From f8ab0bfd7281ec1fe1b7a586f4e389f4b0096bbc Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Mon, 8 Mar 2021 06:42:23 -0800 Subject: [PATCH 01/14] Start notebooks_v2 and and sync to md with jupytext --- notebooks_v2/00.00-Preface.ipynb | 206 + notebooks_v2/00.00-Preface.md | 140 + .../01.00-IPython-Beyond-Normal-Python.ipynb | 155 + .../01.00-IPython-Beyond-Normal-Python.md | 109 + .../01.01-Help-And-Documentation.ipynb | 358 + notebooks_v2/01.01-Help-And-Documentation.md | 280 + .../01.02-Shell-Keyboard-Shortcuts.ipynb | 210 + .../01.02-Shell-Keyboard-Shortcuts.md | 152 + notebooks_v2/01.03-Magic-Commands.ipynb | 241 + notebooks_v2/01.03-Magic-Commands.md | 192 + notebooks_v2/01.04-Input-Output-History.ipynb | 225 + notebooks_v2/01.04-Input-Output-History.md | 167 + .../01.05-IPython-And-Shell-Commands.ipynb | 258 + .../01.05-IPython-And-Shell-Commands.md | 204 + notebooks_v2/01.06-Errors-and-Debugging.ipynb | 429 + notebooks_v2/01.06-Errors-and-Debugging.md | 152 + 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100644 notebooks_v2/figures/05.12-covariance-type.png create mode 100644 notebooks_v2/figures/Data_Science_VD.png create mode 100644 notebooks_v2/figures/PDSH-cover-small.png create mode 100644 notebooks_v2/figures/PDSH-cover.png create mode 100644 notebooks_v2/figures/array_vs_list.png create mode 100644 notebooks_v2/figures/cint_vs_pyint.png create mode 100644 notebooks_v2/helpers_05_08.py diff --git a/notebooks_v2/00.00-Preface.ipynb b/notebooks_v2/00.00-Preface.ipynb new file mode 100644 index 00000000..ce3662ee --- /dev/null +++ b/notebooks_v2/00.00-Preface.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What Is Data Science?\n", + "\n", + "This is a book about doing data science with Python, which immediately begs the question: what is *data science*?\n", + "It's a surprisingly hard definition to nail down, especially given how ubiquitous the term has become.\n", + "Vocal critics have variously dismissed the term as a superfluous label (after all, what science doesn't involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.\n", + "\n", + "In my mind, these critiques miss something important.\n", + "Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia.\n", + "This cross-disciplinary piece is key: in my mind, the best extisting definition of data science is illustrated by Drew Conway's Data Science Venn Diagram, first published on his blog in September 2010:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Data Science Venn Diagram](figures/Data_Science_VD.png)\n", + "\n", + "(Source: [Drew Conway](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram). Used by permission.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say \"data science\": it is fundamentally an *interdisciplinary* subject.\n", + "Data science comprises three distinct and overlapping areas: the skills of a *statistician* who knows how to model and summarize datasets (which are growing ever larger); the skills of a *computer scientist* who can design and use algorithms to efficiently store, process, and visualize this data; and the *domain expertise*—what we might think of as \"classical\" training in a subject—necessary both to formulate the right questions and to put their answers in context.\n", + "\n", + "With this in mind, I would encourage you to think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise.\n", + "Whether you are reporting election results, forecasting stock returns, optimizing online ad clicks, identifying microorganisms in microscope photos, seeking new classes of astronomical objects, or working with data in any other field, the goal of this book is to give you the ability to ask and answer new questions about your chosen subject area." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Who Is This Book For?\n", + "\n", + "In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: \"how should I learn Python?\"\n", + "The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools.\n", + "Most of these folks don't want to learn Python *per se*, but want to learn the language with the aim of using it as a tool for data-intensive and computational science.\n", + "While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I've long been frustrated by the lack of a single good answer to this question; that is what inspired this book.\n", + "\n", + "The book is not meant to be an introduction to Python or to programming in general; I assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks.\n", + "Instead it is meant to help Python users learn to use Python's data science stack–libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools–to effectively store, manipulate, and gain insight from data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why Python?\n", + "\n", + "Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets.\n", + "This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind.\n", + "The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: *NumPy* for manipulation of homogeneous array-based data, *Pandas* for manipulation of heterogeneous and labeled data, *SciPy* for common scientific computing tasks, *Matplotlib* for publication-quality visualizations, *IPython* for interactive execution and sharing of code, *Scikit-Learn* for machine learning, and many more tools that will be mentioned in the following pages.\n", + "\n", + "If you are looking for a guide to the Python language itself, I would suggest the sister project to this book, \"[A Whirlwind Tour of the Python Language](https://github.com/jakevdp/WhirlwindTourOfPython)\".\n", + "This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python 2 vs Python 3\n", + "\n", + "This book uses the syntax of Python 3, which contains language enhancements that are not compatible with the 2.x series of Python.\n", + "Though Python 3.0 was first released in 2008, adoption has been relatively slow, particularly in the scientific and web development communities.\n", + "This is primarily because it took some time for many of the essential third-party packages and toolkits to be made compatible with the new language internals.\n", + "Since early 2014, however, stable releases of the most important tools in the data science ecosystem have been fully compatible with both Python 2 and 3, and so this book will use the newer Python 3 syntax.\n", + "However, the vast majority of code snippets in this book will also work without modification in Python 2: in cases where a Py2-incompatible syntax is used, I will make every effort to note it explicitly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline of the Book\n", + "\n", + "Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story.\n", + "\n", + "1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work.\n", + "2. NumPy: this library provides the ``ndarray`` for efficient storage and manipulation of dense data arrays in Python.\n", + "3. Pandas: this library provides the ``DataFrame`` for efficient storage and manipulation of labeled/columnar data in Python.\n", + "4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python.\n", + "5. Scikit-Learn: this library provides efficient & clean Python implementations of the most important and established machine learning algorithms.\n", + "\n", + "The PyData world is certainly much larger than these five packages, and is growing every day.\n", + "With this in mind, I make every attempt through these pages to provide references to other interesting efforts, projects, and packages that are pushing the boundaries of what can be done in Python.\n", + "Nevertheless, these five are currently fundamental to much of the work being done in the Python data science space, and I expect they will remain important even as the ecosystem continues growing around them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Code Examples\n", + "\n", + "Supplemental material (code examples, figures, etc.) is available for download at http://github.com/jakevdp/PythonDataScienceHandbook/. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.\n", + "\n", + "We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example:\n", + "\n", + "> *The Python Data Science Handbook* by Jake VanderPlas (O’Reilly). Copyright 2016 Jake VanderPlas, 978-1-491-91205-8.\n", + "\n", + "If you feel your use of code examples falls outside fair use or the per‐ mission given above, feel free to contact us at permissions@oreilly.com." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation Considerations\n", + "\n", + "Installing Python and the suite of libraries that enable scientific computing is straightforward . This section will outline some of the considerations when setting up your computer.\n", + "\n", + "Though there are various ways to install Python, the one I would suggest for use in data science is the Anaconda distribution, which works similarly whether you use Windows, Linux, or Mac OS X.\n", + "The Anaconda distribution comes in two flavors:\n", + "\n", + "- [Miniconda](http://conda.pydata.org/miniconda.html) gives you the Python interpreter itself, along with a command-line tool called ``conda`` which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.\n", + "\n", + "- [Anaconda](https://www.continuum.io/downloads) includes both Python and conda, and additionally bundles a suite of other pre-installed packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.\n", + "\n", + "Any of the packages included with Anaconda can also be installed manually on top of Miniconda; for this reason I suggest starting with Miniconda.\n", + "\n", + "To get started, download and install the Miniconda package–make sure to choose a version with Python 3–and then install the core packages used in this book:\n", + "\n", + "```\n", + "[~]$ conda install numpy pandas scikit-learn matplotlib seaborn jupyter\n", + "```\n", + "\n", + "Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **``conda install packagename``**.\n", + "For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/00.00-Preface.md b/notebooks_v2/00.00-Preface.md new file mode 100644 index 00000000..44cca9e3 --- /dev/null +++ b/notebooks_v2/00.00-Preface.md @@ -0,0 +1,140 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) > + +Open in Colab + + + +# Preface + + +## What Is Data Science? + +This is a book about doing data science with Python, which immediately begs the question: what is *data science*? +It's a surprisingly hard definition to nail down, especially given how ubiquitous the term has become. +Vocal critics have variously dismissed the term as a superfluous label (after all, what science doesn't involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters. + +In my mind, these critiques miss something important. +Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia. +This cross-disciplinary piece is key: in my mind, the best extisting definition of data science is illustrated by Drew Conway's Data Science Venn Diagram, first published on his blog in September 2010: + + +![Data Science Venn Diagram](figures/Data_Science_VD.png) + +(Source: [Drew Conway](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram). Used by permission.) + + +While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say "data science": it is fundamentally an *interdisciplinary* subject. +Data science comprises three distinct and overlapping areas: the skills of a *statistician* who knows how to model and summarize datasets (which are growing ever larger); the skills of a *computer scientist* who can design and use algorithms to efficiently store, process, and visualize this data; and the *domain expertise*—what we might think of as "classical" training in a subject—necessary both to formulate the right questions and to put their answers in context. + +With this in mind, I would encourage you to think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise. +Whether you are reporting election results, forecasting stock returns, optimizing online ad clicks, identifying microorganisms in microscope photos, seeking new classes of astronomical objects, or working with data in any other field, the goal of this book is to give you the ability to ask and answer new questions about your chosen subject area. + + +## Who Is This Book For? + +In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: "how should I learn Python?" +The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools. +Most of these folks don't want to learn Python *per se*, but want to learn the language with the aim of using it as a tool for data-intensive and computational science. +While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I've long been frustrated by the lack of a single good answer to this question; that is what inspired this book. + +The book is not meant to be an introduction to Python or to programming in general; I assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks. +Instead it is meant to help Python users learn to use Python's data science stack–libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools–to effectively store, manipulate, and gain insight from data. + + +## Why Python? + +Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets. +This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind. +The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: *NumPy* for manipulation of homogeneous array-based data, *Pandas* for manipulation of heterogeneous and labeled data, *SciPy* for common scientific computing tasks, *Matplotlib* for publication-quality visualizations, *IPython* for interactive execution and sharing of code, *Scikit-Learn* for machine learning, and many more tools that will be mentioned in the following pages. + +If you are looking for a guide to the Python language itself, I would suggest the sister project to this book, "[A Whirlwind Tour of the Python Language](https://github.com/jakevdp/WhirlwindTourOfPython)". +This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages. + + +### Python 2 vs Python 3 + +This book uses the syntax of Python 3, which contains language enhancements that are not compatible with the 2.x series of Python. +Though Python 3.0 was first released in 2008, adoption has been relatively slow, particularly in the scientific and web development communities. +This is primarily because it took some time for many of the essential third-party packages and toolkits to be made compatible with the new language internals. +Since early 2014, however, stable releases of the most important tools in the data science ecosystem have been fully compatible with both Python 2 and 3, and so this book will use the newer Python 3 syntax. +However, the vast majority of code snippets in this book will also work without modification in Python 2: in cases where a Py2-incompatible syntax is used, I will make every effort to note it explicitly. + + +## Outline of the Book + +Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story. + +1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work. +2. NumPy: this library provides the ``ndarray`` for efficient storage and manipulation of dense data arrays in Python. +3. Pandas: this library provides the ``DataFrame`` for efficient storage and manipulation of labeled/columnar data in Python. +4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python. +5. Scikit-Learn: this library provides efficient & clean Python implementations of the most important and established machine learning algorithms. + +The PyData world is certainly much larger than these five packages, and is growing every day. +With this in mind, I make every attempt through these pages to provide references to other interesting efforts, projects, and packages that are pushing the boundaries of what can be done in Python. +Nevertheless, these five are currently fundamental to much of the work being done in the Python data science space, and I expect they will remain important even as the ecosystem continues growing around them. + + +## Using Code Examples + +Supplemental material (code examples, figures, etc.) is available for download at http://github.com/jakevdp/PythonDataScienceHandbook/. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. + +We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: + +> *The Python Data Science Handbook* by Jake VanderPlas (O’Reilly). Copyright 2016 Jake VanderPlas, 978-1-491-91205-8. + +If you feel your use of code examples falls outside fair use or the per‐ mission given above, feel free to contact us at permissions@oreilly.com. + + +## Installation Considerations + +Installing Python and the suite of libraries that enable scientific computing is straightforward . This section will outline some of the considerations when setting up your computer. + +Though there are various ways to install Python, the one I would suggest for use in data science is the Anaconda distribution, which works similarly whether you use Windows, Linux, or Mac OS X. +The Anaconda distribution comes in two flavors: + +- [Miniconda](http://conda.pydata.org/miniconda.html) gives you the Python interpreter itself, along with a command-line tool called ``conda`` which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with. + +- [Anaconda](https://www.continuum.io/downloads) includes both Python and conda, and additionally bundles a suite of other pre-installed packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space. + +Any of the packages included with Anaconda can also be installed manually on top of Miniconda; for this reason I suggest starting with Miniconda. + +To get started, download and install the Miniconda package–make sure to choose a version with Python 3–and then install the core packages used in this book: + +``` +[~]$ conda install numpy pandas scikit-learn matplotlib seaborn jupyter +``` + +Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **``conda install packagename``**. +For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/). + + + +| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb new file mode 100644 index 00000000..44ec590c --- /dev/null +++ b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IPython: Beyond Normal Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many options for development environments for Python, and I'm often asked which one I use in my own work.\n", + "My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or Atom depending on my mood).\n", + "IPython (short for *Interactive Python*) was started in 2001 by Fernando Perez as an enhanced Python interpreter, and has since grown into a project aiming to provide, in Perez's words, \"Tools for the entire life cycle of research computing.\"\n", + "If Python is the engine of our data science task, you might think of IPython as the interactive control panel.\n", + "\n", + "As well as being a useful interactive interface to Python, IPython also provides a number of useful syntactic additions to the language; we'll cover the most useful of these additions here.\n", + "In addition, IPython is closely tied with the [Jupyter project](http://jupyter.org), which provides a browser-based notebook that is useful for development, collaboration, sharing, and even publication of data science results.\n", + "The IPython notebook is actually a special case of the broader Jupyter notebook structure, which encompasses notebooks for Julia, R, and other programming languages.\n", + "As an example of the usefulness of the notebook format, look no further than the page you are reading: the entire manuscript for this book was composed as a set of IPython notebooks.\n", + "\n", + "IPython is about using Python effectively for interactive scientific and data-intensive computing.\n", + "This chapter will start by stepping through some of the IPython features that are useful to the practice of data science, focusing especially on the syntax it offers beyond the standard features of Python.\n", + "Next, we will go into a bit more depth on some of the more useful \"magic commands\" that can speed-up common tasks in creating and using data science code.\n", + "Finally, we will touch on some of the features of the notebook that make it useful in understanding data and sharing results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shell or Notebook?\n", + "\n", + "There are two primary means of using IPython that we'll discuss in this chapter: the IPython shell and the IPython notebook.\n", + "The bulk of the material in this chapter is relevant to both, and the examples will switch between them depending on what is most convenient.\n", + "In the few sections that are relevant to just one or the other, we will explicitly state that fact.\n", + "Before we start, some words on how to launch the IPython shell and IPython notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Launching the IPython Shell\n", + "\n", + "This chapter, like most of this book, is not designed to be absorbed passively.\n", + "I recommend that as you read through it, you follow along and experiment with the tools and syntax we cover: the muscle-memory you build through doing this will be far more useful than the simple act of reading about it.\n", + "Start by launching the IPython interpreter by typing **``ipython``** on the command-line; alternatively, if you've installed a distribution like Anaconda or EPD, there may be a launcher specific to your system (we'll discuss this more fully in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\n", + "\n", + "Once you do this, you should see a prompt like the following:\n", + "```\n", + "IPython 4.0.1 -- An enhanced Interactive Python.\n", + "? -> Introduction and overview of IPython's features.\n", + "%quickref -> Quick reference.\n", + "help -> Python's own help system.\n", + "object? -> Details about 'object', use 'object??' for extra details.\n", + "In [1]:\n", + "```\n", + "With that, you're ready to follow along." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Launching the Jupyter Notebook\n", + "\n", + "The Jupyter notebook is a browser-based graphical interface to the IPython shell, and builds on it a rich set of dynamic display capabilities.\n", + "As well as executing Python/IPython statements, the notebook allows the user to include formatted text, static and dynamic visualizations, mathematical equations, JavaScript widgets, and much more.\n", + "Furthermore, these documents can be saved in a way that lets other people open them and execute the code on their own systems.\n", + "\n", + "Though the IPython notebook is viewed and edited through your web browser window, it must connect to a running Python process in order to execute code.\n", + "This process (known as a \"kernel\") can be started by running the following command in your system shell:\n", + "\n", + "```\n", + "$ jupyter notebook\n", + "```\n", + "\n", + "This command will launch a local web server that will be visible to your browser.\n", + "It immediately spits out a log showing what it is doing; that log will look something like this:\n", + "\n", + "```\n", + "$ jupyter notebook\n", + "[NotebookApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook\n", + "[NotebookApp] 0 active kernels \n", + "[NotebookApp] The IPython Notebook is running at: http://localhost:8888/\n", + "[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", + "```\n", + "\n", + "Upon issuing the command, your default browser should automatically open and navigate to the listed local URL;\n", + "the exact address will depend on your system.\n", + "If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/* in this example)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md new file mode 100644 index 00000000..119244dc --- /dev/null +++ b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md @@ -0,0 +1,109 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) > + +Open in Colab + + + +# IPython: Beyond Normal Python + + +There are many options for development environments for Python, and I'm often asked which one I use in my own work. +My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or Atom depending on my mood). +IPython (short for *Interactive Python*) was started in 2001 by Fernando Perez as an enhanced Python interpreter, and has since grown into a project aiming to provide, in Perez's words, "Tools for the entire life cycle of research computing." +If Python is the engine of our data science task, you might think of IPython as the interactive control panel. + +As well as being a useful interactive interface to Python, IPython also provides a number of useful syntactic additions to the language; we'll cover the most useful of these additions here. +In addition, IPython is closely tied with the [Jupyter project](http://jupyter.org), which provides a browser-based notebook that is useful for development, collaboration, sharing, and even publication of data science results. +The IPython notebook is actually a special case of the broader Jupyter notebook structure, which encompasses notebooks for Julia, R, and other programming languages. +As an example of the usefulness of the notebook format, look no further than the page you are reading: the entire manuscript for this book was composed as a set of IPython notebooks. + +IPython is about using Python effectively for interactive scientific and data-intensive computing. +This chapter will start by stepping through some of the IPython features that are useful to the practice of data science, focusing especially on the syntax it offers beyond the standard features of Python. +Next, we will go into a bit more depth on some of the more useful "magic commands" that can speed-up common tasks in creating and using data science code. +Finally, we will touch on some of the features of the notebook that make it useful in understanding data and sharing results. + + +## Shell or Notebook? + +There are two primary means of using IPython that we'll discuss in this chapter: the IPython shell and the IPython notebook. +The bulk of the material in this chapter is relevant to both, and the examples will switch between them depending on what is most convenient. +In the few sections that are relevant to just one or the other, we will explicitly state that fact. +Before we start, some words on how to launch the IPython shell and IPython notebook. + + +### Launching the IPython Shell + +This chapter, like most of this book, is not designed to be absorbed passively. +I recommend that as you read through it, you follow along and experiment with the tools and syntax we cover: the muscle-memory you build through doing this will be far more useful than the simple act of reading about it. +Start by launching the IPython interpreter by typing **``ipython``** on the command-line; alternatively, if you've installed a distribution like Anaconda or EPD, there may be a launcher specific to your system (we'll discuss this more fully in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)). + +Once you do this, you should see a prompt like the following: +``` +IPython 4.0.1 -- An enhanced Interactive Python. +? -> Introduction and overview of IPython's features. +%quickref -> Quick reference. +help -> Python's own help system. +object? -> Details about 'object', use 'object??' for extra details. +In [1]: +``` +With that, you're ready to follow along. + + +### Launching the Jupyter Notebook + +The Jupyter notebook is a browser-based graphical interface to the IPython shell, and builds on it a rich set of dynamic display capabilities. +As well as executing Python/IPython statements, the notebook allows the user to include formatted text, static and dynamic visualizations, mathematical equations, JavaScript widgets, and much more. +Furthermore, these documents can be saved in a way that lets other people open them and execute the code on their own systems. + +Though the IPython notebook is viewed and edited through your web browser window, it must connect to a running Python process in order to execute code. +This process (known as a "kernel") can be started by running the following command in your system shell: + +``` +$ jupyter notebook +``` + +This command will launch a local web server that will be visible to your browser. +It immediately spits out a log showing what it is doing; that log will look something like this: + +``` +$ jupyter notebook +[NotebookApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook +[NotebookApp] 0 active kernels +[NotebookApp] The IPython Notebook is running at: http://localhost:8888/ +[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). +``` + +Upon issuing the command, your default browser should automatically open and navigate to the listed local URL; +the exact address will depend on your system. +If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/* in this example). + + + +< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.01-Help-And-Documentation.ipynb b/notebooks_v2/01.01-Help-And-Documentation.ipynb new file mode 100644 index 00000000..00e8bfd5 --- /dev/null +++ b/notebooks_v2/01.01-Help-And-Documentation.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Help and Documentation in IPython" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow.\n", + "\n", + "When a technologically-minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer as much as knowing how to quickly find an unknown answer.\n", + "In data science it's the same: searchable web resources such as online documentation, mailing-list threads, and StackOverflow answers contain a wealth of information, even (especially?) if it is a topic you've found yourself searching before.\n", + "Being an effective practitioner of data science is less about memorizing the tool or command you should use for every possible situation, and more about learning to effectively find the information you don't know, whether through a web search engine or another means.\n", + "\n", + "One of the most useful functions of IPython/Jupyter is to shorten the gap between the user and the type of documentation and search that will help them do their work effectively.\n", + "While web searches still play a role in answering complicated questions, an amazing amount of information can be found through IPython alone.\n", + "Some examples of the questions IPython can help answer in a few keystrokes:\n", + "\n", + "- How do I call this function? What arguments and options does it have?\n", + "- What does the source code of this Python object look like?\n", + "- What is in this package I imported? What attributes or methods does this object have?\n", + "\n", + "Here we'll discuss IPython's tools to quickly access this information, namely the ``?`` character to explore documentation, the ``??`` characters to explore source code, and the Tab key for auto-completion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Accessing Documentation with ``?``\n", + "\n", + "The Python language and its data science ecosystem is built with the user in mind, and one big part of that is access to documentation.\n", + "Every Python object contains the reference to a string, known as a *doc string*, which in most cases will contain a concise summary of the object and how to use it.\n", + "Python has a built-in ``help()`` function that can access this information and prints the results.\n", + "For example, to see the documentation of the built-in ``len`` function, you can do the following:\n", + "\n", + "```ipython\n", + "In [1]: help(len)\n", + "Help on built-in function len in module builtins:\n", + "\n", + "len(...)\n", + " len(object) -> integer\n", + " \n", + " Return the number of items of a sequence or mapping.\n", + "```\n", + "\n", + "Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because finding help on an object is so common and useful, IPython introduces the ``?`` character as a shorthand for accessing this documentation and other relevant information:\n", + "\n", + "```ipython\n", + "In [2]: len?\n", + "Type: builtin_function_or_method\n", + "String form: \n", + "Namespace: Python builtin\n", + "Docstring:\n", + "len(object) -> integer\n", + "\n", + "Return the number of items of a sequence or mapping.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notation works for just about anything, including object methods:\n", + "\n", + "```ipython\n", + "In [3]: L = [1, 2, 3]\n", + "In [4]: L.insert?\n", + "Type: builtin_function_or_method\n", + "String form: \n", + "Docstring: L.insert(index, object) -- insert object before index\n", + "```\n", + "\n", + "or even objects themselves, with the documentation from their type:\n", + "\n", + "```ipython\n", + "In [5]: L?\n", + "Type: list\n", + "String form: [1, 2, 3]\n", + "Length: 3\n", + "Docstring:\n", + "list() -> new empty list\n", + "list(iterable) -> new list initialized from iterable's items\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importantly, this will even work for functions or other objects you create yourself!\n", + "Here we'll define a small function with a docstring:\n", + "\n", + "```ipython\n", + "In [6]: def square(a):\n", + " ....: \"\"\"Return the square of a.\"\"\"\n", + " ....: return a ** 2\n", + " ....:\n", + "```\n", + "\n", + "Note that to create a docstring for our function, we simply placed a string literal in the first line.\n", + "Because doc strings are usually multiple lines, by convention we used Python's triple-quote notation for multi-line strings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll use the ``?`` mark to find this doc string:\n", + "\n", + "```ipython\n", + "In [7]: square?\n", + "Type: function\n", + "String form: \n", + "Definition: square(a)\n", + "Docstring: Return the square of a.\n", + "```\n", + "\n", + "This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Accessing Source Code with ``??``\n", + "Because the Python language is so easily readable, another level of insight can usually be gained by reading the source code of the object you're curious about.\n", + "IPython provides a shortcut to the source code with the double question mark (``??``):\n", + "\n", + "```ipython\n", + "In [8]: square??\n", + "Type: function\n", + "String form: \n", + "Definition: square(a)\n", + "Source:\n", + "def square(a):\n", + " \"Return the square of a\"\n", + " return a ** 2\n", + "```\n", + "\n", + "For simple functions like this, the double question-mark can give quick insight into the under-the-hood details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you play with this much, you'll notice that sometimes the ``??`` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language.\n", + "If this is the case, the ``??`` suffix gives the same output as the ``?`` suffix.\n", + "You'll find this particularly with many of Python's built-in objects and types, for example ``len`` from above:\n", + "\n", + "```ipython\n", + "In [9]: len??\n", + "Type: builtin_function_or_method\n", + "String form: \n", + "Namespace: Python builtin\n", + "Docstring:\n", + "len(object) -> integer\n", + "\n", + "Return the number of items of a sequence or mapping.\n", + "```\n", + "\n", + "Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring Modules with Tab-Completion\n", + "\n", + "IPython's other useful interface is the use of the tab key for auto-completion and exploration of the contents of objects, modules, and name-spaces.\n", + "In the examples that follow, we'll use ```` to indicate when the Tab key should be pressed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tab-completion of object contents\n", + "\n", + "Every Python object has various attributes and methods associated with it.\n", + "Like with the ``help`` function discussed before, Python has a built-in ``dir`` function that returns a list of these, but the tab-completion interface is much easier to use in practice.\n", + "To see a list of all available attributes of an object, you can type the name of the object followed by a period (\"``.``\") character and the Tab key:\n", + "\n", + "```ipython\n", + "In [10]: L.\n", + "L.append L.copy L.extend L.insert L.remove L.sort \n", + "L.clear L.count L.index L.pop L.reverse \n", + "```\n", + "\n", + "To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\n", + "\n", + "```ipython\n", + "In [10]: L.c\n", + "L.clear L.copy L.count \n", + "\n", + "In [10]: L.co\n", + "L.copy L.count \n", + "```\n", + "\n", + "If there is only a single option, pressing the Tab key will complete the line for you.\n", + "For example, the following will instantly be replaced with ``L.count``:\n", + "\n", + "```ipython\n", + "In [10]: L.cou\n", + "\n", + "```\n", + "\n", + "Though Python has no strictly-enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote such methods.\n", + "For clarity, these private methods and special methods are omitted from the list by default, but it's possible to list them by explicitly typing the underscore:\n", + "\n", + "```ipython\n", + "In [10]: L._\n", + "L.__add__ L.__gt__ L.__reduce__\n", + "L.__class__ L.__hash__ L.__reduce_ex__\n", + "```\n", + "\n", + "For brevity, we've only shown the first couple lines of the output.\n", + "Most of these are Python's special double-underscore methods (often nicknamed \"dunder\" methods)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tab completion when importing\n", + "\n", + "Tab completion is also useful when importing objects from packages.\n", + "Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``:\n", + "```\n", + "In [10]: from itertools import co\n", + "combinations compress\n", + "combinations_with_replacement count\n", + "```\n", + "Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session):\n", + "```\n", + "In [10]: import \n", + "Display all 399 possibilities? (y or n)\n", + "Crypto dis py_compile\n", + "Cython distutils pyclbr\n", + "... ... ...\n", + "difflib pwd zmq\n", + "\n", + "In [10]: import h\n", + "hashlib hmac http \n", + "heapq html husl \n", + "```\n", + "(Note that for brevity, I did not print here all 399 importable packages and modules on my system.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Beyond tab completion: wildcard matching\n", + "\n", + "Tab completion is useful if you know the first few characters of the object or attribute you're looking for, but is little help if you'd like to match characters at the middle or end of the word.\n", + "For this use-case, IPython provides a means of wildcard matching for names using the ``*`` character.\n", + "\n", + "For example, we can use this to list every object in the namespace that ends with ``Warning``:\n", + "\n", + "```ipython\n", + "In [10]: *Warning?\n", + "BytesWarning RuntimeWarning\n", + "DeprecationWarning SyntaxWarning\n", + "FutureWarning UnicodeWarning\n", + "ImportWarning UserWarning\n", + "PendingDeprecationWarning Warning\n", + "ResourceWarning\n", + "```\n", + "\n", + "Notice that the ``*`` character matches any string, including the empty string.\n", + "\n", + "Similarly, suppose we are looking for a string method that contains the word ``find`` somewhere in its name.\n", + "We can search for it this way:\n", + "\n", + "```ipython\n", + "In [10]: str.*find*?\n", + "str.find\n", + "str.rfind\n", + "```\n", + "\n", + "I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.01-Help-And-Documentation.md b/notebooks_v2/01.01-Help-And-Documentation.md new file mode 100644 index 00000000..9f493bef --- /dev/null +++ b/notebooks_v2/01.01-Help-And-Documentation.md @@ -0,0 +1,280 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) > + +Open in Colab + + + +# Help and Documentation in IPython + + +If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow. + +When a technologically-minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer as much as knowing how to quickly find an unknown answer. +In data science it's the same: searchable web resources such as online documentation, mailing-list threads, and StackOverflow answers contain a wealth of information, even (especially?) if it is a topic you've found yourself searching before. +Being an effective practitioner of data science is less about memorizing the tool or command you should use for every possible situation, and more about learning to effectively find the information you don't know, whether through a web search engine or another means. + +One of the most useful functions of IPython/Jupyter is to shorten the gap between the user and the type of documentation and search that will help them do their work effectively. +While web searches still play a role in answering complicated questions, an amazing amount of information can be found through IPython alone. +Some examples of the questions IPython can help answer in a few keystrokes: + +- How do I call this function? What arguments and options does it have? +- What does the source code of this Python object look like? +- What is in this package I imported? What attributes or methods does this object have? + +Here we'll discuss IPython's tools to quickly access this information, namely the ``?`` character to explore documentation, the ``??`` characters to explore source code, and the Tab key for auto-completion. + + +## Accessing Documentation with ``?`` + +The Python language and its data science ecosystem is built with the user in mind, and one big part of that is access to documentation. +Every Python object contains the reference to a string, known as a *doc string*, which in most cases will contain a concise summary of the object and how to use it. +Python has a built-in ``help()`` function that can access this information and prints the results. +For example, to see the documentation of the built-in ``len`` function, you can do the following: + +```ipython +In [1]: help(len) +Help on built-in function len in module builtins: + +len(...) + len(object) -> integer + + Return the number of items of a sequence or mapping. +``` + +Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window. + + +Because finding help on an object is so common and useful, IPython introduces the ``?`` character as a shorthand for accessing this documentation and other relevant information: + +```ipython +In [2]: len? +Type: builtin_function_or_method +String form: +Namespace: Python builtin +Docstring: +len(object) -> integer + +Return the number of items of a sequence or mapping. +``` + + +This notation works for just about anything, including object methods: + +```ipython +In [3]: L = [1, 2, 3] +In [4]: L.insert? +Type: builtin_function_or_method +String form: +Docstring: L.insert(index, object) -- insert object before index +``` + +or even objects themselves, with the documentation from their type: + +```ipython +In [5]: L? +Type: list +String form: [1, 2, 3] +Length: 3 +Docstring: +list() -> new empty list +list(iterable) -> new list initialized from iterable's items +``` + + +Importantly, this will even work for functions or other objects you create yourself! +Here we'll define a small function with a docstring: + +```ipython +In [6]: def square(a): + ....: """Return the square of a.""" + ....: return a ** 2 + ....: +``` + +Note that to create a docstring for our function, we simply placed a string literal in the first line. +Because doc strings are usually multiple lines, by convention we used Python's triple-quote notation for multi-line strings. + + +Now we'll use the ``?`` mark to find this doc string: + +```ipython +In [7]: square? +Type: function +String form: +Definition: square(a) +Docstring: Return the square of a. +``` + +This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write! + + +## Accessing Source Code with ``??`` +Because the Python language is so easily readable, another level of insight can usually be gained by reading the source code of the object you're curious about. +IPython provides a shortcut to the source code with the double question mark (``??``): + +```ipython +In [8]: square?? +Type: function +String form: +Definition: square(a) +Source: +def square(a): + "Return the square of a" + return a ** 2 +``` + +For simple functions like this, the double question-mark can give quick insight into the under-the-hood details. + + +If you play with this much, you'll notice that sometimes the ``??`` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language. +If this is the case, the ``??`` suffix gives the same output as the ``?`` suffix. +You'll find this particularly with many of Python's built-in objects and types, for example ``len`` from above: + +```ipython +In [9]: len?? +Type: builtin_function_or_method +String form: +Namespace: Python builtin +Docstring: +len(object) -> integer + +Return the number of items of a sequence or mapping. +``` + +Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does. + + +## Exploring Modules with Tab-Completion + +IPython's other useful interface is the use of the tab key for auto-completion and exploration of the contents of objects, modules, and name-spaces. +In the examples that follow, we'll use ```` to indicate when the Tab key should be pressed. + + +### Tab-completion of object contents + +Every Python object has various attributes and methods associated with it. +Like with the ``help`` function discussed before, Python has a built-in ``dir`` function that returns a list of these, but the tab-completion interface is much easier to use in practice. +To see a list of all available attributes of an object, you can type the name of the object followed by a period ("``.``") character and the Tab key: + +```ipython +In [10]: L. +L.append L.copy L.extend L.insert L.remove L.sort +L.clear L.count L.index L.pop L.reverse +``` + +To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods: + +```ipython +In [10]: L.c +L.clear L.copy L.count + +In [10]: L.co +L.copy L.count +``` + +If there is only a single option, pressing the Tab key will complete the line for you. +For example, the following will instantly be replaced with ``L.count``: + +```ipython +In [10]: L.cou + +``` + +Though Python has no strictly-enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote such methods. +For clarity, these private methods and special methods are omitted from the list by default, but it's possible to list them by explicitly typing the underscore: + +```ipython +In [10]: L._ +L.__add__ L.__gt__ L.__reduce__ +L.__class__ L.__hash__ L.__reduce_ex__ +``` + +For brevity, we've only shown the first couple lines of the output. +Most of these are Python's special double-underscore methods (often nicknamed "dunder" methods). + + +### Tab completion when importing + +Tab completion is also useful when importing objects from packages. +Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``: +``` +In [10]: from itertools import co +combinations compress +combinations_with_replacement count +``` +Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session): +``` +In [10]: import +Display all 399 possibilities? (y or n) +Crypto dis py_compile +Cython distutils pyclbr +... ... ... +difflib pwd zmq + +In [10]: import h +hashlib hmac http +heapq html husl +``` +(Note that for brevity, I did not print here all 399 importable packages and modules on my system.) + + +### Beyond tab completion: wildcard matching + +Tab completion is useful if you know the first few characters of the object or attribute you're looking for, but is little help if you'd like to match characters at the middle or end of the word. +For this use-case, IPython provides a means of wildcard matching for names using the ``*`` character. + +For example, we can use this to list every object in the namespace that ends with ``Warning``: + +```ipython +In [10]: *Warning? +BytesWarning RuntimeWarning +DeprecationWarning SyntaxWarning +FutureWarning UnicodeWarning +ImportWarning UserWarning +PendingDeprecationWarning Warning +ResourceWarning +``` + +Notice that the ``*`` character matches any string, including the empty string. + +Similarly, suppose we are looking for a string method that contains the word ``find`` somewhere in its name. +We can search for it this way: + +```ipython +In [10]: str.*find*? +str.find +str.rfind +``` + +I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one. + + + +< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.ipynb b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.ipynb new file mode 100644 index 00000000..55d4bb55 --- /dev/null +++ b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Keyboard Shortcuts in the IPython Shell" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts in your workflow.\n", + "Most familiar perhaps are the Cmd-C and Cmd-V (or Ctrl-C and Ctrl-V) for copying and pasting in a wide variety of programs and systems.\n", + "Power-users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes.\n", + "\n", + "The IPython shell doesn't go this far, but does provide a number of keyboard shortcuts for fast navigation while typing commands.\n", + "These shortcuts are not in fact provided by IPython itself, but through its dependency on the GNU Readline library: as such, some of the following shortcuts may differ depending on your system configuration.\n", + "Also, while some of these shortcuts do work in the browser-based notebook, this section is primarily about shortcuts in the IPython shell.\n", + "\n", + "Once you get accustomed to these, they can be very useful for quickly performing certain commands without moving your hands from the \"home\" keyboard position.\n", + "If you're an Emacs user or if you have experience with Linux-style shells, the following will be very familiar.\n", + "We'll group these shortcuts into a few categories: *navigation shortcuts*, *text entry shortcuts*, *command history shortcuts*, and *miscellaneous shortcuts*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Navigation shortcuts\n", + "\n", + "While the use of the left and right arrow keys to move backward and forward in the line is quite obvious, there are other options that don't require moving your hands from the \"home\" keyboard position:\n", + "\n", + "| Keystroke | Action |\n", + "|-----------------------------------|--------------------------------------------|\n", + "| ``Ctrl-a`` | Move cursor to the beginning of the line |\n", + "| ``Ctrl-e`` | Move cursor to the end of the line |\n", + "| ``Ctrl-b`` or the left arrow key | Move cursor back one character |\n", + "| ``Ctrl-f`` or the right arrow key | Move cursor forward one character |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Text Entry Shortcuts\n", + "\n", + "While everyone is familiar with using the Backspace key to delete the previous character, reaching for the key often requires some minor finger gymnastics, and it only deletes a single character at a time.\n", + "In IPython there are several shortcuts for removing some portion of the text you're typing.\n", + "The most immediately useful of these are the commands to delete entire lines of text.\n", + "You'll know these have become second-nature if you find yourself using a combination of Ctrl-b and Ctrl-d instead of reaching for Backspace to delete the previous character!\n", + "\n", + "| Keystroke | Action |\n", + "|-------------------------------|--------------------------------------------------|\n", + "| Backspace key | Delete previous character in line |\n", + "| ``Ctrl-d`` | Delete next character in line |\n", + "| ``Ctrl-k`` | Cut text from cursor to end of line |\n", + "| ``Ctrl-u`` | Cut text from beginning of line to cursor |\n", + "| ``Ctrl-y`` | Yank (i.e. paste) text that was previously cut |\n", + "| ``Ctrl-t`` | Transpose (i.e., switch) previous two characters |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Command History Shortcuts\n", + "\n", + "Perhaps the most impactful shortcuts discussed here are the ones IPython provides for navigating the command history.\n", + "This command history goes beyond your current IPython session: your entire command history is stored in a SQLite database in your IPython profile directory.\n", + "The most straightforward way to access these is with the up and down arrow keys to step through the history, but other options exist as well:\n", + "\n", + "| Keystroke | Action |\n", + "|-------------------------------------|--------------------------------------------|\n", + "| ``Ctrl-p`` (or the up arrow key) | Access previous command in history |\n", + "| ``Ctrl-n`` (or the down arrow key) | Access next command in history |\n", + "| ``Ctrl-r`` | Reverse-search through command history |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The reverse-search can be particularly useful.\n", + "Recall that in the previous section we defined a function called ``square``.\n", + "Let's reverse-search our Python history from a new IPython shell and find this definition again.\n", + "When you press Ctrl-r in the IPython terminal, you'll see the following prompt:\n", + "\n", + "```ipython\n", + "In [1]:\n", + "(reverse-i-search)`': \n", + "```\n", + "\n", + "If you start typing characters at this prompt, IPython will auto-fill the most recent command, if any, that matches those characters:\n", + "\n", + "```ipython\n", + "In [1]: \n", + "(reverse-i-search)`sqa': square??\n", + "```\n", + "\n", + "At any point, you can add more characters to refine the search, or press Ctrl-r again to search further for another command that matches the query. If you followed along in the previous section, pressing Ctrl-r twice more gives:\n", + "\n", + "```ipython\n", + "In [1]: \n", + "(reverse-i-search)`sqa': def square(a):\n", + " \"\"\"Return the square of a\"\"\"\n", + " return a ** 2\n", + "```\n", + "\n", + "Once you have found the command you're looking for, press Return and the search will end.\n", + "We can then use the retrieved command, and carry-on with our session:\n", + "\n", + "```ipython\n", + "In [1]: def square(a):\n", + " \"\"\"Return the square of a\"\"\"\n", + " return a ** 2\n", + "\n", + "In [2]: square(2)\n", + "Out[2]: 4\n", + "```\n", + "\n", + "Note that Ctrl-p/Ctrl-n or the up/down arrow keys can also be used to search through history, but only by matching characters at the beginning of the line.\n", + "That is, if you type **``def``** and then press Ctrl-p, it would find the most recent command (if any) in your history that begins with the characters ``def``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Miscellaneous Shortcuts\n", + "\n", + "Finally, there are a few miscellaneous shortcuts that don't fit into any of the preceding categories, but are nevertheless useful to know:\n", + "\n", + "| Keystroke | Action |\n", + "|-------------------------------|--------------------------------------------|\n", + "| ``Ctrl-l`` | Clear terminal screen |\n", + "| ``Ctrl-c`` | Interrupt current Python command |\n", + "| ``Ctrl-d`` | Exit IPython session |\n", + "\n", + "The Ctrl-c in particular can be useful when you inadvertently start a very long-running job." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While some of the shortcuts discussed here may seem a bit tedious at first, they quickly become automatic with practice.\n", + "Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md new file mode 100644 index 00000000..af166780 --- /dev/null +++ b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md @@ -0,0 +1,152 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) > + +Open in Colab + + + +# Keyboard Shortcuts in the IPython Shell + + +If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts in your workflow. +Most familiar perhaps are the Cmd-C and Cmd-V (or Ctrl-C and Ctrl-V) for copying and pasting in a wide variety of programs and systems. +Power-users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes. + +The IPython shell doesn't go this far, but does provide a number of keyboard shortcuts for fast navigation while typing commands. +These shortcuts are not in fact provided by IPython itself, but through its dependency on the GNU Readline library: as such, some of the following shortcuts may differ depending on your system configuration. +Also, while some of these shortcuts do work in the browser-based notebook, this section is primarily about shortcuts in the IPython shell. + +Once you get accustomed to these, they can be very useful for quickly performing certain commands without moving your hands from the "home" keyboard position. +If you're an Emacs user or if you have experience with Linux-style shells, the following will be very familiar. +We'll group these shortcuts into a few categories: *navigation shortcuts*, *text entry shortcuts*, *command history shortcuts*, and *miscellaneous shortcuts*. + + +## Navigation shortcuts + +While the use of the left and right arrow keys to move backward and forward in the line is quite obvious, there are other options that don't require moving your hands from the "home" keyboard position: + +| Keystroke | Action | +|-----------------------------------|--------------------------------------------| +| ``Ctrl-a`` | Move cursor to the beginning of the line | +| ``Ctrl-e`` | Move cursor to the end of the line | +| ``Ctrl-b`` or the left arrow key | Move cursor back one character | +| ``Ctrl-f`` or the right arrow key | Move cursor forward one character | + + +## Text Entry Shortcuts + +While everyone is familiar with using the Backspace key to delete the previous character, reaching for the key often requires some minor finger gymnastics, and it only deletes a single character at a time. +In IPython there are several shortcuts for removing some portion of the text you're typing. +The most immediately useful of these are the commands to delete entire lines of text. +You'll know these have become second-nature if you find yourself using a combination of Ctrl-b and Ctrl-d instead of reaching for Backspace to delete the previous character! + +| Keystroke | Action | +|-------------------------------|--------------------------------------------------| +| Backspace key | Delete previous character in line | +| ``Ctrl-d`` | Delete next character in line | +| ``Ctrl-k`` | Cut text from cursor to end of line | +| ``Ctrl-u`` | Cut text from beginning of line to cursor | +| ``Ctrl-y`` | Yank (i.e. paste) text that was previously cut | +| ``Ctrl-t`` | Transpose (i.e., switch) previous two characters | + + +## Command History Shortcuts + +Perhaps the most impactful shortcuts discussed here are the ones IPython provides for navigating the command history. +This command history goes beyond your current IPython session: your entire command history is stored in a SQLite database in your IPython profile directory. +The most straightforward way to access these is with the up and down arrow keys to step through the history, but other options exist as well: + +| Keystroke | Action | +|-------------------------------------|--------------------------------------------| +| ``Ctrl-p`` (or the up arrow key) | Access previous command in history | +| ``Ctrl-n`` (or the down arrow key) | Access next command in history | +| ``Ctrl-r`` | Reverse-search through command history | + + +The reverse-search can be particularly useful. +Recall that in the previous section we defined a function called ``square``. +Let's reverse-search our Python history from a new IPython shell and find this definition again. +When you press Ctrl-r in the IPython terminal, you'll see the following prompt: + +```ipython +In [1]: +(reverse-i-search)`': +``` + +If you start typing characters at this prompt, IPython will auto-fill the most recent command, if any, that matches those characters: + +```ipython +In [1]: +(reverse-i-search)`sqa': square?? +``` + +At any point, you can add more characters to refine the search, or press Ctrl-r again to search further for another command that matches the query. If you followed along in the previous section, pressing Ctrl-r twice more gives: + +```ipython +In [1]: +(reverse-i-search)`sqa': def square(a): + """Return the square of a""" + return a ** 2 +``` + +Once you have found the command you're looking for, press Return and the search will end. +We can then use the retrieved command, and carry-on with our session: + +```ipython +In [1]: def square(a): + """Return the square of a""" + return a ** 2 + +In [2]: square(2) +Out[2]: 4 +``` + +Note that Ctrl-p/Ctrl-n or the up/down arrow keys can also be used to search through history, but only by matching characters at the beginning of the line. +That is, if you type **``def``** and then press Ctrl-p, it would find the most recent command (if any) in your history that begins with the characters ``def``. + + +## Miscellaneous Shortcuts + +Finally, there are a few miscellaneous shortcuts that don't fit into any of the preceding categories, but are nevertheless useful to know: + +| Keystroke | Action | +|-------------------------------|--------------------------------------------| +| ``Ctrl-l`` | Clear terminal screen | +| ``Ctrl-c`` | Interrupt current Python command | +| ``Ctrl-d`` | Exit IPython session | + +The Ctrl-c in particular can be useful when you inadvertently start a very long-running job. + + +While some of the shortcuts discussed here may seem a bit tedious at first, they quickly become automatic with practice. +Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts. + + + +< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.03-Magic-Commands.ipynb b/notebooks_v2/01.03-Magic-Commands.ipynb new file mode 100644 index 00000000..eba5ed1c --- /dev/null +++ b/notebooks_v2/01.03-Magic-Commands.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IPython Magic Commands" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n", + "Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\n", + "These are known in IPython as *magic commands*, and are prefixed by the ``%`` character.\n", + "These magic commands are designed to succinctly solve various common problems in standard data analysis.\n", + "Magic commands come in two flavors: *line magics*, which are denoted by a single ``%`` prefix and operate on a single line of input, and *cell magics*, which are denoted by a double ``%%`` prefix and operate on multiple lines of input.\n", + "We'll demonstrate and discuss a few brief examples here, and come back to more focused discussion of several useful magic commands later in the chapter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pasting Code Blocks: ``%paste`` and ``%cpaste``\n", + "\n", + "When working in the IPython interpreter, one common gotcha is that pasting multi-line code blocks can lead to unexpected errors, especially when indentation and interpreter markers are involved.\n", + "A common case is that you find some example code on a website and want to paste it into your interpreter.\n", + "Consider the following simple function:\n", + "\n", + "``` python\n", + ">>> def donothing(x):\n", + "... return x\n", + "\n", + "```\n", + "The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into IPython you get an error:\n", + "\n", + "```ipython\n", + "In [2]: >>> def donothing(x):\n", + " ...: ... return x\n", + " ...: \n", + " File \"\", line 2\n", + " ... return x\n", + " ^\n", + "SyntaxError: invalid syntax\n", + "```\n", + "\n", + "In the direct paste, the interpreter is confused by the additional prompt characters.\n", + "But never fear–IPython's ``%paste`` magic function is designed to handle this exact type of multi-line, marked-up input:\n", + "\n", + "```ipython\n", + "In [3]: %paste\n", + ">>> def donothing(x):\n", + "... return x\n", + "\n", + "## -- End pasted text --\n", + "```\n", + "\n", + "The ``%paste`` command both enters and executes the code, so now the function is ready to be used:\n", + "\n", + "```ipython\n", + "In [4]: donothing(10)\n", + "Out[4]: 10\n", + "```\n", + "\n", + "A command with a similar intent is ``%cpaste``, which opens up an interactive multiline prompt in which you can paste one or more chunks of code to be executed in a batch:\n", + "\n", + "```ipython\n", + "In [5]: %cpaste\n", + "Pasting code; enter '--' alone on the line to stop or use Ctrl-D.\n", + ":>>> def donothing(x):\n", + ":... return x\n", + ":--\n", + "```\n", + "\n", + "These magic commands, like others we'll see, make available functionality that would be difficult or impossible in a standard Python interpreter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running External Code: ``%run``\n", + "As you begin developing more extensive code, you will likely find yourself working in both IPython for interactive exploration, as well as a text editor to store code that you want to reuse.\n", + "Rather than running this code in a new window, it can be convenient to run it within your IPython session.\n", + "This can be done with the ``%run`` magic.\n", + "\n", + "For example, imagine you've created a ``myscript.py`` file with the following contents:\n", + "\n", + "```python\n", + "#-------------------------------------\n", + "# file: myscript.py\n", + "\n", + "def square(x):\n", + " \"\"\"square a number\"\"\"\n", + " return x ** 2\n", + "\n", + "for N in range(1, 4):\n", + " print(N, \"squared is\", square(N))\n", + "```\n", + "\n", + "You can execute this from your IPython session as follows:\n", + "\n", + "```ipython\n", + "In [6]: %run myscript.py\n", + "1 squared is 1\n", + "2 squared is 4\n", + "3 squared is 9\n", + "```\n", + "\n", + "Note also that after you've run this script, any functions defined within it are available for use in your IPython session:\n", + "\n", + "```ipython\n", + "In [7]: square(5)\n", + "Out[7]: 25\n", + "```\n", + "\n", + "There are several options to fine-tune how your code is run; you can see the documentation in the normal way, by typing **``%run?``** in the IPython interpreter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Timing Code Execution: ``%timeit``\n", + "Another example of a useful magic function is ``%timeit``, which will automatically determine the execution time of the single-line Python statement that follows it.\n", + "For example, we may want to check the performance of a list comprehension:\n", + "\n", + "```ipython\n", + "In [8]: %timeit L = [n ** 2 for n in range(1000)]\n", + "1000 loops, best of 3: 325 µs per loop\n", + "```\n", + "\n", + "The benefit of ``%timeit`` is that for short commands it will automatically perform multiple runs in order to attain more robust results.\n", + "For multi line statements, adding a second ``%`` sign will turn this into a cell magic that can handle multiple lines of input.\n", + "For example, here's the equivalent construction with a ``for``-loop:\n", + "\n", + "```ipython\n", + "In [9]: %%timeit\n", + " ...: L = []\n", + " ...: for n in range(1000):\n", + " ...: L.append(n ** 2)\n", + " ...: \n", + "1000 loops, best of 3: 373 µs per loop\n", + "```\n", + "\n", + "We can immediately see that list comprehensions are about 10% faster than the equivalent ``for``-loop construction in this case.\n", + "We'll explore ``%timeit`` and other approaches to timing and profiling code in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Help on Magic Functions: ``?``, ``%magic``, and ``%lsmagic``\n", + "\n", + "Like normal Python functions, IPython magic functions have docstrings, and this useful\n", + "documentation can be accessed in the standard manner.\n", + "So, for example, to read the documentation of the ``%timeit`` magic simply type this:\n", + "\n", + "```ipython\n", + "In [10]: %timeit?\n", + "```\n", + "\n", + "Documentation for other functions can be accessed similarly.\n", + "To access a general description of available magic functions, including some examples, you can type this:\n", + "\n", + "```ipython\n", + "In [11]: %magic\n", + "```\n", + "\n", + "For a quick and simple list of all available magic functions, type this:\n", + "\n", + "```ipython\n", + "In [12]: %lsmagic\n", + "```\n", + "\n", + "Finally, I'll mention that it is quite straightforward to define your own magic functions if you wish.\n", + "We won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.03-Magic-Commands.md b/notebooks_v2/01.03-Magic-Commands.md new file mode 100644 index 00000000..6dd3af53 --- /dev/null +++ b/notebooks_v2/01.03-Magic-Commands.md @@ -0,0 +1,192 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) > + +Open in Colab + + + +# IPython Magic Commands + + +The previous two sections showed how IPython lets you use and explore Python efficiently and interactively. +Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax. +These are known in IPython as *magic commands*, and are prefixed by the ``%`` character. +These magic commands are designed to succinctly solve various common problems in standard data analysis. +Magic commands come in two flavors: *line magics*, which are denoted by a single ``%`` prefix and operate on a single line of input, and *cell magics*, which are denoted by a double ``%%`` prefix and operate on multiple lines of input. +We'll demonstrate and discuss a few brief examples here, and come back to more focused discussion of several useful magic commands later in the chapter. + + +## Pasting Code Blocks: ``%paste`` and ``%cpaste`` + +When working in the IPython interpreter, one common gotcha is that pasting multi-line code blocks can lead to unexpected errors, especially when indentation and interpreter markers are involved. +A common case is that you find some example code on a website and want to paste it into your interpreter. +Consider the following simple function: + +``` python +>>> def donothing(x): +... return x + +``` +The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into IPython you get an error: + +```ipython +In [2]: >>> def donothing(x): + ...: ... return x + ...: + File "", line 2 + ... return x + ^ +SyntaxError: invalid syntax +``` + +In the direct paste, the interpreter is confused by the additional prompt characters. +But never fear–IPython's ``%paste`` magic function is designed to handle this exact type of multi-line, marked-up input: + +```ipython +In [3]: %paste +>>> def donothing(x): +... return x + +## -- End pasted text -- +``` + +The ``%paste`` command both enters and executes the code, so now the function is ready to be used: + +```ipython +In [4]: donothing(10) +Out[4]: 10 +``` + +A command with a similar intent is ``%cpaste``, which opens up an interactive multiline prompt in which you can paste one or more chunks of code to be executed in a batch: + +```ipython +In [5]: %cpaste +Pasting code; enter '--' alone on the line to stop or use Ctrl-D. +:>>> def donothing(x): +:... return x +:-- +``` + +These magic commands, like others we'll see, make available functionality that would be difficult or impossible in a standard Python interpreter. + + + +## Running External Code: ``%run`` +As you begin developing more extensive code, you will likely find yourself working in both IPython for interactive exploration, as well as a text editor to store code that you want to reuse. +Rather than running this code in a new window, it can be convenient to run it within your IPython session. +This can be done with the ``%run`` magic. + +For example, imagine you've created a ``myscript.py`` file with the following contents: + +```python +#------------------------------------- +# file: myscript.py + +def square(x): + """square a number""" + return x ** 2 + +for N in range(1, 4): + print(N, "squared is", square(N)) +``` + +You can execute this from your IPython session as follows: + +```ipython +In [6]: %run myscript.py +1 squared is 1 +2 squared is 4 +3 squared is 9 +``` + +Note also that after you've run this script, any functions defined within it are available for use in your IPython session: + +```ipython +In [7]: square(5) +Out[7]: 25 +``` + +There are several options to fine-tune how your code is run; you can see the documentation in the normal way, by typing **``%run?``** in the IPython interpreter. + + +## Timing Code Execution: ``%timeit`` +Another example of a useful magic function is ``%timeit``, which will automatically determine the execution time of the single-line Python statement that follows it. +For example, we may want to check the performance of a list comprehension: + +```ipython +In [8]: %timeit L = [n ** 2 for n in range(1000)] +1000 loops, best of 3: 325 µs per loop +``` + +The benefit of ``%timeit`` is that for short commands it will automatically perform multiple runs in order to attain more robust results. +For multi line statements, adding a second ``%`` sign will turn this into a cell magic that can handle multiple lines of input. +For example, here's the equivalent construction with a ``for``-loop: + +```ipython +In [9]: %%timeit + ...: L = [] + ...: for n in range(1000): + ...: L.append(n ** 2) + ...: +1000 loops, best of 3: 373 µs per loop +``` + +We can immediately see that list comprehensions are about 10% faster than the equivalent ``for``-loop construction in this case. +We'll explore ``%timeit`` and other approaches to timing and profiling code in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb). + + +## Help on Magic Functions: ``?``, ``%magic``, and ``%lsmagic`` + +Like normal Python functions, IPython magic functions have docstrings, and this useful +documentation can be accessed in the standard manner. +So, for example, to read the documentation of the ``%timeit`` magic simply type this: + +```ipython +In [10]: %timeit? +``` + +Documentation for other functions can be accessed similarly. +To access a general description of available magic functions, including some examples, you can type this: + +```ipython +In [11]: %magic +``` + +For a quick and simple list of all available magic functions, type this: + +```ipython +In [12]: %lsmagic +``` + +Finally, I'll mention that it is quite straightforward to define your own magic functions if you wish. +We won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb). + + + +< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.04-Input-Output-History.ipynb b/notebooks_v2/01.04-Input-Output-History.ipynb new file mode 100644 index 00000000..7e827a5f --- /dev/null +++ b/notebooks_v2/01.04-Input-Output-History.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Input and Output History" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previously we saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts.\n", + "Additionally, in both the shell and the notebook, IPython exposes several ways to obtain the output of previous commands, as well as string versions of the commands themselves.\n", + "We'll explore those here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IPython's ``In`` and ``Out`` Objects\n", + "\n", + "By now I imagine you're quite familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython.\n", + "But it turns out that these are not just pretty decoration: they give a clue as to how you can access previous inputs and outputs in your current session.\n", + "Imagine you start a session that looks like this:\n", + "\n", + "```ipython\n", + "In [1]: import math\n", + "\n", + "In [2]: math.sin(2)\n", + "Out[2]: 0.9092974268256817\n", + "\n", + "In [3]: math.cos(2)\n", + "Out[3]: -0.4161468365471424\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've imported the built-in ``math`` package, then computed the sine and the cosine of the number 2.\n", + "These inputs and outputs are displayed in the shell with ``In``/``Out`` labels, but there's more–IPython actually creates some Python variables called ``In`` and ``Out`` that are automatically updated to reflect this history:\n", + "\n", + "```ipython\n", + "In [4]: print(In)\n", + "['', 'import math', 'math.sin(2)', 'math.cos(2)', 'print(In)']\n", + "\n", + "In [5]: Out\n", + "Out[5]: {2: 0.9092974268256817, 3: -0.4161468365471424}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``In`` object is a list, which keeps track of the commands in order (the first item in the list is a place-holder so that ``In[1]`` can refer to the first command):\n", + "\n", + "```ipython\n", + "In [6]: print(In[1])\n", + "import math\n", + "```\n", + "\n", + "The ``Out`` object is not a list but a dictionary mapping input numbers to their outputs (if any):\n", + "\n", + "```ipython\n", + "In [7]: print(Out[2])\n", + "0.9092974268256817\n", + "```\n", + "\n", + "Note that not all operations have outputs: for example, ``import`` statements and ``print`` statements don't affect the output.\n", + "The latter may be surprising, but makes sense if you consider that ``print`` is a function that returns ``None``; for brevity, any command that returns ``None`` is not added to ``Out``.\n", + "\n", + "Where this can be useful is if you want to interact with past results.\n", + "For example, let's check the sum of ``sin(2) ** 2`` and ``cos(2) ** 2`` using the previously-computed results:\n", + "\n", + "```ipython\n", + "In [8]: Out[2] ** 2 + Out[3] ** 2\n", + "Out[8]: 1.0\n", + "```\n", + "\n", + "The result is ``1.0`` as we'd expect from the well-known trigonometric identity.\n", + "In this case, using these previous results probably is not necessary, but it can become very handy if you execute a very expensive computation and want to reuse the result!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Underscore Shortcuts and Previous Outputs\n", + "\n", + "The standard Python shell contains just one simple shortcut for accessing previous output; the variable ``_`` (i.e., a single underscore) is kept updated with the previous output; this works in IPython as well:\n", + "\n", + "```ipython\n", + "In [9]: print(_)\n", + "1.0\n", + "```\n", + "\n", + "But IPython takes this a bit further—you can use a double underscore to access the second-to-last output, and a triple underscore to access the third-to-last output (skipping any commands with no output):\n", + "\n", + "```ipython\n", + "In [10]: print(__)\n", + "-0.4161468365471424\n", + "\n", + "In [11]: print(___)\n", + "0.9092974268256817\n", + "```\n", + "\n", + "IPython stops there: more than three underscores starts to get a bit hard to count, and at that point it's easier to refer to the output by line number.\n", + "\n", + "There is one more shortcut we should mention, however–a shorthand for ``Out[X]`` is ``_X`` (i.e., a single underscore followed by the line number):\n", + "\n", + "```ipython\n", + "In [12]: Out[2]\n", + "Out[12]: 0.9092974268256817\n", + "\n", + "In [13]: _2\n", + "Out[13]: 0.9092974268256817\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Suppressing Output\n", + "Sometimes you might wish to suppress the output of a statement (this is perhaps most common with the plotting commands that we'll explore in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb)).\n", + "Or maybe the command you're executing produces a result that you'd prefer not like to store in your output history, perhaps so that it can be deallocated when other references are removed.\n", + "The easiest way to suppress the output of a command is to add a semicolon to the end of the line:\n", + "\n", + "```ipython\n", + "In [14]: math.sin(2) + math.cos(2);\n", + "```\n", + "\n", + "Note that the result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary:\n", + "\n", + "```ipython\n", + "In [15]: 14 in Out\n", + "Out[15]: False\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Related Magic Commands\n", + "For accessing a batch of previous inputs at once, the ``%history`` magic command is very helpful.\n", + "Here is how you can print the first four inputs:\n", + "\n", + "```ipython\n", + "In [16]: %history -n 1-4\n", + " 1: import math\n", + " 2: math.sin(2)\n", + " 3: math.cos(2)\n", + " 4: print(In)\n", + "```\n", + "\n", + "As usual, you can type ``%history?`` for more information and a description of options available.\n", + "Other similar magic commands are ``%rerun`` (which will re-execute some portion of the command history) and ``%save`` (which saves some set of the command history to a file).\n", + "For more information, I suggest exploring these using the ``?`` help functionality discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.04-Input-Output-History.md b/notebooks_v2/01.04-Input-Output-History.md new file mode 100644 index 00000000..a885909d --- /dev/null +++ b/notebooks_v2/01.04-Input-Output-History.md @@ -0,0 +1,167 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) > + +Open in Colab + + + +# Input and Output History + + +Previously we saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts. +Additionally, in both the shell and the notebook, IPython exposes several ways to obtain the output of previous commands, as well as string versions of the commands themselves. +We'll explore those here. + + +## IPython's ``In`` and ``Out`` Objects + +By now I imagine you're quite familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython. +But it turns out that these are not just pretty decoration: they give a clue as to how you can access previous inputs and outputs in your current session. +Imagine you start a session that looks like this: + +```ipython +In [1]: import math + +In [2]: math.sin(2) +Out[2]: 0.9092974268256817 + +In [3]: math.cos(2) +Out[3]: -0.4161468365471424 +``` + + +We've imported the built-in ``math`` package, then computed the sine and the cosine of the number 2. +These inputs and outputs are displayed in the shell with ``In``/``Out`` labels, but there's more–IPython actually creates some Python variables called ``In`` and ``Out`` that are automatically updated to reflect this history: + +```ipython +In [4]: print(In) +['', 'import math', 'math.sin(2)', 'math.cos(2)', 'print(In)'] + +In [5]: Out +Out[5]: {2: 0.9092974268256817, 3: -0.4161468365471424} +``` + + +The ``In`` object is a list, which keeps track of the commands in order (the first item in the list is a place-holder so that ``In[1]`` can refer to the first command): + +```ipython +In [6]: print(In[1]) +import math +``` + +The ``Out`` object is not a list but a dictionary mapping input numbers to their outputs (if any): + +```ipython +In [7]: print(Out[2]) +0.9092974268256817 +``` + +Note that not all operations have outputs: for example, ``import`` statements and ``print`` statements don't affect the output. +The latter may be surprising, but makes sense if you consider that ``print`` is a function that returns ``None``; for brevity, any command that returns ``None`` is not added to ``Out``. + +Where this can be useful is if you want to interact with past results. +For example, let's check the sum of ``sin(2) ** 2`` and ``cos(2) ** 2`` using the previously-computed results: + +```ipython +In [8]: Out[2] ** 2 + Out[3] ** 2 +Out[8]: 1.0 +``` + +The result is ``1.0`` as we'd expect from the well-known trigonometric identity. +In this case, using these previous results probably is not necessary, but it can become very handy if you execute a very expensive computation and want to reuse the result! + + +## Underscore Shortcuts and Previous Outputs + +The standard Python shell contains just one simple shortcut for accessing previous output; the variable ``_`` (i.e., a single underscore) is kept updated with the previous output; this works in IPython as well: + +```ipython +In [9]: print(_) +1.0 +``` + +But IPython takes this a bit further—you can use a double underscore to access the second-to-last output, and a triple underscore to access the third-to-last output (skipping any commands with no output): + +```ipython +In [10]: print(__) +-0.4161468365471424 + +In [11]: print(___) +0.9092974268256817 +``` + +IPython stops there: more than three underscores starts to get a bit hard to count, and at that point it's easier to refer to the output by line number. + +There is one more shortcut we should mention, however–a shorthand for ``Out[X]`` is ``_X`` (i.e., a single underscore followed by the line number): + +```ipython +In [12]: Out[2] +Out[12]: 0.9092974268256817 + +In [13]: _2 +Out[13]: 0.9092974268256817 +``` + + +## Suppressing Output +Sometimes you might wish to suppress the output of a statement (this is perhaps most common with the plotting commands that we'll explore in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb)). +Or maybe the command you're executing produces a result that you'd prefer not like to store in your output history, perhaps so that it can be deallocated when other references are removed. +The easiest way to suppress the output of a command is to add a semicolon to the end of the line: + +```ipython +In [14]: math.sin(2) + math.cos(2); +``` + +Note that the result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary: + +```ipython +In [15]: 14 in Out +Out[15]: False +``` + + +## Related Magic Commands +For accessing a batch of previous inputs at once, the ``%history`` magic command is very helpful. +Here is how you can print the first four inputs: + +```ipython +In [16]: %history -n 1-4 + 1: import math + 2: math.sin(2) + 3: math.cos(2) + 4: print(In) +``` + +As usual, you can type ``%history?`` for more information and a description of options available. +Other similar magic commands are ``%rerun`` (which will re-execute some portion of the command history) and ``%save`` (which saves some set of the command history to a file). +For more information, I suggest exploring these using the ``?`` help functionality discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb). + + + +< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb b/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb new file mode 100644 index 00000000..cfc3efe6 --- /dev/null +++ b/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IPython and Shell Commands" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When working interactively with the standard Python interpreter, one of the frustrations is the need to switch between multiple windows to access Python tools and system command-line tools.\n", + "IPython bridges this gap, and gives you a syntax for executing shell commands directly from within the IPython terminal.\n", + "The magic happens with the exclamation point: anything appearing after ``!`` on a line will be executed not by the Python kernel, but by the system command-line.\n", + "\n", + "The following assumes you're on a Unix-like system, such as Linux or Mac OSX.\n", + "Some of the examples that follow will fail on Windows, which uses a different type of shell by default (though with the 2016 announcement of native Bash shells on Windows, soon this may no longer be an issue!).\n", + "If you're unfamiliar with shell commands, I'd suggest reviewing the [Shell Tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick Introduction to the Shell\n", + "\n", + "A full intro to using the shell/terminal/command-line is well beyond the scope of this chapter, but for the uninitiated we will offer a quick introduction here.\n", + "The shell is a way to interact textually with your computer.\n", + "Ever since the mid 1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating system through familiar clicking of menus and drag-and-drop movements.\n", + "But operating systems existed long before these graphical user interfaces, and were primarily controlled through sequences of text input: at the prompt, the user would type a command, and the computer would do what the user told it to.\n", + "Those early prompt systems are the precursors of the shells and terminals that most active data scientists still use today.\n", + "\n", + "Someone unfamiliar with the shell might ask why you would bother with this, when many results can be accomplished by simply clicking on icons and menus.\n", + "A shell user might reply with another question: why hunt icons and click menus when you can accomplish things much more easily by typing?\n", + "While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can intimidate the average computer user.\n", + "\n", + "As an example, here is a sample of a Linux/OSX shell session where a user explores, creates, and modifies directories and files on their system (``osx:~ $`` is the prompt, and everything after the ``$`` sign is the typed command; text that is preceded by a ``#`` is meant just as description, rather than something you would actually type in):\n", + "\n", + "```bash\n", + "osx:~ $ echo \"hello world\" # echo is like Python's print function\n", + "hello world\n", + "\n", + "osx:~ $ pwd # pwd = print working directory\n", + "/home/jake # this is the \"path\" that we're sitting in\n", + "\n", + "osx:~ $ ls # ls = list working directory contents\n", + "notebooks projects \n", + "\n", + "osx:~ $ cd projects/ # cd = change directory\n", + "\n", + "osx:projects $ pwd\n", + "/home/jake/projects\n", + "\n", + "osx:projects $ ls\n", + "datasci_book mpld3 myproject.txt\n", + "\n", + "osx:projects $ mkdir myproject # mkdir = make new directory\n", + "\n", + "osx:projects $ cd myproject/\n", + "\n", + "osx:myproject $ mv ../myproject.txt ./ # mv = move file. Here we're moving the\n", + " # file myproject.txt from one directory\n", + " # up (../) to the current directory (./)\n", + "osx:myproject $ ls\n", + "myproject.txt\n", + "```\n", + "\n", + "Notice that all of this is just a compact way to do familiar operations (navigating a directory structure, creating a directory, moving a file, etc.) by typing commands rather than clicking icons and menus.\n", + "Note that with just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations.\n", + "It's when you go beyond these basics that the shell approach becomes really powerful." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shell Commands in IPython\n", + "\n", + "Any command that works at the command-line can be used in IPython by prefixing it with the ``!`` character.\n", + "For example, the ``ls``, ``pwd``, and ``echo`` commands can be run as follows:\n", + "\n", + "```ipython\n", + "In [1]: !ls\n", + "myproject.txt\n", + "\n", + "In [2]: !pwd\n", + "/home/jake/projects/myproject\n", + "\n", + "In [3]: !echo \"printing from the shell\"\n", + "printing from the shell\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Passing Values to and from the Shell\n", + "\n", + "Shell commands can not only be called from IPython, but can also be made to interact with the IPython namespace.\n", + "For example, you can save the output of any shell command to a Python list using the assignment operator:\n", + "\n", + "```ipython\n", + "In [4]: contents = !ls\n", + "\n", + "In [5]: print(contents)\n", + "['myproject.txt']\n", + "\n", + "In [6]: directory = !pwd\n", + "\n", + "In [7]: print(directory)\n", + "['/Users/jakevdp/notebooks/tmp/myproject']\n", + "```\n", + "\n", + "Note that these results are not returned as lists, but as a special shell return type defined in IPython:\n", + "\n", + "```ipython\n", + "In [8]: type(directory)\n", + "IPython.utils.text.SList\n", + "```\n", + "\n", + "This looks and acts a lot like a Python list, but has additional functionality, such as\n", + "the ``grep`` and ``fields`` methods and the ``s``, ``n``, and ``p`` properties that allow you to search, filter, and display the results in convenient ways.\n", + "For more information on these, you can use IPython's built-in help features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Communication in the other direction–passing Python variables into the shell–is possible using the ``{varname}`` syntax:\n", + "\n", + "```ipython\n", + "In [9]: message = \"hello from Python\"\n", + "\n", + "In [10]: !echo {message}\n", + "hello from Python\n", + "```\n", + "\n", + "The curly braces contain the variable name, which is replaced by the variable's contents in the shell command." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Shell-Related Magic Commands\n", + "\n", + "If you play with IPython's shell commands for a while, you might notice that you cannot use ``!cd`` to navigate the filesystem:\n", + "\n", + "```ipython\n", + "In [11]: !pwd\n", + "/home/jake/projects/myproject\n", + "\n", + "In [12]: !cd ..\n", + "\n", + "In [13]: !pwd\n", + "/home/jake/projects/myproject\n", + "```\n", + "\n", + "The reason is that shell commands in the notebook are executed in a temporary subshell.\n", + "If you'd like to change the working directory in a more enduring way, you can use the ``%cd`` magic command:\n", + "\n", + "```ipython\n", + "In [14]: %cd ..\n", + "/home/jake/projects\n", + "```\n", + "\n", + "In fact, by default you can even use this without the ``%`` sign:\n", + "\n", + "```ipython\n", + "In [15]: cd myproject\n", + "/home/jake/projects/myproject\n", + "```\n", + "\n", + "This is known as an ``automagic`` function, and this behavior can be toggled with the ``%automagic`` magic function.\n", + "\n", + "Besides ``%cd``, other available shell-like magic functions are ``%cat``, ``%cp``, ``%env``, ``%ls``, ``%man``, ``%mkdir``, ``%more``, ``%mv``, ``%pwd``, ``%rm``, and ``%rmdir``, any of which can be used without the ``%`` sign if ``automagic`` is on.\n", + "This makes it so that you can almost treat the IPython prompt as if it's a normal shell:\n", + "\n", + "```ipython\n", + "In [16]: mkdir tmp\n", + "\n", + "In [17]: ls\n", + "myproject.txt tmp/\n", + "\n", + "In [18]: cp myproject.txt tmp/\n", + "\n", + "In [19]: ls tmp\n", + "myproject.txt\n", + "\n", + "In [20]: rm -r tmp\n", + "```\n", + "\n", + "This access to the shell from within the same terminal window as your Python session means that there is a lot less switching back and forth between interpreter and shell as you write your Python code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.05-IPython-And-Shell-Commands.md b/notebooks_v2/01.05-IPython-And-Shell-Commands.md new file mode 100644 index 00000000..370d26ac --- /dev/null +++ b/notebooks_v2/01.05-IPython-And-Shell-Commands.md @@ -0,0 +1,204 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) > + +Open in Colab + + + +# IPython and Shell Commands + + +When working interactively with the standard Python interpreter, one of the frustrations is the need to switch between multiple windows to access Python tools and system command-line tools. +IPython bridges this gap, and gives you a syntax for executing shell commands directly from within the IPython terminal. +The magic happens with the exclamation point: anything appearing after ``!`` on a line will be executed not by the Python kernel, but by the system command-line. + +The following assumes you're on a Unix-like system, such as Linux or Mac OSX. +Some of the examples that follow will fail on Windows, which uses a different type of shell by default (though with the 2016 announcement of native Bash shells on Windows, soon this may no longer be an issue!). +If you're unfamiliar with shell commands, I'd suggest reviewing the [Shell Tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation. + + +## Quick Introduction to the Shell + +A full intro to using the shell/terminal/command-line is well beyond the scope of this chapter, but for the uninitiated we will offer a quick introduction here. +The shell is a way to interact textually with your computer. +Ever since the mid 1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating system through familiar clicking of menus and drag-and-drop movements. +But operating systems existed long before these graphical user interfaces, and were primarily controlled through sequences of text input: at the prompt, the user would type a command, and the computer would do what the user told it to. +Those early prompt systems are the precursors of the shells and terminals that most active data scientists still use today. + +Someone unfamiliar with the shell might ask why you would bother with this, when many results can be accomplished by simply clicking on icons and menus. +A shell user might reply with another question: why hunt icons and click menus when you can accomplish things much more easily by typing? +While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can intimidate the average computer user. + +As an example, here is a sample of a Linux/OSX shell session where a user explores, creates, and modifies directories and files on their system (``osx:~ $`` is the prompt, and everything after the ``$`` sign is the typed command; text that is preceded by a ``#`` is meant just as description, rather than something you would actually type in): + +```bash +osx:~ $ echo "hello world" # echo is like Python's print function +hello world + +osx:~ $ pwd # pwd = print working directory +/home/jake # this is the "path" that we're sitting in + +osx:~ $ ls # ls = list working directory contents +notebooks projects + +osx:~ $ cd projects/ # cd = change directory + +osx:projects $ pwd +/home/jake/projects + +osx:projects $ ls +datasci_book mpld3 myproject.txt + +osx:projects $ mkdir myproject # mkdir = make new directory + +osx:projects $ cd myproject/ + +osx:myproject $ mv ../myproject.txt ./ # mv = move file. Here we're moving the + # file myproject.txt from one directory + # up (../) to the current directory (./) +osx:myproject $ ls +myproject.txt +``` + +Notice that all of this is just a compact way to do familiar operations (navigating a directory structure, creating a directory, moving a file, etc.) by typing commands rather than clicking icons and menus. +Note that with just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations. +It's when you go beyond these basics that the shell approach becomes really powerful. + + +## Shell Commands in IPython + +Any command that works at the command-line can be used in IPython by prefixing it with the ``!`` character. +For example, the ``ls``, ``pwd``, and ``echo`` commands can be run as follows: + +```ipython +In [1]: !ls +myproject.txt + +In [2]: !pwd +/home/jake/projects/myproject + +In [3]: !echo "printing from the shell" +printing from the shell +``` + + +## Passing Values to and from the Shell + +Shell commands can not only be called from IPython, but can also be made to interact with the IPython namespace. +For example, you can save the output of any shell command to a Python list using the assignment operator: + +```ipython +In [4]: contents = !ls + +In [5]: print(contents) +['myproject.txt'] + +In [6]: directory = !pwd + +In [7]: print(directory) +['/Users/jakevdp/notebooks/tmp/myproject'] +``` + +Note that these results are not returned as lists, but as a special shell return type defined in IPython: + +```ipython +In [8]: type(directory) +IPython.utils.text.SList +``` + +This looks and acts a lot like a Python list, but has additional functionality, such as +the ``grep`` and ``fields`` methods and the ``s``, ``n``, and ``p`` properties that allow you to search, filter, and display the results in convenient ways. +For more information on these, you can use IPython's built-in help features. + + +Communication in the other direction–passing Python variables into the shell–is possible using the ``{varname}`` syntax: + +```ipython +In [9]: message = "hello from Python" + +In [10]: !echo {message} +hello from Python +``` + +The curly braces contain the variable name, which is replaced by the variable's contents in the shell command. + + +# Shell-Related Magic Commands + +If you play with IPython's shell commands for a while, you might notice that you cannot use ``!cd`` to navigate the filesystem: + +```ipython +In [11]: !pwd +/home/jake/projects/myproject + +In [12]: !cd .. + +In [13]: !pwd +/home/jake/projects/myproject +``` + +The reason is that shell commands in the notebook are executed in a temporary subshell. +If you'd like to change the working directory in a more enduring way, you can use the ``%cd`` magic command: + +```ipython +In [14]: %cd .. +/home/jake/projects +``` + +In fact, by default you can even use this without the ``%`` sign: + +```ipython +In [15]: cd myproject +/home/jake/projects/myproject +``` + +This is known as an ``automagic`` function, and this behavior can be toggled with the ``%automagic`` magic function. + +Besides ``%cd``, other available shell-like magic functions are ``%cat``, ``%cp``, ``%env``, ``%ls``, ``%man``, ``%mkdir``, ``%more``, ``%mv``, ``%pwd``, ``%rm``, and ``%rmdir``, any of which can be used without the ``%`` sign if ``automagic`` is on. +This makes it so that you can almost treat the IPython prompt as if it's a normal shell: + +```ipython +In [16]: mkdir tmp + +In [17]: ls +myproject.txt tmp/ + +In [18]: cp myproject.txt tmp/ + +In [19]: ls tmp +myproject.txt + +In [20]: rm -r tmp +``` + +This access to the shell from within the same terminal window as your Python session means that there is a lot less switching back and forth between interpreter and shell as you write your Python code. + + + +< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.06-Errors-and-Debugging.ipynb b/notebooks_v2/01.06-Errors-and-Debugging.ipynb new file mode 100644 index 00000000..2550e5b1 --- /dev/null +++ b/notebooks_v2/01.06-Errors-and-Debugging.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Errors and Debugging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process.\n", + "This section will briefly cover some options for controlling Python's exception reporting, followed by exploring tools for debugging errors in code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Controlling Exceptions: ``%xmode``\n", + "\n", + "Most of the time when a Python script fails, it will raise an Exception.\n", + "When the interpreter hits one of these exceptions, information about the cause of the error can be found in the *traceback*, which can be accessed from within Python.\n", + "With the ``%xmode`` magic function, IPython allows you to control the amount of information printed when the exception is raised.\n", + "Consider the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def func1(a, b):\n", + " return a / b\n", + "\n", + "def func2(x):\n", + " a = x\n", + " b = x - 1\n", + " return func1(a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mfunc2\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mfunc1\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" + ] + } + ], + "source": [ + "func2(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calling ``func2`` results in an error, and reading the printed trace lets us see exactly what happened.\n", + "By default, this trace includes several lines showing the context of each step that led to the error.\n", + "Using the ``%xmode`` magic function (short for *Exception mode*), we can change what information is printed.\n", + "\n", + "``%xmode`` takes a single argument, the mode, and there are three possibilities: ``Plain``, ``Context``, and ``Verbose``.\n", + "The default is ``Context``, and gives output like that just shown before.\n", + "``Plain`` is more compact and gives less information:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exception reporting mode: Plain\n" + ] + } + ], + "source": [ + "%xmode Plain" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", + " File \u001b[1;32m\"\"\u001b[0m, line \u001b[1;32m1\u001b[0m, in \u001b[1;35m\u001b[0m\n func2(1)\n", + " File \u001b[1;32m\"\"\u001b[0m, line \u001b[1;32m7\u001b[0m, in \u001b[1;35mfunc2\u001b[0m\n return func1(a, b)\n", + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0;36m, in \u001b[0;35mfunc1\u001b[0;36m\u001b[0m\n\u001b[0;31m return a / b\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m\u001b[0;31m:\u001b[0m division by zero\n" + ] + } + ], + "source": [ + "func2(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``Verbose`` mode adds some extra information, including the arguments to any functions that are called:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exception reporting mode: Verbose\n" + ] + } + ], + "source": [ + "%xmode Verbose" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m \u001b[0;36mglobal\u001b[0m \u001b[0;36mfunc2\u001b[0m \u001b[0;34m= \u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mfunc2\u001b[0;34m(x=1)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m \u001b[0;36mglobal\u001b[0m \u001b[0;36mfunc1\u001b[0m \u001b[0;34m= \u001b[0m\u001b[0;34m\n \u001b[0m\u001b[0;36ma\u001b[0m \u001b[0;34m= 1\u001b[0m\u001b[0;34m\n \u001b[0m\u001b[0;36mb\u001b[0m \u001b[0;34m= 0\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mfunc1\u001b[0;34m(a=1, b=0)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m \u001b[0;36ma\u001b[0m \u001b[0;34m= 1\u001b[0m\u001b[0;34m\n \u001b[0m\u001b[0;36mb\u001b[0m \u001b[0;34m= 0\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" + ] + } + ], + "source": [ + "func2(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This extra information can help narrow-in on why the exception is being raised.\n", + "So why not use the ``Verbose`` mode all the time?\n", + "As code gets complicated, this kind of traceback can get extremely long.\n", + "Depending on the context, sometimes the brevity of ``Default`` mode is easier to work with." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Debugging: When Reading Tracebacks Is Not Enough\n", + "\n", + "The standard Python tool for interactive debugging is ``pdb``, the Python debugger.\n", + "This debugger lets the user step through the code line by line in order to see what might be causing a more difficult error.\n", + "The IPython-enhanced version of this is ``ipdb``, the IPython debugger.\n", + "\n", + "There are many ways to launch and use both these debuggers; we won't cover them fully here.\n", + "Refer to the online documentation of these two utilities to learn more.\n", + "\n", + "In IPython, perhaps the most convenient interface to debugging is the ``%debug`` magic command.\n", + "If you call it after hitting an exception, it will automatically open an interactive debugging prompt at the point of the exception.\n", + "The ``ipdb`` prompt lets you explore the current state of the stack, explore the available variables, and even run Python commands!\n", + "\n", + "Let's look at the most recent exception, then do some basic tasks–print the values of ``a`` and ``b``, and type ``quit`` to quit the debugging session:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb> print(a)\n", + "1\n", + "ipdb> print(b)\n", + "0\n", + "ipdb> quit\n" + ] + } + ], + "source": [ + "%debug" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The interactive debugger allows much more than this, though–we can even step up and down through the stack and explore the values of variables there:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb> up\n", + "> \u001b[0;32m\u001b[0m(7)\u001b[0;36mfunc2\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 5 \u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 6 \u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb> print(x)\n", + "1\n", + "ipdb> up\n", + "> \u001b[0;32m\u001b[0m(1)\u001b[0;36m\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb> down\n", + "> \u001b[0;32m\u001b[0m(7)\u001b[0;36mfunc2\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 5 \u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 6 \u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb> quit\n" + ] + } + ], + "source": [ + "%debug" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This allows you to quickly find out not only what caused the error, but what function calls led up to the error.\n", + "\n", + "If you'd like the debugger to launch automatically whenever an exception is raised, you can use the ``%pdb`` magic function to turn on this automatic behavior:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exception reporting mode: Plain\n", + "Automatic pdb calling has been turned ON\n" + ] + }, + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", + " File \u001b[1;32m\"\"\u001b[0m, line \u001b[1;32m3\u001b[0m, in \u001b[1;35m\u001b[0m\n func2(1)\n", + " File \u001b[1;32m\"\"\u001b[0m, line \u001b[1;32m7\u001b[0m, in \u001b[1;35mfunc2\u001b[0m\n return func1(a, b)\n", + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0;36m, in \u001b[0;35mfunc1\u001b[0;36m\u001b[0m\n\u001b[0;31m return a / b\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m\u001b[0;31m:\u001b[0m division by zero\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb> print(b)\n", + "0\n", + "ipdb> quit\n" + ] + } + ], + "source": [ + "%xmode Plain\n", + "%pdb on\n", + "func2(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, if you have a script that you'd like to run from the beginning in interactive mode, you can run it with the command ``%run -d``, and use the ``next`` command to step through the lines of code interactively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Partial list of debugging commands\n", + "\n", + "There are many more available commands for interactive debugging than we've listed here; the following table contains a description of some of the more common and useful ones:\n", + "\n", + "| Command | Description |\n", + "|-----------------|-------------------------------------------------------------|\n", + "| ``list`` | Show the current location in the file |\n", + "| ``h(elp)`` | Show a list of commands, or find help on a specific command |\n", + "| ``q(uit)`` | Quit the debugger and the program |\n", + "| ``c(ontinue)`` | Quit the debugger, continue in the program |\n", + "| ``n(ext)`` | Go to the next step of the program |\n", + "| ```` | Repeat the previous command |\n", + "| ``p(rint)`` | Print variables |\n", + "| ``s(tep)`` | Step into a subroutine |\n", + "| ``r(eturn)`` | Return out of a subroutine |\n", + "\n", + "For more information, use the ``help`` command in the debugger, or take a look at ``ipdb``'s [online documentation](https://github.com/gotcha/ipdb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.06-Errors-and-Debugging.md b/notebooks_v2/01.06-Errors-and-Debugging.md new file mode 100644 index 00000000..697ca76d --- /dev/null +++ b/notebooks_v2/01.06-Errors-and-Debugging.md @@ -0,0 +1,152 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) > + +Open in Colab + + + +# Errors and Debugging + + +Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process. +This section will briefly cover some options for controlling Python's exception reporting, followed by exploring tools for debugging errors in code. + + +## Controlling Exceptions: ``%xmode`` + +Most of the time when a Python script fails, it will raise an Exception. +When the interpreter hits one of these exceptions, information about the cause of the error can be found in the *traceback*, which can be accessed from within Python. +With the ``%xmode`` magic function, IPython allows you to control the amount of information printed when the exception is raised. +Consider the following code: + +```python +def func1(a, b): + return a / b + +def func2(x): + a = x + b = x - 1 + return func1(a, b) +``` + +```python +func2(1) +``` + +Calling ``func2`` results in an error, and reading the printed trace lets us see exactly what happened. +By default, this trace includes several lines showing the context of each step that led to the error. +Using the ``%xmode`` magic function (short for *Exception mode*), we can change what information is printed. + +``%xmode`` takes a single argument, the mode, and there are three possibilities: ``Plain``, ``Context``, and ``Verbose``. +The default is ``Context``, and gives output like that just shown before. +``Plain`` is more compact and gives less information: + +```python +%xmode Plain +``` + +```python +func2(1) +``` + +The ``Verbose`` mode adds some extra information, including the arguments to any functions that are called: + +```python +%xmode Verbose +``` + +```python +func2(1) +``` + +This extra information can help narrow-in on why the exception is being raised. +So why not use the ``Verbose`` mode all the time? +As code gets complicated, this kind of traceback can get extremely long. +Depending on the context, sometimes the brevity of ``Default`` mode is easier to work with. + + +## Debugging: When Reading Tracebacks Is Not Enough + +The standard Python tool for interactive debugging is ``pdb``, the Python debugger. +This debugger lets the user step through the code line by line in order to see what might be causing a more difficult error. +The IPython-enhanced version of this is ``ipdb``, the IPython debugger. + +There are many ways to launch and use both these debuggers; we won't cover them fully here. +Refer to the online documentation of these two utilities to learn more. + +In IPython, perhaps the most convenient interface to debugging is the ``%debug`` magic command. +If you call it after hitting an exception, it will automatically open an interactive debugging prompt at the point of the exception. +The ``ipdb`` prompt lets you explore the current state of the stack, explore the available variables, and even run Python commands! + +Let's look at the most recent exception, then do some basic tasks–print the values of ``a`` and ``b``, and type ``quit`` to quit the debugging session: + +```python +%debug +``` + +The interactive debugger allows much more than this, though–we can even step up and down through the stack and explore the values of variables there: + +```python +%debug +``` + +This allows you to quickly find out not only what caused the error, but what function calls led up to the error. + +If you'd like the debugger to launch automatically whenever an exception is raised, you can use the ``%pdb`` magic function to turn on this automatic behavior: + +```python +%xmode Plain +%pdb on +func2(1) +``` + +Finally, if you have a script that you'd like to run from the beginning in interactive mode, you can run it with the command ``%run -d``, and use the ``next`` command to step through the lines of code interactively. + + +### Partial list of debugging commands + +There are many more available commands for interactive debugging than we've listed here; the following table contains a description of some of the more common and useful ones: + +| Command | Description | +|-----------------|-------------------------------------------------------------| +| ``list`` | Show the current location in the file | +| ``h(elp)`` | Show a list of commands, or find help on a specific command | +| ``q(uit)`` | Quit the debugger and the program | +| ``c(ontinue)`` | Quit the debugger, continue in the program | +| ``n(ext)`` | Go to the next step of the program | +| ```` | Repeat the previous command | +| ``p(rint)`` | Print variables | +| ``s(tep)`` | Step into a subroutine | +| ``r(eturn)`` | Return out of a subroutine | + +For more information, use the ``help`` command in the debugger, or take a look at ``ipdb``'s [online documentation](https://github.com/gotcha/ipdb). + + + +< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.07-Timing-and-Profiling.ipynb b/notebooks_v2/01.07-Timing-and-Profiling.ipynb new file mode 100644 index 00000000..2d716b38 --- /dev/null +++ b/notebooks_v2/01.07-Timing-and-Profiling.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Profiling and Timing Code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the process of developing code and creating data processing pipelines, there are often trade-offs you can make between various implementations.\n", + "Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, \"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.\"\n", + "\n", + "But once you have your code working, it can be useful to dig into its efficiency a bit.\n", + "Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to dig into a multiline process and determine where the bottleneck lies in some complicated series of operations.\n", + "IPython provides access to a wide array of functionality for this kind of timing and profiling of code.\n", + "Here we'll discuss the following IPython magic commands:\n", + "\n", + "- ``%time``: Time the execution of a single statement\n", + "- ``%timeit``: Time repeated execution of a single statement for more accuracy\n", + "- ``%prun``: Run code with the profiler\n", + "- ``%lprun``: Run code with the line-by-line profiler\n", + "- ``%memit``: Measure the memory use of a single statement\n", + "- ``%mprun``: Run code with the line-by-line memory profiler\n", + "\n", + "The last four commands are not bundled with IPython–you'll need to get the ``line_profiler`` and ``memory_profiler`` extensions, which we will discuss in the following sections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Timing Code Snippets: ``%timeit`` and ``%time``\n", + "\n", + "We saw the ``%timeit`` line-magic and ``%%timeit`` cell-magic in the introduction to magic functions in [IPython Magic Commands](01.03-Magic-Commands.ipynb); it can be used to time the repeated execution of snippets of code:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100000 loops, best of 3: 1.54 µs per loop\n" + ] + } + ], + "source": [ + "%timeit sum(range(100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that because this operation is so fast, ``%timeit`` automatically does a large number of repetitions.\n", + "For slower commands, ``%timeit`` will automatically adjust and perform fewer repetitions:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 407 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "total = 0\n", + "for i in range(1000):\n", + " for j in range(1000):\n", + " total += i * (-1) ** j" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes repeating an operation is not the best option.\n", + "For example, if we have a list that we'd like to sort, we might be misled by a repeated operation.\n", + "Sorting a pre-sorted list is much faster than sorting an unsorted list, so the repetition will skew the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 loops, best of 3: 1.9 ms per loop\n" + ] + } + ], + "source": [ + "import random\n", + "L = [random.random() for i in range(100000)]\n", + "%timeit L.sort()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this, the ``%time`` magic function may be a better choice. It also is a good choice for longer-running commands, when short, system-related delays are unlikely to affect the result.\n", + "Let's time the sorting of an unsorted and a presorted list:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sorting an unsorted list:\n", + "CPU times: user 40.6 ms, sys: 896 µs, total: 41.5 ms\n", + "Wall time: 41.5 ms\n" + ] + } + ], + "source": [ + "import random\n", + "L = [random.random() for i in range(100000)]\n", + "print(\"sorting an unsorted list:\")\n", + "%time L.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sorting an already sorted list:\n", + "CPU times: user 8.18 ms, sys: 10 µs, total: 8.19 ms\n", + "Wall time: 8.24 ms\n" + ] + } + ], + "source": [ + "print(\"sorting an already sorted list:\")\n", + "%time L.sort()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how much faster the presorted list is to sort, but notice also how much longer the timing takes with ``%time`` versus ``%timeit``, even for the presorted list!\n", + "This is a result of the fact that ``%timeit`` does some clever things under the hood to prevent system calls from interfering with the timing.\n", + "For example, it prevents cleanup of unused Python objects (known as *garbage collection*) which might otherwise affect the timing.\n", + "For this reason, ``%timeit`` results are usually noticeably faster than ``%time`` results.\n", + "\n", + "For ``%time`` as with ``%timeit``, using the double-percent-sign cell magic syntax allows timing of multiline scripts:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 504 ms, sys: 979 µs, total: 505 ms\n", + "Wall time: 505 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "total = 0\n", + "for i in range(1000):\n", + " for j in range(1000):\n", + " total += i * (-1) ** j" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information on ``%time`` and ``%timeit``, as well as their available options, use the IPython help functionality (i.e., type ``%time?`` at the IPython prompt)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Profiling Full Scripts: ``%prun``\n", + "\n", + "A program is made of many single statements, and sometimes timing these statements in context is more important than timing them on their own.\n", + "Python contains a built-in code profiler (which you can read about in the Python documentation), but IPython offers a much more convenient way to use this profiler, in the form of the magic function ``%prun``.\n", + "\n", + "By way of example, we'll define a simple function that does some calculations:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def sum_of_lists(N):\n", + " total = 0\n", + " for i in range(5):\n", + " L = [j ^ (j >> i) for j in range(N)]\n", + " total += sum(L)\n", + " return total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can call ``%prun`` with a function call to see the profiled results:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + } + ], + "source": [ + "%prun sum_of_lists(1000000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the notebook, the output is printed to the pager, and looks something like this:\n", + "\n", + "```\n", + "14 function calls in 0.714 seconds\n", + "\n", + " Ordered by: internal time\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 5 0.599 0.120 0.599 0.120 :4()\n", + " 5 0.064 0.013 0.064 0.013 {built-in method sum}\n", + " 1 0.036 0.036 0.699 0.699 :1(sum_of_lists)\n", + " 1 0.014 0.014 0.714 0.714 :1()\n", + " 1 0.000 0.000 0.714 0.714 {built-in method exec}\n", + "```\n", + "\n", + "The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of execution time is in the list comprehension inside ``sum_of_lists``.\n", + "From here, we could start thinking about what changes we might make to improve the performance in the algorithm.\n", + "\n", + "For more information on ``%prun``, as well as its available options, use the IPython help functionality (i.e., type ``%prun?`` at the IPython prompt)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Line-By-Line Profiling with ``%lprun``\n", + "\n", + "The function-by-function profiling of ``%prun`` is useful, but sometimes it's more convenient to have a line-by-line profile report.\n", + "This is not built into Python or IPython, but there is a ``line_profiler`` package available for installation that can do this.\n", + "Start by using Python's packaging tool, ``pip``, to install the ``line_profiler`` package:\n", + "\n", + "```\n", + "$ pip install line_profiler\n", + "```\n", + "\n", + "Next, you can use IPython to load the ``line_profiler`` IPython extension, offered as part of this package:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext line_profiler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the ``%lprun`` command will do a line-by-line profiling of any function–in this case, we need to tell it explicitly which functions we're interested in profiling:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "%lprun -f sum_of_lists sum_of_lists(5000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, the notebook sends the result to the pager, but it looks something like this:\n", + "\n", + "```\n", + "Timer unit: 1e-06 s\n", + "\n", + "Total time: 0.009382 s\n", + "File: \n", + "Function: sum_of_lists at line 1\n", + "\n", + "Line # Hits Time Per Hit % Time Line Contents\n", + "==============================================================\n", + " 1 def sum_of_lists(N):\n", + " 2 1 2 2.0 0.0 total = 0\n", + " 3 6 8 1.3 0.1 for i in range(5):\n", + " 4 5 9001 1800.2 95.9 L = [j ^ (j >> i) for j in range(N)]\n", + " 5 5 371 74.2 4.0 total += sum(L)\n", + " 6 1 0 0.0 0.0 return total\n", + "```\n", + "\n", + "The information at the top gives us the key to reading the results: the time is reported in microseconds and we can see where the program is spending the most time.\n", + "At this point, we may be able to use this information to modify aspects of the script and make it perform better for our desired use case.\n", + "\n", + "For more information on ``%lprun``, as well as its available options, use the IPython help functionality (i.e., type ``%lprun?`` at the IPython prompt)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Profiling Memory Use: ``%memit`` and ``%mprun``\n", + "\n", + "Another aspect of profiling is the amount of memory an operation uses.\n", + "This can be evaluated with another IPython extension, the ``memory_profiler``.\n", + "As with the ``line_profiler``, we start by ``pip``-installing the extension:\n", + "\n", + "```\n", + "$ pip install memory_profiler\n", + "```\n", + "\n", + "Then we can use IPython to load the extension:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext memory_profiler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The memory profiler extension contains two useful magic functions: the ``%memit`` magic (which offers a memory-measuring equivalent of ``%timeit``) and the ``%mprun`` function (which offers a memory-measuring equivalent of ``%lprun``).\n", + "The ``%memit`` function can be used rather simply:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "peak memory: 100.08 MiB, increment: 61.36 MiB\n" + ] + } + ], + "source": [ + "%memit sum_of_lists(1000000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that this function uses about 100 MB of memory.\n", + "\n", + "For a line-by-line description of memory use, we can use the ``%mprun`` magic.\n", + "Unfortunately, this magic works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the ``%%file`` magic to create a simple module called ``mprun_demo.py``, which contains our ``sum_of_lists`` function, with one addition that will make our memory profiling results more clear:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting mprun_demo.py\n" + ] + } + ], + "source": [ + "%%file mprun_demo.py\n", + "def sum_of_lists(N):\n", + " total = 0\n", + " for i in range(5):\n", + " L = [j ^ (j >> i) for j in range(N)]\n", + " total += sum(L)\n", + " del L # remove reference to L\n", + " return total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now import the new version of this function and run the memory line profiler:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from mprun_demo import sum_of_lists\n", + "%mprun -f sum_of_lists sum_of_lists(1000000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result, printed to the pager, gives us a summary of the memory use of the function, and looks something like this:\n", + "```\n", + "Filename: ./mprun_demo.py\n", + "\n", + "Line # Mem usage Increment Line Contents\n", + "================================================\n", + " 4 71.9 MiB 0.0 MiB L = [j ^ (j >> i) for j in range(N)]\n", + "\n", + "\n", + "Filename: ./mprun_demo.py\n", + "\n", + "Line # Mem usage Increment Line Contents\n", + "================================================\n", + " 1 39.0 MiB 0.0 MiB def sum_of_lists(N):\n", + " 2 39.0 MiB 0.0 MiB total = 0\n", + " 3 46.5 MiB 7.5 MiB for i in range(5):\n", + " 4 71.9 MiB 25.4 MiB L = [j ^ (j >> i) for j in range(N)]\n", + " 5 71.9 MiB 0.0 MiB total += sum(L)\n", + " 6 46.5 MiB -25.4 MiB del L # remove reference to L\n", + " 7 39.1 MiB -7.4 MiB return total\n", + "```\n", + "Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 25 MB of memory usage.\n", + "This is on top of the background memory usage from the Python interpreter itself.\n", + "\n", + "For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks_v2/01.07-Timing-and-Profiling.md b/notebooks_v2/01.07-Timing-and-Profiling.md new file mode 100644 index 00000000..3e9a82a0 --- /dev/null +++ b/notebooks_v2/01.07-Timing-and-Profiling.md @@ -0,0 +1,281 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python [default] + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) > + +Open in Colab + + + +# Profiling and Timing Code + + +In the process of developing code and creating data processing pipelines, there are often trade-offs you can make between various implementations. +Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, "We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil." + +But once you have your code working, it can be useful to dig into its efficiency a bit. +Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to dig into a multiline process and determine where the bottleneck lies in some complicated series of operations. +IPython provides access to a wide array of functionality for this kind of timing and profiling of code. +Here we'll discuss the following IPython magic commands: + +- ``%time``: Time the execution of a single statement +- ``%timeit``: Time repeated execution of a single statement for more accuracy +- ``%prun``: Run code with the profiler +- ``%lprun``: Run code with the line-by-line profiler +- ``%memit``: Measure the memory use of a single statement +- ``%mprun``: Run code with the line-by-line memory profiler + +The last four commands are not bundled with IPython–you'll need to get the ``line_profiler`` and ``memory_profiler`` extensions, which we will discuss in the following sections. + + +## Timing Code Snippets: ``%timeit`` and ``%time`` + +We saw the ``%timeit`` line-magic and ``%%timeit`` cell-magic in the introduction to magic functions in [IPython Magic Commands](01.03-Magic-Commands.ipynb); it can be used to time the repeated execution of snippets of code: + +```python +%timeit sum(range(100)) +``` + +Note that because this operation is so fast, ``%timeit`` automatically does a large number of repetitions. +For slower commands, ``%timeit`` will automatically adjust and perform fewer repetitions: + +```python +%%timeit +total = 0 +for i in range(1000): + for j in range(1000): + total += i * (-1) ** j +``` + +Sometimes repeating an operation is not the best option. +For example, if we have a list that we'd like to sort, we might be misled by a repeated operation. +Sorting a pre-sorted list is much faster than sorting an unsorted list, so the repetition will skew the result: + +```python +import random +L = [random.random() for i in range(100000)] +%timeit L.sort() +``` + +For this, the ``%time`` magic function may be a better choice. It also is a good choice for longer-running commands, when short, system-related delays are unlikely to affect the result. +Let's time the sorting of an unsorted and a presorted list: + +```python +import random +L = [random.random() for i in range(100000)] +print("sorting an unsorted list:") +%time L.sort() +``` + +```python +print("sorting an already sorted list:") +%time L.sort() +``` + +Notice how much faster the presorted list is to sort, but notice also how much longer the timing takes with ``%time`` versus ``%timeit``, even for the presorted list! +This is a result of the fact that ``%timeit`` does some clever things under the hood to prevent system calls from interfering with the timing. +For example, it prevents cleanup of unused Python objects (known as *garbage collection*) which might otherwise affect the timing. +For this reason, ``%timeit`` results are usually noticeably faster than ``%time`` results. + +For ``%time`` as with ``%timeit``, using the double-percent-sign cell magic syntax allows timing of multiline scripts: + +```python +%%time +total = 0 +for i in range(1000): + for j in range(1000): + total += i * (-1) ** j +``` + +For more information on ``%time`` and ``%timeit``, as well as their available options, use the IPython help functionality (i.e., type ``%time?`` at the IPython prompt). + + +## Profiling Full Scripts: ``%prun`` + +A program is made of many single statements, and sometimes timing these statements in context is more important than timing them on their own. +Python contains a built-in code profiler (which you can read about in the Python documentation), but IPython offers a much more convenient way to use this profiler, in the form of the magic function ``%prun``. + +By way of example, we'll define a simple function that does some calculations: + +```python +def sum_of_lists(N): + total = 0 + for i in range(5): + L = [j ^ (j >> i) for j in range(N)] + total += sum(L) + return total +``` + +Now we can call ``%prun`` with a function call to see the profiled results: + +```python +%prun sum_of_lists(1000000) +``` + +In the notebook, the output is printed to the pager, and looks something like this: + +``` +14 function calls in 0.714 seconds + + Ordered by: internal time + + ncalls tottime percall cumtime percall filename:lineno(function) + 5 0.599 0.120 0.599 0.120 :4() + 5 0.064 0.013 0.064 0.013 {built-in method sum} + 1 0.036 0.036 0.699 0.699 :1(sum_of_lists) + 1 0.014 0.014 0.714 0.714 :1() + 1 0.000 0.000 0.714 0.714 {built-in method exec} +``` + +The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of execution time is in the list comprehension inside ``sum_of_lists``. +From here, we could start thinking about what changes we might make to improve the performance in the algorithm. + +For more information on ``%prun``, as well as its available options, use the IPython help functionality (i.e., type ``%prun?`` at the IPython prompt). + + +## Line-By-Line Profiling with ``%lprun`` + +The function-by-function profiling of ``%prun`` is useful, but sometimes it's more convenient to have a line-by-line profile report. +This is not built into Python or IPython, but there is a ``line_profiler`` package available for installation that can do this. +Start by using Python's packaging tool, ``pip``, to install the ``line_profiler`` package: + +``` +$ pip install line_profiler +``` + +Next, you can use IPython to load the ``line_profiler`` IPython extension, offered as part of this package: + +```python +%load_ext line_profiler +``` + +Now the ``%lprun`` command will do a line-by-line profiling of any function–in this case, we need to tell it explicitly which functions we're interested in profiling: + +```python +%lprun -f sum_of_lists sum_of_lists(5000) +``` + +As before, the notebook sends the result to the pager, but it looks something like this: + +``` +Timer unit: 1e-06 s + +Total time: 0.009382 s +File: +Function: sum_of_lists at line 1 + +Line # Hits Time Per Hit % Time Line Contents +============================================================== + 1 def sum_of_lists(N): + 2 1 2 2.0 0.0 total = 0 + 3 6 8 1.3 0.1 for i in range(5): + 4 5 9001 1800.2 95.9 L = [j ^ (j >> i) for j in range(N)] + 5 5 371 74.2 4.0 total += sum(L) + 6 1 0 0.0 0.0 return total +``` + +The information at the top gives us the key to reading the results: the time is reported in microseconds and we can see where the program is spending the most time. +At this point, we may be able to use this information to modify aspects of the script and make it perform better for our desired use case. + +For more information on ``%lprun``, as well as its available options, use the IPython help functionality (i.e., type ``%lprun?`` at the IPython prompt). + + +## Profiling Memory Use: ``%memit`` and ``%mprun`` + +Another aspect of profiling is the amount of memory an operation uses. +This can be evaluated with another IPython extension, the ``memory_profiler``. +As with the ``line_profiler``, we start by ``pip``-installing the extension: + +``` +$ pip install memory_profiler +``` + +Then we can use IPython to load the extension: + +```python +%load_ext memory_profiler +``` + +The memory profiler extension contains two useful magic functions: the ``%memit`` magic (which offers a memory-measuring equivalent of ``%timeit``) and the ``%mprun`` function (which offers a memory-measuring equivalent of ``%lprun``). +The ``%memit`` function can be used rather simply: + +```python +%memit sum_of_lists(1000000) +``` + +We see that this function uses about 100 MB of memory. + +For a line-by-line description of memory use, we can use the ``%mprun`` magic. +Unfortunately, this magic works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the ``%%file`` magic to create a simple module called ``mprun_demo.py``, which contains our ``sum_of_lists`` function, with one addition that will make our memory profiling results more clear: + +```python +%%file mprun_demo.py +def sum_of_lists(N): + total = 0 + for i in range(5): + L = [j ^ (j >> i) for j in range(N)] + total += sum(L) + del L # remove reference to L + return total +``` + +We can now import the new version of this function and run the memory line profiler: + +```python +from mprun_demo import sum_of_lists +%mprun -f sum_of_lists sum_of_lists(1000000) +``` + +The result, printed to the pager, gives us a summary of the memory use of the function, and looks something like this: +``` +Filename: ./mprun_demo.py + +Line # Mem usage Increment Line Contents +================================================ + 4 71.9 MiB 0.0 MiB L = [j ^ (j >> i) for j in range(N)] + + +Filename: ./mprun_demo.py + +Line # Mem usage Increment Line Contents +================================================ + 1 39.0 MiB 0.0 MiB def sum_of_lists(N): + 2 39.0 MiB 0.0 MiB total = 0 + 3 46.5 MiB 7.5 MiB for i in range(5): + 4 71.9 MiB 25.4 MiB L = [j ^ (j >> i) for j in range(N)] + 5 71.9 MiB 0.0 MiB total += sum(L) + 6 46.5 MiB -25.4 MiB del L # remove reference to L + 7 39.1 MiB -7.4 MiB return total +``` +Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 25 MB of memory usage. +This is on top of the background memory usage from the Python interpreter itself. + +For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt). + + + +< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/01.08-More-IPython-Resources.ipynb b/notebooks_v2/01.08-More-IPython-Resources.ipynb new file mode 100644 index 00000000..91d083e8 --- /dev/null +++ b/notebooks_v2/01.08-More-IPython-Resources.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# More IPython Resources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this chapter, we've just scratched the surface of using IPython to enable data science tasks.\n", + "Much more information is available both in print and on the Web, and here we'll list some other resources that you may find helpful." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Web Resources\n", + "\n", + "- [The IPython website](http://ipython.org): The IPython website links to documentation, examples, tutorials, and a variety of other resources.\n", + "- [The nbviewer website](http://nbviewer.jupyter.org/): This site shows static renderings of any IPython notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for!\n", + "- [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks/): This ever-growing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to full-blown courses and books composed in the notebook format!\n", + "- Video Tutorials: searching the Internet, you will find many video-recorded tutorials on IPython. I'd especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferenes by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Books\n", + "\n", + "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do): Wes McKinney's book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we've discussed here, another perspective is always helpful.\n", + "- [*Learning IPython for Interactive Computing and Data Visualization*](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.\n", + "- [*IPython Interactive Computing and Visualization Cookbook*](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython–it also goes into some depth on a broad range of data science topics.\n", + "\n", + "Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very useful if you use it well and use it often.\n", + "As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/01.08-More-IPython-Resources.md b/notebooks_v2/01.08-More-IPython-Resources.md new file mode 100644 index 00000000..6df81915 --- /dev/null +++ b/notebooks_v2/01.08-More-IPython-Resources.md @@ -0,0 +1,60 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) > + +Open in Colab + + + +# More IPython Resources + + +In this chapter, we've just scratched the surface of using IPython to enable data science tasks. +Much more information is available both in print and on the Web, and here we'll list some other resources that you may find helpful. + + +## Web Resources + +- [The IPython website](http://ipython.org): The IPython website links to documentation, examples, tutorials, and a variety of other resources. +- [The nbviewer website](http://nbviewer.jupyter.org/): This site shows static renderings of any IPython notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for! +- [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks/): This ever-growing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to full-blown courses and books composed in the notebook format! +- Video Tutorials: searching the Internet, you will find many video-recorded tutorials on IPython. I'd especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferenes by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter. + + +## Books + +- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do): Wes McKinney's book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we've discussed here, another perspective is always helpful. +- [*Learning IPython for Interactive Computing and Data Visualization*](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis. +- [*IPython Interactive Computing and Visualization Cookbook*](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython–it also goes into some depth on a broad range of data science topics. + +Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very useful if you use it well and use it often. +As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer. + + + +< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.00-Introduction-to-NumPy.ipynb b/notebooks_v2/02.00-Introduction-to-NumPy.ipynb new file mode 100644 index 00000000..40e06ba0 --- /dev/null +++ b/notebooks_v2/02.00-Introduction-to-NumPy.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Introduction to NumPy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n", + "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n", + "Despite this apparent heterogeneity, it will help us to think of all data fundamentally as arrays of numbers.\n", + "\n", + "For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n", + "Sound clips can be thought of as one-dimensional arrays of intensity versus time.\n", + "Text can be converted in various ways into numerical representations, perhaps binary digits representing the frequency of certain words or pairs of words.\n", + "No matter what the data are, the first step in making it analyzable will be to transform them into arrays of numbers.\n", + "(We will discuss some specific examples of this process later in [Feature Engineering](05.04-Feature-Engineering.ipynb))\n", + "\n", + "For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data science.\n", + "We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package, and the Pandas package (discussed in Chapter 3).\n", + "\n", + "This chapter will cover NumPy in detail. NumPy (short for *Numerical Python*) provides an efficient interface to store and operate on dense data buffers.\n", + "In some ways, NumPy arrays are like Python's built-in ``list`` type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size.\n", + "NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you.\n", + "\n", + "If you followed the advice outlined in the Preface and installed the Anaconda stack, you already have NumPy installed and ready to go.\n", + "If you're more the do-it-yourself type, you can go to http://www.numpy.org/ and follow the installation instructions found there.\n", + "Once you do, you can import NumPy and double-check the version:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.11.1'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "numpy.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\n", + "By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Reminder about Built In Documentation\n", + "\n", + "As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature), as well as the documentation of various functions (using the ``?`` character – Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\n", + "\n", + "For example, to display all the contents of the numpy namespace, you can type this:\n", + "\n", + "```ipython\n", + "In [3]: np.\n", + "```\n", + "\n", + "And to display NumPy's built-in documentation, you can use this:\n", + "\n", + "```ipython\n", + "In [4]: np?\n", + "```\n", + "\n", + "More detailed documentation, along with tutorials and other resources, can be found at http://www.numpy.org." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.00-Introduction-to-NumPy.md b/notebooks_v2/02.00-Introduction-to-NumPy.md new file mode 100644 index 00000000..3e9d767a --- /dev/null +++ b/notebooks_v2/02.00-Introduction-to-NumPy.md @@ -0,0 +1,104 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) > + +Open in Colab + + + + +# Introduction to NumPy + + + +This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python. +The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else. +Despite this apparent heterogeneity, it will help us to think of all data fundamentally as arrays of numbers. + +For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area. +Sound clips can be thought of as one-dimensional arrays of intensity versus time. +Text can be converted in various ways into numerical representations, perhaps binary digits representing the frequency of certain words or pairs of words. +No matter what the data are, the first step in making it analyzable will be to transform them into arrays of numbers. +(We will discuss some specific examples of this process later in [Feature Engineering](05.04-Feature-Engineering.ipynb)) + +For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data science. +We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package, and the Pandas package (discussed in Chapter 3). + +This chapter will cover NumPy in detail. NumPy (short for *Numerical Python*) provides an efficient interface to store and operate on dense data buffers. +In some ways, NumPy arrays are like Python's built-in ``list`` type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size. +NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you. + +If you followed the advice outlined in the Preface and installed the Anaconda stack, you already have NumPy installed and ready to go. +If you're more the do-it-yourself type, you can go to http://www.numpy.org/ and follow the installation instructions found there. +Once you do, you can import NumPy and double-check the version: + + +```python deletable=true editable=true +import numpy +numpy.__version__ +``` + + +For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later. +By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias: + + +```python deletable=true editable=true +import numpy as np +``` + + +Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy. + + + +## Reminder about Built In Documentation + +As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature), as well as the documentation of various functions (using the ``?`` character – Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)). + +For example, to display all the contents of the numpy namespace, you can type this: + +```ipython +In [3]: np. +``` + +And to display NumPy's built-in documentation, you can use this: + +```ipython +In [4]: np? +``` + +More detailed documentation, along with tutorials and other resources, can be found at http://www.numpy.org. + + + + +< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/02.01-Understanding-Data-Types.ipynb b/notebooks_v2/02.01-Understanding-Data-Types.ipynb new file mode 100644 index 00000000..22977418 --- /dev/null +++ b/notebooks_v2/02.01-Understanding-Data-Types.ipynb @@ -0,0 +1,833 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Data Types in Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Effective data-driven science and computation requires understanding how data is stored and manipulated.\n", + "This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this.\n", + "Understanding this difference is fundamental to understanding much of the material throughout the rest of the book.\n", + "\n", + "Users of Python are often drawn-in by its ease of use, one piece of which is dynamic typing.\n", + "While a statically-typed language like C or Java requires each variable to be explicitly declared, a dynamically-typed language like Python skips this specification. For example, in C you might specify a particular operation as follows:\n", + "\n", + "```C\n", + "/* C code */\n", + "int result = 0;\n", + "for(int i=0; i<100; i++){\n", + " result += i;\n", + "}\n", + "```\n", + "\n", + "While in Python the equivalent operation could be written this way:\n", + "\n", + "```python\n", + "# Python code\n", + "result = 0\n", + "for i in range(100):\n", + " result += i\n", + "```\n", + "\n", + "Notice the main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:\n", + "\n", + "```python\n", + "# Python code\n", + "x = 4\n", + "x = \"four\"\n", + "```\n", + "\n", + "Here we've switched the contents of ``x`` from an integer to a string. The same thing in C would lead (depending on compiler settings) to a compilation error or other unintented consequences:\n", + "\n", + "```C\n", + "/* C code */\n", + "int x = 4;\n", + "x = \"four\"; // FAILS\n", + "```\n", + "\n", + "This sort of flexibility is one piece that makes Python and other dynamically-typed languages convenient and easy to use.\n", + "Understanding *how* this works is an important piece of learning to analyze data efficiently and effectively with Python.\n", + "But what this type-flexibility also points to is the fact that Python variables are more than just their value; they also contain extra information about the type of the value. We'll explore this more in the sections that follow." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A Python Integer Is More Than Just an Integer\n", + "\n", + "The standard Python implementation is written in C.\n", + "This means that every Python object is simply a cleverly-disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as ``x = 10000``, ``x`` is not just a \"raw\" integer. It's actually a pointer to a compound C structure, which contains several values.\n", + "Looking through the Python 3.4 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):\n", + "\n", + "```C\n", + "struct _longobject {\n", + " long ob_refcnt;\n", + " PyTypeObject *ob_type;\n", + " size_t ob_size;\n", + " long ob_digit[1];\n", + "};\n", + "```\n", + "\n", + "A single integer in Python 3.4 actually contains four pieces:\n", + "\n", + "- ``ob_refcnt``, a reference count that helps Python silently handle memory allocation and deallocation\n", + "- ``ob_type``, which encodes the type of the variable\n", + "- ``ob_size``, which specifies the size of the following data members\n", + "- ``ob_digit``, which contains the actual integer value that we expect the Python variable to represent.\n", + "\n", + "This means that there is some overhead in storing an integer in Python as compared to an integer in a compiled language like C, as illustrated in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Integer Memory Layout](figures/cint_vs_pyint.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here ``PyObject_HEAD`` is the part of the structure containing the reference count, type code, and other pieces mentioned before.\n", + "\n", + "Notice the difference here: a C integer is essentially a label for a position in memory whose bytes encode an integer value.\n", + "A Python integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer value.\n", + "This extra information in the Python integer structure is what allows Python to be coded so freely and dynamically.\n", + "All this additional information in Python types comes at a cost, however, which becomes especially apparent in structures that combine many of these objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A Python List Is More Than Just a List\n", + "\n", + "Let's consider now what happens when we use a Python data structure that holds many Python objects.\n", + "The standard mutable multi-element container in Python is the list.\n", + "We can create a list of integers as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "L = list(range(10))\n", + "L" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(L[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, similarly, a list of strings:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "L2 = [str(c) for c in L]\n", + "L2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(L2[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because of Python's dynamic typing, we can even create heterogeneous lists:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[bool, str, float, int]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "L3 = [True, \"2\", 3.0, 4]\n", + "[type(item) for item in L3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information–that is, each item is a complete Python object.\n", + "In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.\n", + "The difference between a dynamic-type list and a fixed-type (NumPy-style) array is illustrated in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Array Memory Layout](figures/array_vs_list.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the implementation level, the array essentially contains a single pointer to one contiguous block of data.\n", + "The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier.\n", + "Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type.\n", + "Fixed-type NumPy-style arrays lack this flexibility, but are much more efficient for storing and manipulating data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixed-Type Arrays in Python\n", + "\n", + "Python offers several different options for storing data in efficient, fixed-type data buffers.\n", + "The built-in ``array`` module (available since Python 3.3) can be used to create dense arrays of a uniform type:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import array\n", + "L = list(range(10))\n", + "A = array.array('i', L)\n", + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here ``'i'`` is a type code indicating the contents are integers.\n", + "\n", + "Much more useful, however, is the ``ndarray`` object of the NumPy package.\n", + "While Python's ``array`` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data.\n", + "We will explore these operations in later sections; here we'll demonstrate several ways of creating a NumPy array.\n", + "\n", + "We'll start with the standard NumPy import, under the alias ``np``:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Arrays from Python Lists\n", + "\n", + "First, we can use ``np.array`` to create arrays from Python lists:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 4, 2, 5, 3])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# integer array:\n", + "np.array([1, 4, 2, 5, 3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember that unlike Python lists, NumPy is constrained to arrays that all contain the same type.\n", + "If types do not match, NumPy will upcast if possible (here, integers are up-cast to floating point):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.14, 4. , 2. , 3. ])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([3.14, 4, 2, 3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to explicitly set the data type of the resulting array, we can use the ``dtype`` keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4.], dtype=float32)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([1, 2, 3, 4], dtype='float32')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, unlike Python lists, NumPy arrays can explicitly be multi-dimensional; here's one way of initializing a multidimensional array using a list of lists:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 3, 4],\n", + " [4, 5, 6],\n", + " [6, 7, 8]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# nested lists result in multi-dimensional arrays\n", + "np.array([range(i, i + 3) for i in [2, 4, 6]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The inner lists are treated as rows of the resulting two-dimensional array." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Arrays from Scratch\n", + "\n", + "Especially for larger arrays, it is more efficient to create arrays from scratch using routines built into NumPy.\n", + "Here are several examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a length-10 integer array filled with zeros\n", + "np.zeros(10, dtype=int)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1.],\n", + " [ 1., 1., 1., 1., 1.],\n", + " [ 1., 1., 1., 1., 1.]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a 3x5 floating-point array filled with ones\n", + "np.ones((3, 5), dtype=float)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 3.14, 3.14, 3.14, 3.14, 3.14],\n", + " [ 3.14, 3.14, 3.14, 3.14, 3.14],\n", + " [ 3.14, 3.14, 3.14, 3.14, 3.14]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a 3x5 array filled with 3.14\n", + "np.full((3, 5), 3.14)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an array filled with a linear sequence\n", + "# Starting at 0, ending at 20, stepping by 2\n", + "# (this is similar to the built-in range() function)\n", + "np.arange(0, 20, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.25, 0.5 , 0.75, 1. ])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an array of five values evenly spaced between 0 and 1\n", + "np.linspace(0, 1, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.99844933, 0.52183819, 0.22421193],\n", + " [ 0.08007488, 0.45429293, 0.20941444],\n", + " [ 0.14360941, 0.96910973, 0.946117 ]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a 3x3 array of uniformly distributed\n", + "# random values between 0 and 1\n", + "np.random.random((3, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.51772646, 0.39614948, -0.10634696],\n", + " [ 0.25671348, 0.00732722, 0.37783601],\n", + " [ 0.68446945, 0.15926039, -0.70744073]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a 3x3 array of normally distributed random values\n", + "# with mean 0 and standard deviation 1\n", + "np.random.normal(0, 1, (3, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 3, 4],\n", + " [5, 7, 8],\n", + " [0, 5, 0]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a 3x3 array of random integers in the interval [0, 10)\n", + "np.random.randint(0, 10, (3, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a 3x3 identity matrix\n", + "np.eye(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 1., 1.])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an uninitialized array of three integers\n", + "# The values will be whatever happens to already exist at that memory location\n", + "np.empty(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy Standard Data Types\n", + "\n", + "NumPy arrays contain values of a single type, so it is important to have detailed knowledge of those types and their limitations.\n", + "Because NumPy is built in C, the types will be familiar to users of C, Fortran, and other related languages.\n", + "\n", + "The standard NumPy data types are listed in the following table.\n", + "Note that when constructing an array, they can be specified using a string:\n", + "\n", + "```python\n", + "np.zeros(10, dtype='int16')\n", + "```\n", + "\n", + "Or using the associated NumPy object:\n", + "\n", + "```python\n", + "np.zeros(10, dtype=np.int16)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Data type\t | Description |\n", + "|---------------|-------------|\n", + "| ``bool_`` | Boolean (True or False) stored as a byte |\n", + "| ``int_`` | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| \n", + "| ``intc`` | Identical to C ``int`` (normally ``int32`` or ``int64``)| \n", + "| ``intp`` | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| \n", + "| ``int8`` | Byte (-128 to 127)| \n", + "| ``int16`` | Integer (-32768 to 32767)|\n", + "| ``int32`` | Integer (-2147483648 to 2147483647)|\n", + "| ``int64`` | Integer (-9223372036854775808 to 9223372036854775807)| \n", + "| ``uint8`` | Unsigned integer (0 to 255)| \n", + "| ``uint16`` | Unsigned integer (0 to 65535)| \n", + "| ``uint32`` | Unsigned integer (0 to 4294967295)| \n", + "| ``uint64`` | Unsigned integer (0 to 18446744073709551615)| \n", + "| ``float_`` | Shorthand for ``float64``.| \n", + "| ``float16`` | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n", + "| ``float32`` | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n", + "| ``float64`` | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n", + "| ``complex_`` | Shorthand for ``complex128``.| \n", + "| ``complex64`` | Complex number, represented by two 32-bit floats| \n", + "| ``complex128``| Complex number, represented by two 64-bit floats| " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More advanced type specification is possible, such as specifying big or little endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/).\n", + "NumPy also supports compound data types, which will be covered in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.01-Understanding-Data-Types.md b/notebooks_v2/02.01-Understanding-Data-Types.md new file mode 100644 index 00000000..c18d8220 --- /dev/null +++ b/notebooks_v2/02.01-Understanding-Data-Types.md @@ -0,0 +1,329 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) > + +Open in Colab + + + +# Understanding Data Types in Python + + +Effective data-driven science and computation requires understanding how data is stored and manipulated. +This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this. +Understanding this difference is fundamental to understanding much of the material throughout the rest of the book. + +Users of Python are often drawn-in by its ease of use, one piece of which is dynamic typing. +While a statically-typed language like C or Java requires each variable to be explicitly declared, a dynamically-typed language like Python skips this specification. For example, in C you might specify a particular operation as follows: + +```C +/* C code */ +int result = 0; +for(int i=0; i<100; i++){ + result += i; +} +``` + +While in Python the equivalent operation could be written this way: + +```python +# Python code +result = 0 +for i in range(100): + result += i +``` + +Notice the main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable: + +```python +# Python code +x = 4 +x = "four" +``` + +Here we've switched the contents of ``x`` from an integer to a string. The same thing in C would lead (depending on compiler settings) to a compilation error or other unintented consequences: + +```C +/* C code */ +int x = 4; +x = "four"; // FAILS +``` + +This sort of flexibility is one piece that makes Python and other dynamically-typed languages convenient and easy to use. +Understanding *how* this works is an important piece of learning to analyze data efficiently and effectively with Python. +But what this type-flexibility also points to is the fact that Python variables are more than just their value; they also contain extra information about the type of the value. We'll explore this more in the sections that follow. + + +## A Python Integer Is More Than Just an Integer + +The standard Python implementation is written in C. +This means that every Python object is simply a cleverly-disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as ``x = 10000``, ``x`` is not just a "raw" integer. It's actually a pointer to a compound C structure, which contains several values. +Looking through the Python 3.4 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded): + +```C +struct _longobject { + long ob_refcnt; + PyTypeObject *ob_type; + size_t ob_size; + long ob_digit[1]; +}; +``` + +A single integer in Python 3.4 actually contains four pieces: + +- ``ob_refcnt``, a reference count that helps Python silently handle memory allocation and deallocation +- ``ob_type``, which encodes the type of the variable +- ``ob_size``, which specifies the size of the following data members +- ``ob_digit``, which contains the actual integer value that we expect the Python variable to represent. + +This means that there is some overhead in storing an integer in Python as compared to an integer in a compiled language like C, as illustrated in the following figure: + + +![Integer Memory Layout](figures/cint_vs_pyint.png) + + +Here ``PyObject_HEAD`` is the part of the structure containing the reference count, type code, and other pieces mentioned before. + +Notice the difference here: a C integer is essentially a label for a position in memory whose bytes encode an integer value. +A Python integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer value. +This extra information in the Python integer structure is what allows Python to be coded so freely and dynamically. +All this additional information in Python types comes at a cost, however, which becomes especially apparent in structures that combine many of these objects. + + +## A Python List Is More Than Just a List + +Let's consider now what happens when we use a Python data structure that holds many Python objects. +The standard mutable multi-element container in Python is the list. +We can create a list of integers as follows: + +```python +L = list(range(10)) +L +``` + +```python +type(L[0]) +``` + +Or, similarly, a list of strings: + +```python +L2 = [str(c) for c in L] +L2 +``` + +```python +type(L2[0]) +``` + +Because of Python's dynamic typing, we can even create heterogeneous lists: + +```python +L3 = [True, "2", 3.0, 4] +[type(item) for item in L3] +``` + +But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information–that is, each item is a complete Python object. +In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array. +The difference between a dynamic-type list and a fixed-type (NumPy-style) array is illustrated in the following figure: + + +![Array Memory Layout](figures/array_vs_list.png) + + +At the implementation level, the array essentially contains a single pointer to one contiguous block of data. +The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier. +Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type. +Fixed-type NumPy-style arrays lack this flexibility, but are much more efficient for storing and manipulating data. + + +## Fixed-Type Arrays in Python + +Python offers several different options for storing data in efficient, fixed-type data buffers. +The built-in ``array`` module (available since Python 3.3) can be used to create dense arrays of a uniform type: + +```python +import array +L = list(range(10)) +A = array.array('i', L) +A +``` + +Here ``'i'`` is a type code indicating the contents are integers. + +Much more useful, however, is the ``ndarray`` object of the NumPy package. +While Python's ``array`` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data. +We will explore these operations in later sections; here we'll demonstrate several ways of creating a NumPy array. + +We'll start with the standard NumPy import, under the alias ``np``: + +```python +import numpy as np +``` + +## Creating Arrays from Python Lists + +First, we can use ``np.array`` to create arrays from Python lists: + +```python +# integer array: +np.array([1, 4, 2, 5, 3]) +``` + +Remember that unlike Python lists, NumPy is constrained to arrays that all contain the same type. +If types do not match, NumPy will upcast if possible (here, integers are up-cast to floating point): + +```python +np.array([3.14, 4, 2, 3]) +``` + +If we want to explicitly set the data type of the resulting array, we can use the ``dtype`` keyword: + +```python +np.array([1, 2, 3, 4], dtype='float32') +``` + +Finally, unlike Python lists, NumPy arrays can explicitly be multi-dimensional; here's one way of initializing a multidimensional array using a list of lists: + +```python +# nested lists result in multi-dimensional arrays +np.array([range(i, i + 3) for i in [2, 4, 6]]) +``` + +The inner lists are treated as rows of the resulting two-dimensional array. + + +## Creating Arrays from Scratch + +Especially for larger arrays, it is more efficient to create arrays from scratch using routines built into NumPy. +Here are several examples: + +```python +# Create a length-10 integer array filled with zeros +np.zeros(10, dtype=int) +``` + +```python +# Create a 3x5 floating-point array filled with ones +np.ones((3, 5), dtype=float) +``` + +```python +# Create a 3x5 array filled with 3.14 +np.full((3, 5), 3.14) +``` + +```python +# Create an array filled with a linear sequence +# Starting at 0, ending at 20, stepping by 2 +# (this is similar to the built-in range() function) +np.arange(0, 20, 2) +``` + +```python +# Create an array of five values evenly spaced between 0 and 1 +np.linspace(0, 1, 5) +``` + +```python +# Create a 3x3 array of uniformly distributed +# random values between 0 and 1 +np.random.random((3, 3)) +``` + +```python +# Create a 3x3 array of normally distributed random values +# with mean 0 and standard deviation 1 +np.random.normal(0, 1, (3, 3)) +``` + +```python +# Create a 3x3 array of random integers in the interval [0, 10) +np.random.randint(0, 10, (3, 3)) +``` + +```python +# Create a 3x3 identity matrix +np.eye(3) +``` + +```python +# Create an uninitialized array of three integers +# The values will be whatever happens to already exist at that memory location +np.empty(3) +``` + + +## NumPy Standard Data Types + +NumPy arrays contain values of a single type, so it is important to have detailed knowledge of those types and their limitations. +Because NumPy is built in C, the types will be familiar to users of C, Fortran, and other related languages. + +The standard NumPy data types are listed in the following table. +Note that when constructing an array, they can be specified using a string: + +```python +np.zeros(10, dtype='int16') +``` + +Or using the associated NumPy object: + +```python +np.zeros(10, dtype=np.int16) +``` + + +| Data type | Description | +|---------------|-------------| +| ``bool_`` | Boolean (True or False) stored as a byte | +| ``int_`` | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| +| ``intc`` | Identical to C ``int`` (normally ``int32`` or ``int64``)| +| ``intp`` | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| +| ``int8`` | Byte (-128 to 127)| +| ``int16`` | Integer (-32768 to 32767)| +| ``int32`` | Integer (-2147483648 to 2147483647)| +| ``int64`` | Integer (-9223372036854775808 to 9223372036854775807)| +| ``uint8`` | Unsigned integer (0 to 255)| +| ``uint16`` | Unsigned integer (0 to 65535)| +| ``uint32`` | Unsigned integer (0 to 4294967295)| +| ``uint64`` | Unsigned integer (0 to 18446744073709551615)| +| ``float_`` | Shorthand for ``float64``.| +| ``float16`` | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| +| ``float32`` | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| +| ``float64`` | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| +| ``complex_`` | Shorthand for ``complex128``.| +| ``complex64`` | Complex number, represented by two 32-bit floats| +| ``complex128``| Complex number, represented by two 64-bit floats| + + +More advanced type specification is possible, such as specifying big or little endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/). +NumPy also supports compound data types, which will be covered in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb). + + + +< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.ipynb b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.ipynb new file mode 100644 index 00000000..2d5222ce --- /dev/null +++ b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.ipynb @@ -0,0 +1,1575 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Basics of NumPy Arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas ([Chapter 3](03.00-Introduction-to-Pandas.ipynb)) are built around the NumPy array.\n", + "This section will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays.\n", + "While the types of operations shown here may seem a bit dry and pedantic, they comprise the building blocks of many other examples used throughout the book.\n", + "Get to know them well!\n", + "\n", + "We'll cover a few categories of basic array manipulations here:\n", + "\n", + "- *Attributes of arrays*: Determining the size, shape, memory consumption, and data types of arrays\n", + "- *Indexing of arrays*: Getting and setting the value of individual array elements\n", + "- *Slicing of arrays*: Getting and setting smaller subarrays within a larger array\n", + "- *Reshaping of arrays*: Changing the shape of a given array\n", + "- *Joining and splitting of arrays*: Combining multiple arrays into one, and splitting one array into many" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy Array Attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First let's discuss some useful array attributes.\n", + "We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.\n", + "We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.random.seed(0) # seed for reproducibility\n", + "\n", + "x1 = np.random.randint(10, size=6) # One-dimensional array\n", + "x2 = np.random.randint(10, size=(3, 4)) # Two-dimensional array\n", + "x3 = np.random.randint(10, size=(3, 4, 5)) # Three-dimensional array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each array has attributes ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x3 ndim: 3\n", + "x3 shape: (3, 4, 5)\n", + "x3 size: 60\n" + ] + } + ], + "source": [ + "print(\"x3 ndim: \", x3.ndim)\n", + "print(\"x3 shape:\", x3.shape)\n", + "print(\"x3 size: \", x3.size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful attribute is the ``dtype``, the data type of the array (which we discussed previously in [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dtype: int64\n" + ] + } + ], + "source": [ + "print(\"dtype:\", x3.dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other attributes include ``itemsize``, which lists the size (in bytes) of each array element, and ``nbytes``, which lists the total size (in bytes) of the array:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "itemsize: 8 bytes\n", + "nbytes: 480 bytes\n" + ] + } + ], + "source": [ + "print(\"itemsize:\", x3.itemsize, \"bytes\")\n", + "print(\"nbytes:\", x3.nbytes, \"bytes\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, we expect that ``nbytes`` is equal to ``itemsize`` times ``size``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Array Indexing: Accessing Single Elements" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are familiar with Python's standard list indexing, indexing in NumPy will feel quite familiar.\n", + "In a one-dimensional array, the $i^{th}$ value (counting from zero) can be accessed by specifying the desired index in square brackets, just as with Python lists:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5, 0, 3, 3, 7, 9])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x1[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x1[4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To index from the end of the array, you can use negative indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x1[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x1[-2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3, 5, 2, 4],\n", + " [7, 6, 8, 8],\n", + " [1, 6, 7, 7]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2[0, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2[2, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2[2, -1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Values can also be modified using any of the above index notation:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[12, 5, 2, 4],\n", + " [ 7, 6, 8, 8],\n", + " [ 1, 6, 7, 7]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2[0, 0] = 12\n", + "x2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind that, unlike Python lists, NumPy arrays have a fixed type.\n", + "This means, for example, that if you attempt to insert a floating-point value to an integer array, the value will be silently truncated. Don't be caught unaware by this behavior!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 0, 3, 3, 7, 9])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x1[0] = 3.14159 # this will be truncated!\n", + "x1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Array Slicing: Accessing Subarrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as we can use square brackets to access individual array elements, we can also use them to access subarrays with the *slice* notation, marked by the colon (``:``) character.\n", + "The NumPy slicing syntax follows that of the standard Python list; to access a slice of an array ``x``, use this:\n", + "``` python\n", + "x[start:stop:step]\n", + "```\n", + "If any of these are unspecified, they default to the values ``start=0``, ``stop=``*``size of dimension``*, ``step=1``.\n", + "We'll take a look at accessing sub-arrays in one dimension and in multiple dimensions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-dimensional subarrays" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(10)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[:5] # first five elements" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5, 6, 7, 8, 9])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[5:] # elements after index 5" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 5, 6])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[4:7] # middle sub-array" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 2, 4, 6, 8])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[::2] # every other element" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 3, 5, 7, 9])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[1::2] # every other element, starting at index 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A potentially confusing case is when the ``step`` value is negative.\n", + "In this case, the defaults for ``start`` and ``stop`` are swapped.\n", + "This becomes a convenient way to reverse an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[::-1] # all elements, reversed" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5, 3, 1])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[5::-2] # reversed every other from index 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-dimensional subarrays\n", + "\n", + "Multi-dimensional slices work in the same way, with multiple slices separated by commas.\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[12, 5, 2, 4],\n", + " [ 7, 6, 8, 8],\n", + " [ 1, 6, 7, 7]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[12, 5, 2],\n", + " [ 7, 6, 8]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2[:2, :3] # two rows, three columns" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[12, 2],\n", + " [ 7, 8],\n", + " [ 1, 7]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2[:3, ::2] # all rows, every other column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, subarray dimensions can even be reversed together:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 7, 7, 6, 1],\n", + " [ 8, 8, 6, 7],\n", + " [ 4, 2, 5, 12]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x2[::-1, ::-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Accessing array rows and columns\n", + "\n", + "One commonly needed routine is accessing of single rows or columns of an array.\n", + "This can be done by combining indexing and slicing, using an empty slice marked by a single colon (``:``):" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12 7 1]\n" + ] + } + ], + "source": [ + "print(x2[:, 0]) # first column of x2" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12 5 2 4]\n" + ] + } + ], + "source": [ + "print(x2[0, :]) # first row of x2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the case of row access, the empty slice can be omitted for a more compact syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12 5 2 4]\n" + ] + } + ], + "source": [ + "print(x2[0]) # equivalent to x2[0, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Subarrays as no-copy views\n", + "\n", + "One important–and extremely useful–thing to know about array slices is that they return *views* rather than *copies* of the array data.\n", + "This is one area in which NumPy array slicing differs from Python list slicing: in lists, slices will be copies.\n", + "Consider our two-dimensional array from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[12 5 2 4]\n", + " [ 7 6 8 8]\n", + " [ 1 6 7 7]]\n" + ] + } + ], + "source": [ + "print(x2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's extract a $2 \\times 2$ subarray from this:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[12 5]\n", + " [ 7 6]]\n" + ] + } + ], + "source": [ + "x2_sub = x2[:2, :2]\n", + "print(x2_sub)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now if we modify this subarray, we'll see that the original array is changed! Observe:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[99 5]\n", + " [ 7 6]]\n" + ] + } + ], + "source": [ + "x2_sub[0, 0] = 99\n", + "print(x2_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[99 5 2 4]\n", + " [ 7 6 8 8]\n", + " [ 1 6 7 7]]\n" + ] + } + ], + "source": [ + "print(x2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating copies of arrays\n", + "\n", + "Despite the nice features of array views, it is sometimes useful to instead explicitly copy the data within an array or a subarray. This can be most easily done with the ``copy()`` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[99 5]\n", + " [ 7 6]]\n" + ] + } + ], + "source": [ + "x2_sub_copy = x2[:2, :2].copy()\n", + "print(x2_sub_copy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we now modify this subarray, the original array is not touched:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[42 5]\n", + " [ 7 6]]\n" + ] + } + ], + "source": [ + "x2_sub_copy[0, 0] = 42\n", + "print(x2_sub_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[99 5 2 4]\n", + " [ 7 6 8 8]\n", + " [ 1 6 7 7]]\n" + ] + } + ], + "source": [ + "print(x2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reshaping of Arrays\n", + "\n", + "Another useful type of operation is reshaping of arrays.\n", + "The most flexible way of doing this is with the ``reshape`` method.\n", + "For example, if you want to put the numbers 1 through 9 in a $3 \\times 3$ grid, you can do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n" + ] + } + ], + "source": [ + "grid = np.arange(1, 10).reshape((3, 3))\n", + "print(grid)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that for this to work, the size of the initial array must match the size of the reshaped array. \n", + "Where possible, the ``reshape`` method will use a no-copy view of the initial array, but with non-contiguous memory buffers this is not always the case.\n", + "\n", + "Another common reshaping pattern is the conversion of a one-dimensional array into a two-dimensional row or column matrix.\n", + "This can be done with the ``reshape`` method, or more easily done by making use of the ``newaxis`` keyword within a slice operation:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([1, 2, 3])\n", + "\n", + "# row vector via reshape\n", + "x.reshape((1, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# row vector via newaxis\n", + "x[np.newaxis, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1],\n", + " [2],\n", + " [3]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# column vector via reshape\n", + "x.reshape((3, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1],\n", + " [2],\n", + " [3]])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# column vector via newaxis\n", + "x[:, np.newaxis]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will see this type of transformation often throughout the remainder of the book." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Array Concatenation and Splitting\n", + "\n", + "All of the preceding routines worked on single arrays. It's also possible to combine multiple arrays into one, and to conversely split a single array into multiple arrays. We'll take a look at those operations here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Concatenation of arrays\n", + "\n", + "Concatenation, or joining of two arrays in NumPy, is primarily accomplished using the routines ``np.concatenate``, ``np.vstack``, and ``np.hstack``.\n", + "``np.concatenate`` takes a tuple or list of arrays as its first argument, as we can see here:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 3, 2, 1])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([1, 2, 3])\n", + "y = np.array([3, 2, 1])\n", + "np.concatenate([x, y])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also concatenate more than two arrays at once:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1 2 3 3 2 1 99 99 99]\n" + ] + } + ], + "source": [ + "z = [99, 99, 99]\n", + "print(np.concatenate([x, y, z]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can also be used for two-dimensional arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "grid = np.array([[1, 2, 3],\n", + " [4, 5, 6]])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3],\n", + " [4, 5, 6],\n", + " [1, 2, 3],\n", + " [4, 5, 6]])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# concatenate along the first axis\n", + "np.concatenate([grid, grid])" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3, 1, 2, 3],\n", + " [4, 5, 6, 4, 5, 6]])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# concatenate along the second axis (zero-indexed)\n", + "np.concatenate([grid, grid], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For working with arrays of mixed dimensions, it can be clearer to use the ``np.vstack`` (vertical stack) and ``np.hstack`` (horizontal stack) functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3],\n", + " [9, 8, 7],\n", + " [6, 5, 4]])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([1, 2, 3])\n", + "grid = np.array([[9, 8, 7],\n", + " [6, 5, 4]])\n", + "\n", + "# vertically stack the arrays\n", + "np.vstack([x, grid])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9, 8, 7, 99],\n", + " [ 6, 5, 4, 99]])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# horizontally stack the arrays\n", + "y = np.array([[99],\n", + " [99]])\n", + "np.hstack([grid, y])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similary, ``np.dstack`` will stack arrays along the third axis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting of arrays\n", + "\n", + "The opposite of concatenation is splitting, which is implemented by the functions ``np.split``, ``np.hsplit``, and ``np.vsplit``. For each of these, we can pass a list of indices giving the split points:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3] [99 99] [3 2 1]\n" + ] + } + ], + "source": [ + "x = [1, 2, 3, 99, 99, 3, 2, 1]\n", + "x1, x2, x3 = np.split(x, [3, 5])\n", + "print(x1, x2, x3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that *N* split-points, leads to *N + 1* subarrays.\n", + "The related functions ``np.hsplit`` and ``np.vsplit`` are similar:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11],\n", + " [12, 13, 14, 15]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid = np.arange(16).reshape((4, 4))\n", + "grid" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2 3]\n", + " [4 5 6 7]]\n", + "[[ 8 9 10 11]\n", + " [12 13 14 15]]\n" + ] + } + ], + "source": [ + "upper, lower = np.vsplit(grid, [2])\n", + "print(upper)\n", + "print(lower)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1]\n", + " [ 4 5]\n", + " [ 8 9]\n", + " [12 13]]\n", + "[[ 2 3]\n", + " [ 6 7]\n", + " [10 11]\n", + " [14 15]]\n" + ] + } + ], + "source": [ + "left, right = np.hsplit(grid, [2])\n", + "print(left)\n", + "print(right)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, ``np.dsplit`` will split arrays along the third axis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.md b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.md new file mode 100644 index 00000000..34f95f6b --- /dev/null +++ b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.md @@ -0,0 +1,433 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) > + +Open in Colab + + + +# The Basics of NumPy Arrays + + +Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas ([Chapter 3](03.00-Introduction-to-Pandas.ipynb)) are built around the NumPy array. +This section will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays. +While the types of operations shown here may seem a bit dry and pedantic, they comprise the building blocks of many other examples used throughout the book. +Get to know them well! + +We'll cover a few categories of basic array manipulations here: + +- *Attributes of arrays*: Determining the size, shape, memory consumption, and data types of arrays +- *Indexing of arrays*: Getting and setting the value of individual array elements +- *Slicing of arrays*: Getting and setting smaller subarrays within a larger array +- *Reshaping of arrays*: Changing the shape of a given array +- *Joining and splitting of arrays*: Combining multiple arrays into one, and splitting one array into many + + +## NumPy Array Attributes + + +First let's discuss some useful array attributes. +We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array. +We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run: + +```python +import numpy as np +np.random.seed(0) # seed for reproducibility + +x1 = np.random.randint(10, size=6) # One-dimensional array +x2 = np.random.randint(10, size=(3, 4)) # Two-dimensional array +x3 = np.random.randint(10, size=(3, 4, 5)) # Three-dimensional array +``` + +Each array has attributes ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array): + +```python +print("x3 ndim: ", x3.ndim) +print("x3 shape:", x3.shape) +print("x3 size: ", x3.size) +``` + +Another useful attribute is the ``dtype``, the data type of the array (which we discussed previously in [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)): + +```python +print("dtype:", x3.dtype) +``` + +Other attributes include ``itemsize``, which lists the size (in bytes) of each array element, and ``nbytes``, which lists the total size (in bytes) of the array: + +```python +print("itemsize:", x3.itemsize, "bytes") +print("nbytes:", x3.nbytes, "bytes") +``` + +In general, we expect that ``nbytes`` is equal to ``itemsize`` times ``size``. + + +## Array Indexing: Accessing Single Elements + + +If you are familiar with Python's standard list indexing, indexing in NumPy will feel quite familiar. +In a one-dimensional array, the $i^{th}$ value (counting from zero) can be accessed by specifying the desired index in square brackets, just as with Python lists: + +```python +x1 +``` + +```python +x1[0] +``` + +```python +x1[4] +``` + +To index from the end of the array, you can use negative indices: + +```python +x1[-1] +``` + +```python +x1[-2] +``` + +In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices: + +```python +x2 +``` + +```python +x2[0, 0] +``` + +```python +x2[2, 0] +``` + +```python +x2[2, -1] +``` + +Values can also be modified using any of the above index notation: + +```python +x2[0, 0] = 12 +x2 +``` + +Keep in mind that, unlike Python lists, NumPy arrays have a fixed type. +This means, for example, that if you attempt to insert a floating-point value to an integer array, the value will be silently truncated. Don't be caught unaware by this behavior! + +```python +x1[0] = 3.14159 # this will be truncated! +x1 +``` + +## Array Slicing: Accessing Subarrays + + +Just as we can use square brackets to access individual array elements, we can also use them to access subarrays with the *slice* notation, marked by the colon (``:``) character. +The NumPy slicing syntax follows that of the standard Python list; to access a slice of an array ``x``, use this: +``` python +x[start:stop:step] +``` +If any of these are unspecified, they default to the values ``start=0``, ``stop=``*``size of dimension``*, ``step=1``. +We'll take a look at accessing sub-arrays in one dimension and in multiple dimensions. + + +### One-dimensional subarrays + +```python +x = np.arange(10) +x +``` + +```python +x[:5] # first five elements +``` + +```python +x[5:] # elements after index 5 +``` + +```python +x[4:7] # middle sub-array +``` + +```python +x[::2] # every other element +``` + +```python +x[1::2] # every other element, starting at index 1 +``` + +A potentially confusing case is when the ``step`` value is negative. +In this case, the defaults for ``start`` and ``stop`` are swapped. +This becomes a convenient way to reverse an array: + +```python +x[::-1] # all elements, reversed +``` + +```python +x[5::-2] # reversed every other from index 5 +``` + +### Multi-dimensional subarrays + +Multi-dimensional slices work in the same way, with multiple slices separated by commas. +For example: + +```python +x2 +``` + +```python +x2[:2, :3] # two rows, three columns +``` + +```python +x2[:3, ::2] # all rows, every other column +``` + +Finally, subarray dimensions can even be reversed together: + +```python +x2[::-1, ::-1] +``` + +#### Accessing array rows and columns + +One commonly needed routine is accessing of single rows or columns of an array. +This can be done by combining indexing and slicing, using an empty slice marked by a single colon (``:``): + +```python +print(x2[:, 0]) # first column of x2 +``` + +```python +print(x2[0, :]) # first row of x2 +``` + +In the case of row access, the empty slice can be omitted for a more compact syntax: + +```python +print(x2[0]) # equivalent to x2[0, :] +``` + +### Subarrays as no-copy views + +One important–and extremely useful–thing to know about array slices is that they return *views* rather than *copies* of the array data. +This is one area in which NumPy array slicing differs from Python list slicing: in lists, slices will be copies. +Consider our two-dimensional array from before: + +```python +print(x2) +``` + +Let's extract a $2 \times 2$ subarray from this: + +```python +x2_sub = x2[:2, :2] +print(x2_sub) +``` + +Now if we modify this subarray, we'll see that the original array is changed! Observe: + +```python +x2_sub[0, 0] = 99 +print(x2_sub) +``` + +```python +print(x2) +``` + +This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer. + + +### Creating copies of arrays + +Despite the nice features of array views, it is sometimes useful to instead explicitly copy the data within an array or a subarray. This can be most easily done with the ``copy()`` method: + +```python +x2_sub_copy = x2[:2, :2].copy() +print(x2_sub_copy) +``` + +If we now modify this subarray, the original array is not touched: + +```python +x2_sub_copy[0, 0] = 42 +print(x2_sub_copy) +``` + +```python +print(x2) +``` + +## Reshaping of Arrays + +Another useful type of operation is reshaping of arrays. +The most flexible way of doing this is with the ``reshape`` method. +For example, if you want to put the numbers 1 through 9 in a $3 \times 3$ grid, you can do the following: + +```python +grid = np.arange(1, 10).reshape((3, 3)) +print(grid) +``` + +Note that for this to work, the size of the initial array must match the size of the reshaped array. +Where possible, the ``reshape`` method will use a no-copy view of the initial array, but with non-contiguous memory buffers this is not always the case. + +Another common reshaping pattern is the conversion of a one-dimensional array into a two-dimensional row or column matrix. +This can be done with the ``reshape`` method, or more easily done by making use of the ``newaxis`` keyword within a slice operation: + +```python +x = np.array([1, 2, 3]) + +# row vector via reshape +x.reshape((1, 3)) +``` + +```python +# row vector via newaxis +x[np.newaxis, :] +``` + +```python +# column vector via reshape +x.reshape((3, 1)) +``` + +```python +# column vector via newaxis +x[:, np.newaxis] +``` + +We will see this type of transformation often throughout the remainder of the book. + + +## Array Concatenation and Splitting + +All of the preceding routines worked on single arrays. It's also possible to combine multiple arrays into one, and to conversely split a single array into multiple arrays. We'll take a look at those operations here. + + +### Concatenation of arrays + +Concatenation, or joining of two arrays in NumPy, is primarily accomplished using the routines ``np.concatenate``, ``np.vstack``, and ``np.hstack``. +``np.concatenate`` takes a tuple or list of arrays as its first argument, as we can see here: + +```python +x = np.array([1, 2, 3]) +y = np.array([3, 2, 1]) +np.concatenate([x, y]) +``` + +You can also concatenate more than two arrays at once: + +```python +z = [99, 99, 99] +print(np.concatenate([x, y, z])) +``` + +It can also be used for two-dimensional arrays: + +```python +grid = np.array([[1, 2, 3], + [4, 5, 6]]) +``` + +```python +# concatenate along the first axis +np.concatenate([grid, grid]) +``` + +```python +# concatenate along the second axis (zero-indexed) +np.concatenate([grid, grid], axis=1) +``` + +For working with arrays of mixed dimensions, it can be clearer to use the ``np.vstack`` (vertical stack) and ``np.hstack`` (horizontal stack) functions: + +```python +x = np.array([1, 2, 3]) +grid = np.array([[9, 8, 7], + [6, 5, 4]]) + +# vertically stack the arrays +np.vstack([x, grid]) +``` + +```python +# horizontally stack the arrays +y = np.array([[99], + [99]]) +np.hstack([grid, y]) +``` + +Similary, ``np.dstack`` will stack arrays along the third axis. + + +### Splitting of arrays + +The opposite of concatenation is splitting, which is implemented by the functions ``np.split``, ``np.hsplit``, and ``np.vsplit``. For each of these, we can pass a list of indices giving the split points: + +```python +x = [1, 2, 3, 99, 99, 3, 2, 1] +x1, x2, x3 = np.split(x, [3, 5]) +print(x1, x2, x3) +``` + +Notice that *N* split-points, leads to *N + 1* subarrays. +The related functions ``np.hsplit`` and ``np.vsplit`` are similar: + +```python +grid = np.arange(16).reshape((4, 4)) +grid +``` + +```python +upper, lower = np.vsplit(grid, [2]) +print(upper) +print(lower) +``` + +```python +left, right = np.hsplit(grid, [2]) +print(left) +print(right) +``` + +Similarly, ``np.dsplit`` will split arrays along the third axis. + + + +< [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) | [Contents](Index.ipynb) | [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.ipynb b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.ipynb new file mode 100644 index 00000000..a382c03e --- /dev/null +++ b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.ipynb @@ -0,0 +1,1112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computation on NumPy Arrays: Universal Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Up until now, we have been discussing some of the basic nuts and bolts of NumPy; in the next few sections, we will dive into the reasons that NumPy is so important in the Python data science world.\n", + "Namely, it provides an easy and flexible interface to optimized computation with arrays of data.\n", + "\n", + "Computation on NumPy arrays can be very fast, or it can be very slow.\n", + "The key to making it fast is to use *vectorized* operations, generally implemented through NumPy's *universal functions* (ufuncs).\n", + "This section motivates the need for NumPy's ufuncs, which can be used to make repeated calculations on array elements much more efficient.\n", + "It then introduces many of the most common and useful arithmetic ufuncs available in the NumPy package." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Slowness of Loops\n", + "\n", + "Python's default implementation (known as CPython) does some operations very slowly.\n", + "This is in part due to the dynamic, interpreted nature of the language: the fact that types are flexible, so that sequences of operations cannot be compiled down to efficient machine code as in languages like C and Fortran.\n", + "Recently there have been various attempts to address this weakness: well-known examples are the [PyPy](http://pypy.org/) project, a just-in-time compiled implementation of Python; the [Cython](http://cython.org) project, which converts Python code to compilable C code; and the [Numba](http://numba.pydata.org/) project, which converts snippets of Python code to fast LLVM bytecode.\n", + "Each of these has its strengths and weaknesses, but it is safe to say that none of the three approaches has yet surpassed the reach and popularity of the standard CPython engine.\n", + "\n", + "The relative sluggishness of Python generally manifests itself in situations where many small operations are being repeated – for instance looping over arrays to operate on each element.\n", + "For example, imagine we have an array of values and we'd like to compute the reciprocal of each.\n", + "A straightforward approach might look like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.16666667, 1. , 0.25 , 0.25 , 0.125 ])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.random.seed(0)\n", + "\n", + "def compute_reciprocals(values):\n", + " output = np.empty(len(values))\n", + " for i in range(len(values)):\n", + " output[i] = 1.0 / values[i]\n", + " return output\n", + " \n", + "values = np.random.randint(1, 10, size=5)\n", + "compute_reciprocals(values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This implementation probably feels fairly natural to someone from, say, a C or Java background.\n", + "But if we measure the execution time of this code for a large input, we see that this operation is very slow, perhaps surprisingly so!\n", + "We'll benchmark this with IPython's ``%timeit`` magic (discussed in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loop, best of 3: 2.91 s per loop\n" + ] + } + ], + "source": [ + "big_array = np.random.randint(1, 100, size=1000000)\n", + "%timeit compute_reciprocals(big_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It takes several seconds to compute these million operations and to store the result!\n", + "When even cell phones have processing speeds measured in Giga-FLOPS (i.e., billions of numerical operations per second), this seems almost absurdly slow.\n", + "It turns out that the bottleneck here is not the operations themselves, but the type-checking and function dispatches that CPython must do at each cycle of the loop.\n", + "Each time the reciprocal is computed, Python first examines the object's type and does a dynamic lookup of the correct function to use for that type.\n", + "If we were working in compiled code instead, this type specification would be known before the code executes and the result could be computed much more efficiently." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introducing UFuncs\n", + "\n", + "For many types of operations, NumPy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a *vectorized* operation.\n", + "This can be accomplished by simply performing an operation on the array, which will then be applied to each element.\n", + "This vectorized approach is designed to push the loop into the compiled layer that underlies NumPy, leading to much faster execution.\n", + "\n", + "Compare the results of the following two:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.16666667 1. 0.25 0.25 0.125 ]\n", + "[ 0.16666667 1. 0.25 0.25 0.125 ]\n" + ] + } + ], + "source": [ + "print(compute_reciprocals(values))\n", + "print(1.0 / values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the execution time for our big array, we see that it completes orders of magnitude faster than the Python loop:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 loops, best of 3: 4.6 ms per loop\n" + ] + } + ], + "source": [ + "%timeit (1.0 / big_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vectorized operations in NumPy are implemented via *ufuncs*, whose main purpose is to quickly execute repeated operations on values in NumPy arrays.\n", + "Ufuncs are extremely flexible – before we saw an operation between a scalar and an array, but we can also operate between two arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.5 , 0.66666667, 0.75 , 0.8 ])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(5) / np.arange(1, 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And ufunc operations are not limited to one-dimensional arrays–they can also act on multi-dimensional arrays as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 2, 4],\n", + " [ 8, 16, 32],\n", + " [ 64, 128, 256]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(9).reshape((3, 3))\n", + "2 ** x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computations using vectorization through ufuncs are nearly always more efficient than their counterpart implemented using Python loops, especially as the arrays grow in size.\n", + "Any time you see such a loop in a Python script, you should consider whether it can be replaced with a vectorized expression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring NumPy's UFuncs\n", + "\n", + "Ufuncs exist in two flavors: *unary ufuncs*, which operate on a single input, and *binary ufuncs*, which operate on two inputs.\n", + "We'll see examples of both these types of functions here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Array arithmetic\n", + "\n", + "NumPy's ufuncs feel very natural to use because they make use of Python's native arithmetic operators.\n", + "The standard addition, subtraction, multiplication, and division can all be used:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [0 1 2 3]\n", + "x + 5 = [5 6 7 8]\n", + "x - 5 = [-5 -4 -3 -2]\n", + "x * 2 = [0 2 4 6]\n", + "x / 2 = [ 0. 0.5 1. 1.5]\n", + "x // 2 = [0 0 1 1]\n" + ] + } + ], + "source": [ + "x = np.arange(4)\n", + "print(\"x =\", x)\n", + "print(\"x + 5 =\", x + 5)\n", + "print(\"x - 5 =\", x - 5)\n", + "print(\"x * 2 =\", x * 2)\n", + "print(\"x / 2 =\", x / 2)\n", + "print(\"x // 2 =\", x // 2) # floor division" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is also a unary ufunc for negation, and a ``**`` operator for exponentiation, and a ``%`` operator for modulus:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-x = [ 0 -1 -2 -3]\n", + "x ** 2 = [0 1 4 9]\n", + "x % 2 = [0 1 0 1]\n" + ] + } + ], + "source": [ + "print(\"-x = \", -x)\n", + "print(\"x ** 2 = \", x ** 2)\n", + "print(\"x % 2 = \", x % 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, these can be strung together however you wish, and the standard order of operations is respected:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1. , -2.25, -4. , -6.25])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "-(0.5*x + 1) ** 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each of these arithmetic operations are simply convenient wrappers around specific functions built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 3, 4, 5])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.add(x, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following table lists the arithmetic operators implemented in NumPy:\n", + "\n", + "| Operator\t | Equivalent ufunc | Description |\n", + "|---------------|---------------------|---------------------------------------|\n", + "|``+`` |``np.add`` |Addition (e.g., ``1 + 1 = 2``) |\n", + "|``-`` |``np.subtract`` |Subtraction (e.g., ``3 - 2 = 1``) |\n", + "|``-`` |``np.negative`` |Unary negation (e.g., ``-2``) |\n", + "|``*`` |``np.multiply`` |Multiplication (e.g., ``2 * 3 = 6``) |\n", + "|``/`` |``np.divide`` |Division (e.g., ``3 / 2 = 1.5``) |\n", + "|``//`` |``np.floor_divide`` |Floor division (e.g., ``3 // 2 = 1``) |\n", + "|``**`` |``np.power`` |Exponentiation (e.g., ``2 ** 3 = 8``) |\n", + "|``%`` |``np.mod`` |Modulus/remainder (e.g., ``9 % 4 = 1``)|\n", + "\n", + "Additionally there are Boolean/bitwise operators; we will explore these in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Absolute value\n", + "\n", + "Just as NumPy understands Python's built-in arithmetic operators, it also understands Python's built-in absolute value function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 0, 1, 2])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([-2, -1, 0, 1, 2])\n", + "abs(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The corresponding NumPy ufunc is ``np.absolute``, which is also available under the alias ``np.abs``:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 0, 1, 2])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.absolute(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 0, 1, 2])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This ufunc can also handle complex data, in which the absolute value returns the magnitude:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5., 5., 2., 1.])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([3 - 4j, 4 - 3j, 2 + 0j, 0 + 1j])\n", + "np.abs(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trigonometric functions\n", + "\n", + "NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions.\n", + "We'll start by defining an array of angles:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "theta = np.linspace(0, np.pi, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can compute some trigonometric functions on these values:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "theta = [ 0. 1.57079633 3.14159265]\n", + "sin(theta) = [ 0.00000000e+00 1.00000000e+00 1.22464680e-16]\n", + "cos(theta) = [ 1.00000000e+00 6.12323400e-17 -1.00000000e+00]\n", + "tan(theta) = [ 0.00000000e+00 1.63312394e+16 -1.22464680e-16]\n" + ] + } + ], + "source": [ + "print(\"theta = \", theta)\n", + "print(\"sin(theta) = \", np.sin(theta))\n", + "print(\"cos(theta) = \", np.cos(theta))\n", + "print(\"tan(theta) = \", np.tan(theta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values are computed to within machine precision, which is why values that should be zero do not always hit exactly zero.\n", + "Inverse trigonometric functions are also available:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [-1, 0, 1]\n", + "arcsin(x) = [-1.57079633 0. 1.57079633]\n", + "arccos(x) = [ 3.14159265 1.57079633 0. ]\n", + "arctan(x) = [-0.78539816 0. 0.78539816]\n" + ] + } + ], + "source": [ + "x = [-1, 0, 1]\n", + "print(\"x = \", x)\n", + "print(\"arcsin(x) = \", np.arcsin(x))\n", + "print(\"arccos(x) = \", np.arccos(x))\n", + "print(\"arctan(x) = \", np.arctan(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exponents and logarithms\n", + "\n", + "Another common type of operation available in a NumPy ufunc are the exponentials:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [1, 2, 3]\n", + "e^x = [ 2.71828183 7.3890561 20.08553692]\n", + "2^x = [ 2. 4. 8.]\n", + "3^x = [ 3 9 27]\n" + ] + } + ], + "source": [ + "x = [1, 2, 3]\n", + "print(\"x =\", x)\n", + "print(\"e^x =\", np.exp(x))\n", + "print(\"2^x =\", np.exp2(x))\n", + "print(\"3^x =\", np.power(3, x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The inverse of the exponentials, the logarithms, are also available.\n", + "The basic ``np.log`` gives the natural logarithm; if you prefer to compute the base-2 logarithm or the base-10 logarithm, these are available as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [1, 2, 4, 10]\n", + "ln(x) = [ 0. 0.69314718 1.38629436 2.30258509]\n", + "log2(x) = [ 0. 1. 2. 3.32192809]\n", + "log10(x) = [ 0. 0.30103 0.60205999 1. ]\n" + ] + } + ], + "source": [ + "x = [1, 2, 4, 10]\n", + "print(\"x =\", x)\n", + "print(\"ln(x) =\", np.log(x))\n", + "print(\"log2(x) =\", np.log2(x))\n", + "print(\"log10(x) =\", np.log10(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also some specialized versions that are useful for maintaining precision with very small input:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exp(x) - 1 = [ 0. 0.0010005 0.01005017 0.10517092]\n", + "log(1 + x) = [ 0. 0.0009995 0.00995033 0.09531018]\n" + ] + } + ], + "source": [ + "x = [0, 0.001, 0.01, 0.1]\n", + "print(\"exp(x) - 1 =\", np.expm1(x))\n", + "print(\"log(1 + x) =\", np.log1p(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When ``x`` is very small, these functions give more precise values than if the raw ``np.log`` or ``np.exp`` were to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specialized ufuncs\n", + "\n", + "NumPy has many more ufuncs available, including hyperbolic trig functions, bitwise arithmetic, comparison operators, conversions from radians to degrees, rounding and remainders, and much more.\n", + "A look through the NumPy documentation reveals a lot of interesting functionality.\n", + "\n", + "Another excellent source for more specialized and obscure ufuncs is the submodule ``scipy.special``.\n", + "If you want to compute some obscure mathematical function on your data, chances are it is implemented in ``scipy.special``.\n", + "There are far too many functions to list them all, but the following snippet shows a couple that might come up in a statistics context:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy import special" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gamma(x) = [ 1.00000000e+00 2.40000000e+01 3.62880000e+05]\n", + "ln|gamma(x)| = [ 0. 3.17805383 12.80182748]\n", + "beta(x, 2) = [ 0.5 0.03333333 0.00909091]\n" + ] + } + ], + "source": [ + "# Gamma functions (generalized factorials) and related functions\n", + "x = [1, 5, 10]\n", + "print(\"gamma(x) =\", special.gamma(x))\n", + "print(\"ln|gamma(x)| =\", special.gammaln(x))\n", + "print(\"beta(x, 2) =\", special.beta(x, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "erf(x) = [ 0. 0.32862676 0.67780119 0.84270079]\n", + "erfc(x) = [ 1. 0.67137324 0.32219881 0.15729921]\n", + "erfinv(x) = [ 0. 0.27246271 0.73286908 inf]\n" + ] + } + ], + "source": [ + "# Error function (integral of Gaussian)\n", + "# its complement, and its inverse\n", + "x = np.array([0, 0.3, 0.7, 1.0])\n", + "print(\"erf(x) =\", special.erf(x))\n", + "print(\"erfc(x) =\", special.erfc(x))\n", + "print(\"erfinv(x) =\", special.erfinv(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many, many more ufuncs available in both NumPy and ``scipy.special``.\n", + "Because the documentation of these packages is available online, a web search along the lines of \"gamma function python\" will generally find the relevant information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Ufunc Features\n", + "\n", + "Many NumPy users make use of ufuncs without ever learning their full set of features.\n", + "We'll outline a few specialized features of ufuncs here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specifying output\n", + "\n", + "For large calculations, it is sometimes useful to be able to specify the array where the result of the calculation will be stored.\n", + "Rather than creating a temporary array, this can be used to write computation results directly to the memory location where you'd like them to be.\n", + "For all ufuncs, this can be done using the ``out`` argument of the function:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 10. 20. 30. 40.]\n" + ] + } + ], + "source": [ + "x = np.arange(5)\n", + "y = np.empty(5)\n", + "np.multiply(x, 10, out=y)\n", + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can even be used with array views. For example, we can write the results of a computation to every other element of a specified array:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 0. 2. 0. 4. 0. 8. 0. 16. 0.]\n" + ] + } + ], + "source": [ + "y = np.zeros(10)\n", + "np.power(2, x, out=y[::2])\n", + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we had instead written ``y[::2] = 2 ** x``, this would have resulted in the creation of a temporary array to hold the results of ``2 ** x``, followed by a second operation copying those values into the ``y`` array.\n", + "This doesn't make much of a difference for such a small computation, but for very large arrays the memory savings from careful use of the ``out`` argument can be significant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregates\n", + "\n", + "For binary ufuncs, there are some interesting aggregates that can be computed directly from the object.\n", + "For example, if we'd like to *reduce* an array with a particular operation, we can use the ``reduce`` method of any ufunc.\n", + "A reduce repeatedly applies a given operation to the elements of an array until only a single result remains.\n", + "\n", + "For example, calling ``reduce`` on the ``add`` ufunc returns the sum of all elements in the array:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(1, 6)\n", + "np.add.reduce(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, calling ``reduce`` on the ``multiply`` ufunc results in the product of all array elements:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.multiply.reduce(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we'd like to store all the intermediate results of the computation, we can instead use ``accumulate``:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 3, 6, 10, 15])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.add.accumulate(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 2, 6, 24, 120])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.multiply.accumulate(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that for these particular cases, there are dedicated NumPy functions to compute the results (``np.sum``, ``np.prod``, ``np.cumsum``, ``np.cumprod``), which we'll explore in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outer products\n", + "\n", + "Finally, any ufunc can compute the output of all pairs of two different inputs using the ``outer`` method.\n", + "This allows you, in one line, to do things like create a multiplication table:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 2, 3, 4, 5],\n", + " [ 2, 4, 6, 8, 10],\n", + " [ 3, 6, 9, 12, 15],\n", + " [ 4, 8, 12, 16, 20],\n", + " [ 5, 10, 15, 20, 25]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(1, 6)\n", + "np.multiply.outer(x, x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``ufunc.at`` and ``ufunc.reduceat`` methods, which we'll explore in [Fancy Indexing](02.07-Fancy-Indexing.ipynb), are very helpful as well.\n", + "\n", + "Another extremely useful feature of ufuncs is the ability to operate between arrays of different sizes and shapes, a set of operations known as *broadcasting*.\n", + "This subject is important enough that we will devote a whole section to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ufuncs: Learning More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More information on universal functions (including the full list of available functions) can be found on the [NumPy](http://www.numpy.org) and [SciPy](http://www.scipy.org) documentation websites.\n", + "\n", + "Recall that you can also access information directly from within IPython by importing the packages and using IPython's tab-completion and help (``?``) functionality, as described in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md new file mode 100644 index 00000000..e91bd159 --- /dev/null +++ b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md @@ -0,0 +1,392 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) > + +Open in Colab + + + +# Computation on NumPy Arrays: Universal Functions + + +Up until now, we have been discussing some of the basic nuts and bolts of NumPy; in the next few sections, we will dive into the reasons that NumPy is so important in the Python data science world. +Namely, it provides an easy and flexible interface to optimized computation with arrays of data. + +Computation on NumPy arrays can be very fast, or it can be very slow. +The key to making it fast is to use *vectorized* operations, generally implemented through NumPy's *universal functions* (ufuncs). +This section motivates the need for NumPy's ufuncs, which can be used to make repeated calculations on array elements much more efficient. +It then introduces many of the most common and useful arithmetic ufuncs available in the NumPy package. + + +## The Slowness of Loops + +Python's default implementation (known as CPython) does some operations very slowly. +This is in part due to the dynamic, interpreted nature of the language: the fact that types are flexible, so that sequences of operations cannot be compiled down to efficient machine code as in languages like C and Fortran. +Recently there have been various attempts to address this weakness: well-known examples are the [PyPy](http://pypy.org/) project, a just-in-time compiled implementation of Python; the [Cython](http://cython.org) project, which converts Python code to compilable C code; and the [Numba](http://numba.pydata.org/) project, which converts snippets of Python code to fast LLVM bytecode. +Each of these has its strengths and weaknesses, but it is safe to say that none of the three approaches has yet surpassed the reach and popularity of the standard CPython engine. + +The relative sluggishness of Python generally manifests itself in situations where many small operations are being repeated – for instance looping over arrays to operate on each element. +For example, imagine we have an array of values and we'd like to compute the reciprocal of each. +A straightforward approach might look like this: + +```python +import numpy as np +np.random.seed(0) + +def compute_reciprocals(values): + output = np.empty(len(values)) + for i in range(len(values)): + output[i] = 1.0 / values[i] + return output + +values = np.random.randint(1, 10, size=5) +compute_reciprocals(values) +``` + +This implementation probably feels fairly natural to someone from, say, a C or Java background. +But if we measure the execution time of this code for a large input, we see that this operation is very slow, perhaps surprisingly so! +We'll benchmark this with IPython's ``%timeit`` magic (discussed in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)): + +```python +big_array = np.random.randint(1, 100, size=1000000) +%timeit compute_reciprocals(big_array) +``` + +It takes several seconds to compute these million operations and to store the result! +When even cell phones have processing speeds measured in Giga-FLOPS (i.e., billions of numerical operations per second), this seems almost absurdly slow. +It turns out that the bottleneck here is not the operations themselves, but the type-checking and function dispatches that CPython must do at each cycle of the loop. +Each time the reciprocal is computed, Python first examines the object's type and does a dynamic lookup of the correct function to use for that type. +If we were working in compiled code instead, this type specification would be known before the code executes and the result could be computed much more efficiently. + + +## Introducing UFuncs + +For many types of operations, NumPy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a *vectorized* operation. +This can be accomplished by simply performing an operation on the array, which will then be applied to each element. +This vectorized approach is designed to push the loop into the compiled layer that underlies NumPy, leading to much faster execution. + +Compare the results of the following two: + +```python +print(compute_reciprocals(values)) +print(1.0 / values) +``` + +Looking at the execution time for our big array, we see that it completes orders of magnitude faster than the Python loop: + +```python +%timeit (1.0 / big_array) +``` + +Vectorized operations in NumPy are implemented via *ufuncs*, whose main purpose is to quickly execute repeated operations on values in NumPy arrays. +Ufuncs are extremely flexible – before we saw an operation between a scalar and an array, but we can also operate between two arrays: + +```python +np.arange(5) / np.arange(1, 6) +``` + +And ufunc operations are not limited to one-dimensional arrays–they can also act on multi-dimensional arrays as well: + +```python +x = np.arange(9).reshape((3, 3)) +2 ** x +``` + +Computations using vectorization through ufuncs are nearly always more efficient than their counterpart implemented using Python loops, especially as the arrays grow in size. +Any time you see such a loop in a Python script, you should consider whether it can be replaced with a vectorized expression. + + +## Exploring NumPy's UFuncs + +Ufuncs exist in two flavors: *unary ufuncs*, which operate on a single input, and *binary ufuncs*, which operate on two inputs. +We'll see examples of both these types of functions here. + + +### Array arithmetic + +NumPy's ufuncs feel very natural to use because they make use of Python's native arithmetic operators. +The standard addition, subtraction, multiplication, and division can all be used: + +```python +x = np.arange(4) +print("x =", x) +print("x + 5 =", x + 5) +print("x - 5 =", x - 5) +print("x * 2 =", x * 2) +print("x / 2 =", x / 2) +print("x // 2 =", x // 2) # floor division +``` + +There is also a unary ufunc for negation, and a ``**`` operator for exponentiation, and a ``%`` operator for modulus: + +```python +print("-x = ", -x) +print("x ** 2 = ", x ** 2) +print("x % 2 = ", x % 2) +``` + +In addition, these can be strung together however you wish, and the standard order of operations is respected: + +```python +-(0.5*x + 1) ** 2 +``` + +Each of these arithmetic operations are simply convenient wrappers around specific functions built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` function: + +```python +np.add(x, 2) +``` + +The following table lists the arithmetic operators implemented in NumPy: + +| Operator | Equivalent ufunc | Description | +|---------------|---------------------|---------------------------------------| +|``+`` |``np.add`` |Addition (e.g., ``1 + 1 = 2``) | +|``-`` |``np.subtract`` |Subtraction (e.g., ``3 - 2 = 1``) | +|``-`` |``np.negative`` |Unary negation (e.g., ``-2``) | +|``*`` |``np.multiply`` |Multiplication (e.g., ``2 * 3 = 6``) | +|``/`` |``np.divide`` |Division (e.g., ``3 / 2 = 1.5``) | +|``//`` |``np.floor_divide`` |Floor division (e.g., ``3 // 2 = 1``) | +|``**`` |``np.power`` |Exponentiation (e.g., ``2 ** 3 = 8``) | +|``%`` |``np.mod`` |Modulus/remainder (e.g., ``9 % 4 = 1``)| + +Additionally there are Boolean/bitwise operators; we will explore these in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb). + + +### Absolute value + +Just as NumPy understands Python's built-in arithmetic operators, it also understands Python's built-in absolute value function: + +```python +x = np.array([-2, -1, 0, 1, 2]) +abs(x) +``` + +The corresponding NumPy ufunc is ``np.absolute``, which is also available under the alias ``np.abs``: + +```python +np.absolute(x) +``` + +```python +np.abs(x) +``` + +This ufunc can also handle complex data, in which the absolute value returns the magnitude: + +```python +x = np.array([3 - 4j, 4 - 3j, 2 + 0j, 0 + 1j]) +np.abs(x) +``` + +### Trigonometric functions + +NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions. +We'll start by defining an array of angles: + +```python +theta = np.linspace(0, np.pi, 3) +``` + +Now we can compute some trigonometric functions on these values: + +```python +print("theta = ", theta) +print("sin(theta) = ", np.sin(theta)) +print("cos(theta) = ", np.cos(theta)) +print("tan(theta) = ", np.tan(theta)) +``` + +The values are computed to within machine precision, which is why values that should be zero do not always hit exactly zero. +Inverse trigonometric functions are also available: + +```python +x = [-1, 0, 1] +print("x = ", x) +print("arcsin(x) = ", np.arcsin(x)) +print("arccos(x) = ", np.arccos(x)) +print("arctan(x) = ", np.arctan(x)) +``` + +### Exponents and logarithms + +Another common type of operation available in a NumPy ufunc are the exponentials: + +```python +x = [1, 2, 3] +print("x =", x) +print("e^x =", np.exp(x)) +print("2^x =", np.exp2(x)) +print("3^x =", np.power(3, x)) +``` + +The inverse of the exponentials, the logarithms, are also available. +The basic ``np.log`` gives the natural logarithm; if you prefer to compute the base-2 logarithm or the base-10 logarithm, these are available as well: + +```python +x = [1, 2, 4, 10] +print("x =", x) +print("ln(x) =", np.log(x)) +print("log2(x) =", np.log2(x)) +print("log10(x) =", np.log10(x)) +``` + +There are also some specialized versions that are useful for maintaining precision with very small input: + +```python +x = [0, 0.001, 0.01, 0.1] +print("exp(x) - 1 =", np.expm1(x)) +print("log(1 + x) =", np.log1p(x)) +``` + +When ``x`` is very small, these functions give more precise values than if the raw ``np.log`` or ``np.exp`` were to be used. + + +### Specialized ufuncs + +NumPy has many more ufuncs available, including hyperbolic trig functions, bitwise arithmetic, comparison operators, conversions from radians to degrees, rounding and remainders, and much more. +A look through the NumPy documentation reveals a lot of interesting functionality. + +Another excellent source for more specialized and obscure ufuncs is the submodule ``scipy.special``. +If you want to compute some obscure mathematical function on your data, chances are it is implemented in ``scipy.special``. +There are far too many functions to list them all, but the following snippet shows a couple that might come up in a statistics context: + +```python +from scipy import special +``` + +```python +# Gamma functions (generalized factorials) and related functions +x = [1, 5, 10] +print("gamma(x) =", special.gamma(x)) +print("ln|gamma(x)| =", special.gammaln(x)) +print("beta(x, 2) =", special.beta(x, 2)) +``` + +```python +# Error function (integral of Gaussian) +# its complement, and its inverse +x = np.array([0, 0.3, 0.7, 1.0]) +print("erf(x) =", special.erf(x)) +print("erfc(x) =", special.erfc(x)) +print("erfinv(x) =", special.erfinv(x)) +``` + +There are many, many more ufuncs available in both NumPy and ``scipy.special``. +Because the documentation of these packages is available online, a web search along the lines of "gamma function python" will generally find the relevant information. + + +## Advanced Ufunc Features + +Many NumPy users make use of ufuncs without ever learning their full set of features. +We'll outline a few specialized features of ufuncs here. + + +### Specifying output + +For large calculations, it is sometimes useful to be able to specify the array where the result of the calculation will be stored. +Rather than creating a temporary array, this can be used to write computation results directly to the memory location where you'd like them to be. +For all ufuncs, this can be done using the ``out`` argument of the function: + +```python +x = np.arange(5) +y = np.empty(5) +np.multiply(x, 10, out=y) +print(y) +``` + +This can even be used with array views. For example, we can write the results of a computation to every other element of a specified array: + +```python +y = np.zeros(10) +np.power(2, x, out=y[::2]) +print(y) +``` + +If we had instead written ``y[::2] = 2 ** x``, this would have resulted in the creation of a temporary array to hold the results of ``2 ** x``, followed by a second operation copying those values into the ``y`` array. +This doesn't make much of a difference for such a small computation, but for very large arrays the memory savings from careful use of the ``out`` argument can be significant. + + +### Aggregates + +For binary ufuncs, there are some interesting aggregates that can be computed directly from the object. +For example, if we'd like to *reduce* an array with a particular operation, we can use the ``reduce`` method of any ufunc. +A reduce repeatedly applies a given operation to the elements of an array until only a single result remains. + +For example, calling ``reduce`` on the ``add`` ufunc returns the sum of all elements in the array: + +```python +x = np.arange(1, 6) +np.add.reduce(x) +``` + +Similarly, calling ``reduce`` on the ``multiply`` ufunc results in the product of all array elements: + +```python +np.multiply.reduce(x) +``` + +If we'd like to store all the intermediate results of the computation, we can instead use ``accumulate``: + +```python +np.add.accumulate(x) +``` + +```python +np.multiply.accumulate(x) +``` + +Note that for these particular cases, there are dedicated NumPy functions to compute the results (``np.sum``, ``np.prod``, ``np.cumsum``, ``np.cumprod``), which we'll explore in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb). + + +### Outer products + +Finally, any ufunc can compute the output of all pairs of two different inputs using the ``outer`` method. +This allows you, in one line, to do things like create a multiplication table: + +```python +x = np.arange(1, 6) +np.multiply.outer(x, x) +``` + +The ``ufunc.at`` and ``ufunc.reduceat`` methods, which we'll explore in [Fancy Indexing](02.07-Fancy-Indexing.ipynb), are very helpful as well. + +Another extremely useful feature of ufuncs is the ability to operate between arrays of different sizes and shapes, a set of operations known as *broadcasting*. +This subject is important enough that we will devote a whole section to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)). + + +## Ufuncs: Learning More + + +More information on universal functions (including the full list of available functions) can be found on the [NumPy](http://www.numpy.org) and [SciPy](http://www.scipy.org) documentation websites. + +Recall that you can also access information directly from within IPython by importing the packages and using IPython's tab-completion and help (``?``) functionality, as described in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb). + + + +< [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) | [Contents](Index.ipynb) | [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.04-Computation-on-arrays-aggregates.ipynb b/notebooks_v2/02.04-Computation-on-arrays-aggregates.ipynb new file mode 100644 index 00000000..b5eb814d --- /dev/null +++ b/notebooks_v2/02.04-Computation-on-arrays-aggregates.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aggregations: Min, Max, and Everything In Between" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question.\n", + "Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the \"typical\" values in a dataset, but other aggregates are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.).\n", + "\n", + "NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and demonstrate some of them here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summing the Values in an Array\n", + "\n", + "As a quick example, consider computing the sum of all values in an array.\n", + "Python itself can do this using the built-in ``sum`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "55.61209116604941" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "L = np.random.random(100)\n", + "sum(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The syntax is quite similar to that of NumPy's ``sum`` function, and the result is the same in the simplest case:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "55.612091166049424" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, because it executes the operation in compiled code, NumPy's version of the operation is computed much more quickly:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 104 ms per loop\n", + "1000 loops, best of 3: 442 µs per loop\n" + ] + } + ], + "source": [ + "big_array = np.random.rand(1000000)\n", + "%timeit sum(big_array)\n", + "%timeit np.sum(big_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Be careful, though: the ``sum`` function and the ``np.sum`` function are not identical, which can sometimes lead to confusion!\n", + "In particular, their optional arguments have different meanings, and ``np.sum`` is aware of multiple array dimensions, as we will see in the following section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Minimum and Maximum\n", + "\n", + "Similarly, Python has built-in ``min`` and ``max`` functions, used to find the minimum value and maximum value of any given array:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1717128136634614e-06, 0.9999976784968716)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min(big_array), max(big_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NumPy's corresponding functions have similar syntax, and again operate much more quickly:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.1717128136634614e-06, 0.9999976784968716)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(big_array), np.max(big_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 82.3 ms per loop\n", + "1000 loops, best of 3: 497 µs per loop\n" + ] + } + ], + "source": [ + "%timeit min(big_array)\n", + "%timeit np.min(big_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For ``min``, ``max``, ``sum``, and several other NumPy aggregates, a shorter syntax is to use methods of the array object itself:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.17171281366e-06 0.999997678497 499911.628197\n" + ] + } + ], + "source": [ + "print(big_array.min(), big_array.max(), big_array.sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whenever possible, make sure that you are using the NumPy version of these aggregates when operating on NumPy arrays!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi dimensional aggregates\n", + "\n", + "One common type of aggregation operation is an aggregate along a row or column.\n", + "Say you have some data stored in a two-dimensional array:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.8967576 0.03783739 0.75952519 0.06682827]\n", + " [ 0.8354065 0.99196818 0.19544769 0.43447084]\n", + " [ 0.66859307 0.15038721 0.37911423 0.6687194 ]]\n" + ] + } + ], + "source": [ + "M = np.random.random((3, 4))\n", + "print(M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, each NumPy aggregation function will return the aggregate over the entire array:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "6.0850555667307118" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aggregation functions take an additional argument specifying the *axis* along which the aggregate is computed. For example, we can find the minimum value within each column by specifying ``axis=0``:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.66859307, 0.03783739, 0.19544769, 0.06682827])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.min(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function returns four values, corresponding to the four columns of numbers.\n", + "\n", + "Similarly, we can find the maximum value within each row:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.8967576 , 0.99196818, 0.6687194 ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.max(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The way the axis is specified here can be confusing to users coming from other languages.\n", + "The ``axis`` keyword specifies the *dimension of the array that will be collapsed*, rather than the dimension that will be returned.\n", + "So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, this means that values within each column will be aggregated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other aggregation functions\n", + "\n", + "NumPy provides many other aggregation functions, but we won't discuss them in detail here.\n", + "Additionally, most aggregates have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (for a fuller discussion of missing data, see [Handling Missing Data](03.04-Missing-Values.ipynb)).\n", + "Some of these ``NaN``-safe functions were not added until NumPy 1.8, so they will not be available in older NumPy versions.\n", + "\n", + "The following table provides a list of useful aggregation functions available in NumPy:\n", + "\n", + "|Function Name | NaN-safe Version | Description |\n", + "|-------------------|---------------------|-----------------------------------------------|\n", + "| ``np.sum`` | ``np.nansum`` | Compute sum of elements |\n", + "| ``np.prod`` | ``np.nanprod`` | Compute product of elements |\n", + "| ``np.mean`` | ``np.nanmean`` | Compute mean of elements |\n", + "| ``np.std`` | ``np.nanstd`` | Compute standard deviation |\n", + "| ``np.var`` | ``np.nanvar`` | Compute variance |\n", + "| ``np.min`` | ``np.nanmin`` | Find minimum value |\n", + "| ``np.max`` | ``np.nanmax`` | Find maximum value |\n", + "| ``np.argmin`` | ``np.nanargmin`` | Find index of minimum value |\n", + "| ``np.argmax`` | ``np.nanargmax`` | Find index of maximum value |\n", + "| ``np.median`` | ``np.nanmedian`` | Compute median of elements |\n", + "| ``np.percentile`` | ``np.nanpercentile``| Compute rank-based statistics of elements |\n", + "| ``np.any`` | N/A | Evaluate whether any elements are true |\n", + "| ``np.all`` | N/A | Evaluate whether all elements are true |\n", + "\n", + "We will see these aggregates often throughout the rest of the book." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: What is the Average Height of US Presidents?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aggregates available in NumPy can be extremely useful for summarizing a set of values.\n", + "As a simple example, let's consider the heights of all US presidents.\n", + "This data is available in the file *president_heights.csv*, which is a simple comma-separated list of labels and values:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "order,name,height(cm)\r\n", + "1,George Washington,189\r\n", + "2,John Adams,170\r\n", + "3,Thomas Jefferson,189\r\n" + ] + } + ], + "source": [ + "!head -4 data/president_heights.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the Pandas package, which we'll explore more fully in [Chapter 3](03.00-Introduction-to-Pandas.ipynb), to read the file and extract this information (note that the heights are measured in centimeters)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[189 170 189 163 183 171 185 168 173 183 173 173 175 178 183 193 178 173\n", + " 174 183 183 168 170 178 182 180 183 178 182 188 175 179 183 193 182 183\n", + " 177 185 188 188 182 185]\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "data = pd.read_csv('data/president_heights.csv')\n", + "heights = np.array(data['height(cm)'])\n", + "print(heights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have this data array, we can compute a variety of summary statistics:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean height: 179.738095238\n", + "Standard deviation: 6.93184344275\n", + "Minimum height: 163\n", + "Maximum height: 193\n" + ] + } + ], + "source": [ + "print(\"Mean height: \", heights.mean())\n", + "print(\"Standard deviation:\", heights.std())\n", + "print(\"Minimum height: \", heights.min())\n", + "print(\"Maximum height: \", heights.max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values.\n", + "We may also wish to compute quantiles:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25th percentile: 174.25\n", + "Median: 182.0\n", + "75th percentile: 183.0\n" + ] + } + ], + "source": [ + "print(\"25th percentile: \", np.percentile(heights, 25))\n", + "print(\"Median: \", np.median(heights))\n", + "print(\"75th percentile: \", np.percentile(heights, 75))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the median height of US presidents is 182 cm, or just shy of six feet.\n", + "\n", + "Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)). For example, this code generates the following chart:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn; seaborn.set() # set plot style" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(heights)\n", + "plt.title('Height Distribution of US Presidents')\n", + "plt.xlabel('height (cm)')\n", + "plt.ylabel('number');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These aggregates are some of the fundamental pieces of exploratory data analysis that we'll explore in more depth in later chapters of the book." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.04-Computation-on-arrays-aggregates.md b/notebooks_v2/02.04-Computation-on-arrays-aggregates.md new file mode 100644 index 00000000..4e52167d --- /dev/null +++ b/notebooks_v2/02.04-Computation-on-arrays-aggregates.md @@ -0,0 +1,224 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) > + +Open in Colab + + + +# Aggregations: Min, Max, and Everything In Between + + +Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question. +Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the "typical" values in a dataset, but other aggregates are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.). + +NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and demonstrate some of them here. + + +## Summing the Values in an Array + +As a quick example, consider computing the sum of all values in an array. +Python itself can do this using the built-in ``sum`` function: + +```python +import numpy as np +``` + +```python +L = np.random.random(100) +sum(L) +``` + +The syntax is quite similar to that of NumPy's ``sum`` function, and the result is the same in the simplest case: + +```python +np.sum(L) +``` + +However, because it executes the operation in compiled code, NumPy's version of the operation is computed much more quickly: + +```python +big_array = np.random.rand(1000000) +%timeit sum(big_array) +%timeit np.sum(big_array) +``` + +Be careful, though: the ``sum`` function and the ``np.sum`` function are not identical, which can sometimes lead to confusion! +In particular, their optional arguments have different meanings, and ``np.sum`` is aware of multiple array dimensions, as we will see in the following section. + + +## Minimum and Maximum + +Similarly, Python has built-in ``min`` and ``max`` functions, used to find the minimum value and maximum value of any given array: + +```python +min(big_array), max(big_array) +``` + +NumPy's corresponding functions have similar syntax, and again operate much more quickly: + +```python +np.min(big_array), np.max(big_array) +``` + +```python +%timeit min(big_array) +%timeit np.min(big_array) +``` + +For ``min``, ``max``, ``sum``, and several other NumPy aggregates, a shorter syntax is to use methods of the array object itself: + +```python +print(big_array.min(), big_array.max(), big_array.sum()) +``` + +Whenever possible, make sure that you are using the NumPy version of these aggregates when operating on NumPy arrays! + + +### Multi dimensional aggregates + +One common type of aggregation operation is an aggregate along a row or column. +Say you have some data stored in a two-dimensional array: + +```python +M = np.random.random((3, 4)) +print(M) +``` + +By default, each NumPy aggregation function will return the aggregate over the entire array: + +```python +M.sum() +``` + +Aggregation functions take an additional argument specifying the *axis* along which the aggregate is computed. For example, we can find the minimum value within each column by specifying ``axis=0``: + +```python +M.min(axis=0) +``` + +The function returns four values, corresponding to the four columns of numbers. + +Similarly, we can find the maximum value within each row: + +```python +M.max(axis=1) +``` + +The way the axis is specified here can be confusing to users coming from other languages. +The ``axis`` keyword specifies the *dimension of the array that will be collapsed*, rather than the dimension that will be returned. +So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, this means that values within each column will be aggregated. + + +### Other aggregation functions + +NumPy provides many other aggregation functions, but we won't discuss them in detail here. +Additionally, most aggregates have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (for a fuller discussion of missing data, see [Handling Missing Data](03.04-Missing-Values.ipynb)). +Some of these ``NaN``-safe functions were not added until NumPy 1.8, so they will not be available in older NumPy versions. + +The following table provides a list of useful aggregation functions available in NumPy: + +|Function Name | NaN-safe Version | Description | +|-------------------|---------------------|-----------------------------------------------| +| ``np.sum`` | ``np.nansum`` | Compute sum of elements | +| ``np.prod`` | ``np.nanprod`` | Compute product of elements | +| ``np.mean`` | ``np.nanmean`` | Compute mean of elements | +| ``np.std`` | ``np.nanstd`` | Compute standard deviation | +| ``np.var`` | ``np.nanvar`` | Compute variance | +| ``np.min`` | ``np.nanmin`` | Find minimum value | +| ``np.max`` | ``np.nanmax`` | Find maximum value | +| ``np.argmin`` | ``np.nanargmin`` | Find index of minimum value | +| ``np.argmax`` | ``np.nanargmax`` | Find index of maximum value | +| ``np.median`` | ``np.nanmedian`` | Compute median of elements | +| ``np.percentile`` | ``np.nanpercentile``| Compute rank-based statistics of elements | +| ``np.any`` | N/A | Evaluate whether any elements are true | +| ``np.all`` | N/A | Evaluate whether all elements are true | + +We will see these aggregates often throughout the rest of the book. + + +## Example: What is the Average Height of US Presidents? + + +Aggregates available in NumPy can be extremely useful for summarizing a set of values. +As a simple example, let's consider the heights of all US presidents. +This data is available in the file *president_heights.csv*, which is a simple comma-separated list of labels and values: + +```python +!head -4 data/president_heights.csv +``` + +We'll use the Pandas package, which we'll explore more fully in [Chapter 3](03.00-Introduction-to-Pandas.ipynb), to read the file and extract this information (note that the heights are measured in centimeters). + +```python +import pandas as pd +data = pd.read_csv('data/president_heights.csv') +heights = np.array(data['height(cm)']) +print(heights) +``` + +Now that we have this data array, we can compute a variety of summary statistics: + +```python +print("Mean height: ", heights.mean()) +print("Standard deviation:", heights.std()) +print("Minimum height: ", heights.min()) +print("Maximum height: ", heights.max()) +``` + +Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values. +We may also wish to compute quantiles: + +```python +print("25th percentile: ", np.percentile(heights, 25)) +print("Median: ", np.median(heights)) +print("75th percentile: ", np.percentile(heights, 75)) +``` + +We see that the median height of US presidents is 182 cm, or just shy of six feet. + +Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)). For example, this code generates the following chart: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn; seaborn.set() # set plot style +``` + +```python +plt.hist(heights) +plt.title('Height Distribution of US Presidents') +plt.xlabel('height (cm)') +plt.ylabel('number'); +``` + +These aggregates are some of the fundamental pieces of exploratory data analysis that we'll explore in more depth in later chapters of the book. + + + +< [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.ipynb b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.ipynb new file mode 100644 index 00000000..ee0496c1 --- /dev/null +++ b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.ipynb @@ -0,0 +1,807 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computation on Arrays: Broadcasting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We saw in the previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.\n", + "Another means of vectorizing operations is to use NumPy's *broadcasting* functionality.\n", + "Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introducing Broadcasting\n", + "\n", + "Recall that for arrays of the same size, binary operations are performed on an element-by-element basis:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5, 6, 7])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([0, 1, 2])\n", + "b = np.array([5, 5, 5])\n", + "a + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Broadcasting allows these types of binary operations to be performed on arrays of different sizes–for example, we can just as easily add a scalar (think of it as a zero-dimensional array) to an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5, 6, 7])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a + 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can think of this as an operation that stretches or duplicates the value ``5`` into the array ``[5, 5, 5]``, and adds the results.\n", + "The advantage of NumPy's broadcasting is that this duplication of values does not actually take place, but it is a useful mental model as we think about broadcasting.\n", + "\n", + "We can similarly extend this to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1.],\n", + " [ 1., 1., 1.],\n", + " [ 1., 1., 1.]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = np.ones((3, 3))\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 2., 3.],\n", + " [ 1., 2., 3.],\n", + " [ 1., 2., 3.]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M + a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the one-dimensional array ``a`` is stretched, or broadcast across the second dimension in order to match the shape of ``M``.\n", + "\n", + "While these examples are relatively easy to understand, more complicated cases can involve broadcasting of both arrays. Consider the following example:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2]\n", + "[[0]\n", + " [1]\n", + " [2]]\n" + ] + } + ], + "source": [ + "a = np.arange(3)\n", + "b = np.arange(3)[:, np.newaxis]\n", + "\n", + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [1, 2, 3],\n", + " [2, 3, 4]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* ``a`` and ``b`` to match a common shape, and the result is a two-dimensional array!\n", + "The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Broadcasting Visual](figures/02.05-broadcasting.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The light boxes represent the broadcasted values: again, this extra memory is not actually allocated in the course of the operation, but it can be useful conceptually to imagine that it is." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rules of Broadcasting\n", + "\n", + "Broadcasting in NumPy follows a strict set of rules to determine the interaction between the two arrays:\n", + "\n", + "- Rule 1: If the two arrays differ in their number of dimensions, the shape of the one with fewer dimensions is *padded* with ones on its leading (left) side.\n", + "- Rule 2: If the shape of the two arrays does not match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the other shape.\n", + "- Rule 3: If in any dimension the sizes disagree and neither is equal to 1, an error is raised.\n", + "\n", + "To make these rules clear, let's consider a few examples in detail." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Broadcasting example 1\n", + "\n", + "Let's look at adding a two-dimensional array to a one-dimensional array:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "M = np.ones((2, 3))\n", + "a = np.arange(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's consider an operation on these two arrays. The shape of the arrays are\n", + "\n", + "- ``M.shape = (2, 3)``\n", + "- ``a.shape = (3,)``\n", + "\n", + "We see by rule 1 that the array ``a`` has fewer dimensions, so we pad it on the left with ones:\n", + "\n", + "- ``M.shape -> (2, 3)``\n", + "- ``a.shape -> (1, 3)``\n", + "\n", + "By rule 2, we now see that the first dimension disagrees, so we stretch this dimension to match:\n", + "\n", + "- ``M.shape -> (2, 3)``\n", + "- ``a.shape -> (2, 3)``\n", + "\n", + "The shapes match, and we see that the final shape will be ``(2, 3)``:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 2., 3.],\n", + " [ 1., 2., 3.]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M + a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Broadcasting example 2\n", + "\n", + "Let's take a look at an example where both arrays need to be broadcast:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = np.arange(3).reshape((3, 1))\n", + "b = np.arange(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we'll start by writing out the shape of the arrays:\n", + "\n", + "- ``a.shape = (3, 1)``\n", + "- ``b.shape = (3,)``\n", + "\n", + "Rule 1 says we must pad the shape of ``b`` with ones:\n", + "\n", + "- ``a.shape -> (3, 1)``\n", + "- ``b.shape -> (1, 3)``\n", + "\n", + "And rule 2 tells us that we upgrade each of these ones to match the corresponding size of the other array:\n", + "\n", + "- ``a.shape -> (3, 3)``\n", + "- ``b.shape -> (3, 3)``\n", + "\n", + "Because the result matches, these shapes are compatible. We can see this here:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [1, 2, 3],\n", + " [2, 3, 4]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Broadcasting example 3\n", + "\n", + "Now let's take a look at an example in which the two arrays are not compatible:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "M = np.ones((3, 2))\n", + "a = np.arange(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is just a slightly different situation than in the first example: the matrix ``M`` is transposed.\n", + "How does this affect the calculation? The shape of the arrays are\n", + "\n", + "- ``M.shape = (3, 2)``\n", + "- ``a.shape = (3,)``\n", + "\n", + "Again, rule 1 tells us that we must pad the shape of ``a`` with ones:\n", + "\n", + "- ``M.shape -> (3, 2)``\n", + "- ``a.shape -> (1, 3)``\n", + "\n", + "By rule 2, the first dimension of ``a`` is stretched to match that of ``M``:\n", + "\n", + "- ``M.shape -> (3, 2)``\n", + "- ``a.shape -> (3, 3)``\n", + "\n", + "Now we hit rule 3–the final shapes do not match, so these two arrays are incompatible, as we can observe by attempting this operation:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "operands could not be broadcast together with shapes (3,2) (3,) ", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,2) (3,) " + ] + } + ], + "source": [ + "M + a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the potential confusion here: you could imagine making ``a`` and ``M`` compatible by, say, padding ``a``'s shape with ones on the right rather than the left.\n", + "But this is not how the broadcasting rules work!\n", + "That sort of flexibility might be useful in some cases, but it would lead to potential areas of ambiguity.\n", + "If right-side padding is what you'd like, you can do this explicitly by reshaping the array (we'll use the ``np.newaxis`` keyword introduced in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 1)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[:, np.newaxis].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1.],\n", + " [ 2., 2.],\n", + " [ 3., 3.]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M + a[:, np.newaxis]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also note that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``.\n", + "For example, here is the ``logaddexp(a, b)`` function, which computes ``log(exp(a) + exp(b))`` with more precision than the naive approach:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.31326169, 1.31326169],\n", + " [ 1.69314718, 1.69314718],\n", + " [ 2.31326169, 2.31326169]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.logaddexp(M, a[:, np.newaxis])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information on the many available universal functions, refer to [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Broadcasting in Practice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Broadcasting operations form the core of many examples we'll see throughout this book.\n", + "We'll now take a look at a couple simple examples of where they can be useful." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Centering an array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous section, we saw that ufuncs allow a NumPy user to remove the need to explicitly write slow Python loops. Broadcasting extends this ability.\n", + "One commonly seen example is when centering an array of data.\n", + "Imagine you have an array of 10 observations, each of which consists of 3 values.\n", + "Using the standard convention (see [Data Representation in Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb#Data-Representation-in-Scikit-Learn)), we'll store this in a $10 \\times 3$ array:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = np.random.random((10, 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute the mean of each feature using the ``mean`` aggregate across the first dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.53514715, 0.66567217, 0.44385899])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Xmean = X.mean(0)\n", + "Xmean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now we can center the ``X`` array by subtracting the mean (this is a broadcasting operation):" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X_centered = X - Xmean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To double-check that we've done this correctly, we can check that the centered array has near zero mean:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.22044605e-17, -7.77156117e-17, -1.66533454e-17])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_centered.mean(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To within machine precision, the mean is now zero." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting a two-dimensional function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One place that broadcasting is very useful is in displaying images based on two-dimensional functions.\n", + "If we want to define a function $z = f(x, y)$, broadcasting can be used to compute the function across the grid:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# x and y have 50 steps from 0 to 5\n", + "x = np.linspace(0, 5, 50)\n", + "y = np.linspace(0, 5, 50)[:, np.newaxis]\n", + "\n", + "z = np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use Matplotlib to plot this two-dimensional array (these tools will be discussed in full in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LLB6BTSeAm0BtVGgQ2+y9R0HtcXia/X4buD1hWHSqNm/a3V/7CWlmMzTo2HwY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\nHIwtxPGVX5uCmie15H4fjF7PZkv34g0GhNbr2EYra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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(z, origin='lower', extent=[0, 5, 0, 5],\n", + " cmap='viridis')\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a compelling visualization of the two-dimensional function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md new file mode 100644 index 00000000..b9914ad6 --- /dev/null +++ b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md @@ -0,0 +1,299 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) > + +Open in Colab + + + +# Computation on Arrays: Broadcasting + + +We saw in the previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops. +Another means of vectorizing operations is to use NumPy's *broadcasting* functionality. +Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes. + + +## Introducing Broadcasting + +Recall that for arrays of the same size, binary operations are performed on an element-by-element basis: + +```python +import numpy as np +``` + +```python +a = np.array([0, 1, 2]) +b = np.array([5, 5, 5]) +a + b +``` + +Broadcasting allows these types of binary operations to be performed on arrays of different sizes–for example, we can just as easily add a scalar (think of it as a zero-dimensional array) to an array: + +```python +a + 5 +``` + +We can think of this as an operation that stretches or duplicates the value ``5`` into the array ``[5, 5, 5]``, and adds the results. +The advantage of NumPy's broadcasting is that this duplication of values does not actually take place, but it is a useful mental model as we think about broadcasting. + +We can similarly extend this to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array: + +```python +M = np.ones((3, 3)) +M +``` + +```python +M + a +``` + +Here the one-dimensional array ``a`` is stretched, or broadcast across the second dimension in order to match the shape of ``M``. + +While these examples are relatively easy to understand, more complicated cases can involve broadcasting of both arrays. Consider the following example: + +```python +a = np.arange(3) +b = np.arange(3)[:, np.newaxis] + +print(a) +print(b) +``` + +```python +a + b +``` + +Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* ``a`` and ``b`` to match a common shape, and the result is a two-dimensional array! +The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission). + + +![Broadcasting Visual](figures/02.05-broadcasting.png) + + +The light boxes represent the broadcasted values: again, this extra memory is not actually allocated in the course of the operation, but it can be useful conceptually to imagine that it is. + + +## Rules of Broadcasting + +Broadcasting in NumPy follows a strict set of rules to determine the interaction between the two arrays: + +- Rule 1: If the two arrays differ in their number of dimensions, the shape of the one with fewer dimensions is *padded* with ones on its leading (left) side. +- Rule 2: If the shape of the two arrays does not match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the other shape. +- Rule 3: If in any dimension the sizes disagree and neither is equal to 1, an error is raised. + +To make these rules clear, let's consider a few examples in detail. + + +### Broadcasting example 1 + +Let's look at adding a two-dimensional array to a one-dimensional array: + +```python +M = np.ones((2, 3)) +a = np.arange(3) +``` + +Let's consider an operation on these two arrays. The shape of the arrays are + +- ``M.shape = (2, 3)`` +- ``a.shape = (3,)`` + +We see by rule 1 that the array ``a`` has fewer dimensions, so we pad it on the left with ones: + +- ``M.shape -> (2, 3)`` +- ``a.shape -> (1, 3)`` + +By rule 2, we now see that the first dimension disagrees, so we stretch this dimension to match: + +- ``M.shape -> (2, 3)`` +- ``a.shape -> (2, 3)`` + +The shapes match, and we see that the final shape will be ``(2, 3)``: + +```python +M + a +``` + +### Broadcasting example 2 + +Let's take a look at an example where both arrays need to be broadcast: + +```python +a = np.arange(3).reshape((3, 1)) +b = np.arange(3) +``` + +Again, we'll start by writing out the shape of the arrays: + +- ``a.shape = (3, 1)`` +- ``b.shape = (3,)`` + +Rule 1 says we must pad the shape of ``b`` with ones: + +- ``a.shape -> (3, 1)`` +- ``b.shape -> (1, 3)`` + +And rule 2 tells us that we upgrade each of these ones to match the corresponding size of the other array: + +- ``a.shape -> (3, 3)`` +- ``b.shape -> (3, 3)`` + +Because the result matches, these shapes are compatible. We can see this here: + +```python +a + b +``` + +### Broadcasting example 3 + +Now let's take a look at an example in which the two arrays are not compatible: + +```python +M = np.ones((3, 2)) +a = np.arange(3) +``` + +This is just a slightly different situation than in the first example: the matrix ``M`` is transposed. +How does this affect the calculation? The shape of the arrays are + +- ``M.shape = (3, 2)`` +- ``a.shape = (3,)`` + +Again, rule 1 tells us that we must pad the shape of ``a`` with ones: + +- ``M.shape -> (3, 2)`` +- ``a.shape -> (1, 3)`` + +By rule 2, the first dimension of ``a`` is stretched to match that of ``M``: + +- ``M.shape -> (3, 2)`` +- ``a.shape -> (3, 3)`` + +Now we hit rule 3–the final shapes do not match, so these two arrays are incompatible, as we can observe by attempting this operation: + +```python +M + a +``` + +Note the potential confusion here: you could imagine making ``a`` and ``M`` compatible by, say, padding ``a``'s shape with ones on the right rather than the left. +But this is not how the broadcasting rules work! +That sort of flexibility might be useful in some cases, but it would lead to potential areas of ambiguity. +If right-side padding is what you'd like, you can do this explicitly by reshaping the array (we'll use the ``np.newaxis`` keyword introduced in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb)): + +```python +a[:, np.newaxis].shape +``` + +```python +M + a[:, np.newaxis] +``` + +Also note that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``. +For example, here is the ``logaddexp(a, b)`` function, which computes ``log(exp(a) + exp(b))`` with more precision than the naive approach: + +```python +np.logaddexp(M, a[:, np.newaxis]) +``` + +For more information on the many available universal functions, refer to [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb). + + +## Broadcasting in Practice + + +Broadcasting operations form the core of many examples we'll see throughout this book. +We'll now take a look at a couple simple examples of where they can be useful. + + +### Centering an array + + +In the previous section, we saw that ufuncs allow a NumPy user to remove the need to explicitly write slow Python loops. Broadcasting extends this ability. +One commonly seen example is when centering an array of data. +Imagine you have an array of 10 observations, each of which consists of 3 values. +Using the standard convention (see [Data Representation in Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb#Data-Representation-in-Scikit-Learn)), we'll store this in a $10 \times 3$ array: + +```python +X = np.random.random((10, 3)) +``` + +We can compute the mean of each feature using the ``mean`` aggregate across the first dimension: + +```python +Xmean = X.mean(0) +Xmean +``` + +And now we can center the ``X`` array by subtracting the mean (this is a broadcasting operation): + +```python +X_centered = X - Xmean +``` + +To double-check that we've done this correctly, we can check that the centered array has near zero mean: + +```python +X_centered.mean(0) +``` + +To within machine precision, the mean is now zero. + + +### Plotting a two-dimensional function + + +One place that broadcasting is very useful is in displaying images based on two-dimensional functions. +If we want to define a function $z = f(x, y)$, broadcasting can be used to compute the function across the grid: + +```python +# x and y have 50 steps from 0 to 5 +x = np.linspace(0, 5, 50) +y = np.linspace(0, 5, 50)[:, np.newaxis] + +z = np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x) +``` + +We'll use Matplotlib to plot this two-dimensional array (these tools will be discussed in full in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)): + +```python +%matplotlib inline +import matplotlib.pyplot as plt +``` + +```python +plt.imshow(z, origin='lower', extent=[0, 5, 0, 5], + cmap='viridis') +plt.colorbar(); +``` + +The result is a compelling visualization of the two-dimensional function. + + + +< [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) | [Contents](Index.ipynb) | [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.06-Boolean-Arrays-and-Masks.ipynb b/notebooks_v2/02.06-Boolean-Arrays-and-Masks.ipynb new file mode 100644 index 00000000..c78fee42 --- /dev/null +++ b/notebooks_v2/02.06-Boolean-Arrays-and-Masks.ipynb @@ -0,0 +1,1286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparisons, Masks, and Boolean Logic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This section covers the use of Boolean masks to examine and manipulate values within NumPy arrays.\n", + "Masking comes up when you want to extract, modify, count, or otherwise manipulate values in an array based on some criterion: for example, you might wish to count all values greater than a certain value, or perhaps remove all outliers that are above some threshold.\n", + "In NumPy, Boolean masking is often the most efficient way to accomplish these types of tasks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Counting Rainy Days\n", + "\n", + "Imagine you have a series of data that represents the amount of precipitation each day for a year in a given city.\n", + "For example, here we'll load the daily rainfall statistics for the city of Seattle in 2014, using Pandas (which is covered in more detail in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(365,)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# use pandas to extract rainfall inches as a NumPy array\n", + "rainfall = pd.read_csv('data/Seattle2014.csv')['PRCP'].values\n", + "inches = rainfall / 254.0 # 1/10mm -> inches\n", + "inches.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The array contains 365 values, giving daily rainfall in inches from January 1 to December 31, 2014.\n", + "\n", + "As a first quick visualization, let's look at the histogram of rainy days, which was generated using Matplotlib (we will explore this tool more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn; seaborn.set() # set plot styles" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(inches, 40);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This histogram gives us a general idea of what the data looks like: despite its reputation, the vast majority of days in Seattle saw near zero measured rainfall in 2014.\n", + "But this doesn't do a good job of conveying some information we'd like to see: for example, how many rainy days were there in the year? What is the average precipitation on those rainy days? How many days were there with more than half an inch of rain?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Digging into the data\n", + "\n", + "One approach to this would be to answer these questions by hand: loop through the data, incrementing a counter each time we see values in some desired range.\n", + "For reasons discussed throughout this chapter, such an approach is very inefficient, both from the standpoint of time writing code and time computing the result.\n", + "We saw in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) that NumPy's ufuncs can be used in place of loops to do fast element-wise arithmetic operations on arrays; in the same way, we can use other ufuncs to do element-wise *comparisons* over arrays, and we can then manipulate the results to answer the questions we have.\n", + "We'll leave the data aside for right now, and discuss some general tools in NumPy to use *masking* to quickly answer these types of questions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison Operators as ufuncs\n", + "\n", + "In [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) we introduced ufuncs, and focused in particular on arithmetic operators. We saw that using ``+``, ``-``, ``*``, ``/``, and others on arrays leads to element-wise operations.\n", + "NumPy also implements comparison operators such as ``<`` (less than) and ``>`` (greater than) as element-wise ufuncs.\n", + "The result of these comparison operators is always an array with a Boolean data type.\n", + "All six of the standard comparison operations are available:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = np.array([1, 2, 3, 4, 5])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, False, False, False], dtype=bool)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x < 3 # less than" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, True, True], dtype=bool)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x > 3 # greater than" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, False, False], dtype=bool)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x <= 3 # less than or equal" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, True, True, True], dtype=bool)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x >= 3 # greater than or equal" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, False, True, True], dtype=bool)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x != 3 # not equal" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, True, False, False], dtype=bool)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x == 3 # equal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also possible to do an element-wise comparison of two arrays, and to include compound expressions:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, True, False, False, False], dtype=bool)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(2 * x) == (x ** 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As in the case of arithmetic operators, the comparison operators are implemented as ufuncs in NumPy; for example, when you write ``x < 3``, internally NumPy uses ``np.less(x, 3)``.\n", + " A summary of the comparison operators and their equivalent ufunc is shown here:\n", + "\n", + "| Operator\t | Equivalent ufunc || Operator\t | Equivalent ufunc |\n", + "|---------------|---------------------||---------------|---------------------|\n", + "|``==`` |``np.equal`` ||``!=`` |``np.not_equal`` |\n", + "|``<`` |``np.less`` ||``<=`` |``np.less_equal`` |\n", + "|``>`` |``np.greater`` ||``>=`` |``np.greater_equal`` |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as in the case of arithmetic ufuncs, these will work on arrays of any size and shape.\n", + "Here is a two-dimensional example:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5, 0, 3, 3],\n", + " [7, 9, 3, 5],\n", + " [2, 4, 7, 6]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.RandomState(0)\n", + "x = rng.randint(10, size=(3, 4))\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ True, True, True, True],\n", + " [False, False, True, True],\n", + " [ True, True, False, False]], dtype=bool)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x < 6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In each case, the result is a Boolean array, and NumPy provides a number of straightforward patterns for working with these Boolean results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Working with Boolean Arrays\n", + "\n", + "Given a Boolean array, there are a host of useful operations you can do.\n", + "We'll work with ``x``, the two-dimensional array we created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[5 0 3 3]\n", + " [7 9 3 5]\n", + " [2 4 7 6]]\n" + ] + } + ], + "source": [ + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Counting entries\n", + "\n", + "To count the number of ``True`` entries in a Boolean array, ``np.count_nonzero`` is useful:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many values less than 6?\n", + "np.count_nonzero(x < 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that there are eight array entries that are less than 6.\n", + "Another way to get at this information is to use ``np.sum``; in this case, ``False`` is interpreted as ``0``, and ``True`` is interpreted as ``1``:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(x < 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The benefit of ``sum()`` is that like with other NumPy aggregation functions, this summation can be done along rows or columns as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 2, 2])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many values less than 6 in each row?\n", + "np.sum(x < 6, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This counts the number of values less than 6 in each row of the matrix.\n", + "\n", + "If we're interested in quickly checking whether any or all the values are true, we can use (you guessed it) ``np.any`` or ``np.all``:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# are there any values greater than 8?\n", + "np.any(x > 8)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# are there any values less than zero?\n", + "np.any(x < 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# are all values less than 10?\n", + "np.all(x < 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# are all values equal to 6?\n", + "np.all(x == 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``np.all`` and ``np.any`` can be used along particular axes as well. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, True], dtype=bool)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# are all values in each row less than 8?\n", + "np.all(x < 8, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here all the elements in the first and third rows are less than 8, while this is not the case for the second row.\n", + "\n", + "Finally, a quick warning: as mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb), Python has built-in ``sum()``, ``any()``, and ``all()`` functions. These have a different syntax than the NumPy versions, and in particular will fail or produce unintended results when used on multidimensional arrays. Be sure that you are using ``np.sum()``, ``np.any()``, and ``np.all()`` for these examples!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Boolean operators\n", + "\n", + "We've already seen how we might count, say, all days with rain less than four inches, or all days with rain greater than two inches.\n", + "But what if we want to know about all days with rain less than four inches and greater than one inch?\n", + "This is accomplished through Python's *bitwise logic operators*, ``&``, ``|``, ``^``, and ``~``.\n", + "Like with the standard arithmetic operators, NumPy overloads these as ufuncs which work element-wise on (usually Boolean) arrays.\n", + "\n", + "For example, we can address this sort of compound question as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "29" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum((inches > 0.5) & (inches < 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we see that there are 29 days with rainfall between 0.5 and 1.0 inches.\n", + "\n", + "Note that the parentheses here are important–because of operator precedence rules, with parentheses removed this expression would be evaluated as follows, which results in an error:\n", + "\n", + "``` python\n", + "inches > (0.5 & inches) < 1\n", + "```\n", + "\n", + "Using the equivalence of *A AND B* and *NOT (NOT A OR NOT B)* (which you may remember if you've taken an introductory logic course), we can compute the same result in a different manner:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "29" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(~( (inches <= 0.5) | (inches >= 1) ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combining comparison operators and Boolean operators on arrays can lead to a wide range of efficient logical operations.\n", + "\n", + "The following table summarizes the bitwise Boolean operators and their equivalent ufuncs:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Operator\t | Equivalent ufunc || Operator\t | Equivalent ufunc |\n", + "|---------------|---------------------||---------------|---------------------|\n", + "|``&`` |``np.bitwise_and`` ||| |``np.bitwise_or`` |\n", + "|``^`` |``np.bitwise_xor`` ||``~`` |``np.bitwise_not`` |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these tools, we might start to answer the types of questions we have about our weather data.\n", + "Here are some examples of results we can compute when combining masking with aggregations:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number days without rain: 215\n", + "Number days with rain: 150\n", + "Days with more than 0.5 inches: 37\n", + "Rainy days with < 0.2 inches : 75\n" + ] + } + ], + "source": [ + "print(\"Number days without rain: \", np.sum(inches == 0))\n", + "print(\"Number days with rain: \", np.sum(inches != 0))\n", + "print(\"Days with more than 0.5 inches:\", np.sum(inches > 0.5))\n", + "print(\"Rainy days with < 0.2 inches :\", np.sum((inches > 0) &\n", + " (inches < 0.2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boolean Arrays as Masks\n", + "\n", + "In the preceding section we looked at aggregates computed directly on Boolean arrays.\n", + "A more powerful pattern is to use Boolean arrays as masks, to select particular subsets of the data themselves.\n", + "Returning to our ``x`` array from before, suppose we want an array of all values in the array that are less than, say, 5:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5, 0, 3, 3],\n", + " [7, 9, 3, 5],\n", + " [2, 4, 7, 6]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can obtain a Boolean array for this condition easily, as we've already seen:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[False, True, True, True],\n", + " [False, False, True, False],\n", + " [ True, True, False, False]], dtype=bool)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x < 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to *select* these values from the array, we can simply index on this Boolean array; this is known as a *masking* operation:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 3, 3, 3, 2, 4])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[x < 5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is returned is a one-dimensional array filled with all the values that meet this condition; in other words, all the values in positions at which the mask array is ``True``.\n", + "\n", + "We are then free to operate on these values as we wish.\n", + "For example, we can compute some relevant statistics on our Seattle rain data:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Median precip on rainy days in 2014 (inches): 0.194881889764\n", + "Median precip on summer days in 2014 (inches): 0.0\n", + "Maximum precip on summer days in 2014 (inches): 0.850393700787\n", + "Median precip on non-summer rainy days (inches): 0.200787401575\n" + ] + } + ], + "source": [ + "# construct a mask of all rainy days\n", + "rainy = (inches > 0)\n", + "\n", + "# construct a mask of all summer days (June 21st is the 172nd day)\n", + "days = np.arange(365)\n", + "summer = (days > 172) & (days < 262)\n", + "\n", + "print(\"Median precip on rainy days in 2014 (inches): \",\n", + " np.median(inches[rainy]))\n", + "print(\"Median precip on summer days in 2014 (inches): \",\n", + " np.median(inches[summer]))\n", + "print(\"Maximum precip on summer days in 2014 (inches): \",\n", + " np.max(inches[summer]))\n", + "print(\"Median precip on non-summer rainy days (inches):\",\n", + " np.median(inches[rainy & ~summer]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By combining Boolean operations, masking operations, and aggregates, we can very quickly answer these sorts of questions for our dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aside: Using the Keywords and/or Versus the Operators &/|\n", + "\n", + "One common point of confusion is the difference between the keywords ``and`` and ``or`` on one hand, and the operators ``&`` and ``|`` on the other hand.\n", + "When would you use one versus the other?\n", + "\n", + "The difference is this: ``and`` and ``or`` gauge the truth or falsehood of *entire object*, while ``&`` and ``|`` refer to *bits within each object*.\n", + "\n", + "When you use ``and`` or ``or``, it's equivalent to asking Python to treat the object as a single Boolean entity.\n", + "In Python, all nonzero integers will evaluate as True. Thus:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, False)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bool(42), bool(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bool(42 and 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bool(42 or 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you use ``&`` and ``|`` on integers, the expression operates on the bits of the element, applying the *and* or the *or* to the individual bits making up the number:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0b101010'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0b111011'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin(59)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0b101010'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin(42 & 59)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0b111011'" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin(42 | 59)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the corresponding bits of the binary representation are compared in order to yield the result.\n", + "\n", + "When you have an array of Boolean values in NumPy, this can be thought of as a string of bits where ``1 = True`` and ``0 = False``, and the result of ``&`` and ``|`` operates similarly to above:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, False, True, True], dtype=bool)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([1, 0, 1, 0, 1, 0], dtype=bool)\n", + "B = np.array([1, 1, 1, 0, 1, 1], dtype=bool)\n", + "A | B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using ``or`` on these arrays will try to evaluate the truth or falsehood of the entire array object, which is not a well-defined value:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "A or B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, when doing a Boolean expression on a given array, you should use ``|`` or ``&`` rather than ``or`` or ``and``:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, False, False, True, True, True, False, False], dtype=bool)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(10)\n", + "(x > 4) & (x < 8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trying to evaluate the truth or falsehood of the entire array will give the same ``ValueError`` we saw previously:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "(x > 4) and (x < 8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So remember this: ``and`` and ``or`` perform a single Boolean evaluation on an entire object, while ``&`` and ``|`` perform multiple Boolean evaluations on the content (the individual bits or bytes) of an object.\n", + "For Boolean NumPy arrays, the latter is nearly always the desired operation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.06-Boolean-Arrays-and-Masks.md b/notebooks_v2/02.06-Boolean-Arrays-and-Masks.md new file mode 100644 index 00000000..1c0448e5 --- /dev/null +++ b/notebooks_v2/02.06-Boolean-Arrays-and-Masks.md @@ -0,0 +1,391 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) > + +Open in Colab + + + +# Comparisons, Masks, and Boolean Logic + + +This section covers the use of Boolean masks to examine and manipulate values within NumPy arrays. +Masking comes up when you want to extract, modify, count, or otherwise manipulate values in an array based on some criterion: for example, you might wish to count all values greater than a certain value, or perhaps remove all outliers that are above some threshold. +In NumPy, Boolean masking is often the most efficient way to accomplish these types of tasks. + + +## Example: Counting Rainy Days + +Imagine you have a series of data that represents the amount of precipitation each day for a year in a given city. +For example, here we'll load the daily rainfall statistics for the city of Seattle in 2014, using Pandas (which is covered in more detail in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)): + +```python +import numpy as np +import pandas as pd + +# use pandas to extract rainfall inches as a NumPy array +rainfall = pd.read_csv('data/Seattle2014.csv')['PRCP'].values +inches = rainfall / 254.0 # 1/10mm -> inches +inches.shape +``` + +The array contains 365 values, giving daily rainfall in inches from January 1 to December 31, 2014. + +As a first quick visualization, let's look at the histogram of rainy days, which was generated using Matplotlib (we will explore this tool more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)): + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn; seaborn.set() # set plot styles +``` + +```python +plt.hist(inches, 40); +``` + +This histogram gives us a general idea of what the data looks like: despite its reputation, the vast majority of days in Seattle saw near zero measured rainfall in 2014. +But this doesn't do a good job of conveying some information we'd like to see: for example, how many rainy days were there in the year? What is the average precipitation on those rainy days? How many days were there with more than half an inch of rain? + + +### Digging into the data + +One approach to this would be to answer these questions by hand: loop through the data, incrementing a counter each time we see values in some desired range. +For reasons discussed throughout this chapter, such an approach is very inefficient, both from the standpoint of time writing code and time computing the result. +We saw in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) that NumPy's ufuncs can be used in place of loops to do fast element-wise arithmetic operations on arrays; in the same way, we can use other ufuncs to do element-wise *comparisons* over arrays, and we can then manipulate the results to answer the questions we have. +We'll leave the data aside for right now, and discuss some general tools in NumPy to use *masking* to quickly answer these types of questions. + + +## Comparison Operators as ufuncs + +In [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) we introduced ufuncs, and focused in particular on arithmetic operators. We saw that using ``+``, ``-``, ``*``, ``/``, and others on arrays leads to element-wise operations. +NumPy also implements comparison operators such as ``<`` (less than) and ``>`` (greater than) as element-wise ufuncs. +The result of these comparison operators is always an array with a Boolean data type. +All six of the standard comparison operations are available: + +```python +x = np.array([1, 2, 3, 4, 5]) +``` + +```python +x < 3 # less than +``` + +```python +x > 3 # greater than +``` + +```python +x <= 3 # less than or equal +``` + +```python +x >= 3 # greater than or equal +``` + +```python +x != 3 # not equal +``` + +```python +x == 3 # equal +``` + +It is also possible to do an element-wise comparison of two arrays, and to include compound expressions: + +```python +(2 * x) == (x ** 2) +``` + +As in the case of arithmetic operators, the comparison operators are implemented as ufuncs in NumPy; for example, when you write ``x < 3``, internally NumPy uses ``np.less(x, 3)``. + A summary of the comparison operators and their equivalent ufunc is shown here: + +| Operator | Equivalent ufunc || Operator | Equivalent ufunc | +|---------------|---------------------||---------------|---------------------| +|``==`` |``np.equal`` ||``!=`` |``np.not_equal`` | +|``<`` |``np.less`` ||``<=`` |``np.less_equal`` | +|``>`` |``np.greater`` ||``>=`` |``np.greater_equal`` | + + +Just as in the case of arithmetic ufuncs, these will work on arrays of any size and shape. +Here is a two-dimensional example: + +```python +rng = np.random.RandomState(0) +x = rng.randint(10, size=(3, 4)) +x +``` + +```python +x < 6 +``` + +In each case, the result is a Boolean array, and NumPy provides a number of straightforward patterns for working with these Boolean results. + + +## Working with Boolean Arrays + +Given a Boolean array, there are a host of useful operations you can do. +We'll work with ``x``, the two-dimensional array we created earlier. + +```python +print(x) +``` + +### Counting entries + +To count the number of ``True`` entries in a Boolean array, ``np.count_nonzero`` is useful: + +```python +# how many values less than 6? +np.count_nonzero(x < 6) +``` + +We see that there are eight array entries that are less than 6. +Another way to get at this information is to use ``np.sum``; in this case, ``False`` is interpreted as ``0``, and ``True`` is interpreted as ``1``: + +```python +np.sum(x < 6) +``` + +The benefit of ``sum()`` is that like with other NumPy aggregation functions, this summation can be done along rows or columns as well: + +```python +# how many values less than 6 in each row? +np.sum(x < 6, axis=1) +``` + +This counts the number of values less than 6 in each row of the matrix. + +If we're interested in quickly checking whether any or all the values are true, we can use (you guessed it) ``np.any`` or ``np.all``: + +```python +# are there any values greater than 8? +np.any(x > 8) +``` + +```python +# are there any values less than zero? +np.any(x < 0) +``` + +```python +# are all values less than 10? +np.all(x < 10) +``` + +```python +# are all values equal to 6? +np.all(x == 6) +``` + +``np.all`` and ``np.any`` can be used along particular axes as well. For example: + +```python +# are all values in each row less than 8? +np.all(x < 8, axis=1) +``` + +Here all the elements in the first and third rows are less than 8, while this is not the case for the second row. + +Finally, a quick warning: as mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb), Python has built-in ``sum()``, ``any()``, and ``all()`` functions. These have a different syntax than the NumPy versions, and in particular will fail or produce unintended results when used on multidimensional arrays. Be sure that you are using ``np.sum()``, ``np.any()``, and ``np.all()`` for these examples! + + +### Boolean operators + +We've already seen how we might count, say, all days with rain less than four inches, or all days with rain greater than two inches. +But what if we want to know about all days with rain less than four inches and greater than one inch? +This is accomplished through Python's *bitwise logic operators*, ``&``, ``|``, ``^``, and ``~``. +Like with the standard arithmetic operators, NumPy overloads these as ufuncs which work element-wise on (usually Boolean) arrays. + +For example, we can address this sort of compound question as follows: + +```python +np.sum((inches > 0.5) & (inches < 1)) +``` + + +So we see that there are 29 days with rainfall between 0.5 and 1.0 inches. + +Note that the parentheses here are important–because of operator precedence rules, with parentheses removed this expression would be evaluated as follows, which results in an error: + +``` python +inches > (0.5 & inches) < 1 +``` + +Using the equivalence of *A AND B* and *NOT (NOT A OR NOT B)* (which you may remember if you've taken an introductory logic course), we can compute the same result in a different manner: + + +```python +np.sum(~( (inches <= 0.5) | (inches >= 1) )) +``` + +Combining comparison operators and Boolean operators on arrays can lead to a wide range of efficient logical operations. + +The following table summarizes the bitwise Boolean operators and their equivalent ufuncs: + + +| Operator | Equivalent ufunc || Operator | Equivalent ufunc | +|---------------|---------------------||---------------|---------------------| +|``&`` |``np.bitwise_and`` ||| |``np.bitwise_or`` | +|``^`` |``np.bitwise_xor`` ||``~`` |``np.bitwise_not`` | + + +Using these tools, we might start to answer the types of questions we have about our weather data. +Here are some examples of results we can compute when combining masking with aggregations: + +```python +print("Number days without rain: ", np.sum(inches == 0)) +print("Number days with rain: ", np.sum(inches != 0)) +print("Days with more than 0.5 inches:", np.sum(inches > 0.5)) +print("Rainy days with < 0.2 inches :", np.sum((inches > 0) & + (inches < 0.2))) +``` + +## Boolean Arrays as Masks + +In the preceding section we looked at aggregates computed directly on Boolean arrays. +A more powerful pattern is to use Boolean arrays as masks, to select particular subsets of the data themselves. +Returning to our ``x`` array from before, suppose we want an array of all values in the array that are less than, say, 5: + +```python +x +``` + +We can obtain a Boolean array for this condition easily, as we've already seen: + +```python +x < 5 +``` + +Now to *select* these values from the array, we can simply index on this Boolean array; this is known as a *masking* operation: + +```python +x[x < 5] +``` + +What is returned is a one-dimensional array filled with all the values that meet this condition; in other words, all the values in positions at which the mask array is ``True``. + +We are then free to operate on these values as we wish. +For example, we can compute some relevant statistics on our Seattle rain data: + +```python +# construct a mask of all rainy days +rainy = (inches > 0) + +# construct a mask of all summer days (June 21st is the 172nd day) +days = np.arange(365) +summer = (days > 172) & (days < 262) + +print("Median precip on rainy days in 2014 (inches): ", + np.median(inches[rainy])) +print("Median precip on summer days in 2014 (inches): ", + np.median(inches[summer])) +print("Maximum precip on summer days in 2014 (inches): ", + np.max(inches[summer])) +print("Median precip on non-summer rainy days (inches):", + np.median(inches[rainy & ~summer])) +``` + +By combining Boolean operations, masking operations, and aggregates, we can very quickly answer these sorts of questions for our dataset. + + +## Aside: Using the Keywords and/or Versus the Operators &/| + +One common point of confusion is the difference between the keywords ``and`` and ``or`` on one hand, and the operators ``&`` and ``|`` on the other hand. +When would you use one versus the other? + +The difference is this: ``and`` and ``or`` gauge the truth or falsehood of *entire object*, while ``&`` and ``|`` refer to *bits within each object*. + +When you use ``and`` or ``or``, it's equivalent to asking Python to treat the object as a single Boolean entity. +In Python, all nonzero integers will evaluate as True. Thus: + +```python +bool(42), bool(0) +``` + +```python +bool(42 and 0) +``` + +```python +bool(42 or 0) +``` + +When you use ``&`` and ``|`` on integers, the expression operates on the bits of the element, applying the *and* or the *or* to the individual bits making up the number: + +```python +bin(42) +``` + +```python +bin(59) +``` + +```python +bin(42 & 59) +``` + +```python +bin(42 | 59) +``` + +Notice that the corresponding bits of the binary representation are compared in order to yield the result. + +When you have an array of Boolean values in NumPy, this can be thought of as a string of bits where ``1 = True`` and ``0 = False``, and the result of ``&`` and ``|`` operates similarly to above: + +```python +A = np.array([1, 0, 1, 0, 1, 0], dtype=bool) +B = np.array([1, 1, 1, 0, 1, 1], dtype=bool) +A | B +``` + +Using ``or`` on these arrays will try to evaluate the truth or falsehood of the entire array object, which is not a well-defined value: + +```python +A or B +``` + +Similarly, when doing a Boolean expression on a given array, you should use ``|`` or ``&`` rather than ``or`` or ``and``: + +```python +x = np.arange(10) +(x > 4) & (x < 8) +``` + +Trying to evaluate the truth or falsehood of the entire array will give the same ``ValueError`` we saw previously: + +```python +(x > 4) and (x < 8) +``` + +So remember this: ``and`` and ``or`` perform a single Boolean evaluation on an entire object, while ``&`` and ``|`` perform multiple Boolean evaluations on the content (the individual bits or bytes) of an object. +For Boolean NumPy arrays, the latter is nearly always the desired operation. + + + +< [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) | [Contents](Index.ipynb) | [Fancy Indexing](02.07-Fancy-Indexing.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.07-Fancy-Indexing.ipynb b/notebooks_v2/02.07-Fancy-Indexing.ipynb new file mode 100644 index 00000000..26c27f72 --- /dev/null +++ b/notebooks_v2/02.07-Fancy-Indexing.ipynb @@ -0,0 +1,935 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fancy Indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous sections, we saw how to access and modify portions of arrays using simple indices (e.g., ``arr[0]``), slices (e.g., ``arr[:5]``), and Boolean masks (e.g., ``arr[arr > 0]``).\n", + "In this section, we'll look at another style of array indexing, known as *fancy indexing*.\n", + "Fancy indexing is like the simple indexing we've already seen, but we pass arrays of indices in place of single scalars.\n", + "This allows us to very quickly access and modify complicated subsets of an array's values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring Fancy Indexing\n", + "\n", + "Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once.\n", + "For example, consider the following array:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[51 92 14 71 60 20 82 86 74 74]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "rand = np.random.RandomState(42)\n", + "\n", + "x = rand.randint(100, size=10)\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose we want to access three different elements. We could do it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[71, 86, 14]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[x[3], x[7], x[2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can pass a single list or array of indices to obtain the same result:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([71, 86, 60])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ind = [3, 7, 4]\n", + "x[ind]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using fancy indexing, the shape of the result reflects the shape of the *index arrays* rather than the shape of the *array being indexed*:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[71, 86],\n", + " [60, 20]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ind = np.array([[3, 7],\n", + " [4, 5]])\n", + "x[ind]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fancy indexing also works in multiple dimensions. Consider the following array:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = np.arange(12).reshape((3, 4))\n", + "X" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like with standard indexing, the first index refers to the row, and the second to the column:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 5, 11])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row = np.array([0, 1, 2])\n", + "col = np.array([2, 1, 3])\n", + "X[row, col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the first value in the result is ``X[0, 2]``, the second is ``X[1, 1]``, and the third is ``X[2, 3]``.\n", + "The pairing of indices in fancy indexing follows all the broadcasting rules that were mentioned in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb).\n", + "So, for example, if we combine a column vector and a row vector within the indices, we get a two-dimensional result:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2, 1, 3],\n", + " [ 6, 5, 7],\n", + " [10, 9, 11]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[row[:, np.newaxis], col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, each row value is matched with each column vector, exactly as we saw in broadcasting of arithmetic operations.\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 0],\n", + " [2, 1, 3],\n", + " [4, 2, 6]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row[:, np.newaxis] * col" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is always important to remember with fancy indexing that the return value reflects the *broadcasted shape of the indices*, rather than the shape of the array being indexed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combined Indexing\n", + "\n", + "For even more powerful operations, fancy indexing can be combined with the other indexing schemes we've seen:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]\n" + ] + } + ], + "source": [ + "print(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can combine fancy and simple indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10, 8, 9])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[2, [2, 0, 1]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also combine fancy indexing with slicing:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 6, 4, 5],\n", + " [10, 8, 9]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[1:, [2, 0, 1]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can combine fancy indexing with masking:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 2],\n", + " [ 4, 6],\n", + " [ 8, 10]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = np.array([1, 0, 1, 0], dtype=bool)\n", + "X[row[:, np.newaxis], mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All of these indexing options combined lead to a very flexible set of operations for accessing and modifying array values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Selecting Random Points\n", + "\n", + "One common use of fancy indexing is the selection of subsets of rows from a matrix.\n", + "For example, we might have an $N$ by $D$ matrix representing $N$ points in $D$ dimensions, such as the following points drawn from a two-dimensional normal distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 2)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = [0, 0]\n", + "cov = [[1, 2],\n", + " [2, 5]]\n", + "X = rand.multivariate_normal(mean, cov, 100)\n", + "X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the plotting tools we will discuss in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb), we can visualize these points as a scatter-plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn; seaborn.set() # for plot styling\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use fancy indexing to select 20 random points. We'll do this by first choosing 20 random indices with no repeats, and use these indices to select a portion of the original array:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([93, 45, 73, 81, 50, 10, 98, 94, 4, 64, 65, 89, 47, 84, 82, 80, 25,\n", + " 90, 63, 20])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = np.random.choice(X.shape[0], 20, replace=False)\n", + "indices" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(20, 2)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selection = X[indices] # fancy indexing here\n", + "selection.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to see which points were selected, let's over-plot large circles at the locations of the selected points:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YsACHDx/G448/DgDYuHGjlMUTUS/jKzl3rjXu338UqqrOYcyYBJSX17hnW2s0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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], alpha=0.3)\n", + "plt.scatter(selection[:, 0], selection[:, 1],\n", + " facecolor='none', s=200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This sort of strategy is often used to quickly partition datasets, as is often needed in train/test splitting for validation of statistical models (see [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)), and in sampling approaches to answering statistical questions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modifying Values with Fancy Indexing\n", + "\n", + "Just as fancy indexing can be used to access parts of an array, it can also be used to modify parts of an array.\n", + "For example, imagine we have an array of indices and we'd like to set the corresponding items in an array to some value:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 99 99 3 99 5 6 7 99 9]\n" + ] + } + ], + "source": [ + "x = np.arange(10)\n", + "i = np.array([2, 1, 8, 4])\n", + "x[i] = 99\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use any assignment-type operator for this. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 89 89 3 89 5 6 7 89 9]\n" + ] + } + ], + "source": [ + "x[i] -= 10\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice, though, that repeated indices with these operations can cause some potentially unexpected results. Consider the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "x = np.zeros(10)\n", + "x[[0, 0]] = [4, 6]\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Where did the 4 go? The result of this operation is to first assign ``x[0] = 4``, followed by ``x[0] = 6``.\n", + "The result, of course, is that ``x[0]`` contains the value 6.\n", + "\n", + "Fair enough, but consider this operation:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 6., 0., 1., 1., 1., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = [2, 3, 3, 4, 4, 4]\n", + "x[i] += 1\n", + "x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might expect that ``x[3]`` would contain the value 2, and ``x[4]`` would contain the value 3, as this is how many times each index is repeated. Why is this not the case?\n", + "Conceptually, this is because ``x[i] += 1`` is meant as a shorthand of ``x[i] = x[i] + 1``. ``x[i] + 1`` is evaluated, and then the result is assigned to the indices in x.\n", + "With this in mind, it is not the augmentation that happens multiple times, but the assignment, which leads to the rather nonintuitive results.\n", + "\n", + "So what if you want the other behavior where the operation is repeated? For this, you can use the ``at()`` method of ufuncs (available since NumPy 1.8), and do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 0. 1. 2. 3. 0. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "x = np.zeros(10)\n", + "np.add.at(x, i, 1)\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``at()`` method does an in-place application of the given operator at the specified indices (here, ``i``) with the specified value (here, 1).\n", + "Another method that is similar in spirit is the ``reduceat()`` method of ufuncs, which you can read about in the NumPy documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Binning Data\n", + "\n", + "You can use these ideas to efficiently bin data to create a histogram by hand.\n", + "For example, imagine we have 1,000 values and would like to quickly find where they fall within an array of bins.\n", + "We could compute it using ``ufunc.at`` like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "x = np.random.randn(100)\n", + "\n", + "# compute a histogram by hand\n", + "bins = np.linspace(-5, 5, 20)\n", + "counts = np.zeros_like(bins)\n", + "\n", + "# find the appropriate bin for each x\n", + "i = np.searchsorted(bins, x)\n", + "\n", + "# add 1 to each of these bins\n", + "np.add.at(counts, i, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The counts now reflect the number of points within each bin–in other words, a histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results\n", + "plt.plot(bins, counts, linestyle='steps');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, it would be silly to have to do this each time you want to plot a histogram.\n", + "This is why Matplotlib provides the ``plt.hist()`` routine, which does the same in a single line:\n", + "\n", + "```python\n", + "plt.hist(x, bins, histtype='step');\n", + "```\n", + "\n", + "This function will create a nearly identical plot to the one seen here.\n", + "To compute the binning, ``matplotlib`` uses the ``np.histogram`` function, which does a very similar computation to what we did before. Let's compare the two here:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumPy routine:\n", + "10000 loops, best of 3: 97.6 µs per loop\n", + "Custom routine:\n", + "10000 loops, best of 3: 19.5 µs per loop\n" + ] + } + ], + "source": [ + "print(\"NumPy routine:\")\n", + "%timeit counts, edges = np.histogram(x, bins)\n", + "\n", + "print(\"Custom routine:\")\n", + "%timeit np.add.at(counts, np.searchsorted(bins, x), 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our own one-line algorithm is several times faster than the optimized algorithm in NumPy! How can this be?\n", + "If you dig into the ``np.histogram`` source code (you can do this in IPython by typing ``np.histogram??``), you'll see that it's quite a bit more involved than the simple search-and-count that we've done; this is because NumPy's algorithm is more flexible, and particularly is designed for better performance when the number of data points becomes large:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumPy routine:\n", + "10 loops, best of 3: 68.7 ms per loop\n", + "Custom routine:\n", + "10 loops, best of 3: 135 ms per loop\n" + ] + } + ], + "source": [ + "x = np.random.randn(1000000)\n", + "print(\"NumPy routine:\")\n", + "%timeit counts, edges = np.histogram(x, bins)\n", + "\n", + "print(\"Custom routine:\")\n", + "%timeit np.add.at(counts, np.searchsorted(bins, x), 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What this comparison shows is that algorithmic efficiency is almost never a simple question. An algorithm efficient for large datasets will not always be the best choice for small datasets, and vice versa (see [Big-O Notation](02.08-Sorting.ipynb#Aside:-Big-O-Notation)).\n", + "But the advantage of coding this algorithm yourself is that with an understanding of these basic methods, you could use these building blocks to extend this to do some very interesting custom behaviors.\n", + "The key to efficiently using Python in data-intensive applications is knowing about general convenience routines like ``np.histogram`` and when they're appropriate, but also knowing how to make use of lower-level functionality when you need more pointed behavior." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.07-Fancy-Indexing.md b/notebooks_v2/02.07-Fancy-Indexing.md new file mode 100644 index 00000000..f6d2b832 --- /dev/null +++ b/notebooks_v2/02.07-Fancy-Indexing.md @@ -0,0 +1,306 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) > + +Open in Colab + + + +# Fancy Indexing + + +In the previous sections, we saw how to access and modify portions of arrays using simple indices (e.g., ``arr[0]``), slices (e.g., ``arr[:5]``), and Boolean masks (e.g., ``arr[arr > 0]``). +In this section, we'll look at another style of array indexing, known as *fancy indexing*. +Fancy indexing is like the simple indexing we've already seen, but we pass arrays of indices in place of single scalars. +This allows us to very quickly access and modify complicated subsets of an array's values. + + +## Exploring Fancy Indexing + +Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once. +For example, consider the following array: + +```python +import numpy as np +rand = np.random.RandomState(42) + +x = rand.randint(100, size=10) +print(x) +``` + +Suppose we want to access three different elements. We could do it like this: + +```python +[x[3], x[7], x[2]] +``` + +Alternatively, we can pass a single list or array of indices to obtain the same result: + +```python +ind = [3, 7, 4] +x[ind] +``` + +When using fancy indexing, the shape of the result reflects the shape of the *index arrays* rather than the shape of the *array being indexed*: + +```python +ind = np.array([[3, 7], + [4, 5]]) +x[ind] +``` + +Fancy indexing also works in multiple dimensions. Consider the following array: + +```python +X = np.arange(12).reshape((3, 4)) +X +``` + +Like with standard indexing, the first index refers to the row, and the second to the column: + +```python +row = np.array([0, 1, 2]) +col = np.array([2, 1, 3]) +X[row, col] +``` + +Notice that the first value in the result is ``X[0, 2]``, the second is ``X[1, 1]``, and the third is ``X[2, 3]``. +The pairing of indices in fancy indexing follows all the broadcasting rules that were mentioned in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb). +So, for example, if we combine a column vector and a row vector within the indices, we get a two-dimensional result: + +```python +X[row[:, np.newaxis], col] +``` + +Here, each row value is matched with each column vector, exactly as we saw in broadcasting of arithmetic operations. +For example: + +```python +row[:, np.newaxis] * col +``` + +It is always important to remember with fancy indexing that the return value reflects the *broadcasted shape of the indices*, rather than the shape of the array being indexed. + + +## Combined Indexing + +For even more powerful operations, fancy indexing can be combined with the other indexing schemes we've seen: + +```python +print(X) +``` + +We can combine fancy and simple indices: + +```python +X[2, [2, 0, 1]] +``` + +We can also combine fancy indexing with slicing: + +```python +X[1:, [2, 0, 1]] +``` + +And we can combine fancy indexing with masking: + +```python +mask = np.array([1, 0, 1, 0], dtype=bool) +X[row[:, np.newaxis], mask] +``` + +All of these indexing options combined lead to a very flexible set of operations for accessing and modifying array values. + + +## Example: Selecting Random Points + +One common use of fancy indexing is the selection of subsets of rows from a matrix. +For example, we might have an $N$ by $D$ matrix representing $N$ points in $D$ dimensions, such as the following points drawn from a two-dimensional normal distribution: + +```python +mean = [0, 0] +cov = [[1, 2], + [2, 5]] +X = rand.multivariate_normal(mean, cov, 100) +X.shape +``` + +Using the plotting tools we will discuss in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb), we can visualize these points as a scatter-plot: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn; seaborn.set() # for plot styling + +plt.scatter(X[:, 0], X[:, 1]); +``` + +Let's use fancy indexing to select 20 random points. We'll do this by first choosing 20 random indices with no repeats, and use these indices to select a portion of the original array: + +```python +indices = np.random.choice(X.shape[0], 20, replace=False) +indices +``` + +```python +selection = X[indices] # fancy indexing here +selection.shape +``` + +Now to see which points were selected, let's over-plot large circles at the locations of the selected points: + +```python +plt.scatter(X[:, 0], X[:, 1], alpha=0.3) +plt.scatter(selection[:, 0], selection[:, 1], + facecolor='none', s=200); +``` + +This sort of strategy is often used to quickly partition datasets, as is often needed in train/test splitting for validation of statistical models (see [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)), and in sampling approaches to answering statistical questions. + + +## Modifying Values with Fancy Indexing + +Just as fancy indexing can be used to access parts of an array, it can also be used to modify parts of an array. +For example, imagine we have an array of indices and we'd like to set the corresponding items in an array to some value: + +```python +x = np.arange(10) +i = np.array([2, 1, 8, 4]) +x[i] = 99 +print(x) +``` + +We can use any assignment-type operator for this. For example: + +```python +x[i] -= 10 +print(x) +``` + +Notice, though, that repeated indices with these operations can cause some potentially unexpected results. Consider the following: + +```python +x = np.zeros(10) +x[[0, 0]] = [4, 6] +print(x) +``` + +Where did the 4 go? The result of this operation is to first assign ``x[0] = 4``, followed by ``x[0] = 6``. +The result, of course, is that ``x[0]`` contains the value 6. + +Fair enough, but consider this operation: + +```python +i = [2, 3, 3, 4, 4, 4] +x[i] += 1 +x +``` + +You might expect that ``x[3]`` would contain the value 2, and ``x[4]`` would contain the value 3, as this is how many times each index is repeated. Why is this not the case? +Conceptually, this is because ``x[i] += 1`` is meant as a shorthand of ``x[i] = x[i] + 1``. ``x[i] + 1`` is evaluated, and then the result is assigned to the indices in x. +With this in mind, it is not the augmentation that happens multiple times, but the assignment, which leads to the rather nonintuitive results. + +So what if you want the other behavior where the operation is repeated? For this, you can use the ``at()`` method of ufuncs (available since NumPy 1.8), and do the following: + +```python +x = np.zeros(10) +np.add.at(x, i, 1) +print(x) +``` + +The ``at()`` method does an in-place application of the given operator at the specified indices (here, ``i``) with the specified value (here, 1). +Another method that is similar in spirit is the ``reduceat()`` method of ufuncs, which you can read about in the NumPy documentation. + + +## Example: Binning Data + +You can use these ideas to efficiently bin data to create a histogram by hand. +For example, imagine we have 1,000 values and would like to quickly find where they fall within an array of bins. +We could compute it using ``ufunc.at`` like this: + +```python +np.random.seed(42) +x = np.random.randn(100) + +# compute a histogram by hand +bins = np.linspace(-5, 5, 20) +counts = np.zeros_like(bins) + +# find the appropriate bin for each x +i = np.searchsorted(bins, x) + +# add 1 to each of these bins +np.add.at(counts, i, 1) +``` + +The counts now reflect the number of points within each bin–in other words, a histogram: + +```python +# plot the results +plt.plot(bins, counts, linestyle='steps'); +``` + + +Of course, it would be silly to have to do this each time you want to plot a histogram. +This is why Matplotlib provides the ``plt.hist()`` routine, which does the same in a single line: + +```python +plt.hist(x, bins, histtype='step'); +``` + +This function will create a nearly identical plot to the one seen here. +To compute the binning, ``matplotlib`` uses the ``np.histogram`` function, which does a very similar computation to what we did before. Let's compare the two here: + + +```python +print("NumPy routine:") +%timeit counts, edges = np.histogram(x, bins) + +print("Custom routine:") +%timeit np.add.at(counts, np.searchsorted(bins, x), 1) +``` + +Our own one-line algorithm is several times faster than the optimized algorithm in NumPy! How can this be? +If you dig into the ``np.histogram`` source code (you can do this in IPython by typing ``np.histogram??``), you'll see that it's quite a bit more involved than the simple search-and-count that we've done; this is because NumPy's algorithm is more flexible, and particularly is designed for better performance when the number of data points becomes large: + +```python +x = np.random.randn(1000000) +print("NumPy routine:") +%timeit counts, edges = np.histogram(x, bins) + +print("Custom routine:") +%timeit np.add.at(counts, np.searchsorted(bins, x), 1) +``` + +What this comparison shows is that algorithmic efficiency is almost never a simple question. An algorithm efficient for large datasets will not always be the best choice for small datasets, and vice versa (see [Big-O Notation](02.08-Sorting.ipynb#Aside:-Big-O-Notation)). +But the advantage of coding this algorithm yourself is that with an understanding of these basic methods, you could use these building blocks to extend this to do some very interesting custom behaviors. +The key to efficiently using Python in data-intensive applications is knowing about general convenience routines like ``np.histogram`` and when they're appropriate, but also knowing how to make use of lower-level functionality when you need more pointed behavior. + + + +< [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) | [Contents](Index.ipynb) | [Sorting Arrays](02.08-Sorting.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.08-Sorting.ipynb b/notebooks_v2/02.08-Sorting.ipynb new file mode 100644 index 00000000..8213646c --- /dev/null +++ b/notebooks_v2/02.08-Sorting.ipynb @@ -0,0 +1,789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sorting Arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Up to this point we have been concerned mainly with tools to access and operate on array data with NumPy.\n", + "This section covers algorithms related to sorting values in NumPy arrays.\n", + "These algorithms are a favorite topic in introductory computer science courses: if you've ever taken one, you probably have had dreams (or, depending on your temperament, nightmares) about *insertion sorts*, *selection sorts*, *merge sorts*, *quick sorts*, *bubble sorts*, and many, many more.\n", + "All are means of accomplishing a similar task: sorting the values in a list or array.\n", + "\n", + "For example, a simple *selection sort* repeatedly finds the minimum value from a list, and makes swaps until the list is sorted. We can code this in just a few lines of Python:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def selection_sort(x):\n", + " for i in range(len(x)):\n", + " swap = i + np.argmin(x[i:])\n", + " (x[i], x[swap]) = (x[swap], x[i])\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([2, 1, 4, 3, 5])\n", + "selection_sort(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As any first-year computer science major will tell you, the selection sort is useful for its simplicity, but is much too slow to be useful for larger arrays.\n", + "For a list of $N$ values, it requires $N$ loops, each of which does on order $\\sim N$ comparisons to find the swap value.\n", + "In terms of the \"big-O\" notation often used to characterize these algorithms (see [Big-O Notation](#Aside:-Big-O-Notation)), selection sort averages $\\mathcal{O}[N^2]$: if you double the number of items in the list, the execution time will go up by about a factor of four.\n", + "\n", + "Even selection sort, though, is much better than my all-time favorite sorting algorithms, the *bogosort*:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def bogosort(x):\n", + " while np.any(x[:-1] > x[1:]):\n", + " np.random.shuffle(x)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([2, 1, 4, 3, 5])\n", + "bogosort(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This silly sorting method relies on pure chance: it repeatedly applies a random shuffling of the array until the result happens to be sorted.\n", + "With an average scaling of $\\mathcal{O}[N \\times N!]$, (that's *N* times *N* factorial) this should–quite obviously–never be used for any real computation.\n", + "\n", + "Fortunately, Python contains built-in sorting algorithms that are *much* more efficient than either of the simplistic algorithms just shown. We'll start by looking at the Python built-ins, and then take a look at the routines included in NumPy and optimized for NumPy arrays." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fast Sorting in NumPy: ``np.sort`` and ``np.argsort``\n", + "\n", + "Although Python has built-in ``sort`` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes.\n", + "By default ``np.sort`` uses an $\\mathcal{O}[N\\log N]$, *quicksort* algorithm, though *mergesort* and *heapsort* are also available. For most applications, the default quicksort is more than sufficient.\n", + "\n", + "To return a sorted version of the array without modifying the input, you can use ``np.sort``:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([2, 1, 4, 3, 5])\n", + "np.sort(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you prefer to sort the array in-place, you can instead use the ``sort`` method of arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4 5]\n" + ] + } + ], + "source": [ + "x.sort()\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A related function is ``argsort``, which instead returns the *indices* of the sorted elements:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 3 2 4]\n" + ] + } + ], + "source": [ + "x = np.array([2, 1, 4, 3, 5])\n", + "i = np.argsort(x)\n", + "print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first element of this result gives the index of the smallest element, the second value gives the index of the second smallest, and so on.\n", + "These indices can then be used (via fancy indexing) to construct the sorted array if desired:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sorting along rows or columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A useful feature of NumPy's sorting algorithms is the ability to sort along specific rows or columns of a multidimensional array using the ``axis`` argument. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[6 3 7 4 6 9]\n", + " [2 6 7 4 3 7]\n", + " [7 2 5 4 1 7]\n", + " [5 1 4 0 9 5]]\n" + ] + } + ], + "source": [ + "rand = np.random.RandomState(42)\n", + "X = rand.randint(0, 10, (4, 6))\n", + "print(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 1, 4, 0, 1, 5],\n", + " [5, 2, 5, 4, 3, 7],\n", + " [6, 3, 7, 4, 6, 7],\n", + " [7, 6, 7, 4, 9, 9]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sort each column of X\n", + "np.sort(X, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3, 4, 6, 6, 7, 9],\n", + " [2, 3, 4, 6, 7, 7],\n", + " [1, 2, 4, 5, 7, 7],\n", + " [0, 1, 4, 5, 5, 9]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sort each row of X\n", + "np.sort(X, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind that this treats each row or column as an independent array, and any relationships between the row or column values will be lost!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partial Sorts: Partitioning\n", + "\n", + "Sometimes we're not interested in sorting the entire array, but simply want to find the *k* smallest values in the array. NumPy provides this in the ``np.partition`` function. ``np.partition`` takes an array and a number *K*; the result is a new array with the smallest *K* values to the left of the partition, and the remaining values to the right, in arbitrary order:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4, 6, 5, 7])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([7, 2, 3, 1, 6, 5, 4])\n", + "np.partition(x, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the first three values in the resulting array are the three smallest in the array, and the remaining array positions contain the remaining values.\n", + "Within the two partitions, the elements have arbitrary order.\n", + "\n", + "Similarly to sorting, we can partition along an arbitrary axis of a multidimensional array:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3, 4, 6, 7, 6, 9],\n", + " [2, 3, 4, 7, 6, 7],\n", + " [1, 2, 4, 5, 7, 7],\n", + " [0, 1, 4, 5, 9, 5]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.partition(X, 2, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an array where the first two slots in each row contain the smallest values from that row, with the remaining values filling the remaining slots.\n", + "\n", + "Finally, just as there is a ``np.argsort`` that computes indices of the sort, there is a ``np.argpartition`` that computes indices of the partition.\n", + "We'll see this in action in the following section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: k-Nearest Neighbors\n", + "\n", + "Let's quickly see how we might use this ``argsort`` function along multiple axes to find the nearest neighbors of each point in a set.\n", + "We'll start by creating a random set of 10 points on a two-dimensional plane.\n", + "Using the standard convention, we'll arrange these in a $10\\times 2$ array:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = rand.rand(10, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get an idea of how these points look, let's quickly scatter plot them:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn; seaborn.set() # Plot styling\n", + "plt.scatter(X[:, 0], X[:, 1], s=100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll compute the distance between each pair of points.\n", + "Recall that the squared-distance between two points is the sum of the squared differences in each dimension;\n", + "using the efficient broadcasting ([Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)) and aggregation ([Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)) routines provided by NumPy we can compute the matrix of square distances in a single line of code:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dist_sq = np.sum((X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2, axis=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This operation has a lot packed into it, and it might be a bit confusing if you're unfamiliar with NumPy's broadcasting rules. When you come across code like this, it can be useful to break it down into its component steps:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 10, 2)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for each pair of points, compute differences in their coordinates\n", + "differences = X[:, np.newaxis, :] - X[np.newaxis, :, :]\n", + "differences.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 10, 2)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# square the coordinate differences\n", + "sq_differences = differences ** 2\n", + "sq_differences.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 10)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sum the coordinate differences to get the squared distance\n", + "dist_sq = sq_differences.sum(-1)\n", + "dist_sq.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just to double-check what we are doing, we should see that the diagonal of this matrix (i.e., the set of distances between each point and itself) is all zero:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dist_sq.diagonal()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It checks out!\n", + "With the pairwise square-distances converted, we can now use ``np.argsort`` to sort along each row. The leftmost columns will then give the indices of the nearest neighbors:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 3 9 7 1 4 2 5 6 8]\n", + " [1 4 7 9 3 6 8 5 0 2]\n", + " [2 1 4 6 3 0 8 9 7 5]\n", + " [3 9 7 0 1 4 5 8 6 2]\n", + " [4 1 8 5 6 7 9 3 0 2]\n", + " [5 8 6 4 1 7 9 3 2 0]\n", + " [6 8 5 4 1 7 9 3 2 0]\n", + " [7 9 3 1 4 0 5 8 6 2]\n", + " [8 5 6 4 1 7 9 3 2 0]\n", + " [9 7 3 0 1 4 5 8 6 2]]\n" + ] + } + ], + "source": [ + "nearest = np.argsort(dist_sq, axis=1)\n", + "print(nearest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the first column gives the numbers 0 through 9 in order: this is due to the fact that each point's closest neighbor is itself, as we would expect.\n", + "\n", + "By using a full sort here, we've actually done more work than we need to in this case. If we're simply interested in the nearest $k$ neighbors, all we need is to partition each row so that the smallest $k + 1$ squared distances come first, with larger distances filling the remaining positions of the array. We can do this with the ``np.argpartition`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "K = 2\n", + "nearest_partition = np.argpartition(dist_sq, K + 1, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to visualize this network of neighbors, let's quickly plot the points along with lines representing the connections from each point to its two nearest neighbors:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RaLCxqUVMzFoSEp4dvjE3P4eDw358fJywsLDI1mfqU//5TR7rKYQeUalUfPbZ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\naNmyla5LEULkAQluoRMJCQlERp4mPPw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], s=100)\n", + "\n", + "# draw lines from each point to its two nearest neighbors\n", + "K = 2\n", + "\n", + "for i in range(X.shape[0]):\n", + " for j in nearest_partition[i, :K+1]:\n", + " # plot a line from X[i] to X[j]\n", + " # use some zip magic to make it happen:\n", + " plt.plot(*zip(X[j], X[i]), color='black')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each point in the plot has lines drawn to its two nearest neighbors.\n", + "At first glance, it might seem strange that some of the points have more than two lines coming out of them: this is due to the fact that if point A is one of the two nearest neighbors of point B, this does not necessarily imply that point B is one of the two nearest neighbors of point A.\n", + "\n", + "Although the broadcasting and row-wise sorting of this approach might seem less straightforward than writing a loop, it turns out to be a very efficient way of operating on this data in Python.\n", + "You might be tempted to do the same type of operation by manually looping through the data and sorting each set of neighbors individually, but this would almost certainly lead to a slower algorithm than the vectorized version we used. The beauty of this approach is that it's written in a way that's agnostic to the size of the input data: we could just as easily compute the neighbors among 100 or 1,000,000 points in any number of dimensions, and the code would look the same.\n", + "\n", + "Finally, I'll note that when doing very large nearest neighbor searches, there are tree-based and/or approximate algorithms that can scale as $\\mathcal{O}[N\\log N]$ or better rather than the $\\mathcal{O}[N^2]$ of the brute-force algorithm. One example of this is the KD-Tree, [implemented in Scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aside: Big-O Notation\n", + "\n", + "Big-O notation is a means of describing how the number of operations required for an algorithm scales as the input grows in size.\n", + "To use it correctly is to dive deeply into the realm of computer science theory, and to carefully distinguish it from the related small-o notation, big-$\\theta$ notation, big-$\\Omega$ notation, and probably many mutant hybrids thereof.\n", + "While these distinctions add precision to statements about algorithmic scaling, outside computer science theory exams and the remarks of pedantic blog commenters, you'll rarely see such distinctions made in practice.\n", + "Far more common in the data science world is a less rigid use of big-O notation: as a general (if imprecise) description of the scaling of an algorithm.\n", + "With apologies to theorists and pedants, this is the interpretation we'll use throughout this book.\n", + "\n", + "Big-O notation, in this loose sense, tells you how much time your algorithm will take as you increase the amount of data.\n", + "If you have an $\\mathcal{O}[N]$ (read \"order $N$\") algorithm that takes 1 second to operate on a list of length *N*=1,000, then you should expect it to take roughly 5 seconds for a list of length *N*=5,000.\n", + "If you have an $\\mathcal{O}[N^2]$ (read \"order *N* squared\") algorithm that takes 1 second for *N*=1000, then you should expect it to take about 25 seconds for *N*=5000.\n", + "\n", + "For our purposes, the *N* will usually indicate some aspect of the size of the dataset (the number of points, the number of dimensions, etc.). When trying to analyze billions or trillions of samples, the difference between $\\mathcal{O}[N]$ and $\\mathcal{O}[N^2]$ can be far from trivial!\n", + "\n", + "Notice that the big-O notation by itself tells you nothing about the actual wall-clock time of a computation, but only about its scaling as you change *N*.\n", + "Generally, for example, an $\\mathcal{O}[N]$ algorithm is considered to have better scaling than an $\\mathcal{O}[N^2]$ algorithm, and for good reason. But for small datasets in particular, the algorithm with better scaling might not be faster.\n", + "For example, in a given problem an $\\mathcal{O}[N^2]$ algorithm might take 0.01 seconds, while a \"better\" $\\mathcal{O}[N]$ algorithm might take 1 second.\n", + "Scale up *N* by a factor of 1,000, though, and the $\\mathcal{O}[N]$ algorithm will win out.\n", + "\n", + "Even this loose version of Big-O notation can be very useful when comparing the performance of algorithms, and we'll use this notation throughout the book when talking about how algorithms scale." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.08-Sorting.md b/notebooks_v2/02.08-Sorting.md new file mode 100644 index 00000000..6cc46498 --- /dev/null +++ b/notebooks_v2/02.08-Sorting.md @@ -0,0 +1,282 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) > + +Open in Colab + + + +# Sorting Arrays + + +Up to this point we have been concerned mainly with tools to access and operate on array data with NumPy. +This section covers algorithms related to sorting values in NumPy arrays. +These algorithms are a favorite topic in introductory computer science courses: if you've ever taken one, you probably have had dreams (or, depending on your temperament, nightmares) about *insertion sorts*, *selection sorts*, *merge sorts*, *quick sorts*, *bubble sorts*, and many, many more. +All are means of accomplishing a similar task: sorting the values in a list or array. + +For example, a simple *selection sort* repeatedly finds the minimum value from a list, and makes swaps until the list is sorted. We can code this in just a few lines of Python: + +```python +import numpy as np + +def selection_sort(x): + for i in range(len(x)): + swap = i + np.argmin(x[i:]) + (x[i], x[swap]) = (x[swap], x[i]) + return x +``` + +```python +x = np.array([2, 1, 4, 3, 5]) +selection_sort(x) +``` + +As any first-year computer science major will tell you, the selection sort is useful for its simplicity, but is much too slow to be useful for larger arrays. +For a list of $N$ values, it requires $N$ loops, each of which does on order $\sim N$ comparisons to find the swap value. +In terms of the "big-O" notation often used to characterize these algorithms (see [Big-O Notation](#Aside:-Big-O-Notation)), selection sort averages $\mathcal{O}[N^2]$: if you double the number of items in the list, the execution time will go up by about a factor of four. + +Even selection sort, though, is much better than my all-time favorite sorting algorithms, the *bogosort*: + +```python +def bogosort(x): + while np.any(x[:-1] > x[1:]): + np.random.shuffle(x) + return x +``` + +```python +x = np.array([2, 1, 4, 3, 5]) +bogosort(x) +``` + +This silly sorting method relies on pure chance: it repeatedly applies a random shuffling of the array until the result happens to be sorted. +With an average scaling of $\mathcal{O}[N \times N!]$, (that's *N* times *N* factorial) this should–quite obviously–never be used for any real computation. + +Fortunately, Python contains built-in sorting algorithms that are *much* more efficient than either of the simplistic algorithms just shown. We'll start by looking at the Python built-ins, and then take a look at the routines included in NumPy and optimized for NumPy arrays. + + +## Fast Sorting in NumPy: ``np.sort`` and ``np.argsort`` + +Although Python has built-in ``sort`` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes. +By default ``np.sort`` uses an $\mathcal{O}[N\log N]$, *quicksort* algorithm, though *mergesort* and *heapsort* are also available. For most applications, the default quicksort is more than sufficient. + +To return a sorted version of the array without modifying the input, you can use ``np.sort``: + +```python +x = np.array([2, 1, 4, 3, 5]) +np.sort(x) +``` + +If you prefer to sort the array in-place, you can instead use the ``sort`` method of arrays: + +```python +x.sort() +print(x) +``` + +A related function is ``argsort``, which instead returns the *indices* of the sorted elements: + +```python +x = np.array([2, 1, 4, 3, 5]) +i = np.argsort(x) +print(i) +``` + +The first element of this result gives the index of the smallest element, the second value gives the index of the second smallest, and so on. +These indices can then be used (via fancy indexing) to construct the sorted array if desired: + +```python +x[i] +``` + +### Sorting along rows or columns + + +A useful feature of NumPy's sorting algorithms is the ability to sort along specific rows or columns of a multidimensional array using the ``axis`` argument. For example: + +```python +rand = np.random.RandomState(42) +X = rand.randint(0, 10, (4, 6)) +print(X) +``` + +```python +# sort each column of X +np.sort(X, axis=0) +``` + +```python +# sort each row of X +np.sort(X, axis=1) +``` + +Keep in mind that this treats each row or column as an independent array, and any relationships between the row or column values will be lost! + + +## Partial Sorts: Partitioning + +Sometimes we're not interested in sorting the entire array, but simply want to find the *k* smallest values in the array. NumPy provides this in the ``np.partition`` function. ``np.partition`` takes an array and a number *K*; the result is a new array with the smallest *K* values to the left of the partition, and the remaining values to the right, in arbitrary order: + +```python +x = np.array([7, 2, 3, 1, 6, 5, 4]) +np.partition(x, 3) +``` + +Note that the first three values in the resulting array are the three smallest in the array, and the remaining array positions contain the remaining values. +Within the two partitions, the elements have arbitrary order. + +Similarly to sorting, we can partition along an arbitrary axis of a multidimensional array: + +```python +np.partition(X, 2, axis=1) +``` + +The result is an array where the first two slots in each row contain the smallest values from that row, with the remaining values filling the remaining slots. + +Finally, just as there is a ``np.argsort`` that computes indices of the sort, there is a ``np.argpartition`` that computes indices of the partition. +We'll see this in action in the following section. + + +## Example: k-Nearest Neighbors + +Let's quickly see how we might use this ``argsort`` function along multiple axes to find the nearest neighbors of each point in a set. +We'll start by creating a random set of 10 points on a two-dimensional plane. +Using the standard convention, we'll arrange these in a $10\times 2$ array: + +```python +X = rand.rand(10, 2) +``` + +To get an idea of how these points look, let's quickly scatter plot them: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn; seaborn.set() # Plot styling +plt.scatter(X[:, 0], X[:, 1], s=100); +``` + +Now we'll compute the distance between each pair of points. +Recall that the squared-distance between two points is the sum of the squared differences in each dimension; +using the efficient broadcasting ([Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)) and aggregation ([Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)) routines provided by NumPy we can compute the matrix of square distances in a single line of code: + +```python +dist_sq = np.sum((X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2, axis=-1) +``` + +This operation has a lot packed into it, and it might be a bit confusing if you're unfamiliar with NumPy's broadcasting rules. When you come across code like this, it can be useful to break it down into its component steps: + +```python +# for each pair of points, compute differences in their coordinates +differences = X[:, np.newaxis, :] - X[np.newaxis, :, :] +differences.shape +``` + +```python +# square the coordinate differences +sq_differences = differences ** 2 +sq_differences.shape +``` + +```python +# sum the coordinate differences to get the squared distance +dist_sq = sq_differences.sum(-1) +dist_sq.shape +``` + +Just to double-check what we are doing, we should see that the diagonal of this matrix (i.e., the set of distances between each point and itself) is all zero: + +```python +dist_sq.diagonal() +``` + +It checks out! +With the pairwise square-distances converted, we can now use ``np.argsort`` to sort along each row. The leftmost columns will then give the indices of the nearest neighbors: + +```python +nearest = np.argsort(dist_sq, axis=1) +print(nearest) +``` + +Notice that the first column gives the numbers 0 through 9 in order: this is due to the fact that each point's closest neighbor is itself, as we would expect. + +By using a full sort here, we've actually done more work than we need to in this case. If we're simply interested in the nearest $k$ neighbors, all we need is to partition each row so that the smallest $k + 1$ squared distances come first, with larger distances filling the remaining positions of the array. We can do this with the ``np.argpartition`` function: + +```python +K = 2 +nearest_partition = np.argpartition(dist_sq, K + 1, axis=1) +``` + +In order to visualize this network of neighbors, let's quickly plot the points along with lines representing the connections from each point to its two nearest neighbors: + +```python +plt.scatter(X[:, 0], X[:, 1], s=100) + +# draw lines from each point to its two nearest neighbors +K = 2 + +for i in range(X.shape[0]): + for j in nearest_partition[i, :K+1]: + # plot a line from X[i] to X[j] + # use some zip magic to make it happen: + plt.plot(*zip(X[j], X[i]), color='black') +``` + +Each point in the plot has lines drawn to its two nearest neighbors. +At first glance, it might seem strange that some of the points have more than two lines coming out of them: this is due to the fact that if point A is one of the two nearest neighbors of point B, this does not necessarily imply that point B is one of the two nearest neighbors of point A. + +Although the broadcasting and row-wise sorting of this approach might seem less straightforward than writing a loop, it turns out to be a very efficient way of operating on this data in Python. +You might be tempted to do the same type of operation by manually looping through the data and sorting each set of neighbors individually, but this would almost certainly lead to a slower algorithm than the vectorized version we used. The beauty of this approach is that it's written in a way that's agnostic to the size of the input data: we could just as easily compute the neighbors among 100 or 1,000,000 points in any number of dimensions, and the code would look the same. + +Finally, I'll note that when doing very large nearest neighbor searches, there are tree-based and/or approximate algorithms that can scale as $\mathcal{O}[N\log N]$ or better rather than the $\mathcal{O}[N^2]$ of the brute-force algorithm. One example of this is the KD-Tree, [implemented in Scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html). + + +## Aside: Big-O Notation + +Big-O notation is a means of describing how the number of operations required for an algorithm scales as the input grows in size. +To use it correctly is to dive deeply into the realm of computer science theory, and to carefully distinguish it from the related small-o notation, big-$\theta$ notation, big-$\Omega$ notation, and probably many mutant hybrids thereof. +While these distinctions add precision to statements about algorithmic scaling, outside computer science theory exams and the remarks of pedantic blog commenters, you'll rarely see such distinctions made in practice. +Far more common in the data science world is a less rigid use of big-O notation: as a general (if imprecise) description of the scaling of an algorithm. +With apologies to theorists and pedants, this is the interpretation we'll use throughout this book. + +Big-O notation, in this loose sense, tells you how much time your algorithm will take as you increase the amount of data. +If you have an $\mathcal{O}[N]$ (read "order $N$") algorithm that takes 1 second to operate on a list of length *N*=1,000, then you should expect it to take roughly 5 seconds for a list of length *N*=5,000. +If you have an $\mathcal{O}[N^2]$ (read "order *N* squared") algorithm that takes 1 second for *N*=1000, then you should expect it to take about 25 seconds for *N*=5000. + +For our purposes, the *N* will usually indicate some aspect of the size of the dataset (the number of points, the number of dimensions, etc.). When trying to analyze billions or trillions of samples, the difference between $\mathcal{O}[N]$ and $\mathcal{O}[N^2]$ can be far from trivial! + +Notice that the big-O notation by itself tells you nothing about the actual wall-clock time of a computation, but only about its scaling as you change *N*. +Generally, for example, an $\mathcal{O}[N]$ algorithm is considered to have better scaling than an $\mathcal{O}[N^2]$ algorithm, and for good reason. But for small datasets in particular, the algorithm with better scaling might not be faster. +For example, in a given problem an $\mathcal{O}[N^2]$ algorithm might take 0.01 seconds, while a "better" $\mathcal{O}[N]$ algorithm might take 1 second. +Scale up *N* by a factor of 1,000, though, and the $\mathcal{O}[N]$ algorithm will win out. + +Even this loose version of Big-O notation can be very useful when comparing the performance of algorithms, and we'll use this notation throughout the book when talking about how algorithms scale. + + + +< [Fancy Indexing](02.07-Fancy-Indexing.ipynb) | [Contents](Index.ipynb) | [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/02.09-Structured-Data-NumPy.ipynb b/notebooks_v2/02.09-Structured-Data-NumPy.ipynb new file mode 100644 index 00000000..8f3d3e2e --- /dev/null +++ b/notebooks_v2/02.09-Structured-Data-NumPy.ipynb @@ -0,0 +1,606 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Structured Data: NumPy's Structured Arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data. While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``Dataframe``s, which we'll explore in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imagine that we have several categories of data on a number of people (say, name, age, and weight), and we'd like to store these values for use in a Python program.\n", + "It would be possible to store these in three separate arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "name = ['Alice', 'Bob', 'Cathy', 'Doug']\n", + "age = [25, 45, 37, 19]\n", + "weight = [55.0, 85.5, 68.0, 61.5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this is a bit clumsy. There's nothing here that tells us that the three arrays are related; it would be more natural if we could use a single structure to store all of this data.\n", + "NumPy can handle this through structured arrays, which are arrays with compound data types.\n", + "\n", + "Recall that previously we created a simple array using an expression like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x = np.zeros(4, dtype=int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can similarly create a structured array using a compound data type specification:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('name', '``, which means \"little endian\" or \"big endian,\" respectively, and specifies the ordering convention for significant bits.\n", + "The next character specifies the type of data: characters, bytes, ints, floating points, and so on (see the table below).\n", + "The last character or characters represents the size of the object in bytes.\n", + "\n", + "| Character | Description | Example |\n", + "| --------- | ----------- | ------- | \n", + "| ``'b'`` | Byte | ``np.dtype('b')`` |\n", + "| ``'i'`` | Signed integer | ``np.dtype('i4') == np.int32`` |\n", + "| ``'u'`` | Unsigned integer | ``np.dtype('u1') == np.uint8`` |\n", + "| ``'f'`` | Floating point | ``np.dtype('f8') == np.int64`` |\n", + "| ``'c'`` | Complex floating point| ``np.dtype('c16') == np.complex128``|\n", + "| ``'S'``, ``'a'`` | String | ``np.dtype('S5')`` |\n", + "| ``'U'`` | Unicode string | ``np.dtype('U') == np.str_`` |\n", + "| ``'V'`` | Raw data (void) | ``np.dtype('V') == np.void`` |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More Advanced Compound Types\n", + "\n", + "It is possible to define even more advanced compound types.\n", + "For example, you can create a type where each element contains an array or matrix of values.\n", + "Here, we'll create a data type with a ``mat`` component consisting of a $3\\times 3$ floating-point matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", + "[[ 0. 0. 0.]\n", + " [ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "tp = np.dtype([('id', 'i8'), ('mat', 'f8', (3, 3))])\n", + "X = np.zeros(1, dtype=tp)\n", + "print(X[0])\n", + "print(X['mat'][0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now each element in the ``X`` array consists of an ``id`` and a $3\\times 3$ matrix.\n", + "Why would you use this rather than a simple multidimensional array, or perhaps a Python dictionary?\n", + "The reason is that this NumPy ``dtype`` directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program.\n", + "If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, you'll probably find structured arrays quite useful!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RecordArrays: Structured Arrays with a Twist\n", + "\n", + "NumPy also provides the ``np.recarray`` class, which is almost identical to the structured arrays just described, but with one additional feature: fields can be accessed as attributes rather than as dictionary keys.\n", + "Recall that we previously accessed the ages by writing:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([25, 45, 37, 19], dtype=int32)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['age']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we view our data as a record array instead, we can access this with slightly fewer keystrokes:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([25, 45, 37, 19], dtype=int32)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_rec = data.view(np.recarray)\n", + "data_rec.age" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The downside is that for record arrays, there is some extra overhead involved in accessing the fields, even when using the same syntax. We can see this here:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000000 loops, best of 3: 241 ns per loop\n", + "100000 loops, best of 3: 4.61 µs per loop\n", + "100000 loops, best of 3: 7.27 µs per loop\n" + ] + } + ], + "source": [ + "%timeit data['age']\n", + "%timeit data_rec['age']\n", + "%timeit data_rec.age" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whether the more convenient notation is worth the additional overhead will depend on your own application." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## On to Pandas\n", + "\n", + "This section on structured and record arrays is purposely at the end of this chapter, because it leads so well into the next package we will cover: Pandas.\n", + "Structured arrays like the ones discussed here are good to know about for certain situations, especially in case you're using NumPy arrays to map onto binary data formats in C, Fortran, or another language.\n", + "For day-to-day use of structured data, the Pandas package is a much better choice, and we'll dive into a full discussion of it in the chapter that follows." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/02.09-Structured-Data-NumPy.md b/notebooks_v2/02.09-Structured-Data-NumPy.md new file mode 100644 index 00000000..31f00183 --- /dev/null +++ b/notebooks_v2/02.09-Structured-Data-NumPy.md @@ -0,0 +1,212 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) > + +Open in Colab + + + +# Structured Data: NumPy's Structured Arrays + + +While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data. While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``Dataframe``s, which we'll explore in [Chapter 3](03.00-Introduction-to-Pandas.ipynb). + +```python +import numpy as np +``` + +Imagine that we have several categories of data on a number of people (say, name, age, and weight), and we'd like to store these values for use in a Python program. +It would be possible to store these in three separate arrays: + +```python +name = ['Alice', 'Bob', 'Cathy', 'Doug'] +age = [25, 45, 37, 19] +weight = [55.0, 85.5, 68.0, 61.5] +``` + +But this is a bit clumsy. There's nothing here that tells us that the three arrays are related; it would be more natural if we could use a single structure to store all of this data. +NumPy can handle this through structured arrays, which are arrays with compound data types. + +Recall that previously we created a simple array using an expression like this: + +```python +x = np.zeros(4, dtype=int) +``` + +We can similarly create a structured array using a compound data type specification: + +```python +# Use a compound data type for structured arrays +data = np.zeros(4, dtype={'names':('name', 'age', 'weight'), + 'formats':('U10', 'i4', 'f8')}) +print(data.dtype) +``` + +Here ``'U10'`` translates to "Unicode string of maximum length 10," ``'i4'`` translates to "4-byte (i.e., 32 bit) integer," and ``'f8'`` translates to "8-byte (i.e., 64 bit) float." +We'll discuss other options for these type codes in the following section. + +Now that we've created an empty container array, we can fill the array with our lists of values: + +```python +data['name'] = name +data['age'] = age +data['weight'] = weight +print(data) +``` + +As we had hoped, the data is now arranged together in one convenient block of memory. + +The handy thing with structured arrays is that you can now refer to values either by index or by name: + +```python +# Get all names +data['name'] +``` + +```python +# Get first row of data +data[0] +``` + +```python +# Get the name from the last row +data[-1]['name'] +``` + +Using Boolean masking, this even allows you to do some more sophisticated operations such as filtering on age: + +```python +# Get names where age is under 30 +data[data['age'] < 30]['name'] +``` + +Note that if you'd like to do any operations that are any more complicated than these, you should probably consider the Pandas package, covered in the next chapter. +As we'll see, Pandas provides a ``Dataframe`` object, which is a structure built on NumPy arrays that offers a variety of useful data manipulation functionality similar to what we've shown here, as well as much, much more. + + +## Creating Structured Arrays + +Structured array data types can be specified in a number of ways. +Earlier, we saw the dictionary method: + +```python +np.dtype({'names':('name', 'age', 'weight'), + 'formats':('U10', 'i4', 'f8')}) +``` + +For clarity, numerical types can be specified using Python types or NumPy ``dtype``s instead: + +```python +np.dtype({'names':('name', 'age', 'weight'), + 'formats':((np.str_, 10), int, np.float32)}) +``` + +A compound type can also be specified as a list of tuples: + +```python +np.dtype([('name', 'S10'), ('age', 'i4'), ('weight', 'f8')]) +``` + +If the names of the types do not matter to you, you can specify the types alone in a comma-separated string: + +```python +np.dtype('S10,i4,f8') +``` + +The shortened string format codes may seem confusing, but they are built on simple principles. +The first (optional) character is ``<`` or ``>``, which means "little endian" or "big endian," respectively, and specifies the ordering convention for significant bits. +The next character specifies the type of data: characters, bytes, ints, floating points, and so on (see the table below). +The last character or characters represents the size of the object in bytes. + +| Character | Description | Example | +| --------- | ----------- | ------- | +| ``'b'`` | Byte | ``np.dtype('b')`` | +| ``'i'`` | Signed integer | ``np.dtype('i4') == np.int32`` | +| ``'u'`` | Unsigned integer | ``np.dtype('u1') == np.uint8`` | +| ``'f'`` | Floating point | ``np.dtype('f8') == np.int64`` | +| ``'c'`` | Complex floating point| ``np.dtype('c16') == np.complex128``| +| ``'S'``, ``'a'`` | String | ``np.dtype('S5')`` | +| ``'U'`` | Unicode string | ``np.dtype('U') == np.str_`` | +| ``'V'`` | Raw data (void) | ``np.dtype('V') == np.void`` | + + +## More Advanced Compound Types + +It is possible to define even more advanced compound types. +For example, you can create a type where each element contains an array or matrix of values. +Here, we'll create a data type with a ``mat`` component consisting of a $3\times 3$ floating-point matrix: + +```python +tp = np.dtype([('id', 'i8'), ('mat', 'f8', (3, 3))]) +X = np.zeros(1, dtype=tp) +print(X[0]) +print(X['mat'][0]) +``` + +Now each element in the ``X`` array consists of an ``id`` and a $3\times 3$ matrix. +Why would you use this rather than a simple multidimensional array, or perhaps a Python dictionary? +The reason is that this NumPy ``dtype`` directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program. +If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, you'll probably find structured arrays quite useful! + + +## RecordArrays: Structured Arrays with a Twist + +NumPy also provides the ``np.recarray`` class, which is almost identical to the structured arrays just described, but with one additional feature: fields can be accessed as attributes rather than as dictionary keys. +Recall that we previously accessed the ages by writing: + +```python +data['age'] +``` + +If we view our data as a record array instead, we can access this with slightly fewer keystrokes: + +```python +data_rec = data.view(np.recarray) +data_rec.age +``` + +The downside is that for record arrays, there is some extra overhead involved in accessing the fields, even when using the same syntax. We can see this here: + +```python +%timeit data['age'] +%timeit data_rec['age'] +%timeit data_rec.age +``` + +Whether the more convenient notation is worth the additional overhead will depend on your own application. + + +## On to Pandas + +This section on structured and record arrays is purposely at the end of this chapter, because it leads so well into the next package we will cover: Pandas. +Structured arrays like the ones discussed here are good to know about for certain situations, especially in case you're using NumPy arrays to map onto binary data formats in C, Fortran, or another language. +For day-to-day use of structured data, the Pandas package is a much better choice, and we'll dive into a full discussion of it in the chapter that follows. + + + +< [Sorting Arrays](02.08-Sorting.ipynb) | [Contents](Index.ipynb) | [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.00-Introduction-to-Pandas.ipynb b/notebooks_v2/03.00-Introduction-to-Pandas.ipynb new file mode 100644 index 00000000..dfeca68c --- /dev/null +++ b/notebooks_v2/03.00-Introduction-to-Pandas.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Manipulation with Pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous chapter, we dove into detail on NumPy and its ``ndarray`` object, which provides efficient storage and manipulation of dense typed arrays in Python.\n", + "Here we'll build on this knowledge by looking in detail at the data structures provided by the Pandas library.\n", + "Pandas is a newer package built on top of NumPy, and provides an efficient implementation of a ``DataFrame``.\n", + "``DataFrame``s are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data.\n", + "As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.\n", + "\n", + "As we saw, NumPy's ``ndarray`` data structure provides essential features for the type of clean, well-organized data typically seen in numerical computing tasks.\n", + "While it serves this purpose very well, its limitations become clear when we need more flexibility (e.g., attaching labels to data, working with missing data, etc.) and when attempting operations that do not map well to element-wise broadcasting (e.g., groupings, pivots, etc.), each of which is an important piece of analyzing the less structured data available in many forms in the world around us.\n", + "Pandas, and in particular its ``Series`` and ``DataFrame`` objects, builds on the NumPy array structure and provides efficient access to these sorts of \"data munging\" tasks that occupy much of a data scientist's time.\n", + "\n", + "In this chapter, we will focus on the mechanics of using ``Series``, ``DataFrame``, and related structures effectively.\n", + "We will use examples drawn from real datasets where appropriate, but these examples are not necessarily the focus." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing and Using Pandas\n", + "\n", + "Installation of Pandas on your system requires NumPy to be installed, and if building the library from source, requires the appropriate tools to compile the C and Cython sources on which Pandas is built.\n", + "Details on this installation can be found in the [Pandas documentation](http://pandas.pydata.org/).\n", + "If you followed the advice outlined in the [Preface](00.00-Preface.ipynb) and used the Anaconda stack, you already have Pandas installed.\n", + "\n", + "Once Pandas is installed, you can import it and check the version:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.18.1'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas\n", + "pandas.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as we generally import NumPy under the alias ``np``, we will import Pandas under the alias ``pd``:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This import convention will be used throughout the remainder of this book." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reminder about Built-In Documentation\n", + "\n", + "As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature) as well as the documentation of various functions (using the ``?`` character). (Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) if you need a refresher on this.)\n", + "\n", + "For example, to display all the contents of the pandas namespace, you can type\n", + "\n", + "```ipython\n", + "In [3]: pd.\n", + "```\n", + "\n", + "And to display Pandas's built-in documentation, you can use this:\n", + "\n", + "```ipython\n", + "In [4]: pd?\n", + "```\n", + "\n", + "More detailed documentation, along with tutorials and other resources, can be found at http://pandas.pydata.org/." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.00-Introduction-to-Pandas.md b/notebooks_v2/03.00-Introduction-to-Pandas.md new file mode 100644 index 00000000..013b2e36 --- /dev/null +++ b/notebooks_v2/03.00-Introduction-to-Pandas.md @@ -0,0 +1,93 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) > + +Open in Colab + + + +# Data Manipulation with Pandas + + +In the previous chapter, we dove into detail on NumPy and its ``ndarray`` object, which provides efficient storage and manipulation of dense typed arrays in Python. +Here we'll build on this knowledge by looking in detail at the data structures provided by the Pandas library. +Pandas is a newer package built on top of NumPy, and provides an efficient implementation of a ``DataFrame``. +``DataFrame``s are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data. +As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs. + +As we saw, NumPy's ``ndarray`` data structure provides essential features for the type of clean, well-organized data typically seen in numerical computing tasks. +While it serves this purpose very well, its limitations become clear when we need more flexibility (e.g., attaching labels to data, working with missing data, etc.) and when attempting operations that do not map well to element-wise broadcasting (e.g., groupings, pivots, etc.), each of which is an important piece of analyzing the less structured data available in many forms in the world around us. +Pandas, and in particular its ``Series`` and ``DataFrame`` objects, builds on the NumPy array structure and provides efficient access to these sorts of "data munging" tasks that occupy much of a data scientist's time. + +In this chapter, we will focus on the mechanics of using ``Series``, ``DataFrame``, and related structures effectively. +We will use examples drawn from real datasets where appropriate, but these examples are not necessarily the focus. + + +## Installing and Using Pandas + +Installation of Pandas on your system requires NumPy to be installed, and if building the library from source, requires the appropriate tools to compile the C and Cython sources on which Pandas is built. +Details on this installation can be found in the [Pandas documentation](http://pandas.pydata.org/). +If you followed the advice outlined in the [Preface](00.00-Preface.ipynb) and used the Anaconda stack, you already have Pandas installed. + +Once Pandas is installed, you can import it and check the version: + +```python +import pandas +pandas.__version__ +``` + +Just as we generally import NumPy under the alias ``np``, we will import Pandas under the alias ``pd``: + +```python +import pandas as pd +``` + +This import convention will be used throughout the remainder of this book. + + +## Reminder about Built-In Documentation + +As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature) as well as the documentation of various functions (using the ``?`` character). (Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) if you need a refresher on this.) + +For example, to display all the contents of the pandas namespace, you can type + +```ipython +In [3]: pd. +``` + +And to display Pandas's built-in documentation, you can use this: + +```ipython +In [4]: pd? +``` + +More detailed documentation, along with tutorials and other resources, can be found at http://pandas.pydata.org/. + + + +< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.01-Introducing-Pandas-Objects.ipynb b/notebooks_v2/03.01-Introducing-Pandas-Objects.ipynb new file mode 100644 index 00000000..8c156408 --- /dev/null +++ b/notebooks_v2/03.01-Introducing-Pandas-Objects.ipynb @@ -0,0 +1,1566 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introducing Pandas Objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.\n", + "As we will see during the course of this chapter, Pandas provides a host of useful tools, methods, and functionality on top of the basic data structures, but nearly everything that follows will require an understanding of what these structures are.\n", + "Thus, before we go any further, let's introduce these three fundamental Pandas data structures: the ``Series``, ``DataFrame``, and ``Index``.\n", + "\n", + "We will start our code sessions with the standard NumPy and Pandas imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Pandas Series Object\n", + "\n", + "A Pandas ``Series`` is a one-dimensional array of indexed data.\n", + "It can be created from a list or array as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.25\n", + "1 0.50\n", + "2 0.75\n", + "3 1.00\n", + "dtype: float64" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we see in the output, the ``Series`` wraps both a sequence of values and a sequence of indices, which we can access with the ``values`` and ``index`` attributes.\n", + "The ``values`` are simply a familiar NumPy array:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.25, 0.5 , 0.75, 1. ])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``index`` is an array-like object of type ``pd.Index``, which we'll discuss in more detail momentarily." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=4, step=1)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 0.50\n", + "2 0.75\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we will see, though, the Pandas ``Series`` is much more general and flexible than the one-dimensional NumPy array that it emulates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ``Series`` as generalized NumPy array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From what we've seen so far, it may look like the ``Series`` object is basically interchangeable with a one-dimensional NumPy array.\n", + "The essential difference is the presence of the index: while the Numpy Array has an *implicitly defined* integer index used to access the values, the Pandas ``Series`` has an *explicitly defined* index associated with the values.\n", + "\n", + "This explicit index definition gives the ``Series`` object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type.\n", + "For example, if we wish, we can use strings as an index:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.25\n", + "b 0.50\n", + "c 0.75\n", + "d 1.00\n", + "dtype: float64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n", + " index=['a', 'b', 'c', 'd'])\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the item access works as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['b']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can even use non-contiguous or non-sequential indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2 0.25\n", + "5 0.50\n", + "3 0.75\n", + "7 1.00\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n", + " index=[2, 5, 3, 7])\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Series as specialized dictionary\n", + "\n", + "In this way, you can think of a Pandas ``Series`` a bit like a specialization of a Python dictionary.\n", + "A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a ``Series`` is a structure which maps typed keys to a set of typed values.\n", + "This typing is important: just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a Pandas ``Series`` makes it much more efficient than Python dictionaries for certain operations.\n", + "\n", + "The ``Series``-as-dictionary analogy can be made even more clear by constructing a ``Series`` object directly from a Python dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "California 38332521\n", + "Florida 19552860\n", + "Illinois 12882135\n", + "New York 19651127\n", + "Texas 26448193\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "population_dict = {'California': 38332521,\n", + " 'Texas': 26448193,\n", + " 'New York': 19651127,\n", + " 'Florida': 19552860,\n", + " 'Illinois': 12882135}\n", + "population = pd.Series(population_dict)\n", + "population" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, a ``Series`` will be created where the index is drawn from the sorted keys.\n", + "From here, typical dictionary-style item access can be performed:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "38332521" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "population['California']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unlike a dictionary, though, the ``Series`` also supports array-style operations such as slicing:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "California 38332521\n", + "Florida 19552860\n", + "Illinois 12882135\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "population['California':'Illinois']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll discuss some of the quirks of Pandas indexing and slicing in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Constructing Series objects\n", + "\n", + "We've already seen a few ways of constructing a Pandas ``Series`` from scratch; all of them are some version of the following:\n", + "\n", + "```python\n", + ">>> pd.Series(data, index=index)\n", + "```\n", + "\n", + "where ``index`` is an optional argument, and ``data`` can be one of many entities.\n", + "\n", + "For example, ``data`` can be a list or NumPy array, in which case ``index`` defaults to an integer sequence:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2\n", + "1 4\n", + "2 6\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([2, 4, 6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``data`` can be a scalar, which is repeated to fill the specified index:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "100 5\n", + "200 5\n", + "300 5\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(5, index=[100, 200, 300])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``data`` can be a dictionary, in which ``index`` defaults to the sorted dictionary keys:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 b\n", + "2 a\n", + "3 c\n", + "dtype: object" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series({2:'a', 1:'b', 3:'c'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In each case, the index can be explicitly set if a different result is preferred:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3 c\n", + "2 a\n", + "dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that in this case, the ``Series`` is populated only with the explicitly identified keys." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Pandas DataFrame Object\n", + "\n", + "The next fundamental structure in Pandas is the ``DataFrame``.\n", + "Like the ``Series`` object discussed in the previous section, the ``DataFrame`` can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.\n", + "We'll now take a look at each of these perspectives." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DataFrame as a generalized NumPy array\n", + "If a ``Series`` is an analog of a one-dimensional array with flexible indices, a ``DataFrame`` is an analog of a two-dimensional array with both flexible row indices and flexible column names.\n", + "Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a ``DataFrame`` as a sequence of aligned ``Series`` objects.\n", + "Here, by \"aligned\" we mean that they share the same index.\n", + "\n", + "To demonstrate this, let's first construct a new ``Series`` listing the area of each of the five states discussed in the previous section:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "California 423967\n", + "Florida 170312\n", + "Illinois 149995\n", + "New York 141297\n", + "Texas 695662\n", + "dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,\n", + " 'Florida': 170312, 'Illinois': 149995}\n", + "area = pd.Series(area_dict)\n", + "area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have this along with the ``population`` Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " foo bar\n", + "a 0.865257 0.213169\n", + "b 0.442759 0.108267\n", + "c 0.047110 0.905718" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(np.random.rand(3, 2),\n", + " columns=['foo', 'bar'],\n", + " index=['a', 'b', 'c'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### From a NumPy structured array\n", + "\n", + "We covered structured arrays in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb).\n", + "A Pandas ``DataFrame`` operates much like a structured array, and can be created directly from one:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([(0, 0.0), (0, 0.0), (0, 0.0)], \n", + " dtype=[('A', '\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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+ { + "data": { + "text/plain": [ + "Int64Index([2, 3, 5, 7, 11], dtype='int64')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ind = pd.Index([2, 3, 5, 7, 11])\n", + "ind" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Index as immutable array\n", + "\n", + "The ``Index`` in many ways operates like an array.\n", + "For example, we can use standard Python indexing notation to retrieve values or slices:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ind[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([2, 5, 11], dtype='int64')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ind[::2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``Index`` objects also have many of the attributes familiar from NumPy arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 (5,) 1 int64\n" + ] + } + ], + "source": [ + "print(ind.size, ind.shape, ind.ndim, ind.dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One difference between ``Index`` objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Index does not support mutable operations", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mind\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jakevdp/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1244\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations" + ] + } + ], + "source": [ + "ind[1] = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This immutability makes it safer to share indices between multiple ``DataFrame``s and arrays, without the potential for side effects from inadvertent index modification." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Index as ordered set\n", + "\n", + "Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set arithmetic.\n", + "The ``Index`` object follows many of the conventions used by Python's built-in ``set`` data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "indA = pd.Index([1, 3, 5, 7, 9])\n", + "indB = pd.Index([2, 3, 5, 7, 11])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([3, 5, 7], dtype='int64')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indA & indB # intersection" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indA | indB # union" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([1, 2, 9, 11], dtype='int64')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indA ^ indB # symmetric difference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These operations may also be accessed via object methods, for example ``indA.intersection(indB)``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.01-Introducing-Pandas-Objects.md b/notebooks_v2/03.01-Introducing-Pandas-Objects.md new file mode 100644 index 00000000..1d638fc4 --- /dev/null +++ b/notebooks_v2/03.01-Introducing-Pandas-Objects.md @@ -0,0 +1,379 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) > + +Open in Colab + + + +# Introducing Pandas Objects + + +At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices. +As we will see during the course of this chapter, Pandas provides a host of useful tools, methods, and functionality on top of the basic data structures, but nearly everything that follows will require an understanding of what these structures are. +Thus, before we go any further, let's introduce these three fundamental Pandas data structures: the ``Series``, ``DataFrame``, and ``Index``. + +We will start our code sessions with the standard NumPy and Pandas imports: + +```python +import numpy as np +import pandas as pd +``` + +## The Pandas Series Object + +A Pandas ``Series`` is a one-dimensional array of indexed data. +It can be created from a list or array as follows: + +```python +data = pd.Series([0.25, 0.5, 0.75, 1.0]) +data +``` + +As we see in the output, the ``Series`` wraps both a sequence of values and a sequence of indices, which we can access with the ``values`` and ``index`` attributes. +The ``values`` are simply a familiar NumPy array: + +```python +data.values +``` + +The ``index`` is an array-like object of type ``pd.Index``, which we'll discuss in more detail momentarily. + +```python +data.index +``` + +Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation: + +```python +data[1] +``` + +```python +data[1:3] +``` + +As we will see, though, the Pandas ``Series`` is much more general and flexible than the one-dimensional NumPy array that it emulates. + + +### ``Series`` as generalized NumPy array + + +From what we've seen so far, it may look like the ``Series`` object is basically interchangeable with a one-dimensional NumPy array. +The essential difference is the presence of the index: while the Numpy Array has an *implicitly defined* integer index used to access the values, the Pandas ``Series`` has an *explicitly defined* index associated with the values. + +This explicit index definition gives the ``Series`` object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type. +For example, if we wish, we can use strings as an index: + +```python +data = pd.Series([0.25, 0.5, 0.75, 1.0], + index=['a', 'b', 'c', 'd']) +data +``` + +And the item access works as expected: + +```python +data['b'] +``` + +We can even use non-contiguous or non-sequential indices: + +```python +data = pd.Series([0.25, 0.5, 0.75, 1.0], + index=[2, 5, 3, 7]) +data +``` + +```python +data[5] +``` + +### Series as specialized dictionary + +In this way, you can think of a Pandas ``Series`` a bit like a specialization of a Python dictionary. +A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a ``Series`` is a structure which maps typed keys to a set of typed values. +This typing is important: just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a Pandas ``Series`` makes it much more efficient than Python dictionaries for certain operations. + +The ``Series``-as-dictionary analogy can be made even more clear by constructing a ``Series`` object directly from a Python dictionary: + +```python +population_dict = {'California': 38332521, + 'Texas': 26448193, + 'New York': 19651127, + 'Florida': 19552860, + 'Illinois': 12882135} +population = pd.Series(population_dict) +population +``` + +By default, a ``Series`` will be created where the index is drawn from the sorted keys. +From here, typical dictionary-style item access can be performed: + +```python +population['California'] +``` + +Unlike a dictionary, though, the ``Series`` also supports array-style operations such as slicing: + +```python +population['California':'Illinois'] +``` + +We'll discuss some of the quirks of Pandas indexing and slicing in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). + + +### Constructing Series objects + +We've already seen a few ways of constructing a Pandas ``Series`` from scratch; all of them are some version of the following: + +```python +>>> pd.Series(data, index=index) +``` + +where ``index`` is an optional argument, and ``data`` can be one of many entities. + +For example, ``data`` can be a list or NumPy array, in which case ``index`` defaults to an integer sequence: + + +```python +pd.Series([2, 4, 6]) +``` + +``data`` can be a scalar, which is repeated to fill the specified index: + +```python +pd.Series(5, index=[100, 200, 300]) +``` + +``data`` can be a dictionary, in which ``index`` defaults to the sorted dictionary keys: + +```python +pd.Series({2:'a', 1:'b', 3:'c'}) +``` + +In each case, the index can be explicitly set if a different result is preferred: + +```python +pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2]) +``` + +Notice that in this case, the ``Series`` is populated only with the explicitly identified keys. + + +## The Pandas DataFrame Object + +The next fundamental structure in Pandas is the ``DataFrame``. +Like the ``Series`` object discussed in the previous section, the ``DataFrame`` can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary. +We'll now take a look at each of these perspectives. + + +### DataFrame as a generalized NumPy array +If a ``Series`` is an analog of a one-dimensional array with flexible indices, a ``DataFrame`` is an analog of a two-dimensional array with both flexible row indices and flexible column names. +Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a ``DataFrame`` as a sequence of aligned ``Series`` objects. +Here, by "aligned" we mean that they share the same index. + +To demonstrate this, let's first construct a new ``Series`` listing the area of each of the five states discussed in the previous section: + +```python +area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297, + 'Florida': 170312, 'Illinois': 149995} +area = pd.Series(area_dict) +area +``` + +Now that we have this along with the ``population`` Series from before, we can use a dictionary to construct a single two-dimensional object containing this information: + +```python +states = pd.DataFrame({'population': population, + 'area': area}) +states +``` + +Like the ``Series`` object, the ``DataFrame`` has an ``index`` attribute that gives access to the index labels: + +```python +states.index +``` + +Additionally, the ``DataFrame`` has a ``columns`` attribute, which is an ``Index`` object holding the column labels: + +```python +states.columns +``` + +Thus the ``DataFrame`` can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data. + + +### DataFrame as specialized dictionary + +Similarly, we can also think of a ``DataFrame`` as a specialization of a dictionary. +Where a dictionary maps a key to a value, a ``DataFrame`` maps a column name to a ``Series`` of column data. +For example, asking for the ``'area'`` attribute returns the ``Series`` object containing the areas we saw earlier: + +```python +states['area'] +``` + +Notice the potential point of confusion here: in a two-dimesnional NumPy array, ``data[0]`` will return the first *row*. For a ``DataFrame``, ``data['col0']`` will return the first *column*. +Because of this, it is probably better to think about ``DataFrame``s as generalized dictionaries rather than generalized arrays, though both ways of looking at the situation can be useful. +We'll explore more flexible means of indexing ``DataFrame``s in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). + + +### Constructing DataFrame objects + +A Pandas ``DataFrame`` can be constructed in a variety of ways. +Here we'll give several examples. + + +#### From a single Series object + +A ``DataFrame`` is a collection of ``Series`` objects, and a single-column ``DataFrame`` can be constructed from a single ``Series``: + +```python +pd.DataFrame(population, columns=['population']) +``` + +#### From a list of dicts + +Any list of dictionaries can be made into a ``DataFrame``. +We'll use a simple list comprehension to create some data: + +```python +data = [{'a': i, 'b': 2 * i} + for i in range(3)] +pd.DataFrame(data) +``` + +Even if some keys in the dictionary are missing, Pandas will fill them in with ``NaN`` (i.e., "not a number") values: + +```python +pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}]) +``` + +#### From a dictionary of Series objects + +As we saw before, a ``DataFrame`` can be constructed from a dictionary of ``Series`` objects as well: + +```python +pd.DataFrame({'population': population, + 'area': area}) +``` + +#### From a two-dimensional NumPy array + +Given a two-dimensional array of data, we can create a ``DataFrame`` with any specified column and index names. +If omitted, an integer index will be used for each: + +```python +pd.DataFrame(np.random.rand(3, 2), + columns=['foo', 'bar'], + index=['a', 'b', 'c']) +``` + +#### From a NumPy structured array + +We covered structured arrays in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb). +A Pandas ``DataFrame`` operates much like a structured array, and can be created directly from one: + +```python +A = np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')]) +A +``` + +```python +pd.DataFrame(A) +``` + +## The Pandas Index Object + +We have seen here that both the ``Series`` and ``DataFrame`` objects contain an explicit *index* that lets you reference and modify data. +This ``Index`` object is an interesting structure in itself, and it can be thought of either as an *immutable array* or as an *ordered set* (technically a multi-set, as ``Index`` objects may contain repeated values). +Those views have some interesting consequences in the operations available on ``Index`` objects. +As a simple example, let's construct an ``Index`` from a list of integers: + +```python +ind = pd.Index([2, 3, 5, 7, 11]) +ind +``` + +### Index as immutable array + +The ``Index`` in many ways operates like an array. +For example, we can use standard Python indexing notation to retrieve values or slices: + +```python +ind[1] +``` + +```python +ind[::2] +``` + +``Index`` objects also have many of the attributes familiar from NumPy arrays: + +```python +print(ind.size, ind.shape, ind.ndim, ind.dtype) +``` + +One difference between ``Index`` objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means: + +```python +ind[1] = 0 +``` + +This immutability makes it safer to share indices between multiple ``DataFrame``s and arrays, without the potential for side effects from inadvertent index modification. + + +### Index as ordered set + +Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set arithmetic. +The ``Index`` object follows many of the conventions used by Python's built-in ``set`` data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way: + +```python +indA = pd.Index([1, 3, 5, 7, 9]) +indB = pd.Index([2, 3, 5, 7, 11]) +``` + +```python +indA & indB # intersection +``` + +```python +indA | indB # union +``` + +```python +indA ^ indB # symmetric difference +``` + +These operations may also be accessed via object methods, for example ``indA.intersection(indB)``. + + + +< [Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) | [Contents](Index.ipynb) | [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.02-Data-Indexing-and-Selection.ipynb b/notebooks_v2/03.02-Data-Indexing-and-Selection.ipynb new file mode 100644 index 00000000..7ce540e7 --- /dev/null +++ b/notebooks_v2/03.02-Data-Indexing-and-Selection.ipynb @@ -0,0 +1,1607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Indexing and Selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In [Chapter 2](02.00-Introduction-to-NumPy.ipynb), we looked in detail at methods and tools to access, set, and modify values in NumPy arrays.\n", + "These included indexing (e.g., ``arr[2, 1]``), slicing (e.g., ``arr[:, 1:5]``), masking (e.g., ``arr[arr > 0]``), fancy indexing (e.g., ``arr[0, [1, 5]]``), and combinations thereof (e.g., ``arr[:, [1, 5]]``).\n", + "Here we'll look at similar means of accessing and modifying values in Pandas ``Series`` and ``DataFrame`` objects.\n", + "If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of.\n", + "\n", + "We'll start with the simple case of the one-dimensional ``Series`` object, and then move on to the more complicated two-dimesnional ``DataFrame`` object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Selection in Series\n", + "\n", + "As we saw in the previous section, a ``Series`` object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary.\n", + "If we keep these two overlapping analogies in mind, it will help us to understand the patterns of data indexing and selection in these arrays." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Series as dictionary\n", + "\n", + "Like a dictionary, the ``Series`` object provides a mapping from a collection of keys to a collection of values:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.25\n", + "b 0.50\n", + "c 0.75\n", + "d 1.00\n", + "dtype: float64" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n", + " index=['a', 'b', 'c', 'd'])\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['b']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use dictionary-like Python expressions and methods to examine the keys/indices and values:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'a' in data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['a', 'b', 'c', 'd'], dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('a', 0.25), ('b', 0.5), ('c', 0.75), ('d', 1.0)]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(data.items())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``Series`` objects can even be modified with a dictionary-like syntax.\n", + "Just as you can extend a dictionary by assigning to a new key, you can extend a ``Series`` by assigning to a new index value:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.25\n", + "b 0.50\n", + "c 0.75\n", + "d 1.00\n", + "e 1.25\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['e'] = 1.25\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place; the user generally does not need to worry about these issues." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Series as one-dimensional array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A ``Series`` builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays – that is, *slices*, *masking*, and *fancy indexing*.\n", + "Examples of these are as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.25\n", + "b 0.50\n", + "c 0.75\n", + "dtype: float64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# slicing by explicit index\n", + "data['a':'c']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.25\n", + "b 0.50\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# slicing by implicit integer index\n", + "data[0:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "b 0.50\n", + "c 0.75\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# masking\n", + "data[(data > 0.3) & (data < 0.8)]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.25\n", + "e 1.25\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fancy indexing\n", + "data[['a', 'e']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Among these, slicing may be the source of the most confusion.\n", + "Notice that when slicing with an explicit index (i.e., ``data['a':'c']``), the final index is *included* in the slice, while when slicing with an implicit index (i.e., ``data[0:2]``), the final index is *excluded* from the slice." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexers: loc, iloc, and ix\n", + "\n", + "These slicing and indexing conventions can be a source of confusion.\n", + "For example, if your ``Series`` has an explicit integer index, an indexing operation such as ``data[1]`` will use the explicit indices, while a slicing operation like ``data[1:3]`` will use the implicit Python-style index." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 a\n", + "3 b\n", + "5 c\n", + "dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'a'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# explicit index when indexing\n", + "data[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3 b\n", + "5 c\n", + "dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# implicit index when slicing\n", + "data[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because of this potential confusion in the case of integer indexes, Pandas provides some special *indexer* attributes that explicitly expose certain indexing schemes.\n", + "These are not functional methods, but attributes that expose a particular slicing interface to the data in the ``Series``.\n", + "\n", + "First, the ``loc`` attribute allows indexing and slicing that always references the explicit index:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'a'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.loc[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 a\n", + "3 b\n", + "dtype: object" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.loc[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``iloc`` attribute allows indexing and slicing that always references the implicit Python-style index:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'b'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3 b\n", + "5 c\n", + "dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A third indexing attribute, ``ix``, is a hybrid of the two, and for ``Series`` objects is equivalent to standard ``[]``-based indexing.\n", + "The purpose of the ``ix`` indexer will become more apparent in the context of ``DataFrame`` objects, which we will discuss in a moment.\n", + "\n", + "One guiding principle of Python code is that \"explicit is better than implicit.\"\n", + "The explicit nature of ``loc`` and ``iloc`` make them very useful in maintaining clean and readable code; especially in the case of integer indexes, I recommend using these both to make code easier to read and understand, and to prevent subtle bugs due to the mixed indexing/slicing convention." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Selection in DataFrame\n", + "\n", + "Recall that a ``DataFrame`` acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of ``Series`` structures sharing the same index.\n", + "These analogies can be helpful to keep in mind as we explore data selection within this structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DataFrame as a dictionary\n", + "\n", + "The first analogy we will consider is the ``DataFrame`` as a dictionary of related ``Series`` objects.\n", + "Let's return to our example of areas and populations of states:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " area pop density\n", + "California 423967 38332521 90.413926\n", + "Florida 170312 19552860 114.806121\n", + "Illinois 149995 12882135 85.883763\n", + "New York 141297 19651127 139.076746\n", + "Texas 695662 26448193 38.018740" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['density'] = data['pop'] / data['area']\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows a preview of the straightforward syntax of element-by-element arithmetic between ``Series`` objects; we'll dig into this further in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DataFrame as two-dimensional array\n", + "\n", + "As mentioned previously, we can also view the ``DataFrame`` as an enhanced two-dimensional array.\n", + "We can examine the raw underlying data array using the ``values`` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4.23967000e+05, 3.83325210e+07, 9.04139261e+01],\n", + " [ 1.70312000e+05, 1.95528600e+07, 1.14806121e+02],\n", + " [ 1.49995000e+05, 1.28821350e+07, 8.58837628e+01],\n", + " [ 1.41297000e+05, 1.96511270e+07, 1.39076746e+02],\n", + " [ 6.95662000e+05, 2.64481930e+07, 3.80187404e+01]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this picture in mind, many familiar array-like observations can be done on the ``DataFrame`` itself.\n", + "For example, we can transpose the full ``DataFrame`` to swap rows and columns:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " area pop density\n", + "Florida 170312 19552860 114.806121\n", + "New York 141297 19651127 139.076746" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.density > 100]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These two conventions are syntactically similar to those on a NumPy array, and while these may not precisely fit the mold of the Pandas conventions, they are nevertheless quite useful in practice." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.02-Data-Indexing-and-Selection.md b/notebooks_v2/03.02-Data-Indexing-and-Selection.md new file mode 100644 index 00000000..af3d3836 --- /dev/null +++ b/notebooks_v2/03.02-Data-Indexing-and-Selection.md @@ -0,0 +1,324 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) > + +Open in Colab + + + +# Data Indexing and Selection + + +In [Chapter 2](02.00-Introduction-to-NumPy.ipynb), we looked in detail at methods and tools to access, set, and modify values in NumPy arrays. +These included indexing (e.g., ``arr[2, 1]``), slicing (e.g., ``arr[:, 1:5]``), masking (e.g., ``arr[arr > 0]``), fancy indexing (e.g., ``arr[0, [1, 5]]``), and combinations thereof (e.g., ``arr[:, [1, 5]]``). +Here we'll look at similar means of accessing and modifying values in Pandas ``Series`` and ``DataFrame`` objects. +If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of. + +We'll start with the simple case of the one-dimensional ``Series`` object, and then move on to the more complicated two-dimesnional ``DataFrame`` object. + + +## Data Selection in Series + +As we saw in the previous section, a ``Series`` object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary. +If we keep these two overlapping analogies in mind, it will help us to understand the patterns of data indexing and selection in these arrays. + + +### Series as dictionary + +Like a dictionary, the ``Series`` object provides a mapping from a collection of keys to a collection of values: + +```python +import pandas as pd +data = pd.Series([0.25, 0.5, 0.75, 1.0], + index=['a', 'b', 'c', 'd']) +data +``` + +```python +data['b'] +``` + +We can also use dictionary-like Python expressions and methods to examine the keys/indices and values: + +```python +'a' in data +``` + +```python +data.keys() +``` + +```python +list(data.items()) +``` + +``Series`` objects can even be modified with a dictionary-like syntax. +Just as you can extend a dictionary by assigning to a new key, you can extend a ``Series`` by assigning to a new index value: + +```python +data['e'] = 1.25 +data +``` + +This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place; the user generally does not need to worry about these issues. + + +### Series as one-dimensional array + + +A ``Series`` builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays – that is, *slices*, *masking*, and *fancy indexing*. +Examples of these are as follows: + +```python +# slicing by explicit index +data['a':'c'] +``` + +```python +# slicing by implicit integer index +data[0:2] +``` + +```python +# masking +data[(data > 0.3) & (data < 0.8)] +``` + +```python +# fancy indexing +data[['a', 'e']] +``` + +Among these, slicing may be the source of the most confusion. +Notice that when slicing with an explicit index (i.e., ``data['a':'c']``), the final index is *included* in the slice, while when slicing with an implicit index (i.e., ``data[0:2]``), the final index is *excluded* from the slice. + + +### Indexers: loc, iloc, and ix + +These slicing and indexing conventions can be a source of confusion. +For example, if your ``Series`` has an explicit integer index, an indexing operation such as ``data[1]`` will use the explicit indices, while a slicing operation like ``data[1:3]`` will use the implicit Python-style index. + +```python +data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5]) +data +``` + +```python +# explicit index when indexing +data[1] +``` + +```python +# implicit index when slicing +data[1:3] +``` + +Because of this potential confusion in the case of integer indexes, Pandas provides some special *indexer* attributes that explicitly expose certain indexing schemes. +These are not functional methods, but attributes that expose a particular slicing interface to the data in the ``Series``. + +First, the ``loc`` attribute allows indexing and slicing that always references the explicit index: + +```python +data.loc[1] +``` + +```python +data.loc[1:3] +``` + +The ``iloc`` attribute allows indexing and slicing that always references the implicit Python-style index: + +```python +data.iloc[1] +``` + +```python +data.iloc[1:3] +``` + +A third indexing attribute, ``ix``, is a hybrid of the two, and for ``Series`` objects is equivalent to standard ``[]``-based indexing. +The purpose of the ``ix`` indexer will become more apparent in the context of ``DataFrame`` objects, which we will discuss in a moment. + +One guiding principle of Python code is that "explicit is better than implicit." +The explicit nature of ``loc`` and ``iloc`` make them very useful in maintaining clean and readable code; especially in the case of integer indexes, I recommend using these both to make code easier to read and understand, and to prevent subtle bugs due to the mixed indexing/slicing convention. + + +## Data Selection in DataFrame + +Recall that a ``DataFrame`` acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of ``Series`` structures sharing the same index. +These analogies can be helpful to keep in mind as we explore data selection within this structure. + + +### DataFrame as a dictionary + +The first analogy we will consider is the ``DataFrame`` as a dictionary of related ``Series`` objects. +Let's return to our example of areas and populations of states: + +```python +area = pd.Series({'California': 423967, 'Texas': 695662, + 'New York': 141297, 'Florida': 170312, + 'Illinois': 149995}) +pop = pd.Series({'California': 38332521, 'Texas': 26448193, + 'New York': 19651127, 'Florida': 19552860, + 'Illinois': 12882135}) +data = pd.DataFrame({'area':area, 'pop':pop}) +data +``` + +The individual ``Series`` that make up the columns of the ``DataFrame`` can be accessed via dictionary-style indexing of the column name: + +```python +data['area'] +``` + +Equivalently, we can use attribute-style access with column names that are strings: + +```python +data.area +``` + +This attribute-style column access actually accesses the exact same object as the dictionary-style access: + +```python +data.area is data['area'] +``` + +Though this is a useful shorthand, keep in mind that it does not work for all cases! +For example, if the column names are not strings, or if the column names conflict with methods of the ``DataFrame``, this attribute-style access is not possible. +For example, the ``DataFrame`` has a ``pop()`` method, so ``data.pop`` will point to this rather than the ``"pop"`` column: + +```python +data.pop is data['pop'] +``` + +In particular, you should avoid the temptation to try column assignment via attribute (i.e., use ``data['pop'] = z`` rather than ``data.pop = z``). + +Like with the ``Series`` objects discussed earlier, this dictionary-style syntax can also be used to modify the object, in this case adding a new column: + +```python +data['density'] = data['pop'] / data['area'] +data +``` + +This shows a preview of the straightforward syntax of element-by-element arithmetic between ``Series`` objects; we'll dig into this further in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb). + + +### DataFrame as two-dimensional array + +As mentioned previously, we can also view the ``DataFrame`` as an enhanced two-dimensional array. +We can examine the raw underlying data array using the ``values`` attribute: + +```python +data.values +``` + +With this picture in mind, many familiar array-like observations can be done on the ``DataFrame`` itself. +For example, we can transpose the full ``DataFrame`` to swap rows and columns: + +```python +data.T +``` + +When it comes to indexing of ``DataFrame`` objects, however, it is clear that the dictionary-style indexing of columns precludes our ability to simply treat it as a NumPy array. +In particular, passing a single index to an array accesses a row: + +```python +data.values[0] +``` + +and passing a single "index" to a ``DataFrame`` accesses a column: + +```python +data['area'] +``` + +Thus for array-style indexing, we need another convention. +Here Pandas again uses the ``loc``, ``iloc``, and ``ix`` indexers mentioned earlier. +Using the ``iloc`` indexer, we can index the underlying array as if it is a simple NumPy array (using the implicit Python-style index), but the ``DataFrame`` index and column labels are maintained in the result: + +```python +data.iloc[:3, :2] +``` + +Similarly, using the ``loc`` indexer we can index the underlying data in an array-like style but using the explicit index and column names: + +```python +data.loc[:'Illinois', :'pop'] +``` + +The ``ix`` indexer allows a hybrid of these two approaches: + +```python +data.ix[:3, :'pop'] +``` + +Keep in mind that for integer indices, the ``ix`` indexer is subject to the same potential sources of confusion as discussed for integer-indexed ``Series`` objects. + +Any of the familiar NumPy-style data access patterns can be used within these indexers. +For example, in the ``loc`` indexer we can combine masking and fancy indexing as in the following: + +```python +data.loc[data.density > 100, ['pop', 'density']] +``` + +Any of these indexing conventions may also be used to set or modify values; this is done in the standard way that you might be accustomed to from working with NumPy: + +```python +data.iloc[0, 2] = 90 +data +``` + +To build up your fluency in Pandas data manipulation, I suggest spending some time with a simple ``DataFrame`` and exploring the types of indexing, slicing, masking, and fancy indexing that are allowed by these various indexing approaches. + + +### Additional indexing conventions + +There are a couple extra indexing conventions that might seem at odds with the preceding discussion, but nevertheless can be very useful in practice. +First, while *indexing* refers to columns, *slicing* refers to rows: + +```python +data['Florida':'Illinois'] +``` + +Such slices can also refer to rows by number rather than by index: + +```python +data[1:3] +``` + +Similarly, direct masking operations are also interpreted row-wise rather than column-wise: + +```python +data[data.density > 100] +``` + +These two conventions are syntactically similar to those on a NumPy array, and while these may not precisely fit the mold of the Pandas conventions, they are nevertheless quite useful in practice. + + + +< [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) | [Contents](Index.ipynb) | [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.03-Operations-in-Pandas.ipynb b/notebooks_v2/03.03-Operations-in-Pandas.ipynb new file mode 100644 index 00000000..edc45634 --- /dev/null +++ b/notebooks_v2/03.03-Operations-in-Pandas.ipynb @@ -0,0 +1,1041 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Operating on Data in Pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.).\n", + "Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) are key to this.\n", + "\n", + "Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc.\n", + "This means that keeping the context of data and combining data from different sources–both potentially error-prone tasks with raw NumPy arrays–become essentially foolproof ones with Pandas.\n", + "We will additionally see that there are well-defined operations between one-dimensional ``Series`` structures and two-dimensional ``DataFrame`` structures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ufuncs: Index Preservation\n", + "\n", + "Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas ``Series`` and ``DataFrame`` objects.\n", + "Let's start by defining a simple ``Series`` and ``DataFrame`` on which to demonstrate this:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 6\n", + "1 3\n", + "2 7\n", + "3 4\n", + "dtype: int64" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.RandomState(42)\n", + "ser = pd.Series(rng.randint(0, 10, 4))\n", + "ser" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B C D\n", + "0 6 9 2 6\n", + "1 7 4 3 7\n", + "2 7 2 5 4" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(rng.randint(0, 10, (3, 4)),\n", + " columns=['A', 'B', 'C', 'D'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we apply a NumPy ufunc on either of these objects, the result will be another Pandas object *with the indices preserved:*" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 403.428793\n", + "1 20.085537\n", + "2 1096.633158\n", + "3 54.598150\n", + "dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(ser)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, for a slightly more complex calculation:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Q R S T\n", + "0 0.0 NaN 0.0 NaN\n", + "1 -1.0 NaN 2.0 NaN\n", + "2 3.0 NaN 1.0 NaN" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df - halfrow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the types of silly errors that might come up when working with heterogeneous and/or misaligned data in raw NumPy arrays." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.03-Operations-in-Pandas.md b/notebooks_v2/03.03-Operations-in-Pandas.md new file mode 100644 index 00000000..c705da54 --- /dev/null +++ b/notebooks_v2/03.03-Operations-in-Pandas.md @@ -0,0 +1,215 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) > + +Open in Colab + + + +# Operating on Data in Pandas + + +One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.). +Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) are key to this. + +Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc. +This means that keeping the context of data and combining data from different sources–both potentially error-prone tasks with raw NumPy arrays–become essentially foolproof ones with Pandas. +We will additionally see that there are well-defined operations between one-dimensional ``Series`` structures and two-dimensional ``DataFrame`` structures. + + +## Ufuncs: Index Preservation + +Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas ``Series`` and ``DataFrame`` objects. +Let's start by defining a simple ``Series`` and ``DataFrame`` on which to demonstrate this: + +```python +import pandas as pd +import numpy as np +``` + +```python +rng = np.random.RandomState(42) +ser = pd.Series(rng.randint(0, 10, 4)) +ser +``` + +```python +df = pd.DataFrame(rng.randint(0, 10, (3, 4)), + columns=['A', 'B', 'C', 'D']) +df +``` + +If we apply a NumPy ufunc on either of these objects, the result will be another Pandas object *with the indices preserved:* + +```python +np.exp(ser) +``` + +Or, for a slightly more complex calculation: + +```python +np.sin(df * np.pi / 4) +``` + +Any of the ufuncs discussed in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) can be used in a similar manner. + + +## UFuncs: Index Alignment + +For binary operations on two ``Series`` or ``DataFrame`` objects, Pandas will align indices in the process of performing the operation. +This is very convenient when working with incomplete data, as we'll see in some of the examples that follow. + + +### Index alignment in Series + +As an example, suppose we are combining two different data sources, and find only the top three US states by *area* and the top three US states by *population*: + +```python +area = pd.Series({'Alaska': 1723337, 'Texas': 695662, + 'California': 423967}, name='area') +population = pd.Series({'California': 38332521, 'Texas': 26448193, + 'New York': 19651127}, name='population') +``` + +Let's see what happens when we divide these to compute the population density: + +```python +population / area +``` + +The resulting array contains the *union* of indices of the two input arrays, which could be determined using standard Python set arithmetic on these indices: + +```python +area.index | population.index +``` + +Any item for which one or the other does not have an entry is marked with ``NaN``, or "Not a Number," which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)). +This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are filled in with NaN by default: + +```python +A = pd.Series([2, 4, 6], index=[0, 1, 2]) +B = pd.Series([1, 3, 5], index=[1, 2, 3]) +A + B +``` + +If using NaN values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators. +For example, calling ``A.add(B)`` is equivalent to calling ``A + B``, but allows optional explicit specification of the fill value for any elements in ``A`` or ``B`` that might be missing: + +```python +A.add(B, fill_value=0) +``` + +### Index alignment in DataFrame + +A similar type of alignment takes place for *both* columns and indices when performing operations on ``DataFrame``s: + +```python +A = pd.DataFrame(rng.randint(0, 20, (2, 2)), + columns=list('AB')) +A +``` + +```python +B = pd.DataFrame(rng.randint(0, 10, (3, 3)), + columns=list('BAC')) +B +``` + +```python +A + B +``` + +Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted. +As was the case with ``Series``, we can use the associated object's arithmetic method and pass any desired ``fill_value`` to be used in place of missing entries. +Here we'll fill with the mean of all values in ``A`` (computed by first stacking the rows of ``A``): + +```python +fill = A.stack().mean() +A.add(B, fill_value=fill) +``` + +The following table lists Python operators and their equivalent Pandas object methods: + +| Python Operator | Pandas Method(s) | +|-----------------|---------------------------------------| +| ``+`` | ``add()`` | +| ``-`` | ``sub()``, ``subtract()`` | +| ``*`` | ``mul()``, ``multiply()`` | +| ``/`` | ``truediv()``, ``div()``, ``divide()``| +| ``//`` | ``floordiv()`` | +| ``%`` | ``mod()`` | +| ``**`` | ``pow()`` | + + + +## Ufuncs: Operations Between DataFrame and Series + +When performing operations between a ``DataFrame`` and a ``Series``, the index and column alignment is similarly maintained. +Operations between a ``DataFrame`` and a ``Series`` are similar to operations between a two-dimensional and one-dimensional NumPy array. +Consider one common operation, where we find the difference of a two-dimensional array and one of its rows: + +```python +A = rng.randint(10, size=(3, 4)) +A +``` + +```python +A - A[0] +``` + +According to NumPy's broadcasting rules (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)), subtraction between a two-dimensional array and one of its rows is applied row-wise. + +In Pandas, the convention similarly operates row-wise by default: + +```python +df = pd.DataFrame(A, columns=list('QRST')) +df - df.iloc[0] +``` + +If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the ``axis`` keyword: + +```python +df.subtract(df['R'], axis=0) +``` + +Note that these ``DataFrame``/``Series`` operations, like the operations discussed above, will automatically align indices between the two elements: + +```python +halfrow = df.iloc[0, ::2] +halfrow +``` + +```python +df - halfrow +``` + +This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the types of silly errors that might come up when working with heterogeneous and/or misaligned data in raw NumPy arrays. + + + +< [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) | [Contents](Index.ipynb) | [Handling Missing Data](03.04-Missing-Values.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.04-Missing-Values.ipynb b/notebooks_v2/03.04-Missing-Values.ipynb new file mode 100644 index 00000000..4547377c --- /dev/null +++ b/notebooks_v2/03.04-Missing-Values.ipynb @@ -0,0 +1,1302 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Handling Missing Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous.\n", + "In particular, many interesting datasets will have some amount of data missing.\n", + "To make matters even more complicated, different data sources may indicate missing data in different ways.\n", + "\n", + "In this section, we will discuss some general considerations for missing data, discuss how Pandas chooses to represent it, and demonstrate some built-in Pandas tools for handling missing data in Python.\n", + "Here and throughout the book, we'll refer to missing data in general as *null*, *NaN*, or *NA* values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Trade-Offs in Missing Data Conventions\n", + "\n", + "There are a number of schemes that have been developed to indicate the presence of missing data in a table or DataFrame.\n", + "Generally, they revolve around one of two strategies: using a *mask* that globally indicates missing values, or choosing a *sentinel value* that indicates a missing entry.\n", + "\n", + "In the masking approach, the mask might be an entirely separate Boolean array, or it may involve appropriation of one bit in the data representation to locally indicate the null status of a value.\n", + "\n", + "In the sentinel approach, the sentinel value could be some data-specific convention, such as indicating a missing integer value with -9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with NaN (Not a Number), a special value which is part of the IEEE floating-point specification.\n", + "\n", + "None of these approaches is without trade-offs: use of a separate mask array requires allocation of an additional Boolean array, which adds overhead in both storage and computation. A sentinel value reduces the range of valid values that can be represented, and may require extra (often non-optimized) logic in CPU and GPU arithmetic. Common special values like NaN are not available for all data types.\n", + "\n", + "As in most cases where no universally optimal choice exists, different languages and systems use different conventions.\n", + "For example, the R language uses reserved bit patterns within each data type as sentinel values indicating missing data, while the SciDB system uses an extra byte attached to every cell which indicates a NA state." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Missing Data in Pandas\n", + "\n", + "The way in which Pandas handles missing values is constrained by its reliance on the NumPy package, which does not have a built-in notion of NA values for non-floating-point data types.\n", + "\n", + "Pandas could have followed R's lead in specifying bit patterns for each individual data type to indicate nullness, but this approach turns out to be rather unwieldy.\n", + "While R contains four basic data types, NumPy supports *far* more than this: for example, while R has a single integer type, NumPy supports *fourteen* basic integer types once you account for available precisions, signedness, and endianness of the encoding.\n", + "Reserving a specific bit pattern in all available NumPy types would lead to an unwieldy amount of overhead in special-casing various operations for various types, likely even requiring a new fork of the NumPy package. Further, for the smaller data types (such as 8-bit integers), sacrificing a bit to use as a mask will significantly reduce the range of values it can represent.\n", + "\n", + "NumPy does have support for masked arrays – that is, arrays that have a separate Boolean mask array attached for marking data as \"good\" or \"bad.\"\n", + "Pandas could have derived from this, but the overhead in both storage, computation, and code maintenance makes that an unattractive choice.\n", + "\n", + "With these constraints in mind, Pandas chose to use sentinels for missing data, and further chose to use two already-existing Python null values: the special floating-point ``NaN`` value, and the Python ``None`` object.\n", + "This choice has some side effects, as we will see, but in practice ends up being a good compromise in most cases of interest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ``None``: Pythonic missing data\n", + "\n", + "The first sentinel value used by Pandas is ``None``, a Python singleton object that is often used for missing data in Python code.\n", + "Because it is a Python object, ``None`` cannot be used in any arbitrary NumPy/Pandas array, but only in arrays with data type ``'object'`` (i.e., arrays of Python objects):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, None, 3, 4], dtype=object)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vals1 = np.array([1, None, 3, 4])\n", + "vals1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This ``dtype=object`` means that the best common type representation NumPy could infer for the contents of the array is that they are Python objects.\n", + "While this kind of object array is useful for some purposes, any operations on the data will be done at the Python level, with much more overhead than the typically fast operations seen for arrays with native types:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dtype = object\n", + "10 loops, best of 3: 78.2 ms per loop\n", + "\n", + "dtype = int\n", + "100 loops, best of 3: 3.06 ms per loop\n", + "\n" + ] + } + ], + "source": [ + "for dtype in ['object', 'int']:\n", + " print(\"dtype =\", dtype)\n", + " %timeit np.arange(1E6, dtype=dtype).sum()\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The use of Python objects in an array also means that if you perform aggregations like ``sum()`` or ``min()`` across an array with a ``None`` value, you will generally get an error:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvals1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jakevdp/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'" + ] + } + ], + "source": [ + "vals1.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This reflects the fact that addition between an integer and ``None`` is undefined." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ``NaN``: Missing numerical data\n", + "\n", + "The other missing data representation, ``NaN`` (acronym for *Not a Number*), is different; it is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vals2 = np.array([1, np.nan, 3, 4]) \n", + "vals2.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that NumPy chose a native floating-point type for this array: this means that unlike the object array from before, this array supports fast operations pushed into compiled code.\n", + "You should be aware that ``NaN`` is a bit like a data virus–it infects any other object it touches.\n", + "Regardless of the operation, the result of arithmetic with ``NaN`` will be another ``NaN``:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1 + np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0 * np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this means that aggregates over the values are well defined (i.e., they don't result in an error) but not always useful:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(nan, nan, nan)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vals2.sum(), vals2.min(), vals2.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NumPy does provide some special aggregations that will ignore these missing values:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(8.0, 1.0, 4.0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nansum(vals2), np.nanmin(vals2), np.nanmax(vals2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind that ``NaN`` is specifically a floating-point value; there is no equivalent NaN value for integers, strings, or other types." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NaN and None in Pandas\n", + "\n", + "``NaN`` and ``None`` both have their place, and Pandas is built to handle the two of them nearly interchangeably, converting between them where appropriate:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1.0\n", + "1 NaN\n", + "2 2.0\n", + "3 NaN\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([1, np.nan, 2, None])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For types that don't have an available sentinel value, Pandas automatically type-casts when NA values are present.\n", + "For example, if we set a value in an integer array to ``np.nan``, it will automatically be upcast to a floating-point type to accommodate the NA:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = pd.Series(range(2), dtype=int)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 1.0\n", + "dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[0] = None\n", + "x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that in addition to casting the integer array to floating point, Pandas automatically converts the ``None`` to a ``NaN`` value.\n", + "(Be aware that there is a proposal to add a native integer NA to Pandas in the future; as of this writing, it has not been included).\n", + "\n", + "While this type of magic may feel a bit hackish compared to the more unified approach to NA values in domain-specific languages like R, the Pandas sentinel/casting approach works quite well in practice and in my experience only rarely causes issues.\n", + "\n", + "The following table lists the upcasting conventions in Pandas when NA values are introduced:\n", + "\n", + "|Typeclass | Conversion When Storing NAs | NA Sentinel Value |\n", + "|--------------|-----------------------------|------------------------|\n", + "| ``floating`` | No change | ``np.nan`` |\n", + "| ``object`` | No change | ``None`` or ``np.nan`` |\n", + "| ``integer`` | Cast to ``float64`` | ``np.nan`` |\n", + "| ``boolean`` | Cast to ``object`` | ``None`` or ``np.nan`` |\n", + "\n", + "Keep in mind that in Pandas, string data is always stored with an ``object`` dtype." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Operating on Null Values\n", + "\n", + "As we have seen, Pandas treats ``None`` and ``NaN`` as essentially interchangeable for indicating missing or null values.\n", + "To facilitate this convention, there are several useful methods for detecting, removing, and replacing null values in Pandas data structures.\n", + "They are:\n", + "\n", + "- ``isnull()``: Generate a boolean mask indicating missing values\n", + "- ``notnull()``: Opposite of ``isnull()``\n", + "- ``dropna()``: Return a filtered version of the data\n", + "- ``fillna()``: Return a copy of the data with missing values filled or imputed\n", + "\n", + "We will conclude this section with a brief exploration and demonstration of these routines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Detecting null values\n", + "Pandas data structures have two useful methods for detecting null data: ``isnull()`` and ``notnull()``.\n", + "Either one will return a Boolean mask over the data. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data = pd.Series([1, np.nan, 'hello', None])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "3 True\n", + "dtype: bool" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb), Boolean masks can be used directly as a ``Series`` or ``DataFrame`` index:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "2 hello\n", + "dtype: object" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.notnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``isnull()`` and ``notnull()`` methods produce similar Boolean results for ``DataFrame``s." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dropping null values\n", + "\n", + "In addition to the masking used before, there are the convenience methods, ``dropna()``\n", + "(which removes NA values) and ``fillna()`` (which fills in NA values). For a ``Series``,\n", + "the result is straightforward:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "2 hello\n", + "dtype: object" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a ``DataFrame``, there are more options.\n", + "Consider the following ``DataFrame``:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2\n", + "0 1.0 NaN 2\n", + "1 2.0 3.0 5\n", + "2 NaN 4.0 6" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame([[1, np.nan, 2],\n", + " [2, 3, 5],\n", + " [np.nan, 4, 6]])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We cannot drop single values from a ``DataFrame``; we can only drop full rows or full columns.\n", + "Depending on the application, you might want one or the other, so ``dropna()`` gives a number of options for a ``DataFrame``.\n", + "\n", + "By default, ``dropna()`` will drop all rows in which *any* null value is present:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2\n", + "1 2.0 3.0 5" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can drop NA values along a different axis; ``axis=1`` drops all columns containing a null value:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 2\n", + "0 2\n", + "1 5\n", + "2 6" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dropna(axis='columns')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this drops some good data as well; you might rather be interested in dropping rows or columns with *all* NA values, or a majority of NA values.\n", + "This can be specified through the ``how`` or ``thresh`` parameters, which allow fine control of the number of nulls to allow through.\n", + "\n", + "The default is ``how='any'``, such that any row or column (depending on the ``axis`` keyword) containing a null value will be dropped.\n", + "You can also specify ``how='all'``, which will only drop rows/columns that are *all* null values:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "0 1.0 NaN 2 NaN\n", + "1 2.0 3.0 5 NaN\n", + "2 NaN 4.0 6 NaN" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[3] = np.nan\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2\n", + "0 1.0 NaN 2\n", + "1 2.0 3.0 5\n", + "2 NaN 4.0 6" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dropna(axis='columns', how='all')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For finer-grained control, the ``thresh`` parameter lets you specify a minimum number of non-null values for the row/column to be kept:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "1 2.0 3.0 5 NaN" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dropna(axis='rows', thresh=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the first and last row have been dropped, because they contain only two non-null values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filling null values\n", + "\n", + "Sometimes rather than dropping NA values, you'd rather replace them with a valid value.\n", + "This value might be a single number like zero, or it might be some sort of imputation or interpolation from the good values.\n", + "You could do this in-place using the ``isnull()`` method as a mask, but because it is such a common operation Pandas provides the ``fillna()`` method, which returns a copy of the array with the null values replaced.\n", + "\n", + "Consider the following ``Series``:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.0\n", + "b NaN\n", + "c 2.0\n", + "d NaN\n", + "e 3.0\n", + "dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can fill NA entries with a single value, such as zero:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.0\n", + "b 0.0\n", + "c 2.0\n", + "d 0.0\n", + "e 3.0\n", + "dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can specify a forward-fill to propagate the previous value forward:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.0\n", + "b 1.0\n", + "c 2.0\n", + "d 2.0\n", + "e 3.0\n", + "dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# forward-fill\n", + "data.fillna(method='ffill')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can specify a back-fill to propagate the next values backward:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.0\n", + "b 2.0\n", + "c 2.0\n", + "d 3.0\n", + "e 3.0\n", + "dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# back-fill\n", + "data.fillna(method='bfill')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "For ``DataFrame``s, the options are similar, but we can also specify an ``axis`` along which the fills take place:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "0 1.0 1.0 2.0 2.0\n", + "1 2.0 3.0 5.0 5.0\n", + "2 NaN 4.0 6.0 6.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.fillna(method='ffill', axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that if a previous value is not available during a forward fill, the NA value remains." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.04-Missing-Values.md b/notebooks_v2/03.04-Missing-Values.md new file mode 100644 index 00000000..5476fd9d --- /dev/null +++ b/notebooks_v2/03.04-Missing-Values.md @@ -0,0 +1,324 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) > + +Open in Colab + + + +# Handling Missing Data + + +The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous. +In particular, many interesting datasets will have some amount of data missing. +To make matters even more complicated, different data sources may indicate missing data in different ways. + +In this section, we will discuss some general considerations for missing data, discuss how Pandas chooses to represent it, and demonstrate some built-in Pandas tools for handling missing data in Python. +Here and throughout the book, we'll refer to missing data in general as *null*, *NaN*, or *NA* values. + + +## Trade-Offs in Missing Data Conventions + +There are a number of schemes that have been developed to indicate the presence of missing data in a table or DataFrame. +Generally, they revolve around one of two strategies: using a *mask* that globally indicates missing values, or choosing a *sentinel value* that indicates a missing entry. + +In the masking approach, the mask might be an entirely separate Boolean array, or it may involve appropriation of one bit in the data representation to locally indicate the null status of a value. + +In the sentinel approach, the sentinel value could be some data-specific convention, such as indicating a missing integer value with -9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with NaN (Not a Number), a special value which is part of the IEEE floating-point specification. + +None of these approaches is without trade-offs: use of a separate mask array requires allocation of an additional Boolean array, which adds overhead in both storage and computation. A sentinel value reduces the range of valid values that can be represented, and may require extra (often non-optimized) logic in CPU and GPU arithmetic. Common special values like NaN are not available for all data types. + +As in most cases where no universally optimal choice exists, different languages and systems use different conventions. +For example, the R language uses reserved bit patterns within each data type as sentinel values indicating missing data, while the SciDB system uses an extra byte attached to every cell which indicates a NA state. + + +## Missing Data in Pandas + +The way in which Pandas handles missing values is constrained by its reliance on the NumPy package, which does not have a built-in notion of NA values for non-floating-point data types. + +Pandas could have followed R's lead in specifying bit patterns for each individual data type to indicate nullness, but this approach turns out to be rather unwieldy. +While R contains four basic data types, NumPy supports *far* more than this: for example, while R has a single integer type, NumPy supports *fourteen* basic integer types once you account for available precisions, signedness, and endianness of the encoding. +Reserving a specific bit pattern in all available NumPy types would lead to an unwieldy amount of overhead in special-casing various operations for various types, likely even requiring a new fork of the NumPy package. Further, for the smaller data types (such as 8-bit integers), sacrificing a bit to use as a mask will significantly reduce the range of values it can represent. + +NumPy does have support for masked arrays – that is, arrays that have a separate Boolean mask array attached for marking data as "good" or "bad." +Pandas could have derived from this, but the overhead in both storage, computation, and code maintenance makes that an unattractive choice. + +With these constraints in mind, Pandas chose to use sentinels for missing data, and further chose to use two already-existing Python null values: the special floating-point ``NaN`` value, and the Python ``None`` object. +This choice has some side effects, as we will see, but in practice ends up being a good compromise in most cases of interest. + + +### ``None``: Pythonic missing data + +The first sentinel value used by Pandas is ``None``, a Python singleton object that is often used for missing data in Python code. +Because it is a Python object, ``None`` cannot be used in any arbitrary NumPy/Pandas array, but only in arrays with data type ``'object'`` (i.e., arrays of Python objects): + +```python +import numpy as np +import pandas as pd +``` + +```python +vals1 = np.array([1, None, 3, 4]) +vals1 +``` + +This ``dtype=object`` means that the best common type representation NumPy could infer for the contents of the array is that they are Python objects. +While this kind of object array is useful for some purposes, any operations on the data will be done at the Python level, with much more overhead than the typically fast operations seen for arrays with native types: + +```python +for dtype in ['object', 'int']: + print("dtype =", dtype) + %timeit np.arange(1E6, dtype=dtype).sum() + print() +``` + +The use of Python objects in an array also means that if you perform aggregations like ``sum()`` or ``min()`` across an array with a ``None`` value, you will generally get an error: + +```python +vals1.sum() +``` + +This reflects the fact that addition between an integer and ``None`` is undefined. + + +### ``NaN``: Missing numerical data + +The other missing data representation, ``NaN`` (acronym for *Not a Number*), is different; it is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation: + +```python +vals2 = np.array([1, np.nan, 3, 4]) +vals2.dtype +``` + +Notice that NumPy chose a native floating-point type for this array: this means that unlike the object array from before, this array supports fast operations pushed into compiled code. +You should be aware that ``NaN`` is a bit like a data virus–it infects any other object it touches. +Regardless of the operation, the result of arithmetic with ``NaN`` will be another ``NaN``: + +```python +1 + np.nan +``` + +```python +0 * np.nan +``` + +Note that this means that aggregates over the values are well defined (i.e., they don't result in an error) but not always useful: + +```python +vals2.sum(), vals2.min(), vals2.max() +``` + +NumPy does provide some special aggregations that will ignore these missing values: + +```python +np.nansum(vals2), np.nanmin(vals2), np.nanmax(vals2) +``` + +Keep in mind that ``NaN`` is specifically a floating-point value; there is no equivalent NaN value for integers, strings, or other types. + + +### NaN and None in Pandas + +``NaN`` and ``None`` both have their place, and Pandas is built to handle the two of them nearly interchangeably, converting between them where appropriate: + +```python +pd.Series([1, np.nan, 2, None]) +``` + +For types that don't have an available sentinel value, Pandas automatically type-casts when NA values are present. +For example, if we set a value in an integer array to ``np.nan``, it will automatically be upcast to a floating-point type to accommodate the NA: + +```python +x = pd.Series(range(2), dtype=int) +x +``` + +```python +x[0] = None +x +``` + +Notice that in addition to casting the integer array to floating point, Pandas automatically converts the ``None`` to a ``NaN`` value. +(Be aware that there is a proposal to add a native integer NA to Pandas in the future; as of this writing, it has not been included). + +While this type of magic may feel a bit hackish compared to the more unified approach to NA values in domain-specific languages like R, the Pandas sentinel/casting approach works quite well in practice and in my experience only rarely causes issues. + +The following table lists the upcasting conventions in Pandas when NA values are introduced: + +|Typeclass | Conversion When Storing NAs | NA Sentinel Value | +|--------------|-----------------------------|------------------------| +| ``floating`` | No change | ``np.nan`` | +| ``object`` | No change | ``None`` or ``np.nan`` | +| ``integer`` | Cast to ``float64`` | ``np.nan`` | +| ``boolean`` | Cast to ``object`` | ``None`` or ``np.nan`` | + +Keep in mind that in Pandas, string data is always stored with an ``object`` dtype. + + +## Operating on Null Values + +As we have seen, Pandas treats ``None`` and ``NaN`` as essentially interchangeable for indicating missing or null values. +To facilitate this convention, there are several useful methods for detecting, removing, and replacing null values in Pandas data structures. +They are: + +- ``isnull()``: Generate a boolean mask indicating missing values +- ``notnull()``: Opposite of ``isnull()`` +- ``dropna()``: Return a filtered version of the data +- ``fillna()``: Return a copy of the data with missing values filled or imputed + +We will conclude this section with a brief exploration and demonstration of these routines. + + +### Detecting null values +Pandas data structures have two useful methods for detecting null data: ``isnull()`` and ``notnull()``. +Either one will return a Boolean mask over the data. For example: + +```python +data = pd.Series([1, np.nan, 'hello', None]) +``` + +```python +data.isnull() +``` + +As mentioned in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb), Boolean masks can be used directly as a ``Series`` or ``DataFrame`` index: + +```python +data[data.notnull()] +``` + +The ``isnull()`` and ``notnull()`` methods produce similar Boolean results for ``DataFrame``s. + + +### Dropping null values + +In addition to the masking used before, there are the convenience methods, ``dropna()`` +(which removes NA values) and ``fillna()`` (which fills in NA values). For a ``Series``, +the result is straightforward: + +```python +data.dropna() +``` + +For a ``DataFrame``, there are more options. +Consider the following ``DataFrame``: + +```python +df = pd.DataFrame([[1, np.nan, 2], + [2, 3, 5], + [np.nan, 4, 6]]) +df +``` + +We cannot drop single values from a ``DataFrame``; we can only drop full rows or full columns. +Depending on the application, you might want one or the other, so ``dropna()`` gives a number of options for a ``DataFrame``. + +By default, ``dropna()`` will drop all rows in which *any* null value is present: + +```python +df.dropna() +``` + +Alternatively, you can drop NA values along a different axis; ``axis=1`` drops all columns containing a null value: + +```python +df.dropna(axis='columns') +``` + +But this drops some good data as well; you might rather be interested in dropping rows or columns with *all* NA values, or a majority of NA values. +This can be specified through the ``how`` or ``thresh`` parameters, which allow fine control of the number of nulls to allow through. + +The default is ``how='any'``, such that any row or column (depending on the ``axis`` keyword) containing a null value will be dropped. +You can also specify ``how='all'``, which will only drop rows/columns that are *all* null values: + +```python +df[3] = np.nan +df +``` + +```python +df.dropna(axis='columns', how='all') +``` + +For finer-grained control, the ``thresh`` parameter lets you specify a minimum number of non-null values for the row/column to be kept: + +```python +df.dropna(axis='rows', thresh=3) +``` + +Here the first and last row have been dropped, because they contain only two non-null values. + + +### Filling null values + +Sometimes rather than dropping NA values, you'd rather replace them with a valid value. +This value might be a single number like zero, or it might be some sort of imputation or interpolation from the good values. +You could do this in-place using the ``isnull()`` method as a mask, but because it is such a common operation Pandas provides the ``fillna()`` method, which returns a copy of the array with the null values replaced. + +Consider the following ``Series``: + +```python +data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde')) +data +``` + +We can fill NA entries with a single value, such as zero: + +```python +data.fillna(0) +``` + +We can specify a forward-fill to propagate the previous value forward: + +```python +# forward-fill +data.fillna(method='ffill') +``` + +Or we can specify a back-fill to propagate the next values backward: + +```python +# back-fill +data.fillna(method='bfill') +``` + +For ``DataFrame``s, the options are similar, but we can also specify an ``axis`` along which the fills take place: + +```python +df +``` + +```python +df.fillna(method='ffill', axis=1) +``` + +Notice that if a previous value is not available during a forward fill, the NA value remains. + + + +< [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) | [Contents](Index.ipynb) | [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.05-Hierarchical-Indexing.ipynb b/notebooks_v2/03.05-Hierarchical-Indexing.ipynb new file mode 100644 index 00000000..4751d6a1 --- /dev/null +++ b/notebooks_v2/03.05-Hierarchical-Indexing.ipynb @@ -0,0 +1,2807 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hierarchical Indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas ``Series`` and ``DataFrame`` objects, respectively.\n", + "Often it is useful to go beyond this and store higher-dimensional data–that is, data indexed by more than one or two keys.\n", + "While Pandas does provide ``Panel`` and ``Panel4D`` objects that natively handle three-dimensional and four-dimensional data (see [Aside: Panel Data](#Aside:-Panel-Data)), a far more common pattern in practice is to make use of *hierarchical indexing* (also known as *multi-indexing*) to incorporate multiple index *levels* within a single index.\n", + "In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional ``Series`` and two-dimensional ``DataFrame`` objects.\n", + "\n", + "In this section, we'll explore the direct creation of ``MultiIndex`` objects, considerations when indexing, slicing, and computing statistics across multiply indexed data, and useful routines for converting between simple and hierarchically indexed representations of your data.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## A Multiply Indexed Series\n", + "\n", + "Let's start by considering how we might represent two-dimensional data within a one-dimensional ``Series``.\n", + "For concreteness, we will consider a series of data where each point has a character and numerical key." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### The bad way\n", + "\n", + "Suppose you would like to track data about states from two different years.\n", + "Using the Pandas tools we've already covered, you might be tempted to simply use Python tuples as keys:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(California, 2000) 33871648\n", + "(California, 2010) 37253956\n", + "(New York, 2000) 18976457\n", + "(New York, 2010) 19378102\n", + "(Texas, 2000) 20851820\n", + "(Texas, 2010) 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = [('California', 2000), ('California', 2010),\n", + " ('New York', 2000), ('New York', 2010),\n", + " ('Texas', 2000), ('Texas', 2010)]\n", + "populations = [33871648, 37253956,\n", + " 18976457, 19378102,\n", + " 20851820, 25145561]\n", + "pop = pd.Series(populations, index=index)\n", + "pop" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this indexing scheme, you can straightforwardly index or slice the series based on this multiple index:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(California, 2010) 37253956\n", + "(New York, 2000) 18976457\n", + "(New York, 2010) 19378102\n", + "(Texas, 2000) 20851820\n", + "dtype: int64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[('California', 2010):('Texas', 2000)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "But the convenience ends there. For example, if you need to select all values from 2010, you'll need to do some messy (and potentially slow) munging to make it happen:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(California, 2010) 37253956\n", + "(New York, 2010) 19378102\n", + "(Texas, 2010) 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[[i for i in pop.index if i[1] == 2010]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This produces the desired result, but is not as clean (or as efficient for large datasets) as the slicing syntax we've grown to love in Pandas." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### The Better Way: Pandas MultiIndex\n", + "Fortunately, Pandas provides a better way.\n", + "Our tuple-based indexing is essentially a rudimentary multi-index, and the Pandas ``MultiIndex`` type gives us the type of operations we wish to have.\n", + "We can create a multi-index from the tuples as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex(levels=[['California', 'New York', 'Texas'], [2000, 2010]],\n", + " labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = pd.MultiIndex.from_tuples(index)\n", + "index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Notice that the ``MultiIndex`` contains multiple *levels* of indexing–in this case, the state names and the years, as well as multiple *labels* for each data point which encode these levels.\n", + "\n", + "If we re-index our series with this ``MultiIndex``, we see the hierarchical representation of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "California 2000 33871648\n", + " 2010 37253956\n", + "New York 2000 18976457\n", + " 2010 19378102\n", + "Texas 2000 20851820\n", + " 2010 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop = pop.reindex(index)\n", + "pop" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here the first two columns of the ``Series`` representation show the multiple index values, while the third column shows the data.\n", + "Notice that some entries are missing in the first column: in this multi-index representation, any blank entry indicates the same value as the line above it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now to access all data for which the second index is 2010, we can simply use the Pandas slicing notation:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "California 37253956\n", + "New York 19378102\n", + "Texas 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[:, 2010]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The result is a singly indexed array with just the keys we're interested in.\n", + "This syntax is much more convenient (and the operation is much more efficient!) than the home-spun tuple-based multi-indexing solution that we started with.\n", + "We'll now further discuss this sort of indexing operation on hieararchically indexed data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### MultiIndex as extra dimension\n", + "\n", + "You might notice something else here: we could easily have stored the same data using a simple ``DataFrame`` with index and column labels.\n", + "In fact, Pandas is built with this equivalence in mind. The ``unstack()`` method will quickly convert a multiply indexed ``Series`` into a conventionally indexed ``DataFrame``:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " total under18\n", + "California 2000 33871648 9267089\n", + " 2010 37253956 9284094\n", + "New York 2000 18976457 4687374\n", + " 2010 19378102 4318033\n", + "Texas 2000 20851820 5906301\n", + " 2010 25145561 6879014" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop_df = pd.DataFrame({'total': pop,\n", + " 'under18': [9267089, 9284094,\n", + " 4687374, 4318033,\n", + " 5906301, 6879014]})\n", + "pop_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In addition, all the ufuncs and other functionality discussed in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) work with hierarchical indices as well.\n", + "Here we compute the fraction of people under 18 by year, given the above data:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 2000 2010\n", + "California 0.273594 0.249211\n", + "New York 0.247010 0.222831\n", + "Texas 0.283251 0.273568" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f_u18 = pop_df['under18'] / pop_df['total']\n", + "f_u18.unstack()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This allows us to easily and quickly manipulate and explore even high-dimensional data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Methods of MultiIndex Creation\n", + "\n", + "The most straightforward way to construct a multiply indexed ``Series`` or ``DataFrame`` is to simply pass a list of two or more index arrays to the constructor. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " data1 data2\n", + "a 1 0.554233 0.356072\n", + " 2 0.925244 0.219474\n", + "b 1 0.441759 0.610054\n", + " 2 0.171495 0.886688" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(np.random.rand(4, 2),\n", + " index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\n", + " columns=['data1', 'data2'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The work of creating the ``MultiIndex`` is done in the background.\n", + "\n", + "Similarly, if you pass a dictionary with appropriate tuples as keys, Pandas will automatically recognize this and use a ``MultiIndex`` by default:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "California 2000 33871648\n", + " 2010 37253956\n", + "New York 2000 18976457\n", + " 2010 19378102\n", + "Texas 2000 20851820\n", + " 2010 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = {('California', 2000): 33871648,\n", + " ('California', 2010): 37253956,\n", + " ('Texas', 2000): 20851820,\n", + " ('Texas', 2010): 25145561,\n", + " ('New York', 2000): 18976457,\n", + " ('New York', 2010): 19378102}\n", + "pd.Series(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Nevertheless, it is sometimes useful to explicitly create a ``MultiIndex``; we'll see a couple of these methods here." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Explicit MultiIndex constructors\n", + "\n", + "For more flexibility in how the index is constructed, you can instead use the class method constructors available in the ``pd.MultiIndex``.\n", + "For example, as we did before, you can construct the ``MultiIndex`` from a simple list of arrays giving the index values within each level:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex(levels=[['a', 'b'], [1, 2]],\n", + " labels=[[0, 0, 1, 1], [0, 1, 0, 1]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "You can construct it from a list of tuples giving the multiple index values of each point:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex(levels=[['a', 'b'], [1, 2]],\n", + " labels=[[0, 0, 1, 1], [0, 1, 0, 1]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "You can even construct it from a Cartesian product of single indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex(levels=[['a', 'b'], [1, 2]],\n", + " labels=[[0, 0, 1, 1], [0, 1, 0, 1]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.MultiIndex.from_product([['a', 'b'], [1, 2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Similarly, you can construct the ``MultiIndex`` directly using its internal encoding by passing ``levels`` (a list of lists containing available index values for each level) and ``labels`` (a list of lists that reference these labels):" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex(levels=[['a', 'b'], [1, 2]],\n", + " labels=[[0, 0, 1, 1], [0, 1, 0, 1]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.MultiIndex(levels=[['a', 'b'], [1, 2]],\n", + " labels=[[0, 0, 1, 1], [0, 1, 0, 1]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Any of these objects can be passed as the ``index`` argument when creating a ``Series`` or ``Dataframe``, or be passed to the ``reindex`` method of an existing ``Series`` or ``DataFrame``." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### MultiIndex level names\n", + "\n", + "Sometimes it is convenient to name the levels of the ``MultiIndex``.\n", + "This can be accomplished by passing the ``names`` argument to any of the above ``MultiIndex`` constructors, or by setting the ``names`` attribute of the index after the fact:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state year\n", + "California 2000 33871648\n", + " 2010 37253956\n", + "New York 2000 18976457\n", + " 2010 19378102\n", + "Texas 2000 20851820\n", + " 2010 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.index.names = ['state', 'year']\n", + "pop" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With more involved datasets, this can be a useful way to keep track of the meaning of various index values." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### MultiIndex for columns\n", + "\n", + "In a ``DataFrame``, the rows and columns are completely symmetric, and just as the rows can have multiple levels of indices, the columns can have multiple levels as well.\n", + "Consider the following, which is a mock-up of some (somewhat realistic) medical data:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjectBobGuidoSue
typeHRTempHRTempHRTemp
yearvisit
2013131.038.732.036.735.037.2
244.037.750.035.029.036.7
2014130.037.439.037.861.036.9
247.037.848.037.351.036.5
\n", + "
" + ], + "text/plain": [ + "subject Bob Guido Sue \n", + "type HR Temp HR Temp HR Temp\n", + "year visit \n", + "2013 1 31.0 38.7 32.0 36.7 35.0 37.2\n", + " 2 44.0 37.7 50.0 35.0 29.0 36.7\n", + "2014 1 30.0 37.4 39.0 37.8 61.0 36.9\n", + " 2 47.0 37.8 48.0 37.3 51.0 36.5" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# hierarchical indices and columns\n", + "index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],\n", + " names=['year', 'visit'])\n", + "columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],\n", + " names=['subject', 'type'])\n", + "\n", + "# mock some data\n", + "data = np.round(np.random.randn(4, 6), 1)\n", + "data[:, ::2] *= 10\n", + "data += 37\n", + "\n", + "# create the DataFrame\n", + "health_data = pd.DataFrame(data, index=index, columns=columns)\n", + "health_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here we see where the multi-indexing for both rows and columns can come in *very* handy.\n", + "This is fundamentally four-dimensional data, where the dimensions are the subject, the measurement type, the year, and the visit number.\n", + "With this in place we can, for example, index the top-level column by the person's name and get a full ``DataFrame`` containing just that person's information:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "type HR Temp\n", + "year visit \n", + "2013 1 32.0 36.7\n", + " 2 50.0 35.0\n", + "2014 1 39.0 37.8\n", + " 2 48.0 37.3" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "health_data['Guido']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For complicated records containing multiple labeled measurements across multiple times for many subjects (people, countries, cities, etc.) use of hierarchical rows and columns can be extremely convenient!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Indexing and Slicing a MultiIndex\n", + "\n", + "Indexing and slicing on a ``MultiIndex`` is designed to be intuitive, and it helps if you think about the indices as added dimensions.\n", + "We'll first look at indexing multiply indexed ``Series``, and then multiply-indexed ``DataFrame``s." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Multiply indexed Series\n", + "\n", + "Consider the multiply indexed ``Series`` of state populations we saw earlier:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state year\n", + "California 2000 33871648\n", + " 2010 37253956\n", + "New York 2000 18976457\n", + " 2010 19378102\n", + "Texas 2000 20851820\n", + " 2010 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can access single elements by indexing with multiple terms:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "33871648" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop['California', 2000]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The ``MultiIndex`` also supports *partial indexing*, or indexing just one of the levels in the index.\n", + "The result is another ``Series``, with the lower-level indices maintained:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2000 33871648\n", + "2010 37253956\n", + "dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop['California']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Partial slicing is available as well, as long as the ``MultiIndex`` is sorted (see discussion in [Sorted and Unsorted Indices](#Sorted-and-unsorted-indices)):" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state year\n", + "California 2000 33871648\n", + " 2010 37253956\n", + "New York 2000 18976457\n", + " 2010 19378102\n", + "dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.loc['California':'New York']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With sorted indices, partial indexing can be performed on lower levels by passing an empty slice in the first index:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state\n", + "California 33871648\n", + "New York 18976457\n", + "Texas 20851820\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[:, 2000]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Other types of indexing and selection (discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)) work as well; for example, selection based on Boolean masks:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state year\n", + "California 2000 33871648\n", + " 2010 37253956\n", + "Texas 2010 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[pop > 22000000]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Selection based on fancy indexing also works:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state year\n", + "California 2000 33871648\n", + " 2010 37253956\n", + "Texas 2000 20851820\n", + " 2010 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[['California', 'Texas']]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Multiply indexed DataFrames\n", + "\n", + "A multiply indexed ``DataFrame`` behaves in a similar manner.\n", + "Consider our toy medical ``DataFrame`` from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjectBobGuidoSue
typeHRTempHRTempHRTemp
yearvisit
2013131.038.732.036.735.037.2
244.037.750.035.029.036.7
2014130.037.439.037.861.036.9
247.037.848.037.351.036.5
\n", + "
" + ], + "text/plain": [ + "subject Bob Guido Sue \n", + "type HR Temp HR Temp HR Temp\n", + "year visit \n", + "2013 1 31.0 38.7 32.0 36.7 35.0 37.2\n", + " 2 44.0 37.7 50.0 35.0 29.0 36.7\n", + "2014 1 30.0 37.4 39.0 37.8 61.0 36.9\n", + " 2 47.0 37.8 48.0 37.3 51.0 36.5" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "health_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Remember that columns are primary in a ``DataFrame``, and the syntax used for multiply indexed ``Series`` applies to the columns.\n", + "For example, we can recover Guido's heart rate data with a simple operation:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year visit\n", + "2013 1 32.0\n", + " 2 50.0\n", + "2014 1 39.0\n", + " 2 48.0\n", + "Name: (Guido, HR), dtype: float64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "health_data['Guido', 'HR']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Also, as with the single-index case, we can use the ``loc``, ``iloc``, and ``ix`` indexers introduced in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjectBob
typeHRTemp
yearvisit
2013131.038.7
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" + ], + "text/plain": [ + "subject Bob \n", + "type HR Temp\n", + "year visit \n", + "2013 1 31.0 38.7\n", + " 2 44.0 37.7" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "health_data.iloc[:2, :2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "These indexers provide an array-like view of the underlying two-dimensional data, but each individual index in ``loc`` or ``iloc`` can be passed a tuple of multiple indices. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "year visit\n", + "2013 1 31.0\n", + " 2 44.0\n", + "2014 1 30.0\n", + " 2 47.0\n", + "Name: (Bob, HR), dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "health_data.loc[:, ('Bob', 'HR')]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Working with slices within these index tuples is not especially convenient; trying to create a slice within a tuple will lead to a syntax error:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m health_data.loc[(:, 1), (:, 'HR')]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "health_data.loc[(:, 1), (:, 'HR')]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "You could get around this by building the desired slice explicitly using Python's built-in ``slice()`` function, but a better way in this context is to use an ``IndexSlice`` object, which Pandas provides for precisely this situation.\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
subjectBobGuidoSue
typeHRHRHR
yearvisit
2013131.032.035.0
2014130.039.061.0
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" + ], + "text/plain": [ + "subject Bob Guido Sue\n", + "type HR HR HR\n", + "year visit \n", + "2013 1 31.0 32.0 35.0\n", + "2014 1 30.0 39.0 61.0" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = pd.IndexSlice\n", + "health_data.loc[idx[:, 1], idx[:, 'HR']]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "There are so many ways to interact with data in multiply indexed ``Series`` and ``DataFrame``s, and as with many tools in this book the best way to become familiar with them is to try them out!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Rearranging Multi-Indices\n", + "\n", + "One of the keys to working with multiply indexed data is knowing how to effectively transform the data.\n", + "There are a number of operations that will preserve all the information in the dataset, but rearrange it for the purposes of various computations.\n", + "We saw a brief example of this in the ``stack()`` and ``unstack()`` methods, but there are many more ways to finely control the rearrangement of data between hierarchical indices and columns, and we'll explore them here." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Sorted and unsorted indices\n", + "\n", + "Earlier, we briefly mentioned a caveat, but we should emphasize it more here.\n", + "*Many of the ``MultiIndex`` slicing operations will fail if the index is not sorted.*\n", + "Let's take a look at this here.\n", + "\n", + "We'll start by creating some simple multiply indexed data where the indices are *not lexographically sorted*:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "char int\n", + "a 1 0.003001\n", + " 2 0.164974\n", + "c 1 0.741650\n", + " 2 0.569264\n", + "b 1 0.001693\n", + " 2 0.526226\n", + "dtype: float64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]])\n", + "data = pd.Series(np.random.rand(6), index=index)\n", + "data.index.names = ['char', 'int']\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "If we try to take a partial slice of this index, it will result in an error:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "'Key length (1) was greater than MultiIndex lexsort depth (0)'\n" + ] + } + ], + "source": [ + "try:\n", + " data['a':'b']\n", + "except KeyError as e:\n", + " print(type(e))\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Although it is not entirely clear from the error message, this is the result of the MultiIndex not being sorted.\n", + "For various reasons, partial slices and other similar operations require the levels in the ``MultiIndex`` to be in sorted (i.e., lexographical) order.\n", + "Pandas provides a number of convenience routines to perform this type of sorting; examples are the ``sort_index()`` and ``sortlevel()`` methods of the ``DataFrame``.\n", + "We'll use the simplest, ``sort_index()``, here:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "char int\n", + "a 1 0.003001\n", + " 2 0.164974\n", + "b 1 0.001693\n", + " 2 0.526226\n", + "c 1 0.741650\n", + " 2 0.569264\n", + "dtype: float64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data.sort_index()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With the index sorted in this way, partial slicing will work as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "char int\n", + "a 1 0.003001\n", + " 2 0.164974\n", + "b 1 0.001693\n", + " 2 0.526226\n", + "dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['a':'b']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Stacking and unstacking indices\n", + "\n", + "As we saw briefly before, it is possible to convert a dataset from a stacked multi-index to a simple two-dimensional representation, optionally specifying the level to use:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "state California New York Texas\n", + "year \n", + "2000 33871648 18976457 20851820\n", + "2010 37253956 19378102 25145561" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.unstack(level=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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year20002010
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" + ], + "text/plain": [ + "year 2000 2010\n", + "state \n", + "California 33871648 37253956\n", + "New York 18976457 19378102\n", + "Texas 20851820 25145561" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.unstack(level=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The opposite of ``unstack()`` is ``stack()``, which here can be used to recover the original series:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state year\n", + "California 2000 33871648\n", + " 2010 37253956\n", + "New York 2000 18976457\n", + " 2010 19378102\n", + "Texas 2000 20851820\n", + " 2010 25145561\n", + "dtype: int64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.unstack().stack()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Index setting and resetting\n", + "\n", + "Another way to rearrange hierarchical data is to turn the index labels into columns; this can be accomplished with the ``reset_index`` method.\n", + "Calling this on the population dictionary will result in a ``DataFrame`` with a *state* and *year* column holding the information that was formerly in the index.\n", + "For clarity, we can optionally specify the name of the data for the column representation:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateyearpopulation
0California200033871648
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2New York200018976457
3New York201019378102
4Texas200020851820
5Texas201025145561
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" + ], + "text/plain": [ + " state year population\n", + "0 California 2000 33871648\n", + "1 California 2010 37253956\n", + "2 New York 2000 18976457\n", + "3 New York 2010 19378102\n", + "4 Texas 2000 20851820\n", + "5 Texas 2010 25145561" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop_flat = pop.reset_index(name='population')\n", + "pop_flat" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Often when working with data in the real world, the raw input data looks like this and it's useful to build a ``MultiIndex`` from the column values.\n", + "This can be done with the ``set_index`` method of the ``DataFrame``, which returns a multiply indexed ``DataFrame``:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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population
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" + ], + "text/plain": [ + " population\n", + "state year \n", + "California 2000 33871648\n", + " 2010 37253956\n", + "New York 2000 18976457\n", + " 2010 19378102\n", + "Texas 2000 20851820\n", + " 2010 25145561" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop_flat.set_index(['state', 'year'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In practice, I find this type of reindexing to be one of the more useful patterns when encountering real-world datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Data Aggregations on Multi-Indices\n", + "\n", + "We've previously seen that Pandas has built-in data aggregation methods, such as ``mean()``, ``sum()``, and ``max()``.\n", + "For hierarchically indexed data, these can be passed a ``level`` parameter that controls which subset of the data the aggregate is computed on.\n", + "\n", + "For example, let's return to our health data:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subjectBobGuidoSue
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" + ], + "text/plain": [ + "subject Bob Guido Sue \n", + "type HR Temp HR Temp HR Temp\n", + "year visit \n", + "2013 1 31.0 38.7 32.0 36.7 35.0 37.2\n", + " 2 44.0 37.7 50.0 35.0 29.0 36.7\n", + "2014 1 30.0 37.4 39.0 37.8 61.0 36.9\n", + " 2 47.0 37.8 48.0 37.3 51.0 36.5" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "health_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Perhaps we'd like to average-out the measurements in the two visits each year. We can do this by naming the index level we'd like to explore, in this case the year:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "subject Bob Guido Sue \n", + "type HR Temp HR Temp HR Temp\n", + "year \n", + "2013 37.5 38.2 41.0 35.85 32.0 36.95\n", + "2014 38.5 37.6 43.5 37.55 56.0 36.70" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_mean = health_data.mean(level='year')\n", + "data_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "By further making use of the ``axis`` keyword, we can take the mean among levels on the columns as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "type HR Temp\n", + "year \n", + "2013 36.833333 37.000000\n", + "2014 46.000000 37.283333" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_mean.mean(axis=1, level='type')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Thus in two lines, we've been able to find the average heart rate and temperature measured among all subjects in all visits each year.\n", + "This syntax is actually a short cut to the ``GroupBy`` functionality, which we will discuss in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb).\n", + "While this is a toy example, many real-world datasets have similar hierarchical structure." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Aside: Panel Data\n", + "\n", + "Pandas has a few other fundamental data structures that we have not yet discussed, namely the ``pd.Panel`` and ``pd.Panel4D`` objects.\n", + "These can be thought of, respectively, as three-dimensional and four-dimensional generalizations of the (one-dimensional) ``Series`` and (two-dimensional) ``DataFrame`` structures.\n", + "Once you are familiar with indexing and manipulation of data in a ``Series`` and ``DataFrame``, ``Panel`` and ``Panel4D`` are relatively straightforward to use.\n", + "In particular, the ``ix``, ``loc``, and ``iloc`` indexers discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) extend readily to these higher-dimensional structures.\n", + "\n", + "We won't cover these panel structures further in this text, as I've found in the majority of cases that multi-indexing is a more useful and conceptually simpler representation for higher-dimensional data.\n", + "Additionally, panel data is fundamentally a dense data representation, while multi-indexing is fundamentally a sparse data representation.\n", + "As the number of dimensions increases, the dense representation can become very inefficient for the majority of real-world datasets.\n", + "For the occasional specialized application, however, these structures can be useful.\n", + "If you'd like to read more about the ``Panel`` and ``Panel4D`` structures, see the references listed in [Further Resources](03.13-Further-Resources.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.05-Hierarchical-Indexing.md b/notebooks_v2/03.05-Hierarchical-Indexing.md new file mode 100644 index 00000000..551c0729 --- /dev/null +++ b/notebooks_v2/03.05-Hierarchical-Indexing.md @@ -0,0 +1,599 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) > + +Open in Colab + + + +# Hierarchical Indexing + + +Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas ``Series`` and ``DataFrame`` objects, respectively. +Often it is useful to go beyond this and store higher-dimensional data–that is, data indexed by more than one or two keys. +While Pandas does provide ``Panel`` and ``Panel4D`` objects that natively handle three-dimensional and four-dimensional data (see [Aside: Panel Data](#Aside:-Panel-Data)), a far more common pattern in practice is to make use of *hierarchical indexing* (also known as *multi-indexing*) to incorporate multiple index *levels* within a single index. +In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional ``Series`` and two-dimensional ``DataFrame`` objects. + +In this section, we'll explore the direct creation of ``MultiIndex`` objects, considerations when indexing, slicing, and computing statistics across multiply indexed data, and useful routines for converting between simple and hierarchically indexed representations of your data. + +We begin with the standard imports: + + +```python deletable=true editable=true +import pandas as pd +import numpy as np +``` + + +## A Multiply Indexed Series + +Let's start by considering how we might represent two-dimensional data within a one-dimensional ``Series``. +For concreteness, we will consider a series of data where each point has a character and numerical key. + + + +### The bad way + +Suppose you would like to track data about states from two different years. +Using the Pandas tools we've already covered, you might be tempted to simply use Python tuples as keys: + + +```python deletable=true editable=true +index = [('California', 2000), ('California', 2010), + ('New York', 2000), ('New York', 2010), + ('Texas', 2000), ('Texas', 2010)] +populations = [33871648, 37253956, + 18976457, 19378102, + 20851820, 25145561] +pop = pd.Series(populations, index=index) +pop +``` + + +With this indexing scheme, you can straightforwardly index or slice the series based on this multiple index: + + +```python deletable=true editable=true +pop[('California', 2010):('Texas', 2000)] +``` + + +But the convenience ends there. For example, if you need to select all values from 2010, you'll need to do some messy (and potentially slow) munging to make it happen: + + +```python deletable=true editable=true +pop[[i for i in pop.index if i[1] == 2010]] +``` + + +This produces the desired result, but is not as clean (or as efficient for large datasets) as the slicing syntax we've grown to love in Pandas. + + + +### The Better Way: Pandas MultiIndex +Fortunately, Pandas provides a better way. +Our tuple-based indexing is essentially a rudimentary multi-index, and the Pandas ``MultiIndex`` type gives us the type of operations we wish to have. +We can create a multi-index from the tuples as follows: + + +```python deletable=true editable=true +index = pd.MultiIndex.from_tuples(index) +index +``` + + +Notice that the ``MultiIndex`` contains multiple *levels* of indexing–in this case, the state names and the years, as well as multiple *labels* for each data point which encode these levels. + +If we re-index our series with this ``MultiIndex``, we see the hierarchical representation of the data: + + +```python deletable=true editable=true +pop = pop.reindex(index) +pop +``` + + +Here the first two columns of the ``Series`` representation show the multiple index values, while the third column shows the data. +Notice that some entries are missing in the first column: in this multi-index representation, any blank entry indicates the same value as the line above it. + + + +Now to access all data for which the second index is 2010, we can simply use the Pandas slicing notation: + + +```python deletable=true editable=true +pop[:, 2010] +``` + + +The result is a singly indexed array with just the keys we're interested in. +This syntax is much more convenient (and the operation is much more efficient!) than the home-spun tuple-based multi-indexing solution that we started with. +We'll now further discuss this sort of indexing operation on hieararchically indexed data. + + + +### MultiIndex as extra dimension + +You might notice something else here: we could easily have stored the same data using a simple ``DataFrame`` with index and column labels. +In fact, Pandas is built with this equivalence in mind. The ``unstack()`` method will quickly convert a multiply indexed ``Series`` into a conventionally indexed ``DataFrame``: + + +```python deletable=true editable=true +pop_df = pop.unstack() +pop_df +``` + + +Naturally, the ``stack()`` method provides the opposite operation: + + +```python deletable=true editable=true +pop_df.stack() +``` + + +Seeing this, you might wonder why would we would bother with hierarchical indexing at all. +The reason is simple: just as we were able to use multi-indexing to represent two-dimensional data within a one-dimensional ``Series``, we can also use it to represent data of three or more dimensions in a ``Series`` or ``DataFrame``. +Each extra level in a multi-index represents an extra dimension of data; taking advantage of this property gives us much more flexibility in the types of data we can represent. Concretely, we might want to add another column of demographic data for each state at each year (say, population under 18) ; with a ``MultiIndex`` this is as easy as adding another column to the ``DataFrame``: + + +```python deletable=true editable=true +pop_df = pd.DataFrame({'total': pop, + 'under18': [9267089, 9284094, + 4687374, 4318033, + 5906301, 6879014]}) +pop_df +``` + + +In addition, all the ufuncs and other functionality discussed in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) work with hierarchical indices as well. +Here we compute the fraction of people under 18 by year, given the above data: + + +```python deletable=true editable=true +f_u18 = pop_df['under18'] / pop_df['total'] +f_u18.unstack() +``` + + +This allows us to easily and quickly manipulate and explore even high-dimensional data. + + + +## Methods of MultiIndex Creation + +The most straightforward way to construct a multiply indexed ``Series`` or ``DataFrame`` is to simply pass a list of two or more index arrays to the constructor. For example: + + +```python deletable=true editable=true +df = pd.DataFrame(np.random.rand(4, 2), + index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]], + columns=['data1', 'data2']) +df +``` + + +The work of creating the ``MultiIndex`` is done in the background. + +Similarly, if you pass a dictionary with appropriate tuples as keys, Pandas will automatically recognize this and use a ``MultiIndex`` by default: + + +```python deletable=true editable=true +data = {('California', 2000): 33871648, + ('California', 2010): 37253956, + ('Texas', 2000): 20851820, + ('Texas', 2010): 25145561, + ('New York', 2000): 18976457, + ('New York', 2010): 19378102} +pd.Series(data) +``` + + +Nevertheless, it is sometimes useful to explicitly create a ``MultiIndex``; we'll see a couple of these methods here. + + + +### Explicit MultiIndex constructors + +For more flexibility in how the index is constructed, you can instead use the class method constructors available in the ``pd.MultiIndex``. +For example, as we did before, you can construct the ``MultiIndex`` from a simple list of arrays giving the index values within each level: + + +```python deletable=true editable=true +pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]]) +``` + + +You can construct it from a list of tuples giving the multiple index values of each point: + + +```python deletable=true editable=true +pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)]) +``` + + +You can even construct it from a Cartesian product of single indices: + + +```python deletable=true editable=true +pd.MultiIndex.from_product([['a', 'b'], [1, 2]]) +``` + + +Similarly, you can construct the ``MultiIndex`` directly using its internal encoding by passing ``levels`` (a list of lists containing available index values for each level) and ``labels`` (a list of lists that reference these labels): + + +```python deletable=true editable=true +pd.MultiIndex(levels=[['a', 'b'], [1, 2]], + labels=[[0, 0, 1, 1], [0, 1, 0, 1]]) +``` + + +Any of these objects can be passed as the ``index`` argument when creating a ``Series`` or ``Dataframe``, or be passed to the ``reindex`` method of an existing ``Series`` or ``DataFrame``. + + + +### MultiIndex level names + +Sometimes it is convenient to name the levels of the ``MultiIndex``. +This can be accomplished by passing the ``names`` argument to any of the above ``MultiIndex`` constructors, or by setting the ``names`` attribute of the index after the fact: + + +```python deletable=true editable=true +pop.index.names = ['state', 'year'] +pop +``` + + +With more involved datasets, this can be a useful way to keep track of the meaning of various index values. + + + +### MultiIndex for columns + +In a ``DataFrame``, the rows and columns are completely symmetric, and just as the rows can have multiple levels of indices, the columns can have multiple levels as well. +Consider the following, which is a mock-up of some (somewhat realistic) medical data: + + +```python deletable=true editable=true +# hierarchical indices and columns +index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]], + names=['year', 'visit']) +columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']], + names=['subject', 'type']) + +# mock some data +data = np.round(np.random.randn(4, 6), 1) +data[:, ::2] *= 10 +data += 37 + +# create the DataFrame +health_data = pd.DataFrame(data, index=index, columns=columns) +health_data +``` + + +Here we see where the multi-indexing for both rows and columns can come in *very* handy. +This is fundamentally four-dimensional data, where the dimensions are the subject, the measurement type, the year, and the visit number. +With this in place we can, for example, index the top-level column by the person's name and get a full ``DataFrame`` containing just that person's information: + + +```python deletable=true editable=true +health_data['Guido'] +``` + + +For complicated records containing multiple labeled measurements across multiple times for many subjects (people, countries, cities, etc.) use of hierarchical rows and columns can be extremely convenient! + + + +## Indexing and Slicing a MultiIndex + +Indexing and slicing on a ``MultiIndex`` is designed to be intuitive, and it helps if you think about the indices as added dimensions. +We'll first look at indexing multiply indexed ``Series``, and then multiply-indexed ``DataFrame``s. + + + +### Multiply indexed Series + +Consider the multiply indexed ``Series`` of state populations we saw earlier: + + +```python deletable=true editable=true +pop +``` + + +We can access single elements by indexing with multiple terms: + + +```python deletable=true editable=true +pop['California', 2000] +``` + + +The ``MultiIndex`` also supports *partial indexing*, or indexing just one of the levels in the index. +The result is another ``Series``, with the lower-level indices maintained: + + +```python deletable=true editable=true +pop['California'] +``` + + +Partial slicing is available as well, as long as the ``MultiIndex`` is sorted (see discussion in [Sorted and Unsorted Indices](#Sorted-and-unsorted-indices)): + + +```python deletable=true editable=true +pop.loc['California':'New York'] +``` + + +With sorted indices, partial indexing can be performed on lower levels by passing an empty slice in the first index: + + +```python deletable=true editable=true +pop[:, 2000] +``` + + +Other types of indexing and selection (discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)) work as well; for example, selection based on Boolean masks: + + +```python deletable=true editable=true +pop[pop > 22000000] +``` + + +Selection based on fancy indexing also works: + + +```python deletable=true editable=true +pop[['California', 'Texas']] +``` + + +### Multiply indexed DataFrames + +A multiply indexed ``DataFrame`` behaves in a similar manner. +Consider our toy medical ``DataFrame`` from before: + + +```python deletable=true editable=true +health_data +``` + + +Remember that columns are primary in a ``DataFrame``, and the syntax used for multiply indexed ``Series`` applies to the columns. +For example, we can recover Guido's heart rate data with a simple operation: + + +```python deletable=true editable=true +health_data['Guido', 'HR'] +``` + + +Also, as with the single-index case, we can use the ``loc``, ``iloc``, and ``ix`` indexers introduced in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). For example: + + +```python deletable=true editable=true +health_data.iloc[:2, :2] +``` + + +These indexers provide an array-like view of the underlying two-dimensional data, but each individual index in ``loc`` or ``iloc`` can be passed a tuple of multiple indices. For example: + + +```python deletable=true editable=true +health_data.loc[:, ('Bob', 'HR')] +``` + + +Working with slices within these index tuples is not especially convenient; trying to create a slice within a tuple will lead to a syntax error: + + +```python deletable=true editable=true +health_data.loc[(:, 1), (:, 'HR')] +``` + + +You could get around this by building the desired slice explicitly using Python's built-in ``slice()`` function, but a better way in this context is to use an ``IndexSlice`` object, which Pandas provides for precisely this situation. +For example: + + +```python deletable=true editable=true +idx = pd.IndexSlice +health_data.loc[idx[:, 1], idx[:, 'HR']] +``` + + +There are so many ways to interact with data in multiply indexed ``Series`` and ``DataFrame``s, and as with many tools in this book the best way to become familiar with them is to try them out! + + + +## Rearranging Multi-Indices + +One of the keys to working with multiply indexed data is knowing how to effectively transform the data. +There are a number of operations that will preserve all the information in the dataset, but rearrange it for the purposes of various computations. +We saw a brief example of this in the ``stack()`` and ``unstack()`` methods, but there are many more ways to finely control the rearrangement of data between hierarchical indices and columns, and we'll explore them here. + + + +### Sorted and unsorted indices + +Earlier, we briefly mentioned a caveat, but we should emphasize it more here. +*Many of the ``MultiIndex`` slicing operations will fail if the index is not sorted.* +Let's take a look at this here. + +We'll start by creating some simple multiply indexed data where the indices are *not lexographically sorted*: + + +```python deletable=true editable=true +index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]]) +data = pd.Series(np.random.rand(6), index=index) +data.index.names = ['char', 'int'] +data +``` + + +If we try to take a partial slice of this index, it will result in an error: + + +```python deletable=true editable=true +try: + data['a':'b'] +except KeyError as e: + print(type(e)) + print(e) +``` + + +Although it is not entirely clear from the error message, this is the result of the MultiIndex not being sorted. +For various reasons, partial slices and other similar operations require the levels in the ``MultiIndex`` to be in sorted (i.e., lexographical) order. +Pandas provides a number of convenience routines to perform this type of sorting; examples are the ``sort_index()`` and ``sortlevel()`` methods of the ``DataFrame``. +We'll use the simplest, ``sort_index()``, here: + + +```python deletable=true editable=true +data = data.sort_index() +data +``` + + +With the index sorted in this way, partial slicing will work as expected: + + +```python deletable=true editable=true +data['a':'b'] +``` + + +### Stacking and unstacking indices + +As we saw briefly before, it is possible to convert a dataset from a stacked multi-index to a simple two-dimensional representation, optionally specifying the level to use: + + +```python deletable=true editable=true +pop.unstack(level=0) +``` + +```python deletable=true editable=true +pop.unstack(level=1) +``` + + +The opposite of ``unstack()`` is ``stack()``, which here can be used to recover the original series: + + +```python deletable=true editable=true +pop.unstack().stack() +``` + + +### Index setting and resetting + +Another way to rearrange hierarchical data is to turn the index labels into columns; this can be accomplished with the ``reset_index`` method. +Calling this on the population dictionary will result in a ``DataFrame`` with a *state* and *year* column holding the information that was formerly in the index. +For clarity, we can optionally specify the name of the data for the column representation: + + +```python deletable=true editable=true +pop_flat = pop.reset_index(name='population') +pop_flat +``` + + +Often when working with data in the real world, the raw input data looks like this and it's useful to build a ``MultiIndex`` from the column values. +This can be done with the ``set_index`` method of the ``DataFrame``, which returns a multiply indexed ``DataFrame``: + + +```python deletable=true editable=true +pop_flat.set_index(['state', 'year']) +``` + + +In practice, I find this type of reindexing to be one of the more useful patterns when encountering real-world datasets. + + + +## Data Aggregations on Multi-Indices + +We've previously seen that Pandas has built-in data aggregation methods, such as ``mean()``, ``sum()``, and ``max()``. +For hierarchically indexed data, these can be passed a ``level`` parameter that controls which subset of the data the aggregate is computed on. + +For example, let's return to our health data: + + +```python deletable=true editable=true +health_data +``` + + +Perhaps we'd like to average-out the measurements in the two visits each year. We can do this by naming the index level we'd like to explore, in this case the year: + + +```python deletable=true editable=true +data_mean = health_data.mean(level='year') +data_mean +``` + + +By further making use of the ``axis`` keyword, we can take the mean among levels on the columns as well: + + +```python deletable=true editable=true +data_mean.mean(axis=1, level='type') +``` + + +Thus in two lines, we've been able to find the average heart rate and temperature measured among all subjects in all visits each year. +This syntax is actually a short cut to the ``GroupBy`` functionality, which we will discuss in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb). +While this is a toy example, many real-world datasets have similar hierarchical structure. + + + +## Aside: Panel Data + +Pandas has a few other fundamental data structures that we have not yet discussed, namely the ``pd.Panel`` and ``pd.Panel4D`` objects. +These can be thought of, respectively, as three-dimensional and four-dimensional generalizations of the (one-dimensional) ``Series`` and (two-dimensional) ``DataFrame`` structures. +Once you are familiar with indexing and manipulation of data in a ``Series`` and ``DataFrame``, ``Panel`` and ``Panel4D`` are relatively straightforward to use. +In particular, the ``ix``, ``loc``, and ``iloc`` indexers discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) extend readily to these higher-dimensional structures. + +We won't cover these panel structures further in this text, as I've found in the majority of cases that multi-indexing is a more useful and conceptually simpler representation for higher-dimensional data. +Additionally, panel data is fundamentally a dense data representation, while multi-indexing is fundamentally a sparse data representation. +As the number of dimensions increases, the dense representation can become very inefficient for the majority of real-world datasets. +For the occasional specialized application, however, these structures can be useful. +If you'd like to read more about the ``Panel`` and ``Panel4D`` structures, see the references listed in [Further Resources](03.13-Further-Resources.ipynb). + + + + +< [Handling Missing Data](03.04-Missing-Values.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/03.06-Concat-And-Append.ipynb b/notebooks_v2/03.06-Concat-And-Append.ipynb new file mode 100644 index 00000000..0460286d --- /dev/null +++ b/notebooks_v2/03.06-Concat-And-Append.ipynb @@ -0,0 +1,1643 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining Datasets: Concat and Append" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the most interesting studies of data come from combining different data sources.\n", + "These operations can involve anything from very straightforward concatenation of two different datasets, to more complicated database-style joins and merges that correctly handle any overlaps between the datasets.\n", + "``Series`` and ``DataFrame``s are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward.\n", + "\n", + "Here we'll take a look at simple concatenation of ``Series`` and ``DataFrame``s with the ``pd.concat`` function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, we'll define this function which creates a ``DataFrame`` of a particular form that will be useful below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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\"\"\"\n", + " def __init__(self, *args):\n", + " self.args = args\n", + " \n", + " def _repr_html_(self):\n", + " return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n", + " for a in self.args)\n", + " \n", + " def __repr__(self):\n", + " return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n", + " for a in self.args)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The use of this will become clearer as we continue our discussion in the following section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recall: Concatenation of NumPy Arrays\n", + "\n", + "Concatenation of ``Series`` and ``DataFrame`` objects is very similar to concatenation of Numpy arrays, which can be done via the ``np.concatenate`` function as discussed in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb).\n", + "Recall that with it, you can combine the contents of two or more arrays into a single array:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = [1, 2, 3]\n", + "y = [4, 5, 6]\n", + "z = [7, 8, 9]\n", + "np.concatenate([x, y, z])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first argument is a list or tuple of arrays to concatenate.\n", + "Additionally, it takes an ``axis`` keyword that allows you to specify the axis along which the result will be concatenated:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 1, 2],\n", + " [3, 4, 3, 4]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = [[1, 2],\n", + " [3, 4]]\n", + "np.concatenate([x, x], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Concatenation with ``pd.concat``" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas has a function, ``pd.concat()``, which has a similar syntax to ``np.concatenate`` but contains a number of options that we'll discuss momentarily:\n", + "\n", + "```python\n", + "# Signature in Pandas v0.18\n", + "pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,\n", + " keys=None, levels=None, names=None, verify_integrity=False,\n", + " copy=True)\n", + "```\n", + "\n", + "``pd.concat()`` can be used for a simple concatenation of ``Series`` or ``DataFrame`` objects, just as ``np.concatenate()`` can be used for simple concatenations of arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 A\n", + "2 B\n", + "3 C\n", + "4 D\n", + "5 E\n", + "6 F\n", + "dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser1 = pd.Series(['A', 'B', 'C'], index=[1, 2, 3])\n", + "ser2 = pd.Series(['D', 'E', 'F'], index=[4, 5, 6])\n", + "pd.concat([ser1, ser2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It also works to concatenate higher-dimensional objects, such as ``DataFrame``s:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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df1

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df2

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pd.concat([df1, df2])

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" + ], + "text/plain": [ + "df1\n", + " A B\n", + "1 A1 B1\n", + "2 A2 B2\n", + "\n", + "df2\n", + " A B\n", + "3 A3 B3\n", + "4 A4 B4\n", + "\n", + "pd.concat([df1, df2])\n", + " A B\n", + "1 A1 B1\n", + "2 A2 B2\n", + "3 A3 B3\n", + "4 A4 B4" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = make_df('AB', [1, 2])\n", + "df2 = make_df('AB', [3, 4])\n", + "display('df1', 'df2', 'pd.concat([df1, df2])')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, the concatenation takes place row-wise within the ``DataFrame`` (i.e., ``axis=0``).\n", + "Like ``np.concatenate``, ``pd.concat`` allows specification of an axis along which concatenation will take place.\n", + "Consider the following example:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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df3

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df4

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pd.concat([df3, df4], axis='col')

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" + ], + "text/plain": [ + "df3\n", + " A B\n", + "0 A0 B0\n", + "1 A1 B1\n", + "\n", + "df4\n", + " C D\n", + "0 C0 D0\n", + "1 C1 D1\n", + "\n", + "pd.concat([df3, df4], axis='col')\n", + " A B C D\n", + "0 A0 B0 C0 D0\n", + "1 A1 B1 C1 D1" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = make_df('AB', [0, 1])\n", + "df4 = make_df('CD', [0, 1])\n", + "display('df3', 'df4', \"pd.concat([df3, df4], axis='col')\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could have equivalently specified ``axis=1``; here we've used the more intuitive ``axis='col'``. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Duplicate indices\n", + "\n", + "One important difference between ``np.concatenate`` and ``pd.concat`` is that Pandas concatenation *preserves indices*, even if the result will have duplicate indices!\n", + "Consider this simple example:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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x

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y

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pd.concat([x, y])

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" + ], + "text/plain": [ + "x\n", + " A B\n", + "0 A0 B0\n", + "1 A1 B1\n", + "\n", + "y\n", + " A B\n", + "0 A2 B2\n", + "1 A3 B3\n", + "\n", + "pd.concat([x, y])\n", + " A B\n", + "0 A0 B0\n", + "1 A1 B1\n", + "0 A2 B2\n", + "1 A3 B3" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = make_df('AB', [0, 1])\n", + "y = make_df('AB', [2, 3])\n", + "y.index = x.index # make duplicate indices!\n", + "display('x', 'y', 'pd.concat([x, y])')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the repeated indices in the result.\n", + "While this is valid within ``DataFrame``s, the outcome is often undesirable.\n", + "``pd.concat()`` gives us a few ways to handle it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Catching the repeats as an error\n", + "\n", + "If you'd like to simply verify that the indices in the result of ``pd.concat()`` do not overlap, you can specify the ``verify_integrity`` flag.\n", + "With this set to True, the concatenation will raise an exception if there are duplicate indices.\n", + "Here is an example, where for clarity we'll catch and print the error message:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ValueError: Indexes have overlapping values: [0, 1]\n" + ] + } + ], + "source": [ + "try:\n", + " pd.concat([x, y], verify_integrity=True)\n", + "except ValueError as e:\n", + " print(\"ValueError:\", e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ignoring the index\n", + "\n", + "Sometimes the index itself does not matter, and you would prefer it to simply be ignored.\n", + "This option can be specified using the ``ignore_index`` flag.\n", + "With this set to true, the concatenation will create a new integer index for the resulting ``Series``:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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x

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y

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pd.concat([x, y], ignore_index=True)

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" + ], + "text/plain": [ + "x\n", + " A B\n", + "0 A0 B0\n", + "1 A1 B1\n", + "\n", + "y\n", + " A B\n", + "0 A2 B2\n", + "1 A3 B3\n", + "\n", + "pd.concat([x, y], ignore_index=True)\n", + " A B\n", + "0 A0 B0\n", + "1 A1 B1\n", + "2 A2 B2\n", + "3 A3 B3" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('x', 'y', 'pd.concat([x, y], ignore_index=True)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adding MultiIndex keys\n", + "\n", + "Another option is to use the ``keys`` option to specify a label for the data sources; the result will be a hierarchically indexed series containing the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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x

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AB
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y

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pd.concat([x, y], keys=['x', 'y'])

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AB
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y0A2B2
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" + ], + "text/plain": [ + "x\n", + " A B\n", + "0 A0 B0\n", + "1 A1 B1\n", + "\n", + "y\n", + " A B\n", + "0 A2 B2\n", + "1 A3 B3\n", + "\n", + "pd.concat([x, y], keys=['x', 'y'])\n", + " A B\n", + "x 0 A0 B0\n", + " 1 A1 B1\n", + "y 0 A2 B2\n", + " 1 A3 B3" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('x', 'y', \"pd.concat([x, y], keys=['x', 'y'])\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a multiply indexed ``DataFrame``, and we can use the tools discussed in [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) to transform this data into the representation we're interested in." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Concatenation with joins\n", + "\n", + "In the simple examples we just looked at, we were mainly concatenating ``DataFrame``s with shared column names.\n", + "In practice, data from different sources might have different sets of column names, and ``pd.concat`` offers several options in this case.\n", + "Consider the concatenation of the following two ``DataFrame``s, which have some (but not all!) columns in common:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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df5

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df6

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pd.concat([df5, df6])

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" + ], + "text/plain": [ + "df5\n", + " A B C\n", + "1 A1 B1 C1\n", + "2 A2 B2 C2\n", + "\n", + "df6\n", + " B C D\n", + "3 B3 C3 D3\n", + "4 B4 C4 D4\n", + "\n", + "pd.concat([df5, df6])\n", + " A B C D\n", + "1 A1 B1 C1 NaN\n", + "2 A2 B2 C2 NaN\n", + "3 NaN B3 C3 D3\n", + "4 NaN B4 C4 D4" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df5 = make_df('ABC', [1, 2])\n", + "df6 = make_df('BCD', [3, 4])\n", + "display('df5', 'df6', 'pd.concat([df5, df6])')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, the entries for which no data is available are filled with NA values.\n", + "To change this, we can specify one of several options for the ``join`` and ``join_axes`` parameters of the concatenate function.\n", + "By default, the join is a union of the input columns (``join='outer'``), but we can change this to an intersection of the columns using ``join='inner'``:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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df5

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df6

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pd.concat([df5, df6], join='inner')

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" + ], + "text/plain": [ + "df1\n", + " A B\n", + "1 A1 B1\n", + "2 A2 B2\n", + "\n", + "df2\n", + " A B\n", + "3 A3 B3\n", + "4 A4 B4\n", + "\n", + "df1.append(df2)\n", + " A B\n", + "1 A1 B1\n", + "2 A2 B2\n", + "3 A3 B3\n", + "4 A4 B4" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df1', 'df2', 'df1.append(df2)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind that unlike the ``append()`` and ``extend()`` methods of Python lists, the ``append()`` method in Pandas does not modify the original object–instead it creates a new object with the combined data.\n", + "It also is not a very efficient method, because it involves creation of a new index *and* data buffer.\n", + "Thus, if you plan to do multiple ``append`` operations, it is generally better to build a list of ``DataFrame``s and pass them all at once to the ``concat()`` function.\n", + "\n", + "In the next section, we'll look at another more powerful approach to combining data from multiple sources, the database-style merges/joins implemented in ``pd.merge``.\n", + "For more information on ``concat()``, ``append()``, and related functionality, see the [\"Merge, Join, and Concatenate\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.06-Concat-And-Append.md b/notebooks_v2/03.06-Concat-And-Append.md new file mode 100644 index 00000000..7083bd20 --- /dev/null +++ b/notebooks_v2/03.06-Concat-And-Append.md @@ -0,0 +1,251 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) > + +Open in Colab + + + +# Combining Datasets: Concat and Append + + +Some of the most interesting studies of data come from combining different data sources. +These operations can involve anything from very straightforward concatenation of two different datasets, to more complicated database-style joins and merges that correctly handle any overlaps between the datasets. +``Series`` and ``DataFrame``s are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward. + +Here we'll take a look at simple concatenation of ``Series`` and ``DataFrame``s with the ``pd.concat`` function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas. + +We begin with the standard imports: + +```python +import pandas as pd +import numpy as np +``` + +For convenience, we'll define this function which creates a ``DataFrame`` of a particular form that will be useful below: + +```python +def make_df(cols, ind): + """Quickly make a DataFrame""" + data = {c: [str(c) + str(i) for i in ind] + for c in cols} + return pd.DataFrame(data, ind) + +# example DataFrame +make_df('ABC', range(3)) +``` + +In addition, we'll create a quick class that allows us to display multiple ``DataFrame``s side by side. The code makes use of the special ``_repr_html_`` method, which IPython uses to implement its rich object display: + +```python +class display(object): + """Display HTML representation of multiple objects""" + template = """
+

{0}

{1} +
""" + def __init__(self, *args): + self.args = args + + def _repr_html_(self): + return '\n'.join(self.template.format(a, eval(a)._repr_html_()) + for a in self.args) + + def __repr__(self): + return '\n\n'.join(a + '\n' + repr(eval(a)) + for a in self.args) + +``` + +The use of this will become clearer as we continue our discussion in the following section. + + +## Recall: Concatenation of NumPy Arrays + +Concatenation of ``Series`` and ``DataFrame`` objects is very similar to concatenation of Numpy arrays, which can be done via the ``np.concatenate`` function as discussed in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb). +Recall that with it, you can combine the contents of two or more arrays into a single array: + +```python +x = [1, 2, 3] +y = [4, 5, 6] +z = [7, 8, 9] +np.concatenate([x, y, z]) +``` + +The first argument is a list or tuple of arrays to concatenate. +Additionally, it takes an ``axis`` keyword that allows you to specify the axis along which the result will be concatenated: + +```python +x = [[1, 2], + [3, 4]] +np.concatenate([x, x], axis=1) +``` + +## Simple Concatenation with ``pd.concat`` + + +Pandas has a function, ``pd.concat()``, which has a similar syntax to ``np.concatenate`` but contains a number of options that we'll discuss momentarily: + +```python +# Signature in Pandas v0.18 +pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, + keys=None, levels=None, names=None, verify_integrity=False, + copy=True) +``` + +``pd.concat()`` can be used for a simple concatenation of ``Series`` or ``DataFrame`` objects, just as ``np.concatenate()`` can be used for simple concatenations of arrays: + + +```python +ser1 = pd.Series(['A', 'B', 'C'], index=[1, 2, 3]) +ser2 = pd.Series(['D', 'E', 'F'], index=[4, 5, 6]) +pd.concat([ser1, ser2]) +``` + +It also works to concatenate higher-dimensional objects, such as ``DataFrame``s: + +```python +df1 = make_df('AB', [1, 2]) +df2 = make_df('AB', [3, 4]) +display('df1', 'df2', 'pd.concat([df1, df2])') +``` + +By default, the concatenation takes place row-wise within the ``DataFrame`` (i.e., ``axis=0``). +Like ``np.concatenate``, ``pd.concat`` allows specification of an axis along which concatenation will take place. +Consider the following example: + +```python +df3 = make_df('AB', [0, 1]) +df4 = make_df('CD', [0, 1]) +display('df3', 'df4', "pd.concat([df3, df4], axis='col')") +``` + +We could have equivalently specified ``axis=1``; here we've used the more intuitive ``axis='col'``. + + +### Duplicate indices + +One important difference between ``np.concatenate`` and ``pd.concat`` is that Pandas concatenation *preserves indices*, even if the result will have duplicate indices! +Consider this simple example: + +```python +x = make_df('AB', [0, 1]) +y = make_df('AB', [2, 3]) +y.index = x.index # make duplicate indices! +display('x', 'y', 'pd.concat([x, y])') +``` + +Notice the repeated indices in the result. +While this is valid within ``DataFrame``s, the outcome is often undesirable. +``pd.concat()`` gives us a few ways to handle it. + + +#### Catching the repeats as an error + +If you'd like to simply verify that the indices in the result of ``pd.concat()`` do not overlap, you can specify the ``verify_integrity`` flag. +With this set to True, the concatenation will raise an exception if there are duplicate indices. +Here is an example, where for clarity we'll catch and print the error message: + +```python +try: + pd.concat([x, y], verify_integrity=True) +except ValueError as e: + print("ValueError:", e) +``` + +#### Ignoring the index + +Sometimes the index itself does not matter, and you would prefer it to simply be ignored. +This option can be specified using the ``ignore_index`` flag. +With this set to true, the concatenation will create a new integer index for the resulting ``Series``: + +```python +display('x', 'y', 'pd.concat([x, y], ignore_index=True)') +``` + +#### Adding MultiIndex keys + +Another option is to use the ``keys`` option to specify a label for the data sources; the result will be a hierarchically indexed series containing the data: + +```python +display('x', 'y', "pd.concat([x, y], keys=['x', 'y'])") +``` + +The result is a multiply indexed ``DataFrame``, and we can use the tools discussed in [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) to transform this data into the representation we're interested in. + + +### Concatenation with joins + +In the simple examples we just looked at, we were mainly concatenating ``DataFrame``s with shared column names. +In practice, data from different sources might have different sets of column names, and ``pd.concat`` offers several options in this case. +Consider the concatenation of the following two ``DataFrame``s, which have some (but not all!) columns in common: + +```python +df5 = make_df('ABC', [1, 2]) +df6 = make_df('BCD', [3, 4]) +display('df5', 'df6', 'pd.concat([df5, df6])') +``` + +By default, the entries for which no data is available are filled with NA values. +To change this, we can specify one of several options for the ``join`` and ``join_axes`` parameters of the concatenate function. +By default, the join is a union of the input columns (``join='outer'``), but we can change this to an intersection of the columns using ``join='inner'``: + +```python +display('df5', 'df6', + "pd.concat([df5, df6], join='inner')") +``` + +Another option is to directly specify the index of the remaininig colums using the ``join_axes`` argument, which takes a list of index objects. +Here we'll specify that the returned columns should be the same as those of the first input: + +```python +display('df5', 'df6', + "pd.concat([df5, df6], join_axes=[df5.columns])") +``` + +The combination of options of the ``pd.concat`` function allows a wide range of possible behaviors when joining two datasets; keep these in mind as you use these tools for your own data. + + +### The ``append()`` method + +Because direct array concatenation is so common, ``Series`` and ``DataFrame`` objects have an ``append`` method that can accomplish the same thing in fewer keystrokes. +For example, rather than calling ``pd.concat([df1, df2])``, you can simply call ``df1.append(df2)``: + +```python +display('df1', 'df2', 'df1.append(df2)') +``` + +Keep in mind that unlike the ``append()`` and ``extend()`` methods of Python lists, the ``append()`` method in Pandas does not modify the original object–instead it creates a new object with the combined data. +It also is not a very efficient method, because it involves creation of a new index *and* data buffer. +Thus, if you plan to do multiple ``append`` operations, it is generally better to build a list of ``DataFrame``s and pass them all at once to the ``concat()`` function. + +In the next section, we'll look at another more powerful approach to combining data from multiple sources, the database-style merges/joins implemented in ``pd.merge``. +For more information on ``concat()``, ``append()``, and related functionality, see the ["Merge, Join, and Concatenate" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation. + + + +< [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) | [Contents](Index.ipynb) | [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.07-Merge-and-Join.ipynb b/notebooks_v2/03.07-Merge-and-Join.ipynb new file mode 100644 index 00000000..76f2b1db --- /dev/null +++ b/notebooks_v2/03.07-Merge-and-Join.ipynb @@ -0,0 +1,3576 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining Datasets: Merge and Join" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One essential feature offered by Pandas is its high-performance, in-memory join and merge operations.\n", + "If you have ever worked with databases, you should be familiar with this type of data interaction.\n", + "The main interface for this is the ``pd.merge`` function, and we'll see few examples of how this can work in practice.\n", + "\n", + "For convenience, we will start by redefining the ``display()`` functionality from the previous section:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "class display(object):\n", + " \"\"\"Display HTML representation of multiple objects\"\"\"\n", + " template = \"\"\"
\n", + "

{0}

{1}\n", + "
\"\"\"\n", + " def __init__(self, *args):\n", + " self.args = args\n", + " \n", + " def _repr_html_(self):\n", + " return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n", + " for a in self.args)\n", + " \n", + " def __repr__(self):\n", + " return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n", + " for a in self.args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Relational Algebra\n", + "\n", + "The behavior implemented in ``pd.merge()`` is a subset of what is known as *relational algebra*, which is a formal set of rules for manipulating relational data, and forms the conceptual foundation of operations available in most databases.\n", + "The strength of the relational algebra approach is that it proposes several primitive operations, which become the building blocks of more complicated operations on any dataset.\n", + "With this lexicon of fundamental operations implemented efficiently in a database or other program, a wide range of fairly complicated composite operations can be performed.\n", + "\n", + "Pandas implements several of these fundamental building-blocks in the ``pd.merge()`` function and the related ``join()`` method of ``Series`` and ``Dataframe``s.\n", + "As we will see, these let you efficiently link data from different sources." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categories of Joins\n", + "\n", + "The ``pd.merge()`` function implements a number of types of joins: the *one-to-one*, *many-to-one*, and *many-to-many* joins.\n", + "All three types of joins are accessed via an identical call to the ``pd.merge()`` interface; the type of join performed depends on the form of the input data.\n", + "Here we will show simple examples of the three types of merges, and discuss detailed options further below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-to-one joins\n", + "\n", + "Perhaps the simplest type of merge expresion is the one-to-one join, which is in many ways very similar to the column-wise concatenation seen in [Combining Datasets: Concat & Append](03.06-Concat-And-Append.ipynb).\n", + "As a concrete example, consider the following two ``DataFrames`` which contain information on several employees in a company:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " employee group hire_date\n", + "0 Bob Accounting 2008\n", + "1 Jake Engineering 2012\n", + "2 Lisa Engineering 2004\n", + "3 Sue HR 2014" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = pd.merge(df1, df2)\n", + "df3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``pd.merge()`` function recognizes that each ``DataFrame`` has an \"employee\" column, and automatically joins using this column as a key.\n", + "The result of the merge is a new ``DataFrame`` that combines the information from the two inputs.\n", + "Notice that the order of entries in each column is not necessarily maintained: in this case, the order of the \"employee\" column differs between ``df1`` and ``df2``, and the ``pd.merge()`` function correctly accounts for this.\n", + "Additionally, keep in mind that the merge in general discards the index, except in the special case of merges by index (see the ``left_index`` and ``right_index`` keywords, discussed momentarily)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Many-to-one joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many-to-one joins are joins in which one of the two key columns contains duplicate entries.\n", + "For the many-to-one case, the resulting ``DataFrame`` will preserve those duplicate entries as appropriate.\n", + "Consider the following example of a many-to-one join:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df3

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegrouphire_date
0BobAccounting2008
1JakeEngineering2012
2LisaEngineering2004
3SueHR2014
\n", + "
\n", + "
\n", + "
\n", + "

df4

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
groupsupervisor
0AccountingCarly
1EngineeringGuido
2HRSteve
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df3, df4)

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employeegrouphire_datesupervisor
0BobAccounting2008Carly
1JakeEngineering2012Guido
2LisaEngineering2004Guido
3SueHR2014Steve
\n", + "
\n", + "
" + ], + "text/plain": [ + "df3\n", + " employee group hire_date\n", + "0 Bob Accounting 2008\n", + "1 Jake Engineering 2012\n", + "2 Lisa Engineering 2004\n", + "3 Sue HR 2014\n", + "\n", + "df4\n", + " group supervisor\n", + "0 Accounting Carly\n", + "1 Engineering Guido\n", + "2 HR Steve\n", + "\n", + "pd.merge(df3, df4)\n", + " employee group hire_date supervisor\n", + "0 Bob Accounting 2008 Carly\n", + "1 Jake Engineering 2012 Guido\n", + "2 Lisa Engineering 2004 Guido\n", + "3 Sue HR 2014 Steve" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],\n", + " 'supervisor': ['Carly', 'Guido', 'Steve']})\n", + "display('df3', 'df4', 'pd.merge(df3, df4)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting ``DataFrame`` has an aditional column with the \"supervisor\" information, where the information is repeated in one or more locations as required by the inputs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Many-to-many joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many-to-many joins are a bit confusing conceptually, but are nevertheless well defined.\n", + "If the key column in both the left and right array contains duplicates, then the result is a many-to-many merge.\n", + "This will be perhaps most clear with a concrete example.\n", + "Consider the following, where we have a ``DataFrame`` showing one or more skills associated with a particular group.\n", + "By performing a many-to-many join, we can recover the skills associated with any individual person:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroup
0BobAccounting
1JakeEngineering
2LisaEngineering
3SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df5

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
groupskills
0Accountingmath
1Accountingspreadsheets
2Engineeringcoding
3Engineeringlinux
4HRspreadsheets
5HRorganization
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df1, df5)

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroupskills
0BobAccountingmath
1BobAccountingspreadsheets
2JakeEngineeringcoding
3JakeEngineeringlinux
4LisaEngineeringcoding
5LisaEngineeringlinux
6SueHRspreadsheets
7SueHRorganization
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1\n", + " employee group\n", + "0 Bob Accounting\n", + "1 Jake Engineering\n", + "2 Lisa Engineering\n", + "3 Sue HR\n", + "\n", + "df5\n", + " group skills\n", + "0 Accounting math\n", + "1 Accounting spreadsheets\n", + "2 Engineering coding\n", + "3 Engineering linux\n", + "4 HR spreadsheets\n", + "5 HR organization\n", + "\n", + "pd.merge(df1, df5)\n", + " employee group skills\n", + "0 Bob Accounting math\n", + "1 Bob Accounting spreadsheets\n", + "2 Jake Engineering coding\n", + "3 Jake Engineering linux\n", + "4 Lisa Engineering coding\n", + "5 Lisa Engineering linux\n", + "6 Sue HR spreadsheets\n", + "7 Sue HR organization" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',\n", + " 'Engineering', 'Engineering', 'HR', 'HR'],\n", + " 'skills': ['math', 'spreadsheets', 'coding', 'linux',\n", + " 'spreadsheets', 'organization']})\n", + "display('df1', 'df5', \"pd.merge(df1, df5)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These three types of joins can be used with other Pandas tools to implement a wide array of functionality.\n", + "But in practice, datasets are rarely as clean as the one we're working with here.\n", + "In the following section we'll consider some of the options provided by ``pd.merge()`` that enable you to tune how the join operations work." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Specification of the Merge Key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've already seen the default behavior of ``pd.merge()``: it looks for one or more matching column names between the two inputs, and uses this as the key.\n", + "However, often the column names will not match so nicely, and ``pd.merge()`` provides a variety of options for handling this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ``on`` keyword\n", + "\n", + "Most simply, you can explicitly specify the name of the key column using the ``on`` keyword, which takes a column name or a list of column names:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1

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employeegroup
0BobAccounting
1JakeEngineering
2LisaEngineering
3SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df2

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeehire_date
0Lisa2004
1Bob2008
2Jake2012
3Sue2014
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df1, df2, on='employee')

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employeegrouphire_date
0BobAccounting2008
1JakeEngineering2012
2LisaEngineering2004
3SueHR2014
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1\n", + " employee group\n", + "0 Bob Accounting\n", + "1 Jake Engineering\n", + "2 Lisa Engineering\n", + "3 Sue HR\n", + "\n", + "df2\n", + " employee hire_date\n", + "0 Lisa 2004\n", + "1 Bob 2008\n", + "2 Jake 2012\n", + "3 Sue 2014\n", + "\n", + "pd.merge(df1, df2, on='employee')\n", + " employee group hire_date\n", + "0 Bob Accounting 2008\n", + "1 Jake Engineering 2012\n", + "2 Lisa Engineering 2004\n", + "3 Sue HR 2014" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df1', 'df2', \"pd.merge(df1, df2, on='employee')\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This option works only if both the left and right ``DataFrame``s have the specified column name." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ``left_on`` and ``right_on`` keywords\n", + "\n", + "At times you may wish to merge two datasets with different column names; for example, we may have a dataset in which the employee name is labeled as \"name\" rather than \"employee\".\n", + "In this case, we can use the ``left_on`` and ``right_on`` keywords to specify the two column names:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1

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employeegroup
0BobAccounting
1JakeEngineering
2LisaEngineering
3SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df3

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namesalary
0Bob70000
1Jake80000
2Lisa120000
3Sue90000
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")

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employeegroupnamesalary
0BobAccountingBob70000
1JakeEngineeringJake80000
2LisaEngineeringLisa120000
3SueHRSue90000
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1\n", + " employee group\n", + "0 Bob Accounting\n", + "1 Jake Engineering\n", + "2 Lisa Engineering\n", + "3 Sue HR\n", + "\n", + "df3\n", + " name salary\n", + "0 Bob 70000\n", + "1 Jake 80000\n", + "2 Lisa 120000\n", + "3 Sue 90000\n", + "\n", + "pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")\n", + " employee group name salary\n", + "0 Bob Accounting Bob 70000\n", + "1 Jake Engineering Jake 80000\n", + "2 Lisa Engineering Lisa 120000\n", + "3 Sue HR Sue 90000" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n", + " 'salary': [70000, 80000, 120000, 90000]})\n", + "display('df1', 'df3', 'pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result has a redundant column that we can drop if desired–for example, by using the ``drop()`` method of ``DataFrame``s:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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employeegroupsalary
0BobAccounting70000
1JakeEngineering80000
2LisaEngineering120000
3SueHR90000
\n", + "
" + ], + "text/plain": [ + " employee group salary\n", + "0 Bob Accounting 70000\n", + "1 Jake Engineering 80000\n", + "2 Lisa Engineering 120000\n", + "3 Sue HR 90000" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\").drop('name', axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The ``left_index`` and ``right_index`` keywords\n", + "\n", + "Sometimes, rather than merging on a column, you would instead like to merge on an index.\n", + "For example, your data might look like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1a

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group
employee
BobAccounting
JakeEngineering
LisaEngineering
SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df2a

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
hire_date
employee
Lisa2004
Bob2008
Jake2012
Sue2014
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1a\n", + " group\n", + "employee \n", + "Bob Accounting\n", + "Jake Engineering\n", + "Lisa Engineering\n", + "Sue HR\n", + "\n", + "df2a\n", + " hire_date\n", + "employee \n", + "Lisa 2004\n", + "Bob 2008\n", + "Jake 2012\n", + "Sue 2014" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1a = df1.set_index('employee')\n", + "df2a = df2.set_index('employee')\n", + "display('df1a', 'df2a')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the index as the key for merging by specifying the ``left_index`` and/or ``right_index`` flags in ``pd.merge()``:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1a

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group
employee
BobAccounting
JakeEngineering
LisaEngineering
SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df2a

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
hire_date
employee
Lisa2004
Bob2008
Jake2012
Sue2014
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df1a, df2a, left_index=True, right_index=True)

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grouphire_date
employee
LisaEngineering2004
BobAccounting2008
JakeEngineering2012
SueHR2014
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1a\n", + " group\n", + "employee \n", + "Bob Accounting\n", + "Jake Engineering\n", + "Lisa Engineering\n", + "Sue HR\n", + "\n", + "df2a\n", + " hire_date\n", + "employee \n", + "Lisa 2004\n", + "Bob 2008\n", + "Jake 2012\n", + "Sue 2014\n", + "\n", + "pd.merge(df1a, df2a, left_index=True, right_index=True)\n", + " group hire_date\n", + "employee \n", + "Lisa Engineering 2004\n", + "Bob Accounting 2008\n", + "Jake Engineering 2012\n", + "Sue HR 2014" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df1a', 'df2a',\n", + " \"pd.merge(df1a, df2a, left_index=True, right_index=True)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, ``DataFrame``s implement the ``join()`` method, which performs a merge that defaults to joining on indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1a

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
group
employee
BobAccounting
JakeEngineering
LisaEngineering
SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df2a

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
hire_date
employee
Lisa2004
Bob2008
Jake2012
Sue2014
\n", + "
\n", + "
\n", + "
\n", + "

df1a.join(df2a)

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grouphire_date
employee
BobAccounting2008
JakeEngineering2012
LisaEngineering2004
SueHR2014
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1a\n", + " group\n", + "employee \n", + "Bob Accounting\n", + "Jake Engineering\n", + "Lisa Engineering\n", + "Sue HR\n", + "\n", + "df2a\n", + " hire_date\n", + "employee \n", + "Lisa 2004\n", + "Bob 2008\n", + "Jake 2012\n", + "Sue 2014\n", + "\n", + "df1a.join(df2a)\n", + " group hire_date\n", + "employee \n", + "Bob Accounting 2008\n", + "Jake Engineering 2012\n", + "Lisa Engineering 2004\n", + "Sue HR 2014" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df1a', 'df2a', 'df1a.join(df2a)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you'd like to mix indices and columns, you can combine ``left_index`` with ``right_on`` or ``left_on`` with ``right_index`` to get the desired behavior:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1a

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
group
employee
BobAccounting
JakeEngineering
LisaEngineering
SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df3

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namesalary
0Bob70000
1Jake80000
2Lisa120000
3Sue90000
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df1a, df3, left_index=True, right_on='name')

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
groupnamesalary
0AccountingBob70000
1EngineeringJake80000
2EngineeringLisa120000
3HRSue90000
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1a\n", + " group\n", + "employee \n", + "Bob Accounting\n", + "Jake Engineering\n", + "Lisa Engineering\n", + "Sue HR\n", + "\n", + "df3\n", + " name salary\n", + "0 Bob 70000\n", + "1 Jake 80000\n", + "2 Lisa 120000\n", + "3 Sue 90000\n", + "\n", + "pd.merge(df1a, df3, left_index=True, right_on='name')\n", + " group name salary\n", + "0 Accounting Bob 70000\n", + "1 Engineering Jake 80000\n", + "2 Engineering Lisa 120000\n", + "3 HR Sue 90000" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df1a', 'df3', \"pd.merge(df1a, df3, left_index=True, right_on='name')\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All of these options also work with multiple indices and/or multiple columns; the interface for this behavior is very intuitive.\n", + "For more information on this, see the [\"Merge, Join, and Concatenate\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Specifying Set Arithmetic for Joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In all the preceding examples we have glossed over one important consideration in performing a join: the type of set arithmetic used in the join.\n", + "This comes up when a value appears in one key column but not the other. Consider this example:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df6

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namefood
0Peterfish
1Paulbeans
2Marybread
\n", + "
\n", + "
\n", + "
\n", + "

df7

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namedrink
0Marywine
1Josephbeer
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df6, df7)

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namefooddrink
0Marybreadwine
\n", + "
\n", + "
" + ], + "text/plain": [ + "df6\n", + " name food\n", + "0 Peter fish\n", + "1 Paul beans\n", + "2 Mary bread\n", + "\n", + "df7\n", + " name drink\n", + "0 Mary wine\n", + "1 Joseph beer\n", + "\n", + "pd.merge(df6, df7)\n", + " name food drink\n", + "0 Mary bread wine" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'],\n", + " 'food': ['fish', 'beans', 'bread']},\n", + " columns=['name', 'food'])\n", + "df7 = pd.DataFrame({'name': ['Mary', 'Joseph'],\n", + " 'drink': ['wine', 'beer']},\n", + " columns=['name', 'drink'])\n", + "display('df6', 'df7', 'pd.merge(df6, df7)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have merged two datasets that have only a single \"name\" entry in common: Mary.\n", + "By default, the result contains the *intersection* of the two sets of inputs; this is what is known as an *inner join*.\n", + "We can specify this explicitly using the ``how`` keyword, which defaults to ``\"inner\"``:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namefooddrink
0Marybreadwine
\n", + "
" + ], + "text/plain": [ + " name food drink\n", + "0 Mary bread wine" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.merge(df6, df7, how='inner')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other options for the ``how`` keyword are ``'outer'``, ``'left'``, and ``'right'``.\n", + "An *outer join* returns a join over the union of the input columns, and fills in all missing values with NAs:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df6

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namefood
0Peterfish
1Paulbeans
2Marybread
\n", + "
\n", + "
\n", + "
\n", + "

df7

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namedrink
0Marywine
1Josephbeer
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df6, df7, how='outer')

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namefooddrink
0PeterfishNaN
1PaulbeansNaN
2Marybreadwine
3JosephNaNbeer
\n", + "
\n", + "
" + ], + "text/plain": [ + "df6\n", + " name food\n", + "0 Peter fish\n", + "1 Paul beans\n", + "2 Mary bread\n", + "\n", + "df7\n", + " name drink\n", + "0 Mary wine\n", + "1 Joseph beer\n", + "\n", + "pd.merge(df6, df7, how='outer')\n", + " name food drink\n", + "0 Peter fish NaN\n", + "1 Paul beans NaN\n", + "2 Mary bread wine\n", + "3 Joseph NaN beer" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df6', 'df7', \"pd.merge(df6, df7, how='outer')\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *left join* and *right join* return joins over the left entries and right entries, respectively.\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df6

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namefood
0Peterfish
1Paulbeans
2Marybread
\n", + "
\n", + "
\n", + "
\n", + "

df7

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namedrink
0Marywine
1Josephbeer
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df6, df7, how='left')

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namefooddrink
0PeterfishNaN
1PaulbeansNaN
2Marybreadwine
\n", + "
\n", + "
" + ], + "text/plain": [ + "df6\n", + " name food\n", + "0 Peter fish\n", + "1 Paul beans\n", + "2 Mary bread\n", + "\n", + "df7\n", + " name drink\n", + "0 Mary wine\n", + "1 Joseph beer\n", + "\n", + "pd.merge(df6, df7, how='left')\n", + " name food drink\n", + "0 Peter fish NaN\n", + "1 Paul beans NaN\n", + "2 Mary bread wine" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df6', 'df7', \"pd.merge(df6, df7, how='left')\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output rows now correspond to the entries in the left input. Using\n", + "``how='right'`` works in a similar manner.\n", + "\n", + "All of these options can be applied straightforwardly to any of the preceding join types." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overlapping Column Names: The ``suffixes`` Keyword" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, you may end up in a case where your two input ``DataFrame``s have conflicting column names.\n", + "Consider this example:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df8

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namerank
0Bob1
1Jake2
2Lisa3
3Sue4
\n", + "
\n", + "
\n", + "
\n", + "

df9

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namerank
0Bob3
1Jake1
2Lisa4
3Sue2
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df8, df9, on=\"name\")

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namerank_xrank_y
0Bob13
1Jake21
2Lisa34
3Sue42
\n", + "
\n", + "
" + ], + "text/plain": [ + "df8\n", + " name rank\n", + "0 Bob 1\n", + "1 Jake 2\n", + "2 Lisa 3\n", + "3 Sue 4\n", + "\n", + "df9\n", + " name rank\n", + "0 Bob 3\n", + "1 Jake 1\n", + "2 Lisa 4\n", + "3 Sue 2\n", + "\n", + "pd.merge(df8, df9, on=\"name\")\n", + " name rank_x rank_y\n", + "0 Bob 1 3\n", + "1 Jake 2 1\n", + "2 Lisa 3 4\n", + "3 Sue 4 2" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n", + " 'rank': [1, 2, 3, 4]})\n", + "df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n", + " 'rank': [3, 1, 4, 2]})\n", + "display('df8', 'df9', 'pd.merge(df8, df9, on=\"name\")')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because the output would have two conflicting column names, the merge function automatically appends a suffix ``_x`` or ``_y`` to make the output columns unique.\n", + "If these defaults are inappropriate, it is possible to specify a custom suffix using the ``suffixes`` keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df8

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namerank
0Bob1
1Jake2
2Lisa3
3Sue4
\n", + "
\n", + "
\n", + "
\n", + "

df9

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namerank
0Bob3
1Jake1
2Lisa4
3Sue2
\n", + "
\n", + "
\n", + "
\n", + "

pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namerank_Lrank_R
0Bob13
1Jake21
2Lisa34
3Sue42
\n", + "
\n", + "
" + ], + "text/plain": [ + "df8\n", + " name rank\n", + "0 Bob 1\n", + "1 Jake 2\n", + "2 Lisa 3\n", + "3 Sue 4\n", + "\n", + "df9\n", + " name rank\n", + "0 Bob 3\n", + "1 Jake 1\n", + "2 Lisa 4\n", + "3 Sue 2\n", + "\n", + "pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])\n", + " name rank_L rank_R\n", + "0 Bob 1 3\n", + "1 Jake 2 1\n", + "2 Lisa 3 4\n", + "3 Sue 4 2" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df8', 'df9', 'pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These suffixes work in any of the possible join patterns, and work also if there are multiple overlapping columns." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information on these patterns, see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) where we dive a bit deeper into relational algebra.\n", + "Also see the [Pandas \"Merge, Join and Concatenate\" documentation](http://pandas.pydata.org/pandas-docs/stable/merging.html) for further discussion of these topics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: US States Data\n", + "\n", + "Merge and join operations come up most often when combining data from different sources.\n", + "Here we will consider an example of some data about US states and their populations.\n", + "The data files can be found at http://github.com/jakevdp/data-USstates/:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Following are shell commands to download the data\n", + "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-population.csv\n", + "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-areas.csv\n", + "# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-abbrevs.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the three datasets, using the Pandas ``read_csv()`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

pop.head()

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state/regionagesyearpopulation
0ALunder1820121117489.0
1ALtotal20124817528.0
2ALunder1820101130966.0
3ALtotal20104785570.0
4ALunder1820111125763.0
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areas.head()

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statearea (sq. mi)
0Alabama52423
1Alaska656425
2Arizona114006
3Arkansas53182
4California163707
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abbrevs.head()

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stateabbreviation
0AlabamaAL
1AlaskaAK
2ArizonaAZ
3ArkansasAR
4CaliforniaCA
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" + ], + "text/plain": [ + "pop.head()\n", + " state/region ages year population\n", + "0 AL under18 2012 1117489.0\n", + "1 AL total 2012 4817528.0\n", + "2 AL under18 2010 1130966.0\n", + "3 AL total 2010 4785570.0\n", + "4 AL under18 2011 1125763.0\n", + "\n", + "areas.head()\n", + " state area (sq. mi)\n", + "0 Alabama 52423\n", + "1 Alaska 656425\n", + "2 Arizona 114006\n", + "3 Arkansas 53182\n", + "4 California 163707\n", + "\n", + "abbrevs.head()\n", + " state abbreviation\n", + "0 Alabama AL\n", + "1 Alaska AK\n", + "2 Arizona AZ\n", + "3 Arkansas AR\n", + "4 California CA" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop = pd.read_csv('data/state-population.csv')\n", + "areas = pd.read_csv('data/state-areas.csv')\n", + "abbrevs = pd.read_csv('data/state-abbrevs.csv')\n", + "\n", + "display('pop.head()', 'areas.head()', 'abbrevs.head()')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given this information, say we want to compute a relatively straightforward result: rank US states and territories by their 2010 population density.\n", + "We clearly have the data here to find this result, but we'll have to combine the datasets to find the result.\n", + "\n", + "We'll start with a many-to-one merge that will give us the full state name within the population ``DataFrame``.\n", + "We want to merge based on the ``state/region`` column of ``pop``, and the ``abbreviation`` column of ``abbrevs``.\n", + "We'll use ``how='outer'`` to make sure no data is thrown away due to mismatched labels." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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state/regionagesyearpopulationstate
0ALunder1820121117489.0Alabama
1ALtotal20124817528.0Alabama
2ALunder1820101130966.0Alabama
3ALtotal20104785570.0Alabama
4ALunder1820111125763.0Alabama
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" + ], + "text/plain": [ + " state/region ages year population state\n", + "0 AL under18 2012 1117489.0 Alabama\n", + "1 AL total 2012 4817528.0 Alabama\n", + "2 AL under18 2010 1130966.0 Alabama\n", + "3 AL total 2010 4785570.0 Alabama\n", + "4 AL under18 2011 1125763.0 Alabama" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged = pd.merge(pop, abbrevs, how='outer',\n", + " left_on='state/region', right_on='abbreviation')\n", + "merged = merged.drop('abbreviation', 1) # drop duplicate info\n", + "merged.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's double-check whether there were any mismatches here, which we can do by looking for rows with nulls:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state/region False\n", + "ages False\n", + "year False\n", + "population True\n", + "state True\n", + "dtype: bool" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.isnull().any()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the ``population`` info is null; let's figure out which these are!" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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state/regionagesyearpopulationstate
2448PRunder181990NaNNaN
2449PRtotal1990NaNNaN
2450PRtotal1991NaNNaN
2451PRunder181991NaNNaN
2452PRtotal1993NaNNaN
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" + ], + "text/plain": [ + " state/region ages year population state\n", + "2448 PR under18 1990 NaN NaN\n", + "2449 PR total 1990 NaN NaN\n", + "2450 PR total 1991 NaN NaN\n", + "2451 PR under18 1991 NaN NaN\n", + "2452 PR total 1993 NaN NaN" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged[merged['population'].isnull()].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears that all the null population values are from Puerto Rico prior to the year 2000; this is likely due to this data not being available from the original source.\n", + "\n", + "More importantly, we see also that some of the new ``state`` entries are also null, which means that there was no corresponding entry in the ``abbrevs`` key!\n", + "Let's figure out which regions lack this match:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['PR', 'USA'], dtype=object)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.loc[merged['state'].isnull(), 'state/region'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can quickly infer the issue: our population data includes entries for Puerto Rico (PR) and the United States as a whole (USA), while these entries do not appear in the state abbreviation key.\n", + "We can fix these quickly by filling in appropriate entries:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state/region False\n", + "ages False\n", + "year False\n", + "population True\n", + "state False\n", + "dtype: bool" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico'\n", + "merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States'\n", + "merged.isnull().any()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No more nulls in the ``state`` column: we're all set!\n", + "\n", + "Now we can merge the result with the area data using a similar procedure.\n", + "Examining our results, we will want to join on the ``state`` column in both:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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state/regionagesyearpopulationstatearea (sq. mi)
0ALunder1820121117489.0Alabama52423.0
1ALtotal20124817528.0Alabama52423.0
2ALunder1820101130966.0Alabama52423.0
3ALtotal20104785570.0Alabama52423.0
4ALunder1820111125763.0Alabama52423.0
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" + ], + "text/plain": [ + " state/region ages year population state area (sq. mi)\n", + "0 AL under18 2012 1117489.0 Alabama 52423.0\n", + "1 AL total 2012 4817528.0 Alabama 52423.0\n", + "2 AL under18 2010 1130966.0 Alabama 52423.0\n", + "3 AL total 2010 4785570.0 Alabama 52423.0\n", + "4 AL under18 2011 1125763.0 Alabama 52423.0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final = pd.merge(merged, areas, on='state', how='left')\n", + "final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, let's check for nulls to see if there were any mismatches:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state/region False\n", + "ages False\n", + "year False\n", + "population True\n", + "state False\n", + "area (sq. mi) True\n", + "dtype: bool" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.isnull().any()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are nulls in the ``area`` column; we can take a look to see which regions were ignored here:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['United States'], dtype=object)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final['state'][final['area (sq. mi)'].isnull()].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that our ``areas`` ``DataFrame`` does not contain the area of the United States as a whole.\n", + "We could insert the appropriate value (using the sum of all state areas, for instance), but in this case we'll just drop the null values because the population density of the entire United States is not relevant to our current discussion:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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state/regionagesyearpopulationstatearea (sq. mi)
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1ALtotal20124817528.0Alabama52423.0
2ALunder1820101130966.0Alabama52423.0
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4ALunder1820111125763.0Alabama52423.0
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" + ], + "text/plain": [ + " state/region ages year population state area (sq. mi)\n", + "0 AL under18 2012 1117489.0 Alabama 52423.0\n", + "1 AL total 2012 4817528.0 Alabama 52423.0\n", + "2 AL under18 2010 1130966.0 Alabama 52423.0\n", + "3 AL total 2010 4785570.0 Alabama 52423.0\n", + "4 AL under18 2011 1125763.0 Alabama 52423.0" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.dropna(inplace=True)\n", + "final.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have all the data we need. To answer the question of interest, let's first select the portion of the data corresponding with the year 2000, and the total population.\n", + "We'll use the ``query()`` function to do this quickly (this requires the ``numexpr`` package to be installed; see [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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state/regionagesyearpopulationstatearea (sq. mi)
3ALtotal20104785570.0Alabama52423.0
91AKtotal2010713868.0Alaska656425.0
101AZtotal20106408790.0Arizona114006.0
189ARtotal20102922280.0Arkansas53182.0
197CAtotal201037333601.0California163707.0
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" + ], + "text/plain": [ + " state/region ages year population state area (sq. mi)\n", + "3 AL total 2010 4785570.0 Alabama 52423.0\n", + "91 AK total 2010 713868.0 Alaska 656425.0\n", + "101 AZ total 2010 6408790.0 Arizona 114006.0\n", + "189 AR total 2010 2922280.0 Arkansas 53182.0\n", + "197 CA total 2010 37333601.0 California 163707.0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2010 = final.query(\"year == 2010 & ages == 'total'\")\n", + "data2010.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compute the population density and display it in order.\n", + "We'll start by re-indexing our data on the state, and then compute the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data2010.set_index('state', inplace=True)\n", + "density = data2010['population'] / data2010['area (sq. mi)']" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state\n", + "District of Columbia 8898.897059\n", + "Puerto Rico 1058.665149\n", + "New Jersey 1009.253268\n", + "Rhode Island 681.339159\n", + "Connecticut 645.600649\n", + "dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "density.sort_values(ascending=False, inplace=True)\n", + "density.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a ranking of US states plus Washington, DC, and Puerto Rico in order of their 2010 population density, in residents per square mile.\n", + "We can see that by far the densest region in this dataset is Washington, DC (i.e., the District of Columbia); among states, the densest is New Jersey.\n", + "\n", + "We can also check the end of the list:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state\n", + "South Dakota 10.583512\n", + "North Dakota 9.537565\n", + "Montana 6.736171\n", + "Wyoming 5.768079\n", + "Alaska 1.087509\n", + "dtype: float64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "density.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the least dense state, by far, is Alaska, averaging slightly over one resident per square mile.\n", + "\n", + "This type of messy data merging is a common task when trying to answer questions using real-world data sources.\n", + "I hope that this example has given you an idea of the ways you can combine tools we've covered in order to gain insight from your data!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.07-Merge-and-Join.md b/notebooks_v2/03.07-Merge-and-Join.md new file mode 100644 index 00000000..44700a62 --- /dev/null +++ b/notebooks_v2/03.07-Merge-and-Join.md @@ -0,0 +1,420 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) > + +Open in Colab + + + +# Combining Datasets: Merge and Join + + +One essential feature offered by Pandas is its high-performance, in-memory join and merge operations. +If you have ever worked with databases, you should be familiar with this type of data interaction. +The main interface for this is the ``pd.merge`` function, and we'll see few examples of how this can work in practice. + +For convenience, we will start by redefining the ``display()`` functionality from the previous section: + +```python +import pandas as pd +import numpy as np + +class display(object): + """Display HTML representation of multiple objects""" + template = """
+

{0}

{1} +
""" + def __init__(self, *args): + self.args = args + + def _repr_html_(self): + return '\n'.join(self.template.format(a, eval(a)._repr_html_()) + for a in self.args) + + def __repr__(self): + return '\n\n'.join(a + '\n' + repr(eval(a)) + for a in self.args) +``` + +## Relational Algebra + +The behavior implemented in ``pd.merge()`` is a subset of what is known as *relational algebra*, which is a formal set of rules for manipulating relational data, and forms the conceptual foundation of operations available in most databases. +The strength of the relational algebra approach is that it proposes several primitive operations, which become the building blocks of more complicated operations on any dataset. +With this lexicon of fundamental operations implemented efficiently in a database or other program, a wide range of fairly complicated composite operations can be performed. + +Pandas implements several of these fundamental building-blocks in the ``pd.merge()`` function and the related ``join()`` method of ``Series`` and ``Dataframe``s. +As we will see, these let you efficiently link data from different sources. + + +## Categories of Joins + +The ``pd.merge()`` function implements a number of types of joins: the *one-to-one*, *many-to-one*, and *many-to-many* joins. +All three types of joins are accessed via an identical call to the ``pd.merge()`` interface; the type of join performed depends on the form of the input data. +Here we will show simple examples of the three types of merges, and discuss detailed options further below. + + +### One-to-one joins + +Perhaps the simplest type of merge expresion is the one-to-one join, which is in many ways very similar to the column-wise concatenation seen in [Combining Datasets: Concat & Append](03.06-Concat-And-Append.ipynb). +As a concrete example, consider the following two ``DataFrames`` which contain information on several employees in a company: + +```python +df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'], + 'group': ['Accounting', 'Engineering', 'Engineering', 'HR']}) +df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'], + 'hire_date': [2004, 2008, 2012, 2014]}) +display('df1', 'df2') +``` + +To combine this information into a single ``DataFrame``, we can use the ``pd.merge()`` function: + +```python +df3 = pd.merge(df1, df2) +df3 +``` + +The ``pd.merge()`` function recognizes that each ``DataFrame`` has an "employee" column, and automatically joins using this column as a key. +The result of the merge is a new ``DataFrame`` that combines the information from the two inputs. +Notice that the order of entries in each column is not necessarily maintained: in this case, the order of the "employee" column differs between ``df1`` and ``df2``, and the ``pd.merge()`` function correctly accounts for this. +Additionally, keep in mind that the merge in general discards the index, except in the special case of merges by index (see the ``left_index`` and ``right_index`` keywords, discussed momentarily). + + +### Many-to-one joins + + +Many-to-one joins are joins in which one of the two key columns contains duplicate entries. +For the many-to-one case, the resulting ``DataFrame`` will preserve those duplicate entries as appropriate. +Consider the following example of a many-to-one join: + +```python +df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'], + 'supervisor': ['Carly', 'Guido', 'Steve']}) +display('df3', 'df4', 'pd.merge(df3, df4)') +``` + +The resulting ``DataFrame`` has an aditional column with the "supervisor" information, where the information is repeated in one or more locations as required by the inputs. + + +### Many-to-many joins + + +Many-to-many joins are a bit confusing conceptually, but are nevertheless well defined. +If the key column in both the left and right array contains duplicates, then the result is a many-to-many merge. +This will be perhaps most clear with a concrete example. +Consider the following, where we have a ``DataFrame`` showing one or more skills associated with a particular group. +By performing a many-to-many join, we can recover the skills associated with any individual person: + +```python +df5 = pd.DataFrame({'group': ['Accounting', 'Accounting', + 'Engineering', 'Engineering', 'HR', 'HR'], + 'skills': ['math', 'spreadsheets', 'coding', 'linux', + 'spreadsheets', 'organization']}) +display('df1', 'df5', "pd.merge(df1, df5)") +``` + +These three types of joins can be used with other Pandas tools to implement a wide array of functionality. +But in practice, datasets are rarely as clean as the one we're working with here. +In the following section we'll consider some of the options provided by ``pd.merge()`` that enable you to tune how the join operations work. + + +## Specification of the Merge Key + + +We've already seen the default behavior of ``pd.merge()``: it looks for one or more matching column names between the two inputs, and uses this as the key. +However, often the column names will not match so nicely, and ``pd.merge()`` provides a variety of options for handling this. + + +### The ``on`` keyword + +Most simply, you can explicitly specify the name of the key column using the ``on`` keyword, which takes a column name or a list of column names: + +```python +display('df1', 'df2', "pd.merge(df1, df2, on='employee')") +``` + +This option works only if both the left and right ``DataFrame``s have the specified column name. + + +### The ``left_on`` and ``right_on`` keywords + +At times you may wish to merge two datasets with different column names; for example, we may have a dataset in which the employee name is labeled as "name" rather than "employee". +In this case, we can use the ``left_on`` and ``right_on`` keywords to specify the two column names: + +```python +df3 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'], + 'salary': [70000, 80000, 120000, 90000]}) +display('df1', 'df3', 'pd.merge(df1, df3, left_on="employee", right_on="name")') +``` + +The result has a redundant column that we can drop if desired–for example, by using the ``drop()`` method of ``DataFrame``s: + +```python +pd.merge(df1, df3, left_on="employee", right_on="name").drop('name', axis=1) +``` + +### The ``left_index`` and ``right_index`` keywords + +Sometimes, rather than merging on a column, you would instead like to merge on an index. +For example, your data might look like this: + +```python +df1a = df1.set_index('employee') +df2a = df2.set_index('employee') +display('df1a', 'df2a') +``` + +You can use the index as the key for merging by specifying the ``left_index`` and/or ``right_index`` flags in ``pd.merge()``: + +```python +display('df1a', 'df2a', + "pd.merge(df1a, df2a, left_index=True, right_index=True)") +``` + +For convenience, ``DataFrame``s implement the ``join()`` method, which performs a merge that defaults to joining on indices: + +```python +display('df1a', 'df2a', 'df1a.join(df2a)') +``` + +If you'd like to mix indices and columns, you can combine ``left_index`` with ``right_on`` or ``left_on`` with ``right_index`` to get the desired behavior: + +```python +display('df1a', 'df3', "pd.merge(df1a, df3, left_index=True, right_on='name')") +``` + +All of these options also work with multiple indices and/or multiple columns; the interface for this behavior is very intuitive. +For more information on this, see the ["Merge, Join, and Concatenate" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation. + + +## Specifying Set Arithmetic for Joins + + +In all the preceding examples we have glossed over one important consideration in performing a join: the type of set arithmetic used in the join. +This comes up when a value appears in one key column but not the other. Consider this example: + +```python +df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'], + 'food': ['fish', 'beans', 'bread']}, + columns=['name', 'food']) +df7 = pd.DataFrame({'name': ['Mary', 'Joseph'], + 'drink': ['wine', 'beer']}, + columns=['name', 'drink']) +display('df6', 'df7', 'pd.merge(df6, df7)') +``` + +Here we have merged two datasets that have only a single "name" entry in common: Mary. +By default, the result contains the *intersection* of the two sets of inputs; this is what is known as an *inner join*. +We can specify this explicitly using the ``how`` keyword, which defaults to ``"inner"``: + +```python +pd.merge(df6, df7, how='inner') +``` + +Other options for the ``how`` keyword are ``'outer'``, ``'left'``, and ``'right'``. +An *outer join* returns a join over the union of the input columns, and fills in all missing values with NAs: + +```python +display('df6', 'df7', "pd.merge(df6, df7, how='outer')") +``` + +The *left join* and *right join* return joins over the left entries and right entries, respectively. +For example: + +```python +display('df6', 'df7', "pd.merge(df6, df7, how='left')") +``` + +The output rows now correspond to the entries in the left input. Using +``how='right'`` works in a similar manner. + +All of these options can be applied straightforwardly to any of the preceding join types. + + +## Overlapping Column Names: The ``suffixes`` Keyword + + +Finally, you may end up in a case where your two input ``DataFrame``s have conflicting column names. +Consider this example: + +```python +df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'], + 'rank': [1, 2, 3, 4]}) +df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'], + 'rank': [3, 1, 4, 2]}) +display('df8', 'df9', 'pd.merge(df8, df9, on="name")') +``` + +Because the output would have two conflicting column names, the merge function automatically appends a suffix ``_x`` or ``_y`` to make the output columns unique. +If these defaults are inappropriate, it is possible to specify a custom suffix using the ``suffixes`` keyword: + +```python +display('df8', 'df9', 'pd.merge(df8, df9, on="name", suffixes=["_L", "_R"])') +``` + +These suffixes work in any of the possible join patterns, and work also if there are multiple overlapping columns. + + +For more information on these patterns, see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) where we dive a bit deeper into relational algebra. +Also see the [Pandas "Merge, Join and Concatenate" documentation](http://pandas.pydata.org/pandas-docs/stable/merging.html) for further discussion of these topics. + + +## Example: US States Data + +Merge and join operations come up most often when combining data from different sources. +Here we will consider an example of some data about US states and their populations. +The data files can be found at http://github.com/jakevdp/data-USstates/: + +```python +# Following are shell commands to download the data +# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-population.csv +# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-areas.csv +# !curl -O https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-abbrevs.csv +``` + +Let's take a look at the three datasets, using the Pandas ``read_csv()`` function: + +```python +pop = pd.read_csv('data/state-population.csv') +areas = pd.read_csv('data/state-areas.csv') +abbrevs = pd.read_csv('data/state-abbrevs.csv') + +display('pop.head()', 'areas.head()', 'abbrevs.head()') +``` + +Given this information, say we want to compute a relatively straightforward result: rank US states and territories by their 2010 population density. +We clearly have the data here to find this result, but we'll have to combine the datasets to find the result. + +We'll start with a many-to-one merge that will give us the full state name within the population ``DataFrame``. +We want to merge based on the ``state/region`` column of ``pop``, and the ``abbreviation`` column of ``abbrevs``. +We'll use ``how='outer'`` to make sure no data is thrown away due to mismatched labels. + +```python +merged = pd.merge(pop, abbrevs, how='outer', + left_on='state/region', right_on='abbreviation') +merged = merged.drop('abbreviation', 1) # drop duplicate info +merged.head() +``` + +Let's double-check whether there were any mismatches here, which we can do by looking for rows with nulls: + +```python +merged.isnull().any() +``` + +Some of the ``population`` info is null; let's figure out which these are! + +```python +merged[merged['population'].isnull()].head() +``` + +It appears that all the null population values are from Puerto Rico prior to the year 2000; this is likely due to this data not being available from the original source. + +More importantly, we see also that some of the new ``state`` entries are also null, which means that there was no corresponding entry in the ``abbrevs`` key! +Let's figure out which regions lack this match: + +```python +merged.loc[merged['state'].isnull(), 'state/region'].unique() +``` + +We can quickly infer the issue: our population data includes entries for Puerto Rico (PR) and the United States as a whole (USA), while these entries do not appear in the state abbreviation key. +We can fix these quickly by filling in appropriate entries: + +```python +merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico' +merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States' +merged.isnull().any() +``` + +No more nulls in the ``state`` column: we're all set! + +Now we can merge the result with the area data using a similar procedure. +Examining our results, we will want to join on the ``state`` column in both: + +```python +final = pd.merge(merged, areas, on='state', how='left') +final.head() +``` + +Again, let's check for nulls to see if there were any mismatches: + +```python +final.isnull().any() +``` + +There are nulls in the ``area`` column; we can take a look to see which regions were ignored here: + +```python +final['state'][final['area (sq. mi)'].isnull()].unique() +``` + +We see that our ``areas`` ``DataFrame`` does not contain the area of the United States as a whole. +We could insert the appropriate value (using the sum of all state areas, for instance), but in this case we'll just drop the null values because the population density of the entire United States is not relevant to our current discussion: + +```python +final.dropna(inplace=True) +final.head() +``` + +Now we have all the data we need. To answer the question of interest, let's first select the portion of the data corresponding with the year 2000, and the total population. +We'll use the ``query()`` function to do this quickly (this requires the ``numexpr`` package to be installed; see [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)): + +```python +data2010 = final.query("year == 2010 & ages == 'total'") +data2010.head() +``` + +Now let's compute the population density and display it in order. +We'll start by re-indexing our data on the state, and then compute the result: + +```python +data2010.set_index('state', inplace=True) +density = data2010['population'] / data2010['area (sq. mi)'] +``` + +```python +density.sort_values(ascending=False, inplace=True) +density.head() +``` + +The result is a ranking of US states plus Washington, DC, and Puerto Rico in order of their 2010 population density, in residents per square mile. +We can see that by far the densest region in this dataset is Washington, DC (i.e., the District of Columbia); among states, the densest is New Jersey. + +We can also check the end of the list: + +```python +density.tail() +``` + +We see that the least dense state, by far, is Alaska, averaging slightly over one resident per square mile. + +This type of messy data merging is a common task when trying to answer questions using real-world data sources. +I hope that this example has given you an idea of the ways you can combine tools we've covered in order to gain insight from your data! + + + +< [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) | [Contents](Index.ipynb) | [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.08-Aggregation-and-Grouping.ipynb b/notebooks_v2/03.08-Aggregation-and-Grouping.ipynb new file mode 100644 index 00000000..e8ace290 --- /dev/null +++ b/notebooks_v2/03.08-Aggregation-and-Grouping.ipynb @@ -0,0 +1,2663 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aggregation and Grouping" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "An essential piece of analysis of large data is efficient summarization: computing aggregations like ``sum()``, ``mean()``, ``median()``, ``min()``, and ``max()``, in which a single number gives insight into the nature of a potentially large dataset.\n", + "In this section, we'll explore aggregations in Pandas, from simple operations akin to what we've seen on NumPy arrays, to more sophisticated operations based on the concept of a ``groupby``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, we'll use the same ``display`` magic function that we've seen in previous sections:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "class display(object):\n", + " \"\"\"Display HTML representation of multiple objects\"\"\"\n", + " template = \"\"\"
\n", + "

{0}

{1}\n", + "
\"\"\"\n", + " def __init__(self, *args):\n", + " self.args = args\n", + " \n", + " def _repr_html_(self):\n", + " return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n", + " for a in self.args)\n", + " \n", + " def __repr__(self):\n", + " return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n", + " for a in self.args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Planets Data\n", + "\n", + "Here we will use the Planets dataset, available via the [Seaborn package](http://seaborn.pydata.org/) (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\n", + "It gives information on planets that astronomers have discovered around other stars (known as *extrasolar planets* or *exoplanets* for short). It can be downloaded with a simple Seaborn command:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1035, 6)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import seaborn as sns\n", + "planets = sns.load_dataset('planets')\n", + "planets.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " method number orbital_period mass distance year\n", + "0 Radial Velocity 1 269.300 7.10 77.40 2006\n", + "1 Radial Velocity 1 874.774 2.21 56.95 2008\n", + "2 Radial Velocity 1 763.000 2.60 19.84 2011\n", + "3 Radial Velocity 1 326.030 19.40 110.62 2007\n", + "4 Radial Velocity 1 516.220 10.50 119.47 2009" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planets.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This has some details on the 1,000+ extrasolar planets discovered up to 2014." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Aggregation in Pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Earlier, we explored some of the data aggregations available for NumPy arrays ([\"Aggregations: Min, Max, and Everything In Between\"](02.04-Computation-on-arrays-aggregates.ipynb)).\n", + "As with a one-dimensional NumPy array, for a Pandas ``Series`` the aggregates return a single value:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.374540\n", + "1 0.950714\n", + "2 0.731994\n", + "3 0.598658\n", + "4 0.156019\n", + "dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.RandomState(42)\n", + "ser = pd.Series(rng.rand(5))\n", + "ser" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2.8119254917081569" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.56238509834163142" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ser.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a ``DataFrame``, by default the aggregates return results within each column:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B\n", + "0 0.155995 0.020584\n", + "1 0.058084 0.969910\n", + "2 0.866176 0.832443\n", + "3 0.601115 0.212339\n", + "4 0.708073 0.181825" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({'A': rng.rand(5),\n", + " 'B': rng.rand(5)})\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "A 0.477888\n", + "B 0.443420\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By specifying the ``axis`` argument, you can instead aggregate within each row:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.088290\n", + "1 0.513997\n", + "2 0.849309\n", + "3 0.406727\n", + "4 0.444949\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.mean(axis='columns')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas ``Series`` and ``DataFrame``s include all of the common aggregates mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb); in addition, there is a convenience method ``describe()`` that computes several common aggregates for each column and returns the result.\n", + "Let's use this on the Planets data, for now dropping rows with missing values:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " number orbital_period mass distance year\n", + "count 498.00000 498.000000 498.000000 498.000000 498.000000\n", + "mean 1.73494 835.778671 2.509320 52.068213 2007.377510\n", + "std 1.17572 1469.128259 3.636274 46.596041 4.167284\n", + "min 1.00000 1.328300 0.003600 1.350000 1989.000000\n", + "25% 1.00000 38.272250 0.212500 24.497500 2005.000000\n", + "50% 1.00000 357.000000 1.245000 39.940000 2009.000000\n", + "75% 2.00000 999.600000 2.867500 59.332500 2011.000000\n", + "max 6.00000 17337.500000 25.000000 354.000000 2014.000000" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planets.dropna().describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can be a useful way to begin understanding the overall properties of a dataset.\n", + "For example, we see in the ``year`` column that although exoplanets were discovered as far back as 1989, half of all known expolanets were not discovered until 2010 or after.\n", + "This is largely thanks to the *Kepler* mission, which is a space-based telescope specifically designed for finding eclipsing planets around other stars." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following table summarizes some other built-in Pandas aggregations:\n", + "\n", + "| Aggregation | Description |\n", + "|--------------------------|---------------------------------|\n", + "| ``count()`` | Total number of items |\n", + "| ``first()``, ``last()`` | First and last item |\n", + "| ``mean()``, ``median()`` | Mean and median |\n", + "| ``min()``, ``max()`` | Minimum and maximum |\n", + "| ``std()``, ``var()`` | Standard deviation and variance |\n", + "| ``mad()`` | Mean absolute deviation |\n", + "| ``prod()`` | Product of all items |\n", + "| ``sum()`` | Sum of all items |\n", + "\n", + "These are all methods of ``DataFrame`` and ``Series`` objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To go deeper into the data, however, simple aggregates are often not enough.\n", + "The next level of data summarization is the ``groupby`` operation, which allows you to quickly and efficiently compute aggregates on subsets of data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GroupBy: Split, Apply, Combine\n", + "\n", + "Simple aggregations can give you a flavor of your dataset, but often we would prefer to aggregate conditionally on some label or index: this is implemented in the so-called ``groupby`` operation.\n", + "The name \"group by\" comes from a command in the SQL database language, but it is perhaps more illuminative to think of it in the terms first coined by Hadley Wickham of Rstats fame: *split, apply, combine*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split, apply, combine\n", + "\n", + "A canonical example of this split-apply-combine operation, where the \"apply\" is a summation aggregation, is illustrated in this figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](figures/03.08-split-apply-combine.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Split-Apply-Combine)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This makes clear what the ``groupby`` accomplishes:\n", + "\n", + "- The *split* step involves breaking up and grouping a ``DataFrame`` depending on the value of the specified key.\n", + "- The *apply* step involves computing some function, usually an aggregate, transformation, or filtering, within the individual groups.\n", + "- The *combine* step merges the results of these operations into an output array.\n", + "\n", + "While this could certainly be done manually using some combination of the masking, aggregation, and merging commands covered earlier, an important realization is that *the intermediate splits do not need to be explicitly instantiated*. Rather, the ``GroupBy`` can (often) do this in a single pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way.\n", + "The power of the ``GroupBy`` is that it abstracts away these steps: the user need not think about *how* the computation is done under the hood, but rather thinks about the *operation as a whole*.\n", + "\n", + "As a concrete example, let's take a look at using Pandas for the computation shown in this diagram.\n", + "We'll start by creating the input ``DataFrame``:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " key data\n", + "0 A 0\n", + "1 B 1\n", + "2 C 2\n", + "3 A 3\n", + "4 B 4\n", + "5 C 5" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n", + " 'data': range(6)}, columns=['key', 'data'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most basic split-apply-combine operation can be computed with the ``groupby()`` method of ``DataFrame``s, passing the name of the desired key column:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('key')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that what is returned is not a set of ``DataFrame``s, but a ``DataFrameGroupBy`` object.\n", + "This object is where the magic is: you can think of it as a special view of the ``DataFrame``, which is poised to dig into the groups but does no actual computation until the aggregation is applied.\n", + "This \"lazy evaluation\" approach means that common aggregates can be implemented very efficiently in a way that is almost transparent to the user.\n", + "\n", + "To produce a result, we can apply an aggregate to this ``DataFrameGroupBy`` object, which will perform the appropriate apply/combine steps to produce the desired result:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " data\n", + "key \n", + "A 3\n", + "B 5\n", + "C 7" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('key').sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``sum()`` method is just one possibility here; you can apply virtually any common Pandas or NumPy aggregation function, as well as virtually any valid ``DataFrame`` operation, as we will see in the following discussion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The GroupBy object\n", + "\n", + "The ``GroupBy`` object is a very flexible abstraction.\n", + "In many ways, you can simply treat it as if it's a collection of ``DataFrame``s, and it does the difficult things under the hood. Let's see some examples using the Planets data.\n", + "\n", + "Perhaps the most important operations made available by a ``GroupBy`` are *aggregate*, *filter*, *transform*, and *apply*.\n", + "We'll discuss each of these more fully in [\"Aggregate, Filter, Transform, Apply\"](#Aggregate,-Filter,-Transform,-Apply), but before that let's introduce some of the other functionality that can be used with the basic ``GroupBy`` operation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Column indexing\n", + "\n", + "The ``GroupBy`` object supports column indexing in the same way as the ``DataFrame``, and returns a modified ``GroupBy`` object.\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planets.groupby('method')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planets.groupby('method')['orbital_period']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we've selected a particular ``Series`` group from the original ``DataFrame`` group by reference to its column name.\n", + "As with the ``GroupBy`` object, no computation is done until we call some aggregate on the object:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "method\n", + "Astrometry 631.180000\n", + "Eclipse Timing Variations 4343.500000\n", + "Imaging 27500.000000\n", + "Microlensing 3300.000000\n", + "Orbital Brightness Modulation 0.342887\n", + "Pulsar Timing 66.541900\n", + "Pulsation Timing Variations 1170.000000\n", + "Radial Velocity 360.200000\n", + "Transit 5.714932\n", + "Transit Timing Variations 57.011000\n", + "Name: orbital_period, dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planets.groupby('method')['orbital_period'].median()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives an idea of the general scale of orbital periods (in days) that each method is sensitive to." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Iteration over groups\n", + "\n", + "The ``GroupBy`` object supports direct iteration over the groups, returning each group as a ``Series`` or ``DataFrame``:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Astrometry shape=(2, 6)\n", + "Eclipse Timing Variations shape=(9, 6)\n", + "Imaging shape=(38, 6)\n", + "Microlensing shape=(23, 6)\n", + "Orbital Brightness Modulation shape=(3, 6)\n", + "Pulsar Timing shape=(5, 6)\n", + "Pulsation Timing Variations shape=(1, 6)\n", + "Radial Velocity shape=(553, 6)\n", + "Transit shape=(397, 6)\n", + "Transit Timing Variations shape=(4, 6)\n" + ] + } + ], + "source": [ + "for (method, group) in planets.groupby('method'):\n", + " print(\"{0:30s} shape={1}\".format(method, group.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can be useful for doing certain things manually, though it is often much faster to use the built-in ``apply`` functionality, which we will discuss momentarily." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dispatch methods\n", + "\n", + "Through some Python class magic, any method not explicitly implemented by the ``GroupBy`` object will be passed through and called on the groups, whether they are ``DataFrame`` or ``Series`` objects.\n", + "For example, you can use the ``describe()`` method of ``DataFrame``s to perform a set of aggregations that describe each group in the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " data1 data2 \n", + " min median max min median max\n", + "key \n", + "A 0 1.5 3 3 4.0 5\n", + "B 1 2.5 4 0 3.5 7\n", + "C 2 3.5 5 3 6.0 9" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('key').aggregate(['min', np.median, max])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful pattern is to pass a dictionary mapping column names to operations to be applied on that column:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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data1data2
key
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" + ], + "text/plain": [ + " data1 data2\n", + "key \n", + "A 0 5\n", + "B 1 7\n", + "C 2 9" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('key').aggregate({'data1': 'min',\n", + " 'data2': 'max'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Filtering\n", + "\n", + "A filtering operation allows you to drop data based on the group properties.\n", + "For example, we might want to keep all groups in which the standard deviation is larger than some critical value:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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df

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df.groupby('key').std()

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data1data2
key
A2.121321.414214
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df.groupby('key').filter(filter_func)

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" + ], + "text/plain": [ + "df\n", + " key data1 data2\n", + "0 A 0 5\n", + "1 B 1 0\n", + "2 C 2 3\n", + "3 A 3 3\n", + "4 B 4 7\n", + "5 C 5 9\n", + "\n", + "df.groupby('key').std()\n", + " data1 data2\n", + "key \n", + "A 2.12132 1.414214\n", + "B 2.12132 4.949747\n", + "C 2.12132 4.242641\n", + "\n", + "df.groupby('key').filter(filter_func)\n", + " key data1 data2\n", + "1 B 1 0\n", + "2 C 2 3\n", + "4 B 4 7\n", + "5 C 5 9" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def filter_func(x):\n", + " return x['data2'].std() > 4\n", + "\n", + "display('df', \"df.groupby('key').std()\", \"df.groupby('key').filter(filter_func)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The filter function should return a Boolean value specifying whether the group passes the filtering. Here because group A does not have a standard deviation greater than 4, it is dropped from the result." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Transformation\n", + "\n", + "While aggregation must return a reduced version of the data, transformation can return some transformed version of the full data to recombine.\n", + "For such a transformation, the output is the same shape as the input.\n", + "A common example is to center the data by subtracting the group-wise mean:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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data1data2
0-1.51.0
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" + ], + "text/plain": [ + " data1 data2\n", + "0 -1.5 1.0\n", + "1 -1.5 -3.5\n", + "2 -1.5 -3.0\n", + "3 1.5 -1.0\n", + "4 1.5 3.5\n", + "5 1.5 3.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('key').transform(lambda x: x - x.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The apply() method\n", + "\n", + "The ``apply()`` method lets you apply an arbitrary function to the group results.\n", + "The function should take a ``DataFrame``, and return either a Pandas object (e.g., ``DataFrame``, ``Series``) or a scalar; the combine operation will be tailored to the type of output returned.\n", + "\n", + "For example, here is an ``apply()`` that normalizes the first column by the sum of the second:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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df

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keydata1data2
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df.groupby('key').apply(norm_by_data2)

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keydata1data2
0A0.0000005
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2C0.1666673
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4B0.5714297
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" + ], + "text/plain": [ + "df\n", + " key data1 data2\n", + "0 A 0 5\n", + "1 B 1 0\n", + "2 C 2 3\n", + "3 A 3 3\n", + "4 B 4 7\n", + "5 C 5 9\n", + "\n", + "df.groupby('key').apply(norm_by_data2)\n", + " key data1 data2\n", + "0 A 0.000000 5\n", + "1 B 0.142857 0\n", + "2 C 0.166667 3\n", + "3 A 0.375000 3\n", + "4 B 0.571429 7\n", + "5 C 0.416667 9" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def norm_by_data2(x):\n", + " # x is a DataFrame of group values\n", + " x['data1'] /= x['data2'].sum()\n", + " return x\n", + "\n", + "display('df', \"df.groupby('key').apply(norm_by_data2)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``apply()`` within a ``GroupBy`` is quite flexible: the only criterion is that the function takes a ``DataFrame`` and returns a Pandas object or scalar; what you do in the middle is up to you!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specifying the split key\n", + "\n", + "In the simple examples presented before, we split the ``DataFrame`` on a single column name.\n", + "This is just one of many options by which the groups can be defined, and we'll go through some other options for group specification here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A list, array, series, or index providing the grouping keys\n", + "\n", + "The key can be any series or list with a length matching that of the ``DataFrame``. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df

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keydata1data2
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df.groupby(L).sum()

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data1data2
0717
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" + ], + "text/plain": [ + "df\n", + " key data1 data2\n", + "0 A 0 5\n", + "1 B 1 0\n", + "2 C 2 3\n", + "3 A 3 3\n", + "4 B 4 7\n", + "5 C 5 9\n", + "\n", + "df.groupby(L).sum()\n", + " data1 data2\n", + "0 7 17\n", + "1 4 3\n", + "2 4 7" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "L = [0, 1, 0, 1, 2, 0]\n", + "display('df', 'df.groupby(L).sum()')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, this means there's another, more verbose way of accomplishing the ``df.groupby('key')`` from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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df

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keydata1data2
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df.groupby(df['key']).sum()

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data1data2
key
A38
B57
C712
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" + ], + "text/plain": [ + "df\n", + " key data1 data2\n", + "0 A 0 5\n", + "1 B 1 0\n", + "2 C 2 3\n", + "3 A 3 3\n", + "4 B 4 7\n", + "5 C 5 9\n", + "\n", + "df.groupby(df['key']).sum()\n", + " data1 data2\n", + "key \n", + "A 3 8\n", + "B 5 7\n", + "C 7 12" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df', \"df.groupby(df['key']).sum()\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A dictionary or series mapping index to group\n", + "\n", + "Another method is to provide a dictionary that maps index values to the group keys:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df2

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data1data2
key
A05
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A33
B47
C59
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df2.groupby(mapping).sum()

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data1data2
consonant1219
vowel38
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\n", + "
" + ], + "text/plain": [ + "df2\n", + " data1 data2\n", + "key \n", + "A 0 5\n", + "B 1 0\n", + "C 2 3\n", + "A 3 3\n", + "B 4 7\n", + "C 5 9\n", + "\n", + "df2.groupby(mapping).sum()\n", + " data1 data2\n", + "consonant 12 19\n", + "vowel 3 8" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = df.set_index('key')\n", + "mapping = {'A': 'vowel', 'B': 'consonant', 'C': 'consonant'}\n", + "display('df2', 'df2.groupby(mapping).sum()')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Any Python function\n", + "\n", + "Similar to mapping, you can pass any Python function that will input the index value and output the group:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df2

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data1data2
key
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B47
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df2.groupby(str.lower).mean()

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data1data2
a1.54.0
b2.53.5
c3.56.0
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" + ], + "text/plain": [ + "df2\n", + " data1 data2\n", + "key \n", + "A 0 5\n", + "B 1 0\n", + "C 2 3\n", + "A 3 3\n", + "B 4 7\n", + "C 5 9\n", + "\n", + "df2.groupby(str.lower).mean()\n", + " data1 data2\n", + "a 1.5 4.0\n", + "b 2.5 3.5\n", + "c 3.5 6.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display('df2', 'df2.groupby(str.lower).mean()')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A list of valid keys\n", + "\n", + "Further, any of the preceding key choices can be combined to group on a multi-index:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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data1data2
avowel1.54.0
bconsonant2.53.5
cconsonant3.56.0
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" + ], + "text/plain": [ + " data1 data2\n", + "a vowel 1.5 4.0\n", + "b consonant 2.5 3.5\n", + "c consonant 3.5 6.0" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.groupby([str.lower, mapping]).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grouping example\n", + "\n", + "As an example of this, in a couple lines of Python code we can put all these together and count discovered planets by method and by decade:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Orbital Brightness Modulation0.00.00.05.0
Pulsar Timing0.09.01.01.0
Pulsation Timing Variations0.00.01.00.0
Radial Velocity1.052.0475.0424.0
Transit0.00.064.0712.0
Transit Timing Variations0.00.00.09.0
\n", + "
" + ], + "text/plain": [ + "decade 1980s 1990s 2000s 2010s\n", + "method \n", + "Astrometry 0.0 0.0 0.0 2.0\n", + "Eclipse Timing Variations 0.0 0.0 5.0 10.0\n", + "Imaging 0.0 0.0 29.0 21.0\n", + "Microlensing 0.0 0.0 12.0 15.0\n", + "Orbital Brightness Modulation 0.0 0.0 0.0 5.0\n", + "Pulsar Timing 0.0 9.0 1.0 1.0\n", + "Pulsation Timing Variations 0.0 0.0 1.0 0.0\n", + "Radial Velocity 1.0 52.0 475.0 424.0\n", + "Transit 0.0 0.0 64.0 712.0\n", + "Transit Timing Variations 0.0 0.0 0.0 9.0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decade = 10 * (planets['year'] // 10)\n", + "decade = decade.astype(str) + 's'\n", + "decade.name = 'decade'\n", + "planets.groupby(['method', decade])['number'].sum().unstack().fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows the power of combining many of the operations we've discussed up to this point when looking at realistic datasets.\n", + "We immediately gain a coarse understanding of when and how planets have been discovered over the past several decades!\n", + "\n", + "Here I would suggest digging into these few lines of code, and evaluating the individual steps to make sure you understand exactly what they are doing to the result.\n", + "It's certainly a somewhat complicated example, but understanding these pieces will give you the means to similarly explore your own data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.08-Aggregation-and-Grouping.md b/notebooks_v2/03.08-Aggregation-and-Grouping.md new file mode 100644 index 00000000..bbf03a7a --- /dev/null +++ b/notebooks_v2/03.08-Aggregation-and-Grouping.md @@ -0,0 +1,408 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) > + +Open in Colab + + + +# Aggregation and Grouping + + +An essential piece of analysis of large data is efficient summarization: computing aggregations like ``sum()``, ``mean()``, ``median()``, ``min()``, and ``max()``, in which a single number gives insight into the nature of a potentially large dataset. +In this section, we'll explore aggregations in Pandas, from simple operations akin to what we've seen on NumPy arrays, to more sophisticated operations based on the concept of a ``groupby``. + + +For convenience, we'll use the same ``display`` magic function that we've seen in previous sections: + +```python +import numpy as np +import pandas as pd + +class display(object): + """Display HTML representation of multiple objects""" + template = """
+

{0}

{1} +
""" + def __init__(self, *args): + self.args = args + + def _repr_html_(self): + return '\n'.join(self.template.format(a, eval(a)._repr_html_()) + for a in self.args) + + def __repr__(self): + return '\n\n'.join(a + '\n' + repr(eval(a)) + for a in self.args) +``` + +## Planets Data + +Here we will use the Planets dataset, available via the [Seaborn package](http://seaborn.pydata.org/) (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)). +It gives information on planets that astronomers have discovered around other stars (known as *extrasolar planets* or *exoplanets* for short). It can be downloaded with a simple Seaborn command: + +```python +import seaborn as sns +planets = sns.load_dataset('planets') +planets.shape +``` + +```python +planets.head() +``` + +This has some details on the 1,000+ extrasolar planets discovered up to 2014. + + +## Simple Aggregation in Pandas + + +Earlier, we explored some of the data aggregations available for NumPy arrays (["Aggregations: Min, Max, and Everything In Between"](02.04-Computation-on-arrays-aggregates.ipynb)). +As with a one-dimensional NumPy array, for a Pandas ``Series`` the aggregates return a single value: + +```python +rng = np.random.RandomState(42) +ser = pd.Series(rng.rand(5)) +ser +``` + +```python +ser.sum() +``` + +```python +ser.mean() +``` + +For a ``DataFrame``, by default the aggregates return results within each column: + +```python +df = pd.DataFrame({'A': rng.rand(5), + 'B': rng.rand(5)}) +df +``` + +```python +df.mean() +``` + +By specifying the ``axis`` argument, you can instead aggregate within each row: + +```python +df.mean(axis='columns') +``` + +Pandas ``Series`` and ``DataFrame``s include all of the common aggregates mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb); in addition, there is a convenience method ``describe()`` that computes several common aggregates for each column and returns the result. +Let's use this on the Planets data, for now dropping rows with missing values: + +```python +planets.dropna().describe() +``` + +This can be a useful way to begin understanding the overall properties of a dataset. +For example, we see in the ``year`` column that although exoplanets were discovered as far back as 1989, half of all known expolanets were not discovered until 2010 or after. +This is largely thanks to the *Kepler* mission, which is a space-based telescope specifically designed for finding eclipsing planets around other stars. + + +The following table summarizes some other built-in Pandas aggregations: + +| Aggregation | Description | +|--------------------------|---------------------------------| +| ``count()`` | Total number of items | +| ``first()``, ``last()`` | First and last item | +| ``mean()``, ``median()`` | Mean and median | +| ``min()``, ``max()`` | Minimum and maximum | +| ``std()``, ``var()`` | Standard deviation and variance | +| ``mad()`` | Mean absolute deviation | +| ``prod()`` | Product of all items | +| ``sum()`` | Sum of all items | + +These are all methods of ``DataFrame`` and ``Series`` objects. + + +To go deeper into the data, however, simple aggregates are often not enough. +The next level of data summarization is the ``groupby`` operation, which allows you to quickly and efficiently compute aggregates on subsets of data. + + +## GroupBy: Split, Apply, Combine + +Simple aggregations can give you a flavor of your dataset, but often we would prefer to aggregate conditionally on some label or index: this is implemented in the so-called ``groupby`` operation. +The name "group by" comes from a command in the SQL database language, but it is perhaps more illuminative to think of it in the terms first coined by Hadley Wickham of Rstats fame: *split, apply, combine*. + + +### Split, apply, combine + +A canonical example of this split-apply-combine operation, where the "apply" is a summation aggregation, is illustrated in this figure: + + +![](figures/03.08-split-apply-combine.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Split-Apply-Combine) + + +This makes clear what the ``groupby`` accomplishes: + +- The *split* step involves breaking up and grouping a ``DataFrame`` depending on the value of the specified key. +- The *apply* step involves computing some function, usually an aggregate, transformation, or filtering, within the individual groups. +- The *combine* step merges the results of these operations into an output array. + +While this could certainly be done manually using some combination of the masking, aggregation, and merging commands covered earlier, an important realization is that *the intermediate splits do not need to be explicitly instantiated*. Rather, the ``GroupBy`` can (often) do this in a single pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way. +The power of the ``GroupBy`` is that it abstracts away these steps: the user need not think about *how* the computation is done under the hood, but rather thinks about the *operation as a whole*. + +As a concrete example, let's take a look at using Pandas for the computation shown in this diagram. +We'll start by creating the input ``DataFrame``: + +```python +df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'], + 'data': range(6)}, columns=['key', 'data']) +df +``` + +The most basic split-apply-combine operation can be computed with the ``groupby()`` method of ``DataFrame``s, passing the name of the desired key column: + +```python +df.groupby('key') +``` + +Notice that what is returned is not a set of ``DataFrame``s, but a ``DataFrameGroupBy`` object. +This object is where the magic is: you can think of it as a special view of the ``DataFrame``, which is poised to dig into the groups but does no actual computation until the aggregation is applied. +This "lazy evaluation" approach means that common aggregates can be implemented very efficiently in a way that is almost transparent to the user. + +To produce a result, we can apply an aggregate to this ``DataFrameGroupBy`` object, which will perform the appropriate apply/combine steps to produce the desired result: + +```python +df.groupby('key').sum() +``` + +The ``sum()`` method is just one possibility here; you can apply virtually any common Pandas or NumPy aggregation function, as well as virtually any valid ``DataFrame`` operation, as we will see in the following discussion. + + +### The GroupBy object + +The ``GroupBy`` object is a very flexible abstraction. +In many ways, you can simply treat it as if it's a collection of ``DataFrame``s, and it does the difficult things under the hood. Let's see some examples using the Planets data. + +Perhaps the most important operations made available by a ``GroupBy`` are *aggregate*, *filter*, *transform*, and *apply*. +We'll discuss each of these more fully in ["Aggregate, Filter, Transform, Apply"](#Aggregate,-Filter,-Transform,-Apply), but before that let's introduce some of the other functionality that can be used with the basic ``GroupBy`` operation. + + +#### Column indexing + +The ``GroupBy`` object supports column indexing in the same way as the ``DataFrame``, and returns a modified ``GroupBy`` object. +For example: + +```python +planets.groupby('method') +``` + +```python +planets.groupby('method')['orbital_period'] +``` + +Here we've selected a particular ``Series`` group from the original ``DataFrame`` group by reference to its column name. +As with the ``GroupBy`` object, no computation is done until we call some aggregate on the object: + +```python +planets.groupby('method')['orbital_period'].median() +``` + +This gives an idea of the general scale of orbital periods (in days) that each method is sensitive to. + + +#### Iteration over groups + +The ``GroupBy`` object supports direct iteration over the groups, returning each group as a ``Series`` or ``DataFrame``: + +```python +for (method, group) in planets.groupby('method'): + print("{0:30s} shape={1}".format(method, group.shape)) +``` + +This can be useful for doing certain things manually, though it is often much faster to use the built-in ``apply`` functionality, which we will discuss momentarily. + + +#### Dispatch methods + +Through some Python class magic, any method not explicitly implemented by the ``GroupBy`` object will be passed through and called on the groups, whether they are ``DataFrame`` or ``Series`` objects. +For example, you can use the ``describe()`` method of ``DataFrame``s to perform a set of aggregations that describe each group in the data: + +```python +planets.groupby('method')['year'].describe().unstack() +``` + +Looking at this table helps us to better understand the data: for example, the vast majority of planets have been discovered by the Radial Velocity and Transit methods, though the latter only became common (due to new, more accurate telescopes) in the last decade. +The newest methods seem to be Transit Timing Variation and Orbital Brightness Modulation, which were not used to discover a new planet until 2011. + +This is just one example of the utility of dispatch methods. +Notice that they are applied *to each individual group*, and the results are then combined within ``GroupBy`` and returned. +Again, any valid ``DataFrame``/``Series`` method can be used on the corresponding ``GroupBy`` object, which allows for some very flexible and powerful operations! + + +### Aggregate, filter, transform, apply + +The preceding discussion focused on aggregation for the combine operation, but there are more options available. +In particular, ``GroupBy`` objects have ``aggregate()``, ``filter()``, ``transform()``, and ``apply()`` methods that efficiently implement a variety of useful operations before combining the grouped data. + +For the purpose of the following subsections, we'll use this ``DataFrame``: + +```python +rng = np.random.RandomState(0) +df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'], + 'data1': range(6), + 'data2': rng.randint(0, 10, 6)}, + columns = ['key', 'data1', 'data2']) +df +``` + +#### Aggregation + +We're now familiar with ``GroupBy`` aggregations with ``sum()``, ``median()``, and the like, but the ``aggregate()`` method allows for even more flexibility. +It can take a string, a function, or a list thereof, and compute all the aggregates at once. +Here is a quick example combining all these: + +```python +df.groupby('key').aggregate(['min', np.median, max]) +``` + +Another useful pattern is to pass a dictionary mapping column names to operations to be applied on that column: + +```python +df.groupby('key').aggregate({'data1': 'min', + 'data2': 'max'}) +``` + +#### Filtering + +A filtering operation allows you to drop data based on the group properties. +For example, we might want to keep all groups in which the standard deviation is larger than some critical value: + +```python +def filter_func(x): + return x['data2'].std() > 4 + +display('df', "df.groupby('key').std()", "df.groupby('key').filter(filter_func)") +``` + +The filter function should return a Boolean value specifying whether the group passes the filtering. Here because group A does not have a standard deviation greater than 4, it is dropped from the result. + + +#### Transformation + +While aggregation must return a reduced version of the data, transformation can return some transformed version of the full data to recombine. +For such a transformation, the output is the same shape as the input. +A common example is to center the data by subtracting the group-wise mean: + +```python +df.groupby('key').transform(lambda x: x - x.mean()) +``` + +#### The apply() method + +The ``apply()`` method lets you apply an arbitrary function to the group results. +The function should take a ``DataFrame``, and return either a Pandas object (e.g., ``DataFrame``, ``Series``) or a scalar; the combine operation will be tailored to the type of output returned. + +For example, here is an ``apply()`` that normalizes the first column by the sum of the second: + +```python +def norm_by_data2(x): + # x is a DataFrame of group values + x['data1'] /= x['data2'].sum() + return x + +display('df', "df.groupby('key').apply(norm_by_data2)") +``` + +``apply()`` within a ``GroupBy`` is quite flexible: the only criterion is that the function takes a ``DataFrame`` and returns a Pandas object or scalar; what you do in the middle is up to you! + + +### Specifying the split key + +In the simple examples presented before, we split the ``DataFrame`` on a single column name. +This is just one of many options by which the groups can be defined, and we'll go through some other options for group specification here. + + +#### A list, array, series, or index providing the grouping keys + +The key can be any series or list with a length matching that of the ``DataFrame``. For example: + +```python +L = [0, 1, 0, 1, 2, 0] +display('df', 'df.groupby(L).sum()') +``` + +Of course, this means there's another, more verbose way of accomplishing the ``df.groupby('key')`` from before: + +```python +display('df', "df.groupby(df['key']).sum()") +``` + +#### A dictionary or series mapping index to group + +Another method is to provide a dictionary that maps index values to the group keys: + +```python +df2 = df.set_index('key') +mapping = {'A': 'vowel', 'B': 'consonant', 'C': 'consonant'} +display('df2', 'df2.groupby(mapping).sum()') +``` + +#### Any Python function + +Similar to mapping, you can pass any Python function that will input the index value and output the group: + +```python +display('df2', 'df2.groupby(str.lower).mean()') +``` + +#### A list of valid keys + +Further, any of the preceding key choices can be combined to group on a multi-index: + +```python +df2.groupby([str.lower, mapping]).mean() +``` + +### Grouping example + +As an example of this, in a couple lines of Python code we can put all these together and count discovered planets by method and by decade: + +```python +decade = 10 * (planets['year'] // 10) +decade = decade.astype(str) + 's' +decade.name = 'decade' +planets.groupby(['method', decade])['number'].sum().unstack().fillna(0) +``` + +This shows the power of combining many of the operations we've discussed up to this point when looking at realistic datasets. +We immediately gain a coarse understanding of when and how planets have been discovered over the past several decades! + +Here I would suggest digging into these few lines of code, and evaluating the individual steps to make sure you understand exactly what they are doing to the result. +It's certainly a somewhat complicated example, but understanding these pieces will give you the means to similarly explore your own data. + + + +< [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) | [Contents](Index.ipynb) | [Pivot Tables](03.09-Pivot-Tables.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.09-Pivot-Tables.ipynb b/notebooks_v2/03.09-Pivot-Tables.ipynb new file mode 100644 index 00000000..195a65af --- /dev/null +++ b/notebooks_v2/03.09-Pivot-Tables.ipynb @@ -0,0 +1,1382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pivot Tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have seen how the ``GroupBy`` abstraction lets us explore relationships within a dataset.\n", + "A *pivot table* is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data.\n", + "The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data.\n", + "The difference between pivot tables and ``GroupBy`` can sometimes cause confusion; it helps me to think of pivot tables as essentially a *multidimensional* version of ``GroupBy`` aggregation.\n", + "That is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motivating Pivot Tables\n", + "\n", + "For the examples in this section, we'll use the database of passengers on the *Titanic*, available through the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "titanic = sns.load_dataset('titanic')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "class First Second Third\n", + "sex \n", + "female 0.968085 0.921053 0.500000\n", + "male 0.368852 0.157407 0.135447" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us a better idea of how both gender and class affected survival, but the code is starting to look a bit garbled.\n", + "While each step of this pipeline makes sense in light of the tools we've previously discussed, the long string of code is not particularly easy to read or use.\n", + "This two-dimensional ``GroupBy`` is common enough that Pandas includes a convenience routine, ``pivot_table``, which succinctly handles this type of multi-dimensional aggregation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pivot Table Syntax\n", + "\n", + "Here is the equivalent to the preceding operation using the ``pivot_table`` method of ``DataFrame``s:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "class First Second Third\n", + "sex \n", + "female 0.968085 0.921053 0.500000\n", + "male 0.368852 0.157407 0.135447" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.pivot_table('survived', index='sex', columns='class')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is eminently more readable than the ``groupby`` approach, and produces the same result.\n", + "As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both women and higher classes.\n", + "First-class women survived with near certainty (hi, Rose!), while only one in ten third-class men survived (sorry, Jack!)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-level pivot tables\n", + "\n", + "Just as in the ``GroupBy``, the grouping in pivot tables can be specified with multiple levels, and via a number of options.\n", + "For example, we might be interested in looking at age as a third dimension.\n", + "We'll bin the age using the ``pd.cut`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "class First Second Third\n", + "sex age \n", + "female (0, 18] 0.909091 1.000000 0.511628\n", + " (18, 80] 0.972973 0.900000 0.423729\n", + "male (0, 18] 0.800000 0.600000 0.215686\n", + " (18, 80] 0.375000 0.071429 0.133663" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age = pd.cut(titanic['age'], [0, 18, 80])\n", + "titanic.pivot_table('survived', ['sex', age], 'class')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can apply the same strategy when working with the columns as well; let's add info on the fare paid using ``pd.qcut`` to automatically compute quantiles:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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fare[0, 14.454](14.454, 512.329]
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female(0, 18]NaN1.0000000.7142860.9090911.0000000.318182
(18, 80]NaN0.8800000.4444440.9729730.9142860.391304
male(0, 18]NaN0.0000000.2608700.8000000.8181820.178571
(18, 80]0.00.0980390.1250000.3913040.0303030.192308
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" + ], + "text/plain": [ + "fare [0, 14.454] (14.454, 512.329] \\\n", + "class First Second Third First Second \n", + "sex age \n", + "female (0, 18] NaN 1.000000 0.714286 0.909091 1.000000 \n", + " (18, 80] NaN 0.880000 0.444444 0.972973 0.914286 \n", + "male (0, 18] NaN 0.000000 0.260870 0.800000 0.818182 \n", + " (18, 80] 0.0 0.098039 0.125000 0.391304 0.030303 \n", + "\n", + "fare \n", + "class Third \n", + "sex age \n", + "female (0, 18] 0.318182 \n", + " (18, 80] 0.391304 \n", + "male (0, 18] 0.178571 \n", + " (18, 80] 0.192308 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fare = pd.qcut(titanic['fare'], 2)\n", + "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a four-dimensional aggregation with hierarchical indices (see [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)), shown in a grid demonstrating the relationship between the values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Additional pivot table options\n", + "\n", + "The full call signature of the ``pivot_table`` method of ``DataFrame``s is as follows:\n", + "\n", + "```python\n", + "# call signature as of Pandas 0.18\n", + "DataFrame.pivot_table(data, values=None, index=None, columns=None,\n", + " aggfunc='mean', fill_value=None, margins=False,\n", + " dropna=True, margins_name='All')\n", + "```\n", + "\n", + "We've already seen examples of the first three arguments; here we'll take a quick look at the remaining ones.\n", + "Two of the options, ``fill_value`` and ``dropna``, have to do with missing data and are fairly straightforward; we will not show examples of them here.\n", + "\n", + "The ``aggfunc`` keyword controls what type of aggregation is applied, which is a mean by default.\n", + "As in the GroupBy, the aggregation specification can be a string representing one of several common choices (e.g., ``'sum'``, ``'mean'``, ``'count'``, ``'min'``, ``'max'``, etc.) or a function that implements an aggregation (e.g., ``np.sum()``, ``min()``, ``sum()``, etc.).\n", + "Additionally, it can be specified as a dictionary mapping a column to any of the above desired options:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " fare survived \n", + "class First Second Third First Second Third\n", + "sex \n", + "female 106.125798 21.970121 16.118810 91.0 70.0 72.0\n", + "male 67.226127 19.741782 12.661633 45.0 17.0 47.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.pivot_table(index='sex', columns='class',\n", + " aggfunc={'survived':sum, 'fare':'mean'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice also here that we've omitted the ``values`` keyword; when specifying a mapping for ``aggfunc``, this is determined automatically." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "At times it's useful to compute totals along each grouping.\n", + "This can be done via the ``margins`` keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "class First Second Third All\n", + "sex \n", + "female 0.968085 0.921053 0.500000 0.742038\n", + "male 0.368852 0.157407 0.135447 0.188908\n", + "All 0.629630 0.472826 0.242363 0.383838" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.pivot_table('survived', index='sex', columns='class', margins=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here this automatically gives us information about the class-agnostic survival rate by gender, the gender-agnostic survival rate by class, and the overall survival rate of 38%.\n", + "The margin label can be specified with the ``margins_name`` keyword, which defaults to ``\"All\"``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Birthrate Data\n", + "\n", + "As a more interesting example, let's take a look at the freely available data on births in the United States, provided by the Centers for Disease Control (CDC).\n", + "This data can be found at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv\n", + "(this dataset has been analyzed rather extensively by Andrew Gelman and his group; see, for example, [this blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/)):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# shell command to download the data:\n", + "# !curl -O https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "births = pd.read_csv('data/births.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the data, we see that it's relatively simple–it contains the number of births grouped by date and gender:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " year month day gender births\n", + "0 1969 1 1 F 4046\n", + "1 1969 1 1 M 4440\n", + "2 1969 1 2 F 4454\n", + "3 1969 1 2 M 4548\n", + "4 1969 1 3 F 4548" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "births.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can start to understand this data a bit more by using a pivot table.\n", + "Let's add a decade column, and take a look at male and female births as a function of decade:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "gender F M\n", + "decade \n", + "1960 1753634 1846572\n", + "1970 16263075 17121550\n", + "1980 18310351 19243452\n", + "1990 19479454 20420553\n", + "2000 18229309 19106428" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "births['decade'] = 10 * (births['year'] // 10)\n", + "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We immediately see that male births outnumber female births in every decade.\n", + "To see this trend a bit more clearly, we can use the built-in plotting tools in Pandas to visualize the total number of births by year (see [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) for a discussion of plotting with Matplotlib):" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + 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GayRRaIZ8ff2oqChn06b13HXXSEuHI4QQohFtT4sH4K52Q38zCbEu/LW+PNztQUxmEytT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "sns.set() # use Seaborn styles\n", + "births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot()\n", + "plt.ylabel('total births per year');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a simple pivot table and ``plot()`` method, we can immediately see the annual trend in births by gender. By eye, it appears that over the past 50 years male births have outnumbered female births by around 5%." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Further data exploration\n", + "\n", + "Though this doesn't necessarily relate to the pivot table, there are a few more interesting features we can pull out of this dataset using the Pandas tools covered up to this point.\n", + "We must start by cleaning the data a bit, removing outliers caused by mistyped dates (e.g., June 31st) or missing values (e.g., June 99th).\n", + "One easy way to remove these all at once is to cut outliers; we'll do this via a robust sigma-clipping operation:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "quartiles = np.percentile(births['births'], [25, 50, 75])\n", + "mu = quartiles[1]\n", + "sig = 0.74 * (quartiles[2] - quartiles[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This final line is a robust estimate of the sample mean, where the 0.74 comes from the interquartile range of a Gaussian distribution (You can learn more about sigma-clipping operations in a book I coauthored with Željko Ivezić, Andrew J. Connolly, and Alexander Gray: [\"Statistics, Data Mining, and Machine Learning in Astronomy\"](http://press.princeton.edu/titles/10159.html) (Princeton University Press, 2014)).\n", + "\n", + "With this we can use the ``query()`` method (discussed further in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)) to filter-out rows with births outside these values:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we set the ``day`` column to integers; previously it had been a string because some columns in the dataset contained the value ``'null'``:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set 'day' column to integer; it originally was a string due to nulls\n", + "births['day'] = births['day'].astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can combine the day, month, and year to create a Date index (see [Working with Time Series](03.11-Working-with-Time-Series.ipynb)).\n", + "This allows us to quickly compute the weekday corresponding to each row:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create a datetime index from the year, month, day\n", + "births.index = pd.to_datetime(10000 * births.year +\n", + " 100 * births.month +\n", + " births.day, format='%Y%m%d')\n", + "\n", + "births['dayofweek'] = births.index.dayofweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using this we can plot births by weekday for several decades:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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/DBo0iNTUVN5//30WLlzImjVr2LhxIy0tLSxYsIDk5GTWrl3LwIEDWbp0Kdu2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "\n", + "births.pivot_table('births', index='dayofweek',\n", + " columns='decade', aggfunc='mean').plot()\n", + "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n", + "plt.ylabel('mean births by day');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apparently births are slightly less common on weekends than on weekdays! Note that the 1990s and 2000s are missing because the CDC data contains only the month of birth starting in 1989.\n", + "\n", + "Another intersting view is to plot the mean number of births by the day of the *year*.\n", + "Let's first group the data by month and day separately:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 1 4009.225\n", + " 2 4247.400\n", + " 3 4500.900\n", + " 4 4571.350\n", + " 5 4603.625\n", + "Name: births, dtype: float64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "births_by_date = births.pivot_table('births', \n", + " [births.index.month, births.index.day])\n", + "births_by_date.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a multi-index over months and days.\n", + "To make this easily plottable, let's turn these months and days into a date by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2012-01-01 4009.225\n", + "2012-01-02 4247.400\n", + "2012-01-03 4500.900\n", + "2012-01-04 4571.350\n", + "2012-01-05 4603.625\n", + "Name: births, dtype: float64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "births_by_date.index = [pd.datetime(2012, month, day)\n", + " for (month, day) in births_by_date.index]\n", + "births_by_date.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Focusing on the month and day only, we now have a time series reflecting the average number of births by date of the year.\n", + "From this, we can use the ``plot`` method to plot the data. It reveals some interesting trends:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yYhU6hxyIRGOCH7nMkF3wihgGW1eVYHOzGSNWL3pHXSgvVkEqyX+zKjEohYSb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "births_by_date.plot(ax=ax);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "In particular, the striking feature of this graph is the dip in birthrate on US holidays (e.g., Independence Day, Labor Day, Thanksgiving, Christmas, New Year's Day) although this likely reflects trends in scheduled/induced births rather than some deep psychosomatic effect on natural births.\n", + "For more discussion on this trend, see the analysis and links in [Andrew Gelman's blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/) on the subject.\n", + "We'll return to this figure in [Example:-Effect-of-Holidays-on-US-Births](04.09-Text-and-Annotation.ipynb#Example:-Effect-of-Holidays-on-US-Births), where we will use Matplotlib's tools to annotate this plot.\n", + "\n", + "Looking at this short example, you can see that many of the Python and Pandas tools we've seen to this point can be combined and used to gain insight from a variety of datasets.\n", + "We will see some more sophisticated applications of these data manipulations in future sections!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.09-Pivot-Tables.md b/notebooks_v2/03.09-Pivot-Tables.md new file mode 100644 index 00000000..aba98584 --- /dev/null +++ b/notebooks_v2/03.09-Pivot-Tables.md @@ -0,0 +1,290 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) > + +Open in Colab + + + +# Pivot Tables + + +We have seen how the ``GroupBy`` abstraction lets us explore relationships within a dataset. +A *pivot table* is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data. +The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data. +The difference between pivot tables and ``GroupBy`` can sometimes cause confusion; it helps me to think of pivot tables as essentially a *multidimensional* version of ``GroupBy`` aggregation. +That is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid. + + +## Motivating Pivot Tables + +For the examples in this section, we'll use the database of passengers on the *Titanic*, available through the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)): + +```python +import numpy as np +import pandas as pd +import seaborn as sns +titanic = sns.load_dataset('titanic') +``` + +```python +titanic.head() +``` + +This contains a wealth of information on each passenger of that ill-fated voyage, including gender, age, class, fare paid, and much more. + + +## Pivot Tables by Hand + +To start learning more about this data, we might begin by grouping according to gender, survival status, or some combination thereof. +If you have read the previous section, you might be tempted to apply a ``GroupBy`` operation–for example, let's look at survival rate by gender: + +```python +titanic.groupby('sex')[['survived']].mean() +``` + +This immediately gives us some insight: overall, three of every four females on board survived, while only one in five males survived! + +This is useful, but we might like to go one step deeper and look at survival by both sex and, say, class. +Using the vocabulary of ``GroupBy``, we might proceed using something like this: +we *group by* class and gender, *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and then *unstack* the hierarchical index to reveal the hidden multidimensionality. In code: + +```python +titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack() +``` + +This gives us a better idea of how both gender and class affected survival, but the code is starting to look a bit garbled. +While each step of this pipeline makes sense in light of the tools we've previously discussed, the long string of code is not particularly easy to read or use. +This two-dimensional ``GroupBy`` is common enough that Pandas includes a convenience routine, ``pivot_table``, which succinctly handles this type of multi-dimensional aggregation. + + +## Pivot Table Syntax + +Here is the equivalent to the preceding operation using the ``pivot_table`` method of ``DataFrame``s: + +```python +titanic.pivot_table('survived', index='sex', columns='class') +``` + +This is eminently more readable than the ``groupby`` approach, and produces the same result. +As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both women and higher classes. +First-class women survived with near certainty (hi, Rose!), while only one in ten third-class men survived (sorry, Jack!). + + +### Multi-level pivot tables + +Just as in the ``GroupBy``, the grouping in pivot tables can be specified with multiple levels, and via a number of options. +For example, we might be interested in looking at age as a third dimension. +We'll bin the age using the ``pd.cut`` function: + +```python +age = pd.cut(titanic['age'], [0, 18, 80]) +titanic.pivot_table('survived', ['sex', age], 'class') +``` + +We can apply the same strategy when working with the columns as well; let's add info on the fare paid using ``pd.qcut`` to automatically compute quantiles: + +```python +fare = pd.qcut(titanic['fare'], 2) +titanic.pivot_table('survived', ['sex', age], [fare, 'class']) +``` + +The result is a four-dimensional aggregation with hierarchical indices (see [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)), shown in a grid demonstrating the relationship between the values. + + +### Additional pivot table options + +The full call signature of the ``pivot_table`` method of ``DataFrame``s is as follows: + +```python +# call signature as of Pandas 0.18 +DataFrame.pivot_table(data, values=None, index=None, columns=None, + aggfunc='mean', fill_value=None, margins=False, + dropna=True, margins_name='All') +``` + +We've already seen examples of the first three arguments; here we'll take a quick look at the remaining ones. +Two of the options, ``fill_value`` and ``dropna``, have to do with missing data and are fairly straightforward; we will not show examples of them here. + +The ``aggfunc`` keyword controls what type of aggregation is applied, which is a mean by default. +As in the GroupBy, the aggregation specification can be a string representing one of several common choices (e.g., ``'sum'``, ``'mean'``, ``'count'``, ``'min'``, ``'max'``, etc.) or a function that implements an aggregation (e.g., ``np.sum()``, ``min()``, ``sum()``, etc.). +Additionally, it can be specified as a dictionary mapping a column to any of the above desired options: + + +```python +titanic.pivot_table(index='sex', columns='class', + aggfunc={'survived':sum, 'fare':'mean'}) +``` + +Notice also here that we've omitted the ``values`` keyword; when specifying a mapping for ``aggfunc``, this is determined automatically. + + +At times it's useful to compute totals along each grouping. +This can be done via the ``margins`` keyword: + +```python +titanic.pivot_table('survived', index='sex', columns='class', margins=True) +``` + +Here this automatically gives us information about the class-agnostic survival rate by gender, the gender-agnostic survival rate by class, and the overall survival rate of 38%. +The margin label can be specified with the ``margins_name`` keyword, which defaults to ``"All"``. + + +## Example: Birthrate Data + +As a more interesting example, let's take a look at the freely available data on births in the United States, provided by the Centers for Disease Control (CDC). +This data can be found at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv +(this dataset has been analyzed rather extensively by Andrew Gelman and his group; see, for example, [this blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/)): + +```python +# shell command to download the data: +# !curl -O https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv +``` + +```python +births = pd.read_csv('data/births.csv') +``` + +Taking a look at the data, we see that it's relatively simple–it contains the number of births grouped by date and gender: + +```python +births.head() +``` + +We can start to understand this data a bit more by using a pivot table. +Let's add a decade column, and take a look at male and female births as a function of decade: + +```python +births['decade'] = 10 * (births['year'] // 10) +births.pivot_table('births', index='decade', columns='gender', aggfunc='sum') +``` + +We immediately see that male births outnumber female births in every decade. +To see this trend a bit more clearly, we can use the built-in plotting tools in Pandas to visualize the total number of births by year (see [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) for a discussion of plotting with Matplotlib): + +```python +%matplotlib inline +import matplotlib.pyplot as plt +sns.set() # use Seaborn styles +births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot() +plt.ylabel('total births per year'); +``` + +With a simple pivot table and ``plot()`` method, we can immediately see the annual trend in births by gender. By eye, it appears that over the past 50 years male births have outnumbered female births by around 5%. + + +### Further data exploration + +Though this doesn't necessarily relate to the pivot table, there are a few more interesting features we can pull out of this dataset using the Pandas tools covered up to this point. +We must start by cleaning the data a bit, removing outliers caused by mistyped dates (e.g., June 31st) or missing values (e.g., June 99th). +One easy way to remove these all at once is to cut outliers; we'll do this via a robust sigma-clipping operation: + +```python +quartiles = np.percentile(births['births'], [25, 50, 75]) +mu = quartiles[1] +sig = 0.74 * (quartiles[2] - quartiles[0]) +``` + +This final line is a robust estimate of the sample mean, where the 0.74 comes from the interquartile range of a Gaussian distribution (You can learn more about sigma-clipping operations in a book I coauthored with Željko Ivezić, Andrew J. Connolly, and Alexander Gray: ["Statistics, Data Mining, and Machine Learning in Astronomy"](http://press.princeton.edu/titles/10159.html) (Princeton University Press, 2014)). + +With this we can use the ``query()`` method (discussed further in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)) to filter-out rows with births outside these values: + +```python +births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)') +``` + +Next we set the ``day`` column to integers; previously it had been a string because some columns in the dataset contained the value ``'null'``: + +```python +# set 'day' column to integer; it originally was a string due to nulls +births['day'] = births['day'].astype(int) +``` + +Finally, we can combine the day, month, and year to create a Date index (see [Working with Time Series](03.11-Working-with-Time-Series.ipynb)). +This allows us to quickly compute the weekday corresponding to each row: + +```python +# create a datetime index from the year, month, day +births.index = pd.to_datetime(10000 * births.year + + 100 * births.month + + births.day, format='%Y%m%d') + +births['dayofweek'] = births.index.dayofweek +``` + +Using this we can plot births by weekday for several decades: + +```python +import matplotlib.pyplot as plt +import matplotlib as mpl + +births.pivot_table('births', index='dayofweek', + columns='decade', aggfunc='mean').plot() +plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']) +plt.ylabel('mean births by day'); +``` + +Apparently births are slightly less common on weekends than on weekdays! Note that the 1990s and 2000s are missing because the CDC data contains only the month of birth starting in 1989. + +Another intersting view is to plot the mean number of births by the day of the *year*. +Let's first group the data by month and day separately: + +```python +births_by_date = births.pivot_table('births', + [births.index.month, births.index.day]) +births_by_date.head() +``` + +The result is a multi-index over months and days. +To make this easily plottable, let's turn these months and days into a date by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!) + +```python +births_by_date.index = [pd.datetime(2012, month, day) + for (month, day) in births_by_date.index] +births_by_date.head() +``` + +Focusing on the month and day only, we now have a time series reflecting the average number of births by date of the year. +From this, we can use the ``plot`` method to plot the data. It reveals some interesting trends: + +```python +# Plot the results +fig, ax = plt.subplots(figsize=(12, 4)) +births_by_date.plot(ax=ax); +``` + +In particular, the striking feature of this graph is the dip in birthrate on US holidays (e.g., Independence Day, Labor Day, Thanksgiving, Christmas, New Year's Day) although this likely reflects trends in scheduled/induced births rather than some deep psychosomatic effect on natural births. +For more discussion on this trend, see the analysis and links in [Andrew Gelman's blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/) on the subject. +We'll return to this figure in [Example:-Effect-of-Holidays-on-US-Births](04.09-Text-and-Annotation.ipynb#Example:-Effect-of-Holidays-on-US-Births), where we will use Matplotlib's tools to annotate this plot. + +Looking at this short example, you can see that many of the Python and Pandas tools we've seen to this point can be combined and used to gain insight from a variety of datasets. +We will see some more sophisticated applications of these data manipulations in future sections! + + + +< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.10-Working-With-Strings.ipynb b/notebooks_v2/03.10-Working-With-Strings.ipynb new file mode 100644 index 00000000..c500790d --- /dev/null +++ b/notebooks_v2/03.10-Working-With-Strings.ipynb @@ -0,0 +1,1410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vectorized String Operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One strength of Python is its relative ease in handling and manipulating string data.\n", + "Pandas builds on this and provides a comprehensive set of *vectorized string operations* that become an essential piece of the type of munging required when working with (read: cleaning up) real-world data.\n", + "In this section, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the Internet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introducing Pandas String Operations\n", + "\n", + "We saw in previous sections how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4, 6, 10, 14, 22, 26])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "x = np.array([2, 3, 5, 7, 11, 13])\n", + "x * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This *vectorization* of operations simplifies the syntax of operating on arrays of data: we no longer have to worry about the size or shape of the array, but just about what operation we want done.\n", + "For arrays of strings, NumPy does not provide such simple access, and thus you're stuck using a more verbose loop syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Peter', 'Paul', 'Mary', 'Guido']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = ['peter', 'Paul', 'MARY', 'gUIDO']\n", + "[s.capitalize() for s in data]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is perhaps sufficient to work with some data, but it will break if there are any missing values.\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'capitalize'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'capitalize'" + ] + } + ], + "source": [ + "data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\n", + "[s.capitalize() for s in data]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas includes features to address both this need for vectorized string operations and for correctly handling missing data via the ``str`` attribute of Pandas Series and Index objects containing strings.\n", + "So, for example, suppose we create a Pandas Series with this data:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 peter\n", + "1 Paul\n", + "2 None\n", + "3 MARY\n", + "4 gUIDO\n", + "dtype: object" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "names = pd.Series(data)\n", + "names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now call a single method that will capitalize all the entries, while skipping over any missing values:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Peter\n", + "1 Paul\n", + "2 None\n", + "3 Mary\n", + "4 Guido\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names.str.capitalize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using tab completion on this ``str`` attribute will list all the vectorized string methods available to Pandas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tables of Pandas String Methods\n", + "\n", + "If you have a good understanding of string manipulation in Python, most of Pandas string syntax is intuitive enough that it's probably sufficient to just list a table of available methods; we will start with that here, before diving deeper into a few of the subtleties.\n", + "The examples in this section use the following series of names:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam',\n", + " 'Eric Idle', 'Terry Jones', 'Michael Palin'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Methods similar to Python string methods\n", + "Nearly all Python's built-in string methods are mirrored by a Pandas vectorized string method. Here is a list of Pandas ``str`` methods that mirror Python string methods:\n", + "\n", + "| | | | |\n", + "|-------------|------------------|------------------|------------------|\n", + "|``len()`` | ``lower()`` | ``translate()`` | ``islower()`` | \n", + "|``ljust()`` | ``upper()`` | ``startswith()`` | ``isupper()`` | \n", + "|``rjust()`` | ``find()`` | ``endswith()`` | ``isnumeric()`` | \n", + "|``center()`` | ``rfind()`` | ``isalnum()`` | ``isdecimal()`` | \n", + "|``zfill()`` | ``index()`` | ``isalpha()`` | ``split()`` | \n", + "|``strip()`` | ``rindex()`` | ``isdigit()`` | ``rsplit()`` | \n", + "|``rstrip()`` | ``capitalize()`` | ``isspace()`` | ``partition()`` | \n", + "|``lstrip()`` | ``swapcase()`` | ``istitle()`` | ``rpartition()`` |\n", + "\n", + "Notice that these have various return values. Some, like ``lower()``, return a series of strings:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 graham chapman\n", + "1 john cleese\n", + "2 terry gilliam\n", + "3 eric idle\n", + "4 terry jones\n", + "5 michael palin\n", + "dtype: object" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str.lower()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But some others return numbers:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 14\n", + "1 11\n", + "2 13\n", + "3 9\n", + "4 11\n", + "5 13\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str.len()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or Boolean values:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 True\n", + "3 False\n", + "4 True\n", + "5 False\n", + "dtype: bool" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str.startswith('T')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Still others return lists or other compound values for each element:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 [Graham, Chapman]\n", + "1 [John, Cleese]\n", + "2 [Terry, Gilliam]\n", + "3 [Eric, Idle]\n", + "4 [Terry, Jones]\n", + "5 [Michael, Palin]\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str.split()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll see further manipulations of this kind of series-of-lists object as we continue our discussion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Methods using regular expressions\n", + "\n", + "In addition, there are several methods that accept regular expressions to examine the content of each string element, and follow some of the API conventions of Python's built-in ``re`` module:\n", + "\n", + "| Method | Description |\n", + "|--------|-------------|\n", + "| ``match()`` | Call ``re.match()`` on each element, returning a boolean. |\n", + "| ``extract()`` | Call ``re.match()`` on each element, returning matched groups as strings.|\n", + "| ``findall()`` | Call ``re.findall()`` on each element |\n", + "| ``replace()`` | Replace occurrences of pattern with some other string|\n", + "| ``contains()`` | Call ``re.search()`` on each element, returning a boolean |\n", + "| ``count()`` | Count occurrences of pattern|\n", + "| ``split()`` | Equivalent to ``str.split()``, but accepts regexps |\n", + "| ``rsplit()`` | Equivalent to ``str.rsplit()``, but accepts regexps |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With these, you can do a wide range of interesting operations.\n", + "For example, we can extract the first name from each by asking for a contiguous group of characters at the beginning of each element:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Graham\n", + "1 John\n", + "2 Terry\n", + "3 Eric\n", + "4 Terry\n", + "5 Michael\n", + "dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str.extract('([A-Za-z]+)', expand=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can do something more complicated, like finding all names that start and end with a consonant, making use of the start-of-string (``^``) and end-of-string (``$``) regular expression characters:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 [Graham Chapman]\n", + "1 []\n", + "2 [Terry Gilliam]\n", + "3 []\n", + "4 [Terry Jones]\n", + "5 [Michael Palin]\n", + "dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str.findall(r'^[^AEIOU].*[^aeiou]$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ability to concisely apply regular expressions across ``Series`` or ``Dataframe`` entries opens up many possibilities for analysis and cleaning of data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Miscellaneous methods\n", + "Finally, there are some miscellaneous methods that enable other convenient operations:\n", + "\n", + "| Method | Description |\n", + "|--------|-------------|\n", + "| ``get()`` | Index each element |\n", + "| ``slice()`` | Slice each element|\n", + "| ``slice_replace()`` | Replace slice in each element with passed value|\n", + "| ``cat()`` | Concatenate strings|\n", + "| ``repeat()`` | Repeat values |\n", + "| ``normalize()`` | Return Unicode form of string |\n", + "| ``pad()`` | Add whitespace to left, right, or both sides of strings|\n", + "| ``wrap()`` | Split long strings into lines with length less than a given width|\n", + "| ``join()`` | Join strings in each element of the Series with passed separator|\n", + "| ``get_dummies()`` | extract dummy variables as a dataframe |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Vectorized item access and slicing\n", + "\n", + "The ``get()`` and ``slice()`` operations, in particular, enable vectorized element access from each array.\n", + "For example, we can get a slice of the first three characters of each array using ``str.slice(0, 3)``.\n", + "Note that this behavior is also available through Python's normal indexing syntax–for example, ``df.str.slice(0, 3)`` is equivalent to ``df.str[0:3]``:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Gra\n", + "1 Joh\n", + "2 Ter\n", + "3 Eri\n", + "4 Ter\n", + "5 Mic\n", + "dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str[0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indexing via ``df.str.get(i)`` and ``df.str[i]`` is likewise similar.\n", + "\n", + "These ``get()`` and ``slice()`` methods also let you access elements of arrays returned by ``split()``.\n", + "For example, to extract the last name of each entry, we can combine ``split()`` and ``get()``:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Chapman\n", + "1 Cleese\n", + "2 Gilliam\n", + "3 Idle\n", + "4 Jones\n", + "5 Palin\n", + "dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monte.str.split().str.get(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Indicator variables\n", + "\n", + "Another method that requires a bit of extra explanation is the ``get_dummies()`` method.\n", + "This is useful when your data has a column containing some sort of coded indicator.\n", + "For example, we might have a dataset that contains information in the form of codes, such as A=\"born in America,\" B=\"born in the United Kingdom,\" C=\"likes cheese,\" D=\"likes spam\":" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " info name\n", + "0 B|C|D Graham Chapman\n", + "1 B|D John Cleese\n", + "2 A|C Terry Gilliam\n", + "3 B|D Eric Idle\n", + "4 B|C Terry Jones\n", + "5 B|C|D Michael Palin" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_monte = pd.DataFrame({'name': monte,\n", + " 'info': ['B|C|D', 'B|D', 'A|C',\n", + " 'B|D', 'B|C', 'B|C|D']})\n", + "full_monte" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``get_dummies()`` routine lets you quickly split-out these indicator variables into a ``DataFrame``:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B C D\n", + "0 0 1 1 1\n", + "1 0 1 0 1\n", + "2 1 0 1 0\n", + "3 0 1 0 1\n", + "4 0 1 1 0\n", + "5 0 1 1 1" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_monte['info'].str.get_dummies('|')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With these operations as building blocks, you can construct an endless range of string processing procedures when cleaning your data.\n", + "\n", + "We won't dive further into these methods here, but I encourage you to read through [\"Working with Text Data\"](http://pandas.pydata.org/pandas-docs/stable/text.html) in the Pandas online documentation, or to refer to the resources listed in [Further Resources](03.13-Further-Resources.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Recipe Database\n", + "\n", + "These vectorized string operations become most useful in the process of cleaning up messy, real-world data.\n", + "Here I'll walk through an example of that, using an open recipe database compiled from various sources on the Web.\n", + "Our goal will be to parse the recipe data into ingredient lists, so we can quickly find a recipe based on some ingredients we have on hand.\n", + "\n", + "The scripts used to compile this can be found at https://github.com/fictivekin/openrecipes, and the link to the current version of the database is found there as well.\n", + "\n", + "As of Spring 2016, this database is about 30 MB, and can be downloaded and unzipped with these commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# !curl -O http://openrecipes.s3.amazonaws.com/recipeitems-latest.json.gz\n", + "# !gunzip recipeitems-latest.json.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The database is in JSON format, so we will try ``pd.read_json`` to read it:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ValueError: Trailing data\n" + ] + } + ], + "source": [ + "try:\n", + " recipes = pd.read_json('recipeitems-latest.json')\n", + "except ValueError as e:\n", + " print(\"ValueError:\", e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oops! We get a ``ValueError`` mentioning that there is \"trailing data.\"\n", + "Searching for the text of this error on the Internet, it seems that it's due to using a file in which *each line* is itself a valid JSON, but the full file is not.\n", + "Let's check if this interpretation is true:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 12)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('recipeitems-latest.json') as f:\n", + " line = f.readline()\n", + "pd.read_json(line).shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes, apparently each line is a valid JSON, so we'll need to string them together.\n", + "One way we can do this is to actually construct a string representation containing all these JSON entries, and then load the whole thing with ``pd.read_json``:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# read the entire file into a Python array\n", + "with open('recipeitems-latest.json', 'r') as f:\n", + " # Extract each line\n", + " data = (line.strip() for line in f)\n", + " # Reformat so each line is the element of a list\n", + " data_json = \"[{0}]\".format(','.join(data))\n", + "# read the result as a JSON\n", + "recipes = pd.read_json(data_json)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(173278, 17)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see there are nearly 200,000 recipes, and 17 columns.\n", + "Let's take a look at one row to see what we have:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "_id {'$oid': '5160756b96cc62079cc2db15'}\n", + "cookTime PT30M\n", + "creator NaN\n", + "dateModified NaN\n", + "datePublished 2013-03-11\n", + "description Late Saturday afternoon, after Marlboro Man ha...\n", + "image http://static.thepioneerwoman.com/cooking/file...\n", + "ingredients Biscuits\\n3 cups All-purpose Flour\\n2 Tablespo...\n", + "name Drop Biscuits and Sausage Gravy\n", + "prepTime PT10M\n", + "recipeCategory NaN\n", + "recipeInstructions NaN\n", + "recipeYield 12\n", + "source thepioneerwoman\n", + "totalTime NaN\n", + "ts {'$date': 1365276011104}\n", + "url http://thepioneerwoman.com/cooking/2013/03/dro...\n", + "Name: 0, dtype: object" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.iloc[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a lot of information there, but much of it is in a very messy form, as is typical of data scraped from the Web.\n", + "In particular, the ingredient list is in string format; we're going to have to carefully extract the information we're interested in.\n", + "Let's start by taking a closer look at the ingredients:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 173278.000000\n", + "mean 244.617926\n", + "std 146.705285\n", + "min 0.000000\n", + "25% 147.000000\n", + "50% 221.000000\n", + "75% 314.000000\n", + "max 9067.000000\n", + "Name: ingredients, dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.ingredients.str.len().describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ingredient lists average 250 characters long, with a minimum of 0 and a maximum of nearly 10,000 characters!\n", + "\n", + "Just out of curiousity, let's see which recipe has the longest ingredient list:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Carrot Pineapple Spice & Brownie Layer Cake with Whipped Cream & Cream Cheese Frosting and Marzipan Carrots'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.name[np.argmax(recipes.ingredients.str.len())]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That certainly looks like an involved recipe.\n", + "\n", + "We can do other aggregate explorations; for example, let's see how many of the recipes are for breakfast food:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3524" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.description.str.contains('[Bb]reakfast').sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or how many of the recipes list cinnamon as an ingredient:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10526" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.ingredients.str.contains('[Cc]innamon').sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could even look to see whether any recipes misspell the ingredient as \"cinamon\":" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.ingredients.str.contains('[Cc]inamon').sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the type of essential data exploration that is possible with Pandas string tools.\n", + "It is data munging like this that Python really excels at." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A simple recipe recommender\n", + "\n", + "Let's go a bit further, and start working on a simple recipe recommendation system: given a list of ingredients, find a recipe that uses all those ingredients.\n", + "While conceptually straightforward, the task is complicated by the heterogeneity of the data: there is no easy operation, for example, to extract a clean list of ingredients from each row.\n", + "So we will cheat a bit: we'll start with a list of common ingredients, and simply search to see whether they are in each recipe's ingredient list.\n", + "For simplicity, let's just stick with herbs and spices for the time being:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "spice_list = ['salt', 'pepper', 'oregano', 'sage', 'parsley',\n", + " 'rosemary', 'tarragon', 'thyme', 'paprika', 'cumin']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then build a Boolean ``DataFrame`` consisting of True and False values, indicating whether this ingredient appears in the list:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cumin oregano paprika parsley pepper rosemary sage salt tarragon thyme\n", + "0 False False False False False False True False False False\n", + "1 False False False False False False False False False False\n", + "2 True False False False True False False True False False\n", + "3 False False False False False False False False False False\n", + "4 False False False False False False False False False False" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re\n", + "spice_df = pd.DataFrame(dict((spice, recipes.ingredients.str.contains(spice, re.IGNORECASE))\n", + " for spice in spice_list))\n", + "spice_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, as an example, let's say we'd like to find a recipe that uses parsley, paprika, and tarragon.\n", + "We can compute this very quickly using the ``query()`` method of ``DataFrame``s, discussed in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb):" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selection = spice_df.query('parsley & paprika & tarragon')\n", + "len(selection)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We find only 10 recipes with this combination; let's use the index returned by this selection to discover the names of the recipes that have this combination:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2069 All cremat with a Little Gem, dandelion and wa...\n", + "74964 Lobster with Thermidor butter\n", + "93768 Burton's Southern Fried Chicken with White Gravy\n", + "113926 Mijo's Slow Cooker Shredded Beef\n", + "137686 Asparagus Soup with Poached Eggs\n", + "140530 Fried Oyster Po’boys\n", + "158475 Lamb shank tagine with herb tabbouleh\n", + "158486 Southern fried chicken in buttermilk\n", + "163175 Fried Chicken Sliders with Pickles + Slaw\n", + "165243 Bar Tartine Cauliflower Salad\n", + "Name: name, dtype: object" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipes.name[selection.index]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have narrowed down our recipe selection by a factor of almost 20,000, we are in a position to make a more informed decision about what we'd like to cook for dinner." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Going further with recipes\n", + "\n", + "Hopefully this example has given you a bit of a flavor (ba-dum!) for the types of data cleaning operations that are efficiently enabled by Pandas string methods.\n", + "Of course, building a very robust recipe recommendation system would require a *lot* more work!\n", + "Extracting full ingredient lists from each recipe would be an important piece of the task; unfortunately, the wide variety of formats used makes this a relatively time-consuming process.\n", + "This points to the truism that in data science, cleaning and munging of real-world data often comprises the majority of the work, and Pandas provides the tools that can help you do this efficiently." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.10-Working-With-Strings.md b/notebooks_v2/03.10-Working-With-Strings.md new file mode 100644 index 00000000..9c09ef73 --- /dev/null +++ b/notebooks_v2/03.10-Working-With-Strings.md @@ -0,0 +1,375 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) > + +Open in Colab + + + +# Vectorized String Operations + + +One strength of Python is its relative ease in handling and manipulating string data. +Pandas builds on this and provides a comprehensive set of *vectorized string operations* that become an essential piece of the type of munging required when working with (read: cleaning up) real-world data. +In this section, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the Internet. + + +## Introducing Pandas String Operations + +We saw in previous sections how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example: + +```python +import numpy as np +x = np.array([2, 3, 5, 7, 11, 13]) +x * 2 +``` + +This *vectorization* of operations simplifies the syntax of operating on arrays of data: we no longer have to worry about the size or shape of the array, but just about what operation we want done. +For arrays of strings, NumPy does not provide such simple access, and thus you're stuck using a more verbose loop syntax: + +```python +data = ['peter', 'Paul', 'MARY', 'gUIDO'] +[s.capitalize() for s in data] +``` + +This is perhaps sufficient to work with some data, but it will break if there are any missing values. +For example: + +```python +data = ['peter', 'Paul', None, 'MARY', 'gUIDO'] +[s.capitalize() for s in data] +``` + +Pandas includes features to address both this need for vectorized string operations and for correctly handling missing data via the ``str`` attribute of Pandas Series and Index objects containing strings. +So, for example, suppose we create a Pandas Series with this data: + +```python +import pandas as pd +names = pd.Series(data) +names +``` + +We can now call a single method that will capitalize all the entries, while skipping over any missing values: + +```python +names.str.capitalize() +``` + +Using tab completion on this ``str`` attribute will list all the vectorized string methods available to Pandas. + + +## Tables of Pandas String Methods + +If you have a good understanding of string manipulation in Python, most of Pandas string syntax is intuitive enough that it's probably sufficient to just list a table of available methods; we will start with that here, before diving deeper into a few of the subtleties. +The examples in this section use the following series of names: + +```python +monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam', + 'Eric Idle', 'Terry Jones', 'Michael Palin']) +``` + +### Methods similar to Python string methods +Nearly all Python's built-in string methods are mirrored by a Pandas vectorized string method. Here is a list of Pandas ``str`` methods that mirror Python string methods: + +| | | | | +|-------------|------------------|------------------|------------------| +|``len()`` | ``lower()`` | ``translate()`` | ``islower()`` | +|``ljust()`` | ``upper()`` | ``startswith()`` | ``isupper()`` | +|``rjust()`` | ``find()`` | ``endswith()`` | ``isnumeric()`` | +|``center()`` | ``rfind()`` | ``isalnum()`` | ``isdecimal()`` | +|``zfill()`` | ``index()`` | ``isalpha()`` | ``split()`` | +|``strip()`` | ``rindex()`` | ``isdigit()`` | ``rsplit()`` | +|``rstrip()`` | ``capitalize()`` | ``isspace()`` | ``partition()`` | +|``lstrip()`` | ``swapcase()`` | ``istitle()`` | ``rpartition()`` | + +Notice that these have various return values. Some, like ``lower()``, return a series of strings: + +```python +monte.str.lower() +``` + +But some others return numbers: + +```python +monte.str.len() +``` + +Or Boolean values: + +```python +monte.str.startswith('T') +``` + +Still others return lists or other compound values for each element: + +```python +monte.str.split() +``` + +We'll see further manipulations of this kind of series-of-lists object as we continue our discussion. + + +### Methods using regular expressions + +In addition, there are several methods that accept regular expressions to examine the content of each string element, and follow some of the API conventions of Python's built-in ``re`` module: + +| Method | Description | +|--------|-------------| +| ``match()`` | Call ``re.match()`` on each element, returning a boolean. | +| ``extract()`` | Call ``re.match()`` on each element, returning matched groups as strings.| +| ``findall()`` | Call ``re.findall()`` on each element | +| ``replace()`` | Replace occurrences of pattern with some other string| +| ``contains()`` | Call ``re.search()`` on each element, returning a boolean | +| ``count()`` | Count occurrences of pattern| +| ``split()`` | Equivalent to ``str.split()``, but accepts regexps | +| ``rsplit()`` | Equivalent to ``str.rsplit()``, but accepts regexps | + + +With these, you can do a wide range of interesting operations. +For example, we can extract the first name from each by asking for a contiguous group of characters at the beginning of each element: + +```python +monte.str.extract('([A-Za-z]+)', expand=False) +``` + +Or we can do something more complicated, like finding all names that start and end with a consonant, making use of the start-of-string (``^``) and end-of-string (``$``) regular expression characters: + +```python +monte.str.findall(r'^[^AEIOU].*[^aeiou]$') +``` + +The ability to concisely apply regular expressions across ``Series`` or ``Dataframe`` entries opens up many possibilities for analysis and cleaning of data. + + +### Miscellaneous methods +Finally, there are some miscellaneous methods that enable other convenient operations: + +| Method | Description | +|--------|-------------| +| ``get()`` | Index each element | +| ``slice()`` | Slice each element| +| ``slice_replace()`` | Replace slice in each element with passed value| +| ``cat()`` | Concatenate strings| +| ``repeat()`` | Repeat values | +| ``normalize()`` | Return Unicode form of string | +| ``pad()`` | Add whitespace to left, right, or both sides of strings| +| ``wrap()`` | Split long strings into lines with length less than a given width| +| ``join()`` | Join strings in each element of the Series with passed separator| +| ``get_dummies()`` | extract dummy variables as a dataframe | + + +#### Vectorized item access and slicing + +The ``get()`` and ``slice()`` operations, in particular, enable vectorized element access from each array. +For example, we can get a slice of the first three characters of each array using ``str.slice(0, 3)``. +Note that this behavior is also available through Python's normal indexing syntax–for example, ``df.str.slice(0, 3)`` is equivalent to ``df.str[0:3]``: + +```python +monte.str[0:3] +``` + +Indexing via ``df.str.get(i)`` and ``df.str[i]`` is likewise similar. + +These ``get()`` and ``slice()`` methods also let you access elements of arrays returned by ``split()``. +For example, to extract the last name of each entry, we can combine ``split()`` and ``get()``: + +```python +monte.str.split().str.get(-1) +``` + +#### Indicator variables + +Another method that requires a bit of extra explanation is the ``get_dummies()`` method. +This is useful when your data has a column containing some sort of coded indicator. +For example, we might have a dataset that contains information in the form of codes, such as A="born in America," B="born in the United Kingdom," C="likes cheese," D="likes spam": + +```python +full_monte = pd.DataFrame({'name': monte, + 'info': ['B|C|D', 'B|D', 'A|C', + 'B|D', 'B|C', 'B|C|D']}) +full_monte +``` + +The ``get_dummies()`` routine lets you quickly split-out these indicator variables into a ``DataFrame``: + +```python +full_monte['info'].str.get_dummies('|') +``` + +With these operations as building blocks, you can construct an endless range of string processing procedures when cleaning your data. + +We won't dive further into these methods here, but I encourage you to read through ["Working with Text Data"](http://pandas.pydata.org/pandas-docs/stable/text.html) in the Pandas online documentation, or to refer to the resources listed in [Further Resources](03.13-Further-Resources.ipynb). + + +## Example: Recipe Database + +These vectorized string operations become most useful in the process of cleaning up messy, real-world data. +Here I'll walk through an example of that, using an open recipe database compiled from various sources on the Web. +Our goal will be to parse the recipe data into ingredient lists, so we can quickly find a recipe based on some ingredients we have on hand. + +The scripts used to compile this can be found at https://github.com/fictivekin/openrecipes, and the link to the current version of the database is found there as well. + +As of Spring 2016, this database is about 30 MB, and can be downloaded and unzipped with these commands: + +```python +# !curl -O http://openrecipes.s3.amazonaws.com/recipeitems-latest.json.gz +# !gunzip recipeitems-latest.json.gz +``` + +The database is in JSON format, so we will try ``pd.read_json`` to read it: + +```python +try: + recipes = pd.read_json('recipeitems-latest.json') +except ValueError as e: + print("ValueError:", e) +``` + +Oops! We get a ``ValueError`` mentioning that there is "trailing data." +Searching for the text of this error on the Internet, it seems that it's due to using a file in which *each line* is itself a valid JSON, but the full file is not. +Let's check if this interpretation is true: + +```python +with open('recipeitems-latest.json') as f: + line = f.readline() +pd.read_json(line).shape +``` + +Yes, apparently each line is a valid JSON, so we'll need to string them together. +One way we can do this is to actually construct a string representation containing all these JSON entries, and then load the whole thing with ``pd.read_json``: + +```python +# read the entire file into a Python array +with open('recipeitems-latest.json', 'r') as f: + # Extract each line + data = (line.strip() for line in f) + # Reformat so each line is the element of a list + data_json = "[{0}]".format(','.join(data)) +# read the result as a JSON +recipes = pd.read_json(data_json) +``` + +```python +recipes.shape +``` + +We see there are nearly 200,000 recipes, and 17 columns. +Let's take a look at one row to see what we have: + +```python +recipes.iloc[0] +``` + +There is a lot of information there, but much of it is in a very messy form, as is typical of data scraped from the Web. +In particular, the ingredient list is in string format; we're going to have to carefully extract the information we're interested in. +Let's start by taking a closer look at the ingredients: + +```python +recipes.ingredients.str.len().describe() +``` + +The ingredient lists average 250 characters long, with a minimum of 0 and a maximum of nearly 10,000 characters! + +Just out of curiousity, let's see which recipe has the longest ingredient list: + +```python +recipes.name[np.argmax(recipes.ingredients.str.len())] +``` + +That certainly looks like an involved recipe. + +We can do other aggregate explorations; for example, let's see how many of the recipes are for breakfast food: + +```python +recipes.description.str.contains('[Bb]reakfast').sum() +``` + +Or how many of the recipes list cinnamon as an ingredient: + +```python +recipes.ingredients.str.contains('[Cc]innamon').sum() +``` + +We could even look to see whether any recipes misspell the ingredient as "cinamon": + +```python +recipes.ingredients.str.contains('[Cc]inamon').sum() +``` + +This is the type of essential data exploration that is possible with Pandas string tools. +It is data munging like this that Python really excels at. + + +### A simple recipe recommender + +Let's go a bit further, and start working on a simple recipe recommendation system: given a list of ingredients, find a recipe that uses all those ingredients. +While conceptually straightforward, the task is complicated by the heterogeneity of the data: there is no easy operation, for example, to extract a clean list of ingredients from each row. +So we will cheat a bit: we'll start with a list of common ingredients, and simply search to see whether they are in each recipe's ingredient list. +For simplicity, let's just stick with herbs and spices for the time being: + +```python +spice_list = ['salt', 'pepper', 'oregano', 'sage', 'parsley', + 'rosemary', 'tarragon', 'thyme', 'paprika', 'cumin'] +``` + +We can then build a Boolean ``DataFrame`` consisting of True and False values, indicating whether this ingredient appears in the list: + +```python +import re +spice_df = pd.DataFrame(dict((spice, recipes.ingredients.str.contains(spice, re.IGNORECASE)) + for spice in spice_list)) +spice_df.head() +``` + +Now, as an example, let's say we'd like to find a recipe that uses parsley, paprika, and tarragon. +We can compute this very quickly using the ``query()`` method of ``DataFrame``s, discussed in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb): + +```python +selection = spice_df.query('parsley & paprika & tarragon') +len(selection) +``` + +We find only 10 recipes with this combination; let's use the index returned by this selection to discover the names of the recipes that have this combination: + +```python +recipes.name[selection.index] +``` + +Now that we have narrowed down our recipe selection by a factor of almost 20,000, we are in a position to make a more informed decision about what we'd like to cook for dinner. + + +### Going further with recipes + +Hopefully this example has given you a bit of a flavor (ba-dum!) for the types of data cleaning operations that are efficiently enabled by Pandas string methods. +Of course, building a very robust recipe recommendation system would require a *lot* more work! +Extracting full ingredient lists from each recipe would be an important piece of the task; unfortunately, the wide variety of formats used makes this a relatively time-consuming process. +This points to the truism that in data science, cleaning and munging of real-world data often comprises the majority of the work, and Pandas provides the tools that can help you do this efficiently. + + + +< [Pivot Tables](03.09-Pivot-Tables.ipynb) | [Contents](Index.ipynb) | [Working with Time Series](03.11-Working-with-Time-Series.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.11-Working-with-Time-Series.ipynb b/notebooks_v2/03.11-Working-with-Time-Series.ipynb new file mode 100644 index 00000000..d5919a02 --- /dev/null +++ b/notebooks_v2/03.11-Working-with-Time-Series.ipynb @@ -0,0 +1,1963 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with Time Series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with dates, times, and time-indexed data.\n", + "Date and time data comes in a few flavors, which we will discuss here:\n", + "\n", + "- *Time stamps* reference particular moments in time (e.g., July 4th, 2015 at 7:00am).\n", + "- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the year 2015. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24 hour-long periods comprising days).\n", + "- *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds).\n", + "\n", + "In this section, we will introduce how to work with each of these types of date/time data in Pandas.\n", + "This short section is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\n", + "We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas.\n", + "After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dates and Times in Python\n", + "\n", + "The Python world has a number of available representations of dates, times, deltas, and timespans.\n", + "While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other packages used in Python." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Native Python dates and times: ``datetime`` and ``dateutil``\n", + "\n", + "Python's basic objects for working with dates and times reside in the built-in ``datetime`` module.\n", + "Along with the third-party ``dateutil`` module, you can use it to quickly perform a host of useful functionalities on dates and times.\n", + "For example, you can manually build a date using the ``datetime`` type:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2015, 7, 4, 0, 0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "datetime(year=2015, month=7, day=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, using the ``dateutil`` module, you can parse dates from a variety of string formats:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2015, 7, 4, 0, 0)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dateutil import parser\n", + "date = parser.parse(\"4th of July, 2015\")\n", + "date" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Once you have a ``datetime`` object, you can do things like printing the day of the week:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Saturday'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date.strftime('%A')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the final line, we've used one of the standard string format codes for printing dates (``\"%A\"``), which you can read about in the [strftime section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [datetime documentation](https://docs.python.org/3/library/datetime.html).\n", + "Documentation of other useful date utilities can be found in [dateutil's online documentation](http://labix.org/python-dateutil).\n", + "A related package to be aware of is [``pytz``](http://pytz.sourceforge.net/), which contains tools for working with the most migrane-inducing piece of time series data: time zones.\n", + "\n", + "The power of ``datetime`` and ``dateutil`` lie in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\n", + "Where they break down is when you wish to work with large arrays of dates and times:\n", + "just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python datetime objects are suboptimal compared to typed arrays of encoded dates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Typed arrays of times: NumPy's ``datetime64``\n", + "\n", + "The weaknesses of Python's datetime format inspired the NumPy team to add a set of native time series data type to NumPy.\n", + "The ``datetime64`` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly.\n", + "The ``datetime64`` requires a very specific input format:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(datetime.date(2015, 7, 4), dtype='datetime64[D]')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "date = np.array('2015-07-04', dtype=np.datetime64)\n", + "date" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have this date formatted, however, we can quickly do vectorized operations on it:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n", + " '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n", + " '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'], dtype='datetime64[D]')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date + np.arange(12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because of the uniform type in NumPy ``datetime64`` arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python's ``datetime`` objects, especially as arrays get large\n", + "(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)).\n", + "\n", + "One detail of the ``datetime64`` and ``timedelta64`` objects is that they are built on a *fundamental time unit*.\n", + "Because the ``datetime64`` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\n", + "In other words, ``datetime64`` imposes a trade-off between *time resolution* and *maximum time span*.\n", + "\n", + "For example, if you want a time resolution of one nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\n", + "NumPy will infer the desired unit from the input; for example, here is a day-based datetime:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.datetime64('2015-07-04')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.datetime64('2015-07-04')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a minute-based datetime:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.datetime64('2015-07-04T12:00')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.datetime64('2015-07-04 12:00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the time zone is automatically set to the local time on the computer executing the code.\n", + "You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.datetime64('2015-07-04T12:59:59.500000000')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.datetime64('2015-07-04 12:59:59.50', 'ns')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following table, drawn from the [NumPy datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html), lists the available format codes along with the relative and absolute timespans that they can encode:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|Code | Meaning | Time span (relative) | Time span (absolute) |\n", + "|--------|-------------|----------------------|------------------------|\n", + "| ``Y`` | Year\t | ± 9.2e18 years | [9.2e18 BC, 9.2e18 AD] |\n", + "| ``M`` | Month | ± 7.6e17 years | [7.6e17 BC, 7.6e17 AD] |\n", + "| ``W`` | Week\t | ± 1.7e17 years | [1.7e17 BC, 1.7e17 AD] |\n", + "| ``D`` | Day | ± 2.5e16 years | [2.5e16 BC, 2.5e16 AD] |\n", + "| ``h`` | Hour | ± 1.0e15 years | [1.0e15 BC, 1.0e15 AD] |\n", + "| ``m`` | Minute | ± 1.7e13 years | [1.7e13 BC, 1.7e13 AD] |\n", + "| ``s`` | Second | ± 2.9e12 years | [ 2.9e9 BC, 2.9e9 AD] |\n", + "| ``ms`` | Millisecond | ± 2.9e9 years | [ 2.9e6 BC, 2.9e6 AD] |\n", + "| ``us`` | Microsecond | ± 2.9e6 years | [290301 BC, 294241 AD] |\n", + "| ``ns`` | Nanosecond | ± 292 years | [ 1678 AD, 2262 AD] |\n", + "| ``ps`` | Picosecond | ± 106 days | [ 1969 AD, 1970 AD] |\n", + "| ``fs`` | Femtosecond | ± 2.6 hours | [ 1969 AD, 1970 AD] |\n", + "| ``as`` | Attosecond | ± 9.2 seconds | [ 1969 AD, 1970 AD] |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the types of data we see in the real world, a useful default is ``datetime64[ns]``, as it can encode a useful range of modern dates with a suitably fine precision.\n", + "\n", + "Finally, we will note that while the ``datetime64`` data type addresses some of the deficiencies of the built-in Python ``datetime`` type, it lacks many of the convenient methods and functions provided by ``datetime`` and especially ``dateutil``.\n", + "More information can be found in [NumPy's datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dates and times in pandas: best of both worlds\n", + "\n", + "Pandas builds upon all the tools just discussed to provide a ``Timestamp`` object, which combines the ease-of-use of ``datetime`` and ``dateutil`` with the efficient storage and vectorized interface of ``numpy.datetime64``.\n", + "From a group of these ``Timestamp`` objects, Pandas can construct a ``DatetimeIndex`` that can be used to index data in a ``Series`` or ``DataFrame``; we'll see many examples of this below.\n", + "\n", + "For example, we can use Pandas tools to repeat the demonstration from above.\n", + "We can parse a flexibly formatted string date, and use format codes to output the day of the week:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2015-07-04 00:00:00')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "date = pd.to_datetime(\"4th of July, 2015\")\n", + "date" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Saturday'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date.strftime('%A')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we can do NumPy-style vectorized operations directly on this same object:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n", + " '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n", + " '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n", + " dtype='datetime64[ns]', freq=None)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date + pd.to_timedelta(np.arange(12), 'D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next section, we will take a closer look at manipulating time series data with the tools provided by Pandas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas Time Series: Indexing by Time\n", + "\n", + "Where the Pandas time series tools really become useful is when you begin to *index data by timestamps*.\n", + "For example, we can construct a ``Series`` object that has time indexed data:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2014-07-04 0\n", + "2014-08-04 1\n", + "2015-07-04 2\n", + "2015-08-04 3\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',\n", + " '2015-07-04', '2015-08-04'])\n", + "data = pd.Series([0, 1, 2, 3], index=index)\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have this data in a ``Series``, we can make use of any of the ``Series`` indexing patterns we discussed in previous sections, passing values that can be coerced into dates:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2014-07-04 0\n", + "2014-08-04 1\n", + "2015-07-04 2\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['2014-07-04':'2015-07-04']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2015-07-04 2\n", + "2015-08-04 3\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['2015']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later, we will see additional examples of the convenience of dates-as-indices.\n", + "But first, a closer look at the available time series data structures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas Time Series Data Structures\n", + "\n", + "This section will introduce the fundamental Pandas data structures for working with time series data:\n", + "\n", + "- For *time stamps*, Pandas provides the ``Timestamp`` type. As mentioned before, it is essentially a replacement for Python's native ``datetime``, but is based on the more efficient ``numpy.datetime64`` data type. The associated Index structure is ``DatetimeIndex``.\n", + "- For *time Periods*, Pandas provides the ``Period`` type. This encodes a fixed-frequency interval based on ``numpy.datetime64``. The associated index structure is ``PeriodIndex``.\n", + "- For *time deltas* or *durations*, Pandas provides the ``Timedelta`` type. ``Timedelta`` is a more efficient replacement for Python's native ``datetime.timedelta`` type, and is based on ``numpy.timedelta64``. The associated index structure is ``TimedeltaIndex``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most fundamental of these date/time objects are the ``Timestamp`` and ``DatetimeIndex`` objects.\n", + "While these class objects can be invoked directly, it is more common to use the ``pd.to_datetime()`` function, which can parse a wide variety of formats.\n", + "Passing a single date to ``pd.to_datetime()`` yields a ``Timestamp``; passing a series of dates by default yields a ``DatetimeIndex``:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n", + " '2015-07-08'],\n", + " dtype='datetime64[ns]', freq=None)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.to_datetime([datetime(2015, 7, 3), '4th of July, 2015',\n", + " '2015-Jul-6', '07-07-2015', '20150708'])\n", + "dates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any ``DatetimeIndex`` can be converted to a ``PeriodIndex`` with the ``to_period()`` function with the addition of a frequency code; here we'll use ``'D'`` to indicate daily frequency:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PeriodIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n", + " '2015-07-08'],\n", + " dtype='int64', freq='D')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates.to_period('D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A ``TimedeltaIndex`` is created, for example, when a date is subtracted from another:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates - dates[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regular sequences: ``pd.date_range()``\n", + "\n", + "To make the creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: ``pd.date_range()`` for timestamps, ``pd.period_range()`` for periods, and ``pd.timedelta_range()`` for time deltas.\n", + "We've seen that Python's ``range()`` and NumPy's ``np.arange()`` turn a startpoint, endpoint, and optional stepsize into a sequence.\n", + "Similarly, ``pd.date_range()`` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates.\n", + "By default, the frequency is one day:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n", + " '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range('2015-07-03', '2015-07-10')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, the date range can be specified not with a start and endpoint, but with a startpoint and a number of periods:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n", + " '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range('2015-07-03', periods=8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The spacing can be modified by altering the ``freq`` argument, which defaults to ``D``.\n", + "For example, here we will construct a range of hourly timestamps:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\n", + " '2015-07-03 02:00:00', '2015-07-03 03:00:00',\n", + " '2015-07-03 04:00:00', '2015-07-03 05:00:00',\n", + " '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\n", + " dtype='datetime64[ns]', freq='H')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.date_range('2015-07-03', periods=8, freq='H')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create regular sequences of ``Period`` or ``Timedelta`` values, the very similar ``pd.period_range()`` and ``pd.timedelta_range()`` functions are useful.\n", + "Here are some monthly periods:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PeriodIndex(['2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12',\n", + " '2016-01', '2016-02'],\n", + " dtype='int64', freq='M')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.period_range('2015-07', periods=8, freq='M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And a sequence of durations increasing by an hour:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',\n", + " '05:00:00', '06:00:00', '07:00:00', '08:00:00', '09:00:00'],\n", + " dtype='timedelta64[ns]', freq='H')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.timedelta_range(0, periods=10, freq='H')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All of these require an understanding of Pandas frequency codes, which we'll summarize in the next section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Frequencies and Offsets\n", + "\n", + "Fundamental to these Pandas time series tools is the concept of a frequency or date offset.\n", + "Just as we saw the ``D`` (day) and ``H`` (hour) codes above, we can use such codes to specify any desired frequency spacing.\n", + "The following table summarizes the main codes available:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Code | Description | Code | Description |\n", + "|--------|---------------------|--------|----------------------|\n", + "| ``D`` | Calendar day | ``B`` | Business day |\n", + "| ``W`` | Weekly | | |\n", + "| ``M`` | Month end | ``BM`` | Business month end |\n", + "| ``Q`` | Quarter end | ``BQ`` | Business quarter end |\n", + "| ``A`` | Year end | ``BA`` | Business year end |\n", + "| ``H`` | Hours | ``BH`` | Business hours |\n", + "| ``T`` | Minutes | | |\n", + "| ``S`` | Seconds | | |\n", + "| ``L`` | Milliseonds | | |\n", + "| ``U`` | Microseconds | | |\n", + "| ``N`` | nanoseconds | | |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The monthly, quarterly, and annual frequencies are all marked at the end of the specified period.\n", + "By adding an ``S`` suffix to any of these, they instead will be marked at the beginning:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Code | Description || Code | Description |\n", + "|---------|------------------------||---------|------------------------|\n", + "| ``MS`` | Month start ||``BMS`` | Business month start |\n", + "| ``QS`` | Quarter start ||``BQS`` | Business quarter start |\n", + "| ``AS`` | Year start ||``BAS`` | Business year start |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:\n", + "\n", + "- ``Q-JAN``, ``BQ-FEB``, ``QS-MAR``, ``BQS-APR``, etc.\n", + "- ``A-JAN``, ``BA-FEB``, ``AS-MAR``, ``BAS-APR``, etc.\n", + "\n", + "In the same way, the split-point of the weekly frequency can be modified by adding a three-letter weekday code:\n", + "\n", + "- ``W-SUN``, ``W-MON``, ``W-TUE``, ``W-WED``, etc.\n", + "\n", + "On top of this, codes can be combined with numbers to specify other frequencies.\n", + "For example, for a frequency of 2 hours 30 minutes, we can combine the hour (``H``) and minute (``T``) codes as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TimedeltaIndex(['00:00:00', '02:30:00', '05:00:00', '07:30:00', '10:00:00',\n", + " '12:30:00', '15:00:00', '17:30:00', '20:00:00'],\n", + " dtype='timedelta64[ns]', freq='150T')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.timedelta_range(0, periods=9, freq=\"2H30T\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the ``pd.tseries.offsets`` module.\n", + "For example, we can create a business day offset directly as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\n", + " '2015-07-07'],\n", + " dtype='datetime64[ns]', freq='B')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas.tseries.offsets import BDay\n", + "pd.date_range('2015-07-01', periods=5, freq=BDay())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more discussion of the use of frequencies and offsets, see the [\"DateOffset\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects) of the Pandas documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resampling, Shifting, and Windowing\n", + "\n", + "The ability to use dates and times as indices to intuitively organize and access data is an important piece of the Pandas time series tools.\n", + "The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series-specific operations.\n", + "\n", + "We will take a look at a few of those here, using some stock price data as an example.\n", + "Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data.\n", + "For example, the accompanying ``pandas-datareader`` package (installable via ``conda install pandas-datareader``), knows how to import financial data from a number of available sources, including Yahoo finance, Google Finance, and others.\n", + "Here we will load Google's closing price history:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowCloseVolume
Date
2004-08-1949.9651.9847.9350.12NaN
2004-08-2050.6954.4950.2054.10NaN
2004-08-2355.3256.6854.4754.65NaN
2004-08-2455.5655.7451.7352.38NaN
2004-08-2552.4353.9551.8952.95NaN
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" + ], + "text/plain": [ + " Open High Low Close Volume\n", + "Date \n", + "2004-08-19 49.96 51.98 47.93 50.12 NaN\n", + "2004-08-20 50.69 54.49 50.20 54.10 NaN\n", + "2004-08-23 55.32 56.68 54.47 54.65 NaN\n", + "2004-08-24 55.56 55.74 51.73 52.38 NaN\n", + "2004-08-25 52.43 53.95 51.89 52.95 NaN" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas_datareader import data\n", + "\n", + "goog = data.DataReader('GOOG', start='2004', end='2016',\n", + " data_source='google')\n", + "goog.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity, we'll use just the closing price:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "goog = goog['Close']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize this using the ``plot()`` method, after the normal Matplotlib setup boilerplate (see [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn; seaborn.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eLa1T7JqZiwG5d2NAJiKKoM9/OIl7/v6tZD9xsHLHDIBcJi0c8cjP88XHDMi9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\nmzcPMpkMCxcuxIIFCyAIAoqLi6FWh7+16PMtFeL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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "goog.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resampling and converting frequencies\n", + "\n", + "One common need for time series data is resampling at a higher or lower frequency.\n", + "This can be done using the ``resample()`` method, or the much simpler ``asfreq()`` method.\n", + "The primary difference between the two is that ``resample()`` is fundamentally a *data aggregation*, while ``asfreq()`` is fundamentally a *data selection*.\n", + "\n", + "Taking a look at the Google closing price, let's compare what the two return when we down-sample the data.\n", + "Here we will resample the data at the end of business year:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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I/tXpWsrUde30BQghxCjkraqi5l9/QfXPfhoZh021mZg+KXVQ97EnGQf92vtr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\nSDQRrtcwNjWS2oYECk85aPBUMSs9zzfVLYQQQgwHIReQna6eA/KFk5I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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "goog.plot(alpha=0.5, style='-')\n", + "goog.resample('BA').mean().plot(style=':')\n", + "goog.asfreq('BA').plot(style='--');\n", + "plt.legend(['input', 'resample', 'asfreq'],\n", + " loc='upper left');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the difference: at each point, ``resample`` reports the *average of the previous year*, while ``asfreq`` reports the *value at the end of the year*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For up-sampling, ``resample()`` and ``asfreq()`` are largely equivalent, though resample has many more options available.\n", + "In this case, the default for both methods is to leave the up-sampled points empty, that is, filled with NA values.\n", + "Just as with the ``pd.fillna()`` function discussed previously, ``asfreq()`` accepts a ``method`` argument to specify how values are imputed.\n", + "Here, we will resample the business day data at a daily frequency (i.e., including weekends):" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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51bR+3HpVH6rrjLzw/kEOnSxXJIuDC7UelQqGSqEWQgjh5FQqFTOSYnjg5sHY\nbDb+b91hNh3Id3gOhxXqmnojJwur6RMVgL+3h6NuK4QQQlyWkf3DeOT2Efh5e7Bm0wnWbMrGanXc\n9C2HFer0k+XYbLLIiRBCCNcTF+HP43eMJDLUh00HCnjt0yM0GS0OubfDCrU8nxZCCOHKQgO9ePRX\nI0iIDeLQyXJeWHOQs3VNnX5fhxTqRqOZzJxKIkK8CQ+WTTiEEEK4Jm+tht/PG8rEIRHkltTy3OoD\nFJTVdeo9HVKoD2XrMZqt0u0thBDC5bm7qbnr2gHMmdKbypomVr6XytEzFZ12P4cU6pXv7APAQ6PI\nHiBCCCGEXalUKmaOi+W+GxMxW2y8+vFhfjxU2Cn3ckjlPD827vPtZ9ibWeqIWwohhBCdLmlgOH+4\nbRjeWnf+891x1m49idXOG3qobA7YIuSGZV80fx0d5sszyUmdfUshhBDCYUqrDLy69jCllQZ6R/jR\nZLJSXGngi5dmXfa13dtz0uzZs/H19QUgOjqa++67j+XLl6NWq+nbty8pKSntvmFxRX3HkgohhBBO\nKjzIm8fuGMnK91I5XWzf5UbbLNRGoxGA1atXNx+7//77eeihhxg1ahQpKSls2rSJqVOntuuGESE+\nHYwqhBBCOC9fLw1uKvtv39zmM+qsrCwMBgPJycksWrSI9PR0MjMzGTVqFACTJ09m9+7d7b7hzHG6\njqcVQgghnFhxhcHu12yzRa3VaklOTmbevHnk5OSwePFi/vexto+PD7W1F2/mu6lVRIT4MHOcjjEJ\n4ZefWgghhHBCkaHeFOjt+4i3zUIdGxuLTqdr/jowMJDMzMzm1+vr6/H397/oNT63w8N0IYQQwtnd\nNmMAL72Xatdrtlmo161bR3Z2NikpKZSWllJXV8eECRPYt28fSUlJbNu2jbFjx9o1lBBCCOGKJg+P\nZvLwaLtes83pWSaTiRUrVlBUVIRareYPf/gDgYGBPP7445hMJuLj43nuuedQdcIDdCGEEKK7c8g8\naiGEEEJ0jKzpKYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQ\nQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NC\nLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE5NCLYQQQjgxKdRCCCGEE3Nvz0mzZ8/G19cXgOjo\naO644w5iRh/pAAAgAElEQVSWLFlCbGwsALfddhvXXnttp4UUQgghuiuVzWazXewEo9HI/Pnz+fTT\nT5uPrV27lvr6ehYtWtTZ+YQQQohurc0WdVZWFgaDgeTkZCwWC0uXLiUjI4OcnBw2bdqETqfjscce\nw9vb2xF5hRBCiG6lzRZ1dnY26enpzJs3j5ycHBYvXsy9995LYmIiCQkJ/OMf/6C6uppHHnnEUZmF\nEEKIbqPNFnVsbCw6na7568DAQCZPnkx4eDgA06ZN47nnnrvoNcxmC1VVBjvEFUIIIVxDWJifXa7T\n5qjvdevW8cILLwBQWlpKXV0dDzzwAIcPHwZg9+7dJCYmXvQa7u5udogqhBBCdD9tdn2bTCZWrFhB\nUVERarWahx9+GE9PT5555hk0Gg1hYWE888wz+Pj4XPRGen2tXYMLIYQQzsxeLeo2C7W9SKEWQgjR\nnTis61sIIYQQypFCLYQQQjixdq1MJuxnb2Yp63fnUFRuIDLUm5njYhmTEN5tcwB8mPoju/TbMWtq\ncTf5MT5sEvNHTlEkixBCOBt5Ru1AezNLeePLjBbHl8xKdGiRdJYccK5Ib69e3+L4pICZUqyFEC7N\nXs+opUXtQOt357R6/L2Nx8nMqXRYjtRsvVPkADhg2wZeLY/vKtvBfKRQCyGEFGoHqaptolBf3+pr\n9Y1mth8udnAi5XOofavwGFiLqpXXzJoah+UQQrRPZzwy+/bbr8nLy2XJkl9f8s+mpaXy+efrePrp\nlRc8Z9u2rbz++t+YN+9W0tJSee65F3nwwSX84Q+PsmnTBkJCQrnxxtmX80fodFKoO1leaS0b9uWz\n71gpF3rG0DPYm9/NG+KwTH9de5iSypYrxTkih9VmJevsMXaX7abQUHDB89xN/p2aQwhxaX75yKxA\nX9/8vVLjWwBUqtY+6v/Xzp3b+O1vH2L8+InMmXNru37G2Uih7gRWm42jpyvYsC+fY7lVAESEeNM3\nOoBt6S1brDdOjCM8yHGbmtw4Ma7VZ9SOyHFIf5RPctYCMDh0IDQEcKR+T4vzxveY2Kk5hBA/9/Hm\nk+zPKrvg62frmlo9/tbXmXyy9VSrr40e0INbrurT5r2PHEnnd797AIOhnrvvXkxTUxOffroWi8WC\nSqVi5cqX8PcP4JVXXiQzMwOLxczddy9pXmirqamRxx77IzNmXMe0adc0X3fHjm3s2bOT48ez8PcP\n4LHHHuaLLzbgoKFZdiOF2o6MJgu7M0rYuD+f4opzLdaBuiBmJPViUO8Q1CoVA3XBrN+dS3FFPREh\nPswcp3P4p9Hz91Mix+CQgUyLuYJxEaMI9+kBwIepIewq24FZU4Ot0ZcI8xDmXyXPp4VwJhZr68Xt\nQscvhbe3Ny+++CpVVVXce+8iZs26mZde+iuenp689NJK9u7djaenlurqav75z/9QV1fHRx+9z4gR\nozAYDPzxjw9xyy23MWHCpJ9dd+LEyWzbtoVp02YwaNBgaPVBm/OTQm0HNfVGNh8sYEtaIbUGE25q\nFeMH9WT66F7EhP981N+YhHBFu4kclSOvpoAe3qFo3bU/O+6mduOmPtf97Nj8kVOYzxRMZiuPvrmH\ngoZ6cvR6YsPCOi2fEOLnbrmqz0Vbv0++vZeCVsbZRIf58kxy0mXde/DgYQAEBQXh6+uDm5sbzz//\nFFqtlvz8XAYNGkJpac5PxRZ8fX1JTl5CWloqhw4dJD6+DyaTEYB16z5m69YfUKlUPPnkswC4WAO6\nBSnUl6GwvJ6N+/LYnVGK2WLFR+vOzHE6rhoRTZCfp9LxHM5qs3K0/Bib87dz4uxp5vadxZW92t+F\nrXFXM3V8EF+UfsNbaad4bvp9nZhWCHEpZo6LbfWR2cxxusu+9rFj565bUVFOXV09n3zyIevWfY3N\nZmPp0nODzGJj49iyZRMAdXV1PPnkCu64YxHjx0/kd797mAceSGbw4KHMmXMLc+bccoE7uWbFlkJ9\niWw2G5k5VWzYn8fR0+emMvUI9GLa6F5MHByBp0f32ymsyWJkb/EBtuTvoKyhHICBwf3o5Rd1yde6\nekg/vtngTaXHafbnnGB0bF97xxVCdEBnPjIzGpv43e/up6GhgRUrnuCLL9Zx772LcHd3w88vgPJy\nPddeez0HDuzjgQfuwWq1ctddi5t/PigoiOTkJaxc+Qx//vPfLnKnc13f5weTucqgMlnwpJ1MZit7\nM0vZuD+vufunX3QA05NiGNYnFLXaNf7BO8OJqlO8mvYG7io3RvccwVW9JhHp27PD1/s28wBfl3yM\nl7EnL1/zkB2TCiGE48juWQ5S12BiS1ohm1MLqK43olapGDUgjBlJMcRFyBQiONfL8GPBLob3GEKA\np33+x1z23Z9p9Cjlhp7zuSZhhF2uKYQQjiSFupOVVBr4fn8+O48UYzRb8fJ0Y/LQSKaO7EVIgLbt\nC3QxNpuNzMpson0jCPDs/A8oe89ks/rMW7g3BfKXGctxU8v+MUII1yJLiHYCm81Gdv5ZNuzLJ/1k\nOTYgxF/LtNG9mDQkAi/P7vfXZbKY2Fd6kM35OyipL2W67kpujL+20+87Jq4fG7KGk3dKw/6sMsYm\ndLwrXQghXFn3qzytMFusHMgqY8P+fHJLzrX8e0f6MyMphhH9Qrtla67OVM+PBbvYVrCLOlM9apWa\n0eEjGNFjqMMy3D/uJh49sodPfzzNyH490Lh3v38HIYTo1oXa0Gjix/QiNh0ooKq2CZUKRvYPY8bo\nGPpEBygdT1G1xjq+OfM9Xu5eTIu5git6TSDQ07F/J2GBXlw5IopNBwrYmlbItNG9HHp/IYRwBt3m\nGfX/7nnsZvQjtGkQJaeDaDJa8NS4MWlIBFNH96JHYCtbOdnRgdJDbMjZTImhjJ7ePZgRexWjwod1\n6j07miNdf5T+QX3Ruis3J7zWYGT5G7txU6t5Yck4vLXd+rOlEMKFyGCyS3ChPY8pTGRM73iGxIeg\n9fhvAQjSBhLu3XJVrIqGKvQ/zRP+X+09P7vqFBtyN7c4767EBYwKH3bZ12/v+W3lcDZf78rh022n\nmTlOx5wp8UrHEaLbsndDw2Kx8PvfP4DZbOall/6Kr6+vHdNe3I03zuCLLza0OJ6S8ihFRYVcf/2N\nqNVqRo8eQ0rKo7zxxjvMmzeLNWvWodFo2nUPhw4mmz17dvNfYHR0NCtXnttS7KuvvuL999/nww8/\ntEuYzrJLvx08Wh5XhZ9ib1MGezN/fvzKXhOZ23dWi/PT9UdYd/LrFscv9fxf2pi7hVHhw+x2/Y7m\nOZ/D2Uwb1YtN6afYVLSBURXz0IXI0qJCONqB0kO8k7Gm+fui+pLm7zv6vqHX62loaOCtt1bbJeOl\naX3ti9TU/Xz99ffN35eUFP/PwijKrJfRZqE2Gs+tn7p69c//IjMzM1m3bl3npLIzs6b1PY+tbk3c\nEDejxfFY/9afhcYFxHL9ZZy//sxGbK0sYVdcX2qX67f3/LZyOBtPDzeGDjdzwJDDOwe/4Klp9ygd\nSYgu6Yldq1o9/uz4FWzIadkLB7A68yO+OPVti/Pb489/XkVBQR4vvbQSvV6PwVCPxWJh8eL7GTFi\nFAsX3kpMjA6VSs2JE8dZs2YdlZWVzJkzk6+++h4vLy+WLLmLt99+lxdffJ6ysjIqKsqZOHEy99xz\nHytXPk119Vlqamr405/+wuuv/42cnDNERkZhMplayfMnDIZ6Vqx4mMmTryA3N4ebbprzP2coswRp\nm4U6KysLg8FAcnIyFouFpUuXotPpePXVV3nsscd44oknHJHzsrib/LB41LQ8bvTn2rir232duIAY\n4gJiOnz+wbJ0iupLWpwX4RNul+u39/y2cjij20ddxcFN+yjTZHO4IIch0bFKRxKiWykxtL4FpsVm\n6fA1ly1bTkrKo/j4+BAX15u5c+dTXq7n/vvvYe3aL2hoaGDRosX06dOXF154lqNHD1NQkE/v3vGk\npu5Dq/VizJhxlJaWkpg4mEceuRGj0cjs2ddxzz3n9goYOTKJW265ja1bf8BkMvKPf/yL0tIStm5t\n+cFj2bJH2LZtC6tWvcy3337tNEuMtlmotVotycnJzJs3j5ycHJKTk+nbty/Lly/Hw8Oj3ft62quv\nviMmRE5kW/k3LY5frbvSobnmDbmOv+7+V4vjcwdf2y1zXKrr46/jy/yP+CDzK64e/qjScbqFt37c\nwOa8LZg0NWhM/lwVcyX3TGnZiyO6hn/cuPKCr0X7R5BXXdjiuC4gipeuebxD9zMaa9Bo3CguLuDW\nW+cSFuZHWJgfgYH+qNVG1GoVI0Yk4unpyaxZM0lN3U9hYSF/+MPDbNq0CbVazbx584iNjWDt2mxe\nfPEZfHx8MJvNhIX5odVqGDx4AGFhflRWljJ69Mjme0RGRhAW5sd9992HwWCgX79+PP7446jVKsLC\n/PDz0+Lt7UFwsA8ajRthYX6o1SpCQ33x8GjlWWonarNQx8bGotPpmr8uKirCzc2Np556iqamJk6d\nOsWqVatYseLiXR1KDiZTWX6af2vywOZmwt3kz/geE7kxYbxDc/XzGsBdiQvYmLuF4vpSInzCma67\nkn5eA7pljks1LX443538gRrPfD7ZtYspfQcrHalLax6E6XHuyZzZo5qNJZ/T8J2J+SNlv/Du5uro\nKbxTvabF8auip3T4faOysh6TyUJERDRbtmwnJCQKvb6MqqqzmExuWK02Kirq0WiM9O07mP/7v9fQ\nar1ISBjByy//BQ8PD37zGx3vvvsBGo0XDz74BwoK8vn444/R62tpbDRRW9uEXl9LWFgkP/zwPddc\ncxPl5XqKi4vR62t59tmXmvPo9bVYrVb0+lpqaxsxGIzNGc+9ZqO8vM75BpOtW7eO7OxsUlJSKC0t\nJS4ujvXr16NSqSgsLGTZsmVtFmmlpZdlgAbu7LeQpLh+imYZFT7MKQZsOUuOS6FWq7m5z0w+zPsP\n3x1NZ3KfQU7TNdUVXWgQ5q6yHcxHCnV3c/794pcf8C/3fUSlUnHHHXezcuXTbN26maamJh555DHc\n3Nz438FbGo2GHj16EhERCYBOF0twcDBwrnv76acf5+jRw2g0Gnr10lFe/vMZMZMmXcH+/XtZsuQu\nwsN7EhQUfKFEF0t7GX/SjmtzepbJZGLFihUUFRWhVqt5+OGHGTbs3D/M+ULdnlHfSrXUmkwmHtry\nFCqbO3+bloK6G64y1tX8+bNdZBxv5DezBzOin4wA7ywP/PBHWvscZLOqeH3qnxwfSAgX47AWtUaj\n4eWXX271taioKKefmrX15BFwNxFu7StFuotYMHkoT2TvY92PpxjaJ6RbLvHa2T5K3X7B19xMzjuO\nQYiuqMu/wxUUWDEV9mZ89Eilowg7iQjxYdLQCIorDGw/XKx0nC7FZLby7objbNxVDha3Vs9pzI/j\n480nMVusDk4nRPfUpQu1zWYj60QTmvIEpvRNVDqOsKMbJ8bhoVHzxfYzNBk7Pj1E/Fd5dQMvvJ/K\nlrRCorwjeXTEI0wKmIlbUwA2qwq3pgCGaqYRYuvNd/vyeG71AYor6pWOLUSX16WXEM0rreWpd/Yz\nNiGce2dJoe5qPt12mq935XD9pEhmTxigdByXduR0BW9+mUF9o5nxg3pyx4z+eGou0KI2mlmz6QQ7\nDhfj4VdL0kgti5KmyaMlIX7BXs+ou/Rv1sFsPQDD+oYqnER0hmvHxODdO5tN9aspqq5SOo5LMlss\nvPXjVl79OJ0mk4WF1/QneebACxZpAK2HO3dfN5D7bkzELSaTVMMPPLrx75TWVDswuRDdR5cu1IdO\nlOOmVjG4d4jSUUQn8PJ0Z1BUNCp3M//a/6XScVxOSXUVy7//G2mWb/CPrGTFr0ZyxbCodk95SxoY\nzsPj7sazKYxaj3ye2f1nNhw72Mmpheh+umyhLjtbT15ZHQN1QXh5ytaIXdWipOmojN4Ukcnx0par\nJonW7TqVxXN7XqXBoxhvYyQrbppGXIT/JV8nLjScF6c/RD/3Mdjcmvii6ENe3PyRDDQTwo66bKH+\nLHMLnoN2oIszKx1FdCKtxoOJYVeiUtv4z6EvlI7j9KxWK//c/Q3vnXkHq3sDfdySWDX9QXr4B3T4\nmu5ubvxu8hxuj1uE2uTDiVMmnn83VQaaCWEnXbZQZ1cfR+1dx6h4ndJRRCebO3wi7k1BVGty2HPm\nuNJxnFaT0cIb6w9zsGovKqs7syJuZemUubirL/w8+lJMiB/Iyil/ZGzkCHJLann63/vZll7U7v0A\nhBCt65KFuryuhgZNGe7GINm7uBtwV7sxM3YG5tIYtu6tlMLQiuKKep5bfYD9GZX0ODuJZcN/yzUJ\n9l9bwN/Li+SZCecGmqnV/PvbLF7/7Ch1DS23FBRCtE+XfHi7MSsVldpGnHdfpaMIB5k+cARHD6s5\neqaSjDOVDJIBhM0OZJXxr2+O0Wi0cPWIaG69ug/ubp37GT1pYDjxkQH886sMUrP1nKjPYOboAUwb\n4FrrywvhDLpki/pIRSYAU+JGKJxEONLcK+JRAWu3nsIqrWqaTCbW/JDF658fxWqzce+sBG6f3q/T\ni/R5IQFa/rhgBNdPisLYM53PCtfwwub3aTQZHXJ/IbqKLleojSYL1cZqVEZvhkbFKh1HOFBMuB9j\nE3uSX1bHnowSpeMoKrdCz/JNr/Bj2SZ6BnvzxMJRjE3o6fAcarWK2RP6s6D3HahNPuSTziOb/kxG\nUZ7DswjhqrpcoT6ef5bGjLGM0cyRlZK6oZsnx+HupuKzbacxmbvn0qKbsg7xYupfMXqWExys4tGF\nw4kK81U008T4BJ6d9DDB5njMnlX8PeP/8cH+7TKeQIh26HKVLO3EuT1Ik/pGK5xEKCE0wIurR0ZT\nZS3hvX0/Kh3HoaxWK/+3/TM+LVyDTW1isOcknp92P75aT6WjARDk48Oz05cwOXAmKouGTTvP8vrn\nMtBMiLZ0qcFkVpuNQyf0+Hpp6BPd8XmhwrVNTYpgm/nfHKiDWXWjCPG99IU8XI2h0cSLmz5B752G\nyqzllt63MKXvIKVjterWEVOYUjWCf5dnk3pcz+miGhZfn8AAXZDS0YRwSl2qRZ1TXMvZOiND42WP\n4u4sxNeXAV6jwN3EW/u+VjpOp8srreWZfx8g71gwvg1xPDbm905bpM/rGeTHHxeM4OZJcVTXGXnp\ngzQ+2XpKVjQTohVdqpqlnTi/CYfMne7u7hp9LZi05FoPc6a8VOk4nWbH4WKefzeVsrMNzEzqw6pr\n7yMyMFjpWO2iVqu4YUIcK341gtBALd/syeHRL//DseJ8paMJ4VS6VKHeU7oHjV8dg+Jc441KdB5f\nrZakoEmo1FbeOdj1NuwwmS38+9tj/OubY7i7qfntnCHMmRKPWt2+DTWcSXxUAE/dlcTgISrqA47x\nf0df5z97v8dqlda1ENCFCnVWSQGG0HQC4s/g6WGfJRGFa7t95FW4Gf0pt+VyuqRC6Th2c7y0iOXf\nvMG2wwXEhPuSctdol9/K1cvTnaXXXcWkgJmoULGv/nse+/7/oa+rUTqaEIpT2doxP2L27Nn4+p6b\n3hEdHU1ycjJPPPEEADqdjueff77NqVB6fa0d4l7Y6zu+IMO4k9E+U1k0Znqn3ku4ji3HjvPuV7kM\njQvnd/OGKh3nsn1xZA8bi78CdxNxxkn89urr8LjI3tGu6GRZMX9PfRejZzkqk5bb4xcyrk8fpWMJ\nccnCwvzscp02R30bjedWEVq9enXzsV//+tcsW7aMkSNHsmLFCjZv3szUqVPtEqijTtZmY/OAqf1H\nKZpDOJcrBvRj78F60k9VcDyviv4xrjmy2Gyx8JdtH5NrS8OmVjPGZxp3XjVN6Vidok+PCP40bSl/\n3/k52fXHeWvdGTYPzKRUcxizRy3uJj/Gh01i/sgpDs92oPQQG3I2U2Ioo6d3D2bEXsWocGWWRXWm\nLKJztVmos7KyMBgMJCcnY7FYWLp0Ka+99hoqlQqj0Yher8fPzz6fGjqqpLqKRg89nsYQol1kII1w\nDJVKxbwr43l+dSprt57isTtGolK51nPc8po6Vu18g0bPUlRGb+7sv4CkuH5Kx+pUHu4alk6Zx/GC\nSl6r2EiR70EAVIDFo4bt1etp3F/P9YPG/OznAj0DcFe3fFurajyLxdZyAZxLOf9IeSafnPiq+fui\n+hLeyVgDQHxA7GVf/1LOv1gWKdZdT5uFWqvVkpyczLx588jJyWHx4sVs2LCB4uJi7rrrLvz8/Bgw\nYIAjsl7QxuOpqFTQ27drv3mJjomPDGBU/zAOHNeTelzPqAE9lI50UR+m/sgu/XbMmlrcjH5YS+Kx\n+IG/KopHJt7VLeaFn9c/OhhV+MlWX9tfu5X9u7f+7NjjY5YR4RPe4tzXDr1FiaGsxfFLPb81G3O3\nYLFa7HL9y82zMXeLFOouqM1CHRsbi06na/46MDAQvV5PZGQkGzZsYO3ataxatYoXXnjhotexV199\na6r1fpjK+zJn3uROvY9wXYtvHsLBFzfz2Y7TXDU2Bk+NRulIrXrrxw1sr14PHudaj1bPGtCl0cc8\nmefn34K7W9d6Ht0eZo9aWusDsdngyt7jfnYsOjyUYK+W7wHjdCOoaqxucfxSzt96Zner+UrqS5k1\nYPplX/9Szr9YFnkP7HraLNTr1q0jOzublJQUSktLqaur48knn+TRRx9Fp9Ph4+PTrjW1O2swmdFk\nISOrgVC/wUR6h3T6oDXhmjTAqKFa0k0b+cu3ldwz7jqlI7Xqh9zN0MqKn3mWdKoqr3d8ICfgbvLD\n4tFy9Le7MYB5cTf/7JilDvR1Ld8DpkZc1eq1L+X87LIzFNW33Oylp0+4Xa5/KedfLIu8BzoPe31o\narPCzp07l9raWhYsWMCyZctYtWoV999/P8uXL+fOO+/kyy+/5KGHHrJLmI7IzKnCaLIyvJ9rT08R\nne/6Mf1ReTaQVrOb6oZ6peP8TE1DA//Y+TXmVgoSgFnTfacpjQ+b1OrxcT0mODTHjNjWi+t03ZUO\nzQHOlUV0vjZb1BqNhpdffrnF8Q8++KBTAl2q86uRDZfVyEQbogODidcM47Q1lbf3reehKbcoHYni\n6irWpG3ktPEwuF94cwp3U/d5Lv1L80dOgVTYVbYDs6YGVZMvxsLe9Bzl2Clb55/9bszdQnF9KRE+\n4UzXXanIM+ELZRkeNtjhWUTna9c8anvojO4Yq9XG0td2oFKp+MtvJqB2sdG8wvGq6ut5fOcqbCoL\nj47+g2KzBPRnG/ho/y4yVd+jUlvBrKG3xxCCtAGkGja3OH9SwExFpiM5o+q6Jh795x7UKhUr7x2L\nn7eH0pEU12QxsibrE0xWM/cOXqh0HPETh3V9O7PjBRXUGowM6xMiRVq0S5CPD8P8xqNys/CvA184\n/P65JbX844ujLH9jNwcPmVCbfBiincyqSY+z7IpbuXvsNUwKmIlbUwA2qwq3pgAp0r8Q4OvJjRN7\nU99o5tNtp5WO4xQ81BqqGs+Srj9KZsVxpeMIO3PpFvWqze+R13iCW+IWcOXA/na/vuiamkwm/rDh\nVZpKInhm9mzCg7079X5Wq5VjuVV8tzePjJwqAKLDfLlubAwj+4ehce9+I7kvl9li5el39lNUXs/j\nd44iLqL7Pho4r6C2iBf2/5Uw7xAeS3qo1fnYwrGkRQ0UGU+j0hhJ6q1TOopwIZ4aDXf0uRNTeSTr\nOrFFZrZY+PjgNpZu+BOvfPc9GTlVDIgJZOktQ3n67tGMTewpRbqD3N3ULJjWDxvw/vfZWB3T3nBq\n0X6RTI4eR5mhnM3525WOI+zIZT9yHSnMxepRh78xBh9PrdJxhIsZ1T+MuAh/DmSVcbqoht6R9muR\n1TU28mHaZg5V78PmYcDmAdG6Xtw5XFp+9jRQF0TSwB7sO1bGzsPFTBoaqXQkxV0fN53U0nS+zfmB\n0eHDCdIGKh1J2IHLtqi3nE4FIDFkoMJJhCtSqVTccmU8AJ9sPYk9ngDVNZh4f/t+Htn2HGkNW7G6\nN9LD0p/fJDzIU9f9Sop0J7jlyj54atz45MdT1DdeeNR8d+Gt8ebmPjOZGDkGrbs0YLoKl21Rn647\ngc1DxXTZhEN0UP+YIIbEh3D4VAVHTlcwJL5jc/ErqhvZuD+fbelFNJmNeA32QKdJYMGI6UQHy/z+\nzhTsr+WGCbF8svUUn28/w+3TZBnhsRHyntjVuGShrqipp8lkxoswwv0DlI4jXNjcK+I5clrPewd+\nYGXs3EtaojO/rJbv9uaz71gpFquNID9PbhwVx6ShU/DRypQhR5k2qhfbDxez+WABk4dG0quHr9KR\nhLArlyzUR0+fpSlzHLOujFM6inBx0WG+xAwrpEyTwZrUEBYmXXy7VqvVytYTR/j2zBbOFgRjKY8m\nMtSHa5JiGJsYjrubyz5NclkadzW3T+3LXz5O572Nx1l++wiX2yFNiItxyUKddqIcgJH9eyqcRHQF\nC0dew0uHjrG3ahuzGyfiq235bM9stfDF4T1sL96BybMCPCCwpwe3TxnCEJnHr7hBvUMY3jeUtBPl\n7MksZVyivDeIrsPlCnWj0UxmThXRYT70CPRSOo7oAuJCw4lRDyZfnc6/93/Lbyb9d6MHk9nKD4dP\nsL7sI6wedeAJvsZorou/iil9BymYWvzSbVf35eiZSj7efJJhfULx8nS5t7dOcbzyJCerzzAzbprS\nUUQHudz/yRlnKjFbrAyTtb2FHSUn3UDK7qNk2nbzwA97cDf6EWUdSsnpIKrrm/AcpCJM3Ze5CdMY\nEh2rdFzRitBAL2aO1fH5jjN8tTOHW65y7Frgzshqs/LZya/JrytiYHBfegfEKh1JdIDLPVA7mH2u\n23t4XxlNK+znh+NpqNwsqFSgUtmweNaQ57WdBq9crknS8ezkZTwzfbEUaSd3zZgYQgO0fH8gn6Jy\n59ohTQlqlZp5/W4C4OPjn2O1WRVOJDrCpQq10WziUN0OAsIMxPaUzdGF/ezSt76Sk0fUuZZZqH/n\nLr85Q0QAAB/hSURBVDMq7MND48ZtU/tisdp4//tsu8yPd3XxgbGM6TmS/LoidhbtVTqO6ACXKtTb\nTmZA+EmCYspkVKewK7Om9bXou/M+0K5qWJ9QBvcO4VhuFanH9UrHcQo3xl+H1k3Ll6e+o84oPQ2u\nxqUK9d7CwwCMipQ9V4V9uZta76HpzvtAuyqVSsWCqX1xd1Px4eYTNBktSkdSXICnHzN7T0Ojdkff\nUK50HHGJXKZQW61Wik2nweLOVf2GKh1HdDHjwya1frzHRAcnEfYQHuzNjKQYKmuaWL8nR+k4TmFK\n1HieHPsH4gJkEyNX4zKF+lDBGWweBgKs0Wg1suqTsK/5I6fIPtBdzPXjYgny8+S7vXmUVhmUjqM4\nN7WbrP/tolxmetaPZw4CMDg0QeEkoquaP3IK85HC3FV4ergx/+q+/L/Pj/LBphP8bu4QGdsiXJLL\ntKir8npgzhvI9P4jlI4ihHARo/qHMVAXxOFTFaSfrFA6jhAd0q4W9ezZs/H1PbfQfXR0NAsXLuTZ\nZ5/Fzc0NDw8PXnzxRYKDgzstZGVNIwWFFhJihxHiK4N7hBDto1KpWDCtH0/9ax9rNmWTGBeExr39\nG690ZWarmZNnzzAguK/SUUQb2izURqMRgNWrVzcfu+OOO3jyySfp378/H330EW+++SbLly/vtJCH\nTp5f5ERWIxNCXJqoUB+mjopmw758vt2bx6wJspkPwFtH3+No+TFWJP2eKN8IpeOIi2iz6zsrKwuD\nwUBycjKLFi0iPT2dV155hf79+wNgNpvx9PTs1JDnN+GQ1ciEEB0xa0IcAT4erN+dS/nZBqXjOIXJ\nUeOwYeOj45/JwjBOrs1CrdVqSU5O5u233+app57i4Ycfbu7mPnjwIGvWrGHRokWdFtDQaCYrtwpd\nuB/B/jJiUQhx6bw83bnlyj6YzFY+3HxS6ThOISGkP0NDEzlVncP/b+/e46Is8/+Pv2YYhqOIKHEU\nSMFTphhmpqnoauWmlZap5WnDNt00T48Kw2JNxc392vrNdLN26/v1sAuVZlZbqZlSah4wzSREBE+g\nyEHOIMPM9f3Dn/PLRCFl7nvUz/Px6BHMOPf1Bob7w31d131de/J/0DuOuIoGu74jIiIIDw+3f+zr\n60tBQQFpaWmsWLGCd955hxYtWjTYkL//tS35uWlvFlabjd7RIdd8DCGEGBrrzfZDZ9iXWcDJomru\n6nCb3pF090zP0cz4Yi6fZP+H/h164OkqOxI6owYL9dq1a8nMzCQxMZH8/HwqKyvZtWsXKSkprFq1\nCh+fxk3uKiiof4nGhnyQsRb36NME+Xe85mMIIQTAE7FtmXusmOVrDzAvrgcmlxvmxheHMGDm/rBY\nPs/ZxBc/pdI3tJfekW4qTXVxaVANDE5YLBZmz55NXl4eRqORWbNmMWnSJIKDg/H29sZgMNCjRw+m\nTJly1YaupcjWWGqZtfXPGGyuvDkoEaPx1v6lEkJcvzUbM/l63ylGxLZlcE9ZpavWaiG9+DBdW90h\n95k3Mc0KdVO5lkL9ZXoan55JIch2B3MGjndAKiHEraayxsLsFd9jqbOR9MeetGjm2Mmw4tbVVIXa\nqS9Rd+dd2ITjnpAuOicRQtwsvNxdeTy2LectVlK2HNE7jhANctpCbbPZyLcegzpX+kV11juOEOIm\ncl+XIG4P8mH3z2fJOH5O7zhCXJXTFurDeWexVrvTQoVhNrnqHUcIcRMxGgyMub8dBmDN5kzqrDa9\nIzmVWmut3hHELzhtoc7IrqI24x4eCXtU7yhCiJvQ7UE+9OkaTG5BJd/sy9U7jtPYnruLOduTOFsl\n+1Y7C6ct1D8cKcTkYqBzm5Z6RxFC3KQe69cGL3cT67/LprRSriIBPFw9qKyrYu2RDXpHEf+PUxbq\ngpJqThVU0DHcDw+3G2YnTiHEDaaZp5lhfdtQfd7KR1tlxTKAbv530q5FJD8VZXCwMF3vOAInLdT7\nL67t3U7W9hZCOFZsdAhht3mz/eAZsnJL9Y6jO4PBwBPtHsFoMPJh5gYsVovekW55TlmofzhSAEB0\npBRqIYRjGY0Gnrq/HQCrNx7GZpMNKoK8Augfeh9FNcVsOrFV7zi3PKcr1GfLSsk27iA0woKvtyxE\nIIRwvKhQX3p1DuREfgXbDuTpHccpDL59IDG3deWu22QdC705XaHelJmGy20naBlcoXcUIcQtZERs\nW9zNLqzbdpSKaunu9TC583Tnpwj0CtA7yi3P6Qr1waILkxf6tYnROYkQ4lbS3NuNR++7ncqaOtZt\nO6p3HCHsnKpQV9XWUGbMxVDrRZfgML3jCCFuMQNiQglp5cW2/XnknC7TO44QgJMV6i2ZBzC4WAkx\nt5WdsoQQmjO5GHlyUDsUsGZTJjZt9iwS4qqcqhr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, sharex=True)\n", + "data = goog.iloc[:10]\n", + "\n", + "data.asfreq('D').plot(ax=ax[0], marker='o')\n", + "\n", + "data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')\n", + "data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')\n", + "ax[1].legend([\"back-fill\", \"forward-fill\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top panel is the default: non-business days are left as NA values and do not appear on the plot.\n", + "The bottom panel shows the differences between two strategies for filling the gaps: forward-filling and backward-filling." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time-shifts\n", + "\n", + "Another common time series-specific operation is shifting of data in time.\n", + "Pandas has two closely related methods for computing this: ``shift()`` and ``tshift()``\n", + "In short, the difference between them is that ``shift()`` *shifts the data*, while ``tshift()`` *shifts the index*.\n", + "In both cases, the shift is specified in multiples of the frequency.\n", + "\n", + "Here we will both ``shift()`` and ``tshift()`` by 900 days; " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PZ8zRFHy9nGnT1Mvq869NmgI9U94zn/921sO9ad/cu8S+/v4eJCRdZsrCkvPl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yMQCJBOp2lqaqKzs/OHc77/5tjX18fo6Cj9/f1YrVaCwWCxL+HVFNq7UChEKpViamoKwzCoqKgg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\niIgJKJBFRERM4A/3oO5fXZCK2gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3, sharey=True)\n", + "\n", + "# apply a frequency to the data\n", + "goog = goog.asfreq('D', method='pad')\n", + "\n", + "goog.plot(ax=ax[0])\n", + "goog.shift(900).plot(ax=ax[1])\n", + "goog.tshift(900).plot(ax=ax[2])\n", + "\n", + "# legends and annotations\n", + "local_max = pd.to_datetime('2007-11-05')\n", + "offset = pd.Timedelta(900, 'D')\n", + "\n", + "ax[0].legend(['input'], loc=2)\n", + "ax[0].get_xticklabels()[2].set(weight='heavy', color='red')\n", + "ax[0].axvline(local_max, alpha=0.3, color='red')\n", + "\n", + "ax[1].legend(['shift(900)'], loc=2)\n", + "ax[1].get_xticklabels()[2].set(weight='heavy', color='red')\n", + "ax[1].axvline(local_max + offset, alpha=0.3, color='red')\n", + "\n", + "ax[2].legend(['tshift(900)'], loc=2)\n", + "ax[2].get_xticklabels()[1].set(weight='heavy', color='red')\n", + "ax[2].axvline(local_max + offset, alpha=0.3, color='red');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see here that ``shift(900)`` shifts the *data* by 900 days, pushing some of it off the end of the graph (and leaving NA values at the other end), while ``tshift(900)`` shifts the *index values* by 900 days.\n", + "\n", + "A common context for this type of shift is in computing differences over time. For example, we use shifted values to compute the one-year return on investment for Google stock over the course of the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TN8ADznlo7vBi8YV6WdsUOrwfYu3cMRHDejrLm2a0c14UPXSDcw8KKnYSeMfO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\n3AkrUPtYNzSZQnoN8GpH8aR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YZW6ttfXABazdVYovfj0hfTDbNqtkgBJaohgMzF4SiX4O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\ns8WKLX+cR/+C9qIXklwJca4XpW+t/AP3XGvf80A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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ROI = 100 * (goog.tshift(-365) / goog - 1)\n", + "ROI.plot()\n", + "plt.ylabel('% Return on Investment');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helps us to see the overall trend in Google stock: thus far, the most profitable times to invest in Google have been (unsurprisingly, in retrospect) shortly after its IPO, and in the middle of the 2009 recession." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rolling windows\n", + "\n", + "Rolling statistics are a third type of time series-specific operation implemented by Pandas.\n", + "These can be accomplished via the ``rolling()`` attribute of ``Series`` and ``DataFrame`` objects, which returns a view similar to what we saw with the ``groupby`` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\n", + "This rolling view makes available a number of aggregation operations by default.\n", + "\n", + "For example, here is the one-year centered rolling mean and standard deviation of the Google stock prices:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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oI9alHq/MJKtypznncf+w9cmNdc6sOW2AeZXZu0sBnLukErs1N9/ZYprcOtYg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\nX4vfgTqYSFNT4+Jwoier9nFKOB4hkoj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xs3ZDctpMWdnjUHx7vXfpu+kLe9jTvY9/ffE7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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rolling = goog.rolling(365, center=True)\n", + "\n", + "data = pd.DataFrame({'input': goog,\n", + " 'one-year rolling_mean': rolling.mean(),\n", + " 'one-year rolling_std': rolling.std()})\n", + "ax = data.plot(style=['-', '--', ':'])\n", + "ax.lines[0].set_alpha(0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As with group-by operations, the ``aggregate()`` and ``apply()`` methods can be used for custom rolling computations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Where to Learn More\n", + "\n", + "This section has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\"Time Series/Date\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\n", + "\n", + "Another excellent resource is the textbook [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (OReilly, 2012).\n", + "Although it is now a few years old, it is an invaluable resource on the use of Pandas.\n", + "In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics.\n", + "\n", + "As always, you can also use the IPython help functionality to explore and try further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Visualizing Seattle Bicycle Counts\n", + "\n", + "As a more involved example of working with some time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\n", + "This data comes from an automated bicycle counter, installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\n", + "The hourly bicycle counts can be downloaded from http://data.seattle.gov/; here is the [direct link to the dataset](https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k).\n", + "\n", + "As of summer 2016, the CSV can be downloaded as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once this dataset is downloaded, we can use Pandas to read the CSV output into a ``DataFrame``.\n", + "We will specify that we want the Date as an index, and we want these dates to be automatically parsed:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Fremont Bridge West Sidewalk \\\n", + "Date \n", + "2012-10-03 00:00:00 4.0 \n", + "2012-10-03 01:00:00 4.0 \n", + "2012-10-03 02:00:00 1.0 \n", + "2012-10-03 03:00:00 2.0 \n", + "2012-10-03 04:00:00 6.0 \n", + "\n", + " Fremont Bridge East Sidewalk \n", + "Date \n", + "2012-10-03 00:00:00 9.0 \n", + "2012-10-03 01:00:00 6.0 \n", + "2012-10-03 02:00:00 1.0 \n", + "2012-10-03 03:00:00 3.0 \n", + "2012-10-03 04:00:00 1.0 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, we'll further process this dataset by shortening the column names and adding a \"Total\" column:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data.columns = ['West', 'East']\n", + "data['Total'] = data.eval('West + East')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's take a look at the summary statistics for this data:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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50%33.00000028.00000065.000000
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" + ], + "text/plain": [ + " West East Total\n", + "count 35752.000000 35752.000000 35752.000000\n", + "mean 61.470267 54.410774 115.881042\n", + "std 82.588484 77.659796 145.392385\n", + "min 0.000000 0.000000 0.000000\n", + "25% 8.000000 7.000000 16.000000\n", + "50% 33.000000 28.000000 65.000000\n", + "75% 79.000000 67.000000 151.000000\n", + "max 825.000000 717.000000 1186.000000" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dropna().describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing the data\n", + "\n", + "We can gain some insight into the dataset by visualizing it.\n", + "Let's start by plotting the raw data:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import seaborn; seaborn.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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tj8t7zgtGo2U41IqszWJH7s3W28BIHoyFBRDKO+bx0otbqe9dTrXe8N6kSRM8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\neHt7Y8KECfDz88P8+fMxatQo+Pv7IyAgoMr7XVn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.plot()\n", + "plt.ylabel('Hourly Bicycle Count');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ~25,000 hourly samples are far too dense for us to make much sense of.\n", + "We can gain more insight by resampling the data to a coarser grid.\n", + "Let's resample by week:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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W5D7tRhaNGNdRt/jTlfNJwSDkYBD20jLYywYBiMf5FVmGHImAdbri8U0S/l7B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NKz6nnpwC4AphcNvlj73Zv70etFZtUXD1j1tnb72Hush5pyu9afutHdzPMNL3LU1cS4ns9z95cv\nIBSO8qOnt1BebOXfVszO7scYRqhenqrPngs0hbd+QlX6khLYv0+pAClLDimqUQIV4Y52dKorkQe+\nu/ojguEoZ82qpM8bYvHsSqaOjyuJhaJhjDrl99eJyQurv208xt82N/Ld6xciGEN0B3qYXTJd267W\nOY4PWhVWuWCMEMSDjDIp3vjFeIezQDjKoSY/bk8p/opG3j62jovrz8/7mgaDGsWS8mT1W2fNBgFs\ns+YkfZ/o8Ytpws8nEr5QkB+++iqTrDP4+nLl9xUQtCYtTquRf1o+nQ93tzGrfCEb+95lypTkeXZh\nxTwO9h1ma8dOLraWsbtrLw6DnXrneP586A3eOPoOdy78JjWO/HpTZIMfP/sxwXCUH990lvZZod2k\nLWYgXrKayeMn5vFfuLCOyn7ytUfb3FhNes6ek+wIqfMfpI6ivbu1GafVyOlTB4b6p9UWMS1Djt4d\nUt53p0F530PRMHu692LWmZlWPJlp4wuJSEP3wFWUOM3IBcchANOKk+fjQrMyZ/SFXEmG/8mdz7Kn\nex8/Oee+rI2/oNPn7/E/+eSTWZ1kLMMcM/ze7dsApZmK2t846nYhFg9vjkeFLMsEG48hBwO4PthA\n0bKLACW/L0ciGMfFX2Lb3NMouuQLGCsqsM09DX1BbkblotrzqHOOZ1JhPZ19fjY1tDNpXEGSMdl4\n6CCbetexdOkCTitPbawvObOWi86o1oxIOBrmVzuepivQDQKceYbyKBkNOv7xC9OprRiZlqaqSMuQ\nPH77QI8flFB+f8M/gEsgyzkL9STi7usX8Mr6I3y8v4OpNYXoYqI7sizz+31/4kDvIZZPWEaFtYxq\nexXvfNzEoRYXZ0yv4FCLi3++dCZOm5FXdnwMQK0jXoJYYVVCmg2tjdSXlEMblFqV5+Bw31H+fmwt\ni6vOYPa4GUwaV0BrXw0PfvITPu7YfkINv0pY7R/qj/T14j9wAMeC1KTDaGx70WxO2ZExqVHSSWD1\n93lDOK0GbdH8fssH9BZtRFcZARTDL4pCkv77OfPGcc68cezptrF1xwa84eTnqdY4DVEQ2daxizml\nM3GF3JxZuQBRELEarEiyRJuv44Qa/ge+/rlBt8nG8Kse//SJpdjLku/HjLoi/uvflgwIdSca/lQ6\nDB09fnpcwZSGfzBs2tcMMgT8yty141A7TzY+RaV+At9dOpn5eRwzEyZVO2k7cgir3sL0omROzj9M\n/DxXTLoUgy7Z2dzSrtimNl8HVbaK7E6k04Esaxo1qTA6CltHCOaJilymypjXFxSgc8QMv8uN4QQZ\n/qirTztn33vvUHjhMgRBINSitAY2jUsgKBmNlMVU+XLB+ztaaO/x8/nPjddygeGIlz5PaMDL9ca2\nBloLdjOlOHO52UNbHsNpcnDjnOs50HuYInMBy2qXsmbfyzR7FWbtM3/bx1mzKk56MxgVotkMopgX\nuU/SQv3J3qGadgl3DUwBqVwCXUGBpv6YL7EPwGzUc9UFk7mKyUSlqLbQUoyJzHFvK7/e+Qwzi6fx\njdNuwGkzMrWmELNRR6HdRLHThCAISCETdv8EJhfEJWGrHVVM4iz27bRgXqCktwpMyvPuDQXY1rGT\nYkMZH7VuodJWwaUTLqLKWk6rt21Y64z7I07ui4f6ZUni+GOPEDh4AMM99yVJbqtQc/zp0l/J5L4T\nK94jyzJ3/nIDobDEFxbV8eXzJnGgQ+HlbGz9mOtmXJXx95tWNJmHln4PXyDK4RYXR1pczJpQzGMv\n7KVy7niOuY9i1Vu4b9EdGru+3KIsSNt9J7+yprXbx5N/2cOCaWV8/nO1Wj+KSKZQfywydqDFw9sN\nW/nS0knUVcYdBH8wgj8YodBh0uYPQ1GC4mqKUP/K8weSWlV09Pr5eF8Hk2sKmDRuoMP0rfO+hMt3\nGVazYgZn1VZgOm5CZ8pPDCgTfGEffzv6Ln0hF4urzhgQrbPoUzsLDoMdd9jDwd7DWRt+QevQl76W\n/9SL3+cAU3VNnCwnCOgcTq1W9EQS/LQWpIJA6PhxAgf2K58fV1Sj8m0pm4gCuxFBIEmmtarExtUX\nThkQ/rpokfJAWfQW9hztYc+RZAU3XyDMq1t2ctTdiCzLiILIjJKpfOv0f+Xs6kXYDTZkWcYXiDCl\npgBfMEJUktjXOEws1RwgCAI6my0vcp/WxS2Nx5+K+6FGFsy18UXTUAx/Iu7/8D954MP/1P6/fNIX\nKDEr967UonhCC6aVc868cUyqdvDFpTXs8nzMR8e3smLBfH5y6b8yrSSeGrIbbHzr/BV858vnszvG\naFYlf8M+5T1oaD/Kx+3b2d6iPJMV1grCUoSXPtoxLNeUCqnIfa4PNhA4qDRbUkv1+kNl9acjvJ5M\ncp8gCPzi1qV844rZLF9USzgi0XIsPq41m9/nrS1NvP1xEz98ajNHW5NFwkRB5I2PGvn24xtobPew\nt7GX9l4/X1s+nXPq5gOwrXMX5dYyKqyKJ/qntxTCaZuvg7AU4fm9L7KvJ/8GVengDYQJ95OALbQb\n+fJ5k1g4TYki6ZxOEIQk6fP+UMPP63e1M7HKSWlhvMzucIuLx17awQ+f3kIwFD9XUqg/x/cqFJHo\ndgUJhVN7vjpRpMhuwaRXvGyzSU+JuYiuQA+yLPPkX/bw8rpDythlOWM59GBwhzz8/di76GQTsxzp\ntQ7645vzbwTQCJ67uvbyPzueIhxNL5qklUtmCPd/qj1+QafDXD8B/94GdM4CBFHUQv2REyjbG2pT\nmNLOs5bg2rCe3rXvYJkylWDM408M9eeL2RNKkkh6u7v28sL+V7mk/kLOqJyftK0nFl606m28+M5B\nFs2sZEaC2E8wLLGtazvoGVA+JAoi//f0O7n7fz5CN+so112s5C+feGUX7T1+br/6NIwG8cTLVCaO\nyWbLy+NP18UtMdQ/YJ9Y3b6prg7vju3K/nka/s4+P/f+eiNnz62itMBCt9dNuS1+D816E9fOWMnj\n2/6XSYWKByzJEr6In1cOvs5HLZuJyFFkdzGTzp1GaeFAL0IQBKWZkSkEkbjHP3t8DcJBgc6YZr8h\nonhIs4tn0twSYVZdcthz6/5ODh7v44tLJmgloLIsE4pImHLsX96/jj/q89H5x99rcrC+hj0UL790\nwH6ax59GzTKZ3HfiQ/0GvciCmCEE+N6VV9LkXsR/bX6EI92tzJg0A72jl1kmL2bbwEn5wgU1fP7M\nWkRBYOm8+BzQG5xFo7uZOkcNnb1+ip1mej1BppRX0SqLtPs7+KhlM+ubP2RT68f87NwfDOt13fXL\nD6itcHDHNfF5w2zUJ6UL1bkzG4//c7OrmDinSuMKAfzxXYXl/9A3liTtoy8sBEEAWdZy/NsPdrG5\noZ1dR7qZXlvEsoU1KdvnVpfauGZZem5Tqm6QJZYijntb8Uf8zJpQjMmgQ5Il/t/Hv8Qd8nDPmf8x\nwFvPBhW2cq4YfxWit5T6wuw7wFXayv8/e+cdIFddrv/POWd629neN1uyaZtsekJ6QkLvcFHBS7Fh\nQ382hItyRbkiesGGoCIqXhApIoKCoSSQ3sOm191s732nl3N+f5yZ2ZmdmW0pBOT5b2fOnD0zc+b7\nft/3fd7nIUVnQw5VaR/f93sA2t2d5FsSnyc8LjmcXv+/deAHtdzvPnY00juPLvWfLfha1cCfsmIl\n7uqTOHbvIvDxm/A1NyNoNGgzs0Y4w9ihETW0utrZVn2C/qZMVs9VhU4cbj/1XWqGn2q08p1bZsS9\nNtWqx5DWgzggUplREfd8iknPQ59fhNUUkl5VFFYuMZBqzMagk9hzrIOGdgfXLR8dMfF0IZktKl9i\njDavyVj9mvSQWUhnfOAPM/q1mdmqr7zXM2b2cRjpNgOPfHkJsqKo1ZL+ABZdbPCelDqRh5f/ILL4\n/GT7r2lw16HxphHUy2QZMumkm73VrSytKIyUMaPh8gRYlX4lZcV6TKESo1bUkKK3RbKaC8rUFsH8\nvJnMz1M3e3uOdeD2BlhamYvD7UenlQjKMlpEHG4/j/3tAIXZljGzoCOl/lDg71n7OsGBftKvvZ6B\nHduTTlREevxJSv3nMuPv7HNjNmgjLm7he6/AmsePlt6HSat+zi8c38yGtq3MLZoIxNq/hlnza2vX\noxU1rCxYgiRKdHVBcWApJSm5PPTnvbg8AX7wmQV8bNUkjm3PwOV3sbP1PQC8wbFNRowGj35t+aiO\n06TY8bW2JP3dhTP+ykk5GIZsSqM3FdEQJAmN3U6gpyfKmU+htdvFxPwUdFqR7n5PwsA/Eu7+zTZs\nZh3fvXWQQ1JTGwALdHl6WDA1DwW1wplvyWNj01a2NO9kecGiYc6aHGvKxy6QJQoi/7PkXkRBjIzW\nTk+fmjToA2qPn+FL/f/2gd9YWkYPg57pkcB/Fkv9/tbBWX37qtV0PPdn+t5Zj6+lGV1u7rjsZKPh\n8QV4fv1JJhfauaBCNXjJMqlZa4+vGyHqN9fW4+JIYyuY4cXjr1KeWsr1E6+MO2ePt5dUvR2DJrE7\noNcf5NFn9rBmbgG27H4e3fc7luYtZHr6NE42S6SYz53Mr2Q2QzCI4vUgjMEYJzjEmS8MUatTs5kE\nPf6IAYzFgjYjA19T47hn+AVBiASOycVmaACzLn4TEZ1xWHRmcENA341dm8aMzKmsa9jI4Y5qFiuJ\nZbbX7qynd8DLnIlTYxboFG1KJPDnJOgnCgLsPtbO0spcllbGLjxmg4Y18wqYVT52Madwxh5m9Xvq\nagFIXXMRgb5efO+sx1N7CuPE2OxN9nhVG+okErCC3jCYLZ5lVv8bOxvYvL+Fz145jefWnWBiQQoX\nzy9kQo41EvQBukJGSOmGxPLZr28/xVrnOmwGMxcWLgNg/Z5GUkIM+ns+OSdGpe7u+V/FE/Ty3S0P\nAmAW7DHckLMFWVH48Z/3kptu5vbLpgDqGuqtr0N2uxNXvcKBSBJ5/OUDGHQaPn3FoKSN1xfE6fFj\nNWljZMM1qWkEenoiFZzKsgwqy0a+zxxuP1sPtJCXYU4oxvPgHRfEKf5dO3seNQN2dKKWLk83D+78\nGWuKVnBJ8So2Nm3lYNeRuMDvC/oBBZ00elXVZPAGfeiHnCfMDdnZqhJ2F+YO3yoY7PF/lPEnhbF8\nEpLFiqFEzUY1ISOVwNns8be2IlltSGYzKUuX0fXqy3S/8S8Unw9dbv7IJxgBAgJ5mQaea/8VJ8WZ\n/OfUG0nR2dBLOjQGFxfOGQwIZXkpfMV6FS2ONp499hLugIcs11yMeg1zQyM3h0514vS5h2UOp5j1\nXDBPT1PgKFMtc0nV29nWspvNzTtYWbCEyyZdc9rva7SIlu0diyOenITVD2q539tQjyLLMRuzSOA3\nm9Gmp+NrajwtF7iDnUfo9HQTZnCZkpB+wsi02DkS6kpNsOcxOW0i6xo2UjzRl1DUBOCTFyXOyLUd\n01B0TQhSELsmfqGsLEuP0xII4+k3j5Nq0cWUukcLcUjGH3Q6ETQaRIMR0+Sp9L2zHtfRI3GBP+j1\nDCthLQgCosmE7HSe9Yz/kxdN4hMXTqSpv5Pbry3gyHE/f3n7BP/1n3Mix6zdUc/+jka0Rl3MZiAM\nRVE40n+IoMbP9IzBTdnnroqtsoU3h/VtA7xb1cziihx+uOQ79HkHSNNmntGgLysKHm8ArUaKUfUU\nBYEbVpRhNQ3eY1Koahrs6yXY10vP+nVkffymyMYsXHr+66ZaystLmDpB5au0dDnx+WX2Hu9g0/5m\nvnJDZUwGr83OxnOqBsk6tkkhWVbo6vcmlOyVFRmf7MVijH1uceFsFjMbfyDIT998Ha/Bh17SY9en\nYNVaaHPGC3k9uPOn+IJ+Hlz63WGv53BtN9sPtbFqTn7CCsXP9/6GU/31/HzFD+MqJrIis6vtPYwa\nAzPSE+n/DWI0gf/fmtwH6iJf+vDPSLvyavXvSKn/7AR+2e/H39mBLkfNxEWDgZQVqyKL3pno7+t1\nEjOmGPErPsK3jyAIZJky6XB3IiuxZJciawELc+dSYiui093F/lOtRFu3O9xBphOrdLkAACAASURB\nVDk+we2Tbk36P7UakU7xJG+3v0abq4MleQsIKkG0ooZl+eMrjY0X45XtDTodagapj18oNOkZKIFA\nnGWzHB34QzLL4yX3bT3Ywq/3/pkXj7/CiydeAcCoGd6zPXrmN9ecTVmK2vvf1LQt4fEOv5M/HnqW\ntbXr4p77+hUXcmPB7UwKrsaSoNKgkcTI4t8z4OWVzaeoOqG2PyqKU8lIMaIoCgdquiK2raNB2HhK\nDrH6ZZcrsnkzTlY5I+5jR+NeJ3uS21SHIZlMoe/07Fec+v39/Pi9h9nWvZH/WFnGvbfMjVnA7RYd\nWpOHdGPi2fKAHKBGsxGA6elT4p4/3tAbY50tKwo5aSZSrXqsOgsF1tyErZ3TQZ/Dx12/3sbTIX+O\naEwqtJObPnj/DY709dH9xlr63lmHO0RchsEe/8QJ6SysyKYg5BR6rKGX3792mPlTs/jpnUvjgmLG\n9TdS8I27IsTr9XsbeWtXw4jXbjPruGlNOfOmxG9GO93d3LXpezxz5MW452RZQZbBlKb+tifY1JHY\nHHMWXZ6eUIYf+ny8/XS4u+jz9UdMtYa7nokFKViTbMhNGiMBORDhXB3pPs67DVtw+Jx0uLvwBX3M\nzqyMG/kbCkHzEblvVIguFYp6PYLecNYCv7+9HRQFbSjwA6SuXkPPm2shGDwjjH6AjpAZS6ZxsCSW\nZcxQRXc27ufKedNIMetobHfg9QcpyrZSYMnjRG8Nl61MoyRlkMy1cFo2C6cNP0rS6+1jQ+NWtKKW\nElsRWaYM3us4wKqCpejlFLYcaKE0zxazUJwtjFe2N+hwIlksCfuTYWtdf2en6p8Qfk2oPSCaLRFu\nhmQZPjMJBGX6HD7sVl0M6XHBtCyebfdHxrW+s+AbkRZNMuij+ja55mwMGj3XlF2WdHRML+nZ276f\nElsRlxavjnt+1dTJrJqaWOFxaO9WlpVIoAln+gdquvjHllquW1ZC/ijHoAVRRNBqo8h9TjShDbjG\nakOXX4D75Ik4Ua2g1zuikqVpWgWB3t6zKsPa3e+hvr8Vv0YltjU7WjjWVU2rq41ZWTNI0av3Q0W5\nhWBbIKKdMBTRC/qkIXPeOw638dtXD1GWb+M7t6i94uIcG8U5Njp73Zxs7KPf5ePdqib+Y0XZsDoa\n7a4OTvXVMz9n9ogjmqlWPY99fZQ9/shIXw+eGnW6QPYMBsNwBjp3ag5a02ClZuWsfFbOSl7p1Kam\nxhhcCaiTBqeDo82qlHlrR2yp/70THfzmlUPceslkvJouREGkyKpe2wRbIQE5iNPvRCep7/Vwt+q0\nqBW1yIqMXw6wtnYdE1NKmGgv4eE9j5Frzub2ipsoyLRQkJmcZGo3qOfs8fZi1VnY3VbF9pbdTEuf\nTLYpkweX3od3NMZMH5X6xwfJZIrM2Z9p+KL6+2Fo7KkRhr9hwtisWxNhx+E2NjYfBREyTIOl2esm\nXsEUaQkdnYMZ//6aLvYca+f//cdM8kOl/EZHMyUpY7uO/zv8PAApOiuSKGHTWbl3wdcB2FPdyLb6\no1htU89J4B+vQ1/Q6YhkLUOhjZrljy45R0r9JhMpy5aDApZZiYlKYXQPeLnnN2pG/od7Low87gv6\nUKI80Gr7G8iz5MS9PhppRisSWhalrWJmiHh58YTkYjtaUR1ZanN1xD3n9Qdp7nRiMWrJjCJftTrb\n2dK8g+reWgqsedxQfhWpVn1CsuaM0uTtgOEg6HQoPi+KLCM7nYjZg+/bNHkKvU2NeOvrMJYNBkTZ\n60WToC0Tjexbbh/ztYwVJ5v6+MvB9fjS1apEm6uTn7/9GmJmIyUpRZHALwgCmcZ0im2FSc9138Jv\n4g36IuqMYVQ39XHLJZNZOSs+MWjvdfPcuhPMnZzFqtn5ZKQMXyV6pXotVR0HsOmtTE0bvxztH147\nQnuPi3v+U+05h387vpYWfM3qhFK0GmOYbCZIGqpOdPLPbbVcu6yEacVpiIKAPyDjcPsx6KRIOyMR\nVs0ZnUV8UJZZt6cJu0XHgqlq4tLZ6ybFoiMlRf2dTS+M5apMK07jF19dikYDL2xsIt+cE+ndXzcx\nfrLkSJdaCbl7/lcxa020ONtYW7uOfEsuTQ51rbdo49e8QFDmpQ3VXL2kJPJeU/Vqq6TX00eRtYB+\nr0owD4/cakUNWp2GVmc7vqCPIlviz+GjHv84IRoNMQ597uqT+FpaSFm67LTP7Q+N8g3V6M/65C2k\nXXp5xDXwdJCVakTocoEfMo2Di3Cqwc7iqbGB7fILJkTc+gpkdVHpcvbzyuZTZKcaWTgtmzd2NlCQ\nZU6o4R9GkbWAYz0nmZEZ713t1DdwyvgmXn06cPochpEQ7rGPJfArwSCyy4WUn/jHpAnP8g8Z6ZOd\nTgS9AUGjQdBoSL34khH/V5bdyBeuqcDjC8Zq6wfU0cDwolHbX8fivPnDnYrZWZXMGaNDW7gatLV5\nJ4vzBlXZalv6+fGz73HDitIY5z6H38n6hk0ABJQA2iG6/Ydqu9m8v4U1cwsoyx+fXLGo1yN7vWqG\nqCgxPfmwgFJ0FU5RFIIeD9pRelecTSyYms17fon9nTArczpVHQfRZ3TjB9KNgyQ+i9bMl2d+NiLP\nmgiJSJUANyfhZeyv7uTt3Y189sppFGYlrlYNRcNAIwDB3nRkuxKj9TEUgaCMzx9Ep5XQSLHVgTXz\nCojWApNCgd/x3t7IY0qUNkO41P/02ydZvXQSH79wIruPdlDbMsCVi4vZd7KTP799nI+vmhghJZ8O\n9rTv43hvF9O1gz3xR/92AI0ksnK1WjFIM8a2FcKjqCc7GpGDoPcPv4nNNmUyKXUiOSFVzLCY0syM\nikjgL7MXA+rmbX91FxUlaUwqtOPxBXljZz3XLiulq8+DVlHv+Z4QwbbfN4Be0sURqh+t+h2SIPGD\nxfckvKZI4P9onG9sEA1G5La2yKLc+fJLuI8eAQFSlpxe8B/M+GNvbFGrjXtsvCjJtWHp8EMnEQew\n0SDXnMW1ZZezIGMRbzY3kJlqJBBUIo5ZwwX+K0ouIsuUwYKcOXHPhXuaXZ6eMb6T8WGw1D/6PnPA\n6VLZ30lIYNok6n3BcRLHwhlINJ7fcBh0UGiaQIerk1N99SOeZzwl7CJrPvUDTUhCLAlsUqGdz105\njYqSWMZ5XlQwuqr0kkh5eMuBFmpbBrhkYSEzStMwh3qXLV1OalsGWDgGBUdRpyfocCCH/NxjZvBD\nY1zRVTglEFCtWs9B7340aBhowqqzMCVtElUdB/ELLgySIY6cmWk6s2qgVpOOlbPzybQbEQQBd8BN\ni7ONAkt+XNUA1A3TgN9JviWX3792hP++fT6p1uSfYV3bAD99fh8XzSvg2mWxFZ6h7YRwqT8sRAYg\nRxsvhTLQyaUZ5KSZ0Gkl0m0GXny3GllRmDclK2E/Phr+gMw/ttZSlGUZ9lhf0M9LJ17FITkpsclA\nPoqiUDD/GDhT+dvWDsgYzKajoSgKBuxcm/pFctKHZ+pfUXpxzN8dbjXw51vzmGQv43hvNeV29XML\nygrtvW4uD6kV3rxmEoKgTh/85C97KZkYRJREPCGuQJ+vnxRdPAnQprPQ4mxPOjYZsT3+KOMfG0Sj\nUR0HC/gRtLpIAGn/89MYikvR548/a/W1toAknZHMfjjcMeM2+n0DceSw7n4PG/c1U5pno7Isgy3H\nTrKz/x2WFM5lQc4cLpqwEoAbVw6WVK9clpdwjC8aWkkbkz1GI6w2V1VXx8UTlEgw+K/fbqMg08KX\nr4/XDjgdRJf6vc3NuI8dwTp/YUK2fhiBAbWslkzoRRMR8YkN/LLLOWbdhe5+D5Ikxui6A1yzZCJi\ndQWT0iZg0mvRSboxaxGMBl+a+Rn2tu+PE3ISBIFF0+M3nyatiQm2QoySgekhRvHb9RvY1nuABWlX\nkGrVs3j6YAVr68FWOnrdzJyYMWqymaDXI3d3RQSRou11xZAda9A9aLwUlvcVhmH1nwsEZZn3aprp\n8fYyLW0yhda8yMYqwxhvdnWmUZJroySqePjTDS/QLBziW3PvpCSlKO54h9+JL+gjw5jO0uWlaKTh\nr68sL2X0Pf6Qel90GUCJLvWHMtBFM/IjBLQ0m4HPXx2vDZIMiqKgEdXx4eGgk7T8v9mf56d7f83O\n1r1cUXox/b4BqjoOUGqahNGgxSuLuJwiDNmLvb27kde21/Hl66ZTXpC49ZcM4Yw/y5jB5ytv41R/\nPeWpqibGpEI7RdmWSFUhTJQ1GwR+8OmFaLUCsARREJEVGYfPSVZKfJyw6azUDzThCXoTk38/muMf\nH0SD+mHKHg+iVofs9oAkofh8tPzmMSZ87wdJZ4eHgyLLeJua0eXkDsoqngU8t+4EBp0Ut0OPXIdC\nSJFK4d3D1TSnnGCSuzjp+X574Cnq+hv5+cofjkuvPS00szwQ7Mcfpez2X/85F5c3cMYtfKNZ/W1P\n/R5PTTUdf30B+6rVZFx3Q8LP3lGtSnPqshIHccloRDSZY0r9SiCg3iNjzPjX7WnkXzvqqSxL5/rl\npZHMqcCayxdn3QbAQkYv6zlWWHUWVhQsHtNr7pp7JwqDm5BWZzut/npmzbXEqTLesKIs0SmGhajX\no/h8ERGl6M9UTJDxh4mA73fG7/IEeK1qP6RBgTWPYlsRX5r5Ge7Z/IOYMv+5wuSsIpo7DtHibE0Y\n+MNtHouYwvIZp0ckfm1bLbuOtvOV6ytJT1HbXZLFQnBgUPwsWoY5EoiSrH1BWabf6UerEZOOouq0\nElcvjfdtSIQ8Sw6pmgyaPY30OF00Dqht1pL0XL6x8HI8vkDMiGIYq+bks2Zewbg2bR2hwJ9hTEcn\naeM4FAZdfNwQBCFu/QvKQS4tXh1R1oxGuEqRKLGDj8b5xo2wAIvsVksuQbcLXXYOKctX4GtpxrF3\nz7jO62tuQvF6MJSM7sYdL0rzbDHkrGik2Qxcs6yYyUWpiILAJYvVMq51CAHF5Qnw+9cO89Aze2jp\n78aisYzbpEUnabHqLGiNnhg5V6tJy1P/Osr/PvfeuM6bDOGs3VNzEk9NNdrsHCSTiZ61r9O3eWPC\n1/TtV7XoTVPjOQph6HJz8bU00/m3v6IEg4PZ6RgD/42rJnLtNRpMBfUxC9xQ86TzCUM93+2hBanP\n288fXz/Cs28dP73zh3r1YT93KWHgj87439/AHx6rs5p0fPbS2SzPX8TUkNWqgMClxauZnXlmK1mj\nwZwidW1pcbYlfD5Fm4K9dx7ttSn0DHh59KX9vL69Lun5/AEZl8dPIBivdz9vSha3XzYFmzl6ll/N\nkHUhrowcbbwUCKCIEn94/UjC/9Xa7eYHT+3i7d0jj+qNFmbBDih0uLvY36ie1zugD4lladEk2IRo\nJJGWLhdPvHqIPcdi5/Z7PL3s6zgYGbkL40j3cV44/goVGVO4vOSihG2W4aAoCj0D3ohPgVbScmXp\nxSzLvyDu2Ejg9yZWl/0o8I8Tgxm/G0VRkN1uRKOR1IsvBaB347vjOq+nRs0qw2JBZwsLpmazZEZi\nScdf7/sj3950fyTIOHzqDWzRxZa4DXqJ0lwbS2fm4JEdWDSnZ7NbkT6FYltsBiIIAjdcWMBXPpZ4\nfGy8CPeHw8zi9Guupei730PQ6eh69e9x9q8Avfv3I5pM6CcUJz1v9i23oc3MpPv1f9L06M8JOoZv\nDwyH/f27OOHfHSMucu/vdvDfv98x5nO9HwhnIv/acxyzURuj2y4rClUnOuMWzeEQVu/z96jKdtGl\nfskU3ojHZ/zC+0Due/zvB/nO77ZHgmG+JZePT74uMoJn0Zm5qvSSuFbKuUDYwS1Z4E832fnh9R/j\na1esxGTQ4M3dxXHNW0nPt+9kJ3f9eivbDrbGPZedaqI4xxarshfq85umqeX7GHJfMIggSUwrTlwJ\nyc8w87OvLE1aqQRo7nTy90011DSPbtx6er665niUflLS1IrD7OKRJ5Z0GhGdVsI+hP+wvWU3Txz4\nP071xW6Wavrq2NC4hTxzDleUXDSqa4vG2h31fP+PO6lvH1kqPtucRYltQtJE7KMe/zgRVnuT3W61\nlyjLiEYTupxcjJOn4D56BF9r64hkPNnvp+FH/4Nl9hzSr7oGT+25CfzDQRRE3AEPL205SuWEXGra\n1bEuizZWsKXD1UG9YTP5llyUDoUs6+mVLT9RfgMb97Ww5UALS2bk8s57TbxStQP/hO1cVXpJwpny\n8UIQRVWxzeVCNBqxzJqDqNORevGldP/zVXreXEv6VYNKgv6ODrxt7ZhnzxlWLllfUEjRfffT/Ktf\n4jp4AOckdcMyllJ/ICjT0uXCrDHT4mzDLw+y5B/4zIIRe5fnC8KkI43BywXl2TFELwGV+DcWhn84\ncw/0qATQGEvdUAUuXGGBqFL/+9Djv/WSyZgMGkRB4GSjysAuybOeUyOqZAj4JMSggequ5shjrc42\ngoocp++u10oENAM0OjqS2i6PhnAXjTDfxVwxnd633ogt9QcCSDoti06DsR+eQEhUgQhjU9N2Xjv1\nJrdM/ThL8hayNG8hBo0h4m2fOQrC8/q9TTR2OEgxx95f2Wb1/f1m/1N8sfJTTM9QOS9hx8Q2VwfT\n0seeyFyysIjLLhjdCPWCnDkJSdQRaD7K+MeFwVK/O1JeDGcdKStWAtA3iqzf21CPt76OnrffRAkE\ncNfUIOh06JOMjJ0J9Dm8PPHPKrYdbEn4fGpolEhj8DLg8nG0Sc0MLNr4rHVn6162Nu9UX6cf35hW\nGJIo0tTpjBiRzJ+WyvWr1N14XX8j9W1n1hQpXCq2zl8QCQ5pl16GZLXSvfZfMX3IsO3rcGX+yHlN\n5ojKY++778T8r9Gg3+njt68eorNTrbisq6qOPKeRxBjHsvMZ9tD9kJ0lxrG7BUHgy9fP4NKF8T3m\nZAgr6wV61cCfuNQfxeoPk/vOcam/rceFyxuIKGIere/hL+tOIMvnR5vGbNAy2V5OWVphRKHzgR2P\n8KOdPwegqdNJTXN/xGY3w5SBX/bT7xv772/PsXa+/8ddHKgZJLymX30NBXfdg6E45B45ZI5fHIYb\npSjqBFF4iigRctJMXLusNKbCNBQOn5MBnwNRENCgY92uVt7Z28hM60JuLLuBVMPIhL2PXTiR7946\nj4yU2JZpeGwPiCnnhwN/ewJ9jNEgevrF6XdxOKQP8NKGarYfjq+2DAdhFOS+jwJ/AkQyfo+boEtd\nbMKbAcvsuUgWK31bNyP7h3fC8tap5SDZ6cTx3l58TY0YJhSfVWKfViPRad/Bs+2/jOtDAaTq1Zu+\nrFjVVf/66mv53Ixb44hImaYMjBoDba4OhKD+tAO/KArceslk5oeyhzpHA8/XvADAkfZTPP7yQfyB\n5Lv4Mf+/UPndtmjp4GMGI/ZVq1G8HtzVg77lriOHADBNGTnwq8dNRUqxE+hWFzxpDAYwaTYD//PZ\nhVROUIlV1hT1PSuKwuGuY+zrOBQnqXw+IsuUyedn3EahOJ2fv7iPQ6e6T+t84c1ZoFs9T7SpjqDV\ngiTF9PjfL3LfgeoufvznvTS0O2jpcnLl4mLuu21eTLn7/YQoCtw5/xbunP2pUHVPXb8mh9oQJxp6\nefrNY3T2qfwlR48aiOu6EwcXrz+IyxNIuLEpy0/h1ksnU5Y3SEDTWG2YJk9BCLVuokv9BIM4/TLP\nrz8x9FTqscD9f9zFc+sSPz9ahN+zUWNAkgRc3gA2s563N/exY7N23FwlUNdFURBJN6RSbh8ksWaF\nAn/rOAM/qLyq5k4nvz/4DI/t+z193gHMBi0N7Q6ON/QiKwpBeeS14byY49+3bx8PP/wwTz/9NPX1\n9dxzzz2Iokh5eTnf+973AHjhhRd4/vnn0Wq1fOELX2DlypV4vV7uuusuurq6sFgsPPTQQ6SmplJV\nVcWDDz6IRqNh8eLF3HnnnWf8msWQcYPs9kQWm3DWIWq1WOYvoO+ddXjr62OUxIYi7DIG0PnyS6Ao\nZ73MbzJo0BkC4Fcwa+L11sMBvMejkqhyzdmRvmA0VKlKVZTnmvRPsarwzBIS+7yDmvd+0cUDn56R\nkGE7XtgWL8Gbl4dhYuz3o0lTNzhhcR9FlnEdPYI2NRVd7uh8sgVRxLpgIb1vvQGMrdQP6shPOFCE\nfE3o7vfy6JaXkMx9PHrhQ2M63/sBg0ZPZWYFLx48SUO7I04trmfAy97jHRTnWinLG3nTGO7Vh0v9\nknnw3hUEAcloQnZFZ/zDW/KeLayZV8iaeYX89+93YDFq+fbNw5RczwM0O9SKXl2dQGepm5Wz81k5\ne3AcuSwjjxMte+kP9iZ8/Yb3mnhlyym+fN2MuN683aLHbkn8+QsaLQjCkFJ/EJ1el3REThQEfv6V\npQmfC2N/dSenWgZYPjMvqf5AWDPfqDEiCkJkNDlsOnY60IoaHlj8XxgkQwzrXy/psOtTxp3xA/z4\n2b3YTFpmXFDOsZ6TPLbvSb45707uf3I3/oCMJAn88P/2UFGSxscvnJhU/jeSWL5fpf4nn3yS7373\nu/j9KgP2Rz/6Ed/4xjd45plnkGWZt99+m87OTp5++mmef/55nnzySR555BH8fj9/+ctfmDRpEn/+\n85+55pprePzxxwG4//77+elPf8qzzz7L/v37OXo03rzjdBGd8YfLi9FWq7pM9QYaatgyFN66WgSt\nFk1aOv529QdoKD37/X1XwIVZY0o4jhLWg37mnQO8+O7JYXtlYXOKwuLgGZlHbuly8uI7JzlS18ML\nmw4CUGJT+1r1ITWxM4XUC9eQ8+nPxV13uIQc1vH3NTcRHBjAPrNyTO/RtnCQbTuWUn9bt4sn9j3D\nuvqNXFa8moxQpSU9xUBOphazLvH3dr6iINPCjSvLyE6L3WT2Ob00dToJBkdXAg9n7mE77KE2uqLR\nSDARuU///szx//ft8/nsldN4uWo7Lx55jS73uRGnGg3W7qjnrse30tThiKjHLS2fREBy8MT+P7G3\nfX/k2Ck56iag25u4YnPxgiIe+/qKpIS8ZBAEITSiGdvjN5r0zJk0/gCskcQR2yrRGf/ZgF2fklDX\n5MrSS7iu7PJxT+d8/9ML+OYnZjMlNB3S5GhBK0r88I4LuHnNJMryUrj1kskEg/LwXKD3m9U/YcIE\nHnvsscjfhw4dYt481WBi+fLlbN26lf379zN37lw0Gg0Wi4Xi4mKOHj3Knj17WL58eeTY7du343A4\n8Pv9FBSoPfKlS5eydevWM37dUkyP3x3zGIAUcokKDGPkI/t9eJub0BcWYl2wMPK4oWTsM85jwaFT\n3XQ5B9AKiXfDxbZC7ij+OqXifFo6XUjDyHWGA39t/5kZrznZd5JeqRabScuCSjULrMycRpYxk+4B\nV4zz2NlCuAUQDCnE+drUDZm5dGwVDf2EYrQhPfmxsPp3HGmjxdFBpiGDYPMk3tg0GDBcARfmBLre\n5zMWTc9JKK9anGPj1ksmD9uLjUZ0yV7QaOJIe6LROGScT22zncuMv7PXzZG6Hjy+ABpJZMDlZ3fL\nAd5t2RAJNucD5k3J5K6bZpGTbuJwm9pu1AfttDjb2Nd5iNquQf5PobWAu+bdyUVFK8f8f5o6HPzg\nqV28uTOxwqSg18co9ynB4Ij6J/0uH519yT/LacVpXLe8dFi1wUjGL6mB/3hDL4++vJdNB+vPKnl2\nUe485uXMPu2Ne7T9+d83nWLANbgurpydz7dvnoPR5uZId+IR2vd9nO+iiy5CiupnR++EzGYzDocD\np9OJNcpn2WQyRR63hJTWzGYzAwMDMY9FP36mEdPjH1LqB5BsId/pYQK/r7ERgkH0E4ojgV+y2SKl\n5vFCURR+88pB/rQ2caUjzaYnKHgxaxNbw2pEDTNLc7nnk3P46n8Mn+WW20v5YuWnWJq/MOkxY8Gb\nra9zStxGfqaFAb8q1LIkbyFXZ9zO319zc6C6a4QznD6G6viH3fW0KfFCGcNBEATSr7kW09QKtDnD\nOxdGY82CbJD8ZJszyE03URHKpGRZxhVwY07g0/7vgOixvEStE9FkQvH5Inrvgxn/uQv87b1uXt5U\nw5G6HhRFwWLUYk31ISCM6KJ4LpGRYiQr1YQkikhaPyIiMwuKaXGo4jId7YPLvtcDz/+jk39tbabN\n2c5Th/5Cs2Ow3+/xBdQef4IsNj3FwC2XTGZBEudOUaePE/Bp6/Py2rbapNf+65cP8tjfDo7xHcfi\njsrb+J/F90bcDk84DnM05Tme2/suR2rPn8rMUASCMk2dTnr6fdw9/6t8ovBTiIKAKMCuo+28svlU\nJIb+5dhLPL7vDwn5QOFxvvNGuU+MGndxOp3YbDYsFgsOhyPh487Q4hzeHIQ3C0OPHQ0yM0c/h+6R\nM6gDtEoQk6h+sKk5aaSFzmGakEsToPW5kp5X06X+eDIrJpM1pwLP6gsxFuSTlTW2ADMUtS39NHU6\nuWppacL/bbVr0R/SkZliH9N7ToRMrBTnjT6ojYQiey5VrYfRWwQK03JwyU4m5GZRnCdw8eJzM+Lo\nFbPV7zboJTPTildRd9Naq5XUMX5emVdcBFeMbWa3JkReK0jL4Yo5g/yDtTtPICsyQb/2tL+38wFB\nWeHNHXWYDRqWzx55ikXItBOePNdZrXGfQafdhhtINWvQ2qw4JXUBTMtOxXaOPq8VmVZWzJ/Axtod\nVPU38dKLXpylHWSY08jPObMa/KeLmu566nob+a8Lv4g/6Of1LXW8Vv8u6ODmC+eSmaZ+ZvZUE7dc\nPo2sDB0P7/wlDX3N1A7U8aOL7sFmsPLkKwd5c0cdP/v6CvIT9JQL81PjHguj0WzE63REvssTwSAW\ni5GZk7OT3uMPf23FsO/rn5trcHkCfGzNSK6Cg9/HEnESr7cABQeZP+cW7Ibz8/d1qKaL37xykBtX\nT2L1/KnMjVoS1+5uZO/Rdm69soINextRAgZkRcZgE0gZ8n40aRaaAfMwctnnNPBPmzaNXbt2MX/+\nfDZu3MgFF1zAjBkz+NnPfobP58Pr9VJTU0N5eTmzZ89mw4YNzJgxgw0b8DoVWQAAIABJREFUNjBv\n3jwsFgs6nY6GhgYKCgrYvHnzqMl9HR2jrwwEXWqJxNXbT9Cs7hAH/ALB0DkCQfVjc7R1JjxvZqaV\nzkNqRu5Ly6Gz04H9plvHfB2J4Bhws6gih3SLLum5Hln+ALIij/i/Ot3dPHPkBeZmz0qoEHWmkaZV\nf4y3PPRXZEcqX7n+Rjo7RzbScXsD/OzFfVy/rJQpE5IvNKOBHKo8urr76OgYoL9NrTJobLbT/m5G\nQiAos/GYOqZjIfb/Tc63Mtc5iwJL/lm/jjOF99oPsL5hI1eXXkZ5auzGTVEUDp/soDDLMqr34/QO\nZi6KwRj3moCoZm/tje3oMsHZqz7f7wrgPY3P63hDLy++c5I7r59BShKiWjQUReFXO54C4N5PfJ0H\nd7rI1BeeV9+ZxxfgvrV/wm9ppkhfjE1npSzbAq2h0WRP7Oebl2pg/alNNPQ1k2XMoN3VyY/e/TVf\nnf05rlk8gWsWTwCUMb9HWdIS9Hhob1cro0ogQEqKiZy0+O93tPB7AzgcnjG9XucfrCC5egP4pfPn\nu4pGllXHA59Rq6tD398NS0u4etEE+npdHDrZCUa1FXaqpZV8S2zV1ulQFzlHv4tkOKeB/+677+a+\n++7D7/dTVlbGpZdeiiAI3HLLLdx8880oisI3vvENdDodN910E3fffTc333wzOp2ORx55BIDvf//7\nfOtb30KWZZYsWUJlZeUZv86wcp/i8USYxDE9fosFBGHYHr+3rg5Bo0Gfd2ZtaLNTTREb3UR4Y2c9\nzZ1OPrG6HKN++E7OsZ4TnOitoSyl+IxeYzLkhMQvli+wcdXUJTEGLn0OL25fkJy0+BZFW48LURDo\n6vec9jWIOh2CRhMh94XV97RWC6d/9uHh8gTYfqgNU7qdLFMGe493sOtoO1cvKSY33cynp998lq/g\nzMIT8FDTV0eHu4tuTw//PPUmX5v9edJD5jS3Xjpl1OeKLtlLpvh7QByi3hfuHZ+ucl992wAOT4CR\nxvAdbj/vneggO6oA9madquOQbT67hltjhV4rMa+khG0dzfz075tJE/P56n9UsirzUjo9HQn5PzPt\n83iju4kcw1T62UCL2MmR7uPMyEg+4hoIyvzomT0UZlm4/bKpcc+LOj3IMkogEGkpjjTK7PT4cXoC\npNv0CQWRllaObvImGqao9pl2jFK67xe6+z28vKmGuZOymFWegSgK6EX1s/vkxZNYW9vAqZoD9Hh6\n44SZRtPjP+uBPz8/n+eeew6A4uJinn766bhjbrzxRm688caYxwwGA7/4xS/ijq2srOT5558/Oxcb\ngqDRIGi1BN2JWf2CKCJZrUl7/LLfj7epEX1h0bjMfEbCtkOtbKhq5uY15XHiKSW5NvQ6acTRuOre\nWv5y9G8YJD0Lcs+eIUw0ckzqqmlJ9caRcx5+roqsVCNfuWFwI+f1B9FrJYpzbNzzyTM3NiWaLYM9\n/lDrSGOzgfPsqubZzDoeuPFa4FoAXmnbQCCrDbOxHFlRRm1he74gLOLT5+3nYNcRuj09vF2/gY9P\nvm7M54om8yXs8YdFfELqfWdKq3/NvEJWzMobcQ7f7Q1wpK6HupDt6szM6Vw/8UrKUkriFt73G4Ig\nUJaRz7YOWDLPxiSTSly1e8s5dTKVQKUcsz68tq2WLQda+fbHr0WjETlUm8kF03IRBQGXJ4AggEEn\nxU/IiAI3XzQJmynxZEXMLL9WDbin2pwcqWpi5azECdFL71Zz8FQ33711HjbzmZvYmOq5CmEYIvP5\ngqAss+9kF4GgTHmBHa028To+qBvQznRiN13nReD/oEI0GFVyn8cd+Tsaki2FQGfimU1HdQ0EgxH1\nqjOJLQeaeb3hdRZUVCY04plUaB+RSf1u4xZePP4KAJ+quDmiOnW2kWvOYn72bAossT96d8DNf16f\njj2kKtjY7uBYQy//3FrLqtn5FGVbqZyYfsYCo2Q2E+hT55aDDgdIklrRcY7cdjiTqPUe47jrJEbD\n9fzf2qNsP9TGA59dmNRg6XxDWK+/fqCR2n6V2b29ZTfXlF2GQWNgf3UXDe0DXDy/aMSNaHTmnkgQ\nKXrSBs4cuU9WFDy+IL6APKxqYqbdyB1XVfD6qbfgFCzLv4AUvY3lBYtO6/+fLYR/0/3BbiaE/N/D\nGgRDMWdSJrMmZpBi0aGRRBZXDLLKn/rXEQ6e6uYXX12GVhP7+xMEYViNhvDEhezzRqR2U+1mpCTz\n58CwVaKgLPPSuzUUZlkS2kcPhzsvXzam498v9PR7WbuznqsWF7NgamJ+1cmmPk7WypRaSyKGPdEY\nzRz/R4E/CdTxIU/CjB/UDNHX2IDs88WNHvVWqZrQpqnx5a/TRUDbR6/hOG92H+ca/YJxnaPQko+A\nwBUlF0e0ps8FTFoTt1fcxKcfWs9vWM8f7rkQgIaBJn5Z9QSXFa/mytJLeGlDNfuqu3jgswtp6XSy\ncV8zZfk26toGyM+wDDvKMxpIZjO+lmYUWSbocCBZLOdkdr6tRx1ZLMiwoNdJ2ELGSA6fg9suncJN\nayahlT44YprhwL+/U1U+nJs1kytKLsIQmp/u7vckVX0biujMXUxU6h+i1y9HxvnGnxX+a0cdZoOW\nF985ycULirhqcXHkObc3QHVzH7lpZtKjxIkuK17Dwpx5WHVjN2Y6lzh4RN0YHe+sg/Lhj81Nj99o\nhV35vnTd+B0GIxm/z4eiUTdVGekW0sfg4RANRVEdPRWS309trg5+sutRluYv5LqJV4zr/7yfyLAb\nufc/h6/A9g54wWfhUzM+FWPyFUa4yvxRxj8OiAYDgb5eZJcLQa+P602FZ/mD/X2IGbEZc+97VSAI\no9J+HyvmlBbyQnPy53/32n7SbUauX5b8115mL+aRFQ+gl94f8ZPPXF+ES+7H5Xdh0pqwh2SEG3s7\n8fgC/L8bZ0aOzc8wM29KFttD7Y3/WFl22oFfNJtBUdSKjsMRcRQ72zje0Ms7e5u47dIpTMixolHU\nYLbp8CmunjM7xrL4gwCTxohW1JBjyuLGSdeSZcqICYjRCnEjITrwJxJEGqrXr3i9aktumJ7xcO0T\nWVbweIN09Hp49GvL457vd/lYu6OeyrIMLppXwD+31jKp0M7kolTSjadHMD0XWFlZzIbtZpo7XRyr\n72Fy0eivOSjLfPUXm5hRls6Xrp0+7LG/eeUg3f1e7r0lPliFv1PZ641UdEZqfbq9ARxuPzazLu73\noJHEEY1s3AE3nqDnvLa4Hg1eWK+Kq920pjwuKUlmnBSUg0iiNCoBn48CfxKIRiOK10vQ5YzL9gE0\noVn+QH8/2qjAH3Q5GTh+AkNp2Zg03EcLq87CBGsRdf0N/GX9MW66MNYJyp9xlHXO3czt/0pEgCcR\n3q+gD9CrqeH12rcpzPocU9LKI73iI83NtOe743gLABdUJBaKGQ/C30twYADZ5UQqOHumSdFYVpnH\nssrBMmqqQd08Khovu+qPIOg8VGRMPWuKY2cagiBw56zPYdNZIj3HcZ8rSoEv0e9m0DhrMOOXDIk/\nJ68vyP+9cZQ9xzv41deWo0lQRRFFgeuWD04ivNu4hamp5RH3texUE9/6hGqr6/OrrYCTTX1jCqDv\nJ2wmHT9a/h26+zzYzGO7nyRR5LGvL0cUBZweP5IoYNAlDhVXLi5OWi2LlPq9XkSTOlN+qK4X07F2\n5k5O7Pi3aX8Lb+1q4I6rpyWV9h0O0XK9H2RUlqXT1uMadSWyzzvAI3seY2HuXC4yqVWa82aO/4OE\n8EIT6OlBlxXfaxnM+GMJfq6jR0GWMVcMv1MeL55bdwJZawJBYda0+JKo1QY4wZRAp/98QZ9P/czs\noVKxTtJi0ZoxZ4BBr6G+bYDcdHOkL/yPmjdIM9hZkndmhITC5DF/h+oXL1nOTdl2wOegzdVBjikL\ni85Mulnd8LgCTv60aydKaiPfX3TPBybwA0y0J+extHQ5qTrZSUVxWsLNXDTC2u4oSpJSf3zGn4jY\n5/L4ufeJ7SyZkcsvvrosYdBv6nSSlz4ojVzT3cSLx1+hxFbEt+YNjgcHgjJbD7biD8jcsOLsKm6e\nDeg0GrJSzZH++ljglb20Ozr406v1uBwiD30hMZchmV48DPIvFJ830m/OTLdgTk2+Nl08v5CL5ydO\nWFq6nGze38LMiRlJeUyDgf+D8xtKhCkTUpOOLrs8fjbuayEr1RiRP7bpLExOncjrp97ClOElH2AY\nk54PTkPxHCM80kcwOELGH6vX7zp0AADTWQr8OekmcgwFzMiYRqo1Pmt3+tWM6HxWgOvzqoE/3CMG\n1Tyox9PL8foenvjHYerb1TG7rc27WFu7jrfrNtDQ2cdbuxr47auHaO91I8vKuEp64VKyr1WVjJEs\n50bQY/3xKn6299cRX/ASWyHXll3O8knTmFamXsP5/L2NFR5fkH6nL071zR3wEJRjF6WwtjskLvWH\nbbHDSppyksBvMmj53qcWMG9KVsLWiSwr/OG1Izz71qAD3M83/A2AiyasijzW0O7gaH0PB091xxkQ\nfRAQCMp8/dHN/PzFfeN6/faW3fxk96NctNKYNOiPhOhSf1hxMS8nhcKs8W20NZIYMwKcCGdbp/98\ngIJqghVdDBAEgUuKL8SuT+Ff9eqY6Uel/nEgOtgnCvyJMn5FUXAeOohkNp0VRj8QGoNJ3Dtt73FR\n3doJGiIEq/MNQTnIwS5V3MggDV5jeepEzFIKJflmrr1Sx6NHf4KhWo8zZDj0xZmfYu+BPuraBkgx\n6/ju73ag1Yg88JkFCQkuwyEcWPztqrri2cj4FUWhvs0RYVQDVNU1gnHQITHHnM26rX00yB3UGGux\naM3opXPrNnc2ICsyDQNNmFOMfPzCWK5Jn3eAB3Y8zOTUiXxuxi0xzwl6PXg8cQY9EF3qD7H6fV4k\nQ2K1vFSrnlSrHkVRCMpKTNYvigLfvnk2bd3qBqLT3Y1ibyLXlM2MKKJrTXMfOw63cftlU9h4qI7W\n/l4uqMjCqrOclq3ruYJGEvnep+aPeySuoVEVVKrubGXRMJ2w6qY+fv/aEVbOzo/L1MOlfsXri1jE\niiP0+P2BIH0OH0aDJm7KItNu5IpFxcO+/sOS8Q8Hs0HLTWvK8csB9rRVoRG1zMysIMOYxm3TPsET\n234NDF/qP//v4DOEPqePV7ec4odP706oOz0U0eN70Tr9YWhSwnr9Ufay7e0EOjuxV1aOKFRxunht\nWy3//fud9DkHTTBMBi1aQxCDZDxvF6fo64ruX63OvYjmPVPZc6Qbo9aIXW9DQSHLlMkdlbeRZcrk\n0oVFfP7qCj6xupwHPruAB++4YMxBHwad38IGPWcj8Pv8Mr/46z6qmwbvj6nl6j0VHlsEmFxoR8mo\nxh1ws6ZoxXn7vY0FJ3tr+MnuR9nYGG+g9Xb9u7gDbqo6DnCoK9ZvIhwooi15I8+ZBkv9iqKgDOnx\nK4rCW7sa6BlQ2exNHQ7u/PlGXt5YA8Cf3zrOq5tPAarATbj1sKetClmRWR367ANygD1t+7Dn9/Lt\nm+dgt2nY4n+WV3uf4N4t/8N/bz3/LZPDsFv04x6BvXC6Kicta1x4fckzx9x0M1+6bjqrZufFPSdE\nZ/yhILTzaCfH6pPr5R+t7+XHz+7lveOd47ruVQVL+cmy+5maNpKk7wcfAvDU4ed4q+7dyGMpehty\naAn5KOMHHvvbAU429fHAZxeO6scQo9RnSp7xR6v3+VpUur1l0gjzM+NES5eTp/b+g5x0IxdPXM30\nkvQYPWaLUYtZr8WonJ9a1KAG+2/NvRONGLsxslv0/ODT8zlc20OeLo/7F9097Hmy7MZxj+ANZvxq\n4BfH4K43Wuh1Ep+9choW42DW0utVNwGp+sH+ZMVEK89u2YNVZ2FFweIzfh3vB0pTijFIBvZ3HsbS\nMxO7Rc8FFTn0+wbY1LQdi9ZMZUYFRdbYVFIIjeYlzPi1IcVFtwvF71e5AHo9gaDM8+tPYjPr8PqC\nPPPmMb5yQyXZaSZ+8sXFkaxxRmk6XQlc3xoGmgDIkgqQFQUBgacO/4UJ1gJmZlbQ6mrHp3ix61MI\nKkEqM8/8pM75iLBd9K6aWlr37+PbNycW0DIZNJgMiX8/kVK/zxsJQnk5tmGncmaUpvO/X1qS8Ll9\nJzs50djHqtn5MSOW0ZBECbN4/vKbzhR2HW2nrnWALGMGLc42FEVBEAQMkp50cwbQ+dEcP8C9t8zF\n7Q1g1I/uLQtR2UTCUr/FCoJAsG8wo4vIv47R6W20MOg0dGmO0+cQ+VTmNQmP+c7Cb5z3oywlKUUJ\nH3e4A7y9p5GyPBvXLktu2lPdW8vfq1/j8zNuh6AuJriOBpHA36lmFZL17JD7phWn8c7eRp558xi3\nXDKZ5r5ONIIGS5T1rlFj5M5Zn2PA50D3Pk5anEloRA1T0yfxXvt+GuVWUszqCNb6+k34ZT/XT7yC\n5Qk2Odr0dIJOR9LZfNFoRHa5VSU4wBGAPzxXRZ/Dy323zcNk0EaqeRpJjCnxV5YlbgtcVXYp3Q2p\n/PqvJ/n+p9MxG7RkGtNpdrRzqqWfDkElgF48YdWHZmM2Ghg0BkwaE8Z0mW9fPbJqZjjwRCMc+JWo\nHn9xQRqGYch9w6Hef4R2cQBZOb+UEt8PuDx+DDqJDGM6ra52Tvaeojy1lBS9jf+64Juc/NPnPsr4\nwwgHfbc3gEYShpXplEYo9QuShGSxxGT8wYFw4E/hTIi/BmUZjy8YyVrsFh1+3GSZ4stqALuPtrPn\neAdXXDCBgnESaN5PKIrCVYuLKS8YXuDjQOdhavrq+MnGp+k6MJUff2HxmIJ/JMMPBQnpDGf8G6qa\naOt2UT7NT5V/B7MqF9DV58HdZyIvNX78qTRl+NnkDyIqM6bxXvt+jPkNLJqqTmOsLFyCIAgsykss\nPJX7+S/F2LgOhWg0qTLaIbles83MpQuKyMswYQr9RoZW8zy+AHptvNxsGNmmTL59+ZUxj2WZMmhz\ndbCuqpobVpVy69SPU/Ih/I5GQqAvFY9WwOV38/Cex1iQM4dLiy+MO+7Zt4+zeX8LD39pceR7gMEK\njuwdZPWPNMcflGV6BrxoJTHONKmqbwf9DJBpv/x039oHHitCkseP71PXsBdPvMK9C74OjE6y94Pf\nUBwF2ntcOD2q/eqeY+1887EtHB7Bl1k0Dp/xgyrbG93jjwT+UVoFD4eOXjdf/tlGXt9WF3nMHfAQ\nUILYdBYONddzz4vP8+KWQe/q/EwzlWXpmMeYAZ8veHXLKf76bjX+QLzHdDSuKbuMfEsuPWIt939+\n+tgz/iHjYmea1Z+aouGg+DpPHn6Kk84jNAuHmFqcxo+v+AJ3L77jjP6v8xVzs2aSZ85ha8tO6vob\nAFXb/5qyy9CKiRd/Ua9XPROSQFXTdEU2BwaLmVnlGWQlySD/+PoRvvbLzTzz5nGe+MchHG7/qK49\ny6iOSF24OI1Ug52FuXPJMmWM6rUfJjxy5Ve5b/kXWF+/iTZXO/+oWZvwuEvmF/G/Q4I+DCn1hzL+\nd6taaOpILo3d0+/loT/v5e09jTGPK4pCt6eHNEP8iJvD5+QXe39LTV/tWN7ehwJXll5MhjGdW6d+\nPPKYIIogCB8F/je31/Gtx7fSM+Bl6oQ0/veLi5g5cfgfcjS5T0qQ8YMq2yu73ch+lWA3mPGfXuCX\nFYUn/3mY1XMKuHHVoGf7xsMhcpJgptFbw0D6HvKLBhez3HQziypyTlvZ7v3C/AUCV15qQDeCgp0g\nCFxYuAwZmU1N21AUZcTNQjREk4noWZih5L5XN59i075h5BFHQE62xPzCyczImIZO1FLf3zjyiz5k\nkESJm6Zcz3zbSna95x0VoXYkiEYjis8XYfZL+uFbIzetKefxb67govmFTC1KxaBLfF/5A0G1IuNV\ng1M4yLe7Entx/LvA4fJz75Nbeat2c+QxvxzPFE9PMST0ORCiSv3hNmhRcdawG/UMu5GHv7QkTjfB\n6Xfhk/14PSLHuk/iDQ6Smqs6DnC8t5ravvqxvcEPMAZcPv6+qYamOg3fX3Q3BdbYKrAgScP2+P8t\nAv+qJamULT9AjesI2zu28eiBxxPewBv3NfPWbjU7GWmcD6JH+tSbOnAGM/6PrZoYpxJmMKrXbNNZ\nyLOqylcDwR78geB539cfDX6z/yl+e+BP7Os4OOKx87JnkaKzsqV5J+uravn9a4dH/X8EURxs34hi\nzPcrywq5GWZaul20dDl5dcsp2nqS+1onQpYpg6vKLuULlbdTaM2n2dHG1371LkfqekalW/9hQWlK\nMcWamWglKen7VhSF2v56XP6RP+PwBjzQqxosVXe4+eVf99OZgLQHKidGFARy0kwsm5mXUMwHYPuh\nNh58Zg8nGtXzpkrZTDHPQiefvyTZcwGjXuL6y60EBA9lKcV8a+6dSMNMnQTl2M23GFXq93d1AVA5\nf3JcCX806PJ0A9ARaOSXVU9QH6oiATj8qtNmtjmL72//CQ/u/NmYz/9BgygKyIpChj3JVJOk+ajH\nf7j9BKf667kg4KHX00eDo5kNtbu4IHdezO6ztdsVcUaLHedLXuoHdaRPm55O0DGAoNWq4j+O8Tu9\niYJAWX4KvqCfngEveq2EyaBhZlERkvk6Cq35GEMz8O2uTh5/+SArZ+dzvKmLjn4Hn7t8Jlrpg/fV\nyoq6cDQMNDMzc3gBJI2oYXnBEva0VdHY18lVi8dmNiSZTapcr9mslsZCEEWB+VOymDc5k30nu3B5\nAmMaiXK4/Ty37gQzStNZOC2b1UUrmKDppqXAyj+2nGLKTbPHdJ0fdAy1X/3XjjqKsq1UFKfxjy2n\naJSPcND/Lh+bdO2I5Lnw79Dfpuov5OSksqw8d1hXvUBQxusPDnvMspl5LJs5mDEZ5XRonI5iTxvx\n/X2Ycbi2h3XvBrjmgltZNKk4qTGRw+3nvid3MLnIzheuGfzdDgr4+AiEAr8+M5ORGi7d/R4UBVJt\nOgQEBEGgx6NuyibYCqnrb6B+oInyVLUq0OFSz51pTMfhc8YIg31YYTZouX75YFXkZFMfggAlOTa6\nvd0giR8F/l31qnvYRHsp5allrGvYyN+rdpClTIph+34sqqw+mh5/RL0vxOwPDvQjWa1nxOntYOcR\nfr3/j4jVi7lj9TJmlKaTarCzLF9V0QrIAXSSjoNdRynWl1NRkka15wCHgv9ibzsszB3e4el8Rq45\nsR3lUKwpWs4lE1bFfN4vbajmRGMfd988e9jvQTRboKMjaX+/xdnGKWEPFTNLRrTJbelysqGqmRtW\nlCKJApOL7BGFsZmZFczMBGYOe4p/C/Q6vGw50MqikOeCQa9hAhM5HNjIzta9Iwf+0Fht979eA0Gg\neOkCslOSewT0DHj55mNbALjvtnmU5A4GBG/Qx/3bfsyCnDlxLm6leTa+ckPluN7jhwkzJ2aM2BIF\nMBs03HfbPOxDWoxhYx7F58Xf5UVB4LE367h2edmwWf9Df3sXTU4tQXMbJq2R+xZ+iwxjOhcVrSTP\nksOfDj8XGcME6HB3IgoiaYZU3AEPOaNcPz5MaOxwsH5PI3dcXcEvDv6GT8peUv7dtfr3txzHoDNF\nPKrt+hT8mb1MLx0spT/5z0McE9ZzccVMLipePqKAD4AmTX19oEclCgYHBtDlnP6oyUPP7KEl51UQ\nYdK8DmaUqpuT59adwKTXcPXSEjSihouLVvHPU2+QV9GKRpqNxQq0g1n7wZxj/ez0W9jespsZGaOb\nldYkIIlNL0ljZlkGCqrARTKER/qG9vff3l3P290vM6BRF5Y9bfuYnj512E3EjsNtvLmrgcqydKYV\np8UY8YRR01eLJ+Cl3F6KVvpgki/Hg/q2AZ596zgNHU7uumkW3//0fKRQhWXFzDy0GpG9OzPpcI0s\n2BKt15+yYiWWiWW4OwaSHm+36PjFV5fS0O6IE3pqcrTQ7xsgqASRZYVehxdJFGIC0qambVS1H+SG\n8qvIs5wZg6gPIwRBSGwPK4oIWi2yz6dWQ6025s/IH5bDE5ADiGU76fM7EPwCDr+TFmcb3e06BqrL\nyJuTj0EyDAn8XaTp7QTkAArKh1q1LxqnWvpZv7eRJdNzWTkrn5Wz8nni1UMIFkkV8fl3z/hljZup\n6TMii/eUtHK2t+ymydFCoVUtRa5enMZ7VQ38vaaBXKWCiuI0EEWQ5Rgxn2ho09SA7O/uUtWpfD4k\n6+n3Be+4Zirf3/0SKOAM9a9AzUSiSWxripYTUAKsLlwGgMuv9jo/qIF/dtYMZmeN3/8bGLV7WpjZ\nLw4J/FOL05G0V+Ox1nKo+xCn+ut5fO02LplRwcQkY4bXLisdVnegudPJI5tegpQ2frLs/n+rwO8P\nypQX2plUZEcjipGgD0QCgE4w4Ay0ISvysMqF4d+hZLWScf2N/OrFKnr7PXz+6oqExwuCgNWkY1rx\nYMn+RE81f///7d15fJT1ncDxzzP3lckxmcl9EcKRcCccglxqrbdFS6l0lW7Vql2tFet6Vuz2gNrV\nV10V13bXV0WtF96tJ66CIIpQkUPCEQgJkDuTezL3/jFhSEgyBBISwnzf/0ieZyb5TR7zfJ/f9f2W\nvBf+OsOSRm1TOyue38K5E1K4ak4uu8ucuDx+StwHKXbu7ZZsSvTM5w/dmzqvpVD0egIuFz6nE0N2\nDoVT0qmJ8LC217mfZm8LE2Kn4G+JxRNzEG/Aiy3WSmqiGZ1WTUZMKvsaDuD2e9CqNEx2TMCo1ofT\n9QaJjnU0Wo2K3LRYHPHH4tMNl+XzyD8/wa8inCa5J1ER+CE0zH/U6PiRfFGxmU2lezBl2rDFGmj0\nHetx6PUdRUMMRgK9lOUF0CSEbii++rrwqtWBCPztSlMoC5PKwFUjv0fJ4UZyUq1MG9t1CEur1nL5\niO+Gvz5aoMc0TAP/QPAH/NS115+wTOzRvfzH7+FPSzSTlpgD5JBgtHKgqQzFWo0ttm9TJ+9u3sNm\n5+dclF/ItLTQQ4wj3ojR4sWraDEN83KhJys3NZbc1Mh5GRoagqBIncmfAAAgAElEQVSBFncb1l6y\nwAHoUtNAUbD/8EeozWYumJrJ4crGXl/fk7WHN1LaFFr9rVfryIsfQYLByKO3nht+TWllM7sOOmnP\nqkajqLEZonuu/3iv7/s7Jo2R8zPnhrdmvv9lGa+vK+GXP5zcpXKeSqfHW1sDfj9aW89JlDobaxvF\nvVN/QVmFmy9KGlk69+LwubTE0Chdvm00Fp0Fl8+FXh/LD0aFkpmVNYd2z6iiY8066XYL6XYLW3bX\nsPdQI5PzEtFp1WhVGgKKlOXl0YsfxNPpIXNCYgGLkm7m06/qKLC6iLfqOdRSAcA425hw9SiVwUDQ\n5+016YQmNg4UBV99fXgrnzqmfwtL2j0+Ui3JPDz7Idx+N6+uKae8qoaliyYSY4q8fanN11GZ7wwu\nyXs6BYIB/rjpKSobG7jMtoQLinou7wm9D/V3VmAbg82QwJgMW8Qtkn//vBSNWoXFqMVobaeq6VvK\nW+xMIxT4NWoVaoObGG3sgKz/ONvMGZXPvgYDQSVyT808bjy5/7Uy3PMfk52Azdx99KTN24ZOretx\nKuj6gh/RmHc5akWNQWPoMafAd6dlUjjewrKNoUCilh4/ADvrivnvbX8lEAyQZHJwYadqhvMmp3JB\nUXq3nRMqvT485FzapuGD5zZz7YV5XUZ+jpcek0qqJcg5vWQ+7/xzO8uMSee2STeSbuk5wdnZKhAM\nsmlXFRNH2nB7/TibfNLjB0i3plDjPhb5DRo9cwpGMKcgNArwzoYDfLrbx9xp53PJ6HMxaEI3eUNW\ndrgn3xNFo0ETF4+3vu5Y4D+Jgi/1Te20uX1dalo/9uo2fP4A915biE6t5SeXhFart7Z7eeC9v5Cd\n6OCGqT2n61UraowaQ9TMcR1PpajQalR4tU1MGh25l3a06Evn6+VsdvM/f/+WaWMdzJ2UhkVn5j9m\n3hPx+wSCQXz+AB9sKqMgJ4ExE0PTLQ7Lsd5NaVMZLd7W8LYj0VUoG1z3jHA96W3arbN3S9ew4cgm\n7pzyM9JjUmlwN7L+8BeMjs8jL34EcfruIxCNLW7cvgCOjoWcNa5TKxJzNjNqDOGdN9/JmtdlWsag\n6zmUKJ1KJydkpjAl387r+97h3LQZERfxqhQFry/AFzsrQIF9hxqxxxm5bGZ2xDaOSTg9dVLOVBu2\nV7BhewU/vaIAg06Dx+vHEIxBpdLKqv4TufScbIrGOIiP0bNzfz3f7Ctj0fkjSfnZreG0rr3RJCTQ\nfmB/eGV/X4f6W1xefvPsZr43O6dL4L/rmslU1LV220Jm0mto0JVQESF4/GTcj/r0s89mKZYk9jeV\n4lGage7FXo7S2kKrlbWJx1YtmwwaLpmRhUHftYe3raSWD78qZ8GcEd2GrVWKwvdmj+DCGcn4gwHW\nH/4CAFunDGNHyw+Pih+J6K6t3cuR2jZssYY+J59ye/3c/9QGclNiuHxW1xLYu+r3EgwGSTKHcl1U\ntlbzXunHKCjkxfe8FuOx1dsw6jUsXTSRjTuqSLLZOS9jNpMdsrr/qLROPenc2Gy2VG0ly5oZLugT\nCAZxuX1dtk52rruQNjKDKkcdn36+gbWHPueJ8/4Q8eepVQp7yhvIydLism3Hp8sGsgfyIw179jgj\nl8zIwtSRjl6nVXPv/CUc3FCKp6X3xGFRH/h3ldbz4sf7+Pn3x4cTfuSmWVEpof2jnGBoVpuQQHvJ\nPtyHQ79kTR8Cf3l1C8FgkF8tKeq2GlalUkizdx81aPO5CCoB7Ja4bufEMeGsa67aiCuxLVMKSbvj\nl5jGHttBoNOoGJUZ023xXXyMgQunZpBq6/lBYq9zP499/TTz0+awZf8h0HcN/MlmB/dPW0q8IfJc\nd7Tac6iRv39eyhWzssOBv7qthg8OfkJVaw3zMmZR6JjYZZpErVJYeH4e7S5Pl+/V4G6ksrUKs9bE\n6j1vMTV5Co3uUD2Nnnr6Rz3446lAaKqtuMyJs8XE1TMvH+iPOqzp1TpunXgDMToLJQ0HeL74Vf5l\nzEISjQkEg0GWPr6e1ERzl0p+qk49fm1iIrGGjjzyfViAp1IpXH9ZPjtqd/Hats2MSIq+tMkn0nk9\nRWeKWi09/kgCQbh67ghsHQF48qjIi8KOp+lY2e8+WAqA2nLiOf7Syib+sfEgP++0V7ipzYOzyU26\nw9xt/qvOVc8re94EwKqL7mxiJ+Iwhm4Oqz7ZQv6Csb1uHVJUKswFXZMENXtbuG/9b5mZOo3FY64O\nH89wWMLrPo63u8zJt+V+VIqK7TXFuDpGiOKPyyku28F6N2lkIpM67Rf/tHwDq/e+HQ4OdXvrGWcb\nG56Cg9C6iUmjHN1WiO+q3wtAsimJ9Ue+xGGy4+vI0tmXxC4GnYYbLouO0runYqwtVOe+0dPU8d/Q\n719RFB6+ZWa3v7fOQ/2vb3Wi2WdiQmIB22p3dsm9v9dZglFjJM2S0uUBLxAMsKZsLQAJhu5B7qOD\nn2I32pjUz91AZ5OPvionqc2HIdB7GvOoDvyBYABtnJN4rfmUF11pOlaqustCxXT6MtQ/e0Iqsyek\nhtPsvvnZfr78tgqt0Ys2to67Lr0YQ8c8fa2rnie2/oUaVx0GtYEC2+hTame0cJgSUStqivITek3R\n2psX1v2ToCqIXtX3NRIajQq/V0WGOZPSlgOMNs5hTE4RuijasjfQvqnZQZAgCzK/z4H9CiPStF2C\nfiTF9XsAmJk6lZLGAzjbG/B3zEvHRujxt7Z7aWr1YI8znvT/N9HIqgs9RDV5jlUn7ekhW9WRxEdl\nsTBuTApGk56Dnhy21e5kX8MBpiWHAv/qve9Q2VrFH2YvC9/7ANrdAfY27AfA3dp1cfOnhzbwZsm7\njIrLlcDfidcfAHXkBalRHfgPNJbx2NdPMz25kOvyQ9WNShpKWbXlfdJUY/np/Lkn/B7a+I75rfbQ\nHtKT2c539GFjdEYcFxRlsLF6A2+WbGRzVQrnps0AINGYwC0Tf4JBrceqG5isgGczh8nOn+b9LuJ+\n8N5kZSrsOASpFkeX4y6fi//d+D4Hyt089L2ruuyuOLpdbU1ZPqX7DjB9TMqwzpo4FDx+Lxv270Sn\n6Jk1cizegB+rLoaRljF4rU4mp3RfBLbvcCOPvbaNc8clUzj62PXSKBqSTHbyOx6Qne6G8AN2XIQe\n/zsbSvlmXy2piWbmT05j3IgTbz2LZkdHHpvcXUdcWtu9qFUKOo2aKmdbeKhfm2Bj5AgbdnsMltLR\nuHzt4RwqdS4nh1qOkJ8wukvQB9BqFLQBM15VKymWrkP9f9//AUCvqYSj1SUzsjj0hZm2CLXBojrw\n58RmYjPE82XlFvxBP/9asBi3302tsp/s2L5l4NN03puqVodXi/dm695a9rXuwq2v5qLs+diMCYzt\nSDCypWorKkXV7ek16QR70sUxpxLwj3IpoQWayeaugV9BYbf3S+zZjl5zvucnjOYN/sHOumIJ/CfJ\n4/ewuvxvJASzmDVyLI7a87B6faQlWshO7jlYJyeYuHp+HrrjtgBem/+DcKDXqDTUtzfwnax5JJkd\nWLS9L/b84fl5LJyfy2fbKig50iSB/wRidGYUFBo9zbT72nlky0pswRy2fZ7ADZfloygBnv9wH/8W\nE+qoHF1MC5BsTuKyEReGv95eFyqw1VPGTq1GzW/n/JIaVx05sV2ny1LNyZQ0loZ3GoiQNm8b7hNU\nRIjqMS2VoqIwaRIAm6u2AsdWrno0Dd1e7/MHulXBO5q9D0Jbw07UI2/3+vii8is+r/iyy/F3D3xE\necsR8hNGRbxBiROrdrax7JlNvLa2JHxs485KnM3uXt/jcvuoaKkGuj9oGTQGRsXnUtVeSZO3qcu5\nRz9dzf1r/0iQIFnWjKgoEDLQTFojCgoJ8aHb0cUzspic5yDCVm8sRi2TRztwxHd/0FY6FubG62Nx\ntjcwxTGBK3MvPuHfplqlYt6kNK48Nyfi60To3jkteQqj43OpcdVxpLWS2Lggty5JpsW0j5cr/oe8\nsR7UHT1+jc3Gf7+1g+ff29Xte31TE6qlMj6x50JbFp2ZnNjMbsevy1/EqLhcrsi9aAA/2fD36tZ1\nFDfuj/iaqO7xA0xLnsKHBz+h0BGqohKrj8GsNVHZVhV+TWVrNRWtVWz/WkOKzcJ3OiWGUZnNKDpd\nKF1vLwVfOisYaeGFqmqyYjKwdWyDKWko5R8HPgIIP4iIUxdn0fOTS8aGi+v4AwH+9tEe/uP66b2+\n57Oth9lzpAZzjLnHzIcFiWModu5lW/Uu5mTMCB93qepo8NVg1pr496LbBv7DRAGVosKoMdDSkXnS\nHmcMX7vK+jZe+ngvBTkJXf7u+uLyERf1eWrM7fHjbHFjMWoj1osXxxydHt1S9Q0ATZ5mnvzmf8Pn\nLyrKJWZjO25Aa7MxNdNBm6GGQ82qcP34Q81H2OPcR2ZMGvE9LN6LJNFo4/YpNw3MhzmLKEFNKFd/\nBFHd44dQJbhlM/6dH41dGD6mx0JNq5OKulaCwSArPl3F/+x4jh2qf3BOwXHDwIoSTt3bl/n9b2p2\nEAgGmNJpf3BuXDbXjv0BM5KLmGSXRSr9pdOqsZp1vLhmD63toSGvf1swPuIe8QunZ3HDmBt5aEbP\nCXvG2cYA8MHur7oc9+uaMWoMxOqkp98fRrWJRlcL7Z6uaUYtRi0zxyXTbN3Oewc+Dh//9OvD/Orp\nz6mo6z2vRWHSxC5/Z5Fs3l3NfX/+gq17JXHPyapxhcrinpNSRIw2NN+eos4lw5KOPj0DFAXjyDwK\nRzt48+BLPLfrlfB70ywpzEufxeIx3x+Stp+NCrIS8asiP/BGfY8fju39PipOH0u9txq1LrQP0mR1\n0+RVmD9yEhZjKHi43D4MOjWKoqBNsOGtrOy2h/9ARRMHK5uZnp+EQafmqbd2UmMLBY7jb0gzUoqY\nkVJ0uj5iVAkGg/jVbagM7fxzTw2zJ6QyJqujkqI/wHMf7ObSmdnhLG1HdS7RfDyHyU6sJoG2QDUe\nvwedWkdNYys1rlqyYjJk0WU/BbwaXP421mwuZ/PuGhbNH8nY7AQsRi3Txibx5votaNU6Ls45H4CC\nnARyMxOINQ/MLWxGQRJ6rZpJebJX/GQdrayYZHbgr00naNlLsm8yH28+hCM+mYlP/QVFo8Hj99Du\nc3dZjKcoCgtH9ZyJVJwarUp7wh7/kAT+q666CktHqtT09HRuvvlm7rnnHlQqFXl5eSxbtgyAV155\nhZdffhmtVsvNN9/MvHnzcLvd3HXXXdTV1WGxWFixYgXx8X2ryNZXC0ZfiNs/l3iTkSBBmn3N5MRm\nclF26KZT0+DiT69+w42X55OdbA3v5T++x69SFL4s34kl1seU7EwmjzXz4uEK8uJGhPevioFX66rn\noS/+QHZcJlnxEwkEk8OL/nYddKLXqvF3VBIrr25h76EG5k3NOuHw1y2Tr8PZ7kSn1rHq/WLW7t6N\nYXwAq1qKuPTX1Ix8qtvsTM2JZUSGieS4rtMtVl0MVZ3S6NrjjNjtMZRX1PBB6Wd4Al6STHbGJ47F\neAqFkNQqFUVjHCd+oeim2lWLgoLNEM+KK5fgC/ppdXl59MtnSCeZSXkLaWv38vR7WyBWVuGfbkdq\n3ARO0BEZ9MDv8YQyba1atSp87JZbbmHp0qUUFRWxbNky1qxZw6RJk3juued44403aG9v55prrmHW\nrFm8+OKLjBo1iltvvZV3332XlStXcv/99w9oG0fEZoX/3eBuJBAMEK+PC/fqXG4fC+ePDPcYteGh\n/q7DvfoYF2WWNbx9ZCtvfzKXm64cx39k3017R/lIcXokGOJQKSpKm8oobSojKyaD3LhsAMaPsDG+\n04ptnz9AeXULdY0u7JbIRZAyYlLJ6JibnD0xFUdOA+8cBrtBdl3015W5oSpsL+5+nfWHvwhlOiS0\nirvkcCMVVX585lCPsfOe/rp2J2/vfz/89bIZd3UJ/Lvq9rCrfg+zUqeFU/iKgXXDuH+hvt0ZLoqk\nRo3WrKFRdZj4jpwIGrWKMbkm9tUSng4Qp4cWPWq1HnD1+ppBD/zFxcW0tbVx/fXX4/f7ueOOO/j2\n228pKgoNc8+ZM4cNGzagUqkoLCxEo9FgsVjIzs6muLiYLVu2cOONN4Zfu3LlygFvY4vLywsf7SHT\nYUGtd2NoHkGcIy18PjMphsykY717rT10Q9HEdV2csuFIaOX+5LhpZJ6bQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "weekly = data.resample('W').sum()\n", + "weekly.plot(style=[':', '--', '-'])\n", + "plt.ylabel('Weekly bicycle count');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows us some interesting seasonal trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) where we explore this further).\n", + "\n", + "Another way that comes in handy for aggregating the data is to use a rolling mean, utilizing the ``pd.rolling_mean()`` function.\n", + "Here we'll do a 30 day rolling mean of our data, making sure to center the window:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YWIjhw4ejsLAQOTk5kMvlEIvFqKioQFpaGvbu3Yv8/HwIhUKsW7cOjz76KKqqqsBxnI3F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\n4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9NLSBzs3yJ5Tui49oLl82Lb4YoVK3j00Ucxm80MGjSIWbNmIUkSN910E4sXL8Zut7Ns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\n7p2zL7/8ko8//pibbrqJ8vJybrvttl4bd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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "daily = data.resample('D').sum()\n", + "daily.rolling(30, center=True).sum().plot(style=[':', '--', '-'])\n", + "plt.ylabel('mean hourly count');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The jaggedness of the result is due to the hard cutoff of the window.\n", + "We can get a smoother version of a rolling mean using a window function–for example, a Gaussian window.\n", + "The following code specifies both the width of the window (we chose 50 days) and the width of the Gaussian within the window (we chose 10 days):" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eXYN18pR+zeeKoEBbKwQCGLJz4tYGY1YW9uklOLdspnPfXqwTJ8WtLamg/b1N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\nGbuLW6hrOVa7+lntV+w+ut/nlZ5GJSMqRINEIiAIAumhCwhWTr6+NBC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2N+1BQGB17BkB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "daily.rolling(50, center=True,\n", + " win_type='gaussian').sum(std=10).plot(style=[':', '--', '-']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Digging into the data\n", + "\n", + "While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the interesting structure.\n", + "For example, we might want to look at the average traffic as a function of the time of day.\n", + "We can do this using the GroupBy functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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0n1RC80xPyCJy3iTd0BUkG7uSz7AJ+e677+buu+8+7fH169ef9tjy5ctZvny5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "by_time = data.groupby(data.index.time).mean()\n", + "hourly_ticks = 4 * 60 * 60 * np.arange(6)\n", + "by_time.plot(xticks=hourly_ticks, style=[':', '--', '-']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hourly traffic is a strongly bimodal distribution, with peaks around 8:00 in the morning and 5:00 in the evening.\n", + "This is likely evidence of a strong component of commuter traffic crossing the bridge.\n", + "This is further evidenced by the differences between the western sidewalk (generally used going toward downtown Seattle), which peaks more strongly in the morning, and the eastern sidewalk (generally used going away from downtown Seattle), which peaks more strongly in the evening.\n", + "\n", + "We also might be curious about how things change based on the day of the week. Again, we can do this with a simple groupby:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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kZWVBoVBAr9cjOTkZ+fn53Vc1EVEvY5w8BZq0dFj37YXtUF6wy6EQpWhrg3nz\n5qG4uFhqi6Io/T0iIgJWqxU2mw0Gg0F6XafTwWKxBFRAbKyh7Y2I/dQO7KvAsJ8C11N9FfGT+3Bg\n2S9Qufp9DJo2ATKlskf+3a7C76nu12ZoX0omazo5t9lsMBqN0Ov1sFqtLV4PRHl5YOHel8XGGthP\nAWJfBYb9FLge7St9NExzrkDNxq9wfOUaRF+7oGf+3S7A76nAdPbApt2zx4cPH449e/YAALZu3Yqs\nrCyMGjUKe/fuhdPphMViwalTp5Cent6pwoiI+qLoBTdAbjSi6rNP4aooD3Y5FGLaHdqPPfYYXnvt\nNSxevBhutxvz589HTEwMli5diiVLluCuu+7CsmXLoFKpuqNeIqJeTa7TIfbmxRCdTpSt4qQ08ieI\nzS9SBwGHU9rGYafAsa8Cw34KXDD6ShRFnHvhOdgLjiP+4Z9CPzqzR//9juD3VGB6fHiciIi6lyAI\nvsd3ymQo/+A9eF3OYJdEIYKhTUQUgtQJiTBfMQ+u8nJU//fzYJdDIYKhTUQUoqIWLIQ80oSqzz6F\ns7ws2OVQCGBoExGFKLlWi9hbFkN0uVD+wXvBLodCAEObiCiEGSZMhHbIUNjycmE9sD/Y5VCQMbSJ\niEKYNClNLkfZqvfgdXJSWl/G0CYiCnHq+IEwz70S7ooKVH3+n2CXQ0HE0CYiCgPR1y2AwmxG9ef/\ngbO0NNjlUJAwtImIwoBMo0XsLbdBdLtR9sF7CPK6WBQkDG0iojChzx4P3bDhqD+UB9uBfcEuh4KA\noU1EFCYEQUDckjt8k9I+eB9ehyPYJVEPY2gTEYUR1YB4mK+cD3dVJar+80mwy6EextAmIgoz0dcu\ngCIqClVffA5nSUmwy6EexNAmIgozMrUasbfeBng8KPtgJSel9SEMbSKiMKQflw3diJGoP3wI1n05\nwS6HeghDm4goDF2clCYoFChf9QEnpfURDG0iojCl6tcf5quuhru6CpWfbAh2OdQDGNpERGEs6n+u\nhSIqGtVffQHH+fPBLoe6GUObiCiMydRqxN22BPB4UM5Jab0eQ5uIKMxFZI6DbuRo1B89AmvOnmCX\nQ92IoU1EFOaaT0orW/0+vA32YJdE3YShTUTUC6ji4mC++hp4amo4Ka0XY2gTEfUSUVdfA0VMDKo3\nfgVHcXGwy6FuwNAmIuolZCoV4hbf7lsp7f0VnJTWCzG0iYh6EX3mWESMHgN7/jFYdu8KdjnUxRja\nRES9TNzudT7yAAAgAElEQVRtd0BQKlG+ZhU8dk5K600Y2kREvYwyNhZR/3MtPLU1qPz3x8Euh7oQ\nQ5uIqBcyz78aythY1Gz6Co5zRcEuh7oIQ5uIqBeSKVWIve12wOtF2XuclNZbMLSJiHop/ehMRGSO\nhb3gOCw7dwS7HOoCDG0iol4sbvESCCoVyteugqfeFuxyqJMY2kREvZgypnFSWl0dKjdwUlq4Y2gT\nEfVy5quuhjKuH2q+3ghHUWGwy6FOYGgTEfVyMqUScUtuB0QRpe+tgOj1Brsk6iCGNhFRHxAxcjT0\n47LQcKIAdTu2B7sc6iCGNhFRHxF7q29SWsWHq+GxcVJaOOpQaLvdbjz66KNYvHgx7rjjDpw+fRqF\nhYVYsmQJ7rjjDjzzzDNdXScREXWSMjoa0dcugMdiQcXHHwW7HOqADoX2li1b4PV6sWrVKjzwwAN4\n5ZVX8Nxzz2HZsmVYuXIlvF4vNm7c2NW1EhFRJ5mvnA9l//6o3fw1Gs6eCXY51E4dCu3k5GR4PB6I\nogiLxQKFQoEjR44gOzsbADBjxgzs2MEb+YmIQo2gUCDutjsAUfStlMZJaWFF0ZEPRURE4Ny5c5g/\nfz5qamrw17/+FTk5OX7vWyyWgPYVG2voSAl9DvspcOyrwLCfAtfb+ip21mQ07J6Mym07IB7MQdzc\nK7pmv72sn0JRh0L7nXfewfTp0/Gzn/0MpaWlWLp0KVwul/S+zWaD0WgMaF/l5YGFe18WG2tgPwWI\nfRUY9lPgemtfGa+/GVU5+3D6nysgpg6HXK/v1P56az91tc4e2HRoeDwyMhL6xv/BBoMBbrcbw4cP\nx+7duwEAW7duRVZWVqcKIyKi7qOMikL0ddfDY7WgYv26YJdDAerQmfb//u//4le/+hVuv/12uN1u\n/PznP8eIESPwxBNPwOVyITU1FfPnz+/qWomIqAuZ516Jum3foXbrZkROnwFNckqwS6I2CGKQn9fG\n4ZS2cdgpcOyrwLCfAtfb+6r+2FGc++PzUCenYNCvfgNB1rHlO3p7P3WVoAyPExFR76AbOgyGCZPg\nOHMatd9tDXY51AaGNhFRHxd7y60Q1BpUrFsLT4B3/lBwMLQpLHhFEc2v5Hi8Xr+22+PfJqLAKUxm\nxFy/EF6bDRXrPwx2OfQ9GNohyO3xYmvueb/XHn1jGzzNFkH42Z+/82+/7t/+6Wvf+rUf/tP3tx96\ndatf+8FXLm1v8Wvf/7J/+76XNvu3/+jfvvdF//Y9L3zzve0fPe/fvveFzfA2C+Ufv7jFr33fH/3b\nr67NhcPlkdp5Jyv99kdE/kxz5kIVPxC1326F/dTJYJdDl8HQDpL6BpcUMqIo4pU1uXC5fSEjlwlY\ntakAVnvTve8aldzv83qN0r+t828bI9R+7SjDJW2jfzvGpPVr94+6tB3h106I8W8PivOfXDGov397\n8AD/dtrASL92eoJ/e8ggEwQIl20PS7qknWyW2g6nB8cKq6FS+L69XW4vXl+XJ73v9Yr41ds74fU2\n9f+mvef8ztS9PGunPkZQKBB3+1KulBbiOHu8h+w8UoIxqTHQqn132T36xjb8v9vHSWH5xPJduP/6\nERgY67v/Pe9kBTISTdCoFJyV2Q4X+8rt8UIh94W2w+XBtoMXMGdcAgCgrt6Jl1cdwNN3T5DaT/xt\nF157ZDoAoL7Bjcf+uh2v/3QGAMDp8uCT7WewaGYqAF/o19qcMF9yIBRO+D0VuL7WVxeWvwXLzh2I\nu+NOmGbNCfhzfa2fOoqzx0OE0+WBy910ZLryy3ycr2h69N3GnHMoKrNK7amj+sPZbPunfzBeCmwA\nGJ0aA42qQ7fREyAFNgColXIpsAHAqFNJgQ0AaoUc91w3XGo3ON0YkRIltastDuw+WurX/v27Tcv2\n1tU78c7nx6S20+XB6Qt1XffFEPWg2JtvhUyrRcVH6+C28Ps41DC0O+jw6SqU19il9usfHcSRM1VS\nu87mRHGz0L55VirizE1DzjfOSEV8syHm5iFDPUutkmPU4GipHWXU4L7rRzZrq/HwTWOktggRU0cN\nkNqVtQ04W9J0hlFSVY9/fHZUapdW1ePN9QelttXuwoGCii7/Ooi6giLShOjrb4C33oaKdWuDXQ5d\ngklxGW6PFw5n00Sm/+4qRN7Jpl+0u46W4tDpppAelx7jF7z3LhiB8UPjpPaQQWaY9OE7nNqXKRVy\nDGx2gBUTqcWNMwZL7aT+BvzitkyprVMrMC87UWrX2pxoaDYprrjcis92nZXaJ8/X4uXVB6R2ZW0D\nvsu7ILXdHi+czT5P1N1Ms6+AamAC6r77FvaTJ4JdDjXD0G50pqQOZ0qahoI+3HwSX+87J7UdLg9O\nnW96/4pxCRg6yCS1Z49L8BtS5Zlz3yETBOiaTQyMMWkxY0y81M5INGHZLU2hHmvSYsGUZKntdnvR\nz6yT2oVlFuzNL5PaR89W4/V1eVK7qMyKL3cXSu36BhcqaptGfYg6S5DL0e+OOwEAZSvf5aS0ENJn\nLpp6RREOp0eaCLY3vwx1NidmN17rzC+sQUVNA5L7+55ONjTJjDqbU/r8NZOTIJc1zVZO6s9H0FHH\nRBk1iDJqpPaQQWYMGWSW2qkDIxHbbDa/SiHDiJSm4fvCUgvOlDYNxx88VYW9x8vxwELfkP6RM1U4\nca4WC6b51pGurG2A1e7i9yy1izY9A8YpU1G3fRtqNn8N85y5wS6J0IvPtMtr7H7XmLflXcB7Xx2X\n2qLoO4O5aFxGLCaN6Ce1M9Ni/M6WFHIZBKEptIm6i1GnQkKzSYlDBpkxf+IgqZ09JA6L56RL7Wij\nBtlDYqV2SVU96uqbDjjzTlXim/3FUnvn4RK8tb7pzP3ShWqILopZdAtkWi0q16+Du7Y22OUQwji0\nRVGE3eGW2qcv1OHDzU0LApRW1ePT7WekdvIAI6Kbnd1kpsf4TTaKNWmResm9w0ShSK2Swxihktpp\nCZGYMKzpgHPOuATcNrcp1FMGGDC52QGp3enBgOima/Rf7inCui2nmt53uKV72KlvU0RGIvqGRfDa\n7ahYtybY5RDCKLStdpffdb5T5+vwwgf7pbZKKcf+gnKpnRJvxPyJSVI7MU6PG5pNHlLIZZDJeOZM\nvZO82ZOakvsb/YbfZ48diAUzUqW2KPqC/6IPt5zExpwiqV1V1+C3uhz1LaaZs6FOHIS67dtgLzje\n9geoW4VUaDc/c66zOfG3Tw5LbafLgxVf5Evt+JgIxEc3Td4ZEK3DM83uvY3QKDE6tek6IBG17n8m\nJSEzLUZqmyJUGJrUFPLvfpGPQ6cqpfbZEovfzyr1boJcjrjGSWml762A6OEBXDAFNbQ37WmaAWt3\nuLHsz9uk5SN1GgX2Hi+XFiwxG9RYPDddel+rVuCe60ZIn5cJAmdsE3WB66amYFC/pklrgwcYkZ7Q\ndKfE3z49grLqptnqeScrGeK9nDY1DcZp0+E8V4SabzYFu5w+Lagp97ePD8LSOGFGq1ZgXEYsGhp/\n+BVyGV5/ZDqUjetHC4KAScP7Q8bJYEQ9asG0FOkauiiKmDyiHxLifNfEPV4v/rrhEDzNroFvOVAs\nraNPvUfMopsh0+lQuWE93DU1wS6nzwpqaP/stnF+Z8f3XDfc735XpULe2seIKEgEQcA1k5Ola+Ze\nr4g7rsyAXuv7ua21ObH2m5OQN/5cuz1ebPjuNGen9wIKgxExN9wEr92O8g9XB7ucPiuooT1x5ADp\nvmkiCj9KhRxTRjYt6apWyvDgjaOkEbHCUiv2HS+XbpessTrw0dZTre6LQl/kzFlQD0qCZecO1B/P\nb/sD1OWYmCHqYMURHK7MR6W9ChaPBW63BwIEzEuahQn9x7XY/pui77CvzPf4SZkgQIAAQRAwY+Bk\nZMaNarH99vO7cajymLSdrPHPCf2zMCJ6SIvt95bm4njNSWm7i58bEzMS6ebBLbY/VHEUZ+qK/PYt\nQMDQqHQkGRNbbH+i5jSKrRda1JNsHIR4ff8W2xdZzqPcXtGinuHawZBD02J76hkalQLDmk1ii4/R\n4d4FTXNPjhfV4FyzB+ecKanDgYIKLJze8nuIQo8gkyHujjtR9NzvUfbeCiT95mkICsZIT2Jv9yC3\n142qhhpUNlShwl6FSnsVMsypGN5KSJ6sOYNvi3cAACKUWgACRFGEw+Nodd+V9iqcrvWtZy2iaShy\nVMzwVrc/Z72A3PJDLV5PNg5qNbRP1p7Bd8U7W7werYlqNbSPVB3HlnPbWryukqtaDe19ZXmtbn9T\n+oJWQ3vHhT2tbn+nbBEmRk1s8XpB9UlUO2oRrYlCtNYMo8oAmcCJi91No1JgYEzTr5lxGbEYktg0\nqS2/sAb1DU2T2Pbml+FCZT2ubbbMK4UW7eBURE6fgdqtW1Dz9UaYr5wf7JL6FD5PuwuJogi31w2l\nXNnivY2FW/Dxic/8AhUArkicgRvTr22xfaW9Cg0eB6I1ZiQOiG1XP4miCBEiRFH0nbW2Ek4OjxMu\nrwsQfSHvFUWI8EIj10CjaPlgk1qHBfXuer99ixBhUkfCoNK32L6svgI1jlpA2rfvM/10cYjWmlts\nX2QpRll9BdC4nbfxzyRjAvpH9GuxfUH1KRRbL0BE0769ohfT0sZB6zK22P6dw6uwp3Sf1FYIcpg0\nJtyYdi3GxI5osX1vFyrPPhZFEU63F2qlb/7Kui0nYdCpcOV434Hd57vOQiGTYV5j2+sVe3x9hVDp\nq1DisVhw+onHIbo9SHn2OShMZvZTgDr7PG2eaXdQWX25b/i62Vlzhb0Sk+PH45aMhS22j9ZEYXBk\nMmK0UYjWRiFG4/uzny62lb0D0dqoVl8PxMXhYnzP7za1XAW1XHX5DS4RqTYgUh34N1ucLgZxupi2\nN2yUaBiIRMPAgLdPNw9u9Qw/1tT6L46ZCZMxODIJVQ3VqGyokkY85Jc5237n8Ac4XXsWUdooRGvM\niNaYEaUxY2hUOiLVLQ8KqGMEQZACGwAWzUyVbusEgOo6B4YnN/0svPtFPtITIqVHo9odbmhUci4x\n3MPkBgNibrwZZSveQfma1Rhw733BLqnPYGhfwit6UeuoQ4W9EhX2KuiUulbPxArrzuHDgn9LbY1c\njVhdDIyq1oNtbNwojG3l2jL1jJTIJKREJrW9YSOFTAGn14Xj1f6PJXw4895WQzunZD88ohdRjeFu\nUhshl/Huh45oflvnknkZLd5PanYP+Z8/OoirJgySFlKqqLXDbFD7rQhH3SNy+gzUfrsFlt07ETlj\nJhA7oe0PUacxtBudrDmDlUfXoLKhGh6x6R7TIea0VkM71ZSCu0fcLp05Ryh0PNrvRe4YdjMAwOVx\nocpRgyq77wy9tevrAPDF2W9w3lYitWWCDGZ1JO4b/YNWP+MVvbym3gF3XT3Urx1t1GBwfNNB1B8/\nOICHFo3CwMYHrpy+UIfEOD0XXuoGgkyGfnfcicJnf4uy91YgcXLLCbLU9XptaDe4HThdexYVDU1D\n15UNVTCqjLh/zA9abK+Wq1DvtiPBEI8YTRRitNGI1poxIKL1X9JmjQlZGlOr71HvoZQr0U8Xe9nL\nGBfdnHE9yusrUCkNv1ejqqEGOqW21e1/v+tlOD1ORGvNiNZEIapxCH5s3OhW5xRQ6+6+Zpj0d4/X\ni1Gp0RgQ41v4xe3x4oX39+Pln0yVQnt/QTnGpMbwuQNdRJOcgsgZs1C75RsUrnwf8uFjINcbIDfo\nIag1PJHpBmEZ2qIowuKyotJehXq3HSOih7bYpsZRiz/nLvd7TSlTQC1v/RdigiEez09/qlvqpd4v\nw5yKDHNq2xs2MqmNKK0vx8maMziB09Lrl5sU99XZzYhQ6hrDPQpmTSQUsrD88e02cpkMtzcbTne6\nvFg4PUVaC6KqrgHvfH4Mrz40DQDgcnuw80gppo+Ob3V/FJiYGxbBsncPitdvANZvkF4XFArI9HrI\nI/SQGwyQ6/W+QJf+jGj80xfycr0BgkrFoG9D2PzU17vq8e7RNdJZs9PrAgBEKHR4YcbTLbaP1phx\nTco8RDeeNcdoo2BQ6TkkSSHh4bH3AvDdBljdUIvKhipUO2qhVbQ8M3d73dhw8nO/Ow8ECIhUG/H0\n5MegbCW83V53nw91nUaBqyY0PYdcIZdh6ZVDpFA4fcGCb/YVS6FdbXEg92QFZmUGPiGSALlej0GP\n/xreYwdhKa2Ex2qFx2pp/NMKd1UlnMXnAtqXoFQ2hnpTwMukoG8W+hcPAiL0kKn71shUUH+qK+qr\nUFBdiAp7lTSMXeuow8Nj721xtKWWq3G48hhUMhXidLF+s7Bbuz6olCvxPynzevLLIWo3hUyBWF00\nYnWXfyKdAAEPj70HlfZqVDZUSzPgHW5Hq4Ht8Djx6JbfIFJtlIbdozRmxGijMSV+fHd+OSHNGKFC\n9tA4qd3PrMWSuU1n5kfPVuHImWoptM+WWHC6pA43z2s5kkf+VP0HIHZUxmVv+RLdbnhsvhD3WCz+\nf5dC3gaP1QKv1QpXeTkcRUWt7utSgkr1PWfxjW2DAbKICOmsXqYM/M6ZUBPU0H7kP0/B5fV/OpBM\nkMHmrodeGeH3ulwmx/PTnoRWoeXwCfUpcpkcGeY0oOXt7a1qcDcgzZSCqoZqnKkrxKnaMwAAs9rU\nami7vG6crStCgn4ANIq+s5pcpF6NSH3TWdrIwdFIjW96rnjuyQo0OPngk64gKBRQRJqgiAx8HpDX\n5YLXZmt21m6Bx2JtDPzGvzd7z1laCrHwbGD1qNWXBP2lod/sNYMesgg9ZMqW628EQ1BD+6q0mXA5\nRMRoo3xnzpoomNSRl71VRqfUtfo6ETWJVBvx03G++2Y9Xg9qHHWoaqiSLild6rz1Al7Z9xcAQJw2\nBomGgUgwxGNwZDLSTCk9VnewGXUqGHVNZ2DzshPh8nildnG5VZqVTt1PplRCZjJBYWpP0Dvhsdrg\nbTY8LwW731m97z3nhfMQnc7A6tFofNfoWw361ofuu2OJV66IFga40lDg2FeBad5PZfUV+O78ThRZ\nzqPIUgy72/es7OFRQ/Bg5g9bfNYreqW13vuC2FgDvtx2Ciu/Oo7f/2giH3J0GeH6s+d1OJqG6y8J\nea80jO9/Vi+6Wj8AvpRMq21xFj/q8WWdqpfffUR9XJwuBjem+ZbSFUURVQ3VKLIUQ32ZW892l+zD\nxyc+k1ax8/0Xj2hNVK8N8gExEXh40WgGdi8kU6shU6uhjLr8vJJLeR2OS87aLzmzbwz5i6HvKCqE\n6L54KZihTURdRBAERDdO8rwcr+iFUq7Ekap8HKlqejzjlUmzcX3q1T1RZo/rH9V0ac7t8eKDTQVY\nOC0FBl34TmiijpOCPjqwoBdFEaLDAY+18yMRDG0iapcp8RMwJX4CrC4bzjUOqRdZipEamdzq9t8W\n78B5aykSDfFINAzEgIh+YX07Wk5+GarrHIjQhMbEJAp9giBA0Ggg03R+omeHf3LefvttfP3113C5\nXFiyZAnGjx+Pxx9/HDKZDOnp6XjqKS5UQtSb6ZURGBqVjqFR6d+7XW75YRytOi615YIc8RH9cFPG\n9WE50W3isH7IHhInrapmqXfyjJt6TIdWGtm9ezf279+PVatWYcWKFbhw4QKee+45LFu2DCtXroTX\n68XGjRu7ulYiCkM/HvW/+GX2Q7htyI2YFj8RCYZ4XKgvu+xT5nLLD+F49UnUu+w9XGlgBEGQlkUt\nq67Hb/6+G2U1oVkr9T4dOtP+7rvvkJGRgQceeAA2mw2/+MUvsHbtWmRnZwMAZsyYge3bt2Pu3Lld\nWiwRhR+lXIkkYyKSjInSax6v57KT1tYc39D4LHYgRhuNRL1vWH1GwuRWV4wLJo9XxK1z0hBnCq26\nqPfqUGhXV1fj/PnzeOutt1BUVIT7778fXm/T/YwRERGwWMJv6j8R9YzLrcUgiiJuTLtGuv2syFKM\n/eUHcaD8EGYlTmv1MzWOWkSqjEGZuT4gOgIDopsWgvpm3zmMSYtBlLHvLFJDPatDoW0ymZCamgqF\nQoGUlBSo1WqUlpZK79tsNhiNLZ853JrY2NafP03+2E+BY18FJlT7aX7cdOnvoiiisr4axZYSJPRv\nOVPX5qzHg+ufhUEVgRTzIKSYExv/G4QBhrgW23dUIH2Vf7YKX+acw1VTB/uttNaXhOr3VG/SodDO\nysrCihUrcNddd6G0tBR2ux2TJk3C7t27MWHCBGzduhWTJk0KaF/heDN+TwvXRQuCgX0VmPDqJyXi\n5Ymt1lvdUIOxsaNQZClGXulR5JUeBQDEaKLwzJTHu+RfD7SvzFoFfr00C067E+V2J9web596jnd4\nfU8FT2cPbDoU2rNmzUJOTg5uuukmiKKIp59+GgMHDsQTTzwBl8uF1NRUzJ8/v1OFERG1xawx4Uej\nlgIA6l12nLP6htUv9zS/s3VFWH38YyQaBmKQ3rdca3xEfyjlnb99SxAE6LW+/bjcHrzw/n7cODMV\nw5ICXDSeKAAdvuXr5z//eYvXVqxY0al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IycmhsbERq9VKVlYWkZGRbNq0CR8fH6KiosjL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\n8BD/A/9r7TmuhSCKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "by_weekday = data.groupby(data.index.dayofweek).mean()\n", + "by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n", + "by_weekday.plot(style=[':', '--', '-']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday.\n", + "\n", + "With this in mind, let's do a compound GroupBy and look at the hourly trend on weekdays versus weekends.\n", + "We'll start by grouping by both a flag marking the weekend, and the time of day:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "weekend = np.where(data.index.weekday < 5, 'Weekday', 'Weekend')\n", + "by_time = data.groupby([weekend, data.index.time]).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll use some of the Matplotlib tools described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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L0xFCCBFjIvU6paiqtvfwKisbtTy8JrKybHLecSIezxk6ft6uygr23/1zbOdMpMdNtwBQ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\nCAt7cXHruiB5eYH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Ht2eWL5+xzOew42tpCVvxv58pJxcAV7kkx0KI6Ne05XOcB0qxjZtA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fUE102J7+midaqcohqWr81i8vBwxg85Mz5+YvhYthTsZE/xXvaWHGCYve3BsSRJhN5+FzmFhVSu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\nRhAEQRAEQRCEPun/A1rsNddykhZ+AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "by_time.ix['Weekday'].plot(ax=ax[0], title='Weekdays',\n", + " xticks=hourly_ticks, style=[':', '--', '-'])\n", + "by_time.ix['Weekend'].plot(ax=ax[1], title='Weekends',\n", + " xticks=hourly_ticks, style=[':', '--', '-']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is very interesting: we see a bimodal commute pattern during the work week, and a unimodal recreational pattern during the weekends.\n", + "It would be interesting to dig through this data in more detail, and examine the effect of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\"Is Seattle Really Seeing an Uptick In Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\n", + "We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.11-Working-with-Time-Series.md b/notebooks_v2/03.11-Working-with-Time-Series.md new file mode 100644 index 00000000..5c71c3bc --- /dev/null +++ b/notebooks_v2/03.11-Working-with-Time-Series.md @@ -0,0 +1,633 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) > + +Open in Colab + + + +# Working with Time Series + + +Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with dates, times, and time-indexed data. +Date and time data comes in a few flavors, which we will discuss here: + +- *Time stamps* reference particular moments in time (e.g., July 4th, 2015 at 7:00am). +- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the year 2015. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24 hour-long periods comprising days). +- *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds). + +In this section, we will introduce how to work with each of these types of date/time data in Pandas. +This short section is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series. +We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas. +After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas. + + +## Dates and Times in Python + +The Python world has a number of available representations of dates, times, deltas, and timespans. +While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other packages used in Python. + + +### Native Python dates and times: ``datetime`` and ``dateutil`` + +Python's basic objects for working with dates and times reside in the built-in ``datetime`` module. +Along with the third-party ``dateutil`` module, you can use it to quickly perform a host of useful functionalities on dates and times. +For example, you can manually build a date using the ``datetime`` type: + +```python +from datetime import datetime +datetime(year=2015, month=7, day=4) +``` + +Or, using the ``dateutil`` module, you can parse dates from a variety of string formats: + +```python +from dateutil import parser +date = parser.parse("4th of July, 2015") +date +``` + +Once you have a ``datetime`` object, you can do things like printing the day of the week: + +```python +date.strftime('%A') +``` + +In the final line, we've used one of the standard string format codes for printing dates (``"%A"``), which you can read about in the [strftime section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [datetime documentation](https://docs.python.org/3/library/datetime.html). +Documentation of other useful date utilities can be found in [dateutil's online documentation](http://labix.org/python-dateutil). +A related package to be aware of is [``pytz``](http://pytz.sourceforge.net/), which contains tools for working with the most migrane-inducing piece of time series data: time zones. + +The power of ``datetime`` and ``dateutil`` lie in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in. +Where they break down is when you wish to work with large arrays of dates and times: +just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python datetime objects are suboptimal compared to typed arrays of encoded dates. + + +### Typed arrays of times: NumPy's ``datetime64`` + +The weaknesses of Python's datetime format inspired the NumPy team to add a set of native time series data type to NumPy. +The ``datetime64`` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly. +The ``datetime64`` requires a very specific input format: + +```python +import numpy as np +date = np.array('2015-07-04', dtype=np.datetime64) +date +``` + +Once we have this date formatted, however, we can quickly do vectorized operations on it: + +```python +date + np.arange(12) +``` + +Because of the uniform type in NumPy ``datetime64`` arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python's ``datetime`` objects, especially as arrays get large +(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)). + +One detail of the ``datetime64`` and ``timedelta64`` objects is that they are built on a *fundamental time unit*. +Because the ``datetime64`` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit. +In other words, ``datetime64`` imposes a trade-off between *time resolution* and *maximum time span*. + +For example, if you want a time resolution of one nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years. +NumPy will infer the desired unit from the input; for example, here is a day-based datetime: + +```python +np.datetime64('2015-07-04') +``` + +Here is a minute-based datetime: + +```python +np.datetime64('2015-07-04 12:00') +``` + +Notice that the time zone is automatically set to the local time on the computer executing the code. +You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time: + +```python +np.datetime64('2015-07-04 12:59:59.50', 'ns') +``` + +The following table, drawn from the [NumPy datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html), lists the available format codes along with the relative and absolute timespans that they can encode: + + +|Code | Meaning | Time span (relative) | Time span (absolute) | +|--------|-------------|----------------------|------------------------| +| ``Y`` | Year | ± 9.2e18 years | [9.2e18 BC, 9.2e18 AD] | +| ``M`` | Month | ± 7.6e17 years | [7.6e17 BC, 7.6e17 AD] | +| ``W`` | Week | ± 1.7e17 years | [1.7e17 BC, 1.7e17 AD] | +| ``D`` | Day | ± 2.5e16 years | [2.5e16 BC, 2.5e16 AD] | +| ``h`` | Hour | ± 1.0e15 years | [1.0e15 BC, 1.0e15 AD] | +| ``m`` | Minute | ± 1.7e13 years | [1.7e13 BC, 1.7e13 AD] | +| ``s`` | Second | ± 2.9e12 years | [ 2.9e9 BC, 2.9e9 AD] | +| ``ms`` | Millisecond | ± 2.9e9 years | [ 2.9e6 BC, 2.9e6 AD] | +| ``us`` | Microsecond | ± 2.9e6 years | [290301 BC, 294241 AD] | +| ``ns`` | Nanosecond | ± 292 years | [ 1678 AD, 2262 AD] | +| ``ps`` | Picosecond | ± 106 days | [ 1969 AD, 1970 AD] | +| ``fs`` | Femtosecond | ± 2.6 hours | [ 1969 AD, 1970 AD] | +| ``as`` | Attosecond | ± 9.2 seconds | [ 1969 AD, 1970 AD] | + + +For the types of data we see in the real world, a useful default is ``datetime64[ns]``, as it can encode a useful range of modern dates with a suitably fine precision. + +Finally, we will note that while the ``datetime64`` data type addresses some of the deficiencies of the built-in Python ``datetime`` type, it lacks many of the convenient methods and functions provided by ``datetime`` and especially ``dateutil``. +More information can be found in [NumPy's datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html). + + +### Dates and times in pandas: best of both worlds + +Pandas builds upon all the tools just discussed to provide a ``Timestamp`` object, which combines the ease-of-use of ``datetime`` and ``dateutil`` with the efficient storage and vectorized interface of ``numpy.datetime64``. +From a group of these ``Timestamp`` objects, Pandas can construct a ``DatetimeIndex`` that can be used to index data in a ``Series`` or ``DataFrame``; we'll see many examples of this below. + +For example, we can use Pandas tools to repeat the demonstration from above. +We can parse a flexibly formatted string date, and use format codes to output the day of the week: + +```python +import pandas as pd +date = pd.to_datetime("4th of July, 2015") +date +``` + +```python +date.strftime('%A') +``` + +Additionally, we can do NumPy-style vectorized operations directly on this same object: + +```python +date + pd.to_timedelta(np.arange(12), 'D') +``` + +In the next section, we will take a closer look at manipulating time series data with the tools provided by Pandas. + + +## Pandas Time Series: Indexing by Time + +Where the Pandas time series tools really become useful is when you begin to *index data by timestamps*. +For example, we can construct a ``Series`` object that has time indexed data: + +```python +index = pd.DatetimeIndex(['2014-07-04', '2014-08-04', + '2015-07-04', '2015-08-04']) +data = pd.Series([0, 1, 2, 3], index=index) +data +``` + +Now that we have this data in a ``Series``, we can make use of any of the ``Series`` indexing patterns we discussed in previous sections, passing values that can be coerced into dates: + +```python +data['2014-07-04':'2015-07-04'] +``` + +There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year: + +```python +data['2015'] +``` + +Later, we will see additional examples of the convenience of dates-as-indices. +But first, a closer look at the available time series data structures. + + +## Pandas Time Series Data Structures + +This section will introduce the fundamental Pandas data structures for working with time series data: + +- For *time stamps*, Pandas provides the ``Timestamp`` type. As mentioned before, it is essentially a replacement for Python's native ``datetime``, but is based on the more efficient ``numpy.datetime64`` data type. The associated Index structure is ``DatetimeIndex``. +- For *time Periods*, Pandas provides the ``Period`` type. This encodes a fixed-frequency interval based on ``numpy.datetime64``. The associated index structure is ``PeriodIndex``. +- For *time deltas* or *durations*, Pandas provides the ``Timedelta`` type. ``Timedelta`` is a more efficient replacement for Python's native ``datetime.timedelta`` type, and is based on ``numpy.timedelta64``. The associated index structure is ``TimedeltaIndex``. + + +The most fundamental of these date/time objects are the ``Timestamp`` and ``DatetimeIndex`` objects. +While these class objects can be invoked directly, it is more common to use the ``pd.to_datetime()`` function, which can parse a wide variety of formats. +Passing a single date to ``pd.to_datetime()`` yields a ``Timestamp``; passing a series of dates by default yields a ``DatetimeIndex``: + +```python +dates = pd.to_datetime([datetime(2015, 7, 3), '4th of July, 2015', + '2015-Jul-6', '07-07-2015', '20150708']) +dates +``` + +Any ``DatetimeIndex`` can be converted to a ``PeriodIndex`` with the ``to_period()`` function with the addition of a frequency code; here we'll use ``'D'`` to indicate daily frequency: + +```python +dates.to_period('D') +``` + +A ``TimedeltaIndex`` is created, for example, when a date is subtracted from another: + +```python +dates - dates[0] +``` + +### Regular sequences: ``pd.date_range()`` + +To make the creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: ``pd.date_range()`` for timestamps, ``pd.period_range()`` for periods, and ``pd.timedelta_range()`` for time deltas. +We've seen that Python's ``range()`` and NumPy's ``np.arange()`` turn a startpoint, endpoint, and optional stepsize into a sequence. +Similarly, ``pd.date_range()`` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates. +By default, the frequency is one day: + +```python +pd.date_range('2015-07-03', '2015-07-10') +``` + +Alternatively, the date range can be specified not with a start and endpoint, but with a startpoint and a number of periods: + +```python +pd.date_range('2015-07-03', periods=8) +``` + +The spacing can be modified by altering the ``freq`` argument, which defaults to ``D``. +For example, here we will construct a range of hourly timestamps: + +```python +pd.date_range('2015-07-03', periods=8, freq='H') +``` + +To create regular sequences of ``Period`` or ``Timedelta`` values, the very similar ``pd.period_range()`` and ``pd.timedelta_range()`` functions are useful. +Here are some monthly periods: + +```python +pd.period_range('2015-07', periods=8, freq='M') +``` + +And a sequence of durations increasing by an hour: + +```python +pd.timedelta_range(0, periods=10, freq='H') +``` + +All of these require an understanding of Pandas frequency codes, which we'll summarize in the next section. + + +## Frequencies and Offsets + +Fundamental to these Pandas time series tools is the concept of a frequency or date offset. +Just as we saw the ``D`` (day) and ``H`` (hour) codes above, we can use such codes to specify any desired frequency spacing. +The following table summarizes the main codes available: + + +| Code | Description | Code | Description | +|--------|---------------------|--------|----------------------| +| ``D`` | Calendar day | ``B`` | Business day | +| ``W`` | Weekly | | | +| ``M`` | Month end | ``BM`` | Business month end | +| ``Q`` | Quarter end | ``BQ`` | Business quarter end | +| ``A`` | Year end | ``BA`` | Business year end | +| ``H`` | Hours | ``BH`` | Business hours | +| ``T`` | Minutes | | | +| ``S`` | Seconds | | | +| ``L`` | Milliseonds | | | +| ``U`` | Microseconds | | | +| ``N`` | nanoseconds | | | + + +The monthly, quarterly, and annual frequencies are all marked at the end of the specified period. +By adding an ``S`` suffix to any of these, they instead will be marked at the beginning: + + +| Code | Description || Code | Description | +|---------|------------------------||---------|------------------------| +| ``MS`` | Month start ||``BMS`` | Business month start | +| ``QS`` | Quarter start ||``BQS`` | Business quarter start | +| ``AS`` | Year start ||``BAS`` | Business year start | + + +Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix: + +- ``Q-JAN``, ``BQ-FEB``, ``QS-MAR``, ``BQS-APR``, etc. +- ``A-JAN``, ``BA-FEB``, ``AS-MAR``, ``BAS-APR``, etc. + +In the same way, the split-point of the weekly frequency can be modified by adding a three-letter weekday code: + +- ``W-SUN``, ``W-MON``, ``W-TUE``, ``W-WED``, etc. + +On top of this, codes can be combined with numbers to specify other frequencies. +For example, for a frequency of 2 hours 30 minutes, we can combine the hour (``H``) and minute (``T``) codes as follows: + +```python +pd.timedelta_range(0, periods=9, freq="2H30T") +``` + +All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the ``pd.tseries.offsets`` module. +For example, we can create a business day offset directly as follows: + +```python +from pandas.tseries.offsets import BDay +pd.date_range('2015-07-01', periods=5, freq=BDay()) +``` + +For more discussion of the use of frequencies and offsets, see the ["DateOffset" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects) of the Pandas documentation. + + +## Resampling, Shifting, and Windowing + +The ability to use dates and times as indices to intuitively organize and access data is an important piece of the Pandas time series tools. +The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series-specific operations. + +We will take a look at a few of those here, using some stock price data as an example. +Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data. +For example, the accompanying ``pandas-datareader`` package (installable via ``conda install pandas-datareader``), knows how to import financial data from a number of available sources, including Yahoo finance, Google Finance, and others. +Here we will load Google's closing price history: + +```python +from pandas_datareader import data + +goog = data.DataReader('GOOG', start='2004', end='2016', + data_source='google') +goog.head() +``` + +For simplicity, we'll use just the closing price: + +```python +goog = goog['Close'] +``` + +We can visualize this using the ``plot()`` method, after the normal Matplotlib setup boilerplate (see [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)): + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn; seaborn.set() +``` + +```python +goog.plot(); +``` + +### Resampling and converting frequencies + +One common need for time series data is resampling at a higher or lower frequency. +This can be done using the ``resample()`` method, or the much simpler ``asfreq()`` method. +The primary difference between the two is that ``resample()`` is fundamentally a *data aggregation*, while ``asfreq()`` is fundamentally a *data selection*. + +Taking a look at the Google closing price, let's compare what the two return when we down-sample the data. +Here we will resample the data at the end of business year: + +```python +goog.plot(alpha=0.5, style='-') +goog.resample('BA').mean().plot(style=':') +goog.asfreq('BA').plot(style='--'); +plt.legend(['input', 'resample', 'asfreq'], + loc='upper left'); +``` + +Notice the difference: at each point, ``resample`` reports the *average of the previous year*, while ``asfreq`` reports the *value at the end of the year*. + + +For up-sampling, ``resample()`` and ``asfreq()`` are largely equivalent, though resample has many more options available. +In this case, the default for both methods is to leave the up-sampled points empty, that is, filled with NA values. +Just as with the ``pd.fillna()`` function discussed previously, ``asfreq()`` accepts a ``method`` argument to specify how values are imputed. +Here, we will resample the business day data at a daily frequency (i.e., including weekends): + +```python +fig, ax = plt.subplots(2, sharex=True) +data = goog.iloc[:10] + +data.asfreq('D').plot(ax=ax[0], marker='o') + +data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o') +data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o') +ax[1].legend(["back-fill", "forward-fill"]); +``` + +The top panel is the default: non-business days are left as NA values and do not appear on the plot. +The bottom panel shows the differences between two strategies for filling the gaps: forward-filling and backward-filling. + + +### Time-shifts + +Another common time series-specific operation is shifting of data in time. +Pandas has two closely related methods for computing this: ``shift()`` and ``tshift()`` +In short, the difference between them is that ``shift()`` *shifts the data*, while ``tshift()`` *shifts the index*. +In both cases, the shift is specified in multiples of the frequency. + +Here we will both ``shift()`` and ``tshift()`` by 900 days; + +```python +fig, ax = plt.subplots(3, sharey=True) + +# apply a frequency to the data +goog = goog.asfreq('D', method='pad') + +goog.plot(ax=ax[0]) +goog.shift(900).plot(ax=ax[1]) +goog.tshift(900).plot(ax=ax[2]) + +# legends and annotations +local_max = pd.to_datetime('2007-11-05') +offset = pd.Timedelta(900, 'D') + +ax[0].legend(['input'], loc=2) +ax[0].get_xticklabels()[2].set(weight='heavy', color='red') +ax[0].axvline(local_max, alpha=0.3, color='red') + +ax[1].legend(['shift(900)'], loc=2) +ax[1].get_xticklabels()[2].set(weight='heavy', color='red') +ax[1].axvline(local_max + offset, alpha=0.3, color='red') + +ax[2].legend(['tshift(900)'], loc=2) +ax[2].get_xticklabels()[1].set(weight='heavy', color='red') +ax[2].axvline(local_max + offset, alpha=0.3, color='red'); +``` + +We see here that ``shift(900)`` shifts the *data* by 900 days, pushing some of it off the end of the graph (and leaving NA values at the other end), while ``tshift(900)`` shifts the *index values* by 900 days. + +A common context for this type of shift is in computing differences over time. For example, we use shifted values to compute the one-year return on investment for Google stock over the course of the dataset: + +```python +ROI = 100 * (goog.tshift(-365) / goog - 1) +ROI.plot() +plt.ylabel('% Return on Investment'); +``` + +This helps us to see the overall trend in Google stock: thus far, the most profitable times to invest in Google have been (unsurprisingly, in retrospect) shortly after its IPO, and in the middle of the 2009 recession. + + +### Rolling windows + +Rolling statistics are a third type of time series-specific operation implemented by Pandas. +These can be accomplished via the ``rolling()`` attribute of ``Series`` and ``DataFrame`` objects, which returns a view similar to what we saw with the ``groupby`` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)). +This rolling view makes available a number of aggregation operations by default. + +For example, here is the one-year centered rolling mean and standard deviation of the Google stock prices: + +```python +rolling = goog.rolling(365, center=True) + +data = pd.DataFrame({'input': goog, + 'one-year rolling_mean': rolling.mean(), + 'one-year rolling_std': rolling.std()}) +ax = data.plot(style=['-', '--', ':']) +ax.lines[0].set_alpha(0.3) +``` + +As with group-by operations, the ``aggregate()`` and ``apply()`` methods can be used for custom rolling computations. + + +## Where to Learn More + +This section has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the ["Time Series/Date" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation. + +Another excellent resource is the textbook [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (OReilly, 2012). +Although it is now a few years old, it is an invaluable resource on the use of Pandas. +In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics. + +As always, you can also use the IPython help functionality to explore and try further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool. + + +## Example: Visualizing Seattle Bicycle Counts + +As a more involved example of working with some time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965). +This data comes from an automated bicycle counter, installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge. +The hourly bicycle counts can be downloaded from http://data.seattle.gov/; here is the [direct link to the dataset](https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k). + +As of summer 2016, the CSV can be downloaded as follows: + +```python +# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD +``` + +Once this dataset is downloaded, we can use Pandas to read the CSV output into a ``DataFrame``. +We will specify that we want the Date as an index, and we want these dates to be automatically parsed: + +```python +data = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True) +data.head() +``` + +For convenience, we'll further process this dataset by shortening the column names and adding a "Total" column: + +```python +data.columns = ['West', 'East'] +data['Total'] = data.eval('West + East') +``` + +Now let's take a look at the summary statistics for this data: + +```python +data.dropna().describe() +``` + +### Visualizing the data + +We can gain some insight into the dataset by visualizing it. +Let's start by plotting the raw data: + +```python +%matplotlib inline +import seaborn; seaborn.set() +``` + +```python +data.plot() +plt.ylabel('Hourly Bicycle Count'); +``` + +The ~25,000 hourly samples are far too dense for us to make much sense of. +We can gain more insight by resampling the data to a coarser grid. +Let's resample by week: + +```python +weekly = data.resample('W').sum() +weekly.plot(style=[':', '--', '-']) +plt.ylabel('Weekly bicycle count'); +``` + +This shows us some interesting seasonal trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) where we explore this further). + +Another way that comes in handy for aggregating the data is to use a rolling mean, utilizing the ``pd.rolling_mean()`` function. +Here we'll do a 30 day rolling mean of our data, making sure to center the window: + +```python +daily = data.resample('D').sum() +daily.rolling(30, center=True).sum().plot(style=[':', '--', '-']) +plt.ylabel('mean hourly count'); +``` + +The jaggedness of the result is due to the hard cutoff of the window. +We can get a smoother version of a rolling mean using a window function–for example, a Gaussian window. +The following code specifies both the width of the window (we chose 50 days) and the width of the Gaussian within the window (we chose 10 days): + +```python +daily.rolling(50, center=True, + win_type='gaussian').sum(std=10).plot(style=[':', '--', '-']); +``` + +### Digging into the data + +While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the interesting structure. +For example, we might want to look at the average traffic as a function of the time of day. +We can do this using the GroupBy functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb): + +```python +by_time = data.groupby(data.index.time).mean() +hourly_ticks = 4 * 60 * 60 * np.arange(6) +by_time.plot(xticks=hourly_ticks, style=[':', '--', '-']); +``` + +The hourly traffic is a strongly bimodal distribution, with peaks around 8:00 in the morning and 5:00 in the evening. +This is likely evidence of a strong component of commuter traffic crossing the bridge. +This is further evidenced by the differences between the western sidewalk (generally used going toward downtown Seattle), which peaks more strongly in the morning, and the eastern sidewalk (generally used going away from downtown Seattle), which peaks more strongly in the evening. + +We also might be curious about how things change based on the day of the week. Again, we can do this with a simple groupby: + +```python +by_weekday = data.groupby(data.index.dayofweek).mean() +by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'] +by_weekday.plot(style=[':', '--', '-']); +``` + +This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday. + +With this in mind, let's do a compound GroupBy and look at the hourly trend on weekdays versus weekends. +We'll start by grouping by both a flag marking the weekend, and the time of day: + +```python +weekend = np.where(data.index.weekday < 5, 'Weekday', 'Weekend') +by_time = data.groupby([weekend, data.index.time]).mean() +``` + +Now we'll use some of the Matplotlib tools described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side: + +```python +import matplotlib.pyplot as plt +fig, ax = plt.subplots(1, 2, figsize=(14, 5)) +by_time.ix['Weekday'].plot(ax=ax[0], title='Weekdays', + xticks=hourly_ticks, style=[':', '--', '-']) +by_time.ix['Weekend'].plot(ax=ax[1], title='Weekends', + xticks=hourly_ticks, style=[':', '--', '-']); +``` + +The result is very interesting: we see a bimodal commute pattern during the work week, and a unimodal recreational pattern during the weekends. +It would be interesting to dig through this data in more detail, and examine the effect of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post ["Is Seattle Really Seeing an Uptick In Cycling?"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data. +We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb). + + + +< [Vectorized String Operations](03.10-Working-With-Strings.ipynb) | [Contents](Index.ipynb) | [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.12-Performance-Eval-and-Query.ipynb b/notebooks_v2/03.12-Performance-Eval-and-Query.ipynb new file mode 100644 index 00000000..99fd306b --- /dev/null +++ b/notebooks_v2/03.12-Performance-Eval-and-Query.ipynb @@ -0,0 +1,1153 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# High-Performance Pandas: eval() and query()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we've already seen in previous sections, the power of the PyData stack is built upon the ability of NumPy and Pandas to push basic operations into C via an intuitive syntax: examples are vectorized/broadcasted operations in NumPy, and grouping-type operations in Pandas.\n", + "While these abstractions are efficient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue overhead in computational time and memory use.\n", + "\n", + "As of version 0.13 (released January 2014), Pandas includes some experimental tools that allow you to directly access C-speed operations without costly allocation of intermediate arrays.\n", + "These are the ``eval()`` and ``query()`` functions, which rely on the [Numexpr](https://github.com/pydata/numexpr) package.\n", + "In this notebook we will walk through their use and give some rules-of-thumb about when you might think about using them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motivating ``query()`` and ``eval()``: Compound Expressions\n", + "\n", + "We've seen previously that NumPy and Pandas support fast vectorized operations; for example, when adding the elements of two arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 loops, best of 3: 3.39 ms per loop\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "rng = np.random.RandomState(42)\n", + "x = rng.rand(1000000)\n", + "y = rng.rand(1000000)\n", + "%timeit x + y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As discussed in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb), this is much faster than doing the addition via a Python loop or comprehension:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loop, best of 3: 266 ms per loop\n" + ] + } + ], + "source": [ + "%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)), dtype=x.dtype, count=len(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this abstraction can become less efficient when computing compound expressions.\n", + "For example, consider the following expression:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mask = (x > 0.5) & (y < 0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because NumPy evaluates each subexpression, this is roughly equivalent to the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tmp1 = (x > 0.5)\n", + "tmp2 = (y < 0.5)\n", + "mask = tmp1 & tmp2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In other words, *every intermediate step is explicitly allocated in memory*. If the ``x`` and ``y`` arrays are very large, this can lead to significant memory and computational overhead.\n", + "The Numexpr library gives you the ability to compute this type of compound expression element by element, without the need to allocate full intermediate arrays.\n", + "The [Numexpr documentation](https://github.com/pydata/numexpr) has more details, but for the time being it is sufficient to say that the library accepts a *string* giving the NumPy-style expression you'd like to compute:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numexpr\n", + "mask_numexpr = numexpr.evaluate('(x > 0.5) & (y < 0.5)')\n", + "np.allclose(mask, mask_numexpr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The benefit here is that Numexpr evaluates the expression in a way that does not use full-sized temporary arrays, and thus can be much more efficient than NumPy, especially for large arrays.\n", + "The Pandas ``eval()`` and ``query()`` tools that we will discuss here are conceptually similar, and depend on the Numexpr package." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ``pandas.eval()`` for Efficient Operations\n", + "\n", + "The ``eval()`` function in Pandas uses string expressions to efficiently compute operations using ``DataFrame``s.\n", + "For example, consider the following ``DataFrame``s:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "nrows, ncols = 100000, 100\n", + "rng = np.random.RandomState(42)\n", + "df1, df2, df3, df4 = (pd.DataFrame(rng.rand(nrows, ncols))\n", + " for i in range(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To compute the sum of all four ``DataFrame``s using the typical Pandas approach, we can just write the sum:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 87.1 ms per loop\n" + ] + } + ], + "source": [ + "%timeit df1 + df2 + df3 + df4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same result can be computed via ``pd.eval`` by constructing the expression as a string:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 42.2 ms per loop\n" + ] + } + ], + "source": [ + "%timeit pd.eval('df1 + df2 + df3 + df4')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``eval()`` version of this expression is about 50% faster (and uses much less memory), while giving the same result:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(df1 + df2 + df3 + df4,\n", + " pd.eval('df1 + df2 + df3 + df4'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operations supported by ``pd.eval()``\n", + "\n", + "As of Pandas v0.16, ``pd.eval()`` supports a wide range of operations.\n", + "To demonstrate these, we'll use the following integer ``DataFrame``s:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df1, df2, df3, df4, df5 = (pd.DataFrame(rng.randint(0, 1000, (100, 3)))\n", + " for i in range(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Arithmetic operators\n", + "``pd.eval()`` supports all arithmetic operators. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result1 = -df1 * df2 / (df3 + df4) - df5\n", + "result2 = pd.eval('-df1 * df2 / (df3 + df4) - df5')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparison operators\n", + "``pd.eval()`` supports all comparison operators, including chained expressions:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result1 = (df1 < df2) & (df2 <= df3) & (df3 != df4)\n", + "result2 = pd.eval('df1 < df2 <= df3 != df4')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Bitwise operators\n", + "``pd.eval()`` supports the ``&`` and ``|`` bitwise operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result1 = (df1 < 0.5) & (df2 < 0.5) | (df3 < df4)\n", + "result2 = pd.eval('(df1 < 0.5) & (df2 < 0.5) | (df3 < df4)')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, it supports the use of the literal ``and`` and ``or`` in Boolean expressions:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result3 = pd.eval('(df1 < 0.5) and (df2 < 0.5) or (df3 < df4)')\n", + "np.allclose(result1, result3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Object attributes and indices\n", + "\n", + "``pd.eval()`` supports access to object attributes via the ``obj.attr`` syntax, and indexes via the ``obj[index]`` syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result1 = df2.T[0] + df3.iloc[1]\n", + "result2 = pd.eval('df2.T[0] + df3.iloc[1]')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Other operations\n", + "Other operations such as function calls, conditional statements, loops, and other more involved constructs are currently *not* implemented in ``pd.eval()``.\n", + "If you'd like to execute these more complicated types of expressions, you can use the Numexpr library itself." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ``DataFrame.eval()`` for Column-Wise Operations\n", + "\n", + "Just as Pandas has a top-level ``pd.eval()`` function, ``DataFrame``s have an ``eval()`` method that works in similar ways.\n", + "The benefit of the ``eval()`` method is that columns can be referred to *by name*.\n", + "We'll use this labeled array as an example:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B C\n", + "0 0.375506 0.406939 0.069938\n", + "1 0.069087 0.235615 0.154374\n", + "2 0.677945 0.433839 0.652324\n", + "3 0.264038 0.808055 0.347197\n", + "4 0.589161 0.252418 0.557789" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(rng.rand(1000, 3), columns=['A', 'B', 'C'])\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using ``pd.eval()`` as above, we can compute expressions with the three columns like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result1 = (df['A'] + df['B']) / (df['C'] - 1)\n", + "result2 = pd.eval(\"(df.A + df.B) / (df.C - 1)\")\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``DataFrame.eval()`` method allows much more succinct evaluation of expressions with the columns:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result3 = df.eval('(A + B) / (C - 1)')\n", + "np.allclose(result1, result3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice here that we treat *column names as variables* within the evaluated expression, and the result is what we would wish." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assignment in DataFrame.eval()\n", + "\n", + "In addition to the options just discussed, ``DataFrame.eval()`` also allows assignment to any column.\n", + "Let's use the ``DataFrame`` from before, which has columns ``'A'``, ``'B'``, and ``'C'``:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B C D\n", + "0 0.375506 0.406939 0.069938 -0.449425\n", + "1 0.069087 0.235615 0.154374 -1.078728\n", + "2 0.677945 0.433839 0.652324 0.374209\n", + "3 0.264038 0.808055 0.347197 -1.566886\n", + "4 0.589161 0.252418 0.557789 0.603708" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.eval('D = (A - B) / C', inplace=True)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Local variables in DataFrame.eval()\n", + "\n", + "The ``DataFrame.eval()`` method supports an additional syntax that lets it work with local Python variables.\n", + "Consider the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "column_mean = df.mean(1)\n", + "result1 = df['A'] + column_mean\n", + "result2 = df.eval('A + @column_mean')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``@`` character here marks a *variable name* rather than a *column name*, and lets you efficiently evaluate expressions involving the two \"namespaces\": the namespace of columns, and the namespace of Python objects.\n", + "Notice that this ``@`` character is only supported by the ``DataFrame.eval()`` *method*, not by the ``pandas.eval()`` *function*, because the ``pandas.eval()`` function only has access to the one (Python) namespace." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataFrame.query() Method\n", + "\n", + "The ``DataFrame`` has another method based on evaluated strings, called the ``query()`` method.\n", + "Consider the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result1 = df[(df.A < 0.5) & (df.B < 0.5)]\n", + "result2 = pd.eval('df[(df.A < 0.5) & (df.B < 0.5)]')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As with the example used in our discussion of ``DataFrame.eval()``, this is an expression involving columns of the ``DataFrame``.\n", + "It cannot be expressed using the ``DataFrame.eval()`` syntax, however!\n", + "Instead, for this type of filtering operation, you can use the ``query()`` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result2 = df.query('A < 0.5 and B < 0.5')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to being a more efficient computation, compared to the masking expression this is much easier to read and understand.\n", + "Note that the ``query()`` method also accepts the ``@`` flag to mark local variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Cmean = df['C'].mean()\n", + "result1 = df[(df.A < Cmean) & (df.B < Cmean)]\n", + "result2 = df.query('A < @Cmean and B < @Cmean')\n", + "np.allclose(result1, result2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance: When to Use These Functions\n", + "\n", + "When considering whether to use these functions, there are two considerations: *computation time* and *memory use*.\n", + "Memory use is the most predictable aspect. As already mentioned, every compound expression involving NumPy arrays or Pandas ``DataFrame``s will result in implicit creation of temporary arrays:\n", + "For example, this:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = df[(df.A < 0.5) & (df.B < 0.5)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is roughly equivalent to this:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tmp1 = df.A < 0.5\n", + "tmp2 = df.B < 0.5\n", + "tmp3 = tmp1 & tmp2\n", + "x = df[tmp3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the size of the temporary ``DataFrame``s is significant compared to your available system memory (typically several gigabytes) then it's a good idea to use an ``eval()`` or ``query()`` expression.\n", + "You can check the approximate size of your array in bytes using this:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "32000" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.values.nbytes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the performance side, ``eval()`` can be faster even when you are not maxing-out your system memory.\n", + "The issue is how your temporary ``DataFrame``s compare to the size of the L1 or L2 CPU cache on your system (typically a few megabytes in 2016); if they are much bigger, then ``eval()`` can avoid some potentially slow movement of values between the different memory caches.\n", + "In practice, I find that the difference in computation time between the traditional methods and the ``eval``/``query`` method is usually not significant–if anything, the traditional method is faster for smaller arrays!\n", + "The benefit of ``eval``/``query`` is mainly in the saved memory, and the sometimes cleaner syntax they offer.\n", + "\n", + "We've covered most of the details of ``eval()`` and ``query()`` here; for more information on these, you can refer to the Pandas documentation.\n", + "In particular, different parsers and engines can be specified for running these queries; for details on this, see the discussion within the [\"Enhancing Performance\" section](http://pandas.pydata.org/pandas-docs/dev/enhancingperf.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.12-Performance-Eval-and-Query.md b/notebooks_v2/03.12-Performance-Eval-and-Query.md new file mode 100644 index 00000000..861612c4 --- /dev/null +++ b/notebooks_v2/03.12-Performance-Eval-and-Query.md @@ -0,0 +1,317 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) > + +Open in Colab + + + +# High-Performance Pandas: eval() and query() + + +As we've already seen in previous sections, the power of the PyData stack is built upon the ability of NumPy and Pandas to push basic operations into C via an intuitive syntax: examples are vectorized/broadcasted operations in NumPy, and grouping-type operations in Pandas. +While these abstractions are efficient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue overhead in computational time and memory use. + +As of version 0.13 (released January 2014), Pandas includes some experimental tools that allow you to directly access C-speed operations without costly allocation of intermediate arrays. +These are the ``eval()`` and ``query()`` functions, which rely on the [Numexpr](https://github.com/pydata/numexpr) package. +In this notebook we will walk through their use and give some rules-of-thumb about when you might think about using them. + + +## Motivating ``query()`` and ``eval()``: Compound Expressions + +We've seen previously that NumPy and Pandas support fast vectorized operations; for example, when adding the elements of two arrays: + +```python +import numpy as np +rng = np.random.RandomState(42) +x = rng.rand(1000000) +y = rng.rand(1000000) +%timeit x + y +``` + +As discussed in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb), this is much faster than doing the addition via a Python loop or comprehension: + +```python +%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)), dtype=x.dtype, count=len(x)) +``` + +But this abstraction can become less efficient when computing compound expressions. +For example, consider the following expression: + +```python +mask = (x > 0.5) & (y < 0.5) +``` + +Because NumPy evaluates each subexpression, this is roughly equivalent to the following: + +```python +tmp1 = (x > 0.5) +tmp2 = (y < 0.5) +mask = tmp1 & tmp2 +``` + +In other words, *every intermediate step is explicitly allocated in memory*. If the ``x`` and ``y`` arrays are very large, this can lead to significant memory and computational overhead. +The Numexpr library gives you the ability to compute this type of compound expression element by element, without the need to allocate full intermediate arrays. +The [Numexpr documentation](https://github.com/pydata/numexpr) has more details, but for the time being it is sufficient to say that the library accepts a *string* giving the NumPy-style expression you'd like to compute: + +```python +import numexpr +mask_numexpr = numexpr.evaluate('(x > 0.5) & (y < 0.5)') +np.allclose(mask, mask_numexpr) +``` + +The benefit here is that Numexpr evaluates the expression in a way that does not use full-sized temporary arrays, and thus can be much more efficient than NumPy, especially for large arrays. +The Pandas ``eval()`` and ``query()`` tools that we will discuss here are conceptually similar, and depend on the Numexpr package. + + +## ``pandas.eval()`` for Efficient Operations + +The ``eval()`` function in Pandas uses string expressions to efficiently compute operations using ``DataFrame``s. +For example, consider the following ``DataFrame``s: + +```python +import pandas as pd +nrows, ncols = 100000, 100 +rng = np.random.RandomState(42) +df1, df2, df3, df4 = (pd.DataFrame(rng.rand(nrows, ncols)) + for i in range(4)) +``` + +To compute the sum of all four ``DataFrame``s using the typical Pandas approach, we can just write the sum: + +```python +%timeit df1 + df2 + df3 + df4 +``` + +The same result can be computed via ``pd.eval`` by constructing the expression as a string: + +```python +%timeit pd.eval('df1 + df2 + df3 + df4') +``` + +The ``eval()`` version of this expression is about 50% faster (and uses much less memory), while giving the same result: + +```python +np.allclose(df1 + df2 + df3 + df4, + pd.eval('df1 + df2 + df3 + df4')) +``` + +### Operations supported by ``pd.eval()`` + +As of Pandas v0.16, ``pd.eval()`` supports a wide range of operations. +To demonstrate these, we'll use the following integer ``DataFrame``s: + +```python +df1, df2, df3, df4, df5 = (pd.DataFrame(rng.randint(0, 1000, (100, 3))) + for i in range(5)) +``` + +#### Arithmetic operators +``pd.eval()`` supports all arithmetic operators. For example: + +```python +result1 = -df1 * df2 / (df3 + df4) - df5 +result2 = pd.eval('-df1 * df2 / (df3 + df4) - df5') +np.allclose(result1, result2) +``` + +#### Comparison operators +``pd.eval()`` supports all comparison operators, including chained expressions: + +```python +result1 = (df1 < df2) & (df2 <= df3) & (df3 != df4) +result2 = pd.eval('df1 < df2 <= df3 != df4') +np.allclose(result1, result2) +``` + +#### Bitwise operators +``pd.eval()`` supports the ``&`` and ``|`` bitwise operators: + +```python +result1 = (df1 < 0.5) & (df2 < 0.5) | (df3 < df4) +result2 = pd.eval('(df1 < 0.5) & (df2 < 0.5) | (df3 < df4)') +np.allclose(result1, result2) +``` + +In addition, it supports the use of the literal ``and`` and ``or`` in Boolean expressions: + +```python +result3 = pd.eval('(df1 < 0.5) and (df2 < 0.5) or (df3 < df4)') +np.allclose(result1, result3) +``` + +#### Object attributes and indices + +``pd.eval()`` supports access to object attributes via the ``obj.attr`` syntax, and indexes via the ``obj[index]`` syntax: + +```python +result1 = df2.T[0] + df3.iloc[1] +result2 = pd.eval('df2.T[0] + df3.iloc[1]') +np.allclose(result1, result2) +``` + +#### Other operations +Other operations such as function calls, conditional statements, loops, and other more involved constructs are currently *not* implemented in ``pd.eval()``. +If you'd like to execute these more complicated types of expressions, you can use the Numexpr library itself. + + +## ``DataFrame.eval()`` for Column-Wise Operations + +Just as Pandas has a top-level ``pd.eval()`` function, ``DataFrame``s have an ``eval()`` method that works in similar ways. +The benefit of the ``eval()`` method is that columns can be referred to *by name*. +We'll use this labeled array as an example: + +```python +df = pd.DataFrame(rng.rand(1000, 3), columns=['A', 'B', 'C']) +df.head() +``` + +Using ``pd.eval()`` as above, we can compute expressions with the three columns like this: + +```python +result1 = (df['A'] + df['B']) / (df['C'] - 1) +result2 = pd.eval("(df.A + df.B) / (df.C - 1)") +np.allclose(result1, result2) +``` + +The ``DataFrame.eval()`` method allows much more succinct evaluation of expressions with the columns: + +```python +result3 = df.eval('(A + B) / (C - 1)') +np.allclose(result1, result3) +``` + +Notice here that we treat *column names as variables* within the evaluated expression, and the result is what we would wish. + + +### Assignment in DataFrame.eval() + +In addition to the options just discussed, ``DataFrame.eval()`` also allows assignment to any column. +Let's use the ``DataFrame`` from before, which has columns ``'A'``, ``'B'``, and ``'C'``: + +```python +df.head() +``` + +We can use ``df.eval()`` to create a new column ``'D'`` and assign to it a value computed from the other columns: + +```python +df.eval('D = (A + B) / C', inplace=True) +df.head() +``` + +In the same way, any existing column can be modified: + +```python +df.eval('D = (A - B) / C', inplace=True) +df.head() +``` + +### Local variables in DataFrame.eval() + +The ``DataFrame.eval()`` method supports an additional syntax that lets it work with local Python variables. +Consider the following: + +```python +column_mean = df.mean(1) +result1 = df['A'] + column_mean +result2 = df.eval('A + @column_mean') +np.allclose(result1, result2) +``` + +The ``@`` character here marks a *variable name* rather than a *column name*, and lets you efficiently evaluate expressions involving the two "namespaces": the namespace of columns, and the namespace of Python objects. +Notice that this ``@`` character is only supported by the ``DataFrame.eval()`` *method*, not by the ``pandas.eval()`` *function*, because the ``pandas.eval()`` function only has access to the one (Python) namespace. + + +## DataFrame.query() Method + +The ``DataFrame`` has another method based on evaluated strings, called the ``query()`` method. +Consider the following: + +```python +result1 = df[(df.A < 0.5) & (df.B < 0.5)] +result2 = pd.eval('df[(df.A < 0.5) & (df.B < 0.5)]') +np.allclose(result1, result2) +``` + +As with the example used in our discussion of ``DataFrame.eval()``, this is an expression involving columns of the ``DataFrame``. +It cannot be expressed using the ``DataFrame.eval()`` syntax, however! +Instead, for this type of filtering operation, you can use the ``query()`` method: + +```python +result2 = df.query('A < 0.5 and B < 0.5') +np.allclose(result1, result2) +``` + +In addition to being a more efficient computation, compared to the masking expression this is much easier to read and understand. +Note that the ``query()`` method also accepts the ``@`` flag to mark local variables: + +```python +Cmean = df['C'].mean() +result1 = df[(df.A < Cmean) & (df.B < Cmean)] +result2 = df.query('A < @Cmean and B < @Cmean') +np.allclose(result1, result2) +``` + +## Performance: When to Use These Functions + +When considering whether to use these functions, there are two considerations: *computation time* and *memory use*. +Memory use is the most predictable aspect. As already mentioned, every compound expression involving NumPy arrays or Pandas ``DataFrame``s will result in implicit creation of temporary arrays: +For example, this: + +```python +x = df[(df.A < 0.5) & (df.B < 0.5)] +``` + +Is roughly equivalent to this: + +```python +tmp1 = df.A < 0.5 +tmp2 = df.B < 0.5 +tmp3 = tmp1 & tmp2 +x = df[tmp3] +``` + +If the size of the temporary ``DataFrame``s is significant compared to your available system memory (typically several gigabytes) then it's a good idea to use an ``eval()`` or ``query()`` expression. +You can check the approximate size of your array in bytes using this: + +```python +df.values.nbytes +``` + +On the performance side, ``eval()`` can be faster even when you are not maxing-out your system memory. +The issue is how your temporary ``DataFrame``s compare to the size of the L1 or L2 CPU cache on your system (typically a few megabytes in 2016); if they are much bigger, then ``eval()`` can avoid some potentially slow movement of values between the different memory caches. +In practice, I find that the difference in computation time between the traditional methods and the ``eval``/``query`` method is usually not significant–if anything, the traditional method is faster for smaller arrays! +The benefit of ``eval``/``query`` is mainly in the saved memory, and the sometimes cleaner syntax they offer. + +We've covered most of the details of ``eval()`` and ``query()`` here; for more information on these, you can refer to the Pandas documentation. +In particular, different parsers and engines can be specified for running these queries; for details on this, see the discussion within the ["Enhancing Performance" section](http://pandas.pydata.org/pandas-docs/dev/enhancingperf.html). + + + +< [Working with Time Series](03.11-Working-with-Time-Series.ipynb) | [Contents](Index.ipynb) | [Further Resources](03.13-Further-Resources.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/03.13-Further-Resources.ipynb b/notebooks_v2/03.13-Further-Resources.ipynb new file mode 100644 index 00000000..877d9613 --- /dev/null +++ b/notebooks_v2/03.13-Further-Resources.ipynb @@ -0,0 +1,99 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Further Resources" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In this chapter, we've covered many of the basics of using Pandas effectively for data analysis.\n", + "Still, much has been omitted from our discussion.\n", + "To learn more about Pandas, I recommend the following resources:\n", + "\n", + "- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\n", + "\n", + "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).\n", + "\n", + "- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across–more than likely you'll find your answer on a Stack Overflow page.\n", + "\n", + "- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\n", + "\n", + "Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/03.13-Further-Resources.md b/notebooks_v2/03.13-Further-Resources.md new file mode 100644 index 00000000..1d684316 --- /dev/null +++ b/notebooks_v2/03.13-Further-Resources.md @@ -0,0 +1,57 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) > + +Open in Colab + + + +# Further Resources + + +In this chapter, we've covered many of the basics of using Pandas effectively for data analysis. +Still, much has been omitted from our discussion. +To learn more about Pandas, I recommend the following resources: + +- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions. + +- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017). + +- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across–more than likely you'll find your answer on a Stack Overflow page. + +- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters. + +Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across! + + + + +< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/04.00-Introduction-To-Matplotlib.ipynb b/notebooks_v2/04.00-Introduction-To-Matplotlib.ipynb new file mode 100644 index 00000000..9aebeaaa --- /dev/null +++ b/notebooks_v2/04.00-Introduction-To-Matplotlib.ipynb @@ -0,0 +1,535 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization with Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now take an in-depth look at the Matplotlib package for visualization in Python.\n", + "Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack.\n", + "It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line.\n", + "IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months.\n", + "John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.\n", + "It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities.\n", + "\n", + "One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends.\n", + "Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish.\n", + "This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib.\n", + "It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world.\n", + "\n", + "In recent years, however, the interface and style of Matplotlib have begun to show their age.\n", + "Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\n", + "Still, I'm of the opinion that we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine.\n", + "Recent Matplotlib versions make it relatively easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [ggpy](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), [Altair](http://altair-viz.github.io/), and even Pandas itself can be used as wrappers around Matplotlib's API.\n", + "Even with wrappers like these, it is still often useful to dive into Matplotlib's syntax to adjust the final plot output.\n", + "For this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General Matplotlib Tips\n", + "\n", + "Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Matplotlib\n", + "\n", + "Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``plt`` interface is what we will use most often, as we shall see throughout this chapter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting Styles\n", + "\n", + "We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures.\n", + "Here we will set the ``classic`` style, which ensures that the plots we create use the classic Matplotlib style:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plt.style.use('classic')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this section, we will adjust this style as needed.\n", + "Note that the stylesheets used here are supported as of Matplotlib version 1.5; if you are using an earlier version of Matplotlib, only the default style is available.\n", + "For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ``show()`` or No ``show()``? How to Display Your Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context.\n", + "The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plotting from a script\n", + "\n", + "If you are using Matplotlib from within a script, the function ``plt.show()`` is your friend.\n", + "``plt.show()`` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.\n", + "\n", + "So, for example, you may have a file called *myplot.py* containing the following:\n", + "\n", + "```python\n", + "# ------- file: myplot.py ------\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "x = np.linspace(0, 10, 100)\n", + "\n", + "plt.plot(x, np.sin(x))\n", + "plt.plot(x, np.cos(x))\n", + "\n", + "plt.show()\n", + "```\n", + "\n", + "You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed:\n", + "\n", + "```\n", + "$ python myplot.py\n", + "```\n", + "\n", + "The ``plt.show()`` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\n", + "The details of this operation can vary greatly from system to system and even installation to installation, but matplotlib does its best to hide all these details from you.\n", + "\n", + "One thing to be aware of: the ``plt.show()`` command should be used *only once* per Python session, and is most often seen at the very end of the script.\n", + "Multiple ``show()`` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plotting from an IPython shell\n", + "\n", + "It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n", + "IPython is built to work well with Matplotlib if you specify Matplotlib mode.\n", + "To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``:\n", + "\n", + "```ipython\n", + "In [1]: %matplotlib\n", + "Using matplotlib backend: TkAgg\n", + "\n", + "In [2]: import matplotlib.pyplot as plt\n", + "```\n", + "\n", + "At this point, any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot.\n", + "Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``.\n", + "Using ``plt.show()`` in Matplotlib mode is not required." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plotting from an IPython notebook\n", + "\n", + "The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n", + "\n", + "Plotting interactively within an IPython notebook can be done with the ``%matplotlib`` command, and works in a similar way to the IPython shell.\n", + "In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options:\n", + "\n", + "- ``%matplotlib notebook`` will lead to *interactive* plots embedded within the notebook\n", + "- ``%matplotlib inline`` will lead to *static* images of your plot embedded in the notebook\n", + "\n", + "For this book, we will generally opt for ``%matplotlib inline``:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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W/n4vc2LxF9Jhy84JIaSUkvfeU0sL/+9/uhMFtoUL4fXXYYV3erMCyvHjUKcO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\nbxiGEYBM8TcMwwhApvgbhmEEoP8Hf41iEpQega0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "x = np.linspace(0, 10, 100)\n", + "\n", + "fig = plt.figure()\n", + "plt.plot(x, np.sin(x), '-')\n", + "plt.plot(x, np.cos(x), '--');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving Figures to File\n", + "\n", + "One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.\n", + "Saving a figure can be done using the ``savefig()`` command.\n", + "For example, to save the previous figure as a PNG file, you can run this:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig.savefig('my_figure.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a file called ``my_figure.png`` in the current working directory:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-r--r-- 1 jakevdp staff 16K Aug 11 10:59 my_figure.png\r\n" + ] + } + ], + "source": [ + "!ls -lh my_figure.png" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To confirm that it contains what we think it contains, let's use the IPython ``Image`` object to display the contents of this file:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TQGYm7yR8PMp6hIjECDjZiHPbHxnJ3kdqXm4V5BokVFspS0vWrmvbNt5J1MVo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\nq9g5tT78TwWMyCY7P9tgV257ru7BlB1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+ "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image('my_figure.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In ``savefig()``, the file format is inferred from the extension of the given filename.\n", + "Depending on what backends you have installed, many different file formats are available.\n", + "The list of supported file types can be found for your system by using the following method of the figure canvas object:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'eps': 'Encapsulated Postscript',\n", + " 'jpeg': 'Joint Photographic Experts Group',\n", + " 'jpg': 'Joint Photographic Experts Group',\n", + " 'pdf': 'Portable Document Format',\n", + " 'pgf': 'PGF code for LaTeX',\n", + " 'png': 'Portable Network Graphics',\n", + " 'ps': 'Postscript',\n", + " 'raw': 'Raw RGBA bitmap',\n", + " 'rgba': 'Raw RGBA bitmap',\n", + " 'svg': 'Scalable Vector Graphics',\n", + " 'svgz': 'Scalable Vector Graphics',\n", + " 'tif': 'Tagged Image File Format',\n", + " 'tiff': 'Tagged Image File Format'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig.canvas.get_supported_filetypes()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two Interfaces for the Price of One\n", + "\n", + "A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. We'll quickly highlight the differences between the two here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### MATLAB-style Interface\n", + "\n", + "Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\n", + "The MATLAB-style tools are contained in the pyplot (``plt``) interface.\n", + "For example, the following code will probably look quite familiar to MATLAB users:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure() # create a plot figure\n", + "\n", + "# create the first of two panels and set current axis\n", + "plt.subplot(2, 1, 1) # (rows, columns, panel number)\n", + "plt.plot(x, np.sin(x))\n", + "\n", + "# create the second panel and set current axis\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(x, np.cos(x));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is important to note that this interface is *stateful*: it keeps track of the \"current\" figure and axes, which are where all ``plt`` commands are applied.\n", + "You can get a reference to these using the ``plt.gcf()`` (get current figure) and ``plt.gca()`` (get current axes) routines.\n", + "\n", + "While this stateful interface is fast and convenient for simple plots, it is easy to run into problems.\n", + "For example, once the second panel is created, how can we go back and add something to the first?\n", + "This is possible within the MATLAB-style interface, but a bit clunky.\n", + "Fortunately, there is a better way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Object-oriented interface\n", + "\n", + "The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.\n", + "Rather than depending on some notion of an \"active\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit ``Figure`` and ``Axes`` objects.\n", + "To re-create the previous plot using this style of plotting, you might do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First create a grid of plots\n", + "# ax will be an array of two Axes objects\n", + "fig, ax = plt.subplots(2)\n", + "\n", + "# Call plot() method on the appropriate object\n", + "ax[0].plot(x, np.sin(x))\n", + "ax[1].plot(x, np.cos(x));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more simple plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated.\n", + "Throughout this chapter, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient.\n", + "In most cases, the difference is as small as switching ``plt.plot()`` to ``ax.plot()``, but there are a few gotchas that we will highlight as they come up in the following sections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.00-Introduction-To-Matplotlib.md b/notebooks_v2/04.00-Introduction-To-Matplotlib.md new file mode 100644 index 00000000..c5b6796b --- /dev/null +++ b/notebooks_v2/04.00-Introduction-To-Matplotlib.md @@ -0,0 +1,261 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) > + +Open in Colab + + + +# Visualization with Matplotlib + + +We'll now take an in-depth look at the Matplotlib package for visualization in Python. +Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack. +It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line. +IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months. +John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003. +It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities. + +One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends. +Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish. +This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib. +It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world. + +In recent years, however, the interface and style of Matplotlib have begun to show their age. +Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned. +Still, I'm of the opinion that we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine. +Recent Matplotlib versions make it relatively easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [ggpy](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), [Altair](http://altair-viz.github.io/), and even Pandas itself can be used as wrappers around Matplotlib's API. +Even with wrappers like these, it is still often useful to dive into Matplotlib's syntax to adjust the final plot output. +For this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly. + + +## General Matplotlib Tips + +Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package. + + +### Importing Matplotlib + +Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports: + +```python +import matplotlib as mpl +import matplotlib.pyplot as plt +``` + +The ``plt`` interface is what we will use most often, as we shall see throughout this chapter. + + +### Setting Styles + +We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures. +Here we will set the ``classic`` style, which ensures that the plots we create use the classic Matplotlib style: + +```python +plt.style.use('classic') +``` + +Throughout this section, we will adjust this style as needed. +Note that the stylesheets used here are supported as of Matplotlib version 1.5; if you are using an earlier version of Matplotlib, only the default style is available. +For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb). + + +### ``show()`` or No ``show()``? How to Display Your Plots + + +A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context. +The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook. + + +#### Plotting from a script + +If you are using Matplotlib from within a script, the function ``plt.show()`` is your friend. +``plt.show()`` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures. + +So, for example, you may have a file called *myplot.py* containing the following: + +```python +# ------- file: myplot.py ------ +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(0, 10, 100) + +plt.plot(x, np.sin(x)) +plt.plot(x, np.cos(x)) + +plt.show() +``` + +You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed: + +``` +$ python myplot.py +``` + +The ``plt.show()`` command does a lot under the hood, as it must interact with your system's interactive graphical backend. +The details of this operation can vary greatly from system to system and even installation to installation, but matplotlib does its best to hide all these details from you. + +One thing to be aware of: the ``plt.show()`` command should be used *only once* per Python session, and is most often seen at the very end of the script. +Multiple ``show()`` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided. + + +#### Plotting from an IPython shell + +It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)). +IPython is built to work well with Matplotlib if you specify Matplotlib mode. +To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``: + +```ipython +In [1]: %matplotlib +Using matplotlib backend: TkAgg + +In [2]: import matplotlib.pyplot as plt +``` + +At this point, any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot. +Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``. +Using ``plt.show()`` in Matplotlib mode is not required. + + +#### Plotting from an IPython notebook + +The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)). + +Plotting interactively within an IPython notebook can be done with the ``%matplotlib`` command, and works in a similar way to the IPython shell. +In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options: + +- ``%matplotlib notebook`` will lead to *interactive* plots embedded within the notebook +- ``%matplotlib inline`` will lead to *static* images of your plot embedded in the notebook + +For this book, we will generally opt for ``%matplotlib inline``: + +```python +%matplotlib inline +``` + +After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic: + +```python +import numpy as np +x = np.linspace(0, 10, 100) + +fig = plt.figure() +plt.plot(x, np.sin(x), '-') +plt.plot(x, np.cos(x), '--'); +``` + +### Saving Figures to File + +One nice feature of Matplotlib is the ability to save figures in a wide variety of formats. +Saving a figure can be done using the ``savefig()`` command. +For example, to save the previous figure as a PNG file, you can run this: + +```python +fig.savefig('my_figure.png') +``` + +We now have a file called ``my_figure.png`` in the current working directory: + +```python +!ls -lh my_figure.png +``` + +To confirm that it contains what we think it contains, let's use the IPython ``Image`` object to display the contents of this file: + +```python +from IPython.display import Image +Image('my_figure.png') +``` + +In ``savefig()``, the file format is inferred from the extension of the given filename. +Depending on what backends you have installed, many different file formats are available. +The list of supported file types can be found for your system by using the following method of the figure canvas object: + +```python +fig.canvas.get_supported_filetypes() +``` + +Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier. + + +## Two Interfaces for the Price of One + +A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. We'll quickly highlight the differences between the two here. + + +#### MATLAB-style Interface + +Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact. +The MATLAB-style tools are contained in the pyplot (``plt``) interface. +For example, the following code will probably look quite familiar to MATLAB users: + +```python +plt.figure() # create a plot figure + +# create the first of two panels and set current axis +plt.subplot(2, 1, 1) # (rows, columns, panel number) +plt.plot(x, np.sin(x)) + +# create the second panel and set current axis +plt.subplot(2, 1, 2) +plt.plot(x, np.cos(x)); +``` + +It is important to note that this interface is *stateful*: it keeps track of the "current" figure and axes, which are where all ``plt`` commands are applied. +You can get a reference to these using the ``plt.gcf()`` (get current figure) and ``plt.gca()`` (get current axes) routines. + +While this stateful interface is fast and convenient for simple plots, it is easy to run into problems. +For example, once the second panel is created, how can we go back and add something to the first? +This is possible within the MATLAB-style interface, but a bit clunky. +Fortunately, there is a better way. + + +#### Object-oriented interface + +The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure. +Rather than depending on some notion of an "active" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit ``Figure`` and ``Axes`` objects. +To re-create the previous plot using this style of plotting, you might do the following: + +```python +# First create a grid of plots +# ax will be an array of two Axes objects +fig, ax = plt.subplots(2) + +# Call plot() method on the appropriate object +ax[0].plot(x, np.sin(x)) +ax[1].plot(x, np.cos(x)); +``` + +For more simple plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated. +Throughout this chapter, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient. +In most cases, the difference is as small as switching ``plt.plot()`` to ``ax.plot()``, but there are a few gotchas that we will highlight as they come up in the following sections. + + + +< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.01-Simple-Line-Plots.ipynb b/notebooks_v2/04.01-Simple-Line-Plots.ipynb new file mode 100644 index 00000000..46c51db6 --- /dev/null +++ b/notebooks_v2/04.01-Simple-Line-Plots.ipynb @@ -0,0 +1,650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Line Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\n", + "Here we will take a first look at creating a simple plot of this type.\n", + "As with all the following sections, we'll start by setting up the notebook for plotting and importing the packages we will use:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-whitegrid')\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For all Matplotlib plots, we start by creating a figure and an axes.\n", + "In their simplest form, a figure and axes can be created as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = plt.axes()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n", + "The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\n", + "Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances.\n", + "\n", + "Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = plt.axes()\n", + "\n", + "x = np.linspace(0, 10, 1000)\n", + "ax.plot(x, np.sin(x));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\n", + "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3b7o6fO18EZPGzkxERwfw8pLHA0qDo7eOolW9VmhWpxltKaIhhQmTvbk9DHUM\ncerOKbpCRIQbvQhs3UoMieYsBAC6Nu2KJ4VPkP4gna4QEeHhm8phLWxz/z5w9iwwcCBdHQqFgrnV\ncd++QHo6kCXx9riSNvq4OPWXJK4OWgot+Nj5MBVfdHMDTp0CcnJoK5EWgiAwZ/TbtwOuriQXhTa+\n9mytjnV1gcGDSTVQKSNZo3/8mJxRdXOjrYTAWvjG0JB8t3FxtJVIi5ScFBSXFaNjo460pYhGbCyJ\nz0uB7s26427+XWQ8yqAtRTTkEL6RrNHv3An06UPqckiBAa0G4MK9C7hXcI+2FNEYOlT6MxFNUzGb\nZ6WIWWEh6SY1eDBtJQRtLW142XoxNWkaNAj491/gyRPaSipHskYfGwt4e9NW8T/0dfThZu3GVBU+\nT09SmOnZM9pKpENsWix87SQQLxSJ/fuBDh3U12NZGVhbHZuakknpjh20lVSOJI2+pITM6L0kdrqN\ntY0kMzOgc2fSuYsDZOdnIyUnBX1b9qUtRTSkFLapwM3aDclZyXj47CFtKaIh9fCNJI3+yBHA2lq9\njYuVYbDNYBy4fgAFxQW0pYiGjw+P01cQnx4Pd2t36Gnr0ZYiCoJA/t9KzeiNdI0woNUAbEtnp4Ob\ntzeZnBYV0VbyZiRp9FKchQBAA8MG6Nq0K/Zck1kfsbdQYfRyqcKnTmLTY+FjK8EHT0lOnwaMjAA7\nO9pKXoe18E2jRkD79iRUJkUkZ/SCIL34/Iuw9oDa2AB16pCjlrWZpyVPsT9jPzxtPGlLEQ2pTpgA\n0mJwz7U9KCyVWZftt+DtLd3VseSMPiWFxOg7SvR0m6+dL+LT41FWXkZbimh4e/PTN3uv7YWzpTMa\nGDagLUU0pGz0FsYW6NCoA/ZlsLNBVLE6lmKFB8kZfcXDKdXTbVb1rNDUtCn+vfUvbSmi4ePDjT42\nja2wza1bpJZRjx60lVQOa0XO7O0BPT3g3DnaSl5HskYvZVgL33TvDmRmkrpCtZFyoRxx6XFMZcPG\nxZGz87TLh7yNiiJnLJUAl+rqWFJGf+8eaYjRV+Kn24baD8XW1K3MVOHT1gaGDAHi2UkRqBGJmYkw\nMzKDdQNr2lJEQw4TJhszG9Q3qI/EzETaUkRDqnF6SRn9tm0kLV9fn7aSt9OpcScUlRUhNUcGhair\nSW0O37AWtsnLI5magwbRVlI1vna+iE1j58Hr3Ru4cgW4c4e2kpeRlNHLYRYCkCp8PrY+TIVv3N2B\nY8eA3FwykKlCAAAgAElEQVTaSjRPbDpbRcx27ybhOFNT2kqqxseOrd8jXV3yF+w2iaUISMboCwtJ\nOr5UanJUha89WzMRExOgZ09g1y7aSjTLtUfXcK/gHlyautCWIhpymTABQLdm3ZDzNAdXH16lLUU0\npBinl4zR79sHdOpE0vLlQF+rvrh0/xKy87NpSxGN2hi+iU2LhbetN7S1tGlLEYXSUjKblGoeyqto\nKbTgZeuFuHQJBraVxNMTOHBAWjWkJGP0cpqFAKTImbu1O7ZdltgaTQW8vUnt8tJS2ko0B2u1548d\nA5o3B1q0oK2k+vjY+TC1Oq5fn/ST3buXtpL/IQmjLy+XZk2OqmDtAW3WDGjZkmzk1QYePXuEpKwk\nuLZ2pS1FNOQ2YQIA19auSMpKwqNnj2hLEQ2phW8kYfSnTpEYsa0tbSU1Y7DNYOzL2IenJU9pSxEN\nqT2g6mTHlR3o17IfjHSNaEsRDTkavZGuEfq17IcdVyRc57eGeHuT48pSqSElCaOX42weIEXOnC2d\nsfeahNZoKuLjA8TESDONW2xYC9ukpQH5+YCTE20lNYe11bHUakhJwujlOAupwMeWrQe0c2eyiZSW\nRluJeikuK8auq7vgZSuxpgcqUDFhkmr5kLfhbeuNXVd3obismLYU0ZBSCXDqRn/zJqnL0b07bSXK\n4WPng7j0OKbSuGvD6ZtDNw7BzswOjU0a05YiGlKu+loVjUwawd7cHgevH6QtRTSkFAalbvTx8dKv\nyfE2rBtYw8zIjLk0bqk8oOqCtbBNTg5w9iwwYABtJcrD2uq4e3cyib11i7YSCRi9nMM2FbBW5Kx/\nf+D8eeD+fdpK1IMgCMwZ/fbtwMCBgIEBbSXK42Png9j0WGZqSOnokEmsFGpIUTf6o0flUZPjbbC2\nkWRgALi6EvNgkfP3zkNLoYV2Fu1oSxENFiZMbS3aQkdLB+eyJVjnV0mksjpWyugFQcCsWbMQFBSE\nsLAw3HplbbJv3z4EBAQgKCgIkZGRbx2rZ0951OR4Gy5NXXgat4yomM0r5Lhr+QaKioA9e0gFUjlT\nUUOKpUnToEFkMpufT1eHUkafkJCA4uJibNiwAVOmTMHcuXOfv1daWop58+Zh9erViIiIwMaNG/Hw\nYeXd3uW6efQiLKZxDxkCJCSQGkSsEZsWC187X9oyROPAAdKv1MKCthLV8bVnKwxapw7QrRv5i5gm\nShl9cnIyevfuDQDo2LEjLly48Py9q1evwsrKCiYmJtDV1YWzszMSEyvfqGTB6AH2qvBZWACOjsRE\nWCIrLwtXHl5Brxa9aEsRDRbCNhX0bN4TGY8zcDv3Nm0poiGFY5ZKGX1+fj5MX4i36OjooPz/U8Be\nfc/Y2Bh5eXmVjiWnmhxvw7W1K5KzkvHwWeWrF7khhQdUbOLS4uBp4wldbV3aUkRBEMj/I1YmTLra\nuvBs44n4dAnsYIpERZZsGcU200odajQxMUFBQcHzn8vLy6GlpfX8vfwXAlIFBQWoU6dOpWNlZWUp\nI0GSdG/SHesS12FYm2E1vjYvL09y30W3bjr49Vcz/Oc/2RpNwlHnd7Hp3CYE2ARI7ruujKq+iwsX\ndKCl1QB1696DTP6TqqSXRS9EnouEj+XLyxQp/o5UBz09wMzMAtu2PUaXLiVUNChl9E5OTti/fz88\nPDxw5swZ2L5QpMba2ho3btxAbm4uDAwMkJiYiPHjx1c6lqWlpTISJMnwDsOx+9pufNTnoxpfm5WV\nJbnvokkTwNgYuHfPEp07a+6+6vou8ovzkZidiOiQaNQ1qCv6+Oqgqu9ixQrAzw9o2lRaz44qBDcI\nxrQj01DHvA5M9Eyevy7F35Hq4ucHHD9uIVqI7U4NW1gpFbpxc3ODnp4egoKCMG/ePMyYMQPx8fGI\njIyEjo4OZsyYgXHjxiE4OBiBgYFo2LChMreRHV62Xth9dTczadwVWbKshG/2XN2Dbs26ycbkqwNL\nYZsK6hrURfdm3bH76m7aUkSDdra5UjN6hUKB2bNnv/Raq1atnv97v3790K9fP5WEyZFGJo3gYO6A\nA9cPwN3anbYcUfD2BqZOBWbOpK1EdWLT2eoNe+cOcPky6VPKGhVJiMMcah4GlSIuLiQB8do1oHVr\nzd+fesIUa7CWPNWzJ5CRAWRm0laiGmXlZYhPj4e3HTvT3/h4wMOD9CllDW87b2xL34bScja64Ghp\nAV5e9LJkudGLTIXRs5LGratLWqNJIY1bFY7fPg5LU0u0rNeSthTRYDFsU0GLui3QvG5zHLt1jLYU\n0aAZvuFGLzIO5g7Q09bD2eyztKWIBgtZsrFpbIVtnj4lOQ6enrSVqA/WsmRdXYGTJ4HHjzV/b270\nIqNQKJgL33h4AIcPAy+cqJUdselsFTHbu5c0GKlfn7YS9VFR5IwVjI2BPn2AnTs1f29u9GqAtWqW\ndetKI41bWdIfpONJ4RM4WzrTliIacu3KVhOcmjihoLgAaTnsdMGhFb7hRq8GerboieuPrzOVxi3n\n8E1FETMtBRuPe3k52TNhNT5fQcXqmKVJk5cXmdGXaDhvio0nX2LoaOlgsM1gxKUxcgAd0kjjVhbW\nas8nJ5NiWTY2tJWoH9bCoJaWQJs2JBSqSbjRqwkfW7bii61aAY0bk80kOZHzNAdns89iQCsZt156\nhdoQtqmgf8v+OH/vPO4XsNMFh0b4hhu9mhjUZhCO3jyKvKLKC7rJDTmGb7Zf3o6BrQbCQEfGrZde\ngeVjla+ir6MPt9Zu2HZ5G20polFh9Jo8gc2NXk3U0a+DHs17YNfVXbSliAbtNG5lYC1sU9GDtHt3\n2ko0B2vhG0dHss9y6ZLm7smNXo2w9oB27Qo8eABclUkjrcLSQuy5tgdDbGTeeukF4uLI2XkdpYqX\nyJPBNoOxN2MvCkvZ6IJTUUNKk5MmbvRqxNvWG9svb2cujVsuRc4OXD8Ax4aOsDBmoPXS/1Ob4vMV\nmBuZo1PjTjiadZS2FNHgRs8Qzes2R4u6LfDvrX9pSxENOYVvYlJj4G3LTjA7P5/0Hx00iLYSzeNj\n64NdN9gJg/bpA6SmAnfvauZ+3OjVjK+dL2JS2TkH7OoKJCUBjx7RVvJ2BEFAbHosfO3Z6Q27ezdJ\nXHtLHx9m8bHzQcLNBJQL5bSliIKeHuDuDmzT0B4zN3o1U5HwwUqRMyMjoG9fOmncNSH5TjJM9Exg\nb25PW4po1KbTNq9iY2YDUz1TJGcl05YiGppcHXOjVzOdGndCUVkRUnNSaUsRDTmEb2JSY+Brx85s\nvqyMzP5qq9EDgHsLd6YON3h6Avv3kwJ16oYbvZpRKBTMVeHz8gJ27dJ8GndNiEljy+hPnAAaNSKJ\na7UVdyt3ppIQGzQAnJ1JgTp1w41eA7BWha9JEzpp3NUl41EGsguy8U6zd2hLEY3aHLapwKmhE+7k\n3cH1x9dpSxENTbXq5EavAfq17IeL9y4iOz+bthTRkHL4JiYtBl42XtDW0qYtRTRq47HKV9HW0oaX\nrRdTq+MKoy9X8x4zN3oNoK+jD3drd57GrSFi0mKYOm1z9SrpN+riQlsJfVhLQrS2JiGcpCT13ocb\nvYZg7QF1dCQbhJpM464OD589RHJWMlxbu9KWIhpbtwK+viRhrbbj1toNJzNP4nEhhTZNakITq2P+\n6GiIwTaDsS9jH56VPKMtRRRopHFXh23p2zCg1QAY6RrRliIaW7cCQ4fSViENjPWM0ceqD3Zekfj5\n3hrAjZ4hGhg2gFMTJyRcS6AtRTQ0tZFUE1g7bZOTo4Xz54EB7FRZVhnWVscuLkB2NpCRob57cKPX\nIL52vkw9oH37ktBNtkT2mCuKmHnZetGWIhp79hjA3R0wYKfKssp423pj55WdKCmT8PneGqCtDQwZ\not5JEzd6DeJj54O49Diexq0m9mXsQ4dGHZgqYrZzpwEP27xCE9MmsDGzwaEbh2hLEQ11h2+40WsQ\n6wbWMDMyQ2JmIm0poiGl8E1sWixTYZv8fOD4cT0MHkxbifRgLQnRzY10b3vyRD3jc6PXMKw9oJ6e\nJLPvGeU95nKhnDmj37ULcHIqRr16tJVIj4okRFZqSBkbk4qW27erZ3xu9BrG196Xqa72ZmaAkxOQ\nQHmPOSkrCfUM6sHGjJ2O2Vu3AoMGsdFsQ2zaN2wPALhw7wJlJeLh5wds2aKesbnRaxiXpi7IeZqD\nqw9l0qapGgwbpr4HtLrEpMYw1TKwpITsfXCjfzMs1pDy8SGrOHWsjrnRaxgthRa8bL0Qly6RwLYI\nDB1KNpJKKTbSYu1Y5aFDgI0N0KQJGxv36sDX3pepGlIWFupbHXOjpwBr54BbtCBVFQ9ROgRx+cFl\nPHj2AN2adaMjQA3wJKmq6d2iNy4/uIw7eXdoSxENPz8gOlr8cbnRU8C1tSuSspLw6JnE2zTVgGHD\n1POAVofNKZvhZ+8HLQUbj7MgcKOvDrrauvBo48HU6tjPj5xiE3t1zMZvhsww0jVC/1b9sf2ymrbY\nKVARp1d3Fb43EZ0SDX8Hf83fWE2cOkU6edmz0xxLbbC2Om7eHGjdWvzVMTd6SvjYslWj3s4OqFeP\nnAXWJDef3MS1R9fQx6qPZm+sRipm8woFbSXSx6ONBw7dOISC4gLaUkRDHeEbbvSU8LL1wq4ru1Bc\nVkxbimjQCN9sSdkCHzsf6GrravbGaoSHbapPPYN6cGnqgj3X9tCWIhrqWB1zo6dEI5NGcLBwwMHr\nB2lLEY2KmYgmc1g2p2zGMIdhmruhmrl8GcjJAbqxs6+sdlirIVWxOk4UMYGeGz1FfGx9mEqe6tyZ\nbCJd0FAOS3Z+Ns5ln2Oq9vzmzYC/P689XxO87bwRnx6PsvIy2lJEQ+zwDX+cKOJrT2YirKRxKxSa\nDd9sTd0KTxtPGOiwU9oxKgoICKCtQl60rNcSTes0xZGbR2hLEY2K3yOxrIEbPUUczB1gpGuEk5ka\n3sFUI5o0+uhUtk7bZGQAt24BvXvTViI/AtsGIvJSJG0ZotG5M8mOFmt1zI2eIgqFgrkHtHt3Up/+\nyhX13ufRs0c4fvs4PNp4qPdGGiQqiizZtdnpaa4xAtsGYnPKZmbCNwqFuLVvuNFTJrBdIKIuRTET\nvtHWJidG1F37Ji49DgNaDYCJnol6b6RBeNhGeWzMbNDIuBGO3jpKW4poiLk65kZPGceGjtDX0ceZ\n+2doSxENPz+yqahONqdsxjB7dk7b3LgBXLtGunZxlCOwbSAiL7KzOu7RA7h7l5zEUhVu9JSpCN/E\nZ8TTliIa/fuT0M3Nm+oZP68oD/sz9sPbzls9N6BAdDTg6wvospMOoHEC25HwDSsd3LS1yQovUoS/\nu7jRS4DAtoGIvxbPTPhGT4+Eb8R4QN/E9svb0aN5D9QzYKcjBw/bqI6tmS0sjC1w9CY74Zvhw4GN\nG1Ufhxu9BOjQqAN0tXSRlJVEW4pojBghzgP6JjZe3IgR7UaoZ3AKZGYCqanAgAG0lcgf1g439OpF\nEuhSU1Ubhxu9BFAoFPBq7cXUA9q/P3D9Ook7i0luUS72ZuzFUHt2agRERwPe3mQlxFGNitM3rIRv\ntLTISm/TJhXHEUcOR1UqjJ6V8I2ODjk1IHb4JjYtFn2s+qC+YX1xB6YID9uIh525HcwMzfDvrX9p\nSxGNESO40TNDuwbtoKOlg+Q7ybSliIY6wjcbLmxAULsgcQelyJ07wLlzgJsbbSXswNrpm3feAZ48\nAS5eVH4MpYy+qKgIn3zyCUaOHIkPPvgAjx693kDjhx9+gL+/P8LCwhAWFob8/HzlVdYCnidPMfSA\n9ulDjEyM42EASZI6fPMwU71hN20ip2309WkrYYfAdoGISoliKnwTGKjarF4po1+/fj1sbW2xdu1a\n+Pr6YvHixa995uLFi1ixYgXCw8MRHh4OExN2ElvURcVGEivhm4rjYaouOyvYkroFrq1dYapvKs6A\nEmDdOiA4mLYKtrA3t0cDwwY4dusYbSmiURG+UdYalDL65ORk9OlDGj306dMHx469/IUKgoAbN25g\n5syZCA4OxmZ1Z88wQqfGnaCrrctU7RuxjocB5LQNS2Gbq1fJhvXAgbSVsMfwtsOx4cIG2jJEw8UF\nePYMOH9euet1qvpAVFQU/vnnn5deMzc3fz5DNzY2fi0s8/TpU4SGhmLs2LEoLS1FWFgYHB0dYWtr\nq5zKWoJCocBIx5FYe34tM42ue/YEHj4EUlIABwflx7lfcB8nbp/AlhFqrq2gQTZsICsenSp/Czk1\nJcQxBD1W9sAvg35hoimNQkEmTZs2AR061Pz6Kh+xgIAABLxyJODjjz9GQQFp3VVQUABT05eX0oaG\nhggNDYW+vj709fXxzjvvIDU19Y1Gn5WVVXPVDJKXl4esrCwMbDgQQ+OGYqrjVOhoseEAnp51sGJF\nOSZPrt4+TcV38SLhl8LRr1k/PL7/GI/xWB0yNYogAOHhFvjppyfIyqq8y9ibvovaSk2+C0MYoqlx\nU2xK2oT+zfurWZlm6N9fF5Mm1cfEifdqfK1STuLk5ISDBw/C0dERBw8eRJcuXV56PyMjA59//jli\nYmJQWlqK5ORkDBv25roklpaWykhgjqysLFhaWsLS0hKtj7ZGSmEKBrUZRFuWKIwfD4wdC/z8c51q\n9UGt+C5eZNeeXfi026fMPC/nzgGFhYC3t/lbm4y86buordT0uxjrNBY7s3ZiZLeRalSlOZo0Ifte\nd+9aArhTo2uVitEHBwfj8uXLCAkJQWRkJD766CMAwOrVq7F//35YW1tj6NChCAwMRFhYGPz8/GBt\nba3MrWolIe1DsO7COtoyRKNbN6C4GDh9WrnrM3MzcfbuWaZKEq9fDwQF8U5S6mR4u+GIS4tjpnG4\nQgGEhJAN/BojUCQpKYnm7SVFZmbm83+/k3dHqDevnlBQXEBRkbjMnCkIn31Wvc+++F0IgiD8dOQn\nYXzMeDWookN5uSC0bCkIp09X/dlXv4vajDLfxaCIQcL68+vVoIYOly4JQpMmNfdOPp+QII1NGqOr\nZVfEp7NT0XLUKDKLLS2t+bUR5yIQ2iFUfFGUOH4cMDAAOnakrYR9QhxDsPb8WtoyRMPBAWjcuObX\ncaOXKKw9oDY2QMuWQEJCza47e/csnhQ9QW8rdvrrrV9Pzs5XZ7+Coxp+9n44dOMQcp7m0JYiGuHh\nNb+GG71EGeYwDAeuH8DDZw9pSxGNUaOANWtqdk3EuQiMchwFLQUbj2pxMTlWGRJCW0ntwFTfFJ5t\nPBF1KYq2FNFo377m17Dx28MgdfTrwN3anakHdMQIID4eqG41jLLyMqw7vw6hHdkJ2+zYAdjZAW3a\n0FZSe2BtdawM3OglzCjHUYg4F0FbhmhYWAC9e1e/D+bejL1oWqcp7M3t1StMg6xeDYwZQ1tF7cKj\njQdS7qcg41EGbSnU4EYvYQbbDEb6g3SkP0inLUU0QkOrH75hbRP2/n1g/35SoIqjOfS09RDcPhj/\nnP2n6g8zCjd6CaOrrYtRjqOw+sxq2lJEw9sbSEoCqkpwzC/OR1xaHILas1PbZv16wMsLqFOHtpLa\nx7jO47D6zGpmKlrWFG70Emds57EIPxuOsvIy2lJEwdAQ8Pev+uRAdEo0erboiYbGDTUjTAPwsA09\nOjfpjHoG9bA/Yz9tKVTgRi9x2jdsjyamTbDn2h7aUkRj/Hhg5cq3l1xdcXoFxncerzlRaubcORK6\n6c9G2RVZMq7zOKw6s4q2DCpwo5cB4zqx9YB260b6ox4+/Ob3rz6+irScNHjZemlWmBr55x8gLIzU\nKuHQIcQxBPHp8XhcKP+ieDWFG70MCGofhF1XdjFzpl6hILP65cvf/P6GtA0I6xgGPW02umWXlABr\n1xKj59DD3MgcbtZu2HhB5P6WMoAbvQyob1gfnjaeWHeenUJnoaFAbCzw+JXJVUlZCSIvRzIVtomP\nJ+fm7exoK+GM6zQOK8+spC1D43CjlwnjOo3DytPsPKDm5oC7OzmJ8iLx6fFoXbc17MzZccW//wYm\nTKCtggMA7tbuuJ17GxfvqdBpW4Zwo5cJA1oNwMNnD5GUlURbimiMHw+sWPHyaytOr0CwHTtNVK9d\nA06dIp2kOPTR1tLGmI5jsOzUMtpSNAo3epmgraWNCV0m4K/Ev2hLEQ1XV3IS5cwZ8vPt3Ns4dvsY\nvFqzswm7bBkJUxkY0FbCqeB95/cRcS6CmTr11YEbvYwY13kcolOj8ejZI9pSREFbG3jvPWDxYvLz\nilMrMKLdCBjqGNIVJhLFxcCqVcAHH9BWwnkRq3pW6Nm8J1PNw6uCG72MaGjcEINtBjOVKfvee0Bk\nJJCdU4wlyUvwYdcPaUsSja1bSf1wvgkrPSZ1nYRFiYsgvC2ZgyG40cuMSV0m4a+kv5hJ5W7UCBgy\nBJi6YgvszO3QrmE72pJEY8kSvgkrVdyt3fGk6AlOZp6kLUUjcKOXGT2a94ChriH2ZeyjLUU0PvoI\niLrxJyZ1+Yi2FNFITQUuXAD8/Ggr4bw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x))\n", + "plt.plot(x, np.cos(x));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's all there is to plotting simple functions in Matplotlib!\n", + "We'll now dive into some more details about how to control the appearance of the axes and lines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adjusting the Plot: Line Colors and Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first adjustment you might wish to make to a plot is to control the line colors and styles.\n", + "The ``plt.plot()`` function takes additional arguments that can be used to specify these.\n", + "To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color.\n", + "The color can be specified in a variety of ways:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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xR5W7Gc6ekZFBmzZt3I4QsEfz4ObM6jnL/QVadVJRiCHXeywDQNu2bcnKynI/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x - 0), color='blue') # specify color by name\n", + "plt.plot(x, np.sin(x - 1), color='g') # short color code (rgbcmyk)\n", + "plt.plot(x, np.sin(x - 2), color='0.75') # Grayscale between 0 and 1\n", + "plt.plot(x, np.sin(x - 3), color='#FFDD44') # Hex code (RRGGBB from 00 to FF)\n", + "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n", + "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\n", + "\n", + "Similarly, the line style can be adjusted using the ``linestyle`` keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JeEHBIdLSptV6zd09RIiIWwosGBcYSbojid1dd1N8spgbZt5Az+M9af3P1sJE/EhBAQcL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\njfuSk3kvMpIOvr4E6nT8XqnvqMNRFNi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, x + 0, linestyle='solid')\n", + "plt.plot(x, x + 1, linestyle='dashed')\n", + "plt.plot(x, x + 2, linestyle='dashdot')\n", + "plt.plot(x, x + 3, linestyle='dotted');\n", + "\n", + "# For short, you can use the following codes:\n", + "plt.plot(x, x + 4, linestyle='-') # solid\n", + "plt.plot(x, x + 5, linestyle='--') # dashed\n", + "plt.plot(x, x + 6, linestyle='-.') # dashdot\n", + "plt.plot(x, x + 7, linestyle=':'); # dotted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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v3pjUvLnQEE+7mobJP05G5+WdoTVokfRKEr566iurD3GAV+REYul0wPbtwJNPqsfDhwMj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\nYrOz0f3wYcxJS8O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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, x + 0, '-g') # solid green\n", + "plt.plot(x, x + 1, '--c') # dashed cyan\n", + "plt.plot(x, x + 2, '-.k') # dashdot black\n", + "plt.plot(x, x + 3, ':r'); # dotted red" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\n", + "\n", + "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adjusting the Plot: Axes Limits\n", + "\n", + "Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\n", + "The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Yly6VncQ+c+aIewQGnG9DGseiTpozdSrw9ttAebnsJLY5e1YMc0tOlp2EXBGL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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x))\n", + "\n", + "plt.xlim(-1, 11)\n", + "plt.ylim(-1.5, 1.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x))\n", + "\n", + "plt.xlim(10, 0)\n", + "plt.ylim(1.2, -1.2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\n", + "The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Yly6VncQ+c+aIewQGnG9DGseiTpozdSrw9ttAebnsJLY5e1YMc0tOlp2EXBGL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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x))\n", + "plt.axis([-1, 11, -1.5, 1.5]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Tku6Eh0shO3pUtsWjWy1dKqNtfHxUJyGt8vQEYmPlXpkwwc5j2POhJUuWIDg4\nGIsXL0b//v0xa9asW37m0KFDmDt3LhYsWIAFCxawyJtQ6fAwRx87jeyjj4D4eNUpSOtKu2/sXfvG\nrkKfnZ2N8PBwAEB4eDh27tx5w/etVitOnDiBcePGIT4+His4e8a0nDU8zIiOHZMX1t26qU5CWte+\nPXD5MvDVV/Z9vsKum+XLl2P+/Pk3/N6dd955tYXu5+eHvLy8G77/22+/ITExEU888QSKi4uRlJSE\nVq1aITg42L6UpFudOgEXLgCHDwMtW6pOoy2pqfLE421XByqZiYeHNJqWLgVat7b98xXeYjExMYi5\naUjA888/j/z8fABAfn4+atSoccP3q1WrhsTERPj4+MDHxwd//OMfceTIkTILfU5Oju2pDSg3N9ew\n16J375qYO9eCUaPyKv5hGPtalLJagQUL6uHvf/8VOTmF5f6cGa5FZZn9WnTtWgUjR9bGiBE/2fxZ\nu9oSISEh2LZtG1q1aoVt27ahXbt2N3z/+PHjePnll7Fq1SoUFxcjOzsbAwcOLPNYAQEB9kQwnJyc\nHMNei+Rk4IkngH/8o2al9kE18rUodeAAcOUKEBV15203GTHDtagss1+LRo3kvdfZswEAztj0Wbv6\n6OPj43H06FEkJCRg2bJleO655wAAKSkpyMrKQlBQEAYMGIDY2FgkJSUhOjoaQUFB9pyKDKBDB6Cw\nEPjyS9VJtGPJEiAujjtJUeV5eAAJCfIC3+bPWq3qdvjMzs5GaGioqtNritFbK+PHA5cuAe+8U/HP\nGv1aWK3APffIUs4PPnj7nzX6tbAFr4W86+rWDVizxrbayfYEucXQodKKLS5WnUS9zz8HfH2BNm1U\nJyG9adkSaNjQ9s+x0JNbNG8ONG0KZGaqTqLekiUydr4y7yuIbnbTIMhKYaEntxk6FFi0SHUKtQoL\nZVhlQoLqJKRXrVrZ/hkWenKbxx4D0tOBvMqNsjSk9euBFi2Ae+9VnYTMhIWe3KZePSAszHn7YOpR\nSgrw+OOqU5DZsNCTWyUmmrf75uefgawsWaCKyJ1Y6MmtoqKAPXsAM05wXLIEiIwEatZUnYTMhoWe\n3KpaNWDQIGDBAtVJ3I/dNqQKCz25XXIy8J//2L/kqh4dOCBdN127qk5CZsRCT27XoYPsj7p9u+ok\n7jN/PpCUJGuVELkbCz25nYeHtOrnzFGdxD2KioDFi6XQE6nAQk9KJCYCq1cDFy+qTuJ66ekybr5F\nC9VJyKySOh8/AAAJEklEQVRY6EmJO+8EevSQkShG9/77wLPPqk5BZsZCT8okJwNz56pO4VrffQfs\n3Ss7SRGpwkJPynTvLiNR9u1TncR1PvxQuql8fVUnITNjoSdlvLyAp54CZs1SncQ1CguBefOAZ55R\nnYTMjoWelHrqKWDZMuCXX1Qncb6VK2X9cL6EJdVY6EmpBg2Avn2l5Ws0s2fzJSxpAws9Kffcc9J9\nY7GoTuI8R44ABw8C0dGqkxCx0JMGdOgA1KoFZGSoTuI8M2ZIa75qVdVJiFjoSQM8PIA//Ql47z3V\nSZzjwgWZHzBihOokRIKFnjQhLg7YtQv49lvVSRz34YdAv372beJM5Aos9KQJ1arJCJzp01UncUxR\nkTyZvPSS6iRE17DQk2a88ALw0UfA+fP6vS3T0oCgIKBtW9VJiK7R798oMpyGDWWpgJQUP9VR7GK1\nAm+/zdY8aQ8LPWnK6NHA/PnVkZ+vOontNm8GcnOlf55IS1joSVNatADaty/U5QSqiROB114DPPm3\nijSGtyRpzsiRefi//5MXm3qxYwdw8iQQH686CdGtWOhJc0JCihAUJNvv6cWkScDYsYC3t+okRLdi\noSdNeust+VVQoDpJxfbulc2/H39cdRKisrHQkyZ17Aj84Q/62Jhk3DhgzBjAx0d1EqKysdCTZr35\nJjB5MnDliuok5du+XRYv45rzpGUs9KRZ7doBoaGy56oWWa3Aq6/KP0hszZOWsdCTpk2cCEyZos2N\nSdasAS5dAoYMUZ2E6PZY6EnTWrWSNd3ffFN1khsVFcmY+cmTZUtEIi1joSfNe/NNYOFC4JtvVCe5\n5r33gLvuAiIjVSchqhgLPWle/frSFz56tOok4uxZGTc/Y4aspU+kdSz0pAsvvCBr1aelqU4iQymT\nk7npN+kH5/GRLlStKht6xMUBXbsCtWuryZGVBWzZAhw+rOb8RPZgi550IywM6N8feOUVNefPzQWG\nDwdmzwZq1FCTgcgeLPSkK1OnAhs3yi93GzNGnib69nX/uYkcwa4b0pWaNYF584DERODLL4EGDdxz\n3vR0YO1aWdOGSG/Yoifd6dZNXoYmJgIWi+vPd/y4nG/JEqBWLdefj8jZWOhJl8aPlzVwxo1z7Xmu\nXAEGD5bhnZ06ufZcRK7CQk+65O0NLF8um4m7at16i0WeGoKCuA8s6Rv76Em36teXfvMuXYCAACAi\nwnnHtlqBl18Gfv4Z2LCBE6NI39iiJ11r2RJYsUIWFsvIcM4xS1elzMoCVq4EfH2dc1wiVRwq9Js2\nbcLocualL126FIMGDUJcXBy2bt3qyGmIbuuRR6QgJyZKd44jioqAESNkUlRWFl++kjHY3XUzadIk\n7NixAy1btrzle+fOncPChQvxySef4MqVK4iPj0enTp1QpUoVh8ISlefhh6VFHx0NZGfLQmi23m5n\nz8qLV39/YPNmGcpJZAR2t+hDQkLwt7/9rczvHThwAKGhofD29oa/vz+aNm2Kb7S09CAZUtu2wO7d\nMr7+oYeAL76o3OdKSmR5hdatZehmejqLPBlLhS365cuXY/5NwxqmTJmC3r17Y9euXWV+Ji8vDzWu\nmyNevXp15ObmOhiVqGL16gHr1wOLFgGDBgEPPCDb/PXoAfj53fizP/wgXT4zZsiL3U2bgDZt1OQm\ncqUKC31MTAxiYmJsOqi/vz/y8vKufp2fn4+abCKRm3h4SH/94MFS8GfOlK8DA2V0TkkJcOIEcPEi\n0KuXbED+yCMcWUPG5ZLhla1bt8b06dNRWFiIgoICfPfdd2jevHmZP5udne2KCLp05swZ1RE0w1nX\n4sEH5VdF9u51yulcgvfFNbwW9nFqoU9JSUFgYCC6du2KxMREJCQkwGq1YtSoUahateotPx8aGurM\n0xMRURk8rFarVXUIIiJyHU6YIiIyOCWF3mq1Yvz48YiLi0NSUhJOnTqlIoYmFBcXY8yYMRgyZAgG\nDx6MLVu2qI6k1Pnz59GlSxccP35cdRTlPvjgA8TFxWHQoEFYsWKF6jhKFBcXY/To0YiLi8PQoUNN\ne1/s378fiYmJAICTJ08iISEBQ4cOxYQJEyr1eSWFPjMzE4WFhUhNTcXo0aMxZcoUFTE0YfXq1ahd\nuzYWL16MDz/8EG+99ZbqSMoUFxdj/Pjx8OWaA9i1axe+/PJLpKamYuHChaZ9Cblt2zZYLBakpqZi\n5MiReOedd1RHcrs5c+bgL3/5C4qKigDI8PZRo0Zh0aJFsFgsyMzMrPAYSgp9dnY2wsLCAABt2rTB\nwYMHVcTQhN69e+PFF18EAFgsFnh7m3eduWnTpiE+Ph7169dXHUW5zz77DMHBwRg5ciRGjBiBrl27\nqo6kRNOmTVFSUgKr1Yrc3FxTzq4PDAzEzJkzr3596NAhtGvXDgAQHh6OnTt3VngMJVXl5glV3t7e\nsFgs8PQ03yuDatWqAZBr8uKLL+Lll19WnEiNtLQ01K1bF506dcL777+vOo5yv/zyC3JycjB79myc\nOnUKI0aMwIYNG1THcjs/Pz/88MMP6NWrFy5evIjZs2erjuR2EREROH369NWvrx8/4+fnV6nJqEoq\nq7+/P/Lz869+bdYiX+rMmTMYNmwYoqOj0adPH9VxlEhLS8OOHTuQmJiII0eOYOzYsTh//rzqWMrU\nqlULYWFh8Pb2RrNmzeDj44MLFy6ojuV2KSkpCAsLQ0ZGBlavXo2xY8eisLBQdSylrq+VlZ2MqqS6\nhoSEYNu2bQCAffv2ITg4WEUMTTh37hySk5PxyiuvIDo6WnUcZRYtWoSFCxdi4cKFuO+++zBt2jTU\nrVtXdSxlQkNDsX37dgDAjz/+iCtXrqB27dqKU7nfHXfcAX9/fwBAjRo1UFxcDIs79o/UsPvvvx+7\nd+8GAHz66aeVmo+kpOsmIiICO3bsQFxcHACY+mXs7NmzcenSJcyaNQszZ86Eh4cH5syZU+YEM7Pw\n4FoE6NKlC/bs2YOYmJiro9TMeF2GDRuG119/HUOGDLk6AsfsL+vHjh2Lv/71rygqKkJQUBB69epV\n4Wc4YYqIyODM2zFORGQSLPRERAbHQk9EZHAs9EREBsdCT0RkcCz0REQGx0JPRGRwLPRERAb3/xI/\nBk/pWBptAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x))\n", + "plt.axis('tight');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x))\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Labeling Plots\n", + "\n", + "As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n", + "\n", + "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x))\n", + "plt.title(\"A Sine Curve\")\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"sin(x)\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The position, size, and style of these labels can be adjusted using optional arguments to the function.\n", + "For more information, see the Matplotlib documentation and the docstrings of each of these functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\n", + "Again, Matplotlib has a built-in way of quickly creating such a legend.\n", + "It is done via the (you guessed it) ``plt.legend()`` method.\n", + "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, np.sin(x), '-g', label='sin(x)')\n", + "plt.plot(x, np.cos(x), ':b', label='cos(x)')\n", + "plt.axis('equal')\n", + "\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label.\n", + "More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aside: Matplotlib Gotchas\n", + "\n", + "While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands.\n", + "In particular, functions to set limits, labels, and titles are slightly modified.\n", + "For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n", + "\n", + "- ``plt.xlabel()`` → ``ax.set_xlabel()``\n", + "- ``plt.ylabel()`` → ``ax.set_ylabel()``\n", + "- ``plt.xlim()`` → ``ax.set_xlim()``\n", + "- ``plt.ylim()`` → ``ax.set_ylim()``\n", + "- ``plt.title()`` → ``ax.set_title()``\n", + "\n", + "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes()\n", + "ax.plot(x, np.sin(x))\n", + "ax.set(xlim=(0, 10), ylim=(-2, 2),\n", + " xlabel='x', ylabel='sin(x)',\n", + " title='A Simple Plot');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.01-Simple-Line-Plots.md b/notebooks_v2/04.01-Simple-Line-Plots.md new file mode 100644 index 00000000..0024ea23 --- /dev/null +++ b/notebooks_v2/04.01-Simple-Line-Plots.md @@ -0,0 +1,239 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) > + +Open in Colab + + + +# Simple Line Plots + + +Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$. +Here we will take a first look at creating a simple plot of this type. +As with all the following sections, we'll start by setting up the notebook for plotting and importing the packages we will use: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +plt.style.use('seaborn-whitegrid') +import numpy as np +``` + +For all Matplotlib plots, we start by creating a figure and an axes. +In their simplest form, a figure and axes can be created as follows: + +```python +fig = plt.figure() +ax = plt.axes() +``` + +In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels. +The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization. +Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances. + +Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid: + +```python +fig = plt.figure() +ax = plt.axes() + +x = np.linspace(0, 10, 1000) +ax.plot(x, np.sin(x)); +``` + +Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background +(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces): + +```python +plt.plot(x, np.sin(x)); +``` + +If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times: + +```python +plt.plot(x, np.sin(x)) +plt.plot(x, np.cos(x)); +``` + +That's all there is to plotting simple functions in Matplotlib! +We'll now dive into some more details about how to control the appearance of the axes and lines. + + +## Adjusting the Plot: Line Colors and Styles + + +The first adjustment you might wish to make to a plot is to control the line colors and styles. +The ``plt.plot()`` function takes additional arguments that can be used to specify these. +To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color. +The color can be specified in a variety of ways: + +```python +plt.plot(x, np.sin(x - 0), color='blue') # specify color by name +plt.plot(x, np.sin(x - 1), color='g') # short color code (rgbcmyk) +plt.plot(x, np.sin(x - 2), color='0.75') # Grayscale between 0 and 1 +plt.plot(x, np.sin(x - 3), color='#FFDD44') # Hex code (RRGGBB from 00 to FF) +plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1 +plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported +``` + +If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines. + +Similarly, the line style can be adjusted using the ``linestyle`` keyword: + +```python +plt.plot(x, x + 0, linestyle='solid') +plt.plot(x, x + 1, linestyle='dashed') +plt.plot(x, x + 2, linestyle='dashdot') +plt.plot(x, x + 3, linestyle='dotted'); + +# For short, you can use the following codes: +plt.plot(x, x + 4, linestyle='-') # solid +plt.plot(x, x + 5, linestyle='--') # dashed +plt.plot(x, x + 6, linestyle='-.') # dashdot +plt.plot(x, x + 7, linestyle=':'); # dotted +``` + +If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function: + +```python +plt.plot(x, x + 0, '-g') # solid green +plt.plot(x, x + 1, '--c') # dashed cyan +plt.plot(x, x + 2, '-.k') # dashdot black +plt.plot(x, x + 3, ':r'); # dotted red +``` + +These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics. + +There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)). + + +## Adjusting the Plot: Axes Limits + +Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control. +The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods: + +```python +plt.plot(x, np.sin(x)) + +plt.xlim(-1, 11) +plt.ylim(-1.5, 1.5); +``` + +If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments: + +```python +plt.plot(x, np.sin(x)) + +plt.xlim(10, 0) +plt.ylim(1.2, -1.2); +``` + +A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*). +The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``: + +```python +plt.plot(x, np.sin(x)) +plt.axis([-1, 11, -1.5, 1.5]); +``` + +The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot: + +```python +plt.plot(x, np.sin(x)) +plt.axis('tight'); +``` + +It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``: + +```python +plt.plot(x, np.sin(x)) +plt.axis('equal'); +``` + +For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring. + + +## Labeling Plots + +As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends. + +Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them: + +```python +plt.plot(x, np.sin(x)) +plt.title("A Sine Curve") +plt.xlabel("x") +plt.ylabel("sin(x)"); +``` + +The position, size, and style of these labels can be adjusted using optional arguments to the function. +For more information, see the Matplotlib documentation and the docstrings of each of these functions. + + +When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type. +Again, Matplotlib has a built-in way of quickly creating such a legend. +It is done via the (you guessed it) ``plt.legend()`` method. +Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function: + +```python +plt.plot(x, np.sin(x), '-g', label='sin(x)') +plt.plot(x, np.cos(x), ':b', label='cos(x)') +plt.axis('equal') + +plt.legend(); +``` + +As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label. +More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb). + + +## Aside: Matplotlib Gotchas + +While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands. +In particular, functions to set limits, labels, and titles are slightly modified. +For transitioning between MATLAB-style functions and object-oriented methods, make the following changes: + +- ``plt.xlabel()`` → ``ax.set_xlabel()`` +- ``plt.ylabel()`` → ``ax.set_ylabel()`` +- ``plt.xlim()`` → ``ax.set_xlim()`` +- ``plt.ylim()`` → ``ax.set_ylim()`` +- ``plt.title()`` → ``ax.set_title()`` + +In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once: + +```python +ax = plt.axes() +ax.plot(x, np.sin(x)) +ax.set(xlim=(0, 10), ylim=(-2, 2), + xlabel='x', ylabel='sin(x)', + title='A Simple Plot'); +``` + + +< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.02-Simple-Scatter-Plots.ipynb b/notebooks_v2/04.02-Simple-Scatter-Plots.ipynb new file mode 100644 index 00000000..76bc01d6 --- /dev/null +++ b/notebooks_v2/04.02-Simple-Scatter-Plots.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Scatter Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\n", + "Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\n", + "We’ll start by setting up the notebook for plotting and importing the functions we will use:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-whitegrid')\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scatter Plots with ``plt.plot``\n", + "\n", + "In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots.\n", + "It turns out that this same function can produce scatter plots as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 10, 30)\n", + "y = np.sin(x)\n", + "\n", + "plt.plot(x, y, 'o', color='black');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ijo5GSUkJLC0tER8fjyFDhrT7vsjISNjb2yM4OFgvQTvEcQAtzWVUHB2H4dCh\nKKFjEB2FhyciJeU+pNLWgq5PxrjQxokTJ5CSkgI3NzcsX75cpZ/7ihUr+D9InVBb2M+ePYvGxkak\np6ejsLAQCQkJSElJUfme9PR03Lx5E6+++qregnaIijohBpWQEIJXXjmDxMRgFBVNh0w2Hfoq8Ma4\n0IaZmRnMzMzAcZygN1GqLex5eXnw9PQEAIwePRrFxcUqj3///fcoKipCQEAAbt26pZ+UhBBR4DgO\n/v7T4ef3OjIznxZ4fTSJNcaFNmbPno1Zs2bh22+/xa5du/B///d/8PPzwzvvvGPQQq/2w9O6ujrY\n2dkpv7awsFA2s3/w4AGSk5MRGRlJq5UT0oMoCvylS1tx4AAHNzdb9U/imVgX2uA4DtOmTcPevXvx\n2Wefoaampt3cvL6pHbHb2tqivv7pJWotLS0wM2v9eXDq1CnU1NRg2bJlePDgAeRyOUaMGIE5c+bo\nLzEhRDQUBd7ff7rBX9cYFtoYPHgwgoKCur0fbaldaOPMmTM4d+4cEhISUFBQgJSUFKSmprb7vq++\n+gplZWUdfnial5eH5557jr/UPJBKpSq/iYiFItedO/eweXMGfvqJw+9+x/Dhh/4YOvR5QTOJCWXS\njBgzAeLMJcZM9+/f12mhDbUjdm9vb+Tk5CgXaU1ISEBWVhYaGhogkUg0fiGxrUwixtVSgNZccnkT\nFi48qtJT5YcfhOupIsZjRZk0I8ZMgDhziTHT/fv3dXqe2sLOcRxiYmJUtjk6Orb7Pl9fX50CkPY6\n76mSRJcKEkLUojtPRYh6qhBCusNkCztjDGFhYUZ5tQ71VCGEdIfJVoqMjAykpKQgMzNT6ChaM/ae\nKtXnq4WOQEiPZpKFnTGGpKQkSKVSJCYmGt2oXdFTZcGCJHh5RWHBgiSjWoyi5nyN0BEI6dFMsglY\nRkYGioqKAABFRUXIzMyEv7+/wKm0Qz1VCCG6MrkRu2K0LpPJAAAymcwoR+3Gpvp8Ncqiy1AWXYby\nmHLl32lahohBT+vHbnIj9rajdQVjHbUbk/6T+6P/5P7Krx2j218SS4xfUGQQJo6dCP9Z/oI2uRKT\ntv3YMzMzMXDgQLz22mvtjg/1Y++GnJwcjB07VuWgMsbw3XffUWEnpJvyy/ORejcVSQeSsG7ROr0V\neGPtxz58+HDs3bsXH3/8MXx9feHn54dBgwYBgEH7sYMZwLVr1wzxMlqpqKgQOkKHxJhL20y/nvtV\nT0meMoWFCBHaAAAUeUlEQVTjZAh8Z5q0eBJDNBiiwGzesWEe/h7s2NfHWEtLC6+5cnNzmYuLC/vx\nxx8ZY4y9++67LCAggDU3N7Nff/2Vubi4sPz8fLZmzRrlc3bv3s1WrlzJGGMsLCyMLVmyRPlYWFgY\n27t3L4uJiWGrV69mTU1NjDHGkpOT2ebNm5WZtm7dymJiYhhjjHl5ebGUlBSt3pdCVVUVS01NZW+8\n8QZ7//33WWNjo0770bV2mtyInQiv7ZQMMVEcIBsuQy7LxdItS3Ht+2vYGLWR15cwxn7sChzHKXuz\nm5ubd/9gaIkKOyFEewywKbeBa50rQtaFwG+mH+8vYYz92AsLC7Fv3z6UlJRgzpw5+PzzzzFw4ECt\n3jcfTO6qGEKI/jDGYHPbBh7XPXDA7wAuHb0k2AepYuzHfvPmTfj7++P06dN47733BCnqgAgLO10e\nR4h4uQ1zE7ygA0/7sV+5cgWzZ8/GvHnzMHToUNy7d0/tc+Pj45Geno6CggKsWrUKDg4O8PX1xdKl\nS8FxXLf6sUskEkyaNEnr5/FNbT92PuTl5WncU7gsuswgl8qJsUUnIM5clEkzlElzYswlxkza1M62\nRDdiJ4QQ0j2i+PC0+ny1sr9IeUy5crv9ZHu6woIQQrQkisJOdy0SQgh/aCqGaIwxhuPHTyEoKFHo\nKISQLoiusNtPthc6AnmGoqCPHx+MxYs55OfXCR2JENIFUUzFtEVz6uLBGENGxmkkJPwd//73bMhk\nWwFw4LhLQkcjhHRBdIWdaE9RgHNyirBtWwhv+w0PT0RKyn1IpckAqJMfIcZCdFMxRHP6niJJSAhB\nWtoMuLn9F2xsTgGgnvbEOAndj92QvdgBGrEbJcUIPSnpNIqKZuhtioTjOPj7T8e4caNw+XIxEhOD\nUVQ0nRYt6cE2Ll+ORzdvtttu5eyMsNRUARIJr20/9q6+x1C92AEq7Ebp6RRJa0HXN0WB9/N7HZmZ\nZ/Ddd7bqn0RM0qObNxF94UK77dE8v46x9mMvLS3Fhg0b0NjYCMYYJBIJ5s2bZ9he7KDCzgtDryqT\nkBCCV145oxxBy2TTYcgC7+8/Xe+vRUhxcTGOHz+OkSNHYtmyZUhNTcWhQ4dQW1sLT09PTJ8+HVVV\nVThy5AgAIDU1Fampqdi1axcAQC6XK7s9hoeHo6WlBbGxsaiqqsKePXtgYWGBnTt3wsLCApmZmais\nrMSRI0ewZcsWREZGAgCcnZ2xbds2AMC0adM6zbpv3z4AwOeff44pU6Zg2bJlqKqqQkJCAubNm4cV\nK1bo7Th1hAo7Dwy1qozCsyNomiIhpsgY+7F7e3sjNDQUP/zwA8aPH4/169fzd0C0QIWdBxzHKRcd\nWPzVYsEKPE2RGM7HHwfh55/zVf595XI5nn9+HDZs2CZgMtNhjP3YJ0+ejDNnziAnJweXL1/Gzp07\nkZ6ejiFDhmj13ruLrorhk2JVmVGtq8qEx4Yb5mWfFHg+L3UkXXv55YkYNuwafH0vKP84Of2AMWNe\nEzpajyHGfuxr167FP/7xD7zxxhuIjIyEra0tfvrpJ533p6seOWL/+OMg3Lt3Gb1791ZuY4xh0CC3\n7o22DLCqDBEHHx9/ZGYmwc0tFxwHMAb88MNIrFtn2v/mVs7OHX5QauXsbNAcin7sH374IWbPng0L\nCwuMHTsWZ86cUfvc+Ph4+Pr6wsvLC6tWrcLGjRvh6+uLxsZGuLq6dqsf+6pVq7B+/XocPXoUZmZm\neP311/HKK69ovZ/uEl0/dkPIyjqOf/97McaOlSm3Xbtmgz/84QB8fPy13t+kxZNwzexaa0Ff1FrQ\ndZ2CEWNPaMrUsays4ygpWQx3dxmuXbPB7363HQsXLhM007PEcJw6IsZcYsxE/di14OPjj8LCkVD8\nSGMMuHHDFW+8odtoSyyryhDD8vHxx/Xrrsrzx8vrDaEjEQKghxZ2juPw+usrkJ/f+sFIXp4N/P1D\ndC7I22K3UUHvgTiOg5/fOuzebdet84cQvvXIwg4AU6b4qIy2dB2tk56jsbERf1m3Do2NjcptPj7+\ncHV9n84fIio9trDTaItoa1lEBL4YPBjLn9y8ArSeR+vXb6Tzh4hKjy3sAI22iOY+P3oUX9vb4/GY\nMTjRty/Sjh0TOhIhnerRhZ1GW0QTpbduIS47Gw/HjwcAPJwwAbHffovSW7cETkZIx3p0YTcm1eer\nhY7QY32waRNuv/mmyrbbs2fjg02bBEpESNfUFnbGGKKiohAQEIBFixbh7t27Ko9nZWXhrbfewvz5\n8xEdHa2vnD1ezfkaoSP0WNtDQzH8669Vtg0/eRLbn9zIQojYqC3sZ8+eRWNjI9LT07F27VokJCQo\nH5PL5dixYwcOHTqEv/3tb5BKpTh37pxeAxNiaE4jRiBy6lT0u3gRANDv4kVETp0KpxEjBE4mnI6u\nEBIzoRbaiIuLQ3JyskaLcfBJbUuBvLw8eHp6AgBGjx6N4uJi5WOWlpZIT09XNutpbm5WuU2fdE/1\n+WrlSL08ply53X6yPa0Na2BLJBKcDw3Fl/n5mFNbiyUSidCRBLUsIgJfDh6Mx5GR2L9xo9BxBKXp\nQhvqvodPagt7XV0d7Ozsnj7BwgItLS0wMzMDx3EYMGAAAODgwYNoaGjAhAkT9Je2h+k/ub9KAXeM\ndhQwDdkTFweLjz7Crk8+ETqKoFSuELp4EWnHjvH+g85YF9qoq6vDhg0bUFJSgoEDB8Lc3Bzu7u4Y\nMGCA8nsMQW1ht7W1RX19vfJrRVFXYIxh8+bNKC8v73JNP0P9CqIpqVQqukxA57mEzCvGYyVUprjg\nYFRVVXX4WE84TrfLyxF95gweLlwIoPUKoahDh/D755/H8GHDeMtVVVWFoqIifPbZZ3ByckJYWBiS\nk5Oxfft21NXVQSKRwN3dHXfv3lUuhHH48GHs2LED8fHxkMlkqK2txe7duwEAmzZtQk1NDUJDQ/Hr\nr78iNjYW1dXVOHDgAB49eoTk5GRIpVIcOXIEsbGxWLNmDR4/foznnnsOISGtXVM76/pYWVmJuLg4\nVFZWYufOnWCMYd++fXj48CGWL18OZ2dnle8xBLWF3c3NDefOncOMGTNQUFAA52e6uEVERMDKygop\nKSld7kdszXX4bPjD5zqQneWynmWN/g7CTL+IsTkSZdIM35lWxMTgrr9qo7y7fn5I2L8ff39SRPnI\nde/ePTz//PPKaeAXX3wRdnZ2yr7mtra2cHR0RFhYGC5cuKCy0IaDgwNsbGwwfvx45WvY2NggIyND\nudCGYj95eXmQSqUoLCxUWWjDwcEB5ubm8PLyUu5Dk4U2fvjhB6xfvx4ODg5wcHDA9OnTYWdnp/O/\nwf3793V6ntrC7u3tjZycHAQEBABo/TUmKysLDQ0NcHFxQWZmJtzd3REYGAiO47Bo0aIul5AyRYZY\nB5Lm1IkYbA8NRfHmzbj9pB4A+rtCyBgX2uA4TmUls2czG4raV+U4DjExMSrbHB2fzvXeuHGD/1SE\nEFFSXCEUdPEiHk6YIOgVQm0X2pDL5dizZ49GC22cPXsWO3bsQFBQkHKhjXHjxikX2rC1tUVsbKxO\nmTw9PXH8+HGMGzcOtbW1+Pb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(0)\n", + "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n", + " plt.plot(rng.rand(5), rng.rand(5), marker,\n", + " label=\"marker='{0}'\".format(marker))\n", + "plt.legend(numpoints=1)\n", + "plt.xlim(0, 1.8);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YAwcOhJOTE3JycuDj48M6DhEYmUyGHj16oE+fPnBwcEDnzp2h0WhsYuTas54dUy/0x49W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\nY8cOzJo1CxcvXrT48Z577rk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y, '-ok');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y, '-p', color='gray',\n", + " markersize=15, linewidth=4,\n", + " markerfacecolor='white',\n", + " markeredgecolor='gray',\n", + " markeredgewidth=2)\n", + "plt.ylim(-1.2, 1.2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options.\n", + "For a full description of the options available, refer to the ``plt.plot`` documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scatter Plots with ``plt.scatter``\n", + "\n", + "A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x, y, marker='o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n", + "\n", + "Let's show this by creating a random scatter plot with points of many colors and sizes.\n", + "In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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BbCa7IqzNRpMroxfBVGbwSBij4aZYlvNlzs/PMTm3xAOnDuNyOxg42EVXX4vJ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\noNX2QiLxruOqO6TSSGi1NHIlx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(0)\n", + "x = rng.randn(100)\n", + "y = rng.randn(100)\n", + "colors = rng.rand(100)\n", + "sizes = 1000 * rng.rand(100)\n", + "\n", + "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\n", + " cmap='viridis')\n", + "plt.colorbar(); # show color scale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels.\n", + "In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\n", + "\n", + "For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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xWSboCmOzWimUimQX80xcnOTQA/dt4Ce3eRaTSZwbmPrp9PmYWVjARgWn8+qT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "features = iris.data.T\n", + "\n", + "plt.scatter(features[0], features[1], alpha=0.2,\n", + " s=100*features[3], c=iris.target, cmap='viridis')\n", + "plt.xlabel(iris.feature_names[0])\n", + "plt.ylabel(iris.feature_names[1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\n", + "the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n", + "Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ``plot`` Versus ``scatter``: A Note on Efficiency\n", + "\n", + "Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``.\n", + "The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n", + "In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.\n", + "For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.02-Simple-Scatter-Plots.md b/notebooks_v2/04.02-Simple-Scatter-Plots.md new file mode 100644 index 00000000..4de8e640 --- /dev/null +++ b/notebooks_v2/04.02-Simple-Scatter-Plots.md @@ -0,0 +1,147 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) > + +Open in Colab + + + +# Simple Scatter Plots + + +Another commonly used plot type is the simple scatter plot, a close cousin of the line plot. +Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape. +We’ll start by setting up the notebook for plotting and importing the functions we will use: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +plt.style.use('seaborn-whitegrid') +import numpy as np +``` + +## Scatter Plots with ``plt.plot`` + +In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots. +It turns out that this same function can produce scatter plots as well: + +```python +x = np.linspace(0, 10, 30) +y = np.sin(x) + +plt.plot(x, y, 'o', color='black'); +``` + +The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here: + +```python +rng = np.random.RandomState(0) +for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']: + plt.plot(rng.rand(5), rng.rand(5), marker, + label="marker='{0}'".format(marker)) +plt.legend(numpoints=1) +plt.xlim(0, 1.8); +``` + +For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them: + +```python +plt.plot(x, y, '-ok'); +``` + +Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers: + +```python +plt.plot(x, y, '-p', color='gray', + markersize=15, linewidth=4, + markerfacecolor='white', + markeredgecolor='gray', + markeredgewidth=2) +plt.ylim(-1.2, 1.2); +``` + +This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options. +For a full description of the options available, refer to the ``plt.plot`` documentation. + + +## Scatter Plots with ``plt.scatter`` + +A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function: + +```python +plt.scatter(x, y, marker='o'); +``` + +The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data. + +Let's show this by creating a random scatter plot with points of many colors and sizes. +In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level: + +```python +rng = np.random.RandomState(0) +x = rng.randn(100) +y = rng.randn(100) +colors = rng.rand(100) +sizes = 1000 * rng.rand(100) + +plt.scatter(x, y, c=colors, s=sizes, alpha=0.3, + cmap='viridis') +plt.colorbar(); # show color scale +``` + +Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels. +In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data. + +For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured: + +```python +from sklearn.datasets import load_iris +iris = load_iris() +features = iris.data.T + +plt.scatter(features[0], features[1], alpha=0.2, + s=100*features[3], c=iris.target, cmap='viridis') +plt.xlabel(iris.feature_names[0]) +plt.ylabel(iris.feature_names[1]); +``` + +We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data: +the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower. +Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data. + + +## ``plot`` Versus ``scatter``: A Note on Efficiency + +Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``. +The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually. +In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data. +For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets. + + + +< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.03-Errorbars.ipynb b/notebooks_v2/04.03-Errorbars.ipynb new file mode 100644 index 00000000..1b5c958f --- /dev/null +++ b/notebooks_v2/04.03-Errorbars.ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing Errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself.\n", + "For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe.\n", + "I know that the current literature suggests a value of around 71 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.\n", + "\n", + "Suppose I augment this information with reported uncertainties: the current literature suggests a value of around 71 $\\pm$ 2.5 (km/s)/Mpc, and my method has measured a value of 74 $\\pm$ 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.\n", + "\n", + "In visualization of data and results, showing these errors effectively can make a plot convey much more complete information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Errorbars\n", + "\n", + "A basic errorbar can be created with a single Matplotlib function call:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-whitegrid')\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 10, 50)\n", + "dy = 0.8\n", + "y = np.sin(x) + dy * np.random.randn(50)\n", + "\n", + "plt.errorbar(x, y, yerr=dy, fmt='.k');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the ``fmt`` is a format code controlling the appearance of lines and points, and has the same syntax as the shorthand used in ``plt.plot``, outlined in [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb).\n", + "\n", + "In addition to these basic options, the ``errorbar`` function has many options to fine-tune the outputs.\n", + "Using these additional options you can easily customize the aesthetics of your errorbar plot.\n", + "I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(x, y, yerr=dy, fmt='o', color='black',\n", + " ecolor='lightgray', elinewidth=3, capsize=0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to these options, you can also specify horizontal errorbars (``xerr``), one-sided errorbars, and many other variants.\n", + "For more information on the options available, refer to the docstring of ``plt.errorbar``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Continuous Errors\n", + "\n", + "In some situations it is desirable to show errorbars on continuous quantities.\n", + "Though Matplotlib does not have a built-in convenience routine for this type of application, it's relatively easy to combine primitives like ``plt.plot`` and ``plt.fill_between`` for a useful result.\n", + "\n", + "Here we'll perform a simple *Gaussian process regression*, using the Scikit-Learn API (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) for details).\n", + "This is a method of fitting a very flexible non-parametric function to data with a continuous measure of the uncertainty.\n", + "We won't delve into the details of Gaussian process regression at this point, but will focus instead on how you might visualize such a continuous error measurement:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.gaussian_process import GaussianProcess\n", + "\n", + "# define the model and draw some data\n", + "model = lambda x: x * np.sin(x)\n", + "xdata = np.array([1, 3, 5, 6, 8])\n", + "ydata = model(xdata)\n", + "\n", + "# Compute the Gaussian process fit\n", + "gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1,\n", + " random_start=100)\n", + "gp.fit(xdata[:, np.newaxis], ydata)\n", + "\n", + "xfit = np.linspace(0, 10, 1000)\n", + "yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True)\n", + "dyfit = 2 * np.sqrt(MSE) # 2*sigma ~ 95% confidence region" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have ``xfit``, ``yfit``, and ``dyfit``, which sample the continuous fit to our data.\n", + "We could pass these to the ``plt.errorbar`` function as above, but we don't really want to plot 1,000 points with 1,000 errorbars.\n", + "Instead, we can use the ``plt.fill_between`` function with a light color to visualize this continuous error:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the result\n", + "plt.plot(xdata, ydata, 'or')\n", + "plt.plot(xfit, yfit, '-', color='gray')\n", + "\n", + "plt.fill_between(xfit, yfit - dyfit, yfit + dyfit,\n", + " color='gray', alpha=0.2)\n", + "plt.xlim(0, 10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note what we've done here with the ``fill_between`` function: we pass an x value, then the lower y-bound, then the upper y-bound, and the result is that the area between these regions is filled.\n", + "\n", + "The resulting figure gives a very intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained and this is reflected in the small model errors.\n", + "In regions far from a measured data point, the model is not strongly constrained, and the model errors increase.\n", + "\n", + "For more information on the options available in ``plt.fill_between()`` (and the closely related ``plt.fill()`` function), see the function docstring or the Matplotlib documentation.\n", + "\n", + "Finally, if this seems a bit too low level for your taste, refer to [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.03-Errorbars.md b/notebooks_v2/04.03-Errorbars.md new file mode 100644 index 00000000..87a76370 --- /dev/null +++ b/notebooks_v2/04.03-Errorbars.md @@ -0,0 +1,132 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) > + +Open in Colab + + + +# Visualizing Errors + + +For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself. +For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe. +I know that the current literature suggests a value of around 71 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know. + +Suppose I augment this information with reported uncertainties: the current literature suggests a value of around 71 $\pm$ 2.5 (km/s)/Mpc, and my method has measured a value of 74 $\pm$ 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered. + +In visualization of data and results, showing these errors effectively can make a plot convey much more complete information. + + +## Basic Errorbars + +A basic errorbar can be created with a single Matplotlib function call: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +plt.style.use('seaborn-whitegrid') +import numpy as np +``` + +```python +x = np.linspace(0, 10, 50) +dy = 0.8 +y = np.sin(x) + dy * np.random.randn(50) + +plt.errorbar(x, y, yerr=dy, fmt='.k'); +``` + +Here the ``fmt`` is a format code controlling the appearance of lines and points, and has the same syntax as the shorthand used in ``plt.plot``, outlined in [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb). + +In addition to these basic options, the ``errorbar`` function has many options to fine-tune the outputs. +Using these additional options you can easily customize the aesthetics of your errorbar plot. +I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves: + +```python +plt.errorbar(x, y, yerr=dy, fmt='o', color='black', + ecolor='lightgray', elinewidth=3, capsize=0); +``` + +In addition to these options, you can also specify horizontal errorbars (``xerr``), one-sided errorbars, and many other variants. +For more information on the options available, refer to the docstring of ``plt.errorbar``. + + +## Continuous Errors + +In some situations it is desirable to show errorbars on continuous quantities. +Though Matplotlib does not have a built-in convenience routine for this type of application, it's relatively easy to combine primitives like ``plt.plot`` and ``plt.fill_between`` for a useful result. + +Here we'll perform a simple *Gaussian process regression*, using the Scikit-Learn API (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) for details). +This is a method of fitting a very flexible non-parametric function to data with a continuous measure of the uncertainty. +We won't delve into the details of Gaussian process regression at this point, but will focus instead on how you might visualize such a continuous error measurement: + +```python +from sklearn.gaussian_process import GaussianProcess + +# define the model and draw some data +model = lambda x: x * np.sin(x) +xdata = np.array([1, 3, 5, 6, 8]) +ydata = model(xdata) + +# Compute the Gaussian process fit +gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1, + random_start=100) +gp.fit(xdata[:, np.newaxis], ydata) + +xfit = np.linspace(0, 10, 1000) +yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True) +dyfit = 2 * np.sqrt(MSE) # 2*sigma ~ 95% confidence region +``` + +We now have ``xfit``, ``yfit``, and ``dyfit``, which sample the continuous fit to our data. +We could pass these to the ``plt.errorbar`` function as above, but we don't really want to plot 1,000 points with 1,000 errorbars. +Instead, we can use the ``plt.fill_between`` function with a light color to visualize this continuous error: + +```python +# Visualize the result +plt.plot(xdata, ydata, 'or') +plt.plot(xfit, yfit, '-', color='gray') + +plt.fill_between(xfit, yfit - dyfit, yfit + dyfit, + color='gray', alpha=0.2) +plt.xlim(0, 10); +``` + +Note what we've done here with the ``fill_between`` function: we pass an x value, then the lower y-bound, then the upper y-bound, and the result is that the area between these regions is filled. + +The resulting figure gives a very intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained and this is reflected in the small model errors. +In regions far from a measured data point, the model is not strongly constrained, and the model errors increase. + +For more information on the options available in ``plt.fill_between()`` (and the closely related ``plt.fill()`` function), see the function docstring or the Matplotlib documentation. + +Finally, if this seems a bit too low level for your taste, refer to [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar. + + + +< [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) | [Contents](Index.ipynb) | [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.04-Density-and-Contour-Plots.ipynb b/notebooks_v2/04.04-Density-and-Contour-Plots.ipynb new file mode 100644 index 00000000..e35070d0 --- /dev/null +++ b/notebooks_v2/04.04-Density-and-Contour-Plots.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Density and Contour Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions.\n", + "There are three Matplotlib functions that can be helpful for this task: ``plt.contour`` for contour plots, ``plt.contourf`` for filled contour plots, and ``plt.imshow`` for showing images.\n", + "This section looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use: " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-white')\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing a Three-Dimensional Function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll start by demonstrating a contour plot using a function $z = f(x, y)$, using the following particular choice for $f$ (we've seen this before in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb), when we used it as a motivating example for array broadcasting):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def f(x, y):\n", + " return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A contour plot can be created with the ``plt.contour`` function.\n", + "It takes three arguments: a grid of *x* values, a grid of *y* values, and a grid of *z* values.\n", + "The *x* and *y* values represent positions on the plot, and the *z* values will be represented by the contour levels.\n", + "Perhaps the most straightforward way to prepare such data is to use the ``np.meshgrid`` function, which builds two-dimensional grids from one-dimensional arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x = np.linspace(0, 5, 50)\n", + "y = np.linspace(0, 5, 40)\n", + "\n", + "X, Y = np.meshgrid(x, y)\n", + "Z = f(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's look at this with a standard line-only contour plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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du8jPz4/279/Pqe/evTvFxsbSlStXqGXLlvTs2bNi96kEAF26dImcnZ156xUKhVolnqur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N69ezdjxbOgoCDaUUgGVZYmS5cu1Yhokozap6nZxu7duxEUFKSRczGBl5cXfv31V8b7jxo1CuvW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\nhg0bkq2tLaWlpQkNM+PDwcGBTp48ye1NiICcnBz5+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.contour(X, Y, Z, colors='black');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that by default when a single color is used, negative values are represented by dashed lines, and positive values by solid lines.\n", + "Alternatively, the lines can be color-coded by specifying a colormap with the ``cmap`` argument.\n", + "Here, we'll also specify that we want more lines to be drawn—20 equally spaced intervals within the data range:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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GL7/8kssuu4z4+Hj27NlDYWEhW7duJSEhAb/fT1NTE1arlZqaGkwmE36/n7a2NgDC4QgR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NP/SMvXHyCknIu39BCSBEhepKYaZcHdWosgSxi6MxPViToyMhKn06nRwbKysiguLubcc8/V7svz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S1fvpw33niD559/XlSy+nFfu3Ytzz77LHfdddeM8+LMM89k165dghL478SxxUkgEMDj8YiK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\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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.contour(X, Y, Z, 20, cmap='RdGy');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we chose the ``RdGy`` (short for *Red-Gray*) colormap, which is a good choice for centered data.\n", + "Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the ``plt.cm`` module:\n", + "```\n", + "plt.cm.\n", + "```\n", + "\n", + "Our plot is looking nicer, but the spaces between the lines may be a bit distracting.\n", + "We can change this by switching to a filled contour plot using the ``plt.contourf()`` function (notice the ``f`` at the end), which uses largely the same syntax as ``plt.contour()``.\n", + "\n", + "Additionally, we'll add a ``plt.colorbar()`` command, which automatically creates an additional axis with labeled color information for the plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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xwBXYWowRqH32mckJcI8HTO71wOBH5ZuKTmBlFzvw/lHzYANDJYFpwEFUkbXF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\n74F2XWvMJRsEIsQPFAHEWMZ1uSAb2n0hPqdOWNV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.contourf(X, Y, Z, 20, cmap='RdGy')\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The colorbar makes it clear that the black regions are \"peaks,\" while the red regions are \"valleys.\"\n", + "\n", + "One potential issue with this plot is that it is a bit \"splotchy.\" That is, the color steps are discrete rather than continuous, which is not always what is desired.\n", + "This could be remedied by setting the number of contours to a very high number, but this results in a rather inefficient plot: Matplotlib must render a new polygon for each step in the level.\n", + "A better way to handle this is to use the ``plt.imshow()`` function, which interprets a two-dimensional grid of data as an image.\n", + "\n", + "The following code shows this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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thwGGvjLbR40sprGYxuiVlISVhDWCHwsV1oJ+27bsdrsDj1cpM3PMRNpsNgdh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\nCzWzGFh58cxqqmbu7vkxKV2VTW11+r5PG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\n", + " cmap='RdGy')\n", + "plt.colorbar()\n", + "plt.axis(aspect='image');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a few potential gotchas with ``imshow()``, however:\n", + "\n", + "- ``plt.imshow()`` doesn't accept an *x* and *y* grid, so you must manually specify the *extent* [*xmin*, *xmax*, *ymin*, *ymax*] of the image on the plot.\n", + "- ``plt.imshow()`` by default follows the standard image array definition where the origin is in the upper left, not in the lower left as in most contour plots. This must be changed when showing gridded data.\n", + "- ``plt.imshow()`` will automatically adjust the axis aspect ratio to match the input data; this can be changed by setting, for example, ``plt.axis(aspect='image')`` to make *x* and *y* units match." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, it can sometimes be useful to combine contour plots and image plots.\n", + "For example, here we'll use a partially transparent background image (with transparency set via the ``alpha`` parameter) and overplot contours with labels on the contours themselves (using the ``plt.clabel()`` function):" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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/ZL+DKhd3o1opdDLS0igtKXVGIt0wYetgtlXmPtqdQZZlGjRokJSFxY8K3XHH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contours = plt.contour(X, Y, Z, 3, colors='black')\n", + "plt.clabel(contours, inline=True, fontsize=8)\n", + "\n", + "plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\n", + " cmap='RdGy', alpha=0.5)\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The combination of these three functions—``plt.contour``, ``plt.contourf``, and ``plt.imshow``—gives nearly limitless possibilities for displaying this sort of three-dimensional data within a two-dimensional plot.\n", + "For more information on the options available in these functions, refer to their docstrings.\n", + "If you are interested in three-dimensional visualizations of this type of data, see [Three-dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.04-Density-and-Contour-Plots.md b/notebooks_v2/04.04-Density-and-Contour-Plots.md new file mode 100644 index 00000000..6c0f76a3 --- /dev/null +++ b/notebooks_v2/04.04-Density-and-Contour-Plots.md @@ -0,0 +1,141 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) > + +Open in Colab + + + +# Density and Contour Plots + + +Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions. +There are three Matplotlib functions that can be helpful for this task: ``plt.contour`` for contour plots, ``plt.contourf`` for filled contour plots, and ``plt.imshow`` for showing images. +This section looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +plt.style.use('seaborn-white') +import numpy as np +``` + +## Visualizing a Three-Dimensional Function + + +We'll start by demonstrating a contour plot using a function $z = f(x, y)$, using the following particular choice for $f$ (we've seen this before in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb), when we used it as a motivating example for array broadcasting): + +```python +def f(x, y): + return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x) +``` + +A contour plot can be created with the ``plt.contour`` function. +It takes three arguments: a grid of *x* values, a grid of *y* values, and a grid of *z* values. +The *x* and *y* values represent positions on the plot, and the *z* values will be represented by the contour levels. +Perhaps the most straightforward way to prepare such data is to use the ``np.meshgrid`` function, which builds two-dimensional grids from one-dimensional arrays: + +```python +x = np.linspace(0, 5, 50) +y = np.linspace(0, 5, 40) + +X, Y = np.meshgrid(x, y) +Z = f(X, Y) +``` + +Now let's look at this with a standard line-only contour plot: + +```python +plt.contour(X, Y, Z, colors='black'); +``` + +Notice that by default when a single color is used, negative values are represented by dashed lines, and positive values by solid lines. +Alternatively, the lines can be color-coded by specifying a colormap with the ``cmap`` argument. +Here, we'll also specify that we want more lines to be drawn—20 equally spaced intervals within the data range: + +```python +plt.contour(X, Y, Z, 20, cmap='RdGy'); +``` + +Here we chose the ``RdGy`` (short for *Red-Gray*) colormap, which is a good choice for centered data. +Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the ``plt.cm`` module: +``` +plt.cm. +``` + +Our plot is looking nicer, but the spaces between the lines may be a bit distracting. +We can change this by switching to a filled contour plot using the ``plt.contourf()`` function (notice the ``f`` at the end), which uses largely the same syntax as ``plt.contour()``. + +Additionally, we'll add a ``plt.colorbar()`` command, which automatically creates an additional axis with labeled color information for the plot: + +```python +plt.contourf(X, Y, Z, 20, cmap='RdGy') +plt.colorbar(); +``` + +The colorbar makes it clear that the black regions are "peaks," while the red regions are "valleys." + +One potential issue with this plot is that it is a bit "splotchy." That is, the color steps are discrete rather than continuous, which is not always what is desired. +This could be remedied by setting the number of contours to a very high number, but this results in a rather inefficient plot: Matplotlib must render a new polygon for each step in the level. +A better way to handle this is to use the ``plt.imshow()`` function, which interprets a two-dimensional grid of data as an image. + +The following code shows this: + +```python +plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower', + cmap='RdGy') +plt.colorbar() +plt.axis(aspect='image'); +``` + +There are a few potential gotchas with ``imshow()``, however: + +- ``plt.imshow()`` doesn't accept an *x* and *y* grid, so you must manually specify the *extent* [*xmin*, *xmax*, *ymin*, *ymax*] of the image on the plot. +- ``plt.imshow()`` by default follows the standard image array definition where the origin is in the upper left, not in the lower left as in most contour plots. This must be changed when showing gridded data. +- ``plt.imshow()`` will automatically adjust the axis aspect ratio to match the input data; this can be changed by setting, for example, ``plt.axis(aspect='image')`` to make *x* and *y* units match. + + +Finally, it can sometimes be useful to combine contour plots and image plots. +For example, here we'll use a partially transparent background image (with transparency set via the ``alpha`` parameter) and overplot contours with labels on the contours themselves (using the ``plt.clabel()`` function): + +```python +contours = plt.contour(X, Y, Z, 3, colors='black') +plt.clabel(contours, inline=True, fontsize=8) + +plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower', + cmap='RdGy', alpha=0.5) +plt.colorbar(); +``` + +The combination of these three functions—``plt.contour``, ``plt.contourf``, and ``plt.imshow``—gives nearly limitless possibilities for displaying this sort of three-dimensional data within a two-dimensional plot. +For more information on the options available in these functions, refer to their docstrings. +If you are interested in three-dimensional visualizations of this type of data, see [Three-dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb). + + + +< [Visualizing Errors](04.03-Errorbars.ipynb) | [Contents](Index.ipynb) | [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.05-Histograms-and-Binnings.ipynb b/notebooks_v2/04.05-Histograms-and-Binnings.ipynb new file mode 100644 index 00000000..b8927d77 --- /dev/null +++ b/notebooks_v2/04.05-Histograms-and-Binnings.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Histograms, Binnings, and Density" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A simple histogram can be a great first step in understanding a dataset.\n", + "Earlier, we saw a preview of Matplotlib's histogram function (see [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boiler-plate imports are done:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-white')\n", + "\n", + "data = np.random.randn(1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``hist()`` function has many options to tune both the calculation and the display; \n", + "here's an example of a more customized histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data, bins=30, normed=True, alpha=0.5,\n", + " histtype='stepfilled', color='steelblue',\n", + " edgecolor='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``plt.hist`` docstring has more information on other customization options available.\n", + "I find this combination of ``histtype='stepfilled'`` along with some transparency ``alpha`` to be very useful when comparing histograms of several distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x1 = np.random.normal(0, 0.8, 1000)\n", + "x2 = np.random.normal(-2, 1, 1000)\n", + "x3 = np.random.normal(3, 2, 1000)\n", + "\n", + "kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40)\n", + "\n", + "plt.hist(x1, **kwargs)\n", + "plt.hist(x2, **kwargs)\n", + "plt.hist(x3, **kwargs);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you would like to simply compute the histogram (that is, count the number of points in a given bin) and not display it, the ``np.histogram()`` function is available:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 12 190 468 301 29]\n" + ] + } + ], + "source": [ + "counts, bin_edges = np.histogram(data, bins=5)\n", + "print(counts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Two-Dimensional Histograms and Binnings\n", + "\n", + "Just as we create histograms in one dimension by dividing the number-line into bins, we can also create histograms in two-dimensions by dividing points among two-dimensional bins.\n", + "We'll take a brief look at several ways to do this here.\n", + "We'll start by defining some data—an ``x`` and ``y`` array drawn from a multivariate Gaussian distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mean = [0, 0]\n", + "cov = [[1, 1], [1, 2]]\n", + "x, y = np.random.multivariate_normal(mean, cov, 10000).T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ``plt.hist2d``: Two-dimensional histogram\n", + "\n", + "One straightforward way to plot a two-dimensional histogram is to use Matplotlib's ``plt.hist2d`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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DjRs3+uqtAGCCJiIzUSQfZ6GqKoqKivDwww/j+uuvP6VvjMViQUtLS8/FfgZc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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(x, y, bins=30, cmap='Blues')\n", + "cb = plt.colorbar()\n", + "cb.set_label('counts in bin')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as with ``plt.hist``, ``plt.hist2d`` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring.\n", + "Further, just as ``plt.hist`` has a counterpart in ``np.histogram``, ``plt.hist2d`` has a counterpart in ``np.histogram2d``, which can be used as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "counts, xedges, yedges = np.histogram2d(x, y, bins=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the generalization of this histogram binning in dimensions higher than two, see the ``np.histogramdd`` function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ``plt.hexbin``: Hexagonal binnings\n", + "\n", + "The two-dimensional histogram creates a tesselation of squares across the axes.\n", + "Another natural shape for such a tesselation is the regular hexagon.\n", + "For this purpose, Matplotlib provides the ``plt.hexbin`` routine, which will represents a two-dimensional dataset binned within a grid of hexagons:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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IoOWyrphAUUB8CZ3HZaWPaGWTYEk1JTKm7jD88mgdMy0nJIRfHmvgV7MNnDpW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\nPDSEAOCXr3JY1A1PVmiZLliYLFg41nYghMCET8z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hexbin(x, y, gridsize=30, cmap='Blues')\n", + "cb = plt.colorbar(label='count in bin')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``plt.hexbin`` has a number of interesting options, including the ability to specify weights for each point, and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Kernel density estimation\n", + "\n", + "Another common method of evaluating densities in multiple dimensions is *kernel density estimation* (KDE).\n", + "This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now we'll simply mention that KDE can be thought of as a way to \"smear out\" the points in space and add up the result to obtain a smooth function.\n", + "One extremely quick and simple KDE implementation exists in the ``scipy.stats`` package.\n", + "Here is a quick example of using the KDE on this data:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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nEZKeR6tsfaiyLBRazz2brV/6AWBXYsecTjEZD/yxxx7DeeedBwBYuHAhhoeH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\nsOV2xtx6dJ36zz74fbvDydeOgLvMLoFetpcZXIdZKgEwIwV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats import gaussian_kde\n", + "\n", + "# fit an array of size [Ndim, Nsamples]\n", + "data = np.vstack([x, y])\n", + "kde = gaussian_kde(data)\n", + "\n", + "# evaluate on a regular grid\n", + "xgrid = np.linspace(-3.5, 3.5, 40)\n", + "ygrid = np.linspace(-6, 6, 40)\n", + "Xgrid, Ygrid = np.meshgrid(xgrid, ygrid)\n", + "Z = kde.evaluate(np.vstack([Xgrid.ravel(), Ygrid.ravel()]))\n", + "\n", + "# Plot the result as an image\n", + "plt.imshow(Z.reshape(Xgrid.shape),\n", + " origin='lower', aspect='auto',\n", + " extent=[-3.5, 3.5, -6, 6],\n", + " cmap='Blues')\n", + "cb = plt.colorbar()\n", + "cb.set_label(\"density\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "KDE has a smoothing length that effectively slides the knob between detail and smoothness (one example of the ubiquitous bias–variance trade-off).\n", + "The literature on choosing an appropriate smoothing length is vast: ``gaussian_kde`` uses a rule-of-thumb to attempt to find a nearly optimal smoothing length for the input data.\n", + "\n", + "Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, ``sklearn.neighbors.KernelDensity`` and ``statsmodels.nonparametric.kernel_density.KDEMultivariate``.\n", + "For visualizations based on KDE, using Matplotlib tends to be overly verbose.\n", + "The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more terse API for creating KDE-based visualizations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.05-Histograms-and-Binnings.md b/notebooks_v2/04.05-Histograms-and-Binnings.md new file mode 100644 index 00000000..fd37e9f6 --- /dev/null +++ b/notebooks_v2/04.05-Histograms-and-Binnings.md @@ -0,0 +1,168 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) > + +Open in Colab + + + +# Histograms, Binnings, and Density + + +A simple histogram can be a great first step in understanding a dataset. +Earlier, we saw a preview of Matplotlib's histogram function (see [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boiler-plate imports are done: + +```python +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt +plt.style.use('seaborn-white') + +data = np.random.randn(1000) +``` + +```python +plt.hist(data); +``` + +The ``hist()`` function has many options to tune both the calculation and the display; +here's an example of a more customized histogram: + +```python +plt.hist(data, bins=30, normed=True, alpha=0.5, + histtype='stepfilled', color='steelblue', + edgecolor='none'); +``` + +The ``plt.hist`` docstring has more information on other customization options available. +I find this combination of ``histtype='stepfilled'`` along with some transparency ``alpha`` to be very useful when comparing histograms of several distributions: + +```python +x1 = np.random.normal(0, 0.8, 1000) +x2 = np.random.normal(-2, 1, 1000) +x3 = np.random.normal(3, 2, 1000) + +kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40) + +plt.hist(x1, **kwargs) +plt.hist(x2, **kwargs) +plt.hist(x3, **kwargs); +``` + +If you would like to simply compute the histogram (that is, count the number of points in a given bin) and not display it, the ``np.histogram()`` function is available: + +```python +counts, bin_edges = np.histogram(data, bins=5) +print(counts) +``` + +## Two-Dimensional Histograms and Binnings + +Just as we create histograms in one dimension by dividing the number-line into bins, we can also create histograms in two-dimensions by dividing points among two-dimensional bins. +We'll take a brief look at several ways to do this here. +We'll start by defining some data—an ``x`` and ``y`` array drawn from a multivariate Gaussian distribution: + +```python +mean = [0, 0] +cov = [[1, 1], [1, 2]] +x, y = np.random.multivariate_normal(mean, cov, 10000).T +``` + +### ``plt.hist2d``: Two-dimensional histogram + +One straightforward way to plot a two-dimensional histogram is to use Matplotlib's ``plt.hist2d`` function: + +```python +plt.hist2d(x, y, bins=30, cmap='Blues') +cb = plt.colorbar() +cb.set_label('counts in bin') +``` + +Just as with ``plt.hist``, ``plt.hist2d`` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring. +Further, just as ``plt.hist`` has a counterpart in ``np.histogram``, ``plt.hist2d`` has a counterpart in ``np.histogram2d``, which can be used as follows: + +```python +counts, xedges, yedges = np.histogram2d(x, y, bins=30) +``` + +For the generalization of this histogram binning in dimensions higher than two, see the ``np.histogramdd`` function. + + +### ``plt.hexbin``: Hexagonal binnings + +The two-dimensional histogram creates a tesselation of squares across the axes. +Another natural shape for such a tesselation is the regular hexagon. +For this purpose, Matplotlib provides the ``plt.hexbin`` routine, which will represents a two-dimensional dataset binned within a grid of hexagons: + +```python +plt.hexbin(x, y, gridsize=30, cmap='Blues') +cb = plt.colorbar(label='count in bin') +``` + +``plt.hexbin`` has a number of interesting options, including the ability to specify weights for each point, and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.). + + +### Kernel density estimation + +Another common method of evaluating densities in multiple dimensions is *kernel density estimation* (KDE). +This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now we'll simply mention that KDE can be thought of as a way to "smear out" the points in space and add up the result to obtain a smooth function. +One extremely quick and simple KDE implementation exists in the ``scipy.stats`` package. +Here is a quick example of using the KDE on this data: + +```python +from scipy.stats import gaussian_kde + +# fit an array of size [Ndim, Nsamples] +data = np.vstack([x, y]) +kde = gaussian_kde(data) + +# evaluate on a regular grid +xgrid = np.linspace(-3.5, 3.5, 40) +ygrid = np.linspace(-6, 6, 40) +Xgrid, Ygrid = np.meshgrid(xgrid, ygrid) +Z = kde.evaluate(np.vstack([Xgrid.ravel(), Ygrid.ravel()])) + +# Plot the result as an image +plt.imshow(Z.reshape(Xgrid.shape), + origin='lower', aspect='auto', + extent=[-3.5, 3.5, -6, 6], + cmap='Blues') +cb = plt.colorbar() +cb.set_label("density") +``` + +KDE has a smoothing length that effectively slides the knob between detail and smoothness (one example of the ubiquitous bias–variance trade-off). +The literature on choosing an appropriate smoothing length is vast: ``gaussian_kde`` uses a rule-of-thumb to attempt to find a nearly optimal smoothing length for the input data. + +Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, ``sklearn.neighbors.KernelDensity`` and ``statsmodels.nonparametric.kernel_density.KDEMultivariate``. +For visualizations based on KDE, using Matplotlib tends to be overly verbose. +The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more terse API for creating KDE-based visualizations. + + + +< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.06-Customizing-Legends.ipynb b/notebooks_v2/04.06-Customizing-Legends.ipynb new file mode 100644 index 00000000..9b5a6169 --- /dev/null +++ b/notebooks_v2/04.06-Customizing-Legends.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customizing Plot Legends" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\n", + "We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\n", + "\n", + "The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('classic')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 10, 1000)\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, np.sin(x), '-b', label='Sine')\n", + "ax.plot(x, np.cos(x), '--r', label='Cosine')\n", + "ax.axis('equal')\n", + "leg = ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But there are many ways we might want to customize such a legend.\n", + "For example, we can specify the location and turn off the frame:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.legend(loc='upper left', frameon=False)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the ``ncol`` command to specify the number of columns in the legend:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.legend(frameon=False, loc='lower center', ncol=2)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information on available legend options, see the ``plt.legend`` docstring." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choosing Elements for the Legend\n", + "\n", + "As we have already seen, the legend includes all labeled elements by default.\n", + "If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.\n", + "The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances.\n", + "Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\n", + "lines = plt.plot(x, y)\n", + "\n", + "# lines is a list of plt.Line2D instances\n", + "plt.legend(lines[:2], ['first', 'second']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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QDnhxTyv/rCzA09O6DoCPDXoM6y+tR5NO2fHkX1th9QMsR+rxx1Ww8avVAnv3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\n+dZdH19YSK+wsGtDkJ2MuIICeoWF8bLZbF2Djz8mAwPtK5RWpkwh33j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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y[:, 0], label='first')\n", + "plt.plot(x, y[:, 1], label='second')\n", + "plt.plot(x, y[:, 2:])\n", + "plt.legend(framealpha=1, frameon=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that by default, the legend ignores all elements without a ``label`` attribute set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Legend for Size of Points\n", + "\n", + "Sometimes the legend defaults are not sufficient for the given visualization.\n", + "For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this.\n", + "Here is an example where we'll use the size of points to indicate populations of California cities.\n", + "We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+EGzevJm0tDTWrVvH448/PuD9dYVCQtpGIoZJp4d4m3zs/fAIlnHxWC1m6isb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "cities = pd.read_csv('data/california_cities.csv')\n", + "\n", + "# Extract the data we're interested in\n", + "lat, lon = cities['latd'], cities['longd']\n", + "population, area = cities['population_total'], cities['area_total_km2']\n", + "\n", + "# Scatter the points, using size and color but no label\n", + "plt.scatter(lon, lat, label=None,\n", + " c=np.log10(population), cmap='viridis',\n", + " s=area, linewidth=0, alpha=0.5)\n", + "plt.axis(aspect='equal')\n", + "plt.xlabel('longitude')\n", + "plt.ylabel('latitude')\n", + "plt.colorbar(label='log$_{10}$(population)')\n", + "plt.clim(3, 7)\n", + "\n", + "# Here we create a legend:\n", + "# we'll plot empty lists with the desired size and label\n", + "for area in [100, 300, 500]:\n", + " plt.scatter([], [], c='k', alpha=0.3, s=area,\n", + " label=str(area) + ' km$^2$')\n", + "plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area')\n", + "\n", + "plt.title('California Cities: Area and Population');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\n", + "In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\n", + "Notice too that the legend only lists plot elements that have a label specified.\n", + "\n", + "By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information.\n", + "This strategy can be useful for creating more sophisticated visualizations.\n", + "\n", + "Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements.\n", + "For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple Legends\n", + "\n", + "Sometimes when designing a plot you'd like to add multiple legends to the same axes.\n", + "Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot.\n", + "If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one.\n", + "We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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vvEG3b98u9s0wGfz9/WnKlCl0/PhxsrGxKbZt0RkyJo/TarX06aefUocOHYpF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\nqjRp0iTZp0FOSkoie3t76t+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "lines = []\n", + "styles = ['-', '--', '-.', ':']\n", + "x = np.linspace(0, 10, 1000)\n", + "\n", + "for i in range(4):\n", + " lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n", + " styles[i], color='black')\n", + "ax.axis('equal')\n", + "\n", + "# specify the lines and labels of the first legend\n", + "ax.legend(lines[:2], ['line A', 'line B'],\n", + " loc='upper right', frameon=False)\n", + "\n", + "# Create the second legend and add the artist manually.\n", + "from matplotlib.legend import Legend\n", + "leg = Legend(ax, lines[2:], ['line C', 'line D'],\n", + " loc='lower right', frameon=False)\n", + "ax.add_artist(leg);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a peek into the low-level artist objects that comprise any Matplotlib plot.\n", + "If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.06-Customizing-Legends.md b/notebooks_v2/04.06-Customizing-Legends.md new file mode 100644 index 00000000..4a59f415 --- /dev/null +++ b/notebooks_v2/04.06-Customizing-Legends.md @@ -0,0 +1,194 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) > + +Open in Colab + + + +# Customizing Plot Legends + + +Plot legends give meaning to a visualization, assigning meaning to the various plot elements. +We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib. + +The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements: + +```python +import matplotlib.pyplot as plt +plt.style.use('classic') +``` + +```python +%matplotlib inline +import numpy as np +``` + +```python +x = np.linspace(0, 10, 1000) +fig, ax = plt.subplots() +ax.plot(x, np.sin(x), '-b', label='Sine') +ax.plot(x, np.cos(x), '--r', label='Cosine') +ax.axis('equal') +leg = ax.legend(); +``` + +But there are many ways we might want to customize such a legend. +For example, we can specify the location and turn off the frame: + +```python +ax.legend(loc='upper left', frameon=False) +fig +``` + +We can use the ``ncol`` command to specify the number of columns in the legend: + +```python +ax.legend(frameon=False, loc='lower center', ncol=2) +fig +``` + +We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text: + +```python +ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1) +fig +``` + +For more information on available legend options, see the ``plt.legend`` docstring. + + +## Choosing Elements for the Legend + +As we have already seen, the legend includes all labeled elements by default. +If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands. +The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances. +Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify: + +```python +y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5)) +lines = plt.plot(x, y) + +# lines is a list of plt.Line2D instances +plt.legend(lines[:2], ['first', 'second']); +``` + +I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend: + +```python +plt.plot(x, y[:, 0], label='first') +plt.plot(x, y[:, 1], label='second') +plt.plot(x, y[:, 2:]) +plt.legend(framealpha=1, frameon=True); +``` + +Notice that by default, the legend ignores all elements without a ``label`` attribute set. + + +## Legend for Size of Points + +Sometimes the legend defaults are not sufficient for the given visualization. +For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this. +Here is an example where we'll use the size of points to indicate populations of California cities. +We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries: + +```python +import pandas as pd +cities = pd.read_csv('data/california_cities.csv') + +# Extract the data we're interested in +lat, lon = cities['latd'], cities['longd'] +population, area = cities['population_total'], cities['area_total_km2'] + +# Scatter the points, using size and color but no label +plt.scatter(lon, lat, label=None, + c=np.log10(population), cmap='viridis', + s=area, linewidth=0, alpha=0.5) +plt.axis(aspect='equal') +plt.xlabel('longitude') +plt.ylabel('latitude') +plt.colorbar(label='log$_{10}$(population)') +plt.clim(3, 7) + +# Here we create a legend: +# we'll plot empty lists with the desired size and label +for area in [100, 300, 500]: + plt.scatter([], [], c='k', alpha=0.3, s=area, + label=str(area) + ' km$^2$') +plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area') + +plt.title('California Cities: Area and Population'); +``` + +The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it. +In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists. +Notice too that the legend only lists plot elements that have a label specified. + +By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information. +This strategy can be useful for creating more sophisticated visualizations. + +Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements. +For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb). + + +## Multiple Legends + +Sometimes when designing a plot you'd like to add multiple legends to the same axes. +Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot. +If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one. +We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot: + +```python +fig, ax = plt.subplots() + +lines = [] +styles = ['-', '--', '-.', ':'] +x = np.linspace(0, 10, 1000) + +for i in range(4): + lines += ax.plot(x, np.sin(x - i * np.pi / 2), + styles[i], color='black') +ax.axis('equal') + +# specify the lines and labels of the first legend +ax.legend(lines[:2], ['line A', 'line B'], + loc='upper right', frameon=False) + +# Create the second legend and add the artist manually. +from matplotlib.legend import Legend +leg = Legend(ax, lines[2:], ['line C', 'line D'], + loc='lower right', frameon=False) +ax.add_artist(leg); +``` + +This is a peek into the low-level artist objects that comprise any Matplotlib plot. +If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn. + + + +< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.07-Customizing-Colorbars.ipynb b/notebooks_v2/04.07-Customizing-Colorbars.ipynb new file mode 100644 index 00000000..09384541 --- /dev/null +++ b/notebooks_v2/04.07-Customizing-Colorbars.ipynb @@ -0,0 +1,575 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customizing Colorbars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot legends identify discrete labels of discrete points.\n", + "For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool.\n", + "In Matplotlib, a colorbar is a separate axes that can provide a key for the meaning of colors in a plot.\n", + "Because the book is printed in black-and-white, this section has an accompanying online supplement where you can view the figures in full color (https://github.com/jakevdp/PythonDataScienceHandbook).\n", + "We'll start by setting up the notebook for plotting and importing the functions we will use:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('classic')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we have seen several times throughout this section, the simplest colorbar can be created with the ``plt.colorbar`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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BsHGB0C+kAtk7H+JWerTAcgM96mvWc5NFy490DI1j7CcO2irUFo8O4mxrMLYxtVw+cdKu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nx4w76IT9F7Kq15yXS87v2/CvzVlxz7gwVDSDsvSgYZm3XZyMaULCEbsEvAhgPbl89oy0dASiwf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\naZ5hrjPevFxyai8r0OrNl+u86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+An9phfpYvyuE+8XxrtExj+M/B8M3uOmS2Js1Tuu2Ke\nlP4c+GII4R1yzJPPYT35/PUpuvg8UvL57WI0hPA7IYRnhxB+kCjHT4UQfgX4u33kN/H8beDrZvaC\ndOiVwBfYUxkTh8c/ZmaHqTLTK4n5tfvI76ZJE+fiMQ2lHzOze9KzvkH+57uHrjqCoxvwWmKk9mHg\n3qvmR/h6OXEC6gPEaNvnEq/fC3wy8fwJ4GnyP28hRuMeAl59hbz/NGM0ea/5BX6Y2Ck+AHyEGE3e\nW56JExAeItZwew8xC2Kv+AU+AHyTOEP9a8AbgadflEfgR4DPp7b5jqvS58vc5qTrmWaaaSb2a5g8\n00wzzXRlNIPhTDPNNBMzGM4000wzATMYzjTTTDMBMxjONNNMMwEzGM4000wzATMYzjTTTDMBMxjO\nNNNMMwHwf3ceQw5DTt3BAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 10, 1000)\n", + "I = np.sin(x) * np.cos(x[:, np.newaxis])\n", + "\n", + "plt.imshow(I)\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now discuss a few ideas for customizing these colorbars and using them effectively in various situations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Customizing Colorbars\n", + "\n", + "The colormap can be specified using the ``cmap`` argument to the plotting function that is creating the visualization:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7LIfYKEEsSeNm3c9prAX+pe17M3BxM/K9qmGQ2rcEubjKmluBVzmx5WztV62d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\nkUcemVWmyg4hlz8C8COttW8H8A0APw7gUwD+DMC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(I, cmap='gray');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the available colormaps are in the ``plt.cm`` namespace; using IPython's tab-completion will give you a full list of built-in possibilities:\n", + "```\n", + "plt.cm.\n", + "```\n", + "But being *able* to choose a colormap is just the first step: more important is how to *decide* among the possibilities!\n", + "The choice turns out to be much more subtle than you might initially expect." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choosing the Colormap\n", + "\n", + "A full treatment of color choice within visualization is beyond the scope of this book, but for entertaining reading on this subject and others, see the article [\"Ten Simple Rules for Better Figures\"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833).\n", + "Matplotlib's online documentation also has an [interesting discussion](http://Matplotlib.org/1.4.1/users/colormaps.html) of colormap choice.\n", + "\n", + "Broadly, you should be aware of three different categories of colormaps:\n", + "\n", + "- *Sequential colormaps*: These are made up of one continuous sequence of colors (e.g., ``binary`` or ``viridis``).\n", + "- *Divergent colormaps*: These usually contain two distinct colors, which show positive and negative deviations from a mean (e.g., ``RdBu`` or ``PuOr``).\n", + "- *Qualitative colormaps*: these mix colors with no particular sequence (e.g., ``rainbow`` or ``jet``).\n", + "\n", + "The ``jet`` colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap.\n", + "Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data.\n", + "Among the problems is the fact that qualitative maps usually do not display any uniform progression in brightness as the scale increases.\n", + "\n", + "We can see this by converting the ``jet`` colorbar into black and white:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from matplotlib.colors import LinearSegmentedColormap\n", + "\n", + "def grayscale_cmap(cmap):\n", + " \"\"\"Return a grayscale version of the given colormap\"\"\"\n", + " cmap = plt.cm.get_cmap(cmap)\n", + " colors = cmap(np.arange(cmap.N))\n", + " \n", + " # convert RGBA to perceived grayscale luminance\n", + " # cf. http://alienryderflex.com/hsp.html\n", + " RGB_weight = [0.299, 0.587, 0.114]\n", + " luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))\n", + " colors[:, :3] = luminance[:, np.newaxis]\n", + " \n", + " return LinearSegmentedColormap.from_list(cmap.name + \"_gray\", colors, cmap.N)\n", + " \n", + "\n", + "def view_colormap(cmap):\n", + " \"\"\"Plot a colormap with its grayscale equivalent\"\"\"\n", + " cmap = plt.cm.get_cmap(cmap)\n", + " colors = cmap(np.arange(cmap.N))\n", + " \n", + " cmap = grayscale_cmap(cmap)\n", + " grayscale = cmap(np.arange(cmap.N))\n", + " \n", + " fig, ax = plt.subplots(2, figsize=(6, 2),\n", + " subplot_kw=dict(xticks=[], yticks=[]))\n", + " ax[0].imshow([colors], extent=[0, 10, 0, 1])\n", + " ax[1].imshow([grayscale], extent=[0, 10, 0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "view_colormap('jet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the bright stripes in the grayscale image.\n", + "Even in full color, this uneven brightness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset.\n", + "It's better to use a colormap such as ``viridis`` (the default as of Matplotlib 2.0), which is specifically constructed to have an even brightness variation across the range.\n", + "Thus it not only plays well with our color perception, but also will translate well to grayscale printing:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "view_colormap('viridis')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you favor rainbow schemes, another good option for continuous data is the ``cubehelix`` colormap:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "view_colormap('cubehelix')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as ``RdBu`` (*Red-Blue*) can be useful. However, as you can see in the following figure, it's important to note that the positive-negative information will be lost upon translation to grayscale!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "view_colormap('RdBu')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll see examples of using some of these color maps as we continue.\n", + "\n", + "There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the ``plt.cm`` submodule. For a more principled approach to colors in Python, you can refer to the tools and documentation within the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Color limits and extensions\n", + "\n", + "Matplotlib allows for a large range of colorbar customization.\n", + "The colorbar itself is simply an instance of ``plt.Axes``, so all of the axes and tick formatting tricks we've learned are applicable.\n", + "The colorbar has some interesting flexibility: for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the ``extend`` property.\n", + "This might come in handy, for example, if displaying an image that is subject to noise:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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OiYpYGd9flNO4iMqlJxNVxj7JREN2dOnCC6yULAFUAtKJAwIypDjwrEsj3Uu4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OAAAg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\nczUA4OGDB3DjjTfe3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tWFRj5Q6A0KLGfl+ZczjZXMGvZerlGs9CzQC6CPy0WbABbefu5T\nAdj3449/7HUAgM0LszCFcv2OKTOQKxVG5EHl+NSH/8kyThGAKvoZ1MoARW9gf5cHyJcy5MsFsmMZ\nsmM58uXc/W/X7Xa7FCt2e3Y0C+WLFVs+X8lx/RfvteddGUD1Mnudfob//OaPQQ9ymNwyKHmWR6DP\nxkt9+7+9rAoYnKioQzIo9e2f16AwYcDAwIEjz0CtZPbZL7tnvzxQWOrnGGQq6NjqrkB6y7dbde23\nLz18BEWmUDidH3zck4d07dtZL1foZbYeA6Vt+oVI1yYMLigJhHrf61MNkNe1S5zKRW5HW2bTx0RV\nnnV9OWl6WSsnPRM17nHVjXDdwN300Aou2znfcoxovYBrHtXBaeR/7UqdNQoBzD5upgG1xNvV9gvL\nY2c2lNepTbdSMXoeoHTmUAVQw+DprhXCedVbby47JMOB5QRAzi2ULFM4D2Hba99c/k/C5qaJ46Nq\nQe5x2UroVlNNRuT8inVt3DXCbPQ+8NsVUbObRjYmHxzry3eXHkK+oXRdturaXbtpmoLVxaH5Ezfo\neho5BZROuHgNsHPzKD983ZRunlw7o5Yp9DOF5cBQ2OWLH/ggrnjpj0MV1u2klWMRHFNhTNHIxB7K\nSiZKJPadFC6oXCbCMlqpCDmIjAN9ggB0EnCuIYlQCHaLgU6Ey7xt2/Yb7vpyeF9e+eHfA5z7RkqA\nCuf6ckk1beoC5eKiBnjm8y6G6mVQgxyq70BNP4MeOFCVGbxr3wJeO/cwdK7BiqGVBmuGzl2eqAZ/\nPAnLRFlGiiBTCdmRkB2BjiAUvdy68YxLl8CMX/m1Z0AN8hCQnkgJU3QgkhRUpOAkddPpKDB3SqXC\nBnVr7Z9fqWvjAKeBBVq5sekrSt2qwEb1co2VXgbNBK0MtDJI9u9FvmGbZaO0wWDnuTCDZl0Dtp8U\noousryAE4Ywv/wP2P/NFMFrYc3ZkJZaO3Ue1FAQ2Ggl1bSwUEbQgFFqgEIyO9Ayb5dnDI3f9Sxxg\nbpOmaqdzN9pyGmGeKsXBiZKTHkR5sdSjc9VF2yvrHhmjDHq8bEccYL62OvjjK0m8Mcq4jp9fb1U2\nthYv1bgd5RfCefMt5cdfKDpnCajSC56A5QfvxvzpexDm/SNXZoQQuJ0OHl+TIBUmsgaW6yDNUHUA\nwmpE6gzZ4k7U8z616ZpGgGYvdORB8KbTm3dG97Ju2Q6mSHFwStZfmC1Dsf/hY5hZmEMRuXmUQWVk\nlmcoTt8wg289eAwq17j8JT+KIlNQhYHWBkaZAKKse6cEE9Eba38FgQQgtQARQWoBIwW0ZiSawSyr\neZaAEJg8KERIzFhIgmaC0gZaCseyUQMzEbEE3rhqZV1iUR4okxfQWVG68Dxwcr+qp6AyhZ/rHIDq\nGahMwRQ2T5QpLMiAcX2A8xh4lxZJCi49IQk6NZCFhswlHrNzAW/630fwO0/fCDBgco0O7Gg+cqP5\nbBB6DipyIHX5o4wFg57F5Vpf6icZ9rrWpswyXzhde7dsz8VAeTDlda4NQRUaqtDQijGY3wKTa6vz\n1ehaCmy981v43pN/EDLTVtcpB5egFxEHo8sUmbYZ6wtjoFjaBK8uxQUz8NX3/Bmued1PodJDxaxc\nxERBW3cepnTnBRZzxP6TSR417rymqV25xaj6V1m64XKxy6YutxxYiajL6tKG+pmB7x0tqcpJ4Ekj\nyzLygObg4+Zho1FHOEzNrZGV8NSqPe/C6XvKQPcKI9VQ71GNfcRXStthuR+VM8JdOkrXQa/ZsM7j\nc2pZJlutn29aXZsWAHWk7TGshYXCKXfeiZbSBWIZik2bFmwiRZeR2jJR2sXBKPQLjUGhMShMAFAv\nfvwuFIVBkVnjqnKNItcoMo08s7/7vvtN5JlCnikUgwLFoLDrubaunUyjGNiyhSunco17v3GjA2ca\nWaHRz6pGfVCYyBVlXZCKy9QKJsR68fALGOKGHKPgspGzKrB86JjNAZXbRfsReTGAGigUPYWiZ91w\nRU+h6BfWLdcrUKwUYV+2kiN36/lKjmLFlVvOUawoFG5b0StQ9Au8+dIO1KBA0Vdgra0bb1BAZ7mN\nvcqVdeu5uB7PrNjA6bIz+M6nvjykc/8YfIoDpUtAVRiDe+55ELmyz7YMKjdWV7lCkSuo3Lh1ty3S\ntdeffPhARdd5Fuk603jorMdVdF1ktu2oQiNzMViDwoQ2lxcGuctV5ZODatd2tbFRfVe+5uWN7fzn\n3v53JZgydoJpViowkFO/PS5hZ+NykoGoRw0T1fTYWt087le3GNQ4zuairXPDBaLzxOxWHFB85oaZ\nCo6IWYq9yzl2LVjadxRDsWomCgB1ZloZqWEAZSxYSWpDoJkxmqZpyBM1uWsWyQAAIABJREFUWAJm\nFkJgOME9GyKwTG2a/3rQeJMbz0u9TvFh8WWVwUxi89Z05DDmj+GAv/1D/QKbZ90oSOahO+XOsM5H\n6bp+K17XhwcKp80k5dyII263TTZ1oopH+ss0MElqqVY5BZROGrG5lZxLz8B+7RuuBI1nDrQMnLFT\nhcaHvnwPVG4cO2GqbJS2eYsWd50HnfWG2BES0iadFAm0lJCJABsB4aaN2n7hpVCFc8nAjm2XgpAm\nBjf+3Wfwgy95LjIlLKNiDJQRFhQkXAIn32ybbtrFQ7GfrsXNkzc/lyJf7kEXCiqz7judOWaq7wCU\n+1UDhYNHMywSQRcapjDBJeon9I1FEIGUm1yXCEliIDoCRjH2L+XYuWnG5ZbyD8myUFte+INY+tKn\nIfICspNAFwrSZ0zXGuRAoGfaiA0u+YGnVPoe4xgxO90LwoTTt95xH7afsQuFZmzZuR1L/Rz9QpcM\nZK6Cvr2uj+3bi5mN26BUxDz6aXGYsZSkQLZS0bUhAZIJtLC6NkZAJp6BKjsSIkIuNPo54cFbb8Ml\nl1+CfqFsKgQXA6eMgNb217JsLulmg47f+/oXAEUfftJodkDn1n0ruGD2kU+2eSLkUcNEeWkEUzUW\nqtw+zFqYBqMaSxwoWERJhMJX1wjx197ZMLqvWt+Ru2uFPV1aHqQmOd4fF4MVN+P2+EZYZZ8AWAAV\nnxclALzzcHM6hDxqXkEPqzDuDJtotbLNnSftPdwIoABYAJUPxup61HWbdF1npE6bSSp1arrWof5k\no+TyvfdXzrAmAAWM/pKbdmLQU7Iqie21Zyf89C3agSily9xAdli7B0wauuDIwEa/eQad96GyFai8\nhyJbgcp6UFkP+coRqLyPYrCC3uG9UNkKdNaHyixroQpTnqvQ9n+loQuXUbswuPzZ11Xqpkw0ua7h\ncF+tg3ViFsobVzcXnlEK33poUI68c6yUyjR0blA48KT6lo2aNww1UMgHGkczjb42WFEGPW3Q14xe\ntKwog37YbpOVFn17rk1E4bwqU1ADDZ1pqEGBd/zBx2Eym1ZBFwqsDExe4CO39Kxryphynr5Kv9jc\nuxjnylPMOPucM6DY5nzyCVOzwqDzkb9EVpS6VpGuu4vbgGNLQddqsAKd95yul4OuVdYLui6yFeis\n59pF7o41AZhZnft2ZTPlbz///NDulLYANczl53T9ic/dGAAiMGxPAct6P/Dde+0gAsdEXbiJYNS0\nIQU8mkmfolcnotOI6JNEdCsR/S8iGhqiTUS7ieizRPRtIrqZiH5pknOvCUQR0UYi+jAR3eIu/ORR\nlSWiNxLR7a78c9dybS8x0GmpIwAgnvW8SfyHlRSlippYhfo5mJuHXDY1ttGvXrME11l0voSGAUmF\nhWrKGcVRoPfE0vJFEMfwMOP8DWJoOwB0om5WrOHroUlt+dyWxrKUr1hdj5j/kGuLl4SrL32s636x\nug7BH7nJAS3KllvL0pG96OzaXbKSa3TlAafceZPKcevDXAPwgMODEGP8HGnWTXZsqRcMWWxUteKS\ngcqNAz/KAaK+A0596GwAPRhAZwOofs8Ck0EfOuvDFAVU3ofKe9B5z2UEd/FVLrZIK4M9OAbtwNsg\nSvDpk0UqYwIwsB8m9l4w4iOFwPATBwfXjrag6bEbyE7t4oCUygoHbJQDNg7oZPb/QW7Bk2ILlgaG\n0deMvmH0tQVUA+O2+8UBq75mZLm2oCwANAegMgWda/zklhXryvNuRmXn6/uR86UdKW20TSSpqy49\n/xuAMhwb5Z6T1lbXymUlD3PoKYODz/8RGFWyjP75d/bvg1IaA9kNulZ5v1HXqt+zuh7Yfc/d/LAt\nl/eh8wFUYLkM8sEg6Ntfq9SzzYhfaBNceP4enn3t5WAGHrrpO9XO0vdRbPDZ938Mp5+/Cz4Gzo7I\nNNODKMMBkDUuDQMKJpDfAPBpZr4IwGcBvLGhjALwBma+BMBTAfwCET1m3InXykS9C8DHmfliAI8H\n8N22yhLRYwH8OICLATwfwO/RuLk4arKSl0op80KV+9uYiSaXDtBsSON9MaCKpQ6kTMy4NJQfqNUZ\nrdsO1eKeKkPYGv2T7fvHuu4mkCGj23a+MXWb9vJjtlVuvzM/la4ZQEHD9I/X9WwqK9cio1q+xJvv\nt2mS5LBv066hbZkeR2mPEWNGL6fEy3Hpw2Lz6hnR/QeP2JgiLoGU7HQqgEU7V50qbFCxyo0DVgqm\nGEAVA+hiAJPn0PkgsA8678OoDEZZlsoUmXXz5Jk1ug5MGZU741oa8Fv7c8HAhvn7IgN7zz0PQhk/\nnUlkP1se6B994qbQdllr+4FntGOkrHHVSsMU2sVFabtk2jJSmQ5MUaYMMgeKckYASwMHoAaG3f6S\nnepre8zAMAbaYODuJ5w/LwGULgx0bhN6cqFhlAV6Orf5oXyKhnLEWBVABX37zW6QiY8pCqDEuPkP\nXX6wQpsQNK6UCe7apcUtUFlhdaoyp+sMOs+crntDutbFAKbI8LF7hQNPFnjpIoN2OmaWAVBpZUCH\nD9u4PFXmssp1lNbAcAD/ALD98Y+t6Dtu9M961Qst4xhGPWocXM5h1LSMt/eErGuKgxcD+BO3/icA\nfnjoqswPMfM33foygFsAnDHuxFPHRBHRBgDXMvMr3UUVgKNE9GIA10WV/Rxsp/RDAD7kyt1DRLcD\nuArAP016zfmOnNo9M+r/SY6v95SG2Q7ln6BsUyxPm1D/KC7cvHGEAR1+ialpfyjTBGxWn+JgOKFm\n9H9TMJDfNk2gUIs0snu1TRWAy/WJkCe4BqrPs5drzHXkkK65Pu0L25F6BhQg9bhbHzWnX1eOToQ6\nVo7D6Dwi+i8A/hWADMCdAF7FzMcayt0D4Cjsd03BzFete2XWQY53H+bImrCcdtoGHMuUjZtxhrXQ\npWuPtR3OrgsuWQNtHOAYQBUZTGHnmDM+ZsdoGAdQKvcmJISQYC0hkg583irAdvpa2ISUUhpoTRBa\nQGqbzLM6+S1j1+6dLmt26ZYc6lPdzf7Th/4Br3rJtUC+DJ/J2sdFsbZgJSxaW3eeYmgHbkxh3Xra\nx4oZxkAzcrYB2oqB3AHRepyOTVZu041oAXQAQJAblWZsrKPQNuVDQpAdGQCc7FjwJN2ExxxAQQkM\nwo22GHJmQGu7zzN3muFAqQ4AShmGCaxQtK6Njf1SObTKgf6Si4fKXT3adc1aWp0nnUq/wUaBxKJN\nPuri6bQUyOYW0XX1Kd15ljGb65Tz/rUREPbcbpocLz5rudHYnDLy/nQfa366oFaZ7uNyOzPvAyxY\nIqLhecXiOhCdA+AJmACfrIWJ2gPgIBH9ERF9nYjeS0RzAHbElQXgK3sGgPui4x/ABChvUmljJgwz\n5vMjjeXqMkpx48xZM0G0eiPIs6OzaUclpyszUQOssiABiLQc2wwD1s5ArVaG3LUjAFR9usShlAlO\n5joSs8VydX/DrYmGtjMOO7YBKDPojT5wAjlO7rxPAriEmZ8A4HY0U+KAtW3PYOYnnqwAyskj0odx\ncJEgxNvp4NIzJWjxWaqd8TLKgqln/u07oVUOo3ILoFQOU2TQKofK+nZbkaO7fDSsm8IaYq1y6CJz\nxxT2V+UwqogMuQ9Wt6kTPKiLJzyOA7nZ3ZMBwlQnbG8UT37ZD9ZYA1RYKLtoG3dUKLBmNz+eAxO5\nhnaMSebzK7H99YxT7rYP3K9f+rpkqeLtp12yJ6zrQlcZKLdufN2MqdQzfIw4QDUuHoeIolxaHNy3\n8cTRuUtYajxo1gbGgSmjiqDrwgEoD6qUB9BFjqsuObOia+Xag1GWofS6BjNe9Lk/drqOpo9xme99\n3bZ979ZQRw+UaczdlnOKRgw3AytHV1wM3LT9zDAT9bmv3IDfftd78dvvei++etO3AeCChvp8ioi+\nFS03u98far5I630tAPhrAL/sGKmRspbReQmAywH8AjPfQETvgP1aWyvxM5WMukgvWZzsHGL046iz\nFKtho46b1JkXz240xjKtslH7hJhtx9XZqdZt3gcGHFECm0bE3U+COye9C9/ZN4GVeijd0Gg8lDrs\np9YVF5fw5/XljEjWTedipn3E6MRyHOKemPnT0b9fAfCjLUUJj45BK49IHxZrwseb+FiZAFIccNLK\nGjnPTLAxuP4Z/wZGqwoI0i55pXGpQthorEgZUoeQkCBj2QkvRARTELSw202SQmuGdG4n42Kfclcv\nZXxKBov+wpB3/zRankp4D5gjF3KU4NIDKe2AjHbgpjDQuQV0hQtuLhzzFC8e0A29wwA0AWntRbz/\nW3dhRpBL0UCQytbB5p5iVx8OMVFhkmNXfzbWtSoAXPPvP4zr3/nKBh37wHtTmVLFz3unIzBljE0c\nGs+Hp43LB6Vy66pzutb5AMbYUYJGFwDbWLMv33BLed9en87PKhPYdkEELXL8zVN+DFIbCCMsSNYG\n0lAJ7g3j/tMvwCbj26O7p+C+ZRgeBx/dXqMxN9+BXs4w0cwbTWfy+aYiue6KS3HdFZcCAA4+fAg3\n3HzL7UPHMT+n7ZxEtI+IdjDzPiLaCWB/S7kEFkB9gJn/dpL6rgVE3Q/gPma+wf3/EdgOqK2yDwA4\nMzp+t9vWKL/zO78DwKrm2mufjmuf/vTJuJcaCwVmsOtIJmWTRrpgMB4gtWWwXg+hvFcZol+/SmuY\n2ZRG9X41i91Jv3qeJoan5rtiAANDmBXV7ZuSOBlEu4R+eoTS6nFvTce3PY+16joMWBhRZhr5/Be+\niOuv99PrrMF262lztEwsrwbwoZZ9DOBTRKQBvJeZ/+B4V2ZKOW592H9961tCZvJLn/Q0nP/Ep4R4\nE7AFU0Dp1gvzpJnqZLOmyJAlHZh8ANYFjC6gVWGHvOsiuHmaXDwkJEiWXbydkFiWriGlwInNaJ2k\n1l3HkdGvGP8KUztZu+RaWaMN7rxtP87YNgvjwJRxDBQ7FszGg1mGxD8/xXbUtHIASjl2qu7OE7AM\nM1O5/Q/PvAy/+MDNKAhIjB2UkxQaOpeQHec+0xzAlCVBDCBTfPruPp576QYIo+2YHGPwxbf96NBH\nXFucs66Naox1bSKd64iFMsYGtXsXnmFj9dSmaxIgo4O+ox12n5QwWoFUAZPIcF2j4cBbdbH3wyW7\nGOuz3a8X6vX5b34Xn7/xWzCZTaI6lYT4sxH7Vy8fBfBKAG8D8NMA2gDS+wF8h5nfNemJpwZRroO5\nj4guZObbADwbwLfd0lTZjwL4oPvaOwPA+QC+2nb+3/zN36wY0vDY2IRA7oncZROAmfpL4E87yZQd\nMRvlccSoePnk2INQG1oyV48T5sYcR03lRv6/CtktVzANqUCABVCPkPgr9W+6EWLLDnR2767sn+sf\nQm9287rp+njJdddeg+uufpr9hw3e8rb/d6rz1L8CP/fVb+DzX/3G2OOI6FMAdsSbYB/vm5n5Y67M\nm2Fjnf685TRXM/NeItoGC6ZuYeYvTnEbx1WOZx/26298M1YKg74yWMk1jroJged7h3BELEbTbyAY\nVs/22Nw+LrjXGyitLRvl43ScUTUuLmrUR5IBAULYGCmjYbSNrRFGO7CGANrC4oxobFg1M255+7tx\n1Zt+OWxvebClIfTpASxCw57ztiI/smwBlDElWPSMUBRLpIEyCN+xUh5ANeWJMv5eYdGVJOBV37sJ\nhSAkTOEYY8gBNuvWYg+gYN8bozVMNsCzzpxvHoQxpj/lsJRA2T9L3fScA6jS2Hb/bTjWncOxzozV\nrcsPddXtN+Afz3mcfa8rutZgEmUcpmsTVtd2dGFV1yWAM+76MYjyKX58SGbT1DrVm60+n6dfdiGu\n2bMN+ZFjyJd6ePunWk18uxht599rveRU8Z5vA/BXRPRqAPfCDhABEe0C8AfM/CIiuhrATwK4mYi+\nAavGNzHzJ0adeK3JNn8JtlNJAdwF4FUAZFNlmfk7RPRXAL4DoADwOp4ABS1lGgudCGFT+3x3o6Tt\nkFFtxPAUc5+NkUkA1FJusDg61dSQ9L7wd5i75gXuv3X2qLJBAYmU2s/jLe3xknFNReQ9mM4cZh9/\nRWM9mgBULKvV9TrGy5cnBNZv5Fyto3nGlZfhGVdeFv7/f979xy3VaKfEAYCIXgngBQCe1VaGmfe6\n3wNE9D9hg69POhDl5Lj2YV+76Q489uI98ClWlmZPA9y8dj7+xIs3qOy2+4DmwD6wAbOOAp6r+9rE\nBh5rGGmNKaLzGg+SIsMKILAnvp5eLnrDL0T1bb7eP3ztLrzg8cNxuzYWx5Tvsv/RjC/feQiXb52z\njBQ8gxO77uKl3Z1nU0MSiBBikzQDh0WKDlSIS/N1AVACKmOBXZgx2lWSmYdvtuXm41F5ASC75+uf\nZfmMOTyGhcP7cGRmHvt2ngOdDcB5PwSRs1H4x3MutQMJuA6inEk0fj5BDTISxqVl8G0FHE0d468b\nXIlcTvjunn2uGbNpA5vYeNfRM4ljaaeNifJu4Nb905ySDwH4gYbtewG8yK1/CfbdX5WsCUQx800A\nntSwa6iyrvxbAbx1NddY7MqRwH/vUo5di83ZryUNx75U67OamkTH4fjGPC12RPVFqTeohoqXAOr4\nSAVAxQhi3dFEVejAPeBt54wtZ8YwdMdD17RyGDx/WrUe68VWrTWm6fiMznsegF8H8HRmzlrKzAEQ\nzLxMRPMAngvgt9e9Muskx7sPe9Ljz8dypsM0MDG7AwD3334X5nbtDqwAUAKaJ16wBV+7eTkYpxBT\nZLQDXA48uX2NQnaCWRIyHFO6hDxQ4yHDGmJ64i+PCd+h5z9pD5CvVLZx7T/POvnfp56zCdmyi+9y\nbroASKIjPcDSPGzQRTiWKwAKICyoHEYSGFQBrqbBQAQ9aB8szUPvIzfGwg6fa+nTn4V58jUAqs8y\nBszMjKWNW4GsVwHOcDGuJfvEo3UNRPqN6s0euEZtjBndA3uBs88q6wNg0M+A+ao9dSRp08Xim7f/\nGwuAphlUFT2cMe68kytFy6Mh+HOk7FrsNCo4gR4JoIDhhtGcNHPqqq1ZjmRraCwrR8aXWYWoKZvK\nKlNkDenETACgpjnvupyzBqAATA2g9DrDci6KkcuU8t8ALMC66L5ORL8HWEqciP7OldkB4IuODv8K\ngI8x8yfXej/fb+IN6q7z9jTuf+meAl+/db9lI5iDEUTlF6XBBJDqhtHFvlwTEKgZ4/gdqfd7YZLd\nVbxJ7//8Xc6lpytGkds6Zu9WBHAw05U6VUFR6+FhiUUzY6/7yAoAqv5damxMVvjfldu7HD3T6B6G\npthqkcUfeFb1uiPENLoOS+aJeTXghEN5z6SxMYGFA4Bs266Ku84YRqfbPh3XKPnk3bW2txbD6QLL\n25ZTIGoVIg7fH9ZXqxI1hpXLGqz7anMKNckDxxo/0KeSTdO1Zyvzm9Z07brrK5l4TFztuJZHSv3x\nII9GfI189s5DU9Unljh566QyCuqo+kNbhcQZ0PJ7hwaerF5i49W0TCHMfAEzn83Ml7vldW77Xmb2\nlPjdzPwEl97gUmb+z2u/mUenfO09fxrWm/qbUfLXd4+bOmpYh4Wc3LEwM9N8/r3fuXnic4yTV193\n7qrKe4PPALZ01jbvkap9zOzK29OGVEBd7RXetdD8TL/21+O/C7r339O6775v3jj2+DDVDDwIbik3\nEWCxZc49p90uDPpNk9tPJs89p+zob//2g1OfB0AZB9i2nEhmo0FOahBlTiuDgwloDWac5hu+22bd\n6+de5cnP2NA+3ciqpQnU7btr/c4/Qhrjg9SEoy2K8S8jzza/zJU0AqK9I33WeZsnq0vDeb3MT9hR\nJ0XZAY96fZN1CqDrnD2UAmXVMrITOg6uvlMyLE967SvCejcROHxkadXnEELaUXWVjohALru+HZU1\nqi+zx9UHuwwGxVDnRgB2PfbS5npM66L277D7/fxnbgO1vCckCUKIkUapnt+tcjzK3BpJZGj9+Y4U\nEd8bXYT8Sd0JHn641zw4KOqPn/TS8bOWZbvPKetQO9+ZT7hi7PEkpb0mCTeyUjTWq3kgE4Fcfbcc\nfCCc4657jlSef7w+Nzc7tk7tlS2fzQWXnL62EI9owurG5SSbceGkBlF1kesc5T3t6VZ7WH0UiZfG\nSW4HK8MFRTTz3I5z19RAOao9jQA7jfSzn8w4vn5TXdKZaas3JK13usqvkbW80ypdh7xNq5U1sqKj\n6PB6DpZTcnyFyHa0mzcthvbs+zIpCFJQBSgJQSBB5ZB1otKYOtBEQlTaSABSztiW5cv1sMCBL7c/\nvja5a/v6EQH33LuvrEb0RqqGaT2O7X8YQ2+tkOG61z37wrKOoe5xf+KeD/nFXtH/311cgCRCjHv8\nL8H26f5+bMoDt07A5k4yBGaELO9buDpt2TLn9BA9YxJjOxFq6a38YVS7zSqYJAuYRalne4zTzUS6\n9s/Zb7dt6ND2s8I9+PtvhF0ttydoQptHIhRe5YxuVWF2ecSalxMaY9MgjyoQtRZpU2loxw2KWS/M\n1pYzqunrjmbmG8uOrUrF6LaXjlOm8QiwU6nbhAb9eDbtoZey4dl54Of3LBTVL38CcOvBBpCKdl1P\n0gTkHe3DeCeJhbAXqva0B/7sPZMd13rh9XfnnZLphYggBEEQVQBUud/9xhbLGcHqIsr1eH8MiPz/\nFJWVsmS1vIEOxji6diTS1fe8PTsb7ylNh11dG7a3TAzu4lhIltckSeF/kgJCEkhagCSI3AIHmuyi\nllcgYA1XSnYolYD9laGsHTElA5CyZSgqQ6K8tpACJICcLZCK61gClfH9DzDMlPlnCCDoXlikZ0/j\n9ltdcMQ6Sfz+0S9HwDnKA1XXdbwQgaSMdF8eLyKAQ/4ZEGHT//hgpU363p4ArKz0K+CwYgliu+Ce\nlQd0H3zvGgbjGm29Hm3LSdZ/ndQgKvQlqsqY+Hd9TWi3dj4RdSCCJvUzV8FGU3UmesCrNbSrLbcO\nsV6NEg/PRqmvtfisp1Wp/QqtHrycVjPVEwEXb/Mg1dZR0ORgufLdGP2jz2+f2WTVbhBXftvLX7u6\n4+pyagLiEy7+ret89iND+4aYKLeIwJwQpKyBIJlAyMQaSZFAJB34+dJI2v/9NmYDkaR2v0zCry0r\nIZOOPYcgSyD469eYqGvPtaBIOBDj3xcZgb2h0WKhzUdtPwInFjBJC1iEBU5CkgMz9jqJAz6JA0Xx\nkghC4ssJQif634MkXyYul7h9Qljg5EEbSfvMu9IzgRIkRdVNuoo+WoZnVbJ5Xs9NuvbPXSRdq6PU\n6vAXtzwDIil1RzKB7MxCyAQy0nVoF64tiCQN/wfwLKTVs6SKrkHA0ktfHupm6+vZUWBxcVIWvtrP\nvfznn16C0VWKH5HYupxiolYh/mE5xmSUOeo4oBXbrLoBGxTtCHZeVg1qm+twtTZ+InNVq+fE7AVW\nwf5MC6SGjosZqub1qbKm5zYrev3WK6eq7axevnrNNj0d7BVOzzQWPNV3N8YjjD4FBqsdnrhOwkU+\ncjklj4BoBQJQPPul1qXnmZXIoPrlxXd9pgQw0k6Q6116UiYQ0qYoEDJ1hjYtAZSQuPkDbygZh6SD\npDsXASx7DpL+mDSwFCIR9npSwHqGrHH19frHew4hEeW7IohKZsK9jvWYrGUXbxVYEGkZFBGu48GT\nKMGT9GBKQMoSSEkCUgeU/JISkJJdj8FVXKa+3Z7PbU+kvZ4gyEQ6MCXCYpN9l25PuGXpyHKjW48A\nLD24zz2TKBZLIIApKQgzEgFECUHuWTgQ6wCzEEl4biLpWH2nHiylYKPtJMORrmO9k0wcCEwgZFru\nk0kFJFNN14CtWxIxZwDwzvf8dXmfTZ1dxVXsGDVR6nsqGcdEnRqdN7nwKqauKJLxcTizaXsg8UqE\nr4SaboSdn8A4ZI+dEjCvir1oK9sEmlYLpBrPYa/33SUaeb7GZj7q+p3Z+PTN4GSC53KwN7rNbJ0b\nn8V0NSOpQo1aKOaZCQcwrLuccuedcMn6GQjkXEkRK0FVFkoKwt9d+BwHMlAxdiJJLfiRKZKZOZBM\nIZKOZSLSTmCfnvAzvx/WNxZZyUolaWAtYvBFIrEAIjLq8w/vg5TCMimCQEaX7Amcca3Qsc33vVAf\n+RcMqwNW0k5H4kGL7IgSYCUCIhUWLAkKYClxIMiDJM9ATbIkZF1/qSB0JIESwrtnzoLsSFBCoQ6U\nlHU7zB0LqCKAuLh5Q+P9EgEbT9/pPBoiuG2t+7DUMZMI7jwSZLFZIiATd0ySYOXAfVZXkW49kBJJ\nB8nMQsQ4dUJ78ABKJqmNfZIpKEki3QtID159HYiQSmFZukEfUogAqDzz+Ks//1J3P6M/GK2LsIzD\nEnUmbzUyhomaxrAS0WlE9EkiupWI/hcRbRxRVrgULh+d5NwnNYgSevIv5ljBsV+6DkgmgScmKUfY\n3XXYMiRk1NCxdVdefQLj2EX4iMgkIKmlzHcPV4NEualpRMc+ZpHD/Vbyy7ht4jimI227S0EUQBJz\nVdf6vjtaz+en5PDSTYZrH+u6kU4eMZLwRMhIOvyUO+8RkQPfuS2sS1GN9UmdYU0EIZECiTe+QuCK\ni7aVjEwiStCUpBXjGoBU2oHszobtKxu2QHS6EGkHJutDJB10gHCsTLr2XIllgjwTle08HSQIncQa\n1m4ntaBKUGBVrNvcxXgxAqsLwMXEkE1ESQ44RYyOkDGAsrE7smO3idQusiMhUwkpY+bJ/s5E22Zq\nQOl7G7dVgZOwQKwbHd9xzJNMJV4v9qJ7xRWBlbIALsFg25kgKbCla9zoOLL3IS2jM9Z8u76h1LV1\nP8aAOXGuWgu43JJYXW/YfaFztYpSX2kXO5YON+padrrWdZt2kXRmAgtZAWFe14m97rF9B4I7UwpC\nmgh0NixEdfRuz1V+zPsgdzfwYVp3HsCjPwCnYyd+A8CnmfkiAJ8F8MYRZX8ZdlaCieSkBlHcXZjq\nuHEmgjC5W+7c02Zt2TpAWqd4rCD778F39i2vwvdeqq4CYmqTU7Zin/n3AAAgAElEQVQeW9v3mNMS\nOB7bBTfWjqnnWSERmLeKK89Trev9fNDiTqttotp2/7888/zW826ciSZpxXDbGBrR09C5xHVLDt7d\neq1HTEbR4ZOmqjgla5Izn/xEZ1vKr3vr2gHSRCCVEqkUSCUhkRRYiW/dcwjSrUspLGPkQJNMO5Cd\nGYikC9mZhUy7SDqzkEkXSXcWSdetpzNIOrOY2bQdIp2BmVuESLv2HJ2ZAFQsSCvdajIRwbDaOgoH\nACm4JGX95QLwpff8GT7/jTvA/eVyhwcgDkxRmkAkEiKRoERCpHZdOvCUdCRYEmRHQHYlutKCIA+G\n/O+MFJhx+2alwKwUuKx3CDNuf1cQ5mUJoGYkYUUZpJICUBOphP72TRApuf8TCCkwv7QPIi0DshGN\nmLP35Neb+zfP5JGPixJWt57lSaR9th7ECKcDr+skTUsglKSQnS5E0sXhHedAOp3Wdd1dOM0BqC5E\n2nUAuosk7bp249uR1fXWs3badcdC1V3LD7z9XTX3bYvEgNmDKP/M5PQgirUeHYowHZP+YgB/4tb/\nBMAPN98S7Yad1uoPJz3xWufOO2Hy3YMreMzWeRBRYAYIzTFCgqgxzsg3jjbY0tZ4htitmPlqLN9y\noli2n4PHAuP9vXaipNo2CuCLZFrd31S+vm8Mg0V5b2pAe0SJiZKGUr4C7rSPTBzKLt+wDUClPcRl\nvaxW18NxUeV6m67V1j0tZ1ulrGFAwCm26cQKhb9cjYVyQOqzn/gCrrzuKUikZaI6iUCmBLS0Bs9I\nhkmEm3NPgtmnFylTFxhlY1+4/nUeXCuWXbFB6SVzJVOJJJE1oGZ/U2HrksqSKfMjzISLKfJevfjd\nuPbnXw5R9EHZCpCVsUPkGBwfw0MORMWL7EjIwkClAt3ZBApwc9kxui6pKAGQbAPNFTM02z59oDkE\nhAf3mXOZpi4e6mCusWehA9mVSGakA2kCyUwCkVoQZ0FdApHY+DMhBSAl3n/Dw/i3z9psP5Jitrlm\nA25831/gkle+DGB2TF0JODfMpFjJtHuu9vkmiYCSAkliJ0A2UoBTBrMAc+ouIaDG6doOtysHHEgb\nO2V1PYMkTZAkAkla6li4NtZxdekmAokUDtAL7Pn119tYqcj1XO8H7UCiGCyX7dLqOQUlU7LzY+fO\nm4qJ2s7M++zh/BARDU/uaOUdsFNbtbr76vKoBVGP3TY/lFU7NqwRrgDQDqT8cZPKpAyUIGo0/ObA\nAxDbzqjuGNEowqi3+g2FM3K1HDAMnEiU00XUZZyhJjEMoGIWrBLdPXwuD6B4zFNuA1BA86036TrT\nBl1pc6H4zMdD5xpZi6rI/jHw3MTv0uqv0zbvoBBrH0F3Ku7pxEr4kre/MgApCwSe/6LrcLRfBCPm\nf3UiYLSAMcaBJz95rQRRB9qBEdYJtFRgo0rA7F8SKnML+ZFbpQtQWACVUrmelIxUJ7V1ueLMTbj3\nyCC49vxy9J9vwcYrL7P35Q2sLsoccoi2C2kT5npXmPBxTxKy04HpKOi0gOwoyMIgnUnADjwlrnMn\nIlCmIDVQMKEwDOWmf2HYYO34kfuUBtK58FJBOHcxgegIJN3ELjOJBW4diXQmgeykkJ0EopNYIJVa\nQEUyxc8+bXPplqzl5orf3St/9icwUCYAJz8KUBKhp21sWSqtjjNpXaYqlW7SZ6vrwdJRSNfXEhEU\nERISTtdFZYqfuq7jAHLh4qQ2LB/EYOsuy/QFsBzp2i8BQLkAfFmCfQ8Ia7cLCNtGZchLZQPivb4h\nJeTUIMqAR7DlbR+IRPQp2GmnwiZYM/GbTadpOP6FAPYx8zeJ6BmYsBt/1IGoOgOhDCMRzWyUN65i\n6QDM4rYAgFYz+i2WVGfQtQD2Nmai7Qpi2xnDmmkESGGnPVsjkqDKy1T9Gq0DKfft2Aammq4b39zy\nIWBh84TsSEPbmwB8MnPFLdbIQLUwj0RAN6KPydHQ0+paEA0BqDZdj0q1MZKFrB1n559fp+G7p0DU\nCReve0LJSiRCIJHG/grCYGkF3ZnZYNCKRMAY6SYktsdvuPdWHD7j/JCgUQsBNh0I7Q3r8FQYIVeU\nG+ouE4mtD92Jo2dfFFiJJJVIOvZXOhDnAd1tB5axONupMFKCgK2PvyTEnArfYmUaXxiAnUKLAvhI\ngDQFJR0LTlIZgEoy04HRGlJp6EIicVOw+O8LnzdLFAaJMkgJ0ExhEuLKPRNCgk1JsHV3MVZ3LGW4\nZNMMklkLoix4sm5DD55kDKASX3dZjjgLsV5xJqWytysKBZCEEGUahkQSUlE+104i0E0lcmWsrrXV\nNzMwv3kzVK5daJkNAFdCgk0KUkVgobjmWSjzQ6VB10IKZNtPR5KQ1XOTrlOBmVSGeqWOgYzzc4Gq\ncW+VPlDEuo5ceULYukwJosKky5F84Tt34wu33AMAuOHO+wBgaFoHZn5O2zmJaB8R7WDmfUS0E8D+\nhmJXA/ghInoBgFkAi0T0p8z8ioayQR51ICoWwvBUG3XDSwSYxW0A7LQxcfIzw9zqFvLlE1Fm8x0F\noNpdfw31jrfpAog6IV/HSuFaB2kgIHzkVwSWmKg6MWab62+aoO8GAMVE4OXDoIXTmr/QmsDFCBAW\nsiZjNMxjY+yL6v+PLsts9RonuDOH9gOnbRtxRoTrCnDjHIr+Vph5RPLUsZdolmAxRoHpVZ5y+kmG\nW4WIfgvAv0XZAb2JmT/RUO55AN4Ja9Pex8xvW/fKPErEAijLQHnjJIiQCKCTCGzdtglLgwJzSkJr\ntgsz2JQGaOX8i5Eog8HRh9FZ3AwpBbQ2YGMNYj5wuuaoTwDARiHpdGzMExGO7bkIaerdeBKJZ6NS\niTSVmOlIzHYk5joSnUSWbh5RjtjzDIV9BQgHbr0TZ15S2rMDR1awfc4xNrLMcRWYqLSDYybDQprA\ndFKYQkEWKViboYmJPYgSgqCVgcg1EsPQuQYz2Xc9es4Ej3tEGSzuYqAet7ELOSORdBOkDkglswmO\n9A12npZCdlOINIV0YMq6o1IgSV2Mj6z0Z/U+QhDQ7aQYKBOeVeJZKafrbirRKTS6gpElArMd2Tib\nhRqsIOnOgpSNp+seehgri1stsA5MVFXX5AYmnDFbYJ/qhED1JJU21soDKEnopBJdt3RqjJQky94l\n7rkHdx75D8ea50EIq0MAcCMCOekAUkKk003+ytrA5NXBPleffyauPv9MAMDBo8v4+l0PrnaC0Y8C\neCWAtwH4aQB/O3Rd5jcBeBMAENF1AH51HIACHkUgSlA5Ka5nLB5aKbBj3vuP/w97bx5vyVWWCz/v\nWlW19zmnT8/pIZ10Z55IQmbGBBkFBBNRSZiuKApeEPUTxPEqKN4rg6jf5eIV+H4qijKpON8PUD4C\n4g9IhBAICSEJGTtJp7vTwzl776q11vv98a5Vtap21T77nNMtHen316f33jWu2m/t9T71vFPFUCjU\ng8uDbcrsIqyai47pb5AOt0poCdCU5qZNWLKsIqC6ng48sbWNvxA1AWW0A6lp2aeu846DCiaCZUCv\n2YDG1dfHG8nIOPTSpZmsNiwR6z/UzAlna4LmZm6d2tjl/h6XNrdj7emrcU1HpOArKYzpZ7WA6ugx\nUe9i5nd1rSRp2PVuAM8E8ACALxHR3zDzrUdrQMeiKKCMiyHislZQ6ityp0rhd976B3j9L74GeaKR\npw49q2CdhmOuk8oEGKVw8sI+7N14AlgzlCWwA9gxsn4CBoGdZ3IDU0RZmdKuk1B/KgQwq5KhSD14\nipmoLBEDm6oq8D2wUVLVWoDU1nPOqP0+Tlg/BxSLqIKNfWB5IkwUJzk2rUlRLBioIoE2AUDJX3zN\npCQQnLSCNg4203CFg041XNiWIwabYgDlSxeEjL9MS0xUT4BUMptgkPZwwvoUup8h6feg+yl0L4PK\nPIDyLB5Sqcu0f6HA+g1qYqP6mHVUXt+JUkg1o5cozGQJrGMUXMA6xozTGMT7E6DWrYO1DkpZuIRR\nbNmG1Ova+UbE5fwXJS2QIjxCKdL+uK51opBmCRKv65lMYyYVNipLRfeZlvEqD6JSFUo1TJ7jVJYB\nhRF3nvZ1rpIMrFfY0LiFiWquX4G8DcBHiOjHANwN4MUAQETbAbwvNFBfiTxmQFQsId5o+5oUcXPr\npYCUy+a6opGnOm9r+MrYNtVGNUKp7XgTTzY5gLy2vrHtOJCKYN7Uhcqo9YK5PF5USqJGybXtI8um\nbfrcGMXkAHJmLBaM2Sx25UXnXuL31nQhth0jyCRdUxSVtnxdV3qdHAM3vRzFJsNL/ViuAHA7M98N\nAET0IUhmzHcViIpFgconfE2EXqJhGfj133w9FnKL3DKMY1jvwmPIvTXwbIBRBKUcFs+9AD3DsM4h\nsexdfhJDBITYqSobMLT1kBpEwlQkqSqz8JJUI/GMyGymMdtLvIFN0E+rAPNUqXLsqQ699sZ/I+xb\nzVRMtE93TzPQSItLz2TQ/R6ctQKcjAM7LgFUaElifLaizTV0YmELi8R6FsoxXMRasXVRC5cKTKlE\nABQp8kHlEguV9DWSfoZ+P0Pi/1RaxUYFBiqwUQEUbJjtw7VkNgvQCAHlIRAfntFRyLRD4ajG+sxm\nGo4BzQ4P3XkP1u3cJdftda0slYxjkrTruiL8qVXXOhE2LrjwsixBL1GY7SWYzTT6qeh6NtM+fkyV\nug7sYwDO4dpqEhfajGPfkgyU5qDkyDFRtfXNYOhpjsm8D8CzWpbvBjAGoJj5MwA+M82xj3kQtZRr\nZ5KtCbd6E0wFmcZGTcJXk4xq5/Gi97HbaZI4UlBx6YApgRSAOpgK261AYvDUebz4fVxorRn7Q6oq\nhRAfCpN1HbNR8bHXZNXxm0et3HDtx6wBoWXoetJxyn1WQlLFelxpsToAruiehFYpP0VErwBwA4Tu\nPtBYvwPAvdHn+yDA6rtOAhtFhLIAoxgp9lW4FYxmmFQYKOvc2A8gp1CY0UFbB6sdtCMpl+OZiTaj\nQkr4A4mVIei4hIEHGJlWpQsvAKmZLEEvlc/9RKGfqDKDMNUKHqNAEfDgI4dx6rbxApQVmBLwYWkI\nSlIxrkkBTnLoNAX36uwTVFU5XKUKJrFQqYXOFGyh4YxD0k88iBp3/wEo3XihnYzONFRCUGkIItdQ\nvRRJP/MMlIC68Fn3ewL6fKFSKC3vtfYAirAXc9hILfNXBDZSrZBYRkJOMh4VwyaAzQJYDmPPcOq5\nZ2JYWIxadJ2wgjVc6TpmDSJdy9dXsY7yWmVepknEQGUaGTFmegn6noXqZ0nJOKY61Cyr7t+4WGxd\n1yETNACo4AbNQNnKGtEzM5ztfgg81jKPj3kQFUtsRGODW1vutRwHWgZnSYv9XZHc9cgCTt9czyZr\nGtEuFqrVNbQEmov3tyB0lnxrYa84OgcBmKakQW3/5qDjc036PEFoijHEOnWOo1o77bqm0SK4NzvG\nQAYxjpF2uGcnjqNt/NH3UVhXY9hWcktZZhzpUp0147QMmZDh8isA3gPgN5iZieitAN4F4FWrHOp/\nSpF7hMuneEUS6Jy68KeQaoeMFRwLK1HOWR50aUXIjcNAEfLEwRoHnTgfG8Pla9hvLDEjGOXg0vPg\nabh/HzZt2+KNp0Y/VSWACmCqn+gGM+FT3pWC74yCEzfPj113MKocQJTW0GkGZzKQKbDfKKxLe1B9\nh4S5Nk9XY/YlB3QBmxk4D6Bs4TAaGaRENQDlrCtbjJBHrkoLcFIhuDzUgkoT6J7EQNUA1IwAKqQZ\nKBUghSSTV6WiekgKm2gARi+oCoCPdwO867bq8ZdqhQN79mNu3VpYZrhUwzZAryIg3bsHyfpNSDRh\nZETXScrCRKWMw3v3YmbdhvL7iufELl2Tr0nV867Z4LKdzTRmegKmbvn3m3HVlZdWGXqRvlOl5N4t\nzxHNbyXjSDgwKLAuU1UcWZqCTQpKl+4O0SpLuPPa866/c/KYAlGTpMlSNOsFRQ6t6frZtZ3Dv56+\nea7GIk0LoJaU3d8Ctp2OEu5FsVohc0sT5AZ27QxUCU5aXIECiHR17Mb4ONp2oiwFoCIGhbuYqmWI\nVvWctVZd96pGmYWpgE34FvYNDLaume5HHd8rY+uIoPJFuEzOtxIXZf1MDKW0j2EKLr3VZ+mNZbfc\nejc+d9vdS+83IcOlIe8D8Hcty+8HsDP6fJJf9l0nIUM0uHpC6ntgozKt5OGOuQqQplCY0SBRYlC1\nIhTGwaSMwjgUzpV1lFzETJRxMv73GxrMhh5tCYlR3XDSNowOHsCarZvQD8Y1rQDUTKqRaULPu/My\nX5QxFN5UqDK4YiP7ufd9GFe+8upwIcj37Udv3VwtSHvj2lm4EQHOQTEjKWN7PAOlFVRmoPMENkug\ncgM2Fq4wcNYhcymcqdx/sVsvxLCGnnyUEHSSgBKpl6VDCYNeiqSXyWuWegDVA/X6oLQPyjwblWZl\nLBdUEs1ffoYo5/+IcUQAzAqplQe3E7dsxMBYOFZwzJjNqkem4P4bnnwidGGRGYfRQ3uxZv165MZJ\nFqJxeOibt+CsK59WPm+36Tpu6RL0FUpnpF7P/URB33Iz5q64DLOZxpVXXop+2MaXOUjj1j8IDbHH\n50RxOyvY+c1AvhchmQARG7USWcqdN5aW+R2WxxyI6mKjAGEF0kaaO4Cxp53VmD0AGBQOM6latgtH\nEeD2PQi1cVv7Btt9Ve0wXqUjd13Db6kaQAqYDkyV6wM4m1ZagiDi83gxKkHiTcJqb/VYv01dtwEp\nQHTdS1RZ+iKMbvuUAKpzLNG1u2wWtoXZij913gdtbkx2DTftBB/1lDKW3XLaDjzltKo+2dv+7rPL\nPiYRbWPmB/3HFwH4WstmXwJwBhHtArAbwHUAXrLsk/0nkODeCSyFVsJGWRY2qs9KYqAS0fWtX7sd\np559WlngMtMWPf9AkBuH3DgYJ0DK+hiqOAg9ZHrFvfkCo1UeM1G4+5bbcN7F55UZYz3v6umnwloE\nN16mCT2tfM85ed339Vux/cJzW2Oinvrq64BiVDI22QlbATMEtBUgkkmdI2JXzl0a8Kn8Stx5iYLK\nDVxaQGUJdGHgClvFUFnfusiDKBYKT1Ls/XhIqwiUSTmFUExT4p6qulC6nyHpZQKgsj4+eWAjnjvv\nSjZKwIAAKFZJVOqg/QcedB2Yx4yV6Mrr2kUPrypiq1JtSz33T9qCQ4cWMDvbFyCVMi753mdjtqdx\naGA6dY3oeGXVeQ+CM8869hKNmSdejsWDB7F566YqmcBXeA/6llIHIas0dus1bnBS2ACfSOBdn7AW\nyMyKa921lThorj+W5DEBosbS/iOJjWtgBZpu4y4wtRIhotqTRJC24elDD8PN1zPD1MZtGBiH2RqD\nEY3L2c4f6FgsUbMoY1smXpP9WWmdqLHVCnRoD3g+Kh2gVAmgxs/dcU0P3gHaPlbyQ/ZoYIk2IBXr\n+tGhwXrfwiX+eqeJPQugq35+qtbV3LDjmZvU8b7tc3QCr++27Mf2uLFpZVJMwSrk7UR0EYTg+zaA\n1wCoZbgwsyWinwLwCVQlDr5xNAZzLEt4cicCbr7t2zj3rF3QEIbCOqCXwBtV2Z5AuOTic5Abh0RL\ni5KhN6qZsbCWkVsH419tcOcBY+6hIKGNh6KqZpFWhEuecEGZfRey8fqpRqYUeokY3V6isOfjf48z\nrr3Gu3okLmrLBefgo//wObzi6itbGFvC6J470N9xMqC0JDeQLw+QpICzIAnmqlh2kn6BqR755sMJ\nXGrgshTaWDhj4fICzrlaFl9nwUUlowqxVZKh56u2pwl0WtWF+tLn78KTn3uBMGVZHyrr47knOVDm\n2ahEYqNACkwCnoKbMlzvzX/zCZz9wmdHwde+zAKLro0DbALvrg33RlUBPtUOWaIwTBS+9c27sW3n\nDvStw2xvLaxjjIzDYGEAPSPuw3Vz7Q+Dt37hRlz4lMv9MSvgnCYKt9xwE654ymWSied1vnH7Zmmj\n4z/3dQBTFRsVWMaYbWx821g8eBhzc5kU2XS+nEWSgm0KpCubg9gx7MTA8uMxUcuWZCl3DpqgyS/r\nAFOxTAJW0waKtxI0QA1AxfZ5ZpILKGafgDF33ZirZwxIRVNbmxGm8alvWRJXKw8AquXJLHbjmbtv\nRXLKee2H23Z6zW25JOOEbl2vj3vgRePRU4DomMFsSgBXk3Rd+7ycr7fp7qwlB6ycMz0aE01XzZRm\nhouvHXX2ER/AY0zCvXnxuacit85n6QGZJjgmsJ8HVDC8weXm2YO+c56B0hIn46SOlHGuZKECgGoC\nqfDQWe+LpkrDmnpWKk0UMl/GoOfjYnqJQk8rnHHtNegn2rNQqqwE/tIXXglCSH+v/9Z6u84EbO6D\ny8nPZ4m4xuKWJZ7FIKXglLQ0oTSDLnK4vIAtxIXH1pWuvIqFEuM8FvenQvVuideR2CoPopQqGSnt\nY6Oe8vyLBCgF913WB6U9oNcH0p6UNkgSfOWTN+Dxz7+qZNgQAakLrn4OTKiujgpwaCI4r2uOdB0z\nOqE6eHC7XXD+aTXGUdhGBzuTilvPiUFr1pbSivCUZz5pTNeBebzqe64QPUe6zrToOE2U9CX0sVMB\nSAX3cwB7RFQHUj4manbdWrDNJbjc6xope7C8QtIiytZsk5Vk5x1NOXZB1BLBx0sZV6BOP7YkNfht\nlg8oYmO6d9Fg02y9gW3bWAFALe6Hm92wGggjT0MP3gnaemp0Av89NQ1n/P2tqFZU4Mcn+SjbWZRY\nkl3nTn1GtfsW2O11wDWNrnV+GM63TDhaum5b15QmIznNGR9YsDhxTmNogf5qfc1eJsYUHJejLuFR\nJdSJ0orgEBIIVK1NU3D5aXJIiFA4QqpY0uKVgksr9sk6jgysMFFuAhMVXpsuvVCu4PPX34BnPOMK\niXvSNGZkMy2unkQRbv7jD+OyV10n7UzKzK22O9yDJ534bDIH0gzKZJz/8mefxNOvvUoC0LW0KmGT\nAYWkxVOaQ1kLNoWAKFO588CoufPGzuzpoLpLT95L42Nf/ylNAZVIjaO0L2xUiINKe+LOy3qASvD4\n5z1VmBalhZXq0Lciwl/84I/j2o+9DwyJc2NU24fYohAbVyhG6gjr1iT46p98GBt+4GrYlKW1jdf3\nkdR16pelIV5KV+BZ2McoHooE8KuaO8+D1HICFiD8pv/9Cbzj1c/Ae//+Zrz6uY8DsVtxZh4g+p2Y\nXXwcRC1fDv3bpzH/pKeP3bxtxhVoN6CWx901ZZBex3nD1pNsbwBQXZvEp3SzG7oPNEmawePbTpdJ\nuMk0BUDj6ycNnMKMimKqpoRv8QTfPp52Vx+HsbZs7/7tL6Ge9IP+c3Ob6oxu+3kCkpxF3PBzKV27\nqL/fmO8eR0bXzW3lgAL2R8YhPbwPasPmpQ/QONqJc3Kd0gespT7YCuRYo7y/GyUwEzf92V/iwpf9\nIBwBTAAUI41KwuoAoBShUFJTyGiGdQqFc7AOuP+Bh7F56+aSgXIQZrXLlQfUjTaRVEkvC0F6xut5\nz35i2ZpEakJVBjcYWjHIwCWvuq4KLEfLA4b3ZbGrMvSEoRAwRdqBejN45qteKBX1lQaKRCpdmxyc\npGBnQUUBdgYf+fIj+OHHzUM7Czgn7W26WKhIrEqQkIuaH/t4HZ2UBTQp9Wn4SSrLk7R04YX3UIm0\ntFEJ3vqM1+BXPveBkoEp8/+j75mI8Yq/fj+MY9CD90Bv3Sl+Pa9rsiHBoK7roXU47SU/VOraMNdj\n3oAa69glWhE2zmQ4ODIeAAmzWbr4qGpDk/i4p34xgO6tgSkKDB/eh9ldJ/mWNVFRVVR2rDb3EYGJ\n8PbXPh/sDH7iBZcCzoiugVWVOJjIRB2PiVqePLhgsO1JTwfQzkA042LCdkHC8iaAAqoJIKz5p9v3\n4Xlnbpx6bATgqw8dxoVb14yt6wosngrGxKCpjB1oK1/gKeK2opxEmNl3L7D55BJUTStjY5wQSAlM\nAE/R8gCg7nrzL+PUt/x266bX/eGX8KHXXO53Gy/ksJSuy7G0DaNtEliGdO5HQn3PpApoAKjpdE3j\numkrtLpMcRMmoeNy9CU8sb/pHR/E//i5l8CwsDcASUSZYhDE5VJYh0JJc91CEQwLAyEshIJlxpm7\ntsF5V44YVADg2oPFW379f+FX3/zaWluiEAuoGwAq9Hf71D9/ES/43if5XnPeBaTqcTGhdUlCVbBx\nAA5yjvKqI3+V1A4aDhfR71eZbUTGfzcaHDK5TAE2GcgasCl8ALrFzv23Qs3tAKx8JkBcgi5KXAlF\nZZUuf28JUAIncetVhSDhK2ojyeQ1jYCUTjyIkvesJCuPlcavXv9HYFL4vRe+Fj/zT+9v9Xpc+dK3\n4PoP/roA5R27fBYZRbpmJBYoqNK1ZWGerGYYr2vnPGiaoOtmYHnQtXWMtT09putQLLWMlfLgOOmt\nFV33Uui1s6WuSzbK3zcxkKpddHDjwcl3BgZ54LdUpfMuYZamzJPWH0tCx9qAAICIeLBwuNMwlyOO\nYmmCtIL1lu2ax+qsXN2yz6RA5c6sLH8sd/83oXacVbkru9xsXUZ0Kbccu9qYh6zQbykMt1qZCJwm\nrVvm8kl350JuMZNOqLDU0HvXsdgaicmYcK42WUrXreNZlq4dZubXg5mXNTQi4vve/OqJ25z05vcu\n+7jHZTohIj54eLGMY7EsyQnWgyDr/NO2KVCoBNah3LawlfEM761zcCA4x6XxdIySpQDgjY6oM64f\nFIxsGfgMQlEUmO1nnpmoYp0SXVUlD9sHt07om/fNf/gUzv/+Z8MOB5iZm43WQeJgnAHZwgeRG/ns\nDGANyDnAGYDDOisAyeTCNPlXOAs2sv7//vQ9eP1VJ9bBU1yNPwJRNR2Ez2UT4ZiR0qW7kXRSFtWs\nZ+ElVTaeTiSwXKferZeAtd+GFIpIf5ZRvgYAJPqMdA/JJL939yM44YSNldsugCYWt12xsAA1MwuH\n8TiouDq9y3OoLKvpOrgXlddNzEbF71MtLFkFnCpdl8vG1q5xTOAAACAASURBVPkHd1uAbNCx170r\nvK4lgSDddeGy5hkieuOrLjnnHb981cWd27z50zfgT2+6/fXM/O5lHHcDgA8D2AVJi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SUHqXTKFrALCUQLtCCtJ96wbQGZfVGKixrjIdqDL3YAuYoqH1lHK8Yvl3WJix68KzATtCWRPI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\nwjswja6b9Dlw0YPHZtYrS7tbB7a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=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# make noise in 1% of the image pixels\n", + "speckles = (np.random.random(I.shape) < 0.01)\n", + "I[speckles] = np.random.normal(0, 3, np.count_nonzero(speckles))\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.imshow(I, cmap='RdBu')\n", + "plt.colorbar()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.imshow(I, cmap='RdBu')\n", + "plt.colorbar(extend='both')\n", + "plt.clim(-1, 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes-out the pattern we are interested in.\n", + "In the right panel, we manually set the color limits, and add extensions to indicate values which are above or below those limits.\n", + "The result is a much more useful visualization of our data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discrete Color Bars\n", + "\n", + "Colormaps are by default continuous, but sometimes you'd like to represent discrete values.\n", + "The easiest way to do this is to use the ``plt.cm.get_cmap()`` function, and pass the name of a suitable colormap along with the number of desired bins:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ja4kShBC65/tvMrdtrMjF6YHE+EOdO8dXKjI7245/vG8THn+D7W5BKQ3beSqcGRiYYk8D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\n9ZTSxwEgqDmK6qxZDCmlAwC+DqAXgghOUUpfAlAqUcqvEoD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6))\n", + "plt.colorbar()\n", + "plt.clim(-1, 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The discrete version of a colormap can be used just like any other colormap." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Handwritten Digits\n", + "\n", + "For an example of where this might be useful, let's look at an interesting visualization of some hand written digits data.\n", + "This data is included in Scikit-Learn, and consists of nearly 2,000 $8 \\times 8$ thumbnails showing various hand-written digits.\n", + "\n", + "For now, let's start by downloading the digits data and visualizing several of the example images with ``plt.imshow()``:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UYtDKKnq9uq7Ipa5bbs11qmlDBwcHyGQyHjpBiwtYmhyLxQxw+fvJXWYyGctW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\nM/M/h6PUiUNWq1VbXl52MGCP85lxoMv+XVtbS+T5X11deewYr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load images of the digits 0 through 5 and visualize several of them\n", + "from sklearn.datasets import load_digits\n", + "digits = load_digits(n_class=6)\n", + "\n", + "fig, ax = plt.subplots(8, 8, figsize=(6, 6))\n", + "for i, axi in enumerate(ax.flat):\n", + " axi.imshow(digits.images[i], cmap='binary')\n", + " axi.set(xticks=[], yticks=[])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because each digit is defined by the hue of its 64 pixels, we can consider each digit to be a point lying in 64-dimensional space: each dimension represents the brightness of one pixel.\n", + "But visualizing relationships in such high-dimensional spaces can be extremely difficult.\n", + "One way to approach this is to use a *dimensionality reduction* technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest.\n", + "Dimensionality reduction is an example of unsupervised machine learning, and we will discuss it in more detail in [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb).\n", + "\n", + "Deferring the discussion of these details, let's take a look at a two-dimensional manifold learning projection of this digits data (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) for details):" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# project the digits into 2 dimensions using IsoMap\n", + "from sklearn.manifold import Isomap\n", + "iso = Isomap(n_components=2)\n", + "projection = iso.fit_transform(digits.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use our discrete colormap to view the results, setting the ``ticks`` and ``clim`` to improve the aesthetics of the resulting colorbar:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YbNmU7EoUavuj0QjMRg2utOQvNcUcDOu+2MIDj/+bV9/9sM/9p8+ZzgiXAyUc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\nC18AwydDqAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results\n", + "plt.scatter(projection[:, 0], projection[:, 1], lw=0.1,\n", + " c=digits.target, cmap=plt.cm.get_cmap('cubehelix', 6))\n", + "plt.colorbar(ticks=range(6), label='digit value')\n", + "plt.clim(-0.5, 5.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The projection also gives us some interesting insights on the relationships within the dataset: for example, the ranges of 5 and 3 nearly overlap in this projection, indicating that some hand written fives and threes are difficult to distinguish, and therefore more likely to be confused by an automated classification algorithm.\n", + "Other values, like 0 and 1, are more distantly separated, and therefore much less likely to be confused.\n", + "This observation agrees with our intuition, because 5 and 3 look much more similar than do 0 and 1.\n", + "\n", + "We'll return to manifold learning and to digit classification in [Chapter 5](05.00-Machine-Learning.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.07-Customizing-Colorbars.md b/notebooks_v2/04.07-Customizing-Colorbars.md new file mode 100644 index 00000000..62798097 --- /dev/null +++ b/notebooks_v2/04.07-Customizing-Colorbars.md @@ -0,0 +1,253 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) > + +Open in Colab + + + +# Customizing Colorbars + + +Plot legends identify discrete labels of discrete points. +For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool. +In Matplotlib, a colorbar is a separate axes that can provide a key for the meaning of colors in a plot. +Because the book is printed in black-and-white, this section has an accompanying online supplement where you can view the figures in full color (https://github.com/jakevdp/PythonDataScienceHandbook). +We'll start by setting up the notebook for plotting and importing the functions we will use: + +```python +import matplotlib.pyplot as plt +plt.style.use('classic') +``` + +```python +%matplotlib inline +import numpy as np +``` + +As we have seen several times throughout this section, the simplest colorbar can be created with the ``plt.colorbar`` function: + +```python +x = np.linspace(0, 10, 1000) +I = np.sin(x) * np.cos(x[:, np.newaxis]) + +plt.imshow(I) +plt.colorbar(); +``` + +We'll now discuss a few ideas for customizing these colorbars and using them effectively in various situations. + + +## Customizing Colorbars + +The colormap can be specified using the ``cmap`` argument to the plotting function that is creating the visualization: + +```python +plt.imshow(I, cmap='gray'); +``` + +All the available colormaps are in the ``plt.cm`` namespace; using IPython's tab-completion will give you a full list of built-in possibilities: +``` +plt.cm. +``` +But being *able* to choose a colormap is just the first step: more important is how to *decide* among the possibilities! +The choice turns out to be much more subtle than you might initially expect. + + +### Choosing the Colormap + +A full treatment of color choice within visualization is beyond the scope of this book, but for entertaining reading on this subject and others, see the article ["Ten Simple Rules for Better Figures"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833). +Matplotlib's online documentation also has an [interesting discussion](http://Matplotlib.org/1.4.1/users/colormaps.html) of colormap choice. + +Broadly, you should be aware of three different categories of colormaps: + +- *Sequential colormaps*: These are made up of one continuous sequence of colors (e.g., ``binary`` or ``viridis``). +- *Divergent colormaps*: These usually contain two distinct colors, which show positive and negative deviations from a mean (e.g., ``RdBu`` or ``PuOr``). +- *Qualitative colormaps*: these mix colors with no particular sequence (e.g., ``rainbow`` or ``jet``). + +The ``jet`` colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap. +Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data. +Among the problems is the fact that qualitative maps usually do not display any uniform progression in brightness as the scale increases. + +We can see this by converting the ``jet`` colorbar into black and white: + +```python +from matplotlib.colors import LinearSegmentedColormap + +def grayscale_cmap(cmap): + """Return a grayscale version of the given colormap""" + cmap = plt.cm.get_cmap(cmap) + colors = cmap(np.arange(cmap.N)) + + # convert RGBA to perceived grayscale luminance + # cf. http://alienryderflex.com/hsp.html + RGB_weight = [0.299, 0.587, 0.114] + luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight)) + colors[:, :3] = luminance[:, np.newaxis] + + return LinearSegmentedColormap.from_list(cmap.name + "_gray", colors, cmap.N) + + +def view_colormap(cmap): + """Plot a colormap with its grayscale equivalent""" + cmap = plt.cm.get_cmap(cmap) + colors = cmap(np.arange(cmap.N)) + + cmap = grayscale_cmap(cmap) + grayscale = cmap(np.arange(cmap.N)) + + fig, ax = plt.subplots(2, figsize=(6, 2), + subplot_kw=dict(xticks=[], yticks=[])) + ax[0].imshow([colors], extent=[0, 10, 0, 1]) + ax[1].imshow([grayscale], extent=[0, 10, 0, 1]) +``` + +```python +view_colormap('jet') +``` + +Notice the bright stripes in the grayscale image. +Even in full color, this uneven brightness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset. +It's better to use a colormap such as ``viridis`` (the default as of Matplotlib 2.0), which is specifically constructed to have an even brightness variation across the range. +Thus it not only plays well with our color perception, but also will translate well to grayscale printing: + +```python +view_colormap('viridis') +``` + +If you favor rainbow schemes, another good option for continuous data is the ``cubehelix`` colormap: + +```python +view_colormap('cubehelix') +``` + +For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as ``RdBu`` (*Red-Blue*) can be useful. However, as you can see in the following figure, it's important to note that the positive-negative information will be lost upon translation to grayscale! + +```python +view_colormap('RdBu') +``` + +We'll see examples of using some of these color maps as we continue. + +There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the ``plt.cm`` submodule. For a more principled approach to colors in Python, you can refer to the tools and documentation within the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)). + + +### Color limits and extensions + +Matplotlib allows for a large range of colorbar customization. +The colorbar itself is simply an instance of ``plt.Axes``, so all of the axes and tick formatting tricks we've learned are applicable. +The colorbar has some interesting flexibility: for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the ``extend`` property. +This might come in handy, for example, if displaying an image that is subject to noise: + +```python +# make noise in 1% of the image pixels +speckles = (np.random.random(I.shape) < 0.01) +I[speckles] = np.random.normal(0, 3, np.count_nonzero(speckles)) + +plt.figure(figsize=(10, 3.5)) + +plt.subplot(1, 2, 1) +plt.imshow(I, cmap='RdBu') +plt.colorbar() + +plt.subplot(1, 2, 2) +plt.imshow(I, cmap='RdBu') +plt.colorbar(extend='both') +plt.clim(-1, 1); +``` + +Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes-out the pattern we are interested in. +In the right panel, we manually set the color limits, and add extensions to indicate values which are above or below those limits. +The result is a much more useful visualization of our data. + + +### Discrete Color Bars + +Colormaps are by default continuous, but sometimes you'd like to represent discrete values. +The easiest way to do this is to use the ``plt.cm.get_cmap()`` function, and pass the name of a suitable colormap along with the number of desired bins: + +```python +plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6)) +plt.colorbar() +plt.clim(-1, 1); +``` + +The discrete version of a colormap can be used just like any other colormap. + + +## Example: Handwritten Digits + +For an example of where this might be useful, let's look at an interesting visualization of some hand written digits data. +This data is included in Scikit-Learn, and consists of nearly 2,000 $8 \times 8$ thumbnails showing various hand-written digits. + +For now, let's start by downloading the digits data and visualizing several of the example images with ``plt.imshow()``: + +```python +# load images of the digits 0 through 5 and visualize several of them +from sklearn.datasets import load_digits +digits = load_digits(n_class=6) + +fig, ax = plt.subplots(8, 8, figsize=(6, 6)) +for i, axi in enumerate(ax.flat): + axi.imshow(digits.images[i], cmap='binary') + axi.set(xticks=[], yticks=[]) +``` + +Because each digit is defined by the hue of its 64 pixels, we can consider each digit to be a point lying in 64-dimensional space: each dimension represents the brightness of one pixel. +But visualizing relationships in such high-dimensional spaces can be extremely difficult. +One way to approach this is to use a *dimensionality reduction* technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest. +Dimensionality reduction is an example of unsupervised machine learning, and we will discuss it in more detail in [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb). + +Deferring the discussion of these details, let's take a look at a two-dimensional manifold learning projection of this digits data (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) for details): + +```python +# project the digits into 2 dimensions using IsoMap +from sklearn.manifold import Isomap +iso = Isomap(n_components=2) +projection = iso.fit_transform(digits.data) +``` + +We'll use our discrete colormap to view the results, setting the ``ticks`` and ``clim`` to improve the aesthetics of the resulting colorbar: + +```python +# plot the results +plt.scatter(projection[:, 0], projection[:, 1], lw=0.1, + c=digits.target, cmap=plt.cm.get_cmap('cubehelix', 6)) +plt.colorbar(ticks=range(6), label='digit value') +plt.clim(-0.5, 5.5) +``` + +The projection also gives us some interesting insights on the relationships within the dataset: for example, the ranges of 5 and 3 nearly overlap in this projection, indicating that some hand written fives and threes are difficult to distinguish, and therefore more likely to be confused by an automated classification algorithm. +Other values, like 0 and 1, are more distantly separated, and therefore much less likely to be confused. +This observation agrees with our intuition, because 5 and 3 look much more similar than do 0 and 1. + +We'll return to manifold learning and to digit classification in [Chapter 5](05.00-Machine-Learning.ipynb). + + + +< [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) | [Contents](Index.ipynb) | [Multiple Subplots](04.08-Multiple-Subplots.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.08-Multiple-Subplots.ipynb b/notebooks_v2/04.08-Multiple-Subplots.ipynb new file mode 100644 index 00000000..53744352 --- /dev/null +++ b/notebooks_v2/04.08-Multiple-Subplots.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple Subplots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes it is helpful to compare different views of data side by side.\n", + "To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\n", + "These subplots might be insets, grids of plots, or other more complicated layouts.\n", + "In this section we'll explore four routines for creating subplots in Matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-white')\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ``plt.axes``: Subplots by Hand\n", + "\n", + "The most basic method of creating an axes is to use the ``plt.axes`` function.\n", + "As we've seen previously, by default this creates a standard axes object that fills the entire figure.\n", + "``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system.\n", + "These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\n", + "\n", + "For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax1 = plt.axes() # standard axes\n", + "ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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1A4qLaV4gJgZo0kS/x1AqgXfeAY4dAxYvBpYtAxo1AjZs0O9xmGmSfVIozcWF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\nu7u7HkM3P7VrAzt2UNE8V1e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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],\n", + " xticklabels=[], ylim=(-1.2, 1.2))\n", + "ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],\n", + " ylim=(-1.2, 1.2))\n", + "\n", + "x = np.linspace(0, 10)\n", + "ax1.plot(np.sin(x))\n", + "ax2.plot(np.cos(x));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ``plt.subplot``: Simple Grids of Subplots\n", + "\n", + "Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create.\n", + "The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid.\n", + "As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EuP/++0V9fb1tu8ViEePGjRMJCQkdHuNNufrcBUq1tbUICgrqcn99fT3mz5+P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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(1, 7):\n", + " plt.subplot(2, 3, i)\n", + " plt.text(0.5, 0.5, str((2, 3, i)),\n", + " fontsize=18, ha='center')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots.\n", + "The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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q6+vx85//3O51LleoVCr87ne/Q19fH0pKSrBu3Tq89tprNn8uOnLz5k1s2bIF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\niYgkw8FORCQZDnYiIsn8H1BDee8pcOnKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n", + "for i in range(1, 7):\n", + " ax = fig.add_subplot(2, 3, i)\n", + " ax.text(0.5, 0.5, str((2, 3, i)),\n", + " fontsize=18, ha='center')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ``plt.subplots``: The Whole Grid in One Go\n", + "\n", + "The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\n", + "For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\n", + "The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\n", + "\n", + "Here we'll create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner.\n", + "The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# axes are in a two-dimensional array, indexed by [row, col]\n", + "for i in range(2):\n", + " for j in range(3):\n", + " ax[i, j].text(0.5, 0.5, str((i, j)),\n", + " fontsize=18, ha='center')\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ``plt.GridSpec``: More Complicated Arrangements\n", + "\n", + "To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\n", + "The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command.\n", + "For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this we can specify subplot locations and extents using the familiary Python slicing syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(grid[0, 0])\n", + "plt.subplot(grid[0, 1:])\n", + "plt.subplot(grid[1, :2])\n", + "plt.subplot(grid[1, 2]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This type of flexible grid alignment has a wide range of uses.\n", + "I most often use it when creating multi-axes histogram plots like the ones shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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sbNzWYBSNRhkeHmZmZoaFhQXa29uJRqMsLS3h9/txu92kUimRh1e6Nbce9BqN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Icffoju7m7ExcUNv85Hw/JWoaFDJe7u7li0aNGEv3/p0qWTnOi+RYsWISoqatzba7VatLW1Ka46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\nsbyJiFSI5U1EpEL/A5Irq324HXUvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create some normally distributed data\n", + "mean = [0, 0]\n", + "cov = [[1, 1], [1, 2]]\n", + "x, y = np.random.multivariate_normal(mean, cov, 3000).T\n", + "\n", + "# Set up the axes with gridspec\n", + "fig = plt.figure(figsize=(6, 6))\n", + "grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\n", + "main_ax = fig.add_subplot(grid[:-1, 1:])\n", + "y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n", + "x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\n", + "\n", + "# scatter points on the main axes\n", + "main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\n", + "\n", + "# histogram on the attached axes\n", + "x_hist.hist(x, 40, histtype='stepfilled',\n", + " orientation='vertical', color='gray')\n", + "x_hist.invert_yaxis()\n", + "\n", + "y_hist.hist(y, 40, histtype='stepfilled',\n", + " orientation='horizontal', color='gray')\n", + "y_hist.invert_xaxis()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.08-Multiple-Subplots.md b/notebooks_v2/04.08-Multiple-Subplots.md new file mode 100644 index 00000000..bb1d7407 --- /dev/null +++ b/notebooks_v2/04.08-Multiple-Subplots.md @@ -0,0 +1,187 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) > + +Open in Colab + + + +# Multiple Subplots + + +Sometimes it is helpful to compare different views of data side by side. +To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure. +These subplots might be insets, grids of plots, or other more complicated layouts. +In this section we'll explore four routines for creating subplots in Matplotlib. + +```python +%matplotlib inline +import matplotlib.pyplot as plt +plt.style.use('seaborn-white') +import numpy as np +``` + +## ``plt.axes``: Subplots by Hand + +The most basic method of creating an axes is to use the ``plt.axes`` function. +As we've seen previously, by default this creates a standard axes object that fills the entire figure. +``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system. +These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure. + +For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure): + +```python +ax1 = plt.axes() # standard axes +ax2 = plt.axes([0.65, 0.65, 0.2, 0.2]) +``` + +The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes: + +```python +fig = plt.figure() +ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4], + xticklabels=[], ylim=(-1.2, 1.2)) +ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4], + ylim=(-1.2, 1.2)) + +x = np.linspace(0, 10) +ax1.plot(np.sin(x)) +ax2.plot(np.cos(x)); +``` + +We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4). + + +## ``plt.subplot``: Simple Grids of Subplots + +Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create. +The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid. +As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right: + +```python +for i in range(1, 7): + plt.subplot(2, 3, i) + plt.text(0.5, 0.5, str((2, 3, i)), + fontsize=18, ha='center') +``` + +The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots. +The following code uses the equivalent object-oriented command, ``fig.add_subplot()``: + +```python +fig = plt.figure() +fig.subplots_adjust(hspace=0.4, wspace=0.4) +for i in range(1, 7): + ax = fig.add_subplot(2, 3, i) + ax.text(0.5, 0.5, str((2, 3, i)), + fontsize=18, ha='center') +``` + +We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height). + + +## ``plt.subplots``: The Whole Grid in One Go + +The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots. +For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array. +The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes. + +Here we'll create a $2 \times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale: + +```python +fig, ax = plt.subplots(2, 3, sharex='col', sharey='row') +``` + +Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner. +The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation: + +```python +# axes are in a two-dimensional array, indexed by [row, col] +for i in range(2): + for j in range(3): + ax[i, j].text(0.5, 0.5, str((i, j)), + fontsize=18, ha='center') +fig +``` + +In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing. + + +## ``plt.GridSpec``: More Complicated Arrangements + +To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool. +The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command. +For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this: + +```python +grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3) +``` + +From this we can specify subplot locations and extents using the familiary Python slicing syntax: + +```python +plt.subplot(grid[0, 0]) +plt.subplot(grid[0, 1:]) +plt.subplot(grid[1, :2]) +plt.subplot(grid[1, 2]); +``` + +This type of flexible grid alignment has a wide range of uses. +I most often use it when creating multi-axes histogram plots like the ones shown here: + +```python +# Create some normally distributed data +mean = [0, 0] +cov = [[1, 1], [1, 2]] +x, y = np.random.multivariate_normal(mean, cov, 3000).T + +# Set up the axes with gridspec +fig = plt.figure(figsize=(6, 6)) +grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2) +main_ax = fig.add_subplot(grid[:-1, 1:]) +y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax) +x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax) + +# scatter points on the main axes +main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2) + +# histogram on the attached axes +x_hist.hist(x, 40, histtype='stepfilled', + orientation='vertical', color='gray') +x_hist.invert_yaxis() + +y_hist.hist(y, 40, histtype='stepfilled', + orientation='horizontal', color='gray') +y_hist.invert_xaxis() +``` + +This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details. + + + +< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.09-Text-and-Annotation.ipynb b/notebooks_v2/04.09-Text-and-Annotation.ipynb new file mode 100644 index 00000000..a577e69b --- /dev/null +++ b/notebooks_v2/04.09-Text-and-Annotation.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Text and Annotation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating a good visualization involves guiding the reader so that the figure tells a story.\n", + "In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary.\n", + "Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this.\n", + "Let's take a look at some data and how we might visualize and annotate it to help convey interesting information. We'll start by setting up the notebook for plotting and importing the functions we will use:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "plt.style.use('seaborn-whitegrid')\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Effect of Holidays on US Births\n", + "\n", + "Let's return to some data we worked with earler, in [\"Example: Birthrate Data\"](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year; as already mentioned, that this data can be downloaded at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv.\n", + "\n", + "We'll start with the same cleaning procedure we used there, and plot the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "births = pd.read_csv('data/births.csv')\n", + "\n", + "quartiles = np.percentile(births['births'], [25, 50, 75])\n", + "mu, sig = quartiles[1], 0.74 * (quartiles[2] - quartiles[0])\n", + "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\n", + "\n", + "births['day'] = births['day'].astype(int)\n", + "\n", + "births.index = pd.to_datetime(10000 * births.year +\n", + " 100 * births.month +\n", + " births.day, format='%Y%m%d')\n", + "births_by_date = births.pivot_table('births',\n", + " [births.index.month, births.index.day])\n", + "births_by_date.index = [pd.datetime(2012, month, day)\n", + " for (month, day) in births_by_date.index]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Gj1hIRUVRuTIpD64Ykwn47DPgr38Fhg2jiHFFhfJr/fILRWVGjaJkPzmuXgXOnDE32xg8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\narD2Gbudx5itFAzDMAzDMIwapIQxR4wZhmEYhmGYBoe1lYI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "births_by_date.plot(ax=ax);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we're communicating data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.\n", + "This can be done manually with the ``plt.text``/``ax.text`` command, which will place text at a particular x/y value:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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GIcaRI0DXrkCvXsD+/brXvXGDBJ9Qbq1pU5p4wZDjpKZSZDs4mDzYq1eT3YOm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "births_by_date.plot(ax=ax)\n", + "\n", + "# Add labels to the plot\n", + "style = dict(size=10, color='gray')\n", + "\n", + "ax.text('2012-1-1', 3950, \"New Year's Day\", **style)\n", + "ax.text('2012-7-4', 4250, \"Independence Day\", ha='center', **style)\n", + "ax.text('2012-9-4', 4850, \"Labor Day\", ha='center', **style)\n", + "ax.text('2012-10-31', 4600, \"Halloween\", ha='right', **style)\n", + "ax.text('2012-11-25', 4450, \"Thanksgiving\", ha='center', **style)\n", + "ax.text('2012-12-25', 3850, \"Christmas \", ha='right', **style)\n", + "\n", + "# Label the axes\n", + "ax.set(title='USA births by day of year (1969-1988)',\n", + " ylabel='average daily births')\n", + "\n", + "# Format the x axis with centered month labels\n", + "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n", + "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n", + "ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``ax.text`` method takes an x position, a y position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text.\n", + "Here we used ``ha='right'`` and ``ha='center'``, where ``ha`` is short for *horizonal alignment*.\n", + "See the docstring of ``plt.text()`` and of ``mpl.text.Text()`` for more information on available options." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transforms and Text Position\n", + "\n", + "In the previous example, we have anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*.\n", + "\n", + "Any graphics display framework needs some scheme for translating between coordinate systems.\n", + "For example, a data point at $(x, y) = (1, 1)$ needs to somehow be represented at a certain location on the figure, which in turn needs to be represented in pixels on the screen.\n", + "Mathematically, such coordinate transformations are relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform them (these tools can be explored in the ``matplotlib.transforms`` submodule).\n", + "\n", + "The average user rarely needs to worry about the details of these transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three pre-defined transforms that can be useful in this situation:\n", + "\n", + "- ``ax.transData``: Transform associated with data coordinates\n", + "- ``ax.transAxes``: Transform associated with the axes (in units of axes dimensions)\n", + "- ``fig.transFigure``: Transform associated with the figure (in units of figure dimensions)\n", + "\n", + "Here let's look at an example of drawing text at various locations using these transforms:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(facecolor='lightgray')\n", + "ax.axis([0, 10, 0, 10])\n", + "\n", + "# transform=ax.transData is the default, but we'll specify it anyway\n", + "ax.text(1, 5, \". Data: (1, 5)\", transform=ax.transData)\n", + "ax.text(0.5, 0.1, \". Axes: (0.5, 0.1)\", transform=ax.transAxes)\n", + "ax.text(0.2, 0.2, \". Figure: (0.2, 0.2)\", transform=fig.transFigure);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that by default, the text is aligned above and to the left of the specified coordinates: here the \".\" at the beginning of each string will approximately mark the given coordinate location.\n", + "\n", + "The ``transData`` coordinates give the usual data coordinates associated with the x- and y-axis labels.\n", + "The ``transAxes`` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the axes size.\n", + "The ``transFigure`` coordinates are similar, but specify the position from the bottom-left of the figure (here the gray box), as a fraction of the figure size.\n", + "\n", + "Notice now that if we change the axes limits, it is only the ``transData`` coordinates that will be affected, while the others remain stationary:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.set_xlim(0, 2)\n", + "ax.set_ylim(-6, 6)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing ``%matplotlib inline`` to ``%matplotlib notebook`` and using each plot's menu to interact with the plot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Arrows and Annotation\n", + "\n", + "Along with tick marks and text, another useful annotation mark is the simple arrow.\n", + "\n", + "Drawing arrows in Matplotlib is often much harder than you'd bargain for.\n", + "While there is a ``plt.arrow()`` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, and the result is rarely what the user intended.\n", + "Instead, I'd suggest using the ``plt.annotate()`` function.\n", + "This function creates some text and an arrow, and the arrows can be very flexibly specified.\n", + "\n", + "Here we'll use ``annotate`` with several of its options:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "x = np.linspace(0, 20, 1000)\n", + "ax.plot(x, np.cos(x))\n", + "ax.axis('equal')\n", + "\n", + "ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),\n", + " arrowprops=dict(facecolor='black', shrink=0.05))\n", + "\n", + "ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),\n", + " arrowprops=dict(arrowstyle=\"->\",\n", + " connectionstyle=\"angle3,angleA=0,angleB=-90\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The arrow style is controlled through the ``arrowprops`` dictionary, which has numerous options available.\n", + "These options are fairly well-documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to quickly show some of the possibilities.\n", + "Let's demonstrate several of the possible options using the birthrate plot from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UgRkI573atdOIjpaUa+5JhKG44AI5P/9c0rnJlVdKZPXkSflp0yb0NrfeKvmv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "births_by_date.plot(ax=ax)\n", + "\n", + "# Add labels to the plot\n", + "ax.annotate(\"New Year's Day\", xy=('2012-1-1', 4100), xycoords='data',\n", + " xytext=(50, -30), textcoords='offset points',\n", + " arrowprops=dict(arrowstyle=\"->\",\n", + " connectionstyle=\"arc3,rad=-0.2\"))\n", + "\n", + "ax.annotate(\"Independence Day\", xy=('2012-7-4', 4250), xycoords='data',\n", + " bbox=dict(boxstyle=\"round\", fc=\"none\", ec=\"gray\"),\n", + " xytext=(10, -40), textcoords='offset points', ha='center',\n", + " arrowprops=dict(arrowstyle=\"->\"))\n", + "\n", + "ax.annotate('Labor Day', xy=('2012-9-4', 4850), xycoords='data', ha='center',\n", + " xytext=(0, -20), textcoords='offset points')\n", + "ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850),\n", + " xycoords='data', textcoords='data',\n", + " arrowprops={'arrowstyle': '|-|,widthA=0.2,widthB=0.2', })\n", + "\n", + "ax.annotate('Halloween', xy=('2012-10-31', 4600), xycoords='data',\n", + " xytext=(-80, -40), textcoords='offset points',\n", + " arrowprops=dict(arrowstyle=\"fancy\",\n", + " fc=\"0.6\", ec=\"none\",\n", + " connectionstyle=\"angle3,angleA=0,angleB=-90\"))\n", + "\n", + "ax.annotate('Thanksgiving', xy=('2012-11-25', 4500), xycoords='data',\n", + " xytext=(-120, -60), textcoords='offset points',\n", + " bbox=dict(boxstyle=\"round4,pad=.5\", fc=\"0.9\"),\n", + " arrowprops=dict(arrowstyle=\"->\",\n", + " connectionstyle=\"angle,angleA=0,angleB=80,rad=20\"))\n", + "\n", + "\n", + "ax.annotate('Christmas', xy=('2012-12-25', 3850), xycoords='data',\n", + " xytext=(-30, 0), textcoords='offset points',\n", + " size=13, ha='right', va=\"center\",\n", + " bbox=dict(boxstyle=\"round\", alpha=0.1),\n", + " arrowprops=dict(arrowstyle=\"wedge,tail_width=0.5\", alpha=0.1));\n", + "\n", + "# Label the axes\n", + "ax.set(title='USA births by day of year (1969-1988)',\n", + " ylabel='average daily births')\n", + "\n", + "# Format the x axis with centered month labels\n", + "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n", + "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n", + "ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\n", + "\n", + "ax.set_ylim(3600, 5400);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll notice that the specifications of the arrows and text boxes are very detailed: this gives you the power to create nearly any arrow style you wish.\n", + "Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time consuming when producing publication-quality graphics!\n", + "Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather included as a demonstration of some of the available options.\n", + "\n", + "More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery, in particular the [Annotation Demo](http://matplotlib.org/examples/pylab_examples/annotation_demo2.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.09-Text-and-Annotation.md b/notebooks_v2/04.09-Text-and-Annotation.md new file mode 100644 index 00000000..28d03cec --- /dev/null +++ b/notebooks_v2/04.09-Text-and-Annotation.md @@ -0,0 +1,249 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) > + +Open in Colab + + + +# Text and Annotation + + +Creating a good visualization involves guiding the reader so that the figure tells a story. +In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary. +Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this. +Let's take a look at some data and how we might visualize and annotate it to help convey interesting information. We'll start by setting up the notebook for plotting and importing the functions we will use: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import matplotlib as mpl +plt.style.use('seaborn-whitegrid') +import numpy as np +import pandas as pd +``` + +## Example: Effect of Holidays on US Births + +Let's return to some data we worked with earler, in ["Example: Birthrate Data"](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year; as already mentioned, that this data can be downloaded at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv. + +We'll start with the same cleaning procedure we used there, and plot the results: + +```python +births = pd.read_csv('data/births.csv') + +quartiles = np.percentile(births['births'], [25, 50, 75]) +mu, sig = quartiles[1], 0.74 * (quartiles[2] - quartiles[0]) +births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)') + +births['day'] = births['day'].astype(int) + +births.index = pd.to_datetime(10000 * births.year + + 100 * births.month + + births.day, format='%Y%m%d') +births_by_date = births.pivot_table('births', + [births.index.month, births.index.day]) +births_by_date.index = [pd.datetime(2012, month, day) + for (month, day) in births_by_date.index] +``` + +```python +fig, ax = plt.subplots(figsize=(12, 4)) +births_by_date.plot(ax=ax); +``` + +When we're communicating data like this, it is often useful to annotate certain features of the plot to draw the reader's attention. +This can be done manually with the ``plt.text``/``ax.text`` command, which will place text at a particular x/y value: + +```python +fig, ax = plt.subplots(figsize=(12, 4)) +births_by_date.plot(ax=ax) + +# Add labels to the plot +style = dict(size=10, color='gray') + +ax.text('2012-1-1', 3950, "New Year's Day", **style) +ax.text('2012-7-4', 4250, "Independence Day", ha='center', **style) +ax.text('2012-9-4', 4850, "Labor Day", ha='center', **style) +ax.text('2012-10-31', 4600, "Halloween", ha='right', **style) +ax.text('2012-11-25', 4450, "Thanksgiving", ha='center', **style) +ax.text('2012-12-25', 3850, "Christmas ", ha='right', **style) + +# Label the axes +ax.set(title='USA births by day of year (1969-1988)', + ylabel='average daily births') + +# Format the x axis with centered month labels +ax.xaxis.set_major_locator(mpl.dates.MonthLocator()) +ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15)) +ax.xaxis.set_major_formatter(plt.NullFormatter()) +ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h')); +``` + +The ``ax.text`` method takes an x position, a y position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text. +Here we used ``ha='right'`` and ``ha='center'``, where ``ha`` is short for *horizonal alignment*. +See the docstring of ``plt.text()`` and of ``mpl.text.Text()`` for more information on available options. + + +## Transforms and Text Position + +In the previous example, we have anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*. + +Any graphics display framework needs some scheme for translating between coordinate systems. +For example, a data point at $(x, y) = (1, 1)$ needs to somehow be represented at a certain location on the figure, which in turn needs to be represented in pixels on the screen. +Mathematically, such coordinate transformations are relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform them (these tools can be explored in the ``matplotlib.transforms`` submodule). + +The average user rarely needs to worry about the details of these transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three pre-defined transforms that can be useful in this situation: + +- ``ax.transData``: Transform associated with data coordinates +- ``ax.transAxes``: Transform associated with the axes (in units of axes dimensions) +- ``fig.transFigure``: Transform associated with the figure (in units of figure dimensions) + +Here let's look at an example of drawing text at various locations using these transforms: + +```python +fig, ax = plt.subplots(facecolor='lightgray') +ax.axis([0, 10, 0, 10]) + +# transform=ax.transData is the default, but we'll specify it anyway +ax.text(1, 5, ". Data: (1, 5)", transform=ax.transData) +ax.text(0.5, 0.1, ". Axes: (0.5, 0.1)", transform=ax.transAxes) +ax.text(0.2, 0.2, ". Figure: (0.2, 0.2)", transform=fig.transFigure); +``` + +Note that by default, the text is aligned above and to the left of the specified coordinates: here the "." at the beginning of each string will approximately mark the given coordinate location. + +The ``transData`` coordinates give the usual data coordinates associated with the x- and y-axis labels. +The ``transAxes`` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the axes size. +The ``transFigure`` coordinates are similar, but specify the position from the bottom-left of the figure (here the gray box), as a fraction of the figure size. + +Notice now that if we change the axes limits, it is only the ``transData`` coordinates that will be affected, while the others remain stationary: + +```python +ax.set_xlim(0, 2) +ax.set_ylim(-6, 6) +fig +``` + +This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing ``%matplotlib inline`` to ``%matplotlib notebook`` and using each plot's menu to interact with the plot. + + +## Arrows and Annotation + +Along with tick marks and text, another useful annotation mark is the simple arrow. + +Drawing arrows in Matplotlib is often much harder than you'd bargain for. +While there is a ``plt.arrow()`` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, and the result is rarely what the user intended. +Instead, I'd suggest using the ``plt.annotate()`` function. +This function creates some text and an arrow, and the arrows can be very flexibly specified. + +Here we'll use ``annotate`` with several of its options: + +```python +%matplotlib inline + +fig, ax = plt.subplots() + +x = np.linspace(0, 20, 1000) +ax.plot(x, np.cos(x)) +ax.axis('equal') + +ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4), + arrowprops=dict(facecolor='black', shrink=0.05)) + +ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6), + arrowprops=dict(arrowstyle="->", + connectionstyle="angle3,angleA=0,angleB=-90")); +``` + +The arrow style is controlled through the ``arrowprops`` dictionary, which has numerous options available. +These options are fairly well-documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to quickly show some of the possibilities. +Let's demonstrate several of the possible options using the birthrate plot from before: + +```python +fig, ax = plt.subplots(figsize=(12, 4)) +births_by_date.plot(ax=ax) + +# Add labels to the plot +ax.annotate("New Year's Day", xy=('2012-1-1', 4100), xycoords='data', + xytext=(50, -30), textcoords='offset points', + arrowprops=dict(arrowstyle="->", + connectionstyle="arc3,rad=-0.2")) + +ax.annotate("Independence Day", xy=('2012-7-4', 4250), xycoords='data', + bbox=dict(boxstyle="round", fc="none", ec="gray"), + xytext=(10, -40), textcoords='offset points', ha='center', + arrowprops=dict(arrowstyle="->")) + +ax.annotate('Labor Day', xy=('2012-9-4', 4850), xycoords='data', ha='center', + xytext=(0, -20), textcoords='offset points') +ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850), + xycoords='data', textcoords='data', + arrowprops={'arrowstyle': '|-|,widthA=0.2,widthB=0.2', }) + +ax.annotate('Halloween', xy=('2012-10-31', 4600), xycoords='data', + xytext=(-80, -40), textcoords='offset points', + arrowprops=dict(arrowstyle="fancy", + fc="0.6", ec="none", + connectionstyle="angle3,angleA=0,angleB=-90")) + +ax.annotate('Thanksgiving', xy=('2012-11-25', 4500), xycoords='data', + xytext=(-120, -60), textcoords='offset points', + bbox=dict(boxstyle="round4,pad=.5", fc="0.9"), + arrowprops=dict(arrowstyle="->", + connectionstyle="angle,angleA=0,angleB=80,rad=20")) + + +ax.annotate('Christmas', xy=('2012-12-25', 3850), xycoords='data', + xytext=(-30, 0), textcoords='offset points', + size=13, ha='right', va="center", + bbox=dict(boxstyle="round", alpha=0.1), + arrowprops=dict(arrowstyle="wedge,tail_width=0.5", alpha=0.1)); + +# Label the axes +ax.set(title='USA births by day of year (1969-1988)', + ylabel='average daily births') + +# Format the x axis with centered month labels +ax.xaxis.set_major_locator(mpl.dates.MonthLocator()) +ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15)) +ax.xaxis.set_major_formatter(plt.NullFormatter()) +ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h')); + +ax.set_ylim(3600, 5400); +``` + +You'll notice that the specifications of the arrows and text boxes are very detailed: this gives you the power to create nearly any arrow style you wish. +Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time consuming when producing publication-quality graphics! +Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather included as a demonstration of some of the available options. + +More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery, in particular the [Annotation Demo](http://matplotlib.org/examples/pylab_examples/annotation_demo2.html). + + + +< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.10-Customizing-Ticks.ipynb b/notebooks_v2/04.10-Customizing-Ticks.ipynb new file mode 100644 index 00000000..6da68ddd --- /dev/null +++ b/notebooks_v2/04.10-Customizing-Ticks.ipynb @@ -0,0 +1,511 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customizing Ticks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This section will give several examples of adjusting the tick locations and formatting for the particular plot type you're interested in.\n", + "\n", + "Before we go into examples, it will be best for us to understand further the object hierarchy of Matplotlib plots.\n", + "Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the ``figure`` is the bounding box within which plot elements appear.\n", + "Each Matplotlib object can also act as a container of sub-objects: for example, each ``figure`` can contain one or more ``axes`` objects, each of which in turn contain other objects representing plot contents.\n", + "\n", + "The tick marks are no exception. Each ``axes`` has attributes ``xaxis`` and ``yaxis``, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Major and Minor Ticks\n", + "\n", + "Within each axis, there is the concept of a *major* tick mark, and a *minor* tick mark. As the names would imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('classic')\n", + "%matplotlib inline\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes(xscale='log', yscale='log')\n", + "ax.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see here that each major tick shows a large tickmark and a label, while each minor tick shows a smaller tickmark with no label.\n", + "\n", + "These tick properties—locations and labels—that is, can be customized by setting the ``formatter`` and ``locator`` objects of each axis. Let's examine these for the x axis of the just shown plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "print(ax.xaxis.get_major_locator())\n", + "print(ax.xaxis.get_minor_locator())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "print(ax.xaxis.get_major_formatter())\n", + "print(ax.xaxis.get_minor_formatter())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that both major and minor tick labels have their locations specified by a ``LogLocator`` (which makes sense for a logarithmic plot). Minor ticks, though, have their labels formatted by a ``NullFormatter``: this says that no labels will be shown.\n", + "\n", + "We'll now show a few examples of setting these locators and formatters for various plots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hiding Ticks or Labels\n", + "\n", + "Perhaps the most common tick/label formatting operation is the act of hiding ticks or labels.\n", + "This can be done using ``plt.NullLocator()`` and ``plt.NullFormatter()``, as shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes()\n", + "ax.plot(np.random.rand(50))\n", + "\n", + "ax.yaxis.set_major_locator(plt.NullLocator())\n", + "ax.xaxis.set_major_formatter(plt.NullFormatter())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we've removed the labels (but kept the ticks/gridlines) from the x axis, and removed the ticks (and thus the labels as well) from the y axis.\n", + "Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images.\n", + "For instance, consider the following figure, which includes images of different faces, an example often used in supervised machine learning problems (see, for example, [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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kZU5WRrArBJoix2R2b4whkJcZKFivXoyPmKJi6jz5ENi0LZtdLVjBMIBSFFUuLe5dS9u0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S5tk+s6TG9msoyqhbSOmtQ7slGsNWtEccij/hHttgzQ6+06vVMBeXbPJKFOb2dKGdY1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MqlqjPZxx+/Ye21dbdpiW2NxumI91QpzXu+5LBO9hvwf0/t6g1O7PaQiv5E2qDZVF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\ns7I4JVpHKCj4E5+mEf/76nxFlqQMp0OSKCFYhh3CGQQ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eh6YqKK6La1usNiGb+YY8zdFN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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(5, 5, figsize=(5, 5))\n", + "fig.subplots_adjust(hspace=0, wspace=0)\n", + "\n", + "# Get some face data from scikit-learn\n", + "from sklearn.datasets import fetch_olivetti_faces\n", + "faces = fetch_olivetti_faces().images\n", + "\n", + "for i in range(5):\n", + " for j in range(5):\n", + " ax[i, j].xaxis.set_major_locator(plt.NullLocator())\n", + " ax[i, j].yaxis.set_major_locator(plt.NullLocator())\n", + " ax[i, j].imshow(faces[10 * i + j], cmap=\"bone\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that each image has its own axes, and we've set the locators to null because the tick values (pixel number in this case) do not convey relevant information for this particular visualization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reducing or Increasing the Number of Ticks\n", + "\n", + "One common problem with the default settings is that smaller subplots can end up with crowded labels.\n", + "We can see this in the plot grid shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(4, 4, sharex=True, sharey=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Particularly for the x ticks, the numbers nearly overlap and make them quite difficult to decipher.\n", + "We can fix this with the ``plt.MaxNLocator()``, which allows us to specify the maximum number of ticks that will be displayed.\n", + "Given this maximum number, Matplotlib will use internal logic to choose the particular tick locations:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For every axis, set the x and y major locator\n", + "for axi in ax.flat:\n", + " axi.xaxis.set_major_locator(plt.MaxNLocator(3))\n", + " axi.yaxis.set_major_locator(plt.MaxNLocator(3))\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This makes things much cleaner. If you want even more control over the locations of regularly-spaced ticks, you might also use ``plt.MultipleLocator``, which we'll discuss in the following section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fancy Tick Formats\n", + "\n", + "Matplotlib's default tick formatting can leave a lot to be desired: it works well as a broad default, but sometimes you'd like do do something more.\n", + "Consider this plot of a sine and a cosine:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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P/p2v/WhhrPPc9OwJrFtnHE4gJgbo2BE4eTL/+1I1ef/2G/D668a+x35+sh9p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\nTn1sgejMg5O3hrT1aYtDbx0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot a sine and cosine curve\n", + "fig, ax = plt.subplots()\n", + "x = np.linspace(0, 3 * np.pi, 1000)\n", + "ax.plot(x, np.sin(x), lw=3, label='Sine')\n", + "ax.plot(x, np.cos(x), lw=3, label='Cosine')\n", + "\n", + "# Set up grid, legend, and limits\n", + "ax.grid(True)\n", + "ax.legend(frameon=False)\n", + "ax.axis('equal')\n", + "ax.set_xlim(0, 3 * np.pi);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a couple changes we might like to make. First, it's more natural for this data to space the ticks and grid lines in multiples of $\\pi$. We can do this by setting a ``MultipleLocator``, which locates ticks at a multiple of the number you provide. For good measure, we'll add both major and minor ticks in multiples of $\\pi/4$:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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dvniYVlGtnIbeT/kxoccErzP06em3di20b59s6ENDjeFxl6EHqFOqDssGLKN8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\ntNZ3/AOqpvj/RWBKBm21L/PZZ5+5W4Q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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))\n", + "ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But now these tick labels look a little bit silly: we can see that they are multiples of $\\pi$, but the decimal representation does not immediately convey this.\n", + "To fix this, we can change the tick formatter. There's no built-in formatter for what we want to do, so we'll instead use ``plt.FuncFormatter``, which accepts a user-defined function giving fine-grained control over the tick outputs:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9MvWxK3wXKnhWAAAkpycjbGkYVp5bmeNjHOm92bMnsH69aQiGe/eADh2AU6eU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\nDQDf7P0GfZb3wdOUp1a2Th2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+ "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def format_func(value, tick_number):\n", + " # find number of multiples of pi/2\n", + " N = int(np.round(2 * value / np.pi))\n", + " if N == 0:\n", + " return \"0\"\n", + " elif N == 1:\n", + " return r\"$\\pi/2$\"\n", + " elif N == 2:\n", + " return r\"$\\pi$\"\n", + " elif N % 2 > 0:\n", + " return r\"${0}\\pi/2$\".format(N)\n", + " else:\n", + " return r\"${0}\\pi$\".format(N // 2)\n", + "\n", + "ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is much better! Notice that we've made use of Matplotlib's LaTeX support, specified by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae: in this case, ``\"$\\pi$\"`` is rendered as the Greek character $\\pi$.\n", + "\n", + "The ``plt.FuncFormatter()`` offers extremely fine-grained control over the appearance of your plot ticks, and comes in very handy when preparing plots for presentation or publication." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary of Formatters and Locators\n", + "\n", + "We've mentioned a couple of the available formatters and locators.\n", + "We'll conclude this section by briefly listing all the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentaion.\n", + "Each of the following is available in the ``plt`` namespace:\n", + "\n", + "Locator class | Description\n", + "---------------------|-------------\n", + "``NullLocator`` | No ticks\n", + "``FixedLocator`` | Tick locations are fixed\n", + "``IndexLocator`` | Locator for index plots (e.g., where x = range(len(y)))\n", + "``LinearLocator`` | Evenly spaced ticks from min to max\n", + "``LogLocator`` | Logarithmically ticks from min to max\n", + "``MultipleLocator`` | Ticks and range are a multiple of base\n", + "``MaxNLocator`` | Finds up to a max number of ticks at nice locations\n", + "``AutoLocator`` | (Default.) MaxNLocator with simple defaults.\n", + "``AutoMinorLocator`` | Locator for minor ticks\n", + "\n", + "Formatter Class | Description\n", + "----------------------|---------------\n", + "``NullFormatter`` | No labels on the ticks\n", + "``IndexFormatter`` | Set the strings from a list of labels\n", + "``FixedFormatter`` | Set the strings manually for the labels\n", + "``FuncFormatter`` | User-defined function sets the labels\n", + "``FormatStrFormatter``| Use a format string for each value\n", + "``ScalarFormatter`` | (Default.) Formatter for scalar values\n", + "``LogFormatter`` | Default formatter for log axes\n", + "\n", + "We'll see further examples of these through the remainder of the book." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.10-Customizing-Ticks.md b/notebooks_v2/04.10-Customizing-Ticks.md new file mode 100644 index 00000000..473a317e --- /dev/null +++ b/notebooks_v2/04.10-Customizing-Ticks.md @@ -0,0 +1,226 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) > + +Open in Colab + + + +# Customizing Ticks + + +Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This section will give several examples of adjusting the tick locations and formatting for the particular plot type you're interested in. + +Before we go into examples, it will be best for us to understand further the object hierarchy of Matplotlib plots. +Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the ``figure`` is the bounding box within which plot elements appear. +Each Matplotlib object can also act as a container of sub-objects: for example, each ``figure`` can contain one or more ``axes`` objects, each of which in turn contain other objects representing plot contents. + +The tick marks are no exception. Each ``axes`` has attributes ``xaxis`` and ``yaxis``, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes. + + +## Major and Minor Ticks + +Within each axis, there is the concept of a *major* tick mark, and a *minor* tick mark. As the names would imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots: + +```python +import matplotlib.pyplot as plt +plt.style.use('classic') +%matplotlib inline +import numpy as np +``` + +```python +ax = plt.axes(xscale='log', yscale='log') +ax.grid(); +``` + +We see here that each major tick shows a large tickmark and a label, while each minor tick shows a smaller tickmark with no label. + +These tick properties—locations and labels—that is, can be customized by setting the ``formatter`` and ``locator`` objects of each axis. Let's examine these for the x axis of the just shown plot: + +```python +print(ax.xaxis.get_major_locator()) +print(ax.xaxis.get_minor_locator()) +``` + +```python +print(ax.xaxis.get_major_formatter()) +print(ax.xaxis.get_minor_formatter()) +``` + +We see that both major and minor tick labels have their locations specified by a ``LogLocator`` (which makes sense for a logarithmic plot). Minor ticks, though, have their labels formatted by a ``NullFormatter``: this says that no labels will be shown. + +We'll now show a few examples of setting these locators and formatters for various plots. + + +## Hiding Ticks or Labels + +Perhaps the most common tick/label formatting operation is the act of hiding ticks or labels. +This can be done using ``plt.NullLocator()`` and ``plt.NullFormatter()``, as shown here: + +```python +ax = plt.axes() +ax.plot(np.random.rand(50)) + +ax.yaxis.set_major_locator(plt.NullLocator()) +ax.xaxis.set_major_formatter(plt.NullFormatter()) +``` + +Notice that we've removed the labels (but kept the ticks/gridlines) from the x axis, and removed the ticks (and thus the labels as well) from the y axis. +Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images. +For instance, consider the following figure, which includes images of different faces, an example often used in supervised machine learning problems (see, for example, [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)): + +```python +fig, ax = plt.subplots(5, 5, figsize=(5, 5)) +fig.subplots_adjust(hspace=0, wspace=0) + +# Get some face data from scikit-learn +from sklearn.datasets import fetch_olivetti_faces +faces = fetch_olivetti_faces().images + +for i in range(5): + for j in range(5): + ax[i, j].xaxis.set_major_locator(plt.NullLocator()) + ax[i, j].yaxis.set_major_locator(plt.NullLocator()) + ax[i, j].imshow(faces[10 * i + j], cmap="bone") +``` + +Notice that each image has its own axes, and we've set the locators to null because the tick values (pixel number in this case) do not convey relevant information for this particular visualization. + + +## Reducing or Increasing the Number of Ticks + +One common problem with the default settings is that smaller subplots can end up with crowded labels. +We can see this in the plot grid shown here: + +```python +fig, ax = plt.subplots(4, 4, sharex=True, sharey=True) +``` + +Particularly for the x ticks, the numbers nearly overlap and make them quite difficult to decipher. +We can fix this with the ``plt.MaxNLocator()``, which allows us to specify the maximum number of ticks that will be displayed. +Given this maximum number, Matplotlib will use internal logic to choose the particular tick locations: + +```python +# For every axis, set the x and y major locator +for axi in ax.flat: + axi.xaxis.set_major_locator(plt.MaxNLocator(3)) + axi.yaxis.set_major_locator(plt.MaxNLocator(3)) +fig +``` + +This makes things much cleaner. If you want even more control over the locations of regularly-spaced ticks, you might also use ``plt.MultipleLocator``, which we'll discuss in the following section. + + +## Fancy Tick Formats + +Matplotlib's default tick formatting can leave a lot to be desired: it works well as a broad default, but sometimes you'd like do do something more. +Consider this plot of a sine and a cosine: + +```python +# Plot a sine and cosine curve +fig, ax = plt.subplots() +x = np.linspace(0, 3 * np.pi, 1000) +ax.plot(x, np.sin(x), lw=3, label='Sine') +ax.plot(x, np.cos(x), lw=3, label='Cosine') + +# Set up grid, legend, and limits +ax.grid(True) +ax.legend(frameon=False) +ax.axis('equal') +ax.set_xlim(0, 3 * np.pi); +``` + +There are a couple changes we might like to make. First, it's more natural for this data to space the ticks and grid lines in multiples of $\pi$. We can do this by setting a ``MultipleLocator``, which locates ticks at a multiple of the number you provide. For good measure, we'll add both major and minor ticks in multiples of $\pi/4$: + +```python +ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2)) +ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4)) +fig +``` + +But now these tick labels look a little bit silly: we can see that they are multiples of $\pi$, but the decimal representation does not immediately convey this. +To fix this, we can change the tick formatter. There's no built-in formatter for what we want to do, so we'll instead use ``plt.FuncFormatter``, which accepts a user-defined function giving fine-grained control over the tick outputs: + +```python +def format_func(value, tick_number): + # find number of multiples of pi/2 + N = int(np.round(2 * value / np.pi)) + if N == 0: + return "0" + elif N == 1: + return r"$\pi/2$" + elif N == 2: + return r"$\pi$" + elif N % 2 > 0: + return r"${0}\pi/2$".format(N) + else: + return r"${0}\pi$".format(N // 2) + +ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func)) +fig +``` + +This is much better! Notice that we've made use of Matplotlib's LaTeX support, specified by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae: in this case, ``"$\pi$"`` is rendered as the Greek character $\pi$. + +The ``plt.FuncFormatter()`` offers extremely fine-grained control over the appearance of your plot ticks, and comes in very handy when preparing plots for presentation or publication. + + +## Summary of Formatters and Locators + +We've mentioned a couple of the available formatters and locators. +We'll conclude this section by briefly listing all the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentaion. +Each of the following is available in the ``plt`` namespace: + +Locator class | Description +---------------------|------------- +``NullLocator`` | No ticks +``FixedLocator`` | Tick locations are fixed +``IndexLocator`` | Locator for index plots (e.g., where x = range(len(y))) +``LinearLocator`` | Evenly spaced ticks from min to max +``LogLocator`` | Logarithmically ticks from min to max +``MultipleLocator`` | Ticks and range are a multiple of base +``MaxNLocator`` | Finds up to a max number of ticks at nice locations +``AutoLocator`` | (Default.) MaxNLocator with simple defaults. +``AutoMinorLocator`` | Locator for minor ticks + +Formatter Class | Description +----------------------|--------------- +``NullFormatter`` | No labels on the ticks +``IndexFormatter`` | Set the strings from a list of labels +``FixedFormatter`` | Set the strings manually for the labels +``FuncFormatter`` | User-defined function sets the labels +``FormatStrFormatter``| Use a format string for each value +``ScalarFormatter`` | (Default.) Formatter for scalar values +``LogFormatter`` | Default formatter for log axes + +We'll see further examples of these through the remainder of the book. + + + +< [Text and Annotation](04.09-Text-and-Annotation.ipynb) | [Contents](Index.ipynb) | [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.11-Settings-and-Stylesheets.ipynb b/notebooks_v2/04.11-Settings-and-Stylesheets.ipynb new file mode 100644 index 00000000..0bae420e --- /dev/null +++ b/notebooks_v2/04.11-Settings-and-Stylesheets.ipynb @@ -0,0 +1,656 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customizing Matplotlib: Configurations and Stylesheets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib's default plot settings are often the subject of complaint among its users.\n", + "While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences.\n", + "\n", + "Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer *stylesheets* feature, which contains some nice sets of default configurations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Customization by Hand\n", + "\n", + "Through this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default.\n", + "It's possible to do these customizations for each individual plot.\n", + "For example, here is a fairly drab default histogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('classic')\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.random.randn(1000)\n", + "plt.hist(x);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can adjust this by hand to make it a much more visually pleasing plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# use a gray background\n", + "ax = plt.axes(axisbg='#E6E6E6')\n", + "ax.set_axisbelow(True)\n", + "\n", + "# draw solid white grid lines\n", + "plt.grid(color='w', linestyle='solid')\n", + "\n", + "# hide axis spines\n", + "for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + " \n", + "# hide top and right ticks\n", + "ax.xaxis.tick_bottom()\n", + "ax.yaxis.tick_left()\n", + "\n", + "# lighten ticks and labels\n", + "ax.tick_params(colors='gray', direction='out')\n", + "for tick in ax.get_xticklabels():\n", + " tick.set_color('gray')\n", + "for tick in ax.get_yticklabels():\n", + " tick.set_color('gray')\n", + " \n", + "# control face and edge color of histogram\n", + "ax.hist(x, edgecolor='#E6E6E6', color='#EE6666');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package.\n", + "But this took a whole lot of effort!\n", + "We definitely do not want to have to do all that tweaking each time we create a plot.\n", + "Fortunately, there is a way to adjust these defaults once in a way that will work for all plots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing the Defaults: ``rcParams``\n", + "\n", + "Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create.\n", + "This configuration can be adjusted at any time using the ``plt.rc`` convenience routine.\n", + "Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before.\n", + "\n", + "We'll start by saving a copy of the current ``rcParams`` dictionary, so we can easily reset these changes in the current session:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "IPython_default = plt.rcParams.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the ``plt.rc`` function to change some of these settings:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from matplotlib import cycler\n", + "colors = cycler('color',\n", + " ['#EE6666', '#3388BB', '#9988DD',\n", + " '#EECC55', '#88BB44', '#FFBBBB'])\n", + "plt.rc('axes', facecolor='#E6E6E6', edgecolor='none',\n", + " axisbelow=True, grid=True, prop_cycle=colors)\n", + "plt.rc('grid', color='w', linestyle='solid')\n", + "plt.rc('xtick', direction='out', color='gray')\n", + "plt.rc('ytick', direction='out', color='gray')\n", + "plt.rc('patch', edgecolor='#E6E6E6')\n", + "plt.rc('lines', linewidth=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With these settings defined, we can now create a plot and see our settings in action:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(x);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see what simple line plots look like with these rc parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ty4bjMo24fJmxvhCv/zzMT58O0XYxSiQM+UWCY/faeeBXnTQctG+Kh35ZLIsP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(4):\n", + " plt.plot(np.random.rand(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I find this much more aesthetically pleasing than the default styling.\n", + "If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes!\n", + "These settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](http://Matplotlib.org/users/customizing.html).\n", + "That said, I prefer to customize Matplotlib using its stylesheets instead." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stylesheets\n", + "\n", + "The version 1.4 release of Matplotlib in August 2014 added a very convenient ``style`` module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension.\n", + "\n", + "Even if you don't create your own style, the stylesheets included by default are extremely useful.\n", + "The available styles are listed in ``plt.style.available``—here I'll list only the first five for brevity:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['fivethirtyeight',\n", + " 'seaborn-pastel',\n", + " 'seaborn-whitegrid',\n", + " 'ggplot',\n", + " 'grayscale']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.style.available[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The basic way to switch to a stylesheet is to call\n", + "\n", + "``` python\n", + "plt.style.use('stylename')\n", + "```\n", + "\n", + "But keep in mind that this will change the style for the rest of the session!\n", + "Alternatively, you can use the style context manager, which sets a style temporarily:\n", + "\n", + "``` python\n", + "with plt.style.context('stylename'):\n", + " make_a_plot()\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create a function that will make two basic types of plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def hist_and_lines():\n", + " np.random.seed(0)\n", + " fig, ax = plt.subplots(1, 2, figsize=(11, 4))\n", + " ax[0].hist(np.random.randn(1000))\n", + " for i in range(3):\n", + " ax[1].plot(np.random.rand(10))\n", + " ax[1].legend(['a', 'b', 'c'], loc='lower left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use this to explore how these plots look using the various built-in styles." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Default style\n", + "\n", + "The default style is what we've been seeing so far throughout the book; we'll start with that.\n", + "First, let's reset our runtime configuration to the notebook default:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# reset rcParams\n", + "plt.rcParams.update(IPython_default);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see how it looks:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Fc52jN7k6VR0SFEL7Wu1ZsXdFoecGBZm9uItDksy8hHnc2PhGQoNDrW/8zBkY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\nGGNMXBk5ciTFxcXU1ro/haW9ctJz2F66napzVU7qt614wjF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist_and_lines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FiveThiryEight style\n", + "\n", + "The ``fivethirtyeight`` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).\n", + "As you can see here, it is typified by bold colors, thick lines, and transparent axes:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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KNd4lIxdNMsLVq6puBXW9ite5RHikIoBTlz3EkC4bADRzK2z1b3Dz5YmXQEoK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with plt.style.context('fivethirtyeight'):\n", + " hist_and_lines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ggplot\n", + "\n", + "The ``ggplot`` package in the R language is a very popular visualization tool.\n", + "Matplotlib's ``ggplot`` style mimics the default styles from that package:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Upo8/rT2stiiN+tb410n294VobPBTtSS64uOjkUBSCCFEQnh6fXzzfw7y7pFB7rtwLufM\nz45rymws5ubX6L/tS6iqpbjLZtFUMD/h10gU5XBgXHsT+umfoI80T9k4dFcH+ve/xPjbf0AZ0X30\nN/b4KMuyMyvTDkpxpD8w6nFq9TkpETBPJ+MVIR9NTWEadW2DcWe4d273sqDKgcMZfxgogaQQQoi4\nmFrzh12d3PI/hzh7XjZ3nFMWcYYlWrr5EPqXj5Dx7fswLrgMd3bsrRIni3LPQ13yacxH70UHRw/C\nks381Y9RZ1+IKi2P+r6eXj/ubDtKKaoLXewao+ezWnEGun4rerA/3uHOCCFT80ZjH2siWB85rCTT\nRsjUtA7E/jpqbw3Q0xVk/sLoi4+PRgJJIYQQMWvtD3Dbi428drCPu/9qDh9flIuRhCwkhFscmo/c\njfrUFVjmVADj15JMJWrdxyEnD/3MLyb92nrrG9B0KFyWKAaeHh/urHDQUVXgYucYJWhUegbULENv\neT3msc4k9W2D5KdZKc6MvH6jUoqaOKa3tdbUbfVSvdSFxZKYv1MJJIUQQkRNa82fdrbxtecaOLUk\nnX86v5zZWbEVNI70evoXP0TNW4hxxvlHby/NsuPpHaUkTYpRSmFc8ZXwhpRJXEeohwYx/+PHGH/7\nJZQttuensddPWXb4vtWFrlF3bg8zTl8nu7cjFO209rCaQhf1Y2x6moinwY/FArPLEjdjkBpb24QY\nwdLVDp1tSTm3mqJpJSFOBiFTs6ttiLea+nnT00e6w8b3zi1jbm78C/Ynol99Hu1pwLjl3mNuL50m\nGUkAlZmF8Xc3YD7+AMZtP0Bl5ST9mvqZn6Nql6MWxdbVp88XIhDS5H2wYWperpOWfj8D/hDp9lF2\n+y4+FZ58CN16OLwBR4zK1JpNjf3cdV70Sw1qi9J4bm931PcLBjW73vOyYk16QtcuSyApUk9nG/7v\nfyMpp3Zcf3tSzivEyWooYLLt8ABvNfWxpWmA/DQrq9wZ3HxGKUvLC+jvT/56OH1wP/qZX2B84/so\nx7HruvJcVnxBTb8vREYCa1Qmi6peilqzDvMnGzC+/J2oN75EQ+/fhX5nI8YdD8V8Dk+Pj9Is+9HA\nw2ZRLMhzsqfDy/KSE9tbKqsNtfKj6DdeRn3i8pive7Lb1TZElsNCaQxZ/Dk5DrqGgvR4g2Q7Iw/j\n9u/ykl9oJbcgsaGfTG0LIYQ4RudQkOf3dvOPLzXyhd/t47/3dlGR5+L+C+fyg4vmcfkphSzIcyZl\nR/bx9GA/5o/uRn3mi6hi9wk/V0qFs5J90yMrCaA+8VkY6EP/+Q9Ju4YOBsM1I9dfhUqPfvp02Mhp\n7WHhdZJjT62q09eh35CWiePZGOUmm5EshmJRgSuqdolDgyYH9vqpOsUV0zXHIxlJIYSY4bTWHOrx\n85anjzc9/TT3+TmtJIOz52XztbWzR5/CnKRxmT95ELX4VIyVHx3zOHeWHU+Pj0UFif+QTAZltWJc\ncxPmP92EXrgYVb4g4dfQ//MM5Bagxvm9RWLkRpth1YVp/GF359h3mlsBFivs3wUV1XFd/2Rkas3G\nQ318d11ZzOcYLkweSUccgF3vDjFngZ209MTnDyWQFEKIGShkaurbBnnL089bnn5CpuYjZZl8bmkh\ntUVp2BK0ozMe+oXfQ1c76tqbxz2uNHv6rJMcpgqLUZ++BvPRezG+/QDKkbh1pvpIM/qFZzFuvT/u\nrLGn10/trLRjbltU4OS+172ETI3FOPH8SinU6vCmGyWB5An2dnhxWY0TMr3RqClK4/G3WyM6trsz\nSNuRIOdclPjuUiCBpBBCzBiDgRBbDw/wlqeft5sHKEq38hF3Jt/4aCnzch2TMlUdKb1vJ/q532Lc\ncg/KNv4O09IsO68c6J2kkSWOseoszLp30L96FHXFlxNyTq015i9+iLrwb1AFs+I+n6fXT9lxGcks\np5W8NCsHu33Mzxs9AFarzsb83g3oT18z4fM30wzv1o7n760y34mnx8dgIESabewZg3C5nyEWLXZi\ntSXn71sCSSGEOIl1DAaOZh13tg2xqNDFKncGn1tamLSi4fHSfb2Yj96DccWXUYXFEx7vznJMu4zk\nMPWZL2J+70bMza9hrDwj7vPpTX+GwQHUuZfEfS5f0KRrKMisjBNfJ8OFyccMJPMLwT0X3t0Mp62J\neywnC601mxr7+OZHS+M6j91isCDPye720Tc9DTvsCRAMaMrnJa80lwSSQghxEtFac7Dbx1ueft70\n9NPS7+fU2RmcuyCbmz86e9zsRSrQpon5+H2olWeiln4kovuUZNpoHQgQNDXWUaZaU5lypoXXSz74\nj+h5lXFlEXVfD/o3P8W4/rsoS/zPc1Ovn5IM+6jT11UFLt49MshFC3PHvL9afQ7mpj9jkUDyqPe7\nfChgXm78XWXChckHxwwkQyHNzu1eTlnpQiXx70ICSSGEmOaCpqa+9YP1jk39aK35iDuTK5YXUlOU\nNq2CK/2np8DvQ132txHfx24xyHNZOdIfiKmcylRTcytRF1yG+dh9GDf/c8xBoH7qcdTqdag5idm8\nM9wacTTVhS6e2tEx7v3VaavRv34M3deLykzO+rzpJhHT2sNqi1z8tm7s56Bhr4/MbIPCWcmdeZDy\nP0IIMc1d+du9/GxrG1kOC986s5Qff3IB16yYxSnF6dMriNy5Hf3yf2Nce3PUwdR06XAzFnX+peBw\nov/rVzHdX9dtRe+tR33iMwkbU+MHNSRHMzvLzlAgRMfg2E0elDMNtWQFevNfEjam6UxrzeuHemPq\nZjOaqkIX+zq9BELmCT/zeU327fJRszT5lQwkkBRCiGnuB6fauO/CuaxfUsDc3Mmp75hoursj3PHl\n725E5eRHff/p0nN7LMowML5wA/rV/0Hv2RHVfbXPh/mLH2J89rqE7v729Popyx59CtZQiqoP1kmO\nZ3j3toCD3T5CpqZijHWl0UqzWZidaWdfp/eEn+2p81JabiMjK/lLWSac2u7o6OChhx6ip6cHpRTn\nnnsuF110Ef39/fzgBz+gra2NoqIibrzxRtLSwiUCnnnmGV566SUsFgtXXnklS5cuTfoDEUKIVLZt\n2zZ++tOforVm3bp1XHrppcf8fHBwkH/913+lvb0d0zS55JJLOPvssyM6d+6/3Ya+5iZU9fR8r9Wh\nEOaj96LO+hiqZllM53BnO9g9QVCT6lROHsYVX8Z8/H6M2zZEXEhc/+E/UPMXoZacltDxeHp8uGvH\nDuqrCtLY2TbE2vJxpq2rl8JPH0Qf9qBKTiwoP5NsbOxjdVliprWHhddJDlFd+GGJpr6eEM2NAdZd\nmJjM50QmzEhaLBauuOIK7r//fu666y6ef/55mpqaePbZZ1myZAkbNmygtraWZ555BgCPx8OmTZt4\n4IEHuOWWW3jsscekur0QYkYzTZPHH3+cW2+9lfvuu4/XX3+dpqamY455/vnnKSsr45577uH222/n\nySefJBQKRXR+44vfwHz0XsyNf07G8JNOP/sLsNpRH18f8zlKM+14eqZvRnKYWrICtXw15s/+NaLP\nTn3offTGF1H/96qEjiNkalomWHNaXehi1wTdVZTFglp1JvoNyUqG10cmdq3ocGHykeq3D1FZ7cDu\nmJxJ5wmvkpOTw9y5cwFwOp2UlpbS0dHBli1bOOusswA4++yz2bx5MwBbtmxhzZo1WCwWioqKKCkp\nYd++fcl7BEIIkeL27dtHSUkJhYWFWK1W1q5de/Q9c5hSiqGh8Iey1+slMzMTS4TrBNWixRg3/xP6\nP3+J+YdfTasv73r7ZvSbr2Bc/dW4+k6Hi5JP3zWSI6lPXQntR9B/eX7c47QZwnzyIdRffx6VNfbu\n6Vi09AfIdVlxWMd+TirynRzq9uELnrhGbyS1+oOWieb4x53MDvX4GPSbLCxI3NIDgNrCNHa2D2F+\n8DffejjAQJ/J3Ir4d4VHKqq/2tbWVg4ePMjChQvp6ekhJycHCAebPT09AHR2dlJQUHD0Pnl5eXR2\njtNKSQghTnKdnZ3k5384RTja++LHPvYxPB4PX/ziF7n55pu58soro7qGKinDuOUe9Pa30D97EB0M\nJmLoSaXbj2D+7EGMa29CZWbHda5shwUN9HpT/3FPRNlsGNfcjH72F+imQ2Mep//8R3C6UGvPS/gY\nwq0Rx98B77AalOc42Ndx4hq9kZR7HqRlQpRrP08mmw71sbo8EyPB65dzXFayHRYOdfswTU39tiFq\nlrkwJrEzVcTlf7xeL/fffz9XXnklTueJEXW0c/51dXXU1dUd/ff69evJzJyc+fxY2O12GV8cohmf\nz5K8qlTJ3IRgsVhJS9JzkMrPbyqPbdhTTz119P9ra2upra2dwtGMbtu2bcybN4/bb7+dlpYW7rzz\nTu69994T3m/Hfe/MzETf8SADD34PfvhPpN/4XVTa2MWK4xXPc68Dfvofuw/nJz+Dc/mqhFy7PNdF\nZ9BKaZJfj5Pyms+sxveZa/E9cT8Zdz6MstuPubbZfoS+Pz1F5h0PYclKfGmdVl8/8wszjnmcoz3u\nU2Zn836fyekV4/8+vGd/DPPt10hbuTam8Uzl+0wirv1m00H+Ye2cqM8TybWXlmazr9fEOWDgSrdR\nsSg3YZ91kbx3RvSJHQqFuO+++zjzzDNZuXIlEM5Cdnd3H/1vdnb422ReXh7t7e1H79vR0UFeXt4J\n5xxtQH19fZEMZ0pkZmbK+OIQzfgsoeRlFJI55RcKBZP2HKTy85vKY4Pw+Navj33tXSIc/77Y2dl5\nwvviyy+/fHQDTnFxMUVFRTQ1NbFgwbE1ASN579TXfh39qx/Tc9s/YHz5NlReAckQz3Nv/vJH6Mwc\n9JkfIxDDOUa7dnG6lT0t3czNiGlIcV07GfSKj6Lf3kTvTx7E+MwXj167t7cX80f3os65hMHMHEjC\nWPa39lJblHbM4xztcS/ItvDSgS4uqRj/l66XrsL83ZME/89VKEf0065T+T4T77Wbe/10DPiZk66j\nPk8k167MsbK1oQuzO8CqM9Pp7++PeazHXzuS986IprYffvhh3G43F1100dHbTjvtNF5++WUg/Aa4\nYsUKAFasWMHGjRsJBoO0trbS0tJCRUVFDA9BCCFODhUVFbS0tNDW1kYwGOT1118/+p45rKCggPfe\new+A7u5uDh8+zKxZsXU5URYL6jN/j1p1FubdX0d7DsT9GBLJ3PwaesfbGF/4SkJnCaZ7CaDjKaVQ\nn/8S+t3N6O1vffiDt1+H9iOoj/110q49XjHykao+2HAz0Zd0lZMH8xeht72RqCFOG8O7tUfrEJQI\nNUVp0KooKraSnTv5fWYmvOKuXbt49dVXKS8v5+tf/zpKKS6//HIuvfRSHnjgAV566SUKCwu58cYb\nAXC73axevZobb7wRq9XK1VdfPS1rmgkhRKIYhsFVV13FnXfeidaac845B7fbzQsvvIBSivPOO49P\nfepT/PCHP+Smm24C4LOf/SwZGbGn1pRSqI99CjOvEPP+28KbWWqWJ+ohxUy3NKF/+QjGDXeg0hKb\nOizNsrNzf3dCzznVVFoGxtVfxXz4+xjffgBTgfmrxzD+/hsoa3I6lmit8fT4KcuaOHOYn2bDZTNo\n6vXjHqPm5DB1enjTDavOStRQp4WNh/q4cnlh0s6fhYW5ppPc+VPT/nTCQLKqqopf//rXo/7sO9/5\nzqi3X3bZZVx22WXxjUwIIU4iy5YtY8OGDcfcdv755x/9/9zcXG699daEX9f4yJnonDzMR+5GfeoK\njCRszIiU9vkwH/k+6pOfTVgbv5HcWXY8J1FGcpiqqEGdfRHm4/fjLS1HLV+FqqhO2vU6h4I4rIoM\nR2SBSVVhGrvahyYOJJedjv7lI+juznCGcgY40u+nbSBAbVHaxAfHaNd7Xnozg+zr9TKnMLG7wiMh\nnW2EEOIkpxYuxrj5n9H/9WvM//zllJUH0v/xCKp0LuqsjyXl/MWZdjoGg6O2jJvu1Mf/BswQga1v\noC77fFKv1dgzcXZxpKoCFzsnqCcJoBwO1PLT0W+9Es/wppVNjX2scmckbVq7sy1IZ0eQonk26iN4\nDpJBAkkhhJgBVIkb45Z/Qb+7Bf2TDejg2D2Sk8F8/X/R7+9B/e3/i3u5k9aa4Ci1C62GojDdxuH+\nyX1sk0FGHeP0AAAgAElEQVQZFozrvkXGt+9L6k58AE+vj7IJSv+MFElh8mFq9TkzqmViuAh5cnab\na62p2zZE9RIXi4tPLEw+WSSQFEKIGUJl5YYLlw/2Yz74j+jBgUm5rvYcQP/mp+F1fU5X3Ofbv8vH\ni39sGzWzWpplp+kk6HAzGpWZhaV0TtKv4+mJbKPNsDk5DjoGg/T6IujEVFkLgwPoxtTaAJYM7YMB\nmnv9nFKcnMC/6WD4C1PpHBvlOQ56fCG6hia/jqoEkkIIMYMohxPj/92CmlWK+S/fRHe2T3ynOOih\nQcxH/gW1/irU7PL4z6c1h973MzQYwtNwYuYxvE7y5OhwM1Uae/24I9hoM8xiKBYWONkdyfS2YaBO\nP3tGtEzcdKiPle4MrEmY1g4GNTvfCxcfV0phKEV1gYv6tsnPSkogKYQQM4wyLKjPfBG1+hzM7389\nadkhrTX6yYdQC2sxVq9LyDm72kMoBWecm8+u94YIBo7NSpaeZCWApoKnxxdVRhLCZYB2RhjEqNPX\nod98BR1hL/npauOhPtaUJb5YPMD7u33k5lvJL/xwz3RNURr1rZO/TlICSSGEmIGUUhgXXIb6m7/D\nfOA2dN3WhF9Dv/RH9JEm1OXXJuycjQf8lM2zU1DkoGCWlb07j23PV3qS7tyeLP2+EL6gJt8VXT3C\n6g92bkdClbghtwB2bo9liNNC51CQgz0+lpUkfre2d8jk/T0+qk85dod2TdHUrJOUQFIIIWYwY+UZ\nGNfdgvnEA5ivvZCw8+oDe9H/9evwukhbdNmtsQSDmsOeAO654fNVn+Li4H4/A/0fZrZKsxw09fqn\nbGf6dNfYG85GRrshamG+k/2dXgKhyH7vavW6k3rTzRuNfayYnYHNkvgwa/d7Xsrn20nPOLY8U0We\ni+Y+PwP+yc30SiAphBAznKqsCW/C+dPTmL//97iDMD3Qh/mjuzE+dx2qaHaCRgmHPQFyCyw4XeGP\nLqfLYMEiB/XbPsxKZjks2AxFt/fknjZNFk+PH3cUO7aHpdstzMqwc6DLO/HBgFp5Jvq9LWjv1Ow0\nTraNh/pYnYTd2j1dQY4cDlBZfWK9SJtFUZHnjHgHfaJIICmEEAJV7Mb45r+g67ain/hBzOWBtGli\nPvED1PLTUaeuSegYh6e1R5q/yEFPd4i2Ix+Ot1Q23MTME0GHmrFUF7oin97OzIJFi9Fvb4rpWqms\nxxtkf6eXU0sSu1tba039Ni8La53Y7KNnjGuK0ia9nqQEkkIIIQBQWTkYX7sL7R3E3HAHerA/6nPo\n55+B/l7Up65I6NgG+0P0doeYNfvYtoAWi6J2mZO6rUOYZjiTWpplx3OSlgBKtsae6GpIjhRpYfJh\nxunr0Jv+HNO1Utmbnn6Wl6TjsCY2xDrSHMTnNSmfP/bzU1uUNunrJCWQFEIIcZRyODCu+yZqdjnm\n3d9Ed7RFfF+9Zwf6f3+P8cWvJ7wPdGODn9JyGxbLiZmY4lIbdofBof3h4NGdbaepTwLJWMSdkWwb\ninxpxCkrwdOA7miN6Xqp6vVDfaxN8LS2GdLUbwuX+zHGKSe0qMDF+11e/JPY3UkCSSGEEMdQhgX1\n6WtQZ5wfLg90aP+E99G9XZiP3ofxhetReYUJHY/WetRp7aPjVYrFy13srvPi95m4sxwnbVHyZPIF\nTbqGghRnxPYlYFaGDVNrWgciWxahbDbUirXoN16O6XqpqM8XYk/7EKfOzkjoeRv2+0nLMCgqGf+5\ncdkM3FkO9nZEtlY1ESSQFEIIcQKlFMb5n8T49NWYD9yO3vH2mMdqM4T56H2oNeeiFp+W8LF0tAax\n2RTZuZYxj8nKsVDitrGnzislgGLU1OunOMMWc19opVRU7RLhg5qSb7x80uyyf8vTxynFabhsiQuv\n/D6TvfVeapdF1hVqsssASSAphBBiTOq0tRhf+hbmTzZgvvo/ox6j//Ar0Br1ycuTMobhbOREJWkW\nLXHSdCiAK2jQ7Q3iG6UftxhbPNPaw8KFyaPY7LGgCkJBaNgX13VTRbgIeWKntffUeSlx28jMHvuL\n1EiTXZhcAkkhhBDjUhU1GDf/M/q/f4P5zC+OyR7pHe+gX3sB45qbUEZkH3TRCAQ0Lc0BSudMvAHE\n4TCorHGyc7uX4nQbh2WdZFQ8vb6YSv+MFE1hcghnMdXqc06KTTcD/hB1rUOsdCduWru3J0DToQCL\nFp9Y7mcsNYUudrcPETInJ8srgaQQQogJqeJSjFvuQe/chn7iAXQwgNneivmTH2BcfRMqOzcp120+\n5KegyIbDGdnH1dwKO95Bk0UOl0xvR8nT46cszozk/FwHh/v8DAYir+OpTj8bveW1mEtOpYrNTf0s\nnpVGmi1xX6i2vtnNgipHxK9/gGynlVyXlYPdk1MCSwJJIYQQEVGZ2eHyQD4v5g++y8CGf0Sdewlq\n0eKkXXO8TTajMQxF7XIX7n4nnm4JJKMRazHykWwWg/m5Tva0R77ZQxUWw6xS2PFOXNeeahsP9bEm\ngbu1Ww8H6O4KMK8y+uC+pshF3SStk5RAUgghRMSUwxFue1g+HyO/EPWxTyXtWv19IQYHTIpKouv7\nXFRiw56mGDwsayQjFTI1h/v9lMYZSEJ4nWS03VXU6nWY07hl4mAgxLstg3ykNDHT2oP9Iba9Ncjp\nH80bteTVRGoKJ68wuQSSQgghoqIMC8b6q0i/4XaUkbyPkcYDfkrn2MetmzeW8hob2b1WvEMSTEbi\nSH+AHKc1IUW0qwpd7IxinSSAWrEWdm5HD0RfBD8VvN00QHWhiwxH/NPawYDmrdcGqKx2Mmt25Gsj\nRxrOSE7GbngJJIUQQqQcbWo8DX7K5saWIZtX7GSfHmLXe5PbLm66auz1UZYdfzYSwh1u9kS52UOl\nZaBqlqG3vJaQMUy2jY2JmdbWWrPtrUFy86zMrYz9+ShKt2E1FM19yV93KoGkEEKIlNN2JIjDaZCV\nE1uGJ91uYa/VS0tzkO7OYIJHd/JJxPrIYdlOKzlOC4090W32mK67t31Bk22HB1iVgN3ae+t9eIdM\nFp/mmrDc1XiUUtQWTk67RAkkhRBCpJzGA37Ko9hkM5ribBsZ5QY7tkbRtm+G8vT64q4hOVJVYVp0\n9SQBapdD62F0a3PCxjEZ3mkeoCLfSZYzurW8x2tpCnBwv48Va9NjWhd5vJoiF/VtEkgKIYSYYfx+\nk9aWALPnxNevuzTLTndakFAQmhund2mZZGvs8VOWoIwkEHWHGwBltaI+cua0a5mYiCLkfT0htm8e\nZOXadJyuxIRmtZNUmFwCSSGEECml+WCAomIbdnt8H1HuLDvNfT4Wn+qifvsQwaBkJUejtaYpAV1t\nRqoqdEVVmHyYWr0OvemlaZNB9odM3m7uZ3UcgaTfZ/LWawPULHORkx9fVnMkd7adAX+IjsHkfomS\nQFIIIURKORRl7cixDPfczi+0kpdvZf+uyGsbziSdQ0FsFkVmAnYcD3Nn2en3h+gainJ9avkCsNlh\n386EjSWZth4eYG6ugxxXbAGgaWre3jRIcakt5o1lYzGUonoSspISSAohxDRnTlIrtMnQ2x3C5zUp\nnBV/ZmY4kASoXuriwF4/gwNSDuh4iZ7WhnAQs6gghultpcJZyTemR03JTXEWId+53YtSUH1KbGV+\nJlJTmPx1khJICiHENOdpOHk6uDQe8OOea0fFUDvyeIXpNvp8IYYCJmnpBvMq7ex8V8oBHS/RG22G\nVRW62BlDEKNWnYV+eyM6kNqv60BIs7kp9mntxgN+jjQHOHV1Wky1UiNRIxlJIYQQE9lT5yUUmv5Z\nSdPUNB1KzLQ2hLNipVl2mvvCAcmCKied7UE62qQc0EiJLP0zUnWs6yTzCqF8Pmx/K+FjSqR3WwZw\nZznIT4t+U1hXR5D67UOsPCM97rXA41mQ56SlP0C/P/Le59GSQFIIIaa5rBwLB/endvYmEq2Hg6Rl\nGGRkJm6t3uxMO54P6hlarYqapS52vDOEPomWA8TL0+unLAkZycp8Fw1dPnzB6JcTqNPXYab47u1Y\ni5B7h0y2vD7A0pVpZGYn7rU+GquhWJjvjHqJQTQkkBRCiGlu0WIX+3Z6CQamd3DUeCD2TjZjcWd/\nuE4SYHaZDYsVGk+i5QDx8vT4cCeoq81ITqtBWbaD/Z3Rb3JSp66GPXXo3u6EjysRgqbmTU/009qh\nkGbzawPMWeCguDS+8laRGm6XmCwSSAohxDSXnWshv8jKgb3RdRJJJT6vSXtrgNnlCQ4ksxw0jQgk\nlVIsXu5i13teAv7pHXgnQr8/hDeoyY9x1/FEqmKoJwmgnC7U0pXoza8mYVTx23FkkOIMG0UZkQeD\nWmve2zKEK82gsibxGeCx1BSlUZfEdZLJeeUIMQMpqxXL/uSUrAjMmg0Z2Uk5tzg5LFrs5PUX+5lT\nYU/qmqtk8Rz0Uzzbhs2W2E0HpVn2YwJJgJw8K0UlNvbWe6lZ5kro9aYbT48fd7Y9rnZ846kqcPGX\ng70x3VetXof5u5/DuZckeFTxi6UI+YG9fnq6gqw9NzNpv+/RLCpw0dDlxRc0cVgT/94ggaQQidLX\ni3/DHUk5teXW+ySQFOPKyLRQXGpj/y4f1adMr+BIa03jAT+Llyd+3LMzw5ttTK0xRnx4Vy1x8vJz\nfZQvsCd0TeZ04+n1JWWjzbDqIhc/3nIErXX0wVPVKdDTiW4+BItqkzPAGIRMzRuePu7+qzkR36ft\nSIB9O72ccW4G1gR/WZqI02owJ8fBno4hlsxKT/j5p9/XViGEEKNaWOvk4H4/3qHpVSuxpytEMAj5\nRYnPbbhsBlkOC20Dx3b3cLoMKqod1G+b2eWAGnv8uLOSN81akGbDblE090XfXUUZlnApoBSrKbmz\nbYg8l5WSzMgC8IH+EFvfGOTU09NIy5iaLy3JLAMkgaQQQpwkXGkGZXPt7K2fXh1cwptsbEmb7nOP\nMr0NMK/SQX+vSevhmduHO1kbbUYK992ObbOHWn0O+o1X0GbqfDnaeKg34t3awUB4c01ljZOCWZOz\nuWY0NUUu6pO04WbCr38PP/ww77zzDtnZ2dx7770APP3007z44otkZ4en2i6//HKWLVsGwDPPPMNL\nL72ExWLhyiuvZOnSpUkZuBBCTCfbtm3jpz/9KVpr1q1bx6WXXnrCMXV1dfzsZz8jFAqRlZXF7bff\nHvV1KqodvPTffSyoCpGWnvpTtqGQpulQgDPPz0jaNYY73Jw6+9jbLRZFzTIXdduGKJhlTVpR6FTm\n6fUnPZAMFyYf4twFOVHfV5XOgYxMgvXbYE5lEkYXHVNrNjb2c+d5ZRMeq7Vm65uD5OZbmVuR3N/x\nRKoL03jg9cOETI0lwa/zCQPJdevWceGFF/LQQw8dc/vFF1/MxRdffMxtHo+HTZs28cADD9DR0cH3\nvvc9HnzwwUldVCqEEKnGNE0ef/xxbrvtNnJzc7nllltYuXIlpaWlR48ZHBzk8ccf59vf/jZ5eXn0\n9sa2QcHhNJhbYWfPDh/LVqUl6iEkzZHmAFk5lqRO+ZVmOTjYPfqO9lmzrTTsM2jY52f+wsnbSZsK\nfEGTzqEgJRnJzkim8dze2Mv4qNPXEdj455QIJHe3DZFlt0S0HGBvvQ+f1+TU1RlTHgdlOSwUplt5\nv8tLZX5i1yJPOLVdVVVFevqJizO1PrFswpYtW1izZg0Wi4WioiJKSkrYt29fYkYqhBDT1L59+ygp\nKaGwsBCr1cratWvZvHnzMce89tprrFq1iry8PACysrJivt6CRU6OHA7Q15u8bhaJ0nggcZ1sxuLO\nttPUN3rdSKUUtctc7K334vOlzvTpZGju8zMrw5bwDNXx5uY4aBsI0ueL7fWoapeHM5IpYGNjH6vL\nJ86eH/b4Ofi+jxVr07FYUiOZlqx1kjGvkXzuuee4+eabeeSRRxgcDM+7d3Z2UlBQcPSYvLw8Ojs7\n4x+lEEJMY52dneTn5x/992jvjc3NzfT393PHHXdwyy238Je//CXm69nsigVVDna/l9prJb1DJl3t\nIUrcyV07Vpplp6ln7BqbmdkWSsttKf/7SrRkb7QZZvmgu8ruGNolAlBShu7vQ3d3JHZgUdJah8v+\nlI//Ja+3O8S7W4ZYuTYdpyt1tqLUFLqoj3Gt6nhieoQXXHABDz30EPfccw85OTk8+eSTiR6XEELM\nKKZpcuDAAW655Ra+9a1v8dvf/paWlpaYzze3wkFXR5DuztTtK+1p8FPitmG1Jjdjk++yMhTUDIzT\nb3hhrZPDngC93amfxU0UT6+PsiSvjxw2vE4yFsowsCxajN5bn+BRRWdvhxeH1aB8nN+Z32ey+bUB\nape5yMlLrQqLwxnJ0WaU4xHToxw55XLuuedy9913A+Fv2e3t7Ud/1tHRcXSa5nh1dXXU1dUd/ff6\n9evJzIy+Z+VksdvtMr44RDM+nyV5f3zJXKeSzHMbhkrZ5zfVX3sATz311NH/r62tpbZ2cmvSHf/e\n2NnZecJ7Y15eHpmZmdjtdux2O9XV1TQ0NFBcXHzMcdG8dy5ZbrBv5xDrPpabwEfzoXiee601TQf7\nWfXRPDIzo8+KRXvt8lwnXUErxfljTEtmwimnGex6d4hzLioc9+95Kl/zibx2y8ARzpiXG/H54rn2\nqeUmv9p2OOb7Bxefir9hL2nnXBTT/eMx/Li37Ojm7Ir8MZedmKbmrVfbmDM/neol0W8sGu/aiZCZ\nCS5bI10hG3NyI1snGcl7Z0Sf2FrrYyLY7u5ucnLCv6Q333yTsrLw7qUVK1bw4IMPcvHFF9PZ2UlL\nSwsVFRWjnnO0AfX19UUynCmRmZkp44tDNOOzhJKXQUn0N7HJOrdp6pR9fqfDa2/9+vVTOoaKigpa\nWlpoa2sjNzeX119/neuvv/6YY1auXMkTTzyBaZoEAgH27t17woZGiO69s2i2pm67nwP7uyhIQo3G\neJ77zvYgIdPEkeajb4z1i4m8dnG6lT0t3bjTxv47nVWq2V0XYO+uTkrcY2edpvI1n8hrN3QOcumi\n7IjPF8+1y9I1u1oH6OrpxRrDmkxXZQ3+//0DoSn4vWdmZtLb28sr+zv4xkdLx/wd7Ng6hGmGWFBl\nJOw5SvRrrbrAyeYD7eRZJw50I33vnPCdZcOGDdTX19PX18d1113H+vXrqauro6GhAaUUhYWFXHvt\ntQC43W5Wr17NjTfeiNVq5eqrr57ynUpCCDHVDMPgqquu4s4770RrzTnnnIPb7eaFF15AKcV5551H\naWkpS5cu5aabbsIwDM477zzcbnd817UoFi52suu9IdaeM/U7R0cK145MXmu+441VS3Ikw1DULnfx\n7uYhikpsKbNJIhlCpuZwn5/SJHa1GSnDbqEo3cqBGHcNW+ZWQkcreqAPlT752eADXeE1tvNyR8+e\nNx7w0doc4IzzM1ApXEYqPL09yAWVicmYQgSB5PHfmiFcEmgsl112GZdddll8oxIpz9LVDp1tER/v\ns1gjzjSq4MwtDixOXsuWLWPDhg3H3Hb++ecf8+9PfOITfOITn0jodd3lNvbv9NJ6OMis2VNXEHmk\nYFBz2BPgrAsmLyBwZ9l5NYKez4WzbGTl+Hl/t4/KGuckjGxqtA4EyHFak9J7eSzVhWnsahuKKZBU\nVivMWwj7dsLSjyRhdON7/VAfa8pH75Hd1RGkfruXNesyUr7PfU2Ri9/UtU98YBRSayWomD462/B/\n/xtJObXj+uiLMAshRqcMxaIl4axkUYk1JbKSLZ4AOXkWXGmT96E7XJQ8EjXLnLz6Qj9l8+wptes2\nkRp7Jm+jzbCqQhdbmvq5pCq2+6uFtei9dahJDiTDu7V7+era2Sf8zDtksuX1AZauTCMzO/UbALiz\n7HiDmraBAIXpiflieXL+hQghhDiquNSGYSiaG1Mj29/YkPzakccrybRzpD9AyJx4LXN6hoU5C+zs\nfPfk7cPt6fHjnqRp7WHVhS52xVoCCFCVteg9dRMfmGAHOocIhDQVecdmqEOhcPvDORUOiktTI9s/\nEaVUwtslSiAphBAnOaUUVUuc7H7PixlBIJVMgwMmPV2hSf/gdVgNcl1WjvRHFkxXVjtpPxKkqyN1\nyyfFo7HXjzt7cjv5FGfYCIbC2bCYzFsIzYfQvsmt9/mX97tYfdy0ttaad7cM4ko3qKyeXh2RagrT\nqI+xFNNoJJAUQogZoGCWFWeagach+h3SieRp8FNaPjUbWSLZcDPMalNULXGx453E191LBZ4e36Rn\nJJVS8dWTtDvAPRfe353YgU3gL+93sab82PW8B/b66e0OsewjaSmxXCQakpEUQggRNaUU1Uuc7K7z\nEgpNTWCktT66W3sqzM6y4+kdu8PN8dxzw1nTpoOpsSQgUbTWeKYgIwnhdZK74uiuohZO7vR2Y4+P\nPl+QRQUfbhBqawmwb6eXlWekJ72YfjLMz3XSNhCkN8aWlceTQFIIIWaI3AIr2TkWDu6fmqxkR1sI\niwWy86ZmU4I7ig038EEf7uUudr47RDBw8mQlO4eC2AxFlmPyn4f410kuRu+dvEDyj7u7OK8yH+OD\nrONAf4itbw5y6up00tJTf3PNaCyGYlGBk50JapcogaQQQswgVUtc7NvpnZLAqPGAj7J5k1c78nju\nLAfNUQSSAHkFVvKLrOzbdfL04Q5nI6cmK7wgz4mnx89QwIzxBFXQsBc9CWXiPL0+Xj/Ux6eXlwAQ\nDGg2vzrAwhpnUgr8T6bhdomJIIGkEELMIFk5FgqKrLy/J/Ip3kQIBjQtTQHcUzStDdGVABqp+hQX\nDfv8DPafHH24wzu2p2aDiN1iMC/Xyd6OGNdJpqXDrNlwcH+CR3ain29r47LqPLKdVrTWbH1zkNwC\nK3Mqpu41nCg1RS7qErROUgJJIYSYYRYtdvL+Hh9+X4xZoRg0N/rJL7TicE7dx06O00LI1FGvDXOl\nGcxf6KB++8mRlZyKGpIjxbPhBianDFB96yD7O7x8fFG4T/2eOi8+n8mSU13TbnPNaBbmuzjU7cMb\njP89QAJJIYSYYdIzLZS4bezfNXlZyamoHXk8pRSlWXaaothwM2zBIgfdnUHaW6f/xpup2mgzrLrQ\nxa54A8kkrpPUWvPTra18dmkhDqtB44FBDh3ws3JtOsZJ0jbTYTWYm+tkdxzrVYdJICmEEDPQwlon\nB9/34x1KflZyoC9Ef6/JrJKpL9rszo68BNBIFquiZpmLuneGprwWZ7ymovTPSFUFLnZ3DGHGWlap\nshr270SbyVlqsLGxD39Ic9a8LPp6Q7z1ehcr16ZPaTY9GWoTVAbo5PqtCCGEiIgrzaBsnp299cmf\nrm1s8FM6x54S2ZzSTAeenth2rZe4bdjsiv27BxI8qsnT7w8xFNQUpE3dZpEcl5VMu4XGGJ8HlZUL\nWTnQdCjBI4NASPPzbW1cubwIQyl2bh+idlkWOXnTe3PNaGoTtOFGAkkhhJihKqodNB0KMJDETSTa\nDNeOLJ/iae1hpdmxbbiBD9rLLXNRt60Xc4pqccarqTfcGnGq1/klZHo7Ceskn9/XRUmGnWUl6XR3\nBOnpClFZlZHw66SCqkIXezq8BOPMsEsgKYQQM5TDYTCv0s6euuRlJdtbgzicBlk5qVFzL5ruNqPJ\nybOSnWvDc3BqOwTFqrHHN2Wlf0YKb7iJY1q1sha9d0fiBgQM+EM8taODK5YXArC7zktljRPLNCw6\nHokMu4XiDBv7O+P7+5dAUgghZrD5i5y0Hg7S15OcrORUdrIZTXGGnbaBAIE4Moq1SzPZt9M3LddK\nenr8lE1R6Z+RqgvT4itMvrAW9tYntH3l7+o7WTE7g7m5Trraw38TU71BLNkS0S5RAkkhhJjBbDZF\nRZWDXTsSn5UM+E2OHA5QOmfqN9kMs1kUhelWWvpjzygWlThxOBWHPdNvB7enNzUykmXZdnq9IbqH\ngjHdX+UXgdUKR5oTMp62gQDP7+3iM0sLgBHZyBRY15tMNYVp1MexxAAkkBRCiBlvboWD7o4g3R2x\nfaiPpelQgMJZNuyO1PqoKc1yxDW9DVBR42RvvTehGbHJ0NgzdV1tRjKUYlFBvO0SE1cG6JfvtnNB\nZS4FaTY624L095kplUlPlpoiFztbB2PfQY8EkkIIMeNZrIrKGic730tsVrLxwNTXjhxNrB1uRioq\ntqKU4khzYoPvZPKHTDqHghRnpMZzEm9hciprIQGB5IEuL+809/Op2jwgnI1cWONIiSoDyZafZiPd\nbom5kgFIICmEEAIon29ncMCk/Uhipmv7ekIMDZoUFqde2RR3jEXJR1JKUVnjmFZZyeZeP0XpNqxG\nagRIce/cXliL3lsf9zh+trWN9YsLSLNZaG8NMthvTmkrz8kWb7tECSSFEEJgGIpFtU52vZeYwKix\nwY97rh0jRYKWkeLduT2sxG0jGNC0t06PrGRjj39KWyMerzLfxYEuL/5QjEXxi93gHUJ3tsU8hm2H\nBzjS7+eCyhwA9uwYYmGtIyVft8lSUxhfPUkJJIUQQgBQWm4jGNS0Ho4vMDJNjScFWiKOZXhqO96A\nWSlFRbWTffWT12oyHp5eH+4U2LE9zGUzcGfbYy4/o5SCypqYs5LmB60Q/3ZZIVZD0X4kgHdIUzon\nNV+3yVJTlEZd22DMfw8SSAohhABAGYqqJS52vTsUV5DV1hIkLd0gMys1akceL8tpxVCKHm/8JY9K\n59gYGDDpak/9rGSqbLQZqaog/untWNdJvnygF7vFYHVZJlprdu/wsrDWOaOykQCzM20ETU3rQGzL\nWiSQFEIIcdSs2VYMi6L5UOxrJQ+l6CabkdwJ2HAD4SUBFVUO9u5MfqvJeHl6/ZRlp05GEqCqMC2u\nDTexdrjxBU3+fXsbXzi1EKUU7UeC+Hya0vLUKVU1WZRScU1vSyAphBDiKKUU1ac42b3DG1PBbZ8v\nvGFndllqB5KlCVonCVA2z053Z4je7uS1moxXyNQc7vNTmpVaz0t1YbgEUMwZcPc86O5A9/VGdbf/\n2t4uDVIAACAASURBVN1FZb6T6sK0o9nIRbVO1AzLRg6rLXJRH2OnIQkkhRBCHKNglg1XukHjgegD\nraaDAWaV2LDZU/sDObxOMjFrGy0WxYJFqZ2VbB0IkOO04LSm1sd+QZoVq1K09MeWAVcWC8xfBPsi\nXyfZ6w3y7M5O/nZZERBeihEIaGaXzbxs5LDaIslICiGESKCqJU721HkJRdlKMFVrRx4vUTu3h81Z\n4KD9SJD+vtTMSnp6/Cm10WaYUiruepLRFiZ/akcHZ8zJpDTLLtnID8zJcdA1FKTHG/1aXwkkhRBC\nnCA330p2noWGfZFn7Xq6ggT8JgWzUq925PHcCehuM5LVpphb4WD/ztTcwd2YIq0RRxN3Pcko1kke\n7vPzckMv/3dJuBVi6+EgoZCmZAZnIwEsRrjTUCztEiWQFEIIMaqqxS727/IRDESWlWw8EK4dqVTq\nZ3ZmZdjoHArGXsNwFPMq7RxuCjA0mLhzJoqnJ/U22gyrijOQZF4ltHjQ3onX+P18WxufqMolx2n9\nMBu52DktXrPJVlPkoj6GwuQSSAohhBhVVo6FgllW3t8zcZbNDGmaDgWmxbQ2hDMwRek2mhOYlbQ7\nDMrn29m/K/XWSoZrSKbmczMv18mRgQD9/tiWBSibHcrnw/7d4x63u32IXW1DfLIq3ArxSHMQrTXF\npTM7GzmsJsZ1khJICiGEGNOixU7e3+PD7xs/y3bkcICMLIP0jNSsHTkad7adpr7EBZIA8xc68BwM\n4POmTlZSax1eI5miGUmroajId7KnPZ7p7cXjTm9rrfnpO618ZmkBDqvxQTZyiEWLXZKN/EBlvhNP\nr4/BQHQBvQSSQgghxpSeYWF2mY19u8bPSjYe8FM+TbKRw0oz7TT1JDaQdLoMSsttEWVxJ0uXN4TV\nUGQ5UjfIryqIc8PNwlr0vrEDybc8/QwETNbNywagpSmAUopZs1N/Pe9ksVsM5uc62d0eXUZdAkkh\nhBDjqqxxcuh9P96h0bNs3iGTzrYQJe7pFUi6sx0JKUp+vAVVDg7u9xPwp0ZW0tOTuhtthsW74YYF\ni+DgfnTgxDJCQVPzs21tXLm8EIuhZG3kOMLT29Gtk5RAUgghxLhcaeG1f3vqRs9UeA76KXbbsNqm\n14dyaYK62xwvLd3CrNlWDuxN/Llj0ZiipX9GWlTgYm+Hl1AMRfABlDMNit3QsPeEn72wr5v8NCvL\nS9IBOOwJYLEoikokG3m8msLoN9xIICmEEGJCFVUOmhsDDPQfu35Kaz1takceb7i7TTx9xcdSUe3k\nwF4fwWDizx0tTwqX/hmW6bBQkG6loTv2JQHhMkA7jrltMBDi1++184XlRSil0KZkI8dTVehiX6eX\nQBTVDCSQFEIIMSG7w2D+Qge7dxyblezuDGGakFeQuuvvxpJht+C0KjqHoi/CPJHMLAv5hVYO7p/6\ntZLhYuSpHUhCeJ1kXPUkF9aij+tw8+zOTpYWpzM/zwlAsyeAzaYoLJZs5GjS7RZmZ9rZ1xn5OkkJ\nJIUQQkRk/kIHbS3BY3pKNx7wUzZNakeOxp2k6W2AyhoH7+/2Rd0dKNEae1O3huRI4Q43sfV7BqCi\nBvbvQpvh12fHYIA/7e7is0sLASQbGaFoywBNGEg+/PDDXHPNNdx0001Hb+vv7+fOO+/k+uuv5667\n7mJw8MMn/plnnuErX/kKN954I9u3b49y+EIIcXLatm0bN9xwA9dffz3PPvvsmMft27ePyy+/nDff\nfHMSRxcZq01RUf1hVjIYNGlunD61I0dTmuAONyNl51rJyrHgaZi6tZID/hBDgRAFaamfgasuTIsv\nI5mZBTn50NgAwK/ea+e8BTkUZYTrRDYdCuBwqGnReWkqRVuYfMJAct26ddx6663H3Pbss8+yZMkS\nNmzYQG3t/2/v3uObru/9gb8++ebWtEnbtCm0SWuBphQiFwUmCorlom7e8HHOmNOzx+YDPSqomz/P\nUDxD3bFedhhTJuqOHnbYHjpv50w2FbcxoF5QB2grWC62gG3TUnoJTdJLbt/v5/dH2tDSQtMm33yT\n8n7+0yT95vN5p/3mm3c+VwfefvttAIDT6cSnn36KZ555BmvXrsV///d/yzL2hBBCUokkSdi8eTP+\n/d//HRs2bMDu3bvR1NQ07HF/+MMfMGvWLAWijE5xiQ6drhBOdYTgrPchM1tAmiF1O7fkmnDTr2Sa\nHnWH/JDGOIkkVk5PAFaTLiVa4AqMGvhFjvaeoTOvoxXed/srNHT68Y/GLvzzhTkAAEni+LqGWiOj\n4bAYcGgUa3qO+O4vKytDenr6oMf27duHRYsWAQCuvPJK7N27N/L4ZZddBkEQkJeXh/z8fNTV1Y0m\nfkIIGXfq6uqQn58Pi8UCtVqNBQsWRK6bA/3lL3/B/PnzYTKZFIgyOoLAUOrQ4/ABH4593Z1ya0ee\nyWbSoskt3zjGHIsaegNDc8PYk6NYNLr9KEyB8ZEAwBiLfbvE0vC+27+rasU/OXKQoQ2P3W2qD0Kf\nxpCTR62RI8lKUyNzFGuOjulrpNvtRlZWVrjCrCy43W4AgMvlQm5ubuQ4s9kMl8s1lioIIWTccLlc\nyMnJidwf7trocrmwd+9eXHXVVYkOb9QKJ2nR2y2ho82f8tvL2TK1snVt97NP16PukE+RHromTyDp\nZ2wPFPPC5PbpONDSjUZ3AN8pDecp/a2RpbSLTdSm5xmiPjYu/RH0jyGEkNhs2bIFt956a+R+Mg8L\nUqkYHBelwTHLBEGd2tf/XIMGbr8IX0i+xcMtE9RQCQwtTYlvlWxM4q0RhxPrwuQ8Oxe/L1yGHxSr\noBHCKY7zmwAM6SrkUmtk1ByjSCTH9FfNyspCZ2dn5GdmZnjLIbPZjPb29shxHR0dMJvNw5ZRU1OD\nmprT2xmtWLECRqNxLOEkhFarpfgG8AvyvSHl/GKSqmWrVCxpz79kf28AwJtvvhm57XA44HA4Elr/\nmddGl8s15Np47NgxPPvss+Ccw+v1oqqqCmq1GnPnzh10XLJcO41Tw//7QECZiSTxPO9smXp0imrY\ns9NHPniMdc+co0bNl17Yy8wxXStGW3ezN4iy/GwYjWljrnOsdY/F7LR0OHc5odYbkKY53b0abd07\najugSkvDYl899MaZEEWOukNeXHplDozGsSXUSl7jlKr7hpkZAKK7dkaVDXDOB307njNnDiorK7F8\n+XJUVlZGLnRz587Fr3/9a1x33XVwuVxoaWlBSUnJsGUOF5DX640mHEUYjUaKbwBBjP+6a/3kbIlJ\n1bIliSft+ZcK740VK1YoGkNJSQlaWlrQ1taG7Oxs7N69Gz/+8Y8HHbNp06bI7RdeeAFz5swZkkQC\nyXXtVPJ/H8+68zPU+PpEJybqomuVHEvdmWaOgD+E43WnYJk49uEAo6k7IEpo6w7AqArC6439mp2o\n//cFWVpU1bdhxoTTiX00dQdFCS9/1oh7J/jg/+pzBOdfifqjfqSlM6SlB+D1ju1Lz3g5z0fLZDJF\nde0cMZHcuHEjDh48CK/Xi7vvvhsrVqzA8uXL8cwzz2DXrl2wWCy4//77AQA2mw2XXnop7r//fqjV\natx+++3U7U0IOe+pVCqsXLkSFRUV4Jxj8eLFsNls2L59OxhjWLp0qdIhntfCO9zIu3A4Ywz2aXrU\nHvLHlEiORrMngLx0DdSq1Poc7l+YfGAiGY1tX3figiwdZpSVQPr77yGKHLUHfbj40tGVQ0ZnxETy\nzG/N/datWzfs4zfddBNuuumm2KIihJBxZvbs2di4ceOgx5YtWzbssatWrUpESKSP1aTF3qYu2esp\nKNLgyFc+uNpDMOfKP17P6QmgMIUm2vSbZjFg+9HOUT2nyy/i/2o68MSyIsCkBYIBNHzVAWOmPiF/\n6/NZ6i7+RQghhMSBTcZFyQdSqRimlOlQezD67ediEd4aMXUm2vSbaknDkfZeSKMYLvRWTQfmFxpR\nmBleM1MsnYm6WgmlDr2MkRKAEklCCCHnOatJi2ZPYFSJy1gVTtLC0ynCfUq+ceb9Gj3+lFr6p585\nTY10rRD1QvEnuwLYcbQT3595evlBp60cpkArsnOoNVJulEgSQgg5r6VpVMjQCmjvlj+5EwSGyVN1\nqD0k75hMIHVbJAFgWm70ywC98mU7rptqRnZaOGkUQxx1gcmwH39bzhBJH0okCSGEnPesmVo4ZZ5w\n0++CyTp0tIbQ5RFlq0OUOJq9qbUY+UBllugWJq/r8OHAyR7cOO30clr1xwLIsmiR2XoQ3DO6sZZk\n9CiRJIQQct6zmeTf4aafWsMwya5DnYytkq3dQWTqBOjVqfkxH83C5JxzbKlqxfdn5CJNE36doRBH\n3aHwntqYMg2oPZiIcM9rqXmGEUIIIXFkTWAiCQDFdi1amoPo6ZZnRx1niu1oc6bCTB06fSG4fWcf\nbvB5czdO9YawdEpm5LH6Oj/MuWpkZqvB7A7w2pqzPp/EByWShBBCzns2ky7qyR3xoNWqcMFkLY4e\nlmcGd6pOtOknqBhKc9NwuH34VklR4vhdVSt+eJEFQt86maEQx9Ej/shMbWafTolkAlAiSQgh5LyX\n6BZJAJg8VYemhiB8vfFvlXS6AyhM0Yk2/c414WbHMTeMOgHzrBmRx76p9SPHooYpq29rxeIS4OQJ\n8J7uRIQ7rnB/9MMuaF48ISmAq1QQjh6Sp3CzBWJ27sjHETKO5RjU6AmK6AmKMAzY41lOOr0K1iIN\njn3tx/RZse+FPZDTE8CSAV2+qajMkoY3DrQPedwXkvDa/nY8vMga2T0vFAy3Rl5WfjqxZGpNOJk8\nehiYMSdhcY8H/IP3gUnRbYxAiSQhqcDrRuDZx2QpWvvQLwBKJMl5TsUYCozhVkl7TnyTunOZUqbH\nh3/zomSaDlptfDoJOedwevwoNKVu1zYAlObqceyUD0Fx8PqefzrkgiMvbdD/6XitH5YJahgzB38J\nCI+T/AqMEsmocUkEr9wG/Ci6RJK6tgkhhBD0jZN0J7Z725CuwkSrBt/Uxq/eTp8IgTGY9KndVmTQ\nCMg3anHs1OlxpJ29Ibxz5BT+ZZYl8lgwyHHsaz/sw+xiw0od4DRze3S++gIwZIx8XB9KJAkhhBCE\n15JM9DhJACgp0+F4rR+hYHx21ml0+2FL8dbIfmVnjJN8/UA7yieZMNF4+vUd/9qPvIlqGE3DDEmY\nPBVoOAYeSMwaoeOBtPNdsMXXRn08JZKEEEIIAKtRm9CZ2/0yTAJy89SoPxqfZMfpCaAwhZf+GSi8\nMHkPAMDp8WN3gxffvfD0UJxgQMLx2uFbIwGA6fSA9QLgeG1C4k11vKUJaDgGNu/yqJ9DiSQhhBAC\nwJapRVOCdrc5U8k0PY597Ycoxt4q6XSn9tI/A/UvTM45x++r2nDTdDNMutMtj8e+9mNCvgYZxrNP\nkOofJ0lGxiu3gS1cBqaJ/vyhRJIQQggBUGDUoqUrCFGKTxfzaGRmCzBlCWg8HnuLaKMnMG66tvPS\nNQBj2F7bgWMuH66bmh35XSAg4XhtAHbHuVtfaZxkdLivB/zTXWCLvj2q51EiSQghhADQqVXI0gto\n7Q4qUr99uh51h/2QYkxkne7x07XNGMM0Sxqe+bAe/zLbAq1wOm05dsSPfKsG6RkjLNdUMg04dgRc\nlG9v8/GAf1YJlM0Ay7GMeOxAlEgSQgghfawmnSITbgDAnKuGIV2FpoaxJ7LdgfBamDmG1J6xPZAj\nLw2FWXpcUWyKPBbwS/imbuTWSABg6UYgJw9oOCZnmCmNcw6+8z2oyqOfZNOPEklCCCGkj02BHW4G\nsk/Toe6QD5yPrVXS6QnAatJC1bdQ93jwbXs2nrmhbNBrOnrEj3ybBob06BaPp3GSIzi8H2AMmDpj\n1E8dP19ZCCGERGRkZER2/ZCLIAgwGo2y1gGEW0u6urpkrwcIb5U4cN3CRMudoIZazdDSFES+bfTj\nHJ1uP2wpvjXimQQVQ7pWgLdvHpTfJ6H+aABXXDWKc6/UAf6PD4CrbpInyBQn7XwPrPzaMV0zKJEk\nhJBxiDEGr9erdBhxkYhktZ/VpMWH33gSVt+ZGGOwT9fj6xofJlo1o/5gd3oC42bG9tkcPeKHtUgD\nQ3r0narMPh381d+ASxKYijpjB+IdrUBtDdjK+8f0fPprEkIIIX1smcqNkew3oUANSeRoawmN+rmN\n7gAKx1mL5EB+n4SGYwGUTBt+3cizYVk5gCEdONEoU2Spi1e+D3ZpOZh+bFuDUiJJCCGE9MnWCwhK\nHF6/cjN8GWMoma5H7aHRd7E7PeNnDcnh1B3yw3aBBmmG0acv4WWAamSIKnXxgB/84+1gV35nzGVQ\n1/Y4JpxqB1xtspTNQsosj0EIIXJijMHaN+GmzDK2Fpp4KCjU4MgBHzraQsixRPdRHRQldPSEkG8c\nn4mkr1dC4zcBXHnNGIc62B1ATRUQQ9I03vC9HwHFdrAJBWMugxLJ8czVhsDTD8pStO7Hj8pSLiGE\nKM1q0sLp8SuaSKpUDCXTdKg96EPOooyontPsDSIvXQO1Kv6TrHq6Jfh6/JAkETq9CoI68bPC6w75\nYCvWQp82ts5UZndA2voqOOeyT0RLBeElf96Favm/xFQOJZKEEELIAEovARSJo1iLr2t86HSFkGUe\n+eM6Xlsjcs7R3SWhozWEjrYQXG0hiCKQYfKhtzsIv49DJQA6vQp6PYMuTXX6tl4FfVr4py6NQatl\ncUnaerpDcNYHx94aCQCWiQDnQPvJ8O3z3bEjQG8P4Lg4pmIokSSEEEIGsJq0qDyu3MztfoLAMGWq\nDnWH/Ji7YOSP6/DWiKOfaMM5h9ctoaPtdOLIVECORY0cixql0/VIN6pgMpng9XrBOUcwyOHv5fD7\nJPj6fvp9HB53ONH09Ybvh0IcOt0ZCaaeQZ8W/jnwcUE4e8JZ86UXRZPG3hoJhIct9I+TZJRIgu98\nN7zkT4yz2CmRJIQQQgawmXRwJkGLJAAUTdGh9pAHXo8Io+nci2873X7MtY7cDS5JHJ5OcUDiKEKr\nZcixqDEhX4Pps/RIM6jO2pLIWLilUasFjJnnjkkUOfy+04lmOMGU4OkU4fNJkWTU7+MQBBZOLtNO\nt27q0hg0Gob6o35ceU10XfznZJ8OfF0DXLYk9rJSGO90gX/1OVS33hVzWZRIEkIISbjnn38ef/jD\nH9De3g6r1Yo1a9bgmmuuUTosAEC+UYO27iBCEpdlvOFoqNUMk0rDu91cdEn6OY91egJYPm1oi6Qk\ncnS6TieOpzpCSEtTwWxRw1qkxcw56pha+s5FEBgM6WzENR855wgGwknnwATT5+PwnBJx0SWZ0Olj\n24Mc6Bsn+fc/x1xOquMf/hVs7uVghtiTc0okCSGEJFxxcTG2bt0Ki8WCd955B/feey8++eQTWCwW\npUODRlDBnKZGS9fYuorjbVKJFjve86KnS8TZ1mYXJY6mvsXIQyGOzo5QX+IootMVQoZRgNmixgVT\ntLhovgE6XXKt/scYg1bHoNUN38ppNGbEZ4H9giKguwu80wWWZY69vBTEQ0HwD/8K1U8ei0t5lEgS\nQsh5SrzjhpjLEF4eW+vOtddeG7l9/fXX47nnnkNVVRWuuuqqmGOKB5tJiyZ3ciSSGq0KF0zRou6w\nHxPyh/4+GOQ41ujDfMGIfZXd8LhFmDIF5FjUmFKmgzknHRotzVIGEB4PWDINvPYg2LyFSoejCP7F\np8BEK5itOC7lUSJJCCHnqbEmgfHw1ltv4eWXX4bT6QQA9PT04NSpU4rFcyZbZnic5CVKB9JncqkO\nu973ordHRMAvwdUuRmZVd3lFCOkMGXoBZTP0yMoJ79dNhsfsDqC2BjhfE8ld70G19Ma4lUeJJCGE\nkIRqamrCgw8+iDfffBNz584FAFx11VXgPPYxcPFiNWlxuK1X6TAidHoVbBdosO2PLRBFCdk54RnV\nF16UhkyzgD8fcUHoAXInaJQONekx+3RIn+1SOgxF8IajQEcbMDt+X5EokSSEEJJQPT09YIzBbDZD\nkiS89dZbOHLkiNJhDWI1afH3o26lwxikbEYapjr0EDQ+qM6YBOT0BGDPGd3+0+etoilA20nw7i6w\n9DjMBE8hfOd7YIuuARPOPdt+NJJrtC0hhJBxz263484778T111+P2bNn48iRI5g3b57SYQ0SXpTc\nn1StpGoNgzlXOySJBIBGdwCFSTCeMxUwtRqYXArUHVI6lITiXR7wqk/Brrg6ruVSiyQhhJCEW7Nm\nDdasWaN0GGdl0glgADx+EZn65P6o5JzD6YnPrjbnC2bvW5h8VnJ9gZET/3g72KxvgRkz41outUgS\nQgghZ2CMoSCJFiY/l06fCBVjSZ/wJhNmnw5eW6N0GAnDJRG88n2wxdfFveyYzrrVq1fDYDCAMQZB\nEPDUU0+hq6sLzz77LNra2pCXl4f7778fBoMhXvESQkhKqq6uxpYtW8A5R3l5OZYvXz7o9x9//DH+\n9Kc/AQD0ej3uuOMOFBUVKREq6dO/57YjL7k/w5wePwpN1Bo5KpOmAs5vwP0+MN15MLZ0/17AlAVW\nbI970TElkowxPProo8jIOD1YdevWrZgxYwZuvPFGbN26FW+//TZuvfXWmAMlhJBUJUkSNm/ejEce\neQTZ2dlYu3Yt5s2bB6vVGjkmLy8PP//5z2EwGFBdXY3/+q//whNPPKFg1KQ/kUx2TneAurVHiel0\nQOEk4NgRYNospcORnbTzPVlaI4EYu7Y550MGIu/btw+LFi0CAFx55ZXYu3dvLFUQQkjKq6urQ35+\nPiwWC9RqNRYsWDDk2lhaWhrpvbHb7XC5XEqESgawmrRwuv1KhzGiRk9yLJyeavrHSY53/EQj0FQP\nNmeBLOXHlEgyxlBRUYG1a9dix44dAAC3242srCwAQFZWFtzu5Fo+gRBCEs3lciEnJydy32w2nzNR\n3LFjB2bPnp2I0Mg5WDO1KTFG0un2o5BaJEeNlTrAaw8qHYbs+K73wC6/CkwjzxqjMXVtP/7448jO\nzobH40FFRQUKCgqGHMPY8Kvr19TUoKbm9DeBFStWwHi2TUSTgFarTbn4/IJ8A6/P9n+lsuUqXL6i\nBUENQwzndrK/NwDgzTffjNx2OBxwOBwKRnNuX331FSorK/Ef//Efw/4+2munEMd14pQmCMKQ15iI\n885uSEdH7zfQGdKhFU63uyh5zg9Xd5M3iKkFZhiN8rZKJtvrjpU0ax48L61HRpoeTH32JCuVXzfv\n6YZnz0cwrt8M1RjKiebaGVOmkZ2dDQAwmUyYN28e6urqkJWVhc7OzsjPzMzhp5kPF1BcNmSXidFo\nTLn4BDEkW31yrq1GZQ9XuHxFi2IopnM7Fd4bK1asUDQGs9mM9vb2yH2XywWz2TzkuPr6erz00kt4\n+OGHB409Hyjaa2eyJ/ejIYrikNeYqPMuL12D2mYXirJOJ2lKnvNn1t0TFNHlDyGN++H1ytt6mkyv\nO24sE+H9qhpsSlni645CrHVLO94FymaiW6MHRllOtNfOMXdt+/1++Hw+AIDP58P+/ftRVFSEOXPm\noLKyEgBQWVkZ2f6KEELOVyUlJWhpaUFbWxtCoRB279495NrY3t6ODRs24J577sHEiRMVipScyWbS\nwulJ3nGSTncAVpMWKjl7RMax8TxOkktSuFtbpkk2/cbcIul2u7F+/XowxiCKIi6//HLMmjULU6ZM\nwTPPPINdu3bBYrHg/vvvj2e8hBCSclQqFVauXImKigpwzrF48WLYbDZs374djDEsXboU//u//4uu\nri5s3rwZnPPIkmrj1fz58/HLX/4SCxcuVDqUc7KaknucpJMm2sSElTog7d4BXPNPSocSf4e+BDQa\nwD5d1mrGnEjm5eVh/fr1Qx7PyMjAunXrYgqKEELGm9mzZ2Pjxo2DHlu2bFnk9l133YW77ror0WGR\nEdhMWuw/2aN0GGfV6KYdbWJinw78fhO4JIKpxs+4YgCQ+lojZR2/D9rZhhBCCDkrq0mX1GtJhlsk\nKZEcK2bKBoxZQFOD0qHEFW9rAY4eAvvWItnrokSSEEKIIqqrq1FeXg6Hw4EHHngAgUDyJWzhtSQD\n8k6mi4HT7Yctk7q2YxFeBmh8jZPkle+DXbY0vPC6zCiRJIQQooitW7fitddewyeffIKjR48O6fpP\nBkadAK2a4ZRPVDqUIYKihLbuEPIzqEUyJiXTga/HTyLJ/X7wT/4OduW3E1If7fBOCCHnqRtfPRxz\nGX+69ezLpozktttui8xQv++++7Bu3Tr89Kc/jTmmeLMawzvcmNOS6yOz2RtEXoYGGoFmbMeClTog\n/fF34JzLPp4wEfieD4Ap08AsiVn9IbneFYQQQhImliQwHvLz8yO3bTYbTp48qWA0Z2fLDO+5PXNi\nutKhDOJ0+2l8ZDzk5AEqAWg9AUwYurFKKuGcg+98F6p/vi1hdVLXNiGEEEU0NzdHbjudTkyYMEHB\naM7OZtIl5RJATk8AhTQ+MmaMsfGznmTtQSAYBKbNSliV1CJJyHmOqdUQjh4a8/P9gvrcuyiZLRCz\nc8dcPhm/tmzZgiVLlkCv1+O5557DjTfeqHRIw7KatKg60a10GEM43QFcXJBcraQpy943TnLhspGP\nTWJ857tg5deCqRLXTkiJJCHnO68HgY0/l6147UO/ACiRJGdgjOGmm27CLbfcgtbWVlx99dW47777\nlA5rWFaTFk1JuLtNo8ePG6ZlKx3GuMBKHZD++kelw4gJP9UBfuhLqH54b0LrpUSSEEJIwn366acA\ngNWrVyscycjy0jXo9InwhyTo1MkxIkziHM20q0385BcCvl5wVzuYOTW/+PIP3ge75AqwNENC602O\ndwQhhBCSpAQVQ36GFs3e5Bkn2dYdhFEnIE1DH+PxwBgDSqan7DhJHgyCf/Q3sPJrE143nYGEEELI\nCAr6FiZPFo3uAC1EHmes1AHUHVQ6jDHhn+8GrBeA5RcmvG5KJAkhhJAR2EzapNoq0enxo5CWuQ2l\n8gAAEXdJREFU/okrZp8OnqILk/Od70K1OPGtkQAlkoQQQsiI+teSTBbhFklKJOOqcDJwqh28y6N0\nJKPCj9cCnk5g5jxF6qdEkhBCCBmB1aSFM4lmbjvdARTSRJu4YoIATJqact3bfNe7YFd+G0wlKFI/\nJZKEEELICKym8GQbiXOlQwHnHE6PH1ZqkYw7VuoAr02dRJJ73eBf7gFTcP1LSiQJIYSQERg0Agwa\nAR0951h8P0HcPhEMQKZOmRao8SzVxknyD/8KdtGlYBkmxWKgRJIQQgiJgjVJJtw0evywZerCS9aQ\n+JpUCpxoBPf1Kh3JiLgogn/wFzCFJtn0o0SSEEIIiYItScZJOt0B2GjGtiyYRgsUTQaOHVY6lJFV\n/wPIsYAVTVE0DEokCSGEkChYk2QtyUZPAIW0hqRsmN2REt3b0q73FFmA/EyUSBJCCCFRsGXq0JQE\nu9s0uf3UIikjZk/+CTfc+Q3Q0gR28aVKh0KJJCGEkMRrbm7GHXfcgZkzZ2LGjBlYt26d0iGNyGrU\noilJWiRpDUkZTSkD6uvAg0GlIzkrvmsb2BVXg6k1SodCiSQhhJDEkiQJP/zhD1FYWIg9e/bg888/\nxw033KB0WCPKTVejKyCiJyAqFkNPQER3QIQlXfkEYrxiaQZgog2or1U6lGHxni7wfR+BLbpG6VAA\nAGqlAzifCafaAVdbXMryC2oI4uBlKVgoeb9NEUKU984bnTGXcf33skb9nKqqKrS2tuJnP/sZVKpw\ne8a8ecrsyjEaKsb69tz2IV+vTAwNnb0oMGqhohnbsupfBoiVTFc6lCH47h1gF84By8xWOhQAlEgq\ny9WGwNMPyla87sePylY2IST1jSUJjIfm5mbYbLZIEplKrCYtGk75kJ+vTNdywykfbDTRRnbM7oD0\n0d+UDmMILkngu96DauX/UzqUiNR7FxNCCElpBQUFaGpqgiRJSocyajaTFgdbu+ALKRN7fWcvCmmi\njfzs04Gjh8Al5YYxDKvmCyAtHZg8VelIIqhFkhBCSEJddNFFyMvLw5NPPokHHngAKpUK+/fvT4nu\n7bnWDDy/pxXbDrXBqBVQYNKiwKhFgUnT91OLCelaaAR5up4bTvmwsNAgS9nkNGbMBLJygMZvgMzZ\nSocTIe18D2zxtUm1GD0lkoQQWTG1GsLRQ/IUbrZAzM6Vp2wiG5VKhS1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with plt.style.context('ggplot'):\n", + " hist_and_lines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Bayesian Methods for Hackers( style\n", + "\n", + "There is a very nice short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/); it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book.\n", + "This style is reproduced in the ``bmh`` stylesheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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aGNtRa5cD0DmBzhHg7mxLC8IjlZ0uN1+YDRrHlr3HkEYT0QX5dvdxn8x21Hpb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with plt.style.context('bmh'):\n", + " hist_and_lines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dark background\n", + "\n", + "For figures used within presentations, it is often useful to have a dark rather than light background.\n", + "The ``dark_background`` style provides this:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Dv0e1J4NAWUmYadOH1vlcxM1bWH3tLC7/sQtF+QVMhNhgdO7cHpqa6ggIiBdL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\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with plt.style.context('dark_background'):\n", + " hist_and_lines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grayscale\n", + "\n", + "Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.\n", + "For this, the ``grayscale`` style, shown here, can be very useful:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+++/x22+/4cSJE4yOlg2mdY5AxyjqsWPHcPHiRVy8eLHz8fPnzyMvLw87duxg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\niy/w/fffQyQSISAgAGvWrLHYLusvv/wS33//PQB0+zvGPHHUaDQ4ffo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with plt.style.context('grayscale'):\n", + " hist_and_lines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Seaborn style\n", + "\n", + "Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\n", + "As we will see, these styles are loaded automatically when Seaborn is imported into a notebook.\n", + "I've found these settings to be very nice, and tend to use them as defaults in my own data exploration." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VFa/PO18zcn+ROGATCO9S9szk/pM4b6mf8l6+B4GQXS6sr78KktQ9CxngH3RiOVsQBEEI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn\n", + "hist_and_lines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication.\n", + "Throughout this book, I will generally use one or more of these style conventions when creating plots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.11-Settings-and-Stylesheets.md b/notebooks_v2/04.11-Settings-and-Stylesheets.md new file mode 100644 index 00000000..2faa2bb0 --- /dev/null +++ b/notebooks_v2/04.11-Settings-and-Stylesheets.md @@ -0,0 +1,269 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) > + +Open in Colab + + + +# Customizing Matplotlib: Configurations and Stylesheets + + +Matplotlib's default plot settings are often the subject of complaint among its users. +While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences. + +Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer *stylesheets* feature, which contains some nice sets of default configurations. + + +## Plot Customization by Hand + +Through this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default. +It's possible to do these customizations for each individual plot. +For example, here is a fairly drab default histogram: + +```python +import matplotlib.pyplot as plt +plt.style.use('classic') +import numpy as np + +%matplotlib inline +``` + +```python +x = np.random.randn(1000) +plt.hist(x); +``` + +We can adjust this by hand to make it a much more visually pleasing plot: + +```python +# use a gray background +ax = plt.axes(axisbg='#E6E6E6') +ax.set_axisbelow(True) + +# draw solid white grid lines +plt.grid(color='w', linestyle='solid') + +# hide axis spines +for spine in ax.spines.values(): + spine.set_visible(False) + +# hide top and right ticks +ax.xaxis.tick_bottom() +ax.yaxis.tick_left() + +# lighten ticks and labels +ax.tick_params(colors='gray', direction='out') +for tick in ax.get_xticklabels(): + tick.set_color('gray') +for tick in ax.get_yticklabels(): + tick.set_color('gray') + +# control face and edge color of histogram +ax.hist(x, edgecolor='#E6E6E6', color='#EE6666'); +``` + +This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package. +But this took a whole lot of effort! +We definitely do not want to have to do all that tweaking each time we create a plot. +Fortunately, there is a way to adjust these defaults once in a way that will work for all plots. + + +## Changing the Defaults: ``rcParams`` + +Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create. +This configuration can be adjusted at any time using the ``plt.rc`` convenience routine. +Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before. + +We'll start by saving a copy of the current ``rcParams`` dictionary, so we can easily reset these changes in the current session: + +```python +IPython_default = plt.rcParams.copy() +``` + +Now we can use the ``plt.rc`` function to change some of these settings: + +```python +from matplotlib import cycler +colors = cycler('color', + ['#EE6666', '#3388BB', '#9988DD', + '#EECC55', '#88BB44', '#FFBBBB']) +plt.rc('axes', facecolor='#E6E6E6', edgecolor='none', + axisbelow=True, grid=True, prop_cycle=colors) +plt.rc('grid', color='w', linestyle='solid') +plt.rc('xtick', direction='out', color='gray') +plt.rc('ytick', direction='out', color='gray') +plt.rc('patch', edgecolor='#E6E6E6') +plt.rc('lines', linewidth=2) +``` + +With these settings defined, we can now create a plot and see our settings in action: + +```python +plt.hist(x); +``` + +Let's see what simple line plots look like with these rc parameters: + +```python +for i in range(4): + plt.plot(np.random.rand(10)) +``` + +I find this much more aesthetically pleasing than the default styling. +If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes! +These settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](http://Matplotlib.org/users/customizing.html). +That said, I prefer to customize Matplotlib using its stylesheets instead. + + +## Stylesheets + +The version 1.4 release of Matplotlib in August 2014 added a very convenient ``style`` module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension. + +Even if you don't create your own style, the stylesheets included by default are extremely useful. +The available styles are listed in ``plt.style.available``—here I'll list only the first five for brevity: + +```python +plt.style.available[:5] +``` + + +The basic way to switch to a stylesheet is to call + +``` python +plt.style.use('stylename') +``` + +But keep in mind that this will change the style for the rest of the session! +Alternatively, you can use the style context manager, which sets a style temporarily: + +``` python +with plt.style.context('stylename'): + make_a_plot() +``` + + + +Let's create a function that will make two basic types of plot: + +```python +def hist_and_lines(): + np.random.seed(0) + fig, ax = plt.subplots(1, 2, figsize=(11, 4)) + ax[0].hist(np.random.randn(1000)) + for i in range(3): + ax[1].plot(np.random.rand(10)) + ax[1].legend(['a', 'b', 'c'], loc='lower left') +``` + +We'll use this to explore how these plots look using the various built-in styles. + + +### Default style + +The default style is what we've been seeing so far throughout the book; we'll start with that. +First, let's reset our runtime configuration to the notebook default: + +```python +# reset rcParams +plt.rcParams.update(IPython_default); +``` + +Now let's see how it looks: + +```python +hist_and_lines() +``` + +### FiveThiryEight style + +The ``fivethirtyeight`` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com). +As you can see here, it is typified by bold colors, thick lines, and transparent axes: + +```python +with plt.style.context('fivethirtyeight'): + hist_and_lines() +``` + +### ggplot + +The ``ggplot`` package in the R language is a very popular visualization tool. +Matplotlib's ``ggplot`` style mimics the default styles from that package: + +```python +with plt.style.context('ggplot'): + hist_and_lines() +``` + +### *Bayesian Methods for Hackers( style + +There is a very nice short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/); it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book. +This style is reproduced in the ``bmh`` stylesheet: + +```python +with plt.style.context('bmh'): + hist_and_lines() +``` + +### Dark background + +For figures used within presentations, it is often useful to have a dark rather than light background. +The ``dark_background`` style provides this: + +```python +with plt.style.context('dark_background'): + hist_and_lines() +``` + +### Grayscale + +Sometimes you might find yourself preparing figures for a print publication that does not accept color figures. +For this, the ``grayscale`` style, shown here, can be very useful: + +```python +with plt.style.context('grayscale'): + hist_and_lines() +``` + +### Seaborn style + +Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)). +As we will see, these styles are loaded automatically when Seaborn is imported into a notebook. +I've found these settings to be very nice, and tend to use them as defaults in my own data exploration. + +```python +import seaborn +hist_and_lines() +``` + +With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication. +Throughout this book, I will generally use one or more of these style conventions when creating plots. + + + +< [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.12-Three-Dimensional-Plotting.ipynb b/notebooks_v2/04.12-Three-Dimensional-Plotting.ipynb new file mode 100644 index 00000000..cad5e0a7 --- /dev/null +++ b/notebooks_v2/04.12-Three-Dimensional-Plotting.ipynb @@ -0,0 +1,606 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Three-Dimensional Plotting in Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib was initially designed with only two-dimensional plotting in mind.\n", + "Around the time of the 1.0 release, some three-dimensional plotting utilities were built on top of Matplotlib's two-dimensional display, and the result is a convenient (if somewhat limited) set of tools for three-dimensional data visualization.\n", + "three-dimensional plots are enabled by importing the ``mplot3d`` toolkit, included with the main Matplotlib installation:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from mpl_toolkits import mplot3d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once this submodule is imported, a three-dimensional axes can be created by passing the keyword ``projection='3d'`` to any of the normal axes creation routines:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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w+tvf/oYXXngBO3bswJtvvomRI0di4sSJePzxxzU9Rzabxdy5c/Hee++hb9++qK+vx4wZ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\nEaeccgruvfdeNDU1Yfz48di5cydOOuk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = plt.axes(projection='3d')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this three-dimensional axes enabled, we can now plot a variety of three-dimensional plot types. \n", + "Three-dimensional plotting is one of the functionalities that benefits immensely from viewing figures interactively rather than statically in the notebook; recall that to use interactive figures, you can use ``%matplotlib notebook`` rather than ``%matplotlib inline`` when running this code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Three-dimensional Points and Lines\n", + "\n", + "The most basic three-dimensional plot is a line or collection of scatter plot created from sets of (x, y, z) triples.\n", + "In analogy with the more common two-dimensional plots discussed earlier, these can be created using the ``ax.plot3D`` and ``ax.scatter3D`` functions.\n", + "The call signature for these is nearly identical to that of their two-dimensional counterparts, so you can refer to [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) for more information on controlling the output.\n", + "Here we'll plot a trigonometric spiral, along with some points drawn randomly near the line:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VxowZ0+H3CgsL1ewXl8vFvn37WLZsGZIkcfHFFzNw4EDVHSdSHo0yB451gUOq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes(projection='3d')\n", + "\n", + "# Data for a three-dimensional line\n", + "zline = np.linspace(0, 15, 1000)\n", + "xline = np.sin(zline)\n", + "yline = np.cos(zline)\n", + "ax.plot3D(xline, yline, zline, 'gray')\n", + "\n", + "# Data for three-dimensional scattered points\n", + "zdata = 15 * np.random.random(100)\n", + "xdata = np.sin(zdata) + 0.1 * np.random.randn(100)\n", + "ydata = np.cos(zdata) + 0.1 * np.random.randn(100)\n", + "ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that by default, the scatter points have their transparency adjusted to give a sense of depth on the page.\n", + "While the three-dimensional effect is sometimes difficult to see within a static image, an interactive view can lead to some nice intuition about the layout of the points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Three-dimensional Contour Plots\n", + "\n", + "Analogous to the contour plots we explored in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb), ``mplot3d`` contains tools to create three-dimensional relief plots using the same inputs.\n", + "Like two-dimensional ``ax.contour`` plots, ``ax.contour3D`` requires all the input data to be in the form of two-dimensional regular grids, with the Z data evaluated at each point.\n", + "Here we'll show a three-dimensional contour diagram of a three-dimensional sinusoidal function:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def f(x, y):\n", + " return np.sin(np.sqrt(x ** 2 + y ** 2))\n", + "\n", + "x = np.linspace(-6, 6, 30)\n", + "y = np.linspace(-6, 6, 30)\n", + "\n", + "X, Y = np.meshgrid(x, y)\n", + "Z = f(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UCt544w1uvvlmxo4dyz333NOh5+A59+348ePCIXEB4PdHumcjWwndIQ10Rx6u\nJ2l2BjabTVRMTU1N+Pv7nzU3NT8/nwMHDogR6+Xl5URHR5OUlER8fLyQFVpXvOXl5VRXV4tKVxrb\nY7FYRIKY0+lk+fLlInEsNDQUHx8fxo0bR1xcHMePH2fv3r0cP36csLAw7rrrLl599VWWLFlCdXU1\neXl5HD16lOPHjwtXg2T/CgsLIzQ0lICAAFH1tr5cl953TwK22+04HA4hV5jNZhwOB2q1mpSUFNLT\n0+nduzdbt26lrKyMu+++m+zsbN59912io6MZM2aMqHa3bt3aQmIoKipizZo1XHnllZ167yR5QdIk\n2yNReIb8tBfdTbpHjx6lb9++FBYW8vbbb5OTk8OTTz5JZmYm7733Hp988glPPPEE1113HevWrWPR\nokX8/e9/Jy0tjWnTpnHttdcya9YsJk2axPTp0xk2bBh33303mzdv7pBf3XMaxt69e/nqq6947bXX\nuuUYu4jfD+m2JzhcQndUqS6Xq8tdMF0hXZfLJUhEskad6wu5Y8cOTpw4Icarh4SEoNPpsFqtFBYW\nikpX2qqrqwkLCxMNERERESJpLCAggNjYWFG9zZw5k6+//prY2Fhyc3MZN24cdrudnJwc1Go1sbGx\nBAUF0dDQwA8//CAW7uLi4oRH1263k5mZSd++fVGr1eJ9kixctbW1LSYDS4FDnps0USAoKAiNRkNC\nQgJDhgwhJiYGjUZDc3Mz3333HRs3bqS2tpaMjAwUCgXbtm3Dz8+PkJAQgoKCeOKJJ2hoaGDPnj3s\n2bOHgQMH0tzcTEFBgWhJttlsbNiwgYyMjA6/f56aZFtorRdLUoWnRNEeF0V3asdOp5PZs2djMBiY\nNWsWI0eO5IcffuD5559n2LBhzJo1i5qaGh5//HEGDRrE448/TnZ2Ng8++CDPP/88GRkZ3Hbbbcyc\nOZPMzExuueUW1qxZQ0pKSoerek/S/eabbzhy5AgLFizo8jF2Ay5+0u1MvGJ3VKldnVEGnetqc7lc\nQuuULkfbq027XC4qKirIz8+nsLCQkydPUlpaKhoG4uLiRM6CVPFK5FhTU0N1dbW4LSsrw2AwUFNT\nw8GDBzl58iQ2m43MzExOnTpFfX09kydPJjExkZKSEk6cOIHJZOLSSy8lNTWVN998E4fDwUMPPcTi\nxYvp168fDoeD2tpa0XVmt9vFFYlerycqKgqVSiWaH3x9fVvICp5NE0ajUdjZGhoakMlkQp5obm5G\nr9ejVCrse9NsAAAgAElEQVQ5duwYV155JQ0NDfz888/odDpuuOEGjhw5QlFREcOGDWPo0KFUV1fz\n/PPPEx0dzXXXXce2bduora1l/Pjx/OMf/+jwe38u0m0LrSWKM+nF0ufCx8enhbWqKygvL6e0tJTM\nzEx++OEH3nnnHeRyOXPnziU2NpYlS5bw/fffs2jRInr16sXzzz/PsWPHWLZsGVVVVdx77728+OKL\nxMXFceutt7Jy5Uq+//57tm3bxocfftjh5+M5DWP16tUYDAYefPDBLh1jN+HiJd2uZNl2R5UqkW5n\nx+VAxxospDjJxsZG/Pz8UCgUgnDac//c3FwWLFhAcHAwiYmJJCYmEh4eTt++fYmKisLHx4eGhgbh\nZ5Vuy8vLqampQa1WiypXp9OhVCqJiYlBrVYzatQosaBx+eWXk5WVxbp16zh06BDR0dEMHz6cPn36\n0NDQICpqSbZwOp2EhobSp08f/Pz8KCgowM/PD71ej0qlIigoqMWxW63WFlVf66saiXQk3Vcmk6FU\nKhk0aBB1dXV8//33GAwGpkyZQmJiIps3b2b9+vVi/E9KSgq9e/dm8ODB9O7dm5KSEt59910OHjzI\n0KFDueeee8jLy2PJkiWo1WqqqqrYtm0bqampHXrvO0O6Z8LZXBTSayM1mnRWL87Ozub5558nMjKS\nP//5z/Tp04evvvqKt956i4kTJzJjxgy2b9/OK6+8wqxZs7j55ptZvnw5n332Ge+//z5VVVX85S9/\n4d133+XIkSOsWLGCjz76iDfeeIMFCxZ0+Pl4jlR6//33UavV52V0Uydw8ZFuV8i2uLgYs9lM3759\nu1ylAtTX1xMaGtrpbqX2kK40C8qTbCXtqyMNGlLVGBoaSmNjI8XFxRw/fpyqqiqKi4spKirCx8dH\nVLqeWQt6vV5Ma6ivr6empobi4mJhVv/ss8/EoqTBYCAuLo6UlBRkMhnHjh2jqKgIX19fevXqxciR\nIxk0aBBarZaioiIeeughAgICsNls+Pj4oFariYmJEVWt1L4rVdlarVYMsZRen9YVYFNTE+Xl5bhc\nLiIiItDpdKLSq6io4OTJkxgMBpxOJxqNBqPRSFJSEn//+9+JjY3l2LFj7Nq1i++++468vDyio6OZ\nPn06Wq2WDRs2kJOTQ1NTExkZGezbt4/MzEzWrVvXofe+O0n3TJA+O4DoApQW7zwX7c4lUWzatInk\n5GSSk5PZuHEjy5cvp3///txzzz34+/vz8ssvU1hYyOOPP45Wq+XRRx+lb9++PPnkk6xatYoPP/yQ\n999/n4MHD7J06VLWrFnD7NmzycrKYtq0aZ06Ls+pw6+//joZGRlMnjy58y9W9+HiIV2JbM1mM76+\nvmJOVkfw2Wef8e6777J582bh0ezKCnFXSfdspHk2sm3P/Vvva8uWLZw6dYrc3Fyqq6sFsXmu7KvV\nalHBS1WulLtQUVEh0sWkhTKFQsHChQuB3xa9Bg8ejN1uZ/fu3ajVaoYMGcLw4cPR6/Xs2LGDzZs3\nU1RUhEKhICIiQnSpVVZWYrPZCAoKIiQkhPr6eiwWC06nU2QW+/n5iQU0z03KZPB0LUi3Utdac3Mz\n8BvRhIaG0q9fP8LCwjh06BBlZWUkJSVx1VVX8csvv1BQUIDVakUul5OZmcmdd95JSEgIW7ZsYefO\nnbhcLvz9/bn00kvZt28flZWVyGQyPvroI0aMGNHu997T8nQ+0Zrc2ytReHqLN2zYwMcff0zfvn2Z\nNWsWYWFhfPbZZ6xatYo//vGPTJkyhW+//ZbXXnuN+++/n2uuuYb58+fjdrtZtGgRa9as4aOPPuLT\nTz8V7oYrr7ySJUuWsHbt2g4fk0S60uf+mWee4YYbbiArK6s7X7rO4uIhXcnkLX0hOrMa29zczKWX\nXsrIkSMJDw9nwYIFXeqp72pKWFvWNbfbTVNTEzabDblcjkKhOKMftCPWt48//pjIyEhSUlKIi4tD\nJpNRUlJCXV0dZWVllJSUUFxcLOxdUtaC561OpxNNAWVlZbz99tu8//77hIWFYbVaiYyMxG63ExIS\nQk5ODnK5HK1Wi0wmIzw8nJiYGPR6PSUlJfzyyy9UVFQQGRlJRUUF8BtxS5N/IyMjiY2NJSwsTEz/\nlYZfGgyGFp1q0uKZRBIKhQKNRiO0Xx8fH9H+K5GOn5+faJJRqVSYTCbS0tLIyspiwIABIpxn//79\n+Pn50dTURG5uLldccQW1tbX4+/uTm5tLREQEtbW1qNXqDnVE9RTptvdxPMlYqogNBgMrV65k4sSJ\n6HQ61q9fz9q1axk9ejRTp07FYDCwaNEiXC4Xc+fOxWq18swzzzB48GBmz57N3/72N5qamnjllVd4\n+eWXaWxsZO7cuYwfP57ly5dTUlJCVlZWhwuf1slpDz/8MPfeey8DBw7s9OvUjbh4SFcaTNieFX8p\ne7UtfPrppzz99NMMHjyYyy67jDlz5nS62pWyBjqbySoNXlSpVB0iW8/7t5d06+vryc3NJS8vj7y8\nPEFA0gKalLkQHR2NUqnE5XJRW1srpkZImQs1NTXU1dUhk8nYtGkTNpuNlJQUKioqaGxsZOzYsVRX\nV2M2mwkPD+eHH35g2LBhpKamUlxczMmTJwkKCiI6OpqgoCDq6+s5ePAgFRUV2O12AAIDA0XWguRG\nkS6LoWWIvPSzZBnzrNIkuFwufH19RSOC0WikqamJwMBA0tPTmTx5suiaO3XqFHV1dfTq1YvIyEjy\n8/PZvXs3AwcOJCQkhPLycjIyMtiwYQORkZFERkayf/9+fH19+fTTT9v9xb/QSLctWCwWVq9ezZdf\nfsmgQYOYOnUqvr6+rFixgj179jBz5kyysrLYuHEjH374Iffccw9ZWVk89dRTyOVynnnmGZ555hns\ndjvPPfcct956Kw8//DCHDx/GZrPxxBNPdOqYWudJzJw5k5deeulCmZF28ZHuuVb8q6urGT58OD/8\n8AM6na7N/QwbNoy8vDz0ej1r1qyhb9++nXpOXQ2skU4iAQEB4svRHrL1vH97/Mbvv/8++/btIykp\niZSUFJKTk0lMTBQdUXV1dZSWlrbIW/DMWpCaFiTLmFarFZeKfn5+xMTEkJmZyZYtW6irqyMpKYm0\ntDTKyspEGHl9fT1yuVzIGhqNBrfbjcFgoK6ujoqKCiwWC1qtloaGhhbZuUqlUhCxp2VM2lwul5Ae\npM+JdDUkk8nEPDuJeMPDw6mpqcHX15fY2FgSExNJSUkhPj5ekO8XX3xBfX090dHRuFwu0tPT6dOn\nDz/88AOHDx8mLCyMESNGcPLkSfLz85HJZKSkpLT7clk6uZ5v0vVsIugINm7cSFVVFTfeeCMBAQGs\nWbOGDRs2MGXKFG666SZyc3N59dVX0ev1/OUvf6G+vp5nn32WK664gunTp/PCCy/gcDh49tlneeCB\nBxg+fDgDBw7k0Ucf5b333mPGjBl8++23nbpKbE26t956K5988smFEmJ+8ZFuexaf5s6dS3l5OR99\n9FGbvz9+/DhDhw6lV69ehISE8Oabb3LJJZd0+Dl1JbDG7f5tem5jYyNyuZygoKAOfzEk0j6X37ix\nsZGAgAAcDgdFRUXk5+cL21h1dTUajUZkLUijeqSsBckuVlVV1aLSXbZsGQaDgZSUFE6dOoVWq2X0\n6NGUlpby008/YbPZuPrqq5k0aRIWi4X9+/fzyy+/CFmgurqawMBAevXqRe/evamrq+PAgQNiJppe\nrycoKEgEn0vddp6vn7S1FT4jtQf7+/sTFBREUFCQeFyHw0F8fDzh4eEMGDCAY8eOkZOTQ0VFBUql\nEn9/f3Q6HSNGjCAzMxOFQsHatWvZunWryGoYM2YMK1euJD8/nylTprB+/Xp8fX354osv2pUf0Fky\n7Cg68zhut5vq6mq++OILtm3bxogRI7jttttobm7mn//8J3l5ecyePZv+/fvzySefsGHDBubMmUPf\nvn158cUXCQwMZN68ebzwwgs0Nzdz33338Ze//IWnnnqKzz77jEsvvZRp06Yhl8s7dZXZOk9i/Pjx\nbNu27UKZAnLxkK7b7RaX02daPJIuL202G0OGDOG5554744rmU089xWuvvYZarWbhwoXccMMNZx0J\n3RZMJpOY6dXR45BW610uV6fP0O1p8igqKmLHjh2iA01K20pKSiIsLIyEhARCQkKw2WwtGiOk7rO6\nujrUanWLStdgMPDII48QFBREZGQkN998M4sXL8bhcDBt2jRiY2PZsWMHe/fupa6ujtDQUJKTk4mP\nj0cmk2EymWhsbBRBOTabTXhrHQ6H8F86HA7kcrmwlcXGxqLT6YT9SdJyJTnB6XSK8e5Sfm5VVZV4\nnaVK2Gq1IpPJCAsLIzAwEH9/f3r16kX//v3p3bs3cXFxKBQKPvnkEzZt2iQWXa+99lquu+46SktL\nWbx4MTabjT//+c8cO3aM77//noCAAMaMGcPLL798zvfuQiVdSZ8dMmQI48aNw+l08sUXX7BlyxYm\nT57MjTfeyKFDh1iyZAkDBgxg1qxZlJeX8/TTT3PVVVcxY8YMFi5cKPTdRx99lAEDBnDJJZfw4osv\n8sILLzB79mzWrVuHn5/fad7i9sRltu6yGzdu3IWSpQsXI+meTcecNWsWY8aM4eabb+bHH39k/vz5\nfPvtt22+GUajkVGjRpGTk0N8fDxz5szh3nvv7dBz6khgjSfZSiE1EgF1tkmjPU0excXF5OTkkJyc\nTFxcHP7+/tTW1gr9srKykvLyciwWC5GRkSJvISIiAr1eL0jObreLxoinnnqK7OxsBg4cyL59+9Dp\ndPTq1YvCwkIKCwvR6XQMGTKEQYMG0dTUxI8//sjRo0epra3F7XaTnJxMZmYmw4YNIzMzE5fLRVlZ\nGbt372blypVYLBYcDgeBgYEt5qBJFa3na9oarVfgPcNypNbwxMRENBoN999/P3q9nuDgYKqrq9m9\ne7fI0q2pqSE8PJwRI0Zw4403Eh8fz+7du3n33XeFH/nmm2/mq6++YufOnUydOpVVq1bh5+cnKuKz\noadI17Nz61yQFiZramr4/PPP+emnnxg9ejQTJ07EarXy3nvvUVxczKxZs0hPT+eDDz5gz5493H//\n/SQnJ/Pcc88RFhbG3LlzWbRoETabjUceeYS77rqLefPmsWrVKgYPHkx9fT1/+MMfUKvVp4UCtafr\n7gIOMIeLiXTh/yxSZ9Ixf/nlF2677TZ+/PFHwsPDcTqdZ/ywmc1mGhsb6d27N3a7nalTp3LHHXd0\nyHbSnsAaaQSQzWYDEDKCVH11pUmjPaRrsVjIy8ujoKBAbL6+viQmJhIZGUlcXByJiYlotVp8fHyo\nr6+nrKyMyspKqqurxWYymQgLC0OpVPLOO+/g5+eHUqlk1KhRrF69GqVSyY033khCQgJffPEF+/bt\nw8/Pj+TkZIYMGUJ8fDyNjY2cPHmS48ePi442adEzJCQEtVot/k8KtC4tLRW+7KCgIBFU7lnpenZg\nNTU1CQ1Zstv5+PgI50R2djYWi0WQbU1NjWgvDgkJEY0R48aNIzg4mAMHDrBhwwYOHTpEU1MTw4cP\n57777hPy1dGjR5k+fTrZ2dlkZ2cD8OKLL5423bY1rFYr/v7+FxTpHjlyhH/84x+MGTOGcePGYTab\nWbduHbt27WLSpElMmDCBQ4cO8c4779CvXz9mzZrFyZMnefXVVxk9ejSTJ0/mjTfewGg0smDBAp56\n6il69erF5ZdfzoIFC3j55ZeZM2cOX3311Vnbn9tqgfaUjzxzlQMCArjhhhsulCxduNhIVwowaYto\nKisrCQoKYvny5YwcOZL+/fufdV9SLGJBQQGDBg0iMDCQWbNmsWjRonY/n7M5KVqTrbTg1jqkpStN\nGuci7draWp555hkSExOFH1eq8iQClC7BJV9uQECAsIh5dqBJMYkffvghS5cuJSkpifz8fLRaLX37\n9mXfvn3Y7XZUKpVwQTQ0NHD06FHKy8sJDAwkPDyc9PR0+vfvT3x8vBgVdPz4cXJycjh16hSnTp2i\noaFBVKWST1epVIoGDc94ROlL6fnFlEb9NDc3i042aWHOx8cHPz8/ISOkpKSQkZFBWlqaOEFlZ2eL\nYCCTyURKSgqTJk1i2LBh7Nu3j40bN1JSUoJCoWDatGns2rWLEydOMHDgQH766SciIyPZtGnTOT87\n7SXDrsCzXfZskLT9yspK1q1bx4EDBxg7dizjxo3DZDLxr3/9i9LSUmbMmEFaWhoffPAB+/fv58EH\nHyQ2NpYFCxYQFRXFgw8+yOLFizEYDMydO5c///nPzJkzhy+//JJLLrmETZs28dhjj53z+9kankQs\ntTYvXbqU119/nZCQECZMmEBGRgYjR47sUBbGpk2bePDBB3G5XNx111089thjLX6/Y8cOJk2aRHJy\nMgBTpkzhySefPNsuLz7SdTqdbRLN3LlzAdqlp8FvH3r4rfJcvXo106dPR6/Xs2DBAu6888527aMt\nJ0V7yNbzb7vSSnwu0vXMlpWm/BYWFooZaXq9npiYGEGSUVFRKJVK7Ha7WDirqKigqqpKTPzdtGkT\nVqsVjUaDTqcjLy+P8PBwkpKSCAoKYs+ePTgcDoKDg4mNjSUzM1M0QezatYujR49iMpkIDg4mICAA\njUZDWFgYWq1WOBB27dqFw+EgISEBg8Eg7GgSabZ+raT/k9qCJdeDTCbD39+fkJAQwsPDqaysxG63\n079/fyIiIrDb7SgUCmw2G7m5uRgMBiwWCxaLhfT0dK688koGDRpEUVER+/fv59ixY6JB54477iAg\nIEBkMMTFxVFbW0t1dTUAO3fuPOti74VGusuXL2f//v2MHj2aUaNGYTKZ+Pzzz9m/fz833HAD48eP\n58iRI3zwwQckJycza9Ysfv31V958803GjRvH+PHjWbx4MSaTiQULFrBw4UJiYmK45ppreOKJJ1i0\naBEPPvggb775JrGxsV0KVfds+MjPz2fOnDnccsstZGdnc9lllzFz5sx27cflctGnTx+2bt1KdHQ0\ngwcP5j//+U8LN9OOHTv4+9//zvr169v79C4+0pVaUVtXhw0NDXz00UfMnj27XQTWmjCHDBlCTk4O\nvXv3Zvv27e2KffR0UnSEbD1RV1fXadI9V6X89ddfc+LECYqKitDpdCQkJBAfH09CQgJRUVHYbDYM\nBgO1tbUtOtCkfYaFhREeHi4sY7m5uTz22GP4+/vT1NREbGwscrmcEydOEBMTg4+Pj2gWKSkpoays\nDI1GIzrNpMGWKpUKs9lMeXk5hYWFoj1bim+UKlOpcpUW0aQAHknPk+xWkt/Zx8eHwMBAgoKCsFgs\nFBcXC8eFlMZms9mIiIgQzg9pMTQ+Pp6hQ4eSkpKCr6+vaBcuKSnB5XJRUlJCr169GDRoEDabjZyc\nHMLDwzl27BhRUVE0NTUREBAgXs8//elPzJ49+4zv3YVCum63m+zsbNLT0ykpKeHrr7/m8OHDjB07\nljFjxmAwGPjoo48oLS1l+vTppKWlsWLFCn766SfmzJlDdHQ0zz33HLGxscyaNYt3332Xqqoq5s2b\nx5w5c7jzzjv5/vvvSU5OZseOHdx///1ceumlXTomT+9xYWEhzz//PCtXruzwfn788UeeeeYZvv76\nawBeeuklfHx8WlS7O3bs4NVXX2XDhg3t3e3FRbqSgN66Ovzhhx9Qq9Wkp6e3e1+trWe1tbX07dsX\nu93OTTfd1K5AZMn+JH2ZXS6XGP3dXhLtSivxuSrlnTt3otPpSEpKIjAwkLq6OoqLi1tsCoVCeGcj\nIyPRarUEBweflrVQW1vL8uXLycnJQafTYbFYcLlcJCQkYDabxQig1NRUlEolJpOJ1NRUEUZ+8OBB\ntFotOp1O6HJarZaQkBCUSiW+vr6YzWasVisWi4Uff/wRh8OBv7+/mPHmGeDiuUmvhecm/Z/nHC2Z\nTEZUVBS9e/cmODhYjP+RFnOKi4vF4xgMBkpKSoiLi+PSSy8lJCSEvLw8mpubiY+PJzs7G4PBQHx8\nvNh3bm4u4eHhlJWVER0dzeeff37G9669FWhX4RkMc6bfv/nmm9TX1zNq1ChGjBiBwWDg888/5+jR\no4wbN44xY8Zw9OhR/v3vfxMXF8ddd91Fbm4uy5YtY/To0UyaNIlXXnkFm83GvHnzePXVVwkPD+e6\n667j8ccf58UXX+Svf/0rEyZMIC0tjZEjR3bpmDwXIQ8fPsyKFStYtmxZh/ezZs0aNm/ezD//+U8A\ncTJZsmSJ+JsdO3Zw0003iazpV1555Vw8c8Yv/gVhaOsMPC8jpQ9SY2Njh0fvSPuQoNVqGT9+PF9/\n/TVffvkl//73v/njH/941n1I+pJkc+pMHkRXcLbHcjqdJCYmkp+fz969e4WBPy4ujvj4eK6++mpB\negClpaUUFxeTm5srOtHcbjc6nU5ouoWFhQQHB2M0Grniiiv4+eefKSgo4KqrrsJoNOLn54fNZuP4\n8eNotVoKCgqA396f/v37iwxgyevrmeMQHBwsRim53W769OlDdXU1drsdpVJJcHCweL7SF04iLM8s\nBqfTidFoFItpRqMRt9uNSqXCaDSSkJAg2n+lkfFWq5Xm5maKiorENOK0tDTkcjn5+fk0NDSIzGIp\nwEelUpGSkkJaWhq7d++moaGBpKQkEUxeWVlJYWEhCQkJ5/1z0FlIx/bYY49RWFjIt99+y7x58xg9\nejQzZsygtraWVatWsX37dqZNm8bLL7/M2rVrefzxx5k1axavvvoqixYtorKykgcffJAPP/yQF154\ngXnz5jF37lxSUlIYOnQou3fvJikpiYEDBzJ06NAuP2/P777BYOhSLva5kJmZSVFREUFBQXz99dfc\neOONnDhxolP7+p+sdKW20K5mHkDbLbT19fX0799fZNUuW7aMG2+88bT7SjKC5AtWqVSdJtuuHsuZ\n5IkVK1ZQVlZGcnIySUlJJCcno9FohB+3pKSEgoICysvLMZvN6HQ6cfkv5S0EBwfjdrtpaGhg7dq1\nvPfee+h0Ourr6wFISUmhpKQEg8GAn58fgwYNQi6XU1JSgsPhEGPZMzMz6dWrF8HBwYLcQ0NDRYeZ\nyWQSAy4NBoOQKUwmE76+vsJZYbVaha7vOTnCs01Y8nxKC3Dw2xRZl8slLueNRiNKpRKNRiMIXyJ9\no9EoFg+bmpqorq4mOzsbt9tNdHQ0TqeT1NRUBgwYwM6dO/n5559Rq9VkZWVRWFjI4cOHueyyy/jl\nl1+46aabePjhh9t833qi0m2dxtUaJ0+eZO3atZhMJq6++mqGDx+OwWBg3bp15OTkMH78eLKyssjJ\nyeHDDz8kJiaGadOmUV1dzVtvvcVll13GLbfcwptvvonVauWvf/0rS5cuJTg4mMmTJ/Pwww/zwgsv\n8Oijj3Ldddfhdrs7bMtsC57SzMaNGykoKGDevHkd3s+PP/7I008/LRY925IXWiMpKYl9+/adbfH7\n4pIXJNLtauYBnLmF9r777uOLL74A4JZbbmkxB8vhcAiylWxLUnZCZ9HVYzmTPCH5HWtra1t0oBmN\nRhHfqNVqRbCMn58fBoNBZCxIC2l1dXUolUr27t1LeXk5jY2NjBo1ip07dxIQEMCdd95JTU0NW7du\nFa2+V155JRERESIy0mw2YzKZRKOEXq8XhCflAktZuc3NzUILLy4uFhMlJNKQbGLSMULLq5+23AzS\nDLnU1FShA0tSkNR2LXUGAhw8eBCDwUBsbCxKpZLm5mZSU1NJT09HJpOxdetW9u/fT2hoKCNGjGDA\ngAGsXLmSqqoqRo4cyc8//4zT6UShUPDFF1+0SXg9SbqtrwLdbjeffvopaWlpZGRkUFBQwLfffsvx\n48cZOXIk1157rfDqlpWVcccdd5Cens5XX33F5s2bmTp1Kv379+ett97C4XCIKlciv7/97W9cc801\nlJSUEBAQQGFhIQMGDKB3795d1nOh5Wv3ySef4HK5OkXm0gl069atREVFMWTIEFauXElaWpr4m8rK\nSvR6PQA//fQTt956q7iCOwMuTtLtauaBtK+2urlMJhMDBgzAbrfj6+vL448/zl133UVTU5OQEaTg\nlO4Y+9NV0m2rUs7NzWXPnj0UFBQgk8la2MXCwsKw2WwtZp1J888UCoXoPJNupYrv+uuvF4HgJpOJ\na665hr1791JZWYlGoyE9PR23201xcbHIUejXrx/Dhw8nLi6Ouro6ioqKOHLkiBgzZDKZMBqNREZG\nEh8fj06nE6PYpRSwLVu24HK5SE5OxuVyiWrYs6PP19cXuVwuZsRJQyulL7w0F23IkCGi0cNoNFJZ\nWUlJSQkVFRWo1WpUKpUIG+rTpw/x8fFERUXR3NzM/v372blzJ01NTWg0GkaPHs3QoUPZuHEj27Zt\nIzw8nD/96U+sX7+egoICLrnkEo4cOcK7775Lnz59TnvfzqW1dgfORLoul4t9+/bx/fffYzQaycrK\nYtiwYZjNZr788kuys7O59tprycrK4sSJE3zyySdERkZyxx13YLFYWLZsGcnJyUybNo2PP/6Y3Nxc\n7r//fv7zn//gcrmYOnUqDz30EH/729948sknueOOO8jOzu62cTqer93bb79NbGwsd9xxR6f2tWnT\nJh544AFhGXv88cd555138PHxYdasWbz11lssW7ZMxKu+/vrr55JILi7SlbJSu5J54LmvMzUWzJw5\nk61bt4ppAo888gi33nrraeOyu2PsT1dPIG2RbmFhITU1NSQlJaHRaLBYLOTn55Obm0tZWZmYBCGF\n2SQmJqLX6wkMDBSX1BIpV1VVkZOTw86dO3E4HPTr14+CggLsdjt33HEHcrmczz//nIqKCjIyMrjp\npptQKBTs2bOHH3/8kaqqKmw2Gzqdjvj4eOLi4ggODqaxsRGz2YzNZhPaq5S74HA4hCPBarWetoDm\n6cWVmiI8w+1bG+wB4WqQ9GA/Pz/8/f1RqVSo1Wrx+8DAQBITEzl+/Dj79+8Xi40qlYqBAwcyfPhw\nVLBL9kcAACAASURBVCoVGzZsYPfu3SiVSiZNmkR6ejqLFy/GarVy/fXXs27dOgIDA7n++uuZM2fO\nae9bT5Bu6whE+K2B6ODBgwwbNoy0tDSKiorYtm0bJ06c4PLLL2f06NEYDAbWr19PYWEhN910E/37\n9+ebb75hy5YtTJo0iSuuuILly5dTXFzMww8/zLZt29ixYwdPPPEES5YsISMjA5lMRn5+vpCH9u7d\nyzvvvNPlY2otmSxatIjhw4czbty4Lu+7m3Bxkm5XR5fD2T2udXV1XH755SKtatq0aTz++OOnEWN3\nkG5XTyBtVcpGo5Hc3FwKCwspKCjAbDYTHR1NQkICSUlJxMTE4O/vL2ad1dfXCz+u1WoVTRHS9s47\n77B371769u3L8ePHmTFjBlu3buXXX38Vgx8rKio4duwYlZWVBAcH06dPHzIzMwkJCaGmpoYTJ06Q\nm5tLXV0dVqsVpVJJXFwcvXv3Jj09nV69eqHVakVHWUNDA0ajkcOHD7N37158fX1FyLrJZMJisdDc\n3NyCgKVqV5IgPEfSjxw5kj59+qBSqVCpVKJB5NixY2RnZwtrnclkQqlUEhYWRp8+fRg0aBD9+vUj\nNDSU7777jk2bNlFYWEhERARjx45l6NChrFmzht27d5OcnMz06dN54403hP/X7Xa36fH8b5CuFBh0\n6NAh9uzZg9FoZMSIEQwdOhSz2czWrVs5cOAAWVlZXH311RQXF7Ny5UpUKhW33347brebf/7zn4SF\nhfGnP/2JrVu3sm3bNh599FG2bdvGsWPHePDBB3niiSd44IEHeP3115k0aRKbN2/mrbfe6pZjal29\nz58/n9tvv50rrriiW/bfDbi4SLcjmbrnQlt2K6fTKexJM2bM4OjRo7jdbqFBLl26lLi4uBbPp6uz\n1rqbdLOzs9mwYYMgV2mgo1wup6ysTASVl5WV4ePjg16vFx7YiIgI1Gq1mJcmtQA/++yzNDc3i/E5\nBoNBTNj97rvvqK6u5rLLLmPixIloNBo2btzI7t27qampITQ0lISEBNLS0khNTRUJVtKstOrqahoa\nGsSwTSlxLSgoCKVSKRbaJGuep1PB5XKJUBzpVqp+nU4nZrMZmUyGn58fQUFBWK1WMY1Dko+Cg4MJ\nDQ0VC4mxsbGEh4cTHBxMc3Mze/fu5dixY5SVlREUFMTgwYOZOHEiISEhrF27lp07d6JQKLj99tuR\ny+V8+OGHKJVKevfuzb59+5DJZLz//vtERUW1yA/oCdL1jEB0u90sXrwYjUbDkCFDSE1NpbS0lO3b\nt3Py5EmGDh3KVVddRVNTE19++SUnT55k/PjxDBkyhO3bt7Np0yZGjRrF6NGjWb16NUeOHGH27Nnk\n5uayatUq5syZw+bNm3G5XAwcOJANGzYwdOhQysvL2bx5M59//nm3jCZqfSKZM2cOc+fO7ZBd9Dzj\n4iTdzkzRbQvSyr/0RjY3Nws98Oeff2bGjBliYUeaYBsdHS3u39U2XuhYaE5baC1PSBmzDQ0NYlGs\nuLiYmpoaoqKiiIuLE6E2ksXLbDaLSre6upra2loUCgXh4eHYbDY++ugj9Ho95eXlXH/99RgMBrZu\n3Up8fDzXXnstFRUVbN++HYPBIJowIiIiCAwMpKKiooXOK3WHxcfHk5qaSkZGBjqdTqR/lZeXU1xc\nLAZiNjQ0UFBQIK5yJI+vFM4ikZbnracc4evrS3h4OCkpKYSFhaHT6YiIiBBj5eVyOfX19Rw7dowT\nJ06Qn59PZWWlsInFx8eTkZFBVlYWGo2GX375hW+++YZTp06hVqu54YYb6N+/P//5z384evQoGRkZ\nXH755fz73/+mb9++HD16lAkTJvCnP/1/7L15dJvlmTZ+SbIWa7dky5a873a8BttJHCeQhZIGaIGw\n9pQuLKeUGU5LlzPTZdrO16FnOjOdmVJoO0A7lHZYCi1bEhJCQ0LYEsd2Ysf7ItvyosXad8mW9PuD\n774rmwQcJ5mZ5vc95+gkBFvvq+W93/u57mu5a5mZS/owdi1BkatZVHRpsCiXy9HX14fOzk54vV5s\n3rwZmzZtQigUwrFjx9DT04P29nZs374dTqcTL7zwAgDgjjvugEwmw1NPPYVUKoV77rkHg4ODeP75\n5/FXf/VXWFhYwHPPPYe/+7u/w09/+lPs3LkTR48eRUtLC37/+9/j+uuvx80333xBO9P01xSNRrno\nfuELX8Cjjz667Lr8H16XZ9E9nxTdj1putxsSiQSLi4uQSqV8EdCiaO5EIoHCwkLI5XI88sgj3O1e\nqIwXWJ1pzkctKroZGRlwOp3o7OzEzMwMvF4vCgsLWYVmMpkgEAhgs9kwNzfHib/BYBAGg4FVZ+m8\n3Gg0ih/96Ec4efIkRCIRsrOzMTs7y367g4ODiMVizMeUSqU4ceIETp8+DYFAwGnDV1xxBfLz8xGN\nRnkrPzMzg4WFBYRCIcZTlUolFAoFVCoVVCoVlEolZDIZOjs7WYhCuCzBCORCRpACDdc8Hg+kUimS\nySSam5uXMSQikQir0fx+P5+DTqeD0WhEeXk5WlpamB43MDCAzs5OWK1WRCIRNDc344YbboBEIsEb\nb7yBkydPIiMjA7fccgusViveeecdGI1GRKNR+P1+aDQa/Pa3v13m/0u0RJJpr8Xi8OMWYePT09N4\n6aWXUFhYiNbWVlRVVcFut+PYsWMYHR1Fa2srOjo6kEwmcejQIQwODmLHjh1ob29HZ2cn9u/fj/b2\nduzatQuHDh3Cu+++i3vuuQfxeBxPPPEEvvSlL6G/vx9nzpzB5z//efzkJz/BnXfeiddeew3BYBAP\nPvjgqjyGV7NWGpjfeOON2Lt37wU3YBdxXV5FdzWeuqtZ1NmSdPNc1J1HH30Uv/71r7nDqq6uht1u\nxxtvvMFbpQsNp7wYRZcm8sFgECMjI6ioqEBBQQESiQRmZ2c5/8xms7FhudFohE6ng1qthlKphMvl\ngsPhgNPp5EcsFsPhw4eRSCQQj8c50odSOTo6OhCLxbBv3z52CqusrERubi7HA83NzS3jxRoMBjZM\n1+v1UCgU3JFT4q/L5YLX60UgEFhGF6NhYfrwDPhzl5uuRAOwLKo9MzOTC7tKpYJarUZOTg5KSkqQ\nn5+PpaUlxrYtFgt3u4FAAEKhEE1NTdi6dSvy8vIwMTGBt956i70cPv3pTyM3Nxevv/46FhYW0NTU\nhKWlJZw5cwZ6vR4ejwePP/44DAYDn186Lrkym2ylxeHKQrzaYhyPx3HixAk0NjYiMzMT/f396Orq\ngs/nQ2trKzZu3IhYLIZ33nkHPT09WL9+PXbs2IFgMIh9+/bB7Xbj1ltv5aQQt9uNe+65B3a7Hb/5\nzW9w2223Qa1W4xe/+AXuu+8+vPnmm9BqtRwE+vbbb0On0+Fzn/scmpqa1vT9XrlWGpjv3r0bx44d\nu+TKvvNYl2fRPZ9ssPRFxZaGK/F4HEql8px0rVAohB07djAhv6SkBNnZ2fjZz37GF4zH44FarV6z\nuGGt+DRtsYPBIAQCAeRyOUQiEaxWK6ampnibTPaNJGOUSqXLvBaIi0vRPNTpktT3S1/6EgDAYDDA\nbrdzBPrw8DBPpsl3YWJiArOzs1zMKeKGwie7u7sxNjaG+fl5AB/E8Mjlcuj1emi1Wmi1WvZfIHxW\nIBDA5/PhjTfe4M9BLpdDIpFAIpGw2xi9/zRoXVxchEAgwLZt25hnSRAF8a1dLhffYMj8nJgWZWVl\naGxsRFlZGZaWljA+Po7Tp08jEAjA4XCgoKAAO3bsgEQiQWdnJ+bm5hCJRLB161ZYLBbYbDYYDAY2\nab/hhhtY4XguKtfKz5e64vRCTF3xygj1sxXiYDCIN998E6OjozAajWhpaUFVVRUcDgeOHz+OwcFB\nNDc3o6OjAyKRCIcPH0Zvby86OjqwZcsWDA8P4+WXX0Z9fT2uvfZanDhxAocOHcJnPvMZ6HQ6/Oxn\nP8NNN90EsViMp59+Gl//+tfx4x//GHfffTd++ctfoqamBvX19bj66qsvmsfE/3IDc+ByK7rAx3vq\nnm2lF1vicgqFwlXRte6//350dXUBADtU2Ww2/OM//iNaW1svWFG2Fnx6aWkJ4XCYuyG6gfz617+G\nSqVaFqsuEAhgt9sxNzeHubk5Tvs1Go3Izs6GVqtFSUkJxGIxgsEgY7putxuHDh3C2NgYAECr1UKp\nVGJqaopTekUiEbq6umAymZCRkcEwhdPpxNDQEObm5pZ1ltnZ2QwhUMIHddfEVgiHw9yVUlEViUQI\nBAKQy+WQSqXw+/3LPtt0YQTBDFRgyYyIItnphr20tASVSgWdTsecZLVazcbyoVCI5cQk7MjLy8MV\nV1yBkpIS2O12DAwMYGlpCfPz89i8eTPEYjFDN2VlZXC5XHC5XJzF9q//+q98ziupXKtdZyvEZ+uK\n33nnHWRkZKC6uhpZWVkYHBxET08PvF4vmpub0dLSglQqhffeew89PT1Yt24dtm/fjsXFRRw6dAjT\n09O44YYbUFJSgr1792JiYgKf//znsbi4iCeffBJbtmxBfX09Hn74Ydx6662YnJzEzMwMmpubceLE\nCb7W6urq8OlPf/q8X+e51v9yA3Pgciy6lCKwGlFCMpnkqXh6saW1GubA4OAg7r33Xv5iSyQSrF+/\nHp/5zGewcePGCxY3kFHOai5A8glIF2mEQiHGOIm/SOyA6elpdvqi/DNK+w0GgxxCSUwFACyB1el0\nePTRR7lwhkIhlJeXIxqNwmKxQKfTQaVSQSAQIBQKwWazIZVKwWAwQKFQwGAwwGAwMFthaGhoWRcs\nkUg41DInJ4dFDUT1CoVCCAQCiEQiCAQCsNvtsFgsrDQjvi11w1R0afspEomg0+lQUlICtVrNxjqE\n3YtEIuYKE6xA720kEuFwzNraWpSWlrIz2uzsLABgfn4e8XgcV1xxBaRSKbuRWSwWVFVVIRaLwel0\nIjMzE8FgEMlkEk888QRUKtUFFd2zrXROMvGU5+fn2aPYYDCgqakJVVVV8Hg86OnpYWex9vZ2yGQy\nvPPOO+jq6kJrayuuvPJKzM3N4aWXXoLRaMSnP/1pjI+P46WXXsL111+P6upq/PznP0dzczMaGxvx\n05/+FF/84hfxxz/+ETt27MCzzz6Ljo4OzM3NoaOjAx0dHRfldQJgbw6pVMpF93+RgTlwuRbdpaUl\n+P3+c1K1VhZbushWrtUyB3bv3g2fz8eOV0VFRVAqlfjBD34AnU53QeKG1QwF06lsxK6gO3soFEIi\nkcD8/DwmJiZgsVh46k5DNLFYDJfLtSwDLZVKIScnB1qtFoWFhctoUmR488Mf/hAZGRlQKBRIJBJw\nu92orKzk3cbc3Bzq6uqgVCoRDAaRlZUFl8uFwcFBZGZmQq/X801NrVZDLBbzAMvr9cLj8cDj8bDS\njcQJUqmU87MyMzOhUCgQiURgNptRU1ODpaUlzM3NsXENdfsymQyZmZlIpVJwuVzsmhYKhdgJjQoT\nDdVCoRDC4TB3vekFWiAQsHouFoux41plZSXKysoQCATYYay3txfFxcUQCAQwGo3o7u6GRqOBSqXi\nAv3tb38bzc3NF73opq+BgQGcPHkStbW1KC8vh0QiwfT0NHp7e+F2u7Fu3TrU1dVBKpWip6cHp0+f\nRmlpKa666iqIxWK8+eabGBkZwc6dO9HY2IgjR46gq6sLt9xyC7RaLZ566imUl5fj6quvxqOPPooN\nGzZAp9PhlVdewZ133onHHnsMDQ0NbDT04IMPXtTXl+6lG4vFcNttt+HNN9+8qMe4wHV5Ft1zeequ\nttjSWu0Q64knnsBvf/tbiEQiKBQKuN1ufOITn8CJEyfwX//1X9BoNGvmIH7UUHAlBr2SXQF8gAl7\nPB50dnZytLparYbH44HFYoHFYsH8/DwUCgWMRiMP0TQaDUKhEObn5xEOh5mTGwqFoNPp4HK5cOjQ\nIeh0OiwsLGDr1q0YHx+HzWZDTU0NtFotLBYLPwfRsTIzM5mqRc9rs9k4iYKw50Qiscy8nHYvtKV3\nuVwIBAJs9xiNRvm1pw/W6N/SUySAP2OidNNQqVRcBImbq1Qq+XPz+/3weDw8NCWPD+pgCwsLGc4S\niUTQaDSw2WwYHR1llR1h+52dnTAYDKiursaJEyeg0Wjg9XpRV1eH7373u5ek6FJqclZWFne5o6Oj\n0Ov1aGxsRFVVFbxeL06dOoWBgQGUlJSgra0NCoUCp06dQnd3N6qqqrBlyxaEQiHs378fGRkZuPHG\nGxEMBvHMM89g48aNaG9vx+9+9zsolUpce+21ePjhh/HJT34Svb29yM7OxtjYGKqrq3Hy5EnIZDJ8\n//vfv2ivEVhedB0OB775zW9+pIXm/8C6/Iru2Tx1U6kUk95JI70ajHW1Q6x4PI7du3dzWCINM9ra\n2nD//fdzR7eWdbahYCqVYnbF2WCRs70GkUjEkILFYgEAFBUVoaioiJ36fT7fMrPycDgMrVa7LJqH\n1HXf/va3MTs7y45fdrsdhYWFbPAdDAbR0NAAg8HAOWYejwcLCwuor6+HTqeDUCiEx+Nh+lkgEIDP\n5+PBlVarZa8FSuWVSqXLnMLoIRKJcOrUKYRCIZSUlCCVSjHDQiQSMQ4sEAjgcDigUChQW1sLgUDA\nWO7i4iJ3xrFYjB/RaBShUIhjgnQ6HZ8bmeVQp+7z+TAyMgKBQMDUwYqKCohEIrz55puQSqXQ6/Vo\naGjAa6+9BrVaDblcDqfTCalUil/+8pdIpVLLaE8XY01PT+PNN99kTnlNTQ1EIhEmJycxNDQEh8OB\n+vp6NDU1QSaT4dSpUzh58iSMRiM6OjqgUqnw9ttvY2BgAC0tLVi/fj1Onz6N9957D1deeSVqamrw\n/PPPQ6FQYM+ePfjd736H7OxsbN68GY8++ijuuecePPbYY+zXMDU1hW9961sXnT+bbmA+NjaGhx9+\nGE899dRFPcYFrsuz6FKnq1arWaN/PsWW1vkMsb70pS9hfHyccUuHw4H29nacPHkS3/nOd7B9+/Y1\nvZ70oruWm8fMzAxOnjwJu90Oo9GI0tJSFBUVQavVIhAIYGZmBrOzs7BarRCJRMusG1UqFcMTRNUi\nr1vasi0uLvI2dWBgAI2NjWhqasI777wDq9UKr9eLqqoq5Obm8hY8EAjw8InwZIPBgMzMTKa20e6C\nXj8VPbfbjUAgAI1GA4VCwXADeRW73W6OahcKheyHQaIQ6m6LiopYqUZZaeR2FovFGLemok+8X2I9\naDQahrHm5ubYQ5fOKysriweCJFPW6/XYuHEjwuEwDh06BKPRiLa2Nuzfvx85OTnwer34/ve/j6Ki\nootWdIl3XVZWBq1WC5vNhuHhYYyPjyMrKwtVVVWora1FMBhEb28vBgYGYDKZsHHjRuTk5KC3txfH\njx9HYWEhrrrqKiSTSbzxxhtwu924/vrrIZFI8MILL8BgMOATn/gER9LfdttteOaZZ1BaWsrFnShx\nx44dg9/vx1NPPXXRB1zpBuZdXV14+eWX8fDDD1/UY1zguvyKLlF+vF4vAKyp2NI6H5FFT08PvvWt\nbyGRSEAsFkOr1WJhYQHbtm3D1Vdfjfb29vM+PgCWNctkMkQiEe7aVjuYIxVZRUUFhEIhZ6HNzs5i\ncXGR6WImkwkqlQrBYJCtGx0OBzweD1Qq1TKqmNvtxo9+9CMkEgmOWS8uLkZWVhbOnDmDaDSK6upq\n5Ofnw263s39DZmYmNmzYwHjn1NQUp/tSUZfJZMjLy2M2BHW2xC4geIBUVPQgHHZ6ehqpVAparZbF\nETQ4A8DYd35+PkMZ1DmnP9Lj2Yl+Fw6H4ff7OWqeTNYJby4tLYVOp4PH40Fvby8mJyeRk5MDg8GA\njRs3QigU4qWXXkI0GkVTUxPy8/Oxd+9e1NXVYXx8HCKRCNu3b8eePXvYh2LLli1r+t6Q6o7EG2az\nGVKpFBUVFaisrIRCocDY2BhGR0dht9tZ/adUKhn31Wq1aG9vh8FgQGdnJ7q6utDU1IRNmzbBbDbj\n9ddfR3NzM9rb27F37144nU7ccccd+NOf/gSv14ubbroJv/jFL3DjjTdi3759aG1txZEjRxAOh2Ew\nGPDd7373ohfd9BRlstj84Q9/eFGPcYHrrC9YIBDc9xdbdGmqnUqleBq91nW+Iosbb7yRY2p0Oh3/\nGYvF8IMf/OC8VTfU2UYiEbYUPN+BnNvtxvT0NCvMDAYDu3npdDqEQiGmMdlsNiQSiWW2jRKJBHq9\nHoFAgClOhw8fRn9/P3vdNjU1obOzE9nZ2bj11lvR1dWFnp4eRKNRLrJOpxNjY2NcXCUSCXfU6TJf\nt9vN3S11xIFAgNOCicZGlDHqaqio0vP7/X5Oq1CpVBAKhXC73SgrK0NxcfGytGDitxL7g9gadNOh\nZAqK8BGLxdBoNIzVBwIBzM/PY25ujgeGarUaFRUVKCkpgdvtxokTJ+ByuWAymdDS0oKTJ0+iu7sb\ner0eXq+X1WyxWAzxeBx5eXloaGjAc889x3DV+ayenh5YLBa27NRoNLDb7RgbG8PExASysrLYvD6Z\nTKK/vx+Dg4PIzs7G+vXrkZ+fj+HhYZw4cQJqtRrbtm1DZmYmjh07BrPZjGuuuQYmkwn79u1DJBLB\nzTffjL6+Prz//vu46667cOjQIcTjcbS2tuKVV17B1q1b0dfXB7vdDqFQiJtvvhllZWVMZUtX3F2I\n7DndwPyll16C0+nE17/+9TU91yVal1+nSxQr4nNeiKfu+YosHnroIbz//vvcfRUWFmJ+fh7bt29H\nV1cXnn766VUP1KjDpc9Bo9Gc9xfx9OnT6O7uhslkQkVFBYqKiiASibhAzM3NIRaLsWl5Xl4e1Go1\n05lICUaqNhI+/OpXv0IoFEJZWRnMZjOUSiW2bt2KgwcPcvaZ0WiEzWbD9PQ0fD4fcnNzsX79ehQV\nFcHr9bKNZCgUgsvlQk5ODqcOE2UsFouxKIE6YvpMCINN/5P+XzovFfjz0IwWdbDp7mPpdo7pGDIp\n8pRKJeRyORvl2O12jI+Pw2Kx8O9JJBJmzDgcDkxNTWFhYYGhjWg0CrlcDqFQCJlMhvLycmRkZMDv\n92PdunWYmpqCQCDArl27MDU1hampKYyPj+Mzn/nMx8V6A/ig+5+amoLJZIJUKoXdbufnkUqlKCkp\nQUVFBRQKBSwWC4aGhmCz2VBRUYG6ujpotVqMjY2hu7sbYrEYbW1tKCwsxJkzZ3D8+HGUl5ejo6MD\nbrcb+/fvR2FhIXbu3InOzk709PTg1ltvxezsLN5991188YtfxPPPP89WnyqVCidOnIDJZILFYsFX\nvvIVFBQULKOzEXskXfa8shh/3Eo3MP/Nb34DuVyOe+6557yum0u8Lr+iezE9dc9XZGG1WnH//fdj\naWmJB3iFhYWw2Wzo6OjAAw888LFMiHRhA8EiwWBwTfaQZGYSDofhcrlgsVhgtVqRnZ29TGobiURY\nfZbub0tsg+LiYrYitNvteOihhyCTyRAIBFBVVcWGM7t27YLL5UJnZyfi8Ti2bNmCnTt3Ynh4GMeP\nH8fMzAzC4TDy8/PZ9IZwUbfbDY/Hg3A4zNiqUqnkKHfqcKn4EYZLBS29GFPXurS0BGC52U26vSM9\nVsb3BINBPieXy4XZ2Vm8/vrrzIwh2XMymVwmHZZIJMtkqDqdDvX19SwP7+7uhsPhgE6ng0KhwMDA\nAA/n4vE4O6dt2rQJLS0tKCgogEQi4ZvvzTff/JGfdzgcxsmTJ2G1WqFWq9lbQ6VSweFwwGw2w2w2\nQ61Wo7KyEkajEcAHsTyDg4PQaDSor69Hyf/Nzuvs7IRIJEJHRweys7Nx4sQJ9Pf3Y/PmzaitrcWR\nI0cwNTWFm266CT6fD/v27cONN94Iq9XKcURPPvkk9uzZg+eff555wHK5HJ/97GfPeS2kF+L0Yrya\nrjjdne1nP/sZampqPvZ9+29el2/RvVB3LuDc6REfte68804+djgchk6nQzQaRWVlJUZHR/H9738f\njY2NZz3vlcIGMmdZiz1kJBLB5OQkxsfH4fV6UVBQwLCCSCSCw+FgTm44HEZeXt4y+8ZkMgmn04nZ\n2VlEIhG43W4sLi5iYWEBp0+f5qGbxWLBbbfdhrfeeguTk5NYt24dmpqaMDIygv7+foRCIZhMJtTV\n1cFgMMDlcnGX6/P5oFKpeJpeXFzMxjwejwdWqxUulws+n4+ZBGRGQ96vUqmU6XJ0kyVBRLoUli5k\nKtLpXXL63wmWUigUkEgkEIlEkEgkzIYhWplAIIDVaoXZbMb8/Dyr4KgIkO8vMUwkEglLqYnTLBAI\nOJdOq9ViaWkJLpeLv3disZgZJps2bcK99977oc85mUziwIEDbMZDNp0Oh4PjkKRSKYqLi7kAz87O\nYnx8nNOM6+rqoNPpOMONRB20k0n30qDjSaVS7Nq1C7Ozszh06BCuueYaKBQKvPDCC7jlllswODgI\nt9uN0tJS9Pb2MtfabDajo6MDO3fuPK/v80rj+XN1xXTzEgqFeOihh3DNNdec97Eu8ToXptvyF1t0\n6cIKhUJMnl/rWosJ+ZNPPom9e/fywMvpdKK5uRn9/f3Ys2cPdu/ezVp/Osa5hA30es636CYSCTz/\n/PMwGo0oKiqCTqeDTCb7kLGNyWSC0Whkc3C73c5eA36/nztLkvUqFAo88sgjGBgYAPCBOo0KcV1d\nHTIzM9Hb24twOIz169fjyiuvhM/nw9GjRzE+Pg6JRIKqqirU1dVBr9fD5XJhamoKMzMzcLvdCAaD\nWFxchFarZYtFvV4PnU7HuDpJdKnbTPdEoEe6DDYdWkjvdKlApxdt6jQJp7333nuXJQunZ62RB69a\nrWZ/iby8PAgEAhw9ehSTk5MMdVEnmz78JBYFYc4VFRWIRqPQarXQ6XR44IEH2AGMBl46nQ4/+clP\nMDQ0xBi3QqFgy8v5+Xk4HA5otVqYTCYUFBRApVItUyDK5XIOI41EIpiensbIyAikUinWrVuHc5uG\nRQAAIABJREFU0tJS7lRjsRhaW1tRXFyM3t5enDp1CnV1dWhra+OEiauvvhpqtRp/+MMfsGHDBmRn\nZ+PFF1/E7bffjpdffhmbN2/GG2+8gcbGRoyPj8NqteKmm25CW1vbqr/PH7VWFmLaKe7cuRO5ubmo\nr6/H7t270dTUhIqKilVDdAcPHsSDDz7IMT1nC6P8yle+ggMHDkChUOA3v/nNavPdzlV0//Mvvuhe\nDE/dtRQ8uljJeIWivCsrK+FwOJCfn4/7778fBQUFHytsANbuyZtMJhGLxTghwu/3s19uQUEBMjIy\nWJhgtVoRCoVgMBiYj0uveWZmhv13PR4P9u3bx3aNY2NjuOKKKwB8EMpXXl6O7du3Y3JyEm+//TYi\nkQgKCwtRUVGBzMxM2Gw2TE1NweFwIJVKwWg0oqKiAvX19cjPz+fCT+5jTqeTKVw0TCRbR7pBkTyY\n/iTObjojgf5OW//0DpfoYoQfUy4cxQPR7wsEAuYEp8uRaXAXCAQQjUYBgI3RiUImkUiQTCb5ptPe\n3o7h4WFW5kkkEoyMjPD7TMIICgWtqalBQ0MDSktLodVqkUgk2HVNKBQiJyeHrTeFQiEcDgfm5uY4\n+LGwsBCFhYVQqVSw2Wz8/ubk5KC6uhomkwmzs7MYGBiAz+fjkEir1cp0t82bN0OpVLIv8jXXXIPF\nxUXs3bsX9fX1qKurw7PPPovW1lbI5XIcPnwYn/rUp/Dss89i/fr1vCswmUz48pe/fEm8EEhUIpPJ\nYDab8Q//8A8wmUyYmZnB9PQ0W4qu5nmqqqpw+PBhmEwmtLW14bnnnkNNTQ3/zIEDB/Doo49i//79\nOHHiBL761a/i+PHjqznNyw9eIKexi+Gpu9aC99d//dcc652VlcWS5GAwiG3btsFiseDBBx/8WGFD\n+jms1pM3Ho/DYrFgcnISTqeTB2Tl5eVIJBI8QLPZbNBoNMzL1ev1WFpaYh6uy+Vi4YLBYEBWVhbi\n8Th+8YtfMBWrqKgIQ0NDKC0thV6vR29vL6LRKMrKytDW1gav14vTp09jenoaWVlZqK+vR3NzM7Ky\nsjAxMYGBgQHOa0ulUtBoNKxCowdp6Aky8nq9CAaDrCykP9PFDendT/rfqRimU8Oo26RiOjExgd7e\nXshkMigUCi7mBFOlUqllhZrYHQaDASUlJTAajfB4POjv74fZbIbT6WQaYTAYhNvtRiKRYCVcQUEB\nFhcX2fOCzrmlpQVCoRDDw8Mwm82YmZlBJBLBlVdeie9973v83pCrmc1mg8vlYjELPZ/L5cLMzAxm\nZmZ4mFZSUgKhUIjR0VGYzWakUinU1tairKwMbrcbp06dgsfj4eI7Pj6Ozs5OFBYWYtOmTZicnMS7\n776L1tZWVFZW4uWXX4bBYEBbWxueffZZXHnllUzdo6bi5MmT8Pv9aGxsxBe/+MXzup5Wu1YamN9x\nxx146qmnkJ2dfV7Pc/z4cfyf//N/cODAAQBnj17/8pe/jO3bt+P2228HANTW1uLo0aPLdrHnWOe8\niNeeXf6/ZKX7qV7oIs7jahdFltCQiOLGr7jiCuzbtw/XXXcdJBLJqm4I59sR9PX1cWe9fft2RKNR\n9nf1eDwcv7Nhwwb2XLDb7ejr60MwGOSiV1NTA51Ox8wFj8eDI0eO8BY9Go3C5XKhra0N3d3d8Pl8\nuOmmm5BIJHDw4EG88MILKCkpwYYNG7B161YMDw+jr68Pb731FtRqNQdebty4cZksmfx9e3t7EYlE\nOPRRq9UiKysLer0eubm5XBDJWUwoFLIHQvpnT/BC+oMGZ4T9JhIJDtgUi8UwmUzw+XwciLmwsIBo\nNPqhAMxEIoFQKIS+vj5mOxC2SJaUsVgMbrcboVCII4XIojJ9NzY1NQWPx8OFvL+/H83NzaipqcF1\n110Hk8kEp9OJwcFBHDhwAAUFBZBKpTxk3LRpEwDw7uXYsWOQSCTIz89HRUUF1q9fD4fDgcnJSRZj\n5Ofn45Of/CTcbjeGhobQ29uL2tpa7NixY5nxzYYNG3D77bfj5MmT+MMf/oBt27bhtttuw+uvvw6b\nzYY9e/bgtddew3vvvYdbbrkFzz33HG688Ua8/PLL2LJlCw4fPszQysWCFc61Vg7V1pLEPTc3tyx2\nq6CgAJ2dnR/5M/n5+Zibm1tN0T3n+n9FF+df8Gjt3r0br7zyCoAPLnq3242ioiL09/ejoqICDocD\n9913H/75n/95VTLI9IiZj1stLS1YXFzE7Owsjh07BrfbDYPBgJqaGh7iWK1WdHd3M8E/NzcXTU1N\nzC0m5dfk5CRzailAUiQSYXFxEdXV1RgaGsL8/Dx27NiBU6dO4cUXX0ReXh5aW1sRj8cxMDCAV199\nlYv45z73OahUKpjNZpw5cwbvv/8+/vSnP0Gj0XAxramp4RDBaDTKpjd+vx9er5eNbNKpYktLS1xI\nV/rIpj+oS6UHCWlEIhH8fj9mZmaWBVgSMyEvL4+HaDSkSfeB8Pv9EIlEUKvVyM3NZbzabDbD5XLx\nDYKCMmUyGce+04WblZWF66+/ngebxL45evQonn76acRiMeTn56O8vBy7d+/Grl272INiamoKp06d\nYhP4yspKNDc3w+VyYW5ujm0ci4qK0NjYiJaWFszOzmJ0dBQDAwPsqRAOh9HX14eXXnoJ69atw86d\nO2G323HixAkolUq0t7ejpKQER44cQWlpKW644QYcOXIEBw4cwO7du7F//36cOXMGO3fuxKFDh9DR\n0YHBwUF+P0OhEOrq6tZ0Ta1mrbxGaEfxl7L+cs50xVqZhXUxnu98O13yo6VwR1JSEQWsv78ft9xy\nC8siV3sOH7XIa2B8fJzvuBUVFcjNzWUv2vfee29Zt9vS0gKxWAy32w2n04nh4WH4fD5OwzUajSgp\nKeHgwhdffBFCoRAqlQpTU1MoLy/H9PQ0enp6UFtbC7VajePHj+P48eMoKCjAtm3boFar0dvbi56e\nHrz//vtcGEpLS9Ha2grgA6mq3W7H5OQkuru7EYvFGA/NysriBAeTycSCjXSrwnQJL9Gr0tVqNGQR\nCoXMw6WhWTwex6lTpwD8Wc1IdpFU8IluReIFskekwY1cLmcfBrPZzBCBWCxGXV0d6uvrkUgk2OXN\n6/UiMzMT5eXl3OHG43F0dXUxrKFUKrF+/Xrs2bMHJpMJXq8Xo6OjGB4exjPPPIP5+Xls2bIFer0e\nxcXFAMCCDrPZjIyMDJhMJpSWlqKhoQFutxsWiwWHDx+GVqtFcXEx2tvbEY/HMTo6isHBQYaFYrEY\n+vr68PLLL6OxsRE33HADBgcHsXfvXrS0tOCWW27B22+/jVdffRW7d+9Gd3c3Xn31VVx77bX44x//\nyDOBSCSChYUFZGVlYW5uDnq9ftXX0FpW+nV6Idd+fn4++5MAwOzsLPLz8z/0MzMzMx/5M+e7/mIx\nXeD8PHU/bq3FhHxxcRFvvfUWnnnmGeYWEtY5OjqKTZs2obe3F4WFhbj33nuXbVPWeg6HDx9GJBJB\nRUUFSktLIRQK2RPA4/Fw2GRubi6WlpZgs9kYB1QqlbxNzcrKQiKRwMLCAoLBIHw+H0KhEHw+H44f\nP84SW4oyKi4u5uk4FdNQKISBgQHE43Ho9Xq2kQTAePPc3BzkcjkX98LCQhiNRshkMlbJkSKMYnHI\nRpEoWCRKIAYCdbrENliZnEDFN32Y5nQ60dfXt4ySRBAEFWeCN3JycmA0GpGbmwuxWLzMd4EYHxkZ\nGVAqlYzh0qDNZrNx0SdIpKCgAFlZWairq+NBpUwmY9EITeJ9Ph8rCauqqlBTU4Pq6uplXhN0cyLv\nYaLcWa1WCIVC9kum55+amoLX60V5eTnKy8uxtLSE4eFhTE9Po6ysDDU1Ncz7XVpawqZNmyASiXD0\n6FEoFAp0dHRgeHgYQ0NDuPbaazE4OIi5uTls27YNL7zwAnbv3o19+/axWfvMzAxuvvlm1NbWrvo6\nOt91NgPztXjpJhIJVFdX4/DhwzAajdiwYQOeffbZZef+2muv4ec//zn279+P48eP48EHH/z/7yAN\n+LOn7vnSvc62zseEPF3YIJPJ8LWvfQ3xeJxVcWSkQh4Dra2tyMnJwac+9akLPodgMAi5XM7bWoqD\nIQcxhUKB+fl52Gw2+Hw+LiA5OTlcQCiDzO/3Qy6XQ6fTQSKRwGg04uWXX8bg4CDkcjkCgQByc3N5\nKGQymaBUKjE2NsYS1pqaGgiFQkxPT6O/v5/VWsQlFYvFcDqdmJ+f5ySKxcVFtlikQRoxKqiTJBzV\n7/dzIQ4GgwiFQgw7UHFNT1GgQpre7Q4NDQH4c6ZaengleTmkwwjpxyBDHSqitDtQKBQQi8WsjFta\nWkJpaSkaGxs5F428KOh9p05XrVYz5iuRSGAymXDfffchHo/DbDZjbGyMP1ur1YrHH38cNTU1SCaT\ncLlc/PllZGQgNzcXeXl5UCqV8Pl8PEBVKpWs/CMe9vz8PAoKClBVVQWBQIChoSFYLBZUVFSgtrYW\nFosFPT09KCsrQ0NDA3p7ezE+Po6rr74aTqcTJ0+exO7du9HV1cWfs9ls5veSDJW++93vXtKAyHQD\n82Qyieuvvx5vv/32mp7r4MGD+OpXv8qUsW9961t47LHHIBAIOKLqgQcewMGDB6FQKPDkk08yk+dj\n1uVZdAmv83q9FxR/Dnw4wvxs61zChoceeggWiwWpVIodtFQqFXw+HxoaGvDuu+9i586d+MIXvvCR\nyrmPO4d4PM65ZwKBAGVlZSgqKgIA7sTC4TAzFXJycrC0tMS0I7fbDalUyvHnBoMBYrEYoVAIVqsV\n0WgUr776KkurJRIJotEocnJyIJFIMDw8zF00APT29iKRSDBOS/4TZLYTj8eRlZWFrKwslJSUoLCw\nEFlZWUilUpidnWWvCIJFYrEYD83IyHyl3SPlplGnuzKscSWfMx6P45VXXuHiuZLpQNAB4YLEx83N\nzUVubi5HEDmdTlgsFmZhUKQQeQITVEE3zGg0iszMTGzZsoWLO1lmUqw8ubGFQiHEYjGIxWIUFhai\nvLwcVVVVfGybzcZKPb1ezynNPp8PdrsdNpsNAoGAz5dCPokXnZeXh6qqKgiFQkxMTDCli6hRfX19\nWFhYQEtLC/R6PY4fP45gMIgtW7bA7/fj7bffxrZt2zi88rrrrsOBAwewfv16vPvuu2hsbMTo6Cjm\n5+ehVCrxwAMPrOkaXO1K99L1+/245557cPDgwUt6zDWsy7forvTUXev6KDnxxwkbzpw5g//4j//g\nrisrKwtOpxMlJSUYHR1FZWUlEokELBYLHnvssXMW1XMV3UgkgrGxMUxPTyM3NxdlZWXQ6XRwOp2Y\nnp7GwsICX9BlZWXMg7Xb7QgEAnzBEoZI7xlt6UkAkJmZiWeffZZThYk+RvHsUqkUFosFyWSSlW0Z\nGRmYnp6G2Wxm/1mTyQS9Xg+hUMgBmVarlbs8lUrF2/js7Gy2bqStutfrZcpYunl5Ota6MhuMiikA\nVqnR76ebnKfPAoDlCbzU8RKvOt1khzpkchyj8E46zuLiItxuNydCj4yMsDVnIBBgjw2pVMpwS0ZG\nBrKysiCVSvGJT3wCUqkU4+PjHCY6Pz/PRkM//vGPmeLndDohFov5xqlUKhEIBGCz2TA/P8+Qhslk\nQiwWY4cxg8GA8vJyZGZmcjdNKkKv14uuri5otVoewJ06dWoZH7e9vR2BQABjY2PYsmUL9u3bh+bm\nZkxNTfEN/1Of+tRFS/w910ovujMzM/j7v/97PP/885f0mGtYl2/RJU/dC4k/B84e2bOaxAbggwv3\nG9/4BmKxGIP8CoWCMTjqVq677jq0trby1nrlWln4w+EwBzsWFxejsrISIpGIO66MjAyU/N/o8GQy\nCbPZDK/Xy5Z6RLmimBoqaBkZGdDpdMjKyuItLuWXHT16lM26PR4PCgoKAHxgjl1UVMQT+oGBASST\nSeTm5rJZjNvtxtzcHOx2O1QqFT8/eT+Qj4PVasXMzAzLaqm7lclknLxA3gtKpZJFCuk0LoIRaNG/\n0fufSqWYHkW/ky4dBv5sG0lKMoIJwuEwQwnkMiYSiRAKhZgr6/F4GO6gwRzBO9SNi8VilJaWcmF1\nOBzs2zA3N8f+E8FgEPF4HCaTCWVlZaioqIDRaOSbt8ViQXZ2Nq666irodLplvF0SoNDnrVQq2UfC\n5XLBaDSy5Ht6ehqTk5PQaDSorq6GTCZjo/G6ujoUFhYyp3rDhg2QyWQ4cuQIampqkJeXh9dffx2b\nN2/G6OgoxGIxotEodDodjh8/jsXFRfh8PnzjG9+44PnKx610A/OBgQH853/+Jx5//PFLesw1rMuz\n6NK28EKTeIHlkT3pJuKrETYAwCOPPMLm5gCQmZnJsSk2mw3r1q3DmTNnIJPJcO+990Kv18NoNC57\nXir8AoFg2bCjsrISi4uLrDAi7qtGo8HCwgJmZ2e5yyopKWGLRsJ1o9EoY6dUACh00ev1wufzAfgA\nLnA4HJBKpSzHdTgcTLIn1ZrRaIRSqUQymeRBndFo5O0vWUkuLCwwnSorK4tdvKgjpqEYqcQoUcLr\n9XJmWTQaxeLi4rIhGr1n6ZLd9PjxZDKJoaEh3nmkMyDIy4FW+mCNul1KBiGxBBVqGtKRiowUYEKh\nkAdpc3NzSKVSPCikHQXNAFQqFaRSKbKyshgSIz/h0tJSWCwWLCwssDERMRNaWlpQU1PDGDHxrCks\n1OFwsJ0iSZUTiQSmp6cxMzOzLJzTYrGwuXltbS0SiQR6enogEAhwxRVXIBKJ4Pjx42wJefjwYRQV\nFcFoNOLw4cP45Cc/iQMHDqCtrQ3vvPMORCIRnE4nVCoV7rvvvjVfg6td6Qbm7733Hv70pz/hX/7l\nXy75cc9zXd5F90KTeIEPukoAHAl+vqbow8PDeOKJJ5BIJLjT0mq1cLlcKC4uhtlshlarRX19PQwG\nA3w+H3bt2sXbZzL8tlqtGB0dhclkQm1tLZaWljA6OoqFhQUUFxejtLQUANhfQSaTobCwEHl5eXC5\nXAiHw7DZbIjH48jJyUFubi6ysrI4Roem5wBYiKDRaBCLxfCrX/0K0WiUu1y6cGlYp9FokEqlYLFY\nOKaGbkper5fNynNycqDRaPj5xWIxIpEI/H4/HA4Hy5HJL5eKPP0OOXkRlxbAMlUayXwBcLGkQRop\n0I4dOwYAy7BfOg5BOMR+oOejG20sFuP3iUQNSqWS8Vsaivl8PgSDQUQiEQgEAu6OVSoVRwYlk0lU\nVFRAp9MhHo9jeHiYDVusVitisRj7AVdVVXGXm5OTw5FDNIi85ZZbsH79erbJJDWkXq/nQFEappGN\nJu1U6PORSCSorKyERqPB1NQUJiYmUFJSwrTAwcFB1NbWIj8/H++++y7kcjmam5tx9OhRGI1GSCQS\nhsymp6cRDochFAoxNjaGDRs2YMeOHWu+Ble70g3MDxw4gLGxMXz3u9+95Mc9z3V5Fl3aGq5mCPZR\nK5VKMS5HxfZ8C3gqlcJ3vvMdxGIx/l0aqPn9fiiVSuTk5DBP8t5774Xdbsfo6Cja29uhUCjQ1dWF\nRCKBtrY2SKVSjIyMwGq1ory8HCUlJYjFYjCbzRzJQzr7hYUFdvPSaDQwmUzIyclZltRAWWS05aeB\nn8/n4+DFzs5OjrbJysqCw+HgGHESLhBNLRqNwmq1IhAIMK82MzOTo8atVissFgskEgkbtlCXK5PJ\nkEwm4ff7ma4WCASYqUCWiendbTp1LD3xIX2QBgCTk5OYmJj4kGk5PVZ6MhAXl36W0icIKqCbwsrj\nLC4uwmAwoKysDAUFBVxEFxYW2L9YIBDwgI2GbET5WlxcZPiHRBiUtmy32zlvrri4mENE8/PzOalY\no9GwJzIVYADszZBKfRC/PjMzg8zMTJSUlECn08Fms2F8fBxSqRQ1NTWsigsEAmhsbIRUKkVnZyeU\nSiWam5tx4sQJlisfOnQIra2tGBsbg1arxdDQEAwGA+x2O6anp3HXXXddMId1NSvdwPz3v/89otHo\nJR/erWFd3kV3rZ66dJFRlysSiVZtZH629ctf/hKTk5PLJKjEN1WpVJiZmWFbQ6vViu3bt2PLli1w\nOBw4c+YMpx1QjHpRUREqKysRiUQwMTEBj8fDcerJZBJzc3OYn5+HTCaDTqeDRqNBZmYm3G437HY7\nlpaWkJ2dDb1eD41Gw4WOIIWlpSXGLHt7e9HX14eMjAym45B5DXXTfr+ft7AVFRUMhZCH79LSEpuT\n03AsvWuklGEq/oR/0n8TS4HMjMg7lyLSicpFnWl6QaWi6fP52E5xpfeCRCJhzJhwYzoHgiZop0Dv\nESVTxGIxxpaJZkZ4LJn0SCQShlio4yXTdcpqs9lsmJmZ4Ru9x+NhyCInJwft7e3ctUajUXi9XjaJ\nt1gsePzxx/n3FhcX+fPLzMzkmyz5iJhMJshkMk4LSaVSKCkpQU5ODqxWK8bGxpCTk4Oqqiq43W6c\nOXMGeXl5qK6uRl9fH7xeLzZv3sxeG3V1dTh69Cg6Ojpw9OhRVFRUYGFhATMzMxAKhfj85z9/QW5/\nq13pBuaPP/448vLy8NnPfvaSH/c81+VddNfiqZtebCnpdWlpadWRPWdbg4ODePrpp7kIyGQyVqh5\nPB7k5OTw0KShoQG33XYb+vv74XK5sGnTJoRCIQwNDUEul6OxsRGpVApjY2McP1NYWIhQKITp6Wm4\n3W4WOVDmGdG+srOzkZubC5VKxV0qUZOUSiV7Asjlct4p/PGPf0QwGEQikYDRaITD4YBarUZOTg5T\nzhoaGrhY0CCooKAAer2ezXGo4BM8QbgjFTiBQMBFw+12c3GLRCK8jafukgxq0h3G6N9WmlsLBAK8\n9NJL7HOw0hw7vctNN86hbpe+S6QwS1ez0blTl003plAohIyMDBQXF0Mul/MNxul0wmazsRkTDdsy\nMzOXyY1DoRCKi4vhcDg4togoZTabDXq9HgUFBcjPz2dBhFwuR3V1NRdl6nJTqRR/H8gjeWFhgVkO\nubm58Pv9mJycRCKRYIhhdHQUNpsNNTU1yM7ORl9fHyKRCFpaWtiOs6OjA93d3VCr1ZDJZJiZmWF4\nhgai9fX1uOqqq/5bim66gflPfvITtLa2fiwH/n9gXZ5FN91Tl4ZgH7dWJjZQMsH5RvacbQWDQfzb\nv/0bby1JOhoKhZCVlYVAIIDMzEzk5ORgcnISQqEQO3fuxNatW2E2mzE1NYXKykoUFRWxzJfcoijg\nMRQKwWg08oTY6/XC4XBApVJBrVbDaDQimUwygT6ZTC7DbgUCAW/l/X4/FhcXoVAocPDgQSwuLkKt\nVsPv96OyshITExMAgLKyMvh8PthsNgAfRI3T6woEApicnIRSqUR+fj7UajVjpT6fjyWrpBqkXLH0\nPDKZTMbmOtTRkmcuMS9osEZFEsCH1Gg2m40LJ4BlxteE6VJhp06UzoUoc/T+UE4cDffo/Urn+9L5\nEu5LRjg6nQ45OTnQ6/VQqVSciEwWoCQUITpeeipFW1sbi1BCoRBsNhssFgsmJiYQCATQ3t6OXbt2\nMaVPr9cjMzOTh2k0UCUqGQ3lCA7R6XQIBALsuVtZWYmlpSUMDg5CLBajoaEBMzMzmJycRFtbG5xO\nJyYmJtDe3o5jx46hqakJPT09qKqqwuDgIBYWFhAIBHDPPffw7uZSLrrZUdH93ve+h5tvvhlbt269\npMddw7q8iy51rB+lgjmXsIHW+Ub2nG2FQiG88MILLF6gwqtUKllwkJGRgbm5Oaby3HjjjRgcHEQq\nlUJ9fT38fj/Gx8eh1+tRXV2NeDyO8fFxRCIRFBQUQKFQcLZZKBRivqxYLMbs7Cz70tJwRaFQMJ2H\nuK808FGr1ZDL5exIRTSnZDKJYDCIdevWsWF2Y2MjFAoF+ydQdhqlTxBbwWq1QiqVIjc3l49P2CyF\nQZLjmcfjYeiFUhyoy6UBHW3p0wUQ6YuEFkeOHDmrQCI9fSDdFJ26Xep4qZCnQxJSqRQKhWIZdYw+\nQ7FYzF0sTe6JDhYOhxlOIW4x4dnJZBIajQYVFRWseJNKpZiZmUEwGEQqlWJvCZL0ElNCLpdjYWEB\n69atQ15eHtxuN0MpOp0O2dnZEAgEnHsHAAaDAWq1Gslkko9RWFgIrVbL6jWyiJydnYXNZkNTUxP7\nMrS0tPDn2tDQgHfeeQe1tbXc4fp8PkilUtx9990szb2Ui4ou7Ui/+tWv4mtf+xoaGhou6XHXsC7P\norsaT12iAcXj8bMKG2itJbJn5QqHw7Db7fjd736HpaUlHuwRhQj4ILW3oKAAAoEA4+PjKCoqwlVX\nXYX6+nrO0mpoaIBKpcLExAScTieKi4uhVqsRDAaxsLCApaUlFBYWIjc3l/PMnE4nFAoFpzDE43He\nvsfjcS4aarUaQqGQKVqBQAD9/f2wWq3Mo62trYXVamWvVb/fj+npaSwuLqKsrAxZWVnweDx8cZMZ\nC9HIFhcXedtLnF2tVsvdpVKpZF8FMp4hzioxAigpgjjSdKNMH55RR03Zb1RsgQ87xxH+S5ACWW7S\n+VCBp4FdugyZFHPpZjvpuLNMJoNGo4FCoYBIJEI8HmdMlWiBo6Oj8Hg8PKicn5/nIkv2kDKZDNnZ\n2di6dSvzemdnZ2E2mzE5OQmtVguj0YiNGzeioaGBaXeUjefxeKBSqZCdnc0dNvk75Obmwmg0IhQK\nwWw2QyAQcGDmyMgIIpEIqqurEQqFMDo6itLSUh6yrV+/HuPj4wxHZWRkYHR0FFKpFGazGc3Nzdiy\nZQvPAi7lIu48Xet33XUX/v3f//1jfU3+B9blXXTPFqFO2z8ybDmXsIHWWiJ7Vi7CEx999FH2ZZXL\n5QgGg1AqlfB6vTCZTAgEAohEIsjLy2MMsaamBkqlEqWlpQiHwxgfH+csM0qlBYCioiKO856fn0c0\nGmXJajAYZFxxcXGRmQqkQiN2AA0eKZ3hD3/4A1OmjEYjZmdnUVZWxoY0FHw4MzPDwzDQTK7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BIREKZcjFODadJDoODuwmazSQGKMjH9xQxXt+5qrwUAUZkuqQNmt9o0Ry+uVEPwGLr7jJkvpVzU\n9yYmJkZx0aRLAoGATPRNT0+XzPHgwYNSCKR/LtUB+fn56O3tRWVlJZqbm2VnwMKix+PBnDlzUFlZ\nCbfbjfb2dhw4cECmE59zzjn4yEc+IudJhYrNZhOqg7uhYDAouxKPxyNm90VFRVG7LipMcnJy0NbW\nJovT7t27EQgEcPnllx9lwM97bCpDc8ebN2/G1q1bcd999437+bKzs0UvH+t7BlveExIS8KUvfQnX\nXnvtsZ569oLuZHnqAtHTI8YTlJ0RWE3TxJ49e/Dee+9JK3BPTw8qKiqwf/9+VFdXo6GhAaFQCIsX\nL8bevXvh9XqxfPlyhEIhtLe34/LLL0cgEEBrayuSkpKQl5cHm80mrmFULNjtdvT29grfqMGA8iTK\n6pKTk7Fjxw709PRIljQ8PIyysjLs2bMHOTk5qKioQENDAxobG1FcXIySkhLJgBobG5GRkYHi4mLk\n5eXJyCRmfz6fT7rpsrKyRDMdCoVEs6u372zcYDasmyJ4rpxQoAtjCQkJsN6/WqnA7jNNQ1glZZSg\naW6Y0sPU1FTp9qK2mFktPYxpGE/PChYMuWuy2+3IzMwUfwMa82jrzObmZqFQ0tLSZFHKysrCmjVr\n0NDQIO9FT08PSkpKUF5eLuqDAwcOYP78+UL5cKHgNeQ04oGBATEc4nDNgoICGIaBhoYGka7V1dVh\n7ty5qK+vR05ODlpaWoT6oo7b6uqlnb+mMrSB+Ysvvojm5mbcfPPNo/7NeeedJx2dAGSk03333Yer\nrroqCmRzcnLQ2dl51HO0tLSgoKAAHR0dOO+88/Dtb3/7WFTG7AfdiVIDwGHQtdls47anY8bNKQIO\nhwOBQAA///nPRdPJDyZH6gSDQcybNw87d+4UtcLBgwfR0tKChQsXoqKiAsnJycjPzxcaoK+vT5oe\n+CHr7+8Xk5asrCwZpknROg3Gme1u3rxZ3NCys7PR09MDwzBEm+v3+zF//nyZydbe3o6CggIxMSeX\n29bWhtzcXBQVFcmgxkAgIL8nB+tyuWQum27/pRyLbmJcHFhIo+yN7bocv639Fxj6XtZSsXA4fJTe\nlwCurSJjaXIJVvrcuIBy2gXpHC5w5E+pTx4aGoqShbG4SD3w/Pnz4XA40NbWhtTUVJHnkR4oLy9H\nRUUFCgsLAQCHDh3CoUOH0NDQIFTDaaedJtaLNEbnfZGcnCz3BDn1vLw8+P1+tLS0IDs7G2lpaThw\n4AByc3NlNBM70DweD3p7e7F7924Eg0GsXbtWJpjwumvnr6kMzR3/6Ec/QmJiooxKH08sWLAAmzdv\nFnrhwx/+MHbv3j3q39x7771IT0/Hhg0bRnvY7AfdiVIDwJGRPaO5lcUKPcCSnKrmln/729+KBImT\nJDIzM8UxaufOnSguLkZmZibee+89pKamisVeXV0dFi1ahLPOOgudnZ1SILFmNHSSooQuEAjIwEeO\nmWGn19DQEHbt2oVwOAy3242WlhZUVVWJEfnChQtl0nB6ejrmz5+PcDgskwhcLpf46PKDzOnDLPTl\n5ubKUE7TNKPkYpxCYbPZxASHdAMpAH49/PDD6OjoED2nznJ5jZm5WLNcAq7mdK3j2/XEX2a8vK8A\nSObpdrtl+CMAGX9Oz4m+vj7xmeCOic5j9LotLS1FaWkpWltbsW/fPrk2LS0tUvyi9afD4UBGRgYy\nMzMxODgoOwzy6GVlZcjOzkZ3dzcaGxtRX1+Pq666Cg6HQ4CJnrvc6XCxo+McOfzGxkbhkTkhmPcr\nue/GxkY0NzcjISEBn/jEJ6Lub6vz11SG5o6feuopzJkzB5/61KfG/Xy33HILsrOzccstt4xYSNO7\nx8HBQZx//vm4++67cf7554/21LMXdMfjqTtSDA8Pi4v/WCKWz0NfX99RBb1du3Zh586d8kFOT0+H\n1+tFZWUl9u3bJ3rXvXv3Ys6cOXC5XHj33Xdhs9lw+umni96W2z9uy9kJRR8FFqK4TQcO62NZ8WXR\n5/3330dra6uAcWpqKjo7O0Wj6fV6ZYrtwYMH0dzcDI/Hg9LSUumsogtZfn6+jJFheyvlYnQ+4yBK\nbtW1EQ6zMT1vjK9lcHDwKPNyDY7kbUd7fwi4VHVoW0fKfwi8sSYFUwrG8/H7/XIe7ALLycmB2+2G\ny+WSMfJsQEhKShKrzLa2NtmRcVGiRGzBggXYt2+f+E90dXVhaGgILpcLixYtkkWOE5fpmVBaWoqS\nkhKheRYuXCjZNWsJGRkZMAxDZr5lZ2cjHA6jvb1dvB8aGxtl8a6vr0dZWRn279+P4uJiHDhwQLoi\nPR4PzjnnnKjrbDWhmcrQNMb999+PdevWHQv8Ro2uri58+tOflqkuv/jFL8RI/tprr8V///d/48CB\nA/jEJz4hVq1XXHEFbr311mM99ewH3aGhIdlGjzc4CuZYKzYr1cPDw0hISJBsEoAoE/S2NxwO49e/\n/rVkwtSF0n0sLS0NtbW1WLJkiRTS5s2bh/z8fOzatQvt7e1YuHAhli1bhkgkIuYoCQkJ0l3GLJ9a\nUMqauKVmoScYDGLbtm0yC4zG5QDQ1taGqqoqhMNh1NfXw2azydj3lpYWkRmVlpZGfXCbmpqkuyov\nL0+q49YpEcxyWejRX8zCCHz79u3DCy+8IK3V1uGTsUxVrF1nvPbM1jQfTKClNpdAyuckPUEuODMz\nUyYz0B6USgWv1yvNB6RBMjIyZCQR/STYjRYOh2ViAwApgGkT+uLiYlGesIW3t7dXPHaLi4vF8Keh\noQEtLS0oKirCBRdcIKoPGskHAgHRHLNQSEUGtdZut1sy3oSEBHR2dsLlcklG39raCpvNhjPPPFOy\nff0ZnA7QtdIYt956Kz7/+c9j9erVU3rcccbsBV0NgMeTpcaKWBaR1rCaplsLdyOpKF599VXJNAjs\nlO8cPHhQBOl9fX1YsmQJ+vr6sHv3bhQXF6O6uhoHDx5Efn4+5s2bh+TkZMkGOffLbrcL52gYhhRR\nqA0l4IfDYbzzzjty3WjlWFpaKmBLr96WlhY0NDQgJydHbP4Isv39/eIwlp2dLdOGSTX4fD7k5eVJ\nhssGAQCSOVIqxkr50NCQZN92u10mKuguNYKjHsWugzQD/yWtwi898ZftxgMDA+ITS+DlvcW/1yZB\nPA/gcNEtKysLHo8HJSUlyMnJwfDwsMi7mFEHAgH09PSI1WhWVpZoecnRA8D+/fvFC5mL65w5czBv\n3jxZ/FhYS09PlzHwTqdTvBU+/OEPw+fzyQJGn2RSX6ZpoqurS6ZcdHR0IBKJyL3o8XikAae5uVkW\nzaSkJPzjP/7jUbyt1flrqsJKY/y///f/cMcdd2D+/PlTetxxxuwH3bEA5rFitJE91FLSbYrtodYY\nSUXR1NSEmpoacV/iLLO2tjaUlJTA6/XC7/dj7ty5aGhoQH9/v2S2O3fuFKqhqqpKgDUjIwOJiYmy\n6BBsOISS0ipmdDabDQcPHhTFBGVo+fn5OHjwIAoLC5Geno76+nppCU1OTkZbWxsOHToEp9OJ8r9P\nlQ0Gg5L9EmA5x0tPt+js7JQJB3a7XabZMrvlgsFMNBwO41e/+hWam5ujZplZO8n4WmOZlVv/Tz2u\n9sq1Nk2QT6XSgtkuJ01w6oge4U6KpLe3F16vFz09PbJwpKWlyVBQehYMDQ0Jj9rX14f6+nopFHIG\nW35+fpRLW3d3t3g7pKamoqioSOiEnp4eNDQ04MCBA3A4HCgvLxdZWWZmJnw+X5RVps/nQ39/v7wG\nOtG53W50dnbCNE0BWo/Hg+bmZhmMmZCQgIKCAmn71TFdoGvNqK+44gp873vfQ15e3pQed5wx+0F3\nMmacxRrZw1ZVPRZ8tArtSCoK+hwEAoGoLCw5OVk0p6Wlpdi9ezdcLhfmzZuHPXv2wOv1orq6Gvn5\n+Xj//ffR2NiIdevWYeHChdJ2rBUX9IZlvz/Bn4Wk999/X6gY0hAJCQnIy8tDbW2tGKl3dXWhvr4e\nKSkpqKiogNPphNfrFUAuKiqCx+NBdnY2fD6fFJXIE3KCBS0c2RGn3cX4FYlEhAenL6weCKlH/vDa\nEnD5Xli1uvrnBE2t/eX/OaWCnK1evPj+875gm7VuDSblkZ2djZKSEpSWlsIwDJn6Sz8Kgi61yfRD\nrqioQDAYRH19PRwOR5RdJ/nwrKwsrFy5Er29vZLlDgwMoLi4GKWlpaLzra2txcGDB7FgwQKcddZZ\nYmDERYoJCRU2LpdLTNPdbjdaW1vFIS0cDqOhoQHt7e2yIJ555pkjJiTabnGqwgq6H//4x/GHP/xh\nyo3TxxmzF3SBI7TARGecae8E0zTFw3UskycYo6koampq0NbWJrwsFQZDQ0MoKChAXV2dFJ327t0L\nl8uF6upqtLS0YO/evcjPz8fChQtRUFAgHVdOp1MyRL/fL9OHKU8j2BqGgXA4jF27diEUCiE9PR1d\nXV0oKSlBU1MTEhMTUVpaioMHD4pQn9MBDh48KJlUbm5u1LSESCSCwsJCeDwe6Y7jJAkOdwyFQqJ1\npQY1LS1NMspgMCh+DjU1NbL152LK1xDLG9d6/8ZaEK0OVlqjS06X3Dod0AiszKwHBwdFh0wJmj5/\n6o85Xoi0AymY5ORkcYmjQxafLxKJiPE8efTOzk74/X60t7fLQMyCggIpnEUiETQ2NkpjDVuzs7Ky\n0NjYiGAwiNNPPx2meXh6CUGfiQOnUlMdMTQ0hOzsbLS0tCAvLw+NjY0Ih8Oora2FzWaD0+nEueee\nO+LnZjpAN5aB+euvvz7lDRnjjNkNutyCTnTGGXvXU1JSxm2GPpqKore3F2+88YaAiNPpFNs9r9eL\nrKwsJCQkoKmpCaWlpcjIyMCuXbuQlJSE6upqmKaJXbt2we/3o6qqCkuWLBEjbdM8PPZneHgY6enp\nUWDLjit6oDLTTktLQ2dnJ+bOnSsG2tXV1QgEAqirqwMAMdBmgS8QCMjsL85ta2pqirIQ5PggFvto\nkkKvCCoW2NWVnp4uk3y12Qy3wQQxrcsl4FonQ+jfAZDWXaoYyG+TkiFXS/kdDXdYiNR/m5KSIkMq\nOVaImTR9jN1udxSotrS0oLOzU+oAzP55zvPnz0dSUpLMUAsGg+jq6hJtLY9F7W1DQwOam5uRlZWF\n0tJSFBcXy88PHDgA0zRRWVmJiooK2O12AXLejwMDAzAMQ2oK2tmMaouuri74/X6ZEpGQkIDy8nJU\nVVXFvOe13eJUhgZ32jqeoF66wKkAumyQGO+MM/bU09mKGeTxxrGka5s2bRLzFloM0vM2MTFRWjLZ\neltWVga32419+/ahq6sLc+fOlcmtHAhJUGLzgHVMTSAQkBZPFljou0qer6KiAv39/Th06JBMoO3s\n7BS+sLS0VOawkct1OBxR5i7BYFDkYu3t7eIzwIYITjMgHzo8PCyFtHfeeQfd3d2iK+V7SgDVgMp/\ndebLD571X/7f+gVAsl3yunryLwuA5O+Z3XNMEbfo5H71lAh97+Tm5qKyshLFxcXiodDZ2SmG7j09\nPVJYYwbLDLyhoUEek5KSgsrKSjEc7+jowKFDh9DY2IjMzEyUl5ejqKhIaIaOjg7Mnz8fixcvFu8K\nyuPIT2dkZEjrc1ZWlkw13r9/P1paWqJsMM8888wRuz1ZpJ3qbT5pOA26J6iXLjDbQZdvxnhnnJGa\nYFYzEYvIY0nX9uzZg4aGBlExJCcnw+/3Izk5WUaqsMpdUlKC4eFhNDQ0ID8/H6WlpWhvb8f+/fuR\nkZGBefPmYd68eUhMTJRik+7YYsEIOMyHcW5bIBCAx+NBU1OTGJzQRD0lJQV1dXUYHh7G3LlzkZub\nK0MM2SpMs3Jul1taWhCJRKSY5vF4xLmMDRH8ok8EKYaenh4xcUlNTY2ayMtMUnPg/GKwQm+VjjEL\n5msHIJQAf0YfBZ31ktelykEPrtRmNNTlUodLS0u+l2xR7urqkmGeSUlJyMnJQWFhIVJTU+Wey8zM\nFL6WHGxqaioKCgqkSYLcKxtFaIrEJpuGhgaZ5lFZWYmEhAQ0NDSgt7cXF198sRV/QS4AACAASURB\nVNh5GoYhkx78fj8yMjIEhJOTk/H2229LnYDXNCMjI2YBjTGdoMuMOhKJ4GMf+1gcdGcqxuupSxNr\n8qAsZEwEdPlBGqmSGwgE8Ne//lWAIxAIIDU1Ff39/cjJyUF3dzcMw0BeXp6MFS8tLUUkEsHevXul\nsEU3sObmZixYsAArVqw4KrvTpjDUy+rincvlQnNzszQ97N27Fzk5OSgvL0d/fz/27NkDu92OsrIy\n5OXloa+vT4pDqampkuWmpKTA5/OhtbVVOtPsdrtkjeRyyTPTXYxmKyw2cQdgnXWmB07qDDeWkiFW\ngY0Ui540oX9PRQndxTjAk63L6enpQj11dHSIUoG+EVzs2FLNJgkW4fQo9ubmZuHN2WjBBhcCKc1x\nenp60NXVJX7MNL4h10tJGmkGu90u7cEZGRkoLy+Hw+FAS0sLTjvtNOTk5MDv9yMSiSAlJUUkdBkZ\nGTh48CDq6upkAWe3XEJCAhYsWAC32z3iPc928om04I8lNLgPDQ3hn//5n/Hqq69O6TEnELMfdOmp\nOxbA1G27NFfh30x0ZA+BYzS98Jtvvilto9RwJiUlyc1LjpMC+9bWVgwODsoEV3ohFBcXo6ioSLrV\n+F7yQ8OszjAMoRaYhbNzjr3+bHFlYaaiogIejwednZ2yzS0rK0NpaSmSkpLQ3t4uraEpKSkijyLN\nQJUA23/pI0AO1263449//KPItPQUXmvWqjNWDbiaVtA0hJ48oBsprFaQ+m85FcKqcPD7/VHFN76G\nzMxMGYFDDp1TIXp6eiTT9fl86O7ujvJ+KC4uFs51//79klnSspMTnW02G0pLS9HZ2SnXkdNHPB4P\n3G43QqEQWlpaZLdQXl6OnJwcyX7tdjsqKirgcDgQDAZRXV0tC7HT6UR/fz9qa2vh9XpFzUDrT+Dw\n7mDNmjWj3vPTBbr6OM3Nzbj99tvxy1/+ckqPOYGY3aDLrqHu7m7JGmJFrLZdK7hO1Jd3LHrhjo4O\n7N69G6FQSExWdHdYa2urTIWgxy7djxobG1FYWIjCwkL09/dLcau8vFym8ZIzJchwbAw1xuxs4nNn\nZGSgtrYWdrsd5eXlUrWORCIoLy9HQUEB+vr6UFtbi7a2NmRkZEixjJMrOjo60NHRgf7+frGepBkM\nOTgWrAgepBt4HaxZKHCEBrDyu9ZpApqGYGars2V9PUaaOsFjUWdLHTENVnj/9PX1obu7W+wX2ZjD\nQhv9g/W8OA7cpGNcR0eHzFsrKCiQDLOkpASDg4Ooq6sTxzJO/M3JyUF+fr60Gbe1tUl7dlZWFjo7\nO1FbW4tAICCSPmp509PTUVxcjOHhYXR3d2P16tWifmCbsN/vF8UGs9zc3FzMnTt31HveOpRyqkKD\n7t/+9jc8++yzeO6556b0mBOIUwN0R+oGG61t1xoT9eUdi17YNE1s27YNPp8PAEQs73K50NXVhZyc\nHEQiEbS3t6OoqAimaeLQoUPywaFHazgcRvnfx4h0dHSgqakJS5YsQVVVlRTRwuEw2traMDAwIMDO\njjbOvHI6nSgrKxN+tqioSDIscrkej0fAgQMnvV5vVDU/MzMzanwQO83YCUUQ++tf/4pDhw5FTfxl\nWy7BMJYcTIOtBs2RmiSoobU+lqGPozljqhH8fr80lmgLSOp1s7KyZCaa3W5He3u7eFJw4kQgEAAA\naR8GDpu0z5s3D1lZWTh06BD2798vjQzkjktKSpCeni7n4vV6JWPmfDqn04nm5mY0NDQgEAigpKQE\nhYWF4pfc39+PkpISacJpa2tDYWGhjHPnItHe3i7SMVJPzOyXLFlyTK52ukBXH6empgavvPIKHn74\n4Sk95gRidoPuaJ66Vl+GY90YE/XlHWuTBrNGcpUUsjMr7OvrQ2FhIQYHB9HR0YHS0lKZNOHz+VBW\nViacLD0R6D5FE3HgcPbW1NQk2aTf70dubq7MVCspKZECTnV1NRISErBv3z4MDAygrKwMWVlZMiq+\nq6tLwJVOZ8xWCTIcCcNxPMygmHGxaEMVBX0hNCBqMKTxN3l7PfNMT4YAjuhx+VxWWkF7LuhjWjNo\nFtg4mJJ/w8eTI6dJD7lZu92OtLQ0kc3l5ubCNM2oeXJsZSWApqSkoLS0FGVlZcJx0yCernJ0bmNX\nmd/vF71vaWkpioqKMDQ0hLq6Oni9XhQWFqK4uFiaLoLBIEpLS4XzdTgccLvd6OjoQEJCgjiPcXw7\nr1dKSoq02OqdiDXGO5TyeEMf549//CPef/993HXXXVN6zAnEqQG6uhtsrG271pioL+9YmzQCgQB2\n7twZVZH1+XzimVtYWIiuri7JSPv7+8XTlplLU1MTXC6X+CI0Njbi0KFDSEtLQ0lJCSorK2Gz2dDe\n3g7g8PaMvfbMbE3TFDH+gQMH4Ha7UVRUhL6+Pmnxzc/Ph8fjEZClJpfZHkcP8T1g1xmlcMxy9+zZ\ng46OjqP4Um2vaNXcxjIu12PRAcj3DA2e1pE9/KKGWQcfxwyVmTgXBh6bj01OTpZmCm2CQwMcUidp\naWlSTLTb7bLgpKWlybDIjo4OhEIh5Ofni1nQnDlz0NjYiL6+Ppm8wUkinNzMuXVutxuVlZVITExE\nY2MjWlpa4PF4UFRUhMHBQRmCmpubi46ODnlf29vbZaFnAw2vZ1FRkbQk87rqIBDrRWcqQxuYv/DC\nC+jv78cNN9wwpcecQJwaoMstND84Y2nbtcZEfXmtE4FHiw8++AADAwMCDBkZGejq6hKJkN1ul/bM\nYDCI/Px8RCIRNDQ0IC0tTZymDh06hPb2duTl5aGoqAh2ux2tra2YP3++0CpcfKjUaG9vx9y5c9HW\n1obOzk4ZwdPY2Cicb25uLoaGhtDS0gKv14vMzEzhaBMTE9Hf3y/OYTRnoRSM74vP55PWY4IhvV5Z\nyOOugr+nCJ7FrVgjekKhEADEpCJiZbk2m+0oI3N+gAkWuvWXk5RpN0lqgTQEzXJYH9AZe3p6Ojwe\nj1h2tre3i3EMkwGqO3ivzZkzBw6HA7W1taJXZucg9bsOh0MaTJi9FhYWChXV1NSEtLQ0US1wqjPB\nk6PfuYMaGBhAbm4uurq6kJ6eLj68bLThEExqymNx4Xp8kebSAUx6p5g2MP/e976H7OxsfP7zn5/U\nY0xizG7QpWSHxsushI/nTZ+oL284HB5zk0ZPT4+MQKdUiv38oVAIOTk5aG5uRlJSElwulxRuqCAg\nGObn56OgoEDG6XR0dEiFOykpSbbWKSkp6OzsRF5enojuS0tL4ff7cfDgQRkrw+/9fr9YNQaDQWlJ\nHR4eFmNvqkW0hWNfX5+01tKrV49W1x1f7PrSKgOd2VqtGPnBZoeaphI0AI+U6Wq6guBNekI/lkCq\nzXD4gdfUDUcgaQ6d9olstGF3GR3ZwuEwWlpaMDg4KPdcb28vkpKSkJ2dLRLBgYEBKaYODQ0hNTVV\nCpR2u13ka1yAMzMzpdhK6gE4PGnCMAwUFxcLXVVYWCieIuxO0zsIt9uN4uJiAEcP+YylldZgqzGF\n7+1kALE2MH/00UexZMkSXHrppeN+vimO2Q26tMKjKfVE3I4m6surh1MeK0zTxAcffCDFFmp2uXWn\nkoLZUmpqKtxut5iisIgzNDSEQ4cOiWcr9ZjceiYlJUnbbnFxMZqampCfn49AIIC2tjYUFBQgNTUV\nra2t6O7uFjnS8PCw/CwtLU1ae4PBIDo7O+H1ejEwMCAVe3Zwkbd86aWXoiRfGsT0UEo2E2gbRtIP\nzHJ1W64exWPtWANGznStHrp66rDmbjk2njQJFQrUuFJ/S6mfdh4DIJQCJ4j4fD6RzXFnwwYLNpHw\nnuvu7hZrTDqUUYrX3t4uBcrMzEyUlpYiMzNTpkeEQiEBXzau0GC9t7cXnZ2dMrnZ6/UiNzdXFkjK\nJ5m1sukmVoRCITFIpx6ZCxxwxNXNqpfW972W740ViLWB+T333IOLL774KEP1EyhmN+hSd8sP5URA\n91jNDWM5l+PpjGtubobX640qHLFZgtvgjo4OZGdnw2azoaOjQywAA4EAWlpaxKHM5XKhu7sbzc3N\nGBoaQk5ODjIyMmRsO31tk5OTZdCeYRwe8e50OuHxeBAKhdDc3Izh4WHhK6muoAkL23nT0tJENcLJ\nv8xGOCGCM9P0vDFmvez20ubksbLchISEqC28dZurs91YkjI9nFJnusx2dfbGkT4s8nGhSEpKkm40\np9MpbcwEUzZHcNfFLJXeHSxeUU/NLFVTFKQbCgoKkJ6ejoaGBhlymZiYiLy8vCjDGtIMhYWFMvOM\n711JSQlSU1Nld1JcXAzDMNDc3Izc3FwAkGm/PT09cDqdspixKGsNtsoToK11EqsKRGfE1vcsFu7o\nQp0ViLmQE3Q3bNiAr3zlK1i2bNnYPpjTH7MbdCkJG0tjwrFios9hmuaYu9oIsnV1dQgGg7DZbAgG\ng1LQ4PTgvLw8hMNhtLa2ih9ra2srfD4fSkpKkJaWJn4HiYmJyM/PR2pqKnp6euD1eqX1lmO23W63\n0BPkaTs6OmSxoJENv9LT05Gbmwun0wm/3y/TEtgpRR2p3W7Hu+++i9raWlkECaSkfNjxpS0SmS3p\ngZBDQ0Pw+/0ClCx+ERhHu2/5O37I+YGnDIp8LotkzHjZJkwPC1IwHP1OjTfPh6CqZ7wlJiZKp6Pu\n7GK2Ozw8LMdk2zRweGoHxwENDAzA4XAgMzMThYWF4h5HCZ7dbkdubi7cbrcAaU9PDzweD/Ly8hAI\nBNDY2Ai73Y6CggJpoMjKyoLT6URHRwdcLtdR1A6LaKWlpUft9MhFU9Uw1ux0LEBsLYLq0NkyC7MA\n8MUvfhHf/OY3UV5ePqbzmIE4NUB3Mjx1J8MM/VidcToTAyCzs1hRZ098V1cXXC4XQqGQKBqYiZJ/\n5cwszt3iuO22tjYYhiFbUzaPMCvLzMyUuVrs8yfdwK1tdna2vB6Opc7KypJR5PTHPXDgADo6OqRb\niyY32r+VW2vrVl3zhZS1aR7XqmywXlNmR/r3vKf1dSZY60KclqLRX4GLBJ+Xsi56/JJK4Sy0rq4u\neW1sKafpEHXJHE4JQLodh4aGpMEiEomI5jcxMVFUHVQtGIYh1E12drZ0pw0MDMDtdiM/Px82mw1t\nbW1R9BDNifLy8pCWloaWlhZ5j+hqNzAwgJSUFDFFcjqdkhXzGnKhmYiU0nr/jzUj5uP5HrEw/ulP\nfxovvPDCuA2upiFmN+gCk+epOxnAPVIrsRVsgSNqB4KhLsiwC2poaAgej0c+bPn5+aK5tNvt0hmm\nCyvZ2dmw2+2iMPD5fDLZNjMzE16vV+arDQ8Py1DC/Px8uFwu6R5jJxT73bu7u9HS0oJgMIicnBwB\nelbVe3t75Tm5dbdmutwia9UAs35SDhoQdaHMuj0djde1/svfs2FCUxY0RgcgGaDNZhNNsS4C8n1i\nppuamiryOcq9OC1jcHBQtuU04CblkJKSAo/Hg8TERJkqTFlURkaGUBKRSEQyYQJ0ZmYmHA6H7Gb4\n3hmGIQtnYWEhkpOT0dTUBIfDgezsbHR2diIxMRHJyckYGhpCcnIyQqEQDMMQCoO7PGa3LCxPtW9t\nrGKd7jTcvn078vLy8O677+Luu+9GbW3tlHv4TiBmP+hOlqfuZAC3tZWY11jfRJRTAUBycrJU+Jnt\nUdrV09MjDQqUjQ0ODqKtrU141e7ubnR2dsqsrkAggI6ODvFlzc7OFjVGS0sL+vv7YRgGUlNT4XQ6\n4XK5ojJhdv7QuMU0TfF3YIV8//79omQgFaLdw2hvaJpm1IQGv98v18kqEdNqAj0WXXeC8YNvbaaw\nZsCxfBr4cyvPSzWENjfXHhCmacLhcAi4EjRJI9C8h/60AKTbixk/h4iyQ4+LLV3JnE6ncLmkeuhy\nlpqairy8PDidTtkt0O7R4/HA5XKhra0NXV1donAIh8OyAHs8HplG4Xa75XF87fw3OTlZCm2Tnd2O\nJ3iNuPv42te+hj/84Q/o6OjAqlWrsHr1atx1113jchachjg1QHeinrrA8elsRwrdSqw/+FyxqcHU\nxYhgMIjW1tYoYPb7/bL1CwaDMsvK7/cjJycHoVAI7e3tcDqdwu+1trZKS3FWVpYAg9frFRka+Uy/\n3x/VqkuJFzNsnWlzcCSN3ZnZZWRkwGazSRbOxYFZC2kfcoF6uGSsggo1opRhWbvMrGHNcGNlu/p3\nVkqDSgRec+1DrKkJraig3y2B2eFwiAkOfQxIPVA6xsnMfIy24yQgd3V1IRwOi843OTkZAwMDsiDy\nfafBe29vL7q6upCQkCCaYHr9ZmZmIjk5GX19fTBNU1ziIpGI6MBTU1OlUYPz67ijYRZ/PBr3yQor\npWG32/HSSy/hoYcewv33348VK1bg7bffxo4dO7B+/fr4uJ6Ziol66jKOR2c7UnDOF81sGAQgbmut\nNzSzXWp2WUlnMauvr08+4JQzud1uJCYmykQG7fTV0dGBgYEBOJ1O5ObmRumZe3t7kZCQIDO4+LrD\n4bC4gxFQUlJSxNjbMAxRK3CKLmkAqwSM4EWzGj3Bl8Uq4MgcMj3VgYCkgVCrFBij0QixZGVa1aA5\nXGa3GlC1bpXZsW4h5vP4/X4MDg6K7pYFOd0y7XA4RAGiR7WzAEcKhtms1+uVaRQEaTZH9PX1yTgg\ntu7SGD0vLw82mw1er1c4fdIi5HD5PvD6JiQkiDqDO6yZzG5JadAN7+tf/zpsNhsee+yxEzWrjRWn\nBuiOx1PXGscr+bKGaR72TaCGkVQBM4rRuLFQKITOzs6ozIvZrr4RqUnOzMzE8PCwyMFcLpd87/f7\nBSg5w6u3txcAxDoQgHy4BwYGYJqmfOA4KoYf6N7eXlEkaL0tZWGhUEhcoEif6M4tFs40mJF+4bWy\ndovpJgit/dTZbSyA1cBsLcjozJWqAC4AGuytmS7pCCoe9AghBtULwBGv5r6+PrFuJG9OKR/nrpEn\nJpVASiglJQVer1dAmioJl8sl7ycpA1JQHJ3ucrmiZp9xMWXnGblcLjp8HzXNEqtwOVURK7vdvHkz\n7rnnHnzjG9/ApZdeOiNZ9wTi1ADd4/HUHSko+WLl/nj+DjjSFkl+kj9nFnWsm5nbURaQ+NyBQEBm\nmrEYw0kFlJQRsFn44hh0DvPjh5a+ACzaECxIFdAljHwabQ7Z+ECdLbNYbr25hbZ2j9Ezl1QKC2jM\nbKn95L/aH0FzsgRUfe2sma5+P6yAzMdpxYNWSWjQ16DKLJf0AsFaZ8A+n08oFE4KJm+elpYGm80m\ntpD9/f0y8DQ9PV0oH04s9vv96O/vF6UBOwK5u7DZbJLlcgQ8p4EkJSWhp6dHFCimaaK3txcul0u0\nxampqbJAsohIcyJrEUvz6lMFxFY52vDwMO688050dnbi6aeflgThJIvZD7raU3ciJuTHo7PVfzMS\nb8s2XKsvrLVQxPOlHpOvh85g5PAIEmwb5WBK9s9zDExXVxcASFELgFAQpCi4rQyHwzIJgdxuZmam\njOPmhASduVr9Bux2u/gokC4ggOhGCBYKmVXpLjECtvZF0JmXVi7oBUlztda2YP0ecWEg6Ft9HfRs\nNqs2mNk4VRgEZ/LeVCgEg0EARygTdm4RjHmd6FtLEA4EAnA6nTKynsemRwKN4h0OB4aHh+VvCL7B\nYDDKA4PvGw3jSZkRbHkPJiYmSgOMNUZSE0wWEFubLex2O7Zu3YrbbrsNN9xwAz73uc+dbNmtjlMH\ndCdqQg6MfXpELAnYsXhbIPbNDBxx1CIdQVMX/Tf8APE1kjJwuVyS6ZumKdkw5WihUAgul0vcsFh0\npLEOK+02m010p1Ry2Gw2yYYJPpTqsCWUnCabPDSQ6q4uLjb0LbCCnlYwWE3KY32NFlpipr8HjrSq\n6i44ni95bAKlYRjSZaazUWa8bALRTRZ8ndwNEIT1xBJeF44y0tMh6GDGeWCkiEzTlCYH8vDsEmQ7\nLzNbLs7cZfh8PiQlJUVx01RljBXcJguISb9wQQoEArj//vuxd+9ePPvssygqKhrT+ZzAMftBl1kk\ni1gTKQSMZQJFLL3tWHjb0Z7PeiMz27LZjoz0oYIAgGRAGRkZsvWkpwHnkCUmJopBSiAQEDMWbikz\nMzMlu6eEidkuAYfbZQKsdt4iH8hOM26lqfMlbcAskK+P2bH+0gMpNfdrpROsFIPeZWiAJVhFIpEo\nxQS30dYmCSvw83y1lCwpKUn8Gii5AyDXhaA8GhjrRg3y3QR6mp1TFubz+eB0OqXjLxKJyHWleTwB\n2WazCX9LTwVmxtzRMMNmljuaof9Y43iAGEBUdpuYmIidO3fipptuwtVXX41rrrlmyvXA0xSnDuhO\n1A8XOKyzTUtLO8ofdDS9LYtQk+Upyu25BilqXEk5sB2zv78fwOECGdtI+b3mEpkBk99ji6vP55PK\nuS5YUffM5yPokONNTExEIBBAf3+/ADFBSysfCDrac5WgREAmyAGImd3ye13k0u/JSFmVtalipJ/r\nYh5fp6YPeH56SjAXEmvnWnp6OpKTk6WoysWM14bX2jqdmCoIdoaxAYIm8aZpygRi0zRlN0K7TbZP\nk1IgZ8/2ci6iBMGJfk5Gi5GAmO/Vpk2bMH/+fPzmN7/B1q1b8Z3vfAeVlZVTci4zFLMfdJnVTNQP\nF0DMbHmsetvJChbPAoGAgG4gEBATbKoidLWX0iBWwH0+H3p7e5GYmChOWKyoa6AlyOpMlg0BGkS4\nqDGjI+Bwm8ysl4BDgOLioQtPugkiFr9tBUmrjlbzu7rIBiBKaqZ/buWBTfPISB+d+fK6sktLnxdf\nL7ld3iN+v18KjOSO6fPARSolJUWyUAJmKBSK6f3A68b3jobnnNBB/p6OaOSKAcjYHe6Q+P4SrLWh\nz3RwpryX+XrC4TCuvPJKvP322xgYGMDatWuxZs0abNy48WTmcK0x4guZWqv3GYhYgvuJPEcsKoFZ\nCwtSU3GjMFPUoMEblh8WbhvZyw8Aubm5MnwQgEwr4M9sNpu0AwOQhYrG2pzPpfnB9vZ2oR2Sk5PF\nYlKbuxAkBgcHo8CL8ipKkrQ/Aq+rlo/xmuusVj82EAgISFoVCXzsSC5kDP5Oy/r0e629HKzgzGy3\nr68PoVDoKDokPT1dRhnRYW14eFhap1m4crvdYqXI3QY9bbkbyczMFOOdgYEBAW/TNEUaSPUD/57m\nQ2xwYDLAxQPAUW3PUxmRSETuTXpxPPXUU/D5fHjttdeQk5ODHTt2oK6ubjYB7qgx6zLdifrhApAK\nPzk0BjMQtkxOlAsbSzBLoOqBW3c9y8rn84lXBDMtFq1YbDEMQ7J3FnSoQkhJSZEPBAtipExo9MKC\nDjM6brcDgYCAsZZZkXrR2adV86rtFQlS1i40ayPESBIxXquRfqazXP5LUNdFO61UsBruWBcGTQ1o\n6VwkEonqvGOxyuFwRBXkuEtKTU1Famqq0Ea8/omJieKnzPeM1p/cyWiZHgBJBnjfJiUlCafNa2zV\nF09FsCbBe9HhcODAgQO4/vrrsW7dOtxyyy0z1oAxTTH76QWC00T9cE3TFJMSfmiYWUw2b3s856Td\nsLQJDIt3CQkJkilxG0vDbHoFcOKsYRhRW2NeNxbrnE5nFEVAzpVUB7NWZlLAkcIVs1ytStCie4Kf\nVSOrGyas2aVu2dVfsYo2vF7W+1rrcrVrGY8HHOEhdWEt1jHZVEAw0a5kGsw0b035oLaVZCZMWoKS\nNAI0PSvC4bDQQ9RJh8NheRwBnNIxqhN0Uwn/Px2AG4lEoj4vhmHg+9//Pp5//nk89dRTOP3006f0\n+Dp6e3txzTXXYNeuXbDZbPj+97+PNWvWTMehTx3QnYgfLrMfgoXewupW0emurpKj1Ibb/J7Bwpph\nGAKUlApxCwtAFg2tn9VZHQtoLBRFIpEovpHgSlDUhTDNf2oJFq+XBuVYRuJWFYI1442lv7VSBzo0\nd6ubLKy0heaLNTBrzbDenlsXFUrCmLFbdbx8r7gb43XlY3hfUUbH3RQLcwTaYDAoCyJpHdYvqHnV\nkjyer17guGCMdt3GGzq7pVSwubkZ119/PZYtW4Z77rln2n0SrrrqKpxzzjm4+uqrpWYxETOr44jZ\nD7oAoqRJx+OHO5relh9AneVQ7qSLP1PBR3EhockKCye6W4uVaQBCO3BLqxsCuN2lTIlbWz5ONzNo\n+RYbOwgWXIy0zIsgw3Ph+8DH8/xiaWNjZbuaCtDZPYGNng06s7UCMf/VAB6LJuB15DGs8j3NT2up\nG0FYNxfohV9LzwBI8U1fJ03R8Pdc+EhX0NOC9A7pCypNqGihCoFUArN/ni+LanwtvH6xJF3jCWa3\nPDfDMPD888/ju9/9Lh599FGcccYZ087Z9vX14fTTT0dtbe20HvfvceqALm/6saxmscCWPeoj8bax\nNLXkAfWHeqLtkgRH8tM6a+GHlaBkmqZ8kFmlZhbLjIOcIOkBh8MhcjdSFNrzQE9KCIfDAowEGgKD\ntZOLv9fdZQRvnq+164vX0JqJahWC1UtBf3+s93ikTFc/F/9vVSroBYi0Ei0qtZSMel5ebwIs30su\nQhzro2ezMUvldeFCxt9pT4KUlBRZIPUCxr/j6+L9oq+99ZqM1KBzvEDMe4uvvaOjAxs2bEBxcTEe\nfPDBCY3Pmkjs3LkTX/rSl7Bw4ULs3LkTK1euxOOPPz5dHrynBuiO1VM31tbSykMdD28b6wbm9s4K\nxMcKZjMjSdEIEtYtLY/J3zHD0XSJnmarFRhay8ksTYMru9i0x4I2S+Fjec2YGVu7y/R10X+rr5FV\nbaCvLbM3q3ZX/2uNkegJfmm9r37/dLGPgKTpB724aKmXBldyq3wc30/9ODYsWDl2tutqLS1pCV0o\nI9DzHGknqoFTJxWj3cPWnQUz+1jvkb5Xw+GwJAYvvvgiHnnkETz44INYt27djCoSduzYgbVr12LL\nli1YuXIl1q9fD5fLhXvvvXc6Dn/qgC61pCNZM46mt+UNPhk3ykitviPR8jbSKwAAG6FJREFUEppK\nGK2FGIBkYdZtMcGXH3YAQjcw82FWyswVgFACzIL53ACiMjlte8gPs96yaqMaK41gLXTpwpSVe7Xu\nJvgzHVZOMta10tI07gZiAbD+V4OyLkLpBY7/14CsFx5eW03NWIuKVsUBVSC8B7UOOxwOy66L15et\n2dzxEGyZnfO18PzGE6Pt6nivseU4Eong5ptvRnJyMh599NEJ+VFPVrS1teGMM85AXV0dAOCvf/0r\n/uM//gO///3vp+Pwp4ZOV+s1rTESb0u+dLL1trparI/PG5cfJv3h4AfpWFk2Xwc/2FaOl5kQC4oE\ndJq9uFyuKJCnnCwh4cgYdD4nOUtrMYy8cCRypKWUGbHOInkeBHWtBACObGd1pT2WplYvTvp91o+1\nfs+f6X9H+rlVJaEzPZ6XtpvkosjjcdEhwAKIAmC2R2t+XMupqDjhVp3NCw6HI2rHwHtEqygARHG3\nfK8mArh8zlgcON97u92OX/7yl9i4cSOSk5OxbNkyXHLJJWhvbz8hQNfj8aCkpAR79+7FvHnz8Oc/\n/xkLFy6c6dOaXaALHN0cMRJvy64uTkqYjvOy3sDU/fLDRLnaWGkJ3YJKAOQWEYCAOQtfBGPt6UCz\nE253maXq62LV5BKAuKXUAMtClwYJvg7tjTuSzpbZMwFNvy4NlLHANNZiO9bMltllrOYN/X/r7kJn\n85R7aaUJFyi95WcHn17AeH2oQOF11KBKjlf/H4C8B7zWWnUzmcHqPw2SBgYGUF9fj0suuQTXXnst\n6urqsH37dpSVlaGqqmrSjz+eeOKJJ3DFFVcgGAyisrISP/jBD2b6lGYXvcCMoLu7W+iFkXhbzaFN\nd+jCiZVKGImWGIlXA450r2nu0woUBBMCGr/X/B2fX9MU3L7qLTMQndmNRino12VVAlhlYqPxr9ZC\nGoCjimm8l/Uxrf9qKmMkWkMXo6yvSWeSWtHC16czf9INvPc0BaN3BHxenqeWJfKxfC5NJVnBlo+f\n7LAW8+x2O9544w3ccccd2LBhAz7zmc/MKHd7gsapwenyA93d3S2SMWYtlC9NJm97vKGlWWN1Ixur\nWkJnkhrMAESBLx9LukFna8ARQb8GYQKCBg1rlmcFVw1GuiCjs3f+y2Pyuax/r2kDDbCjFdE0OMdS\nKeifaY6c2TwXZCvdEOu16OukC3Kx/CV0Js3fE1D17gA4vKhQAaGpDmumziLoVGW3emqJz+fDv/3b\nv6GhoQHPPPOMDLKcrohEIli5ciWKi4vx4osvTuuxjzNODdDltpb9/5rwJw83HVRCrKAUbTzqCGuM\nppbQ234+zlqh5/fcoloBhSDKa6fpCGumba12a2DXXK2V49ULgZXntYJjrNfPx1mLX/r3Gpj1NYiV\n5erj6ufVXLNeIK1AHEuVYc2IdTGVj9HXX6sjNE2kgZtFMz5W624nM6zZbWJiInbs2IGbb74ZX/rS\nl3DVVVdNSNc73nj00UexY8cO9PX1nbSgO6s43S9/+ctoaWnB8uXLkZaWhvfeew8PPPAAnE6nbIVj\nOVpNZVCeNZlZNoFRA7f+YHPqKzuarFwvwVZvt1lRjwXCukhmzeKY8VL1wGxPZ+hsp9WPB46MNtcZ\nIH+ueVXGSJyu/mJYgdj6fx3a+8EKxpo20OCrC2r6/HThUC9SycnJ8hh9/fh767VnEVYXDHndtKOc\nPsZIXPnxBrNbm80m7mb//u//jrfeegvPP/88ysvLJ3yM8URjYyNefvll3H777XjkkUdm5BwmI2ZV\npmuaJv7v//4PX/3qV9HY2Iizzz4bTU1NqKqqwqpVq7B27VrMmTMHAKK2cjpDmawbV6sD+OGczsxA\nAwC1pJSPAbEr2wQbvcXVz6X5Yb1g6Qza+jjrF4+teXb9NRKgMmIBqZVysP6Nfh36eRg6I4/FJ/Px\n1uugpWhWysRKIcSSpOnz4rWwtpnrv2GRTDfGjJRt834eabcw0j1jNRj/4IMPcOONN+Izn/kMvvKV\nr8xIdsv41Kc+hdtvvx29vb14+OGH45nuiRCGYWBgYABXXXUVrrvuOrEe3LNnD7Zs2YL//M//xAcf\nfICkpCQsX74cq1atwurVq5GZmYlwOCy2gSNtE8cauptsutQR1uCHPBQ67MGqncSY2WkOkn+jAQc4\nwmky42Xw76zbc/1B1wDI7E9nsfpYmhe28q76NelimZXTHY37HQlwrQCvs1vrORIAtcEN/5YAzEKW\nBkruSPR5aB6Z8is+TmfdWpHA68fHjLbbYe0AGFuXWTh8ZHxOWloaIpEIHnvsMfzpT3/Cc889h/nz\n5498s01DvPTSS/B4PFi2bBk2b948Jfz1dMWsynTHEqZ52HF/+/bt2LJlC7Zu3Yq2tjaUlpZi5cqV\nWLNmDRYtWhQltwJGVw8wIpHRu8mmM/QWUbcz6yKQpgv03xFsrCCsMyrrY4CjM02tTLAWrvR9dyza\nYKTv9b8M6/W2fq9BC4g2PLdmqvpvrK/LCsb6eNZFQO+mrNeGHK11sSOokk8fb4ymhuEXqTfes/v3\n78f69etxwQUX4Gtf+9q0u+rFim984xv4yU9+ItRKf38/LrvsMvzoRz+a6VMbKU6NQtp4IxKJoKGh\nAVu2bEFNTQ127twJ0zSxZMkSrFy5EmvXroXH44m6gbV6gBnlsQZSTtdrIfCzYDfauegPn942E2Q1\nn6mzNwCS2elMD4huUqFETfOTI30xjsXFHu+1jVWs4s9jAflISgfrsa1/b82G+RjNSwNHlAh6UbZy\n2tbFcLJC004EW+Bwt9bzzz8Pp9OJnTt34rvf/e50WSAed7z22mtxeuFkD5vNhoqKClRUVOBzn/uc\ncFtvv/02ampqcPfdd6OhoQFutxurVq3CmjVrsGzZMhiGgcbGxqhJAQBkuzidwKs55OOZaKE/3Ho7\nTABhxVw/VtMT1myPH2p67+qOPGuhi8fUIK2zYGvWGAsgGaO9Vr0VHw1AdfarM1LrY3VWzB0Dz4eP\ntwKm5nl5Psx8taJjqrfNmnbSO7KCggJEIhHU19fD4XDgwx/+MK677jo8/PDDU3o+p2LEM90xhmma\naGtrQ01NDWpqavD666+jvr4eiYmJuPnmm/GhD30IFRUVUcWTqSrSWWMkR7KJhgYXXUjS94ymHGIV\niUYCOGvjwrFoA+tzjCWsNMhoESuzthbV9LnHokT064+VuRrGkTllvG7TzU1GIkfG57D77ac//Sl+\n+MMf4rHHHpPs1u/3o7e3F3l5edN6frMo4vTCZMaOHTtwwQUX4KabbsJHPvIR7NixAzU1Ndi7dy9S\nU1OxYsUKrF69GitXrkR6evqYtJzjienmkGMpEXQnlwZeazEqVoY5UnFLRyxwi/U3Vo5Yd3Ixex5p\nQTjWcWKFFZw1GFuvEzNZYOJ+COMN0zx6fE5bWxtuvPFGVFZWYuPGjdNleXiqRBx0JzMikQja2tqO\n6sYxTRO9vb3Ytm2bFOm6urpQUVEhkrX58+cfVcA6XkN0TSVoDe1MBEHFMI74F+gikbXpQet4YxXT\nRluECJzHAsWxgPlIj9d/oymQ0R6rqRZu27WzmKYTxvoeT2ZEItG2pTabDb/5zW/wxBNP4Jvf/CbO\nOeecaT2fxsZGXHnllWhra4PNZsO1116L66+/ftqOP00RB92ZikgkgtraWinSvffee0hISMDSpUuF\nH3a73VFFulgSH34oaJIDYFKphPGEVkjwwwwc3dmlOVurrpSArcFoNAAY6X5lZs3nivU8Y31eq8LB\nShlo7teq2QWOmHrrUTq6IcKqp53Mxgbra7KOz+nu7sZNN90El8uFb33rW9M1uiYqWltb0draimXL\nlmFgYAArVqzA7373O1RXV0/7uUxhxEH3RAnTPDzvipTEtm3b0NTUhPz8fNENL1myRMxNmA3rxgKO\n2TlZFBIMq8xK0w7WxojRwkoFaBvKsYKXFVBjcc/6nDR9MBrwa1PvkaRW1sWHNMxkdksyu41Ejoz2\n+cMf/oAHHngA9957Lz760Y/O2P1jjUsvvRRf/epX8Q//8A8zfSqTGXHQPZHDNE00NjZKke6tt95C\nIBDAaaedhuXLl2NwcBCBQABXX321UBMscFmNVKb6PJk5TTatYX0ea4cbEE3DaE6Z12K0Ilys18J/\nR1NFjCUm47qMpqc9lj7cGtbxOf39/bjtttsQDAbxxBNPIDs7+7hf41RFfX09zj33XOzateu45hqe\nBBEH3ZMtAoEAXnjhBdxxxx0IhUI47bTTAAArVqzAmjVrsGLFCjG+1lvW4x0PNNagYQ8wM7SGVoVo\nkxftdDbdXCkQnVFO1MhIh5aS6S+tiLECsTXTTkhIwP/+7//izjvvxNe//nV88pOfPGGyWwAYGBjA\nueeeizvvvBOXXHLJTJ/OZEccdE/GuOuuu1BaWoovfOELMAwDnZ2d2Lp1K7Zs2YI333wTfX194iux\nZs0azJ07FwAmVKSzBjXLnFg7k7SGtYCoPYRjOXVN5Q4gFl86HTsN3digF1vDOGJ8npWVhUAggHvu\nuQfNzc145pln4PF4pvTcjjdCoRAuuugifPSjH8UNN9ww06czFREH3dkY2leipqZmRF8J7ZR1PIYo\nBBVroWwmYiyZNkFpJKtJnQ1PBCCtfOlMFjOpu2UB9r777sOPfvQjkS5effXVOOuss5Cbmztj5xgr\nrrzySrjd7pPaLewYMXtB98knn8TTTz8Nu92Oj33sY3jwwQdn+pRmLEwztq9ESUmJgPBpp50W01dC\ng5JpmlGFspmasMHXZHW+Oh7A1LTEaK95rNKy6c5uRws9PiclJQWBQAAPPPAA9uzZg0svvRT19fXY\ntm0bPvnJT+KLX/zijJ2nNd544w2cffbZWLx4sSyAGzduxIUXXjjTpzaZMTtBd/Pmzdi4cSNefvll\n2O12eL1euN3umT6tEypG85VYsWIF1q5di/z8/KgMkeoCTv6djiJdrNBTC8YyZWMsoRUPI3GlsV6z\nVes6k9ktF0VtMP7uu+9iw4YNuOKKK3DdddfN6K4kHgBmK+h+5jOfwb/+679i3bp1M30qJ01YfSVq\namrQ0NAAh8OBzs5OLFmyBI888giSk5OnrUgX6xw5cXY6Mu1j0RLMcJOSkk6I7FaPzwmFQnjsscfw\n+uuv49lnn532gZCvvPIK1q9fj0gkgi9+8Yu45ZZbpvX4J3DMTtA9/fTTcckll+CVV15BSkoKHnro\nIaxcuXKmT+uki3vvvRdPPvkkLr/8cjidTuzYsQNDQ0Oorq6WIh19JbThzWR3WTEDZWPBTHba6aKd\nNsMZDy0xWedjzW737NmD9evX46KLLsKGDRumPfuORCIy2rywsBCrVq3C888/P9uaHMYbJ6/L2Hnn\nnYe2tjb5nh+A++67Tyb/1tTU4M0338SnP/1p1NXVzeDZnpzxoQ99CF/+8pejKtyhUAjvv/8+tmzZ\ngieeeCLKV2LVqlVYtWoVkpKSxFFsolMLrMWpmfRw1YBLxQZ/zmyY0qzpMDWyjs8xTRNPP/00fve7\n3+GZZ54ROeF0x7Zt21BVVYWysjIAwGc/+9nZ2Fk26XHCg+6rr7464u+effZZXHbZZQCAVatWwWaz\nobOzEzk5OdN1erMizjvvvKN+ZrfbsXTpUixduhRf/vKXj/KVeO6556J8JdasWYPq6mrYbLaYUwtG\nygw1wDkcDjidzhndvmuVhHXqB/0lGFZaYjIWHx2xiogNDQ24/vrrcdZZZ2HTpk0zWuRsampCSUmJ\nfF9cXIxt27bN2PmcLHHCg+5ocemll2LTpk0455xzsHfvXgSDwSkF3Icffhg333wzvF7vCdXVMx1h\nGAYyMzNx/vnn4/zzzwcQ7Svx05/+NKavRG5ubszMkGDk8/lgGDM31ogRK7sdix0kwVU/jzYIH+vi\nYw3r+BwA+K//+i/85Cc/weOPP45Vq1ZN8BXHY6bipAbdq6++Gl/4whewePFiJCUlTenojsbGRrz6\n6quylYrHYT+IqqoqVFVV4corrzzKV+LWW29Fc3Mz8vPzsXLlSqxevRpLly6FaZqora1FYWEhgMOA\nxInBU12kixXMbg3DQFpa2oSOT67bOheNQHwsWiJWdtva2oobbrgBCxYswKZNm2Sy8ExHUVERDh48\nKN83NjaiqKhoBs/o5IiTupA2nfGpT30Kd911Fy6++GLs2LHjlMt0xxtWX4m//OUvOHToEKqqqnDN\nNddgxYoVKCsri9qmj9bqOtnnNhEN8ESOG0stoQeFdnV1oby8HL/+9a/x9NNP41vf+hbOOuusE6qN\nNxwOY/78+fjzn/+MgoICrF69Gj/72c+wYMGCmT61EyFO3kLaiRAvvvgiSkpKsHjx4pk+lZMuDMNA\nSUkJSkpKkJCQgJ/97Gd49NFHMW/ePGzbtg0PPfQQamtr4XK5JBteuXKltPhONk/KsG7fpzO7ttIS\nBH+/3w+73Y6WlhZceOGFCAaDyMjIwJVXXik0xYkUCQkJ+Pa3v43zzz9fJGNxwD12xDPdv8doKomN\nGzfi1VdfRXp6OioqKrB9+/Z4sW4cMTAwgEAgcNQuwTTNEX0lOKF53rx5UZaIwPgcuGYqux0prONz\nbDYbXnrpJXzzm9/Ehg0bkJiYiG3btqGurg6/+tWvZuw843HcMTt1utMRu3btwkc+8hE4nU7ZKhcV\nFWHbtm3x+VFTGGPxlcjKyjqqq8zawKEBdSTT9ZmIWONz+vr6pLng8ccfR1ZW1oydXzwmHHHQnayo\nqKjAW2+9NekfiK9//ev4/e9/j6SkJMyZMwc/+MEPZsTV/0QN0zTR39+P7du3o6amBlu3bkVraytK\nS0uP8pXQ89lYpOLP2Fgw09mtdXzO5s2bcc899+C2227DJz7xiRk9v/i9OCkRB93JisrKSmzfvn3S\nC2l/+tOfsG7dOthsNtx6660wDAMPPPDApB5jtsVIvhKLFy8WWqK7uxs+nw+LFi2SoZHTMaE5VsQy\nzBkaGsKdd96Jzs5OPP300yeEG9h03Yt33303srOzxdrxjjvugMfjwVe/+tVJP9YMRBx0T6b47W9/\ni1/96lf48Y9/PNOnclKF9pV47bXX8Nxzz6G9vR0XXHABFi1ahFWrVmH58uVISkqasgnNI0Ws8Tk1\nNTW47bbbcMMNN+Bzn/vcCaVMYEzlvdjQ0IDLLrsMO3bsgGmaqKqqwptvvjlbaJW4euFkiu9///v4\n7Gc/O9OncdKFYRhITk7GGWecge985zs444wz8OijjyIQCKCmpgavv/46HnnkkShfidWrV6OyslKa\nIyZSpBsp9Pgcp9MJv9+P+++/H3v37sVvfvObE1rbOpX3YllZGdxuN3bu3InW1lYsX758tgDuqBHP\ndKcxRlJI3H///fj4xz8OALj//vvx1ltvxSvVEwyfz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = plt.axes(projection='3d')\n", + "ax.contour3D(X, Y, Z, 50, cmap='binary')\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_zlabel('z');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes the default viewing angle is not optimal, in which case we can use the ``view_init`` method to set the elevation and azimuthal angles. In the following example, we'll use an elevation of 60 degrees (that is, 60 degrees above the x-y plane) and an azimuth of 35 degrees (that is, rotated 35 degrees counter-clockwise about the z-axis):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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xiWYwGOZVdIWjvoXPd+H7WKVSEY1G5QdbNKkymcw8KSEej8ubkDqEX1hsrtDp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\nFQBQX1+PrKwsjBw5EhMnTkRmZ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9CozYCYtRKAZapB/j2/J7gyq1cDtJzVUqbG4hOBzuVy\noaysDB4eHsJ5jJI3cq3MRsnZyiNzaM9InTNfZzZI8LVhCzGzXJ4vFS5y04ZsZC7Pf6PEjYDbExUB\nJWGkK4CzdZJPPvlkwEjCkpOTcfDgwYE8xHIQdDsT9fX12LRpEzZs2IC0tDQsX74c2dnZAM6NhWlv\nLppau3gh5Witra3C+4AdXe48ChRFQWhoKLKyshAdHQ273S7aWglCDDnrZUbIVlmCgVyIZAecDMis\n7ssTehsaGnDs2DF8+umnwoZQHqlDLwq2KMt6WnWGy/NUB7ek/FoGZmbAzPhlioHZvcz1+vn5wWKx\n4Ouvv0ZzczNuuukm4SlM/2HKvOQuNFl3zGYG4JyZuBpweS7AuQUDQBt5o2ygo85wfX19ERQUJKZe\ndDfIvZO7bm1txR//+EdUVlbi9ddf7zeXMHWkpKQgJydnkNO9VEJRFOzevRtr1qxBZWUlFi5ciBtu\nuEEYzajtGZnhqif0Xuigx0F7wEsgAc4CYGZmJuLi4tpInGSvBqAtULkDX0qWuBtQO7jxsQwGA44f\nP46ioiLRkMGMWK7Ey+BLaoGfyTerXbfkc1V/z+08Fw+CKwGd5ysDvtz6y4XDbrfDz8+vzRh4dvRx\nG0+vYhbBKJGjIoLyORlw+ZqTspFpBi7eMperBl1SN+Hh4W5nD3Yl3LmE3XfffcjIyLhgkrBLKAZB\nt7tRUVGBV199FVu3bsWMGTOwaNEixMTECM0iBerMNvrTz4HNC/X19QJ4STPITQCUXhFgExISEBsb\nKywO+VhyyOArm4uzpZhARO7Vy8sLpaWlMBgMMBgMMJlMgndm0Uf2seV5ycCizlblhg65uww4v6DG\nn8kVfnWm7E6PKu9iZLkUwY10C8GT3Cv5WYIVAFEoY0bK/5H1tGrABSCorPZkbqQTmHGHh4f3iFOV\nnyfblS0WCxYsWIDbb78dd999d79e1xdpDIJuT6OpqQn//ve/8frrryMiIgILFizAunXrMHfuXMyc\nOVNkf32p+e0o6NPA7WhdXZ0AYAKaTDXwfMlrcvseHx+PuLg4REdHt9HzMtrLfAmoHE5ZWlqKmpoa\nUdyjMxcBiu298msl+yqQ3yTQAG0LZTwX/txdENzk79ujGAi2bKLgeZAuIufKBg125fFcSTew7ZrK\nDhlw5cm+BFzKwAj0st+zuugnc7h0cWOTRHdDLvyycaekpAQLFy7EU089hRkzZnT7sX/iMQi6vRWK\nomDbtm1YsGAB0tPTceedd+K2224T+kpmbRey3didPI03E9tumZUTcJmlyu2tAEQBjU0RBOLQ0FCU\nl5cjNjYWDocDTqcTwcHByM/PR1BQEE6ePCkAhLphZobUscoVeIIvXyu5M5CLATNQ0gpyW6/M8arf\nH/VnOTOmmkEe5cPHlkcAsQhITwa+bjKFRDqBmSqBldkuv5YnQZAakTNbPncCrsxlc9FTKxQoQ1MX\nHLsS7iRhP/zwAx588EG8/vrrvWZu/hONQdDtrTh8+DCuv/56PProo7jjjjuwYcMGvP/++7j66qux\nZMkSYT4ja37lLKo3gzc6izXubj4CnuzXIEuwSDXIqgBmwbJJDDWp8vaZygVSAbL6gDw3+Vp5C0v+\nludHcJVBUJZyyaCpBk818Ko1usC5wpS7ohyANmDLLJXPiedH6oGLSHNzM/z9/YVag34R5LOpXuHX\nlIUx8+bjy+cjA66sx5WzWxq987E7a1KjDneSsB07duB///d/hSRsMHoUg6DbW1FfX4+DBw+2GZrp\ncrmwdetWrFu3Dt7e3li2bBmmTJlyXrtxVzW/HYWcpfzY5GMCHq0h5cYDNfBS1sQFg5kvfWRlj96W\nlhYhoVIURXg7EFj59wQt+fd8PdTgxsxWzkDVngruGh6Atl65/F7N48r/Rw6aCgICF7NjmSqQrRUB\nCKDic6XrF9ul+RjUMKt1uADaLDDyosDXRZaF0TxHNgnia9FVi0aZiiLl8eabb2Lbtm3YtGlTjwty\ngwHgUgfdl156CS+//DI0Gg1mzZp13gy2CxWKouDEiRNYu3Yt9u3bh9tuuw2333676LOnoqA7mYkc\nra3dm17Mc1BnvVQ1MOMj8JJykD/LwEtJFLfm1OqS2iB3SQAjKLApg8cnwDDkhgi5UOeOUpA5Xvl9\n4O/k72U1BDNLGfj4N3xOanqBDR3kd9lyTW6X7ysfn2oXmT7g85RlYcxo+TPZcIcFPGbVHYVMb/F/\n1fSWO0nYU089BZPJhFdffbVP1Te/+93v8PHHH8PX1xepqanYsGFDjzrmBnhcuqD71VdfYfXq1di2\nbRs0Go0Yjd3fYbPZsHHjRrzzzjsYNWoUli1bJtqN1fZ5XWk37o3pF2xP5Yc7jlcGYRbLCLh8DJqw\nUDoFQAy8pJSM8734Pf+WAEKgUIMrcC6jlWVjPKY6A5bbf/m/8odcmFIDLbNlZr3MbPl60/hHNjan\npIvvJ3cw/Fvy1nKhjOcrF8vULb2kFZiFyu5mnQ1m5OqWdhY8ZR+I++67D1lZWXjiiSf6vPj7+eef\nY+rUqfD09MQjjzwCDw8PPPPMM316zH6MSxd0586di+XLl2Pq1Kn9fSpuo7W1FV9++SXWrl2L+vp6\nLF68GDNmzBCdYNxS88boiHroTT8HApq6fVgNcPwawHmZL4GKgApAfE3PBNmYXDa3IX9K8CPA87gy\n/ypnrPIEDNnohp9lYJNB2d3j8PwBtOFQgbYTI8i18tyYhfIxWCRjUwhDzmRZRCPQytyt2rSG/K26\nAac77zF3GXK7d3BwMCwWC+bPn48777wTCxcuvOCSsA8//BAffPABNm7ceEGPewHj0gXd0aNHY/bs\n2dixYwf8/f3xt7/9DePGjevv0zovFEVBcXExXnnlFXzxxRe46aabcNdddyE8PLxNuzGF+Wqdal/6\nORD81eArc72yBy9vZl47dCXj36qpB7bUcgtODSypDbmgBKBNdisXzNw1RLgLNeXgjgPmVp+vMykS\nZvCyXI3Pnz8DIJ4nqQYCp9yxJ9MJcuGMz0MGXLmzTM72exrytaPRaPDAAw/gyJEjaG1txerVq3HD\nDTf0ynG6GjfccIOg3y7RuLhBtyPnocceewxTp07Fiy++iP3792Pu3Lk4c+ZMP57tj4fT6cSmTZvw\n5ptvIjU1FcuXL8fIkSMB4LyiCFtnAfSKH29HwayWhTYZfOWGCrkxgVtteYoBv+bPmfXKfgdyQYn8\nMblQNQDLigUeV6YV1J/lzJcAK/PBagrDnZSMICxzziz4yfSIzFMTmAGIrFbufpOzXHeFst7e3rPY\nCpy7dvbv349nnnkGTqcTeXl5uPPOO/Hss8+eN3Wku9HevfrnP/8Zv/zlLwEAf/7zn3Hw4EF88MEH\nvXLMARoXN+h2FDNnzsTKlSsxZcoUAEBaWhq+//77gWz5JkJRFOzZswdr1qyB0WjEggULMHv2bFGM\n4bQJFnX6wiKyvfOS1QTtcb7MduXuNoIigZRAIhewZDoBOAdQctcZs0c1LSDztvyZu88yxeCuXVjO\n1mWeV1Y2yG3CchsxAAGwzNrlhUItB1M7nLHg1lP70I7CXbF1+/bteP7557F582YMGTIExcXF+Ne/\n/oWHHnrogtELb731Fl5//XV88cUXQjlxicalC7qvvfYaysrK8NRTT+HkyZOYPn06iouL+/u0uhxG\noxGvvvoqPvnkE8yYMQMZGRl46aWXsHXrVqGNlbuSLsRNQgBSA68MvgRKGXjVfCuzYeBcNi3zrWoa\ngUUogi8fVy0JA3De9+rz5/Ute0rwGGoKR51pc3FgkYuPw//lOcngrgZadzQCM+O+eg/dScLWr1+P\nHTt24L333us3SdiOHTvw29/+Fl9//fVFkRT1MC5d0G1ubsaiRYtw6NAh+Pr64rnnnhNZ78UYTU1N\nWLp0KT744APceOONuPvuuzFhwoTzNL8Xqt2YnCABhDRAe+ArZ5Ay4MpNFnKmy+/VjRAyKMnKBJmf\n5fnxswzMwLnimbtsl//DYxEkZS2vDMzy3/Gc+LUMtmoKQ1YkqDP3voj2JGE1NTVYt25dvxoypaen\no6mpSQDuFVdcgZdffrnfzqeP49IF3UstHnroIezcuRMffvghrFYr1qxZg+PHj+POO+/ErbfeKkyv\n+2rEkBzsWvLwaDvfTeZm1XQDgVNdvCLtoM5cCcwA2gCymrbg7+W/62yoOV51ViqDPsFUXgzaA1s1\nbyuDtzujnb4OFkNlSdi9996L4cOH4/HHH+/zBXow2sQg6F4skZOTg6FDhyIwMFD8zGw2Y/369diy\nZYtoN1ZPNwZ6PmJIDperc/7AciFK3abbEefLY8igyq/VICsfy122qw53xTX1h/o47hov1N1s/F7t\nAyFztsxoWRDt7U5Ed6Eo57eDm81mLFiwAHfddRcWLFhwwSVhgzEIuj2K5557Dg8//DBMJlO/mia7\nXC5s27YNr7zyCjQaDZYtW4ZrrrlGFHq6O2JIHeotamdDBkRZ6+vuQ928AKBD2kBu9VUfjz//MWBR\n88LM/OSMVg3OMl/r7kOmDdzdSz8mB+xpKIoiCq70ZCgsLMTixYvx9NNPY/r06b1ynMHocgyCbnfD\nYDBgyZIlOHHixICZPqooCk6ePCnajefOnYt58+YhKCiozU3enXZjOoRxi9rTkPlc+bOc9cogLBfg\n1N/Lz78756H+rP5azQG7A1yZeugqxdFVj4TOPKZaEnbgwAE89NBDWL9+vZAhDka/xCDodjd+9atf\n4cknn8QNN9wwYEBXDrvdLtqNL7vsMixduhSZmZkAfnzEkBx93YABnNuuy5ItNfWgzoDdZcRdAV13\n4Cp/LRfd5HNUd8S1tp41iFfz292Jzngk/FhQEibTP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+ "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.view_init(60, 35)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, note that this type of rotation can be accomplished interactively by clicking and dragging when using one of Matplotlib's interactive backends." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wireframes and Surface Plots\n", + "\n", + "Two other types of three-dimensional plots that work on gridded data are wireframes and surface plots.\n", + "These take a grid of values and project it onto the specified three-dimensional surface, and can make the resulting three-dimensional forms quite easy to visualize.\n", + "Here's an example of using a wireframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SKVpB5K+//kKv1zNr1iyWLl1a5Hb8/PyYNGkSAQEBXLlypdDyM2fO0KpVKwYOHIggCOzY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = plt.axes(projection='3d')\n", + "ax.plot_wireframe(X, Y, Z, color='black')\n", + "ax.set_title('wireframe');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A surface plot is like a wireframe plot, but each face of the wireframe is a filled polygon.\n", + "Adding a colormap to the filled polygons can aid perception of the topology of the surface being visualized:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MdpKuItCNa3A0lJSPFh5QRQjIiAjm/HHYyqSobKIyhimKUP0Qrv57rMjN5+24\nUJtsDfOVbe6FdynJ91KM7yiH/z7+RYp+gQHLoNyUODJoChzexP5yME+Qljl8BZIkh6ur2BZ5ZH5N\ngYuF7T0drBf5t0SainK6QYjo/HlV69d1Lpe76nN04RVAut2MwyuVCtVqFSHEBZHtcmK5jMjh/GQL\n86li+ZMd45wsK5JNSoopLPbPBiR3rmyFCqMCweNnC7x6ZJhzTpBvuTs+zq8OP4NUEJEmed/AEg6B\nQiuJSMj5JhnDY0AqZr0o/aY9v09BVWkSApSGUY/65JirFbYSRGXrdzXlt1b+CQRnfZOKFmSkSzKE\na2qTdiUF416KKWVwnTVTj30jxElLQVX52FqRlAaDhiSrbCzhYwkoVu9AyG9imuGvwguZfDd36K1d\nn+3WhTVP2as1Ev6niW9y2q41G2139rJ4pBB8ZiDR6jAAs+o1rG66xgz5Wmz/ifkxkgzE3r+kY2i+\np1dCYQS8jOWFZhmhuXtppVIhl8uhta63w7kYwr1a5AWlFOVymVwumLzIZDIkEolQn97ns6O4ulPD\nPVTIkTIaieUZcwPO/HE9O50nJjsj2ozRT871MfzB+U987hh+Bl9D1ICY1KSMClHpYwgfX5tEhaCq\nNZN+kDtgCYGrG8QSk4KKUpxyLbRoRKmG0JzyEh3HUFThv19WGeyrDrC3spqDTpppfxVlJXAUlJXB\nfjvBM84AE8piUFZadN+kjJNTDo7WZAwLU/gIIRgwJJX5/SVFlWLpNjx1JnT/YWg3bDFNs15cE4vF\nME3zvBLFxaYoLgcemv0RT+d/CkBEGFT8hl4bYzV7i6W6lLQpugpFGSlv4dnyBANGMJdgiGHKKkYt\nL9cw/y0RY2nFBs2km81mL1kJ8HLiZRfp1si2NqtcI7Ra9BeJROrRX5jGtlRcaXmh5gPhed6i2/3c\nP36CjJkk12Ygo4GEsZGiHzSedP1BmG9U6WjFkLWJMftwyzYxHZD0i9MOmQF418CLRITHjG+x1mx8\nL44SRIXFajMgjA2mYMLXHHMNVhkw4yVYawXHo7VmzreYVh5m26loPPK+QdoI3mV9LfBRoZliWkOZ\nCGhJzosx5gFsIC0rHRnDpogDQVQfI0GEMjEZRNAV5SOEJjIvNfQbkqwPMeHTJytki7/CqvSDC37n\n3dAsdS2mKaTjOOfttrDQfpYDh0uH+O5Uwzh+Szzd4p9sq1dR0g3rz7UWGKziiaLGxECpQKY6613H\n5sg0WgVHVH17AAAgAElEQVQuY9J8w0Ud10qJdF82pBvmZdtMtmGEdDVEqRc6hlKq3jXYMAwikQjJ\nZPfJrmY8MnmC61avIed1+i6MlQXR+ativGRCU3fgmUq0492o6saBCmOlKnvW9PPzfScZ9aJssxq6\nQNbXaHyySiKVhwAEGosI6wyDqnbxNJx2Y4DgnC8oYSC0IkNrKpoUMO7HSBsBQc/60dDMBKjNi3c+\ngBRpRFuxR1zkMREkRZyE0JjCqo8bl8EEWVknMSliiYB48ypNhDniTFJwfkRfZPl7tS1Gouhm8F2T\nKZZTnphxitw79QNU0+TksCXx5i/fuLyJrNeaaRIVZxhz91BUU1wbH0LzPFK+htHyHJvNFxEkOe70\n8fPpn13y8TQ/THqa7mVCty4Ntf5jC0V/yxmlXqw142JfGWt2ktVqtT75V8sXXQyOF2Y4Xc6xwQ5v\nu/Nifo43DKWp+AWem22dHX56OseNayM4qnFTnWkKln8xvZcZLUhIn5QAT8M5H0AzaGiiTQReVRYD\nhgY8ksAqXLSGqoaUNMhrnwnfJKDO1u/VwGPMSTISKZFTFmHZCQBeF/Ws6Nv0NQWUg9phqymJiYZ/\nRkEZlPw+1prBq7AQgiRlPC2Y8RUSEMJjUmUYkllm7bvgEpBuGGqRcZjBd7dW6RDYHzqOc8F6sdKa\n/+fQv5BJtDp+xWWRgg8RkeFfp0xe3z9bL4rokwlcHedgJdhmreVh6EF+WqpybXwAjUNZvI7+yG4M\nsTiz8fZzbibdnrxwCXE+sq31YJJSgn8aqo+Btw+8fXjWm5HxDyBE37JEqZdr9rlarbaQbS36WYpM\n8uhk0GX3hZkcawYMvLbuvgpIGZuIillybqvuW1GKNdYmxuzAGUwiOZILiHR1pEB/5CQIGDY0BWVw\n1tf4AtYbraSoNSSFQVhZcFJKUkKzBs1Oy0FriYem6Ct8BFUNFQ3jjsVIBGwdnoML4GiTBC4xoZjV\n0frnMekCmhHDY4vlYAkPranXxQH0SZ8+mWXCiyLQDJsOntZMKUlVFaloiRRBFH7SG2CdeYq8/Qjp\n6BsX9TtcCiwkUdT8Qmp9xWoP+eZJu/NJFF8b+wnHSmfYHplr+dxWgb3hpHMDtqpQ0o3KxrWRYR4r\nNtLDIvo4E/71lNU5hswcBns4WD7FW1d9ZMnn237N5/N5Nm8+f+bDlcaKI90a2RaLxfprdXtLnBbH\nLzWJLNwGcgSliqBOYNinKXjPE4l9ANh1xXMlF4q4FyLbC8GPJwJJoeR5jMTWcqLS6Qd6tiIZigxB\nSJ+zrN1w5hq0VlPygpv3fdt+Qsx0MHTg12CLCoYA248SMVuLEGwdJWO056uCi+ioEBNCILWmz2gt\nE95mVVDa4vpImaxvckYZTcquZlgqXmVVSIoihpBUFcyoCAeqSXZEqmy1fCQ+7rwdixCQVT5xYRBv\nyo5YY9poDc9W4vSZM/MTatCcQFFSNkcdRZ//uSWTbq2/16VELSoWQtRb1yxWoqjJFPvzY3x19FGu\n70+jmx6W6yIpHH2MpLGbuycK3NCXQTdZ3Mz5KRwd/P5rrQxaxHipco6IMJH6JIec9aTNzayKXLi/\nRXP2wqVyGFtOrDjSraXX1F6bajO6oV0atEaW/hNCz4E/hwFo41oUUfrUCaaL/xGt347Wf3xRpHsp\nihtqPr2VSgXTNBcs1ljs/suew9OzjVlmocI14OfzWXYlNhBmcvP0dIFXDZl42iMqBgCXuFHhhv5g\n3NNumnWRfD27TOjWY1Ya0kLQHuWe8RJsinSWEvtatUSgzfBQGEKzynQZ1C62NqhowXoziKKV1vV8\n35iEDdJhfdKplxz7ovUY4lIx5yt8DFJNub3H3ShRY4aKMuibn8CLCkVFC6QQpGSZrEox6x8kUX2C\n1bHXhX6vVxLt5L6QRNGcX+x5HlPlHJ84dDcKzWAEZpu+to3RGKZI8KPpFGCzJhpIRAAZ4zry3mTT\nuimeKQW/8TWxVRTJkPdPcWPfey74nFaaly6swJQxKWX9YqgVNXTtP2Z/HeEFNdqeWItvvB5FBoSF\nMLYwYF6Lr7/BXPmfLuqYlkMbrm1fi2yz2Syu6y6pOu58eHJ6FFc1opBj2U7TcAjcvOYq6dBlRc9j\nTSRwz684QQrZu0b2Y0nFpNtH0mjovUpr1lutGRK2Cl7zJ7wIJ90YB504T1XjlLRm3DXrKWrBcXQn\n3MBRrLGuEBCTPgOGR1FV5wm3czshgtLiSd+jHKKjDxgOJeWRna9qG3MT2MwhhSanYi3j1I5LiuB4\nAE6WPxv6va0UNKe0RaNRYrEYf3P6R8y6we8YNVonyZKiQs65kXNOIDNFZfCglhgcLmQoNbVsmvMi\nFFSQIZI2YxysnEJisiX+8xd0rB1tgVaIprviSNdxHEqlUl1a6NoSxz8O5b+mKncxJ7bjiGEM/3Gk\nHkMgwTuAoafok6/Htb9KzrnwNi/LFek2+z5cilLkRyZasxXGSiXWRAc71lttZSgVu++35KSC7QsK\n0Nw6cAql4bTbT9poRKsVP0lqvtqsqgyeLK3jmGtwwNOcVg4zukqJCiWtcEWBGV3moOvyvONw1HUZ\ndX18Hf4G4naZPAOoas1Z3+0w0KmhoMBGMeFr7JBh0oaLh8sRO0JBzyDnI+J+o0y1KSc4KhqTVGnD\nDkhAHWOy+nTXY2vH5Uo3vFAJ7X+M7+XJuWP1v21yLculjPHAbPDwFmgq8znLUb2bRJOElJbrOW2f\nBMAQFmN2sN666M8RM5ZHErhYL93LhRVHupZlkclkFjSh0VpTLv8Ns5jk3afRaoq4Ci4coSeQ/tNI\nGUfKYUyhiIkBJot34apc1zEXwsWQbi0Rvqap1Ux2lhrZLmb/tUm0ZmRkZxbDgFzFwXPZeglsO56d\nKRIREQ7OlrlxcJShSJEjlTVERCuDxearyg5UVvNYZQ05LYganVkWEdH6mQaKSjOrBS84mhOuqL+y\nQkCqflcNHAoaqmgmfDf0e8mqQKu2hGK0S1v3mPA54YNsOidTKKb9hiQjReC9WzsHhYEhXA6V7god\nsxuu1oqzl/Jn+G+nHqr/nTBM5rxG5BoVUR6bM+vvGyOxFI4uYRHnwRnBULSh5Wu1oZ7Lu8bYQUnN\nITBIm2+4qHun+bsrFov09YV3or6asOJItznVpduP5XlPUbG/g1ITgCBjrEdQQmOhrHfgx/4PlHEj\nWs0QYRyTCBkqvDj3xxd1ASx1fdu265GtEOKCZYTF3LTH8zOcqXRqtFPlzlDPrkQpOR7bk+FGNnOO\ny/rIddhKc8Oq0yBgRiXpNxq+CJ4WRITioeIGpjCRUuGFVI75GpLS7vh8/sxAQE4rXnI0x1xBWQkK\nSnfNWCjpRgJZGc2kclEtk17gN00QxqXNcaez0u6Ym8ASLkU/1fJ5n6zi6sZtExFW3bQnPm89qf2D\nTF/Em9PVgLxj89WTT+I1GRBtSyVbJtFWm9dxqtr4zbckgkk639/NnOciZEDQGWMTyPmqM6x6uklC\nvIq17GypuluKXWY76Wqtr2g5/2Kx4ki3hoVI17YbRspp6/VIqqj4R1GZB9GpT0H8Duj7W0j+Fzz5\ncyTEBCY2lppktPi1CzqWxaKZbG3bJplMLrpN+0L7P99F+sDJk/SZ0Y7PD8xmicvWz09NB+QR9bv3\nEDtb6KPPKrMjNcWhylqQklVmo4/aMXuYo14CaTaiWFOEELy2METnsfttOq4QUNCKl9wgHa0bym1D\nFbVm3PfrxDinOjMyfKqUmpzJbC2ZVQox/zBpRlR6ZP2hpnOy0QSTN33SQWmTlPR4JveZrsd4JbBU\neeGPn32QOa/Q8tlwrPG9Z4wBpsutebVps0pCruIH0xVShkHBPwvAWHkNERnYPqaMVyHFWQSSkt7O\ncHqYZDJJJBKpz9fUumyXSqVFexdfDaZTi8WKI93ahdONaLT2cex7ATCMHZiJj6Ay30fH3geyTTuy\nbsYxfx8n9k/0Je5gQPZxqvQV8u5kx7jnO6bz/ei1TIt8Pk+1WiWZTNa9ei9HKfEDp46zNdEpJXha\ns85qRLRpM8HxeZObU1OljvVrqOQk1w+fJmU4lIhi4dUjvXNOmmmv80HSZ3RmJyjdrYAh3KLP1QZH\nvSSn3M7lvg5Mcdrh4HHGV5QVoZ4TcelyzG0QyFEngZh/EBiigqtaj9EUJfymn8tRjddoQ3hI4aL8\n4xRCOnG042oki68fe54fnjneQbpRo/FGMlvYjDRa31B8ppiu7sDRml19CTSKfnMLz+dLFP0xJAbH\nywZlNUbS2MOG2HVAcP/UvChq+fXJZLKjLVbNu7gWFdfy08vlcj3v+GqVapqx4kgXGjOsYVVcnvsE\nWk8jRIq+vs8jrFefdyyNgRV7K32xX2C1sYajhX9c8vEs9ASukW2lUiEej3d0obiUfroA05Uyz01N\nILxw6cJuIpwhsxHFncmX2RALn5g4N1liV2aKM25/II3IgFCLfpR91RESsnWW29eCAbOTxNv1XAh0\nYCO0FSa488loo36cMa9V1y92MSsHqOJy0vW7Lh80Cxyz45SUQaGJmE2hmFRDLevGZZkzbiM1qd8s\nUlHBAz0jqyhtkDFcDpcWlxVzOYhisZHui9kpPv3CYyRMk2mnVY6ydTDnscbayo/Pliiqhgl+2owg\nifHwbEDU62LBvXm8uJrr0wkUPn3GbtZHA4vPg2WDG/q6d/VtNwZq9i6ORqN172KlFHfddRcjIyMc\nP36c97///dx55508//zS5J1//dd/5brrruOaa67hk5/8ZMfyhx9+mP7+fm655RZuueUWPvGJTyxp\n/GasSNKF7kRj298DIJn6fzHM7UsaJ5X8X8kYQ7jeQ5wo77+oY+lGtpFIZEFTkkuBB0dPoLTm1HSn\npgtwMFtEzr/Oa7tVUhgyO0k3Jk2S/YcwhY8rAiLvN8q4SvLT8hZ8TJJtUZDtGx0ygq9FqJ476yWJ\nG51dHQCcpsj4pJfihButSwfV83x/x93BrtkQAFqUOeJkOohZhTwAzLZzqUXDhtBIkSQqKkzaexc8\nnqsNRdfho3vvw1E+m/oSLWcdk5I5bxpTmDw5niRlGcy4DdLdkUhyMN94Y4oYWfrNrTyTK7Ax7iEw\neDYHqyM5UsZuInKItBmelrgQalV3lmXV//97v/d7/OQnP2Hnzp1cf/31vPDCCzz55JOLHlMpxYc+\n9CHuu+8+Dhw4wDe+8Q0OHjzYsd6b3vQmnnnmGZ555hn+83/+z0s+9hpeVqSrtYvj3Ecs/ttEo29f\n9DjNGOj7KKvEWQ5nv4LSi/MzaD6WCyHbi410zhfpPjAaZC1MlapsSnSm58w5LuujwQ0zPtf69jCd\n7WyTsyE2yNaNE8haJoLWrDKLPF3aQlnHkPhEZOt3F3aG09VUqJ5b6CItKA1+2yV72k9w1Iti66D5\nZDe4WlIkzhmv+41uCsVZv3P22xJlJr3WCbUBs8g5t/FZv5Fjwl2H0oKkyCOFIsJZss7SpKoriY8/\n+xBjpeDBPJhofYvYlkqhUPTrXZwo2OxIJ1om1SQZnisEmr4lBEX/NEcLgZxlyWnS5i7O2GV8fYKD\nZZMbF4hyF4vm6F1Kyfr16/nQhz7E5z//ee64445Fj7N371527tzJ5s2bsSyL2267jbvvvrtjveUK\nilYk6bbPWNbguj/BMK8jkfj9JY3VPEbCGgH5GraZP2bf3JcXPU4t9atQKFAul4nFYucl24WO40IQ\ntn3Fc3l8fKz+95AZnlIT1X3EZITDE60SwNGpPH1mKwkOWgUM6WPN18LGhMtJezUzOhg7Kjp100RI\nU8myDjc4kV3I08Mg7JI95yc46MS6SgcAJRWZXzeN1yXaPeumme5C+HnVOQlpawOlBQcqa/l+cTf3\n5rZyd/4mXrSHmHT66JMOL5X+uftBcfW0YP+nEwf41/Gj9b8ts/WhuSZukjYG+P7J4CE8lGhMqhlI\nxpoaeuxMJkgZI+zLF0hISck/w/6cwZ5UiqixmXG7wA2p5SXdiymMGB8fZ2RkpP73xo0bGR/vLI9/\n/PHHuemmm/ilX/olXnzxxQs7aFYo6UKr0UwNnvtT+vr+K+2tmc83TjtZjWT+b3xtknT+ilz1/K8p\nNUenUqlUdzWLRqOXTdRfaD8/OT1K1W+QYL4Y3oBytOCwNjKEp9slANgSb9U0++KHWibAHG1w2G50\nE46JVoLVWjNgdOq5KaMzilYaBqzOljy1/XTDmDtISXXP3Z6bz8TwMTjjhZeKvlRcg60VpyudN29C\nOhT91vHTRpXv5HfzgrsOBzClh4vBUWeYF+3toKsU3KtfYjgyN8Nf7n+05bOKbq1WjBkO+eJmyvON\n6iyzIQuNWFs558zW/94UlxzMB9/h9ekkfeY1jFZLbE/4HCpF2BzbQr918UUMl9PW8dZbb2V0dJR9\n+/bxoQ99iHe/+90XPNaKJN2wCSitFZHoLyPl6iWP1U66UXMYZb6Fiu5nIv9/4qpC6Lau65LP57Ft\nu9708ULJ9lJlMNSkhRqOTM2RMDrJ6VihiCiHR8FepUF2EkUqliNmBYSpNUy4/S0zWPG2STRbWR0a\nraMkayOdxSizbrKeBdEOdwHSzfkJ9pa3hhKz1jDblPp11stQVa2TihXfwrWCbc85nTevFJoZ1Zr9\n8XhpK7NNebxJ08ardSMW8EhhOxEmybmtVoiXGwtdV7OVCp999kls1RrZTjqtv43QMR5s8vEszRcS\nmcJgfC5KyW9kplgiygvzUsOGuMcL+eB6ixomo3aOG/tuvbgTCsHFkO6GDRsYHW20hz99+jQbNrR2\nsEilUiQSQdeSd7zjHbiuy+zsLBeCFUm6NTQTlRAS09xxUWM0Y0fm91Eyju3nOJr9ZGtE7XkUCgVK\npVLdPHyp3qSLPY6L2V5pzUNjJ1s+c5VmWzL8wVQqdbbCATg0kcOYr07bmi4wV42TsYKbbM5LtFRt\nAS3+CwAqpC36tNOHFdIwsqC667ndIl2BpqRjlHSMF6vrOpZXtIlqavCmhWB/vvWmeqm0BmP+bjBM\njRuSC+z4Hv78hNkJZ4gJvx/dJFVIETSwBPBxmXAzTLgJns1/O/S4Lzfar09PKX7/hw9Q1q0PubWJ\nGCW/EclawuCxM41rK2kaTLuBveNGcxtxq3XclwqN8UwR4USlSL9pcqIadOO4NnH9spxPc6R7MSXA\nr371qzl69CinTp3CcRy++c1v8q53vatlnYmJRvrf3r170VozONhZQr8YvGxId7nHiJj9GOZbmVEZ\nfOdRzpS/XSfbYrFYL0eOxWJIKa94vmXYeew7c465aqepjeV3aqkRaeBkwx8aBdtlWyKQGDYmC8w6\nceIyMB0/UVqFbNosmERrlTBqaWG+FpyorOJHc9fyUO4a/nniFu6b3M3+3AiTbh++FqETaxCQcbeH\nWnPF2Zg7wLTXWtAw6XVG8FVTUnAbOu1E0zqmoTle7nww9VlVni+tJe/18VR5ExBkKzS3hhdNenTE\n8HmqOMi5audMeA1X0lb0048/wVNnz+K2PTTX97VmsOyKbuN4oRHJ7sgElWkRYbF31CcZa2y/NbqW\no5UgCk4ZBoeLwXZb4qs5Vc2yObqLdbELI6t2LBfpGobBnXfeydve9jZ2797Nbbfdxq5du/jCF77A\n3/3d3wHwrW99iz179nDzzTfzkY98hH/8x6WllTZjxVk7wvkLJJY6VrcxdmX+A0/Yj1FQWfK5/4aI\nbmMweV1HBdnV0vanHd9/8TDbMgMcy7WaTo9O56Gt2GxLbJCjL80QGTJwVGfWRkIFUbBllImZQYuc\nrBvH9VojwmibB6/WkHOT3Fdew5SfRs3r7S6SWASqRJlRaV4obkRoxYAoMGCUibXJEUU/hhXi2wCQ\nbfJDEEKwv7qRNycP141qpv1UR/pExNTsy2/kjauOUfIttNW6Sl6FV+P5CO7P7sAzjfn9BVV1CYLj\nTRgOWgdvPXHpYyuLE1WfnDtFxhoKHfNSI4zYv3PoEN84cACAOa/1wZyKCGpdklZbfRTyrb/xUEJy\nxod1xlaeLpexmyZO4yoNBKblm+LDHK2cAsAyJHgwbIV3Tr6Qc2pGLpdjz549Fzze29/+dg4dOtTy\n2fvf3+hM/MEPfpAPfvCDFzx+M1Z0pNutQGIpWIjsDBHH0zcy5Qlc5TDq/xlW9NIksy+3vOApxQ8O\nH2O11SkZnCtWWB9vTZ1KuTFs12dnJjwKGZ0uk7bKFL0oq6PFwNh7ZgTdVlHmuI2/fS14LLeNZ6sj\nTKiBOuECWCEuYa42OOuv4jtzN/NioVUmqPjd44O5tg7BZR3lkB2kwFWU1aVtJRDVFL0IB0trkbJ1\nnYjlkQupesv6CbK6NZJu9pSIGn5dL47KoGx11tM8mf1e1+O/3Hh+cpI/e/QnQCCJnK20zlmoJk09\nWRwGq/W6jJgOURnhiVEXQ8CEG3gsbIwOU9UNaSkx3z16tTVI1ptgjTXCrtS2ZT2X5kh3JXjpwgol\n3UsR6TaP4/s+xWKRfD7PzuSvg0hT0uD4kxzJ/XXXMa4mPH5yjLlKlWKhM0MAYG2k9VWsMB1EKykV\nnsZ1OldiR6bEZDVJn2FzKj9IRUQ6iiDi85kLjjJ4cPY6TtuDoalcUbMzi8Kb12w9YfKMs5V7pq6n\nMp8xYMnuOdOO7pwYPO4MUfCjTIWUI9dgmYp9xY1MhOTmSiE4WemUGI5W1zBjt5J8+/C+qkXBPqZQ\naDyezwV64WLNXJYTzZHuVLnMf7z/BzjzPfXWppMdbzY5P5gE2xlfzzMnchR1629cVjnWiK1MV1y2\nZJLY82XQbqmfiggIfE1kgIIf5PyuNobI+1m0P8x1qfXLfk6wcmwdYYWSbg3LRbo1NJOtYRj09/cz\n0n8NPhuoaM2cE2O8/C0mKz+6JMexnJHuPQePAHB8co6E2UlK5VLjRosbJqfGg5vl3GR4pgaAZbg4\nSBKGw/F5QuqzWm/IoViRuWqCeyf3MOlmMEJeRFxfYIUs8Nsm3GZlH9+euYWfzm0mHkLSACU/gg6x\noNRC8lx1Y0cU3A7HkLhd2uW4sjW6PldNUxJxnLZuGIbQOE2GOc2adtwISHnStck6Ux1mLhBcd80N\nJC8Vqq7Hf/rRg0yVGyl5g32t0XxUSibtHJYwGDsZPDzOVhuVjHFDUlJlfnIy+N3Xp4NrazgywHPn\nikzOT7Al/CEm3CkiwgJ8UkaahFi7bG+JYf3ReqR7GbCcEWapVKqTbSaTaelEsSHxLmwdo6qrCGK8\nlP1TKt65jjEupX/CYqG1Jlss8qMjgWG5r3SoZHBkKktUBjfVlugg/vxs1MRciQ3JzsjPNFwqSpAU\nLpOlPqoigtaadLQxweK4kqwd5/7Z3ZRqonHIOfldXMJUSHaCMgweK27nRDG8e3F+ASe0rEoy7i78\nyjljpzlbDb9Zo6bfkrN7aH7yLGr6LZNnAIqG5BCkjgX/js3LFr7QPFp4pMXMpWbj6boulUrlgi0O\nzwetNa7v8wff/gHPTbSa8ESird/5pnQShWansYXxuQqrUzHyXuPBuiOTZJDNZJ3gwRKPBg/vmDPE\njsE4PoqEEaVY9fG0x/rIJqJWniQbef2q5dFza1iJXSNghZLucskLSilKpVJ9rBrZtveNevXgO/HJ\ngDSZdFbh6QJHCn/fcjxXg8Tgui65XI6Hj56g3NTJNxpCclXPZ3symNhJtPnJboh1lspuGJphzokR\nw+VYOdgugo9s6tCYrSZ4OH8tvmzK65Wd34nqYhzudfncURaPF3eEaqzlLnIIBFkNh8truy4HmLZT\nzDiprssnnOC7qPgWZ+Zzck2pmHVadd1ctfE2IQXY87KIFKWgV5+s8EQ2yJluNnMBiMfjLWYu3SwO\na/0AlxoV+0rxifse4/hcFttvlRLsturBVYkIQ5E0ew/OywT9rQ+14bjFIyeauoNQIGMmeexUgaFU\n8PttMDayOhUc38msoKAmOJRX3JzZsuhjPh/a5QXbtonFwtMNrzasSNKt4UKJrka2uVyufgPULvZu\n++kzf4aq6qeiqigdJ+88wZHiUy3rXYlIt7nzhOM4JJNJHjx1umWdc7PF0G1j823Js9OtmQLVQmdx\nQiLloIXA8SUVGRBdc7mv4xscKaxDt32H5gJtddohupT/KgRKGDwwvafDsMZZwFu34lvMeH3kQ8i6\nhqwbp4qF7YePI+dzdl8obEA33eSFtjHjkUo9hzc4meBcpNAkDBONy4RdZtrunlBfM3MJszg0TbP+\nWzdHxefzmtVa8+f3/4QfHxtjMN35VjDrtmYuRC1FPL8aez5Uj8dav5dKOUFh/oFuScE5Z4ZVej2O\nUgjLRiA4cM4Fs8T6yBpM6ZMx17A1sQFLLr5S9HwIy8hYCbaOsEJJ90Ij3XayzWQyJBKJReXZvmHw\nNmwVIWE6nKyMYPujPJf7Fo6qthzTheJCSLe5SMMwDGKxGFVf8eiJ0Zb1zmVLrA+RDM7MlkiaEUbH\nW93Hjo/PEm/pYKGoyZg5v6GRNts37p9ejxdy+JEQLdYMSf3yfEEsEl6JVks+KMsIT2QbznFKg+rS\nUggg7wUkczxkQgwCDbksA2+M8XL4q6lpwPHyak66bWO0/dymVExVmiSGpgIROf+giEjN/dOP1T9f\nTI5uLSiwLItoNNoSFdd8PRaKij/1g8e490DQqqpdSjCl4Fyl9YEc9aM8e7JxPbhG4/cbjMQZzTVI\nent/EksY/HQ00Ihn/Vk2W+s5W6wy7U3iOgPsyMDZUpQ3LLO00Iwr/Ya5VKxI0oWlvdIrpSiXy+Ry\nQdJ2M9nWxjrfOEPx9cSMEXLeGspK4qkEq8wSP5n91qLHOB8Wu31zkUYkEiGTydTbGN373GFiIS1/\nNsQ7JYOxXJHdiXUtxQUAnq/ZmW5oqMlUBS0ltisRTUOn5kuBzxbTnK4OdOTR+q7AMjsj3bjVSa4l\nNxqa5aA1GE2VayecYV6alwwKfiy0428NeS+I5E9XB6i6nd/JRCWNmNdcZ93wdvQAJ+3VuKJ1MjJm\nejU/WVoAACAASURBVFTacpTtJm+GqOFTnY+eBQGxJUyHffnWsuwLRTfj71gsVo+K//bHP+Vbz75U\n38Zpc81bl0m1tOOJGSYvnmg1mp/xGqW/2/UQ4+XGROvaPpMNxiZyjsfaZJSsV2QmH+WawQRSSB4/\nlycR8TlcKPOa/uVNFetFulcIQogF83SbyVZrTSaTIZlMdsgIiyXMnalfpOQnSVszPJVbj9Qn2Tv3\nPabs0WXJPjgfatkVhUIB0zTrFXHND6Bv7T3A1v5O+0a3Gp5yZeTDNdFYE6H0Z4JIplyNtBBjJlrB\n9SX75oKS2mikrTDC6zwn3xfErc7o17bD83ArntWRQ/t0YQtj9kDX7hI1lP2AdJUwODjTWR48WW1E\n/zYWlS4m7+OVfpRuv8Fhxm7VgtsfJt683JCMOFgYRIwK49XWQpXlRLPX7D/vO8zXfnqgZXnebU0f\nTLfZN94aWceZfINko6ZRz+EdjibJzrj/P3tvHitLep73/b6vlt67z77cfZ8ZDmcTV0m0TFMipTjS\nGIItg4hB0jGMyEkMJ38EsREggQPbkAT/YwWSYEq2ISkK5TiKxaFliqs0pMihOEPNDDnb3e899+zn\n9L7W/uWP6j5dVV195m4zozvhC1zMdJ+q6lqfer/ne97njYF0xvT4wWa4zTNzWVbMOX6w3+XErMaC\nPI4uwPIk7585S8mYPuF5NxEFXcdxMM3p3P5ftnigQXcaLZAE23K5nAq2o7hdwPzQ7EcRokjPz9FV\nJfpemyPmDF/a+zeAess43REt0m63D4x1ouqK0fov3tzm2l6dtMKtm7sN9JTj11rp52Rrd5zRyGG2\nGq0+kyoga3j8oHoUOzAgCNATMrCUlmjYKRknQOCl831WKhBKvt06T9WdPgHmK3HggwCwFcxMZPTN\nCFUihGBrCsWw65ToOpMPtZVwNSuaDm17XFoc1Ra7vgDhI3C52LnGWxWO5/PP/sOzfPPSWuIviu1+\n3OnNjPC1Z/OzdHbi8r+js/kDA/dj7hzFUvwcSJVhpxeuU8z7GE44OjJNi1drDo/N5Xmh3uYDlTdv\nJnAv8VY7jN3veGBBN81TVynFYDCg1WoRBMEB2L5Zh9DbBV0pJYvmE1SdRZZNi+ebqywZsGFd5KL1\n3H0H3VH/p2m0SDI+/2JY47+xO+ne1Xc8ziWkY6v5Ihdf3cZMOT/VVp9TpQqa7iIlDCwDPaJEMJXP\nbq/EraGkSk/trpCixZ3SE01MOXdpxjMAntJ5ozuZvY6i68Xd3jxD40ZzzMsGCvrEQaTuTVIMDTuH\nrUy67iToGppPcqDVjBROFHXnYOJPMHRlw+FPay+G/3+ffRca3QH/w7/5z3zl5av0/HjWPV/OY3nx\nEYY99MQwpYZYE+RLcc/gciF8qayaRV69WMOPeOxmpOTizpiKUNLh+aHWW5ca612L2WyOrMzyvplT\n9+0YD34vYetYLt95F4p3Kh5Y0IUxrxsEAYPBgGazie/7lMtlisXibbdjvhNq4ONLT+OqHAqLusrT\ndbYAxbfb/w8Df3phwZ3E6OXRbDZjL49pYAvQ6Fl8Y5jdNDoDTs5Mvvkria6/J80KluNzbn6SjgBY\nNoqYGQfD9Gj3s7EqsowIeKl29ODG11IANs0rYRrEGGY6/TENpAFududpOOnD1p43aToerTCrWUVI\nnE8HnX6i79rm8KUySPHqNbWAphv//SiGSqEOMvW5TB+loGK4vNKZNMi+17i+U+e/+Y1nePVW2Kli\nvxv3JE5TLtSccJmnjBV2drt4CXmflgkPZtkORwntSGXaueIc11vh+roQCD+D5QecLGXY64XP3Z7T\n5T2Fk6zk0y1D7yXul4H5OxEPLOhGgbLdbt8V2KZt681iKbtIRs6z55bJCsGlfoV5rYKluvxZ8+6d\nh0Yvj1F7ds/zKJVKt308/+n7l/EiaddSLiVra8UnSbxaCKIF0s2/ey0HpEBKFYJuhLNd71awIp0f\n0irMspnJCTM9xc7R9yGbTS9XnnpVlMINdF5uHEurv6CX4qTWM7JUhwqD3cFkZpSmYti1w+V8peGk\nyMpaCdCvZC38YXXawClwrTNPfTCPJgMcVyNv2FSdJq7vgWoggq/huC9NO8rbiucu3uK//ex/Yqc5\nnLDLGtT7CRPyTJymyWiSnUGX0/kZ3ngp9PttuHF6oRNYnMzN8OrlGkLApjVOKubk+P46O5vjle1w\n3eOlLC/stjiZL3NrUONM9shboi6IbvNBoxceSJcxCMXQnU4HpdSBqPxu404nwR4pPcWf1qocywzY\ndU2yfQcy8GrvGzzR/xlO5O+Mw1JK4XnegfSnUChgGNO7ICTDDwK+8FLcIanfm2z4eHO/wexKloZt\nkdV01i+HmtHdnU7q6/fqVhXzEZ/+IDy3uhECpudLur6JpsdtDKMRBIpMbhJIs/okEA8sEyObPiEq\np1g9jhpB1uwiu3aJlWx8lNGf0kXiUmeFhdy1sDw45ZgbXgEIQchydRpu7iB77Tgm87k4mAWJjegy\noG3PsdUyAI3Hcj0qwYBV3+Jjc1dYyAzoVQyc3t+mqAXU/V38fody7n8nb/7N1H2eFu2exb/9oxf4\n4uvXsCLFMEuzRZr9+ISdmziPq7Ml1kQdc0MjCMKCjq3e+BwKFFtWmzP2Ips4rM4VueGH98uF4jwd\nb3xt5zMlLrd2hufDJABOF4q0xQJPFFfo9XoH0rfkv3v1oIYHqwQYHmDQlVJSKpXo9/uHDrtvJ+4U\ndH9u9eN8bf9btLw+CGgFGbIMAMF3Gl/jWO408hD96ChGYNvv91FKHRzTnd6If/L9a9S7cTC4sVUn\nN6szcKM8nuB0aZaGvc2F0hzrTugOtV/rcuRMga1OfKLFLQRUsi6tXpxaaDYKyEzSeSoOusqRyITt\nQRAoiilyMdvOYGQnfX9tT0Obcof2rPFL9qX6UX569eKBr2+gBJZvpnIZNVVg4Bn0VPpL2kXDsTXK\nWYfN+jxZ6eMqSYBk0Dc5VtrnpNllXu+zotsUpUtFc8hJH1MEDAKHvJTkjqWPTpRSuBmPm7akq3ZR\nQw/F9uB/wfMvUcr+zwhx+GMZBIo/eu4N/t1//h6mqcUAF6BQNCHR8ShaygshX/s+tcprW+ELZmmu\nyPUIPbZULlDKwBuvh+A9O5vlxhBnC22Dvdz4XrGHKhVdSNb6HXQhCaTLsrbI48tHDwylgiAgCIJY\nVV0ShDVNu637/+1s1XO/44EFXdM08TzvHTGbyWpZ5s0l9twuRenRFB6Ffo5S3qLl3uSbtRf56ML7\nD93GCGyDICCXy6FpGt1u944B1w8CfvNLz3NmbpZL1dp4+77iwuwsr+7FW8Vobrj9ghW/9MvZ/ATo\nBjnIZnz2OkUqQ1D0A0GjnaO8NAZJpRRmUgaW0i3C7RvImUnQdf30F5RlG1PvUNs2GDWD6Hk5Lm4e\n4T3HtgBodAqT1l+jkJIXaycIUiibrHD56MwlnspucSYjyJTHCgBfgUBgiunZmRN4SOkyUNB2DRY1\nbSIh8Al4dVChZKxNUCcD94tU/WOcKX46fd+B12/s8n/8wXNcXq8CcH5pge39+AtL6sn9U+z04te2\noJm8+vzYP2RmJgcRDe5iJYuqKyDctpYV4MCJfIVbl9rUjoTfnypU2Bqu92h5gVftLR4rrVAL6pzL\nngLGcy/JcxEF4iAIcByHIAgmsuIRECcnz6OZ7pEj98e97O2IBxZ0R/FOOXz95NKP8fvr+/jCQco2\ne4MSpbxF013j/938Eh+afYycNplN+b5Pv9/H8zxyudxBT7W7dZn64guXWN9v8fjZSY+BXErGdGu/\nhTBhfy0+HE8r/VUZH9fVUEqSHYJqq5sjmcRLFFpiEkamGd046bfbtNeMcwjoeoFGpAMPl9xFztj7\nZDMu7UEODmGbrg0WWCiOgUoS8P7CGj9ZuXRQSbbmFrgQmdzTBFiB5MV+lgu5LvOJPnNBENAfKhSE\nAFNzqSqX2SCLMQSbQCm+0ZjjVDl9Im3PP8fm4HOs5D5BXhtfT8v2eP6VW7xyfYf/+O3XYhx2Jpvi\nIOcn/BRKObYiRuWGlDjbHp4/3pCe1WLZcUVmeX5t++DzyN5xyS5gLftUh2B8VJR4bhB2my5rIY9e\nVCZCK/JjyydTj3MUI11xdM4iLSt2HGciKx4tK4R44OiFB3oibfTft9LIfFp8bOHDCJVjoAL8QGBk\nBB0rg4/HSlbj89tx+8eobaSu68zMzBwUNtxtuJ7Pv/1K6P9Qa0520N2rTnouNHoW71tYpbYfX359\ns0XBHD/AvvTJZl1a/bAAwcx4KAX1Wglpxs+3liYXS0yYea6k1c7RauYneqbpRrpywZvihwDgJc6b\nr0le2w6LNPru4fx+38nQt8NjPZvZ579b/gZPz70SK93Naj3+ojeW2O04OX5g6ZQzDXYCl1ueHeuc\nPMCdOAuGgJay6AZha6MXuoWpgKvk+1h3bhHg8EbnN7Adjz/7i+v8i9/8Gn/7f/pd/vlvfo1rW7WJ\nSUM/5dxXe/FrO5tov/O+7BJ71XjmO4j4aAhgUIsD94bVZilb4OLlKvlSeO7KRgbHHvry5ooMlMOs\nkUVIF9wcT80fbjaUFlEzINM0J8yANE07eN6r1SqPPfYYzz77LJ/73Of4gz/4A65cuXJHz/KXvvQl\nHn74YS5cuMCv/MqvpC7zj/7RP+L8+fM8+eSTvPzyy3d8TMl4YEF3FPejP9ndgK4QgqPZIwQKuk4Z\n07C42gjdt+YMjy9sf4Ndu3ZbhQ13uw/PfPcNdhphxrpZa7OQj1dobdc7LBcnVQyVQUoGHijORRrt\n+SWFofv0hpViZsaj3cvieTpaorTXSJGLmcYQpFsFXr1+lGcvX+C1+ip/sn2eL1x8L1+/doEXrx/n\n2uYSMs10F5imFgsCQRoe36BCp5vFehNjlb5r0G5m+dHiVf7rpe+wYqZL/WbNHW7ZRV7rz7Cv+sxE\nluson+ueRT/w6QYKW02p+BPgKJfnexmuTpG3OW6el5pjI5xd+1v80ud+nX/22a/xje9dx7JDAEw6\nhAF07DhXmzU1qgm5WDY7Hi48ObvExiu1iWX2rX5kmWV2O+PPCzM5Oq7DKWYIArCHo56HcwvIfHgf\nn9AqVOlx2pzHyPkczSwcNDO9HxGttht5TszPz/OHf/iHHDlyhFwux+/93u/xC7/wC7e9zSAI+If/\n8B/y5S9/mddee43f//3f5+LFeD+7P/7jP+batWtcuXKFz372s/yDf/AP7vlYHljQfbv6pB0Wf/PY\nR3GdMqhwxqjnmdhulurgJq7y+O0bX7jtwoZR3O5+2K7H73z1L2LfHS1N6iGPlSalUVotHSAMd/wi\nCHIK19dASAzhI4WivhduX3fj+5js6uv7gu16hW9ePscLWyfZtCv4Uh7QAUpI2k6ONXuO7zdWeW7t\nNPXGZHWZSPFtALAtPX2EICXf3z2GZx4+enBsjY+uXuRYrjbRij0augx4cZDFMPfIaClcNIobvs26\nG0ylkCGkG677BVpkJqriAL538wiYcaXHhQ8+i0woQlrduOQPYLcVz1gX54oTue9If3ukUGTvhTpL\ni4nyZVNnd8j5ZjQN68aAve54u0tzecpGhquXhmoXp4dEsLvWoS1sdCHZ3+uzabXZq9koNH7qyFtX\nhTZ6RjRN49y5cziOwz/9p/+Uz3/+87z88su3PXp8/vnnOX/+PCdPnsQwDD75yU/yzDPPxJZ55pln\n+PSnQ479Qx/6EK1WK9YZ+G7igQXdUdxPH9s73c5jlfMYlBDCpWllWSy4XKwvopsdKtLghfZrbIjq\nmxY2wJ2bdfxfX3mReieerfjOJEh5Cc+F1VKR157fpJKfzHY3NpsIIBi6io3a52Skx6CRxR5qX2Uy\n040oG3xPcu36Ejd7c1gJr1ulp5xfT2Bh8Nz2Sa6uLx8Mn31XIjPpoOvY0+V0m14ZO6Vk9+DnbMnP\nn3qZJxY2kALW7OmdadetGVoyw44zXdw/CAz+rH+UwSFUSMMz6ZLB0BWvJaro1vZWyR6fpIbKs21W\nHhlPgmqaYK8ZB9jZco6+HX8ZlIqT17XjOmQ0jZktiWV55BKVZ8sLY6B+qrhENhM/v0ZO8rC5gOX4\nFHMm24Mu7y0vUW8OuNVv8Z7SIpWyyYncDLmMoD2A9y+8tRNb0eel2+3elXphc3OT48ePH3w+duwY\nm5ubhy5z9OjRiWXuNH4IutybO9HD5VPYvqLaq1DKddmwiziuiRq2Yvmdjf+Er26Pc77dY3nl2ja/\n/cff48xSvJJsfb+NljiWte2458IpvYQKFKcWJqvQWh2LkzNlgqEczBveHtnAo1ofZkdKIRKa2uww\nI/NcyZXry/TsFCMapUitwfCH+ysEF7uLPH/1NI6lY3XM6SqBQwAusHVq1WmeDIoPFa/z/oWxKqGn\n0jPdjpvhsrOAFLBup1fsAbw6OIIndJ7rTC9JXvfH+7PljUcetq+xY2ZIm0pcu7XM/IeqjMpDFueL\neIl2FXOzk62IpDH5SO/0ezypLbCzHlo2Jg+5MATh+WyOGy/tkyvHX1qO9Fm7Eo7YVpbDYxENxepS\nib7vQkdgliUVP89KJcvDxRW0e5RxHhbJ8mnf9w8M4R+EeGBB937SC/eynf/qxE9gO0VQWQIlyeCz\n2T5KyQyzkpv9Lf50/4X7tg8D2+Wf/+7X8QNFORvPWPq2y9lESW/fdjk7N/6ufWM4uTZIpxgWswX8\nQhArBQs8jf5Q1yqVQkQxL1AYhofjaly5tsLAzZBWRyY8kS5TSCy67xX45tWzhwDndD8GAN/T2LMK\nBH7yxxQfLN3g4bnt2LeG5tHwJrnW77RPoQ8zc2eKbvamPXfg37Anc1TdybeKUrDujjNlw/RZ64fX\n4/n6SYxsuofwriySm7eonA6BspxSxpvLTf6enWgyOVfKcSE/w5UXx0PiTsJtbNTX82xQxrF9vMSI\npBCYdHrhOtmyzvFCmatrDWYXsixlC1y+VacnLa5utbFRfHTldOox3a+Igu69PPtHjx7l1q2x9/TG\nxgZHjx6dWGZ9ff3QZe40HljQhfvbJudutuO6LiUvi6Eq5HWX9U6JuYzLxX6OUm7AiMD73MYX6bqT\n4v+7iV//j8+xsR9mHbVGb+LvFXMyy6zoIWCeKJfZXQvXvXWjhqFPgle7PiDIxCVfncjEW9LYxnAV\n3kDn6uWVsCABJiRlAGICBKeHpRlc3FvBbqXTCMn+ZNHwfIkvNdrV6ASi4v2lG5zPp3Fxgg0nPjT9\nTv0UmdyYMtE1xU0rTkMMfINb7hzjN4ng2fpx/MQtVPWzDGKppeCKtcBab5ZccZKjBahXVwiG78nl\n94X7bBop/eNSznMtUf67Wiqw8714t4rtdlzV0gkczpRnuPKD8Lfqzni/KnmT9RtjA6WB5nFk2D3Z\nywSc0mcwdElOmnhBQNv1eN/S26+ZvZvR6gc+8AGuXr3K2lrYqfnf//t/z9NPPx1b5umnn+Z3f/d3\nAfjzP/9zZmZmWF5evqd9faBBF96ZTNfzPNrtNr1ej2w2ywcWzjLwFDudGRYKNh6S7c4yhjP0hvB6\nPLP15tnum+3Dd165yR/+2asHn9f3WswX4xlQvTXJDzaH3x0VYyCyLI9TC5OTbNf2G6CJMU1hgxMp\nC9MSrdCVpXH5xgp2lDtIu/+nAeWUZ8VH48r6MkHCk1cp8IzpD9jIw3anM86Uj2QaPJQKuMN1Ig/s\nLXsGO5MyRHfj5+rlzvGJLsQD3aCeOM41b5IPVgZcshanGrCv22PN6cyZNtn5AU6KLLKb4HMNXbIf\nKXBZLhaYbxo4Ea5/bjZPL7HeTr9HqaqBEmiaYLM9VmmcKc9QbYyBvOVZXL0aFuFU/T7r6x1OHikz\n6AScXZjhR2aPHuiS36pIZrp3Sw9qmsav/dqv8YlPfIJHH32UT37ykzzyyCN89rOf5Td/8zcB+Ot/\n/a9z+vRpzp07xy/+4i/yG7/xG/e8/w8OEZISUZex+7GtNwPdaYUNnzz+o3x561XKeoDlmeiBz3Wn\nxGmthjtU6f/nm9/jx+ffy6ni9Imbw/bhue/f5F/8u69h6BLXGx/v8bkKtUgJ8Pp+k9n5LI3BOFtZ\n220ys5SlfiVu+ViSk5MubkUhPFBGuB96WxLFm6jfggqgs1vAKSSrJVLoBdLNa5Q2xUNYQFdkWL++\nxMkLY8D0bR0mKq4i+z8Ewh4ZBh2T+ZkOWc3DUzLVahJClcKGU6GiWVx351MzdSUFbiAxZMCl/hJO\nSuapG4qXO6v8WHmbggw9fTdTOlJsDmZQnmShMDlS2duZoVd2ib6NVt63S+uNSc3rfju+/tJckRt2\nSEfM53OUr3vYq3G97dx8ns1Iy52ZcpbFfIlbL4eTdsvLJS4H4TY0KShE/IwrpQzLWoGq06eYM6no\nWS53Gpw9VeY7a7s8eXKeH188zlsdUaDtdrsUCtO7frxZ/MzP/AyXLsV9S37xF38x9vnXfu3X7nr7\nafGuyHTh3vskHQZ4b1bYcLQ0R0HOkpGKtXaZkuZgK0k70mW3adf4H7/1h3gpWss3i2+9fIP/9V//\nMZ2+zbmVeCtyL6FYUApOzs5MfPdoZY7adnxYubPRnPgttxyg9wX+EHSx4reIjBQyBJtZ/DTz8RTQ\nTctolU+sqiz2t+Gzvu0W2dsZo77Tnp4nBI5ARbpMdOsZljIdhBDUDjE8B9hzi7w8OJoKuBC6QF63\nFuj5Blt+Jf2AgB23xLPN41iBxraXx005wA1rjh03XRGx0VuY2PbCozVqCQObcjFDe5DwUyiF91sl\nm2FpU9DY6dEcxCkMM8EDL84U6F8Zj47Ks+OR0xNzS3TccVa8tFhgfz1c9shqEdkdtbvSKJsZhIQP\nrtwb33mn8aB56cK7BHTfKq3u7RY2AHx4/hQdV1G1DEpmCAxVcqjhnEW25HDT2+cX/u3vcWt/Euym\n7cOffu8q/9u//tJBdps14qCztl1HT7SzESkdIrOTvuY06n1OLo4BWhH6LUgbkCC7IlT3R2Ik41Jd\njUErh0piilLpd1WKXEzaUybXfAgiFMKN2gK9TpiVu1Pa+gAEVqSzhQi4cHIHbeiu1fAmZ/qjccOZ\nw3+TYWrVL/Bi78RE+6Bo2EKnRp4X7QVueZNAvzso4kidgZah5cT592atiLWUdh8L/OPxrHZ+djK7\n0w1JwTQ4XjXYX28jNcluohO0m8j25/0M1f3xttXQQ1eXktblNju98fozRpa9WrhsoWRyea2GlHCj\n3uLC7CwXSnMYd2irejeR9F14kEqA4QEH3beqQOJOOzYAfPrsh7C9DHmhGHgZUApH6rQ64cMuNUVR\nwY2ZPT71r/5vnvnu6wwS3Fp0H165us1//8v/kX/3zPMxqdD2frxzb992Obscpyxu7TZjFzZvGuy8\nuIehT+7/Qm6c2XimAhUOKwH0loxra5WCTIAKwF4vAGJCeys8Uu+qNI2uTLfQDcE4ui6CyxvL+LY8\nVLngueO/PXlknbnCOIPrHdJPreVkeaN35E17rtW8Iu6bVLtdaa6wbVXY8opspXSiWDvIZAXXu4ux\nv92oL5L2FurX55HnLaIETb4wqUX2UZzv5tm5Hr7Ul5aLuIlZx4Y1znyPVkp47Tj90PHDi/LE3CKe\n41PtjamIfsSfQ/clgYLTi7NstXpkMpL3zdx52e/dxIPsMAYPOKc7ivsFukEQYFkWg8EAwzAol8u3\nbYi+WqywbMzS8NusdRVl08XTNDpOhplgAFJhBDaqkKW7OODffOUF/tXvf5P3P3SMv/L4GVYqRW7t\nVKm3Ldo9hy9847XhfoW1841OePPvN3ocXS2zWR2Db8GIP4Dtvs3JY2VuNMJl3js7z40XbnHmiVUu\n3Yy7jrX2xsDklhV6TxCYAQQgLEmQH59XTSmEBH8ri+eHt07StlbzYIJACRSk1St46UyvcJlY3g4M\nblxZIl9Jn/GHsDADASdna5xbiB+nKzT6rkE+xVryhdZpAqFRd/KU9Onbv9hb5XS+ylJm0tMCwn5u\n+xTxHclZaiQBtOVk6Unz4NvdCMXQHmTpL8rUxL8aZJEVH7noEuwPT0zixZY1dDLbHtcujp3mSrN5\n2BlnsZom2G6F+y6FYK4qqJrxidfNTgdT06hfbLKwWmLDCrd3crbCRj28n4SAa9shsM8X8xyVLpoU\nfGD57VEtJA3Mf5jpvo1xvzJdpRS+72NZVigDu4OODdH4UPk4bdcnQBIMmx6KbECzFg4zc0NTb++U\nzW6/w+JckedeXeNXPven/MYffptf/Q/f4f/80ktc2xo/OErBseX4m3ypHB+27tcnQWB+2DlCCGi/\nEWbs2ZTLvbnRYKEc7qtXVmh9gZcJ0NoSoeTBhBqAToDqSwb1YXYcqElONmWuSrhThuPTLllKF2GA\nqijQa0/PRr1AUsn2ed+xZFNGAHHQeicaVzuLdER4PHV7Ou+7MZih7eepOtOXudxaBilpTcmYr/cX\nY9SUpZk07dxw3RWElpLlNjI0hpSOdn4MkF17PExYqRQ51za48epebF2ZiV+cpcXSQXeRH1lYorbW\nYjdS5TY/n6fnuDw+u0CzNsAojd+ox4w8LSvkkN+zush+t0/BNGi4FsczRU5UKmTexgKFKL3woGW6\nDzTojuJuQVcpheM4tFotfN/HMAxKpdJdV7d88sxjaGhovsT1jZDeNBT77QKokNcVw8os77TD/Nx4\n+FksjB/Ute1GrH2X58WRrJfoCrFVbbOQkI61O2HG9sjiArX1EHQ3b9RS+cjjM5UQKxUIJQmyKqQW\nULHqJU0GB7TCtBBpmWuKty5w53efD51mHjXZwR2AQCh+7NS11JZAAE0/Ptx3A8nr1hFGx9NR6d4I\nSsGlbqjNTPZEG4Xja+wNK898obNnxcF54Bm0giQYC671Fum7BnY2/Z6r1coH+6edGMCwe0d1qFh5\nZHEW/cUm2AFBYucHCYvH8ky470cqRTae22bxWIUg8tzMzRcwNY3q62EW2x86jy0VCzj98bZmlbOu\nSAAAIABJREFUhl1aHp6Z50aniUDxeGXpvjbZPCyS9MLs7PSKwb+M8f9b0HVdl3a7zWAwIJ/P37PN\nIsByocQJc46cyjLAhVF3A6lwO7NITZEdAqh/xGVXjfWQu81xttrp25w+OlYp3Nisx/jYm1sNCtn4\n+PvYXPxtv7bbpJwxydfGANRpW5w5Glc/ALgdFz874liH59ER4SRZ5JT4LQMvUnUl0lrppMnFplya\naV4z066kZkMgdLz1SeBTnmB2vk/emEIUM1lZ9r3GSfzId0pI9geTqoIrrSX6KgTMrp/FTlFsXGkt\noSJ87/Ygfj2u9hZTq0Z23RKXOsvxKr9hBL6gXoycbx200wPyOYN23+YDi0vsf30Tq+tQTikJ3mvG\nR0DSlEghWKhJXMcnPxN/CegFjSdmFmgNdbnbQ9ObU0YRlQ938GilRMcJz7EhJCeKZUxT58ePv/VS\nsVEkJ9J+qF54G+Nu6IVkYUO5XMY0zftiEQnw4cpxbC8sozWGTRCNjMfafh6lICeHnKKAG7N7FPMh\neG7V2ixFZqRH3wNYjhcDSz8IODEfBwfXTvQoU4r3Li2x9tJO7PuSMYl0azeqBAWFdCVKD9BaGkIk\nJtF8UP34uipNBpZWRDblXRaYUzS6KcNsgNGp6/RyqMRkmxYElIoWLSs9E4VQY9wc2itWrQI7wSTd\nsGfFz2ugBNescRdhIQS77fh6XiDY8eIPfj2ilnADSd1P15JawqAapMvHmlslAjPRg+1cn6W5Ak9q\nZW587ebBG0okPBfyBZNGwpWs7zk8MTfP5qWQvkqW+7oiYHfYnqc8k6PeH1DJZrj5gz1qXritIzLP\n5qDLyZkKrh5QDkxOzlUwb7PNzr1G8hn9Iaf7DsXtFEj4vk+n06HT6WCaJpVK5aC4YbSN+zEZ96lH\nnsCQGrpl4KoA6ZqYBY++J7GbZfKRrrfBjE/u9LhAYWlh/PDtJUp8MwmwNBLNw25u1zG0+OUsdJlI\nG7fXGhM2hJ4XoBkCoQRBRh3oL6MAajYEJKmJlMKGKAd8EGmprgsqbXJNTU7OHWxmWEqshMRZH4Oa\nQJHLh5RL2zlMgSDYtsIM9C/ap1Izz06Cj32juYIn4zu0n+B+r7aWCBKqhr7KMBgWFlztLk5Ur42i\n1c2z108H3WqKzE3OeBQ9l1t/EfeQ6A7iE4QLy5PbFFKw/d3xBGPDitNUeVfSaYbgurAaHuNDxVlU\noFhvtsmbBo2dLnu9PksiR1dzabV6PDq/lLr/b2X8UDL2DsXoxB+Wpd5ux4b7Bbpz2TwnjTmyQRYr\n4+BVc6ApdOGz2yyTKTmIEfcmYKfYJRiCVz8iRN+qtlmeHz/cm3txbe/6Tit28SzH4/TCeEh7ZLbE\n9T+9OVGz36j3OHUkLjHzdQhGCXggkMOeZcEoE/LBqMrJ6rGJSTRFkNa0Ia1+wk7PioTDJLiP/hah\nKLuDLKorAUXWcBlhXlrr9Wi0/Byvt1boy3RwdqWOP+SgvUByK8X6sS3G6waBYMuZfOiFCCfuAiXY\nm1IIAVDrFtgfTE7O2W2DXmXyxKlbGtcXEqXeAnZqcSP2XDl+IXIZDXPTxR0aomuaZKcxph+KOYNb\nr1cPPsucJKtrbLyyz9KxMl4Q8PDMHOWlHKamsbXWxNR0ZsoFfvLs6Xsqx72TSP7OD0H3HYp7LWyY\nto273Y8PzZ8kUOCrgF5HoAUapunTciS9eo5MRDtpK5vB8fB3b+40yGXGGexyhEKotQYcXRwPYds9\ni1MrcUDIRbLf1b5Ov2dz7sQCyZjJxR9Iu8LBJIxwIs3/hlmrWQ91pckU+XY1ummFEWKaRtc55MGN\nbkYKrI0ChubHJs6ciWqNeNgYXLEP0ZNKwd6Q132tuYonJ+kYT5d07fAcXm8s4k9Ruew7JW5052Nc\nbzQcS6dDqGBwEs05q/vjCbRo9Psmm0uD2GhgfrHIIJHpJs1wnlxeYuPSWBWzeLQc0/A+trxIrz2+\nKJameHRugX7PJTefQQDNmx28nODh2Vkqi1n8jsfZuRn0+0TN3U38kF54hyJZ2DAYDO64Y8P9BN3P\nPPEUptCQXZNCUcOv5jCyYYax3yyTF+MHJDA8BosKt6Dw/IATq+MbqN2LO0YtzsYzoplEe569ekhJ\nnFuaY+27G+GX7mTZ8d56PGv2dYHSRAhqEblWYAA+mDU54cGqlBqXCg9DcyfPn7RVOs87RdEgpigT\nYLJ9j6sLMlp8hUAKBu509cm19gL94PBseN8uYfsaG+60WXHBTjccVaxPXSbMqrec6XKmWqMYJgFC\nsN8Zv2CVgnY2JRN3oFUxCXTonht/Pbs0mUm3I9TBo6vzUI+/5UrzY+ri6FwJvxsH7b1+j8bQq8Mx\nBQ8vzlPd61IPbPymT2EuS73a48LCDL1eD8/z8H0f13Xxff8tA+FkpjuaCH+Q4oEG3WRzSsuyaDab\n+L5PuVy+rY4N0W3dL9Cdz+dZ1kvodoYg79Jq6GTyAShFNzAJ3Mg+FQMEiu6pAIVCRIbW63ttCpFu\nr61OfGKknmhGudfssVIpkN8cP3A3r+6RS3SMre51OL4SgrsCgpFKwY24N4lQg5vZBxmIiXJfEagJ\nGkCm2Epo02sN0mOaBaSv8GMOiYqF05P8NAhadvpkWn9gcrMzT885vHFlN8jyWvMIQZqkYBh1p8DN\n+gJeij3mwXbcLJ0Ur14IgbUe4Y+rjTFwdtZyWLnJ8xDc0gmGKpbOI+Pv9cT1FUIcUAenFivsf3Md\nJ8mrR5zU5lqKQcSHd2Y2z7FCifqweelOv0umC9mcjqcUazcbmIbO0nyJn3/yvRQKhYNyfN/3sW2b\nXq9Hv9/Hsiwcx7lvQJxGY9zuM/6XJR6svZ0So7frvRQ2jOJ+vaF/dOkUpiaxPZ9AScxOjpEgoTXI\njnldDTII/AJYy4qNWvsASDw/4NTRMYWwtt2gHNHzru82mS/F3/LnyhW2XhuL5B3H5+yxSV5ypOv1\nMhxod6NJo/ADhKPIVMNbxLDjE5UiRdAqUs6dNk3BNaWbxrSzrw9ARB6u2dUWmXy6AXhnCqhe3FtB\nIekd0s4HQq/cTffwIWtHmKw50x3jAOqtPO12Ouh2GjncyD1aU7mDLsl7U9QMvYhcz14S2EPmKNmw\ncn6xgOV4LJTzBD+o49k+tVZ81NQeyr4eP7rIxmt77ETkZQurRaxb4fLlmXD+48bFKiunZjhiFDAz\nGtv1DqulMdiOOvhms1ny+Xyse69S6gCIe70eg8EAx3HwPI8gCO7ombtfBubvZDzQoKuUot1u47ou\nQoh7Kmy4H8Y50fX/3vuewlAS1dHJZwWNqk4hCFGtT4agO37g1Oj7Y4qGa8UnuiJv9UApTq7Eh7PH\nI5NnlUIG//KkmY43mASn2nZY0ullCLNYXyEiz65QivwGHFRpJDxs0wB2Qs2gQO8rhJOicpim0Z3C\nuWtRzMj4ZMt26nIQ9i1LRqNVYFuF9Ew/pcNDNHaqlTdt49628hNKh2h4vqA5yNPqpINurR+XkHlS\no1ErYjs6nXLK/vVDaiG2D8Nst5ronTYzXyBr6izseXRrfbIFk/3oRJuArWaXQtag+fI+M4sFmpFR\nVMk02dkIqYWFYyVOmCWUUpgzJls3mpw4OoPTcfmJh04drJPMQKPdezOZzAEQ53I5dF1HKYXrugwG\ngwMgtm0b13XvGIjfrqKM+xUPNOiOgLZYPNy27062d79AdyaX40i+jOrrmDmwPIkemRjx2+MHSJhD\n5y4NeicDyuVIddpOvDrNT0jj3CFnKwSc7Gtc+/4WywmO78bVfcqFOIjsbrVYHU7M+aZCHyhUtLBB\ngDYYvxiSms5Uq0aTUJ9cg/wlSfElA/2miflqlvxfGGTf0DHXNfS6YBpeBWnNKxlrdBEKMe/gONNf\nri5yorLs9frKwU73PYNpl9n3JFtWhb59eDZcbRfpD6Yv0+jlUQjaQWYiqfdcSUtMrrvfLbJfK6eq\nN/wNHRKSwP4pyFRMao04zaTndB7RC+wNzcYXj8/EjndhucTAdnmkNEOn1mfuWJx3tmrjF5pZMtm4\nGKoaMoZOq22Ry5nkcwafeOTOOv6OMuIREOdyOfL5PPl8HsMwEELged4BEI/oiShPHAV3z/PuekT7\nTsYDDboQur+PJGPvVCv2aet//Mx5cpqGbfuAIPCMgxY+vpDIUTuaQsBoYO3MwTVn7J3a6ducOjIu\njLi5VT9wAYNxtdr7VpfYfCnUbkb1vgC+H3DyyOSEz0Ixi9QkSgO9H8dRo0VMx+plk/xtfEgrHTC2\nJYWXTTK3ssi+CUJDaSCEJJA6WAayamJcNSm8oaMlLCOEoyaKAQ5iWKcs5hyErnC8Q0Y0UtKJNMfc\n2p+hGZF5IeTUbHenWsETGoNDOg47tkbbztCzpmfDjXZI+wRC0uvE3zD1ejEVWPeCAs2JUuEwOila\nPL0pcN8zmUkXuz5rz28cfM5W4tucWSpwYqHC9W+H/cFkYXysjxxbYH17PFrKCI1ez0HTJJ2Wha5J\ndrpdjpbLsQzzbiVjUWrCNE1yuRyFQuGAnpBSxnjiET/83e9+ly996Uv37LvQaDT4xCc+wUMPPcRP\n//RPH0zAJ+PUqVM88cQTPPXUU3zwgx+8p9984EEXuG8cz/0G3U899QQmOt5AoAlFtSfIDLlRlVV4\ne3qItaZCi2DYrXyX2bkxV1uKZKl9y+VspDrNdjzef+oIa1++dvDdznp9wgPB6kzOaK3VWoBCBGB0\nx8AqfIWISph8hUp0a4gKALQ+lC5KpGUiErdUGohqtkIpg+wVk+y45x/6ZCOFcQgBeR9RCE+Uk9Yg\nLBLtIegGgeBSZ1K8303JZANfsDUsVBhY07PYerUIQkzlhruWiR2hONoJiqE2xb/BskwGadtsCbqz\nKU0vHcnWkntwpaUUPLEyy+YP4lWISVpd5nXKVQ81TABGRuW6Jsm0ffpW+FlIcUAzHDlW4er1Kg8d\nm2cwcPjZ9z+Uegz3K0b0hGmaMZ5Y13WEEFy+fJlf/dVf5ctf/jInTpzg6aef5lvf+tYd/84v//Iv\n81M/9VNcunSJj33sY/zSL/1S6nJSSp599lleeuklnn/++Xs6tgcedG+nQOJOtnU/QdfQNM7NzuHb\nkrwRSgQybvhQKQE6OsV+CK5aZAzqF6AXMbPea8RTwqga4cRShczNLkFEc1nb73D6dNyrde16ldlE\nR9mWa+NlFGYrCO+EYfmtWQsIzAg/l1Lt5+fDWyezH1C8KtFSnMSUUvjm5PcHPtpCotWyFF6TCFch\nD1E6+Nkwyx2FI+RUigCgOyySuLkzR19OApbVmwS3vWoFd+jF4AgNz598PAJfUHPCa2YHGp43uUy9\nHZ/cbLnjTLPfymBr6Vm639UZtCYzXXdbJ8nnCEfh5DX6RoC1CNmMziOFHLXXtmkm6IZaom9eQUk2\nL4aVaUIKtmpDC9CjC8jseLh+9vgc21sh6M4Wc6gAMhkNQ2r81QifC/fWq+x2Y7R9Xdf51Kc+xb/8\nl/+Sv//3/z5/8id/wqc//WmWlu68Mu6ZZ57hM5/5DACf+cxn+PznP5+6nFLqvrQFg3cB6I7ireoe\ncbcxcjD7Ly+cJo/O0BsaJbQDw1npKewdDQKQxviCKg0a0mbkIrhVbbM8N+att6vhpMiZlVm8725z\n5aV1cvk4iOTM+IMdBOpAJgZDMkOT+BmB2VbjajOlyLSDiUKIWHgBfk5QvO6T29CRSpKGgNJVE50n\nIEX54JrkXzXRG1Nu6iBAHXFjVbtCCFxrOsVgBzq2p3HVmiwOAeh58fMVBLDZj/onCPopFEOrWsA/\n2BFBvx8f9nu+oD2Iv9w6gXnQEr7eTvdgUApcV2cwmKQROtrkd+a+OOB4B6c1jlkBN797k4WEUiVf\nzlKtj4cQszN51iMlxAvHKgxsj2LOZOeFbbyIh8NsPvxd09DoOS4rs0W2mh3OL05SVW+XkiDNYezc\nuXP8rb/1t7hw4cIdb29vb++gu+/Kygp7e3upywkh+PjHP84HPvABfuu3fuvuD4B3Aei+Vd0j7mV9\n13XpdDoMBgN+7tFHKGomnhNaEPTxyQyrLW3dx7Eg28pCPs6ReoHP4Ihg1ChhJVKNtlfv8v6zR+h9\n8xaDloVtuZw5G89sb17ZxczEQakTefhyFRPf8cAPEIE8mMAyWwqRdLJJ3CW6FVB53cfoZMYyrjQx\ngzsFRNN6qAuNzLrEaE6KfXUtQGQnf8B2pvOuvpS8Xl3BTakoAxho43JfgGqtjJ3w9E/LhvcTqoNe\ngoZodPMTCgwlBN1WLnQNS2kGGm5IAyGxA52gFznhVUG/PHkMyhkv0y8rblTDm0pL9EBbiEyS6brk\nZMakFcmEK8vhy/zCTIV+22J32OzywrF52laYKTx8dIEb+y2WzRydgcMv/PhjqYfwdhve3K6X7sc/\n/nEef/zxg3+PPfYYjz/+OF/4whcmlp12DN/+9rd58cUX+eIXv8iv//qv3xWVMYp3RecIuH9Z6r1s\nYzT86Ha75PN5TNNECMHjK4s8u7FOWZm0pEPeMbCVizcDxm5AsGsgLljIjiIYTrAEmgIB/VUobEBv\n+ABUilnOZ/LkbrRx+mMpWLseH0IO+g4PPXmcNy6O+b31mzWWTs6yV+sS5CU4ArMTIISOP5SEZRqK\nZEecpAVjfitAZePgIT2fJDqLKT0409zJwhU0itcFrUcDgqF4X2gBspC+IdedPnPtuDr7nSJamgEP\n4f3S75qUyjZKwUanMqHI6FtxAOs1slgJe8ikyqHRTq+OavezeL4kmCLk93uj7QqseoZ8IdTIOXsG\nzKdTC9GoP1ngyFdbtDtxKZ1ZysDwJf+epQokGpkGGY2l2QI3vrNOeaHA1tBoSe322cj5SCHQfUWg\nFDvNDtmKwQfOH4tt4+3Wy46Asdls3hbofvWrX536t+XlZXZ3d1leXmZnZ2cqRbG6ugrA4uIiP//z\nP8/zzz/PRz7ykbvY+3dBpjuKdzLTjfo8AJTL5ZiD2ac+/CMUAh1zmJ1oeR2zHlaCaZ7C9sG4Lslb\nEV53mNm5JYE7A/2BwwdPLJN7eZ9rX7/Cxo1qzJB842aV1aNxQb+bos9dnS8xP1ugObAJDIU2NJ7x\ncwKtH6C7ItYUEiDIjD/nN13Mbjp/OxlTrBunKBSUIUEzKL8WhCoPBUIGU60hD/NZqO2XCZzD5UQj\naqBWL2GJyay5n7BBq7YmpYl932BE9XW7GezUmuewVU/NSgdkFYAXUWP0B2Net5WdzIyLrUn5mFfU\nCOYzbG/Fddr2cOcePb3I1WevECQNkAYWq5j4rs/CqZA2eOj4Ar4X0O07PHRsHi8jObc4Q1/5PHFi\num/F22140+l07tnA/Omnn+a3f/u3Afid3/kd/sbf+BsTy/T7fbrdcF6l1+vxla98hfe+9713/ZsP\nPOi+k/RCms9DWkni+48foSRNuq6D4Qh6uodZH9onDrWxnp0Dd1wSFmRBHz7Mg0Vof2cdY6OH3Q0z\n3ma9x7lHVmO/MzsT5xJvXtllNmFu3djrMGdKkALhKSRhxZCXE+Tq4b4kQXekajBaHrmaNtkBGGIT\nbwfnJ+0h9BVBNm0DCn/YXkZoWcqveeCF5uTTwlHpk2l23aDfzqIGh4NAdzipudFOz5YCKbGGHYad\nvk4rxYtSIRgM9br1TjpfC9B1DLpTBpaqE58oG3gmgSPwGjp2YXIdtzZZ5ic8Qf9Dc/iJLiPVZp/j\nqxXWn70Sfo5QC4apYeoaN4byMjlsz+NvdSkfC+msoGpT9x32dzooQ/Dpv/bk5P6/TQ5jyd+63Uz3\nsPjH//gf89WvfpWHHnqIr3/96/yTf/JPANje3uZnf/ZnAdjd3eUjH/kITz31FB/+8If5uZ/7OT7x\niU/c9W/+kF64i22MJskGgwGapk00sEzbxodOHuWPb1yj1JPszToUA4HRAlmS+H3wkeg9CZF7SPRc\nKBkEumDvwwUaV+KaquQDduvqProh8YZcahAojh2ZoRF50ALPZ6PfRxR0pBeABooA4Wvow8ViioNA\nEWQEwlWUbg19LtLE+ylAmgbO2sAjyE9mg9rAR0Rm9d3ZDEIplHtIXiAFfk9DL47pB6WgWp0BBL6j\nAemlwgB9DJp7efqpXTPDsJsG2RWfeq04dXKxb2XIZDzadmZqGiM7w2OuTDr6jKmFYQiBu5/B9TVI\nJLrCDrBm4l8KN8ArGOxrgpWKJNMKr39pNsfA8/Ev7+PZHqX5Avv748q0pZOzyN2xZKRhOTx8fIGN\nb65x7ESJ06uz1F6vwWyFpmWxuFyZcLZ7OyP5XHU6nXt2GJubm+NrX/vaxPerq6v80R/9EQCnT5/m\n5Zdfvqffica7KtO9V0nH7bytR21+bNumUChQKpVigDttG3/vJ36EnKfhWz7SB7NkkKlJ7JJCDeVi\nDiYyyrlFTEqsJYM3Mj1ORCbMrl/aZiFSfdbr2pxOTKjVdsZdgzVNou3W6Joy3M+h5aDSIFcLEIQv\nHRXJdIWvQCkqVz3k0Bh3QgbmBCE1kIh0jW76NYoet5tXWItDq8lAwCHOY84gwbvu5rGGGtlASdQU\nXhnANSRr+4c/tIO+SeAJqlOMayCcTGu0CqgpPsAA9CUqCa6A8sBPaSvf62bopEjLMhseJEx2srve\nAd3QfCh7QOosHJvhSCBoDku+F0/HlRyzheyBdMzM6mxW27gbXRCwUW9TdARL5+cJuh6ZnMFHHj6R\nemhvZ6YL42fsQWxKCe8C0B3FW63T9TyPTqdDr9cjl8tRKpUwjMmMbdo2Ti3MsahlsfSAhb6Jm1EI\nW6A5AjkEFaVp5G+N1/ETFGDvmElvafybSsHK0Tin5SR43J3NBkeOhEPFR07PsXV5GzdQCDuAIVD6\npsAYSoHVRI8zRXE9QB9yjkpNVo3JFPtIFUzR6HrTynyH1Iau6B+N8rhiojVPNKKVaYEnqLYi1XhC\nouzpvK6oSqxD2vsA9AOT5n6BYErnBwiNaGr96dsRXYkINIL+5L4EXSM1gx70zIg0LRLO5HfRbsvW\ngo61oCGkoCIkG9/fPPibXhxnyLOzebzGOMtdPjvP+SNz7F6vsXRqllzG4OYPtulqAc32AC0r+Tsf\nfWLqMb4dkQT3B7EpJbyLQPetoheCIKDb7dLpdDAMg0qlcqBKuNP9+Nh7zpLNGHhVF8sIgUrfUZiR\niSrZkxjtYUnwjEBFsnevIPl+xUZbGj/g6zf20SJa2PVr+7HsF2CunOf4sVkuffll3NUZMCS6M04f\ndSu0bwQmsjXNUmSa0cw3mLR09CazV23gppa6phrlEBZMKBS9o8GEGY7WSV0FACdyC7e3SvgJTiNI\nAalReLUs2Ic/An3ToNo53K/Vd3S8wXSmTnaG++RJVKKIxJ+ynjbQ0BsJNYgdYCWaSQpX4RXiL//+\niQxnVvI0Nuqx79v9kAvWNMms67KxPi43zy7ksdfCjLh0rMyRbI4AxU61i6fB/GyRmdKUicC3OdMd\nxQ8z3Xco3qqJNKUU/X6fVqt10HnidjoGH7Yff+cnniBrS5QQ5LoCM6Mj+wIjF+lIm5fkNoYAqAuM\nQQR0SxoKxd6PFA8mu1qNPmcfOTJeX8HiQnxCZ2ejATtVAi/An8mBEAcloAC6Pf5/Ecl0ha/INRLu\nUbd5joWdPq6fOgQXYC2pieweQPmHFEEMh9XeQKPen5zImga6QVfiKQMO44wBranh9g93JZMdDa0z\nzQcY1IGKQkA3cq0dQZBWzqxAswWyGX+BZDY8SKgPMvvuhJJBaoJX6LG5Nu4UYWYNtobVZY+cnCNw\nA/r98YRcTtfYuzkE4azGjZe3CRbDjhH5vMlPP3mOdzqS4O55Xupo8y97PPCgC9wXW8bRdqJm6EEQ\n3Hbnieg2pu3HTDHP0VyRQAezCUZeIoTE7/goRhIxCZ5GbmfIaSYKCTQ7oK8rak+VDvSunhsnPbfX\nmwfVW5ommRM+layOAlTWQB/4+EOgl5YfG976/pieKFzvIZw4eKYdWhBMAmwa5QCTbWRGYVcE9nz6\nefMPedEpKfH6Go3tMipFWyZS2qUD+LsmMAX0IiGbGrJ/WHUeyLZEDNJ/R3Y0RGS/VG+8XJBQLYxC\n6ysEAmHLOJ+d8gJJmsTrVoBlSNqny/QWx3TCyvklfD/g/JkFLn39dUqRDiVCwM6lcX80PRAMZICl\nge35ZHI6P/+R90w7A+9If7TRM/ag2TrCuwR04d4z3VEl2UiZMLKMvN+u9D/z5HnyhoE7CBi0QyG7\n1w7IHHS6BekHGPsSGYCaaFMefraWTFoPh2nhjUs7zC2NNaSteo8z58LSxvPHylx77hKXnr/O7IUV\nlKGhd7wD2Va2bsdBd/i93rAxHYMgEweTtHZfSTMcAJVWdQb4+cmsNdDBWjaGTmIp62jioHQ6LTq1\n/NQuwI4SE9aKQV8jGMrFEBIxxZpX9iDwNcQhRRhaQ4Rl0FM0waKboDsidMJ0amE4ekOiN4YyOldN\nUgu+wi3ElRf63mAsqXq8gpcN79/cXIG5uQJ737sOStGPTFw+8cQx9jZCfe/iiRkuv7GNn9XQpISM\n4PzxBTLm9Izy7QTdZPwQdN+huNdMd1S2a9vh03evZuiH7ccv/LUn0O3QaERailDpKSlEhtDCEAih\nUdmSOJWku9f4c+dsnt7RTDihlrRuDHwePjvHpa/9YPgxoGt5qIwGrkJpAmF5KDsqt1L4BR0CRWHb\nBaVQiVJiz5lEqFTdbYrngh6AyiY8ITTwCkOLv2ldJqSYnm0qaO8XmFpBIQQy0TPNr2Ziy8te+mOQ\n72bDLPWQbFgOFQkKMdFsUzgClTTE8SXKkqiBTG8H5Ctk5BRrrXB9vSYnqYU9F/QE7xt5pJUh6Z4v\n4WckA8+nPLDoNwcYWeOAzz12YhbPGu+4WcnQ1RQiE/ppFDIGf/cnn5p6/G93POhdI+BdArowBrs7\nuRi+7x8oEjKZDOVy+c1Xus39mBa6rvHEkSU0QPMk5pAj8Lv+wcy+Nqy19xug6RoyAoxIDnKcAAAg\nAElEQVRuWcbqvOqPF7FndDYiE2pCCgzXoX8jbvHXFxraICAYFjtk9gexQgjh+Shdkl/voWOCN6nV\nSma+SqnU7PXIY8cmv1uIn18lQ8Ad4Z9McSo72LdB+q0q9nVE//AXZNYdZ4PBQBIklhdWyrYDGAyt\nVRUCI8VNTPQFYij3Egi0boJbbWvpmVhPI+im73OmSawtEZZEuKBSaJJiwghdDjz8UiQbDhRuKUPz\nqXk85bH52hYAqw+t4Lo+ZkbH3ajS7IX2kPpCjivNDiIATwKmZLmS56Ezy6n7Oop3gl6wLItc7nDl\nyV/WeFeB7u1GtGxX13UqlcpB2e79tndMi7/7X3yAHBpKCVRn6A8rFJVmeDl6RjC8wSSzOzpaBHQD\nU6JZkbG2Jmify7MvPM48vEIub3Jy3uTil79Pa79NeT6kHVQ+h182MToeSAVegGGLGIgqz0X2PTLd\nkTxscrwfJIazC5X8xEQOxLvRjqIUKWlVAtwisTvw0E7AaZVpjkDsm6kNMaNhd8bXw6/Fs9xp2873\nx5OmAgHtiUXQ2gnw7stDP48i6Gl4U6RsmpXwWRASrarhJaoE8RWdhC7drNqxxqZGK8yEA1Pj+2KA\ntVIg0CW5+VDdcvZoCatjsb7TxF7M4OckSoCpS3Qp8YXirz51Z90h3sq439Vo71S8K0D3dgsk0sp2\nc7ncRG+ntxp0Hz23ynw2hwhAQ4IKO5Ln+qFeNZAw6loz6IR+CNFISrSUFDQeK9M0fYr9Ljf+PCz5\nbO13mF0qIaRAVUooU0P64Gck5v4AgSSIKickFG9ZSCEPPkfDlAJlxsGiUprMNgxd0uhPmuNmhpSN\nEuAlABeYdDeLRJDcGUBumQglwn+HAPZo9KwsSdCZ5CYnJtMUBLUEgCZLiv0UUI3wurIjUFO8IVRb\nQ6U8esJTqZ3p9V058WLLbPYnqB+ROEeaG22/JBicnaX1o8d4xelTOlXm5e0azYcX6S9mCTIa/nAM\n5UuFpkkKQvKzf+UhbNs+tInkOyEZe1DlYvAuAd1RTCuQGHUjbbVaeJ53aHv2twN0AT725FlmdANf\nF2RaYapmOS5ze+G62aH2UiDIOeM2P8CEC5jIhl4KrxRcxJk4t3vz1Q3OPHkSMsYBTeHldcxegFJ+\n7MHVez5GpCVEcoJs+cjkTZ7PToLYQqmQbnUz/NLPpZcIH3bWvARHXBpkITJJJaZX++Jq4YvNq2XS\nS3mFRETfET2Jl2ym6aZQB8mM2R9P+Mn2dMqj1Pv/2nvzMDnqcu3/862t19mykwVIMIQQQiArKoIK\nsggCcuCAniM/QTzi4SjbQUDE5RwFN1YV+MnLwY1X3uOC4AuiIASPmEkM0bCICSEkZLLP1tM9vdT2\nff+orprq7urJNplJhr6vKxdML1Xfrq5+6qn7uZ/70TAipsIYvV5mW41Yp6doCKP6b6Xo1FALFd9t\nn+l1simCgnDZ6ljY49OgKF59wZUUAF0ITMWzFD32sIkkk17GP1RDJPcF1ZnuvrYAjxRGRdAdTKu7\nq7bdqG0Nx0n0zx9aiG6XBzb0e5lrKQ5unxf8nJAlYdGFpo0DUcVNV67fjA+Ykr802WDcKZVmzq9v\n6MFN6WhFiYuDlrM8o5vwZhwXo9IdkvEzK7m8RCIiS4ygIFqS0Z6xWZ9yqHN4hagtRvmQqhjINh2w\nN1YGPcUcJNNSBEqnFpnlBi8JZa1Kb0S7risq1h00PFStX80p4IJbR/EgALfPRe+rXa8S0XknbIkQ\nKomtoZ27EqfqDqOaWohnzAobyfDdUdIZ+M2YpfItQn8JBOiAoSiYtsOFZ8wLAl14fln1EEnHcTBN\nMwjE/hDJoUY46DYy3QME4YBp2zZ9fX27bNsdbBv7uobBkEzEOGxCG62Ohpo2SObATpeVAx0WGWEN\nbEdAU8kgXa7gm2ml4kfkGiqKPwNNVXh1WpL0idMHdpZKgGkhFAVHlegZ74fmhOLCVEUjPaHyJO4t\nVEZAIyKrjSJzUoaOUp1RSli/swcEdX12ITT1N+q58ufXNumYVXSCGMSNDMDaVsvlVry/XEyLW3pk\n0U4IBbW8f1EQKBGFNfCUEGqvUvd2u6XkzfiqGCmPJwmLYlf0nEQoAq3fG2kEYGwtIqu+i+pjKrOh\n786VOPGBO5hi2alOkRK7TFv4LcL90sHQFQ7XYsw6clL5s3vntOM42LZdE4j9wZJCCBzHoVgsBtN8\nhzIQh9+fyWQaQfdAgP+l+227hmHssm03ahvDEXQBPnLGfNSii6lL1O1etJHSQbEN1IKDEda6ujb6\n2gKi7CAWq4o6qTC/pwg2TUzgvmcGsiWNUNWBLE2CVuYnxh/hGaCMa02h9Nhk+weKXy1tSQqlygho\nRWS1joRZ48ewePxEFiitHL4BUmuLTPm7y7HFNIuaxnFousnL8HxB+2BBd5DgKU0FJSciK/+Dcbpq\nAZTM4Kd6UEzbLmpog2Bt/d7jat8gPr2mgqijTACwdnjHWLgCtThwntSjFnwZnRAKiS3e8VerNMGq\n5eKG59+5Ejc9QDVomRKiLC1TTQe3PNoppagIAcK0KSmCmASpq5RKNqcsOsKz/LRtisUipVKpInCG\np6SE6yi6rpNIJEgkEkFxOjzNN2qs+p4gPKrnYKUXRoW1o19A829z4vE4ra2te0XuD2fQfefCGSR+\noJMumVgKNBUVzLhANyG1xcFKOIGjVA4HXTVoWpOn75g0sqqYViiUIDQXzUzrdGs6WkuC9OtZlJiX\nycSlEvQZKM1xKBSZKjXc8Wm2dw6U6FvHpdjeU2mInS0OEJ9xQ+PY5jZ2tHeS6SkS7vK3JkpKeYut\nr3WT71bIH1JV8HHwLgJR9KojqGt+7gq0DiO6CCXx0u6I2BrfLgYN5t62FYQJVqFeyAVRUlBcieiv\nH3RlSeAaEOVVozkiWKBAYPRAoWyJrJZEDc8tbOlVHcsL0vtUkBI3UdUQsSUHrQNyPH1nHpID/dSq\nO9BbIrImlANyMB+0aENcw8xbxONxWkrwj//8bnQ9ZHJU/n352a7jDFw5NU1DUZTg3K8uZvt0hP+7\n8LfjB2xFUYJ/qqoG26o5tlUG5tOnT695zcGAUZHp2rYdKBL8cc17W00dzqBrWRbzjzwEo89Fb4sh\ntpSwW7x2XUUaFQMc7WYDHBdNJoltKVBSK09su9rsWlVQ+02cJp3MvFZKrRqJuIZelm0JRbCtO8uR\nU8byZvtbxFoqOcJEutZj1qcbjjlkPJM32mz70xYyPbUqhUymgKNBfmJtwPWOEQMTgfcAIqMg7eg8\nQRDdXKH1ex1eXqAf5HsRCupWrWaEfAVsgdap1M2EAYycQKvDL+udTsV5qfuZsxXdf6HnKlUBCgqp\n9XaNjWb1BUUJ2WcKV+KEg3T5Iq7YLv22FziVlIEoN8botuToQ8YEARfKXHV5HHoikQg8SFRVJR6P\noygKtm1TKpUCpYP/G/CL247j4LpuEJA1TSMejwcZsaqquK5bkxGbphlkxFFDKQ9GjIqgq2kaTU1N\nGEZ9M+rdxXAU0sJNGZ+65D3EDA3XdlBLkEQFx0HgBYxgJYqAkukZkGxXsNN6RT5ot8agyu9A+K9Q\nFfJTYrhHpMkXPcpg3NQWHEfirvcuVtXTxquVC4auEtNU5qvNbPvNBvq29zNmYqWbGYCiCTa5ebKH\n6Zjp+hlhso5doxREtvyqeUhsVzx/3zoQEc0V8TJdIBCoddp9AXBljcFMzdqkgOzgPxmjz1trFKr3\nL0yvlVfvkXWohYjPs7NKx1tycNpCRj9V1IKRKQZub0rBwi1zwWnVyyZF0cLSFUTBBk0hnrO56OPR\ns7+klBSLRfL5PPF4nFQqRSwWI5FIkE6ng9b5alqhmprYk0DsK4/6+z0D/3w+z1133UVXV9c+y9R+\n/vOfc8wxx6CqKqtWrar7uqeeeoqjjjqKI488km984xv7tE8YJUFXCBEQ+QfKROCobVQ3ZTQ3N9PU\nlGTR8Yeh9bkYSYXYDjuwNlTUGHrXwC/VH5OuaDrpDSXUXOWvWMlVZp1ulcxqmzTJT45RHKsTH5fk\n2Knj2Lnec6LK5ivTxLxZyefOnTYesayTt0Lju1MhiZLEK/D1TTcojPGkSUIRKKXoY+nkoklYgUCt\nzlhdSG0SKCiDF9qqNqlnQA1ZN9ZtM8YLlnpx8B+xVhSodTrjwMtMVRv0iKCr9bvIKomiEIrH5UYE\nV8WM4F8kGHmFZGhisrE9jwhpeJN9JoSyVLc4cFCUvB1ssVBWLcTKgUvRFZodGKcbzKwaAwXe3WQu\nl8NxHNLpdGSdRAiBoijouh4EZT8Q+6/3A3E4g/WbkoCaQKyqKrFYjHjcO9csy2LTpk0sW7aMD37w\ng8yYMYNPfepTNevdHcydO5dHH32Uk08+ue5rXNfl3/7t3/jtb3/Lq6++yk9/+lP+/ve/79X+fIyK\noOtDUZQhmR6xr0G3Gn6G4FMgzc3NGIYRnGCf/Od3EbMFrgJOn0Oq3PUlgESn5VWr8PS1PlSRRO+u\njCLVdgduU8RMLwVKY3RezfbytzVbcQxPp7m9s9K0NhtSLiyYOh71rRxWlV2jqqleZ1lCkD9Eo3+q\nTrVSSq3WuwYYRElQlbGmNgvUsk52sKBb/b5YVVZYE8x9SEmsmwrPgyjEej3v4XrwfZDVAhW6aiiP\nto94j9EjkBE+FWlbrXl9CgVFCJq7Bg5yMlZpgmNnQh/CcnCbQ9xuuYBmOC5Wmf+3Yyo4Lm5MJbmj\nxLtPmV2xPb+hKJzd7okJ1O4E4jA1UV2sAy/w2eWW9FQqxTe+8Q0OPfRQNm7cyG9+8xsuuuii3V5P\nGLNmzWLmzJmD/t5XrFjBzJkzOeyww9B1nYsvvpjHHntsr/bnY9QU0vz/jnSmG96GEALTNMnn8yiK\nQlNTU03XnG3bCOEwd/ZEVrzm9cbLLhMM1dNNNiVIbSrQf3gSu8VA22kjVO8HmeiSOE0l7LayLrbK\nA8ExFOIFWeEO6I9Tl0C+RYPmJpqLUDRdFM1TAaiqQmcmjyIEx48fwxu/WcvsJQNFC6+FV+W1Qobs\nNL085DL6YldPqVA9/LLee4we0DNhf4i6b0O4XkFNCjC6CQK1j3rFND0Lqg3SGXh/zbYtidbv7SMq\nNgtLejaLwuuyVouhyR9SojhK5HUm1gt2ktrnIi4QbtbLVJ1+h+RWh9JEg3yoeIrjIkP6Xa07Dy1e\nG3jCcjHLwdJwwVLBKFqUkjopF2LdJjguF1zyzuD9vg5XVdUhddzzM1s/GAefL1Ss8//5vyMpJStX\nrmTChAm89NJLvPrqqySTSWbNmsWsWbOGZF1R2Lx5M9OmTQv+njp1KitWrNinbY6aTHcoPXWHYhv+\neJ98Pk8ymSSd9k5+13WDwJvP5zFNk2Qyyb9+6hRiZT9qpwjJ8hqKuiDWJ1FzFgiBCMm4lLYUbVsF\navkWspBSa+r+RhVX6BpKpThACHJxgdWkUmrTKY7TsMZoONLlkHiMV17pID85wZpSjvxEg/wEnd6Z\nCfqnxOiWoekQe3rIFIFWrPOmctRTSpDYWinhUqP6ZP2PgvDG3EuIdUY0GjjUZKAA8bL0QkgwCtFr\nimW8uKgWCY+uCz1fWfTSQhRDrNuN7oQDjKyLnqvqLitJTLPqIuZKCBXI0hmNpn4qpnPonfkKakEN\nffdm18BQ03xZiuiWfZidrn6MvMuSk45E17Wa7HZP/KT3BdUZcbKswFAUhVgsxqOPPsr555/PlVde\nyfTp0/n85z9PT0/PoNv8wAc+wLHHHhv8mzt3Lsceeyy//vWv9/vnqYdRken6OBCCrt8W6TdlpFKp\noGjg/ygLhQK2bROPxwM5zYRDWjhu9mRWrH4LEddwdhRgknfSJZsM9J2S7WlIjonj2xpYioBsiabX\nJL1z08iYht5nYoeq1UIRlYUpVRC3JMXQLa0rQLE9u0eEwESChG1dWexJXuZUkjakNW/iRFT8qBML\nBxvWGHcUcnWitfB53KqyvnQ9KVWUhy9AwlKhz/Uyywjo/RKrKRSosrKCdhBZF5JVHIkr0ctqOiEh\nlpcUU5X71yoHNaP1Q6k8B1LPSYio0aWkinBsjF6JFapJamUT8zCMUmVQl6aEjQWYNpDZJlQdv+dC\nmA52Ou5tRUoodwkmXEnR0MB1cZoS6I6L0W0ixhhcfvUHsG2bfD4fFKdHwq/Wp+MsyyKZTKJpGk88\n8QQvv/wyDz30EAsWLOAvf/kLL774YhCY6+Hpp5/ep7VMmTKFt94aGFzY0dHBlClT9mmbjUw3Yjt7\ns41q3tZvl/R5W4BSqUQulwuohupixKc+/V6ShoYiJdLQSea8rLavVMTJucQ2ZMiUqkhFx0ZTErSt\n9X71sarMtuTW3o9rUREyIgOMOgpCEcEQyYq3qyJyG6gCo052akSY2ID3HSQ7BFqE5aKXbdb/fpSs\ni9FdP1CoVZlsrLvq+YjbeiMLSijxVDKVnImWc4MZc8F2SqBK4V0gonxzAb3c/m1kZcWxi7K4jFcd\nK+FKEp2S5nJGLaSkFLpVV7v7A4tIpbcAMe85tUwDKX1FFCHRX9tJMhlj8bvfAcIhn8+TSCT2SXa5\nL/ALdlJKmpqayOfzXHHFFTzxxBP87ne/47TTTmPs2LGceuqp3HDDDcRi0S3ne4p6v/lFixaxbt06\nNm7ciGmaPPLII5xzzjn7tK9RE3RhcOXAnm5jd+FPmshkMliWRVNTE4qiBO2PfldPLpfDdV3S6XTd\nWWuTpo5hzvTxNEnh8Qzb+r1WzeY4Ukqa8wZOQkeGxvPEyvPQVCvOuI4ShVIl41jUK+ehAZGFmyi4\nWvTpERV0hRBodfjWeD23raggDTT3CMb21W/ZrlsQA+gwa7Ljeu9N5QRatYwrgoM2KntEaqRfRoTt\nowKk8oJ4p1PhiRDAkVgZ76KqSDDKRTi9RE2WKxyJla9cmFJ0UIRAez2PUXBoNgVuaD9aKACn/KkP\nUpIrUwvxpEF8TRcaCmgKF3xiSTBzzL8zG06j8GpKI5FIsHTpUs455xzOP/98fvCDHwy5LvdXv/oV\n06ZNo729nbPPPpszzzwTgK1bt3L22WcDnnriu9/9Lqeddhpz5szh4osvZvbs2YNtdpcQuziww3fU\n9xF+d0t3dzdtbW17fZWWUtLT08OYMWN2+Vr/Vsx1XZLJZJDZWpaFbdsVInFN09B1fdCOG4DNG7r4\n988+TDGmYmeLODiUpjQR35RD0Q2UNkHWNqHJC7aKK9F6PepCAvpkha5EZZCL99pelboMzar1TBC2\njJzeiytrZBGi5GBHaHDjqkK/XnvKpHSNXiMi45aQr+rkjHU5pDcLcB2K46MDr8SlOL42sCZsQfzv\nFoXJWmQxDADHpf9Q773N65xa+gLITx6Y0NFUVFA6qufESfreUe4ssyXpTbV0AICj20ihBA0JYeh9\nDrGQz6+ZFmQPU2kraBSzlccqtqOIqlQeCy1jBtm3klTJNTlYZX2uartglQvMjouSdxCGhprJ4zYl\nEbZL0+YMIlMiMSbNsSccxlX/eb635qoilqqqFf8GO3f3Fn7BTlEUEokEhUKBW265ha6uLu69917G\njx8/pPsbJtQ9SKMm0x1KBQMMPg7E19v6/g7Nzc2oqhrIWsKjfvxChKZp2LYd6HT7+/sD3iqsZphy\n+FhmTBtDPFPESccw+mw000EmyjrGHklzfEAm5CoCyu25ArC2OuidlRRErHrkjkbg4RB8XkVA1Cj1\nqKm+dXhaNar3FbDrDKm0hK9H9aBnXNIdoZbeOqjnu5ve5GV/an99cwcpPBrEyLiIiIzYa0oJ7Xxb\nrUZNCIGW817TXKi1ePSh5gUyIuAC6FXWjHpOIuzajBaouTBoRauC7nD7bdKbTOIF773J4kD9QOnJ\nI8oKB11VSRZMxm7sQWZMpK5hC8EVN5+DrusVRSxPQ94UNDpYlhWcu7lcjkKhUKG13RuEmy38Jovl\ny5dz1lln8Z73vIf//u//PlgD7qAYVYU0GBqdbVjyFYZ/khSLxSDYAhV96L7w2zCMuoWIsDTGP3HB\nu5XRNI3LrzqFG674CUa2SHJMEr3LpH9iEnZYCEB02YhxAlm+/U80xfA18EIKJuxw6TccepvLo2R0\npbJbTYBiuTjhdlIF1KKNU93+G9EB5moiUloVZYgDYLouURd+AWh5F9NQ0fpdmjaGMkZF8bLsiAAv\n8W65wzRJosvFLQcytehGZuLgcdJ6v0u8Mzo7BdAKYLaBmncRddzE9JyLnVZQ+1zqqdjiGQdpCJyq\nOw+l5NbQGF6noYtTJaVLWBJRHbhzFqgDP90Y4JiQXJMjdWiSPsuEFu95DYED6LaD3NyL0u9SUhR0\nTaCnYnzwwgWkI4zogcAPoZ6sy7IsisXiXmXEjuMEUsp0Oo1pmnzpS19i7dq1PProo/tcrDqQMSoz\n3X1tkIDKTDfM29q2TVNTE/F4vKJzZnd5W6iVxjQ1NQWFN9d1GT+5ieMWTMPI2/RZFk6vidPVj1I2\nLLBdQWJdV7A9s+rSWciXUNcXSXR4VZb+iGJapA9BRKYbpT4QQqBGbLLkuNEFOUVUZLRhKKYXJJvX\nS5RQFBdCoBWiM1YhRMXoccWSJHYMbF+N4JzDiHVLRB11AxAYose7ohsawJOOGb1Ordm5D0ei5yVG\npvYz6Dk3crvjI4x0ZHdV16GktmW44C1YCAFv5mjaUCT9cidtHVmasjapV3YQW7mZWAGSSR0hwEIw\nfnILH/nX99f5hNGoPnf3NCP2Exd/LmEymeSll17irLPOYtasWTz22GOjOuDCKMx0602P2BNU+/Lm\n83mklBW8ra+39f1D/ef3Zoqwbx4SNle/8Rv/yNUX//9s6ctjS4t0nyCvF9EMTyKj2YLW7gK9YxIU\nDBUj7wTVatGagn6bZBekDJPOCQZ6r1UxSSCqOSGqwFY9iDJ4re1ClfGKwKML/MGXYSglWTHJOHjc\nhuZ1bmTxSylKSNU87D1nSijLtlJb3AoDHTGIlhcg1uniNA0SdF1RbnYQdZk5xRYYmfrnmdFjIqQX\nYCusc6Usqy+q1A5CYG3KYtg65rgyfeR6BuZhxC0XO6SZFfkSTujzxnAxJSiOQqy7RKnkoriyrN+V\nFIoO0jRpmtjKVf9xXt317wn2JCMG73x/9tlnmTVrFo8++ijLly/n4YcfZsaMGUOyngMdozLTHYqg\n6zhOBW/rT5ywQxNy/WqrYRik0+m9HtseBUVRuPzfT4fOPKieLrMlFrr1b0ogOwqonTlQFeKhIGoL\ngXDL2dRWm/S6HKJUmZq6Ca0mK3Ujpvqiihr+F6hbYq32Pwgej6Ap1LxL00YbtU7WGfWe6v0YGRc9\nV/WkUKLXDOhZh3jf4JMshRCkNjuD3h4rDijVc9NCMPJl7wCr0vVL73NQIiK52lNACOG1fQevtSon\nAwNWtjLzrSiv2Q6lcuYtXZdivrytkolQVYzyptSmJO865SgOK5uU7w+EM+JkMhlIu3xd+g9/+EPO\nPPNM7rjjDhzH4YEHHhhWtcRIYtQEXR/7GnT9W6D+/n6EEDQ3N++x3naocOziGRy36HBShmf3WOwy\nMfzhgbqOris077TQbZdSsbJ4poeaB+L9ColtJmooiEnFE8pXfHZdRTFrA5I/Gr4C9Zoe6h36qsdb\nCgqt6yxUS0TyxlC/YAblbNaRJLfW3qr7XHHtGiTJ7TaKI6I/kw9XYvTsgqLosYnVKdhpfRaK9H1z\nwcgMXIm0fMR2pUQtx0fNFujd3nepFSs/gyjZoIQyX9upyHKTxgCPmjQUhKoiXRehe8psSwpU6dI2\nJsm/3LJvWtPdhV90tiwrcCB76KGHKBaLPP/882zYsIHrr7+eqVOnjogueCTQCLplhHlbKWVgLxfF\n2zqOQyqVGpS3HSp8/nv/TEvCwMBFEQLnrZ1gDwyylDYk3+zDMiq/yoJdmXLqJrSu7ac51FigR6gN\ndjerlaqItFmsFycDhy0pSW6x0dcWUaRXylLqZKVSVet6OkgBqa0uqlNHORDhbhbvdtAsv8GiPu9v\nZGyMQRQQwpGeqqTOa4y+ymNvZL19CcuNHBev9pUq1BqJnZb3uZXKOw+136q4wCiF0N+uS748hkdK\nSaHsQKfjeMMnHRvXhURbiq/+16X7/bz1f0+5XA5d10mlUmzcuJFzzz0X27Z55plnmD17NhMmTODM\nM8/kyiuv3K/rOZAwaoLuvtALvk9CoVAIeFvbtrEsf4SODOY9JZNJUqnUoMMthxKKIrjtR5eTUATY\nNlpTGnWrV0ST6bhXyS9KjM4CIqQekOlK9ykMHcMB7e9Z0mt6EaZDyRnEPSaEKK5XAEklovCjiQrz\n9fDjStGleb1NamdldlqPRhCAVqhDE+Qlsa7637NalckKW5LYGco46wVdVxLvsr391jmPjF4LgfBe\nU7V2peigVhnrqCYopouRiaYs1KqCoWYJ4lvzlU0VUqKGjregihUumkGBTZRKgSmSLQXSdXGEQjKu\ncs2t5zNhyq416PuCsK+IXyB+8MEH+Zd/+Rduv/12vvCFL+zWvMKhQCaT4cILL2T27NnMmTOH5cuX\nD8t+B8OoCbo+9iTouq5bMU/N52198+RCoUA2myWXyyGEIBaLDYvxRzWa2pJc9Y0PEzNNHFdiqBqJ\n/jwkYgGNECsopMNz0xQFpUrsahZKiPJrW9fkcLO1rV2RBTY9WlxldlaTqeVAGbFdI+PQ8rpFLFf7\n3Qw6My0iY9WKDs0biqh1MmRv0UpF0BybFRXqiKhMGCDV56I6XttvmIsN1urKgC4QQqBXZbtGj1nr\nMwvoGQctYp9KwUKpKpYJILG1ULF+NWdWXAOUojWgYihLtvz/18vcf0wHhEIi5lENZ/9/S5izZPp+\nG5sezm5VVSWVSrFt2zYuvPBCtm7dynPPPcfxxx8/5PsdDFdddRUf/OAHee2111i9evU+d5MNBUZN\nRxoQeHL6/FE9hPW2vkFy9Wwn0zQxTRNN09A0LajGelaMnml6WJe4P27X/JO4VPfHpxIAACAASURB\nVCqh6zrP/J+V/Pz+58ih4nb1YU9qQZQstHK3kiyWsJp0nIlem5faV0AJ36LmihXZkgRs1aE0IYHV\nFvOcsFzpBcGq7FbJFnGb4zWPWWOqMmpA7StiTvC0n8aOAs0ZoN/FiSlIPSI7lhKzRYt04pKuQ3HC\nQFYkbJeWNQU0R2DFFazW+hlToQ2chIqWtWjeVJllSiA3Ra/I4oXp0LrRDG71C2M0zLbK7bcVwd08\noEcw0wr5Q7wikSIh9VYxUv/r4mA31/oEGDv6UavbpIsmar+J2RbDnOxpwbWuAkrou1P7ikGRzUBi\nFbwLgezPoxgG0nXBcTxnOkVwxvnH8bEbPxgoCoBIbe3ewnVdCoVC0J0phOCRRx7hgQce4M477+Sd\n73znsHO2fX19HH/88bzxxhvDut8y6n7YUScZG0yn6wcx3yPUb14INzc4jhM8H0UjVA/p86ea+mOo\nh6pd0m+NFEIE6/jQx9/DG3/dSPsf3/CGC27PYE9sQZZ5ShGPoe/MggRnUitKKgbhW9ekAf1l/0jK\nRZ5+B+OtAu62IoXxccyxMVJCUGWahWI5NU1ibqx+wNO7iozpFTjZgXfFHEkx4i1CeCY6UZKy6nO3\naX2hPOAR9JLEkrKubaJWcHESKsmtJkJUDcek3ESRGvh+E9tLnuWa//68ixlu93dcnC2FijlqWr/j\nZaRCkMqYdRsujIyJE9eQRihw+j67VbFOKRvI6z0l1PEuRSrH+QjT9ppHyjAzeYRhIKVEKVs7GsLF\nUlVUx+IjV57Chz91SvB6v1hcr0FnTwOxZVkUCgUMwyCZTLJz506uvfZapk6dynPPPbdLJ7D9hTff\nfJNx48Zx6aWXsnr1ahYuXMjdd99NIhHdDDJcGFX0gm+MHJW9+7xtsVgMeFkY8Lf1edvw81G8bfWQ\nvnQ6TXNzM4lEIhjQl8/nA3F4VKvvYPD5ML81snodV935T0xqjeFIr6Mqni2ghyiBVFsKo7uAsTOD\npSrI8NgdRUFWFdhkOQArpiS1uUDLSz24a3aib+nDCN2+V4+aAcBQUcqyJGG5GDsLJNf3kdpQoGWT\ng1PlIWBZLjjRx0GpZ4KuKChlb9nkW3mM8EgdKSOLgcE2TWgpgO5E5xZhtzKl6GDkq593K7xzY51F\nRJXblyIFapl3dnuix1qIooWSt9CrGh2MbKnGDEeznUBzLADxRjfxYmWWngz5oct8EeHPBiwUQShI\n18W0JaoCl930wYqACwO/k3rTHPy5ZNlsNmhZrx44CQPnqv+bicViPP7441x44YVceeWV3H333SMW\ncMH7za9atYorr7ySVatWkUwm+frXvz5i6/ExKjPdqBPD9+YMuyhV+9vGYrG9kn/5dENYp1uv1bce\nLRGmEgZrIRZCcOfTN/DgF3/Bb3+2Ejdv4RZ6obUZIQR5WwISdWce1XJwNRXVGEgvpVqVi2kqiuMG\nkl0FQSznEi9ZsKOHhJA4MYFjCNx+C1XTcGzHC0ZSgu2Q1AycPiu0XsUzyNarskshEAWztshHHVla\nGXFbYPeVSPRGPFdysWJ1Ov9siba5/gwereQGUyAS20s1WaoCpF1BVvWsF41cbVMDQKzgYpUsVKVO\ncC8rCbRMEWdiEm/KvMTNOShVF3a3r1BZZLRdjC0Z+sekPR8H26ZYGDh3hWV7U0YAPa7jOB7N1NKa\n4MZ7P8ZRC3ev4SBqmsNgd3X+HWImk6GlpYVsNsv1119PPB7nmWeeoaWlZbf2uz8xdepUpk2bxsKF\nCwG44IILhmSw5L5i1GW6YXvHQqFAJpNBURRaWlrQNK1Cb+uT/kKIilbGoUC9Vl+fHy4Wi/T19QVT\ngbPZbHBh2JUUTQjB5f95Abc8eBmJpI4iBXJHl3exUVWk67WvGr0ltKqBkzJhVG8Mt1Cp8RWGjj+D\nQpECvQjxPklTp0VsW5Fkp0WiyyLRbRPvtr0xMlXrjdXR8cbU6FMuahqDj6QtSG+xI2/cRZQhj/++\nHruSXqmCYnscsZq30QvRr1HL2brRWUSp83NJFEHPRCtBRNEKJHEKkCo3N6iZYk3ApWjWHgfTxOzO\nYmzYDr05RF8hONY6EgyPJ5aFAo4DmpAcc9xkvv/Hm3Y74NZD1F2db13qOA6apvHzn/+cI444grlz\n57Jt2zbmzZvHjh079mm/Q4WJEycybdo01q5dC8Dvf/97jj766BFe1SgrpPmWir29vQHH6gew8O29\n37qrKArxeHzY5F/VsG2bYrGI67oBLeK6bmB8szu8Wl93ji999H463uxE1wWFZApKJopvcCMljgru\n5LEBDxjPl7BCASQmvdvRMJK6IF8V0JIxjXxV84SUEnS1hleNxVQKEbf+ugqFRDQXbCZEBecJoBRs\nxnSVKETI08Dbbf8Eo2b/as4isSmHk9ZrCoBhFJog1muhm9HH2BUu2elJ0m/mUeuYrmvS9Ux9qi9o\ngNaZq9Ahu0jyR7Ri7MzX2DUqPbmKdmZcF3ID7LpQwJYSUklIJxH9BYQR8yij3iy6Y3LRtafzD1ef\nVffz7gvCUyUSiQS5XI6bb76Z/v5+PvnJT7J+/XpWrlzJGWecwXnnDU2L8b5i9erVXH755ViWxYwZ\nM3jooYeGKwuvmzWNqqDrS7z8MdF+Vuk7hvkVVillMCpnJOBzZr4bWTjDjhrOB9QE4opKvJR857r/\nzQv/dzWu4yDHj0EtFLHLP3bdsSmZNs7kMdCURHT1oYSLCdIF06loOdWE6/dgDDymiihPHC/wGrW3\n1q6h1XSuScCJ1/GYTWvkQg9rOYvEW30IF5yWRFAArEYxDW56QBkgLJfk+gyK60ng7PH1lSyObaJb\ntZN3w+stxV3iEVMsfKhbu3FjGs6kSnNgUbTQe2pTaDMhUGKJShrBshF9lXcchmtj5gbeL0N3JNJx\nPBN418WIaYw9pJXP/59/Y9rMyXXXubcIj89JJBJomsYLL7zAF77wBa699louuuiit0032R7g7RF0\ns9ls0MLb1OQNnfLpBl9Ktre87VBAShmYf/hZ+K6qw9W8mv8vSi3xx8dW8b1rH8a0bJJTxlHo825l\npetCLu9luhNbsBMx1OqhXdk8Ih4L79jrfKsOdK4L1YoOx4F4bZbnIiMfd3AieV23WMCa6H1vem+J\neEcuyMedhF5LjZRhKhb2hHJgdSWJjVm0onfFkEisQ5qiFQ5Som/uxR3bHLldf3syV0A0RxeEUgqU\n3ur09nPouIqLibYzF1kglNkc7sQxFReqRMHEDFFBcV1Q7BoYSeGWSkHTiWGolHKegYwi4MzL38fl\nX794v5zT4YnAiUSCYrHIf/zHf7Bx40buu+8+DjnkkCHf52BwXZeFCxcydepUHn/88WHd9x6i7pcx\nqjjdWCwWFLR8rtTnS6WUpFKpIeVt9wS+gY7f1ba7E1b3RC1x/Kmz+OZvr2PuoukUN+/ELY/uEYqC\nVL3ih9jRh7KtB1msamCo5mCF8HSeNR8kItWtczjrjmWvY2ouyhRCa9YiEQq43nvqd8+FTXbasnYQ\ncL2lCUQhWlWQ7i+hFC0w6287bVlo2TqEryspbe4K9qP0DsgfRK4YHXD784iShdjWPdD8UDAphQKu\nEFDYmQn+1lSC4y5dl2K2gKopTDx0DHf84RY++Y2PDPk5HTUReNWqVZx11lnMmzePX/7yl8MecAHu\nvvvuA4KX3ReMKvXCF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes(projection='3d')\n", + "ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n", + " cmap='viridis', edgecolor='none')\n", + "ax.set_title('surface');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear.\n", + "Here is an example of creating a partial polar grid, which when used with the ``surface3D`` plot can give us a slice into the function we're visualizing:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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qsdzpWLOKXoiiiHa7Ta/Xo1wub2jZzno/ZulY9H2ftbU14jhmYWGBer0+INzl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\n4Q348Pm8KD4ZxaugeGQ8Xg+yT8bjVZA8Ium4Rqw/TqI/zi8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h7+D4wjUlpEsVErPKiC2p\nJb2yBb2uLKuRp+GilAAQ0lpmcS57UFnPKRGVpsoS6mfkt33SNI1oNIqu64RCIVd7U7fZjUXE1vst\nInZGsM4F11wiliQJr9eLb0Bvraoqe/bs4eWXX+b9738/LS0tfO5zn3M95qGwePFiHnnkEU4+2f0m\nA5lr9l/+5V944okn2LBhAw899BCbNm06rP1ZmBKka0EUxTHpHjFa0s2FFSFYKZCSkhI8Ho99gn36\nqlMx+tME98cxDRN/TmSbq7W0EA37mbOqJes5j0+hdVUz23d0EyzxgSBk2Ub2dGZq8AWg+7UDVCSh\nPDz4eiyRn6+c1+LeIXU8YKkSrDLPcDhMOBzOioh1XSeRSGRFxM6L2Ili6QUoTqy4VAdaKBQhdx2K\nuT7f2ZPvfaDpBo112VrT7r44y+or2ftsG3FVY0vXIRI1PqJNQTZHoqQqfWg+ydXiUogmIehesivG\n0nmfESOpDGGbJmUJA6Vf5f9+9qSs91h9AJ3R7UgWQ4dDxM7UhPM3dC7SalrmZhYMBvne977HjBkz\naGtr4y9/+QsXXXTRsMfjxNy5c2ltbS16va9du5bW1laamppQFIWLL76YRx999LD2Z2HKLKRZ/052\npOvchiAIpNNp4vG4PT3OLRPOLC7ptM6vZds7BzD3REiVylA5mANU/QqiaWLkXDR6iZ9NL25i8Xvm\nsvHlLdS2VDOttoyOjigVTdWIZUGMaJLtA14OtQ1lHGgfzNfWN5Sz8e/teIMeFh8/nQ17u9h7MF8i\ntaClZlTfx3BgubulUik8Hk+e6ZBzAc5KIzkX6qwL11pJtx9D8EOxSFgUi73mHvn39MUJ+BTiOTfK\nrkMxQkEv0Ry1RDgwQJImNFSWkDgQZasZIz4zBJJAZh4yoJ8dolOyEE9hhtwjXTnoIe/TUsbzIXww\njseUqKwvY9nqwZu41S3FKnoZq6ozK7J1LrxCtgLGeljXkWmarFu3jurqat588002bNhAIBBg7ty5\nzJ07d0zG5Ya9e/fS2Nho/93Q0MDatWtHtc0pE+mO1mnMuZ2x2IamaXYUFggEbI2rYRg28VoRWiAQ\n4J+vPwtBFJBMEe8hFdmpxZXETFTiAr2+nA0vbWbZqYtIxVK8/b9bERUPDXPr6DoYYebsGrSBHG15\neXai4lBvJvpKxdK0PbOT5SVlNFZmax99HpmZRbxUxwLW1FVVVYLB4LANcqy0ktfrJRAI2BGxc3FH\n1worFIC86X3WuAp0lgBQXfLsFmqmuXfjrbMq+cyBhwG79/biEUVEA/Ye7KdHNIhL5BnnoJtZGm43\neMvcUx6kNVI5NxchrWHIImUdCYhoJJMq539sdWZ4OdHtSPykR4PciDgQCNjPe71eHnnkES644AK+\n8IUv0NzczNe+9rUhDarOOOMMlixZYj8WL17MkiVL+OMf/zjux1MIUyLStXAkkK5VFhmLxfD7/QSD\nQTsis4gkkUigaZot6hcEgfoZ06itL6N9ZycYEr7tfcgzSjkUynR68PoV3LKF2vRyFpYHef3ptzFN\nk7knLWb7hn1MHyh/tdq5CzmdYCsqQ+zbk6NSSGh0/H0Ps2dPQ2kMs2lfN61NVQWjutHCWjjL/S5G\ng1wzHIPi6YViv3U8WZiwowVkYwAhawHO7lSc+edARwT0bOlaLJpCG6rtPBlPXaNIRwvSGokCUbvY\nF8fI8cqQehP4Yya6AagqJRWlnHX+cXZRkbU4PRHuYbmw0nGqqtoppT/96U+89dZbPPDAA6xYsYLX\nXnuN9evX28RcCE8++eSoxjJ9+nR27x6s7Gtvb2f6dPcGncPFlCHdyY50rVXWRCJDclZZrFPP6BT1\nu53Ql33xNG6+9uHMOGQJ/WCC0j6JeIVCzK9kZF85OUqjqoTOtdtY8J45mIJIT2+cOcub6GvvpnZ6\nmd3AsnlODTs2D/qxVtWG81p19/Vmxn5wWzds66axLszypioikciYFjk4UwmKoozrxT1UTreYv2N/\ntDCxdvXGsj/q+P/eA31Yc3nnUUVjqTytsBHXoBiZDsDvU4i5ttHMIGBCvNBv4nhe1gzkvf0ocT3T\n4840EX1eTn3/YuLxOJqm4ff7J60NjjOlEQ6H6e/v58tf/jKiKPLXv/7VlomdfvrpnH766WO230LX\n/KpVq9i2bRttbW3U1dXx8MMP89BDD41qX1MmvQDFlQMj3cZwYRFIX18fqqoSDocRRdGu3LKqdqLR\nKIZhEAqFCk6fl6xooqIimFEvKDKkVYy0gfdACm9bL6LbIo0g0InAhr9txlMaJhFLs29HJ3s3tjOt\nanCaK+dEQbnGLNOqQnmtuw/tj7BkVr296AHY2tr+/n5isdiIlASQn0rw+/3jGk0NpV7IG7Y5+OiL\nJDLteQzHQ888EnEVwWDwYQ4+4kkVgUI+ZdkQhvm9qcXMfwGxQJpE0HSMiiBoOkpbD/62CEpMt5uK\nCqqGooh84KPL7J5j1sxsIo3Cc1Mafr+f5557jnPPPZcLLriABx98cMx1uX/4wx9obGxkzZo1nHPO\nOZx99tkA7N+/n3POOQfIqCd+8pOfcOaZZ7Jw4UIuvvhi5s+fP6r9CkN8sUeNPbtV3dLT00N5eflh\nX8imadLb2zusEkM3fwer0kbTtCwykmUZRVGKVtwArHthC7d86TeYXg+k0uAdXCXXEwnUMgW1tjQr\n4pX2dLPYFNi5rZu5y5vY/GobobIADe+aw8aNB/D5FXTdsBsTVtWU0JnTAmjB0gY2vpFtpuPxyPzy\nT1fiySkgyK1Qsh7FujmMRyphuDjrin8bzMFapDrwf79PJuFoNuk2IrPA84UgiELBXma5EJN68bQB\nIGhGcY2taWbe4xLVy70xzJSGt99AGlgY9IhgmdopIqw8YSbXfPcCgLxFLLcZzlj/blZ0K4oifr+f\nRCLBjTfeSHd3N/fccw9VVVVjur8JQsEvaUqlF6x/x8LJvtg2nCboVscJK6K1coqWxMVa1LFkL8lk\nEtM0kWXZdbq+8qQ5LFvdzGuvt2cId8D0BEDy+5CiGp6dEeRKD31BJdMNYno5cp9Ky2I/m19tA2D6\n7Gp2v7yFcGM1tdPL2PrOoKNVdW0+6cZcptILlzXmES5kKwmc31eu/4JV4mnpMy3bxYn2XvUoMqpj\nQc3ZXDKZ1EZEqMNBrodGQZgmxhCLYzBghFSEdH26STKXcA2TUFJF3x9F9vhhgHAl0yBtZLalCCaG\nbvLZr3zATicUUhNYJdhjScROHw2fz4csy7zyyivccMMNXHXVVVxyySWTklMeb0wZ0rUwFjrbQsSd\na4JeUpJZnBhJ3haKm8PIssyV3zmPL1z4M+KqiVcRSdmHIyDqmamh0aMR7lFJSRpqTZgeTIwBuVfT\nvDq7k8Cs+QpqToVZd2d2LjcY8rJnZ36LxqWrZg73a3MlYiuCMQzDrjgrlB8er4vLNM2iCoVx2CHp\nAbvOoSBoQysSYOgoW40kYaDnmmQYBA4l0buSCDrISraETIsmEMIZFYupGXz6i6dS5lKiDPl+GjB2\nRGy1yrJ8NNLpNN/61rfYsmULjzzyyKgXq45kTBnSdUa6xbpHDBdO4jZN0/bltRL8bnrbZDI5rGjO\nzRzGeTLLHoHzP76S//fAWlIIoOswcDy6LGfygEIma+jVFZT2OEa5n96OfkRZJBYZ7MGVONhH6azB\n4oea+lIO5DTAbJxZyaa39+aN87jVh2co4lx9zk0lOLW1uRetM/ofC1tIa0aiDFX+OgQUF+ewQhB0\nc+hyW+u9mjEs0tWH2J7hkZATKp6eJGLCAAQkUcYvGSTMwe/Q1A3wZ0hYEWFWUwVnf+yEYY3VwmiJ\nGMiKbhVF4Y033uC6667jsssu4wc/+MGEz4QmGlOGdC0U6h4xErj58pqmmZW3tfS2Vpsa6/XDadvu\nFiVe+Kn38tpzW3lnZy+SpqIPvCbIMl50Ug4plCiKdB9KIgCty2Zk9cuSRIF3/vomC05bxMZtXUyr\nCtOxry9r/85I3UJFZYjGmfnloMVg3ZySyWRBNzKnpMvrzRQGjLUtZK79Y9Gqs2FASxv5utkCEDQT\ncwyvKsnI6ZnmhKpTqpukOxLIhvWewZtbPK0jOFsyxQejXEVV+dpPPzEmYxwJEUPmHHjmmWeYO3cu\njzzyCK+88gq//vWvaWlpKbSLKYUpc0sZ66o0y5g7EonYqQJJkuxcLWCvtno8HrtFzFhBEAS+9KOP\nEdZS6KKUHXm7/WyCQLC+nB2OzhJzlzexZ2tGJrbp6beZ11ROd47toKJItO3ITy0sWzVzRJGm9X1Z\nxR7DdSODfFG8s0wUshUTQ3kv5HoE+Hw+ZnuCtJpemiICZT3uJdUFxwbDJlwYvhoBwBxGCsKfV9Sg\n4+9N4N95iNCuKMb2Qw7CdSCeQMhpVSQM+Dj4DI3PfOl9hEsLuXqMHs7fNBAI2DdYa9bz85//nLPP\nPpvbb78dXde57777JlQtMZmYcpHuaEnXkpzFYjF8Pt9h523HAhXVJXzsC6fx8+//maQkw0CUkkbA\nTKYQfNl19rrXgzpguOLxKezP6SZg9MeQYmkaZlTQPlAo0TSrim2b8lvPLBtmasGZSsg1Nj9cuJWJ\n5qZgVFXNUkzYFWi6bs84rHFE90fo2Jq5sahBGRi+BlUywRjJ4Qz31DNMTM8wUgu6gWKaCJ1RpJiO\npFtpFzET1Aru2/CFfI61APCKJilFQT7Ux7ylDZxywaphDnR0sCovAbsq86c//SnJZJLnn3+eadOm\nsX79enbs2DElF83ccIx0B+DM25qmaasS3PK2kiRNWE+wsz9xIn97ZB1bNuxF9SgIloRM1SCHdBOa\niSCJCLrBrCUNvLN25+CLAvR1Rehs74VtHSw4bRHbdveiuMiMBAGWrmjKe94JZyp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r = np.linspace(0, 6, 20)\n", + "theta = np.linspace(-0.9 * np.pi, 0.8 * np.pi, 40)\n", + "r, theta = np.meshgrid(r, theta)\n", + "\n", + "X = r * np.sin(theta)\n", + "Y = r * np.cos(theta)\n", + "Z = f(X, Y)\n", + "\n", + "ax = plt.axes(projection='3d')\n", + "ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n", + " cmap='viridis', edgecolor='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Surface Triangulations\n", + "\n", + "For some applications, the evenly sampled grids required by the above routines is overly restrictive and inconvenient.\n", + "In these situations, the triangulation-based plots can be very useful.\n", + "What if rather than an even draw from a Cartesian or a polar grid, we instead have a set of random draws?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "theta = 2 * np.pi * np.random.random(1000)\n", + "r = 6 * np.random.random(1000)\n", + "x = np.ravel(r * np.sin(theta))\n", + "y = np.ravel(r * np.cos(theta))\n", + "z = f(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could create a scatter plot of the points to get an idea of the surface we're sampling from:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RDqs89aCXXVui3P9HD8VFRn75gMwp14+gaWcHP32ijX27gtgMUW652c78540E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\nit1uZ+nSpXzzzTe8/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes(projection='3d')\n", + "ax.scatter(x, y, z, c=z, cmap='viridis', linewidth=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This leaves a lot to be desired.\n", + "The function that will help us in this case is ``ax.plot_trisurf``, which creates a surface by first finding a set of triangles formed between adjacent points (remember that x, y, and z here are one-dimensional arrays):" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bhnS3imyVUhP/zr22xDkJ8gLc/i1fGuzslGwP+kGJk8/j3P9NQ3ksLbx4rPTpKE1CQF33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\n0gV9RwbD9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes(projection='3d')\n", + "ax.plot_trisurf(x, y, z,\n", + " cmap='viridis', edgecolor='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is certainly not as clean as when it is plotted with a grid, but the flexibility of such a triangulation allows for some really interesting three-dimensional plots.\n", + "For example, it is actually possible to plot a three-dimensional Möbius strip using this, as we'll see next." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: Visualizing a Möbius strip\n", + "\n", + "A Möbius strip is similar to a strip of paper glued into a loop with a half-twist.\n", + "Topologically, it's quite interesting because despite appearances it has only a single side!\n", + "Here we will visualize such an object using Matplotlib's three-dimensional tools.\n", + "The key to creating the Möbius strip is to think about it's parametrization: it's a two-dimensional strip, so we need two intrinsic dimensions. Let's call them $\\theta$, which ranges from $0$ to $2\\pi$ around the loop, and $w$ which ranges from -1 to 1 across the width of the strip:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "theta = np.linspace(0, 2 * np.pi, 30)\n", + "w = np.linspace(-0.25, 0.25, 8)\n", + "w, theta = np.meshgrid(w, theta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now from this parametrization, we must determine the *(x, y, z)* positions of the embedded strip.\n", + "\n", + "Thinking about it, we might realize that there are two rotations happening: one is the position of the loop about its center (what we've called $\\theta$), while the other is the twisting of the strip about its axis (we'll call this $\\phi$). For a Möbius strip, we must have the strip makes half a twist during a full loop, or $\\Delta\\phi = \\Delta\\theta/2$." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi = 0.5 * theta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use our recollection of trigonometry to derive the three-dimensional embedding.\n", + "We'll define $r$, the distance of each point from the center, and use this to find the embedded $(x, y, z)$ coordinates:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# radius in x-y plane\n", + "r = 1 + w * np.cos(phi)\n", + "\n", + "x = np.ravel(r * np.cos(theta))\n", + "y = np.ravel(r * np.sin(theta))\n", + "z = np.ravel(w * np.sin(phi))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, to plot the object, we must make sure the triangulation is correct. The best way to do this is to define the triangulation *within the underlying parametrization*, and then let Matplotlib project this triangulation into the three-dimensional space of the Möbius strip.\n", + "This can be accomplished as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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00Moo0XVNYwXzVbJTEVlrlGU3pP61y9jSY+DePsIwxDjZu+n9lb9vGAWUWYkG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\nRfQT2Thl1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N8Bv8vx8+OkEnvO423AwpG+XoHxoeDpFiiQG4n+t/jPAlcDSJJ0LtAxlHhu\ngQIA/w9ULKic9q2iRAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# triangulate in the underlying parametrization\n", + "from matplotlib.tri import Triangulation\n", + "tri = Triangulation(np.ravel(w), np.ravel(theta))\n", + "\n", + "ax = plt.axes(projection='3d')\n", + "ax.plot_trisurf(x, y, z, triangles=tri.triangles,\n", + " cmap='viridis', linewidths=0.2);\n", + "\n", + "ax.set_xlim(-1, 1); ax.set_ylim(-1, 1); ax.set_zlim(-1, 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combining all of these techniques, it is possible to create and display a wide variety of three-dimensional objects and patterns in Matplotlib." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.12-Three-Dimensional-Plotting.md b/notebooks_v2/04.12-Three-Dimensional-Plotting.md new file mode 100644 index 00000000..135eebb2 --- /dev/null +++ b/notebooks_v2/04.12-Three-Dimensional-Plotting.md @@ -0,0 +1,251 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) > + +Open in Colab + + + +# Three-Dimensional Plotting in Matplotlib + + +Matplotlib was initially designed with only two-dimensional plotting in mind. +Around the time of the 1.0 release, some three-dimensional plotting utilities were built on top of Matplotlib's two-dimensional display, and the result is a convenient (if somewhat limited) set of tools for three-dimensional data visualization. +three-dimensional plots are enabled by importing the ``mplot3d`` toolkit, included with the main Matplotlib installation: + +```python +from mpl_toolkits import mplot3d +``` + +Once this submodule is imported, a three-dimensional axes can be created by passing the keyword ``projection='3d'`` to any of the normal axes creation routines: + +```python +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt +``` + +```python +fig = plt.figure() +ax = plt.axes(projection='3d') +``` + +With this three-dimensional axes enabled, we can now plot a variety of three-dimensional plot types. +Three-dimensional plotting is one of the functionalities that benefits immensely from viewing figures interactively rather than statically in the notebook; recall that to use interactive figures, you can use ``%matplotlib notebook`` rather than ``%matplotlib inline`` when running this code. + + +## Three-dimensional Points and Lines + +The most basic three-dimensional plot is a line or collection of scatter plot created from sets of (x, y, z) triples. +In analogy with the more common two-dimensional plots discussed earlier, these can be created using the ``ax.plot3D`` and ``ax.scatter3D`` functions. +The call signature for these is nearly identical to that of their two-dimensional counterparts, so you can refer to [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) for more information on controlling the output. +Here we'll plot a trigonometric spiral, along with some points drawn randomly near the line: + +```python +ax = plt.axes(projection='3d') + +# Data for a three-dimensional line +zline = np.linspace(0, 15, 1000) +xline = np.sin(zline) +yline = np.cos(zline) +ax.plot3D(xline, yline, zline, 'gray') + +# Data for three-dimensional scattered points +zdata = 15 * np.random.random(100) +xdata = np.sin(zdata) + 0.1 * np.random.randn(100) +ydata = np.cos(zdata) + 0.1 * np.random.randn(100) +ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens'); +``` + +Notice that by default, the scatter points have their transparency adjusted to give a sense of depth on the page. +While the three-dimensional effect is sometimes difficult to see within a static image, an interactive view can lead to some nice intuition about the layout of the points. + + +## Three-dimensional Contour Plots + +Analogous to the contour plots we explored in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb), ``mplot3d`` contains tools to create three-dimensional relief plots using the same inputs. +Like two-dimensional ``ax.contour`` plots, ``ax.contour3D`` requires all the input data to be in the form of two-dimensional regular grids, with the Z data evaluated at each point. +Here we'll show a three-dimensional contour diagram of a three-dimensional sinusoidal function: + +```python +def f(x, y): + return np.sin(np.sqrt(x ** 2 + y ** 2)) + +x = np.linspace(-6, 6, 30) +y = np.linspace(-6, 6, 30) + +X, Y = np.meshgrid(x, y) +Z = f(X, Y) +``` + +```python +fig = plt.figure() +ax = plt.axes(projection='3d') +ax.contour3D(X, Y, Z, 50, cmap='binary') +ax.set_xlabel('x') +ax.set_ylabel('y') +ax.set_zlabel('z'); +``` + +Sometimes the default viewing angle is not optimal, in which case we can use the ``view_init`` method to set the elevation and azimuthal angles. In the following example, we'll use an elevation of 60 degrees (that is, 60 degrees above the x-y plane) and an azimuth of 35 degrees (that is, rotated 35 degrees counter-clockwise about the z-axis): + +```python +ax.view_init(60, 35) +fig +``` + +Again, note that this type of rotation can be accomplished interactively by clicking and dragging when using one of Matplotlib's interactive backends. + + +## Wireframes and Surface Plots + +Two other types of three-dimensional plots that work on gridded data are wireframes and surface plots. +These take a grid of values and project it onto the specified three-dimensional surface, and can make the resulting three-dimensional forms quite easy to visualize. +Here's an example of using a wireframe: + +```python +fig = plt.figure() +ax = plt.axes(projection='3d') +ax.plot_wireframe(X, Y, Z, color='black') +ax.set_title('wireframe'); +``` + +A surface plot is like a wireframe plot, but each face of the wireframe is a filled polygon. +Adding a colormap to the filled polygons can aid perception of the topology of the surface being visualized: + +```python +ax = plt.axes(projection='3d') +ax.plot_surface(X, Y, Z, rstride=1, cstride=1, + cmap='viridis', edgecolor='none') +ax.set_title('surface'); +``` + +Note that though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear. +Here is an example of creating a partial polar grid, which when used with the ``surface3D`` plot can give us a slice into the function we're visualizing: + +```python +r = np.linspace(0, 6, 20) +theta = np.linspace(-0.9 * np.pi, 0.8 * np.pi, 40) +r, theta = np.meshgrid(r, theta) + +X = r * np.sin(theta) +Y = r * np.cos(theta) +Z = f(X, Y) + +ax = plt.axes(projection='3d') +ax.plot_surface(X, Y, Z, rstride=1, cstride=1, + cmap='viridis', edgecolor='none'); +``` + +## Surface Triangulations + +For some applications, the evenly sampled grids required by the above routines is overly restrictive and inconvenient. +In these situations, the triangulation-based plots can be very useful. +What if rather than an even draw from a Cartesian or a polar grid, we instead have a set of random draws? + +```python +theta = 2 * np.pi * np.random.random(1000) +r = 6 * np.random.random(1000) +x = np.ravel(r * np.sin(theta)) +y = np.ravel(r * np.cos(theta)) +z = f(x, y) +``` + +We could create a scatter plot of the points to get an idea of the surface we're sampling from: + +```python +ax = plt.axes(projection='3d') +ax.scatter(x, y, z, c=z, cmap='viridis', linewidth=0.5); +``` + +This leaves a lot to be desired. +The function that will help us in this case is ``ax.plot_trisurf``, which creates a surface by first finding a set of triangles formed between adjacent points (remember that x, y, and z here are one-dimensional arrays): + +```python +ax = plt.axes(projection='3d') +ax.plot_trisurf(x, y, z, + cmap='viridis', edgecolor='none'); +``` + +The result is certainly not as clean as when it is plotted with a grid, but the flexibility of such a triangulation allows for some really interesting three-dimensional plots. +For example, it is actually possible to plot a three-dimensional Möbius strip using this, as we'll see next. + + +### Example: Visualizing a Möbius strip + +A Möbius strip is similar to a strip of paper glued into a loop with a half-twist. +Topologically, it's quite interesting because despite appearances it has only a single side! +Here we will visualize such an object using Matplotlib's three-dimensional tools. +The key to creating the Möbius strip is to think about it's parametrization: it's a two-dimensional strip, so we need two intrinsic dimensions. Let's call them $\theta$, which ranges from $0$ to $2\pi$ around the loop, and $w$ which ranges from -1 to 1 across the width of the strip: + +```python +theta = np.linspace(0, 2 * np.pi, 30) +w = np.linspace(-0.25, 0.25, 8) +w, theta = np.meshgrid(w, theta) +``` + +Now from this parametrization, we must determine the *(x, y, z)* positions of the embedded strip. + +Thinking about it, we might realize that there are two rotations happening: one is the position of the loop about its center (what we've called $\theta$), while the other is the twisting of the strip about its axis (we'll call this $\phi$). For a Möbius strip, we must have the strip makes half a twist during a full loop, or $\Delta\phi = \Delta\theta/2$. + +```python +phi = 0.5 * theta +``` + +Now we use our recollection of trigonometry to derive the three-dimensional embedding. +We'll define $r$, the distance of each point from the center, and use this to find the embedded $(x, y, z)$ coordinates: + +```python +# radius in x-y plane +r = 1 + w * np.cos(phi) + +x = np.ravel(r * np.cos(theta)) +y = np.ravel(r * np.sin(theta)) +z = np.ravel(w * np.sin(phi)) +``` + +Finally, to plot the object, we must make sure the triangulation is correct. The best way to do this is to define the triangulation *within the underlying parametrization*, and then let Matplotlib project this triangulation into the three-dimensional space of the Möbius strip. +This can be accomplished as follows: + +```python +# triangulate in the underlying parametrization +from matplotlib.tri import Triangulation +tri = Triangulation(np.ravel(w), np.ravel(theta)) + +ax = plt.axes(projection='3d') +ax.plot_trisurf(x, y, z, triangles=tri.triangles, + cmap='viridis', linewidths=0.2); + +ax.set_xlim(-1, 1); ax.set_ylim(-1, 1); ax.set_zlim(-1, 1); +``` + +Combining all of these techniques, it is possible to create and display a wide variety of three-dimensional objects and patterns in Matplotlib. + + + +< [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) | [Contents](Index.ipynb) | [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.13-Geographic-Data-With-Basemap.ipynb b/notebooks_v2/04.13-Geographic-Data-With-Basemap.ipynb new file mode 100644 index 00000000..20c4d657 --- /dev/null +++ b/notebooks_v2/04.13-Geographic-Data-With-Basemap.ipynb @@ -0,0 +1,752 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) | [Contents](Index.ipynb) | [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geographic Data with Basemap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One common type of visualization in data science is that of geographic data.\n", + "Matplotlib's main tool for this type of visualization is the Basemap toolkit, which is one of several Matplotlib toolkits which lives under the ``mpl_toolkits`` namespace.\n", + "Admittedly, Basemap feels a bit clunky to use, and often even simple visualizations take much longer to render than you might hope.\n", + "More modern solutions such as leaflet or the Google Maps API may be a better choice for more intensive map visualizations.\n", + "Still, Basemap is a useful tool for Python users to have in their virtual toolbelts.\n", + "In this section, we'll show several examples of the type of map visualization that is possible with this toolkit.\n", + "\n", + "Installation of Basemap is straightforward; if you're using conda you can type this and the package will be downloaded:\n", + "\n", + "```\n", + "$ conda install basemap\n", + "```\n", + "\n", + "We add just a single new import to our standard boilerplate:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.basemap import Basemap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have the Basemap toolkit installed and imported, geographic plots are just a few lines away (the graphics in the following also requires the ``PIL`` package in Python 2, or the ``pillow`` package in Python 3):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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WivXVVV5/5w5vvLXLy29cI00TPvjkRZ594hzX7k1/6c6t26//5mde/Tv34TRe8g3IMmAu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sc5EwT73lUBwoSxaLWpu3KAqgVcysnQ6TtrqtHp6v3xeKGCwF/ocFzOISKFIAqFy7uXkXQqZtDq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wytc9KBZnnxPLddvZVTi31u2L0ZfI9Ts0cIwzLrp6Zpd5u02n3uve1mPnj/3dywYyvTtTLb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0b0+x0k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\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K1oQYeOt2Zi9l7n70Hu1sznq7YrtZkbJiu91gneJkveXpp6/Sb04I3gKZw8OrLBZzmsZx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\n4YgRo2boeonWwqamFNTK1lg3E6cZrUhR7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+Rdks7he6opGNx6vbevMO9btLe+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rFSCilO0Hra7j7j9Qd2rU4sp1cKqiMlTqZ+pdb9x7Ur2u2b0D/Eb+6zffAYv74g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\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FYkpRJQe1J73kKY5e5M6Up1BrGHKo4TTG5nIgEYtzDVmWLrN+h/q6abw+5E+TNnBkk2Y0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\njTbaaK/D/n95DVTF7823aAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "m = Basemap(projection='ortho', resolution=None, lat_0=50, lon_0=-100)\n", + "m.bluemarble(scale=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The meaning of the arguments to ``Basemap`` will be discussed momentarily.\n", + "\n", + "The useful thing is that the globe shown here is not a mere image; it is a fully-functioning Matplotlib axes that understands spherical coordinates and which allows us to easily overplot data on the map!\n", + "For example, we can use a different map projection, zoom-in to North America and plot the location of Seattle.\n", + "We'll use an etopo image (which shows topographical features both on land and under the ocean) as the map background:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZX/6CYg0Fo1WtJ++cKaXg1ZHini++/Et+8fP/hRyFWAwXWl5//CdYNla+R71DRRHVStMH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TEoS20sNOb5xfnuEggxgjlPPHS0OcOABiHXXdwkoDFSpVFDolRXlnOsAYRgHEeFLOgwcp4e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\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CEIullCZO4iAkdnS8Dg6ZPOVlReaMG34VJ948/lX2Xm+BMlVrB0DIDR4smMi1ONNT3B1L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NMqXE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Ydb0T52t8owQh/TmO3ER3M4GsPX661/9vw3hqxn7Axt8BERWs20bs+pemg3rSR7MaMD70F8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i5CslU1KEqrHt2lbOmagqbdeAbTGuQp4poYjEUAsBao6VbC3Ge9qhwzYGbQq6BgwLXTH4\nHOmpqKWQ5pHT4SvHeMCYwrvtDXFcmE8zBkuz3RDb9yIOahoa20KEmqCmmRzOxGXCKpmf9o3meDrx\n5csL2JZSRbXru47u7oaff/nKOSduG8v+ec+yRJqU+PL0yOfjM+6bW5pv7xifXtj0LSUVzi8Hhm++\nxfWa6ekk61zR2LzQpD1PX/8vjqc9y1Ex7DYMQ8Ph8AhuoDQbcI2EQTSeqSbmBbLfYYcdpr+FZHHO\nYboWrTIlJtnw50KsC8syUbpbjN3RffiRUjTL4YH6/rd435DnQDDPlNNC4YS/3eLuOsrWkOKZcDyj\nm0DTBvo7Q1IVi8U3DU0PPn1Fn79g44k87jG6YZkMh2XgKSv+8C9/5uWYobll9/67v1sP/3FLdi12\nFwHNpXheJeGXgrcuwvrNKRJeY8Hy2i742+e6Ft1V2ZqCeMsupxWKtK9KylK4jAzIL0klZvUjSghy\nQutKXQU2WVYlqXMpokqVkF2nyUqsGEqJL1QrA3W1nlQR/tQUXsU2WQrm9ffT0lot8UA4z2jXoF0r\nCjcqtURqFsTdfElBubT0AO0MKkjRrisLFqXwTQtK2rkpJxrj0VpoMsa9xtnkLDD1UjJxXkRcZYUL\nKkHRSXx7Zo1gsu5KpbkW4HXeeUEfvgZKi0xdKUSgUQpWa2pIWKCWhFEGrQ1hPqGVAJVTiCglfNCq\nNFkZCZv2lhoKX58esF3g/Q/fiwCqaozaMPTfUlTLw/Gv6LTww4eWpk/UzjP0DToHbvoN8zQxv+wZ\n7u4pORDjjN9uYGhotq0oDnPlGCsfhhv6/p79KGKH0Gf8TcOm3aFfJuZ9IabCsN3gbGYJC8syY2xh\nd9MxjpF5kpnldrNlCROScCLzqJAXUk7yO2hDrpnb+4ESBBCw221wzvLy8oLznorC+4622XI6zYQw\nIxmQ0HZSZNqmW1NTMqlm/vqXv/DDb/6JpAvVgbEaZT0ZS7WO0zRymicumZpC5IKwVNo1GaIUUaOf\nppGHr19RRlTOOQaWGFEmEmIlKwM6o0zBlIVSEjXK8znv1xQNRcl17TJIsXsrIxBBzhtdwjquuTZm\n36wN1rm10/M6w9QrNzQlCZs2qpJywBnYekeczjS68mHT44wlJ+ku5VVH5JUGe9FPFLwzqK6lxsTj\np09kY2jvb4W5Taa3mnCOFFUwDjor6888L3hjcCFQlwOugm86giqErLBNT9s7mlZC2q3N5HQgxwMq\njaQxCrbNwvN5z/HwzBImsgu43sB8hFCZ4kwoC3GJNPoDH25/ZLj/SA4jWts15mqilAPbwRKWicPh\nkSVl/vm3v+XrwwN//frMOAeUcWzQPOx/5uHlwPuP9+T5wNPpSHf3ATYbfhnP5N0tdx++ZT+OBKWJ\n2rB/eRYbkWtZppl8HJnTA80mY6zi4fGPPL98oUXR777DlJ5w+Ep+nuh++E+MFGIVHztoYjGkpmA/\nyGlRaUMzdDTteo1RudEWXTM5nNBmQreOJ3XLeRloncLXTNp2JGNgswF/4un0QCTh7gbuftiCc8w6\noXxl9+2Or394QOvMbrAIJGlhp/f04xOtCrSNEJVU22CU4Q+PL3x9euZwjOyfF7r3P3L77T/Ttt2/\nr2BeFthL8cwrceby75fC+LfFlfUmuLQJ/7ZdezlxKqUkg9JalvU0Y4wGCpSAUlqiY5DWY0qJMI7Y\nFU12eZ3We1FKZUOJQQzfTUPJCVKRuCerIUshtLohVfE1pRCu97hSmmY9wYFI0iWhoaCsnHIlrgio\nBdt2oCxGWRHzVE0uAWsdaEWcF2kxpQTOYZwlxPg6e13fj5QLrZfnWcIiv6821xP8dU75dt6rDbbx\n4r0KC9ZZaVenJKdrbaTQqlcIxQXpdTHqX4r45XMVpOFr4kst5VpU31wZgKL6TpLjnUW7VnBq2ojk\nvSS0EtKMazeoeWJeJs6nEZpmDavuUAZuuzu0HiAeGBdQnSfUE2FxvNtaUJHlNNGoNfy1is9LOUPU\nmqQVeMfQdnitcA6+/+Y/oSo8HX/heNjz6P/KJie6pLh5t6Npf8vp9EKIJ6Z5xlq9Kn6lNS7XWr6G\nRxsj70tK6yLbGqrN0onwFdUoaqpgK0Un9ueTLOAWaixojKC9gJvNQMqSNil8W4Uphc5BQMKgX+Jn\n2lPH3e03mGrplcXqSiaxqMihtXyt8MspEDVEowlZY91u/cxnSk3kZPn89Qug8MYxp8K8iDo0VchK\ng7NgzVoE6zr30+uG8/VauHaY6vUSeHNFqF99vbxtz8L1ea5dk2tLV132i2tQgMY6aU/nuLBrO243\nHtNvaQ3srJUJSV1n+qqItUNVQslMYUQZi/EtNS248wk1Qvv9b9FNwXSSurE8HWST4g3KGSHs5CjY\nyyrA+hwU0xwYbgb8ZqB3FusdzgS8mdBqYTk/kZYnGhtprJxGp9OZWDWJibG8kE2k6Vu0F9pRKDPR\naLruFlcy9ZCI0yPHp4nt9obTNPP08kJIC15VLIp+s2G4GZheTvz3P/6faGtp+zswPRjHHBey0ty+\ne0eMmqW08MN/pHQNUyootaUbGsKoGUPBb3ZMS8ButhjrKTmSU6XdvsP3G/qhoS4v7Owtze73PHz6\nyi85UtWCjprcbdDOQA0oa1GuweiCMxq3G9ApMniLqYGmAdSZHEcBFRiNM5USZ24HzxQyjEc29czH\npuP+9j15sXz59InGtLT3Hd3SELOIDXZ3Mv8+TjPFZVzTM93tiDmSpyN328Cgv/CNHVBkCRw4TtgY\niVXxdDiyPwYmf0PQkeHjO9798KMU8fHh79bDfwwuIAtlgVcV7NvCd7WHrK3Xy+OaWbbKofWbVuB1\n9gjXm0drWbQvBVAbcx3KKyXtP5mbZsnuW83/b1uWRUMpIuBR1ouyE0BpfOsxOWBtIs4HacmOC6pY\nsl9tF+uOudR4TUGX+ZD8u1Ei4c5BEue1ESFNvfzH+nqCVsASIqpIG7bpOhloa0MqmZTyNTxYXxMv\n9CsQosoCfRGPXN8LLZsGawUEUXJGWQdYrAKvCrlETC0otMSVrYtdjFLE5URiRZIfAt77VdSi8d4L\nENkghaG8AvXNeiq4fH4FUN5j12KyFBEjlXnGNRVqwKoWXS0Zg2tvmZbIXx+e+fHbjzglJ5tCRzgW\nGvUR5ztO0yPWZkI58Px8ZOs3YtcgYQiMx0LnO5zbMMdEOk/cv39PM2ww3nMxRGw339L80LB7ec//\n/fn/4HjYc/YDjR9AKZqm47h/WU808hkYI9er9wi0HZjnBesU1snmZRwjWmeJscqF8zyRc8EazfZ2\nwzSO5CriGoVimhdO40w/9GhtySkCVUIGlKIbBmKMhGUBYOjl8z3OJ56ev7IZ7ui7FqfXtJnlTKiJ\nkBeMqjRa0xqx/ly5sLmAkk7B8XTi+eVA1/fEUjmdRlIpNF2PNjJXN75BW0dSem3PisDEWne9v+p6\nj19Eer+Ky3qdBlyL4Wu7lqv16fJcF6JVrQJDKbUQwro5XuPWaslshp6bTcfdzmOyxpaMKwW9Co10\nRVq7WuOKZlkixlRORIqWE00KCyrNeC/33tC0bLqOX54OLNNEv7nFONnMGCWGEYVCWYtxN7Rtptv1\ntI1G1YjhjM5nYnyhlpE0P9P7hIozy7iQFiXGfWeYlpGUR4xTqBIIJRJzAZPx3tH0PY3ztFvDsGhU\nKeT0IojOLpDmiePpCEukHuV+broO5x1FKQEjaIWyBZMyVTWkpEF7dLujN44QZnJasNpgK5S80HmP\naTzLSexyKS04Y7GqUPsdSVvCaWb55YFJJeYxM04dybVot0H1hn4zQGNYyohFGNONgU2juesdvfUQ\nZ3SNOLuQlgOoQOMsZQmokOiURo8Tz4evqDRxd/OOXRdQy1dcKvzTnWbYRuacUHeGuSiqU/SbjDWS\nnTuFhQrcfvhAzuDLjJqPFD2jt5Y6TTw+PHGcF3At1Q18OipOqcF0A81OcIRleuHw8sI8zX+3Hv7D\ngpnrelLCXO0L8HrytNZKCkaRWdeFdfr25lj/AojrUilRggnTVb5Wa32D0nv9t7c3a117N5cFnzc/\nTy5zQGmqlpbmxaeojSevxSOVyLJEtHEY16GqJau8xuasJuE1GFvalqCUW0VJihyS8F21nO5qNVTW\nDQOFUiOKfA251t6TU0Jb8ZDmUkiqYFsPdc21XDGAJSZq0Tgni7dkXwpGUOnX6DJrhXWZc1pPhPL9\nVltqnKBkXNehmoGiKjVfoBKKIn6cNTnl9eR/bZVZu25mylUZev2ZSl5TWVWLl3iyWuX0bZ2T2e/6\neXkrLe+Si/BMtYd2x+O4R78cuN32dE6gEUsM9F1D03qWJXNcPtF1hU1jcXnicDxRk0NVsRT13cAc\nM2VaULayHCb6YbP6WSOnMaKMxtkduxvDt/F3PD3/wvEcafoIOdNXQb1N44I2ipplft40BqMNMcvv\nGVNexVoQFknraNs1xitK8eu6hpgSz/sXSim0bbcWAqEUGQulJrEFUJmXmaHvWUIAJfF0F9tQ29p1\nrh8l5eN8xPuOEhPPL4+E+SyQ8xLwu4F3mxtCzrBUhqFfRXKCoUTBNI34pqdqw+l0JqSMdg2+G4gZ\ntG2wbU/R9roB1Mr8f7QG126OMdeOxFsUJqyFkl+vEX/jFfl1oUVM95fIJqWE8lVLofOaDzvP+xvH\nYKXoGmWQxl8lrWQxq5SQoKp8LZaVoxoiKGQjpws1LzStYXCOzig6aznHM62Rw/TpNDKfjrJJbDy7\nzY6mldlc6wutOlPnB3R4IucTxzCR40KeZopW2BWK4ay0wvdfH0klrD977VagSFnhlKFvtmx0w63f\nQgp4L77OwgrQsDvKYlmeM/lwpCqFdZqkI9VUvOuYwiMp/AWvG4ztiLGjsQNdO1BMIdDgdECrStca\nNInpfOZxv2f34X+nup6aMofnPSZrnNLECrE6lpg4W89oO6ofUDeJu80GbTuMVfS9F6vPeSKWCUPk\nxlq2NnPnNIaRx8NPKDXhXCAeT/Suw5SWZim4KOv84jWmVbiSmecvPD1OfHP/DW7t6MRlYpoDMFPy\nRMEyHiJD+w2NHiQs23ne3d2RU6V3laFRzKc9fx6/kh4n9ueIageMuyVWz6I1hUAdI4VIiJFz1ixz\ngWb7rxdD/i0t2TVBoxBlZqjsOsDnKrkvpYrE/c3J8drOq29hBjLfs3oFj6+Krl/tSoG3Ld1ftYTX\nvo2qAh2PpaDXBV6vp5+qFMqsEIA1rTulKuHUuoiybdiSExTlpK2LFMHr6WleBF0XI7q1q9VUoaoU\nCc2F/SoioJKFEqPWD/hS/C/cXEomLLMgo1JEuTexNUaj7MVOsrI+L8rjtc0N0u5KSYK5Lyn05uJj\nVQarNSqL+u/idy25oErC2osfVhHX1vNFlXHJnWNVWdZaqCUR143IZd5k19cLXCENitfTgvMSKh2T\nIS0L6XikphbX9WirJeOwShRYSg2PxxO1ZuxtLxl5JoHxFNNgu++J4xE1PvPdx1u2AfoWvkyFM5Ih\neTrsWUJFVY1VinScCZsT3lcOhyPx8cC77YbtdqDvd/z+u//Cn9lyOH3G1gXX9VhVCTnjXCtimBgZ\nNoaUKmZVbaMkMUdEKYKCGgZN2zbXSmCMnIoqwu5svKMoWKK01a0zuGxYwizXRb2k00vLf55n7u9v\nrzPmC6dZ1cJ8PnLYPzFsdpK96RS2ihisQdMMPVoXmpPFXq1Tl42m+Er3+z3jEnA09Nsbop5o+y1N\nv+F4GklaZqK5iBWLomQWzZvwhL+5F4U/Xrl4Gf/h4zKofPOo65y8AjlICoXKiX7oaDrLxle+u9H0\nHnQWIRxrPCBw3fCJSlaDEcdmDlHyO3MhpEK1Cjs0eF/YbHoaDXVeKNPEMPT0jWdZZqbnZ8IyY9sG\n3zpc5+h9pa0LNh9heUDnF6hHjvuvpHNhaN+zGX5H3+wkys0WUGeOh5+Z93vSXKQTFTPFVHQNWGPQ\nOWPayKa1DMYwTwXdapaS+HrYS9xa59jPE/s0YWtm8C1+11GdwrkGU1vScSKFkXk+UYonBU1jW+Is\niTyL7cgl0HqHNbfkIB02VyMbPeGanjhn2l1DSZElzCi3EtcGTb95h9YeFTPeGdrGs8SEddA2ClsT\nLCNURd/39L7Q2oItgdPpM89P/4LzmftNz+3QYpzjxjh2rWMZZ76MBw41cyoz2meGTceHux23m4bT\ny5FQCqdloZiKbzRmDoRl5PnpmRf9QuPuMX6g3WiGxnEOs1znfsNSez4/PHI+RqZkKaFSjyOlBuIU\nSVOk5iwbGuXRzQa726K63d+9jP8x6adAWgKZIkBKPcuJigalWkpR+MbjnER1XUQlIJaSSxs3r3zX\nvN5cWsmcpJZElrgNLqkjb0VDl5lHqYL0unqnjME1zeuNpxVcio6xksm3UkZyldQPbRLeVAqGphvQ\nqqGQSDFKwUDACbrrIERs21G0JKjoppGgUiWMR+M9iULKiOqrBDmBroAGURRrSUdxfn3dWYzMSkMS\ny8syLVyQ09ZaUaFGUfbmZcH3gxTyWrF2TXkwmvP5vJ5WIJVIbzW6VtAK1wwE5QgxoZS8rnQR9tRK\nDoESEsp5amuuHllrLWGaifOZjJwK2657TYBZP5PLjI91DbRGS7o6FaMNtu8Jq982F9kwpDmhapJ5\njG/JFM5zZHMecUYWd10Tuoi/0N59z09/euDUJT60GwyJr+c9Vim6wXIuHrPZomkhFozVnJ/31Hii\nURpt4TSPHJcTd7tb3jc7vv/wv1GVYg6PBEDlQL/dUIOjbRuUrhwPe+Z5YTN0gkYskp8qGyqPcWI3\neRuOLifNxBICqWSc9uSVdqO1IlPFc1clvNxaBKquCrVmnDO0bUsI4Xpq894xHWdSWUhhoYjZA997\nSmuIuVBS5LhMBAPaidAhpXQtXzkXHh4fSDmxvbmjGCWsYOtR1nOaF85LwGw6QdhVUMpi1rbsJZNQ\nfsdXXzXlDcP1bbH8m4K4foPM72uhqnJt09a1GFslr9s6xVZbdk3HbteincKbzKbTokouSPxTKhIT\nh5CLtHpdL5TWWOfQcaFzHnKm6kh/e0N3o+luG7rB4Gvi8PhIOB7YvN9w3p84vxxI48jN3Y7+dke7\n6/GNpitH7PknSjxgVCTlicNhz+EY2fU/8rt/+s8MvQQpVyLn8QtfHx44Pn4iHCLetiiEPjYtC+Ug\nwildDHVjsO9anr8e2B+e6HYdpzTzdDhSrCbuJQWqD4EyVUqF4IW2ZrynsYZhGEAnTssBXZAZahnX\nEZbC6QO7XY/RIkAMU0ST+XC3g+mBZWpp6sKHvpLjwucYUfaeobfENKNSQufK6RQoFUZnqK3GO4PJ\nia2K7PxMTRmTRlJIHMtMLiPzvOe2a/j27oZ713LTdCQKzAs6nNFIBmdNiyS4+FbGHCQ+Pf1MiJHD\nODPngvOOAc28JIpcALw8P9N1nv5mw3c3Nzx8/sz+cc+8dxyfJQc5csNTeGFOlpJEyeyNJk6JWg2x\naBq/pd/eUX1PxJNt8/fr4d/9yuUbqqLmS8sOQjhTrZGYlJqp1QpwOKXrXEMyGPN15gHSzkmliopu\nbcUaVcXsrkBZx1LF4iHzjfUGLYLaSllOupfTp3NOipvWkpxQ8vU11pIx6wKvtKQNxCRt1JgLSjuK\ncqsPM6xqU5kHphCw3mM2nqI0Ohc5obXtqjiUC7fkgLGGxltSzOQsQHEoGO3BrnNBLSSfnKRNW2Ik\nLsKhjDGinV3bzBnajvWYKzv7nIjjGW0Nxgt31mhZaMx6Er0kkuSwEM9HfCMn7RAzxnqcER9rzitf\nd029qAW0cQKZB3zTcDHcmypZc9ZYSpxZ8iIt3JzR1uGahlpXZijIrJRVnVsyzjnaYRAMVqmr0Fb8\ndFFV0AbjB5bpwPNxpHWK1jueHh+42W1Ii6drtrTN93x9OnB/a9k/feHhuOfmn3+kU47pcaTpN2Rj\nhJGqFFYZpv3EVCLaVKyRNrm2mt40bDb3fHz/Wx6+ZuZwZrfruL97RzyO/PLLX9m/PAoPuO/WgG3h\nklorxU1pTUpxvcZhWRZyluKmFLJpM6KmXlKSqDZrxXvp9IqG1Cv0QKwdSkHf96SUOJ9PApBwDmss\nZz3Ttx1aV87nI0OzBaeIGaK1LESez0ey05IT2zbix9Vyz07zzMvTMzk70rLIDEcbfDuwpMwSEq7r\nydYhvZ810afqqxr2dQbzeuK+kI2viLw3j7+da16JVOq1el6EPta6dUNZaDcas8zc7Ta8vxMSWC2S\njFDlYkMV6VLoVUv0VmCYs1iCuqblOE84rWlRJF3Qmw2mbanOom1lfDny9PkTCiFAzSGgvGZ38x5/\ne4tqDK4pbNWRcvwLxAecgdPpyMvznvMp8e7ut/yH3/xnhuaeVbAPFJSp+Kalb98xtB9wbcNpmcFZ\nDtNXlpCwxWNdSywt//LnB0qdiXmmqRVtFaa2MCW6anj/7iN3uw+UDD99/u/s93tMtTibQWda32Ld\nHa54as6oVDBVwBp911G1qNxbP5Cj5vT8FW8s77qWGl6YQuCuVdwWx8vhyMfaUcIBa3q0rvzpp594\nfk6MxVP6De5m4Jv793y78/h5pI0nbBlZlpFcKiZnqobNMFDbGxqruVUt22oxY2R8eeC0f5IgDu/Q\nzjOUwrfv3rNQeZkCX/afmZaRahShFIp2DNWSTpVSLVZZiAUVCqc4EdTEH/7ywPnpEylUjhniMlEV\nbG5vKLlfR2uGxneooijagh7ot/f4bkc1hVAVSRuhB/2vFsyKQAK01qS6oJXYPWpFFlCtiMsZpRxW\nSzZeWnmxF9+fXNRiuk5ViV1EKVIpYNeQY+Oo2soinMM6MV0DiavMKLiY76+GaJkhxZVpesGbwUXR\nq1Z7SESXvC5mFq0cpRppM1OvsOi63kC1SCvLDTt831JXuPwFAwYyF4zLJGkqJWKNxEHllAVCXWSh\nVmtrtK6yetuIKCXMM1pbAYGvEv05SPCu9V7Us0VabM5ZSutEIbkxwgAAIABJREFUfRnOzC8HSgDd\n9HJcqWLsrt5RkZmbMg7fWFTNhHUoztrCKmW17CgJwrZtS85p9YAq+t2GUhJxlv9nvMe1FqWdLJxU\n0iw4PMlEeGNJyYVExBgraQc14dZTmdFaMlFXj6vpFS/7r5Sj2AU6J8/RWMfneWSKlW/utxxj4lwj\nfuf4+HHL578cyccjIWrMoDDKgaqYdqAmS4wzmkTVlWHTonzLOQX0YhmaO9L2I2H5hVlFzimx3x8J\nKXFzs2FcjmQTCUvA06G1Q6mENY4YCiFkhk1D2zpAkVKWEOiQ0FYyYq0xhJA4nxNDr2i8bIqU1uuN\nCyHILLlpHM45zucTMSYpIjmTVaZr5X44HY/MFT42iqEZJCzbWhq/waSF0zJJkopVq85AABfjNLHk\nDKahGIcbPG3XU5VhPp2x3qGdEUVsCmgMikzKEZQRFN5a+X6FveRX+r7/v8XjjRjIiJjo2puVUHWl\nNG2r2DaGITk2vcYhwIKq5CQmO9SCKlw7NEVBXUVOl4dCiR9aK3SpNMowK82SIiXKuCTHSpxnlnmm\n39yAceimFzN9a1HW0+rCxiTi4RMpPNB6xcvzE/v9C/OU6ZoPfHz/exq/I5cAiJZBqYZt/y3b4R35\n3UytlSUleHlmjIHNbYdeArY2qKo4xDM1BjbbG4re0XU77ntL/7HFG4kZ7JsNVneoqig1cf7pv1Lm\nTOkKgQXvxBdsiiIG0V4UFEuqjKlK2lFMYAquaXGbli2GLk44NNvesW0d+bSnL4GK4jwVwnnEU5ge\nHzhOBvX+e/rv37N9t+N263DjC/nhZ8Y4MgwNNS+0FLaqMmdLWx3KWJZlJISRFwrLNHI+7TkdHsil\n0nYDxrbkYqg6oK0la8UyF8YpUbWiVotRnhg8IcHdu3sa42hqxCwThxnapifQMCa5XlPKKNWy2e1I\nWZHyCKWiiiFli3Y7XL9Buy3atyRrKUq6qEI0+/sX9z8smKFkrDYoCspWnG0kkitLxJPWlmWJwk3M\noEqh1CQ0jvXEZ0RyKQKUKhBnZRypKrT2VM16oooo5PQl1ukCap1Fqlf4wUXsI6e9V6n7K0BhTUVH\nCqa0bkAYtkItMVqUUTEWSnoVG1nnxNNoLNYJASiGQAoB7SxmRWjpNVkihYVaAgoR6BjforRDrXPO\nGCPLPMvzqkpSdZ3xHVBdD0kUkWpVClKrIPO0oWolhdBotJWNSTg9YWrGd8NqEq8YXdC1kGIghoDZ\n3mEbh6KICfxqCL+kRFzCp0WVKYg8KezWipdNF4syCUqmhDVHUwNKOgty6pJc0VqhpkqJMmdKIZDU\ngrWOy5LmnJeiXCBXjdEaO3S0vuPw8Ik0n2RWPGcmCqYabDdQXZJ5TtuuoqbE4eGR5RyoZoejYBsh\nMo2pYN2A9R2n/aMEAkSN8xriQk6Z99ue7XDHl+WBr4c9tm9Rreebmx94/PxXxjHi2sKyZIqyeC/q\nP2s0h4Oo57quME2LtNxzpJZKiJnGaDrnZcZdoXeG1lp0VeQkoINaNSkVUqzXDkQplRDSVXC1LBFn\nK6EazuNEygs2JXzn6TYDzsuCe+MacIHzYeQpTkQqS0yUJDPBECJm2BBdC35AZZhyJaWFXKtkuQJD\n45iWSNVa/KFaPLapXsAeb06O/5ZC+a8+FFz1ywWtC1pXrNO0FrxO3GwaNt5hS6GUS0fJrFfQChCp\nas2+5CpuevvQStE4OW0ZZ9i0lnxeOB+f8OoWYoPTjmGzkxQeBE6hjMJpy04r7kxFj0+8vPyFfpOZ\npszxcKYWS9ds+P67/8j97UcUFqWy3Adl3RBkB6rBNT25KrxPfGx3nKeJxu+Zp4kQCudUmM8j1vXg\n7jkfD+im4b674W54j7MtJYJWZt0owIfdjzztnvl0/IXxZSI2keoyFs20HAlpoeTKklaPupN1Rfke\n12ravLDrPUPR2DnTAonK09Mj8/6Z+90tzy9HfnlaWMyAdy0n05BuB/rf/oi7v6OmmenxifHlkf2D\ngNm/6TZoMkMeQcHGN2xcS9aVOUyrwLMy7/eyqvuOqsA2A3WB837CmonqLVknUoQUPRWHMT22vaWq\nDt97dHfD+XRiOUyEKRFCwUxnUkrMxaN9i2stxEiIgeV8pISIswPav0e5e7BbqnVUq4m6kNVCXnm8\nlzrz9x7/+ITphJQf40wNC9pK3JHSXtBxWYQjyihySeQlIHg1ICuqbSjWkU8PKxrPkpRB2xa98kZV\nregcAAERlFUoo6hoVai+ERUuoipVSpGjIMkkmPcNJm8tlus4T8QJ65tQ9foFKiXPqJiF8Qho93YW\nUzFNK/vgKuZovaaylJTRRcm9EbNQVjRAxFhNUS21yjylZGkp+6YVS4I0l4mhYm62kmhQCikFaqro\npsG1jewSV4ACClLN6DBLe1Y7TNdRszAlbeNYwsJ8PpPPI6rvcY1sQnIKxLjgVoXnZe5orYOMCIDe\nKJGNNSiSyKrrqnpecVzOWHKOpJwwrr0qG23TCdWnyMbEKig5UlWh5pkcEuXkcf2wgvVlVq2sJSkL\nztDefUMZO4yKzGGkYSUU2cJUEvs5kIvjMAXGP34lJo1vtyTfUY0l1sISRATWVEvTOfr7D4RlZCmF\nUCvWwnlc0Cnx7r6j275n/3JmrBU/dMw5cg4Zax1OK+Y0CfS8KlmQ5sA0JW5uBLROzWunRYQyxhg5\nBWTpVjiraX2H0ooUE1Cuqk6QiLVaRSx1OEzs9wvff3+7krWQtqu2JL1QcqGxhhoz8bxgMfTOsi0W\nqudoOmJVBAdajl6iJ1jhFeeUyMsMSlJ8tDG0VpTAxkrUW4yRSiYW8N0g3YdcrraSi4r1emp8O69c\nW6+SnvGWOPXrxwV2IACSinVVoA05UfKC9x2mKsHVIZ7YdY8mNitWCEkp8vONFN/La5CfAX3Xcl4W\nDOL3MzWRxiPZWnQjCnHjGsbziFlmnG3xTUunDTca7lRiXo7U84HDPFGyo+/uudm95+72W1ovyMGQ\nzkzTC854GrfBuU5el5b0mZQ8pSTGwwvPTy8cTmdyrjw+71msp+DodvccT4VxsujW8XisdHZiGAwa\nhbcCcChUvO748Zvfobzh8/FnDi97SpvoTUuNhfv2HU3T8/D0iVN8obay6ey856bvaI1hYx1b4+m8\nRs+Zc8q8zIFQNGmp/OnxzNcT6L5BqQ2ztrjbb9DbdyjrKfPMOWrOZcO8u2VzvyW/M6TzJx7+/N+I\njaXbeF7CSKoF17Xcbbe4nHnXdBz2L5y5h8aRz5HnT3uyccwKShCF/ykoUtky7D6w3X3ANj1LSGhj\nePj6wvl4pleW6hy1Bg6nB5TvMdph2g06LSznPWWeoDY07TfY9h3VviOpRrIxLSSCaAOMRhVFjbIh\ns/XfUTCb7YYcg9yIxRJjRmtLypkwH9HGY6wlFaHFV1fIMZJiwitNGQ+EqlElIzVJ0hWskmOzSCLy\nehMorNWEFK43l+C61ptQa/IKC8CI+KaUcoWslzVHk3X+mYrsYrGvwoDL6SqezlCh6XpCSdRVTFMQ\n9Z1TK+RchjdUxFJgV6Zriller/OUXIkxoK0mZQ1k8mpF0Nqup1agCIRdtw22bbDaMI8Tymq0d5LL\nud71ykiMmjWWKZ7JcybPQU6dXbOuGVHUXiliGo/vP8qmxChKmkgFOcGv6tdS17ao1sSYRLiU8tXO\nY40lTmdAlLYlJwyifkvTRHGKdhiwrhUKjpZFvxR5bt80WKtQVlNLlO6Aa6BotNU493q55TBR5xPW\nWKxS6H6DrvI5xOVMiUf6bcPz4YUYImpKnJbKZmg5ZU2yAyEbvPaElHFNg1X+yh+1zqB1S9M4+q4y\nvzwRTjOlHbBzYffue7q08NdPf+R26Nh5h2oHbKlYBU6vPrnGsYSJeQ7rqSizzAlUwVmBhJdSRYmd\nM8ZC00i7tgK11NXXepn3KTltp0oMYtIfx0CM9doVsdYwzQnV9rAYVK40ztFow89/+jPf/+Y33L3f\nsFEOquWj6dDactSZqgzaGkoO6+dSJImklWQhayUqLiwLS1oYho0ov5V0Xrx169x5zavNItSppZKV\nFE3vHPO8XAV5sEJIsugNNPpfOYmulislAddNC/3G4Fzh9LzQao/HovMaCaYvTGm1vpNQlXh/q10T\net78jLfWFm8dYRU/WWPpfIPVljBNmGI4jSMvhxPKyOdkVKHTsLGVjc34spB1RVXD178c6fo7us7z\nEgvHpz3GTHz73T1TeOTz5/8GNfHh3W/47uPvoVpynvjl00+c94aX5xOfvz4QikE3PabZEOoOjccN\nA8kO5FpQw4Yxa/70cOTp5Rd2wwat4Ltvv+W+72hrxRvDu+03dLuB3XHL15dfcLXl/fA97/y33Jg7\nghn5+eZf+H8//1eO8UTfbPmwuec3NxtykNFCjnA4zjgs51B5mTUvZ0XejxyXltn0GHOH7z9wc9fQ\nf3xH13e0phBdSwgF2xlaO2C3G/4naW/WI1eSZGl+ut7FzHwhg4zcpruBHvT//y2DmXlooFBdmRkL\nGaQvtt17dZN5EDUns6pyqrqKQCa3oMPc/ZqKisg536mhsHv4SKwJv7xyOl84HY80F/jThw+4Ijw/\nPYEUjpcTm1UxKT5yvttT7u4pw8x2PpJT4m73kfjwJ2wYMNaTS+b1/ELOGzlVpvkBWmXLK9kVKpVd\nmIjWk19+Y7ucyJujtTvGw48wvqP6mWodxRWaLTRRTYszDmcDVoTUuoOg/esXvn9XwVyOGrlS8qbk\nFj+q8pFb7qShlg1xXcFgDERwdGqIUYGQH+6oq3agzhhaXpG0UJuCqsU6qrGUvOF8wIagylIjWFr3\nIdKDqvveMy209YodZzD9U+k3YAHEytvuU0VBOsI0AjEORBfwMZBW3b01Ay4GRm9o5RUliQQaauhv\n/WYb/KBpHRnSstJEDftpKbioilwtlip+UvGHBVMxpSjUOiWa91irCekYzQqsRfdY0hpiLc00gjdU\n43F3D1gM3hiGoEPrrVRK+WZWNyIUEUoV/DBiQLMuYyQ496ZYNN7grMcP8W3/WFslOke6XrEx4mLE\nCqyXM6YUhsd3updIWUUiTUk1PkbwHuctOa/9OdDPKcRAtOE7QLgKN3SUDmldFOPnfO9qLQGnSQt/\nfUFa4pQSEYu1E1+Ola1Fqo06ZhxnSAsNQ2yZlBLNeFJqxDEwxZHt/MSvP/1MKQ43HyiXlRoMD/vf\nc41PvFyuGBx22jHFAbOuhHUDGtfrxrplhMY4eKVMpYrBkrfKcPNMVn3e4uA6cELxhNa6HmbeI5pE\nf3+Dtq/rxjDAOEZSyuSsO/hp8jw19QfPk+duvycXXTmUnFivVwZbqJcFuxXmAMkI2fcurBe5XFSp\nHmMAE9+AF8aYt+6ytY5blKZh6FZ1Ajou/mYrub23WtOJguuWpJvoBvhmdfpXLCS1abrNEC2Hneew\ns+SUeJwGfj8cmI3FiX7+CFC6v7mLjeRffth//tIA9WgOXlN2xBh2ceR+v+d8vVI3Jc1YHxh290ql\nygXyRlgSfmf57eUzry9feD4X1nVmORVy+ZXd4x8I3mNsIu4HjK/klkiXZ1pyXF4NxsxcLhf+6R//\nJ4UDbv5A+OF/ME0Hjc6yAe+CTqNo1B5cP+wdlUIeR15K4/m0EKzj+c+/MLfC/RB43O+IDswAd7s/\n8MMf/k+8jHquNcNZHIwTeQtUa9kPA7+/O/C7/R6eCy9fL6zNcFozx/NKq1AbnEridE6Im7HjA9P+\nR+Lj7wj7A7tdZDd7yIWWN9q6UpcTk9MpHGTkslBZcc3gbeByOrGbZuw0s0rlHz79zPOX36jSeF2v\n1GD4+ONHoh/5fFoJ4z122FHHkU0WhukO8YE1rWzrldPxBWvUzubEsV6ulJoVN9rAiiO/FnK5UhJU\n80DYfcBP76kmsglgLMYJxL6WKkY1Is5hm57TVjQUo5j2d56yf0fBdNZhpNFE90S2q9taM9rlNGW8\nqtRZ/WG5ZMq60o0jWJJ2OM5i9hONAM1gy4bNV2reEONoYjGhZ17mghihmUrEYY2hNC2emBtaq2AD\nGNcQIxiryR6qexBwvPnFbjjtvGWNbXIG16OFdN6v3rkYA+Sv5OMX/O6A9ztq6z7SCjRHbt13aT2S\n+57FglTlqdrQ+a89hst5//ZurlW/VnlLzF2NtW0Zmo61vfVI7SCHDp+2RvmawQ+6NysbSGHbMtXH\nNxWGNA3OvR2Gxhh9DZ3E9I2uhCp2S9FMy6b+Ne8MJndrSAi0Vhn3O7YYkJIVTn1ZCHHGu8ias4p4\nUPWxe9sf6+jdB6s0GxRhVuo3jq0LgWoEvJrNW60Er+P5esm0MCHZI3Zgc5nqLNu6Im7GTTN+3uEP\njzTnmR7eU7eN9eWJWrKu3UzFZS32x8+f2FJievwRe3eAJqQs7OPEn/743/ny289cliM7FxjmiVwL\ngnZby7IwjpFhiNg3bF5VMYW1xDCwpYRzlmG0WKseSvvdWNJ2vJxYtUfkXDqlqZGL2kqMQSlLJVFb\nY55nfv30wi5MDNVxPV8458Lw+ECdAlfbqMtFD5TLiW0MtAjNqs+zNWFdFrZWsMPUmcg3zCJv+9Kb\n0lq/P/Kt2IqgQPoOU+8F6XYJdF2hWmt9K6LW3kAX3yD23MAGIoxj4bD3jK7xED0HDEYCMQ48eDDS\nE4FM1+G2bwXy37067T7taB3G6nSqWIdtYJtQykrKhTgMuGHChkiTyvH5mcEVjj8983T6hSqJ6zGx\nf/gTrWjX7u4/gBmoaeO3SyK6xnWNHF8r6fIFZw1hfE+rQht+T9y9R3a/o7hAcQGs74B6sFYwtvV8\nTsi2Yn3EDY4iA+OHkaEVzLpwPL1yumQ+LwteFvY7z335gbs7R/QwOANUillYtieeX3/iYbK83z3i\nU+Hn/+cfeflceH51XLDkGBkPe50eiHBdhCoDfv874rvf43YH/G6nz30UyuXC5fkry/Er5IVh8Bze\nv2eaHI4MacNbITeLGQ64O0OumdN2xTeweSNJ4+7uI2XYeF6+sG2V16dXnn67IjEQTgPTfq+aiEtm\nOf1EympXaWkjhpmyVRyWajZKS7QiGBkRM5HtTGsBv7snjvcQZrZeACsFY4uuAhp4GygU5SMjOpaV\nTq0z8C9ylr/78W/bSoJR8QRoIat6z7upX43wlvJuRYhDQMoG3uLHCZOhbpHtfMUEwbhNdx3W4XY7\n7FLxogXG4MBFcus7EwdSErmj8KR3LT5EfVNaMH6Avp8sHYRgrNJ3DApLQLQImNYwuSgJCBDvVRDk\nXf93uvdLl3NXxDqshbReaWJ1R2EsSOG2yInjhLFC2q4KS58VW1WbqmZNR/qVrIeIiNVFvoVt2aiX\nM9UGvJvwPuKdUwi49ThxQKEWxRMKlloakjbW5aRxQDa8QRAw4IPnFoZ7E0bhHLmnziAqtLiBIGq3\nA4FQ06rZnzFSO2s2tUo1ooSgBDT1g25JLwM+RAgDRdVcnUMLKifophNzK+L9wG6iggTf8YfdztKM\nQWoDH/HTI27S7mIwaJHKiYQjDDMuDrS+y67G4YzHTTOSEtI7umsunJcLJMO4e+T+4QfGccLXgpMM\nWHa797QKn35ZOa9HDoPDTpH7d/dE13eprZDSgrGtWwg88zSqWjhDLap2jdFyXS6INOZ57KCJjnU0\nRmPQUsXg8C5yzZtiD0XFZ+M4c3e/43Q6YYAJy+Mws55WtrzymirDfcbZwuoKgYSPhpwdZo5UElte\n38hR3nsm6wndT5yLuqBvu9fb3l/dX5YQ7DcPc0/CueVg3nbdYGh9fwvdRobBO/Om9u6rUqSoYDAY\nC62x3xne3Xkma9l7x4RCMSQ19Uz28ngLof4bFN/fKY5vv/ybP9bgZv3nKsAK3fuclis5F4aHvWLm\nxol0eSVdLnxdjzz99hNmiiAR6w7I7gNp3QgPDxQc23GBlNkulbyeyClg2jtqmDDhR7K7w8aovlsf\nWd2M9Nei5b9hrIL0RcB65eY2aVRJWFsYnCNuKgKzu0fc7p3GBOYN5wo1Cr9sia/PZ97f7Xn0lik4\nanrmevwH9iERWyQ9rfzln458/bWwbfcsdabtZmyYKckipSHiwH/Evr8nfviAHzXqzNZKqJV2Xrk8\n/YXr8QtpWSh5I9+/o10arFfemZUoV1LNFMkMv/vI+48fsctXzNNfiE0V8TYMmAajs3zc3TG7gZfX\nxPVoiI8TIjOGHbUm0vUEkshl1RSmatgWjV+zOLAesQ1nZ1y8x8Y78DP4CeNHxBpyK5SWMLbDY+j6\nlqYismBuzVx/em7PuPA3yut/UQ//jXpJLRuW79Bo/WFuJata1lls81ATpERtme3lC/sPH5Xf6jzJ\nwGWNGKcFytiCoMQJkxNuGIiDjne9CxjRMZd3qiZM10Wl7laLWEkb1jmcN52VavvJBs0auHFZxSA4\njGj2nawblEJBFWQq35e+9+veLwpmnBjHe6QZ1uOFfL4QH39gGCI5CaUkzI3wYzsU3keCi/iobM7S\nE0Raq+Qt951LjzASq2/o1hA3YPsupbVGWpX04m2gbpWGejcRTVSXJowxIG5HISDGE6JCEnJO/cBp\n5K3iev7m99641hq5aAZm7PYVRMilICkRjIYVG6tYP7xOGIxoYokZoga9VoMPgyL8jOksVvQBtZop\nSiuUVnFW3oKub4xh5zwVw5YzIg3n1M9rrOaatjyAKOOV7sv1rWjhtpq3GbzHutAFXgJvTFy1BokR\nrB9obuhACcu2rOSyMo6GVBoh79hN73m4u/Lrz698Oh95PHiGMHB+ftHXhqNmpUU1DK5nlUprHK8b\n3kOMqogupRKj2qZqVbKUiHb6tXTQurW0fBvzBuIQuVw24EaIakzTxLtJeBhnjkvDzjOpFrY58nk9\nI2Vhn4QfholmRo5l5fPlyDw94oMnxsZu3tOkINZTunf3Vlp0GqPxe+L0MibGae6lWJzRcaaYG7lL\n3lTpeqo4DLZvYcxbRy1WbUitKht3cHCIhoioetJ4dt4TMYQKrhdsWvd99oNLuImEvoOY/L1D6m/+\nok855Js2KVjHGCJSC+vlgp9mwrBDwki9nMiffsZL4fn5lWXzhLADY9k/vGPJIHbAXDbOX59o1SEF\nfBho5o7m7olzolwbMn6EeE/GYLyhWaEFi26nhDh6pKltx8XAllV/MUdDWVfS9ZkwGCLAMsDhjoRH\nnGecB6zsMFbItmH3DdMySyvsBUJt5CVzsI7z1fLXP7/w9Fvi9cnT2o+4+COMI4wDMozgAy4I3hls\ndKT3d4wPE95BS0Kk4VvhenxlOV8oTWgmYuY72H0gmai6lnlk6+Ko/f0BYwomPyHrZ7ie+fqyctm0\nYJKPGDYmb9iWxOW1Mu7eYacHZNiT0It6yg3XgOx1dOpGXaeECR8nrB8Qa7FxD36mEmjGIW/neKHZ\nhLC9NUs0MLU/T6IQHbVgyXc/8zY5+Q8XTETZoc45hrEHJ7fGer2ScsbGHWaYkGLIx6/U6xPWC9vL\nZ3KDcdqBn9XHVg3YGWcj3haqHLG7kRvCS3JmujNI22hbIfcHa9zPCHrolFKR0ihSNbz5xp+1GXwk\neoe1hdoyBatL3cGTjkfq5RU/zJpsLoq50xjA79ITqFRjWLaKNY4sBjvfYXzU9PBc34J74xDV+F+6\nsCNGaqlsy9ozA29FVc3qtVZazrTS8DFgjMdN+5tKXkdc0rp1RsfdPvh+Q+2Qh/77bVGteSldfOFs\nV71CGEdC7Pul/m1844BaCGbQdBFjqK2xratSmUrGBJ0c5JRwRnBjJE4jlERZN7b1ip/2OprLK9Y7\nTCsEq2N6ER3t1VaxUlU7KaKq2bf9sjJ6AWzTkGz1q36zC5jo34RfVaR/fURJTbVAbbpPEkjbwlYL\ntUMGrDXUpiHPwUImU6VpuG7KDFJZloItninMxBB4fPyBnJ758uUfKDXz+/sDWQSHUdBANZhh6D5K\nSwyRZb3iQ2O3CwiJ1/MGOMIQ2BI0Ainr6gLR76n36k/dtsQ0aUTcPOt74Hpdu8LVYGTgDx8nRu/Y\nljPDYc+0v+fZJH7KJ17blZetcJwSNhgu6cJSK+/Cf0PqSEpfuZyvnEph9RXiyBi87kFtwMaBRui3\nf9chIaavC78DrL8VoW+Vyd4SdvroSlfW+meNRi1CTRlXEvvR8nEKPI6qzGwi+CrYKrgmUBvO0MMO\n+jPxHYv6nx1G/8pu9J9N0G5awf5za8IgjoMfiVVwtTBND8hwp4j1tHE3Bc7PZy5rxo13GDcSxolh\nOHD89BkzjsjlSD1fGeYHKoY43tHCnmYHqmzUtmLGR1VhGoOLFrENPwjWN6R1LrQVWlpoKDgthMBA\nIzbh3Xhgu55IlwW5myEGmlepcLOC81ZTZkplHiPT4OB85Pz8wvF64eXzr5w+/8SXT6/UOmPs72j+\nHW64V3vQPGP2M3EXmSZPThewGT9BDZnmRsRBCHqulrSxXI7gB1yYsKGy3888PN5jqXhZedg5Fjtz\nmByP+5GhJdavR9bXK7/8fOHzWSj+wMPjPR/ez/j2ymAy1y8XWsuw22HnmToMpKYZumIjIkGJa2iw\nRPSROO50beciuIFSI0X0Ytc6TdyZRmsZIx1e0zSNlS5gc07XD7XqZKuJfBfy0adhf3+F+W8XzBA8\nJWVaKWq27+O/abcDFylVE+VFLG26R4zDuYI1BSnC+emMGVZFy7mZUouKTlDYtQSDc9qJNhfYrgut\nZGxT0oyUwrZt39JJolfkW/f1KTML2rZBTpS0EGzGtI2WwY97qAZXM/FuT4sjLeypRe0vt2QRHZeq\ncd/6iDFOD8nZ42zQNPfLpY/7PDirwITgFbKd+n6rVoU0GI+OrnXMZTvYutWECR7nIiAd0m4601Uz\nOhWabYjDAKbylm8ZIs6pIMYY7a48DucH/aZ3NoMBtbHkQr+8cwOpWwwYeUsbqbVqsQSNV1sWMBDm\niXG/7xnBwnI+U2vj8O49WyoqWqlC3lZss4h8A9Y75xiVqx9TAAAgAElEQVRDoKWFuq7YOFCtw3SF\nJv01mCoErHZwtfWwb6OWog5YUHWz1dfRqUnWOJpsbMuCQ0VNONe9oT3hpb+WXBOj64kwTqPVJBeW\nLXE3TTpalEYMI/vdHb/8XNjWM+92M36ciK6xHi/UBvfzntfTFdB9Y8or0xTw3nA+J9ZVeP9+0v12\nF1KV0jCDZQja9Zaq6TNxCKxropSC76PqYRhYrhp2kHNjjlY7RFO5XC6MuwemYWTvEsd147pekVrw\nYnRsaAd2815BCiWxbRtLypjDwH6/Z7uuYFQAlGrTvaP3fezaO0Qxf1uU5KZG1Qiub+2c0OMO3v6t\ndVYFdc7gBscYJ353GPhh8uysYGuh9OmYvekK7O1jffs4fPdHf/+HvBVFuSE1dYiDSCOVhdIyExOh\nBXbOM3rt5iUamizYJtTtyPHpN77+9oT/4Q8EmbqeIvLl52eaiVgmGpVpjhqiMAVaGEgCtRpwO9jt\nED/qDg6dRjhn8FYYgqFVo97d6DDeEccATRhsY3t9Qc4KrmhJFe8metygO31vK65kxhApecHWwiAr\nrSaOP/2Ff/r8ifV01vO0jsjwA364w/oZa1RklLeF8cNEu99jA2AzRa7E0TLcBWq5gJ118jQY0mnF\n2oodDfOw4/1+IraEbSuP8xlJR1p6IZ4XZh4QOyDHC19PJ16eX/jy9MLaPHX6gRweMGbEZc+PDwes\nvVAun1nSETt5kq3gmiqZi8ENP4LdgQXvRNcHOK5iEeexPtKcxzuLEeWGGwpQ+yRLc42d9X3lIW/x\nhCHqNC+ljco33OOtYVFl+39C9NOqBhzXnChFU8ad1cfdWh3r3WK8mve0OKqNom7EnYdSKenKdj0j\nYYWxYY0gzfe5vsNYj0WoW8ag+6fgPDEEck5I1Yy5slzVddUjqry1IBWH0MqG9RXToJQrtIyzgXoR\nSoVxd4ePA0vVLo4Q+uK9A6ClIU27LAH8PCNOCS616Re7efV82uC5Iaet99Sc+zhXelxXBxc4R22V\nIkKtWbMqw55a5TturhYDESHn0q0X7Q3SsG2JG73FelUKx2kPAtYGiHqm1dqI49THboWyXWm5YOOg\nr1T9Kog0SlYSj3U6Fg4x4gzI1vdI/STatk2771KoOTPd3XflZCYMI8O853I6KvC96o7O+aAH1rpg\n0wbb8naoiQ1UFxj3e2iZ2hrBB730tKaG/35utlZIy8o4jb2odTHXG3xbi27OiSYqlzfWAdJJRrar\nUwe25UwVgZbVTlNW7qIjhAGD677ZwBBnhjiSy0LGsJ9n5LRyXTWWbRp3nM8rIoUtbd1iZdg23de+\ne5z1ORIABRAAjKPpjGXpQgvUipQK0zRSqwqFDvt70lYYh0nVuEbtU9ZaTtcrZtsIuwOjiUQxVBz1\nsupOBoNphZIviClM08A0j7RxIjy+Q2IgLQlvbrm14LGqB+hGx1tM3/cHyK17vFWwm9L8LQCemx2m\nqhK6NWZTebeP7P3AQzRMNFxVzvOtGxS6ev9WqPWj891vvvv1tyJuaBhTERIlb5y7FSHGqEpro++1\n4/LK8+szf3z/X/jx/k/o6auJRde6MoWMnE5sr79xeXlBhp1Ct1OkpabiwyEShlHD5icV3OVWKBQ9\nVL12Lw2vq4mScb5hyUhJBCP4YglxoohhiIFCYxgiYwzYVnDpyrJdECnYKTD+cA/RU4Nn8MJoLa4k\nZFto1xfMslC2K5e88PzyzMsvnygJjN8Tdh8x/k6xbz7irCdaS7oeYSyIv8AYcfuRkjOmFOZ3d0Cl\nXF6Z2gEKPSYxQXA8/viewy7yoV3ZLUfadiY/XVjXVzBwbnA9nZEU2Urj5bpy3Co53GP375C414mH\nmzllz5Aq9m5ii1dSHLDpSokDJiWkNIwd8cMBYxTXWAydOGVVw2I91VptbKwQBKQL6ECwVmhex/vW\nWCSrjkW6ULR1WlzNWUV4/3z8etud/50f/3bBbA3jHM6OugesFTHqQzQtqTJS+iGW1QSatoS1lmoV\nUu6GkRD2+sI7XU5C0N2dgZqF2goN7SAMjdKEerkCQhim7iNUj5o4i3XgQmC7rJS0qgKqiproi8IU\nnPU98VsRbuuWaDbivOCjxzgDUjA9+7HURF4uGOcx+ztaMxqs2qBtq6aRxIB0daDt+9S6rJgY9O/g\nbeTbbrsYr95Q43TkZS1vCD3ro37uXa37tykthlpRoIBRozm9uJdSMDbr7gcdk4UQkFa0y/N6+2pl\nVeiCDRo+3INxRXoREy0wSMW2Rpwm/foGr0WmVOr5rJ1oKci20ZogKZO94qykJEXshaAewtMJ6z2x\nhzqHccBU+kOvHea6XTEGQlS/qzW27xW062gNzSgUfTZs321K0xGKtQY/RPCG5arj2JZS34cqD9V7\nFbcYHxniTGpgvWEeLXNAu3wxgMWIIhNjHNgyvF6v+OnAHAecH7BWrVTSGiEYUspcLhvOg3OaH+q9\nYUsKs0hJBT7jqEkvtWdu4mFdNlpTwox1plueYFlWRQOOA9Mw4aTgnHaz5bSQUsJKwxurgcIhsqaC\nxxKdV5FWPgEr4xSIMZCbiq3W/oyFGLrQx7xFlt3OjFoKpk9Gbn9Y6233eXsm+8Hh9eJUSwGnz6ZQ\nGaLl0VV+N0IwjQEwt+Qe1EJxq4PfrVRB+t6xLzIN36LFrNySXQrGZbZ05NNvf+Z8eeZ0+kIqq+Lw\nLPq+NoYtb1yWDcqJ/RQpZQIH1QDOME2Bl5+fWV6P5BYx+x8g7klLwdsRY0ed+LigX5cQyTaSa0Js\nQsqKM3pom5ahVFrp65nlBLbR9rorzeuFdduIs47YXXBEByUnWM8Mg6WNAxI8ZTAM+4AvFb+eGZ0h\nvzxx+fqFy+XMmnXaZ8pKWRMlO+zwjrD7ETe+Izf1sutCThWiW1uIe0+SI6054hhVlb0bGOcRKYn7\nwx3L6xNiAwyBwVnu9iP7IbD3hvDyxPb1Ny7nFy5bolnPw/sPXF4Xno5Xoik8L4mzGMp8j9s/YO8e\nMTaQns+0VtnqnterEMbAVQaq9ZRtocU7XKe0DPsdIURS0mfQhoFqfPcDqz/dWMD2otgEZwzZQG4V\ncUJz/XOvTa1F9bZWVHHVTa3/9ph/tycXUbHmf7hgFhU/dv9cF610tWptDYoe0KYJ9XLWg3+e8aOS\ncta0Ybttw9pATRXaTZ2ptwDTu8uGYH2klE2Vua0rObdEo+FdwI8ROygdpKwnynLEecc4DDqd9Xua\ng5IvNDHkZcO5ho2G1gxIxlVDWzNihVw2TC3YMeLHgD+8hyqIJKL3GKPz9OzA2BG6VcP19r1hYIw0\nY1QAJILrCQqlKBHfVosP/SBC/YdiWvezGpwNGKf4vnYbVwtdkelwxnVFZsM6Rau1VrG3ZrArUHPJ\n1LRibMOKpSa1KNjeDVSBnFS5SK6EYWSrimxrJRFMw8dIkT7T7yDv2iohqpK5Vk2KEetUvWvBxqiQ\nPqOvO0SFgJdlIV+vXJcNP+8J40wYJy0QvcirrUfHeYAKktCd6zTuWft+dRwsmKwUKGlMccQb5U5a\n63qn3mhVqFQNCy4VI1UVy8ZoyHAM7KaJ2TeGEPRbIijXFEdKmW1LbCLcT/eEcWTaH9guC9fLpkU+\n6A6yVnj3fqK1Qoia9lFKt3SsOmqN0XdRktGsw1r7CFbVxiVXaqhM08T1csV7JT2F4Jmcp+YN51Qd\njQhbSpjBknNWn6dzHaKRsThyvZDKBe9HYgxc1kJKG5v0APEQ3wqkho8rvL+kRBPRcN92W1NoqLh+\n6dybpeT269TxY745rB0Q1H/qzcadN7RScWJVoCgGNaV1teLt/29nk3wrkvpDw6+NVbV7qSvL+sLp\n8oVPn/+Jry+/YGwBsyFGI/ekqaISUJWvMRyvhs9f/0Kpe4pRJNtuGhmspZ5XtmPG7f8I+9+Tq/aL\nYThQdKiDNaHHuhkamhMa4468dj9IzRgRhjiwVahFOxdjUAjLetHnD3BSKecLDIFiG8vpRPn6hemw\no3bd4uM4ce8M9bLw26cvnJcrp0+/QNrAeFIRWhFcDLS2h/EON73HxANVepqRjdr5+y7CGx3hYUeW\nDSkrLSe1cJkdFMMcJko11PMrVgqHGDQVqCaO//SJn78+E6nI5Ynj6zPvf/wjw3jgkg58ea28blBa\nIoUdMh5gmCg2MPsRgCSZujzRwoEVx2kTTgs0NJbQWYcbJ4wbAU8zaFpSZ0+L9HjAHtGIMyqkMrrz\nLLVSbKHZ/vf9vGu2djRnP5vNjYds3vQdYr4J11AZG7b9J0g/YRj0jQX4OOKtIW2bjiFc92i1BtYQ\nHx+hNXIurMtCiIOOSUBDgrcFsBgbwTikFfC9YESDGGEYZ9ZFxx2gIbpNGtYG4jgQZ0suC8vLE+XS\nRwy7mWyEaD2RmRh21HpHo1Jyx7yFwGA86bqQzq+6KwsWrKhvLTeGaYdpittz1mFFaOUKNmDm6S0g\nW8QRQmBbN5y12Gl8G1Hd2KlyKzhEQPdEeumwesMRT64KmA7Gv+V3GqNjpdI9UnoF11uS7uVujF7L\njbRSctIUEqfd91sl9R5JSny5zfmDaUjNtFUl1140SUTN7ZFcVV1bRA9G5z3FeZzX6DMf9GBcU+n+\nXL0A+HHEB0/aEhhDCJ4w3FP2s+51jKcIpKqFZB52bxFZNwN8rd9CrW8eP2OMxn0FT6oLPnhyylwu\nBdOEtKxMw6ihwfSdX65M+zuuy1UX/d4jrZDWhVw9uXqGxxlnlPHqrRb6YZx4uH/gfP1M7ke7+iQH\nwt7r+sFV3fdg0eBwFRo5Z7mcE9vWNIDaqSezNbURGAx5Kzjve8HV4nu5Joy17Hd7np8ujOOO8+XM\nNM4c9iNrTqzrRoiBeZ55TYkLmUtJGn5gDLklhSpZj5y+cr488/7+jxwOe87lwnlduNbKOOz1Te89\ny3XDRa8j8T5ajTFoELlxXcCllxqNNjNUUVFE9Oq1HOOAhf5cBLCWy7ay6PWWfmKr4EUUc2dvqpzv\nf9xu9NJvL7bq5UhWUr5wOj9xPD9xOn3lsjyzpRMmFoxVh7WzoR+GXYvQdNVipZFL4udff2a3/z+Q\nIdCcYRwD+XTl8vWIqyPz/g8s7hFhIz7uMDlSc8I4Q26aTqOJTaKeWSAOo+7B0qqXeum8bSo2eub9\nniFarsdn9o/vyClTji/kWpgPH5VPXQrVBpyfiYNjPwbCdePpf/4Dx7/+xCVttMFAqepkE0ddAL+D\ncIcNO3y8Iwx7mg06DRPLlhfEV0qrSF0xu4lLLZrzGUfaUgjjpIB6G4lSOf78V2K6MH54Rxwmji9n\n/vrrb+QlcffhA8O7Bz7/X89kC5N75HiG418+szVPPPzI0hrufq8h7MtCdJbly1HBJMbT/CPFRqwt\nJGPABKyPzHf3yDix4rHGY4zHhIAT2zcvFSOGaG+0t9uDpOusZiHXTYvpdxcxfQ6gUbHG9/UX3yFU\newfbzxolSvWRx39KJdua0mdCoIr6Ca3RhfUbCaTv4EqtqqadZ2xKunCtDdOVmyZGnItUue1EfBdI\n0JWSqniycdRPuTW9WYOqvKyhpI3r81fEWIYfPiouLyVqXnU0JAt+mKniGO8e2GygSe17skqcRtxB\nI5Mw2o3cEk/S6aSdo4Ck/HYbFz8gdlF7hFTlXPoDYjWJoopCDPRNrzd3rPpFrThKVlJR6QpbY9Qr\nSjBqwymb3q6d0/FXowdLW/LSU1IsgO5QdYrQlY09E7CJpjDoXtYyjiPHp6e3XVTub27vepbe7JF8\npqZKHEec04NTVcgZ4yxhmjXkO074UfceN5FNKxU/ztR1wyCsIsQW35bogpDSRi4FN0z91u1o9K61\nlm8+UWBbrt2X5nX3exMjGcOw23Gz5Bjo9gxBukhChvHNkB/CbX+sarhGVwhLgXwmLxvpKGzLgdd3\n77guicM4cZgHAoaPP/43fvvtNwZXeJx2PDrPdd3wMXLdMufLhRgLYQjs7cw4D5zOFxqG15eE945h\nCJRsNDquq3YNtnuXoeSCs04h7EVH46fziRCc5mpuue9IPbXpJU1XDPqsn7crboiUknV3fbcjXVfE\nOXJZeD19YTc8ME8z4yVjL4nHwx7nd6SsIrMtXXm4O+CCZUsrTho+BJZVlb7WxT7VsNQiTPuApWJq\nxa5XnSg5i6mms4q7rsFH3f1XRa3bt0vfd8XRfPdrbk2m+uWaJLbtxPPrL7y8/MLx/BtrOlPJCEUP\nUC9KbbEGx4DI0LsFEJpOIYyymI0Z8GFimg58vWw6+TLC6y8/s76eGPf/FTs9Im3A3xJB6qp4x5pp\ncssENVinnWMp6p9tJQOVcbdjGCNLWfVztarYPJ9PRCtIurK8nnHeMe13lLyBNex2M2Yc8U0FUdcv\nX9menzh9+kx6XjBWcGaENtGyp7YB7EiY3mPiHjvMGB9obsDFoO/zTcH61hZwBT9ExHuMNwz7Wc9h\ndPoyegPpzNef/xfr18/c3avO4+vTkZfPT+RzYvfhA3H/yPn5SnJ3xMd3nM2OtWW28ZH58QMmTJg1\nYcMBYwQ3XPFktrQghwdcHDDMmP46ct7YlL5OuHskFQfNUKsiNJ1xCm5ropMnKdieciXOqn3E2P7e\nkLcppenPlFTFVNaUkCoaZNGnpK3p392sULb1vfz/T5H83yqYJaW+6NdO0vuA7zNlQWXb6jXrMVlS\n9OBztv/+Wz6mtX2MIUoMUe/givTxi3OBVlrvLPS/NSFyE7dvKdHWV23Rxxls1AfajQQ3KMElrdRt\nQ5qGvRqjeDYxgbwlfXsOAXFQW8Y4vWUM89S9W0K+LJTaKFV9PC2iFo+6aCq889TlSnMT22ahFYb9\nHS5EqjHUqsZ8kUZta//6KGfUGhWZ1NZwESSrEEQsOn839PGz6f++YL2OG13wmtNYFCF368Zc7+JN\n8Hqo0HTf5Tq+sN+utrxSRceUKWfq5YqfZ9p2RWzEjxPeO3IVjHP9+9sUEoClGUtrYGIkjjoWdEHj\nl9qyYIaolwyBsq2UdNUH13psdPg4YLGqUG6FW55hyRmpTRWM2L6jtG/xcCJCThsuapdunQOpiLPE\nWcOPQcedpiMJcy5valm9UepzVWvjvC58rY27ITBOO6oULkvBm8Juescf//g/cPWFd9PMLBtLu4AJ\nioe0CTsaXk8Xfv/7d6S6sVW9sS+XK3/84x5HY0mZ/T4wzYFWE9YFrLM9iSRisFyvC/MuEAblyc77\nkZeXK63C+XrGtkbwjmoj1hu8NPbNsjWPnWeO7cwqG+NhQryK4NxSOF2/sGwfCSbwMM86JhbhaT0R\nhpHcVv1amqxagR71VLeCRXdBxtleLJQV7Yw+57TaE1oq1k06ZaiqUPQ29h2iKJXLqLThXx1wfe+1\nFAVjCAuvx098/u3PPL/+ypZfwSQtQLbXXaOHpXE31OBAKwH1AlSw9M5ekDoQ3Z7/8vs/4dyev3z5\nhcOHHzBGOH/9WcU1hzsW24VfxvXPSy91TXTKdFsXGDTppdWMdY4mBRc9OB0bx1HzTrflTMmrJjb5\nwHJ6JfiAn2f9uLXgbGA37bFVuPz0//L68hvpetGx+npFWsEwUa8zwoTxe9x8ADPgd3dUPPWmw4iK\nfWttw8TENAtusMRpxnhIZSFER6qJJoFxCkitDKaSjy88ffmsecXjji9fTly2wrR/5OF3f8JapxD+\nccA93CMFrs0yPn5AipBMp6v5iDWe5hxmDnppiSsuKMHMimAaDH7AtoXd4/t+0Vchj1TAD4hxVOmj\n0o42NQ6kWJpXcVWPU+7rHC2YDl0Z1qphEdQeC2lN3yCaN+IZopJN7Zm6tPqfX+r+owXTef92cOoH\no3cQqOCkFhW5QE8jsd/GkzfOVldntqaJJje0HVRy2nRyI6pcwimpRrCahOK6FaE1Wi6IeAiRkrS7\n0j1LUNSa89ix4rUNIi0XJK2YbcMGlXwb72mtUkqiSdWxjoA3av/oc50uTwY7jNh5T5UCS8NUtV9U\nK+pFaqqubednBN35EiekBpqptLIxTjtuYfPO6ZtKatMcQqMeK+MNGDXb15qxNzCCdNGFc2C1yDep\nBO+7b06QDlQXuYmGdFQ7zLN26bWqfeBwT/AOZ1RwEIdR4d+ns2YM9u9bGJWepBi42w1bx8y1Sd9n\n2jdFcH6DNDTqtpG3lXY54YZACBGs7288TTSp24o3uvuU1vRSZjtib1sxzhFjJPSCmftY3vV4qCFG\nxCio3ffbo7VO+bj9cLt1X61VtrRhPYQwkNeN7bKRl0QZPD8ETxhntnUjUxmGmY8f/8R2LDw/fWEp\ni4p1RMfMQ4ykdVNgM5aX1xXvA+vSlLu5i3z9+ow1VZWbHnJrrOtGKf0CiUWHELp7vSwru92MiCEO\nXi+lwXFaFuZ5T3OBaQrMwbEcE3vrsC0QxgOv2RKHgeANOa1IylzWV1JZqO3CNDwyWMPX52fKOOKC\np5TGNA76dfd6gFhnWDeFiNCRcnoO9tgqo+PNmlYcWgwtlSE4SlPyleujL7kdZv3SBW/C6152/vZA\nUt+n8Pz6E//rz/83x9MXGhvOVWzvJJsJWiwxqqrv/xOxiK06HjcVY6sCOYvF2InD4Qdsa3z69Ff8\nMBPCQDk/Ua4X7dJCQDEYer5pohH9ue/nnuiZphfZhutwDmeafn24/ex17N4zX42DDMSdFrviDXGO\nRComFdy68PznX8inn8jbFVMNLUNbPNQZ4YCJ9/j5gJsPtKbnop0njLFspmCCxQ3QSBiz8rA3jAM4\na7ifDZEF11aqcXw960UnlkyTjMsLpVzxwbB7eEd4eEcpwp0L7OcdUjLL+cz9/YG6LnA+siyV8O4j\ndhqwWwEx5C1jErSWyUb0Eo10RCgghXI5EfyMl5nahLJmxHuqaIOh9cN3Iajplx+h2apEMOe6g1CV\n1lUaIh1R6gxODNLBIFRNtnLdG3yLhdPzEb388Z3A57YK6H/2L5Sz/zsFE+e0YndDvRFLlfa2b9KD\nVNtcG277EDrgWUevrSc2aNOo3YEexkU/6b6Xsz7Q6CNNOqOyVBAN4PU2EHaPWKfQbjWd6htqKypS\nsE7ZqrU17HSPm+6oSYUjjYYX0ZskGvRba6LWgslZ00Byt0yME60JfpzIGHKumDhgTaRUEBMI847B\n6qh3Ox1Jy4IdIi1t5G3TZJH9DjsOnD79gmDw8w5pULMKbG6niZGOaTKa5DI4x1oq0Vu8619P7JvB\nFvMt1Nd0upGCKVWR20SoueiFBBXiiIuUzks0fkDQ78tw54ni3oQkfpjIOROCFtScNNar9Z3WTW1G\nF4PEedIdr1HF67jb4XY7nIX1coEqhK5i0v1t6ZmOaugOQ2Q5X9Qq1MPKtUirsm256iTgRuSw6HSi\nGbXICGrBaT055LafMAZc31GUUtiuC7YIzg5sUnk+L4x7TezAOYY44vyAaZmUNsbomePEesrcgsst\nnrxu7PczXz9fqFUhzpfrxg/v71iWjVorh0N8EyPRn+m7w4Hz+apUpWqIMXC9bhhXCWEj90tgrY7r\nciXawPl8xLlADIavT1/48uUJs5u5+3DPaCPbdmEUj59GUhyJO8PL5xN/+es/8sffjezjO3wfc83D\nSM463jLe0Yxe3Fz3rup60XSVt74f5U2Upfi7ZlScNgzKvdWzRiHiNRv0btFoDWzwmPZWMvuZ9M9u\n76LfK2mN59dfOF7+DLYXpZsYwzgw8dvozOpYrtIQ0yjOgSkYCpYKzUOzxHHHfP+Bf/jzr5wWw4f/\n/l8ICOdPv5FOV+zdB2VI14wx/o00JEDNujIwgAn6h9LAx0hNGe8tu/cPqtQWVJ1eEsPgcSZweX6l\nOY8dd8T9O+roGUfB5jOzqaxPX3j66TfWpzPDFJHrTNkcbXHYcIff7QnTHv9whwyOatQSYYxDgopi\nfIxqj/NCDI37necPDxtT+sp9qNzNE5JWtm1lycJh1HDukBaOtnG9nigN3t3v2axi+nZD4PJyZp5n\nPn35hA+O9LxRTmfM9cT9+x8xDwdqS0i+QnNIMljGrsNoGLwiFqvqQ2RL1POZ3buBfD4hbNTUsZDz\nRKkonhTdO2sRFPVJ0lQQZlXUpcQz0L2VTo6a6PNGbdC6dUluEliHEmi1gCKaYiVvjYB+Y+Vtjsl/\nrsO01lLRkY5t/UxGpe4iop2OCCnlfjM13wQc9EPG9hdgfH+hN59c0Fs3jRAGWlPIuvHSD6hbNytY\n6/E2IFIpeaP1TrfXDu1agrbsKRcMquQ03uN8xNKQUhRBtemODqMjOTvP5LRBTXpI5EJeVmrJ2uIH\nS7CN3RDJAldnKSbSKpRlYRwifn9HONxjQWk4w4A4h7GW7fVEiBNunkg56a25AOtGWRZ96Pd7PGCD\nJqg4Z9hWzWccnGWrpe8jrR4Y8g2m3t4uLqV7jqCUoj7EScUt3jpaUWtOcyq/QITaBJ+Fmq4KBwgd\nSzhERBrbddVYMOeI4wD9MkTPCV3TQiuFOI3a6YSIswYjTcVhTTNN9QoplGWhrGdkGPFdzOOMxcyD\njv46Yi2vV8K80+9ByRo9ltTL5/VGhGl6YSo1I0ZDAazV5HmswRrdidioggLrHfV8pSZVN1c/UcxA\nbsr2nOaBMUby0hiHgVgq19cj59dXhmHHuhWiC4z3j+qxLJZpmNi2jd0wMk2BX399ZreP7A87nNOU\nn5wz1lmuy5V1TYjGuhNDYBga837CGo+zsJEpuXFZVuLdPaeTRqC9f6fRacfzC49zhGXh7jAzvvvI\n8+XEbhwIh3teXp744f07fv7LM2I3/CDsdxPgeTHCcjoTxxmMJXURmV5qVWtQSgbjVegjSqxx1qgw\nzBt8cASrFg+LaBC4sRRplG3RVUJUdnGLGiD+/Q8jOm3qJVTl/kbj5pbrAqbSUMtQaVogLHo5Mo7/\nj7T3WpIkS7Lt1uFG3D0iMrOquvsSQACB4P7/j+AVwIzIkCbVVUkiwomRQ/Ggxz2rL+TODBr5UDQz\nwsPdzPSo6t5r97VOpSIHRxmo9N5VKRHXVs0UDifC4j0AACAASURBVBznZ0p13NrA8OMP2MOReD0T\nzytkRbNHsAMaESZJdm63+WpF8MPj65YuXEpbpLaIxmC9w1uHapX3L6+YumNrI19usO2E3/1AVo6E\nZYwRff4C2zuXyzfOv3xmed1QxRPPJypH7HhEfxhRJoi+IUD1G9U48X97UaZHdmpTeGPBVY5PmU+H\nKz+NbxzrymB3ZqU4pMb1/M75+sqWE8aOqBJ4+/XM7h1LrUTlKCqQMhA3armxL+/8GjK3dGZunq02\n9pzh9BH38SfyvSG4jzhLo1QwIYj40DkZm3bxYjOW8PEjw8sz6defGTy0XDHBkXKhoAgtodreuz7d\nVdM8BFwylu+Trv5XpaTLbK2QakVnaOXuu1Ty/Megu5WNLI1E7ZMzerCAXESi3v73dpn/foB0P2VR\nJT1DGUnzVrX2QGBFTKKMVFrL7yulK0Llvynd0xHukV9U0fCYhlGWkhMpis9N8FGyl5OCWfsNrcid\nTlKqQisvp0rdfXqm0fKOokrmowvkXMi5oO83pnG4UbNtKxT5IIyWEVWlj55zpaaGVobwdCBpI3QN\nlUhplaBmM6IOLz1vEmK9PwwK3hqMG0C1LnRREvjcGrUWvOmJLqxQKt4JxJoiJ7GWM8VL2n3LiVIL\nsadP1FJFVef8I6fwnkfYWkPliiqZpsC0iiqC09Pegy6MCvn6NNlHdmuQ7crNXAoYTe6gCmX09xG1\n0VK0snRbzsrv10YzDHNXQwoSL647RktH6IbwGOkaY5iOR6L97j1stWG9SOGX61X+XBFjuBqc5A9a\ni8XQlBa/bBWjsrfSFYQgiSFCkOKhHrY9Ii7TBKiAJusoXjM3UuzM663i7MLvn0dGr1FlY729s95u\nxHjBpMQ4TihlxHOpCt5rNIpgLTFFRm8Z77B1I7aTezQdSuAeMYp9JwyefRMSybYXjJdr2jg5WDrn\naS0z6onr7SLXV4rEtAHw8nzCGYVPibE0dC58chOqWOpaKWtCWcGxvd++8PLhdwzjQNwVxFWA6Krr\nC3IRVaKW+1QsqRaljAjQrOvB6QIIyTFRm0wacow4LQcUY4UgFXMibgtJCeasVTH7P1ha6j6M7f9+\nt5wohdKFEhM6OwgWwkivjegid2f7jV/ut7/uFGjdlIyPiya4GcvA+X2njc9MP/6BWCuf//orl88X\najugxo9UM0JqEjqAplnT7SQyQy4x9kmBINOMH2le3p/WDOm2km4X0r7iDoHzcmWNheGH3+GnCduh\n9uq2sPzlF+p+Zbu8EreCmz8Q/Au5jRg7UCmgEzHfqCZg52f02CMUa5P3QytqzYTBMswR4xPT0Mjb\nO3/9+hU1WVJZeL2+8oObuZ6vvLedzTZyLLSUuMVGy3BtDTNP7EXhBk8wivf3b/hWMNuGK3KwjGYg\neoWeB25rJG5rVwTTYSOFuCW0m+VwlQU5VnMhrRvGa+anJ3KKMt3X8rwanOX2yxf84SN28OKR7Y1Q\nH9TLc7M7Bh7IRlqfgtwL5j1ko1JUpiCuCt0539QiwRxZpm53Pc39Srp/zfb/km//HQUzl4bSRsyw\nte8ja6X0bxRLEXoCqpu37feCCY/xWmlVxD1NUSmknGTn0Md61o60JrByWuda3rcd7Xvlt33xS20o\nFVBK4lm8M9RWSGnr0UsK1TJKW2oT6XmV5STayii0tEpMmRKTiFxqRVknniAgKyhKYUKg5ZXL7Y1m\nA+H4jLKeuCdwvkPRS4+iSYBc3AU5VNScyfW7P6gpWfzfoRD3h7x4vaDEQtoXaskMhxlaD9juKSsp\npe9jAyt7o5IzrBuKhg9BAlGXFa2gpAWcpt6jmXzAaLBaoXPCBRkfGiOnt+ADpco+QOnv6RE5p4fQ\nqClJRpHdjizPrTGU5UatlSGMMrbtUv87VPzu21QdS9hqI2f5zK3p+6AQcM6x3G6UnPDBY7UAz70x\n3a9oqcAWN46DEUB9FfuQ4AEFh5W5MySb7NwraB8w4wxhIisxuJ9NwsQzbV34+uWfmXzk+XCgttTx\ndRZXIaeFVhRjED+sd0aSVspGKmI7uCvGda2UluSgU+4dsCP4kRQL67IBpe/3BaOXi3hlS1V4A+M0\nsC6JbV/6tCVDTrCuvF1uoC2nT59IW2WtCVLj7fJKLpG3y2fOt2+cwh8IzjG0yuB2SdFpDW8suQoe\nsQcOUUuhadUPw2KdkamrjPdNk05LduGyclEK2SXT2OPOvsHmDDk3glXoKt1A0ZWiwN59bt172VoB\ndV81GIFeDEGyWmtD50rKmfgbZfUDyK5A/+aB+dgDGk0zhm2r2OOz+I2vZ27fXsmMuD/8L9TTT8Ii\nLYm6bTKJCFP3mYqGQaYpuj9gDQqPngPUQlXCSL4sF9whkDRUpZk/fJD3cjlDk9FnuZxhqSgzUwdH\nOFjCeMIykq8bajaUdaMURdWW5kb0cMCNI9TCel1Q1uCCobVKmAzWQy2ZuBZKbAxFQtUv53fyliFk\ntl1zUR41jbjpyK8/n9n9ieaOJK0IxxMmZ15eZrbbmcGN7HWDrNhumeePL1RlMFSss6x7wo8TUIi3\nGzVFdFMM44Tyhqo1uVYBaVSJh5yGwBgseVtY6kbShhIcsQBOQrXNcCBX0z27TTrF+3m9Fehrl+/F\nTfQJVEFs6oqQl5rCFPMolK1b6loptCQBDFobGfHyN1Kfvl3//9lhokxP5Egd/ySG0tZZpPU+GlSW\nnDIS68QDmScjFOka7hd7LQ3QD1pIqwrVxT2ifPt+ghAF6PeZ8r2jqtRHqHOthtKLorNGMgV3Ucgq\nbVDa9RR6YaC6QRBmJXdCTE/vkBGR/MUYkcmb1uQ9cDN10GitqGEEa7FBXlrt6CXrAs4atIK4XyFF\nUWZmeU8aXUSV5ZTj7qKcO/zBOqwOpL3KtCDdGH0QBWGTgqm7JeXxsfZuymhFdabvf4EKfhge6SS1\nTwCMEZZl6/sk6y2aLKPDTUQfGst2u/ULS0QctQu67h7QHCPG3vel8rns68r6+srw4UPfS8kNHcIg\nwc619B2lIaV7iLGM751zeDehVHuY/LUWMLuxVqYE/XRYS2H0nm3bqDGRTep2jUiKRkD+rXF6emJb\nVioNqw2m9e5zGjAHi3IFaweu7wv7n955TVdefONpVpxmT91vrJcLad/RVhTBxggppFYhE42DJaVK\naYpWFe4wA7Unx3RRWf0uxJK0FhiGgdYUa0zEmMipse/CA85JRu/jMcgYMIlqfNtXIQkpQ7xeqQX8\nMFFvG9VarLN8OL6ggsHHjcuSOF9feZ7+wDQOjMA0DMSO55M8WLHrNN21CPUOz5CRGkrJ+9dgahqT\nFVpVrLekVlHeg9WUO4/YyLW1A1+XBT3PTPou8vvvHkcdydcQdT1GiYHfaKz3+GHAKU3oOtsl7lw7\ndepv8WWFlrNMaYqipoafRsqmsGZgfvnI6/XM9u0LXik4fqSdfk8liIVKKXBW1jfaSOdtdBchGvEl\np9TvF8U0OPZlJ6bEOFjmH59Y44oyjXny6LyzXd4lDitn8i4EIF0kZzIcrXTUSrMtCxhDUZ7qjTCo\nW8MfZobJQttYrys1VfzTjPaKXDPKgw2GbanEkjiNR07DRxpJ1mTbzA3N4XSUxmGwaBtYt5VoZggv\njB+esEZh806thffzwscPHyixUZI8X8dxoKKhe+tLbYzTCQzEdSVtK0E7/ACxRnLNNERZq7XBzwPj\nqBhs5u3yGcVOVjPFjZSmqIOl+ImmHFVJNKAAV7KsnkzXZ/TVHKpxB7vUXigN4oGV/b88+1qroqt5\ndKryDNU6yJhf/2Za0eDR0co//g9//bsFU2S8fWRTG7lGvBvJUXIljTU9tkv9zckvpYS1d2iBfK27\nReBeSJ2z/YFeKA10D1f+Xrk0Wlsh/9ClwU14rfsmGLGG5PulnjXorMX6IHFgtSslqVC1iG2KYONQ\nGjc4Sk2yS7NGpOi/OcHeExjuHh7VlXoU+QCrpit1Rcyk71D2HAUyj5zYrRMVL90om7YN2w8UcV0x\nwyAYN6Wk+FiNcSPNKfK2s719QRuwH36iKskcdc6RYpIYLXoDHiyqiM8yrytaGUosuClIh1kKpVZU\n7aNqrTCqkrabXAzW4XRlXa/UbcUfn6RQaoU1VmLL+gMg7hGlRd3qO7SgaoU+HNHWUksWH6UWT2WM\nO60UaA60iINa9/6pBlnJKNlZK3xYBL8WudtuKiV1EL9W5P07YYV+TdzFPXcgwrKuQtepMg4tVUhJ\nph/U4nLDzkdOL888v8xMy1d+DI3np5XLtz9ze3tD5cy+bKghMnonCQu1cXqasMbwfj7LeGkYGJWQ\nkJY10ZSRkb0Sz6n3Murc9ygjXVORFZRFUShZItzmKTzEbErDct25p9elmCVCzCloCa0Me2qk98zx\n5QOzcTQ3YZ3nF/NGDoW1rVy3z/wQfuRJKdak2C87SnvBJNaKsYGK7PCq6lMYIwcC7aysPfYNlSKj\nkWg26x0xbjgr96tg/jSuWa7XK/tpJqUdXz3OD9j72uJvHi59905B64YNhmYtdhpw00CYRkbrmbTF\naUPYN1prLMvyKJqiOhZbR6mFkipTOPF8euH114waJMapxg1SRCmHCs805WkxitrXO7CGkhKmdUFZ\nzlCyjP1rEpiCbrgRJlNpKlKU7NcPTzN6gXkcUa/feP/znyl7omw7ufQYKhdoyAFQA3G9opylKi2C\nFwWtFkwQWMh0CKi0cbtc2JvCPj9jjp7WkqzzbGMYDVSBhRRr2JAkj6wGNg5YZTF24jCNxG3ll3/9\nhVYDegy4KeAt5H1lvS18W29M8xPFjSQTcdYyGce6LoTxQI2R5es3zHwUgIrSDKcn2DYmLYk6zVms\nP1JQNArWNgnByIrLObJuG2EawAaUdeTayK1g7CiCvSbP37hL1B1G/JlyDUKritREGKd6ZbsfnFpt\nlFSoMVNLkwYMjUTRijiokdA6cGd4S6+qerHk0XH+jyU//xHRj1KPeTVay/6sNdw4oWUY/VjOaiUm\n5jtNoVaJT0HRzdf9Jrz773IhZRFooBIFjao9jaIzUlOM5JIee6laSu9WKmVdO9/V452jGOlWch+L\ntqY7L1ZupEdwsRFridKSbdjXdIjfTzIlZf9VeiVqfUcsiKzWNVVdtNx/vkZKiT1KTp0ChnEk9jmn\n0qJErK3hhoFhmtiXRVi3xhC3DWrFBY+1oe8DM63Iw7cZ2d9V3QSGsG4CZlcymvTe9ZGuZmuZ5gwN\nhVKOosXnprUAjFVXLBvv0f2CLtvGli7yeq3FW4u1mmLk87x7IsWX6QR83/fkaNlf1ZwxQ6DAI6z4\nPrb1IUAp4k3T4NwkI95+gIr7Tk27FPV+iAAlhRBFXDfivksn6kV8tPcutPXrNITQge5WYBT7znx6\nkjDsKidQax20Rlx3OZi0Ri07JhS0j1STeL288u3rV8rlyil4lBEKklwHleNxYpomLpdLP2hYmjJs\nUdB11sgD3jYZNcciyL7rVTrfaRKAgXMC5q5VYuvUoPHeSmQcMjGxrnA8CcdZKbH0tJrJTQhE636l\n7jfZA1OwyL01+wHl4e39xl9++WfGHw3D+InJaa4U9ryzbjtFScRXbgVtZb/e9B1cb/rUp6C8IxwO\nmBzJJfYQ9sLhOLKuG34aKDmjtWJZIRnFNB/I2pA16KZkx/ibx5Gi0VSh1YT1imH0mNWhg6NpJRsg\n3RGcqRBTfByaQQ7QjwDsu4y1wu9++gkfLEnduMYL568/Ez//Qvx8BvMR++GI9f4xAaG/qtqnPaUU\nVM14I0i2ShXhoq4YlwlOcXgZeX/duF4v/PA8k/fE65/+Svz8hXJb0F5sIeHwQvMzOhxE/VkvFBUp\nJhDmE147bu/vEvygZUplnGd7PbO+vtLGEffDJ/zziJ0MzmhahuPoOQwOny3ZSkSdbgrnDTk6Sh3J\nzfLn9zNl+RNozTVl6tORUgqTa5SU2ItCDQeenj9yPB3Z1gXGwvH5mfXrrwIoMZr1epUEFg37umIP\nE/PTM/ntlcvbGyUZfPAo5BBmB0NLC+nyjqqemleGw5GSI9v1hnv+UdwRtQjQAOFelyQpUtrcM2UL\nzboe/Gx64RNngFaaWrNAUGKm5dyvMY1BACitp2PVLKsPVEbrgVa/Qxolkenebf5b5fI/MpKlUUvq\nNHiF0HkMShtatzm0Ts0xj/2i4LLueYu5lgfEQMOjg5OIqSa7ASXfq6lCabJToakeOC1vDkjn2moV\n6HcfB9eU4GFq7bu4BoUie9Oe5i60FEWrRcaz6h5ZdKfHZCpCMCo9pssYe9+kys92f/3wsDDUHgqs\n0VjjKEp2ZbnIB3UXR9VSpKhYy7osojLr/895saNQI3lP5HXH+AE3HWQPW4vA53vR2pZb39VmDuNE\nqI5b2ogtS6g20v1qJ51hqSI0aUBZMtoaUi04bTDTET8dUa3QipCPSi3Sxdv68FjeT/TGSFSWdODy\nAIvbBrTeSWtR9Rkl3tEcRcGr+ii+PyyNsRgr+5Hl7SthHHHTTAoD2shoVRJaKjWJKKSsO0wFZQzO\niF9wT5HyG2FRSvLwMdaKSldLvqJzTtSY64Iqhfn5mcM4EkyilDeUb1zjhe36SsoVawTndy/6cS9Q\n4fXbhZwkQk1ry75nnDGEp2dCzZB3VN6xKGqWhJCsLFrXxyio9BtZElfkxOaC7H1yql1x7AhBRuwx\n3tm0jrjTg6jF7pFz4Xx+o+TMnC3aeOxUJQZKVfZy5V9+/kd+/FQ5jSe20fLL65WaNToIz9N53/fs\nYie5r0cUUNJO2xZ0LsS4saWV0U2ioLUKpSq1CtUIBcPgGIfAPI6Epmi5fa+TTU71d72jomGdZk8b\ntSWahiz9LjFnyDJmc1XuP2ct8+FAyoVt34Rn3DRgUEje7F///CemOZK0KE5dcIzPz3x7XdB+wDgv\noO4uSrz7yvU9bL0KwN+YRt5XsPJ8s7ri/YhRiba80rZ3ngbPrBWvr29s//JnVLP46QfoO0gzHkhA\nM42mC+tV8I52OpJjIZUklDOlcOOMDzNx24ixoD/+QHg+EeYRNcA4akYHLToOzvDkFMEZEop5nEjb\nxtvnz3JoHGaUCbRpYLIfeTtfqK/vzPMB50eUMqwxoYcDKgw0pUjK0JzDDQNL3Liui/CMsxQl7Sx+\nGMQamCvvX35lvZ5pePyH/4waP5GbdHSiG7O48EyJC9Y7Pnz6wOe//AnTgBzFAlgSmq62zRHKjrce\n5QcKd9tcXyF1BvX3vrAfdHLpEwEZA1vt0MqhqiQCCddFg7mHyd+vP/WwUvV/ezzf//6C2WoX4VRR\n1PU8w1oFNMxvCiRKgMzfdXH0QqW/j2dbe3SLjztI/fbl9p/D3MctCqNcN+a3R5dZW5O95P3P9pPn\n3aeolDxMWtOP0eidGPOAuisxZT/Gzsp1fNldfKQfggzZ/8k+tbb2kJnLt+5tfdcmNaVp2lJoYBW6\n4/Puhw5jRBl8l8WnlBjHERWEHlJLt3uMQjOyo6W2QqFIFmejfw7yfuY9QhQUlRoM3jhSXNCt4XXo\ne2dFRgg+7unYAeE7O2Lr0AqsRuhDWsAC9zGq6UIu6D9rf69bqX2/LTQl2adKSoy07SJ8aa2LvowG\n5zFNsFW5AcpydIVY3vH+CWXlvfHeYb1l7XugwTt0a1zPZ85fvxHGkfl0EjJRFeFNSknoQ93OU7LE\ncOnfXG+133Rl31ne3lE1MwQIB4v2E6+vf8Uaw/zygt8TY2vczm9cLlcOx1HSPkqhFFiWBa014zCh\nhxkVJgwFlTTsBZUi1IhGvKvWymdWSmHfZc1grUARUhJRlbUCNdjWHaMF3rAsq9i2EFW2sYbaGvu+\nYa1nngdizOxxxbYL4/iEGpQIfazinBvL5cLr5z/yw8f/wofZs9y0YAutIgye5hzXde33qiAYm0IO\np9SHoM4bYc2aYJhOR9bthnW6I8wa1hk+fnxiCIEBzVjAlYbp523R+7SeSiKFqNE4X87clovct2gm\nNzAPI74qfJLQ6eoMi/bsFG5pZ8mZSpIpUX/4GSMq0vlwILQn6qKYDieo8K1JB9e0IAfvD0uZ7im5\np5RCWxkBKlMwgwUl6lUfFK1uXN9X1PUrp9MTVMW3P/6R9csrVo9Mp0+0cWbNCRUsOlS8gX1fxYpx\nvuCOT/hpYFmuoDXWSFdWFTI1aoI71IeR+WlCWcWaFloqpNyYtMbVwtE6ToeR97eFdN1ZF/EFn14+\nEKZJOMhm5Dlo4rbxXhtt3XBuEHHWumPC3IVWCmzfCarC7f2NnHea1uQc+/OvB48bS8sF8oqdPCWN\nVKVJRayFNhjovl2rLXvTDGF6uCmsBtJKqRlnFJp7fq18lsZ4qnGPyZBFxtV3SY7uOgcaIubJ5VGj\nxO1WUEj2rQ1e1Ll4Gcfiu17lnqd693f/u5XwP1YwS5FIL6V07+p6KrvqSDbb8WlGPEKtNYz3Inro\ny0vdswnvBe2hdurS7e9hwffC2R57Q2ONFNvWb14lJlbTEDVfL45wh3XTaSNiaLXWitG+J2zTeOw/\nyj1EV4uY5878lMJsHg+B3IOY5b4S+X93zXQhx31Bo6SQ34VOrQMDgJQiKswUBQsalSuGKqSRJnuW\n/XbrSl5LMUZOplph3YjVFdMK3lm2bZMLRMlIuebKErP8Xu86FDpyCJ79l5/J2qIPB1HPUsmppycA\nxQwCaVeQWiWWTEqaw2kWAUQRhahWSpIyuuinNNlzOeMeBVwhu86yJtK2kreV09OBwxS4dYLPngsp\nRxEnGUOwGh3PxPevLNHjTyd5cLSKCRZlRM28Kkg5YgYvRWVf2c6N4XjEaRnLO+fYY+zXj9wEJcuc\nQWtD63mj3nkyK2Vd2Y3Dm4laHXuCrJ5ArUxPT4Q1ctCavEcutwW4p4Yo4p7ZtsLz04zRlvf3C/t1\nR9eEV4XZNmzNUFvf0affqPwgxtSLuEAxhmHEaMu2yfeppXK53BiGIHYVrTmdDo91RK25RxUphmCw\nTpPiTixXRusI2THgcUYzHEbSYFG7Re1npvmZj6eRy/oONeO0xCO1vj5prckkpiu6J2PI3uDdjKqZ\nmhLaKTANVWStr7UcRpzzzFNgaJqQwFfpDk2D+huFY2vyh2SCBLdy5VbOtJaxWXFSgRc745uGkjAK\nNirVKXJN1H3r6x6xvKD64dwojFac387EDMPhdwzK8PZ+peyFNhu+Y7rp9656iP5UN7ejQYe+58or\nSon1JK2Jg+t/9v2db3/5yvvnG82cGJ//QPjwI1teyHlBNclfUUqhVcZZMPOERWOqHAxKk52x94Hx\nOIEy+DFw22+YoTIdNZf1RikrKsvPVkthi4kye56CRc+O81IITyec+4g2Cu8MOkBJiXhdWN8ujM4/\nGorb7UZpEHTFBI1WDWpCk1nP3zCtyFi1NW7rjh9mzHhAHQ/sqVD3glGO2qdf9t68GI1xhqoSpWVq\nXFG6EuMGte/MlUbVLJFeOmCM5Fjmbi1U2uB6OkzMhZoFcHK3+qj+ubUqBbPG/UFma00uuOYr1QE7\nQoxDDvK6yKhX6fL4WnId9Ckif5uZ8/+5YNK6KrYpSY9XGqNlhKCMotJ3lT3A0zkn5IUm+Kx7N5JS\nehRWkd1nKmIpaT3xQt9h7jxcJL2rU6Qc+38X1ZSiCxNUt7k8ujexH5Qmp9jamuTHoXrR148cwPvr\nECVr6xEwUsgFT1q76lTezNpFIz3Z71Gccx/nWiwY1UUl9SGWyil1UZAXNSINPfcRUknQ5+gSym0l\nXgvFXjIRsLVgWsFZTbV3UcDhsVPMS8J4hXIarBEHih+5vJ+J1xXGEb3vDMNAzYW87/2gYvGu/EbO\n7yjWkaokm/frk7jv5G2TfauTzE/1m8/Vmn6goQlCsFtoaskEa8mXG9dfvmAOE20YQMn1FIKFtPP5\n57+wN4sfJrSR8XHKmWZl5+rHiVIaKnjavmG9wWLJ65nrcmM6PcnOuYmgScZtPBIItLHyOdQi3X2R\nMT2lUVMjbnDTYIeJDx//V758+yupNl6OJ9L7O6rzf1uFMAxAY123x036+nrjlnaU15QUKVaL0Mpo\nBMco11qM8TEWrrXhvcUYxxBmnBOxDH2sryxdUFR6wLR0pSklckkMg8W5wL4VUtpJqbHviaYqtILH\n0nYFYWCaXyhNgW6UfCPfGqfxA6d54FoqpauotdLkUvF9eqG1QlvYrgt13xjHkQYcDjNuHigKhlES\n7CkVoxWD0QSlcKUxpjvcWvaRjfqwgDQMCsmWtb6x5o01bXLI1JbBB5y2uCadj22KVCIlF1KKpJQf\n0XDdofcIYq+1cn57Z5hP0tmcb7x/+QbKigdXSzhxrbkPt74XUPoEzFmL8Y19X6jxRjCVUcFzC5Sv\nF97++oV9z6TsCR//Z4oeKH5gJbPHG5BJe6K2nabkgDrNB/KoWL6eCVpsXtpZ/GHi8HRinGdyzcQc\naWkF7TCuEtCcnp5Yv14x8wGUIZbKeS2Y0pis5uPBCZQkJwwKUxuX9zfe397YYhKhlDaE+QDOML2c\nuG4bqAzpxudff+Xl5YmgG8PgRQGbGsPzEbUV2nUjzAEzOrbXV7Yvr7hgoSrqbcd9nNmzBHQ33SuL\ngrjcoAk0pg0DpXXOtnI0/0TM0iG6XjRLSlitoFa81ehm2LdNJl3ePUax8Q4AaeIV7UIGqjboYQAr\n1K+aK6rKmNbioEMRxBNcZPLVpw2626T+rV//PkvWBWH3pSJ7K+6xUkawVCn3By44J+3utm3fO0i+\nV++7rP4eRqycfXSXsmeSi90/Qm47eQTJGbyPtKDfgK3KWLCfEHV/XVopCvKzt95VKi0P+JKlQLRe\nlI0xOCvfT7yHMnpsSNEtpc/LzeOWkvF032eWKirM+461toqq6ntBid8LfSlZQotpjEMgrVdR/Toj\nIpxxkCQLJd1SQpFyJqWNvO9UL/J2baTrvitCjTXYYWCnyHuaDSpr3HhC+1GEQkag6sv1hm6VMM+0\nzv7Mu7Bg6RFhk1PMbSMExbkUrjFhgseGfwxtQAAAIABJREFUICMSa2mohz/zPtqqtcoeTAPOcvz4\ngVwyr3/8F8I8Mx8PFOMoVm5IVystXqkpYadn3DT33NRBdp/9QdOawOOHacaEQF5v4i+NO0o53r5+\n4fjygtUDOUcRpxlJW1cVQt+H5AI5aVoLVHcgt528SREiDag8sN42nH9iiY1v24JedoZp4scfPklX\nVyrjMHKYT3z58oXz+41S4TB6MIXrLgk7l0UmKTVntJVd/v1668/l70zXXDif3zmfV15eDjzA7JOn\nFDkMem8fneZhnKg1su8712tmGDy1Fem8a2JvV1q0pDXTtGPzkctVujo/jNjjBw7TE7//8SN/+noT\nbKSSsWpJsav4K1pXjFUk1dDBsbVMCE4Sf5SBnGhtx1Dx1uG15qgVvlSIFV0s9xwJUS8Y6bqUFk5o\nyyhdeT9/4XJ5x7mBSiW1wrJtODTGBgnxzpU17qQ9UkrCO4lb26IcxIUVKmNkUcIb/tN//j2H5x/4\ndr7gXSAFi7GB1JQUz67nvB9YaY2cI9ZrUtmwpTA6saeVy0q8Zn6+/cr7H/+CHQ9weMEeZ9aY0VMj\npTfWbUN3dbgk/7huUckYM5L3hBk906cnUim4wePHUQQzTpGWG6VFplkaD6saT+PE29c3tmWXw1UQ\nU/5129F7JswOUzO6Npy3QOXr1y/88z/9Ey4E3i8XkgvoEFCj55pWstJsFCiZen7DafCtcfv8yiFM\n7Lcb19uCP7xg1oXb7cz04xHXAuZ2pV4vFP8MOOzLM6XdGb8dm9kaW41oA6oUhtOBkhTaH2jTKIVL\ne8JgqGWVCYzVxNtCLruwvUvDGU2i61q6Z7jcmy83yji12zeVcRgcRjlsM5QUaQl00w/4hBTKKgf7\n34xiZWLYHrSnv7tgNiT7TjuHMY6S+zjyLoR5GIjVQ85fSumqwt+0un2PlLOo0nTfM90XgQoZn2kt\nBfR7pynJ2aaLY+6vKucu5DGm53L2dHitKK08VLoyOu6im36aqLVA71BlfNv6/kNyMO/AgdYELCAv\nUUshqBXVCkZbShNWYOvCEKUB/bcnlEdiC7Ij0U12v0U1SpYOrNXSA7XFN2qULK0pQh6y3oGzWK1J\nMaJqZVtXQDrwaZxpRhF36aC0ct8/n2BxVk7MCggTspdGEW+LYMda6+kLjbTvOGtQ+428nqn7zjTN\n2MGSe86m6kt6ZyxaGZIkXctDojaKavjRY51hvbzDYULNI+e3V1SY8c8CwX4aKq0aknVUf0C7gaqE\nK9l6qkOjQWkEF/BWUWOEWiVqTmuOh4PYVroNwGgRHCljSCXKFEFV9m2naiuOIKXQg6i8hTO8se2V\ni6541ziG37OrwuYih6cBU26cvOLrL7+wXjfKWPj46SM5KS7XxE8/vWC94e38RqsG4yxhkISR2/VG\njB3u0Rq17R0Qr2nN8PXLlZQyznWFeSm8XRe0bpJBqoUclFKVHWcPeBZtSmWczAOCkZLcG4VM6l/f\nGs+393du18zhOJFMYyuBUq68fPqBD2vj/BYpzWAHjzVQy4Z2Dm1lZTA9H9iWK8pYmjZUbfBGUH55\n29CqEYzhMAyCa8u1j7V+y+fsGDKEL4reQVXO16/86Zd/5LacGQ4Txhtianx9e0UfK2HS6OCkH20K\n7xxeN0wwxFqJ60pJGdU08/xC1Yl0jgTnCMEyjobrv76RYsQaL2K/ktFhpqYor6gnWkDF2YZ1BUWi\n7BuGTN02treFtmdqrAzP/4lweGLVmqoa1gNlQ+0LujSsctTSqEWBNijj0cMEwxE3nfCjwZ9mJi/r\nopyziJ10AhU5jhYXAtu2o1Imbom47ZxeTvhJlOANARwkGteYuH75jNWKw+nE+fzOH//4J67rxtEF\nchhRwVGs4n1fwHnh7Noj1gXibRHY+hq5vV5YubJdL1Rv2dcNdbmia6bEnVga5bJKduUwgx6w+kDr\n/Ng7kUyZhq4KWwV+08ZnSml4L8ziTMWQBQNoR8mltQ5jDevtzDAJYk+pRvCOqpWsmkruCn+NsVB3\n0XaoZvHKoZVHpQ6HiU1CzI2lmkLRW0+DamithFTVDJUOoTAItriD+P+ugll7zqAEySY5LTYeaLbf\nKl5ba49T8L1Y/vcin7sHsyoxvN+ZsbWIykkZL92noguFlORxmu9FoFaBDRgvHS1NaDP0NyPtUVCp\n3M2p93GL/E33sTDtu91FdqTtsfzXSgkhyFpaFWGCMTJC1LViW0E3UZ3WnpGZU6YZ/TcK3nsIsvAV\nDepuFI8JUxumNSl+1tC0PEhrrYLuw8iHagzFGGpHBmoFbds767ayrquQ/5F1jjiQRK2qnQgrcpNC\nqV2gVlHC6mGQ97B31UZJLFY1mtv1Rr1eSTGic6NicPNB9gg5SrK80mjvJc/PWgEsFP04xOwlU5wh\n/PQJXQu+NZQPYrHNie31nf3bz6zrgv7df4U+ji0VGcn23bRFkllKUZQKGIufD8SU2HMSRmxOpKgp\n+46fZ2zQmGBBS5pGIuOHEaympYxxSm6WqiCrjrUzPL2c8OZGiVeum+yuci5MWmOHmZHA5XLBXBZi\nLl3NOrDFTKkDp6cnTs8HnFPclgvf4pVtlfG8D8IfLbkSM+QsifGDHyg1E+POOBhiqoyjnNS9M1Ag\nbwWS4BydM7RqWJckkx0LIVicU+wxS+diPMFN7Cu8vy0MYeA4TTANvNvKl+svPP/4e374cECtN7Zc\nQWnsNEnOpbaSHGSFxRnGgDcG3RTWaHywqKpwLRC0IliPw+Ca7PkfUILvFfNv1Ie1Jt6vn/nXv/wD\nb7e/YkewxjOMM2ZobNeVLUXx0ZaKQTO7gYtqLGuW+DAt91XZE1pZPn78Hdv7jW+XLzJGr5W4rby/\nfu20ILE1maDBCSoOKirvGNswFsbR4J0kcNy+vYmn9rZh7IA2A26QDkY1gy6yOhAcH+h8IPiJVjWR\nC+44YKcJPXjsPOHnGeU11iSUTlRVaCpjBoVzstp5eTky9CAFXSXBSSvNEBzeG7SDfZOftxjDlhWX\nr2+8f/7Cy/OJv/z8M7fbjewD/vjEWgAXRBAWAgrHNDxJLnFtxNtOvK6YOUhxa0AphOMTwzTStKVZ\ni24FUxXxsrIvCf/0ghmeBLgQu6Ja9ei+VnDeMA1H9rdvkCQXt2iDaxaFANatETGgNgbjB2repMvc\ndkpJoqEgo1XDD56qOiS/y0l1K8KNztIW1lqxXnQG9/G8DZpmG8UkqlFULc2NswaPoVbdAwUksUa3\nSu3rv7+vYNbad4utF0FRqLbynRxzHw/ei8N3Bez3zvJePO7xP7W27+GsrT6sGXd2rbkXy5IfBU8K\nt5xabQjSFZUitoeu5mzym+Sh3Vvuv8HsIbPqe3f52xDjptp9o9yhCPL6jO20I1E7gdakZSFd30Tl\nFywpeXI1+MMJVeXgILvW73hAkT0rbMdHWS0P+JYz2jspJCgRCjUlwdNKdzWygBeUVigroy6KoyU5\nkZMbyltAd7i3RVkNtoqFodwtMhVlNNaKn/ZukbmLHR4EjBDQ9gUbI0Ur9lpJlwtt3cXw7Z10IUPB\nDQM17uzrgjt+lIb8TpMxgZozFY2bLG6YqcZQI2zXC5evX6hhJDiJlRIhmcC+7zjBljUFyCkR44ZW\nlSE47OEoMU1G9bzGStkjO7LLqUHTrERAGWcY5oHS5HTrnCYMluwNNUb2y5kvX17Z1hvHqTEHjU6V\n23pjVJGX2TE9fUJPctr3xyc+hEFEHa1x+XYBbZmmJ6bhwB4Xvn1ZeX+LeO+ZppFpMsS88+3bRi0w\njQZnrGD+UuN4OGBtA1acMxLqXcEazRAU27YzBg9NqFq75BDjjMdZR0oFrSun2QuTtmpyiRgDz88H\nxuGAOxypY+X1+savX/+JPzz9b3z8eGS/JW4KsJakO7jaiA3LaoPTXsZkrRGMxRtRVpswMmqNbmCb\nwpTWoykbjfSY3sh9INOilDZ++fJH/vzXf+Cy/YKbCtN8FBye9WQq2gvQ3zhRpivAa03QjjEMxLQK\nkavDN45h5tP0ics28q4y6244X3fOy2fW9UbeMzWMjPPIRqGViHWaGldsXTgOA0aD15m6rZx//plt\nWXCcMGbGDSeBnxRNxUj4Q7ZQRRCojXhxq54w1jEcn3DPgTAPmKBQBmLeCcYyzQaU6B60lrg6oxTN\naUbVMNuGMZo9Z263lcPzM06DqkJSK3VnL5FaA8u+k2Jm/PCJdpq4Xc6ocWI0lvPnr9SY8c9P2HmA\nTWGVYzATqRRUqeRlIy877sMzeY8o6+VQNk7oYeCWI2Y+UhfNfivEc6SNRzi+UPVAXRJxL/jZQ4s0\nMsEZnBLhUfIjrBdUXLHjB/TeaHnrKmSFaZUYwQeDDwMpbOQtUUokoUEX9KDJrlFiopVGiw2dJZVE\n7w0h2srzq7XSYQgW5Q3Ow153mgUdPMZKEyKiq0JlJzhF3Rb2y4WUMu3fmMn+h/Iwzd3ojwJrUKX1\nbojvwpM+ar0TV77bO75/83shLbUXXSlTAj3ouDalhGXa6LaGviQuVURFYDoFpttUelGvreF1z3Ds\n31+sLjy6yT5TfYxmH0tJxfddb1fb3YtoTtIxm951ivHdULYFfzpBy6IG66IlXcFamZlXICF0/FyE\ntwkycm4p4QZHNY4aRGVai+yEt7SglKVUzfF4JKmd2r9O3CJuGB6fS+o7ZWM01sleUVn6HkGoIAoZ\nDUnCi8RKARgFKad+uKADKUYZa5tA0ZZwfMJq2HMUoYxCQpqVRtUquZJJDjtjONCMZb3d8CE86FDj\ndKDmSI47zgJpIbETXl5weQfraCVJRilKRllWyEmqKYjfr5fcoMREKllUxkbjjkcMClUbPgyUPVJT\nZpodyjnWPeLHicFqli1SixwCtJE9luS5SkD2+bKy3RL7MWBbxRNQwfNeG2uq5G1DHV5wp2dmpVje\n3zi/vrJsO/PkGYcDwc+UDEM48fHFME0zh0Ogqcj118/QLPM8YrAoGs4NnE4nhtFzvrwzj7sozotl\n3yMhBLyfueyZ3VpSFGO30Z59j0yjo7XA7XalAsPUxXR3H5tqfVUCHodSG6Vd+b//8f/A/k+WP3z4\nb/xumPly20gUYo9Oo1V0gcF3Dm6FGjM2S/ycU2I1sJ28YmqDUimpiHDDbJI00a950MS8s6w3rFXM\ns+cWI9pkvD9yOE6U6njfroRpYJpnrDJYZWl38EhphKo4aPHIbtuKTjvGDuznlY9PP5H/4Pk//69/\n4uvXV5ZdujQzzrj5RM6J1FacD2hdaC1yOlgGHVnO79yWG9vtRi4N7IDxT2gzk5WRnESnJUC+NekO\ndaeXNct+3ak6MT+NjC8ed6g4n0FVrNGkuGD0hLOSD2u8xfXnSstZkmBaE5tETPiSGY2ixU00NK3Q\n0o6qiZIBP6LHkePo8KpQtLz/qjTWtwucd46nI+HpxF4ab+/veGf5+vrKertxOBywxjCGEWSAgRlG\nSAVtHCGMXLaN5hzFz7xfdlQENT+xaYMtknnprKyrchFblPeWuC+87QUXJuqeqO+vHD78RM2JvFWU\nQxJxKjScpAkZEUGZ4Cjrgs6bKF1zoC2FdN0FzZhF5JPIwjn3MklyQxAYSSkYr3Czp+aFeltpLuAm\nS3AjJjd8XGE9k7cby76SlwWwNBzg//6Ced+V2MeYsWGte+wZH1Dt3kkaYx6dJny3nNyLZx/UPBSY\nrcqCXviiSYpza7+Re0tHeE+3qE3UrHfBSYOHP/P+Wu481dZ6WvpjsdtQRn6o+iiivZib+47lXjfl\nn42VHWYuGaudPGi1xr98wJlGLVFEObFAkb2nqG7lQWx1x/tVUfKpJuPnVgtxzZAzRbcOTRAxAgi6\naRiC7HvusgmlsdZ9D0rtHan3nhAMzmv2GNn3CAZ0WiW6SxnQrhuB6cDzhPOW0sVUpXVvk9aUmGnZ\nEJxDV0WMG60VxuOBPDaJJqsVFRN537HOY72TNJEklCPT6UWlNSywvX5DBREybJ9/wQdH1Y02TLgw\nUinoGlFonB2IpU8HcqKuK2aaGMaBMHpKSxJmvK+SQNDkMGFqk25dyXtSLldwHmcsA7C9vpFLBd3Y\nVOW2FIgFUw3D0zNUKNtGWi+8ve0cj4GnTx8pZeHX6yvBaayaUMbwT3/9ymxhNo3pOPGTMeQkkXZa\na6b5wH/5LyMlZ4ERlMiyvmOU5enkeTp9lD11zYTBMAwe6xTTmKglc73eyEXx/pYIA4TQWJdGyRFt\nFONoqQVRyY6VYdAE70ldUatUhCrK2X1vXYCWGHpXZq1C6cSf/vIPeAIfP/5Xfn+yxFrZKiRdSEWu\nPI/GqEawVgpNbfiqMKpPikrr6fVgeuxUSYklvrKmM8uykmLhMJ8YhhE/aLRz1LdMVRU3DqghkIHU\nqS1P05En7TkWw6hEZ5AVeAyD9VSZuQknuVWu64V/ef9X/tv//olPP/xA+Ndf5GFOY9sKuWr0/eeZ\nRrTTlD1zmAM6n/nyl79QtWNfd/z0kWk4kDNUZhqDoD+NQjuD9YbJa6ZjoamF6+uZknbCweEOBnus\nKF1pZce48Hj+zccB7w3OwmAcvk+uay6kGCWg3FrGaaalxGQd521jp+GtI2vFvq38P6S9aY8kSZKm\n94hedrlHRB5V1d3TM7OYBYEFyP//KwiQBMEPy9kl2efUkZlx+GFmevKDqHtm9+yQjVkHsrOyI/yy\nQ0VF5JXnbSkR7KijLggFw1YSdYukn5758P4dn84r4XigHmdyg/XLK4JWV2qu6uMJiLHkkrm8nTGH\nBYKa3jd0XK1dNvLgaSZQamV5eke09m7BVUXn3a1Ycona/ulQjnEKOBdYjcWnRFnPrAkGURP7WDMO\nfe66rlhJlBixNZPTSqqCryOkQt0KPikAX43JheYtw2FSsVvL4CphDgw+kFsF1MTCiODGmcFYDibx\nsBi29YUvP/2O9e2VYidMU9MNNzxQ278dFv8mP0zbg0nrpVMxf6l6vWWO95EMH/pYQc9IzFcrldoz\nt5tRcIypj5oodLnWRrspmPj62rYLe9ptaP4mCurvew+c9/5jN62Wb+cke4TsM1etNaqSrjFm0PLq\nvTxr7s4ciLrJI1pKbVUX51o1+Ofc+yENat360LfpVjxq/Oxs6FCDRgGMD9SiThhmCt2Ga4DWzaQ7\n3SLGHUTh5DfLtFyVyFNL0ht40AWw1UZcX6mtMc8Ltip/1DhDq4mSKrkzf63tKr5adUyo9mGZWmhG\n+5XWKOVJiVIVE7MKe4zDOKE6x/j4cB+ZUDyiwXWj4lobznvSHhHvceOoM1vHg2LwYlSJOaJlZ+sw\nwVNyVjXhOOEw4MDaSkor0oN7MxbCRDFeIdkl47rK+jabW5uBqH55l/hCQQjzgplGsoeaKnmv2GaZ\nh4X9utNioZqBXA1ZBtbqOZ8S29p49/6Bw2CxZeX8+jPL5DCuEETY9sh6vvD5SyUMarSOWAU61IBJ\njmR3DssjrcEYtN8lkmktYo0lbjvblhFxTNPM68ulX/eVbVsZBs1MxykAiT1GrDOIKeS8YV3FeIv1\nt2CiUP95DoTgGadA8BbBMISJYUic3z7z55/+M04Skz8y+JHRj8RmyVgMAdvL/DZWghU8AqnoHK80\nUi3qPtOU67pvKz/++Cd+/PJfWPdX9YA1lvcfPvDhu49A48vbM58vPyHHGfv+I6vznC4bplQGa5lc\nUJUs2rKhtw+siI4DtXKHjJRayURy3KgIb69nxnlhOT5i90pMwjVa5HDEHh5ofiblRMk7Wzyznl/B\nHTF+UvDEcCRmSy0G4yZaNaAe1rhZ8HbHtR25rpT1Qr1e8Q8PHD4+Mj8eEaPX/CknxHi8dzhvGMIM\nDbwRDk6PYdojwVhazkgpuFEwtep4jjVcL2cu+47MswrWasEa1QgEUZKYM8LryyvXtzcchrpXqEK2\nFrNMpNbYXk8s779Ts+2mbQTnPClG9hgJtm+mjcMMon8bAzUzDxMxQQ0LaVp0zegiv1JUaazsWINz\nhpq0j9nEcVmvtGmmbSdMSTTjFZ152Wi+4QdH2S/EvPU+qYYlY0dt4cmAq55qofWxORk8ZgxU2xAn\nOKsis2YaEhoiGUmJoVWkboSnJ8Rk4tsnrmWjGGH9/Jnr+YTIwDJ/xPoZGyZsGFj3/44e5i2I1Zru\nQ+vtVjvtPb5vy66l3YJY57h2FezX5K313pbtAqG/HD0pN+f3LtbROcyvP/+2XHp/XxEFe9+CZc92\nb0i6W5Cl39CtfyTTRymMtZrBUvvnVHhw6aglOqy33T5+05JnjBHjdHbSjZ7Wvhk9Ee5UHvrcam3d\nuaRVZdIaoY0jqYFxI94PlFy1F9s3KKaXrFup92BgEVwYEBNoUtXQt0bSeqFcXnResWgATSkTxBL3\nRMISloNyGvtmIzhLSxFTshJ2xpFkA7FlzRqsUIMDW7VM2hwW2zMWLd3UqqIiby2uz2/exl1aAxs8\n1s7EtOOd4bCMnL78QhgOeqRLL9vljA0dq2h1VEcEihHt9RohODVsbgJidK7ODAFxel7dDDVn9hoV\nHL8nHVsxhul4pFgQaXjVximEIhWkOAYHWYRhPiBSue5Xrj89I1QOD9/hDw/kqvCG5ek9tZ6JZWVb\nT1hbKHXldIksb15xbm7gcHikJB2Hdm5knhs5FVJsDMFhrbBe197TrwQ/4OeJz19eqBQen3RnH7xn\nWRbGccB7S8xXUlkZJ4/zlVxXhXQ4ve9iVPujcXBUb3tJNmFMxXuPK921wUYu6y/87v8+Y5Pj8ekD\nH7/7DTEL1k1MywO2LVQ76yJL67ZJmWoalZXr9oUUV5xp5P3K8+ef+PzLT2zbGaFhRSs6X76cOF3+\nhSJqSJyMEPwRRNi2SImRIJbHw8IUBiVQoddxq+0+93u6nlhJOkN9m8WsjWkZMS5wXl9YHh6JKXO6\nbiomawXcgPcDsRb1dIwbbb9i/YAJD8RrQuxEbYOuY9ZplYik9BqXkBoRSeQWKa1QJoM9vuP9Dz9g\nmmEA4tsb+/Mr/sERbGNwjeDgMKj9VI4ZExvEjC+FqQPZc0rdgKFh+/oxiOByoWyRZgQbAtYFhmFg\nHh0//vETYZ6opzfsvvHh4/fkLZFSRo4Lfhh5/fEXmvNqVI92/EouXK9XnY+3t/ltnf82XbeBaZS0\nEU+FXAP49xoTnJK8jIiCV6B7EgvWwHa9EgZLLYlSM9PHD5Q/nnBpx/YqVL5cmA4zIoW6a4DNVcWF\nJRZq6ehVHC0ExHpwQgsG04Em/o5lzDRRUL5IwtBwkmjbhmmZqa7UeOXy+pktbhg/UvC4xx84Ht7j\nzUQumfnxwDgPuMvbvz9gKvegY9FQI1M17fxaz7z1LZGvgUWJPB0td38tLYncgqSOi5iupPtaIrUd\nv2fMV8FQ7T3HqtHzro69BcJb3/JG2nFdXHTLREHdH/RtGlSoFB3jsI6Sdm7WMeoPA8523J1R9a3y\nXJX/qahbo/3VlLBdaStGZxlTih1GrUNCVTr8F/2spWYtN+aCDaP2MLNm5aaDrW+l6LzvyLrpBRq8\nlqxFsKYzTtdIrjv1esKQMeKo+066VtK2k9YN/Ig/HO58VdBSihQ9Rnm9Qk6YwROckHK5n4dmA1hH\nxegcK9BKhVgoouKmwepCfiuhxxjvxx0x91K58Yo5xPTMYLuSY8OOM1JVVVly1o2MMWoL1x1nvB/v\nvXJrLdYHxRzWRqVArl213EUrYUTEYY3XnqoPrGnlKJYArHGjrrtmLHXHYjBeMH7EGUN826kZrA3k\nPbBdnW4wamG/nqjW4saFLDujRG6mtNfLmZIbhwW264l1jczzgXk5MMvEl8/PXK4706ibtGEY6Zow\nYimcTheen9/48PGBfd/Ia2ZZRt5/eOyth0iulXdPytzNOWOtYZ4mYkqcris5ZUoRWtNzmZNCrlu+\nsiff7znBW0HSzimeKA1IK/M1IdHi/YH9+omwfI9/9086e9wKmExuF/btzKeXP/HnH/8vtu1CcEKJ\nGyVt0Aqm86B1vq1REVLcEGsQE/BuUILUtpNaUQXuqKKe2XkVEYl05YGuGSlGNVfwsF6vCJrp1JIY\n56D3hQuYEDidNoxx1KilvmE+0IxDpDEuM5UIMkOGGsH2nDYX9e2VoIrKcfDUvJHjip8Nh8dHqJEY\nV/yoZgCP08z6/EY8Xzm9vACNg3/i6C3L4BXYcNmYBq/nZttpuTCPE641Rqd90fV8oXrH5A0Dlsdp\n5ny6sqfMhw8fibVyjQlvDIMVyusr59dXZFv5zft3HKaRf/5/fk8dPcf37ympkdfC/O4jdfDU6669\n6JzVFUSE8XBQBa1xpLiBgBsccdfNRpURvxxpMt7NM1qt93W+VvWcVBeiTGsFjGO9nqlUrLcUO3D9\n/IadDS1mvHW0Imw5U1vvCaPMk9oaxjt8MEyHg677o6d5Q5OCC0LpwXccPK05bIbBOr2OvOW8XXl7\nfcahCnqL4fD4gZor/vhEq+Ca42g85eWFbCPLx3c8vhtZzunfHzDpYPTbfCSiZJ1bb/Lbcmzr5cub\ngETuYfK+dCq4t9GpQbeRj3bP2qyz9zLq7e/cszL1cezZZVeemp7h5tscY3/FlJKeXJF71trf+l4u\nNqJGuWnbaE2Vqj0OYztFSKk4Sqap/WK5qYbVpV57lDVpMNW50W48C/1ziwYJVPBkjCjRBsU7hWG8\nA9lr1VJyq/W+82vekteVYL2yJ0Xr/jVrdqaKXAjjgWH25JpYX6/UhCpPw0BzOgTfOmhCRHeZ3mj2\nPx4OUHUkwdxK0XSwPDqUrI4CO0IiDIMCAUolp4Q1I8Gpzdp1vdwHha27CYo6oN52G7haWa9nxmkC\nb1mOj2pW7iy121jte0LSjreGYVZAdClFn2MNpWjmUXJBqm6+yDrY7GcN/GIcuW66UWgdIF8V2L5e\nV6RUjsuCRVhPF7KdVFaWC2XbMc7TqnB9vVISzHPA1EK5FuwyIKkgciSvb+Q0ELxD6oAtiXqNnN6u\n2MePMDSyFNbziWFwfPzwqNefTJhenSXiAAAgAElEQVTudelFKUDBT/xP/+N/4nJ55k9vF1JqvLxc\nSLny/t07ZdlGw+PxA3vcaPXKw3FBpLHl1HvulVKFUoVx9JzPK809cLXax0pJe+NFGqUmqis0D6dy\nYton/u7D3yHZ8Lvf/R47vPHb5T1htOzriXV/5e38C8/PP3O6PJOqBsiaKpAxrtBKIxd1t7gNg1cB\n8br5FGmMg9o8FVERh4rxVEBkCignyWgZVhr7trFvGx/ev+clXigv5V71QgTrIbfKuMw4O3F5W/n7\n//AP/O//y/+BOz4SpoFYMrZVrDR2Y0nVQ6kMTUdoblwvG5xa4klmO6/UdGU6DhyniXw6E0ks3z/i\nqpBOF/7w4/+p/Xtv8FPg6fv3zGPgwcBBBIOQcqVuFyZnKTQyEETxddTKPI9cLhcu153mDA9GsJMn\nSwNj+Pj4QFoTf37+M59+/DO/1Mb5+RmMYZxmTpeNXz69EEvj6Ve/wY0z69sFF0bMNKnBde3K2JKJ\nWedRm5uoVXCtIf0eySlzeT3RGAiPPyBmoNWv/Orbyn6bu4emyuiaEGdJcSetK3ZcWHeo+dBjQsLa\nQLOwrYXmAsZ5jTOmYr2BQVs7yzSyzCPbttIGGA4DKUWcNKoRSt3xgzB6D+dG3SODt5RWyTkyLBOz\nn6AKRSbCYWFuDRkGxiAMOeLfTlyvPyEm0+wH8gZyff33B8wb6Nw5T6u6k63yFQpwI9qUUtR4Rep9\np8235doezPTGaD0p/OqqcSvvfhNaEVHfxNts563v+G0Yvtv9yDfl0P7/0xq5VkqtOKfp5c3MV8ue\nGsBaUYUapXRiRCcSRbWsEuv7cL/rJUfNfmsTpCiHtjmBnL/OpNaC9R5rQ+fJNnLeyEll/sbYzsLU\ngF5yoqRIGCeM1VmkO2zYWlzPDktKxJc3pvfvdffeYBhmrBMaWSETDaSpz55xDuMHmvEqe0eDeS1Z\ny71SMa0wjEGV0MZ2r0ujC1zfVLTauttIU3ut1og56jFKiZo34ibknJTW4wdMtwmLMWnGgcIa0r6S\nt003EEXLQKWoJ2owlnEKVJPIuTJ4HWWotSFdfZxLhqpG2kYE46xmB1UXaRcCvo+L1G2jbhsyDH1g\nuWfJFXyYdDzHOLZ9pzqPn2ZaE2LaIXic813ApY4sNe1YE9WWKUa2uDM5y1RGzGiwwdMsYDLXuGFE\nrXNy3nHiEFHwRdpPpJgJ4ZF5OTBNI3u8siwzx4eP/Plf/sTb2yvjOPL4+MjhsHA5nzmfzkzTiLee\nuEe2bSUMnlIaX748U8lksZptxoIZPUMY2badFHdcjCzHR2IZ2KMn2Z06BhBHRvv5v6wn0vPPLOHI\n2STK/on9j/8zj08L6/bG5y8/sm4nctnVlSJ47au1Rs1afREnlNT7/IKOKnZhEJbuctI336X3Iw1Y\nr0byrgOjDNyBHSklxmHQgfZd1fR7UxBBE8t6uVBi4cPhkbRnPOrYEsKA+/CRRqHlREuF7bpS9kRL\nSZmuoHPhAuItJliaKTRTGR5Gno5P+FxYn18oJfLDP3xHlMLlfKXkhDtOPL3/FVhFyz0sI7MYlmvC\n34RwDZpRc4Qill20x2x7JW7fIodl4Xy58HI+cymFrVZ+98tP/ON//B/YY2R2gq+Z05dn/HJAxhk3\njJhxYhXD6g3Lbx8Vw3naaddIGEb2FAnjQCbz9vkPZAz2+BEzjCoeizuHaWBeRqRk9o7GW777Da33\n9O/jBLf2Wc8sb4JPkUoVSytQEoT5B4wLFJMpT8rD1oxFWwRmtJigVQFjGiFoiVWojIOqb2NcSfVK\nqIHBBqZB2zQisF4iD4MnGEvxhW1daaWQS2FwhjA/EmzAFGHfC29vZ/xomULBxJ2hZUgv5PRF+7ex\n8Xo9c/705d8fMBt0pV+9l0c1c3B3NarikHoQvD3xGyGOyNcZxluGdwt2vUh771tqwFH6vNJNcs8+\n218ESeBeCqh9lOQm0JFeqr3NFlqrKrtW1U7MdEFOK0r8ceMINZFOJ7U2Whb9LsZge6mCpkHu5s7R\neiNUMGp4fFPm5hsrs/dx5eaDWTSYdXNebsFf+rErSqRwHRdovdPA426Uoh48jaHOE80YakzkfQMz\nY80ADfZVVZbGDSqgEgOi/dyK9oQUVt05/bUiNC6vr9S4YY4PNK9weGj4YVBxVLdIq7VqwOr1E+dc\nP0YKcLA3M9ySutgo0vKuZWVnwAjr2yumCdOwaA/KqAcjtZGLzlGVqo7vrvZNW1Wvw2aEtGewBmM9\nzij0vZaCFQ2gfhwoLbJfLrTrhmtaWs9RWaspNrwxODdQS+ay6YC8sZYhBOIeGQ4LPgTI2h+0Q6Cl\nSoqZapsaDjfD4CdijOQCx/GROhiSFKRGiigNBzHk1qjSmKaBy+szX37+jBHDw5NgrOC9xRgYp8C2\nXdm39X6vHQ4L86zv03oVI+VEKcq2NUZVz8fjjPWW1Ax7zMR4plbtWU7jRE6ZfFKCy8MU2P2oYADb\ng1iLHOcjtlm2DGES7NPIdrnw8/M/87I6nBMu+yupRqwTrA9Y59R2Lgul9+Gc7XDxqN/fOJ3VxKqy\nMlfBpKRlXm8wwfSedfcnbK2vDXq/p5gQMSyLjnhM88KhvePyWinNIMURTODd+ARZOL99YR4G0rph\nR215xLc3pBls09nSagqpNqRmFG1RsUZtCkOw+MnTKBwfAoODt/OJ7AqPH98Tt423L89M756oY9D1\npBY8jR/midk6fG6YJki9Mbh7iwOtVDnberAwOPHEzgoW4O1yJafEDoT3H4jG8qdfPlFOZ55//sRu\nPGZcCONMmGZia4hxzO8dTQyxVGTPhDDRBkuLG5MYorWU9Q07PTAMAcSQmpaxrQtIy1xfP5OKEI7v\naOKJSc3F7a3N9W2AkK/Vs1YFwVMztP6aIoJ0dndttQNoBNMaqouzuC5Us65AyzhrCcHiTPf5rcJ2\nPmNd5vA0cZxHvPEcMGouLgYzDqw58nY9E/POFALD5BFxeAm0ujKkEw/eki8JMVVHhEZDDKroevnl\nF+w4YqfDvz9g3nYPt+B36wnqHKC9/06rf6lA/fbxVSQEt1GQb4VC/Qf6+t+UzRoqdjGakunLf6O4\npekC22rtoINvxUFKrmlW0X70UuutZiu9x6rznL18NIxIX/AFdCFvGiScH75i9G4BuWeT5a60NT24\ng3VB1Z+tQNPfMVa6EOm2YTDqTgK4EMg5qQekUWzgbf7UoL2CmwrYL4sGiaQ9UOd9VxcLxg2I8apu\nberpaEQzxEql9l2+ghh0w6KWZwU7DFQxSG2Uvlil1OHwtSgN1BjSvqswIRdsCBCCik0KvceW76xd\nnS2NSMe8LQeVzFss7bqpZ+gyEIW+mBZlwbaGdY142rgZd+eUlRBVK84Nd5QhuZJLJdVCvlwRazCz\nHj8ZRqRWYkpk0Sw1p6I2alVJTeIdZpzwIagdUK34YaAaQ+uw75yzepZWS0obqWc4dhpoZYe8kzMs\n3jN6CKGSJKjDxfXKjlBozF5dYQo6c3s6v5FLYZ4HjAPvLafTK84LoWmPD2DbdkopCjno17i1lhCC\nqspFh+dLLcQ9EmPmZtJ+Op2Y55mGYYuRejoTmHk0gXDw7AaSqbjBME4TNVcu55Vz3jinK2YQDJUt\nndVgwBms8Roow4AYBeob8RCrqr+LQjCWh0Xnf7sYD2tItVBLQ6QHSWN7GqpUpWtKnJ2679jOcC4Y\nwjApfi1nSsw6FuJnmploZseLxzlLzI3rGhmXmVMupFqRPSLN49HNr5XG6XqhpUiLmcZOODxwfHdk\nPk742dEk8Xa6wpZ4izt73DkeFkJwvL6cGOaZ0avd2OgdD6Pj/TTwKA4bgVSpmDubVL5ZA3V56UJC\nUZhJC4Ft33l9feVyXZEwMj484seRVDLbtrJvV/LskbBwsSPTuJCd7xU7o16SfROiaXp3BklRzQIq\niFgGH3AIqVSMVS1AjFXZ1TXgD0f89MC2F2LaUY6MajzuBLVexbthSG9jhTJasnIa7wmSXouVZnWu\n3loB02imEWYdidP0Q+5iHieVwRjKarnuCbMPhAJzE0aE5gOugamVYB3jPBHXV87rCWcOkLXtlave\nH8NgyPGN58sLbhwRhOAnWN5jzMi2V4ItupH4Nx7/vwEzd2n6XQ17o/H8dbm1L3L3x7f//VfBUf5V\nUP1LbJYqbHsAtUZZsUb6Is+dM3t7hjHmzhC9BTP1rKx9h9ovVrHdOUBpga1/n9pJOm5Z7hAG5c12\ngr1xmF4yLrncWbS3kpLp4hiRngmLIQxDd1FJ+BAU18StrKwfSjF3ffHrnpNfS9yVsusiqYPO5n5R\n3r5fmCblzPKtBdkNRei7gverpZR6Gyop6AaiEPEdk1tBhFghvr7glgU3zaoENgZjHaZVyraS1it2\nGAnzjFilLG1576Vtfzul7NuGc7bPrnkVX6yR8nahGF0sq1GHg9IzJ2dVEJVLpNWoZb5asWIJxmKt\nZd83JFVIVxX3iGCqkj8QDSTTpFivdXshxYQZBtwQKKKCL71eDOLVliiVjBlGxDvqBntOtJKxItjg\nSdcrZvQYBjJCy5brtrGnjPOBCFxr4bI2xiQEJ5g2QfNc9o02D1gTuF4vSBXMtJDWlct6Yds3Yp4I\nwWKd8Pr6wvsPj3jv73ZeIjfFum6CbBhIceN6zRirsv7g9TOd18zN09X7wHbdmd4t1CKktwtLCNTn\nE8E5wnEmdvsut3jWfddMlsaWNkpLOOcR62kVKiq2Glwg+KBKZSzBB6bBsw4nLFXnc43heHjo94zO\nVYcw6NxhyuRWKQIlNUqBLI1qCxdZ+clVluZIYebJTxjvdWRBBGmJUBr/8el7xOjs8el65fPbiefT\nF0b/DhcGjscjn//lE3FdmR4d3k3sn5+xxrBtO3G/QMpIbUyHwPsfZp7eLxgLpe3saSPUFVsqmazG\n2I8zSYT53RO+NrbTG08fFt49LRyCZ2yClKbkrdoVqbeV6q8C5q1ypEUI9cWc55kvImpDtxwx01HX\n26p6h2yFEjzVeexwVDJPa8rwrQ1XCtsakTDqwl/r11nrtJNzIhyedKyvVgbnqW7ksl/IKUMpmPBA\ntQe2pHZcziljWKTppqd1YE1fY1V0Zu9OVLfkofRS+y1BMt6DFcwoDJMHq+paNwjW9jEvCmINgwNv\nDB6Dt8LDYeJxnHjnAo/GIBklPTWt87nWGMLAdw8HltmzAVdVbnJ53WhVMNOI1MLBGrCOuBeKmfCH\nQBgfcLngbJ+W+DcefwO4oN3/3EHm7i+fdtsl3XbD7Zvn3UQ432aUvRLeyy5/leJz87fswh/zjUau\nNG4s21uWaG7uDzfBj+kOEd/0V9ut1t6DrkIQpPcPdX5SsNTCHffXeik3DCO5qLBEy5eiDinWUHuT\n27geqIsubBj1hUx1/8rQ7YIlFfUKrnmMSZSqfN7cs9SGloSk6RxdrZXUGs6qrVdMCStCiRkJnmYs\nMeoc382bU9A+bLquxFpww8AwTgxe+5P1hvNynlTBWaczVNYowNwH/KCsSefcnQTUclYD6nHCDQOp\nVtK2MR8OSmOx/XiiN1IplbxvDIeJMA6kmPn0hz/RXl55+sd/UPuuWimiJbJpmYn7Tq0wDSNS6c4E\neh15pzOX8ziR442lq6rdW4VB6o7D4PasmWTt1muDZ28KrXbW4Z2ay5qgAOuWIxnYz5deSpe7Obkb\nAm5ZlIRiLX4+6I45JmpM7Gkj+0rwAZwlt8L+emIePN4Frj5h24A3I2HQXl9wHmcD++sb236l1I1h\nVIuy3KlH+5Y5nc4cj0e892zb3q8hjxO4rhvWNpwTFJli2dYd7wcQFUWpgj2zbTveekYv5LcTwQ0c\nDg/UDYp42jRyzYU1FqTq+Sg1ImjvFuM5HA5YY6m5Ms+LnqMGUoUPjw8MxpLNgsuNZhMSK5frSefj\nMIzDxGF+oGaIKbGZSnYaUPaaedsuNGOxzRC3iGmZazOEJoym+8aWjCmFp3Hgu4f3fP7pE22LhCa0\nDG+XN+qoathcGs+fPiPNMh0f2PdCbIlyulKLcoDHhwMPD0c+fveO90dHyjs//vQT1nucczwtg5b1\njCUsC9N0ZNsbw2xocWN+euDpYcY7S8uVWMEVcA2cCP+63sY9MxPobSKdNIhZy5Hv379ni4mUKyXr\nRNeA5fPzK2b2xJIUfvHuPb/8+ImYE+5wYBxHzqcTp7cTEibefXhH2jf2y5mn40IrhWWZWH7793z+\n4x/ZX145zh/YcqE2IYQZZ4Q9CdWMapWIUMpObDpiZ6zBG60uxt5XrqVo+6InELc575vxxC3BMcZg\nvcFOgh3UNUQquJzxu4LS9wA0gzeWKcBkhDwaZjtxHAYWaxmrxppctf1185UFYZkmZE9QElIFmyst\nN057pAXDMj/x7tETxonzdSMVITeh4hhGT4mXbs/23378TeCCbx/ffvl75vJXStgb3/WmYv22bwlf\n4QW3i0e4lWxvsGa5Z7AG6aUAoAcDi8rlFVggdybqPTj2/qUYA6UgvfTWar3b+GAsjUqRejcm1Xqq\ngWZxVoU9JWXytoP36Cb5ZgikdKAqX8EO3HqCxrDHnZIi1tueJSjfUD0HM+vlgg1QUuwOFK3Te/ru\n09qe3fL1u+gJ0JKh89wsw/ruQc9N956Lm3pe+nGk1srldKLtm/Zjh/G+6fHea6A1hnQTPLigPnE0\nnFerMUV76WZE4fe5W40ZctyxTu783duYRIyRHPWaWb98IZ5XjAjHv/s7wjSxF4WHl5iUPtQHhq1X\nkELeIrUo/KC2pmMETVW5dV+ZDgsyDKyriohu/ey9dReNTp2yU8BOI+VyVnReUguu4FEDZGsxNlBi\nxJWmFQ1rcHagJjWBDkPgel0pVMZxoRbtp8oQVIXcLmxxo5TKGAJ+fkdGXVWSXWjjgd1Z0l4o2XDw\nA+NgmZZKNMK+X8mX7nU5WJ6fv7CtjRiTCrtCUGFULXoNWENK5W6Qnmtli+luar5vqYNBIo3K6Xzi\n8XjEiFYj6p6gwIhVOs8wYscBOxku+8qad4VCBCVxLePEPIy4BqH13lEF31Sw8d048/LpC9v5ynq6\n0nJh8J5aL6SaORyOHKyjXq4YHMcwEKSSVIrDaC2H44AJHoyalXtjmcMEFXLrsO1tZwyWwzwqMKBU\nXGmkdUeq0Kj4cYJt588/fuK0Rw4//COIYdsuWmGYPKOfeff0jsM0qTn1duZPb8+4YSDmxDRNVCzG\nOrz3LMExzxPOO55GaKWBGZjdiLWak0i7ZYLdWALztbj2r+Kmrl/OqeAtpT7nboR5WUgpsq9n/DQz\neU96PeGBlirBenw1vH554fT8zHw8EvqIVSmVMATc4O+c48MyI61wen1h8JZWtGqQrol4vVKDVfcR\no6xpqLSaFJCCVsmkJya0r8rY1tT/GO81Yehr382DyvX7rzVNAtRjVHu3gUZZIy321w4OO1pGJ4zO\nMRlhMjBKxhzUF3XwDV91Qye9Tlibaita18Gcrs/8y8s/UwUOw3fM44xMEyEMmMkzTp66RxyNw+h7\n/BC2LWn7JO/dheq//fibAuY9U2t/7TigO3b18mn3HUatFW/tfSd161veHrc+aLvttKRnZvfs8qvL\nwb9qdfazJq2pYKv2i9daWjfqvZU51PWjq0a5t0m0j9cDa5NGterTp+KWpETrO8FCF1Bjeo8oZwVb\no3OZt893z+6M7cGrQB/Faa1+DUw9UzfW6o2WC60KzjtUaA7WdmEAcs92BFEXltbwXmHNtaqDiu2q\n3tZVs2YYGbtY5+6/WCvFO51DsyooUtuo/BfnFAQjfZYRaGKoGGJp7LWgppIV7yx+GdR/MMcuSNED\n3eqKs5b9esU4S02Z65cviBim44HxeFTp+prITYVF1jpVzopgndeAHBNg7yUgQRiGgRwjqSRSTLSc\nVM7fM+EwDDrHmtQMoBnBT5OKelwfg7r1ko3p40F0wZowWLUAy/281z6nGpzBuZ0Yr6QK2BHmQC0G\nYcLkkZxO5BzZcsK6hohShtwwg5k4XTdaBG8XxEKqkckWzOi0l1ITZdUAc33boc5Mw0zes/I9peGN\nQ4zlmhTegHHkCluEglDFwVV7mOqhWbBWcN6S6650p6abhFJ28vWV9FZ5Kplp+TXeDFATg1HV6jgN\nTMvMYxgItVG2iEeYmp5fFwaGoC4+2/mV589fyPuOEQj+wEBFSsFsO6mdWa977/0e8EPAOUtGy2/N\nGkQcYi3TFDDWEJyHXJAmTMOEpMohBEYjCp7whtPrxhoTOReOzjAtCz///Ikff/oZ7weOD0euVQVp\n9jAiJTGNA8fjQNyvXC5nvDVMhweGw8K7X4+aWe8bxgjvHg94S3exyGAsImq352ujpcretCXgROfW\nVXPwjW7i33gI2mcuWUvvuRaqCG/7pqNbNivcf7vS5hk/TUhrxLVwzW8sj4/My0xrjcvlgvea/Vln\nmYJlGQ6YVri8PLOe3yjOMM8z0zyzX95UA7EEWoeMVAzOG0qNXfEeNAbc4gB9TlJEhV4if7V+cM8q\nb65Nek9b7Ojwk7DYio2JL6+vWr0ZHM2DGQzWwuJgFpilMUhjGAplX1U7gSCo9y2GbuKuB1hs5uX8\nZ9b0jIhnrxMfF5gfDzyJIUpGQiFJpZQT0hLeKox9aY2yV5IteDH/jTOlj7+pJHv7+/bnVgattSKd\nGnNzIrn9bulUmrvA5yb2gQ40uAVSVWPqcb313PpCdu8Jcg/af/nZ6HFa6TdUpb3YXmevtWD7XJd+\nBLn3J533GjRFPf9uQiMRoGgJ1w6BhsIQNL3UUQxr1NCa7h5f+KoGvuHrbqIiEdQOSNTvs9K6bY1m\nVsZ4/KDYPM20Ve1m0NJd6TuqUioldXpOFWJXEnvv+hyU3EsTuZQunlJ4vOs3UTOKlfP9/MVt19+l\nwyJM36tZ7bXWou9RRGii9k+1Nl3AjFNhyejJ/bxN3lOz9n7VPcZqf+/8hjjP/O4JA1zjTouZsuvm\nxI1G+48xauZbFNYwhIFSb+xduZuVG6PHtjbNdF0I3VnDqBgNIdfGvq0YZwnj2AUzummxztwzsUrr\n8HX9t1FFiM4nFqUNNeB61cUIMZSasU6PYzYCVZBmGA8L1AEpOyWtShjylmBH4jUzjQPLw0TwjRyv\nXC+vxLzjqxB8wJBY5plWdlyKuDITrCV4R95X0nbh8XBkjSt7SiCNdUu0otllEZ2rnLyKgbZtI+fG\nsmg/a9+V9Wm8skDP11WFTKWwns9MpxUj8P20kKThYqFaw8P4wKFCuXbIBGBIcN1Z5iPUzH4+YVrG\nmYIdhFISpa1YYPEOqYV4fsOLwxT9fUlDL2/3+eteaWkdaFJLJbWk96Z22ZmniSBgcmTLO59OL/z0\n9swpVaodNIs2wuvbGRkOPP7qN8jhkWAcLljqaWP2qurdL1+I+87D8cC7p0cOx5khGKxBKTUeWomM\ncubl559YTy/YEAjzASRQ1sLRTjweHqh2pBXl9DbkTuH8/15cNQMcquW6R0qrpFZ53S7KwfWeluHT\n+cxbLLh5Ilr1faw54qWBNfdN0Ha9EkZ1cXLWYmgM3rJeVq4n9bYVE3h6fORcLyQS1c4E45F6079q\n8HGuz3w71x1/2j1gwlfB0l8867bON53fxumG3zirDkS2Ka1HGm+vL5xenjl+9xFnG4hWfIJXWtJB\nGrNpUDeeX//M68uP1Bpx1rKE33CcfmAcVSNgO5u2logzjnStYCNj2NmvJw6Tx3tPSSdez5+5bC9c\nthPO0M3JHS0K7x+/ZxpHXPzvDJg5fx12h1v1sjd3+yIkIvcgabv45tb81exO7j3L279vr3/r7Wl6\n1AOg1Z8bUNVlf/9vwe69eUoIvkPftSSiRJI+cynaA71L1KuCoq0YcALG6dv2xbYWVVo6qye6Vmib\nDvrabilGK5gexEUc0uHQugdoX0vR/Wpy3mnpWYrS+ktW+k+9+W1qXxXRWdBciy7qdDNWjf+qnsUo\nXLuDFUq7QR6AenOlUNEHaMn1noFWpa3knDXjTFq2M169RqWXpWNU/FutVV0SakWcCmVKLrSc2RpU\nK0x+VreWPXM9nWi1Mcwzxgem4wNNGtu5MR0OhBBYz2ckV+oaablg5kmb9tZiDopJy7ngnQbBdbtS\nkypUXc/SrTX4ZSFvK1uMOO/Ztg0fwn1mc982ci6M06gl3Rt5yQescewpYm/iM5G7Elm6aMj0vrk1\nUJ1T389QMa6xXncsDe9AUqVRqHUFGjVGGjBMR1pZ2F5feD4/E4Jl98LJFN4/LaR94/RywrfE5LXX\nIq1wXNR4e3l44vrpjXXfeAgLh3cHnvwDad9IqbLMC3uMvL1ckSY45xiC47ptuNFp7zt1tCM3yAO4\nnkmkmCglk0sipkTInv3TC9Z7BhwlRw7NMC0HBgnI5YK7JEJTuHYuGVcFSYV1PTMNnsk6yjSR0k4u\noj34lHFGJyqDH7BOWNerIi9bZU0ZMwTcNGCKbuqqqKOQNLU6NM6qaIXK6D0mZeK68fPnz/zpyxfO\nubJVz8f3f8/D/Pd8OV84l8ry698yfvhIc57JaPYCEw/LoJqB2TFPHxmHwOAdwTfO55/5lx//RNpX\nvv/4Hu8s10vi8vln0qYeqTUE9tJIa2Zqjn/6zT/y9Kv/hLETrfOre4PmL4Kjtk7+YnUFYDCOZBy1\nFc1SnefXv/41133jy89fOO2N8cMj/jCTS2WPGQtM3rOJHudtWxXgHjzTcujnufH85YXT6wu+Vnyt\nTOIZ7MhPb19o0zvasCiSrq/d3O4H2zUN9LZcX7trJ/zchaD3b9G/kYi2M6qlJVE3G2+7MXODAqd1\n43rd8NPMOGtJfBwcizPMzjDVxlgLLkdeXv7Mp5/+K4ejw3vDet14ef09Na/Myz9RCh12o1Wxd0/f\n83p6phJ5XCakrDz//HumeWTjQipfeHv7M+d84vF4QMyR82XjfNpZ8xvff/gPPB0//pvx8G8KmF8z\nP13Y733M3ke7lUFvXpT3Mhzr6zsAACAASURBVKC+QO9Tcu//6aVzU4r1rCglzeSKlhqNuV1cvczX\n2td6+D3DLTqg31+j63hub6yZagcEKF9QB+NvqkOc1SylFkRsv6YNdp507jBFLeOi0AItY/p/lWXf\n5uVab0Ijt+Oh4y7eO/ZNa+OKiGtY6xmnUXt3se/878KgRo2JVgtlj0jweB8wzmg52co3JRKds0Sk\nW65ZrLg+L6rK19Z0vOPbXq/OenpCCL3Xl3vQlPtNMgw621lbt0TLpe/20Wa7VQl7cCqc2a4rbtDv\nVFOiZEeNkSc/g1jyZaOuqrqtJTNMM2aesd4pXzIlcowKOqDhD0eGIRAOB1yHNpSSWddIWq84p2XV\n68uLorVypg4Drm8KbuCGbV3Zt5UwjUjH9pVa++iK8oQxQr2u3EabML0MnFVlOM0Tzjeul6uWi9cr\n0svpQsWbTIqpn1/DVh3WDZjhCd88cTuzvp2Rlnn9fMJQCMHjjwfMYcTZSssrm8kYMrVZ/LFSY2R4\n/8hyPPD28pnmHcvHj5xeT9hiODwMOGMJ1pJj4t1379hl5cvLM6053TFbR6uw75Vl8kr+Oa2EADlv\nil/MkfOXZ57efWB7OxNbwY6B0Xq8dQzLgW2LnE4nnt9eCWFgXma2dYNUcD5wXTNlKzg3Ukm8vL5g\nTMU5VWSnWhiNkETLjtREzAXTOtQ9WPBCExWMjFZ1BgY1WidXRBy5WT69Nf6fn954KZkcRh4ev+NX\n3/8Wh+X3f/wDZvEc3s9Mi2UMnqM1jNnSjgaxmRwbY5jwVq9F0yLbyxtf/uW/cvryicE7ks9MhwPb\n9cKDg3wYaDbyms68rq9446h74fc/nsDPzMvfqUWXdzSTtRfOoGX7v46VcvufRrEaZFLciTkxjJ75\nOPK//a9/5LI1nn74Bx4+fKQNnuvlzOl80j65N5QmvUXgmA4LwzgBvZqVE1jL9x/ekz//zOlypoSJ\n3/3z73i7ZNz33+v4VK33dbov+kr4cvabz/u1WnhbA25r+r3dJbdESHDisM2o+ZJolWgYtH96eTlh\nrWM+LtofnzzHwbFY4QBMgKXw9vKJz7/8icv5E8vjE7lY4l5IUZi+84DC7VVcJIixTMMjv/31P5HL\nK23fubz9zH7eSJtnOBrmsDPYHWzlOHgGhDxCEOEtfiFcZg6H478ZD/8GNB53k2gRwdavjdxbQzhF\nndWz3x7g/rjZcH2bnd4ejdozSYPIDUp1C0JdRdWDYCn5zon9StO5lW2/LuZ6vnvAvQllRJRjKXJX\npAnaL2ii/1bBSP2mzHCTe1tkGO78xOuXLxjv8fOsP+sRWrm3rqsyiyp1bYUS2V/fWF+v2OUBE5SA\nY4xHcKpuFIexfVzHuF4SKlQxuBCgK2SN3Ei3/VjKrecr9+NWasOI4gVvvYZ7W9wZnLuNwHyVfrcO\nJSi1YHugvI+vdH5ubgXbYJomcJoFF1Fg9GAEfCA6p+rcFCGpk4ppOl7SWqO0qu/xeMQvk5ZSxVJi\nZD2fdXTIWaZpVE9OdpZpYDBC3jcuP/+kfpnO69hI/87We8ytZNSvHWplXBYtvcVdCUo9eJZcccFr\naddact8wWWupOZNiQQYP6OvlnMlWZ8P28wkLSEnKeY0ZZw3TMjKNI1EqcdNrMa4bw+FAcB4zTtj1\nQI4rdd9w3jEdF4qrXDJ8//iItMi2vhLjlZJhCYYiO1cZKFvhvDemYYRW2cUxHCdME+ZhUJ/KUPn4\n7h3P1595O60IEMIErXK5XImx8vigM3DWgfOKixyMJwyWVoRzVGZqccI0GEyOpNNKPl94++kXWq24\n4GhWAfbBGQ7DIyYXggwwqIq6isH7J06XZ7b8C0a6v621lNIoccfYgWF+woWgQn5jsS5AcLSaKSZi\nabhScVl67zYTt8gbG/noSKvHL0d+9Zvf8nBYSNcXRpf44d07DkdHLidkPxOmEVMKtqXuirHTLoXL\nvtJKZp5G/vBf/jNWdv7+wyNTGBi8Y/LaGhGx7NL47vuPlMtnLnLV3qM0ytD45fJ7wvWFYW68XX7C\nBcevf/hPzMPfITL/VWL5l/XaaiBJ5bJvrCmyDEf2PVNq4eHhyOPDxH458RgeGL0hl0TJG5GA+IXb\noKd1gVyFtifqfmHfrvjjAQys5zO5wevbhTo8Yn/4B8zycE927qLCv/iY39LXvqkMms59FdREQr5C\navrCfhdTijE0CsY0Bm/UwtE7xnHGTyMuWA6z43GAuQpzafiiZts/v/yeL+sfmD5YbBDipXJ5Szws\nH1mGjzr3ayy1iFrMiSBiGcLEy09/5PXzH3g3H/nw7kDcNvKqrka/Co84vqNtgbVGDocDEnY+fzlx\n2Z65XD//q2Nxe/wNbiX2/ud2EG8zOLrL0KBlTO/bcdup/L+kvdmSY0mSnvnZehYAvkVEZmVWdQkp\n5NVczPs/CWfIbk4PqyuX2HzBchZb50LtwD2ysqRr2EiR9PAIOAA/i6mp6q/f/8p1/eZgvnnkkgDT\nelKqNXBfy7Sl1dW1UaA0ik1CLC+qjUWhr8999dBUbUf0W4ma3HRFSSnUlCS9yVrbIHV+U26g+a41\nZiyt2T0M15LzMk1t0ZffUikpyxptAUcpEzlcKOcnPJ7O3FOrIRsnpJ4i5ZttdjGE3MydBWhutMEY\nf/38pRaxDXtTOnlLPSqt2Y7W1+AqjisaZTyKze5MjkxqknClVNPW5Ku3ZM5N1atfKUy6CRtKkYBQ\nEKxfNdKDK7c3Ug5dVzF/1oaiKosumArOdez6HuMcIYpfY1WF5eWIcRY7jMIZLZV4PlFtZm6u83kJ\nWBQP333gvKygIW7Qiq1HW4oQXc4TMayYw9hweoInzDGSQmrIQtn61tIs5JS4pahahQZVKyVLSdE5\nB7VwfHqi5EI3jHTOU1NinVbiKuxeZQJVWaoymM5SdCarzIxC+x7jemy5Ja8rJUZOi5TGrYFw+krJ\nC5iMJnF7u5MRnm7klOB5XknRMRUlAXi8oXgj2bi2+MGzsx3VOpY103dDgxhEQkysIdF1Du8tuazk\nLKKgruvQSloapzmTVMHtehKFT9OJtJxYs7i9KF+42e2JIbEuK2N3oFgpXqc1Uqrg72qpIka5/xF/\n/MrLY+RyPss9VhVattp0TnPY77H9yBRXnJUKTkLEZs5YyKkFLFo/H6qGi1p4zicWZXn3/ju6/Y5z\nOBHXMw+3jv3djqID//yXf6bWjHq4oy4r4XTCV9gNHaXhKK015CMMBB5ub6m1UKYZPY7My5E0zez2\nBxKFlMEpj3cj2WRWnfmYE8/zR27yI2ZKXJYXcoJl0fzx+467wz+1TOy1R/i2+lZU4bhMvEwXOeeN\nad1Zi3eKmCbmaeKP7w6cT0fKywuLquzG77DeoeYLYUlE5dHO0xlFnBfKumC8JWjw4w4/7FmLwd48\nYA4PgrEr6aonUW18TJaW10C5VZMrrbmlG4yhNlpYfV3ftx8rqpBqxVQNqmAQdfYyzc2iTPxFq5FJ\nAKPA1ErNEIFgCqs+UWzm5uY7vBuYwsLQ7Xl3+ye8uieFjG6Ma6Ur1lZiuvBy/EQl8t13t3QoyjLR\nGcU4HHDW0BnP88uRr+czahTk3hxXVEnMl0dOfvi78fAfHivZzKIls5SSZG2CCW3UtU+2La7XQKVA\nVb6x2NoCrbz2q72TanYiG5R9y3IENduCYctiRb5sWqB8FfVsAqFStx6Cur6mbg35LZvcrtnUZjFp\nN6a2rcTJptatrz1b0zz4QqAGKZdur51zkf5h9WhtoYroxd7d47Sl5kCKkCoyQ5mTbDi0kVLw5tDR\nShpbGXWj/OScSFW1kZNXYdHbsrfZMsP8Omu6bQJsw+yFEChKgp1UDhC3DrNVDYq4kZim8lUVbRu0\nIURMVSgjJJ26kY60wnQdVit051mniRQCRksgqkXEIqlCuszENcjN4kWS7nd7QpurNAihSRvDejqT\nliDXQ9+xlsJ8PKKGPbVlyNt4jdIanbN4i/Yee9iBt2JKWws1SG/XeC+Q91opKeE7LxlhzoLM023T\nVBApvLakdaJUGG9vUSkT50mUyFZTk0X7EW0tMWdyLWgn94TuFLEZblvn6VwHPhLPF0yp6BgJ84UQ\nZgoRpTJ9Z4id5XQ6Mh5GShLLIqc6QqlENM5DNopLXujR3I89etgzLyvGiDtLyaVVDOR61w3TWGvC\necOH999xPk9QNTEl5pS4He/pbw+8PD/yeDkxx0iuFe8tSicoFkpiNzpe1hOJyBTAR/C9ZOWBgNMJ\nSsL1Bx7u/oCpX4kp0nUOKOQS8Z1l6LvrcR9ds8OrBauAVKQlUXSzfdOYzrLEiRDFcLnXHfMy8fjy\nSFpnXj79wv3uhh8Ot3x8/Mj08V8ZDj3r8YxNhS5nsbt7OfLy/MRuN+LHAeccP76/I4bI+XgRRnE3\n8PXLI/vbAyEFlnXGqgFXHPv+DmsqOiykmNj5Hhc0l/OjkIW0avO1sZVjW+pwbWnIGIa1FlTlEhbc\n0HO4vWF/sxfgeFtFY1pwTnN8euT5yxNuOJC0IeuRHArrPBOWSLYC+MD39OMO5TRxWYjWkNZANQZ/\nuEcNowQ5hIyvr70srmuGUltwbGtIa02VN8GR5oG6fSsvoVuHqK1ZKl+TqpRlqsBbg7eGorW4r0yF\nO2U2Kyq0LcTpQg4Jqzsup8CSwXLg/fs/cr//HlN7am2kLl1QdeEyP/Ny+oUQj9zcdOyqxsZK7zs6\n67HGCo/5cmI5nzl0PWa/J5WETTBaSz8MjCb/7wfMLfi9Uunlwq2qxTDUNbPTepvTrCKmkRDXMs1m\nndUC0WuU45qZbeXFqwMB6vp6Egw1RtnmhUjD8b2OpFyzXKVIlWuwU4orp5Ja24l7FbkotQF95d8V\nupmXAqq8XiCqXj9fyUkugnW9ZmKbnNogCr9aMrVG9ncD89ML4ThDf0fWUt7TbEAFI6q4roMqEnrq\nVprOAjtHVJvKiBddQXZkrxsQrn3NTamGEjGNXLzbBuG1h6lbqTdnUdJWlQWMYHTbmGRSWtHaYUxH\npoA2AuI3imy12DmFQKwFNw7iZdmO13K5MKDhIuDv2vfEsS0YOULIWD2y//Ce8+ncysZWfACHjhIW\niu3obwYJpF3HHBNhDWhfcE78DXWrTigFaZpRBbz1eGNZltBm4wp5WUnWkObKMI5425GbEXLTfcoG\npZHCjRbm5xoWwhKw9MRLIk0T/WHPvBSxT/IOBtfITR6DsG07pQXibSo5gfKWrLVwP8cdve+xSyS4\nnpoO5LQQ1zOpFh6fI+uauSwBReFm1xNrQpNR2mONpzsMzEkTcmJBkbymVM3h/p7T41eUqVijpeTe\ndxhVsbli/UB/2JETTFOStoTv+KcfvsONOx6niTkkUjV41+FtR1WZqBeOl0A8T5xQHLoebhx6jfg1\nc9/tGXaeJUrATGnCFBjGGzrnOR6fmeYTSmVE8xN4fvzKZVr503/+M70RyzfVZutKSuhccdZIGbxW\n5rBQteJmuGXJ8HwJ5HXmmALz6YU8n1hr5NNfNV9OXxhs4rbP3JpIjyLPK7lK1eTD7Z6bm/0rjzgk\nlpczeVm5eXdPDCspJ/px4NPnz8JCzgWnNDfjjl1v6eIqGLgkJt9JaUxTvfvBstt3QrHJba1sYVBr\nmZ1VSnE+nZmXhfH2Bu0syii6Js5KneXmu3eE08THT18x1vPuxz/y5XTmNF/EJzcJt1lg6KKY7/qR\nqkQpHtZEWTPu9j3VD1JBIaOqCCVRXGEtUnJV5FqbX2xbU1oPVld11W3UJsxSLbl5OyWxtdRQBaMV\nzhjRC2QjI0fGkHXlFFZi0mAGWRPVynL6ysdf/wfT+Ym1BNZT4A/v/8yP7/8TO/sOUxwUCV7ZVFCR\nl+df+PL4F3I58/27G95pg5oyh/FGgCcpczoeeX56Zp5mDvsd799/4CWs+P7Ay+cvfD/ccHM4UJf0\nvx8wt8cWNLaZnNzKpbWVYLagWWtbdDZjaLV1Jl9LtFuZdmu/STD8Tb1cfRs4X/9BvstNjFK1wmh5\nk025Wmm7phYwW9pLaWqw196flFvtm/dMMYrSTUsPUFUpkdRmGbP9PtUY/DCSU6QCYQ0t2xG/QmMV\nXWcoUXH5+pUwXRgOdxTrqUZjVEVXuaAq4soSYsZawcPFhkSzzrfeZL7OrQJyoygpm36DGnyz3dtK\nlK1mwiaesm9Qe9vPrKcTOQZxNB8Gul5k7WFdyRniHFqgNcy5CN6tKqgCdY854bW+Nv2dE7CzN5Zu\n6JnPZ8zY43cjGM0Sg+DurGc6vpBTwo8CPDdGYyrkoonGg7N0TjL5+XKBfifuK9pijKXWQixVrLXa\nnKJWmjot1BDofYcG9je3zKowp0iKK84K4Jl28+eUro4mtULR+ZqRG2OwjaqkfSeuKE58QrPVRK+l\nz6s0tl1/nbHM0wWjxX4p10JIC6oXB5lU20bG7IkhUMuIPjyIQXaKePaslwmtEqdQSWFht/cM/UAp\njr574N39nuenTzydToQU6L3mvipcv0NRiYuU80qDfixrpq6RVBzPxwuPLyvaava3msLK8dcjExV7\nf8cPP/4Tw7DHa4HrRx2Jy8J6PnN+fOJyOVNL4L7vKeGFaVnIOgKZtJ5IQZEidPs9+90tu93IPB9Z\nw4VcAl8fv/Dl65FaDB8+3DN2jr7zhFRYw0pnFBRNuCwseSaj8IMQl6ZLROeBh9sb1royzc/cuMqP\nf/4jail8fPpKUZHdzjM6g8+JvjjOc8B1hn7oxLUmroRc8X5oJCTP/e0d437Hz7/+yof37wnryn6/\nw/c9c1xxKHw3EJz071YUIa+4ocP0llo7Upg5TUc+P/6Kf/+AYZTRrK102QJMBeZ5pqTENC+UzuPj\nQB8VnfYkYzivM5/++hPeeN69f0cZLKPZcf7lyHo6sR939MOex6cL1lq5h6wixZkaIks0DA/fY4ee\nECI5VWpasBqU0aTaeC00BGlbZnQ1Lcl5XXy3dV3WdhnnM22cbdM8bEuQ1ppqwDqN7y2j0ZS5ki4T\nurP4w55B9byzmr0u9CpwuXzkX3/+b3w+/ZVqk2ATVxj/JNxeVZGSrjSEiHnl8/PP/Pz5f2LHwh9/\nfOBOO5avR1womPGGZZo5n86sy8LQdeiKnGPnWeeFL//PT6TnEx/+6Xvq8UJN/xE0HpJK1jcnuq3H\n10Cnt3Js22lsopTfthA3Mc0GLlDbX76p7W/hFV4dSnSbZ9zmDdGaRhEWA1OQE00Ly3VTdMm7bAHy\n2hPVW3m2vU/j5Va10TektAY0ayvEOLtoMdlVzWTYO+nhIVn3BmdXutB1hhwmclhQpWJdTymVcJnR\nh57BOfK6tL6AIL+qshgj/aSk2/BzE+MoJYDmTWiUVbkKqradh5CM2kalZZJxQ8vpevX4hFd1b84Z\nldtMpLXiPq8tOVVSlMxQayclvbfzt0qhcmvq+16g2NVSYoVqMH7ADxrv5PmDVcRpZjCmKWIzduiI\n6woohsMNKCljphCZLzPxdGZ4dw+9F+u4UrHDiLI9pSgp5avW61ZQlPSnbeclkK4JGzM5LaRaUX2H\nGR29Fqh9mC+ttL95kIq92XYdllJbCV4Rwso6L6gGPO/HgePxTN+L2XRNpXmyapkXbU4xYZkxuxET\nE7lIJle1ubYujLcUBaba63yp6ZxslpYB1Y8ys7mcUKaH7sBxKTyfH5nOE85CzZmwZLzy1AiXywmv\nwTtDrpYVJ31eBaokKJmfPx9ZY6UYD9YxJ0NeFGZ4z77vGT98YP/wjloNJutrVUENhXoTSQ8X1unM\nMp0IlxM5nyjTilkjWid2a2BwIylWHh9XdruR/WFktz9wY3c8P39tNnMrWnc8f/3CMp0Zx5G+72Sg\nvBouxxNPLyd016H7gafTma/HE0V1fPfH/4LpNPn0C98ddricKacJrztuDzsyA2ld6IPHKUfKBdMd\nuDlYQrywzBNLNWjtoBoiIrRZ15Wffv6FfjeIwXUM+GGQ8ra2aA3LGomxsKaVmBOdcwzDjlAL81yo\neWVJC0/PX3g4nNn7oa2Rsurkdg+VUvBOyD3TtKB3I3OInB5fWJcVHXpyTux3O4ZxT3cYKSYz7Cy9\nhRAuDIcdy/lEbUbioAgxsZwn4rJg7/5E3b8nqYyuGtWgBDSqF8q80Xu0dbw5OqHV1SDirYL+mnmW\nCtv8OuqaZUpiJevO0CuGDgYK1RseX5Kwm2tlryr3RqDnx+kL//On/4tPl59QQ8F1jiHCkle+fv6F\n0dxyv++v8/2lBp5PP/HT4//Nqi78+eFHbrsBdZqpS6TrR2JYOb+cCWsbIyuVm90eg+Ll8VEs3l4m\n/nz7jvf7ez4/fSHG/0DAlOCnQb0BCGhQLVjW69fXnuHvKa62U7FloNRN1UTLDl8XcimtSj9zK6tu\nfU6txaexFOnTmTbj+Pp5WwbWOtXbiazXwPw2kEpgSRuWqvXNlJF5SN0W8JKlB6SuQ+8SPEpRKCOZ\njzG6lXcLSmVQKykvbXawp0agaHTv8M5w6B3nRbLKahXW9Gjv8U7cU0qWWUutZEBfStdNzKPa+agN\nDl8hlVeLs7cedaX1PnWFzRpqu/ivRA6l6W4fxGpLgeJ1bEcmYRXDOMpFWuSKqK2UbSzEVKCIAs75\nDjAinMiJ6XxijTNmDihdWS6K819/Rh8OpLBSlcb47hpAUkikdaWERHd7j+565hCoWmhICiUUJm1k\nI9J1LNPU8IFWUIWdw3U9eV4kQwa0s4Q5440XgZFqCEfEzaG2ErvdwPRq4xDLIL3WBrUb6fte0HS1\nCuWoiZRcBVM0zlpiSSKCsQbf9cRauUwT3TCKvyb6yj4uZGIJFC1kFGsNxss1R7+jrAuu7CDfEuYT\nT+uCSgFfC2E5o8hYVRi7Hs0ty7xwnE7kEui9zDoa1bMberw12BxRYYXoRRlrnIg/lGGpDq92dMMd\na+5I5yo78qSgesjitGN1pR93jLt3lBSYzi+cvefl5SMqRsbOEi4Ti03oIv6IMa1M84l+sKzrzBom\nZM2WOeLTyzPTSXNu98Bhv5fFOBcuxzOqizCvrDnjK+KOEy6UELlVRpSVU2QY93jnSdNMqRoz7Nn1\nA8vpwjIvQskJlWm6ENbIzc0dRrsGNAHvO9Zl5eb2wMP797ycjmIz1wwWxn4AbyAHApHOymiWqhBS\nJpAJubBmcdYJeSWmQO0QkcxWYuN1Y2utxWgR1a2XiTOKTkvLRJcq1mz6Qg6BEiNaVYb9yN3Ys6hC\nnk7EpPHDDdaJO1NcF8Ia8Idb1K4jlSTm0FlU5MpYMIrXhs7rl9qqda+9TXW9P7ne+20d2VpjtbXd\nlELY5hVtBLHnm59wMBo3GLqbkX7Xy3u4ijeZkM78/PRXPk6fibZyGHfs+54ezVyeOZ6+8G/pX1jv\nV2537+n7PZ9efuavX/4Hc37mw/sHPgwHxrVigmLf7WTk7HxmOp+xxjLu9uKvai3z5SKWcRX2vuPh\ncEsJmXVJrOk/WpJVqhF9Kls7VF+DzmvT9zUkfRPDfu8FYROktDSzqC2YyoHf3EEkM5Q03zTc3gb1\n3QQ46rev/Wa39BrQ62sytn3SrXTQyprXgFMFdr4F/re0ISUJbJslbPY5SpELUKtg7YoSs1wjvUeF\nQSkDzrUB3sz8/Jn18TMBgz4YlJULaF0XKRVRG2WmXkvY1NbHVOV6jFXrISua0XPrb35zRHSbo33V\nB7V7VrLpbTcpc475iqGToCvD7lrrq/vLlp3yRlVM+5qT7NK1NtTGhu37kRIL1ZR2I0/4m56aAt24\nF6eSkmWhC4kcoDgHQ0/IhZQytvOUqsghSpm8lx372nBZfgOOU8EbVmTRMM5KX6gTpwRnK7VIUM4p\nYawlVjGvNqbHOUuI0m+f50muOyV820ollUxcFrTz2N6LL2cW5rBqaERjrbjVALFskAtx+dgqLFlW\nGLSu4iysKtqJYhIEy7dEKL3H27456ihUN+JUQc9H1ulEXiaMMSxB83J6FpGV9yhnuayZtEZ2Y0+t\nAzZrehzeOPrbG9aUWUJhDpmqNNZ1VBzzHIkh02eD87f0TiDypSqh75SEVoiPofd0Nx6336Of73n8\n/K+c1heBZq+ZvMx0xhDiTMmJcTdwPp/QGlJqnrchU3wSolEIrEYT1yAtgRS4hMDN4DEaVDHUqshr\nYMgTh8OOZS6cjyeZsZ5XzqezINuMYdwfUCUz9E7IODlTSmybQxGL1SIAEmMks7q7v6MbegqVGAIP\nH94zhQWlxFHEGU80ipClYpBUZZlnYlzFVTMnckwCt9QaZbYkQ3QBb9erV1ML6aFenp6ZTmf++OF7\nofZ0HlsV2mpcNYzWE+aFdVoIn79Qzycu00Tdv7ve12FZyDnj7x4YdiMpZXxcqbWwtJaT7dy1LfVt\nhU9dNQHbwqNbprmt6VcU3fYj0lmSal+NmKbRsNagbQdeMYUF3w8oZdjfj+z3HbbXdApKOPP48lee\nT5+wTjEMB26HPff9AZbIxFfWfObLy8S6PKH+8H+guz/yeP7MJZ5xnWWwHp9hSDBojx9Hvjw/cjkf\nMcow+p79MEI7Pqb1mmut9Ic9/e0Nl7Bg97s2vfH7j38/YL7tm6FICCmntlKWYOdee4JF/e0s5m8f\nmzjnusu6/odA0Wsll4qiXMUqpgXs0nB0ijZY3uYjrx8XoAlAtrJkavZC1jZlb0Nv0Xb5tsGD5bO1\nxjWvgWGDBxutqbrNZ9J4sa3HWpH+RE6tIK08ysoCmXKDBBfAQFon4vMn4vmIPtyLACAl6Qk0sY0y\nYlYdYxSnEm1aCStfy79blixMWcmMhXUrpeLNWHtzGZDGfttsaPGtvM5hVTDaoZTBua6VKAvaWJlT\n3Rb6LAEDLSpZEGPcrUwSQxTlNBmbC363g3mGisznxcBwd48/3IjE3TtKKoCWEYhpQSmDvhlZFeQl\nYozHKk+NmZxFcLZO4iigrMV6h/OeNM+C9esdaY2YsRfepdGowROiCJRSipQUKTFCFrCEs056jCEQ\n1pWKqKe9d3RdR1wm7D3cLgAAIABJREFU4jJTug4zDiggLqsoeecLKVZc34n7SRSz6AIUrbDOkYv0\nd8iSzW3XWCKRSpDr2CJIuZylxO00/dALFWqtOLXHKIUuMqObQkUfDljnWOeJuK5SdlUa8kpNlaG7\nww43nNeI1fB8ntBxEtDAElhzBeMwztFbS1WVeZlJxnDJFaUdHw737HsPTbiSC0KHadz/Wg30txx+\nHDBjx5ef/pnL+ZHRCQe41MIyr5SSMN6grCaklaozuxstxCAaG9k6tNJYZ3GdpzjYdxZtFMfnIyFW\nDvtbPnz3nl3fsRzP1BjZGyv86BjRpbBWmWvsrSHMC4dhRNfMui4Mg+P9+wdKgWUO1/sorImb23u8\nF6B/zInvv/+eGIKYig+90KRQqJCxKdGZnlOaWcJE8RVfCq5YirLEkhmHka7zCCBMbWL/17W1fSug\nEE9Uip//+lfe70UAdD6fWZS4Ag3ec/n4leePv1LWlbxcIAaKk82sc76pmpHrBjD9QJwmYphkrCNE\nUAY7SJWjNAGm2B7q17V5SzqVaqXZ1xV2S0AkUsqIiKoabZEsWgiSGK/wXlN0Iq8BjWHXWTrv6H2h\nt4WuZko4El8+MdTI4eaWWAq75LnJPdPLjIoOtROkYignnuZfmb8m5nyBLjPc9PRDj7eWOgUuT2fO\nMfFyPmKHju9//IHBj5Qs3rbUzG70nM4njucLvHtHrpGXvLJYwPd/N3b9+wHzVb4qpcE3J7ogi+Sr\nLOc1ff97j2/7iupVLNvMjCsK6ey+zmJuiKatV1XbC/1+6fd1JyQ9gtfAKT//miVtGdMWeKnSL5SK\ncet3qlfwsJg6S0aX89Xl7qp823ZkW/VXmLhFgMGdhwaW1p2oM/thj+kGiu8ptHKcs6R1oVZxEKHS\n+pbyolLi/RYRKL9XxpRXZ5hr0l+bc0sr3daWJUhpVTYWW7CTspOVMmQpaGWwVmDSMaYrWL8qhfeN\ndUmSINzezjiHKrlZWPXozpOOZ2pVLCGiQsT5nlwEebYsCxXNOl2oVaOxDIeRYo2wYI3GGEctRQAZ\ntVl5tffcEGB1vpAvF4bDSM0K2w/EOhOrsIJLrSLIcZaUE+PhphmPi5fj5TILVs55WcCsxTsraDkl\n2bexlt3tDbXCcj6TcqIkhbaWfuhwfSel416Cr1HgUNQ1Nd4wrfdu2ggRwqG1giJLRdxKlJLys7OK\nUhZhDitD13solbRmkvG4d3+QmeiQqPS4UaMrlPhEji9o1RMTfP18kYxumTFlpbeQlpXLvKJsh+sd\nXnWEalinC1EpsjV02lBqZlomSggMfU/XdRir0YhVU2rsYIXGYLi/+zNed3z6+C98ef5XupuVD+7A\neNpzPD1znkNDwBU6b3BasR/FvPv5+XzVIMQmwMpKTI1f5jMla969+8DD/QPGaM5PT5AEHLGuMsK0\nG0eOxyPWGd7dHlBU1hyYLonBOjQF266DzXRdKkCW25sB52QhDWtgd9jT+Y7p+EJcI856/KHHojl9\nOXKjJEjMUbxTQzNt9+g2b2o57G5knrQUNIYNz6LavWmMRhtRVQ+7HUtKjNoyeM+yzGg03TBQcmFZ\nVpbjmbIksRJTjrLrMA8P+JsPFD1ChaHrsObVJanUyhICKhcZk7JS+So5y9qkW2n2WkWTzYvcB69r\ndqu7gqpNjVtQuoJreYNVovBXha639N5glTiefHfbMeTITcnsVEWHhFoDZb4QXz4zxoWbcY8dB56/\nvPCdP6AvmXkO3NoD8eBQBEzWXMKZ+aLwux4uEsy7oRdvW6t5mU6sp4nDzYG7Dx8Y+o4SA9Ppheny\nwm43UKrh45dPfH05wthhO82xRth7qvv7YfEfEP28LXB+Gxg3oc9m53xN4//O4/XA/+3fvRUR1SpB\nc8uESru4rj9fa0O/8a1V2PY5r38n5QVjdBP6bCXW0kRBryQXpcSRpLaIp1oGtwVmqedLQE0xNXSf\natnUq6PIdSPQBEab0tcYS1UWlKFoz9q9lwXVS2ZoFTLAPk3kecHtRuHAtj5kyYWaM8Z5CRwtq8+5\nXM9MqRLo0iYGagfYtM3AJmS6noXW57RW+kjfGGMjxxgqMWXp87bjYd3G7k3ELJ6JGy7LWsGx1VxI\nlxms4uH7H1ien8hWXCuWlh1SZFfrtCHlwu7hHSkIfSkGadI736EbgH0YBpHwGy0l7vbIKZHmmW63\nI1RxU1nPJzlnnWT5RimM86QlkSqsUcouFsUaZ5ZZxlGsFToRqZIVKKvAWPrbOyn5Fkgp4sYdbtxh\njSWcJ7kWkFkzYz0p5es1lMsrXrKoVwl+VQLj194JaL+JNpxzoKCEi/RZG/YrhUKJWRazzmN8L797\nzNjbW7zx5DUQp0DnPVDJcaWUzDpfqHVA4Yg5SXm7vwdlyNWQkyVmGeWo1qCcxjgJxh+//kzJiT98\n/z3v3r+XPm+tzVBaetmugkmCgtuP32H+qDG94udf/jshnfjD6LF3B84vL+Ql0imFippYCse64iKs\nST6vsw6NgL93g6ATd/s9yxzpu5F1jYRV5qBHb0kxkOOC6ztCXHBec1kX0kWJ1ZVWrJcLu9tbVM3k\nEHDjcK1UlZLbXLbmfJnRWjH0ElyPz4+cT2es79FVqDJrEpcdP3iWlJimhaIymURMgTUklmmlMwc6\n2zcfz4JRbWxDbZd/M4pPhbiu+N1IVbA/HFBV5p6981y+PnNZVopyqHGgv7tDacNyPKKNYXz3nlgN\nKcvaVXImtJn5eVpkxKkfWF5eKFWobMq560jbxr7eWlKqrXVNBiJVLOq1B6uVatg/ULolUaaiDeIc\nRWLwjp1XqChl8P0It4B+fqKcjlK6tUBYYDqTTwv5FFBdYgiF3Z3ll0+/soYVqy3jcKB2GRUz4SVT\nY+Xu8A50ZpofeXo5kvXCezPQPdxKtW4YKBXCtJDjmePzR06nC8Z9z2WNnMNCd7OjdhYzihn6UgtL\nWv9uDPuHWLL62mv8pvr+m2eq6w5Ffu73nvO3r/3257cy7TVo8nYGUrHVBV45s6/l4utn+M1nrJtY\nRr+pLl/LyLLIlZypJQK6UYWAbRuwlWqNA10JcSvvula2rBRdMEqLK1gq19JnjklKTK20qo1gS3Mb\nW0EbinbSh0wrOQRyjELSqFBiIi5LQ09JQM5ZPCedtU3lWlrmX6GUb8rUV3BBFfeYDbB8VTpfj9Gr\nSGhjqcqxKuQsO3BrOzbkoOxAs7gkIApTSpaSpGrzYCmzhgBkzgmW44nDDw/YXFn0IqMZMWGVpkyz\n2CK1mbCspOTvXIdVmhzXpgAWL0yjfRvAr218qKK9p7RNjB8Gaitbl23TpC3zyxNpmfG7kVQK4yg9\njRIjvh/orKOGxHw64bzDDB2mM+A1REvJlRSSZP/NnFlbh/YD1hrpJdUKNUpGYTVoLZZqqpXAspTM\nVcvac9WSdNYirja1CpvVWkwFjCGsEVKCGFEFcbuplbhGigZ90IKri4ESZ/qHe5QV/0xVCjEEWFdM\n14nTTFFoZUFbAU2UiIoTeToC0kcmFqa8EI+BdZnoh46v/ok1Ltzc3OCcGEprg3gnRpkB1UqjjWfs\n3vHnP/ZQOj7++r84ecvDzYF393fU88T85ZEwSwZ1WSp5mum9ICKVNgz9QOc8fugoVNY18OXLI30/\nMw47jHWkqEk5sOYVNzimdSHNifuHO0qYWC8nYvQcxpHnaUbf36GN5jJdqLawpkAMCWUcvt/x+fER\n3/Xc3d0SLieW+UIqFe97fN9LNp8zL+cTKwW04hxX5hSJNRLTIn6KIZJDFTsrM+CNR1XdtBnNmPzN\nBMDpdGocbVlPQk7MOZFRzM8X5pDoHu7Jncwc7/uBGjPLvKL9CGqQDRoQU7huyHLOV0a3qZWNfaZU\nM1KoAlhQCskyt+VzSy7an5vmsyUZkk3q9geBFDTxoBGnF2Kh14VbZ8ixUGPmUAp6eaI+fyF8fcTp\nQtWFeb5wvJx5er7gfMeH9+/5pz/9Ce8sNUVyjqRc0HFg8YYcFsISeTd84GH3PTt3xy+f/1+ePn/l\nOX1GvfuOh2GHKQdeTgun+ZHbncewcjpNHM8r/c3Kp6dnlpJRypK8JjtFqoU5JeLfqEBeH/9QSVYw\neFtA2wQev/fUbwEE//8ekiW9/dlN6Xp9vCnngtDpQbc+p7zx773ra7N6E8u8voZu2WMpGQHuqFZW\nre1rkQwrRZQREY7W4tqBknGSNwV/WcjC2pB5+jVjpWIUUDMambWUq1BuEoX0G43zsrCWSlxWoeX0\nHc5roQcB1nbCsdwyRqWaqlbKh5uJtm6oq9j6mdbaV8eYVoYtOZFzvVKG3qpot3Mq379mdCLu2QRV\nDms9Rkv/NUQBBRgUdtyRUmC6TCjvQVtK1WAsylpULtRUxJPPD1weH7G7A2Y3yCB2zhQF1ntSDcTm\nmqOVYtsB5ZQkS94A6ioTU2Tc7wjzIkKmNUpvpyS6vscPo2Tp2rCuK1SF8z1aG9YlUpVGW4+2DrQl\nV8hrgOaZqhq2sFZY1lWuE6XEXSMmgRQohXMdyjopP28ee7VAKeSSSLkIslAVtKpYoyhxIYUoxKJG\nZDLW0nU99D3r6USKK90wihpXg9/v5TzOM9oU/NgTqqHoZi5tHN3ugPOOMAes9mKFphW6JnIMxKCo\n3kvRcJqJpxPT0yTXre+ofmS6BOb5zNPTE947vvv+A8PQyeYieXTxzRHECO3KOv70w/+J9Xd8Pf+F\nX04zd13Hh4cPdLbj+ZdfWJeFJUsJOqGuKLxpWskuE0tkjishJLSyrSKUZewpJ6ZwlNsvrUzLKtWk\n+UTWVeaKl8L+/QO/5kShoL0ix0JMkTDNdP2I9R1L83O9u78jriKa6bsOkyu7mxvmmLDGMC8zL5cz\nyYpAa6qFZFSbbczUKJsUXRVpTZQIZClhq+u6st1jmpQzx8uF4ixLyZy/fKUoOOWVKWeO00y3O3C4\nvSOmwM47TK08Pr+QzwvqdkeKCmNGcg1AuLZrTPOrVC0YDzc3pBCJRbw+N4W/WPVUYcRqEaCpLbJe\n12V1DZZy+ykJnNv4CBKUvZUWyt4oDhaKMZiu56AjWScu+YLOZ8LzCyUGlpT4+jKx1Mq+FxjH/jBy\n+vpEWheMg6lGau6YzyvhPKED3L2/Y+CGsbMM3498evwLv/z03/j4+YW4S/TacVlmdsbKuFlaOU0T\nS6p8eT6KybzWuN5TOsuqS9vAltf59N95/EMlWahvMsztZG/fv8XHbV/bwVVvX6fylqX4ewH1bSn1\n97NT9c1rbs/aVFvy8vV3n/uqBmsBs33dYADGGmiDxVUJ8/Las61CvSBnGSfJUspxXkxupQTb3AEA\n3wyR80YBsgZKQVHIefNfbJlecwLRzT1cKY1zXuY7leDmFLITLDVQiyaECk1566wFq0XIsn3W1l/V\nWjIctrnJ7c9aBEApxtZ/rdfgu0EN0htp9XXE4toLFoWhNa6NAWmcd21uVqAGmYLtHSoq8rrKSEgq\nrJeJZbpw++4GgyYGQc7VlCkVUi045Hys6yo9RSXzaikljNass8DLt10/8IptLPI60zyLr2mW87Y7\nHKhOSolFGbnytUUZycqtc8R5JVfFsBco9RwSfgSRNtfrzn2jRoWwsswL2hgBT1wuKCtqWGohrCsG\ncNZdL3fv/avgShkyYoGkm8u9XFuK6TJBjNx9/x3OeZZ1YT5fCOczw26P6ztygM5bnLbMy4rSGtdZ\nLi8vJO3QzlOywjmL76TnnEohV0VnFb0qLOcz8zyTjMPf3qGNwg8rtThSEPPmWgyXSbGEia5PVFaM\nEaj+bj/yhx++59D3TKeJZV25vz/gvKZWg7O3fP/hv6BGy8df/xc/fXpk2QU+HAbGH7/DrRM+K0qU\n86RSYj6fiLWiSsfz8YhyMlrVdebaa+uHgekCy6KISVoU2nco7zjGRIyB0Ugf9uXlmVQz5+kifN0Y\nxD/VOFIqxBzYHQ7c3t2jFJzmidE7QghY16GU4nI+Y6xAFaZ1YSqVOzMQvWaNlTUL/lHlVunRgnrL\nsVyJaCBxKQNayYbr5eWFgELvRugcyltx3UkJ6y3dww27d+/xu5EhWGxcef76yNOnR+xwQz/sBIaC\nxihxNZLWymtiIKhQGZ9bl4WIRlVZZ6zVbZ1s4Jk3/3+7cm4aii2ObntWbRTGKqyGrlR6DSYrdlZT\n5oV4fiauZ4bsCXXiuBzxJrOsM2VZmZbEck64mx4zeoa7HZXM09fPxPlCLIbSO4puVavLzFiFSW0r\n1KLY9Tv+6Yf/ymg7Hh//yk9fv7DvHLvecbi94bDr+PrLF1KOzCuoWKDz3N3fMb6/Q/Ud57iypNDG\nSv8DAdOqxhS8Hrrt5KtrH2zrP74O5rbD/qo8Qarg7USob1/jKvh526e8BuB/5PH6xNefaZYv2+f7\n5pPzNu2Uhb4IcEAp2QmW9nm10nKUWpDY+ntb/7Lm9rnbqwverhDCSkmZbLJYSbG5qahrANBV7Kdo\nKDC5bgtVa4Eqo0gpInzXTM4V5zoMpv1u8rXEhLVGhDkbbm+7WdrvbayVfk3raVpjWm8Xsbtq6MMY\nJehv2eYVds8rsED6l5Kx5pxbH2ijKElZV7d5xpJzw83B+nxifnxi3I+4NfP08RMlV+y4xw4DZneg\naAEXmM5j2shQXFdqaXOx1ohJpVLXoC4jL/Lvzjp85ySQaY0ZerK19DcHqracpklKuNZTtcE43YQX\nBkzBdYgJtoZ+6MB1FKVwVl3PcZHmnQjSnEDcU4qYrhOT6Sobmra8SJaXchvPEXKQLPJWAqwx1NxE\nTQ1vZpzj8HDPOs9Mx5NsOHKmPxywXcd0uWC8o/c967KicqUfRuL5TFjEGYccsH2P68QVp5YkJVSl\nqCSmywvz6YXqO/xhxO33GFVZ14SyHeO7gRoy67KKx2XUxJBxzjCMHcsCKc8Y88yLmUmXws3tyO2d\nR6nK8biKB6fveHfzI53u+GJ/4vT4K9WeeXgYsNrRrY66VnSRMZRSM4fOQ0o8HATIn5q1l/grerph\nAG2ZK3glA/K1ZIwBFQJGd9zsBtbpzNfnE9UKoSrnSjWOY4g8DAMhJGrJ9H3PvCx8/fqI9xY3DpyP\nJ9592DNdhC0bYxRbPt+h7ntWVZlqJihEia01VmmqMSjjcMqLVyzSD78uQll6gNJS0FhtsH1PsZrx\n/obz6YS3lhyDgC28KKjXc+Tx10/MlzMVGD98IFBIaUEbh3cWY7wEbq2EGFVlRC5EaSWom/umuQDl\nnFSk3q6jtV5nhLcF822KcwWvqXoNmsYonIUBxa5WnFb05YWnX/+Zj//2L5SQ+NOf/kwyka/TE+9c\nh7sZZXxOF3Z7R3Ea3RnMYJnjzDxduLy8EDywuyepnlQSulTubx8YuluKNlRWakmA5/2H/4oaLV/+\n+wsprtzc7vH7AeUMc0hyDHQH2mPcwHi4RTnPmhILiUgmbsnR33n8A3OYbQax1uuCj9bXxeMaA9WW\nibxmI29fQ3wi4cqUbUGn5XxvgmY7cb/JQH+bsX5btv3bz/z6dQuR295pe8e3z2yft1S05XUhVps6\nVnqdIjQSx3CAuAZAvDKv/5Zb4KgKM47SF8yJGsUr0XcdxhrpDSpFjIFKlbLjdXjeoJtvp3WiuCxV\ndo3GWFH3l9abrBltRJ1WksCNrTZiqso2nN9GYRQiiYc2yypCh7dzlG9V0ZsH6Vautc2lpuR8PfCv\naue351wWL7FvM9hxwCqoa8bf3gq3NomASTuFdh5qJYUVtBUWZ6z01gkkYJ1xtjGElULphvbbNlnG\noLKhIZlI80o4HtGdp9vvqUYRq8AnUq5X5W2ulVQqqRZSkTGSGjOd9XR9B71lUZWQEgObSCNdYQ/a\nbNjEStdLJpJjxPsObTRriM3wN8hx17LBEHW2unrHbr1mGSkqdH1HWGfmaSatC8554rKCAtf1oMD0\nXlS7wyDuJ6WyzBMpJ4Z3d5jmuKC0IsTlmnVoYzG6EJaJ9fxMoTAcdvjDKHzg04V0OWP3PdZ0xCVK\n+Xw2qFgpq+D1FAbrKuuSWZcndIXBeHwHf/m3v3A+H5nnwI8//CeBA7ieu/E7hh96zocbHp9/4uPj\nEW0zvjh23YDtjXB5/S0zshFkDvigGHsZn1HtOIV1ouTEd+9u2R8OrOvCPE1obYhrxBiLMlIlMDtH\nXyuq66/YS2ukX1rCGess0zxxOZ2xqrLvO1JcGYahISQDfd+zriv9aMV71BimHEgUTGcx+Faul+fX\nKhvN4/LCXV4YnBfx1pZoVIFimCagSiGiEL2DHweUUthaGZ3HG0daAl9++ch6mdF+ZPfhHnt7R64V\nVxXOCshjmQKlVWJIqQkxRbVblUZZh9OmGTFIO+v1tq3Sr2wLrugdrqnRdSm9Oie2rFNT8cCNURxy\nwZuFdf6CVidsl3iZnvnLLwE1WC5lYuw8N+MDBoP2CzVGziTsYMlGAlaMK3mNUg3TmlTFnL1XjtE+\n0Pv3lGREdIYVUwDlMP4Gt98Tzyt+9GQyzy8XllVii/cO53fc3D8IaczUJtiCpBTprW7mdx7/Pumn\nvBkcatmYxsi85LVv+DZ41d/UgLescaMB0ex6JIhd9+3qtdz7+5/3bwOorO9vafm/DbSvZI167Wj/\nNpBvIbM03Ux5xcC1l3rrw/kqQuLq/6m0hKHrLKl0xeXYtBKe+HdKn8QZQ4nidI+SAWsx5q6ibmwZ\n6LY4lCSODbaVg0uRjYpW5rrIphCotaC1WF1pJUpd1RwFyAmFuQbwkqX0EEO9km2ElvNtZrmVYLff\n+zqX2jZNb3udW4CttaBKwXuHd04WpnmSgFIKKkW8EgXwukaUF4Sf0hbTdxQjoy1GSY/VWQMlUmNA\ndzdU5eSca4V2hkQl2draMIWwzJRQ6XrL2AlaLIfAnGP7nIqU2/muknFrY/G9JhXxNVXWUrSVGlqD\n4But0c3TT2T3TZzWNpLWWrz3VzGWUpLZV2liihoyRjkebeuuzZvRqPJ6jLVu5KSuByRjl+tYRouq\namM55zPhfJHX8xa338mAeFWtYiDZbEmZru/oHCznJ5bzmWI09vYetduhvSWHleXXXzDGYocdgUrW\nBqU7AaAvjoInhonlHFAElAXnNX3niVS+Pr3w8evCEiZ817NfJ3To2NFhdYdXt3z/cMN+uOXx6Sde\nTp84xhNPy5mKwnmP1ZYyJ1zVqGQYlMFoR9aGd3c31BSYL0c6b9mPHk2lsw76HSlmIhXrBmKNqGGP\nU2IFp3yHTomQMrcfPhCfnujGHX3XkePKfj/ijLjVLIsESWsMnfecl5W5bR6sd5iS8aZSrMZqaZus\nJVO1oibaJmblfDkyLWc6txef2nqNMzRxg7QbQmDoXPNQHZlejnRWXDb0mvn0069cPn7C3t7TffiB\n/uEBvMeWTInS9zVa4TSUErHKiDm20qytatQ5RzXmFZ9ZXkezYowo9JW49rrkfrtmXtfTa+JQcMCY\nCztj8SoznZ94fPqZWi+YvqD6zCU8o5STsY3Bov3ALhu6IbC8PKHVSt1pzvFCWStFV3EzMRYz9EJh\nipm8LqR45jI/0rk7qJZaDapKRcepgf1uRyoXBm94efpCOQXOp0CIit4bfD9I79JA0YWyVSDrtzHm\n9x7/fsBsWYdSr8pKDahNQLMFzm8ClXwvoF4l6Tv6uqhuIwiq9b/VlsnxTYLDv1+WrbwNen/T47ye\n8LcfT73+pLw5lf+PsXftjxs70jz/cW4A8kJSUpXtnmmPe3bf7ff/ONs97Xb7ppJEkcxMAOe6L+IA\nyfK2XZ3+qVwqSVQSCZyIeOK5FCV00PbYr5/92W1Z33+6MchMLxrv/vYdZtnIPrV3kk0c4oScMpfr\nM/n1OxwmwvlMs5rsUmtlmWcttFZp9c50yNAYdLCTzkRFI7asIc4L6XbFBtVfaRwYXQC/QeaNkhPG\n2A5fKpN2w+vFOixb0ZTdZ3aHod8VTOfcnola++6zdIau7j+L6giDx1vDfHmDGPGi9l8+BNZlYb5e\nscPE4XQkVQ2BvZtNJI3JaoXglawSY8TZggnvSGhG9zPiQi9sghktDoNxFlOEPEfevnzB/PgrxsOJ\nlBN7YkvfSzrneiKHUIxC17V1G7FGL5gqEdLpoG4ju6a+GEttlfW2UqrC1TTIJSq5x+jedJMfuY34\nJGr4X1LavYNzyRwOEzY6NqOJJio98EFvwlQypSTWt5mWCn6aMCGQnZBKQkoj59xTd2R3dom3N/L8\noj8/fsAcz9hhpJbM8vUzLS5MP/yaNjhi7uuE0RIGj1kHsvfUdaCsMy1G6lpoS4PBks+RbAylOYw9\nYcLI6+1GFTCnJw5+ZJ4jp4eB8+FHQhg4nz5wXV94vX1nnhdMcwQ70kJlsAE7CTUuPK8LB2eplxuX\nbz9xCIYfxicutyu5zIRhZI0q8QjDgAyOnAqMB8ZJCWXfXi7dLczw7TIzVOHjdKS1SswV2wp2cORa\nqTUTY0Zs5uv3V9Za8YcDNRiqbUxWJx+KPlu2o2W5lL4ObLSWEdtoKNpCt5zcVi+bnrrUwuAHnh7O\nfPnrXxmL4bKsJA/GONrbzPqnnzhPB/zTB3IYialq1m7KOzt+GiecdRTZ3MFUqhRjwvpBoX8jFMz+\nfGtjrO9rSywpTaP4MJb3xRKaUtgLVA9VGqZlcqusLbFUh7ORb9//xOvlCy6sFBNJEmmD4CfLdD4x\nno8YGRjagJgZ5lf8NJJD45pnbLOEaQQfSMNAtYFUle3bJPPl6x+RNvDP/+P/4XT4kVo29UTDF+HQ\nKuNxxMWZP//xD5jkeXvRUPanH04cHwYYihbMXvSl6SDXj+2/+/rleC9zNzLfjABEtDvZzMa3i7lp\nDrdR/+4A1Eku70kj259qjV0lws9Wi2wF8J54oq+tIPwNgri/x/cQrAh/H5EWtoqNMQqHtM3gfZuK\n0Qlyr7si+5ffCv/9y+n/jO2GBd27MVNVKJ10x0cRcBZnDMEa1k4CMp0EZJ2jNYVT1xqxXhRWEfWz\n1OmyQ6spsd7s4JEYAAAgAElEQVSu+GFgOBzuutTWdtGy9Ou8Lf9bg1ySQjdeMww3MpDRj4u8Roz3\nd+iGtkPV2w5Wp3HdYZZS8CFokk3Je5Fdl4W0LByGgbLcduOFnNRQQPp+ZY257zOPiNXvnZKpJWGK\nuqDEeaaUjJUu3ZFGK4XgLE3Uaq2i7GBjJ3JOvFxvmq1nDKfTWR13SunELLtLbaCxxoW1RMwwUUxl\nXVZ1UNkZxJtfsGpX6fdmblXj5GolV7Xba0YZvFs8XSmaixiCuv7YfuCmnHouqk6kznsG76k1k/te\nOQTV3op32BB0r53VMGI4n8iLugOVNQGOCpRVVwBiDM5ZvPPElFjfVigj0+MnwqdHMg1bE+vnv1I+\nf8E9/oroBiSBa/ocu42xPBhy9Yg3hGmkLpE8r7So/pvry0qKutbw3pBt5VZurJcrt2/PtD7l/svx\ndx3TO/DweOBBfsPTcqHUhneBnArXy41pnJjGiRivrOuNGmeu37/iTj9wOh9ZW+HzlzdiKgzTRK6F\nNa58HA25XrmkG7l5WjtQ3UAmUjQpnb98febJW47OcHl9JefEcRwx2SA5k9eMGSxlzsxzVPu0aVI3\nrZIZTVA7O9OAwkLEGouEgBWhxkaNFue1Id/j9bYzDCHXyrIupJQ4hcDz8wvOBT4dzthrJHXnrp/+\n8GeGwwF5ONEMtBRZUibn1JElOByPjMNIrGrQkbOyvVsTcoqIVYax8xpIn0vTpq3LT6ztzVvOGjBh\nbbfIa9xZsyDNYkXdnhrqnZ3ywpoWbquw1u/E/IILje+vXylt7T7JATMEzucHDuOReCmYqj7Sh6cH\n2tSYXeOlFR5Gz/R0Yni+kL06P13W/py0yhJf+cvnfyXGyMen3/Lpwz/j3VFtLW/f8OuNXz94rt+/\nEi831ivkYrBhxAwHXHCYQc8PWtZnum2Mg3/8+m9Z490nP9mLUpN3xJftL+oHz/3PbIkiqkFqcj+Y\ndTjQsOR3Z/w/eB/3X98KYNuL9FZAdeLc/xsdYqUXPdrPCuz2NWqHJhq2O7uYTtTQrl/DsqsGKFPv\nEoG/eW3vx/absPXw5WZ6blsTvHHYcEQmD7WwzDO5Qnh4xEil5rpb0hkjpHUl1Ybz6rBScsUYr41M\nq7rDHEeGwwFrDSWm/rEpRNiaQr0q+3DaQaITiqa7b0YI+jCnqNPXxu7dAeu6WRTaff/SSqGkuzON\n7lCVFJTiquSTWgjOYUS4zQtSM7k5Hj9+wIjler2RYkTEEaaADYEiwjCOpPlKKwpbxv6+KIUSF7KA\n8coKdKK7jCqNatXEvCRR/SMW486M4SNiDGmZdwh967A1kq1QmzY2zjlK3/kF7wnOkdZEygnv/D07\nsK8oCnTnE8BpF0/337W+KWkpKyFlmzxS6obvtWE7ld0YNbiY15W4ruqfW5t+/scT1jniupLmFUQI\n0wFxntSUIe2twzRLKwWD6V9Pp19pBikNEYc7n3DjibJkaJn4+kJ+e+P44yeyHSm1aewZKp+x1jJf\nL1g30ILFeIdBMGPA9MSZcrsh14F4bZqrajNpFqyrGFN5M5WUIufzmSUmTucjp/OJ4+nIECaCcV0M\nL+S8kNaIiGU8nRiGI4eHRokLx9OPpPnGmqI+O8MBOxSuaeG2XIh5Id+ahpSnxDT9hnyNYEd+/M0H\npsNETAv/+ad/46f4jVsOuPPIrx5+y8kfaW+vpLdnmoXTNPHl6wuTH/BiMKVCKkzWMYoji9CMkMrC\ncZwI40AuhRwTsSVNOKJwub5yHD7h/LCfE601btcrv/8/vyd7YY2R2zJzOE2cHib+/O8XOAwMxxOf\n/q//yZwKl+tMu+hqhaBG/s55vFcD+K0BjGuEkjDGU0SRvlqKenLXQq4N+g5ez10Nl19Xva+kFGzL\nCjm3qiQ707FF03qsHwgNZ4RjGPnxYWJKC5fnTGurmkfcZqQ/j36c8OOBMB4Y3ECVhefrK9UZvo+V\nm6mUAq4Bx0A4Wfw4cHCBJemKJ1V9XyrHufH5679xuT5jbOHHH37LbSk8v/6RY4B2u3H58kKOMK8N\nscLHTx94/PSEc0H10aRuMLM5pNF1sn+/bP63jAvu2ry716qIki62YrNBqrWqI43p0MeWarEzNo3B\n1PemAJ0s07ZbaVsy96N6m+j0+eX+u/4GcpY7aUV6Z7Vj8L1Daj1/UN5dkI0Is1XRTeCvRVf3RiVv\nVdZAK3+/DelFO3aCD9t30aE249VqTKE3oxPnoHsKPw7cvn9HxGCDQrQ5ZVpRPV/NTfM40TQO+oFf\nW8VZLYRxTf2gv4dp7+5JsBcB/RjsLiERI3shLLX0G0eZsLXDPX/zbarzT4eirFOm6EaoMKIdrLWG\ncRwIAiYnaJX5eiW0QSfPnMGp8aQNvusgF9w06XSMyjByiqzzDTFCLhHnJi3W64zESPVOiVdWsN5Q\na6YZVK4hQnOOKspOBXYnna3z2m4H3f3ed9Xb9dnvuqZwaVwjCDtxxHQN6H2FoFNsWmM3xVDUYNtN\nb6+cs16zji6IGGKMrMui5t09scKPo+5/i14TOwzkZSWlgilNTc1PZ4w1zNcrOReGThzZvvYa1RhD\nrEWCxVrBlkp8ubK+vGGmA8l7xFgcjlrU0ar0g9ZYq6HiNvR1BkhwmMEjq0UsHE9P1JyI65WSLszL\njLSEsw3rVdu6xMby/MrX768cjhNPHz7w9PjIOQxqXCAW2yppbcxx5vAoDMZTijaK4/GED5na4GSs\nerSmN5b1yvX2wuvlC41EsA8Mo2UcPgEDQzhxOJzxAYx/5sdff+Rf/+OvXG4Xjv6It55wbEyDoSwC\nxjOeJj4V4fWixu6HYWI8Hsi1YI0jtkgumdAMQdQIYDMU0R21BmVbJz+DZXU9Uvjy02e+ffvCwz/9\nSCyReV3IpfKvl5k/fv/MlB+YmpLicimkuCgKkUZM14/avvZqre1s99bAipByRIwjeA9GJ8a2Ec7e\nnavGaMNpOlHQ10JLCfF6RpTtvm4KYeZaMEDwqqIIAscxcAoQnw01Zd4uzxyPJ8IUmGNEjOcwHgjG\n4irI4LkcLc9x5i2vrFnTmdw4UcSqXaCI+lWXd0VNwBq1Oc3lyuUW+enbyJquXC4XJH/n44cDr99v\nvL6qGU2jItYwniZksJjRs7bMWjOpVcpG9KnbN/n3J7f/VlrJHVqVvaAp1Nmnyfe7yVKoOaOsP72J\nFEI0OzSoZ0q7Q73bkdQLjljdZ+2T7BaL87M3xb533Ar25lTzs4rW33ftF+G9acH+W3oGotkgSzWg\nVUZqybtGUVM//E5Y2os6bR9/9wOxT+LqXEL/mhloZDLOQOtfzzhl2GE1WaN2o+7aY6xKaXuCiXTC\n0Kb99CHsrkM5p06sapSidO8GqnGsVX1e+/e/E5YQgu9/v2zxVuoMtH1AmxB6J/j06+Oc1++51d6o\nbL8OWIcNWsTWFCEligh4T7kszLxRncP2iQ3R0OxcKn6a1J2m6s1cllm72TAg1iG2GwmsUScR5/HD\noBMPgSYW57y6M4nQsOg/y7auUTvEvrPd78e6SZtkl8zoFMl+D4NeI9DJ1znXIXu9kVttdwP3zWO3\n1nsGrKgPb2tNTSmcUwZwqcqSbmD6/ZZLYRjUYSml1I33LbWxB4cv1wunhwes99yuF9IaMU5JU6Vm\nfCchpXmhtko4HAijx0mjvl6In79inKU2DRI2peDZoP970IAPA9UYmjOd16ARW82K7skHjykjJhaI\nB3KciMsLpBs5r9QmmKqNovMQBmGOK7e//JHnl298HM88Hh8YBzU+//rTN6YPZ7CGnBomow5YAFid\nEESN+F2wnMMDh8OPPDz8s342xlEKPZNRENGM1Jgu/OnPP+EHmIYz1/xKromX6zMH7zDDSJkc3gRu\nNWGc5fT4QKranOacsdbiMaxLwkghWCBlUlXWbEyJlAs1LbzEZ3748DvG0SNVd2U0eLu+8Mc//x47\nCofHIwXdl6c18+X5hRrOrDJi8BBrZ99WKBmzLrQx0TBICOoTnDO1NmJMilpI//y6RE2zdh1NBINR\nclI/v3Ujsw0CyiJfb5H52pjOR4ztKUB9wVdoPVu10vKqrPPmsa3impBvhXmZ+fjjJz5++sjnzz/p\ntI9gixr+SzXYMeBsoVxWUikqxGoNsZ75+sJtThyOZ5zzWByStDE1RVFDaw2lZT6//YnPty8MNP7l\n8Uy6vPDl8zPznKgVSmsczwcOj0fs5Ig2E5s6+hTRnW3jPvn/I6jzlydMo0kBbZMhdJ/XjQxi5S6M\n3dxmjNXOpBlRLwDuhbK2dx3OBsXu/2z7JKpw7jYp3tm0/8U7ZNtVbgbq74is+yEGTeHW/XWfYtkY\nntsULNJv0K4jrFt4srBNxFtRVmj0nqKyTXe1P2DOeKSpTo+6edAqozPFiAQV/5eqHqo5Zd3Z5oKz\nBhfUGiyEoNFK3VDZWtt3pYWUG+k203pRqUXDpdtGujI9seQdWWn71rcmY5ska6waESV3Ae++5+s/\nrDGESRM75psapN+Xz/pXWu/7jrtqCsh8A2PUFCBfyHOGAENQu69UI01aN8Uu1KwkG+fUCzRH3fXY\nEBAq3ltEBnJrlJSJl0i7NMwQmJ4+MNjQC7d+btZqt1py+dmkvb3tjSDjvf9ZsbTW6k4Ihee3AhZT\n3AtsTXn/Wikl5psSt0LwOnlaNdU3/esaY3SK7Kkk6xopKeo06PTZccNA3iZ/1MrMyMaeBrGWlBM2\nDIi1zLcbJesuz4bQyViqS02djT2MBw7HA27wpLcL3//9/zCMJ8bjidVCLUKtGXwAUAP5zuLNOfep\nSdmIDcAoOc0YQZqj1gBBkBJwaaDNAS+VeLvQYqQsKyVHok+knGkSQSopR9LrjWf52iUEhbf1xuPR\n8Ie//Ae2GJ6GM8EHbtcb4zRyPJ6wXrAIteoP8HszomsCEFN1wkCZ1UYcp+MT13nFmSMfHr3eIqnS\nguGtrKScccaz1JUHO9BEKNVgrbBeL+pvK4JvQhgmvIfaPHm98vXthXmZKSnTYoaakNSwFayom9i6\nRn7/xz/wPV759Nt/ZjoeuV4WfBZevr9QnSd8+IQdjrhxRGpmma+UZhAsLgzgPGu/Zzc41loIQ1CS\nVUuMPRC+mYEijiYexGKNkKWyV+9WOy+kUVLSYcF7XVV0y73az2aM4LzBmQo14awGlUsfGobgcYyY\nYjm7EydzZHZXnm/PzJcLmawrgmzAO40vKwFz0xD7wop5alzfZoKxHKeJOlhsWhHbWb654IzQ2kCx\nlWgb3iceTydG7/n8+5+43VascayLprccz0emhwMyOhKJ2LKy65t+b5tLwMad+XuvX2bJmkAWAclQ\nu9F2P2g30o8Yg+lVavurjLXqVYgyReu7P7MRUTZP01bu0pWNUMK7r6YT7c9DoN//+zb93k9B/SU1\nDN6mo26u3t4VSv1K+y7qvl+Vnf0p1hL6RPGzMfdnQ6bsPwRLrU0z9lpDitpkuaITjCmF2jI5rcoK\nNYU4v+GC37s9nRjrvcCh2Zs5qk/lBgGKCLlkqIVSEtaH/iUqzgliC0YsaV56TFfYp3jd6285l90T\nthYNmzZWGaaYfUKqKWG919Du7gNbctq72HuTosw8nFClEbxjOBxJlzeGh5GjD6RqeH17xSBY53ei\nzvE4UKqwrokw6PdSaqPiqG4iF0FKj7xyem19GHTaLEWvT2sQE7FeMGHCBukQy0Ze04Iv3rMxmrcg\n7u3gST0XdJs0rRjSZpnYYVlrrUZVNdTmLvepohbGMOC3nZKFfuMpgxI1iljXleCDsmt7o2n7DzOM\nalRQlfRTct6bOjqSUtaoOyzvSTmTc8Y4RSj2ycI51riSk+6rp2liGkckrnz//b8DjeNvfiAVbUiD\n6MSoE22kFmVEI4JzFsRSN0mOng50vrLeixaatZhmEG+xBryBGDNWBtzwxLq+UeqFsixQ1eO3zYbs\nZsTdECs6VbrK89t3XpcLp3Agh4W3l1culwu//V+/ZYkzzhnO55OyQ51+nqUbZajbVetNlzbUpahm\n+te/+p9c54Hy58Lr9SfCKDw9fsBPE7fLjeYPGH/kLVZivmHE4IMjFQ3lbkZ3juSCz+qkNFhPcJ7B\nD6yXG+WyQqkcjwfGMHY7TXXsul1vvFyu+B8+8fF3v6PNhevXzyzXlfD0kfD0RBGPtQdFqbp7ln/Q\ndcJwOlOcJtdI54gA3XykUsXg7AFjAtl66oYTGNljA53A5jLWqgapW4FK2TkJNEV9DB7jlIBknEVM\nAKNpM4+HwMlbpGQoBjudcR9+5MksnMKJPEeCUxj/++WClAU3BGyx2OSxjGwKjJoyTirp+4UWE4/T\ngdEPXGvG2YC4AQknmlRYC7bos+ub8Gk88X//+D8wbwtvWD58PJPXxnVJjIPHTwHxnmIaqRUShYzm\n4N6Nef5rbsr71y8WTC8VxNFsoBmvrgpNnVCakjoVhq061bjeEStLq08ldaPGS4e+9PduVnQb/NlQ\nq6USN+Pw+/T491/3X/zZJL1DvXfzgb3C7b/vPtu+ZwO9L6kq+L9DtTs02Q/P2rabVqjd11ahPqfR\nN32QFuv0UqwzpqlriZkCMgzUbp1mrVPNXD9AmzG7uXorXW8lXaoSoy7j6WkkPiB94tSJyqj0oBXK\nckOGQMOp6URDs/ma2YX4OeUOXZkO+727HsZoHqjXZPqSlcpetgO1XzTbJSubLKW2ihk0iquWTMnC\n0g3bzeCpwLwuvfs3tGSoRWhFocOaM2tOeO9ZZ3VZkSbkuNnxTTRbWLPeP8YqCaG2xPL6plR6P3Rv\nWKtTr3f7rmbbL5eaqa2pZtRotNQ2TW/3w2ZmrTveHhXXdPJrVY32W2sE59UDtlTsNEInCG361c0w\n3vTiuJHm1KRfLeBKSsQUceOwOzDx7s/VqkQK0xs7ETpDWbW3sRtwb81n6xIGEWF9uzL/4Q/YCsd/\n+V9kb1hqxmIJEkgpseS5AxNK+tFnB0Q0EUP6Hqzmrrnte/AmCTG1D/WZbCJzitjHUY0iksGkCZOf\naHGhrgstK5kq5QhWIVDjDGIcDYdpyir+evnGssxMh5G36yufv/4V6yyPTw88nB44HR44HA4d6YF5\nnlniwhgCp8OIULHGY62uEc7Hj/zv3w18/vbAl+e/IO3I2y1Rm+fp448MwxMxNq4v/8Z6uyJxZbKB\nc7Cs5YpxhcnBsZsFvNUVnHAeJ/wTXK3nen3DDpY5zhxLwduBZUl8/f4dRLWI357faNdILgb/4Qfc\np08s3pGrqKNXVXMN4yemDweMG0hqpIlI7sQ/PU9TzqRaccNI8yO59CaxN97NCGIVFbB9V1dLI5Ws\n3BJjGJxyGaRoQ51WlTy5oDyFTUZoveV8dDw64WGFY9SpMxpPrYUwHhms5qsGHwhh4OVyo64FqQkv\nnlB1oElUBuM4HU6cZOAonueYOI9naurpMlNgsA/UMGJCZDVvmNigwMF5/un8gR/Didf1xjkM3JaF\n27IqnDsEvA9ghFwLRSq5FUprFIQsoiEIovC9+Vkh+fnrFwtmEKXDVzGUZqjiqAjV9DxG4Weano1B\nmDu5prZ2nyS7JmWHAcu2p9smVQ1ibqYp63zbd+5vs+2Q30bi2cJMW/dhfV9cTX/Qd3OD/ULocbmL\nQnpxuAOt71794NyyIHeB/3aYyt2CzqB7FvpXh8Y2O4t1qmsSC62oxavR6CdQCNb78C7pvFJyxoUB\nL5bUwA4KfdZSwJjd07atC1Iq9njUCctszkkGqMg4ajrIqqQXY103T4dYSmcG9wSCDq+WbmbQ3k3e\nuZufO2t3Nq4m129NB1jfmbkxIc7sZATpjiaxFKbjyDg45uuFXDPeqMSj5KL7xr6vid2LNy2L7sV7\nsVjmWRGMLvguOePHAT8csKKkK1aodaV2c3QjnuYn7DhSndXECKtRZTn3z6A3PaaTqECH0ypNv5ea\n9/u0xESKBdezDmtV8s7WAG59q4ghpaiSnH6vtD7ltqqfsabLaPFpsmlg8y6o3vS+tq87UtLcxZRW\njYbzoe+kGykncs7KBJTtM1G7wBxX1m/PkBuHDz9y+f5GOxwww0Rrpu/OwVidXIFu0NCjyVqH6aX1\n6wB20xzTcEQcgkGIJdJQaUYIg9733mDqgKkD5JGyzpS4UOJKywapWRuGtWBaoRmQIBRJpHJjGAdK\nM3x/u1BqxlVDfalcrjeCfWY6HJgOE845bvPMPN84n854awh+UC/a1sil0YowhAd+82PgePzE5fad\n+faMDwGMp6TIaB2rD1zajSmc8cMjrzESyxtWZs4ixFRo1nOajhy9IVNZ/KCmU51z8O3lO9P4a57O\nR5Y08zbPtCYsLzNvl8gggePTJ5iOJOPJVTohrfT9d6MZtdur4sito21p28OLNsLOYao2h83K3vRa\nt6F5+pmpWTrYvrZRdnLDGj1L47JCNXr96WdAQ+F3J535qvttVxy+3ddo1jROh4G//vXGOjvGw4B3\nnmEcsbMlxpWWGs1mRBqpVqoTjocj5/HAUAyD95yCx7fK9fqGMXAcBowdWY1QrSOaQnQzR5n48fTA\nD4cTb3/5yvXLC+m6sK664hi6guB4PEMTSqsUGkVM31+KelnLnYfxtxyX969fLJjx8qyZhH7EuUAR\nQ2qaSWid0wMzNTSEdWMy3d1/aOpMImx2bPdYm1oKppMX9LfqQaMIVp9kAWoG4/upbHrd3P7MvSBa\n29gnStEbo/Y900a6YH9//Ozn5mc/lf19itwPwfdGBduv7cX/by927/qorS/a+/sUo7KOTqjYCBQi\neiBJTxhppftMbuScItolcd9z9iuA2EHdg6CzK40ShTqs64Zjn/ravuNViHojSbW9EVCT87Tb0G1L\n8NoLq3Ndi2iFoUd+bWGzW8dRW9t32SlGclyx1qrXJTBOB96ev1FbxYcR663KWKwjFTVP3OBHRGEk\n22E2OkRYSiHPM1IrwziCCLkUzZu0nvFwUDlH0ynfhRHrwj4Nbf5e1jmIKmFo3XBfm5auW0XtsnAC\nWWUCukmqrLcrDEGbAekJMcYQzgdyyZigLkTLvOhU3SHwrRinbmHmQiCuCxqXJIhYpDdM4+GwT/Wt\n1jvDnG7iDWD1ey9VkR9tzkawQpOCG4LuwV5fyCliHz4wu4m4NGwN1LWp3s4JZhyxHdbsGHZnDpv9\nWm5kkT1ftlT9PpzDoI3OMs80wLlRVzIt08KEmpHq5Giqx6aRkqPmJq5Cjis1zrS0UGrmdtOVA1bt\nKZeok7xYozaK1ZHWzOt6xb294rySW3LJCq23xiEESq4a0G49tdsgWmOwMvI0TZzGD8znH/j++pl0\nu/J4GmjzG1PLcHrkfPw1h/FHbreF2+2ZNX/j++2Ntxp5eBh49APB0olBEUrD2fGeG2kNc8z86acv\nfHl5YVkKNYwMDwcOjx9gmFjpDVsG1wS3M2vRg70nrtSmaE5OXTstulLwTmF8gFKSGvubzXWsR/tJ\n1TOpVmgVZ9BouE5oMqI7yXWOrGvC+6CxcFYJX4WCM1soPTSrTaUyZyPGFI6nQPupsLTM6A7q8TwM\njMNEnrOy/VOkdi6FVMMqKz+tmXw6c7OVh9PI7a/fSAin8xlvHMmPfI+QQyNKorrGh+nIp+nE609f\n+f5vf8Gksp8f4zDhhjPTwwPDYSJJIVJVBvYOPVJKjvTEoTuy9l+9frFgpvVKXm9KYXYBEwLGgiFg\nUbZlwip7rqJTVI5dOiH7Xqa19jM2LbDDSvTCUWvFGSX61KoHjLOmf7hrdwYy+ilh+ze2FQC5F0oR\nZaZ2l5Sf70W5w69/O1VuPzcGpdhpAd+agHteZJ8cRfZiuf099+6kw7Z9GV43c3qhmyN3qHMrSgip\naJdjrKGJdom5T5HOOYwYJQr1vDodmrXbb9ZpwLRzmpSATpnGqfNPyRnE7UYUzjtK7exb0d/X+s9L\nqwpbbt+P1bSVTbOqJb9gnWf5/oqMWoxMn5SCd9jBMb++aqfb39cwaoG9rDMxRQgOvLJMW2us60qq\ntktJdI9IZ6DuhbvWbnDfCTnOMQwDyzKTs0KcwTmCU4LDhnrQLNvqTZsF/fw3spY1PRqtVKpRJKHV\nrkNzvYljS3UR1tuNuiyYaaQZvUt88Phx2ItIzIm11E6e6ZR9r7q5GCPW6d5pTRHnlfyVYiQMA34c\n9lSZVqsyVDta04pS8L0f7/efdqE4a2kYalNyh0GQ2ph/+kK6XLCnM4SRZgIyVqxTFm3eSEJBYeGc\nck9x6Y5FKSsKYfuz8TewlbEWMbovj9crYuwdWerPRjGCONs9gOlaToftJBEWj1kXSpxpaYUSqWml\nZv3/lCqtbUkchRwLy7IgUjAmk2pBctqb2db0+/r68p2SK+d54fxwIjirLNoqeHGY6hAZ8MeRXBLL\n5TPL7UadZ41Qw/RiEjgfH3g4fWJNr7y8/cTny59ZY8HExGOw5DiTl4XH8YyzmXmdeXw6E0Lg29cX\n/vNPf2atFXs8cHr6BIcTxVqyEcQHWm49gq91J52+j27Sr1vt6yCdjLz0tUB/PnZNOn34EN1XCg1T\nKxtn3IqSEze6i3eemhOtJB5Gx3W+MK8JZx4wdFvGjihK1R8GLdbVNrJZWW5fuCw/8TL/RDOR7Bsl\nOJY4M+fcRTWalGJag5JodmO0B6618i0lfqTwq4+PpK/fGWpl8IZ8u2Gs42yg2ZHLcmEaz/zThyce\nc+Ovr29c32bVzA4WyZUQBg4fTwyfzpSDkEzdpYhCH2K4G+Pvg9s/qIe/DMmeTuR1JeWoQv48I94g\nyVGMo4r6Yo7HB0xQeYNNi1qwYcmoKba8K2r7Q/b+DfYPe98d+dAFswZKppWE4hQVWqGuggnjXu3e\nFyoRuk9i24ta63+fvLu53l+c7SGT/l42hwu1zdv0p5t+VE3Laa0Lg80+iW7T5zYDbxNw7WGt3nlM\n2L5Wh4Wb0ro3arhtpu+d9JBv+4ErpFII1ne4hb1wbu463nmMWGqBMHTHmFzYItC2piKV0g3EN+GA\nwrAlZ4eGJlIAACAASURBVGxwatbQJ8vWd2bOOUVsqeRayWmltUwIBmMb1jY8lXh7Y77M5NywHz6C\nM1jRSbQ09W40xwOjMV0TKR0BEIYQCMOgspIYd4RimCZKrVxeX0kxKpO07091t9jUaq5qyLdYS3O6\n17U+sFwXhSetJZes18Ma2qo5m35w+nk0jXZrtL1gmj5ptdo6gUaL/zSOTKcTtz5B2+6Osq4Kwa4x\n0nqG43g4EIahy380RHqDuDcP37Ku+8S5PR9b2LjA7uK0NZybQcamLzXGMMeoXrCDZQoB1xrXv/6F\n9HrDPX2gDR6a2Z+NLf9VoTedMlyHvjdj+HVZ1S3GWJUZbBKH/l62piQuatEo9l1EnHedMCTglAxU\n+sTsnMquWocGsR4GgykOKaMeqFkNHsylQszkslLLSm0rFXW6IWdEFvw46p5aDGKVD7HmwtwWDWGO\nkdP1jcMQeDwe+PThkSEM1EVlO2FwPD58wknh9ftnbLZYZ4j5wtvlhdGvBDcBjjF8Inw6kaaJv3z9\nD+x1oYll9PDw8QHrjnz5/EK6rXix5Bj5w3/8gTUXDk9PuMMJ/EgNitpZHxBjqS2C61KHPgk612U0\nZZP56HV33uOcYRgH5mW9u69Zfa6FRjAGb0R9lmFPAKq5kFsiFUWkrMA0GobuB8soxFtmvb5hUP9r\n61Tba1pFSkWqgVIpLZPrlc/f/5O/fvl3bvE7fjDMHJCycFlvXNaFGJOGxutilWQhmsjD+MDx+IC9\nrbwuKy/XG1d/wATHWCKmrqRlwdiB03QgLZVfuSOH05GPLmCef0Juq6KYFLwbcIcB9/ED4dMZdw60\nPmg7TLf+q53521Gad8PQ3zaD71+/WDCL9QxPByyNvK7ktOqDUgvxelEtlHXccsT6oRecgnENJ0Vp\n38VS0c6Xvez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cEK/XLrZKpJFoyGB4+/7M2/qZ8fgDpjhanhE816TPwBg6QarV7uCVqSUz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\nCsaLyoLDldY7sT6SVFIVGyOdey5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 8))\n", + "m = Basemap(projection='lcc', resolution=None,\n", + " width=8E6, height=8E6, \n", + " lat_0=45, lon_0=-100,)\n", + "m.etopo(scale=0.5, alpha=0.5)\n", + "\n", + "# Map (long, lat) to (x, y) for plotting\n", + "x, y = m(-122.3, 47.6)\n", + "plt.plot(x, y, 'ok', markersize=5)\n", + "plt.text(x, y, ' Seattle', fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives you a brief glimpse into the sort of geographic visualizations that are possible with just a few lines of Python.\n", + "We'll now discuss the features of Basemap in more depth, and provide several examples of visualizing map data.\n", + "Using these brief examples as building blocks, you should be able to create nearly any map visualization that you desire." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Map Projections\n", + "\n", + "The first thing to decide when using maps is what projection to use.\n", + "You're probably familiar with the fact that it is impossible to project a spherical map, such as that of the Earth, onto a flat surface without somehow distorting it or breaking its continuity.\n", + "These projections have been developed over the course of human history, and there are a lot of choices!\n", + "Depending on the intended use of the map projection, there are certain map features (e.g., direction, area, distance, shape, or other considerations) that are useful to maintain.\n", + "\n", + "The Basemap package implements several dozen such projections, all referenced by a short format code.\n", + "Here we'll briefly demonstrate some of the more common ones.\n", + "\n", + "We'll start by defining a convenience routine to draw our world map along with the longitude and latitude lines:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from itertools import chain\n", + "\n", + "def draw_map(m, scale=0.2):\n", + " # draw a shaded-relief image\n", + " m.shadedrelief(scale=scale)\n", + " \n", + " # lats and longs are returned as a dictionary\n", + " lats = m.drawparallels(np.linspace(-90, 90, 13))\n", + " lons = m.drawmeridians(np.linspace(-180, 180, 13))\n", + "\n", + " # keys contain the plt.Line2D instances\n", + " lat_lines = chain(*(tup[1][0] for tup in lats.items()))\n", + " lon_lines = chain(*(tup[1][0] for tup in lons.items()))\n", + " all_lines = chain(lat_lines, lon_lines)\n", + " \n", + " # cycle through these lines and set the desired style\n", + " for line in all_lines:\n", + " line.set(linestyle='-', alpha=0.3, color='w')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cylindrical projections\n", + "\n", + "The simplest of map projections are cylindrical projections, in which lines of constant latitude and longitude are mapped to horizontal and vertical lines, respectively.\n", + "This type of mapping represents equatorial regions quite well, but results in extreme distortions near the poles.\n", + "The spacing of latitude lines varies between different cylindrical projections, leading to different conservation properties, and different distortion near the poles.\n", + "In the following figure we show an example of the *equidistant cylindrical projection*, which chooses a latitude scaling that preserves distances along meridians.\n", + "Other cylindrical projections are the Mercator (``projection='merc'``) and the cylindrical equal area (``projection='cea'``) projections." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YjQo++fjT9hmbpubtd94BD++9/1tAMBgOGBYFL774YiDxANP6Ng/v3eXCxhoi\nH7C5d8D3fvhDPv7wI+pyzuxgn9t1hfSgs5ylpRWWllY4O5lwkEnK+RzhLd5ZprMDXDHghZdfwdiw\nquuq5JsvPmfj3Hkm4xGSYBDkErIsxznP5bNnQ7+diMIWpILRIEcpRWMdOpNksRJRYuKmOLC1Jn7f\nYpxNE44AlNJxvqJ32UpjOgjGC5w33XEkUlhsRzvENFXYXEZitWRemRYqCorTYaoGhGAwzLl77x6l\n9TTVnO//4B1AsH9wyHA0ikUXkhHkQqGFXsoQCKzoWchxM8NRYkjIORUxF09E98ULEA6sBJyn9gbr\nJUZBY128Vo6UgqqxlFXD+Wee5+6tm+xtPeB7736P/b1dzp2XfPHl13jr2LuzyU9/9jPu37/Pd3/4\nPbOy5K2336EYDqMx28V+Ql4x7Twm1zmpnOi/g5dI0VMxroP3kpTo0qDStMazO3d80efx/XMTQtWl\n3ixacV0oKBhWEq8i/Oe7PE3XPk/ymlIsOJFjaNmsfYh2oUMpz7x/NCqXDgnpP2uAvrNiwBtvvUuC\nOoUPhWHGy3B38w67+/vMDg4RQvD5F39kNBoxKIZsnLvAbD5lUBSRfR3zg63DeB9RipgPHGOhxnlM\nJJkNdDDsx0XWxgp7w5ZwyzgFIs5rSHPTUrShouQI6UQ+VCkuHiBZqSI5LT54PymxK18ZDEMhIZeC\nQsqOEYwHBV3d82goxwXYJw4lxZpSuVyrJH30vMPxpGhTPrVzodpYgLJjHnfsnxChKtmj2hMVZq4C\nacd5T4MEB15aMi+xwrcbRAuB0pKhVgxyRZZ1nqeOHpG14CqPki5Q50UqOdUNjjEqutjQeIuQ7sQs\n3kdBro9rx4lCi5UddLScglcZ4mVFtJqCIaAosmCl5VqSKYWSkskgR7axN6jKEm89o9EIrUO8d3dn\nB0/IWxU4Xn3jjQg5RI8KwacffcTrr7/O2voqILl48UqEvVyE2ILH871330UpxYcffURZlXz0yccc\n7h/wlz/6Ia/eeJnDy1cw9ZT1jfNcfuZZDvYPmTeWlfWzXL76LL/7/R8YDodcOLfBt998x+F0yh0l\nGC0tIaTm4f4OP/zhD2P/oDFBrUsh0HnBi6+8SpaFjfeH99/j/KXL3PrmG156/TW+/OyPrG9cCN4N\nkkzp+IQOIRxSKxrryRpPoxSZVjHX10VINorg5DkCQqiIXIAgBTM4tqwTrGZcJKd5G6EygUOhpQtF\nn5VGeh8IsQl5AAAgAElEQVQNIo3WEuMCtARgGxvKcynN/nTO7u4+RiicLdnYWOf+/S2klKytr3Nw\nMCMf5O3GbSykBes93UY1BMGW8jZjCkrdBGitMzw7DRqvEqEqUE4EASCTFW6wTrb3aWzwNEbZgKnx\nbFy5ykVv2H24xWeffYpWGqVznBD84Ec/pmoMq2vrHOzvs394yB//+BlbDx7wk5/9NcL0qHidTdzu\nI/qHPAhkW+Wg3c299KH+XAXjtFetJXqEJ8qphDr43ndJe7l/mqNPb0zElETQCfMRIUEb7tc+lkyq\nV8Z0qu5ex/q9eKT9Xx8F68ePF8M8nUJuv9siJJ5LFy5z6cJlhJBU9Zzbd25TVjVFkTOfBaa682F9\nu4hsBOMmPlubF+zaylONDb8zKRjmKnrmnbdGVJ6+98yql34moxJUokOAuhSvoEhTTndyjLSgzRRI\nyviIuRHvFeK7SgtUnIzUjwDQ9EytCB+7tvhGRGMiopT2H163hq+L85LGwkYnzdmIbrYhxhTCC1fN\nsn8HJDvIgwsfICeHwaNRHa2X5NKCFoEkJGM8RaJImXwAtWnwHnIVGIHOB8ZfFq0V4x218FgLlQn3\nwwdqvRVHFhqd1/kkxZk+P4kVGyDYLi6ZcP5chxqjw0wyKDKGWWCtJu9ykEm0UtTzKbdv3uLO1i7e\nw8rymJ29A9bPrHL9+nVu37pFWda89vqrRJ8BCGLxq29v8tzz19p+vf7mG0jAGBMnLWyMvb1d1tbW\nFmKzh9NDrLOMx2OsNaysnUEgcdYwGAxoqGmqio8++YTJcMjZ5RFNNefWl18w393BNYbDPMfYmnfe\neJmDhzucu3Ceg6phMCxwLlTj8M5hfSpQ0QkFJw22bvBK8fGHf+Bg/4DJyjJXzl1kOBqFpGWl2rhh\n2TiqusFXNbPShFl0gdmYZRqVaZw1jMZDBD4qL0+uFSDJCo3zGoEjU7L16PHRGxMBBvWEAgLh86zF\nBK2zgI51RoOCSqSqLIskNsLxQaYRSG7t7+Fd8PjmsznXrl1id2eXS1dewFnP7Vu3WdvYwFob0jic\nALrKJA6PjTmZTRt/8dTGUjeWWW2oIlM2Wc3B3F1crynn0MV4rXeheo+3Ibe3isKiMo6qNpRFzjAP\nRt4o0wyGy7z9/R/yq5//nL/6qx9SG9te3zu4cvU5rlx9DoDrN8AFSmPPzRJt5amkvJKHlkhwYUMG\nideWYD4hjNJtQNd+R0SPvH+2hy6e29uzSQZIXCSxRARKpqpa6dyoxPHQzMEabm3tsru3x/Ubr7a5\n3qJfI3bBl4zf7elx2e9j/2bxS+lxu5JvqdhI9F595wErpUI4ivZdQ3F/O371218xGgXmeJFn7Ozt\nUNUVH3/6IT/6wY853N+lMQF9qauKleVVxuMxzksaZzFOxnBEKpAQkLKB7sfzu4HughiLcjE9ZjJa\nkt8pes+aPDKpIFUDC/FokaYXYhWtxRZkyfTgAJ2Nw5G0dnxCLABUVKShh4pu7fXZwU4IEKo9r4V3\noY3VeteteedD6b1EaGtzxl0oK/qoJp6gaPz/8S/fAsQcMhuhucWlHfB5RaEUWRZgyEwFWDMFZGvr\nmVUNVW1DXo51WHwobaVDXKE2jtJ4ytpgIsvQtuyoAAc/tzHh83v7x/rat/Aep0iPLQgSE9WhZcDf\ni0wxzBXDXDPINLmWLbEi0wqtBK6eMR6NKeczyoMdLj1zjenskK2tba49f43vbt4M42Ua6rpB4NFa\ncf36DcBz8+ZNdvcP8M5y46WX2Lx/n6IouHD+AtYaHj58yIMHD3j+hef54osvWVtfY3t7G+c9G+vr\nXLp4KWxI79ne2WbtzDpSCH713m+QAq4/c4nz5y9jHEEY+QSyhGd3PlUqCkUAAmnG8cH77/PGm2+3\nsKFJsEcLhXSbK1mXmRQ4a6jrhvFkjIDWehVxd0mg0JLbt+/w+Re3uHzlEmcmE4xpUFIxyHOUCq79\noMhizmnInUypByoL3mihNd5FVrFUrezqLMu48qMX4Z3DOBaYxqSNF1GGsjaRbGBoTMzqEpK6Clb8\ntK64dfsmozzHS83zN14AL1r6v3EOa0IRAusDMaFjNzpMJGzUjaWKMae6MRhrsV60pfi882gluHp2\n3L7Grr9e29QA4WNMqYPKshgKKbRiUEjGRc5kkDHINbnuCl2QhB4BkpI9gRls965oeMtCFJ6VoWa/\nNMeEX7v3Wi+hxznwaT8mxeR6yhge93o7IURb9ap9/jaVwaOVQmOYz0oOD6csDYfMq4q6MW26k9Ka\n5649x+qZtQ6OS32K11n0IxcVvRDEgiNRngjBo/2PcM5AByOgMi7m0GrKes6tu7fYO3iIloLl5TVG\ngyF7B3vsTffBCQ4OpkwmEwbFgPl8htQh5DMqhmR5hpSCpSJHWMteWZJpTWktZVUyKxtm0znXnnuB\n1dWzLZM02rloBYNMsVeahf5GSdn9EZYWi0UbkqoMJyUyYmBqBxlw9/a37O3vkRcDXnnxZT7/4nMe\nbD/gjdffoBgUDIohddN0yCBh/S0Vmt3SRCV53LFp+9lDCdpjJ/w/sYOTvnUE2edE31DwLdqX5Ibz\nMW3He9YmBT+7sYE/4X2YT1SY//fv77abILBle/hMz/pSsQqNUrKlanvfYcW1scwaS9PYSFkP95UI\nhAJnPaW1NMYG9le7uH2kpgcv9sr6iG/uTxem+6Qn8I/ehy0pIFhUtDR3JWNydi4ZD/KgLFUgCigF\nWcqTVCGXTYkQg5oUGdPKtNbX1oP7bG1tMxiE2ObSZIKSGu8tVV1zMJvhbYgBixhgtsbyyo2XKauS\nL7/6ildffrUVDAJ4/4N/YzwZk2nNs89cJcsz/u33H1AUBUpKBsWQq1ef4eOPP2Y4GjEqNLX1IVlf\nB/hUCsiicXP/zi0GWUamM2ZlyeaDB1x5/gWuXbseSQPBIwrBchchjO5v17dS6RLLRRxULbr6rXEt\nMcgVWgTl4J3lg999yNLSEquTZfAOQWDYTpYnJCq88h6pBcPBqIWBtJTgBCpLyWgirhHXGgV4F+JD\nhNzGVplGQwABxli8cygtmZUNzgZihcgylFRkMkiPpjY82N7n9r27XLp4gbMXzrO1tU2WZ3z5+de8\n+tYbVCbm0PmQ+9cYQ9nY4Fk3NpB0nMMa146vjaQMn2K9zscUJnh2fczXW4dH92O3Z4QgFLtPDM9k\nQEQURIV0mVGhGOYBIclUEnLq8d4fRwQQQQYtDzQHpYnG1qKQPQrbPup6C2lbJxwTPkK7vls7AVYP\nf5fzObgGnEVJzXy+z/60Zjqb8fIrr4KQkQfRE6ZEMd+6nxwTGlIcPT+oiC7Vuq9Ek+fdqRIB3Lrz\nHbNyFnKinaG2gaUsRYjL4zwqy6iaJsbZHHmeoaXGmIZpWTIej1GE9I95VXJwcMhoPGGQZ8yrKqaH\nKDLAORu8IyFiegm889pfBGOXjijjvY+kHMlhZU56/EfMWUBMjj13fOJAZvSYpuHjTz+iGAyomxpT\n1ljv0Epz6dJFqrLGWsP16zfw3nP//ib37t1FSsGljbNsPtxn4+wGq+trcd56c3Viv/qfJTkUZqKr\nzxsVZ2KJtK9Y7FZGuoxr13q4zMpQ8/bVM3+ewvz1lzuxW+nfGCilD6Ecz11KxxLK5FygQjfO4SKu\nHgRntNDb/KMQxHb9Ch3RKpBCcH51yO2HM9oco5MG9KQDYnE/p/WfsHcZqf5FFmCs0UAzyFSbYK2E\npFCC+5t30DoQoBJOf+X8WbzIqI1BSEGRFwvpNsYYqqqiKAryLI/w4CNamud2FIMXmPKepIKmtugs\nlk9wMVE/MhmdD1b7QKuQDO0DtB1g87j9E0gQLWfvPCJ6maHaSPDow+/oNTliLCDFACwm0rhTYXLi\nZVMCc4DnYwqBEIyysMm8j/mBEvb39qkrw8p4HEhSWiFUTFCXQWQF48uitY6xFIUXKW3DLxDFW5Qh\nWUPJm/CBNYiIVHwfmIRSCLSWAfmwDbVxPLj/ECEFBweHDIcF3oNBMRooijzn0y++4uLFC5y/dLlV\nhCYmnlcxd66sgydpIoMvKOpOaSeozCVWuO8StqWAS6tDbm5PF2D4/vNJOpIK0Ba1UAKUkmgtKaQk\nz2Q0AjWFViE/ToKz0ZDiuJ5rdWDrGYaeTQaaaWWeStgeX9fJdTnho55xm7xKYixRJSJOJB/evXuX\npiyZ1xXWOt5+8/WQn2jjeEbBedIDRZH6OL1+opXd9S1KtZ7itcags4zbd2/xcD/IytXJGATM6wol\nu/gYREIMQc6Nipy96ZSmse36sNYhpCSP5fWc9+hMo4CyakCA1hpjDE3T8M7r76KV5t8+fA+tFa9c\nf73nqdH2VcqQijZvXKcZogKUHJeloTLSYxSXEOAMWmVhgEUXx6zqCoGgKIo4v510aO8TRA/DXDGt\n7UJfQ+pJOCGZwL6taySPXDO2I3/37bgW4o1z1j+135+0bia54qVLSycqzCfGMMvGLODA7fWPrP8g\nn/oWCAuTEEo/CZyXbe5b3XQwVXphcGOTBU4rvFOZKClgmGu29ksS1Th1pgsEi1atn9Q6mCdCdLFA\ngI65k4PM0BTBu2qsDxCsFOTxzQMWyc3vbqGyDOcco2JA05Tc3w1vDRcCTGMQUvLmq28ilUQKhcpH\nNM7TVObEPn3y2Uc0xpBlGS++cCNCIxIhDZ9/8TnT+RSBwDqDaSw/+sFf4oGvv/k81DYlsHLxnqZp\nePfNt4LCdJ7bd29yOJ0ipcaUFa+++hrGGsqDHaqm4d7DXS6eXSczNXowpDYWVMZwcobaOIwPxkyC\naBO82OaCmQTdxnmKXniRBdJUngUB3hQZSgaDKVOBfZwNl/j8q0/Be569eIHhYIh1NTJCblJJcp2h\ntWIsMowQNM4jsHgvQYdZT+SEhXn3keEpJHUdYrHCS7yzbaqPjCXztvcOyJRid2+PaVnT1DPubD7g\ntbfe5GBvn2Y+ZXunYV7OOX/pIpP1c+zOahpnaQxUjeWwapiWhnndRJQkQMH9d/oFhelwPr3nM+wL\nkdAYH+JNmZLc3pn3VkncV6IzTpVIm7BLhdCqSx/ItWSUa8ZF8HqLPFb7EZGQYsPePlbat1uYtBAq\noLRjWnVpYMfP9+CT+ZKEsjsm3DpSV+/UGDNMkHmK++kI+b3//m+4fOE8d+7eRWvN8y9cZzieMK9D\n7LZuC3UvyjiJC157GrWo/aaH+1jrWFk90/U1dubgYI+lyUqM1YmoDzyffvExQga0TIpQJrSqaubl\nnAtnz7O7f0DVlNRNhfWOw9mMeVWhlaaqK4xp2Ng4y3Q+xzQNk/GYeVlirGU0HMbwgqCalQwGBc57\n6trgnSXPc+ZlyV++/SOWxks475lOD/nVB7/h4d4OWaZx1jFZPsud+3c4PDxAKYVWmiIfsrG2znMX\nr1BbEYibIkjKm7e/4fOv/0imM959410m4yXu3L/LhY0LAZ3phWFCUYBYVUeEMEllTYssJYOiri3v\n/+F9jGnAC9545Q3OrK7hfXrRXSgyAmEdzqoOETgp5SWYOP0iqMEATkXtOi/z5HYixNuuxaRUu3My\n/WiT6ikq/ahuo0eB3GetHTfX+uwo0Wr0WNQnBnYFVisGeaL/RoVpIiEiKs/EJjQRYxaC4AEOsmil\nL7rTnUJfHMD+dhAQq8qkPMlIPIpCZphpBkWIA4XydMTYkODTTz9CasF4MsI6z97+HuPREKXDhrh+\n7Trz+Yxvb3/LZDDm48//wMHBlL946y/44xef4Zzn+vMvMigKPv3jR7z+yltMZ1O+vvk11lgOp1N+\n8sOftHUv3/vgN9RVyXPPPc+D7S1UrqnnJVLKEPtTGbsHB1TzktdfeZ3pfM54NGY8GuO9p2rmfPbV\nV9i6RknJa2++wea9u9y/d5MHO3uMigHOWgYCNu9v8errb3L3zl0uXb7E4f4eeztbrJ3dwFhJJhyN\nE0jrOhKKC8I3LWXvQ5FyrKeOSiTX4Y3qRaYYKonQKrw9xqm4+RyTyZDzF85jyob96ZxRnpGNB4GR\nrCXeefI8o7EmsB9lSOgXMib6Ry/MRRJRErYxBRjnIMtCtZ9Q6FmEggBVhVfBws6EpDI1+wdTnLOM\nlle4rHKWllf4+qtveLizB0Lw05/9OCIiPhoMUBvDtLZMy4aqMTF5urcjxJGN4luaBz5iCLY9r6ui\n0r6gPX4/JWoloCkY4T2hIRP7Md1HdCgCC9SOdmMEpfuYvvZa/3V0adN50T8metZ8+umUTiudjlw+\nMFMXPT8pAvFrdrjP7dvfsXF2jYd7u1x97irr6+fC/u+Ncsjv7J6yLQgoeoXdo2D/p5//t9akHw6H\nXLv6POc2zpMpzT/98p/46Q9+CoC1FqU139z+mueuXMM5S20atBCUVR0L6xvyPGNnfxuhFdorHJam\nqWmcwXmLVwqZSdZX16nqmvl8hlYaGYlxKtPMynkYJ+dRSlGZOhiBGubzhoEakBWa3376G+qyQqmA\ncmmtyYvIDJWOj774kMQgnlWzON67lM0hu7ub7JclZVmChzMrS1RVw3g0wFjLr3/3rxHpgI+/+IjR\nYMhf/sVPSGzUTuj3iFIireIOZxwORwyGQ1Ym55iMlnA4qrqkKAYRbe+cqoA+RRlODKH01wZ0cc9+\nHxKSlJykuLZEzHIWXrT7pnPk2mW7iDb2rgchPPeo9hSQ7MOweBIenq7r+g+ezMLk351wrdjlNibS\neoOJEhxgQOdEgLEimSKRKhoTKMAbywW3H87bIG2bk+MTjTgp0e7aqQOplF16Q0eoTdlV5sl1VJRa\nxTJ2XRWLupzxzbdf46Qh05pc67YyzV+8/AbTyvL7T99HypCDNBgUlHWNVoqmrlkaj9mfzhBSxIo3\nirq2fP/tH/Lzf/k5xWhIVVeM8yGv3Hg1jlE3dh9++iHbD7cRQDEaUM8r/uanf8vO3jaT8QStM7pX\nqcEwl/zhk4+wiLZihm0MpgkbPMtyrGnQWlNXNVcuX6Kp59jGcff+Fqac8dZLN6iamsl4wkff3OSV\nV9+K6ICntAF6TCSWuraxJmb4PKVEKCHJtGCQac4tDxjkKhQM0JI8jm+mJR+//z7nL1xAaMXyZAlM\nyQe/+wNvvf0Ow0Ee8ltjflem0ttXVFA+yoeYZqytKlXaZN0+SGiEMfEVXdYE2Nl79vf3uXNvB+sM\nn/zxC86trfGTH7+NNY5/+MefM1pe5ez6Otdfeol5U4dEcReYv/OqYVYbysYFtqzzAZ6N6IjzbqE8\nnPfE8mYxTu8TQ6+3YaXj2bNLfLF58IjdRCugklUf0qFC+oBSMhLVFINMMSwyxkUonJ5rFbz7CHMK\nEX27HvR7krjwDpZGKpB+3OIuP+YVdJv9BD0pOp3te8eIEHOcMCU8H3zwHsWgYDwcxHXW8M7rb0VC\nS7jvKFcxRhxSDoTvVbfuwdjJiPrX3/4yxP2sbVnHf/Pjv0MIwe8/+R07ezv81V/+Ff/8L//MT773\nE37xm19gnUFIsMbz93/z9wC8/9F77O3vYBpDMRrgbBMEtQhr3TnHzuEhw8EQ7yLjzNuOOOk94/GQ\nxjRIoTDGYq1BKU1VVeR5TpEpVpeX8UIwygr2ZlPqKijLuqyQUjKrSjwy1NUdD6mtYV7WC8/urWNp\nNGIyHHJvdycUaNCKpm7ACXKt8TjOnjnDwfSQe/e2uPrsZd56+Qc0jYnzGRSjkpHdm+DFnhElRZA1\n//WX/xBZ7oL0Nh1nHefWz/P262/zT7/8R4yzaKm4uHaGncOKd996l8RwTbJbLGgT3/uXBZj1aVIL\nT7DTkm8V390c4F6JY3mgufEISPaJCvN/+z//AZUPefHaayEh2z9dB08MkpPqmHRaPb2yRUrFp59/\nyNaDB/z0hz/DWItUGSCoG0NlLN47locZ24d1m5jr2uTVBHdFMkXMdzvap/AmkPgWEBnjPbEaj5J0\nbwcRosfI66BbIQRlOcNYw8rSKgCTQgWoCtjdu8eDnQc0dRNyjISicTZao5ZMZwigMk1IKjY1EIR/\nXdU46/ibH/9tqDZz0niKThi8/+Fv0DpjeTzm4f4+L169wXgUKNrDXGK9YGd3j+FwxPu/fz88m9as\nrZxhe/dhu/iqqkJrjURF0o5naTRiaThkOpszzHOquqap5ggRYp3PvfxKoLFbT20dVWOYV4Z5bSmN\nCc9mo0ElA0Pv3OqQcaZonCBrx160sLeUku0HD8hyzeHuPvc3t3jpxRc52N9naWnCymRApjO0DmxQ\n41zI8xShjFnKo5ULrPBQsxKI1WDAO0HjPc28ojYV0/mMh/sVt767ya17m9y4dgWD5PV33uHwcM53\ntzZRSmDqGVevv9gaCbOq4bAyMY/SRSOOrqRXTMlJMbphplguGramkllpWiTFEYSx8x4vHErIlg2u\nEL3X6oXr+Fi4NIokiCkWKblcq5AznGlBoQVFphkNktLMKOK4C9GlPqQ1JkSwsJ0zlFXFYDCknE8Z\nDCdMCslBaSMhrIfm0JUBhL5R3BdSgpQX2fqjC55t3GcEdrMgVNZqmprRcMhvPngvlMPznhvXXyLL\nQ+7rMFMhbOCOsyu7vQv//Kv/RqoT6r1v0zqMMfzNj/4WcPzyvV9gbSh4MBgMKXLNhXOX+eiz3wOe\nPMupneFHb/2I6XzKJ199CEpS1zVZFkpj1nXN2tIYYy37sxk2KmYlFUJC3dRR3ulAmLE2xtEj2Cck\nG6tnqMuS82trVHUTDA8ZXmQwyPMQPqwaMq2pnaN0loP5HO+hqmuKPOSF4wXWuUju04zygjsPt1FK\nMRkMqeuS9aUlnA9reTJZYjo9ZF7XnFtaYrcuGeoV3rjxBtY6/ssv/y+MM1FWyBiicQjhObO8zs7+\nQ5TSsS52eDumaUyo6QyxAEYMHyiFVoqL62t8e28TEDgv+J//+u9bfkhd15RVyc7eDpnWDMdjVier\nGGNiGdXw9mLwbXjQ9fVLWoTAcdXXrZGj68aVD/nBK8//eQrzvW/2juDYIQD7dEozCfdFtuSDB5t4\n4fnki4/JdIZ1DQJB09QorYM3KCQiVtNorGGUj2jqmmcunuPbe5ucWVnn+WdfAgFaBfKCFLKtNWhM\nTF/xXXmkMJD9N4N0CbipfqRWIZdRSk3TVHz13Re8/MKrhGoxRx8wXG9SKA5L0z6wViqmk5TUTYlS\nGVIpdnZ3+PLbL/HCkedFq/isC5BGglmCsjFkSvPqi6+xNFlecDeTYHv/w98iBIyLAdPpFJVlrJ/Z\n4OK5KwxzibGhDOGXX3/JaDTCmIpyfsjewZS19TXKeUljQlk77wHneebKZQ4ODrG2IctzlFSU85Jr\n5zYom4bSem5/d5M3X3uVg90d7u9PObsRPMN8OGFWR8XZhFdbGRNglExLzq0MKLSkceElu5kipPJE\nL19ay527d3m4tcVkPCYfjTAOxvmI5fGIbKAolIikoMhSFiC0DiX0HG1h+6P1k/vEA2s9Xgjm8xl7\nBwfMZoG5bOqGuinRxZCLl68wPTxkeXUFmWU0dRNYtVJTW0NlPLOyYVoZTCR4tcxuF4lF+FiQIKTw\n5LG25rwOijbAt6lCUJfCoSVc3Zjw5eZhu/ODF0iAmhdemxVJVdHb1DG1JBkVw0wGhVmEesfjQjOI\n5J+ymZNJhVIZ1WyHzYfbrExy7m4/4PWX3uGTLz5gMBhy78EDiizn4voaX978jpdeeJMzq+s9xEj0\n5EOKOR2PKx1n5qa3TERYT3TpI+mZ+qiViPsrXdd7z+bmt1y5+By1c9Gj7VfgEdH79lhn+MVvfk7f\ngAo5woa/+8l/QivFf//1P8VjTazBG8yBxlqUFjSR3eqd4/tv/IBf/+FfUUqTZYqqrhmNCg6nczZW\nlllfWaWcz1kbD8nxZDqsSyMUB7MZs8aDNeRaM/eOSV4wq0oOZ3PywZDKVAihyHTkSmQZOIO3jmGR\nI13I40UI5gi2DvbI87wNTSidt3mj3glGg1AneF6XeALT3DhHWYdaxkoppoeHrC4tMRgUrBYF66MB\n+7OSB4eHyKJga3cP5xyDvEBJGI+GzGdzXCojGuvVGuuoqwrnHZnOqeuauq7RWoewipDUjWF1aYnn\nLp7j3s4edd1gnaOcV+RZjrEGZ11r4EAIrSQC49/9+D8t6KAFfdTBSl3BF0J2QAjbReD+SDWhBHlM\nCs2LFyZ/HumnX9E95S6ldBFI3KWEsaRl6lpl0P04pFD8/Df/ncl4iNKSjbNnMM4ABUqG3MDamFC4\n2MakdqVCqklTM1wuWF4esVqOQVZs7XzJUpahG4OZT1m7cJFpWeGAsm7CwsgHzEvLlYvXosfpW7LH\n1vYmG6sbfH7zj+zsP+S5S88xL+d8t3mT4aDAe8/SZMJnX/+OG9de57/84r/y0+/9NaYp2T/cQtgG\nJzUvPfcKSsr4JpcQ+/iX3/8C7+Lm0yqQg6whGyhABWZarNThvEV5xeE85N1pnXFmeZVL5y+xurwa\n4oWxQPFX337Bd/du8tLzN3jlhVcpigF3793CWMHB7IDGWm7e+Zprl59BqhyE4PoL18O8+GBUfPrl\nh5RlyeFsxsvXX2F1ZQVjHHt7O5w5s8Z37hbPXHmG7Qfb3LrzHQjPrd1diDD48pkzPNjdZefhQ3IE\ne9v3WFpdIx+PEYUOpJPaopWhrENMTwiBMaEylHUBcvNCxpSPoCz2Z1NufvcdpjG89s7bbG1t4coG\n4x1lU+OkQmSKQmSBPq4EQqhUDRQlwImEBPQ8HR/eXFLWDVVZB7KRLnA2lABcHmcU2ZiNc+doGotx\nhs3NXb769hsQnlfffBMhM6xQMWE8xdjDS7QDZhAikaHQQRdL9ISUF4fDWoGS4VVNxrooAAAfCCRt\nGCHFXWIYoXtbTtxf0RhIMU4Z00kC/K3IlGSQiZhWEsqhjYrwijDpa+q65O7DTXJXs7x8hoeb36C9\nY1AM2doOOay/++y35Fqxd3iAVIL9csp51pC54tNvf8/S1pjzZy+zOjlLUQxbsdMPk3TKLQkz33qP\nId4WEe0AACAASURBVA7ej3wJmrqkMQ1LS8v4WJpPcqSOdIRxfYx5XnvmGtYJfv7Lf6QY5kxGoyDU\nB2OevXCVwWAICOZVzbOXnuHugztUZYVSEmMNFhcNYY8TFustFkNjG4QITE/vHMFJEuS5xjnLr//w\nL0G5mpKyhjxXlFUV3r4hBLfv3WVlUFBRsz6QmNpgvWKgJbk1jL3DKxDSMfKew7LBW8dqoZj6UIxC\nCcIbTvKckbT/D2dv+iTZdZ75/c6556651tpVvaAb+0KCJMBVXCCKI1HSeKiRPfIS9kf/Mf4r/M0O\nKTyeGGuhqDElUuImEoBIAiBAotkNoNF7LVm53bzL2fzh3MxqyDOjCHYEooHqQnXmvTfPOe/7Ps/v\nwZqWRmXMlksW2pDGCb1egdKGveEIhKCxlrJa4Z3BEtaiNE6QIljmysbjrMGriMWqxDnHqmro5Tl5\nnrLby9nJEqxtSa3Ars7ASZbLOQjL/niEtjocuouC3miIK0uMd4yymMmyovaeXhxB3sNZS4kly/pB\n0KZlAGfgybKYLEmpVstweCR8TnSrg37MC6xwyI7AFA46gtYY/uZ7f81vf/6rHE0eslxOibxDJUlH\n9FIUeY8bH97gCy99kaZu6PcG/PhnP4RmxWg4xEQxUkjK5YLBaIz0njhJGQ22KHZ3/8v74b9UYX7z\ntbe5tH8Zx0dVaP/cp/Xovz96Unz35j9RNZosCb7GrWEfYQ1KCDIsy6bG4ZmuGuI4YqUtWRIjPMGg\njsA6Syw8kYrZGQxZTE+IpCAzGkco3ZdVSxpFjLbGfHDvPuOtLU7LFUkxYFqW7Gxvc9a0CAGnZ1P2\ntg/44O77bG2NccbRKzI8AmM0Dk+i4rBxG4O2liyOEZEkEZJeHOOsI5cRrWnZ397mdL7gTGta5zCt\nweExxvD0Y89wuHeR77z6d6GVIQRKKrIk4/ErT9HLcx4c3+P+8V3wik+/+NmgqmtqsrxgWS7I0zxY\nRxy0puXVn/+E1jYoGfPFT3+J+w/vM19OeXjykEG/h9aWr33+FX7w0x9hBVzYusBjF68igLPpCceT\nB6go5mwxYzpdomLFlz/7lc29W99HKSVNWzNfzPnlL39FXqRIGdHr9ZACsjhmMjmjKPpceewqcZJi\nnaOxQUlbtYZVo2ltsAj1s5g4ChSSkOYS2rKRlJ1qNpx8l4sVg2GPo/sPyNKMxjgKJTHGsrU1IktC\nG13iAltWqSBGUgrRKZXXc8ywWWomkwnD8Q73j09ZLlve+sXPefaZp5lNp+zu7XH9l79g7+Aq73/w\nHsPtbZ77+Mcp+oOQV7nOK+zGESGvMLQm1ypYB5vvtTaoUltnN9Wj936zwWnjqLUOYA7ftXIfOcwq\nKbi22+e9ozkIuclpfNSis/G8IjYz+VRF5HFElsTkqaRIYopE8f4HPw8M5LigSCIis4J6xUo35GmK\nlopl3YKKgtIbaESAuVsf3ofwsDsc8+D0lEYb2rpiNNpGCE+aJGhj6eU5rdVkqsfTV184rzgfWSME\nAmPb85i2riUsiTg+fcDWaBsVx+tVhvsP7/PW9Tf5/Vf+EICmrQCB0S2vvvU6++MhTz/xAm+++yaL\ncklW5GRJirMG6x2tMZ1qOOXTz38OpSK++b2/RCDI0hRtDdY6vva5ryFlxN/+6G+wzjEejPntz32N\nv/ju/4M2GudaRIdRXN8Du54/d63d3Z0dBirCdnSaXEZEtiV1LbmS6KahrwTOWjQCr2KiOGIry5hr\nzcMG5ssKmeddBJzuVPbd4UGKYDvplMOpivEujHmW1arryLEZTSml8DaMitI4Zmc05v7kJDyD2qCN\nBsKcfdQfIoBeFDGMJbGu6XtLf3+fD84qWgTSGu7O54yHQ07mc/YHQ1rn2Ov3KbwBFdFPYh5O59i8\nx3w+57RcYqzt2usaKSOcs+xsbbOqGy7v7vHh0QOsZUPiybKMarVCdW3uSIb2bVVXnZ89VJqj4Rhj\nWy7v7XI6m5GlGbZc0ktSdrfHHC9LkIpV29K0ltOzCVmasre9ze6wT9W07Pd7nM7npEASJyzbhv3d\nXT77sed/s5bsX/7wh9ybLNBty2gw5hPPvRTS6LvH/59vno+SMIISVXI6PeEXv/4ZeZ4zXy546tIl\nFmfHuChilCa4SFFZw0hAeXyfZ68cAoLp9IxZucQlPXzdUOQxxWAb25RUFnZ6GZPGMNraQXnL+8dz\nWm8RUjAYDCmXS0gzVkbTj1NoGjLX8PDklA+nS5LhmH4vZzgaYLRFSoFuw3w0jmMSpciydAP5rdqW\nWAjiOALtUJEkjWIujAe8d3SCSFM8npPpDF23PPnYkzx97VkioTr0V2g7H08fcLB9iVW9ol8M+MHP\nv7uhj3jraNrQIv39L/7hRiTUak2RFvzkrX/k2WvP8drbr/Kll7/Cj372Pfq9Ac5axv0Bs+UCEBxu\nb2MQGGN55trHQMAHt29idEPVVBRZjnGWWms+8cxLYRYBHxm1v/bG6xitObhwwK0PbzEajIJlRgl6\nWY5KYpIoxrQt4yJjOptxePkKUsUkWUFjPHXraIyl1ZokDoGyrbWbSkjJ8IyEDTOiWi65eeP9IFxJ\nYsqy5IVnnsGYIGjpD3IElkSFDxFCotYQ646xGisVmizdhtLoFpznbL7g3sMT7t29x6Ku+J3f+90g\n8BKesrHd4sE5h7Oj97j1o76+R5tFKbT/18Id64KNpzWB4qNN2Fi7EBTW/uVW20D56Xx31p8rX9cb\n5uN7fd47KgNsQgX/XhytuZ2PsDsJB428a7vmSUTS0akyFahI2qy4d/dXuKYkbmv2+gnv331IMt5C\nJwXzVUWcxgFIISO01ljv6Oc5jTEI70mTlAvjMdc/vMW4PySVEc18zmD/AmfzGaOsYOXsxiMoOgFI\nvyh46sqLnQjsHJ32D699N4h2sozPvfjFbvMU3Lj1Lvce3kOpYJMwxtC2LYPhgCRLMUaTKsViVSIi\nyYXxmNmypNYtsVLdQcaSJjFxFLynZr7A5QXTsqRtW5IkwVqL1mEU5LzlG1/9Y7z3/Pnf/0fo5s9P\nXnqS67cDAB3vOmW62qx1jx4utTUMih7LuuLTjz9FEQuOJxNa4zienHA4KHj6wnYnovEsqhovIPEW\nsj5lbTmaTqm9pIoUcRzTy1Ok81gfhG5aG1y3pnohwDkSFTNfLMJhPkkolyX7O7scTSfUbUscKbbH\nI6SHUX/ArYcPGA0GnC3mQSchBE3TkKUpaMO1vR1OP7jBVr+gL0EPtiDOOaoqBnkgm+lIYa1jNj1j\ne3ub+ckx//rFp3l4cswbS8deHHFkHdJ6jPA0bdvNi20A20jJ/tY2qVLsDMfcPj3mbDZFG4MQEWma\noFvTdSIinLZsbY+6DV7yzOEF3j+ZsFws8Agu7e1inKUqKy7sbnNydsa46JFjybOcumlZJRk4hxUw\nSFP0qsTJiMR5mrrmYGsL7zwnk1O29/b48mc/85ttmH/2ne+xKufkeY/GOtI04WNPfor3PrzB4f4l\nkji0/f65wAZCq2itoZVScOf+LW5++GtSpWhMRdU2JHmQNJ+enPLU5SsobxgreDhbstUvqH04mS28\n4HBvh2vb21CeMls13DhdcFYuyftDvBSoqJtlWcul3T10XaK8Z5wnHC0rVkiUihlUU5b9be4fHyMi\nGarfNCWRESezGdvDIcZaqqYhzzJiBGmWMZvNSPIcvGOnKLg9mSCE5OLODg/OzlBRxFP7+5RliVMx\nuVKczRehUrOWfprSaEOUhCDhZV2Txn2Eh9oscWKdYiGxziK71Apvgxdu3d2y3Yl/DeEYDHPiOKNt\nW7I0IY0ienFK02oaZ5nO53gi8ixF6xYpY0RHZqqamjjKePG5T+E7nNx6f5CdkMR1WELhYbqY8+bb\nbwKC4aCP0Zo0TYkjSbNckCPZHg959/1bPP3cswz3LyOFDBUMQfVcNjZ4BuNA04nX3kEV8dZPf0qa\nJIg4RbcN+WBMkaZc3BoilSJLoyDKeuSgFtidgZxUrpZEUqFtaI1KHNZYHpwc0bSas9mKj33m093m\n5jbIvrLttivnwn3oooKEYKO+DQvluTrPdXQVt9lMgwJW6xAQXbWh8tAd3Scg84Liey2Msj7MUqw/\nt1VEUvD4Xo/3jhYdTEORqi5DU4puFtoF9UZB3Z3Fglipc9V39/XJ6V1W5RHXtvtcf/dtLu7scDyf\n46OIhciQQjLXLZGKqNuQohNHEUKGWDbZiVqklFw7OOR0OmVZLhFe8LGnX+aNd19DO0eWZxhjwr0j\n2CVwofo0zvHUpec3IjkEfOv7f4kUEf/6lW8gheQnb/2IOBFUjaZtGlqtsdYG4ZyMKPKcVgf1pzYB\nl+ic52Bni+lijvaeulqRZXnwb2uLkJ6RVGhjcGnC2WyBiEC3BhUrdGu4evEan/nY55BS8h+/+x9C\nELwP4h1jLFketAZVVQUxSiSDcC+SyG78kuc5q9WKPM/xzrPTH7CqSi4eHmDbFlUvmTUG7y1bRcZB\nP+XtozlfePIy5WJC2VqIEmZ1w1LmHAx7TOZLiBTOaCIvSJKIybwkShOM8zTOb7Ib024e2VhL1llV\nGq3xQJxm6LYliROGvR63j4/Ah2dfd0CVOI4Z9ge4VtNX8FgeOhNVtWQZ9UiznFlVUVlB2TaM0oQ8\nKzitVwySDHSNEIK8PKWIY4bbW7w5bVkulsRxwlev7jErF9wxCUdlyWJVkkSKQkTs7e/x6zu3aXVX\n7XYkj92dnU48JGlNUOoO04xEKO7Ppkigl6UQKbTWGG0YD0cUSiKsYy+PaYUiTxOOliU0NVujMfdP\nJ0ilEFKybFp2+n3uz2YcjrdYliW9OPzZH33ta7/ZDDOLU5okI00SVosFcZbyT7/4EaPBLu/c+Blb\nvQIDHJ2c8tlPvdLJiNcf/Ii2bfjRG99nVa/4xit/zLXLT6FNyxu//hnbXnM8nzDq9dgfjfHzCRWe\n+6UnUQmxTJgsl6gkIYkiEjx3JnPeuXsf0XFdk+EQqWKMCS2YRMVIFXGymPP0/jYjW+KihMZ69qRl\nXrdkgwH92KOLnEZItHNUdUMtBdoa7p+eUFcVezu7TGZnJGlGT3iWugHddEIdF3LYvAktOtPSGHj7\n3h16aUY/dizmcxoJ0oTMwGpl0N6TijSAo6MI60IGoUpilAcfR0hPyL+zHaTcB9g2rvOPKgXWISJB\nnMQIBNa0KDxVXWGUIlcx4GisIStyVlXD4f4l9rYOePv6G6go5mD/Ev2ij0NsiErhVBfEE4HgYxFC\nMJvNeP2t1yiKHqqIacqGs9mUSAjSJGE6m/HZz36BX/3qHU7LJb3RiNViwd0Pf8zV518kzYpQQQmB\n0t2GY31oTeFBOISFNEl4+vHHkDjKKmwyeR5jrSMvAslGAEnapaB4uhZhy2JVUdUtSsUs5yWtN4Bj\nZzjk7smMCxcv8dwTz1K1JrRPXTDyewgCiu71rfOQpexCceOI2dlDsjQhK8Yb1WsQIITT/mRyn6Zp\n6A+2SbKcOI5IIk2tZXcvQyXaCof1DmkD6ch7gbM++CHXG2bXbhXyfKNG0gG0I5J1ek7HGlUqpOaE\n8IOgApfC8847P0DgOOwrlpMle7t73Fg0JHER3p+Hmda01qCcp6oqRIeW023LYDBAa02eJhhrg1BE\nt6y6DfT1d1/FW88rL/8O/f6Q2WLK6fSE20cfslxOsdayO7rAZz/2W8RRzF99/89JVMLvffEPKIqw\nsX339W9jjCVJYnwTnoskUaGz4gxluSLNUrRtqXWNcyGGrchzqqpEiDFt26C9I00SwPH47h7X79zB\nGMeddtW1ArvILSQqjdke7CAlXNg74N9/+09JkySELstgF4tUOCg2TYNKYqI0RrQGi6e1BozfCFIa\no5FpTNnUxLFCpTFPb1/kxtFDIiIKYRn3UpSAMy2QNYzzjKN5ycl0RZqk1Msl/X6PsfDcP52Sqgjb\ntjhrGGQKZ2GcRshIIKVGSEWU5dyflSRpQrxGWSIDLEHKoASv6y4ezgeBjwswAmvtZmzWti2z5YKr\nO7uk84fopMfidML2wQV2VcTcaO40LXv9At06Lo8UudAMrKE/kEwWgoGtqAQI3bA6fsjFqmH74kXu\n+pQf3p/izo64lAmeBi489RQ3l5rShDzd9QjPWsv+3i6L5RLVNlze2ULLmGXbdDoWDRh2x0Mwhu1+\nn6oskaMhUSQpogjTtug4ojEa51uO5mcgI+bOY8oljnCQjdKU0lp2lWJV17Q4LDDXhgvbO//F/fBf\nrDB/fmvGe7dvcmHnAvP5lAfHH5IWGctyRRwphoNtirRge2sPpdKN9uf2ves8PH2A1i2xjJhXK56+\n+hiL5YKj+ZTYenZ3tnEixGFNFgvKuiZOEi7u7oFuSKUki2MelCu2I89p2fCFFz/Ou7fvcjqf8fzB\nNqqtkCpi2WgmIgej0asl4/6AvWFBvDxDqoiVF4x7BQ9nJXmRcbwy9PKCi3lYnBprWVQNRsSctp65\n1ljrWK5KDgYDjsqSUZazNJrt/oD7pyfISPLshQvUXrLorCYgSGUQosQytBwra8NJU8UkkcT68Gfa\nhxZeI3wYbNPNyISgbWsECu9dJxDyrOOL1rWIIHwpArwQQXIuBd559kYjmtbw5GMf5ydv/RhnPb/1\n8pf4+1e/w2c+9hmG/VFoD3EeRXg+uwS8YLmasz3axlrLZHrKjQ+uI1WoPoQX9Is8AKCbhiIteOLq\nU6FdCfz8pz9GiIgXXvgkTkbU2qKioFBdVAHCbP0aIB6qozQOc7iz4yOkB0dgE+eRCi3aNGZQJPTy\nDO8sRhsQntmi4nR6RiRzylVJg+XCwQXyvNhABKwNs7g1DF0b04Uxh9Dl6UqzDsONY9mxhT2xlJwc\n3yLPBiyXZ2zvXOD07B5O1xRGE+U9ljh6MsIaA97THwzxxRUa69CtoTLn8PWmNay0pWktrbFdjp/7\nyPVXUnB1r88HR2XYtKMuDCBW5B0btpd0fuFH8giDrzhUqD/8yTe5UGQMx2OiZoZSKfcWK+KsR6U1\nkZQ0LtBS7jx8wO989uvsbe9vPvuvvfMqd09uEwlBlqQopbi0s8ud4yOMtZ1tK7ScTdvy7373f4RO\nyHbeV/I8mNzjnQ/eQmsTlKfAsgzVWpoG4L5zIZhgnbVonQsWq67F2zQNHuj3+7RtqDKllAgpuby7\nR99bzlYrLhYJr916AGmYfS2XS7wI1wYPWZZRtw04H9q7OrSOm6YhTRJUF1kX2uYabyyy81RKFTZH\nZ7okF0S3QdPhPW3oaAjB4xcvs51nnM2mOGvppzFLB1XTMoqgRZBJGBQpVd2C9yRpUKRKFQeesTP0\nkzCHbpxgUWsQMjxjCGIlyKMgECtbSxJ5nFBU1kMUsWrDIUFbi5AREiiyjOPptCNoRayqChVFlGWJ\nl4LL+xco2oo0z9l1FfV8GkIRipyTxnJxZ0SlJcrXWOtoRMQo7jqIKsXphod37tFLEvI0YTKbMXdw\nsL+HsJo0K8jSlOvzmqd7ijPjuLp/yJ/+/Jfgw+a5W/SJopAQVa4qVtowHPTpAdv9gjvzJb00Y7Zc\nUDQl165dQziLt5rFqqaXJcRGc1ZWlCrn/qLkiYMLHB2fcGYMy3JFf9APIezeo+tmo/J1xtIbDPjk\nE0/wyosv/ua2kvArfBCUVJyeHfHw5BbVcsHLL321Q3ytl/DzX5GAH77+HVrdUvR6LFclu+M++zvX\neOziVX7y8+9wUMTcmsw4XpZkWcZyuWR7NEbjSSOJVBFZpEBKrPOM8py7J6cIglL32vY2sa4ZRpZh\nHloL7924wWg85khD1q7w2rA3HmGsQWYFejGDrjJwzuG0wTvHolqxM+ixbA1Fb4CwGvKMEzVAe8kv\nbt3imStXmK0qqmqFkJKt0ZjLoxHHJ0csHKzahixJiKMoPNjOoUQwx/pwwYPqUUYYZ8Ncq1MI48NJ\nSztL2+rOEtNRez2dRDpU386HRV8pFYgkUYSSEWtkwP5oi7qpqYylbVu+8vLvsCjn/Ozd1xn2Atw8\nAna3D9jfuryBPohOMfjuBz9Ht4YHJydkacowH3XhsOEEK6UCIehlGYM4IVYBSNBaQ5r22N+/FLL7\nbGDOahvmjsY55qWm7VSmEA4sARYeotPyOOLeh3ewTpOlCUVSMOr36PUzYinIUoV3QT0nZcTZckGr\nWx4ez7nx/k1+6ytfDhQe20EKvEMbT6M1VetpuvmidY48jhjkMZNVG2bWSUTRpcZLGWhQq/mENE15\n+92fMRr1eHZ3i3c/vI0vFzz/7HMkXuMiBQjeee8Ww0vPM9g6CJjHjnPaWkvdGladnaTpNtCw8bDZ\naCDkfl7d7fPh6apLgwlM2DxR9JKgeC2yJIAf1Bp0H+6N94af//IfUVYzTAWTsmErliyMx8YxRaRo\nhGBlLMJ7Zsslz1x9nmeuPLdJtHrz1z/jxr2bxEoRy4g4jtFa88ThRY5mU1ZVFUYFLgjbvPe88tLX\nGA/GzMs5P3jj71kuS55/8gUOdy7xtz/+FmmWkcQhQLyu6yCAkxJjLYMOERerbnanYkpriKSkqmva\ntg3VnAttcxVFQTgWx1wdDllOjnhYG5Zak6SB4xx3FeO8XBJFEamKsQQhjHWOWEjWSgzn3Iaas7Zl\nWK3xXT5rOMSEnF6JIIljRGdXcNYGsciqxHtPryg43NpmO895cHZK1KXQLI1BJQnSWoYSWhHRGMO1\nrT5FIlhqwe2zBUpKBr0exmgUgXzWy1JarYmEwKoY4T26bXFCEnVB5ZUL1CzvBRqII8XKtOjWIKUk\nTxJGvT53To5ZKxXWFaZzQYXuvWOc5jy7O2JfOcr5nChNuTuZcnl7jDeGwWiAbTVCwNl8xs7hRdqq\nIhGSxhiiWHE6mTAYDGm0ZpDnTAwwPWZrewdXrWgajdoeczxfsb97yC+PJ0yaBikkW70es+WSPI7p\nJzG1dbQ+fNYvDQtiq2mqJTNSPJJBpNkuYiZHJ4z2LtCUCyofI+IU2a44NZKldUwWM5pas7OzReLB\nRRGLckkvHzIuCsbjC8yXM/a395DO8Huf+eRvtmH+5OZkIwZZG/fXW+Oj1P71kG0tiFiTNUSH9pUd\n9Fms5eYI/vZHf4nA03pP3VRh/uAcF8ZjVnXDtb1d3rt7DxOFsv2pwwOuDEfcuf8Be64FY2h7feYi\nY2U9UbVgJ3ZYlTAeDilPT5gZR5zExFkRyvhIcrQsGQ0KJkdHNFHEqBgwTBSzk2NSJUl7I3S9Is4y\n8A4rJdMo4/68Zu5ghMVFMStCf/CJ/QvMy5LTxQLfzQdUVxV5a5H4QBCSMXkkQyqLNYgo3iRcrC+q\nA7SxZGlK2VSMsh7zeoW1roOOd5DmTlhh2iCFv3p4japeMV8uaE3L3njEZz/xZdo2KNO89/z19/6S\nLEu7VnnLH77yDb7/+j8wXU7JYsVvf/7rwCN2BSHOZ5rrGR6Of/jx33G4v88gy1msVkwXJdJ7DsZb\n9POMeyfHpL0xjz329GbDbK0jjSStNZwudEdnseebRBTajVkc0UtjBpnizns3GQ/HHO7tYrwnUcE8\nLgUknfjCGMt8teBX19/j3fdv8YUvfZmk6NEYQ2uCTWkNQ6+0DkB0u853hV4aMS5STpfNJsotTxVF\nHBGpbo4nPJmKuX3nF0Qi4e7JHT752CWWizm3jk+YN5qrF57g6hMvhMOO8ZuqcU1f0l24QKOD0Krp\nOMrBs9lFe3V+sFgKHtvtc2dSokQgIaUq5HbmSUQviclTRbKO0ZMRkfS8+uZ3WcwWjHbG7EQOv5qR\njHa4fTLFxglxnJDFQaS1aBuatsU4+KOv/DGiG0f89Q//IoipfAAHWB9i74QQXLtwwNF8RrlahXGC\ntZvIJpXEITQ9L3jp2c/wvX/6Tnd/DMhwel+b89ebViQkaZoE7CXBh7z+f2QUoY0mUaEd78U5pnNV\nVXjnGA0HfPXZZ/jJjZvM6oaz+Yw8y4hkFNB0nRI0TkNqUKAbyc5U38ELXKiWw+c1CmABgA7OEMn1\nQfdcQW6M6XjJoVJzzoXQeO/pFT0e279AqxvKuiKNgvI4TlKM1ngR1sLlaoWvG4xuuLK3y6hX8P7Z\nnFFREAtYaIOTCmHaYN2L4o0YL1YK07aYSJG44GuUKqY2GuVD5RcYx7ZTujcM8oJBXnD39CQos52j\n22uRUcBPPnothIdhltLgwmFjteCw36dZlTTlgmvbI0Z7uyA9pm2xxjBKctJYcbZYkOUZy1aTWEt/\nb5eT92+RDXu4KEI1Bu0MW/sHWBVzuiy5flZzcWvAeyczqtmMpFdwcTRgXrccjEdkaFJaChmhteG4\namgcjDOFSlLuH50Q9ca0WpPlOVVruX18glOSfpIgutZrEisujkb0JNx8OKEs50T9PloHTYGSkv/h\n6/+OZw9Hv9mG+cPrpx81E3dm2I9ip2TwFLoQpSQ7o9i5odrzn374LZ68/AQniwccjIY0yzm353PK\npsEZi1CSKzv7CCwLbTl6cJ/BcMC1g0O2k4hP7G7zrW9+k72nnmIni/DOYOIU6TSJSqiXS5aLOQ9n\nS5KL1+jFsDvs07gYIsE4kVS6ZaDAiZi3Xn2dfP8SA2WojaEfK7Z7OS0x06MHjLfGtK1BCMnKGbby\nHmcy4kzDfQ2P724zW62YL5cc7uxz9ywQNBACa4KqVUpJliabVo2ymnEv52zV0rQtWZ5v5iraGnwk\nWa4qijwj7m6wF2sVaYQXktWq7BSgoeefpClWd6AGY4kTBc7z33zpD6mN70z13cGGDtAdx+i2CTg9\nBG0nL1/joZIOJoF/9D6vj0nh181b7zKZTzjc2SZ2FhHnXLz4FP/05qso6XnuuU9hDF3iu6PRDiU9\nq9ZxPK9ojaNpzIahGncz3URJ+nnMqEgZ5THvvPFzLu1fYHdvlzSW9PIc4cOHvdUtD09OuH7jJjLO\n2Lpwka29Pea1pqw1lQ5BzU1rgt2nq/asX0eEQC+L2OllnCwbkihgwmIVqt1MRV0oru9QfOeWhUxx\nsQAAIABJREFUjjXEwkMHmggz0Y3Xt2vHr78/HCTDJq07dWyjA07Q2vMoO4cnFpLLOwUPpnW3WYaK\nN+7SdNL43D5y98ENHpx8SGRbRv0BTtcoY7g2TtBE3Fy50LJTirNyhUoTTs8m/Mnv/i+0bQtAaxr+\n/bf/lCzLKfIi2Em6cGNtQyWapCmXd3Z5MJkEQ3qcbOZg1gbGqvAQx3GX0wqt1Zv2rXMBHRgrxaDX\np6rrsAHiUZ1wI+2qQ73ehLsNVkqJw5PFKa0Or9k4y9ZgyOP9HjenM6bLJWVdbSrFQNjxDPt9HKAI\n9rRApjmvrB5Z66DbtNckoKZpNsb3PEk3+oGqqkiSZDN7q+t609r13nPpwgX6vR5Hk1NGvT6xs6Bb\nMiWZa8fSai7u7mPqmiSSzKolbZSSCkG1KkmFZ3tri9mi5KxtSZKMqBP2RFGENoZMghPB1iY7Bqsx\nIX0nVhGtdzjraNtgk9sZjiiynJPZtNv4w7ogu+6WisI109Zs4g+9c9SmRSE3gfDaBF1F2x1mekXB\nTp4xqCZsFRmrkwlFphjtXqB2mn6vjxWCsq5oTs4wSYieG/SHrFrNaDDmtRvv08tTsnrJLd9j3Cs4\nblp2sowBDUYkZNKQuhrfaqY24urlA77/7m22+33K2Rn5YIiMUxrv+eDOHXZ2tlmUK55//HHKtiXx\nnr0i5cHZDIfnYGcnMMyrFQLopwm/Ppmh0oydwYD/7pX/vOjnX9ww/+FXx93MQGwmExsYAW7jEfro\ncnr+PVLAX3/vL1BKkaYxEsd0ueRgZ5sHJyd4IekVGfujMWflMogNkoSd0YgnRU3Z1Cy94O2TElTE\nS888hzp6H7dcsnOwx1QKThcNh/v7FIs5N6//isNLV7BY1NYWrUoZ5Rln924j64adw0NOGkNULWi0\nJYoV92/f5vErl0n6fWZHJ4xHI6anp2S9PrPZjCKNGW+NKI0jGo35/vtHtFqjkpjD8RafurjLveMj\naplw/eFJ12oyYUbn6TLxwtVJ45hIRThjydIkAMFdsLRYFxCBpjP2eiHCCdY7nFn7/uymgg/kkS7o\nuTV86vlPc+XgGs5BnggaHRJFzpf28zaMFILXf/FjPvPxL2wA9m9df42PP/0yiVL86K3vMYgTVJxx\nce8iZV2xrGY8/8TLXZ7kplXAydkR7926QRILtvrbXLkSKkvn2aDzWusQnUH74bSiNqGl7NanXNEl\nbciQmznKE8a9hFGe8Lff+haff/kz9IqCXi+mn2cd0s1TNQ23bz8kGhQUwy3mpeZs1bCqW1ZtmC1p\ns46TW6cvrJ9RGOYxW0XK8bLZBDAnnY3j//dsr8U4/pwN4uD857pO1BQFRrGScoNUlFIQd5+jKJKc\nnb3HcHhtk/qitaO14d4mKuLSVs6kbMPPEWE0oaQgVZIkFrz2T9/mSiG4Oh4ynU6o5mekRQ+xtYur\nV4yGfeaV5tenS2ycEquAKjtdlPybL//xR2g5d09u84v33ggElm4TjaLw/QHvF56/xw8vcu/sNFhZ\nZOgWxJHqjPKBgNPr9TbVtfTQWINEMOj3Wa7KzUFSRpJERsRxspkb0h2CIFx/IUT4O2SEkwGQ32od\nRF9JQp6lXB2N+OB0wtF8hmlbenkBCKzRpFlG3dTUVU2e5+iN6ja0gtfvf3OcFOefj82fPfq1oPTa\n+DCjDrUHUOuWIs1AeAZpQZIkLJsV1gSl9LWdbSaTCVuDAZiKXpEzryyt8xgpwIYYPhfFCAFOCrzR\n9FRCYzQNkHow3ca5Fmd5a/FS0E8SklhRNQ1WCJo2oA2llDRGsz/eZpBnnMynpB1iUDuPEwS4ioxQ\nKqJtNUWWoo2jNZr5fEESx2RZ1omFDHmesyxXjIcDvPdcyDNyW3O5n7GanARLVVXjpeDClctETjBb\nTEmTGC3D7P2s9ei24dlrT/Dhg7vcWGouDvoIIUiEoxIpPVsRm4r7K8MogkVVc6+yOJVwuqo4uHBA\nL40xreVzn/gq7919h7fe+xWPHx7yq/fe5/GLF8mShH1lOMwUk9UKpOLD0oWRXpTwwd075L0hn/34\nK6gkC7PyBF688hvmYf7t20dBmEFQ9a0zINcPzfli8ggn9pGH7Gx2wmu/+HF3Eg15ka1usNaEhzeK\nuHZ4SC+OefvOHfp5ThIp9osMPrzOoJczP5txZ+cSs8kpL77wEiMqhqZmNpkgTMP+5cvItODuyYS7\nt2/T7+U8tr2HaRvyNMJ1OLYeFpFmLKoVcV6grSe1Fm8Mbb2kN97FGU3rPbZtA/R9VVLsH1DVFcK0\nNMaylBkfVgaSEDT82O4+905Putme/M9G/0kpKbJsg+FTAnIVY5zpMpAlrdFdaHNAW5nulLxWUAoh\naDuxi+s2mapuqVcVf/S1P9lUMs55iljSmLAYu0eOMsKfZ/Ot14D1zw4UpLDAf/+n36VXJKHacI5E\nKRIf/IU1gs+9+MpHWvA88rtzjuPJEePRHo21YdPsDgGLUnN/WoUPVYeRe/SZUTIEIKdK0s8Ttvsp\nO4OUt159jYO9PdI4wzlN3baMBxmr1YppWfP8p17meL5isqxZVC2rxtJ0oOs143XzGrv3L4Vn0P0d\np4um4wufi0TWSEXPI4fBR++r+KjNBBGg5oFV3PEyJUHFKghVogxKR6tXxHEviGas37xOJYNFZGeQ\nMSn1RyAFcQRpJHn7+k/oUyJnE7JEMRCghj3OGks/zzhe1hRpwpQM2VYsjWMlIuq64g++9G9ZO6U3\nn30B//ff/VnILxRhM7TGEHUzvbWv8vGDQ27euUPZVMQqJk0SkiQ8I1XTUNc1WZYx7PU31ZaUEqUC\n73dRLhn0gnDHWEucJDhjaI0hEoIkDRYO5z6KRIuiiHK1olcUwYvnIU2CsOeZ7TEfHh8zNaFiWo9E\nIMTDeRdmkm3nBez1eqxWq/Of70GoMIQwzhJ11RTROVKNTncQdetnpCKcp8NphmupOlGQ1oadwTC0\nPFX4+8FRliv2CoUlIs8Klk2NWoukuhDwxlmk7DJLOx6xkAJPqPjphFqhGjSBxUxYm1trkErhtMGJ\nLh+3m9MKKcnjlF6WcjyfBeW3FBgbotpEpxRXMuo6HpZat1SrEGgdQiY8SaSQQmKcoW01IBjHES8e\nbGGrJfPVipGKqcoZxXCL48kEmSREbc24P6Zta5JBn/l8wWB7hyRJSXsD7nzwAS5NSaPQudBe0CsK\nJpNTlpNT2rTPC5/+RngdHTXsH3/6bQrfUqdbfP7jX8R7+Kvv/QciFdPLUi5tb/Hw4UOKLEPXFbuZ\nQMiIsrZU1uJ7B7zwzKepW0PTRUwaG6hPF8Y5n39y5zezldStJY6DKVxagXQgZMdjdHQVUBgi2/VG\nuj5hSsHu1h6feeEzPJwcoaTk+q13EV4SxzEX9/c5cCtmWvOwqdkfjsJMQ2vGeUJ2+TKTRckb3vA/\nffHf8lc/+HOeHCgeHDWIJz/HpefGOGt4860fseePSNKcwyJhvL8NVYkxkpW1lLNTkv6QEwR7Wwrj\nBD08k7MJyfY2UaKQKrA+lxoa4SkGA1bTKVm/T+oain5BW0sWixWTpM8zfUjiiEUD17ZGzMo5KlaB\nHSoEdVN3qSUZutU474LvNFY4GeYaUSftjwWBHSsEBnBe4LvWiFIRwnnazjMYfJjdJiAEbVPzpZe+\n0uXzrRWX66QWh/WPsjXPBUTna35ABa5Pzh7C/GVV0roGSQfz7hZGKSXCe372zo9ojOWFJz5Bvxhi\nrA5CIMLcc2/nAsaGOZXrArC9C5WE9Q7j15VZJ3gSPmRCisBktS7CujoQT1rD4594CdqaYb/Aas3R\n9evM5mdM5wsuXr7CWVkxXTXMq8B3rdtg5bCce7s859F0j7ZWQ/xT56PsEnJsV/r6zW4ZwOHr/1wr\nlDeVJ50fVHoi74E1xH9djq43YY83nkgV4TMSCWIESRx1vjOIhCGJJEkUAOvIDn9HuP5lXbOTC2zW\nZxwZGhUh45TTeck0TnCxohWKqlphlKIRhlVVYZ3lR2/8gC998isfIfBMFyH4eH04CyB5R12G9n+i\nYg73LmJ0yGulqdBGo41GNU3gjyYpw14frTVt226sC1EUYaxFAMP+gLquN7NBZ01oL3btTaP1Rnhj\nre3gBaFCy9I0CIw6Gkyi4hCIrVK0iGl1iVRRwB364I80XcvYe49KE4TWrFarzaZnjEXFKtxb51Ey\n5EfSvZb164+6anf9/AghiHzXXXMWISWm8xGGrMiuE6Qdh3mGFpJcKZI0ZV5V9KQkBvAOjaAnofF+\nfYwJMWI+qN9jD8IHQLvxjkSKbuzliYVA2PCc57GiaVqEUuAcWZJwtlySpRlx1BGKrEV6R9NofBTh\njWFV1wEJl2ekAjKl8DLCxzFtluOBlTZobYjj7kAj5GZ2q4HpqmFytkROj7D9PsPxgGnbEEnJ9eMF\nV/a2GA8K7p06+kZyVhlOJktUtOS53oBBEXO8XJEogbeWeW04OT4GGXMixvzWy38Inf0qfIw8V688\nz+HeFfCeu8cf8urbr5KmCaM45m4nbDJVyVmpibOMmx8+oC/hxZd+n73BNnVjuHtWbtTq2rqO+wzJ\nfyUP81+sMP/mzfsBpyVDQG9ISOj8cF06xLpg2cQMiXCzT+fHGK053L2EUhFv/foNnrn2HK/+4h8Z\nD/vkzrJaLvF5j5Vu0cawNRjgjaZpDaKc0Rtd4LkXPs3NW29ymCrmZcv4ysdJYsXx6RGD/s4jbTNP\nHCdECMp6ya8/fJvTyQlP7B8wak5JB0NoWkxTUzU14/EYORiSGM3cGFJnSYQgynMW1rDqZj+2bdhO\nY07KFWc+IYoUKlbB1uAcO1s7VOUsEICs4V4TZgKpCn6gEFXmNtLpSITF0BpLrCTeOIQMVaZ1DmRn\niu4qVe1DJqjsrrHzIeXAdKKLL3/qqxsCjevkrmksaK3HuEdo/Ocy2I/8Cj8XtK5J03xDkXn9lz8O\n7VK6tJZuNqWExAlIVMozVz+2mfOtq03nHXcf3OTywdPhdG8D/cbhKSvN0bymNgZnfWf7CACATkMV\nFqSudRl3G0cSR6RxEL+ksQr/RJKT4yOK8Q6zsmFRtVTa0uiQDuO6+KxHhwWbbkhXzeZpzDiPmaya\nLrS8Q9m581Db9QHk/Hp1eDQZTvdRhy8LMAixAcNvKtkOCC+j0JaNo4hIhZaeEOvN+/xVGtsw6vUC\n0J9HN+jgtXzn/Te4NCiQi4ckMkTJ3V00NHGPaV0RqYim1SRKhXRAH9TKjdabQ8vuaI9PPfMy2pqO\nHCSRSCpdUuQ9XJd7uq6u5os5k9k95lXFw8kRcRzjOlJMGidEnZ/OWofqNqP1i18rXAN7N/xdwS7S\nBv9ld629Pw9pCDkUoWcVRXITqLDGAyolGccph1sj3ptMmS2XrJFw+NBqFN0CaNfQ3m52Jwhz/POH\nbm2C6T4j3TO4vuOmm9uFalKgYtmldYTNUkgZVNsdb7uf50gpmS4XYWYbJ1wYZpwtqi5GDayHSCms\nt+A6qIUIApw1fUgSsQ66UqI77DpHCyRdp8R3FemqbYiRoUugG3qxRKmEB2VoQ0oPvSzhbLUij2O8\nsQwzRW1sF0kYDim+66xoDwbIEkVtuhZmB/vI0hTd6kCfkoLnhgnvn0yRsWIUBVrUtLasnOThbEaa\npORRxJVxj+tnS6S3zJYrhoMBW/0+s+mMrfGI+0cP2VIC+mOWdcOXXvpqaDv7j/KE16NBbVuO7r5D\nWdWkg30u7j+G9gYpQl5y1epNQlESBwpU2eguirALeLfdwZ31Euk5HGX8qxcv/mYt2f/9728wzGPS\nWIWsPRmiWwL9JEjg1zPOsJict2zpTu/nSSXhzU6XE27c+SVtZWh12ZFQAm+wKHJ6acbINQwOnuHC\nzmOh/aHCfCVPI85KTWt8UEJ2WXiPdJeQMpzYsygoC+MogLkXt77P+3ceclZrrl5+mlH7kIsXL3Hz\n7n0eO9hheXZGmqUcnc64cvEis+kZcjCicJqyqjER0Bsz0zBrDbtpTBo5XDziweQIQxSwfInias8h\nnGdeO351ViPSIMZBRlitieKIPMlIOgVw2/kCYxXhtAktExfaqc5Zgq0+XHttDbb7/qZt+P3f+jeb\nimENIMhjSWMs2nUJDv/V+9xdt27h1qbhR2/8A6ITEIjgsCeKFEkS0+rQQjPWMOiN+cTTL2+oQ+C5\nceddeq5iMpvz/Iu/HcACxiAQLKqW26clVWftMLaT9XsI8U/rTMbwrMkotAjXM8FEiS7pJHwNoNGO\nVWtoWkdjTBf9xgZpuG6Re+GJRIQQvmuZCookZpgrHkyrR67heavV01Wmfl2RhoVRRpJMBVWtksEm\nBAGXKEQID/Au/DwZhUzQRCmyTlCUqqCyFFIQda3w2jSoKLQa+4liVms2rfNuEZcCnNPcO7rJuLrP\nykuOpivmQoFU/KvP/R7f//l3MNqE0UMcE3eWCK1bWuu4enCVTz310iasQXTvVVvNN7//5xRFERZ/\n/GZunqqYK3v73Dk5wnlP07TkeU7eJWSEebTfKF6FEKSdAKhpzvMZEWtG7bm/OIhs5OY817aaNA0e\nx/X1aHUbFk/CnD9PU2JjuLK3x4ezGbNlsI9o60J+baMRAb+Mt27TinfWE8cKOnLVo4ESUq690A6c\nR3aiJEEYJ60PhKJbA9efCedDm1SbIFY63N3DuaDVgNBmH+U5Skr6acJkVWM6D7RKgyVFRIpERrQ2\n2HSkCMETRRIzSFKk1ywaHexpPryvpKMdndQ1oyzHrRZczCSHW2PatmXqHKfLlqapiIoR29u7HE1O\nyJOE48WcUZpxbavgqNQ0bcO8ahn1i9B1AlrjKJ1DRDHzasXOaEThDE1T46wl7o9QRhO3C7yAtL/d\nhbxbbk4qzpZz/uTr/3PH4l2PXiTeWWQc47WmSBVlY1k1JdXxrzjzKc1ixbXHnyNJMuIoeXQ/2vz+\n/Z/+v1wbxtSV5sZ8xde/8Ee8d/tNxjvPULeWWncZtS4ckFIVDjurVlO1FuuD5Ut3H/ZHOy6P7fb5\nk89f/c02zP/tL94miwN+Ke78aYmK6KWKNOn67d2MU4iQRbhWha7fnBB+kzG5/to6ZVsgNnPR/+s/\n/R8cXLjA04OYm7++zhe//r9yOr3Hzbsf8PGnvoB2jn4acW9aUdaGVdN2XrZ1ynywsawJLaoLLs4T\nRR5HFGmQ49u2BgF1veDuL75DJgmkk3LG1rWn2IsEaSy5e+8hiYqwBEN4v+hjooi2LrFb+9w6q5CR\n5KmDQ+bLOc4Zbs9LvFA0NqSU+K69t93rU7YVrbGksSKJIlQ3u1GR4Gy5Ii8KFAH91WLJooTKmu6c\nCdZbGh82f4+gbTVtG1pjznv+4EvfII5SnHebnEDdLRZh4T/PPnz0vkvBRvUpZegevHH9pxzPHgQJ\nuzHESRJYjM7RSzJkHFHVNXmaMVss2d3aYTI/o5fn9CJF32uq1nBcrfjK5/+ISodTetm0fHhSMq80\ntQ7S93Us1vqcF4QYwYYku9m57OZFSq6DouWmPWNt8FkaaztxTwhk3sxUu/cW0UW5dVmoQgiKJGKY\nx9w+LbtrxKYVe36NfBe2HDZyFQnyRJEloYsQnmtHJENwsxAyKGC7+C7ZCZnGRUKeqtA+qyfsbh1w\nfHqH9+78kpU2fPXlr3dByDBIFdPVWr18PuOVwm+q4+++/i3KuuSFa5/kqcee7YAT8Gff/j/pFT3S\nJCWOIixhg6vqiv/2q/99aA07t7npwsPf/PiveeWlVxgUQ966+Sbv3bvRoRZTqqpi/P8R955vkhz3\nnecnIjLSlOmqNtM9FoPBAAQIkARoQU9KlETqKK2k1VJa7emFnrvn/q673Xtub6WTeJJWbilSJCgJ\npAiSAAnQwA4wGNPTrrpsujD3IqKqe0YAQa15Lp9nZoCZrOqqzMj4ua8ZDNkeDHh9d5e6qYPYRJbR\n73RXM842tiWVCnZXeUS9OmvRqYY4D116hy73iJUFkxToqKO6VOdp2zY6/YT3mM/mQSvYe4o858rW\nFnemM0bz6eo5aBuzQrTWdb1qy0JA8Xp/0to7AcGdCIcsf4VOh1pRuJYemstjiQh21kbVroAWGPZ6\neASj2XT1/eq6jhq3GblO2OzkzL2IvOxwb2vr8HVFkhe0bctWt0s9n4SxTBJGIsJ7DqcT0FnYQ6RC\nJpKrw5yXrt1gq5Asypphv8PZzU1euXUHnWecOXc/G8rxo/0xHWmYt54Lg4xJZZnVDefWu9iDfYpO\nTikz0kyTesEbdw7IaDl/4RJTY3lztCDNO1SzMZNqwXanYFI7tC25/+J5br75JiPV4+NP/loQRonj\nCRcfqtOYByGCldasXl7TewACRCMFQjIqhOCN2y/y8puvcmVzyPHrL/FiLfndL/w+VesYL2qO5zVl\nEyhlJuo4LwOmlLBoou1grDD9KjkOP98DD2z3+INPP/hfN8MMiiiSUghUa2KLTGBcxsBrXBL4aj5W\nk96EGaePQVDCSZYv/Mqux69KbIf0ku/9+GnWej0yl3Hm/k8i1x9m/84rPPv9f+SDn/wt5k0b/QMT\n9sYLylhWOxsQkMuNTggbW0yxZSbbiCxUdLKEtTxlo5dz48ZzXLjwbh752L/hh09/mWE9ZXtjk3r3\nTerhJsd1hZKSrCg4HI1RmWZ8Z5/1XgeVJmTtjEeHGqE1hWyYVCOGnQKfOY6s4GyacFxZupnm/FoK\ntuH6ZMoo7zNIEpRtaaIdmGkNnSwlsQYrQotIIplZSxbh3kiJsWG+sURrIqI0ngwC43/+jS/z27/0\nu6RKk6ggFhAMusO19qvr5KOLepDcO2l3nCAGn3j4QxxO9nn+le+R6rDZZVkWWouEGVGeZiyqik6n\noGpL8jwjEcHxRWYKLQSbesALr3yXR648gRCCItUMOumq4JUEMXYTHyjnl/NOh7fRMm05N7eeNraL\nV5JxsFr41ofWrgWk95y2CJBeINSyIxLqBmsdxsr48Jy+RqGN6wgOIyL0vsL1kSHzT1SQn3Nx9rTk\nKKtYYUobZ7HeR6vn4FWZqvDw5v11nG+Zjq9zMJ1wfvMM//CDv2U8XfCFT/yrlTvJ6XxW4liaYVkP\nn/3QFxEExKSQ8GdP/Sn/6tO/ye/9yu/zx1//I9IlNy8JbU+tNHiYzMfBkaO3gY8bUVUveO3mqzzx\n0Ae4tXcjIF47HXKdYp1jUZX0z10ItKU4Pljv9WmtIc8zyqpaAWlsdAgJAJ0wrzRtCyI4umSpXlFN\n7k6iZZhT2oDMFQiKPCAXrTE0dc1gOGCxCM4cHkvlPMq2gSuaLA2U9eo1MmSXYe6p0+C6FGeWSqmV\nubEQoaJccjV9/ExLIJZSatWaVTI47jR1TacoVkEgUUG4Xgq1avvLGGh1p4P3nqaqyfodRvMSpSMf\nVQrSVCOFo+iECk/I0D5uZbAD7OugzTtaLDiztY1pGx4dZIyrBYn0vH5jl/u2hnjnGPbXOKgtViac\nWRvSES1yNmLfeTbNjEIJ1ooOuTNYWqbWouYTWp1wa9awnjqOj48ohhtUScb6YJ2XX7/GzsY6G9pR\nzQ/wTYvu9NG9PmvJnEvrGxwdHvDT44Zf//yvRuGVWOF7e9IqvycYWu+j7vPJHh7s7SLGQwq8cHzt\nO3+JUgLnYFAUvKur+Kezl9meLfibb/4pT37wf+J4XjOrQiJuPFhzwnFvfXiOl8YISwWwZXIUfnjc\nU35GEfmOAdNFFRKLxzoZjHGVwPsaY13IsqNwdqpVrDYlyvmglSmjqsQyqxPBDUPGed6dgxtUs11S\nmfC5j/46TetYNC1a90jyIR/81H0s6pZpVVG1LeudlON5G1wglpXJPV9QiOUDGNp6jZEkTVBaqZrw\nugsXHmc8uk1tW9bvf5zd6z+irRoao5hP5wyyhNZZ3GJON8+CruYaNOUcb5acvhZvob+WkjobgDoq\npSdA+RAsNwu4fjTHC0mb9emqJJBkfdy0BSQyIXE26Jr6IOBtfaginA3Qc6LqTxLlxUy07Qn2YybM\nRhPFXz395wCcW9/g4PgYYzxf/PRvrAA2y7URxmsn870lN8t5QyIShICd9bP8kGCr5pyjbhvyJARo\npCTDI5RiXpX0Ot0wzyEAlSovaduasrUoodk9vMnls/fh8fSLbDWnnFUt0oggE2cd1gU1IydDS5UI\ntPH2xDUkhrbV/V7efhd/4QJ46NSKWHEoiQ+JjbZcxlmcD221ZTvRLd8/Xp7gNBNasUl0DPEEgJAQ\nobJUiQqADAe1WdqaudXIWMmlCUEIvEol3LpzDeM1VzeH3D4+pDSOX/vUvw6fQ0iksHchnJ2IMojx\nS3sR5Niee+kZbtx5k0VdhrmaSvjtX/gSf/n0fwYXPFmtczx837sB6HfW4nUIoJg//Or/RZEXXLv9\nGi+8+jypDujXNra3dZKElq5pSZA4IclSjYljlGWaLoRcOWCkOkGJwC8O83azwjc0TRMSDSnw0ecV\nwWrdCSmQNkjP+YgUT7XGSolr2iDcrRMsoIyhyFLmxoL3pDLQYaxzSJUgjMF6y1rRCVWq1FFdyaJF\nQCwvqwvn3F2Gw2HjjihSb0mSE3Sx8LH12zT0u53wHC4deUR4TqV3rHV7JEIyKefM5yUPn9vhqG6Q\nWkc8QeiI9HRKt63JsoRJWbOeKtJUk+DpZ4p51SCE58pmj+uzkod0iWgdLEoWTcvFQYZoFijnMLXn\ncPcIc7BLP1Hst4bH3/M+5rd38bZmXHQZ7x1w/+aQYZbjyxHkQ+R0Qc9UTOoSVXTop5pBtcdxO+P+\nM1vsHo3oKoEsuiSdPqWV+GbGuczzTy/d4DOf/R1+/d0WZ08lsLFN77xY2dwtn10hiHPEEz5svBOh\n2FIhyP3103+KVglXds6yoRxbGv7s+WtcunSBTdlyS4bZ6tK0vnUea1ygzcT5pLHRx/lUzAjPUUAI\nOOFWLI+3KCxXxzsHTC9i5QbEAaxzItgYWYeuWtJIrs50gk4UnVSSJgkSULE6ECLk2RK45w6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dBKPD3HSVWYV1pnAh8JT64MMklpjeNgoQMoxllaO8c5VnOz1QwptsaWGb5b/SlO7MJ8+MLLFiqx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 6), edgecolor='w')\n", + "m = Basemap(projection='cyl', resolution=None,\n", + " llcrnrlat=-90, urcrnrlat=90,\n", + " llcrnrlon=-180, urcrnrlon=180, )\n", + "draw_map(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The additional arguments to Basemap for this view specify the latitude (``lat``) and longitude (``lon``) of the lower-left corner (``llcrnr``) and upper-right corner (``urcrnr``) for the desired map, in units of degrees." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pseudo-cylindrical projections\n", + "\n", + "Pseudo-cylindrical projections relax the requirement that meridians (lines of constant longitude) remain vertical; this can give better properties near the poles of the projection.\n", + "The Mollweide projection (``projection='moll'``) is one common example of this, in which all meridians are elliptical arcs.\n", + "It is constructed so as to preserve area across the map: though there are distortions near the poles, the area of small patches reflects the true area.\n", + "Other pseudo-cylindrical projections are the sinusoidal (``projection='sinu'``) and Robinson (``projection='robin'``) projections." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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gJLO64cz6efrdIV/9yj/i8b077D98SF2VrJ3d4r0f/vC/n08n//my9vnTiyVh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\nl5aKLM+JowjwB6G6qryfcHttAkAIi3RtfB3e1qKTpu1BwpKmMSsr6+z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v7R6w1krZKyvK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xWe5yGFpBtHxCshgjjw2WgWfC835MJJoxVlRV4UPHP+fWwMHmZnvGC0qMirmrpxiFtrxAqg80b1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\nQQiB97EtS9vmbnv88WInLs5suwUWh8faKEyKK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 6), edgecolor='w')\n", + "m = Basemap(projection='moll', resolution=None,\n", + " lat_0=0, lon_0=0)\n", + "draw_map(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The extra arguments to Basemap here refer to the central latitude (``lat_0``) and longitude (``lon_0``) for the desired map." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perspective projections\n", + "\n", + "Perspective projections are constructed using a particular choice of perspective point, similar to if you photographed the Earth from a particular point in space (a point which, for some projections, technically lies within the Earth!).\n", + "One common example is the orthographic projection (``projection='ortho'``), which shows one side of the globe as seen from a viewer at a very long distance. As such, it can show only half the globe at a time.\n", + "Other perspective-based projections include the gnomonic projection (``projection='gnom'``) and stereographic projection (``projection='stere'``).\n", + "These are often the most useful for showing small portions of the map.\n", + "\n", + "Here is an example of the orthographic projection:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Sg2TfrkNgwfMCmo3O5K9MkohL62fo9zaQjkOUpLiuixAKY0o/XM8t66O20KAU\nw2HE/sVl9i7N8tCxJ8i1JQx8oiRGKonvuWwNR+RRxK7lRVZHMWkaUXNcHEeRpQkYQ61WI49jHN8v\na7HWEkiLzbMyVaksXhjiKsVWnND1FVYICuFjMSjHIU/Tcu6nsAyjhJanyA14SuIIuDAYIR2X3Fhm\nAxfPVWhrCf0QqTx8L2B1MGB1cx0/rIN0uTDMOLz7IO3OMspxy50O+Kq04EsKQ5KZScuRHqtoK0/e\ncV0z1xZttoVjYyvAcW+qW1kjljNNK8em6kKnMKWgLPAUWaEZZRpPSUajS4DhE/fcw1/8/u9y6cJZ\nXv3N78EN6x985rEH/+LBj/zBr/49T7EpXuSYEuZLHFIq6QbhrTfe9U2fefTjH+Jld7yRO77p3XSX\n9k6W8RzJQisgcBWrg4RelD9n2vWFpGIvH9y8I4U2NvVWAkeWg4g9V1LzHLo1n7mWTzNwyY0BK1DC\n8PhnP0Kicw5dfYS9y1dzcfUiJx77BPPKkiYxvutQ8z2YmWc1swwKSIuCOIpoBQHK81jvDyoyUriO\ng2fLyKTZarPe7/Pqm+7i5JnjSJmxORjhGku73WGt3yPwPAqtaTSahEGDPYtXlerQqj45HPVYWT2P\nVIYkiWmGAaOsYDiIuOXlt3PfQ/fSCEN8z0NiQefUwhDluOyam+PY2XOlGYE12CLH8QN6oyEiTZmZ\nm+Niv8coilBCEkjo1GpIKckFJHmB1AXkGaHnUhjDXKeJgyaUhlwLokqJWlMWX8BgOCRzfRItSIqC\nutDMeZKg2SSLh/ieT47AFAX9vBwNFuPgpgPm200u9SOcICQtDCob0G51qAchrgWN5ZnVdbIs52Kc\nEzRmuem6O6l5pao60yVRjtOyunIzKlO049aTijir6S+53o5OJePeVHBkaQeolKi8cGXpc5sMObty\nkn3L15QtLxo8RxC6imGakGuJKyUnzz5K3Yx4+syI3/319/H00Ue4863fzU1vfAe/9pPftr+/vnJ2\nGnG+NDElzJcwDt961w8WWfJTKyePXXXD3d/EK9/6Tprd+cnzSgrmWwGtwGVtmLA5zJ63fQJeOGGO\nHXDKWpNESSbDmx0pcap0a+jIMhUbutQ8l3qgaAcuSlqOPvRxljo+fVVjM0rZvXiIBx+/j/jSWW5Y\n6hL4PsIavCBgXQaspprVjQ18P8C1hpl2i/VhRJbnYAxSSraGQw7Odplr1Iitw3ocEUcJ9XoNYQy+\n5+O5HkmHRjG2AAAgAElEQVSuiZMYA3SbXfYtHaTZ6EwUoc/+myGOB9U4MCiKgmajxenzz3Dx0jmk\no3CVIvBcfNclThJuOnyIhx8/ymyzidUFYJGuS2otRaFxlWRQ5AgEgeMglaRIUjxVOvZIx0ECJkuw\nRY7XaBIXBYHnorAoa+iGHoM4ZjTsgxsSeIozqxukro/r+oS2YKFVJ80SWoGLNrAeZXjCkEmPqNCE\nJqcVBvQ15LbsocR1WPQlNs8wJsdzHDyn9Jg1Frb6fSIrcOszjIxLlGR0mm2WFvZV7kfbKmJjt4dx\nb09bKYVBRWViYOx4OktJjk51HJWEWabQjz39EG6+iVCCi7HB8+ocWj7EwuwuwHBx7SQNP6DTXibO\noT9c4fzphwmxPHZ2i4984IM8/tn7ecM7fgCv0fmjo5/5mz94+J4P/fYXfdJN8aLGlDBfgpjddfBV\nN9z11nvv+7Pf4erbvobXvP37aM4uXrZMt+4x3wzoxRmr/aQaQ/XChTxX4vKJGtV0EUk1hqsUjigl\n8JSsostyukgjcKkFDr601AOf1XNP0rB92t0ZTJHTFzU2U8N9D3+CPfNzRHmBKHK6ecyBhRnqu/bx\nyIU1Nvt92q6i2WzTjxOMLXsMjTHUaiEbvT5FmrI8O4srJUYICmOI8wJPOYRBg4N7rqJV7yKF4sEn\nHmDP4l7mOvNlPY5tMQts915eub12/iqAKB5xfuUsg2EfRJn2nmnWWe52OfnMaeIkZqHTwas+k3Bc\nBvEIgSAXkKUp0nNxZTkIGqNx8pRUawaZptUolcw6GhF6DmuZwTiKmuOQZBlO4OEJaCgYRDHKcVBF\nzmoBvucyHAxo1UNcKRnmBVoIakoRJwmp1iw26/RzzSiOy9mhlbgoqDdpuZIFX7LaH7Cr3SJOE5q1\nGnMziwjH4cT5c5zdGnKpP6TVqNFszLFn+SoatXbZwzkWAxlTRpyVuYOh8sI1Fs32hZpk2ypQYjm/\nepqN/hrrW2uM0iFLMzPl/ikK5kOf+dl54sLST1M2RxFzzSZCSnbP7cPx6zxx8hFW1s+xNDPDjCc5\ncSbh19/7Hzhz8jhvedePcuj2N/GrP/WdN58/cfRzf6eTYooXHaaE+RLCwZe/+m2Nzuy/PfXop6/b\ndfhGXveOH2J294HLlql5iqVOSKEtF3vxZKzWF6t8HWPnMOcxWSqx3Wd5WVRZzav0XUXNc6j5LrXK\nE9akWyzNLaB1zsrqeQYrT5OmMX6jjSctZ+KMtX6fui4IemvcdM0R8laLU8OYfpyQRREzjSaDLENj\nSbIckaZ0ZrqsDYfkWYYpNHPdLqHrkeoCqw1hUGd5YR+b/S0wsDi3TJpGeJ7P/OxS+QU+sa0Tlys+\nKVOFO7fFs7cPjNW0cRIjMBw/dYxXXHWAp04+Q7tZRwmBKyTDaERuNJ7vo63BlapstQDiIifNyjaU\nwFGERYbvBxRZSlQY6qFPlOW4ns8wS0jzAqM1qTEIKejWgvKz5zmFtYRhyOZohDaalucx02iwNhzi\nAptpgueWaeQ0ScFxyokpuWam06GI+mALasKwYV1aQYBPQdf30UISRUOktLT8AByfobEcX+uRY9FZ\nTtiYIVQeRw6/AlX59VpKK73xYO7ymGRCqmOjeykAozl1/km6rVmePvcUm8MNXMchzVJmm7Pcdu3t\ngMBzfSxj4ZFBotBVtOo7FiEV59dXcJTCGsP66nHObo0QA/i1/+vnSOKIf/S9P0GUmQ//9Qd/6z88\n9cA9H/qiTo4pXnSYEuZLALX2zKIQ4of8sPGvm7OL3P0dP8LyVZfbaiopWGwFhJ7iUj9h8DyCnmeL\neZ6NbcP0avzWFaKesdWdqsQ9zhXGBmNP2NBTNH0HV2pGo02kCnn0wY8y42oWQofUCEyeMr/nAKlQ\nXFhfp3fmBFfv249ptTk+yOhFEZ4QeJ5PlGUA9Pt9FmoBXq1OhuDi5gZWl6QiLbRabVbW18nyMuVZ\nD3x0oQnDECUcCgs138dxAmbbc7RbnbJdw/XxveBZpgTVRtm+e8W2EjsaOAUCR0I7gMdOnqY/7OEK\ng+u4KAzR1iaNuXmUUvTjUu27ORyy1htgtKZeqyFdRdNVWK3xlcJTil40pFCKehCgKGucQgjiPGNz\nNGKu1SQvcpAlMQdCEgQBVkqUsIgiY6QtveGQ0HOJ04zAdZFSUvd8tBTIIiMVZf25JTR7ZlqcX9vA\nejX2dtukyQg/j0mVhzE5ymr6vT7LS7vAq9HLNKtbW1wajEitYM/MMvOLB+k0uxNjA7Nj217msmTH\n/sDbm1rK8nLF6AKDIctSnjn7NEEQ0G3PIRC4ykEphygeoZRCSg/fD7BC4apS4JQUljjNEZWAqHfx\nUWJb49jDj/G+X/wZmp0Zvv37f4L/9hvvfePJp44+Mthcu/R5T5ApXrSYEuZXOW558zs+cvrx++9y\nXN+9+zt/hP033PEswmvXXBZaAVujjNVBwnP3FJb4vIQ5EfKU9yeR5I6IUspSwVimYUsHn7EpQeg7\n1H0X3xXUfUnoSs6ffAgZ9xj0N5ltNRhsbLC0dxd5HOG7HnGh6Q2HzLY6uPUWvoK+tvRxefzchZLs\ntMZxPYajEarI6TSbOI7LpX6fwPfpj0aErksn9MmEYmVrC1GpM2tSUa+FhErhOC6d7izPXLpEq15H\nIUkLjeM4jJKEfcsHmJ/dfRlhltFQaSe309egrN5WddwdZg5QDmKea3qs9jPyIsdaTX/QYzjcYhgP\nEVKBLlCuh9VlA/8oiti9+yBLc8toqzl+6ihGF7hKMlPz6fX6WCEwrosWgq2tTZTjkeYZWVHQrIfM\nhTU2hgOskvhKARBnGc0gAKXQuqDlOsRRRJLG4PkTR6NhHJPpgrxy5dlb9/FsTqfVIVCC4aCP0Fkp\n0HEdciPQRYYsUhbanXJWp1dnIzJoDBd6fdL+JrWZOaxs8NrbvoYsy3aMVStvDc9xccL4QqS8L4XA\nmIJPP/JxiiKlEfg8eeYcYGg32xyem2G9P0DrHBB02zMM4ozDB2+g2ZzFUeWKepMZrZY8i+k2uzzy\n6Ec48egpfv0//gJze/by1m//njMX10cf+61f+F/e9bwn0RQvWkwJ86sUs7sOvGN298HfWz39FK/7\nxz/MkTveiKiuuMf73HMUy50QJQXnNyOSXH/xKdeqj3FiaQflqKjK33PnzMorR1H5jsKrbPJqvkMj\n8Oj3LzLaPE0oNEWWsbV+kd3NEOotiioybfgO0hZgBOloVNbuBLhhkyhsk2jB8ZULpf+rMeg8p+4q\n2s0myvUYZTm2yMnyAqeKknQcUaQJ9VqdrcGQdqtBrdZEWoPjOKwPR9QcF+m4NLwAx3XIRemX6odt\nZrvzCJxJKrYcFL1taLB9nu1IIY4j7x1f7FAS5nyrJMzJwra0zNvqbeE6DtrkbPR65FmCpwTxaABe\nyDWHrqXX38JYw8mzJ/BdF201ncCl5ThspilbaYLvOGRZTj+OyvFauqBRr9EKfEZ5jiMlnutUbR0F\nWZaRa42Skvl2C5NnRIWh0AXKlurjfhKjjUE6ioNzs+zuzDCMhjSKPiLPcazBKgchLJkV1Go1RFHQ\nW18jqAXoQiMQpMonaHZQUnB2o0dmLaeHCVfvO0K32WWmVUaHky06Tofv2MLjo3gsLgPLpz77N1zc\nvIRUimatNLFQQtBuNQnSmNlGDWTpWVwLPPIkZWUYc3Df9UivRrszT5xbNkdZNbuzEottnWLXzG5+\n69f+D/74/e/n7rd8I2/4tu/lP//sP7/7kQfunZq8fxVhSphfZRBCBK9447ddeOLeD3due8t3cfs3\nvgvHe7bbV7fuMdf0WR+kbIyyL6pGOV6m7J+svrqERUqJoKpNCoGQAleqqj+ubE9xHYnnKALXJfAU\noVs6rqBTLj7zEMuhxc0T4njEiMq+TRS0Z+YYejWyeIBvcpxC01/fYG5+Hhn4mEaH81HBuY0tAsel\n1WxSDHssdrtcWlthttEgczxWo5RBHJMnKTPNJiaJCIqYuVYTTxfQaFJYwVBL4iSm7vvkeU7o+cy0\nOhgvpBdFKMclLjQHlq9GeV4l+BHYcZ1tQpQvYJ9V1CplecHhKMlc02W1n2PFDhWRHUeqV/a3luYD\nVgiUVGij2ext0mq0UFIhhWAYDxiO+oyiTbI8o+Z5pFqTG8nK+gqFLnAdF9dxQAi0KZW4hTbkWYbn\nu+UIrzAgLwpqgV9NbCkwWHr9AbnOUVKx0O2yq9lgVmb4vks/yWhYzXB9FdfzcH0PpRRJoXGFRFqD\nlApTGJQLlzZ6DDLNwasO4oRt1qKU3rDP2a0hC60mUabZHI2YaTS55vDNNMJWta3Hqdqdx+r2Mat1\nzplLpzhx5kk2BwNqvs+emRmGUUQt8FjZ2GD30hI2z0mGPQ7Pd0mtpYnl5NYIN6xjVI1rr7mVrcSw\nNUyJc01aGLAWz5GMehu8/1d+ns9+8m9487d8Y/aGt77r3Pd98+sPfeGjYIoXA6aE+VWE/de/8jf7\n6xffPbf3MF/7rh+nPb9r8tw4leoqwXInRIgyqsz1C9v/E5LccTtRvI4jzMos3VESKSWuLL/83Sqa\n9D1F4DoTojRFSq9/CS9aZU4m1FzFhbVVCuWzsrXFoisJfR/lKrx2m1GSYJMIoS1urUFcb7MRxQjp\nkljoDUeoIqPVbJLkmgPLu1jr9Vjd2qQlDc12l/U4IRlFtHyXutB4UhIocCkFOo1Ol9z16cV56RAj\nFMYYjHBwXI84zbFCkKYFwyjm9pvuLFOkbNu8lRHm5Rcen+88G9cwhTVs9dYo8oj9i7M8efoijWaH\n2ZnFZ7/miv1Svcvzv0e1bF7k5HnCKB4BglEU8eSpYwg5pmFb3pcCISTGGow2lYGDRshyKom15QWS\nqVpyjDG4yuHA8i4E0CZB5jF+nmIEuPUGot8vzRdqdUTgIbTBJClRntHudiniCIxlNBiSJAmjPKfW\n7tLszOB6AYMk4eJwRKsesro1JEMSugpPQi+KqAVt9u3az1asWZzfg+8Hl22A8ZayWNY2LpEVCUdP\nPIyrHGbaLTwsukh5Zr1PEIQYDA3PZS50aElBkucEEmyRQ22G1sx+VmNBIWoMkpw0L9CmPM/OHHuY\n3/n3/wbfV7znf/4x/uJP/tu/+KsP/OHPPe8OmuJFgSlhfhVAKnXjgRtuf3Bz5Zzzxvf85MTv9Uq0\nay6LrZC1YcLGMPuC6x23gYxJsux120mSlVOP3Jbzjwc8e0454Nl3FKFXCng81ymFPY5iONrk2NFP\ncMCTBNLBkZpBv482llbdYxRFWJPjN2do1ELWBkO6rSYbVrGVZEjl4CAIpKXAYZgmtGs+ri1QXogJ\nWhxfXcVDsLvTRY3WCWfmubC6BlGfwPeQjofSOYHVyKDO7NIuLkUZvTQj0aVvqacUjlP2KxoEOi8b\nA4tcc2DvIZYXdm+3P1R9JVbIHbW2L+aCxKKEZTjssXu2RT+T2wOtL1vu88BOXPqqF4z/sxPF7k7z\nCIvl/Mo5jj71GK7rlNNDVCmgyYsCXTkS2coEXTmSLM/Q2qCUQGuN1pp2o8lCp8PGYEBW5Mw1G1xd\nM0hd0NOSpDAMnz7G7uUl2rNd+nlBzfUohkOk5xG7klZQIxn0cRwPXeTkwyFaOVitCRtNwu4MqbEM\n0wxlMnRhCOshwyhlmKbsmekSRwPyNCMxkpO9lPn5A1x3zU3VNizJ7BOf+xi3Xf8qTp57mo21MxRC\nYK3BdxwSDMNRTJzEBH7AaDgsW22UZHFmllmnFKjNBQKTW1StyYXVNeq7XklsFL0oJc00mTagNZ/+\n8Af4o//yS9z15rfwzW+6o/+jP/JTu5M4Hr6gg2KKrzhMCfNFDCFEeM3tbzh6+vEH9r/yG76L277h\nnbjPkX5VsowqXSU5vxmVKaQKl9d7xjW1qhWEKk0o2CbISt06HrvlVGSphMRREq9y6PEdReA5BK7E\ndx3iuE/WX2F1cwUx2mTvwgItkTPY3CRLM4JGHUeUPYK5NnitFpGVWGtIkzJl7EuDYzSBgLBWTuZw\nvYCUMhrS0mEt1qTWsDoY4giBYwyLgcu++Xm2hn2i4ZC671HEMX6zQ61RB9dnLUrJgFGakWldSXJK\n0U6almIQq0uXoZn2DIf2X43v+VVj/fb0jbHd+pXn1bg1YjsI3MmCIOy2UEVJWGwFXOwlY0faHcaC\nOxS1O6Km5zk+JpmFK/lzvJ5hNOS+hz9dEm1VcjXWTu5ro8vJKsYgVdVnqQts5fvrux7dZpN2vcHK\n5jppnBDpgkYQsKfTZlkkbPX61Dsd2q5kOBiWdWUpUF6AyjL8ThuTxAy1Lh2YrMZmOZtrG+X2qIVs\nDYbUWi1qQY3WzBybwwGjOEHYHKN8kjwjs4rZRkieRriOTy+KCaQkiUYY5VBvL4LXpNlexPdLM/7V\njfOsrJ5GKsvplUsoxyGOYzSWmu/zsvkZzl1cYZhlrGc5SjnUazWavs98vY4nYaHZROiM46sDRDBH\na/Ygm8OEUVpQGEvU3+TPfvOXePjev+Kb3/1uDu/d89F/9WM/9obPs+um+ArFlDBfpNh37a2/1l9f\n+f7FA9fw+u/+Mdrzy88ZfYSeYne3Rj/OWe0nz0raXT6wuWpxqL60x/U0IcCVCqnKyNIZO6ooVUaS\nUuG51XBnx6luBTqNePLhv2JfCLVmi8cvXOLI4jyeVFAUxL0taq0GRgm0VEjHIXUDfFswzAvqQUie\njHCVItIQmBwhFY7WjAz4gc8GIWvDIe1mm7XBgDhLS3EK4EnJLBpXWOZaXea6bXob66RZwt59+zk/\nzBkWBcPCkOY5umqQz9K0GhNVDi7WhUYKiZQOc+05FuYX6XbnqmHJwLjlYcd2HTfZV1uZ7QmQdudC\nWLZVx2PSlNUA6Qtb2WX76DnN66/Y5VceAle2sGzfWooixxrDpz/7KbTRk7S6xSKFLKNLAYUuzdxL\nX1ZJXuQYq1FKsdTt4HkuaZZBkRE6LhtZziCJcKTi6laA31ulZyW2Nc++hkt/5TxKCLZyjdSGwnHZ\nu3sXeRqj04Sa7xG6HjbPiEcRSZ7TaNTJbVkbFq6L59XYGsVIm5FYUEGNmtRsRSn4TVAuZCMSowhF\nTjdwyaUiMAXnNgdsFIIjV99Od2aBTz/2KTAx5AWuzpBZzBCX2U6L9c0tDu5aZrh+jq1McHxjgPRc\nLOU5MdPp4LsOV83P42HpRyOS4Rb+3I1kssnmMGWUFlhjOXP8UT7wn36GWuDz4z/6Xfz3jx/7id/7\njV/5xWedtFN8xWJKmC8yzO4++HopxHuzNL7uzd/7Lzh006uff9mGz0zD4/xmzCi9vK9yHEWOVazj\nKFKO06o70qyuklWvZNkC4jkK3y3Trb5buqoMhusMBus0fZeg3mFja5Xh+SepFRFzzQb9KGbXnj04\nbsDG+go2z6iHPsZAu9NiGMeYsE6epPTSHN8pBwCbPKNRq9OLIkI/YBRnSD9gK8mxUpEi6acpWVGg\ndZk+xBgCpei6LgtNH6ENca9H3XOZ330AFdY4vdVjI05JC00cp1C1vOS5xmiL1QatDY5yMKYU2tx8\nw62EYX1i2bazcR626cyOVa1s0+OELneOpLqC/8aKTqkEy+2A85vppPa4c/GdkeP2/ty5lipCnMSn\nz9X7Wa0LQZannF85z9rmKoPhoIzqlETrAqUkjudgrCHJssn7OlKyPDtDlCZsjgb4SuE4DkgYRjFZ\nkeO5HkutJvtFQtLr0VMBkV9jd7vO1uoliv4mTc/DC31ErUHdc/GkJY6ScvRYHFPkBVGaoQIfVwqM\n69Ns1Nnc3CIIa3TmFxnGEZtRgjCmHFqtHKQ1FEIR5RohFbZIcZSDKwSe66BMiicVF/oxQWeZp8+c\n4pq5BvM1n3zQo9FokGc5/a0telawPD/P+ZVLtOo1Hr64jmh1GSalA5ZyJIHjsTw7R6fRJBpsUCen\nlwly1aY5c5j1YUKclcfU/X/5X/mT3/glvunt34LdvPiLnz1+/v1HH/ns/c97Ik/xFYMpYb5I4Lie\nZ4z+R43u/O8dfPmdfM13/zP8WuM5l1VSsKsbIoXg3GY0mVEo4P9n782DrMvP+r7Pbzvb3Xt71xmN\nNIhBwAiMzO6yjaIyAkPKEDssNpAYGzuGxEVIClyYQOJQdlVCwBiolImhEpuyqSyVADaJY2xCESMg\nbBJaRhpG8868e693Pctvyx+/c2/3sAgshFAq71M11e903z59+/a553ue5/kuO2nJtpuUkstYJHUJ\njllP1ElZlJrMiH4HqdAqcn72gPnJB9GxYzaZ0KoxTb3i6dvP8cL7fg4dPQM8Dx4+4lOevsHB0XVW\nbYfzls3ynGuzGautDCGCynIW1hNNjpGpw3nlZM7rDqecL2vysmC5WFJN9+jajg0KGwJNiCAFznms\ntSgpGQCTzDAdlpwfH7NX5kTrOHrqaU67iFMZWZHz8PyCs+WKpm764GYNCLx1aG24feN1KClp25Y8\nyzk6uokPyQx82x1uETHSA1v6R88c7r92hb35m3WEr0HN/sZFCbg5Lbl3Ue/G5Dshirj8lqtsWbH7\n4uVxd1aEVx70OwEngFaK5XrJydkJj08fsdmsd51kFIFqUJJrTW40pdacLJbUzlK3NcYovPe9bjSF\nN4cQGQ0GvOn6PsP1BRupeGnRcTgZohanzAYF3WLBbDJm0TYgFdPxiNA0GCGQyjCfz/FaMxgOqOua\neeuZDkvW6yUyLxnmmjabEU3OxXpFYy1diNh6w2QwQAOrZkMnJCp4qiyjMprDUU5YzDlfzMkmM158\n5T4XuuLWtSOezmMi9wSHsB6hFT6CNhl37z3gmWee4eH8nJNO8eLJSdpZE1FKMy5LjsZTOu+QUjJW\nsF4tuPX6z+buhWVRO6yH5ek9/vF3/2fYZsV/+Nf/Et/2n37r0WbTzLuu/d3JBU/qD62eAOb/B0oI\nMX7qTZ/2wvz4/vU/9bXfyuvf/Fmv6S6uSkIKI7m9N3jNCPY1zNZ+DylFSgfZWtJlWlIYncwDMk1u\nBJlWaJU6ycXqHGMkhcmQQnC2OKXIch49fIXZ7CB1W+d3mU6mrC5OUMGzujjm5uERRTUgAMt6wbCq\nWF2cMdubsa4brNA0bUdV5JAVXCxXSCEJzYZpmazdxsMRXsJ6vWE4nXHRBRZeMt/USfyvkx6yrmv2\nqgrallmZIYHz4xNGVYXKMgaTGUuVcbLZIJCMy4q7xydIqTiYXmc63sN6j3OWohigdUaM4TXd5Has\nGuOVESeXBKkESr+p++OK0D5EgtiyaH/re0/24/Cbs4q7Z/Wu+xdX0G67X/7t3IN+e+/aLdi+ttvc\nPXbXbV4eS8jUYa43K9774ntYb5YYYzjYmzAZDDiZX1C3DdZ72rbDBZdoxjFpOq+el9PRiNfPJpyt\n1kTSqPzIBOLyPMkxUlQNxWiMNCrdvPiIc47gIw0BbQy1C5xHTRQSoRTDzLDa1DhgUA7w0jDfrOlc\nh7fJsH4kBQHBoMjItSJ2Dc52FFXFQAkInpJI3TY8WLXUXrAJkWI04fakYoAj15Jus2ZhA43IkFqT\ni8BFbbExsu4c9+cLEBKlFblSPHXtBt57zldLZPRcr3IWTWQ4eyMbBqzqFGTwjv/9f+THfvi7+ZIv\n+0rGT137qb/3rX/zbb/lpHhSHzP1BDA/xuvgqWf/tm023/LM85/N5/35v/47dpUA49JwfVLy4GLD\nsnE727WUObk1EpC9JV3qGAudIrRKExmWmuXiPsvFMQf7N7h9/Vne9+Ivs2lbSg1NCLzh9nO8+NK7\nOdrbJ0bHKK949eX3kEvBNDQURYEwhs16xY1bzzAajzldnLNqW4RtmY5HND6yaVoGuWbtJMY3CJ3R\nNS0DI1nN50yKgoNr16ltSw3ExmEGQ+bLJaOqYCNyHh2fUU0mnNctldG0PjCQgtB2TAcDwnrFcrVi\nPJkQs4KlUHRKs1o3WBfYH+9x+9o1lB7SuJjE6CERXAJph+m3UpG4BUkAQcqMjv1NSOrKxbZr72er\nkl46CRBjOma8TOOA7aj2ksojSdZ4t2Ylr56nGwJ2Ep70d94Ss9h9528m81zW5eMiCNmD+W+/29we\nZydz6f/9zve+k5PzY2ajivFwwKLZ0HQdUgrO5nO01lRFTtM2SC0JwWOd68e3Eq0Vs2rATARWLrAC\nDgrDLbfCdS15XlJVJQ8fP+Lajes0XYvwAZUZnPVEk7F2npMmMkewsY7gPFlvOiEFWJdMN65NZ7TO\nMd+sIQYmZU5FZFU3GGMoipxcQOU3NFGim5pgO45mY+pNzcp6gtY8toILFxkPBjw1MLTWcVTlfPBs\njo3JiP7Zo33unZ6j8op2cU5jCk6XK1xMmtijyYzpYMi902OklAyD5/qoYh6G6PGzLJvEpl2dPuAf\nfte34uoN3/xt38w/+MEf+op//VP//J/8ni8ST+qjVk8A82O0yuH0E/ZvPv3Pl+fHT33+X/qbvP7N\nn/UhH380LhiVhrunazofe9u1fj+5ZbPK3jhAKUojGZSGcWnw3ZxcSaqyYLlZMh5MmI5mdLbl3oMP\ncjY/4frhTdzmhNXxQ6ZG4LoaLSKz8YRWSC4WS8aZYjgcIYymKIa0tmF1ccrAZKiiYNU0OOso8wwz\nnnLn1Vd55to1mgirxZw8elSEwWjIou0YD4cIpTluHDGCdQEE6H4PtYma803DQMJ4MGRZN+i2Rgu4\nNtvjYj6nHAxpTYFF0mCIImM0nKJNkWKiQmCQazad43zVJdeb2Ocyhtj7l247xPRe2b6mEnHFwajX\nn/YdvJYyPWYLRtu4Knqz9pCOf3nUuANGowRP71fcOW12Zgbbv+cWOLfkrMsm8QqA/g6j190ot//i\nVWP47ePSMfoutjdNCCFwcnKX0/MTTuZzrHeJSSsFkUAUESUkSikGZUHdNjhvsd4hhaAyGQdlQS0k\n1jmUURyFlj3haa3d/V4hesrhEFMWaGtpncPFiPPQCIkTitOzMx4FQxAieb8KML0R/Ha4vTcaY5Tm\ndGOJpe8AACAASURBVDGn9Q4jFQdlTnCWECIDHZG+Y39QkClJt2mY97IlfGDdtSA0x23LQuQcDQtE\nsKydoEExKQtGNIQIJ8sOp1NSTIySRddyslrR9Tllg6Lk6aNrPDh5zGK9IS9yDkcDRtkYl92gDjl1\n60DAz/2zH+V/++Hv4Yu+7Kt4+umDH/vv/u73/7WTRw/u/V6uF0/qo1NPAPNjrJQ2phiM/orO8r/3\ne+kqpYBbexUSwf2LOl2Ed5Z0KWtSK0Wmel2kUQxyzbjKyZVjWA3RvUREyohEsKnXnM+PeXh8F7oV\nVeyYVjmbTc3GebR3ZMFybX8fpyTr9YrZaIjvLPvXb+CFxi0vuFhcIH3AmAwhBUhF4ywyy1FC0rYd\nWZ6jRWS+uODmdErTWYKQ6MGAVZDUNtB5xzAv6FCs6gYtIuVgzEnTgA8EqYhtx1hrlO0ojUpJHVnB\nCsWibrm+f5vhcIZ1kc57OuexLtD5gA+BSWloned40abOMkC/rdx1heLK7lBK+k5d9gbyqXuXInWI\nRisyrXbACuzCkOOWXSsuDSAgjWK33f/Nacn9izaNz6/A36477Z+T30pA2AKe7AGv71xf87XtWHY7\ngL3yvVfOqV2n3HeZXbtksV7ygTsv03QN0AM+6QYmxEheGISU5MaQKYntOrRRuOAQQvFUVWCbFdZH\nTlzgdblE+w4XBdbkVGVOKTznjWOUCfKeaGR9RCvD4uQxXkiO9qa8cLbhYeNSyHVZEULyd5VSYYxC\nxMj+aMowz3h0MWfV1um9IiWToqCUkeuVIY+W5XJFG0E7jycwm41pz+copRBFxXnd8tgJnpqOWLaO\ntQvMffJxOhqUKNexqVtkUdIGKCScrdeMi5KXTk6JAnJj+KSnn+F8teL+8WOiFFRlia0tf+T5t/Fo\nvmG+7vAxMj++z4/8N38T26z49u/8dr7tW771c175wAd+0Tn726chPKmPaj0BzI+hEkLo5z7zbXfu\nvvCrN7/wr3w7r/+Uz/6Qj9dS8PT+gNYGHi9akksLKCGRW6DsWa2lSQYCg0JT5obSaHItEATuP36Z\nYVHx+PQ+N45u8urL72G/VJgY8U3NarXESMEzR/uovOCDZwtGsWY2nuCcp3WeoigwZYUSCmcbgnes\n5gsKY1CDinq9wRiFzAvWqw3KZDghyCU0yzVaChwwmu1x2kUaF3FdQ1kUOBTSZMw7i7eOXEkEEpXn\nNG1LqTTKpxgqHwO6HCDKGV4YtM6JSIQ0tDYBZes8nQt0zuN8xAVP8LA/ygkx8uiiTqNYrhJ7tmPK\nS//X5LeadsDbnaIgSTNypRhVhirTZDppVCURHyK187Q2AbbzER9DutERW8KV4JnDES8fry7NIHTf\nzW5vguS2k00ykBSknNi7PpAcZ648p/782u1Yd5WW3Jem+VwCphKRQa6wIfKe97+fe4/v7di+PngE\nMWVVEtP5ZjSZMehMMdYZmVZcrJZEoxgSOSw0Z61Dr+fcunaN+fyC0y7gygE5garIMVpTSU9F5Kxe\no0zGtMgQm3Uie1nLad3xyCs2KsMFT6YNQggCIGKkzHMgooXk1uERJxfndK7D+UDjHQrBrCo5oGVY\nZawbT/SOUkvatkmWiasli67DlwNO2iRlmfma+1ZQK53IakL2NxbJ+lFIDQRsZ6m7DqMUmshF3aCU\n4hOeeppN27JaL8m95aSx3Jhd4+Dac6w6xdmqobZJgvKOn/xRfuK//16++uv/I7K9/Oe/6z/+lg89\nYnpSH5V6ApgfI3Xj2U/+mnY9/7uTo1uTP/3X/gsGk/0P+fhMS153MGS+7rjYWBDs5CBabjWRyad1\nkGsGecqXTMzX1G3adsO73/NzTDKPiIqh8nQoQoDMrWnqllymjmj/4IDVZkWZF5giJzQtVZETvKep\nG4b7RwTncc0qUfu9Q1UVOs85Pz0j0xnWpTxGHxKDMkpFt7hgbzLlfD5neHSdTZA0puLu8WOeno7p\nvAfv0FKhspKL+ZLrw4xoSjqhaa2jjdC1HdPhmLwaMhgeEaXBurDrIq31yTt1B5RpX5kAK3VMgcjh\nsEBKuH++wQe4JPhsX3mxA0slBbkR5FojRAK6lOWZOnkpFVJEYhT4mJ5LbT1tF2icxzlPIElEJKQA\nba0ojOSN18e8/9ESJRJQbg0iZD9a3xGNelbtpal96nqVFLgQ04g5RPpp9m+SosTXdJbyykhWCsGw\n1HQ9qANs6g3L1QIfPJt6w+PTx9TNOu13BYQYyDKNUoqyyJkWOe16RVkWqBjYBM8gyxmGjrkXTAvN\nb5xdIIsKLWCcZWSuxnUNB9MxLoS0Bw4dpVRkUtOsV6xWS5ZRcs/CMipMlvU3A9sblgRkxmhUjBxO\nZqzbhk3bIIh4H3DeUxnNm2ZDTh8+gGpIpSE6TybTDcwoSyP/uSq5cJDnOXs6cH/Z4rSh8x7rPFLK\n3iowvX5N1+GcR0nBOM8QQiIIbDrHeDBiVBV0bYMW0DQNg0xRDp/GZfusWlg1FhsCj15+Pz/0nd/I\n83/kLTz3qc//2M/+nz/9g7/0s//iJ36/15on9eHXE8D8GKhrr3/T963Pj7/+LW//cj7zi79mJ/34\n7UoIQZUpbu8NOF4kUbTcgqWSZEaRK7mLyhoWW6BMeZObesEH77yX6/uHFJnhV37l5xmIhvF0xl6m\neNwGNAHdrBgXGRooyxIQPDp+xO3rNxBasVytKYzGVBVmMMY5x2ZxTrdcMhmPKMdjVusNMka6EAm2\nQyqFtw6lDU3bkGmNcw4RYTgek+0d8t6TBWuXOhhrLTYGnHUUxpArxaFWTGZ7zDtHHcBkQw73biBQ\ngEQqSeehs4lQ0VlHawOdTyBpXcSFgI9pNxdi352FxIYNIXI4yigyzd2zDd6HS5edLRmmH2EXRrM3\nzJhVOUUm2bSOVeexXW+rl2mGhUH3bkk+RGrraVpH63xPMLo8tlGCzCS28sffGPK++6t+VJ46GCXS\njY4SCTy3o+Ht98t+77jdXRudgDbTyQDf+Z7Y5K/IYIiIfqy7BVMlYZgbWhew/fhx+zXnPXdefZl1\nveJsfkaMgRgDyCSlUUYl4wOpGA8qRrnBCE0mfNphB0+W5TRdR64V9XrFyWaNV4ZcCEZFxqAokDGS\nYRnmik3dMCxLrPWMDAjnOV83HFvPxpQcL1cYnYEUhBDIsyx53CqFEpBrw7XphLrtOF+tKHUylVdC\nYKRgaARHzZKTzjKeTqFpKKsMZz1aK9bec+YUJ23AZIaCwPmmZTIec7ZeEwV459n26EopnE+OSLPh\niIEEEQPzpkVlBQbY25uxOD9nYy23RxnL1Yp5zCgGtxmMbrHYdNTW0dZr/ucf+E7uvPBrfPU3fj2/\n/H/94tf9s//pR37wI3n9eVK/93oCmH+INT269ckE991RyLd90df/l9x+7lN/x8dujQbGpebGtOTh\nRUPT38XuOkqjqDLNsNCMigzCmsPZIc7WeO+Yzx+RiY7Fgw8yG5SoYLExZR4+Oj7l6etHnC+X3BhX\nNJ1DOktZVhDTKHMyHLBe16i8gEFJF+BgdkBdb5ifPmJcFuAcq/WGIs/RWpFpzaZJe8Gu6zjYm/H4\n8TFlVeEjWOfY29+jyQoeNpHzumPZNin7kHTxiSHSNhbvA0Zr8jxDCc0zt57l9vWn+04x0PWA0DlP\n13dxnXV0fRTTtpv0oe8oQ/r/SyOCuDNP3xtmjAvDq6drrIctKUcLwaQyjCtDYTQ2BHzyx8PFSGND\n3y2mUe2g0Jg+WzICTefYtJbWBlwICTB79rJWglxJCqP4hJtj3n1v3pN++vHr1kBiZyyhdsYT8FoD\nii0AXjXNz7YSIpW6z84FrN/KYOiN8wWDXCeRfUhoflXSIoWg6zo+8PL7eXx2vJPQ+JB0hz4kElCM\nASlThFemNeM8QwVHZx21d0il6NqWECP7kzEhCo6Xc6aDEhVhud5QFXkC++jx3nJtVGAEZM4S2nSe\nLKXhVStYNd1uTBqJZCa7/HuGwN54zNF4hA+Rk/lF8ggmook0znMwKNgTHW1dk1cl7XJJg2RYlcjg\nOXewjpq5C6A1Uxk5ry1Bp0g3H2P66H1/LgVCSFrc6WDA1BhWXcd+rlk1DTormE6mXJwfY3uDjFzB\nw3XHU9efxcspbdCsG4sPkV/8qR/jn3z/d/LVf/XreOevvPNrPvDe977j/p2X3v8HcmF6Ur9jqe/4\nju/4w34O/78sIcQbJoc33rN385ln/+w3fy/7N5/5bR8nRUqO0EIyHfSykfMaF0LqJjNNlSlGhWE2\nzDmaVExzz2L+CkbB+ekr3H/5ndjNGn/yG6j6nCJYBoWm6Vp827LxkSp26BAZDgbYzZpRUeKsIzPJ\nBkw4x9pHUBIzGbOyjtFoj8XZMbkGt1qjRWQ5X1LlGdF5jNFsViuMMqyXa4QQONsRfAKZ0WiEGIx4\n5A33a8t5a1nWG2KMZFmG90k0n2KnMmAr/k9jxqqomE0OcD4msHSexnqazqe7c+toXaCzAduPX11I\n4OlDCjoOIfa7vy2DFUKATe8fezQpWDQduZYcjgoOxwU+Bs5XHY+XDbX1xJgAUvTj1UDqBtNoMxmW\n+5AioEKMiXgUAt71vq3QE4Dkjky0P8p5eNEQuQTzEBKR56pfraCXruyElP1YtTdQ6M81EAIf0g1F\naxPtN9fJFF/KdKx7d++iY4eLyQZwd65eYd4mcPTcvn6T0WDEg8cPMFohZSJxPf/xz6OU5GI5T5pM\n6/A+0DiHznMcMlke9nFkbfAJaKxFhGT4HoUkyw3z9Zq2c2hlmAyHeAQFns6nLNN2s8ZFQR4dm6jo\nrCMSMSqNyI3W6b2jFJumpnWOSVkyKSo29QYXI1WeYfBcrGom4wFlbsgFbBYrMiGJ/XGkgEwGhFRY\nm3JBSwJNEMQtu1kmApjqx+ZaK6x1OO+RWmFkChyPUrN0Hc4FTFZhhcLHQN203JoMePn+HUbhlOn0\ngNFogveRm294jk/7Y2/jR37gu9nbO/iSb/pb3/nVn/rGN/ydj+Q16Un97vUEMP8Q6sazn/TD3nY/\n/Ja3f4V461d9EyYvuOr4InYMV4Hod2V7g4xrk4KH8xYEFJlO3WRumA0LDiclA9WyOX6BSgfu379D\n2Z4SF4+ZZQrtVlQyMCgLJrMxHQLrPPuzETk9QchoTPSURUndNJSZJgrB+WpDUVXoQYkeTWidZ1iO\n8IszcgKP7t6jzAsuLpYMyjLt53zAWUteFLRtQzUc0tkOGyErS1RZUZcj7mwcj+uaRd2waVu0UEwG\nw37sKSnygiovUEpRZCXj0QQhBLeObvO6W68nCrnbRbp+P9nakIg9Nuy6y6SxDHh/2WG+trvssyx3\nOklB0zkGheETb0+ICOa15cH5hmXtsT7udn3b3ZUUlwSc2LNRBWk0GiM4v5Wp9CDufepMuNwbpk4v\ncjAseTivE6D7/pg7PWhPvblisbfzcrpqRvCbrPe48hgfwYVI4wNaCUaF5vrelHvHp7x05xVmsyla\n691xt561nbX88jt/ibqpuX3jFpPRlGeeehYhJc+94Tn2pwfsTfbZn+4zm+7xsM/a1EpTGEPXtdi2\npa5rbA/auxsGYDIaEL2ja2omZUEuI5UWKAIHJmKbBhM8KgZGg2HqyGVMRJsg8DGie6CEnoikFVob\nrHWs2pZJNWA2GOC6Deu2RShDlmk6D0JKoncUZUlUmsporPeURU6V53RtkpMoIRkXhqbe0Ma0DlEy\nUae8D2Q62fMVSlEZTfAeLWW6IROC1geWbYcLkcPJlLP1mmluMKTfrawG7IUT5vffjYie0ewaw/Ee\nf/xP/zne+6u/yI/8t99TxDz7xr/zPT/YfOW/82+/4yN1bXpSH7qeAOZHsYQQ+kd+4l89WJw8+Nwv\n+abv4rnPeOsVEsaWmdgzIVWKydJKsD/MORwXPFq0KQ3eKIalYVrlHIwLjsYlj155J8bex0bF/Zfe\nxbMjQ+Yt8/NzPvHZZ2i7lkFhkGVG8J4uOAptiFLi1jW+bTFS4WwLSlPXG7I8o65rbt+4QaYkVBOW\nTcvhdA/VrhCuJcbI8cWc2WCQ0uqJ5EpgO8toUNL6iNWGx8enDPOM4XjERhpO9ZD3HZ+z6rpeBJ8k\nGZvNhtZ2lEVBcJ7hYLh98eisY398yLpZ8aY3PI8UOnWMPamlc9u9pe93mH4HpC70jM4roLjTQ146\n3BGIiAjTQcaNvQofAqfLFq0kx8s27R259IYVIpFMohD00jvC1v5OJEN2I1QCzODxPYGmcZHOh9ek\niUiVPiopOBwX3D+vCRFcTAYKvu+GY9wavYsdcPY955UmM7WEccvuveJMtO0Wd2AIaCVofWR/OiHP\nNKfnF4wnk+3Dd+NdrRRN03DvwV10Ztif7mO0YTqekZmUlCOFpCwqlFI8PH4IMb1GSElVVtTek2UZ\nk9GQIssZVSXCe/YKQ7deUBqd2L7WEbuGIs8osgwtPKWSnB8fk2UFp4s51aACH5BEpMnZOEcX/O6P\nEGNModhEVE/OOl8tqIqCg8kU3zVYH5BKs1jXjIcVA+mpjCEXkbsnp8hqSC4lbdcwGw7IZSAi2djA\nzVHGsnFoodIuN/YkMAK5lEzKkkLAfpWkVF3w1NYTRLrx6axlVdccTGfM1zUPG8t0MMC2NSjJsCyY\ntKc8euVd3Hj9m5FS85bPfSt7B9f57r/1bcUnfcanvv3uBW//rOff+A8+ApeoJ/W71BPA/CiVEGK2\nd/2pl01RHf27f+P7mV27vf18LxXY7qjSnkpLidaSo0nBwbjgdNWRmzR6nQwK9kcFB6MCv3rA/OG7\n0CpinSKe3GECRG8ZlDmDIkNlBo9HZTlGahofCHUN1uI6h3eObFDRhoDRiq6zDMuKtm4ohkM6JGdN\nQxk914+uU28WWNsQo6Dtd2C2Te4sSIEsKtZNjc4LfEwzzlu3b9GajEde82odWFibLPC8R5AuxFVR\nsjeZIpRESd2PZQMRQV23jAdjtFZU+ZDZ5KDfR6ZOyXpH59K4sfN9Z7llicYeLOM23DmNXVNnts2u\nTBewUZFxa69CCHh4UXO6alnUlhgj1ycFi9ruDNfTt6UgZbi0ztuOT4WAGEDI9HnnE0PThtTxWh8u\nzduv7CKVlBxOcu6f1zutpd8RlJLhwXaUHENiqPYHuSToXNVXRuA3BVrT78SNFIwKxbrd7oAjRVlx\ntDdltTjnwcOHROD84pzxaJzG4d7x7DNvYG+yByKBdtj+mP7wEci04anrT1EWFU3bsqrX1G1DiJHW\nOTZNw7Le0FpLmWWpA5eSQsJ+JjHRcTDIyLzFbxbYtuulHJK6swynM2oUviiTMUWEi7rBx7T7pidn\nbX9f5xzOO4osp21bEILZeIJtG6xzCCU5X7UEnZMrkEYzqQq0inipUEqnEbn3+AhCaYyEmRHUPjlD\nCQFagIoRHT3RexxJ6tPYtN8USqJ7SUok7d/X9Yb96YzOOc7qFqcyKmNYWs/+3j7TTNCcvIhDMJwc\n8swbP4HPfevb+R++979mtTi5/XPvetfHf+Hnfd7/8vu9Tj2pD11PAPOjUG/5rD/2l0ezw5+9/txb\nBl/4V78DU5S7/ZYWW9PzLXknCd6zTHE0TqA4rx1lpplUObNhzv6oYJQrfuZf/WOUWzK69hyhXvP4\nxXczzhXT8YC98YC23lAMRyAlwmRpbBUhC45cCLLBgMenZ3ipmOUGfMAISb1piMEzHFT48ZT76xYR\nPTde90bu3b/HWKY9zLy2uLalMIoiyxhOpkilycqSRueIvKCVGeus4q6VvLxsmK9rRoMBQgi6ziKV\nwhiD0RpjTJJahMRWtTbtfqSQ1E1DpnKWqxXP3HoGY/Kd3tD5nvTjQk9kSfIRHyL2KrhEdh+3ndmW\nA1tkilt7FUUmeXC+4XTV4vxlYNf2YndzVrKsu0T0YesJm0AqARi7rjDGBGHbvEzrr/6XRsVbkO2H\nugiRgpqvjwvunTeEXgbhPbvfw8eIjyIRbNgmpuyWjOmD7Hedsbcw2C07t+0viSFaah6dLRDK9L9H\nev4+Cu7fu4+WqXu7c+8enbPMplPKokRrc+mCxHZcDNuud/uflJLRYIx1lvPFeSLlxIiSilFVMh2U\naAQZkVJExkZSiZR5KroO0XUEmxioJq+QJqMqK1pgMB5Rty0vzRtqZZivGoQQ2JgIS1prdL/PTBpl\n0f+dQCpF07YobRgWFZ13aHF5Y3XeBqQxBKXAOqLOyI0iOkfc1ISuwcWAyjIKFdN56yyy/3kjLciM\n4migyURECUFjA540KanKkhD8bj8cY6TuWo7GU+qmpfWOlRcEITi3AWky9oYZ4uRVfum9v8zHfdyb\n2T884LM/76381I//r3SL1fPtuPiOr/izf+FffMN/8HWv/kFcx57UE5bsH3h9zlvf/vcPp8O/PHz2\nM7n9aSkzdrv3SvIAubNVMyrJBYxUTAcZh6Oci9omK7tcU2YaLQLN5hGnj16lC5JJrnjw0nu5Xmpk\n8OwfHWJyQ+haVJbjiDihCUqiQiCXAkekXq6QyrA6PWM4GCCIaGPYOIcuK5oAuqw47SKLTcO0GjAT\njnZ5xqAqWdUNw0GFJOB74sMmCEKwOF3QdI4WSXCOOsLZYsmkKsmLkkwqbH9nTYwUxiClwvfjs9am\nqK5t/qIUihAl1/dvkpucyXhKiKIHHk/nIq111J2n7ixNl8g/iQzkE+CEntQT0ths594jIofjgkGu\nebxomG8+dFjEbJCxN8y5c7zC9aCpZJLsKLkFJwH9qNXo7Yj9klCURrO9vCOhai8NSmznMtN86tMz\n/p8PnhHidqR8Cd7bEG+9NTrQvZyo9wZOcpJENpLiaozbpXzEKMG4zFg3Dt8D3XYXK/oLfPCO973w\nPsoshYEvNjVRarRSfNyzbwQS+DVNQ57nr9F4puP0GI7gbH6K957ZZEZmMs4XJ9y5+wIDrZnmhkJB\n8I5cRJr1CpQmK3LqzRrnHHt7M5aLBUErtNLUXceFE3QxInXO2WaNC6lTA4FUstfGarTWyD6NJ3WB\nAiMlRkpE8EzHM5SAdrMixoAIHikiwQdynYIJurZlbzJiJCJ+vWHeNuSjIRsXmZQ581XNwgZsELRB\nsDcskL5DxMAg01jvOKs9Ngi6KAhSYn2gsQ6koPOeQKQwGTf3D3h4ekrrE+luYDSKyI1RiVuec3M0\n5OWzluEzn0p7/hLYFT/+T3+G0wcP+IKv+Pf5+9/9n3/tO376X//QR+L69aReW08A8w+oisF47zM/\n49O/bzydfcWNz/wzzF73pn782jPo1KUTzzYtJDepu5yUGQfDnHXnMFpBtARf89Kr7+bG7Drrs1c4\nvPEcF4/uUqwe0C0uuH7jOvvXjrhY9pT5csBys8YLQRAR31gg4oXCtA0n8wXjooDgKcoSmed0UtN0\nHbWqWFqPC4FKK0ajKcu25Wy95uzkmE+8dciqi5wvVgy0YFSVrDzkSiGznJXtmM8X7E0m2BhZbjYM\n8pzCZNgQduPCXBmQyV9UCoFUCqKgtR1GG7RK4v913SCC5NOf/6zdHs9HetAJdCHSdpbaBjato+0c\nTU8ASp1mz4rddkFJBcIgV1yblixry+NFvesaf7faH+ZMKsOdk/Xue7S8NGAHdr/j1pFnawqwdeNJ\n+9REAAJ2e2utFGWm+LRnZvziS2ep0+7t+7Zj3q2hvpLbnFJJpgW50eSZThd5pdLP3vrbbr+PBJaT\nSrPpPDZckpN2cV8i2eIpJem6FqMMv/quX+FwNqHIcx6envFJn/gpuwDttm3I8mL3+lyNFUsm9XLn\njrT9OR+8+xvce3yHG7MpuUhZq+vNilImp57GWZatI9cahWfjI8J36NGExaomK3POVyuaLslJmq6j\n69KYtSgKRM+kptdhbkltiXUcKLOUk6qUBO+p8pJSa9brJUJCaTSVjMSQ4r0KGSmVxLVt0gVXJRsX\n2FiPMoahjCzqjrUTrK2nKgqmhWKUCS6Wa6ajIc52LKync4ImgPeRjQvU3tNuszyBIs84muzx4Pws\nrTSAIsvIROTGZMAodIgIi5PHvGgzDqcVq6bhPb/2Hl591wu87Yu+hB/4nv/qc19+6eX3rRYXZx/2\nRexJ/ZZ6Aph/AHX7qaf3Pu1z/61XZXTVU3/yK6mmR7s9penvWLeOLlu2a2F07/OqmA1yNq1lsbng\n0fGrsHzEdDRmLYYc7R+gvOXhC+8gj4FKwrAq0WWBzg1nyzW6GlAVOUopjhcrhlqCc9impW5bRpMx\nXkiMSiDnpWbeWKwqWLlI3VlyBYXW5OUw7RDXc3Q54MUHj5iUOUVRcnwxR0tJZTS+Zzx6BM62lHlO\ni2DdNCipdkLytuso85xMG2JMFwLfs2ViTCxFIeiZhobRYIpzaVR269pT5FmRRp79qNX2Gkzrk5xk\n03maLnWbW8C0W3ZsTBd4EWF/XDDMNfcvNqxbx7/p++BwlDMsDHdOVmkv1d8MbfeQl96t7MBK9KM3\ntZsoSLSg96K9/FxuFB9/Y8QL95e4ELDO75JUnA/YAK7/vZJ37fackimvMru8ETOqZ1z3H7USTCvD\npnU4H3Zjyv5Z7wBNin64KgQiRh4dP8I5y/5sxrgqkgdsVH2nHrncn6bXZ2cWf+V3lwiatmbTrLl/\nfIdMS1zXIpTChcQiLfMcFSMb27Far3FEhkVJ6xyd68izjKaz+OBZ1zWx1z5G0j5Xm8TsNjIZKMR+\n75tnKdmkyPNd1prpjQ6MVBgpmAxGGGCxmiNFGnlrk+wNJxlUIqC8Y2MtRV5yulzhhcYHR57lFErg\nneVkVRNkTmaSbCcTnkopBJF129H61OW2ETZtMvbY+MjGOmz/apZFzsF4xsOzM1wMSCXRWpNJyVgJ\nou0oZHLL0kXFvbNTZJbz4KWXedfP/Dyf/Gmfxeh1b/jAt/7Fr3xz1zbN7/+q9qTgCWB+xOuZZ9/4\n6W9+y2f+Qjac8ro/8RWYvOy7D0muRQJIkzxdh6WhMMlSTQmPEpFRqXjxlRdZNpaL1Rm6XfNHpqgN\ndQAAIABJREFUn7oN158nes8v/cJPciO2aCmwzYbD2YzGZIymoyRO72Uo1nnqIGkWF+REkiN4Tj6e\nMF+vKRRkSqGUZN4JzlpP23mMgsootJJ4MgZVxWp5SpCGZZDcPzlmUhSs6g2H0wmd9Wlv049Yq9yw\n8YH1pkZrQ9s07E+ndM7iQ6DrLJNhAmH6iykhkmuDVJrOOYSAQTHm2v5thlV67G73uCPw0FvapdGm\n6zV8jfXUXXLTqa1POswrY1klk7F5YwMPLjavie/6N61r44IyU7xyuk6gyWWc2o58A+RGMCwMVZas\nCoUQyde2l7+4rdQl9uNQKSkyiXWh37sm4YmU/fi1n0wURuLDVoOajBmUlORZuiEzuwlG6kYzLZkO\nMjZtL4sR8TUmB3AFNBFsIzulfO0utm3XHE6n+BBYt/4ywqwf/W53hmp33EjXtfz6+3+dm0fXuFid\nsFyvOZxOWNY1QkQWiwVZUYKAtu3wwVHkJmkYr4y6u67Deb8DyhRUHXYjYanSa7Izi9hJh2LSSGpN\nleco0msuxDa1Jd3wHc4O2KwWLNcbtNZcbNaMqyqBYdcwKzQDEYhScHqx4nBvltyA2hZiIs1lMXK2\n6bBSUyhBkWeo6BiUBdF5grds2g4nNEWesWlaXBQ8XrUsbcDGpBcelBWT0Zi7p48BgdK6N5eQHFUl\noW1xUnG6mDMYDll1NkWLLVf8/E/+S/b2r/H5X/7v8XVf8vabJ48fPvj9XNeeVKongPkRrOeff/4t\nTz3zhl/Yf+Nb5I0/+oUYmQAp7+3ORqVmXGZMqhwTLlgvHqWw2xC4WM+ZViNMZrjz+BFVWTEeHTAs\nBzw+ecAo16we3yVr1kyUwJQFuRbsHR6gjKLuPL5p0EKQ5wWPz89RQpFnBlPmFEXOwsP5uqEwSSBf\nGoMVhpONY9VafNdwczagCQpnA7cO9zm+OGfZWpoI86bBO8ewKEBIVpsNB8MBbYwUSrO2ydO2adpE\nruj3blpKhnnOqrPkCA5mEzbb8IUAA5MhlSQgsBEmwykhaJquYTqacTA73I0yr14At6xRH7Zs2X5E\naz2NDdTW0XQJRFsXMDLFoB0vWs5W7SWz88METIDr05JcS145WV+xm4NRbhhVhmGuAcGmddSdo7GJ\nHQuXxu4xsjMeSBfERAiDnvwUenP2rRhEXHa0W+/aYWEYFQYpRWILu4Dov571toj7w7x/LeJuTHx1\nFMv2J1yykH5LJR8dkaKr8nTsZWuJV4B226HKvnu+//AeH3j5/fjgGVRF32kHRsNEfuucp+5afJ+B\n2bUt1nY474j4ZPhAREuVWMM+ZV8qlQwTOtuRmWwnfdlqeNMYO+wYxkKwM8IosgytkmbSx4BRCusc\nmTJcm045XSwwRFzX7TgH06rkqIwMYuTk+IRXN45bB1NoW4IEp0yy4stzVPDMNw2th0H/nu+aBqkN\nOgaESGuFTGtc8CxXNVIr5h2sbWDtIg4oi5IiL7h7drI7Sbz3FHnGuCgoZGS9WnLaWJqQnLAOpkcQ\nG97x4/8Hm8WGP/WlX/3yX/zSt73+wzrBn9Rr6glgfoTqEz/pk//Cs2/8+H947VPeysGbPieNyZSk\nyJJmcn+YM600pbKcP3wBYxcsu4hTmtO64XDvkPOzE27OJoxHFb/x6Azb1ijvuak8g9GIdn4KJif4\nwN5sQqFh01hypTi5mCN9yhPsrGVSlTTWMp6NcUqzcpFV3TEd5DjvwVvyomITBOdN5LxpwHZUWYZW\nmsPplPV6QR3heN1Sdx3eWZ4aFcyme3zg+BznHOOqwvfjsGQJp+h8oF4u2ZtNOVmuyLWm9Q6JoMwL\ncmAyGrFpWwyy75wMQiqCUhTFmKP92yh52SVAzwYV0HUtZ+dnlOWAohj0CR0eG/qkjt43tvOBpgvU\nnUWr5MH76umaZW13HV2IIZkW8OGD5q1ZkqE8mjfsDTLGlcGFyLJ2rBqXAFKAYhe6tdNxQq/97D8r\neynEFsBCr7/ckZT677n0jBU7wpCUkkGumFQZ4zLFbLU9W/hoXFDbQGv9zndYCdEL/MVOunl583A5\nUt71njsnBLH7fKEFg1yxbj0uXsaVKbn1s1W88OJ7OT47prVd8q1Vgsl4SJFnrNuWQW6IIdA5R9M1\nuD5azQeP7J+TVskNp+s6lFI7W0DnXJLkbG35Ykx7W0Q6z4XAeYeSkhDiLt0kMwalUvi0jxHvQtI4\n51l6DwjF6WqBInkGB2uRWnFjWNAuzyi1Yiwl5+s1k7Jgvlyhq5JWGoosAXJwvR+sS45CQgjapmEw\nqMhExDuL1Aaj06oi04bFconQGfMm7eSXXjCdzuic595ZiguLPVNaCIHWmolWdEBmCpAa5wOf9IY3\n8947v86v//RP8+CDr/BlX/ef2G/4qj/z7KP7d58waH8f9QQwPwL19j//9e806weffPMzvljsv+FT\nUL0f6KBIQHkwlKxPX6DsVoxLTdM25FmOlxpnLUfXb/PK/ftU0rG5OOfw4AAbQLiWxXLD/rBI3pRS\n423Ho8ePOdzfo8xyHl9csD8aM1+uEN4z25tgiiHZsKImvamyrGDjBLZrOKoKrNJ0XY3JSjZBs7KO\nurWMTTIvH1dDNtbxeLVMAu2mQQpBES3XJiMe1I6L9SZ1OBKk0jRNTaEUQUjaKDi5OEf1F7mrfqaF\nyRjqjEzplLohBJqI0YZBVbHoLEU1ZTa9sQNMhOD40T027SbpO7VhU28SSEfNm970yViftIrOB1xM\nwdC+3/OZnon86smKRWNpbEqZcH5LwOlzKj/M90KVKd50awJR8OrZmotNh+0NCoQQO5u6LShdbd4u\nd4Ds9nxSbl2D+ucWXqtxTJIkgAQOon9tEwiy0/AOMs3e0HBzVlF3nseLbhcmbvrkE7Gdu/ZyktAj\n82WXKLi6i9wyaRFit6fMFYwLw7pzO9BUJDav7IHzzt07vHjnRaRMBvLj8RCIbDY1o+GAMtOE4Gh9\nYFXXFHlG6zq8T5OI1FXKnj2dAFIrRQwBKcCGxJgWJELVdnKwff55lu06YKUkRiryotiZ2CNAyXRL\nkynNtKiwzqYxf9cREQzLnFJERsqjoocosNZC17JarlCZIipNXiWZCiGileBsfgG6xDsLUlAaDSEy\nKDPatiYKRZEn2ZfODJVSXMzneKmYtwEfBbKYsuo6Hs7PCYDvnaRCjNiuYzoeE7qW8ewGz7/hzbzv\nzru4efB6JqMZP/qPvp8Xf/VX+RNf/OWPzpfNr33713/l539YJ/qTegKYv9/641/wpf/3rFKfc/tz\n/xyTW28k14oyV0yrjGvjksIfsz5+gbEITIdDTuYL9scDOpdEzeVwjMwKXnrxvRxUFahkFbZerzBC\nMB2PcUqhRTIEaK2j7VqE81RlmbopGxhPxmQmI0SPNAqXZSzWDZkSzFuLCIEMCxHKwZBXzlZorWii\nYtV1SNvyutkQTIHKKj5wfIyQkrppcc5RKMlTkyHnLtLY5PGqlWRiNChNRGCEQBmNj4IQA6u6wTlH\nlmVs6k3aNRmDbztmZQmAMTmdcxiTYfIBuhgymxxx9bQUQvDu9/0aiUSbHHWsdcTe7k5Lw+ufeZai\nqLDBX/FqDZRaoTQ8nqdkl7pzbFpHY5PRwdYAfecle0W+8btVYSRH4xKjJWerlnndcfXbL4k/2x4t\nAWL6SdvN2dYPNkKUu1Gm6NF160a0Pay4cuytabqSl0xY2e+w9c6gPe3OJ2XOdGDofKS24TVmDTFu\nNaSR2He5CYB7YJICJbZyKNkThbbPUyIBoyTTUlPbxLyVQqJEb+ogBPfu3+HFD74fISVFnlFVBVJL\nmrYlhHQu182Gba4lRAZ5Tuftbq/a2CT5kcn8NhHIioKqH+lv+1/dj2qlkP3oPsV5uX7faZ0lCoGU\nCnpjwhBDD8bJW1cieGr/kNP5OV1ngYBAUBnFoCwZCs+4Krn34ge48fQz2NUCfIfSGdJkyCxLJgNl\nQYiCTVOjTE5nLcF1VEXO8dkpw6rC+Y7OOkbDAXVdMyiKFP21WuOB5aYmZCXD6SGvnp5zslzjgCAS\nSa6zlnW9YTwYUGlNXu3zljd9Rj+zSDdl//TH/hG//NP/ki/4sq/l137tV7//+77zb3zD7/lEf1K7\negKYH2YJIcSf/H/Ze7NY2bLzvu+3pj3VeOY79cDuppoUR5HiIEqiZU0eJNmKrEhyDEtOHGdAHuI4\nloLIBmQgSGLAAZzhwQggBIFsJ4GhxLCD2LGjxLGgQIkoOiJFNps9T3c+Yw17XEMe1qo65zZJk0ye\nQvZu3D731K1TVWfXqv2t7/tPf+znXp3n4onHPv0nmR0+Rm4k48KwPy3R7iG7/X1ozqnPTiknYyop\nqdcN2kiy6QQzmjOa7HD3rVc4vViTC08xmtCtF4zKEte0ZNMZrfUYk7Ozt0u7WDCfjGjWK/qmicL2\nrqPIDOV4hKgKGqE4W3f01uGEoFKBLOkbCQGZFdxb9iBBaEPbDfi+5on5hIP9Q75w75hlEx1Z+mHY\nXpyVjKSJzYrRW2aoxIVYfFS6oFrr0EoxKkpGRQEIciGZjsZ0gyXYgUJrApJBwGiyT1nMEQm7u7ou\nBzvw/EtfQKbkEud8unirZKE24L0nywq6rufJJ59G5wVaxQv/ybJjsIHOJQZtZyObdrDRz/VtpgBf\n7zOhhOBoVjAqNMfLjvN1z9f6ibcXTfnIODZsYckNPiUIMaZLkvSiAZtIPxsbPdhghRFT3OgyN/jd\nhjkbo8FiQVWJMLQ7ypmWmmU7cLaOCTA2xZvFRitGickkWVHJvFxLhVLpPU/ZnJuYs2gaH12DLotm\nzOncvK7gPE2/JtNxNLlan/HKW6+CAmvt1oRemRj5ZrTCW0uWaTKtccEzKkq0kCBFsrtLsWze0Q8D\nLgS6vsP6SPTqh8g5dSmzUgiR0kTi+ZdprrwhM0khMVqTGY0Eiizn2s4Ob9y7h1aSiQrkeGRWYNua\n3emIrG3Q1vLym2/x9LPPsLh7j3I8wSkYVSVt0+KlwlpLMEXsQE3G/nzGuq6RUhGEpK4jhqlVoG9b\nEJ5Ca7TzdHUd5VhFSTaa89r9Ux7WDZsyrrRmVa+p2xYE7M132Jte44Pv/vDWRjEE+P3f/W3+/n/7\nq3zvH/4pWi//8S//az/7Tqf5TR7vOP38vziEEOrjn/6h568f7D31rh/6BSZ7N6gKze44Z38UmIYH\nyNOX6S/OWJ+f8R1PP4XBRRbewT57R9dRxZjxdMZzz30xBtPOJgwObt26wXi6S16NmMx3EXZgdzoh\neMfp/bsIP3Dv3h3WiwsmZY61Fq0leZVhyoJBa1a9QyhNHwTDEEOKa6e4aAaCyThuPV2000EJiZAS\nJzTjckznLHcXqxiLZe0VEoXaGkx771BCbrWBMe4CMhNF7QJJllLpu65jbDIWTUtwjpW13D09xQGN\n88i8ZGd2yN5sL4ZbK4FOI8lLOYZEyUjWWK5XSBnzDEWyF3MedMJA93d3ojheK3KtuKgjxrUJS5bb\nwhIQGwODLWYXb3tEH/G2Y1YZbu3FEeft05qmd9/IernyDchw5eE3c1piF5cu5dv568ad6O2Pd0mw\nYVskN3KWqx0tV/5dS0FnPYtmwOgIG1w0PeveXspWtrrPjYNSSDrWjTQnnqu384I23czgA5NCR9lP\nuOr4I8hMjtLRSEDrjIvleRx3ptEpgAJGZcGkrJhPxoyynCwzcTLhPV3fc3pxwaJesW5bFvWKwVmW\nTb3tIq1zCAFGqfS8BusjUYiwCcoO2++DD1svY+scKrn1uCTpmY7GNF1LJgSVEazrjpGRlLZn6Hu6\nYSDPMuqzc5QxuBDxfBsCxWSCDIFyNMLXaxZnJ2R5Rl0vI26poGuWeO9w1qJNTlWNyPMRShlQimo0\nInhHcBbhLNcPDpC2hmQbiRCUeU6hI8xxtlpghOVsteRo71pa54HDG7e49eTT/KNf/zV29w+f/rv/\n8J8c/pEf/N5/8HUX8DvH9ninYH6ThxAi//inf+TuM+/+jltP/uDPU012GJWGw2nB09d2ePDyb1Kf\n3KUsc67NR+yNcpqmZlRUhGBRWUbdDUz39nnz9j1yk1OOJhRFxXQ8pmkaFqf3qRfnnN6/y/58RtfU\nHB8/RAUPNiY/TKvIMBzPp+giIy8rWqMjVgl0w0CR4r+EgKooaJ2gzHKaNJbaLzSP7e2yHiyj0RiL\n4KX79+Nu3TkyHbMcQ9LjXXaboLWO5vBaoVK8kw5JJC4EhTEYqSikQglJkAILnC5WcZQMjEdzHr/2\nDFrn9CmiyzqPBwbbkWuNMZLlxQm7sxl7sx0Wi0WUGaS0kExrgg/kWYYUghvXblLkGbmRrFu7tVDd\nFJdtAUEkj9eNhVzAb7u/eMerxBwp4MZOxbgw3D6r4+bjG18zj/w9kF4HctvVRTzw8rVuilJ45FU8\n8qDb1yWTdvWRgpmKpEjdYpakJdY76sHxcNHR9Jb9cYEPgVU7JNbxVclOLHqDi8Uyfn8ZiUY6d1ex\n2Y1GdlKY5JWbfp/EUk3vRCTpdB3eRV3uznjM7nRMlrBG6xyrtmHZNpytlnTWsqijF61IXVXbdYQA\ndVNTFSUbyrGAODZOXaMiMraFSLdD2mxFpyKZCFNKqS1sIEU0hOjamslozFgr5plnJuH07IRRVbJG\ncjDK6fqOtpwwHk/IhacNUGWG08WCIQSK0RgtBTYEpsbgiedSSkHTO3JjUATWqwVaCR4eP2BwFuct\nwYMyGdVkzmg8w0iJ9I75ZMJUe4Rz9Dae1zLPKPOcxWKNNCXvfepZfvOz/4RnHnt2+x7N9w55+j0f\n4J/+g19nPJl97PdevvfJ7/vo+//2N7iUv+2PdwrmN3EIIcbf9bHveeMDH/n4/uN/4F+iqEbMRjk3\ndyomcsWdF36LwnXs54J3X9sndHXUezlHVmRIbWit4ODmE0hdsji/wDqL8D3BtjTNCt93DF3LerFg\nPp9z7+EDehtiMcpz+iHKOsqyZLq3Q+8to/EUp1QyKod1N6AISKHo+45xpnDWUSjPKgjO1zWHyvHk\nPKfQinXQGJNx5+Q4emKm0d8m23GDA20uzHmWRUw1dXgyBHIpyJWKTkbpYuQJjIqCnoBFslyuoz+t\nB4nk3Y8/y4OTezw8fcDD03vMJnPqpub3v/x/07YNyIznX/4yD09PaLoW5zx78xneDczGI/LMMEoG\nDfP5Lrdu3Irm7FmU2XjvUCFw/+5tityQ5TFNY+PGE650cRHr8Vu8LL3hAORa8sTBmM56bp/WW0u8\nb3LtpOe+fNxt8RAb1qvc3h7Sf1+NjLQh4ahUeFT6sc1oVGzxxkvLRSllNDzvLHXqKJvecV73zKuM\nSWFYNHYbGr3Fda/qX2GLc25+jc3zXJKDLj11R4WitylXRQqMFuQ6yqyM9GSZZtWuqbuOi3rN6XLJ\num1ph4Gm65LpQBzzD3YAEfWJWkV5STRY35DOWrQ2kWykJBA1qZtCLYVM+KZOGKfAGB1fu7pkJRd5\nhhISKTYxapGJezjf4Wxdk5nIlFXBooOjcTCfTun7DmUMWmrqrqMrKoyUkaMgPL2NWGwvIrY6rka0\nw0A7DCht8DbqNL13ZCYD72i7HusdXdvQtOuNkz9ZnlONZyiTY8JAcJF9bF18P6ZVQWs7JtUOu/M9\nXnrrRW4dPr5d8OPZnPd++KP87m/+I47v33/mNz/z+9f+0B/83v/pm17U34bHOwXzGzyEENMPf+x7\nbn/39//wzs3v+2mKLGNnnPP4/oTcP+SFz/6v5K7myZ0Ru5mmrleURYlQGdPZHKlzsmpOPo4yiLfe\nfJV2vUJLjzGS1brGaM24KvHBUxUlXdPgB4cfOqqqoh8stm+ZTkuKyQhyQzs41nYgrBsm0zkP1h1G\nglCGi9bR2MB5F1h7wekAK+sY9WveczhBSMXZACEbcbw4RyrFMAzJgu2yKyryHKMUZZ7HkWsIWwyp\nQFBKQSYVSE1rLVbKaKsnYw6hUIrD3ZvkecWDk4cIBM++6z08OH7A7QdvMrieKi/Y373Gl19+jirP\nUMpw78E9tFaYzLBsGk4X52idc//klLqLUWfGGA52drl+cIBWglGu6QbP4AIXp6fcv/+Adb3CW8fO\n7s62o0t9XmSihm0JgOATvSN6lVeZ5rG9EQ8XHSfL7ptfN4hHO0wuMc0NueZyjBq2LNRNcDRBfEWP\n+QhWmTxTrxJytIijc5VSMUCw7h2rdkjnxqdYszhivah7jJIcTHPWrUu5nZfJK5BM5gMkcDWRk+SV\nEbfYdu8ivf5CS6aVwSR5lRIi6UrjpOHzL3yBrh9YrldpxC4QSm6JVyGAVpE05JzH2gEpYreoVDKA\n6Hu890wn43i7iJ65UkR7vxDAhuhopYlmHf3QU+ZZGoELcp1hrU2s7ficEXIIaKnphgGlFKUpOK1r\nsqIktx064b4X5+c0aM57zzRTCDzHrWNW5LRtzdC0SB2JScpZdJaxWl6QmQypBL11oAyF0YQhMmW9\nd5jNv0kVc15tj7MWJTz16pyynFCWJfvjCVVmEAKcdcyrnGJoub045WB+ne98+gM0XY1RGYRYvKtq\nwgc++km+9Nn/g9OT4+/+8lunH/n+j3/ov/umF/i32fFOwfwGDiHE7H0f+ugbn/qhPzq9+YmfpMwz\nDuYlh5VnOPkyx699kaf2J9wa5wTv405XF2TFiHIyjc4lQ09dL9EmA9tj8oJ1vWJUFRhtcM7Srtf4\nvkd5z3K1jh2CVoyrEVop9ndmzPZ2UKOKYDQ9gt5HmyyhJCshGayLYcPeMS6z+KGUkkFKWmsjSaKP\n4857vWBFjg+Bk8UFfW9xVxI6hBBkCRfRyTlFES86BsgEGKNxadbpU0BuLgSFioUSpdiZX+do/yZa\nG6qi4t1PPot3nlfeeJHZZIxSisOda5xfXLBYnKCVjqbjzqKMph8c1jryLKMwOQQYBsd0OmM0mlJU\nY3oXyLTYxjadPrzHzetH5LlmvW5ZN2vKoiQvim0Ac9iasF9JMgE2AOIkj5KM26c1q84+ikV+5RqJ\nX3n0PhvJwuX38X+SqzZ6IeGsmztuRsVffRy7GSkrmQg6IjpJXbJj5VZnaZ1n3UZ2sE0YZEDE330z\now6Cunf44Lk2L1n1duuRK8JmhLzBS+Nr2HSVm69aCUqjqHLNOI+WcBtnpd551l10IvLpt6uKgrpZ\n0/YNwXsGZx8pvrFQB7RWWzcflRylNl2wDx4l4yZPS0WmdLS6UwoR2DpClVqjkwxkc1Z9CBghyYRA\npfO/wTEFkSijpcD7SF6r24bpaEIzWC6alqKs0DLaOMzGE5rEgh0ZCd5ThYGTZqByPa7rWZ2fUU3H\nOKFQREtI6xxax26ZlOsqTcYw9KgUSJBphU2EpkxrBmuxIeabds0KbXLaZom0AyNjmM1mCSPW7FUZ\nd0/v8+qdN3j34+8BEVBKc/vhmzjvGI9mfOAjn+CFz/8Or7780rOfee6VJ3/k05/8e19zkb9zvFMw\nv94hhNh5z/s/9MYP/NhPTW9+4o9T5IZrOyMOip7RxRe4uH+b9z52SDl05KMxmc6Z7x+R5xk+WIZm\nyZ233qQfBsrZLloL7t69g9SCqoy5fHW9ZnlyhtaGTAqauqbpWiajEeMsQ+ApigxZFsiyYBCC3kPt\noO4dTgrqoKhXa0wWjaszCXVnmWSCLM+pvWDRtEDAqYwLp2gD7I6n3D8/jdihv9RMbroWo1VkSQrB\nSGskgUzqzdmBEHe1VZaBkJjU4QQZ/UHLouL60ZMIJIv1BS+8+jxv3H2Duw/uMKrKqMkbzTnYu8EL\nr3wJpRWDc/GxtEZKTV03DP3AuBphsoynn3w3R4dHTMYT8rxAiJjWcX56wsOLJW03MJ3vMTjH2ekZ\nN68foYLHDz078xkhiKTB3BTKjeXeZYpJlWmuzUrePK3p0liRK+fm7X+2t3OJQcroPL790e39iQXz\nEYcdse09L8tkuFowr5oJRMMBnYzVC6PRKaVkc9EnRPlP3Q2RLe3fFm2WHjOWpfi4nfUMPnBjXkZN\nZTIvv9qRX46QJWUumRaG+ShjUkTbNhs8zeBph0ge6n1glJuYtnLlHEgh0Npwcn5C27cEIMtMwhdj\nAHmuTdw6iA326yNmrjSFMVQ6ehiXRcFgLVWeR+u+wdLagTLPIw7Zd5jMYIBSK3KRzAyUirIiIchV\nTBRBKpSUlMnfVooonxJInBvYHc84Xy5pkcyqCtt3oATOWtoguWiiN+4oz/GrC5Yu8PjRUTRYGCxG\nSfoQMHlGpRW263HDwGwypu9aBu+Q2iRj+yiPyk2GSSD34BwhbRiy9NkMQhGERVsLrsd7x3w8RqA4\nGJdcLM/57S/+Du951/tBCGbjGZ976bPsTvcpihHv/66P89IXPsubr7364S+8eu89P/x9H/8ffuVX\nfuX/y2XzW/Z4p2D+cw4hxM4z733/6z/yk39ycvNjP0FVZNzaLZn5ezx45TOMjOKJ+RTb1MwOruOF\nZhgGmtU56/WaPM+4WK8xWcZs54BCK1b1GoxBK4EdBqR3aKFRRjMuci7Oz8mrEaMiJzOx86wmY/LJ\nGJRCaM3aevpADG+WEoTGe8dkVLG2DoRi2QdUcExGBc55OhSLptlemJVSzEdj1k3Dsq7xpPxAqbfj\nvTzLorm6jIUwivDD9kIf0oUzU4ogFcE5jNZbVqtQmr35DUbVBELgSy8/hzaxU82MoSwKQHK0d50v\nvfgcHkeR5zjrGOU5CEFn44Xz2uF1jvaPmExmSCm3o8L1ehVF/rbh5dfepCwr1nXNerXCmAwhFS++\n/BqrVctsZ850MibXkjJTqAhxRWlJ2OCFkBvFtVnJ7bOaNmZwpWIZHqWGfvU1s+3GNl83DNAtO5MN\n+wg2RJ3N40Z5pNiSai4ZspdPLIXEyKg3rHId47xUZNb6EBI+OdClNJKtX+6V17npGh99ZEE/xKJ5\nfV6yaoZtxqUQYLRkWhp2xzmH0zzmQwYRC3PvojwlXBbEKMGMm6pRoRmSXd8GTyyKgvkml63GAAAg\nAElEQVR0jhCCVb2KkxYffWLHVZm0lbFgWjvgvdumu0gpMMpE7aRWeO8IInrNFnk0xvDOpwJM0opG\niU3wjiEkzWoysHckBnDwOGAIAZPGv55AphTSe4rMgA90zvLWYolVOUFmMe906JmNR+xWOVII8swg\nnOdisUAZTZ5lkIwXjJKs+p5RWSCDZxiG7ZoJ3scxdCqMm9g74Rw+iOStG9eQtR2japws/hyh6xja\nOkaU2RYpJU9ev8neuOKV269w49q7CEIwq+Y8/8bnOZgdYbKCD3zk47z2/Od48YUvvf+nf+HffPYH\nP/Xd//3XuTx+Wx7vFMyvcQghZu965j2v/dF/8U9Pb338x6mKjJszQ7X8Ahd3X+FolDPPM1RWcO3m\nY7TNChkcoe+ZzGf0fZN2ug7nAlk+4mK1oCwzmnW7pYD3TUNT19EwfRhwXRyXlkVGURYUkzGyqnBS\n4IKgDVBvkttFxDgUcYS16B3eBbqUZiF8xGpWveWtRb11TQHIjWFSVhwvLmKxVKlYRi8ynPfJ7EBQ\naB3jthDYxHqUKU9Qi8hYjA4vEqkNTT+AVAQpOdy7jlYZy/WCuw9vE0TsVkwau/Zdz1t330KZuLPP\nTYbSmsHF32sYAllmODo8oixGl+4/wOnZKa+/+QY70xF109L3Lppz+0DXRVeV+WzGtevXyPM8mnsr\nTT04msERAmRGMiszci2Tf6vncFpw/7yhHi4lI5vOcTtifVt3mdbM5o7x36/cFuX9l4+yKb5X+Txb\ncUv46nKSzaG1jJhypilzlXxTQ/KQdfRDzAh1IbJU4VFd6/a1Xn5z5TUIBhvdc/anOb31zEc5R7OC\nnSoanDeD46IeaIdIMtukrES9ZyQhxfGwwjsbcy51zNckBISE51/5ErnJ6fuON+68HjunTdSZiqHh\nddPQ9alTTL9zoQ0iRKN0iUcLFYlBqaAoKXHWRlMCkUK8hcCYjGlmCLalc4EiyxCb8yMFwnu8EAze\nY60lz/NtAo+4Qj7q+o75zpyLtgEZvY9XnSUvSoTr2ZlNeOP+ScSPQ6C1A9VkTFc3SBHQRpFLwfHZ\nBaOyoLOOzGiyLMdZi8nyiN2m8woQrN3KuYzRcaqTJC9SSoa+YzzZwQ09EKjynPr8nLZtsesl0vV4\n21GOZrxy+2X2d69TFRXrbsXzr3+Ra3s3MFnBB7/7E7z63O/x4vPPv//3XnjjnfHsVzneKZhf5RBC\nTG899R1v/sTP/Pzs8U/+BOMy54ndjHDvd3Drcx4/OOBgZ5eyqmjWF9TLC4K3dF2XmG4ekRXRFzXL\n2N+/hhsa8rLADhbsQNt0SBk7LT/0jIsKnz6MxWSEKnJEbghK4RA4RMzMk4K+j7teKTW9B2cHirIg\nCEVrAwHJ2nka61kHwYPa0juHSPR5hGB/MqMZegZn0UpFKj1s8S8AZy2jzMRdOYHeXTIetYwFYKOF\nzLQmCEmXLMykNgzec7R/EyUlt++/ycV6kXRwEoLYauaqqkIiyfMocQiCiIEC149ucrh/RJGV2/dn\nIwU4OzvD9h278ylv3LkPxFHe7s4uj926GYlSfWQhFmXBer3m+PiY8XiS5BJxbLlqB6wDrQXvOhhz\nVkdR/wbbvCyCl2tEXv710cKZmlC1wfziWUrylrA1LthgpZf9Y6LKCN42Nv2KtUmZKYxWzMqY8zg4\nTzNcxplFrHLD/P3ajN6vKPab1yGg7S3Xd0quz6uYnFMPnKw6uiHKfowUUetqFJlORgkqxogZFf9N\nycDzX/o8x6cPWK+X2GHNa3cjPLG/s0eWZTRtzenFKRCjxjbB4b1NZhQ6slp3RxVGCCZZRiFiMLn1\nIWawCmIYuYjSpcwY8I5uGBiCRyR/YyXj+hgXOTJEC0WpNM7HIh3S2gOJSB15NwwRGhAyJoGkzZ6+\n0sFmWnHR9TiV0Q6eWwe72KFnPViqPOOi6Zgf3STYjnpVb3kJQ9NQ5obeuThN8j5qon1AqhjaHVwy\nA0k4qxKRmKaVwqTNiQdyo9FZxbpeoo1GC0lVlhRaYfuOvu9pXTTqOF2dMtieZ249i9Y5n3vpn0EY\n2Jkd8eHv/iRf/tzv8Nprr374teP6I9//sQ++QwS6crxTMN92CCEmRzcee/WP/ewv7Lz7+3+KUZHx\nzEFOf/u30dby+PVblErhhprge85PT5lMJizOzqgyhTSGuutxCIxzyCCxfc96tYjEn4tzZF5wen4S\ncc5hIDcGrwU6M+STEUIbMAYvJF6ACzFZfvDQ2egtKqSgc6CCi5FRwVPkJRfdgBUglMKnghoE+OS8\nEzuTnPloxPFyEQskAi02+FQ0MrDWoqViUhbxQuQDXlwK/4OPAcOEAFJSO5+KZULolOKZx96LQiKl\n4vXbr+F97NgEgj5hSFrpaH4gIpEnJGsSqRSD9Zyfn1OVI4qiTMVrw9oMGJMRXM+DB6f0veXJJ5/k\n+vWbVFWZOtmIk61WK1584SWapuFdT76LfhjSpiBs5sq4ENBK0vaO+xftZbHk7VPYWPQQm9yODQIo\nti3ilhyTxrFCRvJMtH1L6spwacQeHzU+xlbq8vbxb3rs3GiKTDMpYrGyzlP3jm6ImZmD32Cz35j0\n5REnorAZX8Zif7aOxugX9UBvfXQUUtEBKNMRN87SmtqMhTeB1iI4nvvSF5BKRDNyoxhncdx/9/g+\nx2cn7O/sJS9cwbWDa5yen4KIvrEAs9kEJQRlmcesTG3IpEQS2BkVVGJgWXcIY5IjUpS9eH8pgXKp\n6E5D1JmOC40S0PUdvRfbxJrl0KONwbmYgamF2sp1AoIgAlLF5Jl+6Nkdz1i1DZAKFpLeR8x28JKp\nkVGL2jaMs5znX30TNZoyLzJcb7F2IISYxemdxUEqzIL1asV4NMK5QN+1ICS5iXCP0YrBujjuhaiB\n9h4fHFVW4IaefugpyopAZBPLEOiahixT1L3j7OKMu2dn3Dw44treEYfz6/zuC7/L7niHajTjo5/4\nXn7/d36LV1568dnPfPGlaz/6A596R3KSjncK5pVDCFEePf70nR/7E39q54M//HOURcY1c4o4/TKF\n1Fzf36M9fcB6HSUYp2cXjHLDuCpRwuOcRYxGNHULbYcUhmI8ZbU8wxgTuzKVMZpPYLD4wTObjMhS\nkTRFQZAKodV2lOaBxkarMaSKOIuIbNRCRbP0ZgjUvWVtA0NvmWaazKRgZim3Y6nBDgjg5u4eTd8S\nvEN5yHS8aPnUuWmlsc5itMQ7h5BqO4rdFAytJFJrrJBRqwnolJRw4+gJnrzxNM45Pv/i51FaUeYF\n63pJCFEiIBCRzSji67PWgYdMF9y4dpPd+R7z6ZybN25RlaP0Dj1aBIxWdPWKZed44onHWS5XOOe4\nf+8Bs9k04YGek9NTuq7DaM10Muatt95kZ3cnGRVsBO4xWPl4PeCC39bSBOE9Al9uC6WAIGTaaCRN\nJeKRIhTlDRtGbCz0l/SeK/BoukGk593c93JxxuJbGkVuNOPcEIjj+bqz9ClT85uNKnuEsJQw1Ut/\ndUlvHdd3Khb1EGU8KoYzZyYyOGPRjCxqlSYPKuGX6/Uy5nJmmlxKpA/MpiNOz5cIrXhwcp+LxTmj\naswTN97FqKw4X5zF55aCsiwwMhJdpIoF0IhIsLGIZJUIg4/rqQtp8qLipk8Q0DLGxu2NRxQiFtVJ\nkVEPjrWNnZwRMUc2eI8EHDHLM1o9Rls+RCQBBTaYoqHMcvq+jWkxqfMTMlbZzGgYOrTJGJcj5pnm\n/GLBbFLRNQ2dMpTjCX6wdG2N2jB8TUauJN462r5jdz6nWS7RWYYhbqAzGYu4TOd6M60Y+o7pZJd+\ndcHgBozO4gZsk9CiFEY4xuMZ906O+fLtN3jvU+/jzsNXKfOcz774WbSQ7O1c5xOf+n4+81v/G2en\np9/9xmn7yU995H3vmBvwTsHcHkIItX/j8Zd+6I/88cNP/eSfJc8MT0wsO/Y+Gsmk0KxPH4Lv0CF6\nXjb1itlkAiEy7Xw1RfSWbrmEINDVlKFdI4RgsVwglEAMPb4fED5QFBmTnZ3UPXqch85arLVpdAet\ni3ZlSBnNo5HU/UBh4vjLBcGAprXRqCBXG+9OS6YzhnRF9t7jrGOnKJmWJReLc2QARUAl4g4E/IY4\noU00IEDGJkxGdx/vIUhJH3wcScm04y1GjIoJB7vXuH96jxffeIGdyZw3777JfDylKkacLy8YrCXa\nkkmU1FufSzs4uqbjxtF1jk+OeXDykPPFBWdnJ6xWK6aTCVthfwgIKfG25eHZOU3bsVwskVLR9h2D\ncwy9paoqfIBqPGZ3dw+TZfRD9C1VOkOk7tYT7dzWqVOLTW4qPCERWYiFchuQLIjlUWxIUBvM8sr3\n26790n0Hov8n4QrvdfO/t419337EFBzNqDAYFbHEZTPQDQ6bMh+/yTX/6FcSwipiwZIIXCAFX0e7\nO52SeHKj41ctE3N14z0b16XWmkwb1k1NkWmMkizqGq00QUbsfeONu65XvHr7Fa7tX+Ox64/z4Ow+\nUsf1WBY5bd9Hs4HU2QsR8f4gogGBIIK+3TDg0++iACFltL1TcY1qrVE4Vr2ns5ZAhBJ8SIHT3kGC\nLZzzcTJDNO1QQiSrv3iWBjewO5myapotmQsRJy5111EPjqooKaTH2rhZk8FxYQWjzGBsz8PlGlGO\nyLShXiwwWtP3HVleQIgGIav1mlGRY9uOpuuQQpLlZSIGxYSVYIcrhDwoixHd8gLrHcPQY/IcJQTO\nWXQIrNuGzmsumpqX3nqVD37HR3nu5d/ncLbD8eIYKRVFPuYTn/pe/vHf/3W8D8/87//nPzv80R94\nx0bvnYIJCCHE4+/9yFsf/PBHbv7YL/wFjNG87/qESfMaQkiO33qNs7t3yJVjse4piozxdIrtetq6\nZjrbwWcZ9ekpfoiFZLJ7QJ7nrBenCDewe3DIyf0HCAJVVZJXBTrPCSYSZrwPOOJFVEmFA3ofcZYQ\nJDaFJAdi4VQEtNF0NmDD5gLkt+kWcMW+TEDTdRgluTGf0/Y91rqtPAEhGELEvjagnQyROUjawWqh\ncCIWVCFk6nJjyK3RhrbvWDcrFqszuoQ/aWkosgIIzCZz7ty7gwtx5JVpE3WAAbyLoyOdaZqmRRuD\nRND1lul4GskLzjIZT668Z+Btx50HJyBiV3zzxnV2d3eZzeeUVcUmaHojHZHGoHRGXo3wYnNOXdTF\nGcV5PVzGfYXNny1F55HOUJCwWHFJBNoUxpgewpUiyvanA8nDNrFlt9Z9YXtHIGKkby9/uZZURRxv\n9i4aDrR9LJZfrau8atb+Ndb9o1/ZSEbYRntJKRhc4OZuSds7CqMock2VRa1llgKqJZ7MqCR1Abzj\njdtvsDvboR36OBHJci6WK6aTMSfnC/Iii5h+2tA9PLlP09TcPLrJ+fIsaoJFtD/s+oEgItYYXX8E\n3nqMgnlhkMGxGuLGNVMGJUUce8T5PjiXCHISkXC/uOUJtP2AkJG4tBjs9vOHlLFDTQxcJVVyH7KR\nHa4jhtwONnazSm/XppIpFzZoJkWe4sA8ygdery27Nx5jXhguHj7gzrpj/8YttHfYJsIBsshRxmy5\nEXmeo4Pg5PQUqSXT8Zi+brBuoKrGONtHw5HgMXmFVIKhbVFFjnOBvCzwzpJpw9hIpB9Y9Y5Fvea1\ne6/TdB3HizOOdncZfM26q/m/XvgdPvLJj/P3/+av8ez7Pvyxz7301qe+57u+8299zQX1bXC8UzCB\nX/1v/u7vXtvf+44/80v/MTrLeHoHyosvszw75vYrr7A3LZiMK6azXXanFUPf450l2J6yGpNVOecX\nF1ycnqGMxpicajanX55hXcAUORdnJ4zHFcpoRrMpXkhknuFFCrpFEKSMGMWGsRcEQ5yF4tnYm3mq\nXOMQrDsfu6KEL3lPGocl5ZyM41IXoOs6ZlnO/nyHi+UCIWSyu4tjXpd2rD7EC/o2WDhhlo7UOW1w\nPSGRUrNhzFrvEAicC1jncD6QaYPRGePRlBdf+TK97cmMSQ4xKhb2EOi6HikU8+kOVVmS6YzDgyOO\n9g9YrlYgov1YWZRbrC9LWM7BwRECKMuC6XR6Ra0I5+fneOcxWbYtXttxJxvjdRjnhrp39O6KDVy4\n1GeGR9pBuGTIbgwIUmqH3KR3bMzj03lMT7qRdmzK0yXhJ0GpmydI1/pLbWZkneZGMS6iF+miHr6i\nWH61Avn1CubVYqk2hTKZH2gVu0ejJFWmmRQZUgqqXFMaRaZ1HMtqFQumVrHY2J5VHScr948fEpJ5\nu0y6R6UEzgfarqeqqtScRYeftm85vjgBognFYCPWbbQmhCjJsIOjcY48y+l9dGYaQhyhDiGGRGuZ\nIr6kQOMZZZqMgDGKtusT5inITOx+jWBLDOq9px8sgWizlyWcX+KTWUIsjM45ZuMJi/UybiydRRCj\nzoJ3dNYRgmDtAgOBIs+ZVjluveLOcs3DzjM+vMHBbMz5yTFe51RFJA3WdY2XAqU0xhi8i4SjcZ7T\nNC1CEJm1XY/zDqM0dugRWRbZ7dWY4AbsMKAyg7MuJg3FHSGGwLTIqG1g2TbJ4ctytl4xywu0jjDJ\n3cUJ3/Pp7+NX/9pf4+Pf8+mnf/3v/c8Hf+gPfvt2mt/2BfMDn/6JL4X2/IN//j/6G4zGE97/2A71\n65/h9PareDvEKC4J7bomNxovBJ2VjEclfduTj2Mnszg5ZWdnH20Us71D6tWSdr1gZ2eHs5Nj9g72\nyMqSbDSKKQbVKPpE9j3WRUeS4D1BqsT2ix2QlJEFKYB6COQy0tDP20DrSc4tpPzB6E5itMSHzQgp\naso0cDCdYZ2LMUAAbLIH4+G8IFMSu72SX15sRZJzOELyL1VIJaOG0ccLSfBgQ8xa/OC7P8TubJ+b\nR7d47qUv0nYdm97MB3DOMy7HvO/d7+PWtce4fnidg719dnf22JnvUBQlmcnZmc3j9+UlS1YAuQ68\n/tZdiqJkPB5zenLKYrHg/OyC2WwShfFZRpZFzWvwnuMHDxgVOQ/v3Wd5fs7OfIpWkirXLBrLJRq0\nGcdGT9RNN7LpJDfCkKvGBZsisyHGXBXob6pefMhLg/dHCT9sn+WRsWxiD2U6vs5MSZbNQNMP9C7w\n9j70axfMK6X6SqGMYc+p+KeUGK3iH6MVmZHkOroIXd8po3OO0RS5psw0Bsvt119huVqyv7uLIPDW\nW29xfHbK8ckJVZlTN5Eco7TcruXCGI5Pz+LGLsRxcyyacYRqbY9Skq7rcN5RFAXOJ8cfKbHeooyJ\nQQFIOh+wiK1nq5Cxa62KgmDT5AARcfukWe2cR6u4qRwblbAByxBgZKIpghcCTyQyJS0XSkaSmEgS\nDuujCbrR0YkoAhmAdwQpWHU9vXUc94FytosLgcd2dzhQjpO7d7jnBM4UFKMRg+3RIo6IG+fJqgoZ\nPEYbhrpG5zmjomC5WMQEFO8ZuhZTlvghFkgnQWUFUmtCNzAMA+PplLZvQYYY1SYCpTEc7UyxXcci\nbRAIgbO6Zm86RcoYJr/oa77n05/mr/77v8Qf+emf+fg//cyXPvX9H33ft2Wn+W1dMHWW/RuuWf3C\nX/7P/7a4du2IJw4mrF//DKE7xwvJ4mLBzb1ZzNMj0tNjHmOHT5qpLM/o6zVNXTMMFqkz9g6vc/+t\n11DK4IKnGE/og8MUeSwWNo6hcpMzeI8yEWvceHu6ECOrhqDorCXXCiczVt3A4AIrK+idixc7EX0v\nddJPbqzIBPECYYnU+FIrdmczzpaLWAyUxIYN9iLihSEk4gix8PlEX5cb7eOGBIO8Yi8XzeUJcqNj\nYFSOeOrWM2Qm44XXXmBVr9if77NcL+NzC3jy5pM89fhTSKkjky95gwKs6zWnp8doYzBGp07rkhmj\nRCwgRTWlaVvu3rtLCIHd3V2Ojg63HaCSyf1Ia5p6zdnZGefn50wnE6pxRVGUFEZiPQz+kh16FW/k\nalHbYpKXbOFtd7kpnlJedpfiSmG9su4u9yObIe3lcbXji8UkEleKLOKG687SdMNW4rPpLq+OVzed\n5qPF89FiuSUkJXxaSZHSZ2JxyLQmN5LSxMJYZoZ5maWxq6LQkvXinHa9pCpyFosLnA/M53PKskRI\naNsonBcI+q5Paytixoc7M4RSrFfLLbnqMnXFo4yOJBwh6a2l63oGG3WIRmu8czR9t328zlpsun8g\npHCAiG96QKnI7HbpjQzEc7A3KlB+SJvPuDFUyaWq8z5uGkRkjQsdbe18+tgYFUlxs2pE3bXoNLqN\nLF2F1iq+/95jjCHXgovVkrWX0XzEZBwc7HC9MgxDz6LuuGgt1WRKJol4Y9dSlCWr9ZoyzxM+35Mb\nxenxMSbL0VlGGAakMeTK4OwAwWOKMS5YtIB1s46/m4rGIlHbKajbloMqY3GxoiVCDEEEzheL+F4o\nybyaIHPN+77rQ/zVf+8X+dSnPzX6mX/hp/7OX/x3/8LiG73Wfqsc8uvf5VvzEEL8QFFO/sYv/2d/\nUzz15GMczSrWt59jXjr63jKZTri+N2e2e0hejsjzkuW6pmlblDbUXUcxGjP0LVprBucJzrGzu88r\nX36OLNPUXY0UgdnOnKwa0SciQZCS+mJBIPpd9naIIxch8cQLuEfSDgOjLOIpdW/R2hCkASJ+4n28\nMOQqdsEANgi8NCzT2MoSP3hVluNd2H6YQ4gp8xuMMsQrVqTUG02eFwzOs1iv6YaIGbkQw36tj2HR\ng7WpWKbsQGep8hEffvajSUYhaduGtmvZme/yzOPfgfeeZ598lhuHN3k7w0UKwZtvvsFzz38R7zyj\nsuT8/IKQzOA3Pp+ZjucIAqvVir7r8SGQ5RlN22xHnlu2pxTcvXs/MQsV0505s/mcICIuGNmQMRRZ\nK0mmJJVRjArDqNSMCk2Va4qUtJHpKKPYSCtMsqXTKho5bAhblxuMhAuL6P2qpUCIsL39Kyz2rpyX\nTcxUkWnWrWXZxGJ5lQ27LYxvwyTftt63tytiV7kZI2sp02QiMl4zoyhM7GhHuWZcmAQDwCg3KBWf\nazaboZVisV5SjcccHx/TdR1lUXLj6AYf+eB3MZ/MKPKc0XjEar1iGAa6to+bOinI84y+j5j3arVC\nJHKNIGHJ3lOWJYO1DINlWddctA1VUVKKaGFnEnntkXGyjB6tF01LG6BzAZ82NnFTCqNM0fb9tpjJ\nEMDHFJIQYmRcHgLBO0ZZjkq2dJtNjE1M1kxH31d0xrzMGRuBDHEMLQBjorE6wLjIuTlWzI1jLDqO\nz85Zu8BTR7u8f7+iPT/h9qKmyydMqpLFYomQmqLIMeMJTd8xme+gTR5jy4aermmiVjpNcYxUDF2H\nsy0yL7HBMy5KpBAMtkcYTTke4+zATlkwmkx477U5RwpUiHmtPgTWXU/bdzw4O0UKxXu+8zv5V37x\n3+av/covX/vr/9XfevmbuNx+yxzflh2mzvIfLqrRb/yl//TXxCc++uHoonH+BmLxKk3bRRcb2+Od\n5/79+4QQyMqSqshRRnOxWFAUJdPphLapMUkTJU1BMZljRI/OcnIhqaoRTgiEUgglk6QiMJ5Maazd\nkm08sJGLeCHoBscoUww+UA8pZijR53s3RH2Z0igRYjpDMiTQJsZ8Ca0iDjpYjJTsTCa0Q09jh4T7\nRFJLjEiKO2UtI5EnzWYSgzRiNVLJpI+TW59L2IwQFfiAkZoPPfthtDIgBSdnDzncP+LWtVuUecmD\n0/s8eetd7M/3ccEnZ514WDvw+luvc+febTKdMR5PODk75fTshJOzEw73D7YXfS0DL7z0Mg+OjxmG\nHqTAGEPf9ezt7T3yXnddx3K5Is9zRtWIazeuo1PSSt82hKFDqCxNPq9gk2kUehlifUn22d4vMUm5\nWq/S5FNwiVlukkEiAenR8OUNKegREwFAiPizWsYCVrcDbdKpfjWCz9u72K/4t825i6LQ+NqFiE5N\nyeTf6MgazXUs0KUxlGn0mhuNUVF6U9drGFoWF+eMJxPyrODw6BrjyZh1vY7+vumEzKYzDvYPI5M6\nz6mbGh9clC0pSd31EKKPsTZRwmG0irF3UmzxY2001g5xrThL0/eUZYEIkRxntKGSApxHy4irFyoa\nIAw2SqOMUlQCpEherCLiwosmTm4GJEVmWLQdnZAUKnaUQ+o0N0HTwvn4mYM0zo6bjsbamLpCTEzR\nSqbOGQiBItOs1g15njMrc4auY3c2oW0b6sHihOBwNmFoGk6anrUwHIxyTs7PQWtwA0VZYgeLkJKy\nLHHDEGVRSc5i03RBhsDQN5h8TBAelzgGUmmCixF+SsDQ9fhhoCgqzNBgpKJBERJsQgAn4WIduQQf\n+9B3oUrDX/8P/orKDq7/mR//kR/9O7/0i39x9TWW3rfcob/+Xb61DiHks+Pdg//l3/rL/wl/4Ps+\nwbpzVMMJk/4upz6gBejgWK/ruNsSIsUdQZYbrPMcHOzTt1F/NRpPuDg/pW97ZtduUGaG8/UapQzK\nKEKm6FyPHoAij2NOojA/OIdH44gMPu8HnA84oRhl0FpobEyO11LgHXRDsv3yHuFtNCgABusJ3pJJ\nAUlHKBAYGUelxuScrVZbfadLfqXeRpG4FGIrTYhYnEr6szgqi5FLG3efdDI90SBAxi4ubAtwfJKi\nKFBKkZsCgGefeu92HqnTB9x5z/nFGV98/ovRy9YYLI57D++htabMC5SU1O2a8WicxJGeddPgSYYD\nUtINPQd7+1fe51iwRqMKIQQvv/Iy+/t7GLWXpCyByaiKQvHegYhjyfjDG2cVjxRqWyilgH4QSOex\naYS3YbuyJRhH0NOFSwIRXPkquOLP+nYsU0Sm85UxrVGCuh3S472to/znrnPxtq9RarF5P6+yYJWM\nAdNaxk45N1FGMsqiSUKmBaf37jK+cR1J4OTBfYoi4/jsjPvn5wRref9kxmQ0ZTy6pDJt0dMQuHn9\nBl3fMfiW5XJFby270wmrrqUoM5qmYXAepST90McM2SxjIMqQNsXKOU9wHu8djXM2rQAAACAASURB\nVM2YFSWy7ygzQ47l4TAAGhM8jY3JOso58I6691EG1jUMISCExriY5KLKguAcnQtUWtGsa0yxw7rp\nOJjt8PD8DJPHjZUSmhDiu+QErLuW+WTKRdPQSYkXkkmmwMcC6oMkKImwkYC2bLoYxh4E1eAZup5r\nB1OWqyUWwbW9KV3bcaeuaSrD4EDKWDAXTcvB/j7riwtC16PzktXZCUPbIExGnhu8LKIO2/bkWuFd\njodIAEqWgT4xfbXRNHWNlorZzg7+4TGdKTh1kUw4dANBxbH3vbNTrHX8iZ/5WdYXC/6L//BXnvyv\n/8ffuJMXRdm1W2LEt/TxbVUwTVE9vnPt1u//3J/78/z4H/1DLBtL0dzh4s3PwWyO73vOzs6YzucU\nxjAZVTw8OWW+M4maQ6Vp1kuGYaDKNO1qGYk5KOb719BZxuuvvMCkKkApxrMJVir69QqR5Qg7bPML\ne+9wAbzrUSrD2rjD9elCf9YGJJ48iynwBBnF6SFEvRWkMamP41UPPkhc75AqFtRNZmGRR3LE4N02\n61IJYvwWKSgYthflkHbQZRY1cEIQ3YVE2PqWbkd8IroIOe8Y5WOSxhuAYRjo+yEVzA299mp/BS+9\n8iJd38Yxmtbbi+Hh4T79MND1HTcOr8fHCKAEKK1515NPcXp2xtHhIUYbhBSsVivuP7jPwf4+IUSW\n7J07d6ibhvlszuHBIVIKurbl4vycMpP0/UDvYTqeUDcNk+kMoTXOBQYvsClbUUiHGCRCWIQFYUGm\n+CiSC1LshlIHuWHXsvm6GbemcO7t+djuLxKWJ8DH91WIOIrfHN9Modz8fSsVSd9HYs+mWMrEho0h\nylrFgOfCqCgbSfKRh/fuMK4yzk8ecP7QMh5X3D8+5WBvj+X5OfuPPY4yGh98Wpcb39zAarWkqkaJ\nlKa4dfQY99UdzhcXBGI8V8AzqUqW63U0KRCSoOJJyU3GYO02FHrTwjvnOF8sKUzGfllwsVrh84Ii\n0wgpyYXgugHvBHcuWloPWhvO1ivGWpFrKITlvOmRRqN81EIPAZRKGzXvKbXGDw2lVggEjbVp7Ue8\n1QforU34a3xdKMVJY6kyg9HQDwPBWdY+MMozNJ4qj9qW825AmIKzpmdUTugXZ9xZNDx2uMuRaHjz\n+ILd2Rjr41qYT6as6yZKpaQkBMdsd4/gPcvFArRmaBvs0JMbw9nJXXYOb7GqB6SPOlHvXcJg4wfV\nGIPrWrRU7B/uUzYtL552XGhD46LfrlQSFwIPl2eo25I//ef+VS6OT/hL/86/zF/5L3/189P5zicX\n52enX3eB/v/8+LYZyQoh5Hg8/od/+Kf+1BM//2f/ddrBM/bn3P3cb7B7uM/YaJZtjS4KxlpSGcPF\nqmY6m1GWZcT6QjQpH1UjcBYnsv+HvDeNtTXN7vp+z/BOez7TPXeuqu7qrm4bt91uVzdguzFum9jG\nWLZDEkMgASyTEIspJAEhEEGRkpBBSiKIwmCkxEFJiEIiPkRCUSCMMUMwbUzT3VVd053vGff4Ts+Q\nD+vZ+5zb7uDGiaKu5i3dOvvsM+z37P3uZz1rrf/6/XFRBoaL8T5tvWI6LvEKVBIr9AomkwlN26VA\nIupXZbQ4SrhIFxSNl1EGH0Iqv0rGEwAXNW2A2rnU/xLrq94HXBqBQOvkuBB2C7UGYvBMxxNWnSgr\ntz1LVMKBG5Me80UBSe8cvUtlMZ/c7WFXrrVGxkq2/Sbneu4fv8xkPBXailL84zc/R9M2ZKknuhPA\nXCt97u/v8+TpE7TV5HlOWVZ89LWPkmWW+3fuc3x8i+FQPDNDjOAdb7/7Lk+eP+PG0Q0RmCjFm2++\nwdnlOZm1TKdTlssli+WCj3zkI9y9c4eDg4MU0CJd1/H88UNyo8i0wncd2ohYY7Nei2VWkScnGK7+\nbhVTJinBQGJT3GWMpOdwO/d5/eOubCtSnrTg6msG0lchlUgqhYfd6/FVXuO7j2qr1EzXokq3t33g\nF4Q/qTxrVELfaSPEnpR9ZnnGyckpo8GQpm3J84LDgyPOL+fcvf8yw+H42nPwovo3z4udQMoHz7Nn\nj7lYzCmLXERMdS3XR17QNQ1d3xEglRhlYxGCzDZrI09SVFf3BSDPCqaFJfqeWWEpY89QeQYKVm3H\noCioihxLZJqBJjJUni7K6IROyiebVLrbYBh9pCxypiZytq4J2qBMxsAoEYkpUV93MZJlGcZoNn2f\nNpUyQ90GT5FlWCXELq8U1hhcH3bvqVFhuVytUSYX+7xcfC/zomRSas43PTErmUbHqnMEpSjynM16\nzWA6xbcN3vWEVLK1WrNeLsnygr5t0FlOZguZX00b9syINkIreZ2D86ybFqs1+0f7TLVG5xmDPGPd\n9kR9NWq2aKQC++nv+i5+9q/9LZbzk4N20/3Fn/wd//qDr+pCfR8f/8wEzD/+J3/qL/7K7/rMd/+2\n3/0HMcYy9iecvfF3GI1HtPWamBr3/eIyqVItgyqj3tQyxBwj0QVC3+K8gLvzosD1jmwwpKgGuOaS\naMRrTxcZdlDRu54+DWBvB91dCDTO4aKmCwpr9Y6iIkPTYlyrFZS5SSmBTdkkiXSjCCrir63oRm3H\nRPQup1HGMB1POV/OU0Z6pW5VXDliaCXw7MIKT9N5EQdt1SvGmLTUR/n9aVGPMdJ1Pc45qqLi0bOH\n1JsNT0+eslhdsm5rVusVo8GYIll2QWRTb2i7mqIo2ZvtczDb5+bRTQ73D9ms1zjXs6nX9H0nWUo1\noGkbzs5PaLuWtuvZ29vbBYlHjx6S5Rn37t4nyyyRSDWoKPKctm149uwZl2endG3DerOhWa/4wO1j\nqrKiC5G6c0ymU4ajEcTI+fkZVZmTWeGH7pS6OjmP7GJYWsRTcXabWaVL5st6k1ffsUXrGZNMoJNC\n1HmZI7xO7fmlZJbXPknBOKbgqXZKv+vEIp1UskYbGSvRGm3kPhUDhzduYPOCqiwpsoyT8wvu33sJ\nY7MXVL7x2uN++bG1Zeu9kG8ya9BKUbcdxhqyIqMqcnovBtbBu9Qvl6TVuQQVCEFKolFsrw6GI/q2\nRSvFwCpK35Hlli4quiiq2SrTjE1kUFi6vidoEc25GPFoRmWBCiIs6hTCX/WOoDQ+QTq6hJPQyhCi\nzEoLqxV88AyqIW3XEpO+YO16VGLMlllGphV11xONlU1miDS9w2Q5ZW7ou5bOBSZVQZUZcB1RayaF\npm57+rziaFhwejkXY20lQrU8l/dVVVSs5gtCjIynE5xzTEYjVss5w+kBvhdf2WgMRgktSVmdxkzk\nNbJFQbfZyGaxb9jLDXdnYxato0vrTgTmmxWjquJbf8Wn+Et//n/h+37kh378T/zxn3rwY//Cj/7s\nL3qxvo+PfyZKsuPZwR/6ttdf/8Hf8G/8QUaDAWV/zsN/9DNUVUGW54zGI6FgaMvB8THlYERTL8Xm\nyFoKY9ksFxRlgbKlDARHw6oNzM/OMYMhN03Ea02WFxR5QdCG1abGWivBzXl0ltH0vfjtIfOVIXo6\nJ40tWUzEpkmh6V0ghJY+gFci4rFKERQ0Pog8PEY6gmDuosxYuRCJSubbcmVxvROJvrYJBC5pT1Qi\ntSdlSjrqbRIlfTzEK9LajJ3kZbuQpxJUDLJTvrF3xGg45vnZczKd8/z8KcZYRqMxH0m9y3/w+X9A\nYQRYYDOD0Tn5uUjjB4Mhb7/zHuu6pu1ajg4OJNN2jiIvmM72MNpQ5Dmj4ZhXXp6ilKKuay4uLjB5\nxv179yiKIpWwYVAOCCHy1pfepN1sGA8G5FXB2eU5ZZ5hsownF2s2bc/R0SHaKJ4/ewbAarnCEJkd\nHlFm2Y6AozQYxFhYssOA8rKncUpKs15plI6YwI7vKqLeqwzeJMgBqQTfdJ6mE/Xx9aArP/ZPJvZ8\nxUNd5XpbxpCKyS7qK8mDIrvgCoKE8z7QEIVz2skY07AaUGWakFd0PqJC2GXHOv389fN9ARsIjEZj\nnPdcLk8YZDldYvx2fS8gDZXOLgoAYws0iFFIOr3r0uMpQoJkRCOzy+u65tZwQrNqZeazD4Rmw/7+\nHn3f06BxjUObjExDpoTkhBfv2EgkasVhIf6yl01gVhgWdUuLIkTPJKto2gaMqHC3CmufLLhi+nu7\n1DrZ9qxXzqO8oN6190QrrZY8L9i0vYxFDSuC66j7jsIYssxSNx1tNNzcH/Dmo2cM9AF7VrFebZjM\nJCg65SjS4w5GA5arNXGxoKwqNnVNkVl8s8aYAk0vptfDEQWRtmnAGrI8p/Cei+WKG8dHXJ6eontH\nluccjCqy4PjZszXzGEQhHOCd50955cZtftvv+0n+zB/7z/nx3/k7fuqbv+1TX/js3/vbf/Of7mJ9\n/xxf9xnmcDj8lo9/4hP/ww/99t/Pq6+8TNU+5ukX/jbee0aDAb6rqXRklBesVzV12zOfz2nqTlSE\n2YCqsPiuFmJJ3xKzIV3boHzD0fEBxzdvo7W4o9vcCo/SaGnsK5HrZ9airBW/vSDu9o1TuCBOIMbI\nyHMXlZRaFXil8NrSYa6VUhVbnqbIQ0SpuQUQbHmrKgXEQTWgDZ511wqWLgVZpZTwaZN6LiT3ERd9\nUvqlMmy8yoy3qtaYVr8QRN3YtR3L9ZKzi1NeufdBbh7eZLVZ8aGXP8y4GrFpaj7/pc/JSIXRDIpi\nB3hfNzWz2R7WyKJ5sH8gATDL0cZglBYD7vGMLMvIjeL0/EIUtJcXvPfwgfRPhwMGVUWRFzx99pxH\njx5xfHyINYrgPc515JmiynNuHB0wm07EoNjm7O0foJTm7PQU5z3rywsO9/eEBtO3FHmOsds50asS\npr5G9zHbEuZWdaquSD87ak7yjcwzcRrRWog368bRdMnjFPULlLBfLa1n9zlX5d1tcLlSxsrnOpk7\na6WxJvlXGikX2sSD3c2TbkOvkvLhZJClWVhFZq+Cv0AatuH5y89bqhMmtTRE5NNitGGdskMfvAi5\nnGO9qckzsZbLs1w2QSpitKHtJCMNUQRrB+MRXb3mYDwiI2BjoG9aUcfGiMpy1kks1znP/rhiXddg\nMlxUFFaRa8Wmc4wzBcawqRt6FMEHLpueYVVhjSUPXhTmCPxdRRntCjGQZzkuiCJVK4HGW2NldjMK\nsxnEGk3FQNsLRD4i+L1l05LlJT4ouqDoQxS7vigfZ4OSi03Lzf19msszgjF47yXrL3JRnmstVmXy\nrkas8Ax90zKaHdDUS3RSFmubkaWNcdyOVgFOawbDEYM8Y1iWnJ+LJd7d8YD1esPCCfzBeQFJTPb2\nuHn3Nv/zT/93/Gu/67f/tk9808f/6C9lrX4/HF/XAXM0Gh19/BOvf+7TP/pb7ce/9ZPsmyXvfvav\n03eOYVUyqkr2RhXzywuic2hlaJqWwxvHDAYV8/mS2fF9qOdE3xNCpA6G9fyMzMBkMqAcHxCVo/OJ\nJpKoHJ3zrDc1RW7RSt4wnY+EqFl1gT6mBdYa6aMoRcTQReiiSNm3dBKltgPFaldKVYDd9cDEtcAg\ng/N+K+JRivFgyLppdpnLttQXkzCj947eSUnVh4BPfpbyOHonYtHpscQhQUZMtDJUecnrv+yT3L5x\nh/u3XuL44BZFXnB0cMRoMGZQDXnw5F36vqOw2Q6Nt25bnIu8fO8DzCb7kkVO9vAhULfNTrRktOZw\n/4iqrFitllycn8iGwnu89+xPJxwfHjIeVPhe1JI3Dmbcv3NM8EJF2ma6PohbxWJVE23OcDDgvfce\nMJ6MefrkMa5tMQRc75hNJ3gfdlZJIfhEDboOJiB5iKpdSVOnkqbZBsgUiIySecoi1xRWZPtNn+Yq\nXdruqKuM88tnM7/8uN4HVtfu23099TC3fVbZ8FzxbrUSus6V+EfOLzOCZ9xuAK7/rdsNQ9sHLmtH\n50LiG8vj6ESZKqyhzMzO5iuzaTJVXdmXOeeYLy+xxlK3Db0TZF5dN0lkIi4nUbG7LmOIGJvGm7RC\nRWG2HgyHzMqM9XpFCELf6fueMpcgEpXBe8ey7SDLWbY9tQgNmBQ5ddPgtGGQWdZ1m4b7YbneiKdl\nZoiplZJnlsQSIgQZxVLSJ0Gl3uKqrvExkhtL27VoLZ6dVou9m9Wauu0oi4LFaiUVKSJVntF1Leum\nw6FZ1i0hgT3WTUcTDMq3nHeeW1VBt1pRDYbMF3NMngnUXyuqssQ7R54ViJQd8iInhEhRligV6euG\nPgSKsqTvZONCCGTWsrpcYMqcru+oqgE6+dy2Tc2rN29QdQ2nLhCVZtPWTIYjbkxHaJPxV/+3v8Jf\n/rt/48M//H0/+Bf+v1vJv3aOr9uAqZTKf/mnP/PsY7/ye8rXf/Wv5QPjhrc/+9ek9LG3R5lp8r6m\nLComkynz0xM2PSjfsF5eklvD8/MFI9OTGRFiLOoeayJVVTKcjFmsW/aPbtD7DmMMLgaUkoxxW67J\nrBH1KuBBRhi0xsdAZQVi7kIkzwxNLxfwomt3llmy8KVSnjE72ABRVK1yxG3DTEDuXIHTD8ZTzlbL\nXeayJcG4FJAl8AS00Tvo+Jbs0/c92mwdIUjrrgDDdRp277uOi8UFzy+ecXF5RpmXFHnBydlz3n30\nDllmefjkAd4HMmOJKA72jzk6uMmtG7cpi/KF1y2zGXuzPfZm+4xHY6pqwGg4xmjN5cUpk6pAac3e\nZMTtowNuHx8zGFasN2sePXxMHzxd7/ncG2/TtAKmfufBQ84vLhiPxuwdHjEYjnjvwUMxXw4wne0x\nGAx4+vgxBljNL7l35zZd2xKV4uzxQ4bVgPPLCyaTSZrRTLSf3YhGMt+2CrvLOHWyxNLkmZWsUmmc\nD8w3HaumR9rbuxzuBfHVL3J9725fD2qwG/65yi6Ju5nRbWDbUYpMUsoqUhac/C1T0DdKFLTbx8gz\nIff0Pm6vvESIIrUTIm0faFzYmTpLtUJAD1v4w3Q0FhV4Zti0XXKxUVKJURofPG3TCvg8BIwVZ5nM\n2l11A63IjOVoOmZoFTb0ZEazrhu0sbTe82zTEbVh4zy5MQytYpAZdAwMckvvWrqwrdhAH2HlAW0Y\nlwXOOfbHQ7qmY1hWBO/pXY81JpGIQGmDQsR342rEqt5In1XJdTBIJWenpUcMYI2RcraxeC/jJ0IJ\ngvGwIIaeEIXkFWPAJWzl/iDn8uKSyigGRY4PjtAnZ6JNzXA0wvc90ffk5YDgRWXtQkCHiCmHuL4R\nZWzfo6x40rq+J7MZrm0EJm8sg7yk7hqGkxmq79P6EBiXBc/OFsSqwMfAqt5w5/YdimHO8nTOerX+\npnfPN9/37R//5p/6qhbr99HxdRkwlVLqU9/+6b//wdd+2d1P/dC/wsduaC7f/Tnml3NuHB1QFhkT\nEzEx4oOm2SxYbxoGhebw8DCVynqIsDedsFmvUMYwGMlM32RvD+c80/1DtDU07UYCXEymxkDvheCT\nZbLLDSg2qbxitJbmsdbUvaewiiYqmqbhYFiwdqSxgmtik6SA3WaFQUlZbTtXqZBSUutlZs9oTZUX\n5Nbu3sAhRroEs95maNvZSaUUNssl9nqZQ82L8oobqxR5YSnyjCLLyK2hKHKKspA+UpB+zZPnT3n8\n/BGnF6ds6pqTs+fcPr5DURSU5YC7t19mNtkjz/LdsPX2iElUkFtLmWcMq5LJsGJQGKzZulbUrOqW\npusZjKaE1LNdrjY8e/qE4709FpsaFyJVWfL8+XP292b4vicrch49fsLF2RkxBg5nwstcr1aCOOxa\nuraV3uh6w3Q2JXQt070DRqMBMSJYskHFztKJq0zwuiem1ioFHgGYWyPZ1brtOVu1CUIgWU5iTryA\nG/wqr/PtjV3IvZ4Nyixp3AH0JSO+AjMYpXflVGu2tCJB32U2nb8Vc3KjNUorykxGE3yIO07uDppw\nvW+aAtDW/aX3QQKql9s+BCbDIX3bgFLMRkMGeUGWSUYVvBOGa2bTfjCKvVyUsaPgw45BO6wqcq05\nGpXEtuFwf0a3XuGUBu+ZjAaMc8OsEo/YVdMzHAxwrmfdg7WKune4KAFu656jgHXX0UVF0IZBVYig\nR5tdf9UoKXeXxhCcYzIa07YNmRLxH1rTeX/VRgnyc7k2GKMSg1ZEN9oYjI70vaePhiLPcMEnkZai\ntIpxbsmjY1oWLM7P8Z1jMpnSrDcMRmPqek2W5+RZTtN3jCd7UjXabjy0ldJ39OTW0CY4u80yYsJt\nai0q+WpYQd/Ttg3FaExXr+lFv8idgylLJ5uELgoT+9bBDfy05Pk/focs6+/+V3/mp+f/0o/+yM/8\n0lfyr73j61L088lPfvL33Ll772Mf//7fzEcmDcv3Ps+mj1ibekftCptnXLpAlUO9aDi6cYTSCo9m\nMpvx+PETZtMpfbvBqIiJkGU5xuScn17SB8/+nZe4OH8OVpR7TSulDaU1fegFc5d6ietOdpBZltF0\nHUYpVnXyZjQ5fVBslOHdRU27FQck6EBIGYJg7aR3Qoy4IIrNqMBFj45puVYi5jFa0zongOpwFYC7\nXogpW0iB3A7YFAPapsNoIwrAdA7GCnJLlmhZ0L2XuU6jNYOipOs6dFmgEGhB8J5BOeDW8W2qspLR\ngC2XNh2bzYpnJ48prCXPMzHeRRatwWBENRwBmt453nrnPW7tT8lthkuvh0KwfE+ePGG5WNLWNVle\ncufWTb7whS9KIHzWMxoMaOqG4PpU/jOcnJ5wuLfPcDJl0/dM9/bohyOWF2cEk/H89JSD6YSD/Rnz\nxZpRWfH0/BydFVSDihDF8SVElUrqcZfBpc4QMQpSsO4d56uWZZ1U06QyZoo0u1L5Vxksv9IRd2pY\nvfu921LoVdk2XiuvK7S5Gh/Z/tMCA9rRiUwSeGklIIw6tRC2oKZd3N7dfrF3eV3AtL3uYpAyf1FN\nmT875/TyFKUEaDEeDpgOx9zYy4hIib7uejZtQ910aGMoSyuemCGyalum2YCLumNVO9bUidwDwWSc\nLlZkZYXuPDGKs8d57YmBdP1rykzROjDWkMdI13c8bztUUBwNh7RNTR+l7aF1pDAGqxTndUtVDti0\nDR6NUZEYQlKeeuq2A2A0qKi7Tp5HLTqGqEAbRRfZCZ8ya6nKjL5paLuYqhWK3ICOHXlUXNYbXJGz\nt7fPfH5B9I7KatrlnDzPWM2XHB4dEtqWtm9RWrFuOsajAURPnle09YKIolCKNkHdy0yye1dvKIcj\nTi4vmRUlikCzXpCNJ5TAkydPuPWBD3F8saAcTXlwccqm7+iAV156mdV3LPnC//4zvPLNH/p3gf/s\nl3xBfw0eX3cZ5ke+8WM/+tJLL//Zb/3B38q3vLzHnt7QmDHL5+8wqEpmlUU7n/pbhqBEUFPklsVy\nzd54wmazEZdyPEWey4JoBYnnfKRpNty4cxOlDBEpXyijxZzY+9R3iWRZQe8cjZOBAmsUm176PrVP\n85gxypD4cMTZRug1W7eMbc/IJGTdVpYPadGBJE8UdZ6PW/yaKF9H5YC6aVjV9QvKxRijBM30O0WY\nlJHEpbRdl0qxqY9lpM+Vma2dl8KaLPWuDJnNiCn7zbMcFaVkJplkyeHeDUi9WKMVrmvIDCwuT1lv\n5oI384HlasPp5ZL5akXjPJfLJSEEhsMhSmvWqyUmgQ9ilDEabUUY0nctxhg615MXJY+fPWNvMmJv\nPKawgnYbWsVkNBQFbZ5DCPRtLTOXAcZVxXq9oelldq7vHHXdslytGAwq1psNo+EQ7xxFWSbgQ0zO\nMCHBI65RfZBKw7LuOF81bFrJ6OV5l4Vwm53J63415vOLHV8JULALWIqdwOe6IEeCod7d3ho+Z0b6\nl8aoXcDU+ooIpLTZBcVRaWk6f/U4aicvYpdtf9k5bvusL9KHErc3y8jzir3xjNl0j9lkHx9gvlxy\ncnFB3XQyz5lZBkXBwXTKsCipilKUyTFIoMkKNnXLRdPSRcW6c5y1DpvlOGMJ3lH7iDaWjfOUVtNH\nGFeFQD1ipPWBVeeIGPqUaWqrKayldZ7WO7SxjHPpSfY+su490VgiUGSWqNP39y0+yCY9KlF7Z8mm\nzMRI0/fI7Keh9QJMGCTebKKEpPelIleRcW6oiKi+x9UNpijxdS36iLTB9hF0XqB8T+96TJbJbKlz\n5Nak6glUwyldU4sloPOQaF+kSpIOgbprxTHIGELXpVKBowueg4MDnj19gjGWdd/LHK5S1H3DXjVm\nHhqyIufdn/18+dBd/N7f+MO/8S/83t/9u86+qgv7a/z4ugqYP/ADPzA+2N/7q/e+9XvK7/xVv5qD\n+k3U7D5nb/1fWGvouzW6bcWN3VrpnwXPeDJluZyzXm04OJixmM/RQFXkOB/oe0de5WR5jtGK6f4+\nw9khTdvgopQ3YpQh7S5KD8d7UdN1QebABoU41ndesk6Uoigyzjc12nc82nRJ6fqiQjISdxt2CXpS\nv5OyqiNGxPtPgVZWGJBB3gCTwYDL1WpH8FFIJumTTD+SArHaIu9EdCEWVeLDp7QiU5qyKAASO1Xt\nAqS1MvPonKfrHZtNw42DW3zopQ9zsHfIwWyP3ArMu8wNF5dnPHj8kKIa8Mbb77CsW/oQafskmuo7\nrMlwfYd3PUVZMBnPdufYNhsuzs/ZK6Vc61EYm1GWBUopLpcrhsMhk/GE+ckJmdIsFwtCJ/2wx0+e\nMZzt8/Z77+ID3Ll1gze/9Daz6RRrc44OZszGY+reUQwGOODBg8eUecFsNuPRs2fkVUVRVSnASS9Z\nQOEitorIhqnpJViu6h63Hd+JSCnP6F0JVYyqr6AHX03IfCH4xGvqWIWMDikR/Rijk3IyCXu0xtoU\nHI0mM5Bpg7FSepeZ0BT84IUerU3ekC5GFvMFRZF/WcC8HkC3t+MLAfN6oM+NSmMYlqoaMBmNmYwm\nPHz6kDwryPMc5/v0XPYsN2vWrWwAfRJ07U3GjMqSPM85nM1YrBac9p4b0ymPnj7j4OCAR2cX7E0m\nqBhoUBTG4pWmMJrzdcNp69hE4azm1tK7ng4pv5uioMwy2r6VDaNWGO/xYHbi/gAAIABJREFUUYwN\nMmOh3aCyHLwDbSmznHVTU2aWzvVEVOq/ivtQbkQtbxORa3u78zKyUeQaQyRET5ll3Cg1efCcP34k\nQqK6xfUOi6duOvZmE+qmoxgOOD054ejgkLreEJ2UW7VStJsVWhuc68lsgdaaPlmhVWVFvVwRoydm\nOWVW0mw25IMCrTS5yWQuHIXJMvrgmYyGFHnBsKoYlyV3c8u7qzWVtULemgy4fHLCky+9W45y9fd+\n82/6Vz/71a3iX9vH15VbyZvvPPjLxeG96Wd+4Ie5X845+PB3sHj8RQ4O9jEK7u7vMx2WlMMSo2DT\ntqzXG549fcZkus/edMxqvmA2nlCVpShJs4JiWBGVIcsLyqqSsqIyBLzwT42VLCdhs9AKm4ykI0AE\n5wONg86JWfQwh0lZoKuKZ8GKgfO1OT1AuKJpdEP6DjKwTYhJpap35s29D7RdR9u1eO/pUtm37Tu6\nrsMnck/b9njn2c5QamTBDQGapmNUjRhVY9kJa534otlOHdsmADZKUVlLZa2gzKKMobx85x5VYVks\nntG1y6QIjdS9572nJ/zDN96g9Z7nJ6cJJi+L6fHhDY73j/jAK6/yoQ98kFs3b/ENH/lGjg9v7s7V\nGkPbO1EZbtZoIs1aqCNNXVOvltw9OqDZrIhE6t7Ra83leo0LgdWm5u6dOxiluH3nHoPBgIv5khAj\nT0/OePfBe7z33mMuLuZs1hKYM5Nx+949Nj7yznsPmI7GWPNiJ0OSfI2xBpveUb0PNJ2Uf1Uap8n0\n1UhJYUT8ohO/VivYuVt/2fGLzmCqHV9id8e2MiqbLFFo5slVpTCayhpKq8iNkfvNdrzkystzSxty\nTvqPmVE0LtL1kdF4+uJ5bW9ei/ay8VO7TcCu+gGsVkuUEuPybY81JrntbDKjc7WMbiVFtnceazJU\nlOe6C4FFXfPw5JQHp6e8d/KMR4s5B0fHHI+nNCbn+O495k7mNI3NKIucrt6wcIHT5YrnjYMsF2Zx\nljMwGry0SaIX273CGIL3DPOccZahnEdbS+cijZe+fT4YokKgj4rQdoyqEh0jOkYGNiNXUhrv07Ub\nQkCj6VygCVfKc4VstJyX573KDIWSNkazWlPkFcTA/myKaxvK0Zgyzzg9vyQrCrq6kc1e2xK6njIv\nef7oEV3XM0ibTt/3tM0amw9kMkAr2q6hzHNMQLB6VjEajbk4m4NRdN7tmMqx68iJuK4V5fFywSA3\nvPnoIfes4cn8gnF6Pl7/3m/n4vkZH/jYN/zXv+Iz3/kT/+SL+P1xfN0EzMnhzf/47HLxbT/wG38C\n/eBvMD68z8/9lT/PpHBcnJ+DaylDT1YUxN6RVxX4jsxoRoOC89MTtC0YjEZcLtZAZNN0ZEmR5/oO\n53p657BFSddviEG86Xon3nxGmxd2/uvWpcxCsWwFBj0aFOQadPB88fk5be9237/9GGNMUvrkkZkW\njO2/yJU7gWQWAqWuO3Go7/oe5z1d3wvVpmnpOrFVcp3DOU9bd7j+SjwRgsM7J+CDKG4ICikp2cwS\nY2Q6mpHbnExbhlmZRBkZh5Mx927c4PbBAXWz5mx+xsOTE8pySt375I4iC2ieZUQfWC4XQGQ6GlOV\nFcvlgsXlGW+9+XneevtLPHv2mLfe/hJbBbCIkixuU3NjtsdytWZVN4ymMwBOzk5YLZZgM+7ef4Vb\nt27xLd/2Oi+/+io379zhfLUiGww5vVwwnu1x995dbt+/R1aNuHXnDk4p5qs1z09P0cowGQ65d+c2\n5xeXrJYrrDGM9g7pomI8HQNXJB+jtxZZOm0eJNtUCDw9T/D8LKlEyxS4tsKSK1zd/3N2+SJS8Fqg\n2hYgviymbjci2/Ln9nFyY8SBpLAMypxRmTMoM2kL5HYXPE0q6UrQhRgimVZsup6AABacj4KnS5s6\n8aZ80XpMKhmKEK/mSwWKP0rUnq1Zt6LrOmKMlHmBzTKqwUC8aJVwV7umEwh5mg1WSrwrt3ZqX3rw\nHn//zTc5b9ZkMbBXlRQ24/6t25hqSDXZ4/jomKbeUI4mDIuc5+cXKeMTm7rMGsaZZqw1fV2TIZB9\no2Xuc1Yaeu/pfCDLCmnRtC0uRpZtR5+G+lVC+22fvzzLKXJLZTM65xhWxc59BaXpvRMlsYG9UrOf\nBUYERrHn5MkTsixjMKiIKJbrNbPpmLNnT5lOJxRpFrZrWwiB9XrJbDbDOcfx4RH1ek3wHmMtTd3Q\nrFfioGJsqlZZfOhxrsU1Leu6IRsOOahK5kvRe2gr2gMVI8E5bAQbHLfGFV98403y4ZS7oyHWOU7m\nF3zwxm2eNxte+uQ38NN/8r/h1/+m3/CnPv093/+D/xRL+tfk8XVRklVKvarhz/2b/9Gf1a9/+A73\nXv1lXD75EkPbUtct1kQOCotXQibxSkQzWomwZV13tH1I+KgekxcMR2NcsyYYi7YyCGyMwQWopvu0\n7UYsc7b2WDHSOMem7VEKGq9Y90LuaQN0QZEbEWQ83bSc9TInmc7/WiYZdsrXq4Un7u6z1l59bxoc\nDiHQ9h3eeZyTHfKrd1/l1sFtbh3e57VXPsrd4/vcu/kS92+9zHQ0YzqaoJQRlFeMZCZnPBgltqxk\nzuKVKL3MzGZ4L32MaVUxqEpGRQUx0rQdziu0KXEuMhnv8cq9DwpGKy3kdV0zX1yglMxQapNxtH9I\n27SsVktRoAZPVVWUeU5ppWfVB081HKLQaXZvji1yzhYrhrM9oTI1DbPZAbP9A3rvKauBoNXSxmMw\nHHHz9h2m+wdM9/YpigJjLHlRUFYDRoOCyd4+t+/dl4CVCcUlLwsObxxxfOsY52RjMxoN0Db7shKk\n3L5eWoWrbE1vM0yzzeSE47s9R/nZFz+/dm1/xQzzein0ah4z7hSyOj3utg9ttN4F7dxK0CwzQ5Hp\nZOdlyLUit4Y8MwnSfmWEnVvpeda9wDIkq932Cq6VZNPXtsHiqre5Pe8rqEGZyyjVNroLWzVyOb9g\nXW/EFD39qhfoQfqqteCcjEWFECjyHJtZuq6jBzyKtql5cnrKumvJs5zxYEhVlJRlhQuBqsiJQVTs\nWRJruRhZdS2DJISJRIGu60xeYx+xVoDlXVRMq4LVRtxCYgwcz6Y0G6lc9D4ktx+PQe/ISG3X72hZ\nzkl5WcdIbsTpJDdmp0gelBU6Rur1WrQRGsoswynLerVgMp3Rtg152tw2Tb/rSXddn+ZqUx/VZjjX\nYbMMk+U410n7JVVNjBJrsM45RqMJzWpDNijABbzrd8YOSilCQhUejAbUITKpSgbR87RuGQxkQ1Tt\nTznc3+On/8Sf5vt/7XeN/rnPfN9//1Ut6l+jx/teJau0Hu4d3/2Hn/nnf4v53u94nf7kDVZ+iZ8/\nIMssuTGcPnhCduNAdpD5gM38AqMNg/2b6OB4+OxtlsslWVGSRwk4mbOYvACzFT1E8rLC2gxtLT44\nUEaAAErRek/AoBOYedFJI3/rf1hlijKzzIOmu7bYSjYSkn3WdsFRuxELCZqy4AQfJCCmrBWAKO4D\nzokDwa/61s+wN91HE2WB68KuLLxd3w5mR8QY6PuOTbPhc2/9PFVVMKlmvP3oLbHY6nvyLCe3GYM8\n52h2gDU589UFLsL5ciWkjxgp8yGv3n0VCGIcbVLhIs0Yei+BcHEpCDspUTseP3+GIlIWhfgNelKW\npmjqDpRhWJQpiATyouQDH/oolY3sH9/FpwVJmUyA3VnOaJKJAjGGawP1sjt2zrFeLdk/2JdFPsrY\nQmEzKhR1K9WD6COzgyl911OUOdpYDo+O2KzXDEdDlJYZvOQARlRSmjNagZWenDGymBujkh+mCHFi\niDgf6AAdRAVJVDglr2Vqc/7C6/xabzDKtMguOClSb5mrzJLt1aTSIrijEonJtVV6V47dloW3tB+j\nNKdPn2DKimIwFjbpIKPu/O57t8f2UoyI8bBWMcl1rz7KOV+VXVUaddlWUHbNXYQ8FBGP1wiUec5i\nJUCCPM9FuW01TVtjM0We5wDU9ToZsYcUTJfMWQr1RhuatuWLDx+yP55gtRitT6qKqhwyLnNRP9uc\ns8sLcmsxRouDStNKtqgF/q+NkRK/ySlsRtM5Hi1rdNp8TqdTTPQUCtq4dQySCsRlGvJ3rSffYjK3\n/UvnmBYFwfes2o690QDvWkqb8+zpU2bjMXlRYo1mtVyRl5b14jnTvRl90+H7DluU1Jsaq1UiZ9U0\nm4a8KCiKjK53VJml7yPNesn44Jh2s8IZUf1uNzLdpiYbD+kM7B8d8YWf/0e88pFX0UFG0Yosx/Wy\nAen7DpTiwHqezFfcGxQs0Dy4OOXuwRFnj+Z84js/xec++/P8tb/9937o9/yxP/wzf/wP/Yff4fre\n8T483vcBczga/7e3X3mt/LFf9924ky9wePMl5g8/m3Zn0Kzn3Dm+IbvOzlFE8YVrywl0ns1qzUuv\nfpTN5QkmLymtJnQbWt8BgWEhwg+3WtIbi64GbBpRszrvBB6gjDiKdD0Yy6IVCkZuRQEXXI8lMu8i\nF/3Vcng9UMpxlU2oJAbR2sjsFlv/yCu1rNZaLLT6nqqoeO2lb2A63sN7oaYIKQiILxpMyWKrKfJK\nMi1bcGPvNsF7yqIitxllYdkbi6DCZgVGDXjj0ZtkeZYWdvn9RhW8cu9DnF2cEmNgNp4htruKppWs\nsusajo/uojNLiDGhAKWQliX8mQ+Rum6oMovPMorhiM45Fs+fUg1G0jNO4ipv0jhHuCr9hVT6Swk5\nKHZiFWM0zx4/xsRIXpacn5xhs4zBYCBjJy4yLDTPl0sePnwiZWnXczlf4rqO8XRbPoSLiwWT8YCi\nKijKAYGADgqS0Ebwcqn3pzVZen1jyjxdCHROkHhGeWL627ev/1cKl+rLvno1xnElqtnWZrfZ5u6n\nIpDKq+JAsgUupMClIEu9O5N68b7vOTg8pO17ARUoCezOh1358yt1VSNXNnBbwQ+777/2/whWsRuv\n2T4/KJk1PpgdUZUDYgw8ePwuEcWHX3mN4WDIYjVnvrzkpVsvQwKgf+Gtz9F3PWVVUtcbtBIIh9ZS\n5pVxLI0ymsvVEpBKzdn8grKsyKxllBeMy4LXXvkArm9Zr1diK6YVPRELogUgIxhL5x0ueOn9KcWq\naRiUFb1z1L0jL0uy9YZV5xgNBgTXk2vD2WIpyL/dK7gV0EVa78miYhMMEx/IjWF1ueB4NuP07Izl\nYsHNO3e5cXzMk8ePOTg4YLNZ0WvN/sENnj95zHA0pHOOdV0zLHNCJkGurltya9isG0DRrVZ0oxlG\nG1zfY6uKPMtZLRbynuodq7MzhrN9Xn3tQ5ydnTPdn9GuNlR5ju9ashjBe1CKgcnIvceWQ+6w5tli\nwY08Y7V/yHvnz/mRn/jN/Inf/+/xjd/ysU+9+pHXXgf+z69wCX3NH+/rgGls9lsm+zd++N/+I/8B\nev0Gex/6Ltrnn2eUKVGv9p790YR6dUlhLCqXIFW3DlsZsugZH9yk62uWdYNpOzbdislkiClKouup\nnader2RI22jGWUm9WdI5TwRchBgku4xase6l5zGrLGsXcMn38nEdCTq80Nu5HiyV0rvFbquW1Fqy\nM7E48ruf2fYvu04IKSEGlusVw3J0TWmpdspNOSRTvb4gi1jF8Ilv/CRGQ2Esr969x5cevEHdt1yu\n15zM59zcv8Xp5du44IkdeO9o6p4QI9PJmC++/fPkVviil5fPybKM3gUuFwuKopBh7r7d9WbzPKNt\nOhng3gZMHzjYP+DOnbsorfnCFz9P1vdMbh7Tt50wNtPYTOskMGwX3N3AP1sRUZqDDEFMc4uS+cU5\nh9N9xmVFHzzLTc38csnB4R55nhN6WF7OGacRlq73TGdT2XygKAvL+XwufSvnKaKiqWuUNuR5RozQ\npdGWaDVea4IN12y+pOenvYi3tJYAJmKund4rqV5/YdDUOzWsbAz0C6+ivL4otcs09bV4qZK6ejdv\naXUCL8imJwLROzaLS6YH+2gFi8tL8mqIVopxZVm3jkzr9P1xV4Ldnem2jaC2WfBV6nmFxdt6gQoN\nqfPb6seLI09lWVGVAjn/5o9+QrixzrFq1lwsLjmfn9F0LbePb/OFtz7PN732MX7uC/+ATb3aPXU2\n+XPmuYh62rZNLiiB6Dy2EJ/NdrlAKcVJCJyuRqgYuXNwRAxw4/AGVVOzblrqtqFTCu3crkepjWHd\ntDJCNRpK4NGWvvdEbZlVGV3TsO46MbLOMtx6g9aapmmJyWu1zAuatqELgWhzdHr/DvKCHthsNkTv\nGY1HdF1DGA0ZjgasNg1VOSLPLI8evMfxjRuEbkMXA5kxLFZryrzAdY7gFSrL0LojqAIfatr1irwq\n6dZLOmBYSc/Y5hkqBKL31G3DuCgYDSqauma2t89b77zDS7eO6ZsG5z1ZMie4lcHPXyz4gPF8aDRk\nfnnGN4z3eL64IAD/8r/zk/ypP/zH+AP/6R/9W8e37tx99uTRo3/yCv+1d7xve5hKqRtFNfg/fvKP\n/Cf6pew5dz70LQzDOa6es1rM2cwviZuGIs/IrUUZYbo2dctGVQyrkqcnZ3T1mvXiknZzyTBXjEYV\npqhkF5znqDzHAeVkSlCQlyNW6wWdE1B078Gh6EOgjYYuyAIRY6SNgqprQVB1XPWkQvKgBJXk/Poq\ndYBrX+eFIOuC9C2FAet38ICPffjjHB/clIWJ5KUZBVkmwfKqrLc9jFYUmaHKBYXmQ2TT9rz53juc\nLy4EJO0DRllWm5WIMoCXbr3Car0iy6z0uRCXlM65q16TTkAFL9nxaDDm2clTQiCVcuW8tuCEUVkS\nlaLMxIfy9OlTJkWGzQuK4RgfheW5nXUsMkXdJ+UwaRG/1tczWjE/O2E4HDC/vOT85BRlLPuzWZrf\nFKJMXbecn8+5WKzZn46FXNILoWlQZLJYVCXaKJbrDcv5nCLPWa5W9G3P/HKBtZa+77i8mPP0yTPJ\nMq2h29TkmWFQFslzEYjyPHcu4GOkc57eJ5FXiFwPldte5LbsKr6WKVipuL1Tvq4liG5JP9sZzx36\nTouNXGFt6kdKj0tpyXKKosD3rbxXkBEppRAzdKBxMWWlV1msCIlS2fvafbC9lOWLVy3NREQiMigM\ndXeFzrs+s7ktJa82K5RWXFye87k3Pse7j9+lbRvxoOwanp0+IxJ578m70nM34jEqG5JtCfjqupDZ\nU0tmbeLRigdrkPR/J5Sbr1esupaz5RIfIkVeMBmNsEpTFiVN3+02Zdu+oNGaUS7m0FWRs2oaFp2j\nKCs2bYtT8rr3wWMSTSnPMkqtGGeGTEVcTDPYIQpJK/T4riXGgAdc25FXJXleUDcto+GAerPClBVV\nOaBeLRKtS8rXTV1TVQMUkXazonei0+gDYmNWrxnPDgl9Q9/KXKhzXja1ZYVS4GLElhV4jw6RdbPh\naP8QFyPGGopCsnq0Js8KqtCzsCWT2BHR5EXG1Ba8t1qgiozJeMz/+uf+J37DT/z4q7/m07/qfdfP\nfN9mmJPZ/l/6tu/9Eftrvv11JuMRm8c/RzMqiZslWfDUzmOzkug6WhQWIbLEbIBbzelqw50bezx7\n+oTxwFJNDwnOkVVD+iAZAFlGNJp8MKQLEWVyvHcENFFFGURXIobwUdO5bZoAbVBoa3AJAwZcBcq4\nnYtUOxPZmOgqoPA+JFuguFNcEqVcJbN+Mn8ZvGNvfMCnP/HdEAXsLFMnqUwZrlSJIL9vJ/wwmkDE\nbYev0/NqbcHr3/QpQgwsVnM29Ybbx7fp+5758pIYI4f7Nzg5e07btwI5KHJCEIJKm+YnN5smAagL\nXr73AYiRMq9YrjcUZY4K4lzhXSBLlp9VVbJcXKKtFcPnZk0sR/ioE/s2JiEFVKnkLiOn23Kk7Nit\nUcTgaZqG9XzO3sE+z56U9G3D5eWc0WiIRREThi3PslQS1Lx0a59Hzy8xyqK8oMwCAsnfm0woi4qi\ntIRNQ55nTKdjmralazvq9ZrMaN59+z1eevk+z5+d4V3LjVs3mc0m5MYSfEyFSVGebjcOu9LqV2hg\nGq56gSRPRgk06voea9f/1qkkunVP0eoq+CqVkHjp43YkqPeBpumYn885PD6i7zvG4xFWO1adZKRJ\nzJpEONK7vd5bBSnfbrO89FdKiN+1KiOZFd5yuihlFjNt6vq+p8hzYoS96T4PHr/L6fkJdVOnWdxt\nlUWlkSoHSlG3TRrt0QISSX9r13VyjV4Lntu+oah3Y6q0XGtzpBGsru9Z1xsGpYjbxoMKHRX7Q1FJ\n916usVW9wdgxi64jVzBWmqAMq87hQotSWrjEQSzTrIlsmppBVWGsZd725MaicFgjDY1N2xPLgt6L\nrRnGkw9HaK3Z1A0mz6jGYtzdOodrO6YzmSEfD8qdEcGTp0+4fes2RXVEvalZNR1921IWGV3dYqzF\n5iXz+QkueibjGZfnJ4QYKIdDfL2hrmtKYzAxoJuetm/Aa/p0vsPRmM1mTTCGSZWz2LTEaKiKjPly\nwQdv3uXhasWzZsVHPv1JvvB3fpa33vr8D33Tt7/+Y//wb/7d91XQfF9mmEqpf3FyePN3/lt/4A8w\nHRYsTt7meG/I/OkDDFEGgZXhcDqhcz7tgmoaJzzFykTm8zmHh4eMhxllmeO9x2YZOsuEvag1xWgM\nStO0HdpYlMloXc+qqXFBBp19jHgFjZNFJyggLUIr54UjyVWw3JZItTbSx1OJrpLe0BEBD8RwpQyM\nIdB7nwykZczEKMV3fvy7ee2lj7I1jL5eycuN3mUtWsVrmaSU1ere0zqhg2z7YrJsBaH8pMc9uzzl\n4dP3ePjsATFEjg4OeffRO7T9hszmaK3Iizz1ryRjKLIB5xeXDKuK+7dfYjqZ0jlBhC2WC5q6JXiH\nzTK86xgPhzjvaDdrXFMzrEqUzaimh2TlUEYYQsCFuMswQc63dUlVrMCkzUvf1kTX8/TRY4ZVxXy5\n4vadm1wu1lTJWkwhfS4JJOI4UWWWMrcMiwKFpipzEZpYvUMNFkUmJdsg/brhsGS92nBx9hwfFKPR\ngM4FLi8viL5nOBqjlGZxOWc0GhG0ousDbdrJu3CVXfpdOT29FkocaZRObjFbFN3ORYRrjiRXQXEb\nTKwWg+HMyCYpM6KCzVKGqfX2Pgnh5WhIVZUMRiOi90yrjDIBGnr/YgaPuspOt/8ZRA2sfcfF+Tnn\n5+dMp3vpZ66qK1Wukxfs1Xn/QtVvygqNJctzDvcOWW/Wyf3jit2LQvrgyYpuW0mJCLpR0I5p4xhE\nbAQRl2amvZeMD5UgBNc0BKTf0TSNWI7VNat6w6LZ4JL45Xh/n3FVsdnUgu5zPUEpNm3H5XpF7z19\n7+T9EYO42GhDUQhoo3cdnZPre1DkaCJVZjgcZnTLBaU1bFyPTdQdMafPyYqcPBOl7snZGbOJQN8P\n9vaJMbJZb5hO91jMLykHAxFk5RVd14hqNjis1eKxm+fkWUHve0azqeAljaHrO/JMnFSKPCf2PUSP\nd4EiN2Ta0PadzGAred8FFBnwIGqmXc2i7xlohekd4zxjEQMvf+NH+B//iz/Nr/vxH/v1v/Y7vueP\n/r+LBv//Hu+7gKmUvlsNx3/99/37/6X+2P0Z3jkGLKgyRWhq8rLErVfktqBzHYvFpTT8rbzxLZ4O\nQ9v1zCpZXEIUBWWW57gYBfisBDbtUDS9I2iNzUesNhva3pHnAg/vA/RB03p2u360Yulk/nC7z95m\nlkpJtiLcTvOC6tB7v92Ev5CJaiOYrpjYda9/4y/n9W/45ZTFALgKtJB6ozGSG4UyitKKS0SI0LhA\n00svbScBurZAPD9/ys989m+Bivz8Gz/Ho+cPWG2W9H3PK/c+AASqakhmxSqodw3GWMmUQsBmBbdv\n3Gc2kdnI1z74GsPhCOd6Hjx4IIG/77l/9y7e9wwGA4wRO6iuaQhNK4rZasBo7xBl/m/q3jxGty09\n6/utYc/fVMM5dYY7dt/utptu221327FNcEwTIrAUxGTZSYCACBAZZVCchD8CCokIIAEiQiEoiqIQ\nkAJSFAQJiUGQhAgcOzTttrvd19333r7DOfeMNX3zntZa+eNde1fVtU0SCejr3aquU3Wr6tvf3muv\n932f93mfJxGlpRhQBiFv5z2d81SZYRdl2jRSWeaJ5cn774+C1bePb+F6T9d5yrzAKjtu8INMXJlZ\nZlWG97Cve9JM2M7eBbTVI6xJQOQPic4eWnF+dkFZlrx8/55UDEkyyu8Zm2CTlDLPSLMUmyYEpWmd\n3AN3PVi6od8Zxk1bhyhRF4OlVEJDkLzuy6mvgoiWBMAYuR6J0aRW2OIirB57meb67ChcPn0CQFGW\ndG3HwXyC05ZN40iMpsokCXThKsBdD27Dh1bCUFZdy4OHD2ROMM3l2VBBkITEsu/cL4JiJdAL4ap3\nPbt6R9/3HC2OeOfhO6zWlyxmC1575eMA7JodAG3XogdmdnwW2q5juJRD9RgIo5yhNqL/7L0XOFfp\nUQ4yS6+MAQbyXQh+rEDLogSt2DU156sVvXeUec7RdIY1CakVZZxd20hypgRJMlqP10zG2hRZmqEj\nzN52LYeTCt9JBVilluA8PRqdptjgSbNEkCKb0DU1u20t6zFNmU5mbNZrlE1p2xqbJOx3W8pqwq4W\nycim7VHtHpsm1Ps9WZljbEZXb+m6DpUkGDR910WoX6MU2DTDNS0BQS0UUgAoH8S9JQq4uL4jtZb9\ndkOVF4SmZdf33J0dUBnYt45sWmGLnJ/8a3+LB9uzf/6Hfu2/9Ff+yB/+w+6fRHz4p338ioJklVJq\nMp3+t9/3G3/EfPv9hMX9b+HhF/8mL3z0I5w+eIv5dIJWhiwryLKM9XpF03U8fvacRVVycOsOb7/3\nHkfzkpO5kEjEpgdslhFMVNyQXQqnNfump3WB4ALF5GrxE8Jo8NqKeA8A+76XnuW1cZB47nFj0yM0\nNmTqw7A3A+w1VJbXoFS0IrUpTdvy6On73Dt+gcGr8nqw1BoKY6RwAdEuAAAgAElEQVRH1Hm2jR/h\nr/Fc5Ke5/hUqcPfoDl/VX+bd99+WB8BaDuczQoDj+TGXqyVZWpAlBft6x5PnT8bs3mrLJ1/+BIOB\n9WuvvDbeN2MML7/0MqC4dXzC1978BabTGXmSoKzB9Z38XpLQerg1PyIEge36IaB4mX8bSDIAdaLj\niEAY9VL7vkNby9ffeJOj+QGud8zKShRUhKElSIKGLE+YlJmwqbueLDNo66i7QJVaEh3olcjJuRBI\nQiALaQxygkJMJxNSbdhsa/Iso97tcX3PdDZjt9sL2ccrgmtR2z3FfD6aL7ugaLpBIEDF/mSskmBM\nwFQMlsIslns3SuLpYR1xjR0scKtVg8elVJbWqNGqbahktVacPXvKbDFlt5PqrapyfFDsGmH+b70T\n4+/UkCawa3qxJbvRIxwAWLBJjtMbDg4O2azXTKfzaEWnyBJNEw3Kb/bUr8hLSht0EF1iZxxvvPsG\n3/LRb8H5j5EkCV/5+lc4PX8uwd9aceDonUDFREH1MXAKY3wgInkv7Q4XgpBrgqwp0UMOY4ActJv1\ntedd3D4M+7pmWlWjY4tUnnuUgirNeOHWMVlyC6UU67oWCbqI2AzXTCrOQN215CYh1dDuO7bbLbM8\noWlaZpOUvYJN59A6MFMaqwx4EXjYXq5oupb54SFWa0Lf0bYdddNI8mYMk8kEXMd8NqULWqQntWK7\n3VAWGfV+x/z4LsuLC1Kb0KxWTGYLgutpmxab5+hoK5imKS44cGKthgsoHcitEOGUd2ht2CwvOZpM\n6AO09Q6KlL7ZMT++zSvbHV/ren7gN3yeL/9fX+DsnXc/X1TlHHj+/yUGfLOPX1EBMysm/2ExP/78\nH/yDP4btVizf+QJ3Tm7hmhqbWJr9ntXlir7vOT4+JEkzTu7OaOoWpxJWuz33bh/Qti3KaGyaSsZk\nNMEo2bRDEEk8rWmdDPB6bbFWzF3brpM5wd6zbT19bD52PrD3HhfxTWMGofRYLWjZ7IZKhRjc+rhz\nDdCscz6yY6M4QZCqMrEWF+SBfXL6iF29o8grgZkIo0KLi0HcNz5CPTdoJIB0lQYAFuS1zy6e8+DJ\n28L4BBluL3KCc9y59QLWpsync6yWJfPS3Ve4d/ICp+fP6fqek+M7AkVzxZJsmoaLi3MWBwes1ivK\nomBf76nKAu96uuCw2rBarynzgslkRrU4QGkr5x4toZwLAkn7aFgcN/vlruOgSljXDqXlfWltePmV\nl9ms1lxeXtC7e4ChbaNCkgJUYD4rSayh95LtJFbEJ9AG6wJN7yhyi4rkKq2twPRR9ch5ga3LNEEp\nRdN2NG1H8BL8t+sVm92OoqxYrZbs93tc7zm570gnM1JrcF5hjYsbs78KksO6if1CDWNlqHWcZRz7\nmteg2mtB0ypGM+hksO6KQuxDYuZ8oOslGDb7WmaTE41zPTt3TQQsyM+tnCe1imme0DnPvnVD413W\neIx5QUGWZcynUzbbnYw49ZIQZkazrvsboykqNkUHcXutAru6YTqZROLRlJ/84j/g9tEJaZKxXF2O\n14GY9DofUEZk55QWq7qua4Zmq8xReo/re/oQOQXxwjnnCaETGDsGx+tw7WiBF5WZBlUtjWgpqyDP\noPeedb3j7cePSKyl63tuzWZobVjvd2x2Ozk/FRPlgSVvxHloPptRbzecbRyL6YTaw6b1tBhC65gW\nCU3wHBQlp+dnaLQYou9rDmdTjDZMZlN2q0ta1zG1FVleCIrmPUp58smU/XpJVVSsVpcU0wrX9SjX\n412P1oagNPt9A/QMlDpNIEkT6rV4CO/rfdQphuA9eZ6z32zx7Z4qzbi8uMSkGQfTKaugILiRaXwr\nMfRlxb/+7/4B/uS//R/xY3/sDz375Ld95nNf/bmf+cL/z5Dwz/z4FQPJKqVuJ4n9G3/oT/6X5iN3\nD8AHTPOMKi958ObXOZhPWa9WaKO5fXIijW2refh8Re88ptuSJwqMQAzGikuJVxCUIsTMG6Pw8QFs\nfGDXCbEnSXP2bcdyW7PvA41XeDSOQB0CTYSrRKTakiYihG6MwSgdVUTCSMKI5cHI8AxS/OBCZH76\n6IkZSSHW2Dgm4sizkk+88q1YrUa1lt55dq2j7iUBlF7lTVIJXO8RXWctwvnqjKenT2iaRhQ8orPC\nvJpx+/glQGzFrqTORHy9LCZMq9m4+ShgtVqSJilJklBVExKbAIHL5SWXF2fijOJEGzRNEzSa3X7H\n0ck9islMrJm8p3Werhed3DYGzy5CmTKcDlUmTF3PQG4RJunJ3RO8czx++JCT2yciQRgcVZUznWQE\n5G9rGF08jGL0fZTNF7JUj0P2KlaCg1B5khrS1MbKT9P1nrrpePjwAcZYXn7xHgcHC/IyI00yqqpg\ncXwY60g1jpqI1FqsfsIVsqAUYx/SDr6a6pqhs44qQvqK4DMKE0QFn0G5x2o1fgwkoEGlKC0LqqLi\ncFGitWXbjRo+VwBE/NIHaHsRxShTG1sA0kW/3kNVweO7lm1Tc3B4DATyROMCMYlT13IChfc9P/eV\nL3FweMjjJ4/4xntv8v7jh7x07yWqouLp2VPWmzXr7eVoKTfArNKzlL5k8H7sYw6cAdH+DVH0ww+c\nKSCSmIJs+hBJOdZK4FTi5qGNwaDIkkR0o42JbNLoJ6sHMpKcy6Qocd6z2m1Z73fUbUOVFxzNZuRp\nFpNhNybDIbJ6294xn1aAaE4bYNv2NA6C0jQelEmYmMB2tSIpclyaMJlOSTNhNW/Xa+YHC1ys4nvX\noZWmaVqUNqw2NdNJxbNnT5lPSnzfk8+mZCbBqkBbN6yamnv37vONN95kcnhAmuURuTAijeeiELu1\nBC9iFq4Xw+u8LCH0WJvQd8I69mVFu1oxnc/Y6ZQ7RcKT7Y5XT45RRcHf+at/g09816/a/Ohv/u1/\n+59QuPindvyKCJhKKbU4uv2FX/0bf/jkR37Tr0MrTX/+BtPJhEJrjOpxzlGWJbP5jGazxqrAcrkm\n0Z5UOybzGUVVxT6hqO5jLcoaQswqMQa0wSlFF2DbRbgVSNKSi82OzgdczOwa5+iUjI4EpArUxggF\nJ1z5WVoFNsJrsrnHBx5xt7iaaRvUfaKqTxQnF4NncV5XwOc/93lmRYFW0HSeXeOEEANx1lI0TIVA\nFK9hzOm1XNCxGpDeF8ync168+wq3Dm9z6+iEs8sz0tRy6+geeVbGqu5mtRpiNa3GHUjeSZ7lN/pa\n5xdneO85vziXKk6ZON6QYFDU9RZjUg4ODjBJnI9znrZztL2j6T1d78cq08cSUyvxCj0oU9peoLfB\np1FFfO7JkycyrpIlTKsUpTy7usMaQ5ZYtBUPQNnoxfVFoUY5Me8hSwx5rLxNhDdRUDctbdezr0Vh\npnMOh6esKvKyYruvubhYQggc3zrC+RDHNa6qt2HEpO2H9xavc6wsxVHESOCzEjhHtqtWEXYdepVm\nJPiIaL4lS8wIyxp9vd+px+tkjWZaJDgP28ZdRZN/zNF7CXxFasiMWMsJxKtpm5rQtaw3O/ZNw61b\ntyF4ytSwaa4JdVy7DlobFvMF+/2Op8+e0rYNEHjw6D201nzs1Y9jtGI6mTKfzrBGLLSIr+m9qE7N\nqhmfeu1THEwO4vjFli5KuPV9L8HPmGj+rGU6xxphpUdSkMC6ZpSqHMyrtZYkdCATJUkywtE+BPlb\nSjEtK2HaOiEWtV1H3bWiy6o0h/M501JmXJ3vR9adDjLX7Z0IdFRlTttJ77GLEoLOw8GkwHUdffBM\ny5L1fk+RZzSXS+q+Iy+lJWWtHfujEOi6liJLaJs988UC5TtJjNOctg90exF16PqedDZnXha0wZFm\naewThNjTFEJa14pFoNIKo4QE1/U92gpBSCWG1KbUyyXV3btst2umB0eEriPV4POSbDHlCz/5D7l1\ncvy9f+N//nvr3/abfuhDLWjwK0V8/YeTovqWf+N3/Fay2QkPv/bTmOAobYqhpShL8jThybNTTp8+\nQ+HZ7mtmswmHB3Mm0yllNaEP0h8RNmwq2o/agjIobQW20ZLdOTStE5JJ68DrlDq6svcBWqBR4qmH\nFnd6DQNNT+IvMfMOASWMFQlUcVp86FVdWXgF+t7HcYOAGQkCUCYZtxeHvHbvZSZ5xabp2TRSdclv\nXgXL4TSux7dwY9T9CoqNqJ+ci1bMqhlt1wpdPxjm1XE83eEnb/ZZGc+dGy+olKLrOn76Cz9F73re\nf/wQ53rSJGWWpdw7OMBay/L8DLxcp7Zp6J2jcSEySYVN2sTA2fUyBtM7HxnD0ufsgtinqaCuF9OU\nVUlaTMhSy6TM2Gxr1psa5zpc38dr5CA4vO9wroNIBhqJQUrjerlZRWYwSmyXrFEobURfF2hbB4hw\neFmWMTAl4FqqIufp46dM51PR5zWQpxKws2vV382enqwjkasT9m9qDEnsRVot2qdZYshSYffmmaXM\nUoo0ocgsaaKvBUs9VqSDabRW4pgyLyzbtmdTuxj4rt3T4bZfu8ODX6vzgU3jaH1gkluKVHwzXd+D\n97RtQ2ItSgXy1EQdWmKgHFf+yGItqwmTasp6sx77jX3f88Y7b/DOg3eYzw74yEuv8dorn+DbvvUz\nHM2PpL8Wb/hiuuBjL3+Cr7zxc/zs136WB4/e4dMf+3ZSk3Lv1gtoY0Z5PaMNOokJc7hSzxpGsZq2\nufYsBfqoXTugETAYqA8mCXA1WT08j1fPZt/3EAKbes+DZ095fnFBlqS8cPsOx/MDUmtBazrnmFYl\nBM/peo8Pml0nPfoQghjCo5lNJyxXG7Iso9lu0TG5nhweYLOCLEtJreXs7JwklWCfJimXp0+ZTSp8\nu0MZQUvWy3Mm0ymnyx27rqNILJfnZ8zu3BWx+EbmTp1z9MGjEoMKgmJ1bReTL4PvW1bnp2xXlyIs\nHzVyjw4P2Dx7SqoMxirOvWLe9WxXF7xw/z4/9uM/xt/96z/B8afv/mk+5MeHvsJUSi2KavpTP/4f\n/0n9iU9+ms3zdzkqPYuyjFY5LU3TELTmcHGI9j0eDyYRbcne4YCsyOlc7LkYg84yEROI2XpQ0HtP\nQLPvPZvWCVEkKLxOmeQZKnSkScK27+NsnsVqJTqMUTdSaYGdhhAzbA0DGWOEQSOM6GI/ZYTjIDqV\nCIlhVpTcOTwiTRIuN2usrThc3Lkp0D6+2tVDamLlKBXm1a4XUUW0Vrz/9AE/+7Uvcr485/bhCUZr\n1ts1X/qFn+H+7Rf4+KufRGkdx1WGYKiG+zJ+bwjq55dnvPf+Ozx6/D7nF+fcvnWbo8NDzs7O2O62\nlEWB63oKwcTZty0meGyaMT04wuSTyEr2NJ2j6V3sZQ4wbGQNx75zag1ZhNYXuY2bfVSz0Vpmb7sW\nY1PqwbwZcblPrDjLWDM4zAzX77pUoeztWkueHjwk9qrvJdKEPa6TiqDtrxi7IQRccNJbrztCcPS9\njBX0Xc/jh49R3mHSlLoT2LnzHu8itI9Iu6VWk6U2EneGQKfG80oiMzhNrIioJ2YUJrDXRkusjUzf\nKHBgtGJWSMBe1T39MDZyDa6E2KJU6mbgVNz4wgUxAE+NpkgN2lhJfqKLz52TW0I0iew4ed7U1apV\nVyiLTVIO5gtOz0+lAo5V8Xq74fGzR3zjvbd4fvYcoxUffeU18jxnuV7ifU/Xdbxw5yUePH43smYV\nD56+x3wy5/zyjKZtx2SPAZbm6j0JefVK9OA6sz1PUkk09ZXryvC8KX2VeA6QbNN3NFGWzyjprSZJ\nIjOkSuG8Y1vv2e72GKs5ms0psxxFoO16Ug0qCHJUphbXN7Re7v3xrGS7XpFqEXOvm44sSUQIwzmy\nMkcbjVaG58+ekZcVRHnDLEvRVsf1r8mKCY+fnXLnxY9Qr5c0Tc1kOkEZTeM9mbX0rh/NGIKXhH5A\nqtpW0BrneopyyqoOWOVlHMn1BKXpmoasKtlva55vtyyOT3hW11TOsasbSAzTquR/+x/+J5558wO/\n9nu++y/+0tHgm3986CvMqqr+zOd+8Ifs935sTt/WFM1zSnykbouNVZblGG2ptxtc8KR5ibEWHxQq\nSzHWxGAYUIlFJQaHNNoDQrzpgwSX2nk2naf1mn0vriOTPCX4jrZtOd3ucUFRpJbciCfk2A8JRDag\nQLBpfNgZNqIgpBM79IWGae5rwU+88mAxnfLq7TtMioJny0vefvqY1WbHJz/y6TF0Xc9gw3W0FGHg\nmWsjK/EV40alRmH0PgoSbPYrlNJcrM/Js4Sjg6MIA18PlEOwlFe9Gi+A84vn/MKbX+VyuRRLsabh\nyeNHKKV49uw5VhvwgeAkgXl8fsbps6cs11uU0nhtRTqsDyPZp+1FVP7KkUIIHEozVk5WSyDbNm50\nrDcaJpkmuJ7tviNJ8nGMJ00zrDVXVUJcF+H6W1SxN6ZlsF+rAQaFrpfZyEmRUGSGSSG9o7oTqcCu\nlbm7tuvZbdZsVkuW6yWHx8dMiozVcs2bX/sGl2eXPHrwiP16TWKlWjRKCewV77BGRNIHtutA+tGx\nIk1iXzNPDFVqKWOlWWQJRWrJrInEjDizGHq0gjI1HFYJ3geWUeLw2jK8CircrJTGZRCGRG0QxkBY\nta0QgcrcUhUZ2hi6tiWzml03MGOv/syw/YRwLfHznul0zvd+7vs4Prw9wrzG6OgyY9jVa77y9a/w\nE//H/8q0mvF9n/l+fvB7fh0v33+VaTllNpkLeQWBei/WF+yafXwtqVq7tqNtGkEaIpNM+t9y9btO\nrLeSJCFL0lG4YbQzC9JHH1SCxucuVqHO9eO18sFHURE/zlsPz2/b91yuV7z//Bmr3ZZJUfHKnTtM\ny5JJkZMZzdEk4fYkBdfROc/ZakMXAlWesNvvOD09Ja+m7JdCLvMhsK+b6DOq0VYIh0maxt6po+57\nmqZls16TJJrL8zOmB8eYNKWsKtr9niovaLc7mlqSn0HkwQcvnIUAaSqyg+1+z/uPHpNPZjRtx75u\n0GmF71q6ztOuVmRljtptxIkmL3n59h2mRkiTP/iDv5qmafjSl//+D5bVZMKH9PhQV5hKqe9Ii8lf\n+GN/5s9z6yOf4fwbP8WUBktgdnQL127xIbC+XFJv1iSpOIlg7FhFaZtgkwxvZM4KrVHG4NGj20ZA\nZNf6AHunaXpoPYAmSwKTsmTXtJy3Ipadxn5DEvse47Sl0lFFRWDPK20WCZRXm09AG0vvrzQ5B/WS\nRTXheDanc47Hl+dcbjfsm5rgA5/95Pcwm8qMYxioolxVl/GaMczjJUaYvlf/LX6WU2U2nfPinZe5\nf/IiVTGFEOjaFptk3D66c6PXJpXBkJVfjTIMr5wmKW3bSJZ/9wWOD4+4c+cOP//6V7GJEYgoEeH2\noigpsgTfdRwcHuK15tbJXdrITr0Ow/aeUZx86NsliYmVlBllBX2APDPMckuRWHZ1x3sPn1IWpbhP\naIVRBpuKspK1OnoqqiggYSJL0o4VqlIhzjzGoOBFHq53Htd7kZrLLC6IMsuwqQwf9W5Pkqbcu3Ob\n/WZH3XR0Xc92KyMIt+6dkE2mUqF5H1nZfgweRhmyVAynrYnBL5K0THQaSa0WODY15InFmoHsExmf\nMI6elIlhUQozfF33I5w/9G1/KRT2quq8QjA+OA4yfC1rXNG5QL3bcftwQp5YTi+WpHk5/i0+8PtX\nhLSr9auU4nB+yH67Y7vbjut3mNUczuPdR+9yfnmG0pqX771CIPDCyQtMqzkHs0OarqZ3LSEo0jSJ\nz96QMCl676QHp2MfE0axg+E8sjQd0YhBQMSHoRQPI2o0JBG3Dw653KxvGiuEMJpED68xVKoSYGXt\n7Pbis3v/cE6eyDOT0mNjG8B3LQfzObQ1VV6yWi2xWc5sUoILXGzWVJOpWOK1jtPzM2YHB2gtJvCr\n5YqyrCiLivV2T3COajqjmMx59OgBxwdzNusVJstoes+kKDh99pSD+QHNfkeiJQlK4rPcty3BeZrW\nMzk84XTTRNZtTXA92qSQFlw8f45F4YucThvScsJX3n/I/dmEMktZofjOj73GX/oL/x1/5L/683/w\n+z/1bX+CD+HxoQ2YSik1Wxx98Ud+378z/dy3fZyyKghn30DVexbHJ3jvaJsd2+US13fk0wk2lUxQ\nGYN3HpMkmCRBpRZlZL7SK/CI0ojzItfdBR83Lc2mC6Iegya1CGyRFzzb7kVSSw3ZujwwLnhSa+lD\niKScIBWkNhDEOFkHP0KwBI9DxkbQErQTazmYTJmXUzb1jqeXF6zrPb0TGNF1PS/efplPvPJJrie0\ngehGcr3JNFw/ILOGpr8u8D4EgGHzGxSH7BgAy6IUe7A4BqOQ2caAl5GS4EeW7QA3D7Nqt45u88Ld\n++zrPfu9nP+z58/I0pQkzstNy5K+72m2W7EiSjOcMqRFSe+h7iRYNp2TwOSulHx0DJZF7P1JP0+C\nSJ5oJpmlyAxNF/iZn32dNEkp0pTEWIo8i+MGcZZ2SCqskZERJWzTYbJ1SHeGJEd0XkUtZtD07bo+\n2pulzKoM56XnKutCCD7ei0j+5fkZrut4+OQpRVHwkY9/hHwyiUo/ws5s+0Dr/EjU0iqMLNfERkEC\nJcE8sRIYEyMBtUhsVPPRLC/OpMI0QsBZTAomuUEbzb4V8Yobijoj5Hqtj6quJVhDoLuemHEFqYb4\nM7GdD8iYTV/vmVY5Dx89YTafY2wyrlsYQo38Sw/XenwVOZf5bMbjZ48jPOpRKoxQqY5D9Zvdhidn\nT3jjva/z5OwJX3/na7xy/1XuHt/lxbsv8+T0Mb1vMXHsROs4a60i5ByEqTyIC3jnx2sxvIYZCD/+\nykBhuBr6WjLhCRzPFpwuL8drFRg4CwGbCEN8uK59341/R4Kn4Xia0+9WZMqRKs2kFKa5329QeC72\nLfdvH/P0yWMOqkqCcZ5R5SXL1SVpUYhvbtfSO8dkPhXeRnAoI0+1MYa2bdBZwtGtE3arFc47ZrMp\nu92erCwISmG8KH+VZUlf19g0FTsx70gSi9GGtus5Ojjk+cWKtFrgQ2C7uiDLCsrplMuLC7xS1PUe\n43pUZiGbkBc523pP1XaUZY5aTHn8/mN+4ee/nP31v/m32x/9Lb/l7/+ije2bfHxoIVml1O+Y37p7\n94d/629CmZTTL/0tknpLliaU8zlts8H1PWleUB7MyfIc7QPGSi/LJBashcSCMdIvVJrOQdM7Ou8J\nGtogfcouaLog2XFQMgIyapeqhNY5dBACz6AW0keB9NE1I+p9GqWFKTtklEoxFHo+usQHrbHWcjxf\ncGs+Z9c0vPv0CWdrWbhDr8MaQ5ZmfOcnPzfCX8NxvbL84OED43weSMX7y/80MNTJw64VpObY7Fa8\n8e7P8/pbX+Ln3vhHPD19PO6v8T5dfY7v//jgmIPFAe89fEiaZmS5KL30fUfXNuw2a/quJcsLqvkh\nB4dHBGXoIlu06V0ULLhKDoTNqcisBIh0UEtS0otblCmt86zrHnwnPpdpggaKMsWmlqLMKHLxBhwH\n+ON5G30z7bi+iQ2i6CFCUkRGZAiKtgts6x5rFAfTgpPDCbNJTlWkGC3SeQRPUVVcrne8+OILfOJX\nfRydZvR+qCTlPptrvTC5FxJ4Rz9N5J4msVeZRSm/1ArZZrde0mzXZNbgmprQ7shpKTIJeOu9VJU3\n1oEaru6193/jC65HuDHIhlhNXiftDOsOIEtlbvitB0+xSYKlJ0+joXb8TTViM1eWYSpe56HKtknG\ntJyOBJs+VuAm9p4VitSKp2meZuzrHUor/tFX/yE/8wv/CIDPfMtnKbLJOOYFwoJNjBVnjjj65bs+\nBssY5GIv00WzgwGGDdfOE3+l96yMqAQ1XTuuHRAUakSDBjjXi5G6IBvm6llPLOttQxu0oGDtjmeP\n3hUvy9kxd07uMreB89Wak/v3QFnoOy4vl6RFSho8zW4riXlU63Fdh8fTK0VWTUgzcUeZHx7gggaj\nCcrggqJrRWWoaxpJSBMrSatzozDHpJLg6VxP13fkRUJQnuM799htllgVWMzntEHR2pKDkxe4df9V\nkmxK33SY1RrfbjDGsvKekCbkITBPNL/5d/0wX/p7P8VHP/vJ/+yX3qe+uceHssJUSi2q2cFP/vh/\n8qfUi69+jGdf/bscakee52AMSZbS9q1UR4klTVNCdK4YVHp0YvBG2FsBxvmv1kkVMJBzmn7oXQpV\nftt5EYNWwk7cOU1ZTVhvNiNkJJm0Hh8opbgSd5Y4iUGqWanotFSC8WeSJGMxnTHJc5bbDefrNdvY\nexjm2oYKRwF3Du5y7/aLXHe1HPtKQY372QePzIoMmyKg1BV0NBzL9QWX6zOqvBL4NW4Ow/vxwfHs\n/BG9r+l7R9O2nBzdocjL4T7JNVcqumXI39/utrz59pukiY1VoAHnSWxCW+9xTc3R8W2yakpQRsTt\nXaDueurO0fY9fRRdCEogxcSIaECZJkJ+MZoiMRxOxI+w7aUq8N5zfn7J0bwiOAVe5ugSq8lzizUS\noMSzUSDYQRnnBlLI0FuT/lxAorcnjEStgEcpTZpY8LDbC0kptZppmZGkhrapsanl5N49FkcHTGYz\nvNIyTxoYGdHOQ9v27DonM59IUDJxHSaRtGOMiErkqVTZ6dDLVRBcR71eonxLkees1xvWTYfJJuSp\nHsXeB/GGkSWtED9PZHld16eVb+nxX/hYkKqbP3P1hfRX51XBvvVs1ktWyzXnl0vu3L5FniVyX4cE\nYLjWfIBWdi0Ru3Nyl/l0zrScsttvcf5KRW2wvwv42OuU8ZG+71nv11RFxa2D27xy71Xefv8tOteP\nijsQ0SItDZShqkySJLYb1AjN+nD19HnC6Lk5ZHQq9jjzNEUrzabej78LjOSpgUyUWkE6EisqOgpG\npEcrza7tON029MoyX0x4+OyMoDxZlrKYLrA2YX12KrKLxnC5umB2cMi0mHD6+DGp1uw2W1TwJNoI\nwzX6exqbktiErtnT1k0Uv9cYm6K6PV0vHruT+YJuX6OVxw0m880AACAASURBVBiD7zsRQIhiGPu6\nxpqEvu15frZk2wYmi0PqLrBZXkS/2YI+envqfIILClfv2LuW+eEJ5/sdWzTT7ZajxYJJqtGTKT/x\nV/8aB69+6nd9+8de/c9/me3tm3J8KCvMyWz+Fz/z/Z/X3/3Z7+TszZ/khYM5eZZhjGZycMh+v6Pe\n7aj7FrSia9pRH9LHB8Bri0OCovPCYGydjxY6mq5XtF7R+sDeQ+tg30uwHLQ4awdeG5mVUuAVOKXk\nI4iZ7iiaHjxWy4iARpxAEnU1M6eBIk05OTxmMZ2y3G55cPqc9W43wjzOD7N4w4Ysz+OdW/dvEAuu\nCBm/XKiUw/kQxxW4ESy11jx48g5f/OpPs91vGOAvqw1vvvNVmmaHAtIko+8cq/WG5WZDVUzYbLfj\nHNo7D9/hSz//M7z+5uu89fabfPVrr/P2e2/z+htfi44pSnREEQWj9XIppAOlwaacX67FtsiLRusw\nb+mciKzLhiqEmNTq2MuTDXmRJyzKhLYTMQOjZXN7/72HtG0PGBbzgjzPRBsYMfJ2vYx/WJuQp0kM\nQgIIDv3l6yWVH74XA6ePg/Hey+C6cx6jPdtaZOQ0iqb1rDYtm22Dc5BaDd2eg3lFUSQYNUCXYYQ+\n4/59s7CLhJEuaucK6ULW3CRLWBQp09xwUKXMEkeVJUxmM3ad5613H/Lw6XOqqsIoxb5xZMqJvcsQ\nGa+hsUOQ1B+sLseVOK7ImwSzD/ysVlBmmm3bk5YVk8UBDhHseOvtBzRtzzS3FIm5gry5WdR+cFV7\nH5jPDrl/90W+81Of5TAKumulUUZFBShP27Vj9SnXL/Bzb3yJ5eYSozSvvfhxCFzNUwaHj+/fRrs3\nExmfwPgcXt0PgWS10mMAHUdJ4j6QmoS27+O1uWLaDoSZPlrgNZEkFkBmfiN5sHeOddNwuatpned8\n1/D6kxWNKdgHy/OLFdtmh1aBuy++SpJPcEHGzp6fPqfrW/KiIktSsmLGgweP6JsGV9c0dUOWFvT1\njudPn6JtzvnZJbvzM1zXYKyhmB3x7ruP0MZQNy0mL6TqRuGdQwXHdrkihKiPm2iqqkRlFRgLKKqy\nIJse8nxdo21CGzR17PlPD24xufUS4fSc3XbFrCjZdA2PjWV9eooOgX/x1/8LtG3L//IT//397/qe\n7/81H1yN38zjQ1dhKqW+LcnKP/sn/vgfBZNwkm7plxe4vkMXGYe373F5/hxjDXlZ4hoxIh56lLbI\n6XxAW0vbdRBkXKRHqpDOQ1CiDdo5EU53XtF56JUQJTQx41fim5hYw66uUUqPs5GiTuJJYlYYlALn\nMKix/+e8gxBIbMLBbC5Zf73n6fKSpmulh+EcBK7cSOJ1GHplSmm+9ZVPYWzCoAJzncF4ffj7xnUE\n0Q6NcPAwyjIEzflkzkdf+jiH86PYg1T0rqcsJmz329jfS5lWM7quZ7fdcHF5yWqzpMhy0PBzr/8s\nIMbJ6/WKfdOw2qwxcXDeWIGajJUemzaGzWZLXk1J8oJ7L76EsilN56k7F6tLGSPx8ZytViSJzBvm\niWaSW45mOVpD3fnRFFkrxfnZGXjF4WxGYi1VmTObFHRRxWW0vTJG1HqQ/qCGOJBtblQ5gxDE9QRl\ngCAHpaE0NTS1o+5kI9RKjKG73rHa7Thfrjk+uU1aVmNlZbT0H4tUPmdxXAQEci0Sw8m8wBrFNDdM\ni3SEnQ+qjEWZkNLRNzvaphVXmdNLehdIywmTyYTbJyfM5gvKqhJYLhBJSiJrN8RMiZtX1dU/5rm8\nERxH/drh7yCoxDS37Fof5eYCVVnR1CJduVktCdqQ5qXMj1odCV3q5odSNzL5Kza2rKmT47u43vHp\nT3wbr9x/lcX8gHu37nGxOh91Wwf1n65vefT8fV648xJpkvHw6XtRYUeSoKE3eR3mldfUgphEAtBw\nHjfGSUIY37eIJ3jm1YS6aWj6TurecGXAMHwaXsM58bN1PoxKTQMLV85HRjk0wuY2SpNEW7uua8ms\nrMW8nAJK7AmN6NB2TYvVEvSLsuLZ01PSLCHJMt599xHlRM5zsThEKS1+s87TdXuqMkMXOfvGkWQ5\ni8MDQteixXOQ9XJFkiaIELshBEWTzOg6R5pnaKU4nBQU1ZRmsySZzOmjgtJiNqN3nqZpaZoNi6Pb\nrJs9SZJyYoW4lnY984+8xF/6c/+1+VP/zV/+3Z98+YU/+ssuzH/Gx4cqYCql1MHte6//hh/+ncX3\nfv5f5vIbP8XL04L16pysmlDOFtKc14GQJAQnskwY0X71KJRJ6LyLZsjiYtB7cGj2nR+hsG4QIHDQ\nBlHYEDKPBC2HGD/naQYK1k1DiG4i8iARPQVlpkrHfw+bvPOBLEmYz2aUZcV6t+N0vaLupCr20W1+\n6FcO0JzVZvSz7LuOb33lU5wc3v3gdboiYfwSm5yKkNvAlHWxb3W1GQwOGJrX3/oy5+tnlFlBnpUi\naWcTikxgV2ssZVERvGe5XpJlGRfLc07Pn5OkItCOMSgf7ZSUIsulsrMmwSK2T0mScOvWHRaHx6Rp\nQTWd0Xloe0/TefZdTxPhSIHrpGeZJpo8sRSp5WiSMy9T2s7hvBoH8o3WvPH1N9BBc3J4SJomZFkq\n4vqJjAB5JzBcmiTRqcOK2IRScZBfxi8Y1kCESUdvUWCoh3xkRluboAJs9p38fOz17Ouai9WK07Nz\ntArcuXMCSkUWsBeosu3Zto5901P3jqYLbOqOfScJ1K1ZwcW2xXnZLFsn16nuPY0LoA1PH75HnqRc\nXlyQT6bQ1pxdXJAmCUVZCitZXQn0Ox+oMkvrrvpwA/w5sp6H4PSBAPrBYKo+MLJkFEzyJCY9Q4CR\nas45x3q1JABpljGbzWh7ST7KTKpBF66kNT4Qm2/Av8PLHh4copXG2oSqrCjygkk54f2nD0XCEQhB\nRob6vud8ecYnXv1WtNG8//iBQLlKpO9CJLYMzNXxOiDJlbX2xmjN9YAsyyKMY0pHsxkXa3EwGXux\nA1oSIeMBcdJKjQnfQDySRFWk/EIQh6JJWRCixnTnQhS6EMMA63v63Rof4OjWCa53KKV45923OTqs\nODhYiMhAMcFahfKeJDUsFgcsL1ak1tAGzebiOfdfuM92dUlxsKBznr4FY+WZCZ3wAurNmqooaOpa\nCG9lyb5u2auCsqpEwCAENhdnTCYT6u2S49t3CUqRpCn77Zajo0Nar+jPL/BaEZKEvevpsoyibTiY\niJvJLzx8xBe/8JO882j72g9+32f/2i/a6L4Jx4dKfF1p/fvzslr8th/5V8lDzXGmWK0uRTE/S+mx\n5G1NLTVdfGil+eKDZNGCswdcpBV0HjonAXAYFRkUYlonjNUQB7yHvwOKFsYqp+tl8Xul8ErRuZ60\nF1fzIrU0TtH3kQkb9SEXkylZlnG53bJZLnGEUUVlYJt752IlORBKhH2pkIcpySvuHd8DJQ/dcIQQ\nRgo83HSLGKhASgmEos3NzW742wTPl7/+ZS42Z8zynL6vgTlKKfIsv3FfyrxkPluQFxllmaO1pet6\nUBrf9/RNNwakJEnGMQUdxJqszFKenZ6SlzOmswVpITOWnRtIPj1d70dyC0qk/azV5NYwKyzH05yg\nFLvGxZ5m9INUiu1mg9GWxWRC5x2FtSRJvNcejNUs5iV9j/REQyAxGq1FElET/UgJBCXsZfFEVaiR\neCQM2SFJsVqTZwnLTQ2A6x2uE7h3t6/Z1jUf/9irouvpRbfW+yvC2BDFXJSY27Y967rnctvinWeS\nJ5xv2qhNrKKfqUCyeWJER3c253J5iVKat7/xFscHR2TG8O577zE5P+fWrdsUVSUSgkqeh13rmOSW\n1b6/tqCu/eOaWtQQOG6g1PE/XA8gidFMMsOu6YkKbkLriVCkOLdsWExnnJ6f8fTpE27dPpHn0juK\nRCzIdk0/kobGV7q2tuUdKAkkQbHbb1jv1rz75B2yNGG/38XZQ6m2XHB450mThCzJePz8Ee8+elfg\nUSXPn8giChmHKPum1RWbFiQRvP7sXZ0Xka1rohatrMe6bePJy3rReuiRXxlWDwV6IDrwIFq1wXu8\nGgwBrEhqBk9mFSHuWWvAWgk+2XSKQdHs1pztd5ii4uD4hE9P57z39lvMJ14S2Mmcs6eP2PgtaZbz\n5P0nVLMF26Zje/GM2STHBUc1nfP49JzFfIH3DbMiZ3dxzn634/bJHfKiwkXoO0ms9Eizina9ReM4\nun2Hy/MLTFHS9g60pd3vyPOSphaBmdVyzcHBId12TbOrOZ7f5uF6yWnbcmCFhHWvLPg9v/df4z/4\n/T/Or/6BX/8DR8e3b5+dPnvGN/n40ARMpVRaFOUf/93/3n+q8ixj9Y1/wByHUpZqPkcnGakVA1WQ\nBSi/iBBSIlPVRLm61ntC0LQ9YsPletFp9I6gDF5pfGQlmrhZRqgeR8ArUUvJkpRdU6MHayUPJkk4\nXy6puhajKso8Y+c7gtLM8opJWXK+3fLw2TPAR93YK+LAMPDsYj/MBY+71pE00YuzqRtW25XMSI7h\n8do1u9FbGr6nxmw8EEktfS8bmHcsN5esdksePXtA3dRMJgVBBdKs5KqbdONFgEDftxwtFjSdECqA\nWOFrkkR0dYdgMrAGZ5OC3WpJ3bVMJjOm0xkuCLu4d9KvbMcREhfHXCDRokSSJZrjKmNaJuxbqRYk\nu5ZZ16au2W427LZ7JmlG73qSVPwUs8TI3G0QjUutFUUuAhYqKFSIziph6FOqq8pm8J9UCq8CfRCp\nN4mbss6yxIiyTxzgDs6LsMXZU3Q+Yb+vWRwsWK5WFGUVq9Jw4wZGVDBawV0xQ4c7O8QuH4hiDj7q\nzkrPrqwmKO9YbTZUszmtd2zrmtOzM1577eO8+cbXmM5m3HvxJYzSOKDrPamFPNHUnf9FgfDq8/Vz\nvR5Eb/5OajRlZtg0TnxWh2gQgpipR5Lbiy++yPnpc4wKXF5ecHh4iLUZIQS2bSRK5Qn7CMvfrG41\nA8u7bmreffQOy9UFxuoIfTqO8gobPF0vz2HnOlzTo7TilTuvstyuOFuest6tRiehQVZPxd6l9340\nk1ZhQGOuuYrEY/y5cPVeXQiUWc6urkdo3mj5WzoKIlw9TpGAd+O5hboRkXa59oFJnuH7jn3dYozY\nik3KnH3TiWSiTmgDXK5WTBPLZrXhwGrWp4/Y9YpPfvo7WC8vuHz+iPu377E6z8B7jEkopkf03tG7\nhrIsxWwgQJLlhCBuM0Z59hdn7HYtZxfnLG4dE0xCojWqqccKPAQhRoYQOD8/x7UdfS0iC4tqyma3\np10umR/dwnUimTlJEibHJ3Snz7Bti1WK1nue9op0uWZepHzXy/f5/G/+jfyPf+Uvvji/e/Rp4O/y\nTT4+NKSfanH051765Hctvuuf+35YPyTvHYvFgmIyIU9SkqwAPM7ocYG2Tmb1GhfYNx299zR9T+MC\nu6anDbB3njaIwXPvFV4ZgWS9kw1TyjEJpkoRlMAmidbgw9j4L404jCutSI3FphlbD8/WW9q2I01S\njucHaKV5cPqcx8tLWj8YZcaAiTxs3nmZ4YvGy0O/J1zLPjvnaPqGXbO7kfQPxw04bSAWqWviArGv\n4kKIM3yw2a342Te+yMOn3yBPDZOqIDiHNVbigNJj2L7qGynW2xXvP3uAD0HcB2JvNU0T8iwnz3OS\nJMFaO342BOg7bs1nKKPZbLdSkUZ/y9YFmlaIPkNvT6s4MpFaZrnl/kFJnkk1JDNq6sqiynuCkx5x\nVVQYmwg5IQRc79hstrRNG22NYiIRoEzlgTc2CkyoQY7Q4Xo3Ki0JNBvGGdNxjEFdydP1ncdqTZok\nJEkiIv5pRcDxue/+Dvq+p5pMIsEkml/fbGcR8HjPjZEhH8lA11tfPgR6L5Bu0zlpLSAmAV3v+OhH\nX+POnTtoo8iynDTPuPviiyiluDw/5/LiDBVEum/fOOkpq+EBkBU1/i9cC5ZKSaWkhjLzCi8tU/Fd\nXe9lc7x6T0NiOHx4UJam6+h8wBrNxeUFu/12rAjb3rNpejKrqTI7oiQyOywB5nJ1wdsPvs6uXtN1\nLV3fkaXiINI2LQtrOMhztOuoclEFcr3jwbMHtH3N24/eAkSiLs0ytDWR4GdGItvQHkGpUYAdYPTS\nvMZ8DTGwDSMvRZaK3GNcLyFcmzSNWq+jF24Yqk8R0ACBbJuuxTlHlqV0vZME1Xv20ZKw7R2ptWz3\nDT4otnXDSy++RGFTsiwlADpJWJ49Ybc6x6GYHd9ldfacPEvI8wxlNPv9FldvCF3N2XJJMZ2Q5QXO\nOSaTKXmWcDhfcLHcUS6OUEnKvu0oFwuarkEbST4xFtfssUp0urvec3B0RFKWeOdIqxmdTqibVmT6\n8gKTpGzrhiyxmCQlrDvypuN2UdIqmJU5rm4J6zU//Nt/iKdvP+BH/8C/+Xe++9f8uh/6f48k/3SP\nD0XAVEpNXdf+nt/++/59TmYl/vxd5rMZXd/S1g37fQ0uEJR4LCotZIHeeereUXsPRuMwdEE0K/e9\np/ae1oND4aMxdOsDTQj0EdoBaKPAwFhfRa1ErSQ4au/pe0emxc9vtd0wakimGeV0gclyVusVzzcr\nXBxlyZIUq41UkEH6W13v6IYKLb7/62xXo4WuPlgRFVl5s5/DB/pMXAue3OxTKmSTTozhweN3+T+/\n+L/TuZbee9bbDVlimVZTMYvt9vSupa73NwqKrm94/c2fJ7GJWAYpsb2yxjApK4G70ozZdMZ8OiU4\nR9/19F2H2++lHxMCr33s4/R9L8Gyk4ShcULwkcpSZN4yaziqEk7mOa3z7JorYo+NsnBds+eLP/1/\n8+C9B+x2LWWWMq0KkjQhSaz0JAWThViVSmav6VrEJssQh9eH6uUKGhdYfXCq8HHztCK+YA1FZqnb\njgH+ThKxe6q7Thw88oI3v/6WiFPEjdbHXpb01oiFSRgDypU0XLhKWob/97HlENmyorPrCSYjKys+\n+trHaZuad97+Bl3bUBTiiLJYHJLlOQ/ff0DT7K/IakEEIqpskHUc4EUiKquuLaorxZthXWgFsyLB\naMVy1+GvL5gPJgQBmYH2gdu374yaqufnp7zz3jsyzK4EWQjAthUB+HlhSbUwt7XSnC3PePDkXfHa\nTBKqaRVnF2UWt3aOZfDkWnGrKklCEIcZ79k3O9bbpQgGBElaif3VoRc7JEQD5Coemy6+h2sykHF8\najCfDiNBxzCfTGn7LmoPM/69oX95Q/Dgeo84Jm7Dzxhj6NqeumslKepE3MBGVaum74RZ7jxaG5rd\nBh2ckIbiTObi6Dbr9YbTJ4/Yrtccv/gRkrxicXjAZldTlQVN09D3HVWRxhZJRzWdst3sZIbbOQ7m\nJV//2uvMDqaYJMVrRVqUpFkqjF9gOp+RT2dUkwmz2VQ8QtOMMktZ1y0my5ncuoNOU+q2I7EpNs24\nvLhgMZ+T5CUTp9hstljneKdrZd31nhemM37n7/1R/vJ/8ef4V3737/23+CYfHwpItiirP/2Jz/4a\n+5nPfAeXb/495omBfkdSTTDGs+odVWLZ1zVJkuCco4vBpyfOGioNQViw0p5ReDeoecim1/QOHyWx\nhLAjThc+9kE1Ao/Z2O/pvQwrt75HA77vydIMpQ0qOO7Mj0is5Z1nz9i3LZlRKGNZTFJ0UdD2PQ7Z\ngFwflWu8PLQqVjCDVdWwgXZO+i7BOTSaLM1H4skveYRwo8qEwPvP3oMgAb93HbMy4Svf+BJ5nmKj\n44XRqQiI06OM4XJ1KsSBxQlXdD7Y1zW965hMCrpODJ9lDsugvAQz52BSVez3e46Pb7HbbEh8TxlF\nApIk4+zslMPb9+nafvR/dBGmHKrizGpuz3Iyq8UUWomzh9V6rC5Pn5+yW28oq5LFtEJrw3xaSWat\npfcpkJnGakMWvTiJFT4q0PRSdSdW0SvwTolpydisu5J5GCB7NGht0dEwWRupunzw7PcN+7ahblom\nkxLnWl756CsQ4Pz8knw6FTWfMSgT+5lX4gzOy/eu3+kQ+94mrsfgQ0RRHPumI0s002JGMJpnTx6S\nlxXK97TbLYPOazWZ8u3f/hl2u10Uz5ZasulFWDvRijZ4BmPIMFTdAyw5wPtxDQ4QbNOLYlAY1mA8\nY1nI8cvYw/NDxWYSPvKR13j67AlJkhK6nrfefgtjDIv5gSgYJZY8K/BlyfLyKZfrJacXK+6c3KVu\nGhIvlZtHBEYGeUurYNu11D4wsYYMuOw6tDHyftvuqlURAqEL2OT/Ie5NY23b0rO8Z4wx29Xu9nT3\nnNtXd6vKVYbCuEy5sIEQYkPigCsmCg4mBAhIiSKi/Eh+hSCFRGkgAREUKX8iJBMlipSGHwgpkWhF\n6wLbVeXq772n2/vsbrWzG01+fGPOtfa9twokXFXzat9z9tlr7bXWmGOMb3zv937vm5Aa2QJDhI9V\n7MnsDzHyOXbs9L61xMVg2RN5RMlK00QNWoH4+yqlXCbWRPfXav99n5drpWJ/Y0KmpOfYx9pn13Vo\nUgiKrmtpDDDKITGY8RgdLeJGZUFtA01TcefuXZaLJb/8D/4OD199nW3rOT094fnjd5iMCvLpmKLM\nxRM4eJq24fB4jq1qFKLLXTUtSZZTVy1lWZJkOcakXC0e47ynyDSzsuD84pLJ7IC668BZ1usV86OU\ncZkLQWl2QFXXZGVBmRQkWnN+fsbR4QFZcsTm2ddIRhOaZQOnJxTWstlu+Ynf+ll+8S//Hzy+fPd3\nvv6Rj336m7/25S9+5w3xe3v9wAOmUup0ND34Iz/7R/4kR0lFHmrpnSty6qahVZp8NMF6Kzi7c9HE\n1mO9pnZSMEd5cQrvPNYh4gUKFMIS7YJI4gXEY1Ak6sDGJFsHsEosuWBX6NdaQdBoJVCd9p5X792l\n7TxXqyXPry+FlacUJi9Jgse33S4AE+E0Z4cm6yFgQmwl2UmugbBjvXO8dO8VjmbHO+j4/WOHyPHt\nFuLTs3f4la//EgEG9/jX7r/EuMhprRUtVpOIqW5kBIbgudnccHBwh12tNLBeLbi6ecGoKNBK1GVW\nXc24KASyDmL0nJiU2XTOvTv3UAG+dPacxGguNmvOL6/ktDk/xkYjaLHnCrdO7pPUcDrP6Xxg1QjU\nmSQidG60FsEBrcE7uq7lo5/4OF/+pS/yGz/zaeqmJk0y0mgOnudyMDDEgRsyO3m9BPBWEIsk0Til\nYzM6sQVll8kHgnTVevk+TQzrTY0ikKcJzgfevTxj1VrWqzXlpCTViifvPOfO3RMmsynt4LQiI+sD\nUfEl1rO91Ch7aLq/esQjBGmf7CH91iqazlI1hkRrwHBy5z6r63PWqy6S1OR3FOUIH5A6ahABi772\ntm0co9zQ1WF4vf7e3yqPK1kHo1Tq+lKv9MOcgz47Dew07naC9iquAakLCkO86To2qzX5eITrOp6f\nPxd4fTTm/t37Qhaa3+Ho8JiPvJ7QOcXdk7t8+91v8OT5E0yeEIKnajqKoqC1IlxitKbznqauZNxC\niP6SkulnWUZQDBrSPniSwRhd6o6ur8fHOTMcbPfuTE8K6oN3medUTbN7Dj10u0OChKwnrHGZizJA\ng+BJHE+TJLsxjPOxjfZgANLBKqUcYcG3bFcrTJrQVDXBJChEfWpxdUmnMsrxhMXNgsX1BbOjI978\n+CdZ3FwwHmU0TY3zUCQJl5dLFtslhw8e0K0XuM6RZ4mQ8kKHdZ409qqORmMa1zBO51xeX6O8o6q2\nJEVJlueQaBLbMc0MN8slh8cnpOWIdrPBNi1eKeaHR9RNw2g8YpId01w/ZzQq6apGYGOER/Cz//YX\n+F//x7/I//bX/uEvDTfnB3D9wCHZ+fGdX/z053+KH3rro9gXv8ZmcUnAsdqsUUmCMilZPqLuRLja\nxcykc1A7R+cDJk3pPGzbQOcFfjXGRH3YQOOiQLqWTcfGwNV4sL29Vg9lIrUhS1SC0WKQmxlDmeec\nHB6hUZzfXLFYi+FqvyA3VYWK6jQu1lmtd7RtS2ejfmS819ZarLPRFT72XnmPbTveePghfsdnf4rf\n/Ikfe994yWt5bNcCPhIKIt0+OJ5cvhtP89EJAtg2NdPxJDq4iFKNCp7SGEZac1KOGKcZRTaix+Ks\n7Xj7ydep2jWjomCUpiitKbOM0hgSIEtTEqU4nB9xfHhCkQn8dzQqmY5GFKMR09mMO/df4u79B9gY\nJHqrLpDs7WSSczov2HaOzhINkftgqaI6j7AMJ9MpH/v4xzh/9hw0fPMb36TeVgTfRZd5gegMMZsK\natiY+kNMf4UA1koumSQKZXbtAZL1SgCznpjpQd12MXBJ7appW3GBCDCZlIyKhNlc9DOrphlqlz37\nsw+SzoukoveSsdioJztEmf49sotdnhAfH6g7T9XaoR2nsYGq9SzXmyjUEJ+/x8Luf1+/gVsvddEy\n1QMs2MO2/TxFiWLUvEzxARaVHcTw33ftQYz73w5zN4AyKSbNuLm+4fDoiLvHp0zKEVopyqLk0YOX\nmE2nw3OsN1gHZaYps5Q3X/0Irzx8lbauSRNDWYjJsnXib1p1HVdNw8HxkagsJclQh1RKx9Yn3YdD\nWRNetHV7+LwXX9+/B4peVzbuE1oY4WmakpqEaTliVW3lfsX51kOyIaJAAYbWs/eWToZ73a8Lo2m7\nFnrSUSQViV+ssImNEX/WUZYzKkuUtRzPD7DVVurCrSXLC4rMsNmsCb5DqYBXnnWzYnbnHun0kHwy\noxyVNJ0lK3KSROONZjQ7JADj0YjNukKpVPYxrbHWMppMCA6aZsMsN7TOUVcVwXsxlyZgsDSLF9w9\nPmKzXqO9I80z8qIQtCvLMYmUsGbHx6hgGCnN+cUVTWvRzjHLCz772d9Ilqf8tf/rr/DDn/8tf+CD\nJ+D3/vqBBkyl1Kv1dv2Tv+/f+Q9ozr5IsjhjVmasYv+kyXPGowxlEoLrcN5TNW3sowzULtA4ocp3\nXtF5JW0iKlB1ljYonAKnFY2zOKUilAMWJSofQVix/NqB1wAAIABJREFUjuhTFwJddG8PwZOogPGe\n+WjM0XTGulpzsbjBAOOy7D+JlJq8p/UCF/capBLkIxklKvl03slXlKuy1oreo/OcHtzlhz/6GQ6m\nh7vNuw+yruP85hl1s4miAJJR9Q5izlmWq4XAkUZ6JJVSrKot4zhB8zQTv0YvFkbBB1ZVzenxyxQD\nUzZws7xmW9fYzpJHgeXFekOmNYlGpL1C4Gh+wunxKQGBm6vlNcvlgrrreHpxxeVqRdU0EhBCwA9j\nG8gSzYODgjIzbBqLQkclnz6rlF5MA6wWN7imYX5wwPnzM86fP6dpGg4PjynKEojuI7FW3NepQtQq\nVFHEYdfrtxOYsEER9wGSqPrTk7NCrGcaJJNoW4d1Yt+1WCxZrjdsmgYCHB4d8Nobr3H33h3uvXSP\n0Xgca5e9Fm0k8LgwHB58hOFFyWdHnIHd5gk7yUQfa/etc9Sto66jnGCAO/de4s2PfJQszSU76YPk\nUCuVTKb//ADb1ol4g7r9ehBIjWJWiBfnsuqoun0hf4Fed0IaYccLAtD7WEVcI8h7uHP3Pp/61A9z\n/+59nHPcvXOPh/df4kOvv8F4PBke3QeSzgU2jWOzWYCvSZMEnSRRg1X8Qm08vFhn8UHs/7Lo4bg3\norJGm2Zo6fKdjZ82DIeJwUnk1jP3voufvdeDddZSZBnr7ZaeAYuSmvNuDe8gWc9u3HrlqOH7PeSl\ndzHpAy5B+qITvSMJdV7KSkWRM51OMUEmcpFn1NWG8bjk6sUZ8/mcEJzAr1EfdnH1Amct5fSIfDwT\nMZgyJy9yXHA4DZV1PHrzdZzzXN/cEHwcT6PJsoy6qsScmprMaO4cHQDS6tWQcnVzg2trsC0mzUmM\nYVwW6CQhKwus7cjygrraok3KVeNp6pr57ICTu/cpkhSzXnO/yPn5P/rz/Pn/+k/zc//GH/p9uh/Y\n7/P1A4VkJ5PJX/jR3/0H9OsPT5icf4siVWSTmRTxmxa7XJPPD9G2izBsAK2pnaf1gdZLbcc5gVfa\nKFTlnGSHJgZAqWMIbTrEjCNAZFDKydJEbUgfIE8TOi84WJnmpMfHVNWW88tz0IYyy7nebvHRmQJ2\nhfygDRb5/ULb7oOniCn0tcphsThHolMe3HuFD738EQ5nR3sb1wAMoYBtteZ6ecVyfcNbr/0Qg95s\nfA9f/Oo/JOCFzh6fq2NdpReb7qwlMYnUanygC56gU4piPPye1XrJYnmNdV60MfEYk4FfkxghyRA3\n4cl01m81PH/2mPnBIfV6TTEa8erDgto6Dg6OBnasixnPJBflmrpz2NATGohCBGr46uqa1XrNYrXi\n/MkT3vzYR0HBaDzh3unr9P1+Otaedlt+rMt5TdBhYNfutysERIQCLwiDtUAIpIlI+bkgYt/OOsoi\nZbutJTv2EvhNlpK0cDCbYTZLri6uxHqsKDk+OabprBwS4osFJIu0kS3rQ6CzIbaK9FZz0bljP9jt\nwXj953XRRMC0fZ+fIlGKMi146dHLcZ7F6Rlub/gDuSX+rG4co8ywqoWNnBpNmRiUhioycj/wuh2L\novmAGr7fPSb0CwSQz53lBVopaY0BJuNJJCaHW+9xH/J9enHJZnPNw3v3mY3GtF4IbBJcAyE4kkT8\nGbvoEJSkKdZ2tG2HSaS9QzK2qP8c31Ub2a19G8kwXnGMjDEDkhRiz3FP6JlNpwLHxn/Tu6cNe0wP\nxypA9cE49CUfBui7H1LRX02E8KN6r1Tx2bVeXGxwojTWdA3LqysmZcFms6UoC6rNmpPjA64WCyZT\nMZCuuo7D0zus6pqmcxRZymq5oG1qivGE8eEdbNOwfvwuk9GMtEw4OjzgV7/yVdJiwvxgBlgCKVob\nttstRVnK/LYdB9NDfLUmL+egNGWasl5JpuljO0rbtXgytEnorCNJc4JtaVrLaJLxkbc+wdPHT7Cr\nLb7r0DqQpSm2bvj0x97kzoM7PHv27s+c3L37EHjngyfm9+76gWWYSqlPBJP99Bd+4U9wr7Acao8y\nhiQv0CalSFO2TUvrFN5ZOfUpkXnbtJYmqEGhxwZR6/E6ujsoFR1Keg8OsdGyztEvBed3RXwZCNGk\nROmBqXjn4IgiL7i8uWa5XguF3zmMF0LK7bqGosgziiyLZr+ipCNN65aomQDsAmbwHuccH3r1o7z5\nisjU7X7bXrCM8ODB5IC3XvsEb732Scks1U5x5Evf+iKL9fVAPtDypOiUrllXW2bjMd65gUSCMWRp\nwZ2TR4zKCT1bdDqe8ujBq4yyHNqWNMmYzY5QgcGwOU0SrO2kx9QH3v72N1ktb6i3GzkBB89itWa7\nWnHx4pwuOk14F5iPUuZlyqruYrDcKZ4MBslGUW02nD15ireWyXiMj1BPkmYYnaBNEuvXatD1VRE2\n64MLxGAZlY3isMTxi3uV7xmxQvbobMC6EA2aFWmmaTtL2zla20XnCoVHU45yUgU3V1fkqebs6VO2\n1TZqAgszthcr8DHY9vCrtSI030RCWJ+BflB48n2WSYg1cYe1whKvOksV+1lb50nzUvR4Q5z/PUQa\ndvNvmGUKmii6McnET3ScGxrnWFb2/cEy7L7UXsTsPTdv1T/jawjcqYbv45sYMio//MrbRgLvVRca\nlSW163jn/CmnR3PyJJGDbszMu1jmUApu6pY379+lbRq0NiRGsirnXJwfcl+KshjaRW59zCisHvyu\nzh76Ppk4piqu8UlesG5qIbDF+aT7Nbg3CrdeI9YwQzwk7R8+QtSTdk4UiEzkSPhoJJAkoqbjQ2Db\ndjgU06MTnBMxgbaumB7O2TQtwYjcZ7AdVduwalvKckTbdtS9Eprt2KxuaNot+WTG4dEpthPoPR9N\nefLkKdY7xqOJ1DObiqA0iUlpnMMpRdu2bBZXYCvoak4mGSdHR0zHJVonONvhnCVocUCJuQOdE6H3\n6WxGked0Tcv88JCNFejsehG9UDdrRpsN/+4v/Cz/y1/6C/zHf+7PvM0P4PqBBcz50elf+W1f+MN8\n6OX7nP/K/0cg0HpQRrNa3LCoG3ReMJmMCd7SOU/ddnTKYJVYcbkAQWss0CmBOi2I9ByBDlH/aa0V\nqSpk8rsIp/QLFhh8KY1WlEXBo5M7tF3Di8U127qh6bNa5zlfS+/lfpFmVhbMigJxvdwpeigl0muJ\nNoObBzAEzDcefRijNe8+e/vW6Rrgxc0562oVqe9qsABSe3uPD54vffufcnb1/NbvHvoykcbpqmuZ\nFCVFlg9jgVKYrOD08I5kZzEDB8WL67MhiB0cP4CgGOf5wAp03uFdoNdarZuGTdXQeMXZzYJl68mm\nB8xO7zM6OKKxsjEcT8Sz9KbqBhTAKBV7G6XGk8Y+x65pWC6WKBSd92y3G7xzfOOr32A8GmHbVqT4\n0lScHuIhAiU+n1orlGEgNvVtRD2MLVNl53kosKkCJaL9VSPkmVGRyT1MFEEZdJJS5BnjssA5z9X1\nFVeXlyRpSlAp84ODQdUnBGmrCF6CcBc9PjvnpQ+181E/179P5Wb/kkSt3+hjduo91gaazlE3lrqz\nQqqKQbm/nyFIwAr0En8M2ZFCRAwSJa0idetZVFaMx99Tg9xFs91x7paEnrodRL/Dk9n/xfu11fAB\nj+zvV91WXN9csdluWVcVz66umE0mnMxm9NiCSfTQ6rBtKrok42Q0whjNdDqmKHK8krXuIiN0vdkM\nbV6S/e0hTxCNxGPmGLNPWdNCvcmzjDIvWG03UuoIYU+Fa7eWBc344M/XQ9v9eIQQS0XB0ss5eivs\ncusdSSzHdM6xbTsw0k7VeEtSFGy2FUsXmN45ZTqfUTcty6oin87QypClwva21rGtpFwSXGC1XHFx\n/gytU8azI4JXqMQwPzila5oh6zba0LQNJk/p2pagICkKiukBX/32u9SbBarZYKs1Warx3qK1YbFc\nxJ7VQFkU2K4lM4a2lXYsrxPKyZTpZMp0fsjVzQLnLFvryLMCZS2fefVl3vjEh3n7H32DH/mXfusf\n+w6T7Xt2/UACplLqrbapPvZ7/80/RPvil7k/LcFoysmIdx8/pbYekpTOBdIsYVtX0k+pFIu6FZZe\niDR9pegI2Cgx1jNWe3gEJUw4Y/RQm+hFj/tNIwBoaVS+c3BEnmU8Pn/O+WpJFwLb4FlbK/2bStHs\n7Wx9/SYnkCphsNlI4ukne5qkIuquzbDBeO+5f/ISn3nrN2Gt5a03PhnHRn7vcr3g7/6Tv8k//do/\nZr1dSS1ksBOT27baLPnHX/l7vIjB0kQ9zCRJSIyJryk1OescbdeSpSJKL4HCxEDSD0IPg8HF1QvK\nTHos82LMerNkWhboEEi89ALOD08ky/OBPC84PrnHo1fe4MErb3Dv4avce+kVpid38SolUYHDUcqm\ntWwau9cqMkgBDwQfgYs9y5sbkjTh+cUL5vMZOi94/OQJj15+RYSpTUJZZGIirXebm/wnwJiKwSrQ\nN52HIbtMTE8IChEe0ySJFjZ0zHbzNGVTOZquIzGG2TgXabquYbNZsVqtWW82nJyc8vz5OQ9ffhDh\nZz9o0Q71bOcGv8+67TVkJXj25KD9zXN/ju0HuuAZ4O1e/aexTrLMVpRyXKzB3w5Iu0CXJZpxbpiP\nUozSLGvHsrYkyXcKeO+9PiC6f4eAv/+6/Xrps0nF3ucN7GWiUmMXwo7i4vKcxVZMmX0k0j25uGBT\n19w/OqFIU+EgRCNoEHGSMjV0XUdrLUpp0jRlMh6TGiHAtU2D844sz0m1CCYkUbAgxJQ/OIfruj1b\nMAYT6Gk5YrlZD9m1ioSCHdFor19aqfcFxj7D3BdG6MlHIFmlyDTqQQjeWTfoz/Zaw6u6Jp3OadGk\nZclyU0GS0QZNMR7RWit6rkVOWeaczieYIO49bdtSt604BIXAZnnFYnFFVk7QOqUsSpRWtE1FYhJs\nlP5zxlBmBc5a8rxgkmvyvMC4ljJR6NCRRkUkExzzqZgihAB1XaEieejw5ESEKOoN4/GE1jmyPOPg\npdeYzg9YLLZ8/Z3HZCZllBf83i/8Hv7y//wX+Znf8wd+l+nZjd+n6wdSw5xMJn/qcz/zC/r1e3PS\n518XmC81VJ0XZmxasq0do9FY6g8eqrajDbC1gdZLX2UXAq3t6OnhWZaxqwWLh2G/PIdg2U/KuC57\nZZdxOeJkfsiq3nKzWcfaiIqsXGk3sD7I6U7vL34IkQHrnBv6LhVykvXeY7tOYOCwWxDBOj766lsk\nOuHDr3yEzKTiSGA03jv+/q/+HbTRLDYL/v6X/y4vnbzEycEpiUkhwFe+9SWW1Y0Qe+J76bO/wZFe\nabQKg3j0uqo5ms2pu46m7SDVrOpt3Of2ISOxKHO2o5wexAm+JUUYhpdVzcNHbzKdzQe93Tt378tr\nB8VRXuK9p+4s1gdGmUGrwNWmE8sxI5ZaRgn82gvXS41RApZJU1598026tsF2lu12wxtvvMFquSYz\nRtRsckORSw+c1PFkc+6VUxSSjZn9+pkPIt6PQPU7NZYeopc5Iwo/kWmsFN4rnl1cE4KP2q4JSmny\nVHN0fMj08Eggsm3Ner1lNN9lmc4JOaS1QtTpA1xrpc3GuaguQ48OfPC66UFm3382L6bAxila60ha\nRW10dKkRtEb3d1epyD4W/0znPY31rBs5MCmEADQvU9pOpBp38Ol3Wczf7WffMdvsn6j2XmVHDupr\nuNfLa9qu5dH9R6SZKN9keYYxmrquCQRuNmta23I8m5FuDS/aK2zXMR6NUEExylJMxP+kncwMalRu\nvcJ2FqUlKHZKk5kEg8Lr2OvtRBZRKen5TEyUXIzBbzYa8fhFL3H6XT7vfn2WSMBS7x/fMPyaCMmS\n4L1kwF3XkWR5PHhDsB3WOjZVR54oCjQ+STm6d5/rm2sWqzVFlpPkGQfHJ7SRrwGKUZLybL2BIgdV\nEHwgzWRup2mCbWra7YpgMo7vv8TlxRmrVcXxyTFplrNdLwnacHB8wvnFOeOjjNQ4vLOkWmqwuAaj\nJcRo31HXgdFkgjEp1om/8brreHH+gq6paDYrXn3jI6A1WTnm5sUzUi8HkYOjY4JzNG3HZ157yIPX\nH7FaLn6mKEcjYP3dZuGv5/V9D5hKqUfFePazv/8P/lF+7Zf+H94apehRQRdgVI642dQkSmODIy9y\nsZZqLT4xrGtP7YQkQvBSRzDCJEvTlDLPCVGOrossRFHdcHHjcgNcuQ+x3D86IctSnl5f0HStSGLF\n06WLrgPeeZRW+KAp8py26+XapNm69dIOwR7E2wcxG2sS/SlSBZiO5pweCRSa5wUKRaIMZ5fP+Ue/\n9g/obEveHwCC553n3+Lx+dtDzbDtOoq8kGzJGAnK8SBhlMYFN5xcgQhrysnUqEQOGr7lZDxntVkw\nnxwO9yiEwGQ8Y7t6gcnyIXtdrDdMygn5eEo5GkcCTDyUDKbI0lfYn7CnZULXeS43YnfUk3okw2Ug\nrBithqDXdZbgA1mekCYjttut0NqDYVqOGZd5dJeQ4Ou9jwzEPugahs6AoV6528ziMBFi6NFJFFmP\ntTZFtOlKDRdXC9Caqu4gKNbrLU3bMionomubiOBCgsOkCelsxLa2kl0SWzeiI0vdOqpWhOY7L1q6\nImYRSUAIceaDNt6e/KPCUCnEB3Gf6LwEzcZ6ks7FjFuRGU2eKLJEDLS7GKC3TX+Q7GfGLsOrWsco\nT1jV3fveQ1y/8f2oWIfbBb4PetwOkNx9p/a+do8NQ7bfj0BdV3z97a+RpSlXV5d7vAAnaEsQqL1q\nG+rLmqPphEd37/Hu+ZlkkM5SZglvnh7w7eUGpY30RUfuQKINzkitWcfMsA+MCoVBg5EDU38QdXEC\n6cQwHY9pY1Dtey6HA08s2+6qJxHLClHDNq6Pfmh68uHQDxpHwnk31D9FKawjTxPwjiLPcc5Rt0BI\ncM7ivdSyg9aMi4yzF1fcPz2OmaGIvXgX2DQt26pmUhZUdUOeFVJTNx1N1/dBG97+9mNstSQfT6Ms\nY0uZF2TbDU3Xsq22jMciHNJWa156+DLtdoFJxcFogOhdx2Q8E6P30OKCw3YtF+fnlKMR+cEB7XbL\n1cULsiTBec/ByV3a1RWqqyiLhDzNULajAH7m5/41/qf/9r/h//2lb60+cMF8j67vOyR7eOfBL372\nX/59BH/B3W7LfFISEo3ygaptQWu2dSOms2nGsq5pgmLZOCrraEKARNO4jiTd0cbLNEEHR6p2eqri\nNed2NPEIwRqtSU1CkWW8cu8+1jsevzin7VoSk8roKzW0gfSLKQQG5/L+DqVGc2c2I8/k1NR6Txcb\nn4OXAKH0bsL7aN/0ud/weVlUxEkVAl999yv87V/5W9i9YBki209rHU20jdC/iwKTSIEuMfJ9b+fU\ndu1Qmx2CdNzAVtuK49kBKmg+9sYP8cajjzKbHAz3ZyBBKUWeZEznd9E6YbVa8uD+Iw4OT3nl0Zug\nTGSSgnVSM7MuDH/XRjMpE7a1kEeUkl7KITBqhiyz/7dEaZq65Rtff5er6xsUouxz8eIC6yRTS4wm\nzRJMIkpNciKPWXuIIGzcm5RScT6o9y2psDc2bSMEBx+JMnKwcixWG5I0ZbFYcXl5SdNWnJ+fkeYl\ni9UN59fXNMD51YJ3Hr+gbi15UXAwHzMfJUxyQ5nI+7FOPDMb62mi8LyNJKB9RPK7rv3Qh5MwyOz5\niFgkGopEM8kM08JwUCYUqcYD285xtZZSRmP3gyUybjDU3dredSbR74NIh7cXDyFRdPf9P/6Aj9DP\nv14paVBSimSW5+dP+fY739x/Z5wcnXDv9B7ffvwtnp4/jYYFu0OvBB0xgu685e2z5zjveen4BK0C\nExXIlGekIXGetuti5ixvMk1T8lxaHdbbrUC3bct2s6GqKqzt6NoWay1NbB0iBlvbdhyOpyzWq4HM\nRTyE9x+/J2n14gOd2yFR+0o/wwTYG8P+6vWm+8/svGdb1bj4eaxzVE1LUIqm61hXDa0LzGZHTNIE\ng2PTdjRWylPOeZI0IzjPKEu5vLiGyEEIQcsaiIpSziuCt8zmE549eYf5fM5kMmO13VKWJa5tcdYK\nazjNMGlGUY4Ebg07sqIE/sB6vQECWS6yim3dcHhywnQ6RaE4Ob3DbD4lzXLOnjzl6OAAPTogH00J\nQXOzWlHkOd5ZfvS1R0yOZvz1v/q/8/t/4d/7998/47431/c1w1RKneRF+dmf/yN/gkfuMSHLqZsa\nspJt2+CzEY0L2Lj56TTj7PKSjXVsO4s3BhutfYSqL7W54D2TNGHbdLTBCbFFBXSicZ0dmpN7cWVn\nHacHh4zLkheLGzprhxNk27VRxzUMQdfo3blioKUD4zxjnuXYtiXRGmfMwGDcX/hFmtEgjeppXvDx\nNz7JbDqnrivKvMR5xzvP3+HL3/pVijTdCTfHOqOwRjNZkHoHI1ov/nnizpGijTBkszSVLC0EEQX3\nHh/NrC+WN3zqjU/w5iufjDXMvZpKfL+SORtGxy8BiqrZ8tqrH2Y6FauqXtZtUKoJYlnWk1HK1JAm\nipt1O7AsJZDJlyGyVlERltW0dc351Q1t2zGbF2zXG87Poa5a0jSjzEcYIuu468izZAfBB1Ht0Qqp\n8xBERF8RDzv7MKfAc3ILVYRLd0HLdi0YyHVC5zyr1Zqr6ys26y3z+ZzRbMZmu6TIRD0ly3OK0Yis\nKCDJWdVuYL362JpCEB/KUZbQeUcbDbM7K6zWzgWsDdiYnSsVKFJNmZoIWUtgMVqLvmoSodVEkyfS\nQ+ljYNs0HbV13Gwt4yKlSERoXnnRHf0gBLUv4/aDJFmmYemiukyf/fSP21sLO+rt+37j8P+BExSD\nsn5PEu1D4OLqnPV2w/HBEfPZAVopHj9/l+VmSRVZx01TkxfZsI77vuk+cBpjOLu6pMwLHp6ekrQN\nul2wqbYAgzZwSGTbU0YTnB2gbmstGEiyFB3XeS9q0Evn9VeZFzjnWKzX7PduylDsNJ6JsHzbtXHN\n+qGEkicJLtgdi7kPtqHPsvfGPf68L+1YLypfWWIwJsF5h7UOoxPKPEOpgE4SykHhS1pqRmUhGeMo\nJU9T2uV6SAq8d1jbB37D8uaaO3cP8XUt+xIdN2fPKOZHZMrD5ZXwQ5Shs5bCGEw2ovaaaX/DlUYZ\ng0fjIrnK2o71egXE/ck5jJax6SrLdDLhzt07PH38LpvNhnpxQWECp8dzlmsxtz8tc37yX/9d/KX/\n/r/kP/pP/4vfAfz5D5jav+7X9zVgFkXxJ3/4J363HmXnpJsVs9mIYnaADZ6l7dhUkllWzpEkGevO\nsumEcGORxn2U+DwGoMxyfNvS1GuKNGHrHKHtnT+EzeW1i6LfsrGnSnP/+ATnPe+cnZFEVZQ+c+xV\nTHrY1vULZlgMcgIrsoxJKkavPmayaGHZ6n6jjFM9yzLyLKVOEharFfdP7lM3NSYSD7759Bv8ytf+\nyRAA9r34BoFpndB5OyiFuOjUQTx1dp1IYYUgbNwsEXeI/oAQgsc7+JFPfpaj6QFaaZabLV9950t8\n6JW3SJNsuE8hBO7ffTiMweXFOSenD2JNtm+CZwe/hp5tH5gUhhDgctUMpIZ+0+yJPdqwyzK14vr8\nDLQmSROatkEDtbUIuq5J04zjyRhrpTVI695RQ3IlRTQf1r3bikC7ghTcrgneIl4QA2dAyB9BGJZ5\nkrDe1tjYJzYaT6jajrrrePnVV9is11yenRO0oe06Dk6OKUcjOhcikzHQdtLusWkkA6i7GJgJAyNY\nR4JSZhRF2u+awhSejzJOZzmxMhDbCET0oGk9FfI6nZP5mRgtEGxqKHMDaHLrcEajXBBLq3/OdSqZ\nLxSpoe52XpAfnPv2mWIc0fDex/b3X+2+32GxgKzTN175EL/85S+iYt3Yh8Cm2qAUJEkitUTVC6er\n4TEhBLquRWk1lFxW2w3vXsCH7j8gSxXt1ZIsL6Rm6eyALrh48C3zAquFbzAbT8QiLM6RpmmGsgpK\nGOfeOY5mMy6XCzmUJklEOvb6W/dGoSci7cZAxXF27CPwIYToYrIbyv1DR09oAykVSa03qhG6QGcd\nZZmzrRuMBhU05WhEbSXJ0FqTJglFmbK+usDkBWWWMJuNubxaYpKErhV5P93r+qYpNzdLygxC13J2\nfc3r0ykkKdODY7ZtjdGGut6Sj6YcTMe8298jZXAhUGQlW5WQ5pLBBu+ZzeZUcWzX2w1GKS5fnDGf\nH9B2LcenJwQfyPOMtsipFmd0nRwkJ6OSbdvy2R96i7/+i/8niTH/6gdOze/B9X0LmEqpaTmZ/yc/\n9/M/xyvNAuU9IYWq2tKlKU6lbJo1bVSu0CrharNl2dbY2F8ZenPYIJuEitjQKE2omoZt05GoSIpQ\nKtYow1BrnOY5x7MD1pUQe7z3BCuiBdZael9EABWdErIswwO6qQeDaB88udKk/cKNNbnWx/qqFtIM\nMLQsoKQnclSM+Kt/8//mJz7z23j72bcBz5OLp+I/Z8SNQGS+IvQWiB/Ik6i+gR10klLGE/Yg0O2c\nZJIRQk7TBGstWZIxKiZkSc5kNMO6wCiHNE156/VPxcndxx9ZsIvVFVlWUuRjTk4f0Ps5umhFJfBY\n1EUNwjqdFoaqc6y2It2wOxkzZJdC8GGAYleLG168uKDMc5JiTFkWXF5cgjIwl37PdlOx8J5yPCLL\nEvJUoF0AHTcoHVnIfbal4pvazyz799FfooOvcM6CDgQnWUXTdNSt9M8+ffqMtpUs19qOd7/9Nid3\nTkGL1dK2abn36CE2hKGdw1oh1Gzbng3b/0wObU0X+w+Hw0fs14xjZpQcJN6+WHM72EjWYZSKfY8h\nBtg+oPZBlZ3cXswstQ+91zo79eFb63P4MwB155kUhtZGUtBeBvVdVvmtul3/+3bw4x5f9j3Rd1RM\n+MynfpQ0SWImKu/UeYcy8fNqRRMFBtI0pWcMD4IDWrLNPM+p64avP3vKa4eHvPnmR/jaO28TplO2\n1tJ0bSTLKeq2gRiUVQhs6y1pkg78gzRN8fEfInRDAAAgAElEQVSQ049Rkedkacpqu6FvIbs9Ciqu\n3bDryexRqr2apQ+BzJjBNL6Hym+Nz5DW77Ftd4NLlqWi2BUCPihsZylzI0FzVDAqC7pNFcVcwBip\n1y8vb0gPj5mOJmRaUWQmJgPSTaoTw2gyYnH+ApKcNB3z+O23uXP/hGp1w6KqGR+dMBtPaKo13olI\nx+rmGodC6RSLRpuE2gZUkVGWOU3TUJQjLi4uuHv3ToS6A8ubG7K8oGtbKgVPny6ZjEoWyxXKtsyP\n7rG5PmNcTLhZLsnzgoOm4Qu/+3fyF/67P82P/PhPNr/5Q3fyf8YE/Re+vm81zCRJ/viHP/2j3JlW\nPD+/QAUXTzQdW+tZbCrxtfSBynpCkrFqGxq8qPXHheyDUOZV8FhvabYVeV6w6CSLVFoPhJs+s0TB\n0XTGyeyAFzfXLKstvfqO95627WJmKVMxyzKKLGVWligF4yJjlGVDttTXQ9volKC1mPMKpXxHeFF9\nsITYCB0JNeWIv/3Fv8GTi3d5sTgnTRKyNEVrhQsOkxjyLBOCTDS0NUCpkyEz8bFWqiNc5p0jL4qB\nwdcvWK1FsaV1Na89fAMVeZOd85TZ7tQb9vC2pmtZbdZkeRnVinbBQKT9PF0fHJxH68C4MFxvWm42\n3fDzXjM2EIZsSrpjNEYDUZCi6ywWzdXFC6y1FEXB6fERozyLmrAhKhQZ8sxEwYZeHKKHrZNI7NED\nnHVbFg32d+kQ5J74IP17yhiUNvhgeefJczbbijTNODo8ZnZwwMHhISYTclZV1XzsU5/grU+8xUc/\n/hYevddjGSSQtpamky/nfVSj2qF6+3OJsK9R9P73e2s/Hj7CjjTSZ0wh9OvDxbaTnoC1a2G4PQr7\nAe32S/TKWqMsuRVM959zK5APNcrbjx3eJHsKOHJyuZVohsjmlktTN9vovSrHiDSiD52VOmPXtZFw\nFOj6IBqb/KuqorMdN+sVXzl/wdN1zb3Te7x0fEToWsZlybaqqJpa5Oe8j322CUYb4R4Yg48uR/2h\nu68l3jk85HJxM2R7XdfuSDwKUKIG5Jwj9GWB94xTfyDWxux+tndv5O+7Gz+IrNyCxKFpWjrv2DQ1\nidGUhQiK5FkWA69iUmaUuXiXJirQbDYs2ppNteXB62+QBCgMeOejOIIiSQxZkopkZD7GlDPqLjDK\nMpztaOuKm8szqu2a8eyINC/Fei3LSCeHbKxIGtp0BMWEzfKGxBg2mw3aGMZj8d7suo66rpnN52y3\nFZ3zHJ/c4f5LD0jLMToxXC5WLG8uWa1WbJuGIsuo2xatE378X/lJFtcX/JN/8Hc/GAD5db6+LwFT\nKZUnafaf/cwf/HkeesfdScGoyNluG3SW07TSnKqzVBqvtSLJczZdQ1A6ytvJ0dX1WaA2tN5HayJP\nF6Qm0XlhHiqIdQN4cHRCZhLevXhB62z/nsQqzAfcrTcrp/vcJOgIkUyygqBl+gbvh0lr+laV4GMp\nJ9ZeYyaVvkfu0AVPmiZ4FUizlCzNyFJRBxIYVrLatG/C15Kn5sZEqy5LEqnvRSIBInhHnspm0bat\nFOytY1s3sZYKbddxs1zQtA39m287IdAMMSUebwOBLMu5f/dl3EDi8fJlY73NBWFcdpbMCKR4dl2x\nrLtYv9sLlhGhkjqPZJaJgWq95ltf+zqbxZLJwQGd7ZgfHtC2ltFohEbRNpYsSZjPp8xmU/JU3ELy\nLCNLjZhAG+k57X0Ng5daJu8LlhGGc55+b0OJbm0Sa7/eO+rGsVw3PH73Ha6uLrBdy2qx5PzsnHv3\n72PykudnL2gbKzrBUf5QSBICi1UxWLadx/q4EfUZBzvmcg8f/7Oh0vD+7/b2zj5g9hmmC2qXbcbS\nQA9f7gff2wGvBxrEsFkrEUTIDCRGyDqKXStErwqlVB8kd9D3ewOsPJ73/V2eKxrIOzl0CEHq1Nb3\n5gRSUsjTjLIo8VF0QPoTBV3oum6AWNM0JctS8ogKPL664Ml6w7TIeXR6StN2jMoRRZYP5tE9bKqN\nEeGTCCu6XuQjjvEozwk+sNxsIB7KhD3vBsjUOx/bRuQD9bq7mv1DgjzW2vjZY0KglIp7ibp1j4Gh\nfhBC7NklKpZ5KSOYxOCcSMkJWRCMkcP4bDxinGckzgr5xmRgEpbrNUcndwmxH1UISaJF224ramfw\nOiVLDEmaUltLWpSkaUHTtHjXsllfkRcjVJJhtOfk+IiLs2fM5nPy6Yznl9dkZUnbtozHY9bLNd4H\nNpstWX8PvOflV15hNBnTdpbNasP1xRlGJ5ycHBFMwv2Hj9jWncgfdpZEwfbFFV/4wu/hf/iz/3n6\nUz/3c//hd11Gvw7X9yVgfu5zn/tjv+W3/3T5uYMtVd1IoEHYcS+2NddVi0oStp3FGYXXmjRL2bQN\nnbcEJUGptxQKQYhBvexX44MIL1txJ++CwyNGwQ9P71C3Lc9vrtjBJLtNa7ejxyAa+xqt7WidFaHm\n0Gug9ifCMMAsXjG0BMg20p9Gd5tUbxVW5jlFnjMqC8ajkjwScpQCk4hvX5KIUXGWpNJXGckrqY6Z\nobV425LFGqAGgrXY4CUY6V0Q3K631E1N21k+8srHOJwd02/QAendy9N9A2VR7rFWxMDFckoILJ0L\nklVaR9Na6lYo+y54nl5XbGKA6KzfycKFfuPc+Vma+P5WV5d86NWHvP7KS0yLgsP5nM46inJMmZUU\neU5qNHmaUBQFZSkkBYJCul2FOGRiT6cOAYJsXkYphkSb3W3uv/ZZjb3PoHOe6bgApbh3esi6ajl7\nds67776Nd457Lz2isx11U3N0eCCoQujdRgLWSu2vtsKGHViwwYvSz350Yy/T3M8t+yzwn6vYqBH1\nnv0AzE6Kz4fosclwcHkfXhpfaBcoiQQjeZhRiqbzTGJWvyMgEZGOeC5RvXxcH3jDMKf611HDC+1l\nlyFwvbjk1775q/zSl/4hq/USYqC5Wl5RlgV5kUn2mKWMRqXUatOUQCBJzYCwWGvJoqOO1hrrpNWo\nJ6Wtm4rzTc39+YxPPnwYBdz1UIN01g4EP0XscbW7btR+jE9mc15cX8UDYH94kP86K604gxatkrqh\nj79X79XPhaDW69YyMGlvTQZJvenh2ABDaSQgloEhkp9Euo+BLNi3RvmIoimtKbKcarVmXW8pJmOM\nSRmVBRdnT5mMxwTvokMKFFnO8+fnqCRls1qyur7k9PiArMjRWiTtXHSFCs6yWgkBKMlyquUN04Nj\nmqBZb7YYZdBpzmotgTJJDEmSsljcAIGbmxsGEXoPZ8+fURY5r772JvPjEw7v3McH2ZMn0znPLq45\nnE6YpxnjPOOnf+dPcliUfPTHf+jP/vOsnH+R6/sSMKfzwz/1ud/+O5iWJYXR5JlhXW1YN5aL9ZZl\n07CsahZVTWUtbRDpKoso+fRAUj9h+j/XVQWIc0nnbNSS9NHmCe4dHHNxc8PlagnsJn0fHLWSSZQl\nhixJSKMogQ9hyBgsHrSm60Tdor+xPnjsXrYafy2p6TVLBSqEHSTj++AYF6XWAn0InJSQpokEvRiQ\nVQhoLaoi1nYkEc50IdC0Dcp7GRWtSaNlkfx+0c6cTGVRzEbTGCxv78SthbrZcL14wfMX79B2DTuR\n9JhVetHqdd5HIos04E+LhE3d8eymHhRmOudu1TVDzDoGkotWaBV48va7XF1e8uWvfJUX55eUeUae\nZcwmE4xWjIqMcZkzmYwoyow0U5ggmYhSgejdjIoHiugMjTJRk9Z8d3QmeOiseDK2bcO23lLVFWcv\nXnBxccH5xQVBwWg25/79+0zGJTfXV1jrefjoASd3TuOBr7fIQoydO0cdlXus38ku7jKU7/Km1D7J\ng2Fe7UOfSr2/9tjfUh92WWaI96+/h31P6HtfXxLxoah2q9aslEJp6CJbPEt2NnL6vY+LmZMevuL9\nRuqrcWYOkGMfgNbbFVeLF9TNls63fPXtLw9r6uTgmKZtyLMEbTRZlkbCTsAkRiDapo2kny72WzN4\nW4LcEwKs12thYtuOb15dM84SPnLvHpMkQys9sMLruo7SeiFqJ+4O2ITA4WQqLSZdt0fiuZ1FCxP0\nNlyttdQpnZdD7b7o+8CMjf/+Xii7P3wQA6HUoaObiUlIEkOep2RJesu0QCup99ooxFFVonVbjCdU\naNJ8JFq6RYlGE1ygrSoJuGiaqkalJZ1X1E3Hg5de4snTp+RJAmha7zg4mu/VWwOb9TXWdRzfe8CW\nRPpeneXo+JBiNBLz6cTgvUCxIg5iePnllzk4PKQoC7TRbDZrsjzn4uqSEAIvzs9Yr5YkWrGtKmnh\n85IwlQrA8WM/8inWX3mbP/6H/60/8x3X2K/D9T0PmD/2Yz/2+SzP55/99EfR3lKmYv6cmIQ6y2m0\nxhJYNS2VtdTWopShie7hPaux7dlt8USJgs5ZrHOsqoq2a2k7i7WO2XjCbDTh2fUl267dTTZ2JzCp\nh7pIzU4HkXLvPNaKJmdrLZuqiTVRBiIAEE2BZRtIlEjQ1VZ86oiZZa83IwF+Z40U9t5PkgqkGBBS\nT9Y34BNE7gsh9hChGmGZCqmnSBOMScli4Aheahq1lQ3EB2FHejzPXjzmenlF21VCH3cdT87f5vzi\nMSZsmYznpGkeIb0Qvxhgxqa1VK3ck2mRcrmuebGqRZC8dbEJPz4vZl3ALjOJCzntCRootnUrdYs2\nkGhDWeQcT8YUWRY3JEhTFdtDZCyNEj3XAYINEOIuruMBY6gbDzDke4qAcZW7zlHXNderFU3bsqkV\n19crvvGNb/Lxj7zBfD7h8dPnPLu4ZrXecH29IEszxtOpHKpC34MqIuptZ8V1xPd1yT5IvR+iZAgi\n/ZtleF/vKSneCpph//9DHVSeu4NlJYgP2b7bc98YIPJdJrn7U3E7SAti0VjPKDO7mrG6/fz9ILr7\nWSTq7D9273NorZmM53zo1bcoi7FYtU3F7AAUk9GM06O7rKsaEJs87+N89pYk0bStBDgTbex6Kb3+\ndZy1Q+lls63YNA1dCPzqszOaruNjD15inmcUWS4lkjwb7krwURhFKZTR5GnGyWzGi5ubmMUaVJSe\nFKlKNax1NYyF3F3Tr0/CINu3mxP738mljY6lHT1kqrusHBItHpyCRInSVZ6lFJlC4YbsN89SDHK4\n7Ms0IU9J0oS2lfE8u7hidnpXGPARtcuKlO12S1XVhK6BZsnx0SEmH7O+WbJcrRgfzKXmrHcZsPOe\nTbXENRVvfvgjVI0c6g9mM9IogDIajUSKMMsoR2OapsPoFFA8efKEtmlpoz6tCp7rSyEE4j1XmyVZ\nluK1tPTYzmKbmlme85t+w8d58eSc0x/99B/ie3h9z1myk+nsv3rw4U+rdPVtyjxjuWrRaUrlPRs0\nXRD3kdN7r3CzWXCzvUEbw7appYbAvt1RTF2UGmBOIgstMQnBB07nB2RJyjvn50K9Vkqy1LhxGmOk\nbhCf08Ng+xuSD+KqIYmLp2lbsiSRQrNS0QlFUVmL1opUWTpkwhjdL6IwZJTEbAhubxp5kg7OKEb1\nZAPZAFUQ+oo3elDM8N5HqFFLsHeeTAuUWwdH5z1BBTKT0ljB+uumwWjLs6snvLg+4+TwFOcF/zdd\nx7Lecu/+K5weH7KpnfS1hV6jUoJla6VvMDOaMjM8vd5QtT7CXXKA0RpMiMxgJMtVMdD1NV2tNPW2\nggB37z1gtV3TOM84S8lSQ5oYBpnDWJeFgIon5tQI0Ndn2hoVFV9CzMZ3kOOt/tIgbMWeo9lv5mjF\nuqrJs5zLqxs2m4rxfModd59vffttJrMDHr3xOk+fXTKejPHO0nYdSZ7HOq3Ua9vO07TCjB2gpTj/\nlZJAvlc2fU+m1+sZ9xMlgJJqlx7+fbeh9n/uyxgSIdehHBCimo13tE6TmEDiAj4R8tjuUgPacmvD\nVrBeL5lMZiyWV9TtljuHM7ZVQ915ptNDkkQEPnyM9Gp4/8gBdR+Q3Vtfty95r68+fIPLmys8nqat\nyNKCgFhubbfb2G4lwbfpHEEF2s5iTCpZZZJgYg1foPLoUJOmAIMge2M7jNOkxnC2WtH6wCdffY13\nzp5z0bTYro2llygUQCTYAXcOD7lZr8VwXkmddX8994Sg/hCTqJ58tjsoafTuQBLjoOw/O9NqFxmz\nWimMTmTehr4nW5NoTRHb2RSQZilGKcrcoAPoRJGn8phEhUh+dNRNJYb2m4BJsmGObLdbngeLmcxx\n6xqt4WQy5exiSVNt0WrL/fv3uFqsOT485mq54MHRAVlR7GWX8hlFuq4htB3F7BiTFowzsVvTecHJ\nyQlnZ2cURUGaJlTbWPMNTpTUlKZrW05O7qCC4uzxEw6Oj+iaLQd37nF5/jaTyQSjNNvthnI0Yj4+\nwDYd85Mjjo7mLL/55Oj1T37089/85a/8jQ+YcP/C1/c0YH7+858/OTg4+E0//tt+gnz5q9TkbOqa\niU55tq1ZBjF/nsxP+diHfiPXqyv+3pf+FtpoGtuJXY66Tdt2TixuhlpADEQheB6e3KGzgaeXF4DU\nbjSSlTonp5o+oMUnY4Y7TizMa3rz4f5qrSVLUuq2lUCt5BRoUWQ6QaUpXV2TRAJBD8k69r3w4rYS\ng0e/myTG4PtDgY22YQrpDVB6CDQ+BoQe0tWJoWlbkhBQRmN9oKorsjxn2zZkaYb3gSzLSLR8ykQb\nttWCSZIyMgZtoDU5xfgEEQ2KNTknwbKxlqZ11J1jnAlD9+2LDZ114hQjAy+BMbpxmxCrckNGITVL\nozXKO7721a+TGEVRjjiYzRmlRRRiF8JFmkU3lugf2jdc94FAKYTdGu95wAy9qaL4swtWQpQS0/E+\nqAz3I3jQBqU1m/WSb77zmDTPOJhOefjgntQ2Udy7/4BnZ1e0zjMuysH2yIUgjFgXDZxjzbcnY/RT\nqn8fWkVoNsjpP0S2NwrUUOu9HUzDkA33Yw1e3YaFhnAawlAr9VGlRQ48ns54MqPl3sQ6Zr/ZDazW\n+I9GK5zrWCwuUQpm0xnPv/FtQTF0x/XNNVfXl3zojY8NZJkkCp/379F5OwQH4mEH1ddxQ/9phkAy\nLicczg945+nbPD1/zHxyyGQ85dnFU9Ikkc/mPWlqREQ8Qqh9X++AGiUi+Vhk+aDL7FzvUiQBSezZ\nHKOyZGNbvvz4CT/06AHm7IzL4Gh7drcX1COEwDjLSHXC89WF3KN4VBh6evfqjv2/+dDLQOoofrBf\nXySuD3ZzMtb9jTHoEHDO0jgXvW0DiZGyUaJF2YvgoksP5EZTpAYTRFGq5wp0tsPaDpTGJDn/P3Vv\nEmtZlp3nfbs759z2vfu6aDKyMjMyszpWRxbJUhZl2ZJgyo0GBgQYMAwD9sQQDBuaGPbEA8LNwIBn\nBjzS1PDU8MSADNGwJKqxKRUlksqqrKrso3vxmtufdu/twdrn3BfJEi1KyYEPEBkRGe/dd+85e++1\n1r/+9f91m1i/6d5rbcmcpttssFkBXY1HY8YT2uoZF0cFQTtevrxiMZ/KM7aK6XhE68OwiAYxFaVo\nu475eIYl0PkWbQvGkwmr7Z6maZhOp6+gADZzxCBKYGVVcro4oWnEW9Q6Tbu9JYTAJHf4xTnbYsyj\nUcHNs5LRZMRmvcWOcqbjEd/+zlf5nb/7e+6Nb7zzrwL//wuYeZ7/tdn9t4y5/TH5JKdrPUeLU6oA\nMVeUmx3Z9Igf/MpvorRmW+9ofIOzx1RdMyyyHgr13YH0018RmdV7/eyC9X7ParvHJkgDpHdjlBqy\nUMn4BXpVQaoXm9i1IP2W3GUEwgADNW1L5uwQvFWUXoVAqZGmaQb5ux5ykQUklWCWBAoikvnK4RSh\nF/9OWWvf28yUFhUS0s9C3CVsT4En4D2MnEui8FLBFuMxI+vQSVHDR2HMaaXQQfpJx3kOXcPqZslk\nNOb09BEun7CrAyNnKJPVTtOKUHjddmL71HqeLyvaEIdxjP6A6HsrIShigk6VfLpBYD2zig8/+Jg8\nd1jnmM+mWC1sYLRkxuORxRoZEo/KJKhPJ6bnoZIKKvVxotycgZrfQ5PpgMpHjqpp2W13w3yecw4C\nGGuxznLvbMFq4zg5WnG0WLDf7Vjv9lzf3PDtX/4eEcjysWTEVcXR6WIg+rQ+Huy5Qg96SuYlVm7J\n8zCtwQhgktpLCnwxJl5HX1H2n60/UO/spzi8unxNX30euqQk300lJIme5exlBEkq+HhI3O68dopt\nxOBZb2+4XV6yWCy4un6BMvDs+gWj3HJ2fEYxXuC7lqrZ8+mTT3j04A2Uhn25o25rbte3jNJcMWim\noznj0YTMCmu1KEZoZV7p2X773e9xs7rm/Y/+kJfLSy5vXkhSGgIuE/OAru4wRvZB5jLqRggqEUkG\ntRZHj9ZLldZDewEonB3YyjoE6romeJHH/PjmmvtHc8ajMZ+vVuxrGd5vug6rDedHxzy9vh7cQhS8\n0nqR+yeWX/HOCE+f7PUf0mpNk5w+rNUDlN5X4wZEZs4YTC/RGcE5ESOxymC1Is8soYuMcofTkXEu\nlajSmolLs9yt9Hd9iHjfDu8pBHn+zmUoIovZFDsqWFcR1zXoENitbtiubpjdP8cVY17ebDg+PubF\n7ZZRkdOFgObueif1VM3AXK6qmtF4wrassFnByWLBdrcbnFesFXnLXoHMGMPx8THaaE5OT/FthzKG\n9WbNw/sXfPrzDzg6O8LeXPPzScGjyZzNZoPNC4wyBAWP33jI3/ud3+M77/3qfw38N/wpXOa3fuu3\n/jReF6WU+vq3vvs3fv3P/Xn9S0dy2PkYuVqteRqlh3VTVnzrmz+kKGZEIlpb6q7mwekFL25fJu83\nGb4HCJ0fmvvpZzApRnzl/B63mw2bcj/M+wUCRkmWa5MgwN1+lgppZq1ndMIw9ygatMlFIsoM4aQY\nsS1LWRjWSoAz4kvX9zJ6Cb1DnzQkko7Qwa0RiTKr9B/JSvsgAxHbV6F9QIpBpM2iQL6kbFkqlQSL\nKY1D/myMGSAyqxQ6wshYcqNRXY1FMS5ylvua1978tszceRlricC+7tIcoedo7FiVrQTLRKjqR0ZC\nQhBJh7808VXS6hXptswZCmu4vbrC6CiB0lraquF4OiPPstT3iRCFTWi1ePaJEtIBHiOqtDFFYzf4\nxBDU4soxJPtRNuxyuaTrxDfy+eUl6/Ua5zJc5lhvtzinyaxlvdmhjeWDDz7g7OKCh6+/zoOHD3FO\nKpWXl5d09Z6j+YzRZEJAJ9KQVN+tD0OPOqaDNPTVgyzUBE/L/GmfeA2QZfpPvwK0huNxzs22eQXp\n6FFNqUp6KJQv9CAledKqRyNUmntNLGXTM5XTSMRQnQc2mxtxh+lqXtxecnZyznb7kqppaKMInheZ\nYVfXPLt6ws36iqrZc7O64nZzS9Xs2ZU7IiH1yRvqtqasdyw3Vzy/eUpZrXl29ZSr1SWz8RxnXLo/\nislowuNHb/HkxedE3SvrKHzoEmypkoWbFg9GYxIj2lDVNVYdBAJCFKstH4NoLedFEl3vhqS2h607\nNNum5v5swrzI2ZYVEan6Hpydsq8qtlUJae0J9J/uf0rGD5Dz4bkaY4aRkZ7N27+//st7tKmvPLVS\n5HmOVkp8K7XAu3kiBGZO46zBKOnnZlYIWbFrBz9fazRdK/PQpLOlJ0v6IEz/IndEZMxtmju0GzHJ\nFd12R7Pbsy/36DxjcXSMto6oLc+ffs50kjOdz+QzIEhbf5mU1BqjyLRBZ2O6qIS8lRdkuYzPtV13\nYAzHw8RBuS8ZjUYAdL6jbTqyomD54jOsVXwcI/fmU7zL+KQq+cpoxH63p2pqfF1TnB5zc3PL0w8/\n52/9/Ef/7r/53l/4n35hcPqXuP7USD/f+c53/vJ4PFZf+9pr5Lkj+I7x4pgwmVK3LU+Xa0wx4vjo\nPk2a28tdwa999T2uV5e0vpVssRWqdtOkjCmJEgDMJ1MenZ7zbHnNutoPPazeaaTPenr2mcjMicJH\n1EnaLUoF2dQNZVlR1zVd1w2mskWRg1ZysEepSDMnDfe+Mhxm1zgcWlopCmPJtR6c4fv5KoUofPRM\nwswYXOpPWC1yUgrPyBosQo+3iegjDOCAVlFcMjQobbCp16cUhJ7soDUGyVwLEyk0ZNax3O7YdZrz\nB2/JoRKkGl2VLdaQmMaR44nj5brmci3BsvUH8YJ+GN4jc2DxcF4APTtWY7X0HnNnIEReXl6SOct0\nPBFGMpE8s0xGQr7InKPIZYQkMyLl1Qs19Dq/3geB1rqWummoypK2XxcBYhQGK0rGDDbbHXWrwBa4\n3KGNYTQes9luub5ZMh2PuHd+wne/9z0meUas9hjf0e63KAUPH97j/OKcqm1AycxY5wNN56Xiloh/\nQD5UzwpOlQc94UlRWPMKk7rv/93tIqbu6yvV5d3rbu/o7tX/+H7eMkaR0vPBJyTiYFKt1N1DWiq0\n8WhK11a07Y77pwvW62t021Lu9yKW3dQ8v75hv1uy3i7puhZlFFFFykoOLqVlnOGuSEJMgYooBKkY\nPU1b8U8//Mds91sUovglTiSR1x98BUikPBWS4HoYCE397GsvYq4j6Aj7pqZqmpSA9YmbwSip7LRS\nFFk+9Hoj4JxIQtYh8tlyhdeGr967wKrAxeIEZyzLnbBse9i096o8zP4GYvBpNOTwNHvS0d1HFVML\n45BgHWBalYKl1dKft9pIO0ULcckZldorkcxZohcN6bZu6IU8rFZ0TZMY5IIiGKMY5Y4iE4cf33UD\nHlw3DWQFxjlefPqE0HbsqxI3zsEaXJ4TomK53jOfFLR1fZDzU+KcNCRdUVj7wQeMVlzf3LLf78hc\nlrSAa6bT6WAsMR6PZS5zs2W/3zMaFSilqOsaYwxHixPKfcX05D7HiyN2WmMvzlmHQNl2fB4idjwW\n0QmlKTy899732d9s8XX3arv+S7r+1ALm8eLkv3/83R/qYv0EuoZsPOaTyxtWXaQMcHH/Lf619/5K\nCpbScwkxYm3GYnYilUzXDy6HAYoCID3H8tUAACAASURBVEYWkykPT854sVkO4sKou16I8hCzpB4S\nozxEpw8+h6CGGcueGdql0QitFNPJmDxzZFYgj8warLVkxjAtCkCCnLRlAkrFFAAtuQITAwZxzDD9\npkB6cGLFI0KsXVNzPnK8djTifJKT2TsEAG3EVLmH9AgJVkEUTrQmRyASmyAjYzRWiW7uyDqmTmOQ\ng6rsPGQF2eyEo9PXhs8uASBys645njiOxpany5KbXT3YUPXPop8v7ef74lDdHYavtRJfRmsNVy8u\nuX35kvlsBtqx2+4osowsc4yLjFFhyJ0lzzMh+gRRSoE+OMhBWjUNm+2O1WpF1wjMJOzoVmyLEOJV\nVdWEGCirkrISSKjpWl6+fM7LqyteXj5nt10znc3wCEkkzzKOZxNC0Dx58pQnnz/lyWdP+OxnP0MD\ni9Mz7j98fehd9rOpvfD3K81H1f/WV5YCyY6cYZontSP6qjAtab4Q6O6Wn1+InGpogvV9wDj0Jfsq\nO9Ar/8h7DL4XbejlEvrXScLjbc315adc315SNS2Z1ixvX7LuOopxIWNFXcfNdkXuHCdHx0QlnIKy\nKpN5gR/aKN53yQAgDEP9kOaIg0g4huj50Y//b1CQ5zmfPP2Q/+f3/z6+k7GDru1knMqau7dVZCy9\np6olQO6bmjj09uIr7PrMZQPRr/WetutQqe8WQqCq60GXuQyBpy+vCN7zK4/f4eGJEH0SbIAxengP\nMUhg6A3agUQkNClw2WHeW9157/T7Q7969GqlBq9dYzR5JpBs7pyItHcd1hpmI0f0DTEE8iyDZMAQ\nUxtAW0v0gbpuMNYMsos+HEhMMorTDeNjKAm084uvcHm7pFWB47NzTo+PWd9cY7Kc9eoWX20YjUfD\nOac4fDaV4BRNxHetiKlXe47mc6q6xRjNarWiLEvGo/GwHpqm4fz8HIDpdEpExoSKPGcymbA4PWFT\n1nz44cdcaPhgs8V2Ha/fu6BMPerJ8TFuNKZtGgqnuf+V+9y8uP7a1379u3+ZL/n6UwmYJycnr8/m\n8288fPuC43FOUUxZVi1P646gNW+/82t865u/QdUKs7Dzh8PXB+kNNl07SCfdFVxWwPnxgvuLU14s\nrxILVCeNUn2AmYCYmLTCLpX+jU/WW866NLPVQ1xp2SuRd5qMRuRaM05VYtk0jEdjcmPItSJDMcoc\nOkasQhi3IWKJZMluSoJLEHf2NO8mgV1msyZZRpFZlNa8vF2xvL2mrfacjnPRSo0RZ6FwYvorn1Mn\n5imy0FUg3oHglJLDmRBxWuHbirZpaHwEbVHakY0nLBYP0NrembmUTdSmG1K2Het9Q9f1qj0H6Pru\ngX73LO8tfURhRCCzzMDN9TVVLXNX33znTd587RGz+YRi5HCOAVYzWg0zWn2w810njGXviVGhjBWl\nkbzAZhk+KnyCM6V/J/d9udmw3JVUbUtViRas95GqCXz8+RWb7ZbtZsNPPvgpP//5J6yWN1R1Q1nu\nuV3v+OzpU5bbDSrPwZhhLtennvFdGFY+/KslX4RhLAGkfzXJNUqFV/pafYDrg1xAynX1hS5lv/bv\n+uboA5Z7+IL+pw/PJw6v3wfQ4WtDZLO9JapI29VkeUGhPCZ2lE2DsY6QEButoKwrInCz21Dkjt1+\nx76qhJVqJcsv6xpFpDCGpm2SX2NNWddUdU3d1MIrsE6UaVIPS2nN+x/9U17cPOf9D39fZCuj6PI2\njaBMXdtBZCCZAIOaVb/PVaoEe3H1zndSRXGoevt/04l3IKx5sasLxqGMItdwnDkyKzOgB6Z3rw5l\ncJmF1DKQnpy4IvWB2zlHnczjXx1tSm2YKCbvCoFvnbVYrTkej3BKlHqcSfsaNSSO4zzDKFl7Ico6\n7Hv9Xdfh8oyIMIkH8ZRUGDStJC99m8uHIM4+1rFcr6m7junxgvF4Qrut2G7WjJ3BtzXzo6MDuSst\noV7wop8VBVEfa+odk/GI9WbLbDpmuVyKgpfWw1iJTfdLaRk3AUlExuMxTdcxn89wec7Fa6/z4P5r\nlNs9V6sl9/ICrRUvuhZ9cYH3HV6LQMS26/j2977B5Y8/5t/+j//9v86XfP2pkH5ef/31//T1r32P\ni2lAN549gRdNx1GRk528xdnZm5SNGOqGGMmcGTa2UZpNKQbaIQa0EmKIM5oYFfcWpxyNpzy5vRI2\nXqoojTb4pHbTq/L3FapzThYV4LV8aB8PTC1ITfp+U2gROVchoGKg7jxZYZkUOWUZMAF87Gi6FqtN\nClCJgZY2oNUC0urUa2mToodOh6AmYhHyR57naOdQeKwNjI0nm0+43ZdkqQ/Vden9JpiPVDHHGOla\nT24NtffoKGMMESB4nLFkRtEGxWZfc7PbMZsdMR5NBijce5nZy5zG+8AfPllxNHbMCsfVpkpUe4AD\n7KiGHt3hWI9KCAV9D9MaxW69RRM5Pj2XbF+7BGtLpWW1E7UerYaDThvZVK0PxE5c1lerDcoaiApb\n13RtTdd2FHmBswJTjfUYHWV8YLOv2G5LPv/sKWjHaD6lmM742c8/5vz8hKfPX/LRJ0+Yzebs64of\n/ZP3mc0mrPc1J+fnPDx6g8l0grVCcuiNng9SgT1r98D+7IkVMpZAKjzjECBbr4gxI8Q6KfCEoX85\nKP4MLNZh0OROxZoIP+pQvQ6asndCq/xbHMhEw3feCaxaKbquwfuGutywvnkOrbj+7OqGNkaiBqsU\nFVFIGInE1HnxiVzMZ2z3u7QHRL5yv6u5mExYbtZ0dY1zZlgbUclaCTEQOtFfNb2sHGIWoK2s7bpp\nB/jSOUddV0Kq4cBE7XvB/YjZoUo/VHa+8wSE8Wq0Hpi9/cxlz2HYVyVaaZxVNDi2dct6dcvjkwWf\nr7Zcb0X8xKd9rlNCrBOE0I+WmPTnniFLsg08GJjHAQnoe6BaSS9dWjUW7z2Fc6IVDTgFGFkTXfDk\nmSN6CKGj7dL3aoU2sod8mi2v2halxFJO3vthvEfumbSFjBJ7va6uGI1zohLReTdfcPXyOdOFQrmc\ny5fPuff6a2mN3g2bDKhFnwG2TckkX+CsaMYezY9YrpZDK6Lclzx8+JCmadhut6IBXFVobVivl5ye\nnQmSpg3Ke3be8MBYGg2XVlNv1+Sd58e7Dd92OVVdM3aOc5PhHjoePnzI8vmLe//sKPUvdn3pFaZS\nSs1Pzv/a69/4Lm21Y5xnhKLgcl/zomx5+OiX2LWefd1StqLM00s69afBvt6nQ0Mxm0/IMocxlouT\nU+aTKZ9dvxwyS2sMuXWDWk5vY9MPCct8Vi0EHiVi6CocvrdvxseYZMSCwLc+VTpV21J5z77eM86K\nxA4TBaLGixB5bwFGjBJo6eXIAq3vaOokzpwO3ohCBRFn1ipi6QixHQ60fbmnrssB2oohDBWm0Ug/\nUEkmLfOQQIyMnWOaC7U+ROiiNNjbzhODJ2iNzcfMpvekF5l6lW0IjJyi85FPr7ZUrefpbcm0sIz6\nAy++miXfOc7Tcz+IFFij0vPQ3N5I5ZYZI4PU1uAsryACxJDmJBkIID6IOsntasnV9QofFHXZisVX\nUFy+XPLjDz7k08+fgTW8fPmSjz76iMurl2R5jnEF+6pl13UcnS148/GbnN674I233+Tx22+i8jGV\nj1wuV6z2Fa3SBJvzje98h688fszRYgHaUXeeJll2tX0l3vlBrL/vPfWQbESssXopRemVBRGQ9yJw\n4PtgeQeCfWUPDXdW/YL/14fAAIQBllXqDsyqQKGHWT/dH+Dp+QhBy7DbL1nfPme/X9PtN+jQsSv3\nlHVFSMmnSkpTxmiOp1PGWY5SUDctx5MJm81GTAhCxHeeqTNc7rZc3Vyjc0frO8qmpOsauQ8xDJBs\n3dTMpgu00nz49Gc0XS26slq0TEMi+3Rpplgrxb4q2ZUlVXMwSVcqiXsga/RuBQok/ec4uHp0KSB3\nnR90X2X8qCZ3GU2MfLZa04TIbr/n4XzOw8XJEHR76yutNcbaO5WrJO49fyL0tl4wBIo+YKk7v3qE\nzBojXIXQ8zQOT90myDvGwL5pZSRq+LnSLvKdZ5SPaNtuQDz6z2adk7PnTqAe+rijMeXykvlkzG67\nT7rWMteaO8NoOsYVY65ulvTWg4fbGwfyDpE7TOLAONNUdU2W+sb9548xkuUZm+2G4yMRQLCpwMmz\nnLOzs6TzLclNXdfk8wW+bQjbin3omNct06Mj8jby6W5H0IF9XWGjYtc1fO2dr3D1/qf81f/uv/zf\n+RKvL73CHI1G3793fu7e/drbFE//AWUb+Oluh8nGvPfeX2G5qygbmV0LUYgrPcEhonhy/clAKClG\nwmyz1nIyPWJejPn8+iVt16aDWTNyOW1quGslqhxE0vyTZKiSTQpxRkVoYiAzRnp+WTZYeylEe9F7\nTZciqNKGvMgTY9dgU5+sTP6ZNvVDO+8FljCyDaxJlkpIptlGqUiyVI32vb4u9VSsteQG2i4SdPK0\n7J09lMzUaZJpc6LN1x7aKNV32XpciHRRoW0G0aOSH92qbOh8h3M5rz98h6PZKV2ateyCLOxt2fL5\nzU6sqJK33tPbPQ+ORtTXOyr/Kvw4CBamA7nvj/QsPacVwXcUec7swQPOT46xmSFzCkUv7sDwfTKo\nL/+j168VuTkwRqrQ3b4klB3j0UTMrJViu17y0w9+RoxwNJ9xu3rOkxfXrLclF6894J1f+lpKiCLz\no2Pun58QgLe/9rUB9gzBJ6RCDt4+IA5s4MCgetT3uCMiUJ5UeF/ZA6+0H9NfvDqIQfTygyElisPX\npXKzd1LtD5nh1b5AIBmCZbp3Kmm69vB8P6doNGl+Tw9aw7v9FU3d0IbAJx/9mEXhsLmTiiNzBKUg\nemIrkHkIgdoLdGmUpm0rmtZy/959NrstbfC0TUsXI5mLjI+P2Vc1LsFuIC0SCWgCWRZuzDfe+iY2\nVVXOOXyvOBP8EPjuzlxnxr3Ccu3S3LMZ+vcmkYRkVnMwgI6RLM8J3lOknqa1dvDBNcZwNJkyH495\ncnWJUho3GrGpa6KCo7xALxa8WC2JUWQelRN0QadKt1/PfaWrUyCM6f5p3SdW8hSlnxhQSgJxZgwa\nqbrFxznQ+V5KT/r6PonNd15Y9yHIedF54THUbY11jqbtcNayrxsiin3ZSEtD9ciEjOM551iulmSz\nCWwvOTo+QiW28Wq3o6xaurrBWsd8fpS8fzsOYZ8UIOX99ux87wOh3pBlp6w3a7IsG9ZyVVWMx2NC\nEIP2nmVcliWj0YSXV5ecnpyQuYzRaCTP3Tk+fmZ4Uyk+bhoej8d8/vQZi4sLllHzAMtolrPe7ci0\n4dG33+G3f/t32L37cP7/FbP+JNeXXmG+8eZbv/X6N7+vP3/yD5iPZmxDx8tqz+O3f5nlvmJbNWyr\nlrLpBHIjwVHpsBhn4wS1qkEd52Q6YzYa8WT5kqqpUApGeZasefwr8ExmRd1DRLlTMzzKwRG8p07B\nVvkO33UUmWU6Hh/gHCT4db5jvReafNM0QjKoKoxzBKOpk9m0QhOUoQqBsuuISkNyUOmHlmUGTDLq\nfV3jE7ttmhnRZlRaFIuINEFR1i2bfZ3Ubywe2aBOazJnab30ftso/y9EGOWS6YZ0kEumiszfFSNG\nszM6rVjMznC2kB5RiEwLy7bqeLYqadKIRJcCetl4nq8qHi7Gw72Ux3TokfV5shzQ0t9xRuGM5uby\nJU3bcbpYoIwwettWCACZs4N+r9EWaxxKaXyb7NYIKJvR1J73f/I+zy4veXFzyU8//BmffPoJTy+f\n8+bjt7i4uMfbj9/GuYKy6thXnj94/yd87Tvf4vTiYgi8rT8cvPvKDxKAMUbE6JbEBA4DyWlwZ0ko\ngsCyEjxVWp8CQ8mdkByrF7c/3KYeueh8kg/0feWpXgmUw/3t7+ydaiRNTyZBgDuKMUPvOg4CCUNb\nQR2Yos7K7zp2aAWfffYxq+0tPnqy6ViqfJcRlaAfwXeUTcu+bfBJZCEoaEJH1TW0Gq42ay6Ojhg5\nR5FlXJwsGFlL7saM87Eo9VQVvgvSf0RxPDvlnde+ztff+Dbf/+YPmI1mhBAo6+0w0N6rJTnnhj5X\nTBCoTcG2D469WEg/3hFCED/LEAcj6L5HGBO5xyjFqCiG11VKUbiMs6MjnlxfUTctPnixF/Sw6zxX\ny1vmoxGPFidD28cOYxxiC+ZSIFcxDtBsHzi7LvVfVa/JqwTaRQ/QfUTjjIzR9WvVBxEtCD5grElr\nTJCjEPWAUPTM9qppkoarT8mNSlW6f4ULQoSmadEuk76zcyxOj4mAdRnWaG5evmAxLShGYzyKbS0Q\n8RfywwF9UhEI/VksSdL5Yk7bdoxGo+F+TCaT4TlXtUwmGG24uHeP1nc8fPCAGCP7cs/y5obpdCqB\nMxtx5ByrFy8JiwX3pxPOTk9Zbbf8hDRSl1mmxlIZxeOvfxVThx8+/uZX3/sThrF/5vWlVphKKfuD\nH/zgL3zrV3/Iefw5M2f4eLPj7PQNpkePuNlW7KuOOklVZZghA++PhhD9MEMVY+R4PGU+mvLk9pKm\nFdd16Xn1aj9i7uqDnE4hhMQwkwUZFYQuoKIooWilhZmqFFYlQ1rgaDxmW5bDbKazNinoCJOuyHO2\nVclsNKZqW2H0GkPjO1SATBuMydARXKoQm7ZhlFnapqKNUMWIRcgaTVPTtTDOxYoHIq3XQvxQMrZS\nVgKJ9FWOMUbYbUq0ZxXQpYVaN0J4yLTMKEYUQSuIhnVZ8s4bb3H58gXW5Sl4wKSw3GxrrpIm7OBh\nOYyOwKYUxttrixGfXu2+uFcSySix/BTCjNUGa6WS2W42bMdj8sJgpmNGeSEHOimaxDQKkyydfJSg\n3TQtVdXRxo433n3MzfUt3/rud4kxsLpd4rKMD37yAeV+x7Pnz7m4f5/Vas1kPuHP/6XfBG2GKrHv\nE44zTVknrVfVQ8y9MksKCqny83eCl5wDveTdILAnX6FeJebAoVp8tdI8QLU+RdJ+7SfQ9p+5ryJ3\niB9pr/QjDf176YkkSvXQK1JRGjUEy9xZtpsXTMcPePzWN3jx4ufoFkZa05qMUFdELclgMJpOSUbd\n+I6m62QcIEoCEWOkbVp2Vcl0NGK137EqS+q2Yd80PDx/na+/ec69s/u0bUue5WilybNCNEjT55fK\nTPONN7/DP/3oD3l2/VQSThICk5w++kqzbGpBMqzBplkmdwcWVUBd1/Kcokq6rDElLmFgqA5kQoTk\nd+/khJerpcCAMUAnibgtCnZ1izGWp9dXnB0d8cb5Bc9vr/GopNEuQbhJbZgvQsIoGUtr244id18g\ni93xz42Bphs+vpxfyYgChDgmSmdCcBFVn6QkZC3WKLq2pVMdSmnqtkUbC74dzsrYoxSpFM5sTtvu\nCTbDjcfkRcF4PKHebbk4P6Nta6I2jCZTsixPZKFDlXwn4xvWep8txhgIdcl8PsdYmyQOZcSnn3fP\n85z9fs92t2UymVAjWrJN0/DgwUNWNzfEIGfD9OSC25ef8pDAj1e3fCvP+cdPPudkOqfuWvYx57QY\nsWpa1PUNX3n7EX/nb/5tkTP8kq4vNWAaY/7C29/47miir5hExajIWZWOt9/8ZZalVJZ16+mSAEHQ\nh5OlP4h+9uQnQ7W5GE+YFWM+vXo+zF05aweIqIcOMmvRyfhV+olRejRNI0QTq2kTnKTSrJchDn0k\nr0TIvHBzLlcrYpQDyRnx3Azpga1C4Hg6o+oExsmT1B5R5Ppm1gxmx4PNUCf6rMvdHmUsNhND6NqH\n5GHZYa3jZGxYlx3bco82lqYtmRUZTduhjdDNy7qjRVPk8n3C903wjjZ436IQqLkJAaMs++Apigln\ni3ucnTykE6yHaW652dXc7uWZtP5gVTXYQcnT4WbXYLXi0cmEz252Q5baQ6mq14tNPRhrZP5TBO1B\nKU3XhcH1gRBSgDAYJc/ehwg6UpUNTef55JPP2e72FJMZX//W13nt9UfClo2Rs3v36NqW03sXnCxO\nEnGqSFl3suzqpIrs0YvMCo2mbP0Q4A7ohgSx0Js831nTkV5Kr/9bYiiny0cSG7a/Lf2rDkv7ztfG\nQav3Lst4OH7UXVLVHdBL9cIQ6YX6fmX//FWqXFQvzs9BOCLNfWZWY1THevOSk/kErR31fstRptnu\ndqKFDNJv6l+nk9aDVb3bycGUvUvV7fPbG14/v8eq3LOuxREj+MDPPv9AxkN8w/nxBc5aimyUko9h\nAGm4N+Niwq998wd88On7/Oj9f4jLssHyyjjphRYue0WTuU3OIs4dBuIZ4E89KHv1cpUmCYz4xJoH\nsErz6PSc682afV0ltxvRNAUnguUxYLz4sD5fLTkZT3nz4oJnV1fUEbwSezmbWi+HxROH9xsVSWCh\nIc8d+ETii4ASaNkZi287katLATsqNSh8eS/mDt4LSSoGaV/ozBBCpPIdzogeM4qEikTEZu9ADuvX\npug9K9qmhVHBen3D0fECiJS7PRbPaHHB7WpLADbbLVovkhrRQaSlv3SKwn2C2DUNputoGk+WObIs\no21blssl9+/fl5/dtuR5TtM0LFcrJmk+uw+aZV1RlCUQOTk55Uc/+X3uv/WAn++27OYnvHlzy+39\ne5SriufNDfp8gW8qFvMJxWLBb/9v/wf/wX/xn/zdVxbbv8T1pUKyj7/69f/hrW/9Kucjx+bZc243\nKxidU3nDtmypmo66DVLJDL2bw9bZNztW5ZJRMeJ0dszRZMZnVy+SW0gcDpS+AsysZZLljKwjU4p5\n5shNLzcXGeUZWt31qfMpA2ToL0jv0OAQq5g3z045moyFMecySBuz7lrqVnQoA2E4PGyCZCyA95DY\nt20I7L2n9IEaTRuEmj9zGc46odVrRVFkFMazLivKLqCdw1nLbDwRRqaydAHqLuKRWb7gA0EdHAd9\nlP6mTaMZzhoKm+GTWPjx9ISIHiqrSWFYlS2bSiqHLtlR9ZXDgYxyOPgv1xWND7x2MubA0EwuFaTR\nkNQzswa26zWhaTg+WiQZOpdGew4KS9579lXJerPmdnnD1c0NN7dLbpYr0AasA6Nk3KX1A+zUhYgy\nltPTc5QxBDRl3dB0Aqc2d6BUgVEDuTXsmhbPYeZW/iwELx/iK3xT3fcFiQPM2fcHD4zHxEgdviv9\n/sVeY/r/Ygl2gIb7/979/rvEkMN39gE0DhX9EKAVg3pQT+yxSgt8b0yyrpNEZrW+QUX47MknPH/x\nBK0MTgVGmWXqNC2aTTJiLlsJjIZeSUkNIxK6D0RBkt9dWXI2m4soQBA5xsw58iLnk+cf8bvv/wP+\nr3/427y8fXnn80LVlGzLDU1Xs9mvWW5uePriCVob6ZWlfd+z0Q8/V54ZyKHfpTnsfva6l1+7K6vZ\n3/N+blaeq+LB2RmbqmRb7gXpSEiVc3ao5PuRJZ8g4eV+x3Jfcu/0nMI5xtZg0/0XdjyDTN+hx4wk\n1kqlGciQ+s6y1nznqZtGRsS0eOCGIKxZUWKKNCnpDFH+zSN6uUQhlHUhiBmBgiLPZZ3AADt/EcPI\nspy2bkT8o22pQ6RF/EHbqsKbnKosKfcbYtuQFaOBUPlKBS1Ay52/H9AOuoq2KqVYUYrVasVoNBLu\nxlD4CEmwa1s2mw2z6QznHF3X8eDBa1xdX6G1oRiPefuXvs9muWI+nhC04tG9C+r1lsI6ZqMxviyZ\nZjnT8ZT7xvD9936dn/3u7/PLv/ln/7M/siX/Ba4vrcJUSk2+/Z3v/tJrb7/D049/xNlkwY+j5dGD\nb7ApW8q6fUVz09xlEabCY1euaZqGiwfnjJzj0+tL2jQM7ZN6hDNWZo5CQHUBYs14lBFiYF/WGJth\nlaKL4hzehn4WKQwwbuEyovc0bSselGhCaMQZxLdcTCdUIXK5Xn0hUHtWuy2ZsaxTMNJKKumpNTRt\nizIWbaBLurcoiMNhJrNSY2fZlsJEHNzmjSEmQeUsdpxlgRuVUdaVLFKl0RHaVmYKtRLbrv5eNCFi\nCPgukmnYVh11FEPs+fwYkOCWO83NrmGfeshd+nVXA/MuVDjsMgXPVyUPFyMeLkY8W5b0LIf+kO9J\nP1opnj55QlNVfPXxu7jMCkGmJ3/ESN201HVN3TbUdUPnPfuypu063v/JT3nw6Ct89etfJStGQ+UY\n46GPKgfBIagH4mCeLEH+MO6SW4Fn6zZl9f1zCQd2dCSN/KhXGYRE8ClgonVKURJpKyA6uERU6Bmz\nDEoo/XtEHWBZed0e7u0Pnl+Q/Ma+FlDDKJJKwTHtt+Gz9H1Mk8QiMqvJnFSVfaXptCI/Oia0NdPZ\njOvrJ7w2z9htlkyN4nZfUuic1jqqdEOqthGSXBPInKNuG1GsQgzP2yAC9tuqpMiOmBS5CEkkEo1K\nvd8YRKXpRz/5h/zmn/lLgvYozWq35Ecf/C6+jeSZo+pq2qYmz0V9R8TDPaENFC6jbpuBqRqiMD/D\nXcg2VbdZJudBZt1wz3uCnRCLAtYZHixO2Ox37OoqzW/rwUS6Hwdq2lYgVyx10xCsxVjNttzhfcdr\npxfcrq/RiRkWhwo4DsFXWNQpiKaqU/xlW8ajQua0Yxq3GYg+iQuBEGj6vqAkcxpUL/1JElpv0IkL\nYFUY/CZJo2v0eyYhLlopimJEVe4BWG92zKdzytUtubPcVh27as/p6THNpmFze81RjhB/grgTDWc/\ndwJzv3xDEqyIHmMzuuAJXcf5+TllWb4i3NA7zcQYqaqS5WrJdCoSmk+ffM4bb7zF9c01sW6YzSZ8\nsNqxWS2ZnJyhr66Z5zlNnvOsbXiUjTkb5eiy5ImKvP7uW/ytv/63+OpvfO8vAv/jL4pdf5LrS6sw\ntTH/4bd+5QfqzXsXvPvgAS7TdNqBHrOrWiGpJMh0OJRjDxRIUMpszvF0xvF4yicvn1M3NcSYIJUe\n8jNM8hFWG2bjMdt9yacvrnh6fcvYKjID07wgN5IL9LBDf/AH76l2e3oVENJQvNGKwmh0aPF1yWoj\n7u/9g/WpZ7DabpkUxSDRFULACTAFHAAAIABJREFUeOlzZkn3tmo7scJylsJaxnmOs4ax1dybOKzy\njDJHluVo44ha5h+tVkyUp2kDy32J8Y2IKOQJhgbaqKlb6QGXtdyfnkBQBzH8rVphy673e9b7HevN\nSmTZnGa9b2la0YRtvccnmLMfbE7thzuB89XD/NltiQIeLkavHNbCyJR5SpAO63Qyoar3VFU19Gpi\nBB+gDT55jgbW2z03yw1Pn73k+fWK+48eMzs6RjtH1XaDLF//9W0npKem89Re5mR7e62mE6PrtgsD\nWWeUaba1T6M0CXYWMEAk9BCGqTZCXBIEQqBmoxUm9aqMTmMZqf/dJwcmObIoPQBew3UQSoupj9kH\ny9RE+iPfwfCvh99TQpJEOfoZP9E+lqrGaSG5ibpSgmKtSXJqitvlC7wPHC9O2exu8M2e69tbdnXD\nuiqJzsn3EylSYqCVpkkarnUjkmghBmExdx0hitpMFzyr/ZbJaIy1FpdmD4WMk7aeVlRtyeXtZUqA\nO0bZiNloLi2KpsEZy2wyE/TG++H7uiAjA20rUoh119B1fhBG8D79OQyniWjQ+gMhsK/a+1nJ+4tT\nyrZiud0QQiQvCmxKxkHWvzYa3wl5Bnp2s0+tARFyuLy95nRxhrNGkColSb3RVipI3WMLB6IYIVCk\nUbiqqugbK0opQvJT7WUuB3WmeFh7Q+BUCmISSEkFSNMJEkOq8DRf2MFpsamosS6jqmsRQzHyukJC\nVLjpgmdPPmU2zjm5uM/xxQOevXgJoWO7XA8vqu68/pCcDJKBkRg8x/O5tGFCYLfbkec5eZ6zWq0w\nxvD8+XO01pyenJAXBav1mv1+z3g05vTsjCfPnmKtZToZ8+knn/DGxX2s1lxVJdO33uLUOXblntB1\n/Pz2ln2A3W6Pqyry+0cYpZkdz9+bnRy99gu22p/o+tIqzLPTs//ouz/8i+pm+XOaF89pJ3Mev/Fr\nrMqGqhVDZp/mgLSSoehDJSC/j/KcN+99BWNHA8FnUOmJaag3BLblDotGO08Y5Vg9RsXIs13NsfUs\n5lM6L/0EoxRea/HvS9l/nlwFuhho64bxdIRTvapGDtqymGa83O1w1qSepDBWWy9jIKMspw0du708\nKK0UrqrR1qKNIY+eibVMc4u1Gbdbxzg28n68om47tEay9BiZTWfEpua6armYZFTeM8kcXevJrEMb\nqNrAvm4I2g7nbUjiBEaLIHVVdqjYUEbZnIUteP3+6+RWc7NtaDrZYHeDh493CCivwIWA6ues0l+V\n4sltyWsnI15bjLjaNAMUpodNKuSDbHLMfl9hjIhPdGk0xVpL1bSMJ2N2Vc2zyyt813F8dkbbRR6/\n8zZoTdXJmgmxVxY6oBF3K7ZD37V3UUmHi4dxZmi9+HlqJb2kHmpNnzgxsiWr7/uEERIZKSZhivQz\nkwyZUsImlYggPTPlRbVEh77qFXRh6NP3t/GVBmcfOA/XIU9RiRF7SEgOTFidsvwDrCcIjB5g2MwI\nOUaryOnJBZm1rFYv2e935HQYqzmezNiXeyof8UpqZ991tOHQ4weSY49DRZUCR5AE0afq33uOJlOK\nosDvdjKyYGW/EiXB67qO//N3/yb/+p/5NziZneCc4ze+++e4Wd9yPDvmb/+j32ZbbmlaUdVqfSdt\nDpLEXwqAxpthLYqLkR/gfqMleSxSX7OHaPtAabXm4ekp63LHercb4E7ftsQQsT1SlFAGpRVZ5iAE\n6tBJAh4PLaV92/Ds5prT4xN2uz16v6FMyIVN4gMxBnqVWQ2E9J6MBpTBdx2jPM0qaiE6EWVdkRyP\nVBTJTdLYiSgMyb9XycUFpCdonKBOMfVLDpA+QyszGxU0VUXP6TZaQ+hwTuDwNkJX7yAkUXdrmZ+/\nxm5bsduumMynZAkJ6A+PvsqEhMj5FmLAKkng+1Ee5xy73S7px44Yj8d0XUdd1ZT7Pe+++y6fffaZ\n2IHNZkzGY9abNa8ff4WR09Q3a0IxY9/UfFQ3vDmboJ6/xE2m7HcVP9vueMdZNvs9b7mcX/1z7/FP\nfvQHF8VoNP1jQtg/1/WlVJhKqZPJdPYr73zrVyhuPsMpxYOL1+hCxq7uaJLze9dDZond1kNTvaLH\nbOR4ePKYo/EpcGC1xbRZtNYURcFkPKEYF+yC52wyZTEekzuL1YYA5KFlZDUjYxhbxyjLxKlBKTKd\nNnwU1tlsOsEqkXOq25aR08TgKcu9+E1ay2Q05u0HDzmezTieH4HWzCdTjsYLHt1/k8ZH1mXFcrdn\nvd9TNS1HmeW0UFwUMFIdZ7MjQoDVds++aSRjDpEmCPRT7bfkTuM0lE2LDh7feZyKIgjf1EnQ2zDN\nxAHExyiUdgUjI8LS2jn2ytB4j7UZX/vKuxxPpqzLji6mAzEFmYFFepeE0gcjwQTv9CrudOtU5Pmy\nxEepNPUdpqgCdtsdNnM8ff6Mqm149uwZf/j7f8DHH3+Cb2ti8GTOUZYVly9vOLv/gLMHD3j0xhu8\n9e5jOmDfdJSNF/nE1ks12fa/OqqmE1eVuqOsO8ras689VZNcRBqpNgtnWO4E6hXx8QPkDAzVpFFy\n750RXc1hNjR5kppUeRpFkkYT7U/RJlYDU1gjlaYI8R+UnRKgMgzb/6Kq8hfsrFRJgEGnmUo1sF9N\nP8Kje4KPEOAyI1CssdILU9qglUm9ObF3O5ofSY9TBZr9ltB1BN9Rh0iHkGGMFl3frvPD+EZfH+vU\nS6wbCS4+RpbbLUfjCTHBn23XCUkohiR9qBnlBb/ze3+H6/UVn19+xq7ecTIXokmRj6naRnryVg/C\nI/14SZ7nZJkc0kqr4d+KImdUFIzyfFDe6vtj/ehIT/h5dJ5sADebO+cLwi5O7kOZdQkSlcSkrmvx\najVanDa0xpLGzwLsqpqr1YrF0Zzj6RTXVdgeLiclOiTJSnryVj8opAbm69CHpff9tYlZ7IQfYMS1\npHCyRmPoPSkTcpHmvptOZtLrSswCjDU4ZwZolhgpioIqiaMYo5mMcsRDSqHwOKO5uDilrnaSOHrP\nm4/fpWwDL6+X9NDB3ep1mPGMkl2GpCNcVdUhEU/cjxgjJycneO/F1ktr5kdzsjznxz/+MdZalstb\n9rsdk+mUxfFC9IV1hsks3b7kKC+w+zW3dcOfffN1TIzkLmO539JOphSjgpvQ8Svf/2U++b33+ff+\n87/6P/9zbbs/5vqyKsx/67vv/XmlTctbDx+gQuTz6TFtE6mblK0mBR1ImftQLcjv09yxKzdYYxjn\nhlE+pmx2wwap02HnohwYTfDMRiPmtCyKMTur+SxEurZlX9eMJhllVTMbFeyVFYHxxKCd5TlOqVTx\nSu9ThMsDm7KhCgptHYtpxqrcY2KEpuQoz3FK45zhYn6PP/j4UyFOuAxrHc5l1E1FWdUsV575YkQV\nFPtgMDHjqmxwHhrvhUzUNEyKUXJzaLneiLL/tm4osgyb3FDyPEtqPSLvR+jIXcZkMqFpG5SCNgZW\n1R6cwxmhlJ8fH1FVW253JQE7BMZhKH+AyIE71cQfd5wfgiZcrioeLkY8OB6zKUWpKBCZzWY8nr7D\nJx9+yO3q5uAQg+by6oqTkxOWqzXL9Ra05eTslGI0pu08dReou0N/9W412Qe7gfQQw2H4v0cq6Ht9\nhtHIsm869rVPGr5JbECJZbdO/dfelkwr2KyvmM3P0KrvXR+qTR0VXkm/Ug6ptIlipDMa4wNeK0wU\nNxzV49vplsbQHxqpevhFvUvk+1LrHHoxAnPQMe2hOaVSME3JoLWpX5lmXvtgPyQzCoJvaTrP7WrJ\nXLXsfOT5es/sbMa2k6H0iECBOqkzrbdb8iyj8V76iMYOpJYYPb4TceN1uWc2HjMpRtyslhhrk8uH\nfKDedqxtG/7eP/47dIlH8Oj+a1RNRdXU5FmG957tbk/mpAeZZ2KGrpWi9d0QAIssl+CSFGH6uciq\nPsCzPaHEGcvD01OWuw2r3VaebUIINH31Y5LTUS+/F7BJQaesKqbFiLZtJWg6R2GtsFmR/XZ5e8O9\n4wW5UTy9WQnpxTAEWUJMou/ycBVyT4wRprKzh89kkmm8tUbeh0k7SAuz1tkEe6f5VpFZ1CgVk4JR\nYtimNlEM0naSNSNn1ma9JC8yityRGcO27LAuY5IVfLR8Kq0ql7MvS5xzRN/hijEoRVMnxIE0665k\n1KVXvTJpvI1IajfJOMl8Ph96ltPplO12S1VVlGXJ1fU1i8WCo/mcm5sbTk9Pef78OTGZt6/3O0bj\nMe2+IG9E8OCXT0743cuXXFU1cyJ7IrGLfHC75DtHM7qu4+Fizmwyp9N8/58rmv0x15cSME/uP/qt\n7/2ZfwW7+hRM5IOPPub0hz/k9ram8ZL59E70PbOTeDgEZ4WTYXyvGOUjrNFDX64v43sauCei0zDv\n2HsKYyk3K0zbcT5bsK9qfGwYW1DTnJGznGhLIKKUoW4bxkYYf20MLLcVAJ2HaBTWZigvmUoePXY6\nZblaobRhjKf1LU1bMckyQux4fv1MDlxn8V2aAY0Z26bm8npDa3OqznPv3pvcfzBjOp7inOP69oqr\n20uqusIaw+1mh0YxL4TRWWQZTnVEbeiaGmUyqrQ5Gh9pu4axy1i1rSQCwdOhwHeM84K37t8nywpe\nLm8ZbW6Zzc4Hoon0Xw96qDH1kg8CEl+8fhExRbCeq02NQnF+VLCrpCpuOs84s7z19mM+/egT2qai\n2W957eF9jAp8+NFHXN2sycYTkbHLcvaNIBF116UxlzQTmSLW4L7RM6z7fmC/joZuX7KrskLuuto2\nCY4KmGgOQZVE0EnBcrO6pMhyqnKDywvyfCLi5gohZCCBTsU44DJdGkMJJuJ8oDMaHQI6asydkSXu\nkCF+8fVHYdm+OukrVZUCo0rC+3212/cyxaS7J/r0JDMGYpBOH9i0a8aZQtc1dpxTVyXT+ZT5yJJ3\nmko79m1H2ZZ0XXuw10v9Q2czlBJzBJ1g7BCFWam15na7ZTGbsS33OOtoulYqdaMH55ReJs7ljizP\nWO+klzUucupGCTtaSQXXG7YbJQQ2awxlOjeK7NCDi0gf0ybOgu9abCL9TIsRF4sF1+s1ZVPfIRvG\nxF4NRKWGirhvAVkjwTIi9mF122KUEILqtiUYyyjLCE1N00GlYLm65auPHrDbV6zqFosiJrJhUIcg\nTqpqYxI4MVpk5JwRtSOvwVknMndRk1udAmfGyCnKqpbX0IrYdgSQvq5GxAOswLf9nukJjwYYj2cE\nX2OtZlxkOCs9/KZpmTrHxz/7Gb4qOT1d4INiu91gMkfbBcqypjCG7XrLZFygTG/efifZvsMZQWka\n73FKURQFvUvM7e1tQgcKLi8vOT09FSco56ibhqIoGI/HjEYjrl6+ZD6fMjMz1rdL9l2LUrCpK6xx\nPF/eMFqc8GhU8KJtyLXGR7hCMwvS6vvBb/w6f/D3fpff/Pv/zn/7N/6X//W/+mO34x9z/UsHTKVU\nNp3N3/zGd77PdP9z9k3FzfyMcQNNok+/SnSQq0++Z7lsjuW+Ic+KpLjS0XbNHey9z04lkws24jCs\nyoq82fOV0wVV8NwzgWY+pmo1zneYGCl3JUcuI3OWDkVO5OnNmsbDtlN4LS7mRou7wtRI49z7CqvF\nhWQ8KpiaiIodBtmwP/7sCbNiBAhLtvOekcuICsqm4WTxkAdnr2G0kcw5z4nAKB9TZGMenj+iair+\n0R/+35wenfJr336XT599wk8/eZ88s7TVnmAdwVeczUdsai+U8xjxUcZW6roCorhKRCj3W7LMMR+J\nK8BnL5/TdZ6T4zM6T+p5xuHXH+lXIoHlF3TUeOVL7j7HKM9Oq8j94zFlGv/ovMgPtr7jaDZldHpC\njIGffvwJdeOx+Yj5yYLF+QVVmyrLViDYNhEXwoBAJCp9Eg8YKk1S1aZSRZY2q1GK2ShnV0vwzayR\nfs7wzu8EqBC4vX1OWa5YhY4sz7l8/hlvvPlNvBfHDu87rDIEpTBRzG19khqU7F6qBBN6OTZPUAqt\nhbnrB5j7i/c0/oJ3FIeZyhSrh9GEu2MtgyF0goStPgRL2/ttDpWo/KqqDard8vrIUrkR+I5tE/h/\naXuzH9uS7LzvF7HnM+ecd7635q7qqq5udrdaLapJkDZskDBsQYBgGLD/AwF+sR89PAgG5FcbhgE/\n+MFP/gcEmLQpShBNUqTcaha7u6prvHOOZ9zzjgg/RMQ+J29TAlltHyBv5s08eXKfvWPHWutb3/o+\nZSTXeY0MrRKQbhviMKSpGjccH3H/9CGzyYzjvROUVjy/fM5Hn/8EEQg6owndvbrKN4yzjPFwSFFV\nCCB0SldSCLQ7lk5bHVfptKC91ZYfaNdKu0RDUDl2rtKWrZtEsdWTbZteQMS4BWGdVQSBI/1NBiMO\nJmPO5ldu1lg5UwfZCwZ0rjISwvpOGtfqEECjG9vbjK1Ye9M220pZKEItCcKAxFiYOg4lF+cvOJyO\nMJuSRV6ShJZhfEPQwK1hASBtLzaNE4qycOcDjG7c9bOBPXPHEEgrEtK6NRNGAU1jyUJd64OynVPv\nR/JcZevZsUW+dnOdhrpurFpTKKHpKJuK05NjynxF3gjy5SX7p3c5Oj3l8mmJNhAY4apH1Tsr2Yrd\nClFoY+3GjFEMBylFUfQs5bZtuXXrFhcXFz0c66vO4XDI4uyc2d4+ZVFyfX1tZzLbztrxqZZRFmPy\nirwqQQrKquXpas7d8ZTbV1esBwPmRcmZDNDjhC+efMX3f/Bdfu8f/WPe+43v3+ZXePx/UWH+xlvf\neFcHaRTopubFfMm9t3+LTd32Fcxf+TCQRgFpJLlYVSRRgOw0KhREQcS94/t8cfa5Y6nazQGMpZJr\nQ2cURgpeVh3tl485Oj0i7iqmEsK2oRFQIkmSjE1dkTQ1i03Oi0bQJSl105BFVnmirCsL2QED7IYj\nAonpOkJjCLSibaHpOopNyXmryUYjHt6+zcuLMzZ107ucZ2FAnKaMY8Hl1WPCyNp+nV1VVG1Llg4Y\npAP2p8ccTk/529/6ddurkIK3Hn2DKIr55POfoAcJqWoYx5JN3ZG3hsxleS6FIAxDyrKirEsWqxWj\n8ZSj6SHLvOB6uSQdpCRRyuX8JdPx8Q60uSXR9BdjFz78Kx4909M/32309r4xbOqOi7WV0fOOHlEY\ncnx4RFfl/PgvPiJEE6UZyIBb9+6xd3xK1SqaVt0Iml6vVe/0vA27A/83hRX8v33VKGGYBFysaqyF\nEz0EthugfKQNo5iBmLFczXl5dsbp0T2iIOTZ808YDWe05QIZZoyGM4wU1G3N1fUFt04eIkWwDWCB\ntEQqKZAaDNLCv8ITgHaO10VDV4CCI4TclMczPQTu510FrpeKm/tz/UrvDhO5Xp2UvrL0FlKCL7/6\niNX5c9I44fXbB0TA8eGUF6uSda1IQ6g7ReeaXVrbyjGNMr719rfZpr2C124/4vHZV6yLFaELPtJp\nwc7Xa2ajEXlZWrk6Y3oIt9Oql08zBL2JuydQbRMkTRhG0HUYBGmS2hGktiUQgjSOqZvGCTqIrZm0\nEITuOPZGYyaDIWeLubW6cvq0loVq/07bWmF3z6aVRlj1LrOVr1PGtkNMYM3DQ2GJOUkQuMAA0pZy\nNJ1imAhW80uOj+8igbxq6MDNTts+o5HbkaLQjZyBIQossRAgcmNhWkPZWV3epjMM0wwhAwLTooyk\nV7wzdqzGrqkAhHUyAjBG2T42kqZVLFdrADabkiCQZAND3RlMFnDn4SPW84Kz8wvuTE4RxpBEEev5\nNXWRMxpkSGnoVEccRYBAKetm49dNiCXSqbYB1dK2/mdu5EVKyrIkSVNGo1EPqXddx2Q6QQhD3dSE\nYch0OqWqSoSQvP7mW/yL3/uEOA0ohMBEVjd7XeSsZwcc7u9RVw1xloEQfHI15+00obl/l/xqQb5c\nJ//GTe6v8fiVA+ZgOPzPfu23/sNoEoeUl+d8KiO+KQeW6LOjnOI3Bt94DgPYG8Wcrap+HCEKNNrY\nm71srLyVh3CUtnNHxg2iG+NwqSRjEwSovGbz4ppJHPD6nVNLRc9r8rwEJMNIoAcj9rKA61ahkHSA\n6Vo6ITBhSKw1jZt9CjFEcYRWmk3dolqFFlAHEUejjEBAU+QMsiFGhqA1QSIpmppEK64XcxoHgTSd\nxgSSYZJC17BZ1yzWc+pyzWxyzHi4128WD28/4nJxwfn1MyZJjGwN40iQOkhGAIXSTkfXMBtkyCAk\nimLeuPcGt45u89XLl7T6K+tXWCyRwQvGo5Pe0Jadje9mzX/zyy071oUa6ZVJXLDsg5dEa0FRW+3Z\nO3sDwkBSVQ2Xl+dIYP/wxPU6DKd37zHa26eorQh/1VpBi6ZTdJ0XUdj2JW0rZDvy4gPmDXjVfSEl\nTLKYTd3RaUPkxS/d+7ANQjCudxWGIYeHpwhgOpnw9Lnm+OQ2T559TFWsMHXOQEpWecH8+ozBaIgW\nEAaGTX7FZHxCK40bNtfIQBBoK0toNOBYtb0qQn/Eu0HTvx/f29p9Y+4q9bHeOBjZjpFETlnJC957\nYpBw58IGEXvJBtmI4OQOxwOJNB21kZTrDbEMiMMQ5RRhsjB0c8GG+WrF7bt38TO5nm15tbq28KFW\nRJHVcC2rCiEEm6pkMhgySFLKpra9sNAxuWttZQddH9Kom+pcntHaaU24Qzhr2gaJIIqtMXPXdaAN\nRgrn6biF2I3W3N4/BAEv51d0LgBJaecsQxk4AwfZf3hyUONk5Iw0jq1qg5hW1nw6CkOaprEM2LYF\nA+u6JgoEp7Op9cLVmkGWcPbyGXsHJyBywL5eq60Yu/G9Uiys7pGSNI5QStK0rXUZkYZOtcRxSFVV\niDQmLxuSQNK2lowjwxAhnD+rtIFXtZ0d7/DJmLR6tsPRlHyzwRad/j42rNY5RoYEYUwaxpxVCw4O\nT1hdndM2BfnymnQw5EJDIgRN64Kl2a4L26O3bQvLLg/QXUMooKlqDFa28PT0lIuLC+7du0ee5/11\nub6+5sGDB71soVaawWDAerXi4OCAFy9fIgS8881v8+d/+adIMeLn+YZwOLBs5XzNgyxlVpZ0QrJo\nLGr5s7bmtm749d/4IXv33/hP9g+P/vPry4uLf1NM+7c9fiWWrLCd8d/5zt/6uzx++mNCZVBphiG0\n4wDb5/XZP+7zyTTjelO7iuKmibRAcDI7QeutOWySJAjp+gzKMvBa1SFCSRFF5NGA6OCQZTzkzx6/\nJATyumOtJBdVzbO8ZUXMRdWyKCuiOLY+cMqame4NB0RJitIgVIduKlKpKNsWjeDp9ZxlVTMcDBiH\nlkSQF2sOsoSBUExCME3FLEvJwogoTojCyLlcODk2ZXtCndbEAhaXTzh/8TFlcdHfQAbDu6+9i9YW\nLl5UDRebiqquKaqWVhuXXFhZuU1Vk5cFx9MZsWz48osfMzBr9idD3nj4Ju+89j53jh5gDXe2NYKb\nSMAro/a9ZV7tVu5Mcv1yTHWxyBofa2NoleZ8ZT0096cDTm7dQoYhYRRz+84d3v3Wt8imM/KqZVN1\nbKqOvLJM16pWVJ2dz+xF0zs7L9o5YXQvpN45wpLvxXbaz/YaxlnEomjd/3cHSAw++Nt9xJDnSz76\n6F/y8uUXnF9fgoTPnvwMTEOcRLR1RdtURKIlzCLqqgCtiTHkmyXeTaavbqToLaCCwM9qSsTuoLfY\nBp6+anwFehUuAxA3kADXcxVeXF32jNkogCC0gXRXSMKTg5q2tEY6quWLzz5lVdZcLFeIMKBxIv1W\nh0FglPUgNRiiHZeJPlHCkCUZy3xhIWgXAL3lnjGGZbHhYDrtIcHOedMKIciyjCiK+pGPfLNhkxfU\nbl5XSmtGDVaA3eDcL7QVcfcORmmSEIeRdfoIIzAwzDIenJxStTVn8+teBNwzZr0AgHJf++NtmsY5\npTjTd2MN1QMZWPIRNrD5vqqfgVSdde7Q2nC9KZhXLS0BZVkySkLOL84ZDYZMR2MLlwv7YStj6YQg\nbJCxqmHOozKwbP1Oda4qt0zj1rHFjQh7ey/VWHEFS7DSrjeKnRfV21lOIQRxklBVZX+neyeQUFiF\nsCiUGCLKRhHGKQhBhCEKBIMkpesUQWjt19Db+8muW9mvaRnYiYZqk1PXNbg5y9FwhLVHixiNRhRF\n0c+4e2h2tVph3J6fpqlri2iSJOH87IK6Ljm4e5+2c0plgaRTHZd1xS82G8I04bRpKIpNX6U+W1xy\n/8P3+Vd/+E+pmoqv+/hVx0reT8YH+w8ePWRYX/LZIOaD+9+zFlU+e9rJdP3H8SShbBSrsrsxQycQ\nllgh4OdPfooxos/8utYaNnda9RueNWu2lkKVasmVopMBYjzlL89XzJFsOkUsQ4wMWC2X9sIAdV1R\naDvMvt7kXF5dE+qOQSBpjCASdnygdOSg46NDHh0fkaG5LgqKqmJTWheTQSCJjRUrKPOcvLEC1ML3\nDgI7y1m0HXVnadZtVdkh+82C87MvKIq1XXwGAhny3uvvs8oLWhnSGig6Ta40eVXbSlFpamWz++O9\nA9I45MnTJ1wtFnz1/DGbYs2Tsy8RgWQ2OezPsX8I6TfVVy+pDzA7JRzbjRLYBnezw3R28Kk3Wb7e\n1KzLluODGePxEIBsOmVdK9Zly7pq2VQNedW68ZGOWjnhdS9x1zr3EG0DsXKB1G9+emft+PWwN0xY\nVx1KeZMs/3CVpdE9J8EASTri0aN32RQ16+UZVZkzTiPGSUQaRhzs7ZHGMamQxDJgFMcI5+fYVmu6\nZt0ng1bE4ObMZK8XasQvDZELXzHigiZbOb7dK7H72DJ76XuYUS9kEPTB1465bI8BDFpVSDT7d18j\njQOCKKHsBFVnDchbY6gN1FgmtxV4UI7lyg3YfrlZoI2F3QO38UaRdQpK4ph1Ycey4mALySpt++Oe\nSNQpa+4cJTFRbJnsnvfg9V6VsjOIgbS9Qj976cdGgH4udDYccTzb43xxzbqXutuVe9zeA4GDh71f\nZRSG/ZrGgFHWeD4Qls0yK4hsAAAgAElEQVQuwBrSO+hQa+sA0nZuZhOrjBTIkLJRBHFqYXHdcnF9\nSZZmDNIBUWivSRSERNL2qtFb9R3jVJb6YKi865Ebz9Catu2o6ppsMOxhY/x96IK4Zw7773WtIk4y\nyrKwa07sCHG4hWmP13B2dk65WTBIAqqy4Oj4iEZbolMYxTR1TZTEdu8whl34xKeBQgg6kfCTLy8p\nGsV6tSTPc7TWXF1dIaUkz/OeSd11HQcHBxR5ThTFxJFN1AIpKcuCpm1YzufcunXKpz/914hBxuFk\nxrPlws26Goqq4rwqeIKA6Zg7WWrbIcaQxCnf/v7f4hcffcz/8Sc/Pf+lG+uv+fiVAmYYhn//2z/6\n90ijkFnZsFivmQ4P7SKFvr/iWXwBgllmxx4u1tUOgcPCEcJha8LBZVKKPvvotBXTtp/tSIER3jux\n67NCJaxxchNHEIY0geSiU1w3LQvdMS8L6k7ZHqXTEQ2jiAZ4dj3nfLmmUR1PLuf8yc8/53JTcHtv\nxsNxxma14DKvGA/H3JmOuD1JqdqO8XhGoSVF01ALQQtoYaEab48UxTGDbIgIAkQYsyhKWy0Z2JQb\nVpsrawhdFzy9eML904ck0YBNVZK3ikoEFG1HmCTEUrjEQTAbjggwXC7mNAhqoBIBm6LEdIZxNnF9\nIrc1my0RxG6+vrY0fbVoIc/drd2ZFbuHENvgqd019E4fSmlrrK00i7JlXmlu3bnL2+9/g7LVrKuG\nddWSV62DZDtbVXa6D5ad08X0G6f3j/SjMD4we0TCbwqBFIyziGXR9G2A7fuyDxfrwTiVHyGJwoSD\n/SOSbEQcR9zbm6HXK9IgYC+SGCkJk5iDNKUuS9IoQnctTdfx+PMf9+fGKpxsKzuvzOOj5PaMbitJ\nD7Pe/NieaLF7GdyPvPWdlNvZTBlIx57dEZFw50BKydX1GZeXV4Qo0mbjHHpiy7wMI1oXrFtschJI\na1+FELy8et6TqoywR//k7El//e15dcLsSlGUtoKZb9bMRjZZatq27zP653sBCCkEXWORo6oqqbuG\nKI6InCatD45qJ1j1Dh9CkEQx9w6PGGYZzy8vyKvaie+b/rh2k0XvuSmMhQiVssLtlnmt3TymY0S7\ne6VTto8ZSEkURrayD0NCGW5l4ozVYq1aRZLGNJ1mNopBtVwvrsjSIRN3v8ZSWlQA4zSYreSgr8TD\nIOytzLzvpZX3U9v1jyV8YazesT/eTikLa2L6illpRZJm5Plmi3rgWdgCKQxRGBCLhBcXV2SB4cXZ\nOXEAd+/eYbZ3gBKCYr1kOp7YvdmJrrhWrF1vxs51mrZhfnXF2+++R9Eojo9PKMuC9WbNcDikLEsW\niwVZmnJ0dGRbTUWB0sb1Ky1Z7Oz8nMlkQlVVPHj4CGME8WjM48UFh6Mpq2JjJSuFoO5qtDDMVcvT\nMGQ2TDkxmrwqmRcrvv/e3+bOaw/4X//3/5E4Sb5WL/NXCpjDyd4//PYPf4vzs49pQ0k8nth5QYGD\nSZwHH45BFkj2RwlnS3tD+YTV4D387E1fVDmNrtluyw7C6pkeVp/SjhPYTNpmr27DkYKmaynryi5A\nYWi1dQ0JnTpJWdWUlaWkd0qBlMSDAclwQNl1VDLi4NZtHp6eQF3x+GqOjjOGacLZ/JrHz1/w2Ytz\nHp+/YFnWLIqKDkkiJU1VYLQmjGMCESCMvxE6kihklKa8+8Z3WFQ1DZJ1WfP07Kmbm4q5XFwgpOC7\n732PMIhplR3VCKLIEo/aFiUEe+MxgZScz6/tRiIFBBFVZ4PWrZN7FFXeVxg+sxRsxcRvat7cDC7b\nLd53CY27CW3gtYFK90Go03bUolOKutWUdccir3lytWFTNqSRpG4URe2CpZu7bB0M23a+itwqEHm7\nLb9OdoqcPlj6x94gYVN1zmybbS/WPVcbsxWv3qnjlOkIgwAhDW1d86c//gnXdcNmseDzFy8ZpTFH\nwwypW4bDDN11BEKSBJJ13SGEJViIvrITBMHNHrHvsQrh60m/rndOsy0t+uRxV4Tdi3ts4Va2HzfY\nsNtjkC7xBMNgMGayf0w2yKjqGtU1vRCBRXc0ozgkdWIBnRuKj+OYTbmhU1Z4HQN1WzFfXxM6E3bv\nCuJXGdhzXdQVCEnqmKeekwA20RUOSvWzksYlSE3dkBcFTeNYuq4K0UrTNE1/PgDSOOHO4RHLYsOL\n66teetNfdw/D+vXt/+8FUfw1sKMvW3cTSzyzC8+rBAVurEV1LWEQksYhgbR9xDCw0GZRlhBIisr2\n0JNswCgWtE3F1eKKYTZgMBgihLb9a2xhgVH9MRpskJeOSGaUk5ly92+nOltlVjVSWnN1f34xnlDl\nVbBs8ZJlA1pHkhJ+D/X3r7as4CyMWV5fcPfWLRQhm+XcWqVFGVVV01QNgyyjKHIn+r7dc83OeZUy\nRKYzVJiyWK3JBgPS1LoJ7e/v0zUtURQxHo9ZLBZsNhvAwu9aK05OTuwaUpr9vb1eGahTHZvrlyRG\nUS9XvFxec+/gBKNAGNGfk2WZsy5LvlCaMkm5nQ1o245/8dN/zu/+B3+fX/zJn/IP/4v/6h/xNR5f\nm/QjhLiVpNnk3Q+/w4l5RnzrNq8Pjp2pslVMkdI2hzstMZ3idJZyva5plbnZ08HrY9oeyvXmGm2U\ng3icTZaDcYyxA7nSqWcopTChxe6NNg5qsZmWUhZysSMKmhBJFCVoZfjgjQ+5nF/w7OIJMgigs0Z0\nbV2TpRn7ByMioNhsWBQFo/GYuqnJ65YwCtlI60pAp3m+XBIlKS+uLq2aRxAguqqfHT2ZzggERMZQ\ndx2btmM4HBMEAZumIU0HTEcHYKCscso6Z1OuOZgecffkPl88/5RAWVhXDlKMgf3BiDgMuZxfI4C6\nbuysqQwYj4bEMuTL55+xPz1hb3pyo+nYV/I71/OVVhl92tg/S29/7kkqBrQRfTDqxSE6MKZDaUnt\nyC/zoiGUMEkjOqXYFC2tsRWpcoHMv46rZV45Frh5xNueOFh4aZKFPO9F4X1v1r4x46pm/zeEEz1o\nteLs6gmShqvrS+q24+jWLXTbQpIwGw0ZBIKu2JDFMckgoaoVF0XOYrXh3Q9+3frtuQQCX7njyTzb\nI/ABTTh1dsmulvL2/QlXGtrXcIFTeLDLW4y5sZEdIQMpBAG+yvSzlzZ5WK4viKVmsdww2z8kr3KM\n6ihaTRBHdEpT1LVVvZISpYUzPdbEDiLzG/Lzi2fUTmTAm6T3cKBDi0xnR0Aul3MOJjM2dd0TfbS2\nTh0eQYiimMlwxGK1RBnLXK3rmi7LdlILenJO6ODT49keSRTzcn7FuihcRWxhdylDu9alsH64QqDV\nVvXHWwQGQQDCkr+EECDtaJCv0K1BQ4TqrEmCwTJGu05Zy7RAYozbf4Sk6TrypiPOQoSQlI0mTWI0\nLfO85Gq+YDwaEwYBm82aJApplVVSssxgieq2iI10kKLuWsIwdXOiVsglDKUTbseKIohtEmvvCZtk\nGG1IsyHLxbU73q1xtta2+AjCmCefPwajSScRXVuQjQ+YDEIuX57RKeu0M8hS6vmctm5IosiubC3s\nOrPZAxjDxaahEwHZ3j5lXdMqxdHxMVEUsSpX/SwmYGdpHaS/WCz63mW+2bBZb4jThJOTE7TWvLxc\n8NrJEY+vL8nrkjSMub1/wJOrM7tLac3bt97li/NfQNtCFHFnPGOc56zrnN/93X/A//a//M8k+zO+\nzuNXYcn+Ow/f/baYDTMGOuP/Pv+SR6PX3IYhSCLfZwiIAsM0syMRm7rrNzrPCpRe2Nqpr5RNRShk\n3xNQaltdvtq7AAexOAp807V989kY602ptUZ3HdlwyjAd8/bdd/iLT39iRYyllb4SQBynCBlSto01\nuXbKJpPRGCEEm6bFODjUipdbXdl6teDRyS2iMKTuOgKjCYRV6MjihLwoLFOsaai0JogzNuWGb77x\na2hj4ZO7pw/699Q0NVXbMNKK0/1bfPX8c6qmIQoCuk6xN5liBHz+/LnzBwWEHW5WxlBVFWvXCwqD\naEcS7mbtuIXEwWOY5sbzbnTc3BVz/RLhKzfHchTWnaLDZp6eAORaND3z8XJVMRumnM4yXiwKKrWt\nUHtA0niNVnaC5fZoehNce9AIITgYxra6NIbglcBqDDYgSyeg4ZmygZ2/PTq6R9dUZMMDPnv8MWCY\nZCkgqJqaz85fkrhsf77JebLKmR2e8uY7P8AQOvNyg9cK9f3M3b9vj1g7mNRXN15b1D6pT1E8nIq5\nUTniAqivQKW4iRRYssgO4UcKlO4oyg3Xi3NUU7M/TCmrBqWhrBWNkb1zEEFI55IYtOnJImmS9eQZ\npRVfvfjKDsy7TdevWyO8uLmde/UQrdKKLI7J64q2c/OYbo14+NPP6HW1JWloZWFJ7wjUw7dSkkUx\nR9MZqzLn+dVlz5eQDrL0YulGbWlu/Rp6pbrsA0ZgTd/1zpoz2N5m17Z9WylycnxlWYGRRGFoBUSK\n3PpWGk1VN2yEIA1AqI4oDFC6IYkjyrqibFpuHx3RtS1NU9txIgm6s1W9FXqxgUcbEIFl1loSEnSd\nwQhtJfuE6IlD/riVsk65uPsuSTNr9VeXjEYjutYbaW8xgQBJmEhODu/wR3/6p9y7e8pkOGW1uCIv\nNgTpBCXBhAlCSpqm7ZWsdKeo2ppoPCaQEZ3MiMYD6qJglCYOSo9IkoTVctn3VsfjMXmes9lsmE2n\nPHv2jOFwyHK5RAhBVZRWMKNtyLIMIQST6YSnZ09Zdx1yknG2vObh0S2mwxHzYsX+6JA0HvC33vwR\nf/7FHzGMp3Qi4XB6wLzK0SMIw5jvfOu7fy8Igv9SKfU3cpf+2gFzMpn8zqMP/65MAsNffPJj5lnA\nQ9e/CV0/RRtQgSFIQkZJwOeXm5tQiNtGbIaMVfYXcDg5Zm98wPP5C6fMbxvlPcvNNp8cgUPQ4S6c\ntjcM0g7NKgenqM7SyjGS471TPvriL9iUGx7efsTTl1+CkMgg4K377/DVyy84mp7w8uoFymj29/bR\nSnO9WqGxjf80GRDIGClCqqa01OeyZDoaMc83RKHPvAxlVSMkFnZsGsIoxrRrfv7Fz/nht36dYTrs\n9UW1MYwGEwtJOgHmo/0TXr/7Fp8+/QSAYWrHSJ5dXVlmo6tohnFoB7OBoq4xCNpWcevwnnMh8b1J\nG+S0sapJ3nC5J/Bsy8gb19uFJnsTCovl+4rKB0QhpAvOdg5xC+PSOzx0RpNf52RxwNEkJataLla1\n7Un713TX06+RG4++MvY/99VlzLNFuVPFbd+Cf/fKuD6P1v1vd8YOvsfJiMFowsFqzkBU6Krk2XpD\nWdfkecE0jck3FSf33+KDt++RZSM0kkZtIV58ArgDfe+uc88klMJujJZkaJB+JoLtMLuHW7cMc7kN\nmv3rba+UV//ZDdpCQByGfPbFZ9blXkpapRhKYT0whwNeriu7FoQL3u70a7xfacV7r3/YLwKlNJti\nvT2vLmhGUeRUd3aSLqeKM19vOJhMyGtradd0NtG0e7xxjiD2vo6jiDRJeksrgSP1OLuuW/sHCCH4\n6uKMqmuRBsditZWp/V2FEdZNyIpAiF69R+zsI7tfh/5r9868MhnYABTHMfjksOuIw60LkjKaOLBi\n80JHKAx53dCFIQNj75UkilC6RQlouprn5y+4dXhCIQV1Xdh9S1hFI+GqVuVYx6EQKCHseI4I0cYS\nH8uqcVCwDfbG7YtCSFTboQOFUh2Dga8ugz6R8wHTGFu5Ewju3LnLcr7mZH+fYTbiJx99xLtvP2Jv\nMuSrZ885Pr3DVZmjhXVi6jrDxXyFFAGLq3PeejODOMFkUy6fn1n7svHYCsJ7X08Dm/WG/YP9G2M9\nYOH/OI4p8pzZdMpl21nHGCcl+otPPiFSFbGAwWRE5dbf0+tz7h+ekFcF63JJHEScTE755p1fwwhD\ngCSJE/74kz9kUy35/g9/g3LVvnaj5/HXfHztgKm1/q13PvwBcSi4bmpENiCNUsDqHAqs43cUwGSU\n8mJREGBJBT30hA2WoQwIvBwegkEyZH8049n1czw+roXAuyJ4CMgv+M7NVjXGBhnpFrE2dh5pPJjQ\ndi3T8ZRASpb5knvH9zicHfPFs08ZD6a89/oH3Dq4w4vLZ+xNDhgkIz55+jM2mxyEez2l+cE3f53Z\neJ84imm6hp99+RHjbMzzi6ccTUesq5KitPBQIOw8VRwndiOUAVk84PV7b3Lr8DZREN8gTYCF2vYn\nh5RlgZnZSuidR++ijeH86inCCD579gyjDcNsTF6uGGVW4zJOYrd5xRRFyQ8++BFJPKLuqzibZGiE\nY0C6PqHpw+lOmeeOC1/57Iz9+6cJq0sbSKdm09k5RLkLMRovv7eFmbTRrErFumzYH6U8OBpysapY\nFM1NWBgwTksV4+vb7Rr3m97+yFaX2mzNle1f3z7faFAOSlZutlfjBaJBBbY6v33yGvPrM7589jM2\nQnN6dI8Hj6yzRhoPiZLUzgFqCz/rnWu3e1z0EKxASIPU26Cm3dfbkG9eeU/2+x5utWxYu5nK3UjJ\ndhxFCtvOkMKzH23SmhfX5OWCzhgirUnjjP1UcplXbPIlbWswcYIWdkQpCq3vo2dZfvDWt7l3ch9t\n7MD5H//FH1lavtzCmH7fCd1gfxQENGx7iHldMtMjBnHCfL2iaqycovegtOiOGwsZDOmUYjQY9AFD\nSsnheMZ0OORquWRZ5gSOZepFDvwco7cdC6QVT/eVsYdzPcN2F0kxxlA3Vk0oCKx7iA+wbdsSx3Fv\nUu2h2iiMeluxNE7QWiEcXFvWtbNak2yqhk5FJIEhCEMSWowWFGXJ2dU5t45ObSuo2fSjKgIbJO36\nNA4yDqjLms4YB8XjoFV3T2toW6ez69i3bduSpImtZKuSKLYOLlJIoihmPMpo6hK0QipFFA2Yr89Z\n52sm+1MmkyHJbIZMEm7fOuWisIlCdPKIMg54cnbNYLJPV+akoylXy5zj0ykKyWS6R1kWjEYj1us1\no9EIrTWD4YBNvrHjdV1HlmXkeW5RuDC0z1OK1Wrdk7PiOGZ5dc3eMGX18iXJaEgX+BvI8lWuNitu\n7x/z5eUL/uLxT3hw9Dp39h+gsZyIvF7zxum7hIHgO9//O/yzP/gnfJ3H1wqYQoj7g8ne8YM33ra9\nx/0ZXVNQtTWz2Ips+z1gNoxpOuiM171UdGpbAViKtd0UPL1ZG0FRF/1iNoDQpg+U7hjsz+2TUADG\nVjlK05OCjDEM0yGH00Peuv8N/tmP/4AoCHnjzjsoo/jt7/8Og2RIEqUIIfjB+z/i0ycfc+vgFs+v\nnlE2OW3bMhpM+NYb3+FgcoiXzc6SjA/f/DXbjC42XK/nTAdDqtoOP3fGVpVdp/rNJY1T7p88sH1T\ne/C/tOG+9eAdsiS78f0P3niP5v5DNnXLe28ObIIhAz796uc8efElYWJvbi0kgQj43jd/yCAb03TK\nQq+uIvfVoHKw8g3jaLZbt2FLHADjYFBH7LBUSbSxG7/Nxg1aSKTahoE+YOKrWgtdejsxg+HlsmC+\nkRxPM2aDmLNlSdnsoCTG/BWBcusvGAaSaRbzfF64qsxuiNJBtQabRXdGY5QPYBAYWwnbD0OgNZ0M\niAI4OL7DYDTFYBhkI4zrf2kNZdP1ULaHd7fMz513LvyspHAWcwKhrbiBCaz/qVMUu4G6AD3852ct\ne2KP3LqnCEcy8jCtuCFS4KpRYXh29gVN3ZANpyRSQdfw7KpABiGtFgRpQmcMaWhnp5XWBFFImTcc\nTI94496b/UB6IAPm6zlxFKJc1rTbHpGBhST99bbn2gaq6/WK49mM69XSrS9L7MI5ZJjG+tM2rfXF\nNI4VOxkO2RuNqZqGF/NryqrCOKgaaTkCVnLPuCDR9aNSfpQFsxVK8Gxbf4129xNtNCj7ded8Nr0C\nmMCSUGRo11fbtX0vtm4arL5u4zww7c9b1ZEEAZuqRmQpxinj6KogSxIwmqvFJcd7B33f1iejUlrn\nmaquaeqaJNqaYfvRI3svaqqqI41jlOpce8WiJl3XsT88ZrG4BmMTGiEEgywhjNzMZSsIowCtNJdl\nSVnkXFxcsn98yNHdW2gRMhiP2Fw/YTI8IEvvEoQxRdOSTqFWBhEmIKCSKU9fXDK7bU0hhiOrmz0c\nDMg3OaPxiLIs0dqwXCyIk4S92YwkSUAIDg4O6JRiMpnStC1hHDGLplRVTdfWfP7xT3n/nYc8bSoC\nDJ3uAIvWXK2WjI+HzAZjLjcXFHVOFGQobWg6TRgMeXD0Dn/55J/x7ne/y//wj/9bfv9ff9z1N+xf\n8/F1WbK/ce8b32GYWG+z+7NjW9kEAVJaFZ9AQhJJsjikaNre4BbczY3NmkMn7yXl1tomr3OkcKw6\np/9oBQssxNApTwG3VZKng1syiupNZbWbaZqN9xmmY/70p3/CrcO7/Ojbv83+9Iij2S2mw32CIHbs\nTo0k4N7JQwbpiIe3XuPe8UM0cLJ3i73J4Za5abz4tiAKY9597X3unLzGbDTlwzc/dNTzgOFgQBTb\nYBnIgA/e/LaPkzezXP9hDOPBhDCI+pMdBYIkDAiiEZPhHkmcEAYRgQx4+9G7fO/9H5LEU9oOQhEx\nGe4xGkxdBeQILzukmt7ay2ztvXwfEePrSOPAVCccbjx/zz6MY65qFxB9kmI3XT+P6cZD+jERg3Lz\nmr7qxAjqTvPkquRqXXNrNuDO3oA4DHaW2zYI+QLLk1x8damM7+dtBcstbO+OxTGHm07RtZq21TSd\nvZkab0jdWkuxTdVCkCLDzLqdVC1l01G1LXWj7GcnuKHVVgR+e7TG2TnRK/KE0jmIyG0VuGWzbgOi\nFz2wcnuSIHCvId3Au3ylqpTbcxFIHyQ6QhlwcXXG5eKCNJtwPNsnMRb6DLMxJRGFY4PqrqNpagJ3\nH5VlSde2vHX/na16CzagtF1DIAOSKCaQ23EP5ec1oR/j2F3fVdvQdopRmvUIgH9obL+067oeuUji\nmDsHh0yHI15eX/Py+oqiLG2gVVvhga7zetXWaqx/TRfIfdvAf/3qHKfSlnCTxQmR87yVbq51t0cr\n2M6E1469q7Wmaur+dYzyiZ2FObquo3ZCK0Vj547P5yu0gVGWWm/HquRqcc3xwaENpu69eOaxD9hN\n027PmyftALpTvblFmqRuHdmqfTgc03UtoRQkScwgSxkPE6SuWS6vOTu/omphlVckacb1+QuM7hiN\nxvY8GytnWBQ5w9kBV5eXaCN59vIlSRyTDoYcHh7Stg2z41tWUCTKUFgSVBzFLOZz0jSlLAqUM8dG\nWFUgz5xdLpck3pWqqiybV0oQktF4TFOW5EXOcBDRtjXPlaHbKTSSKEZIwYvrC06m+6RRwscvfgYY\nWm3H1aqmI28Ut/fuc/fufZIs4yd/+S//itD2b398rQpzPJ78x4/e+x6DJMQYxaJc0Dp170BKhCNt\nDBLJfNMgkASBdkO2+FYPoXAOC4HcgZsE5/NzjFG2Ce+CZhSmzIYz9sb7fPHiF9Rdi8QyvqR3A3AB\nUnsFCmPQumM6nDEb7bM/PSEKYhSGVvlNbtuHEQIUgshVm3eOH7AqllwtL3n7wbt9M91XtbZfAwhD\nGMY8PH2Ntis4GM9Y5RuenT9GBJphkpCXJSf7p6zLFdera+6fPrBrf+e8vlJoYowhCiRJJClqO4f6\n6qiBZdaF3Dm9x2gwYZRNaNoajXhlJMPNLxpuBMybZCAfLEUPz27Bc9Nnv0L482Z/ql2AsNJqBuFI\nNf4VfRw27BCM8NfIVbPCsCw7NlXObBhz72DApm653tR0yi6YbR9wW13OXO9yO6BvX1s7Cy2ht4EW\nbdeY7/tK43RfhZtlBAspK91Db9pdmN7Hsn9rdu7TAIF5pdfqKkA7XiIJ3e9HRqLRYKSzCTMIY3xK\n0p/9wG2Uvbi6a1uEoX3PfQUqfMVJ/1lgWG3mPMvnJHHAONtnf2+ffHVGKKzG5yix3pSVUkg3RiIM\nNELb3pwUDJIxg3Rwcy2GMQ9vv8bZ9XMCdx2lkHS66wMMYIlXr7Bnu67jfH7N0XTGuixs8uvt/lwi\nJoUkiSIOxhNG2YDL5YJ1UdCPpPjEDjd2sRMIYdtXFx6qdT1Jn9ht7xmDh8xjGRE6Y3p2IFENTsfV\nKey0LWFoUaGmbcmSxFZBrjdnzDZxCN34Sds0zsReWoss58gSu/GJIAgYppKqKlmuFpwcHvHy4oyq\nsiN1fU/V+DXsr/WWVBbFUT86JLCIBC5JHI2nzK/OGWSpU4US1GVFXlS0nSGMjHUfyRKixMLKwywj\nPjlhtVozPRqBse4mz55dUFclQym5e/8hi8WCtq6Iw5Aojsk3OZ2B/dmM9XqFF8XP85zlcoUxhs16\nTacVbdswnk7QWrNarcmyjKauaduW4WDAepOzvzejLGsW8wWL5YLlxRmz431KIWlC0GabHIXSjjfV\nXct8s+Rkus/h+NAm5U7Fqm41olWk8Yg4jPjw+z/k8U+/5LU3337n8198/HP+mo+vFTCV1r/x2je/\nxyAOiaQgFgFVW9Gp1jq/ByFFeYWK9uk0diZNiX5jAPrNJA4lYRhYdp8jLGyqVS/eXVQVt/bv8IN3\nfkQSxSitmI32+Oz5L2jamkCGXK+vbrASAbJ4ZDcrGXLn8D6Nsj3NqrMEAu9J6EOFhfmsw0Rg2Gb5\nIuLh7Te4mF+wPzmycOxOI09IS3oRGLSAKBygjObDt77FcDDi5fljZGCtcwbpgMV6wdsPv3HjWL3K\nETuBBqySS7YTLF/5JZ6cP2G5OqNsCi4WC472jnnr/jeI4yGqrwB9r2NbSSrts3T3853KcTeQCSPY\n6heInZ85KNIbgRvbHxSvvIXd4HKzet1hKe6+JftMrvOaeVFxOEp5eDhmVTXMN9aZQgg/uA/7w4i8\n2ZEqc8enwQairbIiUmyhNykdZGsUUtv/d8oFIL0d+vdvWe8kC7jrL6XADQH38nWi/5e+dxkABLJP\nhgzQYgO5EtY2aitfBmsAACAASURBVJtEuEQycOhLYC26IikJQm8c7T52qs3tOAko1YHpyFdnbLRm\nVVdMhxChGQSCk0nMZpUzjULyakM9noEbcLGeiBVlVXEwPSFLBzeSOGPsONb/9WfnNF1jWaGOZWrt\npHBVFttzIESP9qyLnMPplIPpjFWRU1alNYuWAYM05WS2TxYnLPINizy3KkNu3VrxehfQhHAMdbtZ\nGmNcRSL6YCFctmfZw44o6JNDYQNoIAL7tVJEYXhTLs8FvrIse/EEISxLOAoCay7v9gjlNGeRAiHs\nPKkMQjD2+YGbGIiisE/eqqZlkCROtD+gaVvW+ZKj/WNenL2g61owmiSJqUrL4vdyef0JNvTwdVs3\nGNd3NcYwmcyoqhLVtRSFDeZZkiCFoGltAqpU1+vY1nXD3nREnpcMJzOefXHB/olEOJ9LGcZ2BrOs\naJuWLBsgjCYATm/dgTBCzq/pRGCl94SVuEvjhMV8zq3bt/n0F5+wd3BIHMckccLLly+Z7c1cbzkg\njEIWywWTyZTNZsNysaRuagaDjKuLF4wfvs+zztA0+U0c1cnwdUXO5XrBo8FdXsyfcDy5Z9EvZfrW\n1DSbEYcR3/re3+FP/vD3+c7f/eHfA/47/pqPv3HAFEI8yAbD5OTB66SxteZ5fTbmJxcBYRATBoI/\n+vgPuD07IAinfYUi3MYtXFYaSEEcCOIosGLR0FcOv3j2McezCXEYk40n/Oj930YQOGxf8Oj0TR6d\nvknbNWit+IP/5/eougqtrL7s6f5t5usr/t0Pf5d1mVO1ilb7gXgnqbazA/p+mKfiaymQxpqeRlHK\n/uQEbRTKbYzaBTgjDML1Yz0caFDM1yXT4YD7p/c5OTjh5fkz8upLNsWGOE54cfGMO4f3+gBR1RVC\nCuIo7QNJKAVZHJA3qs+qrfegYrG54vLyKWWdo5zu5+2jO3zj0fuARBmBVXf1Gbnoe5ReR9NXl7Dd\nsP3mqFyWjdjOQ9ocwYYELYQNlsL03/Ovs80lXKfXeHjXBRPT1+jbNUX/Eg4INkgkl5uGRdGyP455\ndDwir1uWhR0ID6VgNkh4Ni+2MBiWUGOvw256tivY4KoM4RM0awjtN0TZV6SvzoL66lr0QVAE2ArW\nrwXhqVEusBuJkPY1Yr++3QbuIepdqzJ/LkJvFu2MoH3bIgwEW+1Y2fvGSiGRTvElLxbkxZIAw8ls\nyLgOyYwhSgNipVBth0JRtYJsPEZISdVZDV8LG9tZ5W+89k083Wu3vxoGIY9uv8ZHn/0E7QXVBXYj\nNzgBgi0xT7v56CiIeHjrEZfzlxzv7bMurF/mQEoOZ3uMsgFXqyUvrq8IpGQ8HN0wmvPB0VtkdVrZ\nsRGfeDko0ydOQgjQ2/65R5KMsfdRJAOr2yqlNYVuW8silU6gPdgJxMYRGEWIVlYLWkprYdU1DRhr\nct12LVEISIlvYIRBQByEIOxom5KCsuuI45C6a0kCK36A1hjVUhRLjg4OOb88J44D0IZGOCYryibr\nrn8io4jWaeD6mUuwIgTD4ZjL85f23CiFxFDXlTt/Lhd0s7bGJJR1TThI2UtiHj95TiStB3ESx6zm\nV2w2HVpZiy2tDekgo2kqoiyzI0EIxpOZJcclKavVirqp6VRHXVd88rOPGAzHTvJP8OL5M/YPDiiK\n0p67tqFpG9abnNPT2zy5uKCtchbzJVkScvton26TY9KhzQzdhIQRDuEJrOpSq1peLC559+6+Wxdb\nuU6lNOu6I0taPvjuD/mf/vv/hv/oH/yn/E0eX6fC/M0H3/xBGIUBWRzyk8d/zrEuGKYj9kZ7PLv6\nkli2XG0WHO/R9w+8hJp1ibABIY0i4jBwYtLSngcMdVdStymDOONHH/z7GCPdZu92dlduSRESRBG/\n/sFvEQYRTy++5BdPf8abd97mz37xJ+RNwXR4QNk53UenR6qMD5hbwDFwvaBQSnTgBpKxTudGCKSI\nLEnB4j6umnLkC2M3ykBIfvzpvyKvVtw9PEYrGI0OmOdzqq7gjcM3qZqaosytyIA7oZ6yvmXKQhYH\nlI3qA4BAsN6cU+Zzlosz6rpmVTfszW7z1utvksSZu7E9E9UHQFc/mq1qjodkjd4qr/jgsBvKdjcs\ns/PZhxJjtiMU24D36rO9qo7TyOzDpemhJv9sT6/w3WyBDd5X65plXnMwSrl3MKRurQdf0XQuuNsj\nUm4j0BqMK3U9VNor0Ujj+p1YSNRVi8LYz0qIvhq9AZGbbe/dSNuDEQJEYJEKuRO2++As7TmKAghE\n4CTJHItVCUu6Mp4E5VIMYyvKKHCtCmnJGt7ncuuDKV0gtdUoRvPk/FNePv+KvdGA/TREri84HIzI\nwhbVddRSoBE0SIQMqLuWOFLkys4Lt51V0em6hrxYMx1NX0lt7Hl868E3+Pirn9Oqtu8J9qx1f821\n7UkKIRnEGR++8x3uHt3lkycfM8kS7h4/5PNnnxDIgGVZcP7yeS804ofYlVJoYecxfbCzlVbgiD66\nF0P3AS6QWwZsL6JgtuhC6MRE0ii2cnjKJgrSjdwg7AgKxiY00kF9nqBlGauRrQCx1yaQQW91JYSd\nl1TSVrSJY9MaY4jDoFfvsQxfq3IVhVacX3WaQDaUpebo8JDlco4xHWkS9feux2HAjpn4dNboLZt9\nOttns15idiEWv4z9fequl3HWZW3bgpQkacbl1QW37tzFenSGNE3H5fUZ33j3fa7XGzZFSVXZUZi6\naUic3djq+srOz4YB66JgPBwyvzgjCCMOj055+vQxg0FKFGcEYUgcx9RNy2g8YZNvWCwWHJ+c0DUN\nZy+eE1VLknRELCMGWUqlNbnpbrwfKQSpmxIIQ6s9nNcVgYzJooBN3dkeubZz4UXd0aqYo9t3SbMh\n97711n/N/58V5nQ6/Z0H3/w+oZSkkeSwvOCqamnamj/6+J/SdgX7gzFX+RrtLpgXR9ba92ggiQKS\n2BN+bBCtmoJ//pf/p8W7y5IPX/8QK8ButtWSq5T6O1kb4nCAEPDa7bdBCG4d3OODriONJhStVfdv\nOmWl1/R2gzJmG4wCYeXMwsAQ72TW2vit0AU4te3yCWn6jV0KeHrxFc8vnxIGIZ+/eMyjk9u8vHjM\n0ewWebHken3Fe69/i9//43+C0po3H7xjf18E22rGGIZpaEkoSnExP0PomrKqOL9+TlfnKCR3Th5y\nZ++E/emRzXQ7hWeE+huiHxfRBuPYi8pXNnq7kRjv6LETIV4d+AZ6KKiHJncCp9j9HY9nGk8c2gbK\nPgib/uV20d4tIuFev+9NCsG8aFlWLXujhHeOJpyvK0ZJyLrqbh77KxBn/1kYtLJ/RIrt35dOnUpq\n+00hHMzqf9dGdoRyTG5jIV+lAaGtXVPft8QxdF2fyX1fBJJW2f9Lqa1XoNwydJXewo2ht+4KXVXp\n+5nO/SQKgp4IZP8vuV6eExjDo9PbhOUVYZ0zHU0o65K6g9YIQmGoZMBkkNFoQ9Vqcm2xG42xfqSd\ndQJa5ktuceeX14C7dreP7/LViy/6teZn6XxVqbqO2WjGa3ff5I27b/Sr472H75DnV6zLFcoIXlxd\n9L8DWDa5lFS1tfd7VWQA7FzkbpDeojvbHqeHXjEO/XH3syfE9GIHjgD06vGHUiIDCISt2Dabgqap\nyZKYsiyRMnAyc3bvEK4y9f1a3wvtVOfmTCVaW62gViuUlMRRiNAG1eHaJprxMKPMc8IoYjIas1hc\nk6UxRVExyFInmOBk9Pw8qb9rjCHNBgQyIN+setTE71jbG9jffL7PrKnKCqUVXaJ45/33WF7O6doO\nYRR1XTEajamKNZtNwWx/n4uzl4xnM6Io7jWCO9UxP3vJ8e07RAKaYoOuKyazPTrVMR2PreVj2zAY\nDlmt11b5Z2mZ17dOjkkHGavViv3DIz764494570PaNeXRIGgiWM2bYORO8iDg8SjICAMAzoVoJTm\ncr0gCmwrxhh6FbKq6agahTKCb373b7N6tvobacr+jQOmUuo3H7z7HZLYQhpNiJWmE5pVec2btx7w\n+Ysnzgy4QpC6vpnuA1QYSpIoIA6CrXi0hD/88e+RV2sMhrypeOv226wq7YyodwbbzU4l5LI+gd14\n7hy8Rtm2zMan1J2gbjtnTmy9FnsZNn/C8f0mQaSFo7oDKEJHKnL7nrtAfukJhHa9MWGQIuRifm5v\nFqDt4PnlJXvjMZ8+/ZimaRByzmJ9zd/58DcJZLgNxEb0N/soDWg6Q9VYKbG8WPH05efsjcesi5zG\naI5mMxblknVTMBnuI4RllBkH8W2TC59t23BvB8B9NePp/zsbDDtRYudL/Hn358z3edhWlLs9hd3K\nzPdBfyn8CrPzWfbn1V8TxJZB2p9zsT22Ly9z8qplOoiZDWJWZcuqbByZa5tUCTdjIPvK0V493Y+m\n4PrSO20B/56ED99WylEKeuxUCpt4OU8sR3QSvahDLy4gbC9Sq9paUPXEDYGS0tqYYYOqf+9RaEkM\ncRi4QGl7mYHfxHcCaCgly80l15ePKZcvee1wglA1bdfw9KvPGIwmxFlGKyPSQLBqWvKqopMRER2R\nSAjThKKznAHjKp/bJ3d3FoD5pet/MDnki2ef9dWcf3hln2E24rvvfI/jgxOM6/uFgeDp2TN+8sVf\nsj8eUjbVDRKbZ67aaq9DdZbtazC96DrQm5YL6G3qhLT2VMpxFfz1k27gtSf2ucDpYVex83cRkEQJ\nXWNdiGRge3h2xjmi6xwT10jnOSr69X5DHtBBpGEQ0jRtLyGHwCoRCUEUBc43UoOQNqgGkrppicOA\n9WLObLbHdLJHUazQusMQWuJOrW+4q/h0XgrJbLrH/Pqyv092byh7vLo/B4EbwWucNrLRUKqGo6ND\n5lfXxEnC8uIKFY5oijmCQ4xqWV5eMJ1MKPKc6XjMer1xkoWRlQesK4rNGtqK6eEJTZlbtbTxhPV6\nzf7hIdPplPl8jmodQ1sIZASbTY4whs1iwWv377G+PmOQRQwmMx57j098e8Ulqi4RisKQpu1AGY4m\nJ/8va2/yY1mSpff9zOxOb/Q53CMiIyIj56wpq6qrwK5uNkmJFEUQEgQIEAgR0EYb/Qf6B7QVtBcE\nSIAWglbaCNKCAgVRzSZ7UnUVWV3VWVlVmRUZo4dPz99wRzPTwoZ7n2fk1M0bcDwPf9O9ZnbtnPOd\n73yHqjNM8oSLlevwpI2lbB3a2BjBt773I378r/45UillQq3Rlxxfy2AKIQ6yYnzr+N4bjFLJ+eIZ\nqIRV5xRWdsYzat1Qto2Papy4syvZcBu3Ek6EPU+Vh5jchT+/+JRl6foLYuHbD78PQmJM58ojTA9b\nRTQ1QHDhBsH6ekP3fU3XOmPZdr7nponlDAH+Cl6YEmCUjJtniJBi9/oQQQwgxbBQlcAVhqcFJ/t3\nQAlGacHOeJd1eUqSSKralzTojnGhKLKCzucsqnLJ3v4DpkWC1pZHzx/x9NlHzGczpID5eIRpG052\nZog052K1QpLw+mv3aXVLmuTx/rDEFAdEjxtf+mCxXoQ+GtPtGb4Bpm4bSm78bYux+zlr5vP+3r8g\nROgeUBJ9dCaEYzxGp8Ybs51xytOLklYbruuOREp2xil39sbUrea6bFiWrkRBmPA1NvhWMXIJkWCI\nbAXWr4l4ob2xtm7RCdWPgbWe8GUd29XBvGB9PbEQ0hOKatarl8znd0hU2OZDmzEJAoJCV9+0wD26\n0hR6Jm/MW7r/n1095ZNf/ZixEiS2Y2QNlW5pm5YuzcnywsGJXcuFVhSJYjIdc7npWGlJnjjIfqE7\nDNK1sLKWP/k3/5p/+Lv/6BVIg1tTt/ZvMZvMKasNVjgmeyi9ONg55IO3v8vJ7hGpsshE0XSGdW34\n2Se/5PnFKZ3eZXcyZVW6eusgLBCilVGW03nGZ4Rj/cQEI+oaAVhHoNKOVZ9IBVLFJtPCT2BsE0gX\nZfCCKpFTBvLpGOMMV5Yo1wpQKayUtF2H7lonwZkmSOkEDgwGlL9+4cQCwmaOh4qDgyStQxrAIq1/\nbYigBWBdBDTJc4RouLy64GD/kNl0Tte2dE2L0SaWn/S90d06nc/3qKuStqm9kQwgbX9fuX2t53RL\nIWibFhvKuDrBi9NT8vGUO7ePqa6u2MgJi6tf8PD1B76aweWKkzR17GJcGzhdV2Bark+fMJvNGZ88\nZDaf8ezRI2ResDg7ZzabAbBcLsmyjIuzM5SEYnpI07Ssrq+o1kvM9XNGkxxZjGnamrWAjdZYZRBW\n9CktIcjSjFSAtilp0tGZhF89/4iHh+9GhMaV07naa20tVaN561vf5X/9H/47/qf/65+dA19JXPbr\nRpjfv/XgbZIkYZQkjOtzqkfP0Lsz0iThZP+QT06fuEEMBfHYqGWKsCglyJPERZf+5q/aNT/77U9i\nriKRKQ+O3qDpXN5La+s3+WFE1EOAsQOFtYMkr6u7C5FlOzCWEX70yTcJdEKQ+M4b/d4gXU2p7Qve\nrQgbeMhp4Beg5e3775MmqWePOuP46HnDvlBsqoqmrfnzn/0R33zrdyirFcv1BZmEPN3htROFMZZN\no2laTVnVmK5mMhojhWDVtjRlh9VLRJKwt7vL3Vv3PIREGJgIW9+MHp1qSNB1DfWr8W2E2ysY2q2Q\n8XOOVxnTm8fnq085ExVrJkUPbQsp4qYzjCyttczHKVWjqVpN2BBq3XG66HhxZZnmCfNxysE09w2q\nW8rWKx35SYsEqgD5+nPojaftYdlgTQcowFbk7g2ptS4/z+BzwmXm2YjJ4X2azmVzE+s/T4LytGvp\n2pv4yFIMWuN5h86r4gQhBCWdw1dXaw52DlmXa0ay5fz8grkvI9ifz1l1hras2ZmO+GTdcG93wnJV\noltDIzOaqsPSUdYteTFmbzZjUa65ul6yqTaM8jHbXGZ3z+RJzt/+4O9ycXXGrx5/RFmXNLrh3q3X\n+MF732N36iQeF5uS680SKSTPzp+xXF+TqISr1Yr96Zw8Tal8BxJXRiGwytU6Zj4y037/cExYvDNu\nt9Yt1rWDwkd7JpBCrI1NEEL0Gpi1QdQgqPqAY6KiNVXTkCcJAmg6je46V5ImO5/PNLRdy6gYEZyf\n2ouIJ75Ux0nZqT41YrUzeFlC07qoRwpB5g1PZzSZlxgs8pyqrjg/f8GtoxPmsx3W62uWS8eYFdJF\nV2EAsrygGI04e/GsHxTRPwaH3/dn84fx6TAnXGBxKkJaG0aTCXmSsEHx4skT5rMpT58+JZvusjg/\nZTKbU21KroUrizHWsr66ZGc0xqYpy8uXpJM5lZRMd3Y5Oz9nMp0550UHQZeOYjSiq9ZcXlwymU6Y\nFjmnv/0Vrx3NuFosWKgENd/lyhq0kn5rcgbb+r1BG8Pt2Zin12uy1OWXm67GCljVHbMi4eW1KzXT\nJqQeNCf3HrK4vKCwe19ht3PH1zKYUsofnDx8DyUlo0KxM5mxno5YGs29wxPqtqVsHWvMKf4r6s5G\ncoMUjtCQpdI3UgUlJItywWJziZIKYzR3Du6SqJy6dQnDNhBUbCCzDCJFu12M70S+rRdH9y2jYrH8\n4Cbzd5kQFit8Ykq7RRT8L1f71dP8pZVYoQcbvIP2gji0m5DWGQEpkDLl9bvvUJUL8mzMX378C5Ik\n4fTiKW1Tc2vvhIvFBd//5gcIActao4DXTu4zn875tx/+GbJtQUnGoxGZtVRNQ9O0nF285OLqjOl4\nhlKpjy57b7y/a4iQlHMmfB53AMOGTZ941f/ujldFowFUkcI6kylCLqqHMkNN7vAzQPi6y01k+AoG\nm6eF66rjumq9vmzK/iwnVZK1N56b1ivTRIQgQKjW17P5fFjwhmyIfJ1BjKBujHjDe7bvuVAv6s4f\n6qZGyMzXgboPkTaoW7kXOtKZQ2ASKRxbVroRCQYTiN+rhKCuNlTVguNxhi1bni0Wrq6ya9ClYXd3\nh3WXUlvL3VnGk2XFJE3ZWEGBZSMVjQHtCTSrsnTtpLB8+Ntf8Pb99xgXYz/OcL1ZkiUpeVpQZAX7\nOwf8YD6nSBOwmul4B0TC+XLN05fP+OXjX7jcWKdpO7dJ5lmGAS6X1xzu7PL4pevnG5iuaZZSNTXj\nUeHzlUEUvUeUzI11pbyhKqsq5rXC80MpTXDpIeNh01AvGnObRpMF8QJv9BCOmGe1YV1qpDeCAke8\nMbqLZKWYb40KW8bvKyAkjPLMd15yMnoSPLvV14z6/GlAWTptOD97yfHxCZv1uk8dhAXvnfm93QOu\nLs+cRN9gLYYcrUNUJDaSMBTWBr1il7/vPMIipWBnPkOYjrrV7n60BmVa2uUFWTGiLdcIDLu7cx4/\nvmaUF3SjEcV0ztXLJ4wzhUoy6s01xWTGbDolyzJOXzzn8PgY03XM53POX55RLs5Jxi3XVxeczDJk\ntwY5J59MWVYVte5oTYfuFZt7f9TvvYkQ4OULkzRFWleT2mq3PxeZYl130U5UXcc4T3nw9vv89Cd/\nof7J3//+Z/auVx1fy2DO9w//i9tvfRslBaNUcbp+wc8bjZ0W7E/nfPjkt+jWE30wXuVC0QZWq2f1\npYn0jEF30//2xW/6yMjA68dvYaylbg1Z6vQpdecig8AC1f612toBkSWoyRg6g2fEOjg36DLGqMAP\ntsSx7aSATopoOCwGaxVGgUGijEDJPp8Z848+T2KsT+pLXE2cdYXpSgiKfJd37++xO7/Fz3/zMw53\nb9O2NWfLM8cSloLrUhP0ypWAvfkBD+6+w28e/5w8T7GtRiqn31nXDbPJlA8f/Zyj3WP25odMRjMs\njs7eR5rBG3fMWKe64x0H42HZz4KwcUV+ERw7PIYR4PD/229k4Nl6uDVCosFYDiO9/jPDW2dFQtVq\nyrrrPzN8vGAwdy6Kvlg3XKwbUimYFil705xjJdk0mk3Tsa4dScOETQgHrzoSU++Ji63zFv0aEMJr\n1/YX1xvT3vt3kFsSPyOuG2E9xOU2a7fp+/pLJZ3TKXvjKwZjFP5TFCNWK4mRitoI7PgAm41IpeCy\naUi1RmJ4UYHp1nRJxnXdIbOMDhf1aCEY5xl15/rEut6gko+f/YbHLz7laP+Y7737OyQyYbleMCkm\njLKMIkvYHe9EB1UjaLRlubni//vwz7i6vnR5QK8I1HVdFPwHWNU1+zs75GnKpqpI09TJO3YdQjnm\nqZSSBEWWZg51MT4S8msjKGjJQBSyPYEI0XdUCUbRRbHOyUoGkWcoNzHa0OGMqdZOvH2UFVR15dny\nTuQ8iLNrrf389OpBTdtG1rQxxH7AwkhKUzkHPEkcZOznPU2c/KJKnKhB7Td3pSRad7x48YyDg2O0\n0axXS7d+PLI1392nrjbUVekidAJruyc0KSmRSLDC74mOeBa7D/lNQwjHUM7zDDoNxpCmKctFjUJz\ndO8NLpcl16sNk8J1lVkvV46TkhfUTU29WnD/vW85YXZTY1vn5DcSRNOxvjzHSsXp2RNUPmZ3NmFx\ndcnR3gwpLLPJCN1UPFqWNPO5qy0W4f5zCI/znjzcqjXaSg5GOZdNh0pdCz7XuAOWZcvuKOPsusYY\n64KozqGQr7/7bT7+9UeT//Sf/pf/1f/2v/yP//1nN67t42sZzHqzvnf74fskSjDJE5pnZ5Sp4s7+\nEdebDZu6outajLVM0hnWKtpB9xQlHaEhDcw0ITBoXiyexsX3nYc/4HB+Qqeh0YY8lZjOeIUa4+SX\nooEMBlRH2HYYTRrrJNuMsQM4bbDLCh9piL7420qwncO6rSUa5US6fGsQi/YAZswlKWVRwqmCGOEE\n4KVbnxg0thHcOThmXOyAEJTVmqIYc2t3h1XlWFsIF6W6vJvh7vF9LhYv2TTXyETStA0C45PbDWVV\nsyqXSJUym+6iOxvtnY83nYSf30T0MI8b/zEMRAko5FeBW4fHV319j1QODc8NYzn4vACfW5wu8fOr\nMsLmQxtsAmJwA2IGaLTlct1wtWlIpGCSp0yKhFvzglZbyqajbB0a0Z9giCx7Q6gEET0I69e9Puh7\nujmMRtJ/mPGb8fCshHcaQqPpIOShgoSeX1cwGB+Gsn/OIRoXM3Yne0hbsjffYTebMGuvEemIBmiQ\nXFYdrRYsRUZqFa3uGBsnG2ZxfQw741ChLE1puw7h77eyK3n84rdo3fC9d77L26/dRwCdsXHsgoMg\ngLIu+dmvf8rF4jxquYY8svQ5QtdVpCBVisvlkv3ZnMbXQUbWq5SDMXOwm1Iu8gqbvdbal3IQkZWQ\nn+5zg31Hkt542ig4IKWTf8vSFOMlN7UxvXKRz31rraMubuiwkSRJlHEL54WJFxuNtwWMlEg01nox\nAC98kKUp+I5M1lg63bqo1TgkK0sz6qbEWsvV5Rl7O/vUVRl7exb5iCIvODt96leXy6cjeu5FYAA7\nXoBCCUVZl4ByqRufh1QqofAatynw5NkZ66phvbgkkXBwsMuLTz9m/+QB0yKnbDXL6wVpmnL27Bkn\nD96gLEvm+0dsOku5XjC2Gya7c3LVQTLFNBuqi+fIfMTL0+fcPdpjPJtS2ZJ6YxnPbgPQqYylaBzS\n5AXmA74TSEsImI8mtE1LYwTTTKER1Ejy5Uuu1uekyQ51Z8gTwbhIqJc1rQ+4yqbjzfe+zZ/+i3/G\n8a2jz9uyto6vrCUrhDjounZ0eOcBqZKMMkW9XGLylJ18wrPz09hG69073+atO9/B2JQ2Nm11tWOp\n955dPRqcXb+grCusNWRpzr2jBxhkNIp14yKrYU6ybjrqpvMUYbfZVUEP1Dij4OBYp/epvbEY6pyG\niNR4bzHkPXVnaI2l7RzW3XQddeNKU6pGUzat//Hf32qq1lC3rgyk7byWrQ1kJ3c+tTYsq47pKMdY\nyWQ04+3XHiLVjNZ41qp1rLkQDSuV8M7r32A+2UN4CrhSkiR1OZC2awFJ29Q9qcfi85e+hN5vAK12\ndUjBeA5zucFQbgWBN46bbMibzw3WyWfXzvZCQoq+XCRQ/odGIPwY00fI49y1E1rXLhI3/nyNpRdz\nj5D9YAMdz8hqRQAAIABJREFUOE/GQqthUbY8uyr59emK81WNEJKjWc4bt6bc2R1zMM0ZZ4lrBu6v\nqe9L6aNRXwo1qColMLiHsHbPUt66l/z6d/dDmkjnSPq8pRR9Rwopeyk0Z6wH5yPdOJTVAlOvaes1\nyeolulqzLtck47GrNbQCjeDuzow8dXnQ1mjWdc2mc3BpIhwcGQzOZDziZP+AB8cnvHnnNZQwXF5f\nUjaGVa2pOmcwh2ujbp0D9+j5o6juY/xjIPMo4QgYbdfSdi1XqxXjPPcNqokGTAjhc5h+PL1MXOK5\nD/jXKiXpdBejykA8CkeISK3/PYjzD+tHlZTUTYO1xHxmiC6tJerVJkrRdq1/TsSG9VmW0ngBgbjm\ntI/iPEVcGKIudtd1Ln8ppeuuYgxV09L5taK1ez5A0C69BW1bs1wuODo8dk6WlOwdHHJ58TLe/2Ex\n9g6b9dGld8i8zF90Bj3KNJtNGeWKLBPsHeywvLxAKVhcnmONu96zq2uOjo9YXz5DdiWZbbi+uuT2\nyTHnl67xc1uuaOuSIk2oN2vyfELdaHRnWFy9JE0UdVsym6TceXCbO/fvIZSkGI8YzefUpsNOJ3zc\ndGSzmY/OB+t/sJ8IYJJPkEBtDUWSehKY4ejkFj/79CfOaZBwvWnZHacYLK2Hy8u65Y33vsFvf/kz\n6u4rkWS/VoT5/eMHb4skSchTyV/+9k+4e+uQA9OwrEuW1ZqmbXjz5H12JgfMRndYlDUWV0ZiDbFo\nNwhNA/z62S+wvhh1f3ZInk4cUccbvrJz9OdGt04ou+2cAEEo0IctBq0xfbPkSPK54fEPD/fXAPG4\nCFNq1z3d2iChZel0v1lCD8lJ6bxRV5KiMMq4xsR4+TQExghft+cK7vPUwdLXVecnqo8k8B5wgLHG\noynffPMD/u1HP6asS1rvlFgks8kOP3j/d0mzgk7b3pCECDI4AsbSau1eY4iyeTFUG5jKV5nErxtt\nhvcMYUlPBvWL/kYZyQDyBAbzFeBMwd4k52xZ+/OGPpS8ed7+BfZzoGGPNITnykZTd4bFRpImMEoT\nRlnC4SwnUwptNU3n6ly1Jw0EFrXBsR3dCvLRDGzl24Too2QCVCtthGEjW1e6mj9rG6w1SIoeJvbw\n8DACD5e6Oz/k06cpNhUsKoPIXXnVfD6jRWC0YF1VVCJBdx0XyzVpUUBnECpxVTEqoSgKxoXwfRsN\ny3JD3XasN2snX6cU7xQzd22vGNbFasFf/PLP+d1v/R63Dk54cfYsRmpFUdA0DUWe0/kmCsb4vphC\nULUthzu7PL88942pYVqMfHcbUImKRJ/Q8SZNE9/w2aM62sRuJBEVED3cGBwBPFpktHYlNHEN9u8R\nwpGwwmdJqWjaxkf/ikQl/jwcubFrdSwn2x6efj1r2/melY4zIYSga9voIOSZa1TtPsetJ9dsu3VE\nQn+my+sFSkgODg4RQrBaXdNUtUsLeAN4c4KyLEMJS1O31D6SV0Ai3XWMxmOmsxGnzy/Jx/ts1msS\nIZhMZtik4PGvfsYHP/wd2raGoiDJ1lycnzKfzbh7eMiLqwVHR0dsrs4RxrBar9HrS1JpSDPXBFwl\nOYv1JbLIGR/s047HjNKEVkmK2ZTzqysmh+9wvrzkpU3RmQDduXw/bv8U3mhqBMI6G9J2HXmaYZvG\n+weugfnTdcnjxUt++KbjpGxaTZZKxpnyiCPUneW1h++wXFzwD/+T//y/Bv7dQbJSyh+cvPWBlAKy\nRLFeX8FkxuF0l09On5DIjL29I969+11arVhVLW1nSBNFlrh2RhZPXvARRadrrtaX/kIFb9/5pu8z\naGMusmwE09w1R27ajqbVLiKLUYONhJYAy4UBCXmG3jZ4uTZs3MlEnIwgp+XWnBWh/ZPLbUrTw4hY\nt8FJfNcJZTBG9fqtCFIsiRVY2ZcBWGCxabm7X7AsWza1gwD9Gbm8p7AIqZyx1x3SuCbA43xK1VSs\ny5X3gmF3soOUnhk4iMzCRQeST9cZui5A2SY6EWFsBoKx8Vy++Pj8Vw2Nq7eTg3cNcpf0m9PN99ys\ntR35Brmrym8mNqj7hNMYzOuNcwl1Ww6qCoanh1mlECRCxtKNRhu6qmVdt2Ahz5xAxyhLyNOELPFr\nwkOSDuI2WANIi7USI4OoejBw/SYWvjOQFXtChiutef7yMffuvu2dCxHXRzSYcfTcsalWaG24xnKw\ne8CddIXtfI2e6SibjrUW6FTx4uqK6XjOzs4M3XakSUqaOAZ3Zwwb3fD84oxlWaKE4s7RHe4ePWBS\nTPjV018jY01NjxF7ZIyd6Q47k10+evQh333ru/yFMRztHfPLR7+ICj5h3maTKU3bOiKUEFyt19zZ\n33cpGotzGDz86dIKbmE7OFVHJwOIhjRG/0Nj6deHcz6th/X6KNJY32heaxLlSliclJ/BetWfru1F\n3uvOGxsle8RFWNc9yYT1d2PVDzqLhGGzxtK1LYkQJJmXBvX9dodKRVh8lOu6fEhcSmuxuOT+gzdQ\nKuX89MO4siIRSPTlMqDoOo3BsilLEJLdvT0Wl1dOJUsIiiJ3LoVMsJ1hNCoYjwqMzpC249btY+q6\nZr67y8uXLzk5vk3bPObo+ISff/Qx9977LhfnZyzOT5FCsLcz49PHH3N8csvVxUrY2Rkz1XvI3UM2\nVUXdauYCnpY1WZbSjeeU2vJitcYmCbZrscNNAEeSC9iNFi7y7ox2qQQpGFuBEYJRkZNawwuR8ONP\n/oQHh99DCLhcN+xNcjZ1izWGRlvarub+W99gcb14g69wfGWDubu7+/dO3njfR0iKtHOFqpMkB2vJ\nk4Jv3vsdOqNY15qqcUnrqYI00Ygkp+26KA8mcbBQWW+wVjDOxhzt3KbqdNT90wZao5nkzoNstLvI\nzt8k2t9cPRxne4jRBsiu7z6wVbxp+2L7fomH+kznBRpfnC5NgA8HeqHWET60dVqGRurYSstYhTWu\nMD2Rrq5LeaM5Hadcl63Pz+geGsWxJxX4ZLUrIF+sLynrNVW7YZQVrKs1m/WG8WTKZDzh7PIFhwd3\nGJbauLHxP9r99Nql9DR3huMRh+UrHF/8omi6/A1pRcjTDaJL0bNRw31uB2J80dWxgv1JzvnKR5fh\nGdsDnz1lh8Gju8KtSHfwfGAHKilQiSOfOUFzt+GEgnhtYNMYyqZFiM7llVRf9pGnkkwlZKnr+GEi\nVDH4RhliaxsjNElPBgmn7xjDPvJ0v8aQZZjHDJErwHQ85+T4NRLdojan1NbB3WmWkyCZJoK3D0as\nm5brusFKwbqs0KZjeXlB1bpWVVJKijRjU1YerdE8O3vK87NndNZw9+gueV587pwvVgsen37Krf1b\n7M73+Dvf+3uMizG/ff4bmtaVjTRt6zp5SElt+0iwrCvqpuVk74Dnl+cgJVVVkWUpFhfBWSxZmroO\nM0L3hCClYsTqnGcXYSTS5WqlF2IQfklIb+yU7xcplCsdMVpjBSSeAdt5CDYaYilpm5YsST3k6ycm\nGiYbHZmw0UsEdrjn+BUbiGXaw7pdp7Hawe/GeBUjoxkVBYl0ZRsSgUiUizal64TSmIrZfMbq+trP\nwnaGLeRQN+UmCsSPRjmORdxRtQ07szlIQdM0Tgs8y1CJ4ur8gp29W5TrK7LJhOl0ysXVgr29PZ48\nfc6941uU1YZ7b77LxcvnjGYzzOqKxdUZx0e7FOMJIs3Z2Zvy4uwlOs1JignXm9LVyrctl0ZjrIC2\nJR/PqVrXQD50qrJ+DzRsKz4JQNjAaTdkMmVWuH6uddNxVl6SSVdZsCgvI5qzrjsmuUt91K1DMDuT\n8dY3vsNf/ewnwD/93PUdjq9sMJum+d7th+8jpSRPJLtCko0mlNWSXKT83rf+AU2nuNrUVE1How1K\nCC5XT1jWj3n39o9A9UQJIaBqN7TGLeoP3vxhjIBCTtFt+IZV1ZIoQdMZB9UaGzH7EJEMo6YouGwd\nn643AkEQ3G+6QnioxpNtkK5wHUDonpgkPGwrAoQGSG8IRPA2/YRG6EiSSItWgsSClYJZ4Tzb81XH\nOFXMcsXlpotQm7UWpEThRYVlwqiYcnF9wfHBPYSFu7de5+nZY+6dPCBPC5LEwbHWEMfAgutNaSyd\n9VJcxkQj2t+68dbyN9grsLaveQw/NRpO4QwSoemx6CPJGP1Hx8H2jg2+/VsqWVw0wQT66DK8zn9v\nECYgRLD+M2x/Mv3zADKWFSTClXAo0bMdw9U4JKGPXowx1K2TlfPLwOXmlCSTgjQVZCohT4TrTp/I\n2JIrkaGFHf1Gio1nJCxMXnudxHMEOp8jxDuYAnoihxAor1ZUT6eMs4Tlyw11s2GUpZyt1iAFL1YN\natLyYrEkyedcrM7J8zySRozvQymlpJUSqRSJSiOj1RrN7YO7fP+9H6BE8kpXyQJFVnC0d4vvvPVd\nBII0SV2dYj5CezH2Is/RRlO1Ll8YyCgAtdHcmu9yuVxStTUgeoTItwELhi/xjaNj9OmPYMxCW64Q\nobsSChubCgwj0oA4GO80BJUeF+FaL2SQkKnEKXtJ32jaujK5zvdDxd+/zi5GFxgrQPbTGA13cA/L\nqnR56yz1taNeLF26MWyqjTeg/n5IU3anu1yev6TrGo6Ob9O1HXW5wdJL/wWHNWget23rHA4p0W2H\nlIpMunZl9aai6Rp29/acQo9wbe+a8prJZMq6rUnzgvr0Je1oAhhkXpB0HVVdkxYjZjLl5eKc1++/\nxrJckYxGbLqOSZqjRiMu1xWViS4DWEvbemEJqciyjKqqHNrH4HW48dPDvcEjOKlUNGXJbF5grOW8\nLUnTjFmaclmuwFoSnFpWmO+rTcutuavRNsaJabz+zhv8yb/4l69Y2Z89vhLpRwgxr+rm8ODO6/7L\nNQd7+7Ta8M///M+4u/8W1iZs6paqcbU7WrtLztIRL86fs65PUYnyJA+LlII///UfA/DawQPuHjz0\nslZ4D68XH1huWpJERmOpfULdeA8qwrPgpe/o4clgQLaW7HDXxucq8fJ7g6J/29dsxZIVHc7BQXJa\nh3PtiUJ1a3yj4c793hmkhETC+aqi7QzLuqXujIuebfCO+0g59K7M0hFv3nuPg51b3Do4prMdi+UF\nnzz+FUqmvRiB7XVa4/t97tIxiIkNft3NFMxKH5kN47S/7rH1fhFMk/vW+FwwkHbg2FixFZmF8d+d\n5lysa1eO4CNGsxWZh74sLoPbX+HAUkK8UiGINPtEeXk51TduFgPnKNQJK593D588hL/j77hotNWu\nhdymMawbw6o2LCvNsmpZlC1XZcv1pmNdd2xqTdW68qmmNdTaoq2i9qS10Ny6aQ1lq9k0mnXdsfK1\npouyY1FrXlxf82c//zc8X29QCqqmZLG+5vF1yelqw5PLBctygzaGN+++g1NjcwzWodpMohJXG+g3\nlzzNONy5xfff/T5KJv3EvMJsZlnOO/fepciKiAQI4PWTh4ClyHImoxGpTBwhKPTC9N+1Wq9ZlRvm\nkwmZSiIhJ8L1gtizMrxHd75UxZNtws1sjKsp/Qz0PjDQIT8Z4NkIAUc4NGwgzpt1xrTzkZq7l6zu\nxTN693ArzvTO982bww4eDNo4w2OM9tGvRAnJanVNpzUqMPOFYHf/EExLXZcYo7k4P2Vv75AkcQSp\nWAsaERh3PVniGs5jLTJJAKca1XYtdV2TZxmb1YqqqmlbQ5IVaCNQVlNtNggh2dnbZbPZcHJ8Qtu2\npPmIxdUlSZKSZikGyenLMzpjIM1I8xFV01CMxttIkjfkePhbWMOoyCnrjYOVbeA6ONJUsBkhpROc\nlWk+ZlyMyJVkvVqhkK521BiU8Y2lhYvcHYHPcl02FKlCKueQlU3LO9/4IY9/8yH/7f/8f/zJZxb2\njeOrsmTf3rt1128cjuGapAUvl5e8/uZ3ONx9jbLRlK2OUWDIP+zPTsjTKWeXT0lVz1Druo6mrXnt\n4AE/eOv3XW4NN5iB0NNqp/t3XblyAL1lLB3ZJxhYV5dpsaZXYTGILYZk2KCH94PbqEWcSG17wpDx\nDM3we2+cA8PWRnJSZwytDRudI/M0XpbPaM0oVby4btk0xj9nOF/VYB3E14UuKoNrGjZ87ozm4ye/\n5qPf/hWNbphO5n17qGggemZsjNLNsKUZPawxsGyBVDIoNPkbHzFKEwOjLIaAqd2eC4yfL4HGzZ1U\nLn99uWqiDRQIgvZcf7ZhDnsnKGxiW5umDC3cZDSSSooIl29HHc6oOCMa1q37pC32renz6NaXosjw\nXZ7hGrqLON1kRei1GjbXtmvdWteWzuDb0BElJTtP1HIOYcjz9VHparXhzslDlErJsoSyqkiLMVmi\nmM1nVMawt3eb915/n2++8U32pvuuQF71ov+pJ/s0XRsNh7tHK/Ks8JuVN1TRQA0OaymKgh9/+Od8\n9OmHrm7Rwu5sz4l4S0FZ1SAEunPGLMsyUm8cjTFcrZbMx2OXg/PRY8hDJx46xfbKP4i+MF95+bq4\n5rx3FFp4ufmXcd6CkWyaJq7FcIQOKCGS7NqWNE3Z292haZptlnlAnryD0K+24c/QVm7rLoc1IEXg\nG3SRcRvO3Xjy1P7uAbrrWK2uSVIXZXdNw+LqnMNbJwPFomBk+rysK9lxjsJ6uWI6ndC1LcZ0dLpj\nvS6pNq6/cNd0tG1HMRszmY65ffeEqqrACsqyoq5rhEwpyzWJcc2uTx8/QuqGsly5NVc11HWD6ZzD\nXuQpNojS39DnTpQCrdFd5e58b/Clv5eEdVFmeJT4+0lIrBUIq9nNEkSSkmSuEfZ86vL0LojW8d63\n3tm5s1ugrQtypof3OH/+GJOOvrS25KsazHf2T16zCNfRo+lW2DQhzcf87W/++yhV+GhK+zIK3z7K\nOvGAv/Ot/xArBYkIGouuyfT33/hb/Oi9fw8hErfcRDBOrrSj08ZHbZZN1VEoGck9oUVXWPy+uDDu\nl32tIX4C/OtDpBINQ4By/WZr4kdtRxPu1tiOar1jEPqtdca6khRfQ1q3jrI/Hyc8X5QsqyZG4HXn\nSk1Ol6UT2RZgtHVFxdFg91GMQHHv5CGjkcsj5ekYJVNfZ9rD0EGUQPuxN34uhsZygFHHm3boKH+d\nKPNzy0hCYBAeBRFKdZuh94B9ZGBCGYx/rwAOxhmLTTPIubJVUhK+xhI/Jszo4ExsNIDgxTOEiDeQ\nEn2UE94dIM8g/p7IkEoQgwh42/HCEjfumy24nHC6EygIsnZKus89u3qBhVhzGcY0brcDgx80QIXo\nQSuE4FvvfJfpdEaeZSzKimKco4Wk1pbLqiGVKT/85u9y6+AEELz52ts0XUvdNCAEaZIwHU+iUUnS\n1OXP0oTL5ZUvXwonZ3l5dRqjmOHxhz/+f/nNk1/zy0//ij/+2b+m1Q0vr146GLauqRr3gx8bKVzn\nlUCCqtuWtuuYT6ZbsohGm2gAOq/gE9ay8u21pFRxfblm0Xgd1x5DCUYydPmI9ZmD/wdjiNiu3wzd\nRFIvdhCMan8uNkZHIi76rYfe4QvrLP5sH6HkJE1TxwQ2htl0jlKSxdU5AtcacZRnYKGuSqpyw97B\nUZTbc+xbt4qs6NdrUFxaLK+dAY0OiI4t8XTnSGxWOoJQtalYr9YYYxmPR2R5xosXz5jt7DKejlFd\ngzWGWS45u1yTpg5KzpREJI6VOilGUbsX60o/sI5wOSpGlFXpx8cZUwWxY5AdrP0gdiMQZEIgPVS9\n7Ayr1nBVVWRS0VrDuCiYpAVZkhIbzAt4uigjkaszhsoK9g6Pefrpoy9NUX4lgymlfPfg9n2pPERV\niAaZJlRNg7H4ekXjax69502AVmukTJiP9+hsHXN1xliOd+/6zd5S1mtW5bUrmPaTGCDZThsWZcs4\nT/rNM0aUdsuw9BJ4fY7TRSz9RttvvD3M1+chIWD/JrJxbTynCH8OnnNdQEysuWyNrwM1lt1JzuWq\n4WrdeCF4B9VWravvrBrD6aJklCVY+nrRoGakPcvOWMNys6RrDd9564cc7d91heefiZx7h0JbtsbL\nRUd46zGwMGz7wn/zGFNsbRZ93rKvk3Rj7Cji3cB4xihXON3Y81X9mfP5IoMuCJEehIR0uDmiEZRu\nHUsPu7rDrYqtyFuIrR6WInwmEIQthucmhIN+HNQbcqOhZ6KMhlJJRyJJEsm0GA8aQfebrYhhrjeQ\nNzZ+Br8LIfj4ya8om5oXq4bLsuOi1Fx1FlTKB+/+kMzDUwAHOwekSQZCkOc5SZJ6tRnFKC/Ik4RU\nJbGgfVjX+OzsKU9OH6OEV+UYnoswpIkj6iw2V/yff/S/89OPfhKNT4gWhRDRsQs609rzFy6XS+aj\nUayZDJJzgdhjbR8pxjKSwCw1vaxlEEEI9/awjdcWgcQvzlAnGoyk67oSOBUO6RJSOcFxH8lq4zPq\n3uG6uRaC9zcYIbixmo9OXufg9sO4doPb2NQ1tZf5GxdjxpMpy+urSNgJe0SSuKjyenEJVjDf3QMc\nYjDKi+g8hIXuokztz0sQzjAEIsYYurbDakPbtKgiZ3G18BKAmiLPSQTszcY8f/mSrtwgTMuto0PO\nr655653XKUYTHh4fYLua1XLpWMXGtXwLwQtYpIflx8WITbnyGtzu+TRJ3Bnafg8JjngiFApB6p3f\npjNYqeja1nWbwVI2FdNEsaiuKZtl7C4jcchN3RomWeI6mDSa49de5/TJb1O+5PhKBnO+u/8f7d17\nx8EoUnA0yrGrFbfn+2hjXa/JgUBAr/MpeLl47JqtWsuLi0cephLemHoDp2GUjem0oxIHEYHW5wud\niHPDKFPOAJo+TxlKP4aRY4TIbO/9hYkY5kVuGs++9i88Z8PbBkZpG0oMkJwTNPcRp3YSdLvjnFYb\nXi6bmNusvAhC3bbUbUfZtKzqjrNlyTRPXEToo/RglANZp8infPe932MyOYiwtTXB6PtzNjhavE/E\n9tdPnJPgucdF6J6IhcFDi/RlNZivfD6isaL/QCs9/D2YNz5bUhKOvUnOsu4iIzr+fMl3uiuU8U/h\nekPtr/IKMjHitcTxi+/3G6DykWgQEQgGxzkhcdXEa1Ui5EW9gVSCVCq0qVmtLlBS0DQbLq+ec3bx\nhF99+gueXjwj8fnBoKUbz0T0WebhvGxHxG487t95m+eLFZ9elzxZG9YoamO5fXCXW/vHMXIPF3H7\n4I6TZURQ5LljlirFqMj99YoY5Vxcn8fxr+qKZbkargDA1xgLiVQynmPnc3JYp5QTLkKFtlpyIDLg\nx39TOVWbIstAcAP+tbGkA3pJu+BQplnmDIpxOdIQKQ6h5JtlJ84w+8eo7uNIQADWaBLfSqvrWi8i\nIOLcWNs7mjJ6X2GiLGFGh+3b/KQxGs94+zt/wNvf/rtkxTgiK2E82sZDwfsHXF68xFqD6bTfZ9z9\nL72QvBSCy7NTimJEMZnQtK2DUf2H2RhEmP4Uhd9HoSc6+de2Tcvi4pqz8wUHB7fotEElijxLKcsV\n86Lg8uUZk/mcdblh9fxj9vemHB7uczAZsz4/pyor5uNJnFtjTZSSlNY5B0WSgnGiDMJrxQppQRhS\nIXuymx/vRPjG6b4m1nQdEpjnIxQuHSZx87g/zrleLtCmQ6lwz7h9b1F2zMY51kLTGo7vPUS3zcmP\n/sF//J/xBcdXMphad3f3bj9ACsGkSBFJwros+ejZJ713YnrNVrce3M44LfZBKJ5fPuVyc4oKiffe\nhjkjaAVZOsLG3I32eZ3e09vUHZPM1R0GQxdMX/SWvmSDD8fW60RvPIfMzeDv+Tf48w0RtCfS+C0z\nRrl+g5/kiiwRPLnceNEAQ6s1rbY0naHurGts7aHbRdlyuW6ZjhLvfLibPhpN7423A3m7MIxsnaON\n1xSik5ub7lCAoX9v/7a/ybG1kdMbgJ61HIy7/dy5EsDeJOVsWX32NdGp2XZ+tr+zPxc5gF9DXnGr\nTG7bRG1BocO85vBrbkYSwQNOIhQrYiS5Ki/5+a9+TJEXSCF4cfYpv/jNT/j1p3/F07NHzMczL3cX\nrWHsp/llRwxigP35Ed9//0e8+fDbbIykag33b7/JOw/ej6+NOUEh+eYb30apzHX4MK7zhhDCkWUQ\nvl2XO69NvWGxvgLg5PAOb9wNJWv9InRCBE2MHoPsXJamMdoLBkFY36JLm0h0VkrGMo7L1ZK96SzO\nsxDO4XHOtPEtpXqDGYTWA3QcWgQO7wetneELJKdoOP1LQnRpra+hDRGrkC6XGaJV7ODec28O5SzB\nOR0SXMRgsYQIaXf/mHc++APe++7fJUldGcf7v/OPSPMijqm1lizLOTw85uL8FK07jO4wpttagbrr\nyL3DY63h4uULZvMdT2oxcdwj4c8GN1L6fdRt2o4cqGN+uOk6yk3taumzjCLLkFJysL9PdX7O4mrB\n6/cfko9mlGXL3v4u+/M5SWf56FefsKlbpLDkWcJisWCxXDlReWtRth+LcTGiLFcxIJFAKlyJ17jI\nXNs8XI1prhIUrgZ3ko689q4iV5K9RDDxxCchBApFpeFelpNgPIoTHFvBqmwZp+5vWhsObj/gxeOP\n6QZSrq86vtRgCiFEuVkf7N++jxSC+TihMh1pqjjY2+ujNO/lhegrHON8FysUZ6uXPD57SiKV97YG\nkY91EaWSKa12SiBdN5Cws26RLsqW+TgNb4k3Rd9u6cbmuRURfnZzvvlcb4QHjExLJKE4oeIQJbmb\nJHZQ8QbUGKc5uj/JeHKxiTndThu0xjN9g3wftK3rk9m0mst1zapsmY9SjA6lMwNyk/HRtbWeANJH\nRzECps/bxqJ42T8qse2x9WQW+JvCse7mDIYxbOi2N5a2Z/Na+EwkG35mIyey3nRfvHiH7x1+nEAg\nZNho3d8C0WYYVZvgeVlv+OJ4ubyjVPTwnQ1CED26EcYqbOghKguRaeLbEd05us9kNKFpS65WZ042\nsTN88OYPuHt0z0evg7kQ8BXsZfz+MPeT8YyHr73NH/zO3+c/+NE/5ptvfIdEpc4htA6GXldrwJVg\n/K2TsUr5AAAgAElEQVRv/IiybqibFm20ExOoGyeVl6bOmAJV7QQzhBAU2Yi7h/cG3+7g4jRNGecT\nV6KCiN0+kiRBBuKNX2ghmnGT4FSPQms/ISWbpmZcjCjyLJKuwr3AYP5CRBiMHeAFxXtVH5e6MXGN\nhPMwWiOxsfZR+CgzRJPY0L3G5f2sdfWgWOibR/tEj7Wxm1LMtg+i4vB/n2Hl9v13uXP/XWa7hxGh\nGE3nTHcOo5FNlOLk9l2uF5e0bRPP2UWyfs0Jp0IUcpYWJ+O3uDhn//CW670ZI+qhE2b7aNZbLoHt\n4Wer6XRLa5yubTCxRT7CWMP+4QEHRyesrq8RKgEMnzw55fbxEY2WdG3N4ckJSZpRNS1lVVFWlQ+W\nBuQoYRmPJlTlKu48EkGqEnKVUHjd8UwmKAtFkpPIlFQmznAZQ5GmFB51cOxf5yTM05xOpdw7POL5\n+ceeSyCizJ6xlkVVMxu5NN/u7QecPvlkKyXzquOrRJjHaZIynu2ilBOuPltcs7i4IBdejcT2m3Z/\nx7ul0mgN1jAtZkhpsWhAbm024SyNNZSt82paX7oRYEdrYVW1FKlyfSnpjcMwSxCOzzOQn3fYHlth\nuNTDL9YGYg3u+rYMZy+agLDc3hvzfFFSdzpGnJFRaxxc60hCOurWNr6M4GxVU7Wdm0gziCStZ/BC\nfIzRWjBCwV75gQ16paGHohSuGXEyiLhumsib6+VVpJ4vOm6OcHBAhvMVXzgYv+GxP8m5WDVf63uB\nYVnmVhQRE/43jq2IW/Q1lVL09Y7Wb4bB6TEmEMfcFwq/5gMZIUSxSggurl4yGU3Ymc3RRvP0+a8Y\nKZhlCdPRlOlkTt1UdKbDWk0Q9hdhE3vF2PckoG0nwb8Da60rNzJOgSZYSyEEv/z0Q/7wx/9PjCTn\nkzm39m4hlaLtdMzHW2tdJOaJFsvNNUIGpR48u3YQWrsv5+Frbzqj4w0l9I6Ke01ohedKRJIkIU0S\n0kTFaDYYwMVmxXw0dibGlx+ESNZFmttM3eDoBIMTPmfoBDlRA79f+esM15tIRZG6Pr3WGC9SYtG6\n67VMldN3TBKFNd3WSopIw/CvNpwbEcUSMmG+e+zzwwNoXQhme7cpxnMSpTg+uUvX1FTl2jmAFlxD\ncokQzkgqFep8FdqEEheoy5Jys2F//yiOaziCoxIUsLA21jAPWR43qUhpmlIUOW1TMj84wmjDxdUV\n9WbNqBhTzPY5PT1jMps7T164qH+9XLM7n2+Rr4LBHmcFrnGGIbECidfmVQl5olDCVRAoiW8p534S\nIRHGGfk8cfKOy66l8rnlVChkmrBuaj7dbHj28hNX6yr7Kg1jDYtNy2yUoa1l9/g+zx79BsEXa8p+\nFYP5ztHxiRbANE+crmSaks9niNUmChFEZmmEK3yeUhu0FexND0mSMLEhChBUbYlFoHXL2eKUTrse\nZqHQPhCIQqS1LDvmReK/q9+CvyyajFP1ZREn288NzTJhs4wRXQ93BIN2vDNiWXUsS+0iQkt/LcNS\nFOPZxJ4Z22nXPaLThrNVQ6utizRtgLx76Bt66PdV190nyvvNP5RPuNyaL6YXIjZrDpt0jNwHY/JV\nj60NzPb/N/TEq2goufFF/hhnjv6+rvsN6SvN7dbfP2tMYpgVv3ZgvP1mF/Oc3oA6uM/NTePrWbsQ\nSQy+L5CHAtQdyEWzyRQpFR9+8pdsyiUXl2fUVcXp1TWJEvz60U/58Yf/ij/86f/Nv/zJP0frBjE8\nIbY3/Zv5t5tXGKHALUPrDOfV6pI//cs/pelatHWSjFJKvvfO92OEKnzEprWO5Q0ASih2pjtbHzs8\nA+G/8807bzEZTdznW+tp//39GWDu6GT6iCaIJwToViC4vF4yzvP4+sAet3ZAuOMV9wCODDSEucPa\nBhvvhdDay8aSC7eJGd2RKOlJf6ZfB0b7OklNmqYejrJxSblcqeTmnLhrdw62AHb2jshHkzhy1kRM\niNnOLXb2b3Pr+A51VVFuXK5YxohWoITyXWVyH5U7lR7nEBCgElaLBUIIZju7vXGM8yDjOhO+5deN\nO8KvY2dgEpWSZKnPAwLGcLFYYYWk0TAqMiZFwdXVNSeHO7TaYOqWTEmaugTrggTwIg7WoT/j8YRy\nvURaB/EkgcFuIZOKTEiyRLmSEiFIFWTSoqwlTVyE33SGsm64rlqKgDZYwzTNWJcladVQWyfRqhxJ\nACWdvaoa6xW+YLx3xPr6itnu0bufmcDB8ZUM5s6DbxYCwWyU0nYG2XZ0RvtoaRO9kc8sFW9YtNYo\nkXiCUOUgWQlGd3z8/EOsNbRGMy2OaH0dZ2jLNYxeLXC5qdmZZNtG7Wts6lunN8TVXvHc9ncM9lq2\n/x7cx/2paxB8uijdDT6IAK0NJSq9wTXWNXLWAXKN3VQMF+sGbS3TXA6oJcNzczdiOCcXCflHv4sF\nrzduEonwjYkdISXeiOJzh2Fro/7qA9uP0WfnZuDkvOKt+9Pt6PKvN7d2a7z7zwjjNUgbxEDJXaMK\n1+sdtM64fE7rSWgBEnfv7VvDuSiml9wTBCm4lId33+FicUaaJlRVxcOT24xEQyY108RJ6imVxags\nGKEvGnYbHz+Lr7jzc29eV2v+5U//kN8++w15lrlm0BYS5b5rUkz4ve/8PqlKI2HPGQhDqzsssDvf\nixHe8Lh5etpo3rjzFnVd0zStf2wioQYh6EzQc+0hWov1CkOqN6LWsNyUzMaTWBvtIlyfm6Mf934+\niA5lgBfBRWaOIelqQOPJW0ueZT7a8zbQ5/1Sr4rTaY3WrlZRdzqiIn3O04DVfnz6cf/MTAkncn94\n8oDQCDyOo5CsF+c8+vCPEd2KzWbF9eIydhdRXlAj1GXi99SmqWnbhrpt2JRl79x75+3q4pzxZEqe\njyLKEsc8pM/wylnRkQ3n5JybVCrS1OnnZllKrlJEMkYkGbfv3kcbWG1KxrmimM548eKU+XRCuVoj\nmoZ6s2JTVr6MpPdrpRQUxYiyXLv1IFx/z0RKFK6zb0DG8lTRNQ0CS5ZaikKQJoDVdG1N3TYkiSIt\nMkTrrquqazIEb92+xShJaPW678EshS/Vc70yp4XrlHNw+z5/8I//yX/DFxxfajCLoviGI/zAJE9o\ntWWjG5Qx3Nrf548/+qMeG7f9QwDBAmFld7bn2HhCxU2q7Womozl1W4FNqDsHT3ZmW0DATaujlFeN\nxmnPeg/xr2ksw2EHs/iFeVC/EGHAqvVXaiyM0pS9cc7Ti40nAIXzH6r40Eea1he9m14KMGzQoW/l\n+apBWJjmalCq09eYhjrUQLcPEUZvpMM5u0WSSt9O6gYkK+JY8Blc6csi9lcM6DCIixtM/0N/Vw7f\nhIOKR6mTV/y63xuuO8aur5AKHB7Cj8tNwxQcGWOILeJiq7iQRxs4iGEch+IHTkrPsjPdReCK93dn\ne1TVBqsUuW2ha5DLS0xTge547egeAaIbwntS2BvnvR09fvZwnncYu9XmGilgvVlhsZR1yapc0nZt\n1HmYT3Y4mB84x7VtKZuaTmsOd27x7oP3OTm4zWw891HUtgMVo18/mscHx7S6o2rqLRGEkG9MfTSX\nJo4MVBQ5aeIUhoR0DdLD51+ul+yMxl4uLWz0A+dncPkOtg2Rq47QozGd+7/o3+9gay94oJx0mtZd\nlPALn2eMIfMlNlmSkOdZ7EoyvPb42TdQk5iG8JyN8XjO/Yff3kJDrIW2Lnn0V3/MpEip6orrq0uM\n1iTKlfskSeIjbTN4X+8YCOG6QXVtgKmd12as5urynN29A4QXLgjXr7WJpxFcrjCHiXBavEmSMJqM\n6ExLnqeM8pyrl2eslssoXTqaTjBdy6gYUYynlIszbh3u0WqN0RasxnQdjpEb8r0wKsZU5TpuVo7c\ng1PrwQure+KZ8evHtSlTJMq9ZpQmKGUYZSlt45p9jI2r2c1Uwq3phKXpkJnkYvXSM3SdsdY+sl/W\nHdMiwVo4uP2Ap49+/Yp7qj++1GBOJpNv7BzdYZIndNrBLGBZdw26bZmORhGjNyJEbUOo1P2tUIpE\nJqQqi5PTmIqjnROEkLS6dgSZQKSJnrzYmlhj4XJdszvJvuzUv/IRIrT4/8/ZqLeh2t5wJhJu7414\nerGhC4740FCAq6ccuHDBO3X1lr7ezQ5qP70hvdg0gGBWOFKEGdyWQyNkTf/+QD6yxpnDUEcYoNig\nm+qOQUfHv5nvET6u95YGTkbvujL427Y3vj/NuFzX3Myf3DyCQ/DK5wa/DR2HEFn2ke9nURHn0Pga\nWt/btO1cY+nYEm14qSLkPnvou2fWBlUZQ1WXnJ4/ZTTaYZQmLOuGy7Lm0ariydkFZ1cL7hy9dmMY\nwyb8BVG+6F/XX0PIP7mT1cYwzsc8OXuGEIK6q/mjn/4hLy6eu40KKKuS5cYxFVWaIIXkg7e/x+9/\n8Ae8//o3mE/mX+i8DJ+ZjKbsTfeBga5pH7ZggM7oCPtWtTPOSeLajw3ziq6VX8d0PKJPldjeyR06\nVd5wORK0jT0ypfQKN9Z9hyBsem7kjNaxPnDoVAWIOFHOgCvp+kgqpdBaxwj0xnTw2ZuozzZba3wN\n5430grWMMqc6tLi6iP1Aq6qiqhvKssIYWK03cY6NcaLtAhFzwSEtEPK+UkrapmG9vmbv4CieYdDo\nFYP7NKzb1Au853lOMc7JihTp83+2a5lMZ2gDuql4+fwJ11dXZIkin+4gZMLyeuEEA8ZjdNuQJ4pU\n3RwWy3g8o1yt4+IIJTmBIe5KFHUMvpQSVHWLRJAgvEMhoXPdSpSwXJUtOweHJNYhPZlsMbTMZMpq\ns3AsbBzCFhyjTd2RJq58ZefWHZ49efR5yzye3xce1tq7070jpoVjLioF0zzDWMGiM0j6pLjo96MB\nF8AtjFVZ0unOS1y5yfnF45+RJWOkSMCKvqNG8BRtINPQRwq4msxJngyKzl914jcn6esdn5cz+2zO\nBG7vjblY16wbPVAGEn2EFdiutodjrRV9vSg4glMwmD73aayLlBbrBox1udtYwtPXYhnrhBOC2HqA\ndbXfYCLz00OzbgO2r7zBbzoPf+1jCPP2HsTnjKszODvjjMt1s22ZXolL+pVw83PCmvMF0BGisv27\n+g3M/RY3n9Dsu+sFM9pQ2uQh8+EphYhSKelh7l4dKJyyM6aSdb0hVTnFaISxIH17tkWjGe8e8nd+\n8PfJs2Lw+SJGc/2uZrdycT7+/NwlHiLuZbni0ekjT/hyZJEP3vk+BzuHLtdVrvhX/+YPOT449gXt\nMJ/Mee3WvS1v4vMiy3AY2/HJ89/wZz//U6cJKlxkNB6N2ZvPSZMEFVR2pOobTPvxr+qapnUG0zng\njnRztV6yM564iErYWB7h5tnDtC50iveXMU5rFGG9U+pUZbquQ8SCWpef1F0b4WLpDSQ4yFoJSet7\nVEYNWdur8GRpumW0e5SjX2240wALSZpH9Z3BRTCfjrEiYblckmcZ8/mMg/19tNGxnKaqa08+0wQ4\nVQjXTF4KLzPoc6shgg7ntb6+Rrct4/mOcySGjizbbPlEJaSZIkkVeZG5tncSsjzFNA3rsqLtOorJ\nlOlkhq5LduczsJZV1bFaVzTVBm0so9wpRh3Op9i2jrV3RV6AMVR15cfHRucNj1Zor6Ebsq1KCpqu\nc92jhCQVwnFArKBQiolyzNmr6yWpkOSpb+GG5J3xlE27QnkCkRIicm6MtayqlnGWMN074vLs9HPu\nKHd8qcFs2/Z4tnvItEipvTVfLp7xZLWkGI/RdeNhKGL0IIRw5SNx3QgvJWWouxILtF3Hye49X1No\nsfz/vL1ns2RJft73S3NsuWvbjt/ZWYfdBRaG3CAhAoQUfCNF6PPoi+hTMBShIEhKYkgM0UBcAAuz\ndnZ2fNvryx6XRi8yz6m6d7p7egyQEz23u+rWqWMy8++e//PILTfqEJ1tH+pu1OE8LKqO/S8YZT4L\nQPEy40UG5GgctOTOVs02qqbHm+0sjt544gfkaH/sQD6wBU7ZARm8BT5dVR3OeWZ5aPT1bpvuDiTr\nRH3G4Hka22tf0uMAhnplvBs7JPM+GHDx1dtKnndfP+u/CHab1PbKlFXdYdyNM/gajHeYgjuhbv8c\nomNi4kZkrI1oZRv/9PXl65XCkCoNXnFvLHVEgw49n6JPcAmOZ7c4PrzNuqpoO8/jyznrLojrpjrl\n7vG9nSNfH9tXPLt38eZtftZdEsAoLYfapBCCLM3Zm+zhvePR6UPe++RdzhYXTMoZt/Zv0zY1dVPT\nM+0Ibmzw187GD9/Tdh1/++5PefD0E7quRStFnufkWUa0XbG/MbRxhHKCY5wXKCWGXsrh3OMFrqoq\niFCnKX1ppF+PJkakNz/jeucs9haGE/aDkfZ+SzLS86965wa6vkBwwDWig11h6v7fSmuU1lH1iDhX\nt+7Y9WEZjfc+E2llKnBm33vnn3D31XcoijJmiyxJotFakyYpeZ6SZSnWGgQ+vJ6qSDih0TrUGIfA\nZec8rXNcnJ2RphlZXoC/7gBus3jbyEvpLb1ekUcJx6wIsmKbJfVqwahI8bajaTtOz04p0wSpNd40\n3L9zjNahb3KU58wShfQhui5HE1bLRXRy6C0XENKyfXo9z1ISGegTg6PPsKdlSmOdwXtJphQJ4Rg+\nRtZVa/AoOuvYn40jXWXIsPX1y34RrRpDkStGsyOuvorBFEKITVUf7x3eRsmwkWslMN2ae3t7bNqW\np6sFgp7st/+gR9BtN2k8ToTNY7GZAx6lEu4fvIXzYJyhs91OOnKbahwYO3YiCu89F6uG/VH6mY1j\nZw48a53fvL6XNpw3I0sIiM69UcLDi5gqIS7WOCO3KcGeFH3XQO1EzX5b6+wdBssOXZUPztl802Fc\n4Kb1kYqvV0jp4ibfdHZgXtq9j/0J9pGu9X6o+WzfDyf02XrRlx9D/ffGPRc7hhsEB+Ocy3V37bNf\nuKVl90Ljz63D30eT0Vj68KyCs+FpI2dxa9zgePTzsT9LCN72AKKKaMtESZRWQ12s73GVgHWG06sn\nGGt4/e7bHB7dhyTBScG4mPDqnVc5u3gY2xduRHHsLtBnUxncfG33855QK+pLJtZY7h3e4/TqhFW1\n5vTylPc+fY8izZkUY9669zY/ePtH/LMf/jFaaZ6cPw4cn2Lr3AkEm3rDR4/e469//RM2zTpkfVZX\npGnCqCzRSpEmCd45jOmCEkf8bB8x9e6kiYLQfZ8w0ZAOvK7A5XrF3igQGWz1aLZr0dptS0wA+wAi\nbIx9f+IuUKcnNBdsQUIupgB7Avde5AF2BKddIBSxO8YzTZJnzLt+3sfXokM62bvVlxcDJiRVVF3L\nYn7Bk9/+hOXFQ9abFcYa2q4bEMT9NUopB15oCCCi/tz6eyCkpE/JhgjexvnuuDw/Y7a3H6Ls62dL\nn4ly3mONQ0od9nwT6r9lXvDowSOE98zGIxLh0EmC9IbVco4SgiTVZMUI5xxZmpIqRaElq+WScVkw\nShRllqG1Zr1eDhtin0UcGLji6x4fxN17WTcV7rlxFi8EwoF3docrNvaRxkyl7SzSCzZe4jqDVoJM\nyZB5E9sU2LoxjFLFeP+IxeUp/8v/+r/91TOWWrjnz3sjjplS2u/vjak6i4qAkTRNeP/ynMo7Ou+j\nVy2HFGmILHd6oITgYHxEta6G52NsQNmG2FIBKkZTvUe/O27+mwAnbi2z4uurZX6RoaTg3n7Jo8tq\noG/rx9DN5HfrjdDnKb3vE4ZbIzq87kMdzcX06gDqIQCGrjaGurVMyyQycgTSg0Dqbgb2oGHSDCnc\nrWpJeD0CjKIB2bEs17x4eHnD9YUBQrHWNskVnbXU3fUo48sY7OuR7PUNDB85du2WhKC/T8aH+vnQ\nK7vz6f7qhQBFpOdSgXs11cFobiPNXVL3cO/SJEcrRdWs8FKRFyWj0Yiy0Dx++iH/+W/+E3/77l8P\nYJ+b4biI/98qfe5Eyzev/8Y9m4ymfOu174ALjqkxhr/4+//Mz977W9557VtMysnQYnHn8A7vvPYt\n9sZ7KCm5tX+bLEmHa0d4zhan/MXP/xO/+ugXLNeXgzrJzz74+8hO5SlHJVmahDqltbF+79CJ3nLy\neo9UQTKL6DBaa3bIT6JosHPM1yvyNA3yVDA4lX29zrggjuycu5ZB6deVNSY08cfRR5DsRJG9cfQh\ndTO0pfTp2F4rMxgnMxwn0QEQtLtCnjVv9/Zuc3B8D0RwtspM0VqPkHkQr5YZ+d5d2q5jtVxR1Q2d\nCejcqq6GLIGzFq1jXVHEHkxrqWItuDMdQgZ2Imd7QEW4NtM2rFdL9g8Ods82rncX+I2VRsgQvZvO\nYlqDdbBeLlmvl9y+c4fNZhO0TZuKJMuZFZpcS7pmw95kFGxFkkFs0XFdSzkaMS4Lbh8dsbi63K6p\n+JCc9wN4SsigstIZG7M4kr2ipFQqpnUBLwa+46YNbXheSJz3kcTfQefp0MydRRqLlpJEB6djd0vr\nhTMOb91heXH2mX1od3yewbw3mu35Ua6pWhu8CK3w1rM/O6BxljzNAI9UPbFxvBFCbxF/UjAqxshE\nDY3LQwQZ02GtMdv+tj6S69Nn/tmb8cWy5XCSfc4lfP74wm0TwJ1ZzmLTPrNfMIT7RBTi1mgO7SG+\njyr91nj6njfWDffFR2afXuKrr1leVR1X65ZpprHOUnWGurORHccMKcXOWEyvqTigPUMEtY1Aw/ns\ngolujq+lpvmZsb3nB+OM82fR4H3Jo4oY1V8TmKZHv/Z9r3aI0LvI/evMDtgs5m2E2IJ5tOwp3ETc\nKOXwJ4nk6s51LNYXXC7PqJsNzln2xntYF1CSo6II5OZCII2lbhuOD49Y1xc8ePrJsJFfrx8Sas67\nTNTswkl2jPq14jEkSvP9t3/A/uwQhWBvss933vweR/vHOGf5F7//pxRpOVjqft31/ZBSRlF1ITi7\nPOWnv/oJranJ8wznPZdXZ/ztuz9ltZpHdRAihZsb+t62fYTh+InWpGkwxINahrPDvjA8sx3HeblZ\nMyvGQRA5Xu8g88V2DffOhItAus6GumWvaduTNuyqlni/JXIPhtZv77EIoJr+vKSQJFGo2vvgcKVp\naAnaLRtd21MEHNy6R56PBiR41YZ1CJ5icsyr3/4xr37rx7zzo3/F3u3X6bqOru0QKiEdzSLAyFNM\nD0mLKYmM2qVSxr7MBK01WUyBx5xc3Euhj81XizkA48k0zpOQC+lBakKAUprOdGw2NcaClIHebjQq\nMF3MHOoEhEBrxaPHT9k/2GOxWLKua4x11NUaqVUAF7pAYZcoTZ5mVMvF4MDu/gwc2BFVLUSMbiVa\nBHR/IFzvqSN9BAUJOutCGwqgpcSYDgtceQnRIS5GZQACKTH0hIqdzMmqMRzeus3y8vSFBvPz5Ezu\njmeHvkw05+uWSSEIvS+GdDaj9oZJPo41nW3txscJG2ibwuS5WJ5GAemtRFd/swJ4ITTxhs17m66E\nZ2/iAJvOYJ1nnGtWtXnOb73cuNbP9Tljf5SSKMnDi821c9v2OG0XsWe7sXm/rSGK3lCKPmXbG02x\nE/14lPMo6YMgYvysdY517ZiLllmRULWGOrIKxaPHaCXC8YevDfe9s8F7s9YR/utj+C/uOHzRcfMe\n50lYDMuv8PwG4EccTgR5oPDmNtq3focIIL65SwbQR5D9ofooQwuBiNywWgqSRJFpQa4VqdYD8jjV\nmvcf/oZPHn9EpjOyNONydckP3v4hr9x6E+8tnz75Lc1mzbpuMN4xHZUIFxRsfvPxr7l/65Vh0+/n\n5NYg9tfY9w2KZzy163MAYF1tmJZTzi5PeHT+kB99+w+CpqDW/F9/8e/5o+/9Ew6mhwgheO+T31B1\nG7735ve399M7zs5P+OWHPwfpSGUwGF3XhfuiFGme0ZqOIs+DMkVMEVoXJLECiYkdosv+/bCX78ho\n7T5T+rYdydV6xf2jW5wuL+kVSqSSw0bRk75751CJxHQGLQMBQd9Xu+2d9LE9xJNGfll6X4RwPW3X\nkiXZMF8HjUqtaZsmAIR0YOPSSkXAjcdGTt74AIfPP3r0Ad/+9o9ItGTT2muGvgfweO+YHNxlfHCX\ns49/iVSaNCsZH93jk7/+d3SbJbe/9QeUkwMe/t3/zWZ1BaIXZsjjfXO0zuFMR7/zCA9eeIQPUl+X\n52cc37lP17SYro3nERyIzobaaaISmq4h05osT8mSKa6qSfKCTX1CKRVpmvD00Sekec7V2WPSRGBq\nS1ZMOX38mNl0HNG8G6SSZKMJSgajPd4/HFL8Aj9w+oa6sg7gJgFJkmO9xTs/BGsKQS/DqNOEUZZS\n2wYtAy+u8NB0LUmScLqp8EnCqm0QwpMkCtvYAXfTm8aqMcymk1D/XM2H7ePm+DyDee/g+LaQUtAZ\nh5aCXz3+GzampXn8iPLwkDKfAVuyaSlERGcSc42hL23TrALM2RnSIbsfitsBAt7X1rab6pDS7F2m\nZ4yzZc3xJGdVr579C1/zyLTkeJLz4enquYa8H0Naie2mdnODG9KyIvYbOY+TUaHEOswAIgnf1iu5\nNDGKXFYdR9McQcvluh3urCAcU+xsnAMi1EVFlT7CHbzQ7X3/hx/hO/ZHKRer5us7qt86CT1a1sWb\nIfr3xdZQXqsR7pQU1E6dclA5kYJESTKtyLQkTcJPrQMCeb485/LqDGNNiGTWDetNxXsf/4bj/Xs8\nOXvI5WKJ85rJZESiFVfrK4q8IEsSNlUV0IEq2cnUXHfCnjl265YxUpQ7ryU64c7RXd578B5Pzx7z\n8aOP+OZr76Ck5F/+0f/AKCupmg1/+auf4KxnbzqjaRuyLGO1XvKbT97l8cVjnOtIdDKAbXpUKjEj\nMrSRsM2gEKN9KUItsT8vO9QitnXtXbRpSF3Ka6911rA/njBfLXHX4rktKEcKgelcv/WwBdWFZ65V\nALs5FxChXdehZTB4zrmAyhSQpymd6ZAe6roKv6eD2LWMvaJ9VKoiqThJEnr8dsFIMbKeFDkSx34S\nehEAACAASURBVKbpV2e8U595rGGeHr/+3fC3WOcaH73K/MkHFJMjkIp7P/wzTL2hWl1w+cFPSZSi\nM4au62JZSyCl32ZZdoymc4751QUHh8ecnTwO62CH51XGmnwqAg9x17WMioI0Sfnk04+ZTkrapsVI\nQTmasa5WXJydc3B8m43pSLOUqyvLQZKyWKzIi4zlfMHs7mtsFhe89earPDq5GqTgEBJBmD+9rqm1\nFicIQVd0EIUPVJ+CwFIWOH7DPQ4azBJjGrwIjE2N8ySp4mSx4PzqkuZeRaqyWE/frnnvPZvWcmum\nGO8fs7g4e67M1+dGmPdffU00XcwlK8WTk48p04wr10Jd0XSGVw+vty5YGx6UGyYNIINXt9wsKdLj\nHW9SxAK9HYzHsGhgKHA/b8NY1YbjCUOUeTPa2HpwXz16EsC9/ZKni4ruGaz2z6r7XTea4b6EORwW\nWXAIAihARKNpHEjXE9IL2lgT61GxrbGBtD3WNZanK+7NCg7GKSfzeog/+iG92HK6xlqPudHY/6xr\n+IceUsCkSHj/6fLrPbAICajIzBoQxTJuUyLUtnrYQ0jCxfSM7yPJUJNMtdyRBCNGkpJU9/1qEucb\npMhIVUKXpiQ6QSDojAk0dM6yrlc8PXvMrYPb3D68iycQjv/mo79DSc9UC3zXUkvBslpyOD7Yybxe\nn783n8+z5vW2yhlmXaITpuUEIQSdNfz2wW84n58wHe3x3bd+h6Zr+OWHP+fhyad01nC5GGOsYX9y\nwF/94id44SiLkjQJava78nnOe1TUZBSCAW3cl1Ig1E4FgjQJrSU9sGZYGzvlFiXlkAnwEJCoQtKZ\njvlqxdFsj2W1oTMmkHlHY+vw4CwINUST1rrI1mPQYrf+6dGxThaQoAEdm+c51liM6UKk1rY452nb\nbiBUGIgVPAOJQQf42DOo0wTwmC68J5Xm3tExbdtwta7IixHReg01WMGzslsxLxWzRpPbb3D28a+w\nbY3OR0ipScspm/lJ6FxYrQIZhY+hcl/D7Y/mBWIgxFcIb/FYbt25R7tZ0JiONEkxnUGqwNetRWAW\nyvOcRMCDBw8wXajb1nVD27TU1YayHDPKE/ZmY9o60KUKpZFSY7wkUxKUwlpD0zWMxmPSq1WUINwG\nEJJQs46WAGsdTduBs3gRnqVC4fEDJ7DzAW9wvlhgslEgXFCC2hhWbY0RKW3b0VkTlISEwlq3LX3E\ne9TF1w5v3WM5v/hyBnM0Gr1+59590XQu0BQpwaptybMUYyxrKv70e39M3UWvXGzRsteIsAFvHVJC\nmY0ZUKEOrOtwXgI9I467PnmGiPP5Yebp50SZff2kj3b9gLXb8cxfwlAcT3M6G0h7P288c5Prz8OL\nIX8e/zl45Z4eCg6dAGFAe48J82OgaDN9HTLen4/PVtya5tzdzyMQKZoC77Fs68YOIn9l8Cfdc+7p\ny4zd1POXGX0rib3ZSvIlz+XmeYQnPoTXAzm9FLuJy+3oI0utBKkKAAEp5Y5kl4hgn/iegE8ev8/5\n1QkH0z3quma9WQ7RTpYk5EmCEIqiKMnTkrrdkKcFnelYLudsqjVMprTGUjc1v/305yz27vHdN97h\nWfP9Jor2We/Flvzwt/jauBwzycesmxV1U/O0O+Hi6pIyK3l6+YSTi6dDo37dtXz06EPW++uQdoyz\npIm6tj2z1O5975U+WmM+YwCUDBuv8x5sAOh4njFvRNjUsXZAHAu20WNjOgRQpFvcgjFmiFAhEBT0\nd8E6h4qlHissUgRCb7lzXwJYUYCU1E2DNSb2OgaHtW27a6nVumnQWtG2AcUq1ZaZJkTGKrQ+CMiT\njDvHt1gs51zOL/luVDZByCAg4beP+PMyCWk55Y3f/1fMzx5y+Mo7IeKuV7Sr85BWlQKdJNR1g1Ji\npyZNqG2maUiLpilZohEKpG04PNznslvSGROYhbQGEfqRhRBkWUqiJc1yzdPTc16/fzfomJqO8XTG\nwwdrprMp4719qvWS6d6M1lpee+1VmtWCoixoqpoiGbG4ukDqBOtsmGfODIGWFwypdi88nTF4IWi7\nlmleUhkTr0UNDhcIfOR5XhvDqAwsUipNsM6RJSlCSEbjEZVvcd5SJjIAVaPFFGydtqqx3H3tDdYv\nMJgvBP0kafaH+/e+QdNZRKRWsxK8FJiuo+0MB5MgURMuHITsy8hxbsT3siQHD61pQz3JhV4ohBh4\nH/top9/c8bt9gdcn0q5X2tcvJ/nz7H/4vZvyTl9kFKliViY8vqpe+jPPnPwxjO57LoNRi+wcUabM\nAc4waGf2gJ6qNdSdo+0cXRd1MSOU3nrPo6sNy6rj1YOSTKstCcIOUna4zzEFHs7zBef7Dzj2x1kg\nKviaxy6kv++hDCCgwMPZI7F7Zyxko3u0d0i7JklAIKZakURViERJEhW5eGO6dlZO2VRrPj15yNnq\nkmW1CbU6rbCRieWHb/8uB5NDELBYXeG9J9MZ+3t3MEIjnSNLExyeTx895Oe//XsuF+f9KrpmEJ41\ntm06vQPTG6ThjmCt4437b6Llloe5dS0/++DveXL+FKkUSoW+wjQNqbhHpw9puxrnbGzg79V3wlF7\nCa/+/LquY3sWW2MaGvyDsbDe40UgCYhoJlAKoRRSqYC16q/Xb9dtTy6wrDfsTyYoEVCrWzHn7bBu\ni7Qd8lgibI7e960hWxAQEFpgbJA46zV4Q3TakqUpRZ6TpimCEEUrrUjSwIiTJAlJmpLlGUJI0jxn\nfzrj7u07nF2ec7lYIITgJ//fv+cv/+ufc3EW2ohE5CLefb43/77zlMkm++zffxuc5fLx+/z2J3/O\n8unHUbVE4QdHQzIqSiajKQf7e9y5dcAok+yPcvaKhPryjLxrodnw9IP3sBYS71mvFpRFhrdN5HpN\nw3HbjtOzM46ODrHWsVpVtE1NV9cYY5FKBpR7HdKh5ahESJivNuRpgk8yNqajMeAQVHWI3K8R3cT5\nIgmo5v4ZCmAyHtE0DY2zWATCu2HOFxF8pnQ6OFdaCPAOITxKhsjW+0DL6GEgc5ciGsB4DnVnOL59\nj2I0e/MbP/inf/astfbCCNPDbG9/n8ZYypiOkkhqYwJ91ps/3mHe8CBCBNc3mfasMoGHdhzVKoKx\n6I2khZh/7mHgfjAgN5JPO0vxs+N0WXNrmj8XPCLEVtHe3iiKfp6hkCKkYh9fVV9LNAQ3ryZaz7jR\nOevopMdZGWoRNlj50I+5NXxbubHtz7NVy6a13N0ruFy3nK+aaBDY/vQhurzZPvKPOUaZxjlP1b5Y\nTufm+CKpyfAbodrVy9BBTHaJvmITaiNCBocv6VGwsck58MRua5p9c3sIhDou5k8CDykBLNJLl1gT\nkKIHs1vcPro/bA6He7djPc/znbd+wKpec3r6kLwoUFJSdx0JnvPFOfeO9zEvKuAP173deYbrFFC3\nNWdXZ6RJynKz4Ncf/iqA8QZEKFi/bc7vad/6iFFJhRDBkIUUp8X0acx+U5dRcUNAonUgJIg9jkqr\n2N9oYg+h6096SBuqa2ff29BQ7+xrmEIKZPCtWW02HE5mQytMLEJFgoKtMbcRYNSDCS0O64NSRRJ7\nF6UMqV6tNMp7rNsi9Yt8zKZaU+QFUooBoKS1vpY962ufOtEkMoCA9sYTJJ5HT5/Qtb1DKNhsltTV\nkl/+3f/LO9/9p9y+92Z/+s95rs+e1w7P+ce/RGJBaKxzFHkRmYwC5+1kPKZtW/YO9tDSkSuB7TpM\nZ0nLHK8EykHtWiajA5LjferNilGW0S2XXC0vuPfKK5imYbVakk/HHM2OePrwU84ursA1lNM9xqMR\nTnjycY50U6wEkSRU1SY4FHmG7OBifoWUCZ4S00Vav23yh1Qr0iyNSNmwWSURidvF+TTfrMmcI1OS\n1EPddugip21aUAlaCayTOGdwIiDXnRA0XYuUglSroYdWDoGTICIwaYzj4PgWD3/xq+euuBcaTCVl\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xkQfeSxto2jpjIl1hSG9ZZxEicIV2pqUzJq6OnhQPEHC5OB+89STW/4QgULalCR6Hj+mu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\n9PcC9Oo4mc94cf86Z6s54IispMaXmURSs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hPoQiQbj5iURWIzCiTpfcB3TgyUg1Rw3jm8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\nCV1H19Q41w4iAdv7Y4u+9AHSGpNGXySJGI8nRKBQBV3XEvtNNqSNOIjX4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sgj\nw4ZgC5DciDBgSzBkQgJswgIbkaBZFMlSsapUxWL1mZEZkRnde/Ha251mNx6svc+5LzIiM7KYGVnc\niZcvXnfvOfucs9de//rX/48m1FYW+7IsxHQ6u8fHOFhTDXOyNVUZSszZmbA2heTmeo/rHK7zBB/p\nO0/fOmIQr0ppsiepDOVjjWirsYXFFBZbijqQNslQ26iUcSpsKeek0yYiw7dZd9Sk8wgI0Sgm/d7J\nfEY1KXF9S9c2OOeHZ4y4BScnK6wQxCnEOUf0IXmKapzzo4XZcB8xyqtpjbaSmdrCSKD0HkWkLO3w\nPYGCA2WGoFVGAJKEXuq1zPeSIuJ7R9u0IgRfWK7cqvle1HrQ5G3bBlOU9L3DEwV21YIauNYJESiR\nGlXKSjMjGsbsXaX1SymBZkHmyPvAer1BKUNVFrTtBrThZ3725/B9R3QbLo/flUAeI+1qgev6IfGo\nraYyik3v6RKUZusJ02uvsVwumM53qMqS5WIN1Zxrr/9M0jmWOddIolFYhb14m+bB9yitZPvBe/q+\nk+sVJRBOD15NfIg4fE8u4LD/+MDx9KYTRLJzXlsenj9tCv388fidH3Jx/BDvur/3wn/0EccnGjBj\njHersvq9f/Hbvy8qEGr4Qf75+wgtTduw2Ky4vrOPB1auZ16XlIPdTxJJ1pppVTOrJxRKM6srfvz4\nu3R+JT2aKeuwRpiSkqVkvF9glp80SHzY3y02PW8fLZmUhs/dmF9xD/9pGPWnbBg91irVeE+oZ/yO\nErNn6bcT6vuIHpiUYYpYQWkMbnXC4tGd5MBhRbgiZUNGy+ISYyD0Dqs003pCaS1lYYfeyizGLX2N\nuW9R+uaKQioYY0N+HAJbQmBTxia9w0Rwrsf1bvCrVAo0XqzoIjmFvZrZaSHI5Scjsw99sstyiW1q\njMFaMT7OG4wMaYYQMEVBUZX4GJjv7bCzO0fpnMVpjBXFAlEb8tKYbi1lXVJUBba0FIVNZtMRF1wq\njYijyN7hPsYE2mYFMVKXFVVRUKQNg9KjoLlGWlR0YbFlgWs7Ce4hbrFzx80MyAZaKYUySoKi0dJy\nljJBk2DYalphrQSX4Hqyp6rRirTjIPRuYNyatIExhaVtG1zvpX2oLHBdf2VzpNTWhjJB1+JbytC+\nopUYa0vQg23GtHwjoq3c5Pm90y9IrTyM91FQik3b0rWO2WwmnqBlSVHVlIXFNRumk5rF0T0JnkC7\nXhCDwyiYV7JhWnVe2i/SIfiu5frn/jzOeZrWcXyxRlV7fOmv/LsU9XzYxGmlsEqL+Et7Rnv8Dl3X\noMsClbSHhQky3t9muk/v5f2yIFBOhOQjfijJcntYo3j1YMLxZTPaiL3A+NZv/SP+3Jf/7eV7P/z2\n77zwH33E8Ymv5BcXF//dH/7qb3Zit2QHXDSCNPfm+o9SciMCZ+sFzjlu7V5j43t852ldP0h81Ylw\n4WOk7Ttiusm01nzz7T/AmCh6ooUsqCbDZCnLzB/w0bKrj/K7LkTePVlztmr53PU5h/NqyJw+bHzS\nWeikMB8b4eejjveffyIuMBJ+8iJklUCb2arLGvFGLIzGFvJQWy2+g0ZFlkd36F03bKoyMUeTYUSb\nJN6EFOOcwyVj47Zt6Hs3QK3O+fThEvQaUjCNKTAq0SlW40OUa47By991fZfaLLZ2z6m0wMDTUEMN\nVJF9KsXvse07mr6j846m71g3Gzrf4YOjD47OiW8jKvVaKoV3AWkgh7IoCd5jrIUYuDg7G5idKJjU\nE5SCnd0Ze7szdnfm7O/vUtUl0/mUruuo6zKJunthgKb5LMqC4HvpCzWWsiyHmqXUFBXWbotnj9dE\nMnghHeUyyXaghLQ2wGAqbYskSKJVkrNLtVcFRAlsvu+GGqFKEovZENsklEGh8F4k+FwvkLA2IrHZ\nNm1yIzFbGza1lY1HjNVUtcU76enZeqAAACAASURBVMcERIzdZ9UkKT2N3qbpvY1NtcyxRpznxns/\nJKA+KZsFH7h2sEcIntVySVEYjp88YWd3h2sHeyxOH3H/e39AszynX58zKQyxXfDk+BFH99+mWZ4L\nDKrSfBiLLUrK3Zs8Obsgljv8zC/+O2hbkpneGYq1VhPbJfXqPYwK6EKCpVKatndioxeFeasn14jK\n4v3oNTv4skrx+UNbSbbXVa0Urx9MOV12dP4Fw2yEdr3kB1/9LVDqf3+RP/lJx8ugdf7T84vL05M3\n793e/+Jn0EpYsApSb5IWP0DCUP+JIfDg8oSfvf0Ghzt7XCwvmU9mrPuOuqxQXceknrDerAk2mex6\nh9KaZXfJ2eIxu9PbSQlI0/YKH7Qop+RaZoIeQrqgHwf551njfN2zbB239yZ8/uach2ebFypgf9w9\nl9tjUlpOVy9XsOB9I5+byrXM/O1UsyRbdYE1Kbs0ZkAPilTHysbPKnia5RkxSr2ySHXIzN6b1DVl\nURBjwDmZ3z6xKHOjfMwkj22YNB3naFCcfxJTHZBhEdweMeOJWuqHQ9lhqya5jbCMouR5DvTA7My1\nU7l3pW1BpO9kHo0Wp4yiLHBOVG12dvcpJwXN6ZKyqrm8vBR4upKaamEKjNZUpYHg2FxeMpnNicFx\nenZBWRWUZY33PW3bJB9MO8CgIUSCE3FxFbPwfUyQoh/qWCODkwQ3O7quxROpy4K+ySbXamgrYZih\nBIEbKxmmNXRNi+/FgURrhU31181mg0poQn52BjRKqUQsEjGFEKGsxKu0bzqignoygSAqSttBTSsz\nBB0x/Za6crNuROZPSQ21SBJ4Kt3azvnhLCTAG5QXofYcSJRSkOqGgipElA/J+Fvg567r2d3do0i1\n02ldMlGKgsBB3XH0w6+yv3/AW9/8bVaLUw6vXadr11wsGm6+8XOYsma6d4vJ3iFRFbz6L/8Sm8sz\nytkOWpfJVSQT1WTztj69T3v0Y8xOhSNgp1OUsWy6jk3T4xz4COum5fBnfpGNF9uv7LmZ+Gtbwe7F\nyT639ye4EDlZtlyblyO8+yHje7//a3z+L3yZP/on/+Cvv9Af/ITjEw+YMcbw5S9/+e89+c7dv3Ht\nS58tjLEyoUlpo+97chN1CIG279mb7qCU5s7Rfb5w83WavmPVtuKL6D0hwIHWVNMdVs2Gjesoy4rO\niYP7N97+Q/7NX/j3KEtL1QdK6+l9xGmdcHppRI5EdPLs/CRzOucj752u2ZkUvH445XLTc3zZPJcm\n/UkFyryQ1IWheYkZ5rPORyfCCxmWzRuZFIS0UYO6iGSXIm8n7UKpjUQrCmOI/ZqLozv4bsWkEh1T\no0XBJff5RaBt2yFIbjNC8zGOUBwD+xVIrSgxCaGnjFhrlBX7pyHw5UySOPSbhQTf5fOKMJpeP/W+\n8rqpbqvGzUNINl6ZJWqLAkMSOt/OwKoCHxy7O3uUdYFOhJqyrNjZmaf3CVig7zb0KFSM+AFW7Dla\nLNnd30s12WR1VRbEILW6SBDNhBjQQ6acCTKja0pMgSTqXLPLEJ7A2ZPJRDbPKQMUkfKr5gjZeFoZ\nLddNi2qPc9ICpItSMmvfY61BRYNzbsgmt9m2UlNM/apJJGDTNkwqkbGz6bVzZiilYJ2E80eVI9kw\naKBjMpsPQg7OOan9ghAZEfZyWRZDDTfP1bgxi1LnJTm/pIAqrTCGru0oy4KmbfEh1RCNpfOB167t\nsVPWTA5rfvjjH7DYNIQYKU3B/sEufdtRdMe899ZDdq/dZN207F1/hdnBLXZuvoH0AENGTPK+Zvnw\nB0zcCX3cUFVT3HSW1LUiq3VHjNKmo6KI2GNLvJN+Wx+zulocNgbxSuD84HEwK5mUhneOl1vPxof/\nXQSmy3tMC/W/vOBb/cTjpRTXuq7726dPTkJ7vhwYj0pJM7G14gCRb6AsZOz6nk3XcP/siNu712TR\nUyP7atm0qK4Thh0Mi1U9mRB14O7xj6gTLFsVYt9UGFGsGBY9BQMj7iPArT/pWGx63nq8QCvFF27u\nMK9fUt9mGkrJHEg96uWTjwbY5Qr8ulWvVGqEO9VoBJ2zS5uJXDbBsUbjVqc8/O5XWD38sbiI2EL6\n84wZdE6983QpWOa643YdcrsWmaGkK4F0C1SKMWdXXuCzGMYFXn5hrEl52XULtDuq4LB1r20fgw9j\nJipZtgTezDLX2gihKNVjrTaJG6CYzGYE77GFmBVP6wmL01MROFD5NZOgdSFi4yYErNL0mzXK91gi\nNw4OmCioItgQKNFMKsnMi7LEB4+Pae5SlqeMRlkJbBmtCdmnjDR1IQphpOspioK6KvF9P9Y49QiB\nGjN+LusSraCw5bBuGJU0ZI3CFoaqLLFaWlJyhpnZttswL2zB3zGyO5+LyXOMbNabpECUdV412iqU\nVRRlNuAW82Rp2ekpinK8J2IY3Fy8C8SknKRSjViR2brbpC6Sv6kaYUyGpQzve8rSAkHq59ayc3DA\n65/9HNdvv8p6ccHq5JhudcHBwR6bzZrTs1Pee/c+nfN0Xc9kOmViIzuVoezPefj9r7I+ezLcg/Lc\niR3a6vge4fI+zm3Y35mxvDhL7VaBi+UGYwrqumI+E+ShCxofRQDFJ9JPDpBX6pcvEPWmpeH6TsW7\nJ6P3sFY8M8PcXq+VUpy8+Q1CiPGbv/eb//GHvtGfcryUgPnNb37zSdM0v/Lwa99PpIiRKZsn1SYX\nA+fdoDlbFgXrruHo4pQbO/uDqLLSiqA0DtgpK2Iivc9tRfQeoxTHlw8ojKIqDZPCSi1TqVTL5Grg\nfBmTkEaI8PB8w4OzNbf2Jrx+bUphfvIj+Kgwcv0p1i+HobY+xk9P1Y3A6mzDJfWwUqtUx1To6Dl/\n51s8+t5XsKET26vcRK9ErWfo44u5mVoCmUutJnk8aw7z7wY37vqHDVV+LT96UGUosu/7tHhwxXE+\nRE/n3VOBeStYpjp+hl0zUJ3ft7BWsqSsP5taLazVmMISvCyQ8/mcvd0Zl+fHXKwWTGZTiuSdaZQm\n9D2u2QgBTiOm0Eox2z2gax2Pjx6yXC65uLxgdXFJu7ykW63oevF7DKl0glKUtkrPszzDIYRhtR8u\ncZT8TCQLNdYUyQarTdc8DsFy3LCqAYqtqhKlwRRSt3SpxmetbIiUEu5D13VyaYaWl/F3syThYMiQ\nekp9qjWHrb8x1mKKkVVNhM1G2L99P6oreS8bB52yQVTeKMj17l0PmSzlvWjypgwVICo1MIC3STHD\n7ClFWZbMphPKUjZJOzu7XLt2k6ZpODl6iLWG+e4u13b36Zo1CsV0NqX3nsvLBScn59STCfWkIsbA\nZFKybnvq3QMG7kDaqOI2uNN3mFSWSVXTthv6EAmpz7euamazSqQmVyvWTYcupmBKESzIz0uMQ3sK\nMWXUz3nG8iiM4rVrU+6frumzhGpiNXzY8hZj5PQHf8Dul/6yjy+hBeGl0TcvLi7+68f3HnTtQuSi\nrtCu5R9S18xNyYgpq9aK082CVbPmxvyAjeuEvpwvipZ6VoyRVd+SqYjL9oJAK7ZOhaEuLUXeOaZd\n1cDSVJly8nw49CdpCfmg3193njuPF2w6x+duzKUF5QXi5rOO46Mc26S0z/S/fNZ4eif39MeLvsaV\nr+WA3/fzLL6es0wzZBs61STF0UGYkoawuWBzck96bZNtUvbjy1lE9iv0KdvJi2MMMZF7/JDV5UUr\nkFo/QtZgzZnw+889z7vWmjYFAJTCp/funMNFP2ioSl/h1RaVQPZtlUCpoojIj3ekEFcKKzXHspIW\nElmEZaFSCtquoSylLrdpNvTBc3D9GpPplKIuaduOrm9pU5YdFaiMNnjH2ckTzi/OKOoaCksxqaEw\nNL0jOketNevNmsKW2EJ6QL3rifleipEsuCDnl6+rsFG1MfgYMGUhCkgxC8FLywfklhJ1hQCU66el\nMbTrDa53A3O5bTtCiGw2m+H9dK5Zen+ljin9nwJjS0vJSOwpyzIFMgWpZUbw86zIs8WuVRKUPZG+\n77BVhQ9OAp7KZEY1aGWD3HddK9C8cLPUIDOohUwxPicZZVECgdvCUBaWyc4ORV3TLC948vgBk/mc\naTVlcfqEg90ZruvY39ulaVuquqIoC7n2heX07IKoFMtNz899+a/SrS6IvpNnRUVC37J6+D1uXp/T\nLk5ZrheYQpjT1hhm05pJbenalqbtWG461m1HtXsT58W7Mm8SfRzboYAh637e0Ao+czgT8ZfOD6Q3\nYyRQf9i6c37ve/h2ww9+6/+cfOAbfUzjpQXMr371q+84537j4e//icTH7R/GkTEWvFykPrmVb9qW\n4D1Hi3NiDNzYOZAieZLT8jGIr5y16MIOAdjHyO9+9zexBgmYhaEsRS4ta1cmZakr7MwPGx9n0IyI\nStCdowWVNXzx5g47HwDTfhwbqLowLxQwX2QunnUjb//dGAzJ+PfWJoWtTQoDgzFD9uJnqpL0nQRL\nk6DY6BrO3v4GRnSzr5I0Uu3KWskQQvqeVipZWZnBYQIYdr85G5QgmTM/YX0+nQ3mBW9QwvEe7x2b\npHzivMsJohBTCovVViBUPcKC2/DvOH8MczbOrWwkJnVFVVZM6lqCaFlSVhXaaPFdTCzMSBwNmq1s\nMNrVCmMLEQjP76W1BKNJTTWfsHttn2oywZQlurDooqCYz4jGoIPHEiisledVQzZHllQi123T8afs\nwlgjtS4ti+d0Uks7i1bk9gtZIAVqF1WimKTrUg27sBgUl2enmNT/ikwvyotwfma2gqwJJum5GiMB\n2bl+eK/cR2sLS4ijBVkIWQRCFKSc31LCUsKAHUVQpG5ZVRO0FpuwEDw+uuGeUyT5QbZYwEal0lKu\nW6thzVIq+3pK0O26HmNrDg5fEeuxzQLXbsRmLgbu3XuHejbHWkulFQd7M169fZtJVfHqq68yn08l\ne7aWxXJB0DXnb3+NH3/tn/DDr/0693/4Rxzd+TbNo++wYzouHt6nmk6oyhJjFLNZTVmVdH3HarWh\n95F161m3HU3XsXfj8/Te43ygDwEft5ngHz4U8Pq1GavWcb7qhmwUBFl6XkvJ9vNy/Ce/R2tn3//6\n17/+UvrkXmqD4OXl5X/1+N4D7zf9sIjlkdVN0KJOoVBC1043XNf3vHtyRGUs12a7ECNWJXhXQW1L\nKlPQ9T2lloblZbvgcv1E1GAKQ1VYqYXpsY6a6evDkTwne3pWQPiw8SKZmFIKH+D+2ZoHZ2tu7NZ8\n9nBG9SG9m9tZykcZ0oP5/ID5p6nlbs/RCF8yfB4X1CFijjqqpAVQjY4juZVEmqgz8UezOboDvhNB\ngAh91yfZt1F6LPhADALvF8aKkbTWWG2v9MKNBIVUdYljppAPMwfVbTjVe49IuWXITw+wngghVJS2\nwhgR6R58KHN9kxx8x/nRMW8onpp/lScyYjUQPZNJTZGgQFtYfHBJtzSLxstfWKXpVkvmdc3p2Tmh\nl74/yS6ETOR7hzaGalpTlNKKE1CYoiQqTTWf47XBdT0EkQ+0Jpm7xziq3yWUKAd5Ud0ShaW+E4Z7\nWVpUEniwhR42H9JTatFGNjbVRFSDtBY4WmlNdAHUCGsWRbbn8mQ7rtxjqpB0Lnt75uclk388jt51\n0ifrfPId1YkkZtLxG7LZdr4nokICPqKBXVXV0PqitcEa6V1VSvpHcx+rNnqom2ujhwAc0/0lIh0M\nf2erip39Q6bzGYvLYxbnT9AhoI1hs15zenbG9Vu3WK9XgPTArlcbJtOa/YM9jAl417NZr0XaUGmu\nzQx9c8l0MqEMLf7yAXU449rc4jcLTCXHTtpE9C5Iu5X3dC7S+UjTO3oXULpGmYLee+nBjLk2u9WH\nGePw72eNVw4mRODoss03+ViCMEbKFFceg60HErh48CZudcaT8+UvP/MNPoHxUgPmV77yle9473+/\n/85bQJQ+uS3YRCUYxgXJMq/s/JQIWj84e8LudMasmkjTOpoSkePSWqMLg4t+gOb+6K3fp7QFpdVM\niiRmkGoY0kOX2XlPuWl8QND8pMa687x9tGTR9Hz2+oxX9idYPb7ns7K3/O8XObbCZJf4l0v4ycFn\nO5uHDL9KIJAsM/U15tql0Ynwk8XVDe3yhNXxW0BAx9RoncT4s4h3fnDFskoNwW5470wq0DpBv0nS\nk/xw577L5NiQjldtvVaGfEOIWFtQFCVlVVLZEpO8OGNqsBcDadkUhjhWrOKweRizlufOYa6/pl7l\n3vXiP6kiMXr5uvf43guk7CPWlAMk5pzDRvldjcZETegDfdOiEIlJ7SPRpXpbjNIqEMXA286mmGkt\nTiMh4vueKgU/lwK0MgJpKuSaVFWFUoq+61JmrVKml+yhtUIXOmWXVgJMVYhQQiQp6EiL0fr8ksVm\ng7FWrolSW9dgqx3HJKSCcfO0LU2Ya4lERXABoqgXxa25D0GMpoWxnK5TfsYS5G6tOL0UiQWbe3yt\nTRuOIPXNLLYu7SzS/1sUVu45o5P+rMLYtA4Whp29a0ynO6xXlywuTnn06DHWFIJgxEjQGm0LmmbN\ndDajmu/J5iCZZMcYaNYt3kdmsylaa/b29oh4JpOKV25f5+aNfW7f2GN/UtAuzqR9pJ7gUXjErSZG\nIQe2faT3nrbt6IMYm2tb4NF0LsOxwpS9Iof3AanmjZ2K0hrun4mST5riYXEwRswt1Na8K6Qenq/U\n8Z98hfln/wLf+I1f+VvPfaOPebx0CZoHDx78jR//8G6rk1p/YUXdX6WMU2lNiIEyMdD6vqdpW3kw\niJwtLrh7/JCD+S6Vkd2QC36ojWmU6FZq2bGumkuUcpTWUKWPIrUpbPsDoq7WjeCTD5DPGhE4W3W8\n9XiBD5HP35T65k8itPD0qAv93OzyT/O6zxyZJqdGqGl7ctXWTlGpPPOpyZxk1aWTI0lqWF/c/wFP\nfvy1ZDcVR9FuLW0H2cg410FzeDaJTaqTR2WuE70vkUvRdMg20wGGFDxzZgO5VSERcVJWmFuU1PZ9\nlacjjLUcoelnqE5J4Ofpg8nHlFJFFa8EfoF1SfXYRG4JEiyDExKL9158N5WisIr9aU1lLXU1gRDw\nfc9isSQiPo+r9ZK27yTLUjoxXUUgPQsiRAwBsf0CsV8yVgQJlCJlR5ZqUkmw7XvQit4H2r4TBaOo\nUnZoB4cRU4oCUF1WArOrSGENVVGgY+C9++9y7cZ1JnWVgmUSDCAmeDf106ZA6RPZahs6DzHIvWAM\nweUsXLZEmfUrrTsaTybmxKRBe5V9WxTFgExIkBQx+aKUwE8i++TNFVGQAGNlgyC8rjDUU01ZMN8/\nYP/wBl3XcnlxQt+3dF3HrVu3REkoeIzW3Lr9Ge4+PCEogeX73qGMYWd3Tl2Juhkqu8oYIor57gxr\nNdcPr2Fdw6w0mG5Dv17SNU1SsZKCvSgsicCH96MLiQv5a7jx+p+j6z298yJkEEZiXQJtnhsu96cl\nu9OS907XA0dgAJ2UJDCFUTgXh8fQxFEyEwXrkwc0J/f5/f/7f3vtOW/ziYyXHjC/+c1vfvXi4uK7\nT75xZyiQ55Ur4/idd5S2GGAapVRyLo+UZcnp8pLHF6dc29mn1pbSpJuXSGksZVGQnQE8kZPFYyFM\nFLnNRGpYNj9MKgfKmGDBjzdQ/iSEoRDh6LLhztGCwii+eGuHg1n59LL6kUZVGNoXJPx8HGM7a8r/\n30qqnvpdJX2XWg3ZiNGy0FsD6+N3OPrxH9FtLgkEQnBDn17wfguNEIWe3NcrC1rSFU273zhkJNtB\nM7lGqIzKqqEulin/w6I8BMMwfITg6QfloH44nphYgwwMXvmTkOG5Z0zGlXpmnrArvpsKlRgk0iIB\npS2G18rIDVH6IY21TOe7TGd7FMrQXF5wevyYs7Mn2EpUXHyEsqyoqxqtkjRfiCJ+b4zA6V6y7ag0\ngUgfZGMqRBqbXGSEqOK8Y91sUErRti0KLQo7yTrLO5dyGQm4Skes0eAcynlUTAiCj5w+fkLjeurp\nJK0DYWDWotTQ0iHqX2HMXq0ZNi8STGXuhkw9kZ+2H82o5Iq2rXhcFlZgyoiInsj52UElKsbAZD4j\nRC/Qq1VEPLa06T5TyTFF6uohenrvxkpFVExnuxxcuwkxcH76mK7dDEQom5jAynke3X0PHeDu3Xvg\nO+bTGX3T0q0Wqc2IZAcn90WZbNKuXdtFhQ5jNGWMVMbgV5f0zQbXiUShdx4fU3Ds5RlqO0/vIUQ1\nQK+9jxhbYKf7tE5gWxciPiQJxyscgPeveTu15cZuxb0nKyH1pBtdED75T0UxVHA+JFW2XMtPaBSK\nk+9+haUzb717797jZyw9n9j4VERO33zzzf/iwZt3++idWC9ZETzON7cLnsoWQ01IK0VhC9q2Zd02\nNF3L6fKSJ4sLru0egIIi5oK5UO9tElwvixIXHEZDocdaZpnsoXL9QaXFbIsv+7Gd758mAPsAD88b\n7j1ZsVMXfOHWDnvT4id6raowtO79rLWPRPDh+ay13Awhf8D2V+/L3q9km1tBVSH3QYZks+/l5vR+\nWtmy9JZkBzm7Gh1GpM6V60G5IX+1Wg0LXPZoffpchkxRj+iDbHsNkA3Q9UgUShlfVrnZPl791JyG\nkBi5IQ6bApUE4p9JlMrfiltYNldrqSpBgDC2nWRCUfDZAFrqamVRQ4DHD+7z+OgRTe9QhWiUYjTV\ndIIKkbZtaNpG2LtBLMIGM+WIwLhtT9f28u+kaLNcLtmsW7L9mA8eYw1NJ8x1TWKXei89rL2HmCDM\nAK7tpSVMa5rlJfQ9WmkKpTk+PmJS1yii6LWmDU1+LYOcY0gIQBakCCFbdaW6dqofhq0+0u0RcwAO\nAZXWDmOMiKwHUS8SPd8UTMtCsqGyHAyXQwiSfSYAZWufQ9c7vEP6cYOiruccXLuJQnF2+phms0j3\n3bZpeWqjM5qiKjk7OePs8T2+8MZrhN7R9Y7eTFmvW0LXQwj0vWOaRBXE1MZhjBUmuU6tRV2fbqs0\nj4hPqU9emH1I6j1RoFW39fVkPseUczon8KxPOsfbmeW4IRjneFZZbu9PePdkNfaBK1AJqdAq19ah\ntNLfKURAhjlRCtzylJO73+f4Yvk/h+Bfao/cpxIw79y589vHx8d3/JuPcNFTYihtMe4AY6RO9Y+h\n+TjVqoiRzvU0wfFwccblasHhfJ+oNcEF+l5uZue9+MlpxcPT99LuF0prmJSpzcTq5GqRNDyzwgqQ\nL/nHlW3+aV+ndYF7JysenK3Zn5Z88dYOe5OPFjifxZB9EVLSlYCWbuorHyoz/fIjyACdfPBrbwcI\nqSUqlbV+9ehB2DdsLk9SwB7+Wnb5Xij9zsuOX5rJpa3AR7/VmmJRSlMUJYMIgLYYbYTlOGSAY5VE\nzkMPeqJlWT5zIcjnkNtRhHAUrxCaIrJgq9TfZ0xBYewwB1fmWV/deGyBMMOIpMW7767AtZI9JeeP\nJGoQvWdxesQPv/8dzlcLoi2o53OKSY2pStAGF0Xxp/MBZQuIiuilPUAYxD4RQXqikxqp9wLVapXs\n0cqK4CObVcNquaFZtxBlBsSTUsTW+07stfrk0FEXFaqXzNIYy97ePrHv6dcrNqsL6lJTaMPycjlk\ng1l+L7d8eC/C7lopqqIQsldRCmJAQBstWV+qtaEyV0mRl/h8TTNnoveOtutTq8RwORKJS3o2nXM0\nTc90NhsujLVSo9wuysUIQbppKKsp167fxtqKy4szFpcXg6iFiCaYREwThnVInI7Pf+ELtG3HK6+8\nwqQweNdRFxZdlPgYmdVTTo6OMEVBVQkBrK4FbagnNYUyHN2/T9uu5X6JEMVglpgfPpImbIgCxydo\n2ftA76KwrKtdvLZ0LuC8cCJiZBAuIMb31S8npeG1gyn3Tze0/Zjta/L9rbc2hgmS9YkUp+KwNgM8\n+f7voQ4+y3f/2a//97zk8anZaPzgBz/4z9761o/bOiqCllpmmQrmfXBURTXs8suiSDRzQ1mWzKdz\nlqslq7Zh7TuaruFgvkcfHIWxUvCPuZ6luffk7YFIUhhFZS2TItczs8h3WupTL9m2UPqnUct83th0\nnrtPJHDuTUu+cGPOzgsETqXE1qt3gReBh/NuTiCQTIxKBQcVhw+FGHLnGz89CVdfa+sYMlV2rDGO\nz0ncYonmwCkLfsds/9agjpKZpnJ0EjgE9hyb0UENO17xbFTYsiBGj0k1JrknkgMGDPDtlWw6Kkjw\nWEj9g0+tnkObgMB5slh0vqfpWgkww++nvkvkfStbbF2LvEGTf+Zyhc69T8M1USmIZwWZ0fgZSHOU\nr3Fq/FaKqDUHhwfM93aZ7e6hyhJljNSfvGSF0UjQYziixAh2fqveBy4mjV7nCS6wWa5RiEG168Ur\nUto2xNlCkolEDgoR1/sB+uu7HlNYSgxHDx6xPD+jW214+OARj4+OsVYW5mJSEyPD5mbMaJJZsRIp\nvrIshw2GVlIL7VMw3d7lva9mnzd4KmX+CsksY0SRNlVJbUwlllhZliwXS6bTKSDC7GVVEKMj+Exk\n0wMCMp3tcO3GbYqi5uLijMuLM/qul2ATIsErYpRMTymYz2eCSKRMe3V2RnRiTt+0nuV6w6qPnDy8\nx82bN7k8OxMD6qQ7HFPmNptMmNqCd996S76vNLospDVPiwl5Jqa5MLqOyHFJAHUpCGoFNz/7C7R9\nkICZ65db1+Pp5aUuNK9fm3L/bM2m8wMMK2uG6CaP0KtcG2sE0VFp3bZaNl5+dca73/0av/v//INf\nfN7a9UmOTy1gPnz48Nfu3Lnzg5Nv36XrW5QP4lsI9M4xK6vk8q6HWpJJi5nWitlkyuVqxclqycb1\nLJoVrx/epu07altgjcVH6YfzwdN2m0FOq7AiOl2XIuZtzciaHRbsT2tiXnCsW8fdJ0seXWw4nFd8\n4eacvUnx3OOurKFz/oXFjEFujhwIM3RtlMKo8WfDOpN26Xrr4yo4A5kFNGZPKUgwrFXD13l5jjFi\n6x0OP/cLRGUT2zAMGRVcCqakkgAAIABJREFUzdCEPZrURkIYmuatsWR16GxAPGSVqV+yrkpc32Nt\ngTVF2tWOELZKi8pwNtunpq5+QyGB1AVP07XEREYaoFlF0rrNmw8Gw2Kl9BZxaeu9xy+k7tR3qCgy\nfSNUK7MpSjc9Xd9zuVyw8R0UBbqq0BNpd7G2xGgjrh0uAoboo2jdetGKzVJzkKu1MQVAOR6XCCHS\n/5k1osWIO/gxhyNmqcCxNccYg3eepm04vjzl+EIa7J+cnHJysWCyN8cZS9c5CmVpu1bUfrxPjEwJ\n3gEhZhVFQQietncobWnaltVqQ1FUQ9a2aVuarscUxXgpU+1P6ugRa4VEJnVHO8D7qNxiIl+v1xvJ\nbI3FOY/RhhCyr6cE2RhhMt3h2vVbGGu5OHvC5cUpfdcN7y/BUiD9vnO4PqCQVpzsQ9qulnSrc/b3\nZhweXqeY7vPO3XdxruVnfv7P41VJ4zyltTSbhsJWWKOY1FOmRc29730fCJTTqWxvrYWyQBUVtq4H\nNrn3ozxfREQ/fBBB+LIsoZiBrmgz4ceHwb1Eni7FdtGntJrPHM54dL5h06a+1ryhSfNupD11WAeq\nwqTWFVlvsgwmEY7/5P/jbBPf3iwX3+BTGJ9awAT47ne/+9d+8I3v9AZNUKKZaVKBvOlbJkWdoA/R\ni1SQHgphBBbWcL66ZL1eYoOn7TpeO7yVHCo8yidITMHd47ek9yvVjyqTxQzEZy/XnXSixUt28WnO\nzgePHCDWneed4yWPLxr2ElR7MCvfB+HVhR7g2BeBYSGON7bSQyaWiVLZNkmRV/l8TGNNbrtGmGs6\nz9qKxKfg2+0MWJR6NHYy52e+/FcxRS2EHi+vNu5uY7JJSnmQgqIqh4wRsnt9hS0KyqrElkY4kqkf\nse86YW4ag/dhMAx+ziylA0zBEVkIjBkhXJvYo7aw9EFIE7nMIM4f2T8xQ7eKzBR+1s5nTG63JNae\nmqucWXov/ozRRzAGVVSoaoKp0uII+N4n9w+prXZNS2Yv59aQ3Aftt5SPspSfi7KgRiXIhVIabRLz\nNcPkqfYVY0giAqMLT4hyTfoQqXb3eOWNz2DqisnBHrdv30RZS9CGw5s3ePjoIShN03T0PhKVEHyG\n+zVlmOumFZZrCqplVWOtwbmei4slCs1sMsW7XAvdymzSauicEMqKoqSqilS2GVGFGMUiTLJ8SwxS\n/hH4WrJgpRXT2Zxr12+itOH89AnLy3N5363bZ3tTKWQ1yUyzVVYIgaqoKbRmvrNDWVT0Xc/i9DHz\n2ZSDg0PeuvMO58cPObzxCsv1Rlxd+ob93V1mtuDB3bucrpdUkxqIVLMpQSkwhi6I4YWPirZ3RMSg\nwodI74P0YnqPMpr5bEa1e5t174Z+TBeyJB6Dzm6uDRdG8cb1GUcXDcvGEbfWA40wscukEy2etbIp\nnxSGzsekcKWpkg+tWzziR//iDzhbLP/bkHdjL3l8qgHz8vLyn9+7d+8rxQ8f0nlhvxVGdnSbvmNS\nV7LIaT08wMCgO1uYApTivGt4cH7G0fFjKq25vnuAVYY+JPmw4Ll/9q7oYCo9NMVnz8yq0ENvZi4y\nw1XY5qcJln3WWLWOeycr7p+umVWWL93aSR6c8vPKGpr+qg3VB52T2vqdLCRQJn9Rq9QApYxZUV7y\n0+OSAqceuJujL+L4HuP8bgOTIUFUg79elL7Kar5PtXM49EcGPy7CIxEmvV6MGG1o2wYfPJumZba7\nRz0tuVxcsFwuxa8yZr1Ygc8Ka2ibNUbrgcTx9H0g9VqJHgrxMMzC7qgcLO0gZ1cYO9y7Om8KQxi1\nRMMWQSRD31vz/z5SVI7VcQwWypohA9o+1uA8sfcpcyQxdCWLbNtmOL+skjQotWzVCrUSWDVflyvK\nRyESXEiZYkffOyEANS1V4hDYxFYNgjPLNVaK3ovDBwnWiz7StD2z3R0Orl1jvVzjvWfTdZwuVigk\nKJJaXvI17/qeiLSZaGPovQetMIXF+Z6mbbhcrChswc7ODlGl/tqxgDbcM3k+qqoc6oiZcJLFJzLB\nq0hKQt71SYwAyqrE2Jq9g5tobTg/OeLy/DS1EWVymEmgwtUdeQgxuaPIs7S7u4drWmLwnF+co6MQ\nh9599y7TnTmL9Ybzk2Pmtuf64TWMEmvEybRmPp9Tonn7Rz9isVlx8/ZtiumEaAxeG+rZLLVbQds5\nzpYbUAU+kb2E6BPpsh1aUbLaNFQ7t1k3Thi0Q7C8ek/AGCwfXzRcbPrhnEiwrtGK0mrq0kpvvBl1\noOtSNqyi0GapjCYGePSt32H6xl/kzW985RN3JXne+FQDJsC3vvWt/+S3fuv3umkHrZdeutKUdK7H\nKkNpLL0TaFVHoeP3fZ9EjzU70xnrZsOi3bBRgW/ffZOm63hl/7qw8nqHVoqHZw/443e+fqVtoUhB\ns7SGwqqUZW5BY+QF6s9G0ATY9J73TtfcfbKiKjRfur3Lrd2aWW1pe/+h9csx8OUPWbALY7heBw7q\nQF0WKStngGmFaZwzTWSRSQBN1olNuCWZHnR1ZCgoEwhyDSVDgOC6hr5Zp7pLHDKU4RVylpnZmMGn\nRvGS/YM9vGtYLhZUZUVZVgQxpxBrpXTu3jlQIinnt6yihrnJwTItdoMua/rZuJALAUippAWb3FPK\nopCaYBA4Np9HgJGwE3PmOC7ksglJwTF9XyeosEi1wmcxl7OIQ7Zgil4CXIiKIpGYYoJYr2w2kjxl\nURRyfE8tihkuzwE2eI9KAdS7gMHgelFAapqOjD6L/q2cp1KS4W+aRkTyEZ/Fpu3xRprz15s1uip5\n4wtfZDKdpv5SJ9CzNkQ0ZVWlTbRscrKwuhAAJUOeTifM5nOUSuo/xNRONm7ilBI2flmUQlBK96nw\nJxDLuWEznWpthcG5lrKaMNvZp6jmhACnTx6zWJzjgku1C2FvRxVAR8lYB04AGVwgQkIgDG3TEFSC\nm8uKoqrxUXPz9ms0bcAqCFFUmqzVzGdTLlYbqqpmYgpOj47ofM/1a/uUSbXJARhD74NkiT7SpV7d\npu9Tm5AaejD7KP2fLkR8sY9XFU3v6RJy4EMmnKX7DaisHoLl5aZPE4a0+ugcLA3TqqBOTlIjYiUG\n9wGBZqeVwUfontzlj7/+h3ztN/7xv/aBC9gnPD71gBlj/NGdO3f+8eNvvxOlcC4iBl3fMylyHdMk\n8oCQMuqipGvF6T0QOdzZw1rLutlQFAX3T484XV7w+v6t/B4A/OjR93G+EXugIWDqK2IGJimFZGjx\naQjxkwyaH7VX84NG5wIPzjbcOVoQgc8ezrmxWzOrRq3aZ75figXDMjIsELJoFrRUhajvGK2lnqm3\nNGD1KDWo1NaikILlQG7Ji9TwxqMPYiTXKLcIBEqzeHKfZnEiVlmQhLLlJSUgpL/zAg/W1YTptMba\nSNOuEitTp98XYkRO7fKRSXZR45xHawuM7So5gBOfPvIxUOUsV4Jggorj6I4irS1jW0OIEc9oojzI\nDmkz9DteCdRajjUTlnIf6nayJA4g4tYx9Kfm4JyyOWMMTdMmMXA9QNv597OWs089rtsIYowpW43j\ndZPsM21wAkOQzUpIORMWSFcCTwgiHu+CH2BAlKjMiI6qxQchwiht6PpA1wmhyzmXem0jfedpmw7n\nAq4P0hoRpO2mLCv29vaYT6do3+P6lsGoOi3QKY+U/m2Vmapa5jUZRShGoXiVk1IFRVky29ljb/8a\nfddyfnLManEhdXYGTafhTgFDNjZXeosZmjddWqGthuTiUheW0HXMZ3P6YFhcLiimu1xeXLC3t8tq\n3WBsyd7uPucXF9RVyXQyoV1v2Kwvmc6meCVBv3OePpF4VqsNq6Zn3bqB7duHQO8TIxUl2WWQOvWq\naXnjZ7/Muh3h2DzPIfXOBCK1VXz2cMqjrWCZSXwqbVIKo5lVllklhhg6oTkJgGBaiWdymWQxnfe8\n9bVfx02vf+/kwdtfff+i9fLGyzVkfM64c+fOf/47v/ab//5/+Jf+erHqNlyb7HLRrqnKkqDkhvZB\nCvAZipvPZlhtaPqWqBU7szlt17FqNkyKCQ/Pj3lt7xa/9LN/iePLSwms81tDXcpE8CloFtln0evk\ndj7WaV5WTvlxBsvt4XzkyaLh0cWai3XP7f0JMcLpquVy3T//eHIZLRX/nY8sfUmlrbRYWNlQ9F4N\nNPwhM9JZXHx8vSvB9xmzmjP5HAAFAsyLrthjLc8eCZHHChHFJY3MoBRlIQorXdtTliVlUbDZrGlb\ncT4gJgcLRkZeSA+ptRJ0cp1VRBFSfRYIQaG1Ha5Rlj5LbfzyW9oQoxy/3zrHMU9Esoutr/PPdTKD\nZnsO0tDGyDUIWUGIod9zQAPU1mYjBoyxQ69gtqMaIO+UufZth0YLpDoE7JjIMaOTh08tDS5lkgIh\n553GWIvcvqZZVCAHxvy+gdxnpxOcDC4pCOVm/t4lT9CYa5wdsmmRs9CJ1bkt3C514UwKjGhtsVYx\nnU4wSuH6NjnTSM0tKwLlswDpBS9sQe96irJks15RV1ViuAoC1fZCwokoppMdprM5Pjg2myXOedbr\npcyjvnqHX3225XwzcnFFPSqLqSixNlQRFpdLXrl1m/W64ejBfQ5v3ODy6D5aeV599XXW6zVlWXK5\nuOTs7ITD64coFzl59B62KHERdO/pi4Ku85RVzWKxRNuS1gmE7Xvpw/Qh0vUdZVUk2FvWwHoypZy/\nyrp1bLqezo29l8MzimSWrx9OeXi+YbHpU414KO6gFVitmRQSLI0G7yIucxJiZFJIElRoMbpfd471\ngx/x7X/xDVa9+i/5lMdPRcCMMd4/PDz8O2dff+s/3f3Xv1SsXU9hLE3fUVkxeO3cBm0Ms2rCpm0G\nmEupSjKHKCpA2ggE9Auv/0Xun73LrN6nLqdcrhqi91RFhXMBrxTGgPWSZZZW0zrJMH2IBC06sz65\nF0AGDa8uUB/X+Dhfc7uWB9J72vaB83XH+bpjVlmuzUtu7U24WPecrzu6LGiw9aTH4f9SO2o7R7Aa\nawIVXXKM0fitOqT3QZibKAKjX+RwnlvnC1mYbAwSipGhl+FKqRcqrr/xC1w8vkfftwyLZ1rgW+fQ\nQeaw69JCEAJVVYIPtG2faiQBbeWhzAu6CKnLAp7fWzG2amzDrcYKs5MhyOV6Jlea1IdIQe7hk98d\nltKE7agw1rKGGlkMxOBTFmno2i4FA5LbimimQkRvwbcSODSjylA6dqOITmBMRd44bNXkVFLFQYgW\nYitV4LzDBI2L4anwLzO0HdnlFMeG+7yJ2pYHUIhWqsyzbKhcChhJxEV0oFUmkEhwzTZPJORn2yg6\njxAUJkI9KYkxUtclBRHXrCF4fIy0fYdXRlo1jE0bCpm3uq7p+pbJZIJzwqewlRXDbO9FOk4pynqG\n1iVt19K3ay4X52htqOp50sB9/zN8ZUOT7wKdZBSvBBSp9xoN3ju8C9x+7VWa83Muzk85Xa9Rl5cs\nNxv29/c5PX3CZDLFFjUnR48pdODW4XUWZxciol/UqGYDClwU8YHKWrqgiL3DBYhB5r9PZgKdc3Qh\nSMDWCoNhsVzx+TfeYNn2NL2n99J/GRKyEANMKsMr+zUPzzYsmn5AqfJ1z33WhZXs0lpN7wKdj/Q+\nJng3MK1Keh8TcQxUDHztN36FO2/+6G+uzk9+9X2T+5LHpw7J5nF6evo3/69/+A/dZBHpfYdVGh1h\nfzqndb1oTCb4RlhtGucdGsW0qlIdJ4EeRvHo4gHzyS7Hl6fc3vsMs4nlZHVE17cjnJV2pkMt02hx\nMtFqkMhTii0bsE823/w44d7tB7e0egyICEHo3ZM17xyviMAb12d89nDG7nZbShw/SfCSHX/vJdts\nKQlRDXJ2ldVMTU9ls6g9AwwnJzec5ZC6CkyjhmA1ZKFp658zowz56aKmqKZJ6SdlN1swovciQaes\nYbVeDwSUxXKVaoNj3W2A5RgX4pwhxqgSld4Pmq/O+7QhkM8heyZKiievNCB977+OQ8zc/kZkhLHV\n1v1mRJYvKoFws0Fxbo2wSQmpGLJCcf/Itbts4BvTwixG1yrpqcpc+oQahAR3ZkZmRNjF3jmaZpPu\nozhcL623slW19ZEDY9pYiMH1+L3MDg4+e5GGgRVKVMQg/YfBR1yfXC8CeC+bsK7r6F1P13dJrScM\nr5vvd/HGbGjbDu89i+WCzktfoej3khAFYfLmuqfYfHmqssKgaDYNs+kMHSKbxYKymjCZ7zHdOaDr\nW06ePGSzvmS1Xg6wqmT2Zsjq319Plsp9RKUgI5uyoijStZNz6F1P03ZCyAmK5ekZb733HpO9A37+\n534eWxTszHfQwN7+IQAP332HqtBMZ3PKcsLDB++BFmSiLK3YISrpuT2/WIjIS1RUZcHufIbRitmk\nprSShHTOsWpbES5UsHv4WbBz1q27ml0m+H1eW149mPDe6Zplu+WypfIaIMmHTRCzMYqu9zS9H9pT\nsvjBtBSuBRFa5zl9+4/5k+/8SR9j/Lvve6g+hfFTEzBjjGfvvvvuf/PV//e3/eFsj9Y7Vl3DXj0b\n4JIQJXupy0r8DLUmINZf06JMjD5NYS2n61Pun73Lmw9/zJ0nd/n6m9/k+s4em+4yNcpueS4m4k+Z\nvRKvMGZTnePPAOHneUOy5/crSHXOc3zZ8OajBWerjr1pwRdvzbm5Iw/PACWmzxl+cUFgFB9Tj2Yq\n2hfFJAmlCw1/jCdbrTpkmPs5qefw9dVvxCgalqas0m5VDwnONksWwPW95H9K6i/Zj9EnRxsQGHZU\n98kZbj5iqetl5mMOjhnelO9FtiO9OFuoZwTLVJtMkWWAqiUyD8czBqZRaSZDoyDtVGVVYK0wk4vU\nn5d/xzlHSEYEYYsk5EKQ7MKL64gf6osMrFWxuXKpZgrrzUYYtDEMfbvbEHPOHN830lxs3y+iCTwa\nxosKEYkAJDqlgyTbIM2mBrhvgMGtSdJ2Zti8ROIgFBF1TJJ34hxyebmkcdBFhSlLQohUdU1hC4qy\nGIJkXU+GmrNWcHl5iikKyqqi9TDZu4GxFcvFBcvLE9pmnQIBW8FRMjWdYNZnLhVq5MRGkI1d2iBV\nVZLYKzSTSS1tdEXB/bvv8OjRI167+Qrd4hIVPQVweX7OtevXuf/uu9jQUZea6c4Or7/+GR7dvcPe\nbErbOdxmzfnFgsl0wtHjI4qiYDadCDu1LqgqS9+3zGcT6kI2Y7klq0vm6r3XvP7Fv8yy7SS4ealz\n+kQm25tabu7V3D1esulGUqHa2khqRSJZijB87wLrLtD2ni718YakKzstLU0nknsmOn717/9drn3u\n57+yvjh9qZqxzxs/NQEToOu6v/Xbv/FPj9R7Z1htWHcbZmXNrKwI3gvZx4vLifee2tjUI6W52Kyw\nRm40q40E1Bi5WJ/z7be+yabv+NbdP+Z8/YhIJ82yenTFEFjWUBk9sGhTOWJ4APSzHoSfcHxSNUvg\nqZ1tFi14fttSBBZNP2SdgchnDue8cThjf1pgGMG4cT62alakTYc7oVBJJ5Vt/mE6rivvuJ2cjbWu\nTMRI/9yCdRLcByirhMmcUv8Yx6A5tIEYIwxaJXZMvRfPQ220eBgagzH2fXP1vLlUeoRo4Wowe1bb\nydUPzQi8JoJRHIO0UmrIePN7ERFBbO/FN5FA17VDr2muUXpEHq93jq7v6Xo/7v5jyuZcUkJiS74s\nTbCPUlnMgcc5h0ZT1jVlWRG3r6IeA2Z2admeJwn+eiRHpR7d4dqkm0iJHckQWcd5jaAiPrgUMIWJ\n2Xs3ZPZd3w+BM895Jpqh5H273qGUiKZba4naoIxO5ypM6rZtk3BCj7VizND3PdPdA67fuI0pKmxl\naZtLFpendF0rG6YgQdKmfvGsdR2jE21ZPd7fOrFdddosDEwhJc39Pn8vQlWVTOuaGISZfb64pNrd\nYf/GDXyzpvOO2WxO07bs7e5xcb5kPq3xdkLX9+zu7nF80XB6cU5Z1UyN5fLygrKu6Hzg4PCQQoEO\nvSBrRJpNg9KiepZh6Ai0ztE6Tz2Zs3P4OVZNz6b1tHnTlezc9mcl12YVbx8vaZ9WEMvPcfosmybx\n/l13nqaXbDU7y4CQfZpO+nJRke//4W+y8AXf/M1f+bee+4C+5PFTFTBjjO3R0dF/9Mt/+39tb832\ncT6w6Vt2q1lqXdDiwK7kYem8NGZHRFpPIA0vlkAJAtJGc9GeUeiCddPwz3/0dd58+McY7eU1DVgN\nZWGoCkNZJsas3jKZVogvXso4t6GgPwtDINkX0yjufeB40fLm4wVHi5a6MHz+5pzXDibs1oUI2Cfl\njULrpJQkUcubXbZwyqsQa07GUgDZWmq3oNnxb3TO2q7Mt0pSdHErq5NMMEuX5ci7TdBRwHw+Z2c+\nZzKZbAW3q44KH7iJyfXAp15/ezwr+A51WHWVMym2VBqf60AIezGzxMUcO20S0kagLIu0vqZM2ok4\ngYsxScOlOlmqp/roB6UWCWLSk0zKjnLw2PZHNVZsweazXdr1RtoMtuT/0hVLdd98cUWDVyDHOATT\nnFUOGwfioMM6Tp/MTUCyit55+t7T9T2btqXtZDMgqjJ50yOa0ldr9Wr4lLP9DFGDtKtI4n7VP9VY\ng7E109k+s/kBk8mU5cUZT44e0GxWw2Ysk8xI2ZJLLUfG5I1hpEgC8DHN42RSUyXN6tyqdjU7TwIQ\nQUQyrFbQtZRG4/ue3b095jtz9GzKK6++xsNHD6hKw+7uDuuLJ7zyyi2enJxycHCAsTWFhhuvfAZb\n1rx3dISqKhEpiIHGOVYusGgcnQ9sOoeLCmMNy6bBo4lK0zg3iJ6fLxfsXf88y6ancU64H16kCG/s\n1uzUlneOl/T++XKbSomzTFoi6HpP13thugd5vTwjs8oKpBsjpr3kV375f+D43bf+g+c/lC9//FQF\nTIAY46+/+eabv/ut3/qDeHPvGifLS3aqmklRAYrSWFzfMykrIQWYTL6Iaeerab1LwusidF2Ykp3J\nnuxYfeDrd77D99/7FjvTItUxJcusC83EGko7tphkRiVs1zJl/GmC5ssMuE/XMF9kRER+78H5hreO\nFiw3jr2ZKAl9ab/jYKKpS8NeGaisCD230dINOq/xymQpRkgqRZ/hqzFrzSzKMYhmWT6jFb5f47uV\niKYntZy8KVJGJdgrDvCpqEbFrSAaCN4R8UmQXZwhsv7siwbPvFA/63fep7yzNZ9DPZir11+QUZ1s\noFJ2psW1Z5CR02aQBBQmspxr7xwxIIEXCZQhQvRkCdyhs0eMHcbNxwBDxziotPggqi7r5YKuaWjW\na8nSvEMrQ1kWcp22AtXw79RyIa8dx5NmRACUIl2D8VxCyCLeYRB0yDCd2HUpolKUVT2oTA3zrCAX\nj/N1UagkOhCZTafEJJ4u9UJBFqyVdpDd/ZuURUnfrlleHNM3K5pmTZYYFO9Lm9pA5DzFmQSUNnJP\nIS4qSmuiVqiEXNSVFcJd2165F5RSyXQ5teLEpPEcIr5tmaKYEWlOT1idnTGfTLi8uBAkwQfuvP0m\nr7z6Cma6j3ItRVXhVcHRwwfs3XiNH77zHjvX9zm8dQsfwQWBQ9vOsXaB1oEyVjZuIYr5uTVsuo5N\n1+ODdCV84c/9G7TBsO48XR+SUXjg1YMJWinuHi/pMjGOq2hS/mY2UFBK4XygdY6km4ELDEx1gFkt\nAfPmXs2v/h9/h9s//1fOTu6//Y+e9xx+GuOngiX79Li4uPhrf/9/+uXv/Y+/9K9OmkoxLatBuzQC\nbfTUwQnU6MPgyqCVxiVHeGNG5RNjDIvmgi/e/hL3T95j0zd8/c63KW3Fv/T6v8LF2hGSyktdBlpn\nUr3HExLBwCiSFFda+p+zYP60DWvUULN61niaUTt+T1rtFLJ4nW86Fm3PtOw4mE7Ym5VUhcEv3gNz\nSNu1uFDIvA01spTJbf8vL6RDtjk+akOWqUd1IZO0JomR80dv0bcbBmcDMplmDEuyiRKGrIKhp8wk\nckf0Duc9TbNh0zQUSQpPpVaRDCPppCajt1kuaWTI9Ols9socPvV1Jj9s/+BKFrx1DawtxMYKCBmy\njaLt6oNPWWdq5Uiwav4eZAF2PcDTeYqE2BSTuLfauvZSR4xBGLVCCgrsHl5DIzCoLSz/P3dvGmzb\ndtX3/Wazmt2d7rav05OEpCcQ2AgScBz4kFSwy10ljlNUQlUq/hCnXJWqJFWpULGLOCQBYxfpDIUL\nB7soYnAZnEQQgo0RIIFsgbAaGqH2SXrv3vfe7e9p995rrdnlw5hz7X3ue/fpSai5T/PWueecfXaz\n1lxzzTHGf/zHf6w6h40iTiDnLqIN4lBmY5nk4pa+jPkCbRyHlPVJUySVfCobg5fKvCiFiPsL/Kmt\nGe/xmENobVR5e5KKGIrQuThcIlcnULY2hqquaZoptqoJ3uG6DrdeojLBSVnLarUSZrzWW/Wrcg+k\nIYDS+OhprM0RbM69poixlspatBJ0IITA2XIpWrN53aetlENK4uiIj5fwCdq6Yb08wWgRTXduIAwD\nfb9mMpnK7nPnHk5Zrn3qD9nfm7PYPeD05IgLly7xwfe/h6uXD5hOpwzBs3IBHxW6MiJ3l6TpgO8d\ndVWN8358tuKkGxiCQPSXH3sbUU85W3b0gwgcJBJPHEw5XXtuHnWjZmxK0pO1sMQ3iILc1SXgKHlp\nUhHz36z5SS2lQ9PG8tKnP8pvv+893L9x7TH42ZfdW1/N8chFmAAppeeWy+UP/8yP/ZS7vDjg9tkx\njTLUgouitOK4HzBZZb/oz1prBXKppOvJpKrHzWiIA8/deY4r+4/xx9/0TuaTBdpMUMDerKKyCFu2\nMkxGBQqNVcKmVarkzNLY9/Hz5b8ehVGZzx9dvprhL2dXarJWg+fWSc9nbp3xmVtn3E+XaJuGxw72\n2JtWNLZ4/w+pYS3w7NZ7F1si0SSjdJYZHR7N6ugGt5//KKWxbgIhtGQx9pKLK8ScwtSMKVE3jUQr\nXto1DYMnxMRkMqGUQrttAAAgAElEQVSq67y5p3GjLm3DStH+K8WJDxUaVmpTXvJKE5BPvJS1K725\nBYs0nRgOqf0s+U2ljBx3kPxnTIw9K0mKGNTILE2pGEPN+ZnejE3dZo5gs9OgtJbcvbVM2wl939H3\njj6LPoSQxiYI5XRijrLiiCpszi2qonEg/4SUlI0lRSwuz0deBMqoURxfxM/F0RrRfaVG3Vd57eZ7\ngfGrqqapawAWOwfs7V+haac413N6dJvV8ggIKOdZLQVW7J1ns7rKd7Vh2SYhvCktxqd3QxaekObX\n1lZYLU3PtUq4YSAqTcjvs013S/n9ilO2u1iwmExE5MFIm7JJ29DWFf3pKbUR5R+lNBcvHmCVcBOU\nEW3tGDy3XrzOpYMF+xcvQlD0XcebnniKYb1kve4zGzVy1q0Zcr4ykrhzfMZh51g7TwDa6S4HV97O\n6cqxHrzouip4Yn/C4XLIxjKNhLAExO0lVoJ/letjdcljp3Htyhxs9qXFpGbZey7NLH/3b/41di5c\n/amUUv8Kd9BXdTySBhOg67ofet9v/Obd5z7yUSZVw+5iRkGQaiU3t/MOizTnNVr0GY1S40lVxook\nWWk3ZA3X711jt93jm5/+Fh7bf5x/+anf4nh1zMGsps1M2bbSTGpLY7PGoSmyeWqs29KorQ3/0TWa\ntZFGrF/okNxN2Xw3Cz5ESfz3PnDWe24er3n21gmfuXnCqvfsTCxvurTg6t6UnUnuC8gGnhtnasxN\nluBTjRFmYa+Wtj6ayP2bzzJ06ywf5xkGJ30ncwSgsrRayjnSoi5TjPe0Ffm0lGDd9aM8W8hqMyHn\nGv1IsskbQdrePDeQ6gZe3YKez3kBjOe3HQ0XprFSKpNBNrnNmMXEQ24jVlpDgZB8CuM2Rukq4b0Y\n1qKIVFpDxS044cEawO3Hy3VQ44YmzaJ11r/tV9I3saqtiKrntlcCm2YD94A93qC8aVQIKkb5fJNh\nRcqJPRFkKKzR3BQ7gTE2O7yabaGC7Uh0jFRUbsZtLJPpnNl8j2a6yKpNAyfHt1meHtL3KzGsSoyw\nd44+BFJVEZD3CplxHWImTeUSlqL1ixaxB6VFts3aGlQieDc6LFpr1v2Q88NqhMtT+a7kyhcyVNet\nOT45Zrk84/DwiGXvJJdoG2azOcvVmosHFwjecbJyHJ+cEFJiOpuxXHbcvn0TWxnadkptNNH3TNop\n9+7c4OKFfUGDECczIKQpjBLBBe/pg0R/TTPnzW/7Dk47z2pw9D7QVJqrey03jzrunQ7n1lJK6WUu\nWVlaIrAuP/vsdJTrtUXzAmAxqdibNbz75/8xZrrnP/cHH/jLPILjkYRkAVJKnVLqr/zoD/6vP/c/\n/+OfmO5M55zaI46jsB+tUmAttTL4mJmzMVAZS0gRF0IuTk/M2paT5TJLXGmeu/0sLx3dYD6ZM/iB\nF+9d57v++J9hf7aD0htPUgp0IzFpUgroHDoEVWCUR39UuUD4wZEylrVNYnlo/SCyT6StiEAhDYZj\nnguH6NjeO5PNaNaKmsfFnZbBR1a956zzIoy9PTJ0o2HT5QPpUWq1ks4hw5L18V1KDVtRvBEynTBF\nTY6SdN5cY0qknLMbnOf49Iz1ei3GVWs0SfoCZoJKjLlgPimK+LnWKjdp9iK2bU2Gl1T2AbYMUz4X\n+bYF1+eNeft5D+Yv5btArilHHSi1VSojJBnvAkX2rkjfbV9L+T+Vwxujv2K8xbFQW1HwpoA+xoAy\nFlBYW1FpuHnvHu3OTDRxKVHgRqjhgQu5tba21lU2CKORK5GWkmt3HsbbQNM6p1RI8pkxX9/iVJSc\nuEpQNw1N2zJp2gy7O4ZhSRgCZO1ppXPpiNHYLLWpYsQ5n+clywmGAMoQ8kIvx1fyvarA6lpLw/Dc\nZLnrHco4krLElFit1sSyDseVkg1FGi+JdHoJnt4bTldLLi32eOnFj/PWd7wD6we0NhwfHbPuBl58\n6RaewM6sQRvNyXrJG3ffwt07R/TrFY8/9YSsVx+o6pqQAtEYlJJepRGR/1sPgzgYwNFqRefjKGB/\n5fG30wXNWd/Tu8DOtKLWmufvLln1mUDFebLc9mIeSXqQG6Fv8rVk5CRT3mTFJMldTivN2cl9fubH\nf5huefbOl7/5ozEe2QgTIKX0S0dHR7/1Cz/xf6aoDW0zp9UGbYUVSUr00TPJy9Fok3vjbfQ3tZJ8\nlsmNZ2OK3D69jVaKVb/E+YGVX/GLH3wX1++9xKI17E9rJpVl2lgmdSEB5XITNQZHjHWajzA0Wz00\nwtxAcuMjr5SLS0W0QBFjhqcyOWMUZd6K0CRKixyvBl46XPHszVPunnZopXh8f8LXXV5wdW/C7qTK\n0WPayllmKLZIFVopD4q+xweHMnqsddPGYCuLtgayGkxM5HZTubVUEMPjvEMZQ922KCtKM8ZamlYE\nLzKzK0eTcTy/Qp8X9RypOxsjm+I8pKIHu4kezynQpFSahIwRcCzft55fmmL7GEZbVKK4EsnpDJkm\n0khS2m4YPRJLSn6RzTWBLFmXr305h/F1JSrIudyjszNU01BaWcUo99e59TL+pLMhhFTKSlK5HgU+\nVsSkxudtv347mg8lp1mgdbYMsBLyj7KWdjJjd++Ai1euMpvNISVOzo44ProLMaBSIHgnOd8kcPwo\n4ZcEINUoZrMZKsaNID5Aynl45PNS2pQFjfWr2ZimTMwavDjoylhW3YBL8glRKQJqhCK3566QfoJP\nrIcB1bQcrtfMrl6lWy5Zr9esV2uOTpfs7+9zfHZGTHCyHgghsdjZISW4d+sF9vYWOO8xdY1ViXUv\nQurr3nHvrCdVE/YW80xyFORs1fesfMhQdOLCpTey2HsDZ51A8PvzGoPic3eWdLlxw4Pre3uMefr8\ns0LWuE9JnIskefYUFTY7Hgm4sjuhqQ1/92/9Dd74jn/9+Rj8R1/xAx6B8chGmGWcnJz8lXf97Lv+\n8N/8i3928o5nniHcfJahN7jkMSg8CaMtF5qGPkTWPjfV1VCFhMsQkt3Kc6KA0TvPrE6reM9H381f\n+vb/iMZaLu00pCS055ByY9VMPklKEXSELJs3+o9fBiLQ9vu9VqO8KR4WzdeT9cuN4jmoLr16pLnN\n7NRs8n0qQ29sPX/MbOWHE4lVH1j3gTunUBvFtLEs2oqLixatofdRaOZJtCYrI1+1Mag0cPe5P0AB\nRleMzZazUVBKakBDiDjvUbrCeZ/F06W+kGLUQr7pkcJ/ZQxVabWlclPnuIlgVI7CUJIXFwOjxzkB\ncsTFePOPczLO3fZ12ZqkVDJkm2iw/EnyliETWHLUKSEZxJz9LNYbRoetXLvtll2b+shzHwBJ5Mdi\nLMXmG6fvbLkmeJcjrRzJZZWg7aOV89aZPSoWJOb3KgSkdO6kHzwGgdLLX4oRZ2uNgdR8tu2EppHa\nUADvevp+zenp0Ug8MsbQTlqcW4tBzHnwmNEIk9WcjDEk7/Epsr97mU8/+ywHT1wVGT7v8HLClI45\nsA3NyzASHhJTYPCeGCPrbsAYKyIhuQQuuCDShDxsJIKCFKT8p6lrLu8dcPuzn2batBzeu89kNuH2\n/fu0ewusknzn8f1jnnjsCq5bo6xiunvA/eWSg/0W7xN7E+hjpK0sQSVWg2Nat6KUFBUuBJaDoxvk\nXmkmC64+9U2cdgODD1xctBwtB24crgRly2ITm046rzC2LnFZcSFt8uybM44U1KSp4YmDCb/26+/h\n2Y/9Poe3rr/9oVP1CIxHOsIESCl9LoTwgz/y3/7AMAyeyWSPhcmC1UlRKc0yetzQMQwdIQQaW2FR\neIUwbLWhMpba2BwdSi1WEUA2RvQltVW875O/zvHqDKWUREKzikld0VYaa0vNX2YGZocq9zf5as7R\nQxexfQ2kn1d6v4c9LtFShtWKp12+2ACDmyhmY3BJkss4WTtuHnc8d/eMW0c9PiZ2phVPXZjx2P6E\nC/OaeWsxOG48+684Obwhr8/h/YP5MIERLXVd4700vTXGZgg35wxzdBa3ItoYAquuY5VzoyU6K7W3\nJbKT8g2H0oa6qXPR/MsJNWNElCOhVKCI7edlZGL7NeV1RaEnhLAlYF+8+hxJsikHKZ+F1lmKTiDQ\nBBsBB3Xe+JTzM/kcigBDyTmuug7nAtqIALcPcSQmbdCGQtcQaNdWFpXS5jS3HLHNeW79vUSzSurz\nRmdBFUazGMjd3X0uXXqMCxeuUNctzvUcHt7l7r1bHJ8cyXUrUXUmCIUQcN7hckTponxZW4t0IpK6\nKXm8a9c/Q9XUaKSu2ycY5evydQnjY1k6MZI7f4TxcR9FjxWlCUpe1wcPRp27N8Zrna9f+QrFwwSW\n/YoLT78BO5mwe/ECp84xvXDAG556I9F7JnVDZRXrdcfp6TF7e7vUdc20bWisoa0tfUoj4UgpST8k\npViuO6nDdI5174TgliJXHnuGIUg3lv1Zzd3TjlvH64wobfVBfRXTX8Y5FGPL0UhI+gQlkocAb7u6\nx92jE/7eD/43LC5c/omUUvd5P+CrOB75CBOg7/u//eL1F77nl/7h//X1f+E/+W7VD0sWyXMSPAaB\nbk58wCapy1z6QSTvkBY186pm6YaRdVcUYAqD1odAlR+/cfgiH9a/zZ98y79FSInLu9LxQDoduLxx\n5fyFRgTGo3geMVvQL2WUeZ7+/9pfU0Zl1NgB4kHo+IuJiM8RSHKugq3II8evlOCs5C2UKkSNDXFK\noRh85GTl6HrPydqzO6nYn9ak7g7D6UtMbCDtXxAtUefwfkDlAqOYyvmkMSK0lcU5BwrslpJPTIlV\nt841eGY8lyF4kkoUIFTl6KfkXMilRAKJqlwLueWApDIX5x46N4wxo2DA9hy90gtCytcvKfk5X6eI\nhEmSG5LyqYiithWVNbhhIBQDi6KyNh9bHA10SlmKT+X2YEahtegH+7Ata6bxXuoOrbVo5LxdhrjL\nFS7dWiBv+MWBYmMg1fY6yWP7t4jKijw1VVVhKxFPH4YBN/ScLVeEKGhQEYtXiDNTOlaWd4sx4EIa\nof2E9De1Rroc9YPD2ooYoa1q+tUKrGH30sXcaD6NhqzcFhtiUfldlR/yiQoClQDlPe1ExCgos5M2\nx3h+XZTZk/fRxRlEojitFLO9XVY3lvikiNqyu9jl04PHJY+LgfneLn55QvCK1eqMncUc5T1rH3FJ\nmKudS6y9QMyHyyVOyX6wGgaCD7gQ2Dt4gt2Dpwkh0laaa3dXnPaS0iipjsSr5C63VvN2fa4wacuJ\nxxF1iklhgAuLmr1ZzQ9//9+gbtqPPf+HH/zPXvHNH6HxujCYKSWvlPruv/ejP/ahb/1T39FcvnQF\nHa7TBfHiGyzJKGLuWmKVkEImTYXrOwbvmWnLSfC50bHUSVljSEqIJcWAOu+5cf8FPvTc+3nnG/8N\nVr1ndyoNZZ+/e8YoUm0TyWcPX2VIaxuu+hIbzS9mWKPGHoM8bKE/5L23oduHjU1EeT5mSuSoewuN\nGx9TcG7rGAMv2T5chLVLTNoLHN66xr3bL9LUDZNmwmy+oK4r3ODoh55h6BmGgRDkupMjQ60N/TCM\nraps1h8NEarK0DYTQgyjSLcao6LtcCcf1Si7pvApZD0zxvn8fB53MVClPvDBUQy+0ZtIb9RHLe9R\n4PIN4D0+riDPQRw327qusuJMqdXccpZK1EnKRkLKb6oC22aFoZTKJ4pD6WM4B72PBgVya67z51bm\n7MEHrakwVSXG0dYYY3FuoB8GVusVw8nRqM6jtq6HLrDv1l02GuwkTOFoFJVR+OAFBRkc1lphoa7X\n2KoedXq7teNktUJXVY7iISqFD7JS5RxzJL0VURd3p7xmAyKDjz4LqciI4xxt7QtbP22vnEL2Cgga\ncv/uIYudOdpY7g+eS7MZp8f3qNuaejpjZ6cHBXXdsvae6aQmDA5vLMZqacMVNV0Qx6r3gT7/3GWC\nj/OBqp7w5rd8K5VVHPWe6/dXrF3IZMe0Rdh6+DqXGkxJ14zXvpzsOSi7XC+YNoY3XJzx4Q/+Dh98\n369im+Y/f+gHPELjdWEwAVJKf9i27Q/87P/+9//6X/0fv3di7S5ze8JxVsgwStReDAqXveqIhhgJ\nxtCrxKXplFurFZVV2WBaXHSbZHURQNCa63c+x7zZ4a1Xv5GVC1ireOOlGdfuLjlKA9FrkokQFEFD\nCptNv0RzXylhg4cZtUprXPj8hu/VxqvlUEejOkYb5XkbVuC2EzE+JZ3/9VyGJ2UOndI8/uZv5fDw\nBkenh5wtzzKL1tA2DXXd0E5n7O4foJXCO4dzA945gvfQW4L3OSpUrNY9SWlMVeFDEMYsIsJeoGOt\nNl1DtH4gB5gyIQZBhjclAvl5eX4KvB/TBhL13mcjtdlQRlJa9sTL/KaUcr/ImA1dnq7iEGRYWWQa\nde7ykLtgxEjTNGPtaKlLFVuoGPUeVDaFW1q85OgzxJDrPmU+huAlXzpe+1LeIddf0qqb61cCMKM0\nxlZoa7FVhbUVxtisquRwzrFcLXGDy2z2B53MLHSfl08qh6DK8cudpvMkSY56IwFYaSmF6YdBHkNL\nnjAkdhczjo8OSbnPZwkWfRCuMmwEFTRKyk3ywRTTOaYktm4JHyNkAftzC5zCC92CI0qgmv1tuS6y\nzoZhYPfiRSYq8cLxCdPFgj4k2gu7BPUSezv7dEf3qauKkCX6nHMkY+mDwweFS5oADCEyxMRZN4BS\n9P1AShEXAz4l3vyGZ1hMZ7x0f8WtozWDz627csQ9lpQ9LLqUzW5sdjDe4g9G5eXaIXWqTxxM6VYr\nfvwHvpeT+7e/Owb/3pe/+aM3XjcGE6Dv+7/1vve973u+/T3f+fa3fPs3Klu1tPGMPhUxZykgDvkm\nWLmeK4s97qzP2GlazrqOmdIs84KxBtqmEcYhQJLWYdqK/N6nb36CebPg0u5TdE7q3p48mFEZza2T\nThZQUoLa6Q2bVKsNU/crZTRfaVgjclRf7rFtHmEDW0mEsAlFtqHLQhYaYSu1+WOp+TRVxd6FJ1me\nHYpEWhSYfbVe03UdSp8CQoypq4aqamjrmtl8h7qq8d7TDx0xBIa+xyDdS/p+wPlAXVU0dSPRlvdb\nBkbWUiGSFNgsxUgkEpUSg6CU9IrUOqMaEt0U45bIRCNVoqMsejHWHGYnpNSoRelTOUZnSoypSjE3\nPk+jghEUglIRpRdSWkgxl17IYyWqFaOeo02tcq5zgwJExagiJEQjYewqpaVVVI7kXgZWKGEzS51m\nJXKFWe1myA5M1/e4szO8C6CK+tBmTh7MgY8bLRtnSjR/cmkMKSNFUopSpPJCPiejK6w1dFlgPaW4\nEUVQmm4YQJuR4FV0Zkt9YHkcMgx5bq0zIjaS4y7zm5+fpD4zjhB8OZFtBnOZu833mK/BpG2o/UCr\nE6enpzCdMpvNcEQhMzUNR0f3sZOWlRsY/EAfE0POHCQjPWp7L9HuEBOdE53eEESP16dIAN701Ft4\n89PfyPN3T7l3Ooi4eulEFDdOTOThe9gYa5x/dFx/CrXN90EpePJgio+R3/z5n0JPd25orX/hoR/w\niI3XlcHM0Ox/8DM/8ZMf+t6v/59auzPBVDOqNIzRfxc8xNLNIHHaL6HrOY6JFuiUZqoNZyHQDz2T\nqmHatAze0QWPyWzApBTrfslvP/s+vuOZf5u9+VVCjJysHbvTiqbSvHR/zTJ5TPYpBe0Rr2z0wB8w\nmq/ViL4WSPTzDWs0ftv7/wJzoV/oeOVj1qMXKs/Z7BkF6hECTY6/ii4oUkN4cPEJbr30KWKUazNG\n7yU0UrLBrfs1677jNJsco3TOiUntbT1paZuWpm6y7FyirWuCF6JYsD7v4XkzLEYnxXOtwwobyOTI\nVGWxAK8SMXpSjiw3e0w2DKo4ENu9EjcbbYGSY9YsNar0WZTAo0SsEo4odNKELDG2Oa7SGkyNogeF\neNG2LTF5iSJLe6yxPkIOU2dxgBgLSmLycUmu0xibe3VatDa5LhJ8cLjB44Nj3a+FcBM3ZQgxy8CM\n/aoL3Cu+wthzdDvXzfb/GSU9dx9l5DOqbFRLZGukwbbz0muxRgRHmtwvN6bIuu9Hgo6Ue2TnRqVN\nCiN//nj7jKhCXsd5votMZvl8kcjTgnLAuF63Tcrmsa3fs2FyztHYipOzUzFe2akMJLqYmO8sODk5\nppq2KMA0bXZ8FENIuS2X6LWGPgg0GyIuRqIPDCFQ1zVPXrrC448/w0uHS07WjsEH0fP1payqOBGF\ne/DyPUvOSm0QC0of4Sz5V+Zvazu4ujshhMTxzU/x7l98F9c/96m3p5SGl735IzpeVwYTIKX0sel0\n+gO/+U/+6X//zv/w36mu7FzgsB+ExJA90GhUlksTw1fVDckaVIhoIwQeoySScN4JezYmdqZzYgwc\nL88Askcb+cjnPsCf/ua/SLAWXyeOVwNaJd5wacbNozWHy152Ng1ElYvpkSjjyxxhvpphtfrlEeYf\nxWh+IUZ8O1JQDzw25r+2vfNxZ9z8rJXh/r2X2N2/gnc9XXdGjH6M4owxqHT+3RU6l5NEnHf0rpcy\nkt5wlPNj1lj2d3fRKko7uEoKweuqQiHF68EPpCjvEaI0PE651AMEfiv5SRF13z5bxdbZbPRPc+Ra\n9pBUDEF2rooyDinhcy9KKfxOYw2luBZJ8qlbgPdoHJHcZgpBivVJo5A7ibHjhjBKxSkQEftsDJUZ\nFXaqqkLpXHgeI8F7QnAE71j7JcMgc0PaEENGgksSlMWP1S9JSEw52iLfq8Ux2Z677fzt9l/Qm9lN\nSMuoylqGTPJSWUs6hrxha4NLcq8n7yU9ExNaG5I2yKUUYzkQz23u4/Up8K/KV/RcdM02QItCmNda\nC6lsc+edv29eSVUxZTThzA1YraQWUit2Fjvc7ztSgpPBC8GrrvBo9poZL925RbIVISbWuS2aNHmH\nIbfQcjEQMwS7t7vLznyBi5YuTTnrhrEvpQ+bzjbF4BUi2SuNjVuRHT6KM5zOPymPC/OGttKcnJzw\niz/5I3Q+/FBK6eQV3/wRHa87gwmwXq//9r/8F//iP37T29/6NveOQT1+8THOTo6xueg6pZTp1LJo\nrRHau1MQsl6kyfmopCOrvmNqa1SQgnCjDAYRZC/w4Mn6HvP2AiFZKcxfDyzXPZd2WqaN5cbhiuQj\nKKkB1aVQ+QHb8kp1kK80XlF15wuMOq3RrIdNB/QvVXT54HE/lDiUYbyizDyirmrrGWPiY6tUBSSK\nM5ajwxtEP0h+KEW0qcfoclMXKG+hc7uvNDosoLTGJLBVxbRpshi2wSjFulszDD3rvqOpamnblNtW\n1VWNVkb6fNZVFkuwWWDBZAgwa81mGDSGEo1GUv5ZG4EByRuPMaLQ47yTNRi2xAOKM6M2tb0Saee1\nHAIYMxaBS2QueTNb6Ryh6RFKNsaKEISR5sA694mtKpEI9KO0oMB1oyCF61EklqvI4LpMLpJ5NVbj\nncfFOIqwFzg1Ipc6IlGqL2UwWxuoopTFnI/Yy0Us5JoRukcgZG00OklJUGWsNAM3hvXQi3JTjgqH\nDGuXj1ARXExZSUrnyF3IfUWlR+Xj0PmzyohpS/01dzyRJVpIUQ/Ax3lNaKNHQlBeygVoH8+zIFDj\n4k0S8WtjWXnP2iguTGYsT06YGss6JlbBMyNx1Dsqpbl29zbRSsu9s2GNT1Ku4aO0fhtizI6X1KFf\nuXAFFzwv3bvPt33bv8u9057V4BlczFUA4qQVHzvwkP0pxwaF8Fhy8TpXWsW4OdsyB4vWsjeruXlv\nxa0P/jNu3j954fa1Z//6K3/AoztelwYzQ7P//nt/+d0f/lNv+O7mXnPM47MdTrplpnhvFm6IkagS\ntdJ0ZyuiNdk7z/BViFRVxdo7dqxl3k5RpkMpWC5XhBhYxWUuR1C0SYvRjJGTmLh73DGdGN58ecFL\nhytO1oNsZjGhk+SmFOpV8wCf51yBByCcrSjx1QyvNeocJPtlG9u44gPHt/WkEc56+ctT3oS2lG+S\nNBKumjmHZ9eBwoCVvFphOo9lI0XfNEjXDJ3rEosBTSQG70Apur5jaQ37szmrbg3aMARPo6yU4GQD\nXlfQdUNWipJjLZFcTDobITv2SNVa4EBtKpSVSLdpG6ZNgzWKrhvonc+5t6KaU84XUFIQr7RmMd+R\n8oIsz1cUpYwuJTEjri21bTHioheDlVIOZhMpij7uMDhQiSrnJ8/WK0DKT1I2ROVaFKy4GAttDC4r\nvQy9z3DvxqRvyjA2pQShhM/5OXmBnEMe5JQfFKt/0BkTpSWlwWhprtBaQzcEXPDjmou5M0rSW1v1\nlmOmlabKc7h2A2QZxkobVEwYIj4laXRMjsgRgxBFfBatlETU2cIlNpFY+aghZJH6rfPQo+O8KYJ5\n8J41WUTDB88q6xmfrFdc2Nvjsy/dQE/n7E4XnB7eJRlDN3gGpXl6Z5dnr18j1A0x6awetNF9jjGy\nM1+wM93h7tEhd0/u8/Zn/iSrLrDqXa4nlahUkIRNzfSrjVKjPNbO6oxobc1HeYu2Mlzdm3D97pLh\n5if5wG+8m+uf+cR3vfonPJrjdWkwQaDZp59++gee/dUP/ndP/9lvq4PzXD24SDg9lLySlkVrtGaI\nAQOYphIoLYlnWdsKZeTiN2jWbqAOFVNTkUxkqCrSkKhtzbMvfZzFZIcnL7yJWd1mKTPZ7E7XnqWK\nXN2bsphU3Dxc4cgK/rFsDBtD8mpCA682HmYcHxbhWa2/QqSfsh/kLbB4zGWTyFFkoQGMUVMqOZIC\nz6URDkpJ5O2adoYxFSGI5meMoHWJxooY+UYRB8hSdzqzWwWCc15IGJPpBH8W6JzjZL3COYeuKrRC\n5MmM1B0mJCdpjcHmDizeS5eTpKT+NviEIvesLJGUSuO+b7Rm2jesjabvOk5Xa0xVbXKYbGoLUWIQ\njM5i6jFx7/4dCgPUB0dKidrazGAEq6UPaTFcLjchMHlzF9sn37XKikjaMHhJGZVymxElZZOX0llo\nPSWbm49rfDOeQLgAACAASURBVPJCxso5KpGG24LfRxiP3K9TDG9BMtP2+s2b7SaO3i5dKLsx2eYK\n1F4bi07SA1Q8gjiyN1OOdM7Hs1vwpwYXPQ5QGiprBXlQGwjYlibOKWVCVxCFIGMyAWojgxmzVcmC\nX6PP6GMQuUUlKSJjpal0SHGTux0jaGnqUBvDrKqpFZzFyCp4tLU0lWGIATtpCQruHt6n0obHmikv\nnR2zM59x4+YNvK2ISaBznx1GFwOVrbhycJEIvHD7Jp3rmU532N9/A0drJ1CsjyJvuV1GErMjsGX1\ntsGykn8vpD2jFW2VGcc+5pfJ/5VRPHkw5cbhmmF5yO//6s/x6U9/6r9Ynx594tX2lEd1PPJKP682\nrl279jc//vGP/+719/1+CtFzuDxhd76PRtEog8oKFSrBOjo6L7BTMaQxRmZtS61FBSilDN2g0DGx\nO51htaXvVly7/Rk+dv33+LWP/hIfu/5hdiYT5m3NvKmYVpaUIreOVijgTVcW7E6qEaYYmWRfBCT6\nSmIDrx2SLXVlX/6xiRzOkzbOP+MVHkkbB2KsQUzSfzAE+Lq3/ms8+fQ7kJyXbIyhsB5JeO8Yt/ms\nZpKQfFZERLy1UbnYXzaSuq7EiFQ17XQutZpVxWQ2o64rmrq0upLm1AVCjYCyJkNRElvlLZukRMO2\nQJMRcq2hYbVec7zqwFYECZOlD2cuV1Fk1DpGhsHhg6jI+Cx/F7LwRFKwdo6AwGUuSV7Kp5TVaABl\niGghjKSUlWsApQlJJNEGL4agd36TRwaBV5Ua2TnSFUVq8sR4yMEOIYxqNufiKwVRA4bxOgj7U20k\nBMdkF4ixD1uzeH4dFeMSSGAUIQW64FingCMJhIg0ABDN3Y3RPbeesoOjszaxtsL6nTYVmgBKarKn\ntaWpNEZtmnFbayFFMbBb4vBKbzGq83MTwjQ2RlCAoBI+enwSru2o7JNRaG0UtbXM6oaDSYOJjnll\naGJkYi0NisE5qtoQU+DMOYK2dOszZpXUrw5NJeeT1OjEDN6zmC+4evEyy37NS3du4qLHx8i3vvNP\n0/tE13t6lxt35/TBthRgQXzK2DaWxdgrZKlYkx20gmblJ2uteOrCjHtnPcuu5/n3/RNOvL15cnjv\nx1+2GbxOxus2wgRIKcWrV6/++f39/Wf3LuztNN/wJI2tuLp7wJ2Te/ik8NklWBequJLEvMtwWJT2\n4iNnR2lFPziM0sQQmbQNKkm+RmmI0fP8vU/R+xXf+NSfAOothzhw0jmWg+fy7oTFpOLG0RpHhJjr\nmR6AVl/jeY4/v1It5MMMqNFqhIxe7X2/lMzZMZre+r75m9ryWCUEjSlmtSZRsxkjlpTbK4XEE088\nAyRu3fgsKIX3A973oDSmkhKGFCU/WHJ7WstjIUFKwjyNMaB0TdM01A24oUdbQ2UqUJEQpOuNraR+\nz0dFIpC0IkU9FvqPMUze+MYIis18akRj1XnPmRuglg73JnfLIG/koAsxd6xnTDDKAA7BZWNQcrNC\n3lFKDInEVFkkXluJN2Mau5CQIlErfHAYZVERfEo5v2pQUSKSsTwjplHWL+Qc2DZUXsY2bjGWV6Rt\ndymORn58NG39fG6o8Z+smzRuyiXQ8THQWJMnXZxe9EaAYfMu4weNa7C0CKuUwWaoeta24AdIicpa\nZm2DdwMhSk5aoZjWNTEkOhKt1gzBjchASf0YbUCV7qaSCqhyK7QyTBZIiVlcQnKpMDMVRsHBZII/\nOkR5h7WGqVZUxkidqjYYrVm0loN2iut6WK9pphPJSVc1IazBaLS1xKjYu3ARHwIv3r3B4KTONaTI\nxYtPEqlZ9h2dC9KNKaaNmk92XsPGaspdm9h4/TDOQelba0tlwYhoSNnPEwcTTteOw+XAjQ/+Mrde\nuHb2m7/8/z2VUtoQK15n43UdYQLcvHnzzp07d77nUx/8qFsfnfC5uzdIVcXTBxdojcYi3k9jK9wI\nn8ldqLWhqWqG4AkKKm1pbUVb19LNom65ONtl3k5pjMWic25ScfPwOh9/4V+xmFbsTGoWk4ZJY2mt\nNJd96WhNiPDmy3P2Z/UYaYrazRdmoB6kpb+212Q0LD3cMH85S0y2xxiFbN+HbCDYQhphzGXGzLAU\n6NGYmsce+zq+7dv/Ak8+9Qx1MxHjq2Ve0nhDy+/WGqy17C4WzKczduYLqqpGGY3zQVi2RCbTVnJS\netNxxYykmoi1khMacsQ3kIh6oxMbVYEaH4yrpexEacXJakXUqvQvo7Qe62MU6n/+XJ8iUUWJ3HKk\n5IkELSQylTuqqAxThhQIBKKW6C9kA+wJeBVwSST/vAoEFQlaolKP5Ox1joKMNec0lbUpBBpG5qZE\nkhsjVjbPpJQ0JMjH+2CUOJYklP/za4qHmdAIpiMdZ0bt3jyvoq8rJJIUEmdDTxc9fRDCT0hRot2U\npCF1EFauj2nsooMxKCQF4FJgiIFZ27Db1hiEDLM/n6G8o1+v0UrIUfNJjc0wqtVWuoogsO0QwrnO\nMdYYrMotw8hlQbmJgFGK2ljRzM3kpdbKPpNITKqaYX3KZGfB7nxB5RxpGDhoJyyPjtmdTGhjYmYs\nd+7eZX82Y17V+OWKyiQmWrFTNbS1Ym8+58rBJU5WS27euzPq6YacPnrs6ts4WzkpNfEhozjiBMZX\n2ytKVJ3TLVIHm2H+DN37rKBUHJ/HDyZ0PnLntOf4uT/k/mc+wou37/1Xr2djCV8DBhPgAx/4wC+t\nVqv/4/q7P+yc83z4+U/iAlzdmVMpofVbNFNbM7ZMYuO51rnQOMRA8AGTYGaltc1qvUIj3S0qo7HK\nYPNNcPv4BT714keYtYadScXOtGbWVkwqS2M0p2vHzaOOvUnFGy/OmFQGrdJoNL8QeFXswXn4soxX\nyomasV3VqxvG11Qi8kXmXMfP2Pqf8fjzr2xu1I2BV2NEE1OiG3qBB1NgsbiYWy9lEXglEWHTNLTt\nBJsNQF0ZLs5b5sqTomN32nJlb4+2qQhac7Czy/HpKU3TjNc2ZWhPaU3bNGPZCEqhjKGxVlqS6U0z\n49JsWuWcaWkybrVi1XcMWXc2JUZlnVAMg5avmDfTYqCKwRlbpmX4MaWEMrn+UrPZDPP8+RDO5aKK\ngEeMORItxhmBU0SUW+Wa5aw8lHV+fRTjEh80YDrhVcwwqHyXnxNJJ8GWlaAxhRCicqJDomktfSLP\n/V6chG1h8gLpKnwCF0W1xifAWHo34EJCJY2KmhhLr0nxvpIAGMJeVqK5q9FMm5bWaNbrlZC0VCL4\nAR887aQFErXVkNusaZ0wyLlqoyU3WFkUiSo7HVVmT1dWo1JiUje0xlIpzcRWNEZTIZHYhemMi03L\nlemEi8bi7t0jOc/Z/UP6boUyinllOTm8x+OXL4N3XJhNcf0amgYbI1EnZnVF6nqmtqJWiv2dS4Sg\nuH3/Dsdnp/RBes/6KESeNz39TUymF4UVm/OWLjsVm/svjU7LeMtm50jnHrQaqREWY6nGnLs8XZyq\nx/YmhJS4ebSmPzvixd/5f7l7tPwHH/vQ+//BF72JPCLja8JgAgzD8F8OXf/p5W8/G1wI/P5Lz1PX\nLRenE6Gjp0Rra4wWhZBAHFmHkms0NFVD7x2TWozeRBtmTUNjK8lzZmNpkyjOECLXbn6KX/nQz2GN\nY3fasDutWUwqJrWltrKZ3jzuOF073nBxzpW9CVaLhN/47wuIIF/N0G4bNaO+dPnLL8ywvxxy3u7E\nca4fYrlJ2drkRkMpBe0+RJKuMNUMpSoWi4vs718VaD0FTGWxlaWqLZO2pmkqdmZz5tMpSismleXq\nzg46RabGMLOWC/MFQ9dRVxVWa7SSo4pJzNVi0hKip+t7Oj+IYVOJPmRWYQgCzSolDM4c3caMU2kj\n3UNciiSdN3IlDNshiHC29EkEn+GykB258l5kZ4BcLyjZvoTzQaKdgLSeywYzlPdJUqSOlmg6hDRC\ntQXGDEXcoMqPJ9HTtTkiEtKTOicIPgotwNZ1lGutsxNR5fIVlSNhlaPPkrKQKLVEl5vSj4wcjzlR\n+RJjWkbWNBKD7yM+KFI0FCg/KhEe8DoRdSJZMnFJzPW0rtlvay7OJ6TQE2Og957JdIZzjiFK0/nC\nsk0xURlNU1mMUaQkUK3WisZINWxljIAcKVEbS4Wm0oZaaSbaMDWWRVWxU9fMraFS0BhDbRQET1NZ\npvMJ06ahbipsU6FtxXQ2g8FxcvMWDAOV0uw0LU/t7TIcH3F6dIStLDvzBd5FmC4465csuxN673BR\n1mdEctBPPfF2nnj8HSz7QOc8roiqn8tdFof25fd06QNcyGSq9K3NdSQqPw6KyzstWitevL8ipcTn\n3vMzrFfL33r/e375P31NG8gjPr5mDOZ73/vesFwu//wv/D/v6p7/vU9wGh0fvvEis+mCg0lDpQ26\nuMp5uBSEYk6O3qKwEJe9Y71eo1PgwmTCom5pbYVFUymNQaNCosJQaYNJ8KFPvodhuM/utGV3WrMz\nrZi2QiKoK8t6iFy/t8RqxZsvz1lMBKYp40uNjm5HmF/VkTZw3MYqbmDYRKbBp00NmE9yQ4cokdXg\nBXpzuTfpW978LXzDM3+CtpWmuSGr4NSVwWiY1podIgdtw86koSZwaTFj3lZUKaAzEWM2adBa2NIG\nMTB9CNw8OWblHD0BncsuQhYz1VZKHCjRYozYSmOMiPi3TUNbV7kZcUYztMqQaTnPmI0Wo9C6dE3Z\ntCMrZQkJUEaNyIixdiS4aGuzoRSWblS524k2svkrhaoMjkhE0BSlwVZChDIZYiswa4gSRZ0r/shO\nQNQbsgrZmGqtaOqapqqAhMuvL0YTSimQoqktF3ZmzCsLuazr4atTcmQjwzVlaFbl3GluMi5wNJmA\nJEbYaCuNxpXkihtruTRfsLCaiVasz04ZnJRSJEWOVOVnj6y54mdKsJyweW4EftWjk5tSFCdBK1BJ\n0jnGUGnFrK5ojaI2ionRtErRhMiwXHLSrUnGEDXM5nMpAzKGfnBoa4gpMZ22XLi4j192DMOADZGL\n0zk6Bfbmcy7uXaA7HTgdeho8qVviSn1svtYhSG3l5ctvYtl7uixk4EMaay63odgS4Y+535zOki9x\ngEqPUZt71hq9QSUuLRpaq3nx3opE4sP/7B/xwnOfjZ989rN/9Y+4izwy43VN+nlw/Mqv/MrnlFJ/\n6Z/+2E+/6y//0PdO9BXFtaND3nRhHxdPORl6DDoz1Qq7MaGtYWFrumHAKoHnJvWUw+NjTpb3ubCz\ngFhJ782hR3RkJH8Rk+RllHL87rO/weMX38JTl57B6kbyIUrIQApwPnL3tKcyjos7DQfzmltHHctc\n4/bFaM++KuHnK1GD+RqOBdjsjluszHKjxizy4GPCxkgICq8TJiS0KtqpGnIhfKUVjz/+dfg48Ny1\nj5KUsD/P1msMMGRx73ltSCHQO4d1A6veE42isYa161m5gcpUrAfpYN+2jeRjXMr5xgJ7RowRVrVW\nhfYsO4gi0buQxQIUg+sJyhCCI2YShNVGvP1sJK3Sm0iPtOmDmWFAswX5xmysrZFyElf0XnPja22M\nlLjkCZUsWumOIoLjReDdWunKU8TOo4bW1tgc2eoERZg7ZM3cUmNa4FxFyko2UQhaKdA5qemr6kY+\nI0m5RdCJhCEpuDjfwXQ9g++obG4KkEoOOpcIlZ2aLIKARJVKFcJdJidVmvV6jdI26/4iRtSIYU4h\n0OTIrgKqKBF076T3o1IQlVDNQgjZ6QC0pvdhlGCstSYET0zQVBaXySwxXzcfI40x2fFQaJWYVpaJ\nMXRZYUglEbWvrWXXGI7PVswmC2olyAYxorOxNllQIqbEetWxv79HCJqzsw5tEyYEKms4uPAEq2j5\nyId+na//1neyOjmiT4qAlkbo2cPwKfGd3/bvkdSEs1WPc1n+biT6pNFQjvxiVZBYuTYSSZZrJMu+\nMprGitOVoghgHCxqaqt57vYZPiVufvZjvP/n/z7Lk+O3pJQ+90ffXR6N8TUTYZaRUvrnXdf9+C//\n6D8c/BA47ZacrToOZgv2JhNiTlYX3cyQvXmlBRcagqcy4IY1k6ZlMV9wsuwwKVGh2W1aGmOZVjUm\nSa1Va2sIkYvzHc5OrvGBT/xz7h0/x/6sYXfSMGsqGqupKoGtXIi8cG/F8drx5IUpT+5PJf/Bl+6C\nfDUM5oNjhNdS2fxeTvQpecpyE7sQGVxk8F46LRTJrlAkv+RrcJGLF56iaRes1mvWQ08fIhjL/bMz\nzlCcJonI6qrmZNWx2N1BKcPds1N6ErqyKKPAakxjJW/n/SYqTGmMqkrE5kPYyOCVMEhJZ5wQJTbz\nJLqQSMpQ2UZYu6iszakRJduco5MEJgkl5xelJyMowljjqOmdkzKQBJu6jExA0jrDsbKWiwg56Fxq\nA5OmobWVsHSNEJJaWzGxhtpoKo1A0yrRNjWtrZhWNbOqwWRnQHJWmyZOLgSGCNbWoC1RwaxqsUn6\na0agskpILilw//SILuc7iyrMuB626jkL57RE2dpq2taic9626wJa13Ju+VLU1mAzi31qLfOqogGS\nc6IT7R0eKbMZgMoaQkr0wRNQDGRlohxJhxRxKeEgqwxJrXajFCpHlrXWoyC/ySIIGqkfNlqMyqJp\nUYMjDJ77yyVPXL7Mjlbo4IhDT4oBUhRSkAKMZjbbwfcOv1oSuyWt1SgXMaamme9x+4VrPP+p3+Wb\nv+Ub2Zk0DNkRCCmNfVd1ZXjn138Htp7ROU9fWLH5eUXcoBjLsp62k0PGiBhBEbvXSjogTZuKeSsE\nR6MVB4uaSWW4dndJiLA+O+YX/s5fY/fSkz/5tWQsAcz3f//3f7WP4Us+vu/7vu/Xu3X3Z9zx2RNP\nfvPbVaUiE5Wo6xlDlBwUSdp7geQ0u35gp66y3mWiUhLJDMNAU9UEwCB/q6sa1w8kBMJNITBrpnR9\nzxOzOafDwN3TF+m6U65efHKLrMM294XeRY6WjsYaHt+boLRi5ULBvf5IY1KL973sv3hS2heqO7sp\nyC6/b0UJFP6AGJhN0TowPrb1wq0cVzG2ZaMub1bZmicfewtKae4d3gIS01acj56EVgnXDTg/0Eym\nVEazdAPKmJFVisplAiGN0FiiRPtyBjEklDFSawYklckelcYaiUaslTKkpJTIzKGlcXGKWaB8LDpB\na7MFeypcJuVInaNEsJNmyvH6bCxlcTEKCzZIlNv1fdaANWPkK6pG5NcwSrIZU0nPV4TdWWmzEWeP\nAVMsFkCKKBVpjMZqgTX3mhaVj6GqqrGhc0QxbRrc0LEanDR/1pE6KE6dYzqdYICDyZzkHfe6NUMy\nuAgKk/V1dWb/pg0cmC+xtkqIdtZgE3S9RwThizOAEIoUEhFntnFTV0yUJnpHQOEy8Qegz3WS3geM\n1ShlMhRtcm9QmU+0QK5KqcxCZoSr8yHLylbSALtBo3Naoa6nEL1Amt7j+4576xXNfM5EIeVLKUP2\nudQElWuGUazXkU9/8hM8/thl6rolqoqLly+xPDvlM5+7xp3bN3jsiSvs7u5zcnaGqypWIdIHmYek\nZD2/9Y3fgguaVe9YD57ORcn/FhWgnLtMaaNAlJWARQXJ2vG+1TmyXExrLi1a2toSY2LeisDCc3eW\nkhv1jv/7f/mv2blw5dbn/uB3vvM1bx6vk/E1BcmWkVJySqk/98H3vP+jb376jVemf+47VcWKynuu\nzvYAzVm/xFgLMbdDMkoUgZQhJFHo6LsOlGLeWpbrXjz/JELOTVXTWsMwDJiqplIKqzSLumYaE4NP\n3D15gepWy9NX/xgAUYmRzbsuIDmGe2c9h0vRpX3r5QW3T3uOlv2r5Hg+/yhKL3+U8YUay/Lscx+b\nhI0ZM/lC9hnJuZGEdQkKFSM+S4tD2nS7iBIJ1GmjrCS8RWQOjebpJ7+Bg/2rfPRj7+V4ecb+rOX+\nvfuc2YqnLhxwdraEGJg5S4qJTgvbNJHG/Ke2iqJ6lmLKTFgJhZWWswsZEkaRC/8jrdXMmhqtIFhB\nD6KxJJV7D5LTuDkyGyMqJWUVIyEo5egzCLwakVzk4P05uTKlNCHBbDrNOVQ5aGsrYpbwE4F1kXHr\nS9uy7LQ1CryKpCANoau6ydJ3DmO0KAgZiZRszgWqFHB+wGgthSAKzoaBkBTaa5JWKKNZ9z29U7zh\nYI/9Ck7WaxaTlrsnhyy7nqVPIvCe56duFdoovBO5wxCiCKOTi1RyL0cVAquQRmM5pjbVBq7VWpGU\nALmnQy/vHYLU6GppiDCEgK2sGEYNS+do6hqXpLZVBPsFyjZILr2wi8VwepEuBCyGWByQCDGXrFll\nJM9nLcoNwkTOUHY/9HSVZda0uG4tJ5FJRmP9IhrcGX/sm95O1c4I1KizU249/1msMcwbTXuwCyGy\nXJ5hmoYw9AxRJCCkVAne9vQ3o0xDPwwMIUn+P5buMZs0gCy/LU1dZC4lYle4IPdHpTWz1lLru2i1\noLb7VLMaUuLZW6eZxQ2/9tP/G6d3b9y5e+Pak69583gdja85SLaMlNK95XL5XT/7kz+z+uwnnmUd\nNf2w5vDoDo/t7LPTzqUHYgn7lGKtFEqLd7XqpIi4rmpEkkwzayyzic1C217qtiJE58W4olmHSNUP\nTJuWvemU1fJFnr/xu8xby05bM83sWWsF4pIyBYGabh6teemwY3da8XVXFuxO6y/6/Ev+7Ss30ohQ\nZjUzyreUd/uidAJbZSqZiFW0L30oJJ9INwirr3Oe9eBZu8DaBQbncT7gYhLINibms33ayQVOVmf4\nweOHgWA1z9+7z0pbqnrCYDWuks291BsaK83ZgkqS184nIIzXmKNQTemwoU2JjCQXuHKB03UnZCXv\npZuKd6zWHZEskK70Vg6zSAMy5m5jSvTeCfEn5zZLbeemrEQ0buumZmc+FUgxz5etZE0qA8qAqWzu\nNCIboSdlIW4hxvTe0+d86BACfdhstKVMJmkxsrXRRO+ZVDUTY0kujF2AXAClLX2M1FXF/nxCSpGb\nJye8cP+Yu2cdt46W3FwNrJLBoakaaY1njFx3N/jsMEhbrKpJQCAFhcKIqHxSKG0k6stlK6V0pexg\nMUaIOpd3KFZ+YKUiXuf2b96Dyd1MVGI1DCLo4D0BYUAHRKQ/pUSXr4Uw6TcMYTHJWlZzXrshS/TZ\nXPOpU8TEJHArUt9qB3FimmZCcMN5JEYpccyiaN7O9w7YufQEp0PE6kCtpBfsnXv3MRr29/eIMdEN\ngxDF8h6itYhPzCcLrlx8I85HnIuZLb0h+cSy5yVGp3pMy6uU5fo0PmZjaRSz1nJxx9L1L/D8rT9g\nZ2JprOL6vaXUdIbE773n5/ns776fuzeuPfN6r7d82PiaNZgAKaWPrlar7/np/+Hv9Ddu3WYVLVdn\nLavumMuLXQ4WuxhtmNha4Cdj8FrTWEXTWipjqa0aJcysVjRaSA1VZaU1lNVMKisbxs5MciwJFs2E\nGotOihu3nuX49Dq7k4p5WzFpLI01VFZn5ZcN06xzgRfvrbl93HEwr3nTpTmz5gsHArT6yrJkRwJP\nNozjhrDBZcfIMm4eyCSHDFBmoyktiiRn2ftIN2TjOWwMZ+8Czkd8EJgyJnjHM9+GqadcOzkkNRWD\nd8Ta0DQtKjNT05golmhnPA4U2hopAxlZrYCW+sIiZRaTME0lUpQcokuaw+XA0dpzsg6snRgSiYjk\nGpcJSeT8WBCm6OAdy6Gn844hiqiCya3GAGIMDMFhqoq6qtmfTzEmsh7WoMAYS5EXBC0dVpTCuQEX\nPC4J3FgZkYUbhsDpcqAbEiFpjtY9R36gIzJEhzaaxlZUyqCStLxrK0voe1xweJOotGHoHXVVsVz3\n1Lal7zpeuHOHo/WSw65jlRIuRU6jIyhwRHRlWQ0DK+cIScTO68aibZQIn9wmLTstpibDpGrMe2qj\nNmuKhDVFzEGLo5Nk/WldSh0Y5fNUfo3zIeeZPavos1iEGBwfs0OQl68QqgSJ0Fpy0a5I82nJFxit\nqfM+UhuNBXSM4DwqRELvuNl17C12UasVwbvRUimVM9IxYW1DO9sjoVivjpky0J/cZ3V8lxTWTPd2\n2NnfJzhH0ord3V36syVG61x/rPKcTEjKSGuvkvePG65A4Q4IpFxWpcy7qBAphqxBXRkJFC7vTlm5\n6zCccXlnH6MN1++tWLtICIlrn/gIv/6PfoTw/7P35sGypnd93+dZ3rW7z37vnTv7aEUSQkZgI4HL\nduQYsP9wyo6LgpSrkiJVrvifJE6RsgMKiFAkVS6wqQpxuYwTwCY4wRDHCUkVYMdmkQRIrEIaLYxm\nn7ude5be3uXZ8sfv6T5nRiNpJM0kkmaeqjun55w+p7vffvv9Pb/v77sE/9dTSqcvz1Xly299VRdM\ngJTS/+HG8b/7yf/67w2aRDCGR6zBd+ccNTOOJrtCHU+KSimaomDVDxijaSvLflUyBE9lDaU2uGGg\nMIbdSUNhNDEESmuZVhVuHJgWBQc7UybWcH3SclBWzJqGJ5/7Q1bdTWZNybQuaUpDaY10mzrTsw3b\nwtmNgSeP19xdDtyz1/DQ0YS2NC/5dWcP6S/0WH3xBgXqwoR68zde7O9dsPI29xNXnS1MlP1LheRz\n8W/wkcFJ4exGYWYOTsTZLsqM0NqK++55RC52xogMIUaG6GHrxKKptZX3WouRRFNYJkUhZK7s7Vla\n0VIqjfjE5ou1NikXwPS8LkGIQhplLVFDUjq7O6XMqlSZHBRROc2iD27bPfq0cVQV67p133G6nNNH\nj7JaBO1NhU6e4EdsaVFKbNiUknlTVVhIIu9IuYCovBlz2eZu6QZGlRhSZD14hqjoQ6JLAZ91l2Ih\nKASfMHS41YppVVErgxs9y74nGsPpekUfHE8f3+LUDeweHHB1/5CyKNmbTJjVFZOypilLYflm/WpU\nEtpsjdjKCQsz5NchodXGCO1H6QRKIFSVpVICwab83iRMAWWRGZzZdEBiw6XTTFokN0PwhKQISuFz\nkUQpAmk90gAAIABJREFUxiQsZx+DkJVU2m6SY8xh9CCZqIjxiFWaUmnRZWfbvZgilZExTwgBawv8\n2HNjveLa/fcy9Z4YnBBoUOgIKimMLmlmB9iqYbU4wfUrCjTzs1NCjDRNy3z0NNevU1QVDs3O7i6r\n5Ypy2rJrSmzwOO9xKfCG+7+OEFXWq+ZjniR1JW4Yd/kzC5vO8kJPufnE2nwN3Js0TOrI6uxxru/s\ncW3/QZ45WbIe5Xid3rnBv/ixv83O4dUfO731zP/8xV1AvjLWV33BBOi67r+5e3z3l37+R35yuLWc\nc8f1HKlE6s6YVjWvO7rGpKq42k5ZrTucMZx3jlobZoXl3qbGkzg+O+V8FPq81pq2LinLQk7I4DmY\nTAkxctjWpBRYdyuGvqNVmuvtjE8/+WGCP2enLZnWhcCzRrIMTdY3bSz0Mp2BVe/59O0lZ+uR6/vt\nSy6c6kU6zJdSEL8ku7xLFLsXOhG98HlshOgbwbS4+1xAlPFSpynM2MgQIqMPjBmqlbSFjaNJIiTF\nlcN7Ody/d+sc4zPzuXdeQnV7T2sqTJDCXGTEYL9qOKwaamOoU6IxBp2EjLSJ1rJGSD4xbtCmtE2t\niVlyIIJx0fiOPmzj1UTClAlQRgwPfEpYa5nUDWiJRhpTEHcgqxG9h9j1bY7FYpDEDcldjrgwoEzE\nWAUqazwRW3ilRDPoY2DwI50bCCnSjyODk65VIsoke1RpKR4hJW6fn/Pk2Qm6rKi0Yex6ju/c5XTV\ncad3zPsBh+d8XKObisPdPY7amkZBWxQ0pmDipcuaWcu0sLRlyaQQ9m1TF1zbmXGtbqUjUwlrwEW3\nnROniLjqKCFVpUxGMVaBlmzOlETyYxUkNWbP2ZLFOHDuegYVWQdRohojfr6dH+iTJ6pEH51YBm78\naZXCIHCkmPwLMzops8UtVe5eFYpKF5R53l1aS/A+U35lNvuHTz+D3d/hMCkIXqQ7Lojlnq1oZwfY\nqqXvFqzmx8Qo+Mtyfo4uLEVVo1JimeCevSuc3j1mZ3+fk0Xk0U89htWaLoxoa1j2PW984BvQxuKz\ny4/fnp8v4BaQU4Q2VLRcxDddpzWaphAHs6NZzWE85q3VDkZbAvucd47BB7r1mp//kf+C6Md/eOPT\nH/9bX/zF4ytjfVWSfl64UkpJKfVdH/jAB/6g+umd1/+V//Cvqs6OHBY18+4cO93joZ0ZT9++BUPP\nZDJjTHCz67ivFP3lrtG4uqGyBbOqRMVEpzykhDEFo/csVyus1hy0NWMaKOqGwvScdCOnyyXDOPCR\nT/4q73zLtzJt6tyNRRgFhRp9hBTyNkZIO5tQ3nnnmXdLdmrL9f2WEMSn8bOxYNXn6DA/G/v1lfSW\nfbHHjHm3viEGpaTY5IfKHSIaLR7Am7kRsitXSiKfVGaCisgaiJp1vxRUz6oM84pUI4ZEVVScDR3r\nbqBpK2yIHLZTyhSZlhW3FwOeRGtKBuVIxuAIOBfxLlFaucToTJBReRaXslYzpJALf5ALVxThu84y\nn4R0LS4HTxtdcPf8TLrZTEQyOW8ypGwkoDWlKdBG3iPJCxBLMqUsMRPItIqEJLFOIKL8kESiE3IH\nF3Mn6wmcd0smVQNjIASomprj8wVBG7wOVLbgw088wep8zmk/YHamuHEkWZGx6CSdbVmUQkwaA413\nDCkwhshgC6qqxEcPfqAsSo7aGTvWslzOWc9P8LnQ6NJypWq5NT/BloX8faVxwaGMBiXEHG0u/qXk\n0SbhwyjynySkoGU/YguLUpk5S8AqkQ0tg3ScG/hVNhUZMkZh8rm22SRtNFEphTxr1eL4hZZCmZnN\n1mj8OKKS6B9VWXB2fkp97Yir9QQ7jgTnCCFQNhOqRgwLuuU5IcetKWRTFp1oKSfthOAcH3/6WXYe\nuI/F8S3KsqDvR07XZ7zpHW9nERznIXB7NTLbOeK+owd57u4Nqupoa6we0guQn/xowBa23jCTlRZu\nRVUYJpVlb1Khw7OU4yk3QuROZ6l0z7r3jGPgX/6D9zHZPVye3H72+16hS8eX1XpVFEyAlNJaKfWe\nf/2//d8fefAND+9+3bvfSXRLHmp3OenPOGh2efjeBxiHJf0YOPFyoRmKijp6fAJFZHSOQGBa1lR1\njbEFJ4sOlGYdE5ZE0fcYYNWvWXWDEBsStGXFED2fevJDvO2Nf4YYi+wXup0iyMmchdEhCpHGACFJ\nMsO895x3C3aagmu7NTHB8aJn2T+/cF5wVp//3RfuM/+/XJeL5ub25aIp/9nMnfLKRdOniDh9QzKR\n5BVJiSAdFTLiaZlOjrh+5RE++viHmdiWpCJ9cBRB8g997/KMqxCnpkKz6NeMpqBSGh8CdVkSnWeC\nZoiR9ehJGEgSkeUDkISggRYI01hDCNLVgXQnMTgxIdiI8jP8WlY1ZHjsbL0k6rRFFlxwVGUl3jxK\n5RCAkmnV0JaWrl8zBul0AjGPBSLaaiQm0kiyDqJfDFGo/jEKrFzkyLKYRDqxdB0pRmb1BOMiiwgh\nBIyCs/kcpyA1BbEp8CiSkWNUFCJVKbRBK4NKUDYNyfcM3chkp2W3lMCCPmj2yxlGaarR0Z2fUbc1\nu1VNqTXnqzXXDg44vXPMUdGwUpFRy4bKk1ApEEaRb8S8IZEQ6REfElVT0q8GlCkwhWy8Sq1ZdCN1\nXZOSo7IF89WKoAFlqFQOO85GBttptlJUaGIUIZnRF9pErSQ20CAuSSlBWRg0MA4DthS2tE8JFeH2\nes0br18nLhf4BKZpadsZKQa69ZwY3PM+G9ZIh6qyJ7FC0/UDr3vjm7h1che9f8T5suO+19/DvhH2\n9vJsjVkuGWLi4XveilKG3elVVn24YJVvd84XpXKzd80gisDgGYYvC0NTGGZNSQg3eOMkcmvZc/DQ\nu/AreO7umt4FfvUXfoKz289y64mPPxC8P3t5rxZfnuurUof52db73ve++Xvf+95f/+iH/uC73vzW\ntxTt1Sus1UgRAuuxp9KWqmhZDSsetgUQWDjHzBoqLbvIqihwwMqPdP2QpRJJJCqZsu21EvNlI5FM\nRVGSEjjvSTGw7haMvufa0X2QxdcpSxY20gNZio0/WLp0WynF4AKnq5EQI1dmNQdTYfMOTkrNTlMw\neGGcbtblWcWXsl4Isb5YXucXaviutk9Q/m8TD7WJfNrWVC5tBlKGOi9BSwBtPUGpxKpb4KMDlfDJ\nk4xm9I5ktMiCCvF96rNe7+RsQaoMKogv6So4RrVx4RHYeNMhbrSkG4KIzikWNru1KHXBoCzLSp5f\nNhhICgm0RlipPgVhf+bXaYxh1k45WZxhrUUlyU0c+o5VN6CUPFbSmdUaL8yxjdVZDqIgJoyRDlDS\nU4Stu81njJKsobQcj3EYhB1K4nzo0FWOC8uFN+arqzFicC8SD/HOnZYVq75HGcOksDRVwTVbYZdL\nru3usmcs1dBTWy3z/sWcsR9QwH5TM5ydokth98YQxC4wiguPSoqmqjB6Y4Iu88TRS1epyZsYRPea\nkpgG9C7k8O/IuvdEI52rUZkBCtuMz3xibp3ANkb6255MyUYiKkWpDVvnozwnT/n4p5ioqoaT5YrK\nB7TrqSe7tLtClOlX5/T98nnn62bzaI3FDXJMyqx37bqB0SeJ7/IDs50pyY10fmSpNMsYOA8wlg1v\ne+QbWfVrlC5kdJE//5JnesGQvQjxvrAuNNvrm6GymllT8uDhDqRbhMUJnz67S7XzJm6cdsy7kd/5\nN7/Ib/zCT8TFyZ13+nF44rN+2L/K1qumw9yslNL7jTF//R/+8I/9zPf82A824eoRg3KYIpK6cx7Y\nrbm+e8huAn+35/a6Q9UVAbEiWfYrFHDQzpirgbV3QigYE4UVlpk1loPZjHEY6Hxk1XdoknhuehHc\nL5Y3eOyJD/H6h75pe+FX2QMGEsnHjKNo6UBiuui6Etst4qL3LPolk8pyMC25slNzshzF3/L/n+P7\nku+z7TYRyFSnlKUYksG30YZpRIiN0VuMemvlBRA9Memte0lTNbzuvj/B6EaeuvVJiroCQGtHURZM\nyobSFoyuZxUCbVWyHh1Uhm5wnMZAYSAajcFQlAUxBZFRKJVBO/m6IS8Nzm1JO0qJ8b338nguBili\nPmY5gMvm3050lEbm1YGENpohStFS1uCC52i2Q3Qdi85jbZlTO8Q426dAoQt0ytrb4CFl+FpbSevR\nihAtRUr46EhIUV4NHc572rqgcwOdG8SQm8SkaXCDY8RjMKQkYcpa621I8k494YFpy6obiePIbNKy\nh2bdrdizjYxgC0Ml/mnURcGqW3O+WKLrmlnTksYOT8BFjwka7yOdGymrmjHJDLNI4INHKUWpRIO7\nig7nZRMy9h6tNsS5grbSdOuegCIpzTCKBEaniNaVzJ1TQhkIXuGSaE5TlCi1QKAtFT5FiuxJG7xn\n6QdCGOltSYyRWdmyHHtaa6m1BMbXZUHfD+hgma96Hvnat+FWC5Znx8KOVSoHWF98FrbF0jnqpkWn\nSLdaorWmHz1Bex54+CHCcsG674laUymLI6GtpisK3nTfO1h256Rksbq5VBw/E1PabGjlXM1SHK0x\nVpx8msJwfa/hfPVJTlfHTJqa6uit3DyXYvmxD/0Gv/zTP8p6fvKOlNIffZGXiq/I9arqMDfrB37g\nBx79/u///pOPvP/D73nHu7+xqHZ2IEQ8YINjRxtM1XKyXHC779jZmVFnD88hRAZysvoG/9eK0iqq\nsmLW1CSSzCvcwEHbiDOQ3JOrsx1W/cDoRlarM8paszc9yoLtDVy56axy53TJ+efi+/n/88nvQuJ8\n7Vj2nlld8NCVCaTEsvevqLzkS+1Wn/f7m9ub45q/LX6iXBrKSinNW4sLSUsSfWPIIvdrBw9y/9XX\nYZThZHFMJGXLwEgMAa0NMSaW/QjaSueqNHVdYo3BKJFjCCtSMV+tSUZCjDea0ohYoWkjXUdUknif\nSCQtaTgqiRWjNRumbGKMklpSVCXaZGg3bVxfFHvTHe4uTimKkuvTGWfzOUFbEhKvFVLApbDVa2qt\nCCFCJqxoY0CB1Zqq0LS1gRgobZnjpzTeeRKihVQomqrGWgsKejcQVMKaIkc5mXxhz1IZramMoYmB\na7bkmbNTHjo8RK/XMI50qyW2MFRlSbdeE7TkipqyZDIRNyayuw/aoLQhonDaMK1rSKIdrYyBbCgu\ncg6RF61HRcJgtKEusxmBUtJNeek+q7rEB7/1oIXsjWpSdmAS5vSGcCZpKFnzSd4IhUAfIgOJqDSl\nLbYogAKqomCiLa2Rwq6DQhcVRVFyz9Eht5/+NGHstptcySG9GE1sDAsUWQYTAykEYkrMe8fu3h7a\nWgotHS7WYkuJKexT4u7gOB83GtKSabNHSiazyi9Cordjn9xQ68zML40Wtn5hsEpTF5YHj6YU+oyP\n33yUQ6N5dhloJ2/m1tmaT3709/hf/+7fSmO//msx+F/9kj78X4FLfSk5h1/pq23bH9o7Oviv/vO/\n/wNmOpmgYuT1BztY59krK+piyqPdgtrAtQhD9ASt8MlwvFrigqfQmpDE2acuSwoV8d5jlQQRxxRp\nUKJL0yXKO0ICn6DLcVG2mvGOr3kP88Gz7EaWvWM1SLLA6C4y7UJmAW1DfV/w1l1+Lx86mqAQmn03\nBs7WI4vOfcYM8ctlfQasi6QlkEk9Ah3Jh91sBOU5jsoYKWiF0RRWU1q5CNSFpS4sbV0wX9/ktz/2\n/6ALsGbjtjKgC0tpKxpbiN2c1rRlwdB1NFVFm7u+OydnLFDUk8m2+405QklpQ2UL0Ux6R4weZQS8\nEbPuJJpeJYkoMUeFzdoplbXMVyuW40q6nsy+fOjKvXz69tNc3zmgTonjs3NSXQnagMYTtt1gaaxo\n+bUhm91Q2jLLTRNNYbBaklJAZCdd51mMA2jNTjOlrWq8c/gYWLgOlzxGiU2cslK0jLHiAIOh1YYA\nlEXJO5qGxa1bGFvQh5HZ3i4hM29DCGKzVliMEhlWitIZWqWYd2u6zObcqWqiSjnSzRB1ysHPEaMN\na9ezjpH1mAuW1jSVodFwuu4YvdgOOh8pK4vB4V1EGwuIlzOErcwnxEAKmqTF1QayAQJ5AyYpaRRG\nfk8kX5HaCunPKsXMVuxVNY22lLbBj56z9ZKjw33Wt28wDB39KDaGddPQdz1122zP9ZSE5LWpZtF7\nYoz0EfZ3D1jcPWbwPdOdHQorkLJKiqfmZzwbYVCW+dBRVRWWine++c+x7j3rMbHsHYMLDFs7PGF1\no4TYo4G6kPGHSgVKwcNXJ+y18P5P/WveXlQ8eX7GA2/+9/jjG+d84hMf5x+/97t525/+i7//W//n\nP/n6V/aK8OW5XpUd5ma9973v/bfE9NAnP/yRt3/9e75Za2tZDD5rqxJ4xz07u0zKBhcHVJY6WBKH\nbcuYYPA5zaAsJQQ4IXMgoyUoWGsGYKeqCN5RNS0+RYxSDKPHGs2qX3B8/gwJx+HOVTZ2XJftq4Dt\nLI+thdzz1+WiM6sLTlYjt857IHE4qzmaVigkiPfzhUu/EDZ9pddnPM72/zfz0HTBVrjEYbiI8ZPO\nbRNXtA1fzmYITTnl6t69BO9YdOdie2ZExxjzfFkyKaXTGFNiiIlViMxHRywrdCHdV7yYbG07vJAi\nIQY8UVx5kJQSH8OWwJOyRCWkSF1VtMZyNp8zJIHq0GlL/pg2E05WCypbMq0r9puSO4slyQok2Q2D\nkKZCpKpKBu9Ec0fEWIMPYsiNEj1eTIluFL/RwSeUEa/hMYiMpBv6bZTZahSolty1Kq0oi5K6KGh1\nwb3tlKt1yQPthH0Uw3LJeuwZYqRpG5rpBHK3Ll62Jutpg8hncoxeDIG1c3hj0Nawip62qHAxsEoS\nYK2QObAPidOuZ+XFWk9ntIAYOV0NRLR0ibn7CykJGSkTrnTu0LafrQA+QFEUGCWylpQkqi2EQIjy\ndyTGLGG1otSK1pSSZ6ugsiUHk312mh3ZOHdrnnjyj7n/wQdJvef41rOgFG3TkuImvzRSlAUmz0Gt\nFiiYJMbpSSt0VTNtWobVguV6SVXV6AwLh+BZec8dn3BVw6Jbi5lDiOxNr7IzOSRR0LtwUSTT5pxV\nGA27tRheFEZCA0gKazWPXJtSGM+/+sj/xdsODjgoS57yUzq/x+NPPc1PvPe7ufLA6//NR37tF7/5\nlb0afPmuV90M8/LKcpO/cevZG/f8kx/+8W//mz/0X+qqrJgHyZ/zKsHyjKBL2skubj3Hu5HOO1JK\nlONIn0kQR23D8XpFSvDwwRXurJd0wyikBWDuPW1dUWkRod8ZBgqj6b3naLrDQKDvbvKxJ57g4evf\nxKxpLzSJuQiY7BWpExA2wcTpeZ3m1uh9e/tCklIXhv1Jyet3apa943Q50rnwosfm8xXSz3WfL2a9\nsOOV9HeZawYyHyhe2i/kKWYKipRndqLrFDiryF25z5mAPkTqape3Pvxumht/xFN3PsloRMKjUJS2\nwmStnRgdiGSCKBfrlHMsVcrkmowghBSwKhfGbLQuBVpjlGFwoxBulBbDcy/dEgmWw0BRlaTk6J0j\nJUVVlehsp0aGaCtjeezZG8S2IjgnXp9FQQgB7wOLVZcNERRVWdANAwmJ8xrDSGUrQhSmsfOOjSbU\nOc8wOtIwsL+3y6xtee74Fi4GuaD7QFRQGMuVdoZxEbxjn8RMGcqQODk55Xa3Yv/ea1RFiU2JgGg/\nOz+ijMyWLZoxBnTU7DVTnB/wRILWjEn0tU4l0thjSITgGTV0eXA/uIRXMkt1MScMBS/kndxhG6Ny\ncHrIBCeVPYlBRdGzDk68dRUCvfqQjRMycWocAzFpjJXjr5Um+IiymtoK9NrYCmtLbFljgyf0C8Iw\ncHr7JoW1FFju3HmasR8orPj0xiTISFFYClMwjuIVrbKGMynAGGxR4NYD3joGN1LVFZOdKd55hnEg\nJHii72G6w/z4lFFHpmXL2XLOkTZURcPY+a3MymgxatBI8HNdKKx2+KAYo0IlmFSWB49alHb8xid+\nma+97wGmizP+cDVy9d738OiTz/E/vu8/4Z5H3vLpj//Wv3rPy/ah/wpcr2pIdrOUUvVsNvu1b3jX\nn3znd37P3zBWwaSAiTUcqQJNYmUM13evMF+cMO87xhhZ9gM+weF0h5nWLN1IUdVMo+fuYsVaa1wu\neCqTWppCdpZVPWXV95ytViilOZhNWfRrbqyWjMDXvf4vsBrgfOXoskhfbOMEKgopZqbei+str+/V\nrHrPeec+42dGK/bakr2JMGvP1iPna/eS4sBeqYL52f7mJgFlC8vKnTb5zehMalHZ0mtDjdcaCi07\nZ8nv01SFpS4NVWEIfsWzdx/jmbufwhhNYQs2wVWFLeRCmQ3gtdLb452pkJI4knfuIkGQDsYF8UZt\nqxbvHT5F2rKS+WJKcrH0I5NmQvQicSmUoffCfHTBYY3lgcN7+NRzT3D/lWukVc+p6/FKOhSrtZj+\nG4vznqIoicFvSWEpRlQ2ezc58DiEsIWx67JCId3polsTY6SdtOxWLceLUzAb6FRR15U42mjDLCXe\nvLtPGQNWG+anpySVCGWJmU1obEWVi9a661mnSNj6sIoxeWUsOgphKcZInwJByeZviIE+RQqtxYxf\nSVaqC4nCWpx3LIe4hbvZMpQF3o05cSVk8/zNtF8rhfN+m1iScgHRuevasMchYbORRcgbOK2gUDAt\nLfuzHXbKljLBeliT3EClDXH09Islyjts2WCKmmE1Z+gWHBwdcn4+x1YVTV3jo6MqG8ahRxuDtaIP\nFeQB2qbFrVd0XUdAUU9aghsJ2tL1HWe946ydoBZLnnQjUSX2JjO6rufr3vAtzCbXWI8u+zDLvHfw\nAeezRM2oHMwuiM1hW3J9v6FzK/7wqV9np7S8yViWzuEP3sVTdzr++7/z3Qzr5cduPvGJr02v8oLx\nqoZkN+t973uf/97v/d6fO7595zviqt9/0ze8XSVlKFViHRy1KRiHjuNuzf7OAYWxrP3AbtMyHweG\n4FH9yCR7zQ5JZlRVXVNk3ZpSiTHAODqSSljnOJw03Lt/QF2VwpArDJ3zjMmz6ubcc/DQdmifEtvB\nvQCQF/6kPO+WrFld4EOkd5EXrpSgG0WW0rvAtCq4Z6+hKcVY/LIU5YXrpchGXq51mRiRv8NWrXqB\nTz/vxW9VAioTgyJbZ6Gt40mCwtRc3b+Pumg4Wd4mJhH3J0SKkTKcK8HFF5zcmLIUA9EymhwOHVPC\nBU9ISZJDQiQgGj+fMwptlipIKk5i8CMYi/Mhaxots6aFmGjLmuXYEX3geHUuVn+Z2NJ1g8CMSjOO\nTmDGqNDKsj89xOgCIlRFQwyw6tZbokqKQjji0oyxLIpMRlF0/Shs0SAbB5NzI8cYqNspnRtpMCxu\n3WRnb0pVVyy9IxUFQwocz884HXsWSczOxySFXClNoTXee07HNV4rseMDhhRwSSwMxQje0FQFwUkC\nRojCcnVeUAeUyvNj6dC01myCyFGKykr3rY3GKM3gZaZcmEIkMbkQxrwZSxn2t0pCyn2ecRqtmTUN\n+9M92rrFhZG+XxKGDmLA9Q7vPLUtqI2BCH234nx+TtPUtG3NOAzEBG3bMo4DVVVJqozJxCISVmmc\n95hCSEhsjNXrmrZtBUkAjLbcSYGd6ZQb/Zhn1UW29fOs+hXXjx4EVGYzS8h1CBsP2ZQdsWTfd323\n5nBWsRwWPHbrdyAMfEPR0FQlT5v7mXcN/+iH/jNW89M7Nx//+BtSSp/9wvAqWa9qSPbySimdK6X+\nzK/84i/9XtNMrr7nO/+SOrWKXRMZlKTMr8LIM3eeY7K7z950j7vzE4SYqNDThnnw3K9ldlWZKcFY\n1osFMcsL2toSo2F0EV8aTlYrdkNABY9JERXhaFLjl4GuP0Mz0lR2O7Q3MeU5jVQIjVjJAZkGdFE5\nNgSSz7e6MdCNHbfOO3aagsNpJZTytWPejS9acF++leeR6sU3rRuY9kI3ll9hJt2AXPREg5+t53Kc\nkk6aqCPBiNn7JjTXb//JPPrK7sNMmz3+8MkP0o9LrIHBjzS2xhjw0YuGEzDayDzNJ7TexECJTjDF\nhPchX8xFZlIUhhSFjUuIRA2VFqblauxBSYxUjJ5VCGhjaE1NSLJJizFyOixRWoqq956mmHDvwXUO\npldo6xlFUeeLpkVrQ1VUUtTJGZPJ89Stx3j06d9n3a9RGe4NPrKKa5HhNA2l1nTjgK0sMSTqqmCn\nbSBrD7UyDOPIbGeXODomO7usVz160pCqksF75slRtzXbEBGtKFWZdzGJ1dgLvG7s1tt201lqbQlJ\ninZVSOccNXgv71VCSEZoQ8yQq9FayE5cpOCQJNg6gmReakNRCAEq5JnoJoFER2G+bqwNQ54xT+uW\nqqyZ1A3ej5x3S1zosAoObMPtbkVZF1ijmGEJPhCcZ7mcs7e/TzEGCquJYZTuPzlBAqoyM7MvPlNa\niUmCyDvE8N45R58SO5MJPgTmKqHGwPzsHF9VPHO+pCwL9oqas3VPyuxl4sAnn/gQJMOD9/8JyBs5\nGdFc3iwq7jtosEazHBZ8/NnfYK9pecfVe5h4z++vDLq5l//pv/0ennvsY7fO7tx4KKX04rObV9l6\nrWBeWimlG0qpd//Ln/uF31JGHX3bf/CX1bl3VNpzdTJhvZwzT5HV+V3qasL1vXvowy18pp8X48BQ\nFPhVT1nX6OBZjI4hJnRS4AVGszoRggz8exK1sWgCXXA0RhiInR9ZrO/QNvfRFZ7RK4LW+HhRRGRO\nuTGNe37+ZUoCVb7UFROcrkZOVyOl1ey1JfcdTEgpMe8c52uHCy938RRIbPPlRe9xufBnneb2ZaVL\nP1dyLMQEXWBJ6cOzvs5sbPJiTm0w2WIuUpd7vP3Bb+HRpz/Eys0hBLo0YLUFEqP3F9q1lBj9KDBg\nYQlRjOLHYcTHKIk33hGTiMYlQzIbGChL7wbpmkgUBrp+TUhJiDoxsFivWPYdV7RsrKyqSCQq2/Cx\nSnEFAAAgAElEQVSON72L3ekVQOC7mFnYG62dTxBdEtmHEvs+awreeP/XMZvs8ZHHP8SiXwhsHDLP\nSBm893S5Gw4xEoJsJsYwYI3lcDJjv2pIwXO/sdztTrk7DKSYuH/aMhC52/e0kyk6/x2bU1M04tMa\nk8SOqXiRQVoquyW+KKXoYgINzjuUkuMXoiEqKbBymkiWqdGG4EOOlb0gYaFy0LvRVOoiuDzGmIOf\ndYbZFdaqbCAhOsjZZMqsbnFuZDn03Dq7TQriJqWNmFl00VE2JZUyTLRlogvG8yX9ek1Vl4zDSEwJ\nYxpWix5diHZVGY2xNifgKFLOmRRSExJE7yW2zqfI7qQluoFlSkzLhhvnx6xT5NSLKf0D+7scn53R\nVhU+BNpSc7paslgtuH70SB7bwAuzL6e15dpOTYgJF5Z84rkPcO/uHm8oC3a953fPlui9P8WP//Df\n4fGPfvj87M6NN6SUhpf3c/+Vu14rmC9YKaXHlVLv/t//2c//Zmvt4bv/yp9XJz4yLhY80k44B26u\nFtw5P8Eazf7uEcu+oxtWVHVFUVZMgOcWS5yx4jUaNYu1oy41dVVQobizHiiUZk+DCpFpWZCCoouR\nmBTBe27cfYy3v+ERloOjNAEXhK0nnqt5a50ygSGTXragbbpIUf88rxfgBQVBfGrvLAYxYG4LHr4y\nwYXEfD0y79zWVPxLXp+lu3yx54gSFWpEyAqXG2gZyeS4oo25QZJNRFKQkiGajVYzy3RCxAeLC4m6\nnPCO1/1ZFqtjPn37j1j2ZxgbIEWCj7RNQyLSDR0klS84kn3pfaAppzKHCuLso1RkdCNDGqiKCmLa\nkofqqsYPjqqoWLuOpq4ZxkGIMnGgKSbsTq/yZ9/+NZS2ETehKOSmZe+kqOXNwiaWETKJOJv4G6Ow\nQUscl0lc3buPd33NlF/76C+zHhYb3DobMih8FOP/PNAjxURSlvXQk0KiKD0ni3OeMgqnoKxr3nB4\nlcZqnrt9G1W3xBQI2jCzFh3FuCFolTWtcpb2cSSphPMOl99LlGJSlFgSa+dRppSklCTzSJ+SbIIy\nXAk6G6JvDMTlb2yctVJKGCWyESWYOhvb14QUS52lFaWpmDRTlNb0Y8eNuzdwIeYAcbKnrMyxtY5o\n4L52H5Yr+n7Ax4G6rjm/e5e5iuzPZlTlhBA8yhrayRTvRzkvt+9THmnkz1tZFJASbhilA7cGjOak\n77jv4CrHTz/Dc4sl9ugq2o9MqppVP2Crht45DqdT1qslxEAIgSsHD0okWZDN1KZaHs0qppWh855Z\nk3jy5ke4Xtd8jbW0zvGpfs3knm/mR3/w+/jk7/za6dnxrYdSSssv5eP91bZeK5gvslJKjymlvvln\n/+n/8puF1vvv+vf/XbVUiZMQOFCaSbvD6SQwJM8kDdRtzbIsoF+y7tbMnYO+Z5hMsnONXIw6D+uz\nBXtVASmyDpHF2cikMFyva6YFDMPIpCwYfMWyO2OxvkNdzOitpvBBSBQhB7Vvi0HawpWb2rJh5T0P\n0nwJ64UwbucC3Xng1nnPpLLsNAWv26kZXWDRe+bdK9F5fvaVYEv2SCkLbLZdaJ5ZItDsJu0+aU1M\nAZtUvp1NqfM/nyPEKqtp6iu845H3cHf+LLdOn2TRnxBCx3K9wijLpNzHmpK96RGzeo+6aLCmoC4b\nlNKMrkNrmy/4kbPlMcv1GXs7hzz23KOMvsc5IWJ1Yy8pJ5QcTg9pyil7kyMOZtfYnzTcng+sRy/P\nM0CI/iKqKaVtAYBLpCil0EZhvaawiSK7H5E0dbnDTrvHsj9HwOSUZ77CIK1sg8nQ4N7kgNdffxO9\n6zhbHdMvbzBaRUfCKM1eXXP77JSbIVK3E67tTLHJyZggeFy23SMXrrSRcyTpMDFC1OldwAcIwRFS\nYIwqh0tnFCUThmJS22ilSJ4jX9opbLpjUhKGbJ73bn6+0R8WRtJhJmVDIjHv1txZnIqTUN5warUx\nEyATzBTVBjkwBafLc5Zdx6SdMC1qVmfnTHf3OH76Ge59+HXozrOcz7NMJ1BWlVgyKrXJIZfbWpFC\nIuZsVB8Dylq00Ywxcc/OAWmx4KO37zB95HXEvqcxloOmYLVes/aJurB471ApsjuZMO966momOZW5\nu7RWcW3W4ENkNUaIxzx656PcYyyv29nBLhc8fn7OeN+7+Qc//D4+9tv/9vzs+NbDKaXFK/+J/spa\nrxXMz7JSSp9SSn3zT//0z35Qab33577zW9XNceRwOoV+oEkRGyM3VmsemM448ZHd2QFqvWKdAuOk\npe3W3FIWl2CIUoysttzuemH1KYUpLHdWK1xSPDit2K0brBO/0265IPieqt6lMDKzMTkRPW5wuNxX\nSu91AdeGmCjt509v+0LIO6vBsxo8N8862soyqy0PX5kQosC2i84xfA7C0Je+Xojd5m4z31YZ3k2Q\nUydzRmeUjsGFJDKVzDTduAKFnB7SG0PhAqXVTOp7eNN911F4zlbHGGPYa8WRKSIFaAt1JbYevtAQ\noyJEuYrvTu5jf3o/Rive+YZ7UEi6xo3TJ+mGBfcfvZFpu4eKGpdE/tKPkZVxzNfDlrixyTLcZIa+\n2BZIZ7anCRC0kUJrI2USZnZpDW97+E8xupGT5R2sKWjLlsPde7h3/wGOdq5SFuJ7Kx2VQSvNtZ0r\nPPbYCj9tMHXF03ef5KzrKGxBVZbcvzPFxhHvXR4T5OBnrbM3a8LFwBDEBjDFJN0gCaMSDiTxZ4wU\nRYGPkTEf25Ahgk2qRlIX58AmqHxr3p/xV3VpQ6UU1LakKRtmTYNSieV6xY3zY5kzG5GqGC2/F/L5\nIyi/PHYCeueIGkoix8ExbRsm7YxhsaQpCgpTMobAql9xWO+g14Wcn0rel6Is8vMHYsqa7WxaAPhx\nxNgCZTR9iOzu7OHOTvn08SnrK1d4w7TmidWKw52WQwWnIZCS4mS1YlbXzJRindEVY0pcPxBTZFoV\nTGrD6XLAWsvRTsETz36a3aLkkUlDGTzLruNRvcsv/cjf4/ff/8vz8+Nbj6SU5l/ih/Wrcr1WMD/H\nSil9Qin1zT/1kz/zfmLc/3e+4y+oZ8eew5iYGMNNN2CqgqgNpV9DrNjd3WcvTHni9JhljITRszOd\nsR5HlqNjJFCUFZ0baIqS0Tl0WbD0jseXgXF9TDKGZT/ijGjKWiWMPWMUKoieSkWFVqK+S9l79fIK\nMW0vMi/7ceFS8Tzvc7JBwf2HEwBWvWOZf/5yktDTpjrB8zrnpATZTSlees1K4K2NEkTAP4Fp88Uq\nRk3QAZ8SxiuMjlijsNmIWm4ryuIKSimWQyLhMnki6183F+fto176uilguUvRZnO75Nr+14gfbQys\nukCITiDiTE4qjGLVuQsDhkuPeRHTlC49VnZHylmNXids1IRksjZUNgaFnfGut3w7o++oixprbO7c\ndGZRqg3Sn2VLa4IbeMtb/zwxjjz6zEdQCJvaKsVhW1OqiMqdXVZnoDXZpSfk1BHRMoYUJdA5W1UN\nLuEj+ODQymRZjt5KZIRhu5lPXpxMm6Km8w83ZC/pkDV1UdJUNU3VEGJgPXTcPLuL86PA93lDsCHH\nCfQqek2fUjY8UFt3n8JodBCDi6N2SjMklsfHXN3dx5+dcvf4LrODHZI1xKoScwUjMpVNOIDOf3/z\nKqSTleOgtVgV+pjY3d3FnZ/xqWef48Zkyp+87z5untzm6LBl1xqeOp+zjokuOKZti0pw4iPDOPLA\nfW8hRJld7k9KfEjcPu8pS8P+tOCPn/1NUvJ808Eh3XpFjImP2wN+6ad+nt/99V85O797+5GU0qsi\neeSLWa8VzM+zUkofV0q966d++mc/kII//Ev/0V9W80IzGT1VSPTRM1/M2SlKpqakO79D3c5447X7\n+fitx7F5DnRvbVm3Ex4/Pcf5gDKKpRvwgxMLsBhY9EI3j9ExRMfB7r0c7N3P6FLWIKrt/EWrTXZk\n7hK3jFJ53hJofGn+9wquzgU6F7g97ymtZlpbDqcV9+23dGNg2TsWvX9ZodvLkUWbRJON+MNsyla6\nKGTiJ8oWFtRKEZSQsXSUC60xijHk2zozKTcXUqVRmwv2JvT6eaWSi6t4Xpv3ZqPz0xvIVN7MbQEn\nPyefdbYA08rSOS/EjU2B3H59kY2Qkl5bx4RG9JdbyNlrCmsobMR62RRoXTMExehDRhkyhKnk961W\nqDRw9swHqQ7eStV6lDJ87YNfDwr++M6jWGWYlg3juCJlA4cQMjknS2h89BhtGIIHlSh0IRpLEssx\nYWxFSoG9acvZfCXpLVx+vSof7gtij1LCWE7ZZi8hTlttWdPYkrIo6YaO3o+cdUvpdlMeYyi99YDW\nStAHo7MjkU2kEPFeuk2FQLkhRcakSQZmViLLFmHg4Xuvc+eJJ2nrFoqCnUIkI6ooqHZbGl2yOjtl\n8I7JdCKdpdKkHN8HogdNKLpuRRcCewf74Bx3Ts8Ydme88dq93Ll7k4OdHZxzPNstWATFkCJ1XWOV\nnO1FiKyCZ9ocoHXiYFpwZzFwtnI0peFo2nB8+gmmNlGrCq8Vqe95zBX8s3/88/zBb/7a/Pzu7YdT\nSucv9TP4alyvFcyXsDI8+66f/pmf+2Dn/NFf/K5vV31VcbWdMMaRhXGcuzVvGloWqzXrGGC54I1H\nD3Iy9gTvuHl6l+gDJkVWKeB7MeyOJqIpmLVH7E4P6IYls8kB03qfupoRQmL0YRs+vGH9Qb4YXyqI\nso+VD7qP8RVLLPlctnmjj5wsR06WI1qJi8i0LjicVcR00ZmuejFKeLn1nCklQt7JX+42UxJNJQqR\ngZAEuksCjUcV8UHlDi3lziKrXbMb0EbTcplMdUGzev7adpj5vrlGZg3n5V9WeZaYCUlRNkejF+H5\npkzE3KVcRJldft/VtvvSSi7CEsAsMVjeaFwIWG+w5sLcYZv1qC7mn5uvXou03x6+k7qdMYYohDOt\ncHFkYiuuTyYEt8QlMWsISpHUxnHJCwMU0QHGKCSeIQWMUdRai1AkRUpjWHdzEmYrsbjYDl26kY//\n5h1o6pa2rKjLihAD/Thwup4zeLdtRrdIhFKSeKM2cKtsQJXZ2EwmvBMyGFyMMpKC0hoKAzNd0gfH\nehyoreGTzz5FudPgUezu7/PczeeY7kyEBesTnV/R9R22KvEhoJWmWy9ppm1GRBKjE0JQHwN7+3vU\ntmRxeozWmtdfu44icHhwyKrvWIaR3ieS0mhjmJQVyXuCl5zepmjZn7SgNU8er+jGQGEVO03JpDZ8\n7PRxrk3ksa1zPDqf849+6lf4ow+9//j0zs3XvTaz/PzrtYL5ElcmAn3TP/+5f/FB1/dX/9rf/A51\nYiI7XnPP7ApnYeBGGDhsalYh8tzynGXfcf+Va+iqYe/KVT5y4wapKGCEtz/0jfzRs7/H6DveeP1r\neeO9b5eEiM2sKkRcEElDn3PtYtoGSF26GG+uJOl5dMkQJbLolVgvtcjFxJYYBFDnFPfdpuD6XoPz\nEs68GjzrwW81pV/o83ixLjpxQQbadgtBCtYWFFObQxa3LGGdcgEJbN1ylL7cyT+fnXu5Yn7ODNDt\nv+ffZ1P/Ym6HEzlRJXecl6HYFzs+KlfmbRFQoklV+ZF8VOgQJV4syOxWZyatfBUYVSQXaitHMSr/\nTjGRWDMSxipcdMy7OdN6Qu89tsybCyBsHldByiEB3qsM88oTTAmCB11XtLbnZHDYosAoK3ITYwgx\nZq3xZpMiOZ9NUVEVJU1Zo5WiGwfWQ8/Jck5M4XkbF+ko1VZCogBlVCZACURPtii0lmxVl7Cm3Jpd\nJESG4oJHK8VaI0SlYWS0hknTMikKYkpUtmJnMsW7gI8Baw3dck5A0bQNKfvplpMJyXu6YaSoK2xR\ncLZYcuXoKic3b+NN4nS1Ru3M2KlrTuZnmLLktht49mxJ20zxKjEtSgoUnZPrQ1PXHM4OGGPBjdMl\n3RgwWjGpCprSMo5Llufn1C7y1qsH3Do95e//+D/n6ccem5/eufnIa2zYl7Zes8b7ApdS6vp0Ov21\nP/0t73rkW//jv2q+6aEHhb2qRPcUhpG+75kdHPDEySlGK+4/OGCMhlvrJbeGNat+4Fve8m2ECKu+\no612cCHhvMTxxEsMTpd9UL2PjJnNGUISA/V4wYQTMkgmuQCkxJuuz/j4c18es/sXS0fZFNBJZWlK\nw5gLaDcG1qPHhy/+3Hxh+glcgkjJhA516f+33+czvv+if4fMdNzsUS7tWzYX+ee/2ov7Pr8lzUBy\n3uxIeDHcf9jy5J3V82aW8Fkg9vxct3PMXPyVTtuOU2u2s1Sj1XbWKbe1+I7m4GmjJf3FGoU1msIY\nSivhwiEM/MHTH2RiQAcxZ2isIgQHSh4jhMTKOZkaJ3kuUuzVtgZWpWXsezqvUMYSUz6XgaigMgWF\nLajLiqqoMFqxdgPDODK4QYrqZc1EYkuk2bzmy0F4m6+buaG8v/ECKteJmBzBa5SyqAxxJ5BYNoKk\n4aCIKbFf1kyDdKjKe0iKu2dntEf77E/2uXPrGeq6YqeswXvunJ6i20aiuqwVX9mk0KPn9t0T7vRr\nDiYtahhJkwl7uzM671gZuLvuccrk6DGLIVJYRTd4rCnZbaaMzlFW9xPsFRbdCAmqUjOtSnYnJZ9+\n8rcx/oxrk4bz83Pe94P/Ayd3jp+68+yTb04p9Z95Ur22Xmy91mF+gSubG3zj+z/wW7/y1I0b36C/\n/z/V3/bw61kMK2Z1w6mKlFTMV3PuaSqMsZyu19w6PWf36BpXygrLOZ947qO8+d53Yo1hPfpcLBPe\nx+0sS7rNuPV+DFkiILDRBXng+eDcBXMzRNFtvlyayS8lweTFfqd3AjveXUrKfF0amtKw0xRc260B\nWI+BbpAiegFRfmHP9YIkkwslGY5LQDY72Ax/tcrfz62kvjQ/e+FreeEretHjsgEELj2PF3++F/Nd\nk8Tn1fuQf/bisO/F4146A/JsW7IVs+duhoPl+wmfNDpIJ2uNxsSINqJwtZm1ucH+lUqSHynNNWfd\nXVJyHNQzGB2qgOD91kyiGwe6EEnJCnEGIb7oGLYAiFYw+AGPeNtGoCpqiqKgsCVVWRJiZBgHOu84\n65aMwW9hdJut8C6e6MV8elMsc3u4jTgzRoSYRoutnrWa4GXTpLWYtKNM7hbz6ZA7ZnTKXSd4A6Uy\n2KQ4HTt2p1NM0rTJsF+XxJRN5puaaAzKFhjvOCgtq/mCsxC5ds81ZqrADyueu3GLZ4Dx8JBQWK7U\nnipG+hR5op+DqamNxkWxXJxY8MB87TiY7dMWFbfPTmmbe9HVNeYrseKzGozWlIXBDefouOSR3Skn\nxyd879/+u1BNb9x59snXvebg84Wt1wrmF7E2NnpPP/7UL/z49/3ot77tJ/+pdWfPcf9hwQxDaEqW\n0XHzfE4wBVpHotX0/ZxTH7i6c8h66LO4OtGP4aJgZpeVjcG6aAZzWHFI2Vc259pdKpqfsZTCB4Fl\nfXx5PxOvRJZmYmPTF7ibjUVKq2lL6T5325LSakYft4W2GwODD5+TifvCIr/pLjblSScyk1baw7jR\ntG5Q7hfpGNVneUClLlW2TMS6+OFn3Hv7wjd3vfxuxiSIxebYfMYbfenvbed1bN6bXKdTyl1wDkje\ndpwCeUqNltBHpS+KdkqKpC5Jl5IU3JPFbX738Q8yrUrO1omm1GjnWY8ejMoRZwqVCkBno++4DXeW\n4quI3lDalklVUhYWo/9f9t4z2rL0Lu/8vWmHc84NdSt3lrrVrYDUSG5JIAkFkAAzBuEACGzGBi9n\na2aNbWzGeMyAF3iNPdgf8DLBGJugsQDbIBCSECBAIAkhoYTUOVR1d6VbN5+w0xvmw7v3Obeqq7or\n3KouSefpVX3vPWHnc579T89jKOqK2lmG5ZjV4Wa0X2t33Iu24a0NT32bnpbn37iImPqVbbrZtR23\nXUq2I0YhBE1rnh2Vf+LYkQ8Bo2Nzl1RtXdnHzEBuNKJxNER7rq2mYl+/Ty41RktkiCn/3EFaW86c\nXWdw5CATW5NYi5AKJQUHkhQxGTNiggueheV93NJUPHT2LPXRIygfOLO1STrQLPUGNNZR2EA/S8kF\nrBUVUmW84PBBysry6KmnSM0KK/1b2R5XNC4KuQspSbViITMcP/YF7lpZZuPJJ/n7/+hfkS6s/Onx\nz3/y1V/uQupXgjlhXiFCCKUQ4lvCifDTf+Pb/+Jf/zc/+YtqUg25xVnyDNyoQBM4M9yBPENKSdV4\nlpXk9NYaQQgau8YgXWFcNdQupl6dC9G+yPu2btWOFHRp2hCNiwMwbbZj18+2UBQILWEKeKZhyRXh\nentjNi6wXTRTxxUhINOKLFFkRrF8Hol2ZrlV6wW4G9NBdMS5xNStz88G1WO2tiVOziW96bxr91qY\n1TBhmgTsaou7U4azjYHo9Ny+5rwIUrRZBOd2v2H3C9hFxrsqo10dEzElzngz0HVQx9Ss7t4mwXni\neJJXUbC7G1sSHoWMIzvtF3A/67PU28/GaJWFLKdHFB4onEAGQVW5qbh4II615CYnSwxaqTjCIhRl\n3VA7R9lUbI+HFK6ZRsW2Jfm2ytjWVeMx99ORjO7/8Ux0lXoZusMZpjOO0borhoeNCyitCd6hjWzn\nNT3gW3cbsI1HSY1WIir+qGioYAJs+yhtWbmG3GgaZykRKBnNwZ2tGeQZ4+EQUdeo2jKWDpnlKOti\nzVTKaLTd1KT9BcabG6QusJz3GDvPRmJYvvUoizrFVQ2nRcHyQkZSe846z4GFFXppzsn1s5zc2aKX\nrnDrza9mbVhQWQchOpKkStFLAo888jvcspDz4P0P8H3/5F+F2+99/UOf/b1fv485rgjzGuZVQggh\n8jz/4YWFhX/8H9713lywwV0Lnq2NdapexrGyIU8TxmUJ2lA2FcV4QiUCS/k+3viStzGpLCc3JwyL\nSJzOxe7C6bC63z2H13VURpuvQEzbEmZuJt284qHFjNo6Nsb1Je/PtYge9xrnyPkBqYkEmpp4V52a\nqE1a7yLQyraei+7iaWVxDtnt+rt7/jwSnPXLnkN3zMp1bXgWzm++CuyeKWTWfQTEkY7bD/Z57Mxo\n9vpdzV0h7KqHimcSp2gVyaWQ023vRA2ieIZEqTiaoURcn5JRYadrCNJakipNlkjSRJNrxer2kzxw\n8nMUzYhcJ6xkOU3dkGc9bAgMegO0kKQmIVEaJWFUFHgceMv2eIIHtNJoCWXdtC4sMeXraY2/xSza\nzpQhuKivCmJKpl1YLtpjoURsYJoe6XYm0/mo4KNU7HT13kGIN5PGaKR06NaRRktFIiW1s6SJoW5q\nEqVJkIyaigpHog09NN47tE5YMRkHdMb22lkcYHRKg4sNU1pTj8dk2jAe7WASQ5r1GBcTRqnhYNbH\nlxVFWfDp0YhseR8v2b9MVjU0RcWpUJDkOdY2NCLj4NI+JmXBE6tnWS8n9M0Stx59DcMiMKnjzLOW\ngl5qOLSYs3byo9y1L+dDH/sk//qH/j3lZPx3mrr66Wdc+HNcMuYR5lWiTWv8X0mSnPzb3/b1P/bD\n//6n88Nf9RoeP/UxDmvLwUTRS1PKAE+PRtHjzyiwlto2NE5S28DBhZREKc7sFDR0jT9MI83QRpaw\nixgFrVu7mJLk7kRt7fwlqf1cYJ9uWNI8/wYvMKuF7oaSovXAVKRaspAbEh0jjqYd1amtb5utZg1V\noUvDMiPODl2KsOMoL8IumuIc8uyymbuqyudmZS8wy9k979vMggv+vEzuLJ0wG0PtosswjX1DW5/1\nhOkYS0s3KBFVh2UbmcVaV8xBx8srtPrHIHTcKEVsAMoSw91H7mZjdJqAJ9GaleW8rRl6Gm9pnGVn\nXFA0FXiPlipym4tbkKcJiRKE0JCYqL6EgERprHNAK53XEqCgVftpD1CUu+2k66K8nBCx7tt1tlrn\nopi5AKU0RgnqusahYrOT7Eqc8WZUKkVPC4SIYy2DdlxDK0OqDK6Opt0qRHGHitauLQSMC5wdrZOl\nCYlUkPbJlEfUDaPtLfI0pZyMSbIMbWJxdOgdPulFP0xj+PzpU5j9+9mXJWRlg7ANG7ZA9hMCkqy/\nwiGT8NTWGU7uFAybmiMrL+TQ8stY25kwqVwU8BcCoxX9JHD68d/l5uU+v/TrH+Snf/w/h8lo+JYQ\nwh8wx1VhTph7hLquf0IIcfr7/+H3vOtf/Nh/yt/w5rfimi3OPPmnVG5EWtYcXhiw1lSMRxWNswQ/\nwfuYjtqZOCSBW/blrI9q1obVrgYfpjJsbelyamf0bAmCqnEM0vSy9uNGJcoL4dmI3fnApHZM6nOJ\nVAgwSpJoSaJiU8QgjXUoo2I3Z2cp1bjup8d5aNp5WNcVNgNTrdMr34nZL13s2v0Mu5++CDqS7HRV\nYx01zurFxXYRdfvqdtslAqMgkycQ+hBK9pEKkrZRJDOKPNXkRpHqSDRPrW9wevs0ta0pbU1tG5b6\nC9y0uIRqSnBxPChoTSIV1gUypahbEfbcJKRaYZsyGkL7ChkUSWKwdTOV0pOe6NEZonNJgKnEnA8B\nhZhGzyG0J0GCFrHbN2vnTlOT4LwlBB+NtEUkSak8Md+dkBuNlpJcRvnAIBWZUOR5D+EshMCZULKS\nDTi5tU6+0CcNiiVl0CJ2yC70F3DOMh4NuenQzYxWT+DqEiWgqUqUMWhjIAh2JmOaPOH2hRX0ZMLO\neERponn5QCq8ramahtpoFvJFcp2xc+YMj7qaVR/Ymoy4786vpXZ9VrcmjBuLc1GkRBvJUi9hfOoj\n9FPFL/zS+/n1//5r1FX19XOy3BvMU7J7DCHE6/I8f99f+u6/vfjd//D7RS+RrJ/8FFvNkGCLKBem\nFCeGQw4v3sxLb3slgqx1AampGkc/1UgpWN0p2Jk0sygTphFQd9p2y6R1jSPdGdUS7jg44JHT13Ye\n+dm6Z6+ms/ZK13k1ULKVw9MSoyRaCoyOOr67h/67VHnXvdz96x7v/Dc7MQIXdp2faXr9PC4bb1cA\nACAASURBVEKMwVZMyR4a8OiZC5+3buZStM0tgjg/qdq5yjgi0o2NxPSrbjtiTbtfiY6jIiF4QpBt\ns1mMT5HRTDlpyRJRszle5fHVz7M93iG0OrFpmtIzKctpRiIFi6lkXNZIbWhs1IVNtGA4KqgRKK2R\nwTNIFcG3X/RSI0IgSTTjqmZ7XCFNQqokhXW4tuNICtUqOIX2RklOZ2yllMiu2cn7SJbaAB6lBEr4\n6ViLUgHTahVYJ1DBs9zrsxCiIXiiDYkQ4GPzzHhSMtHxRuNsM+Fwf5GbZM5wewuTJdOaZmISDJK8\nt8jO2knKosAYg1AKow3exZT0WQ0HDhzgoJdUxYTtouRxW1PrlMQoXjpYZNRUZEvLFGXBcDxkdVSw\n1jSkyYDb9r8MxDKbo5JxbXE+3iQlWrKvl7GUTFh76o/5mZ/4/3j0wUeb1RNPfkUI/uE9/ZB8GWMe\nYe4xQggfFUK84tfe9TO/e+aJ+2/93h/8j+mBQ6/ElKdZO/4J1o1mH57epGJDrZGoFBcEWaIoraKy\njrPD2EG73EtYygxndgrGldtVn4TdcU1HkudHJE07OiDFhYfer2Ifp78/XxHptVpvR3zPJSIv20i1\nk9BTUk6H/7UUJK3B8VQQoFUOikQ3q7+2FECM/uKylYTDS/nM97NF957puW6jxc65ZOpg4md1bhcC\njSWm+YObGQoHEDLeEIhuJnNX9J0lGqWjYsPjqw/yyMnPARKtFcpojDas5H3KpkEIWMkMyjtMmtA3\nSZwzFIqdqqA0Di0SvA8kJtZGjZAQPIjA0DZ4C2VRkhmDC56y8QQlZw1R7bHwPmrWduMiUghk6MTi\nYz2/nxmqsm47hBWBVqxBuTaq1ozLgn7Wx3rPpKloiPZgi0kfV4xR2lBUFcJolHecKcbkgx6HRIYd\n7+CtZXNjzNLBFXp5n2a4g8x6NGVJU9eYNI0p11Yo4uz6Nmd0YGF5hQMqZbyzgU5TFrI+clRB8BzI\nF2j6C2irGe9sszkZc7K2jJ3l0NKt3H7wFWyMG7aGBZPa03QyiFKQGc1Cz3D8C3/Mj/+bH+fk8cc/\nVtXNd4Tgn9qTD8YcwDzCvGYQQvQWFhbevf/Awa9/5//7i+kLb78NJSZ89sHfo1aefWaJm47exe0H\nX0TRNFjnKeqoijMqaoq2USU3kpVeSmEdZ7eL+EUCs7RsCK36zwy7z+kLDg44vVVQNJc2WnKtIrdn\nW9/1WNf13q8Oz6ZGNH0N59ZLtRTccfCZEebuiFSIWf/u7jpq97dsW2S7bl4p22F+EabCBl0EGoXh\n43p3E2aeaAapQUrLY2f+jK3xOtuTTRCQJym9JKPxFusdA6UZJJqlNCETAh1in2sT4ghLGRy1h1Rr\ncqnAeSZNQ+kCShlc09DrpYzLCUGkCBH1Z20AoeLYhgseJRVyVw1WCEiUoGrHRPLEYFRgNCkxxqB0\nvIHx1qGUYsEk9NrmoaKpp524eZKwJBOUi/szqRvGBJyEtWLMYn/AXSJjY3UVkyb4xhLyhIXFRXom\nY7y1QZL2EUpRTnYQ7bHFe7bLklOhQQxy7lk8iB+PgIBCMikKCitwK8s4F7C+oGwa1ocjVhtLjeDg\n4CZedPQ+zu4UbI8rxo3D2tiJbJQkM3HsavXxP+NH/8nfQSj13vXTJ94edg/3zrEnuDbaaXMQQpgM\nh8O3nzl96kd/5G9/c/Unf/JxNseae1/8v/DWe/8S2cISN+9/IZO6obYe6+JdcqYlmdExhSYFo9Ly\nxNqQSWW5Zf+AI8sZRokuJokO7s+CsnFkibou+3yluB43bTdybbarTXf/usad3Y/5cG72IOzOy0+X\n0v4momfkrhefq4qz6x0u+DZdHKauJp25dqdjLEXCi276CvKkTyCmQDsXllQZcpUwsg1rVcPTo5pT\nZWC1adgOlh3XUAaoG99GgI7tumQSXNtVKjES9i/0cbbCe+ilEq1rcA2pkmRSkQhIhcIQXVKigAEM\nEkWKJTeKRAaMDBRVQZJoUgOJ8PSUZDEx7DcJy0LTF5qeFxwwGYdMzn6dsU/ECNnWFoUgEZJ9Wc7q\n5gaLgz5HZcbxY8dZOHCAQZZQVCW9vIdWmtHWZmwiMrF7tlNOik4qnsfHQyZpwoFsgHaO4CxGarwP\nLCwd4PDBQzxy/Em8HXPyxEnOlpYz1rE5GfO6F72VFxx+FavbE7ZGFaPa0lg/c1lpSwWf+vAH+MF3\n/g3KsvyBtVNPf/OcLK8N5inZa4i2g/aHX/va1z7+wAd/4T+Nn/yz9J6v+04xKlNuP/RKKuuwLlA2\n8UOAaDscZWz9tzIqlHjv2ZrUbI5r9vUNdxwYsF3UnB1VOOtnaboLEE9RO/LLIMznIwJ7dgWcvYsM\ndy/jekacl3tDcCFyuxi6rQ+hmx2ddd+6drSi8wn1tIbbXR2wTW16PN5LhPC4ILE+oFygdq61PXNk\nJuOeo19JolPWhqcJwWKto3QNeZaxqPsUTUWNw9oKBSSNxKCRwqNUgvEgpCN4wah2SBlvDDMp2BoO\nMcYwyA0BR3CCXpowrh39VFE4R2I0i2nKcDIGqcmUZEELhDSMbUMDOGcJIdZeBY5EKPoqIXEBHdQs\nPR5m5uNSSlTweGuRIuAaS9U0nNja4OCBA9xMws7WJgduvQmpNTSy1b+F0DRR+9d7tEloqjJ27UpN\nsJaHz66h9i9yeHGJZTTD4QidZkjTQ2gYupI/evBhXnjLLTy8tU3oDVgfbQOab3zVt1HVirVhwfak\nZlI7rPPTrIxEgHN8/DfexcaxL9Q3Hzn4+oceeuiTl3WxzXFZmBPmdcDHP/7xX3zLW97yiL//U+/b\n2t7e99Jv+B6xXKQs9jIyExtIovN85+cYpg4ZSoBTEm9jzLA2rFkf1+zrJbzw4ICtSc36sLqodVbZ\nOPb1k3Meu95jI8+1vucizW4Z3Wv3Atcz5XzemrlUMrzU5Yu2C6hr/4q8GffPt923chqhzvZb+lZF\nR0bFqaiUE7AEpHBIy1RdByA1fV5266vZGa/z8KnPszE6Qy+LQgWFa6YWWo2tGdkGKRWLaU4uFMJ5\nmiCogVHRYNKUDEnhAqvDMVkv56YsR4eaqm5IhaFoLHmSYYRHKMNylpLgUVmPnojpXe8tUhjWbcVC\nZgjeE4SkcZ5+kpBKhXQeI3UUQAixm9argPTQuAYhTFQFch7vPJPgOV2OOXjgADeli5w+8STL+/dF\nFxwR2B6OyPMsKgR5wago6A96CKUJPvp6SgIndnbYWlzgJQsr9BpP6Sp20pw8z1gb7fDEzg4T79lI\nDcX2Fr0k5ezOFmVd821f/TfYGFes7xRsTirKOjYz+VaAQwpBU4751Pt+lsnOZjHa2XrRQw89dGLP\nLqw5Loh5DfM64jWvec3hgwcP/s7KgUP3vPAt32mWDt1KL9WkOqa4orC6p/O1LK2jblVrXKcv62LN\nsrsz3tdP2NdL2JpUrA+rZyjcCODuo4s8cnpn2vjzfNXzng0Xuw7PJ9Nrvc03wgyqloIXHLq87ubz\nt3mmdxuP3czXMz4e65mtoEEnvt56giolMVKQKElqJInWU/H1xKhpB/FOscmJjccZjlepmzL6SmqD\n8w6EINE6Wm0Rl52rhERG4XWJoG6aqYCA94FDvZxENAxHY3S+gPYOL2Ina56k9EQUFTAq9ssqBDt1\nybZrUEqRymhQLYRESIF3jn06o6lqFvM8Kv7IKKAeWnHY2jZkOsHZGus9NWAD9HoDFoXk+LFjrBw6\ngOhS0YB3FhBIrXFNjTYGKSR5f5nJzgajyZjCw47R3Lq8hLAO0pzTjWW7qtgY7jDxjkCg9hZnHcPR\nCGU0Qmi+9uXfQtko1oclO5Oaos1AuXYOWwjB8MxxHv29/8b21uafpUrc9773ve/S1UnmuGLMCfM6\n42Uve5m56aabfnJpaemvHrnvm9KbXvIaUq0wKqZifTtC0jV1NM5TNw7fWj25VgQ6Wn21KSUB+wcp\ny/2EYdGwPqqod3V53n6gz9qwYlzZq9r2G5Foz8fVEt6VkPNeE/peEObsiV1NQDDtvO2afkT7mJQy\npiaJriVKRceSRLejKFqRtK4lRs/GUhKt2Byf5f6nP812sY53gVRr9g8WwEdxgzoEkAoZPNo1FGWN\nkIp9Cz2EihXbnk44KBNqZ9ksJ2w3gbv2LTGuShaSFAFkUkbt11YPVogYGTfOUQRLUAq8RwmFd47E\nGIQPJFKiid6gWZri6tiV6pwDbVAEyrpCmhRjNM56MueZjIbU1pH3MpCC1CQUkwkohdaRtKVU2Kah\nv7CMs5aNjbM8Wkw4dOgIt/T6mCTDec+DG2fYqC0FgqIsSPKMsqoQMkaoznkW8318zYu/kfVxwfqw\nYrtoKGtLbd1MFjAETnz69zhz/x+yuXb2H//hH/7hv7vS62yOy8ecMJ8nvOlNb/r2paWl/3Ln3S9O\nF1/zDpWkZtqCTjsi0I0RWB8jzzg0z9Svz9OKhTMjzpVByr5+EkXMhxVF4ziwkCKFYHXn6lx8vlgI\nE658G68FYV7uMq+EMC+27N0PReH12ROdj6U8Z65TTmXylJRoEe2tjIqk2c1wTn+2xCmFZ1Ru8tiZ\nBzm1/TRG6Tj3KTUreR8tAmVRMGy9G/taE5xluWdIpGSfTqmrktP1BCsVrvEcHAzoO4/ShlwIEqUQ\n3rVZ52hAIGQ0my6sRZuE2rUp0TbVLAN450k7MQOtsXXZfn4EaZJibYMQCqRAeU9TVjhrSbSiaRps\nCPR6fWxdI2UnfSimerXBe/pLBzi9dobHixG33Xw7RweLOGs5vrnOyWKMQ1CHKDKRq9jXvDoZkec5\n61ubvOauN7HUP8LWuGZjVLJT1BT1LMMEUBcjHv/9X2J05omtJ44d/977P/+5X72sC2SOq8acMJ9H\nfPVXf/XtBw4ceO/C4vI9N33tXzcLywdbn8LOBb7DbM7O+TiL1o0YdOnZc7+UYbmXsH+Q0jjPuLIM\nMsOxszeuR+zzTcbPRxPQxdZ1pYR5sWXufmzqE9kOgZ4babZp2nZ+VEmFkrR6s7L1xpToNiWbtkpJ\n02jTKIKv+czxP+HE5lMQAkv9AQu9PqGsKb0jTRMSpUgFGKm5rZ+Tec+krijw9NIU6aCqaza9Z0kK\nam/ppRkLCLQAGQTB+aguKIhdwDLumSN2+kbtWElZFEgRyd05R5AgpG6l7ogOBs5R24bEpChgtLND\nkkRdQOcsqtW7lVIRgNpZkiRBSoFtHEYrRH8fj2yuc/Phm5A41nc2eGpnTL+fo4QjBIcIkoGWrBUl\np8YVXimqquaVd3wVy4s3szWq2BxV7BTxRrdqYlOgD7Bz4jGe/Oh/Z2dz8zNrqydf/9nPfnZy2RfH\nHFeNOWE+z3jzm9+slFL/bnFx8e+98CvfYHov/boYCYgLt4d0n/HOeskxI8vzz6UAFnPDyiDltgN9\nPv/UJpvjek9FDPYKNwphXo9teN4IM8y0WOPjtKTZEecsfSvb+UzdmU1LgZJgpIw1TiVIdOxG7RSB\nkla3Fxx/8OD7GRcjEp2QGoMIgeXBAo219LQiU4GBExxMM1IZoz0hJcIHTk+GWClYqyrSNGU5NZgA\niZAkKBaTFFcWhLZWGoAg4jxnE6LofBQ28NR13aoexW5zLwRpmoILyLYz1lo7FW4PwdE0DUqZaNCO\nRysTMz6t2bb3vnV2cUit2fSKQ0dv47H102xVE8q6ohGSlywv01RjekpDgJ2qZN1btouG45sb3HLw\nNu65+RX0s/1sjys2x1XbDWupatdKMnpOfvp3WLv/I/702c2f/MTHPvwPLvuimGPPMCfMGwT33Xff\nNx09evS/7b/tnoWbXv8OobSefpm1DsfQNmoIwtQv0YeLE2aHEAJ3HV5Aq5i+2h7XbIyrqXPH84Hn\ngyCfb1K+VOw1YZ7/vKBr+pndkgk5awTqyDP6KopdakZRem93ijbRcXB+SphGkRnN0+uP8vCJ+9FG\nUvuKQdZjIKOKz0IqKScT+mmPOwcDVF1FdSIhGNYlq64hpAmNteSJIQEyEV1DFkyKamqCbaYqQB6P\nUArnA8aksbmHQPBRzEBEv7JodaZUtAHzjqos0dq0ad6AdTZq1WqDJEaqolVoCsxS1t0yVW+BNQcr\nec7ZYsiJrQ2CNOQC7mj1d5UUVHXNcDjm6URSScOxs2fJ0wX+/J97O7WF7aJmsyXMSWmnY2ZNMeT4\nH/4KW2eerk899cRXfvrTn37gsi+IOfYU87GSGwSf/OQn3/fSl770nslk8hui2HjFwde9IzELB+MM\n3bSzf6aJGb/0ACFmbg4XgRCCtVFsuz+9XbCvl/CCgwtMasvmuL5gM9CNTC7PtU2X2nF7OTg/Ar3S\niPRaH9dnZhm6Lp/dz3eOJe1TAoIX+K6WSasqJOPoRXxjS64yDnSKdvTECbBCoqRHeYlznkY27Osf\nIjGPslNsMchzMiShaTBGY20gTQyJjjd8xnnqpkabhEwZjiQpm1VJlhj6wjC2FaW05CbHSIltm4gE\nAZyL9fvQEr630cKLTkYw/l8KiQgebENjI5GmaYqr66jvXDfIxGC0RorYYRvwGJNgrY3rUAqd9jBp\nhpCSYTFhY32N+sgteG+5vddjUUjq8ZAkyRHe4WrPeDRmu68ZVw1D6+KojwvUjaW0ccxmOKkjWdaW\nxnlGJx/h2Ef+JyefOv5bjz/+2F9ePXNmfE0umDkuC3PCvIFw//33nxZCvPbee+/9wVvPnv3+O1/x\nWpO//M+LrvLUoTOMinqgnZP8zO3iQhiVlsOLGc4Hzg4r1kYVS3nCocUMKQVb45rtST1tMLjWRHml\ny+9mDsWuI3K+aMOFBAqudr0X3I7rhPPXdaHU+8XO+wWPSzuz6bqRk1ZmrhMPkjKAiOpCgtaoXPpo\nGdLKH0g81kuk9yjvkU6gpG99NxWJ6fOGF389nz/+CU5tH6dSBhMC0lkIEqM1GZqiqsiTBFlX0VJL\nSHaKMSJVcdTKNngC+9MevSCpq6pV0CGK4PvY/BOcR2uNt65tdOrGadpf2xlGAK0N3gVc01DVFdok\nJP08VndDiDZjIaBNTMsKIckXFtBpjneWYjJkdTTk9KTmhbfewnIA62zc3mKCkRJXlrHnIEmpBTy9\nMabqZ4zGI/Ikpaon1M6zM7FsTSp2yoaibmjqipN/+gGOfeaP3GOPPvoPjj3x+E9d6XUzx95jTpg3\nGFpJqx88fPjw/xiNRv/zztUnb1t69V82ydKh9gXnS6TFn9O0Ubiw2ZTzgaJ2LGSGnaIhBNia1GxN\najKjWO4nvPDwApPKsjWpGZXnRp1XEhnt9Uzj7locAhQxOnIhRkwX2u9L0XK91HVfmWLPudtxJet9\n1m0J7Paefs7tecby2juvroGmS/eL6X9t1BnaFCUxPm2QCBeQeATR2aOeEnI7wmIUtx68i43hKqNi\nwkLep6c0RkEiAWdpVJzdDC0ZSyXYlxgaqVAYVusRC4M+OYrgG4QEpEQHAY3FNk3s+A0B3/7eHRPZ\n1TcJU81YJaMISGe7ZdIMpRXe+XgjEQJCxRtUk+akWR8foG4KytEmUggeO3uWMwTeeNfdqLrG2zpG\nut6RKEVZ11RGUycaO5xwUgjGRuGqijxNqauapd5+RlVgfViyNakpKsv204/y5B//Ok8de+z+z3/u\nM28ejUZnL+9qmeNaY17DvIEhhND33nvvD992223/6AWv+Kqk9xXf0MlpPzsuQpqLuWG5l/Dk+oWz\nO1K0r+mnGNVGnUVzzkznpeJaNNFckDy61Jt4dlK81OduBBuyrob56JlZV/P5xH81n9vd2zJNXHbN\nP8ipxN60axaBaI2mYyNQK3AgBYkS7bhJrF/uns9MjSTVku3RWR548lOEUNMzKSp4VvKU/XkaCdQ5\nQlNNxThigOgJHrbLkt7CIpnSNMUYFwJZkuCdwxhDU5QE7wixvbfNQIhpBzDQupbE/QghYK2NKVrR\nPtcdiwBISdpbIElyynJC4yq8s0ghKK3j2M4OtZDctn8/Pe9Z2XeIycZZcDUiBMaTCVYpNgiUxnBm\nY5tCa8qmjt22zpEnS7zktjdxYnPM+qikLApOfPIDPPbpP3JPHnv8ex9++OGfv+KTO8c1xTzCvIER\nQrDAPz98+PC7Nzc3f/VVwzM35/d+c0pv5UKvvehyuohkWDYcWc7RSmAv0PDjA2xNGrYmDamWLPcS\nbj/Qp3GB7UnNTtFECbVLwHWTnusiyxv0vu+yIvLuPRd7PnRGYHuHQJhmJ2KkGZBIxK5Mhhdt6dIH\nkIKm7dAWARovozQObrrtglgm0EHiAywP9hOcp2oqyrJkcbDI0Af6LrA12uDo8gq5yPCuIQTfjnBI\nhAgs5TnCNVGwXClsUTJpGhZ6PapxnKyQQkx1cn3wSKnOuymIx862aj1Kq6l8oAJ8iNGkSXOc90gC\nq2dPkKUpgUCqDcfPnuW0Nuzft8AhqRgIyHs9gq3BN4gQKCYTglKc8JZ1JxiVE0oRELaJ22Y9trHc\necfreHJtyOakZvvEIzzy4V/l+KMP/NHnP/+Fd0wm47m83Q2MeYT5RQIhhL7zzjv/5Z133vnPXvvG\nrzPh7reJrsv1cs7hkaVZHXP3e3dHMOenH/upZrmfMMgMRW3ZnjQMy4brfenslYLPjQ4jBHccnnXJ\nnr/Xe7Un55CKEFMZPURrBTbtmt01oyloHTKil6ZqjQISJTFGthJ6mjxRZEYhqHnosY+yM9mMYgEi\nLv/Q8gq3LCywIj2ZENA0UYe2qhBGtZ26EkLAO4fSGlc3saYYooF0zCa30ldCtB2uepoS7qLM0NZs\nd++vkgqtU4zJkErRVCVVMcIHCyo2//i6om4anh4X1P2cI70+aQikQKhrBksHaYohRTmmdp6RFDxZ\n1JRCUVjLpJrgOzk7FEdWXsDK0otZ3SkZjic8+YkP8JkPv6957LFjP3TqxJM/skendY5riHmE+UWC\nNtr8l0mS/NLa2tqvvvJVj91xy9e8w1Tp0mUR1/qo5gUHB6yNqqkE3wXWtYs0BePKMqkdQhQsZDGt\ne3Q5Z1RZhkXDqGyuy2znjdy5u5c4vyb53If22dp+nmU9u2qaIcSxDtmuMIhZc9l0G4KYblscZ4qR\nnQ9Rccr70NqQzYQ18mTAwmA/k2qEkoKyqRn0erxgcZFbUkMz3okqVrUlTTTDrU0Gy8soqUDAZDzE\nJClKKpIsx5YlhEBjG4IQKGUQUhC8m5pKTw9Je6sxjdyFxCQZJsmQUuOaiqoY4pqaznA7CEGuE+rJ\nCO89nzp9hrtf9CL6TU1PaepijJQKqaOH53Y5xirNsbqgCQlWaUZlSVmXUebSBpQ03Peyb2BrIji9\nVbB54jE+/Vv/jYce+PwjX/jcZ94QQli97JM3x/OCeYT5RQghhD506NAP3HXXXd//+rd9S7rw8reJ\ncc1F2l6eiVtWeoyrOFJypVBSMMg0i7mhl2jGLXkOL5E8L5v8LpCvvJT37uX1fSm1zr0g9Uufw9zd\nJ3zlOD99KVpFA9GNmbQRZhQ0mAkbqNazVako1N7VL7NEk2nVmlArlPA88NjHWd14mjRN+HO3385+\n31BsbZEkBggkKqEYb9NYS5rlaKUZT0YobVBKkyQJzjWxI5a2o1cIgveRMFvyFzIOywgpI7lrRZrk\n6CRDKY2zNXVV4JoaQkD4lihbQQZCwFvPTlWyTWB5YZElIQmuQYjYFeyaBtNbZGxrTlcjtnxgVHsa\nLyirEucdVdPQS/ocPvACDq3cw5mtCTtbmzz00ffxkQ++xz7y8IPfPRqN3n1VJ26O6455hPlFiDba\n/CEhxM+vrq7+7L33fuKrXva135aJI/dQVY7natFZH1bcvNJja1w/5zjCxb74nQ9sTxq2J81UUWgh\nNxxZzpnUllFpGZbNBWul3XI7Sb+LjU2c0xU7fXL3r9fnZu9ymoKub/R7eft/sY7hcyLN7rWE2dxS\naJmTLqoMsRs7tHOO06jSE4JsI83osNN4idaKl7/odTz21OeYTE6zbDRuexRFB5zH2gbTT3HOk/f6\nSKCajAjOgxJopXDWtsYEHWEGXPBobWZk2YkyaI1KMkyWo7ShaSrqcoy19fQWwwePimoNuCBIkwTv\nHdvjCSMpWNm/n9ubOgq903pnOkdRlph+n5D3eHJjnR0XmFSQpxnVZIJ1jrqqWciXue/l38jZYcmp\njR0e/8xH+P33/CJPPPLQz584ceJ/DyFsXckZn+P5xTzC/BKA1vqb7rnnnv/yxjd/7crtX/PteiwH\n1Bfxx+xwy0qPSW3ZGO2tK5AU0M8MC5lmkJo4hF02jCpLUbtnvP5CxPyshLkb14mbrqdsHlyd0s/V\nYCaZN+sclULOokw5q2WqLsJUYqozm+gol5cYRaYVqdGxnmkkRsGp1QfpMeJgaKBpcMWEACRJirM1\nWmtc0xC8o7EWpCLLciASnLUWqVSsC7ZRLsRoUusElWQkaQ4CmqaisRXO1rN9a/crtDXQLgWrlEIE\nj3UeIVXsnrUWKRVKRN/QsixxUlBJiesvsNNYEtdwcjShsOBcjCoX+vs5tHI7ee8oW6OSU8cf4SPv\nfRef+cTHzzz68APfEEL47HU9qXPsKeYR5pcArLXvE0LcfurUqX/+wo9/7Pu+7lv/anrkK94o1uq2\nG/a81soArG4X3HFwga091pb1gZiaLRqgIDeKQaY5spRjlGRUNYwry7i0WH/hOc1nPPY8lyyf75rp\nlc7AXu57Zm/uSoCijTK7Jy60rOjd2uZI43p3lRFluxjnaqp6m1sziZ9MEMJQO49CUBYT8izFlgWN\nc5gkwYdAamK6VgDONmhtqJsGpTVKaaROyPoDlElwTUNdTSgmW1jbQCtpNx0z6Y5Fe1xcCFHAwNbg\nYkQsQoijJUFiTIIMnqaqmDiHTxLWfY3XikMmZXtzi1O+wQdFCmxWNUIYvuKu17MxbFg9u8qffPB/\n8Du/+Wv+8Ycf+J66rn8hzKOTL3rMI8wvMQgh7jh69Oh/evGLX/I1b/tr/1uaHX0RjHRk1gAAGZtJ\nREFUq1vlbPyCGX8eWcoQQnBqq7gu26aVYJAa+qmmn2qs90wqy6iyTCp7Q4rCR+z1MMdF1tJ+Fo2S\nVx1hXglhTiPMVvlnSjiAEFFXVQkx7ZRVKtYwY4Qpop5sG1nmiSRPDL1UY6RnY/0hlu0I0UzIlKIp\nSrxtEHhEaFV62o5SKeU5adbQpoR1kmHSHJWkICS2KXFNjbU1eE/odF/lLoJ8xv7Hx4KI+xK8mzYo\nhTZq1QhcU+NCwEnJJARc20R0877DnD1xkpFo2B6OmeiErXFJUVnuvvsNJMkSn/3jP+C//+yP8+Tx\nJ359c33tfw0hbF/xiZzjhsKcML9EIYT4xqNHj/7sG9781kNv/tbvVqPeUTaG9Tl1PyngzsMLnNiY\nMLlAuvR8XI3M3IW+wDMj6aUxdZsnispGAp3UMX17qTOf1x67O46uvCO1w3M1DO0FYV4JdqdkoZXM\n6zinbfxR7aiGUjENq6TAyJiONa17SZ5q+qkhN4LJ8ElcfYa+8yxlJhKs99TjCa4du1BaI4WMtUxj\nsNahjUaZDKkTTJIhpMDWNdY1+Kaa1kzbLafb0HCeaEHc+GiXJ9rxEtq0cvDxRsi3jyupUB6srQkC\nnFSEAMoYUgQSSUDx2FPHOLy4yIbzPDWpqELCPS96PU88/Ajv/ukfCw/cf//m6aePvTmE8GfX8/zN\nce0xJ8wvYQgh0iRJ/tGBAwf+xTu+8zvNnW/6DrMTMrYnzVRXc5BpDi/lPLE6fM4Ib3qttKmtvUwR\nCiBPFHkSo888UTTOU9SOSW2ZVNHu6IsVl6J12z3+vNcwO0k8uTvSDFPbOdl2lHb1S9NGlx1ZDjJD\nz3jC+AlkvY1wDUpKekbT7AwRtsZ1pKg1QkSRAqkMQickaU4QEu8amqrEuwZvm2m0KKWEjvB2t/LC\neYQppuTYocsVeCK5dsINRqhYRxWKyjnKpiHNM7RQBNvgAiSmjx1uMhpu43t9nqohWTiKq3N++ed+\nij/67fdura+v/5/B+5+ap1+/NDEnzC8DCCH29Xq9H1haWn7n27/ju80rvv4dYrtRU+I8vJRhlOTp\njQt70l5J08ulEsSzEWmqYwSaJ4p+qhEIiiZGn2XjKOuZG/0NgSvI3D4fhHkxXdzzU7LIrgYZDaUF\n0RNTtiSplMRIRaIFqYk3OwuZoZ9J1OhR/GQdX1YsDDK0UlE9qJxgy5qk10ObBG1SpFIE73G2pqmi\nNmsI0SB6d7MOYjY+Al0adVe9tfu7jSqFPHf/piWJNhIVgEaCd+A9dVUjUoNDoJUhlGXs1DWa5X0H\n2V49SVOVlDphZPaherfy8z/zU/zWe37JVo392dHW+jtDCHvbRTfHDYU5YX4ZQQhx02Aw+NdLS0vf\n8S3f83+kX/nGb2SjCOyMK249OGBUNqy1CkC7camEef6IyCV1wF7kvReCloIsUeTtl3OWKEIIMwJt\nXeqvtc9nO6V4biNV4Bzh+9C98ApwLQjzUmqau1Oy08iy1ZGVgli/lCKq/KgoZJ6oqBfbpWEXc0OY\nHCcpzoBr6OU9FrMerpxgrSPv9bDOEnzAe4v3FltXKCFiZ2w77xk3pCPDGfMFOtWe864rdqVbu+5Z\nMXvtOaYFQiHxtHMtBB9i9kJrtNE0kxItwBiDqyp6gyW8c+wMtxmLFL98F+9617t5z7t/jvFo56eL\n8egfhxBmwr9zfMliTphfhhBC3L24uPjvFpeW3vZdf/MfJHe94e0MK89irjm7U7E1+eK5STZKxAF5\nE6XYUiORQlA1jsr6KYlW1u9ZTXT3F3UrVT6rDYdzq5y7a8bPdlOwm9D2mjCf64bnfIm8zkwuRneh\nrVu2oyRKtanYaCKdKEWWKBZzw1I/pa8taudxvK0QUpEERzPcQkvQSlGXBUEElDHnrNd3TT50xy+0\nSkNdajX2y8op+QUEUQ6vS8E6AkLKNsXLtPjaGUpPm5hCmDYYKSGxtDVMpfGNRQsIdQ3eYvJFKh/1\nczdcxgc+9Al+7j/+mHfID22vr35XCGHuKPJlhDlhfhlDCHHf4uLijx8+fPgVf/57/2nvlV/1Rga9\nlCdWd1gbPr+keTXWYFJA2hGolqQmOmkA1NZTW9f+9NO/n4tLL3tb2vTs7s/X80WYz4XzbwC6CE+0\n9T8pu2OqyYya1ppzo+hlikFq2mOsKVcfoNk5i7MOsPSyDCNB2ZoQHNY5lFbTKHB3Grub/uwSrrtL\n5t1NSAgQvEeqKJDQbbvDI7RGShndOkP0v9ydCTjnxiBAcB4HaGOQ3uOtoy4KtIpbotIF8pVDPHH6\nFL/9sUf5mR//MarJ6L2j0fCdIYRje34i5rjhMSfMORBCvHVpaenf5r3+S7/rb/7D5Gu/9btYGzU8\ncXZE0Tx39+xeYs88NHdf1u3ioh2VbO2n5C4rqijk3VhP4zzWehoXaHz8u3Ee58IzBLwvdX+e67Hz\nl/l8EKYUYJQikVF8QHciBEpEmy6jYwazVWcixPdorcmMpJ9I/Poj1JtP0e/3MErQy1NCU4JzSNlG\ndiEglZyu9xnYdWimzwYXR0tadg3BTaNHH0AYFVV7AHxUAOrmMKcdP11/UDtraa1DaYOzFUoo6skY\nYwxSSoo60Nt3CKtz/suv/Ab/+Sf+o9cmuX9r/ew7QghfuCYnYY4vCswJc44phBBvWFpa+pF+v//q\nv/PPfjS/58+9jq3SszGq2C7OdSfZa3Po3cttt2XPl/1sUIDRcVTCdP90bG4xWkbjYR+wzuMCWB9T\nvM4HrIs/oxzc7Kfz/oLC+NeaMKXo9F5Fa5osZrqvMjbr7P7bKNWKCwScC9h22123HyHgfbTsMlqi\nRWzyyZIYXS70EnpUFE9+kkwJlvYtYuuC4XhEv5ejtaKxDYNen2Y8mnamnr/v3TXVRbhaKCbjMYGA\nMSZK4xHVh3y7DKkkwVl8EJ2VypScA20kuWu5wQeC9yTaUBZjnIc0iUSZ5APGZTTH/pUP/gE/8R9+\nkuCaXxmNRj8YQnjgik7GHF9SmBPmHM+AEOLelZWVH77rrru+6Rve/lfU4Vd8ncgGC2yPazbG9Z6O\nd1wr4r34Cnf9fpmrjTOH7aC+llNrK6VllFATM3Fy1f4O4H0bmRE9JWNqMaYcfZilHrt6p5KCm/f1\nzjH63m21Nf2dWdq0M3fuOmCjg0hcn/PhHIKPfwec8y05xtfKrn4pZlFnt2zV2nlp1Vl4KXqJoZ9p\nFvKUtFzFDJ8EX1PXJYM0YbSzhcl7rOxbYbKzifMepVSM/tpUqWgrlVJIpJS4pon+ls4xHo3J+z1U\ne334thYJgtDOT3a1yq5BqTuvs6armfiCBIJ1UyMTW1vy3gCVZNQ28OiJk3zqC0/yy7/8br+6vv3R\n9TMnvjOE8PTlXSVzfCljLo03xzPQ6l2+XQhxV1EUP3L7xz78rQdf9JXJXfe9lRccvYmycWxOakZF\n86wj/LujxYs1njwXWe55xHmRxTx7Y0xMB3ZkI/CIuiUVKUgNNBasA98ekel2QxvpdV2nMz3WuK7Y\nTLMbWgqK2jEu7XRZ0y5Q2jreVPy8JcYws9e65ENxgWafKZsQZy/FdLtFu78SiZh2zHbRazNcJwme\nNE+AhspbaqG56eAhzj79BASJNBolJcKDajVhZdQUoCnL6FziHOOyJksNi4uLhODwzuGcRbRp18a6\nKKyOj00/IYCU7bgIMdIkRqLTs9dYnPd45zGJwaQ5+SCLQgTHjvPLH/wDnjp+ikceeuDfHjt27N+E\nENYu/UjO8eWCeYQ5x3PizjvvvPWOO+74V4PB4B0m78mjr3m7uedl95Inmp2iZmvSUJ5X67zYvOXu\nxy6ESxlNuZLXPheuVGC9I0HYTWhX95k6PyV7Kft1RVJ4XUwmZ3OOkln6Uk6znK1hdPsvVYLMaPJU\n00s1/SzB+JJm82ko1vHlNoN+j62tLY4cOUhdFi0Z2mmnq20s2hi0lHHEpIlNQT4EEmMQgLUNzsWb\nBilVG41LlNaRGLvREhlr0F1IrKSC4FEBvLVMJgVpmiG1Ic8HICVVMeJ9H/4En/zCEwy3Nqud7e1/\n++CDD/4/a2tr8/GQOS6KOWHOccl41atedeD06dN/azgcvrO/sHTgtX/hr5nXvu3tHNi/nxACW5Oa\n7UkzHd+4UgK7nmnaa1sz7T5bl7fsK6lhXols4fl1RDElTLmrDtqNk8iYjm7nLqNQgSFPJFliSLUk\nUQKtFTvHv4AsTpEmEoFnsDjgzKkzHDpyhLou25qhIvGByXAHIQRNU5JogxAS5yx1XUUXEamQSuGc\nQ5kk7mcMy6eRZNdxG0KIYy9CQOMpJkMwKYuLK1GgvW44u3qW//zu/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 8))\n", + "m = Basemap(projection='ortho', resolution=None,\n", + " lat_0=50, lon_0=0)\n", + "draw_map(m);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conic projections\n", + "\n", + "A Conic projection projects the map onto a single cone, which is then unrolled.\n", + "This can lead to very good local properties, but regions far from the focus point of the cone may become very distorted.\n", + "One example of this is the Lambert Conformal Conic projection (``projection='lcc'``), which we saw earlier in the map of North America.\n", + "It projects the map onto a cone arranged in such a way that two standard parallels (specified in Basemap by ``lat_1`` and ``lat_2``) have well-represented distances, with scale decreasing between them and increasing outside of them.\n", + "Other useful conic projections are the equidistant conic projection (``projection='eqdc'``) and the Albers equal-area projection (``projection='aea'``).\n", + "Conic projections, like perspective projections, tend to be good choices for representing small to medium patches of the globe." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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beWX5xdNv306SGPJi8sZAM1n5IqgmssOQ24oSMokMmuyZyb095Je3qv1GuxG3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SgRQdXK9SNbG8QS3DVIY4mkwZh1gA1ZZeJbdzqRgjjoxzqGbBTaSqSgalacnT8lkSDjhEy0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\nIvStRSmDt67rMU6VJgmBoTExIfJB9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+tDoGTD2sSfXX1jrWBrkHK2fA04lnDRqoexOgLQ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5NnvP/4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\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Ka7duoHyPMs8xuuLmzZs8e/SA4aDPcrFk2OtT5yl+4COkQ5nKKmdvp0OUdDk/PeHWzesI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\nTdtkPdNs55RXaDYZ0XWeyWSK7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DvH0A2JCmQheiV7id4KlsDAC415vmiMGgKo7hG3W0mN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\nUl5c8nBySCcIjJCafUuO2RpDNAfork8OTkDHRBxJketolPOWG5lze8iCV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OD5gw/+iAcn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 8))\n", + "m = Basemap(projection='lcc', resolution=None,\n", + " lon_0=0, lat_0=50, lat_1=45, lat_2=55,\n", + " width=1.6E7, height=1.2E7)\n", + "draw_map(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other projections\n", + "\n", + "If you're going to do much with map-based visualizations, I encourage you to read up on other available projections, along with their properties, advantages, and disadvantages.\n", + "Most likely, they are available in the [Basemap package](http://matplotlib.org/basemap/users/mapsetup.html).\n", + "If you dig deep enough into this topic, you'll find an incredible subculture of geo-viz geeks who will be ready to argue fervently in support of their favorite projection for any given application! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drawing a Map Background\n", + "\n", + "Earlier we saw the ``bluemarble()`` and ``shadedrelief()`` methods for projecting global images on the map, as well as the ``drawparallels()`` and ``drawmeridians()`` methods for drawing lines of constant latitude and longitude.\n", + "The Basemap package contains a range of useful functions for drawing borders of physical features like continents, oceans, lakes, and rivers, as well as political boundaries such as countries and US states and counties.\n", + "The following are some of the available drawing functions that you may wish to explore using IPython's help features:\n", + "\n", + "- **Physical boundaries and bodies of water**\n", + " - ``drawcoastlines()``: Draw continental coast lines\n", + " - ``drawlsmask()``: Draw a mask between the land and sea, for use with projecting images on one or the other\n", + " - ``drawmapboundary()``: Draw the map boundary, including the fill color for oceans.\n", + " - ``drawrivers()``: Draw rivers on the map\n", + " - ``fillcontinents()``: Fill the continents with a given color; optionally fill lakes with another color\n", + "\n", + "- **Political boundaries**\n", + " - ``drawcountries()``: Draw country boundaries\n", + " - ``drawstates()``: Draw US state boundaries\n", + " - ``drawcounties()``: Draw US county boundaries\n", + "\n", + "- **Map features**\n", + " - ``drawgreatcircle()``: Draw a great circle between two points\n", + " - ``drawparallels()``: Draw lines of constant latitude\n", + " - ``drawmeridians()``: Draw lines of constant longitude\n", + " - ``drawmapscale()``: Draw a linear scale on the map\n", + "\n", + "- **Whole-globe images**\n", + " - ``bluemarble()``: Project NASA's blue marble image onto the map\n", + " - ``shadedrelief()``: Project a shaded relief image onto the map\n", + " - ``etopo()``: Draw an etopo relief image onto the map\n", + " - ``warpimage()``: Project a user-provided image onto the map\n", + "\n", + "For the boundary-based features, you must set the desired resolution when creating a Basemap image.\n", + "The ``resolution`` argument of the ``Basemap`` class sets the level of detail in boundaries, either ``'c'`` (crude), ``'l'`` (low), ``'i'`` (intermediate), ``'h'`` (high), ``'f'`` (full), or ``None`` if no boundaries will be used.\n", + "This choice is important: setting high-resolution boundaries on a global map, for example, can be *very* slow.\n", + "\n", + "Here's an example of drawing land/sea boundaries, and the effect of the resolution parameter.\n", + "We'll create both a low- and high-resolution map of Scotland's beautiful Isle of Skye.\n", + "It's located at 57.3°N, 6.2°W, and a map of 90,000 × 120,000 kilometers shows it well:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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N23Yho11pnkakoc/4mWr9Pd2pSV12bPj13dPMWbKy4/hVClSsS2XHnnQZOZkX\nr98mSbuiWBXixdfnDNWL2zJ70iilowhJTJIkWrTrzK5TN7jg/5oSTTpy8fotpWMJgvAfJ4/sx3vN\nElrUKs2De3d+ev/KxfM0qlKUTZ5T8Zo6gkGdmjJpRD8FkgqJERwUSJ2yduRKL5E7gw55M+mS39KA\notnS0cOxAY8fPvjlfk7d+uDi6saoSbOJjI7h8fOXasvYuHZVblz15c2rX7dhmCYNzv2GcuSyPwZZ\n8lCkYTuGTfPgU1DwX7UrilXhjw7t2U5vp8Z4uA5m27rlnD99XOlIghpkzpIVz3W7cHYZR4PuLgyd\nOo/PX74oHUsQhG+ioyJJk8aQOhVK4ORQhTPHDgEQGxvLUo8Z9HZqxMIxgzi2ej6n1nlyav0i9u3c\nQoT4d6zxZFlm9EBnapctRqz/JeSHvsT6XyLmwUVenN1L+IfX3Lt1Pc5jSJJEuUrVOHbeV2050xga\n0rRODXZ5r4tzOxNTM4aOncaeM7d4/lmXPLVaMG3x6kT/ThHFqhCn9cs9Ge/SkwPL5uDUpD4rp7ky\nrJcTHz8EKB1NUANJkmjUoi17z97m7rtwijR04sa9X1/NC4KQvOo1bskg12ms3LqHldNcGdqrHfXL\n56dmiZwc3b6WS9tW0KhW1e/bW2fJTPGC+Th6YLeCqYX42LJ2GU/9bjBrZP+fBkqZm5qgr6/PGq+5\neEwfz7GDe3j7+tUPj/vDw8JwnzyagIAPHFZjsQrQqWk9dmxYGa/uBpZZrZg8dykb9p3lxO1n5KnV\ngiNnLya4Td3EBBVSPlmWmeM2mv1b13Fmoxe5clgDUKdKeRzr12Rk304sWr87Va45nxpkzGRB3cYt\nOX38EO8+fFQ6jiAIfD0vH967jT7tW9GgeiXuHtjEq7df+6/a5bRBT+/HX+lBIaG8eP0GlUqlRFwh\nDp/Dw/kQ8A4Ly6w8e/yQmeOHc2r9ItIYGv5y+/WzxnP28jUu3/Zjs+cRrty6Q6bMWdh+/Ap6enpM\nHtmfwOd+VC1oh13OHGrNXqlUMb6Eh3Dn5jUKFiker31y5c3HgjXbOXvyKI7dWrNg3FBa1a8V7zbF\ncqvCT6Kjo3Ed5Iz/TV/2L5mNRcb0P7wfGRlF+VbdcGjXgw7dRX+olCYmJgb3yf+w13st2xdMo0Sh\nfEpHShSx3KqQ0nz8EED5/FnwP7adHFZZ4tw2NjYWhx4uZLAtyLgZC5MpoRBfk0b2Z9uGlYR//oJK\npWKu6xDAi1C0AAAgAElEQVS6t2ka7/1lWaZWp/48fx+ImZk5WbPl4LX/bfYvcye9makak3/l6r6I\nl9FpGe02N8H73rl5DefW9XDt3Yne7Vp8f10styrEW3hYGL3aNSLoxQNOrl34U6EKoK+vx0b3icyf\nPo57t28okFJQl+CgQLq3acAdn2P4bluptYWqIKRE6TNkpPfgkXQaMemPK9Bt3nuYfcdOER4WyopF\nc+KcQUBIXrGxsRzY5c35Lcv5fPsML8/uTVChCl+7bE0b0gvnpvZULGiL363rfJZ12bT3sJpS/6hD\nk/rs9t5AVFRUgvctULgY6/eeYfqyjYz3WBqv7gSiWBW++xDwnvaNq2FtosvuxbNIZ5T2t9vmsc3O\nrJH9Gdi1FZ/Dw5MxpaAuD+7doXnNUhTJloHDKz3IlMFc6UiCIPxH36FjidQxZOKC5XFu16xODU5t\nXELNAlbcP3cA51Z1xblaQ1y/fBETo7Tkz22LSqVK9J3QUkUKMNS5PbNHDcSpQXVuXr9ChmS4qwpf\na4Bc2a04c/xQovbPYZuLTQfOs/nwWfqOn/nH6blENwABgOdPH9OleW1a16nK5CG94tUXVZZl2g0Z\nC6ZZmTRnSTKkFNTl0J7tjB7YjRnD+9G5hYPScZKE6AYgpESyLPP29Svsy+Tl6aldZDA3i9c+7YeO\n50OkLh6rtqKjI+5TKWnG+OGYRwbg5tI7SY+759hpKpYsirmpSZIe93c813uzz9efuSu2JPoYIcFB\n9HBsSI4MRiydPQ2TvMVENwDh127fuEqbehUY1KE5bi694z1oSpIkFo0fhs+Jg+zfmfgfVkE5sbGx\nzJniyuQRfdi/zD3FFKqCkFItnT+TqsVskCQJU+N08dpHkiSWTR5F8LtnzBg/XCzuorBsNrnwufHz\nPLl/q2GNyslWqAK0rl+bk8cOEhwUmOhjGJuYsmLrYYJiDWjStc9vtxPFaip35tghujSvzXzXQfRt\n3yrB+5sYp2PjnImMc+nFy+dP1ZBQUJeQ4GB6OzXm8ol9+G5fSekiBZWOJAjCHxQpUYa8uXLy7PRu\ndHXjP6GPgYE+OxdOx/fEfppUK87BPdv/+OhVUI/mbTtz/8lzLlzT7sVX0puZUqtiOVZ5efzVSmkG\nhobMW7kVi+y5f7uNKFZTsZ2b1+LSsx3bFkyled2aiT5OmaKFcOnajiHOjkRHRydhQkFdHj3wo0Xt\n0uTKkIbjqxeQOWMGpSMJghAPpcpV4l3ARwKDf16f/U8ypjfDd/tKJvftwJIZrjSqXIQrF8+pIaUQ\nl1vXLhMVGUHObFZKR/lro3t1xOfQdsrmzUTPtg3ZsNKL1y9f/Hb7gHdvWeXlwdBe7Zk9eTTe61bw\n2P8+urq6THP//awVolhNhWRZZqnHDGZPGMax1fOpXDp+86TFZaizE8Z6MvNnjE+ChII6HT+4B8f6\nFRnaqSWeE4ajr6+ndCRBEOIpJiaGLFbWnLuSuJlYJEmiUa2qXN6+kiEdmjJ2cA/RLSAZRXz5woi+\nHZk/xiVFDGItUSgf5zcv4fHx7XS0L8fNEztwqFwYh8qFmTlhJL4+ZwkK/MTWDavo3KwWtcvkxd/n\nIHWL5CBj9HtuHNtGm3oVOH5ob5xdEMUAq1QmNjaWqaMHc+7oXg4un0O2rJZJduzX7wIo3rg9c5Z7\nU6ZClSQ7rpA0YmNj8Zw1mY0rFuLt4Ub5EkWUjqRWYoCVkNKEh4XRp30TMhjIbHCfiIGB/l8dT5Zl\nCtZzZNSMxVSoUiOJUgpxmTZ2KB/8r+M9f4rSUdQmOjoan2u32HP8DHtP+nD/4SPsq1TEyaE2DjWr\nkDbNjwsfXLh2i0Y9XRg+xo0h/br/8pwtitVUJCIiguG9O/DpxUN2LZ6hlo7Y+46fofvYGew6dQMz\n85/naBWUERoS8vX//avHbFswlayZMykdSe1EsSqkJEGBn3BuVY/CNpYsmTwyQf1V47Jk43Y2nrjM\nkk37k+R4wu9d871Ar3YNubln/S/nME+poqOj//jzeu/hE+p0GcCzFy/FbACpWUhwEN1a1kE3/AOH\nV81T24jB+tUr0dy+GqP6dxGPljTEk0f+tLIvS1YjiZPrPFNFoSoIKcn7t29o16AyVYrmYfnU0UlW\nqAI4NanHjSuXePTAL8mOKfzsf4//PVyHpKpCFYjXz2u+XDYc27Lmt++LYjUVePv6FY71K1HUJjNb\nPNwwNDBQa3vTh/bhzeP7bFixSK3tCH926ugB2tQtT/92jVnqNuqvHxsKgpC8nj99TJt6FWhbryqz\nRg6I99SC8ZXG0JCejk1Ztcg9SY8r/GiJxzQK5rSmZf1aSkfRWNZZft8tURSrKdzD+/doXbc87epV\nZcG4oahUKrW3aWCgz6a5E5njNhq/O9o9NYe2kmUZr7nTGNmnI1vnT6F3uxZJ/ktOEAT1un/3Nm0b\nVGJIp5aM7tNFbf+G+zi1YPe2jXz6+EEtxxfg+P5dDO7URpyHEynpniUIGufKxfP0bt+YaS69k32y\nd7ucNkwf3pdBXVux7dhlDNOkSdb2U7PwsDBG9uvMq0d3ubh1eZIOohMEIWmFBAfRvnF1oiK+fC9k\nJElCkiRevXzJvDGDcWpcT60ZLDNlpFGtKmxcuZheg0epta3U6Mvnz9z3u0vJQvmVjqK1RLGaQh3Z\nt5NR/buwZsZY6lWrqEiGzs0dOHT6IlNcBzN+pqciGVKb508f06tdI0rms2HzhkWkMTT8806CICgm\nTVojHj3wY+9Sd8xNTb739ZdlGXNTE3JYZUmWHEM6O1K322C69nVBX190F0pKt29cxS53rp9GwQvx\nJ7oBpEAbV3kxZrAz+5e5K1aowte7A4snjeDM4T0c2rNdsRypxdkTR2hpX5buzeqwevpYUagKghbQ\n1dWlWMnShIZ/pki+PBTNn5ei+fNSrIBdshWqAEXz5yVfzhzs274p2dpMLa5eOkf5YmKFwL8hitUU\nRJZl5k4dw1L3SZzZ4KURy2eaGqdjg/tExgzuzqsXz5WOkyLJsszyhbNx6eHIJveJDOzsKPpFCYIW\nKVKqAqd9rykdgzb1auBz+qjSMVKc65fOUaF4IaVjaDVRrKYQ0dHRjB7ozOl9Wzm/ZSm5bbIpHem7\ncsULM6Bja1x6tCUmJkbpOCnKl8+fGdrTiT3rl3LBeznVy5dSOpIgCPEQGhLCrImjaFq9OGuXeGBq\nbKx0JLJlycy7V79fKlNInGuXL1CuWGGlY2g1UaymAJ/Dw+nTvgkBT+5ycp2nRq7zPqJHB9IQiefs\nyUpHSTFevXhGm3oVMPjyiXObl2BjnVXpSIIgxEN0dDT9O7fgnZ8vHiN6E3DpMCN7dlQ6FlaWFrx5\n/VLpGCnK65cviPjyhZzZrZSOotVEsZoCDOrWmkyGMnu9ZmGczkjpOL+kUqlYO3Mc65Z64OtzVuk4\nWu/C2ZM0r1WaDg2qsd59gui4LwhaZMHMCRhEh7HBfSJVypRAX19P6UgAWGW24O3bN0rHSFGu+fpQ\nrkQR0TXrL4liNQWQ5VjqVymnMSe837GytGDp5FEM6e5IUOAnpeNoJVmWWe3lwYDOLVg7Ywwu3ZzE\nSVAQtEzA2zc0qVU5SVeiSgoZzE35/PkzXz5/VjpKinHN9xzlixZQOobWE8VqCmDv0BLvQyeVjhEv\nDjWr0KRmRVwHOYvlWBNIlmVG9e/C1pXz8dmylNqVyikdSRCERLDIYsXLt++VjvETSZLIkll0BUhK\n1y+dp1zRr4Or7j18wvTFq3j55p3CqbSPKFZTgJr1GnHs3EXCwrXjanjG8H48u3+LzWuWKh1Fq0RG\nROB/7w6GBgZ8CgpROo4gCIlkYWnFoxevlY7xS6WLFGTSiH7cuXmNHZvXMmZwDxpWLEj1Yjbs3rpB\n3GRIIKvsNsxeuZGW/f+hsmMPzj0KoFCDtgx2m8P7D+IJY3yJYjUFMDNPT7HipTh4+rzSUeLF0MCA\nTXMmMWvCCPz97iodR2sYGBqy6eB5WnYdSP3uQ3D+x42Aj4FKxxIEIYGq1qrHodM+3PTzVzrKT9bO\nGEuZ3Fno064hZ3aspqSVESsnD2PVlJEsc59Au4ZVuHvrutIxtcaU+auwyl+KPGVrcezqE2Z7rWf/\nuTsEYIydfUtGz/YkMFjcfPgTKa6rJEmS5IcfxVWUNli/3JPbp/awwX2C0lHizWvjduas28nWI5cw\nEBPYJ0hwUCDzpo5ht/d6xvbrSk/HZhrX/01pQQYWmFlnR5blVNOpV5yztcfGVV5sXT6PC97LtObf\nbkxMDF6btjN27lLqNGrB8PEzSWukmYN6tcGLZ0+YP30cxw7sZvmUf2hUq6rSkRQVoUqLoU2BX56z\nRbGaQrx785q65fPx9vwBDAy0Y6k8WZZp2W8U6azzMmbafKXjaCW/O7eYNKIPIR/esmCsC5VLF1c6\nksYQxaqgyWRZplOzWjQoW5ARPZSftiohPgYG0XbwGApVrEtvl9FKx9F6N65conub+qyYOpoG1Ssp\nHUcxcRWrohtACmFhmYW8dgU4dv6S0lHiTZIklkweyfH9Ozi6f5fScbSSXYFCrN55AmeX8TgOGYfj\noDGi874gaAFJkpg8dxkzlqzl3sMnSsdJkPRmpswY3o+1y+YTERGhdBytV6REaRat30On4RM5eEo7\nuvMlN1GspiC1HVrgffCE0jESxNzUhPWzxvPPwG68ff1K6ThaSZIkGjRtzQEfPzLkLkqRhu2YungV\nERGRSkcTBCEO1tlt6D9iAp2GT9S61f0K2+WmUN5c7N22UekoKUKxUmVZsGYn7YaM5eL1W0rH0Tii\nWE1B6jg0Z+eRk0RHRysdJUEqlipG33bNxXKsfymtkRGDR7ux5fBFjlx7SKEGbTlw8pzSsQRBiEO7\nrr3B0Ji5q7Sv6BvSqTUrPWeJGQKSSKlyFZk424vWA0aLQVf/IYrVFMQ6uw1ZrbNxxvea0lES7J/e\nnVFFhuE1d5rSUbSeTc7ceG3cy4gp8+k9cQ4OPVx49Eys9y0ImkhHRwc3jxVMXrgS/yfPlY6TIHWq\nlCf6cxg+Z04oHSXFqNOoOZXtHeg2yk1cBPyLKFZTmNoNW+B98LjSMRJMpVKxfvZ4Vi1y5+olH6Xj\npAjV7Ruw99wd8pe3p3Szzri6LyL88xelYwmC8B82OXPTa8hoOo+cTGxsrNJx4k1HR4fBnduwcuEs\npaOkKCMnzube87d4rt+qdBSNIYrVFKZuoxZsP3RSq054/2OdJTNeE0cw2LkNIcFBSsdJEQwMDOg5\naCS7Tt3g+stg8tVphff+I+KKXRA0TMceA/gcq2Lxxm1KR0mQ9k3rc83Xh8f+95WOkmIYGBoyd/kW\nxszx4vpd8fcKolhNcXLlzYeRsSmXbtxROkqiNLGvRv3KZRg9UCzHmpSyWFkzZ9kmpnquw3X+amp2\n7MedB4+UjiUIwjcqlYombTtz4bp2LZSSxtCQ7q2bsHKRu9JRUhTb3Hn5x20uLfuPIjQsXOk4ihPF\n6m+EBAezZ9tGrSyY7B2as1ULuwL8z+xRA3h05xpb161QOkqKU65SNXaeuk7lho5UbdeLQZPdCQoJ\nVTqWIPw1WZZZPHcaHjMmsHT+LNYt92TbxtW8eqE9/UDfv3mFdeaMSsdIsL7tW7Jn20YCP31UOkqK\n0riVE8XLV6PXuOlKR1GcKFZ/4/nTRwzo5siYwT20bnR9nUYt2HrwuFYW2vD1Sn3TnIlMHzeURw/8\nlI6T4ujq6tKxR3/2nb/Lm0gD8tm3YtXWPVrZdUQQ/keWZWZOHIX+x0d8fnKVZ5ePcnbXajo0rkZw\nkHYsS/z+9UusLTMpHSPBslhkpGGNymxa5aV0lBTHdep8Tly8js/Vm0pHUZQoVn/DNldedHR0eON/\nk55tHQgL1Z67TwUKFyMqFo1cdzq+CtnlZuKg7gzq1lpMOq0mGTJmYvK8ZXiu38OcjXso38qZyze1\n6xGkIPyPjo4OJiYmDHNuj/s/g1gyaSTbF0yjQZUyDOneVisuxt69eUlWC+0rVgGGdG7DmiXziIqK\nUjpKipLWyAh9fX1MjdMpHUVRolj9jTRp02KV1YoFY13ImSENbRtU0ppJ6yVJwt6hmVZ3BQDo6dic\nXFkzMmv8cKWjpGhFSpRmy6ELNO/cn/rdh+D8jxsBH7XjTpQg/JuJiSmBwT/eWJg9ciBfAt8yf8Z4\nhVLF39vX2lusFitgR16bbOzfsVnpKClKREQEL1++IFd2a6WjKEoUq3HImTsvD5+9YMnkUbSuXZGW\n9mW5f/e20rHipY5DS61bzeq/JEliudsoDu7azPFDe5WOk6Lp6OjQwqkLBy/4EWNqRf66rZm/ZrPW\ndYERUjcTU1M+BQX/8Jqeni5bPdzYssoL73UrNLp71Js3r7GytFA6RqIN6dxGLBKQxJ4+8ie7tRX6\n+npKR1GUKFbjYJs3P/cePkGSJEb36cLUIT3o0Lga504eVTraHxUvXY6AT4Hcf/xU6Sh/Jb2ZKetm\njWdU/y68e/Na6TgpnompGaOnzGP1zhNsOOJDiSYdOX3pqtKxBCFeTE3NCQz5eeUfy0wZ2bd0NmsW\nTKVz89oaOc2SLMuEhIQQpcUXiA2qVyIs6BO+PmeUjpJiPH/6iGxZMisdQ3GiWI1DzrwFuPv42ffv\nnRrXY/PcSQx2bsO2DasUTPZnOjo62DdoyrZD2t0VAKBKmRL0bNOEob2ctKLfWUpgV6AQq3eewNll\nPI5DxuE4aAwv37xTOpYgxMnE1Oy3y1QWzZ+XqztX06RiEVrVKcfJI/uTOV3cJEmi16CRdBoxUWvP\nczo6Ogzs1JoVC2cqHSXFKFqyLL43bhMSGqZ0FEWJYjUOtrntuPfo2Q+vVStXihNrFzJ/6mg8po/X\n6Mcd9g4t8D54UukYScK1Txdiwj6xbL44CSYXSZJo0LQ1B3z8yJC7KEUatmPq4lVEREQqHU0QfsnY\nzJxPQb9fU11XV5fBXdqxYupoZowbpnFFYdM2HTl76Sq+N/9/nuyzvtco6tCeGh36cksLBs12atYQ\n3/Nn2LDSi20bV3N4306lI2m1jJksKF2uItu0fAzK3xLFahxy5cmH38PHP71eIE9OfLYs5dTeLYzs\n11ljRz+WqViVx89e8uzVG6Wj/DVdXV3cBvXg0K4tSkdJddIaGTF4tBtbDl/kyLWHFGrQlgMnzykd\nSxB+YmJm/ts7q//mULMKaVQyh3Zr1mpRnrMm0b1NU8oULQTAXf/HNO0zgk79R3Hp+i1UKpXCCf/M\nKG0aPMa4cPvkLk5sXc7QXu01+qZOcoiJieHL58+J3r+pYxdW7tCsJwHJTRSrcbCwzMKXiAg+Bv68\n9KdlpoycXOdJ2JvHdGtVVyOXB9XT06NG3YbsOHRC6ShJIio6mrRGqXv6DiXZ5MyN18a9jJgyn94T\n5+DQw4VHz14oHUsQvjM2MedTPIpVSZIY378bHtPGatTdVbuCRVm36yDZKjcifclalGjcnmHjZ2Jp\nlQ3b7Nbkz22rdMR4cXSwZ/3s8bRvVIdyFasgSZKied69ec3kfwZy5+a1ZG/7/OnjNKlWnMqFrLnm\neyFRx6hRpyEPn7/GyWUcr96+T+KE2kEUq3GQJIlcufPg9+jXg5TSGaVlp+d0CmfLiGO9irx+qXm/\nuO0dWuB9KGV0BQgJC8conbHSMVK96vYN2HvuDvnL21O6WWdc3RcR/vmL0rEEgbDQICQpfr/WGlSv\nhJGBigO7vNWcKv7adOrBxv3nWL//HId9/bnyOJBmjh3Zu3U9jg1qKR0vwQ6du0T5qvaKZpBlmdZ1\nK3D99CE6Na3J3u2bkq3tqa5DGNnbiXE927Fy2mi6t6nP6WMHE3wcA0ND9py5hXH2AhRu0BY3zxV8\nSWXzj4ti9Q9y5snHvUdPfvu+rq4uC8YNpXPjWrSqU1aRK7e4VKpWm+t3/XgXoP3L4IWEhmNkLIpV\nTWBgYEDPQSPZdeoG118Gk69OK7z3H0n1j/sE5UR8+cK2Davo2Kx+vLaXJIkJ/boyf9o4jbm7qq+v\nT267/Fhly4F5+gwYGBgQHR3Nwd3baNOgttLxEkSWZY6cvUjFasrmliQJN4/lvHj/kTqVyzJjzGDm\nTh2jtvaeP33MtLFD2bV1A7IkYWOVlZb1a+FQswreHm4M6d4uUcdNZ2zM0LHT8D5yiZO3n1Kgbhvm\nr97EWd9rqWLJbFGs/oFtngLc+82d1f+RJImhzu1xH9GPzs1qcerogWRK92cGhoZUrVGHnUe0/+5q\ncGgoRsamSsdIFrIss3zhbPZu38xj//sa88v0v7JYWTNn2Sameq7Ddf5qanToy50Hj5SOJaRCu7zX\nUapwfvLa5oj3PvWqVcQkjR77NHgi+/OnjmGb3QrbbFZKR0mQh09fEBkdQ668+RTL8OrFczo1qcGJ\nQ7sZO30hh89ewnfbShbNmfZXfUh/Z8MKT5rVKIlR+GuObPRi96bVDO3W9vv7VcuWJDgkmIB3b4mM\nTNxA1Ry2ufBct5vxc5Zz2v89facsxKpCfXJUbYzrnJS73K0oVv8gZ5583H30PF7btmpQmx0LpzG8\nd3s2rV6i5mTxVzuFdAX42g3AROkYySIqKorJo4dwZNNiujavSbEcJrSuU45xQ3uzafVSbl67rFHL\n0JarVI2dp65TxaEtVdv1YtBk91RxtS9ojh0bV9KnbbME7SNJEgM6tGT/9g1qSvV3wkJDWTZ/Om3q\n11A6SoIdOXeBClVrKdZf9ckjf9o2qESN4nkxjQhgZP/OFC+YD4uM6cmUIQMfPyR9388je7czz3Uw\ns0cNYo/XTN747MehZpXv70uSRIWSxbAvm5fC1kbYWegxa+LIRLVVsVotpnisYOuxy1x7FoLnhn3M\nXbmBmJiYpPo4GkVX6QCa7k/dAP6rYqlinN6wmHpdB/Hy2WMG/TNZ8c7l1WrXZ9SArgQGh2Bmor2P\n0YPDwjHKqF13FxJLX18fQ0NDNsyeQNo0hnwKCubanftcu+vH5ZM7WbdoJg+fPMXWNicFChcnX5ES\nFChSgvyFimJqZq5IZl1dXTr26E/D5o7MnjCCfPatmOrSm/ZN66OjI66LBfV6//Y1eWyyJXg/a0sL\nAj9+UEOixLl1/Qp58hXkw/t3dGpWk0rFCtCrbXOlYyXYobOXqdi4vWLtd2pak+HdHOnj1BKACQN7\n8PnL1771mTKkJ+D9O7JaZ0/SNguXLMcd/ydxbnNinef3r5dt3sGBa3FvH5cbV33ZuWk1FavbU7Zi\nNTJntuT2g0cUyZcn0cfUVKJY/QObnLl59uIlUVHR6OnF768rr20OfLYsw6GHC0OePmbK/JUYGBio\nOenvGaVLR/lKVdlz7DROTeLXn0sThYR9JqON9hbbCZUuXTqCQ0NJm8YQc1MTqpcvRfXypb6//yUi\ngtv3H3H1jh9X717EY/tabt27j3n6DBQsUox8hf9XwBYji5V1sl00ZciYicnzlnHjyiXGD+vNwg3b\nWTjWhZKF8ydL+0LqFBQYiLlJwp+8mJuaEPhJM/r0x8bG0s6hKvkLFMa+UUsK2Fqzcpqr0rESxefq\nDXqOLfXnDdXEOnsOMpmbff8+bRpD0qYxBMAigzkfApJ+kZMiJcqwceGUeG+v0lEREhKMLMuJOj8f\n3OXN0xvneXTjAoO6tSFWBp+rN1NksSpud/yBgaEhlpZZePQ8YSP9M2Uw5/jaBeiEvqdL89oEBX5S\nU8L4qd1Q+7sCBIWGparZAExMTON8lG5oYEDJwvnp1roJC8YN5fzmJQRePcaRFe50si+HQeATtiya\nTrMaJSiTOyMdG1dn6pgh7PJez4N7d9T+uKhIidJsOXSB5p37U7/7EJz/cSPgY6Ba2xRSJ1mWCQwK\nwtw04cVqelMTAhU+P//PY//7ZDA3w750QaaOHUq6tGmVjpRoDjUqsXX9MsXad+zSlwXrt//yPaM0\nadQy3WSR4qXxvXEr3mMMqpUryavHfrRrWIXbNxK+rPWDO9fp37ENJ9ct5NW5fWycM5E6Vcon+Dja\nQBSr8ZArjx33Hj5J8H5pDA3Z4uFGuXzZaV23PC+eJfwYSaVmvUYcO3eRsPCk71SeXELCPmNsnDr6\nrAKkS2dMcAKX2FOpVOS1zUHrhvZMHdqXQyvm8O7CAW7tW8/Ijk3IYfCF09tX0qdtfYplN6ZFzVK4\nDurO+hWLuOZ7gc/h4Un6GXR0dGjh1IWDF/yIMbUif93WzF+zmWgtXv9c0DyhISEYGhjE++nXv6U3\nMyUwUDMuom5e86VU4QJMGtyTjXMnU79qOaUjJdrYft3YsmYZb169TPa2ZVlmjddcjNL8+onm1Tv3\nKFCoWJK1Fx0dzeY1S0mT1ggTY1P8n8ZvnIuNdVau7lxN5wZV6NK8Nj5nTiSoiA4IeE+nERMo07wL\nMbGxONSsQg6rLIn9GBpNFKvxYJs3/2/nWv0TlUqF+z+D6NPagdZ1y3Pjqm8Sp4sfM/P0FCtRmgOn\ntHfloZCw1HVnNZ2xCcEhSbMedBaLjNSvXol/endh6/wp+B/dyuvz+5k3ohflcpjif/4AEwZ1oXTu\nDNQta8dg5zZ4zZvO2RNH+Pgh4K/bNzE1Y/SUeazeeYINR3wo0aQjpy8l/E6CIPzKjauXMDdL3Ewh\naQwNkGVZLaPDE+rm1UuUKWQHQIt6tXBqXE/hRImXNXMm6latqMjsOJIk8fD+PZa5/fPTe6/evic4\nJCxJZyl4+sgf1yG9qFM2L7IkcfH67Xjvq1Kp6OHYjHaN6jCsdweK25pz6mj85mLdfNCHw5f8sSlQ\nnNHuixIbXyuIPqvxkDNPAe6c3ftXxxjQqQ05slrSrWUdpnisoGa9RkmULv6+LhCwh+Z1ayZ720kh\ntS0KYGxiSlCo+kbUmxino1KpYlQq9f93GCIjo7j78DFXb/tx9e41vPZv4cYdP9IZG1Og8P/3gy1Q\nuBjW2W0S3M/KrkAhVu88wb4dm3EcMoTKJYowc0Q/rCwtkvrjCalAVFQUnrMns27pfLwmjkjUMSRJ\nwms3k8QAACAASURBVNzUlMBPH7FMo+wAznevnlM9d1FFMySlTOamhIYEK9J2rjx5uXXfnywWGX94\n/ezl65QoUy5J+/AHBX6ieOGCzBnZn/6T5/DibcL7w3Zt2YjShfPz+l0Am1YuonyVGujp6f12e3+/\nu2S1zk6GjJkYPmEWdcvlo2sLB4oXTHwRLssyV27dw//pcx6/eEWB3LY41FR+BTIQxWq85Mxjx641\nnn/e8A+a2Fcji0VGmvRy5vXL5zh165ME6eKvdoOmzJw4goiISAwM9JO17aQQEpq6itWkvLMaX/r6\nehTNn5ei+fPS6dtrsbGxPHnx6utArjv32b1yLpPu+BEaHk7BQkXIV6QE+QuXoGCR4uTKmz/OEyx8\nLQ4aNG1NdfuGLHKfTJGG7Rjq7MSgTo5a+XMpKGdAlxbEBAdwbdeav7rgMTM1ISjwE5ZZlS1WK1Sv\nw97jO+jQrKGiOZKKsVEawkKVKVZbODnTw3Ucpzcs/uFn47TvdUqUqxLHngkXFPQJcxNjKpQsiu+2\nFYk6RmG73BS2y83HwCC2HBxMOTsLqtasw6DRU8iW48dldo/s28kgZ0eKFC/F0s0HME+fgeHjZ1K5\nTXeKFSpAhWIF6du+JdmzWv6yrSWbdrDvlA91K5bGvnI5bLNZERIaRpeRk7l4y4+ChYuRNYcty2d5\nsXDDdoZ2aYuVpQVZLDJiks5IkeJVFKvxkDNPPvwePkr0iL1/K1usEGc3eVGv2yBePHvEsHEzkm1a\nn0yZLbHLV5Cj5y5Sv3qlZGkzKYWEhpIuNfVZNTXTiLlKdXR0yJndmpzZrX+4K/8u4CPX7t7n2h0/\nfA9sYqn7RJ6/eEnuvHYUKFyCfIWLf59Oyyhdup+Om9bIiMGj3WjWtgtuowawrEFbPFwHU7dqheT8\neIIWe/X8KQMcHRgxy5PDp31YNGE4TeyrJfg46c1MCQpUfkaA2vWbMH3cML5ERGCo4AwyScU0nRHv\nFCtWu/Ah4B21Ovbj9IbFZEz/dWaAM1duMqJ10t4oCg78hLlp0txISW9mygXvZRw5e4FG3YfQ22Xs\nD++fO3kU10HdOL7OkzmrNtOnQ1M81+2imWNHajdowvXLFzmyfyd1Og/g/JalmJkY8ykomCUbt2OZ\nKQMlC+XH++AJLO2Kc+jmU1znLeX/2DvLgCjXJgxfi3RKqpQIotgd2Ind3d3d3d0tdiMGBiqiGIio\n2N0BGGAgSi1I7veDI+fwCcLCFrDXr3P2fd55ZhF2551n5h59HW1EIhFVajfi3E13NDSTVRMmzl7G\nbuc1zHR24dvXL3z9+oWkxEQKmJlR0NSYQqYmmJsZYW5qTCEzEwqaGGNR0IwyxYtKPKAV/G08okAg\nEL37oRyfKBKJqGxnxGuvo5gaS0bD8kdYOK2HTCK/RRFWOB9I+eWQNrs2ryb48TV2LfmzlkfRMShf\nH5/HH9A3yJ/x4lzAqoUzKJj0g1mjBsrblUwjjI7hyau3PHj+invPX/PwxRtevH5LwULmf5QRmBZI\n/dTv7eXBommjKWVnzboZY7G1tsyWL+EaZuS3tEYkEsn/DEtG5LXP7H7tGnLV5zLtu/ahbZdeTB7W\nk2mDezK6T1ex7AyauYSA0BhWbz+ErpxHOvdp1xAzLRVWTh2V46ZW/T9bDrpx9c13Fq6Vz5CcuLg4\n2tavSIsaFVg1fSyRUUIKOTbjzrsfEpWT3LttAyFPr7F53mSJ2Vy35xDHfB+w5/jFVK9fOOvO1mUz\nuHdiD4mJibQYNJ7KjTswYMT4VOsmDu1J6QLaFLW2YPKKTTjWbUxifBxPH93n/ftArjzwx8KqMElJ\nSbx6/oTQkG/Uqp/xaFxhVBQh374Q8vUz37585tvXz4R8CSb0azDfvgRz1ecyt0/spUrZUmK/59h8\n2mjalEzzM1uZWc0EAoEAOzt7XvoHSixYNcpvwMV9G+gzeT6929TH+eBpjIxNMr4xmzi1bE/71YtI\nWDAFVdWc888vEomIEgrR1vkzQ5db0dU3IPxT5rpKFQUdbS2qVyhD9QplUl5LSEjglf/7f8oIXrLv\n8mkePnuBuoYGpf4TwJYoUx6P68/Y7byGKu37Max7e6YP65eijahEyf+T38iYUva2RIWHUrNuQw6f\n82Ng56YEBn1h5dTRmT612jR7IsPmLKdrU0e2uJ4h5OsXtq9fwuvnT9m0353iJUtL+Z38i/OBU6xZ\nPJOaXQYTfCN7vRLyRk9Hh+go2Y5fvnDWncrVayESiRjVpz1FzU2YO3oQALcePaVk6bIS1z2PCPuB\nob5kv5sGd23LxgNunD99HOsidlzz9uLG5XPoGRjyJTQML9+bNKnjSI0Kpfke/qf0mmkBc1bv3Iid\nvQNbDnpQtmKVlGvx8fEp5VoqKiqUKJ35OmkdXV10dItiY1v0j2tXL53npp8vZ7yvU6FkcYnGGEo1\ngExSpFiJLMlX/Q1NDQ1c1yygQUUHujSpzvuAdxK1nxaW1jZYWFnje+eh1PeSJNExv9BQV89RAXZ2\n0dM3IExM6SpFRFVVlVLF7OjZtjmrpo/Be/9Gfty7yJ1juxjTyQkz0Q+8XLfQv119qhYz5cr5U1R2\nrMOKHQco7tQJN8+L/O0ESEnexci0AF9Cw+nUazCQ/Pl2+Jwffi/e02nU9JSJRRmhrq7GjsXTGdSh\nKc1rlGL8gI40r1SMGUN60KddAx7duy3Nt5HaFw0N9u/YhKlRzj9B0tfVQRgVKbP9EhISGNqzLQ0r\n2dGyVmnqli2Ku/Ny9HR1ALh2T/L1qgDhYT8wyoLG79/Q0tRk5+LpjB7QhTF92hL+5i7jujbHJr8q\nn4ODWOC8h9OXrrL10Mk0H8oqVHFkyryVuF28Q5Gixfke8o1oYfL3SUZ9BVnFyMSU/sPGc+zyTVxP\nZ07RILPknW/+bGJrX4KX/s8lbldFRYVlk0dSxLIQXZvVYPN+dypUka62XuOWHTnm5Z1qGpKiExEl\nlPvxnKzR1dMnIkqyuqeKgkAgwNq8INbmBWnTuF7K6z/CwlPGypqoN+bhi9d0HTODheM/MXVoX7n5\nq0QxGTFpDiMnz8XQyDjlNYP8huw6doFpI/vSoNdITm1ZkakTMYFAwLh+3ejYpD6FzExSHozNjAzp\n16UZa3cdpUadBlJ7L79RVVWlZKnSTOjZVup7SZuEhEQEMh61nC9fPl57HcX/Y1CqEx4A33tP6DZi\nusT3jPz5A0ObIhkvFJM6VSsS8egKWv8pE2zrVI8KJYsxfPZSrt8rTUHLwvQfPuGPe51atgOSVQOa\n1ihFfgMDCllYcdr3scT9/E3pchUpXa4iRR1KsXX3Onq1ayEx28rMaiYpYu/Ai4APUrM/tHsHdi+e\nzpBuLTh/6pjU9gFo0qoDJ7x8Mj1lQxGIFArRTaNJJzejp6evEA1WssQovwENalRh/ICeuKyay7Oz\nB4l87MPYft3k7ZoSBcTI2CRVoPobDQ0NVm51oWLdpjh2HsjbwMyX01iZF0x1gtOqYR2Orl/MhEFd\n6Ne+EZfPn5H6Z+eCtTuZsHQD53xyri42QPC3EMwKyrbuNjExEROj/FgWNMOp7yi8/ZK1zRMSErjz\n8AkVq0q+gTNZDUA6zb9aafSzDOjUhscervTv1JoPAf5/TeRYWtugraVF4JWTfPoQSOj3kGz5c+Pq\n5ZQMbVrE/vrFjnVLGdypVbb2+X+UwWomsbN34NW7AKnu0bx+Lbx2r2fBlBHsdl4jtX3sijmgq59f\nLOFieZPXZKsguQxA3AlWuREtTc1c0RmtRLaoqKgwYdYS+o2eRs2ug/C7n/WMUn3Hyrz3cWdAi9ps\nXjyVxpWLcsnzlAS9TU2pshXYfOAUvSbN4+rt+1LbR9oEf/uOaSHZBav58uWjdr2GFG3YAataLbjg\n68fnkOShJo9fvqWQuQX5DY0kvm9E2E+M8stWqaa4rQ3FihRGS1OdQP+36a7T1NKiRKky3Hv6kuYN\natO3bQNcdm5Oc1LW8jmTGNWnQ6oJg0lJSTivXkQ1e1NWzp9Gr7YNqVPWirmThvPq+ZM/bFw4644g\nMVaiWVVQBquZxrqIHUFfvhIbGyfVfSqWdsDv6A6O7dnMgqmjpDa/3alVB46d95aKbWmQnFnNW8Fq\nchmAMlhVoiQ7dOs7hCUb99J66CSOnbuUZTuaGhr0bt+Seyf2sHPBZKaN6sfLZ9I7Uq1Y1ZGlG/fQ\nc+Jcqe0hbYK+hWJWwFxm+wkEAnYfu8DnkO+oq6uxduYEWv4j03j93kMqVZOOZGN42E8MJVyzmlmq\nVyjLgzt+f11TsXptrt59gMvKuaybOoyHl09St1xh7t26QWxsLJCs3ep5wpWYH0HMGjeIg7uc6drU\nkYpF8rN9wwrmjuqP57EDaGtp8ejUfmy0ExjQoTFdmzpy4tA+hFFR+Fw8x/lTR3jtH0jQF/EHI/wN\nZbCaSdTU1LC0tMz0zN/sUNiiEDeObCfwyW1G9m4n8XntkBysHve6kmMaV5JrVvOOxir8k1nNY2UA\nSpRIg3qNm7PTzYvh81Zx+9HTbNkSCAQ0qFGFtTPGMrJ3OyLCwyTk5Z8UsrRGV0dHavalTfC3UMwK\nyS5YBbh+5SKFTE0If3iFMf26oa+XXD529d4TKjpKvrkKIDw8DEN9+SRTWtZ1ZMPS2dy9eS3dNZUd\n6+B77wkqKio0qlmNYxuXULF0CTo3q0lpC23WLpnNzLEDcV09H/fNy4kIfsfjK6eYPagL+1fMISoq\nighhNBf2bKBD04ZYmRdk/tghfLjqzvR+HfE6vJ2KtoZsWTKVJuVs+XTdA8tCBST6PpUNVmJga+/A\nS/9AShWzk/pe+fX1OL9rHf2nL6Jn67psdfXAxFRyIylLlilPgkjAk1dvKetgLzG70iKvjVqF35lV\nZbCqRIkkKF2uIn2GjGX7kVNULZd9KaqebZvj9/AZk4f1YvMB92wPd4kWClm1YBp9h41LmVj06UMg\nNpayDfYkSfC3EAoUlK3/HscPMrJnh1SlQyKRiOt3HzJiwWap7BkeFia3zGqfDi0x0NNlWN8OOLXu\nSLGS5UhKSqRBk1YUskjWqq5YtQYTBj8mISEhpR476GsIteo1QjMhin1b1zJlSB9qVEqWsPLckboM\nce/KefwIC6eojRX7Vs5NeV1VVZW2TvVo61RP6oMslJlVMShiX5JX/u9ltp+6uhr7V8yhZY0KdHaq\nhv+bVxKzLRAIcGrVHrdzlyVmU5pECoVo6+atzKqOrh5RUVE5qhFOiRJFpl3XPrh5XiI6JnOSVhmx\nZvpYIr5+ZOvapdmyk5CQwJj+nXh1z5fOTapz71ZyY1VSUlKObrL88i0EswKFZLpnsVLluHL7YaqS\nvfdBn0kUif4YWyoJ4uLiiE9IQEdbS+K2M0tbp3o88TiIKZEE3D7P3fNHmDC4W8rJqaGRMYXMLXn0\n4k3KPSHfQ+naZwgPnr/Gc+c6pg7pk679Hq2bMqp3l7/6IO2+AmWwKga29g4895eeIkBaCAQCFowb\nwqxhvejeohZ3/HwlZtupZUeOeflIzJ40iYyKRiePSVepqqqipamJMDpG3q4oUZIrKFDInAqVq3H8\nvGQe0tXV1XDbsJj929Zy3ftClu14urvx/ZM/Pi5b2LNkBsN7tubUURfqO7Xgw+dv3HzwZyOLohMb\nG0dEZCRGJqYy3bdt516EJ6hSpmWPlJKP6/ceUblaTanMtL9/+wbFi9pKxbY4mBjlZ+W0MexYNJ3j\nm5YRHRaC1+njQPJDT1xcLIlJ//bAbJwzkYVTRvArNhZ1NTW5+58RymBVDJLLAGSXWf0vAzq1wWXV\nXEb2bsfpY64SsVmhSnVCw8J5HSCf9yQOEcJodPJYZhVAV09P2WSlRIkEadd9AOv2HeXLP13i2cWy\nUAFcVy9g4rCeBGdi4tzzJw9ZOX8aVy54prxWxbE2H4K/EP3rF83q1eTy/k2sWTCF4667GThqMou2\n7JWIr7Lky/dQTE1Ns10eIS6GRsa8fvUcdXX1lO9r37uPqFi9tlT2O3ZgB/3aN5eK7aySL18+1kwb\nw7I5E4mNjeX+bT9URImpRqB2b92U1xeO4u68gkplSsjR28yhDFbFwNa+OG/8A+XWlNS4VnUu7dvI\nyjkT2Lp2abb9UFFRoXGLdjlCFSC5wcpA3m7IHH39vKe1qkSJNHFq2Y5yjg0o0bQr4xatkUjQWt+x\nMuP7dmVU3/Yp3dVp4efrTb/2jQh9fY8NS2amvF7Q3II6DZzY7XYagDLFi7J1/hTOuLnQqecAbj16\nxtNX6csTKSLBX0MoIOMSgN80cGpOTEwMQV9D+BLyHd97j6kkhWA1MiKCi+dO06uNYgWrAA1rVqV0\nURumjxnIi6cPiRDG4HvnQao1ero6NKxZVU4eikeuDFYTEhK4f/vvUg5ZwdDIGHV1Db6EhErcdmYp\n62CP35EdeB7Zw+zxQ1LpoWUFp1Y5oxQgQhiT5xqsAHR1lfJVSnI/IpGIW9d9iIyIkPpeampqTF+8\nFs8bz/mZLz+V2/Xl87fsB6yTB/fCxkSfRdNGp3n98YO7jO3fiSPrFtGifk0K26VubO02YCQ73M6k\n/H/lMiV58ugB6hoa9BkyliXb9mfbR1kS/C0EUzkFq/NXb2PF9iM8DI7CoUkX3n8KokTpchLfx+PE\nIRrUqJqpCWnyYOv8yRQ3UuXUfmeMzQoydM4KebuUZXJlsHr2xGE6Na2B15kTErdtW9Sel+8CJW5X\nHCwLFeDaoa2E+D9jaPdWCLPRMV61Rh0CPgTxIfiLBD2UPJF5NVjNg1OslOQtkpKSWDR9DBMGdqFR\nJVs2r1wolaA17OePVLqoZgULMXvZRrr0HU7roZOI+ZW9piuBQMDe5bO5fdWLYwf3pLqWmJjI3AlD\nWDV1FPUdK/Mq4AOF7Yr/cb+O1r9NOiZG+THKb8B7/7f0HDgC71v3UzKvOYHP375jJsOBAP9FIBBQ\nvnI1lmzYjc+jD7h6+KKmpibxfY4d2MHAji0lbldSWBQ0Y+mkkZzcvIzPn94THx8vb5eyTK4MVg/u\n3Mi0Yf2YOXYgr55nT1Pv/7G1L8GrgECJ2swK+nq6eGxfTREjTbq3qMXXz8FZsqOmpkbDZq04oeCl\nAJFCYZ5rsALQVU6xUpKLiY+PZ9LQnry+d53n5w5x7dBWgp/50bpOWYmVWzmvXkSTqsWoV64wvVrX\n48COTamuD584E3O7kvSbsjDbe+rr6XJi0zKWzZ7A8ycPU14/vHcbOmqkTPV5/T4IG7tiKdeFUVGc\ndjtICbvCqexVKl2CJw/voqdvwL6T3sxYu42dR92z5aOsCPr2HVMZj1pNCz19fUqVrSBxu29fvSD4\n43ua1K4ucduSxsq8IGtmjGXigB7ydiXL5Lpg9cXTRwR9CGT+2CGsmTGWYT1a8fOH5I7tixQrwYt3\nitGQpKamyo7FM+jSuCadnKplOTB3atURNwUvBYiIEqKXx4YCwO9gVZlZVZL7iBYKGdq9FXE/gvDa\ns578+noUt7XBZdU8wn7+IOznj2zvceLQPk667ODomrn8vH+JO8d3s2XVAs6fOpayRiAQMG3hGo6d\nuyiREq9SxezYOHsCI3u3IzzsJ6HfQ1i3ZBbOcychEAgQiUQ8e+OPja093799ZdWCadQvb0PMl3fM\nHNY3la0qZYrz5MFtvgQHUaRoMfa7X2H2+p1sP3wy235Km6BvoRSQQ2b1c9Antm9YIdHv/bQ4dnAn\nvds1T9EtVXT6d2zNkG7t5O1GlskZP2UxOLhzE4O7tkFVVZVebZvz6MUbxvTvxC43L4n8UtnaO3Dk\nouI82QoEAmaO6E9hi4L0blOPNTsOU6NuQ7Fs1KzbiAlDevD1eygFTIyl5Gn2yItDASA5WFWWASjJ\nbYT9/MHgLs0pWdiMnYvmoab272ezQCCgiLUVHwLeYWiU+c+jz0GfWDF3Ehqammjr6KKhqY3bgR1c\nObCZ0sWLAmBrbcmZbato0n8IRqZmVHFMbrrZsGwOg7t1oJCZiUTeX9dWTfC995gVcyeTmJhAj9ZN\nUoavXLh2i7gkCP70nn4dnejW0ombR3dQ1MbqDztVSpdk9YS57N22kY7d+jBv1Rb2uV+hT9v6JCUl\nMaRbe4n4Kw2Cv32nnhxqVnu0rENcTBRvXz5l2SbpqCgkJiZy8vB+rrpIZ8iAkj/JVZnVyIgIPE4e\nYVDntimvLZs8Eh3iWDprvET2sLV34LUClAH8P73aNufo+kWMH9SV467i/YFqaGpSr2ETTl28KiXv\nsk+UUJg3g1U9ZRmAktzFty+f6d68FnXLFWPP0lmpAtXf2Flb8D7wnVh2k5ISOXvqGA1LmFPOTB3L\nfBG4bVicEqj+pmJpBw6unseoPu15/eIZAMEfA2lau1rW31Qa5NfT5ar3BXwvejJ/zGAguZFs2ipn\nxk5fyMtnjxnbpzPO86ekGagCVC5bkhK2Vvi57SIi6A2DujTDxNSMfe5XWOC8jy0Hj6V5nyIQ/DWE\nAjIetQowZNw08hvoc9PnAreuS+fE8OePUBITEihuayMV+0r+JFcFqycP76NRzWqYF/hXhDhfvnwc\nWrsAX6/TuB3Yle09LK1t+BryXWITUCRJveqVuXJgMxuWzGDD8rli1V81btWJo+evSM+5bBIZlXeD\n1bBIZbCqJPfg5rKLqiVtWTV9TLoanEWtzfngL55Uk4VVYSwtraheoQxj+3Vn5siB1KteOc21jWtV\nZ9W00Qzs3ITPQZ+wsS8h8emEkdHRNKtZCb+jO1Lm05/w8iYOVZq27oj/m5doqKv/1YaBni4+B7dS\npWwpTm1ZQRkrE7o2rYG6ujr7T11h4Zb9bHZxk6jfkuL7j5/kN5T9SZ1AIEBHW4f1M8cze/xg4uLi\nMr5JTKKFUejp6kjcrpL0yTXBqkgk4uCuTYzs8eexiKGBPqe2rGDFvMnZlrRSVVXF2rowbwJlO8kq\ns5S0t+WW206uergxdWTfTHf/1W3UjJsPHhMWESllD7NGZFQUunmwZlVP34DwqGh5u6FEicTQ0NTC\n2DD/X9c0rlmVA9vX433+zF/X/T+OdRpy6cbtTK3t1bY5o3p0oG/7hngcd6VkUcmO4lw/awLbF02n\nsEXyUXhiYiIzVm9l7MzFLJ05nm+Brxjes1Om7amqqrJxzkQGdWhKJ6dqqKiocOC0D0u2ubBx/xGJ\n+i4JStrb8eLpw4wXSpiNy+exaupI2jrVo5hVIXZskLxckzAqEh1tbYnbVZI+uSZYvX3jKoLEeOpW\nq5Tm9RJFi7B76UxG9W3Pl+CgbO1lZ+8g8adwSVLQ1AQfF2eivwYysHNTIiPCM7xHR1cXx1p1OXNZ\ncuNcJcXvGc8aUp49rIjo6ukToQxWleQitHV0icxghHCjmtU4sWkps8cNYu3imSQmJv51/W8c6zbm\ngt/9TPsyeXAv+rd1Yu/SmTSvXyvT92UFF3dP9IzNuHvDh7tXvfDavQ6DfzKumUUgEDC+f3ea1q7O\nmeOHCPsRyoHTPizfeYj1ew9JyfOs0cixIjd8sj6CNqtUcayNz+0HCAQCNs2ZwO7Nq/j4PkCiewij\notDTUQarsiTXBKsHd2xkePd2f51v27JBbUb17MDwXm34FZP1eetFipXkhX9glu+XBbo62rg7L6e0\npTHdmtXM1BjAxi07KmQpQKQwGl1d8T7Ucwu6evqEK9UAlOQitHV0iMogWAWoWbk899338sj3PAM7\nNSUmOuOHtuq163Ptzn3i4zM3LEUgEDBtaF+a1auZqfVZJS4unjnrd1LYrjhXPU9wce8GDA2yflJU\nwaEom1YtpINTdWaNG8SB0z6s2nOUtbslM4pbEjR0rMKtq5dkspdIJMLP15vhPVtz9dI5TI2NALCx\nNKdy2VI8f/wgAwviIYyKRFeZWZUpuSJY/fblM75XLtCnfcbivNOG9qW4hQkzxw7MsqZeEXsHXvgr\nZhnAf1FVVWXzvMn0bd2ILk2rp9L9S4uGzVrj7XcHYSa+SGRJpFCIrk7eDFb1lDqrSnIZ2jq6CKMz\nV/NfwMSYS/s28urZIz4E+me43sjYBOvCNtx+JFl97eyy46g7NvYOWFgVpl3jOpgY/b0MIiNK2dsS\nEx3NgnFDeXDnJvoG+enYcyDjFq5ixXbFmHRVpWxJPn38wPeQb1LdJyEhgQ6NqrBo4mDaOZbmw9VT\nDP1HokkkEvHw+UvKVEi7djmrRAuVmVVZkyuC1SP7d9C5eaNMHakIBAJ2L51FwPMH7Nq0Kkv7JZcB\nKH6wCsnvd/LgXqyZOoq+7Rpy9dK5dNca5DekQqWqnLt6Q4YeZkyybFXeDFaVE6yU5Da0dXQzlVn9\nTWJiEiGhoRSysMzUese6jbjodyer7kmc6JhfLNy0m7Ezl2BgaMyP8Oz3BZQqZguAg50NJsZGeHmc\nZN/WtdjZWDN56Tqa9h2V7T2yi6qqKrWqVuSmr3QHzjy8e4ukWCHPPF0Z0r0DOtr/TgH79PkrIgQU\nskhbbSGrCIVR6OpoZbxQicTI8cFqQkICh/duYXj3zOvNaWtp4r5lBTs3Lv9r8JYetvbFeeMfQFJS\nktj3yovOLRpz0nk5U4b34vC+7emua6yAAwIiooR5srkKkjOrkcpgVUkuQkdHl6hMHOn/RkNDnb4d\n27Bo+phMrXes05iLN+5l1T2JE/w1hNi4eKIiI/j6OYhQCQSrBUyMMTEyxN7GGmPD/BQoaM6vX794\n7nmYzfOncvnmHYX4fmrsWAk/Kdet+l7ypHmd6mmWAN5+/IxyFSr/tTwwKwijItHTVgarsiTHB6uX\nz5/BulABypcsnvHi/2BtXpCj6xczeVgvAt69EetePX0DdPX0CPoi3eMNSVOrcnl8XbeyY81CVi2Y\nlmYZROPmbfG8ci2lqUkRyKuyVZCcWY1U1qwqyUVo6+gSJRSvaXDdzHE88PPB0/1ohmurONbmwdPn\nClPOVNTGiq6tnNi0YCJb1y0j5MfPbNsUCATcOLKT0sXsMDUyJDpaiG1Re+48fsawHh2Je3kzAUgY\nogAAIABJREFUXVkwWdKwRlX8pFy3eu2SJ03TGXl669FzSleS/DjU5DIAZbAqS+T/25xNDu7YkKZc\nVWaoXaUCC8YOZmj3lkRGRIh1r61dMV4FKK4iQHoUK1KYm0d3ctf7LBMGdyc2NjbVddMCBSnuUCrT\n8i+yIK9Or4JklYbomJhMd0MrUaLoaOvoIBQjswrJDaMuq+Yxb9LwDNVcdHR1KVWmHNfuyl42KT02\nzZnIjcPbAPC+flMiNu2LWCMQCOjarAFb1yyikmMdvG8pTkYZoHQxO6KFUXz6ECgV+z9Cv/Pu3Rtq\nVCyX5vXbT15SrqJkhz0ACCMjlJlVGZOjg9WAt6958fQRHZuKN170vwzp1p5GVcsxYXA3sQIC22Il\nePkuMMv7yhNTY0O8D2xGEPmN/h0aEx6W+knfqXUnhSoFiBQK0cmjZQAqKiro6OgQKWYmSokSRSUr\nmVWAauVLM7JnRyYP75XhEXf1uo25cF1xHrh/M7RHR4nb7NWuOdr5kvgQGMCVO48kbj87CAQC6jtW\n5oaPdLKr165coG61yqirq/1xLTExkftPnlG2YhWJ7xstVKoByJocHawe3L2Z/h1boaHx9ykgGbF+\n1gTiwr6xdvHMTN9TpFiJHKEIkB7aWpq4bVxCteJWdGnqmOrJ16lle05d9CEhIXPyL9ImL9esAujp\n6SmbrJTkGnR0kk8LssL0YX1JFP5kt/Oav65r2b4be457KFR2FWB8v+4St6miosLAji0hKRG/uw+y\nrHIjLRo7VsbPx0vidl89f8quDctpWTftY/6X7wIxMTUjv6GRxPcWRkWip6sMVmVJjg1WY6KjOXlo\nf4pERXZQU1Pl2MYleLgd4MzxzAkr2xZ14IUCDwbIDPny5WPtzPGM6NKKLk0defzgLpA8ttDCyhrf\nO4rxQR8pjEZbNy8Hq/pK+SoluQZ1DQ0SEhIyrYX6X1RVVdmxcBo7Niz/6zq7Yg6s2naQ9iOm8vSV\neGNbpYl9EWtE7+5K3G5ElBBDYxNUVFQk3kyUXcyMDRFGildmlxEvnj6iZZ1y1CxTlAGd26S5xu38\nZWrWd5Lovr9RV9dgkfNepq7YSIQykSATcmyw6nH8ENUqlKaIlYVE7JkaG3Jy8zLmTx7Bs0wICNsV\nc+B1Dg9WfzOmb1ec50xkYKcmXPI8BYBTq04c85Ku5EhmiYiKzrNlAPDPFCvlB6KSXIJAIKBAgQJZ\nznoaGxpkqtO9doMmtO7Sm40H3LK0T04iNCyChEQRJsbG8nblDwI/fcbcWrKjbIuVKM2cpes56nmZ\n6auc/2imS0pKYvcxDzr1GiTRfX8zb9UWVmw/gufNx7hfVJySudxMjg1WD+7ayEgx5KoyQ/mSxXGe\nN5nhPdtkKGRsbmnNj58/icwlGa+2TvXw2L6aWeMGsn/7Rpq06sDx81cUQv4kQhiDbh5tsAKl1qqS\n3Mfi9bvoPn42wV9DxL43KUkEIv6otf8vIpGI8LCfXPXyoGuLRtlxNUfwIzySxMQETI0N5e3KH/h/\nCsaysK1EbebLl4+eA0fgcf0Zfq8+Mmbh6lTXvf3uoqNvSKmyFSS6729UVFQoX7kaRYs5JP8+KpE6\nOTJYfXz/DuGh32lSx1Hitjs1b0SfNk0Y1ac9cXHpyzepqKhQxNaO1wE5t271/6lWvjTXD23HZesq\njuzfho6+AbcfPZO3W0QI83jNqnKKlZIcyOXzZ/B0d+PBnZt8CQ5K1cBau0ETug8YSYdR04iLixfL\nrkAg4Hvod+qWK8ypoy5prnGqWhzHEoUoalWAutUqZet95ARCwyOIjf2FupqqvF35g4Cgr1hmI7N6\n67oPh/ft4MJZd+7dukHAuzeEh/1EJBJhWqAgRsYmVC1TItU9O46epmPPgVIviRAgULga4dxKjgxW\nXXZuZGi3tuTLl08q9uePHYyZrhoLpoz86zpbe4ccKV/1N+wKW+J3ZAfPb17hfWAAx87LvxQgL0tX\nAejqKYNVJTmLsJ8/GDeoGxcObWHxpEG0b1CBUuZa1CltSffmNQl494ZhE2agY2zOxGXrxbJdyMyE\nF15uXD24hQ2Lp7Nny9o/1kyZtwIDfT1WTBrBt9AfNBswFm8/ydeKKgot69XA+/wZrt++x4GTZ+Xt\nTioCg4KxsLbJ0r0fAv0Z2bsdr66d4fSetaycPoJBHRtTr1xhShTUwNGhAF4e7rRsUDvlnp/hEZz1\nuU7rTj0k9A7SR0VFhSSR/E8f8wKK9xiWAT9/hOLlcZLNF49JbQ8VFRVcVs6leqeBHNzlTPf+w9Jc\nV6RYSV7kUPmqv2FsmJ+L+zbQZ/J8znhfY8XU0XL1J68Hqzr6yjIAJTmLCx4naFSrOic2LU15LS4u\nnuBvIew8eop5k4ax+9gFVm51oV39ilQvd47urZtm2r6DnQ0Ae5fPZsTijfQdOjbV9UbN2xAVGYFT\nvzFoaWpQ3rEencfM4MKe9WIPkMkJ9GzTjFqVylGry0B6TZhNywa1ya+vGJ+Z7z8FYZmFYFUkEjF/\n0nAmDerJ1CF9/rgeGxvH959hxMXHY17ANOX1g6fOUbdBEwyNZFC/q6KizKzKiBwXrB47uJuWDWpL\nvTZHX0+XU1tWUKPLIOyKl6Razbp/rLG1d+DayVtS9UNeaGpo4LpmAY9fijfdSxpERuXtYFVPPz/h\nkR/l7YYSJZnm3MnDDG3TINVr6upq2FiaM3vkQCq06cX8qaMpXb4yPQeNZvCMGZQpXpQyxYuKtU/V\nsqUIePeW8LCfGORP/Z3QtksvEhLiSUiIp2ufIZxzd6PFoJH4Xz6RbblDRcTG0pxP189y9/Fz9HV1\n5O0OAGERkSQkJGZJPurcKTe+fvRnwqZ5aV7X0FDHoqDZH6/vcPNg7NzVadwheVQEKsqaVRmRo8oA\nkpKScN21mZE9Oshkv6I2VrismsvYAZ0J+vjncX9yGUDuqVn9f1RUVBQiCxEpFKKXh2tWdfUMCFeW\nASjJIcTHx3Pd9wqNa6U9OUhNTRW3DUuwUI3m0UU3fE4fxNTEmF1up8XeS11djZYN67Bs9sQ0M1wd\ne/Sna58hADRt0xEjEzMevXwt9j45icplSyrEqFWA90GfsbKyFrt2NDIigkXTx7Bl/mTUxKjD9fC+\nRsjPMGrUzfqgIHEwMDLmZQ7WW89J5KjM6vUrF9HX1qR6hTIy27NxrepMGdSTod1bcficH9o6/z6x\nFrErxruAQBITE6VWP6sEIqOi8nhm1YD3UcoJVkpyBmpqatSu14j9Jz0Z07drmmsc7GxYPHG4RPbb\nOn8KtboOYfv65QweMyXVtaSkJFbMm0JBcysqVa9FmYrVuP3oGVXLlZbI3kr+TuCn4CyVAKxbMpOm\ntapRu0rmuvlFIhFr9xxi6bb9rNt1VGbfx70GjaZVnTJMGtSDgqYmMtkzr6IYj1+Z5ODODYzs0V7m\nosfj+nWjkoMNU0f2SfX0rqOri6GRER+Cv8jUn7xGVJQwTwerusqhAEpyGBNmL2Xxlj0ykfbT09XB\nY/sqDmxbi6f70VTXfnwPwXX3FoIf+TC0a3P8373h5uMXUvdJSTIBn4KxEFO26vmTh5w55sryyX9v\ncP7Nr9hY+kyZz44TXrhduE3VGnWy4mqWMLe0on2XPizcvFtme+ZVckywGvzpA3f8rolVhC8pBAIB\n2xZMJeT9G5xXLUp1za5ocV7lkuEAikhiYiK/YmNTZbTzGsk6q8pgVUnOID4+njPHDpJPJR9hEZEy\n2dOyUAFOb13J3InDeHj33z6Cz8GfKFLYiu2LpnN660puXffh7GVfmfikBAI+fcZCTNmqU0f3M6x7\nO0yM8me4NvhrCHW6DeVHggaHz/lhYVU4q65mmSHjZ3DwlBeBn4KJ+fWLgI9B+N1/zInz3mw+cJRZ\na7aw5eAxhdAsz8nkmGD10J4t9GzbDB1tLbnsr6mhwYnNy3DdtYmLZ91TXi9SrAQv/QPl4lNeICo6\nBm1tbYUbIShLknVWlWoASnIGx133cvvyGR6e2o+VeUGZ7VuhlAO7l85keK82fHwfAMCHgHdY/tOE\nU6lMCcb0687P8HA8r1yXmV95maxorD64dZ26VSpmuO5NwAeqtO9H3VZdWbfriNwSGiamZvQZOpri\njTtiWLEhdXuOZOSSzWw55cONgJ9E6liww/0yrYZMJPRnmFx8zA3kiGA1Li6OI/t3MKybZCdWiYt5\nAVOOb1rKjDEDePPyOQC2xUry4p2ywFpaRERGoaeXd0sAQDnBSknOQiAQULpYUcxMxO8Azy4tG9Rm\n+pBeDOrSDJ+L51gwZQT9O7RIub54QnKdbPMBY3j66q3M/ctriKuxGvvrF8+fPqZquVIZrp27cSdd\n+g1n2Pjpck9mjJw0h1uvvvIsOIYrjz9w9OIdnA+eZv7qrYyeMpf9p314+MofF/dzcvUzJ5MjgtXz\np49RsmgRShSV7HzhrFCtfGlWTh3FsB6tCPv5A9uixXmZixUB5E2kMBpdXV15uyFX9PQNiFRmVpXk\nEHT19OWqXjG6T1ea1qjIiN7t2LN0Jh2a/Cuhpa2lifOCqQC0GDyBr99D5eVmnuC9mA1WTx7eo5id\nLbo62n9d9zH4C54+N+g9eFQ2PZQMAoEAfYP86QbNG5bNwd6qEMN6dJSxZ7mHHBGsuu7cyMge8s2q\n/pc+7VvSpr4jYwd0prBtUV75B8jbpVxLpDAaHR1lZjVSmVlVkkPQ0zeQ+0nA2hnjeOd9kub1a/1x\nLfRnOG079SBCGM1i5z2ydy6PEBYRSVJS0h/6t3/j/u3r1KqYsdpPyI8w8uc3RE/fIDsuygTv82fw\ncDvA0fWLxZLhUpIahQ9WXz1/yoeAd7RpVE/erqRixZRRqCdEs2/LWqKE0XL/cM6tRAqF6OTxMgAt\nbW3i4uOJj0+QtytKlGRIco21kM/fvjNv/TZmr9nCvSey7cBXUVGhkFnaUkJvP37m588flClWlGWZ\n7DhXIj4BH4PE1lh9cMuXWpXKZriuQqniJCXE8fLZ4+y4KBMO7XFm8fihUh9klNtR+GD14K5NDOrS\nRuGeSFRVVTmybiFXzp0kNjaWV8omK6kQESVEVzfvDgSA5CMmPT09ZZOVkhxBVGQEIpGIkfNXcSsg\nlO+qJjQdMJZVO10UYjRlMRtLHtzxw9BAH00NDXm7k2u5cP025SpVz/R6kUjE/ds3qVEx42BVIBDQ\nuVlDzp44nB0XpU60UMjN61dpkUaGX4l4KHSwGhUZyZnjhxjcpa28XUkTo/wGnNqyAk0NDV6+C5S3\nO7mSyKjoPJ9ZBdDT1VNm75XkCPY6r8LBxpJbj1+w0nk/E2cvwe3iHVzOX6Pl4Al8/yHfjuhJA3tS\nwrYweVhgROqEhP5kl9sZWnXulel7Av3foqmhlmkFiS7NG+F58rBCPAClh6+3F1XLlyG/vvI7LCM8\nvK9x5LRnutcVK135f1y5cBYjA32FmXOcFiXtbTm/ez1mxrLvfM0LRAqFaOfxzCqAnr5yMIASxed7\nyDcuXzyPlqYmyzbtQVMrWWrQqnARXM9eZ/XC6ZRv3ROXVfOoW62SXHxUVVXl2MYlfPz8VS7752ZE\nIhG7j51m6orNtOvWRyyB/od3b1K9QsZZ1d9ULO0AiQm8ePqIkmXKZ8VdqXPR4zjtGiqzqn8jLi6e\nKSs24upxidDQ7+muU+hgtXHzNlz3PkfVDv056byM4rY28nYpTWpUKidvF3ItkcJodJTBqnKKlZIc\ngYmpGb6P3wMCzC2tUl1TU1NjyrwVONZpSJuBXfjgcwp9PfkofVgUNMPiH/1VJZIhPDKKtsMm8yM6\nnp1uXpQqm7lRqb8Rd0SqQCCgc/MGnD1xSCGD1YSEBK54nWXtyH3ydkVheR/0mY6jpmNYqDBnbzzj\n8P4dLJ87Jc21Cl0GoKGpyeL1u+g5fBI1uwzmxHlvebukRMZECKPRzQEdn9ImJ2mtRsf8YvuhE7xR\nSrrlScwtrf8IVP9LnYZNqepYm5MXrsjOKSVSZ/SC1ZjYOOB28Y7YgSqAhZUNgUGfxbonuRTgiEKW\nAnz9HISWpoZMB2NkhzcBH/D2uyvTPScs3UClus1wdjlFfkMjipUone5ahc6sQvLTU7e+QyhZpgKj\n+rbn9pPnLBw3VOynMCU5k4ioaEwKK+t9fndYKzIhoT/ZsP8IzgePU9Dckv2nvPBx2Sx3wW4likfL\njr04sG8Dvdu3lLcrSrLIqPkrOXf1Jp2bNaSQqRE+dx9z2vdJlr+bLa1t+BAULNY95UsWJx9JPHv8\ngNLlMp56JUsEAoXOBf7BkDnLefD0BW8uHsvUqNvsEvPrFxeu3eTSOpeU74j6jZunuz7H/DTLVarK\n8cv3ufrEnyb9x8q9SF9crty8S+2ug5WNWGISIYxGR1cZrOroyV+7Mj3eBHxg6KylFHPqxNtwEa5n\nr3P88j2+R8bg5nlJ3u4pUUAaNm3FrYdP+Pb9h7xdSRdhdAzVO/QlQkH/7uTJK/9AXE97MWfNTr4J\n9FnveorlzvvRycYAF9MCBQmPiCTm169M3yMQCGhUowr3bine+FwVFRVifv0S6/3IC2+/uwQEfaV0\n+Up4eF+TyZ4Xrt2idNnyGBn/KzH39NH9dNfnmGAVkuuhdh+/iF2FGlRq10fm2n3Z4YjnZZK0DKnV\ndTDOB90U8thCEYkUxqCrDFbRVcDMqt/9x7QdPoUaXQahbu7A+ZsvWbh2O7b2xcmXLx+zlm1kwtL1\nRMco/oe1Etmipa1NA6cWHPW8KG9X0uVHWDi3Hj5l8Za98nZFYRBGx2DXoB2thkxkwMhJlClfiWkL\nVnPu5ksqVatJwLs3BH/6SFRkpNjfcSoqKpibW/A+6ItY9xno6RAtVLwHCtMCBalVrxElm3bjzGVf\nebuTLiKRiBlrtzFg5CSePLxHkzqZlxvLDm5ePjRu+e9Er/j4eKaO6pfu+hwVrEJyJ+eUeSuZNH81\nTfqPYedRd3m7lCnOXb3JjEWrcT17Heej52g1ZCIhoT/l7ZbCEykUKjOrgK6egVxHWP4mKSmJk15X\ncOw8iK4T5lG+fhu8H75n7PQFmJgVSLW2Ws26lK3kyLJtygYDJX/SslNPDpy+IG830iUqOgZjYxO2\nuB4n8JN4x9O5lQ/BX4hNSKL3iClUql4LxxLmPLp3m5joaCYP60W3Zo50bVKNGiUK4lBAnSp2xiyY\nkvnBC5ZWhXkvZt2qjpYmMdHy/2z8f/Lly8eanUeYu2YHY5ZspMvYmfJ2KU3OX/Xje4QQNXV1alYq\nT0HTtIdpSBLXU+fw9LlBk1YdUl7btnYplibplx/kuGD1N83bdsbljC+Lt7syeOYSYmPj5O1SurwN\n/Eh0bCzFS5bBrpgDR7xuYVmqKuVa9cTziuIdXygSEVFCdPWUagC6+gaER8rvA/lXbCzbXI/j0KQL\nc7e60H34FC7cfUvvwaPQ1klfWm7KglVs2HdE7C8gJbmfWvUa8ybgAwEfg+TtSpoIo2MIDf1OeHg4\nYxetSXfdtbsPuXo7/ePL3ETQ129YF7ale7+hvHr+BDtrC4b2aEWXpo6ox/wgwPskn66dJurJVaKf\nXuOJhws3vc9x4lDmHlgtrIuI/WCgo6VFTLTiZVZ/U7uBE2euP8Pn9gPeBn6Utzup+J1VHT11PicP\n7WFAxxZS33P1roNMXLGZfSe9KVDIHIC3r16wd+taNi2ane59OTZYBbB3KMnxS/f4EJFA7W5D+Bgs\n3vGBrDjv60et+k4pRcTq6upMnrucldsPMWj2CkbNX5kj6lrkQWSUsmYV/pm3HhUt831Df4Yxf8MO\nCtdtw2Gf+8xbt5tjl+7Rol0XVFUz7s+0sCpM7yFjmLh0gwy8VZKTUFNTo1mbjrie8ZK3K2kSGhae\n8t8el325ce9RmutqdxlImyETZOWWXAn6EkIBcwsAHt6+ztBu7VgzbTTDOjXj4Jr56GhrpaxVU1PF\nvIAph9ctZMnMcQS8fZ2hfXPrIgSIm1nV1uRXtOw/GzNDXFwcY/p3ZuvaJVSo4sh5Xz95u5SKE17e\n/EoUUKFKDe7c8qNCyeJS2yspKYlJS9fjfOQMh8/5Ubzkv53/Vy6cpUuLRlhbmKd7f44OViFZLH3T\nvhPUa92NKh36yVx6ITN4+t6mdsM/u9yq16rHad/HBIYnUrldPx69yPiPOa8RJRSiq5xglSxdJcNx\nqwEfgxg5bwVFG3Xg6bcY9p68wvbDnlSrWVfs7v5BoyZz8/ELrtxUvL9NJfKlZcceHDqrmE14Gupq\nbJ4/FWMjIybNXsKYRetISkpKteZ3XaZ5AVN+xcbKw02ZEvT1G2aFrNi8ciEPbl6lnVN9urduyoie\nndL9XCjrYM/8sYMZO6AzsRn8jCysbQgIEm9Yg7ampsJmViPCw/C56Mmjq2d59OAeT98EyNulFLz9\n7jJk1jJmLt2AWcFCDB83lSrt+7HpwNE/fs+zS3x8Ar0mzcP7wSsOed7A3NI61XWBQIC6mtpfbeT4\nYBWS3+jQcdNY4exCl3GzWLF9v8I0MMXFxXP19j1q1W+c5nWD/Ias3XmY/mNn0rD3SFbvOijxX5Sc\njLJmNRlZDQW4+/g5nUbPoHK7fiQaFubs9ecs3biHYiVKZdmmlrY2U+avZPTCNSQkJEjQWyU5HUvr\nIoQqoLKLMDqGPlMW8DrwI0lJSbTr2od4gSqup8+nWvc64D0aGhpoGJiwaf9ROXkrOz59/c5Z96Oc\nO7aP64e3Y17ANFP3DeveATtzE1bMnfTXdZbWNmI3WOloaxEjVLyaVYC42F/o6+kxf8xgPgd9pGrZ\nEvJ2CUgebdp5zAzW7XajWs265MuXj7HTF+Jyxpe9Hlep2WUwYRGREtkrShhNi8ET+BYDe09extDI\n+I81AoEgw5gtVwSrv6lZvzHHLt7hgOdVOo+eQaQCNKRcv/cIOzv7NP+BfiMQCGjXtTfHLt3F1esG\njfuOJvhriAy9VExEIhFRUVHKYBXp6qwmJSXh4X2NOt2H0XbkNIo7OuH9MJBJc5al1BRll2ZtOqFj\naMa2wyclYk9JzkckEnF431aFEE0XiUSpSrFmrd3K1++h7D12Gn2D/Ojq6TN98Tqmrtyc6oHrst9d\nWrbrjJW1DWbGhvJwXaaE/AjD0iQ/vq5bKWSW+UYcgUDA7iUzOHFoH8Gf0q/btLCy4f0n8WqYdbS0\niIlRvDKAG1cv47LLmXz5VKhZqRxN69akUY2qcvUpPDKKcYvW0G/aIra6elC9Vr1U1+0dSnLw7DXU\n9I05fzX7JQshoT+p23M4hlbF2LT/JFra2n+u+fqFE667sbO2+KutXBWsQnKN3CHPG6gYWlC1Q39e\n+QfK1Z9zvn7UatgsU2utChfBxcOXsrWbUr51L46fvyxl7xSb6JhfaKirZ6o2Mrejq6dPeIRkj7pi\nY+PY7XaK0s27M2XtTtr3H8Ol+wH0Hz5e4qUXAoGAWcs2Mnf9Dn78pxZQSd5lzaIZXPU4hrvzMrn5\nIBKJOH/VjxpdBmNZqxWvA95z78kL9rufY+sBd8bPXobnzZdoaGgQEfYTbS3NVMfdF27co3qdRvi/\nfoGDnY3c3oesWDN9DJf2bcTQQPymV0MDfYyNDIn9FZPuGrOChfgRFiZWSYWOtpbCSVfFx8fTu10j\ntqxdyodPQaioqOC5a53cHsySkpLYedSd4k6d+BKnwZlrTylfuVqaa38/jEmiKfa8rx9PXrzC2NQM\nYdSfmdpA/7d0aepIV6dajOzV+a+2cl2wCsljWpds2EWvEZPlPqb1nO9tamcyWIVkaa5Rk+fg7HKa\nCcudGTBtEVFCxXtqlAXJJQDymR2uaOjpGxApoZrVsIhIlmzZQ5H67djjeZ1py5xx93lE6049UMug\nbig7OJQqS5NWHZi9bpvU9lCSc0hMTKSItTlmxkZy8+Hq7fs07TeKUtXrM27mYhr1GUWbYZOZMm8l\ntRs40b3fUDQ0NIj99YtF08ewfub4lAlN3n53uXb3AbXqNcb/3VscbG3k9j5khWWhAmhraWbdgEj0\n1+PefPnyUaiQOR/EaJYuZW/LuzevCPR/m3W/JIyamhrW1oUZ17+7vF3h1sOnVO04gM1Hz7PF9SyL\n1u/ExNQszbUx0dEM69Ea/XwJjO7bNdt7t21cj64tndi5eQ2P7t1Ode3Jw3t0b1GL6YN7MGvkgAx7\nIXJlsPqbrn0Gs/2wJ6MWrWPayk0kJibKdP8vId95HxSc7hPM36hQpTruPo+IVNWnfOte3H70VAoe\nKjaRUdHKgQD/oKenT0QWhLb/y4fgL4xbtIYi9dpy9/1Pth89z+7jF6lZr5HMRqKOnbGIwx4XefpK\ncb5YlMiH0VPm8SIgGBd3T7n5ULdaJVZNH8uHd6/o3m8oc1ZtY/3eE7Tv1ifVup2bVlHG3oYmdRyB\n5FrVLmNnsnr7IRISEtDU1ERX588jTiX/8jH4C6Fh4RSysPrrOktr8bRWjfIbMK5fN9YsmJZdFyWK\nvUNJalUuT8xz+chTfgn5Tp/J82kzfApdB0/g8Hk/ylaonOba79++cuLwfnq1qUch3Xyc2LQMTQ2N\nLO+dkJDAtkMnsG/UkQgVPS7cfkWdhk1Srl/3vsDATk1wnjORwV3bZcpmrg5WIXlM6wnv+1x9GiDz\nMa1evjepUatelo+xdfX0WLpxD+PmrKDl4Iks2LhT5gG3PIkURiuVAP5B/Z8Pjtg48fWEHz5/Rbdx\nsynfuhdRWmac9n3Cii0HKFG6nKTdzBBDI2NGTJrDqIVrFKYJUol80NDUZMVWF8YtXsenz+J1gEuS\nhjWq8iHgHQD1nVqkmVy44nWa0J9h+H/4ROjPMJoPHM+4mYupUbchZgULYWJWgNOXrsra9RzF9iPu\ntO7YPc26xf+SrLUq3hH0uH7duHfTl8f372THRYlS1KEMT1/7ZyvoywpxcfGs3HGAUs26oWluz/lb\nr2nfrQ8qKv+GeyKRiHu3brBqwXTa1itP46rFuOa+j1Gdm3Fg5VzU1LJeepeYmEibYZO8nIEhAAAg\nAElEQVTZefoKzq4erN7uiqW1Tcp1jxOHGT+4G8c2LqFdk/qZtpvrg1UAYxNTdh+7gF2FmjId0+p5\n7TY1xSgBSI9mbTpxwvsB5+6+oHb3oXlmmkpEVJQys/oPAoEAfT19IjI5GEAkEuHle5OGfUbRbNAE\nrCvUwftBANMWrMbc8u+ZDWnTvf8wvvyI5ISX/MpzlCgGpcpWoPfgMfSdulBuDy+2VhZ8eB/41/03\nHzjFw+ev+BISSoeR06jfoj1deg8Ckku3Zixez+iFa/LkCVhmSEhIYMeRU3TtOyzDteZWNgSIOxhA\nW4u5oweyfM5EhXkIti9RmqdvA2W656XrtynTsjset55z+JwfU+atRE//z/riM8cPMa5/R/R/fWHj\ntBF8v+3FiU1L6dexdaqgNitMWraBsHgV9rl7/5HJ3bdtA0tnjOXSvo3UqVpRLLt5IliF32NaVzB5\nwRqZjGlNSkri4rVb1GnYVCL2CllYsvfEJeq26kbldn3lenQmKyKFyoEA/0VXT4/wyL/XrcbHJ7D/\nhAdlW/VkzNLNNO06GO+H7xkyZgp6+gYy8vTvqKqqMmPpBsYvWa8chqGEIeOmERodz2YXN7nsr6er\ng56eLt++pJ/NW79kFj3bNGPH0dOo5y/A5LkrUl2vVb8xQybMpv2oGTQbOI6IDP5O8xpnLl/DsnCR\nVELw6fEz9DuxceJL3PXv2IofXz7hc/FcVlyUOPYOpXj2xl9m+3l4X6PbhDlMWrCOHUfPYWufvsD/\nnk0r2bpgCksmjqBO1YrZyqT+l+2HT+Lu7ceGPcdS9T+IRCLWLJqBy9bVXDu0lbIO9mLbzjPB6m+a\ntenEQY9rLNlxSKpjWh88e0V+IyMsrApLzKaKigqDRk1i9/GLzNtygG5jZ0lMC00RUQarqdHT00tX\nvioiMoqVOw5QpH5btpy8xLi5azhz/SkduvdFXV1dxp5mTI06DShRtjIrd7jI2xUlckZVVZXlm/cz\ne+02uY2jtLW24n1A+nXUnu5uqKmqcvuFP6u2uaY0Wf2XDt37UaNuI0J+hCkVTP4hOuYXl2/cYfGW\nvXTtNzzD9fdv+3HO/TBTh/QWey9VVVWWThzOyrmTFKJczrZocfzfvyc+PjnwPnDyLDuOSEe6z+/+\nY/pMns8Wl9M0aNoqwx4EY1Mz4uLiJerDlZt3mbF6C1tdPchvmLpp8tRRFy6dOsL1Q9soYvV3iar0\nyHPBKkDR4iU4dvGuVMe0el69Qa0G2S8BSItSZStw4vJ91MxsKNeqZ66dSx0ZJURHV3yJlNyKnr7B\nH5nVoC/fmLxsAzb12uL7MohNLmfYf8qHeo2byaxpKqtMXbiaNbtdFXZMshLZYVfMgeGTZtNh5FQG\nTF9Eo76jpfbFnub+1hYpdatpYWhkiJvXFba6eqSrULLbeQ1f3z3jyv5N2euYzyVsPXgM0yqNmbxu\nN1UatKBZ279LEyUmJjJ1ZB82zp6AaRY1a9s0rouBtjonD+/P0v2SREtbm4IFzXn7PvkBzNP3Nhf9\n7kl8nxdvA2g7fDLLnfdnupm7eJmKPJDgxMy3gR/pPGYmq7Ye/COj+/NHKEtmjWfPspmYmWRd+SNP\nBqvw75jW+m26S2VM67lrt6ndQDIlAGmhpa3N3BWbmb1iC53HzmLayk0Sf1KSNxFRQnTSqLfJq+j8\nZ4rV01dv6T15HqVbdCcEPU5eecCanUcoU76SnL3MPFaFi9Bz4AgmLdsob1eUKAB9Bo+mbe/hFKnq\nRIeBE1i01YWZq7fIpAbRvrDFXzOr7bsPYKurB4UsLNNd8/T+LYZ0batUBfiHGpXKoa2jw/Yj55gw\nawkaGTQaxcfF8eF9IB2aNszyngKBgJWTR7BuyUxiFaDEyN6hJM/fJpcCPH3zjjcSPjn49PkrTfqP\nYdLcldRr/OdI9/QoWaYCtx6/kEgZVlhEJC0HT2D01PnUTGNS59JZ4+naohFVy2VcAvI38mywCsm/\n2EPGTpX4mNaIyCgePntJtZp1JeDl36nfpCWnfB5x681nHDsPlPsQBEkSIYxWZlb/g56+ARdv3KLp\ngHE07DsaM4dqXLr3jllLN6TqtsxJDB4zjWsPnuB754G8XVEiZ1RUVOgzZDTd+w2lSav2HD5/kzM3\nHtB3yvyUo1RpUdTako/+6Weaho6bluGDYNCHQKn7mZMoU7wo7RvXZeOyuZlar6mlhWq+fETHZC+A\nqlm5PBVLFmPf9g3ZsiMJijqU4ekbfxITE3n91p+3Ae8l9vD1Iywcp35j6DFozB9SaxlRuXotAj6H\nYFixIYYVG1KyWTca9R1N78nzmbZyE5eu387YCMmNcx1HTad6g2Z07/9n89z1Kxe56XOBReOGiuVf\nWuTpYPU3Nes35vilu7ic85XImNbLfnepVLlahhIdksLErADbD5+lTa9h1Og8iG2HTihMR2R2iFQG\nq6kwLWjBOb+H1G3biysP3zN8wow/aoNyGto6Okyau4JR81crRJ2ZEsXBxNSMA6d8CIpMpPmg8VId\nn21nbcmHwPTLADLD5AWrmbLKmYWbdpGUlCQhz3I2C8YO4eSR/fi/eZWp9fkNDSUy4W7qoJ6cdN2d\nbTvZpUzFquw8epolzrsxNStAPlVVQkJ/ZttudMwvWg6ZSI1GLRk4apLY95sWKMi5my95FhzDxXvv\nWLXrGD1GzeLttyhWbNvHy4D3mbIzYcl6EtR0mb5obarXk5KS2L1lLeMGdmHXkhno6eqI7eP/owxW\n/8Hc0hrXs9fJZ2SZ7TGt53xvSkSyShwEAgE9B47goMc11rueps2wyYT+lJ2mrDSIEMYoG6z+w+S5\nyzl/6xVdeg9CQzP31MS1bN8VDX0jdhyRrkKHkpyHto4Omw+4Y1qkJHW6D+Xzt+8S30MkEuF+6SpC\nYfaC4ao16nD80j08bj5Bv1w9KrbtS5/J8zOt7Z2QkMC37z+y5YOiYWZixNQhvVk6a3ym1hsaGhEq\ngWA1Lj5eIR7knVq0ZdGGvVx48IYqNepQpIgdbwI/ZMtmQkICncfMoICNA1MXrMpWb4JAIMDQyJji\nJcuQlJTIs4e3uXNiLyN6dgKS+0YOn/Fi+srN3H70NCUJNmfdNhr2HskBd0+mLFiVqqEw+NNH+rZv\nxEW3vdz6H3v3HVdz+z9w/FUiM7TMJNmEtJSEZO89blllb2XvlRHZWqRhj6jMrBBFi4ysjEpmoqV9\nfn+4+d2+Qvuc8nk+Hvfjfjifz+e63sc4vc813tdRJzoatsrT+/1GSFb/Q7Z0aay27s7TMa0ikYiz\n1/wLdL3q79Rr2Jgj529RWbUxXc1m5uiMZUkTl5BE+QrCyOo3UlJSEr9pKjekpKQYO30eC23shNFV\nwU9kZGRYuckB415D0R9kzlo7ZyysNjPcchnTVm7M82fc5/gENjntZ8+xC3mOtWr1Guw7eZXrD16z\ncIMjZ6768/ZDTLaeLdmgFU27Ds5zDJJm2ojBPH90D9/L5/94b8VKlYmJzXuy+urte5SrVs9zO3kl\nJSVF63Ym7HG/gLWtG7XrNeRGyN1ctycSiRi/eB0JmaVYs905zzVR/8vAqAPyCko8DH+B0xEPuo+1\noHrr7th7+hAjo8AQixXUad8X8/mrcD5xDg3DzsTFx/Mp9uP32DyP7KNve0266jTm2gE71FV/vcY7\np4T6GlkYMnIcjZq2YOqo/ty6+4BVMydkWaokK09eRJCankH9Rk0KOMpfk5WVZcm6bcwwG4z5wjW4\nWS8tkklOfGIi5YQTrIo9n/OnWTBtDPPGj8j2vzPB30VKSorJsxejVq8BocE3kVdpRvOWyvic86Sr\n2UxO2K6nYoWsd+n/iVz5cmRkZKBUpWq+xVtBTo4W2nokJsSjUq3KH+9fY/t1ytpnv32+xSApZGVL\nsXHeNOYvnIHH1Tu/LOuVmJBA2IN7NKiT93KPvkGh1KxdN8/t5Lex0+YxvKcRJgY6aDZpmKNnHz9/\nydItjoRFvcfNwyffSxLKli7NkvU7GDdmEEbtO9Jx6HisnLpRSrY0h10dkS5RgvDnUeyOjALA1WEr\n6enpWI4f9u/hPVJIZ6ZxzmkLLZvm7L1lh9Tv1jZKSUmJwj8W/bWPuRXz4T0zzAZRRpTCwU0rUZSv\n9MdntrkcxDf8A2u2iX+9zJekJIZ0NWBkj/ZYmg8Xdzg5ZjB4HNOXb0ZHv424QxEUgPT0dLasWcyJ\nA84c3LySNjqa+dr+Z1llKtWshUgkKnrf1HLpb/vMzsjIYNX8abgfcEFXsxmdDLTp3KYVzRrWy9Go\nk7JeFzyvhqJctVq+xfYp9iPtNdX4HHLpt/e9fPWa2kY96WVihIe9Tb71L0lEIhHGppPp0H9Ulhtx\nAA65OuJ36gCedtZZXs+uSzcC+Gf2ck763qOyvEKe2ioIp44fYuMyS4KOO6NQ+c85Rcj9h6y2c+XK\nzSCGj53KqAkzCvWAl6UWE3h5P5CVM8ZhpNuSjIwMUlLTSE5JITkllS/JKSSnpJCSmkaT+nXydLxs\nSomylK7dOMvPbGFk9Te+HdO6ceV8tPqOxH37WrQ0Gv32mTO+AXT958/FjwtDmbJlsd3nyYCOumjU\nV6ezkb64Q8oR4VCA4uv92zfMGjsEWVEKIR6ueaq/J/h7lShRgqXrdzBr0RpuXvfB99JZHGcsJT7u\nE85rF9O1XetstaMoX5mPMe/zNVl9/SqSmtWyHq31Cw7l9fuv62+td+0HYFC3n8v+FBdSUlJsXjiD\njqOn06P/UOQq/pykBd/0JSb2E4+fv6S+Wu5GV9/HxDJq3kqstjpJZKIK0L3vYO6G3GLwjMWcc9r8\nw2xS0pdkQh8+IeTBI4IePCbo3iPexnxkzGRLltod+2WN3z/5kpREcvIXRCIRJUuWyvII1qxcOO3B\ntQunuOO19/vMhYyMDDIyMpQrWyZXseSWkKz+wbdjWpu31KPzmPGsmzMZs4G9s7w3JSUV31tBrHI0\nKeQof616zVps3XMU0xF98T1on+sPAXFISEwU1qwWQzevX2HW2CGYD+jJsmnmwtS/IM8qyMlh0rUX\nJl17ARDo78sI0z4c2bqadq20//A0KMlXJubD+3yN6fWryO9LAE5f9sXzki8Th/Xj0bOXTFu9GU2t\nrwXcNfSNUVRRR7oILtXKieaN6tPL2JAdG1Ywf6UNaWlpRL18jlrd+gCs3OSIs91m9AeaYz6oN4sn\nj8lRzdqMjAx6TZxNz0EjaGtSuBucc8pyyTpG9zNh0tL11FOtSVDYE0LuPyIi6hV16zegsUZLGrU0\npvNIC5q20Ppjjdo/adNMhYz0dKSlpEhMSuLOy7g/btJ9/SqKRTPHcnz7mlwvsclPQrKaTV16D6Bu\nwyZMMu3NzTsP2LbYAlnZH9eM+Abepl6DRhKxC/G/tFsZMmPhanqOt+TWsT0S8RcvO+LiE4SR1WIk\nMzMThy3rcLGzwXX90iI30i8oOrRbGbJ592EGmg3ipMNG9Fr8viC5knxlPuZzshodFYFKNWXmrt/O\nXi9v+g4dTRfzWSQnJ7PX04dGTZt/v3fRjLFsdD7Eh9hPmA3qU2xPwFo9awKNuwxhyKiJfHj3hpH9\nOuJ05CytDNtRqlQpxk2bQ++Bw7FeNpsWvUx5evFYttt2OupFunRpZi2yKsB3kD9kZGTY7HSE5ZYT\nSYlJR7PjQIZbaKJev1GBHI8tU6IE907uo5qyIjL19Sjxm+OAP3+KxWnHBvY52bJo0mhaa7fI93hy\nQ0hWc+DbMa3zJo+kzdDxHNu+BpXq/z/Nc/aaP4aFXLLqS1IS6elpf1zDMmTkOB7dv8OQGYs46bCx\nSIxmJSQm/rtwW1DUfYr9yJyJpiR+iCbQ3fmHfzcCQUHQNzJm7XZnupiZMqhrB6aYDkSjQdabbpTl\nK/IxJp9HVqMiSEpM5OTl63hdu4u8giKTLRcT8/4tZcv9OGCw0GoLxw44M33OFFppaqDTTHwbdAtS\nFUUFZo8dzvols5ix0AqZEiWYPnoAjofP0kzz6wh4lWrVWW/rRn2l7Kcnn+LiWbTJDsfD54rMZmIF\nRSW2Oh/N1zajIl5w/KAL5crLUUGuIhXkKlK6TFkyMzKJS0hEvpLc92n8/xUfF4ez3SZc7bfSy8SI\noOPOqKnUyNf48kIoXZVDFeTk2O7q/v2Y1ks3Ar5fK+gjVrMyf8oojDXr4H7A5Y8HASxYvZm49BLM\n37CzkKLLvdTUNDIzMymVx+kPgfiFBgfQp50mTWtW5so+WyFRFRSa9p17cObGA8qqatDZbCaGQ8dz\n0OvcT0dTlysjy8cP7/K17/jPsVy4EYB6vQbIKygCXyu1XPc5j4l2Xd5Ev/p+b5myZXn/9jUVypfj\n2LnLhNx/mK+xSJIZo4YQ8SSMHkYtSE5JQb1WDZZZ/rzpKicH2yzb6ohxl940bd4yP0Mtcu4E3cLz\nwG7ingZw/4oH5/bb4rZlBRZmQ2lQR5Wo11//jttvXsvEYT0Z0kWfLnr10auniG59Jd49DMD/yC72\nrF0kUYkqCNUA8uT6lYtYjBvKbLNh/NOrC026DeXm4/e/LM2R3wL8rmE5djD7bVYwbaUN5RSqssLG\ngdp1fl2yI/ZjDP2MtVg9w5zhvSV3XU9M7CfqdRxIYHjxKpL9NxGJROzbvZNt65Zit2JOns78zg2h\nGoDgv9LS0rh4xpN9u7YS/jgMN+ulmLTWY5vrIVbZumC/z4vm2nr51l/yly8E3bxO+QpyNNfSBb6u\nqzTRUkerkToRHxOpVl2FV1Ev2LTrMDVUVLkbEsiF0yc4um83VrPG/3J/RFGXmZnJ05eR6PQbzeiJ\nM/nHbDIKikrfr4tEIuoqSCMKD/xjW2FPn9Nm6HjO+D/8oY2/UUiAP6P6mdC0QT3OOm3+YcmftaMb\nc9ZuAWBg904M6W6CYuVKKFSqiELlishXrEipUiXFFTrw+2oAQrKaR9FREUwZ0Zcvn2Oop6HFVufs\nr7HJi4yMDPoZa7FgzECG9upCeno6m50PssbOhTGTLTGfOpuSJbP+i/fowV1Me7Xj7O7NaDdrXCjx\n5tTzyFe0HT4Fn9C8nfYhEI+E+HgWzTDn5cNQjm1fQ93aKoUeg5CsCn7F79plZowZSDu9ltx/Hs3O\nfZ7Uql2nwPtNTU1Fp64C4RfdWW27h0bqqiQkJbPJ5TC7j3h/r8/9/OljRvUzYcaIAViY/VPgcYmD\n7b6jXLgfhbWt20/XviWrCXev/XbXedTrt5jOWYFhjyGMnjCjIMMtEjIzM/E+eZxllhN4fePUD8v9\njp+7jNMxL5ZNHfvHqkbi8rtkVVgGkEffjmk17NSbfv+YFVq/x/bvoYKsNEN6dga+Lti2NB9O0AkX\nQq+dpm87TUIC/LN8tkFjDVZucqTPpDkFcnxhfvhatqpobAQT/OjRg3v066CFUsk0/I/uEkuiKhD8\njn6b9qzYaE+JyjU5dM6/UBJVgFKlStGqtREXb9xiy2ILJgwbgKX5cNZZTmREn/acdD9EfFwcanXr\nc+D0dWwPn2SRjV2OpsSLiov+wbQy+nXlnKGmZmj2HkFg6IPvr32OT8DjvA9TlltTv9Mgmvc0RVG1\nAcPNJhdGyBJPWlqa6KiX9OzQ5qd9KX07t8fLwUZiE9U/EUZWi6D4uDg66dbjlP2GLEdGRSIRh056\nM8NqM516DsBi8dos66ptW78Mv3Mn8Nm3M0+FfAvC9cDbTLd24LD3TXGHIsiB4wddWbNoJhvmTWNU\n/x5ijUUYWRVIkszMTD7FfuSsxxHCfE+xb+PyH657X/PHevcB/INvo6tvyDrbvYhEIswGdMKoRQO2\nLrbI1+M1xW36qk0kllFiwapNv7zn1PFDrJgzmZ7GhjwIj+Deo8doaevRql0nWrfrSGONFsXq9yQ/\njOjdjp76GswdP0rcoeSYsAygmFm31JKU109xXrf4t/d9/PSZ2eu2c9b3JovXbqdTj74/XM/MzGT6\nmIEolkzHed1iidpFefbKDda6euDknvfzugUFLyU5mRVzpxB0/SLHtq/95a7rwiQkqwJJ4rTTBusV\n8+nUvQ9+Vy7w7ubZLBOtpC/JLN+2i+OX/dhz7ALlK1Rg3JDu1K1aCee1iylZsngU8Xn15h0a3f/h\n3K1Hv11rGh0VicdhN5pp6aGla0DpMr9eFvA8/AnKVar9dbNyK+dO5tqlc5QuXYb79+8B0KiuGg/O\nHRFzZDkjJKvFyPPwJwzqpMf9MweoqqSYrWeu3Axi3OK1qDXQYPG6HVSrUfP7taTERAZ30ce8bydm\njh5aUGHn2OFT53E5f4ttLu7iDkXwBy+fhzN1ZD8aq1Zl1+oFyElIHV8hWRWI27Mnj3gcdg+RSMQy\nywkc2ryKA6fO43T4BHdPH6RxvV8vP7B2dGPrXnec3S9QtXpNpo7qTzmS8bSzlqiBhbyYtHQ9IoXa\nzF66Ls9tvYp8SZdWX6e4VWqp0kxLj2ZarWjWUpcGjTV+uYejqHv5PJwBJjpc2WdLekYGX5JTiE9M\nomKF8n+sLyxphONWi5F1i2cxe+zwbCeqAG31tLjjtRcrW2d6t23O1LnLGTZmIiVKlKBsuXLY7fdi\nQEddmtZTo6NhqwKMPvviExMpJ5xeJfHOebmzZNY4lkwZwxTTQcXmh6hAkFfv3rxmWI826GtqICUl\nxaJJYzA20MHYQIfl08ZS5Q9HDM8ea0rlihX4p6cRjofOsGnXIXTrK5H0JbnQj7rMb1+Skzl29hIB\nd8NIkw7PU1seh/dyPzSI0KBbjBvaj3Wzp3DvcTi3Qu/h73eW/fY2vIyMomHjpl+TVy09mmnqoFqn\nbrFYQuDmsAXzQb1pKgGzWQVJGFktQnwvn2fZTDPCzh766fSs7Hrw5BljF6/lS2YJVm/ZTYPGGgDc\nunGVaaP6c/2gA/XUauVn2Lmyec9+7rxLZfHabeIORZCFtLQ0rJfN4bzXEY5sXY1uc8n7Bi+MrArE\nRSQSMXZQVwwaqrBq1oQ8tXXopDdzNtqz7+RVuhtqEHb2ENWrFM0STXfCHuN42IP9nudo3lKHgSPG\nY9ylZ65PbRKJRGiryzPbbBhVFOUZ2NUky5md+IREgu8/5Nad+/iHhhF49wGf4xPQN2zHchsHFJWU\n8/rWxOKc5zGWWIwn+IRLsahfLSwDKAbS09Pp1aYZa2eY0adTuzy1lZmZya7DHiy0sWOgqTlTZi+l\ndJky7HeyZZ/dRm4e3S32qdyV23bxRloei0WrxRqH4GevX0UxY8xAlMqXxM16KQqVK4k7pCwJyapA\nHL4kJbFl7RKCrpzF/8jufFljqtN/DOaWK3gQGsT9Gxfw3rOlyIwKxickcsDrHPaHPQkOvUf5CnIY\nd+5OuTJliI35QOsOXRk6anyWszIikYikxEQyMzOz3CT84f07uug1ICbwfI5ndd5+iGGL8yH2nbqI\n/YFT38uGFRVH9+3BZuVczuzahGaThuIOJ18IpauKgQN77KimIEfvjm3z3Ja0tDTjhvQl9OQ+PjwJ\noXvrJlz3ucCwMRPRMuzA0FlLyMjIyIeocy8uMYnyfzhCVlD4fC9506+DFn3banHSYaPEJqoCQWFL\nT0/nsNsuOurU5eOzu3jYrs+3zVAzRgzEzX4Tky2XEPslgy0uB/Ol3YIW9fotzXsO56hvKBWUVahY\nsSJDe3REt1Yl2tavwshOerjv2cbYwd1YtWA6FuOGMqafCb3bNqdN0xo0rVGWVg2UMWqmwsUznj+1\n/+zJQxqoq+Vq+VEVRQWsLCexaroZpr3acuXCmfx4y4XC2W4L29YsxGevbbFJVP9EWLNaBHyK/cj2\n9cu45LYjX9cEVlNW5PBWK05d9mXi1JHoGBoze+l6ZpoPZtEmO9ZYiq923eeEJFTKVxBb/4IfZWRk\nsN16OYed7Tlos4L2+triDkkgkCgjerdHJuMLx7evyfeNLQO7mmC5dhvhjx+y0fEAA0x06KCvQ7OG\n9fK1n/z0+t0H2ptORruNCc8fh1GKVPyP7Kaheu0f7uvbqT32B9xJz0hHqW4jlOQNUKxcCSWFyijJ\nV6ZsmdLcunOPXhPMSUraQs/+/78ROPzxQxrWUc1TnKZ9ulGnZnX6TxnJhFmLGTFuap7aK0gikYgd\nG1biecAJ34MOqNaoJu6QCo2QrBYBW9cuYUAX4wIrB9S9vSEPdFuyeLM9vdo2Z+y0ubg5bKV5g7rf\nDx0obPFJSZQTklWJEPPhPbPGDkE6OY5gD1eqKWd/c59A8LcIux/KyyseVJLL/8+tUqVKMmFYX5x2\nWLNuhwtzV2xk2KwlBLjvoUzp0vneX169j4mlw4gpfE5M5tIZD5ZPH8vEYf2zXLogK1uKaaOG/LY9\n3eZNWTVjPO4nDv2QrD578oDGdfK+x6K1dgv8Du/C2HQKtdTq0q6jZB5FbrNqAVdOH8P3oH2ONlkX\nB8IyAAn3OOw+J48dYOWM8QXaT/lyZdm0cCZnHG04fXA3ycnJjJm3kqC7YQXa7698PcFKSFbFLdD/\nOn3ataB1I1Uuum4XElWB4BcK+pSpyf8MJPTWNTatXki/oSOp3bAZ/SbN48jpC8R+jivQvnPi46fP\nmIycSll5ZWSkRAQed2by8IF5XmMb/OAxLfQMf3jt+eOwn0Zqc0tNpQabF07Heqml2JfB/c77j7F4\nX8v6dMriTEhWJZhIJMJq4XQWTRqNonzhrA3U0mhEgPse5pgPQ0pKit4TZ/PmfeEfyRqfkER5oXSV\n2IhEInZt38CUEX2wXz6btbMnIyMjTMQIBL8iEokKtHSbonwlru23x/fscVbMncLqLbvR6TwAO4/L\n1DLqhd4AM5ZuccA/5K7Ykq3P8Ql0HDWN+pqteB35gj1rF1G7ZvV8aftaUCjardr88JqUlBQPn73M\nl/YBepm0RUGuDO4HnPOtzfxksXgN/YePZebqzXxJThZ3OIVKSFYl2KWzXryLfM7k4QMLtV8ZGRks\nzYdz/+whNOrXxcrWuVD7B4hLSBRGVsUk7vMnJo/oi/cxV24dc6KHcZs/PyQQCGNQhgwAACAASURB\nVAqcsqI8PvtsiQwLpptBY95ERzB2xgJW2tgjXU6eFVsd0B8wGqMh4wo9tviERLqMmcHLN+95dO82\ng7q0p7ORfr61L1uqJOnpaT+8tsTalvWOewkMfZAvfUhJSbHWYiK7tq3Pl/by2yFXR07sd+LKfjuJ\nXP5RkITSVRIqNTWVbvqN2Ll4Bl3aGog1loIeMciKTr9RJKWJ0GndDk1dQ7T0WlO9pkqhxvA3uh8a\nwrRR/enWRheb+dNzXc9XEgilqwSFqZlKeaKvnyq0sn/3Hj1lv5c3Z31vUb2KIo3rqNJYvTaN6qrR\nuK4aFcqXK5Q4vlm9Yzf2hzzoZKhHZ0M9+nRsl69Hwy6ysWPfqYsMGjke8ymWlChRAoDTJw6zafls\nQjxc8+X3Pj09nYot2uP/6J3EHdvaVkOF49ut0G7WWNyhFAihzmoR5LjNmjtXTnLa0UbcoYhFckoK\nQXfDuBEcyrXge/gF36GUrCxauga00DVEq5UhDZs0K7ZH6BU2kUjEQRcHNq1awI6llgzu0UncIeWZ\nkKwKClOzmuV47Xem0JNESfEtlyiogQ2RSIRv4G3aDRtP8LPYH+quLp45jsyPUezftCLP/T8Mf4He\ngDGc83+IclXJ2m1v1LQm1w/aFdsqAMJxq0VMfFwc9pvX4HfYUdyhiE1pWVlaa7egtXYLZvP1g+rp\ni0huBIfiG3yV+c47iHgVTbMWLWmp1wZNvda01NGnYqXK4g69yElKTGSpxXge3r6F70GHfNuwIBD8\nbe4+eoqBVnNxhyEWBT37JiUlxas376hfv+FPBwQstNpC/w7aOB31xGxg71z3ERP7iR7jLFiwykbi\nElWARhotOHz6ArPHmoo7lEInjKxKoJTkZLrqN2L7ounCesHfiP0ch3/IXa4Hh+IbfI+g0HtUr1ET\nLT1DWuh9XTpQu05d4bz63wh//JCpI/uh3Vgd+xVzi/yZ4/8ljKwKCtNBFwe2r19GY/XazB9nirGB\njvDZk4/sD7izdNtu7PafpJmmNunp6Xgc2UdCfByduvclPu4zw3saccltR67qz2ZkZNBhxBTqa7dh\n3oqNBfAO8u55+BMGddLj7qn9RfbI3d8RlgEUQTeuXmLexH+4d/pAgdTtK47S09O5E/aEG8F3vi8d\n+JKcSkvdVmjqtUFLrzUaLbSR/csWpv+K59H9rJo3FSuLiYwd3KfY/WAVklVBYUtNTcXz8F4ctq6l\nYllZNs6dQrtWwgEaeSESiVi6xQE3rwvsPupN7Tp1ifnwnumjByCdlkgNZQVuP4ng8LmbXDrric2K\nOQS478lxHdLYz3FU0+9KwJMPErdWFb7+PmxcMY+jB1zp0KolBzatyHEbN2/f45TPdQZ0MZbIAyWE\nZLWIWjxzHGWSP7DLaqG4QymyIqPfcCM4lOvBd/ENvsujp+E0bNyUlq0MaalrSEtdA5SqVBV3mIVK\nJBKxYu4ULp0+jqeddbE9rk9IVgXikpmZieM2awIvenDRZZu4wymy0tPTmbBkHQEPX+B4+CyKSsrc\nvR3EZNM+mPbqyKqZE5CWlmbiknU8fpeA/YGTbFu/jNBr57jgsi3H5fbaDZ/EgLGWdOtTuBV4smOH\n9Qqunj5K65YaHD93mQfnDmdrJkwkEnHpRgCr7Fx4GhGNcZdenD/ljmr1qkwe1pcBXTtQWlb2h2fS\n0tJztDlOJBLx5n1Mnutw/y5ZFUpXSbA5yzdw1jeA875/XwHg/KJSvSqDe3Ri6xILgk848+6WNxtm\nmqEqm4zHns10btUAY001LMcPY7+TLQ/vh0p0Qej8kJ6ezqePMSQkfWGYxTKmrtiAx3kfPsXFizs0\ngaBYkJaWRlG5KlUVFcQdSpGV9CWZ3hPn8PRdAnu9rqKopMzxg66YDejE5vlTWGM5mRIlSiAlJcW2\nJZaIkmJZt8SSqXOWkSFbgcWb7bPdV0ZGBiu37+b+k2eo1K5TgO8q95K/JPHkRQRJySmccdpCubJl\nyMzM5O6jp2x1PkivCXNQ1utCNf1uNOg8GL0B5nQaPQPN3iOZsHIz3f+ZyIWgcJau347PnQhGzliC\n3YmLmIyaRmZmJvA16bTdf5RKmu3xOO+TZRwikQjno15Ev30PQMj9h7QbPonabXvRcdQ0bgTdAeDB\nk2ckfcm/WrDCyKqEu3LhDMstxnHv1H7Klysr7nCKnczMTB6Gv+B60B18g7+uf333IYaWWrq00DOk\ne98h1FBRpXSZ4rOW85uMjAzuh4bgd+UC/lfOExR4k0b11DHR18JEX4fW2s1/+sZdlAgjqwJxsrWx\nQurdY9bPldyz5iXVh49fNzrVqK+B1VYnANYunsU1by9O7FxH0yyOHv/46TN6A8wYNXUeHbv3pW97\nTRxWzKF7e8Of7v2vV2/e8Y/FUlKkS7PR4QBVq9cokPeUHz68e8v+Pbbsd9qJao1qPI+IooJcRVoZ\ntkevrQnaeoZIS0sTH/eZuM+fiI/7TMmSJdE3Ms7yBLGMjAwGdtRl5rBeDOjagXGL1xLy6AUTLBaz\nYs4k/A7vop7a/x9nm5mZyZTlG/C64o+0KIM2Opp4+95k2rwV9B08guOHXLGzWU1Z2ZI8efYcL8dN\nOdp3IywDKOLmTh5JlZIpbF86W9yh/BXex8TiFxKKu7cPfnce8DIyisqVK1NLVY1atdWpqVaXWmp1\nUVGtQy01dRQUlYrFes+UlBRCAvzwu3IBvyvneRR2H+3mGpjot8TEQBetpg2L1ClWQrIqEKd1S2dT\nVeozy6cX7FHZxc2LqGg6jZ5Gh56DsFyy9vv61EqlROy3WUHlir8+2fDJ8wh0+o/G+9ZjXoQ/YcqI\nPgS47/mh1JNIJOJLcgoJiUn4Bt1m4tL1/GM+lYmzFn6v3SpuqamphD8O48Hd2zwIDeZTzDvklaqg\noFiF6rVqY9y5J8E3r1O3QSOq16z15wZ/IzQkkAlDu6FQqRKNWrZi+QY7ypQtyxTTPozuok/39oYE\nhN6nrqoKczfs5MnrWBwOnsLn/GmePQljzCQLKshV/CH2kAA/rJdYsHT8UHp3bJftWIRktYj7/CmW\nbgaNObx5JW10NMUdzl8nIyODV2/f8ywiivCIKMIjonkaGc2zyFc8exlJaloaqqq1UaldBxW1uqjU\nrkut2uqo1K5DDRVVSpUqmoX14+PiCPC7yo0r5/G/coHo6Cja6GjRyUCLDga6NKqrJtFJupCsCsQp\n0P86CycP55H34SxHtQQ/u/3gEd3MZzFu5kJGjJtKaEggU0b0ZWTvzqyYMS5byeSY+aspq9KY6fOW\ns8d2E7u2rqWinByJiUkkJiWRkJhIqVKlKF+uHMpVq7PUeifarX4/+lrQkhITObrPiQehQYSFBhP+\n9AmqKjVp0bg+Wo3qoaxQmfcfP/E2Jha/2/eJTxOxwW4fdeo1yJf+bW1WI6+ozCBT8++f6QunmZH4\n+glB95+goFyFqMgIWuros8XpCGXK/jjLmxAfj+eRvfic80RDqxUdu/fl5bOn2Cyz4I7XXsqWyd6m\nZiFZLQbOnzrBhiUzc/QHLygcn+LieRbximeRX5PZp5GvCY94xfPIV0S/eUsV5SrUUquDiqr6v8ms\nOrXU1KlVu06Rqgv74d1bbly7hL/PeW5cvUhaSjLt9XXo+G/yWqu6ZG1UE5JVgTiJRCJ6tWnG5rkT\n6GjYStzhFAlq7fpQSbk6G+z2EnLrBmsXz8Jh1Tz6dTbOdhthT5/TY5wFr9++o456XarWUEFVvQGN\nNFrQ1qQbFStVlqgZom/lAxuqVqWbkT4tGtenaf26v/w5LxKJ2LnvKEu3OGK5dB2DTM0LJC6nnTZc\nOefFrCXraK6l+8uTLM+fOsG8qaNp10qLgZ3bE3jvIa7HT7Np1yGO7duNqlwJtiyama0+hWS1mJhh\nNpj6CrJsmD9d3KEIsiktLZ2I6DeER0TxLDKKpy9fER71mvCIKJ6/jESmZElqqdb+uryg9r/LC2rX\nQVVNnarVa0rMtFRWIl4848aVC/hfuYDftctUrFCODvramBjo0L6VNorylcQan5CsCsTtgLM9N88c\nwtPOWtyhFAmR0W+w3e+O42EPKlYoj4fteprUV89VWwmJSTx4+oz7T55x9/Ez/G7f5/3nBJas24Fh\n+475HHnunDp+iOWzJ+W4fODtB48YNH0hLfXbY7XN6Y/3Z2RkFMjPkg/v39GzjQbHt6/54TCM877+\njJpvhauHD8N7GmV7VlhIVouJmA/v6d66CV521ui1aCrucAR5JBKJiIn9/DWRjYjiaUQU4ZGvCY98\nxfOIV3z4+JEaNWqioqr2fURWpbY6qmrqqKjWkahagJmZmTwOu/dv8nqeW/7XUVOpiYm+NiYG2rTR\n0Sz0DYJCsioQt6TERIyaqXDb003iZh4kWUpKKtLS0jkqn/QnIpGIk5euMW2lDY2a6zB/9SZqqKh+\nvx4S4E8FuYrUbdAo3/r8ldTUVNYtseDKWQ+ObltDy6bZKx+YkZHBOgdXNu05yNwVG2nXqTvv377m\n3ZvXvHv7mvdvonn/Nvrf/7/m3ds3vH/3lpKlSrHnqDfNWurkS/yvIl/ibLeJ4wddmTFyCEummv10\nz8zVm3j0Lok+Q0aybuE0bnu6/fEoYiFZLUZOuh/Ebt1iQjxckZUtmmshBdmTnJLCi39HYb8ms/+O\nyr6M4mVkFOXKl0dVVQ0Vtf8fla1Vuw61aqujVKWqWNfJpaWlERocwI2rF/Dz8ebenRCaN2mEib4W\nHVvrotusCaVKlSzQGIRkVSAJVs2fRlXpRKwsJ4k7FAHwJTkZa8e9bHE5xKgJMxkyegI2K+fje/E0\nqWlptO3YndVbdhfYrFZ0VCTTRvenRuVyuK5f8tsNY/+r8+hpeF+9QdmyZUlPT6dsmTJUVVaimrIi\n1ZQUqa4kTzUleaopKX597d/Xr9wKxmyBFbuPetOkWe73vYSGBLJn+3quXT7PmAE9mT5yMCq/+BKW\nnJKCTr/RmE6aS9i9EK55e7HbagFt9bR+2b6QrBYjIpGIyaZ90FZTZtWsCeIORyAmmZmZvHkfw7PI\nV4S/jCI88hVPI6J5FhnNs4hIEhISUKmlSq3adaipVu+HEVkVVbVCO8VLJBIxqp8J795EkymSIjU1\nhdTkLyQmxGOgrUlHfS06GOjQrGG9fE+uhWRVIAlePg9nUCc9Dm9ZTXt94TQrSfEiKpoZVls4ecGH\n0QN7s3HeNGRkZOg+dhYqjbVYun5Hvm8gvXrxLHMmjcBi9FDmjDPNcfs3gu4gJSVFNWVFqiop5Ki0\noPu5S0xYas0+r6uo1//9SO7GlfPxOedFu869MO7Si5gP73Davp7oiOdMHzmIsYP6IFfhzzN7dx89\npf3wSRw5f4snYfdYNnsivdq3Zv2cKVk+LySrxcy7N6/p2UYD7z1biu3pQ4K8SUhM+lqtIOLV1woG\nkdH/JrNRREW/Rl5eHtXadahZW/1r9QI19e8VDOQVFPPtQ/rCGU+2r5rDjqWW+Abd4VrQXfyCbiMC\n0tLTSUlOJiMjAwV5edq10v43edVFXbVmnmMQklWBpPD39WH66AGc3mWDTrMm4g5H8B8fPn76YX39\n5/gEjIZOoGPff5hkuShPbV+7dI7DLvakp6WRkpLM4wd3ObBpxW9HFwvS9JUbkanRhMkWvz4V81Xk\nS3q3bcG+jcu5EhCC1+UblCkti8WowQzoapLjpRmb9uxn39nr7D/tS1JiAuuWWOJ78TT2K+bS7X9q\n4ArJajF0bL8z+3auJ8B9T76u6xEUfxkZGUS9eUf4yyieRb76vlb2Wymu9IwMVL9t+vqfmrLVa9ai\nZMnsTd9nZmbSu20LrKaO/KHWXmZmJmFPn3M96A5Xg0K5ERTK84iIH55VqVEdY30dOhpo08FAJ8fn\nfIOQrAoky8UzniyaYcZlt500rieZpyQJvnr97gP6g8yZYLk027vtz3keY+v6ZZzyvUtKcjIbls/l\nnOdhVs4YR2U5OaSkQF+zGcqK8gUc/a8t3mRHQnkVps5Z8v21lJQU7oYEEHDjKoE3fAgOvMmcsaYs\nnDQ6X/rMzMyk0+jpNG3dkWlzlwNw4+ol5k4yZa3FBEz7dPv/WIRktfgRiUSYD+xChxZ1WTR5jLjD\nERQjsZ/j/i3F9erfUlzRhEdE8zwyitdv31G1ajVqqaqhql6fmYusqCyf9ZGSZz2OsstmGUHHnf84\nSvr63QeuB93mWuDXk8Tu3A/74djbRvXrYWKgjYm+Nm31tKiYjSkoIVkVSJoTh9ywWTGHawfsUVPJ\n/UlJcfEJnLrsy+AenYQargUk+N5DupjPxP/Ruz/ee3SvE1aLZlCyZClcPS5jMXYojWtXx2HVPOQr\nVfzj84VlsY0tceVqMGLcNJxtNxF4w4e7d0Kor16HtjrNMdJugaF2C5QU8rekYvTb92j2NmXnXi80\ndb6WcXscdh/TXm05vnMdhtotACFZLbaioyLo006TK/tsc13eQyDIidTUNCKi37B6pxOXAkLxvHrn\nh9NLvsnIyKCnoQY2s8f/NNWTHQmJSdy6c//70oGbIXeIT0gAvp67rtVcA5NWWpgY6GCg1SzLtVtC\nsiqQRG6O23HdaU2wh2u2vnT9r/uPwzEaNoGPsbHEBF2UqGSoOHn8/CVdxlpyIehZltcfPbjL47D7\n3A704+LJo+xcNpueY2eiIC+P9dypjOrfQ6IOTfmSnEyjLkNYvtkJ2w0rqFtFDtPeXdHX1MjW+tO8\ncj93CYv1tnhcuUP5ChWAr2t4504y5cahXair1hSS1eJs/x47PFx34n9kl0TX5BQUH5f9Ahk8YxF7\nva7+ssyL59H9HLBdj/+RXfnygZ2RkcHdR0//s3TgDlHRrwEoLSuLvrYmJvpfk1etpo0oUaKEkKwK\nJNaoPsZY/tODXiZtc/zs0i0OPPiQivdJdz4FX5SohKg48QsOZcqanRy5EPDTtWuXvLEYN5S2etrU\nU61Oy8YNGDxtPgBPL51AXbVmYYf7R8u2OhLw7D3ySsp8fPEAL/sNhT4qP2beKhJLVWLNtj3fX9u3\neyd77Tbgf2Q3ZeWrCslqcZWZmcnIPsb0baOJpflwcYcjKOYehr/AaNgEbBwPYtC2Q5b3pKen01W/\nEfZLZ2LSWq/AYomIfsP1wNv4Bt/lWtAd7oU9QiQSIScnR1s9bdoYtWPO4iVCsiqQOJusFiOX9JrV\nFhNz/KzeAHPa9hyMt7sbtz1cCyA6AYDXxatsPnwOx8Nnf3g9LS2NHoZNsZk9gZ4djAh9+ITm3YdS\nSa4C726dl8g9JC9fvUazlynm0+bisc+BW8f25GpUP68SEpNo3nM4lstt6Nyr//fXrRbMIPyOH54u\nDsjVb5HlZ7aw2KWIk5aWZtWW3ayxc+HJ84g/PyAQ5NL7mFi6mc/Ecsm6XyaqAJ5H9lFNoSIdDHQL\nNJ5a1asytFcXdiybTajXXmJDLnN2zzamjRhEXEIiS63WFmj/AkFuNdfS41pQKL8bLMpKTOwnHjx5\ninLV6qirSN7oXXGiUKkigbf8mDdlFIH+vt//rNwct6FWTYkexm0AUK1RjasHHYkNuSyRiSrATKst\ndO41gD07NuBpZy2WRBWgfLmy7LdZwVLLCbyJfvX99bkrNyIjp0T/cdN++awwslpM7LHbzOUTe7m6\nz1ZYcC/Id8kpKbQ3nYxmm85YLvl1EpiWlkZn3fq4rJkvtvIs33yQroySmrowsiqQOAnx8QzpaoCB\nRj1sl8/NdpJz6KQ3jievoqnXBql3j1k/d2oBR/p3e/shBhf3U+w6epIMkRQ1VVQJvROE3+HdNFSv\nLe7wsuXi9VuMXriWVobtaaVWmVlm4p+BXb7NkQshT3A66v19+eKXpCQOONuxepGFsAygOMvIyGBY\nN0NGdjdiiukgcYcjKEYyMzMZOnMxiSUqsHn3od9+GTrstgvvw7u55Lq9ECPMmrBmVSDJEuLjmWk+\nmMzEj7hvX/P9JKOMjAx8A2/zIurrmmwpKZCSkkJKSgrXE2fQ7zaEx/fv0K5BFcYP6/+7LgT5RCQS\nEXzvITGfPlNVSYFmDeuJO6RsSUtLp3nP4UxdtI6taxbhtmY+2s0aizss0tPT6T7Ogk8psMF+3w/H\n3qrLSwnJanEX/vghQ7oaEHTChdo1q4s7HEExsXCjLWdv3sXN04fSZcr88r7U1FQ6atflkM0yDLSa\nF2KEWROSVYGky8jIYO2iWfhe8GKNxUTOXw/A3fsySlWq0aCxBvA1Ufr2n7R0CWYvs8Zy/FCWjxtc\noGvCBUVfZPQbmnQbytXQCAyb1CA2+JLELFXIzMxkw659WO/ay0KrLfQa+A/w62RVMqIW5Av1+g0x\nnzoHswVWXHDZJuwSFeTZnqNe7Dt1kcPeN3+bqAIccdtFY3VViUhUBYKioESJEixcs4X99Rqy1tmV\ntp16sv/0atTU/3/kLi0tjTfRUbyKfMmryJcccnXgwb1Q6qjMEmPkgqJApXpVlBXk8Tx6AE2NxhKT\nqMLX/TZzxpliYqDD0FmL8fE+yfKNdr+8XxhZLWbS09MZ2FGXqUO6Yz6oj7jDkXgJiUnIyJTI0RnL\nf4vslKj6JiU5GRNtdU7sWCMxx0kKI6uCouzm9SvMmzySN29eo6yoSK0a1ahdoxq1qyvTQK0Wpn27\n/5UDEmlp6cR+jhPrSVBFyZTl1kQkSnPtwmnGD+3HxGH9UK1RTdxh/SDpSzIWa7Zw8oo/UVGRwsjq\n30BGRoa1O1ww7dWOrkYG1KiqLO6QJNr01ZsA2G3167OS/0YPw18weMYibBwP/jFRBTjgbI9mo/oS\nk6gKBEVZ5MvnTB8zkN2r59PFyECiRsTE7bJ/IFOWrSfM+4hQWzwbuhnps9LpKEfP32L/7h1o9h5B\nGx1Npg0fgLGBjkR84SlbpjS2K+bS8fJN+ptPzvIeYdt4MdSgsQbDx05l3OJ1OS6N8jcRiURcuH6L\nQ17neB8TK+5wJMa3ElUWS9b+tkTVN1+SknDYsoZVM8YWQnQCQfF38awXvYwN6dnBSEhU/0dJGRme\nvIjguPdlcYdSJJQqVZKM9HTU6tZn4ZotXLkTgU7ngUxavRWzBVbiDu8H3U3a//KakKwWUxNmLuD5\n6w/s9zz755v/Us8jX5GSlk63PgOxP3hc3OFIhOSUFHpNnE2XfsMYONwsW8/sc9qJgaYGLRo3KODo\nBIK/w7vXr6hTU7KmaiVF2TKlKVGiBFb2bsJgTDZcC7yDlv7/n5RWrnx5ho2ewP5Tvhw9c4FPcfFi\njC77hGS1mCpVqhRrdrgw02oLbz/EiDsciXTxRgD6bdozauIsdu47RmpqmrhDEqvMzExGzVmJQs26\nzFqUvW/ciQkJ7Nq2nhXTzAs4OoHg7/HudSQ1qghLuLJStkxp1NXrEp+cxsUbt8QdjsS7FhSKtn6b\nn16vLK+AYdsOHDrpLYaock5IVosxjRZa9P9nDJOWbRB3KBLpgn8Q+m070bBJM+rUb8iRMxfEHZJY\nLdnswJPXH1m/0zXbB0u4OmzFWE+Lpg3qFnB0AsHfIzkpieSUFHGHIZHKli5NQkI8Y6fOxcreTdzh\nSLS0tHQCbt+lpa5Bltf7DhvDnuNnCjmq3BGS1WJu2tzlhD59ydG/PBH7XyKRiMt+gegbGQMwcqIF\nNs6H/tpppT1Hvdh78gK2+73+WKLqm/i4z+zZacMyYVRVIMhXY6bOZfl2J+LiE8QdisSpXbMaZUqV\npIaqGo9fRBIQel/cIUmsO2GPqalSi4qVKv90LSMjgyD/a8TEfioSP/eEZLWYky1dmjXbnJmyYiMf\nPn4SdzgS4/7jcMqVr/D95Iz2nbrzMS4Rv+BQMUdW+C77BTLXegcOB0+jqJT9qUdnu810aatfZI4d\nFAiKipa6+rQ27szybbvFHYrEKVGiBDNHD8HNfhOjJ1uyRhhdzZJIJGLXEU+0DdpmeW3CsJ48vOXD\n9YMOElER4E+EZPUv0FJXnx79hjFtlY24Q5EYl/wC0W9j/P3X0tLSmI6bjo3zITFGVfhyWqLqm8+f\nYnG138qyKdnbhCUQCHJm9jJrXI6f4sGTZ+IOReKM7t+ToJs3aGXYnmsBt3E8eJyYWGEw5huRSMT8\nDTu5ducRMxas+un600dhPA0L5YLLtiJTr1ZIVv8SMxdZ4XcnDK+LV8UdikQ47xdEq7Ydf3htwD9j\nuOQXwMtXr8UUVeF6HxNL97Gzsl2i6r92b7emd8e21K2tUkDRCQR/N0UlZSbPXsrk5RvyNE2bnp5e\n7DaPli1TmnGD+3DQ2Y7NToc5fuMeau37cvLSNXGHJnYikYiFG23xvHILlxOXqCyv8NM9N65coIOB\nLjIyRacsmpCs/iXKlC3L6q1OTFiyvsiUqigo6enpXAsIopVhux9eL1+hAn2HjGC72xHxBFaIvpWo\n6tx3aLZLVH3zMeYD+/fYsWTy6AKKTiAQAPxjNol3cV9yvWP70bMXNO46BOVWnRm3aG2RWJuYXVNH\nDOSk+0EaNmnGdrcTrN/pyrLtTsXqPebGks0OHL/sj4vHZeQVFLO8x//qeTrqaxVyZHkjJKt/kVaG\n7TDu2huLNVvFHYpYBd9/SLVqNVBUrvLTtRHjpuN01IvEpC9iiKxw5KZE1X85bl3HoK4dqF2zegFE\nJxAIvpGRkWGptS0Wa7cRn5CYo2fPXfXDcMh4Rk9bwDn/R5y+6s+9x+EFFCmc8Pbh2NmLJCQmFVgf\n/1VVSZF6aqo8ffQAAJOuvfiU8IWrt4ILpX9JtGyrI4fPX8PF4zIKikpZ3hMaEoj/9au019cu5Ojy\nRkhW/zKzl1lz7noA5339xR2K2Fz0C0S/rUmW11RU1dAxaIPL8VOFHFXhyU2Jqm8+vHvLYbddLJok\njKoKBIVBu1VrWrU1YcX27G22EolEbNpzgBFzV7Ld9TiDR4xFqUpVOnbvw4nzPgUW57Z9x5i1zpY+\nk+cVWB//633MR5Srfv3SLC0tzejJlqx13Fto/UuS5dsc2X/GBzcPnyw3SviEuQAAIABJREFUyopE\nIvbvscN8YGec1iykqlLWo66SSkhW/zLlK1Rg1WZHzBeuyfE3dUnzLCIK/5C7OX7ugl8QrYyyTlYB\nRk6YxWaXw2RmZuYlPIm056gXbl7nc1Si6r8cNq9heO8u1Kz286i0QCAoGHOXb2DPsZPcunPvt/el\npKRiNn81ju7nOHr+Fjr/KQaf/OUL0tIlCixG+YpyDDObjF9gCEb/TMR09vIs7/MPuUt6enqe+xOJ\nRLx++44qVf9/hqffkJEEhobxLCIqz+0XJat2OLH31OWviWoWM4ZfkpKYM2kEBx1suHHYkf5dcrZH\nQRIIyepfyKhDF/SMTJi3Yae4Q8mR5JQUzl31Y/rKjdTvOJBWg8bSefQ00tKy/8GXkpLKrZBQ9Fr/\nXM7jG10DI0qVKce5q375EbbE8PH/WqLK8dCZHJWo+ubt62jcD7qwYMKo/A9OIBD8kqJyFVZv2U03\n81ns2Hsky3WZ7z58pP2IyUQniTh01u97WT6AuM+fOON5FLOBPQssRoWKcpQpW5bNuw8xbt5azl71\nI/zlj0njxeu3MBg4hkHTFpKSkpqn/j5++kyZMmV++NItW7o03foOws2jaBS6zw9Wtntw9jyPm4cP\nSlWq/nT9efgTBnbUpXRKLDePOlFfTTWLViSfkKz+peav2sTxC1clfn3P0xeRbHM5SFfzWSjpdmaJ\n3X5K1mjCRqdj3Ah7g6paHfxCsl8b1S8klHr1G1JBruIv75GSkmLkhFlscjmcH28hx1JSUrkWEMLK\n7bvoPGY6N2//fjQlOx6Gv2DQ9JyXqPovW5tVjO7fk2rKRWv6SCAoDjp278Ohs37YHjnDwKkL+BQX\nz9MXkbi6n2TC4rW06GWKdrtubHdxp1z58j88e2z/HroaGRTo1G/liuX5HPuRDl16YmBkTPe+g1m1\n04kXUdEAxMUnMGbBamxd3YmXKsPizfZ56u/V2/dUySI56ztkFK7Hz4hto1WXUVNZsc2xUPpaZ+/K\nbvezuHn4oFy12k/XvU8eZ0gXfaYM7cneDcsoVzbns2mSoujULRDkq4qVKrPM2pYx86cTenIfZcuU\nFndIAHxJTsbHP4jTV/04c8WPhC/JGHXoQnfTKVjtNvnpJA5D466cveqPkW7LbLV/0S8Qvd8sAfim\nR/+hWC+fw4Mnz2hcr06u3kt2Jaek4B9yF59bwfjcukNQ6D3q1K2HUrWaBITcRb1WzTy1n5cSVd9E\nR0Vw8tgBHp4TTwIvEAhArW59jnjfxGrRTJR0OqKkpISWrgEtdFvjMH4hjTVa/PRMZmYm+3Ztx23t\nggKNTaGiHA8/xXz/tdmU2WxcMRfdAWbIlpQhM1NEmw5d6di9D59iPxJ21TNP/UW/ff/DEoBvNDS1\nefXmLUlfkgs9OWvefSihD58wY/TQAu0nIvoNS7c44hMYyr6T16hS7cffh/T0dDaunM8Z9/2cctyI\nbvOmBRpPYRCS1b+YSbfenHI/wOJN9mxcMF1scTx5HsGZKzc4ddWfG0G3adK0GW1MurPZZS6Nmjb/\n7ekabTp0wXrBZKwsJ2Wrr4v+wUxcsO6P98nKyjJszCS2uBzCftX8bL+X7Ej6koxfcOjX5DTgNiF3\nH1C/QSN0DNthOmMJW/RaU0GuImMHd2Px5DEoylfKdV95KVH1Xzs3rGTckL5FpoC0QFBcyZYuzfIN\ntlguWfvbGaJvfC+fp0IZWQy0mhdoXPKV5Ih78eL7r2uoqGLjeBCRSETEi2ekpaaiVrd+vvQlEonw\nvn6TqjVr/XQtIyOD9PT0QhuASUhMolzZMtTv0I+nLyM577oDk9Z6BdLX2w8xWNm64HbiDENGjcN9\n/Z6fBnBEIhFTRvRFlPiR4BOuefr5IUmEZPUvt3jddrq3bsLArsa00tQolD6TviTj4x/4ffQ0KSUV\now5d6DVqOuucTZCrmP1/XJo6+jx9EcH7mFiUFH4+//i/EhKTCA17REtdg2y1PWz0RDrpNcDKYiIK\nlXP/Dz4hMYkbwaH43PyanIY+eEjDxk3Rad0eM8tVtNQ1oHyFCj8843v5PC8e32fqlsW57jevJaq+\niXjxjLOeR3ly4Wiu2xAIBPkrO4lq7McYtqxZxNTh/Qv8SM3KcnJ8+vTxp9elpKRQVVPPc/tJX5J5\nFhFFxQrlmb1uOw8i3rDD7cRP9yUmxFO+fLl8fb+f4xMQiURUrFD+h3bnWe9gg6MrGRkZAFx0s8XY\nQCff+v0m9nMc1o57sTvgTu9BwznrH5blRiqAo/uc+BD1jFvHnIpU0f8/KT7vRJAr8gqKLFqzldHz\nFnLb0w1Z2VL53odIJOLx85ecveLHqav++AXdpkmzFhiZdGer23waNmmW6w+WUqVK0aq1Eeev32RY\nry6/vfdaQAjNWrSkTNmy2WpbUbkKJl174XDoBPNzsKkoPiER38Db36f17z96TBON5ui0bs/EBevQ\n1NGnbLlyv3w+IyODNQtnsGHu1Dz9eXwrUeXm6Z7jElX/tXPDCiYPH5CnhF0gEBSu0OAApo7qz+Au\n7RjVv0eu2wl7+hy7gyeoWL4sZgN7oVrj57WRIpGI8zcCKFdeLlttlixVincfY3MUh8NBd5Zu3YVM\nCRk6duvNwTPuWVY0SYiPQ+5/1uz+ybsPH1FSqPzLn0PGppMJe/IMGZkS1KxejVrVq1K+TGlCHj3D\neoczsyaY4ntoF621f16G8V8ikYjgew/R0sjevoHEpC9scTmIjdMBOnTthceV2z9snPtfb19Hs2H5\nXC64bCtWiSoIyaoA6NZnEKfcD7Bi+25WW0zMlzYTk75w2T+Q01e+jp6mpKfTtkNX+oyZibWrSbZG\nBbLL0LgrZ329/5isXriRvfWq/zVywkwmDu2OpdlwSpbM+p/L5/gEfANvc9k/CJ+A2zx8Eo5Gi5bo\ntG7PtGUj0dRulaMyUYfddqFUsRx9OrXLUaz/5XzsJG5e5zly/lauSlR98zz8CZfOeuFw8Viu2xAI\nBIVHJBJxwNmeLVaLsF85l36djXPcRnp6Oh4XrrBt7zEePH3OINOxRMR/pmXvkWg3a8yEwb3pYdyG\nzMxMboc95sBJby4H3uPAmevZat+ka282rpjLtYAQ2uhoZjOmDAaZmjF/pc1v70uIj6NCNpJVkUjE\ntYAQrOzduHTdn/YGujisnPdTMv4iKpqXr95wOyKOpMQEoqMieP0qktevItHrXhqrhTO4ccQJ/ZbN\n/tin0xEPzOevYtmM8SydOvaX96WkpGJ/0B0rOxd0Ddpx8MwN6tRr8Mf3s9RiPBOG9qV5o/xZbiFJ\nhGRVgJSUFMusbenZRoMBXdqj2aRhjtsQiUQ8evaSM1euc+rqTW4G30GjRUvamHRn54RF1G/UtMCm\noQyNO2O7YQUikei3fVz0D2Lhhgk5artJM01U1NRxP3eJwT06AV+nZK4FhHD5ZjA+t27z5PkLWrTU\nRsfQGMvV5jRvqYts6dytl4qPi2Pr2iWccbTJ9e+Xj38gc9ZvZ6/X1VyVqPqv7euWMn3kYCrJVfjz\nzQKBQKzS0tJYON2MhyH+XD/kkK0yRdFv3yNTogRKCpV58z4Gh0PHcTjoQU1VNYaZz8CuZ39Klfo6\nwzNnmTVnPI6wztUeswWrSU5Opo56XZpr67P7qHe2l3CVr1CBeSttmLRsCSEervk6ChgfF0eZ0rK/\nvJ6ZmYnXxatY2bvx/lM85tPmst7lFK4OW2nZewRLppgxxXTg99mo4+d9MO7SAxkZGeQqVkKuYiWU\nqlQjIT6eVfOmcHSbVbYSVQD1WjWpLC/PvtM+iESwbNqPCWt6ejqux0+xbNtu6jVuzq4j3llumsvK\njauXePrgDl42BbuRTlykflfeQUpKShT+8e8+Z/dv4n7ABbftawk87vzLUcT/Skz6wiW/gK+jp1f9\nSMvIpG2HrrTp2B0Dow5UkMvelFB+MNZUw3PnGpo1rJfl9ZjYT9Ru14fA8I+ULFkyR217nzrBjtVz\naa+nhU9ACM8jomippYuOoTG6rdvRrKXO9w/zvFq/bA5fXj3CZf2SXD3/MPwFRsMmYON4MNc7/795\n+iiMf3q0IfziMeQq5GxaTRJ8llWmUs1aiESigl2sJ0GEz+y/m+fR/RzYuY5Lbjt+uxP+fUwsh0+f\nx83Tm8fPXwKQmpKCTMmSdO/zf+zddViU2RfA8S9IqDQ2dqCIiNiBgCKKuBZ2d3d3rR1rd7ciFjY2\nKgq62KKIHSAi0iA57+8Pd1n9KQg6AXg/z8OzOvPOvQddhzPnvffcdnTqPZjyFmlvyAp8+wZDI+M0\nlzSlRZIkWthVYtm4AenakDRh0SpidIswctKsNK8LCX5P+8a1qV+9EsO6taV86ZJoa2uRkJDInqPu\nLNi4Cy0dffqNmEijpq3IkeO/gxKeP3nM1JF98b525asxh42bTm4dHe7f9ObebR8iIsKpWtGcKQO6\nZ2iNqiRJlG/cgcET57Jq4XQ6ONry5/B+yGQyDpw6z5RlGzAuUJhRU+dTrZZ1uscFCAoMoLmtJR67\n1mBRrkyGXptZxOfITc4S5t99zxaVVSGFc4dunDy0l/nrtzN1yLc7xyVJwu/Zy5Tq6Y3b97CsXBUb\nhz9YO3AaZctXUPgi/tTY2Dviftkr1WTV4/pNqtesk+FEFaBB42Z4nj9FrmIlmd5pGBZWVX9qnB95\n8+oFrjs28uDk3p96vTxaVH1p5fxpjO7dKUsmqoLwOzqwcwOjenb4bqIaHROL21kPdh07i9etu9g3\nakLf8XOoW68hmpqaREVGoq6u/k2P1uTkZLw9Pbh09jiFi5akWm0byplXxKRI0V+KVU1NjU+xsZjk\n//4Z9l+SJIlDZy8zf+2eH16bN38Bjly6y7K5k2k/eiavX7+iVPFiRERGUcrUjEmL1lPH1v67P6tK\nmZZj9/HLnHRz5dLZE9y/dYM3r1/hffYINS3L08HOisVDO2FaothP7QNQU1NjcOdWnD7iys4jHnRr\nUZ+AoA/8/cAPNa1cTF64jrr1G/7Uz9GCJoVp4NScQ2c8smyymhZRWRW+Evj2DS3rWXFp91oqlC1N\ndEzsV9XTZBnYOjhh+0/19P93savK2ZNH2Ld2Phd2rPru8wOnLcCoXA36DBmj5MjSb1iPNtQolY9p\nQ/tk+LVx8fHU7zqYyjaOjJk2/5dj8fO9R89WDXh+4XCWbSQtKqvC7+TVi2e0bViDAM/jKRszExIS\nOePpzc6jp3G/dI1qNevQrG1XHJxapKsi+jHkAy3rV6GAsQEtG9jwOiiYqzfvERD0nirVajJ7+Zaf\nTlrj4+OpXMKAyDseaGml/eH//uOnOPUdw6V7rzOcyMXHxfHU/xEaGhqUM0+7401SUhJzJg3nhsdp\nRnRvR3XLClQsVyZddxrTKyIqmuJ2LTh17SE5NDRYNGMs9o1b0Kip8y8Ve6KjoqhvVQKfw1spWbSw\n3OJVJlFZFdLNpEhRRk6ZS9thkyiYPx9/37lPpSrVsHH4g/WDZ2BqZq6y6mlaatWtz5j+nYmJ/fTd\n5Oq8900W95Vvv1R58vH25K7PNVzn7s/wa/9rUVX6l1pUfen4IRdyqOdg0aZdODe0w9LMNFP+vQuC\n8NnB3ZspVig/K3fsA+Dp6wAOuF+gVJmyNG3ThTGLd5An74+rmF/yOHuSGhXNOLz66w/AoeERzF+/\ng6kj+rBpv/tPvTe8ev6UooVNfpioAuw7cZYmzu1+ah7tnDmpYPnjTVxRkREM79kWzaQYvA9sxkBB\nd5QM9HRp69SAI/t30W/YOBas3i6Xcf/2ukw+Y8PvdmvIDkSyKnyjQ/d+JCUmUrBwUZbZ2Gea6mla\n9PT1qWBpxeUbt3Cq9/Van4CgYD6Ghv9wDZaqyGQy5k4azvwxg36qkbW8WlR9acTEmdjYO3L2xCGa\nDZyAOjJaOtji3NAO66qVsl1bFEHI6ipVrUVc3Cf8Ij/fbjY2rcbBsQspWrxkhsaRyWScPnYIXT19\nPNyP0Lpe7W+uMTY0YM6ogVRv1ZNDe7fTulOPDMf7/IkfZqVK/PA6SZJwPXWBhRv2ZXiO9HoX8JZe\nbRrSsGYllk2epfD3t9JFTXgZ+vHHF2ZA3fqNWJ+3IHPWbP3uMr6sTvzEEb6hpqZG175DVB1GhtW1\nd8L9ivc3yeoFr7+paW0rt0RO3o7u342mlEjHZo4Zfq28WlT9Pw0NDWpa21HT2o7Jc5bx+OF9zp44\nzJB5qwl8+4am9jY4O9jSyKZWpjmqVxB+Zw2cmtPAqfkvjXH35g1mjh+MelLc59/7PmLjxH7fvVZT\nU4P6targd/820CPDcz3zf4R56W9PoPp/2w+dQKauQcXK1TI8R3rNmTgM5/q1mD9WOT/31NQ+J+Hy\npKmpyfIt+3G2r0LtyhYKO0VLVUSyKmQbNg0aM67f1m8eP+d1k9p2GU8EleFTbCxLZk/EdenMDCfT\n8mxRlRY1NTXMKlhiVsGSoeOmE/DmFedOHuGvPYfoOnYG9WvXoJWDDU3tbbLN0X6C8Dv58D6IxbMm\ncOXcKeaPGURX5yaoq6sTHhmVatu6d8EhbDt0nGOX7//UnM/9fWlRPe3eoScvejJu0Wp2Hb2ksGVI\nPt6e3L/lzYH5GV+C9bPUUAPkv7a8QCETFm/YS+c+7Xjv7S738VUpc5aaBOEnVLCsTGhYBK8Dg1Ie\nkySJC15/U9v213fHK8KmVYuwrmyR4XO7/Z69pN3wKSzZ6EKZcuk7DUVeChctTvf+w9hxxINLd19h\n16oHrpfvUMreGdtOA1myZTcv3gQoNSZBEDIuOTmZTav+okkdc4rrSDw+40r31k1TPjj/m6h+iovj\n0dMXREXHpLx2ztqttO7Q46c3WD1/4kf5MmkvURgxdzmL1+9R2HucJEnMmzKSuaMGKPUO0ceISEAx\nyXdtm/p8CPmITCZTyPiqIiqrQrahrq6Odb0GnL7sRd8OzgA8e/WWJJlEyTKZ70SPoMAAtq9bzq0j\nGVtgL+8WVb/C0MgY5/ZdcW7flbhPn7h2+Tznjh9iXuveFMyfB+d/1rlamZcTG7QEIZPxvnKRfZtX\ncs11I+XSWD+6aocrs9duIzEhAU0tLYoUKkjg+2BOX3/8U/PKZDKePfHHrHTqcwIEBX/Askr6+5hm\nRHJyMtvWLUMtIZbOLZwUMsf33Pb1Y8uB47i4eylsDmPjPExZso4J/btlm9aDIlkVshVreydOHdud\nkqyev3Yj1Z56qrZk9kT6dWhJiSIm6X5NXHw8zQeOxdG5I227ZK5F9Dlz5cLesSn2jk1JTk7m9t9e\nnD1xiFbDppKcGE8LB1ucHeywqV5Zrq1gBEH4Oeo5cpArp3aaiSrALb+nTJ67nNYduxMeFsq7gDfk\n1tH96eVHQYEB6Onqprnj/lNcHPEJCejqyfdwmc8byA6ycv50DHJrsX3hNKXuZ4j9FId2zpwYGedR\n2BxuF2+ybO4UTBu2YcqgnvTv0Cql60J8fAIv3gby7PVbnr16y9PXATx7G8izV28JDHpPk3p1GdO7\nE9UszRUW388QPzGEbMWmfiPmTBpGUlISGhoanPO+RU2nDqoO6xv379zE84I7W86kf52UIlpUKUqO\nHDmoVqsu1WrVZcLMxTzxe8jZE4cZtXgDr1++oEn9urRqaIujTe0s28dVELK6ytVqERP/+YjPbs5/\npHpdgTxGnD12gJbtumBknOeXE61n/o8oVzrtJQDBH8PImzev3AoNkiRx9oQbK+ZPI5cGLBs/EEfb\n2kovZFhXs6JFAxuszU0wKVKU454P0NZO/XjYn2FSpBgL1+zg0YO7LJo+hmXb9lGkUEGev35LcEgI\nJiaFKV6iFEVLlqFo6So0c2hH8ZKlMc6Tj6MHdtNyyCRKFi7I2N4daWpvkyk2J4tDAYRsp1ldCzb9\nOZqaVhbkr9kYN4/bmBT58a5TZZEkiU5/2NC7WT36/VMBTo8pS9ZxyvseO496yHXnv7IFvn3D+VNH\nOH/yMLdv3sC2ZlVaOdjQzN6W/HmN5TaPOBRAEH7s8cMHdG1uh/vmZalW0xISEmnSdxQm5ayYsWjN\nL8+5bf0KPjzwZO3M8aleM3rucp5HJLF4w49PrfqRF0/9GdmnPWpJ8cwe0Zem9jYqv9smSRK2nQbS\nZdgUGjZpodC5fLyvEvcplmIlS2NSpNgPW3MlJibifvQAW1cvIjYynKWThvNH/boKjRHSPhRA9emy\nIMhZ3QaNcb/izQP/Z+gbGGaqRBXg9NGDfIoIoXfb9LeZ+bdF1do9x7J0ogqfD57o2ncI2w6f58r9\nNzRs34/DXo8wbdgG6w79+WvTLp6+fKPqMAXht1DO3IJZSzfiPHg870O+3/tTS0uTHQuncezgzx0F\n/f9e+D/EWF+XoA8hJCQkfvP8VZ877Dp2msnzVshlvr+9rlAsrz53ju6gWQNblSWqycnJ7D95jlsP\n/AgICiYuLo6nfr4Kn7daLWvq1m9IsRKl0tVDVlNTk2atO3Lw/E2m/rWB7uNm4vfspcLjTItYBiBk\nO3XtnVg1czRG+rqZrgtAfHw8C2eMZfOsceTIkSNdr1FWiypV0DcwpHmbTjRv04n4uDi8rlzk3IlD\nLOzQj7xGhikbtKpWLK/ySoggZFeOzVrx6MFtnAdPwGPnmm9OlZIkidNXvMiZUz475vMXNMF1/042\nHjiOOnB57zrKliwOfF7T2X38LGYsWotxnrxyma+UaTkOh0VmiveQKUvX8+TFS3LkyMHAEePpO2yc\nqkNKlZqaGtb1HBg1dR5N+ozE0aYW+fMYUiCPMR2aNsLY0EB5sYhlAEJ2Ex8fTw3TvFQ0M6XjwAn8\n4dxO1SGl2LhyEfcvn+T4hr/Sdf3j5y+x6TiAJRtdVL7zX5lkMhl3fK5z9sQhzp90Iy42hhYOtrR0\nsMGuRtV0HdEolgEIQvrJZDIGd21JCUMt1s/+72jqgKBg+k2dz/PAEOat3o6lnJvz79+1mXWLZuC1\nfxMF8+Vl+OylvIpMZsnGX6/iHju4l4f3bvPo/i0K6Giwf8UcOUT8a94EBmHdoR+jpi+ieZtOqg4n\nXSRJwuPcKd6+esHHD+/x8bpC9TImLJ86Sq7zpLUMQCSrQrbUt11jLpw7zQ3/4Ayfh60oH0M+4FTL\njGuuG1OqCGn58DGMWm1702/0tEy381/Znvn7cfbEYc6dOMSLZ09oXM+aVg62NLatjZ6uzndfI5JV\nQciYqMhI2jaqgbVlOQrmy0N8QgI7Dp+ic5/BDBw1BS0tLYXMu2rRTC4edWH2yP70mTKf454PfmkT\nlyRJrFr0J8dcttGnbTN0c+eib3tntLUVE39GvHwbSO22fZizciv1GjZR6tyPH95HV0+fwkV//PMn\nLa9ePKNdo5oEeB5PV+EgvUSyKvx2tq1bzoFdmzju+XOnqyjC9NEDMJai0vVpNC4+nvpdB1PZxpEx\n0+YrIbqs4/27QM67H+X8iUP43PCibvUqODeoS3MHWwrm+++2oUhWBSHjAt684uiBPSnHgdo7NsWs\ngqVC55QkiemjB7B72wbW73LD4Rc2HMlkMuZMGs6tK+c4s205BfIqrkXUz2jabwzmdRoxYOTEH18s\nJy+ePWHJzAncuu5JfEIC1WrVpXOfoVjXc/jpnf6dmlgzsUcrWjSsJ7c4RbIq/HaiIiN59eIpFpWq\nqDoUAPwf+dK1uR2Pz7j+cJ2PTCaj08hpROfQYdlm10zRNiSzioqM5NK5U5w/cZBLF85gVroUzg51\ncW5UjwJm1UWyKghZRHJyMtevXqKOrf0vjbN60UyunTnMqU1LUz0qVpVsOg1g4ORF1KpbT+FzfXgf\nxKqFMzh1ZD+je3diePcOSJLEnqPurNx9kJi4RDr2GoRDk5YUKlwUTc30V0ldd27C+6QLbmsWyC1e\nkawKgor1at2QltaVGNGz4w+vzS4tqpQtISGB654enDtxiHOnjqCnp8+TJ/4iWRWE34QkSdhXLsmR\n1XOxMi+n6nC+y6bTAAZMWkhtm/q/NE5iYiLPn/jh+8+a3GePHlCuYmX+aNWR4iXLsHnVQnZtWkOP\n1n8weWAP8hgZfvV6SZK4dvMuq3YfwtPnDsEhIRQoUJCixUpQpEQpChcrRbGSpWnYpCW5cuf+Zv6o\nyEhsLYvy7Pwh8hobfvP8zxDJqiCokMfZU8yfOBjfk3t/eHLTtoPHmb5qK/vP3lDKzv93AW8Z078j\nSBK5dXTR0dUjt44eufU+/1dHTx8dHd2U53RSrtFFR/e/X8u7qfWvkslk3L/tQ6uGNUWyKgi/ift3\nbjKqZyuenj+YKXb+/78jZz0YMOMvjnjcIW/+Aj81hiRJDO7agssXzlKksAmVzctRtXwZzEqXwOvO\nA/adOEfg+2DaNGnInJH9KV64ULrGTUhI5HVgEC/eBvDiTSDP3wZy9eZ9DExKsnyL63f/PEf17Yh9\nhSIM6y6fg3fSSlZF6ypBUKDExETmTx3J4glDf5ioenj7MHbBSnYfv6K0FlU7NiynXCEjerT6g+jY\nT0RFxxAd+4no2FiiYkKICnhDYMwnomI/fX4+JiblupjY2M/Xx8SgpqaGjo4Oujq65NbR+Tqp/Sfx\nza2ri46u/uckWPfz73X/uSa3zn+//vw6nXS39voedXV1KlWtIcc/KUEQMjv3I660dcqcx2vfuPuA\nPpPnsdHV/acTVYCnjx/he8eHD3+f/eb0v+YOdswdPYiwiMgMt5XS0tKkTImilClRNOWxuPh4arbu\nzd6t6+jUa+A3rylgUoS3QR9+7hvJIJGsCoICuWxbT5F8RjS1t0nzusfPX9Ju+BSWbHShTLnySokt\nJjqaA7u28PehLZQqVuSXxkpISPwqkU1JeKNj/0l8Y4mOiSUq+i1R72MJio37/Nj/XRf9TxIcExuL\ntpYWurr/JrK6XySyup+rvXpfJr7/X/XVQ0c39XPHBUHIfi6fO0VwUACbXI+yfeE0pZy6lB4v3gTQ\nYsA45q7Y8sutv86fOkLzBrapHlOtpqYmt/6nObW1ObByLrXb9cFt11OZAAAgAElEQVSqem3MK1p9\n9bzbvp3sWDBFLnP9iEhWBUFBIsLDWLXoT85vX5nmJ/0PH8No0mcUo6fNx7qeg9LiO7R3GzY1Kv9y\nogqfP5Xn0TL8Zl3Uz5LJZHyKi/9uwptS4Y35RFRMBFEhQXx8/YkXMZ/+eezz9RGxcXKJRRCErGHV\njsMkJyVx8/pV/tqyQanJ6os3ATx5+YZSRQtTvHChlDtpoeERNO49ggGjp9LAKf2nFqbmwik35g7p\n9svjpJdpyWKsnDqK4T3bcPjibXT1/tu0NmzCn3QZM5n1s8bTyvHXNsa9CQxixR63VJ8Xa1YFQUHm\nTRkFoa/YOGdSqteoqkWVTCbDsUZZts2dgE31ykqbV5lE6ypB+D3Fx8dja1EEL9eNX93WVqRu42Zy\n7e4jEuLjef8+iEIF8lOqWBGCQz5S26EpE2ct+eU5Qj4E07C6KcHe7krvGdt38lyCEzRZsnHvV8WX\ne7f+ZmiP1rRzrMf8sYNTXe4mk8m4fOMWUTGxNGtgC/y3yWvpdlfOenpjUrQ4fr73xZpVQVCWl8+f\ncmjvNh66u6R6jSRJ9Bw/mzxFSjNqylwlRgceZ0+in1ubutWsfnyxIAhCFuHt6cGpwy4kJSdx7tp1\npSWrz14HMHPJBmrVrUdCQgKBb1/z+sUzoiIjcGrRRi5zeJw+TsO6tVRyuMGKqaOo0boXB3dvpU2X\nXimPW1apjpvHbcb064R918Gs+XMshQvkw1BfD3V1dR49fcEOt5PsPnoaPQMjIiIiePs+GN1cuVi6\n3ZXb931RV1cnV65caCJLdX5RWRUEBRjUtSV25kWZOKBHqtdMXbqek153VdKiqnuL+vRvaU+Xlso9\nQUWZRGVVEH4vUZGR2FQswrTBvWhYtyYVy5VReJ/q4JBQpi7bwJ5j7pz29qOgSWGFzTWoS3M621ej\nq/MfCpsjLeev3mD0ko0cunDrm+dkMhlrFs/m0J6thIWFEhMTg56uLto5c9K8bWdatu+OWQVLXjz1\nx6HGf23FShUvRufmjnRq5kjJsuaidZUgKIu3pweTBnfF7/Q+cqbS0knZLaq+5Od7jz5tGvHSw02u\nR+VlNiJZFYTfy4FdW7hydCfH1v+ltDmnLVvPjecfGD5xJuXMKypsnsC3b2hiXYEXFw/LbW9ARiUm\nJpGvZiPcvfzIraNLWGgIRYuX/O61SUlJRISH8eKpP8FBgSQlJxH4+iUnDu0hLOQDHZo2pHMzR6pY\nmKUsKxCtqwRBSZKTk5k3eQQLxgxKNVFVRYuqL21bu4RBnVtn60RVEITfz9H9OxjR3klp80mSxK4j\n7izZekihiWpw0Du6t6zP9KF9VJaoAmhqatDIpg6dm9kSHPQO9Rw5WLf7KDWt7b65VkNDg/i4T/Tt\n0AQH61rkyKFOfmMDVk8agk31yhluTSiSVUGQo8N7t6OrpU67Pxp+93lVtKj6UsiHYM6ccGPNuYNK\nn1sQBEFR3gW8xffeHZquV976/+t3HqCuqU1Fq6oKm+NjyAe6O9vTo0UjRvfurLB50mvOiH74v3xN\nvZpVuXbrLl16t8P1tPd3K6yHXXbQsWkj1s2a8MvzikPHBUFOYqKjWTp3MssnD/9uq6qQ0HCVtKj6\n0p4ta2jXxEFux+MJgiBkBicOu+DsaJ/qHS1F2HXUnWbtuirkEIKEhASOH3KhSzNbWtvXYeqQ3nKf\n42eYlizGH/XropM7Fw3r1mLygG70buvIjWuXv7pOkiTcXLbRs3VTucwrKquCICcbls+jfs0q1Khk\n8c1zcfHxNB84FkfnjrTtopo3nfj4ePZsWYPHztUqmV8QBEFRggLfYFGmuNLmS0xMwvXkefadniPX\ncT+8D2LbuqUc3LOVCqalmDus5y/3MFWkod3ak8/IkAkDO1ParCJjpi+knLkFt254oaEuUaNSBbnM\nI5JVQZCDwLdv2L15DXeP7frmuX9bVBkXLqX0FlVfOnZgD1bly2JuWkplMQiCICiCmpoaaewXl7uz\nnp9vfRcvWVpuY4Z+DKFTUxsa1arMlT1rKVeqhNzGVhQ1NTU6Nm9MK0d71u45SPeW9bFr2ISY6Ch6\ntmoit6qzWAYgCHKweOZ4BndpQ1GTgt88N23ZBvwDP7JwzU6Ft1FJjSRJbF+7hFE92qtkfkEQBEX6\nnKwqJ1u9fucB4xatxrljT7mN+Sk2lgEdm9LGoS5rZ47PEonql7S1tRjRsyNPzh2grJEmN72v0LWF\n/FojisqqIPyiuzdvcMPzIjvPuH7z3LaDx9lx9Az7z95Qei/VL3l7eiBL+ISjbW2VxSAIgqAo6urq\nyGSJCp0jKjqGITMXc9rzOqOnzse5g3yOPU1OTmZ0v46ULZyHeWMGyWVMVTHQ02XO6IHMGT1QruOK\nZFUQfoEkScydNJw5I/ujq5P7q+dU3aLqS9vWLGZkj/YK2QggCIKgalraufgY8VGhc5y6dI37L99x\n+ro/evr6chlTkiRmTRhKfPh7tm1eprK7b5md+FMRhF9w4vA+kmIj6Nbq6xNFVN2i6ksvnj3hjo93\ntj6tShCE31uz1p3YevAEn+LiFDaHJEmYFC4qt0QVYMPyBdy5egG3NQtE7+s0iGRVEH5SfFwci2aM\nY9nkEV99Gs4MLaq+tGP9Mvq2a0HuXDlVHYogCILcxcbE8PL5E1BTx/XEOYXNI+91sX6+99i+bgmn\nNi/FQE9XbuNmR2IZgCD8pC1rllC1Qlnsav7XEDoztKj6UmREOEcP7MH35F5VhyIIWZ5MJuPp40f4\nP3qAbQNH9A1Ev2JVuu7pwZZVC/G+doVqlhWY3L8rzRrYKGSuqOgY1rq4YVbNVm5jPvF7iHXVyhQu\nqNplYlmBSFYF4Sd8eB/EltV/cf3AlpTHMkuLqi/t27ERJ7s6mBTIp+pQBCFTO330IKsXzUBHVw89\nfQN09fTR0TNAV9+AHDk0eHjXh9s3b5DXyJBSxYowe+JQRkyaTdsuvTN8dKTw62JjYhjZtwOzh/fF\ndf5YjAzkd2v+/4VHRuHQYximljUYNXWeXMaMiozg8cN7lDARiWp6iGRVEH7CsjmT6dGqKWVKFE15\n7N8WVTuPHswUi+STkpLYuWEFR1bL581VELKz9cvmMLxTC8qVKk5kdAyR0TFEREUTGR1NfEIiju0b\nU3v+OPLnNQbg1gM/hs5ewp7Nq5m7cisWlaqo+Dv4vWxbuxS76lb0ad9S4XP53H9ITHwys5dtlNsm\n1SbWFsgSE1g5bZRcxsvuRLIqCBn06MFdzrsfxf/s/pTHMkuLqi+dPnaQEoULULWiajd4CUJmkZyc\nTMCbV7x6/pRXL57y6rk/r58/4dXzpyTFf6JX2+bprpJWsTDDc+96xs1fwe5NK5m3cquCoxf+Ffox\nhK1rl3DjoOL/zCVJwrxMKd68fsmn2Fhy6+jIZdxcObU5unmJOKQlnUSyKggZIEkS86aMZPrQ3hjq\n6wGZq0XVl7avXcKkXuIQAEEAeP3yOQM6NiU2OoIyJYpRplgRyhcvTLOW9pQp3o3SxYtk+Ha+mpoa\nOTRyULiYahOOgDevMClS7LdpTbf2r1l0bNaI0sWLKHyuY+cv06L/aAAe3r9DtVrWchk3T978fAgN\nk8tYvwORrApCBlxwP0bouzf07/D51npmalH1pTs+1/n4/h0tHOxUHYogqJzXlYuM7NOe6UN6MbhL\n21Sv+xQXh8vxMxQrVJCK5cqk3PL/Umh4BOeu3sC8TEnMSpfA/2UA9ao1VmT435WQkMDpYwfZu3kV\nN657MXvJOjp076f0OJTtzasXuLnu5JH7PoXPFRUdwz2/J5QuXYZth89ToFBhuY1tnDcfIWHhchsv\nuxPJqiCkU0JCAvOnjWb15GFoaGhkuhZVX9q+djHDurcVGz+E397uzWtYtXA6LktmYV+neqrXyWQy\nuo75k5chkWjkyIHfI180NTSwKGdKRdOSVCxbilJFCzN01hKMCpjw/l0A7wIDSZbJ6DlhgcLilySJ\nl8+f8ur5U6zrOaCpqYn7kQP8OX4w5UuXZGzXVpiM7kPjXsM55roDiyo1qGXrQP2G2bOv8vZ1S+nd\ntvl3P0jIU3BIKE59RqKtn4eKlatjUqSYXMc3ypOf4I+isppeIlkVhHTavWUNpQsXoLFdnZQWVY1a\ndsgULaq+FPj2DVcunmXndDdVhyIIKrV782p2r1vCVZeNX22G/J7xC1fxOjSGnUcvoa2tjSRJBAe9\n4/HD+/g/us+ZB/d5uv8Uzl3703vIGACio6J49eIp5hWtFBJ/6McQJg/rxb2b1ymQLy9LZmkwed4K\npo8dyOHV86lb7b95X146gs/9h9y495CZo/sRNXUBzdt2VkhcqlSqrDm3zx1S6ByvAt7RsMdQGrfq\nzPCJsxSyvKKWbQNWLZxGzzbNyKmtnfL4p7g4nr8OoELZ0nKfMytTS6vBrZqamvQsVH4NcAUhqwoP\nC8WxRlk8dq3B3LQUnUZOI0o9N8s2u2aKnf9fWjB9LLmiA1k25ffeZRqhnR/DIsWQJOn3WMiHeM/+\nfxuWz+fepeOc2LQszYRj3Z6DLNrqiuuZ6xgZ51FihKnzunKRcQO70MGpAfPGDEJTU4Pth04wYs4S\nmjvYsWPh9FRfe/eRPw26DWH1TjcKmhShSLES2WY9a2REOHaVivPi4mGMDQ3kPn5SUhIWTTrRttcQ\neg4cKffx/yVJEsN7taW0kTbLp/73Xt138lzcL3vx+vLRbPN3ll7xOXKTs4T5d9+zRbIqCOkwa8JQ\ncsa8Z+3M8Uxdup6TXnfZedQj0+z8/1dsTAx2lsXwObyVkkXlt74qKxLJqvDhfRA9WzfEoUZFlk0e\n+d0Plqc8rtJ9wmxcTl2jRKkyKojya4mJiayYP41Du7ewbcFUHG1rf/V8aHgEObW1f3gi3e6j7kxd\nuoHI6GhadezBhFmLFRm2Ug3v2ZYmVcswsHMbuY+949BxVh88w54TngpPFsPDQmlua8maaaMwyZ+P\n8143WONyjOSkJE5vXvLbVVdFsioIv+D5k8d0cKrDI/d9nLx0lWkrt7L/7I1MtfP/X9cuX6Bvhyb0\n69CK9k0cqFW5Yqar/CqLSFYF+FyJ69e+CcXy6NDOqQGFC+Tjgf8zrt3xxevOA4JDPrLR5SRVa9ZR\ndaiEfgxhQMemGOfWYOeiaRTI++tV3g8fw7D4oyPr9pygUtUacohS9S6ePcmG+ZO4fmCzXMdNSkrC\nzLE9M1dso1bdenIdOzXXLl9gSPdWFClSlLLmFek1ZBz7tq3DskBOxvbtqpQYMguRrArCL+jX4Q8c\nq5hSvWJ52g6bzO7jVzLVzv//9/TxI04dceXUYReiIyNo07g+7Zs4UNPK4rdKXEWyKvzrU2wsG1Ys\nwN/3Du8C3lDGzAKrGtZUrlYLU7MKaGiofvtGVGQEXZvXw7GmJQvGDZHrv9W9R92ZuX4Phz1uo6mp\nCZClbzEnJiZSwSQX0fcvf7Xe81fceuDH6AUrkbT12HLwrFzG/Fnn3Y+xZ9UcLu5cneo1kiTx6OkL\nQiMiqVPFMlu8t4tkVRB+0lWPc8wY2Ru3tQtp0G0IizfszXQ7/9PyxO8hp464cvKQC59iImnr1IB2\nTg2oaWWRpX9YpYdIVoWs4lNsLD1bN6RGuaKsnDZG7v82JUmi9ZCJHHY/l/JYPftG/LVhT6ZZo5sR\nIcHvaVLHnJC/z/zyWC/eBDBp8Tou3rjFkLHTadu1T0pCryox0dHULl+Qd9dOoqf73yEECQmJXP77\nFkcveHL8wlUSkpLR0dNDlhBHv/Yt6Nm6GXmNDVUY+a9JK1nN+qm4IChIcnIy86aMZHzfLrQcOI5R\nU+dlqUQVwNTMnGHjZ+B+3Y+N+8+SZFySrhPnUdyuBaPnLufG3Qek9YFVEATFO37IBQMtWDF1tEI+\nRKqpqXFw1TykZz5Iz3xIfOxNtdIFca5fBd97t+U+n6K9efUCSZKYuXITIaE/16v0Y1g4I2Yvoapz\nDwqY1+Ds30/p1GugyhNVgNw6OhQyKczNB48AuOjlQ+shE8lX05Fxy7aiaWLOqt3HuHT/DSevPWTe\n2j14v/hIGYfWdBw5jSt/38527+uqv/chCJnU/l2b0dNSZ5vbKRq17EC7rn1UHdIvKVu+AmXLz2T4\nhD/xf+TLKbd9dBo3h8S4WNo2tqf9Hw5Uq2ie7SuugpDZ3PA8T5tGdgq9lfvlv2sNDQ0WTRiGhWkp\nRvXtgLu3X5b6d1+2vAUjp8zl2qVzXB8/k+MbFmco/uev31KzTW8at2iLu9cj8uYvoMBoM+7m9as8\ne/qE14FBJCUl8ej5S05fukq7bn2YMnf5N9dXqVGbKjVqEx4WyuF9O+g1dRE5pGRWTh1Jw7q1VPAd\nyJ9YBiAI3xEVGUnD6mUw1tOhTMWqmbJFlTxIksTjh/c55ebKycMuJCfG0+6fpQJVK5bPUj/A/p9Y\nBiBkBXGfPtGwehnOb1uBWekSSp1bkiQs/ujMhPlrsLZroNS55SEhIYHWDaoxc1AXWjdOPf7IqGiW\nb3ehVaP6VChbmmb9x2BWqyEDR01SYrTpJ5PJuHjmBJuWz+PDu7eM7t2J63d90Stuweip8374ekmS\nOHP8MPMnD+PJ2QNoaam+WpweYhmAIGTQuqVz+PDhAzpG+Vm4Zme2TFThc7XFrIIlIyfP5szfT1i9\n+zgxOia0GzWDUvVbMX7hKm498Mt2t5QEITPw8b5KM1tL6tWoTLlSxZU+v5qaGsO6tGb3xm+rdVmB\nlpYW+QsURCONk/q8bt3DqnlXLj96i13ngYyYtRjfZ6/pNXi0EiPNGHV1dRo0bsbeU9dYsN6FI16+\nHDl3mZ6D0hezmpoaZctbEB0bx4mLngqOVjlEZVUQ/s/b1y9pVNOM/PkLcODc35myRZWiSZLEowd3\nOXnYhVNurqhLybR1sqd9EweszMtliYqrqKwKmdWn2FiWzp7EiUN7WT1jDK0c7VUWS3RMLMVsm3Pk\n0h0KF1V+wvwrJEmiepk8+J7cS14jQzQ0cqS8NyUlJTFr9RbW7j3MzMXradTUGT/fe4wf1I1RU+dh\n5+Ck4ugzJikpKV1dK+I+fWLJrIkc3reD8f26MaJHh2xRWRVrVgXh/yyaPhZtbW02ubr/lokqfP5k\nbl7RCvOKVoyeOg/fe7c55bYP56FT0FCTaOfUgPZ/OGBpZpolEldByCx8vD2ZMLg7NS3Kcf/EHpXv\n3tbVyU035ybs2bKGsdMXqDSWjHr7+iWaGjnQ0tTAsmlnShY1Yeei6YRHRtN59HS0DfJxxOMOBQqZ\nAGBWwZIjl+6oOOqfk972aifcXPG94cFDdxe59OnNLERlVRC+4ON9lS4t6rN538kst/NfGSRJ4sHd\nWykV15yaOVI2Z1UsVyZTJa6isipkNrdueDGwczM2zJqAs2N9VYeT4qrPHZoPGIe3fzA50rilntl8\nio3FsWY5dLQ1sWvijJqaGicO7CYuPoGBY6bSvd+wbLGES5Ik4uPiiAgPQyaTUahwkVSvnTd1FKVz\nJzJhQA/lBSgnorIqCOn08pk/C1ZuEYlqKtTU1KhoVZWKVlUZN2Mh92/7cOrIPv7oP45c2pq0d7Kn\nXRMHLMqWzlSJqyComiRJLJg6ir/GD800iaokSew9dpoRc5bRsWf/LJfY5cqdm5lL1uN16Rzj//wL\nNTU16js2x9AoD2XLV1B1eN8lSRKREeEYGBoBnzeJnTl+GD/fu0RFhBEZHkpURDiR4WFERIQRERFB\nREQEAIYG+kRFxXDO5wkFTb5/nLaBgTF+/j5K+36URVRWBUH4ZZIkce/W35x0c+GU2350cmnR3qkB\n7Zs4qOx8a1FZFTKT08cOsXb+JG4f2aGy6uW74BAmLl5LUEgowR/DeP8hBD0DI+av2YFl5Woqiel3\nEBz0jotnTnD9yjm8PT0ICwujU49+5NbRZf+uzVQwLYV9jcoYGehhZKCPob4eRvp6n/9roI+hvi65\ncuYEoOeE2RS0qEPfoWO/O1d4WCgO1cpw+8gOihcupMxv85eJE6wEQVAaSZK4e/MGJ91ccHfbj55O\nLtr9sznL3LSU0uIQyaqQWSQmJtKkjjlrpgzH0ba2yuLYc9SdkfNX0aXPEGrb2FOgkAkFChXOFMfN\nZlfJyck41ihLVXNTHK2rU79WNYwM9Bj/1xpyaWkxqHPrDLUs8/D2YfCclRzzfJDqNQtnjEM99BVr\n/hwnh+9AeUSyKgiCSshkMu74XOfUkX24H9mPga4O7ZvY087JgfJlSip0bpGsCqoWFBjArRvXOHvy\nMNHvXnBu+0qVLo/xf/GKuet24HXnAYFB76lUuSpW1a2pXNOaytVqYWhkrLLYsqPHDx/w8vkTNv01\nDZ9DW+Xydy+TyShu14L1+9wxq2D53WtCgt/jWMuMh6dcKJQ/7y/PqSwiWRWELEySJMYN7EJQwFsM\nDI3QNzTGwDgP+gZGGBgZY2Bo/M/jRhgaff61nr5Bplt/JpPJuP23N6fcXHA/egAjfT3aO33enFWu\nVAm5zyeSVUGV/B/54mRtgZO9HXWrWNDd+Q8KF8w83UVCwyPwvn2fq7fuc+2OLz5371PQpDDr9hyn\nZGlTVYeX5W1csYCFMyeRM2cu1s0cR1fnP+Q29oRFqwjTzMP4P/9K9ZpZE4ZinBzB4knD5Tavoolk\nVRCysOtXLzF9RC/WTh9DaEQkYRGRhEVG8jE8itDIKMIioj8/HhlJeEQkYeERRMfEoK+vj4GBIYZG\nRhgYfE5mDYzyoG9ojL6hcUpi+2WSq29ghI6ursKrPzKZjFs3vDjl5sKpI/vJZ2xIu8b1af9HQ8qW\nlE+vR5GsCqokk8lo41CdiT3b0L5pI1WH80NJSUk49h5Jh0ETadC4marDyfIGdmqGXYViHD57CU+X\njWhra8lt7IdPntOgxzAu33+b6vrndwFvaWpjwZOzB5XaHs3v2Uvmb9hJVEwsLktnoamZ/iUmIlkV\nhCxsWI82NLIqybDuHdL9mqSkJCKiogkN/5zEhkVEERoeQVhkFGERkXyMiCL0n69/nw8LjyA8IoKE\nxEQMDQwxMDTA0NAYfcOvE10DQ+N/Krqfk1x9A6OUX2v/swkgI2QyGTevX+Xk4c8V1wJ5jT93FXBy\nwLRksQyP9y+RrAqqdu3yBaYP78kj931ZojF7h5FTqflHZ1q07azqULK8Tk2sWTCsO3Y1qypk/Mot\nujNq5rI0O9dMHt6H0vowe9RAhcTwpb/v+TJ3/Q48/75L135DuevjTdn8uqydOT7dY4jWVYKQRb1/\nF4jnpXPsnnkkQ6/T0NAgj5EheYwy/ok6Pj7hvwQ2IvK/am5EFKER7wl9+oyX/ya6Kc9FEB4Ribq6\nOgYGBhgaGv1XtTU0Ru/f5QpGeb56/N9fW1WrRfXaNkyZtyIlcbXu2J9C+fKmrHEtU6Johr8XQVCl\nOrb2FCtdjnV7D2bow6aqfAwNIyY6StVhZAvhYaEYGxoobPxuLR054rojzWS1/8hJtG5QjbF9u2Kg\npyv3GCRJ4vy1G8xZt4Mnr97Sa/BYZm08Qm4dHeZOGsEb/9tym0tUVgUhE1uxYAZxb3xZN2uCqkP5\nIUmS+BQX/1UF97/KbiQfwyMJi4zmY0TU50Q48vOShbCISCIiI8mZMyeG/yxb0DcwQldfH//Hj3j1\n4jkAVhYVaOdUn8Gd26CfjjdeUVkVMoPHDx/QrWV9xvTqxNi+XTLdWvJ/HTt/mV4T5+B+/TFGxtnn\n5CNVsTYvhM/BLQpbpxz0IQQzx/Zc9Q0kV+7cqV43pn9nqhYzYvKgnnKbOzk5mcNnLjJ3/U6i45Po\nO2wCzdp0Qkvrv6UOxw+5MG/KSKpWMGPZpOHpKjaIyqogZEGJiYns276eM1uWqTqUdFFTUyN3rpzk\nzpWTIoUKZOi1MpmMqJjYlEptSpJrY0VoRMTnJQuR0dzyfcyLt4FUKl9WQd+FIMhXOXMLDp33YWTv\n9njcuMWOhdPJl8dI1WF9xe/ZS3pNnMP6vSdEoioHXpcvEB8XR96fuLOVXgXz5aWmVUXOnnSjeZtO\nqV43YNQU2jrWYuuhE1iWM6WSWSmqmJejST3rdPX7jY6JZeHGnURERdO0vjWvA4NYsHE3OoZ56D9u\nNg5OzVFTU+P9u0Ae+d7F7/4dHj+4jZ/vXcLDw3j9Loi7fv6/fGdMVFYFIZM66bYfl3UL8Ny7XtWh\nZEmisipkJomJiSyZPYkTB3axZ8lMbGtUUXVIAERGRVO9dU96DptEu659VB1Olnfvtg992zXmwMq5\nCluv+q+D7ucZvXAt6/YcT/PErsTERF4+e4Kf7z0e+97F48xxbCqVY92sCaluppUkif0nzzFq3nKq\nW9enhKkZl88cR0/fgL4jJlGrbj1ePnvCphULOHPCDXV1NSzLl8WqXBmsypehUvmymJUqkaG12mKD\nlSBkQZ2b2jCifZMssZM4MxLJqpAZeZw9yYQhPRjapQ2TBvZQ2WlW/zp50ZOZm1zZc/KqSuPIDiRJ\nwtrchHUzxtCyUT2lzLnt4HHGzF/BlPkr0dDQxP/RfUZMnJnma6KjoujWoh41y5eknVMDLM1Mv6r2\nP3zynMEzF/M+LJppi9ZQo47tV6/3vXeb9Uvm4O15kUGdW9OvfUsKF8z/y11k0kpWM+fiGUH4zfk/\n8uXlU3+cG2WOM8QFQZCPeg2b4HbxNie879Ow5zCCPoSoNB6ZJKGjq6fSGLKLiPAw4uJilZaoAvRo\n3ZQzW1ewbsFUDm5azLpl84mPi0vzNbp6emzaf5rYnHmZsnY35Rq1JfD9ByKjohk1dxm2nQZg26wT\nbpfupCSqkiRx/eolerVuyIAOTahvUZSXHm7MHNGfIoUKKLzdoVizKgiZ0O7Nq+jbvkWWaHcjCELG\nFDQpzI4jF1m5YDrW7ftx0207hvqqSxhVeapWdvL+XSAF8+VT+rxVLMzwO+MKQMMewxjaozXzV2/H\nOE/qp1cZ58nLn4vXAZ+PZ+0wYgpXbtz8/PvV22jQuBkaGuHdW2sAACAASURBVBrIZDIunjnB+iWz\nCQ95z4R+Xem6eqZc+8amh6isCkImExUZyfFDLvTv4KzqUARBUBANDQ1GTp5D3YZN6Tr2T2QymUri\nSGspoJAxH96/o1B+5SerXzqxYTGVi+elhV0lvD090vWagaMmY1TElBw5ctCmiQMndq3FzrIYjjXK\n4lS7PKvnTGBcN2cen3GlT/uWSk9UQVRWBSHTcdu3g/q1qmWqoxkFQVCMibOX0umPuizcuJMJ/bsr\nfX5JkkRlVU7eBwVikj/1aqYyaGlpsmjCMBzqVKdHn/ZYVa9D3KcYPrx/R3hYGE1atafvsAnkyfs5\nqZYkiYjwMEzLW2D1zBeXZXPIkSMHycnJ+D55TmR0DNZVK6n8/xGRrApCJiJJErs3r2L9tJGqDkUQ\nBCXQ0tJixdaDtHaoRq1KFahXq5rygxDJqlx8CArEJJ9xyu9lMhkymQwNDeWnWo62tbl9dAful7zI\nl8eIQvnykiunNqt2HcCxRjlGT5vHueMHueVzndw5c2JZvixb501O2fCXI0cOLM1MlR53akSyKgiZ\nyPWrl9CQkqlXS7EtTwRByDxMihRl0ZqddBjUhc7NHDErVRyz0iWoU8VS4d0CxDIA+QkOCsBUTweA\nd8EhtBs2mZqVzPlr4nCVxFMwX156tGn21WOrZ4ylX/sW1GjVg3JlSvH03MFM1/f3e8SaVUHIRHZv\nXMmgzq1UfstFEATlqmvfiGVbDqBeqDyn77+m24S5LNy4U+HzJiQmifcbObGwqsayHQeo3roXVVt2\n584jfyxMS6k6rG9UKl+Wvh2cqWNVMUskqiAqq4KQaQQFBnDt8nn2zhqm6lAEQVCBGnVsU1oF3bh2\nmTlj+zFxQA+Fzfc6MIgxC1YybNJchc3xO2nVsQcx0dHMGD8UA0Mj8uTJS44cX9cEN7m6sffEeeyq\nVWJEjw4pR0e/DgzC7YwHybJkJEnCtEQx/qhfV2HH866YOpqExESFjK0IorIqCJmEy/b1dGjaKF3n\n3guCkL1VrWnNh9BwHj9/qZDxgz6E0KDbELoNHI1zh24KmeN3c+uGF5uWz+XUlhUM79aWN29eMXTW\nYl4FvAMgICiYMfNX4txrJHcDoyhl34pZqzazydWNKi26cfV5CPdCknkQKmPK6p1YNOnETreTJCUl\nyT1WdXV1cmpry31cRREnWAlCJpCQkEC9SsU4v20FFcqWVnU42YI4wUrI6maOH0JpXRlTh/SW+9g9\nJ8xGLU8xpsxdLvexf1e92zSio311+nVsBcCl6zfpNGoaxUwKcmXves56XmfutsNsP3IRgOdPHrN6\n0Z+8efmMP5dsoLxFpZSxJEnC8+JZ1i2Zzbs3LxjXtwu92jTLUglmRokTrAQhkzt74jBlSxQTiaog\nCCmcWrZn36kLChm7WKF8aGnlVMjYv6O7N2/w1O8BPVr/t6HJrmZV7h7bTZUKZYmKieWO3xPKVbRK\neb6UaTkWb9iD65nrXyWq8PmgBhv7Ruw+fplFG/ZxyPMeJes5s2D9DiKjopX2fWWUTCbD0+cOL94E\nyHVcsWZVEDKB3ZtWMrpLa1WHIQhCJmJesTL+z56TkJAo19PsgkNC2X30DAPHpX2GvJB+qxZOZ+KA\n7t/8PeU1NmT1jPEAvA58T9FKdhkeu2rNOmxwOYGf7z3WL53LwvrODOrcmuHdO5DX2DDD44VFRLJ2\nz0H8X76lcP48mOTPi0n+fJgUyIdJ/rwUzJcXTc3P6eGHj2GcvXodd88b+Nx/hEXZ0thWs8SmmhUW\nZUundKt48uI1O9xOsvOIO28D33F80zJKFi2c4dhSI5JVQVCxxw/v8/r5U1o2rKfqUARByESuXT6P\ndfUqcktUQ0LDcTvrwdJt+2jcqpNYqypHjx7cpdGE/mle8yk+gVy5cv/0HGYVLFm6yYWXz5+ycfl8\nTBu2pptzU8b27kSRQgV++Po3gUEs2bqXbQeP08CpOVXrNiX4/Tu8Xr4l2NuX4KBAgt8HERISgqGB\nAfp6unz4GEota1us7Z1oNXAK/n6+XPG6xLKd0/jwIZjaVSoRFhHJ8zcBlDQ1I+BdEHuWzaGxbe2f\n/j6/RySrgqBiuzetom/7FimfZAVBEAAe3rvFi9cBLNmym87NG1Mgb56fGufc1evMXb8Tn3u+2Nk3\nYvCkeTg2ayXnaH9fkiSRv0AhHj17QZkSRVO9Li4hAS05rDktUaoMc5ZvYuj4P9my5i8qNu1MK8f6\n9G7dFJkkERkVTVRM7D9fMUTGxOL37DXuV7xo3akHx67cx6RI6nEmJycTGvKBsNCPlCxTFk3N/z4s\nWVapTptOPQAI+RDMTW9PtHPmQldPnyHdWuK6cj7Ojer98vf4/8QGK0FQoajICOwqFefhKRdMCqj2\nTOnsRmywErK65ORkvD09cHPZxrlTR7GuZkWPlk40a2BDrpzpX29q7tSRTv1H4dy+G7ly/3xlT/i+\nv2ZO4KbHKS7sXI1O7lypXtdy0AQcOw2U+weFsNCP7NiwgvMnD5MrV250dPU+f+npo6Orj46ePvkK\nFKJ5m04YGP58X1VJkoiPiyMqMoLoqEiiIiMI+fAevwd32b5uKVsXTKVp/bo/PX5aG6xEsioIKrRj\nw0oeXD7OgZWiz6G8iWRVyE5ioqM5c/wQR1y24ffwHtf2bUqzivevN4FBWDXvhrd/sMJPw/od3bv1\nN+2crHl56egPCw5OfUahYViQBk4tMKtQiVKm5VRyFOuXQj4E43H2JB9DgomKCCcmKoLoyPDPCWlk\nJFFREURFRn7+io5CTU0NfT099PV0MdDTI6+RIRamJWjn1IBalSv+UiwiWRWETEiSJJxqlWfjn6Ow\nqymOV5U3kawK2dX29Ss4vncjXvs2/nA968rtLpx78Jqlm/YpKbrPFeGnjx9y64YXz588pO/Q8eQv\nWEhp8yuTTCZjSDdnCuSC7QunpXntbV8/jl/05I7fc+76+RP4LogyZcthZmFFOYvKlLeoRHmLShmu\nfkqSxKsXz/DxusIz/0fkL1QYkyLFKFy0OIWLFsfQyPirU8ri4+I4734MN5et/O19lUY2tSlWMD8G\nejoY6umir6uDgZ5uyteXv9fW1vqpP6f0SCtZFYvkBEFFvK5cRENNhm2NKqoORRCELKRbv6Fc8zjD\n5CVrWTQh9RPv1u45wKy129mw94TSYnv14hmDOjcnMS6G2lUsya2tRZfmduw6eilbJqzb1y/j5vVr\n1LSqgCRJaR5dW7mCGZUrmKX8Pjomlgf+z7j7yJ87vldZcWA7vo/9MTAwpLyFJWUtKlPeworyFpUo\nXqpMymlWMpkM/0cP+NvrCj5XL/K3tyc51NSoW90KizIleO/nxe0LbrwKCOJNYCBJiUmYFC5C4aLF\n0dM3xPPSOazMy9G9ZWPc/pqIrk7mXxoiKquCoCKDu7akec3yDOzcRtWhZEuisipkZ6EfQ2hhV4mt\ncyfRyKbWV88lJSUxYs5SznjfYd2e45QoVUYpMXleOMOYAZ2ZPrQ3gzq3SUnc5qzZytYjZ7Jlwtqh\ncW3GdWtJGycHuYwnk8l4/jqAu37+3Hn0hDt+z7jn58/HsHDMzMzRNzTi9s0b5DEyxLZ6ZeyqVcK2\nRmVKFDFJNVGOjIrmVWAQrwOD+PAxjAZ1qlPUpKBc4pUnsQxAEDKZdwFvaVrXgteXj6Knq6PqcLIl\nkawK2Z3XlYuM6dsBl2Wz0NbSQl1dDUmCKcs2kKSpy7ItrujpGygllmMH9zJv8nD2LZv93WVN2S1h\nlSSJpXMms2vzGs5vX0XViuUVOl94ZBT3/J7wMSyC2lUqUjBfXoXOpwoiWRWETGbZ3Kkkv3/C6hlj\nVR1KtiWSVeF3sHXtEk4e3IskSZ+/kKht68DIKXOVunmntUN1GlQ2Y+6Ywamuo52xYiPnbvmx44iH\n0uJSFEmSKJNHnYBrp0QnFzkRyaogZCIJCQnYWRbl4o5VmJuWUnU42ZZIVgVBeXy8rzJxaA9Gd2vN\noC5tv3tNQkIixeyas93NA1MzcyVHKF+REeFULmlEkv910WVBTtJKVtVVEZAg/M5OHztI+dIlRKIq\nCEK2Ua2WNblz58aqfNlUr9HS0qRP2+a4bFurxMgUo7VDDQAK1HQkraKfIB+iG4AgKNmeTSsZ1621\nqsMQBEGQm6jICF4+f0a1imlXTKtWMMPn2GUlRaUY8XFxBAa8oV1TR2yqVUqzA4AgHyJZFQQl8vO9\nx9tXL2jewE7VoQiCIMhNZEQ42traqKunnbiFR0Whb2CopKjk7+H9O4zt35lmDvXYt3y2qsP5bYhl\nAIKgRLs3raJfhxZoaorPiYIgZB+FixaneMnSnLnineZ14ZFR6GbRZHXf9vX0cG7AxN7t2LdslqrD\n+a2IZFUQlCQqMoITbq70a++s6lAEQRB+if8jXwLfvvnqsaZturDNzT3N14VFRqNn8PPn06vSh+Ag\nalWuSLdWTcWtfyUT5R1BUJKDe7bRyKY2hfJnv/54giD8HhISElg6ZxKH925HkiS0tXNStWYdylaw\nYs+W1fRr1zzN14dHRWNQMGsmq32HjqepzS6OnPWgRcN6qg7ntyIqq4KgBJIksWfLaoZ2ERurBEHI\nml48e0KHxrUJeOjDw1MuBF935+KOlTjXMifm5V22zZvE9KF90hzjc2U1ay4D0M6Zk1lLNzJk5mKi\nomNSHo+KjmHu2q08evpChdFlb6KyKghKcO3SebRzqFG3mpWqQxEEQcgQSZI4tHc7C6aNZsawPgzu\n0jblNrhpyWKYlixGjzbN0jVWeGR0lt5gVatuPeraO1GiXkuKmBTEJH8+bvs+oqy5JRv2HeH6gc0U\nyJtH1WFmOyJZFQQl2LN5JUO7tBLrnARByHIWzRjLZfcjXNi5Gksz018aK6t2A/Dx9sTjzAlGTJrF\nrGUbGTZxFsHv3xEcFMiQIsUwMDSiZf2qXL5xm7ZNHFQdbrYjklVBULDAt2+4fvUy++eNUXUog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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 8))\n", + "\n", + "for i, res in enumerate(['l', 'h']):\n", + " m = Basemap(projection='gnom', lat_0=57.3, lon_0=-6.2,\n", + " width=90000, height=120000, resolution=res, ax=ax[i])\n", + " m.fillcontinents(color=\"#FFDDCC\", lake_color='#DDEEFF')\n", + " m.drawmapboundary(fill_color=\"#DDEEFF\")\n", + " m.drawcoastlines()\n", + " ax[i].set_title(\"resolution='{0}'\".format(res));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the low-resolution coastlines are not suitable for this level of zoom, while high-resolution works just fine.\n", + "The low level would work just fine for a global view, however, and would be *much* faster than loading the high-resolution border data for the entire globe!\n", + "It might require some experimentation to find the correct resolution parameter for a given view: the best route is to start with a fast, low-resolution plot and increase the resolution as needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting Data on Maps\n", + "\n", + "Perhaps the most useful piece of the Basemap toolkit is the ability to over-plot a variety of data onto a map background.\n", + "For simple plotting and text, any ``plt`` function works on the map; you can use the ``Basemap`` instance to project latitude and longitude coordinates to ``(x, y)`` coordinates for plotting with ``plt``, as we saw earlier in the Seattle example.\n", + "\n", + "In addition to this, there are many map-specific functions available as methods of the ``Basemap`` instance.\n", + "These work very similarly to their standard Matplotlib counterparts, but have an additional Boolean argument ``latlon``, which if set to ``True`` allows you to pass raw latitudes and longitudes to the method, rather than projected ``(x, y)`` coordinates.\n", + "\n", + "Some of these map-specific methods are:\n", + "\n", + "- ``contour()``/``contourf()`` : Draw contour lines or filled contours\n", + "- ``imshow()``: Draw an image\n", + "- ``pcolor()``/``pcolormesh()`` : Draw a pseudocolor plot for irregular/regular meshes\n", + "- ``plot()``: Draw lines and/or markers.\n", + "- ``scatter()``: Draw points with markers.\n", + "- ``quiver()``: Draw vectors.\n", + "- ``barbs()``: Draw wind barbs.\n", + "- ``drawgreatcircle()``: Draw a great circle.\n", + "\n", + "We'll see some examples of a few of these as we continue.\n", + "For more information on these functions, including several example plots, see the [online Basemap documentation](http://matplotlib.org/basemap/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: California Cities\n", + "\n", + "Recall that in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb), we demonstrated the use of size and color in a scatter plot to convey information about the location, size, and population of California cities.\n", + "Here, we'll create this plot again, but using Basemap to put the data in context.\n", + "\n", + "We start with loading the data, as we did before:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "cities = pd.read_csv('data/california_cities.csv')\n", + "\n", + "# Extract the data we're interested in\n", + "lat = cities['latd'].values\n", + "lon = cities['longd'].values\n", + "population = cities['population_total'].values\n", + "area = cities['area_total_km2'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we set up the map projection, scatter the data, and then create a colorbar and legend:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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W5KZgmWeoaSQvS7Lra0AglGEcUMFT1SUffPA+VmuapqGqFyzqiuvn12RlxbFt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qeslgs++egjPn71iqvNllhB3ba8efeOtzc3uEjx9W9+g4urK6qmZr/bMXQdx+OBy23Jcb9juV6j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Virwo2e12YkiQiVVYmooEQkgcHXXTuKinJfv9gfl8wcMHD3j48CFJkkqhDkIhQilFkogp9wCP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zz+QX7io0+RphlhEHN0csJXv/YVTuZzlLKMNPzgQDOeVly6JHaMthWySVlV3Lp1i+VyObSa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\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a7U7jFVqa8GTHcfB7PXqdrlinaU1VlCRZKjq33T2U0mRJwng0Is1yJklCnEiRnExiRuMx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\nUSRZmO9J9tk++Bfh73Zi1tax81zIQ3Vd0+/3RX6t17NBRiZtdVVR5ZnA/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p46GJRkjojj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\n3TaL7fs6f6y13LhzfeY1MlcRGYTNtGLSaaoC9TgUvws66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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 1. Draw the map background\n", + "fig = plt.figure(figsize=(8, 8))\n", + "m = Basemap(projection='lcc', resolution='h', \n", + " lat_0=37.5, lon_0=-119,\n", + " width=1E6, height=1.2E6)\n", + "m.shadedrelief()\n", + "m.drawcoastlines(color='gray')\n", + "m.drawcountries(color='gray')\n", + "m.drawstates(color='gray')\n", + "\n", + "# 2. scatter city data, with color reflecting population\n", + "# and size reflecting area\n", + "m.scatter(lon, lat, latlon=True,\n", + " c=np.log10(population), s=area,\n", + " cmap='Reds', alpha=0.5)\n", + "\n", + "# 3. create colorbar and legend\n", + "plt.colorbar(label=r'$\\log_{10}({\\rm population})$')\n", + "plt.clim(3, 7)\n", + "\n", + "# make legend with dummy points\n", + "for a in [100, 300, 500]:\n", + " plt.scatter([], [], c='k', alpha=0.5, s=a,\n", + " label=str(a) + ' km$^2$')\n", + "plt.legend(scatterpoints=1, frameon=False,\n", + " labelspacing=1, loc='lower left');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows us roughly where larger populations of people have settled in California: they are clustered near the coast in the Los Angeles and San Francisco areas, stretched along the highways in the flat central valley, and avoiding almost completely the mountainous regions along the borders of the state." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Surface Temperature Data\n", + "\n", + "As an example of visualizing some more continuous geographic data, let's consider the \"polar vortex\" that hit the eastern half of the United States in January of 2014.\n", + "A great source for any sort of climatic data is [NASA's Goddard Institute for Space Studies](http://data.giss.nasa.gov/).\n", + "Here we'll use the GIS 250 temperature data, which we can download using shell commands (these commands may have to be modified on Windows machines).\n", + "The data used here was downloaded on 6/12/2016, and the file size is approximately 9MB:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# !curl -O http://data.giss.nasa.gov/pub/gistemp/gistemp250.nc.gz\n", + "# !gunzip gistemp250.nc.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data comes in NetCDF format, which can be read in Python by the ``netCDF4`` library.\n", + "You can install this library as shown here\n", + "\n", + "```\n", + "$ conda install netcdf4\n", + "```\n", + "\n", + "We read the data as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from netCDF4 import Dataset\n", + "data = Dataset('gistemp250.nc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The file contains many global temperature readings on a variety of dates; we need to select the index of the date we're interested in—in this case, January 15, 2014:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from netCDF4 import date2index\n", + "from datetime import datetime\n", + "timeindex = date2index(datetime(2014, 1, 15),\n", + " data.variables['time'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can load the latitude and longitude data, as well as the temperature anomaly for this index:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "lat = data.variables['lat'][:]\n", + "lon = data.variables['lon'][:]\n", + "lon, lat = np.meshgrid(lon, lat)\n", + "temp_anomaly = data.variables['tempanomaly'][timeindex]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we'll use the ``pcolormesh()`` method to draw a color mesh of the data.\n", + "We'll look at North America, and use a shaded relief map in the background.\n", + "Note that for this data we specifically chose a divergent colormap, which has a neutral color at zero and two contrasting colors at negative and positive values.\n", + "We'll also lightly draw the coastlines over the colors for reference:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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orlfTxiSbFtSN7ZmmwN+htYq1zpHWprfrBdW/u/T0KSXORD42PJfdGbnQxO6Y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Wa+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mxyqCqmFVnBlN88hMGIFKHsDJshGCT8QW1STrpPRULJGYLJ6fpUBap5YYqyjG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\nzdnZge17vT67e3u0O216vf7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 8))\n", + "m = Basemap(projection='lcc', resolution='c',\n", + " width=8E6, height=8E6, \n", + " lat_0=45, lon_0=-100,)\n", + "m.shadedrelief(scale=0.5)\n", + "m.pcolormesh(lon, lat, temp_anomaly,\n", + " latlon=True, cmap='RdBu_r')\n", + "plt.clim(-8, 8)\n", + "m.drawcoastlines(color='lightgray')\n", + "\n", + "plt.title('January 2014 Temperature Anomaly')\n", + "plt.colorbar(label='temperature anomaly (°C)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data paints a picture of the localized, extreme temperature anomalies that happened during that month.\n", + "The eastern half of the United States was much colder than normal, while the western half and Alaska were much warmer.\n", + "Regions with no recorded temperature show the map background." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) | [Contents](Index.ipynb) | [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.13-Geographic-Data-With-Basemap.md b/notebooks_v2/04.13-Geographic-Data-With-Basemap.md new file mode 100644 index 00000000..1154330c --- /dev/null +++ b/notebooks_v2/04.13-Geographic-Data-With-Basemap.md @@ -0,0 +1,400 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) | [Contents](Index.ipynb) | [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) > + +Open in Colab + + + +# Geographic Data with Basemap + + +One common type of visualization in data science is that of geographic data. +Matplotlib's main tool for this type of visualization is the Basemap toolkit, which is one of several Matplotlib toolkits which lives under the ``mpl_toolkits`` namespace. +Admittedly, Basemap feels a bit clunky to use, and often even simple visualizations take much longer to render than you might hope. +More modern solutions such as leaflet or the Google Maps API may be a better choice for more intensive map visualizations. +Still, Basemap is a useful tool for Python users to have in their virtual toolbelts. +In this section, we'll show several examples of the type of map visualization that is possible with this toolkit. + +Installation of Basemap is straightforward; if you're using conda you can type this and the package will be downloaded: + +``` +$ conda install basemap +``` + +We add just a single new import to our standard boilerplate: + +```python +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.basemap import Basemap +``` + +Once you have the Basemap toolkit installed and imported, geographic plots are just a few lines away (the graphics in the following also requires the ``PIL`` package in Python 2, or the ``pillow`` package in Python 3): + +```python +plt.figure(figsize=(8, 8)) +m = Basemap(projection='ortho', resolution=None, lat_0=50, lon_0=-100) +m.bluemarble(scale=0.5); +``` + +The meaning of the arguments to ``Basemap`` will be discussed momentarily. + +The useful thing is that the globe shown here is not a mere image; it is a fully-functioning Matplotlib axes that understands spherical coordinates and which allows us to easily overplot data on the map! +For example, we can use a different map projection, zoom-in to North America and plot the location of Seattle. +We'll use an etopo image (which shows topographical features both on land and under the ocean) as the map background: + +```python +fig = plt.figure(figsize=(8, 8)) +m = Basemap(projection='lcc', resolution=None, + width=8E6, height=8E6, + lat_0=45, lon_0=-100,) +m.etopo(scale=0.5, alpha=0.5) + +# Map (long, lat) to (x, y) for plotting +x, y = m(-122.3, 47.6) +plt.plot(x, y, 'ok', markersize=5) +plt.text(x, y, ' Seattle', fontsize=12); +``` + +This gives you a brief glimpse into the sort of geographic visualizations that are possible with just a few lines of Python. +We'll now discuss the features of Basemap in more depth, and provide several examples of visualizing map data. +Using these brief examples as building blocks, you should be able to create nearly any map visualization that you desire. + + +## Map Projections + +The first thing to decide when using maps is what projection to use. +You're probably familiar with the fact that it is impossible to project a spherical map, such as that of the Earth, onto a flat surface without somehow distorting it or breaking its continuity. +These projections have been developed over the course of human history, and there are a lot of choices! +Depending on the intended use of the map projection, there are certain map features (e.g., direction, area, distance, shape, or other considerations) that are useful to maintain. + +The Basemap package implements several dozen such projections, all referenced by a short format code. +Here we'll briefly demonstrate some of the more common ones. + +We'll start by defining a convenience routine to draw our world map along with the longitude and latitude lines: + +```python +from itertools import chain + +def draw_map(m, scale=0.2): + # draw a shaded-relief image + m.shadedrelief(scale=scale) + + # lats and longs are returned as a dictionary + lats = m.drawparallels(np.linspace(-90, 90, 13)) + lons = m.drawmeridians(np.linspace(-180, 180, 13)) + + # keys contain the plt.Line2D instances + lat_lines = chain(*(tup[1][0] for tup in lats.items())) + lon_lines = chain(*(tup[1][0] for tup in lons.items())) + all_lines = chain(lat_lines, lon_lines) + + # cycle through these lines and set the desired style + for line in all_lines: + line.set(linestyle='-', alpha=0.3, color='w') +``` + +### Cylindrical projections + +The simplest of map projections are cylindrical projections, in which lines of constant latitude and longitude are mapped to horizontal and vertical lines, respectively. +This type of mapping represents equatorial regions quite well, but results in extreme distortions near the poles. +The spacing of latitude lines varies between different cylindrical projections, leading to different conservation properties, and different distortion near the poles. +In the following figure we show an example of the *equidistant cylindrical projection*, which chooses a latitude scaling that preserves distances along meridians. +Other cylindrical projections are the Mercator (``projection='merc'``) and the cylindrical equal area (``projection='cea'``) projections. + +```python +fig = plt.figure(figsize=(8, 6), edgecolor='w') +m = Basemap(projection='cyl', resolution=None, + llcrnrlat=-90, urcrnrlat=90, + llcrnrlon=-180, urcrnrlon=180, ) +draw_map(m) +``` + +The additional arguments to Basemap for this view specify the latitude (``lat``) and longitude (``lon``) of the lower-left corner (``llcrnr``) and upper-right corner (``urcrnr``) for the desired map, in units of degrees. + + +### Pseudo-cylindrical projections + +Pseudo-cylindrical projections relax the requirement that meridians (lines of constant longitude) remain vertical; this can give better properties near the poles of the projection. +The Mollweide projection (``projection='moll'``) is one common example of this, in which all meridians are elliptical arcs. +It is constructed so as to preserve area across the map: though there are distortions near the poles, the area of small patches reflects the true area. +Other pseudo-cylindrical projections are the sinusoidal (``projection='sinu'``) and Robinson (``projection='robin'``) projections. + +```python +fig = plt.figure(figsize=(8, 6), edgecolor='w') +m = Basemap(projection='moll', resolution=None, + lat_0=0, lon_0=0) +draw_map(m) +``` + +The extra arguments to Basemap here refer to the central latitude (``lat_0``) and longitude (``lon_0``) for the desired map. + + +### Perspective projections + +Perspective projections are constructed using a particular choice of perspective point, similar to if you photographed the Earth from a particular point in space (a point which, for some projections, technically lies within the Earth!). +One common example is the orthographic projection (``projection='ortho'``), which shows one side of the globe as seen from a viewer at a very long distance. As such, it can show only half the globe at a time. +Other perspective-based projections include the gnomonic projection (``projection='gnom'``) and stereographic projection (``projection='stere'``). +These are often the most useful for showing small portions of the map. + +Here is an example of the orthographic projection: + +```python +fig = plt.figure(figsize=(8, 8)) +m = Basemap(projection='ortho', resolution=None, + lat_0=50, lon_0=0) +draw_map(m); +``` + +### Conic projections + +A Conic projection projects the map onto a single cone, which is then unrolled. +This can lead to very good local properties, but regions far from the focus point of the cone may become very distorted. +One example of this is the Lambert Conformal Conic projection (``projection='lcc'``), which we saw earlier in the map of North America. +It projects the map onto a cone arranged in such a way that two standard parallels (specified in Basemap by ``lat_1`` and ``lat_2``) have well-represented distances, with scale decreasing between them and increasing outside of them. +Other useful conic projections are the equidistant conic projection (``projection='eqdc'``) and the Albers equal-area projection (``projection='aea'``). +Conic projections, like perspective projections, tend to be good choices for representing small to medium patches of the globe. + +```python +fig = plt.figure(figsize=(8, 8)) +m = Basemap(projection='lcc', resolution=None, + lon_0=0, lat_0=50, lat_1=45, lat_2=55, + width=1.6E7, height=1.2E7) +draw_map(m) +``` + +### Other projections + +If you're going to do much with map-based visualizations, I encourage you to read up on other available projections, along with their properties, advantages, and disadvantages. +Most likely, they are available in the [Basemap package](http://matplotlib.org/basemap/users/mapsetup.html). +If you dig deep enough into this topic, you'll find an incredible subculture of geo-viz geeks who will be ready to argue fervently in support of their favorite projection for any given application! + + +## Drawing a Map Background + +Earlier we saw the ``bluemarble()`` and ``shadedrelief()`` methods for projecting global images on the map, as well as the ``drawparallels()`` and ``drawmeridians()`` methods for drawing lines of constant latitude and longitude. +The Basemap package contains a range of useful functions for drawing borders of physical features like continents, oceans, lakes, and rivers, as well as political boundaries such as countries and US states and counties. +The following are some of the available drawing functions that you may wish to explore using IPython's help features: + +- **Physical boundaries and bodies of water** + - ``drawcoastlines()``: Draw continental coast lines + - ``drawlsmask()``: Draw a mask between the land and sea, for use with projecting images on one or the other + - ``drawmapboundary()``: Draw the map boundary, including the fill color for oceans. + - ``drawrivers()``: Draw rivers on the map + - ``fillcontinents()``: Fill the continents with a given color; optionally fill lakes with another color + +- **Political boundaries** + - ``drawcountries()``: Draw country boundaries + - ``drawstates()``: Draw US state boundaries + - ``drawcounties()``: Draw US county boundaries + +- **Map features** + - ``drawgreatcircle()``: Draw a great circle between two points + - ``drawparallels()``: Draw lines of constant latitude + - ``drawmeridians()``: Draw lines of constant longitude + - ``drawmapscale()``: Draw a linear scale on the map + +- **Whole-globe images** + - ``bluemarble()``: Project NASA's blue marble image onto the map + - ``shadedrelief()``: Project a shaded relief image onto the map + - ``etopo()``: Draw an etopo relief image onto the map + - ``warpimage()``: Project a user-provided image onto the map + +For the boundary-based features, you must set the desired resolution when creating a Basemap image. +The ``resolution`` argument of the ``Basemap`` class sets the level of detail in boundaries, either ``'c'`` (crude), ``'l'`` (low), ``'i'`` (intermediate), ``'h'`` (high), ``'f'`` (full), or ``None`` if no boundaries will be used. +This choice is important: setting high-resolution boundaries on a global map, for example, can be *very* slow. + +Here's an example of drawing land/sea boundaries, and the effect of the resolution parameter. +We'll create both a low- and high-resolution map of Scotland's beautiful Isle of Skye. +It's located at 57.3°N, 6.2°W, and a map of 90,000 × 120,000 kilometers shows it well: + +```python +fig, ax = plt.subplots(1, 2, figsize=(12, 8)) + +for i, res in enumerate(['l', 'h']): + m = Basemap(projection='gnom', lat_0=57.3, lon_0=-6.2, + width=90000, height=120000, resolution=res, ax=ax[i]) + m.fillcontinents(color="#FFDDCC", lake_color='#DDEEFF') + m.drawmapboundary(fill_color="#DDEEFF") + m.drawcoastlines() + ax[i].set_title("resolution='{0}'".format(res)); +``` + +Notice that the low-resolution coastlines are not suitable for this level of zoom, while high-resolution works just fine. +The low level would work just fine for a global view, however, and would be *much* faster than loading the high-resolution border data for the entire globe! +It might require some experimentation to find the correct resolution parameter for a given view: the best route is to start with a fast, low-resolution plot and increase the resolution as needed. + + +## Plotting Data on Maps + +Perhaps the most useful piece of the Basemap toolkit is the ability to over-plot a variety of data onto a map background. +For simple plotting and text, any ``plt`` function works on the map; you can use the ``Basemap`` instance to project latitude and longitude coordinates to ``(x, y)`` coordinates for plotting with ``plt``, as we saw earlier in the Seattle example. + +In addition to this, there are many map-specific functions available as methods of the ``Basemap`` instance. +These work very similarly to their standard Matplotlib counterparts, but have an additional Boolean argument ``latlon``, which if set to ``True`` allows you to pass raw latitudes and longitudes to the method, rather than projected ``(x, y)`` coordinates. + +Some of these map-specific methods are: + +- ``contour()``/``contourf()`` : Draw contour lines or filled contours +- ``imshow()``: Draw an image +- ``pcolor()``/``pcolormesh()`` : Draw a pseudocolor plot for irregular/regular meshes +- ``plot()``: Draw lines and/or markers. +- ``scatter()``: Draw points with markers. +- ``quiver()``: Draw vectors. +- ``barbs()``: Draw wind barbs. +- ``drawgreatcircle()``: Draw a great circle. + +We'll see some examples of a few of these as we continue. +For more information on these functions, including several example plots, see the [online Basemap documentation](http://matplotlib.org/basemap/). + + +## Example: California Cities + +Recall that in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb), we demonstrated the use of size and color in a scatter plot to convey information about the location, size, and population of California cities. +Here, we'll create this plot again, but using Basemap to put the data in context. + +We start with loading the data, as we did before: + +```python +import pandas as pd +cities = pd.read_csv('data/california_cities.csv') + +# Extract the data we're interested in +lat = cities['latd'].values +lon = cities['longd'].values +population = cities['population_total'].values +area = cities['area_total_km2'].values +``` + +Next, we set up the map projection, scatter the data, and then create a colorbar and legend: + +```python +# 1. Draw the map background +fig = plt.figure(figsize=(8, 8)) +m = Basemap(projection='lcc', resolution='h', + lat_0=37.5, lon_0=-119, + width=1E6, height=1.2E6) +m.shadedrelief() +m.drawcoastlines(color='gray') +m.drawcountries(color='gray') +m.drawstates(color='gray') + +# 2. scatter city data, with color reflecting population +# and size reflecting area +m.scatter(lon, lat, latlon=True, + c=np.log10(population), s=area, + cmap='Reds', alpha=0.5) + +# 3. create colorbar and legend +plt.colorbar(label=r'$\log_{10}({\rm population})$') +plt.clim(3, 7) + +# make legend with dummy points +for a in [100, 300, 500]: + plt.scatter([], [], c='k', alpha=0.5, s=a, + label=str(a) + ' km$^2$') +plt.legend(scatterpoints=1, frameon=False, + labelspacing=1, loc='lower left'); +``` + +This shows us roughly where larger populations of people have settled in California: they are clustered near the coast in the Los Angeles and San Francisco areas, stretched along the highways in the flat central valley, and avoiding almost completely the mountainous regions along the borders of the state. + + +## Example: Surface Temperature Data + +As an example of visualizing some more continuous geographic data, let's consider the "polar vortex" that hit the eastern half of the United States in January of 2014. +A great source for any sort of climatic data is [NASA's Goddard Institute for Space Studies](http://data.giss.nasa.gov/). +Here we'll use the GIS 250 temperature data, which we can download using shell commands (these commands may have to be modified on Windows machines). +The data used here was downloaded on 6/12/2016, and the file size is approximately 9MB: + +```python +# !curl -O http://data.giss.nasa.gov/pub/gistemp/gistemp250.nc.gz +# !gunzip gistemp250.nc.gz +``` + +The data comes in NetCDF format, which can be read in Python by the ``netCDF4`` library. +You can install this library as shown here + +``` +$ conda install netcdf4 +``` + +We read the data as follows: + +```python +from netCDF4 import Dataset +data = Dataset('gistemp250.nc') +``` + +The file contains many global temperature readings on a variety of dates; we need to select the index of the date we're interested in—in this case, January 15, 2014: + +```python +from netCDF4 import date2index +from datetime import datetime +timeindex = date2index(datetime(2014, 1, 15), + data.variables['time']) +``` + +Now we can load the latitude and longitude data, as well as the temperature anomaly for this index: + +```python +lat = data.variables['lat'][:] +lon = data.variables['lon'][:] +lon, lat = np.meshgrid(lon, lat) +temp_anomaly = data.variables['tempanomaly'][timeindex] +``` + +Finally, we'll use the ``pcolormesh()`` method to draw a color mesh of the data. +We'll look at North America, and use a shaded relief map in the background. +Note that for this data we specifically chose a divergent colormap, which has a neutral color at zero and two contrasting colors at negative and positive values. +We'll also lightly draw the coastlines over the colors for reference: + +```python +fig = plt.figure(figsize=(10, 8)) +m = Basemap(projection='lcc', resolution='c', + width=8E6, height=8E6, + lat_0=45, lon_0=-100,) +m.shadedrelief(scale=0.5) +m.pcolormesh(lon, lat, temp_anomaly, + latlon=True, cmap='RdBu_r') +plt.clim(-8, 8) +m.drawcoastlines(color='lightgray') + +plt.title('January 2014 Temperature Anomaly') +plt.colorbar(label='temperature anomaly (°C)'); +``` + +The data paints a picture of the localized, extreme temperature anomalies that happened during that month. +The eastern half of the United States was much colder than normal, while the western half and Alaska were much warmer. +Regions with no recorded temperature show the map background. + + + +< [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) | [Contents](Index.ipynb) | [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.14-Visualization-With-Seaborn.ipynb b/notebooks_v2/04.14-Visualization-With-Seaborn.ipynb new file mode 100644 index 00000000..f1619f58 --- /dev/null +++ b/notebooks_v2/04.14-Visualization-With-Seaborn.ipynb @@ -0,0 +1,1802 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization with Seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\n", + "There are several valid complaints about Matplotlib that often come up:\n", + "\n", + "- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\n", + "- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n", + "- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot.\n", + "\n", + "An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas ``DataFrame``s.\n", + "\n", + "To be fair, the Matplotlib team is addressing this: it has recently added the ``plt.style`` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and is starting to handle Pandas data more seamlessly.\n", + "The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo.\n", + "But for all the reasons just discussed, Seaborn remains an extremely useful addon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Seaborn Versus Matplotlib\n", + "\n", + "Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\n", + "We start with the typical imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use('classic')\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create some random walk data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create some data\n", + "rng = np.random.RandomState(0)\n", + "x = np.linspace(0, 10, 500)\n", + "y = np.cumsum(rng.randn(500, 6), 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And do a simple plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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H3PzFzWwq28SW8i2UmEoIVASSGCx5ESgVSqakTOFPa/5EdnW2X/pXairF7DLz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\nQUlKGEKvX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data with Matplotlib defaults\n", + "plt.plot(x, y)\n", + "plt.legend('ABCDEF', ncol=2, loc='upper left');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\n", + "\n", + "Now let's take a look at how it works with Seaborn.\n", + "As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output.\n", + "We can set the style by calling Seaborn's ``set()`` method.\n", + "By convention, Seaborn is imported as ``sns``:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's rerun the same two lines as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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hJGWVk9eYtpea8HujHDvQm7fdUT692tTOSitqjYq25kGe+fcPee2FY+OOa28e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KqqJJ3z+NReVWDndvyvvYL/1Zen3Cc+NDjn2rlBqeCra2tZadEzGI3jDsdIi2JOXjHbdgLkGJop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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# same plotting code as above!\n", + "plt.plot(x, y)\n", + "plt.legend('ABCDEF', ncol=2, loc='upper left');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah, much better!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring Seaborn Plots\n", + "\n", + "The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\n", + "\n", + "Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Histograms, KDE, and densities\n", + "\n", + "Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\n", + "We have seen that this is relatively straightforward in Matplotlib:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MxM/IYDTLsjQwMPkJRJzOmKLRqFze7D7do9GoXJZb0ejZT4gSHYnqU2cWBoCsIsBhtIGB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\ncDis9evX6/rrr1d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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", + "data = pd.DataFrame(data, columns=['x', 'y'])\n", + "\n", + "for col in 'xy':\n", + " plt.hist(data[col], normed=True, alpha=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with ``sns.kdeplot``:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EaRaTY+j29qBYUwRJvPOr8I+ODqK3P4zqchHLFpszVGHu0GhErF6uRyQKnK+X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\nvkYa25Lj24VuKxWlWUwdl4+qpl4S/HpH1Rbx8db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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for col in 'xy':\n", + " sns.kdeplot(data[col], shade=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Histograms and KDE can be combined using ``distplot``:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TzDM4YDA5UfwDx6V7KJAnT67M0Qmx8ZSUwI8dO8ZDDz3EgQMHeP755+ccv3Tp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\nH+Bt7gzy0999mvT2NosJp91MhttGcY4Ll8OCZsTwB0PYrCb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(data['x'])\n", + "sns.distplot(data['y']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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OZPnO1SSmHAcgxCeIcX1GM6hLfxztGu+xVhqq2HFgL78d3MLpglQAlHIlvcO7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.kdeplot(data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the joint distribution and the marginal distributions together using ``sns.jointplot``.\n", + "For this plot, we'll set the style to a white background:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VTaLLkRUDiQi8QJO6DEkKBwD8eLBYcCXyYiCRV/Omruhc3vq5PcHQAeHQANi6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\nSBH+H3jVAOxlSfUyAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('white'):\n", + " sns.jointplot(\"x\", \"y\", data, kind='kde');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PsX///pc096U7JAAwdQWm0fhf4bPPORfjRw7j6OQkhoaH8R8//AHuurv+cfirXvUafOLe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\ngxFAo9Xh7t27XFzJz7y5SivH85/4DMJdnqoVCsKEXggv3rgwNRgNxl8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pSxWWim0e7LdR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('white'):\n", + " sns.jointplot(\"x\", \"y\", data, kind='hex')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pair plots\n", + "\n", + "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n", + "\n", + "We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n", + "
" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = sns.load_dataset(\"iris\")\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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G6huQbitCySVTcZNyNvRne9Bj0iNHWxDR+8arflKKRx3lIMVnkOpaJFM50ZRh0Y7+hmWk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\nqkIzN7UlPwmtkMybNw933nkn1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f+kx2Mh3fuE9zERUWwFmqd6nnlFyB4Pls2UbFghiaFA\n3aajecClLRrdzh68XPN36DU6XDHrYlgdNhYcFFSs7z/pse81Iv0Si2QHYSIiGjuzoVA4nmwoDNvj\nwbKZkk3SVEginUOSzKJ9wH1XyoBHgb9+/joA4JyiyrAb2BHF6v4LNYZ4tNeIdIU5IiIaG9ugXVhB\nyz5oDxuHZTMlm4RUSN5///2Q5xcsWIDHH388EVmJq2gfcN/x+m99MbLfiHSTQxYcFEis7r9YVjAi\nXWGOiIjGpqa7Ttj/SafSYn5O6AVGWDZTsklIheSxxx4Lek6hUGDnzp0oLi5ORFbiKpYPuG9rtVGX\niQ8bPo1JujR+JeILZrTX4MotRETxNSVLMtzWGL7sZ9lMySZpNkZMVb5j6i1ZU7Bl7hWo723EFMNk\nzDCWjTld39ZqD9wwVBlYcFBIgTbHCkU6H2SGsQyf95wMOT9ktF9iXLmFiCi+zsmZi/7Z/Wi0tmCy\noQDzcr8SNg7LZko2CZ1D8sEHH+B3v/sd7HY7PB4P3G43Ghoa8Pe//z2R2Ygp3zH1vhsZAoChyhCT\nh50FB0Ui3BK9UtL5IFvmXhF2rhLvRSKi5PJR+z/x7Kcveo81czV+WwYQJbuELtV09913Y9WqVXC5\nXNi0aRNKSkqwatWqRGYh5nzH1Aea60GUrPzmg/T4zw8hIqLk5ld297DsptST0B4SrVaLyy67DPX1\n9TAajXjwwQdx6aWXJjILMec7hl6r1gY9R5Rs/OaDGDnJkShZuFwuVFd/GTbc1KnToVKpEpAjSlZj\nmUNClGwSWiFJT09HV1cXpk2bhk8++QRLliyB3R5+eTqn04lNmzZhYGAALpcLq1evxq233pqAHIfn\nO6a+2GjGvPzZaHa0RDSGn0hO0vkgM4xlMFYZOVeJKAlUV3+Jg3fcjiK9PmiYRrsdeOQxlJbyWZ3I\n5uecA89cD+p7G2HOLEJVbugVtoiSUUIrJNdddx3uuOMOPP7447j88suxZ88enH322WHjpaWlYefO\nndDpdHC5XLjmmmuwYsUKzJkzJwG5Di3QmPrlpQsiGsNPJKdA9y7nhxAljyK9HpZMg9zZoCSnhAoL\ncxfAVG7gbw9KWQmtkCxduhRr1qyBQqHA//7v/6K6uhoGQ2SFrU6nAzDUWzI4OBjPbMbEaHe0JkpG\n0e7+TkREicffIJRqElIhaWxshMfjwbe//W385je/8e7KbjAYcOONN+KVV14Jm4bb7call16Kmpoa\nbNq0KSl6R0KJdtdsomTA+5iIKPWw7KZUk7CNEd977z20tLRg06ZNIxdXq/HVr341ojSUSiVeeOEF\n9Pb24uabb8bJkydRVhZ6nw+Taexd3dHEBYBmR7Pf8fLSqoRdX87Xnurx5RDrPMcqvf0t0d3HoSTr\na45XevFKM9Fi8Rra2vqhUChChtFq1RFdK1bvaTTpdHZmRhQuJ2co3KkYhRsOGyzvsXhvUv2eTfZy\nIV7pxbLsTpXXTKktIRWSH//4xwCAJ598Et/+9rejSiszMxOLFi3CW2+9FbZCMtaxlCZTdOMwTSYD\nCrQFwt8KtAURpxmL68v52lM9vhxiOe432vfAVzT3cSixzGMqpBePNFP5XlUo4O0pD8bhGAx7rVi9\np9Gm09HRK0u44bCB8h6L9yaW769ckrlciGd6sSq7U+k1xzJNSryET2p/4okncOrUKdxzzz347//+\nb3z7299GWlpayHgdHR3QaDQwGAxwOBw4ePBg1BWbeBvtjtZEyWi0u78TEZH8+BuEUk1CKyT3338/\ncnJycOTIEahUKtTU1OAHP/gBfvrTn4aM19raiu9///twu91wu91Yu3YtVq5cmaBcjw13tKbxYLS7\nvxMRkfz4G4RSTUIrJEeOHMHu3btx4MAB6HQ6PPzww1i3bl3YeDNnzsTu3bsTkEMiIiIiIkqkhK4B\np1Ao4HQ6vcednZ1hJzYSEREREdH4ldAeks2bN+P6669HW1sbHnroIbz++uu45ZZbEpkFIiIiIiJK\nIgntIVm7di2WL1+Ozs5OPP3009i6dSsuu+yyRGaBiIiIiIiSSEJ7SO655x709/fj8ccfh9vtxosv\nvuid2E5ERDTeuVxuNNrtQc832u2wuNxQqbirNhFNHAmtkHzyySfCruznnXceLr744kRmgYiISEYe\n/HGOGvocTcCz9g41FiH0Hi1ERONNQiskRUVFOH36NEpKSgAAbW1tKCgoCBOLiIhofFCpVDCVF8Ew\neVLA89aGLqhUqgTniohIXgmtkAwODmL9+vWoqqqCWq3Ghx9+CJPJhM2bNwMAdu7cmcjsEBERERGR\nzBJaIbntttuE461btyby8kRElGJcLheee+6ZgOcMBi2sVgeuvnoTexWIiFJYQiskCxcuTOTliIgo\nxVVXf4n/73/+gfSMwEOc+m1dWLx4CUpLz0pwzoiIKFYSWiEZq6amJmzfvh3t7e1QKpW44oorvMO8\niIhofJs8cykys80Bz/V21ic4N0REFGspUSFRqVS46667UFFRAZvNhksvvRTLli1DaWmp3FkjIiIi\nIqIopMRC5yaTCRUVFQCAjIwMlJaWoqWlReZcERERERFRtFKiQuKrrq4Ox48fx5w5c+TOChERERER\nRSklhmwNs9lsuP3227Fjxw5kZGSEDW8yGcZ8rVBxXW4PDh1pwunGbkwtysLCykIolQohTE5uZtgw\n8cp7tPHlvHYyxJdDrPMcj/cgGfIY6tmLJL1Int1o8hdOKt6bUrF4DW1t/VAoQpeHWq0aJpMBnZ2Z\nYdPLycmUtdyIJI/AUD5HE+5UhGFzcvT44osvJHlqFI5LS0vHtBJZqt+ziSi3wpUr0ZZb0eYv2dJM\n9vRIHilTIRkcHMTtt9+O9evXY9WqVRHFaW21julaJpMhZNwjpzvx82c/9h7fec05qCzJFuK/9VFt\nyDDRXD+e8eW8drLEl0M0eZaK9j1IRJpjTS/YsxdpeuGe3WjzF0o83kM5xOI1KBSAxxN6N3KHYxCt\nrVZ0dPSGTa+jo1fWciOSPMYj3HDYjo5PcPCO21Gk1wcM02i3Y+kjj416JbJY3bNy/mBMRLkVrlyJ\nttyKNn/JlGaypzecJiVeygzZ2rFjB8rKyrBlyxa5s4La5t6Qx5GGIaLRifa54nNJ41WRXg9LpiHg\nf8EqKhQb4coVljtE4aVEheTDDz/Enj178O6772LDhg3YuHEjDhw4IFt+LAVit3txgX83fCRhiGh0\non2u+FwSUayFK1dY7hCFlxJDtubPn49jx47JnQ2vipJJuPOac1Db3IvigkzMKvHfsKvckoUb11ei\npqkXlsJMVJRkBUzL7XbjvROtZ8IZsKgizy+Mx+PB0Zou1Db3wlKQiYqSSVAg8vkoRKlKeu/PjPC5\nCkb6XJZbsnDkdCefLSIas3DlirTcGj7f9HE9inL0LHeIkCIVkmSjgAKVJdkh54Qcq+nGb1484j02\n6gOPVX/vRKsQDqjEJSbxR9bRmq4xz0chSmXSe//G9ZURPVfBSJ9LQEyPzxYRjVa4ckVabrHcIfLH\nCkmEfFtqpxZmosvmxGmfXg0llHC53HjnaDPqWk+iuCADaxZZ0G7thz5djcY2W8ACp6apN+QxEHj8\nKQsvSkXDz9Fwy2C5JQvHarq9LYnS48Y2mxC/sc2OFeeY0dc/CH26Gi2ddgCIuKWxoc0mxG9sswvn\n+WwR0Wh12hy4dk05GtptmJyXAZu9XzgvLbek5U7DmXKOPbU0kbFCEiHfltoV55hx4ON6n7OVWFJR\ngHeONuO/9x4LGObG9ZUB07UUGiTHnI9C41e4Ho9Ax75ysrT46zsji6FuXlsxqt7DTL1GeC6vu6hC\nOM9ni4hGa3AA2PXKce/x5gvFciVvklhuScudTL2GoyBowmOFJATfXpE+56D37339I//O0KrR1evE\nn974Ar4NGr5hgKEWkD+98QUshQYsLM/D8TOtwFMKMrF13SycbrJiiikTCypMfvmIdD4KUbKT9vaF\naznstjqF+VrVTd247GtlaO92IDdLi5YOsQdF2sPh7bVssWFKQSackufS3jcYcj7YWOZvcc4X0fgm\nnfvZ3tMnlGPtPX0+39kG9Nqdwnmn04U7rzkHTR12FOboOQqCCKyQBPzxMOzzui5UN1nR3u3AZFMG\nMrRq2ByDmJSR5i1cphYZ0dnjQLfNidLJRu/fLQUGHP2yHTbH0A8gbZoaf3nnJABgYLDC25MCjPSm\nZGjVUCiAgcFGYfhJpPNRiJKN9PmyFGYKX8z52Tqh5fD6i2cJ56dNzsSga+icAkBWpha/33PUG37z\nWrGlMcuQhn8ca/b+EBh0uYRnTdpymWVICzkfTNqj8x/fOAduT+ihFZzzRTS+ffCvNpyo6UJf/yAc\nzkFML86C1d7tPV+YqxfOl5qzhJ7ZzWsrUFmSja9WWdDaavVrrmBPLU1EE75CEujHQ77JCABo6OjD\nX9446T137YXl6O93IUOvxh/+OvQj5/2jzVhxjhnvH21Gpm5kOMj7R5tx2XllqGmyQpeuRpNPS25d\nq9iqO9ybMr+iQPjxNPxDhq0nlKqkz9d1F1UIX8wFueL+CL19A8L5GZZJQmX8ivPFjd3aOkdaJnXp\natgdA3jm1c+951cvLhHCN7bbhPA2+0DI/EufvYZ2O5559YT3OFBlg8/r+OFyuXDgwBshw6xY8bUE\n5YaSRYe1368cC3WcnyOWc9K5cZGs3Ek03k34Cklzp907BCQ/W4eOnj4899pxFOXo0dwhDh9parcj\nOzMd9S2BKxR9DnE4SH1zL94/2gxgqBdkWKHkR5gufehjcLndkuErQxPhOYeE4iERQ4ukP87rJZXx\nHptTOLb1iRUE6SIPVrsY3pSrhc6m9g7h6rGJ8SdlpgvHRXkZ2PmyWOkPteyv9NmT5jdQZYPP6/hR\nXf0lfrLvUehzMgKet3fYYLGUBDxH41dPrzPkcXdv6HLNbBLvp0hW7iQa7yZ8hUStUuIvb4y0ePpO\nRr/u4llCWGNmGp7/+79w2dfKhL8PVyjM+ZmAz8p+Z1kmIUOngTk/Ew2tvVgwqwC6dDWm5Om8rSGT\nDGn4sqEHC2YVoKTAiD++NpKX4Qm9bD2heEjE0CLpj/OiPPGLOD9bj+suqhia45GfAYVCrBBJK++T\n8zKFOSQKj0LoxZQOycoxpgnpL51dAFOW1vssqZTAT54J/h5Inz0FgD0+6QeqbPB5HV9M5UUwTA78\nGVobuhKcG0oGJZLFaKTlVKGkRyRvkk4otwpztHHPI1GqmfAVEukkWt/J6M7+Qe/EtPwcHf52cGis\n+/6PanHtheVo6ehDSWEmVEoFdGlqDLrcwnAQq90JtUoJfboKc8vyvD9QzjJP8raIvHKoFq+/XwsA\nUEl+jHVbh1pZfFtPPB4Pjp7mhFmKXiKGFkl/nDe2iUOmBgdcWDV/ijf87rdPSZ6hfiF+eUkWDh1r\nRa99ADnGdL8elJZOu7AAxPwZQ0ty+/JtiXzlUK3fewCIywj7hvfAE7aywdZOovFtQXke3D7lTHOH\nXahw9Pb1C+VYW2cfXv5HtTf+leedhRlmlg9EviZ8hUS67O5wbwcw1JpbWZKNJRUFOHq6E23dQ2uL\nt3X3I3+SDl+bO9kbdsHMfPzjWAtePPCl9283rh9aDnhYoB8ovi3I6Wkq4Vyg1ldOmKVYScTQIumP\ncwWAP/7fyByPO685RwifO0mHPW+Ly2NKf9wvqSjwea7EynhxQabkfGjS9yDLkBby+WJlg4iUUArl\nzJuHG4WhoJsvrMBfPh75LcDlxYnCS4kKyY4dO/Dmm28iNzcXe/bsCR8hDN+x86XmTG+LaklhJiZl\npqE4PxOFOXqh9TOSYRg2n6X9IpkwK013alEmqsrzvUsBBroGJ8xSrMgxtGh4Cevall4U5/svYe3s\nHxSeIafTFTK9RRV5AEbSWxRg2exQAvXg+OLzRURS0vl3DocT3/j6TDR32FGQowc8g0JP7cIKE3KN\nWg7jJAohJSokl156Ka699lps3749JukF6mW46mul3uPl84aW4vMVScvo5LyMkK2/gQRKd3gpwEA4\nYZZiRY7W/nBLWI/2GRpuqbxkRVnQZyaUQD04vvh8EZGU3+qBF8/Cf/91ZDny4dER0hESbNwgCi4l\nKiRVVVWor68PHzBCkfYyjHYVouHW1lA9HNHihFlKZeGevWjv72hXDkvEM0xEqU1ajik8bmHxjIWj\n7KklohSpkMRapL0Mo52vMdzaGqqHI1ocw06pLNyzF+39He0cq0Q8w0SU2qTlmFqtEnp+c41afkcT\njdK4rpCYTIaAf1+em4m0dA1ON3ajpCgLiyoLoVQq/OI2fSz2yjR12PHVKktU146UnPFTOe+xiC+H\nWOc5Hu9BLNKM5Nkbq2if2UDpxVoq3ptSsXgNbW39fks8S2m1aphMBnR2hh82l5OTGZdyI9JrRyrS\nsMPhToUJN5qwY32PUv2ejUfZKi3HTjd2C2ES+Vsh3unFI81kT4/kMa4rJKFaOMsKM1FWOFSQt7eL\n3a8mkwGtrVYU5fivLR5Jq+lw/LGSM34q5z1W8eUQy9b4aN+DeKdZVpiJJbOL0Npq9Xv2xiraZzZY\nerEU6zRT+V5VKIaG14XicAyitdWKjo7w90hHR29cyo1Irx2pSMPGK82mpi5UV38ZNuzUqdOhUqli\nds/K+YMxXmWr728IZ7+4gE2ifivEO714pJns6Q2nSYmXMhWScF9e8cD5GkSphc8sUXDV1V/i4B23\no0ivDxqm0W4HHnkMpaVnJTBnqY3lDlH0UqJCcuedd+K9995DV1cXvvrVr+K2227DZZddFvfrcr4G\nUWrhM0sUWpFeD0smW4BjieUOUfRSokLy85//XO4sEBERERFRHCjlzgAREREREU1crJAQEREREZFs\nUmLIFhERUSy4XC4cOPCG8LesLD26u+3C31as+Fois0VENKGxQkJERBNGdfWX+Mm+R6HPyQgaxt5h\ng8VSksBcERFNbKyQEBHRhGIqL4JhcvClWa0NXQnMDRERcQ4JERERERHJhhUSIiIiIiKSDSskRERE\nREQkG1ZIiIiIiIhINilTITlw4ADWrFmD1atX48knn5Q7O0REREREFAMpUSFxu9144IEH8Lvf/Q5/\n/etfsXfvXnzxxRdyZ4uIiIiIiKKUEhWSw4cPo6SkBGazGRqNBhdddBH27dsnd7aIiIiIiChKKbEP\nSXNzM4qKirzHBQUF+PTTT2XMERERJYq9uyWic888szNkOps2bQYA2FqtIcP5ng8VNtJwY02z0W4P\nGm74/LQIwkYaThqWiChRFB6PxyN3JsJ59dVX8fbbb+OBBx4AALz44ov49NNPcffdd8ucMyIiIiIi\nikZKDNkqKChAQ0OD97i5uRn5+fky5oiIiIiIiGIhJSoks2fPRk1NDerr6+F0OrF3716cf/75cmeL\niIiIiIiilBJzSFQqFe655x5s3boVHo8Hl19+OUpLS+XOFhERERERRSkl5pAQEREREdH4lBJDtoiI\niIiIaHxihYSIiIiIiGTDCgkREREREcmGFRIiIiIiIpINKyRERERERCQbVkiIiIiIiEg2rJAQERER\nEZFsWCEhIiIiIiLZsEJCRERERESyYYWEiIiIiIhkwwoJERERERHJhhUSIiIiIiKSDSskREREREQk\nG7WcF3c6ndi0aRMGBgbgcrmwevVq3HrrrX7hHnzwQRw4cAA6nQ7/+Z//iYqKChlyS0REREREsSZr\nhSQtLQ07d+6ETqeDy+XCNddcgxUrVmDOnDneMPv370dNTQ1ee+01fPLJJ7jvvvvw/PPPy5hrIiIi\nIiKKFdmHbOl0OgBDvSWDg4N+5/ft24cNGzYAAObOnQur1Yq2traE5pGIiIiIiOJD9gqJ2+3Ghg0b\nsGzZMixbtkzoHQGAlpYWFBYWeo8LCgrQ3Nyc6GwSEREREVEcyF4hUSqVeOGFF3DgwAF88sknOHny\nZEzS9Xg8MUmHKN54r1Kq4L1KqYT3K1HqkHUOia/MzEwsWrQIb731FsrKyrx/z8/PR1NTk/e4qakJ\nBQUFYdNTKBRobbWOKS8mk2HMcVM9firnPVbxEy2aezWQaN+DRKQ50dKLR5qpfK/G6r1IpnSSKS+x\nSieWeZFDspetyZ5ePNJM9vSG06TEk7WHpKOjA1br0I3kcDhw8OBBTJ8+XQhz/vnn44UXXgAA/POf\n/4TRaEReXl7C80pERERERLEnaw9Ja2srvv/978PtdsPtdmPt2rVYuXIlnnvuOSgUClx11VVYuXIl\n9u/fjwsuuAA6nQ4//vGP5cwyERERERHFkKwVkpkzZ2L37t1+f7/66quF43vvvTdRWSIiIiIiogSS\nfVI7ERERERFNXKyQEBERERGRbFghISIiIiIi2bBCQkREREREsmGFhIiIiIiIZMMKCRERERERyYYV\nEiIiIiIikg0rJEREREREJBtZN0YkIiIiSkUf//MT/OD/vR9KlSofReXDAAAgAElEQVTg+dzsbDz1\n218nOFdEqYkVEiIiIqJR6urpQc7czUjTGQKe19mPJzhHRKmLQ7aIiIiIiEg2rJAQEREREZFsWCEh\nIiIiIiLZyDqHpKmpCdu3b0d7ezuUSiWuuOIKbN68WQhz6NAh3HzzzSguLgYAXHDBBbj55pvlyC4R\nEREREcWYrBUSlUqFu+66CxUVFbDZbLj00kuxbNkylJaWCuGqqqrwxBNPyJRLIiIiIiKKF1krJCaT\nCSaTCQCQkZGB0tJStLS0+FVIKIY8bjiPfYb+2lpoi4uhqTgbUIQZuTeWOLGMTxOX2wXHoXfgqKmF\nzmJB+sKlgDLwEpsAeK/RxBHsXj/z95rWJijTtXB2W/ksEFHSS5plf+vq6nD8+HHMmTPH79zHH3+M\n9evXo6CgANu3b0dZWZkMORwfnMc+Q/UvfuE9nrptG9Jm+b/n0caJZXyauByH3kHNb3/vPbbAA+3i\nFUHD816jiSLYvT7897zl56Ltrbf9zhMRJaOkqJDYbDbcfvvt2LFjBzIyMoRzlZWVePPNN6HT6bB/\n/37ccsstePXVVyNK12QKvDZ4vOMmc/yapnrh2NVUD9PKZSHjRhInnvFHK9r4coh1nuPxHsiRx5O1\ndcJxf20ditcFjmcyGUZ9r0Wbv2RIM9Fi9RrGYzqJzEuwe3347y6HI+D5eOQlmcWz3Moy6kKGVauV\nYa/Psj/50iN5yF4hGRwcxO23347169dj1apVfud9KygrV67ED3/4Q3R1dWHSpElh025ttY4pTyaT\nYcxxkz2+utAsHKsKzULYQHHDxQl37Wjjj0Ys4sshmjxLRfseJCLNSNPTnlnMYlh68ZSA8YbTG829\nFov8yZlmKt+rsXovkimdROcl2L0+/HeVThvwfDzyEkk6colnudXd0xcy/OCgO+T15SpX5Uwz2dMb\nTpMST/YKyY4dO1BWVoYtW7YEPN/W1oa8vDwAwOHDhwEgosrIuBXlGHlNeSUsN2z1jslPK68cZZzi\niOII8SvOxtRt29BfW4v04mKkVZw9qviUQkZ7f4YJn75wKSzwwFFTC62lGNoFS+E8ejhoeN5rlOo8\nLlfIexwA4HbBZbPCfMVlcPXaoC2v8N7rw8+Aq60ZlhkzMNBt5bNARElP1grJhx9+iD179mDGjBnY\nsGEDFAoF7rjjDjQ0NEChUOCqq67Cq6++imeffRZqtRparRaPPPKInFmWXdTzOY4fEcbkTzVmhZ9D\n4hdn0ujGIiuUSJs1h+OXJ4DR3p9hwytV0C5eAe3iM+GPHg4dnvcapbiO9z8I+wz5za0yTx6ptJx5\nBoZbjsV+EiKi5CRrhWT+/Pk4duxYyDCbNm3Cpk2bEpSj5NdfW+t3PJofX2OJH+01aeIY7b0S7/BE\nqcZ2+rRwHOged9TU+h0PV9qJiFIR1wBMMf5j6ouDhIxd/GivSRPHaO+VeIcnSjUZJVOF40D3uM5i\nEY61Fj4HRJTaZJ9DQqMzpjHyknH6U//je+ivPo30KVMAlRLWV/eOjFUOdE3fOSQlFkCp8MZx2axw\nnKoe2SMi1ka7DwXFV5g5H2HvzzOf58naOmiLi5FetVic03RWORxv7UNfXT30xWakL1kJqEaKqbHM\ngSJKJdnzz4Hlhq3ob2xCel4uHP86AVdrC5TadDi7eoaemwVLhuZWnT4NbUE+VLl5gNsF5/Ej3mfT\nvXRh+Lkogfg848qy6cD0mdy/hIjijhWSVDOGMfKBxukbVl80NB7/pz8T/o58/2UhpXNIfNe39/23\nBR5g3UWjfkmhjHYfCoqvsHM+wtyffp/ngBM1T+0aOe53oGbXMyPHHkC7/PyR649hDhRRKun88CPU\n/Pb3yFt+Lmr2vgwAAfcUURonoeX/fJ6lG7YKzwZ6b0D1k78V4kTyrPg+442jiEdEFA02e0wAgcbd\nh/p7uPi+69v7/ls6rjkWAo2VJvlEes8EI/38+urEvRT6GhpCno/2+kTJbngOSbByFhi676X3vvTZ\nsp+u8YsTCT5jRCQH9pBMAMHG3Uc6Hl8aTqXVBvx3PMYxc6x0col2Dof089RNEfdS0JnNIc9zDgmN\nd8NzSHz3EZHuKZJeXAyFJJ702dKXiMeRPit8xohIDqyQTADBxvVLx+NDo0bNn56HutAsjDcW4k+Z\nAqhV0BQWIX3KFLjtvcjX6Yb2iFg4th2xQ0lfsASWASf66uqhm2KGdkEc5qlQxMLOEQm3r8jw51lf\nD53ZDO2SFZiaaxpJb0YFLArFyOe9dOXorh9OlPv4EMVbzsIqTN22Dc7GRlhu2ApnczM02dmwzJyJ\nga6eofu+vBLOY5+hcN1F0BiMUBgyMWh3oOSGrXCe2XekcNkieDKN4Z8V6TNRXul9xrLKpsE9vTyx\nbwARTUiskEwEQcb1h5obIowbDhA/bebIl5t2QewrIt48njgqzDGYmmvieGY5hZkjEm6OSbDP0zeM\ndvn5wfdOiHKfkWj38SGKN4VSvMd9n4XhfzuPHka1z55cgcpupVod0bMS7JlImzUHuXHYBZuIKBA2\nDU5goeaGJMu4YY5nTi3hPi+5P0+5r08UC7Esu/lMEFEyYIVkAgs1NyRZxg1zPHNqCfd5yf15yn19\noliIZdnNZ4KIkgGHbE1ggeaG6IrNUBWaRz82P06injNACRXu8xo+72qql+U+4/1E40HQeX1juKf5\nTBBRMmCFhAAACoUCmhmzYDp3if+YYcmkR49Kif7q09CWWOBxudFfVydurBjtxGFJ/LSKsznOPwVJ\nVwECALjdcLe3wtHSCn1aGuBywfl5iHsl1pPQo5yDQiQLn+cgLcuIwX4n1Olp8PTZ4WpugtpshuGC\nNXAePwLra38bKqeXL4k4TW1xMQxfv5ALPBCRbFghmcACTWYMuDGiJNzwBMpAm3Uhf1nUE4c58Th1\nhfvsHAf3ixshutzCRoh+k+B5LxD5PQfmjRtwetcL3uO85edC39khLFKSnr4dKJ0VcZp8tohITrI2\nhzQ1NWHz5s246KKLsG7dOuzcuTNguAcffBBf//rXsX79ehw7dizBuRy/ot0YMdBmXaNJN9p8UfIJ\n99mF2wgx2SbBEyUD6X3v7OgQjl0Oh9/GiMMbLEaaJp+ticnlcuGLL/4V9D+XyyV3FmmCkLWHRKVS\n4a677kJFRQVsNhsuvfRSLFu2DKWlpd4w+/fvR01NDV577TV88sknuO+++/D888/LmOvxI9qNEQNt\n1jWadKPNFyWfcJ+dvjj0RojJNgmeKBlIn4O03BzhWKXVQifZNDajpATuUaTJZ2tiqq7+EgfvuB1F\ner3fuUa7HTlP/R7Z2UUy5IwmGlkrJCaTCSaTCQCQkZGB0tJStLS0CBWSffv2YcOGDQCAuXPnwmq1\noq2tDXl5ebLkOWGCzduI4WZuwmRGSzHcPd04+atfQ1tcjPSFSwGlyj+c7wTKqSXInL8A/XV14oaL\nUU6SFOKXWACXG9ZX9/q/9kDzCyi2zrzHNU31fhtmBqKZOQuWLdd6NzZMmykOGUlfvAIWlxt9DQ0j\nGyPm5Y/cK+WVcB49HHCTtphMuOXGiJRsJPNDavodUKZrMejoh1qbBme3FdriYkz9j++hv/o0NFkG\nuPoHYLnxW3A2NkFjNEBlnoK0syow1TjJ+6zkLFyAtnZb0GsNp+msq4c6Q4f+2looAD4TE1CRXg9L\npkHubNAElzRzSOrq6nD8+HHMmSOOYW1paUFhYaH3uKCgAM3NzeO+QhJs3gYQw7G+PhN8He8eEMYf\nW+CBdvEKv3DDfDdGTKucGzTdaPMl3QDM97VHOgeGxm6048wd7x8U54hoNCP3EQDn58fEOSN5/397\ndx4eVXX/D/w9C0kmyUwgycxknSBhSYghBRK2aBKDgEJZIqsii7SoPzAoYqlS0KdCpe626FfBr4VS\nKNaqaP1iCzUIsYIEXICyKUpIMtn3fZs5vz/CDHPvbHe2TDL5vJ7Hx9y5555z5t5zznBnzuceFaet\ndF48Z3WRNm+8H0I8zdpYH50zD9f33YwTGfr445DPmMU5lr+AqGlfEYnNbyostv/ISOoThBCv6xM3\nJC0tLVi3bh02bdqEoKAgt+WrVDp/x+/Kse44XlfOnWtvGq+hK9dCmWn7H96Oln+1uISz3VFcgtjZ\nzr0Hd567Iv55MHnvlva5o3xvcHed3ZWfrfNvib12ZC8/R8szJeQ9O5K/J9pRf2ybfO56D76YjzN5\nmLXJG2O9WZyIA33BWn2sjZm2yunvbdaTY2uIQmYzrVQqtlu+t8f+urpgXHNznvb09fyId3j9hqS7\nuxvr1q3D3Llzceedd5rtV6lUKC8vN26Xl5dDrVYLytvs8bUCKZVyp4911/HSCO7cetOFryQR0Tbz\nd6Z88/nEMU69B3efO7PzYPLeLe0DnL/uhvK9wZU687l6DUzZOv+W2GtH9vJztDwDoe9ZaP7uPIee\nyrM/t1V3nYu+lI+zeVgb6/3CwrivC+wLtupjqf3zH89tWo47z6+3eHJsbWhss5m+u1vv9s9qR+on\nRG1ts9003q5jb+ZnyJP0Pq/fkGzatAnDhw/HihUrLO6fOnUq9u/fj5kzZ+K7776DQqHw+elagI24\njRuxHvXv7YdMo+HEeghiLTZlaBw0q3+BjqJi+MfGIGBCL0x9EhADYisehRb08rxBCUnQ/HIVOop7\n1prxS0jiJtB1o/3EcbSVaBEYGw3/CbdBs6KzJ4YkNhoBaVO4+dlZGNHT15TaDOlrDG2ys6wMUj8p\nOioqoVm2FB0NjdAsvx+ddXUYJJdDJBEDTC84voPpdNx4rMRbrbZ/6hOEEG/z6g3J119/jU8++QQj\nR47EvHnzIBKJsH79epSWlkIkEmHx4sXIzMzE8ePHMW3aNMhkMmzfvt2bVe49VuI2bMZ6CGAvNmX4\nmplu/7ZBaF0sxoDYikehRe48rvPyBU57G6oIcWxdkVAl9/rcuGbKzHTL7czT15TaDOlrbrRJAGZj\nc9E/PkH47beh/JNDAByL76g9fcZqPBY/D+oThBBv8+oNyfjx4wWtK/L000/3Qm36B/6z5tuLihEw\nSfjx1tYUsbTP0+g5+H2fpWtk+g8XIeuK0D90CLFPyHpPjvQn/jok1BcJIX0ZPduvn5FpNJztAI1r\na3yYxqb09nPo6Tn4fZ/j64pE2UxPCLHM6npPTo7RQXFDOdvUFwkhfZnXY0iIY/wnTIEGrOeXEU0s\n/NMmo+bsl2gvKoIsToNgfwWKjpZAGhHNWbuEEyuycjnaiksgi40B/AOgkskgi9MAYhGK/vae+XoT\nrq7dYCNuZej69WbrmJC+gxtDEmMWQ8JdVyQKAZMyoPHzR3tRMWSaWPiNTET7V/k3tjXwHzcB7Sfz\n8X1pKQKjo+E/OQOdP1w2tg1pwmjUnj+F9qIiBMRpEJo8CSKRAzFShPQTTN+Nlq/y0VlSisCICHQ2\nNUOzYhlYdzdEYjHaysqhWbkMYlVET/xgbCz8Ro2+2Z+iItCtA/zUKjCdHh0lJca1ezovX4CuugJx\nv1yFzoamnjhEidjyek5kQNPp9ChrbbW4r6y1lVZqJ71G8A3Jjz/+iLq6OjDGjK+lpaV5pFLEBrEE\nAZMyjNO0as5+iZodbwMAWgDAJCYk3M7fpq/x09hb78ORn/7txa3wn61P+g7zGJLBnGvPX1dE4+fP\njXHq6uLGmNzfhqJ9f725rddztqNWLUfNn/YCuNGec4GwFFpbhvielq/yUXqjrQM942L5wY8Qt3I5\nru+5+brpGMmPIYzOmYemwmuccVzzy1XcPvv44wCAwhdf4rxG07dID4a/jpEiMHSQ2Z7WWinu9kKN\nyMAk6IZky5YtyM/Ph8ZkupBIJMLevXttHEV6Q3tREWebs16Jnb9NX+Nvm843thdHYI+9uBX6YOy7\n7F17/n5+jJNZjElZmc3tDt46Ju1FRQDdkBAfxG/rhnGxtYS/ls/NPsfvX521tWbjOD+Npdg8GneJ\ngUQigTIhEvKowWb7mkrrIZHQL9Skdwi6ITl58iT+/e9/w8/Pz9P1IQ4KiNP0fJN8A2e9Ejt/S2Tc\ndX6tzVV2NdajL8WtEMfYu/b8/fwYJ1kMP8aEtx0Vycs/hps/Lz9CfIW/htvWDeNiYAz3ddM+x+9f\nfqGhYEzPeY2fxj821mytERp3CSF9jaAbksjISHR0dNANiSUC1tLwpNDkSUBuzzfJMo0GwQEKyGKj\nIVFH3Vy7xHQdk5gY6FuboZLJEHDLUAwdn9YTw3EjjSw22mx9CGliEsJyV/fM69doMCgxyXqFLLC6\npgrFjfR59mJIzNY1SEjCUEXIze1Ro6EZNOhmzNO4CdAw1hNzEhUF/ykZGKqMMKYflDAaYUH+xrYW\nOsaBR8gR0ldZ+JwImpiBKIaeGBK1Gh119YhatRwXhssRl7safuX1ZmMkJ4YwMgI6PRA8fDiCDeO4\nSR/kr/VDa40QQvoymzckTz31FABAp9Nh7ty5SE1N5fx8N2DWBLFB0FoaHiQSSXrm2JtMa1HeNsW4\nxoPfqJsfPKZ/B6TdTO+XlGJy7GSz9SGuNF3FjpqPgSAANd8itykcCYpRDlTS8poqpO+zF0Ni8dry\ntk1jni43XsEO3b8BNQDdBeS2aZDAS89vz4T0d9bi8IKnZBtfK2y8gh1n3gH+27OdO/kX5uMsL4bQ\nlOk4bmmtH1prhBDSl9m8IZkwYQLn/6ZEIv6PwAPTQFhLQ9tUZrbt0A0J6bdcjR/io7ZEBiIh/Yj6\nBiFkILN5Q5KTkwMA2LlzJx566CHOvldMvu0ZyAbCWhrR8kib28R3ubt9U1siA5GQfkR9gxAykNm8\nIXnppZdQU1ODo0ePorCw0Pi6TqfD2bNn8fiNxwkOZGZz6H1wbu4oxQjkpv4C2qYyRMsjMUoxwttV\nIr3E0L7589GdZWhLFe0VUAeoqS2RAUHI5wT1DULIQGbzhmT69Om4evUqvvrqK860LYlEgjVr1ni8\ncv2ChTn03qKHDmdqvoH2ehli5FEYHzoWYnAf2cegx5XGHzg3FyLYXiBLBDESFKOcnz7g6sKKxHtu\ntG/+fHQDR9uTiAHDSjoQV94CaUQHRImA2SOAbOQ/UjEc3zdedaj9EuJtTAT8FOMPbUgQouX+GCli\n+L7xilk7TlCMQvot4/DltW9wVJsvrI3T+EoI8QE2b0jGjBmDMWPGYPr06QgODu6tOhEnnan5Bn8+\n+3fjNkthmBDGXbzySuMPPYGTN+SmWgicdDNXF1YkfZej7cnRtsDPf0XKQk4b7432S4irHGnHZ0rP\nebRPEUJIX2Tza5SEhAQkJiYiLS0NiYmJSE5ORkpKivE1d9i0aROmTJmC2bNnW9xfUFCA1NRU5OTk\nICcnB//zP//jlnJ9kbaxzOY2YDlw0tMGQuD/QOVoe3K0LZjlz2/jvdB+CXGVI+24qEFrdZ8lNL4S\nQnyBzV9ILl++DAB45plnMG7cOMyZMwcikQiHDx/GF1984ZYK3HPPPVi2bBk2btxoNU1qaireeust\nt5Tny2JCojjb0QrzoEhvBE4OhMD/gcrR9uRoW+DnZ9bGKfCX9ANm/URhvd9oQqKt7rOExldCiC8Q\ntDDiuXPn8Nvf/ta4PWPGDLf9UpGamgqtVms/oY+yNAff2uuW5hGbphs2OA6bh9yFruJSDIqNhir0\nZ2Zp4hTReHrITHQUl8A/LhYtYj/kaY8hRhEFxvQ4XllpDKg0lGetLtbqzjcQAv/7A8Z0qD33Vc+i\ng3EahCZPgkgksX3MjWt8vLLCrF0A5g88GCmPR83ZL41lDEmeiO+bfjTuH56YAPna5egqLoVfbBRE\niaNQUHMa2sYyxIREYVzoz/BD44+cmBFO/orhkKfK6QELpM+yNC4OUwzFvclzUdZUiUi5CoHwx9Lk\nHFQ0VyEmJAqt3a04VPgpUusDEVHfiecD7kJjTSX842IRKh9uszwaXwkhvkDQDYlMJsMHH3yAu+++\nG3q9Hh9//DEGDx7s6boZffvtt5g7dy7UajU2btyI4cNtD9D9iaU5+CplquC5+abp1gdnomlXz7zk\ndgB+uX4IS0k3S9Ngkkb84EJ82Hwc6ZpUfFl0xmJ51upire5m+lDg/0BWe+4r1Ox4GwDQAgC5NxYh\ntMFeO+Q/8KDm7JecMrrWdmFH3afG9Pcmz8WBun8BwQDqzuHean8cOP+xcX9Xchf2nz9oVp5pmS49\nYIEQD7PUZ6o7qjntfMmtc/Duf/9h3E7XpGJYSQca9r2HQbffhuov/gMAaAYgf1xue+yk8ZUQ4gME\n3ZC8+OKL2Lp1K7Zt2waRSIT09HS88MILnq4bACApKQnHjh2DTCbD8ePHsXbtWhw+fFjQsUql3Oly\nXTnWkeOPV1ZwtivaKzj/N3399njzf+ybHi8pq4HeZF9HSTGUd8ptppGU1QByoL27w2p5lup4e3yq\n1br31rnz1PHe4O46W8qvtIQ31/xG+7DF2rW3hl9GZ0kJEHRzu6ypkrOfv13aXO5QeaZ64xz2xTx7\nm7vegy/mo1TKLfaZyuYazmtlzdx2397dgeCqFgCArr2ds09XroUy0/YXB7bq46r+3mY9OS6EKGQ2\n00qlYrvle3vcqquz/8Aib9ext/Mj3iHohiQ6OtprMRxBQTf/NZOZmYnf/va3qK+vF/QLjaXHlAqh\nVMqdPtbR49UBaovbll63lKdpOl1kOGeff0wsqqqabKbRRYYBzUCANMBqedbqYq3uvXXuPHW8N7hS\nZz5r58A/NhbNpts32octQtuh1TJiY4Dac8btKAU3v0i5irMdFRzhUHkGrl53T+fniTz7c1t117no\nS/kY8rDUZ6Ri7tTIqGBumgCpP1qUIvgBkMi4Y7EkItqpurnzPbnKm/9g9OS40NDYZjN9d7feZvl9\nYdyqrW22m8bbdezN/Ax5kt5n84bkoYcews6dO5GdnQ2RyHyxgLy8PLdUgjFmdV91dTXCw3v+EX3u\nXM8/bHpzupg72IoHsbbooNDFCE3TiRSxCMv9JTpKShCgiUN3dyeK/rEPQ+Ji8XjagyhsLDGmaS8q\nRoAmFvXDInFPYxBiFdEYp0pGRXtPDMlIxXBcvvGc/FhFNFamLEJJYyliFFEQi0TI0x5DtDwSj6at\nRnGjFtHyCIhFYrx/4ZDFWAPifaHJk4Bc9MR3aDQIHTPJ7jFmi7XJh6Pz4jnjmgfSxCRcabq5LsiI\n5AnoWtuFzuIS+GtiEJYyBStqg6BtLEO0IhIpYclgycw4lz5VOQ7iZDFKm8oRpYhAWvh4SFOkxvQj\nFb4zPZP4Fv64HhY+DgB3TI4NiUZ9Rz1a2ltxb/JcVLfUIjwoFHWtDbgveS7q2hoQ6CdD8KAg1PvX\nI27tSsgau6D55Uh0NTRZjwmhtUcIIT7G5g3J1q1bAQB/+ctfPFaBDRs24NSpU6ivr0dWVhZyc3PR\n1dUFkUiExYsX4/Dhwzhw4ACkUikCAgLw6quveqwunmJrHr61RQeFLkZoli4lHso75Th/5BM0vrEH\nQE+siGLtSkwdm2VMg5SeP8MAxMvjjfndHp+GqqomXG68wqmzIcbEUqzJ1OgsXG68gj+cftvieyR9\ng0gk6YkZsRM3wjnmRvu6PT4VVVVN6Lx4jrPmQVjuauyouTk3fkXKQvy57tOeGJHac1hRG8RZb2Fp\ncjdnLr04WcyJGZGmSDnpFakKakekT+KP6/7+UtziH88ZkwtqTvPafw6nvadrUvHPq8cA9IyZEbGj\njN/4cn8n4aK1RwghvsbmDYlK1TOd4uGHH0ZmZiaysrIwfvx4i7+WOOvll1+2uX/p0qVYunSp28rz\nBktrNXh8McLiEvPtscKP59fZEGPCjzUxvBdvvEfS+/hrHLQXFXFiROythVPaVG5z29L6DNSOSF/E\nH/OKGrS4RRXPTWOn/ZuOp460dUtrj9ANCSGkPxP0G++f/vQnDBs2DPv27cOMGTPwxBNP4NNPP7V/\nIAHgnbU//OO4z6L3i41x6Hh+HQOk/jf+H2AxnTfeI+l9/DUPAjQazra9tXCiFdwYkSh5BG8/tSPS\nP/DbJn/9EMC8P0Tx2r9hXLWUny209gghxNcICmpXKpXIycnBiBEjcPLkSezbtw8nTpzAzJkzPV0/\nnyA0HkQPHc7UfAPt9TJoFDHo1HVA21iOoYM16NJ3QttYjmhFBCaEp0Fi59KFj5kC/VrdjfUeYtAY\nH4ULN+I+xCLxjbgP7t+m9RqpGI4VKQt71odQRCHETwG1TGWMNdE2lVuMeTHGGtD6EH2esb3dWANk\nfOhYiMENvjVbhyQxibPmgTRxNFbU+hnzSAlNxr3JHShrqkSUQo3ksNHG9ReiFCr8LDwFLLnnaVpR\n8ghMCE9FaGooZ50RRaqC1hkhfYqlOEDDmFfdWg1IgPPll6ANLEdNSx3Cg0LR3N6M4IAgTI/PQJBf\nIOR+QfDT+/WsP9JSBXVQOLp03Zg+PBMjB8c71NZp7RFCiK8RdEOyevVq/PTTT0hISMCECROwa9cu\nJCQkeLpuPkNoPMiZmm+M841NYzXmJMjxj8tHjOlYMjBFOdlmXj80/YQdN9Z7SJf74cszN3/RMs3b\n9G/TdUS+b7zKmftsiBUxSFBwrz8/1oD0fabtDQBYCsOEsDROGovxTyZrHlxuvMLJ497kDk6MCEtm\nvG1wtkNTQ2mdEdLnWYsDTFCMwomOWuw/fxDpmlQcPp9vTDMnYTr+atLW0zWpCAsM5Yzluam/QGZE\nhuMVorVHCCE+RtANyejRo9Ha2or6+nrU1NSguroa7e3tCAiwFXZHHGU639h0bnFdWz0nXWlTOaC0\nk1eT5bz42/w5zJb+NmzTPxJ9i8V4jzBeGjvtgL/f3joj/G1qV6Q/sNUPDHEh/HGWP263d3eYvUbt\nn/gSnU6HwsKfbKYJDU3ppdqQ/kbQDcn69esBAC0tLThy5AieffZZlJaW4r///a9HKzfQmM43No3V\nCJVxH3PMn3dviel8ZH7ch+m8ZWtzmCkmxPfZi/cA7LcD/gcNba8AACAASURBVLa9dUb429SuSH9g\nq90b4qL44+wQ3rgdIPU3e43aP/ElhYU/4cT6dYgMDLS4v6y1FaF//hOGDKF2T8wJuiH54osvcPLk\nSXz11VfQ6XSYMWMGMjMzPV23AWd86FiwFAZtcxmGKjS4ZXAMtI3lCPMfgvvH5EDb2DPvfqIyzW5e\nnGfhc+I+IiAWSaCWqXh/c+frC417If2Xsb3dWPMjNWycWRp7sUH8djJcMQyiZFHPuiLyCIxT/gxI\nhnHdkQnKVISnhlOsEelXbI2HE8LTwJKBqtYa3Js8FzUtdQgLGoKW9pae7dY6KPyDEeIXgrbONqxI\nWYim9mZEy6Oo/ROfExkYCE0wLSxIHCfohmT//v3IysrC8uXLERHB/Xb+woULSEpK8kjlBhoRRFAM\nUqBN1gaZRIafDRkDUbjYGFDZ6t+BUP8h+K72HIoaSqwGIvPpmR6jFCM5cR8j5SMs/n2zLsLiXkj/\nJYakJ2YkzHoafmyQHjqcrjltDGIfF/ozXp5ihPqHoq2zA6H+ofCHP25TphunGDLoPfiOCPEMS+Oh\nYVyuaKmEzC8AgwMUCPcPxxTlJHzfeBUdnV0I9w+HKkCF4kYtAqWBGBuaAhFujulHtflmi+USQshA\nJOiG5K233rK6b/PmzTh48KDV/UQ4a4GT/NdNA9EtBSLbyosQV/AD4buSuzgLva1IWWj2MATTdkft\nkvgKQ1tO16Tiy0s3F4vl9wH+g0MsjenUDwghA53LX8kwxtxRDwLLgZOWXucEovMCk+3lRYgr7C30\nZmlhQ0e2CekvDG3XbLHYRhvjtZUxnfoBIWSgc/mGxJ2rtg901gInrS1SCFgORLaVFyGuMFvozcGF\nDaldEl9haLv8YHZ+H7H04BDqB4QQwiVoyhZxnKWFtOzNETYuRthchujgSEjFUuRpjyFWEY3c1FXQ\n3ggUbu1uhUwSYDUQGaCgdOI4S22WgRkX64yRR2FsaAonEH582FiHFjakBTRJX2K28KfAWA4GPcQi\nERaMnonWzjYsTc5Bp74LETI1RiqGQ54qv9EHLD84hMZnQgjhohsSD3FmjjB/MUL+3GPThQnHh1q+\nETGgoHTiKEtttrGr0fLiiSaB8I4sbEgLaJK+xNlYDktxfZNjx+MW/3gA5n2A/+AQGp8JIYTL6zEk\nmzZtwpQpUzB79myrabZt24bp06dj7ty5uHTpkkvl9RZn5gjbjBWhOcbEwyy1WYuLJxLiI5yN5bA0\nVhc1aN1WL0IIGWhs/kJy+vRpmwenpaVhx44dLlXgnnvuwbJly7Bx40aL+48fP46ioiIcOXIEZ8+e\nxTPPPIP33nvPpTJ7gzNzhG3GitAcY+JhltqsojuY+5qVmCVC+iNnYzksjdWakGi31YsQQgYamzck\nf/zjH63uE4lE2Lt3L2JjY12qQGpqKrRa698s5eXlYd68eQCAlJQUNDU1obq6GuHh4S6V6y6m8+5j\nQ6JR31EP7fUyxCli8UjqKpQ2lQueI2waQxIjj8IQvyFQy1SIUUSBMT3ytMcQLY+EWCRGcaPWLDbF\n2fnQxDfYu/78GJGRiuH4vvEqZ1HDpck5PYsaKiIwQhEPEUTGxTqjg3tiRi43XrGaB7U50p9YimnS\nQ9cTN3VjrR0xxMZ1n8aF/gw/NP4IbVMpVv5sEWpb6xAwKADBg4JQ0VSFquZazqKH1BcIIUQYmzck\nf/nLX3qrHlZVVlZyFmNUq9WoqKjoMzckpnOJ5yRMxz8uHzHuW5GykBP3YQ8/hsQQN3K58Qp2nPmT\n8XVLz7Xn14W/j/g+e9efv5+/XsLS5BzOmiLSFCkmhKVhQlgalAlyVFU13WiL1vOgNkf6E0sxTadr\nTluN5eOvu5Ob+gsAuLkeSdEZzj7qC4QQIoygoPYzZ87gnXfeQWtrKxhj0Ov1KC0txdGjRz1dP5co\nlXKPH3u8ssL4d11bPWeftrkMygThdTDNCwAq2itwe3yq2eumsSWGNLaOd5Qr580XjvcGd9TZ3vXn\n79c289YUaS4322/afpVKud08HGlz7r5OfT0/T+XZ29z1HvpqPtrr1mP5+H2kor3CYjrDPmfGX9O6\nuMod+fT3NuvJcSFEIbOZVioV2y3f2+NWXV2w3TRC86yrC8Y1Aem8/Z5J3yTohmTz5s1YvXo1Dh48\niGXLliE/Px+jR4/2dN0AACqVCuXlNz8EysvLoVarBR3r7FN8lEq54GPVATfrEiobzNkXHRzpUB1M\n8zJsV1U1mb1uGltiSGPreEc48t599XhvcMcTp+xdf/7+GLmdNUVM2q/hvPLziA7mzqUX2uZcvU79\nLT9P5Nmf26q7zoUn8uH3C9Pxlt9HTPsDfz0SZ8Zffl1c4Y583FkXb/HkuNDQ2GYzfXe33mb5fWHc\nqq1ttptGaJ5C8nIkPyE8NVaT3ifohiQgIADz58+HVquFQqHAtm3bcM8997itErae1DV16lTs378f\nM2fOxHfffQeFQtFnpmsB3OfJxyliOeuIWFsjxF5e/DUauM+st/xce1vHk4HB3vXnr33AXS8hEiMU\n8ZCmSI1rjFhqv5bysLXuCCH9zfjQsca1dmJDoiGCyLjuE3/dHUN7z039BWo6qjE8ZSEnhoQQQogw\ngm5I/P39UV9fj1tuuQVnz57F5MmT0dra6pYKbNiwAadOnUJ9fT2ysrKQm5uLrq4uiEQiLF68GJmZ\nmTh+/DimTZsGmUyG7du3u6VcdzF7nnxwvHG+vbN58ddosPTMev5z7W0dTwYGe9ffUjvib/PXGHEm\nD0L6MzEkZv3AdN0nS+09QTEKSiWNu4QQ4ixBNyQrV67E+vXrsWPHDixYsACffPIJbr31VrdU4OWX\nX7ab5umnn3ZLWYQQQgghhJC+RdANyZQpU3DXXXdBJBLhww8/RGFhIeRymmNHCCGEEEIIcY3Nh6SX\nlZWhtLQUS5cuRXl5OUpLS1FfXw+5XI7Vq1f3Vh0JIYQQQgghPsruwoinTp1CZWUlli5devMgqRRZ\nWVmerhshhBBCCCHEx9m8ITEEkO/atQsPPvhgr1SIEEIIIYQQMnDYnLJlsHLlSrz11lv49a9/jebm\nZrz++uvo7Oz0dN0IIYQQQgghPk7QDcmzzz6L1tZWXLhwARKJBEVFRfjNb37j6boRQgghhBBCfJyg\nG5ILFy7g8ccfh1QqhUwmw/PPP49Lly55um6EEEIIIYQQHyfohkQkEnGmaNXV1UEkEnmsUoQQQggh\nhJCBQdA6JMuXL8cDDzyA6upq/O53v8Nnn32GtWvXerpuhBBCCCGEEB8n6BeSmTNn4vbbb0ddXR32\n7duHVatWYf78+Z6uGyGEEEIIIcTHCfqFZMuWLejo6MCOHTug1+vx8ccfU2C7BYwxXCyqR/m3WkSG\nBiIxbjBEoKlthHgK9TnfZbi2xRXN0KiD6doSQogPE3RDcvbsWfzrX/8ybmdnZ+PnP/+5xyrVX10s\nqsfLB741bm+4dyyS4oZ4sUaE+Dbqc76Lri0hhAwcgm5IIiMjcf36dcTFxQEAqquroVar3VKB/Px8\nPPfcc2CMYf78+WYLMBYUFGDNmjWIjY0FAEybNg1r1qxxS9nuVlzRbLZNH6CEeA71Od9F15aQgU2n\n06Gw8Cer+4cOHdaLtSGeJuiGpLu7G3PnzkVqaiqkUim+/vprKJVKLF++HACwd+9epwrX6/XYunUr\n9uzZA5VKhQULFmDq1KmIj4/npEtNTcVbb73lVBm9SaMO5mzH8rYJIe5Ffc530bUlZGArLPwJJ9av\nQ2RgoNm+stZW4NU/IiJinBdqRjxB0A1Jbm4uZ3vVqlVuKfzcuXOIi4tDdHQ0AGDWrFnIy8szuyHp\nLxLjBmPDvWNRXtuKiNBASMTAvwqKaf4zIU6yF0fA73Oj4wZ7sbbEnQzX9vvieiiC/CAVAwyMxlFC\nBpDIwEBoguXergbpBYJuSCZMmOCRwisqKhAZGWncVqvVOH/+vFm6b7/9FnPnzoVarcbGjRsxfPhw\nj9THVSKIkBQ3BFmpGhw7U4QX9tP8Z0JcYS+OwLTPVVU1eaOKxEMMNx6f/Oea8TUaRwkhxDcJuiHx\npqSkJBw7dgwymQzHjx/H2rVrcfjwYUHHKpXO31W7ciwAlNe2mm1npWp6rXxvvvf+frw3uLvOnjgH\n3qhj+bda7raNfjQQz6E3uOs9uOv692Z9eiOPvpZPf2+znhwXQhQym2mlUrHd8r09btXV2Z8KKTTP\nurpgXLOfzG35hYYGO5Qf6du8ekOiVqtRWlpq3K6oqIBKpeKkCQoKMv6dmZmJ3/72t6ivr8fgwfan\nZjj7jalSKXfp21alUo7IUO6cx4jQQMF5uqN8b773/n68N7jz231Xz0Fv5Ck0P6H9yFv182ae/bmt\nuuv6u+ucuiOfvlQXd+Xjzrp4iyfHhYbGNpvpu7v1NsvvC+NWbW2z3TRC8xSSlzvzM+z3xFhNep9X\nb0iSk5NRVFQErVYLpVKJQ4cO4ZVXXuGkqa6uRnh4OICemBMAgm5GvM0w/7m4ohmx6mCa206IE6gf\nDWx0/QkhZGDw6g2JRCLBli1bsGrVKjDGsGDBAsTHx+Pdd9+FSCTC4sWLcfjwYRw4cABSqRQBAQF4\n9dVXvVllm0wXaYsOC0RTaycaWjoR0tplNRhTp9Pjy4sVKKlsQYw6GOm3qqzmSwuEkYHGECNiiBvQ\n6/X46kolisqboYmQY2JiOMQQWz2e33cSNCG4VNRg3B4VG4KCK1WC8xOC+qtj+OdrZEwITlysgLaq\nBVHhQWhu60CA342gdl7a28PoyVuEEOILvB5DkpGRgYyMDM5rS5YsMf69dOlSLF26tLer5RTTANyM\nsdHI58x/TsLkRPO1W768WIE9hy7dfIExzM8OsZovQIGdZOA6daUKb398weQVy/3KgN93Vs9N4hy/\nclYit//ZyU8I6q+O4Z+v5TMTsffTm9ekZyz9CRljo1Hb3Mm5fn7+gzA8gm5KCCGkv3Ptq0DCYbqQ\nV1tHN2dfUbnluZAllS02t/n5WtomZKDg9yNr/cqA31f46fn9zV5+QlB/dQz//GiruNuGsbSto9vs\n+lwva/Bs5QghhPQKuiFxI9OFvAL9uT8+aax8ixfDW+wrRhVkloYWCCOkhyZCztu23Rf4fYd/PL+/\n2ctPCOqvjuGfrxgld1t2YyyV+UvNrl9cJPfXZEIIIf2T16ds9SWmMSCRoYFm880Nc8GtzRFP0IRg\n9dwkFFc2Y2iEAsOiFSiqaEa0MhhpiUqLZabfqgIY64khUQUhPdl8uoghX8M898Q4+hAmrvNGrAO/\njzla5oSEcHR1Jxr7S+ooJU5eqkDx8R8RqzKPAeEHRY+KDUHXrJvHT05WY5BUfKNvBWOilX7qCArE\ndsyo2BCsnJWIyto2hA+WobKuFctnJqK6vg3hITI0tXZg0dQRCFUEYPyoMCgCb57biUkRqKmhX6AI\nIaS/oxsSE/bmmxvmglubI36pqIGT3jSOxH+Q2OLcdAnEyEiONHvdFD9fRSDNSSeu80asg6tlXi5q\n4MV8wGYMCD8o/uQlbszWIGlPv3Q1bsQUv0xiW8GVKuw5dAkZY6Px6T8Lja9njI3GpycKOeOoob0Y\nzq1YTA8LIIQQX0BTtkzYm29u2G9tjjj/ddM4ElfmptOcdOIJ3mhXrpbJT+9oDIijMSjE8wzXgB93\nZxo7YkBjHyGE+Ca6ITFhPt/c8lxwa3PE+a/LTOJIXJmbTnPSiSd4o125WqZZvIGdPmt2vIMxKMTz\nDNeEH3dnGjtiQGMfIYT4pgE/Zct0HRCNOhgbl46FtroVEaGBGBkTgo6ZidBWNSNWLUdbZxf+9vmP\nGBEbguU3Xo9RBRtfHxU3GMvvToS2uhmxqmBIJSIMkooRowyGn1SMv33+IzQRckxICMflG7Epsepg\ntLR34VppE+JjQtDVrTObD8+fky4RA/8qKKY1DohLeiPWgR+nMiLmZt+JVgZjeEwI8s+XGdfhmTBK\nha8uVkBb3bN/SrIaP5jEcQ2LDDH2sZ7YLBX0OtazHR6McTdiSgzxVmmjwnHaZJ2RVNMYFHUwJvBi\nRuzFuNAaI67hn99RsSGQy6RYdncCWts7sfzuRJTVtCAyPAi1DW1YPjMRNQ1tWD4zAWHyACRoQnDh\neh2tQ0IIIT5mwN+Q8NcBWTkrEUumJ6Cqqgn558ssPA9fi7bOm3OaTec3BwcOwgefXzVLDwDz7xiO\nw6euAwC6urlrHxjSzQ8czjneMB/edE76het1eGE/rXFAXNcbsQ721pgAA/b+8+a2Xsc42/z9y+9O\ntLkf4G538Mrj970wuT/n/duLcaE1RlzDP38rZyWis0uPvx65gvl3DOdcu4yx0fi/L3tiSA59WYhl\ndyWg4HIVrUNCSD+n0+lQWPiTzTRDhw7rpdqQvmLA35DYWgeEv8/SnGbTv2sa2i2m5++zli//+KLy\nZrNgW0tz8OkfRKSvsrfGhLbaw9tVtmNO+P3HXv+i/ucaSzFAjDEA1sdPw/9La1ogFXNnGV8va6Ab\nEkL6mcLCn3Bi/TpEBgZa3F/W2gq8+sderhXxtgF/Q2JrHRD+PsNcZtO5zqZ/h4UEWEzP32ctX/7x\nlua3UzwJ6U/srTERHc7b5u+3l97ONr88/roj/P5jr39R/3ONpRigzi4dAOvjp+H/UWFBCODFmdA6\nJIT0T5GBgdAEy+0nJAPGgLwhMZ0HHh8djJUm6xKYrgNiukZIrDoIAX4SyPykiI+R45YoBYormzEs\nWmH8O1Thj5WzElFc2RNDMkgqgp9Ughh1EAIGiXFnmgYx6mBMuVWFMLk/J4ZE5idFRKjMuI5JrMry\nmgi0xgHpT/jtdURsCBiDMeZj4hg1IIIxpmTiGDVgsn/KGDWUIQHG44ffWIPHeHyKGmIxUFLVghhl\nECbdemP7RozI5FtV8Bt0c52RCYlKhCkCrPYfQ33La3viyBJ5MQsJcSHU/1xgOL9lta0ICpBC163D\nkOBBWHZ3AhqaO27GkIQFoaaxDcvvTkRNYxuW3Z2AOLUMQyNCaB0S0u/odDq8++5+s9fl8gA0NbVj\nyZKlkEgkbi8zP/9zm2kyMu5wa5mEuGJA3pBYmgduaS0QS2uEpI1U4cL1OvzPB/8FAHR134wTyUPP\n2iWPLRmHqqomAMDkRODC9TpOeYZ566ZTPSaMUhn/npMx3Hg8H61xQPoTS+uAmMYJiMXgxHgoQwKQ\nlcLtc/z2zt+fkRwJpVKOqqomXLheZxYjwl9nxFb/MdQ3K1VjzM9SzAj1P+cYzq+//yA8t6fA+PqG\ne8ciwE9ito7ToS8LsXpuktXrR+uQkP6gsPAnvPn3k/APMv8Co6OlHpMmTUZ8/Ai3l/lC3h8QGBpk\ncX9rbQs0mji3lkmIKwbkDYmr88BNj+c/O9/SugY075yQHvz+YS+mw1Hu7mvUdz3jelkDZ7u4ohkN\nLZ2c1wxjq6VYOkL6m6hRUxA8JNrs9eY6rcfKVCZEQh5l+VfcptJ6j5VLiDO8vg5Jfn4+7rrrLsyY\nMQO7du2ymGbbtm2YPn065s6di0uXLllM4wh3roXAf3Y+xX0QYh1/HRB7MR0O5+/mvkZ91zOG8mI/\nYtXBZm3DEDtCa8UQQojv8+ovJHq9Hlu3bsWePXugUqmwYMECTJ06FfHx8cY0x48fR1FREY4cOYKz\nZ8/imWeewXvvvedSua7GYZgePzQyGKM0g3H9xhx1ivsgxLqJieEAbsZJ2YvpcJS7+xr1Xc+YkBRh\ndl4ZGIAkFFc0QxUaiLrGdqyem2RxTCVkoLIVGxISEoiGhlaKDSH9kldvSM6dO4e4uDhER/f8jDlr\n1izk5eVxbkjy8vIwb948AEBKSgqamppQXV2N8PBwp8t1NQ7D0vGTbEwpoLgPQnqIIcbkRDUnTsqd\nfcPdfY36rmeIxebnVQSRWbwPIYSLYkOIr/LqDUlFRQUiI28GqKrVapw/f56TprKyEhEREZw0FRUV\nLt2QEEIIIYT0RxQbQnyRTwe1K5XOP+PalWP7+/H9ue7uON4b3F1nT5yDvl7Hvp6fp/Lsbe56D76Y\nT1+qi7vy6e9t1pPjQohCZjOtVCqGUilHXZ3tOKjQ0GDB9bSXlyE/IYSnC8SPP/5oM018fDzq6oJx\nTWC59tIZ6mYrnSFNf2+jpIdXb0jUajVKS0uN2xUVFVCpVJw0KpUK5eXlxu3y8nKo1cJ+0rf26Fx7\nDI8QdVZ/Pr4/191dx3uDK3Xmc/Uc9EaeAy0/T+TZn9uqu85FX8qnL9XFXfm4sy7e4slxoaGxzWb6\n7m49qqqaUFtre62c2tpmwfW0l5fQNI6kO336rN2V1ac4sLK6u96DIY0nxmrS+7z6lK3k5GQUFRVB\nq9Wis7MThw4dwtSpUzlppk6dio8++ggA8N1330GhUNB0LUIIIYSQXmJYWd3Sf9ZuVAhxhFd/IZFI\nJNiyZQtWrVoFxhgWLFiA+Ph4vPvuuxCJRFi8eDEyMzNx/PhxTJs2DTKZDNu3b/dmlQkhhBBCCCFu\n5PUYkoyMDGRkZHBeW7JkCWf76aef7s0qEUIIIYQQQnqJ1xdGJIQQQgghhAxcdENCCCGEEEII8Rqv\nT9kihBBCCCF9k06nR1lrq9X9Za2t0Oj0kEjoO27iPLohIYQQQgghVjD8dYwUgaGDLO5trZViIlgv\n14n4GrohIYQQQgghFkkkErurw0skkl6uFfE19PsaIYQQQgghxGvohoQQQgghhBDiNXRDQgghhBBC\nCPEauiEhhBBCCCGEeA3dkBBCCCGEEEK8hm5ICCGEEEIIIV5Dj/0lhBBCCPEinU6Hd9/dbzPNkiVL\ne6k2zhGygKJOp+vFGpH+xGs3JA0NDVi/fj20Wi1iYmLw2muvQS6Xm6XLzs5GcHAwxGIxpFIp3n//\nfS/UlhBCCCHEMwoLf8Kbfz8J/yDLa310tNRj0qTJvVwrR9lfQPHuXq4R6T+8dkOya9cuTJ48GatX\nr8auXbuwc+dOPPHEE2bpRCIR/vKXvyAkJMQLtSSEEEII8byoUVMQPCTa4r7mOm0v18ZxtIAicYXX\nYkjy8vKQk5MDAMjJycFnn31mMR1jDHq9vjerRgghhBBCCOklXvuFpLa2FuHh4QAApVKJ2tpai+lE\nIhFWrVoFsViMxYsXY9GiRb1ZTUIIIYQQM4EBMkibv4ekU2ZxP9PVGf9ubai0mMb0dWtp+Ptaqpqs\npjPd52o6d+bF32cv1uQWO+lM0xDfIGKMMU9l/sADD6C6utrs9cceewxPPfUUCgoKjK9NnDgRp06d\nMktbWVkJlUqF2tpaPPDAA9iyZQtSU1M9VWVCCCGEEEJIL/LoLyS7d++2ui8sLAzV1dUIDw9HVVUV\nQkNDLaZTqVQAgNDQUEybNg3nz5+nGxJCCCGEEEJ8hNdiSLKzs/Hhhx8CAA4ePIipU6eapWlra0NL\nSwsAoLW1Ff/5z38wYsSIXq0nIYQQQgghxHM8OmXLlvr6ejz22GMoKytDdHQ0XnvtNSgUClRWVmLL\nli3YuXMniouL8cgjj0AkEkGn02H27Nl48MEHvVFdQgghhBBCiAd47YaEEEIIIYQQQrw2ZYsQQggh\nhBBC6IaEEEIIIYQQ4jV0Q0IIIYQQQgjxGq8tjOguer0e8+fPh1qtxltvvWW2f9u2bcjPz4dMJsPv\nf/97JCYmCj6+oKAAa9asQWxsLABg2rRpWLNmjXF/dnY2goODIRaLIZVK8f777ztUvr3jbZXf1NSE\n3/zmN/jhhx8gFovx3HPPISUlRXDZ9o63Vfa1a9ewfv16iEQiMMZQXFyMRx99FMuXLxdUvpDjbZW/\nZ88evP/++xCJRBg5ciS2b98OPz8/we/d3vH2rrszNm3ahGPHjiEsLAyffPKJ2X5HyywvL8fGjRtR\nU1M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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(iris, hue='species', size=2.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Faceted histograms\n", + "\n", + "Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n", + "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\n", + "
" + ], + "text/plain": [ + " total_bill tip sex smoker day time size\n", + "0 16.99 1.01 Female No Sun Dinner 2\n", + "1 10.34 1.66 Male No Sun Dinner 3\n", + "2 21.01 3.50 Male No Sun Dinner 3\n", + "3 23.68 3.31 Male No Sun Dinner 2\n", + "4 24.59 3.61 Female No Sun Dinner 4" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tips = sns.load_dataset('tips')\n", + "tips.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AbgeayWSyrXMiInIqu3fvBgAcOXIEwO0/FBZj9QKLhoYGrFixAsnJyXBzc4NK1fFOkJ0f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\n4jGsiIhI8f4/8A46ouL3SiQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n", + "\n", + "grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n", + "grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Factor plots\n", + "\n", + "Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style(style='ticks'):\n", + " g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n", + " g.set_axis_labels(\"Day\", \"Total Bill\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Joint distributions\n", + "\n", + "Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+Le0cYmEV4TpYnEcf12UNC5Yrarr3M7roTpbqSrSQ8JVN8yqXt5GfZREaTQgg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\nTvb4fjjQyu9+uY3ya29iyg1/jT/Wf8zB9MljWLJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('white'):\n", + " sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The joint plot can even do some automatic kernel density estimation and regression:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SUhIFQlG+9fg/CUfjABgNOuZNzsag+pha6MBigsb6GjbW1wzqfK2tTZgcLmyyB58YJcGA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\nMu7ntLaOPzOnWkFBVkrizfM4UDUDNRpG0zQc87y8dbCZ1i6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bar plots\n", + "\n", + "Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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methodnumberorbital_periodmassdistanceyear
0Radial Velocity1269.3007.1077.402006
1Radial Velocity1874.7742.2156.952008
2Radial Velocity1763.0002.6019.842011
3Radial Velocity1326.03019.40110.622007
4Radial Velocity1516.22010.50119.472009
\n", + "
" + ], + "text/plain": [ + " method number orbital_period mass distance year\n", + "0 Radial Velocity 1 269.300 7.10 77.40 2006\n", + "1 Radial Velocity 1 874.774 2.21 56.95 2008\n", + "2 Radial Velocity 1 763.000 2.60 19.84 2011\n", + "3 Radial Velocity 1 326.030 19.40 110.62 2007\n", + "4 Radial Velocity 1 516.220 10.50 119.47 2009" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "planets = sns.load_dataset('planets')\n", + "planets.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('white'):\n", + " g = sns.factorplot(\"year\", data=planets, aspect=2,\n", + " kind=\"count\", color='steelblue')\n", + " g.set_xticklabels(step=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can learn more by looking at the *method* of discovery of each of these planets:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Pf/5j/SciIiIiIiIiIlIWBoOBvn37snbtWq5c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\nSp6Gh4cTGRnJ5s2bMZvNzJkzh+bNm5OWlsa4ceM4e/YsLVu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('white'):\n", + " g = sns.factorplot(\"year\", data=planets, aspect=4.0, kind='count',\n", + " hue='method', order=range(2001, 2015))\n", + " g.set_ylabels('Number of Planets Discovered')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), a [tutorial](http://seaborn.pydata.org/\n", + "tutorial.htm), and the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Exploring Marathon Finishing Times\n", + "\n", + "Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon.\n", + "I've scraped the data from sources on the Web, aggregated it and removed any identifying information, and put it on GitHub where it can be downloaded\n", + "(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell).\n", + "We will start by downloading the data from\n", + "the Web, and loading it into Pandas:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegendersplitfinal
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" + ], + "text/plain": [ + " age gender split final\n", + "0 33 M 01:05:38 02:08:51\n", + "1 32 M 01:06:26 02:09:28\n", + "2 31 M 01:06:49 02:10:42\n", + "3 38 M 01:06:16 02:13:45\n", + "4 31 M 01:06:32 02:13:59" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('marathon-data.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, Pandas loaded the time columns as Python strings (type ``object``); we can see this by looking at the ``dtypes`` attribute of the DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age int64\n", + "gender object\n", + "split object\n", + "final object\n", + "dtype: object" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix this by providing a converter for the times:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegendersplitfinal
033M01:05:3802:08:51
132M01:06:2602:09:28
231M01:06:4902:10:42
338M01:06:1602:13:45
431M01:06:3202:13:59
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" + ], + "text/plain": [ + " age gender split final\n", + "0 33 M 01:05:38 02:08:51\n", + "1 32 M 01:06:26 02:09:28\n", + "2 31 M 01:06:49 02:10:42\n", + "3 38 M 01:06:16 02:13:45\n", + "4 31 M 01:06:32 02:13:59" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datetime\n", + "\n", + "def convert_time(s):\n", + " h, m, s = map(int, s.split(':'))\n", + " return datetime.timedelta(hours=h, minutes=m, seconds=s)\n", + "\n", + "data = pd.read_csv('marathon-data.csv',\n", + " converters={'split':convert_time, 'final':convert_time})\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age int64\n", + "gender object\n", + "split timedelta64[ns]\n", + "final timedelta64[ns]\n", + "dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks much better. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegendersplitfinalsplit_secfinal_sec
033M01:05:3802:08:513938.07731.0
132M01:06:2602:09:283986.07768.0
231M01:06:4902:10:424009.07842.0
338M01:06:1602:13:453976.08025.0
431M01:06:3202:13:593992.08039.0
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" + ], + "text/plain": [ + " age gender split final split_sec final_sec\n", + "0 33 M 01:05:38 02:08:51 3938.0 7731.0\n", + "1 32 M 01:06:26 02:09:28 3986.0 7768.0\n", + "2 31 M 01:06:49 02:10:42 4009.0 7842.0\n", + "3 38 M 01:06:16 02:13:45 3976.0 8025.0\n", + "4 31 M 01:06:32 02:13:59 3992.0 8039.0" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['split_sec'] = data['split'].astype(int) / 1E9\n", + "data['final_sec'] = data['final'].astype(int) / 1E9\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get an idea of what the data looks like, we can plot a ``jointplot`` over the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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GG2/EsGHDMoxa2JvpqRYqhmHgu9/9bhBSveCCC/Cxj30MSqlA6PHFL34Ru3btwtKlS4N5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('white'):\n", + " g = sns.jointplot(\"split_sec\", \"final_sec\", data, kind='hex')\n", + " g.ax_joint.plot(np.linspace(4000, 16000),\n", + " np.linspace(8000, 32000), ':k')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dotted line shows where someone's time would lie if they ran the marathon at a perfectly steady pace. The fact that the distribution lies above this indicates (as you might expect) that most people slow down over the course of the marathon.\n", + "If you have run competitively, you'll know that those who do the opposite—run faster during the second half of the race—are said to have \"negative-split\" the race.\n", + "\n", + "Let's create another column in the data, the split fraction, which measures the degree to which each runner negative-splits or positive-splits the race:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegendersplitfinalsplit_secfinal_secsplit_frac
033M01:05:3802:08:513938.07731.0-0.018756
132M01:06:2602:09:283986.07768.0-0.026262
231M01:06:4902:10:424009.07842.0-0.022443
338M01:06:1602:13:453976.08025.00.009097
431M01:06:3202:13:593992.08039.00.006842
\n", + "
" + ], + "text/plain": [ + " age gender split final split_sec final_sec split_frac\n", + "0 33 M 01:05:38 02:08:51 3938.0 7731.0 -0.018756\n", + "1 32 M 01:06:26 02:09:28 3986.0 7768.0 -0.026262\n", + "2 31 M 01:06:49 02:10:42 4009.0 7842.0 -0.022443\n", + "3 38 M 01:06:16 02:13:45 3976.0 8025.0 0.009097\n", + "4 31 M 01:06:32 02:13:59 3992.0 8039.0 0.006842" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['split_frac'] = 1 - 2 * data['split_sec'] / data['final_sec']\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Where this split difference is less than zero, the person negative-split the race by that fraction.\n", + "Let's do a distribution plot of this split fraction:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(data['split_frac'], kde=False);\n", + "plt.axvline(0, color=\"k\", linestyle=\"--\");" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "251" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(data.split_frac < 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon.\n", + "\n", + "Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a ``pairgrid``, which draws plots of all these correlations:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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r1y6+/OUv8+yzz/LMM8/geR4/+7M/y7Fjx7jnnnt47rnnuPvuuzl69OhaT9Uw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UUul6OtLUoPuH37dl5//fXyMXfccQcAW7ZsYffu3dPZndkn8LBP7cX71X9dWAMQeNgf/ILUvpdI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\nRCKRSCSS3/h/L/d2XtJO7wgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'],\n", + " hue='gender', palette='RdBu_r')\n", + "g.map(plt.scatter, alpha=0.8)\n", + "g.add_legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time.\n", + "(We see here that Seaborn is no panacea for Matplotlib's ills when it comes to plot styles: in particular, the x-axis labels overlap. Because the output is a simple Matplotlib plot, however, the methods in [Customizing Ticks](04.10-Customizing-Ticks.ipynb) can be used to adjust such things if desired.)\n", + "\n", + "The difference between men and women here is interesting. Let's look at the histogram of split fractions for these two groups:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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v8lV9XyIvYxAT1bg/vGDNhs+cM6n4sURhxbQmVW/rElA4+MHwwQCDmKhSGMRE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\nrbfeKv7hH/6hWuWVpZwxdHd3i61btwo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.kdeplot(data.split_frac[data.gender=='M'], label='men', shade=True)\n", + "sns.kdeplot(data.split_frac[data.gender=='W'], label='women', shade=True)\n", + "plt.xlabel('split_frac');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The interesting thing here is that there are many more men than women who are running close to an even split!\n", + "This almost looks like some kind of bimodal distribution among the men and women. Let's see if we can suss-out what's going on by looking at the distributions as a function of age.\n", + "\n", + "A nice way to compare distributions is to use a *violin plot*" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MTAoLCykrK+OOO+6gqkoLLshH6+zs4I03XuO5535NQ0MdiRk5LLztTlx5RePa3R4eE8vs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\nnNklyRRmt4dQUFDMzJlFVFV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We'll start by creating a new column in the array that specifies the decade of age that each person is in:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age gender split final split_sec final_sec split_frac age_dec\n", + "0 33 M 01:05:38 02:08:51 3938.0 7731.0 -0.018756 30\n", + "1 32 M 01:06:26 02:09:28 3986.0 7768.0 -0.026262 30\n", + "2 31 M 01:06:49 02:10:42 4009.0 7842.0 -0.022443 30\n", + "3 38 M 01:06:16 02:13:45 3976.0 8025.0 0.009097 30\n", + "4 31 M 01:06:32 02:13:59 3992.0 8039.0 0.006842 30" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['age_dec'] = data.age.map(lambda age: 10 * (age // 10))\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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zz/36eiiwOioOg4MDi6J3X1ZWgvv378HLPxAxmbNfV2YjFonwteVrEOXnj6qq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\nQ9sxs0ahYPxuhkGeW0ssbsN4cvA8uixmSbcrKNhPEIuJQZ5YX15jcbV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "men = (data.gender == 'M')\n", + "women = (data.gender == 'W')\n", + "\n", + "with sns.axes_style(style=None):\n", + " sns.violinplot(\"age_dec\", \"split_frac\", hue=\"gender\", data=data,\n", + " split=True, inner=\"quartile\",\n", + " palette=[\"lightblue\", \"lightpink\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\n", + "\n", + "Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(data.age > 80).sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use ``regplot``, which will automatically fit a linear regression to the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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5xMfKpiraGn14XCpVHg/ORfyBQQLiCjM6KzB6Ymh1OvlmU1vZKt/pGl2zuzsV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ULs7PNj/9FO7D/aiR538qU+AcBabcS0eWX4j7IwujWFstnnyMIApmk0cKsqVADTdXp8z1SCQwEf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\njJyPZ0Q7m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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data,\n", + " markers=\".\", scatter_kws=dict(color='c'))\n", + "g.map(plt.axhline, y=0.1, color=\"k\", ls=\":\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apparently the people with fast splits are the elite runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.14-Visualization-With-Seaborn.md b/notebooks_v2/04.14-Visualization-With-Seaborn.md new file mode 100644 index 00000000..ccab7c17 --- /dev/null +++ b/notebooks_v2/04.14-Visualization-With-Seaborn.md @@ -0,0 +1,398 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) > + +Open in Colab + + + +# Visualization with Seaborn + + +Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired. +There are several valid complaints about Matplotlib that often come up: + +- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows. +- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code. +- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot. + +An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas ``DataFrame``s. + +To be fair, the Matplotlib team is addressing this: it has recently added the ``plt.style`` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and is starting to handle Pandas data more seamlessly. +The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo. +But for all the reasons just discussed, Seaborn remains an extremely useful addon. + + +## Seaborn Versus Matplotlib + +Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors. +We start with the typical imports: + +```python +import matplotlib.pyplot as plt +plt.style.use('classic') +%matplotlib inline +import numpy as np +import pandas as pd +``` + +Now we create some random walk data: + +```python +# Create some data +rng = np.random.RandomState(0) +x = np.linspace(0, 10, 500) +y = np.cumsum(rng.randn(500, 6), 0) +``` + +And do a simple plot: + +```python +# Plot the data with Matplotlib defaults +plt.plot(x, y) +plt.legend('ABCDEF', ncol=2, loc='upper left'); +``` + +Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization. + +Now let's take a look at how it works with Seaborn. +As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output. +We can set the style by calling Seaborn's ``set()`` method. +By convention, Seaborn is imported as ``sns``: + +```python +import seaborn as sns +sns.set() +``` + +Now let's rerun the same two lines as before: + +```python +# same plotting code as above! +plt.plot(x, y) +plt.legend('ABCDEF', ncol=2, loc='upper left'); +``` + +Ah, much better! + + +## Exploring Seaborn Plots + +The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting. + +Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient. + + +### Histograms, KDE, and densities + +Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables. +We have seen that this is relatively straightforward in Matplotlib: + +```python +data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000) +data = pd.DataFrame(data, columns=['x', 'y']) + +for col in 'xy': + plt.hist(data[col], normed=True, alpha=0.5) +``` + +Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with ``sns.kdeplot``: + +```python +for col in 'xy': + sns.kdeplot(data[col], shade=True) +``` + +Histograms and KDE can be combined using ``distplot``: + +```python +sns.distplot(data['x']) +sns.distplot(data['y']); +``` + +If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data: + +```python +sns.kdeplot(data); +``` + +We can see the joint distribution and the marginal distributions together using ``sns.jointplot``. +For this plot, we'll set the style to a white background: + +```python +with sns.axes_style('white'): + sns.jointplot("x", "y", data, kind='kde'); +``` + +There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead: + +```python +with sns.axes_style('white'): + sns.jointplot("x", "y", data, kind='hex') +``` + +### Pair plots + +When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other. + +We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species: + +```python +iris = sns.load_dataset("iris") +iris.head() +``` + +Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``: + +```python +sns.pairplot(iris, hue='species', size=2.5); +``` + +### Faceted histograms + +Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple. +We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data: + +```python +tips = sns.load_dataset('tips') +tips.head() +``` + +```python +tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill'] + +grid = sns.FacetGrid(tips, row="sex", col="time", margin_titles=True) +grid.map(plt.hist, "tip_pct", bins=np.linspace(0, 40, 15)); +``` + +### Factor plots + +Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter: + +```python +with sns.axes_style(style='ticks'): + g = sns.factorplot("day", "total_bill", "sex", data=tips, kind="box") + g.set_axis_labels("Day", "Total Bill"); +``` + +### Joint distributions + +Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions: + +```python +with sns.axes_style('white'): + sns.jointplot("total_bill", "tip", data=tips, kind='hex') +``` + +The joint plot can even do some automatic kernel density estimation and regression: + +```python +sns.jointplot("total_bill", "tip", data=tips, kind='reg'); +``` + +### Bar plots + +Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb): + +```python +planets = sns.load_dataset('planets') +planets.head() +``` + +```python +with sns.axes_style('white'): + g = sns.factorplot("year", data=planets, aspect=2, + kind="count", color='steelblue') + g.set_xticklabels(step=5) +``` + +We can learn more by looking at the *method* of discovery of each of these planets: + +```python +with sns.axes_style('white'): + g = sns.factorplot("year", data=planets, aspect=4.0, kind='count', + hue='method', order=range(2001, 2015)) + g.set_ylabels('Number of Planets Discovered') +``` + +For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), a [tutorial](http://seaborn.pydata.org/ +tutorial.htm), and the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html). + + +## Example: Exploring Marathon Finishing Times + +Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon. +I've scraped the data from sources on the Web, aggregated it and removed any identifying information, and put it on GitHub where it can be downloaded +(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell). +We will start by downloading the data from +the Web, and loading it into Pandas: + +```python +# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv +``` + +```python +data = pd.read_csv('marathon-data.csv') +data.head() +``` + +By default, Pandas loaded the time columns as Python strings (type ``object``); we can see this by looking at the ``dtypes`` attribute of the DataFrame: + +```python +data.dtypes +``` + +Let's fix this by providing a converter for the times: + +```python +import datetime + +def convert_time(s): + h, m, s = map(int, s.split(':')) + return datetime.timedelta(hours=h, minutes=m, seconds=s) + +data = pd.read_csv('marathon-data.csv', + converters={'split':convert_time, 'final':convert_time}) +data.head() +``` + +```python +data.dtypes +``` + +That looks much better. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds: + +```python +data['split_sec'] = data['split'].astype(int) / 1E9 +data['final_sec'] = data['final'].astype(int) / 1E9 +data.head() +``` + +To get an idea of what the data looks like, we can plot a ``jointplot`` over the data: + +```python +with sns.axes_style('white'): + g = sns.jointplot("split_sec", "final_sec", data, kind='hex') + g.ax_joint.plot(np.linspace(4000, 16000), + np.linspace(8000, 32000), ':k') +``` + +The dotted line shows where someone's time would lie if they ran the marathon at a perfectly steady pace. The fact that the distribution lies above this indicates (as you might expect) that most people slow down over the course of the marathon. +If you have run competitively, you'll know that those who do the opposite—run faster during the second half of the race—are said to have "negative-split" the race. + +Let's create another column in the data, the split fraction, which measures the degree to which each runner negative-splits or positive-splits the race: + +```python +data['split_frac'] = 1 - 2 * data['split_sec'] / data['final_sec'] +data.head() +``` + +Where this split difference is less than zero, the person negative-split the race by that fraction. +Let's do a distribution plot of this split fraction: + +```python +sns.distplot(data['split_frac'], kde=False); +plt.axvline(0, color="k", linestyle="--"); +``` + +```python +sum(data.split_frac < 0) +``` + +Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon. + +Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a ``pairgrid``, which draws plots of all these correlations: + +```python +g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'], + hue='gender', palette='RdBu_r') +g.map(plt.scatter, alpha=0.8) +g.add_legend(); +``` + +It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time. +(We see here that Seaborn is no panacea for Matplotlib's ills when it comes to plot styles: in particular, the x-axis labels overlap. Because the output is a simple Matplotlib plot, however, the methods in [Customizing Ticks](04.10-Customizing-Ticks.ipynb) can be used to adjust such things if desired.) + +The difference between men and women here is interesting. Let's look at the histogram of split fractions for these two groups: + +```python +sns.kdeplot(data.split_frac[data.gender=='M'], label='men', shade=True) +sns.kdeplot(data.split_frac[data.gender=='W'], label='women', shade=True) +plt.xlabel('split_frac'); +``` + +The interesting thing here is that there are many more men than women who are running close to an even split! +This almost looks like some kind of bimodal distribution among the men and women. Let's see if we can suss-out what's going on by looking at the distributions as a function of age. + +A nice way to compare distributions is to use a *violin plot* + +```python +sns.violinplot("gender", "split_frac", data=data, + palette=["lightblue", "lightpink"]); +``` + +This is yet another way to compare the distributions between men and women. + +Let's look a little deeper, and compare these violin plots as a function of age. We'll start by creating a new column in the array that specifies the decade of age that each person is in: + +```python +data['age_dec'] = data.age.map(lambda age: 10 * (age // 10)) +data.head() +``` + +```python +men = (data.gender == 'M') +women = (data.gender == 'W') + +with sns.axes_style(style=None): + sns.violinplot("age_dec", "split_frac", hue="gender", data=data, + split=True, inner="quartile", + palette=["lightblue", "lightpink"]); +``` + +Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter). + +Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range: + +```python +(data.age > 80).sum() +``` + +Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use ``regplot``, which will automatically fit a linear regression to the data: + +```python +g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data, + markers=".", scatter_kws=dict(color='c')) +g.map(plt.axhline, y=0.1, color="k", ls=":"); +``` + +Apparently the people with fast splits are the elite runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split. + + + +< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/04.15-Further-Resources.ipynb b/notebooks_v2/04.15-Further-Resources.ipynb new file mode 100644 index 00000000..069f3390 --- /dev/null +++ b/notebooks_v2/04.15-Further-Resources.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Further Resources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matplotlib Resources\n", + "\n", + "A single chapter in a book can never hope to cover all the available features and plot types available in Matplotlib.\n", + "As with other packages we've seen, liberal use of IPython's tab-completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API.\n", + "In addition, Matplotlib’s [online documentation](http://matplotlib.org/) can be a helpful reference.\n", + "See in particular the [Matplotlib gallery](http://matplotlib.org/gallery.html) linked on that page: it shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it.\n", + "In this way, you can visually inspect and learn about a wide range of different plotting styles and visualization techniques.\n", + "\n", + "For a book-length treatment of Matplotlib, I would recommend [*Interactive Applications Using Matplotlib*](https://www.packtpub.com/application-development/interactive-applications-using-matplotlib), written by Matplotlib core developer Ben Root." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other Python Graphics Libraries\n", + "\n", + "Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well.\n", + "I'll mention a few of them briefly here:\n", + "\n", + "- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets. The Python front-end outputs a JSON data structure that can be interpreted by the Bokeh JS engine.\n", + "- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free.\n", + "- [Vispy](http://vispy.org/) is an actively developed project focused on dynamic visualizations of very large datasets. Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations.\n", + "- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization. The reference rendering implementation is JavaScript, but the API is language agnostic. There is a Python API under development in the [Altair](https://altair-viz.github.io/) package. Though as of summer 2016 it's not yet fully mature, I'm quite excited for the possibilities of this project to provide a common reference point for visualization in Python and other languages.\n", + "\n", + "The visualization space in the Python community is very dynamic, and I fully expect this list to be out of date as soon as it is published.\n", + "Keep an eye out for what's coming in the future!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/04.15-Further-Resources.md b/notebooks_v2/04.15-Further-Resources.md new file mode 100644 index 00000000..4cacd5b8 --- /dev/null +++ b/notebooks_v2/04.15-Further-Resources.md @@ -0,0 +1,63 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) > + +Open in Colab + + + +# Further Resources + + +## Matplotlib Resources + +A single chapter in a book can never hope to cover all the available features and plot types available in Matplotlib. +As with other packages we've seen, liberal use of IPython's tab-completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API. +In addition, Matplotlib’s [online documentation](http://matplotlib.org/) can be a helpful reference. +See in particular the [Matplotlib gallery](http://matplotlib.org/gallery.html) linked on that page: it shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it. +In this way, you can visually inspect and learn about a wide range of different plotting styles and visualization techniques. + +For a book-length treatment of Matplotlib, I would recommend [*Interactive Applications Using Matplotlib*](https://www.packtpub.com/application-development/interactive-applications-using-matplotlib), written by Matplotlib core developer Ben Root. + + +## Other Python Graphics Libraries + +Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well. +I'll mention a few of them briefly here: + +- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets. The Python front-end outputs a JSON data structure that can be interpreted by the Bokeh JS engine. +- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free. +- [Vispy](http://vispy.org/) is an actively developed project focused on dynamic visualizations of very large datasets. Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations. +- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization. The reference rendering implementation is JavaScript, but the API is language agnostic. There is a Python API under development in the [Altair](https://altair-viz.github.io/) package. Though as of summer 2016 it's not yet fully mature, I'm quite excited for the possibilities of this project to provide a common reference point for visualization in Python and other languages. + +The visualization space in the Python community is very dynamic, and I fully expect this list to be out of date as soon as it is published. +Keep an eye out for what's coming in the future! + + + +< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/05.00-Machine-Learning.ipynb b/notebooks_v2/05.00-Machine-Learning.ipynb new file mode 100644 index 00000000..1654ecfa --- /dev/null +++ b/notebooks_v2/05.00-Machine-Learning.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In many ways, machine learning is the primary means by which data science manifests itself to the broader world.\n", + "Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation.\n", + "\n", + "The term \"machine learning\" is sometimes thrown around as if it is some kind of magic pill: *apply machine learning to your data, and all your problems will be solved!*\n", + "As you might expect, the reality is rarely this simple.\n", + "While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.\n", + "\n", + "This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package.\n", + "This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here.\n", + "Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).\n", + "Rather, the goals of this chapter are:\n", + "\n", + "- To introduce the fundamental vocabulary and concepts of machine learning.\n", + "- To introduce the Scikit-Learn API and show some examples of its use.\n", + "- To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable.\n", + "\n", + "Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences.\n", + "Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years!\n", + "\n", + "Finally, if you are seeking a more comprehensive or technical treatment of any of these subjects, I've listed several resources and references in [Further Machine Learning Resources](05.15-Learning-More.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.00-Machine-Learning.md b/notebooks_v2/05.00-Machine-Learning.md new file mode 100644 index 00000000..a4d616cf --- /dev/null +++ b/notebooks_v2/05.00-Machine-Learning.md @@ -0,0 +1,60 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) > + +Open in Colab + + + +# Machine Learning + + +In many ways, machine learning is the primary means by which data science manifests itself to the broader world. +Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation. + +The term "machine learning" is sometimes thrown around as if it is some kind of magic pill: *apply machine learning to your data, and all your problems will be solved!* +As you might expect, the reality is rarely this simple. +While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts such as bias and variance, overfitting and underfitting, and more. + +This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package. +This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here. +Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)). +Rather, the goals of this chapter are: + +- To introduce the fundamental vocabulary and concepts of machine learning. +- To introduce the Scikit-Learn API and show some examples of its use. +- To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable. + +Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences. +Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years! + +Finally, if you are seeking a more comprehensive or technical treatment of any of these subjects, I've listed several resources and references in [Further Machine Learning Resources](05.15-Learning-More.ipynb). + + + +< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/05.01-What-Is-Machine-Learning.ipynb b/notebooks_v2/05.01-What-Is-Machine-Learning.ipynb new file mode 100644 index 00000000..ca004908 --- /dev/null +++ b/notebooks_v2/05.01-What-Is-Machine-Learning.ipynb @@ -0,0 +1,515 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What Is Machine Learning?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Before we take a look at the details of various machine learning methods, let's start by looking at what machine learning is, and what it isn't.\n", + "Machine learning is often categorized as a subfield of artificial intelligence, but I find that categorization can often be misleading at first brush.\n", + "The study of machine learning certainly arose from research in this context, but in the data science application of machine learning methods, it's more helpful to think of machine learning as a means of *building models of data*.\n", + "\n", + "Fundamentally, machine learning involves building mathematical models to help understand data.\n", + "\"Learning\" enters the fray when we give these models *tunable parameters* that can be adapted to observed data; in this way the program can be considered to be \"learning\" from the data.\n", + "Once these models have been fit to previously seen data, they can be used to predict and understand aspects of newly observed data.\n", + "I'll leave to the reader the more philosophical digression regarding the extent to which this type of mathematical, model-based \"learning\" is similar to the \"learning\" exhibited by the human brain.\n", + "\n", + "Understanding the problem setting in machine learning is essential to using these tools effectively, and so we will start with some broad categorizations of the types of approaches we'll discuss here." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Categories of Machine Learning\n", + "\n", + "At the most fundamental level, machine learning can be categorized into two main types: supervised learning and unsupervised learning.\n", + "\n", + "*Supervised learning* involves somehow modeling the relationship between measured features of data and some label associated with the data; once this model is determined, it can be used to apply labels to new, unknown data.\n", + "This is further subdivided into *classification* tasks and *regression* tasks: in classification, the labels are discrete categories, while in regression, the labels are continuous quantities.\n", + "We will see examples of both types of supervised learning in the following section.\n", + "\n", + "*Unsupervised learning* involves modeling the features of a dataset without reference to any label, and is often described as \"letting the dataset speak for itself.\"\n", + "These models include tasks such as *clustering* and *dimensionality reduction.*\n", + "Clustering algorithms identify distinct groups of data, while dimensionality reduction algorithms search for more succinct representations of the data.\n", + "We will see examples of both types of unsupervised learning in the following section.\n", + "\n", + "In addition, there are so-called *semi-supervised learning* methods, which falls somewhere between supervised learning and unsupervised learning.\n", + "Semi-supervised learning methods are often useful when only incomplete labels are available." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Qualitative Examples of Machine Learning Applications\n", + "\n", + "To make these ideas more concrete, let's take a look at a few very simple examples of a machine learning task.\n", + "These examples are meant to give an intuitive, non-quantitative overview of the types of machine learning tasks we will be looking at in this chapter.\n", + "In later sections, we will go into more depth regarding the particular models and how they are used.\n", + "For a preview of these more technical aspects, you can find the Python source that generates the following figures in the [Appendix: Figure Code](06.00-Figure-Code.ipynb).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Classification: Predicting discrete labels\n", + "\n", + "We will first take a look at a simple *classification* task, in which you are given a set of labeled points and want to use these to classify some unlabeled points.\n", + "\n", + "Imagine that we have the data shown in this figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-classification-1.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here we have two-dimensional data: that is, we have two *features* for each point, represented by the *(x,y)* positions of the points on the plane.\n", + "In addition, we have one of two *class labels* for each point, here represented by the colors of the points.\n", + "From these features and labels, we would like to create a model that will let us decide whether a new point should be labeled \"blue\" or \"red.\"\n", + "\n", + "There are a number of possible models for such a classification task, but here we will use an extremely simple one. We will make the assumption that the two groups can be separated by drawing a straight line through the plane between them, such that points on each side of the line fall in the same group.\n", + "Here the *model* is a quantitative version of the statement \"a straight line separates the classes\", while the *model parameters* are the particular numbers describing the location and orientation of that line for our data.\n", + "The optimal values for these model parameters are learned from the data (this is the \"learning\" in machine learning), which is often called *training the model*.\n", + "\n", + "The following figure shows a visual representation of what the trained model looks like for this data:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-classification-2.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now that this model has been trained, it can be generalized to new, unlabeled data.\n", + "In other words, we can take a new set of data, draw this model line through it, and assign labels to the new points based on this model.\n", + "This stage is usually called *prediction*. See the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-classification-3.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This is the basic idea of a classification task in machine learning, where \"classification\" indicates that the data has discrete class labels.\n", + "At first glance this may look fairly trivial: it would be relatively easy to simply look at this data and draw such a discriminatory line to accomplish this classification.\n", + "A benefit of the machine learning approach, however, is that it can generalize to much larger datasets in many more dimensions.\n", + "\n", + "For example, this is similar to the task of automated spam detection for email; in this case, we might use the following features and labels:\n", + "\n", + "- *feature 1*, *feature 2*, etc. $\\to$ normalized counts of important words or phrases (\"Viagra\", \"Nigerian prince\", etc.)\n", + "- *label* $\\to$ \"spam\" or \"not spam\"\n", + "\n", + "For the training set, these labels might be determined by individual inspection of a small representative sample of emails; for the remaining emails, the label would be determined using the model.\n", + "For a suitably trained classification algorithm with enough well-constructed features (typically thousands or millions of words or phrases), this type of approach can be very effective.\n", + "We will see an example of such text-based classification in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb).\n", + "\n", + "Some important classification algorithms that we will discuss in more detail are Gaussian naive Bayes (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest classification (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Regression: Predicting continuous labels\n", + "\n", + "In contrast with the discrete labels of a classification algorithm, we will next look at a simple *regression* task in which the labels are continuous quantities.\n", + "\n", + "Consider the data shown in the following figure, which consists of a set of points each with a continuous label:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-regression-1.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "As with the classification example, we have two-dimensional data: that is, there are two features describing each data point.\n", + "The color of each point represents the continuous label for that point.\n", + "\n", + "There are a number of possible regression models we might use for this type of data, but here we will use a simple linear regression to predict the points.\n", + "This simple linear regression model assumes that if we treat the label as a third spatial dimension, we can fit a plane to the data.\n", + "This is a higher-level generalization of the well-known problem of fitting a line to data with two coordinates.\n", + "\n", + "We can visualize this setup as shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-regression-2.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Notice that the *feature 1-feature 2* plane here is the same as in the two-dimensional plot from before; in this case, however, we have represented the labels by both color and three-dimensional axis position.\n", + "From this view, it seems reasonable that fitting a plane through this three-dimensional data would allow us to predict the expected label for any set of input parameters.\n", + "Returning to the two-dimensional projection, when we fit such a plane we get the result shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-regression-3.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This plane of fit gives us what we need to predict labels for new points.\n", + "Visually, we find the results shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-regression-4.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "As with the classification example, this may seem rather trivial in a low number of dimensions.\n", + "But the power of these methods is that they can be straightforwardly applied and evaluated in the case of data with many, many features.\n", + "\n", + "For example, this is similar to the task of computing the distance to galaxies observed through a telescope—in this case, we might use the following features and labels:\n", + "\n", + "- *feature 1*, *feature 2*, etc. $\\to$ brightness of each galaxy at one of several wave lengths or colors\n", + "- *label* $\\to$ distance or redshift of the galaxy\n", + "\n", + "The distances for a small number of these galaxies might be determined through an independent set of (typically more expensive) observations.\n", + "Distances to remaining galaxies could then be estimated using a suitable regression model, without the need to employ the more expensive observation across the entire set.\n", + "In astronomy circles, this is known as the \"photometric redshift\" problem.\n", + "\n", + "Some important regression algorithms that we will discuss are linear regression (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest regression (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Clustering: Inferring labels on unlabeled data\n", + "\n", + "The classification and regression illustrations we just looked at are examples of supervised learning algorithms, in which we are trying to build a model that will predict labels for new data.\n", + "Unsupervised learning involves models that describe data without reference to any known labels.\n", + "\n", + "One common case of unsupervised learning is \"clustering,\" in which data is automatically assigned to some number of discrete groups.\n", + "For example, we might have some two-dimensional data like that shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-clustering-1.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "By eye, it is clear that each of these points is part of a distinct group.\n", + "Given this input, a clustering model will use the intrinsic structure of the data to determine which points are related.\n", + "Using the very fast and intuitive *k*-means algorithm (see [In Depth: K-Means Clustering](05.11-K-Means.ipynb)), we find the clusters shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-clustering-2.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "*k*-means fits a model consisting of *k* cluster centers; the optimal centers are assumed to be those that minimize the distance of each point from its assigned center.\n", + "Again, this might seem like a trivial exercise in two dimensions, but as our data becomes larger and more complex, such clustering algorithms can be employed to extract useful information from the dataset.\n", + "\n", + "We will discuss the *k*-means algorithm in more depth in [In Depth: K-Means Clustering](05.11-K-Means.ipynb).\n", + "Other important clustering algorithms include Gaussian mixture models (See [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) and spectral clustering (See [Scikit-Learn's clustering documentation](http://scikit-learn.org/stable/modules/clustering.html))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Dimensionality reduction: Inferring structure of unlabeled data\n", + "\n", + "Dimensionality reduction is another example of an unsupervised algorithm, in which labels or other information are inferred from the structure of the dataset itself.\n", + "Dimensionality reduction is a bit more abstract than the examples we looked at before, but generally it seeks to pull out some low-dimensional representation of data that in some way preserves relevant qualities of the full dataset.\n", + "Different dimensionality reduction routines measure these relevant qualities in different ways, as we will see in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb).\n", + "\n", + "As an example of this, consider the data shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-dimesionality-1.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Visually, it is clear that there is some structure in this data: it is drawn from a one-dimensional line that is arranged in a spiral within this two-dimensional space.\n", + "In a sense, you could say that this data is \"intrinsically\" only one dimensional, though this one-dimensional data is embedded in higher-dimensional space.\n", + "A suitable dimensionality reduction model in this case would be sensitive to this nonlinear embedded structure, and be able to pull out this lower-dimensionality representation.\n", + "\n", + "The following figure shows a visualization of the results of the Isomap algorithm, a manifold learning algorithm that does exactly this:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.01-dimesionality-2.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Notice that the colors (which represent the extracted one-dimensional latent variable) change uniformly along the spiral, which indicates that the algorithm did in fact detect the structure we saw by eye.\n", + "As with the previous examples, the power of dimensionality reduction algorithms becomes clearer in higher-dimensional cases.\n", + "For example, we might wish to visualize important relationships within a dataset that has 100 or 1,000 features.\n", + "Visualizing 1,000-dimensional data is a challenge, and one way we can make this more manageable is to use a dimensionality reduction technique to reduce the data to two or three dimensions.\n", + "\n", + "Some important dimensionality reduction algorithms that we will discuss are principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) and various manifold learning algorithms, including Isomap and locally linear embedding (See [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Summary\n", + "\n", + "Here we have seen a few simple examples of some of the basic types of machine learning approaches.\n", + "Needless to say, there are a number of important practical details that we have glossed over, but I hope this section was enough to give you a basic idea of what types of problems machine learning approaches can solve.\n", + "\n", + "In short, we saw the following:\n", + "\n", + "- *Supervised learning*: Models that can predict labels based on labeled training data\n", + "\n", + " - *Classification*: Models that predict labels as two or more discrete categories\n", + " - *Regression*: Models that predict continuous labels\n", + " \n", + "- *Unsupervised learning*: Models that identify structure in unlabeled data\n", + "\n", + " - *Clustering*: Models that detect and identify distinct groups in the data\n", + " - *Dimensionality reduction*: Models that detect and identify lower-dimensional structure in higher-dimensional data\n", + " \n", + "In the following sections we will go into much greater depth within these categories, and see some more interesting examples of where these concepts can be useful.\n", + "\n", + "All of the figures in the preceding discussion are generated based on actual machine learning computations; the code behind them can be found in [Appendix: Figure Code](06.00-Figure-Code.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.01-What-Is-Machine-Learning.md b/notebooks_v2/05.01-What-Is-Machine-Learning.md new file mode 100644 index 00000000..9d6804b8 --- /dev/null +++ b/notebooks_v2/05.01-What-Is-Machine-Learning.md @@ -0,0 +1,300 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) > + +Open in Colab + + + +# What Is Machine Learning? + + +Before we take a look at the details of various machine learning methods, let's start by looking at what machine learning is, and what it isn't. +Machine learning is often categorized as a subfield of artificial intelligence, but I find that categorization can often be misleading at first brush. +The study of machine learning certainly arose from research in this context, but in the data science application of machine learning methods, it's more helpful to think of machine learning as a means of *building models of data*. + +Fundamentally, machine learning involves building mathematical models to help understand data. +"Learning" enters the fray when we give these models *tunable parameters* that can be adapted to observed data; in this way the program can be considered to be "learning" from the data. +Once these models have been fit to previously seen data, they can be used to predict and understand aspects of newly observed data. +I'll leave to the reader the more philosophical digression regarding the extent to which this type of mathematical, model-based "learning" is similar to the "learning" exhibited by the human brain. + +Understanding the problem setting in machine learning is essential to using these tools effectively, and so we will start with some broad categorizations of the types of approaches we'll discuss here. + + + +## Categories of Machine Learning + +At the most fundamental level, machine learning can be categorized into two main types: supervised learning and unsupervised learning. + +*Supervised learning* involves somehow modeling the relationship between measured features of data and some label associated with the data; once this model is determined, it can be used to apply labels to new, unknown data. +This is further subdivided into *classification* tasks and *regression* tasks: in classification, the labels are discrete categories, while in regression, the labels are continuous quantities. +We will see examples of both types of supervised learning in the following section. + +*Unsupervised learning* involves modeling the features of a dataset without reference to any label, and is often described as "letting the dataset speak for itself." +These models include tasks such as *clustering* and *dimensionality reduction.* +Clustering algorithms identify distinct groups of data, while dimensionality reduction algorithms search for more succinct representations of the data. +We will see examples of both types of unsupervised learning in the following section. + +In addition, there are so-called *semi-supervised learning* methods, which falls somewhere between supervised learning and unsupervised learning. +Semi-supervised learning methods are often useful when only incomplete labels are available. + + + +## Qualitative Examples of Machine Learning Applications + +To make these ideas more concrete, let's take a look at a few very simple examples of a machine learning task. +These examples are meant to give an intuitive, non-quantitative overview of the types of machine learning tasks we will be looking at in this chapter. +In later sections, we will go into more depth regarding the particular models and how they are used. +For a preview of these more technical aspects, you can find the Python source that generates the following figures in the [Appendix: Figure Code](06.00-Figure-Code.ipynb). + + + + +### Classification: Predicting discrete labels + +We will first take a look at a simple *classification* task, in which you are given a set of labeled points and want to use these to classify some unlabeled points. + +Imagine that we have the data shown in this figure: + + + +![](figures/05.01-classification-1.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-1) + + + +Here we have two-dimensional data: that is, we have two *features* for each point, represented by the *(x,y)* positions of the points on the plane. +In addition, we have one of two *class labels* for each point, here represented by the colors of the points. +From these features and labels, we would like to create a model that will let us decide whether a new point should be labeled "blue" or "red." + +There are a number of possible models for such a classification task, but here we will use an extremely simple one. We will make the assumption that the two groups can be separated by drawing a straight line through the plane between them, such that points on each side of the line fall in the same group. +Here the *model* is a quantitative version of the statement "a straight line separates the classes", while the *model parameters* are the particular numbers describing the location and orientation of that line for our data. +The optimal values for these model parameters are learned from the data (this is the "learning" in machine learning), which is often called *training the model*. + +The following figure shows a visual representation of what the trained model looks like for this data: + + + +![](figures/05.01-classification-2.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-2) + + + +Now that this model has been trained, it can be generalized to new, unlabeled data. +In other words, we can take a new set of data, draw this model line through it, and assign labels to the new points based on this model. +This stage is usually called *prediction*. See the following figure: + + + +![](figures/05.01-classification-3.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Classification-Example-Figure-3) + + + +This is the basic idea of a classification task in machine learning, where "classification" indicates that the data has discrete class labels. +At first glance this may look fairly trivial: it would be relatively easy to simply look at this data and draw such a discriminatory line to accomplish this classification. +A benefit of the machine learning approach, however, is that it can generalize to much larger datasets in many more dimensions. + +For example, this is similar to the task of automated spam detection for email; in this case, we might use the following features and labels: + +- *feature 1*, *feature 2*, etc. $\to$ normalized counts of important words or phrases ("Viagra", "Nigerian prince", etc.) +- *label* $\to$ "spam" or "not spam" + +For the training set, these labels might be determined by individual inspection of a small representative sample of emails; for the remaining emails, the label would be determined using the model. +For a suitably trained classification algorithm with enough well-constructed features (typically thousands or millions of words or phrases), this type of approach can be very effective. +We will see an example of such text-based classification in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb). + +Some important classification algorithms that we will discuss in more detail are Gaussian naive Bayes (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest classification (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)). + + + +### Regression: Predicting continuous labels + +In contrast with the discrete labels of a classification algorithm, we will next look at a simple *regression* task in which the labels are continuous quantities. + +Consider the data shown in the following figure, which consists of a set of points each with a continuous label: + + + +![](figures/05.01-regression-1.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-1) + + + +As with the classification example, we have two-dimensional data: that is, there are two features describing each data point. +The color of each point represents the continuous label for that point. + +There are a number of possible regression models we might use for this type of data, but here we will use a simple linear regression to predict the points. +This simple linear regression model assumes that if we treat the label as a third spatial dimension, we can fit a plane to the data. +This is a higher-level generalization of the well-known problem of fitting a line to data with two coordinates. + +We can visualize this setup as shown in the following figure: + + + +![](figures/05.01-regression-2.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-2) + + + +Notice that the *feature 1-feature 2* plane here is the same as in the two-dimensional plot from before; in this case, however, we have represented the labels by both color and three-dimensional axis position. +From this view, it seems reasonable that fitting a plane through this three-dimensional data would allow us to predict the expected label for any set of input parameters. +Returning to the two-dimensional projection, when we fit such a plane we get the result shown in the following figure: + + + +![](figures/05.01-regression-3.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-3) + + + +This plane of fit gives us what we need to predict labels for new points. +Visually, we find the results shown in the following figure: + + + +![](figures/05.01-regression-4.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Regression-Example-Figure-4) + + + +As with the classification example, this may seem rather trivial in a low number of dimensions. +But the power of these methods is that they can be straightforwardly applied and evaluated in the case of data with many, many features. + +For example, this is similar to the task of computing the distance to galaxies observed through a telescope—in this case, we might use the following features and labels: + +- *feature 1*, *feature 2*, etc. $\to$ brightness of each galaxy at one of several wave lengths or colors +- *label* $\to$ distance or redshift of the galaxy + +The distances for a small number of these galaxies might be determined through an independent set of (typically more expensive) observations. +Distances to remaining galaxies could then be estimated using a suitable regression model, without the need to employ the more expensive observation across the entire set. +In astronomy circles, this is known as the "photometric redshift" problem. + +Some important regression algorithms that we will discuss are linear regression (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest regression (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)). + + + +### Clustering: Inferring labels on unlabeled data + +The classification and regression illustrations we just looked at are examples of supervised learning algorithms, in which we are trying to build a model that will predict labels for new data. +Unsupervised learning involves models that describe data without reference to any known labels. + +One common case of unsupervised learning is "clustering," in which data is automatically assigned to some number of discrete groups. +For example, we might have some two-dimensional data like that shown in the following figure: + + + +![](figures/05.01-clustering-1.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2) + + + +By eye, it is clear that each of these points is part of a distinct group. +Given this input, a clustering model will use the intrinsic structure of the data to determine which points are related. +Using the very fast and intuitive *k*-means algorithm (see [In Depth: K-Means Clustering](05.11-K-Means.ipynb)), we find the clusters shown in the following figure: + + + +![](figures/05.01-clustering-2.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Clustering-Example-Figure-2) + + + +*k*-means fits a model consisting of *k* cluster centers; the optimal centers are assumed to be those that minimize the distance of each point from its assigned center. +Again, this might seem like a trivial exercise in two dimensions, but as our data becomes larger and more complex, such clustering algorithms can be employed to extract useful information from the dataset. + +We will discuss the *k*-means algorithm in more depth in [In Depth: K-Means Clustering](05.11-K-Means.ipynb). +Other important clustering algorithms include Gaussian mixture models (See [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) and spectral clustering (See [Scikit-Learn's clustering documentation](http://scikit-learn.org/stable/modules/clustering.html)). + + + +### Dimensionality reduction: Inferring structure of unlabeled data + +Dimensionality reduction is another example of an unsupervised algorithm, in which labels or other information are inferred from the structure of the dataset itself. +Dimensionality reduction is a bit more abstract than the examples we looked at before, but generally it seeks to pull out some low-dimensional representation of data that in some way preserves relevant qualities of the full dataset. +Different dimensionality reduction routines measure these relevant qualities in different ways, as we will see in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb). + +As an example of this, consider the data shown in the following figure: + + + +![](figures/05.01-dimesionality-1.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-1) + + + +Visually, it is clear that there is some structure in this data: it is drawn from a one-dimensional line that is arranged in a spiral within this two-dimensional space. +In a sense, you could say that this data is "intrinsically" only one dimensional, though this one-dimensional data is embedded in higher-dimensional space. +A suitable dimensionality reduction model in this case would be sensitive to this nonlinear embedded structure, and be able to pull out this lower-dimensionality representation. + +The following figure shows a visualization of the results of the Isomap algorithm, a manifold learning algorithm that does exactly this: + + + +![](figures/05.01-dimesionality-2.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Dimensionality-Reduction-Example-Figure-2) + + + +Notice that the colors (which represent the extracted one-dimensional latent variable) change uniformly along the spiral, which indicates that the algorithm did in fact detect the structure we saw by eye. +As with the previous examples, the power of dimensionality reduction algorithms becomes clearer in higher-dimensional cases. +For example, we might wish to visualize important relationships within a dataset that has 100 or 1,000 features. +Visualizing 1,000-dimensional data is a challenge, and one way we can make this more manageable is to use a dimensionality reduction technique to reduce the data to two or three dimensions. + +Some important dimensionality reduction algorithms that we will discuss are principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) and various manifold learning algorithms, including Isomap and locally linear embedding (See [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)). + + + +## Summary + +Here we have seen a few simple examples of some of the basic types of machine learning approaches. +Needless to say, there are a number of important practical details that we have glossed over, but I hope this section was enough to give you a basic idea of what types of problems machine learning approaches can solve. + +In short, we saw the following: + +- *Supervised learning*: Models that can predict labels based on labeled training data + + - *Classification*: Models that predict labels as two or more discrete categories + - *Regression*: Models that predict continuous labels + +- *Unsupervised learning*: Models that identify structure in unlabeled data + + - *Clustering*: Models that detect and identify distinct groups in the data + - *Dimensionality reduction*: Models that detect and identify lower-dimensional structure in higher-dimensional data + +In the following sections we will go into much greater depth within these categories, and see some more interesting examples of where these concepts can be useful. + +All of the figures in the preceding discussion are generated based on actual machine learning computations; the code behind them can be found in [Appendix: Figure Code](06.00-Figure-Code.ipynb). + + + + +< [Machine Learning](05.00-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.02-Introducing-Scikit-Learn.ipynb b/notebooks_v2/05.02-Introducing-Scikit-Learn.ipynb new file mode 100644 index 00000000..d0805519 --- /dev/null +++ b/notebooks_v2/05.02-Introducing-Scikit-Learn.ipynb @@ -0,0 +1,1590 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introducing Scikit-Learn" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "There are several Python libraries which provide solid implementations of a range of machine learning algorithms.\n", + "One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\n", + "Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\n", + "A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.\n", + "\n", + "This section provides an overview of the Scikit-Learn API; a solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters.\n", + "\n", + "We will start by covering *data representation* in Scikit-Learn, followed by covering the *Estimator* API, and finally go through a more interesting example of using these tools for exploring a set of images of hand-written digits." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Data Representation in Scikit-Learn" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer.\n", + "The best way to think about data within Scikit-Learn is in terms of tables of data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Data as table\n", + "\n", + "A basic table is a two-dimensional grid of data, in which the rows represent individual elements of the dataset, and the columns represent quantities related to each of these elements.\n", + "For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\n", + "We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import seaborn as sns\n", + "iris = sns.load_dataset('iris')\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\n", + "In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``.\n", + "\n", + "Likewise, each column of the data refers to a particular quantitative piece of information that describes each sample.\n", + "In general, we will refer to the columns of the matrix as *features*, and the number of columns as ``n_features``." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Features matrix\n", + "\n", + "This table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*.\n", + "By convention, this features matrix is often stored in a variable named ``X``.\n", + "The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, and is most often contained in a NumPy array or a Pandas ``DataFrame``, though some Scikit-Learn models also accept SciPy sparse matrices.\n", + "\n", + "The samples (i.e., rows) always refer to the individual objects described by the dataset.\n", + "For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n", + "\n", + "The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\n", + "Features are generally real-valued, but may be Boolean or discrete-valued in some cases." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Target array\n", + "\n", + "In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``.\n", + "The target array is usually one dimensional, with length ``n_samples``, and is generally contained in a NumPy array or Pandas ``Series``.\n", + "The target array may have continuous numerical values, or discrete classes/labels.\n", + "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array.\n", + "\n", + "Often one point of confusion is how the target array differs from the other features columns. The distinguishing feature of the target array is that it is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n", + "For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n", + "\n", + "With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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1ICc4Ty+XT+7pp2qjJejc1eV0Y3d24+r28OQbR4lSyFidMnxDL0NOWnQqn5gO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3/+E4B169axaNGiVucvLS1l4cKFrFq1iiuuuKJFedpTTOZEdoXvf7PV0ehaSUUt2YU1JMXqCQkM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\nK2P16tVs27YNSZJ48cUXOX78OA0NDTz77LOMHz++K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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import seaborn as sns; sns.set()\n", + "sns.pairplot(iris, hue='species', size=1.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(150, 4)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_iris = iris.drop('species', axis=1)\n", + "X_iris.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(150,)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_iris = iris['species']\n", + "y_iris.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "To summarize, the expected layout of features and target values is visualized in the following diagram:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.02-samples-features.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Scikit-Learn's Estimator API" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n", + "\n", + "- *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation.\n", + "\n", + "- *Inspection*: All specified parameter values are exposed as public attributes.\n", + "\n", + "- *Limited object hierarchy*: Only algorithms are represented by Python classes; datasets are represented\n", + " in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and parameter\n", + " names use standard Python strings.\n", + "\n", + "- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\n", + " and Scikit-Learn makes use of this wherever possible.\n", + "\n", + "- *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value.\n", + "\n", + "In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood.\n", + "Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Basics of the API\n", + "\n", + "Most commonly, the steps in using the Scikit-Learn estimator API are as follows\n", + "(we will step through a handful of detailed examples in the sections that follow).\n", + "\n", + "1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\n", + "2. Choose model hyperparameters by instantiating this class with desired values.\n", + "3. Arrange data into a features matrix and target vector following the discussion above.\n", + "4. Fit the model to your data by calling the ``fit()`` method of the model instance.\n", + "5. Apply the Model to new data:\n", + " - For supervised learning, often we predict labels for unknown data using the ``predict()`` method.\n", + " - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method.\n", + "\n", + "We will now step through several simple examples of applying supervised and unsupervised learning methods." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Supervised learning example: Simple linear regression\n", + "\n", + "As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\n", + "We will use the following simple data for our regression example:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "rng = np.random.RandomState(42)\n", + "x = 10 * rng.rand(50)\n", + "y = 2 * x - 1 + rng.randn(50)\n", + "plt.scatter(x, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this data in place, we can use the recipe outlined earlier. Let's walk through the process: " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### 1. Choose a class of model\n", + "\n", + "In Scikit-Learn, every class of model is represented by a Python class.\n", + "So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### 2. Choose model hyperparameters\n", + "\n", + "An important point is that *a class of model is not the same as an instance of a model*.\n", + "\n", + "Once we have decided on our model class, there are still some options open to us.\n", + "Depending on the model class we are working with, we might need to answer one or more questions like the following:\n", + "\n", + "- Would we like to fit for the offset (i.e., *y*-intercept)?\n", + "- Would we like the model to be normalized?\n", + "- Would we like to preprocess our features to add model flexibility?\n", + "- What degree of regularization would we like to use in our model?\n", + "- How many model components would we like to use?\n", + "\n", + "These are examples of the important choices that must be made *once the model class is selected*.\n", + "These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\n", + "In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\n", + "We will explore how you can quantitatively motivate the choice of hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\n", + "\n", + "For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LinearRegression(fit_intercept=True)\n", + "model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n", + "In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### 3. Arrange data into a features matrix and target vector\n", + "\n", + "Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n", + "Here our target variable ``y`` is already in the correct form (a length-``n_samples`` array), but we need to massage the data ``x`` to make it a matrix of size ``[n_samples, n_features]``.\n", + "In this case, this amounts to a simple reshaping of the one-dimensional array:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, 1)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = x[:, np.newaxis]\n", + "X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### 4. Fit the model to your data\n", + "\n", + "Now it is time to apply our model to data.\n", + "This can be done with the ``fit()`` method of the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n", + "In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.9776566])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.90331072553111635" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "These two parameters represent the slope and intercept of the simple linear fit to the data.\n", + "Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n", + "\n", + "One question that frequently comes up regards the uncertainty in such internal model parameters.\n", + "In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves: interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\n", + "Machine learning rather focuses on what the model *predicts*.\n", + "If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### 5. Predict labels for unknown data\n", + "\n", + "Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\n", + "In Scikit-Learn, this can be done using the ``predict()`` method.\n", + "For the sake of this example, our \"new data\" will be a grid of *x* values, and we will ask what *y* values the model predicts:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "xfit = np.linspace(-1, 11)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "Xfit = xfit[:, np.newaxis]\n", + "yfit = model.predict(Xfit)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, let's visualize the results by plotting first the raw data, and then this model fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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cLiMwwMzc6YMZN7QHgQHmS34feveuAH5sJ+rdu7LTfL/87Wd/ofZ+GHEqkP/whz/w/PPP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\n6dJERKSTUCC3wjAMPttXwr+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x, y)\n", + "plt.plot(xfit, yfit);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Supervised learning example: Iris classification\n", + "\n", + "Let's take a look at another example of this process, using the Iris dataset we discussed earlier.\n", + "Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\n", + "\n", + "For this task, we will use an extremely simple generative model known as Gaussian naive Bayes, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\n", + "Because it is so fast and has no hyperparameters to choose, Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models.\n", + "\n", + "We would like to evaluate the model on data it has not seen before, and so we will split the data into a *training set* and a *testing set*.\n", + "This could be done by hand, but it is more convenient to use the ``train_test_split`` utility function:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\n", + " random_state=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With the data arranged, we can follow our recipe to predict the labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import GaussianNB # 1. choose model class\n", + "model = GaussianNB() # 2. instantiate model\n", + "model.fit(Xtrain, ytrain) # 3. fit model to data\n", + "y_model = model.predict(Xtest) # 4. predict on new data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.97368421052631582" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "accuracy_score(ytest, y_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Unsupervised learning example: Iris dimensionality\n", + "\n", + "As an example of an unsupervised learning problem, let's take a look at reducing the dimensionality of the Iris data so as to more easily visualize it.\n", + "Recall that the Iris data is four dimensional: there are four features recorded for each sample.\n", + "\n", + "The task of dimensionality reduction is to ask whether there is a suitable lower-dimensional representation that retains the essential features of the data.\n", + "Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or higher!\n", + "\n", + "Here we will use principal component analysis (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique.\n", + "We will ask the model to return two components—that is, a two-dimensional representation of the data.\n", + "\n", + "Following the sequence of steps outlined earlier, we have:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA # 1. Choose the model class\n", + "model = PCA(n_components=2) # 2. Instantiate the model with hyperparameters\n", + "model.fit(X_iris) # 3. Fit to data. Notice y is not specified!\n", + "X_2D = model.transform(X_iris) # 4. Transform the data to two dimensions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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wa3u/dhBgQsq5nW53WGBDez/2Xs+af+RumKCczNxD8lZ5QW+ohFKpgNbPGyGBLTCodwiG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\nLK8xl0DS6XR47LHHsHHjRgQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iris['PCA1'] = X_2D[:, 0]\n", + "iris['PCA2'] = X_2D[:, 1]\n", + "sns.lmplot(\"PCA1\", \"PCA2\", hue='species', data=iris, fit_reg=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n", + "This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Unsupervised learning: Iris clustering\n", + "\n", + "Let's next look at applying clustering to the Iris data.\n", + "A clustering algorithm attempts to find distinct groups of data without reference to any labels.\n", + "Here we will use a powerful clustering method called a Gaussian mixture model (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\n", + "A GMM attempts to model the data as a collection of Gaussian blobs.\n", + "\n", + "We can fit the Gaussian mixture model as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.mixture import GMM # 1. Choose the model class\n", + "model = GMM(n_components=3,\n", + " covariance_type='full') # 2. Instantiate the model with hyperparameters\n", + "model.fit(X_iris) # 3. Fit to data. Notice y is not specified!\n", + "y_gmm = model.predict(X_iris) # 4. Determine cluster labels" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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elWKRGhdPwtSraz3m6NtLuQf2O9cd+vZq1j0G8/uD0OL3BNCAAQO0YcMGXXnl\nlfr666/Vu3dv57GePXvq4MGDKigoUFRUlLZt26Z58+Y16Lr5+dapFIqPj2tUPN/+cFxl5Q6X9aU9\nvNf3qLHx+JqV4rFSLBLx1Id4PPPVN3Or3SPxuGeleHwRy54jB1ReXuGy7hfXL2DxNAfxeGbFeHzB\navdIPO4Rj3tWikXyTjxGv0GKPXPOWRlk9BvU5GuG4vvjTSSj/MvvCaAJEyYoPT1d06dPlyQtWrRI\n69atU3FxsVJSUnT//fdr7ty5Mk1TKSkpSkhI8HeIfpcUH+Oc7FW1BgAArrw5Lh4AEPxMh0OnN29S\n4ZdbZZpS3JAh6nDtVc2+rmGzMQ0MIcnvCSDDMPTwww+7PNa9e3fn12PHjtXYsWP9HFVg0d8HAID6\n0d8HAFBdQcZmnXj/PVX8t29sWd4R5bVuJVv/IQGODLAmvyeAWpqGNHi2GYZG9e8UoAgBAAgOzR0X\n35Am0gCA4FGSnS2ztMy5NkvLVPTjIcWRAALqRALIx9KzcpW2PUeSnNu8SPYAAOB/W3IztSknQ5Kc\nW8mak1ACAPiP6XCoIGOzy8SuyKQkFW63SyXnJElGhF0xF3QNcKRoqd5991116tRJQ4cODXQobpEA\n8rHs/CKP68ZqSEURAACo7XBRrsc1AMC6CjI269SGNElS8b69kirHuJsO06UHUMK4y3XsePN+5wKa\nYurUqYEOoV4kgHzMXYPnpiZyqCgCAKBpaCINAMGrJDu71tqw2dR29Bi1HT3G+bhhY2svGm7btm16\n8sknZRiGBg8erO3bt6t79+7au3evunXrpiVLlujkyZN64IEHdPbsWcXExGjx4sWKjY3VH//4R/3w\nww+SpMWLF+v9999Xjx49NH78eD3wwAPKy8tTeHi4/vKXvygyMlJ33323TNNU69at9dRTTykiIsLv\n90sCyMfcNXhuaiLH2xVFAAC0FDSRBoDgFZmU5Kz8qVo7ysuV9/pylRw6pMiuXZWQOidwASIopaWl\n6aabbtKkSZP09ttva/v27Ro/frweeeQR/elPf9KGDRu0detWTZkyRRMnTtRHH32kf/zjH7r44ovV\nqlUrrVy5Ut9++62+/fZb5zVXrVqlPn366IknntDOnTv1xBNPaMqUKerZs6cefPBBff755yooKFCH\nDh38fr/1JoBOnDih/Px8XXjhhbJVy6bu3r1bl1xyiU+Ds5qmVO24a/BcXyLH3WsxMh4AgKZpbhNp\nAEDgtB4h2a+YAAAgAElEQVQ+UpJcegAdffUVndm2VZJUevSIJOn8P9wdsBgRfG677TY9//zzWr16\ntZKTk2WapgYPrvxZ4Re/+IUOHjyo/fv3a/v27XrrrbdUUVGhrl27Kjs7W8nJyZKkvn37qm/fvnr2\n2WclSfv379eOHTv0+eefS5LCw8M1ZswY7d+/X7feeqs6dOig/v37B+R+PSaAPvjgAy1atEht27ZV\naWmpnnnmGfXu3VuS9Kc//UnvvvuuX4K0Cm9uv6ovkePutRgZDwAAE70AoKUxbDa1GTna5bGSQ4c8\nrutqHM0WMVS3bt063XjjjerZs6duv/127d+/X998840GDhyorKwsTZw4Ubm5uRo9erRGjBihb775\nRgcPHpTdbtcXX3yha6+9Vjt27FBaWprsdrskqXv37urbt69uuOEGHT58WBs3btSWLVvUuXNnvfLK\nK1q+fLk++OADzZo1y+/36zEB9MILL2jt2rVq3769PvjgA82bN0///Oc/1atXL5mm6a8YLcOb26/q\nS+S4ey1GxgMAwEQvAIAU2bWrs/Knal1dXY2jayaR0LJdfPHFuu+++xQbG6vzzz9fPXv21GuvvaYn\nnnhCF198sUaNGqVLLrlEDzzwgF544QWVl5frL3/5i3r06KGNGzcqNTVVkvToo49q7dq1kqTp06fr\nvvvu07/+9S8VFxfrvvvuU48ePXTXXXfprbfekt1u18KFCwNyv/VuAWvfvr0k6aqrrpJhGLrtttv0\n1ltvyWiBk6e8uf2qKpFTtdVr5affs9ULAIAGYqIXAKCq54+7HkB1NY4Gqhs4cKDefvtt5zo1NVUP\nPfSQzjvvPOdj7du31wsvvFDruX/+859d1nfccYfz66VLl9Y6/7XXXvNGyM3iMQHUo0cPPfbYY7r5\n5pvVsWNHTZw4UceOHdOsWbNUUlLirxgtwxfbr9jqBQBA4zHRCwBgCw9Xx1tudXu8rsbRElvD4F6o\nF7p4TAA9+uijevHFF3XgwAF17NhRUmVGLDExUc8884xfArQSX2y/qm+rl7sKIQAAWrLqE70SozvK\nlEOr971HPyAAaGFqJnM6XDPReayuxtESW8PgnhWqdHzJYwIoOjpad911V63H+/TpozFjxvgsqJak\nqc2gAQBoyapP9Mo4vE2bcv4jiX5AANDS1Ezm5MVFydZ/iKS6G0dLbA1Dy1VvD6AqDodDaWlpWrFi\nhbZs2aJx48b5Mq4Wo6nNoAEAQCX6AQFAy1UzeVP04yHF/TcBJNW93cvd1jAg1NWbADp69KhWrlyp\nd955R4ZhqKioSB9++KG6dOnij/hCHs2gAQBonob0A3KYDmUc3sbYeAAIMTWTOTEX1D8JzN3WMCDU\neUwA3X777dqzZ4/GjRunpUuXasCAAbriiitI/vgAzaABAGia6v2AqpI7NX12YEuDxsY7TIe25GaS\nKAKAAGtoo+aayZyEcZfr2PGfd03Utd3L3dYwINR5TADl5eXp/PPPV9u2bdWuXTsZhhHyXbEDpb5m\n0AAAoG7V+wG5c+h0jsva3TaxLbmZDUoUAQB8q6GNmmsmc2omiVrSdi+mmwXO3r17VVBQoEGDBgU6\nFI88JoDeeecd7d27V2vWrNFNN92khIQEFRYWKj8/X/Hx8f6KMSQ4TFObdxzW1u/yJElD+p6vkdUm\nerHVCwAA3+naprN25e5xrt2NjaefEAAERs0KzJ7ZP7kcb2qj5pa03YvpZlJZuUPbvjmi0nKHBvZJ\nUFx0hF9e9+OPP1aHDh2COwEkSb1799Z9992n//3f/9Vnn32md955R+PHj9eYMWP09NNP+yPGkJCe\nlat/ZRzUmbOlkqSjJ4pl6OeJXmz1AgDAd8Z2H6YzZ8553CYmNayfEADA+2pWYEbGtlWHasebWrnT\n1O1ewVhN09Knm5mmqZfW7tTeQyclSZu25+ieWQMUHWVv8jV//PFH3X///QoPD5dpmnriiSf05ptv\nKjMzUxUVFbrlllt06aWXas2aNYqIiNAll1yigoIC/e1vf1NkZKTatWunRx99VKWlpbr77rtlmqZK\nS0u1YMEC9enTR0uXLtXu3bt18uRJ9enTR48++qi33o46NXgKWHh4uMaPH6/x48fr+PHjeu+993wZ\nV8jJzi9SaXmFc11aXuGy7YutXgAA+E7VNrGqvzCv+X5dnT1+GtJPCADgfTUrLg/0bqtebccFrHIn\nGKtpWtJ2t7qcOVvmTP5I0omCYh04XKBLepzX5Gump6erf//++v3vf69t27Zp/fr1ysnJ0RtvvKHS\n0lLdcMMN+r//+z9dd911io+PV79+/XTFFVdoxYoVio+P1+uvv67nnntOw4YNU7t27fTYY49p3759\nKi4uVmFhodq0aaOXX35Zpmnq6quvVl5enhISErzxdtSp3gTQO++8owsvvFDJycmSpKVLl6pbt266\n5ZZbfBZUKEqKj1FEeJhKSiuTQBHhYWzzAgDAz+rr8dOQfkIAAO+rVYEZ20ltevvn87iuap9grKZp\nSdvd6tIqMkyREeEqKS2vfMAw1DYuslnXTElJ0Ysvvqh58+apdevWuuiii7Rr1y7dfPPNMk1TFRUV\nyq72b+PEiROKi4tztswZNGiQnnrqKd1777368ccfdfvtt8tut+v2229XVFSUjh07pnvuuUfR0dEq\nLi5WeXl5s+Ktj8cE0Ouvv6733ntPS5YscT42atQoLV68WCUlJZo5c6ZPgwslI5ITZZqmSw+gEcmJ\nKnc49OoH3+mnvEJ1SYjV7Kv6KNzipYUAAAQrevwAgDUFsgKzrmqfYKymaenTzezhYZpz9cV6+9N9\nKi2r0P8b2k2d42Obdc3169dr0KBBuuOOO/T+++9r6dKlGjFihB555BGZpqlly5apa9euMgxDDodD\n7du3V2FhoY4dO6YOHTpo69atuuCCC/TFF18oPj5eL7/8sr7++mstXbpUs2fP1pEjR/TUU0/pxIkT\n+uSTT2Sappfejbp5TACtXr1ab7zxhmJjf37TBg8erH/84x+aM2cOCaBGsBmGRl/aWaMv7VzZEDor\nV0tXfq2c/CIVFpcpzGboyImzkqR5ky4OcLQAAAQvh+nQf3K36aujWZJMDUi4VFM6XC6JHj8AYFWB\nrMCsq9on/obpzq9bYjVNsOpzQXs9OG+o167Xr18/3XvvvXr++eflcDj0zDPP6L333tOsWbNUXFys\n8ePHKzo6Wr/4xS/0+OOPq2fPnvrzn/+sO+64QzabTa1bt9bixYslSfPnz9dbb70lh8OhO+64Qxde\neKGef/55paamSpK6du2qvLw8de7c2Wvx1+QxAWSz2VySP1Xat28vG1UqTZaelat/pf+oM2dLVVru\ncD4eZjP0U15hACMDACD4bcnN1Ec/pqmwtPJ7al7xMbVu3Ur94vrR4wcAWri6tnvVVe3T0qtpUKlL\nly568803XR67+OLaBRtjxozRmDFjnOvLLrus1jmvvPJKrcfefvttL0TZcB4TQGFhYTp+/LjOO8+1\nadKxY8dUUVHh5lmoT/WG0IYhmaZUVejVJaF5JWoAALR0h4tyVVZR5lyXVZTp0Okc9YvrR48fAGjh\n6tru5al3TjBOAwPc8ZgAuum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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iris['cluster'] = y_gmm\n", + "sns.lmplot(\"PCA1\", \"PCA2\", data=iris, hue='species',\n", + " col='cluster', fit_reg=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n", + "This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\n", + "This sort of algorithm might further give experts in the field clues as to the relationship between the samples they are observing." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Application: Exploring Hand-written Digits" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits.\n", + "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Loading and visualizing the digits data\n", + "\n", + "We'll use Scikit-Learn's data access interface and take a look at this data:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 8, 8)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.images.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\n", + "Let's visualize the first hundred of these:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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uXboY2qKjow1tc+fONbS5XC7L4pDGmZQwBcj9r5tA09Bkg7riUCXVSPuiSslL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\nUPhNIf76779i8KrB+OUbv8TX5742HZZX24q3YeiPhqJvWF8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n", + " subplot_kw={'xticks':[], 'yticks':[]},\n", + " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n", + " ax.text(0.05, 0.05, str(digits.target[i]),\n", + " transform=ax.transAxes, color='green')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation.\n", + "We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\n", + "Additionally, we need the target array, which gives the previously determined label for each digit.\n", + "These two quantities are built into the digits dataset under the ``data`` and ``target`` attributes, respectively:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = digits.data\n", + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797,)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = digits.target\n", + "y.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see here that there are 1,797 samples and 64 features." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Unsupervised learning: Dimensionality reduction\n", + "\n", + "We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space.\n", + "Instead we'll reduce the dimensions to 2, using an unsupervised method.\n", + "Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 2)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import Isomap\n", + "iso = Isomap(n_components=2)\n", + "iso.fit(digits.data)\n", + "data_projected = iso.transform(digits.data)\n", + "data_projected.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that the projected data is now two-dimensional.\n", + "Let's plot this data to see if we can learn anything from its structure:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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J83zHi8Pt/bHzz3x+0ueGJxx9qD/RP5xEYWh1nIHkIMUcfWFyIc53kkiFOI45\nOZfyes+bw0Xo67Jm0BxpPvRcpoGKiZSeIJrWsZvsxDIJTIpKd6yHjJ6hJdJKhbOCjKHzdOsztEZa\nQVFIZhJs822jNdrGDt9O0kYa96Ei9NnmLHLMQ8Xnsy1Zw4/QrO/dTFOkhWgmhgLUZ83k9rJb2Bc6\nQCAVwMCg2lmFPxXAoTkod5ShGzozPNNpibbyctcrDCZ9JPUEFnVo1lLKSJJtzgKGitR/mEQB0kaG\nYCp0RCJ1a240xUT60Go1ZkXDpY3N8lRCnAskkQpxHLNyLqbQVoAv5afcXoZLc9EcbcWkaphVM2bV\njNvkJmWkSBtpVBSSepLmaAsGBnkfeezEolpQFZWeeB/BVABN1Xhs3y/JkKEr1o1JMZFrySXPmjc8\nTmlSTHyqYjnbfNvZHtpKwUcKOQwmB/nCpHvZ7ttBf3KASmcFXms+T7Y+QygdZkbWDEyoBFNBUkaK\nIlsRaSNDIBXAa81HAW4rvWV4LdEcSzb51jz6EwMAZJk9eK35R7wnLrOLm0o+wdt976IoCku9S0Y8\n1yrEhUYSqRAnUGwvpthePLxdbi9jl+l9LKqVpJ7AY3EzL3cOhgEbfZuJpCNkmbNwmOxUOMrpiHXQ\nFGmmwl5OgdXLB8G9JDIJbCYbawfWY1EtpIwUsUwMs6Lxzan/TFu0neZIC/nWPKZ7plHjqsFistId\nb8JlcpJtyaLYVszvWp5gm387GSPDUu8Sriu6llWT7sGX9A33JLcMbuO9/rUEUgHsJhvljnKu9F5B\nhbOceblzhq/LpJj4VPkn2erbho7Opdmzjrlgd417MjXuyWP7xovzXlx7d1THO89QHKMliVSIU3RZ\n3nx6470U24pxaHYuy5vPEu8ibCYb/777P9gX2k/ayDCY9HFx1kzWD2wglo7Rl+gnkAqQb80jlAqj\nqSaimSiRTIRKZyW6oVPuLMOkmniy9RkCqQDxTIKPF19HU6SZGnc1B3xNhNNhFnsX4jFnsW5gPQfD\nTQC8lnkDHZ3by24dnokLMC9vDn/pfoVdgd2k9CR1WTP4eMmyo/YiHZqdRd7Lh7dTeoqmSDOaolHt\nrDqpGb1CnKzB6sWjOv74ZUbOHkmkQpyC3YE9/KXrFVAUKpxl3FF2G5XOw0uShdOR4bHDpJHivf71\nhNMRFEXFl/ITSgUps5dyIHMQg6ESgqqikqW5sWsOprmnsi+0n45Y53AFpN+29FPtqKYh1kCuJZdQ\nKsSe4F4c+IUVAAAgAElEQVSsqo2OSDudsS7MiobT5OBvPW+joDAvdy5lhyYsmRUzdtWGgoJJ0WgM\nN/Fq92vcVnbLca81rad5svUZuuLdANR5ph/12VghLnSSSIU4BbsCuzAwgKEVUd4P7B6RSC/Knnmo\nPm6GAqsXk6Li1lyE0kML3JfZy7g45yI0VcOsmKnzTEdVTWSMDJpi4pqiqxhM+uiKdwGgG5mhKkoJ\nP37dRyKZxJcOMJjy0RXrZiA5SFJPogChdJh5uXPYFzpAU6SZL0y6F4/Zg6Io+FMBVEVFVVQMYG9w\n3wmvtTXaNpxEAXYH97C04AoZDxXi70giFeIUOLXDozKGYeD4u6SyJH8RwVSQjKHj1lxcljcf3dDp\nSfRgGAafqljOgrz5pPU0MT2GzWTDpJgYTPpwmBw4NQcZI0Olo4LmSAvBVAhNNVPqKCUZSxDUI1gU\nM4ahE9fjQ4+3WL3o6MMJdf3ARsCgwl7B8orbAFiYfzlNkRb64n2YVY2+RB/xTPy41Yv+fnzUdKh0\noBBiJEmkQpyCpd4ldMW6ead/DRomCmxe2qJteK1ebCYbUz1TKLB5CaSCFNoKsJvs5FpzaY+2U2At\nGJ6gYzaZMZvMw+f96OxYk2Li/in/xHMdL7J+YCMmxUS5o4xSTwFvd66jPdaOpmjEMnF0dPKseZgU\nE6F0iM54N2bVjKaY2BsaWj6t0FbADcXL2O7bwQ59J2bVjEtz8Xbfu1xXdM0xr7XUXsK83DlsHNyM\nSVG5tuiaY04+EuJCJolUiFPgNrsptBVwUdZMoukof+58hU2DW5jumcad5XdQYPOSY8khx3K4OEGF\no5wKR/kptVNkL+JLNV9kXu4c3ux9GwCbxcFU1xR64r0k9QSFtkKcJie5lhwcmoNrCj7NKz2vo6NT\nYivBarKS1JNkjAyv97xJT7wHm8nGdM9UHJoTfzJwwjiWFixhYf5lqIoq648KcQySSIX4iN2+Pezr\na6XSWUG5o+yo+0QPlQxsj7WTMlKk9KFHV9YNrOfm0hvPaDxzcmfjMXvoiffSr3bTG/BR6azAMAyK\nbAXMzKrjq7VfAoaWOtNMGg2h/QCU2UsosRezzbeDD4J78Fiy6E708kFwL5Ndk1jqXXJSMWiKxtqB\n9RwMN5JryeXqwiuloL0QHyGJVIhDNgxsYnNkA5FoknUDG7ij/NajFqG/KPsiWqJtxDIxdD1z3FVV\nzoQp7lqmuGt5I/gq2ZYsyuwldMa7URUTnyj5+IhHUm4q+QRNkWYyRoZqZxUmxUQ4HcYwDALJAL6E\nj0gmQomtlF2B95mZVcdm3xb2hw6QbcniusJrcJlHVinaGdjFmkNrnnbHezDQubHkhjG9ZiHOJep4\nByDERNEQOjyT1cBgf+jAUferdFagKioezUPG0AmlwthNdhbkzR/T+C7JuwgFhUpnJQvzL+MbU7/G\nJFf1iH0URWGSq5padw2aOvQ9eZp7Cr2JXnoSvUQykaFl11JD9XFf7nqF9QMb2R8+wGvdb/DYgcfR\nDX3EOT+sdPShvkT/mF6nEOca6ZEKcUiW2UMoc7gY+9/XmP3Qdt8OMAwSegKTaqI92s4NxcsotBWM\naXzTsqdwd+Wn6Ix1U2IvGlFt6XiK7EUsyl9I2siQ0lOYVTMJPQkMFcVvDDex41CB+754P2/1vs3H\nCq8cPr7KWcmWQ2uofrgthDhMeqRCHHJV4ceo8UzCrbmoz6pjbu7so+6nKArBdIiBpA9VUbGZbOwI\n7CKcCo95jMX2YmbnXnLSSfRD8/PmUuEo46KsmZhQ8VrzqXFNoj67jr2hBhJ6grgeJ5gO0hRpGXHs\nZNckbi29iYuz65mXO5vuWA+PH/w1b/W+g2EYZ/LyhDgnSY9UiENcmpO7a++kLzt03P0uyZ7FxoFN\nwNCjKh/20D5cymwiKrEXc3flCpoizXg0D5XOSpyag73BBqa6a9kb3IemaDhNTjxm9xHH17prqHXX\n8GTr07THOgDYOLiZPGse9Vl1Z/tyhJhQJJEKcYocmp2v1n6JIlshjZEWzKrGwvzLxqXiT0+8l22+\n7ZhVM/Pz5uH6SMGIQCrAmv51pPU0c3JnU2IvHlGDF6DYXsR09zTMqgVf0kexrYhPlHz8mO35kv4R\n24GTeIRGiPHyq1/9ijfffJNUKsVdd93F7bffPibtSCIV54VEIoHFYjlrRdU1VWNF5Z0MJn1oiumY\n46ljKZwK81TrM8QPLT7eEm3l0xWfwmqykjEyPNX6B/ypAIZhsGlwCzeV3MBF2fUjiipkmbNYUflJ\ntvt3YlEtzM+dh91kP2abU921bD40XmpSVCb/3WQnISaKjRs3sm3bNp588kmi0Sj/8z//M2ZtSSIV\n57RUKsVzzz1DS0szDoeT229fTnFxyVlrP/cjhRfOtu54z3ASTelp/tbzNp3RLorshVxbeBX+VIC0\nnmZN/zr6kwM0R1q4omAxn6n6NBbVMnyeQlvhERWOBpM+OmNdeK35IyZRXVmwlHxrPv5UgFrX5FMe\nqxXibHnvvfeYMmUKX/rSl4hEInzrW98as7YkkYpz2rZtW2lpaQYgGo3w2muv8NnP3ju+QZ0ledZc\nNMVE2sjQEesgoScwqxqDSR9v9b1LUk+wL3iArngXCgq+pI9d/vdpjbQddy3R9mgHT7f9gbSRQVVU\nbiz+OFM9U4ChiVYXZdefrUsU4rT5fD46Ozt5/PHHaWtr4x//8R955ZVXxqStiTs7QoiTkEwmjrt9\nPsux5HBL6U1UOMrIt+QxwzMdRVEJpUL8rfctMoZOd7wbs2LGa/ViUjWimdhRqxK1Rzt4P/ABwVSQ\nHf6dw0vB6YbONv+Os31pQoxadnY2ixcvRtM0qqursVqtDA4Ojklb0iMV57SZM+vZvn0b0WgERVGY\nO3dsiyJMNJNc1UxyVdMZ6+Kp1mdIGWk64114rV7sJjtTPVPoinWjo6MbOku8i4bXKf3QlsFt/LX3\nbwDYTDYq7SNLI9pMVtJ6mr90vUJjpIk8Sx7XF1+HTbXi1Jyy2LeYkGbPns3q1au555576OnpIR6P\nk5MzNkMxkkjFOS07O4d77rmX9vZ2srKyzur46ERSYi/ms9Ur6Yx1UWorpuPQeqbl9jKmuIZKDJY7\ny5nhmXbEsVs/Umwhnonj1FyU2IvpjHWRZ8llqXcJm31b2Huo8tPeUAPv9L1HrbuGcnspt5ffOmLM\nVYiJYOnSpWzevJk77rgDwzB46KGHxuxL36gS6Y4dO/jpT3/K6tWraW1t5Tvf+Q6qqlJbW8tDDz0E\nwNNPP81TTz2F2WzmvvvuY+nSpWcibiGGuVxupk2bfkbOldEzvNL1Gq3RNrzWfJYVX3vcWawTSa4l\nh1xLDuWOUp5ufRZfyk+2JZtPlt9OtiX7mMfZTDZIHd7OsmRxddHHSOvp4TKD4XRk+PXGcNPwB1Jb\nrIMd/l3HLF4hxHj65je/eVbaOe1E+utf/5oXX3wRp3PoubX//M//5P7772fOnDk89NBDvPHGG8ya\nNYvVq1fz/PPPE4/HWbFiBQsXLsRsNp/g7EKMjzU969kZeB8AfyqApectbii5fpyjOjVZ5ixWTbqH\naDqKQ3OgnqBQxLVFV/Fc+4uE0mEmOau5JPtigOEkCjDdM40d/p1kjKFbxMW2w7N103rqiHMKcSE5\n7URaWVnJY489NjylePfu3cyZMweAJUuWsGbNGlRVZfbs2WiahsvloqqqioaGBmbOnHlmohfiDBtM\n+EZs+1PnZsEBVVGPWMXlWApthfxjzT+M6IH+vVJ7CXdX3kVrtI36rDreD3yAgYHH7KY+S/49iwvb\naSfSa665ho6OjuHtj9bcdDqdhMNhIpEIbvfhcmMOh4NQ6Pjl14QYT9Oyp7C2bSsGQ3/Pte6acY7o\n7DlWEv1Qoa1g+JnSObmzCaVCFNuLzplb30KMlTM22UhVD98+ikQieDweXC4X4XD4iJ+fDK/3yHqf\nE8lEjm8ixwYTOz4vbv7hortpCrVQ5CigPndi1ZGdKO+dl6PHMVHiO5aJHN9Ejk0c3xlLpDNmzGDT\npk3MnTuXd955hwULFlBfX8+jjz5KMpkkkUjQ2NhIbW3tSZ2vr2/i9ly9XveEjW8ixwbnRnxZSS+z\nrF7ITKy/w3PhvZP4Ts9Ejg0kyZ/IGUuk3/72t3nwwQdJpVJMnjyZZcuWoSgKK1eu5K677sIwDO6/\n/34sFpkmL8T5JJQK8Ur36+i9Cdw9XuZY5pFb5USzmsY7NCHOilEl0tLSUp588kkAqqqqWL169RH7\nLF++nOXLl4+mGSHEWWYYBpt9W+mOd1NmL+WSnFnH3PcvXa/SEm0lsj9OX9MOesIx6lzTufTuakmm\n4oIgBRnEWdfb28vate+i6zqXXbbwgi2iMN6SepLuWA8W1UyhrXDEw+rrBzbwbv9aAPYEGzAwuDTn\nkqOex5fyYegG/o4oAGFTkMhAgoHGMIXTs8b+QoQYZ5JIxVmVTCZ55pkniUSGJqG1t7exatUXh59H\nFmdHU6SZ/973GI2RZiyKGa81nxlZ01mQN585uZfSGm0fsX9btH1EIk1G0/jboljdGjWuyWxJbkM1\nKSioFCWHShBqVinlLS4MkkjFWeX3+4eTKEA8HsfnG5REepa90vUaTZFmMAyaos0MJAfJsmTzZu9b\nlNlLKLIV0hJtHd7/o0upJcIptqxuIhEaKsRQs2QmeVNzGZg7SN+zOuYuJ65LbORWn9xzrEKc6ySR\nirMqOzsbt9tDKBQEwG53kJubN85RXXhSh6oR6Yf+M4APb+yG0mEWeS/HwKA73k2pvZR5uXOHj+35\nIDCcRAHaNg6weMEsNIfKG9ouMkUG4d44bRsHqJiffxavSpxr1urvjur4k3sGZOxJIhVnlcVi4c47\nV7Bu3Vp0XWf+/MtwOBzjHdYFZ4l3EQcjjTRHWnFrbsrtpWSZs3BrLkrtpZgUE0sLlpBO6uz5Uzvv\ntTTg9Fqpu7l8eAJRKpahd08AwzBwF9gprMzC0EE1DaXk7t1+SaTiuN6PLh7dCY5dQvqskkQqzrrc\n3DxuuOHG8Q7jgjYr52K+73iArlgXWZqHwaSPpJGizjMdh3a4UlHbxn5aN/Uz2BjGMCA6mGThl6fS\nfyDEB39sJ5PSKZjmwdcaQTNGjolaXVJTW1wYJJGKs6ax8QDPPbeRcDjBkiVLqaqqHu+QLmheaz5e\n61CPsZLKo+4THUzQ1xDEyAyVTGzbNEA8kOSi2yuIB5KE++IYukH//iCZcAbFpmB2mHDm2fBO89C2\neYCcSicu75GLiQtxvpBpdeKsCIdDvPDCc3R1ddHd3cULLzxLNBod77DECeRWu+FwGW1cXiuJcBqA\n8rn5qKrCYFOEyEACi92EqqlULiigcEYWDa90cuDNbrasbiTQKb9rcf6SHqk4KwKBAOl0Guuh8bVk\nMkk4HJbx0QkondQB0CwqhXVZ1F5TTP++IGaHRt4kF+6ioVu/xfXZOPIsbP1dE1mldmweC5FIEn9/\niE2+zQScIarjNeSk8+jdEyCrRH7X4vwkiVScsmQyyUsvPU9LSzP5+V5uueU2srKOP+rv9RaQnZ1N\nKhUDIC8vj9zc3LMRrjgFLev7aXq3B4CqRQVUXeZl/udr6drpw8gYFNVno1kO38gyaSoWpwlfawR3\n9tDt2/X577AveZBYMkmLtYlr/DdgcRaOy/UIcTZIIhWnbMOGdTQ2HgSgp6ebv/71dW677fhlIC0W\nC3fdtZLGxj0EAjEuvXQOmiZ/fhNJzJ+k8Z2e4e2md3spmOrBkWulfM6RjyjFAkm2PdFEOqGjWVUS\n4TTT7ihmkzFAns1Nb0OAVCyNXhulbI58aRLnL/kkE6fs78c2jzXW2dnZQSKRoLy84tDi7m6uuuqq\nCb3KxYUsk9JP6mcfCnbESCeGXncX2nE4LXirs8lpy8aHn5KLc1FQmFc5HZMm0zHE+UsSqThldXUz\n2b17F+n00KSTiy66+Ih93nzzDTZv3ghAaWkZd955l/RAJzhnvpX8Gjf9B4a+6ORNcuGMdqJv6kep\nqEQpLBqxvyPPgqKAcWgykj3LgmZRua3sFt7sfYt4Js4lObMothef7UsR4qySTzZxysrKylm58nO0\ntbXg9RZQXl4x4vVEIsG7775NJBLG6XTR0dFOc3MTNTUTpQ6JOBpFUai7pZy+hiCtG/qxHNxFaP1G\nXAU2DJMJ9ZMroKyc/X/tpm9vEFu2mapFBfTvD6FZVObeOZn+3jBKxMKtpbeMGEsVYiLq7Ow87usl\nJSe3oIYkUnFaNM1EdnY2OTlHjn11dnawY8c2MpkMqqoyfXqd9EbPEaqq0LPbT7g3jnPXTvpDIUxm\nlUxKJ/zke4RmXkHXLj8wVLheURXmfGYSAP0HQ2x+rhHDAHu2GXexnUhfAk+xndqrizGZJbGKieXu\nu+9GURQMwzjiNUVR+Otf/3pS55FPN3Fc8Xicvr7e4Rq5AAcP7ueFF54jk8lgtztYseJu8vMPl4Lb\nuXM71dWTOHjwALquo2kalZVV43QF4lQFu4ZmVmcsQ4+rDDSFSccyBOIZWlo6sDg1HHlWUrE0B//W\nRdSXoHRWLv694eHbvJ07fJj2BPEU24n0J9CsJmo+VnSsJoUYE7fddhsu19DiCWVlZTzyyCMjXn/z\nzTfPSDuSSMUx+f0+/u//fkc4HMJsNnPLLbdTXT2JDRvWk8lkAIjFomzbtplrrlk2fJzFYqWwsIi8\nvHx0XWfWrEtHrHUpJrbscid9+4L4qi9DS8Ug4SeaV06w9CIccQj3xnDkWena5SfuTxLuTdCypo+y\nulwMzSDcEyfQESW74vCKPpGBxDhekbgQJZNJAH7729+ecN9AIMBPfvITWltb+dnPfsaPf/xjHnjg\nATwez0m1JfdaxDFt2bKJcHho4kkqlWLt2vcAMJmGiip8eDvEZBr5fWzRosXk5eWjaRpFRUUsXrzk\nLEYtRmvax0spn5tH3sUl5D/4ZRKf+Sr9067GMGlYnBp1N1dQvbgADAPThwU2ImlMNhP9+0MMHAyB\nAsHOGJlDxR3yJrvH85LEBWjv3r1Eo1FWrVrFPffcw44dO46574MPPkh9fT1+vx+n00lBQQHf/OY3\nT7ot6ZGKY1KUkd+zVHVou6ZmCi+99AKRSJgpU6Yyb96CEfu53R7uvfcLxONxbDab9EbPMZpFpebK\nw7dhs8ud6BmDSH+CnEonNR8ronO7j0QoTaQ/jtmhYXFp+NsiJEIpcmtcuAvsRAcSeMrsVMzNp6hu\ngizTIS4YNpuNVatWsXz5cpqbm/nCF77Aq6++Ovw59lHt7e3ceeedPPHEE1gsFr7+9a9z0003nXRb\nkkgvMAcP7ufAgQNkZ+cwZ87c4d7l0cybN5/GxgMMDg5is9lZsmQpAOvXr6W+/iJSqRQWi4Wurk5q\na6eMOFZRFOx2+1HOKs41ZruJmTeXD29n0joH3uym5JIcOrYMkopliPYn8LVFCPXHSScz2NwWbB4L\nU68tIavEQbg3zt4/tZAORimaV0rV5QXHaVGI0auqqqKysnL4/7Ozs+nr66Ow8MgqWyaTiVAoNPyl\nv7m5+agJ91gkkV5Ampoaee65P5BMJonFYvT2dnPjjbccc3+Xy80993yeQCCAy+XCarViGAbxeAxV\nVbFarQBEo5GzdQliHKUTGRpe6yLYEaF/f5DcSS6qFxXQvnWAZCSNooLZrg6Nme7ooXd6Cy3bd3N9\nzhV0/6aBrI1/QU2niGwtZiD/C+RNyRnvSxLnsWeffZZ9+/bx0EMP0dPTQyQSwev1HnXff/qnf2Ll\nypV0dXXxpS99ie3btx8xMel4JJGOo0DAz8sv/5lAwM+0aTO44oorx7S9lpZmQqEgu3fvJp1O0dLS\nxPz5l1NQcOzegWEYDA4OEA6HqKysQlEULr54Flu3bgGGbuNOnizPh14IDvyth949AQBUs0qgPUp2\nuZOsMifRgTjooJgULA6ND+o20EUX29/S2d61i3u3pVHTKQBsgS7SW7bClKvG83LEee6OO+7ggQce\n4K677kJVVR555JFj9jIXL15MXV0dO3fuRNd1Hn744RFPIpyIJNJx9Je//Im2tlZgqH5tfr6XurqZ\nY9Zefr6X9vZ20oc+0MxmMxs2rB3RK21vb2PjxvWYTCbmz7+M1157he7uLgBmzbqEa6+9nquvvo6q\nqklEoxEmTaoZnl4uzm8x3+GZtzkVTrJK7Uy6oghfS4SDb3WT8qeJBBNkjAzdShepQ8ut9bcECSVi\neBiacKRqCh6vLPotxpbZbOanP/3pSe2bSqX485//zMaNG9E0jYGBAe64446Tnt8hiXQc+Xy+Edt+\nv+8Ye54ZM2fWM2NGHdu3b8Vms1NdPQk4/IcSCgX5wx+eGp42vm3bVtrb24hGo2RlZaHrOkuWXInN\nZpMqReexZCSNyaoeUR83b7Ibf9vhuspls/PILnOQXebAmW/FklFRshR2v9SBfZ+LJH5Us4qiKiRq\n5pGrt6MndRxVXmwLLjnblyXEMT388MOEw2FuvfVWDMPghRdeoKGhge9///sndbwk0nE0ZcqU4Vuk\nJpOJyZNrxrzNz352FXa7g0gkjMvl5vLLFw2/NjAwMJxEYWhi0uDgIJqmEYmEsVqtUqHoPJZJ67z/\nXBuDzWE0q0rdzeXkVh2+21AxLx+LUyPcEye7wkl+zeFHWgqmevB63fT1hZj/hRr6H72Z11rfIK2l\nmZmqI2/WXLKvcUIoACVlKDIRTUwg27dv549//OPw9pVXXsnNN9980sfLp+I4uuqqa/F6CwgEAtTW\nTqGo6OSKe7e1tbJx43o0TWPRoivIyztyiatj8Xq9fP7zXyQQCJCdnY3FYhl+LT/fi81mIx6PA+Bw\nOLHbHfT0dKOqKlOmTD1nEmmP0U3ECFOqlGNVrOMdzjmhe5efweYwAOmEzr7Xu7j43jJimTjZ5ixU\nRR16jKXu+OcxaSqf+MYCLtpQQ/+BEK58K9WLC4kmMrR8kMT4YICKefm4C21n4aqEOLHCwkLa2too\nLx+and7b23vMiUlHc258Kp6nhibunNotrmAwwLPPPk0ymSSVSvHYY/8NGBQUFPHd7z7EFVfMP+E5\nrFbrUScYuVwu7rzzrkNjpBqXXjqHrVs3U109CUVRWLx46SnFOl42Zjbwlj5UIzNXyeXTps9iV6QH\ndCIfFk/4UG+oj18eeJmUkabUXsLy8tuwqJZjHD2SqipUXeal6rKhD6N0UmfLbw8S8yeJ+ZN0bhvk\nim/OwOKQjyAxflauXImiKPh8Pm666Sbmzp2Lqqps3bqV2tqTH76Sv+JzTH9/P8lkEl3Xef31V2hp\naUFVFVpbW/nGN77CO++8TVdXJ3v37sHj8XDJJbNP6XmowsKiEZOPCgoK6O7uory8kunTZ4zFJZ1x\n6/U1w/8/aAyyx9jNpcqccYzo3FBYl03HtkHiwRSKAs01e0kZQxOGOmKd7PLvZnbu6Y1txgNDCbRr\np49UbKi85J4/tXPxJ6vOVPhCnLKvfvWrR/35vffee0rnkUR6jikoKPj/2XvzMDmq897/c6qqq/ee\npXt2jUYzo22EJLRLIJAEYgeDMWAbG7wlduwk1/llu7ZvkmvHjn+xY8dObOfa2PFN2AzGBhssQOxm\nk0AC7ftIs2j2paf3pdZz/2jRQkgCYcQ+n+fR80x1V506XVWq71ne833xer0MDw+Ty+VwXQchNCzL\nIpFIsH79ejZv3lrOFTo6Osqll17+B59v/vwFzJ+/4HRV/y1BwwMUy9seJiNETwVvSGPJJ9tJDeTx\nhjX2ZJ4B++j3LidP8v1a+Cp0rIJTFlFFUxg/lMV1JYoy6Xw1ydvDsmXLyn/v2bOHfD6PlBLHcejv\n7z/m+1djUkjfgfT29rBhwzMIIVi1ag2NjU3l70KhMB/5yMd4+OEH6eo6SCqVxHFchICpU1uOLG+x\nyWaz5PM5tm/fxqWXXk6xWOTBB9fx9NNPoigKjuPQ1DSFOXPmcskll72uXus7nYvVS7jP+S0WFm2i\nnTnizVtS9F7D41fLQUTneleyfvBRrKe8+PojmK0Bch80CEZffc7ZKjh0PjpEfsIg2h5m2soaNF1h\nzgemkBooRf1WTAmgB7VJEZ3kHcGXvvQltm7dSiqVoq2tjX379rFo0SKuvfbaUzp+UkjfYQwNDXLT\nTf8H27ZQVZWBgX7+7M/+Ap/vaGBGfX0Dn/jEZ2hrm84dd9zKli1baGpq4oILLuLss8/mRz/6CZ2d\n+5FSMjY2xtDQIBs3Pss99/ya4eFBOjs7y6nNMpkMTU1Nr3uu9p1MuzKDPxf/HwZFgoQmvX7/QOZV\nzkXdG6QzPkwwHMSMuxx4eJCF17e+6nH7Hx5kbH8agMxIEW/EQ/3cSqYsrubM61oY2plE1RU6Lmt6\n1XImmeStYvPmzTz00EN84xvf4BOf+ARSSr7+9a+f8vGnXUhfmf/t85//PF/+8pdRFIUZM2bw1a9+\n9XSf8j2BaZrcccft/OIXtxwxQJDU1NRRXV3NVVddTVvb9ON6jeecs4pAIEB9/e8wDINAIMiCBQsI\nh8P4fD68Xi/t7dPZvn0bg4ODpNMpuroOYRhFTFNw8OAB8vkcwWCAefPOPK58y7IQQrxrInVfjkd4\nJod0TwNeI0DEczSVlJmzj/k+3pWl68kRpCtZfNU01JhGbtzAytuYORs9pNGzYYzOR4ZAwPTz65lx\nQQNCFa+7NyrsJJ7UUwhpYYWX4XqbwcnhyW4BJFZoMajB1yxnkkleSW1tLR6Ph/b2dvbv38/ll19O\nLnfq1qen9Q15ovxvX/jCF/irv/orlixZwle/+lUeffRRLrjggtN52nc9lmXxs5/9hLvu+gUjIyMk\nkwmklGQyWSzL5Pvf/y7t7dO58MJLmDdvPsPDQzz22CMYhsGuXTuorKzC7/czODjA3r17mTt3XrkH\nOzQ0yPbtW/F6vViWRbFYREqJlBLXdbFtm3y+wK5dO8pzoYZh8OMf/5DNmzdRVVXFJz/5GZYufe1o\n4GZO5j4AACAASURBVEnee9R2VDCwdQLHKs2PNswr+eMm+nI8/9NO+l+IE4h5MXM2Bx8e5syPt+Dx\nqQxuTyBdietKqqeFqGwuCVznI0NE28P4wqfYyHENtOwWhGug5bYgnNLct1rsolD3abzjd6NY8dJn\n+T0U6/4IlMkG1CSvj7q6Om666SbOOussvvOd7wCQz+df46ijnFYhfXn+N8dx+Mu//Ev27NnDkiWl\niMlVq1axYcOGSSF9BYODA/T392GaJrlcFtctvbSKxQLxeOklYds2Dz/8IG1t7dx9969Ip1Ps2bOL\nzZufxzRNNE2juXkqHR0zaG1tJx6Ps3PnDhKJODU1tRQKBXRdJxQKUSwWcRwHj8fDwoWLCQQCGEbJ\n/m3Pnt18//v/wrPPPo2iqEQiYRRFYfbsDsLhU0tyO8l7h3Cdj8WfaCPRk8Vf5SXaFsIqODzytR0k\n+7JM9OSwdyfxV+lEan10PT1C9bQwVS1BjIxNPmkwfjCDL+JB86ukBwp0PjrE9PPr8Ve8xlIaKfGN\n/RLF6AdpomW34XinodhxEB7UzI6yiAIo1gSKNYbrbXyTr8ok7zW++c1v8uSTTzJ//nwuuugi1q1b\nx9e+9rVTPv60CumJ8r+9lPwZIBgMkslkTqmsmpp3diLg01k/162lvj5GMBjAtm2EEEgpUVUVVVWY\nOrWJYLAU4KHrLmCRTI4Tj49RLBaxLAvLsujqOsTNN9/Meeedx7x582hoiNHb21s+T2trC7FYNS++\n+CLFYpGOjg4WLJhHOBzmnHOWEY+P8+ij97Nz53ay2SxCCAyjyIEDewmH9dP2m99P9/Z081bWbeJw\nlqHdCfwVOmdeOBVFVXAdl76tccy0hV0oBbk5tkturEg+bpIeLjLrQkHDjEp6XxhHFQKpKsQPZJCu\nJFDlJT9QpPO+Ic7909n0bhqna8MIRtYmFPPSOK+KlqU1+CM6WGmYGAPNC1KHogRzF6h+QMGndkIo\nBLLkHY3QCNY1gufk12jy3r6zeHZ84xsr4A22mQYHB8t/L1y4kMHBQdauXcvata8vocJpFdIT5X/b\ns2dP+ftcLkckcmq9mrGxUxPct4OXrNBeycREnG3btqLrOkuWLDsmQOiVSCl5+ukn6e3tIRarYfHi\ns1i37gE0TSuLqcfjIRgMk8sVEcJzxFkoREVFjHy+k0KhiBAKQggsy8a2bfr6+shkCjz33AvMmzef\nXO6o0fgZZyzg5pt/jpQQiVRQX9/E8uXn0NExF8MQ7N59kFQqi6KUcpQ6jovrSjTNQzptAG/8npzs\n2r1TeCfX762sW3q4wNbbu3GdUkO4/0CCGRfUs+2XvUx0ZxjtTGNkrJJVswQJICX5pEn386MoPkF2\nooge1Gg8o4K+FyYopk1s1yU+kMOft3j8pt3suruPid4sdtHBX+mhrqOSpkVRln6qHY/XxV8QCLc0\nnKsoDShuEqSGozch0xMUo9ehp38PUmJVrsFJwsme08l7+4fzZon8ytSbm/HqtbjhhhvKHZeXeGlb\nCMFjjz12SuWcViF9Zf63bDbLypUr2bRpE8uWLeOpp55ixYoVp/OU7xiy2Sy3334rhUJpXL27u4sb\nbvjkSSNGX3xxM889twEozWPW1tbR3NyMx+Ohq+sghmEwffoMVq8+n7q6elavPo/29ukIIbj22o+U\nk9OmUimGhgaPBG4IstksxWKRUCjEOeesxuv1MTw8RFPTFIaGBslmjw4dj4wMU1lZTTBYmr9qbGzE\n5/Mzdeo00ukUUkJjYxOrVp2H1ztp5/Z+ItGTLYsoQLwrQ2inj8xwgULSIljrxXVcEAJEyRVJAEJT\ncEznSIq1AEbOZmhHEtdyCVR5wYV4d4aW2hq6nxwlPZSnmDCxDQfHdPFXFYhlLNJDeSKNAai+Bl/6\nUZAWZmQFemYTyNLz6+oNuIHpFAMn9qgWdhJhp3D1OlAmn99Jjufxxx8/LeWcViF9Zf63b33rW1RW\nVvL3f//3WJZFe3s7l1xyyek85TuGoaHBsoi+tJ3L5U6aYmx8fPyY7dHREVpaWsnlclRXR3Ech3PO\nWYXH46GtrZ0ZM2aW9/X5fFx44SWcc85qfvnLX/DDH34f13XRNA0pXaSUXHjhxYTDYc4//+h89D/8\nw5fRdf1IgFGe/v4+Dh48wLRprWiaxpQpzcye3YFhFKirq0NVNZqbm7nssg+Uk3hP8v7AKjhkR4v4\nK3VUXSEQ9SLdkrC6tovu16jtqKRmRpihXUnG9qawCg6u5ZAbk/Qkx1BVBX9UJxj1IhRBbGaE7EgB\nzavScUUTid4sjuUiX3Zex3CRruTAI0MkenP4KjwsvuF6KpoCAEh9ClpuG1IJYFauOabOan4ferL0\nYrR9bXgym1GNXqTQKTR9EVj6Fly5Sd6PnFYhPVn+t1tvvfV0nuYdSVVVNYqilHt7JcP3k/u7trRM\nY8eObeXtRYuW0NPTdSRF2UxyuSw+n5/a2jqWLz/rhGX4/X6uvfYjbNnyIr293QAsXbqYP/qjPy17\n8Xo8RyMY6+sbmDatle7uLmzbZurUFnbs2I6maVxwwcVs3Pgse/fuQdd9NDVN4UMfum4yXdr7kENP\njtC3OY5jusQPZZixtp45V0xB9SgM70yS7MuRnzAIN/gx8w6BqJfKaUHy4wbFjI1jOGi6hl10KKYs\nms9tINWVRhHQOL+KmRc3UjMjwtQVMVIDeaSbLfVYo15iM8KEG/zse2AAu+iAEEhbsvbv5gHgBGbi\nBI40Kp0CwhxBatUIWcQbvxdkyTnJP/Jf4JoINw/SwT/wb9DwIyaXzk/yZjD5VJ0mYrEYl19+Jc8/\nvxFd1zn//AtQVfWYfQqFAj093dTX15d9a0tzpDEWL16KYRh0dR3C5/MybVobhUKBQCDwqoYCoVCI\nr33tG2zY8Cy6rjNlSi233XYzUkpisRo+9rEby3O1a9deRD6fx+fzUyjkWLBgMQD9/f0AHDzYeUzZ\nhw4dnBTS9yEDWyaAkvsQQN3cSnyRUoOsbU0dEz1Z2lbXkYubpPqz5MYNxg+WxFACqkdB8yrYpkvB\ngJ39Lp5AhDMvb2bG3BDBWGl0Y96HWgjX++l8ZAjVo1DZHGTetVPZ/svekogCSMlYZ/q4OmqZF/AP\n/gghLezAHIzqD4BroRYOoNgTCHMUhILiZAGJQEKuG5h8nic5nlQqRUVFxTGfDQwM0NR0aqYhk0J6\nGunomHNSY/fh4SH+1//6W+LxOF6vl4997EZmzpzN6tXnlXuuPp+POXOO5qh6ae4yk0lz332/ZXx8\njNbWNi677APHmCRUV0e54oorAfjP//xReeJ8fHyMffv2sGDBIgBqamqZP38BkUiEeDxeLqOxsbFc\nTskMokRVVfVpuS6TvLvQvEp53SiAx3e0QZiPG3iPrAE1sjbpoSLJvhyOcXR/13axDRfbBdevoWYt\n5JpW9tlBFsSOThFousL0NfVMX1OPY7nsH3B5bJeD1HwoHgXXckFQzomaGzfoemoExzRY2PJDlNAo\nwjXw5nah5g+gmAMo1hggkMKDYg6DGkAKHSk8MLERf3odAhszfC52xXszXmOSU2doaAgpJZ/73Of4\n2c9+Vn53Oo7DZz/7WdavX39K5UwK6VvEXXfdUV4TOjw8zL//+79y6aVXUFVVxQ03fOqkw8CWZbFu\n3b3s3LkdTfNgGAaxWM0xCblfzit7wapausWpVJLbb7+5nGvU5/PR3DyVWKyGc85ZBcDatRfiug5j\nY2O0trayZMnknNL7kY7Lp7D73j5sw6FuTiU1s45G2ldMCSAUQSFhkBkpYObs8tzpS6hehYpmP+Qg\nX5C4nePYtkOxcgYHn0hSTFkomqCYtvD4VaafV09vSmHds6UIc6lW0HhGHdFiHl/Yw+Ib23AdyfZf\n9WJkLISTpT+lMGOxwCsmENIGJ4Ow4gi3gJA2rhLAVYIIwNVi2L7p6OmtKIZAK3TiSf6evP15rOgH\n3spLO8k7jB/84Ac8//zzjI6O8vGPf7z8uaZprFmz5pTLmRTStwjXdSkWi8Tj4ySTCSoqKpBSkkgk\nOHiwk3nz5h93zIMPruO2225h8+bnEEJQVVXN4sVLWbDg5L64V1xxBbfeege2bdPcPBWQ7Ny5HdM0\nyyIKpRbXNdd8+BhbQL/fz5VXXn1af/ck7z6qWoKs/B+zcB2Jqh1rG1nRGGDqsigv3tKF5lUJxLwY\nGQvHtEtrYBQQCtgFh4CmUMiZuK6E0Rw81knPrBC24TK4PYE3XHr9bLuzh7H2BobdADWzIqi6irVk\nKmtXaXj8GpquUMxYpeU2gFT8jMTPoKU4gc8vkYoXKXQUN49wDXDzqHYGV/GB0BGaheKkQZ+JltxY\njvrVU0/jhJfg6g1v6fWd5J3DP//zPwPw05/+lM997nN/cDmTQvoWcc01H+bOO28nn8/jupJwOMLE\nRJxoNHbC3ujg4ADf+96/0Nl5oGxVlc3mME2Dv/3br5Rt/l4Swnw+T29vDy0t9fzpn36RYrHAww+v\n58EH7wdKgWCu65b3j0Qi76mML5OcXoQQqNqJ5+ZtwyU2M1JqHCZNNK+KUEoRt6pPZeZFDWQGC8QP\nZQl6JULXqJqqoxg2tuFi5m3yEwapwTy4EteW+CIhih4PE91ZamZFqI+pxzgf6UENS/cwNlgkHBB4\nKs7HbmzAYHvJvMQaQAovRctFlQVURaLJIlJaCHsMxfSAmFcWUakGkWoE4Zy6n+ok7z1++ctf8pGP\nfATTNPnRj3503Pd//ud/fkrlTArpKTI8PMS6dfceWRu7nCVLzikHARmGUTabOJnBuxCC2to6gsEA\nrlvaTiaTNDe3nNC4Ydu2LfT0dB/Xi3Qch5GRYX7727uxbZsVK85mwYJF3Hbbf5NOpwkGvSxcuJyO\njjn09HSXj7Usi7lz5zM42I/fH+Ciiy49zVfo7SElkwzKQaIiRq2ofbur877AdV2SfTly4waFhIk3\npKHqOl6/h3zKQDrQvDSG5lMxMjZ6UCM2I0w+buAJqCiqoJAo+XJbR4KK6vM55raESOsaC2ZorFl4\nrH3gwX6HbZE6nNEJKLpcdYWC3zuGdCopmi5d6Q7ywxH89kHq/IOoik1NMIVHlQhpI+w0hwZtRsaX\nE/OPUd9Qh9DrcLxT3/LrN8k7h5cbMbwRJoX0FFm37l4mJkrRjJs2bUIIL5lMhuHhIZ5/fiO9vT2E\nQmG+8IU/P+H85cGDnTQ1TWFwcACgvEYzn89xxx238cEPXnPMWtFnnnmKYrFYXk4D4LoOuVyBhx56\nAJ+v1IvdsOEZcrksqVSqvN/mzc+zYMEiVFXFcUovKiEEK1acTSwWO/0X521iRI5wp30bBgYKCleo\nVzFb6Xi7q/Wew3Ulh54YJtGbQ/OqJPtzWAWb8YNp9ICGr1InN1bE6/fgCWik+nPYRYdAtZdZlzQS\njHrx+DTq5lYwtD1JPmEQmxFh7EAaRRO4tiQ9VGD2fJOOy2ppmHf8muVdXTZ4NdQzSo0lM38/wslT\nMCQ7DjkkjAIb9y0Dt4OPzLqHCm+GjOlQ4SuiCC8Js46+tMOmxNUowqFDcVm2bB4oxwq2Ygwi3FxJ\nYJXJtdPvdT760Y8Cp97zPBmTQnqKvDyljuM4/Pa3dxOJVLBp03McPtxLfX0D6XSKu+66g46OOcdF\nvFZUVODxePB4PCiKihCliLGGhgZ03cvu3TvLQrphwzOsX//AcXUQQuC6Nhs3buC88456QXZ2drJx\n47MYhsHUqVOYPn02gUCAtWsv4r77fsOhQ520trYzPj72nhLS7e4WDEoBKi4uL7ibmK10UJRF4nKc\nSlFFUEym1Xqj9G0ap//FUiMyebi0htQqOgjAsSWaKPUsPQGF5jOjZSOFSIOfzFARPeCh47J6AMIX\nlhqAE91ZimkT13IRqkK0Pczcq5qpnV1xwjpouSL2rgmErqK0VeE7on+jCRfbAUOG8Hh9PHFgPlkr\nynlTHiYWSNER66SeUcKkmeYxecS8gYQRhQmNZa8QSk/qWTypJwFwPVGKdZ+cdER6DxCPx7nmmmv4\nr//6L1pbT5xLd/Xq1YyOjpYtbNPpNJFIhClTpvBP//RPdHS8egN9UkhPkblz5/Hiiy8ApaEtXS/9\nJ7RtB9u2sW0Lj0fHdd1yJpWX47oSyzIRQjA6OkwwWMrCMj4+xoIFi45xQNq2bStSSnw+3xHjeOOI\nCOsEgyGy2Qx9fYcxDIOamlry+TxjY6OMjAzT3X0IVfXwL//y/+O6LgcPdjJ9+gx8Ph/r1t1LVVUV\n3d1dFItF5s07k2g0+tZcwDcBnWNfhF68TMg4d9i3kyOLFy/XqB9mitL8NtXw3Y3rSHo2jHHg4UHM\nnE243o/mU0gczhGMevFVekgNFLCLDqpHJTVUpKrNpuHM6tLSlSMUkib70wd4aORRHNfmrNgKFnx4\nDuOdGRzTJVTnI9oaIjYzQvezowxuTeDxq8y+rIlwvY/EUArvjm6iGZd4WqFSmrReswaZGkZT0xhu\ngBfjq1CDKebUdRMvVPLznZ9mzdSnEdJERA1qQykiSi8znZ9xU+ffUDAgmXGpDJfiBKTrkjz8NK7j\nUh1R8BBHy+/FDr13Et6/H7Ftm69+9auv6nsOsHTpUi655JJyZrInn3yS9evXc+ONN/KP//iP3Hnn\nna96/GS0ySmydu1FXHnl1Zx//gV84QtfKLdc2tvbCQaDqKqGz+dj8eIl1NbWHXf88PAQra3tnHHG\nPCoqKolEKqioqMSyLKqqqlm5clV535qaGkKhEKFQmEAgQCAQwOPRiUQiTJ3aQlVVFFVV0XUv2WyG\nXbt2IIQgHI4QCoXo7Oxk797dWJZJKpWkr+8wUGoA3HXXHTz11O/ZtOk5br/9FjKZ4xe7v1tYrpxF\ngyitgY2ICOepF7DJfZ4cWQAMDDa4z7ydVXxXYeZtEr05ikeiYw89OULvxjHsosPw7iRjB9IEqr1U\nTAlg5m3S/SURNbJWKdjIcsjFDc68diriSNLu/ITBwL5xfvrA7aRTGSxp8/uRp3khlyJ8aTvCr5EZ\nLKCHNJK9OXqeHcPM2+TiBrt+20exmGesK4Fr2UytdVnWoTArbOIJ1FBo+BMiHX/M/YOfZkeXwlz9\nV3xu5XNcvXA/82sPsKR+BwJJT2oKAvCIAs3hftobBdEK2LTXKv/2B5+32N0rODjgsP2ghWWDFK+R\n5m2Sdzzf/va3uf7666mtffX4ic7OzmPSe65evZr9+/czZ86cE3aMXslkj/R1MHt2qXtfUxPm6quv\n5emnn6SxsYkbbvgkyWSSaDTGGWfMPWE0bHNzM7t370TXdTRNo7q6mpaWaWiaxsc+dsMxkbtXXnk1\nvb09rFt3L4qisHLlKnK5LBMTE0yd2kI0GiOZTNDXd/jIEpoJhBAIIcrlaJoHXfcSCoVwHBsoLW9J\nJBK4rksul8Xr9dLX13eMCcS7Cb/wc6P2KQxpoKMfCf4ysZT9SIooMoqg7e2u5ruCXNzgqe/vJT2Y\nxxvycM5fzCbVX4oWN7I2uJLsaJGqqUGWf3Y66/9uO6pPwTJcrJyNogp8IQ+FpIke1Fjw0Wn0vTDO\ncz8dJidzDHjjZBMFWlfVsb/PYWz3AMEnJdpQioZ6DzvvPkwufuwLy8yW0gMGoh6EAClLcQJ5zU/X\noENrg07CqiUW7OeDi7/HosqH8SgOBe8lBIML0TQVbIFAYjuSrOFl33CUj9b/LdOjw6Sdi0D+Kbar\nsLvbJqZdzLzA/RiWzUBxOrWByfn2dzP33HMP0WiUlStX8pOf/ORV941EItx5551ceeWVuK7L7373\nOyoqKjh06NAxcSonY1JI/0BaWqbR0jLtlPfv6DgD13Xp7e2ho2MOyWQSgHPPXU0kcuy8kNfr5W/+\n5sssXLiYTZueK5ssCCG44YZPsmfPbn7wg++VP2tvn17O8uL1epg1aw6BQBAhBPPmLWD69BnEYjGE\nEPzbv/0rfX29BINBQqEwwWCQgYE+li8/67h6vFvwiqNDvPO9ababE0zIPFVKjuXK8aMDkxzP7vv6\nGN6ZAEruRZv+8yDTz68nPZgnPVRA86tEp4fQfCoer0a43odVcPBXuUx0ZVFUhXCtn+q2MI7lMt6Z\nZvdv+xg/kEbRFYI11STlBHbRYXyfjucRF3k4jWZZZH1QWe0hM1SgsjmImbNxcWlaHsZxLIJ1Om1r\nY4zuytCT0OkP1rD9iSIrmrazJLqXKQeGUasG8UTyeFSbs2rWcTg/nYPOlbSLX1GwHZ7oWUbKiDCz\ncg+ZQgXStWmQT/DsU/NJastBwpg1nQFzHnWe/fg8NsLJIrXJZPbvVu655x6EEDz77LPs27ePL33p\nS/z4xz8+4XTWd7/7Xb75zW/yne98B1VVWblyJd/+9rd56KGH+Ou//uvXPNekkJ5GpJSMjY2hqmr5\nZk1MxLn77rtIJBJMmdLMhz503XHj9ePj4+zfv5f+/j5qampZvHgJFRWVnHHGXLZv34ptl3qUZ521\nkoaGRioqKrn77l+SSqUJh8NMm9bK5ZdfSSDgp729GfARj8cZHh6irq6eWCxGPp/nP/7j36murmJg\noI9cLo+qamzdugVV1eju7uIzn/ncSZfvnA4cMUHCfZicJ4PXXoEmT83H8vUQl71YOAgEXqERVPPg\nnPbTvKtwHUnXUyNkRopUNAWYtrLmSNq9o7y0HKW8nTJpP68ORRMcfm4cI+cw0ZUl1Z+nkDARikCo\n4PN5iE4PE6zx4vN6qJwaIHE4x5bbu+nbPI6Vt6EArRvOxJg7wQV/tJBD6y1IONiKgpCCfFFSCVS3\nhZlzRRMbf3IA0yoy8KJNeIqOr0qhcX4VVbNrWf9rl329DrXeThY6DzO0Z4yFNb3EIoOM9NXR2DqE\nosC1y0cYV6fy2KPzeKG3hpFMJcP5GhbV7+LCtufw6xYHRhoYd5PsLzqoCrQGdjFN30pDVKHe24Mz\ncT9G7fVv3Y2a5LRy2223lf++8cYb+frXv37SmJC6ujp+8IMfHPf5jTfeeErnmhTS04SUknXr7mXv\n3lIi86VLl3PeeWt5/PFHSSRKLf3+/j6ef34jq1cfTWY7PDzE7bffwubNm0ilEgihEA6H+dSn/phL\nL72cj3/8k3R3H6KysorZszvo7DzA/v37WLRoKfH4OEIIZs/uYO7ceQghygmCo9HoMQ/NoUOdbNr0\nPD09XZimiaZpaJpWthBMJpOkUqk3LfhI4pDz3IHrFrEUA1vvIWx8FoXT2+J/zkrgIgkIDylZZJM1\nxiVCvqrx/3ud7mdG6dtcsqdMHs6hegQtK2qO2ad9TT2HN41jFxwUj8K0s2pQNYWG+VXUzIowdiSR\ntx13aZhfyZRFUbJjRVpWxEBIhnel8Pt1XMtl5z2HmejOghAouiBYr2MXJKGBRg79fQG/40PKIqZP\nR/cqeMKCaFuIOVc0Mbo3jTeskR90GdmRxkjbdFxVR+eWNLaj0LcngOUPUBUex5cfx+8ZwrQ0VI+D\nrlj0JRqpqNKpdhLUF25lILkQVXEYyNZjuxojuRjP9i1keu0TBPUimmHiFVkMN8S15+bxZT0o1hhq\n5hBadhtpZQ4JMZfqiILuef8+Q+92Tvb//0/+5E+46aabOP/880+4z9uS2Pv9zODgQFlEobSWc8mS\npWVDhYGBfrLZDMFgiFWr1pRv2p49u8lkMhQKecbHx8lkMgD8wz98hdHRYdasWcuOHdswDJN9+/bS\n2bkfwzBIp9PMmjWTq6++jurqVxe/iYk4Dz304JGcqQUcxylHBL+U3SUQCBIOh9+MSwOAJIMrUnAk\n0lZi4ChjKG7kFfvZOGIMRQZQeP1DzZo7G40iKcbpdOKMs4EJVeE69aPorzN4xFIOUNAeRAobn30O\nXmf5667PO4HMcOHY7ZHicftMWVTNef/zDIZ2JAg3+Jl+Xsk2zzZKadKmVsdI9edJHs4hbdACpSUr\nC69vZfN/H8IX0QkEdXI5E6vgoHoEelil9ZIYekABAclDFrlRg3qvynjUh8d2CUbDLJjvwaMJdv2m\nj0hTgELCYuJQHqTEMlwyPy5SO6MKIQTNI1kyTVMYLjQTnmqRH5e4LnQNT6OiKsWezGxihTSt7iht\n0STtVYdJ51vwqiYe1aY2kMCrmkgXor5xmsU2QvSxrv/j/G7bVC6se5oaeQCky1BxGv99zyBJrZH+\nuM7MqRodLRoXLtVR1UlRfTdxyy23nPDzb3zjG8AbT/U5KaSniZO1eBYtWsLmzc/T09ONqqqMjAzx\n4oubWbJkGVASsFQqycREnGQyQaFQBCSmafLjH/+InTt3liPOHn10PYFAgK6uLpLJJNu2baG1dfox\nybtPxOjoKLt27cSyTBRFwefzcemlVzB//pmYpomqqqxatQZdf/OiFCUutugjL/M4IoYmG1Hdmlfs\nY5DVb8MRIwgU/NYV6O7c13WeVcp53Osk2eWOolNBvWhkQPaz1d3CcnUFEpfDym/oltupES3McT+B\nwHNcORKTvOdeJKXIzoL2GJo7DVW+uXOutjiMo4yiuc2n7VyVU4Mkeo+ug65sPvHa2imLokxZFCXZ\nl2NkT5LK5iDhOj/hOh+HDvSTqkpgeySav3TfGuZVAlDRFCA7elSco61h4gczVLX58IY1NG8p+K6q\nVWU8Z9JkFZnZ4UOPhQlYJp4jVoRWwSEQ1RkZsRhPCTyKJKK5FMeLBCsMJnpy1KQdFtX60aZP4e6+\nT3Jl9D+RKYuJQoBn+i8lq4WYV3uI2dX3QiHLdR0HaQgtZsKoxavkqfanqPLnmFbZi6tGsfUxElmN\nmbEBdg/NYmT0g/zxGcME/B4e2LUK0xYcGnGJZ12EcLBsqI4oLJtz/DMzybuPDRs2vOr3k2nU3mIa\nGhqZM2cue/bsAmD58rMIhyPMmXMGc+fOR9d1QqEwfr+f/v6+spCGQiH27t1DPB4v+/Dquo6Uklwu\nS3//4bKQSgkbN25kYiKOYRRRVY2vfe3vaG6eeowr0ivRdZ10OkUkUoFlWaiqSiAQ4KKLLiEUZVaI\nLQAAIABJREFUevN6oS8nr9+DSgzBIK4Yw2feiEAnrz2AKxJ43FL9HTFS+q24FLXH0c3XJ6Ttygw+\nJ76Aa7tY0kIVpUAt+4ggdovfcYfzf3GkC2xmQs1xrvziceVIimURfQlXZN9UITWVHRQ89yORCFSC\n5kfRZMsbLrdlRQxVU8gMF6iYEqBp4cnT4/W9EGf3vX2oXgVv0MPsyxo57Pby7KzHQJNEzlKpNzyc\nq66iZkYEKSXta2rRvAqqpSAiCr2bRph5VTXC4+A6LtIVmFmH/FiRZH8OTddYsLiSGWvreeYH+xnt\nzeINe4g0+rEiQUZnNGEnBjBHMhTGoMYP3c+OEar1UR3VmFph0tBssdVx6Mk3YXsMnuhfzHC6mlgw\nTd5U8IgCtivxCodVbXuZ2lzFs7u9VGhJzmvbTEAkkDJHU7CKw8Em3EwYHBi32xngfNr1TkAg1RBF\n+0gDU7oouKRzk6/N9wrPP/88AIcPH6a3t5fVq1ejqirPPPMM06dP54Mf/OAplTP5RJwmhBBcccWV\nrFhxNqqqHONsNHt2B4nERHm7vr6x/PcTTzyK67r4fD4Upbq8PEVVVRRFwbZtpCzN8aXT6SM2g/3k\n8wU8Ho3BwUF++MPv84Mf/PikdTOMItFoDK/XS3NzM9FoDTfe+Kk3VUQdMYqkiCqbAIkjRhHSj1/M\nwXUNBAp5zwNYyj4AbKUXj/vKpMt/2PBZSIS5WL2M9U5JlMJEmK+cCcA+ueOIiJbY7e7i3BOcRhDG\n47ZjKYcAUGUUzX1zjR1MdTsSySNf1tn983qqZ23ii4+9cSEVQtC89LXnvo2sxcafHCA3VkSogppZ\nEfY9MEhvQxd2/ZHECeMayYVD1GgRTNOgUMgipaRmoZfm5nqGDqcZHxxFaDqOY6PoErsgKSYthrfk\nkJZEr1LJjhRJHs4zEhxgS/1WhKWw1rsG73ARKv2os2LIdBHpUZm6PEjv02OE63xUTg2SnzCIDj3I\np1tuYTDhoEqDZXWbGcrWsC/ezniukhcG59ASTTAlMkE+F+H3vY3Yrkn3RBstlX3Mr5lAKgo+Z4Bh\n+wwyRR8NvheYEhqgtqYaM3ABZy/38qtNrcTyMMXcxzWzHsGr2UyrXgLuRSjWCFINI7V3Z7T7JEez\nv9x4443cd999VFeX3tupVIo/+7M/O+VyJoX0NHMiC76zzz4HKSVDQ4NMmdLMsmVH59p0XceyLBzH\nQdM0GhoayeWyVFRUMG1aK2ecMY9ly5YTCASxbRshBNu3byWXy5dN7Pfv34vjOMflIgXo7DzAunX3\nEQwGGRsbpampmWuu+TANDY3H7Xu6KKpPU9SeBkB1m/Dbl6C6NTjKGAACDVU2UhTHmiUoMoLmNmAr\nQwhUfPba48o+VeYp82kQjaRlkgbRhF+U1tdWyFbg6HBORDafUK8FgoB1LZayGylsPE4Hgjd3gb4g\nyH+fGyO5owIQTOyEr9X+iq+NXvemnvclBrZM4NpHsqM4kmRvjuppIULG0XlsoUAllUgpKRSyuK6D\naRpIKRkeBqF7Cdbo5CZKQ72qohGdFeT57w+QGyrNTVrFHPm4weGBQV6s34CLi1V0uD+5jqu3XI84\n6CJmxFCnVhLxgT+kUjc3QqjOS6I3Sz5RxH/ms3jyWaoDXkJanExRw+8p0lrZz87RGYzkYgznm0hP\nyZHJFNFEEZ/uIJw8//j0F1nTuo3rl+yg0mdxUftz1PX10OR5kYb6KD6zEVubQ1PbB/lsoySTtQkM\nPI5lulSGNcLqC9C//cgFUTGiV+JMrjl9VzM6OkplZWV52+/3MzY2dsrHTwrpW8BLc5AnYtWq87j/\n/t8xMNCPbdvU1dXS0TGH5cvPKpssLFy4mMrKKsbHx9m1awcVFZWkUklUVcXr9eL3B04oogB79+5G\nSkljYxMNDY1MndrCihVnveHfJHGwlP04YgRNTin3JiU2Re33FLWN2KIPRJGi/Rgedw6a24xfqUWY\ns1BlDM1twVST5TI1tx2Pcx6Pyrs57I5Qy14uVlvLIvhyXPJIkUWR1YiTPMYxESMmjm3YrOB6Rklw\nUO4kJpq4TJzcrFqgorvH54k9EaayHUs9hOrG8DorEZz4frwafusCkjse4A/tiZ8OqtvCjO5NYRVs\ngjU+Fl7finKPIDOSYqSmnznN7VykXgKU0vg5jlPOoCGlxLYNGs6oJjmYxjJNAjEdMy7w+I5eDyEg\n1KziNKdQTImbBTNno/pdcEzmtfnI6AY1N7YQGU7guEVmXNWMVXDYdms/NQ1BDLcS19WIhktLV0zp\nQREFdKVAtb+WO/asxRU+DL0fzCTVvgnqfX3UNgoGCu2MGc082aNw5ZkHqPCYrGo/gGokgSwWjahm\nPwBBnyCoQyDvwpF7qhjDoPhxPTGQDnryCQqTQvquZs2aNXz605/moosuwnVd1q9fz6WXnnqGrEkh\nfZvJZDJEozFisZJbUSaTpaIiz759e5k7dx5r1pxPZWUVAJdddgXJZILW1jYsy6RQKBCNRrn44stO\nWn44/LLehBDlst4IDknS3u9haBuQFFBkDUHrw4SsjwECS+nEUXqR5JHCwFR3obnNOGKcCvHH2NLA\nUDdjKZ24IoHmTMMVBQrag2x1LXbYBUCQZD8eV+dy9QPHnN9SDpH33IPEQpUxguYNKAROqe6qULla\n/I/X/Zu73S72yj1EiLBcOQuPOBpsYip7yHvuP1I3kKKA3774dZ+jtBTo1QW4yz3EuBynRWmhTtS/\n7nO8Gk2Loox1ZvD4VKR0qWgK0vXUCOFaPy2HpxOVNXToTQQJIYRA133YdmkeWVGUI77QBVRVI1Tn\nR1GCSOniUaCyKQSOgl10qGzzEazTqWmZSrQQIpMoomiCcLESb8qH8AqmzfIyZ20ljhMikzna2Kpq\n81OIWxweXYXXk6IqOIzpBqnyDVMZjGNYksUN+yg6j7AjsYLuiSZmhEdoi3TiuC5pO8yMyoPcue8a\nDqSzzJg1lRn+QYRzxCpTlAKjXL2RXFEynnSpjqjo4cVomRcBkFoVUnlZ4+59vLTqvcJXvvIVHnro\noSOZvQSf+cxnWLv21EfEJoX0bcI0TW655f9y883/xfj4GKZp4vf70XUdXddpa2vjs5/9/DFuQyU/\n3TALFiwkGAwwPj5OfX09H/rQtSc9z9lnn0MymaCr6xCRSAVnnbXyDdXbJU3K+y2KniePRNequDJF\nVr8Z1a3D65yD4lbikkGKLODiKH3kPevR3BbG3AiOOJeC9ggAiqzC1F5EddtwMRlTDuCI4JG5VUjI\niePqUNSeKAcCOWIcU30Rn3PuG/pdr8aA28/dzl24lIY9J4hzpXp1+XtHOXzM/rbS9wefa9rqKD1P\nxo9+8DK3yc3GZn7t3AOA6qp8WL2eZuX05dP0hjSWfLKNwoTJzt8cJj1YYHhXku79gyTnD0BSMnBf\nP+7HbVaoZ+H3B5noKlAsGESaPOTz+XIPVQiBogiywxap/gJtF0Y5uB7y4waKqlDZ4ifsCfLxWdey\nvWc/rqtTOVGHIgShWh9ta0pBXS+NyrzU6+242Effo8Pkcxp7tS/SlapmKo+xwP4F0udS6ctS68ny\ngY5NLMkP0JtuYiLnw3YElvTTmWjFdi0c22FRbCODXX001m+lMqwhPZVY4ZU4wQ4G5CruXFegWMjj\ns7v48PIsUxqW43qbcPQGfOO/RjFHQPFgVr561Pwk7w4uvvhiLr749TeAYVJI3zYeeOB33HHHbUxM\nxLEsE8dxyOdz1NTU4PP5CASC5ZyjQDmrTGtrG/v27WXmzNnMnAmrV59PXd3JeyZer5fFi5fS29tD\nIjHBXXf9guuvv+GYnuprIXERR97oproTKfIgVaSwkBgIYQFjZPVbyfErXGkgKQDyyD8TRwyhUI3t\nxjHV544p3xFpFPKAj1YlygGRKB0GTBcnikZ+ZTLe05Oc92T0ycNlEQXocXuO6TiqbiOoW47UxMYW\nfWQ8N6PJJnz2eccN80pMXDGBkJHjetKf+tX5bP7lAR7937toX1PDh2862kDYam0t/+3gsFfuppnT\nJ6TZsSKD2xPgQm7cQPUoWAWHgltA5hSE30HGVXplNys4iz2/6+fFW7twTBe9QnDuV1rRKlxs08DN\nW+xdl6LvmSyu6eI6Et2vUT+3Ei0I3U/EqWj24xkJUvX71vLz1XZ+HVOXHB2OVxQVvz9IoZBHseM0\ne+6j7aJxFHOMnfF5bO/5I7oL1VQGayFSQCII+0MM2WdiuUNU+9P0JWNYRgyheXGlwlC6koZAN9Ic\nJTGRoEfRmRuwcMLzeX5oGbnkAIPx9Zi5GajWBLab57kdWW5U7saMnI2svpxi3acQ1gRSDYJ6aqMh\nk7x3mRTSt4kdO7YxPj5+xLBBUlVVRTQao719OjNmzOSqqz5UXgbzyCMP8fOf38T4+BjTp8/koosu\nYWRkBI9HQ1EEruuWjfJN02RkZIS9e3dz+HAvHo/Ovn178Pv9KIpCMpnkhRc2H5PPNJVKMTDQT3Nz\n8zECK3EoaPdhqnsAScC6GoEHRdagyGqgGzABgZBhpCjgiBFUpwlBBZIjw2XSBQIgXBwyqPJMXJnG\nFemSSEsVU9uKkCqtYjbXWZ+inyK1opEzlLk4Io6hbsIR/XjcuXjtlRQ865DYqLIK3Vl0Std8n7uX\npEzQqrRTJ159GUtpCUppyK5WHJs54pXbujsfaRcx1E0UtMeRIo8l9+Bx5iKkhs9ZU97XJUVWvw1X\npBB4CZrXocljxXDpR2ay9CPHNyAi4tjGT4jTF3VtZC223dGDVSz1KBM9WWIzwvgqPHgVHSNQaqyI\naRY1orSOdO+6ARzTRSLJxy261k8w45ogMpfn4Lo43c8YjO0p4hYcVA1Uj4qme2haWIVZcHhog2B0\nq0GDIamtgr4+kxf/c4i5RpC1y32YhsmWrXs42A/BqilcMHsIj5tAy+8GKWnzJqmxpvNI3wrSkTT9\nmV0IaVLlzxOr0gjbw2wZmsEzfYvxKhmaqgocGGvg8a4lLG3cgeuCImw8ikU2neDJrgDbBvqQWgWH\nRiPURYYJ6xmk8KJbfQgrgVY4iDL+awoNn0fqNSe/oJO8r5gU0reJYrGUS7RQyOM4LqFQiO9+999Y\nsOCoKHR1HeKuu37Bb35zN0NDgxSLRXbu3MEDD/yO6dNnsGjREhKJBPF4nEsuuYx9+/bywAO/4dZb\nb2ViYgLHcYhEKpg5czaxWIyZM2cBlIfJoCTo3/rWN8nlslRWVvKVr/xvZs/uQCIx1W2Y6m5MdSeu\nSGOq24kYX8TjtmEpB9CcDgQ2jjKKIn0oshJXjCPwobl12IoNwga8CBkE6aJSheosxxUFbHULttKJ\nKzIgJYqMIWSIRn2EGqUTRUawLB9p778dmY910NwWgtZ1jBTWspnHsV2F1coY05TQKy8xAC5ZbKWX\njfZenncOALDBfYaPaTdSLxqOvy9yP2n9LiylB1VG8dmraOUcLuRi9ri7S+nalOOH8rzOMixlNwoB\nHAq4IoujDOIow8d4/Rra80ccnkoGFEXtKULWDdiiF1ek0dxpKCcRyEv9lzKUHmdcjjFNtLJUOX1O\nS+mhAlaxVFEhBBXNQapaSnOhMy6q5/HsY+wObiewGFq5AgDliGWeQIAQaLqKlkjiui6jPRa5hIM0\nLHBLfr8Cl2KqiJl32BD3sT2tEhqGylGLpmCB3l6TfMDPM/8xwu+3VPLczjizw7uZVtHHSC7KP/78\nAla2X8DfnxenLTrK/9lwAT/fspB03uGgbzZ14XpGsrWEvVk+d/YzeCyVhw8txuNRCHkMJtwWLE8F\nddWC3RPzOLNuDz6PTY1vmFTWy8Z9OhMFC0vTiQULHBytwKuFmB6Lc860XeQNBdcbwutaCDuJVN+a\nNdiTvPOZFNK3iXPOOZd4fJza2jp0Xefiiy87RkQB7r//d2SzpfRpL1kNAhiGQWfnAaqqotTV1dPV\nVVrr+PDDD/L4448zOjqGZZVMyBOJCbq6DuL1lpZuVFRUsGTJ0nJZt99+C7lcKX9nMpnkV/fcxF98\nbTpSZHExsNTdOMpAqceJhaE9S8T4nwipk/fch6OMo1CJFBlsOYRLHkvdiea047XWYKibcJUsYKHK\negQKBc96bKULV4zhKH1I4aLICFKaSJHGUjoBcEWajPcmTHUPriil2LKVbibEPu5zDuGiIxnm1/J7\n/LFyJRF3xTEiVOr93YwrsmxzN+O4DahyCjY2h3iSKrUV1Z1SNs+X2CTcX2MpXVhqJxbgihxSmCy0\nL2ShsvhV76kUNoqsxBEvzXFKNHfaaz4LhvocBe1xABQZJGR+AoXjg8IqlApu0D75muX9IQSqvQhF\nIN1SIysU8zL/uhYURZCRafLWMMuLc/GaOhvdp2kINLL00+089a97MQsO1S1h5l7VzIFbNpAdzJMb\n82FJG0W1cBwPigaBiENFrYMtFMYTDp5CimQ0hFI0MA4nsXxeUtURpGHz4DMZ5tfv5MzaPbhS0J2Y\nQn1wkKcPzeRf1SuYW9fNLdsuZiztRTomfYUqxjIBpBQMyjq+/eRHaYiCmR8n4FPYOBAg4ity5pT/\nx957B1lyXWeev3PTPF/+la/qrnbV3qHR8IRpgDAkSFAESQAEQFCUoJFmQtqdidiVQpqd4W5oqd3Y\n1Wq4EkcaxWokUfSggSFAEL7hTTfQvrvam/L++ZeZ99794xWq0Ww4SSSIIOuL6Ih+9V7mvZX5Kr97\nzj3n+0ZIODlmpJ1/2vc5Xh9ZS3Wd0JgskCvHmC6nqVrLKyd7aK8PiLe1UnHiHJ1q54zfRTDu0d+X\nIu4tRKMLOIsFIv0l4eqrryWfzzM4eIaOjk4+8YlzFTSstQRBlXQ6g++fL0cWRZpyudYk39TUhLWW\nMIzm1JHO7ucZYwiCgMsuu4LPfvZ2mpqaz5ECtNbieobVF02TaQxpac2hJQsENRKUGYzkECrE9FZq\n1S9CTF9Gwf8GAI6ppXpDOQISYahiJMKnkbi5kop6FK1yaEYos4vAOYZr20FCLBot4xgZRiSDG3ad\nk1a1NgQESwmIAJ+cDTHEAEvo7KFKkSnnGZR7mEzwJWROzzd0DqBllkidIKEmmFEzJHQ3Dc4ozbFh\nynIMQZEMb8Uzy7CEWBvWIuR5hGg5877uaTy6AiNTgIANSYa34f+MPm8s2kqoBuauaYx4dAUl74Gz\n90uKBM4B4vrS9zXmzwup5hhrPtHNqVcmcDzFsqvb5x1iZuwMrZUs9VEttRwP4uS8WbovaGXbf1xH\neTqgc0Mjk7vzFCvNMDJCXbHAkJ+luT8gLES42qG7v0Lb5m5yBUgWizSOFTFL2pnuzVIfizFb+zqT\ndibxVQONsdn5RqDZIEPCraIlwe6J9QzMrmaikEFri1gwVigGCZQyOGLJFwJWL0kxGLSy+4yhUoUo\nKPDkvha66idY1zrD7pGlDEwtZvf4apY3n2JVywBnTnRzLLeIsk5hfI+RGYeJ3CKq6o+4sONVBE1h\ntIEru/aik/0LUem/Es9P5X7ZU/i5YIFIf0lIJBJ87nN3vOP7IjKv07t58xaefPJxwjCcfy8W87nk\nkktZtmw51113PSLCpZdexv3333cOkVrLnEKSJp1On6en+9nP3s5PXvgPtPQUifk+l2yLoWW4pj8r\n4Oq1WDuEkVKtXzRaRdl9BLF1xPRFWCkgNk7gHECrYZAAKxrNNGJ9FC1YeTOa1kR2AsfURBDE1GGd\nPGLjICFikzUOstMITRhr2R/0MxjtosN3WOKCsk202g4SzhFm7TSGWZpVPfUSx8gMWsZwbCtl96cE\nzk6qzktYsVwVi/M4OQKTZ50Xp21uv9FiCNU+PLMMRQJPVqPsSTQjKJtA2Xoc+/7EKzyzknTQimUW\nZdtRnN//qmggE9yLlkmUrUORQogDZ8m79voXgwFziGdMLfq9Ul3DCtU//152RR3ZFecXoWWllXpT\nz5tFXQkSJKI4+x45w/hA7UFYGKuQ8D1IZ5ALttBcDdi2rIXEkhWkzjxLc2aIjt4hHvmbHMWZBvqT\nSxgghY4q9K9PsG1ZA9/86zGGyooVzcNctX6YF4+0I3IIARrieQYLi2jOaBJulSWdcGbKUgkUguCr\nkMC4YEFbhbGa/l4HY2DglEFrTSX0sAinZlqJO6dJOjnq/AKn851c0vkaO4dWkfBCtNOAg1AOhHJg\ncJTg+kkOl69kTfInLHK340872NyLlNu/CM7bbyss4L1xWetv/bKn8HPBApF+iHH11dtYvLiPG2/8\nOOvWbeCHP7yP6ekpMpkMd955D3/wB+cazq5du45sNsvw8AiFQh6tNb7v0dnZSRSF7Ny54zxhiC1b\nttK17lZmi0doaGzETQ5jKdaIwPp4tgP0YsQ6JKKPU/EexEiJUB0A66FI49lleNFKqrEX50gzBClR\ndZ9C2XawdUCAlQBDibrwbqyaIJQBrOQJ1QmQCCGO4OPrLVjdy38Nvs8es5e08miN2kn4S1mm+jDe\nXj6lutkdVTGOZrOsxhGF4KBs3RyJ7gFcjJoFq6iXJm7zLyChtmEpEbB3/hoIZx+EjepWysEiAvU6\niMGxXcSjj7zt/dEyisXMpaxrsZNjm4B31rKtjefj2rP7s4nwY5S8+zBSwDP9+HrDe301/tnYZ/Yy\nZM7wnHmWlNRE6x/S93Ov/B5peWciOGoO87J5ibSbojXIkpAEndKFzql5EgWYOJxnzTVdsA9wXCTl\nsvTCZlr764AVJAa/ytjeFLFYGRchGYyztdtjy2/HaVsRJ5FIs35TPa998yTRiVnc4RIbtrhUGzdR\nnwyoG+mgfqyD5dkpUvlH6as/zVVtbTwysIXTs+2cHE9gAkVkHASIORUO7J9gOmgn4YYEoU9oHFyl\nMUZRCV0qoUt9so4bl73AeNBFVWXpbtFUEgWGZ+vIlSxKYHmPotN9mQsbXqA/9SKNTcuADkTncSrH\n0Kn3J9qxgF9dLBDphxx9fUsAWLduPTfddDMnThynpSV7jszgW5FOp0mlUvMp3XQ6BQhhGL6jQ01j\ncgOJujl3ENOHr7fgmixaCkTOfsTGiEfXEzr7sVhCdQQjRYQErl5HPLqqJhnn/YRIHQcJapGlRFgz\nTSxag1FJrAQkZC1Vbzvp4B58LkIkDvIYkZzBsU2ITeGbtTwRHeZ1M0DRFpkxFZQWBrXDMlVGbB1p\niXGptwQjjTimEazGtT1oNTwvRQiCp/sBg2uWodVpInUUP7oMV3IEzhuITeHq7vlrIaLwzWp8s/q8\n62QxBM5rGJlFyyjRXP+oZ1aSDD+FJU/JexAjU3hmGfHo+vm2jneDazupC34fS/SOKk3vBxN2grzN\n0SldxCQ2//Pt0dN8U3+dITvIuB1juaxghhk0mg2ymW3u2/dBTtlJfqR/gEaDDwUp8Em5Bd+LE73N\n5xdtzWISUBiv0rQoRVPfWwjaRkQVTd/6KbxDBjsuZDdl6VnbgeO4CJqpo3mqkxUktYpK6RRmwGf1\nby7DqhitS2GbqtBtXyV26p84PZNhZabKlpZnOTi5nPsOXMvgTDNnZuqxKHrqJ4nrSVQ1pL0uRaHa\nSDWKYZ0Ax62iiRGqJk7nhYJp5Yr+QbZ0/SOzlSSvj73OP01+mr7WJro6GlnWPMRnWv+KxQ2TKJvD\nRprQtoB4WPX2TjoL+PXCB0Kk1lr+83/+zxw6dAjf9/nTP/1Tenp+sQLgv4pYu3Yda9eue8f3M5k6\nbr/9doaHR5idncF1XXw/RjqdprW1lc2bt7ztcTF9JRCj5P1wrnp0ilTwBRL6Cqy+mEgdx0oZx7TO\n9U/WCpmUrT1EjBTxzQYS0XWUvIfQcpKazJ0g+Chbj6eXAuB6ipzzNHgVfHNhbX9SbyZUexFiHAhD\nJswRXjc7SJKiSBGxKQKToctcQSq8kIr3KJZa8ZFrukiEn6Lkf4dQjhKqoyh79gHumaW4upfA3Y/Y\nBFomqXgP4eqVcy08NWeaZPgZPLMMgIItcNqeos4Zo1mBaxbh2j7K7qMEzutYqgTOq3h6PYo6QnWQ\nUA6Qj32NSJ1G2ZZab6xtIabPFna9FwSXiq0wywz1NBCX81O8x80x9to9pEhyqbqCuMQJbMBes5sn\nzGNYLA3SwOedL8xHns/YJxm2Q9TuXMAO+xpx4ri4/GX4/+DjsdHZhKB4XD/KLLOslNU0S3ONRGuT\n44x/BusKnvh4DdB3eSsnnh/DWlh8aZbjL45x4JkhHE9R13FuWjusv4xk8wOE5RId/YZK01L6+yPU\nj+7Dr9uDt8TDjDRxOJakMXOK/jBOqbQYjQvG4CiHeHiCWHE7fiLDMneKcqUMpQSddRP8waav8dPj\nVzJVSnFsuoeYG7C2+QA96QYSbpVSt8vR6W4Gc23UJ/J01s2SrRbpbg7oadEsdp7C1luGaMA0lEiv\nn+H1sXWkE5cwdXo//3B8K32NQ9y0/DmaMmNQrwjrLsEklr7v+7uAX118IET6+OOPEwQB3/72t9m1\naxdf+cpX+NrXvvZBDP1rh7vuuotksoGvf/3vmZ2dQURx8cWX8MUv/jaue/7tHhoa5NChg/iN+1hy\n8QFERQTsw/r/Faf6H6m4jxKp2kPY1xtr0ScVInUS1yxB8PD1ahzbTCa4F2UbKXjfwKgxwMOxbfh6\nE8YZqlmjsR+jZtBqkrJ6DGUb8M1yfLOMl/VL8/t3J80J0qRRoihR4mq5mQvt5xEjOEE9Vfd5QOFH\nmwjVLuw5MZJLIroOLRN4pg/PrGTW+XMiihgMCgjc1zgUFTiiJ2iQBFfIAepZxqyZ5R+ivyPuHqLD\nPUafLKHT7SYZfppIHZkfwYrFqOl5Y/KK9xRaDWMlQMsQyqbm21zeLybsBN+OvkGJIgmSfNa9/Zx+\n12E9fI7C0rgdp0maeN3sZId5jV5ZRFayzNgZ9po9XORcDEBgAybtBAZLnDhFivh4pKnjFCf52+iv\nucBuISEJZmxNjm/UjnCdup44cSrU9riz0kqKNGFYJYpCOrdk6NrURBgGzA4VOf7TmgWuo41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MnVcyQ6SWzie3iFVzFuEzq+FPG7EYnV6sFtiHHqkV+wtd8CPlxYqNf+FUZXVze/+7v/bt5o+b1g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\nUiqCoNpIQOVyodazrPcQ4zgc85qRpHh8AhonfdaGi5MkJo6jxr9H7ql1g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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n", + " edgecolor='none', alpha=0.5,\n", + " cmap=plt.cm.get_cmap('spectral', 10))\n", + "plt.colorbar(label='digit label', ticks=range(10))\n", + "plt.clim(-0.5, 9.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n", + "Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n", + "On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n", + "\n", + "Overall, however, the different groups appear to be fairly well separated in the parameter space: this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n", + "Let's give it a try." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Classification on digits\n", + "\n", + "Let's apply a classification algorithm to the digits.\n", + "As with the Iris data previously, we will split the data into a training and testing set, and fit a Gaussian naive Bayes model:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "model = GaussianNB()\n", + "model.fit(Xtrain, ytrain)\n", + "y_model = model.predict(Xtest)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.83333333333333337" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "accuracy_score(ytest, y_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n", + "However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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a1qB4uaKMWzKSj6f1w9HJkXFLRvJak2rk8737C6i01DT2bT5A0bLP5Xr+vVQ+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\nLzLMGdy6nsSyT5eSeieVln1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "mat = confusion_matrix(ytest, y_model)\n", + "\n", + "sns.heatmap(mat, square=True, annot=True, cbar=False)\n", + "plt.xlabel('predicted value')\n", + "plt.ylabel('true value');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights.\n", + "Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\n", + "We'll use green for correct labels, and red for incorrect labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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x0a8+sjs0JX/sZ+01zLr6OgBAdUM1AKCmvgbBjmBrorLAor8uQnR4NNKT0u0O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\nyCbkjM3xaFcdS+nce7NKJ2pfYSZ0TcCuE7tw7tI5ANcyZnt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n", + " subplot_kw={'xticks':[], 'yticks':[]},\n", + " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", + "\n", + "test_images = Xtest.reshape(-1, 8, 8)\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\n", + " ax.text(0.05, 0.05, str(y_model[i]),\n", + " transform=ax.transAxes,\n", + " color='green' if (ytest[i] == y_model[i]) else 'red')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n", + "To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API.\n", + "Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n", + "Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n", + "\n", + "In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.02-Introducing-Scikit-Learn.md b/notebooks_v2/05.02-Introducing-Scikit-Learn.md new file mode 100644 index 00000000..63d06471 --- /dev/null +++ b/notebooks_v2/05.02-Introducing-Scikit-Learn.md @@ -0,0 +1,626 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) > + +Open in Colab + + + +# Introducing Scikit-Learn + + +There are several Python libraries which provide solid implementations of a range of machine learning algorithms. +One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms. +Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation. +A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward. + +This section provides an overview of the Scikit-Learn API; a solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters. + +We will start by covering *data representation* in Scikit-Learn, followed by covering the *Estimator* API, and finally go through a more interesting example of using these tools for exploring a set of images of hand-written digits. + + + +## Data Representation in Scikit-Learn + + + +Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer. +The best way to think about data within Scikit-Learn is in terms of tables of data. + + + +### Data as table + +A basic table is a two-dimensional grid of data, in which the rows represent individual elements of the dataset, and the columns represent quantities related to each of these elements. +For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936. +We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library: + + +```python deletable=true editable=true +import seaborn as sns +iris = sns.load_dataset('iris') +iris.head() +``` + + +Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset. +In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``. + +Likewise, each column of the data refers to a particular quantitative piece of information that describes each sample. +In general, we will refer to the columns of the matrix as *features*, and the number of columns as ``n_features``. + + + +#### Features matrix + +This table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*. +By convention, this features matrix is often stored in a variable named ``X``. +The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, and is most often contained in a NumPy array or a Pandas ``DataFrame``, though some Scikit-Learn models also accept SciPy sparse matrices. + +The samples (i.e., rows) always refer to the individual objects described by the dataset. +For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements. + +The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner. +Features are generally real-valued, but may be Boolean or discrete-valued in some cases. + + + +#### Target array + +In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``. +The target array is usually one dimensional, with length ``n_samples``, and is generally contained in a NumPy array or Pandas ``Series``. +The target array may have continuous numerical values, or discrete classes/labels. +While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array. + +Often one point of confusion is how the target array differs from the other features columns. The distinguishing feature of the target array is that it is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable. +For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array. + +With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data: + + +```python deletable=true editable=true +%matplotlib inline +import seaborn as sns; sns.set() +sns.pairplot(iris, hue='species', size=1.5); +``` + + +For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb): + + +```python deletable=true editable=true +X_iris = iris.drop('species', axis=1) +X_iris.shape +``` + +```python deletable=true editable=true +y_iris = iris['species'] +y_iris.shape +``` + + +To summarize, the expected layout of features and target values is visualized in the following diagram: + + + +![](figures/05.02-samples-features.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid) + + + +With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn: + + + +## Scikit-Learn's Estimator API + + + +The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238): + +- *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation. + +- *Inspection*: All specified parameter values are exposed as public attributes. + +- *Limited object hierarchy*: Only algorithms are represented by Python classes; datasets are represented + in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and parameter + names use standard Python strings. + +- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms, + and Scikit-Learn makes use of this wherever possible. + +- *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value. + +In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood. +Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications. + + + +### Basics of the API + +Most commonly, the steps in using the Scikit-Learn estimator API are as follows +(we will step through a handful of detailed examples in the sections that follow). + +1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn. +2. Choose model hyperparameters by instantiating this class with desired values. +3. Arrange data into a features matrix and target vector following the discussion above. +4. Fit the model to your data by calling the ``fit()`` method of the model instance. +5. Apply the Model to new data: + - For supervised learning, often we predict labels for unknown data using the ``predict()`` method. + - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method. + +We will now step through several simple examples of applying supervised and unsupervised learning methods. + + + +### Supervised learning example: Simple linear regression + +As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data. +We will use the following simple data for our regression example: + + +```python deletable=true editable=true +import matplotlib.pyplot as plt +import numpy as np + +rng = np.random.RandomState(42) +x = 10 * rng.rand(50) +y = 2 * x - 1 + rng.randn(50) +plt.scatter(x, y); +``` + + +With this data in place, we can use the recipe outlined earlier. Let's walk through the process: + + + +#### 1. Choose a class of model + +In Scikit-Learn, every class of model is represented by a Python class. +So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class: + + +```python deletable=true editable=true +from sklearn.linear_model import LinearRegression +``` + + +Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html). + + + +#### 2. Choose model hyperparameters + +An important point is that *a class of model is not the same as an instance of a model*. + +Once we have decided on our model class, there are still some options open to us. +Depending on the model class we are working with, we might need to answer one or more questions like the following: + +- Would we like to fit for the offset (i.e., *y*-intercept)? +- Would we like the model to be normalized? +- Would we like to preprocess our features to add model flexibility? +- What degree of regularization would we like to use in our model? +- How many model components would we like to use? + +These are examples of the important choices that must be made *once the model class is selected*. +These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data. +In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation. +We will explore how you can quantitatively motivate the choice of hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb). + +For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter: + + +```python deletable=true editable=true +model = LinearRegression(fit_intercept=True) +model +``` + + +Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values. +In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*. + + + +#### 3. Arrange data into a features matrix and target vector + +Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array. +Here our target variable ``y`` is already in the correct form (a length-``n_samples`` array), but we need to massage the data ``x`` to make it a matrix of size ``[n_samples, n_features]``. +In this case, this amounts to a simple reshaping of the one-dimensional array: + + +```python deletable=true editable=true +X = x[:, np.newaxis] +X.shape +``` + + +#### 4. Fit the model to your data + +Now it is time to apply our model to data. +This can be done with the ``fit()`` method of the model: + + +```python deletable=true editable=true +model.fit(X, y) +``` + + +This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore. +In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following: + + +```python deletable=true editable=true +model.coef_ +``` + +```python deletable=true editable=true +model.intercept_ +``` + + +These two parameters represent the slope and intercept of the simple linear fit to the data. +Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1. + +One question that frequently comes up regards the uncertainty in such internal model parameters. +In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves: interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question. +Machine learning rather focuses on what the model *predicts*. +If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/). + + + +#### 5. Predict labels for unknown data + +Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set. +In Scikit-Learn, this can be done using the ``predict()`` method. +For the sake of this example, our "new data" will be a grid of *x* values, and we will ask what *y* values the model predicts: + + +```python deletable=true editable=true +xfit = np.linspace(-1, 11) +``` + + +As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model: + + +```python deletable=true editable=true +Xfit = xfit[:, np.newaxis] +yfit = model.predict(Xfit) +``` + + +Finally, let's visualize the results by plotting first the raw data, and then this model fit: + + +```python deletable=true editable=true +plt.scatter(x, y) +plt.plot(xfit, yfit); +``` + + +Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example + + + +### Supervised learning example: Iris classification + +Let's take a look at another example of this process, using the Iris dataset we discussed earlier. +Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels? + +For this task, we will use an extremely simple generative model known as Gaussian naive Bayes, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details). +Because it is so fast and has no hyperparameters to choose, Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models. + +We would like to evaluate the model on data it has not seen before, and so we will split the data into a *training set* and a *testing set*. +This could be done by hand, but it is more convenient to use the ``train_test_split`` utility function: + + +```python deletable=true editable=true +from sklearn.cross_validation import train_test_split +Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris, + random_state=1) +``` + + +With the data arranged, we can follow our recipe to predict the labels: + + +```python deletable=true editable=true +from sklearn.naive_bayes import GaussianNB # 1. choose model class +model = GaussianNB() # 2. instantiate model +model.fit(Xtrain, ytrain) # 3. fit model to data +y_model = model.predict(Xtest) # 4. predict on new data +``` + + +Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value: + + +```python deletable=true editable=true +from sklearn.metrics import accuracy_score +accuracy_score(ytest, y_model) +``` + + +With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset! + + + +### Unsupervised learning example: Iris dimensionality + +As an example of an unsupervised learning problem, let's take a look at reducing the dimensionality of the Iris data so as to more easily visualize it. +Recall that the Iris data is four dimensional: there are four features recorded for each sample. + +The task of dimensionality reduction is to ask whether there is a suitable lower-dimensional representation that retains the essential features of the data. +Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or higher! + +Here we will use principal component analysis (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique. +We will ask the model to return two components—that is, a two-dimensional representation of the data. + +Following the sequence of steps outlined earlier, we have: + + +```python deletable=true editable=true +from sklearn.decomposition import PCA # 1. Choose the model class +model = PCA(n_components=2) # 2. Instantiate the model with hyperparameters +model.fit(X_iris) # 3. Fit to data. Notice y is not specified! +X_2D = model.transform(X_iris) # 4. Transform the data to two dimensions +``` + + +Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results: + + +```python deletable=true editable=true +iris['PCA1'] = X_2D[:, 0] +iris['PCA2'] = X_2D[:, 1] +sns.lmplot("PCA1", "PCA2", hue='species', data=iris, fit_reg=False); +``` + + +We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels! +This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before. + + + +### Unsupervised learning: Iris clustering + +Let's next look at applying clustering to the Iris data. +A clustering algorithm attempts to find distinct groups of data without reference to any labels. +Here we will use a powerful clustering method called a Gaussian mixture model (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb). +A GMM attempts to model the data as a collection of Gaussian blobs. + +We can fit the Gaussian mixture model as follows: + + +```python deletable=true editable=true +from sklearn.mixture import GMM # 1. Choose the model class +model = GMM(n_components=3, + covariance_type='full') # 2. Instantiate the model with hyperparameters +model.fit(X_iris) # 3. Fit to data. Notice y is not specified! +y_gmm = model.predict(X_iris) # 4. Determine cluster labels +``` + + +As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results: + + +```python deletable=true editable=true +iris['cluster'] = y_gmm +sns.lmplot("PCA1", "PCA2", data=iris, hue='species', + col='cluster', fit_reg=False); +``` + + +By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*. +This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm! +This sort of algorithm might further give experts in the field clues as to the relationship between the samples they are observing. + + + +## Application: Exploring Hand-written Digits + + + +To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits. +In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library. + + + +### Loading and visualizing the digits data + +We'll use Scikit-Learn's data access interface and take a look at this data: + + +```python deletable=true editable=true +from sklearn.datasets import load_digits +digits = load_digits() +digits.images.shape +``` + + +The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels. +Let's visualize the first hundred of these: + + +```python deletable=true editable=true +import matplotlib.pyplot as plt + +fig, axes = plt.subplots(10, 10, figsize=(8, 8), + subplot_kw={'xticks':[], 'yticks':[]}, + gridspec_kw=dict(hspace=0.1, wspace=0.1)) + +for i, ax in enumerate(axes.flat): + ax.imshow(digits.images[i], cmap='binary', interpolation='nearest') + ax.text(0.05, 0.05, str(digits.target[i]), + transform=ax.transAxes, color='green') +``` + + +In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation. +We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit. +Additionally, we need the target array, which gives the previously determined label for each digit. +These two quantities are built into the digits dataset under the ``data`` and ``target`` attributes, respectively: + + +```python deletable=true editable=true +X = digits.data +X.shape +``` + +```python deletable=true editable=true +y = digits.target +y.shape +``` + + +We see here that there are 1,797 samples and 64 features. + + + +### Unsupervised learning: Dimensionality reduction + +We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space. +Instead we'll reduce the dimensions to 2, using an unsupervised method. +Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions: + + +```python deletable=true editable=true +from sklearn.manifold import Isomap +iso = Isomap(n_components=2) +iso.fit(digits.data) +data_projected = iso.transform(digits.data) +data_projected.shape +``` + + +We see that the projected data is now two-dimensional. +Let's plot this data to see if we can learn anything from its structure: + + +```python deletable=true editable=true +plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target, + edgecolor='none', alpha=0.5, + cmap=plt.cm.get_cmap('spectral', 10)) +plt.colorbar(label='digit label', ticks=range(10)) +plt.clim(-0.5, 9.5); +``` + + +This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space. +Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle. +On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with "hats" on them, which cause them to look similar to fours. + +Overall, however, the different groups appear to be fairly well separated in the parameter space: this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data. +Let's give it a try. + + + +### Classification on digits + +Let's apply a classification algorithm to the digits. +As with the Iris data previously, we will split the data into a training and testing set, and fit a Gaussian naive Bayes model: + + +```python deletable=true editable=true +Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0) +``` + +```python deletable=true editable=true +from sklearn.naive_bayes import GaussianNB +model = GaussianNB() +model.fit(Xtrain, ytrain) +y_model = model.predict(Xtest) +``` + + +Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions: + + +```python deletable=true editable=true +from sklearn.metrics import accuracy_score +accuracy_score(ytest, y_model) +``` + + +With even this extremely simple model, we find about 80% accuracy for classification of the digits! +However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn: + + +```python deletable=true editable=true +from sklearn.metrics import confusion_matrix + +mat = confusion_matrix(ytest, y_model) + +sns.heatmap(mat, square=True, annot=True, cbar=False) +plt.xlabel('predicted value') +plt.ylabel('true value'); +``` + + +This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights. +Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels. +We'll use green for correct labels, and red for incorrect labels: + + +```python deletable=true editable=true +fig, axes = plt.subplots(10, 10, figsize=(8, 8), + subplot_kw={'xticks':[], 'yticks':[]}, + gridspec_kw=dict(hspace=0.1, wspace=0.1)) + +test_images = Xtest.reshape(-1, 8, 8) + +for i, ax in enumerate(axes.flat): + ax.imshow(test_images[i], cmap='binary', interpolation='nearest') + ax.text(0.05, 0.05, str(y_model[i]), + transform=ax.transAxes, + color='green' if (ytest[i] == y_model[i]) else 'red') +``` + + +Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally. +To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach. + + + +## Summary + + + +In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API. +Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds. +Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data. + +In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model. + + + + +< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.ipynb b/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.ipynb new file mode 100644 index 00000000..4dcd13e1 --- /dev/null +++ b/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.ipynb @@ -0,0 +1,1179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameters and Model Validation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "In the previous section, we saw the basic recipe for applying a supervised machine learning model:\n", + "\n", + "1. Choose a class of model\n", + "2. Choose model hyperparameters\n", + "3. Fit the model to the training data\n", + "4. Use the model to predict labels for new data\n", + "\n", + "The first two pieces of this—the choice of model and choice of hyperparameters—are perhaps the most important part of using these tools and techniques effectively.\n", + "In order to make an informed choice, we need a way to *validate* that our model and our hyperparameters are a good fit to the data.\n", + "While this may sound simple, there are some pitfalls that you must avoid to do this effectively." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Thinking about Model Validation\n", + "\n", + "In principle, model validation is very simple: after choosing a model and its hyperparameters, we can estimate how effective it is by applying it to some of the training data and comparing the prediction to the known value.\n", + "\n", + "The following sections first show a naive approach to model validation and why it\n", + "fails, before exploring the use of holdout sets and cross-validation for more robust\n", + "model evaluation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Model validation the wrong way\n", + "\n", + "Let's demonstrate the naive approach to validation using the Iris data, which we saw in the previous section.\n", + "We will start by loading the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "X = iris.data\n", + "y = iris.target" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Next we choose a model and hyperparameters. Here we'll use a *k*-neighbors classifier with ``n_neighbors=1``.\n", + "This is a very simple and intuitive model that says \"the label of an unknown point is the same as the label of its closest training point:\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "model = KNeighborsClassifier(n_neighbors=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Then we train the model, and use it to predict labels for data we already know:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "model.fit(X, y)\n", + "y_model = model.predict(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, we compute the fraction of correctly labeled points:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "accuracy_score(y, y_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see an accuracy score of 1.0, which indicates that 100% of points were correctly labeled by our model!\n", + "But is this truly measuring the expected accuracy? Have we really come upon a model that we expect to be correct 100% of the time?\n", + "\n", + "As you may have gathered, the answer is no.\n", + "In fact, this approach contains a fundamental flaw: *it trains and evaluates the model on the same data*.\n", + "Furthermore, the nearest neighbor model is an *instance-based* estimator that simply stores the training data, and predicts labels by comparing new data to these stored points: except in contrived cases, it will get 100% accuracy *every time!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Model validation the right way: Holdout sets\n", + "\n", + "So what can be done?\n", + "A better sense of a model's performance can be found using what's known as a *holdout set*: that is, we hold back some subset of the data from the training of the model, and then use this holdout set to check the model performance.\n", + "This splitting can be done using the ``train_test_split`` utility in Scikit-Learn:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.90666666666666662" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "# split the data with 50% in each set\n", + "X1, X2, y1, y2 = train_test_split(X, y, random_state=0,\n", + " train_size=0.5)\n", + "\n", + "# fit the model on one set of data\n", + "model.fit(X1, y1)\n", + "\n", + "# evaluate the model on the second set of data\n", + "y2_model = model.predict(X2)\n", + "accuracy_score(y2, y2_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see here a more reasonable result: the nearest-neighbor classifier is about 90% accurate on this hold-out set.\n", + "The hold-out set is similar to unknown data, because the model has not \"seen\" it before." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Model validation via cross-validation\n", + "\n", + "One disadvantage of using a holdout set for model validation is that we have lost a portion of our data to the model training.\n", + "In the above case, half the dataset does not contribute to the training of the model!\n", + "This is not optimal, and can cause problems – especially if the initial set of training data is small.\n", + "\n", + "One way to address this is to use *cross-validation*; that is, to do a sequence of fits where each subset of the data is used both as a training set and as a validation set.\n", + "Visually, it might look something like this:\n", + "\n", + "![](figures/05.03-2-fold-CV.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#2-Fold-Cross-Validation)\n", + "\n", + "Here we do two validation trials, alternately using each half of the data as a holdout set.\n", + "Using the split data from before, we could implement it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.95999999999999996, 0.90666666666666662)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y2_model = model.fit(X1, y1).predict(X2)\n", + "y1_model = model.fit(X2, y2).predict(X1)\n", + "accuracy_score(y1, y1_model), accuracy_score(y2, y2_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "What comes out are two accuracy scores, which we could combine (by, say, taking the mean) to get a better measure of the global model performance.\n", + "This particular form of cross-validation is a *two-fold cross-validation*—that is, one in which we have split the data into two sets and used each in turn as a validation set.\n", + "\n", + "We could expand on this idea to use even more trials, and more folds in the data—for example, here is a visual depiction of five-fold cross-validation:\n", + "\n", + "![](figures/05.03-5-fold-CV.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#5-Fold-Cross-Validation)\n", + "\n", + "Here we split the data into five groups, and use each of them in turn to evaluate the model fit on the other 4/5 of the data.\n", + "This would be rather tedious to do by hand, and so we can use Scikit-Learn's ``cross_val_score`` convenience routine to do it succinctly:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.96666667, 0.96666667, 0.93333333, 0.93333333, 1. ])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import cross_val_score\n", + "cross_val_score(model, X, y, cv=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Repeating the validation across different subsets of the data gives us an even better idea of the performance of the algorithm.\n", + "\n", + "Scikit-Learn implements a number of useful cross-validation schemes that are useful in particular situations; these are implemented via iterators in the ``cross_validation`` module.\n", + "For example, we might wish to go to the extreme case in which our number of folds is equal to the number of data points: that is, we train on all points but one in each trial.\n", + "This type of cross-validation is known as *leave-one-out* cross validation, and can be used as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1.])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import LeaveOneOut\n", + "scores = cross_val_score(model, X, y, cv=LeaveOneOut(len(X)))\n", + "scores" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Because we have 150 samples, the leave one out cross-validation yields scores for 150 trials, and the score indicates either successful (1.0) or unsuccessful (0.0) prediction.\n", + "Taking the mean of these gives an estimate of the error rate:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.95999999999999996" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Other cross-validation schemes can be used similarly.\n", + "For a description of what is available in Scikit-Learn, use IPython to explore the ``sklearn.cross_validation`` submodule, or take a look at Scikit-Learn's online [cross-validation documentation](http://scikit-learn.org/stable/modules/cross_validation.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Selecting the Best Model\n", + "\n", + "Now that we've seen the basics of validation and cross-validation, we will go into a litte more depth regarding model selection and selection of hyperparameters.\n", + "These issues are some of the most important aspects of the practice of machine learning, and I find that this information is often glossed over in introductory machine learning tutorials.\n", + "\n", + "Of core importance is the following question: *if our estimator is underperforming, how should we move forward?*\n", + "There are several possible answers:\n", + "\n", + "- Use a more complicated/more flexible model\n", + "- Use a less complicated/less flexible model\n", + "- Gather more training samples\n", + "- Gather more data to add features to each sample\n", + "\n", + "The answer to this question is often counter-intuitive.\n", + "In particular, sometimes using a more complicated model will give worse results, and adding more training samples may not improve your results!\n", + "The ability to determine what steps will improve your model is what separates the successful machine learning practitioners from the unsuccessful." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### The Bias-variance trade-off\n", + "\n", + "Fundamentally, the question of \"the best model\" is about finding a sweet spot in the tradeoff between *bias* and *variance*.\n", + "Consider the following figure, which presents two regression fits to the same dataset:\n", + "\n", + "![](figures/05.03-bias-variance.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff)\n", + "\n", + "It is clear that neither of these models is a particularly good fit to the data, but they fail in different ways.\n", + "\n", + "The model on the left attempts to find a straight-line fit through the data.\n", + "Because the data are intrinsically more complicated than a straight line, the straight-line model will never be able to describe this dataset well.\n", + "Such a model is said to *underfit* the data: that is, it does not have enough model flexibility to suitably account for all the features in the data; another way of saying this is that the model has high *bias*.\n", + "\n", + "The model on the right attempts to fit a high-order polynomial through the data.\n", + "Here the model fit has enough flexibility to nearly perfectly account for the fine features in the data, but even though it very accurately describes the training data, its precise form seems to be more reflective of the particular noise properties of the data rather than the intrinsic properties of whatever process generated that data.\n", + "Such a model is said to *overfit* the data: that is, it has so much model flexibility that the model ends up accounting for random errors as well as the underlying data distribution; another way of saying this is that the model has high *variance*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "To look at this in another light, consider what happens if we use these two models to predict the y-value for some new data.\n", + "In the following diagrams, the red/lighter points indicate data that is omitted from the training set:\n", + "\n", + "![](figures/05.03-bias-variance-2.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\n", + "\n", + "The score here is the $R^2$ score, or [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\n", + "From the scores associated with these two models, we can make an observation that holds more generally:\n", + "\n", + "- For high-bias models, the performance of the model on the validation set is similar to the performance on the training set.\n", + "- For high-variance models, the performance of the model on the validation set is far worse than the performance on the training set." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "If we imagine that we have some ability to tune the model complexity, we would expect the training score and validation score to behave as illustrated in the following figure:\n", + "\n", + "![](figures/05.03-validation-curve.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Validation-Curve)\n", + "\n", + "The diagram shown here is often called a *validation curve*, and we see the following essential features:\n", + "\n", + "- The training score is everywhere higher than the validation score. This is generally the case: the model will be a better fit to data it has seen than to data it has not seen.\n", + "- For very low model complexity (a high-bias model), the training data is under-fit, which means that the model is a poor predictor both for the training data and for any previously unseen data.\n", + "- For very high model complexity (a high-variance model), the training data is over-fit, which means that the model predicts the training data very well, but fails for any previously unseen data.\n", + "- For some intermediate value, the validation curve has a maximum. This level of complexity indicates a suitable trade-off between bias and variance.\n", + "\n", + "The means of tuning the model complexity varies from model to model; when we discuss individual models in depth in later sections, we will see how each model allows for such tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "### Validation curves in Scikit-Learn\n", + "\n", + "Let's look at an example of using cross-validation to compute the validation curve for a class of models.\n", + "Here we will use a *polynomial regression* model: this is a generalized linear model in which the degree of the polynomial is a tunable parameter.\n", + "For example, a degree-1 polynomial fits a straight line to the data; for model parameters $a$ and $b$:\n", + "\n", + "$$\n", + "y = ax + b\n", + "$$\n", + "\n", + "A degree-3 polynomial fits a cubic curve to the data; for model parameters $a, b, c, d$:\n", + "\n", + "$$\n", + "y = ax^3 + bx^2 + cx + d\n", + "$$\n", + "\n", + "We can generalize this to any number of polynomial features.\n", + "In Scikit-Learn, we can implement this with a simple linear regression combined with the polynomial preprocessor.\n", + "We will use a *pipeline* to string these operations together (we will discuss polynomial features and pipelines more fully in [Feature Engineering](05.04-Feature-Engineering.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "def PolynomialRegression(degree=2, **kwargs):\n", + " return make_pipeline(PolynomialFeatures(degree),\n", + " LinearRegression(**kwargs))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "Now let's create some data to which we will fit our model:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def make_data(N, err=1.0, rseed=1):\n", + " # randomly sample the data\n", + " rng = np.random.RandomState(rseed)\n", + " X = rng.rand(N, 1) ** 2\n", + " y = 10 - 1. / (X.ravel() + 0.1)\n", + " if err > 0:\n", + " y += err * rng.randn(N)\n", + " return X, y\n", + "\n", + "X, y = make_data(40)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can now visualize our data, along with polynomial fits of several degrees:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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4vA6cPhdOrxOdGRpaW3F6/ducPieOM753ep3YvU48Z1wbvRQGrR6zrrM33nnK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\n7q/G4ewhPiaMf//+JGZmJGAJ+fZ9PQzDoHXHNjC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn; seaborn.set() # plot formatting\n", + "\n", + "X_test = np.linspace(-0.1, 1.1, 500)[:, None]\n", + "\n", + "plt.scatter(X.ravel(), y, color='black')\n", + "axis = plt.axis()\n", + "for degree in [1, 3, 5]:\n", + " y_test = PolynomialRegression(degree).fit(X, y).predict(X_test)\n", + " plt.plot(X_test.ravel(), y_test, label='degree={0}'.format(degree))\n", + "plt.xlim(-0.1, 1.0)\n", + "plt.ylim(-2, 12)\n", + "plt.legend(loc='best');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The knob controlling model complexity in this case is the degree of the polynomial, which can be any non-negative integer.\n", + "A useful question to answer is this: what degree of polynomial provides a suitable trade-off between bias (under-fitting) and variance (over-fitting)?\n", + "\n", + "We can make progress in this by visualizing the validation curve for this particular data and model; this can be done straightforwardly using the ``validation_curve`` convenience routine provided by Scikit-Learn.\n", + "Given a model, data, parameter name, and a range to explore, this function will automatically compute both the training score and validation score across the range:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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NdiGAkhKp6RHxLheMn6+EO7sdXH37h644IopKXDueQiFmg726Gqivb3rVOcO3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\ni4iIWMgPEUMpSKY0y3EAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.learning_curve import validation_curve\n", + "degree = np.arange(0, 21)\n", + "train_score, val_score = validation_curve(PolynomialRegression(), X, y,\n", + " 'polynomialfeatures__degree', degree, cv=7)\n", + "\n", + "plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')\n", + "plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')\n", + "plt.legend(loc='best')\n", + "plt.ylim(0, 1)\n", + "plt.xlabel('degree')\n", + "plt.ylabel('score');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This shows precisely the qualitative behavior we expect: the training score is everywhere higher than the validation score; the training score is monotonically improving with increased model complexity; and the validation score reaches a maximum before dropping off as the model becomes over-fit.\n", + "\n", + "From the validation curve, we can read-off that the optimal trade-off between bias and variance is found for a third-order polynomial; we can compute and display this fit over the original data as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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YOnUq/vSnP3n8OpMp2pu3CzlKjlNtQys27y1CbJQB35s/ClHhesVq8QS/pzzD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\nHP6qCokxRgzuH6t0OUREFCQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X.ravel(), y)\n", + "lim = plt.axis()\n", + "y_test = PolynomialRegression(3).fit(X, y).predict(X_test)\n", + "plt.plot(X_test.ravel(), y_test);\n", + "plt.axis(lim);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Notice that finding this optimal model did not actually require us to compute the training score, but examining the relationship between the training score and validation score can give us useful insight into the performance of the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Learning Curves\n", + "\n", + "One important aspect of model complexity is that the optimal model will generally depend on the size of your training data.\n", + "For example, let's generate a new dataset with a factor of five more points:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X2, y2 = make_data(200)\n", + "plt.scatter(X2.ravel(), y2);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We will duplicate the preceding code to plot the validation curve for this larger dataset; for reference let's over-plot the previous results as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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aUFofqx+afmZ7OCkUghdecFFYCOefn3xv5t8/Ae++9gxqjWZ6t++USXMTkOHA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\nLIufeRZaZU1fMaNlTWJ/6c8whzrqyrz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "degree = np.arange(21)\n", + "train_score2, val_score2 = validation_curve(PolynomialRegression(), X2, y2,\n", + " 'polynomialfeatures__degree', degree, cv=7)\n", + "\n", + "plt.plot(degree, np.median(train_score2, 1), color='blue', label='training score')\n", + "plt.plot(degree, np.median(val_score2, 1), color='red', label='validation score')\n", + "plt.plot(degree, np.median(train_score, 1), color='blue', alpha=0.3, linestyle='dashed')\n", + "plt.plot(degree, np.median(val_score, 1), color='red', alpha=0.3, linestyle='dashed')\n", + "plt.legend(loc='lower center')\n", + "plt.ylim(0, 1)\n", + "plt.xlabel('degree')\n", + "plt.ylabel('score');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The solid lines show the new results, while the fainter dashed lines show the results of the previous smaller dataset.\n", + "It is clear from the validation curve that the larger dataset can support a much more complicated model: the peak here is probably around a degree of 6, but even a degree-20 model is not seriously over-fitting the data—the validation and training scores remain very close.\n", + "\n", + "Thus we see that the behavior of the validation curve has not one but two important inputs: the model complexity and the number of training points.\n", + "It is often useful to to explore the behavior of the model as a function of the number of training points, which we can do by using increasingly larger subsets of the data to fit our model.\n", + "A plot of the training/validation score with respect to the size of the training set is known as a *learning curve.*\n", + "\n", + "The general behavior we would expect from a learning curve is this:\n", + "\n", + "- A model of a given complexity will *overfit* a small dataset: this means the training score will be relatively high, while the validation score will be relatively low.\n", + "- A model of a given complexity will *underfit* a large dataset: this means that the training score will decrease, but the validation score will increase.\n", + "- A model will never, except by chance, give a better score to the validation set than the training set: this means the curves should keep getting closer together but never cross.\n", + "\n", + "With these features in mind, we would expect a learning curve to look qualitatively like that shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.03-learning-curve.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Learning-Curve)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The notable feature of the learning curve is the convergence to a particular score as the number of training samples grows.\n", + "In particular, once you have enough points that a particular model has converged, *adding more training data will not help you!*\n", + "The only way to increase model performance in this case is to use another (often more complex) model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Learning curves in Scikit-Learn\n", + "\n", + "Scikit-Learn offers a convenient utility for computing such learning curves from your models; here we will compute a learning curve for our original dataset with a second-order polynomial model and a ninth-order polynomial:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+vOrO/a4869cr85nPyQqH7HEmTFMyLcmyZJimva727fn9ZmG/ZR+rbLZwTCik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\ncPKYnE9+qb/wa4DRo0cPffjhhxowYIAkKTMzUyt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.learning_curve import learning_curve\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "for i, degree in enumerate([2, 9]):\n", + " N, train_lc, val_lc = learning_curve(PolynomialRegression(degree),\n", + " X, y, cv=7,\n", + " train_sizes=np.linspace(0.3, 1, 25))\n", + "\n", + " ax[i].plot(N, np.mean(train_lc, 1), color='blue', label='training score')\n", + " ax[i].plot(N, np.mean(val_lc, 1), color='red', label='validation score')\n", + " ax[i].hlines(np.mean([train_lc[-1], val_lc[-1]]), N[0], N[-1],\n", + " color='gray', linestyle='dashed')\n", + "\n", + " ax[i].set_ylim(0, 1)\n", + " ax[i].set_xlim(N[0], N[-1])\n", + " ax[i].set_xlabel('training size')\n", + " ax[i].set_ylabel('score')\n", + " ax[i].set_title('degree = {0}'.format(degree), size=14)\n", + " ax[i].legend(loc='best')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This is a valuable diagnostic, because it gives us a visual depiction of how our model responds to increasing training data.\n", + "In particular, when your learning curve has already converged (i.e., when the training and validation curves are already close to each other) *adding more training data will not significantly improve the fit!*\n", + "This situation is seen in the left panel, with the learning curve for the degree-2 model.\n", + "\n", + "The only way to increase the converged score is to use a different (usually more complicated) model.\n", + "We see this in the right panel: by moving to a much more complicated model, we increase the score of convergence (indicated by the dashed line), but at the expense of higher model variance (indicated by the difference between the training and validation scores).\n", + "If we were to add even more data points, the learning curve for the more complicated model would eventually converge.\n", + "\n", + "Plotting a learning curve for your particular choice of model and dataset can help you to make this type of decision about how to move forward in improving your analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Validation in Practice: Grid Search\n", + "\n", + "The preceding discussion is meant to give you some intuition into the trade-off between bias and variance, and its dependence on model complexity and training set size.\n", + "In practice, models generally have more than one knob to turn, and thus plots of validation and learning curves change from lines to multi-dimensional surfaces.\n", + "In these cases, such visualizations are difficult and we would rather simply find the particular model that maximizes the validation score.\n", + "\n", + "Scikit-Learn provides automated tools to do this in the grid search module.\n", + "Here is an example of using grid search to find the optimal polynomial model.\n", + "We will explore a three-dimensional grid of model features; namely the polynomial degree, the flag telling us whether to fit the intercept, and the flag telling us whether to normalize the problem.\n", + "This can be set up using Scikit-Learn's ``GridSearchCV`` meta-estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "\n", + "param_grid = {'polynomialfeatures__degree': np.arange(21),\n", + " 'linearregression__fit_intercept': [True, False],\n", + " 'linearregression__normalize': [True, False]}\n", + "\n", + "grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Notice that like a normal estimator, this has not yet been applied to any data.\n", + "Calling the ``fit()`` method will fit the model at each grid point, keeping track of the scores along the way:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "grid.fit(X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now that this is fit, we can ask for the best parameters as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'linearregression__fit_intercept': False,\n", + " 'linearregression__normalize': True,\n", + " 'polynomialfeatures__degree': 4}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.best_params_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, if we wish, we can use the best model and show the fit to our data using code from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = grid.best_estimator_\n", + "\n", + "plt.scatter(X.ravel(), y)\n", + "lim = plt.axis()\n", + "y_test = model.fit(X, y).predict(X_test)\n", + "plt.plot(X_test.ravel(), y_test, hold=True);\n", + "plt.axis(lim);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The grid search provides many more options, including the ability to specify a custom scoring function, to parallelize the computations, to do randomized searches, and more.\n", + "For information, see the examples in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) and [Feature Engineering: Working with Images](05.14-Image-Features.ipynb), or refer to Scikit-Learn's [grid search documentation](http://Scikit-Learn.org/stable/modules/grid_search.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Summary\n", + "\n", + "In this section, we have begun to explore the concept of model validation and hyperparameter optimization, focusing on intuitive aspects of the bias–variance trade-off and how it comes into play when fitting models to data.\n", + "In particular, we found that the use of a validation set or cross-validation approach is *vital* when tuning parameters in order to avoid over-fitting for more complex/flexible models.\n", + "\n", + "In later sections, we will discuss the details of particularly useful models, and throughout will talk about what tuning is available for these models and how these free parameters affect model complexity.\n", + "Keep the lessons of this section in mind as you read on and learn about these machine learning approaches!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md b/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md new file mode 100644 index 00000000..8f43d089 --- /dev/null +++ b/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md @@ -0,0 +1,562 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) > + +Open in Colab + + + +# Hyperparameters and Model Validation + + +In the previous section, we saw the basic recipe for applying a supervised machine learning model: + +1. Choose a class of model +2. Choose model hyperparameters +3. Fit the model to the training data +4. Use the model to predict labels for new data + +The first two pieces of this—the choice of model and choice of hyperparameters—are perhaps the most important part of using these tools and techniques effectively. +In order to make an informed choice, we need a way to *validate* that our model and our hyperparameters are a good fit to the data. +While this may sound simple, there are some pitfalls that you must avoid to do this effectively. + + + +## Thinking about Model Validation + +In principle, model validation is very simple: after choosing a model and its hyperparameters, we can estimate how effective it is by applying it to some of the training data and comparing the prediction to the known value. + +The following sections first show a naive approach to model validation and why it +fails, before exploring the use of holdout sets and cross-validation for more robust +model evaluation. + + + +### Model validation the wrong way + +Let's demonstrate the naive approach to validation using the Iris data, which we saw in the previous section. +We will start by loading the data: + + +```python deletable=true editable=true +from sklearn.datasets import load_iris +iris = load_iris() +X = iris.data +y = iris.target +``` + + +Next we choose a model and hyperparameters. Here we'll use a *k*-neighbors classifier with ``n_neighbors=1``. +This is a very simple and intuitive model that says "the label of an unknown point is the same as the label of its closest training point:" + + +```python deletable=true editable=true +from sklearn.neighbors import KNeighborsClassifier +model = KNeighborsClassifier(n_neighbors=1) +``` + + +Then we train the model, and use it to predict labels for data we already know: + + +```python deletable=true editable=true +model.fit(X, y) +y_model = model.predict(X) +``` + + +Finally, we compute the fraction of correctly labeled points: + + +```python deletable=true editable=true +from sklearn.metrics import accuracy_score +accuracy_score(y, y_model) +``` + + +We see an accuracy score of 1.0, which indicates that 100% of points were correctly labeled by our model! +But is this truly measuring the expected accuracy? Have we really come upon a model that we expect to be correct 100% of the time? + +As you may have gathered, the answer is no. +In fact, this approach contains a fundamental flaw: *it trains and evaluates the model on the same data*. +Furthermore, the nearest neighbor model is an *instance-based* estimator that simply stores the training data, and predicts labels by comparing new data to these stored points: except in contrived cases, it will get 100% accuracy *every time!* + + + +### Model validation the right way: Holdout sets + +So what can be done? +A better sense of a model's performance can be found using what's known as a *holdout set*: that is, we hold back some subset of the data from the training of the model, and then use this holdout set to check the model performance. +This splitting can be done using the ``train_test_split`` utility in Scikit-Learn: + + +```python deletable=true editable=true +from sklearn.cross_validation import train_test_split +# split the data with 50% in each set +X1, X2, y1, y2 = train_test_split(X, y, random_state=0, + train_size=0.5) + +# fit the model on one set of data +model.fit(X1, y1) + +# evaluate the model on the second set of data +y2_model = model.predict(X2) +accuracy_score(y2, y2_model) +``` + + +We see here a more reasonable result: the nearest-neighbor classifier is about 90% accurate on this hold-out set. +The hold-out set is similar to unknown data, because the model has not "seen" it before. + + + +### Model validation via cross-validation + +One disadvantage of using a holdout set for model validation is that we have lost a portion of our data to the model training. +In the above case, half the dataset does not contribute to the training of the model! +This is not optimal, and can cause problems – especially if the initial set of training data is small. + +One way to address this is to use *cross-validation*; that is, to do a sequence of fits where each subset of the data is used both as a training set and as a validation set. +Visually, it might look something like this: + +![](figures/05.03-2-fold-CV.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#2-Fold-Cross-Validation) + +Here we do two validation trials, alternately using each half of the data as a holdout set. +Using the split data from before, we could implement it like this: + + +```python deletable=true editable=true +y2_model = model.fit(X1, y1).predict(X2) +y1_model = model.fit(X2, y2).predict(X1) +accuracy_score(y1, y1_model), accuracy_score(y2, y2_model) +``` + + +What comes out are two accuracy scores, which we could combine (by, say, taking the mean) to get a better measure of the global model performance. +This particular form of cross-validation is a *two-fold cross-validation*—that is, one in which we have split the data into two sets and used each in turn as a validation set. + +We could expand on this idea to use even more trials, and more folds in the data—for example, here is a visual depiction of five-fold cross-validation: + +![](figures/05.03-5-fold-CV.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#5-Fold-Cross-Validation) + +Here we split the data into five groups, and use each of them in turn to evaluate the model fit on the other 4/5 of the data. +This would be rather tedious to do by hand, and so we can use Scikit-Learn's ``cross_val_score`` convenience routine to do it succinctly: + + +```python deletable=true editable=true +from sklearn.cross_validation import cross_val_score +cross_val_score(model, X, y, cv=5) +``` + + +Repeating the validation across different subsets of the data gives us an even better idea of the performance of the algorithm. + +Scikit-Learn implements a number of useful cross-validation schemes that are useful in particular situations; these are implemented via iterators in the ``cross_validation`` module. +For example, we might wish to go to the extreme case in which our number of folds is equal to the number of data points: that is, we train on all points but one in each trial. +This type of cross-validation is known as *leave-one-out* cross validation, and can be used as follows: + + +```python deletable=true editable=true +from sklearn.cross_validation import LeaveOneOut +scores = cross_val_score(model, X, y, cv=LeaveOneOut(len(X))) +scores +``` + + +Because we have 150 samples, the leave one out cross-validation yields scores for 150 trials, and the score indicates either successful (1.0) or unsuccessful (0.0) prediction. +Taking the mean of these gives an estimate of the error rate: + + +```python deletable=true editable=true +scores.mean() +``` + + +Other cross-validation schemes can be used similarly. +For a description of what is available in Scikit-Learn, use IPython to explore the ``sklearn.cross_validation`` submodule, or take a look at Scikit-Learn's online [cross-validation documentation](http://scikit-learn.org/stable/modules/cross_validation.html). + + + +## Selecting the Best Model + +Now that we've seen the basics of validation and cross-validation, we will go into a litte more depth regarding model selection and selection of hyperparameters. +These issues are some of the most important aspects of the practice of machine learning, and I find that this information is often glossed over in introductory machine learning tutorials. + +Of core importance is the following question: *if our estimator is underperforming, how should we move forward?* +There are several possible answers: + +- Use a more complicated/more flexible model +- Use a less complicated/less flexible model +- Gather more training samples +- Gather more data to add features to each sample + +The answer to this question is often counter-intuitive. +In particular, sometimes using a more complicated model will give worse results, and adding more training samples may not improve your results! +The ability to determine what steps will improve your model is what separates the successful machine learning practitioners from the unsuccessful. + + + +### The Bias-variance trade-off + +Fundamentally, the question of "the best model" is about finding a sweet spot in the tradeoff between *bias* and *variance*. +Consider the following figure, which presents two regression fits to the same dataset: + +![](figures/05.03-bias-variance.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff) + +It is clear that neither of these models is a particularly good fit to the data, but they fail in different ways. + +The model on the left attempts to find a straight-line fit through the data. +Because the data are intrinsically more complicated than a straight line, the straight-line model will never be able to describe this dataset well. +Such a model is said to *underfit* the data: that is, it does not have enough model flexibility to suitably account for all the features in the data; another way of saying this is that the model has high *bias*. + +The model on the right attempts to fit a high-order polynomial through the data. +Here the model fit has enough flexibility to nearly perfectly account for the fine features in the data, but even though it very accurately describes the training data, its precise form seems to be more reflective of the particular noise properties of the data rather than the intrinsic properties of whatever process generated that data. +Such a model is said to *overfit* the data: that is, it has so much model flexibility that the model ends up accounting for random errors as well as the underlying data distribution; another way of saying this is that the model has high *variance*. + + + +To look at this in another light, consider what happens if we use these two models to predict the y-value for some new data. +In the following diagrams, the red/lighter points indicate data that is omitted from the training set: + +![](figures/05.03-bias-variance-2.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics) + +The score here is the $R^2$ score, or [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models. +From the scores associated with these two models, we can make an observation that holds more generally: + +- For high-bias models, the performance of the model on the validation set is similar to the performance on the training set. +- For high-variance models, the performance of the model on the validation set is far worse than the performance on the training set. + + + +If we imagine that we have some ability to tune the model complexity, we would expect the training score and validation score to behave as illustrated in the following figure: + +![](figures/05.03-validation-curve.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Validation-Curve) + +The diagram shown here is often called a *validation curve*, and we see the following essential features: + +- The training score is everywhere higher than the validation score. This is generally the case: the model will be a better fit to data it has seen than to data it has not seen. +- For very low model complexity (a high-bias model), the training data is under-fit, which means that the model is a poor predictor both for the training data and for any previously unseen data. +- For very high model complexity (a high-variance model), the training data is over-fit, which means that the model predicts the training data very well, but fails for any previously unseen data. +- For some intermediate value, the validation curve has a maximum. This level of complexity indicates a suitable trade-off between bias and variance. + +The means of tuning the model complexity varies from model to model; when we discuss individual models in depth in later sections, we will see how each model allows for such tuning. + + + +### Validation curves in Scikit-Learn + +Let's look at an example of using cross-validation to compute the validation curve for a class of models. +Here we will use a *polynomial regression* model: this is a generalized linear model in which the degree of the polynomial is a tunable parameter. +For example, a degree-1 polynomial fits a straight line to the data; for model parameters $a$ and $b$: + +$$ +y = ax + b +$$ + +A degree-3 polynomial fits a cubic curve to the data; for model parameters $a, b, c, d$: + +$$ +y = ax^3 + bx^2 + cx + d +$$ + +We can generalize this to any number of polynomial features. +In Scikit-Learn, we can implement this with a simple linear regression combined with the polynomial preprocessor. +We will use a *pipeline* to string these operations together (we will discuss polynomial features and pipelines more fully in [Feature Engineering](05.04-Feature-Engineering.ipynb)): + + +```python deletable=true editable=true +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import LinearRegression +from sklearn.pipeline import make_pipeline + +def PolynomialRegression(degree=2, **kwargs): + return make_pipeline(PolynomialFeatures(degree), + LinearRegression(**kwargs)) +``` + + +Now let's create some data to which we will fit our model: + + +```python deletable=true editable=true +import numpy as np + +def make_data(N, err=1.0, rseed=1): + # randomly sample the data + rng = np.random.RandomState(rseed) + X = rng.rand(N, 1) ** 2 + y = 10 - 1. / (X.ravel() + 0.1) + if err > 0: + y += err * rng.randn(N) + return X, y + +X, y = make_data(40) +``` + + +We can now visualize our data, along with polynomial fits of several degrees: + + +```python deletable=true editable=true +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn; seaborn.set() # plot formatting + +X_test = np.linspace(-0.1, 1.1, 500)[:, None] + +plt.scatter(X.ravel(), y, color='black') +axis = plt.axis() +for degree in [1, 3, 5]: + y_test = PolynomialRegression(degree).fit(X, y).predict(X_test) + plt.plot(X_test.ravel(), y_test, label='degree={0}'.format(degree)) +plt.xlim(-0.1, 1.0) +plt.ylim(-2, 12) +plt.legend(loc='best'); +``` + + +The knob controlling model complexity in this case is the degree of the polynomial, which can be any non-negative integer. +A useful question to answer is this: what degree of polynomial provides a suitable trade-off between bias (under-fitting) and variance (over-fitting)? + +We can make progress in this by visualizing the validation curve for this particular data and model; this can be done straightforwardly using the ``validation_curve`` convenience routine provided by Scikit-Learn. +Given a model, data, parameter name, and a range to explore, this function will automatically compute both the training score and validation score across the range: + + +```python deletable=true editable=true +from sklearn.learning_curve import validation_curve +degree = np.arange(0, 21) +train_score, val_score = validation_curve(PolynomialRegression(), X, y, + 'polynomialfeatures__degree', degree, cv=7) + +plt.plot(degree, np.median(train_score, 1), color='blue', label='training score') +plt.plot(degree, np.median(val_score, 1), color='red', label='validation score') +plt.legend(loc='best') +plt.ylim(0, 1) +plt.xlabel('degree') +plt.ylabel('score'); +``` + + +This shows precisely the qualitative behavior we expect: the training score is everywhere higher than the validation score; the training score is monotonically improving with increased model complexity; and the validation score reaches a maximum before dropping off as the model becomes over-fit. + +From the validation curve, we can read-off that the optimal trade-off between bias and variance is found for a third-order polynomial; we can compute and display this fit over the original data as follows: + + +```python deletable=true editable=true +plt.scatter(X.ravel(), y) +lim = plt.axis() +y_test = PolynomialRegression(3).fit(X, y).predict(X_test) +plt.plot(X_test.ravel(), y_test); +plt.axis(lim); +``` + + +Notice that finding this optimal model did not actually require us to compute the training score, but examining the relationship between the training score and validation score can give us useful insight into the performance of the model. + + + +## Learning Curves + +One important aspect of model complexity is that the optimal model will generally depend on the size of your training data. +For example, let's generate a new dataset with a factor of five more points: + + +```python deletable=true editable=true +X2, y2 = make_data(200) +plt.scatter(X2.ravel(), y2); +``` + + +We will duplicate the preceding code to plot the validation curve for this larger dataset; for reference let's over-plot the previous results as well: + + +```python deletable=true editable=true +degree = np.arange(21) +train_score2, val_score2 = validation_curve(PolynomialRegression(), X2, y2, + 'polynomialfeatures__degree', degree, cv=7) + +plt.plot(degree, np.median(train_score2, 1), color='blue', label='training score') +plt.plot(degree, np.median(val_score2, 1), color='red', label='validation score') +plt.plot(degree, np.median(train_score, 1), color='blue', alpha=0.3, linestyle='dashed') +plt.plot(degree, np.median(val_score, 1), color='red', alpha=0.3, linestyle='dashed') +plt.legend(loc='lower center') +plt.ylim(0, 1) +plt.xlabel('degree') +plt.ylabel('score'); +``` + + +The solid lines show the new results, while the fainter dashed lines show the results of the previous smaller dataset. +It is clear from the validation curve that the larger dataset can support a much more complicated model: the peak here is probably around a degree of 6, but even a degree-20 model is not seriously over-fitting the data—the validation and training scores remain very close. + +Thus we see that the behavior of the validation curve has not one but two important inputs: the model complexity and the number of training points. +It is often useful to to explore the behavior of the model as a function of the number of training points, which we can do by using increasingly larger subsets of the data to fit our model. +A plot of the training/validation score with respect to the size of the training set is known as a *learning curve.* + +The general behavior we would expect from a learning curve is this: + +- A model of a given complexity will *overfit* a small dataset: this means the training score will be relatively high, while the validation score will be relatively low. +- A model of a given complexity will *underfit* a large dataset: this means that the training score will decrease, but the validation score will increase. +- A model will never, except by chance, give a better score to the validation set than the training set: this means the curves should keep getting closer together but never cross. + +With these features in mind, we would expect a learning curve to look qualitatively like that shown in the following figure: + + + +![](figures/05.03-learning-curve.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Learning-Curve) + + + +The notable feature of the learning curve is the convergence to a particular score as the number of training samples grows. +In particular, once you have enough points that a particular model has converged, *adding more training data will not help you!* +The only way to increase model performance in this case is to use another (often more complex) model. + + + +### Learning curves in Scikit-Learn + +Scikit-Learn offers a convenient utility for computing such learning curves from your models; here we will compute a learning curve for our original dataset with a second-order polynomial model and a ninth-order polynomial: + + +```python deletable=true editable=true +from sklearn.learning_curve import learning_curve + +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) + +for i, degree in enumerate([2, 9]): + N, train_lc, val_lc = learning_curve(PolynomialRegression(degree), + X, y, cv=7, + train_sizes=np.linspace(0.3, 1, 25)) + + ax[i].plot(N, np.mean(train_lc, 1), color='blue', label='training score') + ax[i].plot(N, np.mean(val_lc, 1), color='red', label='validation score') + ax[i].hlines(np.mean([train_lc[-1], val_lc[-1]]), N[0], N[-1], + color='gray', linestyle='dashed') + + ax[i].set_ylim(0, 1) + ax[i].set_xlim(N[0], N[-1]) + ax[i].set_xlabel('training size') + ax[i].set_ylabel('score') + ax[i].set_title('degree = {0}'.format(degree), size=14) + ax[i].legend(loc='best') +``` + + +This is a valuable diagnostic, because it gives us a visual depiction of how our model responds to increasing training data. +In particular, when your learning curve has already converged (i.e., when the training and validation curves are already close to each other) *adding more training data will not significantly improve the fit!* +This situation is seen in the left panel, with the learning curve for the degree-2 model. + +The only way to increase the converged score is to use a different (usually more complicated) model. +We see this in the right panel: by moving to a much more complicated model, we increase the score of convergence (indicated by the dashed line), but at the expense of higher model variance (indicated by the difference between the training and validation scores). +If we were to add even more data points, the learning curve for the more complicated model would eventually converge. + +Plotting a learning curve for your particular choice of model and dataset can help you to make this type of decision about how to move forward in improving your analysis. + + + +## Validation in Practice: Grid Search + +The preceding discussion is meant to give you some intuition into the trade-off between bias and variance, and its dependence on model complexity and training set size. +In practice, models generally have more than one knob to turn, and thus plots of validation and learning curves change from lines to multi-dimensional surfaces. +In these cases, such visualizations are difficult and we would rather simply find the particular model that maximizes the validation score. + +Scikit-Learn provides automated tools to do this in the grid search module. +Here is an example of using grid search to find the optimal polynomial model. +We will explore a three-dimensional grid of model features; namely the polynomial degree, the flag telling us whether to fit the intercept, and the flag telling us whether to normalize the problem. +This can be set up using Scikit-Learn's ``GridSearchCV`` meta-estimator: + + +```python deletable=true editable=true +from sklearn.grid_search import GridSearchCV + +param_grid = {'polynomialfeatures__degree': np.arange(21), + 'linearregression__fit_intercept': [True, False], + 'linearregression__normalize': [True, False]} + +grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7) +``` + + +Notice that like a normal estimator, this has not yet been applied to any data. +Calling the ``fit()`` method will fit the model at each grid point, keeping track of the scores along the way: + + +```python deletable=true editable=true +grid.fit(X, y); +``` + + +Now that this is fit, we can ask for the best parameters as follows: + + +```python deletable=true editable=true +grid.best_params_ +``` + + +Finally, if we wish, we can use the best model and show the fit to our data using code from before: + + +```python deletable=true editable=true +model = grid.best_estimator_ + +plt.scatter(X.ravel(), y) +lim = plt.axis() +y_test = model.fit(X, y).predict(X_test) +plt.plot(X_test.ravel(), y_test, hold=True); +plt.axis(lim); +``` + + +The grid search provides many more options, including the ability to specify a custom scoring function, to parallelize the computations, to do randomized searches, and more. +For information, see the examples in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) and [Feature Engineering: Working with Images](05.14-Image-Features.ipynb), or refer to Scikit-Learn's [grid search documentation](http://Scikit-Learn.org/stable/modules/grid_search.html). + + + +## Summary + +In this section, we have begun to explore the concept of model validation and hyperparameter optimization, focusing on intuitive aspects of the bias–variance trade-off and how it comes into play when fitting models to data. +In particular, we found that the use of a validation set or cross-validation approach is *vital* when tuning parameters in order to avoid over-fitting for more complex/flexible models. + +In later sections, we will discuss the details of particularly useful models, and throughout will talk about what tuning is available for these models and how these free parameters affect model complexity. +Keep the lessons of this section in mind as you read on and learn about these machine learning approaches! + + + + +< [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) | [Contents](Index.ipynb) | [Feature Engineering](05.04-Feature-Engineering.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.04-Feature-Engineering.ipynb b/notebooks_v2/05.04-Feature-Engineering.ipynb new file mode 100644 index 00000000..9588260f --- /dev/null +++ b/notebooks_v2/05.04-Feature-Engineering.ipynb @@ -0,0 +1,914 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Engineering" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The previous sections outline the fundamental ideas of machine learning, but all of the examples assume that you have numerical data in a tidy, ``[n_samples, n_features]`` format.\n", + "In the real world, data rarely comes in such a form.\n", + "With this in mind, one of the more important steps in using machine learning in practice is *feature engineering*: that is, taking whatever information you have about your problem and turning it into numbers that you can use to build your feature matrix.\n", + "\n", + "In this section, we will cover a few common examples of feature engineering tasks: features for representing *categorical data*, features for representing *text*, and features for representing *images*.\n", + "Additionally, we will discuss *derived features* for increasing model complexity and *imputation* of missing data.\n", + "Often this process is known as *vectorization*, as it involves converting arbitrary data into well-behaved vectors." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Categorical Features\n", + "\n", + "One common type of non-numerical data is *categorical* data.\n", + "For example, imagine you are exploring some data on housing prices, and along with numerical features like \"price\" and \"rooms\", you also have \"neighborhood\" information.\n", + "For example, your data might look something like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "data = [\n", + " {'price': 850000, 'rooms': 4, 'neighborhood': 'Queen Anne'},\n", + " {'price': 700000, 'rooms': 3, 'neighborhood': 'Fremont'},\n", + " {'price': 650000, 'rooms': 3, 'neighborhood': 'Wallingford'},\n", + " {'price': 600000, 'rooms': 2, 'neighborhood': 'Fremont'}\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "You might be tempted to encode this data with a straightforward numerical mapping:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "{'Queen Anne': 1, 'Fremont': 2, 'Wallingford': 3};" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "It turns out that this is not generally a useful approach in Scikit-Learn: the package's models make the fundamental assumption that numerical features reflect algebraic quantities.\n", + "Thus such a mapping would imply, for example, that *Queen Anne < Fremont < Wallingford*, or even that *Wallingford - Queen Anne = Fremont*, which (niche demographic jokes aside) does not make much sense.\n", + "\n", + "In this case, one proven technique is to use *one-hot encoding*, which effectively creates extra columns indicating the presence or absence of a category with a value of 1 or 0, respectively.\n", + "When your data comes as a list of dictionaries, Scikit-Learn's ``DictVectorizer`` will do this for you:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 0, 850000, 4],\n", + " [ 1, 0, 0, 700000, 3],\n", + " [ 0, 0, 1, 650000, 3],\n", + " [ 1, 0, 0, 600000, 2]], dtype=int64)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_extraction import DictVectorizer\n", + "vec = DictVectorizer(sparse=False, dtype=int)\n", + "vec.fit_transform(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Notice that the 'neighborhood' column has been expanded into three separate columns, representing the three neighborhood labels, and that each row has a 1 in the column associated with its neighborhood.\n", + "With these categorical features thus encoded, you can proceed as normal with fitting a Scikit-Learn model.\n", + "\n", + "To see the meaning of each column, you can inspect the feature names:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['neighborhood=Fremont',\n", + " 'neighborhood=Queen Anne',\n", + " 'neighborhood=Wallingford',\n", + " 'price',\n", + " 'rooms']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vec.get_feature_names()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "There is one clear disadvantage of this approach: if your category has many possible values, this can *greatly* increase the size of your dataset.\n", + "However, because the encoded data contains mostly zeros, a sparse output can be a very efficient solution:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<4x5 sparse matrix of type ''\n", + "\twith 12 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vec = DictVectorizer(sparse=True, dtype=int)\n", + "vec.fit_transform(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Many (though not yet all) of the Scikit-Learn estimators accept such sparse inputs when fitting and evaluating models. ``sklearn.preprocessing.OneHotEncoder`` and ``sklearn.feature_extraction.FeatureHasher`` are two additional tools that Scikit-Learn includes to support this type of encoding." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Text Features\n", + "\n", + "Another common need in feature engineering is to convert text to a set of representative numerical values.\n", + "For example, most automatic mining of social media data relies on some form of encoding the text as numbers.\n", + "One of the simplest methods of encoding data is by *word counts*: you take each snippet of text, count the occurrences of each word within it, and put the results in a table.\n", + "\n", + "For example, consider the following set of three phrases:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "sample = ['problem of evil',\n", + " 'evil queen',\n", + " 'horizon problem']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For a vectorization of this data based on word count, we could construct a column representing the word \"problem,\" the word \"evil,\" the word \"horizon,\" and so on.\n", + "While doing this by hand would be possible, the tedium can be avoided by using Scikit-Learn's ``CountVectorizer``:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<3x5 sparse matrix of type ''\n", + "\twith 7 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vec = CountVectorizer()\n", + "X = vec.fit_transform(sample)\n", + "X" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The result is a sparse matrix recording the number of times each word appears; it is easier to inspect if we convert this to a ``DataFrame`` with labeled columns:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " evil horizon of problem queen\n", + "0 1 0 1 1 0\n", + "1 1 0 0 0 1\n", + "2 0 1 0 1 0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "pd.DataFrame(X.toarray(), columns=vec.get_feature_names())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "There are some issues with this approach, however: the raw word counts lead to features which put too much weight on words that appear very frequently, and this can be sub-optimal in some classification algorithms.\n", + "One approach to fix this is known as *term frequency-inverse document frequency* (*TF–IDF*) which weights the word counts by a measure of how often they appear in the documents.\n", + "The syntax for computing these features is similar to the previous example:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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evilhorizonofproblemqueen
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" + ], + "text/plain": [ + " evil horizon of problem queen\n", + "0 0.517856 0.000000 0.680919 0.517856 0.000000\n", + "1 0.605349 0.000000 0.000000 0.000000 0.795961\n", + "2 0.000000 0.795961 0.000000 0.605349 0.000000" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "vec = TfidfVectorizer()\n", + "X = vec.fit_transform(sample)\n", + "pd.DataFrame(X.toarray(), columns=vec.get_feature_names())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For an example of using TF-IDF in a classification problem, see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Image Features\n", + "\n", + "Another common need is to suitably encode *images* for machine learning analysis.\n", + "The simplest approach is what we used for the digits data in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb): simply using the pixel values themselves.\n", + "But depending on the application, such approaches may not be optimal.\n", + "\n", + "A comprehensive summary of feature extraction techniques for images is well beyond the scope of this section, but you can find excellent implementations of many of the standard approaches in the [Scikit-Image project](http://scikit-image.org).\n", + "For one example of using Scikit-Learn and Scikit-Image together, see [Feature Engineering: Working with Images](05.14-Image-Features.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Derived Features\n", + "\n", + "Another useful type of feature is one that is mathematically derived from some input features.\n", + "We saw an example of this in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) when we constructed *polynomial features* from our input data.\n", + "We saw that we could convert a linear regression into a polynomial regression not by changing the model, but by transforming the input!\n", + "This is sometimes known as *basis function regression*, and is explored further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\n", + "\n", + "For example, this data clearly cannot be well described by a straight line:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "x = np.array([1, 2, 3, 4, 5])\n", + "y = np.array([4, 2, 1, 3, 7])\n", + "plt.scatter(x, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Still, we can fit a line to the data using ``LinearRegression`` and get the optimal result:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "X = x[:, np.newaxis]\n", + "model = LinearRegression().fit(X, y)\n", + "yfit = model.predict(X)\n", + "plt.scatter(x, y)\n", + "plt.plot(x, yfit);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "It's clear that we need a more sophisticated model to describe the relationship between $x$ and $y$.\n", + "\n", + "One approach to this is to transform the data, adding extra columns of features to drive more flexibility in the model.\n", + "For example, we can add polynomial features to the data this way:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1. 1. 1.]\n", + " [ 2. 4. 8.]\n", + " [ 3. 9. 27.]\n", + " [ 4. 16. 64.]\n", + " [ 5. 25. 125.]]\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "poly = PolynomialFeatures(degree=3, include_bias=False)\n", + "X2 = poly.fit_transform(X)\n", + "print(X2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The derived feature matrix has one column representing $x$, and a second column representing $x^2$, and a third column representing $x^3$.\n", + "Computing a linear regression on this expanded input gives a much closer fit to our data:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = LinearRegression().fit(X2, y)\n", + "yfit = model.predict(X2)\n", + "plt.scatter(x, y)\n", + "plt.plot(x, yfit);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This idea of improving a model not by changing the model, but by transforming the inputs, is fundamental to many of the more powerful machine learning methods.\n", + "We explore this idea further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) in the context of *basis function regression*.\n", + "More generally, this is one motivational path to the powerful set of techniques known as *kernel methods*, which we will explore in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Imputation of Missing Data\n", + "\n", + "Another common need in feature engineering is handling of missing data.\n", + "We discussed the handling of missing data in ``DataFrame``s in [Handling Missing Data](03.04-Missing-Values.ipynb), and saw that often the ``NaN`` value is used to mark missing values.\n", + "For example, we might have a dataset that looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from numpy import nan\n", + "X = np.array([[ nan, 0, 3 ],\n", + " [ 3, 7, 9 ],\n", + " [ 3, 5, 2 ],\n", + " [ 4, nan, 6 ],\n", + " [ 8, 8, 1 ]])\n", + "y = np.array([14, 16, -1, 8, -5])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "When applying a typical machine learning model to such data, we will need to first replace such missing data with some appropriate fill value.\n", + "This is known as *imputation* of missing values, and strategies range from simple (e.g., replacing missing values with the mean of the column) to sophisticated (e.g., using matrix completion or a robust model to handle such data).\n", + "\n", + "The sophisticated approaches tend to be very application-specific, and we won't dive into them here.\n", + "For a baseline imputation approach, using the mean, median, or most frequent value, Scikit-Learn provides the ``Imputer`` class:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4.5, 0. , 3. ],\n", + " [ 3. , 7. , 9. ],\n", + " [ 3. , 5. , 2. ],\n", + " [ 4. , 5. , 6. ],\n", + " [ 8. , 8. , 1. ]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import Imputer\n", + "imp = Imputer(strategy='mean')\n", + "X2 = imp.fit_transform(X)\n", + "X2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that in the resulting data, the two missing values have been replaced with the mean of the remaining values in the column. This imputed data can then be fed directly into, for example, a ``LinearRegression`` estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 13.14869292, 14.3784627 , -1.15539732, 10.96606197, -5.33782027])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LinearRegression().fit(X2, y)\n", + "model.predict(X2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Feature Pipelines\n", + "\n", + "With any of the preceding examples, it can quickly become tedious to do the transformations by hand, especially if you wish to string together multiple steps.\n", + "For example, we might want a processing pipeline that looks something like this:\n", + "\n", + "1. Impute missing values using the mean\n", + "2. Transform features to quadratic\n", + "3. Fit a linear regression\n", + "\n", + "To streamline this type of processing pipeline, Scikit-Learn provides a ``Pipeline`` object, which can be used as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "\n", + "model = make_pipeline(Imputer(strategy='mean'),\n", + " PolynomialFeatures(degree=2),\n", + " LinearRegression())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This pipeline looks and acts like a standard Scikit-Learn object, and will apply all the specified steps to any input data." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14 16 -1 8 -5]\n", + "[ 14. 16. -1. 8. -5.]\n" + ] + } + ], + "source": [ + "model.fit(X, y) # X with missing values, from above\n", + "print(y)\n", + "print(model.predict(X))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "All the steps of the model are applied automatically.\n", + "Notice that for the simplicity of this demonstration, we've applied the model to the data it was trained on; this is why it was able to perfectly predict the result (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for further discussion of this).\n", + "\n", + "For some examples of Scikit-Learn pipelines in action, see the following section on naive Bayes classification, as well as [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.04-Feature-Engineering.md b/notebooks_v2/05.04-Feature-Engineering.md new file mode 100644 index 00000000..02e87d46 --- /dev/null +++ b/notebooks_v2/05.04-Feature-Engineering.md @@ -0,0 +1,327 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) > + +Open in Colab + + + +# Feature Engineering + + +The previous sections outline the fundamental ideas of machine learning, but all of the examples assume that you have numerical data in a tidy, ``[n_samples, n_features]`` format. +In the real world, data rarely comes in such a form. +With this in mind, one of the more important steps in using machine learning in practice is *feature engineering*: that is, taking whatever information you have about your problem and turning it into numbers that you can use to build your feature matrix. + +In this section, we will cover a few common examples of feature engineering tasks: features for representing *categorical data*, features for representing *text*, and features for representing *images*. +Additionally, we will discuss *derived features* for increasing model complexity and *imputation* of missing data. +Often this process is known as *vectorization*, as it involves converting arbitrary data into well-behaved vectors. + + + +## Categorical Features + +One common type of non-numerical data is *categorical* data. +For example, imagine you are exploring some data on housing prices, and along with numerical features like "price" and "rooms", you also have "neighborhood" information. +For example, your data might look something like this: + + +```python deletable=true editable=true +data = [ + {'price': 850000, 'rooms': 4, 'neighborhood': 'Queen Anne'}, + {'price': 700000, 'rooms': 3, 'neighborhood': 'Fremont'}, + {'price': 650000, 'rooms': 3, 'neighborhood': 'Wallingford'}, + {'price': 600000, 'rooms': 2, 'neighborhood': 'Fremont'} +] +``` + + +You might be tempted to encode this data with a straightforward numerical mapping: + + +```python deletable=true editable=true +{'Queen Anne': 1, 'Fremont': 2, 'Wallingford': 3}; +``` + + +It turns out that this is not generally a useful approach in Scikit-Learn: the package's models make the fundamental assumption that numerical features reflect algebraic quantities. +Thus such a mapping would imply, for example, that *Queen Anne < Fremont < Wallingford*, or even that *Wallingford - Queen Anne = Fremont*, which (niche demographic jokes aside) does not make much sense. + +In this case, one proven technique is to use *one-hot encoding*, which effectively creates extra columns indicating the presence or absence of a category with a value of 1 or 0, respectively. +When your data comes as a list of dictionaries, Scikit-Learn's ``DictVectorizer`` will do this for you: + + +```python deletable=true editable=true +from sklearn.feature_extraction import DictVectorizer +vec = DictVectorizer(sparse=False, dtype=int) +vec.fit_transform(data) +``` + + +Notice that the 'neighborhood' column has been expanded into three separate columns, representing the three neighborhood labels, and that each row has a 1 in the column associated with its neighborhood. +With these categorical features thus encoded, you can proceed as normal with fitting a Scikit-Learn model. + +To see the meaning of each column, you can inspect the feature names: + + +```python deletable=true editable=true +vec.get_feature_names() +``` + + +There is one clear disadvantage of this approach: if your category has many possible values, this can *greatly* increase the size of your dataset. +However, because the encoded data contains mostly zeros, a sparse output can be a very efficient solution: + + +```python deletable=true editable=true +vec = DictVectorizer(sparse=True, dtype=int) +vec.fit_transform(data) +``` + + +Many (though not yet all) of the Scikit-Learn estimators accept such sparse inputs when fitting and evaluating models. ``sklearn.preprocessing.OneHotEncoder`` and ``sklearn.feature_extraction.FeatureHasher`` are two additional tools that Scikit-Learn includes to support this type of encoding. + + + +## Text Features + +Another common need in feature engineering is to convert text to a set of representative numerical values. +For example, most automatic mining of social media data relies on some form of encoding the text as numbers. +One of the simplest methods of encoding data is by *word counts*: you take each snippet of text, count the occurrences of each word within it, and put the results in a table. + +For example, consider the following set of three phrases: + + +```python deletable=true editable=true +sample = ['problem of evil', + 'evil queen', + 'horizon problem'] +``` + + +For a vectorization of this data based on word count, we could construct a column representing the word "problem," the word "evil," the word "horizon," and so on. +While doing this by hand would be possible, the tedium can be avoided by using Scikit-Learn's ``CountVectorizer``: + + +```python deletable=true editable=true +from sklearn.feature_extraction.text import CountVectorizer + +vec = CountVectorizer() +X = vec.fit_transform(sample) +X +``` + + +The result is a sparse matrix recording the number of times each word appears; it is easier to inspect if we convert this to a ``DataFrame`` with labeled columns: + + +```python deletable=true editable=true +import pandas as pd +pd.DataFrame(X.toarray(), columns=vec.get_feature_names()) +``` + + +There are some issues with this approach, however: the raw word counts lead to features which put too much weight on words that appear very frequently, and this can be sub-optimal in some classification algorithms. +One approach to fix this is known as *term frequency-inverse document frequency* (*TF–IDF*) which weights the word counts by a measure of how often they appear in the documents. +The syntax for computing these features is similar to the previous example: + + +```python deletable=true editable=true +from sklearn.feature_extraction.text import TfidfVectorizer +vec = TfidfVectorizer() +X = vec.fit_transform(sample) +pd.DataFrame(X.toarray(), columns=vec.get_feature_names()) +``` + + +For an example of using TF-IDF in a classification problem, see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb). + + + +## Image Features + +Another common need is to suitably encode *images* for machine learning analysis. +The simplest approach is what we used for the digits data in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb): simply using the pixel values themselves. +But depending on the application, such approaches may not be optimal. + +A comprehensive summary of feature extraction techniques for images is well beyond the scope of this section, but you can find excellent implementations of many of the standard approaches in the [Scikit-Image project](http://scikit-image.org). +For one example of using Scikit-Learn and Scikit-Image together, see [Feature Engineering: Working with Images](05.14-Image-Features.ipynb). + + + +## Derived Features + +Another useful type of feature is one that is mathematically derived from some input features. +We saw an example of this in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) when we constructed *polynomial features* from our input data. +We saw that we could convert a linear regression into a polynomial regression not by changing the model, but by transforming the input! +This is sometimes known as *basis function regression*, and is explored further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb). + +For example, this data clearly cannot be well described by a straight line: + + +```python deletable=true editable=true +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt + +x = np.array([1, 2, 3, 4, 5]) +y = np.array([4, 2, 1, 3, 7]) +plt.scatter(x, y); +``` + + +Still, we can fit a line to the data using ``LinearRegression`` and get the optimal result: + + +```python deletable=true editable=true +from sklearn.linear_model import LinearRegression +X = x[:, np.newaxis] +model = LinearRegression().fit(X, y) +yfit = model.predict(X) +plt.scatter(x, y) +plt.plot(x, yfit); +``` + + +It's clear that we need a more sophisticated model to describe the relationship between $x$ and $y$. + +One approach to this is to transform the data, adding extra columns of features to drive more flexibility in the model. +For example, we can add polynomial features to the data this way: + + +```python deletable=true editable=true +from sklearn.preprocessing import PolynomialFeatures +poly = PolynomialFeatures(degree=3, include_bias=False) +X2 = poly.fit_transform(X) +print(X2) +``` + + +The derived feature matrix has one column representing $x$, and a second column representing $x^2$, and a third column representing $x^3$. +Computing a linear regression on this expanded input gives a much closer fit to our data: + + +```python deletable=true editable=true +model = LinearRegression().fit(X2, y) +yfit = model.predict(X2) +plt.scatter(x, y) +plt.plot(x, yfit); +``` + + +This idea of improving a model not by changing the model, but by transforming the inputs, is fundamental to many of the more powerful machine learning methods. +We explore this idea further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) in the context of *basis function regression*. +More generally, this is one motivational path to the powerful set of techniques known as *kernel methods*, which we will explore in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb). + + + +## Imputation of Missing Data + +Another common need in feature engineering is handling of missing data. +We discussed the handling of missing data in ``DataFrame``s in [Handling Missing Data](03.04-Missing-Values.ipynb), and saw that often the ``NaN`` value is used to mark missing values. +For example, we might have a dataset that looks like this: + + +```python deletable=true editable=true +from numpy import nan +X = np.array([[ nan, 0, 3 ], + [ 3, 7, 9 ], + [ 3, 5, 2 ], + [ 4, nan, 6 ], + [ 8, 8, 1 ]]) +y = np.array([14, 16, -1, 8, -5]) +``` + + +When applying a typical machine learning model to such data, we will need to first replace such missing data with some appropriate fill value. +This is known as *imputation* of missing values, and strategies range from simple (e.g., replacing missing values with the mean of the column) to sophisticated (e.g., using matrix completion or a robust model to handle such data). + +The sophisticated approaches tend to be very application-specific, and we won't dive into them here. +For a baseline imputation approach, using the mean, median, or most frequent value, Scikit-Learn provides the ``Imputer`` class: + + +```python deletable=true editable=true +from sklearn.preprocessing import Imputer +imp = Imputer(strategy='mean') +X2 = imp.fit_transform(X) +X2 +``` + + +We see that in the resulting data, the two missing values have been replaced with the mean of the remaining values in the column. This imputed data can then be fed directly into, for example, a ``LinearRegression`` estimator: + + +```python deletable=true editable=true +model = LinearRegression().fit(X2, y) +model.predict(X2) +``` + + +## Feature Pipelines + +With any of the preceding examples, it can quickly become tedious to do the transformations by hand, especially if you wish to string together multiple steps. +For example, we might want a processing pipeline that looks something like this: + +1. Impute missing values using the mean +2. Transform features to quadratic +3. Fit a linear regression + +To streamline this type of processing pipeline, Scikit-Learn provides a ``Pipeline`` object, which can be used as follows: + + +```python deletable=true editable=true +from sklearn.pipeline import make_pipeline + +model = make_pipeline(Imputer(strategy='mean'), + PolynomialFeatures(degree=2), + LinearRegression()) +``` + + +This pipeline looks and acts like a standard Scikit-Learn object, and will apply all the specified steps to any input data. + + +```python deletable=true editable=true +model.fit(X, y) # X with missing values, from above +print(y) +print(model.predict(X)) +``` + + +All the steps of the model are applied automatically. +Notice that for the simplicity of this demonstration, we've applied the model to the data it was trained on; this is why it was able to perfectly predict the result (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for further discussion of this). + +For some examples of Scikit-Learn pipelines in action, see the following section on naive Bayes classification, as well as [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb). + + + + +< [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) | [Contents](Index.ipynb) | [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.05-Naive-Bayes.ipynb b/notebooks_v2/05.05-Naive-Bayes.ipynb new file mode 100644 index 00000000..bb2de18a --- /dev/null +++ b/notebooks_v2/05.05-Naive-Bayes.ipynb @@ -0,0 +1,752 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Depth: Naive Bayes Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The previous four sections have given a general overview of the concepts of machine learning.\n", + "In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification.\n", + "\n", + "Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.\n", + "Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem.\n", + "This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Bayesian Classification\n", + "\n", + "Naive Bayes classifiers are built on Bayesian classification methods.\n", + "These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.\n", + "In Bayesian classification, we're interested in finding the probability of a label given some observed features, which we can write as $P(L~|~{\\rm features})$.\n", + "Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:\n", + "\n", + "$$\n", + "P(L~|~{\\rm features}) = \\frac{P({\\rm features}~|~L)P(L)}{P({\\rm features})}\n", + "$$\n", + "\n", + "If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:\n", + "\n", + "$$\n", + "\\frac{P(L_1~|~{\\rm features})}{P(L_2~|~{\\rm features})} = \\frac{P({\\rm features}~|~L_1)}{P({\\rm features}~|~L_2)}\\frac{P(L_1)}{P(L_2)}\n", + "$$\n", + "\n", + "All we need now is some model by which we can compute $P({\\rm features}~|~L_i)$ for each label.\n", + "Such a model is called a *generative model* because it specifies the hypothetical random process that generates the data.\n", + "Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier.\n", + "The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model.\n", + "\n", + "This is where the \"naive\" in \"naive Bayes\" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification.\n", + "Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Gaussian Naive Bayes\n", + "\n", + "Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.\n", + "In this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\n", + "Imagine that you have the following data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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/EkTvg4do3DDjw71ffDSOMvMXs/PwGeLikyhTNJCX+nShdo3M660CvDpkAJHR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\nYbOnE37lKobvT2CvaNCjcOvpWgya9e4jncsuK1f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v7c2bNxMVFVVkLCwsDD8/PxITEwvH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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_blobs\n", + "X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\n", + "This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution.\n", + "The result of this naive Gaussian assumption is shown in the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![(run code in Appendix to generate image)](figures/05.05-gaussian-NB.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.\n", + "With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.\n", + "\n", + "This procedure is implemented in Scikit-Learn's ``sklearn.naive_bayes.GaussianNB`` estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "model = GaussianNB()\n", + "model.fit(X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now let's generate some new data and predict the label:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "rng = np.random.RandomState(0)\n", + "Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)\n", + "ynew = model.predict(Xnew)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now we can plot this new data to get an idea of where the decision boundary is:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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C/SI7u0AIwcXkNEZ4VdWYxZlxeqRGM1s8qElx9LVFC9EqpJ2ugfAeP5r2qdgl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hAAAg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\nqVV54uXPoVGvE3b6ub0dGb1kjdb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tFLpwYHT9F+kHKTLKGAwC0iihvrxIIAY0jRJhnJ/r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\nCiussMIKK1xnWAXnFVZYYYUVVrjO8P8DYfQSJAwZ8HAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\n", + "lim = plt.axis()\n", + "plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)\n", + "plt.axis(lim);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic.\n", + "\n", + "A nice piece of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the ``predict_proba`` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.89, 0.11],\n", + " [ 1. , 0. ],\n", + " [ 1. , 0. ],\n", + " [ 1. , 0. ],\n", + " [ 1. , 0. ],\n", + " [ 1. , 0. ],\n", + " [ 0. , 1. ],\n", + " [ 0.15, 0.85]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yprob = model.predict_proba(Xnew)\n", + "yprob[-8:].round(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The columns give the posterior probabilities of the first and second label, respectively.\n", + "If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach.\n", + "\n", + "Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.\n", + "Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Multinomial Naive Bayes\n", + "\n", + "The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label.\n", + "Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\n", + "The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\n", + "\n", + "The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Example: Classifying Text\n", + "\n", + "One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.\n", + "We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories.\n", + "\n", + "Let's download the data and take a look at the target names:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['alt.atheism',\n", + " 'comp.graphics',\n", + " 'comp.os.ms-windows.misc',\n", + " 'comp.sys.ibm.pc.hardware',\n", + " 'comp.sys.mac.hardware',\n", + " 'comp.windows.x',\n", + " 'misc.forsale',\n", + " 'rec.autos',\n", + " 'rec.motorcycles',\n", + " 'rec.sport.baseball',\n", + " 'rec.sport.hockey',\n", + " 'sci.crypt',\n", + " 'sci.electronics',\n", + " 'sci.med',\n", + " 'sci.space',\n", + " 'soc.religion.christian',\n", + " 'talk.politics.guns',\n", + " 'talk.politics.mideast',\n", + " 'talk.politics.misc',\n", + " 'talk.religion.misc']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import fetch_20newsgroups\n", + "\n", + "data = fetch_20newsgroups()\n", + "data.target_names" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For simplicity here, we will select just a few of these categories, and download the training and testing set:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "categories = ['talk.religion.misc', 'soc.religion.christian',\n", + " 'sci.space', 'comp.graphics']\n", + "train = fetch_20newsgroups(subset='train', categories=categories)\n", + "test = fetch_20newsgroups(subset='test', categories=categories)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here is a representative entry from the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)\n", + "Subject: Federal Hearing\n", + "Originator: dmcgee@uluhe\n", + "Organization: School of Ocean and Earth Science and Technology\n", + "Distribution: usa\n", + "Lines: 10\n", + "\n", + "\n", + "Fact or rumor....? Madalyn Murray O'Hare an atheist who eliminated the\n", + "use of the bible reading and prayer in public schools 15 years ago is now\n", + "going to appear before the FCC with a petition to stop the reading of the\n", + "Gospel on the airways of America. And she is also campaigning to remove\n", + "Christmas programs, songs, etc from the public schools. If it is true\n", + "then mail to Federal Communications Commission 1919 H Street Washington DC\n", + "20054 expressing your opposition to her request. Reference Petition number\n", + "\n", + "2493.\n", + "\n" + ] + } + ], + "source": [ + "print(train.data[5])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.\n", + "For this we will use the TF-IDF vectorizer (discussed in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "model = make_pipeline(TfidfVectorizer(), MultinomialNB())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this pipeline, we can apply the model to the training data, and predict labels for the test data:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "model.fit(train.data, train.target)\n", + "labels = model.predict(test.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.\n", + "For example, here is the confusion matrix between the true and predicted labels for the test data:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + 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hXYsObNmyhccff5wFCxbQq1cvVqxYwaVLl4iPjyc2Npa9e/cCN765/F5CQgI+Pj7MnTuX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\nkBERkar9AwXLSNBV/8O+AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "mat = confusion_matrix(test.target, labels)\n", + "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n", + " xticklabels=train.target_names, yticklabels=train.target_names)\n", + "plt.xlabel('true label')\n", + "plt.ylabel('predicted label');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Evidently, even this very simple classifier can successfully separate space talk from computer talk, but it gets confused between talk about religion and talk about Christianity.\n", + "This is perhaps an expected area of confusion!\n", + "\n", + "The very cool thing here is that we now have the tools to determine the category for *any* string, using the ``predict()`` method of this pipeline.\n", + "Here's a quick utility function that will return the prediction for a single string:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def predict_category(s, train=train, model=model):\n", + " pred = model.predict([s])\n", + " return train.target_names[pred[0]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Let's try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'sci.space'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_category('sending a payload to the ISS')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'soc.religion.christian'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_category('discussing islam vs atheism')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'comp.graphics'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict_category('determining the screen resolution')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Remember that this is nothing more sophisticated than a simple probability model for the (weighted) frequency of each word in the string; nevertheless, the result is striking.\n", + "Even a very naive algorithm, when used carefully and trained on a large set of high-dimensional data, can be surprisingly effective." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## When to Use Naive Bayes\n", + "\n", + "Because naive Bayesian classifiers make such stringent assumptions about data, they will generally not perform as well as a more complicated model.\n", + "That said, they have several advantages:\n", + "\n", + "- They are extremely fast for both training and prediction\n", + "- They provide straightforward probabilistic prediction\n", + "- They are often very easily interpretable\n", + "- They have very few (if any) tunable parameters\n", + "\n", + "These advantages mean a naive Bayesian classifier is often a good choice as an initial baseline classification.\n", + "If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem.\n", + "If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform.\n", + "\n", + "Naive Bayes classifiers tend to perform especially well in one of the following situations:\n", + "\n", + "- When the naive assumptions actually match the data (very rare in practice)\n", + "- For very well-separated categories, when model complexity is less important\n", + "- For very high-dimensional data, when model complexity is less important\n", + "\n", + "The last two points seem distinct, but they actually are related: as the dimension of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\n", + "This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information.\n", + "For this reason, simplistic classifiers like naive Bayes tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.05-Naive-Bayes.md b/notebooks_v2/05.05-Naive-Bayes.md new file mode 100644 index 00000000..b77dfd03 --- /dev/null +++ b/notebooks_v2/05.05-Naive-Bayes.md @@ -0,0 +1,306 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) > + +Open in Colab + + + +# In Depth: Naive Bayes Classification + + +The previous four sections have given a general overview of the concepts of machine learning. +In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification. + +Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets. +Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem. +This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets. + + + +## Bayesian Classification + +Naive Bayes classifiers are built on Bayesian classification methods. +These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities. +In Bayesian classification, we're interested in finding the probability of a label given some observed features, which we can write as $P(L~|~{\rm features})$. +Bayes's theorem tells us how to express this in terms of quantities we can compute more directly: + +$$ +P(L~|~{\rm features}) = \frac{P({\rm features}~|~L)P(L)}{P({\rm features})} +$$ + +If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label: + +$$ +\frac{P(L_1~|~{\rm features})}{P(L_2~|~{\rm features})} = \frac{P({\rm features}~|~L_1)}{P({\rm features}~|~L_2)}\frac{P(L_1)}{P(L_2)} +$$ + +All we need now is some model by which we can compute $P({\rm features}~|~L_i)$ for each label. +Such a model is called a *generative model* because it specifies the hypothetical random process that generates the data. +Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier. +The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model. + +This is where the "naive" in "naive Bayes" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification. +Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections. + +We begin with the standard imports: + + +```python deletable=true editable=true +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +``` + + +## Gaussian Naive Bayes + +Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes. +In this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*. +Imagine that you have the following data: + + +```python deletable=true editable=true +from sklearn.datasets import make_blobs +X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5) +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu'); +``` + + +One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions. +This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution. +The result of this naive Gaussian assumption is shown in the following figure: + + + +![(run code in Appendix to generate image)](figures/05.05-gaussian-NB.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes) + + + +The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses. +With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point. + +This procedure is implemented in Scikit-Learn's ``sklearn.naive_bayes.GaussianNB`` estimator: + + +```python deletable=true editable=true +from sklearn.naive_bayes import GaussianNB +model = GaussianNB() +model.fit(X, y); +``` + + +Now let's generate some new data and predict the label: + + +```python deletable=true editable=true +rng = np.random.RandomState(0) +Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2) +ynew = model.predict(Xnew) +``` + + +Now we can plot this new data to get an idea of where the decision boundary is: + + +```python deletable=true editable=true +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu') +lim = plt.axis() +plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1) +plt.axis(lim); +``` + + +We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic. + +A nice piece of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the ``predict_proba`` method: + + +```python deletable=true editable=true +yprob = model.predict_proba(Xnew) +yprob[-8:].round(2) +``` + + +The columns give the posterior probabilities of the first and second label, respectively. +If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach. + +Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results. +Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method. + + + +## Multinomial Naive Bayes + +The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label. +Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution. +The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates. + +The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution. + + + +### Example: Classifying Text + +One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified. +We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories. + +Let's download the data and take a look at the target names: + + +```python deletable=true editable=true +from sklearn.datasets import fetch_20newsgroups + +data = fetch_20newsgroups() +data.target_names +``` + + +For simplicity here, we will select just a few of these categories, and download the training and testing set: + + +```python deletable=true editable=true +categories = ['talk.religion.misc', 'soc.religion.christian', + 'sci.space', 'comp.graphics'] +train = fetch_20newsgroups(subset='train', categories=categories) +test = fetch_20newsgroups(subset='test', categories=categories) +``` + + +Here is a representative entry from the data: + + +```python deletable=true editable=true +print(train.data[5]) +``` + + +In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers. +For this we will use the TF-IDF vectorizer (discussed in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier: + + +```python deletable=true editable=true +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.naive_bayes import MultinomialNB +from sklearn.pipeline import make_pipeline + +model = make_pipeline(TfidfVectorizer(), MultinomialNB()) +``` + + +With this pipeline, we can apply the model to the training data, and predict labels for the test data: + + +```python deletable=true editable=true +model.fit(train.data, train.target) +labels = model.predict(test.data) +``` + + +Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator. +For example, here is the confusion matrix between the true and predicted labels for the test data: + + +```python deletable=true editable=true +from sklearn.metrics import confusion_matrix +mat = confusion_matrix(test.target, labels) +sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False, + xticklabels=train.target_names, yticklabels=train.target_names) +plt.xlabel('true label') +plt.ylabel('predicted label'); +``` + + +Evidently, even this very simple classifier can successfully separate space talk from computer talk, but it gets confused between talk about religion and talk about Christianity. +This is perhaps an expected area of confusion! + +The very cool thing here is that we now have the tools to determine the category for *any* string, using the ``predict()`` method of this pipeline. +Here's a quick utility function that will return the prediction for a single string: + + +```python deletable=true editable=true +def predict_category(s, train=train, model=model): + pred = model.predict([s]) + return train.target_names[pred[0]] +``` + + +Let's try it out: + + +```python deletable=true editable=true +predict_category('sending a payload to the ISS') +``` + +```python deletable=true editable=true +predict_category('discussing islam vs atheism') +``` + +```python deletable=true editable=true +predict_category('determining the screen resolution') +``` + + +Remember that this is nothing more sophisticated than a simple probability model for the (weighted) frequency of each word in the string; nevertheless, the result is striking. +Even a very naive algorithm, when used carefully and trained on a large set of high-dimensional data, can be surprisingly effective. + + + +## When to Use Naive Bayes + +Because naive Bayesian classifiers make such stringent assumptions about data, they will generally not perform as well as a more complicated model. +That said, they have several advantages: + +- They are extremely fast for both training and prediction +- They provide straightforward probabilistic prediction +- They are often very easily interpretable +- They have very few (if any) tunable parameters + +These advantages mean a naive Bayesian classifier is often a good choice as an initial baseline classification. +If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem. +If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform. + +Naive Bayes classifiers tend to perform especially well in one of the following situations: + +- When the naive assumptions actually match the data (very rare in practice) +- For very well-separated categories, when model complexity is less important +- For very high-dimensional data, when model complexity is less important + +The last two points seem distinct, but they actually are related: as the dimension of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall). +This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information. +For this reason, simplistic classifiers like naive Bayes tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful. + + + + +< [Feature Engineering](05.04-Feature-Engineering.ipynb) | [Contents](Index.ipynb) | [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.06-Linear-Regression.ipynb b/notebooks_v2/05.06-Linear-Regression.ipynb new file mode 100644 index 00000000..0185cdcf --- /dev/null +++ b/notebooks_v2/05.06-Linear-Regression.ipynb @@ -0,0 +1,1400 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Depth: Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Just as naive Bayes (discussed earlier in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) is a good starting point for classification tasks, linear regression models are a good starting point for regression tasks.\n", + "Such models are popular because they can be fit very quickly, and are very interpretable.\n", + "You are probably familiar with the simplest form of a linear regression model (i.e., fitting a straight line to data) but such models can be extended to model more complicated data behavior.\n", + "\n", + "In this section we will start with a quick intuitive walk-through of the mathematics behind this well-known problem, before seeing how before moving on to see how linear models can be generalized to account for more complicated patterns in data.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Simple Linear Regression\n", + "\n", + "We will start with the most familiar linear regression, a straight-line fit to data.\n", + "A straight-line fit is a model of the form\n", + "$$\n", + "y = ax + b\n", + "$$\n", + "where $a$ is commonly known as the *slope*, and $b$ is commonly known as the *intercept*.\n", + "\n", + "Consider the following data, which is scattered about a line with a slope of 2 and an intercept of -5:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(1)\n", + "x = 10 * rng.rand(50)\n", + "y = 2 * x - 5 + rng.randn(50)\n", + "plt.scatter(x, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can use Scikit-Learn's ``LinearRegression`` estimator to fit this data and construct the best-fit line:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "model = LinearRegression(fit_intercept=True)\n", + "\n", + "model.fit(x[:, np.newaxis], y)\n", + "\n", + "xfit = np.linspace(0, 10, 1000)\n", + "yfit = model.predict(xfit[:, np.newaxis])\n", + "\n", + "plt.scatter(x, y)\n", + "plt.plot(xfit, yfit);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The slope and intercept of the data are contained in the model's fit parameters, which in Scikit-Learn are always marked by a trailing underscore.\n", + "Here the relevant parameters are ``coef_`` and ``intercept_``:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model slope: 2.02720881036\n", + "Model intercept: -4.99857708555\n" + ] + } + ], + "source": [ + "print(\"Model slope: \", model.coef_[0])\n", + "print(\"Model intercept:\", model.intercept_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that the results are very close to the inputs, as we might hope." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The ``LinearRegression`` estimator is much more capable than this, however—in addition to simple straight-line fits, it can also handle multidimensional linear models of the form\n", + "$$\n", + "y = a_0 + a_1 x_1 + a_2 x_2 + \\cdots\n", + "$$\n", + "where there are multiple $x$ values.\n", + "Geometrically, this is akin to fitting a plane to points in three dimensions, or fitting a hyper-plane to points in higher dimensions.\n", + "\n", + "The multidimensional nature of such regressions makes them more difficult to visualize, but we can see one of these fits in action by building some example data, using NumPy's matrix multiplication operator:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5\n", + "[ 1.5 -2. 1. ]\n" + ] + } + ], + "source": [ + "rng = np.random.RandomState(1)\n", + "X = 10 * rng.rand(100, 3)\n", + "y = 0.5 + np.dot(X, [1.5, -2., 1.])\n", + "\n", + "model.fit(X, y)\n", + "print(model.intercept_)\n", + "print(model.coef_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here the $y$ data is constructed from three random $x$ values, and the linear regression recovers the coefficients used to construct the data.\n", + "\n", + "In this way, we can use the single ``LinearRegression`` estimator to fit lines, planes, or hyperplanes to our data.\n", + "It still appears that this approach would be limited to strictly linear relationships between variables, but it turns out we can relax this as well." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Basis Function Regression\n", + "\n", + "One trick you can use to adapt linear regression to nonlinear relationships between variables is to transform the data according to *basis functions*.\n", + "We have seen one version of this before, in the ``PolynomialRegression`` pipeline used in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) and [Feature Engineering](05.04-Feature-Engineering.ipynb).\n", + "The idea is to take our multidimensional linear model:\n", + "$$\n", + "y = a_0 + a_1 x_1 + a_2 x_2 + a_3 x_3 + \\cdots\n", + "$$\n", + "and build the $x_1, x_2, x_3,$ and so on, from our single-dimensional input $x$.\n", + "That is, we let $x_n = f_n(x)$, where $f_n()$ is some function that transforms our data.\n", + "\n", + "For example, if $f_n(x) = x^n$, our model becomes a polynomial regression:\n", + "$$\n", + "y = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \\cdots\n", + "$$\n", + "Notice that this is *still a linear model*—the linearity refers to the fact that the coefficients $a_n$ never multiply or divide each other.\n", + "What we have effectively done is taken our one-dimensional $x$ values and projected them into a higher dimension, so that a linear fit can fit more complicated relationships between $x$ and $y$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Polynomial basis functions\n", + "\n", + "This polynomial projection is useful enough that it is built into Scikit-Learn, using the ``PolynomialFeatures`` transformer:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2., 4., 8.],\n", + " [ 3., 9., 27.],\n", + " [ 4., 16., 64.]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "x = np.array([2, 3, 4])\n", + "poly = PolynomialFeatures(3, include_bias=False)\n", + "poly.fit_transform(x[:, None])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see here that the transformer has converted our one-dimensional array into a three-dimensional array by taking the exponent of each value.\n", + "This new, higher-dimensional data representation can then be plugged into a linear regression.\n", + "\n", + "As we saw in [Feature Engineering](05.04-Feature-Engineering.ipynb), the cleanest way to accomplish this is to use a pipeline.\n", + "Let's make a 7th-degree polynomial model in this way:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "poly_model = make_pipeline(PolynomialFeatures(7),\n", + " LinearRegression())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this transform in place, we can use the linear model to fit much more complicated relationships between $x$ and $y$. \n", + "For example, here is a sine wave with noise:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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2ZFm/NAYKQcCHR6t4rK0b4T1tN5SRsRaikInhqf6AUcQrf1iBjN9bvwrWnlbNi8AHR6vw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\nILz+aSlaOwewacU0q2e9nb1D+O93zuB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(1)\n", + "x = 10 * rng.rand(50)\n", + "y = np.sin(x) + 0.1 * rng.randn(50)\n", + "\n", + "poly_model.fit(x[:, np.newaxis], y)\n", + "yfit = poly_model.predict(xfit[:, np.newaxis])\n", + "\n", + "plt.scatter(x, y)\n", + "plt.plot(xfit, yfit);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Our linear model, through the use of 7th-order polynomial basis functions, can provide an excellent fit to this non-linear data!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Gaussian basis functions\n", + "\n", + "Of course, other basis functions are possible.\n", + "For example, one useful pattern is to fit a model that is not a sum of polynomial bases, but a sum of Gaussian bases.\n", + "The result might look something like the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.06-gaussian-basis.png)\n", + "[figure source in Appendix](#Gaussian-Basis)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The shaded regions in the plot are the scaled basis functions, and when added together they reproduce the smooth curve through the data.\n", + "These Gaussian basis functions are not built into Scikit-Learn, but we can write a custom transformer that will create them, as shown here and illustrated in the following figure (Scikit-Learn transformers are implemented as Python classes; reading Scikit-Learn's source is a good way to see how they can be created):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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PRUbePGR+5cqVWLly5egqG6Gqeg8ERnWjMi8EgMJh1uzZsgfn6HEkuwp7ThTj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\ngM4Frs4WfU0RjZqrsxLJMb4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "\n", + "class GaussianFeatures(BaseEstimator, TransformerMixin):\n", + " \"\"\"Uniformly spaced Gaussian features for one-dimensional input\"\"\"\n", + " \n", + " def __init__(self, N, width_factor=2.0):\n", + " self.N = N\n", + " self.width_factor = width_factor\n", + " \n", + " @staticmethod\n", + " def _gauss_basis(x, y, width, axis=None):\n", + " arg = (x - y) / width\n", + " return np.exp(-0.5 * np.sum(arg ** 2, axis))\n", + " \n", + " def fit(self, X, y=None):\n", + " # create N centers spread along the data range\n", + " self.centers_ = np.linspace(X.min(), X.max(), self.N)\n", + " self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])\n", + " return self\n", + " \n", + " def transform(self, X):\n", + " return self._gauss_basis(X[:, :, np.newaxis], self.centers_,\n", + " self.width_, axis=1)\n", + " \n", + "gauss_model = make_pipeline(GaussianFeatures(20),\n", + " LinearRegression())\n", + "gauss_model.fit(x[:, np.newaxis], y)\n", + "yfit = gauss_model.predict(xfit[:, np.newaxis])\n", + "\n", + "plt.scatter(x, y)\n", + "plt.plot(xfit, yfit)\n", + "plt.xlim(0, 10);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We put this example here just to make clear that there is nothing magic about polynomial basis functions: if you have some sort of intuition into the generating process of your data that makes you think one basis or another might be appropriate, you can use them as well." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Regularization\n", + "\n", + "The introduction of basis functions into our linear regression makes the model much more flexible, but it also can very quickly lead to over-fitting (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this).\n", + "For example, if we choose too many Gaussian basis functions, we end up with results that don't look so good:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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wFKgaZwvjLLtep+Urd87jK3fOi8LIUo9GUbhhQQ4vfNDM17/zATb/ANXVq1Oi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\nMB8d7ZjStXoHRsm2GtFq4uNXtqnRykBHFhk5DnQ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Q3NyMxx9/HE899RQKCwtFN8eUtm3b\nhtraWtTW1uKOO+7A+vXrkZGRIbpZppKbm4uPPvoIAHD16lUEg0Gkp6cLbpX5pKamwu/3AwACgQBU\nVUU0GhXcKnOaNm0aPv30UwDA4cOHkZub2+s1wsvj8+fPR319PUpKSgCAE9GGQXV1Ndra2rBp0yZs\n3LgRFosFW7ZsgdPpFN00U7JYLKKbYEpz587FZ599hqKiouSqE77XQ2/ZsmV45plnsHjx4uRMck6u\nHB4VFRV47rnnoCgKJk2ahAULFvR6DfceJyIiMgjh5XEiIiLqGwZtIiIig2DQJiIiMggGbSIiIoNg\n0CYiIjIIBm0iIiKDYNAmIiIyiP8Dl2lHrVvT3fgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = make_pipeline(GaussianFeatures(30),\n", + " LinearRegression())\n", + "model.fit(x[:, np.newaxis], y)\n", + "\n", + "plt.scatter(x, y)\n", + "plt.plot(xfit, model.predict(xfit[:, np.newaxis]))\n", + "\n", + "plt.xlim(0, 10)\n", + "plt.ylim(-1.5, 1.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With the data projected to the 30-dimensional basis, the model has far too much flexibility and goes to extreme values between locations where it is constrained by data.\n", + "We can see the reason for this if we plot the coefficients of the Gaussian bases with respect to their locations:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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dotHe7NkG+gi7w0lJZT3REcGXtZCQ6XQ9jwKZiQGcCSCkB8L3TRzWh/9IGoNG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def basis_plot(model, title=None):\n", + " fig, ax = plt.subplots(2, sharex=True)\n", + " model.fit(x[:, np.newaxis], y)\n", + " ax[0].scatter(x, y)\n", + " ax[0].plot(xfit, model.predict(xfit[:, np.newaxis]))\n", + " ax[0].set(xlabel='x', ylabel='y', ylim=(-1.5, 1.5))\n", + " \n", + " if title:\n", + " ax[0].set_title(title)\n", + "\n", + " ax[1].plot(model.steps[0][1].centers_,\n", + " model.steps[1][1].coef_)\n", + " ax[1].set(xlabel='basis location',\n", + " ylabel='coefficient',\n", + " xlim=(0, 10))\n", + " \n", + "model = make_pipeline(GaussianFeatures(30), LinearRegression())\n", + "basis_plot(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The lower panel of this figure shows the amplitude of the basis function at each location.\n", + "This is typical over-fitting behavior when basis functions overlap: the coefficients of adjacent basis functions blow up and cancel each other out.\n", + "We know that such behavior is problematic, and it would be nice if we could limit such spikes expliticly in the model by penalizing large values of the model parameters.\n", + "Such a penalty is known as *regularization*, and comes in several forms." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Ridge regression ($L_2$ Regularization)\n", + "\n", + "Perhaps the most common form of regularization is known as *ridge regression* or $L_2$ *regularization*, sometimes also called *Tikhonov regularization*.\n", + "This proceeds by penalizing the sum of squares (2-norms) of the model coefficients; in this case, the penalty on the model fit would be \n", + "$$\n", + "P = \\alpha\\sum_{n=1}^N \\theta_n^2\n", + "$$\n", + "where $\\alpha$ is a free parameter that controls the strength of the penalty.\n", + "This type of penalized model is built into Scikit-Learn with the ``Ridge`` estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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4aejX3YcHpoXJ3O2twJIlY4G6sbWeFaX4+3YgLuUclZU1JH6XQVYrXgxDtA4J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\nlxcuLi4AODs7U15e3uh1Gxubhq13ly5dyh133EFAQMBN28ktrKRfT2+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Ridge\n", + "model = make_pipeline(GaussianFeatures(30), Ridge(alpha=0.1))\n", + "basis_plot(model, title='Ridge Regression')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The $\\alpha$ parameter is essentially a knob controlling the complexity of the resulting model.\n", + "In the limit $\\alpha \\to 0$, we recover the standard linear regression result; in the limit $\\alpha \\to \\infty$, all model responses will be suppressed.\n", + "One advantage of ridge regression in particular is that it can be computed very efficiently—at hardly more computational cost than the original linear regression model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Lasso regression ($L_1$ regularization)\n", + "\n", + "Another very common type of regularization is known as lasso, and involves penalizing the sum of absolute values (1-norms) of regression coefficients:\n", + "$$\n", + "P = \\alpha\\sum_{n=1}^N |\\theta_n|\n", + "$$\n", + "Though this is conceptually very similar to ridge regression, the results can differ surprisingly: for example, due to geometric reasons lasso regression tends to favor *sparse models* where possible: that is, it preferentially sets model coefficients to exactly zero.\n", + "\n", + "We can see this behavior in duplicating the ridge regression figure, but using L1-normalized coefficients:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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1lctWXOVzvGHXCT7acISwAC+eTRjhULMupGivA0ZjJT4B1dwweyu+QSZUFQ3c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\nfP7553z44Yd9HvPy8kIu7y3J6OzsjELRd97CxsZGW3p306ZNTJ48meB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C0JdVJ94RhNHO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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Lasso\n", + "model = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001))\n", + "basis_plot(model, title='Lasso Regression')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With the lasso regression penalty, the majority of the coefficients are exactly zero, with the functional behavior being modeled by a small subset of the available basis functions.\n", + "As with ridge regularization, the $\\alpha$ parameter tunes the strength of the penalty, and should be determined via, for example, cross-validation (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Example: Predicting Bicycle Traffic" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "As an example, let's take a look at whether we can predict the number of bicycle trips across Seattle's Fremont Bridge based on weather, season, and other factors.\n", + "We have seen this data already in [Working With Time Series](03.11-Working-with-Time-Series.ipynb).\n", + "\n", + "In this section, we will join the bike data with another dataset, and try to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor.\n", + "Fortunately, the NOAA makes available their daily [weather station data](http://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND) (I used station ID USW00024233) and we can easily use Pandas to join the two data sources.\n", + "We will perform a simple linear regression to relate weather and other information to bicycle counts, in order to estimate how a change in any one of these parameters affects the number of riders on a given day.\n", + "\n", + "In particular, this is an example of how the tools of Scikit-Learn can be used in a statistical modeling framework, in which the parameters of the model are assumed to have interpretable meaning.\n", + "As discussed previously, this is not a standard approach within machine learning, but such interpretation is possible for some models.\n", + "\n", + "Let's start by loading the two datasets, indexing by date:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "counts = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\n", + "weather = pd.read_csv('data/BicycleWeather.csv', index_col='DATE', parse_dates=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Next we will compute the total daily bicycle traffic, and put this in its own dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "daily = counts.resample('d').sum()\n", + "daily['Total'] = daily.sum(axis=1)\n", + "daily = daily[['Total']] # remove other columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We saw previously that the patterns of use generally vary from day to day; let's account for this in our data by adding binary columns that indicate the day of the week:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n", + "for i in range(7):\n", + " daily[days[i]] = (daily.index.dayofweek == i).astype(float)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Similarly, we might expect riders to behave differently on holidays; let's add an indicator of this as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from pandas.tseries.holiday import USFederalHolidayCalendar\n", + "cal = USFederalHolidayCalendar()\n", + "holidays = cal.holidays('2012', '2016')\n", + "daily = daily.join(pd.Series(1, index=holidays, name='holiday'))\n", + "daily['holiday'].fillna(0, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We also might suspect that the hours of daylight would affect how many people ride; let's use the standard astronomical calculation to add this information:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(8, 17)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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bA7KOZTzixaW5DKpgMKjweZN7zgBOa0GvQrSWI621w7MXFwEoQGvDXlycW0YV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\nDBoiaG0HaU0SutqWvulNb8KlS5fw1re+FTfeeCP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def hours_of_daylight(date, axis=23.44, latitude=47.61):\n", + " \"\"\"Compute the hours of daylight for the given date\"\"\"\n", + " days = (date - pd.datetime(2000, 12, 21)).days\n", + " m = (1. - np.tan(np.radians(latitude))\n", + " * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25)))\n", + " return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180.\n", + "\n", + "daily['daylight_hrs'] = list(map(hours_of_daylight, daily.index))\n", + "daily[['daylight_hrs']].plot()\n", + "plt.ylim(8, 17)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can also add the average temperature and total precipitation to the data.\n", + "In addition to the inches of precipitation, let's add a flag that indicates whether a day is dry (has zero precipitation):" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# temperatures are in 1/10 deg C; convert to C\n", + "weather['TMIN'] /= 10\n", + "weather['TMAX'] /= 10\n", + "weather['Temp (C)'] = 0.5 * (weather['TMIN'] + weather['TMAX'])\n", + "\n", + "# precip is in 1/10 mm; convert to inches\n", + "weather['PRCP'] /= 254\n", + "weather['dry day'] = (weather['PRCP'] == 0).astype(int)\n", + "\n", + "daily = daily.join(weather[['PRCP', 'Temp (C)', 'dry day']])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, let's add a counter that increases from day 1, and measures how many years have passed.\n", + "This will let us measure any observed annual increase or decrease in daily crossings:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "daily['annual'] = (daily.index - daily.index[0]).days / 365." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now our data is in order, and we can take a look at it:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TotalMonTueWedThuFriSatSunholidaydaylight_hrsPRCPTemp (C)dry dayannual
Date
2012-10-033521.00.00.01.00.00.00.00.00.011.2773590.013.351.00.000000
2012-10-043475.00.00.00.01.00.00.00.00.011.2191420.013.601.00.002740
2012-10-053148.00.00.00.00.01.00.00.00.011.1610380.015.301.00.005479
2012-10-062006.00.00.00.00.00.01.00.00.011.1030560.015.851.00.008219
2012-10-072142.00.00.00.00.00.00.01.00.011.0452080.015.851.00.010959
\n", + "
" + ], + "text/plain": [ + " Total Mon Tue Wed Thu Fri Sat Sun holiday daylight_hrs \\\n", + "Date \n", + "2012-10-03 3521.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 11.277359 \n", + "2012-10-04 3475.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 11.219142 \n", + "2012-10-05 3148.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 11.161038 \n", + "2012-10-06 2006.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 11.103056 \n", + "2012-10-07 2142.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 11.045208 \n", + "\n", + " PRCP Temp (C) dry day annual \n", + "Date \n", + "2012-10-03 0.0 13.35 1.0 0.000000 \n", + "2012-10-04 0.0 13.60 1.0 0.002740 \n", + "2012-10-05 0.0 15.30 1.0 0.005479 \n", + "2012-10-06 0.0 15.85 1.0 0.008219 \n", + "2012-10-07 0.0 15.85 1.0 0.010959 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "daily.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this in place, we can choose the columns to use, and fit a linear regression model to our data.\n", + "We will set ``fit_intercept = False``, because the daily flags essentially operate as their own day-specific intercepts:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# Drop any rows with null values\n", + "daily.dropna(axis=0, how='any', inplace=True)\n", + "\n", + "column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'holiday',\n", + " 'daylight_hrs', 'PRCP', 'dry day', 'Temp (C)', 'annual']\n", + "X = daily[column_names]\n", + "y = daily['Total']\n", + "\n", + "model = LinearRegression(fit_intercept=False)\n", + "model.fit(X, y)\n", + "daily['predicted'] = model.predict(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, we can compare the total and predicted bicycle traffic visually:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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6yhK2sKdrC3CCss1mX2PL+fj9zULn9EVMEWc7U9f/ggKYoH0o0hkQStE3lsPY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PYvuUgHUicN17a0JmvgILXslElv1eskpB38jF4CBgN3HImFTo3V3snk2m9ph8BOtYkj8j04It/D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\nknkuDQ357TeRqvGYqPYwzqjcGGPWMm6j0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "daily[['Total', 'predicted']].plot(alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "It is evident that we have missed some key features, especially during the summer time.\n", + "Either our features are not complete (i.e., people decide whether to ride to work based on more than just these) or there are some nonlinear relationships that we have failed to take into account (e.g., perhaps people ride less at both high and low temperatures).\n", + "Nevertheless, our rough approximation is enough to give us some insights, and we can take a look at the coefficients of the linear model to estimate how much each feature contributes to the daily bicycle count:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Mon 504.882756\n", + "Tue 610.233936\n", + "Wed 592.673642\n", + "Thu 482.358115\n", + "Fri 177.980345\n", + "Sat -1103.301710\n", + "Sun -1133.567246\n", + "holiday -1187.401381\n", + "daylight_hrs 128.851511\n", + "PRCP -664.834882\n", + "dry day 547.698592\n", + "Temp (C) 65.162791\n", + "annual 26.942713\n", + "dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = pd.Series(model.coef_, index=X.columns)\n", + "params" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "These numbers are difficult to interpret without some measure of their uncertainty.\n", + "We can compute these uncertainties quickly using bootstrap resamplings of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.utils import resample\n", + "np.random.seed(1)\n", + "err = np.std([model.fit(*resample(X, y)).coef_\n", + " for i in range(1000)], 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With these errors estimated, let's again look at the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " effect error\n", + "Mon 505.0 86.0\n", + "Tue 610.0 83.0\n", + "Wed 593.0 83.0\n", + "Thu 482.0 85.0\n", + "Fri 178.0 81.0\n", + "Sat -1103.0 80.0\n", + "Sun -1134.0 83.0\n", + "holiday -1187.0 163.0\n", + "daylight_hrs 129.0 9.0\n", + "PRCP -665.0 62.0\n", + "dry day 548.0 33.0\n", + "Temp (C) 65.0 4.0\n", + "annual 27.0 18.0\n" + ] + } + ], + "source": [ + "print(pd.DataFrame({'effect': params.round(0),\n", + " 'error': err.round(0)}))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We first see that there is a relatively stable trend in the weekly baseline: there are many more riders on weekdays than on weekends and holidays.\n", + "We see that for each additional hour of daylight, 129 ± 9 more people choose to ride; a temperature increase of one degree Celsius encourages 65 ± 4 people to grab their bicycle; a dry day means an average of 548 ± 33 more riders, and each inch of precipitation means 665 ± 62 more people leave their bike at home.\n", + "Once all these effects are accounted for, we see a modest increase of 27 ± 18 new daily riders each year.\n", + "\n", + "Our model is almost certainly missing some relevant information. For example, nonlinear effects (such as effects of precipitation *and* cold temperature) and nonlinear trends within each variable (such as disinclination to ride at very cold and very hot temperatures) cannot be accounted for in this model.\n", + "Additionally, we have thrown away some of the finer-grained information (such as the difference between a rainy morning and a rainy afternoon), and we have ignored correlations between days (such as the possible effect of a rainy Tuesday on Wednesday's numbers, or the effect of an unexpected sunny day after a streak of rainy days).\n", + "These are all potentially interesting effects, and you now have the tools to begin exploring them if you wish!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.06-Linear-Regression.md b/notebooks_v2/05.06-Linear-Regression.md new file mode 100644 index 00000000..3588f27e --- /dev/null +++ b/notebooks_v2/05.06-Linear-Regression.md @@ -0,0 +1,542 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) > + +Open in Colab + + + +# In Depth: Linear Regression + + +Just as naive Bayes (discussed earlier in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) is a good starting point for classification tasks, linear regression models are a good starting point for regression tasks. +Such models are popular because they can be fit very quickly, and are very interpretable. +You are probably familiar with the simplest form of a linear regression model (i.e., fitting a straight line to data) but such models can be extended to model more complicated data behavior. + +In this section we will start with a quick intuitive walk-through of the mathematics behind this well-known problem, before seeing how before moving on to see how linear models can be generalized to account for more complicated patterns in data. + +We begin with the standard imports: + + +```python deletable=true editable=true +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +import numpy as np +``` + + +## Simple Linear Regression + +We will start with the most familiar linear regression, a straight-line fit to data. +A straight-line fit is a model of the form +$$ +y = ax + b +$$ +where $a$ is commonly known as the *slope*, and $b$ is commonly known as the *intercept*. + +Consider the following data, which is scattered about a line with a slope of 2 and an intercept of -5: + + +```python deletable=true editable=true +rng = np.random.RandomState(1) +x = 10 * rng.rand(50) +y = 2 * x - 5 + rng.randn(50) +plt.scatter(x, y); +``` + + +We can use Scikit-Learn's ``LinearRegression`` estimator to fit this data and construct the best-fit line: + + +```python deletable=true editable=true +from sklearn.linear_model import LinearRegression +model = LinearRegression(fit_intercept=True) + +model.fit(x[:, np.newaxis], y) + +xfit = np.linspace(0, 10, 1000) +yfit = model.predict(xfit[:, np.newaxis]) + +plt.scatter(x, y) +plt.plot(xfit, yfit); +``` + + +The slope and intercept of the data are contained in the model's fit parameters, which in Scikit-Learn are always marked by a trailing underscore. +Here the relevant parameters are ``coef_`` and ``intercept_``: + + +```python deletable=true editable=true +print("Model slope: ", model.coef_[0]) +print("Model intercept:", model.intercept_) +``` + + +We see that the results are very close to the inputs, as we might hope. + + + +The ``LinearRegression`` estimator is much more capable than this, however—in addition to simple straight-line fits, it can also handle multidimensional linear models of the form +$$ +y = a_0 + a_1 x_1 + a_2 x_2 + \cdots +$$ +where there are multiple $x$ values. +Geometrically, this is akin to fitting a plane to points in three dimensions, or fitting a hyper-plane to points in higher dimensions. + +The multidimensional nature of such regressions makes them more difficult to visualize, but we can see one of these fits in action by building some example data, using NumPy's matrix multiplication operator: + + +```python deletable=true editable=true +rng = np.random.RandomState(1) +X = 10 * rng.rand(100, 3) +y = 0.5 + np.dot(X, [1.5, -2., 1.]) + +model.fit(X, y) +print(model.intercept_) +print(model.coef_) +``` + + +Here the $y$ data is constructed from three random $x$ values, and the linear regression recovers the coefficients used to construct the data. + +In this way, we can use the single ``LinearRegression`` estimator to fit lines, planes, or hyperplanes to our data. +It still appears that this approach would be limited to strictly linear relationships between variables, but it turns out we can relax this as well. + + + +## Basis Function Regression + +One trick you can use to adapt linear regression to nonlinear relationships between variables is to transform the data according to *basis functions*. +We have seen one version of this before, in the ``PolynomialRegression`` pipeline used in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) and [Feature Engineering](05.04-Feature-Engineering.ipynb). +The idea is to take our multidimensional linear model: +$$ +y = a_0 + a_1 x_1 + a_2 x_2 + a_3 x_3 + \cdots +$$ +and build the $x_1, x_2, x_3,$ and so on, from our single-dimensional input $x$. +That is, we let $x_n = f_n(x)$, where $f_n()$ is some function that transforms our data. + +For example, if $f_n(x) = x^n$, our model becomes a polynomial regression: +$$ +y = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \cdots +$$ +Notice that this is *still a linear model*—the linearity refers to the fact that the coefficients $a_n$ never multiply or divide each other. +What we have effectively done is taken our one-dimensional $x$ values and projected them into a higher dimension, so that a linear fit can fit more complicated relationships between $x$ and $y$. + + + +### Polynomial basis functions + +This polynomial projection is useful enough that it is built into Scikit-Learn, using the ``PolynomialFeatures`` transformer: + + +```python deletable=true editable=true +from sklearn.preprocessing import PolynomialFeatures +x = np.array([2, 3, 4]) +poly = PolynomialFeatures(3, include_bias=False) +poly.fit_transform(x[:, None]) +``` + + +We see here that the transformer has converted our one-dimensional array into a three-dimensional array by taking the exponent of each value. +This new, higher-dimensional data representation can then be plugged into a linear regression. + +As we saw in [Feature Engineering](05.04-Feature-Engineering.ipynb), the cleanest way to accomplish this is to use a pipeline. +Let's make a 7th-degree polynomial model in this way: + + +```python deletable=true editable=true +from sklearn.pipeline import make_pipeline +poly_model = make_pipeline(PolynomialFeatures(7), + LinearRegression()) +``` + + +With this transform in place, we can use the linear model to fit much more complicated relationships between $x$ and $y$. +For example, here is a sine wave with noise: + + +```python deletable=true editable=true +rng = np.random.RandomState(1) +x = 10 * rng.rand(50) +y = np.sin(x) + 0.1 * rng.randn(50) + +poly_model.fit(x[:, np.newaxis], y) +yfit = poly_model.predict(xfit[:, np.newaxis]) + +plt.scatter(x, y) +plt.plot(xfit, yfit); +``` + + +Our linear model, through the use of 7th-order polynomial basis functions, can provide an excellent fit to this non-linear data! + + + +### Gaussian basis functions + +Of course, other basis functions are possible. +For example, one useful pattern is to fit a model that is not a sum of polynomial bases, but a sum of Gaussian bases. +The result might look something like the following figure: + + + +![](figures/05.06-gaussian-basis.png) +[figure source in Appendix](#Gaussian-Basis) + + + +The shaded regions in the plot are the scaled basis functions, and when added together they reproduce the smooth curve through the data. +These Gaussian basis functions are not built into Scikit-Learn, but we can write a custom transformer that will create them, as shown here and illustrated in the following figure (Scikit-Learn transformers are implemented as Python classes; reading Scikit-Learn's source is a good way to see how they can be created): + + +```python deletable=true editable=true +from sklearn.base import BaseEstimator, TransformerMixin + +class GaussianFeatures(BaseEstimator, TransformerMixin): + """Uniformly spaced Gaussian features for one-dimensional input""" + + def __init__(self, N, width_factor=2.0): + self.N = N + self.width_factor = width_factor + + @staticmethod + def _gauss_basis(x, y, width, axis=None): + arg = (x - y) / width + return np.exp(-0.5 * np.sum(arg ** 2, axis)) + + def fit(self, X, y=None): + # create N centers spread along the data range + self.centers_ = np.linspace(X.min(), X.max(), self.N) + self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0]) + return self + + def transform(self, X): + return self._gauss_basis(X[:, :, np.newaxis], self.centers_, + self.width_, axis=1) + +gauss_model = make_pipeline(GaussianFeatures(20), + LinearRegression()) +gauss_model.fit(x[:, np.newaxis], y) +yfit = gauss_model.predict(xfit[:, np.newaxis]) + +plt.scatter(x, y) +plt.plot(xfit, yfit) +plt.xlim(0, 10); +``` + + +We put this example here just to make clear that there is nothing magic about polynomial basis functions: if you have some sort of intuition into the generating process of your data that makes you think one basis or another might be appropriate, you can use them as well. + + + +## Regularization + +The introduction of basis functions into our linear regression makes the model much more flexible, but it also can very quickly lead to over-fitting (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this). +For example, if we choose too many Gaussian basis functions, we end up with results that don't look so good: + + +```python deletable=true editable=true +model = make_pipeline(GaussianFeatures(30), + LinearRegression()) +model.fit(x[:, np.newaxis], y) + +plt.scatter(x, y) +plt.plot(xfit, model.predict(xfit[:, np.newaxis])) + +plt.xlim(0, 10) +plt.ylim(-1.5, 1.5); +``` + + +With the data projected to the 30-dimensional basis, the model has far too much flexibility and goes to extreme values between locations where it is constrained by data. +We can see the reason for this if we plot the coefficients of the Gaussian bases with respect to their locations: + + +```python deletable=true editable=true +def basis_plot(model, title=None): + fig, ax = plt.subplots(2, sharex=True) + model.fit(x[:, np.newaxis], y) + ax[0].scatter(x, y) + ax[0].plot(xfit, model.predict(xfit[:, np.newaxis])) + ax[0].set(xlabel='x', ylabel='y', ylim=(-1.5, 1.5)) + + if title: + ax[0].set_title(title) + + ax[1].plot(model.steps[0][1].centers_, + model.steps[1][1].coef_) + ax[1].set(xlabel='basis location', + ylabel='coefficient', + xlim=(0, 10)) + +model = make_pipeline(GaussianFeatures(30), LinearRegression()) +basis_plot(model) +``` + + +The lower panel of this figure shows the amplitude of the basis function at each location. +This is typical over-fitting behavior when basis functions overlap: the coefficients of adjacent basis functions blow up and cancel each other out. +We know that such behavior is problematic, and it would be nice if we could limit such spikes expliticly in the model by penalizing large values of the model parameters. +Such a penalty is known as *regularization*, and comes in several forms. + + + +### Ridge regression ($L_2$ Regularization) + +Perhaps the most common form of regularization is known as *ridge regression* or $L_2$ *regularization*, sometimes also called *Tikhonov regularization*. +This proceeds by penalizing the sum of squares (2-norms) of the model coefficients; in this case, the penalty on the model fit would be +$$ +P = \alpha\sum_{n=1}^N \theta_n^2 +$$ +where $\alpha$ is a free parameter that controls the strength of the penalty. +This type of penalized model is built into Scikit-Learn with the ``Ridge`` estimator: + + +```python deletable=true editable=true +from sklearn.linear_model import Ridge +model = make_pipeline(GaussianFeatures(30), Ridge(alpha=0.1)) +basis_plot(model, title='Ridge Regression') +``` + + +The $\alpha$ parameter is essentially a knob controlling the complexity of the resulting model. +In the limit $\alpha \to 0$, we recover the standard linear regression result; in the limit $\alpha \to \infty$, all model responses will be suppressed. +One advantage of ridge regression in particular is that it can be computed very efficiently—at hardly more computational cost than the original linear regression model. + + + +### Lasso regression ($L_1$ regularization) + +Another very common type of regularization is known as lasso, and involves penalizing the sum of absolute values (1-norms) of regression coefficients: +$$ +P = \alpha\sum_{n=1}^N |\theta_n| +$$ +Though this is conceptually very similar to ridge regression, the results can differ surprisingly: for example, due to geometric reasons lasso regression tends to favor *sparse models* where possible: that is, it preferentially sets model coefficients to exactly zero. + +We can see this behavior in duplicating the ridge regression figure, but using L1-normalized coefficients: + + +```python deletable=true editable=true +from sklearn.linear_model import Lasso +model = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001)) +basis_plot(model, title='Lasso Regression') +``` + + +With the lasso regression penalty, the majority of the coefficients are exactly zero, with the functional behavior being modeled by a small subset of the available basis functions. +As with ridge regularization, the $\alpha$ parameter tunes the strength of the penalty, and should be determined via, for example, cross-validation (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this). + + + +## Example: Predicting Bicycle Traffic + + + +As an example, let's take a look at whether we can predict the number of bicycle trips across Seattle's Fremont Bridge based on weather, season, and other factors. +We have seen this data already in [Working With Time Series](03.11-Working-with-Time-Series.ipynb). + +In this section, we will join the bike data with another dataset, and try to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor. +Fortunately, the NOAA makes available their daily [weather station data](http://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND) (I used station ID USW00024233) and we can easily use Pandas to join the two data sources. +We will perform a simple linear regression to relate weather and other information to bicycle counts, in order to estimate how a change in any one of these parameters affects the number of riders on a given day. + +In particular, this is an example of how the tools of Scikit-Learn can be used in a statistical modeling framework, in which the parameters of the model are assumed to have interpretable meaning. +As discussed previously, this is not a standard approach within machine learning, but such interpretation is possible for some models. + +Let's start by loading the two datasets, indexing by date: + + +```python deletable=true editable=true +# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD +``` + +```python deletable=true editable=true +import pandas as pd +counts = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True) +weather = pd.read_csv('data/BicycleWeather.csv', index_col='DATE', parse_dates=True) +``` + + +Next we will compute the total daily bicycle traffic, and put this in its own dataframe: + + +```python deletable=true editable=true +daily = counts.resample('d').sum() +daily['Total'] = daily.sum(axis=1) +daily = daily[['Total']] # remove other columns +``` + + +We saw previously that the patterns of use generally vary from day to day; let's account for this in our data by adding binary columns that indicate the day of the week: + + +```python deletable=true editable=true +days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] +for i in range(7): + daily[days[i]] = (daily.index.dayofweek == i).astype(float) +``` + + +Similarly, we might expect riders to behave differently on holidays; let's add an indicator of this as well: + + +```python deletable=true editable=true +from pandas.tseries.holiday import USFederalHolidayCalendar +cal = USFederalHolidayCalendar() +holidays = cal.holidays('2012', '2016') +daily = daily.join(pd.Series(1, index=holidays, name='holiday')) +daily['holiday'].fillna(0, inplace=True) +``` + + +We also might suspect that the hours of daylight would affect how many people ride; let's use the standard astronomical calculation to add this information: + + +```python deletable=true editable=true +def hours_of_daylight(date, axis=23.44, latitude=47.61): + """Compute the hours of daylight for the given date""" + days = (date - pd.datetime(2000, 12, 21)).days + m = (1. - np.tan(np.radians(latitude)) + * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25))) + return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180. + +daily['daylight_hrs'] = list(map(hours_of_daylight, daily.index)) +daily[['daylight_hrs']].plot() +plt.ylim(8, 17) +``` + + +We can also add the average temperature and total precipitation to the data. +In addition to the inches of precipitation, let's add a flag that indicates whether a day is dry (has zero precipitation): + + +```python deletable=true editable=true +# temperatures are in 1/10 deg C; convert to C +weather['TMIN'] /= 10 +weather['TMAX'] /= 10 +weather['Temp (C)'] = 0.5 * (weather['TMIN'] + weather['TMAX']) + +# precip is in 1/10 mm; convert to inches +weather['PRCP'] /= 254 +weather['dry day'] = (weather['PRCP'] == 0).astype(int) + +daily = daily.join(weather[['PRCP', 'Temp (C)', 'dry day']]) +``` + + +Finally, let's add a counter that increases from day 1, and measures how many years have passed. +This will let us measure any observed annual increase or decrease in daily crossings: + + +```python deletable=true editable=true +daily['annual'] = (daily.index - daily.index[0]).days / 365. +``` + + +Now our data is in order, and we can take a look at it: + + +```python deletable=true editable=true +daily.head() +``` + + +With this in place, we can choose the columns to use, and fit a linear regression model to our data. +We will set ``fit_intercept = False``, because the daily flags essentially operate as their own day-specific intercepts: + + +```python deletable=true editable=true +# Drop any rows with null values +daily.dropna(axis=0, how='any', inplace=True) + +column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'holiday', + 'daylight_hrs', 'PRCP', 'dry day', 'Temp (C)', 'annual'] +X = daily[column_names] +y = daily['Total'] + +model = LinearRegression(fit_intercept=False) +model.fit(X, y) +daily['predicted'] = model.predict(X) +``` + + +Finally, we can compare the total and predicted bicycle traffic visually: + + +```python deletable=true editable=true +daily[['Total', 'predicted']].plot(alpha=0.5); +``` + + +It is evident that we have missed some key features, especially during the summer time. +Either our features are not complete (i.e., people decide whether to ride to work based on more than just these) or there are some nonlinear relationships that we have failed to take into account (e.g., perhaps people ride less at both high and low temperatures). +Nevertheless, our rough approximation is enough to give us some insights, and we can take a look at the coefficients of the linear model to estimate how much each feature contributes to the daily bicycle count: + + +```python deletable=true editable=true +params = pd.Series(model.coef_, index=X.columns) +params +``` + + +These numbers are difficult to interpret without some measure of their uncertainty. +We can compute these uncertainties quickly using bootstrap resamplings of the data: + + +```python deletable=true editable=true +from sklearn.utils import resample +np.random.seed(1) +err = np.std([model.fit(*resample(X, y)).coef_ + for i in range(1000)], 0) +``` + + +With these errors estimated, let's again look at the results: + + +```python deletable=true editable=true +print(pd.DataFrame({'effect': params.round(0), + 'error': err.round(0)})) +``` + + +We first see that there is a relatively stable trend in the weekly baseline: there are many more riders on weekdays than on weekends and holidays. +We see that for each additional hour of daylight, 129 ± 9 more people choose to ride; a temperature increase of one degree Celsius encourages 65 ± 4 people to grab their bicycle; a dry day means an average of 548 ± 33 more riders, and each inch of precipitation means 665 ± 62 more people leave their bike at home. +Once all these effects are accounted for, we see a modest increase of 27 ± 18 new daily riders each year. + +Our model is almost certainly missing some relevant information. For example, nonlinear effects (such as effects of precipitation *and* cold temperature) and nonlinear trends within each variable (such as disinclination to ride at very cold and very hot temperatures) cannot be accounted for in this model. +Additionally, we have thrown away some of the finer-grained information (such as the difference between a rainy morning and a rainy afternoon), and we have ignored correlations between days (such as the possible effect of a rainy Tuesday on Wednesday's numbers, or the effect of an unexpected sunny day after a streak of rainy days). +These are all potentially interesting effects, and you now have the tools to begin exploring them if you wish! + + + + +< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.07-Support-Vector-Machines.ipynb b/notebooks_v2/05.07-Support-Vector-Machines.ipynb new file mode 100644 index 00000000..ab685639 --- /dev/null +++ b/notebooks_v2/05.07-Support-Vector-Machines.ipynb @@ -0,0 +1,1047 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In-Depth: Support Vector Machines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression.\n", + "In this section, we will develop the intuition behind support vector machines and their use in classification problems.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "\n", + "# use seaborn plotting defaults\n", + "import seaborn as sns; sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motivating Support Vector Machines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As part of our disussion of Bayesian classification (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), we learned a simple model describing the distribution of each underlying class, and used these generative models to probabilistically determine labels for new points.\n", + "That was an example of *generative classification*; here we will consider instead *discriminative classification*: rather than modeling each class, we simply find a line or curve (in two dimensions) or manifold (in multiple dimensions) that divides the classes from each other.\n", + "\n", + "As an example of this, consider the simple case of a classification task, in which the two classes of points are well separated:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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gkfH8fCA/vye8vAqOcUt5DpVNJhj55cndu3fxxhtvYPjw4Rg4cGCZnpOUlGHM\nS0mGp6eLxc8B4DykxBrmAFjHPB7PYe/eFQgPHwMnp+KP2bvXBR4ep+Du7m7+gGVkCfvi2rXjuHTp\nLfTrdxJubsCVK2rs3Nkd4eG/wcnJySLmUBbGvrkw6hNzcnIyxo4di08//RTt27c36oWJiKSoRo3G\nuHbNAS1a5BTblpzsCR8f2/0kZyq+voGoVy8Ou3atQE7O36hRoy0GDuwsdizJMKqYZ82ahfT0dMyc\nORMzZsyATCbDnDlzoFKpTJ2PiMisGjRogfXrQ9CixfYi41otkJbWnf/PmYhCoUCnTsPEjiFJRhXz\npEmTMGnSJFNnISKShPbtf8bcua8jJGQfGjbU4OhRV5w+HY7evaeJHY1sAE+nJiJ6iqdnLTz33Gqc\nP38Ux4+fQePGnfD88w3EjkU2gsVMRFSCpk1bo2nT1mLHIBsj7XP+iYiIbAyLmYiISEJYzERERBLC\nYiYiIpIQFjMREZGEsJiJyCJlZ2fj6NFtuHz5tNhRiEyKxUxEFmfnzu9x8mRHBAcPRtWq3bBpU1/c\nuMGCJuvA65iJyKLs3x+Ljh2noW5dLQCgenUtmjXbg9jYcahVK45LZpLF4ydmIrIo2dmrCkv5SQMG\nnMaBA4tESERkWixmIrIoavV9g+MuLkBe3m0zpyEyPR7KJqJiNBoNdu+eDqXyAGQyPTSaQISEvIcq\nVaqKHQ0aTW0Axb9PTk6WwcGhsfkDEZkYi5mIitDpdPjzzyiMHbsDSmXBmF6/B/PmHUS3bmvg7Ows\naj4PjxE4e3Yf/P0zCscEAVi7ti369h0sYjIi02AxE9mIy5dP4MaNpbCz00Clao8OHYbAzs6u2OP2\n7YtFTMw/pQwAcjkwcuRhLF36M8LDJ5gxdXGBgf1x8OADnDkzF35+55CW5oyEhBC0b/+1wfmYgyAI\n+Ouv36HTbYOdnQY5Oc3Qrt2/Ua2apyh5yLKxmIlswI4d36NRo+8RHZ0JAEhO/g3Ll69A//4LoFar\nizxWpzsMF5fiP0OhAJTK4+aI+0zt24+AIAzH3bt34OvrjIAAV1HzrFv3FgYNmo9q1QQAgCDswKJF\nuxAYuByenrVEzUaWh8VMZOVu3ryE+vV/QFBQZuGYh4eAsWO3YPny71GjRjskJi6CUpkErbYOkpPT\nS/xZer29OSKXiUwmQ61atcWOgXPnDqJTp2WFpQwAMhkQHX0aCxdOR69e34mYjiwRi5nIyl28uATR\n0Q+LjavmoKsWAAAYo0lEQVRUQHb2cnh5zUCPHv98X7t7txvWrVPgued0RR6fnCyDUtmz0vNamoSE\nTejSJafYuEwG2NtL4wgDWRZeLkVk5eRyLWSykrYloHnzjCJjXbqk4fZtL5w65Vg4dvWqCqtXj0Bo\n6PDKjGqRBEEBQShpm9LwBqJS8BMzkZWrXj0cV67MQoMGuUXGBQFQKjUGnxMQ8BC3bi3A2bO7AOSj\nevUIDBjQufLDWqBmzWJw4MCv6NjxQZFxnQ7Ize0oUiqyZCxmIivXsmVnrFo1GB4ei1D10WXIggDM\nmeOLoKBrBp+j0SjRpElruLry0PWz1Knji507/43jx79HYGDB0YfUVGDZsh7o0+d9kdORJWIxE9mA\nAQNmYtu2YOj1OyCX5yAnpwVCQ9/CsWMvIDg4vtjj795tj5YtxV9MxFJ07/4uLl/uhoULl0Ch0ECl\naosBA4YVuXwrPz8f+/cvhla7G4AMKlUXhIREQS7nN4pUFIuZyAbI5XJ07foygJeLjNeq9Qk2bHgL\nffrcglwOaLXAqlXN0LjxZHGCWrCGDQPRsGGgwW35+flYvfpFvPDCGlSrVjCWkrIEy5Ztx6BBc1nO\nVASLmciG+ft3R0pKHBYvngWF4j70eh+0b/+y6Kt7WZt9+xYgOnoNXJ+43NrdHXjhhZXYtasHT6qj\nIljMRDbO3d0T4eEfix3Dqul0u4uU8mPVqgF5ebsBsJjpHzx+QkRU6Uq4nuqZ28gWsZiJiCqZnV0o\nMjKKjz98CCgUvAyNimIxE0lQZmYGDh7ciIsXuXKUNQgJGYkFC/oWKef0dGDhwucQEhIjXjCSJH7H\nTCQhgiBg27Yv4ea2GJ06/Y3ERBU2bWqHpk2/Qf36/mLHswkajQYHD66ATpeD4OBBcHNzr/DPVCgU\nGDAgFlu2zEd+/l8QBBkUis4YNGikaHfEIuliMRNJyJ49v6JHj/+gRo2Cdard3LRo0mQP5s9/DXXq\n7IBCwX+ylSk+fiWysr5C375XoFYDO3d+i6NHxyAsrOitLi9fPo7r1xdCqUyDVuuH9u1fhaurW6k/\nW6FQoEuXMQDGVOIMyBrwXzmRhOTlrS0s5Sf1738cu3atQKdOw0RIZRvu3LkFhWICBg9OLBwLC7uH\nmze/x+HDjdC27SAAwP7981Cr1qeIiSlYglOnA1asWIfGjRegTp0GomQn68LvmIkkRKW6b3DczQ3I\nyblh3jA25tCh/6FHj8Ri497euUhPXw0AePAgDcD3aNPmn3WxFQpg2LBzOHNmqrmikpVjMRNJiEZT\n1+B4QoId3NxamjmNbVEo0kq8C5dGcxnbtvXFypX+6N37psHHODkdgVDSbaaIyoHFTCQhbm7Dcf58\n0VW3BAHYuDEEwcERIqWyDXZ2zZCVZXhbbu4VREfvQcuWmShp9UyZTM9iJpNgMRNJSOvWg3D58tdY\nsiQYBw86YfPmGpg3bwi6dJkHWUkf58gkevT4F5YubV3s3spr19qjVy8tACA0FNi50/Dzs7KCuOY1\nmUSFTv46efIkvvvuO8TGxpoqD5HN69BhJARhBO7fvw8vLycEB3PdanNQq9Vo334hYmOnwNHxIBQK\nLbKzA/HgwQk8//zfAAAXF0AmA86dA5o1K3ieIABr1jREo0YfipierInRxTxnzhysXbsWTk5OpsxD\nRABkMhm8vLzEjmFzPD1rIiJiFgRBgCAIkMvl2LEjDMDfhY8JDwdOnACWLgXS0lrByakrgoNfg6dn\nTdFyp6YmIz7+D8hk2fD3fw41a7YSLQtVnNHHXby9vTFjxgxTZiEikgSZTFZ4WFoQIvDgQdHtAQFA\nfn4LPPfcTvTu/YWopXzwYCyuX++IYcM+Q1TUt3BwCMPq1S8hPz9ftExUMUZ/Yg4LC0NCQoIpsxCR\nBTp//iAOHtyPrCwV2rUbBWdnF9GyJCRcx9mz86BQZEGlCkaHDkMrvLJW9+7/xurVd+DruxKhoSlI\nSpJj69ZANGjwDZRKpYmSGycp6R7U6s8RHv7PZV4NG2pQq9YyrFvXDD17vitiOjKWTKjAaYQJCQkY\nP348lixZYspMRGQBdDodFi0ahXbtVqNx4xzk5QFbt/rA3f0btG8/xOx5du6cDZVqEkJCkiGTAamp\nwNq1PTF06BqTfOV29+5txMdvgJtbXXTo0EcSJ3qtXfs5+vefbPBM8TVrumHAgBLOVCNJq/DKX+Xp\n9aQkA7dXsSCeni4WPweA85ASS57D1q1fIzJyERwcCv6sVAJ9+17HunXjce1aB7i4VDFblrS0FGi1\nk9G9e3LhWLVqwMiR27Fw4YeIiJj2zJ/xrH2hUFRFu3YF901OSSnhuiozy85OLfHyrfz8DIv9u2XJ\n/y6e5Olp3NGjCr/l4yUcRLZJqdxZWMpPioi4hcOH55k1y9Gj8xEWdq/YuJ0dYG+/36xZzMnNrRNu\n3TJ8OD0np6mZ05CpVKiYa9euzcPYRDbKzs7wp0alEhCEdDOn0aKkr5Llcq15o5hRUFA4Nm7sjdzc\nouMbNvihadM3xQlFFcabWBCRUXJymgA4XWz8yhU1vLy6mDWLn19/HDv2XwQFZRbbptFY71KmMpkM\n/fv/juXLv4FK9Rfs7HIABMHbexzq1WsidjwyEouZiIzSoMFr2LHjIHr0uF04ptEAO3b0waBBoWbN\n4uvbDOvXD4O3929wd9cXjq9d2wBNm75j1izmplKp0KvXx4V/ruzvZ9PTH+LIkRWws1OgXbuhcDD0\nfQZVCIuZiIzi5xeMK1fmYcGCmahS5SI0GgdotV3Rv/+EZz+5EvTr9z127WoOnW4L7OwykJPTFC1b\nvo5atXxFyWON4uL+iypVfsGQIXeg0wGbNv0HSuUHaNcuWuxoVqVCl0uVl6WfZWdNZwpyHtJgDXMA\nrGMe1jAHoPLmcfz4Vnh7j0LjxkXPLdi71x1q9SbUr2+6Q+fWtC+MIf6FeEREJHnJySuKlTIAdOqU\ngkuX5pk/kBVjMRMR0TMplamlbHtQ4jYqP37HTERmp9FocODA7xCEU0hOzoJWq0KNGtVRo0ZfNG8e\nInY8MkCj8YMgFNxd60laLaDTNRQnlJViMRORWT18mIrdu19AdPShwgVKTp4E7t4Fateeg3XrYtC/\n/3SLWbwoPf0hDh2aAzu7VKhULdChQ2SF1+eWooCA17B+/RY899y1IuNLl7ZAp06viJTKOrGYicis\n9u37EmPGHCryyatVq4K1rd3cNOjb93fs398BISFDxQtZRqdPb0V6+vsYOvQ6FAogORlYteoPdOu2\nEFWrVhM7nknVqOGN7Ox5WLDgOzg5HYNeb4esrLYIDPwUzs68Z7gpsZiJyKwcHQ8XOxwKAF26ABs2\nAM89p0dOzhYA0i7mvLw83L//KYYNu1445uEBvPzyPsyf/zH69JkpYrrK4esbAF/fBdDr9ZDJZBZz\nVMPS8OQvIjIrmUxfwjjw+OJNhSLX4GOk5NChtejT51yxcZkMcHLaV64b/FgauVzOUq5ELGYiMqvs\n7ACD40eOAIGBBScT5eUZfoyU5OSkoaQjuEqlBnq94TcgRM/CYiaiCktNTcKmTR9g164wxMX1wubN\nk5GVZfgmFwEBH2DpUn88+YE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "X, y = make_blobs(n_samples=50, centers=2,\n", + " random_state=0, cluster_std=0.60)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A linear discriminative classifier would attempt to draw a straight line separating the two sets of data, and thereby create a model for classification.\n", + "For two dimensional data like that shown here, this is a task we could do by hand.\n", + "But immediately we see a problem: there is more than one possible dividing line that can perfectly discriminate between the two classes!\n", + "\n", + "We can draw them as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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x97PhNXEy5tLmAqQH8501Bzf+8nek/rAdgZkZKDE0RErASAz67B8wNjZu/Qm6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\nHgGCrJhG2CJ5R7dhmZmZrFixhKVLF3H16hWcnJwYNmwEUVE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xfit = np.linspace(-1, 3.5)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n", + "plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\n", + "\n", + "for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\n", + " plt.plot(xfit, m * xfit + b, '-k')\n", + "\n", + "plt.xlim(-1, 3.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are three *very* different separators which, nevertheless, perfectly discriminate between these samples.\n", + "Depending on which you choose, a new data point (e.g., the one marked by the \"X\" in this plot) will be assigned a different label!\n", + "Evidently our simple intuition of \"drawing a line between classes\" is not enough, and we need to think a bit deeper." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Vector Machines: Maximizing the *Margin*\n", + "\n", + "Support vector machines offer one way to improve on this.\n", + "The intuition is this: rather than simply drawing a zero-width line between the classes, we can draw around each line a *margin* of some width, up to the nearest point.\n", + "Here is an example of how this might look:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ly5cjKioKMpnMY9a0H1cFhUIJrVYLlcq38eOeQMJMDBi++ugYHnL0yXVFDUB7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xfit = np.linspace(-1, 3.5)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n", + "\n", + "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n", + " yfit = m * xfit + b\n", + " plt.plot(xfit, yfit, '-k')\n", + " plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\n", + " color='#AAAAAA', alpha=0.4)\n", + "\n", + "plt.xlim(-1, 3.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In support vector machines, the line that maximizes this margin is the one we will choose as the optimal model.\n", + "Support vector machines are an example of such a *maximum margin* estimator." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting a support vector machine\n", + "\n", + "Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data.\n", + "For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=10000000000.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.svm import SVC # \"Support vector classifier\"\n", + "model = SVC(kernel='linear', C=1E10)\n", + "model.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def plot_svc_decision_function(model, ax=None, plot_support=True):\n", + " \"\"\"Plot the decision function for a 2D SVC\"\"\"\n", + " if ax is None:\n", + " ax = plt.gca()\n", + " xlim = ax.get_xlim()\n", + " ylim = ax.get_ylim()\n", + " \n", + " # create grid to evaluate model\n", + " x = np.linspace(xlim[0], xlim[1], 30)\n", + " y = np.linspace(ylim[0], ylim[1], 30)\n", + " Y, X = np.meshgrid(y, x)\n", + " xy = np.vstack([X.ravel(), Y.ravel()]).T\n", + " P = model.decision_function(xy).reshape(X.shape)\n", + " \n", + " # plot decision boundary and margins\n", + " ax.contour(X, Y, P, colors='k',\n", + " levels=[-1, 0, 1], alpha=0.5,\n", + " linestyles=['--', '-', '--'])\n", + " \n", + " # plot support vectors\n", + " if plot_support:\n", + " ax.scatter(model.support_vectors_[:, 0],\n", + " model.support_vectors_[:, 1],\n", + " s=300, linewidth=1, facecolors='none');\n", + " ax.set_xlim(xlim)\n", + " ax.set_ylim(ylim)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9vQWXTxzDqVvJiOzeE0kBgdYOi9gJrVaLBw/uG565lk4fMwzDeRepVCqwcOHv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\nAH6sncTGlP8LAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n", + "plot_svc_decision_function(model);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the dividing line that maximizes the margin between the two sets of points.\n", + "Notice that a few of the training points just touch the margin: they are indicated by the black circles in this figure.\n", + "These points are the pivotal elements of this fit, and are known as the *support vectors*, and give the algorithm its name.\n", + "In Scikit-Learn, the identity of these points are stored in the ``support_vectors_`` attribute of the classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.44359863, 3.11530945],\n", + " [ 2.33812285, 3.43116792],\n", + " [ 2.06156753, 1.96918596]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.support_vectors_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A key to this classifier's success is that for the fit, only the position of the support vectors matter; any points further from the margin which are on the correct side do not modify the fit!\n", + "Technically, this is because these points do not contribute to the loss function used to fit the model, so their position and number do not matter so long as they do not cross the margin.\n", + "\n", + "We can see this, for example, if we plot the model learned from the first 60 points and first 120 points of this dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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5RaW+A4rF04j/oigKp14fx6Dz57KmDZkCNaKj2Hv7FpXyuUPIs+5BpZatOe7j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\n5InHiusuvV7PnTu5eHh44vKgyaVNTKvV4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_svm(N=10, ax=None):\n", + " X, y = make_blobs(n_samples=200, centers=2,\n", + " random_state=0, cluster_std=0.60)\n", + " X = X[:N]\n", + " y = y[:N]\n", + " model = SVC(kernel='linear', C=1E10)\n", + " model.fit(X, y)\n", + " \n", + " ax = ax or plt.gca()\n", + " ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n", + " ax.set_xlim(-1, 4)\n", + " ax.set_ylim(-1, 6)\n", + " plot_svc_decision_function(model, ax)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "for axi, N in zip(ax, [60, 120]):\n", + " plot_svm(N, axi)\n", + " axi.set_title('N = {0}'.format(N))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the left panel, we see the model and the support vectors for 60 training points.\n", + "In the right panel, we have doubled the number of training points, but the model has not changed: the three support vectors from the left panel are still the support vectors from the right panel.\n", + "This insensitivity to the exact behavior of distant points is one of the strengths of the SVM model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are running this notebook live, you can use IPython's interactive widgets to view this feature of the SVM model interactively:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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aZqe7u4vS0jKuuOJqv3HJySnv+Tgnn7yFu+56gE9+8lrOOec8duz4FOvXb/Ab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\n/lxbt27V+vXrTceylebmZjkcDl28eFEtLS06c+Y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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ipywidgets import interact, fixed\n", + "interact(plot_svm, N=[10, 200], ax=fixed(None));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Beyond linear boundaries: Kernel SVM\n", + "\n", + "Where SVM becomes extremely powerful is when it is combined with *kernels*.\n", + "We have seen a version of kernels before, in the basis function regressions of [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\n", + "There we projected our data into higher-dimensional space defined by polynomials and Gaussian basis functions, and thereby were able to fit for nonlinear relationships with a linear classifier.\n", + "\n", + "In SVM models, we can use a version of the same idea.\n", + "To motivate the need for kernels, let's look at some data that is not linearly separable:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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k5IeYMePsbaXrWLPmDGr1WgIDw+wWmxCNkSRuUSG9Xs/mf36A8uc1uKWmUhga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\n7DNhxYoVTvv4+/n05ZdfCn/4wx8EQRCEQ4cOCffff7+4zWKxCNOnTxc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets.samples_generator import make_circles\n", + "X, y = make_circles(100, factor=.1, noise=.1)\n", + "\n", + "clf = SVC(kernel='linear').fit(X, y)\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n", + "plot_svc_decision_function(clf, plot_support=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear that no linear discrimination will *ever* be able to separate this data.\n", + "But we can draw a lesson from the basis function regressions in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and think about how we might project the data into a higher dimension such that a linear separator *would* be sufficient.\n", + "For example, one simple projection we could use would be to compute a *radial basis function* centered on the middle clump:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "r = np.exp(-(X ** 2).sum(1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize this extra data dimension using a three-dimensional plot—if you are running this notebook live, you will be able to use the sliders to rotate the plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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nrnAkzWNAb2/6yFK388tf3o+HHvo1VFXFmDFj8LWvfR0XXHBe9F37o0KNSFse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\nkJQsgUAgEAgmQVKyBAKBQCCYwP8DzZfIxLKdt6oAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits import mplot3d\n", + "\n", + "def plot_3D(elev=30, azim=30, X=X, y=y):\n", + " ax = plt.subplot(projection='3d')\n", + " ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')\n", + " ax.view_init(elev=elev, azim=azim)\n", + " ax.set_xlabel('x')\n", + " ax.set_ylabel('y')\n", + " ax.set_zlabel('r')\n", + "\n", + "interact(plot_3D, elev=[-90, 90], azip=(-180, 180),\n", + " X=fixed(X), y=fixed(y));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that with this additional dimension, the data becomes trivially linearly separable, by drawing a separating plane at, say, *r*=0.7.\n", + "\n", + "Here we had to choose and carefully tune our projection: if we had not centered our radial basis function in the right location, we would not have seen such clean, linearly separable results.\n", + "In general, the need to make such a choice is a problem: we would like to somehow automatically find the best basis functions to use.\n", + "\n", + "One strategy to this end is to compute a basis function centered at *every* point in the dataset, and let the SVM algorithm sift through the results.\n", + "This type of basis function transformation is known as a *kernel transformation*, as it is based on a similarity relationship (or kernel) between each pair of points.\n", + "\n", + "A potential problem with this strategy—projecting $N$ points into $N$ dimensions—is that it might become very computationally intensive as $N$ grows large.\n", + "However, because of a neat little procedure known as the [*kernel trick*](https://en.wikipedia.org/wiki/Kernel_trick), a fit on kernel-transformed data can be done implicitly—that is, without ever building the full $N$-dimensional representation of the kernel projection!\n", + "This kernel trick is built into the SVM, and is one of the reasons the method is so powerful.\n", + "\n", + "In Scikit-Learn, we can apply kernelized SVM simply by changing our linear kernel to an RBF (radial basis function) kernel, using the ``kernel`` model hyperparameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1000000.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf = SVC(kernel='rbf', C=1E6)\n", + "clf.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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if4KlpRXZbm5QS+I+b21DlwGDtBCV0BKalLg1Gg3vvvsucXFxmJiY8P777+Pm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n", + "plot_svc_decision_function(clf)\n", + "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", + " s=300, lw=1, facecolors='none');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using this kernelized support vector machine, we learn a suitable nonlinear decision boundary.\n", + "This kernel transformation strategy is used often in machine learning to turn fast linear methods into fast nonlinear methods, especially for models in which the kernel trick can be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tuning the SVM: Softening Margins\n", + "\n", + "Our discussion thus far has centered around very clean datasets, in which a perfect decision boundary exists.\n", + "But what if your data has some amount of overlap?\n", + "For example, you may have data like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+8yHWIf25vW0LNR56Hrylugctxo0vp8gLysrKwnLB3PtJ+C7vzAyOzJ+L5rkJ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\nzPbxxx/D3d0db7/9Nm7evIkZM2Zg/PjxomOZbM+ePVAoFNi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y = make_blobs(n_samples=100, centers=2,\n", + " random_state=0, cluster_std=1.2)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To handle this case, the SVM implementation has a bit of a fudge-factor which \"softens\" the margin: that is, it allows some of the points to creep into the margin if that allows a better fit.\n", + "The hardness of the margin is controlled by a tuning parameter, most often known as $C$.\n", + "For very large $C$, the margin is hard, and points cannot lie in it.\n", + "For smaller $C$, the margin is softer, and can grow to encompass some points.\n", + "\n", + "The plot shown below gives a visual picture of how a changing $C$ parameter affects the final fit, via the softening of the margin:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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fnqtxsTHs/GU2ltQU7rl/CBWqVC3QeO2hpL9/sqN6sU31Yi2nbYtcJyKGDBnC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y = make_blobs(n_samples=100, centers=2,\n", + " random_state=0, cluster_std=0.8)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "for axi, C in zip(ax, [10.0, 0.1]):\n", + " model = SVC(kernel='linear', C=C).fit(X, y)\n", + " axi.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n", + " plot_svc_decision_function(model, axi)\n", + " axi.scatter(model.support_vectors_[:, 0],\n", + " model.support_vectors_[:, 1],\n", + " s=300, lw=1, facecolors='none');\n", + " axi.set_title('C = {0:.1f}'.format(C), size=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The optimal value of the $C$ parameter will depend on your dataset, and should be tuned using cross-validation or a similar procedure (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Face Recognition\n", + "\n", + "As an example of support vector machines in action, let's take a look at the facial recognition problem.\n", + "We will use the Labeled Faces in the Wild dataset, which consists of several thousand collated photos of various public figures.\n", + "A fetcher for the dataset is built into Scikit-Learn:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n", + " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\n", + "(1348, 62, 47)\n" + ] + } + ], + "source": [ + "from sklearn.datasets import fetch_lfw_people\n", + "faces = fetch_lfw_people(min_faces_per_person=60)\n", + "print(faces.target_names)\n", + "print(faces.images.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot a few of these faces to see what we're working with:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MISKFWAk+vdFS+MaZAjX4AgDkggyg5BaR18xI6fq7ubMx11oDhWofSknG6Zhh3AKAknPd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\nxZQS4kbDrmmu3bcKnBk5NzpIPfWUSvsDhgAloo9RNg7gJsMlJyGCBB1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Ah6oM6D08HrT/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Z0jpVGWHZRlJxpM5xysKWGtgbUGUhI0oHUGrTNI2dvi6f6BEfQNDqLR30BeyyOpyjkHVwb4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\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8LHWmYUTGkPNr7z1gSReWLOIs/bK07woABkF7W0vQCoACX54XBOUxIzkfFRCPVmJTJpKK\n2LcNodgo8jAg+lBApZ+XKkP615SYvE5Pm4fpHQuaJ29UfmWluJj3g1EWDaM9XzMXA6cxGYpyhPn+\nHLO9GbIyh/MeeWYwLqnP1jYtqvpsh8pZlyAfcb/AfUup7BQlM8vlEYzJMBpN4fFQDGZFS56dWZHH\nr/cu8L2rEXJDEOFAiHxYreteR63Yob4sU4sj0iDvVq6LEFTi4nNM2hKKoX56P1Tx9Hygx/c+LrB/\n+QDnzu9jdzKJ3sTxum5pScuJJEUdFywUPE+ODnF6dIy2vswMeQ3fe1RVBZ3paPgtspz5KI8kJzG7\n1ooFCzhxFNEJqzW8z6IDCngyQcAvPQiWkRsgQZd3nsbwCIVs+Vzvux71smbiYseiLArwdO80q4YC\nKpOFPJMq63qNqlqirhdn2r8zB04+wuG8R9P3OD5d4saNW3jl69/AjZdfwK1bLyHLChSjErP5Dqa7\nUx6IVfBWGH0BGB4sgww6UfwT+YYCVzqMNpZK/9dGw/ik4UkXM4vaiPLFscKxVMl2bc2ODDVXlB5A\nFg8vIeAkrVGNPC8xn5/DuUuXcfn69gLno49/DwKkfyhHLwdOSSqche06eO+RZQWEsj3cW5lhktkz\npVSEy2UPQwzIg31WwoylP8p1kP9rQ8LNWvHsLetZ0jewQL4kRqz7GV87pF8+eCiv4JWPh4+QweJn\nkbejt0+cGOoUk8RdjqIcYTybRCZxaEWXVGBSIWxtusfItaNWJ/fgwObMQSOEBBNJsjZMDoaQOgIn\nGtbDK89BO30PeX2mHimd0SJ5xkpXlipIk+cYjSeYH8wx2Z3EwfJMU+C03qNuWixPz8Y4/E6WMPbj\njTF4BptmhZMToCjGkLk+MbumHjS1hjKlknVYntFrBPbntcJupvtdBRXvS+U0nLa0p5kkkQx/D+Jm\nDLrx7KG2g49B08VqRxAHy+zw4FMgzssM050JLl4+h/39HZRZhq7vKXDeB3JQmgkW1aWAvm+xXp1i\neXqCalnRCA739buGlXxYPKV0I2r/jIhNW2QZEXkEmVLiNuNjMHQmactKtTj8pIq/1zBPIIBIqEIc\nEpJdz7PqUiD0LQXOZt2wsHwgFM552LaPvc2kMOUHDlOr7Y+jAHSwWu+xqGvcvn2MGy++ihuvvojD\nwxs4Pb0DrRQmkx3s7B7g4BIJGxCrk3tcvCHB+zQ0LDCqF6hGQ+sQD4TkiuKTXY9RMJ0mIQX+fs0a\ntQDYViiRB4ZNfGF2dl3DjgQiEk9KIzHrD4GVXXpm2gaCiyZznD9/DZevX8PDb7ryRrbx21oPP3H9\nrqApF4EJH6zfWi1rml3tWyCoSMQZXjPv2PatJ9FyYWmGoU2YOLbzZ0cICMHA68CtsjCwF/MUQPIM\nKk9wCrjyl6rUsvaljJXQGJGNHoBSBQ/7QEMnFAm692slf0vaM+l1F8UEJsswGpNM4d17jJAg53ut\nz1REQ4R1G5SGDh4+bHpAbgbN14Ns/eD1eT5ZCBnGwHuz8RqpshhYKoEGyGe7O9g5v4vpzjQyiMsi\nxygvcLReoapb1OvtVpzDRZXFkBwln5/ui7pe4vbtF+lwNRmKokxi4GXOc8pkBBGt83Ta142+8zD4\n0Q+kXqXygOProBPnYvgaCZLns4yhXdvRKEzAECYkhTTvfAz0pjAYzcfYO7+LKw9dwHw+gfUePSMr\nymx3NlySbuntDfembWusVwusjlcYT0bIeH+lreWtY5EbFXkjEjjzLItQreHNDcYgCwGZcxui7EPr\nOtr+ECFeqTYDf52Noh/cimCyDxEHid3brBrYjhIPkxkoT1ZzTd2Shjb7BFMQ9rH9Rm40W4ZqtVKw\njoLm1167heefexFf+59fxa2bLzEb0SHAoKlXOD2+g+Nbx8jyHLO9aaTPRwUQnaoeYrsSjZlRJ3qD\nRRYPdYE2SK6vT7N9Xb+R0SlN1abm+cWU+acHgKjIljdO5jZtPHzyrETGMmqJTeuQ5wVms32cv/gw\nrj31KC5cvYTp3hbJQWWaoRsKSAPgnolDs26xWi6wXp2iaSqU5QiT6Q7yMoMyOQVZ7jv2MfeDmQAA\nIABJREFUHQ0Hy40PYBCkAmv3ZpHyre+GrgBmClLwLSY0NA6AhZc5uFqeia1aFslu0VQN2qqJAXMY\ngCVOS/DxgVAJgEX4M/La24TNtrOGmbj3DqrRMDpDnhXMzM5QTsigOzK9JXPXCt5xy0Ao9t5DsYeR\nZrHvGDxDmgeNsBVwTxCNpIrADhL8ugBR/qG44opQbfqaodqQBN2iGGG2t4sL185j99wuykkJpRQm\nPEPonEfDQ+QixbjtFT/rIBuh4DnYixDQNhWWq2NMFjvY2T2P0WSEjtsOpMucoRgV8VDu2x5aabjM\nsWVcGpWSvrSQTeTnaJ3Gq6T/f7f0ZJxTBji5TkTG4AKCI7cQsdwT7eO8zFFMCpy7fA7X33wND188\nh3FZoh+QsLbNHB/+nKFkqFIBXVdhvVxgcbiIDHulQEmED7HayxnaVwowhoMnw7NEytQx8MG7+PfS\n+/TU80o/O+69htEKzgf00dyamcq8x13boeWA2Hc9KQE1HZIIyMCAYlnDthbwAws7HuHr+zYmlWdZ\nb6jibPoeh4slvv7Cq/jq81/Bi1/+Mo4Ob6Jp1nRIGI2ua7BenuL0zgkm82liY+mhaICQT9INLDuo\nBXbi4Jpl2WDgnMZP+qZPc34DTUJlVFTP95ZElAVCEadyItJ06PuGf7URsjCGFIcU96uc6+G9gzEG\n8/k+Ll6+hquPPoarb7qG/Yv7KMfbU/mwnU1MzRCimbezHk1VY71Y4vToBMvFEVarE7RthdFoigCQ\nW0ogjUw9kNmTwCR7GSUMLR0seZGzk8pg/AKI1RXNR9EBLjJiQoKQw5rE+OnGrpakPSnOKaJrKSLM\nQ39NOdAgh5co4RhKqCgh2nb/h0UuvIf3Cko1UMpAmwxlOUFRkvWaZusua3uC5HSqJodqONJ3A+Rw\nSO9f+ptS4Q4DJ113rnh8gEeSfqTnJnCfSEX1HJPxc6QUdEdsZWKD9xCei9Ya0/kc+xcOcP7h85js\nTJCzcMWYZwgbdktpG/LnvB/rbmbt4F8g/dsAck1qG2LAA3R2QAnhjwKtyU1UM1MAzQo6HZNwRCjV\nxOcLSPeeUgNYXat70KuN98fPgevT/Sz3t8wgA2HDbGHnYI7LVy/gkeuXsTefwYeAtu83vCS3uaT1\nIb+XSQHAo2kqrNdLLI9OMdufRd6DfK1j2busyFB2ZSJZ8S+Zr1QAglKA9whKAzpAhwA/qG5lz4UQ\nJkHTKI3eufT+BKYNqY0jMoqSgMuIC8Cwc9Nx0k7CMAGJrOecZa/mLvZ7z7LOLvIeAtZti1uHJ3jx\nuW/ghS8/j1df/TJWyyNYrthCCLCuQ9NUWJ6cYHY8RzkuMd2d0kUAaNYNqWdmwNlGpsmqianmrnQo\nbXlPz66r04By1/YxKzZ5Mqa1rUXbtIO+IJFPPIsrULXZsuhBHw8xzf6INIJiOagGFMUEFy5cx2NP\nvRmPfu/jOHf5gNwatniTLw4XkY2qAJ6x69GuO6yWp1gujrBYHKKul9ynJXp1Zgo0kz0OvDp6a2ZF\njq7O+ZChAegIofYWOVecEjwlSJEZbGLAAlQ9icBz3/UklKAI+pXKtl7WqBYVjQpVTapWfeCeK8HH\nAOIMXhxTUkZuE/p3IJKMtrmSdRz1EK3toVChUgrL0RTleIzxdIrgPPqODAiIhJYBJouHDL3WEC8f\n/C9+STqkh5WVzLQqpaD44Egem4jfIz13EdnP85xQAq6WLDNincsi6qJ1hr3zB7jw8EUcPHSAvCTm\neJFnyI1BZy0WVY0qKkRtn1WbtigRqPgvsPGheT+ddwjwpDo2n2A8G0PMir33UWtZ9rVve6C3A7tD\nCoYbEoWDYCnXTcf7UbNwOb+tAScjKpmJ0AGzcYl42AIBpM7F/cByUuLc5XO49vAlPHrhAsosQ933\nUEohZ+Wdsx7kZ10+JJQDTDwkQZKAtq1QrRdYLujszrIM5bgAWMjK9Q5t1cJkGqOJnM3ptSWI8kbR\n77WGlvMbqS1DXysG1DoGzsja9x5ek26y52Dshvs9GFHLkM5iGaGp1w2xmweyfaRi13PgTD65Z1ln\nJwcFxLmctmrQrCu0TQXrpIciVRr1D6tqiWq5Qr2kIXIaT6DKRoFuTNvZDaabzFcReYJJQQNHdmnw\nmixDXloUknFw1RPAruqskxuvITfvu5Y82FqWqRPnFSKC8Hv0pP/Zdw0Ahb29Czh/8WE88X1P48oT\nV3Hu8kGSotuiW8ftG6+SlBtDDGT/1KKu11ivTskZpVrSDeATeakoRxhPx/CefPLqehWdTfKi5Crd\nxn21nDF3TRd7XRnPa4kziGTVRMQigoy1Dn3TIVsTsxGgG7PvSdO2WTWolhQ42zVDI8MkKMJm3KvL\npU/FCjwhtRElcbhPKBa/R5L3cl6T/2m9xOnpIUIIZAbQtfDOYm/vEsbjeeoPq83gmYhRACAWe3SP\nD5nS8RDnKnPYRBUI0Xvp4xMXQCzElCbxBQ0dWw4kOZZeuygn2N0/wJXHr+DCtYsYzcYR4SnzHHXX\nYbFY4+j2MZZHK3Rtd//6y7G/OYBqwdWhEnSKKu6+71Ctl6hWa0x3J7hw7SKqZRUrPQmIJjfIQw6Z\nq/UMLRKKoRPxSpAOrVhoAtFZJstTtSnVvwRp56mq7FgXVWQiu7bjGWqSCs1KSkKnOxNcePg83vzm\nR/H4tSs4N5uh6joYrVFmWTSPbrdc5d9tliBEMiCwXWGFqlqgWq5Qjsj0uu86rqgdeu/RVBrlukHP\nAcx7D8dexuF1xqnUXcHU03YzhKuZxBzgPKAQYn8644SFYFsZT+yT5CqET8HnCvMM2qpF3/QbZ4YP\n0qIjE5A3Kqhy5sApfmzjokA5LlGUJbvRK+4henimAEvzVdzFwQelQHXUe2Q2bL5J4pGy3wfCur0i\nRhx40Fh8JqVHOhwsDmKX5Nisl549EkroerRNjaZZR2F0GYJlwABOW6Bn+EIpzOf7eOjqdTzy5JN4\n5Hsewe6FPZSTMgZi57aXHd65fSPCfeKGQYSmNeqaBRs46FOz3qAoRpjv7uHg8nl0TYflyQLr9WmU\nlcqyApaJUCGAHQ0C9y3TyIv4b8a5Nh77UZoPJKajm0xH1RWAiAc9y/1J8CRj65qHjTf3iwJCgsCk\nghqOEIGHFBRA/cItrqFwfxoVocez6xqslsdoGzpYlFKYzfYx3ZliPJnCtgJ5payaH4z4fAikdDfk\nl6pI6jUhqEHBJceDwLf0f83vVV5rg6nOwVpg4DwvMd/dxcVrD+H8wxcwP5gx/BkgoxeL5RonRwss\nj5aoVzWRt3B/AqdshbQFZAQrrRRAnetRVUsc3n4Vzj+J6e4U5aTE8mhJs3pM6pKllaYxKSQBA4HF\nhyzyyDY3zKxlhaZYcXJl5gOdRyIcvkFSqesYRMkEgRyGxrMxzj10gEffdBVPPXYVl8/to8xzrJoG\nnbWouw7LqsbJyRLLxQr/+5vfvLW9ThXWXZgzEIueulqia8jc3GTit5wcXrShPqPownYc2IzyzEIW\nrC8FTPmJQh6Sezi2onyy7pORFbnzrXVoubcpv2xrBZ3l3jKhWJGR7yXB5CTMi7+vHVSb9yFwllmG\nWVlif2eG85fP4eDiBcznB1iujtF1dZwdo54KqfNAYYM2HgLQNWIN5nkGK0MoAlTGKinSGwqI4yBR\n2N3KzKYaHNZ02Es2QhkfzRv5QLAgaUR2ZNxaL1HXSyYGtUTRlyxJgftVGUbjGS499Agee/opPP4/\nnsDBlQNkGdkO2WC3zvg8OrpBny/4aCEl2DyxgJO7OVUeY4xGU+yfv4CHHruMZt1AZwonR3digqC1\noRuGA1iWF7GqtIM+zZC8NTxkJJPPioyzc7VB2iFza/EbZMHlpnkdOUMVh/OVkJAG6jzQisYH+GBT\nCgQXbRepJcEMZRA0HZCa7wWtM3hnUa1P0dsOTbPCdLaPy1eewKXrV1CWYxy/doyubiMcK8mhPJsh\nbBIy0uGe4Cn5uiGJSG2ePXz4c9CUviaTwJIkpRhnE1N8NJ5i//w5PPToQ5if20FW5ASxMxLUdB1O\nDhc4OTylebiGVae2u90Rgk0tlcF4WqzGueetKHlwzqKqFrhx4ys4PXkLAGDv0h4sC3zb3kFr7uH3\nqYeVTJoHe+0TQSsGzpwt5YxhCT9h0hJ0Tt/H9ziTE/umR7tuUFcVEIC8KFBOaGSjnBTYu7CLy488\nhMefuIbrly5iWpZo+h5V2+F4ucLh6QI3bh/ixos3cPjqHfyf/8c7tr3zG8maLJr17VA3K9i+T9rh\nfDbISFvfWbQV+2fWLdq+R18U0Foh46vpQ4K15UzXSCpAWm8GTSukIaQ+rAsBvbVoWoJexa+zWREx\nKL0G+51amRqgytZEI3qakJCfZweyg2ddb4gclBmD+WyK649fwWNPPolbL72G5eoIp6cuOmkT/Ncz\nsYaC4+xgjvkBKeEc3TzC+mSNvu3TAaqpXPdMSIEPsLEP5lN5bm10olBGxaBIllcduiYp4Du2vmmb\nFm3doGGFIOoJrpNmIwL300I8aGazOS5cuobH3/Ikrj11HfuX9pHnOcHFIenvblPJZrk85N9xputs\nNDBOF13FTJx0eXOMJxPsXdhFuLCLvMywPFrCO4fV6gTWtqjWJHDc256MgrMcgIoUddoTrsF1qggV\n9AZxJ143owaiEcnGyfUi7yY9I4UQEnkAsT9XIMsLkmk0DNu4u2DKYVDd4jImg8p0bBVIdUIeleQm\nIUS48XiCK9cfwdUnr8FkFIAOXz2MGsFAqmjocBZ9UKoaqbrUG8FUvof+n0ZLxHA6QteaqqKhwIc8\nJ2TQ2zFrkNjg8/05di/sYbZPLPC+IwPmjBVgVqsKJ3dOsTxexr4mjXj89/jNAvce6vJ3Qqhark7w\n6tdfwtVrN3Hpoe9FM59G+U9ZQ+Y2AKjcYNiHjvAsaIQtH6hmUdwO8I6QDgVEclFiqnNbomrQti2M\nJgOL2f4cexf3MN2dYjwdR+vE3jmcrNeo2harqsZ/fe1FfOVLL+LrX/oavvGNL+O1Gy/h5PgO/u//\n65e2vLuvv8TWra5X6G1P9nqzUSwSvCOBga4m0fvqdE38BWvRW0utDZVE2+OzH8k/Ov6Zfh6PpiAF\nTGHdktOKQ9W2WK9rrE/XWJ+uI2/CWZeeAS1jeByo9WBsiIUZoIBCkUuWmJJQu+Ns9/gbDpzTUYmH\nL57HI49dwyuPP4abN15A21b8oEop3FEPsSHoIssznNvfRVESOaVdt1gv1lgv1mwnBQAhDuXTyEKa\n5+uaDo7p3hFGzAwHTdE0JcPrtmrR1S2PPli0dYNqvcZ6vUBdr9C2dexrCgRGsBqRJ6bTHZy/dAXX\nnngUlx97GHuX9lCMi3hho46logNsW6tpVpDsOMFwr2/mLJBnlueRjDUal8iNweErR2jqFdpmzRV2\nTX1p79GVDbKsIBk5+kkbIxIA7Qkd0MPPGojAY6SnlrGZrY49Ou+HuqkqEn6U0hF8FbuoTYUiAmjC\n4CGQcYNtq6rk+Si5PLjksmMtzfPS4HSNohhhujPHQ49cwUMPX4Q2Gl3dYX26ZhKU3SCUyOe5u79E\nn1f6vvR1wGamTuSNpL9M7igmtitENzrOwPY9uz4w6SQvMd2ZYbo3pREnvj6moKDZVB1O75zi9PYJ\n6lUDpRU55oxJXm3ra0D8GQo+DOdbhysEsD7zGq+9/ApufOMlPP2Db8ZoPsa0nmJ9uoLtHLPQEWeF\nAUsH9123kCR8enDY0p47KJ+qIygWauckva1atCzVF3xAnucYzcY4eOgAl65fxKWL55CNcrTOYrVY\n46Uvv4RXnn0RL+7vI8tyrOsKX3ruOXztua/ipa9+DbdeewmLxSE/99tf6UzB4J4MHDiXaGoqLkaT\nEVu65aiXdXSP6ZoOq9M1lscrzA/WyJTBeFRsOJdElEqTHF8wBmAClpNZ8hAiNGt57KR3Dj27naxX\nNZbHK6yOV1ifrlGvCRHx3iMLFMZEiUxeD8PPFei6ZppIWpNqhvF4BmNyaG0QwtmS8TdADqLyd1wU\nuLizg4evXsSVJx7Guecv4/TkNlarEzbWpQOmaZZYL5eolmvYzmJnNMLe/hxV22J5tEC1WKNdtzFj\nABTLwIEw9YaF3FkdwloitORlFoNn1K4Vk9Ja5gdrDp59NCtdr08jqUNE3qVSQyCZvtFoigsXH8bV\nxx/F9acfxcHlA4yn40guSJT2VAlta/U9VS5D1mX68709Gun95mWBcjrC3u4cpclw59oFLE+PsVos\n0LQVetfDsfRabzsKnCaDGCZvBujAUKW4UCQfyggXcqUr7NLh+xVImPqGSQggjf8k/VqlX+dzCtRf\nsh3clr0K87zkczz5VtqehKCt65hY0GEy2cF8dxfnHz6Pc+f2YHKDtu1x/Nox6nWNetltVEyJDMW9\nd/rbWHFKwnL3Gs4SAwwlmyKONwjFnqQqPY9dMSrhLCVSoxHG8wnKaYnAPydj3eK+t1gcL3HnlUOc\n3lnAWYtiXGJixijHJcbzyVb3O94L9Kd7oPjXSxJFbrLvPe7cehWvvPQCTk+X2D3Yxe7+Do5uHKGr\n0/zkkAVrMs+sYzU4WAFoFecFlVKxwhKWczBpFK5dNzzqQIo0CCDPzjLHweVzePjJK7j+1FVcmM6x\nqmq89MpNvPKVb+CFZ7+GO6++hvnOPkPlLV568Tncvv0yTk9vo23rwWjI9laCwP3G+aGYmSY95PXq\nFF3bICsyTHYmsL1FNa1QnVZYnVAfvFpWOL19ivFsDCgF6ycoCxoFksrRaI08y+CZvCnBVHqaw2ts\nvUfb92j7Hk1LPInV8QqLQ+69Lyv0IquHdJ3C4Bj2LCQvrRKEAK3IRm40G6HtdjGb7SPPSzZ12HLg\n7JyN93WRZZjuTLF34QC7e+cxGs0i6028zsiM9gSnx4dYHJ3Ctj3KPMfB7g5O9ndQ83yf7XrUywoI\nAXlBM5QyL0TCyH5jYL5v5bA11M9sOnQ1u2nULZqqJbeTpiafyr5B05B7CMHJnkv1NGunoDCbH+Di\nQ9fwxPc+iatPXceFa5dQjHKaI/SkthNcGsm4H2sowSbVigzSbzLjEt1b1rQsMTsocPnxh3B6eILV\nyQp937HpdQvnHWAJzqOgmX7W0FpNawcZhxg+bAlmz2BtjjzPkWVlrIiUIlKF8iqSAkQZCoot3/IM\neVEgL7KNCo16qfT9QkYaz0YYTUZb3W+B752zsDwkbbmXbG0Hx6SCspxgtrOH+cEM0/EI40kJXAUW\nd07RVA3qZR29Q6Vi9PzaxBbnOUIlptSb/c803+k5GXVxT3MepM+KDIoPBsfwoXMOniFhbQyKcoTp\n7pS8FANJp+VlHkcSlodLHL16hKObR6iXFXRmUI7BXpH/HV6caQ11bNNBn0TzvXdYLI9w85Vv4MUv\nvoD/8QPfg3O7c5xc2ENbtagWa+JRMMwvLR6A/DiF1ewttSa01vC5gc7NBrIR2ETZsplEvaphOyIg\nzfZmpF40KjCajvDw45dx4dI5jE2Or3zlG/jqF7+K5//zWXzjhedx6+bLWC6OUZYTMsBwDqvVMbq2\ngWVzCTmXtrlEn3YzYKr4e+oNt1jwuFvXdNg9v4tiRJB/vaqxuDPG8a0T9E2PxRGNzXnvYc/1mMwn\nNMrGLRkRhOdMBU6mBJxj305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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3, 5)\n", + "for i, axi in enumerate(ax.flat):\n", + " axi.imshow(faces.images[i], cmap='bone')\n", + " axi.set(xticks=[], yticks=[],\n", + " xlabel=faces.target_names[faces.target[i]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each image contains [62×47] or nearly 3,000 pixels.\n", + "We could proceed by simply using each pixel value as a feature, but often it is more effective to use some sort of preprocessor to extract more meaningful features; here we will use a principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) to extract 150 fundamental components to feed into our support vector machine classifier.\n", + "We can do this most straightforwardly by packaging the preprocessor and the classifier into a single pipeline:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.svm import SVC\n", + "from sklearn.decomposition import RandomizedPCA\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "pca = RandomizedPCA(n_components=150, whiten=True, random_state=42)\n", + "svc = SVC(kernel='rbf', class_weight='balanced')\n", + "model = make_pipeline(pca, svc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the sake of testing our classifier output, we will split the data into a training and testing set:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target,\n", + " random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can use a grid search cross-validation to explore combinations of parameters.\n", + "Here we will adjust ``C`` (which controls the margin hardness) and ``gamma`` (which controls the size of the radial basis function kernel), and determine the best model:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 47.8 s, sys: 4.08 s, total: 51.8 s\n", + "Wall time: 26 s\n", + "{'svc__gamma': 0.001, 'svc__C': 10}\n" + ] + } + ], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "param_grid = {'svc__C': [1, 5, 10, 50],\n", + " 'svc__gamma': [0.0001, 0.0005, 0.001, 0.005]}\n", + "grid = GridSearchCV(model, param_grid)\n", + "\n", + "%time grid.fit(Xtrain, ytrain)\n", + "print(grid.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The optimal values fall toward the middle of our grid; if they fell at the edges, we would want to expand the grid to make sure we have found the true optimum.\n", + "\n", + "Now with this cross-validated model, we can predict the labels for the test data, which the model has not yet seen:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = grid.best_estimator_\n", + "yfit = model.predict(Xtest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at a few of the test images along with their predicted values:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RjcKTHWqWE2XtSwnBOUWYWgMw0Iy8T8a4MwyMMTKWo+7RbWIGyNMcwhP2PQqH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mAqC8ElAKX0HlxjLQM8yGl4Rx8IydxQJJsRosTMQ+w6MUbuge77is9qv/RLv4Qf+ZEfAQBMJhPE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+65B9/4xjfwvve9D9dffz2+8Y1v7HmdaITNsJWgA8uAu5RUcFc4lpIEvZCoO9xhkKrVnrgt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\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1ssu9XC977SuZLWcsDpcTOzaygnoXSB5nMeV5RXkucMcz3/gjzk5vUVVrjBmI44w8XwhD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\n4+9gvTmjSsB7AikZg6KsiPDvXQL09E0POx1n2VIr6ChTFsASb2QK4Uv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(4, 6)\n", + "for i, axi in enumerate(ax.flat):\n", + " axi.imshow(Xtest[i].reshape(62, 47), cmap='bone')\n", + " axi.set(xticks=[], yticks=[])\n", + " axi.set_ylabel(faces.target_names[yfit[i]].split()[-1],\n", + " color='black' if yfit[i] == ytest[i] else 'red')\n", + "fig.suptitle('Predicted Names; Incorrect Labels in Red', size=14);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Out of this small sample, our optimal estimator mislabeled only a single face (Bush’s\n", + "face in the bottom row was mislabeled as Blair).\n", + "We can get a better sense of our estimator's performance using the classification report, which lists recovery statistics label by label:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " Ariel Sharon 0.65 0.73 0.69 15\n", + " Colin Powell 0.81 0.87 0.84 68\n", + " Donald Rumsfeld 0.75 0.87 0.81 31\n", + " George W Bush 0.93 0.83 0.88 126\n", + "Gerhard Schroeder 0.86 0.78 0.82 23\n", + " Hugo Chavez 0.93 0.70 0.80 20\n", + "Junichiro Koizumi 0.80 1.00 0.89 12\n", + " Tony Blair 0.83 0.93 0.88 42\n", + "\n", + " avg / total 0.85 0.85 0.85 337\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "print(classification_report(ytest, yfit,\n", + " target_names=faces.target_names))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We might also display the confusion matrix between these classes:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bv9lhmNe5e3vWx30HQNb1bDZt2Eavvv8HwONPP0bLtk+xcuMSPotdwFMtmhst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\nLOvWreP69es0btyYuXPnKk5XvK3VkSNHcHV1pVq14n0LVH4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "mat = confusion_matrix(ytest, yfit)\n", + "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n", + " xticklabels=faces.target_names,\n", + " yticklabels=faces.target_names)\n", + "plt.xlabel('true label')\n", + "plt.ylabel('predicted label');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helps us get a sense of which labels are likely to be confused by the estimator.\n", + "\n", + "For a real-world facial recognition task, in which the photos do not come pre-cropped into nice grids, the only difference in the facial classification scheme is the feature selection: you would need to use a more sophisticated algorithm to find the faces, and extract features that are independent of the pixellation.\n", + "For this kind of application, one good option is to make use of [OpenCV](http://opencv.org), which, among other things, includes pre-trained implementations of state-of-the-art feature extraction tools for images in general and faces in particular." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Vector Machine Summary\n", + "\n", + "We have seen here a brief intuitive introduction to the principals behind support vector machines.\n", + "These methods are a powerful classification method for a number of reasons:\n", + "\n", + "- Their dependence on relatively few support vectors means that they are very compact models, and take up very little memory.\n", + "- Once the model is trained, the prediction phase is very fast.\n", + "- Because they are affected only by points near the margin, they work well with high-dimensional data—even data with more dimensions than samples, which is a challenging regime for other algorithms.\n", + "- Their integration with kernel methods makes them very versatile, able to adapt to many types of data.\n", + "\n", + "However, SVMs have several disadvantages as well:\n", + "\n", + "- The scaling with the number of samples $N$ is $\\mathcal{O}[N^3]$ at worst, or $\\mathcal{O}[N^2]$ for efficient implementations. For large numbers of training samples, this computational cost can be prohibitive.\n", + "- The results are strongly dependent on a suitable choice for the softening parameter $C$. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size.\n", + "- The results do not have a direct probabilistic interpretation. This can be estimated via an internal cross-validation (see the ``probability`` parameter of ``SVC``), but this extra estimation is costly.\n", + "\n", + "With those traits in mind, I generally only turn to SVMs once other simpler, faster, and less tuning-intensive methods have been shown to be insufficient for my needs.\n", + "Nevertheless, if you have the CPU cycles to commit to training and cross-validating an SVM on your data, the method can lead to excellent results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.07-Support-Vector-Machines.md b/notebooks_v2/05.07-Support-Vector-Machines.md new file mode 100644 index 00000000..e6b78726 --- /dev/null +++ b/notebooks_v2/05.07-Support-Vector-Machines.md @@ -0,0 +1,448 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) > + +Open in Colab + + + +# In-Depth: Support Vector Machines + + +Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. +In this section, we will develop the intuition behind support vector machines and their use in classification problems. + +We begin with the standard imports: + +```python +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt +from scipy import stats + +# use seaborn plotting defaults +import seaborn as sns; sns.set() +``` + +## Motivating Support Vector Machines + + +As part of our disussion of Bayesian classification (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), we learned a simple model describing the distribution of each underlying class, and used these generative models to probabilistically determine labels for new points. +That was an example of *generative classification*; here we will consider instead *discriminative classification*: rather than modeling each class, we simply find a line or curve (in two dimensions) or manifold (in multiple dimensions) that divides the classes from each other. + +As an example of this, consider the simple case of a classification task, in which the two classes of points are well separated: + +```python +from sklearn.datasets.samples_generator import make_blobs +X, y = make_blobs(n_samples=50, centers=2, + random_state=0, cluster_std=0.60) +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn'); +``` + +A linear discriminative classifier would attempt to draw a straight line separating the two sets of data, and thereby create a model for classification. +For two dimensional data like that shown here, this is a task we could do by hand. +But immediately we see a problem: there is more than one possible dividing line that can perfectly discriminate between the two classes! + +We can draw them as follows: + +```python +xfit = np.linspace(-1, 3.5) +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') +plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10) + +for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]: + plt.plot(xfit, m * xfit + b, '-k') + +plt.xlim(-1, 3.5); +``` + +These are three *very* different separators which, nevertheless, perfectly discriminate between these samples. +Depending on which you choose, a new data point (e.g., the one marked by the "X" in this plot) will be assigned a different label! +Evidently our simple intuition of "drawing a line between classes" is not enough, and we need to think a bit deeper. + + +## Support Vector Machines: Maximizing the *Margin* + +Support vector machines offer one way to improve on this. +The intuition is this: rather than simply drawing a zero-width line between the classes, we can draw around each line a *margin* of some width, up to the nearest point. +Here is an example of how this might look: + +```python +xfit = np.linspace(-1, 3.5) +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') + +for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]: + yfit = m * xfit + b + plt.plot(xfit, yfit, '-k') + plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none', + color='#AAAAAA', alpha=0.4) + +plt.xlim(-1, 3.5); +``` + +In support vector machines, the line that maximizes this margin is the one we will choose as the optimal model. +Support vector machines are an example of such a *maximum margin* estimator. + + +### Fitting a support vector machine + +Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data. +For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily). + +```python +from sklearn.svm import SVC # "Support vector classifier" +model = SVC(kernel='linear', C=1E10) +model.fit(X, y) +``` + +To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us: + +```python +def plot_svc_decision_function(model, ax=None, plot_support=True): + """Plot the decision function for a 2D SVC""" + if ax is None: + ax = plt.gca() + xlim = ax.get_xlim() + ylim = ax.get_ylim() + + # create grid to evaluate model + x = np.linspace(xlim[0], xlim[1], 30) + y = np.linspace(ylim[0], ylim[1], 30) + Y, X = np.meshgrid(y, x) + xy = np.vstack([X.ravel(), Y.ravel()]).T + P = model.decision_function(xy).reshape(X.shape) + + # plot decision boundary and margins + ax.contour(X, Y, P, colors='k', + levels=[-1, 0, 1], alpha=0.5, + linestyles=['--', '-', '--']) + + # plot support vectors + if plot_support: + ax.scatter(model.support_vectors_[:, 0], + model.support_vectors_[:, 1], + s=300, linewidth=1, facecolors='none'); + ax.set_xlim(xlim) + ax.set_ylim(ylim) +``` + +```python +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') +plot_svc_decision_function(model); +``` + +This is the dividing line that maximizes the margin between the two sets of points. +Notice that a few of the training points just touch the margin: they are indicated by the black circles in this figure. +These points are the pivotal elements of this fit, and are known as the *support vectors*, and give the algorithm its name. +In Scikit-Learn, the identity of these points are stored in the ``support_vectors_`` attribute of the classifier: + +```python +model.support_vectors_ +``` + +A key to this classifier's success is that for the fit, only the position of the support vectors matter; any points further from the margin which are on the correct side do not modify the fit! +Technically, this is because these points do not contribute to the loss function used to fit the model, so their position and number do not matter so long as they do not cross the margin. + +We can see this, for example, if we plot the model learned from the first 60 points and first 120 points of this dataset: + +```python +def plot_svm(N=10, ax=None): + X, y = make_blobs(n_samples=200, centers=2, + random_state=0, cluster_std=0.60) + X = X[:N] + y = y[:N] + model = SVC(kernel='linear', C=1E10) + model.fit(X, y) + + ax = ax or plt.gca() + ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') + ax.set_xlim(-1, 4) + ax.set_ylim(-1, 6) + plot_svc_decision_function(model, ax) + +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) +for axi, N in zip(ax, [60, 120]): + plot_svm(N, axi) + axi.set_title('N = {0}'.format(N)) +``` + +In the left panel, we see the model and the support vectors for 60 training points. +In the right panel, we have doubled the number of training points, but the model has not changed: the three support vectors from the left panel are still the support vectors from the right panel. +This insensitivity to the exact behavior of distant points is one of the strengths of the SVM model. + + +If you are running this notebook live, you can use IPython's interactive widgets to view this feature of the SVM model interactively: + +```python +from ipywidgets import interact, fixed +interact(plot_svm, N=[10, 200], ax=fixed(None)); +``` + +### Beyond linear boundaries: Kernel SVM + +Where SVM becomes extremely powerful is when it is combined with *kernels*. +We have seen a version of kernels before, in the basis function regressions of [In Depth: Linear Regression](05.06-Linear-Regression.ipynb). +There we projected our data into higher-dimensional space defined by polynomials and Gaussian basis functions, and thereby were able to fit for nonlinear relationships with a linear classifier. + +In SVM models, we can use a version of the same idea. +To motivate the need for kernels, let's look at some data that is not linearly separable: + +```python +from sklearn.datasets.samples_generator import make_circles +X, y = make_circles(100, factor=.1, noise=.1) + +clf = SVC(kernel='linear').fit(X, y) + +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') +plot_svc_decision_function(clf, plot_support=False); +``` + +It is clear that no linear discrimination will *ever* be able to separate this data. +But we can draw a lesson from the basis function regressions in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and think about how we might project the data into a higher dimension such that a linear separator *would* be sufficient. +For example, one simple projection we could use would be to compute a *radial basis function* centered on the middle clump: + +```python +r = np.exp(-(X ** 2).sum(1)) +``` + +We can visualize this extra data dimension using a three-dimensional plot—if you are running this notebook live, you will be able to use the sliders to rotate the plot: + +```python +from mpl_toolkits import mplot3d + +def plot_3D(elev=30, azim=30, X=X, y=y): + ax = plt.subplot(projection='3d') + ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn') + ax.view_init(elev=elev, azim=azim) + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('r') + +interact(plot_3D, elev=[-90, 90], azip=(-180, 180), + X=fixed(X), y=fixed(y)); +``` + +We can see that with this additional dimension, the data becomes trivially linearly separable, by drawing a separating plane at, say, *r*=0.7. + +Here we had to choose and carefully tune our projection: if we had not centered our radial basis function in the right location, we would not have seen such clean, linearly separable results. +In general, the need to make such a choice is a problem: we would like to somehow automatically find the best basis functions to use. + +One strategy to this end is to compute a basis function centered at *every* point in the dataset, and let the SVM algorithm sift through the results. +This type of basis function transformation is known as a *kernel transformation*, as it is based on a similarity relationship (or kernel) between each pair of points. + +A potential problem with this strategy—projecting $N$ points into $N$ dimensions—is that it might become very computationally intensive as $N$ grows large. +However, because of a neat little procedure known as the [*kernel trick*](https://en.wikipedia.org/wiki/Kernel_trick), a fit on kernel-transformed data can be done implicitly—that is, without ever building the full $N$-dimensional representation of the kernel projection! +This kernel trick is built into the SVM, and is one of the reasons the method is so powerful. + +In Scikit-Learn, we can apply kernelized SVM simply by changing our linear kernel to an RBF (radial basis function) kernel, using the ``kernel`` model hyperparameter: + +```python +clf = SVC(kernel='rbf', C=1E6) +clf.fit(X, y) +``` + +```python +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') +plot_svc_decision_function(clf) +plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], + s=300, lw=1, facecolors='none'); +``` + +Using this kernelized support vector machine, we learn a suitable nonlinear decision boundary. +This kernel transformation strategy is used often in machine learning to turn fast linear methods into fast nonlinear methods, especially for models in which the kernel trick can be used. + + +### Tuning the SVM: Softening Margins + +Our discussion thus far has centered around very clean datasets, in which a perfect decision boundary exists. +But what if your data has some amount of overlap? +For example, you may have data like this: + +```python +X, y = make_blobs(n_samples=100, centers=2, + random_state=0, cluster_std=1.2) +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn'); +``` + +To handle this case, the SVM implementation has a bit of a fudge-factor which "softens" the margin: that is, it allows some of the points to creep into the margin if that allows a better fit. +The hardness of the margin is controlled by a tuning parameter, most often known as $C$. +For very large $C$, the margin is hard, and points cannot lie in it. +For smaller $C$, the margin is softer, and can grow to encompass some points. + +The plot shown below gives a visual picture of how a changing $C$ parameter affects the final fit, via the softening of the margin: + +```python +X, y = make_blobs(n_samples=100, centers=2, + random_state=0, cluster_std=0.8) + +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) + +for axi, C in zip(ax, [10.0, 0.1]): + model = SVC(kernel='linear', C=C).fit(X, y) + axi.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') + plot_svc_decision_function(model, axi) + axi.scatter(model.support_vectors_[:, 0], + model.support_vectors_[:, 1], + s=300, lw=1, facecolors='none'); + axi.set_title('C = {0:.1f}'.format(C), size=14) +``` + +The optimal value of the $C$ parameter will depend on your dataset, and should be tuned using cross-validation or a similar procedure (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)). + + +## Example: Face Recognition + +As an example of support vector machines in action, let's take a look at the facial recognition problem. +We will use the Labeled Faces in the Wild dataset, which consists of several thousand collated photos of various public figures. +A fetcher for the dataset is built into Scikit-Learn: + +```python +from sklearn.datasets import fetch_lfw_people +faces = fetch_lfw_people(min_faces_per_person=60) +print(faces.target_names) +print(faces.images.shape) +``` + +Let's plot a few of these faces to see what we're working with: + +```python +fig, ax = plt.subplots(3, 5) +for i, axi in enumerate(ax.flat): + axi.imshow(faces.images[i], cmap='bone') + axi.set(xticks=[], yticks=[], + xlabel=faces.target_names[faces.target[i]]) +``` + +Each image contains [62×47] or nearly 3,000 pixels. +We could proceed by simply using each pixel value as a feature, but often it is more effective to use some sort of preprocessor to extract more meaningful features; here we will use a principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) to extract 150 fundamental components to feed into our support vector machine classifier. +We can do this most straightforwardly by packaging the preprocessor and the classifier into a single pipeline: + +```python +from sklearn.svm import SVC +from sklearn.decomposition import RandomizedPCA +from sklearn.pipeline import make_pipeline + +pca = RandomizedPCA(n_components=150, whiten=True, random_state=42) +svc = SVC(kernel='rbf', class_weight='balanced') +model = make_pipeline(pca, svc) +``` + +For the sake of testing our classifier output, we will split the data into a training and testing set: + +```python +from sklearn.cross_validation import train_test_split +Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target, + random_state=42) +``` + +Finally, we can use a grid search cross-validation to explore combinations of parameters. +Here we will adjust ``C`` (which controls the margin hardness) and ``gamma`` (which controls the size of the radial basis function kernel), and determine the best model: + +```python +from sklearn.grid_search import GridSearchCV +param_grid = {'svc__C': [1, 5, 10, 50], + 'svc__gamma': [0.0001, 0.0005, 0.001, 0.005]} +grid = GridSearchCV(model, param_grid) + +%time grid.fit(Xtrain, ytrain) +print(grid.best_params_) +``` + +The optimal values fall toward the middle of our grid; if they fell at the edges, we would want to expand the grid to make sure we have found the true optimum. + +Now with this cross-validated model, we can predict the labels for the test data, which the model has not yet seen: + +```python +model = grid.best_estimator_ +yfit = model.predict(Xtest) +``` + +Let's take a look at a few of the test images along with their predicted values: + +```python +fig, ax = plt.subplots(4, 6) +for i, axi in enumerate(ax.flat): + axi.imshow(Xtest[i].reshape(62, 47), cmap='bone') + axi.set(xticks=[], yticks=[]) + axi.set_ylabel(faces.target_names[yfit[i]].split()[-1], + color='black' if yfit[i] == ytest[i] else 'red') +fig.suptitle('Predicted Names; Incorrect Labels in Red', size=14); +``` + +Out of this small sample, our optimal estimator mislabeled only a single face (Bush’s +face in the bottom row was mislabeled as Blair). +We can get a better sense of our estimator's performance using the classification report, which lists recovery statistics label by label: + +```python +from sklearn.metrics import classification_report +print(classification_report(ytest, yfit, + target_names=faces.target_names)) +``` + +We might also display the confusion matrix between these classes: + +```python +from sklearn.metrics import confusion_matrix +mat = confusion_matrix(ytest, yfit) +sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False, + xticklabels=faces.target_names, + yticklabels=faces.target_names) +plt.xlabel('true label') +plt.ylabel('predicted label'); +``` + +This helps us get a sense of which labels are likely to be confused by the estimator. + +For a real-world facial recognition task, in which the photos do not come pre-cropped into nice grids, the only difference in the facial classification scheme is the feature selection: you would need to use a more sophisticated algorithm to find the faces, and extract features that are independent of the pixellation. +For this kind of application, one good option is to make use of [OpenCV](http://opencv.org), which, among other things, includes pre-trained implementations of state-of-the-art feature extraction tools for images in general and faces in particular. + + +## Support Vector Machine Summary + +We have seen here a brief intuitive introduction to the principals behind support vector machines. +These methods are a powerful classification method for a number of reasons: + +- Their dependence on relatively few support vectors means that they are very compact models, and take up very little memory. +- Once the model is trained, the prediction phase is very fast. +- Because they are affected only by points near the margin, they work well with high-dimensional data—even data with more dimensions than samples, which is a challenging regime for other algorithms. +- Their integration with kernel methods makes them very versatile, able to adapt to many types of data. + +However, SVMs have several disadvantages as well: + +- The scaling with the number of samples $N$ is $\mathcal{O}[N^3]$ at worst, or $\mathcal{O}[N^2]$ for efficient implementations. For large numbers of training samples, this computational cost can be prohibitive. +- The results are strongly dependent on a suitable choice for the softening parameter $C$. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size. +- The results do not have a direct probabilistic interpretation. This can be estimated via an internal cross-validation (see the ``probability`` parameter of ``SVC``), but this extra estimation is costly. + +With those traits in mind, I generally only turn to SVMs once other simpler, faster, and less tuning-intensive methods have been shown to be insufficient for my needs. +Nevertheless, if you have the CPU cycles to commit to training and cross-validating an SVM on your data, the method can lead to excellent results. + + + +< [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) | [Contents](Index.ipynb) | [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/05.08-Random-Forests.ipynb b/notebooks_v2/05.08-Random-Forests.ipynb new file mode 100644 index 00000000..04c71263 --- /dev/null +++ b/notebooks_v2/05.08-Random-Forests.ipynb @@ -0,0 +1,759 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In-Depth: Decision Trees and Random Forests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n", + "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n", + "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n", + "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n", + "We will see examples of this in the following sections.\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motivating Random Forests: Decision Trees" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Random forests are an example of an *ensemble learner* built on decision trees.\n", + "For this reason we'll start by discussing decision trees themselves.\n", + "\n", + "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n", + "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "![](figures/05.08-decision-tree.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n", + "The trick, of course, comes in deciding which questions to ask at each step.\n", + "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n", + "Let's now look at an example of this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a decision tree\n", + "\n", + "Consider the following two-dimensional data, which has one of four class labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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jQJ0sHpMKry8RiF9j3b6ayX4jI4z27sYpLJQoVzfSe/Si+xczSrprerOzs+di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\nx7fzqr/V11bxzvBMfMrHmrU3CuX0fOsIc568/7Ib7hU2icuOcdckLm3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s7KmtVrN6RSaL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VODe/cl6vOFZHTqVO/arvsPMKdny9T63bgnnb+fOkb+8GEoBgNeWzdwt0bP45NubYnVO4HsI25l\nbqf9eSUlxaTufIth0fZDmFuSLjF96mv8sPKfDIspR+9a/5lw/aoC5t37er09xfsyt1NsXEWf/pcX\nuJhqLKxZdZBpMy4XwDh/rgxrxTiGDGm43OGOXUmYbFuI6HN5qDvnWCk1xsHEx93dpO8LsPT794kZ\na3b4WupGmHHP003+7KZq6u/UF8lJ/MvXG1uPy/PYLtnZ/LJG5Z5RDlJENtKBo9lsOnoEvaIwc1g0\nHdrb17G+lpS9e3jRTYNy1R7c8uRkXEND0Xe5vJ9WOX+eh87ls2Cc/bzrT75PS+Gv3m7Yutc+DFQf\nOYKq0eAWXn8rk/ZIFv+r9+CDAxmcvXtKXa9ZVVU6/7CSF4ZGs/roEWo00N/Hn8nRjksTqqpK1rGj\nVJtq6B/Zp95qcbPZzFP//JC9ig2ztzduVVVMcPfkhfsexmw2897qH9hjs1Cp0aA/cxoXHx9svr60\ns9oY79+B2XFjbuheNtbFggLmp6dSedU8tXL2LM+VVZE4YlQD77yzyT5i4XTt2vlSXdEFY3lpXUUm\ngPwLFXh59EOn0zHlrkf5Zsk7dOtZRnikH8VFlaRvr2DE0Aft/ogNiBrJvkxIXr8ZjbaE4pJi3D1V\n7ppSfxV2p87epG3OYAiOA7Gqqpw+u4UxE+rPN4eG+bAlaTsWy11N3g6i0Voafk3T8Gut0f3x4wjc\nvZNVSZso0moJsFqZFhJG3JBBdueazWbeX/MD6RYzVRoNXVWVuWGRRDsYbu3XK5x+N5FZa3veWZS4\n+n/4VZMJLJZ6QRhA7dSJlVlHecBsbrD046r889j6X97LbYiMxJiSQvn583jGxoKq4rI1hcEFhfSb\n9zAf9gjlw00bOIINFIVIFX4+dQbtfNrRO/z6vVNFUYhs4Pvf997bnEuIx9CrFz+NFa0rLqZo4ce8\n+8jjPPfjlqtvUpL5R0j3uoeki0B2Xh6Fa1fy81vQQ/1yRxoVcdF28+Zqly5s2JhMYrNf8c4igVjc\nUjPu+QWrVn9KpekYWm0NVos7Ab4DmPTjViy9Xs8D835PTk4W6al78fFuz7x7xzaYb3dA1Mi6mr+r\n173DkGjHK6I1mobnzc6dO0vHzo57rb166zl0aB9RUU2bZ3bRtcdUk2vXw7dabSiK/dasllRdXc13\nKckUmmoYENSJ2EGDr7uifNyQ4YwbMvy6n/38t1+we8K4umHTEuBoZiYvH9zf7JmqdDbVrkZ1zalT\nuIY5Tm6R26M7x3KO0zvCvmIWQL6D/cuesbFYjUaC/vI+BQZXqhInsX3kCOZu3UAiWrs9yM0hbd8e\ncgLb49Or/ry21teXHe39uVRYSIC/PzabjWUFF7ANqJ8IRunYkdXZR3m4qqrZE6SUKarDeXOAUs3t\nk6LVWSQQi1tKo9EwdcoCoHaBTUNJG0JDIwgNvbG5LoM+GLP5XL10llC7BcrTveFShK6uBmqqHc/I\nVFVZ8XNvaIbw+saMnsG3y15j0tT2dYFCVVU2rbvE5ImPNvlzb9b2g/t5O/sghXGxaAwGvsvNJWLR\nv/jzrHm4uzuex2+sjCOH2BsZUW/uEqA6KoqvNmxs9kB8T9QgVmdmYhlweTpC6+2NOS+vrpDDlQzF\nJfhHdG3w89pZrZQ5OK6WllIY1hPbtLv5qS9tjI3h27w8OqYkMyM2/ua+yFVSjx9D19FxyVDdyBEk\n79vDrLETyMvL5VxwkMOFdEV9+7D70AFihzRv3ulueldsVVUOV24Ht9xasduWrHsXLaa5MydNmXw/\nG1YVYbVe/ktgNlnYmlTN6LiGc/V26NCBogLHczcnj2kIC2v64hd3d3emJv6WlI16tm4sYuvGQlI3\nujAu/knatbPfetUSLBYL7x7OpHjCeDQ/ZsVSgoPJmjqZP65ZcUOfYzbbjyTsOJkDPR1nejt7C7Jl\n9ejajTmVJrQHDtQd0+p0KNu2OTxfzczkLzvTSNq13eHrcR7e2EpK7I67LF2GaZKDueWOHUm6dPGa\nbVRVleTdu3hr1ff8ecUyTp87e83zAbxdXbE2sA/aWlBAsH/tKnxPT09cy40Oz9MUFtL+FvyezYlL\nIDhpk91x9/Q93NsCublvd9IjFm2Wh4cHs6b/D0mbv8aqXkBBg17biXlznrzuHO+QQbPYuG4hcWP8\ncdHrsFispCUXENXnvptOAOLn58+s6f99U5/RnJZt3kx+zCi7p25Fp2Ofcv3uTM6Z03yQvp0jLlpU\nFMLMZn4WNZj+P24Naueix1ZdXRfkr+Rms6KqKl9t2kBqeSmVGoUuVpUHBg4h/CaKyP9s4mTiTuaw\nPDkVswYG+QbQdfocXv5mCRfHj0Xj64ulsBDj1q143JXItsBA0s6dI/OH73jm7voZ6h6dkEjR8u/Y\n4uFKWZ8+KAUF9Mw6SmBIKNuv6uX/pLSBnNpQ+8Dy28WfsXfoYJTwUaiqyqq9GTyQfYT5Yyc0+L77\nRyfwr08+QI2PtxsGrti0iaoRtduefHza0aeohH1XDc8DhJ04RcS9DS9SbCqDwcCfEiby3vqNHNLr\nsOldCKmo4sGwSPrdwBYx4Zismm5lbqdV07fazd4ro9HI1tTlWK2lKHgQF3vPDZdLbAu+2bqeDwYM\ncPiAYdi0mTV3z27w4aO0tIRHNqyicGL9AOK9ZSt/HxJNp6COVFdXc9/qZRSPr5++0lZTw4ztuzDW\nmFg/bBAa38s9Ne9t23m9ZyT9Qm++aEFlZSV/WLmMvT5elHVoj8vWFKwFBdTExeIxfHi9oKY9cICP\nO/Ug1EGKTp3OworNaQQHtCcqsjdLkpP4a1hIXf7oKw3euJl37rnXYXv+sWo5Xw4bZPdgotuzl096\n9qb7Naokfbt5Pa+lbsFr9mz0nTphLS/HmJyMoW9fgo/nsHjqLFxcXMjLz+e5pFWcjhmFxs8Pa3k5\nnVJSeWVYDBEh9nuNm5PZbMbX1w2jsXGLDxuq3X0naOyqaQnErYwE4saTe9U4xuoS7klPrzen+pPw\n9Ul8NLPhUprvr/ieJTHDUa4aYVBVlcTNW/ndtNpFS1szM/hzzhEKY2LQuLmhHDvO4Kyj/Gz4KJ64\ncBprf/vhy8EbNvHOjDk39F2sViv/SVrL7upKahSFEKtKbkkxh6ffXZc+EqBs7Vq8J02ye7+qqszc\nuo1fT7HPzXz175PFYmH+4s9qtypd8aCiP3yEV9x9iG4gc9fPln/LsYR4h9eetiWNp6dOb/D7qarK\n1O++pCAoEEtBAYqbW+3DhE6H1Wjk6ewTTI+vXaRls9lYmZbCyfJSOrq5MT0mvsGV4c3tev/2bDYb\nf131A9tMlZTqXAi0WLgrIJC5o9vGPvrmItuXhBAAhHTpQsyK1WwqL69XTN71wEHm9rj2sOJ5bHZB\nGGq34OReEZziogYyLDySZSnJlJhNRPcII2rew/xj5XIscSPttr0A5DRhte2ViTwAss6fx5qXh9tV\nc9HKteamG9n10Ol0/GnCFP64NolDHgZMbga6FZUwWKPjGyWPP+ZkY1BVBmt1/HrSVPQ/1jautlgo\nT04GiwXVasUQGYm+a1cURcHciO9s1elwi7IP8hoPD0qrqi7/v0bD3Y1IVuIMf1j6NRtjRtb9nM4A\nH50/j2XzBh4YM965jWuFJBCLNufkyaMcztpLj5AeRIQPdWpRh7bipZlzCV63mu3VFZRrNQRbrNzb\nM4KY/va95Ct5XSN9otdVg2kGg4H7xtfvhbrpdGA2g94+g5r+BgfjtmXuJX1A/7o/7gCmU6dwHzjQ\n7lzVbLbb4gTgsn8/U6Psz29IUIcOvHPvPKqqqqipqeZ4Xi4v5Z+jcvDlvdQrTCbOfvMFf3lgAZeK\nCjlz7iwe982tW2FcsWsXVQcP4j1gAEM7OF4V/RNFUehhg0MOXnPds5dJg4Y2uu3Okn/xImm+3vV+\nTlC7p3vVoSPMu4OHqhsid0O0GSaTiS++epOz+Z8yNCYX1XUdX3z9AidPHnV201o9jUbDY4lTWDh9\nDkvuns1fZ8y9bhAGmNlvAC77Mu0/7+gxJne/fk3smTFxeG2zX62s2mz0U2/sAWrbubMoXbuiqipV\nBw5QkZ6OLiiImpwcu3MN/fpR+vHHtYk+fnLqFFOKjYTcQDrNn7i5udGunS9fZR2sF4QBFL2ezAH9\n2LE/g/eTk2DB/HrbfDyGDUNxccFz8TeMacS2ovt7ReK+Z2+9Y2phIQnFZQQFBjbwrtZjx+EDVPfr\n5/C1fL92lJbar1C/00kgFm3GilWfMHq8hog+tYkxAgI8mDA5gNQd/6EFlzrcUSJ69OTnuOCzKRlb\nVRW2mho8t25lfomR6Eb0LD09vfiZfxCGbdvrfka24mJCl6/gqQl33VBbdDYbVUeOULZ6NTp/fwxh\nYZjPnKFixw7UH3vuqtVK6cqVmE6fxn3KFIxff4Pm088YumY9r5kUfnNF3d6qqiry8nIdbslqyJmG\nhpa7dSP93FkOKjgcofEaM4YCv3acv5B33WtE9+3P6x27MmzDJjonbyFi42YeyznD89NnN7qdztQ9\nsCNKnuPv6V5egYeHZwu3qPWToWnRJqiqilU9jd7VvkcwaJgb6ekpDBsW54SW3f5mxo5mcnU1q7el\nYLFamRw/CQ+Pxic9mRYdw/D8fL7eso1KDUR6+TD1/gVotVrMZjPZx47SztubztdYTQwwISyCRRk7\n8JlyOYWj15gx6AIDUd7/G9pRo7h06iQ+iYl1w6L6Bx/AqqoYV64mbnBttrSamhreWLGM3QYXyvz8\nCNiZSoKLK688fL/dNfPy81m0I5VcDXjbwFrueIGSraYGL60Wa0ON12qxBAfz3e5dPDnVfqHY1QaG\nRzIw3HEmsNYuKrI3Pb/6lJyrKkWpFguDTOa6uXRxmQRi0SZYrVa0Osc9l4D27pw+ev2ehmg6g8HA\njISmL7IJCgy0C0CfbVrP8rIS8nr2wOV0PhHbtvDbEbH0bGDoeNPRLNxjY6k6dAhd+/a4/FgP2a13\nb/qfz+N/IgfwyIULVF71kKAoCkciwtmfnUX/8AheWvYNO8YnoOj1aIAi4JviYjy/+475oy8n8DiQ\nc4wXD2dSMiaurpdr/PJL3B1kmPJJ3cbs8ZM5tG4l6Q7aXrVnD25RUZiz7YfRb0f/L24sr6xczYnB\nA9F07IiSfZSoo8f4/fQbWyV/p5ChadEm6HQ6LGb7/ZwAhw8W0ad361/Ecqfavj+DD1Yt5+uN6zH9\nOGe7Ynsq//L14qSpmuqsLIwX80lXVH79/TcOh4pX7tzGx+nbqdq3D52fH+Zz5yhdvhzrjxmxyl1c\n8PT0oMbfcT5vtUcIh0+d4FxeLnuCg1Cu6pVpfH1ZW16O1Xq5T/vPzD2Uxo+uN9TsMWcOVf/5D0p2\nNlDbE/ZO2shTXXvg4eHBfw0ahnXFynpTJea8PCyFhbhUVzOqS8OpNm8n3Tt1ZuHch/hDWQ3zU3fy\nvk97/jJvfrPnwL5dSI9YtBmdg4Zz6sQOuve4nHSjpsZM3hkfxkTf2iQGoj5VVdm4awdnCgsYEtqL\n/g6qDlVVVfHk4s/IHjoEJS4aW0UF3/zwLb/rPYDVeecpLS3CZ9o0lCv2vhbs3s2fv/mS5+5/uO7Y\n2dzzvLYrDe/HH0PnU/uzd+nYEXXgQEq//55206cTaLXh4eFJgLGCfAft1WVlMzg8koyjWZjCwxz2\nQPJ9fCguLiYgIKB2yNzBnlxFq8VtwQKmrduIPr8Qbxc9MydNqwsw4d1D+GNpCU99/Amm0BBUqxWt\nry+esbFErV7HGm9v3s7JxqQo9FRhgN5ApqmaCxoFH5tKgm8Ac+LH2l23LVIUhdFDhtI6N1i1LhKI\nRZsxalQiaWmwed1OtC7lKIoBja0rs2c+4uym3VGOnznNK6mbOT1iGNrePVl09Bj9v1jImzPm1gWk\nk+fO8tgXCzE99rO6fcgaDw8KJ07gj6vXUpJ/Affx4+oFYQCPIUPY+ukinrvi2OL0HZiDgnD3qZ/1\nTFEUDH36YF29hpn9BqEoCgnunnxZWIhyRc9YtVjof/I0YSNGg6qiPX0Gtbf9/BLt8rkAABgrSURB\nVKtveTneV2TRUhracKyqdOvUmeljxjl8eUTUQL7uEMinu7aRo3XB1VjJoN372KaBfeMT6nrY2zMy\n2KkoGAZEA3AByL54kQsrv+dJBwlHxO1LArFoU0aNSgQSsdlsBAb6SGatFqaqKq9v28K5qZPrqv+o\nvcLY1yOEt1b/wCsz51BdXc3zqZspDO2Bt4NkIBdGjcT81/dx7dzZ4TVMfvWLFpSoVjQNZIxyDQuj\n84pVDJ3zEACPTZyMZc0KNpmryQ8KxLuomIHGSl74MQNYWEgPeu9I4WBkRL0hZ1tNDTEaTd1CIhcX\nF8JrzNhv3ALf7TtJnDj1WrcJBVBUFb2iwU1VOXXuLCcmT0J7RUUuy4ULeCdeVcm3QwfWHT3GgrLS\n2zLdqnBMArFokyQhgHPs3J/Biah+dpmyFJ2OvVoFi8XC4q2byBszGmW742pHirc3gS56ik0mu7la\ngI6G+vOI3fUGLHmOqxfVHDuGR3Cny5+tKPzyrrt5zGwmP/8Cvn397FZ4/2HSVF5evZJDXbtg7toF\nt6NHGXapmNcee5SSkuq6854YPJzfbUiiKGFMXaYulwMHedCvAwYHBS5+kn3qJM/v30NxwuX5ZUt+\nPpVpaXjfVbtly2Y0ovV1XCXJOGQwSzZu4JHpzV/zWLROEoiFaAVUVcVmszV7qcjmdio/H4Y6TgRS\n6eFOdXUV580mtO7u9ZNpXMElM5P/e+hRfrl+A+qU+uUqbUYj/W3w3oplFCnQXlWYNWQYH+xIw1JU\nhM7Pr+5c1Waj+tAh2gUF2V/DxaXB7VB+vn68OXUmf/p2Mft2pePbPoDO7QLs9qKHh/TgEx8fPkvZ\nQi7grapMj+xH37BrpwX9d0Y6JePG1HtY0QUGouvYEXNuLi7BwSj62opVjliKi/mkuIDc777mhRn3\nSua4O4AEYiGcqKSkmHVJ/0bRXkCjsWGztKNXz3EMiBrp7KY5FBc1kH9lZmK+KrsUQFBZOR4envii\noFosuHTqRNWhQ7j16VN3jrWsjLgLBfSOHc9LlRX8JSmJopgYNAYDmsNHCMvIYFOnYIxx0SgaDarF\nwsbkrUwMCmb9zp1o9Hr03bphuXgRy6VLeIwaRf+cMzf0Haqqqvjl0q84OW0yik5HMXA4J4elL75I\nv/DeBKpw//DaylL+fv48NXXGDX3+McXx3LLbgAEYk5JwCQ5G4+qKrbzcYRrOqj178J41kw1lZXTc\nsIZHbzDxiWh7JBAL4SQ2m41lK/7IXdP8UZTLOYgP7FvF4SMGekc2PidySwkODGLk1k0kV1TUyyWs\nnDrN1PZBKIrCvFFxrNmShJIQT9WBA5StWYPi4oJaUUF8RTWv/OwJAMYMHMzIyD4sS0mm1GQiPqI3\n/+d/lor4+LrepKLTUTIugcKVqxlcVUN2bCyWggIMffqgGAxErFrDnHnzb+g7/GfjOk7elVi3iKxy\n3z5UsxnTz3/OHmpHJ7ampvFStzCG9el7w/dI57DEBWC1Yjiegy02FsXVFR8vb5R/LcQy4x60fn7Y\namowbt6Ma69etcHZx4c0YxmP3nALRFsjgVgIJ0lLW090nLtdj6jfAD+2bEhqlYEY4OUZc/BbvYId\n5mpKNArBVhuTOwQzM24MAO3a+fI/oeG8v3YdZ/r1Rd+lC/4ZmUwL6sKCcRPrfdaVhSIOZ2dxoldP\nh3+UjnUKZlH3Xqzak8FBmwVOnqafzpUFcx+64dJ/WTZL3dy0arPVLpq6omSioigYY2P45/qkJgXi\nvqrCRputXh1kAPftO1j40M/Ysj8TY00NicNiCJo8g1998Gd2dg4GrRbP+Ph6dYzLdK17qkI0jyYF\nYlVVeeWVV8jOzkav1/P666/Tpcu109MJIeorKTtLuJ+7w9c02ta7Glyr1dblbHY0tAowsm9/RvTp\nR/r+DMqKLxIzYco1FzgB1JhNqA2kP7S61h7/RTNs63G5YuS4JisLQwMFCo4G+HPx4kU6/JjBq7Ge\nTBjP8R+WcHLcWLSenqiqij5jHw97+BAUFMScq+a0Z42I4aCPG5qO9pWZgi0NJs0Ut5EmLT1NSkrC\nZDKxePFinnnmGd54443mbpcQtz2Nxg2z2fEfWtXWNvLxXmshkaIoDIsaxLiRo64bhAH6R/ah05Fs\nh6+FnD1Pl2bKSjXU0xtbaSnQ8IMEABpNk4qJ+Pi0419zH+aJw8eI35LK5ORU/tG9F3NHJzg8f8yQ\nYUTsTK8rXPET10OHmRUafsPXF21Pk3rEe/bsITY2FoCoqCgOHjzYrI0S4k4wOnYaazf+gbiE+j2k\nwkuV+Pr0aeBdty+tVsvcDsH8IysLU8TlTF2G/ft5oHtos60enj1mHHu++oydwwZjiIig/McFVFfr\nebGAwNimlR10cXHhvrETGnWuoii8O30Of1y3kn2KSpXOhW5mM3O69yTuBmoni7arSYHYaDTi5eV1\n+UN0OmxS7FmIG+Lp6UXP7tNJWruc4aO88fDQszf9EjXGEO6ZdmdmVpoRE0eng/tZvjGZIo2GAKuN\nWb37MqAZKxFpNBremvcwG9N3sj3rGKcvXuLErnRsw2rzlauqiueOnfxXr97Nds3r8fT05NWZc2ur\njFmt6BwkQmmrTpw5zQ+Ze7EpCuPCIhymQ73TKWoTxl7efPNNBgwYwKQfFzjEx8eTnJzc3G0T4o5g\nsVjYtHkN5eWlxMZMuOE5SXHz9mdn83l6OoWKQiDwaGwsoV3vjAINt9IflyzhC50OU79+KIqC5tgx\nEnNzeWv+fNkffYUmPXYNGjSIzZs3M2nSJPbt20evXtfe4P4TSUd4fe3be8l9aqTb6V4NHBBf99/N\n/Z1up/t0q9RUWAjU6/GrtjBzZAzebr5yz66hMb9T6Qf386mnF2qvsLoNXbawMFb6+hK6dAUzflxl\nfztr397r+ifRxEA8fvx40tLSmDt3LoAs1hJCtEmqqvLmsm9JCvTHMngwWK18t30LD3v4MOcOCBS3\n0toTx1DHxNkdVwIC2Lb/EDeWJuX21qRArCgKr776anO3RQghWtR3WzexZmA/NAEBtb02nY7KmFF8\nsmcvw86cJqRrN2c3sc2qucbQc7UMS9cjq6uEEHeslOIiNAEBdsdNgwayZN8eJ7To9hHh6oatosLu\nuGqzEeKE9rRmt8/SPCGEUyTtSefb0zmc1Si421SGKFqeuutuXF1dm+Xz9x05zPHcc4zs049OQfZJ\nLxwpKSnm220pVNlsxIWGMSDS8XawKq3jvoiiKFRqpNd2M+6NH8vGxZ9x4u4pddWrVFUlaM06Hpl0\n7TKSdxoJxEKIJtu0dzdvWaswjx8LQCWwxmTiwpKveO/++Tf12WfycnkteQPHekegRvXmo0MHGL51\nE6/MmHPN7T3Lt6XyUXE+FdHRKDodS4/nMPzLz/i/OffbVbfqbLFx1MFnWMvLCTc4znomGkev1/O3\nmffxwYY1HMSGTYFwm8JjYxPxbee4BOSdqknbl5pKViFen6xwbTy5V41zK+/TE0u/5vB4Bxmjjufw\nZ4MXAxvoiTZEVVW+T0lmW9Eldpw5hXbB/Hqv26qrmZK2k+fucVyr91JhIQ/uSqEqZlT991VUcP/e\nAzx+V/2e2Onc8zy5dwcloy8vKlJtNrr/sIJP5j58w3ms7xTyb69xbumqaSGEADjX0PBtz1B2pey4\nZiA+eeokxooKIsMj0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_blobs\n", + "\n", + "X, y = make_blobs(n_samples=300, centers=4,\n", + " random_state=0, cluster_std=1.0)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n", + "This figure presents a visualization of the first four levels of a decision tree classifier for this data:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](figures/05.08-decision-tree-levels.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n", + "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "tree = DecisionTreeClassifier().fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's write a quick utility function to help us visualize the output of the classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n", + " ax = ax or plt.gca()\n", + " \n", + " # Plot the training points\n", + " ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n", + " clim=(y.min(), y.max()), zorder=3)\n", + " ax.axis('tight')\n", + " ax.axis('off')\n", + " xlim = ax.get_xlim()\n", + " ylim = ax.get_ylim()\n", + " \n", + " # fit the estimator\n", + " model.fit(X, y)\n", + " xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n", + " np.linspace(*ylim, num=200))\n", + " Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", + "\n", + " # Create a color plot with the results\n", + " n_classes = len(np.unique(y))\n", + " contours = ax.contourf(xx, yy, Z, alpha=0.3,\n", + " levels=np.arange(n_classes + 1) - 0.5,\n", + " cmap=cmap, clim=(y.min(), y.max()),\n", + " zorder=1)\n", + "\n", + " ax.set(xlim=xlim, ylim=ylim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can examine what the decision tree classification looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LE2ruPffcCJgBWhnkrVxPcJcgHBooUWksMpmMmf/+C5u+W4FZajoqdzfCn1lg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\nTzdzeWwIb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_classifier(DecisionTreeClassifier(), X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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QuXrzxPNTKC4s4cDxg0yf2Lx2cu9RDVGxU1tt4/j+bayYW9K0PMnDTUGgVwEK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\nVZJV3OI2c0lWcafb2XdgH4YxPlZXmtW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WaMBPi2ZoKHVerjdHo2LAIJlocFhb/IHOhXjd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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# helpers_05_08 is found in the online appendix\n", + "import helpers_05_08\n", + "helpers_05_08.plot_tree_interactive(X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n", + "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n", + "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision trees and over-fitting\n", + "\n", + "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n", + "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](figures/05.08-decision-tree-overfitting.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n", + "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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xItv2fMKidZvve6+C1mtoXHQ2jI3GNq5+cKPGTeBU+m6mT2wZYr9VaMPbP6Jb\n6nc2FxcdJTVB3P2ZMxptGCWx+YYg9CZi2LmHjZ+5gXd2+lBTa8Vkktl+wJWQUWudHVaPkWUZd3W+\nXZlWq0Ajd2z3Kr+wyVzKsv9YpmUPISy8ew6oDxkSTG7VHM5caGrj8jU4lDqBmCmOXWbVk0bEbeaD\nL3zJvmHhVBp8cCCauSs2OjssQRDuInq+Pcw/0J9Fj73KkeQkjI0Gpiybd8+Tf3qTtFOnqC4+j9mm\nJXrSEkKGhNz3HkmSMFtdAPueqsXWsfc8ZkIMr//ahctXi9DrJMorbVRZIzsTfrsWrtnErdyZfJSQ\nxpDwKFZt7l8zvIeNiGBoxH+RfeU67hGDWDVHzHQWhN5GrPMV2pS4fztxoQcID23qze5PdsEn6vsM\njQi/773JB3cxcfBewkO/fH1Wg83vW4wcO/a+9549cYLYwLcJ8m/p/WZkQZXrvzFiVPcmYUEQhJ50\nr3W+YthZaMVms6GoP9mcPCVJYvlsA9cvHujQ/bOWrOVqzRY+PjKerYfjUIZ8v0OJF6CmPN8u8QJE\nR8rczLn6QO+hPTabjTPHT5F85Bgmk/n+NwiCIPQAMewstGI0mnHTtZ4xq6Ljy4Zip02nM7OHA8Oi\nybp+hKjhLbORT6apiJ7Q9WeyZSWlpOz7A6vnFqPXwe7t+xg68XmGR4ketSAIjiV6vkIrer2Wkppg\nuzKDwYZZObTH244eP5bTOdM5c0GBxSKTdEZJft18ggcHdbnutMRPeeahEvx8FLi5Kti0sprcC591\nQ9SCIAgPRvR8hTaNnvYk7+1+k4mRhVTWaMi+M4Zlj6x3SNtLNjxDQd5iPkvJIHpCLFGB3bM0S68q\nabWLlYuyuFvqFgRBeBAi+QptGjJsKIOH/oqb1wvxCXNl1QIvh7Y/OGwwg8O6Z3nRVxotXsBt+zKb\nOElJEATHE8POQrskSWJYxGB8/R2beHtKVOwaPt3visUiI8syB5M1+IUvd3ZYgiAMQGKpkTCg1NbU\nczbpADarhfFTF+AX4OvskARB6KfEeb6CIAiC4GBina8gCIIg9CIi+TqYwWDk/NnzlJU4/2QjQRAE\nwTnEbGcHSj2RgKl4J1PG1ZB1QUtq9WQWr9/Sbw5xF7qXLMtse/MoGYl5aFyULH1yMnEzRzs7LEEQ\nuoFIvg7SUG/AXLKDtYsMgIqgACt5BSdJTxnNxGlTnR2e0Av9/Zc7OP77PFQ2DQBZR/by3Xdh0myR\ngIXWsjI9kgqFAAAgAElEQVRucuSzVLQuKh56di7ePh7ODkm4B4ck34sVhY5ople7cvo8T8TVAcrm\nsrDBErsvnkUVNXDOWh3nff+TkdqTEn+IxvJTKBUmGmzDmbf6CTQadTdG5zwXKwrJK6tsfm2z2oj/\n9DJ6W8syL6lUz5t/PcSdQFtbVThcwdXbFFy6zcjZI/AMEH/onen8ngwu/t8N9DXuTcvo3n+NJb+b\nQUC4v7NDG9Cen9D+hCuHJF+NKtYRzfRqwUP9uXzjC2ZMbDksvrHRhqwZM2B+PyZLKhcrCjuVgM+d\nSCLa7zMi4pom5xuNd9i63cDyx17s7jAd7mJFIWmX86iqjiTc1rT0yWoxY63Z0+paS7ErctYYR4do\nR5Zl9r75HpUnS9A16sn6azIRq8YzfeUyp8Y1kF1+/zD6GnegaX2+Ps+Ls3/JZ8U35zs5sgFuQvs/\nEsPODuIXMoSUM9OIGJJEgJ8Co9HGG5+HMmr1Q84OzWE0qlhMltRO3VtbfIaIcS2r4rRaBZ6aq1it\nVpRK5T3u7BuqqiNZ5x9tV5YYHUTF8brmOQEWycykKTGMD3Tu+bwnk5OoTSxHb3MBCVxq3bi5/xKP\nrF+Ft5dPl+s3m03s2PopRdeKcPVyZfH6ZYQNHdYNkfdPNpuNj6qMgP0okLbB5vTPitA+kXwdKHbt\nv7P91HhIvYxZ4cfIlRvQ6LTODqtPkGi9HP1B56mdPR5PXXEKSsmMRT2SuSs2oFD03gn/T37/Wd6T\n3+TOlRLUOhUjZ0axar3zv6zlXs1FbbP/3CortFxITWXewsVdrv9v//0nSuIrUUhKyqnhjfS/8/0/\nvoyfr0gkbVEoFPgM9aGu3NhcJssyPmFd/yIk9ByRfB1IkiSipy8Fljo7lD5H7xvLzfxshn75eNxs\nlqkwRna415t6MpERgz4manxTEq+uzePznfUsWb/lnvfZbDaOHz6ItfEmVjyZOm81bu6uXXovHeXr\n48fLr/5/NBobUSmVqFS94/l2YGggGVxBdVdPy+puZGR09D3ual9B/i12vruNioIqNIPUFGTk4y0F\nNv9cKtRwaOd+Nj/3dFdD77fWPrOej6rex5hjRVbb8I5xZ8NTjzk7LOEeRPIV+oQps+dz4kgjpy+n\noJSM1FmHs2DdUx2+v6YohajFLb1nj0EK9LZL971vz9Y/s3HeRTzdFVitMu/uuMD8h3+Bi4uuU++j\nM3Rax7XVEXMXLiQ16SwVKbWoZQ0mdSOjl44iKOjBn+VbrBbeePXvyNea/hRZMGORLZgxoZaaZnlL\nkoTJYOrW99DfRI0axc9f/xXnzpzG3cOdUaPGiCWMvZxIvkKfMWPhcqBzByFIkrVVmQLLPe+5lXuL\nieEZeLo3DU0rlRKPryxlZ8IB5i1f26k4+gOlUsmPfvUKxxMSKLp1m9ETxjB2fEyn6jqZlIQ5W0Z1\nV57wI4QyivCj6Uxpk7aRmBkTuyP0fk2lVDF12gxnhyF0kEi+woCgcB1DafkN/Hy+nLxkkamxjLjn\nPUUFBcwaauXu5WFarQKbubL9mwYIhULB7Pldn0krt/EsH0AdrKCxoQ69l46Zy6czITauy20JQm8i\nkq8wIMxavIrDXzSgMqajkMzUWYYzb+037nnP2NiJJB4YxNpFDc1l13JlfEPG93S4A8aM2XM4FnkE\n27WWMmtQI6/+7X/RaLRotbpePSlOEDpLJF9hQJAkiQWrHwUe7fA9Li46dMEb2XZgJxOiyrhe4Ea5\nZQYLVosh0O6iUqp45pXn+fy9HVTkV+AR4MHiRzbi4dE/zpAWhPaI5CsI9zBh6kzMsVO5cS2P8BkB\nxHi4OTukficsbBjf/fnLzg6j37h6JZPEL+IxNhgZNjaclQ+t7dLkq6wrl9n97i7Kb5Xj7u/O3PXz\nmT57VjdGPDCJ5CsI96FWq4gaPdzZYQjCfWVducz7P38fZXnTTPGi5NNUFJfx1AvPdao+s9nEh797\nH3LVqHHBUGzh84JdDBsR3qnZ7UIL8TBFEAShn0jcG9+ceAFUspqryVkYTcZ73NW+k8nJWG7Yl6kr\n9CQdjO9KmAIi+QqC0A/V1FZjNg+8tcGmhtbv2dJgxWzq3O/CxdUVWWF/kIeMjEanaecOoaPEsLMg\nCP3Gtawstr3+LypyKtF6aJiwdCIbHu/4JLu+LiImgsLEE6jklt3HfEf64OY2qFP1xU2ewoGxezFe\nkFueG4eZWSQO0egykXwFQegXZFnm49c+wHxFQs8gqIdzH6QREhbCtFkDY4LQklUrKSsu43LiZcz1\nZvxH+vPEd5/udH2SJPHSf/6Az977hIr8Ctz9BrHs0VWdTuZCC5F8BUHotLq6Wg7v3Y/FZGHesoX4\n+jrv/NibeTeovlqPCy0z0tVmLZfPZg6Y5CtJEo8/twXLFjMmswkXfdf3Iff08uYb3/92N0Qn3E0k\nX0EQOuXG9Rze+n9vQL4aCYm0L9JY//JG4qZMcUo8Hh6eKF0lqGspk2UZtUvvOJCio86cPEX89qPU\nltXiE+rN2i0bGBb+YLPtVSp1rzmIQ2ibmHAlCEKn7P/XHhQFWhSSAkmSUJXpObrtsNPi8fbyYdjM\nMKzyXXt2DzazZG3n9gN3htu3C9jx++3UpRuR8jVUnKjj3f99E6u19d7kQt8mer6CIHRKTUltq7La\n4tZljvT8y99hd9h2Cq4WoHfXs2T9cgICg5wa04NIOhiPqlwHd+2JYcy2cfZ0ClOni0MT+hORfAWh\nDyouuUN1VSUREVFO2/vYe7An9ReL7XZP8hri3G0hlUol6x552KkxdIVC1frfUlbY0GjF0p7+RiRf\nQehDLFYLr//Pa+SdLEA2gFuknse++zhRo0Y5PJaHnn6Yv+W9huGKGcmmQBVuY/VTjzs8jv5k4cql\nnD9wHmVR0xnOsizjNkbHhIniVKf+RiRfQehFTCYTKpWqVW/WYGigrr6OxINHuX24DJ3UNIvVmgXb\n3/iUV/7wnw6P1c8vgJ+99itOnUjGZDQyc85c1GrH9tBkWeZa9lVkZCIjR/X5A+S9vXx46mfPcGjb\nfupK6/AO9WbDM4/2+fcltCaSryD0Ara6FF7+zYfYbjSgdFMRNmMks9etRpZlCq++y7ihqQT5GbiU\n4oFSCre7tzSnglPXc3BxfbC1l+MDA7oct0KhYMasOV2upzPKykv5x6//QlVG0/Rmz2g3nn/lBfz8\nuv6+nClq5Ciifub4kQxnys66wsWz5wkZOpipM2YOiC8bIvkKgpON8w7htXc+xuW8DnCHGijYlc3F\nmH/h5dPAtOEJlJfBmJkq4mJrOZNpf7/Kz4pu7A0ktfKB2t15rYxwm2+3JGFn2PbmxxjSregkFwAM\n5618+s+PefGV73ep3uPxCaQcOIWx3kjwqGA2PfcUWo22O0IW2vCvdz4gbdt5tI0unFOkc2JaMj/4\nxY9RKh/s89zXiOQrOETJnWKOHj+G0VaEKS6WuJgoZ4fkVOkpp6i8fQaQkFxGU5HaiBu65p9rzDpu\nnSjicmEux6+MRGl0Y+sf81nzbDEpwTeQCochSRJGTQPhq0NQPmDiLS+pAQL7bOIFKL1RatdDkiSJ\nstyyLtWZeuYMe36/F3V9079FTmYe/6z+K9955YddqldoW1lZCem7mxIvgNqmpfxEDfGHDrNw2VIn\nR9ezRPIVetyNnBzePLkTyzg/JMmHX2Wm8kRxGRuWDMylE6cTDhHl9RlLF8kAnEo/j1UV2uq64uw6\n3K4NRyUpQQJrQRgHPzGz8Yf1HD7mB2aZsAkziIqZgJz1YDF4A/P6cOIFcPV2pYr6VmVdcfbY6ebE\nC6CQFOSnFtBgqO+W3aIEe1cyM1FUqe2WVqlQU5RX5LygHEQkX6HHHTqbgHW8f8v/r1Af9l64NmCT\nr6HsBFGxcvPraROUeEWXY07xRCk1/Ze0+DQS6h9MdY7B7t6ya34U3vHj3370PYfG3BkJh4+QevQc\nFpOFYeOHsf7xR7t1KHHeugV8mv0J6ko9AGZPA3PXrexSnbJNbqOs7XKh68aMG89en71Q0bIbl1ky\nMSRiiBOjcgyRfIUeV28zAfZb3VXZLMiyPCAmVnydUjK0Klv2iI7Tnt5cSbqMucGCS6MLcoUFlexq\n/zvytLJg/o8cGG3nHI9P4MAfD6E2ND0rPZ+eQUP92zzdyUPd2zJx8iR8/+BH0v6ms2VnLZ1LWNiw\nLtUZM2sCeclfoDY2xS3LMkHjA3F1dbvPnUJneHl5M2X9ZE59fBpNrR6zxkjI7ABmz5/v7NB6nEi+\nQo8L1HhQbDUhKVuWz4SpdQMy8QLUW4ZitVagVDa9f4tF5npFCGVZ5fgZBjcNwdWD4Wod5sFVaAsG\noZRUmFwMzH54Pjqd3rlvoANSE841J14ApaQi+1Q28re79wtXaOhQHv/mlnZ/XnyniJMJyfgG+jFj\n9pz7bkgybdYsal+s5dyhsxjrjQSNDOLxF9qvX+i6dY89zJQ50zl7MoXwyAjGjotxdkgOIZKv0OM2\nrFxP8UdvUOBrRtarCCqs45srFjk7LKeZvXIL7+5sYIj3dWyyRGZJCBWKOAy5J9BLLSMEetwIiwki\nZONgqsuriZ0xmRGRPTtR7cypk5w/no5SrWTWkjlEdnLzDpvV1qGynnR4736OvHkEdZUei8JMUkwC\nL//6J/f98rJ45XIWr+w7+0H3B8HBg1mzYYOzw3AokXyFHqfV6/jhN75Lwc08KqvOsn79dKdtidgb\nuLjqWfn4j6iva0CSJJTGCvIOGMj3AGparpNlGXdfD5atXu2QuPZ//gWJryehNn450zfpbR756aPE\nTIx94LpGTh5J0umTqKxNXyZsso3Q8aHd1us9d/o0F0+dR61Xs2jNUgIDg+1+bjabSPo0EU21C0ig\nljU0pFnYvW0HDz+xuVtiEISuEMlXcJjBQ8Pwt5QN6MR7N1e3puUVGMHVw5Oo+SPI+fwmKlmDLMso\nIiwse2iVw+I5e+BMc+IFUFXpSNqd0Knku3TVKgy1BjKSLmE2WggbO4QnX3ymW+LcvW07J986jdqk\nRZZlriT9ked+9U27Y/dKSotpuN2Iy11zDRSSgorCim6JQRC6SiRfQeglnnnpWySMOsKNS9dx9XJl\n+YbVDBrk3iNt3Sm+jUajxdvLp7nMUGNAhf2QrKGm9eSwjpAkiYc2P8JDmx/pUpxfZ7PZOLf/HGqT\ntrkdZZGWIzsO8tyPXmi+zt8vAH2IFm7dda9swyfE5+tVCoJTiOQrCL2EJEnMW7SIeYt67nl4cfEd\n3v7tPyjPqELSSAyOC+JbP/kuWo2WgMgAym5XNw8N22QbwSOD71OjY5nMJgyVBnTYb6XZUNVg91qt\n1jD3kXkc/udh1JVNz3zdY/WsevghR4YrCO0SyVdwqJJbBRw+9hk6ZRUNZn8mzX8Ub1/nHkPXFbIs\nk3nhOiqVipFjhjo7nPv6+G8f0JBmQY8bmKAkvopP/D/kyW89y+YXnuTt+jcozShHoVEwJC6ER57u\nXacU6bQ6fCN8qEs1NZfZZCtBka3P7F24bCnj4yZwIiEJv0B/ps2YJR55CL2GSL6Cw9TV1GK78gFP\nrDQCIMv5vLWjkBVP/rJP/lG8nV/Kfz/3EcWnjUgKCJ6h52fvPI23T9NQcd6NIrb/I4HGajPRs8JY\nvWm205dXFecU2w0tKyQFd67dAZpOKfrx//6M0rISNGo1Hh6980vRhm8+ykd/eJ+arDoknUTotBAe\n2tT2Gb5+fgGs3bjRwREKwv05JPleunXHEc0IvVzhqe38akMjX+0lJ0kSK2bd5u/btxM6vu+d4vL5\nKwcxndCi+/K/UdkxmVe+/zYrfraQ0pulHPjeCXQFngBc/Pgc8SmXWfDSLMJ8vRjnHdLj8V3PyeZ6\n9jWmzJyBh3tTHHp3Peav7dynd7d/zuvn69/jsQFU11Sx9fX3Kb5WjN5dz/QVM5g1f16719tsNvbt\n+pybGTcJHBbA1NVTmRA7qUfjPXf6NMl7EmmsbSQoMojHvvGkOGRB6BYOSb7aXEe0IvR2qkozX99d\n0M1Fwprnh6wb45yguqA28yB3/xmWJInaSxJy1hhS393anHgBNDYtBXuquTrfC6IrAXosAdtsNl7/\n7WvcTMxHbdBx7N145j8xj8WrVjBl2VTibyU2b4Bh9TYyd41zdhN64zd/pfqUAUlSUIeRvdf24+nt\nxdiYtjdZeOcvb5CzKw/Vl3+20vefJ3lUEt/88YsMHtJ6b+yuupyZwfb//gxVTdMM8GsXbvKP8j/z\n3Z/1/h3GhN7PIcm3L5+cInSfYOUGDiUeZ8lcY3PZjv1+LJ++zuGHsHeHA37u1BUY7cr8AjwZHxjA\nCYuC2q9dL9XLBNXpaTSFAIU9Flfi0aPcOlSEVm5a46oo0xO/NYGZC+eyZPUKfAN9SUtORaVWMXv5\nXIZHRPZYLO0pKy+h+EIpeqll4pS6TkvKsZNtJt/6hnqyk6+hwaW5zFcOoiSzkA//9C4/+d3PO9Ru\nwuEjnNxzgvrKevzC/Xj0m5sJDGp7UtnJg8nNiRe+PGThXCHV1ZW9dkhe6Dscknwv3Cl2RDPCA8rL\nz+F6WQ4+em/GRsY54LmriuL8x7n24X483SoorQ7EqNuIqbyyh9vtGWFzJ3Au5xj6WhdkWabRq4Gw\nuXO4cKcYVag3ZopQ37XOVBPmwiAPL0z3qLM73MrKQy3bf5mx3ZHIvHSJSZOnEjt5CrGTp/RwFPfW\n9Oy79fPv9p6JNzTUY61rvUOWhETZ1Uoqqyrw8vS+Z5tXMjM48NpB1PU6FGgpL6jhrep/8Mrvf9Fm\nu23tyCVbwGq13rMdQegIhyRfY9f2Ohd6QMKxfeR6V6Me6821qkIupWXy0JonUKp69iPhOWwqMJUq\nmo5aUAPGe9/Sa4UPi0M/wYes/WdBITFt5TT8hg7GCIx/fhFldaWUxecjV9vQjNYz7QdrMIVLRAX3\nXK8XwDvYB6t8A6V01xi/l5XwiIgebfdB+Hj7ETTRn8rj9c2JzzLIyNQF09u83tfHD6+RHjReaEl8\nRrkRJSpUrgr0HdjvOiX+pN1xgQCVmbXk5uYQHj6i1fUTZsVyI2FH8xC9LMv4j/XF29u3w+9TENrj\nkOQ7NjTQEc0IHVReUsItXTnqsKaJKipPFxqmKim9nsmiRUucHF3fMjY0kMULprX5s3F/epm6mlrq\na2rxDwlqTjImS2GPTrhasnIFF0+epy7ViEpSY1I3Mm7ZWHx6WdL45k9e4qPX36U4pxiXQXqmr1zM\nmHHj27xWkiQ2f/dJ3v7tG5RfrcQqW5CR8ZL8iZw5rEOHTbR1nKGkBLWm7Ucek6ZOpfLbFZw5cBpD\nTSOBkQE8/sLTD/QeBaE9kizLPX5QZXzRA570LfSolORktikuo3Kz7wWMyVHzxPpNTopq4DBZUu2S\n78WKQo6d0rLOP7rb2rBYzBw7dJjyojJGx45hfMzEbqu7PWVlJRzbfxilUsnClUubZ1h3t/Pp50g5\nfAqzwcywscNYsW5th5Zw5d3K5fWX/4aqvOlzL8syHtP0/Nt//UePxCkIU+eObPdnYp3vADR6zFiU\ne0/B2Jbka6k2EObT/TNGBedQqdQsXu64k3nSzp5l228/QVna9JlK3ZfG0z97hsio9v/4dFbMhDhi\nJsQ98H1hocPY9B+bObbrCA2VDfgP9+eRZzu+icj5tFSS9yRirDMyJHoI6x9/FJVS/AkVOkd8cgYg\ndy9PZvuOJuHKFRQj/bAV1RBepGbm03OcHZrQRx359BCqMn3zHCrlbS0HP9lH5M+7P/l2xdiYmHaX\nMt3L5YxLfPLqJ6iqmp7/Vpy9TFXZ3/nmyy91d4jCACGS7wC1culKJt+ZxNlzpwkfGseoZX1vna3Q\ntsZGAx+/9QF3su+g99Aze9VcJk6adM97TCYTJ5IScXVzJW7y1Aee+V5bWgt3zewGuJySye5tO1i9\nse/vp3x8f1Jz4gVQSkpupNzEYGhAr3e5x52C0DaRfAcw/8AAVqx0zFmxguO8/ps/U5pYjUJS0ICZ\nbRnbcPsvNyJHtr2L2JXMTLb+3wdYcyVsChsHx+7nOz//AV7e9166czfvId5U3LJf2Ww12Dj1z9O4\nDHJh4dKlXXpPzmYxW1qV2Yw2zBYz95/qJQit9b0NdQVBaFdFRRmF54pQSC3/tVVVWo4fTGz3nj3v\n7YKbapSSCrWswXhBZvv7nzxQu2u2PATDzVhkMybZSJGchye+qCwaMk9e6vT76S2ip4zBompZoS3L\nMv5jfHEf5OHEqIS+TCRfQehHLFYLNkvrBQxtlX2lPN/+gHlJkqjIf7BD58PDI/jpX35BpecdGqgl\nkFC0UtPkK0UbS3z6mjkLFjBlSxyKcCumgHp853iw5eXnnR2W0IeJYWdB6Ef8/QLxH+dD7Vlj8/Ib\ns97IxDntzw5293fHUGw/rOruN6idq9un1eqInTOZ3M8LWjbO0BmZMCf2gevqjdY+tpG1j21ElmWn\nn04l9H2i5ysI/cyzP/4WAfO9sA02oYtWsvDF+UyMa3/C1fwNCzF7G5BlGZtswxZqZNmjqzrV9pbv\nPMfoTSPQj1HhHqtn8fcXMXNu/5pF35HEa7PZOLhnL2/94XW2fbCVBkO9AyIT+hKxyYYgOJgjNtl4\nUMXFRSQePIZaq2bxyuW4uro5LZavlJWVsOvD7VQVVuEe5M7qTQ8RGBjk7LA65K+/+QMFh0pQocIm\n29BEw09+//N2jyMsKSnmX3//gOLrpbh46Jm2cgbzlyxycNRCdxObbAhCD5BlmYtp6ZRXlDN9xkx0\nLn133mtAQBAPP7nZ2WE0s1gt/OU//4jlsgJJkqimgb9l/Ymf/vmXaNrZDrK3uHnzBreSCtF8OQ9a\nISkwZlo5vGcfKx9a1+Y9b/3P6zSkW1CgobHAyqGbh/AL9GPs+Adfkyz0DWLYWRA6wdDQwP/+4/e8\nX3mS/R55/PJff+J8epqzw+o3kuPjMV622Q3xWrIl4g8fdmJUHXMr9yYKg32/RikpqS6rbvP6/II8\nyi9V2ZWp63WcTTjdYzEKzid6voLDFN8uYl/yYWptjQRoPFi3Yi0aXdvDcL3drv2fUz7ZG5Wy6fur\nNTaIPakJjI+ZMKAm41itVj5590NuXriJSqMiZm4Mi1eu6HK9DXX1KLCfJa1AiaGuoct197S4qVPY\nH7QP7rRsOmJSNxIdN7bN6zUaTdNf4q8tJVYoRd+oPxP/uoJD1FbX8Od973F1pEzhaC3nhtbz5w9e\nd3ZYnVZqqkH62h/HSp2Zhrq+ObGmwVDPFzt38vlnn3HwwF6S4o9hsbbeWOLr3n/9LTI+vIohw0Jt\nWiPxf0ni6IGDXY5n7qKF2ILsTz62+Dcyd8nCLtfd01z0riz/xgrkISYMcj1mXwMTHxtPzMS2Z30H\n+AcRGOvH3dNvLN6NzF42z1EhC04ger6CQxxOOIxpYkDz8ekKlZLCYJmcrCwioqKcGltnuCv0FMj2\nw6JujQr0rn1vq8HsrKu89+u3Ib+pp1ZKIXrcOBp5mOd++m0GD2n/wI3rZ66jlFqewapMGi4ev8CC\npV07mtLV1Y1Hfvgo+z/aS2VhJZ7Bnqx6eD2enl5dqtdRZs2fx9RZM7iRm0Nw8GAGubnf8/oXX/k+\nH7/1AcXXinHxdGHOmrWED+895y8L3U8k3wGqtLiY8+npREdHEzxkSI+312gxteopSu5aKsofbDOH\n3mLl/GXc2PEWhol+KDQqbNllzAmf+MB7IvcG+z76AkWBtvlQhACGUCwX4H7Ni13vfcZ3fvpDAM6n\np5KTmU3E6MjmXpzNZuPrW2jI1u5ZQBETF0tMXGyfXVerVmuIihzdoWv1ehee+c43ezgioTcRyXcA\n2vnFDk413ECO8ObgmUzGnfDnyUef6NE240ZPIC17P8qhLfsFu2TVMPGpBz8arjfw9fPjlSe+x9GE\nI9QbDUybtIEhQ8MeuJ5LqTm8/X8HKMw2UxYxhA1bHnugPZW7Q3VRNc2Z90uKL59IVeRXAvD6//2Z\n3EO30Jh1nFWnkbLoBN/60XcZNmEoNwtuN29naVGaGDWtYwmno/pi4hWE+xHJd4C5U3ibk4YbKEb6\nNf25jfDlwu1KMi5eZMy4cd3aVkNdHTv3f06FtQF3Scs0zRAupt2kTmvFp1HD6inLUKnV96+ol9K5\n6FmxvHObUQCU3Cnnd09vR77pih41eVeL+GvBn/iP3//CoQnHM8SDsus1dmU2bAC4B7iTcekCuYfz\n0Jibls5ozDpyD+eTsfgCT7/0HB+q3yHv0i1UGhWxsyaxdNVKh8UuCH2VSL59THV5JWWlJQwdEYGy\nE3vmpp4/hxTpa1emCvbgam52tyZfWZb50wevUznVF0mhQZZtuKTk8JMnvovNZsPFzXXA92h2vp2E\nLdeFr34NkiRRm9HApQvnGRczwWFxrHpiHW/f+ie23KbPUym3GYQn1qBGljz8KFczMtGY7Ncwa8w6\nrl3OYsy48Wx5SQyXCsKDEsm3h8iyzKFDB8ipLESLinmxMxg+YkSX6nvn43e5rCjD4qHC84SF9ZMW\nM3bc+AeqJ3J4FMeu7UcV2jJxxVpZT4jv8E7H1pbUM2coG+mCStGUWSRJom6CL0nJCSxZurxb2+qr\nrBZbG4USRqOxW+qvqCjn07e2UpZbhpuvGwseWtTmQfLhwyP42eu/JOHwERoa6jE1jkatUrNgxRI8\n3D1RqhWk6M6gaWxJwEatgdEx4gxoQegskXx7yEfbPuJCcD2KwKaTXa6n7eZZVhExIrJT9R05cojL\noSaUg/xRAoZg2HHmENFjxj7QJJ8RI6OIOpNEtmsdSh83LNUGBmeZmfr8jE7F1Z6KygoUfjq7MoVW\nRV1j31yK0xOWPjaVk+9+iFTq2lymj1Tdcx/mjpJlmb+/+hqGdCuSJGGkhq1ZH/G9P/kTGBjc6nqt\nRsuSFW2vz42MGsWolVFc2ZOFplGPSWdg1IoRRI3s3me7gjCQ9L2pmX2AqdFIpqEQxaCW5GMb6UtC\n6lCCYL4AACAASURBVIlO15lbVYRykH0yq/BXUJSX/8B1PffEN3jYZSITc3WsZRTfe+6lbh8CnjVr\nNqpLpXZlclYZ0+KmdWs7fVn4iBCe+dMCtNOM1ATW4D3DjS0//kanHid83dUrmVRn1Nv9uypLtBzb\n07kdop5+4Tmef+154r49nuf+9DxbXux7Q80VFWXkF+ThgO3sBeG+RM+3B5iMRoxq+PpUIiPWTtfp\nKmmQZZPdH1NdtRVPX58HrkuSJCZPncZkei4R6l1dWT9mPvtTk6jSWRhkVDF3eCzBgwf3WJt90fxV\ncfjOCOr2gxUsFgtf/7hJkoTVamV3yl6uGPNR25TMCIphysiO9bSHR0QyPKJzIzfOZLGYef1//0xe\nSj42g4z3aA+e+OEWQsOGOjs0YQATybcHuHm4E2DQcvcKVlttI8M9O7+eduncxVzZ9Sam2EAkhYSl\nop6JusG4DnLM6TONDQZqq6rxDQrocC85Lm4SsbFxNNTVo3d16ZNrYB3pwp3ibqtL9gtEMUIF2S1l\nBo8GbnhWUzzMjMqjaTOQ3NxTcEVmyqjJ3dZ2b/PZh/+i6HAZOqnp/0rjJRuf/P0j/u03/9Ftbdy4\nnsORHQcw1DQSHBXMusceRqUUf16F9olPRw95fMl6Pjq0g2J9I2qzxHhdMEs2Lut0fT6+vry87nkO\nJByiwWYkMiCamRtmd2PE7ft016ek1dzE6KbAqxI2TF3K6OiOTbaRJMlhXxB6g5LCIlQaNd5+vve/\n+C7DR9yh0fRg99ybxJz/eoyUv++h4UYVGl8XYjYuIKXuAiqPltESaZgnx0+d79fJ93b2bRSS/VB+\nSU4ZFosZlarrS90KCwp482dvoLzTtE958fEKSgqKefEnP+hy3UL/JZJvDwkZPIR/f+Z71FZVo9Xp\nuuUAAS8fb/7/9u47PqrrTvj/5947XRr1ikBIICGKAFFN782mGIwbbrg7sbNJNrvP/rLZ12a9zz6/\n55fdze4mu4lTbBP3io1twKb33kE0gYQKqHdppBlNuff3hxLEgJCEpJlROe+/zNEtXxXP955zz/me\nNasf74boOu7woYMctZajS47FADQAnx3+lp+PGCl6sreoKCvj7W8+pDjCg+xSGWyz8L0nXsRoNrV7\n7l/29jXo4ro3qMQ45kxtmd2saRrH1p2747DS6vI72voSS4iFamxebeZQE0o39Ux3btyKXGy4WadE\nkRTyD12nqrqSiPB7fy0k9A8i+fqYNSw00CF0yaXCHHTDvHuuNQlGci5fIXXk3TeK7m8+/G49VZMi\n+MsjVqFH5bON63n60acCGtetJElikC6cglvKNbrr7FR+k8dn9R/x6NonAhxh97ialcXmj76htriW\nsAFhjJ2ZQcGZ6yhlzb8dl6GJaUumdNskQ6fdece1PHaVhoZ6kXyFuxLJV2iTWdajaU1eHy66OicR\nnZjo1REet5uPvviEHHspMhIjwxJZ/eDqHl+Qo8RTB7QsGZIUmSJndeACuou1K5/g9X/+ZxrCdWD3\noDtsIz4/ljPbTvPgmtUYDb1zi8e/aLQ38M7/+3ZzrWokKrJr2VGwnRf/78vs/24P7iY3o6eOYfLU\nad12z/T7xpC9NRe9q+VnFzY8hIEJ915uVOg/RPK9i5qqKtZv+YoyTz3BsokFGdM7/J6zL1k8eyEX\nvn4b54TmiVZqQxPDnOFExkT75H4fffEJ55IdyMbm959H6ivRbfqalctX+uR+3cUiG28b2ATzLbv9\n9BTW0BCGNw2i7HflgA5JMoEEzho3DbZ6jBG9O/nu3LKteXemW57V1FyF7EtZrH3tRZ/cc8r06ZQ8\nW8TJbSdx1DQRlRLBo99/osc/MAqBJZJvKzRN43efrWsujSiFUQu8d+pb/joiitj4bn4v18NFREXx\nw6Vr2bp/Bw2qk8EhCSx+svMTx9qT3ViCbGxJ7IrVRFbuva9l9repielsvXEReWBYc8OVSuaOnh/Y\noO4iIWMQxRtK0Ekt//uHDbES3oeHSH29snflmkdY/uhDuFxOTCZz+ycI/Z5Ivq3IunCBkhgNx9Fs\nkCWs6YOQR8ew+/BeHn/osUCH53cxcXE8/Yh/3l3KrfQWfN2DqKuu4eyZ06QOSyMu4c7qT9C8dd6R\nQ4corChmRPIw0sd6l/VcMG8hUaciOHE5E0WTmDVheZfKifrS4udXcvFkNtW7ipAaJaypFlZ/r2/0\n1OYvWcSRDYfhRksPXk72MGfhAp/fW1EUFEUkXqFjRPJtRXZ2No3FVYRPHYamadQczcaSFI2q9bwh\nufxruZw+f5pBcQMZP2lSr/8ATQsZyImGGpSg5lnCnqoGRkcP8dn9vtv2LXvKz+MZGg6HTzHGHc0z\njz/jdYyqqvzqrf+hKM2AkmzhSNFexl4663Wcx+3m1KVz5DSVoqHhOLyb5wcO6tBsZ39TFIXFP1uL\n4/5KBuh1DElO6TMz1y3mIJ752XN89/GmmxOulj31YK9/ly30PSL5tuJKfRERM5pn8kpAxPQ0qr47\ny4zHVgU2sNt8+c2XHHLnogyNYn/Fcfa9eYQfvvBal8sTNtpsZJ49S9KQoX4fZn905SMYNm3gSnYh\nkiQxOmYo9y/u+kYMmqZx9PAhckuuEx8WxczZc6mrqWV3xXmk9NjmDeFTozhbWsuZkyfJmDDh5rkH\n9u+jcIQRXUhzr0Y3IJRzjeUUFlwnIbG5cMpnX33OpVQPsjEegFyPyvsbPuLFJ57vcuy+EhoWSUpc\nbKDD6HZpw0eQ9s8jgObf+9aNm9jy8WaMwQbmLl/I0JSeOSIh9C8i+baiRrVz68xVgIjQCBKTkwIR\nTquqK6s40pCDMjIGAF1UMIUmB3v27GL+/IWdvu7O3TvZfv0EriGhSAcOk+6OZu3jz/itRy3LMg+t\nWN3t1/3je29xJdGFbmgQJ+uzOfnWeUYnDkNLi/LaRl4XG8Lla9leybe4uhRdsvdwopwcwaVLF24m\n3zx7GbIx7ObXJUXmurNvr5/tDd7/wzoufX4VndZcTGPd0bd4/l9eYGgnNzgRhO7SN8aaulmkbLmj\nLdEaE4BI7i7r0kU8iSFebUqwieLaik5fs6HexvYbJ9HGxqGzmlGGRZMZY+P4kSNdDTegsi9ncSWi\nAV1E8wOVYjVTNNxIg60B7Uat17Eem4PYUO+JR0PiBuOu8J7LrF2tYNy48Tf/refO0Qa91PUNEnqK\nhkYbn777AX/8t9+y4dPPcLmcgQ6pXU3OJi7tvXQz8QLIZQZ2b9wRwKgEoZlIvq1YPnUhhuMleBwu\nPHYnxiMlrJi1JNBheUkbMRIlv86rzWNzMCC080uAzp4+jWuod1EQXVQwV4vyWj1e0zSOHjrE199s\noOh6z5uRrKoqmqZxJecKyqAwr6/pwiy49DC8PgR3ZXNi9TQ0EZNpY9bsuV7HTrzvPtKKDLgLqtE0\nDc/lMqZZUoiMaXkgmzh4FGpx/c1/eyptZMSk+PC78x+328V//v2/cu6dS1zfUsLx35/h1//7lz1+\nd6CmJgfuhjs3M2lq6FkPDg6HnaNHDlJaWhzoUAQ/6pPDztlZWRzOPIGMxNz7ZjJg0L1taDAkJYV/\nHPTX7Nu7G0VWmPHcWvSGnrVmMzwygqnWFA5ezUFOicJTYWNQrsbs5+e2f/JdJA8ZgnT0GKS0TBLy\nOFxEBN2Z0F1OJ79a9xtK0kwoiRYOHP6UORdGsHRJ63vC+lPRjUI+2b6BYq0OC3qGWwagZVUgDW/5\nPtxFtYwaOp6R6ekcOXyQ3JzrRFvjmPfi/DvemUuSxMtPv0ROVhZZ2VeYMHMxsQPivY6ZM2su+oN6\nTl24gAqkx6Ux/37fz7D1h13bttN4zoVOau5BKpJCxdEazp09zdiM8e2cHTgh1lAi08JpOOm62eaW\nXAwZ47sJfPdq746dbF23BU+xhBbiIW1BKs//4JVeP3FSaF+fS76HDh/gq+LjSKkRaJpG5v5PeXrM\nYkalj76n6xiMBhYsWuyjKLvHquWrmJiXz6lzp0hMSCdj/oQu/U8bPzCBkfsiuFBpQxcZjMfhIvxU\nNfOff+aOY9e98xZl40PQGZs/kKW0aA6cu8C8hjmYg4LuOB6gsqKCDds3Uqk2ECqZeGDaAhKTkjod\n7928u+VTaiZFImPFAZwsrSYpx0DBhVK01Ei0/BoyPLGMGt38NzF12owOba44NC2NwUOGsH/fXo6c\nOMKMKdO9er/Tp89g+vQZ3f79BFpNefXNxPsXOpeBwoLrPTr5Ajz5V2v58L/fozKrCl2wwojZI1iy\nYnmgwwKae7zb/rQVpcSEIgH1cPWraxwcvZcZc+YEOjzBx/pc8t175SRSRgTQ3GPRRkWz88zBe06+\nvcWgpMEMSuq+MnbPrXmWI4cOkpNznQhzJAuefwaD0bvXn3X5MqdqrhFm9P6Z2uNM5OfmMTz9zn1p\nVVXljc/fpn5qLJJkpQr4w45P+IfHf4AluPt2PSorLKY0QuXWhSVKrBVjtcZP5z/J2TOnGT5lCfED\nE+752rXV1fzqo99TPy4SOdLAlq9/S1pTGD967cc9oqfibHKy7esdVFTZSBoczazFs7tlCdHEmfdx\ncv1pDA0tk87c0Q5mzJ3T5Wv72qDEwfz0l/9IbW01RpMZk7HnLP06n3kWdxEYbvnT0WtGss9fFcm3\nH+hzydemNXWoTWidJElMnd52T/DQuWMQbsbjcKGYWnpExsJGBk9LavWcY4cPUzMqFN0tSco5Load\ne3awfFn3lY40mk0oTeod7XpkIqIimbug40PBVy9ncfzCKQyyjoWzF/DNzs00TI9H+fP3EDJ5COe2\nnuXb7zaz9IFlnY75XFUhAJkFJZ2+hsft5tPffoIjKAVZF8zpggoOHv0dK56/+8zxtNEDOZt5o/2L\nW6wMWTWGq9vOopZ5UBJ0jF4xjdwGBzQ4Oh2z39mdQG27h/lLgyUYt9WFwdbyqOjRPNjNum7d21kI\nnCncffOZPpd8Y2Urhbf8W1M1YhRrwOLpizyohIxLpnLXeUInDUUfHoTt/HXmBg2765Czw2FHCr7t\nXaoi43S7uzW20IhwhjaFkOt0Ixua/7ylSxXMmXRva7R37N7BltpMlJQINI+D0xv+QIhkQpLCvY4z\nxoay5cD2LiVfgKyi5p74mOCBHTre3tjI9o+/pq60hqSxKTQqKnbzEBRd8yiFzhREeV0QwaUOhgy9\n+8SvtNEdu1/a6Bdo+pGDirISYuMT0Om7vg+uMJCrK0dx9eMrGDxGPJoHw3iFJ378DEZTz+mhC76h\nvP7666/7+iZ5tkpf3+KmwVEJXNh1BJvswlPVQOwVB8+uegpjN+ynKzRTG51cqMglKCMRe145jTml\nxNfoefXFV+96zoCEgRzcvgs14ZYHofNlPDl7RbcOOwOMGzWWuhM5eApria6QeChjPinDOr6uU9M0\nPtizAc/I5s0dJFlCHWCl8UQeDIvyGmJuyC5BirQwO3UiBmPH/sY8ajGx5pZlYqX2eirrQzqceB12\nO7966Z8p/qqYuvN1ZO+8zFVHPlrcbROJdEaiZBtDU7pn1rVOpyMkNAy5i0VcejK3y0VFWSkms9kv\nVb9GT5+IZZgZolWSFw/lyZ++0mcTr8ftZtM7n7Ln/S1cPH6GqMRYQsLC2j+xF0uKibjr1yTND+sF\ndhdn+foWXjRNI/tSFnqDnqSUoX69d09kb2xk09bNVLlsRBmsLF+yHEMXH0a279zG0RsXaFSdxCuh\nPPHAw0RGRbV5Ttbly2w8up2KP0+4GmZNoMJVj1N1kxaVyMKFi3vEu1OX08nff/RLlHHe74UjTlST\nlZ1F+ANjUIJM1J3ORQk2oViMDL9h4IFFS0ka2v5MWqf7JGMiWq59rqqQrKIEjLkdi+/gxs0UfpSD\nLLUkh9LgUrRlM9GHtvwOPGXXeGbJIoKtIa1dRrjN8ZNHOH2tAIdixuxpZGLqEMZlTAp0WH3Gxt+v\no3FvHcqfN/Swx9lZ9rNnCY/yzQ5pPcErj8++69f63LAzNL+3FBu9N/N4PPzXu7+l5r4oJEUm21VH\n9ju/4e9e+UmXEt3C+YtYyKJ7Oidt+HDShjf/XjLPnuGDnN0wIgJQyK/Jpearz3l01aOdjqm76A0G\nojQLxUXV2AsqMEQGY06OYVBIDGNmprLh0mEkIHjkQHRWM1X7L5E3LY03znzF0qLxzJ45p9P3HtuB\nco9nnKpX4gUIrw8lQq2moLIRpzkcc2MlK8aOYHoP3dyhpykuLeJUQSnEpGAEVOBETh4rpkwjIuzu\nvRehYyory6k7VY5RanktZSo2cePQcea8/FwAIwucPpl8hRYH9++narQVRWn+sJb1CmVpZk4cPcqk\nKVMCFtf+iydgZMuHmhJm4dy1PB7RtB7R+w1zG7lRV0P4lFQcJdU0rD/Nyn/4BSaLmRsfFZNlqMbj\ncFF1Nh9jfDiSIkNKJPtOn+5S8u3IRBt9YixNchZGtWV40h3nYd4DK3HYGykuvs7gpEmYTBYxcaeD\n9uzbgxY+0KvUqCcykY93bmPG9M6vnReaFRbk4mnwHmSVJImiipo+/TfaryZcCd4qayuRB3u/Q1LC\nLJQWlnXrfRrqbWzbvY1GVxPj00YzIj29zeOdmhvwnrTThBvV40HRBfbPsrKsjOwQG0HDm7cXNMWF\no1sykpOnTjBz1mxeeupFyktK+dV7v0U3PxVZ1/IOtF61o3XyAaKjk5+GpSdgrywna+MFtCoJfZLC\nsr96iNET/vKKpW8uq/Ol4vJBXDxdjmJsWU6lOhpJnzC0w78X4e5SRw3g6AebUM+3tDn1DqaumNpv\nf76ivGQfd9/4yahZtxX4v1TG1Pumdep6l86fZ8vmzVRXVt1sqygv5xcf/4bDA2s5l+LkrdwdbNqy\nqc3rJFvjUBtaloBpmkaCFBrwxAuQmXkOKdl7VrMuzMKNypalQAeOH6TcVoN026ScGNnq8567JEk8\n+b9e4a+//DmPvfMMP/3y/2PqYtE764qZs2cT6SxCU5uXqWmqh1i1jMlTO1J+RWiPLMs88rO1GCfo\ncVgb8Ax2Mv57k5g0d2agQwuYPjnhqi/Iy7nGoTNH0ckyC2bOJ6KdyUxt2bZzG3tvnMUWJhFSrTEv\neQJzZ8+7p2t4PB7eePf35MV7UOJC4GIZC+IyWDR/Ee999gHnh7m9ko7+dCn/9MSP71qWU1VV3v/s\nAy45inArMMAdzNrlj7c7acsfyktL+bc97yPfWo6y1s6D2nBmzZ7DyePH+bjyMISaqD6YRfCogShm\nA9arNp6es4qUdnbM6eqEK8E37I0NHDiyH1uTmxCTgZnTZmHoQUU5+ooGWz0mk7lHPGj7Wr+bcNXb\nHTi4n6+LjyGnRqGpGqc3r+PF6Q91esnIovmLmNM0h4qSUqLjYztVp3rXrh3kDdehC/rzsqD0WHaf\nOc3MxhnUaw6k28oPNgZJ1NfWERHdejKVZZm1jz+Dy+nE7XZjtty5k1SgRMfGMsk4mGO511GSI3GX\n15OYqzHjhVkAnM/PQklt3oAicsFo7Hnl2HPLeW7qo+0m3rspzK9gVdydlcG6i81Wz2frPqLsWhlB\nkUEsengJaSNG+ux+t8u7nktJeRnj08dh6GF10m81ZUjPqfvcd/W9PaQ7QyTfHmhv9gnkjFvWmGbE\nsu3YHr7fhfWaBqOBAYPvbYOJWxXWlqGL8e4FOAYFkX35CnHGMPKctTeLWgCE1cuER0Xefpk76A2G\nHrdpBcCjKx9hUnYOZy+eZXDCKK+62SZZj6a6kWQJSZKwJMdgcEvExPSsbSdv9Zt/+S9sx5xIkkQD\nTt658Cd+/KufEBsX3/7JXeD2uPnVB+9w1aFDMwbx2eETPDFzGhPHjPPpfQWhpxPvfHuges+dJfvq\n1cCW8Ys0h6I6vatRGUrsDE5OYsUDK0g4a8dVUIW7rhH98RJWjJ/XI2Ytd0VyylBWrniIcRMmen0v\ni2YvxHCy9OaWeh6Hi6G2IGITBgQq1DZdzc6i6kyt1/eglBrZuXmbz+/9zY4tZCvRKGGx6MzBOKKG\nsP7QEVT1zhKggtCfiJ5vDxSjs3Lr5HtN1YjWBbZE5uIFSzi/7tdUjglBCTbhLqhmalAyIeHNFWp+\n/OIPuHb1KhVl5Yx7ZmKP7M12l/DICP5q6Vq27N9Og9ZEgjmKZU+1XV7y2y2bOVmShVNzEy97WLNi\nBkFW76H2riy5aGt9cJPDgXZbFU9Jkiipqff5Mo+zRaXIFu+HknJVz95LF4iI9O1IQZPDzpmzJ7CY\ngxiVnuGXilWCcKu2lhqJCVc9UG7ONd7duZ661CBwuInJd/PampewhvqmUlFpcQmlRUWMGJ3eZtL0\nuN3s27eH8rpqMoalM2zkCJ/E09fs27eHjc4LyNHN78s1VWPg2UZ++Kx3Oc7ObqxQmF/BEDWq1QR8\ntqQUVVX5+t/ewHOupd0Z4uCZt77P0BFpnbpnR73/wcecqrZ4T8aryeNf/tcrHS7H2RlnTp/ho017\ncYYMQnU5iHSV8uPvP0doHy9nKPQsc0bdvcKi6Pn2QMlDh/CPSX/DuVOnsERbGLZkpE+GcFVV5e0P\n13HZVIMaaSLogx08OGYOkyZObvV4Radj7ry+sUG8P527cQV5VEv9akmWuC7X4Wi0Y7K0rCsdnRjX\n+Zu0MVN6xNhErP/yCl/910dUZFcQFBXEnMeX+TzxAiy7fxFZb7xHQ0gSkqKg2iqYMjLJp4kXYNOO\nQ7jDk5EBWQmmxhjEl19t4rlnn/LpfQWho0Ty7aEURWHcJN/Wld25cztZyW501uYlNa5IKxtP7GF8\nxvh+sQzAXxTpzuFOWcXnGxRUVVdyaMsW6irTmDh7Bj/4n3/w6f1aEx4Zyc9+8hJbtuygweFg7Izx\njMnI8Ok9PW43lQ1NSC3PNUiSRGV9L9r+UOjzxCdsP5ZfU4ISY/Zqq43TUXAtj+Rh3bMTjgCTUseQ\ne/0I0qDm5Umq002KEonB6Lv34ru3bWfrH7egqzST/+EF9k3Zxg9+9Q8B2THHEhTMQ6u7b8/m9ig6\nHWFm/R0794YFiZ3NhJ5DzEDox6yyCU31fuVvqnET3YHi/kLHTZw4mZWR44nNbCDsXC3j8sy88Piz\nPruf2+1i9ye70Fc1v2vVe4w0Hmhi87uf++yePc2C6eOhugBN01DdTsw12Sx/YGGgwxKEm0TPtx+7\nf/4SLn76e+yTYpAUGXd5PRNNiQSHBHZmdV80beoMpk2d0alzC/Lz+WLPJso89VhlI7OGTWTGtLuX\n5SuvKKPhuh0LLb9HWZKpzvffvtqBNn3GNFJTh7B//2Es5iDmzX+1z+6T2x1s9fVcvnSJ1GGphIaF\nt3+C0GUi+fZjIWGh/N0Tr7F111ZsbgfDB0xg8oKu1bKtrarG2eQkOl70nruDqqq8s/UzGu6LAYKp\nBb7JOcGAq3EMuct2gVGR0VgGmOD6LdfRVEIT+tdM35jYOFY/vCrQYfR4mzZ+y54z12gyRWDYeoyp\nIxJ4WPzcfE4k334uyBrMQw+u7vJ1XE4nb360jmuGWjwGmbgaHc8+8DixA3xbQamvu3Quk5pkk9f+\nT9LQCA6dPX7X5KvXG5jx8Ex2vb0Lfa0Zt+TGPMnI0uce8U/QQq9RWlTEzjN5SOGJ6AHNHMyBK6WM\nz77KkBSxF7QvieTbzzgdTXz81acUOqsxSTqmp03gvsld39f3y40byB2lRzHEoQBVwEfbvmDNwof4\nZMdXlHpqscgmpieNYd6c+V2+X39hNJnA6V0NStO0VmdQ32rx8qWkjx/Lho3fMWzcUKbfvwDFx7Or\nu5OzqYn3PviUvNJaFEVmbOpAVq1a0eurpvU0R4+dgLAErzY5JJZTZzJF8vUxMeGqn/njx+u4MMxD\n7dgwSscE80XxEc6fO9vl6153VHjVdgYodtfy9uaPKc4IQp0wANu4CL6rP8/5c+fuchXhdinD04gt\n9HhNjFMyy5jfgQ3eExIGMnPlCmYtW9yrEi/Aunc/4oLNSmPIYOqDBrH3WgNbvt0a6LDuiaqq2Bsb\n8UMdo04bnJSI2ljj1eax2xgQ13PrlPcVIvn2I7baOvIMdUhKy69dSgrn8KXTXb62Rb5zGYdW7aA8\nzrunIieGcSLrTJfv15+8+tiLDL8M4Zl1JJ5v4tlJK4jp4zPSr5XUIt3ywKCYgrmQcyOAEd2b7dt2\n8PNf/Ia///e3+T+/fIPMc5mBDqlVY8aOJcnUgLvJDoDH1cQAqYIp0zq337fQcWLYuR9RVRVVgtv7\nQKrW9SL3c8ZM4b3zW9GGN+/G5CmtZ0LCMI657qwdrATwmU/TtF43dGkNDeH5Nc8GOgy/UmSJ28pR\nIyu94/eWffUK3x6/hhSahAxUAx99s5P/PSINvb5n1TyXJIkf/uAVdu3cRVFpJTERoSxc9P1urYOt\naRo7tu/g2vVSgs16HliykPCIiG67fm8lkm8/EhIexkC7mZJbEpBaXMf4IV2vpDVyVDovG03sPXkQ\nNypjEidw36KpFL/135R41Ju9beliBXOmPtzl+92rzVs2c7z4EnbVSZwSwhOLHyY2vgvlHAWfSkuM\n5nRlE4q+eURFbaxm/KTeUfjl6PEzSKHNf1uapuF22HDqozh+5AjTZs4KcHR3UhSFhYt8twb67XXv\nkVljQDGGoDVqXHjjPf7uB2sJ6+dLmsSwcz/z4uq1DMl0YThVRsiZKhYbhjOpGyZcAQxJSeG5x9by\n0mPPcd/U5iVLrz7xEqOu6ojIrGPQ+SaeGbOYQUmDu+V+HXX0yBH2yLnYx0fDxARKxln50+aP/BqD\ncG+efvIxpg2QiHIWE+cpYcWERObMnR3osDrEbNSjqR4cNWXU5JzBWVuJvSyf02cvBDo0v6uurOB8\nUQOKMQho7mnbQ5P4bsuOAEcWeKLn28+EhIXyvadf8tv9TBYzax972m/3a825gsvIad6FQ0rDPJQX\nl4r1yD2Uoig89pj/R0i6w+LFCzj+X29RU1lHeMq4m+1XGuvYv3cfM2f3vN6vr1SUl+NWzNw6vzSO\n+gAAIABJREFU2C5JMo0OV8Bi6ilEz1fwUllaRlH+9fYP7EV0rfyZK07Vp7WVhf4rKNjKo0umYYrw\nfq2hWEK4lNO3/t9qz5DUYYRpdV5tHnsdw5IHBiiinkP0fAUAmuwOfv/xW+Rb7ah6mdjtEs8/sKbN\nIhmNNhtGk6nH74A0e/w0sk59jZbWPBlMdboZ4gwlNKJ/v3MSfGdI6jBM3x33atM0DZO+f/V3FEXh\nkQdms/67fVSqZsxaE5OSo5gx6+7lUfuLnv2pKfjNZxvXU5gRhF5pHp6tSYZPtm/gR2tfvePY3Jxr\nfLr3G8oNDkxumYmRKaxa/pC/Q+6wISkpPNO0mD1nD9OoORloimT1E/cWb86VKxw+dAgXHhIGJDB3\nzjz0BtFzFloXGhbOiPhgLtY7kA3NNaUNtfkserj3VBmrqijHZDZjCQpu/+A2jB2XQfqY0RRdLyA8\nMopgq6gdDyL59ltVlZVs2vkdtaqdGH0IN+yVSIp37d8Sz+2bsjU/vX+w60tsk6PRAW7gYFkR8YcP\nMWVqz10bOHJUOiNHpd/zeZqm8eZ7b3ExpBbD2ChsF4o4dO0aR3LO8tdPfB9raIgPou1/3C4XqqZi\nMPSdbf9efOEZNm/6lrziCoKMOhatfIi4+J5fbvV6fj7vfbaREruMHjcjE0J5/rmnu7T8SFEUBiUl\nd2OUvZ9Ivv2Q09HEf3/+Jo1T4pAkA9c9jTRsLiA4wzv5tlY4ozA3n8pYiVu/osRYuXDlKlPoucm3\ns44cPkhWogtjRDQA1tGJ1J3NpzrRyqYd37Jm9eMBjrB383g8vPPuh1wurMbjgcRIE8+vXUNISO9/\nqJFlmeUrlgU6jHv2wfrNVJoT0f95q+/ztQ42bdzMigeXBzawPqZ/vYAQANi5ewcN46NurvWVFBkt\nMQx3ZtHNY9QbNUxNHH3HucGhIegaPV5tmqZhkPrmc1xuyQ10EUFebcHDB2DPLaPW0xigqPqOL774\ninO1ZjzhyRCVTD5xvPdB/9l3uKdpbLBRYvP+/1s2mMgr6j/bUfpL3/zEFNpU72hENuq92oJGDmDC\nJT1NVyQ8msb4lJlkjB9/x7lhkRGkNIWR7XChmJqvocssZ8HcJ/wSu7/FhUZxypaDEtyyF2xjbhnG\n+HCiHb2/dxZouUWVKPqWWcGSJFFQaQOah6J3bN9BSWUtg+KimDNvbq+rUd0bVJSV8dmGzZTV2gk2\nKsjupjuOMRvFz727ieTbD03JmMSxk1+gpEbdbNOdL+fBx36I2WJp9/yXnnyeb779husN5VgwsHD6\nauIHJrR7Xm80e85cTr2ZSdEIFX2YBfuNShz5lQwPNbP82RWBDq/XM+juHHwz6mRUVeU/fv07iuQ4\nFIOF02UVZF56kx//8HsBiLLv0jSNN97+iBrrELBAHVBXlUNwSDyKuXlilK72BvOWLLqn66qqyuef\nf8mV6+XIksTYYYksW/6AD76D3ksk337G4/GQmJzEkryx7Dt1GptFI6xB4f4x89pMvLnZORw8cxQJ\niVkTp7FqRc+d3dydFEXhJy//iIMH9pN/+Qau2iAmLHiK0eMyel2N6J5o+sTRfLL7PFib36mrDhvj\nhiVy5NAhCrVIdH+eKawYLVyzOcg8c4bRGRmBDLlPOXfmDBVyhFcisKZNI6b+MuFhAzDqFeYtvZ+k\n5HubLPXxx59zvFRCNjU/lG+/XI0sb+GBpUu6MfreTSTfXq7kRiFGk4nwqMg2jzt05CC7Lh+jRrMT\nKVlYOm4O/zTrJ9hq6wgJD2szkRw9doQvCg4i/bmnfO7o56ypmU9Gxri7ntOXyLLMzFmzESsTu9/k\nKfeh6HQcPpmJx6MycswgFi5ayPrPv0Rn9l6SogSFk5dfIJJvN9JUlds3PJQkieQhQ1mzpvPLoi7n\nlyGHtJSRVcxWMq8WIPq+LUTy7aWKbhTyznefUhaporhUkhuDeeXJF1tde1paXMJX1w4hjYtBAWqA\nT05s4eepaR0qNLEv6zjSmJYhaoZHszvzcL9JvoJvTZg4gQkTJ3i1ZWSks/+zvSihLeU/tZpCJj+y\nyt/h9Wljxo0jcut+6mhZ6SDX3GDO6q79nLVWnuXVnrutcUCI2c691Cc7NlA7ORLj0Gh0w2PJH23i\ni41ftnrsgaMHYGS0V5trTDQHDuzr0L1s6p0TMGyq896DFoQOSkkdxoxhkUjV13Hbbcg1BcwfO5jY\nON+vk3U6m9jy7Xd8/Ml6si5f9vn9AkmWZV5e+whJcjmW+gLi3CU8+cBU4gcM6NJ1UxMiUV0tnxGq\no4FRQ7t2zb5G9Hx7IU3TKPHUIdEyLCfrFQodrS8HMBtMaK5aJEPLr1trdGIN7lilmRjZSsFt94/V\niSo1gm898shDLKiuIifrKmkjlmINDfX5PRts9fz7r9+k2jIIRW/kyJf7mTvyKitX9t01rgMSEvjh\nqy906zWfeuJR5E8+J/tGCbIskT40geXLl3brPXo7kXx7IUmSsEh67Le1m6XWqwPNmzOfIx/8mqb7\nmnsNmqYRdrGeSa90bCvBRxet5M2v3qd8kA5UiC3y8Mjq5wC4npfPwdNH0Esy82bOJzxSbJItdJ/w\n8AgmTrnPp/c4sP8AR89cxun2YK8upT5qNIrcvLRGCYnlYGYeixc1dmglgNBM0el4+qk1gQ7jnqmq\nSvGN60RERWG2BLV/QheI5NtLTU4Yxa7iHJT45rWm0qUK5ma0PpPQZDHz2rK1bNq3lVrVToQcxKrH\nX+5wubjo2Fj+/uW/ISfrCrIsM2RZKgCHDh9gw42jyMOi0FSNkxvf5KWZj5A8dEinvy+P282ePbsp\nr68iLXEoGeMniFnFgs8cPniYLw5eRQqOAT1U28oIj2lZ06qpHsqKi/jtH94hJiqc5UsXEx7R+QfM\nkuJi1n/1HeV1dkIsBhbNnsLoMXcWsxH87/SpM2zYso9K1YRZa2JCSlyXJp21R3n99ddf99nV/yzP\nJqqjdLfUlFQiqsB9rYLYCpmHJy0idVjaXY8PtloZn57B1NGTyEgfi8lsuuuxrZEkiYioKMIjW2ZV\nv7fzC5zpkTe/rsUHU3E6h0mj7yzO0RFul4tfvvXfnBvUSGksnCnPofREFmPTx3bqev1FWa0NXQ3E\nBd9ZAL/U1kBUrCgGcjdfbtpOrb5lMqGzrhJDUBjSn4t5VF89RXjKOGz6cIodeo4f2M3UCaM7tamG\nqqr8x2/WUWJIwGkIpV4K5tyZs4wfkYwlyLe9rO6kqipbNm3k203fYrUGExPb8T2xK8rLObj/ACaj\ngRA/vEboKLfLxW/f+ZzG0GR0pmA0cxjXKxuIkBoZOGhQp6+bFHP3BzUx4aoXmzj5Pl54ZC1rH32a\npCGd7212Vr3qaKXtzslZHbVr904qMqwoQc3D57rYEM4pZZQWFXf6moLQFs9tU3BDBg6n5tJB3PWV\n2KtKMIXHIeuaE60kSTRak9m6dUen7nX65EmqdFFebZ6wRHbu7tjEx56gtKSE1370/7DhaC55hiH8\n9qvD/Ou//Sea1v5U5i+/+Jr/+4fP+fZKI//+3ne8++6Hfoi4Yy5fukidznvlh2IJ5eLVPJ/dUyRf\nodOiFO9JV5qmEa3r/PZj5bZqFMtt760HhpB99UqnrykIbRk5NAHVYWtpkCQmjUvn+ysmkxHpxGD1\n/kCWFAVbY+ceMDVVg17+CuXDj7/AYx1AcFwykiRhCo/jBtEcPniozfNKi4vZf6kIwgYiyQpyaDyn\nil1knjnjp8jbFhMTi87V4NWmqSpBZt9tGyqSr9BpK6cvxnSsFHdNI67SOsKPVLD6/s6vDxwSNwh3\npc2rTcmpYcxYUVShK1RVxd4oNoFozeIli5mZHESQLR9DTS5ppmqeX7uGEaNG8dxLL2FxlHkdr9ZX\nkDF6eKfuNX7SRCJc5V5tSu115s6Z0en4/S2v4AaWmESvNr0llNyCwjbPO378BIR6LzVSgiO4mJXT\n7TF2RkxcHMNjTKhNzdNYNU3DXHuN+xcv8Nk9xYQrodNSUlL5edJPOHnsOEFhFkYuHNOlyVFTpk7n\nwodZXK6rREoIRbpcwby4MVjDes67od7m8NEDvLexjAYXRAfpeHjZPNKGdy559EWSJLF69YOsbuVr\nBoORR5bM4OvtB6lyGwmSmpiSnsTosZ2bg/CXNbVffL2Fsjo7IRYji5bOICY2rv2Te4j42BjyKgsJ\nik262ea2NzAwru3vYfjwNHZc2IdkjbnZ5rHbGJSQ2MZZ/vXyS8+y9butFJRUEGTUs/TJZ326vE3S\nOjJY30W7i7N8fQuhDynIzePq1StMnDipQxW4+rvMghKMuTA2znviS+al8/zPvhPowlrag2qv8fpP\n/0rsDnQPVFWlorSEsIhIDMbWl/P1F1ezrvCLX/0RQ/wwzBFxuGy1hNkLeP3nP233b+oPf/wTF2t0\nKJZQPA4bg+QqfvLjVzu86qI3mjNq6F2/JpKvIPRyd0u+b3/5GSdd3u/lXbZqXlkylnRRH7nTLl+6\nxK59R2l0ukmMDWf1Qw+i6HruIGJ21hWOnDiNQa9j4cK5hId3bS1+bU01H33wMdcLi7AGBzM4eQiz\nZtzHwMTBbZ6naRonjh4jO/86A2KjmDlrVp9OvNB28hVLjQShl7vbUqOLV7MocCnerwLstcy7bxSh\nYWH0RIcOHGLrzn1cuHCR2OhIgq09q5Jabk4Of/x8J5X6WOqlIApqPeSfO8qkiZ1bXudrO3fs5sPt\npyjRwrlukziybx+pSXGEdeH3bzKZiYyM4NjlQupDUiiy6zhy7CTBsovExLsvy5EkiYSBAxmdPpKk\npKR+sX5fLDUShH7ogZlzkEpbJrSoHjeDgz0MGpwUuKDasH79Bj49lMPFBiunqs3819ufcz0/P9Bh\nedm9/wie0Ja9q2WdgStlduprawMYVes0TWPf8fNIoc3vYyVJxhmezJbtXV/a9N2OfbjDk5FkuTmJ\nhg1k1+GeMXO5txDJVxD6qIjwCFbNmcUISy2DpAqmxnr4wfefD3RYrXK5nJzIuo5iaZ7gIkkSrrAk\ntu7aH+DIvLk86h1tbmQcjtuLvQaeqqrUNXnuaK+3d31TlLpG151t3XDd/qTnvqgQBKHLYmLjmblg\nUqDDaJej0Y7dLXP7lJ1Gh9sn9zt44CB7jp7DZncRE2bmsVUPMCAhod3z0oclc/HQNZSglmHbeLNK\ndA+csawoCjFWA7cubtJUldiIzq/F/4voUBNlDZrX0HFMiLnL1+1PRM9XEISAs4aGEhPk/XHkcTWR\nGNf9s91zsq+yfu95Ko0JNIUlcZ1Y3nzv8w5VaZo+cwZz08Ix1+ZBxTXi3UU8+4T/9hg+eOAg//mb\nt/nXX7/Jhi+/RlXv7InfavXS+Vhqr+FurMddX0Gs8zoPP7Siy3E88tAKIhtycTdU43E0YKm9xkPL\n5nX5uv2JmO0sCL3c3WY7A5wtKSVt9MAARHXvsq9e5YP131LusaBXnaTFmHj5pWe7fVnU+x98yuka\n716aq76S11ZMZvioUR26hqZpqB6PX2c5Hzp4iM/3ZyEFN9dTV5vsTIzVeOqpx9s8z+N2c+b0aUKs\nVlK7cY23pmlknj2LvbGRiZMn9+gZ34HS1mxn8dMSOmTXrp1cKr2GXlKYnj6JUeliJxahe6WkpvJP\nP/0hBXm5hIaGEhYR2f5JnSDLzYnj1iFTSVPRG/QdvoYkSX5PNkfPXEIKjr75b9lo5nxebpvn2Bsb\nefeDTykor8eoV5iYncvSZfd3SzySJDFGLFnrNDHsLLTry2++5Fv5CgUjDeSMUHjvyg7OnRUzG4Xu\nJ0kSg5OH+CzxAsydPQNd7Y2b/9Y0jXhdA0NTh7V5XnFRIRczz+Hx3DmJyR/cnjsHKT2q1uZw+Zvr\nPiTLEY4jNIlayyC2Xaxg7+49PoxS6CiRfIU2aZrG6YpslPBbNhIfGsH+i8cDF5QgdMGAhASeXTmH\nwXIFEU1FjAqq47XvPXvX4z1uN//z2z/yi7c38rtNp3n9X3/DxQsX/Bfwn41IjkdtaqnRrakqSdHW\nu66XdTqbyKtsQLqlkIViCeXs5bZ7y4J/iGFnoU2aptGkubn9f+8m1TezUAXBH0a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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# helpers_05_08 is found in the online appendix\n", + "import helpers_05_08\n", + "helpers_05_08.randomized_tree_interactive(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ensembles of Estimators: Random Forests\n", + "\n", + "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n", + "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n", + "An ensemble of randomized decision trees is known as a *random forest*.\n", + "\n", + "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bZE4OqCl77rkVMAG0Mshes5nAoADsGihR2VZkMhlz//1Xtn33MyZJKahcXRj9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import BaggingClassifier\n", + "\n", + "tree = DecisionTreeClassifier()\n", + "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n", + " random_state=1)\n", + "\n", + "bag.fit(X, y)\n", + "visualize_classifier(bag, X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n", + "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n", + "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n", + "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n", + "\n", + "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n", + "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bZE4OqKh477kVMAE0MshZu5mg4EDsmihRaSgymYx5//4r2775EZPEFJSuLox5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\nP8ctGsamB3K3AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "model = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "visualize_classifier(model, X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Forest Regression\n", + "\n", + "In the previous section we considered random forests within the context of classification.\n", + "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n", + "\n", + "Consider the following data, drawn from the combination of a fast and slow oscillation:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7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\nLrsMa9euxT/90z/52Z7IRJl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L4+PjePfdd7Fnzx688847uO222/Dv//7vUTdLKwsWLMDR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\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(42)\n", + "x = 10 * rng.rand(200)\n", + "\n", + "def model(x, sigma=0.3):\n", + " fast_oscillation = np.sin(5 * x)\n", + " slow_oscillation = np.sin(0.5 * x)\n", + " noise = sigma * rng.randn(len(x))\n", + "\n", + " return slow_oscillation + fast_oscillation + noise\n", + "\n", + "y = model(x)\n", + "plt.errorbar(x, y, 0.3, fmt='o');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the random forest regressor, we can find the best fit curve as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nSia0OFMpA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "forest = RandomForestRegressor(200)\n", + "forest.fit(x[:, None], y)\n", + "\n", + "xfit = np.linspace(0, 10, 1000)\n", + "yfit = forest.predict(xfit[:, None])\n", + "ytrue = model(xfit, sigma=0)\n", + "\n", + "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n", + "plt.plot(xfit, yfit, '-r');\n", + "plt.plot(xfit, ytrue, '-k', alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n", + "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Random Forest for Classifying Digits\n", + "\n", + "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n", + "Let's use that again here to see how the random forest classifier can be used in this context." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['target', 'data', 'target_names', 'DESCR', 'images'])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To remind us what we're looking at, we'll visualize the first few data points:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Hl946Ie3vrVjRTqXjaOSeNH36dC+r8R6f+IiIyFHY8RERkaOw4yMiIkdhx0dERI7Cjo+I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\nXIjFixfbXYqpXn/9daSnp+P++++3uxT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# set up the figure\n", + "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", + "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", + "\n", + "# plot the digits: each image is 8x8 pixels\n", + "for i in range(64):\n", + " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", + " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n", + " \n", + " # label the image with the target value\n", + " ax.text(0, 7, str(digits.target[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can quickly classify the digits using a random forest as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "\n", + "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n", + " random_state=0)\n", + "model = RandomForestClassifier(n_estimators=1000)\n", + "model.fit(Xtrain, ytrain)\n", + "ypred = model.predict(Xtest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can take a look at the classification report for this classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 0.97 0.99 38\n", + " 1 1.00 0.98 0.99 44\n", + " 2 0.95 1.00 0.98 42\n", + " 3 0.98 0.96 0.97 46\n", + " 4 0.97 1.00 0.99 37\n", + " 5 0.98 0.96 0.97 49\n", + " 6 1.00 1.00 1.00 52\n", + " 7 1.00 0.96 0.98 50\n", + " 8 0.94 0.98 0.96 46\n", + " 9 0.96 0.98 0.97 46\n", + "\n", + "avg / total 0.98 0.98 0.98 450\n", + "\n" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "print(metrics.classification_report(ypred, ytest))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And for good measure, plot the confusion matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "mat = confusion_matrix(ytest, ypred)\n", + "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n", + "plt.xlabel('true label')\n", + "plt.ylabel('predicted label');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We find that a simple, untuned random forest results in a very accurate classification of the digits data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary of Random Forests\n", + "\n", + "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n", + "Random forests are a powerful method with several advantages:\n", + "\n", + "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n", + "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n", + "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n", + "\n", + "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.08-Random-Forests.md b/notebooks_v2/05.08-Random-Forests.md new file mode 100644 index 00000000..163041d3 --- /dev/null +++ b/notebooks_v2/05.08-Random-Forests.md @@ -0,0 +1,328 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) > + +Open in Colab + + + +# In-Depth: Decision Trees and Random Forests + + +Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)). +Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*. +Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators. +The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting! +We will see examples of this in the following sections. +We begin with the standard imports: + +```python +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +``` + +## Motivating Random Forests: Decision Trees + + +Random forests are an example of an *ensemble learner* built on decision trees. +For this reason we'll start by discussing decision trees themselves. + +Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification. +For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here: + + +![](figures/05.08-decision-tree.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example) + + +The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes. +The trick, of course, comes in deciding which questions to ask at each step. +In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features. +Let's now look at an example of this. + + +### Creating a decision tree + +Consider the following two-dimensional data, which has one of four class labels: + +```python +from sklearn.datasets import make_blobs + +X, y = make_blobs(n_samples=300, centers=4, + random_state=0, cluster_std=1.0) +plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow'); +``` + +A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it. +This figure presents a visualization of the first four levels of a decision tree classifier for this data: + + +![](figures/05.08-decision-tree-levels.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels) + + +Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch. +Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features. + + +This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator: + +```python +from sklearn.tree import DecisionTreeClassifier +tree = DecisionTreeClassifier().fit(X, y) +``` + +Let's write a quick utility function to help us visualize the output of the classifier: + +```python +def visualize_classifier(model, X, y, ax=None, cmap='rainbow'): + ax = ax or plt.gca() + + # Plot the training points + ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap, + clim=(y.min(), y.max()), zorder=3) + ax.axis('tight') + ax.axis('off') + xlim = ax.get_xlim() + ylim = ax.get_ylim() + + # fit the estimator + model.fit(X, y) + xx, yy = np.meshgrid(np.linspace(*xlim, num=200), + np.linspace(*ylim, num=200)) + Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape) + + # Create a color plot with the results + n_classes = len(np.unique(y)) + contours = ax.contourf(xx, yy, Z, alpha=0.3, + levels=np.arange(n_classes + 1) - 0.5, + cmap=cmap, clim=(y.min(), y.max()), + zorder=1) + + ax.set(xlim=xlim, ylim=ylim) +``` + +Now we can examine what the decision tree classification looks like: + +```python +visualize_classifier(DecisionTreeClassifier(), X, y) +``` + +If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process: + +```python +# helpers_05_08 is found in the online appendix +import helpers_05_08 +helpers_05_08.plot_tree_interactive(X, y); +``` + +Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions. +It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data. +That is, this decision tree, even at only five levels deep, is clearly over-fitting our data. + + +### Decision trees and over-fitting + +Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from. +Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data: + + +![](figures/05.08-decision-tree-overfitting.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting) + + +It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters). +The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result! + + +If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data: + +```python +# helpers_05_08 is found in the online appendix +import helpers_05_08 +helpers_05_08.randomized_tree_interactive(X, y) +``` + +Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further. + + +## Ensembles of Estimators: Random Forests + +This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*. +Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification. +An ensemble of randomized decision trees is known as a *random forest*. + +This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here: + +```python +from sklearn.tree import DecisionTreeClassifier +from sklearn.ensemble import BaggingClassifier + +tree = DecisionTreeClassifier() +bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8, + random_state=1) + +bag.fit(X, y) +visualize_classifier(bag, X, y) +``` + +In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points. +In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness. +For example, when determining which feature to split on, the randomized tree might select from among the top several features. +You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within. + +In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically. +All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees: + +```python +from sklearn.ensemble import RandomForestClassifier + +model = RandomForestClassifier(n_estimators=100, random_state=0) +visualize_classifier(model, X, y); +``` + +We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split. + + +## Random Forest Regression + +In the previous section we considered random forests within the context of classification. +Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier. + +Consider the following data, drawn from the combination of a fast and slow oscillation: + +```python +rng = np.random.RandomState(42) +x = 10 * rng.rand(200) + +def model(x, sigma=0.3): + fast_oscillation = np.sin(5 * x) + slow_oscillation = np.sin(0.5 * x) + noise = sigma * rng.randn(len(x)) + + return slow_oscillation + fast_oscillation + noise + +y = model(x) +plt.errorbar(x, y, 0.3, fmt='o'); +``` + +Using the random forest regressor, we can find the best fit curve as follows: + +```python +from sklearn.ensemble import RandomForestRegressor +forest = RandomForestRegressor(200) +forest.fit(x[:, None], y) + +xfit = np.linspace(0, 10, 1000) +yfit = forest.predict(xfit[:, None]) +ytrue = model(xfit, sigma=0) + +plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5) +plt.plot(xfit, yfit, '-r'); +plt.plot(xfit, ytrue, '-k', alpha=0.5); +``` + +Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve. +As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model! + + +## Example: Random Forest for Classifying Digits + +Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)). +Let's use that again here to see how the random forest classifier can be used in this context. + +```python +from sklearn.datasets import load_digits +digits = load_digits() +digits.keys() +``` + +To remind us what we're looking at, we'll visualize the first few data points: + +```python +# set up the figure +fig = plt.figure(figsize=(6, 6)) # figure size in inches +fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05) + +# plot the digits: each image is 8x8 pixels +for i in range(64): + ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[]) + ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest') + + # label the image with the target value + ax.text(0, 7, str(digits.target[i])) +``` + +We can quickly classify the digits using a random forest as follows: + +```python +from sklearn.cross_validation import train_test_split + +Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target, + random_state=0) +model = RandomForestClassifier(n_estimators=1000) +model.fit(Xtrain, ytrain) +ypred = model.predict(Xtest) +``` + +We can take a look at the classification report for this classifier: + +```python +from sklearn import metrics +print(metrics.classification_report(ypred, ytest)) +``` + +And for good measure, plot the confusion matrix: + +```python +from sklearn.metrics import confusion_matrix +mat = confusion_matrix(ytest, ypred) +sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False) +plt.xlabel('true label') +plt.ylabel('predicted label'); +``` + +We find that a simple, untuned random forest results in a very accurate classification of the digits data. + + +## Summary of Random Forests + +This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees. +Random forests are a powerful method with several advantages: + +- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities. +- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method). +- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators. + +A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice. + + + +< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/05.09-Principal-Component-Analysis.ipynb b/notebooks_v2/05.09-Principal-Component-Analysis.ipynb new file mode 100644 index 00000000..73319df9 --- /dev/null +++ b/notebooks_v2/05.09-Principal-Component-Analysis.ipynb @@ -0,0 +1,1111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Depth: Principal Component Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Up until now, we have been looking in depth at supervised learning estimators: those estimators that predict labels based on labeled training data.\n", + "Here we begin looking at several unsupervised estimators, which can highlight interesting aspects of the data without reference to any known labels.\n", + "\n", + "In this section, we explore what is perhaps one of the most broadly used of unsupervised algorithms, principal component analysis (PCA).\n", + "PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, for noise filtering, for feature extraction and engineering, and much more.\n", + "After a brief conceptual discussion of the PCA algorithm, we will see a couple examples of these further applications.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Introducing Principal Component Analysis\n", + "\n", + "Principal component analysis is a fast and flexible unsupervised method for dimensionality reduction in data, which we saw briefly in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb).\n", + "Its behavior is easiest to visualize by looking at a two-dimensional dataset.\n", + "Consider the following 200 points:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(1)\n", + "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n", + "plt.scatter(X[:, 0], X[:, 1])\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "By eye, it is clear that there is a nearly linear relationship between the x and y variables.\n", + "This is reminiscent of the linear regression data we explored in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), but the problem setting here is slightly different: rather than attempting to *predict* the y values from the x values, the unsupervised learning problem attempts to learn about the *relationship* between the x and y values.\n", + "\n", + "In principal component analysis, this relationship is quantified by finding a list of the *principal axes* in the data, and using those axes to describe the dataset.\n", + "Using Scikit-Learn's ``PCA`` estimator, we can compute this as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, n_components=2, whiten=False)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "pca.fit(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The fit learns some quantities from the data, most importantly the \"components\" and \"explained variance\":" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.94446029 0.32862557]\n", + " [ 0.32862557 -0.94446029]]\n" + ] + } + ], + "source": [ + "print(pca.components_)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.75871884 0.01838551]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "To see what these numbers mean, let's visualize them as vectors over the input data, using the \"components\" to define the direction of the vector, and the \"explained variance\" to define the squared-length of the vector:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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OR+h135AGgIceeghGoxEAsHjxYhw/flxRUGdlmYb9DLGeRoJ1pcxEqacTJ9qg1xdCrw+8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\nY1ATEREJjEFNREQkMAY1ERGRwP4/bV+C7ucCrxYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def draw_vector(v0, v1, ax=None):\n", + " ax = ax or plt.gca()\n", + " arrowprops=dict(arrowstyle='->',\n", + " linewidth=2,\n", + " shrinkA=0, shrinkB=0)\n", + " ax.annotate('', v1, v0, arrowprops=arrowprops)\n", + "\n", + "# plot data\n", + "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n", + "for length, vector in zip(pca.explained_variance_, pca.components_):\n", + " v = vector * 3 * np.sqrt(length)\n", + " draw_vector(pca.mean_, pca.mean_ + v)\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "These vectors represent the *principal axes* of the data, and the length of the vector is an indication of how \"important\" that axis is in describing the distribution of the data—more precisely, it is a measure of the variance of the data when projected onto that axis.\n", + "The projection of each data point onto the principal axes are the \"principal components\" of the data.\n", + "\n", + "If we plot these principal components beside the original data, we see the plots shown here:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.09-PCA-rotation.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Principal-Components-Rotation)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This transformation from data axes to principal axes is an *affine transformation*, which basically means it is composed of a translation, rotation, and uniform scaling.\n", + "\n", + "While this algorithm to find principal components may seem like just a mathematical curiosity, it turns out to have very far-reaching applications in the world of machine learning and data exploration." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### PCA as dimensionality reduction\n", + "\n", + "Using PCA for dimensionality reduction involves zeroing out one or more of the smallest principal components, resulting in a lower-dimensional projection of the data that preserves the maximal data variance.\n", + "\n", + "Here is an example of using PCA as a dimensionality reduction transform:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original shape: (200, 2)\n", + "transformed shape: (200, 1)\n" + ] + } + ], + "source": [ + "pca = PCA(n_components=1)\n", + "pca.fit(X)\n", + "X_pca = pca.transform(X)\n", + "print(\"original shape: \", X.shape)\n", + "print(\"transformed shape:\", X_pca.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The transformed data has been reduced to a single dimension.\n", + "To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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6XS5oaZVMpDEF9datWzn33HMBOO2003jjjTd6n6utraWiooJgMAjA6aefzpYt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\nme1er6+PUlpajGkmiMdNduy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_new = pca.inverse_transform(X_pca)\n", + "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n", + "plt.scatter(X_new[:, 0], X_new[:, 1], alpha=0.8)\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The light points are the original data, while the dark points are the projected version.\n", + "This makes clear what a PCA dimensionality reduction means: the information along the least important principal axis or axes is removed, leaving only the component(s) of the data with the highest variance.\n", + "The fraction of variance that is cut out (proportional to the spread of points about the line formed in this figure) is roughly a measure of how much \"information\" is discarded in this reduction of dimensionality.\n", + "\n", + "This reduced-dimension dataset is in some senses \"good enough\" to encode the most important relationships between the points: despite reducing the dimension of the data by 50%, the overall relationship between the data points are mostly preserved." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### PCA for visualization: Hand-written digits\n", + "\n", + "The usefulness of the dimensionality reduction may not be entirely apparent in only two dimensions, but becomes much more clear when looking at high-dimensional data.\n", + "To see this, let's take a quick look at the application of PCA to the digits data we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb).\n", + "\n", + "We start by loading the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Recall that the data consists of 8×8 pixel images, meaning that they are 64-dimensional.\n", + "To gain some intuition into the relationships between these points, we can use PCA to project them to a more manageable number of dimensions, say two:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n", + "(1797, 2)\n" + ] + } + ], + "source": [ + "pca = PCA(2) # project from 64 to 2 dimensions\n", + "projected = pca.fit_transform(digits.data)\n", + "print(digits.data.shape)\n", + "print(projected.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can now plot the first two principal components of each point to learn about the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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62XbkD8PoD6vbdkVXwcw4S3oO0tOxjxEjwtiyNogE8CslgkIQNSz0UDNhexK/\ncQ1W9GpAg/Ig4IGbBn8GQp3g5kCPEWmMVmvZEgNZj5tthvIoGhm01ncT7PsfZ/4BOTkoHIJchKam\nVef9eZ6JlJKD2SIV32dlPEzwEs2kXSy/e6dajG1+I7vkgTk1NcUDDzzA5z73udpC0dWrV/P888+z\nefNmnn766XktIAWYPEM5sdezpqbYomiz5/i88L+OUkzZRCIWh7aPsumjy2pl6+ar++Ymum+uTgDJ\nVyrkJ8+8M4hT9jAC1WUdz3/1KPnJMkK4BIvPsG5TmZLbTSof58XH+ln77tm1UjrYuRJmvUGkI4QH\nFAp2dfeRoMAHPHt2CNeEsnRq773vS4Z3pChnHZpWxEl0zi1zZ9qNmIVjtct2oAEvVSI4Vl2ioVWG\nEF4eP9CJkR8GPYSbncbI78SJbqoWG8h8H3smT11XPc3LCkwNmkSSHks255mayiL1LYSWuYS7p4m3\nbWDSXgaTOZA+oeFv1ma3aulfoJXG0O08Qnq0+kWus+/h6WKBoBtnSyGOcLJIArQ7SSynjbTXjVHc\ni2a/8n6b+NY6tNwgerkfP9iLt+dLeGYHtufjFsfRveWYRgMgEJUk7hk+p8LLERz7GsLLEYkEyGjr\nsJN3nHbc+fxkPMXeXHV7sgbL4L7OZgIXufD6YvndO9Via/PlEO6XPDC//OUvk81m+ed//mf+6Z/+\nCSEEn/3sZ/nLv/xLHMdh2bJltXOcysIoTFfm1HEtpmyKqQqxlt/vAnTP8dn974OkBwtYYYPV7+yo\nrbNsTT5LILQP09BJxIfxpIX0N572GA3LYgzvSNUWy7euTpDwHY7+eoxiqkJjX4wrPtiLf0rRg0O/\nGGV0VxqAkZ0prvrwEuJtJ1+bk3graAE0ZwIv0IsbvQIj92ItxHyzBbO0H+GmEW4KN3Q9tRWQAjR7\nDL10GLQQUguz8pYG1jizheZjG7GNJFJKSj+bRBx4DrF6Em4LE/R+g+ZMohcP4AX7QAh8I4bu5UGY\nSAReoJtrtavZqK0jlPpXHPc6Rr39JJwc8fCNCL+IPvMUXnAZWuUFhF/CNxuxmz5E0PkuXrjaK7Qy\nv2XGWEeFBNIrUhH1hIXEoETFXHrmCkSlI7WNsKE6McpO3H5By2zKnl8LS4Bp22WgWGFFVBU3UF7/\nLnlgfvazn+Wzn/3sadd//etfv9RNUc4iEDXRTa1WfFw3NawLHJKdj+EdKdKDBZySy/j+DBMHMsTb\nQ2iGRth2Cx9gAAAgAElEQVSaxAyYaEb1yzgWnaL1+sbTHqO+N8rKOzvY/eggQhMIHY4+NU7ruiQt\naxKM7Z3h5e/1E6v3WHrLEtqvaGT6yMkvfd+TpPrzcwIToePUzd3h3T+l4o3mjIOewAuvQVZOILwZ\nfKsZJ349CAPNOYQfaGdmOkJuyiC6pJVg77uq5witZgDkt76B9uP/iWYWcUefJih+jnbDW6pPIH2M\n/IsgDHwjgTQbq8OzWhCEhm82o5ePI7w8llFPj3EDpr0VRwTRS4cQbhatfBy0EAgP4RUJTP0QvbgX\n3+pC6tU1qq6QCFGmem5TUgyuQwQ7MCJrz/jFILXIqy6HzxuWWuUEenkA32zGC6/A1ASmJnBO+QMm\npHYpURYJVbhAOU0garD2PZ0c+80E0ViAZVfXVZdq/J65to+UkvF9Gdyyh1tyibYEqesMoid7aO/2\nsKIGTsmjsW0zWsOZeyEjL6VqtWIP/HKEzHCRcEO1sLtfyrG8/XHq6vN4+yOI1X9KuMGaMwM3XH/u\nc6vS8/CGPGynGyt0APwKbngNUo/ghVfhG3VUmj6ENJJozjhm6nGmDkyy64koUoJ8QWfdfbE5e3rK\nPc+im7M9LU8ihgcQfhmpBZF6BCFtfLMZKXSkrOCGV5+8zkyCO/fn4VntaPZQtdC60NHt0eqem0g0\nexTNmcQLdKIX9iDNepBlDHcS2+xE4CFEGN9sxgi3E5STSL8VtLnvixdegRvbWF1yY8SoNJx7JEgr\nHyM4+b1qoXjATt4KsWt4R3M9P5tI4UjJpkSMrtBrKzmoKJeaCkzljBqWxmhYGruo51Fa1yU48cIU\nbtkDUe3JDu9IYRdd2t5/J0bPXnCm0FqWoUWvqN3PtX1GXkrhe5K29QmK6ZPDx5ouCDe88gUs6e7d\nT6wuBwgMvYiV+RWr33k3hx4foZxxaF5dR/Oqsy8LkZ6HfOQ7yBNH0VtfRraGkUsb0ZxxnMkG3Gf7\ncc1lyFsmECvr8a1W7KY/YPB//28kRaRZh2t0Mrajn7bA8+AXceNvwu9oR0yU0YQNBJCJ9tpzClnB\nCy6rBSfOVG3dpzTqkHoMaSSxEzdj5p5DagHKzfcSmPoeAvDNJvTifnR7BGnUIaSHcKaQkSvxAxWk\nHqViLWHbxC854h6gJdDCneEokcoQOMHZ50lQarkfXrWptZ28AztxO5HmOP55PhdG8UAtLKuX9+PG\nrmFFNERfpB0JaGq3EmURUYGpXHJSSkozDropuO4/9+E7ksJUmclDWYQmMEMGh381RePHbiSQME+7\n765HBsgMV3tnoy+nSXSGmZodZtUMjY0fWUpuvIRdcGGwrvalXN8bBd8lGDPZcE/P/Bo7NoocHMAI\np9DMIkwXkT29eFJSfnIG6bbgWzH48Y/QmpsRiTqM/C6MSBgv1I5vNgGSaOmnBKaeBulh5l9E37IE\npjTkjIPZreO/5RrKzfchvDT4NxJI/QK9dBAQ2Mm3gQAz/wIeEiv1E+z6d+DGr8ONVyfImZmtVJwx\nHHcY3UxCoBNvII03ITFb42gdIUDiCh1Nj/NMfpQX/Do05wQZmSdpLeX24nA1qI0Ewp3BzO88bWga\nmPc5S6nP/UPE10/OfhdCoKJSWWxUYCqXlJSSvY8NMXkwixCw7K2t3PDxlRz46TClGYdYaxAzqCN9\niVv2CbxqJUgl59bCEqCcdVh+SxuJrghj+2ZoW5ZAtzQ6rpwtkrDxnWjHpzB0GyNoUYlfe2ENtqq9\nVemf/FURsohvh/C0Znhl1NLzIJPBkr/DyO9k9ZUa5bEYM5UNxLpaWbFyK95EETmVQ2+cJNA8gbh7\nOZoziSEq2JX9lI0EMrwCvBKB1M8ADaSHlXkK32zAx8LKPos29X2CY1+j2Pl/4da9Ca18jJmZRzgs\nDhGyCgTcF2kfeg+B7aMIL49z2GDinV18q2MvOR3emT7BkBNGCgPfbEQadUz5EhBIceowrAbSRxYK\nyG2/hYqN2LgJ0T6/vUCd+LUId7o6M9dswn5VHVtFWWxUYCoXTTFtk58oE20OEk5Wv4hT/QUmD1aL\noksJR389TvsVSdbd1Y1r+2SGqmFY3xsl3HD6uUUzpGMEtFo9WCEgnLTInCiQHy8znE9x9IUJNt63\nlEhjAE9votz8x4SsaZxAA/IMlWvKOYehF6YRQtC5qZ5A9GSvVjQ1IW7cgrf1aZxCK6E1Y2j2btzg\nGoyojZuffbxoDFpa0WYeByAY9bn+ngyVSBmvfgnicZ/KLwcRXhlHkwTf66JFjWpFH10D6WLNPEml\n+YM49ijTrke9FsIs91fbYY+juWk0Nw3SQy8fJzTy/3DA0hj1DpAVB2nxLHyRJG04OEdOsDSyAeHO\nUBQ238sO80S4BMBMQ5AVRPGy3YBALx9lSaiBSvzNmKV9+B74gRaMzNMEJr6FsydF8egq8E3k0cNo\nf/THMJ8lBMLAbnj3PD4pirI4qMBULor0YIFd3x/AdyW6qbH+7m6S3RGknFsAXPoSKUHXBVe8v4ep\nQzmEBo198TNut6WbGuve283hJ0bxPUnP9U1EGgOM78tQnK4wsWuGUsEh2hSg79Y2dj0yiF10CTcE\nuPIDdQReNdnXtX12fqufcqa6ldXk4Syb/3DumlPtTTciN1+LzO9AG/4LkGDYxwndvIbC+FVIgogr\nr0KEQviFFjQndfIJgq0gDEoj69A5DMIA6eHsF+ibKtUeXagN19qAcNMMTj/Dj058Gz/7PB3C5iMh\ngRXswjdbMUr7EF6pWqVHjzDs7uGR4l/jRK5gJpjmtnwLfW6citmKn0xAGqTZwIwcIZMQCCmRQnDI\nqrCycxPvnKhnNL+Ptvp3sTZxJXr6J/h6HeAj3Ey1Dq/noQeHkM02pbGNYNswPg7Lz793oKK80ajA\nVC6KoRen8d1qOHqOz/COFMnuCPW9UZLdEdKD1U2ee65vqu15qRsaLWvOPgHnFcmeCNc8sHzOdb7v\nM7wzRSlt43s+L39vgOxoqTbnpDhdYfC5Kfpubp1zv+J0pRaWAKX06WtOZ04UOPqbcSKlF1m/BsJx\nwCti+i8RWLsZJ74JaVWXndiJW6prOO1J8HKY+R1o9jiVxs0QPlBdn4kPegA3sgE0A6NhDZOFSWbc\nl/nl2FPI/DHQQgz7Gi84Za6NtyL1GL7ZjsZ4dS2kdHgppIEsVSfPBDaxzx2gxw/jmevouOUOhP4M\ncnqacHOGNzcMsGkow/5IHb+r7+XmUoK17mEImuAPISb3I/VYNdABM7+jOgSgaWAYGOHp6pthGNB4\n+vIeRbkcqMBULopXVwXSZ0NR0wUb3t9DbrSEbmlEm4JnuvsFa12XZO+PhtA0gRkykUBmqEi8/WQV\nH9/1T7tfIPaqNaeWRiB2shvqlDx2PzqIW/EpFuo5VOll3fVHsRjDs7qrmzyXj1NuvJtg6scId6Za\nb9Zqw8wPg5etFlm/+mrs6dvQDk2hN1TQr1mBF1qBF1rC0XqLb+q7wJ1gV/00vaUiupuhYDWSDa/E\nC69AalHQgggnhZn7HVIzIbQcKdIgNMJejuVujF4/ydL8YbzoNWh33YORe4HeE78iWAkxojl0FQJ8\ncOd62iafQCamEH0rIRZH+IVqYM7ygsswCrvAdZENzXjeKsSSpYhN1yDqVUF25fKkAvMyU/JKTFdS\nJKwEUSNy/ju8Rkve3ExurEQxZRNuCLDkxubabZomqOuYW47OtX2yw0XMsDFnb8v5cG0fzRDUdYZx\nCy6u4xOss2i/MkkxZeO7EjOk03n16V/0gajBuru66H9mAulLlr6lBSt88teikndq50u9QDfDo+vo\nyYzQGPerk0X9MgIITv8Q4VV3PdEqQ2ilo6CdXF+oB3Jof/Rf8Ct3YU19D98rINHxzFae1nbimUl0\nd4JEQHA0brM8LTAMF68hQaXpQwQmv4NReAnQSEWWMXHsME27DlO3tof8FXE6XYubKu0YaOgaGO4J\nHFahza7HbPOjtPlR3BM6+R2jeA1BzGAZefAAYtM12HVvRXOmqus1gz2UzS0EdnwGgxO4TiuVmz6B\n1rbkgn4uAMKdQasMVycXWS3nPb7s+bycLSCRbIhHCF+iOrOKMh8qMC8jKTvNtwe/S8EtYmkmf9D5\nXrrDF+dcVKjO4poHluOUPMyQfsbzka9wSh47vtVPcbpaem75W1vp2nzuXoxws+jlI7h+mJ2PmuQn\nysRagpSnbMy4Qde1jax/bzeu7VFK2USbg1iRM3/cE10RAjGTyUNZ9v94mHV3dVHXEUZmM4ROHCXk\nVyhpcaRRh5VsINS+As/Oo1WGsNwUbnQTbmQ14uTuY0gzifCKs21Ng5evBkegg3zjH2KMfpVg5kni\nk9/ELJgYRhCBSwt5ltYlGWpIkotFeMJq5JaZpzBzL1aLu8sKg/snGDxSHVpePpqnN7qevsYeTPEb\n9MpRkD6VcBcO4Ae68K226tZegGdH8c0m/HyIivDRQxlk7DrcxJbZ86t+dbeVX/6c0rHV6MEWrLoT\nBF78O9zbP4Nvtc33I4CwxwlOfKNaUlAIKvXvwYusOevxnpR8Z2SSiUp1iHxPrsj9nc1YF7nOrKLM\nlwrMy8jzqRcouNUvcdt32Da1ne7uizd5Qwgxp7d2Ks/xOfj4CJmhIk7Zwym6aEb1i/H4tolzBqZw\nMwTHv4bwCnhpm4jdRZ7riDQGae6Jc8VHl9QqE5khnUDM5PhvJ8iMlIi3hVhyYzOafjLAx/bMMHmo\nOnPXLroc+PkI19zdgP/1/wWlIle4gqHkZsTqdSw1htAGTyBDOQjrgIHUAnjBVWju76qBo1lUmj6A\nZo9iZn5brS1rj6JPfJ18/fsp5WfQKhpFbSNRD26b2Mu3GnXywQ7aZSNTgRwH6nSQZSJekWxlkGZ3\nphpkIkjk2AiarMPTqr0vOXCcyrI/wJp+DKmFkEY9uXKacmGYWPRKyq0PYORfABGk3PlWGPghFPI4\nuQ7cJe9AS958yps7G06+ROhlgk37EcIHXUOf/B6l9v9aO895PmZ+Z63+LlJi5p8/Z2CmbLcWlq9c\nnrQdOoKqEtDlrmw885rv+/scR1OBeRnRmPuXuiYW7i/3gWcnGd+XASA/UaacdWhcXj2Hpp2ntqhe\nOoTwqpOGNFOjPnqA8ZnqAn7N0GrnS099roHtU0B1I2pNF3OGiF/ZiPoVbtlDHjgApeofF5YlWco+\ntI5O+Nk4sjmNSGbx/Rhu1/VIow7faqbU8kfVPTJlBSO/E2kkGNWKjBlDCH+YXrEUvTCA70dwCx6W\n7lAxOukoPMef+h1kG64kojfwHetljlHPBns9HW4H42aUsDFO1D2M0CyMpi7keDXww4Rpbd6AECZe\nqFoN6Ml8gu0zBl5uP1e1aNzSsBw3sr5W6k67/w+Rhw4iQmFYXQ0wKSXb0zmOFcs0WCY3Xb2Z4OCz\n1bDUdERnF3gFhJdHnlJXF0COjyG3bwMhENffiGiq7lAjtblD63PXeJ4uYmhz6szqQhBTQ7IKkFpy\nhgIa8/T7POOuAvMycm3DZvoLx5lxMoT0EFsab3hNj1PxKoyWx4gZMRoC9bXrfb+62fPZepWnKp0y\nMzXSFKgtN9FNjRW3n3vY79Qi4IGoQby7CTEi0AzBhvd012bdviI3Vq792ym5TB3JzQnMljV1DL04\nXa0MBHRubECES8xZABMKIYeHkHac4ugmpDTRHB+xpA6ph/GD3dWSdX6J4ORjICUVyoy426nM9gQP\nyv30idvxHR/P0UETaLKE44XQPIOY3gRCYEU2c32uTF8qSClYxohfRSb2LqzyzxBmHQ3vvpVlzx/E\nn56kMdSFaYbxKzpueA3Z3CF+l3UQ2Gj2OLuPH+TafJamgEm58Q/wg0sRsThi4+Y579GuXJFnUtVe\n9nDZxouFefsH/0/8gf+bMWuYvDVK0AjQpM/dq1YWi/jf/TaUq2s85YkTaH/8J4hAACd+HXplEK0y\nhDTqzlu4IKzrvLulnqemM/gStjTEiZvqK0p5/VCfxstI3IzzR0vuZ8bJEDdiBPQLH+oquEW+NfAd\n0s4MAsGdrbexPrGOzEiR3f8+iFPySHSFWX93z2nBdaqmvhgT+6s9TCEEV9+7hMa+OIalYQTO3avw\nwqtxKwMYhV1IPUJ083vZ8qYOhIDm5vhptW8TXWGmj+ZI9efJjhQppmxiLUFWva1asSYYN9n00aWk\nBwoEYmZ1vahfjxgYQB7cD9Eo2tvfCTMzSEB6QYrDmzE3BAkYYTyrtdrz0mPo5eO8stdYRdrkdYu8\nGSXmFJgyY6yp7MDwW8kQwClpyCM5HPsK4hua8eu24AW6uC0zSO6Rf8ButzBFFqtnG1pjCGk1U0m+\nHSu3jb7lL+NGXYrP5ZBHjyNjcSr33U9Z70FO7UBqIYSXr+7b6RXAD2Olf0G57WNnfE8nKvbcy7aD\naGnh6eWbGM8N4Ykgx6M6N8mdXCU24o2P4/96KzKXQxbyiFd6goU8ZDLQ3AxakHLL/dWdVoQ1r5J6\nyyIhlkUuzlZfu7IFtqWzmEJwS2OC3vDvZ4a2cvlQgXmZMTWTpsBrX0e3J7OXtDMDgETy26ltrE+s\n4/AvR2tDmzMniozsTNF97dmfp3lVHbqlkxkqEGsN0bQiftZjTyMEdv3bsJN31r6EzzWI27W5Aafk\nMnkwS+Myi9XrniGcmsQ/vBpt6QdADxCImrSurQ41VvIO+388TGF6FfVrNrHyjnaELtCSPtptV+Id\ny0KyEWPNBMI+juHOYJSPUmr5T/jmyZ5rRERwrHZeaOgFYGVZEEu7CG2YcLwEhx6nkgkSSUawX/Jx\nW7sRXd2En/wnAkMTFELTOIkmGBwi3FiHhk9k+Ivo5QGEm8JyMxjLW8kdu4MdoVG2Tf85gfZOlkQ0\njpXCgGSdOUGrGUICQrqnvzmzukMBdmYKtcs9szuIHDMynEgsq10/JIe4cmYpxe9/E5nKIh0H+o/B\nipWzLzoKda9aS6st/DnIybLN45Pp2r6pPxqb5r/0tmGqCUXKBVCBqVwQXczt/RmzE0AqeZfcWAnd\n1Ag3WLj26WseAYqpCuWsQ6w1RMPSKA1Lo2c8bl5mw7KSd5ASgrEz79kphKBtQz1NK6dpr3+aeOgY\nmj1BOLULXd9Nsedzc9YgHvrlaK2wwtjuaSINOstW7MbMPQ8N4HX0UGm8CWP4b08+ie+gl44iZAVf\njwECGeziqrqPYbnPkywcYYUTwyhvxzeTmJE4vtuOTjeyGMNORxCDA+hNYFT2I90U0f0/xQs3okVC\nBK/eAoCWPoKwJxFGtfarHshTrB/gVz0psFZQEkWCXXk+krLQvTKdUiD1EAiBE7/xrG/lymiYd7VA\nf6lMg2myOVH9ubSKNk7IwdpxbaINhoaQlepkHmGayI5O6FuBMAzE9TdS8ExGBl2ScY2mxOsjkDKO\ny6lFpiq+pOz7KjCVC6ICU7kgGxLrOJg7xHBpBEszuaXlrdWwHC0yfSwPUpLsjdK2PnHafcf2znDg\nZyNIXxKsM7n63qX/4X02+7dOcHzrJACdG+tp+uCZa5yGkxadG+vRT+TQ3BliDRUCYR/fHsfMbCUV\nvJ3hKZ9kTKtV/tEqQ+iV49CfI1h/FC9QnVGslwfQKgP4RgOaM0VmuMjM8TziwP8kGbcJr2tBW9uL\nU3cTdfmd3DL2zeo+lV4BhIkuXfxAKyVrGW6xAUsEgAqiqRmj8DJi/Qa0/mn8kQy67+G/+Som5SRW\nqkD8RIRg/Sj4NugGvh+loIFsakI0Vnu3Y8Eogfa30zz1E6QdB+lTbvoAfmj5Gd8bgBPFIabKA7SZ\n9cStXqZtl6aAyRbtJjQ0xuUY3aKHjWIzNIzOWSYk2tvR77oHgOmMz7d+WqZUkWgC3v6mAGt6F/5r\npjMcoM7UyTjVUZDOkEVUTShSLtDCf5KVRcXSLD7U/X6yTpaQHiKgBxjZlSYQt2i/IolT9gjVWQTr\nTu/tHd86iZydAVnOOIzuTtN7fdNrbks559TCEmDoxRSZm4pwlu/BvlvacIbeQnR8J0HLBiHwzSay\nBZt//VWZku1RCaS4NiEIjJXRK8cRQtLcbVcD0mxEarPn14RBpfFu5OBPGDk8iLe9RMvRXVQcD23v\nEMH7DfT4gdllJRNozjh4pep+lloE0DDXxPH0ILrTgWjvRaxYiUyfACuCfvc70PMlconl/L+NKTRn\nnFJpihvsJdxUmsKKTCE9HW/1HcTb/wf1+ndIyzQA3aKH+sxLCCddWwJiFPdinxKYUkp830MIwUDp\nBN8/8QPKvsbufAttoSyd4Q7ubEqyPh7hLfpb576Rbe0E3/1uCk/8BhEMIm69o3bTy0dcSpXqz9iX\n8Nw+54yBWXEkMzmfuqhG0Lr4G30FdZ17O5rZkyugC8EV8cg51wYrl5evfOUrPPnkkziOw4c//GHu\nvvvuMx6nAlO5YJrQSFgne5CvzIq1IkbtvzN9GZ269hGqFX/+I6QnT7vOd+VZAxPA7LwWL/qf8Sa/\nj9Sj+FYrO0avolhx2Nfz/7P3nkFyXWea5nOuyZveVFWWL5SD95agA+gAihSNpJZaogxFqqVRt9TR\nszs9ip7e3eiRYic2FKHVzG7EhCJG22p1b3MlkaJE0chQJEiCDoYACBK+UADK+8rMSp953dkft1hA\noQCKZKubpFTvv1t58ua5ps57Pvd+j5ENDdLjV/mrrmtpP1eittUi3giO2QV4bmY7vAEQGOmnKFVL\nTGU303j6n6FcBeHiDmewjw7BNTFwLUR5EighZBUsB9tfQ0ntxtZb0K5pJrTqM5RmE5Ws6A0o1WGM\nzNMgbGacLGrWoObAFNp0jgnfJBfUIFWrmbpQG7VaBL8vxud5gJPucTShsU5sQJFPzrtuIS9mJUsp\nKRbz2LaJEIKTuZNIJBNmiKqrMl1N0xJo4UAmz7rolavY9E2bUFsXWqw+7e2PwbNCH3muQqEsCRiC\nT9/qp6HmvblGCyWX6aykLq4QDrz9+xTWVK5NvItY+SL+KPDaa69x9OhRHn74YUqlEj/84Q+vOnaR\nMP8AkLO8coCofuXFwJUueydf5EKxnzqjltsbdhPU/mWZiP3FAXrz54j5Ymzt3kzrlhpG38igB1RW\n3d16xe8sva2RE48P4Zgu0aYAzRsTb/sbjuWSGSii+hQSSxYu3IG4j6b1CcaOeZZV3bII8ZYg09OF\nuTGKOYaeeQ4hLazo9TjBFdjxWymGN3k1k04RvedlsmqBvHEaiKOqLvuXvs7Opk60ci/gEVm15l4E\nDlLxERj9HsKtEgpKutoHKasuFauGQLAAqoId3QiVJMrLFxBuFUV3UdvDCL+PrH8HFWMlSAVXthJz\nHOTkJO6Tj5GauYCzpkTjqg5CShSfHOPe/c8g8xJHUxgZNBlv8GPWBRiLWmyggCEUggTZpnq9PqVt\nY0WuQa1c8Fy3ig8rcrEPqGWZ2LaXFSulJOAauI6DY1Vw7BC6NmuVXmVDYzuSbMHFdeWCTc/WVTp9\nYw5jKZeQX3Db1oW1lwdOWhTK3manXJXsO27yiZvefcbq6LTDo89XqVoSQxd8+laDprpFN+si3h1e\neeUVli9fzte//nWKxSJ/8zd/c9Wxi4T5IcdLU69wIPUaAFsSm7it4ZYFY45kjnI4cxTw5PFUoXJP\n810Lxo2Vx7CkTWug5W1FDYZKwzw69BhytlIxY2b4yG27WXpr49u6uWo6wlz/teVYZQcjqr+thelY\nXtut/IRXQ9myuYblu+bXZ7quZOUdzTRvSOA6klhLYP7vSxtj6qdzIgdG6nHK+leQei1SS+CoMYIj\n/43tHQ4vl3302FkMn5/OpjBSVnF8TSB8OMGVOIFlntIOIExPcxUANUrzugjjH9+Buu8wut9EbVdR\ndmzHfnkvp4pRIEH7mEFIdqDuXEdR245WOYtEg+oY5tCvCBz4PjPGBAOqIDpYolDjo6vxehpMnYKS\nZVTRMBQfrW0qB+Rq9rtrsGY0XqtbwV9KCakU7uGDcOg1MAzkipWU7vwyqjONqycXiA1cio3RdczY\nM6jOMIoIUGM04xOSXbUx9Mwe1Mo5pF5HNXEnU/kAjz5fQQobv2bzmdsMwsGL74rfJ/jCR/yUq+D3\nXdmLcFmHtwXH7xSvnbKoWrMlPJbktdMWH9vx+yNM03XpL1XxKWKxBOUPGJlMhtHRUb7//e8zNDTE\n1772NZ5++ukrjl0kzA8pXOlyvtDHi5Mvz9VTHskcZX183YKykYyZmXecvuz4+cm9PD78JFPVaZZH\nlrI6upo/bfuTq5Jmf3FgjiwB+or9AO8oJlSeMZEuV4xxzptjf2GOLAFGXk/TtaMezVCRUtLz2zHG\nj2fQAxqr72kl0b7QAhVOaY4sAZAOip3BeauJtLTBNfHr8D8vLWKPOozrM5AV3K7bGOZL3nlkBSc4\nWzYhJXr2RdTyOYRTxPXV0xOK8qsHba7tzLHVzmAmJSXx9xyorXIoVkU4OtFSngfyQYL1n0ZN9+Jq\nnuCDYk2jTh/FsStUtAwtdS6Dk/UkzCrTTFMki6OE8M/eX7+m8avY9aTDcSSCgNnB+b5+Op/4GfLg\nAWS5hOjs8qba0Ulp9Ubvfl9yX3Tdh6bp2LaFEIKQP8KuYhfqvlGkHCB3TROJZd1ErRPoeW8zhpXG\nQLD32EcplCWhIKRyLgdOWuzaNr9sRAjB2/HLtWt1+scdShWJ3ye4ft3bvwtXg3oZGV9+/C+B6br8\naGSKqVmpvs2xMLuSV990LOLDi3g8Tnd3N5qm0dnZiWEYpNNpampqFoxdJMwPIRzp8OjQY/Tkz3I0\n8wZdoU4aA54YtyudBeOXhrt5c+b4HMktDV+sq+vJnWXf9EEGSkMAnM2fI6SFGSwNsSTYxmuTRxia\nnmRFZDkNfi8L81J1H4Ba3zsTnzr77BgjR73myskVUdbc23pVkr28PZiiCsRsDHTqbH7ODWuWbE7/\napjrv75iwTksJ0gl00jQmMAISmzFT16PMdcnRfFhh9aiFU9w+tk426dvZqq+lWA1z9prf4kWv4BU\nY86ZHqcAACAASURBVJ4J5FZBMRDWJGrPM1ijBVR/kVyrygsNcVZmTxBcnqKv3MeMKnHtYZ5vNQkO\nLkMlRDYRo2f7x9nobyOi7qNsO0hUdPc0RTmA0xBDG1TRVJuyCPBm9yZ2RrZwml6WiBJ1A9Motku1\n5Xp0/yqibpU6kSQhahGn3wTLAsers5QT44jmFk6fKfCrkyWkhGvX6Ozc6LlHhRCEQlEcx0v6UUwT\nfvsbnHIZASSeeRpj6QoU92Ij7FJVcuTMOL98zUQRsGmldy7rstJOx5HkSpJwQKBrV362dTGFr9wT\nIJNziUcUAsbFcSNTDhdGHRIRhbVdGrYjee2URbYgWdGu0dV80YK8fp3O8JRDviSJBgU3rH9vxHsl\n9JUqc2QJ8Hq2wE210cUylD9AbNmyhYceeogHH3yQiYkJKpUKicSVw0WLhPkhxNl8L4OlIQJqgHqj\nnr5iP43+BlbFVlJv1C8Y3xXu5JOtH6e/NECdr5Z1sbVzn+XtAgoCAUigOiuWrQqVp8efoc86R7Fk\nciT9Ol/o+BxJo47V0VVkzSw9+V7ivhi7Gm6b93tDpWFmzBmWhNqI6V4RezlrzpElwFRPjtxYmVjz\n/DZfb6GmI0zd8gjDh1IYEZ0VH21BnRVnt8rzV2mrsnCTUC3YHP1JH+X0ZnR3hPqPTPH8qgFy8u9Z\nYa/kHvXjcPgQ5VfPolAiPb4KN9FJbVGnOf4MsjSFiNieFalGQHiLsRwbh+FBQOCUwpQnptkQt2gu\nT2LYVc6rZTT8KFIifYL02hrqrQ4IhQgEl3jniG0jPv1zcuYUx+QJdL+K1VKkO7iacVPl7K5ltNau\nZ7nyUU47j7GvxaKhZhpL1Vkbvp/PzSzjQMZLEqr1abTU1uBKyEUa0UYGCMZ8mHqQPflu5Gyo+sBJ\ni5XtGvUJ7x4KIdBmY5WyUECTAkWf1ZpVVJRikVP5DkoXDgAS24GBajctdQqnBxxGJm1a6gRbVl4k\nqULJ5eE9VdJ5l6Bf8Ke3GDTUXNlF6veJBfHGoUmHR/ZUmE2kZibvki1KTvZ5z/tEn819u/y01Xvf\nq40p/Lt7A+RLkkhQoKm/Pwvz8g4puiJQF7Nq/yBx8803c/jwYT71qU8hpeSb3/zmVTfyi4T5IYEj\nHaarKULqfIJZGummOdDI59s/S3Og6aoPuivcSVd4YT/DpeEu9mkhOkId9Bf7SRpJ1sXW0BZs5bHh\nx9FmXWuWtOkvDsy5e6+ru5br6q5dcL4j6aM8N/kCAH7F4HPt91Fn1CKu4C670t/eQrqvQKaviC+k\noYc0os0Xk5TqlkYZ2D9NNe9ZAC0bF7pORl5PUc6YIHQstYPHj47ArOvvuH2MxKTK9heOo6HgEiQ6\nPUY61g0qGH4TX10rKF4vSYSKnnsZK3ItsurHzLXiiw4DEJu0qLemcZQyZtxhLK7TCjiKj6AS5UWj\nh2rwLOvEBv6KdgBcfxfFye0M/vZ7FHDIb23EbbCYiXdxc8e3WH/JM7xdvZNngIyepkssZYOyBVEr\n6Az6KTsu7UEDrbGWfXsGcRwXGtuwtlzP5k9uprJ3vsVl2Rfd6FJKKqYXZySRQNTWoaY8gXricYrB\nWh55MQLaJ2jTBjl3IYpWs5F4RGHTcsHmlX5u3si8zNSDpyzSeS+TuFSRvPC6ycp2DduB1R0aQf/b\nE07vkDNHlgA9g85cjNKbMwyOO3OECaCpgkRk4Xkt1+XpyQz95Sr1hs6XElfemF0NnUE/G2Mh3sgW\n0RTBHckEyiJh/sHiG9/4xjsat0iYHwKYrskjgz9jrDKOKhRub9hFW6CFofIIAsE9zXfTEmx+T+eO\n++Lc3/E5egvn0YTK0lA3UZ+XbRvTYxTJXhyrx652GlxHMnhwmn1njuM2aygdNhW3yqncaXYmb8Qf\n0em4Pkn/Pi9ZpnlDgmjj1TN1+/dP4VguiqZglx2Gj6RZvttL+jHCGlvu7yLdV8AXUqntuoJYwbzF\nTeIKBwWYqkzTM9NLemKU3niBz2RXIssqtpjmtG3QWuOnYftW9IBDMRfDMN+EgIN//IcExn5ANbCD\nslyFNdKMnJ7GnyiSLCvkK6DV6rTEtvP88klknZ9j/jQ1ShgkpJjmN+ZTfMS5AyoVjOdexTV9iNQ0\ny//v80yuaSZaC+7tr6Ns2jI387AI8yfioxipJ1GsEzhGHrPmLlpVgdz/CmQyZBqXsb/zbrhkP7Q+\n4GdVu83pAc86a29Uaar1rKbcyfM88k9nSZc1apY18ZmvrCZy3+eRR4+AlIhNmxksCV430tgixCFW\nUxMM02KDIiSVqmTdMh/hgMWlcC4Vd5KSAydtBie8P75x1ub+O/0Y+tVJJxaa/1ksLJBSULikm8w7\nVQ46OJPndKHMTApOT5mUMxM8sDr8rmovb08muLk2hioWrctFeFgkzA8BTmZPMVYZB8CRLi9OvcLX\nl36VqeoUftU/5/Z8r0j4ElxTs3XB3+9tuZtXCy8yZqVYE1vNssjVlWJ694wx+mYGN2tgnwmi3V5C\nabUJqBdJsfPGeprWexmtwcT8coPB0hA5K09HcAlh3ZNlM4s2iibQDHUufvkWjLB2RTUh6+RJnJ/+\ngqaKZLK0gb6ATU7PEtkhmDFtjveewS0Iyn1RhvUix/1TaGfqGIt2UOhsoVeYrI/sYOKojZreRyTQ\niD9aItI8xqPhaQaC0zR8op27Jx8gcOAA1tAkijqKoihkpgXn0tezu3ALXfd18Kb1FSrSc0P7XYNw\nNYglTCgXEaUiLSdncKwSSqlKcGyGjviNyIP74RLCBPDNPDfXAFornULqCUp7U8hTJxCKSuBMLzUB\nSDcsB7y9gqEL7r7Bx7puDVdCR6OCogik4/DSj4+QLkYBh3TPMC8/n+Cue1oQN+68+DyKaUJhyBY9\nzeBgl8mnumM8sqeK4RP88uUS21bANasvWrGbV+j0DDqUKi7mrDSiaUl8uiCddxmddulsUqmYEl0F\n9bJnumm5Rjrncn7EIRFVuGO7D6EInj9ski26rFiisXzJ2y9Z7qyJmrMdZqbh1CEVCbwyadFlWXNx\n3HeKxebVi7gUi4T5IYREogiFBn/Dv+rv1PgS/NmK+xd0/wAoTFUYPpxCqIL2a5NkBrxs1O5QJ2fy\nPdhjJt0r29gU3zDve/7owsSMA6mDvDT1KgAhLchnmz9DOWMyfnIG15a0bE6w5JrfnVgkSyUqjz0G\npRI+oNV4kgN3x6m2+sDvEj+fZO3gFvKZCsJVmQl3oKxcxcnyMqabVrAu9CsafWcI9+v0nd+M495M\nZ8NTWJMp9i2ZYcDnkFcscsowL7YeZXfvi/BGL6YoM1znI+WE0BOP4Js8gb/vP7B9yXU87fyaUrpK\npFiDKBhcaL5ALBSlvr0d482j1JhhzKDKknItvrIDPgOl0oeR/jW4JnZ0O8LOzbtOuzxNtf8CwvKE\nB3Sfwc6GKX6tdrHc/wJbOyaot9ow5W46mrz7fcDZz2HrNYJVgVEJAxdrdkdHy/z9kyVcF25Y72Nt\nl4ahKqzuVJme8Qhja5OPYgbikYsE8nqPNUeYUzMuvUM2y9tU9p9wOHnBYXTapS6u0N6gIhTB6JTN\niQs2p/ttdBXuudFgaevFJUhRvLrN69ZKAgZcGHWxHPjIdh8SOHLG4qWjVZa2aTRfod7y8BmLF4+a\nCAHLVhlkpipzudz1fh/nhh12bvydr9EiFnFVLBLmhwCro6s4lj3BRGUSRSjcUn/TezrPRGWSN2eO\n4VN8bK/dNs/6ezcwSzZvPNw/150k018kWGdQnjEJaSG2JDazYnMTza0LY4tXwpHZGlHw2ocdPnIS\nvZigdUstjungj/gwwu8gA7JSRjrenIoUOc2b9PsjGP5uwlaIqr/IXcqdvMw+ekaHaAwm6bzhLsau\nDSDGT9KonISxIqKYYmnNa4yX7qQvs4FqYC/7A3kOBi0UYeHIBP1HfknTK3XUVBpIxAdIlE1GupsJ\nBzWq/hzuSy/yFw/+JYmjUY71nyMp2hhbOkZmJkU46ad892aG3T0cC09xIV6gbajErmw9G3d9hfD0\nLxCuV1Kjz+zFCm9Crc4KoAtBWetANpYR2awncec4dG5r4T9GD+CbPoAIBKCQQgofVmIXw+4QL7le\nXLlkQMO6IdRDTThSIAyDUSeGPuNZdi8eNfnULX52Xx9mqGKiCJOorrKrPs5gcf7t9s9mt07NOPz3\nR0tUTBiasLFsieUIwgFBrig53GOxfbXOE6+YFMuS9kYVy4Ff7zf59396cQmaybs8+kKFTF4yPOlQ\nF/dk83QN+kYdBsYdQgHBui6N++8I0HpJLHMm7/LC6+ZcTeeZk7C7O8beiSohVaElYJCILEwOA6iY\nktEph3BQmUuKeguOlLyUyjJaMWny+7hp1kW7iD9OLBLmhwCGavD5JfcxXZ0mqAWvqujzdshZOR4e\n/ClV11N4GSoNc3/H597TfIbHxjmgvAohwcryGpgJs+ruFnS/SnnGpG5phOY174wsAfyKnyKluWND\n8eHilZIoAY230VCYj3gCrb0d91QPp9wTZJIqg/UVHPcUa+RalqorUScMNkxsQ3m2mc6lrfT8fJzb\nH+hmuMZFP1kmFM5RE+7DLLuEy8eZ9qvs61hPql5hxH2JiOJHSxnUDeUwrAKOpVMoxlFqsvgTXs9H\nv/QSTKxf/Yb2/z6IbyrOgdtP0SMEnTWt1IXCTCSnmPrkMg7lhrCy7YzVBciZa9l7pkT9aDf1jRPc\nvHqQWhlkMNuGqnfSEpnGMZZg2zGcW5ogGKSSHWPvylH66n/Mztf3sSkLZcMl3LGOUMBL4skz30Mw\n8dk4D6xqJpURGEs7+PlBlXPDNtMznhv1SI9FY63CF9fVU3Zc/IpACEFiqWRg3KF32CESUti92XNv\n/refFHn5TYuqKcmXPTm8ZELB0AW1IYGiQFOdykTaZSrj0t7oEZ1lS6SUc3HFV45ZZPIS0/J+p1SV\nLG/TOHTaolCSCAHFsmQ05dI77MwjzIop5wkgSAk3LvcTV3UujDp0tPq4bvXCDjqFsuRHvy2TLXrn\n373Nx8ZlFzdn+zM5Ds14ylEjFRNNCHbW/stCIIv48OJ9I8w333yT7373uzz00EMMDg7yt3/7tyiK\nwrJly/jmN7/5fk3rAwtN0eZqLd8pevPn+O34HpxZ9Z63yBJgrDJOxangV+dXmB/NvMFLU68ghMKu\nhlu4KXnNvM/LTpmnKk/SFxpCOpJx3yj3VD9BqM7Pqo+2vKdru6Ppdn4x/CQlp8TyyFKu69rE8b4h\ncqNlhCLovsW7brNok7qQRw9q1HUvTPQRikLgC19gYu8eeqxxplcmaTPGSTspumQXm1PXUEiaFCdM\nmqINUFWwqy7ldJXlq9YweTZMgBEQYJMg2TJDqWMSdWmSGM3U00ZExKiZaaJzfBi/nCZUzuKTkqrR\nQnLIYsbuxuwocEEeILTPYcnyMtFujbQleHpSYPqqDJz3cX1gK6bmo5yqQ06qCE3Q9/RaZup1zsdh\negherJtmtVDJ9DRgySBbVnRy21YDv2lSOXUSt7aWl28s0BcKIXt7OOovUQpPkagGoHiAupadtOGJ\nsYeJUJglzmXqChq2b6ABL+bXdK7C8fPePQwYnmWYznnsYyhwUp7AlFVWKKv4xE0hHEfS2Og16t5/\n3OTwGZtsQVKqSjQVbAeGJ10iQUFrvcLSJo/YaqKCYvmidbZtlT4vCcee1QZWFVAEuLP8JqU3r4op\n547j4flWXn1Coa1eZWjSsyLbG1XqYgq3blW5FUgmw1cMLZzss8kWL55333FrHmFOVeeXME2a8xOd\nFvHHhfeFMH/wgx/wxBNPEAp56izf/va3+eu//mu2bt3KN7/5Tfbs2cOuXbvej6n9waDqVPnl6K+x\nZpsGH8ueQBHKnBs2qkcwLmvsmzYz7Jl4YU7g4Ddjv2Vb+9p5Y1LVNFW1QsOqGDPDJaQwWXJNHM33\n3pMjWgLN/OXSP8eRDprivZKbPttJcbqCHtDwR3WqBZsjD12YKyVp3VrLslsXbiCErhPccD2OcxpH\nTtFCK93aUj4m/4SyYZGSefbU7iH1sUkazRYanY8iwgY2OoXaB5k68wLN8edwK0WamzM0qz70Sh8E\nulmurCQuEqiNPppnIObMIMIRgjJLpBDCqb0RZcnzVBt0Sr4SgdX9JFM+ojkDvZSgWmmlz6ghka7j\n2v6bKZ4TBIxz5ESWpqFOzHItoqgyXm8gnFqm80t5MpZhk+pg2HCkx+aGdTq+x39GYKDfuw9nBrlw\nXwuOEBwOu0w5YTaWA8zoDUQqM7Q6KUI1tdyvPcBp9zR+YbBGrJu7X4oi+PRtflzpxQgbahRUVdDd\n4pHcU87j9MgzABwSB/mi+mf0D+mcGSmTCDoMTzlEgoJc0SM0VRXUxQSGLti0XCUSVFjaqjGdlTTV\nKvzZXV7dZMAv5pWHAGxdqdM/5mAiWNmhYegecd5zg0H/uMOZAY8Mb9/mY+Oy+UuXogj+9FaD3iHH\ni2G2qu9I3F+77LXVL1sRlwQMeovleceL+OPF+0KY7e3tfO9735sTuT158iRbt3pZmjt37mTfvn2L\nhPkvRNWtzpElMBtb3MR4ZQJNaKyPrSVv5+e5d0t2aZ7knSNdynYFuLjjjvvi+BQdYtAY8+FX/TQ3\nzZfiAxgvj5MyMwyVhpixZmj0N7IjecOCBtRvQQgx14waPHdspOFijDV1Pj9HlgCjR9MsvaVhfl9G\nKwX9D6MNj3BPtZUj7a0oYdikbCWpJ3FXSZ6t7qWSzOArCnL+CX594ggH/2uI4NokH7uthZqaj2ON\nmDS1v4wvEaPB6OArpRCj4btp1pqxpY1dYxO54QhOWqLnUzABA3YNqdO9LImPoo6qZJL16HUudm6C\nSNWgpOjcaHWz+s07ONv5Jo/HHqFkwF2vfB5z3MGuStRGQaZwgXypSDEcItKmkcpdzOpUFVBzGeQs\nWUoknVNhjk6UyLa24e/vo1oOc1qL0n0+yMrfvspoepKjsc2kNt3Czg1baEmqqJeVdhi64IE7A2xd\nqTORdlnS4JFcRVbmyLJqSs4WMvywZ4jiUAuhoKRcNuloUmmsVRhPueiaoKNBQdcFLUmVurj3rHds\n8M25T58/UmX/CQtNgZs2+1jRpnKy3yHkF6zt0vjSXQGmZlyScYVoSOC6XjbtWyIGDTVXbwemqYJV\n77L35vqlGr1DDoOTDoYu2H2ZzN+WeBhNEQyXqzT7fWy8SveWRfxx4H0hzN27dzMyMjJ3LC8JPoRC\nIfL5ha6TRbw7RLQIHaF2+osDANT6atiRvAGB4KdDP+epsV+jCIXbG25jfdyzOBr9DdQbSSarXq3k\nkmArCSNOKl9ESkl/aQBXuny85V4Opg4hhGBn3Q1zWrZv4djMcX47voeh0hAj5THWxdYyWBpGILip\nfse7ug676lBKm3AJkQNofnUeWUrXxXfux2Qr04z35IBhmg7cQvfHbydc77mdFUUQXq/SLuuoZC3O\nHcpQNGdIjOYolS32xJbw7+6No3bdiJGaRpoTKOYodf5riSgr6XMv8JTzBFUqrNtQx67TrUiriWPD\nOuPTQWqrE9gvOQQjRWrbLSY+5kNR64joUVoNH5QMpjZnmF51HqfqMB7PYu+wuOb529DGe9ns7MNS\nApw9N8prn29CtKu09VyHa0coBIeJbD/A44bLTiXDK8keDreOEyXGjuhfEQ93klj+ZV4tP0Mq10/3\ny+epq7RzetQhOnqIZ9nEU68EuHaNzi2bffMUet7Cmk6NNZfUcvrwYWAwU61w7LyN7cCFl3wsCTss\nb/f6Xfo0iaoIulpUAoagoUZhVYfGyJTnT12xRKUl6Zlxj+2t8H89UiKdc/H7YP9Jk2IZgn5BfULh\n1i0+7tvlJ3ZJU/G3ejzHIwrxK/cGZ6pqMVCuUKPrdIXenUi6rgk+s8ugWJYYvivL+W2IhtiwSJSL\n4AOS9KNcUutULBaJRt9ZUksyeZX/oA8w3umcR4qj9GR7qTESbKhZ956a3f5F3f0cS5/Alg5rE6sI\naAHeSB1jRkwTCnqWy8Hifm5bdv3cd/593Zc5kTmNgmBdzRoUoZBMRvhZ3xOcSJ8CoDPSztc3P3BV\ncfYzUycJBnWsahVFk+TJkAzGKPty867fci105erZr8V0lQM/7aWSM1F9Cm2ra8gMFdEDGps+1UE4\n4Gd8rIruF8R+8zDu8RfIjufxrWlHNsYJ+iqUelK06Qqipga1ro7rzW1Ml8Yw0zaqolI/2Y1PV6Fs\n4zMMb37J7eAehqkzoAcJ6CkIZfhH+7eorksQH+e7c2z84rUYPx7lYGM3dm6GjvM/ImsnkXoae7LI\n49EGVmhVOnWL68Y20b1uFdnlBZR/OknpXJTJ2jD9N5ms3N3C8sljuGerWK7NkuYOdpaWozd8lNrm\nWqavLfA/Ks9gC5NJ4Lt3HaecHkVIGO8IYjSc4O9inyCoBNnIOpyxMYqR73NqyiRfMinrISayGkJX\n0H0+Dp4R7NgaIhT43W70L1n38197f4ZOge7cdnJGklTOI8NQ0Eci7mNlpznvO/ffFcOVnq5sQ623\nsXFdyU/25JkpSEzLS/g5N+wSDijEIyozBegdERjBMLHwO3fvj5aqPDY+hS0llMtsd1WarBDNSY36\ny2T53sn/3lCxwm9Gp7Fcyc76BOsS4Xc8l38NfBjXuD9kfCAIc/Xq1Rw6dIht27bx0ksvce21CyXX\nroQrBfE/yEgmI+9ozmPlMX48+AiO9BamszWD77mUpBWvc0UhY1MgTzpboli6uMBZilwwpyV44uyZ\nVJlkMkLvyBAHh9+Y+/xEqZc3jbO0Bq+c5GOWJMWyid8NYlqTWKakWDKJh+qYmsqTtbL8bOgXpMw0\njf4GPtn6CULaQumys8+OkRqb7W1ZBDWisuXPu1EUwfipLM/878cZGLOJu5PsCJ9lfXMCRWYRxwao\nJBKkR0LEj/8j0/sVUBSUez9B2/IV3Ol+nPOin85zktGJOqqWgxr3s67DnbsX/kIJRVs5+9sm1vAR\nUuEsLhczLafq66jWdXKmXtBbybFaeZZYOYd/qo7JNXXUZGs5N9nLOdelrmeEto+tp+bH/8DhV1ow\nywZKH6yym9j8rRrGH3iR0ql+kDBZ20asYz1q2scUecblGFnbm5cjHY53VBGdSRSpUSKPUzzOeXOI\nRmVW7UkLc0Z0MjZ2jFzR5XB8JamKSnNYUq2aVKswNp5/R8R0+Eic4v77ccYd9DaVlqRkMi2JhBQa\nYjabuh3ePGtRqngegEREYFaKaKpABaZn1fbSORfTsrFcF9MFRYLhkygCTMuLTVYqFoVcAbO8cHPo\nSMmrA0X2HnQIS431SzXKrQWenZ4hbdosDwWY7lf58bNp6tQCDQmF/3BfkFUd3obs7f738rZNwXaJ\nayp/PzRBZVayaDBT4MG2Bup8vz9R93eDd7pefFDwx0DuHwjC/E//6T/xd3/3d1iWRXd3N3fcccf7\nPaX3FecKF+bIEqAnf/Y9E+blWBlZzrGZ44yUR1GEwq31N//O72iKjkDMi2++nWW4u+FWfj78OE2B\nJpr8jSyLLKMt2MqWxCbA6+GZMj0FnPHKBPtTB9jVcOvvnry42F/xzLNjDI55MVonZzOUN2hJdlLf\nEiXdl+XCxL3UVQaI180uvq6LPLAPsXwF9aIeWefS9VGDXJMgbyss3ZmkufHiNUk1BtZFsXipJdis\nbOGwewiAmIjRLZYx2JJGbT1F1oUXUrezwTrLTNxlpm2AvFqC5hawbYxUJyI7g54xSFSWUJZ52qbP\nknhlnJn/eJxCXkGqBqpdRVSKDLGMVbO/XUMtURElJ3OoQiVRWMpQn05N+jUa3DLq5iJP1/6a+8WX\n5mLER7rvJCM2UbEFFTPBkiosa/M+W92hzZHl6LTDs4dM+sccltRLkjEXFI2tK/2UKpLDZyySMUEm\nJzg75HDbFh9fvTfA2hXxucX8s7v8HD5jISWs7lCv2N+yVJEUsLFwEapA80l2bjNQbIWxlEssLPji\nnQF8V5DOk1Ly2FiKR5+1qFYgpmn0vaESqlYQMcjbDhdKFQYORJGOAipMZFx+uc9E1lqMVKqs1aDp\nsvPmbZv/b3iS1zIFGvw6bX6Dgu2gzXpzXAk5y3nfCHMRHzy8b4TZ0tLCww8/DEBHRwcPPfTQ+zWV\nDxxil9VZvp2G67uFrujct+RPma5OE1AD76imM6yFuLX+Zl6YehFXulxbe81cq68rocHfwNe6v4rp\nmgvimwBlp/K2x2+h7ZpaUufzVHIWml+l88ZLfvOSVbkYqafqREGk8dUuof5jt1K3Yh3KAQd5YPCS\ni9eZklP8xHqIiqigtWp8vP1P2KQslPwza+7Cl34KtdyLkC6KOcFO5UZCw3VY/iqbl6zHj5+jO/aS\nip1G6atyaG0jK8514o+M03Tb9fRnXkQBrpvsJmlGQdXwb11PfN8hVg7tJ1waxa2tR2UZvswIqaY1\nIF2sUA0ieDHhySd8fFb9AofcgwBE33yA/Iv/D+GKRJdR4q+7DH9tkHRNmiRJwOvkMRL17lc7cMN6\nnaYaTx6vvdEjS9OSPPp8hddOWeQKDs8ftlnTIdm9FZ54yWXTCj+OI6mYEsvxyjzqE4Js3uXsgEnM\nL1EUQW1MYdsqnX94qsxjeysE/YIv3+3nZL9LoSRZ3alyut8hUOtSY0ukK9my0+VzNym02yGqlmRJ\ng3rVdmApy+ZCsYJleoSftW0UQDeh2e+jaDuULIkuBYFL0l7Hqya/nPDUFs7aFtcFg2yOXXSxfq9/\njMfHUlhSEi2r+GoEmqLMEWZYU2n0L5LlIi7iA2FhLmI+1sXWMm2mOJvvJa7HuLPpI7/X86tCpdZX\nO1fC8U6wpWYT6+NrcaW7gAQnKpMMlYapM2rpCHkdOYQQVyRLgM3xjQyWhnCliyZUNsbXLxwkJWGt\nh+s+ladQ7cCoaUAPXIxJLbu1kaHRYQbGbGQijHLPZ6lZnSXU2UzF0VEAuW07su8CTIxDMIS4bv5X\npgAAIABJREFUfj17J7/GkNKDpYYJhW7goDhA1xUIU2pRzPguAtVhpHRQskeZfOEEpQt3AT6GNs4Q\n360yJAZZuSaEZeg49cepS0RZ1xChOeGww7wNceYQOlPYG7qw2zswOjpZs+81iuUgWiGCEXUw7TKj\n6wSZyiBGNYmy4RZarzc47/aSFPVERYyYiLNL9d6DMavE8iGb8UQS4ZgEpjPUvT5IaNfFxJRbtviw\nHZjMuHQ0qly3Rl9QZlGqSGYKkkJZYrsuriuYKUCu6BI0TKazOm/02oxMeS7T1R0aT71qsuewSSLq\nEA85fPVjfsbTkucOmxw4aWLPiun8L/+jyI6NXp3lP//GZDTloMYVmjs8r0AwKonrGq1Xaf91KQzF\nEz+ob5GMD3mt6JbEdPxJE4Gg2Qli9oSIBhVOT9lEgpLaqErH5iol5GzzOrhQqswRZtlxOJjJ43qv\nGlnLYcq0+UJrEgWBJSUboiGC6u+e3yL+eLBImB9ACCG4tf7md+QufbcYKY/y+MiTFO0SKyLLuKf5\nrqsm71yOK7lhh0sjPDL06JwLeXfDrWxKvL1g59JIN/e3f46J4hQt4aYFDakBfJmn0QqeZF5AMaho\nDyK5qCfbvD7BJ//XEOlpE59boGbgIPQbiDVLYbb8RAQCKF/8EpnCIE/ovyE887/xqnIOgUBxcoxX\nD7MhMCt0Lh303H6ENY0T6MYJrUM1x2C2IXd5xkS1UuBpEDH2Zoa6G2phahRVOmzqakBpddnVlSAu\nvUzNUPwC7g2rwXVQfQ6Ue3CCKwl11hP0rcV5w0SWijxTd47nVodx/GEi62q4s1Xh/3T/C6PWKEGC\nfEP/W1YoK+eufdMKH+O+OpLpJbj2a8T8sPo1DaPwa+SyFdDYhFFXx903vH3NYDQkaKpVvHIVBXRN\nEvB5Oq5VR3D8vMO6bo1yVVI2JQE/TIy5VKqSaNikajocP2+zulPljV6byYxLTdR7l7IlL7nnVL9F\nz4CNECAnFVqWasSSLruXBlgTWRi3dl3J6QGHquk1jA75BRFNY1ddnMllU+SCkg2BCH+5JUpJDTJQ\nrnLgDFSEQk2LZ1m3NSvIVXlOlApMzVisDAcI4aP2kiJLV0JIVYlrKqnZLtiNhs6WWAS/uii4vogr\nQ/3Wt771rfd7Eu8VpZL5uwd9gBAKGe/7nB8d+jlZyxPzTplponrkbUXcf9ecD6Rem+ukAlBySmy4\nksV4CRzbpe/JFKnnTbKnq8TbQhjh+Xs3I/XEHFkJ6SD1OJMEyNt5QmrIEx33q0T0Cv6f/b8wPATD\nQygjQ1grL4otCCF4Uv01o2KMcPEcI2rOE69HwdICPFj8EuHhDL7qPvTyYRRrCq18FlerRfqSTGZe\n5aeZsxwojlOYDpM/uZLscAnXcViX2Y/RP8KgHEBMp9htbqL9NyewT4yB5aDW+7DcINP9ZSampxl2\nHILJFbgBA/ou4Mbi2JrBwysMMqEQTtWkmirR09rLeaUXE5MiRfplP7erF+P69QmFUFcrDSeOszxl\n0ZZYQUzE4cUXoFREHnsD0dyCiF+5a/yl92Zlh4auCWxbsKzVZdtKaKrTaKwLceiUg1AgHBQUSpJE\nRCGVk7guGD6FUtmlYkpqYwqRoELvsFdPqWuCljqFZELhSI+NbUvqEwo+XbC83se370+woeHKOsY/\n2VPhH39Z5oXXLV4/Y3HjBh2fLhitVOkZtYj5VAJJB2EpFMd1oo5OOuVJ3IHXmNpKVKnGq/gVwdli\nmb5ylWTA4BPJBP5Zi9GnKNhSMmPb1Ooa1yYi/M3SVgIfIIvyg7BevBuEQv96og7p6nPv+bu1xu+v\npn/RwvwjQ8V9Z/HDq8GRDgPFQTRFY0mwjbA2vz7t8uMrYeT1NKkLXvZrJWdx9plRttzfNW+Mq0aR\n7iQv6YNMKAUyRZPxkQgCwYrIMu5tvtsrtRkdhcrFa3BGRpDlsidAPovSrE6t4+8kYk8gRJCIkuDm\nmVuo/+mvcF0X0XoMNnVByHPZqdVBqsFV/LhqUJF+ZChC/3gzLWMpQkqQaJ3CyOtptjS3sHGsEXn6\nFPmxl5m0svi74hh5KPiXkc9dYMrpZzg8wHOnR0jXPk9rVxfBz5W5dWITDaP1+Ab2ACMI10VWTERF\ng0u6UJXkZarnQMOaNuSf34/7qycBkKdPITXdcz46DsUDRziabUHXBJuXa1dMpgEI+QX37fKzsl3l\nucMqfROSI+ckfaMVZvIuhs8TA9ixwce1a3SmZ1z++ekKCEFrvUoq5+LTBNGQ4NYtPlqSCjVRhV1b\nfYxMuZy44JDKgk/3ksbq4oKrCfCYluQ3+6rkSx75nR91eO6wyb07/Pz85Qon+z0yC0ckvXaF5bNl\nMW9ZyY7rSeg1dkr6XBiqmARUlQZNJenXOThT4CP1FzcRn2qqY0M0hCUly0IBjMVWXh9Y7HNffs/f\nXfZ7nMfbEqaUkmKxSDg8vxZpamqKZDL5e5zGIv6tsCWxaa6VVlANsiq6Yu6z8fI4VdekNdhyRUUe\nV7o8OvQYg6UhANbGVrO74TaOpI/Sk++lI7TkHWW72lUXYWdQrClQDKxy14Ix1dqP82ruuxxUJqhq\nNewvHqHTt5Rms42efC/D5RHagq1QUwuKMic8qkSj4J9fvL5R2cSzzm8Zi3TQXI2wUnbS6N/C8mfO\ngetdi1vyU+ofpT/UhRAQ767HcKsUlBCE1gAQ0kPUbQqR9NeC45If9AMVlHSG6YEMpVQFXAUlk8NM\na+RSMdLlDgZuOEaf3cBQoMz0TB96IUXbMLwgz/Bl6wF29q/j1aJF0ZeiQ1nNDZGP8l/Ef6boFsmL\nHI008UvnST6i3IkuLnGLr16DGB1GnjwBiTgi4iWHWTbsPaVyyvFc0xdGbD6723/VWt5iRbLnkIkr\n8Uiuzybk9yxLnwYNNYL/6dPBuaScZUtUDp4RlEomQQNO9Tuc7LNZsUTjI9sNVsz2rGysVfn2X4T5\nzz8ocPycTbEikdJkcNzlW18O0Vo/f/nRVHAuy7C1HUhlXbLjF8lsdFDQmVBgdk80U5B86a4AM3mX\nhlqVgtAYG60yMwOj44KKptJrW7Qumf9OK0KwIrzQLbyIDx5OlN6d4Mk8LGyb+55xVcI8cOAA3/jG\nNzBNk1WrVvGd73yHhgbPdffVr36VX/ziF7+/WSzi3wzX1m6nOdC8oFnzi5MvczDtlUy0Bpr5dNun\nFiQFDZdH5sgS4ET2FEE1SNmtsCTUhotkuDzC6t+Redu0tMjU3hNYs96mzu4KsHLeGOmrZzC+AUvG\nsV0LitPktRzMfkcgkP19yLNnoLkFmc0iYjEC932S8mXEsEnZQi11HHD30WNUOUyeGxQHofvmCmXy\nU92cqAhybS1MW51MTa7kq/catAeXMFDyMm39zSqx/iggQFVJ3LMTxvZBtcqIbMYQEzT6LqAKi4oi\nKNTfSO5IGydGazHbTCy1RFmmOa0WcGP1dGQTBM6e4c7P3E77Hh/SSNDy2R0YAYNvO9/ln8r/wODA\nOIFqDQdqDxFtjbIiuwPXhcZaBSEEYvcdsPsO3MlJ3O/8H8gL5yk0LqV35UUxiuEpl2IFQuYMlEqQ\nrEdo3rNNZV1eesPk/IhDS1LBlfOFz326oDU5P4N1TafOzdd4NYKZvMvUTIlYSEXX4Ff7qrTVqwT9\n3vhQwJO86xt1cKUknQO/z+XJl02+/smFerD33mjw2N4qjgut9QpGZ5V/Hs2QsVRqdA3TlQQDgrbA\nRRM8EhTURJW5+GkIH19qrefcgQxlXx5VgdFJh0Ty3akALWIRl+OqhPmd73yHhx56iPb2dn7wgx/w\nhS98gR/96EfU19fPk7JbxPsD27V5ceplRstjNAeauLl+51V1Wt9C2SlzOP06lmuyPr5+jixN15wj\nS4Dh8ih9xX6WReZnj/rE/KQfgWCgODjvb+cLF1gdXcXVIPM5wtNH2H7nJDMzQQJRl3hzmfIVxraI\nVgblAD5Fpy3Yip7yTIpV0ZU0p13cnz2CzOWQp04i4nG4YQciHIbSwjZOdSLJoBzAwEAiecV9ie5b\nPkHd9BTkchQjTbwc+hh2YdbikC65dIk/af0YR2fexHRM1nx8Fbk3HAqTZWJNBk2De5HpFLKhEdQJ\nQv4cUhVU/WFkSCcZe4Psho9xc08nr3X2kJdptIKfvBtjIJTn5oFOVKkQvG4bnZvnd4XpVLuInWoi\nUCphYpPOFfjNMZMDOc/9vHKJxj03+i5aja+8SKm2hX1iMwVLQ06MQ7v3/Pw+gb/nTdw9T3tM2NCI\nct/nKUkfP362QqniUjXlrJWoUjVVLEdSKElWdWgsX6LxxMsV/D7BjRt8hGbJUErJqT6b105ZFMre\n+MZahdu2+FjSqJLJS/IlF+eSx+E4EseVFKRNwXYIa/Pf2ft2BVi/VCdbcNFrbZ4vprE1idNdYnhM\n4XpfgluvC2DZcKrPIRIS3HX9wthZQFFpk0ESVR9FxaKGAANZk6cnTdZFg7T4F0XUF/HucVXCdF2X\nzk5PWPKrX/0qPp+PL3/5y/zkJz95TzJti/j94tXp/XONl8cq4/gUHzuSN1x1vJSSnw39Yi5B50Tu\nNH/W8UXCehgFBUUo5K08eStPSAvNpeIDnMye5nTuNBE9wrrYWo5nTyAQ3FJ/E2kzzUR1cm7sdDXF\njwceJqJHubX+ZnyKjiIUVKEihwZxf/5TrNI4mt1D493rUOrCOL4rKwbdoOxARWVCjnNrZBfd/mW4\nSGqNGtz9r3qLf38fWCbMzMDUJOa+fbBxoVKUhTlPpQegmgii/PlfQrlMUAng+1UZuyLxF1JsPvZz\nYsNl1OYmtn3qM4igR6SJ2VO7+15Bnj8HgHjzKF3GNGk1ipabRAnr6JhYR45hN95N6zWfQTt+gZeW\nP0WxsQk1M0y0mmPjZDPi2us9N/IVFF2SQ22cq/X6bpkVP5lTbSRavc/ODNpsS2k0WhPIF1/AeW4P\nPxW7SGm1SAlTMxo1zZKWeoXd2wzEw3svmo0T48jTJ5ls2EC56vWjXNGuks5J7r7eYPkSjVROEvaD\nRPDQ0+U50pvMuNx/RwApJU+8XGXPoSqDEy6FsiTgg+ms5NHny4SDKoonsITjeCUpR3ospJBU4lUK\nnUW+P5Dno/UJVs1my6ZzLq8et3BdyTWrdCZ0E1mQnC6UqCQcAjEI1+hs6AwT1zV2bbva2+4Jsa/v\n1njzHIQcjTHLRPqKjOXgVKHEF1vr31aQQErJM1MznC6UiGgq9zbUkjQWazL/2HFVwqyrq+NHP/oR\n9957L5FIhAcffJDJyUm+9KUvkc1m/y3nuIgrYKo6fdnx1NuOLznledmsFafCaGWM5foyNEVjTXQV\nf3/hh9iuQ9WpMlgaoiPUzu2dN/GzC09xodgPwIbYOv5q2deI6lEM1aDqVKm6JpOVSVShMl6Z8MpU\nyqO8njlKQA2gCoXbG3ax5uBJME2kVoPjtmOd/v/Ze88oO+4r2+/3r3Bzvn07B3RCDgQJkgAI5ihK\nFEWKWYmSRp739GSNtTx+Y9lr1ozHbySv0Sx7Se95vJ6W5QkaSZQocshhTiBIMIlEIHI3Qgd0Tjen\nin9/uM1uNBEV30jExhdUdd26VdXVteucs88+VdRbL8eM33zGY3akQ2aqwEwlh+qbZl3jhoXeTlGX\nqqVTPyCBQABTcRhmFOQcCZFcsq8IUZaLFRyV/UAtem0WLbWXv0CAAHD/jT7eO2LR9NpOljeVURSB\nnJyAd17Du8FCsWZxfF1Y0WuhsjjwmmyWUFxBxHrwHRxELRdRvS4VvYGAk6FYijJe6KY6+THc9Ufw\n690k/VE67n2AseM6h779Lt6DO2juVglsuQRlUy3avDx4OeKYRiY4SzDdxEhwqZpZ2Bbuz38GlTIV\nV2N2pgyNMY6Zdcx6kqwsSzZEVVpSKs6Hrq0QgkRYoCoS266RZluDyoZeHV0TfOA1fuCEtSRCnJhz\nKVdd/vGZAo+/ZmBakkS41tMZDqg0JARDk5LuFpegX8F1obdN4ZJejWs36lTDBsP+Kr6AwJGSHXM5\nVoUDWLbkp69UFwQ/e/ptLMXmgN9mNmlSF1Vo8OlIpTbIOfbhOVxnwC1XeOhpValUJa9oZYz5YTe2\nKxmtGOckzIOFMvvyNbHVnGnzzHSah9vOria/iI8GztpWsmXLFh577DF8Ph9dXTVRxlVXXUW5XObN\nN9/kK1/5yu/yOM+I3yfJNfxmZeJlp8LgPIkBbIxdQrP/w+Zfi9CEyr7cfiy39tRQhMLm5JULHq5H\nC8dQhIrE4XjxBFkrR8Eusju9l7xZxJlv8bClTVdo2YJBgaZorAj3cmn8EspOmZFKbQpNwSpwKHeE\nlkAzeSvPztk3WTZhkSzWGsmlGsbt3Iq7/pOgeM5wxLUoem92H1XXYNacw3ANukO1e1Ekk6B7wDBq\nhLG8nR9tOsreywW73PcJiwgNYvEBJ4RguVhJmDARolyjXE9QWaroDfoEvW0aDSf3oZXyC+t130k8\n9XMIp4hqjCDVAG6sB3n4UI2wi3lEIonekEKcnEC4KlJLYGVNCqF2qvFWnJKFeTxOpHU1yXIPX+q8\njYDt5fBTo/j3PUFxbC/pkVESxgxaQwsimSTRGSJqxmmqtNDR0Egg5We6XKvTXdKrsT5VQr77DgBq\nPMrhQpxsrI0BTztKwE9bg0q2KFneriGDQUbePkom7+Bb1ornpptQVIeIr0ymYBMNSu7YFjyDv6zg\nwAl7odbbkFCYmJUcGXI5NmLOD3UW2E7N8aenRcNxa4YIQ1M2ZVNy2XINwxRMpl2ypoNMmgtTSLyK\nwuWxMJm8y5M7DebyknzR5Z2DNrNpiZr24cZN4h6NNXV+FCHYHAuflso9E4So1TbrEyqj0iZTMefX\nw+Z4mIh2dtIdLFcZrhgLyxK4Iv679Uq92FayiJcKv3pbyS3h30FbSSqV4jvf+c5p6x9++GEefvjh\n39gBXMTp+KBGfK7U96bEpeiKxnhlghZ/88KIrrNBEQr3tN7Nq9M7MFyTKxKbSHkX51j6NT+6opG3\nC0jkQj00a2bRXR+WtPCpPvyqH89ZCK4t0IbgHSQSRzrEPFGKVpGDuUO4SF7ojiLG5liptEAshtiy\n9Yz7+QBZM7tkOfOhZeWKK+GKK5GZNIeLr5JJeAhYJq5R4Y3Q66xTlvaDTjHJDnc7BgYHnf18mvto\nVzpO+16x6Qrk+FiNDL1etBU1NezC91oziMZNKJ//EnJkGHH3vUz963tk9wxR0a4m4c/TGksj2upR\nq2Vm+vOUZw1agx7iwzE23VVP4y+eZHLfLOZYgrniQZBVKAv2DRyhY/sR6soGmlejeW0XEwcyVHNF\n/GT52KUp2q6sIxZWkLYKiQSk06iqwj2XVnmuew1D+yTNdQp+77wnqiP5p9Fl7IjeQnbcQj1Rx7cH\nJV0NJToaoaNRAC6BgAUsJaKGhMJd13p5/5iN3yu4ZoPOf32yjO3oNNcpjM+6NKYUbrpcQ9egt01j\nbMbmJzsrZKoOekUS7LNpUmqCG7uskXF16ldZqEJwbbKm7H33iMXotIthSaYzLlKCrikoDnScrKMp\nZdDkU7k0GqTRd+b771x4cFkDP6valB2HDdHgeWuYvUE/v8gUMNza32LKo7M7W6Td772Ymv0I42If\n5r8xHMod4aWpl3Gkw1V1W9mcvOKs226IrT+vScCpaPDV80D7fWf82dbkZqarM4yWxwioI8T0KI50\nCOpB/AQZKA4SUP1sS21lffTM5NwWaOXTrZ+ir9BPUA0yVBpid2YvLpKUtw4nHGP7nSlWN30WwhHE\neZrEe8Ld9BWOLiwv/5AI6QOIeAIl1AIHf45VKSJNG6WhDB/SHu2ce5O8UsQb0rGExXvuL5YQppSS\n19xXOd51lIbP1nFDZiP+qsAtvgXKDIRrEYbjq01zEckkumcIY/hljmfDOGuuRx0cRvS/Q0NDBF9L\ngmjdCrI/L+PYLvGISrBSIfzGK8ixQWJ+gSxWqVo+fI4BBYXKhE3upbdQXniKhDLDcLaBE5470BqS\nJLtDZA7Ose6mmkes0DSUBz5TizIdh7rLLufziQjtbSZv7TepGpJrLtFBCIa2P0PPwRHyepg3Ktv4\nLy+P8L/dFSN0Zv8AnMwcNqCGI3S36HS31B4VT71p8PYBm2LVoT4uWd+t4kjB4IRDU1JhYMzBFC7B\nFgufA8Wq5I2jFVb4FFa26JiWZIUMc0erRlBTCM9HeQPjLmu6NE5OOVQMSTwsMKyabV1E0fj3q6K0\nN9TuF1dKduWKpE2broCP5Wc7iVMwXjGI6iouksmqRbvPJnKOtG7So/O51npOlKuMVgyOlWoRp6YI\nHmiuo/miaOgjiYuE+W8IFafC85MvLNjMvT7zBl3BTup9F9bzars2QojzqmXPBJ/q44H2e7m39W6e\nHn+WX6Tfw3ANGiN1SFNlfXQtilB48CyE+wG6Qp10hWpisa3uZpr8Tbw6/fqCgXwikDqvA80HWB1Z\nhVfxMlIepcnXyIrI8rNuu34kxpExl9kEaK7K9Ts15LJFA4P+F8YZys8yGktjVRx8MZ2AN4W7SaKo\ntUhsn9zLu24txZlOQnDoRa59XWIgwXcCz+Y4btsmHF+NZBVzAk/2RcoVFeEIvJk91M0OI9QCVCxQ\nFMpta9G9R9Bys5QPWWQmh1HurNU/vR7JpvVFdgTizBhR9AEbGYjTMZRBGT9OKR7Cnkwj1BEqwsus\nI+nYXLfkvEUojLhhaQ04FaulVYVSE+GETh6hp+9dylWN+uoMHtdiqHQzUvgx7TKHBiWKonL5Gg+6\nlDjPP0vRqiA1DTqW4e9egc/nZzbr8sPnKkxnbdJ5mJqThP0eJuYcHLfWorKhRyMQrX13Nu+SzruE\nUg4TUw6Hjjo4CZNYwWY66OPO7hgjEwbxiEIkAOWqYEW7Rlu9iiLAdiQBn+Azt/ppq1+8p1+by/Fe\ntmZ8sT9f4q6mJL3Bs5Pm63M59lYrvDGRwZWSdZEgJ8oVvtTWgH4Os4KERyfh0TlUWKxX267ktbk8\nZcfBdCVXxMJcFvtvOzPzIn53OC9hvvnmm1x11VL15Ysvvsgtt9zyWzuojypM11wy1gtqJHoheGfu\nF+yceWveh/ZaLp0fpXUqBoqDGK5BV7DzrMboqqJyZ+sd3Nl6B4fzR3g1+wol00QIhbD+y9VwdEXn\nlsabiOhhDuf7CGthbm385eoJ3aGuhbrlueBRfTx4eD1mXELexefqNYkmNR/Y8X0ZVns2cqI4QFXL\nESFC+9AaxuQUrVc2IIQgIzNL9qn29QPLUcxJrEwRjobwNM7gybyImbwDYWexpUO6IQM9Fp53LRTH\nILAshb4yggCcfIW4N0/GspBA1FfGpyzWxuJ+H7d89laesUfI/1ijuxKDgcdQFLBsBc0j8fmgZDgU\n0zbL1jYgpTxnuv7l90w0TaABA+MO03PTNNdLRiwX11FoUqZR64M0JHz8+CXJZNpFEYITEwafWzuN\nNTmGbJifDDM8hNHQhM/n562DBv3DFtkS6KqgDBwbtYmHVRxTUq5Kqibcud7HlFrluQNltHobr6JT\nykuMoIXdXmTMhR8dNnj1kMFN8TiKIuhtUfHUQ77ssmWtzrb1OqYN3jM4FA2dUlsEGC4b5yTMg4Uy\nJSEx59OradMmoKpkLId67/ndfYKqCtRq/xLJrlyBBk8tLfzKbJYWn+dXShNfxO8fzkqYzz77LKZp\n8r3vfY+vf/3rC+sty+L73//+RcL8LSCiRegOdXKiOAjUUqjnEvJ8gDkjveDeI6XklakdLA/1LvRZ\nArww+RL7sgcASHoSfLbjwQXStF0bRSgLJuyWcoKqtp3WpMNKJcae8gxNkWmuaQxSVd/A62xFcOE2\nYpuTV7I5eeUFb/8rob0Ddc16EkNHKbsm4vobEd55Re18BBk0w2x94XZKlOjsrcc7PIDR/wvcvTrc\n8wlWh2cJzL6DlIJjkWUkI13IKTjcH2Zm5hK8mTAbu1VindO4rmRsKMgz7nGK9VPwCdjYvZrlzyfw\nx+Yt6sIREisTRGKSUEvtxWdFbwlR38rRdR/n2HsjWA0t3NG4ngepMH5bhtFdc5jqzQQPprELJQa1\nVQTiYfo9KWRbnJ0jOuXdJjduOvMLz2ilyh5tjpIjSZUCtHt8VBuXsbkzhp8iBwt1HK3bzDp/gvf6\nbKazoMy/WMzmXOayDlF7qaZWCOgbsvnBU1XyZTCsWj9lOADFsuCK1SpHT9aMCS5bobGyQ2PzaJhC\nncqxEQetojJuu/ibHLLz74O2LclpFpmCJBkVzOZdvvLJpa47ZysV1nl0Zj6QvAJ1nqWPMcN1mTMt\nIppGSFMJaSoGLqqoKXM9isCnKkQuQDgEcHMqxpOTc8xZNq0+72nzPvO2Q+MF7eki/q3i7rvvXnC0\na21t5Vvf+tYZtzsrYRaLRfbu3UupVOIXv/jFwnpVVfnGN77xGz7ci4CayOeuljvpLxzFkS7Lwz3n\nHNT8AQx36Ru3RGK4Jh/QpemaC2QJNdP1odIwKyLL2T69g93pvWhC5WNNt7I80k5Z/xckJgi4tHGW\nStUmHBzEVAyq2hxg4XOu/5XOcao6xdHCcaJ6hHXRtb+xnl4hBOLjdxDSHarZKiK4qID1hXWWbU0x\n9NYM0foQQcOPNzeHWspQ32niME155puEInmWJ0zMTAOrcxC+4WEmBnYyXZxDTeRx4jEO7pBsXtHO\nwcdPsi9zgPd7QviKKk1rvOzanGBb4hOI3buhWkVWKzTs+Ht8a2LMBi1isTw+v810spnnxuuxGmqP\n2R+/UORzNys0r4/TvD4O9HCw2k7foZ/jTGpUx3sQ042o7TWPr75hhxs3nX4NTNfl8ck0hs9mYNpm\nUJpUc0laPt1GPnYD05Pb2bdvDfXRNipVl9f3WQjkwtgvVYFAdzue4wnsXB47GkFpbCIQTfD2OzWC\nSkQE0xmJokAqrtBUJ2hIQGvS4fKVLg3JKoblIRVXafV7CTW7ZPKS9nrJBA45KghR24+tUcQ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K5QJ0cLtaiwYOUp22UCWgAn6TLePsCtE58CoP3KRUeb0d1pTrw2BcDciQLSha6r61GFyifVu3jB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\n5p577mPr1o+Jj0+IrIcKgnDxRGo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(projected[:, 0], projected[:, 1],\n", + " c=digits.target, edgecolor='none', alpha=0.5,\n", + " cmap=plt.cm.get_cmap('spectral', 10))\n", + "plt.xlabel('component 1')\n", + "plt.ylabel('component 2')\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Recall what these components mean: the full data is a 64-dimensional point cloud, and these points are the projection of each data point along the directions with the largest variance.\n", + "Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the digits in two dimensions, and have done this in an unsupervised manner—that is, without reference to the labels." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### What do the components mean?\n", + "\n", + "We can go a bit further here, and begin to ask what the reduced dimensions *mean*.\n", + "This meaning can be understood in terms of combinations of basis vectors.\n", + "For example, each image in the training set is defined by a collection of 64 pixel values, which we will call the vector $x$:\n", + "\n", + "$$\n", + "x = [x_1, x_2, x_3 \\cdots x_{64}]\n", + "$$\n", + "\n", + "One way we can think about this is in terms of a pixel basis.\n", + "That is, to construct the image, we multiply each element of the vector by the pixel it describes, and then add the results together to build the image:\n", + "\n", + "$$\n", + "{\\rm image}(x) = x_1 \\cdot{\\rm (pixel~1)} + x_2 \\cdot{\\rm (pixel~2)} + x_3 \\cdot{\\rm (pixel~3)} \\cdots x_{64} \\cdot{\\rm (pixel~64)}\n", + "$$\n", + "\n", + "One way we might imagine reducing the dimension of this data is to zero out all but a few of these basis vectors.\n", + "For example, if we use only the first eight pixels, we get an eight-dimensional projection of the data, but it is not very reflective of the whole image: we've thrown out nearly 90% of the pixels!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.09-digits-pixel-components.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-Pixel-Components)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The upper row of panels shows the individual pixels, and the lower row shows the cumulative contribution of these pixels to the construction of the image.\n", + "Using only eight of the pixel-basis components, we can only construct a small portion of the 64-pixel image.\n", + "Were we to continue this sequence and use all 64 pixels, we would recover the original image." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "But the pixel-wise representation is not the only choice of basis. We can also use other basis functions, which each contain some pre-defined contribution from each pixel, and write something like\n", + "\n", + "$$\n", + "image(x) = {\\rm mean} + x_1 \\cdot{\\rm (basis~1)} + x_2 \\cdot{\\rm (basis~2)} + x_3 \\cdot{\\rm (basis~3)} \\cdots\n", + "$$\n", + "\n", + "PCA can be thought of as a process of choosing optimal basis functions, such that adding together just the first few of them is enough to suitably reconstruct the bulk of the elements in the dataset.\n", + "The principal components, which act as the low-dimensional representation of our data, are simply the coefficients that multiply each of the elements in this series.\n", + "This figure shows a similar depiction of reconstructing this digit using the mean plus the first eight PCA basis functions:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "source": [ + "![](figures/05.09-digits-pca-components.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-PCA-Components)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Unlike the pixel basis, the PCA basis allows us to recover the salient features of the input image with just a mean plus eight components!\n", + "The amount of each pixel in each component is the corollary of the orientation of the vector in our two-dimensional example.\n", + "This is the sense in which PCA provides a low-dimensional representation of the data: it discovers a set of basis functions that are more efficient than the native pixel-basis of the input data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Choosing the number of components\n", + "\n", + "A vital part of using PCA in practice is the ability to estimate how many components are needed to describe the data.\n", + "This can be determined by looking at the cumulative *explained variance ratio* as a function of the number of components:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA().fit(digits.data)\n", + "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", + "plt.xlabel('number of components')\n", + "plt.ylabel('cumulative explained variance');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This curve quantifies how much of the total, 64-dimensional variance is contained within the first $N$ components.\n", + "For example, we see that with the digits the first 10 components contain approximately 75% of the variance, while you need around 50 components to describe close to 100% of the variance.\n", + "\n", + "Here we see that our two-dimensional projection loses a lot of information (as measured by the explained variance) and that we'd need about 20 components to retain 90% of the variance. Looking at this plot for a high-dimensional dataset can help you understand the level of redundancy present in multiple observations." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## PCA as Noise Filtering\n", + "\n", + "PCA can also be used as a filtering approach for noisy data.\n", + "The idea is this: any components with variance much larger than the effect of the noise should be relatively unaffected by the noise.\n", + "So if you reconstruct the data using just the largest subset of principal components, you should be preferentially keeping the signal and throwing out the noise.\n", + "\n", + "Let's see how this looks with the digits data.\n", + "First we will plot several of the input noise-free data:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_digits(data):\n", + " fig, axes = plt.subplots(4, 10, figsize=(10, 4),\n", + " subplot_kw={'xticks':[], 'yticks':[]},\n", + " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", + " for i, ax in enumerate(axes.flat):\n", + " ax.imshow(data[i].reshape(8, 8),\n", + " cmap='binary', interpolation='nearest',\n", + " clim=(0, 16))\n", + "plot_digits(digits.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now lets add some random noise to create a noisy dataset, and re-plot it:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+fftkTbly5SLX/NMWLlzo5mqQq5keqtqjRw9Zo96fZmYNGzZ082LFismaRH36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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(42)\n", + "noisy = np.random.normal(digits.data, 4)\n", + "plot_digits(noisy)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "It's clear by eye that the images are noisy, and contain spurious pixels.\n", + "Let's train a PCA on the noisy data, requesting that the projection preserve 50% of the variance:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca = PCA(0.50).fit(noisy)\n", + "pca.n_components_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here 50% of the variance amounts to 12 principal components.\n", + "Now we compute these components, and then use the inverse of the transform to reconstruct the filtered digits:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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AAEDy/gcjsffkWE6IPAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "components = pca.transform(noisy)\n", + "filtered = pca.inverse_transform(components)\n", + "plot_digits(filtered)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This signal preserving/noise filtering property makes PCA a very useful feature selection routine—for example, rather than training a classifier on very high-dimensional data, you might instead train the classifier on the lower-dimensional representation, which will automatically serve to filter out random noise in the inputs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Example: Eigenfaces\n", + "\n", + "Earlier we explored an example of using a PCA projection as a feature selector for facial recognition with a support vector machine (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n", + "Here we will take a look back and explore a bit more of what went into that.\n", + "Recall that we were using the Labeled Faces in the Wild dataset made available through Scikit-Learn:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n", + " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\n", + "(1348, 62, 47)\n" + ] + } + ], + "source": [ + "from sklearn.datasets import fetch_lfw_people\n", + "faces = fetch_lfw_people(min_faces_per_person=60)\n", + "print(faces.target_names)\n", + "print(faces.images.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Let's take a look at the principal axes that span this dataset.\n", + "Because this is a large dataset, we will use ``RandomizedPCA``—it contains a randomized method to approximate the first $N$ principal components much more quickly than the standard ``PCA`` estimator, and thus is very useful for high-dimensional data (here, a dimensionality of nearly 3,000).\n", + "We will take a look at the first 150 components:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomizedPCA(copy=True, iterated_power=3, n_components=150,\n", + " random_state=None, whiten=False)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import RandomizedPCA\n", + "pca = RandomizedPCA(150)\n", + "pca.fit(faces.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In this case, it can be interesting to visualize the images associated with the first several principal components (these components are technically known as \"eigenvectors,\"\n", + "so these types of images are often called \"eigenfaces\").\n", + "As you can see in this figure, they are as creepy as they sound:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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2nG544liMyEv71vue3z0380r1rnzpJgUQPC1V4s4GAc8Cc/x3+znwENr42U0B\nKH0NxW4G86TQpgscnvAPKoMQRfnDjQ5TMOO5Oo9KpdJ4S5sKlYpWRjEzJ0rOTG91/sxDtKJnzBpi\nbSILPcVLlb41d1nPK9a6gnn0MwE0tU9v0HgyjQitd6/4+bNx21PCDNkDeqe+C+MHVRfxhiHgMRP7\nUX81B+Ygzs4jOyoY9exJQTVDAhxN1jDh+Oyb0MUdw2o8tCo9Ug6K1u5pMu5aHeXpAUwGme6jpL+3\nOQxTTQA3oBitHsmAZvilFBEXrS7oFSaNV2AI90BApcKshFitvPr3Mj1+ovzlf0QQFj+iQKeslbPQ\nnRWJpsZCY08NarWilN07z1nTCIt/2Ua79fylgEqfBMBQKJbHbZ6oQyS62J06QpeQBIVhsFhMLL6j\nxKurVhqQ/5gbdivWY5d47YEyjn/4ZiySMNj8k1cQahhCtWlKzZwP7FS6AcUb63VgTdMgcoXz02dX\nqGIgNyMlb1b68yfYz4zR7x6/EYO0aMXt/Nh8BCKQpmJ2ZnCMI5zSO1KIyLGDE/ua+16q8hnC8Rj3\nbhAQ0fsq3Mk+m+4tRFciQ1l3NzTMIDBP+1WFMvd2g9f+H8JsVv6275sw5TWfmXUjvUIGenMPShbE\n1nhGAIyJfL8kNITqGIbDUIguxOjt92GEf6h1dDrC9aO2vSqVQFrSlyVko3tD1r+jMwnJcTL+rfTz\neM/31fx7PH9TKgRo+E29aZbUT0bVroHVO0zuF+l7YlVOq8V8rca+LU8Y8kz2viiQpj1Syr5LNlXv\njgLGFJGXBctpxfqw4vx4wnl/EEgbox4BMIx9Amk57I7S2t0xf9s7pvRHn5Ujmz70oXyPSpYO+x0Y\nRruhU8feB1PV127OoX12h8VtBnJmqZNyZTEcqnyKsdymXTAb6veMtCQntDOzpyw6esFHX1cQF62s\nCEKPVvwuHgjzliZrf+P8KeNtJCU5WgaBIz7RMw6kwJrJDHjaeu/BHfGUkiAo+sxRSZ9vPuuPJiIk\nKV1oQhxsRTp08/bhTfUm3aPKrg0RSkXZd+z7FaVsaL1M3v/w8GVixdsz754iobWhYH1SaDDHbYFs\nIqyJBSrDwM0YoyMXlvP6Hu/vdVzJlMoQjNGg7xDQKAB0LFvsLYxn2K8Pctd8fWtKQpBqaTGPDQcW\nNnys2p2wd1BorkBs83tjJNZ8WlghGfgBuvPhR6qiwd+zQgE8HEP6fheY8kQY6ub1nN92+bPNasZA\nj1KTgGMEWPjOAAAgAElEQVS0CR+IQpiKXthca3wtaMdIS0tiZs8rfl/sL0nxKHRwiAgxAWQksApD\nBWwPyhoezaIhsERojTa+UYsELVPMVPeG1rBjC5Hpz6xp0cwadgHZm8aZrUaOna9xdpklbfHeMUO7\nHkqbvO9ocWd5M2zx/My07l489Hz22BFbBOc+kAkVnla/j2goLcN7One0wN4a+qD8exePDDOnCMd6\nA7fG9k+GeGGykkHhVnFSGCgV3KWRijQO23B9vuLy7YLLtwuuL1fpBleqKP5mDXb0jE/tjQ1etmyq\nUiZ52QpMRsaYsSwnnE6PePj0gLI9CjOeROnkNaOdmtt/UQnBrK2fa63Y3+kCm+KfvfBeGxqRy6MI\nKSUcI0BRHROGVHFkAIHBgdG7eqdtyh6rwxns2pbX0Aq5VZUftk9Ah+JYco965tUbFiRYrkk0dMN7\nujqmlFC5IgSrkXJEaBEIiSI4RqTMSIsYeNXW2p5xamVfqxTIk9LYDk24oR5TQI5ZFL7WFLA+Ah7y\nmVMJdXbs+bvuIUBCVmXP2PeCUkUTpj+i/POSQAzth22xKAVsmF2BkQoc6SetZYDLhn2/Yt9fUMru\nit+su3lCzSCwFpI9VYSYkGKSVCFZWvTevaCKLHRzz8EOd+8AaUMGjiJAQgzITXK1w7vyXf0mBZaH\nQfSivMyiYtY65GHybhi6qZu0Ri4NTVuFWl+D2xrNUtBBmp1EjkNw6X+5J1dysSf03rS//DgIY46V\nPapCgiahee9wuD8aZCUEzNG4Z7TwDRpbyikdavyPgypraBtYDCcT7+RkPFP4zAykdDCyslqxWZtW\nXGfl3zu4J60/0afDf1TQ944ByY8YmxS2lPkNlrZHUkXt+418Fi/3Ozf2yXlBytoi9QaNOsZSh7HY\ne/f6GiKUqwiWJt+LBzQUsD23Kf93lTsNNJrPTPd09O7HsP0n+dwTIhYILTRBflJECw2xaUEkRQ0D\nB8QIBA5ovYonEwJaCKCm6ABmvggdntHkAzCIv2Z4ewfC++W//G01L00ru4F0vps2NpPOppdvF7x8\necG3377h+fdnXJ8v2PcNre2qiIbBFWMEUVQZNlJCS9lRynX6uqHW4uuXUsa6PqhRIEo+xoh8yjg9\nnQ58glkuASydWZs4Cvc9e5fslF5R645aM2paEFtCKYTWunQN7GLIxRSRc0JcEoL2oyDCcLSIZL+S\nrEOtFVWfc9831LpLeKQV1S2i5KMa3BHaMGtqhT2cRnF4ZB7l+skRYzGsiIDO7yzzS6pv5px/kuY+\nRNLm2fYua4igNW0GVKUXjjfB2wuwwZ2RgwGgKEDKGfmUkZYk3SNPGcu6IOeEkAdabZ819rbW39ml\niFfTz96ed7ycrnh53FF+UNTuh8pfKl0Rcm0jlYFHDKvV6rDWnBmwbRfsu72uvsAOT6s1ad6qlQ0V\nS1aE55JX5LwgxIzUMmotiHFT4Wls6LmLmZYI1SYYUvyGUVtD0JxLgN/l+ev6DAgUyilowwMKgYTo\nhdFVLCg8DxC4MdreUHbNlCjNhbTF0Wby4tyJLNFQfuIlMGLrSD1NxYqmEEgf1rrNt8yLvDOnkRJ1\nz+jMrpQpADGIYFlTwilnnLRyWI4CRyVLM6Kb+YGhuoPF7vNrChpDkJPBnWAQBA2Kk7A0wzOnhLpY\nMSAxpkJtCKV5GdlZSbynqx+RsmRJG+PoLQ9PMPj+S1Eb+UzlfKUk8hvd/XL2RiF5yUhLRsqmCAdr\n2+KrBwRJDXCvuaHeRFW0rZYyhdjMCDRFrOTAu56dDn9HgbTNLXwuu+XQ2lIewhFHTWtGgGXupAnG\nDCki9QgkQc9ClKyMHvqrSx+IezzuTRAEuGF5yLjpmrL4Hs+/aC+EMFLm3MvSImL7dcf2vOHy9YLL\n1xdcn6+4vlxxuXxV731XhMiQ0uhloHtvzvkQz9wcpeskLwXKttTOFDN6E8QJzKP2ReuKMIzqi2Z4\nw+Hh15UZv/vsrQljvBFqLShlQwhSO0LaRQfEPSEmKWudckbLCbGksa5T8S0L+QEQT7hUlH3Dtl+w\nbVeUclHnsPm5FyN5RSaSqpiwjIbkhpOdi9Y6YmT0nh3xMgSykeqc9r70ZnT2e1XLE3lJyOsiXR5P\nGXmRkr92Vuw8lk1b3r9suH67YHvZfA7KXnV/CXpkDkpMActpwenxhPOnMx6ezlgfT1jOC3JKmsXF\nKEWMiplnEvcqBnaR/dJqw3bd8Px8wdfHC14eHvDpdHrzWX/s+Z8WKS4QSWL+Zu0UI7vIQZCCBvZA\nmHI+2S0yR80dqqiH7837T70hKywIIkQ1FkLYYaVTrSvaHDsVKSRwXQgdKRlUb3DbbUzyvuHENUC7\nzsnhkjgWDT5CmMhOhzazABS1kM1RpAyxeWUxIiYc+q1TICRKaNRgbANu/XCQZjfeHRsDHXzeO7Q2\nnv7tO9IcNcwDsBo1US3ygKTe/5IS1pyQQ3SB41D8fC2aOBEmoNWjJYPr+JgtYe0r53g229MwO9Kw\npOQwr81xKxW9EZpWfGGG9kC/+/EPkPYoR006l0GEU16Q8wnLsiLndTTySVPRnzgZqEFisxajTTmN\nrnHafU2IXfFATLWuek2hRfEoBpRYyxR7vm7YLpt6kN3jgjTtj3uf3deNyBXVnLY1X5LVOwfB63bI\nGg/kxeZDSqbq8y8ZvIrRlxLQ/L26rz3zQGs6GHjkkGn0tF+KitB5ZgL7/PV3lPUGRjc3kUNS04K7\nIAKtDWV7DOd1NbwrukHaqvyTyp/R/ClDUkhFMQkyWj00JGE2ICaB/Jf1jLycFDGSbnvyd10bq9VD\n8bUYAk7qsOy1esXVnw1BK2z9gyrbK3qvsLDPbNCmvCCnjJjVWzVjVg3YsV9kH9RWsJcrtu2C6/UZ\n2/aCfb/C0r9zWlSGp3EPKSOlFWkqXQwMOefhkZCAOEKuXKEhgPsPvp0la1dvJeRTltbOp4cVy2nF\nac3IKSHFqIgUo9aOrRRcrjsuLxdcHlZcvr4gP2dsLzsuzxdxmsvoiGvVA9eHFZ/+/ISnPz/h06dH\nPD2ecV5Xye/vjK1WPF+veIGcL6oAFBEOIcjh0d+10rC9bPj27QW/PZz/mPIPMaiHb9W91CuAwGvS\n8MeEjsb6/YDSFONszpRsTZSfQP/mYYyYtVnKury+wK02NJIDYl6UbYBbuDNqD2uB2VR9qrAK7/D8\n7XqW1wpmF4YzeYmU8BI8rj7FS8P43o2dqoWNAjlxJiICcXhJIGhbYJsf+OKOilFHToJ5ovPcieEB\nFSzl7oPgnhKRs7gNxjMB7AVEppbHfv0DLEtOVLSfHVOPWOO3qsA1pFP6IBlayInA7tXY/ck1RSin\nJaGWhFY7Qm3ooo2cVXzviDEdYHJraxpCxLKsOK1POJ0ecXo4Y304Yzkt4tVbLrZ67mM6eJwLVSx5\nzchrUu9f2dynBXlNiCkp1GyGdkXZK+pW1GBUAWIx1LXI9bK2X33pqoQkBHQsj/zjEUJAZ+tMqYrY\nnsHIRHpufRlCGOlWE1ox6nzAe8WnlJBWeVawkKpCDOAQvPSr/L3ETGfinHn7QclQMrkBHDXWPBHb\nZL+pvHrH2gNwprUVfBkkNXlGz3G30IUaIqIUxbGhJnUuiCTMczo94nR+wHJakZcMClK4pew7Ls8r\n0sszYswo5SrOFBhJlf+6PmBZVsSUPXQpsnnKNqjNy2obORqAn6F7hnANSPdBVHkdXYlL62WrfJcQ\ngiIAacGyrFjWE5Z1kU6IOXqoBo5kddRasG0XXC7fcL0+o5QrmBk5L2IAmGM0OXsmy4GjE2nhTUNQ\nAyIiC19HGlod4/Y/G/u1YLvu6kkTKJOT7gypy0tCStrx1ZHPAc3X1nDZdnz7fMHXr894/vKCy9eL\nEJJLxeVrx3bZdY4DmB+xnhY8/fkJf/d3f8JfPn/C5/MZaxY0Y6sFXy9XXLYN3DrKVrBddim6V4+c\nEmYxLvbLhudvL/jyeMbXx9cd/YB78vzV+p6tShNKZZ9IL5crahkkjln5z8Q+iVPvEA8q+QEdrU1l\nY1nM1Lz8uRWwWMsKc7TZEhTPP2tcVQR4d9blews+zJC7oEHsscUDdK1z4obBGwiDISKGaEh8VAgx\nUCs/EIEsb5cA7iTsYKv8R/aCe8iGqozPHfX+iSbCk/M17hMCXZEGu7fZ4JmLGjHbvbBW5RtzZ/tH\ncuC7z5XX7HcDQ+NZRiziUd/fTJ+5pXJLjNo6ShjZA7137Dm5VynWddAWvzrf5e6l1zK5w3OQV1Ih\n/oSHh894eHzE+ekBp6cT1vOCfBKvbHT/gkP3XZVYaw1gqPJPKkwyllPGcl6wnFcsa0ZQxdatpfZ1\nB7BJ6Kc2dEvpUSKrpYSRQoTugfYm6bB3en6AQumHczWa/RyUqD6fIQNO/qUOtKmTn3vMFb0xaqzI\nLatnG5GM1KofaQSmpoz6Wo1xLlZnTBHxJswQ7UYjjqE9UqPlHcq/taaKSxR8M9nXJcxhBCxDFllh\neIfW2VKOF+V3rDg/PODx8yc8fnrE+nhCXiV82VvHft3x8uUFz1+/4fL8jO0qcX/m5q2jrVOkrG+/\nMawUlWAlPTI7WTEBSK2h3in3WtOeKCFgpJDZGRqZWq1VEO2OwKaUUcoJtVb0/ohV95GhWUTyrPFq\niEXBvl9wuXxFKZvvlZQWMEZfFynkFieCqTlR8Myo3glx6ikgTbQqWpPMhbtJzoBnbvTWkZbkSFVe\nJKxBQZCloo19QpDW5ktMyElCokSEUhu+PW34/eGM3x6+4bf1K1qt+Pr7N+zbjuevX1S+dTz++QkU\nAk6PJ/zl8yf808+f8el0Qo4RrXc8bxta63jOGc+KwDUlGbZSXa8dKpkSYbvsuFw3vGzbm8/649r+\nzVInZhhfYVolGpRNqv9JvHEfHhoFpJSdzdm5I/BI77NUn+HxD2vPYj7LckKMSb06g9ajCmZRNG1i\npHo6yhSjZTBCCljb4gSOe8ctI12dGv9dCAGh99fv61N6R2P/GwqjHGZlTedpADh6PNrYnwJ9MXiK\nA6cc0XJEqwl1r95cRhpwzHF240FM5WKJ7lT7Y+2ZCdFKSprRA7MwMQkCGmkYIQxjYRI4tUvVO/CU\nn23Yrl7dFL9BWSIAhgfTehfyZO8o1GSJdbPXlJBzQlmSxOta9linGVrvgX4NRbF9JVB/xrKccTo9\n4HR+wPogcbnltGB9OOH0KEZAXJKm5MANmxmaZ03bNKWdloTlvOL86YzT4wmndZE4H8s6lFJGMRcN\nI1Eg1BKcC+CG4WxolBFOa+6t3/Ps+s1s2HXWvTwZmxNXZV5vIgLPuobVGNEc9qCcmbok4foAnv4E\niEdbdyEPS1XR0UpYlL/W7UgjFaq37kaBIJZTnYT3Of0+pzTJEIO7LAXLIO1Wq5KuCsqusXqCOi4Z\n6+mEh6dHPP3yCZ//7jOe/vwJ56cT0ppBRA7Rvnx5wfPvT7g8X7BfN+ybkvu6QeYAITgLXGqXWKho\nsMPd2JlQS+cA3DHcU+YROhletl135JUTEVobZNIYE1pdwSx7OKmBC0j2WN0LXl7EoxVS+MV5BTmv\nBx1jVE7bt0Fr7Mse6egtDPJr6+jdcvIbZk7Oe/geVR1a7uwGnu2zELV7bJPsGiICa0w+p4jzsuCU\nMyIF1NR8zhszrntBzAmtNLx8+4rffvsPaLWg9YKnP31CLRUhBKwTlyrqBlxSwsO64lMpuJ6FaFq2\nIuX3mxDtbZAEUpE6Cw9or98l/f2ksY/GmaNBX/2QmlJVuBBBYMoAj4eRKvqUusazzO2yGKoI9VaL\np33EmJDzSZV/8raq4sFUSI/0FTlnEEX0VtH2i1/fSYS9oXNTK1QKrpTT4kzJe8dcIxt4DZt6a1tm\noDU0lrSOshWU6yiHLMJEn15zOEFwY4RCGJkEU16xweHUrH53Ql7l4Ml1M3pe0FpFremgbN1TvzHe\n7ib+VGFot2iQn1wzEiEp5GkGxeHFR8UNwD0R86A6j3SeeV7NwBLGcnDBRSTpX3spaDQIgmZkRI39\nn7KkPKFbvQmJiQdqqO9UAIaimH1CJMS+JZ+Q8zqE8AThnx5PWB9WLKdFvf+BftS9YnvZADUEvGhS\nUEUTCXnNeHg44fG0SmU59bhrW7GfCtbTgu1hxfVl83zyslUnk5oxXUvDcsqo++pnwIqn3DMO6+Gx\nbPUwbeZta03jlmXvRLSpIlqvDVAhakQVm0OLbxqcXXZ7tmP/j0KEEMsgTq4L8in7ulmcz1uevuPM\n24IZ2ueGrML6gPQ8GfMkZzbmKOu+iEzIy4LT+QGPnx/x9OdP+Px3n/D5L5/xyy+fcHpcpf00M/a9\n4OXliuevL7h8eRFHaq8eF661eT2BXhtiTmpsrkIQezqPfbdkD8HZ2hj7/95zb0rXcyh4hGO9Qmtv\nbsATSTZCDMnDwhZyTYsUJFrOq4RRckLdCr59XVUp9ynTK2o4Nzkx1RwpU+4xYRTGiQEhGukvaLod\nIfSBOH0Phf3RMCOdmRFzPBidNkjn1EjPa5JXVqe0tIZrKdiUa8HK+SpbwfXbFV9++wf84z/+P5Ld\nUXc8ffoFz7//Fdtlx3Xb1dOX+h2tS/p2Ve9e9ntGShHXyakwDooY5gF97VhOi+yfP6L8hzVOI7ZY\nhLneFIoLIWA9n7BCFP9+3bFvV9RalMgnjNFtu2BTRS1w3wLjDxjZhWikbAgv4AWtNWzbC3qvyHlV\nFrOQq0TpFRdu0re8o1FDbRUpSWggxoTlcsJ+3VHL/d6fdcRKWmxm3lSH/aBKtdbuaR67pjzWvXjF\nLhGYBk0FdIWQAJNXrE1xfAXk8hq2MNhz5IbTBLsllHJDiJmuIopfytTeM8RLntpKatgiKdFviREp\nkJfsndseA/B0yPEUABO5J9kajzj0JJyNXDgOrjyJFSvZa0Gx6mk82M2rEaDYlEdH2QtiLAp5Mt6j\nA+YQihmsg6y1uOJxlu7TWQTdafHDOXK5WRtzNOxbQC3s56e3DooBy77I+0JAjmmkkep917ZgOy3Y\nns64XjfvtVE07LZdNlxfBN7La0PZNJNgz6/2xc8G9+E9jj4dfdoLkuHCoY+4t8Gxmn7UlWV/IAnK\nxI668HH2XsWY4toOVoUQvliuxZb10F2xA0BIDZnzzTPeoHHvWHyHmacsk0iEnhNqE1JpXq84PQqR\n6vS44nP5RXuekBuCD09nPH5+wKc/PeHznz7hl89P+LSuWLQgz14rXvYd365XfHu+Yr9uUmPAvX3Z\nJ/t1lzh07Z4lEi2v3pjiDyvWZZDQBnk4YFHj+Y8Ndrkq7P99qkEQnbiIhTQ8IfF+q0a4nBas51Xm\nE8ByXpHXOQV2RUoZ5/MnPDx8xun0hNPpAetyRk7LFDbW0F3D4NNMqdXcGeQZProDHAW5H/Gtuzi1\nUNStteZ5+j0n5BywLhmP6+qe/pISAhFqa3jeBGZ/vl6x14q9Njy/XHD5csHl6wXPv3/D16+/4uuX\nf8BeNgCMX//xL/j17/+KX//+VzycFlz3HTEEVEU6bVe/XDc8v0hWyXbdvdDUftnVsRXURhALaGp5\n+2Oef1qSWBVdCvhsWsVqe9lQ94qYEpazWLDbi2ze7XrV/NCCbXvG8/PvuFy+Yd8vEutnFuZqtoUl\n32DGRr9enwE8q+Gw43r9hlp35LxiXR+ncIDF0i0Pe9QLiFEswFolHFHKhv2y4/pyvXsj5JSwpIQU\ng7B9WWB4MuhH+RCilCTnmtuIxRvfYFjEARy7wpbRiU2Wx2kdFM0g6L1LmqAhCWp01b1qbmyFlUm2\nGgmGokhdeis7OeLiRPcdhFoaEgO8CMkxTuz+NckrRWFDMxTWJ4L2WUIggegsTcrh/WZs/o5ShwL3\n6n9KNrPCPNrDTd4TjgWGGAEcujVFQyDCmhLqmlFLRbomR1mA18jNj8aIH8ZBPEoZMSXNx13w8PkB\nj396wsOnB5weTwJxpihEMY2/W+gARcpht9K9GlxXaLF3RkwRp/MJL+dVqrGty/CA1A+LJEZOW4QL\n4eU/k8RFBZ1j1L0MaDhaVsz91R1trgYCMH5udeeFwxnQA3tdDSLNs1f0jzVsADXoEqRrXUwRaYlI\nGkeVlD8xIqLxIZYshlEgqd/g4TQJRdp5ipo5YcaEZfXoJhHlwK+Lav1opByRgpRGNZTLyFSlVrSn\nilofvfVvVrJm0jXPizLDl4yHZcWn0wm/PD7g03qS7BQwtlLwsu3qNSY8riv2KoLeUlvNeK69Y68F\nrbMY3knSmF/2Hddd9lHWc2nVNX2t8OMqb2+t+0za9bTuVrFrDYLeq8qbEVOPUUiNy3JCXhZNhUva\naVBDjop6prRgXc44nz755z2cP+P88Amn06OSGyWzIWo6YdTaH60294K9+I3e69wnRuTv+4w+AA6V\nE5EWrmvYnzeRT7rXWuvYSwF3xl4qrmkXHdE6vlyu+Prygqsq5N47yrXg22/fcPlm6Y07qla83fcN\nz9++4Ne//xX//v/69+De8esvjwgput5tRULIl+cLtperOg5QJ0rQzaL34yRPLSbXWv9jyt/a9UoM\nUZj9RYtb9NpAIWC/7Li8POPb19/x8vJFC1HIQ12vX7FvF1jKCzOr57TidHqEpZKktAzmpsUHW8W2\nveB6+Ybnly8o5aoM08VjOVbwJ6WsFqRwBQyKsmYyZsVJHPF+1lekUZbW3XG18Ey5V9YqYx6XIqQl\nIdfsGRJ2gAjqBSZGXkV4yzyObAJrgASWHFarlGiFRTzFa28KTwKm3SQundB7doJfQ3XI/j3Cv9eG\nrrA1RcnRFsWfkWfPXN9v8OKaE5IadSLALJ4vgtPi+nsVaKwqFB3IigRFAAk5WUMnuUbEQBICVa2t\nroZXr/59ZwbxzDkY0Pv7OB82X+r5TYV71vOKh8+POH96wOl8EkGXNI/bBd5QGHWvuKrFfn2+4vJ8\nxfYi0F4MEb2KQZgXKSJS9oLTw+ppRB3CbWlaItTqRRzg9Kll9lHeSY55CPluw0+u8ZqUaWW8BVIH\ntGG2GAJxanUco8fv7Vro7GGfZJkOWedLn9MhY43jpzaarLhSsjLRMbiHY5C8KIPoWUoHrOMdhD+K\najSFoGxugdItXe58OmH964IUg0O/OYoxHIlkHxBpSmxSozlj0XMjMKyFzBiB5H0pRM+eWZRJntS4\nNhljBknvHVutuOw7LvuOOhk3zGqMQwR8ihHnnO9+fudQQTgAnaFNoVjlqoRml3ySQlXqwVvp3Xnd\nexXnBST8BgDCnVkfcH745CjQ+fyEdX3Eup6xrmcsq2TQeM0LaNiZu6CGltVgxcH6nDYuYeL3ePw2\nyrVgv+xK7IxO+gu6Z8GMCwO/OsMezo2RDLgREvfzWSuuz1eUbRf0YznhfH5CCBfEGFHrjq+//4b/\n8H+f0WrFwy+PEnJorMz+Te5rM93bEaIamYuQg01PI0LIqh525D8G+1cXMJZyMzov1dKwby94ef6K\n5+ff8Pz8O67XZ+z7Fb037PuGfX8Rz1arNc2EqXV90HS9oeyNMDhXrpPiPgm1BrV4No2PGynLmKY7\n1vWssaSMEWS3VB9ZhH3b794IEpYYRDmLaQN8UH5OTLMmHMzg0wJANpBZZ3UfKYlvWaS9i5AcFbts\nvhVN6AOK6mw13+cXIIEFqYjVmaWlLoDRUe4++LfWJoxz1ivODWKma0i8PYoHk7MjAjbMSCrUlb8g\nhlNpDVutKJrSJALQavnr/KeExfgFIIQAEYYxonVNObSYGndURWFaP1r9pvitsuI9Q+oVREcN5lx9\nQCC1/bKLZ2ohnWB5/cbYkAN5fb7i8uWC59+f8fzlGZevUvyjqeEnIS6557JXPLxsOD2dhPUflcTl\nGSuDTGodxSTut+P6vOH6csV+EWSOG09cknC/8acCbY6be4VPe4sV1WFoAR9RviBC5DiuY8KxD4Ed\nY/BiR25AqjcPJ7emyXgefRPMyAgpjKqaIUxzr/1C9CzYmW3vMPzyInvYlL73FAG8wNV5WbAoojJ/\njqfZ0TH7p/eOrUu/jVIrrqXgWnZsRQhZtlc9o8iMIYO4Mdjtxq8JgbBmKSi1q+HLaiT0rumdkLLI\n+c6GZnPXVQBqOCqPIZ+QYhrwvsXoQ4Dl5Xumhu7LfQsih0JALcWRSkkNPMM4QstyxrKsTpRM2VCh\n5PPXAa+hPyOaLvt41FmwTBd5hvudnu1l08wajHK7iiqBhbTYqhDdLdXWuTEWEguj5wXCqDshacIn\nD3GI7loRQkKvFZdvV3z77RtqaYoIducKbS9Xr6kj9xaxp+KFskyuz8WHJJT2/XX/KeHP4tBWSazu\nwlLvtWHfNi/UUOuuXmtTJV4PcLQs9gkPD5/x+PgLTqcnLPmkD9m0AdAm+a3MaL0507/3ikAkvAAN\nDZjHO2cAjHRBAoU5zsnugY/KeD8fghqMtLY5Fc2GQdBJ0zIspzZ2xoJFLTvx1LwI0NR9d05D8uIh\nN2k8bgzYJmujKYa8x4SHwq8hIHBEjK95CveOVhpa1Dxx/azau9dStxi/xagXD5EMLw6QnNemc+dM\n/Vax1erKv6vA9OtbE/bpOuZFmyA2RTSnHA5uw4gz++udef7WDU8Dh7BKa71JDm0I5Ja+dfXy9WqL\nM4PLXnD5esHX377i6z9+HSVgr3JeiIKGesxLaOp9bFgfVi0lGtWz6L6H53rhUla7uJDYt+LkVjdO\nw/DMfzpma1cxfku9okijYJKHRkibZomhEsJQWJbtIDF7RcDmPhuKDEkfKi0ik5S9n6OiYRqzZjiR\nUu5lIABBq/yZcRrAwwibztg9Y4npsIc7M0LvUlRKPXKD2Pu0t8WQBaI20lmSKMZmhEvdq3utUgxm\n33EtRfoY2PnUa5qx1TS0NrcOl3uS9trezVQNhQbl65jBAAkDWCntny49D8dm5NmLwhVukSn9ofgJ\nwQ1AIwk64VPTUo2HI9X2yFHK1hbPpPFrK4P+LWfFDECEcDzfqjNarehTEzn5m/uVv8f8AdRNDHyb\n/2f0J0kAACAASURBVKrV9LryiQaZe6SRhzDq83uWgHFcUkA+L3h4/IRt+5OmJCZxWmPStM8icxrC\nkAfOsRvOjGfxVAJQHKkgIixNya9K1v/e+HFXPxVoUI/TiGwWYxAFH1SxiyCLKTnDmLkrVC+bZV3P\nkh/98Anr8oC8LggxoteKUgpKWVHrLtarwjatFnDviCEJZ2BSBqQx4Xnz5LwixWUqBWnV/2zi3hf3\ndchtUl5OfIJC7ar4mxkAmp7nC5ACWmpejcxT5cybMyatkoksVmR1oI39POdae2aDxoP1jh0NMYQi\naP1pX9M7n7/sRdPJNJVJc9Q9de6mVK6hK9XWRn9W1cPf9WUCbysFtYnXY7BUihHN6vq7AhreFGMU\nLDkWLhleknu3rDwDdZPeS/qaEQ4GgC6Ep32/+jw6uqAernmYZSsaq5bD/PzlGV//4Qu+/MNXXL5J\nr4taBOUKFIRQBV1nrRFe9oL1smM5SewUwFD+bXhXTQm4lp9crsXr/bMaLYGsEuadz0/TV/NkMJWG\n5pFtYb8/1OqflD/r+zuxNpthZ8ebxzKvTQgEpADu0tKblGxsYQ1HMSKNkthuVJOvW0d3RODwTHeM\nrEaMhfeS8ilGKEvT/AAnotbWHGq3vWrIXFXv0Ahcm3r+L/uGrUgs34wNgpyDqgz9mpKmjEkRLCvi\nY50Mmcd5k6yYUYRpcAfCKyX63aV3QzFp9tXqIVSRp3PRHc3DV0RsGAuWJTMMAYuhHzlQ2uPB/9ZC\na9FLaOsWk3PNQgUKWkzKrg1AiXkFpe4oynuyqoHv8XsMpSVg1NcguBduadiHqo7NMmEAjjyhhWp+\nGdchS+3+88MT9u2qZ5Kwrg/Ii2REmJ6Fhrt6YzWYAwJ34Tm17iHiWbc5v2pGH35g9P0Y9t/rQREZ\n7DDSCgKW5eQx0ZzF4wcGa9/g0hiTQv6POJ3kYfOS9YGTN0epNTts01pDO4lVk1LGXjaMik0MUfyz\nhZqRpxrrIWQMROB9xR6A4W12lriJKX/PmaZZ6Yzv3YPRdDX2+KQxohmxNLzqe95fC3e3LK2866Tw\nxkv+LR6x5bZa06EBdZtXfM9opaGlNiC8KsJtRj1MGdfeEZWVaml8Qbm3RZX+pqkvW60OddamHpNC\nlnWO7ZruZblmUtKVFQialf+cceBZBzfhCRPE71h9Xy8JTUl4Yd+3+S0HBKfXjrJVT+Mypvbz78/4\n9utXPH/9hu168SwVMc6iV74DW4qiVvE6b5I9sCRJB52gf+8Xv1cvtmV1v2d2vXgcEdHB4jvG9DaD\nMQECMeAkDkcEFCXRcIf1KHDSHUvcGFpwCsE8EkntG5krcMEVwtyiNhzq1tszmZdvxZQM/jfjj/sQ\nwvI392uAnJJ4kfqZlivee0cnkt9Bw0nq+TMEBUsHsh4d5EbtHVsteNl3XDYh6+278Ffm8uBGrq29\nY+3dOTYAJgRg9LQwZFO+Yzcm5gZb8U7ZNxNcs6K1y7Ji7qNixgT5e6XWiNT6N5ItqTHbQUEVdB17\n05R/slLtQWpjJOWBeKVMRYekMV/wENgMtYt82rUhkmQjWG+E96y7D5ZMpDlU25V/FZOU5x59SoLc\nHAkPhuy+POyozmoQGH45LTg/PKDsn1wvCcdhHamkWrinz2EFttDvcGhsxeX6GnK2QmdGOqbw3UyP\nn8b83QDwgikDPrXqe+L9JtS2qJcWHO4zAR5iRM4nrOtJFP+avYqWWSfWda/3pgUzClo7K5SYENMi\nSIAsx5hYZZvGmKYceVPIdqiixwTfO3iytk0BjnjyXO3uqJRmIh8gvZ6NlTyX/RXlwq/iulayc4QB\nBnw5oYQwotSt4hcIbvb2R7e/nz4zj/vxrlVtlA8Vxa8KiAiVRl5/DAo/6nzNyIl5Sa011C4vC5eA\nGS0EacZk0K7F9SyzwA4CGPYk5vUcJuX2Wd7p+Y9CSVqhTiF/gFHKBElOCE7dqsTbAjmKI53fXnD5\n9ozrVeqYS02KpnszHRAM6+Bn3rzU/s/ChofyPtj4IG00+diq94E3kROCGJ6JkyIo9639ITVP59a8\n/mGYjSZNBsV6jf3Z42BACnod5z+4sn4D3iVS5n8UL97uh8fny2yp8XGI+dMkd+bzcr8SMM6KKXWe\nFLwggfLZJuMAeLvhqF6/7XtHDltD6Q0v1w0v1w3bXrzPh7VGj9rBLaaIkhJKTqg5O4k2KRrgFTb7\nCEVanN9jvzEi88Q/uPP55zBqVCRVHDwtWuUnUmsfTN32LOsEGpNvrYM0dZNAw5nBUP5B8/qtd4iV\nbY55yEnZRxrCYnKUzKpfln3zTogG97MRYOl9a28GJfpALUYnvYaUpfJfnFArk8+YwMagRjIBHqaK\nWToxLqcFp9ODZvMQ8qIpwkv2Co4S/tvlTPPYw/NLf6hImvYJUXlhJcND/H43159W+KtleKCvrA0K\niA69RcS+YHoTDAEQCCoiLytSXmSTa1wELAvq8CFFhC5ejnEFpJ6/NalRTwLR0QXdGxrj0dhgspgn\nadqZWav3bwSDUdwzAQ9BoApIFFoXL9ZYsiZwLEWoMygYW9mEFk1wNPwZ0Mdmagq5W074MX7dbzaF\nVbSKsJQ+QQL0zhUifFexD43TzQzWovn1vTMaMQKNnuqNOwKCkq2OXAl/KTzaHDYfxokhCWZAmcFg\nwithHGQeaPvBw2NDZSaFz+KivSvua94MkbTynfuat1ZRyvBWu4Zl0pY89Yib1Koo1x3b9Yrr9eJd\n21rbwQwVsrIvhNi6I5YkMKMZXaWi5HLwNuz6dj8uUAMBpF6e1sgn62zW7y/04ryTPuBV2D7lIYQc\n3g/kRm1Ur2/2/MEsrbmn8ItcAxoeOJ5JMqVqMmFqSDWjBK+EoW+G2Ut6D9ojI8c4YHTju0AId5Z3\n7dwCfUU18huAMhu5qgRLrdj2gsvVqrPVUZudrYCLkdzktS8Z5bSgrot2zzT8RvbcbEx7IS6txWGh\nh1sE7GdjhFFGmfWchZQ2h2gM8YkhDadKoWppCUwg3Zsm67rKMb2xIbfC6JJpn++GYbDUNWnTbd9b\nLLzsm3ZC3NBacTkH2wrvdPzNkJWw0eSgMWu2gu6/Ls6nnz8jlAZCYiAlMVwF7rdS3U3TQqUwlVpF\nWt5b2nubvkAHChV4u2MYKpP8jBnXxvuCnBevOJrXkUb7PePnxzF/tWpmD3RmNepsIXRhRiKRC93O\nVoffFKi0ffU6yVoR0KqGuZCePJTBULZN93qhhjdhTFdZ/NiSQ7WMIcTe4/1FJXowILXpecBuzFIy\ntTTjAjSBbFXg0ARpHjYSRqEbs94PrUonS1XY/UJisRiuTQKj+0afD6yFWpinsIDnvY5GGD8bA+mR\nbA/WrANR4ho+UIHnKNQ0tf555qlMsLvf2SQ4XZj7z605zoBWzbsikm6PbIIfAxJ1TsZUKMXIfu8p\n7xvj4p8lSmAwsrk37SkhL2blxOzlAGOL0VSUxDqqpLU2hMi49rEc7Th72iirjUZVPscYJCPKBGSF\nzWkIzLorwzpYwaKfD49jejc9EW4MIDAJ2UobTvm+tjoCtiaOak0GWRvEqKGQxvk/biAc5oPmtabh\nefvnzrMynSm77nu8P8uJl307kJbKfIBQichhfmk2JfvgWoTbUvTcWmG0/aKhGTOmtZKfI6lTt8OY\nglTIe1ixacnnJSvznW9CkWX0iDd2dzbuDN7n+dr7/Qx6LF6yl+Z1MnK1a1hFyEyxS3lyM/wm6Fpl\nvMmjPhWVsnCfOErqrOmUz6l00nlvRynaAlm7yt7ygI7Nzn4+TA5Z6q7Uohgd8+Q+ZmdsEMmZu5Zc\nD76eooxXRyuCGnYpj/WxdTfvn5n19x29jswF0R0NnQhRm2FZq2Gv+vgg1R7zuiBm7QXxHZ33U+Wv\n3wCuoI+QoBMM3DsGeq8oZXeBR5rDbaUpl5NW9zNorFs/AHKz37xY6UdfUevmrXBtgXi6r64FDghD\n8I2yvypc3xn3zTGKN89HA1IUv+W/dm1CM4RMcIanCqh2zDH3sqMHzwqK1Q6yRkgEKpZG9HZ5Vpl3\n62JoSleac5Arfi2ROaVQ/mz0SYma8rQ57Dy34D0a2LYfDC5VB87RDuFCRCSDp0lKcvpBiKM7oGUS\npBiRohiPxrCePb7Zwzdlr7Pj9yTPct+6A9AS0sHXRzwfRZbIXVqZ11ZRmRG6Zp2kAWdLIZqExNnP\nxSBkDkFofA8ATg5KixGf6LABzWumEEYhm8PvRo9xZkYoDdoD9K4x4svdUwpt/3YiKauqc5xCmNZW\np4ZGQaZZgfeDQgZ6CLB2vaMNr6J4PPXH4H5Yby+Bbcb05IxY2MTgcTNK33Puk2VqEGkMvatc40Nr\n3BCCh7uIRBZspWkFxh1tn8qgT+28jaRZt4KijhUYr+K1aUk4PZwk/ezpJGVyg4VUp+qLjjQSKHQw\nD2M6qIFyb6qfDVkyk2kY8uzV+xT5asOxSCwoVOhHmcuKxpgssuqvlonS2noI09jassa/XelvO3aH\n+vdD6rMbwzCnSBC8e4cYyhqmPkl5YvPIjbNgtTVG98nu58MQ3pgT1ocV508PWM6LnKMiLYJJkY04\n9w6YqoWGELCfFoA0o6gxtu0FzbIXQgAgsiGvUt1xOS9aSnnxKqNWQKyUt8/+T5W/dWaKKY3qWQat\n9a4e/hBkwrrc0CYlE1MG0UjTYYWozGPiPmBAKQuqNaTZvHthYZaySVrb5CEYtE8UlGVvBsMx39M9\nqndAv0YeCxhkIo+fTXCoHwmS1ArCKAsKABWEoPH+VzAUTcLcvOAgaU6pS2va2MxAAvpE4Dta9KOR\nBVEw1aTKesDVQsi8Y8zksinMwNNzu3CdjL/Doxki0Y9xJ/OUZiTA+p5Hg/5okJWsxLIRqZgZVQWz\nVWv28IJ1IpsIATM6cO8gip7iBCIwD+/c45X6ApF71mLFazwwSVOpUJTIU4v+TZj2rSrq3mAFSojW\nQwtRDyXofB3Y7ROPZfa0DW6VHg3B0YD7Hh6uYKydsIUAACjTXmqzg1myEWhCcuZYrdyQV/1zNEjv\nNaSA1Gfeg/2JFtFSgrE9n322QeNMFu4Yf+cFX5oYL+9t52tzbGd4VvzzfVqsndULL9cd1xepImpc\nKTGcmoeGDPK3ol37tTi7PMSoVSLFgMxrHmXCt4LlcfeCUjOn4tXazf+0NXmX9y/h2jaHEwgjn1zP\nPbOS0qYOkoA4KsZhmHWD1yhpljk0voawI+cdZZfSwCFFUJwMAFYHqDV36pwUrg4epkJX+vATevye\np5c9vGiZ5nyS0tHcupfVrqgjhdMJ8OS1AdbzioenM85PZ6ScsF22w1wA0IqH4vVLVcjkxkY+ZTE8\nJ5JkrZuGfBqYk89xVofaXnnNSNZOWY2tt8ZPK/yZ1UdKpLEiPGBGbcVz+5sKLivtG2hig1rdbVX2\nFRhQXiBwVXJhrZKn2SX1zxicdt1SruiaFmVexMz2D0iAIg0Wl7LJZrA/x92bgCyWPogz3sxHGbeK\nPRz/xmAvFRzzJ5JDpaO2OREJjFyUp1BF+vp7YkBvwQ2AQ9gFQ+h7HHXyeM2zHIbQvaSvQapxAdYm\nsqMq5xQIMQxFfXjWSfCkKMWPkoVMNHfdlIZmr3ilQBO887PZ/CuUgNaDNxO08I7DiuZx3IQi7h2j\nHTVAlMC8+v53AqkTo5IXprGStWSkV83d7b0hbNGNM2V5YcZN3BgK5g0IiWeOBdpei2HqJU+TUcnD\nY5e2zJKb/659b+eEJ+5B7f7ZoWnNALH83Pgz49ayWNzTYwu7aCMXzfUn73w5hz1U0dAIX3jaIgih\nS61/3wYEISN6woR6FWyhte7I1XuGNbBKFNDwNmrYjciqWR2Xrxe8fHnB9eUqZDTNBTcvsZZ6yM6w\nDA22sIxmBQncHLE+nCR7o0ia2WkvDusmhXTfNLphlTGV8W/n5p61h6XGDWjeEE0zvphJOTSydrXu\nUsDHEBrrCTJ5xsIfEtSjtoruTllD79Lit9ZNlP+eHQUxpKe7LDdU92gEcB/e/9iToxLsu9ZeK3VG\nPc+n8//L3rsrSbZlW0JjvfbD3SMyz6m6dcEMM/oD+IUWWkDADA1DQgUJNBREBJAxAwEZMyQkUPiF\nFvozMLj3Vp08JzMi3PdjvRDmY60d+fJosW+sMq/Mk5nh7vu15pxjjjnGBOtp/1X1zrV7Zm0FDF23\naR5xephxepgxzMTgLyWrEZf4BghPwg1O+/30uYQ4iHR0SR36diNTJTkHrRAx9LNDaxmphsYP9ry7\nYP9esYgycgr4YvKQeb4y8avWyrP9RFAg0l6mDDbnRg5iwl/cCcqJkaCczPPUiYP+tt2wbwu2bUEb\nJaSwSxtwZ+jAXvayWbw+jrf0v5w1KAyhua7nLDa+ADQRUZcrdAQg4EDU43+g30suXK0GJnPQ6rTv\nD+RB0zbZ/oIe+qp8DtrDy0IhucH+9/b8c86w2SKlBlvmXA7M/P54NdHin5U/MGhVVLAWNQRQm5B+\nLuWMiGZeEZxTlUBnvq7X5XgtoyvfgyMFrVDE4o0tH6lejaFGknNkP1tsobl5Z1vg5yqdmLa0aehG\nWSqc37mKFd8Fd0AQ+pnq6cS2vidyQ5ONQRTGpIo8WOkK9N/BwRlJfQ3ehnng+L6ma1NxcG7OU1VZ\n9tXJ+WVdicL3sGhXKHmV5KalLZBigosWdudeaa0qz6vwPScyMCAycHV8+7UMQFsVAvcX0LPUoQD3\nLssJenAOyXuV7pY+uySZhQPavkYsLwuun694+fxCBmJs8EVjWxz8YlaJ1m1dsG8bT5C0QoOmokaM\n80TPsvS9jezDTUNEERYpSF4VHtou66SXf3rswtbXfT51qF9FLW2/SSkjxZ3Y9onIds45kqwuQa+9\ncEfinpD2HXFbySdgXxFT5OLHNWvffUAeB/jiAVGSpNtN97TGoclfocF0S7QE/V6ui/68TI5w+y1M\n9FwTX8U25JeFjKqzcADCHDBdZpw/nHF6PGGcB77Hi5pdWUtGTPJsOa36gxqDDdOgiIVMNIh2R9x2\nRdxq5sSKv7OTkXIrRlv4YdL7E4W/CvG1lgqUHrLKWRdd+D1udCHjhqzKfrTJDcPU5po1KeOeJpNI\nUoo8rrEqI3pdr8gpotSs70vZYWpMVmgMArqA+vWoUquK3xIADvCzc5QAcOA3ts3y0vdo79tn2a37\nzNB+wye1Sm+ENMlo6YFRIReuvDXgdtXw4bNrC/jqc90F/bdoXYsiYT/nrzDg4VEDvlW9WvqCvDFD\nNcupiggU9KxFTIl007l9Qv4AgTcyfVu8+i3TI9jyl+oV7vO1M9+TvuS83Lt60poxNHZWa4CtJDhl\n1FTHK0w3TAOGkQ1NnIxdViXeuOAwnkbkyEpwjkhdMtfsvcc4T5guE8Z5QGACkIhDoZLscn3NX5D7\nmwEFUQN7nezem/i2mXOWM9akn3v3ANALt3QiPMd7kDb/xGNtcW/9bevYwEkeh0JVjlR8gJBdGSZO\nrBvizLHPrc82Jz+lHhOHjrh675KqWfQlRHhKiXY8niq9++22Yn1ZyHhl2Vo/f2OzL/5OQvq7vbxg\nWZ5ZCr20JNA6+DDAwDIbvBVeVvgNhYIuITNOjXPoe7dxR9Ec6A2K7jv2NsImwjn0ooS+WmrPEZFx\nxx6JbR+56POebG3p+1WkGPU5SIn8Wpb1imV5pj52zjrrb61H8H27pBwIf7X7vwPH5xXyKvtRj9Dd\nu3LKMEH2EuHskD1xv8eLtX1hHQNjDcZJbJZPGE8TwhioPYaKcR61FRemoCqCvS31fJkxXSb4geyR\nrTVIe8S2zB1KVLWV1z/OxggPBtqq+oqc/2r9VN63FAZSrGUim5ErwH2whKQ3wKY3c85ZBXaE2NH3\nHlLysJZEewTej3FjIseq89CShTZbx9yUxroDB5qUYwt6/TicXLS3wX9yA/hakTpmr6tVe9C6+UjZ\n3QVlgsipT0ra/wLvOYaEOChF6Vf21RKxxWl+VbgV37uYPSGzvAr891f8sugGMyhdApJzYda+cC2g\ngUFejZTFyIcxKOhFeCyCE5Y0Q6s5IXJlJuxpCf6yuUvCJedVro0SfLpErM8SmhxyQyvuWQrDsqyv\nXNNSqyqQCdSvgX8aMEyBlCt9C/5+oARhfpgpMOeCagDvPYYpcAJAHANh/goJyGkmT8fihX3/KrGh\ndlL3wAsa8PZ2txo5Od8JrRCsQOe+ANXQJM+RGCryxtzakfHQnbTQxTGNzq9pEwVM5gobQ63e6abZ\nzIvovOVsFOlrbSERFzJKnBSltswSs28a8+TEpwLwDO0Xa7v5eh5VzWwWtjcjFyp8LGqgICh978Ju\nhCUVLqKsBlpCRwOGgSr+6XzC6ULV4+nhhPE8YhgHZZ6Tn0QfDBtC0Af+wFyZ4LxaXv/02J3vkuWk\ne3MIIwBxUOUxVt6zY9qY4C39aPp13zcWBOJJiEL27Ov6gm1b9GekvUPCPs2JU5/d2grR13wvPnq+\nH+h8VBj0lb97Y/C3TETtpwfkOZC9V+6vWisJnDlLCMEU4AfRurAIwWMIHtM44HSZsS4r1iuNeyqn\nzjtl64cxwMAg7pH2WYAJiFYnQYzh1pyT1iiOHBD5wZ88/D8O/vLQ1Nb3Z8xWHzzKiKnn30wh5GJY\nDe4AmtoejJI0YmRYf1+4jZC4D9Rgffk8uuBtjlP01unPjG5GEvyEECKkFfqhu+8DWGNRTbPFJKUs\n1vHnqqAnrwkTuNY2IlQ4SDrngAFa4VNrxMI6IkaWnJH21iqQUSCCfCKPh3VkM9MqMr7j6VyUY+Dv\nIbt2zn6+ciJzCZGQFVtJDTqvbiyZAHBAl5V2iREvAn/ovx1bwuZiYU0jEJZa4Yy0FKy2FUz3Wfpe\nfQLAf98jIjqp0BHW7lnijCUjXDYXGEPjRhqYO7h/mNqMbe9GJn33MAR6HyHnWdPc7RgtOFTqkm9w\nUinH4FC17aKIkfT/QBaeyrTveBBvWdZZ1F6b3DlYS2I0kvjV2qrs2lXYkqRKhfq1+QmP+xXTYFw1\nDstwPtFzwaQ28+o6go+zpIxsW2/TwcFyi0505XOUKpKlsu9cA5vJGJDEryT9KuRlLVLPswBtxn6Q\n7XSkIkgqYEDJfsvzguk84nS7IEUxkHEYxhHTecbpPGN+mDGepgNz2wmBi84IB/omXKYvw3sS37te\nrbjvc/UTXRUAHdeKgjwAbVnlHJHijtwJ66jOCqoWbv1qhd4OoKpIXAgjpumEabpgms8YxgFOP6fj\nS3R6J0Brlcj8P/WTCmxtSoTujZV/LVURJ0pcY0vqAitt1tqSSm7FGUfPOKGllBSmkOC9wzAOGL1H\nuVQs24zltDAPiPYUVeZjT4tt3XB7vuH6+YrrlyuW55WQAgMyz4Jve54m+bL31xYT+P6TFvRX1/pH\nJ4LIOfR76YP0/Wrpf7SKs43oOUea/8ZQ37jkzDeqh7PkaZ3SjnW94XZ7wrq+IHH/R6FW60FidRUp\nxa9Yi6rCdij1Osix9KN+VX/m7huh1tbbN1ytOgfL/X/XBX+R/uyrsaPsp1yEBiXtwcPYVSEktycY\nzjqpaor6sJBMb1MrBGe3AE9OdFDYcW782AK5d9EDYGkDj1L5U/UvyWXpjlVgUZXXRVehy3vWJuDT\nzymLGlr/99URXGmqCAWRoJC8Tw/9KdAnPU/+fesPap109yLIjgRmBMkCDLLNyleRTJwCuKeZ28Hr\nz9ZSgYzGcQls++udeoOHIcANThn9BuYAc6OKMFPV5EHOceW/61n0tbYT3j8Xb7kHrKUevuPv6IKD\n3cnSVvZZhfi7BCSnAiCj2KJBmMCwTuyEq18JYNJOEIdDI+8NNEdDOhg+R+0eKDmjpC5h4lHXmtmB\n9EBWfVvw1/vAOUQWmpI2kyT/OTX0Z5gHvYYi5U33QyAkoFTsW8RyXcnYaYt6HqwjmH9+mHE+E+dD\nSJ7NtZASTyJF835mGptd0UTX5HyNPSYA96wQRkoiO15XC/6MalQm6Clpj362FYTCM5JksH8G6R97\nP4Bc7cjd73R6IFvfeVJ0o+SMmqAIC+2HpGLJnwjRzwf4Hq8GRgiK3LYyb9jzS87I1rAj4U4qe3tz\nI9TnmJ9zSgSs7um1VuzL1hFlM8q5wJ+I9T8OxOL3Q2itKEP8FGoJ3fD8+zOePz3h5fMV221TMz1B\n4J032oq3jAKRBkLRlp+is5y0fGv9VN63sTxFxUk2ktbDNhyIpLInnX9S5zMw1BpIESVKf8sfUIGe\nha7uaTxRUMEjbplm1yWLI/2Arteh8FLb8Hp7x4OIyhuXMVCWv2O4WoJ+cKSnn62F5epX+4MsEGDA\nUCKAOvD7SUWQacrBx3QY6aLRRtGrFoUnAOAROf55gXYLj12WrwR9+uM1/en54co5w2RynOtJf728\nqFTpuVZYfoH/DCLcgf6Rp+slyn3Ngrd7T+nVcgJwXJV7sWjnmD8DrxAGPTd0QrXKvHeN86jfF2iw\nmkmAyEmLljkFdK/ypKhNF7yIux5vQNZY9p1v42o0+9uuI5HDkmbxfe+uyj3Psp2m0AaYk0HSZKud\nC0VD8DZ9e3rmWxsiegubLSo6FA2cdMpYqKi5ZdmIoedOWMihSFEhf8mEUG2H2dYi05HBltH07Ru5\n/0wp0vUDuFps1X9Wlcy3HDtdK6NoX48wuUqoX+JZ/FqYpDhXVenzwWMcB4xjICQBBjFnLNuGjQmB\nqutgaaxvPo04DyMCOwaKEVCvlLkl4hLkTNyJXtbYurYvUbLS2mbfq/5erxAm2nfjhswOrXFfEcPI\nSbC4W/J4X+3RSLKgpsAjTH4iDWogYmMgH5rTK/m9nHVEjZLCPnkjO/YUI02YcTJMZOlG+K3d3iej\n2RoX7lz7tiFwQWGdxbbs2JYd854QBq/7DLXoPEIlJFD+PEca+YRZsbwsNLt/mbA8nmjsj6ecrLcw\nhdqqaU9Yrxuun1/w5dMTvvztC7789gXL841isDU09ivoIE/vOG6PobKjoY7TdnsGCGH81voJ+cUo\nywAAIABJREFU7F900xPCn7WNCQqAsk9mKpdS1PZxHE8IfkBmYZNYK5BlXnHXTJA0zg1CGAi1sdyz\nzzRHWbNIh3o4fljI8Q+g2fZvb2gypiJVsFQmb2H9ag+bg6ZU+wRFF37QHJwt8ExgKtXwPHD7edmk\nIH1vZ8ly1zdXq75abq82zkGnuq/kMgC20ERTjztm2W0RDNaVhT9ZJWeUyMFfZltjbHOnOFbytTs+\noEMFumOS0aPKSIlupqioMGz7SskEtOffglimLEwDv6j5oXvvxsGgH5Ipibeu8TS2ZKFWHXXTwOAt\nbLAK/0tyXEohgZeY1QET4OfHW4aiB4SOtAZA+9VZ+sg8AqboCc8Ek+Sw9PgNYIGcQH8mx9lHfDkf\nb5h0kXtOxFccExtzKuSocYz+uuHKvdEfU188GOsP/16WEtU4CfJdu0FY0Y3NX9vcP5q2ujFGDZKE\nI5BSVgQt7veLHPVLNnrPQjIorQUYvEMFkVP9QLKrYSKIdwwBUwgqyyvBfJ9nbDGqmRXQxltlykX2\nDMeIQ+IKGtbAW2ofmsQW18pNMayFYQ6cGcFF733yx2FE5TZu5uKJKv9VEVm+SbQA0/vEeUhbIiXA\nmNQKNNO4DSGM6hkwDDPGcWZRGkZOGWFU34oUdeqg5KTXGdwWlPYWIQC9INSxT3/PIuMuqwVFGAK2\nZcO+7qrpL6ZR5PDnYV17HggtkFYBCTet1wXry4rTwww/MoHXGkK8GGFYXlY8fXrC829PePr0hJcv\nZAJWSiE1XEGMWSfEeSHgN7RLrN4PXAVrvov6/DD4W2/5rmFo1ZFamTUOqVI12hMrjLHwLmAcTxiG\nmYJaNhoIJdhL8kAs0v0A6R/1+iuMKTBkBUY3lwGQoO8hBBvd8Pk9slbLlnrmKb0p8ANkzuH6m8cY\nnjzhnrVAgAxbO2NRTPOmB3AIhuiQB9ksVfSCFaBkrMlA/LMDzfjXeghiFFBT9/vW7uAv26EyYvV4\nfwYc0wYYA89ujpIA5JiQBJVRlINUxfrvpseMYxqigiPew1UiUaZsYWXkj7+rbFilFGRjKOD0I5Y9\nwiIB0nRSr28HeA5rOk0KpZdSYR0RtpKhKsZ5ciRTwRV+CHNMNPd927GtW+sXiqRnbjC5QCLECzAK\nf/dumtrbqwCQGX2zmtjQdcX3d3ap/pggdNdS2N1QFcuERYGbCVBq96NC7Xz9dDStsCgPIziakEES\nGcPESj7GQ//aNIns7hm0VYRljihe/3sZD2yeFCyBe+dSQRzLiJ4kQoU4QMUQ2uLZA0A4P945DMFj\nFFVKvhcj847E2vq13GqpFXtOiIJudgVDqeQbIuiYcG2o98vJiZirCdwvSCWO3Kx7guAwnli7IKqJ\nWi4FMUaQBW0j1/Y8Iu88kfU4AZZlreN7hb4jWQQPasHuWQCOkri2/xNkTrovqVNp5avN721RTV+M\ntjZT4wtRm/ktq1YKpDLNEVcy2RomMq6DcE+MhXMV1VJqpShFZn4UCIUiH4J0SCB0AiKS4NNyXXH7\nciMy4E4TItaQRkd/rmHQtYJ6LlzPx6H7RBC36Tt8jx+elWEaEPcI1Ab79x7ZfYXZ4NGmqNT6UpIV\nFuRsUWuT/gWEBETVvPTF5T0luND7twdeKuN2xfT/IIRDAMiuafwLRHbv2lNixix9J6lEtbqsFdY0\nRy+BBCUoAV0lXJgpz7CnGntECvza9rDc1/UOLhEako30fPpjb8QOTZi65EIrXmOoD/bG6jelHc6x\ntWkSYRIe12K2s1T1pUK1+3Mph8/SvrygAnKfVOEIdJWUc9r/B6DnW65sqQW5fONYuuTMAFC889V3\neIvQyzDTg95PO/CbUTJgzaGylSpUiF3burUKwIqXORNCu8wkJ7YJdU3QpnQIFfnbs6gPjk56P1rG\nyIhYJwZ0J/RbctYRP+OkqmN0I5PpyQFcqgz9y8RNR2rsk0LAdmNprU9tXHt+VFtBWgCcHEA3NoNq\nK0wlDobpPh/2GPiV7PdG2F+/rW0CVgdEiT9ZRa34D4NzsDD6fKwxKrcl7gnbShr0sMeg3CcuPUlQ\nMmAhDRtuiTjv4SwHN/7V2aaOqW2fV8/APQjYNJ252l+RkldV0FIiSgkQslnfQhQeEhH4GheMbH5b\nJU7/zrMGjFcWPkkDt0Kmyf/2Y8r9ObKaSBLXqU1C6Unj47/nWfneomQ+qyBT2qMmr7qXSMFhKGmw\nlkaAU9fCSzHrlIjfvP57UbCMGyUYaSObb+KQjEoApHPMfX61UzbdGK60WHs7dCoeg/8+3+OHwX+c\nR6BWxJra6JMVhbL61Y0rF46ETMSPG/zlLYAB1rZADgiM1IgihlIbkAKaaZlgFsGJrEQ+SSCIIFbh\nQKNzlEnSCRAoXOG775AfvrXWKOiB4YetU/gzTb3PcDX7epaWDPVaUGzEKPJhV6JWpe/snIUPAX5I\nHZuUoP1ivtYv6M1iaj0KXehN0t34b4O/ViXkpI75GveEqO5c/XfhKh3Q/mI/BunoRGolUVHVIKjY\nitAhKJEZ42LSBH5wKl9XqcyadjtXAvKZ4ADbZcKkP/4G0tfUgn+KqSW8hSpgeci0tYBOUz4X3n/M\nIVGupSDHAmszjOGZ3WhhXaIgzyRBgRVp3MwotGq9jP4dgwdfgUPiZyz0mXX8quVtyBddMhpPlRZV\nNnzsjLK2KhBQEwxue5liqDN1fEfqd9oW2MUMRWaUbeflLsdaDYDcqh95yCsn/YbPMboZf6kee+31\ne1bmpBSgiRQJ/rKksjYAjyRCn/Eoe1UqNA7HFd+6bFivqyIQGvg7hra4JOqIpfhZMPFS2P9ChnbO\ntoBvutDX7cvyvXLOd/X9h2FWhn9KssfWw94j+5VW2RJoO04WtQL6ilPGgJtWjBi/AQUpccumtD1N\nAj+RugG82tP0/IEQ4lqNJmP6WW8k/LX3l2SegrOYMqlT66tCS8jwYaRk33pO4mWfNFQApBJbUpeL\ntqakSPDMK/CDp8JD/RuKajrQ6eu4Nq4pZ9buOJyzGELA8B2Bpx8H/9OoimJ6M4paFF997TPrRaOM\nzdrIN4Jl4YegxARVozIOpSTtF/WsfLkRRO+8J+z1WtLWFjjXWgq9ixN9ftU/k2O4d+0sQBO8g9NN\nuBFpwFVvlZPdbQQANMClnPUBOgqP1IZKGKtmHmEMesGrbnCpq/hLu/Fr1vfSG9FagsrRUBRZ9yYA\n63qFNTSxkfaRkAqe1Y6JbX1fbTKlVkJDatVrLAmA/CpQJipUh7+38I2sAJlyAjI4AejaJRbq5td/\nvqIEsguWqtKu+pCk+wMACW1UZCVhSoLTJby1EWsIeaE/ttbqPG7JHfLBla0GC74vbK/Yx58vAVAM\nfnwIaoMtPV66ntD2gV5jrgp6oR5Jst+yDhWbJh3d34OTDHke+r/kSuRb+YYRZI6/e0U9JFE4vo2e\nWyUyHQhN/Ln884Su9ZV/Z0l+5yqlcEXHGh/u6NWhlTXafacyv3vUSnG7bVhfFtyeFywvC9brSqNj\nSaYhsu6fqGCCpWdPB9oHhpHd2i4TzjjrOCFNEFGPX9CBXAuq7EOyV/Kem4rFcMexj9OEUmJTW2WC\n3etgR9dG7u0CawoJAFWn95vtUNxj9c7VPQoMKyY2bpYguu25cc5ApOLb2J7oLQBQWeF+dajcG4oe\nGaOWpL2U2qrznVA6w8qdmrAxn0e4P8p/SRKUWf+iVtUtqCxwRcieHJfX8yotQBm1TjHqc1BrQ0uk\n7eeY1CetXhUn6pD41+unlb8cdE/KaeplVHHKTVxUV18SAPGEHrT3IzOkdCMYlOL0hDcmJy3ngFKa\n13POCSU3fsBBdrK2IF8r8w889U1IPtXpCM69S0fQivR1upOLzrlOHwooFCgPYDIdA7V7aPq+r0B9\nUilavpFccs2auBaIq1wvb9knBLJRV55zNdbAlFad0Xd8Q/C3Dj6MGOOkPf+4EvFP+pj96pMeOSEa\nPCDBBLCVeqUeTuEwIfA1GB0w3EMutVVHkkW3gNtVg931IIGQ2vr23MO7d/ngIZ7d1bfPb173/BBz\nYiGB3VoLBArALjgdv6NAZQ5KmX28NEBX6dKDG4YAP7K+f2C3QPEMkHnkLpEUtEECYW+1+xZt/1pB\nHAMLbpXx6zV5ig9AnyupxvV6cOLVtaUELs4c+YvlKZECVDYOqwBbwbb7tsp7cSWlRkO2tdaEj5BT\n08kQyP8to36Rq+Rg3EHhk+5FKMOdDlMKj4I9Rqy3FbenG25PN1w/v+CFJX9vzy9Ybjfs64YUdxp/\nFhlyA77mNCUVhgHjNGGaZ8wPJ1w+XmCMwXSadG8R1T5JeER4iPLeHgOkc5W+Y+7yek2XCaWSQA8J\n8aygwoMveY84SdJeI193KmQcQleA9clz/WrPVkfLymPE3YSS+NdTYcUTNRz8W6H4qiBC24v5G+s9\ndM/yPjDC5rilwK08FqgqI6HTUpwVTa4NWTBPg2oBHJ7RLhEQV8BerEtgG/reRpX8BHEV0mHekkpG\nZxAabKwB5qbFI8+smKF9b8//afDfbhs2SzOeGpykkjACKxEUTxe5cAWeUKvXikMr9nKc+6zaqjEK\n08pNI+OD3g9a7ce4kQiM2xUROAoCtcyQ4BhimPrQhFfuXQJDC9nmdRVLx1u7CpR8lp1x2qO2ubHT\nS600BtWpoR16Wa8qLTKMKcjJUYasMHb+6iHid4C1DqQh3XrRckO1NsvP17bdIPLMcT+p/4LchFL9\nV0lFu+9v9HpTBfWa4KRVOtrEgCSPQGUhpZZECVogbTZroPrt6D8TAnF2CEs37y2SmvcsUl+kc1YK\nyTrX6jSrbyhUUejN2QbXalUK3vg4QIsGd4O9v/1yzqmim5CEKDBWVcWLMSn5reTWhpHKlO5V2RDe\nNupX0bwxGnLmvkoiaE849uv1vCRBBSsLBAlPhBjTJTHJz1kUV6gdwHtMcwZUeEArf7mJ5Lrrd+bz\nkFJC2iIZ63TywPeuLUYdtzPWwuJojlN5L+j3rMz3QdoTttuGl88vePrtCc+/P+P65RnXlxcsywu2\n7Yq4b0pia9fJ6fhbCBOm6cRqiAbDNGjLShBGUSE0gHIMSinUWuuCvyQD+52ch9PDiTUJVgzrxPP9\nm947B9jeFCr8KhEEbbJwLsL7oH4VIuQj8FSTHS/dPiaVce9YaiGtWhkdt54JhZIkVGkdtOKzRyLp\nfd6m7TKMg07WSOLaK66WUmjCgCF7cIGSB48wDir2Ffi7AmBlyqIouuwFkshKDKz8v5xbwhFXCvw+\nkDjeLWZtF9AB8v1o2wSAjMqK5sP31g+DP0FPXjP7Hrpsm7xAOjyGl1PLDDngOJeASi6A5ML0ym63\nkCBPn1FLsqGJBoJW/DFuOiKXtQo7IgC1St/D82jJoMIZ967CgT+VglCL+slr2wJopDfeLAlyoc/I\nfFG9c0glI6FtUHIT6IZtjcK8qkZXG0cgpePG2me7gFT8dCO1c9bB4GgtgXuWBP9tm7HvF8QYsW+R\n4cwde0xIOSNzn+k1xFY5YCe+qfvALYFa3d6kaukSmazXkoVVxJObN7Ykm4d8HohZm3LWzJokOFPn\nqX5/8PfBoRSLbI6Eo1IKJwalXUtL96CDw1dMdUlyGIGA4Z6uod639UKoY2IemCEvULtpVYCRZDuS\nSVDijUA3kO46aC9A4fSjIdSP1qG4FwRBExMHY49yuYJWaVutsliKyci50+LPBNPWUlEE5WB4srgM\nm5y2F4oEfykIWEyogp08Gcnro5xoRMiopFRrVPnfd+wABX99WxYa0qSfz4n+/Svwqe//8umHZfnW\namZYB/gQ2ImukXgNu8J5P2IYR4zThJEd/MgzYqBqVNokwHH/KY1Q2gtp6Ujsndf+9HBCKQVxvWDf\nVlJnlape7zFS1SvFoFSa2pLqXaywvR/gXYDjRECkjAHhbwmZr30vKlqsco0IBZkwDBNCIB0NoCIB\nIJkDCaSvPUwAq+ZTb2t1DVPzVBByNkD3FqE1hZJ78Dg0P3/WGYRxgHEW4zTi4TTjNI4YFDGqvF82\nFdge5ayoiLlgTwnbHrGum/JE7JNFyRnbbQcY8hdRLEjgD449RdxX4kb538bS13vuNbIi2UFvnDPf\nHlKWqjQlgeAqUAsLP7A3uFgwVkCqc1jHjHmn7wMOHqL/zLcHi05sLA6UVUFKNtrCY0jGQCH/EOjh\nCYPXEZl7Vq40c95gS04oZCPgkytBzKDyONVh9+Txtk52sxMHkcqph/0bglq6eVePZOIheemzW8Mj\nPqWYjlQp1ZYkBe0c/2wJ4Y+CPzmQ7Qv1Mbdlw7Jv2OKMPIrlJ29Gtareivif98Fdvj7FgiboQ0z+\nlrVLC8A7h4E1yr2wWzmZ6I8xl4rIs90iz9m/RCr53jVMA22ce5L6FwDgc0H2jqp/tO97kD0WmNh1\nveFqgMqqa1xBS/++d+PqN0NR6wK/ryR/ORUlA9H1l8Srff9ezlj4IXfu/9o6kMPu0ShNSNDeX58L\n52j2uXTfKWVkw2ptnRgQ0JKGUgqKc/CBNkGR6VVilXBjOIA7T1UQukRWWh9kJJQoUWVXPVVLvHNt\nKaGAoPLkaE4ftarYzxFta4mB9WTHOl9m0vD3DvPDrHoPtR6nRw5iLIbQDj8G9YcYpoDxNJHhy8OE\n8TzC+eYxIBW+cmZqUT6FJNc5F+xIGnR+tqbLRAJja8S2XRDjyt4sJMlLCKsQehOfe7Jgj3Gjvdda\nDt6jBnHvB2X3C1r8LfVRo3v2iHGcKfBPgYRqTNPCqFWEzYomAMo946JUr80bEK8wDqoj0HsJqCx7\nqa1dV8HaMUQEHMYd02WCNQbzEPBxnnEeSbSpVhrn3FPWfRGQvZvuqZgy1hixSMJQCuImxkg4mFSJ\ncRfxixxC8KrDI1wfQFCfb+97Pwz+wTmE0WNg7fI4RfYXD6CZzwajN3ZmhXNUfYdgNfMTuJkyqF1R\nAIIsSK6WskISBUo5srnP3vTCGV5aVnKEKl0WReNXMurAFf8wYprOmM9nTBcSkjjskHcsqab7Xp9C\nb917CcRtSoWx7YYmPgdVhMF7bOwCF/eAOFIf3a077CIVU0FSIaVjL+g1aiHQWKv061cPU0NEWoVy\nz4pxg4HB4keM4xnz+oBt3YjEdFuxLzv2S2KFvpYY6fXgAN5XINrbOhwDzzWTVZy2WRL/rE0JOwug\nDN7DM6myJ95VzqpjzjpnL0GAeArE1JVgec9ywcOhVXnatmLVO5fcwV2uMsydDXWzE0AaF50indx7\nKviTZNa9nyBp50irOUFNut596QK/9abN1huDGpPeP4pQvGHMEYD2MeX7HubvjYHhirzkgurK4boa\ny9wXbTdRlZJTZqGvdjpe07S++32MAWzV84XueOkCUCBNqY1OxTWqkt5bEL89ZySG5EfnMQ8DCQ+x\nzTT4fpZk1/A5ojtmRAge82XCL3//i+5dxhgtPISboAEARp9x6TfLaVHE1VuSC3Zep476Cj+XQs8Q\n50OFk+EtRZUZv2dN54nO457oeV8XbNuqRRZ9J953XGD31ghjIh8bfY/eVKyUolwvZ6ky9fZrxjwA\niBCQd4HbaK1NRhA3FA2SmCPFWe14Z3rfMGp17/KjZ8OpQskUX29B3yr32C2YvFvZZW8zZNV823Cb\nV3zxFNNiKRj74B8T9pwRE+2dsbAgVW7JfMmFZv9fVlyfrqTx/3TFtm7kCMr/jlqDzRjMONIFaMqY\n4KTv34LwJ32l8TSq8hiNm4wYhgHb5g7QJl281t+hLC6wI5SMaxkMdoIrHjE66hVx8JIg5VzGvvdu\nfzsKcwpKZovfLCYSRrNmK+pHzuvnjuMZ0+mEcR7pJH1H6vBbSxAIkaMV8g+M6PzbgwiQjLr1D5pB\ng7qLZdKUstOg/aR93bHeVqwvK9bbShX2jdyf9nVH3AUx6VTmbBNYAjqI6qD2V5TsVH9A/ni9KIsH\n1u1KLlzrFfvyiO22YruSM9XtYcdlikjjoH17yz36vtIwBhQsvvE5kvUaEDIh8Vm90ys52VVGYSTh\nkoon80aduOpPkcYoJQDsq5y/HSltdx070Prjzjlklxn2dnChINTAG2RkOLlqJWISBX4wJF9E9U5Q\nMiNjULaNz3LGDq6eKcBBg7YELyOz3YfeuFMHQakWhIYhwVmkYO9eiiRInxfaRxT4H9agpoySgcwi\nTbZYuH6jMYJidcxrf0x4jvP8BH0L8bH/LkB7HrVd4Gg8UBKdHBMlpeuOfdtZR4NQxrfk/JErJQOg\nePrhmb+HKvDVou0u1fcwFQiADR7+cqIxPHydtMuxtM58+3uONeqeaQ2PGguZWX62NjhfIX3T3jvm\njGXfUQFGzu4LgMM0oOSCaZ1wuszYbids2w0p7dpbB3iixYdurwGk3SriZM4NfD1dcwuEcGGkrUvI\ncZsO4p/1HoYh7MotnVort/Aictq5MJTWcUsC5H2JaMzTaXcuP3jq74NDZm3tJiHvVUY3iJPFiXam\n/WC7bXg2T9iXDU/TCyHnYsedMwsHtVakilAxMkZa/aIvQHoh23XDdl0PuiFenABnagtZ7/S5p5sA\nBxL1N4/1RydCpGiHeUQpFfu2Y7yOGE9MalhFrckjJXHCEvUugvNS2vnBtygloWTyMm/92L0jeoiH\n9I5tu2Jdrxzok2adbS7e8A1H5iskHBFUOWoYZszzA07nC6Z5pszImDfN+QM0q0/VaEaptLnJA+/Z\nk95JNcLytL0sq5ADK0NwMtpWcmMkx45JH1keUqqWuEe19RXOgzWtDylkKyFQNnGMooYmhQVRbP0a\nGfjeEj+BbVuwrles6w37umJbdqzXDct1wbKuuO0jTpEkTZ1kxOBNEVACUjGkVNb3HuXcOJB0b82s\n3S8wKlc3Ovsv92QXDCJX/FErfybKbBH7RkGAfOSpZ3fvsmzSYSr31JxDcQXFc3bCxyHOXj29QkbL\nhGjWV+8w0IAtMraojsZ9nAS8il6oSiC8ViHyxiZTIc4pDNn/HCWWnSfDnciHjg1J8LctURF5UU14\nJcFxFsU5JJMaWlIqjzb1ypPQ9+5taK1r6IVOEeiEkbTSemb08T4oudBM/bZr4ryvO2IkJvpbov8a\nIyUdAGJhmVZG7qQgsoXmywHx/TAw1RLRh4uR4BycoYAt/V6R6lWZbOYFvD4eWEIKvPMwsIfArpC+\nEJK5Paj7DD8vWrh4j3Bn0SNjxtNpxHaeqHC6nbDvZLMubHwD2nerH3Q/HgL1y61r+7Fws9r1a8mg\nHJTRtiUbp7H+v5DVckrIIMg/xYR937CzDoFwxioXl3JvKTLNRLh7lw9etSKQ6ctJIp4YTSy5wgXD\nqITTcd6cC7aFCtNt2Q8TOnSP5mbOFAmlVDvo1Lhr0KT9GCMy7zXU42cr8Zm4bO41Cs8tcSlav3ms\nP7wRvGcDg4IwBkzziOk0YZlGjPOI8TZjXW+cAJASlEHrhVP/f4dkdqVk5BR5dIt+v+8rSUkWCvDC\n6N/3Ffu2HMgKAv0D0D6IBkBj2RN7wjjOOM0XnE6POJ3PGOZB+6vNdvO+1cPmAjcJVO25F+2chcsW\n1dRDZg50bdEO3uorf/kHBtC+v2xytTKzm+EzqZChm7LTwNC+pxBgekGk3LUF7gsA5L9QYK3Dul6x\n3J5xu73gfH0khOK6YrmtWE4z1jFiCgHeOlhTDsQoacPYatpD0J0LkTKVKr/qsMYR1tcstqt+Mgf/\nPWXs7HZGsC8Jq2zL1j00b4O9m0xwRXEWuWPQmu7yJWsUhtN/X7i3y5B7H/gloIkxCDn98fy+7wI5\nCLp1kIpXPpHfykI1N4j4VnUDJFJgE2USr/nXAfh7q6Fj8rldle6bCU9OdOxiPkIaBl6TFPneFOCa\n9j70mNrf1woYhlchvBdJOpS8JO2r7lqWyo5mlPTtSzeRslMf+q3Xflk2CKponUXmFsCF71dnLZIx\n6Lp7tP9YKKmz1EqBHoRg7b1I1k5qblpFmu54GeL33B4MQ8EweBTv6P35OmcWrUpCeM4FxpG+h/gG\nJGsRGR3Idz73w0R6LGkfMZ0nTKcJ4zRjXUdy1OMgVm2g/dR4Ru2corMSeB0jk8147XXbUpIdRjFF\nKt41NJO4IlByX0pNg4C+T2FOE+1tfSvUOa/P1b1rGAdKmlKTq6bvWJlLQvwNmumn9zbC52CyqaC5\nySeNO/IsaatM97duj+vI6+IKSEqwSQsKoBK3ZGTHSNYWkEkBmqKhRHhbdtpnvkPy/vGoH2eMKWea\nO56aX/kwjxjnCeN2wrYtIO9nubj9AWVkE1GKkDU4KOXEfX/6VTT+xUM6pcjs0Z5k06YKJJhJ1mgZ\naqKK/4Lz+QNOp0dMp1ndogLPTN+7+iAFw1AcoIHNGTLkIAQgK4FNiDh9oJbMXq2AxTFt8PDJww0e\nfj8amtCZ/HG/vq/4m1AGT1OIpXFOIB/u+wMAZdVUTazLC27TE1kv3z5S4H9esL2sWC4bltOOOQ7a\njwyuI26iq1QBPTfozmMpBdVa/Tk9//0xmnY+hGAZS+bgn6iHxuxumUogyczEbY/7tM1l0Yx+RS1W\nJW6Ll4rAwHfs+WJyOy4WKalcHcEBpnaVuqOsXeyARbhH3f0cs39rl9CVfjQIek6bc5lk/K03GffE\nXIf94IV+zxKrUh1FzcJTIIXMHAjuJH4FuCLLgKBxMhaG1uow1iiRT64puV6WDvW2gCmwxUBVgTV5\noOCIWvsbQ2fcEyvp7bcN+7LRvdD5htyb9ALAuqwQO2JrDeI4oGa6nmMgLXprzOH+lLaVtAXkGqaS\nsccm/KNtqC1qEJDNQs5xGAPqWNt5Y/njahp3RlqRgiyVUmAk8UtQQrJ3jv/+vn2Pgh+QYsK40MTB\nMI0M41stKDwbi1nrAE+flXMjagr6UyrpOEj7p1X7x1YHBX+nqDHQeD3yDJA7LLXvmvBbE5crpSgZ\nXYK/D+FNaO84DwDD7jmTEBEqUCXBXIlEGiZKflxw8NlRv57buFq46P6HA1nW872fBw8XNzn4AAAg\nAElEQVQfxXyKUAAhxErCrjG0ax86JuL74HWsWC2xu2SlVhLn+17L58fa/t4TSW2Pyk6WOUaRmpym\nGft24qzPEKxfu5lLA7BsM/25BKWUVB+gjWoQetDcojzv+UYh76+zeMOsfoH7J8zTA+bTI07nC4Zp\n0o12mAaE6f7gL8vyTasbsnyyod5/cA6R5WlfG83oCwCsha0V3jU70JILfOJAwFmq9oB11e53XegU\niKgWbank7kXJVNZzR8JL9837yoYZI7BuC5aF5pSX2wvW50cszwuW64pl2XBbd5yGiEFmo7tjNkAn\n8GOODz0EwW0VVb8kgZIZf2Nw2PxiksBPM+/SJolr8+GWjLm+kfQlIj+1FPXpFig7gaBtebiToYfe\nVJBqGd8vIldLnBTX1PpYwc0Hry0ACf5yjVWZsBQ1PJHqWd+/Oxz6bgT3CUy4q2a49EXvq4BHljaW\ncUkAcL5q8E/8PKU9wdjSWhyxBekDrA/qzTtvdUNv6Bcfq+FfLU/OVNGJAAV7A1iGv/sRwJbsRCaj\nbtiXyEI6sbUN7xS5AUBSrnuTXN3CrpMiHy6nQyKunXsD2GroGZdEl4+vh7l1/5KHQ9BECYodW1tf\nplMYFBRMYGGWLxZBKtF9AKBtTmkN3LOIFE3jZOOyU3t3IAjfGAcSdUtIOXGglll+SUDzN2b2jfKx\njHHa53/dJtb7xrTz1NDMhMzXs5cdlldf1OjItSOr5LcUfNN5AozRMdqE1EnnFk3cpvMEN9DzUL/y\nJgAs2JDMFhRn4QKJflnmjAG0l+Qs5OQIvzeIX8nsPN0gybvt9gsZdc0p615UmZu3byQkZS2Zc31r\n/TT4+04ZzzqLMAWMDP9v00pKVNuZ2aBA5IOvRfTZM3j3or453zhizSjyve3iET+AzlGD+siytBED\nteJn4gm1HkYMw4xpPmGazhhPE8I0NH9t/t5vXRKcvhU6rKHsOnTBH12CQP+mZWMGkpVLz7urjLuR\nP+iG0QV+2TgYAq41NznTTLyAFHcep4wszckWmNVognXPkocuJSDGFfu+YNtuWJYrbs833J6vuD3d\ncHo84XaacB0HtS41pjNDoU49jGyCaEFdDkaOX86ztjX4XCXlXHDGzFWP9Pl31lAXrgQRvljghaui\n/MbqbxyDjhTBiIqdbaM2pqJWp0ZOLes/JqhCRtUxWZZtpbFTzxB6I+3phmYqVR3liAJ8dZ1AlbDq\nQezNiCSunADs+3d//ltrnsjOmJKqqKx2lxxssJpMq9NfKUx8JBVK66AbPGABnv6hwNZ1MEzX+2eE\nQO+9UgHbJQZSyXTiYpXPedpTG0O9bbTx8egZtRX3NwV/IdmmSPuWsRbbstH9VAvmcaR7lANxhTzT\nxF+xwgMyBlH5GnJDUBKk547vMRl7DMFrchg4QQws02qMQQaPFnPQyLHxSwBAxh2JlEbBP6WElO8R\n9wXCRLPqpVSMtxHDONC1VtEaClg2R+TkYIMDGK6XAyxVCrojUquwvrRzjIV1HqbahhJ1yIDs87XK\nKDDHDZVFrq3KRo8myHQFSyRP9x07AMwPJ8CAybyF+WlCmq3dvZHYxtkhM/TfJnCAWtq4oaA61lKC\nJvwWffG9X9GmeAT2z7FJVaOCn63G5q+VYH5CHIEI8ESCRdoTTZmN3z7+Hwb/eRBvaq/ZZZ4Gml3d\ndmIg7hEpnjnAVKAWxBThUFpPpoh8Y9Pkl7EMunAGYglsbcuy2qhTRbVOA6SMjVjr4F2AD6QANY4T\nxvFEr2nGMI7wwXGPJGA608zsW5bpgpkEtKarTkErOIfsPVeodMVlzE360wAUBkuloKSWvfe9HtTW\n+xep1+aSxe0PSAuEs+KckXJCTDu9ODvOSRIrrrYOD+nPj5uy2KwV1L4T+e92fcH16YTrlytOjyfM\nlwnjNGDwTglRAFAt9fphWHoYLQEApIpvSZFA2QUV1TZPbfDZq8wLSEycksB/0FOXfu8WdWMUfYm3\n9H7nYaDpBWMQnaVxHktiGyXSZIHCi9ahOg5YHV+Dnn5LEPaxI3YI6GQhatrIksCcPM//erNTZ00j\nFXDzPt9W0mHYFyE7RiW9VdyX/MxsAbq7fFCzpBl1j+wzIRfRI/mIkto8fnM/FLtbuoa9aJHgV7Lh\n960BEbuS4+vvF7kvpWqm0b6s8qfbbeWNmbRASJ/ihsj8lXtXjMIZ2bWqst5hWzbUUvHh1weMY+BK\n/Os9gjRLuPrsUC3xfEg8jkojZcyAFy4ImzcJKuQZuhe5cK3+S6HnOyaVj6216qQDMcdpBjzt492+\nFtM8IlqLkgqPdXvmoTjm7FB1bxJb/DoeP7RO96mEyvyO3MjPpvAzXVGrhYVju1q5ph0aoPsruJBs\nbc3So8rS5+4SAEkKSWuASHHjabz72k+XiRLfznhtX3YlzRpj2eEvoc5VJ3CMYUKiykgbWJfhsmt9\n/JSJ5+OdTvdAChqR8hWiMqOXcZeplUyJY+eU1ZMca7XIqWBbN6Q9EeKTqF3/vYL3h8H/YRxxm8jX\n3ELmsSvSZSLb0ttGEqMCJ3L7CtsNRBZj8pOcDiNB3kDYMv3FE/im9XDyIVFAbjcIgG6kj9j903TB\nPF8wjifM55Mq+llrEaYB02XG/HB/8O+Dvbj2UYUKoBpYDg4C/efiOaAVlNqp8fH3FbYv9afpopL+\nOPVkhQAk54rQFmaUFgfpk/bnSvkTHPT3nUYjSUtBIKtCD1yXOP1siXoiQA97jDu2jav/9QXL0wW3\n5xuWZzIuGbXyt+pv4NHJwco11k/o2ih8zCpcIqqB8mKEPdcmuSxzsntsD4s+NKLuFo8qkm9ZcyDo\nOziHzSdsMREcuEfEzQGdxK9WsF2AF9jecAuLhydgDBANSwanJgd8UM1EVfavQI7o7wthwSvUStVY\n3JKKMLXzQD1SkVC9Z40hwBqDoRS65x1BlZnJfbaTEBXFPxQhPYFm+cEy1x3hkSoW4ED+Mm3D730I\nnL43t09sE0dCRSMNM+IjwZokfSP2uGLbbli3aydQc9/Kkc7lel2pfcRV1+35pn3Y88czhkAsa8/n\nh5w/ZSKFEmCB68UgLA0Zey78bFZlqjfiMiUO/SRRm+ah54XIfsIDqZr4pT2RgdBtpckZR+5wke/d\ne9Y80H1vV6vXWfvIVMrwnmOYT+RRvei+WOTc+FmoorbKFS9X+0YLOXdIGpT4yF+V6j4hh6eDqBuU\n6Nf0ZQBSmC2VEvVhHDCfZ5wfz3df+/lCgVISs7gnVFTizvC0kCSawxTguao2MMTkj0lbMNIqF3Oe\n6Dt+j2+8KB333nYlU2/XtY2trtS+9TzOd0DLwO2DQojfyx/P2NeoxM1xHrHe1m8e64+D/zyT2lVt\nfatSKvY9YjpNOD2cGnHloNlMMDSdBN7Yi4ExFdY2hiu4im0Bv8Dawj3rqBdVbjqjmyzBRNLnD4EC\n/+n0iNN8wTw/YpxGeJ4plt7PdBoxDm/r+eumzqQUIaXlUlBZuRAQlT+eRc0FkcfaKmdE0qeLqc2j\nx0361JQAlNS83AW2IxnTAlc8V/sOgCjnZW6bRA7+K1LaOkvOXSteUf462mz+8MiZJCjXL2n1v60L\nltsN68uK5UpuZbfTSFmtayNR+hRbSzavaJthl/jq+ZNefpZf+ZVyUVtSFcgoBVsUueGNmf070kZV\nYNqk30+TEm9dUwjqK7DFiOtG6mVxDPDrjrg3+11tQym7maR4ZeQ9p6LVWUkUQHvb1mYK1FXZmZUl\nu/6ttRZuaLA7SZC2zUNERvZlx7ZurIRJzOh7iZ4ABX/PVZp3TT9ANi8ZX+pH8RS96mR023RHaap9\nMA0J0DYXV398/K/n/49a/80pMcWji17aqaKOcce+L1iWK5blWceN712UiBN3YuWRwRwz3JNFiVn7\nrKeHmWR7xwG2FORidVyzR5mEfGcM4K2DdwUpH02t9BrzfmON/aaHSENPZc8lhHW7bVheFly/XLG+\nLCiVOBrDGFByxXDnvjd6j5gSJZaasEtrMWnBAVSkxLr7tcCAIHxri871Z9+mlOg8HJn/Tpz6xNK5\nR5k4nqQYEVkULqWIkiM902jJkPgEpCTS8hMMF3znjyc8/unh7mt//nAmjQFDstTrbWUkgO6pWggR\nWV4WDPPAksOtiM2RUFhUQHwI5F7qtT0MI0OS4MuI8n4j9FIS+LRH5d0QwbI9c8Y0d8EcM17+eMHn\nT39gX1dM8wwfPE4fz981NPth8D+Po27EAsHHIWMbAuIcMD/MNNeYGrwqloxE3KuoDEcJga0xzhuB\nSi4iFMbuKlbN7nplMKNV/zCMmOcLHh4+4uHyCy6Xj7h8uGA6TxjnUdnUYQxwwb8h/29LCCQAdMQv\n1wrHM5SWxXeksvdWqjI5d1Uf9FwbxC+QvWSGOTVIq+cBtAfDMprSqND087FV+xL4Y0TvnyD9tJzj\nXcfcWyzL5i/vnfNOSEAnQLGdJgxDwJWnH6wBTqiAZ5tKa+D4RvXOEYerq5alr/x6HfTJpeovrIG9\n7lgl8K8RcUvak9vXqA+OwID3tjwACoCiyz2FwBKvwLZHhmQtNibYyT0sIz2+eoijl0q4ohLEWwpS\not6ulWDKQa50wf9w/0ngD0QWDCNNCUjVUErhMbeNXw0ylL53KYWttX++BucweCLbDo7NVGoldTJH\ngVRScgOj1XnNFZW/jytOL6wxYPKTgTG+ESEl4dE2V9vUen+EHhmBtKIOQk47QbOJOUVpZ32KFyzL\nM3JOcO5tI74U4FtQTVtENaBRr0zn+/FPj7h8vCBfCupE0LI1gbkKR+la+Z3cwxLMDyI9tarGRTVF\nraul1ZVzxhYT0k6tpxwT4rJr0L9+vuLljxes1wUACdZMpwnGWZzuRDz7tl3hMUoh8FHCIYhsgTE7\nBfk8oYKZ9tbBV4+SBy3qGpppdR/SaywBXxFCQDwajohmU3uV+KHnU4ufDd4NAAzCEHB6mPHw6yM+\n/OXj3Zf9l/MFix/gnEWOCS9fXnTKYd2u2OMG571OvPkQEIagJG1jwOOnha5hqXCFNEKSIhtNjbXw\naJ4K/jB/RUWqEiEP1jr4kT+Xp9dkX84x4/Z0w9Mfn/Hl8yfs24I9XjCeJjyuH1or7tX66Zz/wzRh\n2Xdctw3eOUzjgH2esG8k9TtfZiat8AZo5YbtVeZ6C8Z2Qwtkrb8yGVCY6s3XGQojibues6TZP00P\nuFx+weXyKy6Pv+Dy+ID5ctKJBNHRN8ZQn+aN8O+Bvc6bsHdE9EoGesFzqXAlw2W6qR33glBfSdyS\nTjAA6MWXdolks4eAb/vPtyBbX+jP55IRtSe/Mcy765hfTpHnlCnjTnG/67hDGHRaoMFw1P+XBzFu\nOzaGqabzhmEiZS4aL2FfbCH6VIPKpBZJBg2g6IAFmj5EyXDWoNZGtKwAcqWe2p5Y+YqVEOMSNehv\ny4YoD08vjMTCIfeuwTmSdeXAFxxVamukKtMtdB7FNEZGsnRGvlbEDdzrZC6HACFMWmv3lDmo/ElV\nAEB74c46uIFtfrnqL5wgJw38e6v6I22GYsva/NV/vrxzGKT6r5UQkFIRY8I2bAppS0STap7ukSpa\n2A3WkX9l6Abwsu3IRli55y//0kql3BIEeX+x65XWDo13Rm2dUVJ6w+1Go6nL8kIJp78f8RO4u1aQ\nzOr1hm1dUErC7eoQN5rTF9noy3ZGupyQ5hFpypw4NflauecLP6+9DXPv7NnLPPcwuJBcay7ER1ha\ni2t5XvDy+QXPvz+Ti+CXz9i3Dc55jCP1r6eH+W5XQyXdohlKNWRWJoiSFi/WOlb/O8EYsrVtSVo7\nvoqm7KfXXidWTCNxZprs6cfBZbyvjW12LZCcEOOKbVuQ0o55psJwvpzx8KdHfPjTB/zy6+Pd1/5x\nmnAeR4zeI20RT78/6wSE3EvURvZkuiQxxnvUadDjjSUpIlFrgS2uO9as50eS95wySizKLdBni2OB\ntDCmM5HYrbMsKZ6x3TY8/f6EL3/8hqcvf1OS6/nhAXGN3415P94Na4W3loh/ISDEHblWTGPALXj4\nQMYVAovLeIUw8vu+vYjOHObQOchLxdoSgaPpg5A7pG/kfEAYJszzAx4ffsXj45808J8ez1rlhyGQ\nNgGPeqQYAbxhEzDNxKf/vWxS3op6ExBdgUs8liMBj5OFUml06evVkZ3kYe9Gfo59MNPB5I0o13QR\n6OGIbHiUEhGfksD+5m09/3E8wZgVBrvCteLLsO8rCW1sVGFvN0oCxnmADwHGovUsO6Kkqc3Nz3IS\nR/QJ2tx7YmStgDEdUZLh/0PFf2tiLjreJkz/GJVwSkiRQwj3X3vHLZ55oJ6ezEsv+45l3bG+LBCh\nqZyYjFMdPLtryS2rDzKTF4yh0RvnvYq5UA+QpXO5TSXBTioFJYN5gg4LM+yVJCRz5BuN9gnhjVpB\n+5uSHzFTGrn69ywiEnPCbduwhKW17brWliza9JrzXY9lCMLnqpc/gLVsiMXniZCFAojkd6mAqaiF\nIfm9VUmSfInQz76vuC3PHPyfse/8Xd+A+YlwCkCft28rluWZEZSMbV24tdDaTpePO06PJ+znsWs5\n0n1caj2aTHXjp/I8A0c4V1shpiOHMuKw3ghtW15WXL9c8fTpCz7/7TO+/PEJ15cvyDlhGCZcLh8x\nnma9j+5ZiQWCeuKqJiq1aDCmfZrg+mGYuE1LAV7tpxmZQnd/CCHv9XrN+5JEIymqGbupMPoumaXe\nSYH0CqBinh8wnc54/OUjPvzdB3z88yP+7vH+4P8wz3DW4jQMiDHi86cnjKcJ1jlKptcbaq0Iw0A6\nN/OIYaAJuHEelbgIbK3/z9fPdEmzuLpK65BuATakc0TUFO8A5ywZPD1Q8Hfe8T1VsW8RL19e8OWP\n3/D581/x5ctvyJkmdLZ1QeSphW+tH+4GuZCoADFOKaiRsA1XdyGQlKOSF3B40kWxr5+31X4+V/mv\n9ftbT6nBwFL1NrenE06nD3h8/BWPH/6My+UjTucL5vOM8TSqCYcfaMyDslEOKPbOpwDQcR0R5rEd\nAZApHTCGDZCYmSsB3zuHmDNsKYdMvj8/HPOOAV5hUP47tHOqzHAmvPT9frKbTJpopbQjpuaBQApc\nXtGFn61xPMNah916VmkkZCKlxBvfSrDytmNnhvm2bPAjnesbn5PACoh9/9IUmoGFrdrjBJ/Pai2L\ngrRep5L8csa2R2w81rWvXPFx6yTFxKNtEU0vwnAPkqZC7l3GkFiR9P4l+K8p4bptuI1BJaNFOlce\nMj94fSZKJsKfZPBN1tfr+B9pPEjwbyQriQvy84bREun3igKYykNvSUl++75y1b/R81Sh42A/W85a\nDN5hDJ5HWWmb2FPGdd1wnQa44Dslz1a96n93FS1k7K8jeEo1XJnYZ2sFHGdMFTwpwq1AW/m4ST+d\nRIta31t733GnaRSu+tf1ipwinPfw/v5xL6e2qKw+mSI21rqQYBMjtVX2dSPuy8uCh18fcP5wxnSe\nGAUTJzrWpGeXQZkhl8TQcGKowR/H5B+gSZAcM/Zlw+15wfJMUP/zH0/4/Nvv+OPT3/D09Anr+gLA\n4Hx+xDie+HisJjM/W3tKqrZ54BtwUSb7NbUSiawnDnwh8HSVs+rhYGE1SQSAyq6rh3ulVk2EJPD3\nkH9fGMoeKEqwcr237YZhoO/w8OERH//yEb/+3Uf8+eMj/vzwhp4/V/2XacQWI377+JmqbS4g1+2K\nXBKC5+myeSL9GNYTmOxI184Z7OuOkkha3bhGAhUOVi2WOW6toKwAvDGozsFXGdd0GE9T+x6GzLtS\nTLg9X/Hl0yf88fs/4vPnv+J6/QxUYBxmUtPtjLRerzuCP5rFIf+5BCYXHIKjmWWgKlvdMssRhtiX\nMnMrN00ToGnZnfSRmnlE1WxJZvmHYWJi3wMul1/x4cOfKLudTgjDwLOxQcVIpN+vc6VveAjoOI0G\ncwn68t/gDJVaEG0SQMZ6PL8yV2mVEwYIiNEnNr3GucKcaC+0h0SU3uQBEREf6cVV9temV0JlXX/J\nOu8lPs3zha04F4bUImesWTUF9n1j/Xwx0IkY1p2OYXW4eYcg43/CmbBWpx8EUj4Qm/glff5UBOpP\n2GLE1jH6e1vUnDJtrntikt+rpHF427yvkFwDSzhbY5DHEY8p4Xo64fl0RRi8cllkLleRho7VS/8t\ns/5HdT+9T53j/nfrb2tQrdwSqmzny33CLMRRVgeT0T7RZCDCnyRBKjvz88XH7iwncByEtynhZZ7x\ndJ6wPA/Yhg1m4aDeER/l2tZcqZKP37jn+GeKt3CFbXyLgy2VXtbq7w2PipbMpihc7feVYk5JA/Sy\nPKsvSK0FDh5H/OHHS66LCG9RgpeR0oZ1fcHtllXrflsXrNcbbi+LIlHzw4zpRBu17DclZUpMeQQ1\nZ/GCb8ndYepDXpaqlpzJ3rXv7z9//oIvv/+Oz3/8DZ8//xUvL5+R0k4S58MEoDa76Dv3vch68L1Q\nkAT/lCLivmJjjX+5p6Qo827Q1pIrzfpXL3nfBtCgXw8IASUYUdtWpObX4H7hAkjgXxZK9HJOmOcH\nnM8f8OHXXyj4/+kD/u7hEb+c72f7D97jPE1wxmDZIz4+nDE/zBjnCd4P+tnOeQzjhGGaWPU2MB+n\nFcRiEtQnxvpc8z4ue1dOGTZl2GxRPe2CxhhYfp9xGjHM5JWQmTS8viz48vsf+PTpH/D77/+Ap6dP\n2DaS25fYLZyZb60fBn+xVW0EldKEbBi+9MED00Abdq4N1uzYyDJ+IT3/Np5hlRlKVZ+jz+iMfKxt\n3s7TdCazntMjHh5+xen0AcMwtQ2UhUc8Q6ptnEhcqJqK2j2rEe86EZ4ODdALKgxmcxzX0aTBksiN\nVjsdpMZfr/V6mSWvueAr7FTOXZKxl+7atHZK1EyZqn75/uZwI/5onU8fkPLOyl4W27ZANBsEwYlx\nw77SSAoFXgq+fgjElN4igt9UoKRUIvt53hRsbRwOZfnWyqIkTcBnjZFeMhqZkh63bBxCmKEpCRHM\nIcjc6bzv/cE/shGLfD/vHKZhwCUlfDid8PtlxvM8Ep9kYUMVntgwxmKYBhLwsVaJfaLqRyI/oav6\nG+lP7jdA5E052IvEp2h+d+YgaWtudnEjstu+b0g56j0AvsfuO/aElIlsK8kZAJxTwuM848vlhOWy\nYFvbPHK/kSshkBOAjKxtwX7VWuEqjQPKrzZ3TP/CL65+cyZ0RzlGPAOeEukbrOsV6/J8cKETx9C3\nPPfDNCDOkaDcacAwDOpKRyOvVz7HlGStC6ld7isF/8svF8yXmdtgDSFJbDgVxchFtf250HBW/21P\nhiVUIyOuO14+U7X/9McXfPnjN3z5QhX/8/MfWNcXLZQoAAUel7yf7xFT6kZku2eMp322fcG63rjy\ntwQxG4PgBzhLBV8pBSEMh8JLrneV+KHEvsbWp5HVyBMqq8L9bYqM/l4D/+0Jt+UZ+74hhIB5fsCH\nD3/CL3/5E379yy/40y8f8Ov5jIfpfmE3z2hfcA4P04SHywnnxzPm84RhmGGNxbpf8fLyGSGMGAYS\nQqJneoDzMycCNGmhKE8/alsFveOJjc7sR/4tgCYBH6SobVK+y/OCz5/+wKe//iM+ffr/8Mfnf8Lt\n9gygwvtBnRXD6L/rZ/PTyp/EVErTU+eqygcitVD104KiDx63Zw9jrgxhGc0SvR+wLC/s2HfTUbSU\ndtrQOTD2dr3ehxb4pwvG6YxpOuN0ekQIo2bMouQ0jEHJQjCAYRKWZtVvgP3FtQsVh6pf3kHnrI3h\nlm4zqqE+eevbHVZtr1bddf3T/t+hVbDg9y05dw8EMXAFBUipQaL0sxZitAEeLblnnS8fsO8LRNUL\nYM6E4eqfuRr7RgFHKrLMamMym7vFiIVH/4x8f2c1EaDeP30E6bSzEUrOWGLEsu9YYySWc5KqvrHn\nJPDn2CwxvXeoNainQQiBBUvuh/03/dzIrQtiwJ/HEY/zjI/nM54fTrg906jjVklwpsj8f6laDVhG\nIawl7kGD+6mHb7245ZlDIgQUnpumTTJLtb8ThKzuhTIatGxU8ceNID++PwzzBu4d89wS6SekEFAD\nbYgAcBoGPEwTPswzbg8nbNvetRyoBSM6FLKkWjPJICPxs9C4PLUSG7qWqiqHtli4YlE5Iaj8zGYe\nKe4rfmJHb1R9356wyvFzi8MYC+8Cwhtg/2EekLZEomAPJ0znE4aniYNdwratABZtrQipMqV2PtbL\nivE0Ev+IgyAxs3m0N0vfvyrPwRqr3g7SLlCRqpix3VY8/f6EPz79hi+f/4YvX/6G5+ffcb1+0amG\n0+lBW6MUjAT9vG/f23n0OO0t2ZQJDiKQ3vizMnOIYru3OOnLOVNR5jgJEb0G01oAtOW1vaJwDz+l\njUSaolj2tsBfckLcV674SW5834nMOo5nPDz8ig9//hUf/+4jfv31Eb9eLniYZ0zD/dceaPt2cA6n\necLlwxmnxwvm+QwfRtTbE9b1BU9PASFMCMMEPwya0LswI4wDwjgcCX1Z+CltSkjOhY0WJTTBN0kG\nBYGyLI0dlw23pys+//YHPv3TP+H33/+BUZ8/kFKk884o+TCywu2/dfBnIZWUSxeWqtokeucQWAZY\nRA2IiVjhblsnUGEQ/IjT6UFlYkk2dlOyWquG6ZMoeyHJXnHrk987FzRrFt3+YaYMrBT68zDQn4ux\ng4oRvWHletTrR23h2XDQb4TANrFSaptdV2gPUvkLiYdfXMEc/gKAML1F4ERG1WT8Eax2JbLJJSfU\nIqiJoBaMrLDYhvApfrbG8cSJW2vnADdFZDL7MJCLXoMzKwcphfP4HoqlYOA2SakV1dH5EQRF5/wr\nzdXvLOKzpYQ9J5bnrV32zCMyO1W+4lxIvfTM5COjY55hCHdvgACwMeKwpYQpZ5VwnkLAaRzxMM84\nMfv29jTAmoVg6S3SA84VzSDVMD8bPdxZSoXJBGsXutg01sNkORmvlWq/KGGMPImSojQAACAASURB\nVAz2dcfGLxE5ksDfMkzQDLZ1dzPe95Sw8fmXZFGQj/M44jKNOJ0mrOeZJwzEPjcB+Wg+RNeKjsVk\nAIanR7pZbbqcnBCYAlS2B8+FBcIsc3aEJAWFS/d1w3pdsKwE9dMkSgIhXlYroGG4v/oLQ9CxKqr+\nJwzDhBAI1q7qltlIaMZQJUuGLKS0Jz1aQXcAqeI7lzbeH/Aa7jdGRW1ypJbWy5crPv/+V/zxxz/h\n8+e/4vn5d+p3r1fEtHOiM/B3pSBALaWvHfW+txqhklC2JJwaIWOniH1bkUvk/YgmKbY1HDkKJRHP\nxgU4H+BUBbB9DxqBLhr4c96pqMi9PLDYUZOC6R5XbPuCbb8hpo2ulx+I/P34Jzz++hGXXx5wuZxx\nmSbMMqZ75xJSsezp4xBwfjjh8vGC8/kXjOMJznlsGyWbwzBimlhOfh7Z8yZgYNJ5g/kZvdkjnd9k\nDomsEXJo575pHaHVYi8etx23pxu+fHrC50+/4cuXv+L5+ROu188qZCVF9jDwPTuE76I+pt6LA7+v\n9/W+3tf7el/v69+JdT/77X29r/f1vt7X+3pf/06s9+D/vt7X+3pf7+t9/TNb78H/fb2v9/W+3tf7\n+me23oP/+3pf7+t9va/39c9svQf/9/W+3tf7el/v65/Zeg/+7+t9va/39b7e1z+z9cM5/3/xL/4j\nnhUnB71xPGGaLpjnC8/bj2yt6+ED2Qxaz1rdzqlGefPtbi5Ohgbkv+FeJ5K3RzvP3tmrf091QpMv\nbYzO6aq0pbEHDYL/5j/7T+86Of/lf/0/qNOWDw7DPJJu90wKSkasyAwL/hgR4TkKnTSJTJrrlRlv\nUfhSdTQ10KjN+IPtgNGLm1Qy+SCfeJ4DFo33fcP1+ozn509YlxckFeGgOVtrHf7Nv/m/f3rs/+1/\n/78AINfCMA4Y5wHjacLp8YTzhzMeP1xwmScM3qlvu9g/9+p8+97NC8dE8rRZxIBkfl3EPkQfACrs\nIeelNwSRe0h+L5agtVbENWK9ktNgXHfVKKi5IqeE//1/+x/vuvbjOKs/+DSd8fHjX/DnP/8H+Pt/\n7z/En//+38eHP3/A/DCrlPQwBtWZ8CHADeT0JRLTYhZDIj/2YHwk4lCsdaSKgl5MkURYiu+jmFIT\nP9oj1mXD7csVX357wpe/fcFv/+/f8Ld/+Cc8ff4d63olaWYAwzDhX//r//Onx/6v/tV/AbHk9jxD\nfTpfcP5AUqeqm9Bp0ZPwUrvfRWjLWsu65uJVQBfx4PjWvVCaRsLBH0Df/9V+orbAbMQTM5brgtvT\nDfuywxio9vr/+j/9d3dd+//5//i/MM4jrHdIe8J2W3F7Ilnd5WXBvu0oqag4T6+BL7/q3mXafdoP\nVYuiI+2RliV+m62x+EA48bq3TTeht4mWczxMAz78+QP+8ve/4tfHB8whIOWMp2XBp6dnXJ9v+K/+\nk//4p8f+L//lfw7vB3JMnS84nc8YTyOmy4SHX8i7AAaIa2QrbRL4yp1ZkejJG4ODSRVqEz2TfVWe\nHy+OlSzDPUwDwhTYnC2oOqbrVBBzzohrxPXzC37/xz/w1//n/+fszbIjSZItsaujmbkDiMyqx+ZS\neiXcBv+5CW6Q/+zD19WVGQG426ATP2RQNY/J8SwPTmQgAHc3NVUZrly58p/4X//jX/jrX/+Jr1//\nJz4+/mI9mR3/7//7/zz17P/P/+v/xu3vD3z7X1/x7e+/cF+/oZaCEGf2eRHeT/AxYJonnewnOjOR\nxcTkzDtWlR2HtNEeYYEyVvYThVIZWlVyt5HjECgRDDLWIM4Rl5cFy9sFl9cLXv/xij//tz/w3/78\ngss04cgZt23DmhL+j//+37+71yfGfBn90xhRIutTm/qGPo+M0d/6TuFODgpgWpfQHQcHnAdKVFhY\nmoLLU49gDEprMNWQup4GDF0cwxaSBW0Wp4NzOoG/u3Nr4JyFYxGh+UoPO4hS3EmyVBzV96+jk91O\n6yUfR37vbPT6hzCwAJozfb1aI1EYdPEgmYXQWkWcFkz7QipZrPk//vszVy2FDREp0YUpYrpMWF4W\nvLxe8LrMuMwTgggPsfPPpahKXW0N1dchsCG99lZlZvl52JHoG9XapU9VEazyfmiVlNDGZzoY4XGu\nQ05WRWca2tP65gBUKIac3wteX/+BP/78b/jzn/+BL//xBS9/vpC6pSPZ3q7v7fvQHlFXE11/DohH\nx2+tgcUwMloEo3iqoHMyKbIHv35wrGNwIA6YNPATqsogd0nmp549D3F5FJ0xD6pz3dnLsSIxL2MA\n0yoPxmmw4HkOLHAlrzOqW0ID3P45RCJYvznMODCNom5jDQ0HYqU8cbyy9gDp5uMTAk8yXwGgtTy2\nA9uNlBzTkVCTzLPnoJPDNg1UxOHL/8r6yf1DHL3R9eo/DJW+bcWgmYZmGlANYB/lYZsqiZZcYJ3F\ntEQaKXyB7g0JIJ6+f2M4qXM8qtqqhC0MONkQqe2qz6cHcRIIGf18JFuO8+wCK87wbP/kSwIcCRoM\nnwF5Nt6SLHx9XXDdD7y8v2D9dsf9Y0G4T3BOBG6et/nbx4r1Y8W23nGkjZ4zC2R5H2AMCabRuPIh\n6GvyXEh8ip4/+bWaa98H/D4k7tZ0KJmI+6h66eCvVAZelCAtjZsumWZdBJ5nQsEEJV4iS66v84Pr\nN85fMncyXI4z+jHapSeDH7wJb/PWIPO6daHkxxuA1geY0MAcoFawo6cxsGh909BLPRwyXhDDETP4\nIEgGAjc4ik9c6vinQIdqnjTj0aCF76OWpip1+hBlGejGNfPpetZjZtsDgdMTMCQ4SUkPGwFwRK0y\nqYCtDc431OIQQ0ScFkSe61xr0WE3wHNqVzVXuMDOnwdWTMuE5Trj5bLgMk2YfTg5eqtzBggBkH/T\nQ4sufXwKgvjsgNdOZDBbZVVJWRtWyqoWfPiA1igoAuiwWWdp4FQMJLXLh5GmaD1vBEopPAZ4xuXy\nhre3f+KPP/8Db//8Ay9/vtBoTUe6/XGiqJ/mSpAiF2X5glx1+WsdFGU52xscv2bKYAcpd2WgcyLE\ngPRzZNT7VjGgmRxWPhJptfP89WeNoEyONIYkoel90JGtWtmgDWeKj7vGY6xcKZ/rdA2BAHAOgPXv\nYlDRAwu5hSrnwAC28ihgPu99HkcfaCKZ9LOX5fkarTakI2O771g/VuzrTsObxlG8FYODQnf0/PzE\nr8t9y7OVWRtGkMvBIao9NUMABAqaZa1Pdob/f72teP/6gWkiWd85BBjQSOZnB5qJ01c1PgOdS+Gc\nG4I/zkbRkTqRK+5jrBuqsaDZRBQM8jhUwILOZBOlSxrOZIxBdrmjShIsOHNCUxRRiQaxTbi+XbH/\nY8P67Y7b+ztuHzNr3Iu0+XPX+rFhu6/Yd1KKlKmFIUwDesoTGP2Azjh+nkOmW1sFBkFVPS6c7Ooz\nPK3Z42AnCyP7zRjQTDlLksuF5KJLImn1zHMZjpRRYlGb8TPf90vnb42F4zngpA8vEo32/DUYt26o\nQZGuePlH26OC7jT721ijWX4z4lgbYCxI/PT7m9CFNqSvb2vjsaAUnVprUJ2FazRb+zO6/gBt+jDA\nUD56PUTdqdFD7pKtAtd3ozBeo6EbDfKPhBbFmI13TFYVw8ZoJ8chn3maFhq7y6OUaytAq2jtOSNQ\na4MDdI584ABoXiYsIWAKLOksBk0CODNCW5wFjFrutet6tyF7OUX+pbJEbs8qMKyF/jyGrJOzXsn+\nQ/SoJZymhn1OzLLBWo95vuB6/YLXtz/x+scfOrQlTJRV+Oh1nCeNM+3Zkjr+saT1g0EPTYJkQCP2\nyghPqRWAhUEFrFWSjmVkQJxDixXlUjWLSDuNPt7WDenY0Vr51P33TJ6/5BiL8Telnyf+Nw2GLQXw\nxvD5bx0G1ksDdyiqI59P0S/N+tv5Z8Y9YTk75ABJVnQMRGRfPHtZRwFFSRlpO7iMtCPtVD6RjNQa\nw6yp4bX5fxWxBJXOxP4YmHP2KwGdBixDMGBHFBT9vvXRDEGToZLX+r7i43LHFIOCHb9yAI8XSYG7\n4e90lvwwqO2EcLDjL6nwVMsexJFtqICzau71DsT5FTzsTSnlnCWJ5Xt6roY18sFjuky4fLni5c8X\nvP99Rfy6kLwx+65nr33dcey7Ov4YA6bpghhn8n2CKsnkRx4iZ10/C+OzG4N0vcXvkJJ68geyV6w1\nlBw7h2Z6ImSagaksvZ4p2xeZ85wyEktzB/M4K+R8/dr5O3eqFcuErD4ljtEAhUAk29dHpg+a/jhv\n3o5wNLRGN1v5JST6hqXoGhpI9EsNP/9/rRZotHGLMbDOwDrSRf7MZCu5nPc6MEgGdJwcsumQj2ZE\npUPW+uCZG2BghgcuRmxAUH5gmxVA+Inh1hoabzjrHFwEYp4xHRccx4ZcEnL+jOOjj0XrZzXyn+YJ\nSyRY8XEUL05rwhtbvq+Gq38GyhY6zNfkzzJM+hoRFHEW/MtiXGEZGZHMk0s11RNi0QdpVB4b+vwV\nAkH+1+sfuL5+wcsf5PjjHIlTYnlSX6Qyw1iL7s8HD5wXMebk9OljNw1uBDkxPBND9lXm35EBMTJZ\nU8oD1XuU2CgAKAXXP17w+r7i9u0D6+3G09Geg/3lLMtzRBuyu0rZjLFG11MyXsm6TRsd1zm4HWvg\ngtyMZ0Wdx8N+71nSUBYT5y8/63g1Nbh+vKfnLmstaqk49kRjeu87abLnwsEK721LTm20QzK3u7Fj\nlGRGbOSp9nsKhiQ5MR0NQl//MSOUn29AzxoBpEZ8l/v7HfM8wXsaRvV9wPnza1wn4jp5rWGf7W3f\nxwpb86yC05k1th/7B6SoVSlpAGgNBQbGCvJXUIs7DTZSRMAamGBOQafzDtMy4fJ2weX1imleOPMP\nCOH5gV4ybhkwrJFPE2WdD3qWfSQugnLcZBLrEMQ97refBt4S43acT/1fc41jS4NaOm9MEaLaAy/h\nVaWDpkYemc66wv8/uH7p/MnxW3X6Rjdwd6IKcTWCpZrtBxunZ63gtWYMj1FtbYCxVB+hiNny/zRo\n6YBOBBkaLQCI0QEy+vuSMyw6OUsGqzx7ucjGfQpw3vNDRg9mNGvtA1hq7jUrDAbPWNuztMGgjoel\n/cD7nwzLcPDGAMTInwaKTNAGnRDjrHPNn53oBzDUF4JOlQpTwDRHdv4Obqh51UqjnstIWBwztIdL\nA6Whtie/d67zt9PzMgan2i3xHx4DwiEI8hU+OJTs4crgNJ64rPU8TOqKy+UVlxeC+uPUJ6UpoU8C\n4AcHz58IAvdrkNb/6cEhGD2okkSf1swYtErlGxmzbUCTAn2t8L4S/2CKmJmfsbxcMM8L9n19OgAc\nSxEAD6nSwJZwTOsoCOn3IHsYFGiz03t8NvqMHrLxR0c9BgSnSwEBQQSGfeNdR4jYVhiYU332uYvm\n1wt6ko6s51qybePoPFdJTEYCHpcZJDARB21Oe0HScn7H0jhoAmAMbDPqCGA4CxycyoiE6v1X0Ge+\n77jfV8TogZlt75O337PPHvQTj0UQz+9//rtzKykqoCU5QSe++8yg511Ng2kypXQIAJs886ZBQNEy\nhtXXbo2GzU0XmsR4uV4wTQv2fXp6mJlcMgLaOa8DnQyXep33TEz0sEziVTRCPpf5/vk8ogBi1601\nAP9OKw3N8b2zra7GALkAsHqfNKCM9hcNmmp91PeReSIpTTKsrf107//S+fe6o3xgIjqczRI0UpG6\njLAbzz9ivnP6j+/FKwWK3isKZzqmSaQ8ZI6V4BI1UpYWrIKyfhgDK2MUuZ5ixEs+eckoWOcJyh2z\nfN3sFTyes5M21Ejw/TQARhzvaND0oAxr7M6f75EYpUhAad8dIGMNV0oY+g4RMUxIYaL1NERie+re\nuc4fJmLbTnPEFAOi9/BumFbVGnIlkkkqnemfa0XmiYNFR1kOdcrhcD8a+R4UjZkG7wvJgnTRzusn\n6I4LoICnOPjsUPLnAr/gaVznNF0wL1fMlwXTMumENoUguUbbeC8Y24NbiejJSVqa1teoXOE4M2TQ\n+jtDIfd2godlffBgdNENiqI1wTNHY0G8zPC3gJyP526ez3p/S3ZgpaBkZluLU0M9rbv4q0a4//ll\nmQCoPzigC98Fpmwd28MZGS/5HQnAx7XTDAySgD5/7mV8cj4yk9rKCZUCAGfJ4NK9fv8ZjTG6PuK4\n5Dk6bxWJPEH7bN9oDzGyJj9DRXJ95hqMDehYKxV5zzzm+cA6H+y08DTZtdYKy8RCKvcJ5D9yKvo9\nSnKoz6FBk62Rs9AaYFrVsoZkyoJ4WFkHzhrJRJyDp5oriisoxcGWCj+WeRo5bR895stM53VaEMKk\n3S7PXLKWNAUzIgTq+tD79VYDIh88Qginv+tYan22HfSUoFSRHxDypXV91wOoWhuKLVxiM0DKVOas\nRJS2ljcK3z/V/wuPuE7Yp8RJCf5rmX+/eBa5dTDWnQzSuInH1jwMG2Tc4L+KwI2e1O8fBn0Kc9r8\n4yULblFRC0O/P4xIn7+kVWP8zLVWZSV3kosQbx5eQI0CR72PBkjOAQxaM99lunRj4ICrQ3yt8iGC\nQwFgHIMjtcGYotmF9x7OR/oqCS3/wMj+5KJatteZ8zLS1j2UTijL7xB+qRW5UptfOjLSnnur3wAP\njs/jcW+MtTAxtgIh017rMOhpOU0v9QDglhg2tJ7mxD97OR/6aMyZM37H89at6S1YAwomTlk+y2P2\n/t26Db9HzgFwxvIaS4b3kCED3+2RKudh+La1Bi44xCViXmYEP2HD7en71xbc4Zmc2lNrQ7PtlOEM\nN/d9iiifu6FzeobvnX5uQJAe2wEVWRL4XwKvZtBKZfY9vb0PhAR8pssDQDekiUZWC8lvJC622jRL\nhzXEean99wFxpGwbjUHjfWPMuQ3wu3WqAFDROMgCny9rGo22HgjCUPtG/1sqfe60Jxz7gcBEvc8h\nH+T8HNe0nQS51sCcaDjmZOvHe++l3orC/0/dF93xK0o3wuSNgvZSCmy2KC7DesstoFXZ8TKmfSRf\nt9bgnONRzDPPtifS37OXBDdKcvd2QD0MdfRM7Oy13t9bM733PXg7BUXns6J2go+KtcRtQ6NowTJX\nAg2Aa6jVwrQiMfPpkva/kjKRfHca7+28Q/AO/icjjZ9g+0uNn9qapNZP33fnhy+H1bTOzhwiIHq9\nn73VeTMIdPKd9ZRNXx8NoJhSC6CgFG4rK2eD8ZlrrPELW3500KXITO7BYQmM/UAwMwaMYIyp6mjY\nac2k+4GX8xwl8vq0ypArzvdTW4WBhWmUdVGbHrWo5BzOXQhP3LvoNRDMzT3p+l7yGTvMXxpl/rkQ\nfCeIkHVWWdmtNlTTEZsfBYX6fSPZYesG1VZ+Tf6cxp1QJVlsDUaHwNR9wgk46zn7j4gC80mHhTxr\n/k/eXyHAoe9c75NXrrRKLGf+vgCS0t5nLBEtRdsBrcC2hrGronHNnxIKan8UjQXRNahMBBUCaIgR\n3j9f+6SszulZN1Jk1h+QBO68n3/1erIfvvv+cFF2ZGEly7VNz5OUGbSmz+hCo+I63bMGHhQIGn7u\nn/F9p+xaNCgGvsh5f3Z7rGerjgFOU2dvud3TBwfjba/Fi62T90fT1z8FYIp0DGtAH0hwFNRctQc/\nLhHTHOGHtsdnLoX8g4cLBG877wFDbaDGdESL4Hn0z8vEbTReB14QQQ4eH0S3y5S4NUe2xMo+H2y4\npcVRFKqWnpFrRu0MwuQxXSho9z5+ivBXckZrlHBZJ507hBpaY7QUKgx/ecYSxBIXiQvS4tkxHA0J\nYGu3IcoZKwPxT34OTc9NM1Vfh0qYEmEZYvkfCfGgpOvYE1xwgIlqqx+v38L+5y/bMwJ0qEY2rhgj\ngtms9rKSUx82iHxsPafm9H4iDsRrNVyNo2yjcBYESByce4OBZWNRSo8W3Sg48cTVHf8AL0r2wlBL\n47BWDkN9yFaGVxuiStms9oSQoHVn0j8D1LpohCtQWSPEwDaKfoWoNEbVznl4F9SQP+v8tTfd08En\nCOkcvMn9Sv15dA6EPlhUU08QoN7rL67TOg8BVQPonmGI/WooIGqVv0zjjMwoD0Le7xE6/+39W0fZ\nv4t64IUFLiiHrUbrsNL+44aWM7SGyq2IrVFNV+q94vw1s5e9r5+THnoFZ8Km74rK7PkqQVetrOfQ\noeB+H6bDk+455097Uci9PYA6/Tt62+apnslBwtDdq8+0ownyPXm9898B9BJBAyEMEvzUbgCrlCNq\nBXLnu+iz11r/57JeuVSEJdfhs5rhTPJpZRs2xjXWGQ2apfXTOkLQ5O/itPqa9zPS3+uh5NMeevyH\nn2mV+7z3hGM7kPaEnArq3L5D7H52yfMmro+Dj47aV5njcS55ti7O1ZruZUnaOjIwBPkSKJvvEzwJ\nBGwxqHbo+AH0fGlJjXvk9cHI84GB5W6fOBF65/3zzh/K22DUg0t8tRKq9Nh1MCI4kvAV/rxmXPPB\nHzS1a+cESpBR+R4FOlXXpWjZafw9Wj6p93dBtYKcC6w/B67j9Vvnr0tqBMboUe+jyhYtBmeL3PIk\nwd53kax8avRN0jdCb4tQpzKcX+uI5GD038cj0zeGK47qlAyHfPdAfnPRxxxr7gDGOuVAInv8mZ+t\npwZEQ2b4+PunEoUYfSOfR4yl+eG7GM6ENJCSrgzrUJ3HsyI/Yqgds/2tOjUMm7gp69xaC9eadgE4\n61BMG9OiM4TL73NCTIbIt5c45Pf45znTNc0JItp/3wDNWmhlmTMSOaS1PO8EfIiqdBYitXkKfNz3\nhHwi/v/h2alxZkjYVDbQ8uyt5brd8HxxDozkHhokoyXjJgS8wryY8f/HfSCaBy56uBgQwvTk3RsJ\nOwAMsCw7vY7kdYMtzsu4bvgfXvK7vxg9/9C11PMgjl8DRg54YQHXz6Ds5x5sD5nWYEM+A/pJaark\ngnIQyUzusWf3RvkdvfWs3ze1g3lu/+wqo8QQd1xCPa/Dd3H5Q6JsYKj1s4D5AWIbaQ9V52AyfW6p\n+x/bQcqT4bnMX5I8Cfrl2Yv+Rs6FVTqHzLyeA//x8w9/8FqRczYDiihZbK2AyQWZX8xaC1dcP2vD\nGaOArxCKCOF/NN0vlgnLgc/xs5exDs4YhBDguL3xvDamB37MMWvNYBT3oZ/F6eH1DhX5Rt/jALra\nae2iZKPaJSonWWPpiX2fqZT8ieM/oc6NguQfXb92/nqQRH705zCt1frIGNn2Vxod/+jwRlj0sSY+\nQuxjNvxD4/JwtSIMSCLuJJ8g0pnPXqMRH526RN+j8wfGnztffc16RPijtkPRRBiNQKvtl+pkp/on\nembSYW8H6zycD6i1oH5C8IIM/QMpqf8TOX35O7emiXKdPlv0YOUndwCBuCS6HY3Kj8hUUm/ua/AY\nSfPBkHXgAGY8nL+7YqSaYZxmhClyK99wD3L4i8WAb6Kx+EzjnlXpydU1ZCTAgBAcSpT760p/P5pA\n/YNDZ+IrlVia1voJ8h/OCjt+UWaME8mOhvic8xeRn9asOjT588QFeMhURbgIPzibp/M77CL5eWMY\nLgBIy6oO2U/7fg9IIDQ6ZNlzsgYwPVDFJ52/ZFLU4sdQsLdqe6y12utNwXE/1z0Z6hl/lwnvHSLy\nOTs4cV63ERmQBTh1xYzBdKH2ULFL+UjY1w37naS5jX322beT/YChUhNKQU5F+/mVBDmUVPXsFnI3\ntrL6oqUyjgSThp+7wQOHi5+15SDOutJb/XIm/6BRNgmbdZ8C9G400TzxrMz3vPP33qOhaXsjwCVl\nfu6tcWmltvPzAfDo0M/PTx7qOeFTFJmDtjFBOiF5Q9LVfSivk7ewxSL7RIEqZ/rSgfQT3/8k4Y+z\ndspwOgT2CPMaS+0Ojtsezi/xPex6CgR40eSW5Ht6oB8dPh9s2UiaCQ+GQjZOTgZmo+jbPRkB00sJ\n5D+6vIH49FBD1+gUPw5OxCmZMaORlO/kVQejxQHICP2LqFAtBVW18at+/9HpEWnHozD0/+xlgLPj\n10AMyqSWTZpr1bKA1QNotP1FPsu4sU0lq0fL1te1ZxWiItY/C/Qz9XWir/OBkqz/tA7hefhvnq8k\n7jEvTPbrancAKTpSTJ1hq4WrtpccNKNl58+tbyJ+Y1FRrYGBg95c4zp/qzDCmXrYQ9oixs+BRGYs\nmmTe4phE5ZD1xqfrhOkyY1qfY/uTrr/8jffnD4J3sJEeSVu/C8q1T3kIUsX7GXXi0sRlyfDyngYw\nID0/Kq3xJ7Ydbpaf+fWneviMpfCcjKRQrLHmVAqT9tcwByXFGdXiP5/pPqfEMBrQ24Y7cto7R4Rn\nZWxPoMgG9BBKzp6UNdOesH5sqK1iv+/U8nXbsc075muC/YTdGzNcRTgLuuNPmfhOkvhoIFxP0HW1\ngKkkCQ80lGxhLXeHNCPcNnotnpVgK9sV17U5SsrI3MaJAXVzjQnZDoDwFFRGm1Fo6z/FdRH/4MIg\nYV97r/wjNH/ai4+tyXIutAz5fXKrrc6lB1R99glJs1c977Z3gbC/dczrcp7Ksse2Ix+JW3KlLfDH\nu/+X1nCMpKmFrYDa6/CdIRBHrXAnG98xMxgj9MdaoS7YDwIEkcF8POe0AHRY9HVBWb8ERq00lFRg\nTIa1BrV+IvNFbycynJ20ik42ks8r2YZmn/0e9J6l7acaNEdQDcxg0OmHIdnQCH1D7queo0HVFBAE\nYjCG1nDU7S3X7Hu7ztOX6eUcuU9riLHsbG/HwZhxoUvYemtRnUPz5Ni1bPLgrPRZ1UYDiySoGVqr\niOMALSkpFN2+dwJSriCjRE4ZvqE929wCGuwzzxdM80xZwFCj1UxFogzHGUutp7KSsUTiE76RheUg\n11D9sBg1DFVQlAYii+FB0RA48QSkpVbfl3+u2q5tH2LAfJlxeb1g+0K69M9c1HnQNT0MmNfgOs+n\nNc0Jui2wfR/3oH2ASiH/3PpzR8+o9BwZA1IwaRp4yOsCA+SPfrbGcsTIEw4uUgAAIABJREFUUZAW\nvc9cBJsfyKyZDhCMHOeIaZ4QL1Elnadl0nZgcT5gpybJRw9guWvKe23plWclTHErbbqepaJ5/Rwb\newmwZX/kWrEfB263Fe/Th3Yq7PedSH/3Hcd2wMXP2L1ORlQ7pNk9wf4jjC2OCBgz1wpUwJiKZisa\nvAY1ANAYGRZ7TZ1KQLMVtZ0RAbEdpm+4U2Av7XXVVthkueNHODjuc87fkZiRc/wcpUOKEZCuw1/O\nTrpIVs7LYgT5cTyMiM7SCfavPJytFrRSkVJCKQk5H0jpYHXWBNEp8C4Qijt89Y6kCa01+Mljvi64\nvO245Aur9P4Y7f6lNcwlwcHTjVaRYCTDLjURyzW+WivMUFM1pWt/i+HQlflRS5v8nj5gcfwWo/zp\n+GuUMNgeXrBhNJ6dArg+XEmq8wDgPgH9jsEMlQuMBjkwZ61p+XmFaUpHSMQAtdbQDNXttD+Ws2OA\nMgTJckwz1Pohn6V2CU0SHaH620j8GddM6pG0+T2cI6nLZ4e76PpCMn0yQN6SopxI0KJSS5Jt5HQ8\nHxpbqkbLXBUng4UGmyuMKR3JKMTRyDkjKVkl65oJR6IJrGqMdpP0oKNfY7Aie6MO08Ceuabpgmm6\nYlpmMu5BplRaheqa4PYgB1Rqg3kQtqnO0loMkqTWGfjWSMHLWcBThN4aDcAZy2v0HIZzZAxptRsD\ny6iJHO7Ejkp0HjBxdpgyqdRtz2X+rTV28gJjD+U8ZY03cOQ9ZNcPmQ0oIFahEYvTfWn0gPP3v4M3\nLbiMdxaD+o71X422Ygo5s9WGVH9OevrRlZgxrYNrDNWA58uM5XXB5fWC+SoiSguJP4kGhqUAr9SG\no5DOumgF5ERiQXpv/JykpThEj+g8oqevyXtMIWDy/XvenR3zUQretxX/mj5QW8N227F+3HEYCj6O\n7cB2355GPLVUJ9wkRn6s7aRi+f9a6ne2fIzTWi20BzhYFC0Y+nvvABDehpxzsas+OARGSlxw2kuv\nf+cuhO5wHYwTDQVJEhzck0RXgDJ+mVmASpNAcy56n7U2lJIH6fSEwrMzuj2iThmSFw4c1InDPtsg\nCqgyck44jhX7Ll93HMeK49iR84HWKt8PdW+RANmFy5MLluMFJV9hnEFcJtqnb1dcXirmnyCev3T+\npSS01rhPkhy/cx4h0BhDK9ERR2YSFZEMJjsjhQU52xk2rmw23TgKHzLCA4K1aj4bBjIIgjwMA0Yk\n6n9IbjVDzRnlE6QvtJ7518GAi9SlwH9aC2LSR04Zmd60Z+nDWEZwZKlwOJrW10YugEio6iFDR00y\nZ8cy9lHu3xijw2U0Sxxkmj+T+feaOz1fHRQx/Ixh9MLzvwXnVO3vyBnOdAZ7YudPQ4O4Bs8ZkjJV\nj4ycEkrqJRUy5jiTb3hfdWKpwPtDJsUtOYcxNPXsF9yJx2ueXzDPg7iP1AAl6xjfz7kTHH16frU/\nHwlaZECK9VX3T7XUAikjfr0KdFh4RwGXdyKrzHAps/wTB0nHGAyZ3uY3XSbMrwuW+/bccx+QFCV/\nOaujm/Us8L443fvpGTWFamttMBXdZkjA0PAdHErr5CCFekG/aq3n0dCMfDU+Px5AcSSYouUqcUaf\nYPzpeOw9oZYK5y3iErG8zLh+ueDljxdcv7zg5e2C18sFr/OMZYqYQoDjoPjIGWtKWI8De0rYjgP3\nbcexHmfbN6JejYaJRe8xh4AlRlwiqWouMWIOQUdo11qRSsF6HEBr2JaE9+uC2yUSR8UfZIeOjP2+\n90mkv7msIY5QV/ckNENaJmUtleQ57Bn6E9C5Ha1qC3Zrpf9ck1biTow0hltd+fzGGBCmSOeOHX2I\nwq84656M/fRyLomDIfvpczoPrQEt93JKOg7OwCtyPganvLNsdtXsXFBJaz0PBJqp04Yz9FplRg4F\nCVT2qEhpx7bdcb9/w7p+YN/v2Pc7Ujo48Wb9Fi5jiHLrPL9Q8FF7ohSniOVlweV1wXyZftri/Evn\nn9KB4OXBchYZZGa5V5JXaYV7MQdZWznUIyzID0YuY6AkPFHyExlMdZSaZYkjYplMZ0+Zn0Dolttr\nNFsRZ8qL/BmJV90JgBoT4TM4RxrPtA5UAxbt71orTGZYboj8BUKEOA7HpBSeZ+081RWFFUtdC5Tx\nU3fDAxSnNfKu9GWMQc0FjmdfC3GO4FD3tOCFIhX8fqVWpJyx50zcAl5TWn6jWancn7EW4BYbmU+/\nu4RN74s2MwlUEJJREjn+fJzXyllyPt5zZP8wc8B7f2q98UOGKj3eVL993gFcr19wfX0hhbwlavYv\nWbkQvsJEvdACw3YJWCIKKTkqy/MBinNo1cO3Xp6RtTYcSEXnNNubQ8AcAjyXW2pt2DPpd+8JyIVb\nTAWIGIM2DohD9Ijzs2x/9GxMnivv65q6ot/I6WimodkKiEJl69mtljAM9+LDoNqife/OU1Bw7vjp\niYEEobRHhHjWZ54DnJmeSK8cSLMN+cwljj8fhJL5GIg3cZ0p039ZsFxnXHis9TxFzDEiMiJWONDI\ntWJn9Kk2KmlJG9ZI0u17KaBcZuSlUHDfWufXGINcsmbRuRQchcS0KMCgz+oCCdBYb4FE9udYD+xx\nf+refaBSxnSRWfUTBb2NS1OOR32fOrx6tq7xgOnrTtyEAmCHsdDykf1hFt85K35QVs0HDdqxvvT3\nGzrMJNEU3RWYnmh+qtQ5QPslZ+zHhmNfkTNB8sex0qh0HpFdS+5cKwCtMtrgyd5amwZyp4MLASGQ\nZo4BeP821BoUnZWvECblaNF+liTOMpIQ+N4aaitIacN291jfJ9y/3nB7JVTqZ1yn32b+jqVcO9RA\n9QUX/CkrLNJSlzNFKoOIAfkHcZ6iECiqSE4zi147Ivaq1Jl0fGEpSkaSQUOnTMxa1jYPsAwZGQDG\neGqxQheneW4fnOuQ2joVPMFRU2eECpO8lgrnKrLJSlY5Nmq5KanQeojz4Gl5YQ5oLQIwGtkKtGY0\nOy7Q6kdtqnglBBGpjRkDGgJRG1ogkpoEb7T+z0NgFHxRkJFywZpS3+SMBMn8+bEGP9alpUQgXQDG\nmA7z71m5HNKZkY6Mws7fcH3UcqAVpwAnU/RYeCfEPnhEBHaEZQ1D5R4bHMzdIB3Plzy+/PFPvPzx\nivk6I/Drk3qXsLb7qGMfPN8bo0zMfC5cxkh7QkbiFilplepcjcZBsPMOFgTjj9nfwpCyNZbPBBmc\no1B2ed933I4D274TXD2Ug6o4EUdn45lLDY2Vme6DpLfW2ytKMzAcgIoUrRU1NIACA/Q6J9WOqwaj\nrlQgNBgTYF3nKlCpD6pZITXXjimzBC/3MgvkqnyXsf+6V2aevjq6wLXW0Ad8eR7pXWvFtlMWv6eE\n4L2eBYD2wJYSbuuG9b5h2w7s9w3395VsATsYCdgFoVkvE6ZlQpwCYoyYWVbbOQe0ymeHa868dxoo\n0DhyUvTAWmk/S9jc9vQaTNOM5bpgeaWSxnShgLHmorpqIh8tSCUFbgdls5x9kgCaAUovGTcJhlMh\nATK+dyGthSlowCilS0GSj/sOY9fvgg3qnKDPIKWNnPOJB/KZq5aKxIS5UjJyOpDzgZyT/gn0uTeN\n+QQdcaP9631A8BPNV5lmTPOCeVmo+2aOGlDlnBFWD786RgQsgo9IywtIUEmcPsH9WnIZyOZkex0j\nYzTJ8/btjumvD/LVPxF4eorwR5DFgnm5YLkumJYIGFIV0jo3w90UFRFJoZSRKwB90FL/8D5wO4an\nHtWBqNM4AqOaS6IDWRJyTpBZ42NdUqKiECeUFOGngBqLOvDoLIxzMJ/I/LvAxBCkeHI4IfY+0CYt\nJ2MkzAhHrRS1SgBQS+YAQKLsGXOZaZ2j73XAYQhRSVmz/HyIbG4igkjOvBHENtoeaRujkfPI13ju\n3sV50HNIKeNuduz26N0OTVjUDO0z4crxNLGJs1WRGpbeXtm8aU8QeU4RJ0kbtasIz8L5rjEel4i4\nUMkpzlEDAMkelFHNSEEDGXLH7Gr3ZM0bAF6+vOHydsV0mdT5i2Ny3iJMkYMQDgSd03utlZQOD1bd\n2p3FbgywJ63B150DN86InSOoU+v6zJ8QbY1SK/ZCyMt2HLjz13ocWPcD+37gYJh6FGMR5AGti+D8\n7nLOI/hIWgdR+A59T2smRRgvBeocXIuegeq1D90iqI9saQ40vAW1FVKw5wLfMwcxNg2S4VIyUPIr\nB778HtZaEvSqFc4QRO6c5ZbM566xlc0YKHHMGJIQPtYDeU/4oMWARSdCKjEUQMqZnP6NyHfbbcP9\n2w3bbVe569aaTqRbXmbMMjUyDGgW25iciRhWpQzn+khp+Xk5Oz2gLsB9kN39zTUtPBjn7YLldUGc\nAtmwRN334rTkHJTsOBs3SIy45YPEhXLKMAdNFJU9SdkzrZskP7Sp0J21oEyDZkE+ukMXhNcPPIDA\nBExp6RVSsRmCwmeuKsx7yex5kBaV4oKS7HoJtZeq6X0kASFhrThNiDN128wX4g/FRYSHGqnx8d6I\nTC4+tqs6fko6AjH6hzJ7zplkfI/ubwEglwP7uuL+jWxknMJ/DfanzUzwwzxfMF8umK4zXPRnJTle\nBEoUub0u7wqP1FLQUCGSwFL/0KCAn7xpDZbnzZdSSKc4HUyu2E6BRVcbtD2QcAElJ5Q8IRwRmTeu\ndZbnrxs1Es9cY/ToeLOp3r+1VNsqhgd+dCUqFxx89l0VzlDknI4d+74qlyKECfm4orVGjh/QA0EG\nq7LjJ7hwX3esHyv2bcOx78hJ1oIhJSaUCDRYK0nJfn6W/aOBpb7hkf+gYhKj0Aegaz0vEy6XGXMM\nmHxAkNkAhlp8Ui7YI0WyGtQMdVZBWpS0GDziHDFfZoIjL+x8uc3FCeTPcLm3lCWnnKkMYD/X5jlf\nJ0wcZPjJawAAAyUfieMP3itM75kYmnLG5hIFXwAk9RKkTAiNJRUUT2s5EjYlSCu1Ys8UZG/HgY99\nx23fCeo9Eo6UkVKiWd7M5peSTGMEoubPlbukVjnNC+JEbHYpuYz7gw883Z1kYoOanWbfbSDqVZkM\nSEbeZgObHXzoAbQSwYTtP5T9VHxn6AYhgSeDkjNK6cZf9qOLHp/Z/qdygrQuG4OSMrb7Btx3ErxJ\nmRT0shAtRQee7rnwOUn7gX09sL6v+Pj7K+63G/Zt1e6LaZ5xfXvD9fUVl9cL8am4ZKGsd75vCbgt\n24npMmG+ztp1IBC5Bn/poeT4m2tiUuPysmC+zPDRg2aJ9Dr7WH40VursDmEi21dzRD4Sjj3hcBbY\nwM+GE7lMNWpjwVoUoQeI7NxKYplaJiwe66Gls7HkJ4jMfJlRckGcI72G7MlPZv6cnShnAZDOmonG\nA8eIeVkIARqG3ImtFH/ovIOfPA1Eu3Cr7WXCzKUUHz3QqBy5vq9YP1aE+QM+eOWFCG/JB5bYFmSR\nNReOcMCuK459JfJhzsi1YNvucB+EUBM/7b/o/IW1GKcJ0zxpdooysKoBZdbXEpQEUXLGkTZmQ1YN\nJoCmDlsWV6AcoLPjS0lIacOx37EfK3I+ONMl4yFtX86RgI1z1IpYKrHFc44wxmCaI8NRFtZ9Avav\nlWrMxnRY05KscM5Z65r80xSMBGbvMwHy2A9q/2EoLKUNx7Gi1opjX1FLhgsO17crtRMttJGtNUh8\ngFqjHttjPbB+3LGuN6S0EgtVyE7DWhpDjFWJ9aVbg57pZ0SOWCI5FyRjYLkXlwIzrtOLrCTXMa2z\nmJYJ+YWClnKZ0aYGayZFAioa7uywNZJlsp9kruJEhA/hGd736nhDH6/L7TSe4fIlBgTn0Zh4NZaH\nnr2EbCSOX7X9G1jPgstA1moHRPQegdvvpMQkZaaeLQPZZthMnR1Ss6ThSbS/hL1fGxE7C0O8GxPI\n7seB/ThwsKLX+CWdhNpJwUbskW/zy3sPE6Zppra2OWorm6AaTQhqhnXrhaXNDG05l2JIad4AT3jM\nzGFB7+k2jtrcaqRARYIAKWsJqVUgY+JQSJ85N/zVitwa7MGvVdlJDojFs5eSbA1UsKy1imNL1Gmj\nTj3pnh27WcZ+dwlU0n7g9n7Dt7/+ha/f/oX7/Rv2fYW1FpfLG/7443/H29s/sX68YFomtoWUwQu5\nVb6cc/CgREH74F2X301HJ4BlKS88SXicrpMGErL3pX1ZgtXMCckpK2e43wcPEwyX47wS4NJ2YNsq\nStk5q06orcI6R7wC3gPGGQ1y8kHjibePDfu6a6eEZ16DcEGkxCiSytZZ7Vj41QClH13yszSNlO7L\nuYAYJ0zzBZeXBfPrgsB19DLuy9JJf2EKmJZIwdTLrHwRIuHNCNGjAdjXAx/LhxJ/rbNIW+L7ZG0S\na5D3xIG8oBojIiWEx4rMaIW52Y6O/yQA+q3CH2XWBDv4GIiQBgM4MMu5KUmtZxsBOXvGoRu6PoAZ\nWkYEQo8aCQm7XQ54Yy4Bwf7U96g3Zyx8I7labf1pXTtAHkTOsc8+lxaoJ69SKmdyHIGXhmK6hrox\nRh+Q9V24o2YinaQ9dX18hoaIMUrIiNTLMqtXxTni+nrB5csV1hjs20GBxkFZv4+inCiKeLIW3ShT\ngEVZmWl9UwDSW/18y49Ah/nIJ9i4l3lYBW0/iFCXKozrpBsx4t5ZTD7AAARnW9fJgYOTUtUwKTfF\nDvUHdvTgA+1F0IPX1rv+p7eEMkgm3cCdGJ9gfE/XCXEO3YhxWUHqy7InhJk/DaQ8YSAL1yFwSYD4\nGGwgBkJiXCiwnnygVrFG/dvg9XIcdEbvcYkR3jms3uNmN6z8PNB6mUazZPLSkAl0/slebwn4fehl\nFSnPUETZyYSC+hmcCXrVyCAsVtlk4mvak9bShdRamcPC0odoLVDdOBdGNPJJWa4T+HpPudgYgJxD\nzZFKBnLmP5UACkG2k4obl79gOpwu0zWd95qRpaNgX3fs910dY06EYK7rN/z11/+Hf//7f+D94984\njg3Oeby8/Kn3cRwbYlyUqGos8V2mZcbluiAsQ7lpJr0Bml4IJRLmoRtC1u3ZS5CHwGRmyVAFffC5\nKGchbQeOjTP51igTnnkcuOdyEROwdw5OUtq1rS1nKvst1wW1NYSZgkxKLAbNjNa01Y6cWS8PtNaF\njkaEQ9QGlfj55CWBnCRM1jpCvpcrLm8XXN8uCHOA4VKccFMalylgSCXQRxoudHnjEsrbguvbBde3\nK67zjOg9Sq34iKs+s9oo6ckpU+l0ofJPSRn39xXbbcN22/jnixLJBdWtXFbJuftC0QP50fVL5+88\nwaqqb84Qp3UWLTZdZD0cvnAtpLMSKXhwaK0TzkRAZbkQm5rqql4z6mM9Tq1ZlbOGlCkAkJtNxhAp\nyVMdptaogQVpOnuCm1JRpv9nokAlZDHrHibDNSYwNZZVdE6JfxJ4SNQtGfKxJxzHjuPYtIRBxEWv\nJD1yAhEvf77gyz/e4KzFdhwIc1BSYJwCpnnCx98L7h8r1vWjQz4cpQIN3k8UcTfgUfLy2as1Ylfn\nIzORh9WvmESXGYkQLkNOGQZAmOkZEAok0CzFgU5EbxQtGrkdRcV9wBFwYEKdGCIV7YgeLnrtrRbY\nXYhyzho4Q/3W1Rqa0MUZ07PX5fVCEJ1kvzGoU9XaPwcbcwi4TtSOFZzXjD2Xgj1n3I+DerCt1QwZ\nkGEldD8xErPfADhyRi5FSxiCKEjLl7MW63Hg7/sd/3Yf+FqJpNQqcRxGpTExmMT4f07mVAaaaAsl\n/ymkUnH8j1etFfXovd9thK0lAEiSlQK1ADn17LEyLyUudE7TkbDdNuwsVCMO9cQFyokNddbP6Far\ntdUJk7aSPXvFKeIIBxPTpJwJGEeZbSsVLXoAkzLNqaVuo46SyhKwe8Kxb9j3FcexYr1/w319x7q9\n43b7iuPYuFzpcb+/IIQJpSRY6zWgAQiJWZYXvLz8geV14WxygbEWIXjU2vX3W6moSdC4Hjg9e/8C\n908yEZBLCd3RErReeJ8ZZ+EOh5I4oLMdkhcio7TC7uuOWgv2/YZ9X5XAdnl50bWe2OE1NC11CmnZ\nMjm45ApjMpeT6tnReQ/rGJksgzb+k1dJglJ3Urmc0Wmh512SiDf1c+Y5SfDRY7rO5Oi/vODyduGu\nCUJ0L9OkyGQuBYcngvryssA6i3mZtQw8XwiBSUfG/dsd9283fPz9gdtXBxgKGswxPlcu9bCWi3Me\n2xoQ3n987n/p/L2fEMJCvYqRbmC6zNzeZpS01WpDMgz1V2HkU42fJspJEECO/3r9gpcvr1heL10h\ni9vS7GG1tlfLzLCLR4wz1nViyFzY7UWhaWFhttYQPBlIytBFnvJzKl+6nEyukMyU+vPZaFuvhrxw\n+1E6aKLW7esdH39/4P3f73j/6ytu79+wbTfkvGvN39rexiMZbQgBr8sCby0m72Faf7jWWsR5wvWP\nK4l53Fbs9xXbbce+bdqL2rkY9tQqMvajPnMJXCldBqVQlHlsB/bbhvsHRaOEDDS46LEwxyJOgUh8\nR8a27/jgWri3CVvO2AfYVKA7Zy0ak/OWlxkvf77g+uWK6RKJNLPE3m41RT5IEUsI1GNtWfiGDVXK\nBaOo+2fafad54npiYMTFoPJAFZEmNsYgsvOfQsQSJ1wjlZqOnPGx7yi1qgaCKNIJE7vWBuOaftb7\nTu1YO8Oq1llE7zB5Yv1LIBC9R+I9KdoSaaN9d6yH7gFRPzOWEZIn791aT3A3Ezjx6Dia1Ht77Tdn\nmbzY0HJV5y9QtWSOY/Fd6vXFMPEtEPFNIHeB30c+iHBdBPEjvgs/XxD/RzQu9Lnz+Xr2Elb/sSdA\nWjvZ8YcpKKmCSl5F69Lbx4btvmH9WLHd7jiOTcsurTWUSmU6sqsTlyqpI4eIcNtgz3ZuL6M97P2E\neb4Sc5zJ15frF7y8vbHuwAVhigAa0pbo83DgVLm+/swl3KYwBZ0JYQAcvB8UbWPtA8pA6RxbHlcs\nLZxkK3aUUhGOwCjnjtvtG+73bzwue6FuEU/BaZyjlhNak3tZsW0r77UKoHcIxHmCdbYjSwe11slk\nQ+IqPW/zUiIida3SIcVqojAoqWDLJKFsrYFnp02JwkyJGqtqzi8zpgvJgo9Df1Ih/ZNSqUvktm5M\nBKeg0QUKukMMjAhGxJnO87REajN9veH2N32F4OHvAfvdn4PtWji4jNjWH+t7/Mb5e836IxMXLm8E\nCxlDbP/93vtHBVaPMSClhHkndruw/F2gXuPL61XbSEIMypgX5roPDm0KMAZM9Jox7Qum6YLjYCdX\nMjLXjqSrgEoMlZGCLgAyGpzPtv0o3M+wUnGFN15DYqOGG3TDSi1w/VixfazY1127IgiRmJUw48OE\nGCdqp+Qe1ZQy9pRgQkBW3QTohsArqGd7mbC8zkjbC44tYd92HCuxitNOgZBEwmRIHTOjn7MCeU/E\nXZBaOX8GubfbXx/4+Hqj91I+hcV+IwOYtgOXLanRTnvCOu9w3iKngo9vN9y/3XFshxKKfAwk8mGd\nsum1vs8G2XnXM2ImEXr9kwJIHXozlIIoS39e3tfPnktRFIzmVB5EmqwetMLs/pQzknMIOhCEFNju\nLPCyr8eZtWwtqYiljK0BhyFOy7YyksLscceohmTwYlhTyti2XYOwtB1akxbjNF0mDdafzf4UtXNW\nnaZkViYYEm6pPbMXB11yPnErqCRCZQMpEZTjLOXbCp1V2WOqoCkBBvDgyMnBdwJr6wRgY6kdeZ4Q\n5sia5/5TiBe/CaRdFW0QbmIOQy1NiVq3r7SPt/uqgclx9ACFSLgB02S0W4mCtqxt1DHO8K6T3qh1\n1sN7ut9SEnLasTYSmdm3FR8ff8P9+z8xzRe8ffkn/vjHf+DtH28I0ePY6IyutzuObSd+xbOdHkOJ\nxFqj5NngPbx1SHNWWWMJLrb7CsAw1D2pXkNOGWa3undapbLGtn1gXd+R84Rtu+HYNy1PZEYb93XD\n+u2Oj6/veP/2b6z3DxUMku6zWGdIICBIkV3JDh3rwShRRs7Pd/l0hn/tiRk6Qum8RYwBy9sFr/94\nxeufL7j+8UJqj/OEKQZGxw1KI5LuneF6czfYw4Z3DiZyLlhvK+7fbgPJj4KNHDIjp9QWKeeMpOv7\nWhdu5SWF0Yxas3IqWitIace+3X54r78h/BG5zXAriw+ela4WdQQC88yXmaImNvjiBHXYifRHR0/C\nKZeo0GKrDeXIKAlKLun1eSETUR1yzhdUJfSR8ELJCZm7AAQp8N4rpGZFHMY+T3oCoBleb5UDTDbI\nJiPtFua2EXw51KvFaFHZgqDraZ4ZeiK9hBoXwND/T9MFIUYYhtP3dcN93ZBKwbEnbCs59bwnFsBJ\nSDkzWcwhzDQsJC4RxzIh3CjzKInKJnZYYxnJ+cx17Ic+H5E2rrVi+1jx8fWGj3+/0yARNrzMcAOM\nwf19ZaOw47pedbb4vuyw3qHmgvv7ivv7HftKmQHdS4BvXo2FZn3OwbquSy8qg601lEaON9eCJRBL\nWp1xKcgMC1pD8sTPXiFSptJqow4ERqOURNQAgNb1yBm3fcccPC5xgncOqRR8bBve7ys+bnfcPlZs\n942EoGxnxUN4LplKXcd2YPt40OFvQB+oNczbKFT7S0cnngkc6bzF1BpBqHOffvbMpRKqqlZGma+x\nXV1N9MxrIU0L14jTIOJbAAbCJjngMhXNciQYIbnq1HVBROddWueC59Zi7iTZA0rOg/Pn3uxEryHc\npDhRychF6tAxzyd/QxmKarnpIKKfT4WGp+wJ9293fPz1gY+/3nH/uCMnQlxIS90jLJFJzIySZj4n\nEHlbIpLVkuHDRH3hIWKaLkoc1C4qRjohAYkxautqKdjud3zYbxRITJQ5b7cV+7oiHbvammeuHiR2\nKWlneahOBELzyIHQj2OjIEfKMtKa6QKLHXF5RqbyHceOlOhLUJGdy5bS+qjk1YPs37FS2WTb7xAC\nujEWOR9MdPbIOcNlp+fHOsuzGag3f9/Xp5/9yB2RZzWuzbQQ8vrxRociAAAgAElEQVT2H1/w9s83\nLC8z9+4HmGCRa0HaMpO9E9ZVENpd7RBMFzpLOwWR+7qTTWfVWB97p4BlHoTMa5D1lqSqFUEUyUcS\n9E/co1YrUvpx8PNL51+Z8Zj5AY6sYWsNGgv9CBlIWsIyC41Izykg5ChS8pq4lgHpRW1CVOkPv5Re\nXzQGRJiaqF9cIGhpH6klo9T+GWlTTFguvawgdUz/mal+paFZEjTREbOU7ijBh2Ro6WA6Fr+Qdj3q\nP+cBHSzPmjMbOjArNERFP3Iq2G47bh8rwkQw1nYnpiv1CffMvtXaW6GM6SjJFOgQWYIsRzlMUiB8\nTujmWA812jV4FEMbdbtRRL6vB7dwAkBvwxKUZQ+bio3I8y8pq/PfeAMLU9g6Cw93AmkKOzZqlxMO\nRaL+1j1hWyKmGBGDxzQFvEwzYuB2p1qwp6xKaUV66p+8Rq2FY0+dOGXo35K1cPHAfve4xYAQHJMN\nOaovBfuesO30zI6NhEMoamciIT+bWhvqUDLaRQaWe7pVEIghT6MtPwMLfiD+ACAFwcC96hy8P3t5\nlVEVsRWZkGaV+FiEz1EqQgm91WngCYguhihu5pRhvdX6MMBoQCnDpMrKSBDzDiDiWoQACbQ7Ttts\ntSHkSIQ07xAnCgBE/dFYA/t00YMuZeznAmMP5Z4YY3BsB0P7Gzs9Eh6jtuioZ17mIQB09jSD4+zM\nAEj5UH2SabpgWd6YLGrhAtkt5RKxs1expMadPoGQMXkfYuQndrYbjLEI6cmRvtLO21g1ciDkAmRu\ncspYP1a8//sdf//nV9y+3pAPQgql9U7khNN+IG0H1o8N63rDvm8aBBABcCPxHJ4UiEqS1trmFgPJ\nycekynfex77Wzp26ISTxyrwG6aBW8aefex24C9Z3RIr5YiQy1vUE1vcN68emQYIkvvq151NwLvoX\nQhTMiaYvHhsFj9IpIF+SgNVCJXbRizjWXYmWgpqLaqr3Qc8VQCTAH12/UfgrPGFow7EfSHvu4hSO\nWlGsNYB3WmcEoAFC5dnI43Q6Ecsxxiisoe1iR0IWoZfSe1r5RQk6kv5gnoJVG5NcODpu/OB8CJgW\n6oElyNgpC/Mz16m/eDB43VAxo5gdvQ6BkT7tiYmLhoxh4f74UkkVrRsILqNs9HBlLnPauF92PTib\n5hpR7kEHGeguo+qcAwJHg851KM904aLfXelgg+YdApMvqZODWqwEVh5hMVmXPrKUI9xEzGA0gstL\nrjh2gcC5BdQ7bY+jDJJr5EyeyoY6DoojHoFkCWlKpA62e+xzQvRULiq1IZUyOPx2co6/u6RFjFqO\nsmY2InAiNeCD659WWuE4aNUglvvAa2vUzsRBYZxlzrrtPeXcVSHXaYKjaDWMjHohpI4BthhCYxRq\n12EqT967OP6x1m+tUeY/+PnIvAn5KR3xauQM9iCiNcBsRrtktE3LdBnW1hrbF08IgpGBYE5iXHpf\nQ7oZ+mRbI8XR1sj5z12CXCa+NfuJZz9wFHIqMCbpiF8h92nPuXeI06TdMKPapGc1UGuNBjwEQ+9K\nygrs/C+XN1yvX7AsL0Q045q2tMtJAKBte6a3/blgeQ49zq14hboMPjPQi0S9qhKVN39ogNpA5Y73\nvz/w9V9f8fV/fsXXf33F+r4q0uknj/kyU2nYGs1Y13dqUT6OVYfhkINi4uZQrnIhYLkuuLxd8Lq+\noaFhXi8AhoE5PEaZAlI6TyEEbbWUzDqXz9X86fk7/VLnLyW+XLDdN9RScfv7A4BR1cqSeum3sp3U\ntlFOWB1PWaXnSftrX2noFvF8HHEt5oi0J7avnXR+bAchCeuq5QzpzBpHzIsMOYBz2Xu4fivvmxIJ\n0xCxjGo8crBgoPWGNmTpUjOzbMCb4X7c1hnnNYsEahfLEIOZjoMZnb1Pk+7D6GQwYwxMkHvrrE6t\nk/KGIEMwakh/wvnTuncCYu6wqjgsUdkSqNQH6lWVISjGGNQpkvHnaFGkkGvtLNVaKztJyq6lZ5ta\n6MqpnUUPeDlPCJMAwBhwrbN3JMgmeLbtR7KMkguR1iDCIhHTJVPNe0gmG5NaFPoMnPFxkFhSAVpC\ntlA1PzKIhpynNajWotRCCn2RB3noND3DiAt1D4iBMjBoVVooM6TnlebAdyf+2bqvIha1aXCqIitc\nc4MxMKUip67yJXCxqJy10gNSQnqoVre8XDDxa+RcYEC/u687c0oIRi+lwpaK6lon1DXpsZdn5TRA\nAMAOQTJgr/Knz14ytwNc0tKhIRxMSE87OIsRdUch+QHQ8+G4DbiWXr+XMcal9a6B1hpq6qqPtTSI\nVnzTz8EtgeidIvIXQRWlM0bPpCBfnyj5kOJg79eXejKx1anNVfQsSFOgT6XTfWZ6+UScNDmICxP/\nDOuTZDgXcLm84uX1D8zXi+pW2MATOX2fSClBgCBQcn+tNuxcMmpt2CcDf+KZS/Z6YhJjQ29LraVi\nva34+OsD68d2QsTELgr5TgLQdFDiQnwIGVIjZajeOpyPjHJkwBrEmQy7cKXCFLF9rHSeWpeBlgFe\ngrSKDkfJBYnLhJ91/BRg2K6bz3wmY8i5CpdAZ1tIFp/J/lSRnJYEUf2iJGZWFUcb28V923FsG7cW\neohqZaxR115bZplb0yftEhpoB0Znf+7sd39yp79x/plrJnfcbzfcvt2wvC6kyHQZhs/UMkyd4yV0\nneHcSu+NljnWov+v0CVrnlNPMC2ebHhR1BMCjkDs8u+Sncvry9CdLv8a1Bl9Bv4UeK2Uolm/6JL7\n6BBiJEPD8qddBKhpBkcZkwMMkRsrt/8lbsER1rRkPft91w2iugciA8rZRM2eDbAcas6dRHhFMgI/\nbDTeCDl99jD0LC5MZOidszooZ8xMyWh3/QNrHQccFa1ldnKG+BoiDOIMnA3qaFx1Wr4Io8yptMhx\nQCZBoYhLoVBkXYc2HMPZapwja4A/7wAFupYWVaA7IHV0st7N6P4T/kfJvcNEuC7UfjZhuk6Y54jg\nSIJYOjG0zi4Gwjk4yfqlJi4BQJXvgYIiQD+DqA/GZdIsjFppn3MAhFxxi1uRZ0zP0dhBrpuRpmYM\nTO3BUoOc94bWOmxZhmC1CSrBSF5rDbkkYOP14P3EHDsypI1FZh4CMdHXsMboiOyuNOh0rzx7Wc60\nC6v4tdbgVpH3bcxMJ3hbPqMmQWJ4gVPQpbM5tOPDssopkQLn5YqXP16JUD1FHvTlFFECwAPFWMCr\ndaU96boyR9JSzbiPNWB44jo2KnHkVHBsVDq0nkmvR8L9g+rXVTJ93tc2GbUxksg0EGpG7eBdKZGe\nG3dmWKfoGvE2yO6HKWB5XRQqd852wqyWkKUd2GvAKjySzOXCz7T5AUCrBYZFqiSjllkTObENM0bP\nqUyrbby/RZmwZulIGwZPtYZsDOyRTnvs2Hbs+53REIucZ0W+48QqqLVxIsjDhKo4ffaNxhL/Cjxc\nyRTUJhLVP977v6n506CAfbvhfnvH7dsLLi8LpilqdkErxr6OEQBhSUowUm2fbQ9IZCKCClBGbWVY\nmaImALVy21YnEI0MTGP5vZsgAtCygtQtRR5RjdknoF8iThTN+qWn1Fpy/BOLE4lTGssevQ3KKgwq\nutHWUUacbNLNLRlm2hM2Z2FZGhYM94hTDRNBxT55FbUQ4yiXOC4qQbDmNyt9+fD8cJtae6AV54gw\nU6tdXCLV7FcS9xFERGByGXxjuKUNggi4jvrI55UoXhwgABpqsrDIzjREv0MWI5mFZGgaRHLrkUDB\nxhjUUPQ9nr2MlQCsIvs8iFhRRC290yJ0JZ9PGLkAtOVMJI+lFcjzEBgBvEX/gBCboYSDfpZqJYY9\nfIfzNegdn721CDOVvKi0QC26Ljj9XL+7BB3r5YQeCMv3xvPwIzKh8w4Nlo2n4ezc6JpVI2UM6LpK\nW61Nkmn14L6Owc9pr0s5wPbzLpPPnPk0yReAltFI2IcgXMOwqjEGAYEV3GgAjxA3haTapYdZspmz\n0daAtGeEOWKp1JLcGim5zS8kAvPyxwvmV54kGcIpcDEGFNw00aCvSPtBiGwRCeehDMfPh57bc05w\nVFKUdjsRXEqs6aHQNt+ftQZFni23+cl50XknlsiPHOnxPvDauSEZrOw1eQ6S8PgYKNng4FpsuagK\nhil0pnzKWG+rBiOfCfo1sGVkwhjwvRD3B5UQXNAcNvqZ1gNU2Zq1tUGHQmZZVMiQHgmOhZC47ytK\nPgBj4f0d6ViQjgPTvCh/Qka403v0rF5OnrUO1PRGXL2U0k/r/cBvnD9QUVvBkXZs2w3b7Y71fcN0\nWQkCnsPJGTeOAqTOKC1JgPT0sjGpgypf7QcbClXwJSd7fCACb//A4PTv91+naBuD4fj1Hf/s6i1I\n3dDL90TRTQ4CIMiHRW3k3KurjHRQFFkykTvEmYlMZ3KCdoiWNpNvCmui8+cXJ9GqO0W3Uusxrg8I\nks8k6MQzl6yjNXS4puuMy+sFMMC2TIjvK9a4Yl9JDEVg9vFZqWOwRqNyjdqVGMaa8M4C8B0ujcNM\n8UitM60McqW87ihAMUWRAymnAIADIQuWpYT9Z5yAEYU3Kks44VUwp7HwCFUEgomN4VIPq9SdGe8E\nzwLQbDKzgyXH3qG50bGJ8aa/8LoykU6GWonSmQR8VIPmchcLQ4l0qGTev711N2SLDKmjKb7En5u+\nTl0Q/IytJ8KWc70nHADaJl0CHAybLuXrnEOB7FHeL/y8TAOMYdKt4VKa67M0dMTskCV3NPC/duAJ\nfXMatOdSYBniJl0S7ofXAJWInonrveKotHTC6yg2SlQjjXRAvSy4frliebvQJEl+D9KxF04JBTR1\nSBSEdKv7xEj5gYTWasn0WT/B9geIm7CvRGCDRRctWnclXeYjAwz1e+Y2RBYGcqzoJ4EDjRgnsl6I\nExoavI/ckdWJvqPzxfD0nKegVl4P6GUVzyiM8D5qIZKrsxYxzJ/aAw3dN32ncyFLzLa+KarJehWM\nYOeDNBuok+EA0JNfut8MoLGD3k7O33A7uPI6TA+cRTMCECSBba4GfFWR0EduyY+u3071IyNLxL/9\n2LDdV6zvEdZbxBT7xC85+LxZRQWt8BAPEa94HAQzPpfxEVVSVOkOd3D8FoNinem/rFFupQWh52ZR\nXEGJ7EDt8zDQ4zCLMcgpler2UmsEwHV+o0p01hk4EPQtRvLYD4CH15QimTsHSJy5Uv23S5+Khn4p\nVTelGAbjqH+9j8GU++7rBWNgWx+I8cx1CqwMZyfXqbNRoxy6vQ/5aTxCVgzfwMKldawouWeTspaC\n2HSHOQSTktkx34N+t2lmpXwTDjwFEh3VJX0QNcDnMwAx7jBg8g3Bl3lAKDQjk4DLAA69BivBD9jx\nVibwhImIgtnJ2WC2bqb2uSESGIby0BkIA5ol3SSqZ85Zlg8E+0snhHBKjif73TxrQyipsPXgtxWe\nd+EsPY4G4n4waiEEUx21LA6UWwOpY4KJstUqZ8YYA1+JLxN4vKuMB9bAnb+EYCV7c7waKMCy2TJi\n1mBs/VT2Z51RODttnuv+fd99976NZZVrlyAeSwDCEZKOCWst4Kl9ztrOkI/cBTWqhUq5wDrHQSbd\npZZMB1jZDa8v+hDVk87A086fky0h9o6OLe+JoHdu4xbyMoxRTQ4pMVklslKb4x7Z8YeIGCeIdC6d\nHqufW5IByep7t1AvnwkyJXvHMTLgguXSFJ+DSIFGfRL1AGjveN8DbSHOWmt7YsNIQGU0h9AS7orL\n+aTkKlNoaW25FMZaNNTySAJG+35DKZnXLTMKSB0NpXgukRi05nrSZD1a68gv7TnmAqAqcv8ztPu3\nff693kmtE/u2U2uDs8hz1nYax60Zxni01gkiAh/pKM/WqN4Ho8QGJfNwFCObvqGhGotWXbeHtaF5\nqhfBQx9OJyeNiALXxqxRst9ntP0fHSB/kz5frkiG52eHpn3OsN0IB55XIBskHVTTFGg6baJz3uBc\nJzhWx+IvRbogcieQNdLQDrO01HhYvi8JuEQGs6dtQDNVg4JnLmnr5O1OLNQYMMeIZYoInlqv9jUO\n7S3MdrUZELlPZ/WwFAlyBvQEAqYMG7Q1grgqk6TGy1pLraEDvCrwnuwdADDecLuQwxQDLqyQ9+zl\ngydmvuvDnIwxOPajt7XpITScCRsYR2WoCulCaUAhxEkCtHRkuJC0Dij7qbA2uBkMnsCcApVKq6qQ\n2yK3soZIIiwwrEbnHKbgEX2g3vSc8Wyro3SHyHntpN6KWg0cB5TC6wHo3mXipejOi3Jnaw3pgJKz\nbDU8d6ChRq97DbUB1tBAmYmCB8lwJJOWtR/bbkcJV+3MYei6hExB/Cc6/YRBLsGV8Grk9aWF6+Dn\nl3j4lsoWC/lSs3EzBFKNg1tW3vwRisnnQdo8wTaA7CuhhpJ5yxhn+jXJmiUYIPIYOY7n9n7l9lJK\nKnsbaU4ku77dNyXryoA1sT1xCjq2VgJgsoFgIqtX0SNru+QyQfdOg6BpIWSAuAwH/BaQdqr1j0Jd\nY0eJoGGVkbdxXT9T7hNkckz0DD9G60TyeoD5C89qSQTxk2aB6BkcyPkYsnhKSJprqCLAs9Ogt5QO\n1Jp1UqBzATHsyDGRMJzp5asGC1PotYpwnVg4qjARUNRcM+vg/Oj6tbY/EzIkCi2FtKq3G/cR5qKk\nOtI+BwuYMDO+FhX9kdG05Eh6HVP03x8h4gYW2TFdKlQj6eYgo8tk6l47GSiGbbQVjyJEjdqf3Qin\nckTfHMIopb93qMo6C2c61CukPdSGzK9D7ZN9XKUYdtLNlzG29JAFXusqeV0gJWbq2zUMd1K9zZ7q\nbepkB4b0s+egFGLtj10czjkskYZaeGauhylwH/vBvf/fIywnstqwlrKeUtOV8aAUyDU14JVhf8P3\n2+Vi+8wGxVWB3gLkqV1qChS0fMb5u8jM/ECKXcqj2MO5pjv6U96nOlxFdC7YeVjPGXDqMxOkFNWh\nRh6FO3Q4SHuXMdQ65aMMQIIGBHGi0cmOEThnjCofGkNDSGRa4G/vPTh2QEABZbEFBTZRim8Yfh6N\nrHCAhGAp7XYG1N1hbFFjLTVezW4roXXNcA3ZGM3qpKtoHOyj7bbaAtk7HSQJMAbI3sEnBx8+d+5J\nUKyLrdCj7Xom+cg4DLVmpa1PpmwQclgvl1Ag14m7lNE5tmvQREH1EaSUAwomJPCnVlEZDjXMw8jc\nT577TAeyCeRkiys82+JJZc+UyVaHcTy4tD1mlZEWuzV200jmPy2TBpBpp7HqPn50LpjpSLFchvfG\nfJ3x+nals+Y9c6Gqtj0LT0RaWsdOHrUvtTEBT4Tgnuc58acBP3RGtnqQovwRtjmVf67H1Z2QS7D+\nAZG6t9YiZw5mS0Y6NuS8g+Tw+UybBiPKtVUm2LZT+axyoiyoMQmAFZ1EON63kPZ/dP3W+YvKHwn+\nkGLSsR+9dUg2HDt10RtHzjp5SIZMCFNalrey0dcll4fpLEdJJOTRWgENa+rkouoqbOt8gPODY0gM\nZMNk8MRjFvm7a+QJfFfzr01HTEoG2EIbDKFXkZiK3jMsgzZ08AYPxKFDa7knmpj8iR1I0RZIiuIa\naOSk1ku9zMS2PdPmZ0NBwDCG9cklqJk24phdW2MQnIcbI+rhnJRM5LicshJapNyhWg+ARs56+NnR\njAQ32dw5Zfjk+qCZarTe9vjaYhC0BdM7hOAweY+JBwA9e/noVUa4DTVJHz2yPpPuiEad/ZKytkGJ\nM6Nn4hUdGZUTaRmkXHOGG+W51dIAQ+Na3Z5IJjn4AVEjxyPOXlnesm/R+Si/uxxPJux1zT4YpgHK\nM5Ayk8wQcMHpMKY4E9m01QbLjkvgfYXCbdbnKM+bHFfvnJHOA+cscuplAFRBRjrJstSiztOASkml\nBHwC9aV14m4LyURFmlazf0bxSs76eWR/iP36/zl70y25jSRL+PoKICIySUrq6pk53/u/15zp7lKJ\nFJlbRADw7fthi3tQCyML57BVTVHMzADgZnbtLtI4GGN5IJGpn+65RIsba9TwyTnJYuBhK1G2xsqB\nRmiicuEGQCY+UYPoOyB8Cw9X3mdvvF8pgpyIqVRwShlUUq3pz6Nfi/lf08KZ9cusZHBjOA/AOeUh\niJuhtZ7cA1t3VXTB4fF4wBwDlinSJ15p+Li+rQPbX1a/fzwPSiab9LTt6ih4970fFWTctMsx1f9p\nANMAS6uv1hycoFAusPlOVY8cMd+h94rQklrHAi3TegZgQdH03yGew8q2cfiQTPekRpPArKp/lxb/\nv2h+fgD7dyKB/GVCOqh8yGXPO5rY37DKu12Z+AWyBgj6Qx6n0XGfV7UQyEglX08aAZstmuuQmdol\nSkdoQQeDrBLQH6z3yj4g32HrO//vCVlCOHPe6r/TA5x/ilIK0pCwRVOM7Hj5YC0GYOc+v2cYa9Vh\nLOv3z6YVJTNERNN+mMIAf2MY73mqblX3pH+1//n+knhKNavJ36VntaYZ9tln3vdafUg7pF/0RVIy\npndA7rtK8M5YdpVgpjUA9RqopcIzVKgIEQQl6KEyzjptABynUMov/45D0HuvMcEGQAkOuQYOYCrI\npqtHWh78KraO1pBpiWWvBQuY3hhggMm1UeIpuObblYZ+fjCKKqTNwYeEnEiWlXNB9kVjhKkQAJmf\nw/KOxpeCh/j/GT/f1guWgQE84NiAx8oKZGDdW9/XNnLfsssQfo5L3fdd8tmVs2L7Ok9Y5A3slJcN\ninz2tRejViqKabBN9v3sUfBO0p8wyOMSabr3fW0pn0PJBZlh31rHBstqNK0+p8bBG4Nqefq3vXgZ\nA20IVPEkDeSVCv+V5XWtVh0qgAFRA5uhNTr/OonWaX7Ave/9tlJ0eExR4XsfHEpy2pBSY9a9TqZl\nwvKwYD7NmI+zTv5jMBi5rgbEeMBheYT3EcZYxGlhFDmr9XNwDh8OByzTJI8gqzk8RdrKWSfHHbp5\nWS2kt89bImO6dxZ/AFpYtWmq4OlapKeNYXzQeSMfPoDaaFLPJatHDoXONUXSIX8HX6LZF6lfaxHi\nNUCkSJY7s2KHkGF6OajnIP4Yhnj7HihVUf+d4i+71NbGh4dgG/pxB+Ke/A4zrhMfgtK9qmlP7kxJ\nuXli8iDQ8bauWNcL6gB7GOP0gREJF7GdB3MDIQQNpEIzdN3A/Ttv+bNGOj+RX7Xb/74xxFRF8y6H\nQanEVxByTu77M9E8C+dBPmv5HKzdYZzlg6XyTrPwny/IhbKf7ZVMQNQOMnpU72ANH4ilHyayhrn3\nEMg81eQhmCez4Y+VQ0t33mNR7YVKDl037Fxba1T4WfXjWFMrhiWeLVTrwIGQeyaSSpfp566h39vK\nTFdtTqQIeacBQPf+7ADgGDp3fOAJdCoKFlMsNaJtcESTeGPWoQNAc1zQfN+Nil0xAP2cROpZhsIv\nk55AqAY0AassdM/wa1KzE1iDXKqqGho9trqX78SVv7/8HFCvDbYIX4Om3QIqjM46FEuohjEGVom5\nfd8sHv9iRiSueeOzUninHueo6zpj0D0wakcdLPt3OG9Ri4XJY4M7KCNa601Z6yuV91w+eJVLShNq\nYCh9kN+rcfXTCa5Q5Cp5svEWS3Hrb58/maSLIIKpN8mOEZ31suL6elXv91YqGf8wqiBrAENViMnQ\nTVec8hyOevMfXZSgSGvJqVZuUD18LEyu83C56jsh0cnzQrv+GINKoPNO6gDjLAw7sk7Tglo/YCoH\nAEDwE5/r1Nju646UM5y1eAyBVqWt6QrTOtt/rlJumkv5fBurM2gHv2vi670Xkf56zWitwWSgmKqI\n1MgpqHpGGeRM6yCynS9aQ9GI3S+FWTgZ1jrImGgMnYUxUnLjshxxPD3gcDoxkuaIwGpYassTfx8z\nod/veP1V8/tDb3/6YEfTCKgMRzpX+cFb6yS/nRPGRmvUkXwhH4K8PBLXKdnX63phjWLjQ89rlxhC\nuHmYpXCOSgLRkgvZDDJ53gl9AtRFG2dh6HRhxucfLXIlQU5/HtXlMxOzVj0UBSIlGaBkLzeU0qcj\nKQha2JjMUYfiXWtFLtTd7mtC3BJSZFvUKrHIEr5Ck8R7Yo1lh5qSNGUbtnXHfsxq3lQ4PbFIo8He\n3Bi7T141GMa0R4ays/0gE9lOnKOyvEXGJbIrIruJeqJAkgqpwzXqCU4KFIJRnaVf1hglHN5zpVwQ\nfKX/pvUVlSIYGP3Wq663BO2i56dLBcMUMB9nHB4ozVLIQ0r2a0UnqciJlnUSLXfS9ENBuXLKMFdW\neCg/AKixYBveM2tp908oxn3P/jRT0Ssp98MLfF+ZbV8rxxoXgyJnQslwyen3dstaZ1taQHsQ6yy8\n8WoOJe55jYvAaBkbgkT1dre+NtwT+hpyf5sSiN9b+PX74iyBFBJcsqjOoMECzgLIqLWvRGqmHYTh\ntWdOCfWNza5YxjepxwXlVdxwWkpRxGC7HjEtNPFu1w3XV0oH3XcKcDG7QZyoKJoJ+hlLU0XWsQU1\nd1RJ4OF7LsnTIHSywke2+I4R04HWlQAhMM5ZzMeZgtqYfEwOoJxD7y22y4bIWRai/HBOeBRUTMWk\nBmhI64638xXn04bTPHMap1NCeWD3P2OAYqFD5fdX4zV1Stu7Uv3k+6qloFmvgyo1uVRHpCGXFWtr\nDcXRc1nSxKqGCcvygGk6QJj3QDf7kf9tjKCkPCj5gHk64Hj8gIePn/D40wccHg+UdNvI88WerSIc\nUpsJMZB6bf6kufjj9bfFP+cdwlDshh68S+UD23mvBxk9FLJz6JgMsTA7uacwjExwMnVn+75i31ak\nTCzJlFZQN0Q/hOGHmHgHWTsfedE11pDtKXXH5jmWdiDV3HvpTsk71GJY1vLnf7YbzGQkTx7goq9u\nDXDBYz4a2NBh8dYa1iuQ2YxBZD2kaavDmkUO4D5py/4MMKr53XnH6LxTb4DOfq7vOgil+JO5R9Jc\ngevDTsUXDblSDn1me89amzYCinBk9reXosFyNh88zEKfbybYZg8AACAASURBVJwCJnaiU9c0SyFO\nxnUfeJk0XfCwe6ZGxznY2iV1467ScdETwlt+T673lpC9R/aVYfPSm6hx0hxg51J6cyYuZdMy0R70\nMGE+LXj46YQPxyNZ+3IDkQqFEK37rmuW/bozgpZ0ahbTFS0aeWgOxeRoDp346IVgR1a696Yaxjki\npwS7uj8Qs0a2/S2BlqYrKfbblaHWYYV3A3XemOF0KJ2etRXreaPiz41TiBRP7Lwb5I/yTbXecJZh\nXzEiNu8I9FIWursdFkajKPlanQkxfBJ7BjhWO8xRSbitNkptPDNjnsm/JdN9DYEkpcePR/jgqQhL\nPkUl0xhk6Kqhlsa6egPT2o0Z1kie1s/ojittO1xwGp4kDYyQHUsqahpG/h8T5lN/d8mZsNsyy0dj\njWXJ7cwTcGXpMrPSU8Z+2fH29IYvy1c0A3z8cIIzFjujkDIcOS/+KACEI2W6BFcKY+IAoX+r+Lei\n6xSxFBeZnXAcPJMiAap7ga2pjQOmaVbEwRhaFzgf+HtvzJ3phErZs4UpYj4tFBF8WDAdyazLWIvC\nzrCo5La4rTs/fX3dq1wI0zkWxv75s/+D4p+IlOFGRzZDKVLL1E0WJjas0BeDp9yJWfZz1NxuQQLa\ndQcSTYsikUh5JZgmURiDhCtQfvPMKU7dJWpM/hPYUSYIQRq+3zm+p/gDzCsA0JqBaUZtTOXfoUEP\nQoLse4KeylBEAjUFTG3CNNMNnQ8zzs9nXN8uHNizksUpipJA6DO3/FmwX7+hm+p89w5vlQlCNkG0\n7roz1p2kwTvW3nQvS+vhHOcVl8uKsoh5SaMY5y0NUFyHX+k+VEUOSCtfyHzjMHFjNtNunmHWwHaW\n1hl1wsvMNBb5pxbYgeCHZrXxkabA8+QPALkUPUTu+rn5+5cXtVaBaLM2UyLP0xVO4eamNljTlLE+\nLfRCHx4WLMuMwzxhYQ0+GrDnjGtKWPdA+d+XFXnLyIKklV7c1VOh0t8vpC99BlOgbt8CPgZwx0Sf\nmb+f7R9ihI9JrbWB3jj9sfDzl8kFZjNcnPmzyfnmzxZ+PwUpUdeyKt7lFKCTE7s0+qDqgcN+0Lz4\n1qrqpVv7/nvqjbLYfb/L2pklmSohcyPSaeCN1+efhh1ZeXRUptU6NP9NOSDkc2D0vJQpjQptl34C\n0LWXZBO0WmnlV8uN14FxTB6lH7sXRozn3n33nlDT7tUhFrrjLtkHWitYbxE5R8EFBxdpEKylIacd\n19crzi9nXN+u5G+CUUEm0z7t+6/nC16/vZITZWtIKeN6XTEfZx0kfXQo2em9Fs8X1o5DjECEa7Cn\nlY1u3uHtMhTKkVjY/8AgS9TUPfrs854R5oDDw4K0f0BhBQZg9HOMSyQVxaBSEPSotUrIH7tzSjCV\n43VnAiGQlFgp5l2dNyMraQmGsoXJpX9x6P9t8U9ph/eSDdwzpaWQBTbxEP91edBpOrO6LxKGaimV\noKy3q+6rWmPDnBJ0TwLfYKuBdYFNIRbEOLM7UtBui8g83QhohBdpUhxIIeaPhL0fXXrTDW4egO8b\nCJla6N91p7nMkHZsEf7glUAT54jlNOPweMDb0xvevr3h/HzG5fWM9UL51uODJ85YFE/ambwjwQpg\n+0dpSFq7OUjoobh/7aHmGiDLVZlYrpdVO0061LKGXYxZBdZZWG46ciqEHJw3lJzhvKeHODikOSIv\nEyQcxFWWM1UubMYoapATQcLq8jeqAwAl14nBkmWyXgNNvqncP/kbA1JptKYcB7QxNU0S6CRhUBQc\nTOLkVUXYA1LKCOz5vW073hyR1yrf4z1nbClhZe/08+sF55cLBaKsG7YLcwkSE2dlmrKG0a/hOUyZ\nkBFPL/67gqz4CtGjzAFhDZ2QOD7zjHY0Q34G9Hkxoc1IcFXRdYWsD1ptXbq608Qrk2/meG5iLDOZ\nynmEMGmRuw3noqZGCYnfFc3+7vAa6M7GB4ByKAT6372H2fv06ZjI6HLnIEijKOZdzlKyKJn29MbB\nBU9FkguGhHWRJNmQhfZhUm95IjJyQwHORBHTGEiOiVcJpayjxIq582Dub37GJtMYMqUyg7IjTd2W\nXHg6kkvRWtNm/+3rK56/POPl6yuur1dq9ljiKe9sKRnregEa1Lista5mOH16IPc+7xDmyL9f+5ls\nDBFbAFiWgJD/wcZSOsonuPveu4BWy3DGd1WBFFnrWebMMnfriJMTJhpidABlYjc1pdTwiYJEaibQ\nw5REOtwJwsxfqPR3EBeGnkNZb/rgkXO4ef7HVEIZHP/s+tuTYd9XSPCE7psbx7nyxK/xrTcfoNWb\nJdnOstfbrps+KOI+119W8j2uTGiTzOYQZspw5oLn2AdAoGDI7k/IfbXxC2L14Rd+wr+zA7w5+Nrt\n5DmSF8Wwh9IKLRxrkw3YMawG0o9PAQaTpiMKQmCGwq2ohaF85hAjQoxKYrPhVtI1xpDiT5zcCALs\nVr8//JFFksnfR9oJ+t8um0rXyrCPlkhOgOVIoOI2EvaakA9rg70arJ4e0AaooZG86KocEG1zrcg7\nv0y83pFGQ5QYqjlW7XFv+KTY3ns535n+wqDX74ML/E1u9/CrpKyFCcLg5mK4XVaclwv5DnBhzqVg\nTxnbuuPyesF6XjUrfl/37pWRJRO8/3zkj9CniFoqfKmcaOeHA8H85e7v+yvOkRq7NdHh5rtRE4D+\nWdYKCyL1ScMt0KtC2kxmk4ZgXzfsa0JOO0mRMumQU+7ELGnyxQteOBZqBobegEtTUVl3Le+qTO8U\n7OO6Pvveez9khPjoYVerhQumEzGdp0kXBYrIgNd8cY4U0sNJn+B7JYe2IHbSwMkz7BgRGoOs0paw\nDUxx+ryrNhujo+DogyC20O8JNFMfAW7m3UDEFQlt5bNXvBukmNVCyXdvHPv78uUF5yee/tdVHeeo\nMMkad8dmDK6XC+bzjDBFxJnkooEjksMUEJ3TRijvGQXl5mxuDf1M2lbsaWPXvPc4e3pURkrGJkV0\n+hJsZj2HLrl+VsE7+Mgcq1wpWXADWuPizedk3jNxINjlUdd4jXNBTJeMapOU+5pPnjHLCgjKR+js\n/t6wdNfEP7t+MPnT3t37wMWfYFcY6jzE3Q/ou0BjSF/rmeQhTHTRDivTuUFJeCINCtPE+2/OebcW\n3kl2M0NFdrzZAxGpdZ1nJ2Z1iZ6VhuE9e+/KnswD5DWG7IAVFwY907k1Mhcq2cDYjJD7SyM3krTD\nRuG/yN0+HYyR4nwBVFOGm+h0EqG9mteDgT8EjKRL6YxvGgRmS99zWWG1C5eDSZz7SjpgWKMFLW27\nspVl19gM4KrrHuicFNbQ+RHreeWXdSdk4LRyGBE1OOqyxpOTrjLqd1Oe7dbGo+nIiApUJibee3k2\n93HOwVuL0qpOJQr9s9a/iB+DBKLsTOIUZzQu+pfXqLa3EsWq2mRufvZtJ/e4jYv+mtTNTaZLy5+p\nHB5u74lp9KH0MB36jHA34gMA02EimHFLiBcqXsl1J7nxGg+jESGjvSMjMYPjp+jCgdGS2sLYLktz\n/M6LFaxYxyq0jeFrtd543LCz0eFw+Tr3XiGS/bKslATWbnt/H7T4M6fCZoMEeg7E08BPHi46JbqR\nbTBPftH3vAgmhsIYTYMruXbCZG1Ic0K4BpRCZ7EQ5bTJxFD85fN4h7R3vJ8iJ80ps5eJUThePnc5\nY1Spw/cn7xnbdcX5+Q3npzdSKlxWMgLbV3K8GxrS1hxLvBtq5bRTVb1wOqBwD7xDmyvyHtRVtOWq\nj4Sk+e3bTvyxtL7b4Cf4gFLdTfHUs1Q5Y3/eRhMC0FVc36NfsvY0/H7KKkmI4jzH8mfTszGMI8dX\nSXKkRpb/t66EHQiYH9fbFmyG/6c/6w8jfSlicND41+4s5oaOuqLC8O5Fp7BBEtFJCej778N8UwBl\naqJ9atHYz+8dnGTSvoH2uUhLoe7EK6MwnujC771GiFUlPbyj1OLipJlpaFUOQ/5+DHf7RvzbC3d9\nAZYnZzHvEWhIpt1aexSvdqG2Twbuu0mmtdtmSJoefYgdQY4h3gcDW29ZU9o7T3UanNjh8Xuo2xiF\nB2sDjKW9oGMWsLCxpZCJ7bMUyPW86qQVbiKZeVLyVnhcJL0cCr80V90hj4me8izX+i7YP3iSB3pr\n4ayBt4QEiAnTjcuc+lS0GxgXEBvnHdsl3FiRyoOlO/3hwJbPRoo+oSoiebXcmBHrmchXhKg4X9Cq\nY95Fn35h2O7Z3PfwH04HGGuQtoSJiVxpS0g13bx/esgY+VrDmqEBiJwI50sn78VA6xwmfqrLZ+7N\nmbKVNb+AOTPhln0NdCmfSGK1xptBXeP+evr503svzZmVrAT6+kW8SizD6Iw4GGNQHKl3skmKTlr2\nIbH63tK5N59mQgTEi4Kbm9YatsumyXnKJM+FnPPWiFoJ/bKO4faBRU9uov3n+H7td8/VGMEcjchK\n6VbdIv+0zerPJLtpQWek4PUEwKJqpdF6VlwejWPoHH8k98qxjgZ4a9F4leKDJwk5P4u1EQmOZIob\ne+vvLKu7/zMIU4SvVK1USaNSvGGQKBWu0JpL32VGNgWpE5L0ft2Vy7JvQkLMur4RQre46RLK4BTx\n9TEo2m2N0cRabXJvEAoJ2HJad//qGfhh8afwAOko2T5QDjfT7SxNNbpT1n0FQN7HMskyM7zlSgdF\n9Ki1wjmLMguBKjNszsYywi4u7DnNL7u4Jo379z/7GWXa1TXDOy6RtQiEPZKvAHLOs9XeNhQNijwA\n0jHuSnJaLyuR0AzQKjggqPSUrMbICRhGZGtImQD6vZC/XyB9AyNKKmOUUCLwsEB3cbkv4MN7h2K6\nfe0oOSNo3nbi0fDBj4oKPcSF9BgcfPH0vCTorrwwWXK/7lT4Z/YtCKE3A1MgvTQH9VjbO3MrE558\nXemIh+ejvrP4R+FU8ATrbUPwnWuR0ScKKYDd06HbDrfWYF2CdduNKZUUvZK7BM6A0CSVZ4qKovRd\nqbMezoeOcggZkA+scQ1knBkOr/uv02Gm3e2yK/lovazq0ikToFyyB/V8vzRYZRJ71u6ECPRiLaz1\nUTM/TqvUCDPZDkYbjNYaWhlQIDb1IlOlYWq7QYHu/wzCFOAiEWqdr4o8domieDUAoTVFni3DwdIE\nCwFZ5GgGHrykomZmoZWon9hGuDQls9VSb0yzwkxSUUGeaBfPJNmZv19uBEf+FaGxRhMSf3RJLojs\n/fdtV7Ki3LvGfgyjk6FICpX/wlJB4udwoeNCDQhCRL+sdYTwhEnhcEETAq+drGXzKkc+CESYZH6A\nWMmz1HbfKFinlB6qc+8Vl8iDJUjqrYMnoVbEu2Ckjt+HUgysLb1p5+K/r3tv5vadk/4uWNcz9p1t\nfWvVAk3sfAfvJ159jWvvoGTJUqq6ZRqe/G/OPjPe//KXwUY/MPnhHQJHB1IXKnAQwTFiaWu9RavM\nvmZ4TnbgJmUmt/B+qnARY8JWs40QdJkinGg3E0oSSEikIXw41MZpYv2A/97MoMPARACTv//eK+9k\n1CERjqVy0yN7Rjar+f6vZDSSC3bViXn8l6On+6hxzrL/udHmtuGBSwMEBdXBCyNYvdb9ADfaLk+J\nc7zrZw9TgFHYr3990ihX2MbWtONPf9OISUAG78wZCpVVTzaZPtPUQ3rIypfCSGoqKLEglKBEmFYr\n4kxfUSCxm92YfPB8ydQF8GTwjuLvzG3JNIb18pHWEDZZVNkpc2DNWCBkxz1yUOQX2XEmNf2Qv18+\ntw5fD9p4PiRDiAiosDtxa0T/K++g9bSTlZTEsTm25r6H/3FeUGvDvmyYFnJ6895jN7s2OK00NFN5\n79nvr3A2+v3vGe2j2Zb+rGpElRWxqKyeEIWK8noEKRhgYVrnlEHjD3oPXH8vxHTo3us4TdgXWr+0\nRoRXP3m4jdwMxbTHsZeE3R2MJU5GCQ5y1uZMay5bCmoNfLYZ+K1L5+TZTyvB2MKroYLRlTTkuGmU\nNGytZR99ngzZCErOHXoOZXfdbWt/dBknJNnGEb7duMpaoz4MDQYOgPhYWOdgsqAi5jZjhBFROZ9s\no8mUXmriNMXpgMPDEccPRxweSeo2H2cspwWH44IlRgTnGPruAyCpjDhUTIrutunU/95rWiZCyvic\nF/SBmh96psXYTc245PthYrOkHtIadwzvIURiXc/s/EcE6LE4i7ItxhnBR/gQEeOOaToghhnON2Dv\nzbG8Z3r/jPgmDMX/LyTOP8aAGztmcXdSakFOZOBTSoWvTTszOKNTqHS9tVaYNk7O/PAUsF6//AH2\n1MCO0jvnkvo0JdaFls0h+tccJlD+ELQ46LR9PwS0b1Joq0Ja8n2aJsgDut489BWF/ExojffCRSFh\nOdxqY9e+JsWB9/ZC5tGvQd+/3wJNV24nx7jv4lO9mGh4kUh6NRQSzXec7iv+noOaWqUGCNzsiL7c\nhMHb3RoUlpmICYwULesI9o+gVUJyjiZhvjcCCcuL2ougPH/Qqc95chjT/G4uPPp1v7+3pq9DSm3Y\n0/37vymGm+JvDQWNeI4PVVSqVHjvUULp8LIxAMudUtq50PeCX3JGQ/dvEMnTyOKmz6CyIgA8FdDE\nbzLJXwmadPo5OL7nst5RXbo2YfcVgJ9OJ7TWcJ2vNJkG0W7fNncyyNGajw2KeE3gXG+MtPCM9Zm6\nfW3qihymme1ZWQYoiX0l9TCsTZrFPaPsZF71Pfok+3jhVrxH9fCPx0fkUrBdqQB7T01N3hIK8xa8\nd2hewoyyNmGtNOxcsNPOBEaWsuY9oiT63rfr9gffgMaMbikeEg5FqhLRnRvlUU2HiaRjou8v9HdY\nblwNumV2iPchfuO9Er8J2VmbSCodQauMNQhzgCR+IlB2xXrZEJcrkb3nQBNyozTS6j03KIA0JyFE\nTIcZh4cDlocDltNBZb9xjpinHspVarc/ls9GCIDULO1I+8ZOe/c3+3LFw9RrV+m+LrSClfAoydoo\nvHKoinBt102DzvaNzOpo0qcGQIq++Pqrio5fDvElyDlRA8DrdtnryypP+TdtzDlwaMbANqpXEiBE\nZnl/vP72jaBC4rnIcrhPTmSesCUlp1CGubuFm7ioy0sqD7PqoVmXLsUPFWpMoy996Xp31Vw32bNR\ngXfOwYH0jnTCQbX4YvChkyEfNPdeNZNkS8NktDGRD7zcTN+BFRCWo2gldcuwO1tOpGOmm0s3RX42\nKf5tIO/IXkwnO+s5IIMgyRgnxLiQkQwYhrN9CvWR/mkEOowUU3vP5ZxFDbQ/rii6a9VCbQ1M4z/n\nnLKtq5j9yJTdJOzIAs0DkeVHw9qns6TpVLBDo0L6fyJETsusqWECNTb+mnIowAC+eYy0nML66PKe\n4u8Ivq61W4sabgBCDCRfkx19qSiRGLxxjvoMp9S/v8ryIQo1mRTC09TMKvbLt2l3Es4BQP9b7wOT\nP5lQxpOX5CvcSmyNfhZj8f6763FZkHLGy7IgzudO2g3uBpofCXbye8YY1jOzFpnzEWQPTi6DRlEB\ngBUUuWAvElLS0+wyE6W2dUNrREJsVRjfSXMn5P7IWkidI5l4LOl891yfjkectw3nwxWJjZfEfTIl\njgi2hmyUg0et1GjV2nRPvm079m1DqYUtW8kXxTGfRc/K2pE/Qti4yS7l5vwzMOSyt8zwB4/lNGM+\nLlR8eY0k7HzLahN578IcMZ/mu352KSSCSiSOHs8sIR3PNYAQQjQykLLOYT7OOO5HZD3v2ab7bJA2\ncRusWkgtm7ipRTWoDqg65rqToyHoPb4yebBP/JI9knVVkXWYMBCTuHuv5bSwC2Nfbxtj0GxDq8Kd\nYeVJHuTlraHq97xiWy8M81+56F9wvb5h31dqSuSsYzmeDClaZ0tCKZ6Vdt0dUPxdqBm3w9Dk4BwV\n/FrlvJbgn3/D29+5yPrywPsVspRNaWcjjoI4yLs6O5Uh7JSxXzZc3q64vF6wXVbs+4acVtIss65f\nHnyC/MqNU5o8kPKDi/8xqQCCdqoC+3Yy0B/lEuOu/J6r5IKRKyISGyH5IPM9tAYATQG0g6PJp5ZG\nqVzntRMjGc43poLtg2BMA2BhjKwC+gSoBUCslgGAu+UyH4FmdLJpc+s7du8JJQhOd5FyiN9zEe+g\nsTyvr0yk4Ampk34msRCuyKWg7EW7/bEwgD+rEIIWbmEq9+VJU0Z4nCP5hh9nLMcZE4eGTJxgWGrV\nqUhRpoFvIKSwUknjn99R/EtrCKCmF1UmcIb+Jw+fQn8eCqXHTQfiZ0iqXVwilu2A1gqrY8gYJC5k\n9EREHqcwoza6g5Jg3zZs1xVpJUtYaWh9iN1VjQljcoA6aQR0z20UKr3nOk0T9pxxXGbMbEglJKua\nC6rp75ggLkJeleInPJuqRlh0i+sgiZP7X9ntbXSKJCllYfY4QeEScCN7aJmwRZWiBLTo6fOXzIs5\nYor3IV4AcJgmPC4LXo4HbOuOtGVtJDrSYbXBAsDa/77GyrnApAQwr2OrG9brFUTsYiiGHk/mCNWh\nARQmt7i0kelS5CK+PCw4PhwwH2c2ciJ0MVlK/MzJK/LhgsN8XHB4PNz1s8s6sVWR13JmxZYIPWj9\n92tt8FOgQhwjjKXPYT7OyJ9OhH7mqjLhWgubuNFU3kAW36UVwLQb8q4dEV3QqqXUqkjEzveFkMii\ng5nwZMArZec8vLv/3h8fj9jXHcbuHKE+NLhjfWm3XBPI+pb99gV1UK8KfvdCmOl7G3Jp+qDHzEZI\no0/w/zQdMM9HTPOCOE83KpAbbplxaE08HYzWGh3Evrv+tvh7H9lUJ+iEIjcwrezqVqp2LCJfaNXA\nsr49Z9qDXF7OOL++4nJ5wfX6in27IjMMKhC4Fgv+gWRnKTaFIUwIYcY0LZino/6Q3R9a0v2oq5JJ\nSPOxuRDde+VUwMmbfFD1Pb3hSbg2NruI9DV9DFhO5HVtDFl9TucV8XVCnC4IU8B2idQ85XSzB5W/\nU9nLhTpssT9OadOH6uaz4tpJhV9S1UbCjLsppvdcCkk2wNrByEUmbP0z0O9XWMLycqrDIO/prO/6\nZm89AJEG9vtEUhaaIsIcsBwXLA8LDqcFy2HGPEdE51FqJVMc7nxlIiWDDCIHOf7+S61kQ5zuhwEv\n24ZlirAwGixiAHhHcadlKoO2XQg7VjXyNT+QxwPvZo/HBQ+nA46HGcfDjHmZEdXoo0/6KRfsKWFb\nE9Z9w+Wy4vX1jNfnM9nCrjurUMhLXsiQNJl7Db+Rd0cmbGPMXxJ/vr+mEHCIEcdpos97jggTyRRL\nyjC1v0cy+eRERdpY0i2vomO2A1GVp8Y0TIVK9CtCDiv63EkzLzny8t8ILJ44BRNmkBX6Hissv2IM\nmN4B+wfncJwmPBwWXC4rwf8xIUxBD2iK7e5BTYJEGWcQl4jD40GNmYTLs11papXh5g8s7eCYOE2N\n4jRNmA4LIV58H7R5PEyIU2SuVYPdkzbnIp2eFrLKXh4OWB6Wu39+OeMEYdnXHft1w7xMA6eFGrbt\nsmGdN1hnKXOFIfCONBEKvG8bLpdXXK+v2NYLx8w2WOu1wB2uJ2zXE+/Kd45NpgbQB6/ohnAj6PPt\nvBlFJksB2dCTI6wP9w08AE3+skKWPb6etUYMqEaeGf0f6asJeaKVYZxmRUwbht27AezIxhfFS+3k\nXUHdvQvwIWKaZvI8iN1MqbASg0AEy9+NOCdCC/+/xfb3nog+zvmbaMicmVTBN6cJbGuYzewtMEUi\nDW07v4QRYQ/wycNuTslhrVUUjQseD2dDMAs65F1u4NOIiPmG1UtPpdwEq/a3Y5jCewh/BG0Ne/gm\nEyq/uA1AJehKUsjkIJgOEzzD5vNhwnJasD4esJ6v7IiX1bAG6DuwkQkta5O00dRwvVzUJMMYw53h\nxH4LsvvlX76n5Ikh07RMGhryo8sHj+ZpShFjHgB6gPdC2w2XSi684ujNgGZCMNe5eTH+oQfWur6z\nlgZOTDTiHGnKeTww6SdgiRHWWKTBrrfx5yXyMzFCEpe0Uhvvke9P9zqvG2DAaYB9Xe0srXdy9Dql\n+sLkVd+9v6U4HE8LPn16xC+fPuCXhxOO84LFe0RmkFvTo4FLKVhTIqvflHDZNryuK55e3/Dt6RWv\nz2+4vlxxPV8JCSjceAbPK56R4zIUFZ40pWn74b23FtF7HGLEPE3wU/fsSFsGSiFnPzMU/y3rQb2+\nXdHaYLO67tj3DWnbsO/kWihwOql7CsTspq+4WD0QCOUxYKfLQHGncuDKzyfKj27M05MufXhnnLNz\nmGPEaZrwPEcKFtoC2txJi2Jkpva/vO5YHmZ0sgkALiDbtuPydsX6tmoTA959G2uYQEjfo5eAHEa9\n5mWC5bWCrIWEyGxM9/UQpYnzhE4Zx9/TacG83Af7K4GtVtTGQT/rTnK1XJhXZJQTsJ5XJnhW5gmR\nXPHtG5n8PH95wuu3Z7y9POHl9Xe8vn7F5fLCLqZVi/Q0LViWB5xOH3E8fcDDx4/4cP6orHlZb7Tv\nuAijxLg1sEdMYQXBDLGHv/eKc7hZRSagc82A2zUmZG1Fa2vrrK4kxX5bZMqaRGv7c0t/HaNfjBDJ\nGkMN7PirEMokvhUWYmgnlxBr+9qmrwz/LcIfFf9pmPw53IGzitO6KxGvsvSGoBYDNweV5RhL3fDD\n5YT18hO5mF3fsO3k+JTTfuMlILtuYSrewGCDN/RY+HXq54u654H0xJ/ieyQ/1ElWnXhbF/DTC8jN\nQEbfPY1pgrIK8ZE8DQ6PB2bvJpU8UlcIQGRibGupRMhcVAO/vlHjkAtPftb2KWeOtP91QwEaNPNx\niYRI3En88RPJkkpwSq4RE6fCjF/VFjNHIdaIkjKTYUDGMOr3j/4w2949W4YZATOQWHDjpS4EnJQL\nrCGZ054Stn3UEXfGu+PiGvjvKLUisZzy3uu6UjDNFAP8IBu0Av0Hh5J91/u3impl5+wooOM448Pj\nEf/58SP+88MjPh2PmEKE9OgjepNrRcoZW85IuWur2tmN4wAAIABJREFUg7OY5wnHByJ8OSbRrX5F\nSWwYwoXDsPoA3FDI3ldgyntXXo6L/xIj5mnwW4gBLuyqqhknrpwzkMgVEIZ28+vbisvrG86vLzhf\nXrDyznPnjHXaayaFaeWSZj+EiTM9Jk5JO1FSWly646cUYTcU/2Hf72NAGM+AO3/+yXtMISCGgBA8\nEnMGRG1Aq7QuIxZUK84B8xzhQ8AyRcyBPDH2nEnbLXK+LM8sk+wGVYqxXa3gHRXbPRec11UJgfIO\n0oFf4QO7/eUAHxIReycwV4Y+q3uukb1uWpe0ierA84BhnEHdCIYHqCCHGFBywfnljOfPz3j67Qkv\nv3/Dy/M3vLx8xcvLFy7+z9h3uv9q5uYjluWEh4ef8PDwE9brP2ilw3Xl8HggFNM5aip58ASg6Ius\nzhoaT/2Od/73T/6GEeMQAyFWALAnVNP3+xL4JNN2LQYVgLGOpa7dl0WSDsNEu/qG1iOgW3+HRO0k\nEerKI2DUTDx1ZC1TqqzTRti/N/xUP7P++rPrb58I8tQngpG1jpKCGFKgMJ7bfav8so3grzAFHC3/\n88PxxuJ0u1I3KdyBwvJBymEmXSQxHxN/81J0LUlD4oIQZnoQ6XG9WRkI/G2dGdfJ7776Ad2bEgAw\nsGjiOligL8i2bqpXb6V2vT0/CM5ZCjqqHd7XfZUyngkmrAxVW2EsR69EF8MSKGPZg3wKiFOEC7ZD\n/syWDXPAvEyYl4jo7yz+jJi46pCthbV02Bhm19fCMLh3srqk369V0Q+FdwfdOxoxkhvAHWwDctGd\nmTEGnk2EciAoubWGfU9K5DKGPO3TnrCvSZnQjsleUwyYQkBwDqWKQiUhXe8v/vt117XOJG5/QqKy\nlE1efCW1gi2A0OpM/xxyyriuO35/fcWeEn5/eSN3RJClrxwuDU1NiFImRCOVQt+3SDz3MhDboFOi\nToG2o18Gw6Fgad//Hq6LsxbROUTvMAWCzIVgKGjW9+5x1CA3nUgKv9M5CUdoxbpd2OUt3ZBdwYoH\nymIwgPxvY5WkS+vBDI0tZYmZQO+02rJDM+C0ONvvBoMfXa01BOcwBY8peMQpKNGvNZLAwYyJgfSu\nzYcJx9MBp2ki3sA84zTPmEMnPno2jrLcSOpKplZsOWPLlPGw5axx2WvOeGW+wFn4IYnkjTQ8NJXz\nisRX0jE7Annfzy8eAq3R5CqERGk6GqI6jYoB1X7dUFKG9TQonJ8veP39BW/fXvD68oy3tydcLs83\nK9/C3hW1FoAJ2iKDi3EhpGiXzBB2fB2QLSIaC+LilWQsxnQxTvRMGQv7DsJfKYRGuOAQWtAzGqWj\nAc0Ifwnq0Ig6+M7w+kHWIs6veh4bRuCkAZBVRdoTqUmGYUlUNNY5IDYADqaxiRrXCiUkttYH2yZe\nBLQyFtvs76+//VTm+cimA4FMBrzvhhd8iI+hGsp8Flva4BA9wYaVIQ3xCw/TjrT04l/ZpUwsGreN\n9kzjnltkb845Yj0rcQy9m6pNDUdo6u962loq6jvgP7JMLErIEGRCDiuHCvIfh+7G1rdVQzb2SPsm\ngbn5vgwM36Ja5dGOszNqy40RjLVWC7okPQG84uBDKvC0E1kOJ+z4mZPk3J1yL+UPgF60EvuBb6wh\nbTWvelzoVsMyOdCz4pQAJv+Un5tuaA/QUJImd/ACvZVUkEAvhTr9sdRFPqPGRDjnHeI0TlwWue7Y\nmHR5Pa933/v1spKRFKCFyVmLwBr2wvK+nJ3yDGppXPQKrByM6463lzM+B6ckJqBzFAT1AZOHSu0T\nhhiAyB/RNdDO6AqjKQZQyaUcBB0tY1QM9+37+UvRoWodouMsCT+uFPhPtf4NSBPSbFO1TTxMtIX0\nAfNywmkjfXP3NxB1y+3kIpcCdszYJg30AT7QJDsWA7EJHrMyiAxMnI32Do8H+X4m5zHHiGmaCJlh\nrw7xsJcCqYY0U8QSAuYQMHl/U/DH3xO0zxijDWVhhGqjT0WbTEGIMsc+p3VHSkmnf64SNyiP9w5m\nhnJ/hDdxz0Vr1cEwywrRl95FAwM/eSyVjKA2s6nLp8mV7NlZbWC9V8K49xNl27eC4CM9A8M0Zg3B\n9PNywjQdEEJk2apTzlIIdM9bayiMYMqZuK87eSHUxsOqZXWUx3u8/RWuR19zFyYxN1+RkyAMVSd8\napIqrx26r4v49VtDw7Cs38QjRrxTSBm0Ie0bRFLfw9zI7KzVqqtc4tV0KaL19JlolsdG9SrnpBLD\nP7v+tvgv85FlbJ4lRvSLCm+koitFlZmOALPUHSMAhrXvrsEWo2Qs6y1scXCFzTvM7eFExC8L3zwa\ne4MbkJzPq9f/cJDKRNmg8J8+uHyYkj3n/ROA9RbIuFlHyP+mry27pIaSjKIajh9QIkplSuNTgkgP\nehkLvBiYQJooaVgGrTMaFG60gbPquTPs+Qi872S2c5wjIkOfdnipf/izO3pQdbLhblLvNeuxQwv8\n8xEaEDgApPgC5yx2RQoqbDGQ2EmgH/ik0qTC6D3J1/zkGco1/OJ0vbimNg7fq/UO02EiYt2yYA4B\nuRRctx1vb1e8fXvD67fXu+/9+fmMMHcLXe8ICg7OkaN0ayiBzWYCT7m529ba2o+2kgo29OcUfHh0\ntcN3BE65mkzvPNU3sElWRwCMNSS7VEMrmf5vPzcPaLzxj65131FaJdShNXXWhOxWB/8NYwALC+PE\ntpjvNU9ncQpYHhbVZqdtRyk9KvnmZzWyP6X8dHnfFG1j1E+Nq6yD2GGTPLRbzcoqsNaKbU/vmvzl\nstYiWIsYPHKJSmITa27h+Ixrx1wrci3IlRAcYwxyrdhygjNWi39mBYmERrXWsJeCbd9p6m/UXKVS\ncNk3XPYNOxtsYZBaGgOd0HsT5lR+bIxRf4R7LiGLkVWz2LcTqiNxxJRl7xSR3C4bMe5bgylkqBQm\n4ho0Ia85cqiLcUHJO4ra/QqB0iOEGfN8wOHwAYfDI6Z5UatlYcaDhw9BoeSc39cdrVVY5zDFhetE\ngPexexfccUkOSWsNeS/6DO1tQ2sezhcNMSum0LNoCAVWlQ5/3iUVJWLbaomOxtwMNf4a7qU0egBu\nzgKqXw0wFZkZhoK2i8OpETQyS5ARredz/jcn/8Pxgx7QwUfa/7MEgbTKlotV1cMAoGhPgbKbEhv6\nnrNygaOwgm6WIlO/Qkzlu2nFiAKAiwA6qUasQvUQFMYzjO5Waq0w5f7dX4gBxRbUTfYvUvwzRuag\nQDRpS9jkQbV9AmtT6w8wfxAKD4umWTINBsOjcSXQuPB74+GN16Is3t5C+CO70J6BHmPoyXit3hTf\nv7tGuaQkdglERS9a49z2BmMB73oCm3UW2WX6mpVkSEJQks4X6Lv/DuU5TYocEw99cKpdl5fHgDtq\nAxhD/gWH44KPxyM+Hg5w1uLlesV63fH69IaX31/x+vUdxf+Jir/wJ4L3aIyc2MBGTtyg5exRsqep\nvDXY1pQfoBsnfuFLKeoJf6M+EeSkftcEGyKW3jQ73kGGGdktOiZ6ynMlkcZyeV2P/fh6uV5RasXb\numLdieglpF5ZhUieAAA4/nnbMH0S8sfkS37mZV+uBx5/LpIZIrwhQcRKKmqGIkQ3QKRxgnb04B5x\n8lRnw0a79Vzzu2SeW85IJeO679iZhd1UOkYQrB12rd1NkyfF2pBLxW7oa5ZasSb6d44hf3oXuTBw\n45AKrT0F7i+1Ys8Z15Sws1ugkNsMP1syXNUmhcCqnNcYo06T9xZ/YdVLUqTBqNagZ1MjdsVt0Fjs\n207ntTE6HAgZ3DC5l0jKESUnRg7Fx4QbrTCRkms+YZoWhBhhPZ0Z4vBqwSjcEhXtGVMRvY+YZkJt\n6QwJ7+J5SaQyGpD8ruu7vGVFKgUNuHmhaFbqBVzWBUDnCwkp3cl777SwW2c5QI+aIUG4JaNE1Rfi\nCNjYXZGblRADN0HEeRJpvmQq/Om9/rsP4nB41APEOeqivKOwgThNPZby5r8aD/bviHitS+VkupXV\ngUyUcqgI81f+PN3cP+/exy6pO9xZnfwBPmTa+4r/xKYeJfebTlLHBAwObc7JKsBgv/L+1Q8FLXid\nzgE28ijcUfPPbUtFQd+XagPwnX5dGgd5+YLEXoaeoOfF/Wum8BBjgFwkG+H+CUgOFgkeAdAP0dY5\nCwY0sauDnHyrpXbmu+f0rtxQx29hgIwlJERY2k4KP8N/hKgAzbRBwUG7/nmZ8PF0xE+nEx6XhabW\n8xnn8wUvv7/g+fMTXr483/2zvz29YVom1lgHTFNEmgvDaw4Aab6rQPW16hqk+aYohqx85FkeCRJ/\nmPSBm9XPzWfJz4KgaePfY4zpsCJLX0V5UGtFs5QJcO8E9Pn1FbU1vK0rztuGnVGN0aNfYWcArYqT\nYF87iAZqfH/5mwU4bXOc/kVXTioRnqByUiIwUCFe7mQN25/P0QN/jO+VRlXY4fdeT+cz1pTwul7x\nfLngsm7Yt0RwPLjY8zPbTYwMYiCSoERBVy7oYhJVhd0ucLERVUDrCAvf1gZqLkvtHhPBS7w1B24Z\now+INNJCdgT481zpM5Wckh9dEuIkZDNdrzGpFq0heI95mVBj9+swb4YIegBKKPoulxgQ5e8oJ0JC\nctJ1qrzDANUYIXjKuhToyK1MusJpEmOl9cIx4c4hzhNJw4Mj62OuUfdecQ7MKyKEUdUc49/BjSwJ\nkwHdvWHkonjISyqkb/HjvxlaBZXO4YY7d6MGABgdLhhlxdYRyiv+JzIkei7+kO/tL64fFn+R+N24\nCzl3E34hH461ornvSVZW0AE9+aA/1PcQh3RMekoaw0mBQm6yWtRvtPvDfyJFd/yZhbCHalDbO8Jd\nDhPs7viw25GSdKCJuleWHoYw2osyBOsJhvHBI7ZIE4JOBwTfajyoNQzdGH2JZSVgGq9ATJcWCXlk\nWiIiS0t6BrklpvlhxsQvZ6lVXa/ynXKvWhucJeKkjx6RO21phDIfEGDo0luLZow2GK02FCe5AsQf\nqIUibE0rUOGE3mpza/LhxJudTYq+d4aTCdcaxBjwcFjw0+mEj4cDDtOEy7YhpYzXlzOefnvCt399\nxbcvX+++99e3K2qp+rPPhxn7ISPXimhITUBoCjsASvPpLE07fEhp5gI6JCshJfJ7VCubHrjSVFVu\nWGVlpRIgvm5CcIZ3ghjolldMTZGpe739f31+QmsE/7++XbBdtxuP+TbAm4BB9WKDahQh+j7xcNxv\nElGtZ5MToiZaZ1mHZZYAF1BUnWFo2HLza294CF3i1xPP9jWhNTaEuXPyBYB/PT/jsu84X1dcLivW\nlfbaxhjEiZA1H5yqAYTA5x0lQYqjoXzeklbY0OB4SPBWHA/pOXCtIQ9oUZEVEf9esBbwnomyWd8X\n/SdM93dg3kxOmcpPub/4z0eOc1535hTQ9+2DY28S4mtM3sOx7HYk8+b8/dcRfgPXD0dGQcUYjK6m\nRuBxhtEBKNJICqLwBym1dVYJv/KuCsHRB4/5NBM68Q7YX9fJhtdttpd4qVEyrEoBh94Ddiz0HmGC\nrsGcs7qKFbdcRb3G5koI3wOPbrSDF5TEGAPDfDexeZ4PszYHntFeax2ZiP3F9YPi/6Afnk5nskuH\n6bCsMm271lj/POvAHYDqi76sylCWX64XdXJ4A2ohKBEM8Rnb0/nGzuhm6uebpzeM2ZnNyM717ucA\ny2nmbHag5IR933iaKtjTCpN31VG22i16YYRs4ztDfy7w1cMINCkkJSPFkeM9xa+a1x9iI2v5AYoz\nE/gOE+bjTMWf95zi7BbniGWe4B3ZsaZKbHnyvb6T+FN61+uDI6tdhgStMVjdbZqhpNUF7yAafzFf\nUk2yLTC2EmOVDwUhqVFx6hHQOtUMzQTFFo6NEv3MyzThcTng0/GAh2WGsw7ndcV13fD67Q1Pv33D\n189f8Pz8+e57n9YdBsB2CdjOE9bTim2fNRkwOgfHMG/lSbfxgVEyMY77TpLZ3byHdc4iOq8QsCAI\nbZgAayW3xCxSqz0z0az0gmoq0OsuVDomE1OpaE7UKfdf//ryDTAGiXXcl5eLOuvllPX+po0c5Vx2\n+g6Kh77yWYTEO9iwCiu7sqJBpb2lMAO8hyABvXDI++JDt+0Vtr9nhnuco9oQr+eVsgDWTeN47/r5\nvz7hum7Y1p0an5UafWst6nHGIThYS4X/yL7zlonF1lol8Ukhr6BzyoCVIpaklHL/BS0xTDDNN59D\nNwMy6Kip/L74DdDnZNCJZwVIYOfF+2H/06cHWO/wmgsRrvcNdicd/3xasF6Ie2AAHCRsR5DbXLBe\nN9QynGGbsNiz3lexiZd7P95jijMvii71SOtOsBQPh9aAVjcUjgj30WNqE2qhf394OGA+THDhHYS/\n1hNjAXqflTcmGTOCBluL6p0S+aSGachSYQifh0FBLaR+kuV76WdcY0SndrRDGuGcKaHQGEJIYIKq\nOubjzMWfmu0Qo8rzrRME4o/X37P9jwumw0T7BMcuWgxjllx5ygycKuVv0s0MT2uWoSoxhhAoSTXd\nkBpCHIDGk0qtdHDRpCOEJjMQP/qLYCD7vy610j8jf3sDy6XuPwROH09IWyZ4ieUYaV+xGqNFXyRL\nFMLSDzLyrO4yPHIAk8nMKTdhjGZ0weuOLu8enqUfjff9IXrdSc2nBdORnL/IeKNLm+YYMbHL1MYv\n0X6leElNF/zBpagMAOMsYvSYJ4KWwJ1xTmWAl3j/aSn0pCgSwbC9rwz9c8dwk7t+y/QW8iZlZdO9\nExmgsQYO1NFabjaWGPGwLHiYFywhMsEq4/x6wcvvz/j25Xd8+/orXl7vn/yleO3sL75dNvrnKaG1\nGd5ZuGZRPe14a60obcgp4M8OfEAL2dIOv9SF0ACVESEnhV8Oe9C7IM1iq5Uh5W4SI59ZLQ2OHd90\nVTUiBXd2vr/99xc4T3rq/brjKvbc5y6/SjvFvdZaVb88Nv6K/hRCm/JOOvecScIrEiTV+TeJNpVn\naUD6xNLYRYQ4Ic5kVhXnoKSzyO9FmGjqqbni+nrF2/Mb1vN68zn86Pry2zckbnS2dVdDJecsNQGc\nnIipwTt6/rx1SEWkwEanegAwfE8b3zCBfgUlk8IuyZN7yST3rAW5VCYRkg+EWCAD6LwfVtsI4azw\n2VoKGfRI0Mw916f//ATnHbbzhsvrK9b1AgODkhPiHPHw6QHXX64oHx/JCGoi07A9ZWyXlVDBwRiI\n3D43lu6RdJt8HlaVcTfe+YsZj7Vk3e52jxLL4CJr1DzLWIsq0cGFhspgOd2w0fri9OmEw+NB5bX3\nXPuaboizYiAlSErakyJh1tNQJE6ntdB5bgV1U0IfqdEysqIXQOfAiJ+L2gNnIRBnDebJic5t5z28\n7/bny2nB8fGI6TBpEyY12ft4cz5/f/092/80Yz4tWI7L0LEAMEBJdEOiFDYnGt0BjpIDT0l6nQTX\nyXvfdSWmS2l6R8SHAfMIhHxSi0yE8nV7QIJyD3gf0xrZId77EgDA488fkPakrO9SKlJKWLcLjHlF\nSleWIo6xt5kJSlCzDiv72EaMeCLo9RdWOQ1gw4/QIWIhjUgwj/i5z8cFcYlqfKH8AmcViqR9dMHO\nD+x6Wd9V/IWZbmDgncfERiHStu3b3tcwphPMgvcooaJOf5SCAg0mUfOosC8ajCVY2Ox062qusE6S\n8kZEiF5u64h4N4VAVqzThOM0YQoBaV1xXlc8P70S5P/5M75++xWXy8vd91786gtnmu9XmiCve8KW\nsh740TkkR8oLz/eBnrUCbA0lZGR+PwTZsM5gtwXeyeSnPFD6bxkul7AiiXYlAlxWuPDPinlrQNrk\n+bPEJSkVxTfcewT+9n9/Y9KY2LsmnaILM5nTljg2NaNWcl/Lgf3kmXPTXINrdL9kWi1FGr3xjGCL\nXljAdHOn7m0fEEPENB+xHA8a9xqi19Cq+UBoWIi0481rxuWN+B7n5zPqO5r+l68v7MKZNDyoVuJ6\niEW0MQZTCHhcFgTnFQlKpTuvGcMwMIDmOjqSSlEEyUAaOFIF7CmRIVCp2NnzgRAgOnvyzkggFyVR\n+ABEOM4VinrmPeP6esX17Xr3uffL//cLjDF4+/aG+rngenlFKQnbfoExFoeHIz78xwekf2R45/Aw\nzzAGOK8r3mZaDVaeaCXWduVwG4q0pWS7XvypWRKZmnMOKUUEH1EjNRYyUIaJzj/P/CE1O+JiDwCS\n9rqcDvjwyyOOn05/rDF/c61vV1rP8npOY+QrnQXbZcPl9Yx93+A9SfACNwBWzy2rdQeATvCjh8JI\ndJXGRhQVmf0xSiZDt8oOuMZY+O8m/sOHAx5/fsR0mBQpmQ8z5nlhr4O//ln/fvI/LTh+OOL4eMB0\nIPITwAzuTK5v1hmdMBQasQWu9Ydd97Q8CY9duO4CS0WrAQTrDjnoAnGNH2attAVs9EFXgWWYza/d\ntJgwGKNGFdtlu/tB+PDLB+SU+WeX7i9hvV5wPj+htYp9v6p/Mv0sARLdKusAaWIIgiOPcDfI//pD\nACVFAhi4FLaHlbBF73JalG0r8iPviJXueZUixWNfSee+nq9kz3rPxf2TmMM4ZzEzmUkuawyaIaMf\n2XVWa2FLQYuhcz10p91Z3TADe7xWCknSHVhnLo/Wv84Tf4QMbgKidzhMEQ/zrIXfGjpEn65XfP39\nGb9//oyvX3/Fy8vv2LbL3feeJumih9h22QjyvO5YFzqgw+Qp5a8W+GT5c6d7Sva1hSZ8PkxkNyvM\nYGVtG6EMNC2URQmwXUYooSVi7PFnPAk5YGA7lyQXj1wK7J0ub1/+6zN87CzpUggByRtB9fuekNNG\neeQsfXXOqwmR5fhfMboqpcDvHmklEmfMM5ZSda8vZj+9KeCp31gYDrbxnva5h8cDoV5sYmOthZ8C\nlhOF3BhejezXDefnM54+f8PLtyek/X6Ph9evr7g8X6jZyd0b3XmL7bpr1G5rjZrPeWYZqIdBwQ4h\nLzZYhxsCoEz3ud42b5WbPmH7Z24QVAGQe9NBMD99JvIMEqkvkQlXA9resJ5XvD2fcX273q12+OX/\n/IxWK54+P5FRV95wPj/Dnl9Qa8H8Pwt++t+fcL5ckUqBsxanacaHwxFPhzdGIgnlrMwB2Pcrx9pu\n2gQI4kMwtuf9tIc1Xtd+lpHlMJFD6cQhU4JKFV5BCd9DFCl+Cnj46YRP//kJp9OC63tsvV8vurIW\n4p+8y4kNjdb1jHW9wFqHWgqWeiIOAqsumhgqMRVoVG3pyq50kyapj7JyyJkyIcgIaXj+nIP3E+JE\n5//p4wmPPz/i4ecHTMvEhks75tOM+UAmeCKx/LPr7+19OcZyeTjg+IGgBectSqq0P94SJIErcWxv\nrRUmGWSXVX+rsC5P78IFkH2V9x4tVCZPgKUwbIEpIxGgHRgAPTQFGxXXOCe8A2+VsAF0n/F7J18A\nOHygJKzD44EIb3ywXM8XvL5SUAY9GAmNIf/gI0FWbmdSIO22yLe5IJ1mxIkMLKg5kWIqk/bwDQwT\ntTyMot0XKZy1DCE7h+AsvKUJFCC2cNoztvOKy4scAvdPQG0o3hYGwTlE71WGJIzu6Ej/7p1FaY3I\nTM7BDb4CynClGwzr7OBm1YmfgvgUfgEV8nYUCWwtPSPei/86uagtMcJxw/N6veLp92d8+Z/f8fuv\n/8LT02+4Xl/wV9GWf3bVUrHz7s8Hj30h6P96WXE5LXgsNPF45zHVgt177CXrZ0+JjknXFR0hgR4A\nuoqTFZYUfz5AbhqAwUlTGc/sGd78yBrOigRla2GShfMZ6R0Wt19+/QxrHJussKNirajM+C6seMm8\ni6SpOCo6EWdGpKygfSLjLbeEpoHdrCFQta/wtBFgxGc6TFiOC6bj1KFcazgil///2rBvCZfXK618\nPn/Gt2//wrbe3/hd3644P59xfj6TJTSjeI6TGS+vF6yXFXlPsCBTHW8tlhgROURm52IOdLKlNT2r\nhI6tJq+QFn9pEJgHq5fyrkyX9YXgmW9AJkGCUG4Mt19fL3h7esPl5YLyByLen1//6+NH1FTw7T+f\n8Nv/O8LA4np9Q9pXpLxhno/4+q+f8e33Zzz/8gkflgWHGPHT6YinxxPOD294O864LBN8uPIz1/S8\n7uucHmntfVCm/zQd6Nc8I86TFn8fPexAmO5DCevya0NpAExDiB6H0wEfHx9wnCe0dr773m+XFRLZ\nHiZ69tfLxvc7I6WEbbvien3T90JIiJE/f5nexO2ya/nReVLGAEYQAEn1KxyYlIjgh+H5sQ4hEN/r\n8HjAw08P+PiPj/j0j4/46dMjgvfYUsL24Yr5MCPEyNLJGTmf/vRn/UGkL3VU0zLh9HjEw+MRc6Tg\ng3Xbcd123oNTx7EagpdqrSh7RrJsQMAkrTYW8aaf0aDXtbCmojoDWwHjHcxAhkAR5qghi8Vh4r8J\nObCWyQ6iA5VoynQ34Q0ADstMrG4AhwMR62ppuLxe8Pz0O56efkOtBbnsGk4U48JmSJ7NLEZUgMlP\nh8wGPfamKOjnAVCB5Gm3eZHQGSXQdZKcTPw0fRtyzEEqBduecL2sOL9ecHm54Pq23p1pL6zUfr+6\n65ggDAAdWoFZzt45faBiawyFdmlaJ4E67H5nq2JBfcRTHPq1AIaLUdH4XhtLTekyRZzYPvUQo64k\n1n3H59dX/Pbr7/j8P7/i98//g5eXL1jXC/yd1sZylVyxrTtc8JjWCft1w/XtgstpwX48AiDW9uQ9\nUgjENfBZw09apQaApramcKzwXhpaR7e08N/6O3Ttuzj+ASF6TAeytrZS9FkemrcMP3mdip2zZK7j\n7mc8f/3yTxjj2FOf3fRgv2vSJH6YLHf33cP70K1Yjew/nU41f7aquHFzVLtS+bfDc8NE1okLghy8\nAFhuTM9oTgWXV5Z3/v4V3779C1+//optvb8A5C1jXzdcz2ds69qNhnhKjdOM69sHFCZhxkjOff94\nfMRhmrQByIVMcYS1Tz+RYaKohaGtGoSZJKjGiP0qAAAgAElEQVRaa005A4HDnyRVsjlqcC2TBsPg\n3+CdRzb0Tm2XDefnC96eX3E5v9397P+fT59QasHvv3zA8fERPgTs+4q3t6/Y9hXLcsJP//Mf+PK/\nf8HPv3zEx9MRM6/e/vH4iLefr7i8kZvmel6xXiakfbk5+yWH3hoDx0Y85N44Y4oLQpwwzTPHowcy\nLgtdugo9C0ge3FpF3ouei2EKmKaAOXi2qb7f3ne/0pQvMj/SzidcX68ULb2tur4Q4qp1DmGaKUWR\nm1JV7jDyC5nwRRo/7OLFp4DqWNbnxUikOIQsHBGniMMDQf2f/vERv/z8EZ8ejnDWUSjYw4q4RF6V\neMR4UA7I99fffirWsxwlOMxzxMfTAY/LAmsstpxx2Ta8XVdc1g1X6fIM6z0bFVyROrnW3b+UsVqH\nqV6+KBc1IYTZBtQqk1EvkAAXhlEbKTJDJ7GmDKUI4Sj1wJR7rmWKzOYNeDwesMQJpgGX1zOevnzD\nt2//xPOzZcvNXckZMS6odUK2ibv/vtMHaC8kMjCZ0nQq4+6YvAIcAA/nx8/q9vAUyN+ZLpGptWJL\nCeczEZ7evr3h/HLBdl7vJjwKqUaMaXIu2FJSQxJnDIz3qPK/ddo3yvzPzGimm9W0+OsUY60yZ4v4\nGSjHQ3bCGBoh0tzHOeIwEdR/nCbMkUI89pzx+/kN//zyFf/6r8/48us/8fT0Gy6XF7ov4b5EQ4A+\n/wKy9dzXHetlRXyL6uF+fjhhP2ayv3UOcwyEtOSM6zxhmzc4ziUgmDgrzKcSnkqyufH3pRii9alA\nmy+wwc9gICUFs7PpC0IOPeFvkN/+7QJwuF5fvwKg1MjgI6wVxzgPZ52qNNAqwZy5wZoVKUTs24Qp\nzYi8mvDsu38jSzP9V0Pj8JZudT1yffQZ92Ld63iv3JQV3bkjBdt1x+vvr3j+/A1PX7/g6ek3PD//\nhv0dsL/8fTlnPuhXbNsVOe9UXPzESEIlAu4yEemZ2ftLZAUA77/Hz12USM5Ziu+lLwgLwNYKKyl1\nfE5pQ8DNYWFiqSgHRE4on6W4+RFJ84z1ckEpCdN0X6Tv//rwAalk/PrzBzz+9AHz4QBjgG27YttW\nfP36T/z2z1/w4f9+xKf/+ICfPn3A47LgOE346XTC208rtsuK9bJy7O7Gjo9OJ/2UdrRKv0fOsbTj\nD5H0/SFQEBqFEk26SpC9OCD8IeiAB3Qlmax/FKF8x7WvqUtDTUcoL68XXM9nrNdXpH1j0uqGUoj3\nFENEjFEliPwgDYZmAzG6NRjDRPXhmavFMgcG6J427N/gg6ayHh65+P/0iJ8fH/DxcIC1Bpc94eV4\nYZJ+RIxkmvS9cZhcf1/8rdViHLzHcZopmcyTdeqaEp5nMsJ45iIPgNmQ5AKXd9aaenoJxA1Q9nzC\niLzFRMBTLggGNLcdQpdd1JupUaR+jnflrUFZ+jmlP6gMfnRFZpKf5hnBOjzOMyyAt5czvv76Db9/\n/gnfvv1LIypLSch5Z/1/ZfZqw7Z1jau1HmiNswdYHil2pAp1GlhHBC3hK+jkJ8VAlvKGpgnZKbbW\nsOeMy+WKt6dXvHwhZ7vz0xlp2xUu+9ElYRkS13pdN7xGjyzkxgZlrIukyfJz4riwV55aSA5XFbWR\ne2gMkJ2FzQVWffpZyzrssp2jvW5c6OU6LDOOMxV+mfpzKfh2PuO/v33DP//7N/z2X7/h65fPeHt7\nQkobTaLh/mhPHz279xGJZrtsuMarSsqeP5zx8XhQy9/oSXpUSsF2IHgwbZl/JZRcdQ1QW3dAu8m0\nr8PUz9OCvhaWCkYYzEakqRVCYGXyUy1VjaUMGHEztwfQ313X6xm1Fnh/1X2htQ7TtCBymJb3UTkq\ntRakvGHbLgjXiOk6KRHYR4nX5jWFF9nn4NUxKIA6/6d/Bjdol6UzhFQxhGIZY1BMQakNl+cznr88\n49vvX/D09JmT5L7hr5LN/urq6oqGfV9xPj9j2y4gQplDzjuc95gPC5lBMcnRGYNPp5PyT8T7ovI0\np7Jk9J/fMOxXrEU2Br41ZJ7624gYNTYNqt2wS/7uUmov+i8XnJ/P2C6kcvA+IM73Nb4P84yfjif8\n/PNHfPyPj3j4+IhpIpRr2y54fv6C3/71/3B8eMDjzx/w0y+f8OnxhDkELCHg54cTLj9vWFlhUFJn\n85NffaDi3xpPptP/z96b7FiaZWtC327+7pxjZu4ekU2lhBDPAhJM6gEYMYAaMUJiQI2YFYVUEySE\nYAhSSUh3AhOeAl6BqryXm5kR6e7WnOZvdsdgNXsfiwiPY3dY13bKIzLczc3Ov/+9V/Otb32Lnb5n\nUSxCm8f9iOkwEbdp7BVBovPyCkGSZMFCW8Fjypg3Ijmu4fZSb9gC5uMFl9OFuSx0Z9ZlxeV8xOVy\nxLrNrPtCHStVdrrDPh0wjAOL/EB9ngS75AwNkHlQkTE6wVQDBZPUYYs0cj8Qs3+6m7B/2OHuYY+H\n+wM+7Hd4mHaMFC0YtRV80imY1wWkur7p/KVeUwoJuQzeY9/TxCqpUY19j16gPX4ThmEZGeQSuE+T\n2PhFMxSRf7yqiaCSH/TdChEuVwW8sAXOpgg+Ndxi5rgrgSCX2MjnJoXdb10Cu+37AYdhwP00AaXg\n+d874+ufv+LLD79T579tC02rioEOO3c3OOdYYzkghAXbxpAls/6dT8iZB1jYauDA/aHZ5ZohMhGs\n3TPDwUIGACYKneeFJW1f8PLlhTP/s2pN37J87xn+KtjWDZfzDBhgHTbNRqTmaKyrbUtAbWdzDiOg\nRkpK/gJvG2tgecKVauMzVNpyHUTNcNwN2B0m3E8T7sYRh2HA2JEO93FZ8PePj/jbP/+IH/7uR3z+\n8w94efqCZbkg54K+J+3wW1c/9ledCpG5E5JZvDyc8HzYY+p72HGANQT/p2FAyNSuJdMqRRAnrJuS\n9trJXm0AUEcEV6NvLDHhjUyws9QyKax7+bcQAXPOOpWRPn8m2Hy4LfjZthkxBli7qA1wroOosnUd\ntRBp1pITAk81c65Df+GWOxmty1lbFePpFFJVIlzDb2j3AA1kDsi0xIS4smSv4Xp5okDo5csRz18e\n8fL8FafTIy6XI7ZthmgG3LJKKRzgdIABQlixLCcsy1m7eWLcYJ3DMFDWL90o1lnkAjzsJvQsyiOG\nnIA7hqubjN3wO0apgbHo/befKeaqNCqEQNGDWLYNl5cZ56czXr6+4Px0Rtwil22oG+LWNXUdPtwf\n8N3vPuLTb3+Dh/vv8OXL32Ndzzifn/Hly99jHPc4PDzg4/cf8fHTPXZ9j/vdDlPf4+P9ActvWRaa\nu7G8d1jnHtsycM86JUD90GT2Dbl5PIzUp38YidNhK2IoMLqiukL8c025bQuY1w3FAOsbpJ1TiFjO\nM04vz4gx6FjgnCLW9YJ1uWBdLwiRkj0AWJYzkxY9MfPjHcbdRPoCNbWvqDYAWOoCgePzJu87AEgG\nxtCkUFJt7cmZH4h7t7vb4cAo/N1Is0ycocFQu67Hbjdid7fH7nDHKr0/3+fz7cyfh7LIDGpBAEbu\nIc+lqFQlgzFVkYyZv+LohfgkLyysUWu+dKFqW5hsVmHmM2UG9CuFhI25BjnS9CjK9rnNiIk/YQ0K\nuwjsSj2wtx8EcWYdC8lYQxPCnn53wef/4Pf4+qcvePpKsPK6zjiHBSmsMNYRXOo8ch4ACJOTRiyK\naiJBeQ5CbILj0aao8721DszBjzLBY0bpoLCfZAPLFnB+ueDlyxEvX444PZHjX84zM6pve3YvdUsW\nHRLFu4WJXNYaDCwm1Av8z3wAERtx3IEw9X0tZ5jKeHcyDCMmpOjhggSJpjoL7uvthw7TbsTDfocP\nOzr4U0/Kiad1xV+envC3P/wVf/7jj/j895/x9PUzzudnytCsRc+z4W9d/djXGjdH5NsaUF7OcN5i\n/7DD04cD9tNI35+JV733mLoOd8OIdJcVBZJfwvSth4z+pVA4nQb6vUYlkxAHgv1c55jRvmE+L1jn\ntersW6cMaLlvMoPjVsIflbCuh+HIWG9BegTClS6OlBPWlWr9wzChG2imvZO5DCy8U4qr5R/OeFwB\nzdBwZCOyTUjWVARAEJHCpNklaGIBUBkNKJjPC16+POP4/Ijz+QnLckaM25scPyDOWjhFtX1XSgDL\ncsSynIBS0PkBvhuqvr8XBbeC/TBc1fFF5le13o20QYPt6fXnMPV4MIRd9J6nwkOEUsYWInF7ns90\n35/OWOcVMBb9MOok0JvePQffh2HAp0/3+O53v8V33/8BX77+CfNMff8vz5/RdSOm3QH3Hz/g4ft7\nTNOgd383DPj04V7r864jsuZ8nGlaJssA+446OIhIXs+FY1ExaWuWxMhxqbRkVmvlTqZ1XrUsmKUf\nn7UoPHdC3LpSTNjChnW9YFtngDX2S86kTxBru2LJCcaS/Z7nE7wf0La492VgJKKq3MqEP3q/NdjL\nTlr+ABMMcraMKHj4waMbO9V3GfcTpmlQztN+GFBKUe7F4TBh97CjAGC6r7Lrr9a3M3/um89c28t8\niTomwYnmtCiSiXxs4LYIMXYJiYVHIlIgRrI4lcQZuYgcaLTfOv6cVTVKxyWmDYCBNdRGRWpqtLEi\nmkAPUaVyyVi84SDkgsj91gbA2HV4mCZ8/3CP7/7wHX777/8Wn3/4A16enzTDWHlmeddR3UXnj7fM\nd85UrLXIhqK8xAGtcwAE4pTDodyGUh0SytX3y2DIed2wnAn6uxwvmE8z1nnBti1vMoLOWzqAbIAF\nsq4Ki0DYjwRFDh16qVFyZE7jSKn9rXMONAGxljWMswg+6LtJIdHlZeffZv2+9xjHHvfThO8Od/h0\nOOAwktNdQ8Dj+Yy/PD7hxx+/4PGvjzg9njBfzohhI40CJhONw+7m5++nnt+b0aA0x4z1ssF1xAY/\nnS543k9w1mLX9+g7T73/vsPYJxwyt7PFWtdXLYw1IBqWac0ULNlCrG3r2UlYo3MNROOh6+lzBR5T\nfHkh+V0iKFHXTOZMX4IGhZrtjZEfaJJleWU0Cb3aVDWs6wYyaqgTH1dD5z9uE8K2wa88Ytc6DXBs\nU+aSINhYA1NMrZPjWrRGynthDSRRzV5RbEwM1NN+OZ0wz0fM86k580aTgluXyCLLnaOrVhgFuGBZ\nLnDOY5wOGHc7Csx2Q+3G4YBYCHmtX2cMjETTXqGcSpAFmpkARc9g5ExfhH9iTFjW2sq7XBbqUDA0\nmMx3ns7Ojc4/ZlIXdM5h3I24+3TAw/ef8PDDb3E8PmJdZ2xhwcvLZ/z1h7/Dn//4Hb77/fckqMMc\nHKBg7Dzu7vY0kCgXJY5fjheWHTbcwUEwtRDlZC6KdIH53qMf+iuSczY00jYEQuPCFrXsIsmE847a\n5crbSL6iHZNTpPMDKk9QW/fCd4Dq/QDgYOCczHxZEMLA9p+4MuhKw0uoJTDt9y/SwWZVBK0UwLIY\nUPv+RuFATD1GHlvec2t3LoUSj77HNI3YHSbsDntM00HP2+tlymv67ft6X+/rfb2v9/W+/p1et/f/\nvK/39b7e1/t6X+/r34n17vzf1/t6X+/rfb2vf2Tr3fm/r/f1vt7X+3pf/8jWu/N/X+/rfb2v9/W+\n/pGtd+f/vt7X+3pf7+t9/SNb787/fb2v9/W+3tf7+ke2vtnn/2//+lcVHlliwF9fjvj7z1/x5a+P\nOD2ddDxuq9dNQ9uo317mMItUpvRvyoQ60R1vVd9U3Kc0et86D5mEGxKPWY08SXBdaNTqelmxnEnS\ncrqb8PH3H/HdH77Dw/cPGKYeMvjjv/hP/qObNuef/8v/hSf5ZRZUmWmu+YXGelpvSWt+6NDvBpaj\nHNFPAw01EVUqy33+lqZywdLzqoJcO8CFJU5VAIn1Cej/07NHUThk5UTpBxbRDBr1eIfDhwN61sbu\nxx7G0szv//w//g9/9dn/6T/9L9GPI8ZpZFEZmp9Nylx1Zrt13LcOkBa26MjLYmlZK0omYOVI/m/R\nMpBlbRXBAFC/r2gagCa/6bRBL+OOq/53KVmldrd5w3KaceG55v/7v/6XN737f/W//g1iSFjnlRQS\nn8/Ylk17c62xOrr2avw0iu6NYUle13t0fYeu8yx447VvWXUtIAOpZIhTI/xjofoAuq18blKsZ0NG\njuZE5//+u3scPuxhnUNYA5bzgn/+z/7TX332/+n//L9QuI868ve8vFxweqJJd8t5Zr12UjscdgNN\nEmNJX7nj0q/eDnZqhYaudCpYJ4Dkgku986yMuC0bjRUOkaYLpqzCYNbSMJd+GDDsepVWJd2QzDog\nHv/Dv/ivbnr3/81/9z+TjvpugAHJlR8fT3j5/ILT0wmBzwGpFFZVznov6nPxg14JV7VKnq3tbAcn\nQXvA6SyI4BWdN5quKDYzbgHzacHzl0ccj191dHXfj5imO4zjHl3X4W/+5l/96rP/Z//sv0U/9tg/\n7HH/3T0+/OYBd5/u4DqvA4NWGW19WVjKPegAq6rW2sypAKqYlU4qJQVQ14m8OWlZuK6eH8u+oRXD\nIWVM0nwhgR8S+Uksc11yqSqSrIjpOof/8b//r2969//H//N/424kFdFcCj4/PeNv/78f8Of/9894\n/MujiiidTy8q624Mz8HoBvTdCM+qiqRMyraJP0/Xeb4nHU+8lXvCInYsb51CUvsftqCKlqIESqPa\nV1zOJ8zzC06nJ6zrDIDlnPsJ03SHu/uPePj4Ef/6f/sXP3nWbzr/LUYVndh4sMsWAuJGjldncCce\nPti8YJriRwfAZhE2AIq1gG3kfBshEXH68qJF2fP1MBCgajlLsCHBhfUWG4+0XM4L1vOKcBfQDR3P\nhb9d6MSympnKFqtAjUFnOxau6NGPA8Y9GcB+V1+0szxnoBE0qRPOSB2K9sjAmKxGUhwI+NnpQFvE\nYGFdgutIxCU6SxKyIqMJVFEkFScx6jOss3D9bWInXSdjh00zbIYnPfIFvXovts6W/4lcmX6G9t01\ng3va4KFxGqXdc2MAw4NuUrkKJFQpztMwJBmC5DJp22d2kir8dMOSz5BCrOOrEykktvMj6Gsdii1V\nclnOqDw7vVTIpC/EpIp7IvlaAJhckE3WYKDuC1Q6VgNmeXZrUKxB5jvgvEeOZIzXy8riIPL3bjv7\nKZCBTSxPHLaIbQ3Y5hXrZUEKGTCNdAgHsikl2FTvqIoK6bkWbXP+LWOv5U/RfHlzpiDBIuR8lxoY\n54KsQ4v4bvJsCHWiJr9N1tvXM64T1tRh8cCtguZ9VGlfUkU1/MjVyVtbAyB5Rtk72QMN/IDqQC3f\nAblfzpIYks4+4H02hqc+kirpui1sB2j6Yko3ihzJHefBRAVgcTQ6A+tlwXohh7stm/oCSVb0cxcZ\nRvZK1pb0bMjsmwyk+nMj2wI5T9bYq8BXAl69HjLEzVrEQkqPpKh5rfv/lrXvB50UuoSAlLIGNyIp\nH2PkSY+kyOqch3eelFttlbHuWKBIRhNTsEyiPY4HXonzJ6GrTH4zFyQWPMspk9R6HxC3dOX8fVen\nd4q9TynCWkqCUgo0hOgXxth/0/nHlDSrXMKGZduqtG5KV9KzqkfOL9p5kiek4RZAzgY5GxhRcMts\nPOoAJHX8elDEMVieMFg8ZBa6aJZ7n5F9olnbniIrOYjbQtn6dJlIKtJ1VxfslkVG7VpdT9SnOh42\nM+5lAlWvuuWEenB2aKs6lXxPUywBAAUoVpwbj6vMBcXVvRClNf4tkmvVTCrAGJY4NZIZyxhYFl1m\nlUZb7M3P75vsVHT2TWd4spqHcVWBUA67qLYVdWL4ScYnDr/NmF4HEOb1eZCgAgU2F2RbatBgxEHQ\n57bWAPyMjkdippgQh4gUbh/sI9ln2Mj5yRQ+nZbn66AR3WNGAGp2Y1lv3LJULKM8aD4re0Oy4zwX\nI1/r2dMzcmDhPcATwfgPq3NiRC1yxr6cF0ZEnH6fW5ZkV6KbHlYy9stlRdgiBfRNICGS3jKh0FiW\n6eWPVwP6659jLU/ilK/RLzA1AJDgv/l5EkTJ3TRZHEvdP1FRMwbsbG5Xt+z6imCILDm9A6sZOAw0\ne5Vz73hIl7wXAwBNptvugQbD8lj8nHVT+bcZLbxy/jBXTtZYg96S00wy6MxYHZ72Fh03cVCuZ8VU\nGLV/NEeCkFaa2EfOX5AnmQUCPssSnLxGfkzhwI/3IYslMQbGJk0YiyuwrPp4dXjUL7BSpBMJZVHo\ni7qvtD83Pz6GrsPgaUQzQqDJjiuNH9eZEznpsB7nOpK+Zsl2CgasqvL1E/kFcv5OExTbnCWa61CQ\njVX7bZt5LuRvHGKfrtA+x6PDUWgscEoB63rhe84zIOKGdfn5iZbfdP6pFB3msYaIdaPoh7L+GoUJ\nVC0DCgTyzCkjO85UChSuFedUYOCMUzsnF6JmqxbFSHTbjL0tzdQz1rp3KcOnjDyQ5KVkLttC5YCw\nD/Dd7XOd6cdU+FGCG2st3GDRDR26ocfAk+YGhvrbaEycQPv9WkcHAKYAkECAv8Y62xhDGhRibSHU\nxIOkXLNDkVG/AJJJKh8rA5DqpefnMOlmB3DlfKyB56jWd77J1qBa3DKYg7LYn0K7rQGQ76kyvrYa\nVzWARWD0mgWiAHAGznJ23QYVgBoeveyGMuGOo+10o8QpUCHGxEND5Ge0Y6PluTQoNIYitCZgsiw5\nTV/HgZ1Y9kKZjRhJzaXF9zcGzMpdeP2amv2xhc6H945g8oWkngX6vHUp5ChZ/xKwzRtN6hSE6Wqz\nwKWWoncGAGwxV+foJ1lYIaMvErc/66Pkvrz+BQkqBE2BOohSaCqlOGbbTMa7ZfUjGWvfOSQDDfiM\nq7LTbYYnKJj+mXnl5NvMt0GG6iMaRbh0WcCUNvj5qU0RG5Hlz3ZATntOFgqWZa5fd+OS0o2ie65m\n4jpBUsberpvKswuy1iZJ7ZkW5y6BsLU054G/jJ+ZpNjhikT/gHMQWXINEBr7KfsnqK+JLDvP9g78\nZ7cuQXsBIGYqfWnwn64RVWc9YGjcrnMdo6LkF/qpVynefuzR9TToqpaBDU31E5vHZxkAoT0JJP0u\nyYQ1jGpSwJtCVLup01fTxiiPzK+hAFmkiF+vX838vSNIIaaELdCIUq3Ds+67QHAwABxQ66AZOVtY\nvQn1e5eSgWzpQY0FBXctJGZ0brm+ZImQckUArKcJZnUkakEfM4KhwSSJeQHbusEP/nbnp5+zedl8\nySmy63iWN/3qen81t7wabv5HY8zFMVxnIzWT1tvQZj7FwjpyrKWwc+FsTy6RDO6RAEt0ogW6k6z0\nLcuw8fNcjwOgZQZjjdaxrLPQR7XM+5Bv8JO95OfVP3tVNoAYrFyPTIsKvUISZMskI7SydwB/RkJp\nbh1nDEBnSUjmR7MOrgfS6B5Znsve8FcAGozVBq2vz14phWbSowZxvB3VGXBJQIxoOx9Av9bUYAQA\nXOdheJDVfJoBY0g/3d1mBOMWdWSu8GrCsimsCzQDmuhD0XMUXAVKuVy/q3YZQzNBbC4oP/Ox1GmY\nei6cs0iyn1eBNQ8XSkApgUspgOsKPBxQ3JvufS+ZGt8VQfEcIznZM9/BX/M36K5ez/DQZzCCgNez\ncGXbrpAe2kFxErKfipJZ2Xn6pwV0JkZNxqLCwEDBrQGAOH7febjeK8qhvKOQmnp0vnKIGtw2P+uK\nx2CMooElm+tk3hjK9vk8aInM5IqCmGpbpaRAKBkhlannqaBNiUfLDzeuxIOTYs4IMWILVGtvR02T\nb3LwnoIyGVXsfcdDlMg3+KHTvRT7KfZN7Trbs7ZcSHtGhsQwwlR0oqGFdZmHQ1nAGEUktnDgwVNn\nDgLq+Ouffdff2oiYEpylumTMGVtT65fMUqDBIpCbNSjF0thZHWlcnZi+lFwAU6GrNjKQcwI1APT7\nMvvYeYcUktbSBD7JXYZLNAhGDh2NAOYAYN7eBvsb6PcvpcAbr9kukcz4JasBgP5MmczXHn4CM4zC\n0uotBcLLGdBaXoW2Zdyjya+cnhhGV2ExYwQGahCLXDTDfMuAMwOKhP3g0Y/kPFJIiIUiS8n4ZSiH\nfmYAP3F0AKMS5SoIbH9ae7mRgXKVNf7UUMrXa+ZcJAByQhGi4MVbdGOnJaxbVov2SOBnS72wwne5\niuSby1tQUYL6Z8JiaB+reW/yTFojNlAi7Ovd0qDn+s8EOXLOYgsRy3mlzCBmdMNtQ07iRrVOJdxt\nATGkOpyoCTwEgpWyxuvsvt2X159TzkMtD1z/nRYtqqWlpM7vNapWSmIKkdHsPLs6WvvWpVmvIQdl\nHN1lywhKQdGhSdY1Af+r51XCGr972T/5vLoPPwkKdfNePV/l8NR3YGiIli9qg6hcRbZ6W9efR1R+\nYWn5qPdEUOWsWZ1/SkiZnKxMGBRHLGUsSeL0czOCd0VyhSQoGQCXaHD97iWRkWdtkyVJbgAen+6I\niCyoEwVP9ir4vmUlHpqEGLHGiMCjt4uUU0D32vkONIPNwfLwLe94NLtzzfltgpyru2yY/9DY/194\nT1TegNpOU6w6/lIy+qFDCgnDQKTDbVs020/M+fi59auwPzl+IfvFK4dSyWWZ64D26vJfHVLgyoCX\nAhg5GObnL4Bk93KBWiNYs6/qAKXGKsGBEL1iqOSl/IbMt+s7Okxcd0uWai5WavpKfKvOvA06yFk3\nh7cUmGK4Nlkvr/6LD8uVd+D6rjxru+SAWWMAZ2uWZVAjVS0fmCtjctMylNlLtwBATlENxGvW+qu/\nS3tSahZuICPL62dXB9m8Y8mcGxTgl+zX6yxDJg9SPUU+Fxnut5Z95ENba1D8zzuPIu+Uv1aNmK1Z\nvxgxAYAowMvK9ZA90PIH76k8v5B8XpeRaI/YuCRQlB8iBzkGJQMpBs0MhhtnugcO5gnOjZVp3Bhf\ngzqSlj5/Q37Tc/wqoCs/fV/0gNAR1lcOnXBjDkIZJvWWu08I/bO5aJYr9qUIB6FBHK27/ex7HR17\nXbcmiJ+mXWq3C0Pyr4Oc1lkz/t0kOg8taGMAACAASURBVNfP3yIY+oEh2f63kxVjKnNezks/ZgzT\ngHUeOOu7PfOX0bgUaDnKTxhmzinphNGSq10ixIGD21cBXClk32UvKs8FWs7QwEmRDf2Sq/3R7ydd\nUs3PRzHKxdKgwtTnuXXlUrSEvcWI2Ey3NNZQF5tzQOnYDjbdbK6iw3LmdYqfoIIGGv4T8mcAcx0Y\nys+yxlakrRjlChljCGFPwnughLcbegzDiGU5I4QVJScknsT5c+ub1rCAxtqGmLBxvT/FhMJjD3VM\nbvPC25em7ShCaMoFxWaCT629dkYtMpDrISpgdjftFkpqDFDjz9poUZwJ/ZW2TdC/6SD0Y0cEJzTP\nYQj29x2NEfbS2mOMGjB9fiELSWsfp93OOarpNQ5SCTxNrb4lN2rUrUxx1KzCGhhwbVM2pdS2kcKb\npUz8G5e0zHR9r+Qny4QW13AbfnYJbFdAAZCtzqBcfVkhPw3zM8bw+uvApYbyM1/z2vAi132XLPNW\n2BsAETe3yJfaXX2+X8rEpTxgXT3br51Ca+jI2XNbrOW6sTNXf1dQH4XTfyGAK2ha5Lgtk859gs1F\njdAtK8eEUsBtRhFRCL7XD6zP4NghC0nOOX/VWXOFiBSj7+N1hi/7IshVmz3J93HOIbuM3IkDkPsR\nlEuRUoSNQtZrAutb188Fs/zzrXfwjEzqmRanBWiQk0tBicS+bz+/7IMifMaoc0QW+4VqT2RfWmQ0\nA8ahIkuWSG8OQCkeechcc+4Zng/4CSTzC0va4wQ5AZ8DKv8wox8gtM8YGE5yhGyoDl4cYIHWqRXK\nb8oVbSeG8w6WbW3lFVFnFNp7ZK6TKtlPAMTL8BY5yvuyb6r5x5yxpQTL45KF6NueH0p4/FVwK2N7\ndd/Mq/ctCCKXUSCICb/PUgp1IzV3QbsloI8tW8L/fW1biHPQEwHRWOSS+X78AzJ/gDLYLSUi0Ukf\nek4NAtAYJYPq5I1Rp0ufGCjFKZcDHkBEPfgALKy2LplcyXEwBFtKbUmiegn25fGNRa2HWQMkaH96\nWCOcD2+q/zjvG8iGP6eD1vqctMIVACVzy169ZopO5OaA8stycGq4NMsKsq+JDkSuf95mxJpFqjNl\niwM0ZDRuGUmUsRGxshrkX1tyicWZSQuT7Sw8qEfVe6+/L33a8tkEDQKgiJAY4defoOD6PUqWIM9O\nWgYUZNrCEBh/nbbK8NdSxpYBa5kfUWor5BvgT+01Fmeun+v68lmBg6Xu615BjUbeE2qt3jJc6IzC\nxq/1EaS1UrKXtq7cHjIh3VKrKAWMMcSGmEv3s9Yrf30ldlopkNFXyJ+NkZEgojF+joMk5/1PAyB+\nx6bUz3vtW4n3o3tlm19q8WoHhrWGa54UNBVP3CKxRRTwR6TokFOn7+rW1b47gIN2PqDWWhRboWV6\nB+WKoyCBfObfp+C3ZnHtuzAW2gGiZ6DZ1/b7oaCSPk0liwliJPst2a7vO/RDRMDr/f7l5TuvHSry\ns3POVAvPmSDwfJ3wWWuBvtre2p3Bfz9mGM6mr2B95k9JKdd1dI9ktTbPtKCy2v62bMxXS74nANFW\necu7DzFiY/u5hVA7GACyP5YSv1KEqG749xj6H0jTQ1AIss/UEef4DLVIqZTPJWhXlDhl8gPybA3h\nUxB3SRRlL60lLoJzHZzvYZj490uoz7cz/0LEhy1GhEgEosp6lLo/vxyN8rjNi1fcIkoGH5yExO0O\nuRFzkKzfGMOwsLl6uSR+UMsLteWiVKcsn6E1OLKpoX72tyznLWCqYIhN9UW07Ue1rpUlqNPft9aw\n0ZdLYa8MvSkGGbVdSR2edESUXC+BHJz2ZwoE9pMXLIEDGDo1b7oItcZaYSwiOXlkW1vexBG18B/5\nNxYzUud3neVdQVxGarpSy6r7lyMf8GQq6a0xtsI1EReTIE6hOh0AV2S12xYHLmLc2OC0wYvjlj8V\n9eFnawWN+An1XwXE3Si2ANwHLiRCLXNkboZu9qd9L/IZjDHImQi5lHlWwyEsbAkc4xoQbyx7SCAn\nLYOV/5A1Y38dwbU7K/ewZiYGRqDnIl0P0pPOe1Ykk4aWAcWxSPApTi2z6JQkA7Y4Yo/njALJMKUl\nijUe3tDqJ4a0SAYqPqixdZnh7ysyGl7dT/llACNBWBMk0zZZSnZs4V5eCwvDLD5ot5MYlQTuqEoG\n2RA0nHOBAT9/5vdkjOoVoP71X11tR4EkLILuinYCCZIROtSWt6y11da1QZvNsPnVeW6CWvklgnCa\nMYMTYQ6qDWowoslOYxPlrKE9o+WnJeVvrXnb9OsX7mYgB1/LcRKEUzePq63dHesjsPGR8kHLTyj8\n9+Suys+SpEVaWNXHSZeZo+4uWyzz2AjVU/soewAKiL33SIa7tMzPI37frvmnhAhgC4HIfmJQUuOQ\neBFxh5wYqW5Z2Ez1sZQyXHT1JXsHFxK1y/UsjMCHWeTvckqqeJRCDThqNiiCEtBLarjmb5qsQwIA\nQS7ecA64rY2eV+p78rPs6+ZRAyATW1QMrs229oUzLOR0DzyMpWzGJNNkLVXEpK1j1sMuhrWwMwEd\ncDYYGpBkKpfInmmP8I3P7ztHbN/O6Wc3pcB56r3VtjxBbaT9SVp5mkCMDn5RpyWBXXuBhGAkgkgA\nO7EtwhiDZJOiBxVpoSxPj6EhGFIjddPUFEvRPb5ltcxeoNYX1akJ0ZRVLFuI+6pdUZCw17CDGLVM\nv1T0oUEK6FtUgSXoJ8FPyxiFAwsx2m1mlAoCItz282Ifr5ewuinrj81ZhLYtvsZvxOxa27a81WAz\nC/plmpCMP7N+72x+AplWI8MOg+9O6Yue/ZIzkjXI0lJMB4SNaGKl0NsDf2lbzTHXfcyFYFTN6ttz\n9jrAbb6ZvH/DCF9I+n0oIM+aAQMehe+0LTWYbJ0/MkVKyVT0q7DuRS5V4MsYQijT0P0qb+DqPYoN\nMfThBZJW596gT20BT8pTQD13+qYNrp16m9Hr/3i7mkRCgqlKmgWETCioX3EOLst9zTW4Yqg+GfOm\ne78sGzxzHdZlU/SMj1TVljAGzrPuSU8+xznHqp+ofDhz3SYuOg3UNspCbhzUWGMQY6S21VwAa5EB\nLnVXpEBa66+T8MrBsM7D+x7OFVj7y50u32b7M5tzjZGkPkNU+F1rVqiXo83As8lc96OMJrmk/+2Y\noFAvjUU2FsZSACH1DyLrsQFq4JdCRTWFouTnSwYqsJUIw4gBFsjw1uW4hRD1p3I0rL9Vo1+QKIWJ\nBhlJM2BaksUInFnhOrpgtVbWGj26ZFbtpbGcCTOMpOx/iaSbNrRSmPTXqP+JGtYti/pVuytSH6xh\ncmq5upBtNgzOwqsUqSA2Nbip/fgGznviTvTcDuNFQAiIMSE6B+vjlQPUqLjU9k4xUPIZauxh9LPj\ndhvwCnki1MowqUeV3V4bNFSnZSDGmNGZBnKXLPL1vaF2HtrP3Ih8VKi8XP0seS8U3MaaUUr2BVMd\nFsP4tywRbhHHn5k8JWdXPH1FcuqdEti6Zl81QIS++xax4n1gXogpDWJQCrPJ89X+GlfLK0S4Iglf\nmzNKMQ27uX7/HG9/+ULakhZmSj4oy6qs9wa6b+9fS/57Fb8U+Z8EZ6DATIBSYyl5Kka/siIPberO\nwbgcaM38ckX6YCrJVVC/W1brbOWHV1i5Jm+CANSSRNH3ecVPaWI90gyombDhz0p7YtVOo+NW4RY9\nKJkzZmjSIN6rgBOoQGJnQk61bDhTvJ3oKxLTMSUW+ApNayORX1NKNZFNCSmSXTRg8rU11SZZagNW\ndNNVlEDJgaD77ryFCRY2JpTkKZgR9JODmdYO6uI9NUz89L4DMIHuIP5hrX6pMHkoVohBjHl7+OWQ\nXsMypOoHJGVCSiCQPYuASHZg5UIbYjVmgZZqu5Fk/5LdSBQmjiCzkRJiiWZgpcmGBbq9cTnvENcq\noiAiFgJbF5Ra89XWoFLVD/nFVGWwigBo1sYQp6jG2Zj4YHvoxeOLWEpBcgkmGlbFpD/Ljg6sME9b\noycGTEoQ3Y3Qrx88+sb5W4HjHb1v0hhw6gzbjgcYaGsVOed0RV6kQIBhSREU4bZB+nn0vmJMiB0H\nfo0DidJ+U3J9N8yTaGF0Y41KAVNHxO1kTzE8ifkuKSRCPcTAc2YiGaE6bWTYQuUr6oMWHfbK38jM\nTZAz4bmrpFWlE7IdEWwJ3VBxrFfOv0UH2iy9oJaGUF7rSvzyiqKdr9LR9U7p/tgmqAKjVYk6a0oB\nbL4WMJF6cdXtl2ClOi/3Sg1TPn+bZUvwfZVsWCYCJpKzNYrKVefxluxPWntzzCxd20jYBtF/aH5+\n41wN6N1TksP3lo2BMNCFLZ/57MgnvYoX9Lz/vDqhdYXuopDCxNZJYIYWoTI3Z/+y/1bON2pHh9S2\npcUPEHS2Bqrtu6roVQ3akAVFERud4KLT7ytJg349yPGLbxE0z1niTWWfYTZD814QdC6KOP9SCkIX\nf/5hf+HdW2sB9id1rkzUttecM2ymNrsUIjtcxzM8yIaBEz1JdLupQzeSIqzn1kCZA1HvdkKXek1q\nJLBKMevshBi5zbo4TcBT5I1m5991PZwjnYqcC2v+/3T9Sp+/wChJnb4Y2hToQ7QEp5wzMVwly+MX\nnw05LOqPtCglXxFVtE/fZoC1jfXUyAYw/E/wSJVLbV9O5ohcej2lDtKut9R9jSUhEmFNJ2ZBW2fY\n8VsVkFGRH8LfAAOF79tD1ZKZSi6wiRx5AeAS1dKztzBJ4E/wgacI0RcasJG8u3J8mR2q1OUEVlcR\npiCa17dlACJ0QiQcgacAY5wGPZ5JjyQz+VrghtvUcqbuEM0M6qAPxxfGe19rZhxsAECXMtKQrpjm\nORXEGDV7oOeLjBARykHwm2MosrYmvkXmUzNPySQiO3jB/2C07kzfn19Wkr/b8FykFJSLCuiI43ed\nRxcSfPDw3mnAGGV4VQhEwBMYMWdlNksAASOGn15SSglXgXHOLARzW+Yv2b5mvol/pkD+7FwAzrSQ\nmNdDX98xguN4cEmBoFBZIfhWMU1RoK6SUjXYlH1snqeFPNsAgMpyUtq6RiXfsqw1CFuVN95Y1pbO\nWFDHf0XKhIVJLFVvHJw3ugdy53JOMM6q80+pzpvQ8tErwqgE7a1AlfxsI11TMCSZy6iLZfuj5xg1\nAfr1Z6/IoXJJTGXN+94hZ8r8k6HkLnMgQ4FRQIy1NExJUkWJahBW0QTfeVVVpPNSFGmqpEarXVU6\nx8XSHdyWDSXT8DVBlVJIyIbQN/cG5++8Y/Jqld4tJVffkiM9X5BuFL7HzsF1HYaJ5rx0QwfnPMv8\n0u+Tzn/PSQ7bKEZ26RixzWfZFrUZMeq8GpkxENeADfx83iGGqHbOdx3AZUFKXv4BrX5bpBqhQm9y\nAWM9iK/rOAIHtUujPFMhTGOjXnTnHZJnKMXW7EcGdqiTZ75B1owDGp1XqckmSubgQQKInDMcbs/+\nIg9ykJ+dosCfHc0R6DuVcLxylCB7LJwFRSKaXwLJp1fwYCl0AaWuJBFwaxAEEpbMPoSKjCgbNFWC\nUVVpC1fZ27dWP/Tohv6KwFIKteUJOc93HVzvqvNmAmebJbbdC7lcZ3AyJMj3VSzIKwnUaMYjdWv5\n/7kxLDnR84mcreoQSNnH1yzxLU5A0GrlnGRSdaMzEBlVaJ3/tfLfTwJTDgAkg1Rdis4hjhFd6BTC\nl7ZOyTSrbnqTgXN21k5Bk7PXEvVaZbLob3P+gnBdaXqUcnVzUkooW2GxLeiz+M4jjz26ISMXCuzo\nezYiMeE6ABAH7jar99oae4VsvK4na/DXOH/jLKzmDYQpi/N5y7rqmFirQqhk/LoEztZ3LgEynYeu\n9zS9je9PThm+j7qn4lCAlmgHfb/6vNkA8br0AUF1TEU5DUP9BkBODqLyBkCTtV9brplUaUt1vMYY\nlEHYw5Rhxi1qtp1yxrbSMLWwbtRtkWkAjrTqWVtnTND9dOwgO5SyJ95D7zWx6Ie+QQWd7mUlTNMZ\nXS8rSi485W+FsZu+RzTJ4K3P752DS6kmT0yui1vAui5Y1xkpbQCEXe9grUfX9UhxxyUgh25gBdSh\nI0VYLm127fwX7aa6Jg23JcEUE7ZxQ+AAVCZ0GlszemX/y7NyUGCjQfA/L2v+bdif4RnKXKCqX5pB\nvHIkRcLgZonRKzybUmRgxVgJOUciPcW+BC4XjgCLNyQwbBZLdWwLaxCkxEQJhsNl8+QAvNEK0GYn\nhBC0ZihRquo2s3bzMPQYOo/Oe3hL+tApF6Sc6q40znuNEeu6oSCS0/eA50tRxo6QAO7fFUfUHgza\ncIKEJCJUAxsTIiIHPIU6HRiyujHxJ/XCoVMoul00ypjHbzLJpY3eK+EN0LHMuUpBtwNwVEqUs0Q/\ndM343lY86RoKlWEIKSaEZVMDGEPUlkpjqYxEsDNd4LetooFUikm3LqWkvBMUuuiuc1R64f0S5y2k\nOXk3op7XZmSeZT8Lew+B2inTLRr0inFX9rtrJGYtBcQllTrdrIHuKci+HfpoFSIhgX3KSABiyFyH\nlRIOPUfHPJFu6NGNDWfEW+LoxJaJz85fSiNFJkLQGVcmeIOWWc6apfZeUS/ukW6CAaBmTm+B/OXZ\nBe4PG92tK84RI5GiO2FMrYX7zpHYikz5nHp0HTmtXDJ/Hy6nSlknF73bUqrUz6L6HlnvUltnv7Jp\nbD/cIK2pQI4UcF5N2fy1589cVnMFtrN8tvkPmacT1oDNbmpXcxQl1Q3Lcsa6zghhQdhWpBQZAue2\nSxAK7H2HrpswTjv4rqOsuAmGuoFGlA+7kYanTQO6zsNzgrGFiHXZKBDhOS7bLOOFs2qi4A1ob+88\nhq6j7raYsF4WXI4XnJ6OeHl+xPH4BZfLkXQlmGxsLUHtfT9it3vAtj6oJDAMWFmzlmY0aJQgp+/g\nG5EmAVDFd62W7HbXeeLuxIRxT2drPl6wnNefaHhkLnc6b9CPPy/u9U3nL46/Nd5FHiLXul+NxCky\nbNW0rHVc7yU4p0I4VR2rLXYJBNLCx85ZJG9pmE0pFJQIAWMLmqHrxecRw+JojKmiIW9R+qIMKiBt\nNavuBsr2xbj1Y49pHDANPYauU8dfALickRmGlJpVAXcEoCBmx/UsqBCF1nO5M8AwtJXECIMiWpl7\nkGPWACmsAQVFDbZkGMESdGm9u7nu6/s64EM+l7D2K5lSarA1gBOHVFvkcEXMlLoVGTwextEEFykl\nFCOSmJWs2dax6ZxwgMB1VN85GDtUiU/eczVOQoa69d1HqfVzmyifn5I3wBiN3H3vtPQhrT4wgN3I\n4ck0MEUn1o301gsQIw3igA7soSEhMpJTnApBeZQFiXiLbybPWQ50YyB40DgDBNobUvgKsNYihhtF\nfrj+2LaREvQOIAIxBqQUkVJoRpsaOEefp+sG+K5H1/eUvfX+iuUMMGkzZkWDJAtu+QwS/MqZol55\ncqAhEPkqNy2c7Weldjerd+cN9l+Dk8BZv/AoxN5l7v4RrlI9C17VMIcdOf9hN1wRZ+VzKsITKvrR\nOnVBXmScrDEBoRQYW8j2sgMVLQqp1ZdC3B/fd3omBEm89d2rAeZgRETJLPMHgqX7G0NUtELuZc4R\nYVuxbTPW9YJ1XZDSpohmPSsd+n7ENBZ0Xcd7XkmrACiY8U5hctdZDL1H58gmeUa6who0IRt2g44b\nLqUgF7rLty7vLDyXMFNMWOeNnP/LM15ePuPl5QvmyxExbiqdawzg/Yhx3OFyOWGej1jWj9iWj0gx\n6SwY33VIju6Li2SLJQAwxrCmU9WwKAAPJ2K/yEPDSimEurLInAT/YvdLBk/eJTvY9T/v5r/t/LOQ\n/aTWlTSazikDCUiQXkaBJCt8JbKf3tRisLFEcOoGnrbG2aO5CgBMhbP4vwkMKOrwpAtAIqz2wNCH\nJ7RBRiAqE/oNbS8FVVgEQDOjucluOo+h7zB0HTpH2V/MVRRDngcgJ5hyRkgJUYgpUaJHFnzRC1wz\n/iK1zpD0HUCJVO3e12BMjAt43+QzdOk2fXeBqSSizCkjoRK0YuQBGnypPZceUqA5095XxTdrDZUG\nrGViWEJqMpGUMwVxISEv1ZBfkTqzDv7UzBdo+A28h6bzKF7EeCoZpgBvgv1zTAhLIEOyBr5UjFZ4\nx8aGRnb2I50Fz/wFY4BtCVjOs2bqK1YdFLQuM2LYMC8nbNus7HTnOozjHuO4wzDsMUwdxv2IcTfw\naNAB092E6TDVLgxrkCOPrz4tKKUgbIHQE9k/nsx5K/JRYeeKVIlYVM6JnX/g3xO+SU1pRGjEe1KH\n7PoB3veUHY091faFwFXjnoo2ZFHmq739UcpBqLA88SFqWe06CKAAICWr5+fW1RK8kiJ+pPIoNkrG\n7ErXh4zz7TjYkfqu/BLxl67zcDz0KueMEMkWEMGTEIbMpTwaplTH01prYbp6B3RsuHR9NBEO1efp\nZ26ev88bl5YdLavmwcAwUpFCJO2IV6UQsv8sMlYKBQNhRYyB2esZ1jr0/QhjDMZhVwMX5rVIm2lY\nA0vpUiLWzxvCNKDvPUQ0SWyctt52PIqYa/XI5k2w/8gJXM4FIUQuJdCwnHk+EaqxXZBi0P2mcltG\nSgEhLJhnIkhb00jOO4dSgGE3KNIppcS+65Ccg0HlNQjnSI6cs67q5xSDwrLrxJeRUk1RO9XqSdhf\nIDp/m/AXE7Z1w3pZsF42Ir2sgSQ/GcoEqgKWwJLCZlRyRjMKU1ndTArquJfcKRveMTuWOAIQG8Sl\ngKzBiPQfg9mftbWolIK08fjawpONItee3pAC1Freq155KUfYSmqTACRlEkVKKSNRobshKmW66Imy\nnMRdAcL49gPD/rkgZXIUYYtY5xVh2ZR0CEZgSNwD2jooCAdFvBQUVLGgxqnesHrOLoVsZApp2Bfp\nwhA1wixzrblfn4kszjtFRoaxxzgN8NOAofMoXadOO6aEZaVnW04zwho1G2wRpQKoaInKjzb1YGEo\nt1oGhestlDk75Hg732NbA9YLzS2Pa6Bsv4BlnQmO3B0mjIcR42HCyBme9RQAhi1im3cYDxfMxxnn\n5zNggHVZkS8Rl/kFx+NXzDNlEcY4DMOEUjK6jqZEHj7scffpHocPe0yHHcbDiN39DsM0aFBL2SFl\nOdsa6v40MydSSjAm3+wAyDBV7QKCH3myWY5syFO989aqYY8xaDAjGV7XDeg6yox26Q7jbiQ0wJI6\nZZv1psjfX8qE3M5HA1Scompyn5QEiXoOAOhneN1mecsKS6giSaUQAtd3OthH9vi10BUA5TAJoiVJ\njbWEXuynESMnCsYYmpYaIy7rhtP5gvW8YmXEiWBsqvVSqdUor8h6W5X4JJ9pnJwxlFHKiN5b3722\nMRYGZNnuir7/xpnw5fmMy3GmcelbQNqYhe48+n5kvkFAjBtry9O5SBwsUgmgAMbCsghN4iBW4G5C\nslayjZ1HN3j0EyMpcsb579HeNyI8fG5L8+e3rN53gKHy4XpZsJxnbMtGrbSZJiRa62A89dMT3D9h\nGKZXQe8AYyzZtfOC88sFRYKYke53YXuy9Ru1ljoH51gts2Eny0TJXAoSdzhZSzX9fqRkrpTCBGGW\n9W66vH6J5/Vtwt9GxIL5OHNtYWFIhaCouFX5QMNQqB+8Mk+v1Ptcw4wfPMEWEqkJa9xVwlguIIdj\npCOgnZbURHoNE11r6ikhaNZYNOvZloB+uv0gKNyZshoRgqHpvymToqwwpkQiDZlGQeYknASZfU3w\ncWhg5JIJQu/HHuNh1KgQ8nnnFctlxXJesC1bdeLMxXiNSPjeowCqi6DBAvCmPmcAGKZBa3106Knb\nAAzDS2aUU9KsmjLw2s3gO49xP2B3v8fdxwNxN0aHvtUA4OdYzgtevh5xeblcR/PWKqRqjYHpjTpz\nGCLdJX4/MYPGRws1QIy+EULa7f2+83HGfJqxLQSfybjmbugx3U3Y3ZEzFmh32I3k/B0Fb93QU9Z+\nGDEfZvRjD2OA9bLgdLQIYcXl8oLT6RHbtsA5j5wfME13cLbDdLfDw/cf8OF3H3D38YDpsKPR0XzZ\nl/OqJMe4BayzqJGhCaSFI5IVcbllvR5LK5l0zgT15xwV8TG2TnRMmRwUZXpc0sCCbVvQ9xuAgq4j\n1rMxBo7hSB0Uw2cipRoAGCaKCTmMCMGCikmAmPhnU7Qnf2YMj5l+I9lnvazY1o14BIzCOea0jPux\nzmfvGY0o1yRLgM5eCAFmqa1+Il07dB0Ow4DOU3vbvBFB7bIsxF6fV1xe5PwRibUO7zE1ANEWW2nt\nxVVABgdqLxv7mx1gChExOIiwjaKfW8RyWXB6OuPlyzOOX0+4HM/YllVLkhKMC6QvyFApgPcdQeUs\nOUvtaKRDb6wlxz8TauWcg+fEoR86HS3cDR3dNQ4AhGQtZ1VFdFpydIpIb3j/MSfkUDCf6R2s55X3\n36LrRkxTRtcRatH3I8bxgN3uDtNuz90BnBjyiF/feXb0Bdu6AYbOiowYJ2SFNA4EySOFPlJTHbxH\n33XoLHUCBQ4WVwRGfB1clxWllWBZRhx/a33TGq6XFctpweU4Yz7O7PwpGl0vK1KUmo/VbEzqq9qS\nVHBNiOlpPKyoaKmso8LxtaarAys6r5tqmOHccX0IgMLBAiNGZraKYyFuQMR6WW8eawpAmZXSQpY4\nIo8M0cEYhRQjcwIAYqZL5LqcZoqQ5xXbTJnkcll1DLLvPPYPe9x/d4+SCvqpZ9h4o30/UXTd6rQr\n8U8ISC0rnrPOdVk5g4kAZwG53M587UZquxH2fPv9BZYVTQHKQCtMqiiJtVjnAZEJZ/3QYeg7zXxi\nzpg3gtZPj0d8/dMXPH9+xrZucN4TzH2YsLufGIngUtHQwfedQsWW2/xioClchIzQEsU5qdPfus5P\nZ8zHWeuaMt1wkux7N+ilDktQKNR5B3CZQ3p+OzbUOWcs5xWn5xfSu8gJ63rBspw1Q845wvsO02GH\nw8cD7j7eYf+wg+8JLbm8zFgvdjanWgAAIABJREFUC86ceakCmdw5QPUerlnjlIndumqZSfQUxPlz\n54MRxUpPTrYUGM6MADqjZOwTlwo2CggSnQUZiW2MueqmqIOMIlKkoVrUTkUBACmXeZp4Bm4vzE2w\nAFFOq0JTWmK4cV1OF621U63coOs77O4m7O73mA4T8y68wq3bumE9r5hPMwIjWTJGnHhCPYCCaTfC\nGoOp77EbBoWOny/zVflmPpO9lRYuYxzPmZDODpkWV/vEJchoGfoYiLwridqvrRAiXHAN0ZNs6LZu\nOL9ccPz6gqcfn3H8+oL5fMa2Ldx7XvQsGJC4lHMeQ7/jwDZz4CiZv8c4HjAMEzrfEVoWItJMgU63\ndczrSuhCV2vaRpBSVfjRBLP1JRSIXCNRt6zzSp0D59OZfFwiAam+n7DfP2Ac98iZgoG+32HaHbDf\n32F3t0M/DbXzpilTaV2fn2GdV02ehUCfQiLel4FqA4zjgG7n0DuPfd+rj1xCwLOdEVMiFULJ+ENk\nhCIr8mOM+UWy5zed/3Imx0NEpQZSEJ18yfY7bmVoIrFaw6+oQDd2mPYjht2oWSVB6gI3JdhIDyh9\n4957zvqs1jNFbCHMQTNzYc2K46G6d2HGbkFeA72E4fbsLzD0G9YNBgbRWX5pPCGQN1clSW2NuPMW\nkBe5zDOjJzOOj0ccH18wny8opWB3t8en339HL/swYjCDdj1ITUvgdSHNCVogfaja6gMSlokbwXPL\naab+dGeROmmzuhH65e8tREnQq9SAgxQAqaSyKdGQA4GUSKIS0GCj6+nd7+/26JzDru8Rc8bpMmO5\nLHj68Ql/+eNf8PnPP2LbFgzDhPuPH/Dxtx85IOJg0Eo7WJ0MR+puBQjQcyC4ZXGWS0Ov5HB/ZV1O\nF6yXGTkleEcwYy9kT2bvChojEXw3UK3X9RK4jDRcZRoUHo5bxHy84OXlEd3jDxysbogxYF13zBCm\nbgol93UeKSXMz3R+zs9nzQqFiKrz1zsP1/EkQnZ8NaO+zfmXIu1N5ICF3CdGXnrLpa6LUh1wy18g\nxyv8AIJ/Synoeo+R0RJjjJYtpJ3KBYcYhRzH5FXls1TFwqJ11k2RBmMcOt+xI2LUwF0z6H/13T9f\nlGyXYlJSXT/1mO5GKsPsRg1UYpI20AScyG5sLA1bQDK742ECjMX+wwHOGBzGEXfTRGWvjfrU13nD\n5XTBclno70rNV4Il5hPUIThOVSYLCnxi28bEMRjAREMTKvuf7/V+veIWEb3jdr2Bq6QsNLNsWC4r\n1nkjvkWKCGHDthGHhbhLhNBIIOB8B9/1HCDyu8wZxlqM4x77uztM+4n2cRMdl9o6K8NyvPfaWdVP\nvSIAFEQzcuSMol1CICRC6htG+uaCZd2wzCtKyeiHjj5fJrhfuCTWUnljGKkLYdhTUjDuRxZHo+Fn\n4NKDTEWdjxe6v2dCdMW/jvOKrusI1XbUPbVMPZG4cwb2BbtxxMits5dtQ44ZC5cU13nF+fmM5TTT\nJE9jtORTfiHh+zbsP286xleVm5hpTpvfY9iPV0xW0FlRVSnpCe96qpNOd7RBrnMoiZSL1mWln7Uy\nxOosjZHtnNZ5+rGH9w4xJqzziuVIjq1sNRttHZscHMOHI650md6S+S/nhYzsvKnx3gaPfovoI2UZ\n0ucuWWhJ1Oa1XhbMR+JKCEdhuSx4/vyIH3/4Ozw/f0YpBZ8+/R7d6PHwmwcYGPS7XuUzl9OiIhbb\nvLFATNS6Vz/2cH2FvYQopO1+G6EUmvGaqrz3a8t3Xss5kjURnA+YUqHHwoX1FBKioT0uKWsmbrh+\nZp3FuBvx8N09Bt/hw36HmDOeTids84avP3zFn/74R/zlL/8GMW7Y7e6wrn+AcQS1W2exLZtm/gKz\nOxaRUYi/IWgaU1CM0bbAW9scAWA9L1jXFSgG3nTalSK19XXh97oSAqUcBzZQcYuqgjhOA/q+A6xB\nWAPOzyc8fv6I3e4eXT/yGaV6eimZyyUT9vd77O73GPcjlVdixnJZ0I+9BoFho8DLe5rnbb1VYiIg\npS8a63or6Y3Kbi0DvXI7rPVaBqDMqpYDZHa8YZWSFInsFcJKZ896pBzhOofpsMN0NwEG2C6UGYYt\nIqwdzMKOXz4Pvzxr2anAKNOaAuLEAUoEaIadQr9COn5L5n9+PqtNKTnDs5mkJKa2uErNXmrV7S8y\n6kGRjm0JsM7i4ft7AEQsuxsGrJHY+CHR9yAUiWyg7z1soudotTRcJ6O2qVx6hQLKXeWzGpgH0ve3\n2b31ssKA2tOStEdbsadU+tjf77gXn6B7ax1Wc9HgTuB+5Xv0A4Z+0iAUhVCCYT9i/7BXDsu2bPAz\nlXWkY2LYDRgPI/qGcyEkQO3AkDHRDUKZc0IuSev0t67OOZxjRObS3XS/Y/4U/TndUSa2GwOZHglA\nyemOAxXq8nAK/a/MZ1jOK+JGCNF6XrCtG/bLDtPdTt+laEU8d894vJuw/7DH4X6Pw0QB87xuWNYV\n67Lh+HjC6fGI0/MZ63mBcZZKjmPPXT4/n/B9m/AXmlYUPly2cxi5dWfaE3Gn1R9nDgf9fW03MOhG\nMuDd0GHcDXDWYd02ROYUvHw54vR0wjavMNZid7fDdBgx3e+wv6faW2Gjlxl2F6a/tOZsHDzQCYAa\nSAOjsH1Yb4c+55eZg5JNyWY50LCZbV2RviYcH4+1+4EhqQJoi1eOSaWHwxZwPp3w+PgDvnz+E8Op\nCfu7e3z3u98gp6Q1bjpYRII7fnkhiHcNyCWjHwfENWL7SJCiXPQUE5bjjIVJOKR8VRmpdDBvy36t\nt9pbDlSVNQBK4BSBjdPTCcdHqtcvp5na2XLAtpEh2R3ukFPG4eGAFBJ677EfRsSU0FlqUTw+PeHL\nlz/hxx//iG1bsN8/KDxYYsHLlxclWbWZ9bDjwLDvtKZKnBCrIhrSgfKWzJ8g18CwtEPY6MznnOFO\nM0R213lHJYCxw7gbMe4H9LsBu8MO492EaTdiPw7wzmHwHtu8Yf9wwOH+DrvdPXa7e6zrBTEGjOMe\nw7DD4eGAj7/7iE+/+4jf/uYjPh0O9Hf/ScJpnnG8zDivK+bzgsvLBfNpRkoJzjnEEHB+vlDttKMa\ncS4JMUXYG3sdi9RwIVWD69KcTitTgyt1ec+Epx4xrgRph4WMcErYmAuAUuA7h3E3EJnPOW1TdL7W\n8wEmfCIDCA2RMyvMT84mUIABynhTDMieCWW4neQq6/R04rMn4is1YBbybojSQketqyIGRj8PGuwv\n5wUpUgdM2ALGw4in33zCGgkVUNLrtmG7rFhOdH9LKdQxw3ZTSIQA8XdCoUzeWOa/WGm5vuY0F1Qi\n9i3r8nwmqH3oqTRZCryj+7Z/2MN1Dvef7rFcFpxfyNmE7TdsfymJU2GvQkzzYSRujDHUgoYCdGOP\n3f0Ou/sd7Y2UWOaNgi5QkimjcWFr2+6aKAB3x0qsjCFg5hL1Nm/aNvrWd++syBg75XhIMmUsFAnS\n0cfe0d582BMJ+I7LdZ/u8HB/wP00Yeg65FLwp69fEUPC+fGEbVlx/PoCGIPdslHCzITpFJKS6kWj\no+POjWEa4GUgnrNqf18+H3F8PGJdZ3Q9i7MdDLrR/2K589uZP6taCVNehit4zqhb+c/MbNAYWLSG\nCXcU5VPUNx1GQg2Y7Z22iOW84OnHZ/z17/6Kxx+/YpkvsNbh7uEeH377ER+STNRz2gIhetJJHGsr\nKcy1VwAEe3EEZVx1kLeu+Uy1etIQYEGZEHA5EVS9zqRotc2b1oYJAiJOwzANGBiep1aPjG1bcLkc\ncTo9EqzUj3h+/IKXx2ecXy44XBb0qVfYMEYiKs6nC5blgpwT/NypyhO1INX2p21ZCYIrBaXQSEch\nXlJgcaO2f8eje1OGWY3W/nJiwHUhHsL5+YyXzy94/vKE4/MT5ssJ2zpTt0ImyLzkgrsPD5SlCpTJ\nxE0A3NWwYZ6POJ+fsW0LUAp2uzucTo/wvkfYAnxH3yulQHW4sccwkQDI7mGH/f0e091E0Kir2tkS\nCLzF+Wv3RkrI88xiQgEy1KfwHu0f9pXctxvQjT3Gifu7+w699+hYxtNag37qcf/pDh9+9xHfPf0e\n6zrDWocQFhwOH/Hhw2+xf9hjd7fDp4c7/OHTJ/zm7g5j1yHljMu24byuOM4zjvOM59MZR2Ykl1yw\nMmS8nBYm0Dp2zLc/OykSMtHNeXjf1To8E7a8p8lhfT+wLgENwvI9BaPrZUF3HCAtSylF9P2Iruup\nZDQKakiw/LZsWM4dk+lGuGVBipTFU8li00yfMsia+Us2BqDqhtB/aQfIm0o+xwtKzvT+poG/E9sd\nFpMpOWtpMkeeGirKhSEibBGX4xnHx2es2wznPGIIOHw44Md/8og/P9wTsTdnfHk54unrC54/v+D4\neMK2bHRWuE1QRY6sjL3m1uEYsc00uKYV45K9EDXFGMLNQ53WeYVhlE3smqC8fvDYxz1KKpjPM+4u\nd5TFs0jVfJrx/Pm58pTYDkvCZ1mLJKdM3+uezrl1lgIuRnVTrDMtJAE1zminUx0Fb5TsXUrBeqFO\nhOU0q+6LoAC3LseEuW7oMR0mANDunrBS4DLuCcHuOTCz1mLYDxjGAfu7HT7eH/D9wz2+v7/D/W4H\nZwxOy4JcMs6PJ4DFyJb5gsS9/ru7HQo/m+iphDXw8ywACstFO9ZesKrbQgnACZfzGTGuGKc73H24\nRzfSM/xSqfebniAsXLuS7J8Nf+R6qspU5kzKTvOMbV0AANY4WEd1KusEemAtZ2uxRarbX14ueP78\njK9/+Su+/PgD5uUM5zzWZaZMup341l3Lx1KvrNTFWSzHWRhDaoD097OyIN9U+AOVPRIPKkEBM3EZ\n0ptXzOcL5ssF67IgbHRhx90Bh7t77O53KB+ozNB1tX5OtdMV6zYj54jz5QXn8zMuxyMuL2csp0UJ\nSn7wnE2OWC6LsqjXRUQ2tkYbgLocqPbZaC70DdmJ64e3LOUygGvATGBsCYMbkz+1NJSytvNENtzG\nD/B9p6In4zhg6DoYACERUZKOk5CUcGXYCb0x6Poe426gSz6DuyE2yORHMhYV6XEjq0eq7kQzM+Km\n5+d6btxQQkEMAV3YtKbZDVSWioECWKpZRgy7qD9HFcYAdN4hZZI0nu52+PCbDzg9/gbbusI5h2U5\no+tGbhmy7NQoig4pki5EzljChsu2Yd42LCEglsruzjTZiJ+16iO0UPAtK3EwTVmlV7Z8CPTvvvPw\nPQVewzhQptFIl6aUcHk6XznpGDeM4x7T7oBhP2rNlowd1NGFNWAYeqxdz73UGaYUAKL85vSXnHVB\nHuS9SbIgfCHvvWbNt6ywBuqt9l4RxNT04uMEbDKUpdDXz8cLLscZl+czzs9nnJ/OOD0/4+X5C9b1\nohyEpx8/4PNfvuLv7va4bJQl/+WvX/H44xPVgo+UWAC0J6ISWHKBdw5GZG6VGJl1BoS1rupySL88\nkw7Xeb3p2beV1OK2lQWpmOk+TAPGaSCBtZzQTz05ZSb+Zi6p6hhodjiCjO3udlRK4ITReacBcwFg\nj9xbv61Y5hnLcsEyk0OdzzsM46Doo7bzmVrNE7RlmzciO4eN6/0ZwO3vHqAAYJh6lEwdNvHDgRKX\nnOF7T223uwHe13b2oScy834acTeN+LQ/4LvDAVPfI6SI00pTcreF3se2rtjCipwTwjYicfJoPQnQ\n+5goMOb3TMltlccO64oQST9h21YWVlphjYH3A4wzGHfEQ/gHOf92nK62uxUgpyq+ItOI1mXFuiyI\ncWPSm4W1UAOiRI2xR+cdNh7GMp9mnJ9OOD4/43j8ioUvClAwDBPGPYmaTPsJcYgkNpHqARACiYw8\nlEy4Yl9GSwRvEXsAoNGjkBdTSsgLEeqWy8JZdkRhdIKyIZGrbcYKc62LREmk7FB06EIItHeEIqzK\nb9jf77hbgtnmjz0uxyOJuFjJ6pp+98ysYCtjTasynjEiJHTbRWiHqYiw09XiqNo6YvFPhx2MAbqh\nxzoviJGMzTjt8fDdR9x/d4+HT3f4cNhj3/daL42ZyyXec2Y4IoSVWz+pZWi3P+D+0x2m+x0RxLZA\nugeM4nhubWkDm8JM5ZIzTdjLpItw66qZVOF2pQyAVOO8p+y1pEJCLCFhPs3wvccwUbS9fNhjutux\nGpyB2U1wzmIYehw+HpSxL33Np+MTZyrU6XF+OuHr12eMQ09ZA0D15ZwwbwHzQvW+jQmwiTsehElc\nR3Bzry+oK+eWpa1iHkDhAVKuCkn1fY9uZHnrode2K0FcwhYQp4DuQv39fU890MO4w7gnTQQhTgoR\nathRZwdxXAJCZG2FbVEioTEWQz/Cuo64Ed6z4BDVdpV4yVKr/UhITKtUecuKIZAkrxWxGMrCxIHa\ns0wrZCE0djoLl2EuLxecX444n15wOj9h5YTGOo/nzy94+vEJP37YYwmE7j1+fsbp8aQtvfP5ghQj\n3NljuQwYdxOmdQJKwZBHjEzmMtYATGoumTJw4XtQaVRUJSmJu2Uty5nIeKxxsc2rzqawQhyNYhNY\ntyRSSXN+ueByvHDWn1QRdTpMOHw4YNgNOuBJSsC+94hbrATjkqlktGUiwoaInKgUJboVGtBKUpYb\nOevIgnRs/xzPtr91XbYVKSeC83cjhl3lTxgDJdUWgGd7ZEa2ElISxVbDehQFW4w4ryseT2c8vZxw\nfD7h8nLBtq56pq11ylHRRJVtQQGVksMWrngcKUWkNWOZL5jnIzaWUR76kQmJFMDsH/b/sJq/LDFK\nHGMpcUoirzY767qeYRm6cNMdvfjDwx67w4SBa9QSBS3nBZfTGZfLERdWUJL69DgesH/ZY/lwwLZu\n6ENP5Btmb7vOwUUHFxIKw0DKgBe2K5cnMh/QW7OfX9oHgGrePbOwcx5BkIxDP1QdalGAo35OnkGw\nbdwm4rhfuX7fnKqqlUSY0tImGte7+x0uL3fY5k2DHTlkcgHjSnKuUnOrrS8slnJjBiQT6UrOnAXR\n5xX9gsy1r36kiyW1r2nZY7tsXNsFht2Ih+8/4P77e9x/usPDbofec4uclJKcRTcMGIY9xnGPGDZt\n6fK+Qz/Ssx8+HujiFVYY27hFjOFQIUFWAiqPY+a+9LcEf9pWZT1ERIYMSlKiTwyWM+TIKYjBcvYI\na2Q0g0iOORGDeew6dN5j7DsOGBZqx2QBl7CtzH4POD4e8fmHr4AF9vd7JYEWQJXHhCQrg3/CErjO\nvLIefRW2MnwGbn72xDoKAv97T/3DLOClGTWXQQwbg9qdQgEHzeXo4azD0FOLnMyAkPfkmcOR7ncA\nDE1NY1LYtvQ8t4MMWD+MmhzkTM6EJsmtVKqw/z9nb9rlyJFki11fYwGQWcXuNyPp//8xvSPpvWaT\nrEwkgFh80wdbPJLdJJEdc+qwp1isxBLhZnbtLhYxDhimiZPUesbAs1fOG3F3ZLLek6JH+0rKn4aD\nk1olq+J93bE+SKb3eNzxeNywrjcsyx3OWoQ44H69kU/8r1dVTn38+GCJIGeUpIxtW9Baxbb2FWDJ\nBSPneHTURGKUG4IgaM7qWjRxRsGzKp9luQMwiFdG604jrT54bahEYuYU0a/Ea5sFjytB7sZZcj+d\n+h5ciKslsxSNuWPbY+tZImzdW0tGOaDLraGbKjHKqT4upatTKktLqSY5eBcQhn/vbf/vrvfrXRVD\nYtjk2T5X0MO0J1I9HFYbPnpMpwkFDVOIJMNLGRsy3h4P/Px+xRsrddb7qrVIXDA9y4YbE+pJ2gsm\nylpeh4hU22hjUGtRB0XAw7HSxXmrvIo/WnX/6RNxdE2jA0EOOyk6jpnO5F1c+RVJUlEYo2rYz9/P\nmE4jrLFYth2P+0Id8vsdj/uNrRPJF1kgvGE44fTxgovImnKFCQbWG6B5DfJoaLA7rwN4ByM6Z2E7\nyr75K9Cv9Q4mdXIThU18DrsRDafE+npOcJLdDACst1XDQYRFTVGOVhsBMJdBjHn0gOQkK4LJBqy3\nlQ1dcnfxYydAKQh2oX08GriJEG9s97TRTc2U1S0yI1nzqLmPqZ0BDKMIjzQbtVCTN8wjNX/fzpjP\nk+r7a2vYWJPqPJGCpumCabogpU279aINQncMNLwe6EZJRr93shUOynoXlys5MJ694hCR96hOduWw\nhjCGJE7GGCB4Xb0o2aWvnFWTHZzDEEidkGNALsTc13xwY7HcH3ovyPRPzxXZgupOF+KGR+5+acvK\nOF+Yj7KzGoGCpbos8pnrKO0EnwESPiTmSrLvFnRJ/ekrTaLyuZOV6wDAYBhnnfYsO1IeeTmkYoB6\nYMgk5EQ21xpbR/tPJFd6huj9OUfW27RGIHLUVyd/WuE5uI05JkLeZYKnbA81pZKbsrxl9vG4Y3l8\nYF1v2LYFOW9ollCKnKlgLtcH7fxTwfIhO/Kkxl/kk1BQKylOTKPnL230jMcxKqeFEE/6fjQjpDZt\nLkR588y1bQvxN95Jwh25cSICpNP1lsgZJUlPVguZTXpIqkrNw/wy4/TthHEe1JWShsWgBOfltmCc\nR4TIskC9F+mXqGmORlrCrXCJfS1Wg7wDLVeYauG9JTnePD393X/89kHfs6OQMXmGYSxxEh4blo9F\nuQU5ZThPQ+5RIVUbTf2pFLw/HvjtgwjtK/tAoEFXU86TUV3mpt57p6vmnuHgiFvXJD/HIsYInF9g\njcUwzCglw/uIcZ5pJTcNmE8T/zf/ev3pE+E8WaI6LaiG/J2tBUzXYwrELki7C466PpZynF8JAvUx\nIJWCZdnw8esV11/fcX+7Ybk/sO8r9m3Bstzka8c0feDxcSMo6b4hv2YmWfAuBI2thYFsmaQkTlgH\n/ataYlqDrwD/gSEpCfIIMXRrUv5MwhDI11281o1R5IFYwNKRilEKdabOB0SAoG6ejACJLq7s0hT4\noK9odeTD1yNOu5oPCTlGMwv4OxCbYJWfSezwk1I/cUakxDoAxuj6R75sgdYB6GRE3gwOJnq46DFf\nZjKqeZkRQ0AuhckvDevO0H0DQggYxxmn+QUp7UqSpIx4dhKsTZsM6y08H7y6K3NC8KMTo1ICyr/I\noJ65psvEnyWtUrprnUjckhLqjGNmshMJVvi09nGHf3prWec94NvfXnpiXGsI7xGtVGL0Rkp23Ldd\nd8ACvInqgAotQf1ycGzLxnbQhACVIihQVRvVv7pkotUAJmvh/dFEpTdb6vrYKHOjpq7EkUIWwkBN\nwDDAefZlByMx4jzIPCKBg4d5+NRQiKJGHCSVwMgNhHNk/euCJ1c7zt6QhlSUIM9c5FlACXWUI+K0\ngSTttnxQ9LOl+aHpd8W6Pkj7ri6HgGE1hGU3N0rjpP3vcluQ1p0QImcRQiT42HR+Uylktd7YqCvv\nSQlgUhBLKTCJPfJzDwUqv/Pf/9P3XhK2jWSZj9uI8X2ihhucTsd8EskZIS5Y1aFQUBaSdU84fzvh\n9I2MkUL0yKkwg99hHCjNzjuH5WXBdJ4wTiN8iEi8Dye3RzGuodWl7NqP/ioig9xXB7ux+ZQ1GBiN\nffa6v92p+AfiKpB0mRp6IdKKiiNt5O/vg1GkduSEvtoa1pSw7Ds+HgtuPOiuj1VRLTpf6XV2EzlO\nuqyEkAiJXp4lay0NIjzIhBjh3DeM+xliEHb+fsH5pzPOLzNe5olWq//m+st2WJimzllUMfcwpPUG\n+EAVshY/lJ41/TPboIoXQANZBi/3hbWJNyx3MogohdzZ9n1Vjei20UO0Psjpbr2vdDByyIZC2YE6\nb6t2iTStgP6U7CYg7N9nL8lKN4Yeeheoow1jUGIV/R41AM5ZncLFG7yWcrDbzSg5wRjLfucDhmGG\nY4tLQQqMMbCBpqxWG6r38IGaApcLzJ7pfbe+kgmDMOj5+/IWtTT2Sog6cT1L+BNYStYo9DH2A6RW\n9vtmUx+Z1HT/GxziFHF6PeP17684v5C5z5YzCt+M923DynJQ+kxGjNMZp0xT9TDMyjRPOyUTCpu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lAB/bdyHVYWz156czNTnGEl8T0wjjrQYaLkqfk84Xw54XKecRlHGFAD5TztsGspSPuKfV+w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\nv+Q7SJlBBqMbPoSZbT1Zu2ji3QzHkO0PhDqNIUVzDA6F3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 8, figsize=(9, 4),\n", + " subplot_kw={'xticks':[], 'yticks':[]},\n", + " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(pca.components_[i].reshape(62, 47), cmap='bone')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The results are very interesting, and give us insight into how the images vary: for example, the first few eigenfaces (from the top left) seem to be associated with the angle of lighting on the face, and later principal vectors seem to be picking out certain features, such as eyes, noses, and lips.\n", + "Let's take a look at the cumulative variance of these components to see how much of the data information the projection is preserving:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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G0Wj0ZEnkJqcQyD9ajm92FWNfYQUAwKAPwC1XxyJpeC/ERBp8XCEREUnxaMjn5uYiKSkJ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\nCwkJgcFgAAAYjUY4HA4MGTJENv01GzJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", + "plt.xlabel('number of components')\n", + "plt.ylabel('cumulative explained variance');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that these 150 components account for just over 90% of the variance.\n", + "That would lead us to believe that using these 150 components, we would recover most of the essential characteristics of the data.\n", + "To make this more concrete, we can compare the input images with the images reconstructed from these 150 components:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# Compute the components and projected faces\n", + "pca = RandomizedPCA(150).fit(faces.data)\n", + "components = pca.transform(faces.data)\n", + "projected = pca.inverse_transform(components)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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pLP4/AaxAgjrBavD03zTWJAIxrR1CEWgq/HgtTR1+LrcsHKX4mGdkLZDYtA8xQtSFwvjI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "fig, ax = plt.subplots(2, 10, figsize=(10, 2.5),\n", + " subplot_kw={'xticks':[], 'yticks':[]},\n", + " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", + "for i in range(10):\n", + " ax[0, i].imshow(faces.data[i].reshape(62, 47), cmap='binary_r')\n", + " ax[1, i].imshow(projected[i].reshape(62, 47), cmap='binary_r')\n", + " \n", + "ax[0, 0].set_ylabel('full-dim\\ninput')\n", + "ax[1, 0].set_ylabel('150-dim\\nreconstruction');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The top row here shows the input images, while the bottom row shows the reconstruction of the images from just 150 of the ~3,000 initial features.\n", + "This visualization makes clear why the PCA feature selection used in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) was so successful: although it reduces the dimensionality of the data by nearly a factor of 20, the projected images contain enough information that we might, by eye, recognize the individuals in the image.\n", + "What this means is that our classification algorithm needs to be trained on 150-dimensional data rather than 3,000-dimensional data, which depending on the particular algorithm we choose, can lead to a much more efficient classification." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Principal Component Analysis Summary\n", + "\n", + "In this section we have discussed the use of principal component analysis for dimensionality reduction, for visualization of high-dimensional data, for noise filtering, and for feature selection within high-dimensional data.\n", + "Because of the versatility and interpretability of PCA, it has been shown to be effective in a wide variety of contexts and disciplines.\n", + "Given any high-dimensional dataset, I tend to start with PCA in order to visualize the relationship between points (as we did with the digits), to understand the main variance in the data (as we did with the eigenfaces), and to understand the intrinsic dimensionality (by plotting the explained variance ratio).\n", + "Certainly PCA is not useful for every high-dimensional dataset, but it offers a straightforward and efficient path to gaining insight into high-dimensional data.\n", + "\n", + "PCA's main weakness is that it tends to be highly affected by outliers in the data.\n", + "For this reason, many robust variants of PCA have been developed, many of which act to iteratively discard data points that are poorly described by the initial components.\n", + "Scikit-Learn contains a couple interesting variants on PCA, including ``RandomizedPCA`` and ``SparsePCA``, both also in the ``sklearn.decomposition`` submodule.\n", + "``RandomizedPCA``, which we saw earlier, uses a non-deterministic method to quickly approximate the first few principal components in very high-dimensional data, while ``SparsePCA`` introduces a regularization term (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)) that serves to enforce sparsity of the components.\n", + "\n", + "In the following sections, we will look at other unsupervised learning methods that build on some of the ideas of PCA." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.09-Principal-Component-Analysis.md b/notebooks_v2/05.09-Principal-Component-Analysis.md new file mode 100644 index 00000000..79227e02 --- /dev/null +++ b/notebooks_v2/05.09-Principal-Component-Analysis.md @@ -0,0 +1,454 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) > + +Open in Colab + + + +# In Depth: Principal Component Analysis + + +Up until now, we have been looking in depth at supervised learning estimators: those estimators that predict labels based on labeled training data. +Here we begin looking at several unsupervised estimators, which can highlight interesting aspects of the data without reference to any known labels. + +In this section, we explore what is perhaps one of the most broadly used of unsupervised algorithms, principal component analysis (PCA). +PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, for noise filtering, for feature extraction and engineering, and much more. +After a brief conceptual discussion of the PCA algorithm, we will see a couple examples of these further applications. + +We begin with the standard imports: + + +```python deletable=true editable=true +%matplotlib inline +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +``` + + +## Introducing Principal Component Analysis + +Principal component analysis is a fast and flexible unsupervised method for dimensionality reduction in data, which we saw briefly in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb). +Its behavior is easiest to visualize by looking at a two-dimensional dataset. +Consider the following 200 points: + + +```python deletable=true editable=true +rng = np.random.RandomState(1) +X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T +plt.scatter(X[:, 0], X[:, 1]) +plt.axis('equal'); +``` + + +By eye, it is clear that there is a nearly linear relationship between the x and y variables. +This is reminiscent of the linear regression data we explored in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), but the problem setting here is slightly different: rather than attempting to *predict* the y values from the x values, the unsupervised learning problem attempts to learn about the *relationship* between the x and y values. + +In principal component analysis, this relationship is quantified by finding a list of the *principal axes* in the data, and using those axes to describe the dataset. +Using Scikit-Learn's ``PCA`` estimator, we can compute this as follows: + + +```python deletable=true editable=true +from sklearn.decomposition import PCA +pca = PCA(n_components=2) +pca.fit(X) +``` + + +The fit learns some quantities from the data, most importantly the "components" and "explained variance": + + +```python deletable=true editable=true +print(pca.components_) +``` + +```python deletable=true editable=true +print(pca.explained_variance_) +``` + + +To see what these numbers mean, let's visualize them as vectors over the input data, using the "components" to define the direction of the vector, and the "explained variance" to define the squared-length of the vector: + + +```python deletable=true editable=true +def draw_vector(v0, v1, ax=None): + ax = ax or plt.gca() + arrowprops=dict(arrowstyle='->', + linewidth=2, + shrinkA=0, shrinkB=0) + ax.annotate('', v1, v0, arrowprops=arrowprops) + +# plot data +plt.scatter(X[:, 0], X[:, 1], alpha=0.2) +for length, vector in zip(pca.explained_variance_, pca.components_): + v = vector * 3 * np.sqrt(length) + draw_vector(pca.mean_, pca.mean_ + v) +plt.axis('equal'); +``` + + +These vectors represent the *principal axes* of the data, and the length of the vector is an indication of how "important" that axis is in describing the distribution of the data—more precisely, it is a measure of the variance of the data when projected onto that axis. +The projection of each data point onto the principal axes are the "principal components" of the data. + +If we plot these principal components beside the original data, we see the plots shown here: + + + +![](figures/05.09-PCA-rotation.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Principal-Components-Rotation) + + + +This transformation from data axes to principal axes is an *affine transformation*, which basically means it is composed of a translation, rotation, and uniform scaling. + +While this algorithm to find principal components may seem like just a mathematical curiosity, it turns out to have very far-reaching applications in the world of machine learning and data exploration. + + + +### PCA as dimensionality reduction + +Using PCA for dimensionality reduction involves zeroing out one or more of the smallest principal components, resulting in a lower-dimensional projection of the data that preserves the maximal data variance. + +Here is an example of using PCA as a dimensionality reduction transform: + + +```python deletable=true editable=true +pca = PCA(n_components=1) +pca.fit(X) +X_pca = pca.transform(X) +print("original shape: ", X.shape) +print("transformed shape:", X_pca.shape) +``` + + +The transformed data has been reduced to a single dimension. +To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data: + + +```python deletable=true editable=true +X_new = pca.inverse_transform(X_pca) +plt.scatter(X[:, 0], X[:, 1], alpha=0.2) +plt.scatter(X_new[:, 0], X_new[:, 1], alpha=0.8) +plt.axis('equal'); +``` + + +The light points are the original data, while the dark points are the projected version. +This makes clear what a PCA dimensionality reduction means: the information along the least important principal axis or axes is removed, leaving only the component(s) of the data with the highest variance. +The fraction of variance that is cut out (proportional to the spread of points about the line formed in this figure) is roughly a measure of how much "information" is discarded in this reduction of dimensionality. + +This reduced-dimension dataset is in some senses "good enough" to encode the most important relationships between the points: despite reducing the dimension of the data by 50%, the overall relationship between the data points are mostly preserved. + + + +### PCA for visualization: Hand-written digits + +The usefulness of the dimensionality reduction may not be entirely apparent in only two dimensions, but becomes much more clear when looking at high-dimensional data. +To see this, let's take a quick look at the application of PCA to the digits data we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb). + +We start by loading the data: + + +```python deletable=true editable=true +from sklearn.datasets import load_digits +digits = load_digits() +digits.data.shape +``` + + +Recall that the data consists of 8×8 pixel images, meaning that they are 64-dimensional. +To gain some intuition into the relationships between these points, we can use PCA to project them to a more manageable number of dimensions, say two: + + +```python deletable=true editable=true +pca = PCA(2) # project from 64 to 2 dimensions +projected = pca.fit_transform(digits.data) +print(digits.data.shape) +print(projected.shape) +``` + + +We can now plot the first two principal components of each point to learn about the data: + + +```python deletable=true editable=true +plt.scatter(projected[:, 0], projected[:, 1], + c=digits.target, edgecolor='none', alpha=0.5, + cmap=plt.cm.get_cmap('spectral', 10)) +plt.xlabel('component 1') +plt.ylabel('component 2') +plt.colorbar(); +``` + + +Recall what these components mean: the full data is a 64-dimensional point cloud, and these points are the projection of each data point along the directions with the largest variance. +Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the digits in two dimensions, and have done this in an unsupervised manner—that is, without reference to the labels. + + + +### What do the components mean? + +We can go a bit further here, and begin to ask what the reduced dimensions *mean*. +This meaning can be understood in terms of combinations of basis vectors. +For example, each image in the training set is defined by a collection of 64 pixel values, which we will call the vector $x$: + +$$ +x = [x_1, x_2, x_3 \cdots x_{64}] +$$ + +One way we can think about this is in terms of a pixel basis. +That is, to construct the image, we multiply each element of the vector by the pixel it describes, and then add the results together to build the image: + +$$ +{\rm image}(x) = x_1 \cdot{\rm (pixel~1)} + x_2 \cdot{\rm (pixel~2)} + x_3 \cdot{\rm (pixel~3)} \cdots x_{64} \cdot{\rm (pixel~64)} +$$ + +One way we might imagine reducing the dimension of this data is to zero out all but a few of these basis vectors. +For example, if we use only the first eight pixels, we get an eight-dimensional projection of the data, but it is not very reflective of the whole image: we've thrown out nearly 90% of the pixels! + + + +![](figures/05.09-digits-pixel-components.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-Pixel-Components) + + + +The upper row of panels shows the individual pixels, and the lower row shows the cumulative contribution of these pixels to the construction of the image. +Using only eight of the pixel-basis components, we can only construct a small portion of the 64-pixel image. +Were we to continue this sequence and use all 64 pixels, we would recover the original image. + + + +But the pixel-wise representation is not the only choice of basis. We can also use other basis functions, which each contain some pre-defined contribution from each pixel, and write something like + +$$ +image(x) = {\rm mean} + x_1 \cdot{\rm (basis~1)} + x_2 \cdot{\rm (basis~2)} + x_3 \cdot{\rm (basis~3)} \cdots +$$ + +PCA can be thought of as a process of choosing optimal basis functions, such that adding together just the first few of them is enough to suitably reconstruct the bulk of the elements in the dataset. +The principal components, which act as the low-dimensional representation of our data, are simply the coefficients that multiply each of the elements in this series. +This figure shows a similar depiction of reconstructing this digit using the mean plus the first eight PCA basis functions: + + + +![](figures/05.09-digits-pca-components.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-PCA-Components) + + + +Unlike the pixel basis, the PCA basis allows us to recover the salient features of the input image with just a mean plus eight components! +The amount of each pixel in each component is the corollary of the orientation of the vector in our two-dimensional example. +This is the sense in which PCA provides a low-dimensional representation of the data: it discovers a set of basis functions that are more efficient than the native pixel-basis of the input data. + + + +### Choosing the number of components + +A vital part of using PCA in practice is the ability to estimate how many components are needed to describe the data. +This can be determined by looking at the cumulative *explained variance ratio* as a function of the number of components: + + +```python deletable=true editable=true +pca = PCA().fit(digits.data) +plt.plot(np.cumsum(pca.explained_variance_ratio_)) +plt.xlabel('number of components') +plt.ylabel('cumulative explained variance'); +``` + + +This curve quantifies how much of the total, 64-dimensional variance is contained within the first $N$ components. +For example, we see that with the digits the first 10 components contain approximately 75% of the variance, while you need around 50 components to describe close to 100% of the variance. + +Here we see that our two-dimensional projection loses a lot of information (as measured by the explained variance) and that we'd need about 20 components to retain 90% of the variance. Looking at this plot for a high-dimensional dataset can help you understand the level of redundancy present in multiple observations. + + + +## PCA as Noise Filtering + +PCA can also be used as a filtering approach for noisy data. +The idea is this: any components with variance much larger than the effect of the noise should be relatively unaffected by the noise. +So if you reconstruct the data using just the largest subset of principal components, you should be preferentially keeping the signal and throwing out the noise. + +Let's see how this looks with the digits data. +First we will plot several of the input noise-free data: + + +```python deletable=true editable=true +def plot_digits(data): + fig, axes = plt.subplots(4, 10, figsize=(10, 4), + subplot_kw={'xticks':[], 'yticks':[]}, + gridspec_kw=dict(hspace=0.1, wspace=0.1)) + for i, ax in enumerate(axes.flat): + ax.imshow(data[i].reshape(8, 8), + cmap='binary', interpolation='nearest', + clim=(0, 16)) +plot_digits(digits.data) +``` + + +Now lets add some random noise to create a noisy dataset, and re-plot it: + + +```python deletable=true editable=true +np.random.seed(42) +noisy = np.random.normal(digits.data, 4) +plot_digits(noisy) +``` + + +It's clear by eye that the images are noisy, and contain spurious pixels. +Let's train a PCA on the noisy data, requesting that the projection preserve 50% of the variance: + + +```python deletable=true editable=true +pca = PCA(0.50).fit(noisy) +pca.n_components_ +``` + + +Here 50% of the variance amounts to 12 principal components. +Now we compute these components, and then use the inverse of the transform to reconstruct the filtered digits: + + +```python deletable=true editable=true +components = pca.transform(noisy) +filtered = pca.inverse_transform(components) +plot_digits(filtered) +``` + + +This signal preserving/noise filtering property makes PCA a very useful feature selection routine—for example, rather than training a classifier on very high-dimensional data, you might instead train the classifier on the lower-dimensional representation, which will automatically serve to filter out random noise in the inputs. + + + +## Example: Eigenfaces + +Earlier we explored an example of using a PCA projection as a feature selector for facial recognition with a support vector machine (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)). +Here we will take a look back and explore a bit more of what went into that. +Recall that we were using the Labeled Faces in the Wild dataset made available through Scikit-Learn: + + +```python deletable=true editable=true +from sklearn.datasets import fetch_lfw_people +faces = fetch_lfw_people(min_faces_per_person=60) +print(faces.target_names) +print(faces.images.shape) +``` + + +Let's take a look at the principal axes that span this dataset. +Because this is a large dataset, we will use ``RandomizedPCA``—it contains a randomized method to approximate the first $N$ principal components much more quickly than the standard ``PCA`` estimator, and thus is very useful for high-dimensional data (here, a dimensionality of nearly 3,000). +We will take a look at the first 150 components: + + +```python deletable=true editable=true +from sklearn.decomposition import RandomizedPCA +pca = RandomizedPCA(150) +pca.fit(faces.data) +``` + + +In this case, it can be interesting to visualize the images associated with the first several principal components (these components are technically known as "eigenvectors," +so these types of images are often called "eigenfaces"). +As you can see in this figure, they are as creepy as they sound: + + +```python deletable=true editable=true +fig, axes = plt.subplots(3, 8, figsize=(9, 4), + subplot_kw={'xticks':[], 'yticks':[]}, + gridspec_kw=dict(hspace=0.1, wspace=0.1)) +for i, ax in enumerate(axes.flat): + ax.imshow(pca.components_[i].reshape(62, 47), cmap='bone') +``` + + +The results are very interesting, and give us insight into how the images vary: for example, the first few eigenfaces (from the top left) seem to be associated with the angle of lighting on the face, and later principal vectors seem to be picking out certain features, such as eyes, noses, and lips. +Let's take a look at the cumulative variance of these components to see how much of the data information the projection is preserving: + + +```python deletable=true editable=true +plt.plot(np.cumsum(pca.explained_variance_ratio_)) +plt.xlabel('number of components') +plt.ylabel('cumulative explained variance'); +``` + + +We see that these 150 components account for just over 90% of the variance. +That would lead us to believe that using these 150 components, we would recover most of the essential characteristics of the data. +To make this more concrete, we can compare the input images with the images reconstructed from these 150 components: + + +```python deletable=true editable=true +# Compute the components and projected faces +pca = RandomizedPCA(150).fit(faces.data) +components = pca.transform(faces.data) +projected = pca.inverse_transform(components) +``` + +```python deletable=true editable=true +# Plot the results +fig, ax = plt.subplots(2, 10, figsize=(10, 2.5), + subplot_kw={'xticks':[], 'yticks':[]}, + gridspec_kw=dict(hspace=0.1, wspace=0.1)) +for i in range(10): + ax[0, i].imshow(faces.data[i].reshape(62, 47), cmap='binary_r') + ax[1, i].imshow(projected[i].reshape(62, 47), cmap='binary_r') + +ax[0, 0].set_ylabel('full-dim\ninput') +ax[1, 0].set_ylabel('150-dim\nreconstruction'); +``` + + +The top row here shows the input images, while the bottom row shows the reconstruction of the images from just 150 of the ~3,000 initial features. +This visualization makes clear why the PCA feature selection used in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) was so successful: although it reduces the dimensionality of the data by nearly a factor of 20, the projected images contain enough information that we might, by eye, recognize the individuals in the image. +What this means is that our classification algorithm needs to be trained on 150-dimensional data rather than 3,000-dimensional data, which depending on the particular algorithm we choose, can lead to a much more efficient classification. + + + +## Principal Component Analysis Summary + +In this section we have discussed the use of principal component analysis for dimensionality reduction, for visualization of high-dimensional data, for noise filtering, and for feature selection within high-dimensional data. +Because of the versatility and interpretability of PCA, it has been shown to be effective in a wide variety of contexts and disciplines. +Given any high-dimensional dataset, I tend to start with PCA in order to visualize the relationship between points (as we did with the digits), to understand the main variance in the data (as we did with the eigenfaces), and to understand the intrinsic dimensionality (by plotting the explained variance ratio). +Certainly PCA is not useful for every high-dimensional dataset, but it offers a straightforward and efficient path to gaining insight into high-dimensional data. + +PCA's main weakness is that it tends to be highly affected by outliers in the data. +For this reason, many robust variants of PCA have been developed, many of which act to iteratively discard data points that are poorly described by the initial components. +Scikit-Learn contains a couple interesting variants on PCA, including ``RandomizedPCA`` and ``SparsePCA``, both also in the ``sklearn.decomposition`` submodule. +``RandomizedPCA``, which we saw earlier, uses a non-deterministic method to quickly approximate the first few principal components in very high-dimensional data, while ``SparsePCA`` introduces a regularization term (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)) that serves to enforce sparsity of the components. + +In the following sections, we will look at other unsupervised learning methods that build on some of the ideas of PCA. + + + + +< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) | [Contents](Index.ipynb) | [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.10-Manifold-Learning.ipynb b/notebooks_v2/05.10-Manifold-Learning.ipynb new file mode 100644 index 00000000..d4bbbb38 --- /dev/null +++ b/notebooks_v2/05.10-Manifold-Learning.ipynb @@ -0,0 +1,1066 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In-Depth: Manifold Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task—reducing the number of features of a dataset while maintaining the essential relationships between the points.\n", + "While PCA is flexible, fast, and easily interpretable, it does not perform so well when there are *nonlinear* relationships within the data; we will see some examples of these below.\n", + "\n", + "To address this deficiency, we can turn to a class of methods known as *manifold learning*—a class of unsupervised estimators that seeks to describe datasets as low-dimensional manifolds embedded in high-dimensional spaces.\n", + "When you think of a manifold, I'd suggest imagining a sheet of paper: this is a two-dimensional object that lives in our familiar three-dimensional world, and can be bent or rolled in that two dimensions.\n", + "In the parlance of manifold learning, we can think of this sheet as a two-dimensional manifold embedded in three-dimensional space.\n", + "\n", + "Rotating, re-orienting, or stretching the piece of paper in three-dimensional space doesn't change the flat geometry of the paper: such operations are akin to linear embeddings.\n", + "If you bend, curl, or crumple the paper, it is still a two-dimensional manifold, but the embedding into the three-dimensional space is no longer linear.\n", + "Manifold learning algorithms would seek to learn about the fundamental two-dimensional nature of the paper, even as it is contorted to fill the three-dimensional space.\n", + "\n", + "Here we will demonstrate a number of manifold methods, going most deeply into a couple techniques: multidimensional scaling (MDS), locally linear embedding (LLE), and isometric mapping (IsoMap).\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manifold Learning: \"HELLO\"\n", + "\n", + "To make these concepts more clear, let's start by generating some two-dimensional data that we can use to define a manifold.\n", + "Here is a function that will create data in the shape of the word \"HELLO\":" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def make_hello(N=1000, rseed=42):\n", + " # Make a plot with \"HELLO\" text; save as PNG\n", + " fig, ax = plt.subplots(figsize=(4, 1))\n", + " fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n", + " ax.axis('off')\n", + " ax.text(0.5, 0.4, 'HELLO', va='center', ha='center', weight='bold', size=85)\n", + " fig.savefig('hello.png')\n", + " plt.close(fig)\n", + " \n", + " # Open this PNG and draw random points from it\n", + " from matplotlib.image import imread\n", + " data = imread('hello.png')[::-1, :, 0].T\n", + " rng = np.random.RandomState(rseed)\n", + " X = rng.rand(4 * N, 2)\n", + " i, j = (X * data.shape).astype(int).T\n", + " mask = (data[i, j] < 1)\n", + " X = X[mask]\n", + " X[:, 0] *= (data.shape[0] / data.shape[1])\n", + " X = X[:N]\n", + " return X[np.argsort(X[:, 0])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's call the function and visualize the resulting data:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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4hvhdWFLSVFJNwpQBALh6uVPUVh3E5O5+9efN/1JBwaGMue1uAPZu+4qE+q3c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IhcFGyGYRjt3YiTtAd2dtpTdZ9q5R7VyX2qlXtUJ/e4e0StAU9EREQsTEEtIiJiYQpqERERC1NQ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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X = make_hello(1000)\n", + "colorize = dict(c=X[:, 0], cmap=plt.cm.get_cmap('rainbow', 5))\n", + "plt.scatter(X[:, 0], X[:, 1], **colorize)\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output is two dimensional, and consists of points drawn in the shape of the word, \"HELLO\".\n", + "This data form will help us to see visually what these algorithms are doing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multidimensional Scaling (MDS)\n", + "\n", + "Looking at data like this, we can see that the particular choice of *x* and *y* values of the dataset are not the most fundamental description of the data: we can scale, shrink, or rotate the data, and the \"HELLO\" will still be apparent.\n", + "For example, if we use a rotation matrix to rotate the data, the *x* and *y* values change, but the data is still fundamentally the same:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ftzlGq46dWDlyDI3WxBAEVLRpS78lMXj5+PyjmDQaDQE+PpwqLeEpYBvKlK+mNjb4d+yE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\ntEFPmDCBpKQkJk6ciFqtZsGCB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def rotate(X, angle):\n", + " theta = np.deg2rad(angle)\n", + " R = [[np.cos(theta), np.sin(theta)],\n", + " [-np.sin(theta), np.cos(theta)]]\n", + " return np.dot(X, R)\n", + " \n", + "X2 = rotate(X, 20) + 5\n", + "plt.scatter(X2[:, 0], X2[:, 1], **colorize)\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that the *x* and *y* values are not necessarily fundamental to the relationships in the data.\n", + "What *is* fundamental, in this case, is the *distance* between each point and the other points in the dataset.\n", + "A common way to represent this is to use a distance matrix: for $N$ points, we construct an $N \\times N$ array such that entry $(i, j)$ contains the distance between point $i$ and point $j$.\n", + "Let's use Scikit-Learn's efficient ``pairwise_distances`` function to do this for our original data:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 1000)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import pairwise_distances\n", + "D = pairwise_distances(X)\n", + "D.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As promised, for our *N*=1,000 points, we obtain a 1000×1000 matrix, which can be visualized as shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9Pmx5JYWMUjgfX0/F419jl337a2t87XLEJx6ti9f8peSTOR4Y6UwhFQ/7rzzb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\nq8xg5k9g3db0NRrTGQVcOv0+mk+pMxpzmWReHWNSNvPJg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8FAlZWVREQfyrq4uEg2m42I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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(D, zorder=2, cmap='Blues', interpolation='nearest')\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we similarly construct a distance matrix for our rotated and translated data, we see that it is the same:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D2 = pairwise_distances(X2)\n", + "np.allclose(D, D2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This distance matrix gives us a representation of our data that is invariant to rotations and translations, but the visualization of the matrix above is not entirely intuitive.\n", + "In the representation shown in this figure, we have lost any visible sign of the interesting structure in the data: the \"HELLO\" that we saw before.\n", + "\n", + "Further, while computing this distance matrix from the (x, y) coordinates is straightforward, transforming the distances back into *x* and *y* coordinates is rather difficult.\n", + "This is exactly what the multidimensional scaling algorithm aims to do: given a distance matrix between points, it recovers a $D$-dimensional coordinate representation of the data.\n", + "Let's see how it works for our distance matrix, using the ``precomputed`` dissimilarity to specify that we are passing a distance matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LS0MGLAttQ+Sva7B3dkarzcPZ2YXK8nJObtmI3NKSmswM1I6O9Jo0tcksZflP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8aJ4QHejB9w++8BRwVH8lJQhCiR2kOUSD2feR5VsDMAfgMiaa1uJGJSx9jL3YzsyzuO1xwxqE3t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\nCM1B3Cq3EncM7UxJ67UNr4sCN9JtaJtmbJEgCILQHMQVt5Xw8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.manifold import MDS\n", + "model = MDS(n_components=2, dissimilarity='precomputed', random_state=1)\n", + "out = model.fit_transform(D)\n", + "plt.scatter(out[:, 0], out[:, 1], **colorize)\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MDS algorithm recovers one of the possible two-dimensional coordinate representations of our data, using *only* the $N\\times N$ distance matrix describing the relationship between the data points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MDS as Manifold Learning\n", + "\n", + "The usefulness of this becomes more apparent when we consider the fact that distance matrices can be computed from data in *any* dimension.\n", + "So, for example, instead of simply rotating the data in the two-dimensional plane, we can project it into three dimensions using the following function (essentially a three-dimensional generalization of the rotation matrix used earlier):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 3)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def random_projection(X, dimension=3, rseed=42):\n", + " assert dimension >= X.shape[1]\n", + " rng = np.random.RandomState(rseed)\n", + " C = rng.randn(dimension, dimension)\n", + " e, V = np.linalg.eigh(np.dot(C, C.T))\n", + " return np.dot(X, V[:X.shape[1]])\n", + " \n", + "X3 = random_projection(X, 3)\n", + "X3.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize these points to see what we're working with:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ziYIs+yihb7agAJJZwDg/MFC6Vjbf+ehHb+OTn/wkPT3LALfALN9DozLmYKe2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EfM//XCxbCJl8/n2j1bL3U+zzeWXM30ao2bV2Ov5sa7Gvt1JXN1rbHkemDabK6lz0oiSfKSaMHa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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits import mplot3d\n", + "ax = plt.axes(projection='3d')\n", + "ax.scatter3D(X3[:, 0], X3[:, 1], X3[:, 2],\n", + " **colorize)\n", + "ax.view_init(azim=70, elev=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now ask the ``MDS`` estimator to input this three-dimensional data, compute the distance matrix, and then determine the optimal two-dimensional embedding for this distance matrix.\n", + "The result recovers a representation of the original data:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LS0MGLAttQ+Sva7B3dkarzcPZ2YXK8nJObtmI3NKSmswM1I6O9Jo0tcksZflP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8aJ4QHejB9w++8BRwVH8lJQhCiR2kOUSD2feR5VsDMAfgMiaa1uJGJSx9jL3YzsyzuO1xwxqE3t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\nCM1B3Cq3EncM7UxJ67UNr4sCN9JtaJtmbJEgCILQHMQVt5Xw8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = MDS(n_components=2, random_state=1)\n", + "out3 = model.fit_transform(X3)\n", + "plt.scatter(out3[:, 0], out3[:, 1], **colorize)\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is essentially the goal of a manifold learning estimator: given high-dimensional embedded data, it seeks a low-dimensional representation of the data that preserves certain relationships within the data.\n", + "In the case of MDS, the quantity preserved is the distance between every pair of points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nonlinear Embeddings: Where MDS Fails\n", + "\n", + "Our discussion thus far has considered *linear* embeddings, which essentially consist of rotations, translations, and scalings of data into higher-dimensional spaces.\n", + "Where MDS breaks down is when the embedding is nonlinear—that is, when it goes beyond this simple set of operations.\n", + "Consider the following embedding, which takes the input and contorts it into an \"S\" shape in three dimensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def make_hello_s_curve(X):\n", + " t = (X[:, 0] - 2) * 0.75 * np.pi\n", + " x = np.sin(t)\n", + " y = X[:, 1]\n", + " z = np.sign(t) * (np.cos(t) - 1)\n", + " return np.vstack((x, y, z)).T\n", + "\n", + "XS = make_hello_s_curve(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is again three-dimensional data, but we can see that the embedding is much more complicated:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SCOXyMGaVxaaDSIToC/90gUgw5mNdCTQ2t9DQ3MJv3FZa7Kn4bfGYhgF5ZQRa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\nYAKWgRwGBwcYHtCoP9+HLdHkwEObpg3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qCs0Xv1l5fQOcjrdhm5XGoD/IYFstFbkThZPJ\nZKKwNJ/BzgPIikSReR5WzyEAVBqRxqEjFCe2ICsKDe37aXy1mub6DlyGXDLmqrnujrGUiq7DAiYh\nAwCNaKC/0o63z88LPzhNvNOMNivAkntsdDeEcGQZWXdbSlOW5ThvPFtFb5WAyhJj80MV2OwpM213\nVzdDA0EKS/POW55NrVaz6f7FE94zGo2Toqq1Oi2SEBt9rSgKiDNFdXkhXP2uopnATNbdvGYEpiwr\nQEojG8FMlNq6kkE/k3Mpz66AMjMpLOPJ0c9WZNu6utlx7CSvxgxYdXoURSGaVcbv9j5J4/xb6PUG\nkAQRo3s2r5+o5qObU8Lv/utWcf91Y+0o//17ntj7PBGzHZUjg2whyh1Zeh7+6N38Z3UtyZyU2U/T\nVsP8RamizZsWzGET0Nffz/ebx8p4KYrC5CSasXB4rVZLRNMzQstJUomhmMITrj648xSHHrHgjNxK\nXX8jg+pa5jm2sPvlEyS+XsPidbMnXH/r3y7ksc6n8VfZ8YaD5DoqcAllxPbm8i8f/E8WDf0D4aE4\nPeFdlKUXYE64qPrFTqxSNiPuNGfrHNqPbadgVhbxWByDD3RO3ZT3xGgwskAqommgHXNYwaY2UZRT\nMEqxdzZGUj3GoyXQjX1WSqs0WE30D/onBaoYDAYWfBxO/kZAE84gOmsP9z6U8r0OdkYpyqygrf91\nYn4Bq5iN/2A5/W+5iVrM9DytJhLex833p7RmwZg63EVCMfw9CZpVfXzvgRqWaD+OXdQRaY/x2GtP\nsyrzPvrx0nn6ILd/cgW7XzhB92/nEffqifrgW08/xQPfm0N3Y4COp3LRJ/N4I/8wH35k7qggPR9i\nsRjdnT043Y5JPu+s7AzM6w8SeFOHTrAwmLGPD95ecY6WZgrvrA/z4jBzwnwmrWnXjMBUFIlEYmwT\nFQQRjWYmK4i8/Yf8YnMpZ65A9sT0lR88/jQ76toISTJeRzaWwtl0hAawdveS63YgiiIWtUh3Xx9S\nRiEAgXCAt0438NHNG6bs4Vsf+yC6HZVES5chyRKSJJHRX0O6282nZ8m80VSNIMCGiizsNtuEACS3\ny8XC2mNUhUyIeiOm1mquO0d+o6LIvPy7fURtbVSH/4JBZSdqaeVDfz9xUzz5fABnZC2yLGGK5uEV\nW1DsCq7IfGp2bGXxuont2h1W/vbnN7Dj5bdo+7dN6FQpGjklJqJtmYdgE4mFkhTFbqG+fysL02/G\nIuXhtRzFHUtphYIChZIdVY0fu6ChuHD+ee+K1WxhgXkOAIlEgmPt1cR0Cuo4zE8rxWQ00TfopTrQ\njKwTUIVklmTMwWRMjW3k+ent6aU37CXsD2Lyq1lWsmBCP9fduZAlm4IEg0HS0q4bFbxFczNoswQo\niCwjGbLSL57GksjFmSinzfcmhar17PrZbjbdmyo0vfHjhTzbuo3B3ZkklDC5znLU7bnUmvdTkXEd\n4cEkpnjqMGQU3HS8bkN6GPpOiigBE/FeHRq0WHsXsft7Q0iSSJGqDDRg6riRXY+/yu2fXsb5Eu87\n23p45f92ou0sI2buZOHDXSy+rmzCfG/7xDLq19QTDETZvHD+OQ8h1yKmKjU23m0x3cjIyLzi/svz\n4ZoRmIKgQqVSI4oaEonIjN3QMQ3z0r97Lhq+s/lqx/U28s1L7+xt4q9/+iivlmwmtukW5OpDMNCD\n2DeEEPITam0mFAqx0t/A329ew64/7WIwGkVQqbH0t+A6DxOIoiiEVTpEQUCtUqNWqYkMV3HISU/j\nI+kpzTSRSPBfbxymWdZhRuKDxW5m5+Vw36pFLGhqJhjtZd7K2Wi1Wk5WnkalVjG7opSUsIftT+6l\n8xeLWcsWmmz78ZoqefBHFSxcUT5s8h6+f8NmOEEQQEiRX4/UJhR05zaDr7huIcf/8CoZLXcgCAKd\n1p24yMLvjSAmDSSJIUc0BHxhpGXHueWhTA79/iWIq8lal2TLXesmtdk/6KXH34dBracou2DKfivb\nq9GWe9ANP3+Vp86wpnAxNf4WHHPGfGunqutZXpASiDnGNE41ttFnCGIp8GD32RCNDmpbGinLKZrQ\nvtlsnlQguKQ8n/6/q2Lvo9V4AwY81mzU3QWEZS86tRVFUYh1mPnBwy9TutLFnZ9axd2PCPz4A5UU\nsgGn2cVAMISSSB0EZUVC1kbG9ZAqh6a2RwmEE6hIaYNx9RCO0DwaY3vAOU4oJjWMDzIKhcI8870T\nRFqsaJxRbvhsDgef7iTNewPogEQmlX9+g8XXpSJjTxw6g1qtYu6SMkrKCxkJbHkf58d7tXg0XEN3\nP+WATp0ME4mZtHtfuhCbml3oYmj4ZgZerxdJSmCzOdlnyEO2OlEQoXwF7HwSuXg+Qt0xdEN9uKx6\nZhnA6w+QrZYwuh1o1WpUaU4WiK3n7EMURQqUEC2yjCCKSCE/s82TH9cXjp2iMWc+gigSAB6vP8nX\nc7IQRZHyopQ2m0wm+cnnX0TYfR2ykOD1m17g4X/biCAIdFXHMSuZKIJCkWkVjmQmFlcvqfs1xkO7\n4n4nr5/ejbl9CT3qEwQ0LfiSbUTKDvLg3yyi5ngjex7tRE6omHWTlus+kOKGNZmMLP+4hb888jOU\niIbC1QJ61RCBP7ox4OGU9o/YtZnUpf+Wr/7wFhxOG0vWjdz3yVHVnf3d1Kl6sZW7CQTDDDZWsaRo\nMklDQgva4Welp6ubHl8HaW125LPcetK4LjxON/ktAQKaGOaAgMOeDoJIhInm6fNh5eZ5rNwML/56\nP00vDtAZq8fr7SdfXE2N9Bx2inA0uYm1u3lGeZXuSiBggWg6vQEfpuw4SuZxjnW049W3Y5HySMpJ\nwqoeCm+NIYoiNz+0kJ8eeY7Q3iIUlURGZgZRRxPuPD9SbRKVqKFfd4K117lJbXEpofnK/5zGfmoL\nDkGAEOz4+WuYHDoCg0ECPTIoAv1pIWKxKH9+5AjWhvXIikz1/D3c+4/LEMV3/rf3bsB7tbQXXEMC\nE8b73Kamc3qnMTVpwsXnUk43SYLX2883//wCJzIXMBAMo2t6kSFLDoICCsOLKyWguwlFrcFpt2Eu\nnU/l7j/zinU2sWW3EX3rZZZlOrm5wM09a89fReTh1fPYevIUflSUmrWsnzdn0jVDshphnC8urDMR\njUYnBG+8/Kc3Mey+A53KiKJA5BUz+68/wqrrF+Iu0tOBHx2pH3gsrYGMzGJSGe0wstlWLCnmxJbt\nnPivftKN83Go1xPZ8Ayf/dd7CIUivPhVL5m9qfqSVZU12Nw1LFw1m2g0ys6feklbNBdNhgp/e5j0\n+T4a5vwFa8cyVhsfQIWW+NpnLirtoDXUQ8IF/T19uNLceI2+Kf2R6njqOehobyfqFLCUZzKgh869\nbdgK0xFFkUQ8gTE5UYLmZOXQ1juIPSeVLxfxh8jUXLq2cNtDK5E/LhMOh/ntVw6gOqlHaS1AsvXh\n0qWCbWp3BSjx3Y8la4iGvtcQJS22RdVkkUPy2HrSfWYCUjdPDH6Vz/2/G1l/Yyq6WFEgd7aL094z\n4LegrYix5D4HFctuZtfTb5AIiVy3Ko3iObnDo0n9LpIDBnSiOPrTjw8YcS3uo/ZPnbiUOSSIEAso\nPP6fr+Bu/PCoJUesWk/lviMsXlPB+QLdZhpXw+F5DGNjea8Wj4ZrTGCOIGVKm5lIt4sRYlPlUl5N\npAk+3yA/evI5flk3QPi6exFFEZVZg5hRivzyb0lmFiK4s1Eqd6OLhBDOHEEryLjv+ms07bX0xWRk\nTzYaQHPzA7gbdnP/htUX7Fej0ZBtNRIPxIgmklMSJZSYNVQH/ajMKZNfetSHwTDGJHPkzdO8+L1a\n5nTfSFQTweTQoFVZCflTwV+3fnQ1v23bTtOrAkM+P46sEIN9Hkwm64RgMEVR8FU6mKu7dfS9vtoG\ntFo1R/fW4+heCWJqM3VEZ1N/6HkWrCyjr6+XeKaK7OXDm3cetPS9yoOPrOb579Zz2l2NkuVl7Q3F\nUwq+8VAUhZqOesxFBQB0nz5FutpOR383/ZFB9IKG2bmlCILAopxyjlVX0xvswWnOJNPsRqvVkpab\nhVLjI6KW0UYUynIm+nY1Gg0VpgLqa1pR1OBULOTlTM2YdCGIoojZbOaTP1pP3ekmnvtONXP6Pg6k\nzK2CI4Q8mMSkdjA/83oScoysjTEO/CyB0WdGrRhwCIUUR+7khZ/sHxWYz/zwCPp9W1goiMhGmUTW\nVhauTvkdN394IvlDS0M7Xa19zF5YhLUkTqQ6glZlIJmUaOk4Q+8fHMSTvUTMPZhteoqENZzc+RsW\nShEcw7l4KkFNMj6yX0jj9o4LEy9MD66mw/7ksbyvYb5HMTM1Kkd7m7J/WU5ewVzQK+fDTCQSPPrk\nX3jiWA099lw600uQzQIYzMg6I8pQP4rZDnNWQHcL2q2/5obSPHLWLMWjxHFqBPxd+1iR7+Ebg9kM\njmtbuai5Cbx+ooZXNPmoPQ6keIzuPUf42PqJG+LaijISVTXUdndiJMkdy2ZNOKRs/2E7FbEHaVS9\nRknyVsL+EOEVW/ngjSmTqSAIrLmrjK6tA+TG74Qj8KcvPM/DvzHjcDpG2xIEAdGQnKBbCLoEoCK3\nNIv9pjr0kSWAQljwkpmnASQ8HidKvndsXYUw1kINFUtLaP1mJx26JMb8ufQFIjx7YBt3r7plytWo\nra+lrq2R3DWz6PT1E43FSIgxlJNN6NaZMOZbOVJ9moPHqimx5zI/vYzlhQuQWsHg9Iy2o0JgUUEF\nhxqPM+iU2ReuwdIlsqxoLLDHZXfitFmG5335ZBsajYby+WXo/8XIaz/dStKrx1g6xN9++Rb++C8v\nYj25BVDwz9+GO2amN9BCoaICBWRFRiKJ/7SZQzurWbaxnEirGb0M3p4ASlKkd1cU+R8nHzZ2PVtJ\n8+8zEQcXsT26D886L451rxD3OmloaGAB96IgUS80YfXno5JjDERD5OVt5EzPAUqSy3FmWPDm7OTG\nNeMDn5Sz/p2avSi1fu/8gXemEQgE3rMapuob3/jGN871YTh89ZbfeTsQxZTVUJZT2spMFUyWpASK\nktIaYYx0IJGIjQb0qFQatFr9ZWqVKZOuIIjnCQy6MP7w4jZu/e5/s81QSNeim/HHEijBABTMgdqj\nCLllKPEI1J+AgjkI4QBkFtAfitAVipMlxPjiB29lZWkBOWkefG3NVCV0YDBjaTrB58pc5KZ7zjsG\nSUryYkMv4bQCQEBUqfENDrCpMGPCdYIgUJjuYWluGgtyM9DrdKOm7UQiyp5f9+IKz8eotdMm7KU/\n/xW+8uebMBr1QOoZeOLnr6PbswVRTBViFjpz+PNTj1H1ZITuQCPlywsAsOTIHD14HGnIwKDrCGu+\nYCKnKB2rzUrM0Ux9yxkC+kZct9Vx28dXjyZuDybbGLQMkjQMoS3sYX5WOhk2J7vajpK+pgydyYDB\nYaF/0Eu5KR+1WhyeWyr45dE3nqS1NIkvS+H4nkNYHTYsBR6MFjN9TR2ULq6gvq4OU0U6gsNAWm4W\nzQ2NFDiz0SVVtPS2I+hVBLoGKRTcDA75CBSoMbvtGGxmEmaBREcAh3U8bZk8YQxXAg63lSW35LLs\nnkwWbixAp9OxcFMeoZwj2Na2M9gbJvbUDZgSmRz0PUZCieKlBlAw2tUIgooFm7PY82IlQ/uzEAMO\nlKgGb6KFhKWb0gVj1UAURWH7d3qweCsItWtwhCro7fChEXXc+60yAh0Kuu4y4kEZ/0CYzngV/oiX\nQVMVS/JvwmVPp9H8HMUf8nHzJ+aj02mG10NDylwvMpGRaLRnxvhV5XGvx3D5e86VvzdvHyPzE0Yt\nMpWVlQiCikWLFp/3m1czTKap07euKQ1zog8TZs4fMVbd4+wCzpNzKS+3H3i7GuaZ+kYe/MUfqStb\nC+WrYflNqUVzpsOxnRAcAlFE3Pci4pnDJEsXIzScQPRkkxzsw2tOw7v8Zn4x2Iv60T/zzb++D4DP\n3Xo9cw8fo6WvntVLiinNz7vwTATQyfEJuZ664bSac2Eq9iPn4iGSL8exqNPItywi+6+S2Ow2YrFU\nIMtzv3qTqt8LFA7F0KgU1EYFX7iHXNVmMmPz6flVC4fmn6B0bj4HX2jCUpHAec9ONt+1Ftsw3d7g\nkI/sxVaW3piO2TSZUuuO9TdS2XyKqFrCJBlZUFieomqUlOG5KSiyjF6vIx6P0N7vpSM5gKCIRNp9\naNdkY/E4kGUJ7+xBYgZI1xsJDw2h81hJxONI6hS7D7KCAMSGMx/SXB7WxKx4ewawWzIxGk2caq5B\nN662o85kJJIMXvCeTAfUajWrNy1maGiI498fwCaY8BgK2VD2ANta/gOHUoTJYGZJ5t1EhJ0kEgli\nPhXN0jbsShEJwuRp5jFwunZCu4qigCQSCSbQyMMHAUXE2reYupPVZC3U0nCgi8iAmXSxgqirnSx5\nOX2JOiQ5iUFjpmROLhvuXDzc3hi37OUXax4vaN+uSfdq01zH+zAD2GzO81z77sU1JTBHcL7E++nE\nCAcrXH11Kdu7urnpv5/Br7aDWgM6Y4qaTlQNL5SIZqAbW2cNCZMd3U0fxt/agDR7KUrYD4dehZVb\nQBBRnOk88VYX3xzX/oall879+YFZ2fym+hj91kzM4QHuLnSd89qzDyIjpu1PfecWnsp5hVCnmpIF\nGm5+YD09nf089vV9xHuNnDlVz1I+S7XuRczxPKKxXiRRoSx6E9FEDIspj/a6g+z8fx2kHX8AnSDQ\n+sZRGks7WbTGxpm2BpqMA5hLndS3nGJuKI/ctFTahiRJhEIhTCYTS8/KZVQUhRWuuRyqbMaS7UKM\nK6QF9Axp/bTaglgyUgQCR2uPk2VcAKRo+Cw6EwQTCL1R8iwZJIQBjr55gIAQw6GKUeBI+Rs1Y/wc\n6HQ6stIzR1/nebI51FCLozj13mBDF8vcE3MPZxKnjzVw6Ll2+npMOG0JNBoNWsWKRtTijizAGS/m\niLQNLV5+dFeCaEcaDnOCwvhaAGRpCLUjOqHNSDhCR/QUpr4MrDEDA0IdskakN1bHuiwHWTkZKMop\nXvtZC2p1DvMyV5GMiLR29jIU7yaR0cz190+mBjwXLq481qXUmJxaiM4kq83FYepamLm5BTM/lBnA\nNSkwZzJfUVHk0YdclqVhwgTdtArKi/1RBYNB/rD3CDFF4MU39+KvuB48uaDRwhtPQaActHrwdqGq\nOYinqIyM0jl4V92RMmnb3GTv/jWrnAZ+YjBBRkGq4SEv6sTlmvMFMt0uvrYhA78/gMUyG51usplk\nalIH3ajGrlarWXtXOS/9tIqWgxIHMqt489E2bG/dh6IozB2Kckb7CgvN9xCWBzlp3Ymj9TqQdMiK\nwonYy5j3NmKpvAthOK3AFVzMqR3PsmjNHJqkHhz5KSHlLM2i7kQ7uWlZ9Hr7ODxUi8ptQGqNsNBS\nTJZ7zJwsCALzSspx9jnobO9DJQvMK13CybYzWIpGKPwEFm5ayf5te6m4ew2dDa301bdh05qJaR2E\nE2qiyRiLN6wiEAzQVNdAqLcHjT7CovMIQIvJwoJEIY3VqbJkC2yFWKbQjGcCzXXt7Pq6gjt4B4Ky\ni6aOWvJyCjjY+xQLE58ioY7SIx3HH/CTO1hEoX0NRyLbcBvzaWY7JLWoi+u479MTa3G98LNKFsU+\nSUP6EY60/BJNwopzqJDTseMc3ZFO1oMZrNhUQXaJi+3f7gOvQszSxoZ/1jJrhR9PWtkVISi4nGLN\nU/tFrzaBORnvB/28xzATNSonF6AGtVo3rQWoL7bdYDDIV371OC91BpHX34265RRD+pxUoWabB7Ra\nyJsNJ/ZASzXOimU4HvonhPY6Eq2nxubjTKO0YgFfu2sDz3z5W7S99QIYrYi9rXxqxeQUkLcDtVqN\n2z2Z6HpyrmqK1CEUCmGxjG10oVCIX33iBBn1H0QAduw6TLs8yAIlZY7XaNUoUurwErE1YTFbMBpN\nnAm9gIiaoNxF6Z7PcUL4HW4xJYQkJYHeNpyjeXZu3vDrE74GTKVuetq7MbpMVHbUku7woFJNPChl\nezLJ9oxpfxaNie5gBJ3ZgCCIJANxPlS8mdce24M8x8K6Vesw6vW0N7Yy9FYDc29dgEolYLdZWbR0\nEaqqIRYWVoyu0bmeCZfdicv+zpvNTu7pwB1MCbs5zg106E8Sue0xrH+xYY1kAgJOCmhU3kBWUgwy\npRmLaHdso6QwH0d5kA88fPekeca6TBhEkTRxDhZxKc3iHooMqxCFtbz16P+w5SMSKpWKnIIMPvQD\nMw3Vp/BkOcjKOU8F7ytkBr1ck+7IdeOJNd754KKJaSVW65WpzHK14ZoUmNOpYZ6dSzlywkxVEZkJ\n4oHzn0IjkQgf/tWz7HMsgNJsqD0KizYiREOgN6V8laWLoOkUj8xz82zprfhKliLHIqwdqiMjy8qf\n/ANgcWBoO83tBS7UajW7vv55vvSbJ+nz9/LBZYV87Nabp2V2Yyk4IzZHAY1GS0tDF7//4nHk5hyE\n7C7u/XYpFYtL+cN/Po+28lZiYgJRDcbOBfTJrxERk2iNIlqDiHlhA4aVz3Ljmix2/sqFuX0ufZ2o\nQwAAIABJREFUdmEOIhrOqJ5FpzLhytfSObQTVcSGemkVH/nUjQC440ZC/hB6q4lAr49sMbVRBCIh\n+toHcczK5Pj+E4hxBblTRb7kZF7B1IcJSZIwanREjzQTTNMhIJKHi6L8QlZIEeLzx07tBWUlRHta\nCftCWNypPuPRGC5VKjp3bL1Sa/TOpUCcHyaHiE8JohNSuZ5Gk54Nty7jldpmEl4/mrgVmSS9+sOU\nWx8iFo3T4+1E7dZiK4vygYdXTTkffXYIuVFGrRGR5ASKKoEoqIgrIVToSSQSo4cXs9nMguXnPuDN\nTPH3SzHpjuBquM9TpZUE3tcw3wsYee6ng3N1cgFqAbVag0qlGdaERiLmpheCcP55vbT/CCdnb4Ro\nDEJ+MNlAFFFpNMjBQWSVBl1rNR8tcfDZBz7M5pZWXqvei0UtcP9HbkcURWbv2UdTxxCrivNYMS+l\nzTjsDn71vz55Becx0QowVUDP+FzVZ799ivSq+1NfroEXvvMUwU9Haf5VAUqyGxNZxONxZBK4NEXU\nKi8hiCFm3arif//7fWiHyzdZnQYeH3icvv1WElKcHMMCkkqMso0W7vxsMeFwCKNpA4ebq0AtkG/N\nJNIeYSjhI1NW4/MPcmjgMN6+Hhyrymk5WotrXh5CRMJhz6K9vZ88/xC2s07g/oCfx468gCrXgs4i\nMitqZmXZmLaTbnVR3dWDJTOlFfrbvSwqLqe1u4seXxeCKGL2CZSVpVJbLtXUN9NCVFEUGuqbSC+2\n0rxhK4N7i5DVUQrv9lMyexWmrxl5zrCdjpNJFHcfn/uH5Zx6eS/HX/KRbq4g3/8RQn8ZYof7EDfc\ns3RS+3d8fgnPKduItxlp4ATm1sX0KWcIWmqZtcn0ruGDnbo0WpKJEbqXYtKdfm00pWG+LzDfQ7hy\nGuZILmUicbWQDoxF5E4FjSgiylLK/Np2BsIhKKjAlAxRkuHCXtfFIzevYVZpSoMqzc+bFNX6gTUr\nicfDl5W6cik4u0rLVFVmkr6JG2ByUM/pPT3kxe6kTvs69YltiIoGr6qGVbpPIwoi3lnP8fkf34Ak\nyaPpPTkFmXz58TSqKxt49UcdJAZqkCr286G/vwGtVoter2dr/V5cS/ORkhIvvrmHAtGDSdDy0mAl\n2lIXKo2avs5OpD91YCl0wYAOuS+CYitE7zDh7w4wEPTRFR1AK4ksLKzgz5VbSbtzHqIoEuzzUVvf\nS+mAF5czFeiU4U4n2B6mfaALFCjRpePKTJlVk8lUmpQmfSRn8tJNfZM31+mDoij87pHXCW9bjErR\nk1jWzH2PWdHp3KOmvMzcdD71Hx5SWpSIIKjIyOkitFuFW5UyjesFGwONUz/rer2eD//jSG3NRezb\neZTmYyfw5FrZePfkeqLvHozcM2FCWsnbi9KdHiGaCnR7n0v2PYMr4cMc03jG16WcmnRgJnymU40v\nEPCj1eomnKZvWb2M73zjxwyt+hAUz4eaw1ie+C4bb76VzNY6/tfH7sDtuDjf1kxMJ5lMDFeYP39k\nsXtxhMDRADosJJQIjoV+rJkWhghSqr2BpCZOg/AKJVyHKKgIC/2krzk3R2r5wlIqfjt70vtdvd1o\nCmycPnGKts428tfPJZTQ8cZre3FUZJO2oIC2yjrMFRk4izLRaXVEfEHUWQaG+gehK0xQo6bNE8ZS\n6iQmSbz85k7UedbRQCWzx0Z/8wDReGxC3yU5hZRQOGlM48tvnY3LT4EYr81cmc218lA1iVfW4RRT\nUai+HVv41oE/kGefi3NdN+48E0pSZOWtpbg8KQEajUbZ/cQZWgNxdEIOFpuRqDJEWuHFjWXVxsWs\n2nhZw76qcWVSXUa+f+lWh/HXXoix6t2Ma1JgXi4mpzBcqK7mzEXlCoJAPJ7ga395kSPGbPSxEA9m\nqrlv/UokKYkkxSlasISIUUUoMoBxVjnXeRR+cvf6S+hjGifA2GEk9X/pIqq0wMf/+Wb+bH2NwVoV\ntrwE939xCyqVip+efpau3R4UXYRNnzIgiu101zSQUSRw0/0budR7YtQbOFlVhXlWGlnlFUiiQMgX\nQu+xIqEQD0YwptkI9Q6h1+rQi1qUEFj1JoJH27l54Uaeq3yVLm8QdbMOt9mOLcOEIRTG29iDJEiY\nPVYSLUNkXHfxaQ2Xgovzl41obldeQwkNRdDJVpJKEkmSUfwmbMIcsjTr6Hy0kw5zM8WW1fxh21b+\n6j+LcXnc/OHrb2E9fAfZmmbOeHdidkaY8wEDm+65MMXitYpLT3UBzmt1mOpeX/1Ru1cS15TAHPNh\nvj2Nb3IB54kpDOfCzNWphF2HjvHVZ3bQ6y7E7NLjXrqe/2k8wXVd7TidqXQFvQjZnrTR35ExcKkp\nLtNzAJjKvJ06jOguuCGLosj9X9g86f2/++GdJBIJ1OqJ0cnxeHQ0FeVSYNAbsNttRKMJlEAEk82C\nzmjAJKsZ6hjAlu0iMhDEZDIRrO/DMbcYk9mFtiXMpuU30T/QT73QS86auSRCMbob+4gPBMgRnHSr\nB9FmWGjccYKPFG+cFFE73RjvLxvjSx2r9nFxZj4Yr6EoisJffv4m3ft1iIY4130qE7PVyGHf48wO\nPIgsJmhiO7NMy4mEoxijWfh0NQiCQGbXFva98BI3P2gnVO3BLqhIMxSTllNMZOGr3P3Z80W0Tgeu\nnmCpyxnL5aS6pK69kOn+alqnK4trSmCO4dI2/LNz/S49l3JmNMzdlVV8+lSIwJ1fROppI9rbhnBs\nD0RC/Pb1Bj56wzqO1zczUFtNZ20jnnVbcPa38JF5b49c+0pihHx+jAFJNZy3evmRxRrNZD7UieQV\n49s/f186nQ632oY9L4vTx0+hmmUkGY5Tqs1GE1E4+MdKwmIcxWnHY7IzUHeK8owS5ucsRq/Tc6Du\nGOW3riAsx9C4dcTDMdT1AZKzNSyfuxRJklDdXEbLsS6KmLro9Uzi3JvrxWys8NpThxn6/VqcpHyx\n2//tFVSuAAutH6JZ2I0sK4SEFkzCFgYGQvQmT6MfyiWojmC0ahE1SioVyxqCyFj/KstEooLpxdWk\nRU3PWC7XpBuJBHniiSew2+2YzWai0ei0BFYpisIPfvAd6uvr0Gq1fOUrXyM7e+b2r2tUYKZwIQ0z\nVcA5MSnX72qpS3k2fvLafnxNXdB4BjRaJE8ug12t6FdvYUdmOs/9zy/pyZ2LfMMnIDRE+r7n+NnD\nd+JyTc5zvBhcCZ9s6jASG6e1q9FotMiyNPre1QSVSsUsVRa1tV3keDIYeKODhVnlzCovpWegD7nE\niqMgVdy671gLN2Yvw2gYKzXmcrroCMSwOc0kpSRJdMzLyaRTDCGIAn3tPXhDAyR8QTJbPczOe+eF\n5tkQBIGW+k6e+/YZEr1GjCVDfPTf1qDX63n5j3vY91QTBhykl1gQDDHMOBnZYDXtpQwKb5CmMjPH\nfj0A7VlBTgV/hegtpV+soyxxN35fkMCy17nj3jUIgsC6zzjZ/ZNXUA15EItbeOAz716e0ncLLsV0\n39TUyK9//avRa268cT15eQWUlpbxwQ9+iLlz51+RMe3evYt4PM4vfvFrTp06yU9/+h98+9s/uCJt\nXwyuOYE5WaOY6pqpCjhrEcW3RzowE0E/h0/X8kZ/DG55KJUq4vfCMz9Hd8MHKUr3IIoirSozyqxl\n6ABMNprL1tA/6LtkgXklDgtTEQ9M1NrH+9CuHlQ1VjOYDJKj2Ci1F2JZv2J0PdqHunEsThu91jo7\nnfb6TsoKxsqNzc0qo7v2GFKxiKzIaI8PsXj19UTPHKavtYtBVRhzcRrWnGyaAwHMPZ3kpGfN+Dwv\nhGceqcF1/B4A5DaZJ7/3NPF4gsSTN1IQuZtO5TC91QLRskMUM4gqZiYWlumwHKZ4MUReHsIg2ogo\nPvLXy2T056AJbkRRNtAePUGjfz9lUTd/euQgd3xxMTX7uhAwEvc0seH+bOyO92Zi/LsBY7nlIwJT\nYM6ceTz66O85cuQwTz75BOnpGdTV1dLc3IhGo7liAvPEiUpWrEj5rSsq5lJTc/qKtHuxuOYE5ghG\n/CvjcbkFnM/T20gPl9HGuaEoCn+oakLJnwX24SogRgsaixVjyym6kxEMBj2KJKHIY2H4QmgIiyl/\nWsZ0vrGeTeyQ8gPPtNY+/p5cXL97Tx3CV6TCmu5hwB/mRHUNa8uXj36uQ0M4GkerT1WliXgD9Awm\naJZ6QVKY5ygm05PBJhbR2NSOIMPCFanc1jWzl/HsGy9iX5uBKaHDYrYgWES6j/RelQIz3mlGSkrI\nsoJGoybQpiVyJgtTwoQiC2SxjIbQdpQWO5Hbn6HxSQca2YgnPpeh+uPM+syb+FtFskpg072reeqX\n22mJ7ydXsxxDMBeT2Ehmy50ozQo/rv4vSn0fJXOY3GDPD3cxa3HwPZu6cHG4uixcgiBQWFhEPB7n\n0KHDfP/7P0aWZXp7e3BcZNT9xSAcDmE2j913lUo1o1G516zAHJ+vON0FnKcr6GdCkIxKRJVMoAwT\n/Sj1J1DMDvqX3wayjFZO4PG/TuzwViKzlqPy9XJ7opWszMk5ad29vby0/wgRjZESl5Wbli2cYh3O\nzyg09VjPTTxw9WHi3A7WVrJfqkPjM5OsOc2SdSsZ1A9MuGZBSQU7ju6l1wWdLa1IPWHS1pSQM1zw\n+cCJM9xktuF2unE7J2r1giCwonwZxyOdWOwpXtdgr49Sk4OrEb3xOszezYiCmrAqgDbHR6jJhKxI\njGxdMhLRWBirxcoS592j3x08ZWL21/ooebB4NCcz8fKNiLEeDqp/jpiVYGni80BqXaRuDzqDiZF7\nousvoK+3D2OBafSa9/FOYmz9h4aGRkkLRFEkIyPzXF96WzAaTYTDodHXM53Ccg0LzBRSPsorVcD5\nXLjyGubZQTK357t5q6+d5srdJB0exKAXQatHaamBRBRDfhk3rF3DPUVuDtccZUlZHmuXfmhSu9uP\nVvHvhxppz56H2mClIBqn/fW9/M2mtRNndAFGofE4VyWRi1njK23Gbmhv5sjgGZJqGUtAZMvi6y8Y\nvHW6/gxvBE/SH/ahIoijIJ0dW7czz5ba8MPhEHq9AZVKxQ3z1rLzxF7Mq+bT2taGlGfEGxjAZXFi\nLHDR3d5D4TkqOWSlZeBtGqSltxVEyFZcFBbNrAVgIiben5rKRrZ+r5lIn5qB5gSS+nl0mJHVUeab\n9WR8JMrx757CkMinUXgdQZPEZFeBNpESpMOJ9nF9H7bhQ0FLcysDz1ag91kxJR2UBUpoKP05QkLF\nyKFMl+MjONCNWUmR1yfyasjInEuK8ebycwjf7nq8M7iaXBSTxxIITC8t3vz5C9i79002btzEyZNV\nFBeXXPhLVxDvC8xhTtIL51JeHZic2pIKkrl+6SJ+47Tz2+27aG6p4dBggsD6e8CRgSJJxI69ir3A\nwpoFc1mzYO4523+ydYhBkwchLQcJ6I0MsT+s4m+mvPr8P94LVRI5H6Zjw0skEhzwnyZtRRGyLBOL\nRtl7/CArZi1kzDQ78XCjKAp7+k6QedNsYifrcc/PJT4UQb+4kMh+P89UvQaZRuSuKAt0hZTmFuEz\nJ3BbTWjVGhRFwRcJ0lXfRigUIpJwk+FJx6A3TDnGeYVzmKuUDK/B5OjedwqKovDcN5vIqLuXWDSO\nNSbTyKvkqzeg04uI0ovc/jeryZ5byR//z2Pkt91EIDyA5A/QsV0hVvBLHM0bSOgHKP3YAB5PMQCJ\nWJL4oAFzctjMlgBBVhFa8yyRFgvazACf+coGju48Sce+U6CPc+vDueh0BibnDp4/h/ByCrO/j4uD\n3+/HYpk+gbl+/UYOHTrAZz7zEABf/erXp62vqXDNCUxJkpDliekLF7uJv11cGWYhmUTibOEzkfWm\n3TtEZcl6JHc2kX27EUQ1hP0giqiHenErOp7dc5APrFpyzhy/BCLCuPxEBdApU0Wrntske+GAnncG\ngYAfVVrKjKegoNFpCTHAxA13/KabIByOYC9w4+v2o7Po0Wm0xCMBCiyZNAvHyVuzePT+Vh5soEQp\nRBh2E+eXFVFz/BSNZ+qZc9MystR52Ew23njrIDfPu27mJn6RUBSFbY/vo682gatEYMsDq0bdCZFI\nBKU7FdAkxUBARECNKOmoDj/HX9+cMr0tWbmQihdm88i9z+A68wH00XSogeai3/DQ03HM5nQcjjEG\npeKyQlotj2EdKEIt6GhSvUq6O4uHv7NxmOFJBlTceJ8b7jv3uIf/d46/kevgygnR9zGGsTVMaZj2\n6etJEPjSl746be1fCFe3OnWFkfJVjqQwpG5ySujMxDJcms9vBIqikEjEiMXCyHJyWPjo0WoNkwTQ\n3p4Aca2Z3oO74PRBqD6ARwqTF+7F5PTwcvH1/JexnP/zl1eQ5ak5OFcbJdIddlRnDiN7O/F0VHPf\neQo3nz3WZDJBLBYaFpYCGo1uyrG+E7DZ7EidASRZQlEUIv4QDpUZjUZP6uyo5uyfhMGgB1+cfEcG\nkZYBwn1+/Ke7aDp2mpDXj69vYGzDNqqQJIlSTSbeuk5i4SgelZV5OWUU2rOxm+0pJibTTM/84vCn\n/9hB47+uQHr8LpofWcvjP9w5+pnRaERV2DX8SkAijpca6pXthJM+tv7sNIlE6oCk1+sxJbMwhLNQ\nSXpUkh65JYeW6l4cjjGfrCRJ+P1+FtySRo/9Tdqtr5LtLiFv+aXl7wmCMPwnDuftjtxLNaAidU/H\nWw5kUhy1SRQlgaIkURRptHbt1VekeTKuDkE/lUn2vUu8DteYhikIAiqVDkFIBaCMr1U5/X1fWtDP\nVBG7Gs0FUltCQzTV7iEoySj3fhGG+kkc3oFLTKDZnKrkIWp1HHbNoa2jnfzcvElNfGLzWgqPHKdR\nE8OlamTTLYswGIzsO16F1WigonTEXCigKGOn+ysf0HNl/b4jAVJrnHM4/FYNilaFM6Fn2bzVwxvt\niBVAZCRUHlQIAqx0VnC08gwlYjpVzx1j1uYlVO7Yj31JNpVdNQiHQqy9aSOGQRlVrkh5XhnZ/nT6\nGr1keZawO3hkQm1KdeTq3JC79uhxKKkoa6PipmuPEb409vkD35nP899/koYDfiLtelbIf88QzSAJ\nJF4q5uFFP+aeL67i9ofWIOc1Ez7uwyR48NOOXmugtykAQDweZ8dLezn46yBKfQFejR8xo4m5cytw\nzTrB7Z9Ye9bI3n4q1+Vxq478XU3362oay2S8l4tHwzUmMCFlyhTFMeqvmTtNjkXlng+XU/2koaWN\noLEIxeKAfS+jsjkRXZmUJzpo02nHRpKIoVZN/VALgsDmpQtHXwcCAb78wh5aC5dAV4Ab6nfx+S0b\nONHQzLHOfjx6LVuWzrsEXt3pwaGaY3QlB1HFYcPsFZiMw6bXsyKg093p3JmZiyzLSFLivGs68llu\neg656TnUNNbivLuAQzv2UHbPKvRWE8lQjJBrkLanD3Pn6ps4fuYERp2OkvxibFYzILA2fxG79x0l\nbgRtBBZaCumtr8ecnoZxGv09lwrBNJHsXTzrdXZeBrd/GX73yRrqO9qp5xX02ClmMwPUkendxIEf\nhSlaVIvdbeGY8Gfscgk6lZE0j5tZyy14+wZ59AuHSb61mGR8kAQx5qo+TUP4BeRFYe769Kbpm98l\nc6uevTdIw9deHIduJBymq6ESUVAwpxfj9mRcgVlc3XgvF4+Ga8wkC+O1vJkjRJ/Y/7n7k6Qk8XiE\nRCIGKKhUGnQ600VFlO47XkVNxQ1oypdCRj7oDEjFC/DPWUlHRCLzxOvIsSiyt4ebk51kZ6Vy+x5/\nYz8fe3oPH396D0/uOTip3acPVdE+Zx0qgwmVO4PXtDk8v+tNvtch8Er6Mn6nL+PH295EFFVotQY0\nGv0VEZaXkopz+EwlLXkxtCsyEddm8PzJHUBK643HI8OBXQpqtRadzjjF4ePiNBiTzkA8GCGhSOgs\nJhQFNCoNdpcLj8nNtpZD9C/WU5cfZfvxPYyY/8wmA7dUrOHOwjUs16TjPnaEnNoa5F07GezsJBaL\nEYvFLtT9tOOGz+XQnfsCXrmBrpwXuf5zk1MCXv/tabJa7maZ+ePExSE8zGGINro5QT4bCfcpPPvb\n15Ce30QeawGFiOTnlOYPlC8qZut/Hye99l5s5JLFchRFIqFEUMt6Io1nHx5m5rd5tjk3FWw13pw7\nHmPm3HOZdAGSySTtx19hnjPIXGcYue0tBvp7ZmQ+M4+x3897uXg0XIMa5ghmuuTWiAlzKpwr8vVS\nBE+Xz489awnBQT/9Z46gLLkBVXgIqyQRWXs3H1U1oci1ONLNLFi/EYAjp8/wuJANZRlIkRCP9rRS\ndqaOBbPG6NiS48yVALJGz5udAaS5KeJrUWegUnAgCOpp8lNOvWidvd3UdjVgVhvolH2YPanNXRAE\nYh41rx7cSUAbQx2DjeWrMJstly3IdUmJ5PbTWOxamndVkb9yNjqtgdZXjjI4ICHku+msqiI7LQuh\nQEvtqdNkZGVjddgZ0ViE+josGjWxcBBNOEzLc38hfzhXbSA3n4wlE4shh3w+IvV1oCjoCouwuN8e\njeHFYP6KMkqez6a9rZPsnAqMximcrXLqWTCIVuaabuVY4ClMZDCHu2ljDwD1r0isiLvJoJAMMWWt\nONXRzxO/3MqhJwLM9sWRSKBGhxYLQaUPQZtAlx6ZtrldKiYS0Y9omyPP92TTbiwaoa3mMBqSqKyZ\niFojJW5x9PtFGRaqepqIhIZIDjQjA5bMCtwZ2W9ndJcxsyuJ932Y72PaMJlZ5mIiXy8W6+bN5vHX\nDiHMWoFoUDMY8JIpJnC7MlECPiw2I0sqyid8p7FvEJxFdFfupw8tsc4W/vr4EPcv7OBT6xZjtVq5\naU4hbx48SqB4MVIyTkX3cexmA00jsxJEtHJyGiprnHtTaGxv5q1kLc7VOQwGI9Q8c5qFKzJI0XXJ\ntJysxbR5OUa7GxTYunsPH1px62WNpmPfW2Q3NfGAWsvpujAHhsz09tQTTcJsYyb+Gy1oclO5hc17\nqimr6iY9YMVgtdCSnUfWkiVotVpARaCjHX1bG7pwCOOAF+2WLZhNJkydbXgzPLiyU5toNBwksW8v\nWcPPw2B/P+E1azFO44ZkNJooLSthJMfxbKy7v5gndm8jo/NmUMuYlnTSe7QHv9IGiOSyEiEWpdH+\nNKXRj6XWQ9yFQXCy/5Eo9sQcepVa0lVzSegGaFVvw+G0UbIok9v/qXzKPq8eTI6qHQkSajr6Okvy\nU5Ygr+8Mdb40bLoEZqMOgERCYmBwgFl0k56eOog0dh0ibLFhvEjGondDMFIwGMRstrzTw5g2XLMC\nc+Y1TIb7A5icdnGheo8Xgsfp4jurCnmqaj9noh3sqeqjP6uUYM0x7nHBotV3TPrOooJsHn1zN72W\nXBLeXhKLrqffZOQvcpi+7Qf47j2byclI51+XSuyo3odepXDbLWvwDgVp3L+fnqzZ6Pz93JNtnFG2\njVMDjThXpSoU6MwGHGVZ9G6vRkrXowTjeKwuTE4rAgIIAiGrMiHoJoWLM8knEgn6+vqI73idmCgi\nOV3McTgwOLPIWLsOgFdr9qJz2On2DqJ3mTH0eclu6Mczq4Ch09XY9x3A11gPCxdBURGR/ftwaTR0\nShLZnnRCHd2Yy0rRqbXI0THTbKC3myxBIWUCBIco0N7VgcFi5mJ8aNOBwtJcPvobDQe2PU2kt4es\nFzbRzx4W8CBqdChAq+YlCpap2Pvyd3FLc7AaHDSoDrIk+SUMop0eqZpGcTvGNaf5n9/8zZTVZN4t\nEASBSCRCmj6GKKRya112E929cXrIJtbThkEr0BLUYbW5SHcEGYllyHNpOdPVQn7xbC7WL3q1Q5bl\nGS9LN5O45gTmO+fDTPUnSXEkKTn63gUjXy8Bxbk5fCkrkw//KUpu+ToCkShiYSnOgaOTBNrRmjqq\nOvuY72uiyVVMIBlDbzahElVIskCTaCKZTCLLCdKcVu5bu3jUVGw22/n+ZjO1Lc3kFWeSljY9hY7P\nBWFc7JSiKKhUAnfNvx5ZljHmm9la9SYMC0sAVexsYXlxaO5s4a2h0ySrq1hw5gjFlnTo7SE0azZk\njZnSbIKR/phEpt7FUJ8fan2sKltCoLODtEgYv1aNrFYTq66GW27BW1xCRzyOkJtH5NgRIt1dJAry\n8Wq0WLNyR9vVmm2EkxImjQZQiElJVCYDIwI0Nf+JeYUw/RtuVm4Gd30ig199eTdu/3Jm6UwciP4E\nC1mE6MESyEbeVcxq6xzaE0fwWU6Ro5lDT/AoBfL1pKvKUenibPmM5V0tLEeg1Wrxjg+4VyApaCiZ\nu4pgcB6xeJxZdjsdLXUEQoMM+rzEomFCcRXuRYs5Oxjwnbinbw+pvfPqHNv04JoL+hnBTBZ1TkW+\npn4UI8Ly3AEol4dYLEZAa0EQRWxmMxaLmaConXDNiweO8i+dKv7sWUZlxY1kVe3ArRFQCSJiNIRN\nr8WejJBM/n/23jtKrvM88/zdUDmHruqcI7objQwCIACCWRApkspx7HGYsS15bdmj9fGZ3ZHt8Wq9\nu+MztrzSWrKtsa1RoihmUqQIRkQio9HonHOo1JXjvftHdUADDRChAcKCnnP6VHfXrRu+79b33Dc9\nb3KhJ6WIVmtAq11O6DEYDLTU1eK+RTG1RXH86ZlppmemV3gCNhWvY+74ANlMmtDELCURE3q9EZvN\niVar57767QTe6Wf2wggzR3rZ4brc1ScIMOf38X77KWZ8syuOu4iTgR4cG8tpUAXSG2oYi/kxSRIj\nc7NMmXW833GSUHierfUbMHckSJ2bwdAZZ//eT+MTBYRsFkVV8VmtmPR69KhkU2k0zS1YCwrQjI8h\nGU3kLFb6RscQtm5DyWWZGx4mHo1icxcQqq1nNqswl1WYLa3AWVLO1eoK84kot6euUNDm72VHroad\n/DEyBrbyZZrVzxLMjGGQbNTp76cy+Qia2kmc1iIGtK/Qp30B88ePsnFX0y05r9sNWZY0dz7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5LNEk2nsThKKK9dR7i0hvPDfbibi2guKr1t130tuDKJwurzqfKP//gdfvCDH1BcXIzZbMFoNFJX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kz5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\nBUEQBMEKZEtWEARBEKzg/wFMIO7l5jtiWwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits import mplot3d\n", + "ax = plt.axes(projection='3d')\n", + "ax.scatter3D(XS[:, 0], XS[:, 1], XS[:, 2],\n", + " **colorize);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fundamental relationships between the data points are still there, but this time the data has been transformed in a nonlinear way: it has been wrapped-up into the shape of an \"S.\"\n", + "\n", + "If we try a simple MDS algorithm on this data, it is not able to \"unwrap\" this nonlinear embedding, and we lose track of the fundamental relationships in the embedded manifold:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lQrs0iIiFoZcYjw/FtYWaKroeWcv2V55j2oSm9QjRz6FtciKv77+JNQH3cZLN2HFgxU4I\nrajPjuIvd75mdkSRn5XKV+QM7Dx4mL/mW6m78GpjX91dayDzCHy9FPIzYMGTxmnliDhjVauo1rD2\nYxg8EWtVGVcc+owFV04kICDA7KH4jNjYWO54YQpeXHRgLIkM4gCf0IrebH+vmIO7j5odUeRno/IV\nOQPv7DxE7aCJxhs2OySlwXmXGjdUDbnIWCzD6zV2J3KWGotrVJZCaSGp7nKeuf4qWsfFmTsIHzRm\n0gjaX13AKh5gNQ8TSCSBhBPuSuH5ORt0A5Y0WypfkTNgx2MsnrH6fVg6Hw58Des+NQq3dTJ06W88\nuxvf3thIIakThMcQ9dHf+OzBX5sd36c98OSvmPpAH2KsHQkjgUCiCCSSgJw0/nT9W2bHE/lZqHxF\nzsC0Lm0JmHcvVJRC5eklI8OjYPkbxmpWB7ZBwUnIPARjrzSe47X78+zsywgNDTM7vs+7+o4p9JgD\nXjw4CCOVSTg5RenSNFYtWm92PJFzTuUr8iMeePMDJm0rpmb0TGPxjHZpp/ffjYCifONxIu/pmXFF\nCSycB6FRtCvJYNzwoWbHbzJ+/cQsvKkHqCCXvbxPEkNwe1x8fGsBGSdOmB1P5JxS+Yr8gFWbtzA/\nuj/1PUZAaj/jed78TOg5HOqrYeqtMOMOaNcVRk0zTkE7S2l9ZBPL5/7S7PhNis1m48G3Z+KOzqE9\no6mmBC8ekmrG8MD0V8yOJ3JOqXxFfkBOUQn1VpuxeMbq96DnCLjol7BnA9TWwCcvwNFd0LGncQ24\n72ior2Ppb+8iMqL5rNPcWBLatuGC++LIYgMOwujMReSzh8CMnjzz29fNjidyzqh8RX5AYU0t1vR9\nYLEa+/N63LBpCYy/GuKSoLzIuAkr4xCMvQIObqVToJeEhESzozdZl149luJWGylgH/v5iCBi8COY\ntS/nknMy1+x4IueEylfkO3i9XuYtXMKz36TjqauDL94y1mz+8Flw1cLejbD1CwiJguGTIfc4bF5K\n8OGtfPX838yO3+TdM38KJRzHih/FHCGWbgR5W3Hv1BfNjiZyTqh8Rb7D/W9+wG9tnalM6Gw8s1tX\na/yX2MnYCnDDYvAPgtAIKD1lXOvNOsziu2/Ez8/P7PhNXs++3Qnpk08dlfgRRC47iKYzFek2amtr\nzY4nctZUviL/w+v1sryg1nh8qNsQiG4FYZHGTVbVTuyhkcYsOK0fnD8dQiOhvIjgwEC6d+5kdvxm\n48VlD5DJGqooJIBInOTiIILH7njB7GgiZ03lK/I/LBYLZRlHwIIxwz203Vg28utl4OfAVVUBvUZA\n10Gway2kDQC3i/tG9jY7erNisVhI7BVEOVmUcYIqioikE/uXlpsdTeSsqXxF/ofL5cITmwjnXQZj\nLodLf2VslDDnQfC6Ty8nWQO714OrHg5uI7i2ghunTTU7erPz2Du3UE81wbQmnv54qcevKoZNq3aY\nHU3krKh8Rf5HSUkJJHf5zwE/B9RVnz7VPBCO7YFxV8HwS2DwBCzHdrHw1jnmBW7GYmPjiOzqoowM\nijiCBRsRtGfd4p1mRxM5Kypfkf8RHR1NWsVJ442aKmMt58hWsH2l8Z/HbexmtGU5BAQzsEMiPdNS\nzQ3djN334mz87H6E0gZ/gomiE1veKeT3kz/mrtEv89pfF1BXV2d2TJGfROUr8j+sVis3pLWm9dKX\n8V/yKrRNNa7xDp4I0W2Mm7D6joGUXlg2LGRqj85mR27WOnftSG14Nl7cePHioop2jOKrTV/g3BvD\n13/x57bzXmDbhl1mRxU5Yypfkf+Rf+oUj5+oIW/CDdRd9msICDIeLzq2G8qKjB2NFr8MdTX0rCng\nmgtHmx252XP5ldGW4XRhCsmMoJyTOIigOzOpw0mb49N47cpDLFuwweyoImfEbnYAEV+zZuduMlOH\ngccDFgu07w5rPoSiPOg+1Jj9uupg7SdMGNrN7LgtwpUPjmLBbfNoTS9c1FBNKckMw44DDy4CiSak\nqgPfvFvM+JlmpxX5cSpfkf9SX1/Pp3uPQG2OcUdzYS7kpkNCRwgMMdZx9nfAsT3YQsO5ffIEsyO3\nCFNmTOKF227DTS0eXAQRR09mUcAB/AjhCJ/Ri6txWj41O6rIGVH5ipz2ybpNPLthJ/vskdCuE+Rn\nGNsEhkdDeIyxwlWN09hEoW0qQR/8DZvNZnbsFsFisRAS4Y+n1IMNB9UUs5YnsGKjlgpG8ltOsokB\nkzxmRxU5I7rmKwIsXL+ZuwpC2Df6Whg2GZa/aexkZLVBu27G87wZB4yZ8OLXwFnG4LatzI7doky5\nsy8WrCQwgCg6Ekl7hnMP4OUbXuMU+zi2P8vsmCJn5KzKd9euXcyePftcZRExzZdZBVQmpkJ9Hbz+\nqLFWc2gUnMqETZ8bS03GJhp7+joCsH3+GndNON/s2C3KuMmjcZLPEZZjx0EYiWzhRSrIAawEEoVr\nwSSWv6+brsT3Nfi086uvvsrChQsJDg4+l3lETBGOyzjFvPxNiGoFU24Gq9WY6R7cAttWQ3RrsFog\npg3ewmz6pukRo8YUHx+Pm1qCiAQslJBODaUkM5JKCujEBCLq25K9dzfMMDutyA9r8Mw3OTmZF17Q\nAufSPNw0aggh8x8Bmw0SO8PKd41TzTFtoLYG0vrDsEnGs75uN3g8WCwWs2O3OP5BUE4uGayhhGP4\nE0wZGQxjLhG0pYJcWqcGmh1T5Ec1uHzHjh2rm02kWairq2PW/E9xXvN7mHANBARCfS0snAfp+6Ao\nF0ZPNx47Ahgwlji309TMLVVMQhg9uYoAYokihUBiqKeGzTzNQRayzf9p0vonmx1T5EdZvF6vt6Ef\nnJ2dzdy5c1mwYMGPvtblcmO3q6zF92za/g1Dd1shLtE4sHcTbFwE8R3AYgWvBRwBxraCrdtDqySe\nrNzM3XN0brOxlZeXc0H4A7SiJ6WcoJh0RvIABRzAQRjBxBF98UEeXzzL7KgiP+isHzU60+4uKan6\nzuOxsaEUFFScbQyfoLH4ph8bi81jI7D8FNVxibB+EaT2hxuegK0r4NA2HHVOaq/5g3ENeNc6hh5c\nwVUP3GHK96cl/bl8NwsEVnKieg1BRDGAG8llJ1UUUsUuLuAxDu/Y0ejfo+b05wLNazxmjiU2NvR7\n33fW5avrXtLUtW/Xnpu27WXe3vVUWa0QG2+8Y8BYqK2i9the+OhZY3ejkEh6pnbWJRcTRdWnUUM1\nsXSnjEwchODFTRQdqaGcCscxsyOK/KizetQoISHhjE45i/i6+6ZezC2OU9iP7ICl/4LCHFjzMVRW\ngKsWxl9j3AHdui2btm41O26LVuQ6QT3VZLMZP0JIYjjlnMRJHif4ioocC2VlZWbHFPlBWmRDBLj3\njfd5Mt+Oa+gl4B8A33wJJfnG9oETfmFspLBzDXQbQkFwtNlxWzSr1YIFG27qyWM7m/g74STRnxtI\n4xICalvx+GWLqKhoHqdNpXlS+YoAq51+YLPDuk+NWW9+prGoRm01HNsFs+4xlpVc/T5Brlqz47Zo\nntAy7PgxkF/RlWm4qSWNKQCcYh/VFFOwx59l72qxDfFdWttZBAh018ChA8ZzvG3awe51EBQKpQUw\n5SbjRaGREN2aP45ob2rWli4hIoXSMhtfcA+JDMFGABv5OwGE4cFFLF1xU49fgOYW4rv0f6cIcFPX\nNpCcBl0HQmQcjJwK1U5o3c64A3r7KnC7CfOz0btrF7PjtmzhZUSSTAoT8COABAbgpppwkoikA7F0\npYZSqkvdZicV+V4qXxHgov698Ss9BTu+NBbYyDoCR3ZC9lFjc4XqSnj3L8ywFRIVpWu+ZnKE+lPA\nIXLZihcPDkKJZyBhJNCVqVRSQCt6sGdZodlRRb6XTjtLi+dyubj1rYXUJ3eBQ9th3UIYNRWuuh92\nrzVON6f0htgE+kQXmx23xfMLrcONizi6U8kpTrGHACKowMpeFlBPNd25nO3fbKGwsJCYmBizI4v8\nfzTzlRZv4Vdr+aLEA3kZ0KEbpPaBbSuMO517j4JNy2DRy7B7PfPXfG123BYvtL4dLuop5jjtuYB2\nnE8hhyjkICWkE0g0h1hMJ/dk8nLyzY4r8p1UvtLi1bvdUFZgrOs8aAIMucjYTnDfZti8FCZeDZfc\nAJfcwNbgtryx4kuzI7do+UVZdGA0NZRykIUUcIDh3Esl+aQwni5MpgczyfBbScdOHcyOK/KddNpZ\nWrzxgwbAQed/Nk4IizZutso8BDnHoTAbgsMhKARvVTm7CrWpgpksDjf1OOnGNA6yiFJOsJV/0JmL\n2ceHlJFJja2QATc4CAzUDkfim1S+0uKFh4cTV1XEqX8fWLcQJlwNcUmQdRh2rYXRpzdRyDpMTdY6\ns6IK0GNwBxZ9vYZEBpLIINoxkiIOE0E7IkmmgmzG/TGYWddONTuqyPdS+UqLZ7FYeH78AO5Z9Q5Z\nufm43fVQmAWnTkKf0RDV+j8vTupMoveEaVkFLrpyFIue2cdJtpDMCE7wFZF0IIuNdGUaNvxx1b9n\ndkyRH6RrviLAqD69WHPL5QSEhcF5l4LLDb1Ggr8D9m+G7avB6yWgIIsebXT3rJnatk3G7uehDf3I\nYQdHWIYHD72ZQxDR7Le+z9jJ55kdU+QHaeYrctq+I4ep7DsO1nwIl90Kdj/jud/aGtj/NcF71nDH\nuBFcPGyi2VFbNKvVyiMLZvPo9HcI8SQSQCT77G9RaNkNjhqu+HNP2rRpY3ZMkR+k8hU5LSE2FtvS\n1bj7X2AUb201ZByA8XOgopTKzIPER4SZHVOAgSP68MTiQLYuOoEtyM20224kJCTE7FgiZ0zlK3Ja\nmzbx3JXoz5OlBcaB7auNR4xsp39M7H7M236Y6aOGmxdSvtVzQBo9B6SZHUOkQXTNV+S//ObKGcyN\nqcOxfzPUVP6neAEi46ir1Y5GInL2VL4i/+PeaZewcUw7LqzNMnY3ysswthrctYY4TxVer9fsiCLS\nxKl8Rb5DUlJb3nzkft7uHkrIji9gxBSY+Es2DJrJa8tWmR1PRJo4la/ID+jWNgFX2sBv33aHx7Kn\ntNrERCLSHKh8RX5AdHQMCaVZ/zlQU0mSv3l5RKR50N3OIj/A4XDwp8Ep/HnLIsot/gywV3HnnOlm\nxxKRJk7lK/IjRvbuycjePc2OISLNiE47i4iINDKVr4iISCNT+YqIiDQyla+IiEgjU/mKiIg0MpWv\niIhII1P5ioiINDKVr4iISCNT+YqIiDQyrXAl0gBf7/yatTs3km4twBNmw1VYxciEAcyeMNPsaCLS\nBGjmK/ITvbxkPqs7ZsIvUsjJyyZ7Xzo5OTl8tP8LJv3qMk6cSDc7ooj4OM18RX6Cr3duYVXGZryZ\nUFVQRque7QmMDCHnm2O4a+oJ6BbHLW88wEUp5/GrWTebHVdEfJTKV+QMeb1e5m18l9Qpg4jr0Y7M\n9fvJ3nqYHleOouDgSTqN70tlQTnVRRWszd/H1IJ8WsW2Mju2iPggnXYWOUM1NTX4tQunda8OWK1W\n2p3XHf+gAPZ/uIGQVhFkbTzIyU0HyVi7j7rqWm5+bi55eXlmxxYRH6TyFTlDgYGBWN3/efv46l2U\nZxdRmVdC9tYjhLWNwVlQRnRqAq7qWuodcN28u9h/aJ95oUXEJ+m0s8hP0MOVQPHhHPwjAsnZdoTz\nH52F1WYjb9dxdr/1FW2HdqG62InVbsFZ6MQREcgjy57hvrqb6dejj9nxRcRHaOYr8hP86tLrGZWe\nRPnft9NuZA+sNhsArXt1wC/YQcnxPGpKnDhCgkgakkZUh3hKswv5w2dP8/bS90xOLyK+QuUr8hMN\n7zuMW664CWdW0bfHPB4PlafK6HXV+Yz90zXYHP6c3HKYvF3pxKYlYAvyY1XtLt5e8b6JyUXEV+i0\ns0gDtGkTT9fNsRz4aBMhSdEcW7qDdqN6ENctmXV//Yia0gq6Tx9OTWklWZsPkTi4M5nr9rEw7BRv\nL3qXfm178Pu7H8VisZg9FBExgcpXpIFuuvRanM4KysvLsUy9mL+te5WqonKq8ktJubAvJen5lGWc\nwplXwok1e6h31lBbUUOnKf0pC3Qw+/EbeevBl80ehoiYQKedRc5CSEgo8fEJtGnVmikpY8hfeZD6\nqjocoUGpg9giAAAgAElEQVS0P78nIa0iGfnby4lo24r4QWnYHX7s/2A9O/61gjJrNUePHjZ7CCJi\nApWvyDkyrPcQ7uw6i0CLP4mDUsn75jg9rhzFjn+upKbUSWVOMcFxYXS9bCghcZGEJcYw970/sPeg\nHkUSaWkaVL5er5dHHnmEmTNnMmfOHLKyss51LpEmKb51PFOHXEx9dS1Yrex6cxWtuiczZO6luOvq\niewYT2B0GJEdWlNd7KSyuJy5b/yO9z7XjVgiLUmDynflypXU1dWxYMEC5s6dyxNPPHGuc4k0WTNG\nX0rN/APkbT1CRHIrojvHk75qF6N+N4s2fTpgsYDdYccREkBMWgKO0ABeW/cet/51LsUlRT/+BUSk\nyWtQ+W7fvp0RI0YA0KtXL/bu3XtOQ4k0ZXa7nXun/ponLvkNdaVVFB3OpuesUThCg4jvm0LpiXyc\n+aVY/WzUVdbRdkQ3el09mhM1edz81kN8vWer2UMQkZ9Zg+52djqdhIaG/ueT2O14PB6s1u/v8sjI\nIOx223e+LzY29DuPN0Uai28yYyyxsQM4XHqMd3cv+z/Hy3NLaDu0C/ZABx0v6M2R5dsJDA9m4jM3\nUnQkm78+8zxPxz1Cr249v/NRJP25+KbmNBZoXuPxxbE0qHxDQkKorKz89u0fK16AkpKq7zweGxtK\nQUFFQ2L4HI3FN5k5losHXIRfnT/r1x2izYhUqgrLcVj98Hq8dLygNwDuWhete3XA4/GQueEAHS4f\nxCNbXsd/uZvpXcdzfv/zfGIs55rG4rua03jMHMsPlX6DTjv37duXNWvWALBz5046d+7csGQiLcCF\nw8YyjUGEvJtD6horg9v3weP24PV6AfC6jd0ajq/4hk4T+lGeXUTfX46ly+0X8NRnLzH9gdm8s2iB\nmUMQkXOsQTPfsWPHsmHDBmbOnAmgG65EfkSvtJ70SusJQE5+Ds+sfZ3t85aRdtkQXDUujizeSn19\nPeXZRbQd1hWv18vK+9/gvEeuICQuglWvLeer361j7tU3ktq+p8mjEZGzZfH++5/fP7Pvm/br9IZv\n0lh+XnV1dRw+coicglyS4pMICgpi/Y6NbCrbR1D/eAKjQqjILiblwr7k7TpO1qaDhLSKwD84gNot\neTx1zaM4HA6zh3FWfPHPpaGa01igeY3HV087a3lJERP4+/vTvVsPutPj22PJiclcUJDP0wv/QYbt\nGJE9EgHI33OCyA6tSRnXF4DKfuXMnfcIrdOS8Sv3cvWAy2gd19qUcYhIw2iFKxEf0iq2FU9c9wiv\nzv4zp1YeoCyrgLqKasISor99zfEV35B233iiLutG6DXdeX3LhyYmFpGG0MxXxAfZ7Xbevu9lfv/c\nY5SfyCTDA7Fd2xqPHnm8/+fpgtow41eXy8XrK96hJLAGa5mLyd3HktIhRTsnifggla+ID3v4tgcB\nWLdlHR//aTmRcVGEFXvxuN1YbTa8Xi8B5Ua5/mvFu1ROa03J7hNUWMt4peoLgj5dxO3n/4LIiCgz\nhyEi/0PlK9IEjBg4ghEDRxAbG8qJE3m88sbbVIZ5CKiAa4dcDkBZYC2OAH/KTxbSbdpwALzne3n3\nzU8Z02kYpRWl9O7au8nfqCXSHKh8RZqY4OBg7rj4hv/vuKPKQn11LQHhwd8es1gs7M06RFEff/zb\nhbDks/XcfcH1hIaENWZkEfkfuuFKpJm4+rzp1L93lILtJ/CcXrgja80+YsalEt2jLVa7jVMhVTy0\n/Gn+9s7z/2eVOhFpXJr5ijQToSFh3DPpFiorK3n7nY+o9/eSfMpG4cQQAI4u30HK+L588Zt/kh4Z\nyuq/baS1O5xXf/eSyclFWh6Vr0gzExwczA3j5wDGHdCPL3wWV2IUARHBrH74beL7pTDgVxdhsVj4\n+rnFfPT5R0y9aKrJqUVaFp12FmnG7HY7v5l4M1EfFeNKL6O+sob+N07AarVisVho1bMdHx7+gpeW\n/YuS0mKz44q0GCpfkWYuMDCQq8bO4JaBs3BX1FJfXQdAxrq91DmrcTksfLFvDVc8fj27Duw2Oa1I\ny6DyFWkhunbowrJnP2XNb9+mPKeI4mO55O1Oxz84gLCkOOKHdOaPq15g2ZfLzY4q0uypfEVaEIfD\nwScPzKfvhmD8d5bjrffgHxJAbFoCcd3b0bpPB94rXM3HqxeZHVWkWdMNVyItjNVqZdyocfTp1odr\nX7oDm8NOaEI0dc4aukwZAsDODYdJO7qfrildTU4r0jxp5ivSQsXGxnLvxbdQfDSP3B1HSejfibqq\nGva+t5bSohJeWfk2NTU1ZscUaZY08xVpwYb1Hcp7Hbryy7/cSl63dhTsz6TbjOFYbTbcE1y8/Nab\n/Pri682OKdLsaOYr0sJFRETw8eNv0XV/MJZaD1abDQCbnx1nuNvkdCLNk8pXRACYecE0ki2x/+eY\nX7nXpDQizZtOO4vIt2b1uYQ3/vUxNWEWAsvgFwOmmR1JpFlS+YrIt5LaJPLgRb/+P8cqKir4zXMP\n4ax3MrbXKK6eMtukdCLNh047i8j3qq2t5Rcv3kHMVT3peNMovijfwd/fes7sWCJNnspXRL7XivUr\niT8vlbyd6Rz7Ygd1FVV8mbWFlZtWmx1NpElT+YrI97JhpSQ9H0dkCPVVtcR1S6b9+T15eft7HD9x\n3Ox4Ik2WyldEvteF519ITW4ZxYezie3Slm7Th5M6aRD9bp7IC4tfNTueSJOl8hWR72W1WrlzzHWU\npufTuk+Hb4+Hto7EG2wzMZlI06byFZEfNLT3YO4cfwOZa/d9e8yZW8zgpN4mphJp2vSokYj8qHHD\nLyBibwQfv7wStz909rZh+kVzzI4l0mSpfEXkjAzs3p+B3fubHUOkWdBpZxERkUam8hUREWlkKl8R\nEZFGpvIVERFpZCpfERGRRqbyFRERaWR61EhEGqSwsJCVyxZSUphLh/A6bHY7ib0vIq1HP7Ojifg8\nla+I/GTfbF7J/KfuJibMQWlVHTl+Nnp1iKboxC4cQU/QvmNnsyOK+DSddhaRn+zDlx9jaNc4auvd\ntIkMIsDPTkVVHZH+dXz47O0sevdFVnw6n+rqarOjivgkla+I/GSh9loC7FaS40IZlBpLgL+VhOgg\nLBYv0Y4aftH+EDNabeezeb+hpqbG7LgiPkflKyI/md3fn4LyWi4ZlERlTT1jesWz4UABp0priA4N\n4J2vjvH+unSSbDl88NfryMvJMjuyiE/RNV8R+clqHG0Y1QPW7TsFeNl+JIvE6EAsVggJ9KOovIZb\nLu767evnL32Zidf+wbzAIj5GM19pUoqLi8jMzMDj8ZgdpUUbPv4K1uzNZXd6ESt2ZFNcUUdmQSXV\ndW6O5ZZTXlXPh+vT2Xq4AIBgq679ivw3la80GV8+8xRZQ/piH9KXT66Yqpt5TDRs1AT2Z5TToU0o\no3q1ITTIj6hQB8H+dqwWL87qOqYOa0dBWQ05RVVU+rcxO7KIT1H5SpOw6Kk/E/nYo1xQUkKv+nqu\n+3IV6559yuxYLZafnx/JrYKxWy3sOFqExwvHc8spctbg72cnKTaEFTuySU0M541DcYy7/HazI4v4\nlLO65rtixQqWLVvGU0/pL0H5+Rzds5vyv/6Jdv91zA4UHdhvUiIByC2uorCslt4doqmsraeqppYA\nPzuFZTUUWy0cyS0nqdCPmXfdj92u20tE/luDfyIee+wxNmzYQJcuXc5lHpH/T+be3cS73WwAOmKc\nrtkEHM/PNzdYC1dV5+HmiZ0YkhrHiXwnq/fkMqp7a47klDNxgHEX9D1v7Wf9J89ht3qJSx1Bj37D\nzY4t4hMaXL59+/Zl7NixvPfee+cyj8j/p+jEMaqAUcAngB9QD8R26WZmrBYvPiYMu9XKP5Ye5GBW\nKW2ignjzy6P42a0MSo3lVGkNccEeQou/ZlyfBHYcfoPDgUF07trX7OgipvvR8v3www+ZP3/+/zn2\nxBNPMGHCBLZs2XLGXygyMgi73fad74uNDT3jz+PrNJZzq7a2FueiT8kEPgDSAAuwDZhy8fgzzugL\nYzlXfGUslrBE3vnqKDX1bupdbnJLqpg1siPfHC/mpSX76RwfwSNX9MHl9vDGqqPMHp3C0hM7iB05\n8tvP4StjORea01igeY3HF8fyo+U7bdo0pk2bdtZfqKSk6juPx8aGUlBQcdaf3xdoLOfW/nVrybvv\nLmqOHaMvcBLYD7iAMYAlotUZZfSFsZwrvjSW+LRhnCw5TnJcMKWV9VTV1LMzvYiKahe5xdU8NLMv\nFVX1BPjbmNA/kY0H8qlpFfhtfl8ay9lqTmOB5jUeM8fyQ6Wvu53FZ2X95TEuP3KYzsBejBlvD6Ab\n8AWQfzLTzHgt3uYVHxDob6N3hxgGdoqhotbFnuMlzBndEbvdyguf7Wfb0UKWbMti9a4c1uTGcN6E\nmWbHFvEJKl/xWda8XABGA4VAKUYJFwNxQEKKds4xU01dPd2SItl+rJCc4mpiQgMIDrLz1Cd7CXLY\nqa5zERJgx2KxkFNay4ybH8Vm++5LTyItzVmV78CBA/WYkfxsjnq9OIFYIAgoByKBIiADqHQ2j9Ni\nTVXvQaM5nu9k7pTu9GgfyYBOMcSFB5GaGE7XpAjCgxx8vCmDif0T+eWYjnw+/09mRxbxGZr5is/q\n3DmVZ4DFQC0QCOSd/jUeyD09MxZzzL7+LuKjg1i6/STxUUFcOrQdE/onUl5Zz4BOsdS73QzsFMPf\nPt0LQJ+oQkpLS0xOLeIb9OS7+KzQy2bQdd1aqmtrGAEcxTjdXI5RxrXaqs5UYeER7M+tw5JVwle7\nc7HbrUSFOCirquNQdhn9U2LZl1nC7ZO68smmTCKjo3XaWeQ0zXzFZw2cNoMDV10NwFKMZ3vrAQ9Q\nDXz59pvmhRPKykopr6ggs6CS9PwKsgsrSY4NIj4qiMmDkxmYGsuVozqybEc2FquF/MA+hIaGmR1b\nxCeofMWnBWWfJBNwANFAHcb1XzdQk5tjZrQW761/Po/d4uGuKd157qah9EuJYcG6E4QE+n37Gn8/\nGx6PlyxPOyZc8WsT04r4Fp12Fp9Wv+VrUoAajCUlo4AyIAKw5mabGa3F2/vNZu4Y3YEBnWMpddby\ny7GdKa+q49DJMjbuz8Pfz876/ac4ZUng5of+bHZcEZ+i8hWf5Ha7WXLfXKqLC2kLJAH7MGbAiad/\ntXi9ZkZs8TqndWfH0e0s355NoMMGXi819W5mjezIc4v3ExMeQL+UGGylhRzYuYk+g883O7KIz9Bp\nZ/FJq//+JJfP/yc1GKeat2DMejsCKRinnb3aKcdUPXr2oaC8hkGpsXRrG4mf3U5wgI1FX2fSq0M0\ng1JjKHXWMWNIG7YuednsuCI+ReUrPql880aCMUp2N8Zsl9O/3wc4gf5VVTj1rK9ptq9fwnXjOnMs\nt4JTZTWEBNjZn1lGl6QIxvSKp6bOQ2llHRXVLsL9XWbHFfEpKl/xObmZGVR/s4MiYCKQA5QArYEr\ngOHALRhrPe/ZvMm0nC2dvyOIb44VcX7PNswY3p7EmGC6JkUyOC2W1MRwZozogM1qwW6zUBuYZHZc\nEZ+i8hWfc3Djem6uKGctRumWA4dO/34LxpaCCwEv8Okff2dSSplx3b1sPFhIQnQQn2/N4qIBSfz6\nkm68ufoYHo9xPd5qtbDwSBDTbv6jyWlFfIsumonPad+nH9vDI7i0rBQwVrTagrGs5L/XeHZjzIo/\n27/XrJgtXkJSOxL6TeYfSz8nLjyQpz7Zy9AucVw+oj3fHC8iMSaY4NQJTLrqTrOjivgcla/4nHap\naXz1wCO8cN9dJHi92IApGKeZDwB3YGwr+E+MZ36Li4uIioo2L3ALltKpCyFso7SqjvjoQLYdKeBU\naQj7a5JIixzGxVdebnZEEZ+k087ikyotcKHXy2GM0p2PUbTVwO+Bf2/nUQb8467bzAkpZO3fQGlV\nHZMHJzOqRxtcbi+7suu57p5nOG/CTCwWi9kRRXySyld8UuGhA7gwbrCyY8x8jwCZwHGMEj6Gsb+v\nze0xK2aLV3Qql8mDkwEIDvBj4oAk/CMSsVr1V4vID9FpZ/FJnQYNYfM/X6G710t34BUgFeM5Xwcw\nC1gBbAYuvlXLFpql1hLMg29sw+uFQH8bQ7vE0a7PZLNjifg8/fNUfNKQyZfhvfZG9kZEssVqZRTQ\nAeiJsdjGb4BVGI8f7b3lRqqqqswL20IdPbCLAdH5PDanP4/N6UdMeACLdhRxwYQpZkcT8XkqX/FJ\nFouFix//Cz1Xrad9VDRJwA7g89PvvwFjNmwFWmdmsGXlCrOitlhbVn+Aq97Fws0ZHDpZxqSBbQn2\nt+g6r8gZ0Gln8WlFWZn0KywgAeOa74XAmxhLTi7BOP28BnCUFpsXsgUqKS4ipPIwl45qB8CKb7Kp\nrXNR6w0wN5hIE6GZr/i0gOAgvjy9hvNsjMeL/AAbcC2wCGOpyeqiQrMitkg7Nq9izvDW3749tk8C\nS3fkcfmdfzcxlUjTofIVn3Zy6ef0d7n4F/AxxtKSdwMFwEtAPHAeUPr3J03L2BJFxyVwsrju27dL\nKmo5b9odtO/Q2cRUIk2HTjuLT/MGBtIRSAPeA9pgrHJlAR4GQk6/bnVtrTkBW6je/Yex9N1tHCvY\nicMGB+raM/mX082OJdJkqHzFpw2/4Ve8um4Nk9Z+RU9g6enjJ4EsoD3wGZDl9bLkxeeY+CstuNEY\n3G43/cZcgdU6C7BwaUyM2ZFEmhSVr/i0wMBAJr/7EUtef5WYxx/lrqoqioHXgH8AARhLTc4BPL97\nkI/LSrns/t+aGbnZy0g/zL4lz9ApvIoTZf4knXcdMSpfkZ9E13zF5/n5+TH5hpuxPv4k89omsxxj\nZas4oBfG6efBwFAg6+WXTEzaMuxc9irluYdZtzuDeP8SMja+bXYkkSZH5StNxpBZs4lObscVwHhg\nEtAVCD/9/nQgpNLJ8SOHzYrYrLlcLl5/7ves+WoFldX1zBmdQnZJNceOHzM7mkiTo/KVJqU2JPTb\n3/fE2Nf3XeBT4EsgGXj3oXtNydaceTwenrxrMm1rtzGsSxyVdS4efGMbw7vEUVNTbXY8kSZH5StN\nSud77mdeUlt2AW9jPGrkB+zEuPmqFgj/chUvzdZWdufSF59/xMgUf3KKqwgN9GNg51hmjGjP85/t\nJyA4yux4Ik2OylealHbdenDRxu0snzqDCKAV8AXGLLgUGHL6WMjypWQcPGBi0ubl4LZVOOw2rh6d\nQmSIg8xTleQVVxPksDPgouvNjifS5Kh8pclxOBz84qVX2dSpM0OAuRh3PBcChzHK1wF89fKLJqZs\nXmJtRSS3CiHIYWfigCRaRwVR5/JQF5TEgGFjzY4n0uSofKXJmvTam7wWGso+4ADG3c9VGDPgk4Bt\nyWLyT2aZGbHJq6mpYf5fb8fhKWfD/nwWfZ3J8bxy7DYLh07Vc/Mjr5kdUaRJUvlKk9UhrQvjV6xl\nq8VKBHACOARsxbgD+tbiYvZ+udLMiE3eF2//iWv71DF5UDIzhrfHbrPy1Z48duZ4ufEP7xMYGGh2\nRJEmSeUrTVpCh454OnQkDuN0sxVj398JwB7gxJ8f553Ro8k6dNDMmE3WoZ3refWLQyzeksGHG9Jx\ne7yUe8OY89CbREZFmx1PpMlS+UqTN+4PT5Dh7082kASUA0eBHKDHqXxmffkl666eZWrGpsblcvHC\nE3cxpGMIcy/twYhurTlZWMX2YwUk9r2EkP965EtEfjqVrzR5PS8YR/cXXqbcasUP4wasyzBWvQo6\n/ZrY40f5+Ik/4vV6TcvZVNTX1/PpS/dyS/9KBqfFMX/VEVITwunZPhJ7QAQjJ+ofMiJnS+UrzUKB\n08klHg+1QD7GDVgvAaMAL8aev53//hdWPf1X80I2EWuXf8gv+tQSGx5Im6ggLhvaji/35OLxQtvk\njmbHE2kWtLGCNAv+9XUcBK4BDmLs95sPLAYqMbYgHAIs3vq1WRGbjIqyEhYezCTQYcfr9TKhXyI7\njxfTKT6S1l3PNzueSLOgma80CwMvuYwch4MPgSPANoy9fw8D/sAlGM/+1ugmoR/k8XgozdzJtGHt\nuGRQW0Z0a8V9b+7CP74vtp5XM/SCy8yOKNIsaOYrzUJEVBStHniYU797iESvl64Y2w0WYjz7uxk4\naLHgr/L9XksXfcCGj54kyM/KvekWrjw/hT4do+k/aDhjf/G42fFEmhWVrzQbE26+jQ8KC8l/4RmG\nejwkAkuBbhgbMFzn9bJk3gvMTz9Oz9nXUHAinRN7d9Nx5PmMmjIVm81m7gBM4PV6cTorKCzIo373\n68y7ZSiVNfW8+PkB3vnqGL3aR+HyizA7pkizo/KVZmX6bx9lfedU3r3nLnpUV9EVWISx21EM0Mbr\nJXv5EvyXL6ETcAXw6oK3eee9d7ninQ+w21vOj8TBPVvJXPcaZYW5HDxZyjWj2gIQHODHJYOS+ceS\n/fxzZyBjrrzJ5KQizY+u+UqzM/zyWVy37yj7R45iqZ8fKRgbL2QAJUACcB6Qh/EDcB2Q+NUqnps4\nBqfTaVruxpax8U0C6/KY1C+WZ67tzfH8cg5mlQJQU+eirN7O5Jv+REhomMlJRZofla80SyEhIdz2\nwSJ+kVXAprg41gDLgHYYM2APxmYMYDyK5AcM2vkNr40cTPre3aZkbixer5dtm9dw9MgRCsuqSYoN\nwWKxMG1Ye/ZmlJBTVMnrq45zy+/fNjuqSLPVcs6xSYtktVqZ9c47HBk3jgs8HvZh3ID1KHALUAc8\njVG+lwDFWZnsuGI6AYuX06ZdO9Nyn2sul4sPXvkjxSd2k38qj4n92tA5zk5ooB+rd+Uwulc8AOnV\n0ZRXj+Lel15qUafgRRpbg366nE4nd999N5WVldTX13PffffRu3fvc51N5JwYPGYMJ373R958/mn8\nTp3iCMad0K9i3A3dAwgGIoAwwJ6fy8e/vIrUaTOwRkQSGteK/mPGYrFYzBvEWXr7uQfpFXScsVPb\nUupsxd8X7uPRK/vy/rrjVNYa5wC+2F/NkCm3ktqtr8lpRZq/BpXv66+/ztChQ5kzZw7p6enMnTuX\njz/++FxnEzlnxtx0K+7rb2bxPf+vvbuPq6pO8Dj+uRe8PApj+JTlU7XSKjskOVMqNIwjJi/HWtZs\ncIRYddaHphYTBR9KHR0GcidyLTQfNnPxAX2Rs7q92m11UWdjKzZTFE0TZMp8BNwExitwu3f/OGTT\nrGbei+cAft//wL3eK99zvfi9v/M753eeY0XBG2wFPgNGYMz9XgCKgSRgKxBXfogB5YfYi1HOS7p3\nJ2FpLsOSnrBoC7zndDqpLNvLOYedt0o/o5OfjVExd1Nz6QqBDn+qm8PZcjaGyB8Pp7dWsBIxhVfl\nO2nSJBwOB2DszgoICGjVUCK3gp+fHyN/lc1Lh8vod/AAfTCuAdwE3IdxNaT1QDPwc4xVsRowLtbw\ndxcusGbaZPasyOOHjyUxfNovCQ4Ovs5PahuqKo5ScaCYj97bQ5+uQTw9ZiDhIQ627Kvknf2fM6hv\nFz6rbaR37FgtniFishuWb1FRERs2bPjGfTk5OURFRVFdXU1mZiYLFiy4ZQFFWlNoaGeSCrfzfvT9\ndGpspAZoBP4R42pI9wPZLY/1YCxNCcZu6p8BhUfKCT9SzsZVr+IYN57hyRP5i+jBZm/GDa36TTrB\ndUew4SHY1UjDlxASaPy6T/jRvbx3rJr1H3UiNmkxg6J/YHFakduPzePlZV6OHz/O7NmzycrKIjY2\n9oaPd7m+xN//9lvEQNqmd3fu5H+mT8dRX0/55cs85HbzcyAHGAj0wlia0o1xLvBWYDSwDOM0paFA\nEdCtUydOjRhBz7g4zn/yCc6jR7krNpbxS5cSHByM3d46JxRcvHiRkpIShg4dSteuXb/1sds3rqbT\niSLG/tA4b3ff4bNUnq2ja1ggjz3cF4Bf/9v/8vyrmioSsYpX5VtRUcGzzz7L8uXLiYyM/E7Pqa6u\nv+b93bp1vu6ftTfalrbpRttSeWA/xT9LossXX9AMXMQ4+rk3UAb8GKNwX8EYGfu3fG0G3sQo6ieA\nFRi7qw8CezHWkr6EcVCXf797GPfCYkb+9HHOnTuL3W7Hz8+f0NDQq9M2NTU1fFpVSb977uNfN7/C\nuRMfUnuxhi6BNprdXzKgVxhg53LEg/xiVu51t+fVX03lhVGB+Pt9Xfyr3v4Yj9vDtMT7Wb2riqF/\nu4K7+/S/+RezFd1O77H2piNtj5Xb0q3b9a977dWcb15eHk1NTWRnZ+PxeAgLCyM/P9/rgCJWunfw\ng3i2vMlnfz+DASc+4ajdziW3++oFGQqBtzCK9S5gH/ARxug3DGNu+MOW2x8AKRgXd/gJ8DvgDqDm\nDyfpMeUp5gDhQAJwFGO++STGOcd3tjx2M9DoD8NjehMS3IngO7/HmYuXqXe6SBh8FyUff4jL5bru\nqUDd+wzk2KkDRPXrAsCZ2j8SERpApT2KLecH8ZNfLuSOiG8fPYvIreVV+a5cubK1c4hY6r4Hf8Cd\n/7GPU384yeDuPdk3ZyadP/yAT640cm/dJcKA3UA90BOIxCjgMIyR8hWMJSyDMOaKAQ5hjJ4/BZxA\nKRDdcvsDIBVjTvktjNHzHzEuhZgBvOmCoNJT2IGBaUOIi7uHbe+e5PwXVwgPceB0XqbzdVaeenLS\nTNb8ZgYHqirxs8GRz+voHRXP5F8sarXd4CLiG51FL9IiJCSE+wf9FQBPvLGJxsZG7j5zhqrkJP66\n6iQAr2FcJ/hT4HOM84NfANZiLN7RqeUxMRhLWX7c8nc7MOaMBwMvYhzk5Q/UYqy4NQX4F4ySfgCj\npLdjjJKP/PvHzB//fbb8/iTORheHP63nsRss+Th1/ircbjeNjY0Mc7sJCQnx+fURkdaj8hW5joCA\nAHr170/TG5vZ/M+v47HbuSv2R4S9X8K7mwt45tIlBgK/xyjiDzBK92+AzsA8jCOom4FEjOIFY9f1\nOWswJO8AAAcfSURBVIwRbyLwn3/yM+9o+WrDKOwwoMpmo/5yE/XOJnYf+YI5eTu/U3673U5QUJD3\nL4CI3DIqX5Eb6PeXA+mX89uv70gcw8jF2bxXVMh75eXsLniDHg31DMM4wGoP8F8YpyeFYRRwSstT\nPRi7qeswRr+/BXKBTRgj49+1PO4QRhG/D7ge7MOqUj+WbdpPz57f6zAHwojczlS+Il6w2WwMGz8B\nxsOIOXPZvHA+h4q2EXrFyQmMo6MDgZKICB66VMcsVzPRGKPgbsBh4CFgf0AAc8PCubehgbwAB87u\nPfmH0FAufn4KR2go358zjyGJP23zC3qIyM1R+Yr4KDS0M1PzXoG8VyjfW0zFijyqz5/H9kg8mb/O\n5dh/lxDxT6s5fLiMpoBA/Hv0JN7Ricvh4Ty7JIduPe+0ehNExGQqX5FWFBU/gqj4Ed+4b1DcIwyK\ne4SRFmUSkbZH5x2IiIiYTOUrIiJiMpWviIiIyVS+IiIiJlP5ioiImEzlKyIiYjKVr4iIiMlUviIi\nIiZT+YqIiJhM5SsiImIyla+IiIjJVL4iIiImU/mKiIiYTOUrIiJiMpWviIiIyVS+IiIiJlP5ioiI\nmEzlKyIiYjKVr4iIiMlUviIiIiZT+YqIiJhM5SsiImIyla+IiIjJVL4iIiImU/mKiIiYTOUrIiJi\nMpWviIiIyVS+IiIiJlP5ioiImEzlKyIiYjKVr4iIiMlUviIiIiZT+YqIiJhM5SsiImIyla+IiIjJ\nVL4iIiImU/mKiIiYzN+bJzmdTjIyMqirq8PhcJCbm0v37t1bO5uIiEiH5NXId9u2bURFRbFx40bG\njh3L2rVrWzuXiIhIh+XVyDctLQ2PxwPAmTNnCA8Pb9VQIiIiHdkNy7eoqIgNGzZ8476cnByioqJI\nS0vjxIkTvP7667csoIiISEdj83w1hPXSyZMnmTZtGrt27WqtTCIiIh2aV3O+a9asYceOHQAEBwfj\n5+fXqqFEREQ6Mq9GvrW1tWRlZdHY2IjH4yEjI4PBgwffinwiIiIdjs+7nUVEROTmaJENERERk6l8\nRURETKbyFRERMZnKV0RExGRtonwrKysZMmQITU1NVkfxmtPp5OmnnyYlJYXJkydz4cIFqyP5pKGh\ngenTp5OamkpycjIHDx60OpLPdu3aRUZGhtUxvOLxeFi0aBHJyck89dRTnDp1yupIPisrKyM1NdXq\nGD5xuVxkZmYyceJEnnzySYqLi62O5DW32838+fOZMGECEydOpKKiwupIPqutrSU+Pp6qqiqro/w/\nlpdvQ0MDy5YtIyAgwOooPulo612vX7+eYcOGUVBQQE5ODkuWLLE6kk+ys7N5+eWXrY7htd27d9PU\n1ERhYSEZGRnk5ORYHckn69at4/nnn6e5udnqKD7ZuXMnXbp0YdOmTaxdu5alS5daHclrxcXF2Gw2\ntmzZQnp6Onl5eVZH8onL5WLRokUEBgZaHeWaLC/fhQsXMmvWrDb7An1XaWlpzJgxA+gY611PmjSJ\n5ORkwHgTt/cPRzExMSxevNjqGF7bv38/cXFxAERHR1NeXm5xIt/07duX/Px8q2P4LDExkfT0dMAY\nOfr7e7VcfpswcuTIqx8eTp8+3e7/D3vxxReZMGFCm73inmnvlGutEd2rVy/GjBlDZGQk7el04462\n3vW3bU91dTWZmZksWLDAonQ353rbkpiYSGlpqUWpfNfQ0EDnzp2v3vb398ftdmO3W/752SsJCQmc\nPn3a6hg+CwoKAox/n/T0dJ577jmLE/nGbrczd+5cdu/ezYoVK6yO47Xt27cTERHB8OHDee2116yO\nc02WLrLx6KOP0qNHDzweD2VlZURHR1NQUGBVnFbTUda7Pn78OLNnzyYrK4vY2Fir4/istLSUrVu3\n8tJLL1kd5abl5ubywAMPMHr0aADi4+PZu3evtaF8dPr0aTIyMigsLLQ6ik/Onj3LM888Q0pKCklJ\nSVbHaRW1tbWMHz+et99+u13ulUxJScFmswFw7Ngx+vfvz6pVq4iIiLA42dcs3UfyzjvvXP1+xIgR\n7Wq0+OfWrFlDjx49ePzxxzvEetcVFRXMnDmT5cuXExkZaXWc215MTAx79uxh9OjRHDx4kAEDBlgd\nqVW0pz1e11JTU8OUKVNYuHAhDz/8sNVxfLJjxw7Onz/P1KlTCQgIwG63t9s9Kxs3brz6fWpqKkuW\nLGlTxQsWl++fstls7foXcdy4cWRlZVFUVITH42n3B8Tk5eXR1NREdnY2Ho+HsLCwDjFH114lJCRQ\nUlJydR6+vb+/vvLV6KS9Wr16NXV1daxcuZL8/HxsNhvr1q3D4XBYHe2mjRo1innz5pGSkoLL5WLB\nggXtcjv+XFt9j2ltZxEREZO1z30KIiIi7ZjKV0RExGQqXxEREZOpfEVEREym8hURETGZyldERMRk\nKl8RERGT/R+73qklHmTvMAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.manifold import MDS\n", + "model = MDS(n_components=2, random_state=2)\n", + "outS = model.fit_transform(XS)\n", + "plt.scatter(outS[:, 0], outS[:, 1], **colorize)\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The best two-dimensional *linear* embeding does not unwrap the S-curve, but instead throws out the original y-axis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nonlinear Manifolds: Locally Linear Embedding\n", + "\n", + "How can we move forward here? Stepping back, we can see that the source of the problem is that MDS tries to preserve distances between faraway points when constructing the embedding.\n", + "But what if we instead modified the algorithm such that it only preserves distances between nearby points?\n", + "The resulting embedding would be closer to what we want.\n", + "\n", + "Visually, we can think of it as illustrated in this figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![(LLE vs MDS linkages)](figures/05.10-LLE-vs-MDS.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#LLE-vs-MDS-Linkages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here each faint line represents a distance that should be preserved in the embedding.\n", + "On the left is a representation of the model used by MDS: it tries to preserve the distances between each pair of points in the dataset.\n", + "On the right is a representation of the model used by a manifold learning algorithm called locally linear embedding (LLE): rather than preserving *all* distances, it instead tries to preserve only the distances between *neighboring points*: in this case, the nearest 100 neighbors of each point.\n", + "\n", + "Thinking about the left panel, we can see why MDS fails: there is no way to flatten this data while adequately preserving the length of every line drawn between the two points.\n", + "For the right panel, on the other hand, things look a bit more optimistic. We could imagine unrolling the data in a way that keeps the lengths of the lines approximately the same.\n", + "This is precisely what LLE does, through a global optimization of a cost function reflecting this logic.\n", + "\n", + "LLE comes in a number of flavors; here we will use the *modified LLE* algorithm to recover the embedded two-dimensional manifold.\n", + "In general, modified LLE does better than other flavors of the algorithm at recovering well-defined manifolds with very little distortion:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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zJyCkKZw/ji4+jjKtE7ryElpHhlNeXkVMeAjdLLn0tWTzzqCeuFcUsz4gGrQO\nYLVC8nGIbAPbluKYm0LzrGP8rXtLlqcWURjQEBpEQtZ5muyOY9mzI3l7QwI76rWnTOeEqeNACgIb\nk2BzxZhxgZIGyhYi3DxxW/s9Va5ekJsG2alUufux89gJFod0J+58AV6pRzkX2Aw8/aBBJI4aNf/X\nOpggPz883N1Z83McedVAYCjqomzGR/jSrmmT6/7MXFz0pKVlUlpahrOzMyqVCgcHB6IimtMyogU/\nbV1C3kAP3Do24Mya/fg1V84KyN18mgf92uHr6XNDf0Z3k/R8I9V5Sfi5ath5rgJV+CCCGkQQ3DiG\nPQeOkJJyATcnDa6OOlwcddhsNr5adZpI1wK0BUc5un0paQVVRDRpXddNuaaj+/Zi/Pv/MRqlJx4E\nFFks+D7xNE5//z9MFRWYgVMovfW4i2WigWVAJnDGy4t2cxehPnKYVomHa66r8/KoAjagDOunnEki\n4rEn0Ts63vmG3gAXl99O530jpOcurspms7E2biuF2Ua6DWpNvdAg3NzcePWrYbXKDf1XLjPGz6Vl\n0UtQDTsc3+Ow+Qda8Sg2bGQ1nc/rbzx91fzzv7ftG/aSdb6ALv2jeWf+yCuG5uZ8sBYsytC1+r//\nSlRpUbuYal1SOd34F47PRvfDsHQ56SpHGqkr+cfDyv71Zk5qkpo9APFxYIPqMX9kBbAj+SDhBxM5\nkZzNlGOZFDp7EV1ygS6tohjYrQvTl+7gXMs+0CAS17zzDD27hg4dwxnSrXPN59xlzyHOlpeCsyt6\nd2/GdmyNp4cnaTjCzpXw4Jia+lV7BqAvylZ6/+ePw951WIqzUBu8sHQbBhVlsPJbSke9CkBFg6Yc\nzkyi3/6f2OIQhN7ZhQkGI21bDAWUEYdAb09OtFHyzlubtmf1wWU8fwOf2aeLZnDapxSVToPHjmpe\nHTKxVi71XJdy3D1cqCwpp9pYxY5/LKKBoz+j2g2kafj1f4m4G3XpM4rstJbMS9hHw3YxxDRUtioa\nDK6Me/Ujzp87RdLelUzblkCLIBPns8toHGSgYaAbTesrSaX2JG3m9PHWNG7W6n89qk5VVlay75fl\nNQG4E2BCWRyZmpyM3mYlGjACrsAvXOzZA61RktksV6tp9f08ImLacVbvyCaUrXGFwL9QFuL9OpFm\nLSnhh//7EwM/r52bxR5Jz/0uVtc99z+P/Q8Hv9STGe/Cqrk7iOjqjH/QlektD+44hmbJaDToUKEi\nyNyJys4qA/8VAAAgAElEQVSruWBYRZE5FXdTIxL27SemX/h1HYryq9vR/m/eXcHp/4umal0n4tft\nxLV5Ecu/2sfuRRfIzksjMjoUi8pE4tpsnCqDyOYoznjhgIFil5M0faaE1v0DOLD/AKYiLSUR8Qx+\nN4SA+r7XfvhlnBydGBjdnEdaR9KnVXO0WuVLxAORoeTt2Yg5N528riNrtrFVeAYScGYv088UcV7n\nhqm0mPNmLUkJOxnXqzttnaH08E4i8s/yerMAJg0bTPPwsJr3BejdqhklGxZRfHgnEcUpjGvfikBf\nH76JW0JxdG9IOQVBShIpQ0YS7zV2o3zvOs4nnYIRk6jOScfW7zFltEGnh8zzEH5xDmH7Moy5WZxz\n9sfiV58mhef4x0MPYLjs9Lc5R8+T5tuw5mfX3As80Sbyuj6vXQd2cbR1Jd6tQzA08MYc7kxB/Bka\nh1xKa7vrxD60UT4c/WkrrR57gJDuLah2sJG0K5Hj+efIy8qhUf0bG2G5m4RFhBFYvwmeXlf+rnl4\n+hDRohNtew7Hv0U/HP2bcGjrCoZ2unTSYbCXnu2pWiIiW97Jal83k8nEqrEjeX7JIhYCr6H0yP2A\n+kBSt+7kGtxIOXyQ0yhz6keBLOAlIB44DhyIaIi2uIgLvyxD1akL5fGbMZvNbEf5sgDKXD0osxtJ\nvn6Ejnr4zjX0JkjPXfxusrKyyNnkTTRjAbCWD+TrP3zAl1ubX1HWw9dApSYPnaU+ADasdOzfhMTv\nNQQVKXtsLWvNzJ26iGf+NuiK+38v5eXlJC/0JLhKmZf0Tx7MjOem0irrj2RxgOPLT7Jv+7/505dP\nU/ThYRJ/XkxDjRlDo1Xoql1p2d6Xrn27A9B0YympKenUb9CxJoHTjUjPyuLvq7dToNbT1gUmjxjE\ngi3bmX02D6vKgd4hfqRlnaU0XBlGVZcW4q9Xk1NcAg0bQktlTjl+z0qyc3Jo3aQx73q684/V25if\nlE1y7hpeHNy31ohEenY261R+nG/3AGdOJjBiwUam9crFJSxSSVpTWgDLv8bgoOXdTpEMe6AnOw4n\nYus+AlZ8DRqHWvP4KhdXPE/soMCigsi2cPoA1i7KHPqhiFZMXbuSf40fxpx1mzhXbER17iiqBu2x\nGTzBVEEbjfG6P6/sojycgy6dV+HoaaDElF2rzJDIXsz4Yj5+XUJqevTpJ87R7MWu2BwdSLyQR0X8\ncoZ3f+iG/7zudkcStnNg7Xd4qQvxcnfhfKU/Kq2OI+cLiApVFjzuOFVIWIcbyytxp5SVlTJz1FD+\nnLCPHEBH7Zzx3oCtvJzBH37KzJ8X805xEauAwYAvMFOjwdXTE2N4Q8Jzchg39weswOIN68iKaYfr\njm20AxyAM1zKXlcOVDRpeiebWmckuIvfZLGYcbZd6jGo0aCt+O3DXjr1aMf+sUvI/SkatcURdZ8t\n9BjWm6MfpdeU0aClKs/hN+//vVitVlS2S8O4JspwL2hJCttwwgebVY1xRQxvb9hKmxesvDrr6ovk\nDAZXmja7+aHeFxZvZFc7ZfdIfFkRRTNnsdi1KYUtlVXmp7POMix9F1sT0qnW6OirN/LSH57k043v\nkNJgDJQVQ+J2yq0W5q5dz2vjxzJx0Xr2XFxpv6kwG6c1m3iq/6Vtaav3HeB8eHs4uBm6DcUIvLL3\nF0JslUpOeoCYB1Ad3Ejf1kqPvIG3FyRsAr8QaNoOtv0MXYeAsZj2Jed4r88gpsQtJz4yRslW9yuV\nCqNax1/nL2WGT0dsZzdA21GQsJmA0iwebhbKm49efzKVbtGd+XL5fHyHK73O7NXHGdC0H0DNtEpk\nWGPe9X6Vqcd/qLmud3dB56j8nrmG+HB+b9KN/2HdhY4fPURpcT4t23QmJysN06HviXQtYnAHpad+\n5HwumvqB5BRV8POuC9hsNpJMQTwReeWX8bvBlnfeJDZhH1ZgOzAZJcNcQ5SeebVOR+HunVjHP4aX\nTglTGUAJymExrhYL5gYhBDw3iR5PjseEknM+piAfp317yHB0pHFlJT8Ag4CFQLmDnrLBQxj5l/fu\ndHPrhAR38ZuCg+tRGbQcW8ZgVKgoJ5/G/X57qEilUvHyJyM4+/xZTKYSmjR9BJVKhbrFFmw7u6FC\nRZlDCk073Nk5d4PBQNCQbMpnZ+Ns8ae43jZU5FOZVo2FahrSD2e8oAJSvzrJge6JtOnYksz0LLas\n3YlvkCupCSYqc3SEtHdmwLhu137obzAajZx2ujSdYTN4sDc1m8KBl+a8jQER1DefZe/QAVitVrRa\nLWq1mifbRvJu+lk4uU/JP69S8e2xeNrtT+CEW4OaXrXZ05+EM4d46rLnNvD2Qr1zI9bOl0ZLitoP\noteOmRwtVmOJVebDS4Mi+HDtSj4ZP5yXHh3Hv/78GUbnECXZTpuesG8djsW5fPDIQzRr1Ij3J4zm\n4S17SCvKUbbK6RzQZ56lh78rXyWXYKs8DW17g7sPBISQVW3CN39brSmDa/H29Ob1dmOYO28lNhU8\nHNYdFycD/1z+BeVeoC+FR5r2p3FoIzrbGrF7RSIaX2dM6bWPE1ZXXnmE7b1m5Y+f0MH5BFHuOpZ+\nsxh8W/GgP+SVXMq30DDQjZ92ZfFEDyWvQGahCdS9rvaWdc75wnnSgO9QhuDdgGCgABgJUF1NyeKF\nrGoRRfWAwZz74XsqgF+PALIBU1NTaRIewVlnF1LLjYxH6am3qqriC62WQrWGUKuF+UChjy9tPvqM\nQQNvfrfGvUaCu7iqv68dzWfPfUZ1njMRvRyY+NfRJJ9KYe5bB6hMccUpopinPutKwMV5+IiGtec3\nJ83sxU/vz8Nc4kijTjoGP3bn038+P2UoGzruoCC9jAF9mpFy2olZkw6iM/opgf0i18pw0s4d59yx\n1fz8zgWCrB3I5jAxTESLA0eWXKDSuInhzz5ww3VwdnYmoLKQ/F8vmKtp4u9Nxtn95DVSjpJ1SztF\nTFg91Gp1zRDz6eQLzCtQw7nV0HNkTSDPbd6dTedW41dWTcll7+mnqn0OfL8unRiwfQ+/5GVAYKhy\nsbyU6JB6bCg2UBMGVSoq1LqL/6tiXLMQvg7pALtWgZMLjuXFvB3dgGaNlPnuRiEhTI8x8r2tmONr\npxMaGMiQ5uGMiO3Bt18vAVMlOF02yKp1oMJy40E2rH4oEx+8dC73v1Z9jceTrfC8+Dl89/lC+qZ3\nJsDRi5frj8Zms3Iq4gzrlhxEF+aGJbGAp1vfG6lXryblQjItNEdpWk8ZNXuyvY2p289ijNBwLKWI\n6AhvVCoVqQUmLPV6MPdoOnq1mSq35jw4fEQd1/7qjlZV8TbKcPxfUIbk41CObZ2GErx9gKTVKwmJ\nac8v4RE4J59TpolQhtj9/fyJaNacOWPHk/zdTIZctod0mNnMFwMG03vtanwsZsLycjk76Vm8glYS\nGX3lAVf2SGWz2a48N/Eucy+kT/w93I3pZ6eMXob7FiXblw0bFYN/5PVvhrBxxQ4KMkuJHRxTE+xv\n1e/V/v07D/HpyG14mZtQjw64EkRq/YW8vroDb3Sah3NpBGBFhZoWXFp4Y+q7mNfm9LmpZ+48coy/\n7zxOPnpaqcv4Yvww1h9I5PtTmVhUakYEu/JYn9pfft5btpqvAntBxjklYU34xXwC1VX8KXsLLRoE\nM2XfGQrVetqoSvlywggcf2OLzx+/n88idTAWB0f6l5xi+jPjeOI/P7K61TDQ6XFNOcanASaGdFVy\n3VdVVfGnn5Zx3KzHz1LOewNjCQkKuq52Ltq6k/87WUR+dhY8OBZUKpocWMGikd3x972xRYj//ef/\nQfzXGEZEknXoHFmHk8FiRe/hgl9UKMaDGYwOeICYZtFUVFSQn59HQEDgDY0W3G18fV3ZsX0P3sc+\npkm9S1tIp262YrFU40U+ZUYjTm4+1IsZQpfeN3daYV1Y9fFUHvvwfVJQhtuPoCymy0eZbx94sdwM\nlYpONhvHURLVvIwS2CuA/zw8BmNSEqUH9jMSqAS6oXwx+C6mHfo2bdHNnM4oLmWwm9aiJaM2bb9T\nzbxpdZZ+Vty/qrIvzbOqUFGd48KXk5dQNXcAjhZfps1axmOzIoiIDK27Sl5DSVEZLuYgXPClmBQO\n8h3te3ji6+dNdaUNM+VE8wTHWFTrPrXrzWfX6xzVnNVRtec/H+rSgYe6XP0e26+ZS4LCla1r5WVo\n3DzpmX2Q559+BL1eT++20dfMuvXhE2N4IycHs7mawMB2qFQqZj79CJ+vWEuBGXqEBtK3Q8ea8g4O\nDnw8YdRV3+9/GRXbmW6NM0k4foIDZ5ahc3RmwvBuNxzYf4t7qY59364jvHdrtHodap2GZiOUD9Cn\ncTDr5u0iplk0Tk5O1KtX/5afV5eqq6s5dfQgZ7d8y5Zzmbzh74Jep+Hj5WcY2zmYBj5OHLzgQZrP\neDr2vPcWDMY8PJYlSxZR70wS7sAulHzxTVCG6X8VaLNxEngEKEJJPVvo6YWp30C8t8djSE0hEmiH\n8gVhObDXyYmRX8/i1Iql6Kidmta3qPAOtO7uIMFd3BBDkyKsJyyo0WDGhCY0k9wlrQmwKL31gLND\n2fDtAiI+DK3biv4PmanZRNAbN5T5SR+aUmpU9r26NazCekLJ7OVOCCdZhkEVgLbNCZ57+39E4t/B\nxG7RrFq6kfNRvVC17MJDh+J4s3sfwsMerbXf+3rSafr51R5NcXBwYPKI32f+MSAgkIEBgTW9r9ul\ngWsgxl5+aPQ6sIHGofa2SqtefZU77x2njiaQvPVb3DXlpOcU8nzfMKpaNGT5nhROVfgTEehHAx8l\n22F0iDNJJ3YB91ZwP7FrB+mL5pNogwpHR3ZWVtIVSEMJ7sdQTn4DZQ7+18kzD5Rh+8VhYZjq1aNb\nagrxKD12gCiUPPTNTSZyHoyl8IHeZDRszIAzp3FE6dGXRN4fK+VBgru4Qc992p9ZrvMpT3XCtVEl\nI55/gBkrCzBTxXHicMCAZW02J8edoUmrhtd+wzrg7OiKgUsnEjrhQWAzZb3A1J+f5sVO07EWWKlH\ne8rJh0fn89oH4285nWd2djaZebk0iWj4m8Pn/61lZEP+2iiJaRu/xqC28Y8nH7ktPeB7lclsQq1z\n5NiCbWj0Okyl5Rhzi3HxdceYXURQ+b15lGdZWRlb4j7HQAnZqWeY1Lc+ecVWTjooQdxBp2Fk1zAW\nnvGlMCOJ5XtScHfWEdsiAIv13sqTfvZgApZnn8CWncUrQABQDDwJLAZmAw2A8yoVWk8vjA8NI3n9\nanqkp+OGslq+tG179AYDPigJb3KBn1Dm74OBflYrawvy8YlbgPHxp5jfLRbX06cw+gfQ9e9T66DV\ndUOCu7ghLi4uvPjxkFrXfIbt4ficczRjNFr0kAnzJs/hvbURd+UhDd0GxPD5N4sJPKXkHs8OXcFD\nDyn7yz08PXj563788PK/URld0TZO5+Opk245sM9cvZGPctQUedaj9eYlzBrViyB/5fjSqqoq/rVs\nDdlmFZ2CvBjVXRkhOHQyiXfOVZLR61mw2Ri/II5lTw7D2fnGT6WzBy3CmjDjlXcYMO15NA5azqxN\nYPuHi3E0a+gc2obHx7xQ11W8KZsXfMRjzQrRaNQsK7FisVg5cr6A0xnFdG0eAEBxeTVZpRDsZGZw\n2/pkF1bw0bIk2o36Sx3X/sacX7MKXXYWpSiBvQpqFnaOuPjf546OjE9Lw2pVtjRaLB/xy2cfoz17\nBkujxvR/5XUsFguzdmwjdt0atqhUxAcEoIlozN+3xzMPZSzDDdg7/0fyv5xB2w8+vfONrWMS3MUt\ne+nj4bxzbD7aA5e2ullT/aisrMTJyel/3Fk3fHy9eeaHlqz5+idsVjXjJ0RSL1RZMFZRUcHKP2Wj\nyQgDbFTvc2XK87P4y8yn//eb/g9VVVVMSymnKLovAIf8G/CHb6cREdKAIIMTBzLzWRE1FHR64nIu\nUL5uM4/16cnivYlkNOuhvIlKxeGmvdl28BB9u3S+xU/g3jR13X9oOqoLVrMFrV6HpcpCh0kD8Qjx\np+hIGqt3rWdA55tb8FiX3G0FNV8ei8qq+H5DEsM6heCk1zD5u/3UC/SjzCGI8BANIxoq+fIDvJxp\nHeFLSMO7cx/71WSbLXQCWqGcxV4I9EFJK9sXOAC4PP8y3t7eNYspNRoND77+Zq33UavVDJs9n6Tj\nx2jn7MzoiIaUlpawcsCDuJ86wRaUhXcqk4my5UthyL2z2PB2ufcnqcQdYbPZyM/Px2KxXPGaSqWi\nSU8PTJTVXNNGZNV5YK+oqGDRzDUs+noN5eXlNdctFgtJh1NwcNYS1taNRs3Da17LyEgn77SZAFrR\nhCHKGe+runL80KmbrofJVEm5/uJCRJsNln7FJv/WzGw0mPfcO7Cu0rEm7WylXwhbcpTP0V2nVk57\nu0hflE2Al+cV72+v1mzfyNz1C0k8fQQAfZgX4b1bc3TBVkyl5disFjxClNEPj6h6HKu6UJfVvWll\ntkuLVL3dHIgO9+an+HOs3JfG+xPa8Er/BoxqXk1uxn+1T31vnPoGYDabWf/JB5xZ8TNeKMlqXIGz\nKCfBxaDkli8CIgcPufobXUaj0dAkqiVhEcr0n6urG02/m0OCVktPlGx23YDTJ0/c9vbcC6TnLq4p\nJTmdb17Yjep0Q6zBuxn6j1BiYmv3GMa90Zdvy5aRf9AJrVcFj//fzZ/bfTtUVFQw9ZHl+Ox6HICp\nK2bz1oJBzP1wI7t+TCOsZBjeNOKwQzpZ51Yx4c0BAAQGBlHptRaPgksJZvyq23AqcSnNWl9fXvT/\nllNUjO+ZfRQ2bAvH94JPMERdXJznZKCqvPZ57QZrNQCvjBzExn/OYJNvS/QmI4855NGq6dCbqsO9\nZva6+ewyn0Lv5cK208dpeWgXxopCNFoNLR6JZd+0leicaq9bUFnu+l29vyl6wAt8/8sXuKtKSEh3\noEV5Ma7OOhoFu6PTKv2vxgFOrEuG5YkVDI5yJLOoigxdc6Jdr/+Uxbr0y0sTGb94EY7AVpQz2VsD\n8RotCxx0dKiooB8wOzqG3o0a3/RzGjRqTJi/P67pSnZMd6CRgwMWi4Xi4iI8Pb3uyqnC34MEd3FN\ncf9MICDhceWHk7ByyrwrgrtGo+HZv989q3ZXL9iGz67H0Fz8Fffd8xj/evtTHOLGY6jejzdKQhaX\nqmAurHOAi6N+zs7O9HstlEPv7sBisWGiGCf/aobEtrjao65gNBp5f9laCtERSgWLy11IHjAJ9q7B\n/cRuipt0vJSzXaXCobIUnwOryfWoR1T+af44vAcAOp2OWc8/SmpqCo6OQfj7d72tn9HdZsuBbWwp\nPITVQcW55JO0eLIXbkHeWKrNbPtoCUHtG7P7i+VUlZTTefII0nafJHXXCQKiIyjcdo7+fnf/8aa/\nJTC4AYMmfgxA24oK4v89FncXKLns0CSbzYabZwBhvcczb+9m3L0DGDj+3piCsFgs+O3cgRPKivUx\nwCqU+fYiF2cGlZSQpdHwQ2RTRs6ed8unR2ojGkH6pdTXGQ56NvbsTIOMdHY2akz0l18THHF3Lva9\nnSS4i2uyFNfuIZmLLi3oiv9lHwkLCkBto9tTwbSLjbrT1ftNarVygM2vbFgpL6zGszoIK9W1yqr0\ntU+eGz1xIAlrp+G1/VFc8Oe8/odaW8+u5dnZS1gfPRI0GtSbFmB9YCjkpoPVRrFfKAZTKWXxi6FV\nN1TZKTzbxJ8/DOlNQUE+gYGtaiVeUavVhISE3tyHcA8pKMhnk+0oAY8ox5OePZuEW5A3AKk7T9D2\n+QE4eRqI6N2ak8t2o9XrCO0eRcG5LE5+tI4/D32F4IDgumzCbeHk5ITRpRGqsuOE+hlYsSeFIC8n\nNp+uYOCkP+Hj60/A0AnXfqO7iFqtpsJgoAolYc13KNnn9jo48EFJCVogwmJBe+4sxvJybnU/SPhb\nf2Zefh5h58+TFBaGQ3Ym4zIyAOiWsJ85U/5G8Dc/3OJT7n4S3MU1BXawkrUtF2erL2ZMeLZREkEc\n2X+SbZMNeOUrKVlXJq4jYGk69UPr/h/Z/g93Z8ry2XhuVf4hLOg6m4df7s3iA1soy8niOEsIIZYS\nnwR6v+BHZlo2mxcfxNFVQ0yvRjju74kryiK78JQnWPX1Tzzzj2vv3K6oqOCQgx9cXCBldTRAZTkc\n3wPdlUU91fvX0dWYiuOhJTzZNYbeXZQUqS6XHZd6v7mQnoJjs0v/rGsddRSn5ODewA+VRo2l+lJq\nXXPlpS9jXuEBOIVE2EVgLy0pZueaH9G5eJJeFkr2hUz0Oh3HqusT+UAftiz/Hg+DE6Ete9Cw6d17\nRvuvLiSf5mT8XMoL8khy0JCs1tDJasEHaAE4VFVhBdJRjnP1q6zgTEEBhN/aMb0N27YnbMM2ZY3Q\nl58R/J8va73uWFR0S+9/r5DgLq5p3Ot9WWzYRHaiBdfgasZNVobfE7edxyv/UiYzn/Re7N+69K4I\n7nq9nj/NG8GahauwWK08GNMaF4MLlWHbiM6ZRBVlJDusIvY1Ne6B4Ux/+DABSaOopoI9sZ/iZB1A\nJSWYqcQFX7jO/cR6vR6P6jJyf70Q1Rn3hR9T3OdSb8vUtg9Nk9fyz4cvJZHZtP8A+y5k0NjHk2Hd\n72yynLtBo7CGLN2zDRooyXaaDuhA0ezD5Ac54aJyJHfnAbTPdUTrqEOTXEHarL2og1zQppt4Ivre\nXQldVFjArlXfobZWcvrIPpr5WXHX6ygpraDLhH9SUa3j6C8fk7X5A57tG4lGo2bDvmOc4cW7OsCb\nTCZOrfmMPiHVbP/3GsJTi+kIbAJ6XCyjB+ahBPqtQEqr1jza6vZMraSdO8ue9WvwXLmMCpREN44o\ne+Kr23f83zfbCQnuopaioiJ2bzxEcKgfUTHNAGU1/MjnrjxhKqiRO+n6FLJMJ6imAou6AuuJkivK\n3UkWi4Wl326kvNBK+/4NGfBITz56dhFH3mqKSVOAmSbocESHI05VQax/9zQLzcvobfsAAB1OGLaO\n5lzYt3gm90SPO0dcvuYv467vHHq1Ws1brRvwz/0ryDf40KLkAn977mGG7jhLaUCoUqiyHP/LsqnN\n2bCZv5b6Ula/D/r8DJLilvPHkXfP+oU7wWBw5ZH6D7J63nZsOmisCmDiXz6r2Q5lsVhYv2EDJnMF\nT495Rxm+Nhpxae1yzy6QMpvNbPnxXZ5ur+LA2XysXmYeiVXWgthsNl7/4m0aeEDPFn64OddDo1F+\nZ3o3ceano/F3dXBPS00h2q+C3dtS6ZxazH+ADihJZ349Wz2DS6e8tQVm+fqh0+l++w1vwO6f5rHj\ntReJslgoBx5D2WqnAY40bsIzk9++5WfcCyS4ixrnz6Ty7RNH8T41iP2OyRx8cQ0T3ux31fI9B3Vi\nx6oZuC3ui58tCqxQPP80e/odpEP36DtYc4XNZuPj5xahXzYWB1z4ad5GvIbuwumXx3HHGYu5mpP8\nDEA5+RSTind1C4wUUkYOaexGjRYbZupnDiOADgAEGFtxYPMvNG5+fcOFgzu1o3+7aMrKSnF3j0Wl\nUvFedgFf7l9Buc6JrhTy+MOD+dOPi0m16TmWnkXZA0pv3eQdxMq0w/zx9/mI7mrNIprSLOK304Nq\nNBoiQxuz8+Q+NuzbzKBu/TEYDL9Z9l6RmnqBjgGlqFTuuDpqcXK49M/xybQi2ofqcdZrcXdxIK/E\nxP+zd56BUVxXG362StpV7713gSQQovfeTMcGjBtxT5zYCbGdL4lrbEiM7bh33Cmm9y5RRJMoklDv\nFfWulbbP92OwZBlsqg3Een5JuzP33pndnXPvuee8J8BdVOAzmcyYUN6sYV8Rbu7unEuUY2mrZA/w\nd+BrxJS35wEzYq77D7HWaK67X5PJxN5nnmK2ycRYxFz6o8BI4KhfAKNef+u2nQxeLb157r10sfP9\ndDxy70SJCkdtFLlfqdFc5gcXEu0rGvYL2HWEUpJV80sP9ZLU1tbQsS8aJeLetWvVOIqON6PAiiIO\nkM8uTBgoVe+iitNIAH9GYokDuWwljDsIYAwZktVYa/272lVghfYq6k2cyy/g0+27ySwu7XqQLB43\niuOPzeH4olGYBOj7ykd8GjiZPWETqVDa9ThfIVysJfBbJ7soh9eTv+SkdSm7TWd5fcN73AYFLX8W\nBwdHjuU1sz6pmNzKVtKKGjGbxWs6ml2Lp6MKT0cV9a06yuvaSc6tpbCqlU9OSRg29Z6bPPqfx9ra\nBmXkfE6caqJDKkEBTEAUqXkR0cibgO+fLrVA+4BB191vwpuv0dnZeSEXRsxzDwFeielH+J4EwgYN\nue4+bhd6jXsvXQjGnqIYEr0lRqOBtJQs1ry3i9NH0y86J3Z4CA0ux7r+b3A+QfSw6wuIuVaUSiVm\nZfdkREDAI9ieZJvluBNLODOwVFgR/3ID6tG5tCjyxbK1NBLHg0iQUE0ascISitiPgPigrXdNov9k\n/ysaw/bjySw4VcdznhNZXKrgwx37ut6TSCS8s/MAm6Jm0OkdCnKFWM61vhrp0W2QvBfVjk+5z8/u\nZ3r430aj0fDV3jV8sX8NucX5Xa9/c2QDDoP9CZ8xiLAZg0jvKKK2tvYmjvT6USiU2KktmTc8gDsG\n+fLEHRE8t7mKz09LaJW6UVjVSl9/BxQyCbUtWjbk21Li93tmPLritgi+zFu3mT8fycTWLP6SzgD3\nI7rkjcA84ABiJbdPnJ2Z/H/XL6XbcCqZsRfaLQc2AvsA/yFDcXB0uu72byd6jXsv1NbU8687N5C5\nr5ZixR4AajhHY8B+vnp9O9vvVtL44p3svdeBLSsP9Tg3rE8wo1/T0TFuHR1j1zHi3xoiokMu1c0v\njoODI0H31dFomYmWFqr7rGL2H4fgyQAK2EMKH6IyeLL/P9W4HfwDfQ1LOMnb2OLdZcjbqSKICQQy\ngWw2ker4X6a+LyM6PvyKxvB1bhV1QXEAaDxDWF3Z3uP9aqNEVKPTaWHzB1BVDHc8iLm6FPoMoWPC\nYl+Kk3IAACAASURBVNblVvRQ1PutoNfreW3Ph7QucKN1jgvPrHudLzZ+hdFoRKPU4xErKgkqrCxw\nifJFr9dfpsVbm/r6OiK9utNM3RxU9I2NZ/pjb9J/6qP4+Xix+3QFORWtNCiDWfrSh0THxt2Qfelf\nmlNJu7E/sx8J4t7vi8A+iYTvfS3DgU8Ae5UKnX8AA5avuOb6DUajkd0vPcfBxXdyKvUMCxFT7bYA\ncxAnFMPWfEvmoYTrvKrbi94991749rmjOBy8D0ckNJBPkstSfAyjiUp9hYy0NfQVxP1zu7ZI0tdk\nM3NJz/NHTB3AiKk3YeCX4P6/TyVjWjY1FccYNHoMgiCQ07GKeH6PPf5UkYq00RUJEpwIxoaHyI96\njRLTx/jmLOnKgbfGlUjmUOexgQFXIWBzMT339wa52rKhrhydXica+bhxkH4EJt0DKnFP9UT8fL7Y\nl8jzDy64jn5vDwRB4Is9qyi3aqEurxyv+wdScSKXsqPZDHpiOk0mM//84FW0HT23h4wNnXjEevxE\nq7cHnp5e7K+1ItpPvA9rjpRSAWSlJRMVO5hmL3dSjuzD0saZB8ffXsqErQUHEVyt+CZHDGQLANwF\ngReBUMABaHd0xPjNdwyL6XddE5bP5kznLyeO0YIojJOMmFrX/wfHxDU3sybxAFGjxl5zP7cbvca9\nF/S1Ksw0U00advhg3RGAn0aMDpcLP9KHl5gv0cKtRZ/YCPpcyKhJO52Bh2kA9vgD4EEspcKBrmPl\nWBAZF8S9z48lccs2jJk1NG04i0NzP1pUeUTOuzrn1t0h7pwrPkN9QH/UVQXc6SHeP71ezzvb9tBo\nhLtbk1jbUo3GXsyjx2zuyosHQCrDZL6995OvlG1Ju2icZIezqw9Cth3VpwtpLK5myFMzkSnkaFs7\naHA3EzhuGKlfHUDlYk9bSQ3OHSoqqivx9/a72ZdwzSgUCqJnPM03CV9RnJvGo2PccbGDU7mfcVrT\nwuSZc7B38b/Zw7wq9Ho9e9e8ib4yF4W7DRaAGtENfw/wIXD3hWOnNjby1Ttv0vfL1dfc34k9u/A/\nIW4LvoIYrJcHpAPtcEGp4sLevqvbNfdzO9Jr3HvB5FFBBQcJYCwN5NFm6srSRoUT5ZIknIUoqtSH\nGHbXre8S/CHtLRp+vHpWOutoCvoGQ60adUQLj70wCWtra2YsngBA2uwssk6up1+kC0PGjb+q/mYM\nG4R/bh5J2Xvp4+PJyLhJCILAQ5+uYVfMbFBYYF+SjpObFE1tHWSegKghsOcrmHwfSGVEn9rA4sW3\niCvkF6ZO34yVqzcALhE+ZKw5hEdcCK2VDdSkl1CfXcbgJ2dSdCANXbsWG3cpcY+J9+az1ZtYans/\ndrb2N/MSrgsvH3/cF/+dE58+jIudJTVNnXjZChQVJiE6lbtpb2vl0Pq3sKaFVsGOMXc+ibX1rVXD\n/sD691kUVMFpwZJte6p4BdiGuHo/C/x419uy7vriJk6+/zaRwOvAn4BTwGRgKLBKrmC1vR0OEill\no8cw45HfX1dftxu9xr0XVGYXIpgNQCdNuGoHUMIh/BmFndSLmkErqcqsxbYtkrOrztBvZDn+wT43\nedRXxoBhsXwd9AFlhUdxpx+lsgTu+GsEMxf/tNGOGRhJzMDIa+4zOiyU6LDu4hfNzU0cs/YDTStk\nnaRZKsWpphhl3/Ho888hPZvIUCsTk+oOYhQE7r5nGvZ2t6/Buhp8rN1ILWvA2ld87NupbFGoLSk/\nlkP0olHkW8g5+8UBbDwcMGi0BE0Ut4jKjmXTKtOw5cA27p19a0eOXw6pVIrOBGsOFeHrqkYqkZCV\nnXFRNkDid69zf1QzUqkEs7meL797g+lLnr9Jo740VsYGLBQywtxt2NVp6JpWZyEaeBNisJsGCATK\nPK5P8MrdzQ0D4qQhGHG1vgEx+t7xH88z8uHHMRgMxN2Cpad/aXqNey9Ijd05s0a0hDODJorJYSs6\nx3zcmvri3nphFZEezuYVq3nyw9vDuFtYWLBi34N88spGaioSWPjICAYOj/tVx2BpaYVlSy0t1ZUw\nai5IJFSdlvNPVR3tMV7EBwxiZL8bV/SkubkJuVxxW+SBTxw0jsaEDZQkZyPRm5HXGSjef5bxy+4H\nIGB0X46+tpHIuUORKRVoWzSUHDyHe79AfIdGUHQoj2NpJxkac/1pVDcLiURCqcGLu/tI8XISo+BD\nvfUc2LGBmEGTuo6zQzTsielVtHbo0baUkJZyiJj4UTdr6BfRIbWjraOBnS/v5/HSZr4DFMCTwJuI\ne++hgA+isEzAjCsr7/pTxD36BxJ2bMPKIMbKhAFpgJNEgmnNKjICgoidcnnZ6P9Feo17L8TMduDI\nkRQcm+LRIyqCORCAAwFUu6zB1NazSpOg+eUFNE4fT6OtsZ3owRE4OjleV1vW1tY8tezmFduwsrJi\njLGGNfELxEpwQEfcRJrL9/LM3CtTvrsSTCYTf/h0NfstvVHqO3nAyczSOTf+wZaenUNhVRWj+sXg\n6HB9nw3AgrFzAfhg7ccYwtTYGrsjyKUKObZeTqid7Qia2I+0rxOQKeTYX5CpdR0Vysm15xjK7Wvc\nAXwCw/FwaOn639FayfnSQspTn8ZO2kqLYIveIOdsYS1u9paMiRaDCfdnfUuVZyAeXrfGZHvsvD/w\nxstP8HBePR7AWOBbRNlZNeIG2fcjnQ6s2bkDZs695v6C+w/gbHgEnefSWYGY/vVHQC4IkJPF2pf+\niXHCpB7FmH4r/PauuJeLGDUtHluHLM4dWscAqZay3auRZ/bH4JXPuL+4cmZvKfrvOlCiosWygKhx\norFvbmpmw9tJCDo58bP8iBl4aXWxrav3cuDdcqylzviOkvDgi9N+Nu3l839tp+qjOKx18SSEbmPJ\n5xH4h/ii0WhY+Y+9dJZZY+Wn4Xf/mohKpfrJdm4lHpg4ho05VejVF+pvaztwUMowGo28sm4bOQY5\nHoKWF+dMwuYKa3SbTCY+2L6HKq2Job5uVDU2sSF8KliJK/Z3SjOZnJtLn7Brq0MPUF5RTlVdPdER\nEVhaWvLm5l28ZfSgw7UfId8d5NMJ0UQEBl5z+wAVVRVsTtvL/pzD+Izvi6WDmoy1hwmfNYT26iY6\nq0VJY6lUSuy948j4MrHH+be5lg0AMYPGsuzNNfT1lOPvak1JmwqjsZAl/U2Iquh63jkuYes5Hc/P\n8uw6b0SwBVvOpdwyxt3CwoIhsx+i8puteHR24oLoim8FZiDuu/dAen1qcR0dHbRWVhKK6PYvoNuo\nVQPaqvPU19fi7u75k238ryJ74YUXXrjZg7gcHR23dz7rtaJWW/xq1+7h40LMiGD6D49iyAJfWryP\nI7XuxGDUc8/fJlCm2ocpOJs+D3YwZeFwdDod/1mwE4vNizGf6cPphDyc4ttw83Tu0e6WlQfZ/nQ9\nfRoex7YhGt3pIAoku4kZdulc+Pb2NrY+0Ypr2yCkyLFuiCRfn0D8pFDee3IbsrWLsSiLgvQ+pJzf\nyOCpl55QfE9Jfhnr3jzK2YN5uAfbYmN3c1zVHq6utJ87Tk51HZK2RqaUHeO5u2by0nfb+MB7DMXu\nEaTbBZC48i3Ky0pYk5bPznP52Bh1qJVy/rt9H4ey8vB3sMHORgyievLzNbznOZozLpHsq27DnHeG\nwuBuBS6jwpIxnWWE+Ple0RiNRiPPr97E22eK2Hc6lXM52TxZLPCFyZXEA3sZ7eXA02craQofAnIF\njR4hdJ5NYmrMlWkAXIrOzk7eOvkV9d5mWqrrkcik6Fo6CRgfQ+XJXCr2ZzLHfyzZWZkYFALNiYV4\nN1mjsTVj4WRNfUIe42364+V6ez+8965+g98NlBEd4Eh+TQdlqqFQc4r+Ad2CRhUt4D9kAYrGc9ir\nRROWXNKJY8xsHBxuvkBLS3Mjuz77P7zak0jUGNHWddAqkZAlkzHYZKIIaAGsAUdgj48fLv98EScv\n74vautJn3+dP/QHHU8ksAiIQJw+uEgknEQP5DAYDJV98RrnBSOiwETfwan9Z1Orrq2kPvSv3Xi5Q\nXFhMY10zffpFcvLAObJfDqG9WU8r5SR9sInxf3Hn7n9354hmpmZjlTwR6QUdJNfqMaRsX0ffAT1X\niadXN2Nn7l7ZKVHTkt9z1W42myktKcHC0gK1Wo3E2NPtLzGJx2vy7XBE/FuKjPa8i1e4TY1N7Flz\nAqWVjLjREay8Nx/3wrsAeP/QKv6yaTgOjg5dx2s0GrRaLY6Ojr+45vRzd83i8fp6dDotnp4DkEgk\nZOrlYKkCkxEOrufc8MWcK0iDIWJE+K6T+3DYm0xu5DgoSmfjyk1sf2gebs7OHDHZdq3SOzxDaC8+\ngWv+CWpDBkNHG457VrItMhi95ASzhl++EtayDdv5yGecOJ7OdhRpBzEMHgBAqtNc/rt3M0ZZz0A/\no/TahEe+J68wD8tRPtQcyST+8emcfHsrSmsrjq7YBLUdLBw1l6kjJjNer6fifAU+8T6o1WpS0k9R\neqacaRETb+t0OACtVos3xdiqREM+PNyJQ9sT8FVpMZnMyGRSTCYzdXpb5g6bwP5NpZxKO4tJkKIK\nnsbgwNDL9PDrsHf1mzw6QEAqtWPY0sF8ejgY+3kv0zF+BPZ6PQMQdd4/Bfr+aznRE6fi4e9/zf0V\n5eZgs3E9E4Hvv4W/B5709sG+opwwQWAhQGcnJSuWcdzbmyGLbu/gy6uh17j3whfLdnL0nTYURlsE\nVS7WwZ04Ns/GTAlRzIcmyHs1j6P+pxk2UQxGc3C1R2dVg7bTlnx20UwJ1qssaK3bwt3PD8fFVVxJ\nKOWW1FLd1ZeBTmz9Dd3/Gwy89uA6DPuGYla24HXPaZyn6ulc3YgcK5o9jzNrkR8VpeepMxTxwx1e\nC8+e6m91tQ28tSAJj4y7MaJjV8hL9Ct8tet9t+w7ObR9C7PunQjAmrf2kfGxFVKtNapRe1n68fxf\nZG+usamRd/ceJq2wFJmTK35qJf83fSwO9g64mDtFv3LmCRg6DbJTIH5C17m1Rim1nn2hPBfixlNR\nkc9fPl/Nqmf+hMqk69GPh5Mjf46yZ33GLhJzCqiZ9Uc2SKXsrsiFpMsb+DydTDTsAHotBuvuSRAS\nCUZLFZO0jXzb3oxgbY9LfjJ3hl+86roa3F3cqD22G7PBiK2HI6P+sYDqtCJKtp+l/+vzqLZQ8Oqq\nt3l2+h8ID+32EMRHDyCeAdfV962CXC5Ha/xRuqZcwpyh/qw/WoLKQk5FQwdRd60AYPzsBy/VzE3l\n7In9KBpTkUq7pacDnBU4OTkRplASfOG1YcBRRycmPfz4dffZUFHOMKOBGsTIexDz6UOmTkf/0fs9\nCtP4AyfPnobfkHHvdcvfwvwabvmammpWPVyMl34Y/ozC1hBITs1xpMjxYSiyC/M/C4MTHYEpxAwT\nf6YODvaUalM4kXIEK8GJKO7ERzsaaVZfjmRsZtSdYiqZRlJN5VEZFfpU6mTp6Ifu58n/zu/ac9/w\n6X50H8/HxuyFtcGbxnRLAu6uJz37FB3GekzeJbhFKtj8eAsOhePJkK6m3SEHxdA0+s9z4sSObBqb\nGvAP9Wb9u4mUbbGjnhzqycLc6IgD/sgRXVyd0lpsJuSisJTR0tLK/scs8WgZg7XeD2leJKWq3fQd\nFHyJu3TtdHR0cNeXW9lidKQseBAlwYNJcwwlffcm5sdHM9DXndyE7bSU5tMZGANmE+g6wfbCNKay\nABqqxZW8VAr2zjQX5WLXWotFUw1lBTlodTr6lJ3i1fED6B8eRj9XO95rt8XoJLqqDbbO2JSkI7S3\nsDEllbbmZkJ8Lk5BSj6XwVn7ILEfCyvsD61FGxIHMjlOhaf5c4gDj0ydgHv+CaLrc1ka7cPQ6OtR\n7xODHZP2JaKMdaWjoRV7P1cK950l+oFxqF3skFsqaZF2cmDbTsqqKwh288PCwvLyDd9GSKVSDien\nkXwmDZVcwsFiKbbhE5G1FjEu2pVQL1t2ZBlQoKeyvAzf4KhbrrJZ1p738VN3YjCacbK1RKc3svK7\nEgzltcjzcujb2h0sWD5iNIFz5v9se1fy7LN3c+d0wj4MtTU0A53ANzGxzHj3Y/I//RAMBr73abQB\neTNmEXibFI7pdcv3ct20t7Wj0+twJowaztFCOSFMI5/tyLAggNEAtFoVEB3Tcz/93mcnk716Px3V\nMiwRXYoSJHTmOWM0GpHL5Uy5ezg+ETlkpRTTZ3AIkTF39GhD1yKgoDsH1crgwfGPmwmveEx8IR02\nvfAmfc4/BUCc+WHO22yk3ywVKU+7Y98cS4VFCZVP7iEzpQhPHsQaV8yYOM0nVA//ANXJKQhSAzWB\n22h/bgq5OiuyPN8htqO7UMXVVn67EnQ6HbsOH+Z01ETIPA6uF4KeJBLSbHxobW3BzdmZNY8uwmAw\ncP9H37IvZjac2o+8NAs7OzumWrVxoLGR89832tZEu07HUlUc1JdDRAxqTSMTHOSE+InuaRsbWxza\n6+n8/hyTieL8PB6xDkXrHYtlTQnPbN3N72f0LOf73JwpNK/eSoagwtWs5Z8PzuFIzhEajQITo/y7\nDPk9k65O2OdyTOg/ipMBjXRqNORsPYmuuQO5lbg1U5NRgqFTh99fx9BpNrPi00/4x7Q/olTe2iVP\nr4b9Gz/hDu9KguNC2JbajEP/RfQbOIKiLF/Wph0mNz+fe4c6EOhaSl1LPjtXVzF10ZM3e9g9qCwr\nxqjSkVXexPaUMir3FPJyeSNWe0/whq0t6+RyLIxGOhwccbr3/hvSp1qtpt8Xqzj95gr2HT2Ch5Ul\nfuMmYmFhge/Ly6l85s+sMhgwy2TUTZzCwsf/eEP6vV3oNe6/cfwDArCJ3U7VmVSaKCQSMS3FiVBO\nu7+A2qMShcSSsJkyhk8a1+NciUSCpUcnrdUKzJiQIqODBprV6XR09MPWVjT4ffqH06f/pYOuhs2M\n4Mu1O3Ern4qAQGP0RlTtLgCUcIhOmtD8WMTKLCVtQzv2zWJuuI3On/wtZ3D2DMIaMUVKigxrS3te\n+e4uigoL+fyVHbjumY2bEIOAgG35UIpJpC8LkSCh3CKRuyZc297tiXOZHMkvxt/ehnljxPrt3yYc\n5o38ZhqbW5AE1yIYDGAydcnMOmkaUKu7g/vkcjmRznbkpmxFru/kH4PDmD9lNJ2dArtOpPCnvGSa\nQwciTdmLYcoSOLELJt4NMjkak4mPM45wb2UFXl7eqNVqlgbZ8PrZPbSonYhryqfVIxCtWwAAWjd/\ntmdk82O9LpVKxfzYMCpPF9Eis2Rbag7/vGtWj1ViwplU3jxdSIdUwQgrI88vnH3dq8hR8SMp2v0t\nBTYN2GBBX4dYyr9Ow/XBATQVVhM+U9xOkEqlKMf5kl+YR1TE9XkMbjYtzY0c3fw+lkI7psZCQkaI\nnpQZ/RxYnZMAA0cwaNQkAiOHIvviGQJdxS0YFzsl1qX5P9f0r87xhC2gb2HxNPEzKTrfQtNnKV1T\n9sDWVvpKJHgDJxHQ3cD0BjsXV05t3sAL7W24AB1ZmayurmLyf94kNzoGXVMjYf0HXHEGyv8Svcb9\nN45MJmP55kd59bHP0e6zFisvAAosCXYcxHN7Ll6lbf/6MEWHtUittYx9yo09K0pJzlsBJgkOBOFT\neB//GZ/M7DfciRve96Lzf0hgmB8LvzDwzSuvoWk2MuaeSPKPNFJVdBZL7PFnFOeNpymVHMJPGIVG\nWUHgrE4asn4UyCUzYelqQEBAckEXyzHEjFwu58DnORh3j8LxgtJ0DRnIUOLPKLLZhBQ5quhiYocs\n+fHwLsu2Yyf5a4WERv+JyJtqyFy9iaUzJvJaYRvn+11YGR/eiNTBFfOuz1G4+uAr0fK3/v5d+/sZ\nubl8k3CELwMnYQp0A0Hg6W3vUdDazoiQYKYMjsfdLpdDOftIVbazUyIRU4hkcsg7A7UVaGzseeDb\n7Xx57wxcnF1ZPG4U84ZpaW9vx8lpCLNWbu4xboVwcY2AlpZmnj5bRXms6F3JbGnAZ08CD0wWJ3Xt\n7W08e6qcku/fb23Ee9d+Hpw64aK2rpYHJt+N0WgExImORqPhwI4DnDxYgGlaPIYOHfk7UxD0Zra1\nVRHoH4TVbaw6dmjtv1kSq8VsFtiWbOzx3o/nSnpB/K6X1LSRXtJEUZMVI3+tgV4BnXVFhHiKxlMQ\nBLZ9eRp/AUqAKsRHSqcgUADUNzXR9vH7xE66MfLKKx97kNEXDDuACqjctoWvt27CV9NBqa0tlt+s\npc9t4o6/kfQa916wtLTkpc8fY8NHB8h/pQhbbSAd8mq8x3c5dmmob+S7/xylKOM89mkzcTCEICCQ\nUPg5L++dh0Qi4ZVpu3A8NZc8dmIukfHlwkpS7iti8bPj0Gg0uLq6XnKVl5pYjM3R+Xjq/MnKKsL3\nLylUDN5KyAlRWtOTOOqFXMon/IsJC+IZe8d0jh9IZU/WAZyqRtFsf5a4B1QMmRLP+xWfo8/2ApcG\nZj4nrlRbc1R4M4hM1mGDB2pcMaGjliyaKEKNC425jZSXVeDje3UBYusLamgMmwTHd2KUwGfN9UQm\nJdFo49p90Mg5TE/+hj8vGouPlxdqtRqpVMwyeHXdVj7EF22HLThcKGxxeCP1oxbyqp0TLqmneKv9\nNOPj4+gXEUZldTVFm7aR4xoEu78CuRzGLwKzmdSD1Qxcl4KLAh7yVfPY1PFYWor70w9GeFGYc5Ra\n/364lqbyUMTFe+7F5eWUu3anKJrtnMgv7i49W1ZRQYlLd0yC2daRgmLtVd2vn6O2oZZtqftALmW4\nXxxTBozhhHUFaV8dwKjVM+DRqUilUkxGE598/Q1/nP7QDev718aBBiQSa2QyCWZBoKxOg6+Lml0Z\nrRTXmdj12d9xDxtAv+Gz8Rs4j7c3LqOPu4Q7BvoQcr6DhC0rGfvj8ow3CamNGy3Feto6DFTWtjH8\neAlJwBPAekQjv/AHx7+el3tD+k3ZsY3aMymYfvS6ub2NZwQBCSC0NPPvh+6nT/qN6fN2ote499LF\n3EfGkehxkpKzZ/APsWLKQlE9zWQysWLRLrxTH0HHNhwQDYAECUJaH+rqanFzc8fcqaSGDOzxw5Uo\n0EH2J1t4/rujWOt9kMYn8pfPp18ki1qwQ8BR5w+ArTaQ4j1nWfjsOBIeyMG2TXTny+w6WfTnifSN\nE/Pah4yLxXtLJWeStjAhNoDwPuJa5rmN8+no6MDKyqprIqFw0yDHkjBmkMMmgpiAK31JYjkj+T8A\nzK1m/jV3GR+lXN2+nByzWLI1rD84uqMDlqfuIVpzjuSAaJBIUFXmMT02ksjwnlsTDQ0NfK6xQRsZ\nCY210FQL1vagtgM7MdugLmgA3+XuYXjfKNYnHkYqkfDNjMEs+fAb0r37ge6C8T2TAIOnoFPZUAG8\nXniGSSXFBPqLE5w7hg4kxq+SlOwzDBgTSml9E1M+Wk+DEfpJNbzzu4UE+/sTeCyRIndxe0LZUEVf\n5253pp+PL8GH9lHgJUZEK5pqiHK8MboBrW0tfJC2Fo/FYjbGuj1HmJIZhZWPPbGjIsjedLxrQiST\ny2ixvfWrE/4c7YI1J3NrqWzoQCqBFbtrGTRmJnk5m3hutjUymYayup2s+SgbZydHJEobxsaIn0WE\nl5qMMycQhAduicC6UVMWsr3hPO/uO4KmVYPEDH9ADHBLBX4srOxyA1QN9y57mUHvvcV+vZ5yYDVi\nxHwq4CuTIbngBZIAHm2t193f7Uivce+lB2NmDBKlpH5AyolTyFMHIkGCGQMmjF1R9Cbn89jZiQbE\na2wnp7MyCBamks1mjOhQCFYENc8DwHxoAGtXfMfvXugpuSpV9px7Sy1MDBwZQ8u/jrPvw8M0d1Qz\ndKEvfeN6ylT6+Hvh43/xCvTHqnWLnh/C23VvQpk3ijro6GygkL3Y0a3qJUWKotn9Ku6UyCNxoRz8\nLoG2mG5HaYVXH/5jW8GBwp20SRSM9XJk1vChF52r1Xais7xgHG0dYf9q7O0d6LBQ88M4YYley10f\nreH4gLlgNhPz/rukO4eDyhbqz0NrExRnwoDuLZRWZz8KK0tIzC4ko7kTP0spKgsl26s7+bbkJEXN\n7ZyXWIHKhhKzFSWv/Jc9Lz7Nf4cG8+bJbXRKlYyxl7LwB7rcarWaN4eF8sbJbXRIlYyyhXvm9gyQ\nvFaOpZ7AemwAOVtOAOA9NIItH++jMaUT+6c9MBu7vyOCIKBsu71l6ewiptKSu5I5Q/0BCPbpYH9T\nK4P8LJDJxElMWW0bA9XZDPJ1Yk1JPdA90ZJIbp3rl0gk3HHPUmApjY2NHF8fRInJhC/gh5iepgMs\nEAvGdA68Phe5IAhYblxPqV7PFMTa7VuAREDdPx6rglyE1lZx5Q5UXUIk57dAr3Hv5bKc3lZOO+JM\nOISppPEVthYOSD1aaJJncl/waWwFbxwiDYQ+bCL503cZYnoaDTW0UtHVjhQZhtaLo5yHPOzIN5lv\nYtHuhdJJy/xH/dj+9WEOfl6IpDCUKP29lH6WTGJQMkMnx/DpP3bRmmON0rWdxf8ahpuHy0Vtfk9J\nQTlvPbCf1jw1FnIdGrdcsivX0194mCT+3bVHb8aE4F511fdmYFQkS2MKeLH+POamGmisxqX5PPF/\nXMj4ET9f2c3T04txjQnsqK4An1CY+wTK9APcIdSxsSwTjVsgQTmH8ZJ2sil+PsjFcrtpfgNFgx7W\nH3xDYftK6Dcadn8NJj0E9CGss4YUewnvOA/GFOQC6UeQO7ph7BMKZw9BSxUMH9u1FZBaE8bWhESs\nLC15bkRfokIvLYwyqE8ka/tce8W8n0KtsCJ/xxFULraYTWaS39nGyH/chavRxJkV2/GXu1Lx/lEU\n7jZYtUh4cOiCGz6GXxKTyURWRioKhZKwiD50tDczrW/31k0fbxVrM/JoU3VP6+pbtcwa4o4gCAS5\n25CQXs3YaHeKarXoXeJviVX79yTt+Q5TVQqNrZ0ESqU0mUykXnhvDmKRGAWQplBy70uv/nRDgSCL\nkQAAIABJREFUV4hZJqEeGAkkAbMRjbvFmRTmAq8B9jIZTWHhzF639br7ux3pNe69XBZLSyvkWJLN\nZqxwoJ0qHMM1FGaWY28MYwgLUaKGNMjWLieU6ciQY40HhezFk3ikSGmyOcfwsaIwSn5uAQ01TcTE\n9+H0rlL6tD+EBdbUSBMpKaig9I1YtG06IhGD0lzqRnLsk+/IPbkH05d3YYeSRgp5seRLXt3wIPb2\nlzakG5enos11IY6FYABThZEs9edINBL6cBdH+Q8qqRPK4BqW77j/mu7PY3NmcHj5WyQGjcY8aAqt\nlXmsPZrCw5cJNJNIJHz04AL6vrueJh9xq6M2dgItOTvZHGVFTWsG/ecMZUPSCZCIqzkEAXQ6pK31\nmAGkMjAbIWWf6MqPHglGHa6V5aRYBWGycwGjAfLTMM79A2SfgqYacXLw/R4/ICgtee54LucHzcIy\nrYrH07fx7Lwbsyq/Eqra60EGHv2D0DZrcI8NRK5UgFJB/LMzsVxdwZLxi3618dxI9Ho9Wz/6G1MC\nWujUC2w57k/0qHmkpOxnSJDoZcqv0RI9eDIVJ75lXVIxTjYWnCttoa2zADu1Eq3eSFaNmRqXfji4\n+jJhyJibfFXdpJ85RmjHAaIirdj0TRrFBgPTEQ3vMmAVojRsmVSK5+//eN31ICQSCcKi+zD8+xUO\nGPQEAI1AE/DchWOeBppMJo488w8cXX568v+/zDUZd0EQeOGFF8jNzUWpVPLKK6/g49Pt4kxISOD9\n999HLpczd+5c5s+ff9lzerl16OjoIP1UJm7eLgQE+qM1ttMsyyPKdA+tlGHvJ8Ev7Q9UsgI1rqJh\nB3S0U1OoQSWrBZPo6g5nNvnRywiPCmPYOAdGTR/IF6/upPTDUCy1kWyMWYuyqC+eiO5pt7oxnNzw\nKuFtd1NNQY9xCQYZ7QUy5Bg5xrt4M5igc0/xxrQDLP44mNCoiwuYmNssUKCghMNoqEWGkmaj6E1w\nIIBhPI1+ztc89f715cC2uvpj9hPjAXReoWzKKOThKzgvv6wM/QUJ364xS6TERITj4mJDXV0bi8eP\nZutn60iJnw8nd0FYHGaJAKVZcPogBMdAZSGMvRMsxc/iiL0Lsem7xMlA4joIjoXacshOhhEzwStI\nTKcbPAUA66QNnJ/+KEgkaG0dWZnZyCNNjTjcgP3RK6GttRW9ToudjwtGrR59e3cwp9lsRmK8ddzQ\nV8uR3WsZ6FTDyZxWrCzkmBrPU1M1BKn7dNamHUAmETC7DmHMxFlUhEeTlbSRUr0elXcNC4cIyC+4\n6T85WM3YO+7pij24VagpycRc18i2d9MwZNUQBpxCrOHu4OGJctE9nKuvJ3jWHMbeIH33MX98inMD\n4tnx+4fQVlbiDEQjRuZ/7xtskMmwdvx1vr+3Itdk3Pfv349er2fNmjWkpaWxbNky3n//fUAsPrF8\n+XI2btyIhYUFCxcuZNy4cZw+ffonz+nl1qH6fC3v33sUdfpEtOpyLGd/hmn9VMJNrqTzDR3U4i6x\nv+DKNqKlCQNapMjIYh0jjS+Qz07KOIoNnugGHqDvEF+a06Sc2VqNjcdZCj/zxEPbDwBF2n2UKvZ3\n9W/GTENTLU0UIMeSBvJxIoQWi3xCZ0iozG0kg1XY44cv4j62Vf5cdr+7ltAPLjbu3sMlpCUW4EYs\nUYh7/666SPIiX8dJHogqsI2Hlo276LyrRfqj1DKZ2czBM6lszS3HQjDy1MQRuDr3FAGqqavjocQs\nNI31kLgeVGocbGxZFNUzjkCtVvNkfBifJ7xPqsSGeicPcPKAglToaAWvYCjP7zLsANg4Ms5FRUfC\nSvICBogTgJO7xej6inzRjV+WC4c20t/chIenIzt+4ObVWqjR6XQIgsDbW3aR2m7CSdDx3MwJXfoF\nNxJ7tR2uof60VNTjFOJF8gfbSfvyACpnO+RNJv465RHyCvIIDgy+5Yzb5TDqOyk438pdI8Xvp8Fo\nZsXhLTz47DswpmeAi7dvIN6LlgJwYsNy5LJu6eYAFxUaTfstl7NdVVZE54kCnjxXzWrgzguvTwVe\nNZmY9Mzff5F++w4djsWq9aSPGoIZaAdeB+5GrEK3Y9xEHhh4+ZoK/6tc06/k9OnTjBghzsBiYmLI\nyMjoeq+wsBA/Pz+sra1RKBQMGDCA5OTknz2nl1uHTf9Nxi39Hmxwx0UTT9ZmHTbaQCo5iRk9rvSl\nsrSKIhJQ40416SSxnCSW48copMjwYRgN5JMX8A6VtSXUvjWS4oMm0rY28Z/pCUjabLr6K2IfeoOW\natLR0sJR1XPEVb5ALdkY0ZIlX0PVhNcY/kkFd/5+Ah5RKnwZhUBPY9ra0sLZlDS02p6pWQuemEDk\nvXqc6S5o40gw0aP8+Of+8fzl49nY2l3/w/J3YR645CeDyYj9qV3UFGSz8GwT3wRN4rOgqdy7es9F\nY9t98jQFCkeIGgxj5kHsaOyzjjA+Pq7Hcd/sP8hjVVbsn/AEjdIfSK8Gx2JlYwMlmaC0gKSt4ko9\nPQnHdSuYMCCW7+6bgbWuTTx+0GRRJc9kgKPboCKf2LZSdj39KI+OiMM995h4nK6T8c15uLm589/N\nO1lm3Z8doZP4KmQ6j3+77brv1aWIDAzHzs6O6tQiTry9jeaSOkb+4y6GPzsPeZAt66xPs9b+FMs3\nvt2VD3+7EBQ9Anub7s9NIZfi53n5Km5yx0BqW0TxGkEQKO2wueUMO4C/dRsurToKgR/6YmWAe8SN\nj8/4IUc//gA3IA+YB/wVaADWWlqxeOXXt1Rcwq/NNa3c29vbsbHpfkDL5XLMZjNSqfSi91QqFW1t\nbWg0mp8853K4uNhc9pj/VX7ta7eQqtDS/YNwE/pQF7iN6qI8hvAXZMhxEAJolOTRX3gAAI2ikuBX\nj5D2fCO6Dlfy2Uks95FRvAYFKso4SjgzKWQv8cLjpPElzkRQxH50tNCPJdSRQzWp2KocseywJwxx\nv9eJAJZ+bIv/hepRzq72HOYsZox00kQjheSzg4DEkeza78v2gXv58+oRePt6donEvPTeH3gmeTe2\nOeLKvVWdy/BJvtd9b4ULSlsSiYSHZk9gZGExR9KSWSm0ctzSCfoO48IBnAkYQmVtBYPj+nWdHxvm\nizQrFfPoeRduvhV1wQOxtKTrt+LiYsOWag2toaKXwhzQF+WRjehD4ghsyOfRkRF8craEUsGMLOso\nFrknaJnzJxqjh/Pw2SRWj7XmT656Xi/NQGvnRryyk4XBzhR3yAl3VPPYrHuRSCRMdx3EdtccNp4+\niJNSxh///ghyuZxMvRTz9zr3UimZcnucnNQ3fPXs4jKQ8/vPk6RvoUOpxK2vP1YONpSfyCF02kDM\nBiMmgwmreyNIOHSAu6fOu6H9/5I4O8ezck+316a+VYe9d/hlv3+T5y9h+yo9QlE+Wqy449E/3FLP\nwiN7NlF/bhfNdRXUd+pxQKynPg4xBS1NocA0oD8KheknY2J+jiu51sBTJym98Pf3O/khwFilAisr\nCY6Ot879+rW5JuNubW2NRqPp+v+HRtra2pr29u5qXRqNBjs7u58953LU1bVdyzBve77fc/01+HbF\nHnI3SWnsLMNWnYi3ZgwGOnEcU8mMP8Xx7iQtMrP4ddHTjo/QvXemNnjRUirH76ECkt5PJtqwBAkS\nFKgxY0SBCj3tWOOBFCl9uZtk3iOCmRguKKC7EI4L4ZyzS6KjsQ6VWQyCMQVnI5eP67oPI6cN4juX\nL4ip+zOpfIUaF7wYiJ9pDGZMpCcb+WfUWSydTxH/qILZD4/my2W7aDjfSYX8QyycDUxe6k/00NHX\ndW/f3rqbVVVazEiY6yjhmfkzcLR15o5hI3ly63/AyRsMOlCIBSBU+afY1w7Wlra4uYpR0jEhkYR1\n7ib7B+3aCTpaW/VotW1dn79J311Fj4AowuuyeT1IR+CY4djY2HLfRIHOzk4EQWDA6iSEC4FyZWHD\nefvgbt65ewaT8vOpaqhg8H0zeyi71dd3/1Z9Xb14coq4JdDUJH4u1lqN6A24sAJyMnbQ0ND9O74W\nzGYzOw/vRmPoZFzcaJwdxVXsuJhxjGMcLyW+S2mLqKavbW6nPrcS95gAZEo5Wd8l4aLsc9s9EwbN\n+z++2v0ZVlIdButIhkyaTnV1c1cBpUvh4mLD4ImLe7x2q1x3SXE+kuy1zI5Qk6BXYl/WwgigD7AO\nSHd0or/JxPTXX+fQ+g3Yvf4OUSNHXXH7V/rsK2hqRomY8lYN7AZ8gXKtFs233zFowd3Xcnk3nRsx\nibsm496/f38SExOZPHkyqamphP4gbSYoKIjS0lJaW1uxtLTk1KlT/O53vwP4yXN6ubkc3p1MxVsD\ncNf54w4UWm2hfd6H+EY6Me9RsQyq0v0MnAcBAS0tlMj20cck/nDaLIsJj7Zj7KzB2AdtJ+/JehRm\nbwxoCGc2ybxHGDMoYA/eDEKOEhupKw7mQHS0kcMWnAhD432GR96YwNnEA1Qfs8DS0cSdfwrGwqK7\nQpJSqWTBsmgO/fEkVh0OeBFPA6LW9ik+IoZ7sdBaQwWkvpaEnd8hyj6IJEIr6l7rqtvQ6/dd1/06\nejaVN8w+dPQTI9zfra8g5tgJJg8dzP7jJ9CMXyyK0SSuA79wpBnHMEUP5TnvKD7dfoz3Bvoy6EI6\n2TcPL+S+devICByMXWMFj/taX1QU5f4wD3Lzk6kLGoBDWTq/i/QmJqpbW10ikaBSqejo6EBKz8Az\nyQXvQnhICOEhXDXPzRhL1Zr1ZMrscTVqeG5oxNU38gMEQeC1Te+iXBSK0saZt9d8zWN978LDzaPr\nmACjCzkNueRsPYG2WUPguBjsvMWVr72fK4ZV1T/V/C2Lm4c3Ux54Hp1Ox46Vz5P33RO0aGWoIu5g\n0JiZN3t4V01OegqVZwvIK7HifIOGYRd0CIoR97tdzGbmtDQD4F1awqq337gq4/5zCIJATU01ra1t\n+GraqQbuAVYiuuUVAHo9W//9Cu3TZ14kmvVb4ZqM+4QJEzh69CgLFoi5psuWLWP79u10dnYyf/58\n/va3v7FkyRIEQWDevHm4urpe8pxebg6CINDc3ISNjS1yuZyK3EZsdN1BZQGdd+ARv4G5D3RXDVv4\nRiTr/voFVZXV9DEvpNVUxRnrt/ELcyNsuoKxs8S0r1l3TePtkxuoXxuPqzGCdM9/4+fsSUb1f1FL\nHDgrLCcowhd3uyo6t9fjbo7BgSCKXb/lbztG4+HhSewQ0YD81Ox97IzB2NpnsO2zHAx7+tJgzsUS\nOyRIsaD7h6xqCSI3bT3W2u6HigU2aBp+LFh5dWSVVdDh0Z3mpkPK8oQUPsuvw+Z8LgwNB4VS3ONO\nT0JtZ09biLiPXtZnLB+d3tFl3H08PNjx0Bwy8/PwigvBw8Pzov5mDBtEmGcxxzMTiYsNpm/opa20\nSqXiLhstn9RVoHPyJCAjgQdHRV3XtTo6OLL2sbsxGAwoFIrragsgOzcL/RgXbGxFJ6rbgn7sWpXI\nkondaW73TLgL95MuHElPoiCvBPWCboEgpdoSS/Xtqyl/cMtnPBCt4XyjlNSiBgoSP0Zh7YLOaMbb\nywcf32srXvRrk3NsC0unhLEtuZzoAEf2u9uSXdxIKDAf2Nzcs8SiUtt5yXauFrPZzObfP4zrti3U\nGvQECgItgDei1+CH31CvhnpaWpp/s8ZdIgg3sETPL8St4or6tfkl3PJ1NQ289/ABzFnBmJ1rmfKC\nG7ZOVmxZDI6N8eIxHgncv8kdv8CeqYp5ufmsGiPD1RgDiKt4qyfWsuSf03ocJwgC+bkF6LV6IqMj\nurZfBEHoCnARBIFPXthG9SEVEpWOsX/yZNikfj3auZLr/3L5TnLWS8ipTaKPdgkg4IHYTo77x/xt\nzxg+WHga9yxxUlnvfpA7v7EjPPra67YXlpUx92AB58OHgyAg3/U5xqmizrekvgqf5M2URY2BmlLo\nMxTJqQMII2d1nT8pZzdf3zvjp5rv4lo//wMnkimprWPywDi83K9ede+XJCs3i43OaTiGi+5/QRCw\n+raCJZMudp+6uNhQUFjOimOf4764PxKJhJpN6TwcPBsv94uVCW8HEtasYIxjPseya5k1xI+UvFr2\npVZx53B/zjdpyTT3Zd7vREnkX3Nb7mrZ9OpsnKwEZgzyRSGXojeY+HTulzx+Ic51EzAI8ATKlUqS\nlj7L+CeXXnH7P3XtB7/6nElL/8Q+IAjYgZj6thQ4BjgDYYhu+s8GDWH65p0/u/Vxq3LT3PK93L6s\nfiUJl+PinjgtsPvV1bx0cBr1/07mzHfrMQkG5Oo6ti5rwiEsk0V/nthlnI1GEzJzd7qVBAmYLo6b\nkEgkhF7CB/zDyFWJRMLDL17ewF2O+56divCMwMmDbhx8QE1LRwM5bEUra+COvztRV9mE54QW6v0+\nwMnRmTvu9L0uww4Q5OvL29HNfJ6+C62mnWN+YXwfvy04e9DP1xNV/hFyxouFTYTGKtixEiRSVLa2\nzB/of30XfRnGDR74i7Z/PUSERmCxKZFONzss7FRUrzrLE/1/el/UztaeJwYsZtuqvSCFB8Km3baG\nHcDOJ5rNiQd5aIKoz7/v7HmenR+DVCoh2NMO07lz1NRU4+Z2a03Kfkxtu4CrWoz879QZ2b01C6sf\nJLDMAl5QqYm45z7UfWMYf+fCn2zraqhOS8UWqABMiIY8DXgWGGlpyWEbWxyDQ1CFhjHi6b/flob9\nRtFr3H9jmFuskP8gGt7cZI3JZGLMzEGMmQlvP7URi28fRoYlJRTyr8IP+esbD2BlZUV4RCiySWsx\n7PJFgRU1QZt5YPHNr6stkUgYPKY/ZUv3k7nGjBKIWmCPQrBi2yI5Ds2PILFNpTRmN1V/lrPZopRh\nj9sz8c5r17geGRvNyNho8d59tJGc798w6PBVK9B7+4ivVRaIKnJB0aC0xHD2AMHuA67/om9TJBIJ\nf5n1GHsT99Gua+buQffhYP/zQiOuzi78buLtGRj1Y+KGTSQzLYWa5jLcHaxQyKVIpd2/R3d7S5pb\nW2554z5o9lL2rPw/ogMcOfRWEvecq+ZF4GtEBXw3wHbaHYx7efkN6zPn+DHaNqxlE6JYjQswBjEF\nLgWo+eQLlkyYfNvpIPxSyF544YUXbvYgLkdHh/7yB/0PolZb3PBrP19XSvURa6RmCzJYi17WTPbp\nYnz729LS1MLuZVW4tMdRwiE6aUKVPZx9+/bRpijCQqVg2n1DqXLai3xgJnP+0QffwF+uKMPVXn/U\nwEDGLAlizJIgwuP8+PihE3hWi1sG53XncC2bimNTHNZ1UWQnlxExR4q1zfXtx0mlUkIsBApPHcWq\npoTx9ed4ecFMZJoWkiob0KYegZiRopHXd2CWW1B68hDzx42+bNu/xOd/KyCRSAj2CyYyMBwry0vv\nn+cV5ZNw9iD1dY1IgMTkQwgmMy5Ot5+UqMlk4vCe9eRnnEBl68ywsdPZcTSTuvMlZJc2YCGX4udq\njdFkZvmmAmxNNRSkJmBW2mFj73b5Dm4C7l6+xI1bwIef7uSxA8nIgFJEAZkg4AOVGo/hI3EICsba\n7upFjy713d/0yBLuKy3BHjgAPES3UIsXkBIaSvANUsC72ajVFpc/6DL0rtx/Y8x5eCxbFAfZ88k5\nIguWIm9Rwh5YUfFvHJvi6axSICDQQT2RzKWNapozLcl/ahzpqnJC/5TIwqcm3tRrOLg1mRNfNoJZ\nQsx8NVMWDe/xvtls5rWH19FZ3L0iNNCJmm7DoK6PpDg3A3eP618hDY/uw87onh6M2SOG4JJ2jmXH\nK0gpzoBhM8QgOyBr6zvX3ef/MsfSTrLfIhPnO4JJ2HkcaQn4zovlXPop+iQVMWv4tMu2cavQ2tLM\n1/95lKUTnbB2UrB5VzKtw36Po28MJ1JaeOVeJ3Iqmtl6soyC8y3cP9YPqVCFySxQmfAeyjtextnF\n9fId3QSsrKwYMn4W2lXrOYvoipcA3wIvdWiweO8t1u/dhfnb9Xhc0Km4Hv6fvfMMjKpM//Z1pmQm\nk94L6Z2QQu9FuiCCYEFQULCsbVe3ueru/l333bWsu7qWtYuCSFGa9N5CIEB67733TJLpM+f9cJDA\nohRhRTAXX0jmnPM8z5nJ3Oe5y+92aWni24DMXOAkUse5NsAG1GVlnZfX83On33/xM2TesluICIlG\nQV/JVW+ZI171U/BjCNl8CWdKqmpIJo57ccQbL90w8j7R0NNz/ZJ8yooqOPq8Cueku3FOvouMFweQ\nfjznvGNys/Kxbp+CA540nOlNZZF302bXp4rYE3aC2CH/23LM8YnxrHvpBex6Os8adgD8QrkB8liv\nGyltOXhOkPIijAYjoXOHIVfIcR8aTLq57DrP7vIxGAyse/NpFiQocbSX8rgnh8s5ufZFpgrb0bSf\nwmy1ERvkxtxRQQR5OpBV2oRaKcfVwY6amkoKclKv8youzujZc9hw21x0SP3bs4HpSO1dAe4qKSb3\ny5XXZCxV4hBOILV37QA+VCgwInWduwt4YN9ukteuviZj3Qz0G/efKZoQA1b6xFEUZ7JhPIkmgfuo\nUyWTzya01EqJc2eQGZwwGIw/+ny/JSO5CI+WcWd/du1KoPB03XnHyGUyRMFGCJOQoySPjThMyyX6\nzwXopmxEP2sdd78bgIvLlatmgfSlfSozk6rqqvN+b7PZMJnOdyU6OTkzP8AJrH2SqRFyc//u4jKR\nyf/rK0p+49y39FNHuX2gDbO170HuYFY9v54dRHF9F2OivVl9qAytzkRnj5G9RUYSQtzIr+kko6yN\nOSOCaKmvuI4ruDQNFeU4xw4i7Z7FfDp0OFpAd87rNkBUXBsHsduMmcjlciYCXYC/xcKIc173sVox\nVJRfk7FuBvrd8j9DRFHEN0bNeu/ncOtMQFQbCZxmpP3gKdzbRlKi2Eai7QG8GUw96VTI9xJqnYEJ\nHZrJeXh4xF+3uQ8aFkahaxrunVJSWrdDCUPivCnMKmXD/+VjanbAMbYd+RwT+q2eqHBBb1+Lz74l\n5GRWMf6Pcmbe+8MT6Vra2li6ZhdpYeNwyKvnl5o8fjN/Np/vO8R/SjrQK9VMooO3l997NlP3H0vu\nQlzzDQVo8Lbp+eusHz7+jYrNZmPzkW102noJdR6At4M7KpWayPALqyrGeCaw70gOHhMjEEwijSdK\n8B0TSU91GxHGGyfm7ujogpODPSkFDfi62uPtak9WdS/zxwrUtemYOyqIkdFeJOU1UlCnJ2b4DCqa\nkpg/NgRRFPniYCnKkGGXHug6UVVUQObCBTjV1xELpPr60fLx5ySt+JhlJ0/gZrOxbtgIbnnsyWsy\nnjk7m5FWK18B7kg93I8D3wZpspRKvEb9fBvF/Df9CXU/Yf5XCVX/eXYz6a87E957B0HWifgYh9Hd\nbGbcP7uwxmXSQxM+5ZK2uxN+NCkycX/gJL53VPPQi3N+tPKS71q/l68H3W4FlDdnovMuJPqRVmbe\nM47/LE/C8dRcGjrK0RbbY/EvxxSSS3nPCUZqn0MjeuHUG0luUTpTHgr/wTvnl7fsYXv8HeDgjNnD\nn9yqKmY6WniyoIfGxGn0+oaR7xqCY/ZhhoSH8PsvNvBGVjVW0cYrExNwllnZWFTLydw8RoUHX1QY\n5mZKqHtv+6e03eaKNdGFXXt20jJMQTbV5Bw6xcgoqYbdarWi1XYRERROgM4Fa1odo5zjiBeC0CZX\nEdPlyR0T51zvpVw23r4D2HM8l+G+RnKr2vk8pZeoCYtJPnaUpvYexsR4I5fLCPV1oqlXjsXen/vO\nSD0IgkB0gAu5+mCiYgdf34V8D6nv/hvlwf0sAmKA8T09bBdFghMHc9rbm5L7ljL95ddxdLrymu3v\n+uyX5+ZgPnqYAqABycAHAceAAiBl9hxm//rZq13WT4L+hLp+rhidTkfjDl9ETLjQl+nu0joU0ZbE\n4qdv5ZP27RiOWLFgpJRdGBQNhCQEM+e+W67fxM/htvsncNs5kts2mw19nYpaNpHIUkz0kHe4mWHi\nL+hm6/kn9zhgNpsvkHi9XIwyhaSz3lwL+Sl02qlZ9vlm2scu7DtI7UCT0cqrm3awOmIW2EkdwZau\n+BdNY+/EGh4CVgvlKzfyxeNLftA8bjQaXfX4uTlStO0kwx67FYVKeqjpcmrmdHYqVpnIjqZkZP4O\nyI728NjoxTw4d9FZIZPBAxOv5/R/EIIgcPuDz1NUkIvZtZ7R8aDP+5onZ4XT1KFn1cFSpiX6U9Vh\nw+g3GUdBjsFUgtpOgc5gIaWoFYcQz0sPdJ0wy+S4nfNzF2DbtZ37tluRAWtOnaTr1jlorkEyHcAt\nT/ySDe+8SXdXJ1FIjWLyATWQ5ubOYx9fm9j+zUJ/zP1nhlwuR1SYccSXVorP/r5rwHHiR0htURc9\nO5n2W1aQLn+faOYxVP8UOX+MYvf65Os17Ysik8nQ+RQSznRkyKjjJAniUgDUuNBKETrayGMDrR4n\nLmi9eiXMiwnGq+QkFJ6GW+6CsXMoufVxHFJ3QWMlmI04ZB6muKGJLeVNkmGvzIeUndSbBay+IdKF\n5ApScMVovH75Cz8mMrMUdxZFzhp2ALWnI9oeLXsaU1CN8KOjvZ1WNyNvbfvoek31miIIAqbeDhyq\nNhBQu5J4b2k36uNmzz3jQ/kyzwHNpBeZdPtSJs68h88yNRzMbmDTiSpCfDQ4Vm8iPXnPdV7FdzPm\nqWcoPkfa9QPgV1brWaOyuKGe3A3rrtl4XV2dhNrZ4YxUdjcSSep2DKC5ZfLPWrDmu+g37j8Dtn1+\nlD+P28kj8R/w0qPvE7moF43aiXbKyVZ/StPwz5n9hiOiFV5bspl/zUlBUFkIdEhEfsa546KLpuyw\n7hIjXT/uf2k8vTKpoYgGT7TUAhDCJJopJF/zBbHcSXzBS7y+cPd5HQqvhAmDE1iR6IE3JslSpe6H\ng+vQy1QIum4c961E3VbDQTyp6dBCdhJYzBAzAjRO0jlnELtaf7AH4UbCZrOhrDFSvj0DNWW7AAAg\nAElEQVQNR19X8tcdA6Tcj/av8hgzeDRtPZ3UnSrGNdQXm8VG70A1T3z4Zzo626/z7K+eiuQv8FDq\nqG7pJau8bz16s42QgcMYEBAEgFKpZMETr1Kk8+X+yeGE+TozO96JjpxvrtfUL4qruzvjtu/jzcFD\nWRUdQ5e3N+e+WybArLj6fgQmk4lNy+6nduwwiro6cQWeAj5HahTzLJCw6OfhAbsS+t3yNzkVpZWk\n/c2VBm0TkSxBvktJeuXHLFnrR0OVjjHTpuLlLbXcfG3JZpz2LMUJsOaZKdKsOVtXKiIic/rhO97/\nNSPHDCf78e3UrLCiMrhSNmAVxs5pyC0aOn33MabyHYQz/7zSlrBv0zbuWPLD6vVHxcUyIzWH1ce3\nQdw4MOiwjZ8LokhPxlF63INg4AjwD5OM/51Pgb4HXL3g4FfgFwIdzQyT9f4ssuY/3LkSzcNx2JnM\nNGdUEFLvgHptLYIVfjtpOQ4ODtSlFzPlvYfJ//oYcQulRjGiKPLlqs08Nfuh67yCK6OluZHTOz7A\nQdDRYnGjpbqUCUEh3BUdyvGCJl7bmEtE1CB67aO4ddH5LV1lMhmero5A38OnnczCT5Xw2EGE7z2M\nxWKhZvwI1jc3449UCnfMzo4/PPHLq7q+KIqsXHYfv923BxWQAHwC1AE9wONAGJC8+C5OvvMBo+68\n5+oWdBPRb9xvcsrya+jQ2vAgmjpOYcNCcMFDVBUc4a6HZ553rLHGmW9TX+Qo6XIoIkN8F6tJwHOI\nieefu/PHX8AZqspr2fRqBtYuFQHjBRY+Ne0Cw/jwi3OoWV5Dt7aLqOjf0NHRwa4vjlPxrxCy+RIV\nzpjoJpBxqDVXt2N+dfF8Dr35BXUuHvBtUlzKLogaAs010NsFHc2SIQewd5R27i4eoLJniEzH28vv\nvao53Cg0O+vxcpV6EjjOHIK2u5Dlkxafd8yA8GA6K5qwc+xTrBMEAbPmxnv4ObHhdZYPsyAIAsn5\nWWQ5KokOkFTaxg70obhBx8SH3vre8+XeCZQ1HSLcR02XzkynXeiPNfUfzMlDBwgvL8MR+DblcazZ\nTNrmDYxZuPhip16UPa/8P0LPGHYjkmEPAj4DhiEZdoBxFguffvwB9Bv3s/Qb95ucwWNi+dTpfYK7\n5xHDXGzYyGIV/roLjZs6pAsxX8RED6l8hGvLUOQosWHF1FuFxkFzHVYgyXd++sQpfNMfAKAmqYFt\njkeZu0xq5Wqz2di/9RhGvYmp88YSGCh1s/Py8qJypz0asz8JSF8wIiIZ/n/nuTueuqo52dnZMcTL\nmToAowF6tSATIH4sfP0WlOfC+LlQUwKn90L8ODTOrjxJNVPjvIiNvBu1Wn1Vc7hRkBlsF/0ZIMQr\nkPKMMroqm4mYNUzKo2jqItD2w7QIrheiKOIh70AQnAEYGOhGSlEr646Uo1EryK5ox9/DkUMfPoEq\nbApjp991wTUm3LqQ1CRn8hrL0OPMbUvvv+CYnxLlWRnk/OaXOAHnmvEAUSS5qOCqrq05epggJHGc\nPUiG3QmpvasFKWveDSmpTvgZhLiuhH7jfpNjp1LiGqAiqGAsADJkBDGWiOHVAJjNZioqykn6ugC1\nh436se/QkK9H1emBPW6EIfV5zy1YT/rpDMaM//HrSFtbWxAKBtJNI7WkgAXE5DbmLpMM+2sPr8Nu\n+90o0HBq9Zf8Yf3s83o4253T411AICIo9pok37wwbRQN2zZS5DUA+70rEJUqWgEWPAXr/gmjZkJg\nJDi7ozy2hXdHBDJn+sJLXfamY7rfSHZsOokyxh1rbhsLI6ZfcMyDY+/m0+R1tLrKyHhpC1ER0cR7\nhjBr8o1T+gaSt6HL2tc50UWjpFYr55dzgjlV3MIDUyMI9JI+j6fK91BeFkdYeMwF1xk+YdZPuuUr\nQH5yEnXvvUNzRhoTW1sIANKBW8+8XiKX45RwdWV8RgcHRgDvAM1IojgDgRFIMrcDkGRoGxVKwp96\n+qrGutnoN+43Me1t7bw4YyvymkSsWM4mx5kcGwkOC6K2sp4PH0mhKquD4TyBAjvUbifxtC+nprOK\nMB4GpN2uEwPY/sUeBiXE4uzs/KOuw9XVjV6vY7RUVxLHPYiIFBz/iLbWdgqySpBtn4s9ko68z+kH\n+eaTjdz3zCwA4u+1Y1teDTarDRkyrJhxjPphyXT/TURQEDseD+DgqVT+pQuhplOL496VGKKGERQV\ng9uRz8mKvgWVXsuycDfmTJ92Tca90RgdP5K4nliamhvxHz0Ae/sLm8V4uHnw7JzzxU5+6sbt+4iZ\n9hhfHPoUR7medtGT0cMSsVOaaO8xMX5Q34NmYqCab0ryv9O4/1QRRZEjH79P5+lTuB4+wOKuLrYC\nUUhlab7ARsAiCNTccScP3HF1obyAXz/LxvpaesvKcAaWILnkO4A/wVntzFUDBpA4/dbvu8zPkn7j\nfpMiiiJ/vPsT4mr+ihm95IpnOCaHRhKe6sbXdwhvP7ENp6xbGUDFWZ15j45R1PucxIcEdLShwYMc\n1hLMRBw2/4l/lH7Fr9aMx9vnx6u/ValUeE3qQv/FKHL5CgE5ra3NvLJgO6qwDtwYevZYARmite/c\n+Q/fgkdwCpv+8Q+cCcBnsMhD/+/afQkYjUae2Z9G84wzSV9GPXNT1/DOYw+iUqmoqCzHQeOJr++4\ni1/oJsfR0ZHS2l7eSl6JqBIYoHdmyfR7EQSBxuZG1qR+Q0VjFXKbjJmxE5k19vo2J/qhpCXvRVuW\nhFLtiFviPYwfPJodK1/Gam0k2MuB7Ip2EkKlB9EjJSYGzhh5nWd8Zex69f8x+603yLPZOKO3gxvQ\nCjgCRUCPpyeaRx7ngV///qrHGzh+AoH7kqgbFoe1ox1/4LdIZXfnZmN46PVXpV9xM9Jv3G9Smpub\nMBR6IyJih4bBPEA9adhGnOTYageS3l+NFTMDccBA59nzREQCxsiwNLSSmf0fFHp3BjD6rOCNX879\nbPtgPQ+9+ON25gqIdCWJDBK4jxzWMo5nURSqMBR2k+//Fgn1zyJHSUPclyxcen6XuInTRzNx+v8m\nnPDEyo00O/v3/UJlj94j4OzuNDws4n8y7o1CS1sr21P3YjAaqHHsImCRJKfa3NjJtmO7mDthNh+f\nXE+zm56AGSNx8nMns6qFjv1f8/Si5dd59ldGUV46brUbuTVGeu/3ZKyg0TuASXf+ipXrX8cVGwXV\nHWR1gsJOg0/iPfj6/+9aJl9rKsoKMW1ehZfNRhiQC4wDJgD7ZDKypk4ndMYsbl+67JpWgTg6OuIU\nn4jp6CE2IcnOBgMlQCRgBuqHj2Rkv2E/j37jfpMik8nxVkWRbVlNPIuxYqTEZS3hh+/GEwd8SaSc\nA/TSgIBAFUk44Y9p+FF+9fc59HTpWPGUSGe+EqWhTwpRQADrjyuP8OGfvqF0tSeOSAloShxQnOk7\npcaJQO9I/J/Yitlo495FE/DwdL/Y5a4ZoiiSJbiArkuqXxcE6NUSb39+x7faxka2p6Ti6+LEvFsm\n/izK3wC03V28lbIKvweG0VJQg1rR115X4+tKk7kai8WCwUuGIMpx8pPeN5dgL6pS86/XtH8wBWmH\neSSmL+QwNUbNhszjTJ19D7c/9BIgGcIbleKkNbj6qimolAxrOZBnb49HQCC6WXN44E9/+Z+N7fng\nckpPnqDZaOA3gDEhEdmyR0k/eRyTpxe3PvvC/2zsG5V+436T4uXlRciiZDSfDeW49Z9YNO3EJITT\nldRKEFJyXRhTqeAwdqMLCByiYfw8GQPj5qJUKvnk14fwTV+GNzayWIkr4dihoTFsCw/eF/ujreN0\ncgYdn48l2BRCLmsBsKA/7xh7byt3PTr7O883GAx89PxOekucsPPrZenfJuDl43HF88guKWVXdiEe\nKgXLZk1HLpcjCAIeNgO1I2fCsW9AqSKmuYBn/9qnb11YUcGD+3MpT5iBTNvOsc/X889lP48SuH+v\nfQ+fZ0chiiJN2ZUginhGScoJupYuIhSuKBQKlJ02bJrza7kF043XErehLItslYjRbMXLRU2L1kTA\nkBsnnn4pVIIZlzHBVJ+uYa5VxAz8OyaWof9+l9w/Pcee8SMwxsYx9c13cXBwuOT1LkX2gb00799H\nWVUldqUlPGs0UAgYAFllJV0lRUx/4x0U16jr3M1G/125ifnF3+by+4wPGZ32DEqdmsyst3HAjk6q\ncMKfbhroVBUhK7fQUNRExoEy4qJrKS4qwVI6ABcOY0RLAGPI8XyLab+IZO68eAJC/C89+DWiqbYd\ne9NIZMgIZCy5fIXFu4Zc2b9xtYWjDu1gwiIfXrl7G7oWUEc38bu3l6BSqbDZbPx66nvElryAC0pE\nRD7Vr+K51QuuaA6n8vJ5JK2RhpgZYOjl9Cdr+eDR+xAEgRdGR/F/xw/T7OFJTG89H/5qOTJZn2dj\nRUoO5YlSjN/m4sGmxgCebW7G29v7Wt6mnxybjmylNQrsu3qpPJxD5OzhdFY0kvd1EjKthcHKcObd\neh8Ad0dO58OjayjclIL/6Eh6U+uY63fjdffSCDoOZHYQE+iKxWrjm9MNPHln1PWe1jVDdI+lefc+\n7rKKbAQ6ASEjjS0zpxBq0NMGdBQXsVWhZNF7VycfnLJ5M66/WE61VstjSN3f0oEjwDOAoO1C997b\nbBBFZr3096tc2c1Jv3G/iamtrcEpZwbKM+7sRO0vKU58hYzqndh3hONMADajDP/mmTSTS0zHfMqK\n9xLD7znOv4hgNo74UMkRvONg0dMzLzHitWfCrcNJHriW3gIPQMTJHx77ahahUUFYLBbkcjkv3roF\nXUYAVozY8qP41amPef3IUg58fRp5SRxyJJEZAQFt0ZXX6q/PKaMh5sza1Q7scQynubkZHx8fJg9J\n5H2NmqLKSqaPuR1nZ5eLX0y88XakP4RqWom+ayzZqw8hU8pRO2swanXIVXb0tnYw745ZZ8MTg8Jj\neTv8b7S3t1GaV0ZkzCTc3H6c0Mq1wmQyoRJ1DInyZPwgKfwwLMKTr7evZMZdv7jouaIocvzAFoyd\ndTj7RjJ8/I//d3Y5DBo5jU+e/C1WYAGwG1gOvGHQ43zm/0bgr9s2Y33n/R9cblqWkUbKn/+Mi1aL\nPeCJtFsvA8LpS6TTAPYFN1745sei37jfxKjV9lhVtWCCfDbRSxNiqw4nSyDDeZoSdgECPsTTQcXZ\nhwAQ8CURR3wASZ/d5NJ4XdagUqkQVTqiWIqAjArNJ7h4SOVECoUCnU6HscoFCwaikFzzfnVDWff6\neuzsFFixYkMqgwMwuzRd8RwUNut5P6tMOlQqKXnnjc07edvsg85zKAO/3Mdnc0YRdkZEB+Ch0fEk\n7d9DWcIM5N1tLLDW4e19I0deLw+Z3oa+owevQUHUnSqh5kQBGk8XgifEId4u8u5nq/jTrF+e51J1\nd/dgpPuVh0x+ChgMejT2Kvw9+h4e7ZRy5Db9OccY2PXV+zRUlRCWMB4/vwG0VqRTXJTHI6OU+AWp\nKGvK5fCOFm657acnXPP1vNk8bzBwEEn+NQGwImnI34VkdNXAE0YjuSdPkDh2/Pdf7BxEUSRpxcdY\nsjKotYkMO3KQ9qZGHge2nzlmPpKW/LnNY0Wg1+fm9oBdDf2NY25ivL29CVveQIr8TbwYyAgeZ0Dd\nfIRuqVGjL4Mx0EUZ+9Eiidq4EEQjGYicryImV16fJLCDW4/jm7kMGXIEBDxLb+M/f/mStrY2ADQa\nDbbAShzwOnuODBnWbiUDx/nh4xhELusoZCupmjd56K0rd/c+NXkUsalbQNeNuraIBxx7cXV1Q6/X\ns6JFRBcSD44uFAyby7tH09Dr9SSnpVJWUU5USAiTjHUEffki046t4PUHfx4iNiH2ftQczEXj6Yxo\nslJ7MB+vgdJDjyAIMNSd+vq66zzLa4ezswt6TThHcxsRz3hnjhZ24hc9BpDElr5841e4thzm2Vtk\nqErX41L6KfeGVpHg0oqfm5QgGu6jRmjJvm7ruBhera14IxlxP+AgsAM493E5Bcl1XvL6qxSdSrms\n6x749z8Z96c/sHDdl4R8tQb/pkackPTpA4G9SEp0rS6uuNnZsR7YBLwWHsn4v75yrZZ309Fv3G9y\nlv3xNux9zHgxkAYysKCnh0a6acQJP3ppRoUzYUwnh7XoaKHeZweGiAxa5XnYsNIcsp0pj4Rfl/nb\n2SuxIjWsqeY4rRSiXv8ob83IJe1YHgCPvTOFevd92JB22B3OWcROc2fUlETGvd5NzG0qwubreH7H\nFGIGRV/xHAL9/Nh6/0w+EXPZEi3jhbvnAmA2mzDZnS/IotXpmPfpZuafbmXc2qMEPfp7VrgNofr+\nv7Inbh73vfb21dyO60J+eTkvb9jOW5u3X3aL2ixzBdF3jcEl0AvPQQEY27qxmvuS5iy13bi5uV3k\nCjcetz/8Eg2a4fx9Ryvvpciwxj1CbOIoAHZtWoWHpZp7JoRiNFtp7zYwIkIKPdj+K1JjFn+arUtb\nfHzpACKAL5CU4uYBvwNeATKQDMp9wOPJR+l48lFami7t8VMcP4avVfrbFYBCYBZwAqmt6zDgk9Aw\n7jiRjvzzL7H95llUH3/OQyfScLnBwjc/Jv1u+Z8BCkczFkx0UU0M83DCn0w+Ry4osQWU41vzaxTY\n4UEUJnoYvLSDJb+fRdrxLKpLNnPntMH4DfC5LnOfcts4Tt+2HtuO+WipIQ5p5yuvmcAXL72PzwpX\nwmNCeCd1GWtf/wpti5Humk6Ovx9E5t7NPPzKDKbfefWZu87OLsydPImiykpe/GobCmw8MW08080N\nfK3rBo0T3iWnMPX2kjlwFlQVYJu2GOPuVRB5Ru4jKJrjpenfeX1RFHl53WZ25pWiwcK7y+/Fyyvx\nqud9KdLz8qhrbmXyiKE4Ojpd8Hp2SSnLjpVTM2gGmE2kfLyW1Y8vuWQ81XbG05O7/ihh0wYTOC6W\n1Le34zcoFHm3jQn2g3By6lM6rKqupLunm5iogf+z7Ofi8hKySnMYGBJFXFScNE+bDUEQrro8saGu\nmrQtrzHCoZ3MFi2i6IlcIYVurFYrpvL9tHcbqW/TcSy/EVfHvvJSf3cN/9legLujHQarQK/HqMse\nt6uzndNHtiNT2DFhxp0olVfWYrW5qYHM5J3I7eyZOPPui55/99dbeGvBHAY0NVIJPHWmbXIA8BDw\nanwir+ZksQGwAwxVleQc3M+URRcPMejP+dyNBFbI5cy1WskAtgKVSiUzVnyJp6cnntNmwrSfZk7C\nTw35X/7yl79c70lcCp3OdL2ncF1wcFBdk7V7hqvYum0jRksvAYzCHjeCGY/Mr4XnN88meXcGjtpI\nKeHMNZfxTzvjF+iFyt6OiqJ6erp7CIkM/NHrsx0cVOj1ZsbNHYQ5/hRVp3tw606gjRLqOEVg0zxO\nflVNj0sJcSMiGTY5imMbCvE89AvU9bEIuXGkNW1h9KyB12Q+5TU13L8vl33RMzjlEkHy7m/46L65\n+JacJKGthN8mBlDWYyK7Qw8hsaCyh5JMiOzT11aVZ/P0hCEXXPutzdv5Z0Ez7dMfoClqNBvWrOKZ\nGWMwmS5ssnKt+M1HK3mh14ctDhEc2L+XaYEeOJ/R5O/q6iQlK5uv0vJIGiRJ+SKXU4mGObJ2vDwv\nrlBYWlBIp5cVfXsPfoPDkCvkBIyNQXGsjeenPEZUUJ+4z4rdqznsXkaJbxdrPvmMTH0Zx8pPY+7o\nJcAz8CKjQFlVORtObyetLBsfjQfOTt8tjXw47Sg7lVnYZg8gs60YbU49JwpPs7H+EIeqT1FfUkVC\n2KAruHvnk7z5be6M6iGjrIVHZ4QzMlCgPO8k3apgVBpHuvK2IVrN5FS2s3xGNB5OKradrMbeTk5+\ngwlPVwfuGhvIkDA3PIR28trtiYiOuejff2dHO8dWv8D90c1EKKtYt+MIUUOnnFetcTEa62vI3/pX\nFkW3nzk/iehh33++g5MzIx9+jJhnfofNyxuffbvRnAlBrFYqsevupsxkwgcpAU4OpNVWMeaB72/Z\na7PZUMcOYl96Ko0dHey31yAPDibdYmGm0Yjazo6Wh37B6IWLLmtNNwsODqpLH3QJ+o37T5irNe7Z\npwvZ8Opp6vN7uOVJf+S+bVTVl+LUE0mXfRFRv2hhwqyhuMb3UNp1HEtYIcN/aWXsjKHUVTfwn4Wn\nEdfdSfU2Z9IatjJq5o9X3w596xcEgdDIIOray+hK9aDOlkosC7DDAQdDMIUVGUxeHoEgCOz/dx0O\nLZIxF5DRoyljwuJrE1L4dP9RdkSdkUUVBBo1HvjmHOS+WTP5Jj2HFVVaGmqqMckVGLWd4BsCRalQ\nkiG1fy3LYokHTBt64Y78xfVbaZz2oPRAIFdgCo2nZdPHeDk54ePldcHxV8vGfQd4tdMRW/RwUKpo\n9ovGkn6YafExpBYUsnhXBh+poijJPI0lcpgk0AOom6t4LNwNF5eLd2sbEpFA69FSKnT1eCYEAaBr\n06I/Xc+k+HFnHxQrKss56l2Bz4hw2ssbcBsWhOekSJRRbuzasZP8siK6O7qIDLzwPaxrrOPTym1o\n7onGluDK4cMHSXCJRGPfl9Sm1+sRRZENJftwnRWFIAjY+7lwfOt+5LMC8RwbjlOsLy0OOuT5WgL9\nfphiXG32fnrbaxkT44NGpZAEjkoaqC48SWNpGhllnYS7i7R1GxkX64O9nZxgb0eS8tvIN0WyeIiA\nSil5Q9wdFZyuFYgfMQ6dzsTRXWupSNlAQWYyrv5RODhID2BJO1cTaisgo7ydknotHvIu6q3eBASF\nfe88bTYbxYX5aLVdFJ7ayb0xWgAUchleyi6KDQH4+F661LU+L4ftJUW0GIzscnFhusHAPIOBw0A0\nMBXJbR/Q0kJR4hD8ws9Xasw5doQt0yaR+veXyPnsE1S9PZQr5Dzf0834tjZ8jEbev2MBzn//B+OX\nLvsB78iNzbUw7v1u+ZuUytIaNj/WjleN1N84OWU7j26cjf1vVKQc3EtQhB9xQyRDNWRsLEPGnm+4\nd36Ujm+hJLbiYPOlaUM8Nb+uOdtO9Xqw7IXb2BWWRP2bzVDR93vR0Cc7qQ7ohnxoo4QWCtCoi4EL\nu5D9EJyUcjAZwE4NeSnQ3cmffaN4+8U3aZz7lPT7BAje/RHjHORk7ymmXaFEf+uDADiWpDFz4Pkx\n+qr6ev66dgv55ZUgnNkxGXohaQsfDZ/LZ/la7j/9Fa8tvbZ9qr/Mr0b0OMejIQiYBcm4vJ2ST3mi\n1I1NP/U+7Ld9gH7S3dhp21hGPUFBl3YbC4LAHZNvx3DQRGl2Dc0VdShVdjhN8uZvm//N72c/jr29\nPV3dWuzCJWPVXd9OzFzp2nnrk0h4dAYKlZLsyhaMSTuYO24WjY0NODk54eTkTFLOCXwXJ5wd0+eu\nBI58lcSd0+5AFEXe3fYJTf4mRJMVXZcWJ/rWazQZcQrpe2hyCvWiOqWOMT/wfsq8YjHWlVHb2ou7\nk4oDWfWMifHCx00DWGmoseLlqmFwmAdvf5NHsI8jKqWcnCYbvp5NZJT1Minej1NFLRTUdtLu6oEo\niiTv38gI8QhB0WpEUWTFVy9z+5NvSZr8jQ1Eucq5Iy4YgHVHy2g+8hnGom3oHCKZec8T53nbzGYz\n33z4R6YOaMNgESnMNCIGeZ49xmgRUSgvLeGavPJTIv/4B4JMJpyAU2HhKFtb2YyURX+uXypGFEnP\nzYEZff0cent7OfrIMlw7O1ABDwC27m5OnnNeCBAjQsJlZtz3cyH9O/efMFezc9+z7gTC1rl0UkUV\nSRi6wBZQxYiJiUQOCsXb7+K7wfT9pdjS487+rJM1kbhMwNX1x+uv/V3rj4wLRubaQ9kxPfZGHwxC\nO67zcxg5Q/rijhjlwTcpHyG2uhImzkBsc6HNIYuBQ0Ouej6JYSHk7viKMtEeaopgwjxEV296tF0Q\n3Gc4RIuZjQsn49ndzI6QiaCRYoomD388K9IJd3fm8bU7+VdSBm8fSSXPdxDWyQth28cQPQxO74OJ\n86GnE1tJJpkGGanHjzE7ceA1a4yxsaCKyro68A8DhR2K1L38fXQE/l5erM0qodLrzE7LTkVQTwMf\nhAg8GuTAwslXVsYXFxqLmNVOlWMnoTMG01nbSrfKRG1GCcNjhuDh7sHhXXuxH+yLWW9E16JF4+WM\ntqYF71hpx69ydaAhtZQD2Umk+TWQVJWKtqIFe7maJn8TCrV0T/TNWsJbXAgNDGFH0m6ab3XEPSEQ\n5xhfKk7k0pheRld9Ox35dSTYgmjuacMhVCq9aztUwm0B43BxduXzPWvYVn+MpNKTCFozwb5Bl1xn\ncGQ8FZ0KjqUX0N2tpbhOy5REv7OvF1U1MX9MMHqTFZWdjJlDA4jwd2ZUmANVTd14OcpYc7iMQcFu\nTB8ygCB1O+9++hX62jRmJEhJY4IgoO/VIvqPw8HBgUPbV7NkjPT32K0zU9emY+lEP2K9wc1ay4bk\nCgJDo1GrpQfKIzvXsTCkAl83e3xcVeh7Oth4spGWjh6qm3vJNEQyYeY9lwy/pb38V7RlpUQiJdGV\nNzdhEgQWIdWeFyIl3QGsUqnocnSkXduFR3gk2x9cjO2lP1PV3kY04IrUm70bSAUGA+1I7V2LJ06i\nMvUkNR+9T96J4wwYO/5n0ximf+fez/fiGeBEjuwUvbZ2orkdC0ZObn6Tux+xXpa4xPRlcXx2aAs+\nZXdgoAvH29MIDr62u8cfyoy7x+LskUnBsa9obajE0zOE/IwSYodE4jvAG3/XcBytkwBw60kge00x\n8x+++nGVSiWrnlhKZk4O9zU50/btC3I5NFRAWQ4o7XBtKmLNMR1/PVECgzzB84yb06DDUynjD9uO\ncmDwfEjZCUF+Ukze3hHm/QLSDhBYlUHNuNuh8DRMklpmHrJaeXHTDv5x3wLa29txdXW9qsSze8O9\nybT3pTPnOHJtKw96CQyPlXbrs/0cOdVQhs4vHFlXGzNcBCaPvvwkr//Gx9sXR5WZj0oAACAASURB\nVK8GctYdIWB0DF4DA8lacYTW9jY83NyJdQjl5CsHcXd0wVPjRlXKSbqtneddo7Kigtg/zkJ25rOb\nujOH30UupnD9WpqG2SNaRLzzbdwy53YAqtpqsfeQKiPMeiNKV3sS7puMIAg0JRczwn0YVquV4+uy\nAIFZPkMICQxlx7HdtM1wxtNH8lDt25VNbFsMHh6Xrr8fPWUeo6fM49iBbzDVfUJjuw5fdw0msxWt\nVUNenR5dr44Ivz6hIwe1kvJ2uHOEO02degYFSxUEJwub+NXEAezNaMNssaFUSF6dum45oa6uZKUm\nM8a3i4JqE61aI9kV7cwcJoUU6lp7Sc5rZFqoiZIN2RA1nxET52A167FXSZ+ZNq0Bs8XK8wuke3S0\nqIu4YXMvK6+ms6qCR5Fq3I8Bs4GJZ2LvE4BTwH+CgukC7q+rJWjHVpp37+CD1Sv5Y2Y6cqAW8AEK\nkErddIA38AIwURBwlskoPbCP58rLcDoz1sr2Nm7/ZOUl59ePRP/O/SfM1ezcQ6MC2bp7PVHNUrxK\nhgKavfCZ04iX16Xbtbp6uBA9U0OT7wH8F9Sy9NnbLjtR51rxfes3mUxoXOxI3V+M8qvF2I4PJ3Vv\nGeqBTQwI8eHImiLsa+LPHq/zKuCWB69N3F0QBPx8fclIO02xZwTIFahEK04ntmKY+QAERKJXO5Fa\nUoZRFMCog4p8yE5C3lSN2Wqh2qqk0z8a6sogIAJqS8AnCBRK7OQyfhuspqyyki4U0usAMhn2pRl8\nkV7IqzVmNh1PJUJpJdi3r4rBYrFc9ns0MDiQ0YKWUFMHj8aH8ujcPm3+wRGhRHVUMKAmi3vsOnn6\njtlXlUzp4uzC1jUbkAc70VneSFdNK1a5jY7KJlKK0+i43Q3vmQPRy8wMU0XSbe5FiHOjPr0MU6+B\novXHifEJQzm0b636bh1x5gHMHDmVyB5PRqqimDpsEoIg0KXt5Ov83eisRlyCvGjKrsQrNpD2knp6\nW7qk+P7JCuaMnsnosKGMDhvCAG9/bDYbO9P2YT+pb6duc5DjWmphgO+AS67TarVyaNsqGk+v4+nZ\noWw/VcOutFqau4xg50ipJZTuXj35FQ0MC5d242mVOjxGLSe1VqChvpooHzvSS9vo7DExNMKTUB9H\n1h8tp7ihh6xmFR5D72VAcAQ5p4+wIKqblQdKGT/Il9Ex3hzKbSQ+2I296XUsnBiGl4uaSB8VaZk5\nhA6/HXtnD04cO8RAXyVJuY1MSfQ/+9AQ7KkmudxM2MBhl1xn45FDJJaV0oAkWuMKaJHave4HuoCG\nuESiNRrG1NYA4CCKZBv0jDZIZa35wG1A5pn/ewD1wEykB4RAUUTb0cG32SkyoMJsJvjhxy45v5uB\n/p17P9+LIAgkTAzBkCNKndwAq10P9vaX/6HxD/Rl0VOz/ldTvGwyjueTtFIS2XEdaKR4nRpbkwc6\noztRovQl6dk8nuQ1X7L3o3xqM6BXOE6AOBatQxGD7rnyj7nBYGDV3oNYbDbunzLhAlnZ95ffQ9Q3\nu2m2CgxSC/xl8OSzSWcm72BslYUQ4AtDp8ChDTDvMaztjSQXpaGqLoBhc2DoZDj4Nbj7wOGvcbeZ\neCrWl0fnz2VSZSVLV2+j4ttucyYDzZ2dlN8qfbkVAa+e2srEwfGczM7l4a1JaB29CDK0seaBuQT6\n+f33ki5gxKBYRgySci1sNikrXyaTUV5ZgbNSxrNzpp1tXXs1KJVKloy8g/cqNzH0YamMyWIyk/PK\nXlyGBDDAXYq5u48MIXN9MVa1jIBRkZKbvlWL3wB/Rrkmcii9DPehQVL5WmobfnP9EQSBgIDz80BS\nsk8T+tB42orrKNx6ku66Vkw9BuIXT8JqspCxYh9T7M9PajSbzfzh05eoo434Ug/cI6T7pz9dT/Sg\nyZe1zq2f/Y0BhixkDlJ5nUIh43fz45HJpM/FmtRapj35IfW1VXy2byXtbW0MiJvB1JGTYOQk9u9w\nZ2faGm4d4sfp0lZEUURtp2Dp1Eg+T7Ux+/G3zj5kRSeO4cDhJKICXAj0kko9BwW68t7eGgTO98xp\nFFasViv+ASFYZzzL2pM7qLM44d3YwJBg6d63ak2onS4vcdNu2kzSk44Qq9dzCkk9bgWSS/7bJr3t\nyUf58pwkunKgAoECuZxyq5Vy4E2ZjCabjUFICnfHkGLt32JCUqH79rGy16tfje5K6DfuNzELfjmO\nN058DumDaRIyUbuZOPGNJyHPhFzvqV02lSXVbH2yG6+6uwFI3raCRMv9WDBRxp7zji0rrCCm6Dk8\nUNBMPiedXmH5e0OZNHPKFY1pNBpZ/NE6jg2/G2RytqzayNdLbjsvQ9zOzo4/nBGz0el0vPvFAWrP\nvqgmsC6HimHS62gcwKiH3BMwaQHGhPEod31KuKc7g/3tmRfjhotTIMPi4/u+vENCSHr+IZ5cuZUm\nQU2swkhJYDDl58yzAztEUWTJxkN03ibplxcBj6xawe4/XP4O5/VN21nXYsXS0w1VBbQMn43FO5DB\nn27mi4XTr0m2fsKgROxb+94vhZ0S7yA/jMb/KvWziAwQXahp6MDBzw2NlwuqzmbG3TIGMiFnfSl2\nJoHfTH/4e8NL/h6+nK7OwX9oBP5DI8j7+hgeMQHUphQht1MgCOCpPj935KPNK5CN9WHS1BmU7Eql\n8kgOHkYNC+NmX7IyACRvkr9YhdkqIorQ0WNEpZSfNewAbnYmDAYD3dpOdHWZzE90o6ByA0d2mpk0\nexFiawH3TQrFbLHh4WjH37/OIWyAFxa1DwOnPnKe98TLZwAF7lOwNX+FVmdid1otajsFjb1K/AaO\np7A+lxh/e/RGC40EnA3hBAaHMyDwKZL2f8PO9MOUNHehVgq0qmOYs+SOS64zc89O3N58nQ69ng8d\nHGgZMhx9WyvHSov5k9l89jh3oN3Tk802G64V5fTIZPyzs4NXgYeR3OxzbTa+oC8BbwhSXftCJAW6\nTkHgnyoVIXYqDOERRPY3iLki+t3yP2GuthTO3t6eIbf5sX9LMrHa5Xj2DCfvWA1r131B8pYC3IOV\n+AdfH3Gay8HBQcXmzw4jbJXiqBbMdNsasGGhiqN0UI4CFSpcaYnchlekCnXJUOlcvFDK7JnxR+/z\nxFIuhy2HjvB+wGQp+10QaPQbiEv2IUYNjKa4soqXV61lX/IJxsQNRKVSoVQqcde1kZ+VjtDRxITq\nk6x/5mH2bdtIa+hgqCyQsuzD46TYup0amw3cWivxdXNlelwUCdHRF7i+fX3cmRodwaIh0UxLjKWm\nvIQTuCGqNWAyML09j3FhAfwrrwHxnIQ+c2U+T46/sJb+uzh4KpUX9P60+8fQU5ZHT2gCtvhxYO9I\no/9AjKcPMC3h6nUCZDIZqflpaIZIO2KL0Yx7vpVQOx8quuuRO6lo3VHA/JDJTBo6gebkUiylLdhn\n9PLwTEk0J8g3kOGhiQyNSEBlp8JgMLD1yA4KKooI9Q9BoVCQU5xLbk0h7RnVdHR30tvQQfOOfBxi\nvIm6bSSe0QG4BHlRuSeLScP6MrG/PvoNYcukVsgekf74DA5Fvr+Ru2feecm12Ww2dq35N4aWQtQK\ngTvHhXA0t5GCmk6CvR0RENh0vJKGLjO19Y3kH/6S39wWhJujiih/R1JSTuAbN4O8I+sYEWrPJ3uK\nWHxLOFMS/Aj0UFFkjWLkLXPR6XQc3PwJqUe20nBqLSOdysio7Ca3opUHp0ZiNFtRo2esbyc7sjrI\n7PSiQohl5qJnzoZrRFFk80f/xzyffKZFysisE7EETEVl7aY4Lx3/sPiLCtnk/uaX3FGYjxzoNZvR\n6nqpj0sgqriQXjhbj7AfKAgMIujXz1JhNTOluIhVSK74ZqQduhtS4t1eJMP+barcP8IiuLezg1tE\nkXEWC+1qe/zWbCB0UDw/F/rd8v18J4e2pVBb2EnMqAF4Bjrg0jASgCZyaBWLGVnzMkKNwLpFu1Bt\nKSR++E+357R/mDunFDtptBTgjD8dVGKih3gkUYtm8qga/Rp//mw5mUeLSTmUj6suFhERYWgmPj53\nXfGYCrkMrOc0ixFtKGQCWUUlzF+5jZ4Rs8Ddh63/WsXRJ+7B19ubuyeOZcE4K3q97qzS294/Pc09\nr71DuZ0r5lPb6Bw+C5u7L1Tmg0ygZOpySoCs3dvYeZ/7JR9CfrtgDo4795FZ0ENzZSkhsVH09OpQ\nt9Sgs5hBoQRDL97dzeedV1lby3+OpGKRyVkYF8bo+D6xltLGJgw+8ZB5BBLGg7a970RBwCz74V8R\np3PTyG8swVPtxuxxM3hw6ALWrdqB2UHARatg6Yyl2NnZMaSijMrDVQxNuA83VynMMn/iHLy8nGhp\n6f7OaxsMBl7e8S5eD0kPc6+seJexvoM57VuP++IQlOUCvge6aWlrxegk4jkwkPayBtpL6/FJDMM7\n8PywRYL/QBoaO3D0lRLausobGRc/8rLWeXj7FywIrCDDpCa/qoOCmk5mDgvA1c2V1QVudFWl87dF\ng5DJBEzmKt7I6WLzcSsymcCwCE+wmTjw2R+I9TCwK7UGR7UCtZ10310d7LDXV2OxWNj16Qs8PNzG\nltoq7hofSnmjlggvJb1GGTKZQEZ5Ow9OiwTg137OrMkWmX7nI+fNtaggl8k+DbhonCip66KtoZY4\ndRed3QbUcoHPXzvF439Z9b05FnYmI+1AGjAR0LW2Iu7cxkzgK+BrpGS5oYLAr1KOk5ORTmPMQI4D\ntwIdwGkkVbswwAHJwL8eEISPRoM4azbDBRl+//7n2TEHd3VyoCCP4IjIy3o/+pHoN+43Gav/uZu6\nt0biZAxmn3M2sX8uwxwEVMSTz2aG8+jZGHyYaRb7vvzoJ23cu5qMWEUrYUzFj8GY0JPNFxSwBYAQ\nbsHZOQF3D3emzB+N1XaC4oOFyB0NPPPczB/UdnLOxAnM+GA1e+NuB4WSMembefDhu/n9FxvoiRkN\nA6TkvM5Zj/D6to3866HFAMjl8vMkXD/afYAT4x8ARyle7/PNW8jMWjrqqjHMefTscUUR40jJyWP6\n2ItXWQuCwPLpt7DwgzUkT3+SY3IFW7d8w5Iwdz7Z8BZWJ3ecG0vZ8PJzffevq5Ol21IoPBMiOJiR\nzGp7FfERUjx06uB43jt8jHonV0CA6iIIHQR2ahxzk5g3NPiy71tWUTZbK49g1QhocxrxmBuD2+Qg\nWpu11Oz4nMfmLONZ/8cvOC88NJzw0CtLeNyZvAfP5UOQK6WvMM/lQ9n38XHC5k0EwDHEk6MNR3GM\n9mbUrDtJ+3AXwZPiCJ4UT+XBbLyM52e/3z93ES+veYPiQBvNhVV4Gh0ImneZFQK6Zlx9lUxO8GNg\noAvrjlSwtz2WEeNn4qbdSEy401n3vNFsQ0DkjjHBCILAa19noTfb+NNCNQq5ho93F+HicP7Oua3b\nQmlxIVMDu1HInbBTyimu66K6pYc7xgSz9kgZu9Nq0ajO/6yrZBb+G5vNhohIZnkb3TozAV4O1LV0\ns2SKJALlU9jMicO7GDt59gXnAphmzmZXdhZ3WSx8gxQX1yN1a1uIpAcvyuV0WK3UA5FGA1vycvgL\nUlLcX4DnkMJH38rUJg8fSVxkFCq9HseBg1C7e5Dz2cfEd3UBcDg4hOhRYy/vvejnLP3G/SajdKsc\nb6P0heyqTaB4ezFzXvZlzf+9gVhipJs6nJD6TVswYu9+fbq9XS4F2wzIrc54EAWABT02LMQwD4BM\nVjIipm8N0+8cw/RLe1IvikKh4PPH7uebw0cxGqwseHQharUa0WwE13MSzAQBm/z7/4SKdFY4p+xJ\nGz2a93x1pMkdefeMHj2AU3MlYWN8L2tu+0+eIjnuNmmXDhQOnk3dnhVY7/0dtDbQnXmEO1buYF6w\nO79bMIcDqekUDpp69vyGmHHsytp71riHBwXxTkInn2eXc2zPJjr9ohDX/YsgexnvLFnAmITLc4Xa\nbDa+rjqA7+IhWI1mWunBbYiUdW7v7Uy1QylNTY14eHheVgmfXq/nRGoeKrkTgQGXrjMXRRHRJtKc\nV0VzXjVGrY6g/9/encdFVe9/HH/NsMywgwjIKosirqyamhq55F6pUJimqWlZdss2895+Wfdexba7\nlNl2W9QslbRy19S0xA1JcAUVBVEREZB1YAbm/P4YHaVyG1Fz/DwfDx8P4cyZ+X4YmPc53/M932//\nDlQUlKJ1dcTF35OATqbbvloOiKPkmyzzvmdKivk09WtqAu0pycim45TBaJwdmLdmLYmGHrQJu/Rl\nCUVRyD99llMeOpp5ONDMw5FmQWF0G/0ctra2FG9+j+raC71Am/efYvIDbVCpVGTln6V/XCAFJdXU\n6OtxdlDT1FVDqwA3Fmw6QjMPB7JOlBPYdyourm6cqaona/sxth0oZF9eKVMfMg0KbBPozq85xbg5\n2XP6rA5vdwdOlerQOf9+0OD+jV+x9/RxvN21JHYL5T/f76WFnwvLduSjAvrHBbDw6H7gj8O99+SX\nWGQwcPjfb3Okvp7JmKaZ/QbTgLpjzXwprazAr7KS8/0eiXV1FANewN2YDggiz/2rBHYcPcLJnTvw\nBbK+X0zo2//G9v33SZk3nzpbOwImTqKptwymu1YS7lbmdOURis+d1TriiaeNkc69Ikn79hRBhx5j\nB7OopRwNrpwKWs7sKZNucYuvwK4eX2I4ygZaMYhjbKYjE829Dx0Yiaf/d436kkajkU9XrSO32kCM\ntztarWmd+5ce6Mfq/86j0jcEtI44b1vOmH4Xrm0fO3mSeanp2KDwRJ/uhGnVUF0BObuh6AS60kLG\neA5F4+BGm3WfUhQchcZQw/ggJ8JCrm4pWkd7e1RVOhTOHTT88j0VbbqBosCeX1B6J3EQeLesGMdl\na7grNADtwRPUBJgOjqiuwMuh4UQg3aM6kFN4htWDJ6J4mrqrfdJSuKvd1c+1XlZ2lqLqEkqXbEHj\n6sjZ3AsLgRYfPsnpM4XMrliBcXc5Cc17Exl+6YOGU6dPMXvn1zj2DkaXV0qbjU14OH5Ig8cMuLsv\nyZ/PwnOs6edf/Pku7g+L56sta4gefx8VBSWUHi2krkZPTVkV9k7ahi9ic+GAcO7Wb3Ed0wFVQQmB\nQQ5onE0HcF59W/PLgp2XDPeamho+/ftonujmwrasYqr19ZTihb1fLD+vmEtQq1iq6my5u7U332zK\nwUlrx4Z9ZUSHedHMw4ZTpTpiWnjSJsiduesPM6hTIG2DPFi2q4jHewZypFCHJrQjnTqbeiM+yXNk\nQEg9Xu4OGOqNKIqCSqUiMtST/fllDOnSnPWZJ9HV1rOvxJGxr5l6SfR6PT8u/C/5WTt4qb8P9rat\neWfJHip1BrzdHfB0daBLhDe62jrmrD+ENuLy00w/9PJfWVtfh3H2+2jOrRL4KKYR8Sfd3Yk4VcDF\n/SK9gTfCI3gw9whNjUbe8fLi+YICbIG/t2uP+949vAwYMZ3dv/bG/zGyooKiflce4CcuTcLdiuzP\nPEjT0i40424Ajqu20byn6ayhqqYcV5rQlRc5ygZOu//Cp1sm/elnfLr3yUCWZe/AJd+PX+0/xOCd\nR+HxYIrYjx2OVHOGAPvfr2ZmKYPBwAP/eJed8ePAx42vz5ygeNkanhrcl5DAQLZNeZy//Oc/nKmt\nZ0T3TnQIN10HPFlYyCNLt3IwZjAoCj/N+5aU0YM5OnchKZpg6rWOMORpUKup9fInT6nnp25+BAYG\nXdOlg3s6deTBT77ie9W9KBoHPMsKKC5yAS9/aHphXnSjvoYvf81mX6WB+GOH2Zi+jjp7R+62rWL0\n1Gd/97w7i6swtLhwHXq/WwhnzpzB+yrPmKp11aid7GmbYBqk5tzMg33zNhF2fxx563bTdlQ8hupa\nnDq3YPn8TZcN9+92rcZnVCwqlQpX/6bsXpbJ4OpqHB0vzBmv1WqZOnASK5eYRuBPGDgJW1tbVtbs\nBMDFtwnHUvfj0zaII+szKdx+GL/oFrgGNeVs5nFiHELMz6V3Brt6I4dW7sQj5Dc9KHWXXrjnp+8+\nIqaZHv+mzvg3Nd1SNn3pccZ22IOvh4Zte3ZyqMyRsl9zcNZCxkkY+39fsGz1HLpWHsXNWcOCLQVM\n6N2c0b1a8OmGfGzCBjHk5UQ2pm+heZcgBjW/ELQtQoJIPbCeAXEBNHXV8tVPOQzqFEj+GR2HqpqS\nd0ZP7yh/th7RER2XSNbedDZ+/xk1ZQVMGxLImrP1aOxsOHSyHH2dkfWZJ6msMdAlwvQeO2hs8fdy\nJeieK3d93Tf1NXbd1ZWNT44j/mwpCvBdu/Z0yzlMDPAJ8CSms/qfmvnS59/vY2jqhaIoTAxqzvoV\nSznwyYfcvX8fecB8TPfLVwDOuporvr64Mgl3K7JnSw7Nqi7MIhegdIa6Y7z/4mJOrfUgnxRa8QA+\nDmF0mlCDvb09axZt4cCqSlRaPYOfa09oq6u/xnozxPVoR9DKIvakZVNW4sXxbU3Y+cMaOhteAsCI\nkZO7vjYtIn2d6urq6PS3tzjh39Z8nbzW0ZUVO48x7r5aNBoNmbn57Gvbh8IWHTlYlE/R4uW8PGwQ\nKVvSOBhtmuENlYpdMfezcus2HozrwIKKQNNscxdNMFPj5E6twXDFYC8rK2P2ynXU2tozqF1L4lpH\n8NGEkSRs3UZVbS3p7UL5uM4Tln5qCvj2XWHXRijMI2/ARPIMemyy8qm/fxyoVGQd2s7OA9l0atvw\nbNTHph4Mejg3t7hPxSnc3eOu+mf35foFeMZfmOhFbaPGLruK1mtV7C+u5djm/WjdHDm0Ig0/h8vf\nWldj1KO5aECXjZuW2tqaBuEOpoDv3K4j32eu5fMtC2lqcKbk+EmClBhUKhVhPaMwfJbFAy3i6PLk\n02zLTKNgcwEdfVvRsfOFyVpcKmzZt2QLdk4aTuzIxjXAE7cgL4q/28cTkYmXbKfWWIbxosXYFUUh\n2N2Ir4dppHOwh0JUcQH3dzSNJ8gqqCH/xFEeGPsqhw9mYagzENfFmfmbF2GjUgjvn0T7GNO15a7x\n/RoMKFQUhT27M/CxhRa+rtjYqBnWNZj0w2dYluPCS9M/5detP7H1ZC6B7aI4nXOAwwunMzWhLWt3\nqdHY2dAlwotvU3M5VVpNYrcQWgW4sTg1t0FNtSonXF2v7u6S6J692fvJFyxc+h0FFRU4Hz3CIb2e\nuzDNF78YOBoVQ/cZb9EyrhNGo5FDB/aRn5dL/elCJqdtxwl4DfjLRc870/bPuZ797UbC3Yo0b+NN\nliYTr1rTtbYyl/1oDWXo5g8gvN6fKoo4yDIiHi9gxIuj2bw6nfRXAnCvNHW/fnngG15Z6fW7D9Fb\nzdvHC1eXU2x93gvXsx1wZZl5mxo1tYWN0963vpzHiS5DTDPKAWTthIpS0oLvYsBnP/DRwLv46sBx\nCiNME/vUeAXy7a49vKQoONnZgKHWdPscoK4sw93JkdjWrQhb+DM5fmGwe7NpRHqdge7H0wgbPJJ/\nLVnOtgoFZ10Z3bydiQxvQWw705z+r372DTN/3En98JfA1o7F6Vv5tK6ezu3bmgff9dXp2PaPd8kc\nOMa08tzKLwAF/MJg60o4nU995/7mCXYK3fyYsmQ5d+05xJjOHWgVHAzAyw/2J2/Ot6QrLrgrel6J\nDbmmXh2Nvxt5e/PwjQ5j/+JUvCICCJjcjR+/3IZn5xCa9zIt8OIb04JD09eYlg27iF6vZ876BWQX\nHaVUW4P3FoXArq0x6Gpx3KfDPcLjd69ZXV3NR7sW0uzRWKpLKti4ehetJsSzL2Uzxmo9Lco9eWnU\nhdvAenf+4/kOHu8zknEfPEfk8wNw8nKjYFcO6R+vZnL7EZedma7e0R8nu91szz5N2yAPVv5aAHZO\n5u378s7Ss92FA5kIXy27jh1A1ekeWra6cHAV2PwVrmTn1o082cOFZdtKWbj5KI/cE4aj1hZdvS1D\nRz/HxhXzUc7mUlZrS9n6z8jevZPBnYPQ2tuiq62jvt6Il5sD3dv68MIXmYzt48jpszpCfZz5dHUW\n/WIDyC0xYvDviUZz+duwKisr2DD5Gdyzs6hq1oz2099C/+KzPLo7g+2YRs0rWgfK7uvLiA8+RaPR\nmBatGTuSnj+u4ZBazTZPL86fhkQA32PqkncHQgOvPMZCXJmEu5XQ6/Ws/u8hymqDKCIHg7aYHs+4\n4OTZhKp6U7ebE160YRgebikAZG8uwr0y3vwc2v1dOXjgMFGxHf7oJW6pPetP4HC2N3v4+tygunpy\n+JFaKgj1Pn3lJ7gKZYZ60NeCdwCkrYXKMrjXdOa2JyiCt39aBqqGZxXqc3Nqj+7bm+9nvscOGy/Q\nOuBfdIge//ccjo6OzO4Rwfvb91JUXorTz9nEBfvz9OMP89maDbztEk29M7BpMcvLbVAd2UfkohX8\nc1g/Zvy4E6X7EPPgucLwLvywf02DW9kcHBwY2imKTLem4O4FJYWmue4BugyA08fhzAnT9LalpyFn\nN/v6TmQfsGnNKhY/6ICfjw8ajYb/TRhhvo57rbzrXdB1ac3Oj1fh0yEY73bBADj3DkbjcmEQoq3G\njsiIC79few7tZfnRnzmYe4joFwehW3ac2IR7KdyTS9bS7dTtKuK9CTP+sE27s/fg1Me0vGnOj7to\n9WAn7B21tHvItLiNy9cFVzUdr0ajwcupCU5ept4a3+gw3EN8KN1Udtn9ej4wlrUpteQe383X247S\nrk0EhaVVZByroq2fA8cr7VEdraJnG9MkOMfO1OLiZVlw1dbqcHWwY+LA1mTln2Vx6lHyazzo0Pcp\ncvdv516nNHxaalj48xESu4Xw2mE7avSmS3KDOgXyzc9HMKDBxiOU3gkTmb9pEbYq8G3iQO9oP77Z\nXkxE70nc06PXFVoCm16dypgflqAGyNrPnCnP41J4CoC7gHDgy7ZteWj2/8wHiD9/8Slj1qwiC3A3\nGplQWMAmTJPd7MY0il4L5AA/uTTeZbY7mUWThSuKwrRp00hKSmLUqFHkvHZOoQAAFktJREFU5+c3\n2L5hwwYSEhJISkoiJSXF/P2hQ4cyatQoRo0axV//+tfra7loYNOqbbhufpiW9KUNQ+lQ8ziKoiZ+\nYCdOt7nwHhSG/kCPB0xn9i6+amqpNG+raXoY/6ArT1t6K2g94TBriWI0bUjkZ/5JIF1oRyKVG1qy\nZ2f2db/GQ/Hd0e7eBO7eENIe6hveSlSttmdM+2B8s7eAouBQkEOSrxaVSoWdnR2GJn7QOwm6PUD+\n/c/xjyWrAIiOCOfz0UNZ9vw4Frw4kRcT7sfBwYHMslrq3bxgTyp4+ED8MJQeQ8iIH8vb875BadMZ\nqi4KGKMRjVLPbw3sFEPInnWmL4JaocrZDVGmhXPwDoDyErSZG1FvXQmdL4yCzonqx7KtaQ2ey9I5\n5Ef2eojgNBX+tW5oXS+cvTaNCOT40kyUcwdBxduOEuVnuo6s1+tJObYetxHtcGnvh63Gznxrm0/7\nYCLuv4vQNuGXHF3v27QZ1UdNy/dUFZ6l5NBJ87bqM+W4a64+JBI6DqQw/cL8f2fWH2xwEPJH1Go1\n/R5+GrfASKYNC2Z8RyMv3WvPisMOLCnrQeyIt6gJTWRhppFvM+vYXBPNXff88Sj0K+nYtRff7Lal\nrt5IqwA3ajW+9H38TdrHdMWmPAcfN9PZtoPGFrVahdbeBpUKthwopLRST1VNHUH9/kb/CW9jfyad\nCX3DcXOyp39cICE+rrw8qDkVRzZdVVucTh5vEBwu+fmUtmlLDfA1kArcl76T9YPu41ReHmBa0lUL\n5AHdMJ2tl537+m5MwQ4QBrTyvro7R8TlWXTmvm7dOvR6PQsWLCAzM5Pk5GRmz54NmK5bzpw5kyVL\nlqDRaBg+fDi9evXC2dk04GTu3LmN13phZq+xpZ4awHS9zEg9Nrbg7uHOU191YsXHC1DqVYwa3Qb/\n5qYAH/ZkL97LTuHkz57gUMvdk1zwaoSpRm+ExKd68cvc/6HKV6FgJIz70J4bMe594j5Sv0mhfVyr\n63qN2Nat+HJIFR+sW0FdrQ4XFxfW1lSB1gnN6TzivRyIj4kipUkum3avo1WALz1iTV30dXV1nLS7\nKExsbDmhXL5b29em3tSVX1MFwReNUHZ2Q9XUD7VahbGyFA7+Cu7eNNmyhMlTfr+8XZCfH5/fq+PL\nHatRAXW+9swvzEPxMY2f0Ia05r+aPIpcPXitvASjm2kss7qiFG9X5+v6mZ2nVqt5tM/DGI1Gkr97\nj7qWprAu+ekQo1oPJvOrgyj2Knp5RhAXaZp45vTpQtThprPaOp1p1LVBV4vubCUO7s5UHC4kwubS\nB5vNA5vTblMTMpZmonFwoOJkCWdzT6O2s6Ek8xjPPPrWVbf/7piuVGyrZl/OQVR1CsOb98KzyZVX\nggPQVufSxNn0XmvsbGjtWUv3/qbJk3x8/KDHwKtux6VoNBr6jktm4ZoFqJR6ohLup6mXaXZJXb09\nUAVAM3cHdhwspm2gOwUl1bT0cyXzSDHHDd4MPfdzd1DXAo7mRWPOs1dfevDgxapbhKPfuAF7THO/\n53h7o9HV8K6dHSEGA+dGntA241e+emcGzd7/mDZDEvhh0TcYjx5p8FwDMV2bv1jdb9ZxEJaxKNzT\n09Pp3t3U9RUZGcnevXvN23JycmjevLk5zGNjY0lLS8PX15fq6mrGjRtHfX09kydPJjIy8g+fX1y7\n7vd1ZuughVQvH4otDpR0ns+4caZ7wX0DfHj8H7//gLGxsWHyewnU19ejVquva+WvG83Ozo6RM+5i\nw6Q0HMrCMKBr+ACb35/RWqJnXAw940wfgvX19by/dDUn9EY6+XiQGN8bgPDgYMLPXas+z9bWlhZ1\n5ZhvANNV0lqrcDlThgzg5NxvSVVqOXVkL7Q8d1tdZRndW7cgvLyEz1zCqFfAb9PXLH9uzCXnOW8b\nFsbbYRcmgvFOWUrKrv2oFIUkbzuGDB6Ioihkf7mQxYVBoFIxVJ/Lg48lXfPP6HLUajUvDX6KJUuW\nocdAn7DetAxpQRd+P0GPt7cPxl/OQgz4dQwnY+56nBydyPrPj7QJaEWftnHEdL/8oL7Eex5kcE0N\na7evY29AOa5tfKktryYgX3vFa8e/1a9zb/pd0x4m1UYNpru3L/668Tk5OXHf0HG/+35Mv3F8vvgt\nwlwrOFnphK5JNE7NqjhxNJ85e+rxCYrhL5OeMT++xMafWsMZqmvqKC6vwdNVy4ECHXZ+Pa6qHb1f\n/yfz6wy4ZB2gqEkTAg4cwD5tO4PgN6s9gKa6GgD/0DDq5n7DL/96i2/X/cjQinKKbG3R19XhD6wA\n/IEdbdrRZYr06jYGlXK+v+wavPrqq/Tt29cc8D179mTdunWo1WrS09OZP38+//rXvwB477338PPz\nIzIykoyMDBITE8nNzWX8+PGsWbPmpi8jas2MRiNrf/iFWp2BvkO7me/PtiabVqXz64oTZKbvw2PX\nMJxqgyhrt5wXvu9McFjAlZ/gBjpecIoXF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.manifold import LocallyLinearEmbedding\n", + "model = LocallyLinearEmbedding(n_neighbors=100, n_components=2, method='modified',\n", + " eigen_solver='dense')\n", + "out = model.fit_transform(XS)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(out[:, 0], out[:, 1], **colorize)\n", + "ax.set_ylim(0.15, -0.15);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result remains somewhat distorted compared to our original manifold, but captures the essential relationships in the data!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Some Thoughts on Manifold Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Though this story and motivation is compelling, in practice manifold learning techniques tend to be finicky enough that they are rarely used for anything more than simple qualitative visualization of high-dimensional data.\n", + "\n", + "The following are some of the particular challenges of manifold learning, which all contrast poorly with PCA:\n", + "\n", + "- In manifold learning, there is no good framework for handling missing data. In contrast, there are straightforward iterative approaches for missing data in PCA.\n", + "- In manifold learning, the presence of noise in the data can \"short-circuit\" the manifold and drastically change the embedding. In contrast, PCA naturally filters noise from the most important components.\n", + "- The manifold embedding result is generally highly dependent on the number of neighbors chosen, and there is generally no solid quantitative way to choose an optimal number of neighbors. In contrast, PCA does not involve such a choice.\n", + "- In manifold learning, the globally optimal number of output dimensions is difficult to determine. In contrast, PCA lets you find the output dimension based on the explained variance.\n", + "- In manifold learning, the meaning of the embedded dimensions is not always clear. In PCA, the principal components have a very clear meaning.\n", + "- In manifold learning the computational expense of manifold methods scales as O[N^2] or O[N^3]. For PCA, there exist randomized approaches that are generally much faster (though see the [megaman](https://github.com/mmp2/megaman) package for some more scalable implementations of manifold learning).\n", + "\n", + "With all that on the table, the only clear advantage of manifold learning methods over PCA is their ability to preserve nonlinear relationships in the data; for that reason I tend to explore data with manifold methods only after first exploring them with PCA.\n", + "\n", + "Scikit-Learn implements several common variants of manifold learning beyond Isomap and LLE: the Scikit-Learn documentation has a [nice discussion and comparison of them](http://scikit-learn.org/stable/modules/manifold.html).\n", + "Based on my own experience, I would give the following recommendations:\n", + "\n", + "- For toy problems such as the S-curve we saw before, locally linear embedding (LLE) and its variants (especially *modified LLE*), perform very well. This is implemented in ``sklearn.manifold.LocallyLinearEmbedding``.\n", + "- For high-dimensional data from real-world sources, LLE often produces poor results, and isometric mapping (IsoMap) seems to generally lead to more meaningful embeddings. This is implemented in ``sklearn.manifold.Isomap``\n", + "- For data that is highly clustered, *t-distributed stochastic neighbor embedding* (t-SNE) seems to work very well, though can be very slow compared to other methods. This is implemented in ``sklearn.manifold.TSNE``.\n", + "\n", + "If you're interested in getting a feel for how these work, I'd suggest running each of the methods on the data in this section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Isomap on Faces\n", + "\n", + "One place manifold learning is often used is in understanding the relationship between high-dimensional data points.\n", + "A common case of high-dimensional data is images: for example, a set of images with 1,000 pixels each can be thought of as a collection of points in 1,000 dimensions – the brightness of each pixel in each image defines the coordinate in that dimension.\n", + "\n", + "Here let's apply Isomap on some faces data.\n", + "We will use the Labeled Faces in the Wild dataset, which we previously saw in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n", + "Running this command will download the data and cache it in your home directory for later use:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2370, 2914)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import fetch_lfw_people\n", + "faces = fetch_lfw_people(min_faces_per_person=30)\n", + "faces.data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have 2,370 images, each with 2,914 pixels.\n", + "In other words, the images can be thought of as data points in a 2,914-dimensional space!\n", + "\n", + "Let's quickly visualize several of these images to see what we're working with:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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X15HYx3gjqPV6Pe7krNfrC0dmIUM4hCyo3WlSL8TBwIJYmf/BYBDRMGez+vm4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(4, 8, subplot_kw=dict(xticks=[], yticks=[]))\n", + "for i, axi in enumerate(ax.flat):\n", + " axi.imshow(faces.images[i], cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We would like to plot a low-dimensional embedding of the 2,914-dimensional data to learn the fundamental relationships between the images.\n", + "One useful way to start is to compute a PCA, and examine the explained variance ratio, which will give us an idea of how many linear features are required to describe the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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w+ctQ3WjBusID0IWqcN/kgZDLZP4uiYiI6Bxee/JXQgiBBQsWoKKiAmq1GosX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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.decomposition import RandomizedPCA\n", + "model = RandomizedPCA(100).fit(faces.data)\n", + "plt.plot(np.cumsum(model.explained_variance_ratio_))\n", + "plt.xlabel('n components')\n", + "plt.ylabel('cumulative variance');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that for this data, nearly 100 components are required to preserve 90% of the variance: this tells us that the data is intrinsically very high dimensional—it can't be described linearly with just a few components.\n", + "\n", + "When this is the case, nonlinear manifold embeddings like LLE and Isomap can be helpful.\n", + "We can compute an Isomap embedding on these faces using the same pattern shown before:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2370, 2)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import Isomap\n", + "model = Isomap(n_components=2)\n", + "proj = model.fit_transform(faces.data)\n", + "proj.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output is a two-dimensional projection of all the input images.\n", + "To get a better idea of what the projection tells us, let's define a function that will output image thumbnails at the locations of the projections:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from matplotlib import offsetbox\n", + "\n", + "def plot_components(data, model, images=None, ax=None,\n", + " thumb_frac=0.05, cmap='gray'):\n", + " ax = ax or plt.gca()\n", + " \n", + " proj = model.fit_transform(data)\n", + " ax.plot(proj[:, 0], proj[:, 1], '.k')\n", + " \n", + " if images is not None:\n", + " min_dist_2 = (thumb_frac * max(proj.max(0) - proj.min(0))) ** 2\n", + " shown_images = np.array([2 * proj.max(0)])\n", + " for i in range(data.shape[0]):\n", + " dist = np.sum((proj[i] - shown_images) ** 2, 1)\n", + " if np.min(dist) < min_dist_2:\n", + " # don't show points that are too close\n", + " continue\n", + " shown_images = np.vstack([shown_images, proj[i]])\n", + " imagebox = offsetbox.AnnotationBbox(\n", + " offsetbox.OffsetImage(images[i], cmap=cmap),\n", + " proj[i])\n", + " ax.add_artist(imagebox)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calling this function now, we see the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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QdR2PPvoo7rvvPo6soqqbxWLB+vo6FhYWsLS0hKGhIRZSDocD0WgUlUoFFosF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drYtChQVWo7aG/jdrIl6e7uZisLyvOklrfVasWZM2dgMBhw/vx5pNNp7N69Gz09PchkMlCr1ahW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Rj8cxMzODhYUFHDhwYMXf0VhSJpNx1/CJJ55gW5lyuYzR0VFotVqoVCrMzMzwoq/T6ThyhwQe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XV1eXqlUgkLCwvI5/PYunUrOjs7ZfNlJFepVILFYmno0wagjjdmNptRLpfrNm02bcpYMZ5K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fj3Q6jc2bN8Nms+HXv/419Ho9hoeHYTKZUCgU4PP54HK50N3dvA3q8/mwbdumRZ+BCkEq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\nYdG+SqXC2bNncfbsWdx+++3vwjO2Ob70pS9tqWaLdHbDw8O49957t31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hPPDAAxgcHITf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "plot_components(faces.data,\n", + " model=Isomap(n_components=2),\n", + " images=faces.images[:, ::2, ::2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is interesting: the first two Isomap dimensions seem to describe global image features: the overall darkness or lightness of the image from left to right, and the general orientation of the face from bottom to top.\n", + "This gives us a nice visual indication of some of the fundamental features in our data.\n", + "\n", + "We could then go on to classify this data (perhaps using manifold features as inputs to the classification algorithm) as we did in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: Visualizing Structure in Digits\n", + "\n", + "As another example of using manifold learning for visualization, let's take a look at the MNIST handwritten digits set.\n", + "This data is similar to the digits we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb), but with many more pixels per image.\n", + "It can be downloaded from http://mldata.org/ with the Scikit-Learn utility:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(70000, 784)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import fetch_mldata\n", + "mnist = fetch_mldata('MNIST original')\n", + "mnist.data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This consists of 70,000 images, each with 784 pixels (i.e. the images are 28×28).\n", + "As before, we can take a look at the first few images:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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2JOFEo1EcDgeDg4N8/fXXmM1m0tPTOXDgAGVlZUkp/oB4ful36bU6NTXF/fv3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\n5coVLl26hMfjSRrP8YtIF7SFhQWCwSBWq5WmpiZaW1u/1nMmF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(6, 8, subplot_kw=dict(xticks=[], yticks=[]))\n", + "for i, axi in enumerate(ax.flat):\n", + " axi.imshow(mnist.data[1250 * i].reshape(28, 28), cmap='gray_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us an idea of the variety of handwriting styles in the dataset.\n", + "\n", + "Let's compute a manifold learning projection across the data.\n", + "For speed here, we'll only use 1/30 of the data, which is about ~2000 points\n", + "(because of the relatively poor scaling of manifold learning, I find that a few thousand samples is a good number to start with for relatively quick exploration before moving to a full calculation):" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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QPH8Z2z/t/9iLAEH4X+bg4EBqaioRERHk5+ezaNGiJ5YXnwLh/4V9p0+zKKCdtQWWnMjC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\nG6NRi7W1CYMhC9AAZvL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b3d2egD9xWT83cCqrDmwmW8jFzWDNrMH1HhKz2cy+1G9o87jo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Ny5c4HSm4NiYmKw2WyEhITQqVMnAIKDgxk1ahRKKeLj4wGIiooiNjaWDRs24Ofnx5IlS5wV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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# use only 1/30 of the data: full dataset takes a long time!\n", + "data = mnist.data[::30]\n", + "target = mnist.target[::30]\n", + "\n", + "model = Isomap(n_components=2)\n", + "proj = model.fit_transform(data)\n", + "plt.scatter(proj[:, 0], proj[:, 1], c=target, cmap=plt.cm.get_cmap('jet', 10))\n", + "plt.colorbar(ticks=range(10))\n", + "plt.clim(-0.5, 9.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting scatter plot shows some of the relationships between the data points, but is a bit crowded.\n", + "We can gain more insight by looking at just a single number at a time:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZWLVqFf773/+iW7du+OijjzjpgIiI6P8xtJHDOH78uLwzw4wZMzjpgIiIqBSGtmowGo1q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\n06fGvh519fdaZerzuJVkUdD2/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.manifold import Isomap\n", + "\n", + "# Choose 1/4 of the \"1\" digits to project\n", + "data = mnist.data[mnist.target == 1][::4]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "model = Isomap(n_neighbors=5, n_components=2, eigen_solver='dense')\n", + "plot_components(data, model, images=data.reshape((-1, 28, 28)),\n", + " ax=ax, thumb_frac=0.05, cmap='gray_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result gives you an idea of the variety of forms that the number \"1\" can take within the dataset.\n", + "The data lies along a broad curve in the projected space, which appears to trace the orientation of the digit.\n", + "As you move up the plot, you find ones that have hats and/or bases, though these are very sparse within the dataset.\n", + "The projection lets us identify outliers that have data issues: for example, pieces of the neighboring digits that snuck into the extracted images.\n", + "\n", + "Now, this in itself may not be useful for the task of classifying digits, but it does help us get an understanding of the data, and may give us ideas about how to move forward, such as how we might want to preprocess the data before building a classification pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.10-Manifold-Learning.md b/notebooks_v2/05.10-Manifold-Learning.md new file mode 100644 index 00000000..c492d0b9 --- /dev/null +++ b/notebooks_v2/05.10-Manifold-Learning.md @@ -0,0 +1,461 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) > + +Open in Colab + + + +# In-Depth: Manifold Learning + + +We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task—reducing the number of features of a dataset while maintaining the essential relationships between the points. +While PCA is flexible, fast, and easily interpretable, it does not perform so well when there are *nonlinear* relationships within the data; we will see some examples of these below. + +To address this deficiency, we can turn to a class of methods known as *manifold learning*—a class of unsupervised estimators that seeks to describe datasets as low-dimensional manifolds embedded in high-dimensional spaces. +When you think of a manifold, I'd suggest imagining a sheet of paper: this is a two-dimensional object that lives in our familiar three-dimensional world, and can be bent or rolled in that two dimensions. +In the parlance of manifold learning, we can think of this sheet as a two-dimensional manifold embedded in three-dimensional space. + +Rotating, re-orienting, or stretching the piece of paper in three-dimensional space doesn't change the flat geometry of the paper: such operations are akin to linear embeddings. +If you bend, curl, or crumple the paper, it is still a two-dimensional manifold, but the embedding into the three-dimensional space is no longer linear. +Manifold learning algorithms would seek to learn about the fundamental two-dimensional nature of the paper, even as it is contorted to fill the three-dimensional space. + +Here we will demonstrate a number of manifold methods, going most deeply into a couple techniques: multidimensional scaling (MDS), locally linear embedding (LLE), and isometric mapping (IsoMap). + +We begin with the standard imports: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +import numpy as np +``` + +## Manifold Learning: "HELLO" + +To make these concepts more clear, let's start by generating some two-dimensional data that we can use to define a manifold. +Here is a function that will create data in the shape of the word "HELLO": + +```python +def make_hello(N=1000, rseed=42): + # Make a plot with "HELLO" text; save as PNG + fig, ax = plt.subplots(figsize=(4, 1)) + fig.subplots_adjust(left=0, right=1, bottom=0, top=1) + ax.axis('off') + ax.text(0.5, 0.4, 'HELLO', va='center', ha='center', weight='bold', size=85) + fig.savefig('hello.png') + plt.close(fig) + + # Open this PNG and draw random points from it + from matplotlib.image import imread + data = imread('hello.png')[::-1, :, 0].T + rng = np.random.RandomState(rseed) + X = rng.rand(4 * N, 2) + i, j = (X * data.shape).astype(int).T + mask = (data[i, j] < 1) + X = X[mask] + X[:, 0] *= (data.shape[0] / data.shape[1]) + X = X[:N] + return X[np.argsort(X[:, 0])] +``` + +Let's call the function and visualize the resulting data: + +```python +X = make_hello(1000) +colorize = dict(c=X[:, 0], cmap=plt.cm.get_cmap('rainbow', 5)) +plt.scatter(X[:, 0], X[:, 1], **colorize) +plt.axis('equal'); +``` + +The output is two dimensional, and consists of points drawn in the shape of the word, "HELLO". +This data form will help us to see visually what these algorithms are doing. + + +## Multidimensional Scaling (MDS) + +Looking at data like this, we can see that the particular choice of *x* and *y* values of the dataset are not the most fundamental description of the data: we can scale, shrink, or rotate the data, and the "HELLO" will still be apparent. +For example, if we use a rotation matrix to rotate the data, the *x* and *y* values change, but the data is still fundamentally the same: + +```python +def rotate(X, angle): + theta = np.deg2rad(angle) + R = [[np.cos(theta), np.sin(theta)], + [-np.sin(theta), np.cos(theta)]] + return np.dot(X, R) + +X2 = rotate(X, 20) + 5 +plt.scatter(X2[:, 0], X2[:, 1], **colorize) +plt.axis('equal'); +``` + +This tells us that the *x* and *y* values are not necessarily fundamental to the relationships in the data. +What *is* fundamental, in this case, is the *distance* between each point and the other points in the dataset. +A common way to represent this is to use a distance matrix: for $N$ points, we construct an $N \times N$ array such that entry $(i, j)$ contains the distance between point $i$ and point $j$. +Let's use Scikit-Learn's efficient ``pairwise_distances`` function to do this for our original data: + +```python +from sklearn.metrics import pairwise_distances +D = pairwise_distances(X) +D.shape +``` + +As promised, for our *N*=1,000 points, we obtain a 1000×1000 matrix, which can be visualized as shown here: + +```python +plt.imshow(D, zorder=2, cmap='Blues', interpolation='nearest') +plt.colorbar(); +``` + +If we similarly construct a distance matrix for our rotated and translated data, we see that it is the same: + +```python +D2 = pairwise_distances(X2) +np.allclose(D, D2) +``` + +This distance matrix gives us a representation of our data that is invariant to rotations and translations, but the visualization of the matrix above is not entirely intuitive. +In the representation shown in this figure, we have lost any visible sign of the interesting structure in the data: the "HELLO" that we saw before. + +Further, while computing this distance matrix from the (x, y) coordinates is straightforward, transforming the distances back into *x* and *y* coordinates is rather difficult. +This is exactly what the multidimensional scaling algorithm aims to do: given a distance matrix between points, it recovers a $D$-dimensional coordinate representation of the data. +Let's see how it works for our distance matrix, using the ``precomputed`` dissimilarity to specify that we are passing a distance matrix: + +```python +from sklearn.manifold import MDS +model = MDS(n_components=2, dissimilarity='precomputed', random_state=1) +out = model.fit_transform(D) +plt.scatter(out[:, 0], out[:, 1], **colorize) +plt.axis('equal'); +``` + +The MDS algorithm recovers one of the possible two-dimensional coordinate representations of our data, using *only* the $N\times N$ distance matrix describing the relationship between the data points. + + +## MDS as Manifold Learning + +The usefulness of this becomes more apparent when we consider the fact that distance matrices can be computed from data in *any* dimension. +So, for example, instead of simply rotating the data in the two-dimensional plane, we can project it into three dimensions using the following function (essentially a three-dimensional generalization of the rotation matrix used earlier): + +```python +def random_projection(X, dimension=3, rseed=42): + assert dimension >= X.shape[1] + rng = np.random.RandomState(rseed) + C = rng.randn(dimension, dimension) + e, V = np.linalg.eigh(np.dot(C, C.T)) + return np.dot(X, V[:X.shape[1]]) + +X3 = random_projection(X, 3) +X3.shape +``` + +Let's visualize these points to see what we're working with: + +```python +from mpl_toolkits import mplot3d +ax = plt.axes(projection='3d') +ax.scatter3D(X3[:, 0], X3[:, 1], X3[:, 2], + **colorize) +ax.view_init(azim=70, elev=50) +``` + +We can now ask the ``MDS`` estimator to input this three-dimensional data, compute the distance matrix, and then determine the optimal two-dimensional embedding for this distance matrix. +The result recovers a representation of the original data: + +```python +model = MDS(n_components=2, random_state=1) +out3 = model.fit_transform(X3) +plt.scatter(out3[:, 0], out3[:, 1], **colorize) +plt.axis('equal'); +``` + +This is essentially the goal of a manifold learning estimator: given high-dimensional embedded data, it seeks a low-dimensional representation of the data that preserves certain relationships within the data. +In the case of MDS, the quantity preserved is the distance between every pair of points. + + +## Nonlinear Embeddings: Where MDS Fails + +Our discussion thus far has considered *linear* embeddings, which essentially consist of rotations, translations, and scalings of data into higher-dimensional spaces. +Where MDS breaks down is when the embedding is nonlinear—that is, when it goes beyond this simple set of operations. +Consider the following embedding, which takes the input and contorts it into an "S" shape in three dimensions: + +```python +def make_hello_s_curve(X): + t = (X[:, 0] - 2) * 0.75 * np.pi + x = np.sin(t) + y = X[:, 1] + z = np.sign(t) * (np.cos(t) - 1) + return np.vstack((x, y, z)).T + +XS = make_hello_s_curve(X) +``` + +This is again three-dimensional data, but we can see that the embedding is much more complicated: + +```python +from mpl_toolkits import mplot3d +ax = plt.axes(projection='3d') +ax.scatter3D(XS[:, 0], XS[:, 1], XS[:, 2], + **colorize); +``` + +The fundamental relationships between the data points are still there, but this time the data has been transformed in a nonlinear way: it has been wrapped-up into the shape of an "S." + +If we try a simple MDS algorithm on this data, it is not able to "unwrap" this nonlinear embedding, and we lose track of the fundamental relationships in the embedded manifold: + +```python +from sklearn.manifold import MDS +model = MDS(n_components=2, random_state=2) +outS = model.fit_transform(XS) +plt.scatter(outS[:, 0], outS[:, 1], **colorize) +plt.axis('equal'); +``` + +The best two-dimensional *linear* embeding does not unwrap the S-curve, but instead throws out the original y-axis. + + +## Nonlinear Manifolds: Locally Linear Embedding + +How can we move forward here? Stepping back, we can see that the source of the problem is that MDS tries to preserve distances between faraway points when constructing the embedding. +But what if we instead modified the algorithm such that it only preserves distances between nearby points? +The resulting embedding would be closer to what we want. + +Visually, we can think of it as illustrated in this figure: + + +![(LLE vs MDS linkages)](figures/05.10-LLE-vs-MDS.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#LLE-vs-MDS-Linkages) + + +Here each faint line represents a distance that should be preserved in the embedding. +On the left is a representation of the model used by MDS: it tries to preserve the distances between each pair of points in the dataset. +On the right is a representation of the model used by a manifold learning algorithm called locally linear embedding (LLE): rather than preserving *all* distances, it instead tries to preserve only the distances between *neighboring points*: in this case, the nearest 100 neighbors of each point. + +Thinking about the left panel, we can see why MDS fails: there is no way to flatten this data while adequately preserving the length of every line drawn between the two points. +For the right panel, on the other hand, things look a bit more optimistic. We could imagine unrolling the data in a way that keeps the lengths of the lines approximately the same. +This is precisely what LLE does, through a global optimization of a cost function reflecting this logic. + +LLE comes in a number of flavors; here we will use the *modified LLE* algorithm to recover the embedded two-dimensional manifold. +In general, modified LLE does better than other flavors of the algorithm at recovering well-defined manifolds with very little distortion: + +```python +from sklearn.manifold import LocallyLinearEmbedding +model = LocallyLinearEmbedding(n_neighbors=100, n_components=2, method='modified', + eigen_solver='dense') +out = model.fit_transform(XS) + +fig, ax = plt.subplots() +ax.scatter(out[:, 0], out[:, 1], **colorize) +ax.set_ylim(0.15, -0.15); +``` + +The result remains somewhat distorted compared to our original manifold, but captures the essential relationships in the data! + + +## Some Thoughts on Manifold Methods + + +Though this story and motivation is compelling, in practice manifold learning techniques tend to be finicky enough that they are rarely used for anything more than simple qualitative visualization of high-dimensional data. + +The following are some of the particular challenges of manifold learning, which all contrast poorly with PCA: + +- In manifold learning, there is no good framework for handling missing data. In contrast, there are straightforward iterative approaches for missing data in PCA. +- In manifold learning, the presence of noise in the data can "short-circuit" the manifold and drastically change the embedding. In contrast, PCA naturally filters noise from the most important components. +- The manifold embedding result is generally highly dependent on the number of neighbors chosen, and there is generally no solid quantitative way to choose an optimal number of neighbors. In contrast, PCA does not involve such a choice. +- In manifold learning, the globally optimal number of output dimensions is difficult to determine. In contrast, PCA lets you find the output dimension based on the explained variance. +- In manifold learning, the meaning of the embedded dimensions is not always clear. In PCA, the principal components have a very clear meaning. +- In manifold learning the computational expense of manifold methods scales as O[N^2] or O[N^3]. For PCA, there exist randomized approaches that are generally much faster (though see the [megaman](https://github.com/mmp2/megaman) package for some more scalable implementations of manifold learning). + +With all that on the table, the only clear advantage of manifold learning methods over PCA is their ability to preserve nonlinear relationships in the data; for that reason I tend to explore data with manifold methods only after first exploring them with PCA. + +Scikit-Learn implements several common variants of manifold learning beyond Isomap and LLE: the Scikit-Learn documentation has a [nice discussion and comparison of them](http://scikit-learn.org/stable/modules/manifold.html). +Based on my own experience, I would give the following recommendations: + +- For toy problems such as the S-curve we saw before, locally linear embedding (LLE) and its variants (especially *modified LLE*), perform very well. This is implemented in ``sklearn.manifold.LocallyLinearEmbedding``. +- For high-dimensional data from real-world sources, LLE often produces poor results, and isometric mapping (IsoMap) seems to generally lead to more meaningful embeddings. This is implemented in ``sklearn.manifold.Isomap`` +- For data that is highly clustered, *t-distributed stochastic neighbor embedding* (t-SNE) seems to work very well, though can be very slow compared to other methods. This is implemented in ``sklearn.manifold.TSNE``. + +If you're interested in getting a feel for how these work, I'd suggest running each of the methods on the data in this section. + + +## Example: Isomap on Faces + +One place manifold learning is often used is in understanding the relationship between high-dimensional data points. +A common case of high-dimensional data is images: for example, a set of images with 1,000 pixels each can be thought of as a collection of points in 1,000 dimensions – the brightness of each pixel in each image defines the coordinate in that dimension. + +Here let's apply Isomap on some faces data. +We will use the Labeled Faces in the Wild dataset, which we previously saw in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb). +Running this command will download the data and cache it in your home directory for later use: + +```python +from sklearn.datasets import fetch_lfw_people +faces = fetch_lfw_people(min_faces_per_person=30) +faces.data.shape +``` + +We have 2,370 images, each with 2,914 pixels. +In other words, the images can be thought of as data points in a 2,914-dimensional space! + +Let's quickly visualize several of these images to see what we're working with: + +```python +fig, ax = plt.subplots(4, 8, subplot_kw=dict(xticks=[], yticks=[])) +for i, axi in enumerate(ax.flat): + axi.imshow(faces.images[i], cmap='gray') +``` + +We would like to plot a low-dimensional embedding of the 2,914-dimensional data to learn the fundamental relationships between the images. +One useful way to start is to compute a PCA, and examine the explained variance ratio, which will give us an idea of how many linear features are required to describe the data: + +```python +from sklearn.decomposition import RandomizedPCA +model = RandomizedPCA(100).fit(faces.data) +plt.plot(np.cumsum(model.explained_variance_ratio_)) +plt.xlabel('n components') +plt.ylabel('cumulative variance'); +``` + +We see that for this data, nearly 100 components are required to preserve 90% of the variance: this tells us that the data is intrinsically very high dimensional—it can't be described linearly with just a few components. + +When this is the case, nonlinear manifold embeddings like LLE and Isomap can be helpful. +We can compute an Isomap embedding on these faces using the same pattern shown before: + +```python +from sklearn.manifold import Isomap +model = Isomap(n_components=2) +proj = model.fit_transform(faces.data) +proj.shape +``` + +The output is a two-dimensional projection of all the input images. +To get a better idea of what the projection tells us, let's define a function that will output image thumbnails at the locations of the projections: + +```python +from matplotlib import offsetbox + +def plot_components(data, model, images=None, ax=None, + thumb_frac=0.05, cmap='gray'): + ax = ax or plt.gca() + + proj = model.fit_transform(data) + ax.plot(proj[:, 0], proj[:, 1], '.k') + + if images is not None: + min_dist_2 = (thumb_frac * max(proj.max(0) - proj.min(0))) ** 2 + shown_images = np.array([2 * proj.max(0)]) + for i in range(data.shape[0]): + dist = np.sum((proj[i] - shown_images) ** 2, 1) + if np.min(dist) < min_dist_2: + # don't show points that are too close + continue + shown_images = np.vstack([shown_images, proj[i]]) + imagebox = offsetbox.AnnotationBbox( + offsetbox.OffsetImage(images[i], cmap=cmap), + proj[i]) + ax.add_artist(imagebox) +``` + +Calling this function now, we see the result: + +```python +fig, ax = plt.subplots(figsize=(10, 10)) +plot_components(faces.data, + model=Isomap(n_components=2), + images=faces.images[:, ::2, ::2]) +``` + +The result is interesting: the first two Isomap dimensions seem to describe global image features: the overall darkness or lightness of the image from left to right, and the general orientation of the face from bottom to top. +This gives us a nice visual indication of some of the fundamental features in our data. + +We could then go on to classify this data (perhaps using manifold features as inputs to the classification algorithm) as we did in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb). + + +## Example: Visualizing Structure in Digits + +As another example of using manifold learning for visualization, let's take a look at the MNIST handwritten digits set. +This data is similar to the digits we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb), but with many more pixels per image. +It can be downloaded from http://mldata.org/ with the Scikit-Learn utility: + +```python +from sklearn.datasets import fetch_mldata +mnist = fetch_mldata('MNIST original') +mnist.data.shape +``` + +This consists of 70,000 images, each with 784 pixels (i.e. the images are 28×28). +As before, we can take a look at the first few images: + +```python +fig, ax = plt.subplots(6, 8, subplot_kw=dict(xticks=[], yticks=[])) +for i, axi in enumerate(ax.flat): + axi.imshow(mnist.data[1250 * i].reshape(28, 28), cmap='gray_r') +``` + +This gives us an idea of the variety of handwriting styles in the dataset. + +Let's compute a manifold learning projection across the data. +For speed here, we'll only use 1/30 of the data, which is about ~2000 points +(because of the relatively poor scaling of manifold learning, I find that a few thousand samples is a good number to start with for relatively quick exploration before moving to a full calculation): + +```python +# use only 1/30 of the data: full dataset takes a long time! +data = mnist.data[::30] +target = mnist.target[::30] + +model = Isomap(n_components=2) +proj = model.fit_transform(data) +plt.scatter(proj[:, 0], proj[:, 1], c=target, cmap=plt.cm.get_cmap('jet', 10)) +plt.colorbar(ticks=range(10)) +plt.clim(-0.5, 9.5); +``` + +The resulting scatter plot shows some of the relationships between the data points, but is a bit crowded. +We can gain more insight by looking at just a single number at a time: + +```python +from sklearn.manifold import Isomap + +# Choose 1/4 of the "1" digits to project +data = mnist.data[mnist.target == 1][::4] + +fig, ax = plt.subplots(figsize=(10, 10)) +model = Isomap(n_neighbors=5, n_components=2, eigen_solver='dense') +plot_components(data, model, images=data.reshape((-1, 28, 28)), + ax=ax, thumb_frac=0.05, cmap='gray_r') +``` + +The result gives you an idea of the variety of forms that the number "1" can take within the dataset. +The data lies along a broad curve in the projected space, which appears to trace the orientation of the digit. +As you move up the plot, you find ones that have hats and/or bases, though these are very sparse within the dataset. +The projection lets us identify outliers that have data issues: for example, pieces of the neighboring digits that snuck into the extracted images. + +Now, this in itself may not be useful for the task of classifying digits, but it does help us get an understanding of the data, and may give us ideas about how to move forward, such as how we might want to preprocess the data before building a classification pipeline. + + + +< [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) | [Contents](Index.ipynb) | [In Depth: k-Means Clustering](05.11-K-Means.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/05.11-K-Means.ipynb b/notebooks_v2/05.11-K-Means.ipynb new file mode 100644 index 00000000..96b82f59 --- /dev/null +++ b/notebooks_v2/05.11-K-Means.ipynb @@ -0,0 +1,1010 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Depth: k-Means Clustering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous few sections, we have explored one category of unsupervised machine learning models: dimensionality reduction.\n", + "Here we will move on to another class of unsupervised machine learning models: clustering algorithms.\n", + "Clustering algorithms seek to learn, from the properties of the data, an optimal division or discrete labeling of groups of points.\n", + "\n", + "Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as *k-means clustering*, which is implemented in ``sklearn.cluster.KMeans``.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set() # for plot styling\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introducing k-Means" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *k*-means algorithm searches for a pre-determined number of clusters within an unlabeled multidimensional dataset.\n", + "It accomplishes this using a simple conception of what the optimal clustering looks like:\n", + "\n", + "- The \"cluster center\" is the arithmetic mean of all the points belonging to the cluster.\n", + "- Each point is closer to its own cluster center than to other cluster centers.\n", + "\n", + "Those two assumptions are the basis of the *k*-means model.\n", + "We will soon dive into exactly *how* the algorithm reaches this solution, but for now let's take a look at a simple dataset and see the *k*-means result.\n", + "\n", + "First, let's generate a two-dimensional dataset containing four distinct blobs.\n", + "To emphasize that this is an unsupervised algorithm, we will leave the labels out of the visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AnAS6f/WKqLLXWU9zc3hhDqNWa26uhT//eVf3HGZjsbwL7MHtno7d/pmm1WhG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\ngGOiB2iF4tuTrary7ckW94wz2l6tUhX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ZermhoSGrzxg/s729DYlEgvb2dhQW\nFiInJ4d1JF6Fw2GMjIxQb/EsIpPJcH5+DoVCgWAwiMrKStaRMibbZ9k+8/r6ir6+PphMJtTW1rKO\nw7vj42M8Pz+jv78feXl5EIlEgpmtcbvd6WWdTgeLxfLHIgx8UyH+iOM4wX1wOjs7MTExgcPDQ6RS\nKcHdGGO32xGPx2G1Wqm3eJZoamrC5eVl+tq+0I7Jj36dfQjJ1tYW3t7esL6+jrW1NXAcB6fTmX4O\nfLZrbm7G1NQUtFotEokEZmZmBLNtH3312KRe04QQQghDwpgLIIQQQrIUFWJCCCGEISrEhBBCCENU\niAkhhBCGqBATQgghDFEhJoQQQhiiQkwIIYQw9AN8zzj2XEOmNgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "X, y_true = make_blobs(n_samples=300, centers=4,\n", + " cluster_std=0.60, random_state=0)\n", + "plt.scatter(X[:, 0], X[:, 1], s=50);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By eye, it is relatively easy to pick out the four clusters.\n", + "The *k*-means algorithm does this automatically, and in Scikit-Learn uses the typical estimator API:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "kmeans = KMeans(n_clusters=4)\n", + "kmeans.fit(X)\n", + "y_kmeans = kmeans.predict(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the results by plotting the data colored by these labels.\n", + "We will also plot the cluster centers as determined by the *k*-means estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UeKCgRybHuLFt0x907dmNhT8vYNmU1cipWlK5ihd+6CR3LKQ47IjkQwAXOYWE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5BVJ4U6AGxFJcQFlv8cWnhEsIvy/fw7xFCcmkne5iMoJVZuCGnL1IH3EdekeaX9mzVsgnp0MzRy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\nPtPuQBvsO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n", + "\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The good news is that the *k*-means algorithm (at least in this simple case) assigns the points to clusters very similarly to how we might assign them by eye.\n", + "But you might wonder how this algorithm finds these clusters so quickly! After all, the number of possible combinations of cluster assignments is exponential in the number of data points—an exhaustive search would be very, very costly.\n", + "Fortunately for us, such an exhaustive search is not necessary: instead, the typical approach to *k*-means involves an intuitive iterative approach known as *expectation–maximization*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## k-Means Algorithm: Expectation–Maximization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Expectation–maximization (E–M) is a powerful algorithm that comes up in a variety of contexts within data science.\n", + "*k*-means is a particularly simple and easy-to-understand application of the algorithm, and we will walk through it briefly here.\n", + "In short, the expectation–maximization approach here consists of the following procedure:\n", + "\n", + "1. Guess some cluster centers\n", + "2. Repeat until converged\n", + " 1. *E-Step*: assign points to the nearest cluster center\n", + " 2. *M-Step*: set the cluster centers to the mean \n", + "\n", + "Here the \"E-step\" or \"Expectation step\" is so-named because it involves updating our expectation of which cluster each point belongs to.\n", + "The \"M-step\" or \"Maximization step\" is so-named because it involves maximizing some fitness function that defines the location of the cluster centers—in this case, that maximization is accomplished by taking a simple mean of the data in each cluster.\n", + "\n", + "The literature about this algorithm is vast, but can be summarized as follows: under typical circumstances, each repetition of the E-step and M-step will always result in a better estimate of the cluster characteristics.\n", + "\n", + "We can visualize the algorithm as shown in the following figure.\n", + "For the particular initialization shown here, the clusters converge in just three iterations.\n", + "For an interactive version of this figure, refer to the code in [the Appendix](06.00-Figure-Code.ipynb#Interactive-K-Means)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![(run code in Appendix to generate image)](figures/05.11-expectation-maximization.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Expectation-Maximization)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *k*-Means algorithm is simple enough that we can write it in a few lines of code.\n", + "The following is a very basic implementation:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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pMmPnrSFgTaEsazOmhHR0CVYCW1egKkrIRCBVVXAVZiPJMuW5O7CkNsTvLMO+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YS6T/vSSy+9dKVOVlJirnqjq5Svr1e9vb76em1+/oHsTMkn0Hsvgf8bbVNVlQXLImie+AKBgaEX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\nB19Pl89QA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import pairwise_distances_argmin\n", + "\n", + "def find_clusters(X, n_clusters, rseed=2):\n", + " # 1. Randomly choose clusters\n", + " rng = np.random.RandomState(rseed)\n", + " i = rng.permutation(X.shape[0])[:n_clusters]\n", + " centers = X[i]\n", + " \n", + " while True:\n", + " # 2a. Assign labels based on closest center\n", + " labels = pairwise_distances_argmin(X, centers)\n", + " \n", + " # 2b. Find new centers from means of points\n", + " new_centers = np.array([X[labels == i].mean(0)\n", + " for i in range(n_clusters)])\n", + " \n", + " # 2c. Check for convergence\n", + " if np.all(centers == new_centers):\n", + " break\n", + " centers = new_centers\n", + " \n", + " return centers, labels\n", + "\n", + "centers, labels = find_clusters(X, 4)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels,\n", + " s=50, cmap='viridis');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most well-tested implementations will do a bit more than this under the hood, but the preceding function gives the gist of the expectation–maximization approach." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Caveats of expectation–maximization\n", + "\n", + "There are a few issues to be aware of when using the expectation–maximization algorithm." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The globally optimal result may not be achieved\n", + "First, although the E–M procedure is guaranteed to improve the result in each step, there is no assurance that it will lead to the *global* best solution.\n", + "For example, if we use a different random seed in our simple procedure, the particular starting guesses lead to poor results:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UKgWkkojQKzS5pxFvffo2wcHBLsvyL4PBwJD/G+ryXJXwcJ5/rWgK179zGc8e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\n0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "centers, labels = find_clusters(X, 4, rseed=0)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels,\n", + " s=50, cmap='viridis');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the E–M approach has converged, but has not converged to a globally optimal configuration. For this reason, it is common for the algorithm to be run for multiple starting guesses, as indeed Scikit-Learn does by default (set by the ``n_init`` parameter, which defaults to 10)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The number of clusters must be selected beforehand\n", + "Another common challenge with *k*-means is that you must tell it how many clusters you expect: it cannot learn the number of clusters from the data.\n", + "For example, if we ask the algorithm to identify six clusters, it will happily proceed and find the best six clusters:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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8vZF1W1bQt/tg5i6fyRbHHMzd9Bxdl0PcgAiSzEZSF+SheFSv7RVVRSV7nxVZ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\nV3yHxl3Bp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = KMeans(6, random_state=0).fit_predict(X)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels,\n", + " s=50, cmap='viridis');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whether the result is meaningful is a question that is difficult to answer definitively; one approach that is rather intuitive, but that we won't discuss further here, is called [silhouette analysis](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html).\n", + "\n", + "Alternatively, you might use a more complicated clustering algorithm which has a better quantitative measure of the fitness per number of clusters (e.g., Gaussian mixture models; see [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) or which *can* choose a suitable number of clusters (e.g., DBSCAN, mean-shift, or affinity propagation, all available in the ``sklearn.cluster`` submodule)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### k-means is limited to linear cluster boundaries\n", + "The fundamental model assumptions of *k*-means (points will be closer to their own cluster center than to others) means that the algorithm will often be ineffective if the clusters have complicated geometries.\n", + "\n", + "In particular, the boundaries between *k*-means clusters will always be linear, which means that it will fail for more complicated boundaries.\n", + "Consider the following data, along with the cluster labels found by the typical *k*-means approach:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import make_moons\n", + "X, y = make_moons(200, noise=.05, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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cxJ6JQ4frMC5duon1K39n47fb8pI25FZXh1OeWPUiXjjPi512m4ZkNG3RjIc/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = KMeans(2, random_state=0).fit_predict(X)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels,\n", + " s=50, cmap='viridis');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This situation is reminiscent of the discussion in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb), where we used a kernel transformation to project the data into a higher dimension where a linear separation is possible.\n", + "We might imagine using the same trick to allow *k*-means to discover non-linear boundaries.\n", + "\n", + "One version of this kernelized *k*-means is implemented in Scikit-Learn within the ``SpectralClustering`` estimator.\n", + "It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9rYiqVg2Hw0HCqUR0uE7j0mWZWLPsd9Z9u7kwaUPB4+pwKpGgnscL57rYmTdo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.cluster import SpectralClustering\n", + "model = SpectralClustering(n_clusters=2, affinity='nearest_neighbors',\n", + " assign_labels='kmeans')\n", + "labels = model.fit_predict(X)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels,\n", + " s=50, cmap='viridis');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that with this kernel transform approach, the kernelized *k*-means is able to find the more complicated nonlinear boundaries between clusters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### k-means can be slow for large numbers of samples\n", + "Because each iteration of *k*-means must access every point in the dataset, the algorithm can be relatively slow as the number of samples grows.\n", + "You might wonder if this requirement to use all data at each iteration can be relaxed; for example, you might just use a subset of the data to update the cluster centers at each step.\n", + "This is the idea behind batch-based *k*-means algorithms, one form of which is implemented in ``sklearn.cluster.MiniBatchKMeans``.\n", + "The interface for this is the same as for standard ``KMeans``; we will see an example of its use as we continue our discussion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examples\n", + "\n", + "Being careful about these limitations of the algorithm, we can use *k*-means to our advantage in a wide variety of situations.\n", + "We'll now take a look at a couple examples." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 1: k-means on digits\n", + "\n", + "To start, let's take a look at applying *k*-means on the same simple digits data that we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n", + "Here we will attempt to use *k*-means to try to identify similar digits *without using the original label information*; this might be similar to a first step in extracting meaning from a new dataset about which you don't have any *a priori* label information.\n", + "\n", + "We will start by loading the digits and then finding the ``KMeans`` clusters.\n", + "Recall that the digits consist of 1,797 samples with 64 features, where each of the 64 features is the brightness of one pixel in an 8×8 image:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The clustering can be performed as we did before:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 64)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=10, random_state=0)\n", + "clusters = kmeans.fit_predict(digits.data)\n", + "kmeans.cluster_centers_.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is 10 clusters in 64 dimensions.\n", + "Notice that the cluster centers themselves are 64-dimensional points, and can themselves be interpreted as the \"typical\" digit within the cluster.\n", + "Let's see what these cluster centers look like:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 5, figsize=(8, 3))\n", + "centers = kmeans.cluster_centers_.reshape(10, 8, 8)\n", + "for axi, center in zip(ax.flat, centers):\n", + " axi.set(xticks=[], yticks=[])\n", + " axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that *even without the labels*, ``KMeans`` is able to find clusters whose centers are recognizable digits, with perhaps the exception of 1 and 8.\n", + "\n", + "Because *k*-means knows nothing about the identity of the cluster, the 0–9 labels may be permuted.\n", + "We can fix this by matching each learned cluster label with the true labels found in them:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy.stats import mode\n", + "\n", + "labels = np.zeros_like(clusters)\n", + "for i in range(10):\n", + " mask = (clusters == i)\n", + " labels[mask] = mode(digits.target[mask])[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can check how accurate our unsupervised clustering was in finding similar digits within the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.79354479688369506" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "accuracy_score(digits.target, labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With just a simple *k*-means algorithm, we discovered the correct grouping for 80% of the input digits!\n", + "Let's check the confusion matrix for this:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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P5TQYDEydOhVXV1f27dv3P1/W9qQDsodNmITfM8/875ehPeaA7Hv3H2D25/Mx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\nk9JqMB4taTlAS376Dc2ybVzM9xSD/0X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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "mat = confusion_matrix(digits.target, labels)\n", + "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n", + " xticklabels=digits.target_names,\n", + " yticklabels=digits.target_names)\n", + "plt.xlabel('true label')\n", + "plt.ylabel('predicted label');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we might expect from the cluster centers we visualized before, the main point of confusion is between the eights and ones.\n", + "But this still shows that using *k*-means, we can essentially build a digit classifier *without reference to any known labels*!\n", + "\n", + "Just for fun, let's try to push this even farther.\n", + "We can use the t-distributed stochastic neighbor embedding (t-SNE) algorithm (mentioned in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)) to pre-process the data before performing *k*-means.\n", + "t-SNE is a nonlinear embedding algorithm that is particularly adept at preserving points within clusters.\n", + "Let's see how it does:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.91930996104618812" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import TSNE\n", + "\n", + "# Project the data: this step will take several seconds\n", + "tsne = TSNE(n_components=2, init='random', random_state=0)\n", + "digits_proj = tsne.fit_transform(digits.data)\n", + "\n", + "# Compute the clusters\n", + "kmeans = KMeans(n_clusters=10, random_state=0)\n", + "clusters = kmeans.fit_predict(digits_proj)\n", + "\n", + "# Permute the labels\n", + "labels = np.zeros_like(clusters)\n", + "for i in range(10):\n", + " mask = (clusters == i)\n", + " labels[mask] = mode(digits.target[mask])[0]\n", + "\n", + "# Compute the accuracy\n", + "accuracy_score(digits.target, labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's nearly 92% classification accuracy *without using the labels*.\n", + "This is the power of unsupervised learning when used carefully: it can extract information from the dataset that it might be difficult to do by hand or by eye." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 2: *k*-means for color compression\n", + "\n", + "One interesting application of clustering is in color compression within images.\n", + "For example, imagine you have an image with millions of colors.\n", + "In most images, a large number of the colors will be unused, and many of the pixels in the image will have similar or even identical colors.\n", + "\n", + "For example, consider the image shown in the following figure, which is from the Scikit-Learn ``datasets`` module (for this to work, you'll have to have the ``pillow`` Python package installed)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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zVhNJ3QgAIA38CE2UM8c7A91jNf7tvU/vUtpUIHwok9F4iy1lNL4WQ2nYVSc5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HbmPPlsCmN2npHLV1CkZPpcH1ohjT5XYvT6Nda0tHLyo4Ns2bAWdbHM9av93HvzzRx98x1uu2MX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VXiEARJRC9JyKsSZyBuKxCCIPDZrkVegGjyZY2hrkpv6xAe8l9VOXVdM51CGIWcjA+eZ2rO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\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YPDRFoVxg78QBAoEAdXODYnkex6rQaBQo1TZJ9iSxHJdKKU+5mCXVE2d1bRHTaiBxsD2T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\n3iSnV/jwzXdZv7WCbijMVPOEYky1WUWWJRRBY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Note: this requires the ``pillow`` package to be installed\n", + "from sklearn.datasets import load_sample_image\n", + "china = load_sample_image(\"china.jpg\")\n", + "ax = plt.axes(xticks=[], yticks=[])\n", + "ax.imshow(china);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The image itself is stored in a three-dimensional array of size ``(height, width, RGB)``, containing red/blue/green contributions as integers from 0 to 255:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(427, 640, 3)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "china.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way we can view this set of pixels is as a cloud of points in a three-dimensional color space.\n", + "We will reshape the data to ``[n_samples x n_features]``, and rescale the colors so that they lie between 0 and 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(273280, 3)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = china / 255.0 # use 0...1 scale\n", + "data = data.reshape(427 * 640, 3)\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize these pixels in this color space, using a subset of 10,000 pixels for efficiency:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def plot_pixels(data, title, colors=None, N=10000):\n", + " if colors is None:\n", + " colors = data\n", + " \n", + " # choose a random subset\n", + " rng = np.random.RandomState(0)\n", + " i = rng.permutation(data.shape[0])[:N]\n", + " colors = colors[i]\n", + " R, G, B = data[i].T\n", + " \n", + " fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + " ax[0].scatter(R, G, color=colors, marker='.')\n", + " ax[0].set(xlabel='Red', ylabel='Green', xlim=(0, 1), ylim=(0, 1))\n", + "\n", + " ax[1].scatter(R, B, color=colors, marker='.')\n", + " ax[1].set(xlabel='Red', ylabel='Blue', xlim=(0, 1), ylim=(0, 1))\n", + "\n", + " fig.suptitle(title, size=20);" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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qbBVFURRFURRFUZT1GhW2iqKsN6xIXJ/y/qD3W1kV9G9WURRFWVNojK2iKIqi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cbG5ui22+RyDRrjs2TO6EAceH3aO0tkClUKtWcDQ3FiGqdQwsxmicOA3tY9dDuHlSkTeQMAIt89\nlbCNBm1k4CIwUEDc78ITwlb2B6ggQGpnE4ZKSdj4NaBg+kfHZs+/LozBDnrQRbrmsff1YRvDO/ja\nz5+SkpLyptg56PBwc5dzE3VK2QxfLq9TK+W5MH1UeuQHAa3eAKkUB70+8xfrDB54eJ0AYQhma3Xe\nuTAPJLHyh5NX8KIhZSfPxLtVNptN5sbH+C+f/v5wzHImhyYFmq7hM2QQeCAEcqSUW702ChiGIdVS\niXevXEVogiAMKebz3F99iB/5GLrBZG2c7YMdxMgKI2NkMPJG0qFgFEI0XUPJmOXt+7h+Eu8s3WK8\nNkkYBWw1V3HsLGOVoxZwhmGxMHeNYehTGXUryGVKzE1dRRMaURSQyRyVew29AWHg47k98qUqpmUx\neeUqSkmsF2Q3hf0+8XBI6A6+ylf5ldAsB2PuGiiFZmfOdKwYDhDREOH3UGcQtiJysbwNYrNE5CTP\nD3p8gCX3CbRxYv3rWTVOhW1KyneQmpPlV1v3eNTZw42Gb1TYrncOuLOzTNPt4IXBNyZsu+6Avucz\nWa3iBwF+qJgfn6fZ7tH1fEyjCEoSxRkEA9yhj2MVQJkEUQTKJYwDHLNIuVBG0xWe7zP0d/CHbSBA\nKBfDqFAtTeIPPfqjuFSrVphoJJ9TypiDg7tE0ZBstkCxeJRGPD7xJ5hGnmLpbfqdAZ1WG8M0qI1f\nPfOKbbe5itveJvR75KtTuO0tMmaNxEEjeeCw8wUC16W/vYrf2UQzTLJXf0p+Yi5ptZA/XfqPPTY/\n6p6gI5TCGps93Bb3NsFxMeoNtPwZa7y769D8MnldmE565Pk9qM6/3lY+YR+z9Slh9UP8ifdRmkFU\nPGpHIUIXY9CmP3GDV29wlJKSkvLt5fcry2xst+n1fYZBRDnvsLLTZL/TOxS2Pd/l5s4al2YmEUIw\nN1bnP21/jhsHySqrkuQyzjF/fX8Y0HNdSnYOyzSYn0zGemtxkbvLy7ieT6vVS2pgR6ur+VyWqfFG\n4nEBXFu4yK2l+1RGXhC5bCIMs04iuoShgQ6aoXFufA5d6Ozs7xAHIYauc2Fmke39LaI4ot0/QMqI\nYRARyGRCWyAI4iEHnSZRFNDqNTFck0Z54tiEajabJ8vxOObYybVY1vEyr9rEHP6gS7lxJI5N5+Wl\nYIWpaYSu41Se9aDwRzXFzisaRZ4FzXq1sjVZmUV4bVTpbL4fpr+NGeyjRYNDYWvHW5iqB0i8VNim\npKQ8yWJxjEEYfO2i9vEMq/Yc4bHVbfPLpXtATMnJMVN6/S59UkkEx9OupZR8NmopMAyH7Hc7tAd9\nZuoN5hpTbOzv0SiWKeXyPNzcJZYaMMTzdzF1wYeXf8zK9hK9QR8/EPQGEdNjDXZ21tE0B8cS+ME+\nMEkc5XC9mIW5K6yub2CZJnPTU0m6klIIoVEqzeD7XQqFaaSUh6nItl1ncvrPRvdvgDsYYGfsV0pD\nzpbGCL0+Tr7C4GCdg9Uv6G5lGL/8UzQ9WY1UStFaXiEeBmhWDadgo+kGufGzpf5ohklm+lljBxn0\n8Zf+P4gDrPk/wSidbtzE/ENAfhIKI5GcacD9v4agn6Q+N75aXfCTZFb/HVb3JsFgCW/+f8Gf/tGx\n7YWVX2P1tvCr51/bOVNSUlKkUqMylzdbJvTF2hq397cghmI2w1SjTCWfo9UfUM7nkpReBL9YucXQ\nCGlFPf7byx8CcK7cYD3cRygYegF3llfpuy7vX7lIFMd8eu8eg6FPEEYsTh2JvHq5RO3GO3z58CHd\nnos2aoenaRrnZqZoVI7EW9Zx+PDq81v2KaVoFKv4gUcxW0QTGnPjs7Sa+3hRiAoltmkzN36OLx79\nHinjxODRAAyBjo4tHFBQylUo5ksMvB629eKVyqStjjoxkyhfrJAvVlBKjWK/OGzFc9L3rZRCt23K\n5+af2Tbsd+is3AMh0C9cw8y8uhmoUvLry4DKllHZs6c/x1aVKHaJjSMBG2lVhFRE4uszmkyFbUrK\nd5SrtSmu1qZevuNXYBB4/N2DjwH45xc+JPdUYLB1A1PX0YTJz86/Rc6yX+v5m90mX6x+QcbK8IOL\nP0B7/MMtBIauI5TPg83fIyiBsNjcb9Luuby7uMit5Y9Z2YqRysbQDd5evMCtRx+jaQa6bnBl/i02\nd3dZ3twiDPZ5tLoFJD31wsBFI48QGZSC4TDk7r3bCLHL0HP44uYaKB3TtDh//hqzsz9ke/MhD+/d\nRNc1rr3902OuyACd1hpe//cEXg5mZ5/+qC8lW6yTHa0Gu51dhG6gG+Zhbe1jNE0n1gTFyQWy1dcb\nPIRmIHQr8QoxTmcsJbu7qNWPwcqhXfwDxOKfJhuUBN0EzRg5ML5GjBxKCczuGsat/5PB+b9AZp5I\npdYtFAJpvN7/rykpKd9fmv6A/3flHpam8d+dv46tv7lH7JxlJaUzNvz48gUqo+yaP668xa8+v8N/\n+PuPQUFcjUEHQzu61h+fu8Kd4QafrDxAiqSI1TJNlta3uL+6RkQMAobe8JnzCiF468Jxs6WB7/H5\n/Tssba7x3uVrWKb5zHGPieOYL+5+SRRHXD5/+ZjRVC6bx/M88qNuEEopomhUIwQQg9IV9fI4M9Xj\nxowXZq+ztnGf2/d+x1h9hnrt2dXHjdV7eIMe9fFZKrXx516f5/fY3b2HrptMjF1mZ/0OSkoaM5ew\n7eOGTlEY0FxNMpTqs1cxnlox1XQDTdeTWt2v8H9l6C4TDvcw7THs7LOGlG+K2CoTW8cFcWBMEnC2\nld+zkgrblJSUE+n5Lh1/ACh6vvuMsK3m8vyrK+8iROKIeBrabo87Ww85159gMv/iFkNdr4sXeMQy\nJpYxmp6IOE0IPrp4hU8efEJnEKNog6ojlcD1PfregJ7bQaoCAojiiEK2xAdXf4YQGuboWqfGxqgU\nS3xx99dIGfO4b20UB9hmAce2QVXwvJBYHSDwETz+nDFxHOP7Ho6TYTDoAIlRRtIO6Ljg9AZNICCO\nxbFV3VchWxrDuvIzxsbLHLSS1Kthv0l/+w5OZZxK+TKG/fpFmzAcMpf/LUrFaOYpHZO9NgQuyBik\nBP3x5IQGF/8FRAHYZ3N4fOkp5/5HwvK75B79e8RwH83bxmp/ie5u4k7/KUGhhgj2CfI1Xu+ZU1JS\nvq80PZf20MMQAjcM36iwPT8+TiWfx9A0Cpkkbrf7A75cXmW/3ScIo8QcqSl4/63ztNpdfnvnLu9d\nvMCndx9yb2mDge9hGgY/ee8aazu7LO3tEzz2qVAQDCPWdnbY3d9nfmqK5fUNmu02GcfmZx9+eHgt\nA9fF9T2EEAzD4IXCNoxCXN9FSonrDo4J28X5RabGp9hub/Jw/R7zk+eT3vKPF0uVIkuegnk8xbU3\n6LC6eY84jiBW+P7za12DoUcUBfj+gPXt2wyGB2TtMrMT15/YZ0AUDYnjiCDwCIYuoAh9F695QOj2\nKc/OAwXi0CcK/GT70HtG2JqZHLXLN4AkS+pVkZELKkTG3iuPcSaUQvOWETIgzpxPJqi/RaTCNiUl\n5UQmijV+OHsNNXr9PJwXBKnnsdxcZ6u9hxt4TF59sbCdH5tHKknOzmHqJlsH+ygFU7Uahq7z/uJ7\n/P7R79E1mzh2MDWFZVoUc0WUqoAKUcToIzdGyzwSe/vtA8IwYLw+jkADNPLZArlsgVZLEoY+YbgH\nxNhWjfHqRTStjhAWMgZdN9E0nVIpSa86t/AOK48+I5srYjwnSM0u/pD1R4JsfuzMonbQPiAOYwr1\no963huUkK7YkwnbQfIjf2SAKXArjX09/OABh2JwlyU6MXUhcp+3is7PSuvn1BEWhEZWu4s7+OSL2\nicrXyd38P9DDLtKuYvV9zME27Dtw4Vm3zJSUlJSzcrlcx40CHN2g4pzNoOfroPJUW52lrR02mgc4\nhsmV+Wk0kbgadzoDlrZ2EEJQKRRY3t6i7/lUGwVmqnXWdvZY3Uy2N6pl9tttEND3Xdxtj06vh67p\nNFstADzPRyrF2vYmxVyeRqXKpbkFNE2jkH3xVKJjOyyeWyQKA8bqx8ushBBIYvbaSUuhQraYCFsl\nEZoBSuK5PdY3lwiqPrGIMHWL7eYqURQAUK9O4TgO7d4e5aeMkManztPvHWBmTXbbmwhD4A7bh9u9\noEcsIiqVOUzTJpsrU584j5Qx2UKNzaXfEYdDYisiX9Sws0XKEwsoBU7++am8X0XQPsbKzRMN9zDs\n568ynxbV2U1Wj4svKW9TAfpwC4FC6TlkZubF+3/DpMI2JSXlhVxqHLVziaUklhLLOP1PRxhHSXui\nUZPz2eoEg6HH7NjYS44ETWhcmLiAUoq9dpsvlpZQgG2aFBwHPwz48NKH/PbOQzr9HoIhmhBMNWpM\n1mcS52I1oJDL4w1dLNNBCEEcR9xbuk8cxwihMV6fpNvvcG56kbv37xLFWVBx0kzdLtOoV5iaHANO\nDhyGYbB46YMXbDeZv/STU9+3x4RDn71Hj1BSoukauUoVGcfoT30H2eo54sDDKb54suCbRggNMXH1\nxTtFPmgmaPqL9zsjYf39w9dB9V10b4th9T2U1URpBn7l4tfQgj4lJeX7iBCC9xtfbyuTr8LcWIOu\n61Et5nl7IUlZ/eXNW2ztH5CxbSqFPLNjDQxTsLl7wPnJSR4ur7Pf6ZCxHSrFPO9evcSvPvscbxhw\nYW4afxhgaBpTYw0O2i3CMEIIwfLmOg/XV8jaDj++8QGz489PP1VKEcXRYRYVwHgtEVZhHGJoxuFk\nrlKKWEYwqoWNpUQIUEJRLzXYb22jdPAjl7W1h2AphJb0zxUC0ASFQomVrduJ2aKZxbYyIw8IRTZX\nIBZDNpt3k/NFiswTq7+7B/cJI5dSfopyLikDK5SPnglytQbucJ+ADktLnzMx+R75ShKP4zhEe+Kz\nvE50PYv+FVOQ1aAD20lPX2XaiMwLjJ2EhbTGEDJAWi9/jnsRQoUojNdqHpkK25SUlFMhleJv7nxG\nP/D50bmLzFRebhR10O/yq4dfYGg6f3z1Q0zDoJav8JOLFRqNAnt7vVOd+4vlJbYPDhKhaZhYus5/\n/ux3gGCsXMIxc3RHdTZKJWlSl+dm2W5u83B1j96gzebOIzSRI2OXeOvSZWzLJooiHNtmf/8+g8E+\nrlfBNE2iKEjMKJRiarJKo/7Vfry/CppuYFgWUkpM22Hn7peE7oDK3AKNxpGnr1Oc+NaJ2lNxcBex\n8jfgVFFX/ufX6478BN7Mvzx87Wcn8Mfe+lrOk5KSkvJtpFYq8Ic3jmeoZG0bQ9eZqdd450JipvfR\n9Uv8MrjFr29/mZSvauA4Jh++fRWlFIapIaKk5+z89BTz04nIm5ucYnljg7FalazjYBomlvXi9nI3\nl27R7XeYn5pnun7kGbJ1sMnK3jLFbIlrs8k1L60+YG9/BywFAlZ3HqI92T9WkIje0TUDT/TUlWhC\nw9BNTMNCCA1N01la+owg8hCmIuMUKOXHRx0BBOenPsAyj6Y+Dd0iin167jZ+0Ga6fgPticnYyuw8\njltif/8eluUcmjn1eqv0eus4TpVq9cqpv69vFMMC0wJE8vpFCEGcu/DifU6BFa6TCVcJ9Qqu/ZLJ\n7zOQCtuUlJRTIaXEDYb4YUhv6L/8AGAw9PCCIYauE8Qh5lOrjF4Q8NnyEo5pcmN+4cTZTH84JJaS\nyWoVZMQvv7wJSISw6HseP3n7CltNh9vLD1DAb2/doZgtUK/YhPHIXEIJpDJxvQApJe9efQepJIZu\nsBS6xHGI7/d5+9oNOt19Hjz8ZFQrq45dy+7uEq2DTcbGF6g80RPP9/tsrnyO7RSYPvd8t8dXQTcM\npq69BUohNI04DJBxROQfr6fxO216W1sgfTRdUJy9iPmStK9vBX4LEbmowGT0RPHNnFdGFB79JTT+\nt2/mfCkpKSlfA0opfrvygEEw5KNzF8jbzjPbP759n+7ARSjFWLXCW6O+tDcunOfquTks83hs7vT7\nx/6dzSRjKpK+t0EYMvCOx6AL5+aYm5rENJKVyVqpgq5pL1ylHIZDIhnjPR3PwphG8UQAACAASURB\nVMRbIwiH9N0et5duEschSk8ihEAcekYBGLqZOBTHozeNx07FgrHGNLvNNQSwtb3EZG2BQj5xNw7D\nIZIYoSCMguSYSIGr2Nq/TXlyitJksio7Vb9OZ7DJfvcRUTxEqhiN41lGmWyFKecDGo0i+/tJn/co\n8lEqJo6fNdt6VYbby0SDLvbEPEbuq7fNEXYGNZ9kOAmhwf4DiDyoXgDz60mr15WHRowuX999gVTY\npqSknBJD1/nx+ct0PJeLY6dzY56pjhHJGNswyT2nKfj6fpOddhtNCC5PT7PX3iSKQxYmLh4LhufG\nJpASzk9M8evbN0lCm07eMWiUyqxs7dDpuyiZBFGBTs91+eDaJXRdJ45C9vYP8IfJDKpUSQsCbTSt\nuzj/AZ3uHiid/dY2jdoUjdoFwjCkMko1UkqyvXWfg+Y6vt/H0K1jwrbVXKPX2cUdtJmcuYp2gnGI\n223R7+xRnZjHMF8yMzriyZrc2sIFhv0exbHjqV1uc5+g20HgAQovu42ZXTzV+G+UyR8gNQOyY884\nPJ+KaIi59TFxaRZZnHv5/iPM3iOcg8/Ofr6UlJSUbxFBHLF0sEsYx9SbO7w9fTwt1RsOWdneIZYK\nJHjD8FDY9l2Ple0d5icnyGePYvT7ly6jgIxpYekmuiZYWt9gfnqKdy5eotvvc+6JVj+r25sIYHbi\niR6vT09k+z6bu1uM18fIOA6re6tM1saJYwlDyc7ODuPjSbw911jANCx0pXFv5RZRHCaDaIAEhMSx\nM1SLYxiGQafbhCg+OpkQKBS2ZTNWncXrdvGGfQZuGxnFxF5IdXyS6enLBIGL0iRZp4Rj5+nnmvit\nHn6/R1fbPRS2QghKuSk0oaHrFoZu0WtvIVVMsTx9+MyiacaxldxicQFdz+A4r69LQdjaRQY+YWv3\ntQhbAPH4mmUEgx2EilGDXSh/PU7LnnkeiUOov97uDamwTUlJOTUTxQoTZ2gkLoRgoXGyCD5Xb9Ae\nDMiYFkHo8sXyJ0gVY1sOE+Vpgiip11na3qbV67O0tcVUtcp6c59iNst4pcLD9RU0kUMpnYxdBBUy\nDCJKhQKapjE3MZesvAqblfVdEKPZ3ico5Ku4bp9Hj75A0w1sM8vubgulFJXKAfV6nd2dR2ysfYmm\nmRRLY9THjv/YVxvnGHpd7EzhRFELsLN6G3/QJY5CJuavEg5bmHbl1D3onHwRJ/9sIMuNNVBxhFI5\nNA2yT9x3GSYzopr5LWxvIzSY+PDl+52AtfFLrO3fEbfu473zvx4fOvJASpT17Mp1WFjEq3/Em7d4\nSUlJSXl1LN3gUmOS3tBnsXE04ekNAzRNkLFtFmem6PZdGK3YPubTe/fYa7dp97r89N0bh+9rmsZH\nV5L00Ha3x99/8gmgyDg2E/U6tVIJz/cxDYNOv8etR/cRQDaTpZTPE8kY56l483D1IbsHTXqDHoVK\nnrXmGlkrw1xxjntLd9E0jWKxSCaTSYym7AIP1u4yDHw0Tcc0TLJOhjiKCKIhw8CjOzjA1E36bgdQ\nCASappHPldA0jVKhRnN3jV6nhaYbONkcXqvDRquLk81RKFSAo/vR6ezQ6+4hLEGmUqI0dby8RwhB\nMZfc42A4oLlzH1DoukW++PySJV03KRbP3uLvRZi1KeJBB6t2dH1Khklml366CfMTEToUJlGhDy/p\nXPFVzzO0Xu99gVTYpqSkvEEs0+SjC4mDbxAOyWcKxDKmkCnyj7dv0RkMEBgYuoZlGOSzWZoHmyDb\nDIdDCtkZHMtCKR2UwezEGEO/w9r2Os4T7Wi+vPsbDjq7GEadjN3Afk7dj5QREKKkxLQsMhkHKSXZ\nbDJOJlvCsrJYVobFiz98xtnYtrPMX/zhyz+zkyMKfOxsgd2V/0h7+x8pNt5jcvG//wp3EuxCEbvw\nrOCNvC6tW38PAipXf4aRKTzn6O8uMjuGNPPIp2bDRTAgf/P/BhUxuPRvkIWnzEs0nf75/yEVtikp\nKd9phBC8N3v+2Hv7nS6/+OQLDF3j5z98n/cuPT97p9ltgwa7ndYLxk/+KAVylOm7tbvL53fukstk\neO/6NfKjOOnYNr9Z/hQv9Hlr8jLjpSOxl8vmMLsdspkseSePZVhkrCz5fJ5MJoOu64c1ubeWvqDV\n209qZIGsk+Wdi+/T6jS5t3oTAF3TcewsWTtLt3+AkIBQ6MLg4vw7h+ftdJqYpo3tZKiNT7HmJL1l\nMZ9NkbbtHKaZQbN1pueuo2snyyTDsLHsLFJKrNfcsu5l2GMzwJEbsZIhXvN3KBXjVK6jW893YT4V\nQkDl/Mv3+5aSCtuUlJTXyl63xRerD6jkCry3cHqjBMu0+dlbP0eNZl3DaBmlFAqJrtn89O0rWKbJ\n1u4SAEEUsLyxxTsXr9FsrXLQ3sUxy+ztJwG6Nziy6e+7HVCKUt7g+uWjNOd2p8nq2l0K+TL5XBEQ\nCKG4d+evCIY2oKHUPADFYoO3bvyLJNX5JQZHURiy9uA2QmjMXbyaNGEfMbX4zmG97Ob9fwQUcfj1\n9Z+TYYiMQ5AhnXu/wKnNk5v5p2OcFDWuE9WuPJvGLENEPETICC10sZf+K8ZgH/fcj4gLX60tQkpK\nSsq3ma7rMgxDAl3xnx99xmJ9kkuNZHXsb3/1MT1vwLX5+UPFKoTg7qMV7i0vY9oGWSfD9cVFauUS\nQtPQNA2lFOaoD3kQBMRxTDgyYPzJjSTrRipJNOo7HzxOHx6xMDPPuak5NE1jfWudqBfiD13y83k+\n/OgjgMPYGo7a8yASJR3LiK29Nbb21hKDSAGFTInz05cRQjBWm+KLm78ijmI043gsKJXqFN+qAoJ+\n0ELoGgKB0J/NknKcPOcXjl/LSWi6wdS5D06179eNlDFKRaBipAx5vf0FvlukwjYlJeUr03U7PNi5\njyFMdrt9DgYu7tB/qbCVUnJ3fZWMbTM/Pjmqj00CxEyjQbPdZrLWoJjLYhoGSxtrjFUn6bs59tsu\nrV6PvYMWO80dPD/mwepDUC6ogDCUPFq5y/zsxdGsqxi5IR4FoL29DXrdNr7nMn/uGkIINjd/jTto\nIUj69m5t3OHCpR8AnNh/9mDvS3x3j4mZn6DpJv1um347EdiDXpcoaBN4fRqz15Lam9E1jC/8OZn8\nDIX6O88d93VgFWuULv4Qd+MLot4OQ834JyVsgWfaBGm9LazmTdy5P0CLfJzVv0b3A3RlY+0/wkuF\nbUpKyncYpRQ3H62ia4Kr87PH4trmwT5Lu1soS0JG0BkOuL+zwfAg5PrFedpuH4Xk7s4qF85Ns7Pb\n4oNLl/jdzdtEsST2A/xhwObeLrVyiWIux0fXrxNLSaOaZMbMTU+jj7Ko9CcmbnWh8870NdzAZbI0\nfnity7sr6JrO3Ehcb+5uIIkZ+APguDDc2lunlCtjGQ6dQROJJAyHrG0vIVXMY5PBaqXB3ZXf4w37\nXFn4kIsX3mF7Z41zs5eeuV+PS30KdpW5yjWE0MiYOVq9TYLQo1FZQBvtcxaRepZ93eY2odenOL1w\nVM96AkN3n8g9wCnPohtHZmAyChjurmDkypilo36zuuHglK8hVYTpvKQP7T9xUmGbkpJyarzAxw18\nak80G++4Az5d/oTt9gYCDdCp5GZYGHt57cTyzjb31tYQms5EpYYzSkNSSrG6tYUXDHEsm3q5xPLm\nFneWHyEEvH/lLSoFn77nMTc5wU5zFwhxPYkgCzwgDA5YWRcYhsns9EXanT1qlQl838VxsqPzJCZU\nSukIIajXZ9D1kN2dL/E8DaV0ao0F4jhCP6FuVsqIzZX/TBT00YTBxNxPKVXrDCamEAgyuSx37/wC\n4hDNMGlMH4l93XCoTP74Vb+OU2M4BTITVwisDHb12e8lctsIoaOfIk1ZDruoKEDPvbzd06nptcDO\ngvV6aoCdtV9gtpcQ1csY7gaGv4vCYNj4A/zJ1+dYnZKSkvIm2Njb59P7DxFAtVRg4om62c8ePqTV\n71PMZSmXcnhxQHuzyy1vGceymKk32O7vExghD/fW+dN3fkTWdrh+4Tyf3r5HsZAh42RYnDlKdX0s\naB8jhGBm4vn1l+VskXK2SMfvYgmTzZ0tlg6W0IRGOVeimC2Sy2Zx3cGxbCYAb+jyaPMBSkkuzF6m\nNzhAxjFRFCbzwTqYlkWt1KBcqLO8fRsE3Fn6He9d/imLC6Vj44XhkDAcks0elekUM0nsiuKQ7YMH\nSBmh6yb10unNB8+KkjHdtYfIcIim6RSmF164v7f/kDjoo1RMvnH0zDDcWSForhF1948JWwDdrn7j\nK7XC7aCsLDzRh/hNkwrblJSUUxFLyd/e/CV93+VHF26wMDZLs9vhF7c+R+KRsbIINCzd5oPzV6gV\nai8dU0oFmAiV2PI/RghBKZ9DdjW29lx2m18SywjLzJKxNYr5PGPVo/Fr5SrNgxagI3CRcokovA50\nkNJncuwaGSvHl3c+RtcN3nvnp9i2g5J9UAGo6HCsSmWBSiUJOhurD7h/6xMKpSpX3/rouZ9BCJ1s\nboKh3iI3MogQQjC9kNQOx1GIkBoKExmr547xdRIO2rRu/j0IQfWtn2Fkj9fhht0derf+FqHpFG/8\nGbqdP3EsFXp4n/87iIc4l/8cvfIa3BK3HyEefQqZAurdn7+WPrZxbgrNaxHnJ8Ew0P19YqfK4OIf\nf/XrTUlJSXnDVAt5asUCCEE5d7y+s1YoEkYR1+bO0dzpsL66i2NbOAWLerXExYUZOm6f3zy6hakb\n2GYiSrqDAUE0xDILfHj92le6vvXOOnea99C3NeJBjFWyyFQzZO1kUnmsPkF/OKCQPR5vLNOmkC0Q\nyZhCrkw5W6HTaxMySk1WClvYLExdJo5jBAIlFcXnmFpKKXn46FOGgcfszBWqleM+C7qmk7EKRHFA\n1i49c/xrRWiYuTyRr2MWXl7/atgFlIwxnOP76rkiWjdzqknorxutuYqxex+VKRIuPP/56E2QCtuU\nlG8ZXjjkr+78BoB/c+WHOE+1hBkELv/x9n/C0A3+7OqfYL7Agfes/Je7v6bt9fjhwg3GS0+nsyik\nSv5EMrHVl0oiUWiiwB9d+2cUs9lnBx0RRBF//fHviCX86OpF7qyvsry5g1IRUsE/fH6L81MTLEwl\n6Uu60NCEQCmI5RCQ6HqWH99475mxdeFiaB3mZy4yMXaDKP6Q3376/zAMXBzb4cGj37F/sI2UoFTM\nZ1/8X0xNvEUmYwEbOM6RwcXm+jLN3R0aE1NIKZNPLk8WpEIIzl99vvHTwfYSzY0HCE1DxQrLPvn+\nnISUMbu3foOMIxqXP8B0jo8h45D9W79AqZjqlT/AsI5vV0olNUlKoWTM0ygpQUlQAkaf93kEGx8T\nbn+BCj0EoGR04r5n4vD8T91jJdFv/yUi7BFd+HPIjYPfwrr7lyjdIbz2F6A9f5Z4eO5nDM/97PDf\n3oU/fz3XmpKSkvItIJtx+Nc/fr6Y+MGVoxW+3a0DAOrVEpVqjr/73W/J2Q7/6ic/5udvJSU2cRzz\nD598ykGniwoVrVbvK19frCQKRRzHYCqKdoF3L757uL1RadCoPJsyq2s671z84PDfl84flc3cvfU5\nrYM9Mtksy6t32WtvkLFyvHX1ZNNGpSRKycNY/iRCaMxPPvs88XUghKB26cbLdxyRH7v63Pet8jhW\n+VtSSqNGzxNPx+43TCpsU1K+ZWx0D1jrNAHY7reYrxz/EVttbbDZ20EAB26b8cLrSQmVSrLV2cUN\nfNZb288IW13T+aNrP6DrDZipJilIY6UKf3j1bXRNf6GoBegM+gRRUhuzvLNDq9vHDwIed1nv+z57\nnS5jlRwP1m6xc9Anik0mq+PsHgxQQBxG3F1aZnl9nXKhwPvXr/FgZZ39gy38YMB+e4+JsRkM3aRR\nm8b1WjRqc6yt38H3u1Qrs8h4g253m1Y7x1vX/4JstkI+fyRs2+0Wrtun2z7g0tV3yOYKFEpVZByz\nvnwLJZNAOTa1QDb/4pnXfnuXwOti58pMnLtOoTpBd2+TQXuP2swFrMyzToq93Xt4nXUqsx9hOgUi\n38Vr7wLgtXcxJ+aP7R8OWgw7WwAE7R2MseMpTla+QuXaTwAw88/OalvlSfLXfo6mGeiZk/vhRXt3\nUd4+ItvAOf/P0F9Xb7vJRZSdgWzx+Gpt5KF1HiHiAK31AJkbR28/QO+totCI/BYq+/z2CikpKSlv\niu1Bhy/3N7hSnWT6Ob+53yQfvXOFiUaN6Yk6f/Pb3yCNmF44ONz+cG2Vu8vL+P5js6Ync6denbnS\nLBnD4cHBA1zXxXSOJiHX9lbwA5/zkxfQX1Jruru/SW/QZnZykcVLV2kdjFGrj/HFnV+hdIk37LG8\nfpvpiQuYT6XDaprGwvwNhoFHqXjyc1IUBOwuPyRTLFGZOLk94UkEQ5fu7jKZQo3ct0V0fgPI+jyh\nnUW94LnhTZAK25SUbxmL1Ql+OHMJgeBc+fiDuxsM0USO96bewjIsxvIvT/c9LZrQeHf2GvuDNten\nj5svSClZbW4zWakzUz2eAjNWenngjuMYf+hRydsEYcwHFy7yt598BkqQH/W922/1mRur8XDtFms7\nj3CsPFONy9SKeVrdLkEYknEyLK+vA9Du9Vjd3GZ9awfDcJgcqzE/cwGAIPTY3PqcOA5Y32xwbvYt\n2u1tZmevEQbzbO9kGWu8M6qrXSQYeuw3N6nWJpmZmWfPdhgbn0pmWUd9AbfW7rO7sQxCIVDIOOL8\n1ZPTbwadJoXKOLpuUKxNk6+M02vusbd6h8Dtg1JMXnr3meMOVn9D6LVA6Ixd+EPMTJ7yuSvIMKQw\n9mwNkFVoUJh9ByVjMo3ni02r+OL/J1bp5b3qlDJAaSilAafruXsqhIDa9LPvmzniuT9C+C3k5Ghl\nYex9QrcJZgaV+X4bZKSkpHw7+e3OEkvdPXqh/5WFrVSSle4uk7kqjnH2/qS6rjM/k/y+V6p5+gcD\njJGY3N0/4Oa9B0RxjK5rZPMO/YGLeMXONVJKdlo71Et1TOP/Z+9NYyPLsju/331b7HsEg/vO3Pes\nzKyla+tuSa3u1rTlsQTLtmBgZjAe2IP5ZPuj0DAgCLIMGIZgwR+MwSwWLGkEz2hpSSV1q7uqujIr\nqzIr933jTsa+R7x4qz8Ei0wmySSZS1V1K35AVRLx3rvvvsvgO/fce875q/QEe3BHXQrFAiNDIziO\nw+LSAg/z93Fw8KpehnqevkA6u/QAw9AxrTbD/VOkejrPMtq/l+mFO1hSm1xhHkVWGOybWr3OdR3K\n9QwhXwKv9+kPVJibprw8T6OcJ7pSxHI3VLPT1AsLGI3Kto6t67q0a3kULYDi3X0E11cKIXDDXz1H\nvuvYdunyFUMIwZujBzc99oNbl1moFDk5OMarwzuX0tkpe/s217q78PAmdxZn6I8l+cbh7bVan+Ti\nvZvMZZeRhIokNLLlCiN9aZZzZQ6MDvLJjbu4Lly89YBje3sp14okomkiAR9Xbl9FlhUCvhRD6TR3\nmjVsywYB8WiEfLGM3+vlwNTkqkFSFQ+x6ADtdoN4bJhQMElPquMU+rxBwuHBdf27ffMT6rUywyP7\nGBzZSzi6cTLiOgLQEFh4/T5C0a0dq0a1wPT19xFCMHbk6/iCUQrzM2Qe3kVWXDR/iMAmYVgAvugg\nQpLxxzr9FUIQG9q75b2EEERGdh7i9KyoqSlM28DVK+jX/yOe/d9BSWz+fXlROAOvrf9AkrHGf/ml\n3rNLly5dnoehUIyq0WLwBezWfrJ8l5vFWfoDCb41enL7C57CaE8/Nb1BPBhmMZPl/KWrnfrCsiCV\nTDA23MetpYcMpZ4tEub27B3msnMkI0lO7j0BQDqVJp3qOD83b1xneXER1a8RSAWJ7aAOhy1ZILmU\nmjka0zWOTr2GIqtEokmORpM8nLlGq90gHFy/Izufv0u+MkvIn2Cy/+njFoglaFSKeALBZ5Lt8YUS\ntBsVPDv4fTcL81TnbyF7/KT2vb5arbnLi6Pr2Hbp8hXGcR3+7Np7VNt1fmHPm8grL115C9mZv79z\nnqVKjjOjR5jcZHcPIF8r8ZM7F/BrHr516GtbStg8zloZ/Gd7Cc9l8oDWKdUvWVy5d53hvjTvnujk\nzwgE7ooS+0BqmIHUMBdv3GYpu4jryng0lTdOHESWZBrNJjMLyyRjEWKRMGeOH+b2vduc/eQso8Oj\nOHaZuYWb9KTG6E8f5NatHxEIxBkf+xq3bt5EkiQOHzmyTqJArOxAipWxsG2T21d/hNF2EIQJx+JE\nYzGEJKF5wuw79vq6659EfK5oL8Rqm52xE6ieEGMnzqwaUMexWbh2HrNVB7eOL5pk+MRvPNM4v0y0\nwROo6X00L/57sA2EeA7zsXgF5i5AYgIm33lhfezSpUuXL5tjqRGObRE9s1vkFZsrPWNRvb/88EPq\nzSY+j4eT+/djVyxms4s8cucRCPyal6+dOclPLp8jey2DV/VgBZ6tfkK9WQPHpVgp8NH1n3Jg5CDL\ni0vkczmkZmdH15VcoqEYh3eYb+qL+Kg3TDA6+vGObXPjwWcYzRbCEkSiKUbSe5m+cwPN42Pq0Il1\nWvPiscDqhZu3qOXz9IyPEx9cixIKxhME488e/RaIpncegrwyD/iydW9/nuk6tl26fEVZrOS5OH+b\n+Uoe02mxUFnmOweOk6tXGYjEN70mUy1QbtVZqua2dGyXKnlKjQoNXaVtGfg076bnPc7J8f0MxHtI\nbVJ58Ekaus71R7MkwiEmBz6vQrhSpx+XgUSUhdwSc8uf0WpeZv/YL3J0cpib07OowuHvzp7FdevY\njozjeBlID7FvbGQ1F2fv+DA98SiR8Fo1xXwhh643yeayqHIZXa9RrWWRsGk2yziOTaVcpl6vI4Sg\n3W7j9/tpNevMTt8lluhnaHQv+cxDZh81SaSGqVdzQBiBTrNeZXzvATSfH03zPNWpBfCH4kwc+wYg\n8K5UIY4PDOENhdC8/nVGzTFN9FoJ17aBNu1afl1brutSenQBx2wTn3wV6TmLhTVmPsNuVghMvoas\nbvzdt6bP4+h1/JNvIp4IfROqH+/R/xLsNnLwOfJbK4ugV6C2/OxtPAdq7iqe7CVAgnf++y+lD126\ndOmyHa+kpxgMJkn6N89jzBVL3L4/y8hAmmQ8wuU790hEIuwd6zjWzVYLXBe9pXPp5i2auo6QOouv\nHo/KgX0TXLp3HdOyEAh0o02+VIJt/HLDNLj18A6u5YADo8OjeNSOXJvjOjRaDUq1EtVahXZTRzQB\nAXuPHaBvcC2P9fbDq5RrRaZGDpJ4LAqq2WwwN3+fZKiX0eG9SK6MKquYlkG9UYY2CBsajQoezYve\nbGDQ4tHiZXriYwwk9hDxpwh416odN8tljGaTRqm4zrH9nHJunkalQLJ/Ao9/a3WA5yGQGEDxBlA0\n3z+Y3VqlPIuslzESU7iq7+Xf76XfoUuXLs/EZ/N3uJubI+qNMNWzh+ODh1EkmcHo1iuLr44fZb6U\n4eTwWkW9xXIB3TQYX8kVPdA/QcvUCXsDO3JqoWME+2LrQ33qeovFQoGJvv51O8h35xaZWc6xXCyv\nOraqpGLaBqqkcnRyH15NYyl/iflsCU0NUG+M0G43Pi/oD1hINBkbHCPo96K3Dbwe72pf4rH1pfk/\nv7skwdjocVTNS7pnHJ83TD5XI5ZIku7tpd3uWMNS8SJ6c4ClhUXKpSL1WglJ9JNbfoAQEum+SUYm\nTtLWW0CQyMpqbjC0c0kArz+CY1sU5m8QTo2hevz4w+uLTTmWTS1XIDGyD6vdAreBP7o+39VolqjM\nXu6MYzBOZODZZRgcq01r7gqu1Ub2hQiMvtK5R34BJAUlEKA9exEci7Y/hnf4xIY2ZN8LkEUYfQ1U\nLyQmOxUVl69AMA2hvo3nug5i+RJuaLBzzgvAN/9jtMpDuiawS5cuAPWWzu2FZY6ODaFus3C5GeVW\ng9lSgYO9g1tGVD0LQgj6gpsvZC9Ucly/+ZBstkSjpZNOR3m0uEimWGTP6DBCCA5OTHDj3n0c16Gp\ntzoXOoAAx7Z5uDhLqV7Bo2r4NR+JSIQ9Y9vruU4vzLKQWUBYK+0BU1OTaIrWeX5JMNI7QsQbJhfO\nIhkSXq+XwZH1OurZwhIuLg/nbq9zbBeXpsnnl2g0qpzoG1393KP5GB7Yi97WaVUrpFKDJJK92JZF\n2Vmi1MiAEIz3HyPkXz9uvXv2UM1lSY2OshmFxWkMvY4kyfQ9Vo15MyzDoLG4SGhwEEnZnR3xBLaX\n+/l5Qis9QrZauLKGkXrxKXRP0rXqXbp8RZlIDlDVG0wkBzkzsjNnZiwxwNhjhXjqeov3rl3AtC2+\neeAE4z19yJLE6bHDz92/czdvkauUqTabvDK1VmzKsl1AXslJ7RDw+inXwe/z49E0Do3vwec7xlLm\nIf3Jg1S8Hlp6m6beoFMl2SYaThL0e7l+5y5ej4e3zpxGWZlwOI6zLoQ6ne6jWMzT29OH3x9h71Qn\nN/PypfM0m01arQUmxo8wMjrKrRv/lnz2KoJJwIvPP0gsniaWGKBUWEBVPWiaj97Bzpi7jrMaTrwZ\nrmMjtqjsuHjvLOWlu9QKc4we/daG48t37lLL5ggk4wwe3tyQar4I/uQojmUSSDxfeJuQNbTECLZe\nw5MaB8AoLFG/8REIidCJb6Amx3GMJmpy/Lnu9VR8UZh8t/Pz3DnE/ffAl8B95V9sEHqXZj5AefRD\nXH8a8/S/fCE6t2b8IMI2wRXsvhxLly5dft74ywvXeJjJs1yu8t1Xjuz6+vduX2WpVqbcavLWxO4n\n75/Lsu0kNQggUy/y/sNLOK5DJBJksDdFuidGrlQiEgp1JN6EoFqr4dh257XpspIiA+BiWCaVSo1w\nKMjEwDDj/R2HNpUKkcs9XfKnN9VDvpzHNixkZNKpHgLeAPvH1svUJOJJEvH1i+LuijyMEIKgP0RD\nb5CK9632GSCRSNNo1AivLAY/fk1vaoSZhZvUrSJUXFI9AwyN70EpyJQb598NtwAAIABJREFUWaKP\nRRO5rttZPBUQTMQJJbfeGAjFUjRqCqHY9guo+avXaGaz6OUy6WPHcF1ndQe2IzH01ZLA+TKxAinc\ndhU78MWoGHQd2y5dvqLsT4+yPz36XG0osoy2spro1V7sFF5TFASsirt/TjwUZDaTJxxYCzmJhQOU\n61ViIT+zSze48fAjJDEG7jF0I4CuN7FMG1nyIEsyp4+8QiwcYjmXQ5ZlVEVZNXgzczd4NHOdVHKI\ng/teB2BibJL+3jRXrnzA9LTg+LGvo2lePCu7vJIkr04YVNXf0WylEzbVN9BPtZTjxqXbDE+coKd3\nzaHLLcwx/+ge4ViciYMbKxhn586Tmfkp4cQkI/u/t+G4onTuL29RzVJeGTtZ2VyPFUBIMulDv7Dl\n8d0ghCC8/+vrPpNUDSErCElGUj0EDmx0wF8qqr+jR2s2kT7+P3BG34LB048dD4CkgOJ5IU4tQGvk\nm7RGvglAt7Zyly5dvGrHnvmf0U5qioKEwKdu/S5/Gu9duUixXuW1PQcY69m+Sr1HVlFlBUtY6LZO\n02iRio3zi6+9ytkLl/mz937MoX1TzC9mwAZXWknvdF2E3Mk8FZKE5EC71qZVb+2qv4Zt0FZa+P1+\nTk+d3nHOqOPYXL5xHsM02D91lBMHX6ferHJr+hLFeoajk2eQZYVYNEks2nGIa7US9x9eQVE09kwe\n5+69i+h6A1dyabeaq233JSboe6ygoeu6PPjsE/RGDcLgi4QZHz21ZV97hrcu0vgkkrZiu1WVanWW\nSuUeXm+CkHeI4uw1Sv4AsdGT/2DCjZ+G0fPsUWbPQtex7dLlS2KhUuD8zD3GEmmOD4xtf8Ez4FU1\n/vErb2LZNkHvi81t+NqhQzRaLUJ+P0v5MvcXMgynE6SiQVJhFYHF2avX2D82yvE9U0wODhDy+7l2\n78fo7Rqy5OI4bcq1GvWGTtu0SCfjjPQluD99k1Q8RTziQeZPkUgiSx1pnWqtRNtodfJsVlhYuMHC\n4l0azRZCSLRadTTNy8jIIM3mfaKRtdCfaOx16tUwjVoW17URQqXVqGC0GzRqBXjMsS0X5zDbjyjl\nFrh/Lcvg+GkatTqlzDI9gyO06stYRg29sT4v9nPSE6eI9u3Bs4XOW8/UBNGBPrRtNICfhus4lO9e\nxrUdYvuOI3YZRqeEE4Rf+RZC7ji2AHajgP7gI+RwH97RrSWNXgi9R3Ejw4hL/wahlxHF+7iPObbO\nwCmM2ARS7ibq1T/EGn0HN7yJPFCXLl26PCO/cvooXzswRSL0bFo3v3LwBDVdJ+bf/fWu61Ju1Knr\nOvlqZUeObdQX4jv7XufDi5fIGiWWims2qFpr0NLbXLpxE9uxOwWUREdDHmDv4BhjI0PIksTFG9fI\n5vNU61vv0DqOw/U7N7AdhyP7DvHgwQOWaku0RAvHcXBcB1lsbnfmZ2co5HOMjk0QicWwbItGs4Fl\nm9TqFSKhGPVWlVa7gSRkDMvA90QdiUajiq43kWUTXW/S0uu4rgMNUH1rCxGFpXlKuWV6BscIxxO4\njkOzVsaxbGhB21tnZdt62/HdjtThw0THx1EDAQqFG9h2G9OsY7gVLKOJ7tqdaC6569h+0XQd2y5d\nviQuzU9zN7dEpdV8bsfWdhyuzd2nL5ogHVkfauNVNVhZRM5WymTKJQ4Ojew45Olxlot5Ko06ewZH\nkCWJcCDAo6Us9+YyFGsN6q0mXs0lVyp2jKmroCoqpw7sIxzoGPw9I69RrbdIJwcwLR97RkZotnTC\ny1lGBvq4P32TTG6JWq3KQ5ZoGzkMo8Wtu1eZGNvH1PgJPB4fPcm1PKD5hWvUajlCoWF6e/dSLmbQ\nNA+Li59Rq9zA0COMT76JEILlhQfUKmX8/hjp/hF6+6fw+UJUyssMDK2XWZKkBtDAtU1K2Ydo3hCt\niku9UkIIwfC+b6BqAVRtkNzcNMnBkXWrwUIIvE/JpxFC4AlsnAi5rktt8QGyx0sgObjJlWu0K0Ua\nC9MAeOI9BPo25ke1lu8iBHjTezYcA5Cf0NMzFq5h5e5j13LbOrau62IvnEf4YsiJ7Ve83cz1zq5B\n70o4vOtA7i7CdjtzDjbZ8fDHURYvIjWzuGoAq+vYdunS5QUiSxLJ8LMXDFIkeddO7XRumbZlsqd3\nkDf2HiRTKXFsdHMJteVcgVKlxt6JkdUKyX7NixpQoObSlgw+vHKRNw4fZ6C/h2qjhuVYHRdO0MmD\nlQHLYW5xiSMHOuHSR/cdYHZxgZGBAWzb5sHMI4Q8yuPuQalaZmZ+FoBENM7s7Cztdpv4UJyxobHV\nwo6bsTA7S61WRVEUIrEYmuphz/hB9HaLgd5Oak1PrJ9CMYNH9eLzdGyR49gsLk8TDSdIp4exHQvN\n4yMSSZBKDmCabbwiSDS5Ft6aW5ilUSkhCanj2OLihlwwXPypGOnUxAvbQRVCoAU735dYbA+yrOLz\npfB4oriOQyKVxJKfbff+SWyrjVGdRQsNIn8BxZee3pk2UmMWxz8Iypfcly2Qv//973//y+7Ebmg2\nje1P6rIlgYCnO4YvgBcxjookUzd09vT0P7Ug1OO4rkvbMldzTT/n/IPrnH94g0ylwOGhyU2vbZtt\n3rtykfvLi7guDOyyvH1Db/HjS58wk11CU1VSkRgL+RLnrt9DN0yiQR/NVoNas0U44MO2wXVV/B4P\nA6kEkiSwHYdbDx+xkGli2zKvHDyIJEl4NI1UPIYsCSQh0TYMWi0Xw+gBHARTVCpgGG0G+gdJxPvX\nia7bloHruoyNHqdUyDI3e5d6vczwyCH0VolYYoJ4Ynx1DE2jTd/gFP1D+zrOpy9ENNaH9MS4KooH\no11H9QTxBWL0Dh9FVj04jkOidwB/MII3OMD8zduUM0tIskwwunmhj91QX35E7uZZmvkFQn3jSFuE\nKgcCHtqWwGo1UP1BgiN7NjxDu7RA5epf0849QosNIntD295fKF5cvYoSH0GNP72QiL34KdbtP8Up\n3EXuP4WQ11bQXccBy1rdRXbLc3D1jyF7B0K9CH8cZs4i3ftbXCHhRoZwB0+ANwJPTpasFiCwh14F\n3/PrQ35OIOB5YW39Q6ZrV56Prm1+MfysjGOl2eAHV8/zMLdMLBBkNNVLfzyx6YKz7Ti898F5Hs4t\noCoK6eSajZElmbrVpNaoU6nXKVYrPJqdx3atjtyccFcUZlyEgHAgyFB/P+lUEsuy0FSV3lQKj6Zx\n/fYt7ty/T75QZHhwrciTR9NoNhsE/AGmxiYxTRNFUTgwdYDEU+Yupm1irWjODw6P4F9x/P0+P6Fg\nZNUhzuUXmZu9R7NZpyc5gKKoTM/cYn7+HvVGhb7eEcKhOAF/iGarxsz8dVp6nf7BCaKPFZxybAvH\ncUgNDOFd0aQ1aaP4Nfp79xEMxl+KzI4kyfh8SRTF15lPBOMk0qkX9j1sZq5gVKexzQae0Je7qCuX\nryA3phFWA9e/SV9cFxyTldLbu2pbuCa4IFwLhPzMtrm7Y9uly5fEeDLNeHJ3VV7/6uo5ZosZzowf\n4MTI2u5YxB/Eo6j4PZuvoN2Yu8mnDz5FkMKjqIT9u1tpu/bwLjemHyBJAo+qEfJ1DFTAq+H1dKog\nfu3wXs5evUnLkDixZx/355bJlKrkSlV+dP4qbx7fx0eXL1NrNDoSNpXqunu0jTZnL3yMbdu8cvQE\nt+7OUanVAAtFyDiujG+LcOqRkROMjHQq+N6+eROIUqvohEK9HD3+36w7t1Gr06jqNIKNbZ87HB8k\nHF+/YxoIJYkmktz77P9j7lYT17GR5TFkVcXzHCHFj6P4gsgeH5LiQdpm1VdIMolDp7c8LnsCSN4A\nIJA8O+ufEulFOf6Pd3Su8MVBCyO0IDzu1Lou9mcfQb2GtO8IUu8geILgCYGpw9UfQnoGUv24qh8C\nSdw930K+8ocgJOxX/mnn3BXssXexx97dUZ+6dOnS5auMV1UJeHyYtk3Y+/T3siQEfp8HyzIJBdaf\nW65WqJZriJVijcuZXGdBEYCVKJiV4klej4eTRw7Tk0hQKJX4+OJFNFXlnTfeQFUUAn4/qqLgf2J+\nIEsyJ46sVcg/cGD7nMnl0iK3564R8IZ45dRrqw6l7dh8ducjTLPN/tHjxMJJvB4/muZFURSUz2uC\neAPIsoKmrnduFEVDU704ro32hKpDz9AYPUNr0W9CCIb6D1EuLTF9/zweb5CJqdd/5jRkJdUHQkZS\ndqZi8TJxZR8uMq68eV/Uyg2U5hxWYBgzcnDTcza9zskQsK8gKg7CcGkFJyB18pn62HVsu3T5GaLW\nbtG2TMrN9U7Zgf4xxpL9aFvs7C0WM7QNgU+r8euv/+quC0nVmk1M2yIdjvP20VN41M71Aa+HaEBD\nkWV8Xo1vnDpGoVLh9vQ8hVIW2zawkajbJueuXKHRbK5WC3TtS5y9+CkHJn+daGQMwzBo6S1s26al\ntzhz4iCWbSM4ipBU2m2TTy+8z+zsFULBOdI9RxkZ6RRCmpmZpZAvMDIyjGPb4LZxnfWvN9M0uHvj\nYxrVJrZtoetNnhXTaGDodVzHROCihRpMHP8eirq7ca0uT1OZv0u4b4zIwNTq575oD0Ov/SOEkBBC\nInf9pziWQfLAG8ja7lYxFX+UxJnfAEDaoojV8yDHJ5Fe+x+xl65iXvp/kIdOIadXKjzrLTDbuM06\nAMIXwz3zL+DW+4jlu6DXIX0QNz7WcYoLDzr6tkKCdn2dY9ulS5cuPy+oskLSDmFa1raOrRCCRCKC\noglikfXvxJmFRcyGCXJng8wx6ex6SQIcFySxmlL6y2+9g7ZS3KpWr9NqtbBME8uyUBWFidExBvsH\n6O+LUShsv/D7NJp6HdMyaBvri1I5jk3baGIaBvfuXCWV6GdsfB+vHH8bIaRVjfiAL0xACxN8Ql5O\nkVUCWgjHsfFonXHTWw3mHlzH4/MzNH5og+PabtexLAPJ0Hlajm1+7i6NcoHk8B4CkQSu65K5ewXb\nNEjvOYqyS9v7ovAl9uONTqyLhvpCqd5H6Dnc8CROeD9OcAKkzfsirCbCtRDW7uZXkttApo3rCCTX\nRd7l9Y/TdWy7dPkZ4hv7TzCdX+bY8NSGY76nvHQ1JQCoKJL3maojn9x7gJA/wHBP76pTCzCfzbOY\nKwCwnM9yaHyUetNgOV8C11rJZzHxaVCqVokEg4z095MplKjXZ8kW8lhWmImRX2OgN0k0BKZlk06m\nkSQJTZIAlWp1jlu3z6PrMiBTLBVoNM6Ry1U5duzbLC8uU6vV0DSNUDhEqdDA51uf75RbnqaQnQOg\nf+ggg6Ob55s+jmW2WJ7+KZHkHkKxNamdQCRNvO8EtmngD3mJ9Ezs2qkFqC49oFlcBNx1ji2sVVJu\n10vUF+8BUI/3ERne/2QzNOYv4jgWwaHNq1O+DIf2cYTqw8lcxy09wpE15HRnciEdPAGVEmJ4LW9M\nKB7Y9zYE4tCzUqhLXZnYpfbiHPgerqxCeBNN2y5dunT5OaBUqXF/ZgGA+zOLHJwa3fJcy7Z5tLCA\n3m5zf26OkwfWbIDeNDr5s467JuhuAQLCwSB9/SmmF+Y5NLV31anNFfLUG3WOHjpIwOfH513bffNo\n2rb1NzKZDNVKmYnJqS3PHe2dRJYUJCF4OHub4f4JVFVDVTT2jx5ndvo+lWKBRWsGFJeh/kmMdovM\nwhzp/iFymXmq5Tx6q1PwaWBoClmWaTYqFAqLABQLi/SkRyhm56mUMoiyoFEvMjJxBH9wLV2lJz2x\nEi4cfmqObSW7gNGsUfX6CEQSWHqT6vIc4FLLJogNTnQKfRXuo6p+gpEBaplZHNsi3Df20naChRAd\nu/klITXmEVYVp+HD9aZA3rovpn8cTBPTv7ZzLjeyyM0iRmJqY4rRCm1pDBA4UQlZN2gHRnjWDN5u\nju0/MH5W8k++6nxZ4xjw+BiIpXYtAB/weGkaOhO9I6Sjye0veAzdMAHBQDKF9wnnOeT30dB1qvUq\ntm2SyeexLAPDtMAtIWgCy1hWkEgoxMTwMAPpNONDg/j8EvW6Tbk6Qb5goKktpmcv0G5X0fU2Pakh\nhBDoeotr1/8tpfKnKMowqiqIhF2adZVWq0almqOvbwrHsRkeGSYSiWJZJr19o3g8XpSVXWx/IILe\nqhOJ9jC5/zjqNo6oZbaZvf1XZGY/ollbomdoLdy3Xi4wf/Maeq1OavQwodhjunmOg9lqIT0mUbQV\nkqzg2CaR/kk8oc3zRmXVi23qqP4wsfGj6zRzAwEP5aVpClf+X9q5O2ihPtTg8wnYuFYb19QRO3CG\nXdvEbdcQqhdXkhGujTR4CinQ+Y4Jnx8RTWwcB1mBWD9om5iuUB8EdxeivyNsA2HU4Ylwrm6O7Yuh\na1eej65tfjG8jHGstVoIIXZtd5+G1+Oh1W4TCQU5eWjPU9uWJAnTtPB6NI5MTeJZWZzW220c16Fc\nrWE7FsIFIYuOg2tBKhnntZMnODA5RTK2Zl9++snHLCwtEgqGmBrfqFf+tDGsN2pcuvApmeVlhCSR\nSGw+nxBCEA3GuHPvKpn8ApZtkop3qj37vUHCoShGW8dwWpQqWWzHJr+4SGZxBl1vMTA8gWm0abcb\nlEvZThh2OAbCxbZtfP4w/QMdx9rjC6C36ujtBpauU63kSPdPrOuLqnrRPIENjrhtmdiWiSwrHS1a\nSSI+MInq8SIpKo5poHr9xIY79Suq5RkKmeu0Gjm8WpzsnQu0ihlUfwgtsKZ+8PP09+ziAAI3OL5t\nwSgtcw2lnkXYJna4k8blm/8Ytb4ErosdWD8/kcwGrqR00o+kGI4cxfIkQOrm2Hbp0uUppCIJvnX8\nrV1fV200+dtPL4Hr8gunTxB5Ir9HkWVeP3yAQjFHo9WJgao16ivBPsEVvVgfQlhUqiUuXS9yQ/Vz\n5vgxjh38L/Bqr/PZ1btomko0kkQSKo7bqdpoGOcZHhzh6rULCIZQ1SXGRiNMjH8Lx3H4yY//byzL\nIBrto5hfoFxYohwNMD65n2gsxcVzf8GDO+c4cOQtkj3DyIrC/iNf29FzG+0WNz/+cywjg6wE8PrX\nF8jouGkrIVaus+7Y7JWrVLJZesbH6J3auLP+OMGeYYI9Ty/OJIQguf+1LY9L3jCKP4Hr2ijP7dQa\n1D7+Q5x2g8CR76Kmnl6tu33h3+FWFlD3fQtl+DT0HX2u+780XJfQp/8WuZahuf87GIPHv+wedenS\n5WeAm/Pz/O3lS8RDIX7zrbdf2K6cJAneOrXz9+WJlSrGn1Ou1vi7jz5CCMG7p0/xt2d/uq5tT0Cj\nv7fnyWYA0HUd3JV/d8HdR7e59+gOqqLi9XqJhDaXsXscvy+I3m4R9K8/NxAIc+jwac5e+GsADFPH\nHwhRLZfwBYIEQ1H2Hz7NrevnqVUL5PIzFBqzOFj0JscZHTyy2lajWaJu5RE+cBsuXt/66tal/Bzz\n05fx+MJMHXhr9Xfo2DaPrn6AZegM7DlJYmCCxMB6h7hn6vC6tjRPBEX1IysaijeA5gviODaq/+c4\nbSY0jhvauACyGa4WxNE9uOraeDhqABwL27v+O+Ap3sFXuo3pT9Poe/WFdbfr2Hbp8hUgV6vww5uX\nifkD/NKhk194cYPZ7AxXH1xiIDnI8alXVj9vm2Zn9xUXwzQ3XNc22py7epFI0MMbR47wo08/wXGc\nlTSWlZVRV8JxDYTw4uJgGiaXr9+mVq/Rm0rg95TweLyEQxF++Zv/hOu3rjE7N41pGhhtHcsy8XoD\nvPHa/4JnpTiWJEm88+4/w3EsFEXjpz/5m06IULnYuaXrYJltbLPFg1vvU8oPMzJxmjtXziOrKvuO\nvoq0RUjM8sxdsvMPMdoGrhtk8tB3ifeud/CEJJAkCdd1kZ7QqbNME1wXy9h+tbYyf5Py3E3C/XuI\njRzZ9vzNUDxB0q//Dx0JnafILuwE17FxTR3MNk57BzlWZgscE7fdyaF1bQvr8vvgWMhH30bSdlHs\n4t7fIfJ3cMffhfTOi07sDBdhthC2gdTeWq+xS5cuXe5kF/lk9h4TiTSqrWDYNrpp0jTa/M39z5CE\n4Lt7T6PuUjP8RfG3Z39KtlTAsR0UoWKsVB7GdemNJ4lFImRyOR4sPmK5lOXVoye5cPkS1VoNSYjV\nDNOAb3fBnuVysWPzNJl33vrmaj6saZpcvXYBgKNHTq0WgAI4uOcErutsaW81zYtptvF5fIyNHGBk\nfO+6yv77Dp4mm5nh4fRlHKezG23a622raerYtoWm+th35i20JyKBTFPHcWxsy+DxHFvXdbBtE9s2\nsYz2+mctTVMpzxCJDBGNrzl1Pn+M4clvAAIhBP3H3lmxvT9berX2zUu4zTrSnsNI4a0lCXeL2XMQ\nM7lvXcixPnimswHwxHdAstsIXITzYne2u45tly5fAe4uzzNXzJGvVfj6/mNoyhf7pzmz/IhsOYPt\nWBwZP87l+1eIBqNMDIzz5tGDtNotphenWcio2I4LSCQjUSy7zVIus1J5UcG27LWyDK4JtFkRJyXg\ng3jUoVB8QK0+wr2HJrbdIF/MIEkyuq6znDmLLFWIRWMMDwwxODiGJMsE/MFVp7bVqjHz6AY96RHi\niU4epiR7wW2jrYTJKIrGgWPvMnP/PKX8NPmMhT/QT6mQAaA5USO4xcu8lJmnWS0RjPTQN76HRO/G\nXUt/OM7YsbdxHYdwYn0u6ODhQ1SzWRKDT9efBahlHqGXl5FkZVPHtl2pUF9YJDg4iCe89YqwENKL\n0JxH0nz4j/4jHL2K1rcxl/dJ1CO/hlOeRRk6iVNexHp4EbI5ANzsHAw+fcd63b2zNxDVhU6u7nM6\ntlJ5EW3xOu2h47ihFAiJ+tFfQ6ksYAyeQCndQlv6EH3kVyD1c7zS3qVLl11zN7vAQqXjxP3mK2/j\nUVV6wmHmqnkelTo2ZLlWZCj6fBEyz0q2XMR2bBRFZrS/n2QsikQnzTYZT7CwtES5VkGoUKpWyBVG\nWFhawrY79tnn9XLi8FHGRka2u9U6VEkDy0XxrBV5AiiV8uTyywAUS3ks3aBRqzKxdz/lWoF8eZnh\nvils02RhZpr0wCCRFVm8/VOvUK7k6OsZBdggVyeEoCc9giTLSLKMYbdIxYbWnZNIDFOuLOP3RzY4\ntQCp3klkRcPnX8uxbbfrFCsPSY7uRXYVIqn19rpeW0RvFZEkZZ1j2+mT9NjPYktZG9d1qc/dx3Vd\nQsNTX5lqzK5j4+aWwGjjZhbgBTq2wMY8WiFAbFzYaCUPYqtBzMDmkQXPSjfH9h8YP09x/18mL3oc\nE8EIzbbOWE8vmiwT8vq+0Jdg0BvEsAwm+vewmF/myv0rZEtZ9g7vJRYKcevRHe7PPSJXrJAvVSiU\na+RKZWyrTbVeR6BQb3SKPAghdQpHoa+87yUEKqePHubWvX+Nac4BHjQlyYnDJ9DbLdKpfvw+lwuX\n/k9KpTu0WhatFsSivfSk0vh8nRDoZqPCnZvnWFy4R7NRYXCoI3kkSWBbOqOT+/B4vFTKRcLhONHE\nAKbRItU7Sf/QPgxDJ5pI09M/suX4KqoGrkvf+H7iPUObngPgDYTxBiMbPldUlUA0uukKbquSR0gS\nktxZuJA9PhzbJjp8EC2wMcc2d+0a9cVFbF0n2N+/4fjL+HuWfWGUUGpH3z/JE0SODHT0Aq/8OW7u\nNsIbhtQQUrIHFxeaeYQ30snHrcyBN7zWdrsGjTx4w52CUZKCO/omeLcJcXNsROk+eCJrkQGP4bv6\nZ2iL1xDtOlZ/x0l2PSHsSD8IQfDK/4Z36QNEM4869Qu7HqMuG+naleeja5tfDDsZR9d1WSiV8Krq\nprmtYY8Pw7Y43DdMTyhCOhol6PMR8wVpmQYD4QRHe19esaDtaLZa1JsNLNuiVC0zkO4l6AsQ8Pk4\nsncfmqohSwKfz0tvIsWe0Qkcx0HTPETCIUYGh5gcH9+y/1uNoc/nx7JMhgZGCYcjVKplBBAKRTBN\nk0g0xkD/MJfOn6OQzSIrCnP5B+RKi1iWSXEhw/L8DHqrSd9gJwVHUz2Eg7Et+2KaBk29Riyaxu8L\nEQrEN+z+Zpbvkc88Qm/VSfWMbTguhMAfiKI+5vQuZD6jXJjGkS36+o9suL8sa7iuQzQ2huZZH9q8\nEwIBD8WFBUo3PqVdzKKFYqiBjYuoejmD3W6heAObtPJy6MzRXNC8iLG9iC3UNF5+RyRsb2ydTODj\ndHNsu3T5GSbg8fCdo6f5j5/+lLN3bnBqfC/v7H+20NRnIRFJ8u6xbwKQKWaIBCIEfIHVVdlULE6m\nkEVvd96HqiLjOCbzmQYbtgpdHTARQkbTFAzDQQgJVfWA6wVUNLWPgb40iqJy4kgnf9SyWkQjo7T0\nOtBHq2Xy0UcfcfDgQUZGRmjUCpz/+I+xbYHHEyYcWStakV2+Sik/Qz6ikZkPsDDzgFTvAIdPnuHA\n0V9aPW/PoVPbjkWsZ4BYz4sXQS/O3GL5xjm8oThjX/seQggCiSECia2dZ08kilmv44ludKC/aoho\nH249B2YOlpexF3+MJBu4jgkHfw1yDyBzA3fsLcSB74Jjwcf/FzTzcPjXYfAk7sDOdOu0a/8aZf59\nrME3MY7+dxuO25EBpEYRO7pxMQDAikwh17N4CkvP9cxdunT52eODWzf54PZtxnt6+K+/9uaG432R\nON+LbNQGlyWJX5g89kV08am8euQYR6b28vcXzq0UaQrTm1jbPZ4YGSEcCvDBhXNUqxX0qf0c3r+9\n9ux2RCNRThztjMvC4gzXrl/C7/fztde/yYH9nXxh13UJR6LozSbRWIL5wgOwXYyWTiyaolYpE4rs\nfIfw1r2PqDdKjA4dpr938wigYDCBxxvC4/EjSTtza6xyC8qdeQeblLkIBNMEnrOIoRoIo4ZigIsa\n2vjMzewMtYedEO7I3q/hjb2EoolbII1trwrxs0rXse3S5Tn5wfVPmCvm+freo+xJP59DZK0Iq1u2\n/SK69kyk42l+9e3/DIC2YfD+xfO02jqqFMCSDEyrgGkAeBFodF4jYesjAAAgAElEQVQj7mP+rYkA\nVEVBwYPhPAChI8RpVHU/ptnmxOFjHD18gkeP5vns8kdYlo0QMq5zHL9X4/ix1/nwgx/iOnWq1RL3\n7uSYn7+F0Z4D4XD0xD9neX6ejz/8S/YfPkO9mgegVsni2B0nsF4tfbED9xiW0Wb2wlmEJBg6+TqK\nquHYFrgOjrvz3218zxTxPTsP5/2iMa7+OU5lCXXfNxFSHKQBXHsJwUrOresgXBsco+PIAtgrudou\nnc8cB+xd7lQ5K23YBnLmDtqdH2HHhjAO/woA7b3v0t777paXN/f/M9oD3yL68f+6u/t26dLlZx5z\nxb5atrPNmbBYLvCjm1eIBQJ858jmcmovimKjwod3LhLw+Hhzzyv8/SfncFyHr596DZ9n/e6V3+fj\nu29+nftzj/jhJx8w3DvAkak159W2bRzHQUCn7sULptN+5x7uSroRdHZHT772xlo/HwRo11v4kyFG\nJvYwMtFxqIrZDA9vXSMUibH32NYLmq5rAy6OY7OwfJtCcY6e1Di9qbUiT6FwksNHO5E3lmnw6PI5\nXGDs6KuoW8ggetUwLYp41N3vxu4UWfOQPr21HcJZmwu4n9vHLxCnUcSduwSeINLoLr7broO8/DHC\n0rF6Tn3l9Oa7jm2XLs/Jw9wy+UaV+7nF53Zsv3v8DA+zy+zr33wX7/7yHDPZRU5OHCC6SVjL4ywX\nM9yee8C+oUkCPj+X7l1jINnHRP/ojvpy+c5t7s88orkqsK7iURQECuAB18bFYbUUxefvRMdHwG9i\nmQZNQ4CbxHVrKIrKG6f+c9pGk/RKtd3l7Dylcm6lTQlwadTbXLr0x0AYcFAUyOcX0FvZjnyQC9MP\nPqFWtrBtm1xmHlURtF0dVVNwbAGujmO3uXf93zC+/79CfgHC5o5tMX/rCt5AiNToFMt338dxbPr3\nvbtBG69RyNEoZgHQy0WCqV4SY4dQfUG84U3kb77CuI5N+/b7CM2PZ3J95UI7/xCaJezsPaja0Kgg\nEmPIEwfBrOLO3YRaFid5FGngDGRvI/pXKoHKCrzyT6CZg57d5dQaR/4pdvIwdt8ptFt/h1xdXHN2\nd4gdHqTyyr9ic5GlLl26/Lzy9UOHSUeijKW2z5F9mF1iqVKk2KjxN1cvcGZiH/HgzifyDb3Fp49u\n0R9Lsqf36RXwZwtLZGtFtKbKciHHYq5jQ5bzOQzJIF8r4tYhFY2xd7zj2C3mM5RqFVRFZe/wBFdu\n3SARjTExOsabr7yGqigE/QHuPLhLq9Xk8P7DyLKM4zhcu3sNr+Zl7/jeHT/P5wwNjuHRvAQCQeSn\nFC08fOwMhUKWdO/6HNZidpl6uYxlPP29vXfyder1Aon4ILfufUBTr1CpZlYd21zpIU29TH/qEKqi\n0agUqRU7dR6a5QKRns2jdvomj+GPJDCNFkuPLpEePrIhv/dl4+8dx3VshCTjSzzf3NF1XczGHVzH\nQAsdeqpe7yqVJWiWwGiCa4N4wiU0DZi9DsEYpB+rNWK3kZqZjrxfcwnnK+bYdnNs/4HRzePZOa7r\nciezgCLJeJ/QPH18HH2qhk/18ObUwQ3n7RZVVugJR5G2cHz++uJHPMouYlkWw6k+HizPEPZtblje\nv3qWB4uPaBk6pWqZWzN3KNXLHBzdt0nLG/nRJ+dom21URWGsfwhFkqk3a4CMQAc0xIoivHAB10SV\nVbxqkZa+hJBsAr40hlFCoDExepBIOE7AH2E5c4VQKILfl8S0DPy+AJZVwDJ0BHPo+iKK4iceHyPV\nkyAYjKJqYSKRJK6jUSnbqKqHWKyfvQdewRcII8kKYxNniMbS2FaLWvknVEufISt+ovHdG+7HaTdr\nzN/8jPzsQ2rFPP6Qxty1v6BRnMUXSuMLrZ8geYIhXMcmkEgRG+7kMgkh8IZiKFusID8L2/0920aL\nduYuSiCxM0O3CebMFYzb72MX5lD6DyA9lqckFA/C40eZfAsRjIOQUCaPIScHIZjEvfzXYFlQXkIe\nP4UI96/vhycIwZ5OcQmjBZlHHSP6+fe/VkBUcxB4IoxLUnAjIyApOOEesAyswWO44d5dPZvji3d1\nbF8QXbvyfHRt84thJ+MohCAdiaCp2+cW9oSitC2TWqvBfDGPYZlM9W50QpqGzkx+mVggtG7h8uMH\n17k2/4BCvcKRoU70jWlZTC8tYJsW1XqDUKCTX5kIRjEsk9FkP1P9IzT0JpFQiENTe/j7W2fJ1PIU\nS2UKmSL7JiaRJImQP4jjOgykern74D6PZmYolsvsm5wiFAji9/potVr89JOz5Ap5NM1DwO/n2u2r\n3J+5T76UZ3hgBO2xuctmY1hplGjoDSrVIh7NgyIrBIMhtG3smawohEIRhBA4jk22uIDPEyAYiWFb\nFumh4aeGJiuKit8fWdGi9SGERDTUi2NZaJqP+7NnqTayCCEIB9N4fAFc1yUYTZAcmthyEVkICdXj\nY/7uxzSreWRFxR/eXJP3Wdjp37MWSqAGn3951bFqGJVzOGYeIQeQ1e3bdH1RsE1EdBApuMmzz91E\nWroH9RL0T63ZZUkFIXC1ME5s36Z1Lp4XpVnAF322olbdHdsuXbbgo4c3+asbF0iHY/yrt39lyxfk\nkcExjgw+Xe/zRTGY6MFxXYZSvfz46jluzd1jsm+Eb5/6+oZz+xN91FoN+uO9RIIhMsUMvfGdV5+L\nhUMUKhWGe/tRFZt8aRqBHxC4aJ2dWtHRsnUsG/BiWW1saxqIYlsJ6mYdyCBJM0jStwG4ffcH3L7z\n5zx8NMUbr/3PHDl0hvfe+99ptzOAByjg8fTg2mny2RL57H0CAXjz7d9EVlQ+PfcnNKpZzLZOIWey\nvDjN0OheenrXckYOHH+L69antHWNeOr5cpVd1+XeJ39Fq1oC/LiOhTeUJpgYxXUdgomNq/BCCHr3\nf/marpXP/gNG9j7m2KuED3/7mdqQk6NI0X6E6kXyrV+ZVYaOw9CKJqwvjJxam/BJkoLjC0K7CQPb\nL6aIC3+JlJ/FnjgJB98Bq41y/k8Qeh3r2LdxBzff1XV9MYyjv/pMz9alS5cuT8OrafzioRP85JbM\no2yGkeTmeZA/uHKWhXKeU6P7eGNqzeYMJ3pZKOXoDa9pof/40ic8mJtHbktIQuKX3nqdof5eVFnh\njanO+7RcqzKztIDruhTLZdKRFMV6CWG4xKOx1aJX8UiU0weO8YP336Neb+L3++lJJNfNVzweDz3J\nFG3DoDfVw9nPPqJYLuLz+oiEoni3kWWrt2qcu/0+tm7imi6JaA+vHXtn12N55+FFlnLTpGIDHN73\nxlNDkDcjGk7j1QLcuPYjXNdmz943CAZS6O0q4ZV8WCEEA1OHdtSerKgEwilMo0Ug+sXlt74MJCWA\npPXguhaytrN5nqRoMHxi6xOiadzyMq4/vKHysxPb2QbJsyC3SkSmP4SR0We6/qU6tq7r8v3vf587\nd+6gaRq//du/zdDQWojl1atX+d3f/V0Akskkv/d7v4emPX/IYJcuLwJZyAghkF6Ejso2zOXz/N3V\nK1h2G0GN01NHODq6UW7l7UNrGrPzuQWALR1ux5ZxTA+2IzHWO8JY78bS/oZp8t7HH+DYDl8/9ToX\nbrxPuV7i9KE3SUX96HqeVDTI7QePwA2uKMA5wD0gj9/zTY7s388nl/4TMA2kARsI8XnurUCgaV4k\nIfjgo/9AvX4PEAhJYm7uBpcv/ztc17/SIwlF0Th+/De4+OntlbY6n3/+Yg2FwxSzV4E94PpoNT4P\nlV5DCInDp/8npm+f57P3/wTNE+W1X/rnT/8lPIXOLqMDVNC8QYQkcO0wruPi2PDw/B/SbhQZPPJd\nQsmdL3LMffyHNDOLSIqG5o/Se/LbeMIvUELi85VUsabbVz3/IXazQfDYKbT49veSgzECb/63z3R7\n5Zv/cucnC6mTqbW6+is6PwvRKXvd5YXRtc1duuyOd/Yf5Z3HTLLjOPynjz+krrf4peOnVu2w9MTu\n1Wiyj9Hkekm4z6NWhABJCCRpow3vqMistCkJ3t336oZz1k7u/E+SJY4fOszo4PrFVhcXR9i0zSYf\nXvgA2+rY1YnhCfZPbl9UqtVqYS62OyYwyDNH/9QrFai71KXKus+b9Sq3r5zDsk2EDOn+MYbHNl/I\n7IzJyn9CYmLwzK76YLaazFz4CCEJRk69yfD+N7a/6Dmp5x5Qz9zGG+kn+vlC8AtGCBlf/K0X22i0\nB/fYy1MNUOuP8FVuYnp7aSXWFjlcxGNZ27vnpTq2P/zhDzEMgz/6oz/iypUr/M7v/A5/8Ad/sHr8\nt37rt/j93/99hoaG+NM//VMWFxcZHR19mV3q0mXHvDa+j75onOQToUUvg7lCgXythiTAcSvMF5dX\nHdtP796iWK/x9qFjeB+bXL575DWmBsbo32IXNlPMU2nWyRQLW963XKuSXTm+VMiymFvAsHTmM7MU\ny2VqjSqZYgbbkQGZjmWTAB1cL41Wk2u3rxLw12g0G0AW3CEgsiIYH6S/9zRHDh3AdSXy+XnAw/jY\n90inoly9+hc4Th1c8f+z957BcaRpfucvTXlfhSp470EQoPdsmm4228z0dM/OrGZtnNbc6nRxCt2c\n9EVxcbv7QbERZz5IEXsfpNONtKHVrmZ2LkbT0zPbM+272fQkCIIEAZAAARC2gEJ5m5nvfSiaBmFp\nu3unfgwGicrMN998s5BvPs/7PM8fh7OVttZdqCaZyfEBDGMBhJnq2gbaO3ej3JXIae96mUCgmYHL\nQxTyeRSlGAplGAYjA0PIskzLljYkSWJxbhQhsuSyEQzDQF7DQJq9fYN4eBohlrC7fNR2PlgBlySJ\ntn3fIJuKIkkSZouDXCpxN4dWkFqaIb00hZZLkAyPPZJhm4uGARVD08jF50mHJ56qYevd+ZsUlu5g\nLmsAQGg6haVFRD6HtjCPkQpTmB/D1roH9Wka1I+B2PUNRHQOyu4aWKoZbf9vQS4Fvsr1Dy7xSJTm\n5hIlnoycVmB2aZFsocDEQphv9B4knIhS41v9OXpt+hYzsTC7G7o5vn03HfUNOMx2hK4T8K8MG/U4\n3bx+6BiGEPjda1fFDy8sMDQyQldDOz6/lzJfYMU++XyecGQewxBIgNPu5MCuA5T5gswtzDI+NUZD\nTROhwOqrlul4EvKAIrBZbaiSzJW+s3R09mCx2BDC4MaVPiQJ2nu2rWn4WhQbCVH894vElsIk49Fi\niqcM8dja7ywWi4Ou7mMIQ8f+cIrKJkhFwmRiEQByyTjqJpy7T0o+uYCeS1JIrX1dv46YsmEULYmR\njyz73LB5iTYdY+U3eXM8U8P24sWLHD5cLKXe29vLwMDA/W1jY2N4vV5+8IMfMDIywtGjR0sTZ4mv\nHA2PELr7MLqhc2V8iMZQDT7H+rqcu1tayBcKIOloepBdTcVQmmw+z/mRQbKFAh6Hg33tD7yYsixT\nF1y9MALArs5uXA4nnXXLDS0hBGNTYzjtThAy91xj03PTGMINwoQQLrZ1NjMxfRufpxyfU+P21BS5\nfIZcLoUwUkh0AkOkUjVIWKmu2MnM/CyQwelI4PW0UsiBxaxx584khiEjDAlJUnA4qhgc/CmZzDyq\nWgfCSTqZZmrqNnabwtSdAcxmBz5fNT6fh1w2ju1uGKwkyQQrWmnfYiYRj9PQWgxBnp+e487oRPEa\n9QgNHT107nqd6+ffxhOoWdWoFcJgbnyIO8N95NNJIM6S3E+ofgcW+4NJ02SxYbI8mIxNNidVXXsR\nho6vqgX0l8nEZyhvPbTufX6Y8q0nWBj6BLO7CrPdh7fx6UpJyKoZS/CBuLxsMuHYsg09HsPW3EHs\nk/+MHpsHScHZc5zc+EVMNT0olvU19YSho01cQilrQnY+7vTzECYLBB8K67a7i39LPFVKc3OJEo9P\nQdcYCo/TWdtALJVkW2MLJlWldp33hcuTg8SyScyqiRdad1IbLNYD0A2DwcmbVJdV4LYtr9DrdW38\n7BscGmZ8YoJkKsXJphcZHR/F7w3g9XiYmptCVVVcThfCJiAvCHjKaK/vIOgPMTU7ydDoIEvxCAUt\nv6phm8tl0ESBYEU5cSNK1sgwF5mCpMBitdHR0UN4Zobxm8MABMorCFas/l7S0NhFoZCjrv5BGGsm\nk0AoBrVNnQhhICSDUGXDqscX8jkWZ8YJVjehqA/MFyEEkalxrE43Dq9/3fESqgBzUcVByE+yLrh5\n3FVbkBUT1jXk5+5RWFpETyWxVNfdX0zJL0whKSqm5ygF9LzIeLcgJJWCbaXj2rA+vsThMzVsk8kk\nLteDnCxVVe+vmiwtLdHX18ef/umfUltby5/8yZ/Q3d3N3r2PFlZQosSXgRACQxgrwo6+yIfXz3N6\nuI/qQDl/cGT9HECTonC0e2VeiMVkorG8ingmTUtlzSpHrk3I6ye0ykP+5uRNPr74MTarjTePvkVt\neRWR2BK37gxjtzjw+1torqkl6PdjUqz88rNTABzbt5szF/8SoS/gsHeQyZwCZoAuJKmdrvZvYlLP\nsBAZJ5k4QzatYRh2wgtxJBKADwk3kEaWzNTU9HB77CaZtBkogFCIRsI07jxMLDpLNjXN4vwlFuev\nYLMHeOHF/wlVfbBiXVVXDK0WwkAIgT8UwB8MkEyEmRz9iHRynN4Dv8muo79z/5h7sgf3jNyJwYvc\nGbqEyWLD7vGDALuzCrPVhRACEEiSvOz/cDeHtvVBSJG/bhuweaNUiKK2r6t6C67qR6sI/KTY6h4Y\nuubyJgqSgrmihVTfTyhMXkabH8G5//fXbaMw+B7a8EdovhpsR//HZ93lEk+Z0txcosTj8/7IBa7N\njqFoMmJJMFQxQXdj07rHNASqmE0s0li2vPDUuZEr9I/foNwb4M09Lz9yX2qrq0imktRUVXF9eJC+\ngSu4XW529G7j1OVTKLLCSwdeoqayhmwhx56WPTitTianxznXfwZZlvG4vFQGV6/K23flHHPz09RU\nN7C1die3Jm9g5HVUk0rF3SJavrIgZeUVIEl4/asXYBJCMDlxg1hsnukplbJg0ZgZHPqcRDJCbXUX\nTRs4dm9dPUt0fopkdJGW3v33P18Yv8n41QuYbQ62HH1thdH7xbnb5a/EWVGBJEvYXesbwU8L1eLE\nu14uKyC0AvHzn2Fk0ghdw1bfTH5xiuS1UyAreHadRLE9O2miLwOhOsgEVhkXYTxRQapnatg6nU5S\nqdT9n78YCuj1eqmrq6OxsbiadPjwYQYGBjacPIPBr1ZZ6a8jpTF8MvJagf/jJ39FMpfhD4+/Qf0a\n5eTTmg74iWekJxrz33/16eY4xDJeTGYTNosFn89OPh9FGClkSaKqPMh3Xnn1/r6KamAxmyjkBafO\nXyObqwF0NK0K6AchgaQihManp/8tquKjkAeJJELkADv3l4RRMKkqiuyitqaK69euYOhZZBmELiMo\noCpmzCYNLZ9CkizIchZQsVpthEKe++HI95ieuM75z36IyxPk2Gv/lOo3j9F3+h2G+gWpeIoL7/8t\nu4++TrCyhqmxW5x972fIksTJ3/oDbA4nsYCXKVnBEyjj0Df+0f12C/kMZ97+N+hajp0n/nuGzv6U\neGSargPfpqLxyYpC3fjgPxGdHqZ+56tUdj7aCu9qPNHv85EHBaXmU7eIToLV5dqwzZjfR1RWsNgd\npefJ15DS3PzVpDSGT4dnPY6+KSfMgiorYILyMs+G53wreGTVz3MDWRCQTGceq9/B4Fb27N4KwMDg\nDVRVxWazUh7yYTKbMCkqleV+WhpfuX/Mu7/6FVMz08iAw27n26+9geWh6sb3+uJ02pmbh4WFGVKp\nKC+9+Apu98qV5Kpvf3PNPiYSUT797JfkshkEAofTfr99m81KMiXj9bo3vP5Jp53oPDjdzmX7akkv\nd1QTFquFUMh9X7ZHCINLl35OOh2nq+sFAoEawEVl1dp9fZo8yv00NI241YKmFfAFvbiCLtKSl7Rq\nQlZVAiEPJqt944a+5ojrn8L8ODT0wGMW/nymhu2OHTv48MMPeeWVV+jr66Ot7UHV0traWtLpNJOT\nk9TW1nLx4kW+853vbNhmOJx4ll3+B08w6CqN4ROSyKSYjobJ5vMMjN3GLq3+8DLhABTsJuczG3Mh\nBKcG3ieVjnNk+6tYzbZV97s5eYPT/e9Q7m/k5f3f5ttHv43ZZGZ6ZoHwUgTD0Nm7dQ+dTR2MT8xz\nvv8KbqeT7Vu6efWFFzh/ZZCJ6XlkyYUQbvK5KNCOJNnByAK3yeVi5CUDDA1JMjCM88h0IIQLcOL3\n+Whu7iAYrMZsdhFdmiWbTWC3O8GwkM0mkCQzfRcHSSXjeHzlHNjzh8Wy/GY7kcjKIlGjI/2kEotk\nMwnm56PIskpV82EcvhYGTv89yUyUybHboHq4PTKMMHR0YGJ0An9FLZ7yVrYdD2KxOZbdo0x8nnhk\nGmFoTI4OE1ucJpeKMDN+E8W5vmd+I+LhKfKpKOHJUdSyJzOSn+rvc8sJXOXbUJxlG7dZuQfL8UYk\nu/fZfLfnhpBHP0fUbEM8o2Ib9/h1NCZKc/NXj9Lc/HR4HuO4p2oLLd5arIoFTdfwOJy8d/4ikwuz\n7GnpXjVSai2UggIpgVUxrdrvuYUwA8ODCMNAkRX2bNuBw756qojd4iboDhDwBFAlOyf3n0RRFNIp\nnXTqQdvhxUWy2SwN9U30dm8nHstTTKItEgy6uDO1wNXr57Fa7Wzfto/L/Z+T0zKMjk5QXb2yEOV6\nzM1PEY8vIUky3VsPEQzW3L/WtpaD1FTFmbp9g8jcIs3tO1fUNCkUcoyNXMBktrH14KvYXZ5lY6U6\ng3S+8Aqq2cxiJH3/c8PQiMcjaFqGmZk7GMbjh7c+Ko/zPXTtP46Rz5O1O8mGE4AD186TSLJCNKFD\n4h/+88G9FMacT5ENz2Br+AoatidOnODUqVN873vfA+Av/uIv+NnPfkYmk+G73/0u//pf/2u+//3v\nA7B9+3aOHFndo1WixFcJl83Bbx16hTtzYXatUbkP4FB7OwuxebbWP8hxjaUyXJ+cZltTLbanUGU0\nlU3Qf+scuq5R5q1gZ/uBVfe7OPgh6ewM47Mx4Nu4HMWXeYvZwv7eveTyeQxD4vbUFKMTk0zNTaMq\nEpKUwWk3EfQLcjkrfl8145MZslkbCDNCaEhMg1RJfU03Hk8LIyP/lVwuCzgR5O9r3UaWZjBPmqip\naWVkuJ8tW04yNHyWhfl5ZEmjsrKTmak7SGQIVeygtWMrjoe01WbvTCKEoLK2mIspkQIRA6GCKK4M\nS5KEN1BF67YXyCSjVDdvJRW7g9Wewlteg8Vsx1/xoAKs3bWyAIXNHaKh9000LU1Z3XZMZgfJpQmq\n2o/e3yc6NYShG/jrVlavXo9QzzdIzQ7hbz287HNh6ERvXcJaVovN92h6rI+LFpuiMNuPtfk4kmpB\ndZdTmJvESCcxN3SsWzRNcT3lohvxeZi5Ac37kEdPo0wPYOQz6M/YsP11pDQ3l/h1YCq6yGRknl0N\nbcXV1aeEJEkEHMuNpP6xYRaTMawmMy9516lg/BB7tvRiMVmoKV/9mX9jdJjx6cmivJ4mcDtd7OxZ\nPWR3ZGSEmekZliJLmGSVto520tkUpwdOUemuwCSbaWxsZvvWncyF52hv7Vyz2vn45E2mZsZRFJVd\n2w4g3R0+475SwXJmZscBqFxFfSEUrKWlZTuKolJevryOgqKoxJfCzM+MAVBV04rduXxs52dusjh/\nG1lWqK5dfV6yOlaG6cqySk3NTrLZGMHgo8nTaFqGWGwUt7sRk+n5rJTKJjOyafn9UL6sVdrELGTj\nUPYF/drnQKp6B1p0kkyojdWXaTbmmRq2kiTx53/+58s+uxfeBLB3715+9KMfPcsulCjxTNjb2k2T\nd33v2enhPm6HJ8hrabY1NAPwi0sDjM4tEI4neWPP2qt1BV1DAjTdKOrECgOzulJM3mF10V7TTSqX\npK12bSN7S9Mezl9fIuCpXbGto7Gdzy+d58boMIpswjCsuBwODH2BK9c/RSJ+V7/WBKKGXb0vcO7y\nGbS8CSG2YLU0Uh4KsGfncRRFwWbJ0Nf/HxCGA5Bw2N2Y1KKwenV1E4PXLjAy3E+gLMTuvd+l79K7\nWC0OKirrmZ06BQgczgb8ZcsnwGhkgf7zZ0AIzBYLgVA5VQ0HScbGsbsrkZXl41Ne03L//0MX/hOJ\npdtUt7xIy7ZXeRghDHQtj2qyIoRAL+QJNuy6P4F6K9vxVrajazkMQycbCzN27m2EEKgWK+7yxi+0\nJTAKOZQ1tAEdZY04VqmcvHj9M5ZunMbsCVJ/4g/XupXrYhSySKp5zaqURj6NZLIhSRJGIU/i4g8w\nIjcx0gs4tv8eRi5L6tz7kMsg9AKW5u7HlnZ4JApZpIs/Rl6cwEhFELXbMQppjOqnW0xrBXp+433+\nAVKam0v8OvCTK6eZT0RJ5bO82PFsHWRt1fXcWZyjvbrhkY6zmC3s6e7FMAzy+fwKQ7O5tpF0JoOh\n66iyieb6lXNHvpDHpJpobGwkEokQi8bou3yZdDrFVOEOGSnNbGQaKQwSEo1NzYRC6ztPa6rqWYzM\nY7PaKQtUUlVRh2EYBHwhhBBIkoRWKCArCtFomKsDnwNgtdrxeZc7PSVJov4LDmDD0DGEgXp3zg5W\n1LG0MINqMmOzr4ygKQs1EI/OYzbbMK0RlbYWXu9KnfnNMDd3nmRigkwmTE3N0WXbDL2AJMlIm3CW\nCEMHIZCU9c0tQ8sjKaZnrsCxKfQCjH8OhXQx3zX07DRrV5zaESDteLKClM/UsC1R4tcZj92FWTXh\n/ILHzWmzYFIU3La1BdGjyTg//vTvyeaz6LqMqphRZTMv795LQ8XDengSL+56Y8O+dLfspLtlbTF0\nl8OBSVWRJRNCipLPXUOWK5ElM6pqxdDAEIJUOkJ9TQv1NS18evo8M3PzdLTtoqv9gRHZ2HCcxobj\ny9rvv3KK22ODxGNluFx+VJMZu92OzeZi/8FimOPi/G3u5Sm6Rt0AACAASURBVONaVvFSmi02LFYr\nCLDYitud7mp2HP6XG16/2epFUW1YHasXtrhx6gckFieo73mdTFQjPD5CqKmT+p499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kJF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Xkfwe5HAIKeBHlqSTNOn/e1DWL0NxcDXRjAlEcqaduMI3wIAr8n8QZ5WVk5KQyLxZF/a7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\n3YJSG528CQeD7Hn8IXrbDhH2efHZrbjM7Rx88ylCQS9hhQ+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ft9PZqeeBh24AHDgc9YCu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_pixels(data, title='Input color space: 16 million possible colors')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's reduce these 16 million colors to just 16 colors, using a *k*-means clustering across the pixel space.\n", + "Because we are dealing with a very large dataset, we will use the mini batch *k*-means, which operates on subsets of the data to compute the result much more quickly than the standard *k*-means algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PpbDYpnU5wNZCKGYbob/tIjOqh7FN1QqtNCYyDa8tzmGT3Nr2vNgoCDFhnK9s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yz9dw1i6ve75VLAUVNP/u36w5zmh3I0hGDHY3eRsPwsb11XPdCgarg+rH/phk9ztkJi4hmPPu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CWcOHGU41Vd6PUyX/jsY1jt6WhawlA7/3qfOLaDmqoDzJh10zAlgAuprzvM0fffoLh0PuXT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\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vIU9Rn8+LKIoIgoharUWpVOLxuFCrNYSHH3FraGisJXBIOLG5uYnurqAlUHVNJQ/+9W6u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\n5Ed+Qa5dBfpuMa272bxsLtFHbCKYMoUKhco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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import warnings; warnings.simplefilter('ignore') # Fix NumPy issues.\n", + "\n", + "from sklearn.cluster import MiniBatchKMeans\n", + "kmeans = MiniBatchKMeans(16)\n", + "kmeans.fit(data)\n", + "new_colors = kmeans.cluster_centers_[kmeans.predict(data)]\n", + "\n", + "plot_pixels(data, colors=new_colors,\n", + " title=\"Reduced color space: 16 colors\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a re-coloring of the original pixels, where each pixel is assigned the color of its closest cluster center.\n", + "Plotting these new colors in the image space rather than the pixel space shows us the effect of this:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ls+h9awnjQQcdhJtvvhmrVq3CEUccgYcffhirVq0CY8ysawTwnLw4DjroIHz/+9/HLbfc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\n9lPIrjD2w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oImEphMZqQENNTVM3L7FqM3Wyj1xvCaIDkzTnfPDXqn+uge66O9s5v23l76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WLPfv99kuzVsXLw3u0em0BuOiH/wGYGN9hYfOnWNt5S6maZBMJVgMfMTcFAB7uxXozd9nf/Q8\n0ymbo4HHaUJ2wpCGaVIpVSAmmZEm84ZFI5siISXX3rrIdK3MrGmwm4hxe30TgIniAr7rkU4YfGy2\ngIOD1tFatGZGqSKWAg8R+gTKwQnWsDY6BPMJ5mcEBGucc2OseIrQnAQE9595lrteQCxm8+HP5KiV\nN3nj979DOLuA07yM7e2w51gULItKo4sUAf/nv7/IL3z1KVZu3GRnp8rnvvR16ulZWp0OO9kTGLuX\n+KDcZC4t2So+Rrm8Rx3B/FSSqfmTeK11pJliZ69FrdbmuT97huWjRzAMk4m04tGPfJyTp04zmZ/h\nyNIRLMum7foY1uE53X4WJW4OSYm+NByFFFGAG/MnmJS+DxTfF4BWo0zFMkknU0xNTXHs2DleeuVZ\nPvnhT/DCiz/k7EMfAKmJpYrkEgad6g6vPv97nH70F9jevMf03HGMWEg69u7OrXccnfFcAS0kgR9g\nWBYRDOsZjOqImYkUSNXzx9Hjpl4Mf6xVfw/8kFgeB4HiUPEZltSXvjLbvzYKvCPQiP0x1CMq8YBP\ny2F+Lv37Qj9MTh8k9pRcHZmNhSryZzSV4uyZh0jYca5fv8rf+Ju/wuzMJP/0H/9DlA8T+Wm6TodO\nu067E3BnrcJeq0W70WF7Y4cTJx7BVx71wMQJHZq1Ep/66Meo7t5kfn6C0l6Lt15r0HUbyHQF7Qp0\no4ORTmB0rpKRcSwTbCmJpRII22IylkbHk5hegDYc2jqO4wuCTgNTwaU/+R6peIGkaZAXSZyWQ8uX\npFJ5JpI5nLaDoVPEtYXlayzLRIcBxZkpnvzPPki90UaGATevX8Vp10BqUhpSfogjBE2tMJIm3/jM\n3yaRSHH92g1Sk2kCp4vwNZubW3SaVeYm84hEGsfv4NSaSEPTatTptDs4to0nAGGg3BBbSprKxw/r\nzOUyxIwGGbOM6Ar+/b98mozf5fhSjpX1bdqGJoEkEw+Ymp7nxZ0d4pZJ1tIYO9uYnktl6yr/6Jmn\neeSDx5g+soiWU1hWivx0Hs9ok5/OIhM+2pnEcTsUSSwjjQAAIABJREFUcwaWVWRrpwbTCYozZ3l7\n9QYf/OAjLOQzPPLkR1heOENiqoCwLfxQoQwDtBiYFPbNJ6Ox1dtS7DOJA1w3biI5mD8ChFCDcTxC\nIg6GZqTyi+FnjICiMSQ65LfGTh4Bb2Osoh6WLIYzeGAmKkdn8mC+6sE86gPL/nwempgfDlBGFfx3\nAiUP3Fga42BH+/PgtQOmVkQRTvt3i9oox9pzIDJqDzf3U5TAMFjPYDOg1+7+cxVy9KH11xpBFKAr\nqqRmPCfj0IyWwTXRfYbr36ClQg2sJQbAtHeREGrkTNFjiwERHgpMo2IO+m0eDsAGPTnor/7NfaUw\nhBkZBwvJ2ls3+Fff+W1aKY+TR4/z6NkPM5+fIVjboVOr0yyVePbH6zjVGt1jE/zqJ56ivLXJ/PQM\nGgPTjtFstJlKZQlEiBAmpgIR6ig4Nwdjxv40WUWAQrb4Uy3/Z1qU4vjxR5BScvfONb757V9hdnaB\n3/yNX8c0bSan0nTbLTqdLs1mi5XVVVY2Ywgh2dup8ND5s/iBIh638EpdNjfaPHT+EbzWNeYWltje\naVAu3ceyIjVmc30Hk2gr+faV65hSkEzGiU9kicWNgWnyZHKCIIg22vrXlnbKpBNx/vT7P2Rmtsgp\np8t9S7JhWrSaHfKFCeYXZvF9H9M0iMVgwYvYQc8LyUwd4aOf/jCh3yR09rh+c4vwxgaOkJwxBZlO\nGyEkXe2SyTT59q/810hpcOvm/UEE11QiQL/+NhVfMX02xeluAWFXUXcdZCaLCgL0apediQLOegs3\nbtOod6jOxGmXHZrNW3xgPs6GKehUu6Rsi+/92+9yRGkenkxzZf0ObgAmmpwZkD1zjO03bxNvdZkJ\nAxLhNpYp2Lh3m7//v/wPnDl/gqXlRS6WlkjOPsr1TBr0KhOFaUw7QTpbiEBfq4HpTtKq72IsZFlc\nNrlz8wbnzp/lyNEZzpz/KCdPPsZEboLATKGJCFjjPbbJ4gSapNJ0fUXXj9bByaRBy1NjQNEyxKH+\ni+/L+3Ln7m2e/oP/GyNWJJMt8MQHP8dUcYpqo0KjUaVar/DDZ/8E32lixdP8ta//dXar20xMn6Ld\nbjNZnEN57gPTSf1V5J1HbCKOr30sU6BUgJISJXsqo4oUDSEloVYMwtfTUzJ6uonSemhOd0BZHZW+\nUjl835fING6UIeizAJrItzDsKUojSp8mMiN9IJNwUCS6F449qmxfIe4rIENFREc/So7D8uICc9NT\nlEt7XLt5lXOnz/PUl36RugrY2Nni+uuvcvvK29jJWSzPIHQdlmeOcefGXe6bu4SOgyEFofb4zBc/\nS7NpU7m/TrtWIWkkyKVi+IFASg8vY6IJCJMCCNHSQoQhjvIwhMFEJklpbYuHL5yn3Gzy8Kmz7O5U\nObpU4PWXXiBl+HhBiCs6BCJAhxbLy0scOfUQnufy9ptvgNckCNr4dhyLFIEfoIRB0srSTMVJJ+LI\njQ0StSook1B7BEEXy0gSUwLLiJOansQzbI4+8RiNbptjM/NUy2Vi+Wnur98mf+woy2fOsb23jk2S\nTDHH5p1bvPwn3+NLf+3rJCbyVGodrESK0HVACirVOm9974+JxVIQhKSSEsttYUrwfM2Jc48RJJKI\nUHPv2luU1+8RaI30BQvZAo3tGtp3mIoryhmL0loFoSRzuRleeO0NQsPBzBQpLlaZnp9hsmCSyabR\nqk46rZjUitxUGq1m+JVf/i/Y3b1FEO5x4ty3UF4OZSdQKhiMN6kijV4K2dtgUeMY5s9D0AzAQQ88\nDeaYHhQxTFC/D0BpBqYw41NqHED2o5hGJLoeAYx94DJiMdDb6Rkmj2ccK4zMkOFmTv+NHv+Cw/z1\nxkHJYd/vZ03H2jwAcvoQQDNSRm99GpClerixNR5OSI8c4+UoNeL/OWAPB3B62OQeeTdmsT/yQA5f\nmvogcfg6aoq6r9MZAvt+e/RIGfQGSS99CBEQpLducpjfpHqQWed4v+73aR2AaxRKg5QGhtC4nRbY\ngp1KnfnJGW7WblDbXsU89yTteouYsFhdvY+pfMpbK2ivy/aVu7w2MctUIk2uMEHxyDLZeJZcOkO3\n2uD6nVssHztOMVnARiC1OIB7f7ow8X3584jbqrC8eJr5hZPsbK9y99ZFjp86wxe/+hROt0Z5r8Td\nm9d57dWLCGuJ/KRi7f4K+ckcN6/fQAh4y7bwXJ8gDPny17/CxlqGWvUe1Uod0zTITURRT6dmJgmr\nDdpSktQKW2vwfMqlGplsCs/1SGcnUKHL+Qvn2dkpc+HxR1m9e4dPffrTvPpMBBQBdi0bvxdUYmFp\nkbNnpnA9wdtX7uJ7UUoQR0is3sZMLq0Q0sSM5THsCULRwWADgFXL5uFWY9gpVY9kZhLTinHhsTyd\nTocLH1ikurtCsHCM9uXbLB7PsnDyo1y/epXiB7JYsSTdVpU/+r1/yze+/DkyuSLra+vMzi/idBok\nUik6rSY/+v4fcSTo0inkWPJd7PItIKCUiLHwxAXKRpqk0txa28K8fRvXcSEZg9kY5ZVVDJqc8Lu0\nbROvs8X2JsSSU9y4cpGC4yCXl8jdLbGwNENmYpZYLEat0uWL6SqXA8ntMEcskeEb3/omzco9wu4m\nD515FIwEgZn6DzTq/uNJpWdTm7YlbnB46qH35X05TBxfUWmUmZh9mMrmmzQBKRRdp4Ntxdgt7yKk\nTWnrGqE2McQOl996lUw6T2FqkcW5JRLpPFYqR7vdYGNti5niLIV84V2p3zuCRRmGSAP8MECaMXQY\n5ROEHlBEEOnAEcrrGzUNgm7Q3+0dKniRsvJgpXDoXzUejGEY5XT8mkhZ7qnPPb8lAC30WB6usXtp\njTHyfqCUCTEIvjOq28m+cqmHO/QqVPSjPRpCUJyc4mtf+wZ3rl/n6ltXyM9N8Vvf+Rc0NtY5dmyR\nUEs6TZfGXgsZS5JLJEkk4uh0jDDw6XqKiy/9GKfeJZPKYCeSpNJxtnd2yM8cRQYuSwtzJLRBfmma\n1154g1/65W9x89JbHH/0MW7dW6NYyPNc5/vMnzrDHAl2yiWOPXSGrbVVQpkipI7QCh16GFrjewG2\nFaPT6kahRQwblMQMTGRoIKWN8rq0O1UqnQZGOsedrQ1aboeM6aDRhFqiAh8sTUd55DSsbmzRiVks\nLM6Qyc1wY3UNw1BoQxFKk1DYbFRq2HYSO5ZhtVLDTueJp/JMF+fZrJWZm51le6fMkaUl1tZXOXni\nGG8oBys0eoH/I4XXFALHd5hbXMY3EsTjBqtrWwShi6cEfhjgBy6mZWFYBrXtTXxfMycyTLuSrWcu\n8qH5JeRESFVJNlfWeO6lH1GLa+YmZ/jIYw8TKonWBapVj1TWZG5imltXX8EqKDqdkJil8VWITZQ4\nXSEgDBFGn8XRY3ipPwb72G18k2Q4HuWIIj9k9xj7bHzu7B/Th8yv3nl9cKTpM58jAJDRROyjZQ7B\nyNBicsjUse/cSPpzRhz4fH900QNRUfe1SYwWMMKgDtYe+aCNoX5fjOSS7NdqZI0Y3KTf/4gRRnBf\niWKkVfvvOUr79V77z6Mf7GasrDFz1YP32x+0pn/NaPsH4HDsxv3+G4K5gY/jcE/sgfIghnd/HUfZ\n2ui3IWqpZQpuX3+LN668SmKmwOncEvWdHR69cIHMdI7ADNjcXKfVrLO3s4ptKE4eXeL111/BMhTP\nPv8cJ6eP02o0mLNOM5E06FR32dkt88Lt18nNZ5nI5AfBgiLrEoHsuT0Mfj3e19j+o4rvtACYLkxz\n/Cu/wonTF7l1/U1y+Vme/uM/YXdnk5PHp/B8je+57O3uUpzKD9JhKKVJpZPUa01ee/kl6rUmE/ks\nsbiNbVuU9qrML8yQTKVZfOQxTNMklUpw6Y3X+co3vsK1q3f4wJMf4tLrF1k+fpIf/PEfcubMPGfP\nH+ftt1c4e+4Md+6sIdMF6IG6mpRopXtWSJq96kGTrkwPKGILWjs7g88vX7pBs9lm8rDOSBrgKjrN\nMgDF+VNkJorsrkf5OrUKiMdtzOQc1fIexckc+ekj1EprmFYcO5YkP32MWmmd42ceobq3wuTcCcpb\nt5mcPUa12mRGjKh1zSgS6XZMcvbMR8iqKAfldr2D9neolHdZTk7Dtsvcok+JWTrlt9CYZMo+5xMd\nuPIKv3hymaxyeSMxTbfxFn/8u8+QyhfJpULOfuAz/FCfQkxIwu172BNJlo6d47XVixgpk2a9TDyp\niWfeHaX1P3VJWIK2p4iZohez48Fr6X75c5v6vy/vCVFac+PePS5d/DHFyWmWF49T27rE7LGPkkik\nCJXg/to9lFLs7m6hdUi+uMDO6kW6nV2+9z2foyfP4LtNlueWySbidOs7bG+tcuvedT75sS8ykZs4\nEJX+LyPvCBab1V1yxSzS6jEaWiKUQkg5/CFGMLqMRqTEUGXRaGRfs4KhkgwjZml9JfUggIveqBFS\nprf7rxnJ7TYyufp6Wp9AGez2D82yDpTf/34AFA8qZn0uc1CONAFN6AeYhoEWUKs3mV8+yvTMPDJj\ncf6Z01xrbiOsgFdefZmYEUeGBrYZw+82Ue0WhekpzHQa04D7V6+RSiTRWiG1RPqaqeIsH/vUR1Gh\nxAs08UyacmkXw46zurrF8qmHWNnYojA9xfbaOoadwGm7GIk4xdlZmk4XM5nGlwLl+xAoAk/jh5pk\nNknTaVEwBG7goURIiCI0I8bBIEAqn/lUloQVw/EFZigpJnPokoHldTCUARhY2kBpg2Jxirn8NCut\nCkYAcVtiBT6pWAxpW+yGHsmYjd/tMrMwRafjI+pdkgkbP3TZq1VodFx0vcbS0SVWVjZwlaC7t40t\nYijlIQJFGGjQEh0ESGlSa9TITGXoKA8jEcepRGbJUhto02IiN0Umn+fSvVUsKbGEYvbsccx0nt21\ndUq3bjCRmyLrm3x2dhElbRwtufb0y9xev8OJh88RmgnOXkixc/MeU4V5zl2YJz8zj9s0CUWIVr1k\n5kGINEyUVlEM7cFgPDhU+4DxcOmN8/HLBhcf9sPyk6N89uddBF6GczBiPiOAut/LsX//KCrroD19\nkCYe0ISI3h9t6Vh9+sxl5FO3PyDNsD0H2x2xiH2gG50iesFUDr92GLW5B6x6gGZgBcFw7o+ZbepD\n+p6xS3rt6d1z3/tBN/S+HQWLQ/PS3ko5ssk2uHb8juPfjLGU+yu37yMhEFr21uZ+SiHJYQzvoZsM\nI76AB8eX7q3zilBKTCUghM27K1TLW+zs3ObulS1KT3yEcDqGU+tw7fVLzJzZ5uTZRwjrAeceOsNv\n/vY/I5GwUUrjeYpWu8TeaomF6SI763coedd58YWXmY7lcVMhv/eHv8svfOLrPPbQI3g6IK7NiEGl\nH7hJDwOsCXFIoLS/uuyWdpguzrz7Bb8XRStapXUW5k8wmZ8hlkjz0rNPU6ns0XFN7r5+mXQ6SSIR\nw/cDfM8nlYgzk8kwOR/HdXNcemuDiXwWANu2MAxJbmKCDzz5JIaM3EQyuSk21+6jVMj2xhrnzp/k\nyuXLzC4scevaFQAajTbtbpflI/O4XmRaqkci6GqtaTba5CYyuO7B3I5j4mnyhfzYR8em8rQ2Vsio\nkGDk85Zhs3R2aLrsdhok0nm0VlimwHFqtBpNBOB2GxRmjuE5LarVJpmUQeB36NTXCb06tb1VpuZP\nD4Dm7spr5PJFyo3OgXXBNAKcxn0S6alhG1vj6l8yt8CpqSSrazaBkojiIlMnjlHO5dhZv8fltVss\n5na5mlzk24sum6ZJOm3w6is/ZvXKdc5+7DFcz+CMHePm2gaJ9CRzR88wOXuM4D0UxAYiqx3BwRQZ\n/ainSVtgSmh7ilBHPuzvEMxyIH6o38+9+HMk5VKJ0u4a3Xadl65dpPvBTyITR3G7De5ceYby7hpL\nR87Qbrc4vnyc3/rnv870hItMLNEJ5wgaW7z9yk1m55co18+yuXmbV156lpm5WdxOg6e//y/52Kee\n4uGz5//KdX1HuHnj+stUyvfZ2rqJ49QwTR9jEPJfIaRCSI0cQ4sjPkuib/LV999hjIGUPeA3ykb2\nK9U/omt6ebp6+boE/WhTowzIuIrTD/Eve8pNn1ERjILM4bnjH4zURYx20mgeu0jxNExzsGsvpcRX\nGqUk0rTAEtSbZRaOHsVAYPgay7AihjLwCV2XyUKBQnGSXGEKGUujlQnKQKEIg4BEKkWlUkMJgUjE\nWNvcoNNq4XfbeF2Xvd0Ks1Mz1CpVTMMmCF3QPm23hR03cJwuWnVIWBKpo2TWVsxCAHYsRuh7SBSx\nmEW300YKFYWv1ya2NlHCpN118V2FFCapWIpKrYEpkggdgQepFAkrTqAUccNm7d466VgGqW0SsQxh\naGIbadZWNqiXmsSERT6ZBk+RMWPkszmUUihCmu02E5kcwrRRwiSRSGFaFtnMJIbsJeNWIup/HSWJ\njmczpFIpWp0Ooas5ubhMTEu042AhUFrRdrvcX7nH5EQaU2mkJ9AijspNkjt5iqaSPPn5L/D4Fz/P\n/WoFD1hdvcdCYYqj2Tm8tQaFliZTq7N7+zZvv3mVZ3/0Cv/8//stDNNGiCivZMRyDDkcgR6yV1r3\ndfXBcRg7OAwuMwQZ+4en0FEewZ/k2zfK2g1NBYd5SoXo+7ipkfk5biwuB58MgdbY3Onh4f1H/3OU\nHv9c6V4+hR6g6/8fqsG5Y9cgev6RYmQODrdu+v0MCqEVQodIFJIod58QEXiTQiFFtHaIkUMKPfK+\nDyrDEeZQgBbReB/5k4N7M/xe9zad1PCI1jHZa6+KxroaAa77IKE40Jn7zx2er9FjYyZKCTTORvaf\nveox03pwpR58t5/NHQ3Eo7VGKTX2On70x3uvfC0JHR/D1/y7P/pD7t+5QbO8yW/8xv/Dc28+z0pr\nFzMRw/I0ItDk4kluXrmGZZg0SiX8wMdfr9Js1vjIE4+zOL+I73m8+IOnKXWrXPjoBxBxgw88/ihC\nu/iui4XA0mBoQRD0IgT35pBhit768u6jxetXX2SnssVOZYuu23nXy38vitdpILUilsyilc/qvXWm\nZ+fxvQispdJJLMskmUqAFEwvpQiNaaZn58lNZAflRGDRYGl5jk67y/TMJHNzU9y9dQPHifx29sod\nLl+6wYkTS6zdv8vCYgTsQ69Otdogn89SqzUwLRPVc/SplGtj9XW6zuD/rY29A+2JWyZmbggoH37k\nDHd3yiigOaIc+SceopNKcLRrcPPGfQDazQoAUprYySJXrpdoNasoFZLJzw6uTSUkWsNksYDng7SG\n/dAXM15E4lIdQSU5ZZJVBoZhjEVIvPDwaVRmvIzLvuDGrYjxPOpHprat9Ay5hfNY5z7Cbtpm4mv/\nDeef+DhvVnaopTus3rnHVKHDR9MW2/dL+L6i06xw49o1Lr+1xp0rz/Cd3/mn2MmD9f1ZFqUPAkWI\nwGH/cANN0hIUEuYD8yU2nXEU3U+Z0fUVwU/D8ex9+U9KqrUqz/7oafbu/SlO9RK//g/+N1559rs0\nKutkMjks28SOx8nlC1y5+TZz0xaCAN29x+bmLtVqk0c/9hQzS+coV8q89fLvEGedU2efxE5OcOED\nn0K+S7977wgWPV3i6e//K5Rb5fXn/xSLIFJchI4ULA06DFFBGIHHXrjTUR+bsRD9/Yimo+aho4eO\nFOuB8hJRB8BQ4REjis1PoutHFab9Jnr7ldfx92rk0IN6Ca3RSkU7kL06KD3cjUQrhFJIU+L7ARP5\nKWR2gvsrmyS0SdYw8btthBQYQhCzbLQ2EPEknVAQyjg6jNgeKSOKuut4BKGB4yuS6QxKCNLJNDFp\nYUiFGRMgNe1uE9s2sIRCKh8TjW2amAIsKRBhgFYhhpT4nhf5FCkDKSwMI47QJpPZSUJfAxItINQh\nodbIVILMZAFlKKZmJshNTeCg8CWEqqcAew4icGiU9zh9dplsPoYRh5Wtexw7e4yO7nLq8bOk5/O0\ndReRMdhpl6g6LdyEoCF8zFicfDqLRDGRTVGvlkin45EvqQoQoUJKA+izhoK41si2D52AQnaCdCrD\nxtYWfuiTTFiIMOoL5XjYpoXf6WIS5fSrbm9zdOoIuAamStD1BK6dRGcLnP3ql3jk65/n1duX8aVA\nBJqYgLdffJ583CamJKeOnufzn//ygGkTRGlehAYRaqSOEpFLJXuHiOqtNFKDQcR29I/+ewMx+L+/\nORJ5zqrehklv02REoe+P69HX0fE+zDvXAx8HjvHzRxl5rXWUV3Hk6NdZqIPA4bA67J+XEUVIxHYx\nsqkjRvtEYGgR9ZfSGCOH7AEDOVIXQ/VyuGpFlFcyJAKQehCNuX8MP4uOfpsi4NpbD+gB6R6Y1oyD\ntv573ftfoYbrVr+v0YONpf3PZLQ8TRg9BxltmvTfD40nDnlm8pBDKPTI9aOHiB5Y7xX2L//DZzgE\nhIfJoQBTS8BAS9BCYSZjtNpNKqUyk/kCWcPk9OQ0c7kULd0lsTDLL3zjm5zOLeC3PZq1NmePnWVn\ndZdyucLJR87x6IWTfPDR83SqbQrmBPV6g1K1xPW713A6Lou5OSaSeSxMZEjUzypEmJFJqlZR9ObQ\nV1FU4geOyL+8+IHih3/yrymVdnj55R/+FO7w3hWv28SyJ5hfnGFzbZVsNvIR3NupoJQi8AOkIdC9\naOzl+iHR8YByqYbn+dy+tYIQAsMwmJmZxJSQz2rOHAkGv/+BH2CYBt0w30uDEUk61iGZiOZDYTIy\n28pNZJgKfHJZm3w2Wj/nFqbY24kAVb1n2uWogG0/Ryo7hWnFEQKmp5JABGj70YmtO9eZbtZZ9xo8\n+sTjZAvzxJMZ6qV1ivMn8dw2H/vUJ0ln8khpEPoenfoqaEV2YhppmLhuSDyZI5ZIU5xZpN0sk0wX\nsOwEiXQenJCiP8plRlJsCbzmCOjdLuG3dgcsLUDcaiCkieGFyEBh3rtJZfMKhUKRdqeNGyTptpvk\ncnlasw9x4RP/FcXPfZOtZ++wnssCgrnYDq2n/x0fdcpkVMj0/Cm++KVf/EuNj59lSViCbNwYWNBU\nOgFeoNhtBXR7eTP2W8KMiinFOyvn78t7Qsx4ivLWDYxYZLR+/PgMR47kKJUaZLJ5PvuZb7Iws0zg\ndgl9l4Wjj3P3/io3VwQf+uAJzlz4AA+dOk+n3aFYnGJjN+DmCqzeewuntUsukyedzb0rdX3H8dhW\nbaqVXW5evsSjJ07TLFUQhiQEVJ8qlAJhjBTTU7j6LAEjCthB0b2d9l6uucPM50b8ecZdgcZ32P88\ncpgf1v76HMbi9AHAgVP6bYNebnCFCD1MAbVqnb/7d/8erpFiZ3WLyWQW6YcYhqDrdVFCIIXRy60m\nMUyJKUJE6EHgEgYeiXgM3/PJTGTRhFiGxhAh9U4FR/g9HTR6DplUGqU08ViaZCrH3PwiN27eZnH5\nKK4bIswYvo58LYWKGJdE0ibwXbQIot2KwOv5aIUoHaKMMGqqE+DX9jDbFdydFYTTwm23MYRFoAVK\nGBgStPLw/A5OqUZBGNx99VXmE3Haq3c5mk0Tcz1k1yWFpJjIcHRqiYId5+TsHCkEJgIlNNPFWbpN\nF0PYlPeqxJNpXC9ECIVlxdFCobTCNE2koQj9kJhlYlsGpm2jDQNhG7hBl77P4FR+gmMLi4R+gBAa\nRznEYyampTFti3QiS8JMsH57BautEJ6JKWLkklksw8btehRmZ6j4DoZhIDsOU9k8s/NzGKYZbXZE\n+RKQUo6wV70fBDEcuYeNy/2bGUNFvP+D0tuMGRmrQ4ZqfHz/ee7xIBm/d9/HTgyC5exn8Iet6n8z\n3CwaPUapPN1jXkdBhxpZHxQjoGXAgx3Gq43XeSSU1sh34xeNWR/QB+P72qTH3/ejv0bTXQ+OHuG4\nb8eLnh/nKFCL3kdjoXfI6EZR4KPRBQX22VWMt3HfcZgc/qwj8NxnNg8rZ+xZjbC4D0qxEbHfYe/5\nMdgoVIRIoQg7HT712c9hZPN0HEVaQMpzuXHlbR46c57p+SWWL5znkSc+xNTsAkkjzaOnHsMy4ly5\ncZm6U+UHzz1NqjBJfadJt+IwmcnhtDsUCzNcOPUIC0vHSBCZfPmhTyg1QeghjGEfGEJCCOqnYAvn\nOS2a5Rts3Pwhpx96jFLtIPP0vhwUK5HB6zT47/77/wnX0XS7LY7qIcgJQ4Xn+chQ4+8d/tza7S7z\nnTah3+TUyaEpsBQBbqd86DVBGG2YptNJsrk0b791ixOnL1CqSfwgGi/VSn1w/oQKabZ8Gq3h+J+c\nikBmurf2xqXJpB2j3dgj8F3c+g2EEQFf3w8OzNO9zQbdVhWAq29dJZ3JUt29z2RxlnZjL9qABYrT\nc8QzcyAkc4vHMAwLxw1wu01iiSzV8g6JZA6n08COp3A6dYhJshzsrwxJPDOqUzo3Ta1SJ5YyqFWH\ngXceTs5x7vxpQstA9dhJaaWj6ydmCPQEhgF3r79GfLdEKpFEmkk4mSGbUph3bxKbe5SSNqktZTA3\nVkhPLFMsTB2oz3tRUrakmDIopgzS9rhqPZEwsAxBMWXQdBVuoJBCPDC9gWWIYQC19+U9K6HX5fGP\nfRk7NQeAQQVDBJQ2X+XkuU+ytLDE8WPHOXvmMWLJLG0n5MTZzyKkyaW31hH+Fi8+/8ekJ6bZ3d1l\nr+QynXfougI7tcjZU2dZnl98V+r6jj6LfheOHztHsxty/dodvvDFC9QDRd/1ZRD7r6coDBUUPaJA\njqhvfXAlBqpIBNxGzJ36MghTrzS6B0wHn8GYMv0gGfoz6QHLOV6/cdlfkuidqcXw26EOpnqGebr3\nGoXBNwCtQtLxBL/1m79NoTCLbK9TqVVJGhaGkBha46oQYZmoMMSUEo8QoQMsQgzLJJQC13FJpHLU\n6w2mZ+fZ2igzO3uEyu4uwkyQyk+Sm5xkfW+XqfkFGuUq9XYbM2axvrPOwvIcd+/fYWZumvVbNxEI\nDEMSBh46DPAdDxH43L99HdfzUJ6LFhD0+keSamY3AAAgAElEQVQJhTIEftvh8p/+EMs0wICYZZPK\nFNiqldAWoCRaagxpEU9muPTaSzjKI2VbPHvvJsl4gpsqwJYSw3Eo373Gxu236Ha6WNhkC3ma9TqT\nKNYvvUE1d4ea02GyUKC8u8vEVJEw9EklYzRD3UvNFwGDUAkyqQxOrULTaSISKZLCo4skacVQaFwd\nUmvWyMdtTBlD6XZkOi0NAitESRdfB7jSQKfiiISFbcYJHUjoGLaUeL6PLS1iOoYpBAYBgeOBCqIg\noRpMEW2kREr5GNyIxk7v92OUAYyA1QPyxY0Yf44q60MfQcVoSM8hrBq985DV6qdUGKCf/izUDEy0\nR6/u+/Do/pl6OAHG508/VcO46ezomcOVoQ/Q+mvHyPei/9qfUf05N1qrEQDVK02IYeuH9xvpr0E5\n49GS+75swwJ7a9h+hvYdgL7uF9YrP2pD//o+wj7kWj0C7kf+H9UrdT+P4Oi5o0X0TUDFsO0DFwCG\n10Tr4OgY6/ebGClnfJNgpEseuLkW9V9/FEUMrNAgkWyt3Ofi5dd4e+cWmAHpiWnmZgX1ao2/9dS3\nMIwEjUoDI58jPz+NcSvOJz/1BW5evkGn42NZkup2hcmjRWZPHuH2v3mB5TPnMJZSfOyJj/DSq2/y\n4zdeYXpymrhlMzUzRUpYNDotLClYrexSyE+StON4YYAwIpb63RbHDTl64iFarQbXrrzJ5z/79Xf9\nHu9F8btNAH7/D36HmblJatUKa1aUV3B6Ntplt22LAKhJg8P2xrXW3Ldsitk8N26sc/LYBOVyjeMn\nj7O7W0EjsRNTGLkl1lbvcfrcw1RLOxFgNCAMXM48dJTy7ionjk2ysxVFL80XorvVqg12skmygced\nGzeQKiBE4LoeiUR8EKshVIrw/l2uXb8OWRPSJuQtTkxPcPv+Lh4jKSICzWwuxduvPAd1l0I+x4+/\n/yoBU0g6KFIU21XCW23+zRtXELpJTMPxjOSuG3Ai47O78jzeZpHadgfm77G1vsPs8jI10WIiTFDV\nwYHlKq41lXqTShDQ6boUsxXWShFo9lwPTEmrtosRer0J3V87onXD7TRH+l1hThcQ0kD5bUKZAhRC\nhVixFKYVJ1F1EEoRhiGBHo9r8V6VyPwUbEPghXosPYYUAjdQxExJvBf45n35+RWlFKvrq7x99WXW\n79/G67ZI5haZWbpAbfcWT337f0ULQbVWYSJXYGFhmdTVNJ948jN85zv/ENuWxEyH7e0yc4sTTE/P\ncPW173Lq9DKF4gxPPP5hXn75OV5983UWZ+eIxePk80UMAR3HJfQ9gjD4CwW/eUewOCEnmF6cpaE1\n6SCNgYHRM5Lqm6n1FYlQDNcYgUD0FNOBvjWyOy36odlH2AolewEJ+jfXo4Fw9ABQjgLGd5Ihi3hQ\nidYaxDvs2gzMZtmv50X8ghhJJt73nUREwUF8GYFpO4Bf/aVfxk7b/Otf+z9IJZPYAvBDzDCkq0L8\n0CNuxwn8ECsWR8mIqjC1iVYBVszCVR6nTi6wXakyOT/F2voGS7MFXqjsMDeR5tLFVynOzXH1xedJ\nJePEg4CbF9+g5DsoQkJfcLvVQAYKGbqRmYmwAU27VieUmkq7jlIaIxQoAUoJYkFkemkaMeq1Bm7g\nYmpNF4VSFhKNacXxuhUQUfJ5pQ1W1/YIXIFhCXwNoSXpqHrk3R0qDMukuXsDTLCkiRBQ21wnRGNL\nyc7KXQwlUIagpkKSpsHW/buYhiSViFHaqw1NCHWIlUxQq9aI+wHSgo4XkkTjpNN4gY+2BMrzwXWY\ntE2OHz1B+dY1whCy6WmcwCeXjeN5DsKymFleovbWZbQIMWNxAl9hpyRB2MHXPo4fYJoSrXw8x0Wa\nJgIDoSIvtqECPRrdUjGqcg/9BfuwYl+U0x7oGMMq+0bz8KMe5yX2g6b954wU1EvrQQ+o9qOI6tGx\nf8CUlQEwUDCIGtyfE1II0Cqav300qzVCjqsJw5hW/c2Wfj16G0c9KlD0eUI9Aih7c3/w2isH+r6b\nPcAiAC0HQE6NgsSRuvSjJfd9P4fgfSTQjhiucgeYvH3dOoC9ugebNZEJHRFof1D012EH98F9n+VT\nvTHSM1PdL2MgdgDpGR0x4ycfBIP9e423bf8YO6S4se97vqFKgZYoX/HmCy9z7fY1dnWV1vYOS+eO\nI2SAo0LeuH6D80fzhJUWHUuQT+coTE1RabRBxsCwMW2DDz36QYJ7O+yVdyjGYrxSqTFfM3jphZeZ\nnV/kxe/8Hit+jRMfeZJvfe0pGqstths1Nu7dpRZXfP3LX2W3VMFMJognEsSU4Cdli/qLSmGyQCp3\nlqznIMTPg0r87spT3/jrxEzFP/5///Eg+mm5VGOyOAGAoTXHsz4VIShM5rhza/z6pcCj2vU4feY4\nGxt7zM0n2NqusDgb5/vbJYpTBa5cfpPZKZPrb18iDHx8z2PthVdY6eVM9L0Ay9R0nYNzrCskQmhC\nFVKtNsnls6hQRQCrNy/8UOGHkY8fjQDdCKhWQ+j6TKTilA0j2lQEcBXbuw2MWhMCjVMpg1CYRBFj\nJeBjsbLbwBIGEPlCrmuJEiGWlpS2y9T1KgaCmzdvAHDzxnUCE5KY7B4CRNa29ljWkZ62YTos+QaW\nK3nF9TCzSUCRc+rM5iSVCwvsvr4CQCY/i2VZ5ItzJKxdrFiKc49+nDtrdxFoJiYXkCt1OBqlLwkD\njzDwaE9G5q1hZw3jsLXrPSzpmKTaCSm3h0x5Lm4Mci1m4++vEz/vEoYhV6+8yq03v0u5HFBrCo4e\nP4YKKsTjce7dusTUzHEaC8dRdo5MTLIwu0Sl3SFKU2jQdQM+9vGPsFOq49fvk8wu0159m4m8z6U3\nn2V6do7n/vT32N6u8tjjD/OLv/h3qFWr1NsN7t67iVIhX/7C12l3WqSSaeKx+DvW+R1/OX/8wrN8\n/jOfpglMTxfQCkIhMRSRh44gUtqljF57yZ4jHWZAo4yUqHu61L5k0KKv+g0N7Q4oMKr3mdJjycBH\nFa/hGjmiOPfrI4c79wKimBH7RPQAoO75Y8qe0qv7hYihL6bQCqQkVGFP0ZUoIC4ELgpbWjiBpo7E\nCCMfTwef0BIEYVSeMjSYihghpjQRSmNIEy8MMCxN4DtYQnDj1ZeoNlsoBaHvsxN4HJ3O8t3f/Rcg\nBbevvhHdXymShsHK7TIhkWmYCvwoV2QIwojMRl2lkWaKpq8JZYgyJEllIVIJ6HTomoKugjxxVuu7\nZPNFzBBsU6BEgGXHCH2BQLHbjczogtCn02mRzmWIxSTStFHKiPwyVYhhWgSqZ9YaA8M00Cik1hiG\nSdh7NYiesWVE+TvdMDLVCzyFVA7Hi8tseDso5RBo8NG4TghuHd8PIjDjB/gYzC+coFspkydOMZml\n3XGYnioSuxxgZlKIao2dNy+RScSYVJKwUqKwMIUwBClDUsgmiMUlhmUQoLEAS4gIbGuN02lHppIy\nQCiNL3wMaUXjT46Cgd64GmHf9zNFh/keohVj9tf9Mh6g7PdNOQUjLFpvU6fPsCNEby705lsfeA/K\n6dV3JGqm7M070cMjcl99B23pgd/Bd0IM2cz+eQO8IoasnCAyZ1RykJLCGLTk4Fow7gdNZNbJMCrq\n6HWMsYnjfp4Hg/8w9p3u951Sg3YdYPj2b2qNbAIIOcoLH3y+48xftH4OAOe+ekn5kxWMoc/o8D7D\nOh+eI/FB78fLPHxzbvjcI+AdCoWFQaAUrueRTCfYvnaFTNpk7cYa5UxAOp7ieK7IuVNH2PMq5Noh\nmViGQjpPfbfE7OIUoe3QNNuslSrkrRhbK3dRG+tklif5G7/0X2K1A25srvCVp87xT37913nuez/g\nC5/9Asl6nVjcIp6IkxQ+KlBcuX6V+flF5o4sE8P6iX34F5VnfvA9Pvv5T+Erm6VjD73r5b/XRQkr\n+j0Jh4DCNIdjPRSCbqrIXCbAsIOxLQ+AimES7za4cvFNTK/EpQ2TcK3GfSG4kEvw3d//IwCuXwnw\n/Qh4zQUBl9pRMCJDGnS7Dul0kmqlwdTMML3DRD6LD7SaHdKZJGYsTqgjVsDzfDpxk6TWtA2JYQgC\nX5PuRW4vdKN72ULgOC4HqDUvaoWrDgKpEI8QEL04qgpwZkzYCXCmplDFDGp1i/DoPEa1jGzWAQnN\nAGX5LMYzNNutsTLLXR9nu0xLRnOgFPU0J08fx6mUySRjpBMxOqbgycw0f6ZWI5haXuWNF/6I7OQR\njpQ97MYGeuo4nYSHFoJASYwTCQZ4ULljG4RWq41h2j9XcNHp+SROpkxKfcAoeGCQm/fl51E0tXoL\nIz5NqfQ2pj3Jyq1L5FJtkqkMmbkUJ089RtfpYraq5OJF8vkC5doeMzNLuJ0q9bbJ5tYayCSbO9vc\nvr3N3OIxnvrW38b3PO5vrnHi2HH+2T/6NZ57ps4Xvvif0+w0o/gQUmAbJq7rcuveTQrZIqdPnn7H\nGr8jWJyez3Dj+is88uGP0qhv4oVRjkDCnoLT21FWvaVtlD3Zr2CMgzoxAHIMTLfosSmjCtVBFrEP\nAkd0zt4/DC3rBiZwekByjOizQBSYpX+dGKhoI/5DIuJ7pJAjDMKIwtdjZqQ0Ih+gXnFSa4QR+c8Q\ni2EkkpGiHSgMw8QLFVpGUQBnC0XW7tzGkxoMC1saOPhY0kIH0Go52JbN7fptVBiie7yuR4BpGahA\nE4YhlmWjwhApBO0gAlaRyamJ0AIlBZ6hSUgDGQSIIIhMiQWgDUIMrKOLtA3BZDJDIMHshJQqFdzj\nC3gLizTvrFCoO8SVJJAKz4zUYMuwkMJACgMhBZ7vY0oTvA5CGIRhG2GA0EZvA0GjggAjNLBMk1AL\nwjCKVqhNA9OyEDKKZRkEfsT6IFBSYQkLLQysmI3b7iDCAMfpYBkx0okk2AaIKPhLoASZ/5+99w6S\n7LrOPH/3+fRZmeVNV7X3DraBhiFAEgApGlCiIFESpdBQGkVoRruzknaMVsOV2VjtjmJXu9KMZlaj\nkaFIiqKRQIKEIQDCo4FG+6425b3PzEqf+fz+kVlVWdXVBhQmJFI4ERlV9cy9971899X5zvedcxWF\neckhEWtiW7KVbNygnDc5cs8xhi+ex1BlMktZqrrPzgO7SM+N4yzO0NvUxOzgEK5R7xtAUrAth1Ag\njGM7NebLcZH9FWX1Chu4suyCt8o0bAQ6K/NhDRRSZ9fYADyutRWGax1w2YThEv6154nVSVJn49ZG\nxRozVfuOVkDcCuBsPPpG47uRrQfLtVGta2Vd7t4KM7t5G5vd0836q/UlbnrsrY5/3XAb2r2WnXu3\n7W7Gfd6471st9NV4zLsZ482OXQe4axGKGtAXEnfcfYyL/e8Q1gxcCTKZAsf2H8X1PYQjkx6cJF8p\nk9zVzVR6iXY1gptUue/xxwjZPi+9+DxL1SKTQ8OonXHu+olHCcoB2tu6MVCRjQjx5jiffuRTvDJ8\nlmrVJBHU+fa3v8mOA/vBdrl8/gJGIEjFdSgVSsT+O6zz1tHZwujlV9h37LMU8oWbn/C+rbPNJM6e\n52HbDo7jsHNbiOGhETy/BnIan8lAQCeTWkZVFEoT0xRSy4RaEswv1aqZtrYmqRTLFAtlki1NyHUQ\ns6jJq9hNSKIGCm0Htw7c8rki0Vh4tZ9wpFasJtGcxHUt4k3JWnRflWFmgrlAiI7OLqbGxuh0bWLe\n+kh0bW3qTaLT78YWasylvDSPXFiEqoc0OnztcT44nVtg6PI1O9ykApEGSWzGpj3Rg+kss6MlQXNL\njMHWfaQyGeL3Hmfp5ZfpdvKkvRi59CzHju7k9GIaKZ2lO9TKyOVTBJo613fj5lHqcmKAyHzp7/XO\n/UG2TNnB80FXxCqr+L69bwCqqnHsngd4cvoCSDEQgrmFArsfugvPyqAbYYbGrqIoKtFoE2NjEu3N\nnQhPcOyeR7n/4cd5440XSC/OMzp0lWRrO/c//AjxWILO9k4cx0FRDULxJI9+8me52v8WlUoZ3/M4\n8dpXaO4+gqyqDI5eRVN0ZEkmnU6TTG66OixwE7BYdRdZmp4n3hKlo2k3jm/j+Q4Kq7F7VrKqVuL5\njXK26ztpdee0gQlEeKvFIhqdWB/qlQlrrqXfIAuDNfDns5LjVN8v/FXmYk3etnKOYK0mT+Mkrjvs\ndQpF+GKVYVntbMW/932EJOG6LrKs1OpT+j6e7+F7Lqqs4Qgfy6lL4wDHcUBICCEjXJidmsYTHq4s\nausQ+aD6KpblIukKpqFS9l1k10PRVEzHRZIkZE/FtRyEUJCQsM1af67jYklOLS9SyLiuhy8kEAqy\nb+E4FqpUuybX9XBlH9sVSKpGURIsWiah9k4ybhUbCaWgkpdlZAd0PYoRDKA5DkKpsSC1WpNFLFeA\nouMLDQ+NsuuiCAnJ9RGyhGmaGJqM59jIkoQkydiWh2PZyEr9H6kAx3JRfYEqq4CLcAU4tdCAqihU\nZJAUCV9ToCqDLxFQApieR0kS+LJUWzrBcXBlmZyV59gnPkxoarEG/g0Jo6UTe87n7ugd9M9P4bW2\nEauYBISD71p4so+sGCyPj+AEVW7bv5/x+VlmHRdcCCgqkqgxvLLv1Spo+rXnwcVdJ5OuVeOs7ZMk\n6RrgszI/VorUiE1cpxWwds3T6tfuy5rcemPbK090Y5BjheXya/d3HbO10sZ6p6Y2r1acnc217Zs5\nA9cDJ2vBoHqcaF20Z3Vy1YrceCvXJzacey1DtgI+bzS+jQBrs3Fv1sf1rvV677fGNm4E5K4BgOve\nibfW7/XaatzueR4bge3N7EYO3joW3PPwhcCvV4J1HQdNaJw+e45CvkxECzKamsP0XPRoC9t37KRd\nizJ5fpjhhWncoX6W5qZIxBJoW7v52X/2S7TKCR7SgriKgnR0jq+8+hRjC/M8ePd9eKZL2jQJtbYQ\nDwZRJQ3FVzj5wisc6ttGd183Li6xcIT2lhYU3UAPBklG47V39U2v/N2ZW50lN5liOvYdgsl9cOju\n97iHH24TktwQ8a3ZytIZ5VKF6bn6O9SzKZUqq8BtxdRYBIGgUKogR0K4rkd7R62gyuJ8mmg8TFtH\nM6VimUDQQJIklhYzJJIxhBBYlk21aiIJCUWRmZlaoLU9ie/7mGYNoJlVi3AkhOs6FHJlks1tSLKM\nJ0kMGEF828GyXUKBIAksGouRxgAvU8RLhNnMfN/Hc11s20ZRFBRVxXEcZFlenad2uYwvanmHAFTr\n7+jKJgC020BYZexkG97S/Np2RaUot+P7QZTKDMIvUwl0U8zmuP9HPoYYGiEQCuJ6LsFkB8ulEnd+\n/A5eGCoSjcoUCgWuJuKo6QuAQFMSVPKLLBeXObLrXqbTi6BOI4kQcd0nsOQwV+/ac2+yTuUPma3U\ndRSAKoHp+GTKTu2/rgeGIt6Xor5v9J8/Q2rZJRSJMjp0BdcuI2tRDtzxCImmJGfffJWphTnCAYls\naoxE63bCsVZ+9Cd/mZZokPuOPYSkyBTyeZ785l+TSi3S272TgulRzS8TaWomossIzyQWi/Li977F\nwcP30NRxkFisCduyiAbjhENhjFgzybB+w/HeECwqhsPW3m4CagDbBs0IUq1X+RNCrpf2F/Ww8voF\nbTeTegE1ELeKvRoc64Z+G+PsK7bytwTgefW6HhtA4OrZa/lMtZLuDdSLEBtYl4Y/6uBQSFINZPp1\nSWp9V6NJdQdcURR8SaJaraJqKlbVRAlo+LaHUGqA2QJsAULTcGyPoukgUauE6uLVCiYKCdcFV3HR\nkfDNCjIOOj6+C7gOiuvVci3rN8jxvXrUEky3Wot24uAK8LCRVAUkGde3QVLwZA3L94iEI2hCwZXB\nLFZQdINYJMTB3beRNx0KqYVa7oRhYCs+mWoFKRGk0h3C8l1820XzJRzXIblnB4aisGxVoacVCzAt\nl4AeQJdVstlcbU0iQ8exLJriTZiWhWWZKIpa+/580DUN06wihI+mSNimhVFnTF3bJhQIYpllfEmh\npbWd3PwMBeETjDcRQCKXzaFrGr4nIVQVkNh3cC8DU1P0hmJMB2SGJic4+PA2pqYH2L1rO9nUIq9+\n92X+9U88wW7TI4nJXH6JnGtTcX0qxTKaJ9Miy2RDQQLC5fZD+xgZG8U2q8i+wHdXlilYkYfWvhyv\nQV4kSeuljRvtRqzUZoBjo2R1M+d/fZqivxY02RSQrEi4Vxi92nErMszVwAtr83glKfpGCoJbszXp\n5Gqe58r4bwHUbOzr3QChWwFdt3rsjcDxzcdUv9+sZ1quUWNcB8jeiHXcOPbGvMz3xFZkvdSfCReE\nJHH/Qx8gHFJ4+8zr5NwKebNAqpTn/kQzYqHIudErfOiTP4Izv8S3L5ynJMtcvpii/Bd/yvF9d/Pg\nXcdxUnm+cfEdPvmjP8HZ8csoRpBYJEwyoVFX8rPryAEyAYcFO0//m2/R2dfLc6dP8IuPforFmTkC\nrUn6WptRkBi8con9tx99b667bq7jkdjTgWI0YwRunPPxvt2a+d76OVcolKiWqwTDQZYWM4TCQXRd\no1Ix8T2fSDQEVR/X88llC7jOGlpbnE8TCNScoHKpsrp9aSGDEILICoMoeUSjYdzwmux6Zd3FQNBA\nliWCQYP7HjxOJpVmKZVF140ac12u5xHJMvnmDhQtwvzsFG0d3SzMTbP7yHYcZXNXy/M8XNfBskw8\nRcPXNIJBnWrVXh2HVcjjC5C9W2An64fs2bOdsViMnFuLQre2NzM7swj4uG4EiOD7LvuP3MboyDDd\nXVEuSs3ML06z/cB9TE+M0tJ5CH/wdZ799gv8q888ym1RD1q3szA9j2UW6JFNSkIl5NuEzTIpX2Kf\nuUDs0BaeWcrBUE2ebrl+Y4mfH3qrOrXvrSlY+84Xiw4RvVYx3nZ9dOX9RTHeN/jghz6KpwjOvPUq\nZrUPz5pBUTWi0Ri5bJbU7DAf+ODjWJUsz337LKlUjonJGSqlP+D2e+7jA/d+gIXUEqdOvciPfuqn\nGB2/iqSoxEM6cqQLqOVG7j9wN3owxHJmkVdeeIZDR47ywjPf4tNP/BylahFPuDS3NANwvv8sD9x9\n16bjvSFYNAIamghhVlQ++MCHqZgSnu6DtFaqvkG4BqwwitJ1nEVvE3ZlFS+ssxV2cuWvRoDZCNzW\nRfS51qnzcBt8zjUWY/0qNivb1yvr/RXfWlCryirVon2u6yL8ehl9ySNbyJNoa2No4Ar7t/Ri2Q6K\n0JEkmXK1hKlIqIkm8q6NECFcu4jq1SKKsqLWi7WA57n4olZuWwgZB7n27ldkXM9DCuoIVcUV4Hge\nkqJiOQ5CUfAR6MEAWA6haATLc7HxCAQCeI6PbbsEgyGMoEGhmKdYLhGOxth2qINSYRmznGVxYoL4\ngf3I2TR6xCAteRSKeTShEo41gaqBJKhmSzi2jx4PM5BN0xQU+MiUXBvNMEDzWbIcVCEhmiKUiiUi\nQZ2K5DJbyWEEDBxJwnFMAmoIIfkouoqna1i2h+v7+IZBQNNQ8HBtu7YAc9ygXLHRJJ/4vl0o0TAD\nqWUc00ILG0RDOm1tzezesYN8ucjk/Awlz+FctsDgcpau1g6eeekNOvs6MGNxlqsu2WKVb751gs8c\nv51ELEhHIMr2UALTKmMKgZ0pURBRAvkce7paiLd3cuDgPnynxtZYdhVZCdZDFDUJqiRAlqVVp1yS\nJBynViF1IwDY+AyvA0gbwMHfB5DcOEdtDeQ2Mn1raoHGObLW52Z9bNxeY1SvBxxXwkJrjOJ6ULqS\nA3lzsHYzML7Z8e8GZN7sO9isz0Zwd410ePNe8Dz/mmfkRgzojfrdzBq/j+9HHraZBLjGnNde5JIQ\nLCwuMDw1giwHKGYLJOIxtoRDNGsSgxfPYC7mSVkZposLHN+/j+1XDnBufJhPPvooly8PseOhZgzX\n4uzwFY5//EN0t3XSsbUH39CQFZViNoOZLTBq5ji0Yxd77RLFwXNEtm+j/+xZDh07RK5YoFwq8+qF\n0/zi536Jhblprl6+8p6DxebmMKqiIrwihw4/9J62/cNkuVKWZKyZ2Zkp2rfuxStnEbKC18DgKYqK\n49johoZt2QQCOpPjs0RjYWLxCIsLGaQ6aFuYSxGJhVB1jUw6SyIZJ7W0THNL001Gcq21tHWwtFDj\nwXp6twO19ZNnJsdQVY3Onj48zyWbyXC5f4Bde7aTTucwqxXiTUls2yK1OMeOXXvxPA9ZluneshVF\nkenbtoPx0SH6tu3EsmwW5mZobe9E01Rmp6dobm1HVQ0WZqfp3b6TuelxVC1AW0cHhVyOhflZ+rbt\nrPlIvo+qKtgN1KUsS7hurc/GvM/Z5TK6EaS1p53pqXlmChZE4xiGTs/e3fTu2E8pn2JhZpxguJmB\n0QwDo1fYuq2b8yf+jmT3HZgd+8kq55mdnuKpE6+x+9F7iAYCdG3rWe3Hqprk0ll2d8aQD2+nPRKh\nraeDX9oj8eWlIrbk49nmpvfd92rLpgkhkOT3tvDUPwYrWR4Vu6Yqy1ddVLnGKDYWvoFabuP79k/L\nMstpRidGiMebsKwqfe1lXL8Z1ypy8cyr2FaJkdkUO7MZ9u4+RKLtdrJLZzj+4E9x9tQ7dLY2U66U\nmZ+b5N57P0JzspmtvdtwqRXUy5fKpNNpcoUl9u08gOfD+dI77D3UxtsnXub2Y8dZzqUxTYvFqdP0\ndP4qy9llpmcnge8DLNoZiwN3P4Cq9yAHIqDWcpd8T2ElhFXzI0W9SEY9Lr7Cxq0yGrCiL93g5tYd\n6DpjuNF3WZVPrSxSsd6BupGzV9slIa6pZFOTlzpybRFvz/fx67/7AhRf4HgeliphOKxW3vRlCdl1\ncRWB69oEPY2S4rKYyxJPJHju9Rf4/377f+f/+rV/R98Hj+NVXAp+EVVTcDWdUEs702Mj+JaHIsto\nQkZWZWRNASHQNY3meJxqpYoiK5Qdm6pn43gCVVFBkrA9B0lRMCSFpnCY5UIeSZJwfUCWybsuigyO\n0Kh4JrJqkC+7yKqGoRvMlqsIx8Eum0u+Ws4AACAASURBVATVINWyzdWxMaoLczS1hBkYGOCTdxxF\nV1Qc04JwjJjmEDKCFMoVHNuit7uXUSx8W2BlS3hI5EsmQg6gRZNYVa8GlDQXFB1ZEfTozeTNKpFQ\nBFdWcGSXFlXGIYRlFsGTcD1QNY941KAiLBxLwxMmYVfBi3moAkouBDUJ4djkbHBMB1uNEQxJdLQn\n6GlpJhgOUbSrIEeoSDoSQYItIWRZ4e13LjCemiIUD/OSHAHPpaornBgb5fLoFX7/3/yPtCailIsW\nAQ2cShk1qhH1JeLRENV8DjfZBAJ6klH8WJjp9CJ+0qHVCGNIIXxcZFmiYlYJRMKUswV0RUOlljvq\nNzDrqwAEr+7Ee6uS1bqGGhC1OVJnvVcCIqtBFrGS67h+8ngI6kuTb8LSiw0Eo9/Q3/rgy8b5Wp9s\nDVwYqwVy1uZ6w5xs2HYNOKmPf2VUK+2uRmgaSf9bkIVez67Lvl4HgK5t9xoPXh3m6p8NxXt8xAYg\ntV41Uau+fO0dZUVd0RAM2zieG437ViWvN2rvXZnv19/jdWYZb22tXccjPTHN1558Eiso6Ag2EWxp\nwc647N7RTQSZq6OXsIomlpNjfuwKTw5cYrq0xIcefYSepjbCR8M8/fWv4vgyD330o2xt6cKWfITn\nozswPzTI4MQIO/fswsvmObP4JtnJMUZGLnH8Iw/hn/OwKyUiW5tJBm0st8D4wJv0X+gn3P7erDfV\naKl0njvu/TjReCuh4OZSw3/qlitmCYRiXDz/Or/z+c/za5//HY7d9TAAxWqZSrm2WHxbZzdXLl4E\noJAvIcsSiWStKqplObS2J+nq3kKlUiLZ0kZ6aQFg9ZiNQLHxmBvZ0sIciqIgKwpTEyPr9hmBAKND\nA+SyeaKxKDNT09x25x3oxtp6mqFwlGRzK8V8Bsuy6entI7W0hFV16wBPo1RYJpcr0NreSTaTIhwJ\n0dzajiQJFEWhs6cP26qye98BljM5ggEDVVHQjSCyLOP7Po7jouvaKlgUonau61qoqrIOLK7Y9FRN\niqppKn3bd9DS1kooFCWbmUNRdWxfx7Jray02JWKcevs0I0PDRGPnaW3vRAhBrClC/5V5/uX4N/i3\n/8MvE9bjtGdrVVgVVUGvM7fB4Bqz7klyzZ/Zt49SMUXBNInHW1H0IK5VRdZ0fDNPyNBIpTMEm9qQ\n5Pe+ANU/pLneSgG22rqJAI7rvw8O/wmb6/nMLs7x7He+jKZr6MEEPZ1hMplmdu86jCOFmR99Bd8p\n47tVJkcvMDP6OsVCljsf/CzNre0cvl3w9NNfxzR9Hnvs4yTbetAVibLjEzUkpmYnGR4ZZPvOw8hC\nof/qRSYnrjJ29TQf+PCPoWsBqsU0PfsOU6lWscwS/Vf6Gbn8MvHW6xdpu+FTe3j37ahagv0H7qJs\nO3i+heTLCKGwukwA1Fi6xuIUK1H0ulRTEmLdotuNuVINd3GddHU1V8lf+3EjR2dj5H8tOr+B8q/7\na44A4dUidWYtQwrH95FlGc0TVP1aXp8uCUxRcwZVVcaSfFxP4OoqRluCbGWZr/7Jn/LFZ75On6yw\npbUNDBnPMgkHgtjFAloszpnLV9F0A0lSibY1YVctFMOg4jlYngsSZEwTzTCwTRshSWh6CBUFLRjE\ndSwMTUFIgkKlTL6SR9N1vPoi21XHAgSGruB4DhXbpFIs4iPQDIP5Qgl8qDgmihDoqk6olOPXPv/r\njM8OY3sm/acuksovkXMdmluThBbnqaCCkEmLCrKQmFpaxAxKLKWWastH+BoBTWD5EFI0qtkismxg\niiKSEsbVbaJekKXcMrLk4PkqbkgiYgQRToGSaeG7Noop4XgFmnQJjBiWWcEXtSqumlkinEiSKhTw\nXYHn5LEdma72BHnbRLM9rlyycRFEfZ+i7SOqVZqC0N7Xg+5FWKhmmLWq4Agm5tP4CQ1RLKP4Mrlq\nnn/1uV9G2nWAi5kZtraG0XWVID6q66KXq7QkE+SXC2RcD7+QI2LILNsVnn7qKYxIkE9++DF8pZlo\nMkK1WMLxHX7zN3+Pf//vP0+xWCGkG5hmFV2v/WNdySPzfb8mJa6DKlmS1vatSqplaqrPhpw7AOGt\nSheFzyrjXsvx9VeBWCPjLoTAX5lrK/OxEeL5a3yfoFbkaQWcNh60LlgjNiwg3AAeG0HMNaB1XQ5h\nw3kr+zYBuhutkYXbCJhuJNfdGGja9N3ScN1r0rQ1gLjixNWP2PBz7YJFw/aNvayst9WYf9pom7GA\nm13nyjW8m/zEdytFrS014tUDF/U2ZAlhOxTLRV55+llmZ2cQmoSVy/Pi4GV0X7BweZiSVOFoRyfx\nUISs46EUBUFFJZdP0dXVQXdPH5fOXaW3uY2F6SV+5X/9PFdOnaHS2oUTkNBUA3CZGBrCdIpcGDrP\n/NgEalDHKhSYWppDVSRKjsmhvl40X+bk88/hR6u8/PQXMBVBdOc+nvjoT7+ra76Z7Tr0GMJoY/fu\ne255vap/KuYDyUSS8YzD1/7mD/nbL3+ZQNCgs7Mbz67ieR4RVSYc0lEUhdNvnaSlLYEkSesKzDTa\n0mKNASwW8uvmbXtnD/OzU7S2daJqGjNT46SXFvA8b/V7qdUXWJ8rVq2a4IMRgPm5RaoVC9/3EKI2\nhumpef6X3/1tCtkFZqbnudzfj1lZX2k02dyKEIKZmVkSyRaWl/PIis7E6BBePaBUKNQAWXNrO6ZZ\nZXk5vXq+LEl0b9nGxPgoE2Ojq9uj0TjVahnLstb1p2k6hmGQz+fWbd++cw8jQ1fXbQsEQsiywDIt\nRoauANSVX2vvsa3btqLpBhNj42SX0wSDAeZmFpFlCUmSCAQMUkvL/Pwv/grhrccZGx/C2v2x1SWH\nAHbMv0aXJ+PLEvPzWXzPpT0kMVyY5tlnn0XVLD70yBPoaoBAopNiaoZQyOA3/t2v8bu/+wcsL88T\nTHRiFtIYkeQ1Sy79INqKHNX3a783rrvYaEtFh5br7Hvffjhs5X/zN576G3LpGaxqmfm5JaYn3yQZ\n80kvDlAsVOjash0ttIVSZph4KIsiTDzfoKuzjebWNmanBtEDTeSXs/zz/+k/MHThe1Qsl0ouRSKR\npGz59F+9TCGXxvFOMzJwlliyi/TcIKXly9juJ6hUS2zdcQjTtHjrjacxq0VefeY/Ua769O1MwY89\nsek13PAJnV2cING8B6taQVIMPOEhhIfvO+A3TObV0vXrAdvK76tuzooXuokJaWO9xQYnrh7N3swJ\nul7+zo3YBB8fyfWRJYHtOCg1nRuypmHaNprnY5i1SqgVz8OWfGS3liPomjYRLYgt+Xz1yW/wJ7//\n/yCCOm1NLZSzOYbmpkm9scTgxSucPHWWiXyOeCxMqVoG4WNEQthCIAeDVC0LSZFRoFYUR5Epl0p4\ngGLooMoUSxW8chnfdZBlgaqplMpVPB9kxUIWMr7rIEQtf9IzgsiKhORBUPNwXZtYNIquqVB1iGCg\nyAJfkWgPq1TNZeQWCW3O4mhbJ46uMWvoWK7ErniUc1OLNIfBNzQs26dZMZkqltnf2YIkVSmWJGKq\nx5LkoAuPWGeIRVvQHmmm4qqomDg+RCWFzmgbxaqJr3howSCWa6HaASzJQjFdVDWGpLgoXpiqVMBw\nDXJSlWYrgRwJkYqHcCwL328CzyJqhIl3d9KiqVQMHWyTqFlB0UMs5bMkAiHS+SVaAwnyA0vENA1P\nC2DrEqXSAmEpjDAkOoMdGNt6+LOvPsnRY7dz5tJFjh48wvTMFMJ32LdzK2ZHK827duIoKnpAp33/\nFkI5h8tvnaOcS5GppOnq7aDiVdBUmWqlysVz5/nDP/sTfvanPwueSTQUxKqayLKMIsv4nrda+Man\nxijWZIjS+uefmizKx2OlpNTKRJKg4a9rHvZrwMZK8GazOSOEqC2r0SAHla4jKV8vqbwBy98A4jbL\nrbs1lcCts2E3YthuVb67dtxKZdv1Y16xRuD4bmTAtyKHvZHM9EbtbSb7fbd5shvbh4ZXt6i3Vw9I\nuJaNpigsjk/S3d3J1NIkAzPDhFBJBgIooSCRI0fZd/sBwpk8r518nVcGzrGrZxtnz55HC2ls3ZLk\n+Zdf5ROPPU7SksmVqvSfu8z8wFX88iJ2PETZU2kOxujtSDJ++iKpuSKRWBMzqVn6duzlYz1b0csS\ndzx4L3t37aFVJMj07eTb3/sGtm7gqgq7unde93q/X1uavUR7WwemXSWgB29+wj8Bq5hlAnoQIWk8\n+Z1v8h//z39PNBahs7uNatVkdnaauckJRocGuXTpFLPTM3T1dDI6NEapWCEYNFDU9a6J7/tUqxaB\ngM5ypgaSWttbcB0Xz3MZunoVx3GwrFrKQqlUwbZsjICOYazlLEaiNRAajsQoFnKr+2RZoSmRQJah\nWjEx6mxZNBamXK4ihKA9YeHs3o4QglAoQKmeAzk5PsaWvl56t+4gk07h+z7p1CJb+rZjmlVUTV8F\nqeVSke4tfSwtLhBvqlUeDIeDFItltm7fve6aDaPGIrruenWULMsoikzSXA8iE4k43oY2ALZt7yHW\n1EypsIyqqvhCRRYO83MpOjpbGBmepG9rF4V8AUmWCQQNks1xKhWTUCiA53kkm5tpbuvhi3/5Xzl6\n+xFeeeESh++4h/npcapVk+zuA3i922lu78GxbKK6QueecYxskRcuDJJIhFheXmTn9gO4dolkUxOF\ncp7By5f4whf+iE/95L/EN0sEowmsagU18MPB0gdVsfr/1vF8KraHLARBbS2w5AOW46EpEpbrYzq1\n7zukSuuDsO/bD6x5PoxMjLGjbydvzc8yOzNLJBKiqSlKpCmJ0Ns4cvQ2ypUSg2e/zYsvX2X3nj70\nqbOUnC527OjhrVef40OPPk4wFENXPC6ceoH5qxdZTqfQQxGQVaLRBN0dXZyZOkdmfoBoyzaWFyfo\n7DvC7kMfQAjBbXffR8+WvUR0mdbOnbz87F+jKM0EwgF6t26/7jXIv/Vbv/Vb19v5hS//KqlUmq29\newmG4ni+vLoE23quYMWVWFtQesWuiYKzIoPz1/2+0WFaxwo0dnUD2wxMbupcCVB8kFwfy3dRvBpb\nN5daQFMU8NxagRfhoUkKsqYhKQpBSQIcIuEAnmmx97ZDXLlwgdz8AiWriu36PPfdF/juiddIlwpM\nl/JQtmlOJFhaSoOkkikWWS6VybsuFcknZ1YoejZl38XTVUxF4Id0JEUmaOh4Hsiyii8JFE0jGokg\nCYWwFiDgC1qjEQKeR5OqE6TuwFWrhDUFYZuYuWU038YsLJOMB9F8i0Q4gLmcIR4IEeuIUVycRc1b\nPPrhx4iFo+Q8wcyp8zQVUiiygj8/SoehMXV5lnt6g2Sm07gLy4Rlm+Grg+yKNjGRXibsmiQ8mcsX\n+tndmeDs6BDhXIWOsM/IcJp7D/ZxYWiMdiVOi1vizOQUB4wo4yMX8csmna5P/9VxugMKr59+lQ5V\nI2wtcebSIHdsbaG//xSdzTqLw2Oofo4Dna08+ewz3LstyTNPP01LuYpdmee5V09yZ5vBn/z5lziw\nu5Un//YbhINhtEKWmVyJ9piB7oBvC2zbpiPazOzsAsNXBnnkwx/mmW99i1YjyfjwJPPLeWzF4OWT\npxlfWGLZ9BhJlbD0IFY0yZ677iWbSjM6P88f/eWX8FUJwwgTiMX42pPfYr6U5+23TrK8lOLo3v3I\nQuDY9hpz57ooioKoO/kbwd1KsRkE65mLVTnkenlk7feV4Mra3BRibV6tMV1ilTVcP0/WFr5f4cJE\nHSmsSWD9Vdls45zdaOuYxU2O2Ywha/z7eixa47G3KjG9kW02zhVwdL3ravw+bhTI2ozh3Hh+Y78b\nx3KjMd9MfrrxPl3v3m32c3OQv/Lyr7HZuqYyNTjM8Ngwp86dZrj/Ahkq/MjjH2f8xBlEPIypyHQl\nOlBMm9HxUbqO7qM9EGVkbIhlr0T7lh52bN2DIWvcfuxOtvV08ebTLxHqCjE1OQSeS3phmaP33A5W\niZdf+hapQoaunm3gKuRNEy1XYXF0hJcGTuB6ghbX4MWv/R1Ga5xgc4KgESOVyfPIQx+57r36fuwv\n/9tvUVgeo3f3cYJG6D1t+x+TrTwDqdwiQT10w2fOCEYQkowQsO/IPZw/8xq5bAYARVH427/+G158\n7lkWF6YoFAr4vk+iuZWFuXmSLU2klpZJp7LksgVi8QiZdI700jKyLKEbGoGgQSBg1JaTqo9L1zUC\nAWMVlGmaihGoMZYrpuva6vGWWV3dvrSQQVFkLKsm51SUmmIgnytiGDptHT2MjU5gOjqPffInkRSF\nUEBifGwSy6wQizVhDl5GTrSwMDfN3UebmFswmZ2ZRDcMZqcnaEo0Mz05hqZJxJuSnHnnDB1dXbUq\niK6HpmqMDl/lwYeOM3h1EEXViER0+s+fpynRzOzUOIVclmgszsTYMKFwlJeffxFFUYhEw5w5eZLb\nD0Z46aWzxJsSXOm/QDAUpre3na9+6evs3beTJ7/2FJoRZHZqjFMnT9HV3c2f/Ze/4OCBbv7mS08S\nDAXwXJeF+SVi8QiaVpOFLsyliEQDzM8vMnTlAo8/8XM8962vEw4KrlweYGlxAQS8+r0XcKwMFcsm\nUygyX/GJdu/gyJG7mRi7Qjab5c//9D+jqA6RaDPBQIAXnn+K2Zk5Xnr+myhylZ6tB1D0INV8CmWT\n4Mu7fa//Q5vtgeXWivyENQlNrr1702WXkuVRsmo+c9XxKVkeUUNGkyU8v1YYb2Ng9337wbTR0RGG\nx65y6cKbnD11GtOy+fAjnyQ3/yaBSCuRsEEwHEULxFiaPs3Ru+6nSQ3wzluDRGIVjFAn23YfImwE\nuOO2e+jq3Mb3XvgaseYAMzOTSIrKwuwoB/bdiSqrvP7dP6aUmyHethdF1cksTiBkjeFLJ1gYe4m8\nqaBpBude/1OS7btoSrYiyQqFfJYPPbz5/8gbgsW3zvwRqlCZnUqxdcteDCOK4/n1BMOVBafXy682\nc3Ju5vBc75h1jtCG3ddr8xqguQnTKHyQEdjCry3Uq2u4wiesGQQMg7BQWNZ9wqiUi0UUSWbBKlCa\nnsNNaHz1uW9x5PBhypkst99/D88/9xzxQAQj1kSkrRU1HCZfruCHdLxKFV0JYFYtdD2AYYQw9ACh\nUJSwpmMAMV0niExY1gggoSPArBCWBE65REQRUCnREtTJL84RUDw8t4SmuuQycxgBQbmyjKy6WAuz\nJBQJO7NEk+ojCsu0BhQMu0JI9bCyS2i+i1y2aDm4G8+z+eT9HyLc182CX2I+nyZYLBGdH2N6Yob7\nt0UYGp5ie9RAtitMjg2wt6WFmdlZOrQinZpKbnqEHQlBaniSfW0qCVGkOjvOba1h8sNX6VELBEo+\ny0Ovs68lzsL5S/SqRQJ2CWdskGN9CVKTU+wJC4KKSXlqnHv3bmdpaIA9IQPZMyldvcDD27Yx9PJb\n/JtPf5TpUxfZFw5xd7NBdXCSn7n3IJMn3+CJw9sI5VPs1GU+dXQruSuDfO5H7mXs9bO0BqM4Vh6l\n6CL0GAWrgCcUCvk0MzNTBFWV1576DmFF5uy507R3tDI9PkEi2oSKSnpumfPvXOLOux5gbH6Gywvz\nvHNlCAnBcqHAG6fOMTg6yrlz/Zw8e5aiYxMMhliamcMvVzh08ADxphimbWHoxppE2/OomtXV59Z1\nnYbnuU6sN7B46ybDpoEbVte92zCj1h25HgjU5rLnededk2sg0b8GdDTOtcZz/z4yyBu9G97tcRuB\n0o3eHw0tX7f9za77Vt95Nzpvs/fWrbb3bo7byGjeSu6jD7X8VFErYiYQlAo55mZnUWUZUTUpCYst\nnR2klxYJ6QYDY2P8+I//JNFgmMXxMRaLabSAjmZ55JwSkiHTnmylq3ML1WKRidkxLp06SaGQ5e3T\nb0NQ4+jh23Dn07x28gSnTr1J1a0gAiF2bDuIKgdRXUFElXn7xHdJbIuz78jdTA9PoyJRkuDQncfI\nFYpEYlGO3/3gLd2fW7W3XvoamhBMzU7T0bMHQw+8p+3/YzGhGVQrRcLBCDY+ipDw6uvmWtUikiRT\ntWwWl2YxdI2nnvoKR+58iFJ6hgceeJjXX30e0zSJNyXRDYVYPIKQBIFgqBY8E2CaZh3QQUtbglg8\nAtSqkcbiEYyAvu7ZrJSrq0tsqJpKqVhG01SKhRKarlHIF9F1jXyugF5nEC3LZm5miUA9v87zamkc\nuqGvOuf5XHF1f0tbB6ZZ5WM/+gRNzc2UyxXMShHXtamOXGJufIoHdgS5OJPncNJlGYXF/hmaeuJM\nTiywQ3doj/jMLi4RDXnMzKboanUAH3lqhgO9PUzMzNAqMgSMKLm33sZoiTIxOU2bk0eqOGTyixxp\nSzKdznAwDimrSKmQ4t57jjAzNM4dfoU5xSA3NM99e1p4u3+E/+MzH+Sddy6yq0njzr1bSZ8/z08d\n28PU6XP89N07ySwts0My+cjh7djTCzz+2GEun7hEVyIBro1ZD06WSxVkWaZaNVmcn8cwdF5+/mks\ny2ZwYJTe3q0szM3Q19tHS1sLgwPjjA5cZt/evSwszHHl0kUmxy+jyBKpdIEzb79O/4XzDFw5w+lT\nr1PIFYhE48zPTZFJ59i//zDhWBK7nEcLhJE8G1/IuHaVcmYWPVTLT/WqeRyriqz+465AbCi1Z8r1\noWz7lG1/VZ4qqDGPqlz7xAMyZdsnV3UxXZ+AKr0PFn8IrGJ7zM1Posgq5ZKJbqj09cTILI7iqa2M\njQzwwY9+hqARZnG6n0wmj+xl8GQPNBddtmjv7KC5bSuWYzM9OcaZc+9QKWc5ffIk0RD07LgDy7I4\nc+oNTr/zMpVKlVAkSWv3PoxACEkINEUwfPFp9HAfB4/ex/z8FIqiYJkWu/cdw6ymSCabueuO+ze9\njhsmWSiqRCAkMzs7TCY9i/AcZFnBFwo118FDvIvFZlcdyOt8YM2JWefIXJPpc63TunH7jUwWAtv3\nqMo+vusht0QZn5kmrgV46pvfJNKW4OLYIJPvXODVkX7e+t7LhENhfu/3/wPlfImhsVF+89d+HbNU\n5jf/9b/F8j2qVYdqqUo2vUxhOY9XNNGyVTQhSM2MExIu1cUZIm4VqbBM2CmjW3liVAlV8iT8Knoh\njV5IEywv02TmiZUyJMoZQoUURn4JtZAi4VZoN4tsNUtssSr0Ojad5QpdVpWOaoVODeJ+hc6ARMKz\n6QooRNwqnbqMUSnQGw2iWhU04MSJ13npqWdw5os02xpibIatIYWJ55/nibvvBNvmrt1bOLBnG6XF\nND/+wd1MLOXZndCJRiXc5Swff+B2cpUljre0sG9LO/Ojl/m5+w5iTed5IAJdrRFmxmY52FkmO1Jg\nv+oRZomJhQnuDOsU7CXEcpa+pM7E8GUOhF38YgZ1apD2liBXLl3ipx44TGEhxbFdCR47foiBU8/z\nP/+zT3H6zBv81P3dWFYJd+4CP/uZh3j5udf47c8cZmzsEs2GzJaWMENvvsDjDx1kaXqUOzp9dKuK\nkstzbFsnmekxdNUjDqiuS1F4pBbSaI7NiRe/y9TQIF/+q7/kmeee5Z1LF5jPLbCtOUzSztOh+9y2\no5e93S1sjQToi4UxFIfl4iKDY4NUbIv58Wk0BP/5j/6Arq3dnDx9GtNxKFTKVKwqsqEhKTKJRAJV\nVYlEItSKGTjgebhOYzXfldnTEKSpSwLx/foCT7VPbXnQzWSSN54bjfPJ92vL5KzNrXUzsL5vZf+t\n/1P7fli/7/e4RhC08fP36etWbDOAejOQ/f203djGrbR1MyB/XQns2hGrORhGNMTI8BAD/VcIxKIY\nqMxkMxTGpxmfmSLR0cbswDD9l/oZvHqVWE87n3380yhIqMEAEVRy0wu8+dbrzEyNM3nhIqPzoyzM\njzA2MsqUbTE6MMrFN9/k0jtnGR4Z5+LwCEXLp1ixaW/v4LVnvoca1lkqLhJVfObm5rnvo49y7KMf\n5tiDH+Dwlt0ouSr9Z95+V/f4VixbLqKEQizMDpDJpW9+wg+oaYEIE9OjuK7LN7/xRcLN3UzPjHL2\n4klOn7/AmydfJRQ0+OP/9/eYWZxn8PIV/rff+CVyxSy/8sufZXl5uc7WLa9rt1opAzAxOkEkGmJp\nIU04EmRhLgWArCi4bi03Tkg1ZrCQL+G6Lq7rrkpNAdKpLJVylWCoBthD4RozFQ6vMb66rtHV04am\nqSiKzFY84k1R9nk2B3DRJEFvtHaepmlcPHuRV174HkuLCzS3dJBNTaNqBuPPP8/HjvZRVuED27ay\nozNJbrbAJ+7o5uxMiqOuQzwaYnm5wo8fOciV4Xke37GNQ/u7mH17il84uJOJTIH9qsOWiI60ZLLd\nqvDS/DIHJRdDlTk3Pce9nVEwPTqqJZqafS5fmOBDgTAdmQrehUF2N8f466tT/MIHd9O/uMwHtm3l\nXxzdzoWzV/n9X/4EpwaneHRvNy2yhGOafO6zj3Hy5BV+/acfYW5int3dCRIRnTefPcvnPnkfk7ks\nfVGZcjFPPl9h74FD5LIFVE1dBe9CCMxqDTSfPPEKczOT/Lf/8sd8+2+/zpWLpxkfHSbe0omuSSQT\nQbp6d5Ps2E4y5tG3fTftnT1Uqi4DVwYJhqOMDV8l2dzG//2Hf8nevQcYuvo2kuRRTM1gWRZCCBQt\nQF9vH5pboskAxxdUi9n/jk/8e2OmU2MVNzMfqNj+6idVcvF8n2RIJqxLLJevLVj0vv3gma4Irlw5\ny8jIJbp6e4mHTcr5WRaXJS5fHiEcaWF2dobx8UFSM+cRWgePfeo30GSTgO7guA7Li0OMXPgOEyOX\nuHL5LRanT1PNXmF4aJYrQ8vMz4xy8e1v8NpL32N+8hTvnBqiZOqYlRLNsTin3zlFON5KLp9HlW1m\nZqa47/6Pc8/xJ7jr+CfY2reHdCrPm2+cue513JBZPH3221QrCrcdfJAt3btR9SiOL9WkqMJFFiCQ\ncWWr5tDWP5IQdZ/1WjmVEHV9+8rWfQAAIABJREFUXMOnkZ/c1K4N9q/t2uB43SyPSAiB7IPte5i6\nSlBR+fPv/h1f//MvcqCrj3GvwCt//lUOP3A3X/nCX/HoY4/xX7/xVVRJ4rUTJxgZn2RpMc3Y3Bxf\n+MpXKBYruKZDxbNxqmUcu4rhu3QGdKKeQ1gGO5smqsgE8YgoAtWtYvhVlEqekGeilAo0KYKAZxHH\nI2BVCAuXkOyj+y6665A0NOKaRAiHiAya8NAUgYyNLAtc38P3PVRJoPo+kqjlOCqSwHFddEVCkwx8\nS2K2msNTAzS3NvOjDz4AQRjJzxH1bIrnz5FIhBg6M8DjHznG3zz1XT77sYc588pJju/ZQlNbC6WF\nNP/is5/mxdfO8iO7IoTjCeavjvMzj9zOWxcusa85gR5TyS0s8dgHb6P/zBAfu3s7UiiIqBb42U8/\nxDunxvjnn34QTZMpTJf4+Sce4Ez/IA/u3UU8FGBofJ4H7jzE4NA8t29p5dCebVx48wSf+fjDvPbG\nebrCMsmmJCOnL/LBB+/ixRde5+iuXiaXSkwMX+XoocP82def5xMP3sm5d4Y50NuHLPs4vsfPP/5R\nTp89x7F9O9jb2cri7DxbggEqlsOi56O7MjnHpOILFM0gGWvCtUxwysQNlZmBt3l4azMdVQtleZFw\nNYXIzEG1gleqEpRlwqEIkgStzU0c2rGVaMBgYnaKs+fO07d9O9/4xtc5dsdtfPGLX6Kzp5Onn/oO\n+/bs4cWXXqKzq5NsJkMsGcc2bVRFwcWrza21aYbEBhnkim5yXUmVDYxXw45rJZ4gJJ/VCsKrUtc6\nMBQ+QvJWt60V21kfc7pZvt1G24zN2giCGm1Fvnkz0HkjBvFW9q/Id69n1xvH9VQSN7O/D3t4vW3v\nNSOJt/aseaIWMNQVja7eLfTs3IaXynHlzHnirUnirQlsYLR/iCNH7yCsG8R7uhl54xyXhoepBlW6\nAgmO7L+Dgcsj9PZuZXJ8jL7uTq5c7acp2Un71v3ce/AgVy/0c2FuiEIhh4yPLavEk23Mzk3D5Czf\neud5jGQE07IoVyWSiRb6du2gvbmT7PAMp19/laXyEp9+4udv7Tpv0U6e+BYAHX33sn3bfgLGD1fe\noo2Opqn87Vf+I1/+iz/l6N33kstk+e4zX+fOu47zzJN/xQMPfpDvPPk1ZM3gxGuvMjzQj2VVmJud\n4itf+DOEqAW/hBBUKlXMqkm5XK3lEwaCeL5HuVRBUxWMgI7reqvAJJPKEgxpLKezxHQZV5JoUyRs\nSaZVkQhJggA+hu/RFgsRUSTC+PQ5Fq2eS4vroAoIex4lIdVZcUHEdYl5Lp4QhDyPWUUj6nm0eg6e\ngPFMAUkI+rb18aFHH8JQiqQWF/A8h5YzL+MEQ4wPTPDJRw7z7e+c5rOP3Mlfv3aRj+zo5UBHkrJp\n89uf+xgvnB7m2O4eepIhxkZm+cl7DnPm6iS7u1oIBQ2E5XDnPTt46/IMHzt+gG1hg5Tp8KsfP8a5\nM6M8eu8hrLDH4ESaX//4w5y4Osmxfb1YmsbwTIoP3NHH0Ngit3e3c+/Wdi5eneDhBw7z5skrxMMh\nug2Fy0OT3HPXHp577QJ7ejsYm1niSv8ohw9v5/e/+go/9uBt9A9Ns6evlS5NJmO5/M7PfJhz54bY\n0dvHrp44wzNLtBs6FVRKhSK6oSEkQbFQQlEV4k1NeJ5HqVgmEouRGj/DPXt2EvJK6GYGo5pBZOcZ\nWcphmjaqquF5Lrqm05RIsHNbnHCkjdnZUU68/iLb9xzl5Re+wt7dB/nyX/0ntnR18+xzX2Pn9n28\n/NJzJFs7qVSLBEIxSstzaMHoP/BMubnJol4S4AbHOB4E67mKpuNTrEtVq7aHrmwuS7VdH+k66RLv\n2z+8CSHQtCBdXT209+wlnc4wcPkKyYRGd0cC0/Y4884pjh1/iKolsX1rFxcuDTA2dAbHC2KE+9hz\n8C76r6bp2rKNq5evsGP3bi6cP09zSyt7DtzOkcO3c+rsIItz46QzJtEwVEzBlq07mZ0eJTU3zBsv\nfRvVaEL2M1jlNM0tfXR0dBOPNTExMcDIlZfJZ2f59I//wqbXcWMZ6qkvoYcU5qbmmB8ssX/3fVRx\nMSW7dhNcuVbBUSg1Z0hINf2bf62jevMHue6MNoDOld/XZHMg8Nd+1j/43lrlyFUHqebIrLAwq6o8\n4ePhoQoZ2fFZlh1eOv0WlUyeyeL/z957BkmWXXd+v+fTZ1aa8t50V1d773ume7zBAIMBSGJAgkYr\nrkIKbSj0QSExZEhxtSFKG9oIKaRYmiUJLgwJwg4wg3HdMz3d095VdZnu8jar0nv/jD5Uezc9wIDg\nInAiXlRm3pv33ncr7333f87/nJNhPp9maHKS0bEpxpJxfvTDtygrMHj5MlZdgLlommQ+jyUJqOUK\nPlVFrxQQRYNaKoVSLeMWQC4VMEp5MCqokogqgEfTMEp5JFPHoSiopoldFBAtA0WSVoGzXkM3dZxO\nFataQrFAAgRRx6wVcKsygiUg2WzYRANd0NFlsAQBsWbic7iQFYuAQ0UslagZUBMlZAEihSR2w4lu\nq6NgE1jT3kEgGMLe1MzQ6CQL1TKOjjZUpwtZNGjzNeD0+CgszLJ3/2be/elZ/vArRzg3MkVk7hrb\nBtZz5sQH/PahnXw8s4K3FqW50c/kSJR/+ftP84N3BulzKRx5spfTb07xB6/v44fvnWZLSxPBthZ+\n8oN3+I2Xd3DlegQjs8CB/Qf5wffe4Xee2c2Fq0sEpAKvvrSfH/zwPV7/8nMMzycwi1nWbljP++++\nz+cPbePj8xN4XEXcoR6OHj3JS5/fyd98b5jn9m+mWNKZn1/h1T/8fb71jX/kt155kqHpKEuzM/z2\nS0/x9jvv8pWtvaStGp6iwL5Dm5hcWEEvJpBUDZfDSa5QQjdgc/8AS5MzOBwK/9v/9N9hT4fxFpOE\nRIsOxaSurNPr8rF7bTsbm+ux1cpEwvNE4mnqAvWE5yaoFaNMhWcZnbqOw+NhdmYGyzTo7uljJbaC\npqkkMym6+nr4oz/+Y/rWrcHr8aJI8qob4k066q2nzYMild5edzcD4dxrvX8UFVO4udBuvb8JT8Xb\nYNQSwBJZDcRz99PvvnaFm6qgG2v7jhisD9oDrBv7gHVr5LfbvVMsjNW63LFn3NPunYDz/ns1uTWp\n3HkJd9X5JAr9Zw7GHlL3QX6GjyOPAu73zek9/qcPuizTRBIFSpUKkqJQEy38dXXU1wVRFQWlaqLK\nMpbfzeG9T/DuT97nT/71vyGby9CxbgOR0Wne+8mPGXhpL7sO7qNN8nH+yhlsLpHZ6AK//zu/y/C5\nC6zfu5H2xlb+7uv/EbtLYXpmHtGuIusmqqIQTyewZJFsNEmovYGyAcVUBbfHy66du3nn7bcpu1Ta\nRQ+JpRgji1P0rl/DvgNPP/bcPY4ce/9bABTjYVIrWXoGtnym7f+yRcBCsExGh8+RL5RZnJ+hVi0w\nOzXNpfOnyGUL/OA738bpdvHR0Z8SrA+xMDdFJp3CYjXaONzet+LRJIVCiWC9H1EUyGSyCFi4XA5E\nSUSSpLsiltpvWA3tdg1LELAEAUMAA4EuvYbbMu+7nNY9AWEsKIgiJVHCYxi06jXcloHLsnDcuNKS\nhN80mFZUnJaJ4XKiKDKt7V1oNjv+YDMjV68Tj6XQQw1YoQZ00mwJNWJTJa4vJ3h6QyfvXhjnD7/6\nHO+eHyO/GGVgbRs/ff88L+1fz+hUmHyhzIb2Bq7PR/gXv/kU33/nLG0eN4c3dnJxcIrXv/IMP3rr\nNBt6muhpa+Ctoxf5nad3M7MQJZnI8htHtvCvv3eS/+qlvVwemcEvKrx2aCPvfnyVP3j9WcKLURLL\nSQY29fLW0Qsc2TPA5dE5Ak47rfV+Pjg7wpG96/izdy7y5b3rkUpVcqUSr33+EN/+4Ulee+UA5y9P\ncG54hv/s8wc5c/RjnlnTiuBz406m2X9gF9cWlogsx3G5nIiSSDyWxG5XaWhsIRmPY5oGf/xHf4Jv\n9hyeco4G2aA36MCn1+j32jjQE2LAJeCQRS5Nz5PNpGjvXsvC9MckUhmmp+aJLE8hyQIr0TCa3UVv\n7xoWwws4bSqZQpb2tnb+7F//93R2deD31iHZ/vkHxPlEo8gNKevWLaKQfAc9VZUEMiWDUu1G/kZh\nNSXHTT3xr8HiPy9JlQzsyqpC2ePxEArUo0hQrekIskKgvoOedXs4fuwo/+uf/jvSuRxr+jayEF7h\n4jf/A9uef5Xegf3UN4a4Pvg2dtVgeWGC3/vP/wfGhk/Qu/Fp+tb08Z1vfB1Vk4lGYpRLJUCgWtPJ\n5wqrFPJSieaWemqGSk03kSSF3k3PcvbUUTSnB6ennmQ6QnRpjK6BI+zfve+B9/NIsDh+boKqEMJQ\ndFrafIQCrViSArKBYIlIgowomJjG6iYONy0Xq34tD4p++CBZ/aGbt8DcnfVvarFvfvWmn9e9h51b\n1hZhtX/xluHyNni8Wb0mWKDriLLCXD7BN/7D3+I2JRam58hmcziCAWKFPFJZxzRrUCqgU0UvZ7BS\nUQKYeCoVMAoY5QytDicaFpphYjdMXJKEioXH6cCyDCxZQjBNrGoZmyQhiSY2RcSsFZEwEDGRJIFa\nrYrLriKYVTR0bBjYAUG3UB12csUSDllBMUwULLRaEa+soVUFHKIdWZBwOOxYiFTKFQpGEYcGvQ47\nG4J++voGWHZ7WSpWKSbiBIMOFpZXaAzWMz05SUuonsXJBYTGeootft5+5zh7BjZx6fog+zfvZjK8\njMNlJxAMcvKjizz10gHevjCLT6ixeW0nQ1dG+dJLBzmxsIAz6MLf2c74pXO8sH8L5+ciJJJL7Nh/\ngB987wOefnYzl6YXKWVKbN+1gWMfXeTgvo3kqhZzC4u8/OrTnD91kcP7dmEJEmc+/Jg9B3bx/R+8\nx/5D25iLFEkk42zZt4WPjl/mqRf2MjwcwzSKHNx2kHffP85vffVpTpy+Rodbw1fn5/jpEV48soOT\n569TqpTp7m3m3YujPH1gP0fHrlFMl/jcyy+ynEiTMy3aunqob2xmemaaru5umtpaMGWF8YlZWjxe\n6p0iqt3CpIbk01AdYGTjGPkY3S1+ntjST58H0tcvU4zFmBhfZHYuQa5oEEnnWIhEmFkOc/TkSUxF\n4cTZcyxEV7i+sEBFEskm4wz0rUUSVkOY3wRQwq0cGfdb4B7HgvVoqrZw0+ER7gV01s2yRz+Y7gOL\n9zVl3V7rNxREq0r/O4EJt8vuqHsb3N2WVQvfzX7urXf365t9PWJ27ujvxicP8MW89/3PCgYfBTg/\nCyD6qPE+qOxR1l8LkCQwajXmZmcZvDpEsD6EIivEkwlUl4O8oNO2podqpkSorZ1gqAkyZS6MXmHz\njp34NDuFfJyh6+fRBIHUUpL5xXl0l8XmDeuZGBljZnGSVD6MJcukJlewNYbwB5vxy04yuTyJWJqm\n5gYsFdYGW5mILrNlz172bNyJV3Zy7txpnG4nm3fvXE1V5AvgEVSoc7Bl047HmtPHldOXR3B7AxhY\nuJv78Hr8yIryK5NGw0RiLhLjG3/x/2B3uhgfHaJYKOF0uynkcxiGQT6XJp1KoGoa+WyGdCq7Cu5u\nsHzK5cpqsBRhlfkjSiIu96oF1i1LWPeks7CbJnbLxLwRFcFtmWhYqFjkIkkGNImaIOC+BxRWEJhR\nNALm3fQ9CXBZJiFDxyNbSE4JsbpqaTQF0AWBAafG2qYAazsCRD1+lhZXyGbytLS1sbQYxlMXIhWZ\nJNTYTDwVQ3O4cdY38/X3zrBjbSMfXJ3mhR3ria8kkKoVutob+ebRi7y8fwM/uDRBp9vJmtYQI5NL\nvPTCHsbH55EFkbqNLcxenuHgE1sZnw6TTabZu3OAb7/xMc8e2MTycoJCtsSWvjbOXp5g/dp2NN1g\nZSXOiwfWc+rKFE/s24xTlfnBu+fZu6Of906P8MSBTQzH0wi5EpvXdfL+2REO7tvA1OwyFUReWN/F\nj04O8S9++xk+PDuG1+ukvd7LqRNDPLl3A1OTSyi1Gi2Nfo4Oz/LS2hZ+vBRFqWbZ8+yTpFJ5ctkc\nHV29dPf2MDM5zbYdW9BsThwOG+cHr9Le14vfKuNvCBAPR/EFfQi1KkK1hoLJ+o4Qr+7ow9NUz9zQ\nFTJLCUbHJlgIh7Esk/BSjEwqzrWREY5/8A4uh8CJjz6kUkoxdn0CvVqiUsoxsGkvgqz+wtbAP7VY\nrO7FDlVEkQRMa5XKqIgCTk3CoYq3yuDTMUh+Lf80YpgWU1PDjE9PEgw1o0gy4WQGh82Gpsg0hNrI\n5NI0N7et5mXNpJieHGLdpv247SolOc746BkMUyQTn2RsPIMkq2zYuoeZ6WGiS8PkYqMUq07Ghsdp\nam2mobEZ1WYnnUpSLtWoD7nRHF4a6p0sLSXo6u1ly5adOHytzAy/h9sl0r/+AJZRpT7URsWUURSJ\n7Zu3P/CeHgkW84tx+jYdQPS6yJYTjI1dw6G4qHPXYRqrOQkNUwdBunEIu2W7uE8jDQ8/1Fhwx4Hu\nAW3cVfnhh+HVg6V46/UdJfdVVEWFoqHj8nsRvXZG3juBIomkikWyhTzpeJxUJUO1WiSVSOEqGdRn\nM3T6NFwYmMUcrmoF2w0LRy6dwSur1Ip5NElEkkQMwUQHEKooloFHkZH1Ci5NRJUtbJqC1+1CkWWc\nmo06lxujVkURLRyygE2SERERJQm3rw7LEvG4PRiyTtHMo0gqot1JqmKAasft82DV0lQLRYKhej53\nYD+NosFrX/kcsfo6/u74KaYyKfRYgrpcDkWFw19+hXeOvUvk8lWeevUF4tEEuWKNy2NzbHn6aY4e\nu0BozRbefPNNdhx5ku8fO86GgS0Mz2fIZ9McfuZlfvLGT3ny4FamoxHKsSK+9Y1MJAps7OwkGs0z\nPT3Lxt3b+OmbH9I/sItwPs/Y4BKvPf8k73xwgb5GP61NQd567yT7d2zio+tzNLkkNE+IH310iYOb\nOvjRqQnWbe5FavDyvXfO8Prrr/DdD85jDwbxeEIMXhnltd97he+9dZY1vV0YksC7R0+yde8O/vbt\n02zbuZ3ppXnC2Rx1nV1MTk3QsW4HkxWDxfAKhw8/x6XxeY5NDTJw8EmuDU+QLhdp623HIYtk4jEW\nFuaomgZN6wawe704jSIhxcISLQSqKEYJl+Kgzu7Co4JglRFrJTZ2trC5o5ENTV662xowMzHiCzNU\nSkUWowmikSTZRIpCoUQ6kSa6uMzI5cs8cWAf69f037Kw3Uxvd5ty+slg4NMAiU9b/qi6t8bxCWdm\n4QFKpcfp65Oono9DP30YYLr3+4/q63FB+uOAhweN5Wa790ZevXd8jzuH9wLVe+Vh4PXmPmyYBkal\nzNjVqxTzeUTTJFfIc+3aGJfPnOHElXOomsbS1euoDUEOPf0MejiF6NaYWwmDpeNyKgRlBblcQ/K4\nqQgS8UiYlaVF5uOLoBgYxRSupgb2dmwjrQg8feRZzESWeKXM7r17cdhVJiauk16Jk6ma9A8MEHS4\naa1rZGZqnPb+PhYiEQZ6Bujs6EGoWQwODbHv4KHHmqfHlVS+Qv+6fSjOILphMj15BbvNgcfj/0z7\n+WWJiInb6cJT5+bY228iySLVaoV8PkMmnaSQz1IolEgm0thsKqZp0i+BzzRISzIhvUZQr2HJEovR\nJL46D4V8EZfbeaN9C/OO31tnrYLfNPCZJm7TxGsZ+E0Dj2niMU0a7aug4F6gCCA3a7gyVeRWG2T1\nu8rSoohqWQghDUoG6BZd9XW88tR2pHyJVz9/kLQ3yJ+/d4ZIJE0inloNtoPOoaee5cSxt5mamOXI\nC58nm1phbm6FpcUw+w8/z9GLg3Sv7+Yb3/uQJ54+wEfHzrN1XSeD4wtEayb/6ouH+LffPc4Xnt7J\n/OQS8UiKgQ1tXMwneKKhiWiuRCYcY+2aVn507Ao7BjoZtzIsjy7z0vN7+OH751nX24LbqfHRuTGe\n27eec4OT+GwaLr+XD05cYV1nI6dG59i1tp1is5O//seT/LdfOsw/HL1Irc1Hn9vJ9bF5Xnl5P8eO\nXaK9wY/X5+LYiUH2rO/k/zt+lcNr2xieXaaQKxLY2Mqlq/M4N29hqWySnJ5j43NPMXJxjPNDo2zb\nvZvBS1fJ5zL0ru1HEGBhbo50Oo1pGGzYtB6rro3WShSnx4XD7cSyIJ/JYXfZ8dcHucmM8ZYKrK/3\nsaElwLrmAFsaPESzZSoz08RqJtFImPDiErFoFEFUyOeLLM3PMzw4xJ59e+ls70HUfrUiEZvWqr/j\nnZcqCWTLty2LNy+AfPX2e8O0UOVfDWXVf6pSKBQYuz5MLpdaDWxTzHHt2iUunj/G8PA5qiZEFi6j\n2AMcPvQcxXwBxeElujKPbpoIqg+bzYFoZlDtddRqVczKAlOTi2Tiw+i6SS6XoS5Yz5r+9TjtFgeO\nvEKtkqFaNdi8fSsut4/J8QkiKytUqrB2wxbqAk2EgvUszV6irnGAaHSRvr6NtDS3oUoCE1Pj7N25\n94H39Eiw+Oab/y92l5t4KoMsW5iVCmJNpT7YDqKMYelg3bZ6AHdEZ7xbPlEDL9yIsHcnuOQGFe3m\nIfmOth5HG796iLq/TDQsVF2gJFgINoW/+qu/IFPMUhVNCopFsVhBNsCURCqFIgGvB4ciYnNKlHMZ\n5EoV0TTQHCqyIiBVKthsEpJsIWkSLq8Lxe0gr1fJ6WWwTMxyFZes4dRUFElCFC28AT81SyRXqqLZ\n7cQSMWRJRRCgrNfI6jUkXwBTdbOSzGOKGolsHkHSkGQnOVFiNlvA1thMMpfBoVhIdpXOtmae2r8V\nZ4OHWmsH3706x/evL7K8lIWlGCGbHW+9m1I1R1kWyOll9rV0cOr4SYYHR9i6dRsgksxmEWWZvj1b\nmc0VsKsKGUMmXKrSu3cnV85N4msPkrMHGVpaoHf/Pj6+GCewaT3XCmlSszF6tm3n2AcXqW/txNXd\nzvDIAs/+F3/IsTPDBDrsOFs2cu7jU/Tv28VYpEKkZNG/7SBvHf+Qvc99jsvj02B3cui5Vzl+9CzP\nf+m3GF+OUMyadO09wBtvneTLv/FVTo8OYcp+fD3reOutH7H1mZeo+ZtICjUaerZyamyEF37/S7z1\n3ghPvvo8sbLI4HKcjc8/w+lLw4znk/i3bGLx/BgFQ+DZZ57i+uAl1HIeTa/QUh8ikUyRK5WZmp0l\nHomzq68PuVrBUFy4FDcOQ0eQTUxVRMZAkxT8bh9UdChXcEkyajXDQJOXvV2NdGsSbYKMt66OxUqe\nRLlAb38foqDjUAS6OrpY09OLIq1aKm4lcH9IFM0H0QofHtn07rZuWvQfTv28WU+4/foWYr1N+b7N\nDnhc0s2D+np0+eOA2E+ij35SIKzb8ybe2EMensLjYf3/LID9s9IQ/yza5k8cK9aqYVmvsTg1RWR5\nmZmJCf7q7/4GRRS4fu4SokNDlgVUZMaWZvA2NODWHJiVKnomh6chQFNrGz7sLEUinJoYoq6+jka3\nj0hkGcsukE2laW3vZnF6nlqlyvWZ67T39lAv2ZgKz6GrBgFZY92aAUqKxFd/+/fYu20Pra4QSzNh\nErkojsYAz7z0Ml3BdvSagMPuwMgX6Nkw8HPO7N3yve/+OW5viFQmST6bwDR07E43wUDTZ9rPL0t0\nw0BVRP76L/9vCvnV/IamaWLoOvlcEUWViUeThBoCKMpqqoWEJeA1DPzWagqqlNeOJQrU2exogoDm\nduKwLGqAKYhUKlV0XUfXDUIi3LQRSYDolsnKYOoCliJQXtUsU7mhvCgJAopDoWyKVHOr/obTFYWc\ny4OvUiIsKUwZFrbGFpZ1naDbj0uxEehq4LkXtlOtiVT7t/DO9WucGY+TTKZIxFM47BrdQR95XUeS\nZcrlEpvW+7ly8iwnT19h/4HdlCs1ysUsgiizadtOCiikZDdT+RzLJZU1T65n4swYy/4gqsPPcHS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vG3/hzBUGhuqmdxOYzL00idu401XdtpbezCAERR4mY0GckEEBFvULdWbY33HH5uBY64I5AF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\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3rnH+ozcY+jFhp09sU3l0rVbBuoaHTz3G+z99k4vf+1N0Yx+joVIucnJugdmxWd57+ScU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cxLb5FegK0JTMMiXAbUXAfLcbBM/dxJBmbNIs1zMA0sxyaKo9LFZFgUCNIsRROK2WKCrhmovCxS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RwTSqxHUNr2mQiqcwShUy0TiV4hKZRBLPreF5FpoiE1Yk0tEQgmeQjAaJSQLpYICkLLIuHCVi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\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DQt8nlJIwkhiJBKqqx1MSAUKGZFQdTdVpuQPQdaJIEPk+BD6GpiGjmB4ayoMppUCGUZxL\nNwqPPZjeRICijORgqiBhqeQUDU0IBjLADULshBVTaxEYqoaIJJqu4wYBQRjhOS6u6zIc9GNyrhzF\nYSgqiFgXmTUt0ppB0tDjm0e3RVYzkLqg3B8iBIdgMYogaWsEoYdp6AgkpmliJ2x81yN0fbKZLG4Y\n0BsMMDQdQ1eRkYoahWQTGhoqZyaKBAmLu40m9a7DdrXGXrVJud6mUu9Rb3UZ9rtErhODkDBEUwWa\nJtAMPZ66BhEikoS+j+84uI5LMIzBref42IZFKCP6kQ+KQtaySdpJInfAyWyKMGGzUm5iGLGeUzdN\nup6LbZokDYNmr0XSNOm4AzSh4xowmM4iS0VypQILM3nO5MeYO3qa04VxEvttfvC9N9m9t8kYgkI2\nzcTJaXaTJg03wI4iMptV1NVNomaFTqtB0+swVsijKSrqkXGGlkF/skhqcYH80jEmFheZW1jEUCAi\nJKhX2P7xW2x99CM67QoDQyVUTJJmjrYc0hUCVdOYsHOgJ+hGNgM/wvUD8qVxFFUfTeBjvWI0mpKH\nUXRIf45PQREDyFHExsNaRhlGcQ7iaIqvwMhtNz5mY7r4J8AxkvF0OZQ+6+sbbG1uMz05gzMc0mo2\nSFgWmqYThE3WtlZIZnNAkky6gKFLht4+vX6LbGoOz7Gp1ztIIuoNF0NLU6/vYhpJZuemiKIu5e0u\n6w8qJFIZ6o02Uiosr6ziui6nTv//xL1XkGX3fef3OfncnDvdzt3TPXkGM4PBIBAEMCBAgqS42hVV\nosq1tVX2i8tSWdbaVq1dfrBda7/trta1u1pbWq6kJRUIBjCTICDkMJg80zM93dPd0/n2zfHk4Ifb\nGIIkQFEU1/5Vnaqurj733Hv7hP/v903z3FpYpDg8zOjIMBIQTSQYLU5x9cYSE6OzqHGdc4+eYa98\nD0H2cLyQ0+c+Rj6W4db1OzSbPRLpJEPFQTpGD8v1yGTSvHfxGql0Htf3mZ87iKqKxCJxjK7Lpas3\nkCQZkDl69DA3Fq4zkh/i6jtXGZ+YotmtU5weJV1IM1Es8tIP/wZV1RmcGWJ8ZoxMepx0IosshMxO\nFpGQMA0DTVNp1pscnJsnFATOPfowohxw9PA8mqoxMTqJpqoUh0dwLIdmo83S8jLPfvIZdio77O3u\nkMmn2dq8R61aYXJimOpeA9MMMAwHMwhpNtrkU1lsx+E3vvB53r14iWw0xtLKIj27i+Va/cbU9pFC\nEU2WEaUQCJDEPrvB8wIESURRZFKJCKIkYxoGiaiOY1oEnockymhaP4bDc30EBGSpr23tdR10XSU3\nkKLd2sN2DQLfJxGLcXdlCdO0QAzwQhdNltBlMFp1zG4P23EplzsEgYvnicRTcfbKLf6H3/uff+7D\n9+9a337hj7FtG9dy8MSAQAyQaPYbyHgaSYRO18YwRfIZKIyfxbE6uI5JuVwnFo/x3ltvMDg0gKL+\n/0uD67Yq3L55g52tdbY2tuj1en9no5aPqkhUR1GU/2zNIvRNHz6Y8/erqA82i8sdk0FdJZBESrZL\n+FOoajIVp9Pu3dcoRaI6ekTD93xcoKhKCKnEhy4AZ5JRAl1jPptAiUa4vtegJ0fY3Oqxu2dRKtv9\nzQyo+CGtRud+o66qyodurUbnfsZbp/3j5jCbS/3EAjatyuR1hTAaYTIeIRbVKNVajOTTGJ7PmAg7\nlks0qhPbX7dF1L5ExnBcHD9AzkXojmYoDOSYGsozFlE5NTPFg4NR5M0lXn3rIubaEpOZBLG4TnFy\nmCUxxzU7S761Rr69Qsbu0CjX6DTatFo9UukY0XiURDpJJp/CdV0GR4eoTD1D5OCTGJl5MpkMnU4H\nu1Wl/O63ab75fH9wtV+JdBJ7X4MbC10OZdKQzNPSUgQB+IFEKjuIqqr392k2+zpdWf7/poEMw5C1\n1UXKW7cYGJ6h1WrS6/WvE12P0KyX2dlYIJEeJAghlsyh6gkCa4/A6xHPjOE6DoulNmLYo9PqosfS\nVHcWUGSJ4tgBFNHgznKZrY0NoolBmu2+nnpv+x6drsnBoye4e/NNxqfnGBopoqkhkWicobEjXHj7\nMiPFEeKpDI987ONUtpcQ5BQCPsdOniGdTLO0eBvblSnkY2SyGUzTxXZ1ItEcV67eZXhkCMNyGZ0+\ngyxLaKqK79lcvngVTVPpGCIHDh3jzp1lBgcKvP7qG0xNT2C0tzkwP0NxOEFhaJzvfucNcmmRI0en\nGR6bRtMTpHKjKLSZGB9AkJNINJCUBHh1hidOYPQMPv7kI2jCLgeOnMH1FMbGi8RiUYaLwwRByPpm\ni5vXbvLrv/4Uu7s1us1VstkCe9s36TRrzIyp7GyXMb00na6BF6hUaxaFQgrECM985jPcvf4iqp7i\n9sISrmNjGBbNRvtv+e//uAYGc6iaSqfdY2Aw179m/7Z72b6ELZNNUa009odBEEtEuXXjNu1WlzAM\n8byAaFRHlCSaDQNRkmg0LFrNDqZhEfg++UKCWqXG7/13f/Chh/q5NNRYOguaRWmrTqvbQslniUQE\nalsWipri3nqZ+ECcsUIK1VG4deEaXiLOSrVJcWaYb377TbLKIA+diGCHdp+MFqr3NSphsO/OGQYI\n+wjG+yHygijuZxSCvB8wG/oBBMF9sw1ZkjB6LarVPQYHh9FEaZ/OpO0jlgK+CIoq0aq1QAroGHU6\nlToZMU5UkBDDAF3VSCUz2I6FrAmInohneSQTMWwFfFXD8T1sx0MWpT70F/T1idp+qLkkiqiqghME\nRCIxTMcmFARc10MRZQRCJKFP6bzvPvlBRPF95Gb/Z3GfSttHVN/XbIbIioCuKISqj+v2kUoJsf9S\nQZ82GNUjiLKEYfXu03RDod/cBoDjBnh+P6tQkkR8vx9tEQrhj9GhEHhfk0mIH5gcmBnD9lzaPQvT\n6j8QbcMm9DzaHYNQlZEkmdDfpzu+r6cMQ5J6hIPDw0i9Ll6zSTSaQjIdzAB8z0NgP/8dcZ/WGxKI\nfZ1qgIgXBui8r3vz7+sqCYO+3jUIUEQBIeznM+JbiLJKKCp0W10yUt/1NpGKY7kGIQGu56GECiOz\n84ymM0ynMiRTCkfGx1GiEQQkNN9FDH3a1R6KA06vzrbRo9qs8tijD3J9a5nLN3exNQnD80DTqK/v\nIlZt5mNDREp7tMvb+JUa2blRukYD2RAxw5DGUEAjW2YgOUm51OXoI+eYKk4Ttg1EZJB99q5f5vqV\n11GFLjFcVMuiHYYEQ4NsmyZpVyAr6uzUqziRBG/eukE1XSS0BB556CGcIAQ/QBBBEoI+3RRx3xCk\nT3foo4Lv077vp3HeR/37GjB4P0BDEvpIZegHhOL7p7LwE38viGJfTN1uk8kMkEsPEfoQjcQojozS\nqDWJD6XYXpeZHn0KRxAQlQDP7DI0NIWqJxCUHoEjsVu7zerWXc49+ChXL92mVi4TjSscPDxG4Kts\nbbpcvfgagSDz9oVLzB6YZXCwb0iTz+ep18qcffBBLl98j1fu3iGfilNtGHiuyInTZ7jwxluc/9yT\nXLz6LkPDKqXNZZptkNJ5zh46zvp3XmR8cpwL1y/z9McfIxdPs+0KXLp1mYPzD9BodahU2nz3Oz9g\naCgLyHzq05/jz//Tl4knY+RzQyhKwN0li9TgKCeHpnjxnYscnp1GEmxS+KS1kHwuheOGbFxZ4dlP\nP4kdNNgqbfDgwx9j4+JlLl67xczhg4hCyJ2V2whiyKtvXOBgq87W5hJHD8/xm5//AssLa3QaTV56\n8SVyAwPEY0lCBCzLJJVJ0CzXmR6c5dPPFXnrte9Q2XYwuwqnH3yA0w8OIAoRhgp5Op5LOp3D7Rg8\n8eADPP/8D0jECuxUNvvnTCD2mSJBfxChRiRc10FXdGzPJRJV6HZtxDCg2/EQJBXXtHGjKqqi4Egu\ngS8SBD66rhGGYJouQeggiQHjo9N0ez0sq0MyEyOXzZBJ5Vi6tUw0KqELIYbt4PYCskMRuo0GSS2H\nZfQIAw/PDansmShRkb1SC+k/w+IvFosBsHZnBawAIa2QzWRpNBuIosBuo0Eul2d8NIEsi1y58A6W\nI7C7vcFwcZyLFy4giiKPx577pd/DTw9efxqhtM0uzeomiqqTGZj8UAQzDEPMXh9F3Nna+9CQ9+Hi\nwP3BZhAEbG/uURwbpNc1fyHK1AcrlU78hNHCr6L+Lrllv0z1PB83CJFVBVmSfobyqgoCnxjOsPgB\n45Rup0ej3l8wlgDaH44UBEFAu2MyPDlIqdXD6PQQZYl67aO/15+nafp5FYZQHBtke7OvIUxnk4S+\nh9EzadQ7TEwO0vVD7myWAdjUFIZHBhidVTlp9w2xHnnyAVK5NPVynexgjm6rQ3WfKhoGAZbp8HLJ\n5zdOFliSUlxbuIkpiFxY3eHTg2k2l9fJp+vMD+YxOj12d/rHGigO3s9MBOh1ehg9k/G5STaX1+H8\n73B4dIrVtVXGRsdwHZvg6vNsXn73Qz9ru9H//oIwpFHv4tgu/3HtCp4wTDwe5dS5R+h02kSjsfv7\npNPZD32tX+67/oAXwEf8fnt7k8JAkU4kAWFAKpkkk8yyvrlCOpXC9WHu6Mfu71uvVxkdnbhviCUI\nAp1Wg17jNsWph6nsLrO00iAeLzI9Ndr//L7D0uIlGvU2b795mSPHj5DJ5llb22Jyao56rc6RUx9n\ndeUeL/7wDXK5NDJb7JQXOP/Meb7/7e/zyc98kpVr3yAWKeAZ11he798vjp95gu2dFxgbT3D58j2e\nePo88ZTC8p0Vrl66xJmHzlGvVdjdqVGp/ohcYYBsbI/jD/829XqX8YkR4vEoQRDQqV4kM/g4T55P\ncf3d73Ho5OO0eyGCEhBPpkhnUoRhj+s3Onzq6QMIgsz6+h1OP/Q4O3df470rb5MfmofA4bXXbnGi\nK3PtynWq5RI722Xm5pb57Of/MZ2961itRV56+SqjY0XSmSzNRg3P2iOXyrN4t83Dj59kfPYYV1/7\nY7ZbLsv3BB77+GMcPnmGwPeZmhrB7W2hxEZxzRKF44/yjee/wdj4MBv3dv7O58r7OYzQX37H4tGf\nbw4m9AdStu3cv//Ozw/huCFLd7b7UUJWHcu08VyPkdFBNtd3yWRT1Br+/X3fHzptb9b2PT8+vH4u\nsvjCD/81hhlSbXYZyoyR8nWigcBYIYHjmKi2xFQ8QS4TRcklWBNc2oFDbrBAuWSTy6aYPzBCOhZH\nxIbQQ0IhFEP6LNO+K6KIg283EAIBVYzj+yAoPqFo0KxvsndvEaddpr63ThhYJKIyYhhQKq3huXso\nXg23USYWjeKKOoKogBcSKgJeEGKYNlub6xjtKu16Dce0wRGICxqe6xJqCpYs0+vZHDx8jAs3b3D2\nsYe5t7VDKpNlaHiQdrPB6NQ4fgiqICEKIooIqusQkSR8AmzHRZQU/CBElMC3LWRR7C+kJRFnH1mU\n6QdsSGJ/0S3uRzeIoogc9OMzHBECSUTxQJVVJEEh8AUM18MVQlzf75uyCDIyAZIqYIbgBj66IhHT\nVMIgwAt8CLmPLCqahqYppASZlAInB4eYHE5woVQlLUvERZ01uwcBaAEomoImypieRK1r0Wr3cA0P\nLwQvBEmUECSFlB5D1SMQ0wkcj3gIjuQghpCXEziiy0O5IgbwzuoGguNiEuATICGj+DKSBKIUgCYi\niALxQMATPEQpRCPoR1YEPgECbrj/eRUdlAiBooAgEYlFSA3kSKYz5HI5dFkk5oVIIhwbL+JGI9ie\nTG56ijOjw/zarz/Nsblx8vU2QyNZwrEMmzcWWby1y04xyw+WFim3LPbmx3jbN/nyl77O/KEpBs+d\nZOnGKp6gEGzZtLZ2GMvo5HWf+UMTSI5PfXMdp1XCtw1kT6TbbhJPp9lumBhxlVqqynp3l9GZ05x9\n+jny6RxOy0QOLOobCyz96Fvsrl7DlQVEVQWzQ7drY+hZwoRE3umx2evynTdvMXviQcrNDhV5kEhh\nnNLmJoVcjmQshiZJKIh9BFoU9x2EAwT8/bxOEEIRYX9I0dd/SsjIhGFAiEsQSAjIBIJFHz3S+MH3\nXyIaE4nGZUQ0BBSC0MRzm1hWnSBwiCoJ1CAgpjeolF6hWb5KY+8uQlim2y1jeS3iySihbyGFHo7Z\n4ta1S9RLJcyeg2PalNbLqKJCq1XDCiwGpwboNpus3t3l+rUl9ip1Hnn8QVKZAfRIDNvz2KvWmJqe\nYWdvjwCFr339BSZnZ9kqVUhnB4klY3zs4w/TqJc4ee4Y5Z0KUSGJqmapNjzOHD9Ed+segtTl/MfP\nQmjzwKGjLFy9zeLdFUJRpOPYbG1ukE0mMWyLg0eOYQkCh48c5d2XX8NwXIojE9i2y+LNBQbyI0i6\nxszcFEalyoHJcW4vr7JbaRKNZdiuNKj1ehyYmSefj3Hk8Ay6HiMMI9y8dZNoWiaVjVKu7DI7e4yd\nUgldTzM3N0UuqxM4Cj/47kukUlEWl9Y5+9jjrGys8sCDp4jHE2zv7mI2DAYGYkyNTvLqd1+h4QSM\nTY6TzMUZGIgTej0ESWJ3eZv19T1OPXic9s421XabVr2L0bVwfBsxFPD2zTv7Trvvu+KyH9UDnuPj\nOSGKrKDpKp2egScKiJravwN6/UggLxCwXZtQEJEkBV2VODCR4cB8DsNoIAkKihRnfLxIubSN5Ie0\n2z4oAqoC+bzGxGiR3Z0KsqJTbXbRowlsxyWe0kFzCKW+I/Z//zsfPjX9Zet73/4iruvS6vUYTqaR\nExEieoRxoqiGh2+JFNUII4k4pp6gawj4vsnY2Cidrk1EjzA5M0s0IuF7DgLChza1nutg9hoEnov8\ngWYkCAK21hbZXFumWa+xu7VCGIZE4wl8z2V7Yx27cw/PLOEYZZDiBIGHokbotav3j2d0auxsblGt\nVOl1O1j7aEw0FsF1PSRJRJIlWs0uxx84yc2rt3jiE+e5efUmo2MjjE6MUa/WGBkdAQI0XUXT1X5E\nVBD8DLJoW7/6LERJ6uvdP+g++PetmUSEI8UCiiRytdJiPKaTEAX2DOtn9IsBsO0G2JZzf2s1OySS\nMSIRHV3X0PU+0vjTWs/33VCPDmUZKuZ4dXmbTu/v5iKbSMZw7A9vICVJRN7/buJBgJbKksqkmZ5M\nYtoCqcAjpimcf+ggnhfQ9SVG5wp8ZjjDb3z2QZ4+N49dDxmaGkMfH+PGjdu8em2HnaFRfnRrhUXD\noTN0hBtGhH/3p88zURgkfvZxllbWqGsZrOYm8tYeZ6dHiMV1Dp86hOe4VLb3fsLhdGu9RGEoT3Wv\nge8HlBMKl80u0eGTzHz2d0mnsuzs7pCPCHSXLlD+wR9Ru7eGJEv72bw/K3Naslz+/MoCxTNPYtpV\nmto44+PDVMpl0tlB9EgE13WJRCJ0Om10/VfjkipLMq/88JtIgomkxO+jl77vUy6XaLVa6LpOJpOl\n3W5xJO7SuPU9vNJN1jZ3GLSbGI1Nyj0bTY8hihKddosw9Ll9/Q26jS3MXnv/e9tEQMC229i2y/TM\nKN3GGlubO2ysXGR1rcbJM49QHBslHotgGAbVco2Tp05RqZTxPJ/vfOMFxifG2d4qMTyYpGuleezJ\nx6nt3uHoiVO0yrcJo4eJJ2PslerMHDxNo97Gs8ucffQ88ZjA7KET3Lv1QxYW7pFLtml1YH3tHuls\nFss0OHh4moTuMTz5GFcv/JBO1yaXHabVbrJ08zLxRJ4AjenZObZ3WoxOTLN06wq7OzXiMQHHFdkt\ndRmdmCKRP8DI5EmSCRWfCG+9tUA8pjM+FsPrrVMoHqXZqBKPJ5k7MEQ2n8cPVF558WU0XeXGwi6H\nj5/i7t1NDh85RD6X5NadCka3Tjo/zuTEJN/52vP0TMgNzpHJxMgXsjjdbRIxhfLuOltbNQ4dO0Or\nWaFa7dHrGtiW+fcehHU7fffSn0fzH58c4cSxLPWmSyqpkR8YJJbI4jktPL+vk37/HpwvJJmbTXFv\nve+uvLm+i6LKeK6/H7sT4roevh/wP/7B//Th5/PPe8NBx2NiagS/YzIzViQ0A65deY9A1UikisRl\njezUAI5uU++tYNo7jBUnqe5VmBmeR/RFrlx4nsIT58nmhmiXTAZyI7iyjKQncQUdy7W5s3QFwTZJ\nRFIMjkwQS8Tp9drcXV2kUd0losrU96mre9V1esY4puHiuiFmt0YxFyEqh9RLd0kUxkDQSETSfV6+\nICGpGo3KLrbVRZACEPoU1SDsoy6KIGLYNr12h/XVDfAklpfWsB2PVtcg8Dzaps25uYOUL1xkfKRI\nLJVmY2ONtJqjtbmNLAtIkoCiyHiSih5J0RFF0vEkUq+H6dm0jB6W4yCFIQ59Ux9EAR+PUBQBHyXS\nn1jKqkboBgiej4yAhICuavjdNm7ogSQiA6ogohIgBQHKfmMYhCH2/kNDDN/XdkIQ+DhW33I3E4kQ\n2gF6RGMomSKqagReiCADErhugC8oOD44rossyDiugxcESF6AGgqIgUtddZAUmZgvEvUF9KiCKroQ\nKHiSBqKILUjInkNqOI8khxw8eIi7d1eQsInKMQIpxBV9AlFE7Pq4pkUdDwkVRxJRrP4xk9kUI8Ui\nmXScqCwzmSswkE4yNDxEIhpB1lXKzRZeGBANRYxSk7YY8NqlSwiNFr1eF8txSMRizHzqk/z7f/5/\noDz5KI4c8NbNJU6lcjzwD5/icsVmzVzkKUXm4UPHKORyOIbDnSu3SReyxNoGOdsjGo1zeWedyvId\nElKA7LootkTQdmlslpB9gUhKQRQ19lyf2Pgw1zcWEA8WuVpeImor/NZv/zPGsmcwRQ/ZCRD1Drf+\n8v/GaO4gZuJ4vo+spHC9GJZvY+g2ei6DHOS5XXIpR9qcfewklZjCO/Iu8ZGD6GIcq3eRG1cvc+ro\nEdrtNrIeIQQ6HYNItP/gCcL+ZFwS5X0rbfB9j/6yx92nMIsIAYhiQBAYtJtlBnIpHCegXN9gbV1i\ncnKCbqdNzyoTj0lYpkGz0WVm6gDdygaddp3dvWtkCxLdFmTSPnFgLgAAIABJREFUc9iuTzqeQlQd\nWs1dLAsU0adZriLJPigqydwAly5e4vChOb721a+QyUXx8Lh04xrHJo/RbrloWgo1qlNrWOQyA5w9\nc45/8a/+EEXTUGWVs6cfRNUVdEXA7DTIpFQOzA5h2w622cVzu4RuDwGLH770Mo4t8Bu/+U+4fX2d\nwG3T8xaQQpHAcRkp5BkaHOSNi+8xlB8gqqeZnp9m+sAB1u+uECsMkpk9Tk5NcChUGOq0KSSybGxv\ncfbsGe4u3sFsdxgtjPCO0WNtY5O3373I0MggO7slZg/MsV3e4/biLQgnuXLxIr/5uU/z0ve/z6nH\njlNrbFOvd2l3amTyeT73a58i8Aw69RayNMLw6Ch/9dW/ptzpUu52+NJX/4LnPvE0muQyNZzg/Klx\nXvjhO3jhCN/57ossvHWdJz73DLJlsnBzAbvZwHN8inOTDA0N4soR1pc3GRwokI+EWC2Li5cuUBgb\nxDR6BJaJL3oIgtg3sJLUfXfrPjotSRJg4zgekhniegFKVMG0TUJRwbEshFBAVkUQBSRJQRIUCAJc\nP6RSqTI2OkKjZTEwOEOn16LZtBjIpGlbZSRZJV9Isb1VZnREwAsEeqaL7YgEmIQimI6FFu1bkIf+\nr96MpdFs8PToNHvlPQ4WMtyTYfPyCuW8QiKV4oClog2lqAU+rtVCcEvMjA2zV14hnZlFEgWWFi4z\nXJCRtQw9avdRiNzQDOI+c2X52vfYKQvMT2toiSkKxTnajRK1zXdZ2fxJdHGvVOWw26XXMwnMdept\nkWKhf60brS0kNUmvVSE3PIPRqdGsbjI8OkNpHxlKpuL36VMfnGwbPRPHdrj07nsEQcDVSxf7z+RS\nhWzOQVEVxqcmWb59i5GxCURBZOPeCooi/wwKlkjGSKUTbG2UGBop4Dgu9b8nMihJIqIk/kppqACJ\niEYiopHJfrRTIPCRxjV/m6ENwF1FR8Pm8LFJRFHk/Gie76z30bZcPo0oiVT2PjrGBH68wBwaKXB4\nrt/wTExMAPBxLUlhuIAQT/FyQ8Oz2pyxdpFEWHcsXv3BFcq2x92lbcIQjsRkpE/+Nv/qn/+v/NfP\nfI5btS4/uv46j2bGOP/UP+BC5a/Yc+rkxZCB4hwDxQMIgsD1d/+EIVUm4vZ4yLvHruTwrdVNjMu3\nfub9Nn9KD6tFdIZHI1y/tMTk9CBXbqyyHep84b/61xyYPdjXzu8PAta/+i/p1qpoer8Bk2QZSZJw\nP3Ce1bIzLBsmW80qpx55FjuS5q3NkAPzWbIDE1x87yqbiz/i8NHfp1zeQxQLBIHP9vYWxeLo3/o/\n+6gq7axRGBgDYG+vSojE45PHqVTKCAJYltWPK6tuUCyOsr29SbdV4ftbrzMuyKx4NuniNMuuTSY2\nTlwUabWatNutPvrWKOF5AV4YZ2x4mlvX3uXg8TN87yt/SDQCsiJw6/YGs/PHaLU3UNRxxsdsqtU6\nAwNZnvtHX+AP/8//nXQmRSwW4ey50yiCiSScobK3SzIZZWp2aj/yysN0o+REiOjw4ovfpdVs8Zu/\n/XluXF8iCAJM0yCansAPJBLJPFpilmvf/xYjxUESyRRTM/PMzB9iZek2iVSRwkCSVCbPvHCa0l6b\nwWKeO4ttHvr4J7j83hXSuAyPTqDJL9JplFlcWEEQPBr1Kg8cTbOyZLJ57y66HuHSu2/ziWfP887r\nf8Mjj57C662yvAm9RoMTx7Oce+y36LWreOYucmSY3MA0f/4n/5ZWV6bb7fK9b32X5z77LOlMHEmG\nRybGee+di8zk4rz+wy9z6b0b/KMvfIF2q8Xm6hJ2dwVBEBidfpBILM3YhEq9tkcyO4kW6eD6Ai/8\n9fOMjg+xu1P5exlufZReURRForH+9V2pGjxwIsXyqsfY5CT1ao1my0dVFaqVBvFEgkw2S7VSwxiL\nA9xvZN+/L/2iw7Wfiyx+88/+BaqokYqlCAMXVZcYnsxSGEmjJrLc2lglMpxkz63g2gZ2p8qApHJo\napa7i3dIRlQmBkeQBAXHa2KbZWQadO0WzU6De5sbdLsNyuV1cE1ct4Nh17GcNqurt3EdA0kIcH0H\nX7CRlQBB9OlZfYv0TquLLMnYRgtZchBkm0Z1A0k0Me1On/eezPDVr/wFo6MF6vVtLKvXH4U7Qd8F\nNfCxPBfH88nFE/iWQy6ZQQh9VFFA8n1C28YyTRw3oN3sEnoh3V4H0+yiyxJWu4lr2SBKnHroHKVq\nja5p8cDp06xurOO5FtmREeSojhaNEk8miSVSxKMxEnoUVZGJyAoJWUMOBd7PjvQ8H00UiSoSkiAR\nCH00IwBCrx/5INGnVwpiiCuI2F4Afojv9sPWhZB9xKi/AJEEESUMySoaaUlgKp8hl0zw5vYuMiKC\nItHqWvhBP49RFQW6gUc0cFA8j4gYIog+suijijKaoKGIKnIoIkgyviRhuwGRUEYJRETPRZV80hGZ\nQ/EoYhByaa9JiZBUJkdE10ikkkyMTVAYHWN2eppDU5M8fuAwjz9wnOeOH+fZk4d57MHDnDxzhOOT\nRXIh5GQFRRIYmZjk9Xff5vIPXuV7F99kbH6O9WadP/zTL/PSu++QGx2h124jmh0+VhxDcHycSIyr\nG6t05YDedpVRJUFqqIDoOUx5IkKjxm89eZbBbofxbAyvVcHY3WJAhAMjBRLdNg3DIBWPkEloqJ7C\n5uYqBU1jPJkhFVcIBWj32kQH41ipCIGkYaQgfvY4wnCBR5/6JOef+nVyWpbAtQGf+sIrrH7jT2k2\nGlh6kqZpoyhpXCGJoLjIgo0n5Ghp06yaAduJLANDeW4sLdF2uzx28mGaBsR0mc1rF6iW9ri7vAKC\nRG5wiFCQCQUJcZ8yKoQiYRASBP18vH4kjYgf9i+RAAh8EUnUEUIPx6zRqG7TaGyjReD6zWvoqspg\ndgw/aFOtreN5Ju2GST4zTLm0x067QTQZx3EtGrU6oSdhNno09naR1ASG5TM0UERBZOH6AlbHotO1\nqRsGO6UKV6/eJBpPAAKmbRMEATMTRzE6JpbpsrqxSyyZQouqhH7IzvYWlmVy5vRput0upd1dREI0\nQcB3HQrZFPGozMbKCqokUiwOoEdVxiZGGBodpjCUodEpUW5uEMoeg6ODaKrOgJ7mysWrbOzuYbnw\nyU99hm6zw061zNuX3yMxVODZ5z7FaHYIv2Nx984yG1ub+GJAJKbz6OMPceL4IZ7/66/zja99i9Gh\nAj3L5OjJo3zs8cfptju88/abHDw0TzSRZqtaQYrLtOwmH3/2IdYWt/Bdn3bLZGxsmr956Q1su4/E\nm7bDO+/cpGP0mJ0Zp1Lp0G12eOpjZ+nUuwwNjvBXf/ktllf3GJ+cJZPLMzk9RTQb487aKgfnZ7h7\ndw01ppMZGGF6fhohDKnt1NirNfHFgNFsAttxOHLsGAu3V3BsmzAMcByHMAj39YbivmbMR0Dej/ah\nH8OjSIgyiEqfnkzYNxVDAFUV0DUN3w+ZmZrBsS3a7X5+XKXcRVZivHfxJrZjIMohghCnXKnRMzy6\n3R6+39dWxxMxWm0L1w8Q9/NhbcshEY+iqxJmz+QP/un/8gs9GH/ReuHr/566EHI0mYVohKKkMjiY\nYawwwGg8wdutHUbSObY6LQRBoNVuEe9A8cARSjs3KGRUpieSBG4HzyrjWWUQJALPoNvcZv3uTWqV\nKls7fe1Jte7hei6N8iZrd5f4YFazEFpAP0KnUq7TbLZpdUUMw8PzAhIxkcBt0apvoKgaptHDMRok\ns0W+/B+/SCQaxbWb9HoWvufjef6PzeeCANf1SGeTiKJ43zXvfU2eHtGoVhq4ronRNfE8m3a7iWla\naLpGu9m9T5V64hPn2Vxfp15tcu6xR1hdXsW2XWbnZnAcu+8GqmvIsnRff+c4LqqqEInoH0m/9P3+\n8POXpWd+WM0kIoxlk6QzcV7fqqJJIglRYNuwfyFn1F+02p0ek4U0accjmYryerdNz4JsLocsq6Ti\nMmfHB5gbzvHo6YMcmxnhieEsTz71AA+fOMGjJw/x7GiSMw8/xK+Np3G7IVkhgm7A0YlDvPLeIl95\n+T1efvMa4xPjtDt3+N/+7G/467dvo4/PU/Y7+C2TzzxyGFlX2BE8lncW6FgyrWabYkQhOTREUNtj\nRBdwqyU++eiTTDdXKQwcYHjrXaS7V5kQZSYGM6SiGuWdGqPFLCczIrEwZG23TnFfsxjsO+3eubXB\n0Ehu38pdQtUVZk8cYiU2xKOPP8Pjn/9nJBIpTMvEdV2MS1+j9IMv4jvWPiLSL1mWESUR13ExIhk6\nsQJ3vQA/FMjmiqzfW8Hs1Hj8mX+A0WsDIWsraywurrOyvEQ6kyWRTMH+kKu/fTSW4u/n3P20i2qn\n02br3iKN6jpeILF49RWisSTDo+O4ns/u5h0iakBtb4fs4BTbG2vYZgtV03ECnYqxixwZxLFa1Hau\no8cHadW2GBmbxbEM7i68gmGC4O3RbLRo1CosXF8gpTfxfQEvkGi1A8anjlCvNel1u9QqDbRoru+V\nEYY09xbo9XocO/UYjeoalUoHQYqi61Eso8b4WJZoLMvS7ev4vkdxJI2kJEjmJ0lkhpieHqRWa+JY\nNQYyBtHkOJLgkU7FuHPjVVqNMp1uwPlPfZZ2Y4+Ne/e4ffM60XiG8899hvzAGAKwsnSbSrmKIjSR\nlShnzp7noXOP8Jd/9if84NsvkMmm2N2pcPDocZ759HPUqzu88soVZg9MkEvLrG+U0VQFz3M498gZ\nKpUGkmCzXbIYmzrIa69dJXTLOK6MY5u8994C5b0tThzO0mm38FyXU6ePUqm2GJ2c5Stf+gvqjSYH\nDhSJp4sMjc6RzmW4t3KX40fHWFm6wUA+jR4bYXzuDKIcY3dzlaU7q3iBTGFwANMwODA/x92lFVRF\n+pVpvqEfiyNLEkdOHKXTbrNXqtJs2VTrPpKkcOf2EmavDiFEoknarRa+5+HYNp7n4/gy2WyKRqNN\n4Pfj3z5oOBpPRHEc9yORxZ/bLP71F/8ljUYT1/ZJ55KsrK9SqVWRVZVUOsb47Dh73SYdK8Ao10gF\nSc5MH8RwtnGEFnpMQETC89ugV7H8Fj4+u/Vdyo0ajufRrLcIbAtZlbB9EycwqTUa9EwL2/EI/L4e\nJhB8BClEUSUCHxRZQddjRGMxHNdiuDgEgokfGLiBgaTrXLqywLXL12m366TSEUyjQbfTxnV8ZCQU\nUSQSjeARIMgSgW33lzieTadZw+m1kQMPs9Ukqmv0Wj1k+oYyjtWDwCN0HETbQREl/DBEjcbYq9Ux\nLQMBga2tLbA8pmdm6TkOlXqTVCbP+KFDbJXLyJLCoSNHERGRQpGUEiERSxKLp0hEooiBR1JW0ESF\nUJZxZRFfkpA0GVHSCDwPWQQ/9PGR6IQeoSLjCxAoEr4s4BL0taFS30GUICChKAzkkxwaHkQLA4xU\nkq3tEvFohG3ToBd4yBLoEniExBQVRZYRxb4eLfT7qBO+iOH7dEWo2SaO7SEkEpDQELJRSGi4gUNa\nCHlkbAo9CDg6kePUA8M8ceggzw5MMJ1PkdE0NEll59Yi5Y0tUnOTRGbG+dIL3+TGhRu8eOUazzz7\nDDu1Bl994fusleqsdQweePpT/NFffhU7nUMcLBIbmWKl1MIyfKxQwLNcpiMFnHKFB/IZDN/GFULO\nHJnngdEC05pC0mqRUUPGMklKmyucPjhPTAvo+j3UqMygGKFSrXDrb15D3qzhizB2+hS2JhDLxLi3\ncI9apcqQqjIajzE8lqGJReHINBXNQhzMwMQYTiHDwVMPcWjmJDk9hhjYqIpId+82S1/9I0p3b2Ar\nKk2rTVT0CcIYRixB3XHRcJD1cXbVPBc3XMTxIqePHWRgfoCZiQmcQKG9tYfbM1m8+g7GXploNM5e\nuczi8jJnHjyHrGmAhGtZiKKEY9n4nt+PivE9BEECJPxAwA/72sYwEBEFgW53Bd/bQZFUbl5dJBLT\nIOxhmGsUR3LcW7+FaTYw2z5zMyeolveoN1bB3SAVU7hze5l4fAAtHqfnddit75JMpREIUGSRW4u3\neOTck/S6HtduLuIEfaH2/IE5ZBnGR0dQFYVex6DZaDFUGKTVa6NGItzb2MDzDA7OH6ZWr1Eq7/Hm\n228zXBxianqKRqdDo1lD1VSefvYTeKFLpV5CVhXS6RzVnQbV7TpL11ewujavv/4Ohw8dZWeziizK\njA2NEVEjbN7b4sbtJaRIhG9+89s0axXGD07ziWef4oGPnSWnR/n2H3+JZCrJ1u42J04co+10uXDh\nLWZmJ1m4eYN210KORChOjpLOpEgnk1y5cBGza1Cr1ag32vihhSrLPHziFE8/dIKtxQXi6TSm2WF0\nPE8ul+PpZ57kztINHnnkFGPFSYIgQr1a5fCBabLRHHFJYefeInvbVW5cW0RPpfgn/83vkUxE+MbX\nv8nK+ibRQoLR2UFss8NwsUiqkCI3mKOxvcvqzg6RXI6YJ3HjxnVyxQHsbpuJiUkWFpcZGy3SM/vX\nu+t4OHY//9ZxfNiP4fF8HwRxP69PQJRERFkgEtGRwr6GG0JURe7LEgJotZoYhkXgC5iWQ8/w2C1V\n0XSRnmESjWjUGlUs2yaZTOIHPqom4bk+pmVimCZqTANBxejaEAgEXkAiHkFXo/y3v/vhgcO/bH3l\nr/4vekaPtm2RTaZ4a32VUqeJnkwSFyQeTBdYChxEUWRnd4cgCHhidoYdq0m31yabSeJ5Hsa+e3Oz\n1UQRHVqNHUo7KzS6Cdo/hUyZhkWnY+L5P6mDEmkRov+MWdxQPqDWkhkZHSVw2329t98jlDJcfO8a\nVy68y+7OLoPDw/hOiW4vpFFv4fsBqqowMJSn2+m/B8u0f2ZzXY9etx+jYRoWvt/Xw7SaHQI/JBLV\n7xstJJJxPM/C7Jm4roeqiTSbbTzXY3JmBqPXoVZpMDQywNyhI9xbXSMWj3LsgQfwfZtYPILneeQL\nWRLJGIlk7P57yxcyCIL4K12gvd8s+n6AM5TgyvIukxH1V9IsZrJJMtnU/mcwGE5GOZCOI0kSDx8o\n8tT8GOfnizw5N8yULDGhKlipAs69DW5vVpBm5smNTfH/fPGvuLywxKt3tnjk2WcpuRW++L1LbBgu\ny9U6809+gj/9+nfx41GSEzPEBkbY3u1gmg7tVhvHthkqTrC9co+iqmB2bVTD5eHZUR6dKHA4qeBt\nbTPkdHl4MkP56nUePTLKgNrDsS0GIzYQ4joOL7xyFckL0GWZUw8fodfqkEgnWbq9zlatfb9ZTCSi\nRBMxDhyZodvqUBjpSwOSmST2sc8zMvcQfnoE0zTJZrO077zO9vf+A617twiDgFazhwD3G8ZbUoa0\n1caVde4kJym5Aaoe5+DRs4yMTTI0MoGkSLRqO/iew6svv0S71WJ4ZJzN9RWWbt/g+OmzyLKEbdsI\ngkC9XqPT6fzElkgkAe4bEr7PAvB9n9Xlm3h+SCwWo7T6Go6nkox02dtdJJYepVFaxrE72LbL1Nxp\net0G1e0bON11ZjSLndo6opJA0rK43XXK5RLJTB/hlBSd3ZXXeeiJ38Y22txcuEcYeKT0FrMHj6Do\nGQaKs8RUm8Brs7vbYGRQJvDbaNECS7dvE/guh4+fZnOrRr1W5d033mFsfILDxw6xtb1HYK0RieZ4\n/OnPEAoyndptbC9JJjeA01mlVqty9dJNTNPj1Zdf5/ipk9y6vUkkmmRi5hCCpFPZWeLGzR3C0OfN\nV1+nXKpw+PgJnnrmWR585GPEYnG+/B/+HYVCirWVDY6ePEqlavHOG28yc3COlZvfp9Z0iSezjEwc\nYHzQZWC4yI3rdyjv1eh1uzQbTWQtR6tZ48FzZ3jsyfOs3/4RsfQEhtlmejxOMjvCqXNPsrOxyKlz\nTzMwOk+72cJzDManD5HL59E0ldaVG9xrdFi7u4QeifOb//i/ZCAX5RvPf5U7txZIZXIcmMkhuCUG\nioeI6R5ybIJOdYnyzi2U2CTDOYvrN9YYnxjFbi5QnDjI3eUlRsenMI3Or+5+tJ8QWK1UsS2bMOy7\nQFumTafdJQgCbNsnlU6ys13q7xKG9/Nydb0/0OsDA+97Uvz45WOJKLF4lN/93d//0MML4c/Ykv64\nnnsqz/j4AIl0iKZHQBQwDY+JkUE6rRqKnqQZmmzulphOZIiGMRKqSF3fxZM1HDeB74R0W1XSAzF6\nPYV8NocSibOxVWP9XhtFTBPYAXIkAopHgI9jC1TLHZLJFHrEQ9M0ZMD1beJRnTAM0NQ42dwgO3u7\nhL5HKhqjVdsgn4uSzGd468Jt3n19hRCRuYMHiKc0mp09DMOk17EIrRDZCdBEsR8YLSqEHqQiMWzD\nxA88NE2+bzVtmQ4gEyL1ub5CgCQL+F2DpKyACL4kEcoRnH1X06iu0ajX8QkZKo5SbTaRRBl8GJuc\nYHP1HhFNZe7EUbZ3d6jvlRk/MEM8leLixYvEZZmpfJrK+iq6oGJ6IcRjCIoCASii1qeeOl3CwMFB\noepa+L7X1xQFfp9q6AcIhFhBgBgKSIFPQVM4lk7zxIFRipLKshrhG2+/y7ga5z2jhSlLJCIRkEPw\nJARZJB6JkojF0FUV3+u7nE7kE0RkHVnTiCk6SV8mlo4iegHNroWYSPDK6y/jlnb5Lx45hyBCtdHB\nPnuUL33tZXqlGl5a4vd/5/d47c13uPDyK+iSgjY7xqc/+1n+zb/5twxkMvgSnDlzjrWlJdTAJyLL\nSGLfxGXt7l3S8ThRPyAtCuiqjOha2L5Hq95jeWkb223zheOTxBSdkimy0Ouiqxq24yKpIXEtRmFs\niGa7QVpSaTR7ZDIxSrvbhL5IoCkcPnqIRDaNGoXCzDQ932H96k3efukGW5Uys1GNh8aGmDs9RUOV\nKbkmifEcci7B2MRJhgenQFDAqSKKIY4pULrxOt3LL2KoEm6oY9kGoqpQQ0fVVaKqR0MuYkQPsbZZ\nwZIh88AZpgfTFB2XimvQbOmUhRqvf++rLLx1k7mxCdqVClIkSrVR45/+wR+wuLzMQ2cfYntjk1Qm\nQ7PZZGx0hCDw8D2HwDdxvRBBjKBHkgSC1M9fFGwUSWBj7RK5jEQqFme3tMLqvUUKAxnazf+XtTeN\nkSQ/z/x+cUdm5H1n3WdXn9Nz9tziSCNSFCmSklaUpbUkaGVgtYC0hhdYwF8Mf1z4iwEbsGHv7gfv\nYcsray1KICWRmhHJGc7Z0z09fVR33XdVZuV9xZFx+kO2hiJFcnflfYEEqpCRkRFViYh8/+/z/B6L\n2YUK57UmRiyBGCXQFJ1u/wzX6yJKPoKg06z75PPLuEGXdD7EsYYIYQxZkYhQKRRnSOgF7t7d4d6D\nTXRNoVIpQyixubnB6vIChpHm/voO8ZSCGiiM/REjxyOIJI4PDnjqyadpdVvUajXKlTKvvfYaR8eH\nfOubbzI3VaCQz5EvlnjqmWscHexjjxx2Hu0wWyoSM1QCWSDEIxJ17m/soRsJbjx7hcPtE4KhTzKf\nBUHk3vo6ufkZgrHDC889gyxEVHJ5bt+8jRpPkk9leO1nf4bbtz7iiWefoHZ8hDUaEFdi/D//759S\nXljECX0atVPmq1XGA5PtrQOMbIaha6HqcHxwyu/85t9HlQIatT28KE0yqXH3wV2uX38SPwr5s7/4\nNs89u8rPvPAc7a7H9sEZ+wf7RFGCRrNFdTZBqVhifrZK6A9p1TtoySSp3DK6qlI/3McoJnhqeYHd\nzW1kXcMf2xBPUTTydLp93rr1Pi/eeBo58BkOW6w/2GXx0tPcvXsH0/N49GhrsuAQCROAVxAgiBOi\nM4AsqTjOGEEUiMUUAsEnm0sgReCOx+h6HNseo0gqURjR7w8IowhFimOPx/i+RCqlI0o+xWKMCJfR\nyCcKFbSYxGBgAT6qEkdVFJIZnWa7j+8L+F74GMDjUyjH6Pcs2rX/vBLFLz27RPWJCkZ8Enwei8ew\nLZurSpwTIcBXZNKuz2avTT6XRxAEFmSNjVH/UziOZVn4gU8qmWI0GmEYBrJeJHC77O/v4DKPQEAk\nfJ+WOh47nBzuUZmaIWEIRELyB47LiIUYekSxXKZ21qAzEClkAnrdc5YXSkhqlje+fZ+tRzsALK8u\nEwTupxnI9VoTd+yhaSrj8ff9haIokMtnaDV/cqTGT6pCMfvp6wvFLJ32RF5XrhY4r7UmOcKSzMxc\nld3tfYqlPLMLi3Q7bc6OT7lwaZVcocK3v/UGuq5RLBc4Pjz9FJpTKOZAEIgbOq7j4fuTSWMURYiS\nSKvRJYqiiYQx4gfO74friazBL15dAsCUBN68u4cmCWwNbAZ+gKZriICh6aCqJOIRJVHGEhUsQSGr\nS1zXQJzNkhyDVdJYcyWUdAzZ9uiKCkNd5dbX3gbgxtIUxVKGbnfIeOky/9vX/gLHcdBjGr/3T36f\nD969za2330ITIVYs8gtffp3/5X/6PygUs5imzas//Rm2N3cQI5tcsYzcbvDCyzc42DsgVyhh94bc\nSEEiGcNyfRzXYzCwuL1xTBhFPDFbIpdJEAQBrZ5JKqZh+T6W71E2DKZnCxwdNkinDfq97y9iHHT6\nxOI6rzy5Qq6YQVEUsqUc5/E1xvfe4M2P7rO3N5E5P788zauffZbA8zjar1NerJAvZImvvgJzzyMI\nAt1uF1VVUb0BO3duoWy+8RM/U5aaYLd4hZO9h4iSyNVnfppUKo2iKFiWRRRF1M8O+eB7b3Prg/e5\neOUqjfMzHNsiDOG//qf/HVubd3j+5c9SOztBUWXOa3XmFpcxDINet42iSJimiaLGiMXi6LqOrsdw\nHBtN09l8eBtVi5PNFTnavYfVvjc5OEEmU7nK2fE2lVgCR08QixsMz++B6xGqE4+yaZoks9MQBSQR\nkCOXoT9GkA1CWUZLrRCLqZydnrP76D0kUaZSKWC7Ah9/vMlTTy5RKKR49/09Zso+djiNRIdG00eP\nGWysr/PCy8+zs7VJrztgamaaz/386xzt7/LGt95mZm7/zVKPAAAgAElEQVSGdEKgUFnk4uULnJ0c\n4Y97bO50KOTjKGoS1wuJK11yaYl3braIxQxevDHP3YcdsgkXX8iQTwXcunPI9OwMtZMTfuFLEzhP\nPD3DO2+9Qz6fI5dP85nPfpVPbv0Vly8+w9HpLoE3IghFvvvGmxTLFaIwoN3uMjOd4uR0yObDhySS\nBkYihWNP4lR++dd/HUWBQeMRtpcgk81wsP0h04vPMXbGvPPdt3jl5RVWrrxMr++yt71Dt7GOG+bo\ndbqk03GWVtcoliro4ZDO0T5hNkc8WSSTK3K0e59EKs3C0hqD5gbjQMMfjxDVAolUBsfxuHvzTZ5+\n5jKmExK6QzY2Dpmav8L9Tz5B1RKs37v/n3iFnFQqnWDQH30KohGYwD5/nN9bj2moikgYRoxGDpqm\ngvCD/nBBEEimjE9zaSd+9ImXOZ1SH08qfzSx+idOFr/+9v9Oy64zPVel3WmzuXWCaboc7dVAFajV\nz0mkVdJpldbpAdlynFrtjGFgE8YEAl/l5KTGlUuLNOtdYkISt9+jVCwgx6DTHaDIKq4fIQgSoSjg\njm1GA4/xWMIyB7hel7E3JoxEFCXO2A1w7RGW0yXAQhFkBHTO621EV8QcjghEnVZ/zOLqKpuP9lm+\nsEQmk0DTdEYDkygA8bGpE0FAURX8KERRdRzXRdY0TMdB0lQEScGPRERJBlEgFlfRFJA1Dc8bIxJO\nIC8hBMBwNESRFELHJ3QslNDHiFTGloUcBSiihAKYtoWkuoRRiO+bjBwbYSwROh6h4zEa9AndgFI2\nTbPZRJYibDegsLBEN3AgYbB4/RrvffwhMxcWWLx+hXv7uywvLjBVKOLbDsl4glgsTiKdRlNjxI04\nST2GIYkYioTmOSzmM6QEhaiUIrU0zdrsEq++8ArX1y7wyqVLfOGJJ3l5ZYFrM0Wezhe4FElcyaa5\n9MQCqgz7H26Qu36RCzee5Rt/8Sb3jk9Y/ZVf4O1WjT/82l9QWL1Iq9ci6LR5tlJFU0LMcYR98SLv\n3H5ITDGwdBDVOOcdh9LMNJWVGQqawXIqz1KlzLOX13hxYZGpboenEikuKwoXRFglIjg7RqqfMW62\n6dfOGfR7NNsj9g93OWlZHNTrjPwxchRxvZREEgL8MKKyMM3i2hQXXn+OuetrLK0uU82XqB2d8vxn\nforq5QVihQJTF1ZYXJxneWWFYeQxO1fAkAV2Nu/z/votptNZbDvgtNaimEiQSauolQz63CrG3CL7\n56dcuHKNmeocYuAj6jKKEWPv2++y9Z0/x27tY0YQeSJCNGSoJugKBglJRxMUOp5BIyhSr43Qp/LM\nv/YshgKxsU/kB5x2LfbPe6zkNS6qPRwzoFKsEIYByWyW5bVVRuaAZCxGKVfgo/c/pFQtsf7wActL\nK3x88yN6vQaNxhnDQYdStYSkG3hOgCSIRMIYe1Rn2BkghDE++fhjDg8esbSaRRFUIleg0+7S7nQp\n5coQjBm7bebmK3QbPaK4SrqYRYsbjHoWcS2EYIQqxggDmSCMSKWz3Lv3kHc/+pDK1DSrqxfpdDs4\nzpjt3V1ULc7RUY2TozoHh8foiRSNeoNao83C0hIH+wdcvLCMORzROG9QyOXotFqYgwHWYEAspvGl\nz/402bhBLpvljTffolooo8suC1MZ5GDAcrnIVFqmPzhj5HfI5OPY/S7l7BSGmuR7H99kqjrDBzdv\n8lNfeJ18IocX+BQzKVRVZP3WPYhrICv0+wP6/Rabmxu8/fb3cCKXVCKHrsQ5OTjl2eeeY/twh8HI\nIfQ8HtxbpzIzy0mthuO5BKJPtVrlzbfexxJD1HIas+5w7/4OWjyBbas8erSLEE0Q4KIVYJstqvOL\nxEOFTFpC9ocEkUOlnEKVhsT1kMCHlblZjg57jMcOlXSJ99/9kCB06PcdNFlm7+EB9+/ukIjFORqe\nsroyj1lr8d69uwyHI6rlGR492sANAtrdHmPLwQ8fX79DAVmZQEZ8JtEpsjKJ0CCckIs1VUGVZbyx\ng6YqKKqMbY8RiDG2PeK6Shj6uL5ISIRuiLjemHIxz9qFJSzTQUJldq4KRPR6A0QxRBJVTNMiilxc\nbBRDIiKkPKUxvRAjXRRBGvOP/+F//3e6cf+4+u4H/57BoEs2k6E/6PPgUQeBEfvDHrZt0R+ZKJk0\nCSPB6dkpsViMzdoZw9GQbGaSa1U/r1MqlqjVa+RzeWq1GiupBE7o0el1Ccgg0yF83BBKUZPTkxZB\nEDAaDrDMIYPBECImi7qAKpzhuz06vYiYJmI6IpYjEqIxts4QcKmduywuz3N2UmN2foZ8sYQoyTi2\nRSwew7GdSQTV3/C0RBFYP4nQB6iagihJZDKpSbzJD9XffL1lOZ9KXd2x+5hmPiF9DgdDfD/Ashwk\nKaTd6uC6HoPBgCBw6bS7CIJAJpvGNE2sx0CYfCkHkYSiSiwsX2BjfYNCucz07BxH+0dcvnaVQjFP\nv9ejUMri2GMKpRyBH5AvZkkk45gji0QyzpQis1LMAJDLJnGmkiyuVPnKlSXyT9/gy5en+fmLU7x+\nZZ4ZSeTJYorr+TRruRSV69MUFXj/0QmL156n8PSLfP1P3+DOfoP5n/0l7tUG/M9/8A2Ki2s4QpP+\nIGQ5k8AwdBzHpT59kVsffECpkkcUBBKJBI3zc4rVKTLlCql0mkKxyOrFFS5eXuPX1vLojSaX0zGu\nF1OsxHUuJ3WMQZ9+o03QbmE3mxyed9k+arB1eM7OcZOzVv/T6Mdy2oBwMo1YXJ2iVEzzxHOXWb04\nx/ziFOl8huP9Gs++cp255elPH5dWpnnq+ioP7RHXluaRZIm7Nx9y+vEHGIZOrdGl1TW5Ml1ElSWM\nuIK+eJnZuRIHzT7B2uvI1auYpkk2m52oBm7/35y99xdYu3dQlB+UhO55DtnHMtENz6amlTEdC1mN\nceOVz2MYE3+WKIr0uw0GwwHZXJHV0QEtwWKmmgQxjZFI8cxzT9Fut4jHdXLFKf7yG19jeWWJRu2A\nyvQi2598g3Z7SKd5hjPYpzp7iXjcoNVqYhgJms1zbMuk3z4llsjw6ME9TvfeI5MtoxjTjN0Qs7ND\nr1OjUJzDFyTs3h6fXZhnt9sFUUJSM0ixKrhNEETsYZuQEMt1CaIIOVagdniHWx/eZm6uSLa4xGBo\n0xtJrD/YoDo9z8bDHZotk61HO0SCxnAw5OCgweLSEjubG6xevEin3eLsuM7M3AxnJ3XGjkmrUSef\nS/Diyy8yXYrIpGT+5E/eI5fPE1ctCqUS4fiYxfkS1bJBt33EyBxhxDO0Wi0Wl6fJ51T+8lu3mF9a\n4qObD3j1My9PQJNCxOxsFUGQePu77zFVVglCDcsy6bWO2Xi0wzf/7GuIuBSKOYx0lZ2NB7z02uc4\nPT7mYG8b2/FZv7fOlWuXqJ2eE0UT4FapUuXW++8hCgLJTAF3eMSdT3aIJSbZyffv3EOSVIpTy8jC\nELO/y9KFJzC0gHyxyNgZYtseM1NxQveMjOFDZJKqXmZv9whzZLK4NM1779zGsjzaHZNcKmL90REH\nuztIaopup8vFS4scHzf4+KO7NJoDiuVZzo7W6fYcLNP8VPnwn1p/vYglChOljiCKSJL0t0jVf12l\nSp4rlypYdoQsS8wtLkLkMxpZP7Cd7/ufUq2jKCJu6OQKGVJpA1XX+b3f+29+5P5/YrP4x2/+M2zP\nQldcBt0hqqQhozFyfVxHQtIkxnRB6lMupkkWEpRzU/TtDoE6oFQscXZ8SiqjYjkDfM9FU2JEjAkE\nj7Omg+Nq4EHojbE8sK2A8ShAkTRyGYVry9PMlDKMnD7DYRvwmC5XmJ2tYFlD+kObfs/EtEYEQUSz\nPsAxRVoNEzFQaNWbLM/NQxDgWA5j1yMMQxRpEmeBIOD4E8CMK0r4UYisKBMyqSoTykweQsTYdUEI\nUHUZJ7KJBIgiETGCEJ8gDJAkFVmRiBsaYeAgiyGEY8aBjx9NZK6u40A4xlBAdQX63Q6y7SFHLsF4\njDnuokgSyYyOJZo8c3EFVdcY2TaqHqPXHWI1h2C5mM0maUXDEFXO9o5JL8yRmqqwfXrKzMVVFq9d\nZufslNWLF6nOztDq9yiVijijAeLY4/JUhZQuk4wEDEXHT8T51vvvky7n0RYL/It/+wfc75i8/ju/\nxQftJv/rn3+LTjbLL/+Df8S/eud7PNw+JGnkmZpf5Q//7JsIaKSFOPWTI9S4xt72Ds8uXKC/ucsT\nxQIGPpHnEybjLBWKvLQ2z3MLVaYHA57TY1xRBSq+Q7rbw987wD89wdzdYbi1xfnYpjlscjRq0JdC\nmq6LIinEFQ1Z00jHVOKyRz4Ro5RMk5YVcrkMrqIgix5LhRSGqCAW8nTyOrGEQWf7kKjeQhi77Bzt\nEkQubv2c7vYxpjckkm18v8XB/gbDTpuTkzMOaid4skUqJdHYO8RSE1iRj5KQyMxXSVxYwI1n6PoB\nTz3zPFPlReRAQ0BjeLLByTf+NacHd/H1FIEU4nldwlSKthOh+woZyUBKTHE4iLE1iNPSsiw8s8za\nxSmikY2ix8FzOLRcuk6XC/E4qYM7FM6OWcmkuH10iImMF7gUCyVkVafRa3K4vcVMZYaDsxNyhQyh\nZ5JJG+ztHzDsjzg7O8UaDZiZm8KPImKyiiJ5SKKLaZ/hOF0IYpwe9JFlBS+ySOUkHq3vMDe1xqDr\nIyHR6/YwLQ9ByOB22xCOKOR1ut0+Zw0LzzU42u2Rz84SBQqiKKPHEqwsXyebKuO7IQdH+5zUTlFk\nFd8PaLQ6BNGEHpzLFGh3upw32thjF13TMOIxjk5PiBAoVcrEEwmazSYPH24xPzeHrIw5P2+yf1Cj\n1xvR6XZJZRR6vTaFSg5JF7h190O8UGJj45x2x6ffC9h8tMVJ/Zx+rYnpeuQrRTpnNewoYvXiClee\nvEohV8TE4+mnn+Wb33qTV158hf3NDVJJg1defYn9zUfcufOAdq3JyX6NDz64iRv45DJppkslIKLW\naqMlDGaqU5xs73F17SLlfAF3ZJKPx6lMV7GdEdOVHA+3t3n9lVdYWJphZ3eX3eNzTC9g+cIFHmzu\nMLekc3ElTbU8Te2oSejL+GOF9969yWjkkzQSxNMJrj9xjf36GclMno9vP2BkOuwc1kllk3S6HZKZ\nHLtbRzz31NO0+h2S8SSJRJZEKsPDrR3ssY0iKdhjd+KBkKXH8LDgcfyNBOFjyVYEoiwiySKqKiNJ\nE3JqGIJl+oiCMplKCiHu47zYiAncYzwe449dhqMBZ8dNokBF1iROaycT6XQoY9pjfFegUimxtDpF\nrz9ElHyymRiSFqHoPplUmt/+tX/6d7lv/9j6d3/wP+J64LkOUidAz04aK9MK8cIQCOj1evR6Pebn\n5pFlmadzJc6sEf3eZLo4HA1Jp9K0Wi1Go9EkeiauYzkOw5GN7ai4voah9XD92MQ73LNRVJV8Nsb1\na4skMjNkEy0ir4Yq9ZmqVsiVV9DEHvXWGN/z6LQaSGLE5nYHEGi2BkSItJptpudmESUJ1x0zdmyi\nKCSRNDBN61PCpKzI/1EwhCCYRPSYP/Ql5YdL0yZAJFVVCB5nDgOfkkIFUaRcTOBaYzrdIfJjD5Ag\nCBCF+N6YfM4g9F2efPoqiYSK4/goikyn3aHfHRIEPoP+gHQ2Syab4XD/kKmZaRRVod1sUSgWWLl4\nkd2tHZ546knK1SlqJ6fMzk/TPG9zbDo8P5VHlkRc22VOVhF9ne98tE55LY88fYF//qffZv3c4sXf\n/j3utkb8y2++xYkU46u/89/yjTff4pONPdRUiqWLV/nGn/wZStIAQeHw5Jx0UuBs+5ALi2VOdupc\nLGU/bRarmsnKpTKXLz7J61NJ4kcHvDybYk2PSHdazAYWRv2Ug0/u4x3ss717xtiy6feHdDoD2u0u\n9e4QTRIpJw2yMZ1SyiCpq0xnU1QzSWZyKWKqTNd0UCSRctpAU2SmZwsI6RiBImI1e5jdIbF4jPpx\njV5nyLA7YGv9AEmMCNxJluvpWZPhcZtGs8vReYNAFtAzBttHXSQtga0ZDESVtWoaY2aBqDjLRwOV\nF596GooXURSFTDqDf3iPgz/6H2gdHhC64x9oFFvNPnFDJyvJDAWVc+BMLyMbOYqVRS5dffbxgoOA\naY44P69jmQOiMCJTf0TR7TCdzHBvu4YVimgalCoVYjGFg70jjrZvsrJ2gYcP1inPLCNGJnK8zNHe\nFpF7zsbOgCCMiCcShGFIMpnC81zGjs2wd8bYHhEGLueNIalkHGd0yooc8OjsmPnlp7GtU0TFIApd\nDscqgpYkcAd4rsnVwKYtKTRaLWLJFLtnLlOLl3G9CVdA1eIsrlxEM4qIQkC7/pDt7RNSSYF2u0en\nPUSUJn7iRLJAo16n1+kTBGMkWaRYrtBq1IkQqVSnqFTLbG1scXR4TqE8RSbhcXjc4rg2ZuxYiOEZ\noZDF7J8wVy2SI2Ln3YeQ1blzt4c1DnBsl49vb7G7U2c4GOKOnceZfy1kSWJhcZGlS89RrC7i2j2e\nf/nzfPev/pIrTz7Pyc6HqLE8X/zKl/jk43Vuf/Qxo9YGB0dtPnznbSxzRHVqmoW5LK4vcHZyRqFY\nJVfIcrh/whNXK+TLi4ytDlE4plhdYDj00OMxdrfW+Xu/9AKVuTUera9zenJOTLOZX73B5uYJldiQ\n6xfKPJGKc9BvYro5UomAP/7GHTzXoVQqky9WuHL9efZ2NlldneKdtz/EdiL29upoMQNzNCIdd7h7\n/5AXXnyW45Me+WIJI5HESM9wuLvPaDicNHv/PyTr4uOYniiMfmyjCBNgTbc3nqg2Iojwqddaf2u7\nubkU166WqNdtFEWmWMphxGXSKZFMWuY3fvP3f+T+f2Kz+Fff+xcICqgCEEh4TkQ1O8f2sc/8VIlI\n8zGtEclMAqunIYYmqi4wsh2qxSVCR8ayuihqhOPa6EacbD6POWhieSan5yZDM2Qqr3HpYgkr0Gg0\nXSRXQhZd5pc0ivoYIwbVuSqeAqmYjmgG2KbD/Xs7rCxeYjho8tKrT3FQa3B0MuLgtE3XsjitNxhb\nLjduvICuJYklMmRKVdLZAhLRxPOYzqDGEoSighcFiKqCrCsEQoioTvLoJpF+IkHgIesSgRwB0oQW\nGboQTaY8gijh+iHIKpbjIogCgiyCqOBpCrKigBNRXVlgfjHN5StpStNFdk8PWJgvMw4DyuUqe6cn\nVEoav/obT6MoIZVEDCcSGHQt+u02kSCiipO8O0EIJyv5fsDYdvGcCAUFb+wxtsd4lo3T65NWYwTj\nkNagz/T8FGanSTKEhXSSjKjgRpC98SzvnZ9z/6hOKEokFhe5/WAdopCkEmd374hIVGidNvj86g32\ntrdYq5ZIaQrzySRrxTw35mbJ2jar6RTzxTwLkkZz6wF4FqvlCjECIkni0aOH0GozODym2axhOzan\n3TYNy8KVoGMNkeMasYSBoqnkSkWSMYWsDHPTJURDIZVPY8gCyZiGTUAogm7EsT0XFAkhdBFzBuee\nSUqVma6kiKkqQgTJXBZVVGm3BvS6I/YPzzg7PScKBWqdDq3RiLrZRY7LDBwHS1DJzKwSZKbQF6YQ\ni2AqDk/+/Je49uoXee2Xv8hLX3mdy6+9THFlgbnVSywvXUBVY4iBiyKN2fr2N9l75xuMRB9fEohC\nB0kMsYIIWckh2TIxo0pXybPVkTmSNYxLC6xdfwoXBcKIeBii2yInPR+9lCTT2qd0ep9S6ONaFnHJ\n48npOCvzC0wlVKZklXe31jlrNhBcH02JM4pCWsNz+qMRu4+OGIcyoa4SjSW6A4d4IUEqLiN4Ps3m\nDs3WXSxzn0xaRZRD2r1zHDciVypjBWOGQ5uEniKhGfhuQC6X4/B4H0mNcCKHWDzDzsYJMclgulRG\nUVTs8Yh4IsK0T4gnRIxEjJPDHSyzh+95PLi/gSSn0PQkmXSWVrtLMp0hQqBWq+F6PoIgk8sVyKRT\nJJMGiVQS23FxPZ/dnX2WV9bYPzjh+vXLgMzHn6wTS8ZIZzNYrsN5q8HZWRtf1Kk1TVANjGQSy/S4\nePky+0cn/Mzrr2H2hjR6HZaWVjk4PCJTKrG7s8dLn3mOkp5g2Ghx+8ObHB0fEfVdHjx4yPrONqqm\nc/PDmxhZg8XlZewo4v7WNmfNJtliAc8M2N/ZJpNJ0R31eeWnXmFxfgHHdtnY2SIQImRZ5vKFNd69\ndYvXXn+FZvOI1376Z9h9tE8kSOQyCVzHZdBtMzU1RWG2zEffvcegJ3DWbLG0dhVBFEirMWaKZT68\ns0Wikubatcvc+s4bVApZzJGDqukszC8xXZ2m0Tln92CPfKZIJMrcf/QQ17YxTZsPbt3htN7kv/yN\nv8/e/t7EPB/4hFGEJEXEdRXPdxEEEdcOHjeM0YS0JYEW05BkIAqJ6TogceniUzQa52hqhCSBIMm4\nnoeuqXz+5z7PzvYO47GH64QEAQSRz8CaNDpEAqqu4/sR+UKFbqdDq9djfinP1HSMYX9IMpkj8MfY\nQ4V/+Nv/eZvFr//pvwRkosjDVyNEUaRULHF3vc/sVJIomsheC4UC3W4XTdPoCxGj0Yjni1P0COn3\n+6iqimmapFNpioUiw+EQURQZmTa+NySfEZmdmULAZe+gTxD4yJLM2rJGLmOQSamkslVUKSQkzchW\ncWyLDz5Y54mr8xyfDnjx5Rdo1E/odIZsb51iWQ7tZpswjHjp1ZcY9AcoikoyNZm4jB2HdCaFkUig\nacqnofOJZJxUJoFlOhRK2U8nen+zZEn6D35BkpXH5G9ZfkzR/X5NTVdYWytxZS1DtZRk77DLwlwe\nWdWZm5/j6PCEudkcP/9z18hkBGLamOQooukE9HoDBGECiEtnDPq9IbIsMnZGj1frA4xEkjB0sazx\nJJPT94kbSXrtFp7nsnRhjXarwWVDZSmXQnlMpX325Wv8ZXfAzd0zTFunUJnjo/c/wLdN9GSCg91t\nwGc4GHD9medYv/cJL2d1KobAYmzMk8UEz80X0dtdnpvLkprWuR7J7K8fkVRkprNJfD8g8APurh9h\nnnTp7uyxvXdKWle5t3NK5PpEQBBEDByXaiZBJZ0ga+gkdPXT34spg2LKQP0bIBgALwg/PR8AMW/Q\njHmIsshTS7OPPeoOWtYgAo43Tul1RhzunXHz4SG5RIy7nRattslONMBJ6ozGHnoQsXplhX19mkFl\ngaQSsR+MeOZnf4XSi1/g5S//Gq9+5de49urPkHryZ8nNX+HypacYSimCIGA6rXPyxr+i/uGf/1ii\n7V9TIHuixkOtSF9JkchOMb98hTACTdOxbRtVVel0OuTzRczRgPLpHaZFkyiMSMjwzFSWpUqSJc0j\n4Tvc2tmj1eoyskLKOR9FzzJs3KPXs9nf3cdxfMaeythxaTUbzEyXkRQd3/cYtnfo1O7jmnUkLY8k\njBm01gl8l3h6hqYfMuidk04oyHoJ3z5HiU9zdnCbuK4QRT6CnOLW4RbZbJ5EIg1SDF22GNt9At9E\nFR0UPYVv1Rj0GxCMeLB+jOtF5IpzJJJpzNEQTVOwLYfz+jmu6yGJIolknEyuQCKRIhaLI0nQapxz\nsHfA/OI8rUaL529coNWX2d4+JJdy8AIdz4toNTscHA5IpQOOuy56SSFupPDcAYuLi5w3enz+C6/Q\nbLSwLIeVtTUO9w7J5PJsPdrgxsufIZXO0O/UuX3zNs36Br2eyd72NtvbJ8SMBLfe+zaZXIXVtRVM\nO2T9/kOGQ4tCIY9le+xs7qLH4oyGI15+7TUqU9OYowGbG8coigiCxJXrT7J+7w6vv/4incYmz7/6\nOW7f3kKSRFYWkjiuTL9nUZ2eIp2v8N5HD+laIQ9bPjNLT2MoXUIpz9K8zscfb5NPh1y7/hQ3v/dH\nVKtl6ucdYvEMK6vTzM+mOD3tsr+7R7Y4TSyW4P69B0SRgG0NebT+iEbtnK/+5m/x6P4nP/L6+B9b\nmWwKQYAXX36ew4PjHxmP88WvfJ7tzYmd4K+nkWEYYo4sfthkWChmaTaG1Bs2S4slLqymqJ2bLMwo\nuJ5Asx3yu7/7j3/ksfxEz+I/+v0FxIyO0x7gOiKGksVtRoy1GLmESmJOoH92wPJTq7zxR7d5/mKO\nzFyOWqNOMhYnmyuzvbOHF7qsXJzl9KyNbWo898QzdKwapqjz4MEhi4UE2bLCUS/Fh+8fEXMTiCGs\nXkrit/sY6ThKOstW30QZiHzwzff57Je+zLfffpdSOkM6J9MftzhvmQyHGkEYoikBKgKS5/HlL36J\nXDqLIImMA5dQFuiaQ5yxSyqbQ5BVoggG/T690ZB64xyr10cYe9jDwYToh0BCV3DdEV7ogiASBQqu\nHaBEAQoQBC6CFJAvZ5iZmeXstEkUKvSOz3FCBzkIKBplvvpLn+X2yQfUgg5Xr0yTLxaQRgqdUxdp\nbJPNlBn06px7dc47Y67OzPPRnX26RyaCFxHoBrIoMh5bRFGIJIoIEeiKjqjE8UUQdRk/9EjEYriO\njZ5MoqsJzro1SrkEQqtLYjDisxcXWRBlooxB/MUX+Tff+g6WGZDJGTx59TrN42NEMcQIYWZ2hoHg\nEwxNBqd19EICudfH6g8IbZuu2SfyfeJRxNgZ0xMV6iMPNB1F9PnKhQssyTL9AHbwGCIQQ2ccFxgN\neswUSrhjG99ysKwRakwnEiN8MUSJqwiDAEOUaY0HaHNpMqU0dmNA66DJ3OoK9rBPMZfDc+zJpENU\naMpj/KrG3scbPF+dIRUpBOOA0yjAi1S6gz7pYolkoYRRyqGk4pSmp0hPVYmEkJE7YC5XYMbIIiMS\nxpMMXB+3c8poWGdp9TKyGycwTTy7j2f7CGHIYGjRHfVxbItiTuX49rt4kY2p6gidAUVFwY3DIAJV\niiMEKp5qcO6l2LRdwlSai2vzpFI5XFFE0OOMg5DTkxquN2YqmUDe/IQ1Q4BRjZODQ4qVeRwhIO4K\neGaDRL6C0xsSX73Mt+5vc/f+I/KVCqemTT+QMTznIgEAACAASURBVHJl7FDADyLkICShx0iIHp95\nospsWsIejTh3zmk7DfxojGW6zM1cYmfvlFanx8LKMqPRgEIqTbdm4fsqw2GPVEJgeqZIf9An0FPI\nkoxrWoSBjyiqHJ3VefWl59ncvMN0JYuiahiJLHs75xzXz2i3x5ycDHnyxos8fPQIZ2iyenGZzd09\nbNtBRkBEZHlphfX1ByzMz5AwVM7PGxTyBURRIZlIY1k2t27dJpNPM79Qot3s8sQTVxmafcyxTbWU\nJp8qkkwbfHjzIwqpBO1eD102uPdoi9J0lbSR5v4nDylOlQlCSGeTRErAc9ef49ryKueHZ0hSSMts\ncHx+zFajRUpKsDa7QOBHeIHP8kKVeqOBoiUQiPN//Z//Dtu1Wb2wQkwRkRUBJwj4hS9+mXt37pCM\nGZyen4IqoykKhzt7zC/M8cVf+wK9foeTjzdoORHNeou93SO++pu/zPe+/RaJGLz40g3CXo+ObSEJ\n0Kg3afaGJPI5Lly9jNnrc2GmwsbNe/Qdi1EIiUIF3dAY2w7Vconj0yM+/uguX/niLxNJIm+9/23y\niTjbO0f4CFy6eBl7aPL000/i+wF/+O+/RhAGGLqKaY0YeyFRKCAIEhISYegjiBGiKiNKEboukk0l\nJtTUUCKfncayu4ztCehk7EeMbJcoErmweoHjo2NM0yTwICKcyATHDpohUsqniBkGtbM2Zs9GlARs\nF37ul1Ywh3VCK8b5eQdRDUmni9z87unf+cb9o+rXv3qZXC5Pr9fB8yIUItIYnAcmldg0xpTEYNCn\nkC9w860tPlPJYa8VqNVrzBdKJFSN23vbSJLEzPQMZ7VJmPNPX3uCw34fx484PT0kHouTz+dx3IgP\nP9rD90M8X2BxeYWYdIQkSVTKFfZOwAskPnjnPV7+zE9x56NbFEo5oijCMk163cEPxGGIkogkinzh\nK1+kUi3+wLkdHdY+/Xnt4iIAmxv7k+cOdqifNXEcFx6LGMMgJJNL0mp0foCk+qOqXC2SSqmY5hhN\nldnfq3/6XDaf5ld/8XPcf/AJ3aHNM9crJBIJECQ67QH4DqlcmuPDIxBg79jnmSdn+fDWEft7tR/7\nnn9dxVLu01y+Rr1FrpAh8CdE6IXlVbYePSSfS0Onx4WEzosrE8hIuZJl5coS/+zfvkFflkiUyrz8\n7JMc7O1D6HJ52EdYXeGMGIHbIbW5j5XTMVrfR+DfP2ugyTJj10eVJSRRYKNrMm9MPK83lqaQ5R/d\naO82uiyXsjiej+16GJpKe2RTzST+1rZH7T5TlSyjskHy3OTgtM1qJYcrC0xlUzTOe7h+gB+ECKUE\nw4rBycYhl4M4pXwSURRpjyf/v0f1HgvFHIV8htGFZSQ1Q668wOzMPEEYTkLip+dIxlN4nociK/iB\nR7vfYrZ9D+3q50noCv1+n8FwSPj4by30DxHO7yKPA1RNpn74Hw4yV1Mp3nV1xpGIHk9Tnbv0+HMW\nkcsV8H2P06MtbGtEJpmnf7bJa4kxtmnz4JMdFpeqKKrMeOyhaQrJbJpht08ik+LrBwfcfWeLxKUr\nDM5OaFoe+VIVSfo+8VSSZDRN5YWn57giivj9OrfMHmNRxPd9Rs0m+fnLnJ7sEYYB2dIVhu11SsUi\npmlNSMa9IZVKCV2dTOqDMCIQ04hBF9uVMHQ4OTli8cJL1I4+plwqM/YUMuULdM5u0Wg0GI58dvbG\nrKxdpHZ6yOlxnRdefZnbH36EKPKYUBxn+cIFPrl1i3wxy9TMPPWzY6bnFhn2e1Smpmmen7G3cwBE\nVKfKADx1rcDBmUAUhWQyWaamsihqnHsfv4URi2h245RyNt9754jqdIlydYaH9x8wNV3BsW0yucn1\n5uWfeoVcaY7m2Q6SLBOam5zVR5zUxiQNSOfKSEoKx7ZZWZnm5KROQjMxvRzf+NrXAZhfnEYQFBx7\nRCaX47Nf/CV2HryJT4FBe5+R6ZIpzLK5vk5lqsKv/tbvcH68TuPsgGZXpN04oV5r8+Vf+Spvvfkm\npYLK008tMBx5jMcWkjCmft7mtOYTN3I8+1SJRk9naVbj+PY658GQektmbmEJmEg3K+UMWxsbPLi/\nxc//4n9BwhD45tf/nGRS4+ysjzkyuXHjAs3WiNdfu4jnB/zrf/PWf/Cz/cNVmSrSbvVIpQxyhRLd\nTpMwiOi0fzBiqFwpcF7/2xNEmKhBCsUsiiLTbnZ/wAJw48Y8g6GP60UcHTZIpRLMTMl8+62DH7mv\nn9gs/pPfW2R/0CcTi3F6MiRvVOhsdxmrcdauqAyocXlhhr5rc7Y34ukLi5x1WpQKGfrdLpEY4bkR\nsiRRKMZIZiuYngN2HCf0EVSF3e1Tri5f4rR3xklX4fSkgxomkcUYly4lSEgghCGOAA3H42CzgRDF\nEGWNnb1ThkMLSQxxrAGKECeuxgnsIXElmkQ6iDKyKCEJAolkAlXTiScMZFVldmERWVFJJFL4no8g\nKihGDNv3CGUBLwzwQx8rGGOPxxzvb2D12yAIjO0Q340YmkN83yF0QgxF45mnV4loomgB/VFIpxtw\nXjfB6eONPGQvxisrRV752Wuc+B0ycYmjVpdE0uAv33qIisTzTy4yHPaZulBh/6CF5qfZenREu27h\nWB6aoRG5PlpMJ/RDosDHG7vE4zpCJIMgICkyY3ciP9J0DVFSGLsRckwgCmx0H3KOzxfXllmSQU/o\nHIQRnUAh7irUxS6mEyDJKkPbJo0CvsdIcEmE4gToo8roUgzHGpGPJxgHLrYE5XgaJxzStgcosSzn\nrS6jfptfXFjkYjpJzw3Y8Me4iFg4BHKIHCoIvoSj+SRTcWKazqjfI5FMoGkqxUKBbq1Du2XRctsU\n15LocZnxQORo64z0TJ50PkmzN2Dk2sh6klQyTTqVxGaEECoUEllKpWkS2SR9yadamqFSLmDEk8RR\nH8uSRcSxi6QnEIMQUfDxXQclCvH9MZ7v49oubtuh02izs72B6Y5wmkO6nRaRqHNwWsMSdURJQfBH\nfPX1p4AONg6iEyDGY/h+hKYk8PUEga9jUmTb8mhiceWpKvOLRbQwhecb9Ow+tuVyMhCIJbNk7QZT\njbuU3Q6K6+FJIsQMhrZAhIjTraPHBUIrwnJCdFmhn0zgiwmU0KFz0iJVnOc7HYvv7e4za+hUp9Mc\n9lpo7T7//Pf/AU7nIR2zSSCUqA1CNg/u8uxzl6jXeiiJPD0roGf2GXZHZHIp+taYu+sHJNNpMmkN\nIRyh6zK79R6rqyuc144oF7KMzD6FUgkhlNjfPZg0qHEVIpdUaZYHm3t0uj5CqNPqDBmObFYW5lBV\nkb49Zv/kDDyfwI/wPRcRAccaoWoK2XSa+lmdTCbL3NwCnh/QarU4b7d44onLGLqCO+5wcHyCH8b4\nzEvP4Tkm9d4ZhcIcD+88pDcwqUxX8EOXi2ur7O/WyedTpDJJ3nn3JpevXGBpeY6HDzaoVioIkkq7\nfsTfe/kZAnuIVipzeNrgtF7DdnzuP9ggW8lQKlbpdC16PYet7QOK5QLplI5rm4xMm0KpyvM3nue9\nt95ClmF2YY5YMk69fk4mk8N2HNaWKoSyjK7A/OIq416PVDnD2+/c5XOf+wzf/OM/x3UjxMhn9coS\neS0GYcRQsjmqH7E2u8jRxglGsspRrcZu6xDN9nnts6/zne+9Syqbxg5FdMUitD1GQ3D9gHI1S6N5\nQj4zxYd3blMulcGNqE7N0O/22N7dx48CVBl8L8D1JhPR0I9YWVllc2sTXVeQZAkvcMnlk4SRT+gG\nEIloskYspuA5Fo7jIqsaohKj3+sTBqBpCqIoYI58TGvMwsoMlj9i7I7AlZifK9AfjOi1LSRJwnZj\nvP6lIrgWg5pP47wDchohNuDBR383/8iPq9/9r16i2+39f6y9Z4wkeXrm9wtv0tvKzPJVXV3Vbrw3\na2a5XO4dl+Z4JMFzpAz5gYROgqCToBUIETx9kE6ATgAhHXCgjjrxsEdypeUuydndWXJ2dnrHtZn2\nrrq7unxlVqU3keEj9CF7enbA4epA6AUKlahEZERkRWS+z/953udBUQQOGgG1gsjBep++luTEysS4\nolatMbJG7B+MOLFWw7IsNFWj0+2QTCbp9XqkUimKuTx5UaYVfcyqhLGCbXXJFOZpHd5nZGvcutMg\nk5LRkxWWZnUSpowkeHje5OfcpQ6SrGEmDHa2dhgOBggIeN6E8U2lE/S6AwrF7CTqJAhRVQ3tYRh5\nNpd+mJEJaydPkEtHpHKfnjvX7fYfrV73e3329w44ahygGyn6vS6SJOE6NqPhGPfhd9QLzy1g+Wky\nxhEHhwIQsLXV+cRsz6urs3z5yTnuSz4LocZVp0Mynefb371BSoo480wNZzCiNHuSjXvXSCZN7qx3\n6Q3cR/lhgiCQL0yyEe2xw3jsUChm6XYG5PJpdENnf3cCUkvlPIIo0DzskM4kJ6BHUXjeVHhhefrR\ncaXSJle7DpnIpRUpZAIbURLZGTkUH84BNfojsqZOIfmxIdHI8VAkkVACy/UpGQYpXaXRtygkDW7u\nN4FPGtyEwceAca8zYCafpt4bUs1OZlejKMYNAgxVYWZuAvRHQ5ted8T9ww7luSJRKYHZcTjYbVHN\npeiu5BjtNBAFASOZJlAq5PSQsWaDKJAIckzPrjJVLbNhBVTKNXK5KarVGklzst84jmm2miQTk6B5\nz//YQMPr1slZkwUF23awLIu7772P6A7pWDZdy8F3PTzXowU4oc+UE/Hys8cfsYZ/U91TcoxFlX5g\nU6qtMLt4GkWe5Cx2um0EAUajIelUmsB1KNz9AZpnoWoqsqpgJk06R20S6SQH2w0UVWY0sElnTSIk\nVBkcXWMwthBaY+SlBb5xs0WndUgYuCwuLTLo7tHpBfxPv/r3oLtLFPgoqsKu7/LBlVt8fmaGgang\n+Sp3lQLq5k2aUZ9ssUDHVdndvo6s5kmlUmSNELtr0XZ7zMwdZ2/nHoXyPEI4QtbzaJJLvdEhlUrg\nuhG6qaFoGTpH9zhq2mi6zoOtEWEI1VoN0wTbidjd2kWSRdqtHpqmMuhPDEskWaJYynFYb5HJppie\nnX54zQzY2Trg+ZdfJAqGxOGIW7cPJ/fh517CHx9wf3PMqVM13v7BFXzPZ6paxPcDnjgzzW49pJgN\nSCTTfP+tK6ydXGN2fmYi5a1MUypn2dk+4De/sMyg1cNIlrjh9mk392l0DG7duMV0LUM2X2B/f4zv\nO2xv7mImDArFLPbYod3qMlWZ4tkXn+a9s+8BMk89tYikmmxvNiiVizQPD1g5voAkSeSyBonCSUJ/\nQDZf4eIH7/Lq57/I2Te/y2BgEUchz7zwJKYY4w1HRHrMzvYuMzNFdveGVGs51u/s0W4+wHYMvvCl\n13jn7fdQtcRDFY1A6DYZDl3MVJGEqTC22iQSGT68eId8IUcQBMwvzlE/aNI4aHzqNQ0wtzDHzvYu\nP0oDZnIpxpb9SHqaSieQJJHx2MFzfcyEgW5oj7JpE0kDa/TxolShmGXQHxFFMZIkUp0uMux36XQ+\nBotf+snTiILF7fUh/aGPIsvExNy+vfOpx/ljweLvfvUJWmKE4IhIgsmDW/dRBxK//t/9Fn/++tcQ\nJXBdG0k0aDY7DMYuahRRKSYmzqXZJHZfwRp0efqZKURdx5cjgpHMcKyQTMpoioiMzsFwwPsX90kl\nyriWQqFQYHUtxrF2MLwCsZFFlQ0OxxZmOs9b33kHVS1wfWOXyI0wkUjpCmlVQBddJEViHMLM/BJ3\n764zGA4I44m9uz920SOZMI7RNR0QKGQKmLkk5WyeSr6AbpiYuTSirjGOY3wF6o1Nut0Wx9dOUy5V\niHyJWIwIBAvPHdNtPyCX8Rn19ymU0oiaiRMIHHRlfviNSyQzJmfO1JhTEkjDEbFkU0yWcDSJSAu5\n6zY5uVjD8gb0hj2ioUk88Ckni/TbfW6tNxkEBp1+F9ELkRWR2J+YRozHFomEThCCIskE7mRl3g8D\nJFkmjmJkSUWMPNQ4QApFUnHATy0vcVyQEKeSvFOv07EECpGGuZBBcQ0838bPx1hbdWZqVQJVIBiO\nEEQBX4DQ9vF9h+l8kTCMaONTyVUYHhzgRQKepNCoH2AWTR5XVZbTBnIuww/HR+jJNLEDhqkQ98EU\n01jaGEEL0HSdVq8D2QQPDvYol8t8eHkDZySQK8gsrKaYWZgnodaIRyJCAorlLLKRpFibQk5msEQf\nQ0gwW6mi6RpJI4MgqgiRjR96SH0Lv9vC9yZWwvghrcYBvV4b37UJQpf63U0SWoqjvRZWZ8jO/n3s\n0KcXGFgDl8KUQTPuoopJjFySbDqHbTvEkjnJCQssXn56DiMckhAMmpFCkIrRExLaUCX2Z9hTNDY6\nFnqhxvNfPIOq7hH3WkRBhY6cpHsYM3RhOgPy1nnK1iGltAleg8iWkBNJ2n0HSVWRBYlIFBmPh2ii\njhDZ2D4onsI4n6C5PeDYUp7ff/M8g3yNky+9SHt3h253n2q1yGptiopjIfZaSFmTRrvFuBtQNosc\nX13iw1vXECSfmw/6LJ+Y4+igTaE2T9MZ8f75y2QqeXq9A6byGeIwRjdMOr02uqpSzOc5cWqN/YNt\nXG+MrppsP9gloWrMz06BLnHrfofRCE6trXH18l1CRPq9OmfWVmkObXaO2qweXyOXzvOX33sD2xoi\nCiJhEDE7N83d9dvMzy+ysbHN7NwMru8R+hLZvM7Kwgzd1gGl6RxGVmN4NCQY6zyoH9AdWizPV+i2\nbO7cPyCRkMkmNNqdNo8//hSePQJZBMHld3/7n/H9sz/k3I0d8rPzCJ0WS+UMKwtVbj24g+dFNDtt\nWu0BupZkeqrEUatNpzfmYL/H5s4+c4uz9HtdcpkMe/u7HF9bwR4PWVla4O69B6weOzaJAxLhlc9+\nlgfrB7zxxp+yt7PPL/3SV3BHQ5KmiTXoc++gQ7JW4Ld++R+wceND/vm//neYeo7TyzO4kcdeYw8p\njFgtF/DRef/GBl/96j8jKbr8D//r75MtqfheyG/+xj8mtCPeO/8WpUKFt945jyAq+I5HvdkmaZoM\nnCGmmWDct/FRKGXTdHt9ZE3FtibgLgzEh2BjYo5SqVY4ah4RxzHFQhbNlHHcSdTCsDtCVRQMQyMO\nQwbdIbliDlHWGfR6ZFM5gtDBC3yOGiMkReKnvvIab73zHrl8irQpY8gayVSS3Z06B/U2XqTwy//k\nKbZu3sNuOVTKZdCKdMMbvPu94d/cjf4t6nf+65/GCYNJdiRw7/4DLBv++5/7Mv/mwgcMBr1P2Oxv\n7nhIYsx0SUI1NCRZwu6MEH2YPjkJZP/IeOBHrfnzosz+UZPLD/qIIgxHMDu/wNoitNttMpkMsiwj\nJZZw+/cxc8f4v//4z9F0g82NHSRJnMz7FjKYiQmIicII23Y5+dhjXL98mV538Deep6zIlKcKyLLM\nzNwSghCzPBthZNcQBWFiACfAxv1ddrc3efHlZ0kkDBCkSVwTE5v3Vn2DTMKl3++TTCYfvW/1wwFv\nn10njODUYyc5MS0ybI9wsFhIl3AyGrIss7m1ycLyYwjRGGs0QFVVmvU6+XKZw619jhoxvVikcXCE\nNXbI5lIMBxaJpMmgPyKbS6HpGpIk0Txs4z+UO4qiSExMearA4cM5n0w2xdOmwmwmyXxxAjpv7Tdx\n/MlM7XM/AiIjUeDygwOeXqgSxTH7nSGzhTSHD4Hj2PPRZJng4Vxm2tAebbvT7lPvjZgrZqg8bAqX\nV6a5t74HQCg/jGfwQ2ami+zttlBkkYXlKg+2GrSWc+xe3aB6Yo7dH9xlS1FZDAKkJZOFlUWS+TVc\naw8lUeOFcESzcIynyyIPyi8AYGomCSNJwkyQy+UeHZcYTJrPra0tYuJHs6uJG18nfMiIjfpDus1J\n2OfmRh3X8bnX6GC5HrIkEgOB8CMt5vEEqWQay7aI/BCfGcphgy8vr/yNYNERFW4VTmCPukiywqnH\nX8bzfRZ719jKPY4gCBwd1pEVlXwuh3zjLTLjJslsisHDbM9PK83QAIEwCDk67FEspdlXYHhlm1PP\nr/IHX3+PfjXFc5/9OY7q29TrbZbmdMzMAs+MNiEOUSSRa/YIc7tHaabMwtoslze3KSLx7cY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dc4ag5p9VwOD9vMLc7yyisvc+3aJAfp/IcXUeMVvvDqC1xev871my2WFmdYemWBmfwU/9sffo1e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\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ffXTE2mqROPKpVsps793BX5yRL9T47nc+QFSK6HrAw82HvGWZDCWB/PoBaTTDquwgEGFV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "china_recolored = new_colors.reshape(china.shape)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6),\n", + " subplot_kw=dict(xticks=[], yticks=[]))\n", + "fig.subplots_adjust(wspace=0.05)\n", + "ax[0].imshow(china)\n", + "ax[0].set_title('Original Image', size=16)\n", + "ax[1].imshow(china_recolored)\n", + "ax[1].set_title('16-color Image', size=16);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some detail is certainly lost in the rightmost panel, but the overall image is still easily recognizable.\n", + "This image on the right achieves a compression factor of around 1 million!\n", + "While this is an interesting application of *k*-means, there are certainly better way to compress information in images.\n", + "But the example shows the power of thinking outside of the box with unsupervised methods like *k*-means." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.11-K-Means.md b/notebooks_v2/05.11-K-Means.md new file mode 100644 index 00000000..25085eb4 --- /dev/null +++ b/notebooks_v2/05.11-K-Means.md @@ -0,0 +1,437 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) > + +Open in Colab + + + +# In Depth: k-Means Clustering + + +In the previous few sections, we have explored one category of unsupervised machine learning models: dimensionality reduction. +Here we will move on to another class of unsupervised machine learning models: clustering algorithms. +Clustering algorithms seek to learn, from the properties of the data, an optimal division or discrete labeling of groups of points. + +Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as *k-means clustering*, which is implemented in ``sklearn.cluster.KMeans``. + +We begin with the standard imports: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() # for plot styling +import numpy as np +``` + +## Introducing k-Means + + +The *k*-means algorithm searches for a pre-determined number of clusters within an unlabeled multidimensional dataset. +It accomplishes this using a simple conception of what the optimal clustering looks like: + +- The "cluster center" is the arithmetic mean of all the points belonging to the cluster. +- Each point is closer to its own cluster center than to other cluster centers. + +Those two assumptions are the basis of the *k*-means model. +We will soon dive into exactly *how* the algorithm reaches this solution, but for now let's take a look at a simple dataset and see the *k*-means result. + +First, let's generate a two-dimensional dataset containing four distinct blobs. +To emphasize that this is an unsupervised algorithm, we will leave the labels out of the visualization + +```python +from sklearn.datasets.samples_generator import make_blobs +X, y_true = make_blobs(n_samples=300, centers=4, + cluster_std=0.60, random_state=0) +plt.scatter(X[:, 0], X[:, 1], s=50); +``` + +By eye, it is relatively easy to pick out the four clusters. +The *k*-means algorithm does this automatically, and in Scikit-Learn uses the typical estimator API: + +```python +from sklearn.cluster import KMeans +kmeans = KMeans(n_clusters=4) +kmeans.fit(X) +y_kmeans = kmeans.predict(X) +``` + +Let's visualize the results by plotting the data colored by these labels. +We will also plot the cluster centers as determined by the *k*-means estimator: + +```python +plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis') + +centers = kmeans.cluster_centers_ +plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5); +``` + +The good news is that the *k*-means algorithm (at least in this simple case) assigns the points to clusters very similarly to how we might assign them by eye. +But you might wonder how this algorithm finds these clusters so quickly! After all, the number of possible combinations of cluster assignments is exponential in the number of data points—an exhaustive search would be very, very costly. +Fortunately for us, such an exhaustive search is not necessary: instead, the typical approach to *k*-means involves an intuitive iterative approach known as *expectation–maximization*. + + +## k-Means Algorithm: Expectation–Maximization + + +Expectation–maximization (E–M) is a powerful algorithm that comes up in a variety of contexts within data science. +*k*-means is a particularly simple and easy-to-understand application of the algorithm, and we will walk through it briefly here. +In short, the expectation–maximization approach here consists of the following procedure: + +1. Guess some cluster centers +2. Repeat until converged + 1. *E-Step*: assign points to the nearest cluster center + 2. *M-Step*: set the cluster centers to the mean + +Here the "E-step" or "Expectation step" is so-named because it involves updating our expectation of which cluster each point belongs to. +The "M-step" or "Maximization step" is so-named because it involves maximizing some fitness function that defines the location of the cluster centers—in this case, that maximization is accomplished by taking a simple mean of the data in each cluster. + +The literature about this algorithm is vast, but can be summarized as follows: under typical circumstances, each repetition of the E-step and M-step will always result in a better estimate of the cluster characteristics. + +We can visualize the algorithm as shown in the following figure. +For the particular initialization shown here, the clusters converge in just three iterations. +For an interactive version of this figure, refer to the code in [the Appendix](06.00-Figure-Code.ipynb#Interactive-K-Means). + + +![(run code in Appendix to generate image)](figures/05.11-expectation-maximization.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Expectation-Maximization) + + +The *k*-Means algorithm is simple enough that we can write it in a few lines of code. +The following is a very basic implementation: + +```python +from sklearn.metrics import pairwise_distances_argmin + +def find_clusters(X, n_clusters, rseed=2): + # 1. Randomly choose clusters + rng = np.random.RandomState(rseed) + i = rng.permutation(X.shape[0])[:n_clusters] + centers = X[i] + + while True: + # 2a. Assign labels based on closest center + labels = pairwise_distances_argmin(X, centers) + + # 2b. Find new centers from means of points + new_centers = np.array([X[labels == i].mean(0) + for i in range(n_clusters)]) + + # 2c. Check for convergence + if np.all(centers == new_centers): + break + centers = new_centers + + return centers, labels + +centers, labels = find_clusters(X, 4) +plt.scatter(X[:, 0], X[:, 1], c=labels, + s=50, cmap='viridis'); +``` + +Most well-tested implementations will do a bit more than this under the hood, but the preceding function gives the gist of the expectation–maximization approach. + + +### Caveats of expectation–maximization + +There are a few issues to be aware of when using the expectation–maximization algorithm. + + +#### The globally optimal result may not be achieved +First, although the E–M procedure is guaranteed to improve the result in each step, there is no assurance that it will lead to the *global* best solution. +For example, if we use a different random seed in our simple procedure, the particular starting guesses lead to poor results: + +```python +centers, labels = find_clusters(X, 4, rseed=0) +plt.scatter(X[:, 0], X[:, 1], c=labels, + s=50, cmap='viridis'); +``` + +Here the E–M approach has converged, but has not converged to a globally optimal configuration. For this reason, it is common for the algorithm to be run for multiple starting guesses, as indeed Scikit-Learn does by default (set by the ``n_init`` parameter, which defaults to 10). + + +#### The number of clusters must be selected beforehand +Another common challenge with *k*-means is that you must tell it how many clusters you expect: it cannot learn the number of clusters from the data. +For example, if we ask the algorithm to identify six clusters, it will happily proceed and find the best six clusters: + +```python +labels = KMeans(6, random_state=0).fit_predict(X) +plt.scatter(X[:, 0], X[:, 1], c=labels, + s=50, cmap='viridis'); +``` + +Whether the result is meaningful is a question that is difficult to answer definitively; one approach that is rather intuitive, but that we won't discuss further here, is called [silhouette analysis](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html). + +Alternatively, you might use a more complicated clustering algorithm which has a better quantitative measure of the fitness per number of clusters (e.g., Gaussian mixture models; see [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) or which *can* choose a suitable number of clusters (e.g., DBSCAN, mean-shift, or affinity propagation, all available in the ``sklearn.cluster`` submodule) + + +#### k-means is limited to linear cluster boundaries +The fundamental model assumptions of *k*-means (points will be closer to their own cluster center than to others) means that the algorithm will often be ineffective if the clusters have complicated geometries. + +In particular, the boundaries between *k*-means clusters will always be linear, which means that it will fail for more complicated boundaries. +Consider the following data, along with the cluster labels found by the typical *k*-means approach: + +```python +from sklearn.datasets import make_moons +X, y = make_moons(200, noise=.05, random_state=0) +``` + +```python +labels = KMeans(2, random_state=0).fit_predict(X) +plt.scatter(X[:, 0], X[:, 1], c=labels, + s=50, cmap='viridis'); +``` + +This situation is reminiscent of the discussion in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb), where we used a kernel transformation to project the data into a higher dimension where a linear separation is possible. +We might imagine using the same trick to allow *k*-means to discover non-linear boundaries. + +One version of this kernelized *k*-means is implemented in Scikit-Learn within the ``SpectralClustering`` estimator. +It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm: + +```python +from sklearn.cluster import SpectralClustering +model = SpectralClustering(n_clusters=2, affinity='nearest_neighbors', + assign_labels='kmeans') +labels = model.fit_predict(X) +plt.scatter(X[:, 0], X[:, 1], c=labels, + s=50, cmap='viridis'); +``` + +We see that with this kernel transform approach, the kernelized *k*-means is able to find the more complicated nonlinear boundaries between clusters. + + +#### k-means can be slow for large numbers of samples +Because each iteration of *k*-means must access every point in the dataset, the algorithm can be relatively slow as the number of samples grows. +You might wonder if this requirement to use all data at each iteration can be relaxed; for example, you might just use a subset of the data to update the cluster centers at each step. +This is the idea behind batch-based *k*-means algorithms, one form of which is implemented in ``sklearn.cluster.MiniBatchKMeans``. +The interface for this is the same as for standard ``KMeans``; we will see an example of its use as we continue our discussion. + + +## Examples + +Being careful about these limitations of the algorithm, we can use *k*-means to our advantage in a wide variety of situations. +We'll now take a look at a couple examples. + + +### Example 1: k-means on digits + +To start, let's take a look at applying *k*-means on the same simple digits data that we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb). +Here we will attempt to use *k*-means to try to identify similar digits *without using the original label information*; this might be similar to a first step in extracting meaning from a new dataset about which you don't have any *a priori* label information. + +We will start by loading the digits and then finding the ``KMeans`` clusters. +Recall that the digits consist of 1,797 samples with 64 features, where each of the 64 features is the brightness of one pixel in an 8×8 image: + +```python +from sklearn.datasets import load_digits +digits = load_digits() +digits.data.shape +``` + +The clustering can be performed as we did before: + +```python +kmeans = KMeans(n_clusters=10, random_state=0) +clusters = kmeans.fit_predict(digits.data) +kmeans.cluster_centers_.shape +``` + +The result is 10 clusters in 64 dimensions. +Notice that the cluster centers themselves are 64-dimensional points, and can themselves be interpreted as the "typical" digit within the cluster. +Let's see what these cluster centers look like: + +```python +fig, ax = plt.subplots(2, 5, figsize=(8, 3)) +centers = kmeans.cluster_centers_.reshape(10, 8, 8) +for axi, center in zip(ax.flat, centers): + axi.set(xticks=[], yticks=[]) + axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary) +``` + +We see that *even without the labels*, ``KMeans`` is able to find clusters whose centers are recognizable digits, with perhaps the exception of 1 and 8. + +Because *k*-means knows nothing about the identity of the cluster, the 0–9 labels may be permuted. +We can fix this by matching each learned cluster label with the true labels found in them: + +```python +from scipy.stats import mode + +labels = np.zeros_like(clusters) +for i in range(10): + mask = (clusters == i) + labels[mask] = mode(digits.target[mask])[0] +``` + +Now we can check how accurate our unsupervised clustering was in finding similar digits within the data: + +```python +from sklearn.metrics import accuracy_score +accuracy_score(digits.target, labels) +``` + +With just a simple *k*-means algorithm, we discovered the correct grouping for 80% of the input digits! +Let's check the confusion matrix for this: + +```python +from sklearn.metrics import confusion_matrix +mat = confusion_matrix(digits.target, labels) +sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False, + xticklabels=digits.target_names, + yticklabels=digits.target_names) +plt.xlabel('true label') +plt.ylabel('predicted label'); +``` + +As we might expect from the cluster centers we visualized before, the main point of confusion is between the eights and ones. +But this still shows that using *k*-means, we can essentially build a digit classifier *without reference to any known labels*! + +Just for fun, let's try to push this even farther. +We can use the t-distributed stochastic neighbor embedding (t-SNE) algorithm (mentioned in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)) to pre-process the data before performing *k*-means. +t-SNE is a nonlinear embedding algorithm that is particularly adept at preserving points within clusters. +Let's see how it does: + +```python +from sklearn.manifold import TSNE + +# Project the data: this step will take several seconds +tsne = TSNE(n_components=2, init='random', random_state=0) +digits_proj = tsne.fit_transform(digits.data) + +# Compute the clusters +kmeans = KMeans(n_clusters=10, random_state=0) +clusters = kmeans.fit_predict(digits_proj) + +# Permute the labels +labels = np.zeros_like(clusters) +for i in range(10): + mask = (clusters == i) + labels[mask] = mode(digits.target[mask])[0] + +# Compute the accuracy +accuracy_score(digits.target, labels) +``` + +That's nearly 92% classification accuracy *without using the labels*. +This is the power of unsupervised learning when used carefully: it can extract information from the dataset that it might be difficult to do by hand or by eye. + + +### Example 2: *k*-means for color compression + +One interesting application of clustering is in color compression within images. +For example, imagine you have an image with millions of colors. +In most images, a large number of the colors will be unused, and many of the pixels in the image will have similar or even identical colors. + +For example, consider the image shown in the following figure, which is from the Scikit-Learn ``datasets`` module (for this to work, you'll have to have the ``pillow`` Python package installed). + +```python +# Note: this requires the ``pillow`` package to be installed +from sklearn.datasets import load_sample_image +china = load_sample_image("china.jpg") +ax = plt.axes(xticks=[], yticks=[]) +ax.imshow(china); +``` + +The image itself is stored in a three-dimensional array of size ``(height, width, RGB)``, containing red/blue/green contributions as integers from 0 to 255: + +```python +china.shape +``` + +One way we can view this set of pixels is as a cloud of points in a three-dimensional color space. +We will reshape the data to ``[n_samples x n_features]``, and rescale the colors so that they lie between 0 and 1: + +```python +data = china / 255.0 # use 0...1 scale +data = data.reshape(427 * 640, 3) +data.shape +``` + +We can visualize these pixels in this color space, using a subset of 10,000 pixels for efficiency: + +```python +def plot_pixels(data, title, colors=None, N=10000): + if colors is None: + colors = data + + # choose a random subset + rng = np.random.RandomState(0) + i = rng.permutation(data.shape[0])[:N] + colors = colors[i] + R, G, B = data[i].T + + fig, ax = plt.subplots(1, 2, figsize=(16, 6)) + ax[0].scatter(R, G, color=colors, marker='.') + ax[0].set(xlabel='Red', ylabel='Green', xlim=(0, 1), ylim=(0, 1)) + + ax[1].scatter(R, B, color=colors, marker='.') + ax[1].set(xlabel='Red', ylabel='Blue', xlim=(0, 1), ylim=(0, 1)) + + fig.suptitle(title, size=20); +``` + +```python +plot_pixels(data, title='Input color space: 16 million possible colors') +``` + +Now let's reduce these 16 million colors to just 16 colors, using a *k*-means clustering across the pixel space. +Because we are dealing with a very large dataset, we will use the mini batch *k*-means, which operates on subsets of the data to compute the result much more quickly than the standard *k*-means algorithm: + +```python +import warnings; warnings.simplefilter('ignore') # Fix NumPy issues. + +from sklearn.cluster import MiniBatchKMeans +kmeans = MiniBatchKMeans(16) +kmeans.fit(data) +new_colors = kmeans.cluster_centers_[kmeans.predict(data)] + +plot_pixels(data, colors=new_colors, + title="Reduced color space: 16 colors") +``` + +The result is a re-coloring of the original pixels, where each pixel is assigned the color of its closest cluster center. +Plotting these new colors in the image space rather than the pixel space shows us the effect of this: + +```python +china_recolored = new_colors.reshape(china.shape) + +fig, ax = plt.subplots(1, 2, figsize=(16, 6), + subplot_kw=dict(xticks=[], yticks=[])) +fig.subplots_adjust(wspace=0.05) +ax[0].imshow(china) +ax[0].set_title('Original Image', size=16) +ax[1].imshow(china_recolored) +ax[1].set_title('16-color Image', size=16); +``` + +Some detail is certainly lost in the rightmost panel, but the overall image is still easily recognizable. +This image on the right achieves a compression factor of around 1 million! +While this is an interesting application of *k*-means, there are certainly better way to compress information in images. +But the example shows the power of thinking outside of the box with unsupervised methods like *k*-means. + + + +< [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) | [Contents](Index.ipynb) | [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/05.12-Gaussian-Mixtures.ipynb b/notebooks_v2/05.12-Gaussian-Mixtures.ipynb new file mode 100644 index 00000000..c1f0d0a8 --- /dev/null +++ b/notebooks_v2/05.12-Gaussian-Mixtures.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Depth: Gaussian Mixture Models" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The *k*-means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application.\n", + "In particular, the non-probabilistic nature of *k*-means and its use of simple distance-from-cluster-center to assign cluster membership leads to poor performance for many real-world situations.\n", + "In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Motivating GMM: Weaknesses of k-Means\n", + "\n", + "Let's take a look at some of the weaknesses of *k*-means and think about how we might improve the cluster model.\n", + "As we saw in the previous section, given simple, well-separated data, *k*-means finds suitable clustering results.\n", + "\n", + "For example, if we have simple blobs of data, the *k*-means algorithm can quickly label those clusters in a way that closely matches what we might do by eye:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# Generate some data\n", + "from sklearn.datasets.samples_generator import make_blobs\n", + "X, y_true = make_blobs(n_samples=400, centers=4,\n", + " cluster_std=0.60, random_state=0)\n", + "X = X[:, ::-1] # flip axes for better plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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NHX/LXduMjAxeeOEFZsyYUatgDDffuDvZlNH3MWV09Wt1q7tPZ5LP8f3mZRQ7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\n2qkiyS239RV69fnKf69Z8TZ9O8wntpv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/Pz2PffpPjre1u3zW5ISZbx+aVftC3SJZmWmc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\n7cVXE+ZRcNyJQlLglJ2Ymqt47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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the data with K Means Labels\n", + "from sklearn.cluster import KMeans\n", + "kmeans = KMeans(4, random_state=0)\n", + "labels = kmeans.fit(X).predict(X)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "From an intuitive standpoint, we might expect that the clustering assignment for some points is more certain than others: for example, there appears to be a very slight overlap between the two middle clusters, such that we might not have complete confidence in the cluster assigment of points between them.\n", + "Unfortunately, the *k*-means model has no intrinsic measure of probability or uncertainty of cluster assignments (although it may be possible to use a bootstrap approach to estimate this uncertainty).\n", + "For this, we must think about generalizing the model.\n", + "\n", + "One way to think about the *k*-means model is that it places a circle (or, in higher dimensions, a hyper-sphere) at the center of each cluster, with a radius defined by the most distant point in the cluster.\n", + "This radius acts as a hard cutoff for cluster assignment within the training set: any point outside this circle is not considered a member of the cluster.\n", + "We can visualize this cluster model with the following function:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "from scipy.spatial.distance import cdist\n", + "\n", + "def plot_kmeans(kmeans, X, n_clusters=4, rseed=0, ax=None):\n", + " labels = kmeans.fit_predict(X)\n", + "\n", + " # plot the input data\n", + " ax = ax or plt.gca()\n", + " ax.axis('equal')\n", + " ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)\n", + "\n", + " # plot the representation of the KMeans model\n", + " centers = kmeans.cluster_centers_\n", + " radii = [cdist(X[labels == i], [center]).max()\n", + " for i, center in enumerate(centers)]\n", + " for c, r in zip(centers, radii):\n", + " ax.add_patch(plt.Circle(c, r, fc='#CCCCCC', lw=3, alpha=0.5, zorder=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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otG40Go3ExU2kpKQEo9EXeh5r8c0+vYem2k+QJAXNMB1np51Lly6i6zqq6kVV\nfaI7c+ZsZs3KxM+v/0Pc7Yau6wRbL1wR3l7SUjVOf7AHpmYRGhrG0qXLmD9/AWfP5nD69Ck8Hjey\nbKC6uoqtW/9IfHwCq1ffS0hI6Bi9EoFgZLhrxbeoqBAAVdVITEwc8bDdxnn3cHT/b/DM7BUdzekm\n0z+uzxqX3R7AV9c/Pqh7v3XkY1yzI/oUlrQmRdJ8qAA1uJ2wNgO62Ujw3BTasktx5FchGWTctS14\nnV1ErMroUw/a4GemzNBGsDWh31h6gIXm1hYMBgPPLd/MP775Ii1mL8ZOjZUpWXxzw+br2moymfj+\nmq/z+wPbKNNaMGMgwxrD1zYM7jXfCgkJiRQXF6HrGsXFhcydO2/Uxr6afbv+i+kJL7Nqhi87ub5x\nHz/+RQRG6wJUVUGSJNLS0sjKmovdbh8zO4eCJA3cwlGi73Gz2UxW1hzS0tI5ffoUublnr3jCRsrK\nSnj55d+xePFSMjOzRCha8IXhrhXfysrLeDxdnGwq5ERQI29vzSZRCuVbKx4lYpBFLW6G8HHhPJv2\nAFtP76FCa8OGkbnBSfzJfY/e8r2bdBeS1D9Zy6oZ+cuA5ZTPqOLtrssY/MwEzewV1JYjF7FEBGOJ\nDO53rWV6NOq5GowZfUPIgcVOZm+cyeXqSv7lyKu4H5yE7UqIPPd4JS2tLYwLG9fvfp9nfEQkzz/y\n7SG+2lsnPj4e8D141dRUoyjKmKyZOhwOrNLbpCT0ilHEOJknH6zl12/WMitzPnPnzickJOQ6d7k9\nkSSJVudkdP1UH8EsLofA0EX9zm9oqKOhoZqsrDlMnz6d48ePk5+f15NUtnfvbgoLL7FmzTrhBQu+\nENy1aYW1tbV81lyI8qU0tIWxeGZHkT/LxD9v/+2INV2fMTmdf330L3j1sR/w68df4M82fXlYMjtD\nJf8BqxQl2SPJypjFA4vvJfJkK7rWe05XZRMGux+W8cF0VTb1uzagA1bJyejF3UUbdKQL9TwUNx+z\n2cIbx3bgnBvZ4zFLBpm2+eG8fvijW349I43VaiU4OPhKAQmVxsaGMbGjIP8ks9Ob+x2fM8tI+rSw\nK0Jz5wlvN+mzn+W3W+KpqfNt5Tt03MT+M+tJn76k5xy3282u7c/jbfgK06P/kktnnqbg/HZWrVrN\nY489QVhYGB6PG6/XQ0VFOS+//DtOnTohmmMI7ngM//AP//APozGQy+UZjWFuCoejg+07P+JyugVT\nRO8mf0mI9ZBTAAAgAElEQVSScARKhFV4SYgZufrO3Z6A2WzE61VvcLZvP/LJs2eoqK4kJDCYpqZG\nrFZrj3DHhU3g0NHDKBNsvWOUt/FI1FwSJsRhMBhYlDSTthNFFJw5h6OuGckoE5AWiynERsv+fPzj\nw5GMPu9Za3Wx0BHJMw8+RboUhZZfT1KjhT/NeohZUzIAeO34DupqanDXtGIKC0A2GpAkCanGweop\nIxfGvdk5uxE1NdU0NTVhNBqv1CQePwzW3Txer5d9+/Zj0o8TH9v3AayuQaPVvYnxE4YnCXC45myw\nWK12ElLu49SFOI6djSc87s9Iy1jd55xDe/4fX33oMxLidIICDUxO6kL1nKO0KpWJ8SlMnToNSZKo\nrq5CURR0XaesrIza2hoSE5NHLGJhs1luq++s2x0xXwNjs127oM1dGXaura2l3eXAMKF/oo0h2EpN\nYeMYWDUwuZcu8N8n36chwUJbbhmWXD/MUUGEtxm4d/wMNi5ZS1R4BN9fsJmtJ3dSpzkIwMzqpMUs\nmtFbNSogIIBvP/g0Cy9l8o/7X8ac6KuC5SyuI8xl5J6SUIrcDRiQmBU2iQfvXwPApMRkJiUm97Hp\nnf3bqfXrJDgzGV1RaTtVgiUqCGtiJDZGbp/ucBIeHsGlS5fQdZ3a2qH1Oh4qHR3tvPvuO9TW1vFh\nSQgLZrdjMvkeyHRd5/3dCSxft3xUbRopJElixsxl1/z3QL+zGI1913GnT1HJfn8XqVNmYzAYmDdv\nPomJSeze/SmNjY0YjUZKSop5/fU/sHHjw4SG3n7Z6gLBjbgrxbeurpbxoREUnK3AtKSvsOjFTcyf\numqMLOuLqqq8dPJ92ueF4zxWSOjC1J4tOh3AWxUXiM6NYE5GJvExcfxtzDdveM/GliaMqeG055Sh\nazp+MaGwKY2Ogk5+vOnPbnh9Q2MD77ZmY8vyrR1LZiMhCybRfDAfq2xhVeKdUS85IsL38KHrGnV1\noye+1dVVvPvuOzidHXi9HtzaPH70yzKmJjcjyxrtnZOYueDP7ppCE7KsDHjcIHn7/B4REcFjjz3B\nsWOfcfr0aTRNp7Gxkdde+wP337/hruhSJfhicVeKb21tDSaTkYgyD80JrRhjfQlHarOTeR3hJMb1\nz/IdC45mn6A51ep7k1St395YKTaIvbmnmZNx/f7DvuL6Dmw2G8eq8zFmhBAc03ctMd9d1/N3j8fD\nzs/20trZwZK0OcTH9obgPz65H21a38xqAGtyFMtqI1mwcs5QXuqo071HVtM0GhsbRiXp6vz5c3z6\n6cd4vV68Xl+hkZUrV5ORMbadtMaS9s4U4EyfY7X1GiZr/8+0wWBg4cLFjBsXzp49u/F43IDO229v\nZdmyFcyePeeunUfBncddKb4NDfVoms6UsIlMM00n+3wJGjqzImey8qElN77BKOFVFPC74gEZBvaE\n3NL11/K2HdjBrtqztPorBHYa8FS3QkZKv/O0K9s/CsuK+fcjb9A2PQTZz8TOvDdYkh3NMw88BYCf\n0Yyuakhy3+xqg1dj+ez+Way3KxaLheDgYDo6HKiqSlNTE5GRkTe+cIgcPXqEQ4cOoGkaXq8bi8XC\n2rXriY0d2f3ltzuTp/8pv9v69zx0byUhwTK5+RJvfxxOasphDu4+T2Lqw8TEJvW5JjV1MsHBIWzf\n/iFOpxOTycy+fXtob29jxYrVQoAFdwR3nfjquo7D4ejp+rJ07mJWGm/P9bVFmfN44+2DOOf4ow+Q\nMKO5vaT4X7se9b6Th3nbUIA0exwy4AC8DRacxwoJntcrwLquk2T0bQ/63fEPcMyL7C2ukRzG/spa\n5pzPYVbaDNbOW8GWt36E/9K+Ah5bI5O84s4K/QUGBtHe3gGA09kBDL/46rrOoUMHOHbsM1RVRVE8\nhIaGct99D/QrRlJdVUxh/hZslgZc7lDiUx4lbuLNtZK8HdF1nZzsfTjay4gaP5OU1Jn9zomMjCHs\nnt+y8+RO2tvKaKrdzw+fbcRg8GXZ7zp4nKKu50lOmX3VdZE8+ujj7Nixnbq6WoxGE6dPn0JRVO65\nZ40QYMFtz10nvi6X68pWIh2LxTIq+zu7urp4Y8/7XPY04YeR1alzmTV1+g2vM5lMfHnqan538lOs\nyZE07r1A6MJJyBYTaquLhPMeHvnStTsBHajIRZret2qUKTwQ28lqtNJm5IRQ1CYnE/I6+ebG7+Bw\nOCg3O5DpW8zBEBPM4QtnOV9ZxEdlx3EZvbRtO4E1JQpTkJXOc5V8e/71i2vcjlit1p4tKw6HY9jv\nr+s6+/fv5eTJ46iqgqJ4iY2NY926dZjNfZcQqiqLaa16nq9ubO05tuvgWcrUfyA+cWS7bY0EHR3t\nHN33t2xYVcT4SJn8wjf55INMVq3/p369fI1GI3Pm3cf+3b/ib7/djuFzfadXL3Hyyntb+okvgN1u\n56GHNrFr16cUFvqS586ezUZVVdauXS8EWHBbc9eJb/eXrK6DzWa7wdm3jtfr5YUtP6V6XgjylW4z\n58o+5an2Fh5aee8Nr184fQ6ZqRns/GwP7pgUlDKVdt3NpLBJrHh6yXW/YDrxAv073IyPGs+fJm7g\n5MWzTIxIZ+GX5yJJEp2dnWhdnn6bv3VdZ9+pQ1g2ZmBKmUR3bmnTvgvIFiOh62eQff4ii5h/k7Ny\ne+B7/0dOfA8fPtgjvF6vh4SEBNauXT/gA1/JxTf4kw2tfY6tXuLg5fe23pHie/roL/nWE8U9iWNT\nUnRiJ5xg2/4tLFzy5IDX+Jsq+whvNzbLtVtqGo1G7r13DQaDgYKCfADOn8/FYDAID1hwW3PXia8v\nvOgTFJtt5Mv1fXT4U6oyAzCYep/29fhgtp88zkb9npu6h5+fHxtWrB/02LGGYCo0N9Ln6uvquk6o\n20R9SwO6pBNoD0CSJLxeLz9++yVa21sI02P7fGm1515GTQjEelUThdAlU2g94esM1SkNnLV6O2O3\n29F1HV3XcTqHV3yPHfuMo0eP9Hi8iYlJrF27rp/X142fqW7A41bT6G6DGi7slov96jrbbTIGLRcY\nWHy7PIEDH/dev7a5LMusXn0PBoOBCxd87SLPns3GaDSINWDBbcuQxFdRFJ5//nmqqqrwer0888wz\nrFixYrhtGxGcTifgEyGr9eZa9d0K5Y4GDHH9N1o3+nlwOp1I0q33XnW73XR1dfVbQ/zyio189vt/\nQl2ZgMHPjOZRaD6Yx5FqB3kpGsYUO9srP2bqmb1MDIqkONOPYG8qzfsu4BcThjHYirOwFoOfCWOA\nf79xJYMMkoTqcpMSMHCGeNnlMoory8icOoPg4P5lLMeS3vdfH1bPNz8/j4MH919Z4/USHx9/XeEF\ncHsHLpnY6b0z97Bq+sBfLfo1jgMkpm5i9+ETrFrk7DlWWiFhtK6+5jXdSJLEihUrUVWVgoJ8JEni\n9OlTBAeHkJmZNfgXIBCMMEMS3w8++ICQkBB+8pOf0NbWxoYNG+4Y8e39ktWx20c+7Bxk8EdX3X0a\nFwDY3DL+/v50dQ3dY/R43Pzs/Ze5INXjNktMcFl4dOoK5l3ZehQQEEhQSDAXd2QjSRKaR0XXNCZs\nXtDjDcsxQVwI81D0yXHk1MnIFhNhK9Jw17fR8tlFIu7LRDLItBy52G981ekGVSPhTCf3P9k3hO52\nu/mXt18i395OW0MzcvY7yC1dpERMZEVqFvcvuWdEPBJN0ygpLSUwIOCGLfe6lx26k/CGg9raGnbu\n3I6maSiKh5iYWNauXX9d4QWIjn+YA8dzWTrX1XPs2BkL4RM2DItdo02nMovOzsv4+/d+7iuqwWK/\ndkZ8bFwKRV1/xyvvvoHNr5oubxBG6ypmz334psaUJIlVq1ajqipFRYVIksS+fXsICxtHfPztsX1Q\nIOhmSOK7du1a1qzxVUDSNO22bOR9Ldxu3/5KXdf7Jb2MBA8vXstnH76Ia25vJq3a1sm8gKQrX8hD\nF9+ff/AHstN1ZFMUBqAOeCnnY5ImxBE+LpzishKKLpcQvn5Gj+fafLigTxgawOBvpstP6vNhsEQE\nYYkIRr5SctIcZsd5qQbbJF8ZRs2joH5yiWey1rJ+8T19xEVVVX75/isUpBtoO9ZI2PK0njFLC6r4\nTf0BLr1dxl898q0hv/aBOJR9nC0X91IXJWFwqSS1+vNXa/+E0GsU4u9uzafrvr3Nt4rD4eDdd9/B\n4/Hg9boJDg5h3bqB13ivJiEpnaKiv+cP772F1VxHpyeMsPEbmTz1zlpH72bhsm/xhw8amRx3ktTE\nTs6cD6LBeQ+Ll6+97nXJk7JInjR0T7U7BN3e3k59fR2SJPHBB+/x1FNfFg0ZBLcVQ1JNf3/fF7nD\n4eDZZ5/lueeeG1ajRpLuTGfght7IcBAQEMj3Fj3JH098TKXWhr9kJCs4mS+tvTWPxuNxc16vRzb1\n3R7jzYjgvWO70IFdrgIsqVF9QsbX8jbHWQJpqXMgRfaugxusJrw1rZjGB2OfEkNneSNNB/OJdJlZ\nlTSbx579ap8HmK6uLl786A/kq/U0uDtwvF/H+Efm9xF7++Romg8VcCa6laKyEpLjh2d7UlNzM/9T\n/ClKVhTdFpXpOj/d+Qo/euIvBrxGknq9Mk27tdrHiqLw/vvb6Ohox+t1Yzabue+++7FYbv4BLzl5\nFsnJs27JjtsFo9HI6nU/pLGxnuNFpSRMncrkUejX3D32+vX3sXXrFlwuFyCxbdvbbN785UG9HwLB\nSDJkl7Wmpobvfve7bN68mXXr1t3w/JAQK0bjyIvdjQgM9MNqtaBpXqxWy3ULXw8XGVMmkzFl4P2a\nA42v6zrbD+7mdF0RBh2Wp2SycFbfylGq6sZt1NDbXRgC/HtEVZIlatsbyBvvprNTwT4lpt/9NbcX\n2dK71qw1O9k0YymNjjY+zTlPS4QBW7PKvaZJhLmD2JWTT0e0BatLY0FgKi88850Bvbl/ffslzmXI\nSIYogohCM4Bs6v+eyyYDJISQU3KO6dOmXH/ybnLO/rhrH96MvpW3JEmi2O7E7XYSGtrf6/F6/TCb\njfj5mbBazYSHD10cPvjgA1pa6pFlDZPJwMaNDxITc+092KPNaHzOBx43lokTb76QSHlZAYV5b2I2\ntuFWYpiz8E8IChp8ZyebzcIjjzzE1q1bAY3OznYOHdrF448/ftPLHbfyebgbEfM1OIYkvo2NjXzt\na1/jBz/4AfPm3VwHm5YW141PGgVaW504nW48HgW3W8HpdI/4mBcKC/j4wmEceIkxBvH4sgew2+3Y\nbJYBx//3t37DqQQXhsm+NckTVbu5771inlz9EODz3n+9fSuuljYwqHhbnZiCrASkxaI2OnC1dSLP\nDcfPItFV0YgtpbdjT/C8FJo+ziE4fjx6TCB+l50sNcUTMDGAM1VFxEuhzK3x59F7HyQoyJcg9aDr\nXi6VFBGbMoGwsHG43Spud19PsaWlhXPmZiRD7zqrrmromt4vzK0pKmqjkwnBUYOe/2vNmcPdf10d\nQDFLNDe3YbH0X9/v7PTi8SjouoeOjk4aGjoGZUs3Fy8WcOjQURTFi6J4Wbx4CRERE0bls3UzXD1n\nTqeDk5/9FpulCEW1YLQuImvuQ2NooY+iSycxdv0rm+/zJVxp2in+8M4JZi78OQEBfZMJLxWcoqFm\nJwapE1Weypz5j2Iy9U1eDAwMZdGiZeza9Qler8aZM7mEhY1n5szrl2MFn5AM9fNwNyLma2Cu90Ay\npOrtv/71r2lvb+dXv/oVTz31FE8//fSwrJmNBrIs0/3gO1J9ez/Poezj/Fvx+5xKcXOio4RtLdk8\n9V9/w/mCCwOef6mkiNMhLRhCesVCjg5kV1teT6b2yx+/ycH4NgKWTiZgWgyhC1OR/Ux0ZJeTUWwi\nPmYiuq5jiQyms6IJzdO7rqzrOpkhSfx83jd5jvn8YsV3CbUH89OmvWSnaeRPN7A7pY0fvPqzngIU\nVquVGWkZhIWNu+brbG1twR3Q18sNmBZL6/HCPse66lqR/c1EF3Qxf+bw1YFePDkTrbR/b9wJLcZr\ntgvsff+lITcycLlc7Nr1CZqmoaoKqampzJjRv5LT7YKqqhze/dd8ZcPHPL6+iM0PXGDxtF9zeP9v\nxto06iu3snJhb6azLEt8eVM1Z46/2ue8MyffI8r/hzz9wCGevP8UDy9/md3bvzdgj98pU6Ywc+Ys\nFMWLpqkcOLCPtrbWfucJBKPNkDzfF154gRdeeGG4bRkVfOt8PvUdDfH9oOgI3mkBtBzMJ3T5tJ4E\npn88+xbPywozJ/WtdHX8Yg7ypP4hUle8ndyC88zPnMsZRzkGW99zbMlRRO2t5+++/qfU1tdx+Pj/\nok0L9+3FPe7bi+vn1Fk5Pp1vPPYVLBYLUZFReDxudlSdosXbhlRRBxLoXpWupCCe+c+/4wdPfJcP\nT++nXe9igjmYTUvX96z5f564uImEn9DpiOs9Zgq2YvIzo719nq4gA4qiYvHoLI6dyjMbnxjWbOcp\nyamsKIhlX/FlpKQwNK+Kf04jT2Xcf81xPv/+D1V89+z5FJfLiaJ4sFptLFu24rbeV3rm1Mc8tq6o\nz+uNHg+B5t243WO7Jupv7l9MQ5Yl/M1VPb+rqorq3MbMtN4HSqtVZtM9uRw+vYtZs/vvnZ8/fwHl\n5WW0tLQiyzIff7ydxx770m39Pgm++Nw5acrDxNVZuSOJruvUqB20Z9cSunRqj/ACmKdH82bOvn7i\nGx0SgdJahTG47x5kQ4OL2OnRAHReI0M6JHwckiQxPjKKpyMX8vapIzTFmrD6+WMsbSM1MQWz0URX\nV1fPl2xJWSmXG2sIXzOjxz5d12nafY7ycBPPvPJjAjbNRJIlsj0NHH7933j+nq8QFzexz9gGg4EN\nE+fx2sXj6Km+valqo4NFWhx//bfDm9V8Lb5x35dYXlbCwQsnsJot3H//U9etYtb9/ksSyPLg8xEu\nXiwgPz/vilelsXLlyts+ocfTWUpYaP8HjaTYZurr68e00YPbGwK09Dve5endH97Q0EBSXC1c1Vdr\nfKRM54k8oL/4Go1GVq1azVtvvYnX6+Xy5XJycs7cVPhZIBgp7jrx7fbaJEm6kgk5ckiSRJBkoRkn\nsrn/VNeo/ddIls1ZxHuvHqZxYW8Sla5qpLbYiJngS56KMwRz6arrNLeXJGtveHX13KUsz1zIzgO7\neUs9gXdTGsWSRJHq4MyHv+DH675NWGgYXq+CPSmqz4OBJEkEzIin9XghcnRA755gs5H2JeP5xlv/\nSow1jBC/AExBVhJMYTy9ehNr5i0npXwiH589hBeNmVFZLH1k4a1O46BIjk+86Qzqzs7u918adMGV\nq8PNU6ZMvSP2kpr8JtLcohEa0leASypDmTTr+vuiRxqT7R6Kyn5DcnxvRGLXQTuJqZt6fg8ODqa4\nLABfm5Beuro0dOnayyJRUeOZNWsWp0+fxmAwcODAPhISEgkOHnwyl0AwHNwdHbs/h/3KdgdJknrW\nUEeSRWGT0RxdA65HBcl+/Y7Jsszf3/cNpmYrWE7VYT1Vz+xzBr73UK/3+OSctViP16IpPs9NaXcR\nd9LBpmV9S1AajUbOtpWhzBnfmw1tkGmfF8Ebhz4CoKmjFUtM/zC3JTwQc6jdtwkW6KpupvV4IZ7G\nDoxRgXTdE885pYbyDD/2pzr4/pafoWkaSRMT+e4DX+a5B77CsjmLbuvQnsPhe/8lSRp0qdFDhw70\nCTcvWbJ0JEwcdjKz1rNleyKa1vt5rKmD1q4VY+61Z87ZyJnib/H6B4m8vSOUP7ybgRbwAjGxyT3n\n+Pn5Udc+nw5H3/9Pb+2IJHPOpqtv2Ye5c+cTGhqK1+vF43Gzd+/uEXkdAsHNcNd5vnZ795fs6Ijv\nE6s24Gzv4MOj57Ev6P0S0ZqdLIkYeJtNeFg433/kO+i6jqZp/fYjJ8cl8tMHnuPdQztpVV0kByVw\nz+blA+5brtbaAd/Tva7paB7fNqNqtR1d10mOmYj5+H60GX3XcZ2FNZjDAvC0Omnccw5rYiRBWcm4\nimpxltYTNCeZgKkxuIrrsCVHUZHuz55jB1m9YNmtTdgo0l3PWZKkz30ubkxTUxO5uTmoqoKmaaxY\nMfbCdbMYDAYWrvx3fv/eb7BZilBVC7LfQhYvf2RU7bhw/hCt9bswGV24PInMXfgV/P39yZyzAbj+\nHvhlq/4Pb+22YjWcwGjopKMriZRp3xgwF+HzdIef33xzK4qiUFRUSGVlBTExd3dPZcHYcNeKry/s\nPPLiC/D1h55izqULvHVmD9VaB3bMLBiXytMPbBpwO4rX6+VXH75KrrsKt0EjjiCenLWGacm9e4Xt\ndjtPrR247F5TczN7Th0k2BqI0aWg6zptJ4rQFQ2D1YLi7EKv8vLs1p9Qb/XgKqnBEmnBPN63tuZp\ndeI+W8UkaxQFxi5Cl03pCZvbJo3HEh1C26liAqbF4q5rA8AYbKWkoGpAe25XXC5Xj2c+GPE9fPgA\nuq71tAhMSLiz+hjb7QFkLXiGMyfexCg3omq+xhpXb9Xpxu12c+zw77GaLqKqFsz2xczKGnyjj25O\nn9jG1NjfkTbfl7ugKLn8ZksuK9b94po2fB5Zllm68jvAdwY9dlTUeFJTU7l06RIGg5GDB/fzxBOb\nb+sIjeCLyV0nvt3hxe6ws67ro/IfL2PSNDImTbupc3/23u85k6Yim31FGsqB/zjxFi9G/UVP2Hwg\nPB43f/XfP6Y8QsE/I4aWw/tQHF0or+YTsX4W5rBegXEW1nJZ8mBLjiJgWhTt2aXYs5uYGBXNOI+N\np575CUFBQWz+7d+hX7VebbT5oasaHecvEzgjHgDV0UW0PY7hxOPxoGlaTxnI4cbX2ML33t9s2Lmm\nppqLFwtQFN9DzYIFI7OmfTH/FI21nyLhwWCZSVzCLAoLDhIUEkvG9MW39Jmtrb1Mybnn+dJ99ZjN\nEh0OjVff282Se37az3vUdZ19H/8133y8AJPJN2Z5ZQ4HDtawcMnXBz22pmmorg9Im9SbNGg0Smx+\nsIgPP/uAeQuuHzoeDubNW0BhYSGq6qWysoKSkiKSklJGfFyB4PPcdeJrNpuxWCxXWr2pdHV13TBc\nNdJ0dLSjaTqKqvBfn77BMUcxnDKiKypBc5Ix+JnonBXOu4c+6fF2C4ou0tLWSmb6TMxmM7qu83/+\n+5+5nGTAnhpP465zhK3wbW1qOXKxj/AC2FKiaDlyEVuyT+ADZyZgURv5h0f+rM95EcHjGKjZnbfN\nhd+EEGSLCV3XichuZ83mlYDvIWDbgY+p7GwmWPbj4UXrbqqj0bb9OzjacJE2t4O2mkYMMcFIJgMT\n1UC+Pn8DiXHxg5/c6+ALO9+856vrOgcO7EPXdVRVISVlEpGRkTe8brCcPPYmU2JeZt0c35p+Q9Nh\n/rDVxV9+2051rc5H25OYteBHhIaGD+n+F3N/x1c3NdD92gPsMt98vIRXPnqZpSu/3efcnDN72HRv\nPiZTb3rIxBidgAuf4nY/hcViobW1ibOnXsHfXIXbG0RM/AYSkgbuQdzR0cGEcf0/UYEBBnRv6ZBe\nz2AJCgoiLS2dc+dyMRh0DhzYT0JC0pC3mwkEQ+GuE1+AoKDgnkzXpqYmYmL6l2AcDSqqKnjh5Z9R\nH6BgGmfHdakW+8YZhEhTAV+Wc9P+C4xbmY5sNNChdVFTX8t/fPoKFTGg280EvbuHhycuwG7yp8jS\nRtjkdBwFVQTNjO/NYB6g8tNAxzvx9jtlsl8UNZ72PtnaqtPNZFcQ/moY7uw24g0h/MmGP8VoNOJy\nufi7N39K/Zwrwqx2cGznL/m7BU+SFHftbOBXP32HHSEVyLF2GveUErYxrce7Kwf+/eDrvPj43wLD\ns7aq6zrNzc09PWevbsc4EGVlpVy+XI6qKkiSxLx5w9/0wOPxYPC8y/QpvdvgwsMMPLjGQm6eh+nT\nLHzz8RL+952fs+zefx7SGHa//iJnNEr4m0r6HXd1XCIqov/nJzWhkerqKsLCQjl3/Dm+sqm25/06\ndOI0F/P/htQpC/pdZ7PZKG4OBvoWRHG7NVR9aA8TQyEra07PNrHGxgby8/OYNi1t1MYXCO7KR73I\nyKieovoNDfVjYsPOY/t4+vUfU5thR40JoPZ8CZalyX3CiZJBxn9iOO66VtRGB2mRifxizx+pmR+C\nMTYEU4gN1+xwXqs5THbpBWSrBV3XUVpdmMN7G5Pr3v77mXVVA61vxmic3N87/cqaR5mc7UErbUbX\ndPSiJmYWGPj5X/4zP3n4OX7+8F/x3MavEXLFs31977s0LBjXUztaMsh0ZkXyxxMfX3MuNE3jcMtF\n5FAb7rpW/CeG9wurNmUE8unRfTcxszdHW1sbbrcbSZLx97cSGHhj8T116kSP1zt16jRCQoZ/m8rl\nyyXMnFrb73hKopnySt/DkSRJBPoXDJhBfzN41YG3VXmV/hEgk18szS39i9EUXw4hIiKS7BOv8fRD\ntX3er8VzOmmsfnPAMYxGIy5tOXUNfW3f8lEUs+aMXtKXzWZjxoyZaJqKrmucPHl8yPMpEAyFu9Lz\njYyMRJIkJEmmvn6goOrIcrHoEj89vAVTbAhKqwul3YUp0Io5rP96riUyCEd+FXO1CUxbM4mXavch\n0/c8NT2Cul11+MUH4rhQiSHQH0+zw7dVCLBNnkDzoXxCFqQiGWTUTg+uD89hXeZb59JVDf8zDXwp\nq/+Xn8lk4odfepai0mJyC/PInLaGiTHXXtutUFqRDOZ+x6v09mte43K5aDeryIC31YV5XP95MFgt\nNFW1XfMeg6X7fZdlmaioqBuuoTY3N1FaWoKq+tYqZ88emQbtYWGRlF+2kZzQNwrR4dDw9+u1UdeH\nvuarGxdR31hCeBjkXfIgAYruT0ikr01oc3MDRYVniImdQmbWOt7c/iHf+lJZzxw1t+jUty9mqs2G\nv7m6J3rweex+Ndccf+HSb7DrsA1JOYzJ4MLpTiR1+lcHvdf6Vpk5cxY5OdkoikJ9fR3V1VVER49N\nFCpTxBsAACAASURBVExw93FXim93rV9Zlqivbxj18X/0/q8JezCzT8OBpn3nac8tJzCjb+Uo74Ua\nvh65mAdXrKOxsRHV0D9cIUkSMTGxdNZVUmDvQnN76CxrIPze6UgG+f+zd95xUeRp/n9XVSdockaS\nJAMKAioGFCPmMKOOk+PmdHd7u7d7d7t3u/e7vbTh9m7z3u7OTp5xTDPGMecsoCIoQUAEybmbbrq7\nqn5/tIBtoyCCOju8Xy9evqyu77e+XVVdT32f7/N8HvTBPiArSB8WMjk1nUiPMFZ883MczjlBcWEV\nPhoPnlz6zD1drwmx8STExvf73TzVvqNVjdw9itVoNBJo09ECeMaG0J5b7vYiIpQ2k5W8pN/jD5SG\nhvqeF7DQ0P6rD124kAeALDsYPXo0Pj4+/bQYHP7+/uScmsxs2yl0ut77Y9seE+tXOc+Joqi0WZIG\nHXQ1fdbzvLXpGp7iARbOlpAVOHbSg3GpPhza+9/EhR9mxXQLhcU6Dn4ymfTMH/HD//kBvsZr2O0S\n6Gezdv1fA2C1+fcZtGix3b12riAIzJj9PPD8oMY/VBgMBhITx3DlSiGgkpeXO2J8R3hofCaNb3Bw\nyK0CCyItLc3YbF0udWmHk8bGBjrjvPC4Y7bgnzmOqjePYBgV0DPzU2raWR00mScXrgAgJCSEqFYd\ndzol1eJGFqY8zUsh4by+90NK7HU0dTqwvJWDFOGLXtCwInISL3/v2y4PySWZCxg6c+Zk0dhpFFTs\nRRnd68KWm81MDxxz1zaCILA4Ip0PblxEjPJF9NDRcbUa73FOOU25po25toh7zrjvl7q6+p5zcbfC\nC93Y7XYuX85HlmVUVSU5OWXIxtEXWQu/x9vbf4G3PhdRtFN+Q2JUsIYum42r10RO5o1n+txvubSp\nvF5MRdkFomNT0Wg8CQoKumuUuCzLhPiV8tKaXjfz+EQzv3rjH3lqWSehIRIgMi3dwaSkU/z7r0v5\n8vONjAoTAIXcy8c5f2YjU6evJ3HCenYdOsPy+b1qbcVlIjqvob6zhofk5BQKCwuQZZmioivMm7fg\nnpKkI4wwVHwmja9WqyUwMIi6OqdrrL6+4aEFXbW2tiH4u6+tiToNkl7L2NwuvMK9EAWRzMhMMme5\nlmz8/NRV/M/pjbSl+CEadIhXGlnmMZ74GGeu6ddWvwzAO/u2cLTpCo0aC9ZGM4eu5eCx34P1C1cN\na2pVetIkXupoZef5szRIFrwdGjIDxvLUopX3bLdyVjYBeT4cupSHRQjFs17G0yog6bRMj5lPxqyh\nKzKvqioNDfU90a39RSxfvVqI1WpBlh34+voSEzN6yMbSFzqdjrmLvt3z/zScEfHbTp4gODiGhSuS\nej6z2+0c3vNDMpJzGeXdQWOZSmKcjtK8QBrNWcxZ8A23/nNzDrByfi13+lDWLjNz7bqd0JDe+7Pi\nhoMnF9cwKqx3KSF9okx51VZsticID4+hy/ID3tj6HkZ9FV12Pwy+i0mfumLoTsgwEhoaSmhoGA0N\n9ciyhvz8S8MSSDfCCHfymTS+4Jzt1NfXIQgC1dVVD834xsbG4nfahvUOUR1z8U1emriI15568Z7t\nxyeM5Vcx32XvqUO0dZrInvkkwUGuUaJbD+9ip28lYlwI3Y/Rjqom3mk4S83WJv5mzWtD+I3cyZ42\nh+xpc7DZbGi12gEb+8y0aWSmTRvWsYFTnL+rqwutVo+np7HfYKsLF/JQVQVFkZk4MfmRCDJ4e/sw\nbfpSt+0njvyBV548S86lLhLjdMTFON376ckdNDXvYOcxXxYt/aJLG7utE88+suu8PEUsVtego+Iy\nG6sWu6dhTZlQx+VrVxk3PoXRccmMjvsPl8/NZjM3b95g1Kiox34mmZycwv79e1EUhYsXc5k2bWA1\nykcY4UH4TEY7A8TGxvWs+ZWXu6dYDBeiKNJV20L7pcqe6ErrzWbIqeGVtQNbA9NqtSzPWsRzS9a4\nGV6AUw1FiIGuDzyPyEDkzi5ypBqampoe/IsMAJ1O52KoTCYTv9v2Dt/b8kv+detvOZZ7+qGM407K\ny8sQBGcN3+774G60tbVSU3MTWZaRJImkpIEJpTwsPLX5GAwi9Y1yj+HtJjBAQKO4n+O0yYvZddg9\nsn3Lbg3J4/UcPN7Ju5vb+dWfWsi5YKG9wz1avvKmgcAg97VyVVU5euBXVF5+kcSAr3Cj4EUO7//F\nYx1JnJiYiMFgQJYdtLW1UVt792CxEUYYKj7TxleSJCRJor6+no6Ou0fjDiVnLpzDMTsaQ7gfradK\naDlVjNxpw7BkHCeGyBhZhb5LDiKJdEUaKa4oHZLj3A82m43vb/oFR8eZqZhk4GqKht+ZTvDRsT0P\nfSxO4ysiCAIJCfdWNiotLQFAUWSioqIeuSDL3bi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dOokgCGg0GsaMGcuoUf2fW7PZmcal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\nKT9Z+gJrZjzCT77zE468cRqlW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PcFt1FfX4/DYSc+PgFBEPjpl7V88et2GjQxCC4baeoV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=4, random_state=0)\n", + "plot_kmeans(kmeans, X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "An important observation for *k*-means is that these cluster models *must be circular*: *k*-means has no built-in way of accounting for oblong or elliptical clusters.\n", + "So, for example, if we take the same data and transform it, the cluster assignments end up becoming muddled:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Bslln8KIY1FCAvLT4blLh8XhiLc0rqVymqiotLWdpbKynvb2d3t6eWCv6XA3w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\nNnzdtplwck8mJSWFQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(13)\n", + "X_stretched = np.dot(X, rng.randn(2, 2))\n", + "\n", + "kmeans = KMeans(n_clusters=4, random_state=0)\n", + "plot_kmeans(kmeans, X_stretched)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "By eye, we recognize that these transformed clusters are non-circular, and thus circular clusters would be a poor fit.\n", + "Nevertheless, *k*-means is not flexible enough to account for this, and tries to force-fit the data into four circular clusters.\n", + "This results in a mixing of cluster assignments where the resulting circles overlap: see especially the bottom-right of this plot.\n", + "One might imagine addressing this particular situation by preprocessing the data with PCA (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), but in practice there is no guarantee that such a global operation will circularize the individual data.\n", + "\n", + "These two disadvantages of *k*-means—its lack of flexibility in cluster shape and lack of probabilistic cluster assignment—mean that for many datasets (especially low-dimensional datasets) it may not perform as well as you might hope.\n", + "\n", + "You might imagine addressing these weaknesses by generalizing the *k*-means model: for example, you could measure uncertainty in cluster assignment by comparing the distances of each point to *all* cluster centers, rather than focusing on just the closest.\n", + "You might also imagine allowing the cluster boundaries to be ellipses rather than circles, so as to account for non-circular clusters.\n", + "It turns out these are two essential components of a different type of clustering model, Gaussian mixture models." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Generalizing E–M: Gaussian Mixture Models\n", + "\n", + "A Gaussian mixture model (GMM) attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset.\n", + "In the simplest case, GMMs can be used for finding clusters in the same manner as *k*-means:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+X7z0f0Rvu4TNJAhvE0TPEZ1Z9/tm4vemgwoi2wUzeepL1G9U36m9/xn1Asdm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nMPq3btp8YFsQY1F5w1nLGnndbm129NosXIgRBTtvp2i8ccrmFlbjkYY/C5XVidJdx+y5Ud9tpC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\nU1FJdHQUKbERHCwJzGpVFIUec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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.mixture import GMM\n", + "gmm = GMM(n_components=4).fit(X)\n", + "labels = gmm.predict(X)\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "But because GMM contains a probabilistic model under the hood, it is also possible to find probabilistic cluster assignments—in Scikit-Learn this is done using the ``predict_proba`` method.\n", + "This returns a matrix of size ``[n_samples, n_clusters]`` which measures the probability that any point belongs to the given cluster:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 0. 0.475 0.525]\n", + " [ 0. 1. 0. 0. ]\n", + " [ 0. 1. 0. 0. ]\n", + " [ 0. 0. 0. 1. ]\n", + " [ 0. 1. 0. 0. ]]\n" + ] + } + ], + "source": [ + "probs = gmm.predict_proba(X)\n", + "print(probs[:5].round(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can visualize this uncertainty by, for example, making the size of each point proportional to the certainty of its prediction; looking at the following figure, we can see that it is precisely the points at the boundaries between clusters that reflect this uncertainty of cluster assignment:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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DiIh/nenz36dT68OYDG7+3FEXtekWevTqe7GrV2UqlYro+g+xeNXrDOpVhEql\nYLcL5i6LpkW70y+a0qjJ1TRoNJttW1dQtCuTNm3706rHxZ33L0kXmiLOIZN7aWkp48eP55ZbbmHI\nkCFn/Hx1As3lqLS0hNcffp2jq1NRl+pwmezE96jDCx8/T+PGcf/aTm6322fSiPmz5jH9wV+85hMD\nuOOsaIWeYylHiFMaV2w/OcieKlNJIVrEVfydJzIJIASdokcIQR6ZuHGjQk1IXCCJo3sz7rH7Km4y\nFsyaz08Tf8Z9QoNAkEMG0T4SkOSLbPwxccvb13HznaNP32g+REQEeLXVr7Pn8/3Ds9G4TmmHunY+\nXTmJ4OAQajMhBAf2J1FmLeGq1l1qJFuXr3Y633KyM9m+ZRpaVT4uYuna8w5MJu9elVNZreUrRPn7\nn7oE6Pl3MdrpUiXbqmoiIs78nT9ZtR9tMzIyePDBBxk7dmyVgjGcfeUuVxERAXz962ck7Uxi28Yd\ndOnRiRYnrRt8ajvZ7XbeevYdti3bhbnAQp0mUVx/73CuH125aEOZpYQi8nELF2rUhBBZERyLcooJ\nsUahx/NHzh8jZlHi9T7bJVw0S2xAzqZSNKV6FEUhUBuMPbYYVWogGqeecGJw6Kx0vbstb0x6zeMm\nIS01jZ/+bxZk6VEpYBVmjPhegcpEIFbM5J7Irtb349Rjdq1N8g7GgDtVw+/zlzD+idrfBRoZ2bPG\nyzy1naxWK8eOHSU6OobQ0Jrvzo+ICKBFy9er/PmDB7azc/P7BOp3A1Bsa027zo/TpGlbr88KIbDZ\nbOj1+hqfmih/o6pOtlXNq1ZAzs3N5e677+all16iS5cuVT5O3lGVO3bkGFPenMLRLcm4nW5+b7mW\nMY+MpnOPLj7vPJ+//zmOzElDpahR8CMjvYhJW7+mpNTG4BGDOX7kGIu+W04AwegUPQ5hJ5MThIhw\n/BQDdnf5KBvllDlPAUowWSIVBBVB2aHY0bRygtmES2+nTFdKQF0j45+5h159+7B2xRo2r9iCSqXQ\nc2gPOnfvSl6e2aPcL9+fhsjUVSxSr0OPxceyjgBWzOjwR+2vO+vvh6+2Ki32XoMXQKWoyMspuiK/\ngye3kxCCZYsnEqD9neYNstl+IIDkrKtJHPgGJh/z1y+EnOxMDu14gFED8k7auol5y+7H5fqhYoUo\nIQSrfp+MKPudQGMOJZYwXJpE+g36X40EZvnUV3Wyrarmgjwhf/HFFxQXF/Ppp58yefJkFEVh6tSp\nNfIO8HJf1/GKAAAgAElEQVRnNpt5/d7Xse4R5FH+A1S8ppjn/nyRYfcP5s2PXvb4/P49+zi07Dha\nxXM+sLpUx5Lvf2PwiMF8/OIknPvU6P5eMlGr6IghjkyRQnR4HQxqA2T9HRhFKQal8mk1SonFHJNH\ny74NUFDhVNvZ+/MRMsy5lFI+kMudr+PzN76ga8/u9O7fh979+/zrNVpLyzx+INWKBodw4BZuVIrn\nGrkWzATXC2TUXTdVr0FP0aBtAscWZ3icB8AZUEbiMDnwcPlvHzK46yxCghRAR6MEG273Br6Z8wQj\nbp5yUeq0bfNX3Dool1MnyV/bL48ZS79i0LDnAFi+5H36dphORNg/n0snv/B7liyyVnxGki5l1QrI\nzz//PM8//3xN1+WKMPObn7DscZNNGhHEVKSxxAHrP9nKF/FTueG2yqXnNq3bhNbsnZwDIOdoLunp\naST/mYYeo9d+g8bE2DdvZuuKrez/6TjBSjh5IhOzKCaIMJzYieoQxhOvvEz7Tu0BGH/teLLN6RgJ\nJJwYrJjJIhXtPj2j+91CfGx9sg/nojfpadmzGQ8+/xB+fp71a9K2MZtJQnPSMpYR1CGLVPToCRSh\nmCmhlCLqNY3l3hfv9pqbWl1j77uNHWt2kr/BUhGUnWo7HW9pQ5NmV/aiBW63G7Vzxd/BuJJKpdC+\n+Q6OHtlHg4YtTnP0+aNTp/t8wlWpFHTq8sQgZWVlGNRLTwrG5UKDFYL1yzGbH8Fo9P4/IEmXEjk8\n+gLLPJqFEzt6/CqD8d/0wo9lP6xixJhRFe9k69SLwaHYfa6qZAjxp7CgAJdZ+MzApXHpSGicQIcu\nHXnh8AsUbS0jiHCySSNdOYbBYCTOvw4FueVTbdxuN/t37SeK+IpgZsCEARMZIoXiQw72HTpIuBKD\nDcHWfXsZvWw0D7w6nk7dOlFYWEhMTB2G3jCMZTOXkbfWUvFDq1bU1Ksfy31v30NWZiaZ6Zk0bNyI\nvoP61egofX9/f9778T2mT/mBozuPodFr6NS/A0NvqL0jrC8Ui8VMWJDvhUdaN3Mxe/X2ixKQna7g\nM+5LPZFCk/qZ4GOt8lZNcjl6dD+tWl3ctcol6VzJgHyBmUKMlFBEEL6fCHMPF5Cfn094eDgA/YYM\n4Oe2v2De4fL4nEu4aNOnDY2bNCW4mQn7Qe+yQpoF0LhJEzQaDZ/M+4TZP/zMjEkziEmLK3+fbIHc\n9SVM3fstej8tnXp0QevQe3X3AgQQRAmFKFQOylcpKsQxLW/f/gE2nZlgdzghjYK45sbuvPXtRD59\nczIHNx7GaXMSd1Usox8cTYtW5/8H39/fn3seufe8n+dSYzAYyS8KAzK99u0+qCGhwWnyV55n9Rvf\nyNZdq+jQ2jOjyOadehKajAIgLDyclD0mmjXyzjqSkmEgMsF7BL8kXWou70VXa6GRd41EH66lDLPP\n/YZwPwICKgcCqFQqHn3nUUztNThUdoQQOALLaHZzPOOffQCtVku/sYm4/DwXdXf5Oeg3pk/F06dO\npyM2Pha/nCDvtJcFWn79bhFmcyl6xXf3uD8mtOhx4yJbpJMj0skWaTiwI3ATY0+g1FmC/SD8NnEV\n836cy1NvPs1XK7/kuw3f8PoXr1+QYCydnkqlwqlOpLDY7bHd7RZs29eGho0u3ApdRUWFFBSUJ4pp\n0rQdmeaHmbs0nJJSN8UlLuYujSCn7BEaNylfISokJJTkrKtxuz1naQoh+OtEW6Ki5Jxl6dKnfuWV\nV165UCezWHwvGXglCQwMJKJ+GCuWL8fk8Oyqcws37W9qQc8BnnmqI6IiGDJ6CLnqdMyGInqP7skj\nz/+vYsnG1le3xr+eltyyLFx+diJaBTPisaGMvG2URzlLfllC6vryFJlO4SCPLMx/j352qZ24tHb2\nJ+1HZ/GeA1pILlr0FJFPHepjUgIxKoG4cFJIHiFKBBZKMCqBqNxqMgvTGT52WI21my9Go15+p6rg\n5HZKaNiFJSvySUlJR6spZfd+f1Zt7UzioIno9b5vxgBKSorJycnGaDT5nAdfVUf+2sGW9S/gKv6A\n0pzv2bljLTZnFO07DCE67iY2bIvleFYfOvV8kfoJbTyOrRPbnTm/7kanziEizM2Bwyp+XdWaXv3f\nxq8G5i3L71PVybaqGqPRe+2Af3NOiUHOlhwmXynleDLP3vksZX+50Tn8cQbYaJQYz6QZ71Ba6rlS\nTnFxES/d9xJp63PR2vU4FDvB7Qw889FTNGzS6F/Pk5uby2cTPyUvOZ+jRw9TnGJBix4FCCUKlaKi\nRBSSq06nnqsJReRiIAB/pXKAjEs4yVRSKBNWEmju1aVdKHLxw0AJhUQo5SkwnVEWZuyYXqV1nqvr\nSph6URNrcp/aTjabjSW/TqAo9w9CgwVa/xY0uepuGjRs7XVsQX4uG1a/Sp3Q7YQFWzhyIhZdwA30\n6H3XWdcjPy+XPZtu5fqBeR7bl60LJKrhVGLrNaxSOX8d2sWxo9uIi29Ds+btz7oep3MlfJ9qimyr\nqjnbaU8yIF9kWzdt4dC+Q3Ts1onGTRt7fdGFENx/070cWXMCI4EeSTxCuxv5ZM6k05b97Wff8MOE\nGUSU1SWfbFw40aLDgd0rQ5dD2Ckij3AlhjyRiRMnOp0OlVEhKNbIvU+P492HPiCoMMLrPEKUZ+IS\nuIn6OxuXoZWGqcurP43GYrGw+Y9NhEeEk5mewbH9x4ltWJeBwwdXPKFdzj8Km/74meKcOfjrUrE5\nAjl6Ioy4egFoNW5cSkt69L67ytmsTp2H/MuP93LXDdvR6SoD/dK1IYTV/4iEhMpuayEE834ay92j\n9nvcFBxNUbE//Um6dL+p4nMFBfno9X7/OtJ56aK3uWXAT6hU3jcY0xcPY9DwV6t0PefL5fx9qmmy\nrarmgmXqkmpGh84d6dC5Y8XfbrebJfMXcWTPMdxqJ7vX7SN3SwmRSl1KRREZIplIYlErajK25JK0\nI4nAwADmTpuHtchCdMNobvnPaHZu2cG0N6YT7YijgBz8MWJQTGSLdCLwXshBq+hwi/J3i2FKNEII\n1PEupq37tiIAzoibSWmhw+tYACulFeU6FQcdB3cCYP5Pc1n582ry0goJigyk2/DO3HrPmH996pv6\nwZesmbGeouNmipRcwkUd9IofDsXO3C/m89zk56ifUL86zX1J2LhhJo0i36dp5396SixYrenMXVLK\nqKGB2O2b+G7uegaN+BqDwXBWZW/fuoIh13gGY4CB1xQwfeHXJCS8V7Ft5/ZVDOyx3+vfqkGcmy17\nFgA3sX3LQvIzvicm/CgWq57sotZ06P4cUVHeg6x06kyfwRjAT+s90EySrjQyINci+/ft56nbn8SS\nYkOFGjt26ir1K7qPTUoQBhFANmlEUw+1TcPCnxewe/5BlBwtiqKwWxxmw7w/CY4OQmXXgAIO7IQo\n5U+2Cpw2GJ6cyUtRFBwWp8dnG7SvT1LSIa/jC1V5+GsNqG0aqOOg63XtGPfYffw4dTrzX1uC2qYD\n1BQcMzN/21KKC4r571PjfdZh7o9zWPb+GjR2HaUUUkckVJxPK3SUbnPy0XMf8cGMD6rZyrWbEILS\nvF9o2sXztYW/v4oG8VpS0x3E1tFy18hD/Lz8cwYMfeysyi/I3Uy99r7//f21xzz+zs3eS7+2vj+r\nU2eye9daQrUT6D+07O+tVmAT38x+mCEjZ3lNZ7M5gk/bBW9znH7qkyRdKeQo61pizbLV/HfgeKwp\nDlSo0aAl2Me6wypFhRp1edaraDe7ft+PKldX8SOnUlSU7RH8tf1wxTEnB1otOmzCd3pJN56jb2Ob\nxXj8eN7zxL0Y22sqnqQBHHobwx8dyFfrpvDoz+P5Yu1kHn3lMdxuNyumr/o7GFfSOLVs+HkjZrPv\nUebr5m9AY9fhEHZ0+Pn88U7ZmEFKcrLP4y91NpuNIGOqz32d2/uxY0/5tB+NRkGr7D7r8t3C6DVS\n+R9Ot2cXeGBwAtm5bp+ftbvCyDg+k6tblXntu2HAUf5cP9tre8s2Y1iz0Tun+bbdeuo3GuW1XZKu\nNPIJuRYoKyvjtQdeI9gWgUEx4RblmbwAH/m3QIMWm2IlukUI+SstPpOCWCwWQIUQAnHS3OEgwsgg\nmWgR5zE4K09kEcBJTylhTq675zpOFh4RzoezP2DGlz+SvO8EeoOOXtdewzV9y0eFx9evX/HZ9PQ0\n8g8X4+9jUQlrspOk7Tvo1rOH177iv99LOXGgxXcqVmGGnKxsn/sudTqdDrM1APCeb5uW4SQ6svK/\nrKjG/XSHzmNYtvYX+nYvZeV6Cza7wOWCRg10CE03du1cS1baEtQqB2ha8uvKBO6+yfPmJytXoDH0\nR7Et8XmOoEA1Nuthr+314hqSk/U0Mxd+Ts+OJ9CqBWu31MUv5A66tJVJPSRJBuRa4MWHXyCiqB6q\nv3NRqxQV0dQjU5zAKZxoTsno5TI4GP74AAyB/sxbucxnmTp/HQa1iczSFLToKRMW/BQDiqIQJeqR\nQzputYu4BnHENq9Ly4gGZOzNwlJoJbJBONfddR1dr+nqVa7JZOLeR8ed8ZoCAwPRBWrAR2IoxSCI\nivE9bzQ0NgTL3hz8MJBDuudNwj91aOhHy9atzliHS5FKpcLi7IzdvtDrPe/ajVZGX18+SMRmc+NS\nzj6RR2hYOEncw6Sv3uTesf4EmMqD+uo/nBz5awPtG31Nn/Klzik1r2DqzDhefEdH8wb5+PtDVq6G\nUnt37hx3N8sXbgSOe53DZnOjUnsP/gNo33EoLtcgknasx+V20H1grxpZYlKSLgcyINcCJ3aloVK8\nR8xGUpcc0omicoCMS2/nPy/fyS133UJJSTELPloMad5Pkq07t6Jjv44s+HIhKQdSSCMfP+FHGNHY\nVGWENgji7WkTSWjU4LxcU1BQMA271+fYvAyvbufYztE0bOR7utbA0QP48s9vURVrUQuN12IYTp2d\nvrf09sqffTnpO/h5ps3L5+rmm2nX0kV6poulq8306e6PoigUl7iYsagdw28sz0aWl5vNlo1foVFS\nOXI0j7DQKIJCG9O5+20EBgZ5lW8tOcT/xhk8Blj17qah1LydiFA/oPzG0GRUEWrax32jTfj7V/4b\n7Ny3m53bFmMKHURy6k7iYz27wBcsj6RLv9tOe31qtZr2HXqddr8kXalkYpBaYOG3i3Hle29XFIU0\njuIXrkMVCKEtAhn28CBuuqN88Qm9Xk+Js4hDWw6jcpT/iAoh0MQL7nv9XhIHJTL8tmGY6vqT/VcO\n/gWB2LHhFE4Ki/JIOZZMiw4tCAo+PwNq2nZty6bdGyhOL0XtLs80FtzBnyfff4LgEN/nbNC4Adoo\nFSeyjuMsdmH2K8YZVIYh2o+Iq0IY8uAAxowbC1y+yQk0Gg3NrxpCXmk31m2JotAxClPoII4ma9l3\npB7Hc0YyYOhz6HQ6jh3by+Gk++nfZSO79+zn1muLuaZTKs3id7B+3XwKS+vSoGFzj3ZKPfwBLZuU\nep23YX0tK9ZZaNqw/AbvyHE7wYEq6tX1fIKNjnCxeXs2vfo/z/rNNo4eO050uJmMbMGi1Q2Ib/oC\nMXUSzm8jnQeX6/fpfJBtVTUyMcgl6Kk7niT1tzyv7SWiiEbX1uX9Lz8qX6nnNEk21ixfzcpfVmMu\ntBBZP4ybxt1cMS3I5XIxbuB9WHd7Ds4RQpBNKg07JPDZr5+dtwQeQgjWrlzDX7sPUa9RHP2HDKiY\nRmW328nNzSE0NMzriVcIQXZ2FkajEZPJ91w+ORcSfpt3P2OGb2HWghJuHGbymlY0a2EUI+9YSWFh\n5eCrNYsGc8Mg73fwQggWLDXT7xoDGzZb2XvAxqP3ew8sBJi7NIBrhqwGypcUTdqxClNAKI2btOPP\n9T+CKwWnO5TO3W4nKDik5i74PJLfp6qTbVU1ch7yJcjqsJAvsglVIiu2uYSLkoA83v1iFoqi/GvA\n7NWvN7369fa5b/mSZZTstnkshQjlT9+KUFGw1cKSeYsYNvLaGrmWUymKQq++venVt7J+brebT96Y\nxNbFOyhNs2KI8aPtgKv438uPVkyVURRF5ic+A7vdTpBhH0IIdFrF5xzfwb0zWL3yF9q2H1qxzeps\nBngH5I1bbZSa3Uz/pZiwEDWKSmHpqlIG9DZ6vXZwuiq7sI1GI916DCM97RirFt3MjYNO4O+vwukU\nLFy5gLB6r9LiKu8BfJIkeZLTni4yt9tN/l/F6PEjS6RWLNqQRybhJXVZ8dvvVS5LCMGepN2sW70W\nq7V8alNuZi5q4fu+S4UKDVpOHPY9zeZ8+ej1D9nwyTacR1X42Yy4j6vZ8sUe3n3hnQtaj8uFEHC6\n+zWjQcFqKfDY1qLt/cxbFs7JnWMZ2TB7cQBmi5senfwZOSyA/40LoVljPd/NKvGYKmWzuSkpa+t1\nrp2b3uK269Pw9y//WdFoFEYMKCT18PtcwI44SbpkySfki8zpdGK3OjAqgRgJ9NgnhCA/t+A0R3ra\nvXM3n7/8OZnb8lDsKgwNviLxtt70v64/C95dgrrAexCUCxcuxUndBnV9lFg9QgjWLF/N1lVbUalV\n9LmuD+06VOYbtlqtbFu0E/UpXz21oiZpyV6Kni0kKOjyTxKRknyYbZunU1iYT4tWw+jcpf9Zl6HT\n6SiytECl2oLd7jvgrd3kT+duIzy2xcU3Ra+fyvRFX+KvS8bhMmIISiSmzmx6dzPTpGHlIMH4WC3X\nD1Yxd1EpI4cHkLS3jK07bcTUWcOSBa8zaPgLKIpCaWkJUSG+50V3a3eM3Ul/0rptt7O+Rkm6ksiA\nfJHpdDpiW8SQtabIe2eUg/7DBpyxjLKyMt57+H3sBxX0GEAB5zFY/NYKDuzdj91gpTi/fP5RMOHo\nFT+KRQF+GMj1S6deQlyNXIvb7eblh17iwLxjaJ3lgxn+/H4b3f/Tif+99D8AMjLSKEmxYsT73Yot\nw8lfBw/RoVOnGqlPbfXbwg/ITf2cq5opxLfTcvDwUj5+O4b/PrrsrKcANW3zEHOXPkbD+mX8scVK\nt46Vo/WzcwWp+YNJjIrxet8XGRVLz75PYTAYKt7p/7X7PY9g/I+gQDX7D7v4dVkpTRrouHtMEGAh\nv3AuK34PJXHAA9hsdvz9fKdVDTAK1s//mmYtOqDT+Z5bLkmSDMi1wvX3juDzvV9BbuU/h1PjoMdN\nHQkLCzvj8XN+mI31gBu14tlvmW3LgNkK/koI/koIQgjSOY5TONChR0Eh2BrB129+w6Q55744/azv\nZnJodvm8539orX78MXUzXfv+SefuXYmIiMQQrYcM7+O1YSriEy690bln48jhPRSkf84DdxnRaMrf\nyzZK0NG7eyEffjaG+x+edVblJSS0JMA0ja2bviY/J4kN2zOoG21Ao4tGa+rDoOGe04+EEKxe/hmi\nbCnBAdkUl4ZgU66h36AnQTEAvkfOhoXC8AGeSV5CgxWEbRXwAKGhoWzPbgQc8jr2j61WHhq7hVmz\nH2LkrV+c1fVJ0pVEBuRa4Jr+vfCbomfBdwspSC1AF6Cn65DOjLr9piodn5Oe5xWMS0QhwYShUSqf\nuBRFoS4JpIojhBCBn1K+MEHa1iyOHD582rnBVZW0ZpdXVzSAxubHmgVr6dy9KwEBgbTo04S90496\nZAoTQtC4TwMiIrwTSjidToqLiwgMDPLKj3yp2b75e9q1UlcE438YDSoa1j1IWVnZWc+xDo+IYtCw\nZ6v02RVLP6ZPu++IDP/n/FmUmmfy9bTjhESNIDfvK8LDPL9LLpcgNMj3cBOdpny+nqIoRMTeyaad\n/0fntpaK/UeO29FqFIKDNCR23squpPW0biMHeEmSL3JQVy3RqXsX/m/K/zFryzQmfv8Wikrh07cm\ns2jOQtxu3/mE/xHbqC5O4dldWIbFI6HGyXT4VwRjAFEGJSXe81LPltPhqtK+Jyc8RZOb4nCFluEQ\ndhxBZTS4PoZn3/EMKm63m8lvfcI9ieMY13k89ySO45O3Jp2xPWqzwoIMmjXy3W3bsL6LvDwfqc1q\niMPhQOf+7aRgXM5kVNGo7lrCI+rx7ZwEysoq29ftFkz6NoCmjU0+B2aVOSpHwre9eiBO/3f5YEp5\n9/acRaVk57oY2Kc8AWyj+pCV/uf5uThJugxc2o8bl6FdO3bz3O2vY97jQKNoWK1s5NdvFvLql68Q\nFe17GtB1N43gt2nLMO+oXCFIIP5lcXvPH9aQ5iauanXVOdc9vlUcqb/neE+RwcFVXSvX2fXz8+O1\nT14jMyOD/Xv207hZY2Lr1fMq75M3JrH+k21o0KDDiL0Y1h7cgtP+ccU76UtNnbje7P9rCz06ey+b\nmHzCgS3jZ+rWfbTa5dvtdlQqlc+ehKysTOLrZPBPJq6TdWyjZerMKdx5/zzmrZiC4tyMorhx0JLY\nhnVZv+lDUlLNuP6+rxo+wEhympqAMM98581adCb1SFOGD/DOZe10CpTT3CRKkiQDcq0ihGDio+9j\n2ysq8ldrhY6iTTY+fOEjJkyd4PM4jUbDy1NeZNLLn3B0UzIus5uYhpGUHC/BvzSAIvJwYEeHHtMp\nuaFdJgeD7x5eI13BdzxwO7vW7qZkq70iKLuFi9iB4Qy9YZjX56NjYoiOifFZltVqZcvC7WhO+Ypq\n0LBt4XasT1rBx8Cw2m7w0DuZ/N5ndLna5dFtbbG4QRG0iJvBoYN9aNK0clrR0SO72LT2TQL9j2N3\n+aEzDmTA0Cc8BoAd3L+J44emEOR/EKdbTWlZa9p0eoKYOvEVnwkODmFHkpb2rbx7GJJTncRGZ+Fy\nueg/+MGK7Zv/nE390Le5sbcCfy8UYrG4mTjZTcv2j9Cz981eZblUXbBYDmEweHbALVkTROfuY86+\n0STpCiEDci2yd9ceTvyZgx7PvNaKonB0YzIlJcUEBAT6PDY2rh4Tv5lIUVEhFouF6OgYPpk4idkf\nzSHMFU2wEo5VmMkJSKF119Y4i9wERgYw4Ob+9B7Qp0bqHxAQyLs/vc23H3/LsZ3HUWnUNOvShDvG\n33nWmcDS01MpPm7xORq7JNlKenoqcXGRPo6s3VQqFbfdu4zXP+hBn+5u4mO1/HXUTmGxmxuGmNBo\nXMz4bX5FQN6/bytZh//Dg7f/E3wd2Gyz+OTr7dwxrjxpTEryQczZz3LrsJNH6v/BD/MeImjwT/xz\n42IymfgrpSEu1yHU6sqbASEEB4/YCQyM8LoxK86ZTfNOnq8iDAYViT11GGPK81GfSDnEnu2fYNDu\nQwiwl7Xgq9nt6dd1F80bu3E4BItWBWMMf+y0319JkmRArlVysnNQ7Cqfyyk6zS7MZvMZf9CCgoIr\n5vEm702hrrtBRXn+ihG/kvrodX58+OvEmq4+AIGBQTz8wiPnXE5kZBT+UTrI8t7nH6UnMjLqnM9x\nsQQHh9C6TUdaN99JWqaTrh0qV10CUKsql17csOIJnh7vORVKr1dxXb+DTJl8G03r51BmzcJPZ2H/\nIT3Nm1S+nx41+ATz100jPv7Jym1jv2Ti5G4M7uOmTUs9Bw872JpUxuBEA4vXt/F46nY4HAT4n/B5\nDV3a2Zi5fBV+fkM5susRxg4/OfPXBqbPr0Oq+T2Slm1FUZno0nO0VwrUsrIyNqydhuI6gMutJyp2\nCK3b9DyrtpSky4kMyLVIp26d+bbBNBzHvPeFNw05q1SSqaknOLYhtXxe8kkUReHohmTy8vJ8Tqkq\nKyvjxy+nc3j7EVRaNe37tGXEzddXzFW9UAICAmnZt6nXaGy3cNMisfEl/6RldTYiJHgXoSGeyefz\nC93oDeVPx0IIosNzAO9R143qa7kqYQvXDjJR/k44gFUbLOh00LB+eVDW61Uobs8vU0BAAN0SP2Lr\n7hc4kZ5Ho/parhtkZPZvDeiW+IzHZzUaDZYyE1DGqdKzBKFhcWzZOJVbB2Vx6l3kzUPT+HnFdgYM\neczn9ZcUF/H7wnsYe+3hisxeB48s5/fFt9B/yOOnaTVJurzJgFyLGI1GBtzVmwX/txy1vfJJxx3g\nYMidw08zQMu3nKwcRCk+n7YdhS7y870DssVi4fExj1OwwVKxNvOh+cfZsWEnr016zef53W43s3/4\nmZ2rd+FyuKjfqh63jb8Dk+ncB+88OeEp3rS/ycGVh3HlKajDBE0TG/HkhKfOueyLrUv3+5nx6yZG\nD0+raFenUzD7t1aMuGUkUN62drvvEeUul/D6t+3T3cDshSUVAVkIgcPtvfxiqzaJxMUvYvOf09iT\nXMDBrEYMGXmzV1ISRVGwurpgt//qtTbzyo2NGHxDIqt/+9nn90KjUdCqjrNz+2pyM1cCEB6dSJt2\nvVAUhfWrP+LuUUc8bvSaNnRTUPwzKcnXERd/blPwJOlSJANyLfO/Fx7Cz2Ri/YI/KM4uISw2hIGj\nB9B3SL+zKqdZi+aYGuhx+njaDm4aQP363gk4vvvkGwo2WCuCMYAGLQfmHmXNdavp3d/zXbMQglce\nfpkDPx+vWLwieWkmO9bu4t0f3/a5Fu/Z8PPz47XJr5GZmcHBvQdo2qLZaQeBXWpCQsNp3/0Lpi/6\nFD/NQQRq7KItQ254pOJ9u1qtJisvHIul1GuA1Ip1Fnp08l5DW6etDI4r/zDSsvUtLJjzPpai1WhU\nVsqcCTRp9R8SElrSf/CZXy30Hfwc0+bl0bHlZgx6K5t32DmWWodBI15FURSKik9/7KEDuxndbB19\n25T/fezEIhbM7s+1N07AT73bazEMt1uQEGth6cZfiIt/+ox1k6TLjQzItdDIMTcycsyN51SGv78/\n3Ud1ZcX769E4K5+2nXoHA25O9Jmi8ciOYx7dw//QOvVsXr65IiCbzWYcDjs7t+xk/7yjHpm5VIqK\nks12vp30LQ8/f+7vkgGio2OIjr48AvHJIiLrMPi6//vXz3Tq+SxffP8oQ/vpaNJQh+v/2TvPgLiq\ntAE/dyoMMPTek0ACaaQXQzrpvZnE3l1dV3dd1+66xbaW1fVzLWtL1BRN79UU0nslpNASCL0PDNPu\n/ZGU6+0AACAASURBVH6g4DhDAgkJEO/zb86995z3nrkz7z3veYtNYsUGA/6+Sny8HR3lzGaJikob\nm5KD0Qc+xsE9HzJ77Hbc3X7+XrPYsOMYSO8T3a7rVWXUarWMm/Iui+ffj5fuMN6eEBN1mdVL7ie8\n/WwUlgOknjfTKcbe9H7wGAzul090eH17dDi4umxi354BCNiv/Ddtr6KqWiIkSIUri1mzrJjhY15F\np3MMD5ORuVWRFfItzCNPP4reS8+ulXspyyvHO8STIdMSr5ABrGGTuKAQuJiZxX///jHpB7IQTSI1\nuir0FkdPZ0EQyDia1Ux38dumV59xaLQ6Vm/7EHFjJiarDk/fSXh4bAPsk7nkFcD53LGojw/jttHj\nyEg/xYBuu3+hjGsZM6SMheu+JLrdvxslw7oVf8Nk2EPSGA/CQmpf5CwWM+9/9iEP3uHO3sNWcvJs\nDB3oiijC6s1WzmVF8tzvLjv0FeQvUHV4BxZrPJKUgSAIbNlZTXyspq7vfj3BZtvC18vLmXK7nGpT\n5rfDdSnk48eP88477/DNN980lzwyzcycB+Yy54G5jTq3Y/8YsrcW2JmsASzqGvqM6MNfH3iVyyfz\nqcGIGRNCuYBecB56JCgbv98tc2W6dhtK125D7doOH+jFsg3/YeyQIlxcBHbud+VSyXgeevT5uj3d\nixnbmT3KMXtaVbVIVtp2tq2bC0jU2DozaMgTeDjZYti57Quqy35gwihdncIEUKsF/vyYJ//9qozH\n7/emtMzG+h+rEAQBq82XqKgIwFEhAwiChX6D/sD8ZSe5Y/JFyitshIXYr4SVSoE+XY5w/twJYmK7\nAbV76pIkNTmETkamrXDNCvnzzz9n5cqVuLm5Nac8Mi3I3Y/ew6l9p8ndWlKXA9uiMtHjjs4c3H2Q\nrJOX8CcEpaDCLJnIIR2DVI67YP9HLko24vp3bIlb+M3Qq+9EjMaRrN2zDIvZQGzcSIJcLnMxK43I\nqFqHKEHQYbVKdglITCaRH1YbeOYxD5TKswCI4lm+XnqMkRPn2f2eTxzfSUzgZyiqFXTp5OjpLQgC\nup88pL29lExIqnXkW79NoKSmM0bjnjoP6p8xGkUEdXd8fP0ZMmYeizd9hpb5Tu+xWyeRRZsP4u7u\nydH97+CmOYVCYaPaHEeH+EdpH3P9BVFkZFoT1xzLEhkZyUcffdScssjcAEpKSvjPPz7gmbnP8ML9\nL7L0ux8aLBav1Wp595t3mfHOBDrOiCR+djse+fwennvjeZKX7iJIiED5UwYxjaAlik4UkEO1VG86\ntWIhKMmLex6/92bc3i1NTU0NG9a8yY9rZ7Fj3VQ2rn6O3MuZdcddXV0ZOnwugmQg99zDdA7+PUL5\nbNYsvYfLORn0HTiHDTvsw8O2JBuZNdHdLjGIQiFw5+R0du/4wu7c/Eur6NLRypX4tfc1QFl1J4Yl\nPcA3K+OxWOqfNYtF4puV8QwacjcAHnpPxkx8BlGIctp35iXw0Iezc9NDxEX8yLD+pUwZVcXcCYco\nynqG3MvytojMrcU1r5CTkpLIyclpTllkmpmC/AKem/s8xpNinRnz/LoszhxJ5aV3X3Z6jUqlYtbd\nt8Pd9W3HjxxDWejo0SsIAp6SLyaMlEvFdB0ez7DJQ5k0a4psVrxORFFk9ZJHeWDGCdR1ntMXWbbh\nOArFZwQG1eb+Tt7+FUN7LiDIHyoNCrKPV+Kj28eS7+7m0ae2otQ/wcYdHzJyUAVKpUB5heTgsQ21\nilUtpNq1qZS1mb/iYzUcO2UioYu945YoSqResCCKUp3H9MadnkTFPlzrDDbtc5b8+AlqjgNgoTvj\npj3qUBPZphxChWEBend75b5lbwfy899hcJ9LhARpOHLSRGGxjenj3Rk7tITv1n1J8KS/XdsEy8i0\nQm6qU5e/f9vLPdwSNNc8/efv79kpY6jNjX1iaSoZj6XSt3+fK15vNBqZ/+l37Fy7mzJbMRKSQwUp\nFSpccMNV6cZTrz1Gv/59m0X2xnKrPlPbf1zC9KTjqNX2ynPq6HyWb59Hl661mdYk01aC/OFEiomM\nixbGjXBDrRYY3L+Sb78bx6x7lqNUTmDdjq9AqsJgOQGccjqmWq2zm0+FOgpJOkxMOw3L1hrQeyho\nF1m7lWE0iixeWcm9t3vy7y9jiOngiU0Kp/fAh4iMjPmpBw/uuOeVq97rnLv/yvffVePvvoVeXSq4\nmKPm+IWuWKwKnnvsPEpl7f5ySJCKmhqRFRsMzJzogad7XrN//7fq83QjkOeq+bluhdyQ+dMZhYWV\n1zvcLY+/v0ezzVPq/jTnSRuMWlYv2Eh0+04NXluQX8CL97xIxREzSkFJsBBBuVRMiVSNzy8cuWow\noscHfXct0e063dTvuDnnqrWRl72HwfGOK1lBEJAsZ+ruWyEVIIq1uahnTqz/g/TUK3nsrhK+WfEC\n9z7yNQMHPwrA4YNbyLj0LNG/Kq6VWyChdE20m8+YuDtYv30b44aVMm28OweP1XD8tImcXAvBASpm\nT/HAxUVBaERfbhtRn13rWr6TEWNeobjod+w4tY+AwGgS+npRdnGGnWkdwMVFgZtOgdEoUlWta9bv\n/1Z+npobea4aR1NfWq47H2JTskfJ3FwUSudfryRJKFVX/uo/eeMTDEesKH/hce0p+CJiq6u9bJDK\nUaNFCLNy97N3yM9CM2IVtQ2+7NrEegcrkzWQA0drSOznfEvB1/04NTX1qS979RnJrhNTOX6m/vtP\nOadg497x9B84ye76kNAofMLf4r/fhLNsbSUFRTasNomJo9yZPrFWGZeW2XDRRVzv7QLg6+fPwEET\n6RDThYsXz9CpnWPKToDgACX7jwn4hzhWEJORactc1wo5NDSURYsWNZcsMs1MbN8O7Nl31CHZh9Wz\nhrEzx13x2gsH0xGcJAnxIZDywHw89B6EBPvRpWcXZj4wi8DAliv2UFlZwVfvf0VuWj7eIV7c+dgd\nhISFtpg8zUF819vZc3g1t/U227WXVYgImgF1n919JpKWdZhucc5fsFxdzJjNZn4ZYz5u0kucOzuB\nhRvWASIR0WOYOK230+tjOvYiM3MarsIZ4mM1RIbbJ5RZvNLIuNvrn6UTx3aSn7MRpcKKRtebAYOm\nXZM/QXR0N06fd2NwP0elfC5dRHS9j1GDRjS5XxmZ1oycGOQW5sE/PcT5438hf0d5fRiTroak3w2l\nXYf2V7xWtIo4M6AICNz+2Czu/d19N0LkJiOKIs/e8xylu4wIgkAWeaTseYn/rHr/ulN3tiThEe1J\nTn+IbXu+YOiA2ns7c17BnhPDmDTj/rrz+t92O5vWF7Fxx3+ZOtYxNKmgrAN6vd7BvBjbMcGu5jJA\nQX4uRw/NQ60owSoG0nfgfXh5+9AtYQRFaZ9yMrWIU2fN9OqmpbDYxqlUMzYhti5Uav2qf9IvfjnD\nxtT2V1K2ie8XrmfyrE8cHLmuhn9AIHt39mOAZfsvnNqgolKkmtuZNPaJJvUnI9MWkBXyLYxOp+Pf\nC99n5eIVpB48i0anZuS0kfTo3fOq10YnRJCW6ZjYQQowM3balVfXN5NNazdSuKcCtVD/h1+TIrH4\ny8U89NTDLShZ0yksyOPIwa9RKwqwiH4k9L4HGMOiTYsRsBAeNZwpsxxXsqPGPs6m9Waysr8jMqze\nzH3gmCv+YXc5nG8wGDh6aBM6N2969BqCQqHg5IntVBf8jTmjyxEEAZtNYuGKrykxdKF9p9spyhvG\nlOFrUSvh1FkzPl4KusT7kFP5AAApp/bTK3YFMb9Ike7jpeC+aUdZvvUzksb+vsnzMXriG3y39hUC\n9fuICCkj7ZI/FeahTJjyQpP7kpFpC8gK+RZHpVLV5sW+o2nX3fnUnbx+4k3MaUJ9NSKNmWF339ai\n5ulfk5edh0pU22X9VAgKDCWGhi9qhaSm7KM0+wXmjC6rddySJNZv34x70N8ZNe6PV72+a/dp/LCu\nEJ0mgyB/KzXmYCI6zCGhc715e//e5Rw78B86RhcyZpiaikqJraujCIr+I3kZHzJ7QgU/T6RSKXDn\ndA3frzpM17Dz7Cq9nQ377gPzTrTqctJyQvELmUmffuMByM5aT+IYxz1vrVaBUjxs12Yymdi7ayFW\nczYqTTgDE+c4XUFrtVomTnsLg8FAYWEBPQcH4+rquFcuI3OrICvkNkhJSTGH9h4kIjqSTvFxN2SM\njnGdeP37f7Lwk4XkpRfgqnchceIgRk0YfUPGu1bGTBnD+v/bjLKo3lxr0ZroO+Lmhl9dL1nn/sPc\nieX8rBAFQWDcsAoWrv6Q+M6DGnSYq66uZvPaZ4mLOsQjM2tIuaDhdFovRox7DXf3eg/P7Vs/haoP\nmDtZRdRPBR/cdDB7wiWWrH2JjlHlgGPBkbBgFS5aC8H6lYTGLcfX7w9O5VAIzstE1t5LffrOjIzT\nnD36LFOTctDpFBiqRJavWkrXvu8QHhHr9Hp3d3fc3d1JObWXS+k/4KIppsbsT3TsHGI79WpwXBmZ\ntoaskNsQoijyzsvvcHTVCWz5AqKrlbB+ATz99tOERzaPp+svCQ0P48+vPdPs/TYnQcHBzHh2Kqs+\nWkNVhgltsIrhdw7itiGDWlq0RnPp0kU6RZ3B2Z5919hzXDifSoeYTk6V8tb1L3P3pN0/pcdU0C/B\nSp9u+5i38gUmzfgQqFXaCtMSJGxEhTuuMKeMrmLBsmp6dHHcc9eoBSwWGNK/mu+3riBpzENO78HL\nbxCXLq8hPMSxpKJJjK/7nHr0Te6cnFt3r+5uCu6amsP8lW8QHvFVQ1PEgb1LCXJ7l7njTXVtuw5s\n58ctTzJ8pKNZXkamLXLdYU8yN4/P3vuUw5+fQijQoBLUaGpcKdhRyZtPvtWkePBbjZl3z+Sz7Z/w\nyoZn+TT5I373zO9aWqQmYbVaUKkcv78TKSYOH6/i8rn72LluJOuWP01pSVHd8YqKckJ9D9jlqoba\nVJjtgw+Tn5/HhjWv8/G7A+ifUICqgddvlUqguMSxCAVAVraFkCAlFouEUuXoNPYzvfoksX5XImUV\n9Stlq1Vi3rJ2DBryGAAXszKIb5fi9PoOYafIz89zesxms1FZ+DU9Opvs2gf1lTDkv8aqpS8iig2v\n0GVk2gryCrkNcWjDUZROvrKCQ2Xs2bm7Ta0KmxtXV1cSerbNYgNRUe3YuroD3eIy6tpSz5upqBS5\nZ5YesABlSNI2vvjhIhNnLkSlUlGQn09EiHNTc1SYgfnfv8if7j3CpctWSssku7zSv8RQJaJSCw7p\nMfccNBIZpkYQBDbs9KH/4OkN3oMgCEy9/d9s3fYNVuNeFAorVime4eMfqjOdGwwVhHlanMrrrTdR\nWVlJYGCQw7HUMyfo1SXL6XW9uqmQpJVs2xzMiNFNdxyTkWlNyCvkNoIoilQ2kBlHbdGSfjb9Jksk\n01wIgoBf6EPsOlhfgvDUWRODfpXsQxAEpo++wL7dSwEICQ3jfKbz8penznuREJuCTqegYwcNJ86Y\n6RSj4cBRx7jeb5caeOQuL5RKWLi8go+/LmPxigr8fJT06q5lxz4XNJ6PotPpnIxUS1lZKVlZmdw2\neC4jJ3zC8HGfM2r8n+z2sWM7duZoSlgD8kYRFd3O6TGtVktNjfNY5hqThL+vEsw7GpRNRqatIK+Q\n2wgKhQKfMB/K84wOx6yuNXTv270FpJI5emgdhZeX4qLKxWzzQesxisShd1/9wl+R0Gs06WlhfLf2\nO7TqIioMxwHHSkvengpMxtqyiTqdjjLTMMorl+DpUf9uXVUtcvJ8NI/OPg4oST1vprDYwq79Nrp0\n1LBkTSU+ngpKK5QUVfZBpclBoykhtp2a8gqRiFCJ0nKRbXts7Dw2kb4DH6ZLWJRTuctKS9i59WUi\nA48S4FvFnk2hiNqJDBvpuG2gUqlQuM3kbPondGxXf28p59W4eM1uMIFI+w5xbFoRQ+eOaQ7HsrIt\n9O3hgkZVdoXZlZFpG8gKuQ0xbGYiS06sQWWuDxGRJImIISF0S5AV8s3mwL6lhOvfZuR4y08t+eTk\nnWHLhlJGjnmyyf21a9+Zdu1fB2Dz6geBow7n2GwSolTvfDV6/POsXa/Ehe0EBxSTV+hNlW0wQ5Om\nkZF9D9FhFn7cVU2gv5oBvbQcP23mcp6VbbtsRLUfSbtIMwUFEq+/X0J8JzWJfXXUmCQu5xkprUrg\noXtfb1BeSZLYtuFJHph5+ieHMxVdOuZz6fIX7NrhVldm8ZckDr2PwwcCOLpuJRplEWZbAP4hUxmY\n2LD3viAIRMT+kSVr/8zUMUaUSgGrVWLtliq6xdWa2I0W5ytvGZm2hKyQ2xCz7p2N2Wxh+/c7KU2v\nQOOlplNiDH9+/c8tLdpvDkmSqChYTJc+Frv20CBwPbUGo/Hh64qZdfUcSU7eUUJ/taW6brsnfQfU\nKjqj0cienfNQKwqoMvWg2DycAUlDUatr91pX/9CF1LPJhAYpmTy21nQcHqqhuMTGuq0G7pxxtM5z\n21DlxZI1Bny8FQiCwMxJHqzbdpnCgjz8Axz3dQGOHtnOiP4pDt7f4SESe46tw66G5y/o1Xc8MN7p\nsYtZ50g5Ph9XTQ4mix6/4An07J1EXOcBBAat4tV/T6VPl3wkYMgAV7w8lZxI1eAfMrMRsyoj07oR\npJvonitXB7k6jamiYrPZKCoqQq/X/6YTJbRkxZmqqirOHkwiKdHkcOxSjoW0si/o2s15eUuz2Yxa\nrb5qMY5Na/+Fv/sahg0wUFUtsn5HKAGRf6RbwkhKS4pI3vwwcyZk4OJSa64+lqJk857e9Oh9Owk9\nB1NUmMvn/zeS1593QaOpH2vJmkqmj3d3GP9itoXcAhv9etZ6U4uixOJNsxk1/i9251WUl7Fz6z+4\nfHEzzz/h6GgFsHyjnsHjtl3x/n7NudQDVOY9z6jEevPzuQwVJzLvrTOBV1VVsW3jP/HQHsJVW0VZ\nVSQ+wbPp3Xdyk8ZyhlzBqPHIc9U4mlrtSV4ht0GUSmWrypb1W0Sr1WKo1gGOCvlyoQu+QY6ryv17\nfqCyaAkertkYTXqqrf0YOe6FBvM8jxr/F0qKH+CHbatxdfFkyLgJdavfvTv/zb3TM+0KgCTE2ygt\n+RFfzS42Lu9AdNyz+Ad2QqPJsutXrRKcvgxEhKk5eqr+fhQKAbWy1O4cSZLYsvb3PDAzhQNHreTk\nCoQGO/6NmG1+Tu/pSmSc/ZS5E+z3gmOjrZzP+B6D4U7c3T1wc3NjwrQ3EEURi8WCVqttoDcZmbaH\nrJBlrou83Fw+e+t/pB/ORBJF2vWM5r6n7yUiKrKlRbuhqFQqKk19sVo32MUBS5JEakY3RncL4sD+\nzQiCQI+eQzl6eDXRvu8Q1+9nZ6YaTKaVfLuilCmzPmhwHB9fX5JG3+vQrlOfdKpUhwzU8dGXZYQG\nn+bkvgcpr+xESakNH+96h6kr2cR+ecxslrBK9VWzbDYbq1fOo2fcCRQKNf16uvDtkkrumqm366Og\nSELpOrLhQZxgsVjQu551eixpUDkrd69m+Mi5XDh3hPTUL3BRncMmumC09mBI0rN1BS5kZNoyskKW\nuWYMBgMv3v0y1cdtPykHBWcvZPHKyb/x3rJ38PHxaWkRbyjDx7zM18tL6Nv1MN06iWRcgm374/Dw\nGcTujVMY3CcbCdixPpysSybGPGLvNa3VKujSfh8Xsy4QEdmhiaM7T4QhCODhoWDaeHcArNYLvPuJ\nkb887lanwAUBamrEOlP3zxw+XkOXTvWr9YUrPRg64V4A9u5agLF0EQPiMqmqtvH9qhr6JLiQNETH\ngmUVxLbTEBKk4vBpPwyW0SSNsy/sIYoi27d+hWTeh1JhxWiNYWDio3h61T4jCoUCq83535HZIqFW\nuZCRfpLKvD8zZ3x53TGbLYfPv09n2tz5KBRyFKdM20b56quvvnqzBquuNl/9pN84bm7aNjNP8z+e\nx9kfshzrLRdKlKmK6JfY74aO39JzpVariesygfyy/iQfCsGqvovomImojK8wdlgZbjoFbjoFnWMM\nlJWXonMFvYf9XIUE2NhxIJj2HRIaGMU5KSnH6N4xw6F9514jfRJc68ZRKATiOij5z5du5BdpyLwk\nUlbVhS27dHSILMfdrVZJ7ztsYfN2A34+Ki5kWjh4rAa9u4LKmg7k5aYT7vkmQ/uX4e+rJDRIReeO\nWjZtryahs5YeXV2wWiUWrg5hxMQVxHUeZrd6lySJ5YufYsrQpfSMzyO+fT5d2qewZt0OAkKScHHV\noVAoSDl1iG4dsx3uafXWQAYM/SuH97zL5BH2q2iFQiA8uJCDJ0MIC+/YpDn8NS39PLUl5LlqHG5u\nTdtSkVfIMtdM9tnLDsoYasNU8i44T4N4KxIT242Y2G4AbFzzD+aMqcau/BQwdriWFeurmBrsXtdW\nUmpj+XojqNexec1J/ILH0aPXiEaN2av/EyxYlcLsCZdRKGrHSs8yU1Jmc9jT9fVREtPejdtGr8Fi\nsaDT6ZAkiQP71mI4uh9DlRW1uIkX/+hLcYkNtRr0HkpAYuGab0GhZ8Q4i4MME5Pc+HG3kXEj3HB1\nVdGu4wynpuOjh39k9MDd6N3r50ShELhrahYLNnzMmAkvAZDQ7xm+XfE4M8fmoNUqasOq9rrhHvAo\nGo0GrSrLoW+AQD8Bw5ETwKRGzZ2MTGtFVsgy14xO33BuYxf9b9P7W62scLq3KwgCNrF+g/ZynpXd\nB43cN9sdhSINSOPM+R/57MMQItsNpU//+/Hx9W1wnMCgcPoNnc+iTf+jxnAQ0XSaqmoTj9/n1cAV\nStRqdZ1TmCAI9BswAZjA5o3zmDlsIyDg62OfnMNTl4HR7NyBUKdTYDKJvP+ZCZ2bO/5+C9i86kf0\nflPoN3BW3XnFBbsI6yaRdcmKp16Bl6eyTgYX5Zm684JDItGPWczK5PkIYiYW0ZOuCXMJCY0CwCY6\n3yeujc2W95Bl2j6yQpa5ZsbMHsOhpSdQVdibZayuJkZMG95CUrUskqI9JtMWtFp7y0F1tcip813x\n2lVA3+6VrNls5OG77EMi4mIU5OSm06NLNskHNhEY/TqxnZyHTgF4efswevyzQG3GsKzzX7N2ywkm\nj7FXTpIkUW3t2mA/HvoASsokfL1hz8EaSspsdGyvoWMHDTVmF8y2AMBxdWo0iuw5EsQLvzfg7WUB\nyoFy0rLeZvdOE7cNrq3ClJF+mtWbqoiOUJN+0UJ+oY2RiTr8fJWIov1fkJubGyNGOy8OonQdQknZ\nSXy87F94Nu30oHc/ueKTTNtH9oKQuWa6JXRn6rPjIcSCKIlIkoQUaGbsn0cwcPDAlhavRRiQeDeL\n1kTaVd+SJInF66J56PffEdV1BTtO/RMPT+de6MNuc2Xv4RqmjCohI/U/jR63R+9xTJnzPWb1n9h/\ntF7JGY02Xv8/LyxiIGdOH3R6bd9+o1m02o9FKyqJaadm0mh3LFaJeYvLKTF0xz9kKmcuOL67f7PC\nj6TBEt6/UpDtI0WMpcsQRZF9u39g5th0pox1p3tnLcNu03H7ZHfWbq2ipsaGVejZ6HscMvw+Vu0Y\nx4FjaiRJwmQSWbHJC6X+z/j4Nj3MSkamtSEnBmlltMWA+4qKclZ/vwpRlBg/Yzw+Pg2bWpuT1jpX\nRYV5HNj9b3SqU0goMFo70z/xaXx8/evO2bZmKjPGXnS41mgU2bHXyJjhbuw9IuAZscZpBaQrkXbh\nFOfPLKHakIehPIWZEwyEBQukXhBIPtKdURPftyv6IEkSS+aP49E7C+z6sVgk5q1MYurtb7F75zeY\nyhfTI/4i5RVqzmTEU0N/bh/xOR7uju/1O/daOX8xFKspi0fudtzayMm18MmCCB57almDcdgNsTt5\nDccOfoxKUU1IWBzh7aaT0LNxe+9XorU+T60Rea4ah5wYROamo9d7cseDssnwZ/z8gxg35a0rnlNj\n647NloVSab+63LyzmqTBtVWVVAoJm815neIr0b5DF9p36MKaJXfzx4eq+NnBrFMHidh2R5m/6lUm\nTn+37vxjR3Yyblg+v3ZEU6sFvN2OIooitw2+C6t1DufPpeDu68nYHpGknjlKTt6XdHISsVVQbMTD\n9QK55VYkSeuwrx4arKZz16QmK+MTx7bio/4XrzxZ9VPLflLOH2bn9kcZPPS+JvUlI9PakE3WMjIt\nQOKIp/nih06UlNXGE0uSxI+7qvH1VuLqWvuzvJAdS0hI6JW6aZD09FR6dEpxaFcoBPw8DmM01lcN\nKyrKIizYeT9urgZMJhM2mw2VSkVcfDfCI2rN7Z3ienDgVJzDNaIoYbXCrEkeTB3nzvwfKhFFe0Nc\nYbGIh2d0k+8r/+KXDOpTZdcWH2NFMC7CZHLMmnazKS0p5vDB7VRWVrS0KDJtEFkhy8i0AO7uHkyZ\n/Q3Jp57nk4U9eO8zC3Exam7r64okSWxK9iA4+tFr7r+w4CJhQY7lGwF8vauorKw3N3bpOoS9R5x7\nzKdnWdi/dTz7Ng9l48r7OH0q2e54976vMm9ZNLkFtSv5c2kmvl1SyfiRtY5lEWFqJibp2LKz2u66\ndTva07f/hCbdU2lpCSG+550eS+ydz9Ej25vUX3OTkZHCib2zSYh4kj1bZpOX67glISNzJWSTtYxM\nC6FUKhk0ZBYwi9zLWWw9NA+tuhCTxZ/uve6uC/e5Fjp16sPO3TBljOOxS7khdOhV7wQVHBLJob2J\nJMRvwk1X/45+OtVK51gzQwb8nBXsBMkHXuL82feI6dgLgLDwDgQGLeTrj4ch2bIZO9ydu2fZp9L0\n8VaRlmkgr8BKTp6NQ6fjGDD0rSZn1lKpVJgsKsDRjF9dAy5aXZP6a24unFnK7DElgIq5k/JZtPEH\ngoKfblGZZNoWskKWkWkFBIdEEjzplWbr7/ixdVQZqikt0+HtVR9bnJZpwWAZ6KAMx015nVUb/FDa\nktGoy8kr0BAZcomxw+1DqBL7Gliw9ts6hQxQWFjAqMHVXMrRktDFeWYif18l3y4pZ+o4D/TepDkI\nBAAAIABJREFUfQgNa9fke/Lw0JNf2gU47HBs1+EoRk66rcl9NicqdSBlFSJeegV5hRKu7nKNZpmm\nIStkGZlbEEtVMndMd2ftlirMZlCpar2mfX2UuLg4Zt1SKpWMGv8M8AwAWzf8k7HDlzvt20Vtn97S\nw8OD8+d06FyrKa+w4am3Ty5isUicPGPixad80WgEDp1zHL+xdO75NAtW/Ynpoy+j1SoQRYn12z0J\nin6yxXNZDxnxIGvXFeGiTMNCZ0aOmXX1i2RkfoGskGVkbkGUChOCIDAhyd3h2OKNV3d+sol6rFbJ\nrpLVz+TmFrNp3b/p3G0qoWFReHjoySlO4I7xu/lmSSV3TPOoS4xis0l8/l05z/7eB41GIOMS+AVe\n+0o2IrIjvn7fszJ5HoKUjdXmQ69+9+Dr53/1i28wCoWCMRNeaGkxZNowskKWkWkihsoK9iTPQxAE\nBiTei7u7o9JraYzW9kjScYdwo4IiEZ2+VwNX1dNv4F2s3baCyUnldu05uVY6tbvM8EHfsPfI92w6\nPp1R4//MoGGvMn/Fkwy7LYWN26sxmSRyC0T07hJ3z9Kj0ymoqhbZvHcQ02YPua57q83m9dh19SEj\n0xqRE4O0MuSA+8bTEnNVXV3NxhV3cv/MTCQJvlwSzdip3+Lq2rpyd5cUF3Jw5/3MmZhTp5TNZokv\nl3Zn+twvGmXePXFsK/lZ7zF2cA4e7gq2JhsxVIlMHVf/AnIhEzJK36Jn75FIksTRw1spLjyNh2c0\nkdEJHDvwOa6a84iiFlHZl2FJj6BUKhsetAVpyd+e2WwmLy8XHx8fu6QtrRX5f6pxNDUxiKyQWxny\ng954WmKutv/4PeP6vlEXK1xVLbLp8EsMHjr9psrRGAoLLnNo78fodRcwmQUs9GDoyD9QUV7KkYNf\no1GWYrYF0f+2e/H08nbah9VqZd+eNRw98DVP3JOJr4+S3Hwr59LMtItUEx6qZtH64SRNePsm313z\n0xLPkyRJfLJiHtuLT1PsJeJWKdFVCOKFWb9Hp2tZr/ErIf9PNY6bkqlLkiReffVVzp49i0aj4bXX\nXiM8PPxaupKRaVPo3LworRD4eUFcUgZubs6VWUvjHxDC2Mn/sPvzPH0qmcrcV5g9qhyFQsBqlVix\naQOR8W/Trn03hz5UKhWDBk/BXPUjbrosFiyrIDpCTUIXLWfTLOzYa0RUl93sW7tl+HLNAlbp01FE\n+KAFrMARm4W/LniPtx98qaXFk7nJXJNb4pYtWzCbzSxatIinn36aN954o7nlkpFplfTpm8T6XcM5\nfELi0HGJLftH0bP3sJYWq1FIkkTOhQ8ZP7yiroaySiUwY1wR5099cMVrrVIMy9YamDnRgwG9XfHU\nK+nbw4W50zy4dDHzJkhfT20t53VsWf8am9a9T0lx0U0dvznZkX8ShZf9doegVHDao4zMmzyvMi3P\nNa2QDx8+TGJiIgDdu3fn1KlTzSqUjExrRRAEJs98m0sXsxAEgUk9nVdtao2kXThD947nAMc93GCf\n05SXl+Hp6byeckLv20k98Alqtb2TmEIhcFufSgoLCvAPCLgRYtthNBpZu/RRJg4/SUgfAZtNYtPO\n5Sj0f6J338k3fPzmxGKxUIIR8HQ4JobpOXkhhaiIqJsul0zLcU0K2WAw4OFRbxtXqVSIonhVR5Gm\n2tN/q8jz1Hhaaq4CAhquL9wa8ff3ID9fi7qBFMsqpYS3tw5fX+fzWVlZSEw7538XHSKqKazJx9+/\nfXOJ2yA/LHiTB2aerAvHUioFxg4zsObHD3F1nXLdHu83+3kKULmR76RdlWsgcXSvVv1f0Jpla6tc\nk0J2d3enqqo+wXtjlDHITl2NQXaWaDzyXDWOn+fJ3z+arfuiiGl3yeGcnKKOxIuaBudTrfYg/VIA\nXTqWOBw7fd6b2N4RN+W7EGv2OY2NHjWohBUrP2fEqAeuue+WeJ4G+MSxtOI8Sn292VqyicSVeuDl\nEXjT5LHZbCgUCocwuYaQf3uNo6kvLde0h9yzZ0927NgBwLFjx4iNjb2WbmRkZG4iCoUC35CH2HP4\nF3/+ksSmZD2h7R654rUuLi5UWkZQWi7atVdVi+SVJaLXO5pdbwQqpfOkJhqNgGircnqsNfPgxDsZ\nVxyB2/ESarKLUZwuJOG0klfn/PGmjL/z8B5+/+WrTP3iT8z639O8Ov9dKirKr36hzA3hmsKefull\nDfDGG28QHX31UmryG9XVkd88G488V43j1/OUnnaaC2cWolYWY7IG0rXHPYSGXf33K4oiW9a/i5Yf\nCfIvIr/ImyrrYEaNf/6mxRZvXPkEcyfscWg/dEIFXl8THe1YDrKxtOTzVFNTw6VLF/H398ergRC0\n5ubAicO8nvoDlvb1L1OSKBF+oJJPHnv9iqtl+bfXOOQ45DaO/KA3HnmuGkdzz5PVaqW0tBQvLy/U\nanWz9dsY0i8co+TS04weXB9qVVouseLHUUya8eZ19f1be56enf8mpzo7/v1biitJTPfk5UefbfDa\n39pcXSs3JQ5ZRkbmt4tKpcLfv2VyR7frkICg/JBvV3+JqyYTm+iGoE1k4vRr3zv+rXLZWoYzD2+1\nrwfrT5zEb/nX/G7qvTddrt8yskKWkZFpU0RHxxMd/U5Li9HmcRdccHTRA9FsRXDTsrnsJHcZKttE\nKs9bhZatVyYjIyMj0yIMDIzDVmF0aC8/lI5HtwiMHT3ZvHdbC0j220VWyDIyMjK/Qe4cO4vB2b5U\nHs1EkiSshhpKks+gDfRE6aJBrDLj7eE8UYzMjUE2WcvINJHCoiK++n4Zl8uq0LuomTwikV7du7e0\nWDIyTUIQBF6460ly//MSh/efR+mqxXtgRwRl7TotIN3M4IcGtbCUvy1khSwj0wSKiov5wxv/oVgX\njiB4QA0cmb+WpyZVMHJIYkuLJyPTZF64/XGeW/Ie+R3cEJQKRKsNtxMlPNZvdqMSPq3dsZFd2cep\nkaxEany5Z9QsvG9S6NathqyQZWSawLwly39SxvUxmha3AH7YkiwrZJk2SXBgMJ8//Dortq0lMz8X\nb407s+c8eVVnrsLCQt5a+CGnOtpQdnIHBM6KxRxc9BrvTvszQQFBN+cGbiFkhSwj0wRySiprV8a/\nbi+tQpKkRqcelJFpTajVamaOmtKocw2GSv763dvsKj6H1d8Fd7+QumOCQqC0tw8fr/8WV7WWFONl\nLJKNdtoA7k2cQkxUhxt1C7cEslOXjEwT0LtqnLZ76rSyMpa5Jbharqi/Lnyfg7EWjAor7rEhDscF\nQWB77gl2xlRSnKCnooc3x+ItvLjtU7Kys26U2LcE8gpZRqYJTB81lMNfrMDsFljXJpqqGNotpgWl\nkrmVsFgsCIKASnXz/p7NZjMfLP2co4YMDJKZEJUnE2MTGT8oye68zEuZpOorEBQ+INGgVcjsKuCu\nsG+v6ubDNztW8NIdT97Qe2nLyApZRqYJdI3vzDPTK1i0aQfZxQY8dRqGdIvlgbm3N7mvy5dzUKs1\nLZb1SqZ1cfTMcebtW0W6rQgBgVhlAL8bMYd2EVfPM369vDz/bU50FlFofAC4BHyc/SPsgvGDksjJ\nzeGH5DWcyUmjVFWEZ5QnbjFBGFJy8OgcZteXJErQwCI7x1p6g++kbSMrZBmZJjJ44AAGDxxwzdef\nOXuW9+Z9T7pBQolEJx8tLz/+IP7+fpjNZt759EvOZBfholEyqm83Zk4c34zSy7RGsrKzeO3Atxi7\n+QC1zlApwEvrPuKzO/96Q7NlnbmQyknvChQa+5hjKUzP6mPJ2EQbn2dtxhrni9DeG321jpJtp/Hq\nH4PNYMSYVYRrpB8AthoL4ubzeAxz/hLhItzc3OdtDXkPWUbmJiKKIm98sZCLqmBUXiEIXqGk2nx5\n7eMvAHjzv/9jWx4UaIO4KPjzxY4Utu5MbmGpZW40C5JXUt3ZMVSotIcX325aekPH3nvqEEKU8wQg\n2TUlzDu3GVu8X51pWqnT4juyKxXHMvHqF4MkipTsSMFlUxZzy2L58I6X0OZUO/RlKzfSP/Daq3H9\nFpBXyDIyN5G9Bw6QI3rY/fAEQSC1sJqyslJOXixA4R5ed0zSebPj0AlGDG6dIVW5l7M4fuhjXFQX\nsImuCJpBDEt6WHZwayJ51koEwXF9pFApuWy6sWbeEJ9AbOXnUHq6OhyrySnGNjHeYeUmCAKCVcKY\nXYxvpZL+IQP506xH6uKW78pK5LsTO7B09q1NNJJeSmJNCLPunHpD76WtIytkGZmbiMViASd/vCIC\noiiidlJXWK1qnYaswoJcUg//jjvG59e1lZafZvWKHMZP/XsLStb20Cu0gMWhXZIkPBTaGzr2qEEj\nWPDpZor72itk0WwlRONNkdp5retAD1/+Ff8woaFh6HQ6u2OzRk4mqTSRpTvWYhGtjOw9k5hoOeTp\narTOX7qMzC1Ifn4+qelZKIvSHY7FeGvx8fFlcNf2SKaqunZtVT6TRwy+mWI2mkP7/sf0sXl2bd6e\nAlGBW8nPz2khqdomozsNhJwKh3bV2VKm9xvT4HVn086xKXkLJSXF1zy2QqHgLyPvxfdAKbayaiwl\nBizHc0hIUfLPB59DedZZTSjo4BpITEysgzL+GW9vHx6cche/m3afrIwbibxClpG5Ccz/fimLd5/C\nog/GqPPDfPYAbiExCIhEu5p55pG7AXjkrrn4rFzNwbMZuKhVTJsxiW6dOzdpLEmS2H/wIDZRZEDf\nvjfidgBwUV9yapq+rbeRJdu3ETjqzhs29q3GoF4DuT3/EitPHKS6kyeSTcTzTCV3dhxJu6h2Dufn\n5F3mjVWfkOZXg+jrisuaDfRThvPcnCfs0l2aTCY+XP4lx6uyqJbMhKt8mJWQxMCEfnb9dYmJ56HK\nqXyw7TtKAwSUGiXlJhPFlaUMU8WwuSQbhU+94nVPLeeufnfduAn5jSJIV4sCb0YKCytv1lBtFn9/\nD3meGklbmau0jAwe/+BbJH1wXZskiZgyT/DivVNIGj6i2fZcJUni5bffZ0++FQEFPbwsfP3BPykq\nMjRL/79k0+qnmTNuu0P7hUy4bPwvnbv0czjWmmkNz1NVVRXrkzejUasZk5iERuOYiEaSJB799EWy\n+9h7XovVJsblh/HEjAfr2v706d84012J4hdmZ3VaGc92nMGA7n3q2i5kpvHMrk8xx9k7lrkeL+aj\niX9hz4n97Mo+SbVgIVjhye8n3Y6nWwDFxcXo9XpcXFyaawpuKfz9m+YdL6+QZWRuMKu2bLdTxgCC\noEAb1Z0zGdmMakYHqMzMTPZcMqD2rh3vSEkxh44cJSqi+ROXhERN5fiZ3XSPq9/7lCSJ5MPxTJrV\ntpRxa8HNzY0ZY66cwnLv0f1kRcCvd3YVOi37Ss/xxE+f9x87yJnQGhRqvd15lvZeLDm6yU4hf793\nrYMyBqju6sO8jYv5y51PMJWJQO13vGjbElanHaTE3YqbUaCbOpRnZz4mK+brRN5DlvlNUVRUdF37\nbdeCTRSdtguCgNVma9axtFoNyl9mZRBtuLjcGKegLl0HcansSZasD+FsmoU9hxXMX9GTAUP/dUPG\nk6klLScTZaDzlVeFYML20zN1JOMUimC90/NybOV2n4ttVU7PExQCmy8csEun+fXahcwXT1Le0xtl\nrD813f3YF1vNK9++ey23I/ML5BWyzG+CXfsPMH/1FjLKLSiQiPF15eGZE5q8P3stJPbqzsZzW1G4\n+dq1S4ZihvVv2GHnWggJCWVctzDWH89CRGBkx0C6du58w0yxAwbNwWabRdqFs3hF+jChr1zh50bT\nI6YrC88cRYhwjB32Q4fyJ099T607osmCQuuYjEOHfZte4QoYHc6TJIlKFxtb925j5MDhSJLEj7nH\nUfS0H1uhUnLSrZAdydvp3rU7XtdYftFgqGTtzk1oNRrGJo5Cq72xHuatDVkhy9zynE9L4+3Fm6hx\nD0Lx0//EeRH+/vn3fPbyH/Dx8b1yB9dJv969GbxrHzuzy1HoPAEQq8sZFq2nZ/fuzT7eHx+6n7uK\nihBFkYCAgGbv/9colUpiO8bf8HFuVfLz80nNOEundp0IbMT31aVTZzru1HE2VKyN8f0JsbiKkWE9\n6z5PHTaBFd/so6qXn931thozvbza27VN7TGCbYc+xS0+1K698uRF3BMiOZ2XzkiGU1VVRYnW7CBT\n2YELiFYbf9Oswm3VKjpZfHlh+mNNqov81dqFrC44hDHeG8kqsmD+Du7ulMSExNGN7qOtIytkmVue\nxWtrlfGvqXAL5dvlq/nDA/deU78VFeV8+PUCUrILsYoSHQK9eWDmJNpFRTmc+9KTj/PjzmR2HTuN\nIAgk9ujLsMQbl+zDz8/v6ifJtChGo5G/L/qAk9oizIGuaDevpJvZn5fnPHXVvdh/zH2at5b8l9NS\nPlWu4F+lYkRQAneMn1l3jqurK3/oM4MPDy6hvLMepYsGKbOMXmXePHrvPXb9dYvrivdSE3mlZ3GP\nDUYSRSpPZaP2cUPt5YanpdbDWqfT4W5S8ksXwfLD6ejaB6LxrTWji8BpSeLFhe/yz9l/4vMNC7lg\nykeBQLxbGA9NuBNXV/uY5+0HkvlBPInQza92H1WlpKqnL5+lbqbLxY5ERURd4yy3LWQv61ZGa/D0\nbCs0dq6eeu3fnDE533PzL0vF3duPokojXjotw3rEccf0q2cTslqtPPziP8jWhNplWPKsyuaDZx4l\nOKj1mG7lZ6px3Ox5evHrtzgab7Vf5Vpt9EnV8vd7nnF6TcqFM3y5ezkXTAUIQAd1ALf3SKJH1551\npupfYzKZWL1jHaXVlQzq0pe4Dp2QJAlJkuxCpL5es5BFuhQqz13GXFCBJsgLpUaFLbuMl4bcz9jE\n2spP7y7+mM1hhShdas3eJbtS8RnUyWFcW24FmoN5WCfG1EURSDaRiAMG/u/Rf9hVs3rh27c5Hmd1\n6EOSJIalefHM7Y9dZTZbJ7KXtYzMr/DUacDk2F5xKRWLlz9qyRfcoRKYtz+Lsor5PH7f3Vfsc9na\ndVwU/FH+KutWmS6U+ctW8exjD9e1lZSUMG/JcrKLK9FpVYwa0IvEAddenEKm7VNaWsIJRT6C0r7S\nl0Kl5Bh5lJeX4elpv097Of8yf93xBdUJvkBt+c8U4L39i/m0fUc8PGoduM6kpbJg7xqyzMVoBRU9\nPdvx0KS7UKlUXMy5yHPz3+JsTS4SEu01gdx/2xQ6x8Rzz/jZXPz6XdYVnSdgfL3pm64RfJq2hcAU\nf3rGJ/DUjIexLvmIXZYszBHuCA0s6ZTBeor8cvH+RRSBoFSQ0U3Dsq2rmTW6/sXXIJlw9BuvdXys\ncpLB7FZFVsgyzY7BUMl3y1ZRUG7Ax92VO6ZOvGYnj+Zg0rDbOPTNeqxu9X9+kigimapQe9i/2Qsu\n7mw9kc591dXodDokSeKzbxew+3QG5dUm/PWujO3fnXMXc1FqHM2KgiBwqbg+49LF7Gz+8t5nlLiF\nIQjuYIQDS5OZmZ7Fg3fMbtJ9JO/dx5ItyeSVVeOp0zCsZxxzpl45REamdXIx5xJGPzXOXJaqfBTk\n5uXh5ubO8h/XkFF+Gb1SR0F5EVXdffh1kFxZD2++3byU3027jzNpqby8+0uMnb2BWgWdbbpExpdv\n8srcp3hu1X8o71uv0FOBV5O/5H2PPxIaFEqkfyhesY6FISztvVh2ZAs94xNQKpW88/sXOZ2SzuGU\no8xTVeDMrmDOK0Pt67hCVLm5kHI5y64tSKUnDUdPb9FiI0TrvPDFrYgc9iTTrJxMSeG+l99m6VkD\nuwqVrEwz8sCr73Po2LEWk6lXQgIPjuiBd3U2lupyLFWleJScxcUn2On5ZQoPTpw6CcC//vsZS06X\nkq8JosYrkkuKAD7dnkpGRhoN7fboNPXvuf9bvIxS9wj7wgE6H1YdPEtpqfOUhM7Ytf8Aby35kVSL\nN2VuoWQJ/ny1N4PPvlnQ6D5kWg/tIqPxKHA00QJ4FolotFoe/PR5vnA5xo525awKy2FT6Umq0/Id\nzheUCnJ+KkDx7e7VPynjehRaNSeDDbz51fuU9nRUblXdfPh223IALleX1Jmif02hzV7tBvj7M3bI\nKJIiemMrt/fQliQJ24FLuMU6/43VVNkr/bmDJuF62rGIhvfRMuaOnO60j1sRWSHLNCv/XbSSCo8I\nhJ/2swSFEoM+nI+/X9OgArsZTB03hm/ffIl/zLiNt+YO5/PXXkSncB4frLQaCQoIoLS0hF3n81D8\naiUs6DypFNUoyy87XCvVVDKkZ30o1bnLziv11LgHs2bz1kbLv3RLMhY3ew9cwcWDzcfOYTY7er3K\ntG48PPT0UoYjmuzNsbYaM321UXy+4wcK+nmjdKtdQwtKBZ5DOmHKLUOyOT63bj/VGb5odR5jrwjx\n5ExpJgqVc7Nwrq3WquOl1DntH0Av1P8OJEliQ/IW/r7oA7KrCok5WIP2eBHmokrEC8XEHLMwt8to\nbBWOoVTG7GIyLl+0a4sKj+LFPncQe8qG8mg+6iP5dDkl8MbEJ3B3d3cqz62IbLKWuW6sViurNmwk\n5dwFUguNaPwdz8moUnDhwgViYpo/Y1RjUalU3Na/f93n+CB3Thglh7SVMZ5KoqKi2bBlC9Uab0wF\nF7FWVyAolLiHdEChUlNm03DHgI4s25+C0T0UFApUlbmM6hzG+KSRdX0pfmVflGw2LNXlCGotSmXj\nw63yyqrB1dOhvcikJCcnm+hox3zHMq2b5+b8nne+/5iDNVlU6CU8KwT6ukbz1IyHmD3/WQTBMQTK\no3sklacvoe8WWdemSCtlcq/arQut4Hx1K9lEXEQVNQ3I4i7Upugc0X0QP6x5E9dE+2IQlpwyFCUu\nlJWV4unpxZMf/I3dAaUoY9wAsPlr6HJezQzf0YR2CSU8NByDwcAX/7wH3W3tcAnxAaA6LR9TXhnl\nXQJIvXCWTh061o3RI647PeK6Y7VaEQShQSe1WxlZIctcFxmZmbzy0VfkKf2w1NRgE1Q4Zt8FSaGk\nyui4N9WSPPvwvTz/7kdkWT1QuOqxGQ2ECaX85fH7AQjw9aXswhH0kZ1xC4hAtJqpuJiC1tMfb5WN\n26dNYfqk8SxftwGTxcL44Q8RHGxvoosP9ye5qNYyUHExBUmU0Hh4YyvJ5fhZM5OrqnBzc7uqrJ46\nNSW/MDDUlOZjrirHUl3JM//+klkj+zNr0oTmmxyZG45KpeK5uU9gNBopKirE3z8AFxcXzGYz5gY8\npVRuWqrOXsY9PgxECbcz5cyOHEx8TBwA3T0iyTHnotDY/7VrThfz1OQHeDV1McTavwhaLpWQFDMZ\ng6GSf2z6FDHYnZLkM6h83DEXVCBZbEiiSLKnjtSlf6dztTdH4i0oveqfW6W3G6fijAwoLaB/79qX\nXoOhEn2PKMxGC6V7z4EErhG+eN/WEWulkey8HDuF/Mt5ccbeowdYe3onZaIRP6UbM/qMoUvsrRX/\nfl0KefPmzWzYsIF335VTpv1WeeerhRS4htfufZhqqLh0Bouhdm/UPTQGpbrW5BasrKFr5y4tJ6gT\n/P38+N/rr7AtOZnzmZeICI5i9IjhdaEga3fuwTd+IMJPnxUqDV7tulN64RjFFgOvf/Q/Hpg1hbtm\n1cd+lpaW8tX3y8goKEOrUtAlKpigS6c5m1+BW2AUat1PTi5+oRytFnn53f/jvVeevaqsI3t34X/J\nZ7GIApXZ59H5h+EeGEVlbhoXi8r4cvspwoICGdi3z1X7kmlduLq6Eh4eUfdZo9EQKXiR6eTciuNZ\n+AyNx29DDpP6JTF+9hi78oePTr6HjC9f50x4JYpADySbiPp0Mfe1G0nvhN6olv2XwpJiPHu2Q1Aq\nqDiWiVhlIkuTw+mN5yju7Y2bUoHCRYMxswC/YfXbL7ZqE3n7zlNkysPLq5uDbEoPVw6nnuXnHV9/\n/wACDGpKEvzQtQ+0v+esKvpM6enQR0P8sGUl8yv3IcbpARUZmDh2ZB5PVUxkaO9Bje6ntXPNCvm1\n115j9+7dxMXFNac8Mm2Iy5dzOFdqQeEFVXmZiKKVwIThCIKAZLNRlnESt6BIXAWRGcP6tEoTlCAI\nDB88mOFOSg6fyMxH8IhwaHcPi8Vw+QIbj6Wx7fA/6BUbRb/49gzs35/nP/iCIl0YguAJFjh+LJ8B\ngT4UlRuw6ew9TgWFgtMlNs6dP0/sVUz5MyZOoLK6ho8XrcSn65C6ds+IOKwmI2V56azduVdWyLcI\nsxNG87cT36HtWp85y1xUiWiyoHJzYVzf3swcM83hOrVazXuP/JU9R/ZyIP0krgotMyc/io+PL3uP\n7Mc0IBhPLxcqT2QhiRIencNRumnZfuwEfi6edTHRxswCh9hipU6LW/tAyk9mOYz7Mzapfv9ZqVSS\nFNKTRQXHEQJq94FtNRaosTBU094hrKshLBYLSzN3IfbwsW+P8WLBsY2yQgbo2bMnSUlJLF68uDnl\nkWlDlJWVYRE0aGw2LMZKvKK71h0TlEq8OyRA5iFeevx+Bva7cXV5bwSSJGG0OC/8oNK6YizKJqjX\nKBRKFWdEOLLzPP/6chH+fcbZ7UkrtG7syy3CXGNyGuIiuftx5MRJO4UsiiJrN23m/MXL+OrdmDlp\nAjqdjn7dOjN/V6pTeSTRRmXNbydeszVSU1ODWq1ulhfPxJ4DeKaqitdWfYE1wBVsIkoPF7z6xRC8\nv4wZj0y+4vUDew5gYE/7WPczF8+jDK99KfTsbZ86s9hmIEzwBWqfecGJ8xeAa6Q/5YfSsFWbUOrs\nn2hbjYWC9EJKSkvw8a5VnnePnYXrFi0L1q8mnypUvu5ozVDppaeiohy93tEv4tfsP3qAkkgNznbH\ns3RV5OfnExgY6ORo2+OqCnnJkiXMmzfPru2NN95g7NixHDhw4IYJJtP6iYmJJUBVQ3ZeOh6hzld4\nGk8/evdIuMmSXT+CIBDhpyfNicOp4XIafvEDUSjrfz6uPkFYQmKwGitR6+wr7AjufvxdQlm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jP7UHNgMxEZPVHpWiNLq/dtJCw+jbCUrtgAmwTPfbUZh9PFpZMuBCAmPAyl3hfQb0VRiGojmCYY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\nSy+8gKVPvYG7vtqv7jE0B4ZZ0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "size = 50 * probs.max(1) ** 2 # square emphasizes differences\n", + "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=size);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Under the hood, a Gaussian mixture model is very similar to *k*-means: it uses an expectation–maximization approach which qualitatively does the following:\n", + "\n", + "1. Choose starting guesses for the location and shape\n", + "\n", + "2. Repeat until converged:\n", + "\n", + " 1. *E-step*: for each point, find weights encoding the probability of membership in each cluster\n", + " 2. *M-step*: for each cluster, update its location, normalization, and shape based on *all* data points, making use of the weights\n", + "\n", + "The result of this is that each cluster is associated not with a hard-edged sphere, but with a smooth Gaussian model.\n", + "Just as in the *k*-means expectation–maximization approach, this algorithm can sometimes miss the globally optimal solution, and thus in practice multiple random initializations are used.\n", + "\n", + "Let's create a function that will help us visualize the locations and shapes of the GMM clusters by drawing ellipses based on the GMM output:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from matplotlib.patches import Ellipse\n", + "\n", + "def draw_ellipse(position, covariance, ax=None, **kwargs):\n", + " \"\"\"Draw an ellipse with a given position and covariance\"\"\"\n", + " ax = ax or plt.gca()\n", + " \n", + " # Convert covariance to principal axes\n", + " if covariance.shape == (2, 2):\n", + " U, s, Vt = np.linalg.svd(covariance)\n", + " angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n", + " width, height = 2 * np.sqrt(s)\n", + " else:\n", + " angle = 0\n", + " width, height = 2 * np.sqrt(covariance)\n", + " \n", + " # Draw the Ellipse\n", + " for nsig in range(1, 4):\n", + " ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n", + " angle, **kwargs))\n", + " \n", + "def plot_gmm(gmm, X, label=True, ax=None):\n", + " ax = ax or plt.gca()\n", + " labels = gmm.fit(X).predict(X)\n", + " if label:\n", + " ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)\n", + " else:\n", + " ax.scatter(X[:, 0], X[:, 1], s=40, zorder=2)\n", + " ax.axis('equal')\n", + " \n", + " w_factor = 0.2 / gmm.weights_.max()\n", + " for pos, covar, w in zip(gmm.means_, gmm.covars_, gmm.weights_):\n", + " draw_ellipse(pos, covar, alpha=w * w_factor)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this in place, we can take a look at what the four-component GMM gives us for our initial data:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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/y6bNc5k9d/mI2jUtD0k7T1NLB1MnNaBppfUdkEhKwRErvs3bmxFxpcihyHBN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SeeuHplfLrDQ0tbcNQ1rP+0p7ljXFWSqdSfjOEFuP/mB5A3y2w9vZ84GaqEj/tnf4q50+bR2t6J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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gmm = GMM(n_components=4, random_state=42)\n", + "plot_gmm(gmm, X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Similarly, we can use the GMM approach to fit our stretched dataset; allowing for a full covariance the model will fit even very oblong, stretched-out clusters:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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SYwkMx6F3Bf/mZ3fw8HceRM/VuuR33L2D7e9/iU/+xZ/WHafpOkXLYmAkQm+X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\n3ClIquFiZFyUoBSEN8IhB99bb72Vvr6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6WlvaEsmxinQ7SyRHCNN6OrDKk5egNE2DjrCP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\nsBUHw6MxbLt1EpThcDEwmqJY2v/aS4493rHJOjQ0xBe+8AVuvPFGLr/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sh8OB0xCcd/Ip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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gmm = GMM(n_components=4, covariance_type='full', random_state=42)\n", + "plot_gmm(gmm, X_stretched)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This makes clear that GMM addresses the two main practical issues with *k*-means encountered before." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Choosing the covariance type\n", + "\n", + "If you look at the details of the preceding fits, you will see that the ``covariance_type`` option was set differently within each.\n", + "This hyperparameter controls the degrees of freedom in the shape of each cluster; it is essential to set this carefully for any given problem.\n", + "The default is ``covariance_type=\"diag\"``, which means that the size of the cluster along each dimension can be set independently, with the resulting ellipse constrained to align with the axes.\n", + "A slightly simpler and faster model is ``covariance_type=\"spherical\"``, which constrains the shape of the cluster such that all dimensions are equal. The resulting clustering will have similar characteristics to that of *k*-means, though it is not entirely equivalent.\n", + "A more complicated and computationally expensive model (especially as the number of dimensions grows) is to use ``covariance_type=\"full\"``, which allows each cluster to be modeled as an ellipse with arbitrary orientation.\n", + "\n", + "We can see a visual representation of these three choices for a single cluster within the following figure:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "![(Covariance Type)](figures/05.12-covariance-type.png)\n", + "[figure source in Appendix](06.00-Figure-Code.ipynb#Covariance-Type)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## GMM as *Density Estimation*\n", + "\n", + "Though GMM is often categorized as a clustering algorithm, fundamentally it is an algorithm for *density estimation*.\n", + "That is to say, the result of a GMM fit to some data is technically not a clustering model, but a generative probabilistic model describing the distribution of the data.\n", + "\n", + "As an example, consider some data generated from Scikit-Learn's ``make_moons`` function, which we saw in [In Depth: K-Means Clustering](05.11-K-Means.ipynb):" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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py1/+su66666xx/1Sf/H2T0q//lyfnBbPyZMnVVdXJ8uydPHiRXV0\ndOgv//Iv3S5WVpi+lvucOXPU1tYmSWpra9PnP//5qO2m1l3kfnV2dkadaMyePVs9PT06d+6choeH\n1d7ers997nNuFdWRRPs3ODiompoaDQ0NybIsHT161Ig6i8WyLQbph7qLZN8/P9TdmTNndN999+m7\n3/2uVq5cGbXND/WXaP+c1F9eWtzpiFzvfMWKFaqtrdVVV12llStXavbs2W4XLyN+Wcu9rq5OW7Zs\n0fr161VSUqInnnhCkvl1t2TJEr3++utat26dpPCQx8GDBzU0NKTa2lpt3bpVGzZskGVZqq2t1YwZ\nM1wucXqS7d8DDzww1lMyb968sXkMpikqKpIkX9VdpFj7Z3rdPfPMMzp37px27typp556SkVFRVqz\nZo1v6i/Z/qVbf6xVDgCAQTzbVQ4AACYiuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGOT/\nAwS7zXKJEUjLAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_moons\n", + "Xmoon, ymoon = make_moons(200, noise=.05, random_state=0)\n", + "plt.scatter(Xmoon[:, 0], Xmoon[:, 1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "If we try to fit this with a two-component GMM viewed as a clustering model, the results are not particularly useful:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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JayeBMArp6kuRKXiYVrzuPcXFLpKLCH3k9g/ffA8rtLGFQ5IWPpTv0Se30Su3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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gmm2 = GMM(n_components=2, covariance_type='full', random_state=0)\n", + "plot_gmm(gmm2, Xmoon)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "But if we instead use many more components and ignore the cluster labels, we find a fit that is much closer to the input data:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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QHHzuCK6vJKK0DBGN4hjPC1DmHXtg3yH+5St7+fZdNZ7cfZDe/oi+gdmuYVEU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AEeeeAuivkYqXnFa+BVODctL6/o174kymGKJf+QuRIOc2SiyJrlQ2dE3rYTsUiEWAS0ZhPDsLAd\nBxDAsQgpYotgu6471T7mIokiSode7tMJ13U5fCyHIEe69p97ol3CFVVEUaB/wH+aYXZAAxfKlRqm\nLSBNVaTf+43dVCZvbtm3VrmSr/71p+kfrFEqHvQ9XqcqcgDBtYjH2u18JdckFo9hTF07FUx6ki++\nKvL5BMI9D0kUsBberSvnnre2bSznonBMepJBbvt0YGggw+6DYyih9tVFLBahoemYuGQHGvB0+/NH\nzimxeuSlM/+e657WCVmWqdQ0X+EWBIF60yHWSdiVKOO54hk3RcyPSDjM3JqlnkQUXbNmbsgFQJTO\nrGl7rutyZCyPIEe6vi/HdZnIlxCk8Iy787U3LOWpJzaTnzNeNpPdzFVvGCRfqiBIKuIcpdi3278g\nrFJeRqX8R8B2BOEhXPd1M491qyK3TINMb/vvkaU36J8TErd0jaVDZ06XxLMhEO55SJJIB9Oqk4pl\n6mT7gnnbpwuCIDCUSXI0X0cNtV/IMuleDo3lufq6JWx5cnPLzO10djM3vGklLrO9wn593L5CLqlU\nanWS8fYLnayq5IpVlg6F2iz3RVGk2jBINjSii/AROJMQRbHr6MkzgWPjBZDCC4r2eL6EKLU6oW3Y\nuIq/+Kt9/ODer5Mbj3jjNK9O0790GYLsF4noNMRjevsGXBey/Z8n3b+iaxW5ZdnEo6qP97hBTzI6\ns92yDFK90TP+c1wsgXDPIxxSqVeMjv2dJwPXdQkrAuFQMEzkdCIei5GoNtCcdmMWQcDzHF+7hFs+\nfYT7v/dPM60wV1+3hJGzNjKRLyPIoY593NPtYnMRBYFG0yQecRB9WnxEpbOrmhIOc3SixJoV3S/w\nAacXh4/lvJx2l8/UdhzG82XPvtSHDRtXeRXkLhTLVWwk3+8XwKo1Fk/99id4tRrT/ASYm+bbQLp/\nBX/5N62jV+cjCw6xSOtNqOM4hBVmWiAdxyEsQ8LnZvXFSiDc84iEw7ilOjyPwm2bTQYHghDQ6cjg\nQIY9HYxZopEw0XqTdRtW8pE/b2+rSfXGyZfrHfu4p9vF5iPLKqVqjbTPiE9BEGgYLrpu+NrqimqU\nsfECQ4NnziCSFzMT+SLRZBJR7JzgW0i0p3FdKEyHxrvc1/3eazM889ROLCsHKHgtjgZwSct+3XLa\nALZpkEn5FOLaTfpSsykd0dYZyAbXx7kEwj0PQRCQ5eev2N62LHoTkTPKE/nFhCAIDPf3cThX9Q2Z\np9O9Xu7Rx5hFlmWSMbVjtW2xQ3/33p0HGL1/jEopzsBgc2oFP3tjIKsq+VKFJT7iLIoiVd0hUa+/\nqKYpnYkUimUapki8S/jYC4+X2bXzGD+49zAT4xFfgxXbsilWaohy96jf7u37ufcbK7GspcBWAGRl\nD6qaoVHfMLNft5y293omiVh7lMDUG2RTszekltFg2ZJVFArdC3tfbATC7YMqi5gnXFp+fCiiRSL+\n4ppsc6YRjUZIRhrUTbstBycIMJDp5Vhu0tdKMhqJ0N9fZ/f29uOmfPq7Z8Pq7wVg+xbY8uRmbvn0\nvhbxdlGpVGskE+2RADUUZjxfCYT7NKZUnqTWdLsO15hu+dq18xh/9RcC+dzbZx6ba7BiWRalyQai\nz9yF+XiTvt499S9PqC3zWs4+9/Mkeu6mMBEh299g0+sHWbdhje8xHNshJNPm6GdNifb0ezINjcFM\nT7Co8SH4ifigKtJxWZ+eKJahkU33nfTXCTj5DPanEWz/XmlFkelLhrEs0/fxN7xxBX2pH7dsm9/H\nPc33v72XUqHV3KKQu5T7v3ekZZsoS5SrTRzH/3vsSiHyxVLH9xNw6jJZqXpTvhYU7RKCFOIH9x5u\nqRiHWYMV07IoVRqICwwfmXleh+iQYSznfR8+n9u/8go++f9ewtp1w/7XUBdE12rzqrCMOv2Z3hnR\ntiyDdE80qPvpQCDcPsSiUUzTf37vc4VtmkGI/Axj2VAGo+kf0kvEY0RlfC9mI2et4hOftfm9136V\nkbO/2XFW996dB9j+tP+Nnt8FVVbDFIuTvvvLskyx0sS2X4AWioATplqrU6oZC47mzBfLuKIKAkyM\n+3cRjI+HKU82EBc41lw65a1TmTo98TDSlC1vqq8H12rizPt+2ZZOX9+saFuWgWA3GcikZivIbZt4\nSAyK0boQhMp9kCSJRUy0O2E8r2Qh+GKeYSiKwmA6zlhRO+589/qzV/OJv1zFRHESqUOe8ZEHxrEt\n/yId/wuqQMNwOhaqKaEoY7kig4MvbvvI04V6o0FhUkNZYHpXvjiJ5cqIUxexTgYrPX2VRa+0p7n8\nmiG2Pd0+6eva6wZbvmOCIJBO9VKr1Wg0dSQljGPbpHpiiIKAZRhIokNPLNwyQMd1XRRM0qmgeLIb\nwXKvA9GQfFLC5Y7joAgWqd4gr30mkkwkSIREX9tNQYDBbC+W4X8hFUSBTF8Cy/KP9njDSDbitd7M\nIsk/4urrlvg+R1ZDFEr+fbeeaYtLveF/PgGnDprWJFeqLyjaxfIkhtPaynXtDUvJZDdP/Ws7cC+y\n/M/Uqhq7t+8/rvNYu2ElN/+5zqsvvpsN53yTV190Nx+5tcFLzj/bd/94PE423YuMTl/UJSy7qKJF\nujdCf6avbeqdbWoMDZz5JkHPlmDF3YHengSHxoqooefWrEKwdQaCVpwzmsGBNHsPHoOQ/+zuuZao\n85EkiUxvgny5ijxv5Z3O1NnFdOXuvUy34qzfuJPV697gey47t+3jh989RCmfYMlSnT98y0rOOXe2\naEgNhzkyVqQv0d5aFnBqYNs244UKSqj7hMFqvU7TFJDmdcWsP3slt352D9/4p7/mqd+di2XegGXB\nU7+BwwdGufnP97O2y9Su3dv38/B9x1pGcn7g468CwLUMMn3dvzuO4zKQTtLjUyg5F1PXWDLQF3gM\nLIJAuDsgiiIRVeK5zADahsZw0K99xiMIAsuHs+w7kkcN+1uimpZFVbOQ5vgF7Ny2j/u/d4TceJRM\nf50LLull5KxZ69xLrhpgx9bRqeI0T8D70qO84c0bqTe0tslKO7ft4wuflinkvCrgp5+E3/73I3z+\ni3taxNsRQxSKJdKpoFDyVMN1XY5OFFtE27IsDMPAtGwcx8VyDcbHJylWm4iS5A2bccH1/oMrCGSH\ns4RjfVhm68CbYn4T9937D/zJB1IgeIZqkiggigKSKHJ4/xh/9/nITCX5jq2w7WlP7FevGSbdl6Db\nLGTXcVFFm55E9wijaepkU7GuVfIBswTC3YXeZIyxYg1lEW0SC2HqTQaC1oYXDV6+O8FYseGb7+7t\nSWCZZZpTrmuzIvvOmX22PDnKuz+8m5GzPfHuNozEtNsNOO7/3pGW4wFMjF/Cf3zzXzjntlnhliWZ\n8UqTvt52B7iA5x/HcWjqOoZhcmyigG7LuOg4jovjuAiChCBJSJJ3+TZsiXLdQfGJ8MylU0V4uZhs\na1V08OxIf/i9IxTz/3fLY8X8Jh76wZ3c+pdrEbs4tbiOi+iaZNLdayhMUyeVCBGLdo8oBMwSCHcX\nVFUlJIHtMwXqeDANjaGVw1Sri5vJHXBmkEzEaWg6dcOeqbadSybTy9GxPIhhX5Et5jfxy0fvYc2I\nPlOw1mkYiTXHYH965f7rXzh4IfWNwKw5xvhYe/pHCUU5NlFgyWAween5xLZt6g0Nw7SwbAfTcnBc\nECWFhtakbkeQJclbCUvgZ7VSKFZnJnd1wjQtelI138dSWf9OCFGSKBf8RwwXclFc16ajhLiAo5PN\ndI8wWpZBX1zx9RsI6Ewg3AuQTfdyZLzka56xEK7r4lg6Q5kewuFQINwvQgb70+w/dAzw7yAY7E9z\nZCzfcfxnIRejJxZmolDCRgZhKhSKizMVEgUwDB1RFNi76xD/+L+SlIpzbwKmi9k88e7pqzBRmJyx\ntXRci8nJBq5tEA0r9CSTwcr7JGBZFg1NwzBtTNvBshwcBGRZRRQVEGG6M0trNqlq1oJtX6VylWgy\nDnRuX3Ucl0pd4+KrBtn1zCjl4mxFeG9qlIuuaPcLmCbVX/fq2eaRHdCZrBlIgkY8FkFVW6OS7lSL\nV7f1jmVZJCISPcmgvuJ4CYR7AURRJNMbI1+uH5d4W5aFKtoMDaSCYosXOcuG+9l7aBzFx89cFAUG\ns31ksv6V37GeClXNJpboo1avoxn27MVcYCa/KMo2oqTy2ENFSsXr5x3lQryV9wb6Uj/m6uuX4aAw\n7c3StCV0W8ZxRbbsHmcwa0zNqRaQBAFREpFEL/cpiQKqIhOJRJ7XQTynIw1No9k00C0b03RAEJEV\nFUFoFen5mKZJcVJDXiBFV63XMd2Fx5NWaxqSrLJq3XLe+7GDPPbgnRRzMVLZOhddMcCqdcs7Pvei\nKwbYuaVV7KftTPftOcrD9x0jNx4m21/nxjetZsPGVd7shWxfd9G2baKqQ6o3qPk5EYLfvEUQiYTJ\nALlyDUXtXmXuui6W0aQ3HiaZDFq+ArxK8aUDKQ5NTLZ0Kbgu1Ot1Gk2TS6/M8NTvfky5eNnM433p\nUS57/RDilDFFPBYjFDKp1TQcJERp9tdXwCuKK+b8V+6RaJHzXnE3F70uw9IV7QNPAERBxJYi2I5L\ndE6+0QEcF0wbsKHStLFKRURcFElEkkRkSUCRRaKRMKFQ6EV5s9rQNLSmjmE5GKaDKCnezY0os9gy\nGddxyRWryAsMBNENHc1wFxxz2dQNbHe273fVuuVdhXo+02L/6ANfpVJKeiM/pzzIv/Q5lWL+XYBX\ntPbM06N88rPb+f3XnLfgpLKwZJNNB6J9ogTCvUgikTBDskShXMGwBJR5oSHbtr1QY0hmcDAVhBoD\nWohEwgymTMZLTRAkypM1dMtBlFREUeWlLz+HP/2Lbfzwe3dSKsZJZRpc6jOTW5EV+noVdN1gx7bd\n/OThEpOFOOn+BpdfM0wq6+9sdd4rBN7z4VcCUG80OxYCKapKqVJvEe75SJKEJM3egNiA7UBTdynW\narhOGUUSkSURWRaQJZFwSCUaObOcAg3DoN7Q0E0bw3QQJAVZ9lbT6gnWs+aKZaQFVN7FZbLaXHA/\nXGg0dUTp+ExW5rNi9SA3f2J1y03CV/7mP2dEe5pifhMPfO9uLvn9l3U8luM4KBgMZIOW2GdDINzH\ngaIoDGbTGIZBraFh2w4gIIoQiYaIRoJcTUBnEvE4h4/mmKg4hKNx5HmdL+e99CzWjKyYym12b4v5\n9U+f4Jt3DWMYbwJg707YvW2UP3iTy44toy1+5n3pUS6d43tuuSKmaaB0KGiyHJl6o3HcVb6CIEzl\nOj1BcQDD8f5UGjp2voYiCSiyhCILRMMhotHTS8zr9QZ1rYlhuTju1A38cayou1EqT3pztbv1VwGl\nch1JDuG67swf33PVNC8sf4K4jguuSW8i3lY93qlCfXw8hmmavm1drusiOjqDgY/FsyYQ7hNAVVVS\n6rO7iw14cWHbNgeP5oj1ZIgZBQy7fZIYQDwWxXXr1DSz4xCJvTsP8M27dAzj6pbtpcImtm+5mw/8\nmcbmB+6mmI/6rtwVRaVa00j1+X+HFVWlPFl/TttzZEWZeT82YNtQmzSwCzVkCVRZQpVF4rEIkchz\na3r0bHBdl1q9TqNpYJgOiAqyHEJS/Cu8T5SGptEwXATBoWmaWLY90/7luN55uHgOao2mgzDjySxg\nWCb1enOm3kEQBERBoFLVUBQFWZGRZeW40heObaPIkIj692l38iwfGNKpaxq98767ruviWhpLhrIv\nyjTKc00g3AEBzwMHj+aQVK+yfKA/w+Fj47hizPcilojHcN0qDd1E8ll5b35gHMPwz1MW89GOLWNz\naZo2bpc2R8uVqNcbxGInr7dWluWZAjcb0GyoFBrgVFBkEVUWCYdkkonE87oqd12Xaq2Oppvoho0o\nq0hSqGMx2YlgGAa6bmLZDoZlMZ6bRJBDXo+2KM70aCPO1iDato3uyKiR1su2ooZQzNbPUW/qCHIE\nWxAwmhauayAI3uTDcCjcVTxtyyQWUQn7+NtPc/k1QzzzVKtneTa7mWtvWIZtt0VSzaQAACAASURB\nVEcAHEtj6WAmEO3niEC4AwJOMvlCCaTWYqPhgSyHjuU6mmYkEwlct4JmmkhSq3h7BWj+I0JTGf+V\n0Fxc1wVBplwuE41G0Rreig9AEkUEUUAUJfLFSSKR8PMqml743hMMw4VmwyFXzqHKAiFFIhZRicfj\nz7kAOI7jVe3rFrppI8khRFF9TkLgnkgbXp+27WDZLqIoT7nmiZSrDdRokq4WZExVh0sKe3ce5LEH\nxynkYqSzda66bimDy4Za9tVNB2EqouO9jnepN20HvVpDlaW2OgbXcRBcm75EtMXr3I9161dy8627\nePyhr5Mbj9A/qHHtDcs4a+MqcFvbXi2jwdLB9GmVEjnVCYQ7IOAkU9NMpHkTwURRZDjbx9FcGSXk\n3+Pdk0ziTE6iW9bsCgymCtBehteffeHMdlW9fyaX7bquZ41pWl6o1XGxcXFsF1wBRKg4BilUbFGm\nrrtTz7MAF8dxsEwT3ThKOBoFXERRQBRAEkQESUAWvMEokiigKAqqoixY5Xy8iKJIKOwJjOFCo2Ix\nXppAlUVCikQiHmkbVHE8NBoa1YaGbjhIypRYP8uVta4bNHXPktS0HIQZkZYQJFDm/IgaDQ3LlZAW\nEO2GpmEjcWDnQe68PUqp8B4Adm+DXc+M8p6PHmT1yPIZUR8fC9OXqfPqy7KsWLNs5jiiKCKKYSzH\nYbJSIRGPI4oitm0TlgVisUUYobiAY3DBBefwqgvaz3s6H+75WGgsG8oEov0cEwh3QMBJxnZc3zF8\niqqS6YuTLzc6thn29fRQrlRoGrNh80uvGmDHlsOUCkuZHjaiqgd5y3tCDK94CZVqA8txEUQRUZxS\nCdFrCZp7/TR0E1EUkUSx7cIqSd7qt2HUObbnGPd99zC58SjZgQbXXL+U9WevwnABG1zLxdEMHLuB\nIIIsCIiyiCx6LWKRcGjBYrvFIssyTIXXdQdqhQY4k4QUmXBIIhmPoS5Qf+I4DpOVKvWmhSvIyHLo\nWa2sbdum0dAwLAfTskGYEmpR6hpet22bqmYuaLLi4qLpNpKs8NiD4zOiPU0xv4nHHrwLgK98vkyt\nkgIK7N/Z4MlfFli38QCXXb+iTcARI1SrNZLxCMlomP17DrcNE1k3b/iIZ2NqkeowWMQyDXp6wl5O\n226ydDAQ7ZNBINwBAScZqYufcywaxTQtJusGSgfB6U0mqVSr1HUTWVZYPbKCD/zZATY/8LOpArQS\nr75kkKEVw2iG44VhF3GtlCQVw9CBzj3De3Yd4x+/mKCYf8fMti2/28ytn93H+rO9fnBBEJBkuWVg\nynRFua67lOt1XMdGlgRkSUKWBMKqZ+LybEPe06F1B6gbLqWxMrLgElJleuKRlhz99Oq6aTgoahhJ\nOfHLn2maNDQd3bSwXc/9DEFCOo4hGeXJ+oKiDVCtNmbSJYWcf3SmmIvyw//YSq3yaqAfmAAuxHFg\nx9MwduTHvPVDh2bE23VcHMdCVSTiUZX9ew639GUDPPPUKB/+5P4Z8bZtG1Vy6U3626ACiNgoiopr\naSwNCtFOGoFwBwScZGRJpH069yy9PUlMq4hmmh1Xpl6BVoNqQ0dWQi0FaJVqHRsJURQ5sOcgP3s4\nTykfpS/T4DWXZ1ixxr+QTZJlmnpnq0yAxx4uUMxf17Itn7uU+777tRnh7oYgCC1tZ9OCrtUsCpMl\nRBEUSfRW5pEQ4dCJL30FQSA0ZXBjAePlJnZ+kmO5CSqVJtF4D7IcOuEea69vu4lhO7iuiCQrCJJ0\nQhfRaq2OIyq+kZi5mJaF4cC01X06W2f3tvb9UtkGW3+nMOuSd0PL45PFy/j5j7/K0pVLEFwHVRGJ\nxmIggGmYPHzfMd++7Ifvu4d1G1Zi2xaxsESsS1rCtkzi0RCuHYj2ySYQ7oCAk0xvMsJEyX/+9jTZ\ndIrxiTyGLbTks+cSj0URRYFyrYky5axVrWs4gowoCBzYc5Cv/22SyWnL0x1eb/fbbz7YIt5zxb03\nVeHGt6xhaPmw72sWOjix5Tr08fqx45l9vqH26RW6gxf2bkzqOE4dRRJQZYlIRCUSPv4ZAQCmYVCp\nNTBsEEJxKrrNpFYmpEhEIyrxaGyhWjDAsy6u1hteKxgSoqwgLjKNv23LXr5/7yHGj0UYGNL4gxuW\ncdY5q73Vum4j+wyemU+9riPP+T6MbHR54pdfnuoqMIGNpDKHueiKAZ753fTtof/N32QhRjwko6iz\nx3NdF0ESOvZl5yei2JZJT1wltNAdj2OQCMcZGkgHon2SCYQ7IOAkE4/FKJZrTFdLd2KgP8OxsRyW\nLXQs8opOhZdLFS8vblk24pQA/Ozh/KxoTzFZ3MTPHr6TFe/3hLtN3IH9uzfzgT/bz5qRlW2vl+7g\nxJbt0Mc7nx3P7OO2T4nkc51D7dNIssz0+tVwQasYuJMNVFFAVSVi0ciC85p1Q6da07BsAVkNoUjT\noXwJiGAD5ZpNqVpAlUViYZV4rF3Ea/UGmm5g2SArIcTjvFJu27KXz34SchNvB7xZ6E/+ZjO3fnYH\nvdkeEBUaTRPXAWfaSIU5pyGAbdo0DAtZllBVhV89/gTfvmdJS/++qt7PDX88xuqR81m+ei9bfwed\nOg6yA00O7T/aUpH+2k29vPT8szr2ZaczVfqSEZQFfOn1ZpVl2STDgbnK80JQNRAQ8Dww1J/CNBYW\nu6HBLJLbxHE6B9cj4TD9fQlsU2OuaVYp779qKhVmt3vivqnl8WLuUjbfP+773EuvHiCVHm3Zlslu\n5prrly70VgC477uHyecubdnmhdoPL/hcSZaRlTCOFKJpy4wVahwZL5IrlKnWWkdU6oZOvlCmWGmC\nFELuUqAmyZJ3XEGl3HA4NFZgIl+iVq1TKlcYy5W8KnsxtOCgj058/95D5CZa33cudyn/8a39mG6Y\nnTuOcvffPcHf/OVOvvaV33Fg3xiyEkKa/iOH0G2QlDCuoLBtyz7+/Z520x3DuJpnnvL+//V/uJpE\nz4N4Y1x/0rJfX3qUkY0ud94e5Vc/eQ+7t72FX/3kPdzzpV727DzI5dcMkcq0fs6pzCh/+JbVC4u2\nVmdpJhaI9vNIsOIOCHgeUBSFgVSc8WIDZYEpc0MDWY6MTeAK/gYt4DmRDWT6qNYOY9ueYUdfpgE7\n2vftS8/eMHQS904hcYDh5f9Ns/kbDD2KGiozvFSmVttIadITz+l7B0kAUfDagabNVTqNKz2eUPs0\n0/MBbKCi2ZRrRbBNLNsiFImhqqEOQeLOSJKIbonkJpsYRh1VlQmrEonYrGB3CnnPx3VcNL2Jadkc\nPuR/45CfiLNv9yH+8X/NtnSxDXZuHeV9HzswU7fg2DaWO3uB/s9HSpiGv6lOYepnuXpkOR+45SCP\nPfhTjhysozV+TjQ6wPBym4uuGOCxB4UWK1yAUvEyHr7vHv7Hx1/Fhz+5n4fvu4f8eITsQIMb37TK\n68vugq7VWZoNs2So82jQgOeeQLgDAp4n4rEYrgsTxTpKqHORjyAILBns5/CxcUSls9mIIAgsHepn\nLDeJbti85vIMu7eNtqyoe1KjvOby2ZVQJ3H3C4nv2bmfL98WoVz8xMw2y/oJTz3Rz+FDh/kftxxm\nzfqVM4/NzAe3XRzdxHaaJHonfc99saH2TliWRb2hT01JC1ObbKJKOqGQRCwa6zpScppmU6fR1HFc\nCUkOMf2RNC2oFyYJySKH9h/l859WW0Pev93Mpz63l/17j/CNr49Rmewjlijwhjemuezq14Agkx3U\n2L61/TXT/RqP/khra+kqFTbx6I/umhFuTW/NbZcLMTqFwNNzfparR5azesS/GPHef635bp/Ob6/b\nsJLVa4ZJxEJEwgtHGrRGhZUDMYYGBxfcN+C5JQiVBwQ8jyTiMYYyCSxd67rftHjbhv/FdppIOERP\nIkwirrJkWZq3fWiS8199J6s3/Bvnv/pO3n5zpaUw7TWXZ+hJtYdEL726fcX04P850jKH2eNCYCvF\n/CY23z/W6eTZt/swX/v7JzhySEFVvwxsn3m4Lz3Kqy7uZaIwSbFUpVKtYRiG/7HmYZkmxVKZasNE\nlMPIsoIoiChKCFdU0UyRsfwkhVIVTWv6HkPTmhRKFWpNC0EKtbSxTZ0+shLCFhS++x9H2kPeE5fy\nlS/9hi9+YYCxY39Go/EecuOf4O4vL+eRB38BwBXXDpPKtv6ce1OjXHzlAMVOLV352e2m1fpYb7qO\nXwhcVe/n8mv8Cwvnk87Wfbdn+hs4toNrG6R7Y4sS7Wa9zIYV6UC0XyCCFXdAwPNMNBpheEDk8HgJ\ntYNrGngmGUuH+jl0bAIl1Ll3NtWbpFCqoPT2ElJklq7o72josmLNct5+80F+9vCdlApRenor3PiW\n1QyvWDmzz56d+9l8/zhP/7ZT1bMXkM53CK/v2bGfr3whTDH/7pltqno/A0seZNmKOJdePcia9euA\nue1hBnu2b+exB/MU83EGh/WZ6nOY8g+vVtFNF0WNdFxxCAieiOOF0yv1SWzXAkfEsixqjSau4K2w\nF0Mh7//57N4RxXVe17LNdV7Hvd/6PJdcCevOWsmf/sV+HvzBPeQnoiR7qlx89SCrR1aQyv4a/Fq6\nMp6wGoYBQus7fM3lGfZsP8RkcRlzTXfe+C6VNRteS73eva0P4KIrBtixpXXmeyozyqarsoRkh2Ri\n4emGtm3hGhVesmEF4ROs+A949gTCHRDwAhAOhVi1JMt4rkTTEjq2iomiyNLBLIfH8h19zQHSfUmK\n5QqxWJyoa1Op1LEFydfgY8Wa5TNV5o6psWLZALWpC/+enfu54/MRioV34wmEH17INtOh4nzz/WMt\nog1eEdWyFXfz3o9e4Puc/bsP89XbkxTzXrX79q3w1G9H+egnn2H92auoNXRkJYyiLr7NSBIlECV0\nU+DQkWNIgkQsFiUcXvxlr1O1teNUfbfXKumZ/1931krWnbUSrdGkaYIw5SB28ZUD7NzaPnr14iu9\nqIcxz+IWYOnKYd7+oUP88tH9FHNRUtkiF13ROSzux/JV/dx86wSbf3TPjDvaZVdmeNn5Iwu3egGm\noRMWdTaesy5wQ3uBCYQ7IOAFQpIkhgczVKo1cuU6suLvJCZJEsP9KY5OFLuKd6o3yWSljm65pFK9\nNJtNGs0mlu1ODc1ovdiahkZfonXVvPn+8SnRhtnQ7IVz9vgJXu/wKJde7R8m7bQS77Tde912sS8W\nNvF/vnMHb3pvH2FVRbE0otEIwrzerT079ntWnbkomaxn1Tmde280GuiGhKJ6EYt606auVQmHpEWN\nLd10VT9bfte6Su1Lj2Lbk1TK7fvHk4XWDS40dAtpzg3U6pEVXPPGX3H/d26jXs0QS+S5+sY0q0d+\nz3uKTcvMUMsyCSsCZ587wtnnLnjKbTiOg2vr9CYTpM5JsuGctdi2hSJCb0+s7efpR1OrkUkorF21\n5vhPIOA5JxDugIAXmGQiTjwWZTxXpGGC4tOCpCgKSwczHBnLIamdC9Z6kjEMw6Bca6KqKuFwGMdx\nqNcbGJaXR3ZdF1kS6UtE22xWW6vLN0z9fS+qOkY8WSEcCzO0JMRV1y1vKUybSybbYGeH7Z3oJOrl\nYg+hcAwXMGwXrVRDVSRikRCSJLFnx37+9rbQjOjvALZtGeWDt+ylfzgDkkJcDYPpRRQ873aJpuWg\nlaqEVYl4h9GlDU0ju2yQ933sGI89eNfUSrfBRVcMcPTQav7tqw/hurPhckF8iBvenG05Rr2uIc6b\n7rZ35wHu+/ZqSoX3AtDU4L5vjzK8zKsqt11Ptx3HwbENYtEwygl6vVumTkgRiSV6AK/y3XVNemKh\nRa2yHdvCNuusHuqlPxu0e50qBMIdEHAKIIoiQwMZavU6E4UqkhptE2dJklg61M/R8RyuFO0YrlRV\nlf6USqVaRzMNZFklkYjP5K4LuSjpbINLrx5oM11pry7fAGzg3Jd/lbe8/1JEUcI2GmTSfTN77Nmx\nn833j82seNefA9u3tM5q7rZCh85ir4Qm+Jcv1ynmY6QydS58XT8r1y2nVNFQZIGH7jvWvlLPb+LL\nX7iNVGY5fZk6V1y3lKGlrWMvRUSQVHTboVmqcvTAEX7645IXQs42ePWlvaxYtxJZUnwrtb1//5oH\n7r2NRjVDNJHnyut6eekFL8cwTFRVAQc002lzSHv0R+2DQqaryletXYbjujhmk7AiE4l1rm3ohmVb\niK5FTzw6U3xnWQZRVSIRXziXDWAZGhHZYfmqIRLxzrUYAc8/gXAHBJxCxGMxYtEoE/kSVc1EDbWu\nBkVRZMlgP8fGc9huuOsYzWQiRtg0qdU0du48zFf/JjYnDA47nh7lA5/Yz3kvXT+z7dKrB9jx9CjF\nOfnXVHqUK69bQkSBhmFgWzaO4yCKYlsh2k480f6DN+1nx5a7Z8TcK0hb2fFcL716kG1Pt+Z948kH\n2b+rB63xTgD2ANueepD3ffwgK9ctxwHGjvkXSBXzGynmr/Wet32Ud3/Ye858RET27x3jzi8mKc9x\nk9vy5Cjv+9iRrjnk11z6Sl4zVXA+PU7z0Yd+jlbXiEX7WbrC5vevHJxp8Zo5ty6DQgy9iYRNPB47\noTyy67rYlk40rBIOewJt2zYSDulk1JuutgCWZaBKLumkyvBAitAC09YCnn+kT3/6059+oU9imkZj\ncS0hpyOxWCh4f6cpz/d7EwSBeCxCTzxCs1FD12dHek4/nojH0BpVTJvZ0Z0+SJJEJBLi37/2DNuf\nubHlMU1bjWn+lFdduALDtAFIpXtZe1Ye0/wp0dh2Rjb+ije+M8SakZUoikI0rOK4NpZeR1ZU7v3X\nneycf9zGaqLR3/Lej17AazcNcP6rlpLK9HZ9z4lklOGVh7DtXxCNbmfNhl+Cu5X8+Edb9jP0tZSL\nP+IVr10JwK6tBzh68HyfI24Bzp45H8P4CS/9vSW+r/29b+5k746bWrY1tdVYZufnzGXv1Izs3dvP\npVpOojX+mMrkyzh84Hy2PHGQNRvy9KVn3//2p/dz5ED7Oa8/+5dcfPkIluN29Kv3Q1VldN3EtnRU\nyUu9KIqCbTvgmsTDCj2JhW8EHNvCtXWS8RDJiMyyoeyCrmnPB2f6teVEOKFPRdd1Pv7xj1MoFIjH\n43z+85+nr6+vZZ/Pfe5z/Pa3vyUW8+4u77jjDuLxRQxpDwgIADzRHRrIYJomucIkmuG0GLcM9mfI\nFYoz4fBuFHL+IVc/x7Q1Iyt9fcunScQTiI5GOCyRn/BvO+tWiDafpqah6SZrzhphzVmz2299r+u7\n/46nFf7ly//Nha/r58LX9bPrmdF5/eZeAd1cSh3auro91um9zWd2Rnb7VK5y8TIevu8feN9Hl8+k\nPi6+coCdW0YpFVtTCVdfvwxREBZlHjON4ziYRhNFtInGEwiCMCXYBvFIiGhk4bC44zi4lk48GiIU\nipEIS6RT3W+0Al5YTki4v/WtbzEyMsIHP/hBHnjgAe644w4++clPtuyzdetW7rnnHnp7gy9AQMCz\nQVEUhgc9AZ82DlFUrwI9m05RKJaoNTvP8wbIDjTg6fbtqXQNF3+B7IZhOuTHxigXDwM/YHpS1XRB\nW7dCtLk06jUMW0T2tYH1b7lyHIv/+tkH2fXMKO/6SI23fbDI4w/fQaWYpFQ8QDl/A7OFdR59Uz3S\n+3cd5NEHxynlY/Rl6lx8xcDMY/NJ9tVoNDWi4e4CPjsj27+ArFxIUKrUCSsiIVVizbphPnjLER59\n6J6ZVMIV1w7PzL1WZQHbgb27DrYMBLnoioGZ0P104VpElcmk+qjVdGzbQsIlHlaIRnq6njN4IXRs\ng1hEJd7Xh2VoZHsji6q2D3hhOSHh/s1vfsN73uMVV1x44YXccccdLY+7rsuBAwf41Kc+RS6X48Yb\nb+SGG27wO1RAQMAiURSFwf40tm2TL05Sa5pIcph0qg9pssJkTetopXr19UvY8uTmloEfmexmbnjj\nCsKSS9U2cBEXHaLdtfMQ//S3KfK5W+Zs9Vy9UpnDXHZVBsvUkSRlpn95Pk1NQ7cEZMX/NZetMtn2\npF87mneDUi5u4vEH7+QP/mQ9b36fl0fev1vl3//hEOXirHCnMqNcfMUA+3cd5M4vxigX3zv7Pp4Z\n5fU3uXNW7duBrUhylXqtwa5th1i7YVnXOdSzM7L9LUl7UlUUyWu6EoBEPMo5541wznkjvvvHY1F+\n+9/buOuLPZSK3nV295Sf+bs+vIc165YQUmUiiSS2bWFbBrJgkYipqIvIR9u2jeB4K/J4PIVl24iO\nzvLhTNCffZqw4G/pd77zHf75n/+5ZVsmk5kJe8diMWrzJvU0Gg3e+ta38o53vAPLsnjb297Gueee\ny8iI/xc1ICBg8UiSxEA2Rb/rUp6cpNpoEo2oKIpMvlRD8XFjW3/WKm75zD7u/+7XyE1EyPZrXH39\nEtaftZpkTwSQ0HWDRlPHsFwkWek6U/nxh4rkczfN23oh/YNf4Ja/fBnrzz4H13VpaBqmaWHZNqbt\n5W737T7Mj+87ysRYhHS/NlMpPp+rbjyLg3v3U6/m8FazJmAAl8zsU8iFZ2aTAyxdOcS7P5rn8Qfv\nnFlVe1Xly/na3/9Xi2iDJ/47t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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gmm16 = GMM(n_components=16, covariance_type='full', random_state=0)\n", + "plot_gmm(gmm16, Xmoon, label=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here the mixture of 16 Gaussians serves not to find separated clusters of data, but rather to model the overall *distribution* of the input data.\n", + "This is a generative model of the distribution, meaning that the GMM gives us the recipe to generate new random data distributed similarly to our input.\n", + "For example, here are 400 new points drawn from this 16-component GMM fit to our original data:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Zj3QbKMrgJvIhHzTY1dWCL3zhRr8SP8We/0Y87lrlm3A6f4CeHvEamUwtkI48\nH5t5HgcQJiP/6/oZxFp3Hs6fPwWHYwmMRoNPwDsBtOPoUaCr6zew2R6EezaBe/vXdLvODG4iH319\ns+CdcjQCm60MNttquEv86VSqTzTfMQWlpRcBTGBw8BD6+y/D6fTW2IqKKnDLLeLzzp9/DzbbAzOP\ncQBhMpJf17/9bRCffvp/QmwtWY3GRvHvzBvw7QA2Ynw8Y2aHOO9e7eLfafpdZwY3kQ+7fQCAb7/q\n0zOPsPYWb4G6k+Rr+y9adMnzPIdjCRobX+cAwgRQ2+8sv67V1cfw6afSGTZ2uxMu1wQMhpcwMjKI\nqSnvoiy+e7UbDB+gqsqRdteZwU3ko6ioQtavWjHz//Qr1SercHcZo/jQukCR0gybpqYOtLdvhdIA\nNpOpFyUl0zPXfmNadl8xuCnt+dYUhoY+hG9/qcn0AUpKDuL8+ffR12eGxXKAfd0JxnBOTloXKFIq\niG3Y0A3pADYXgNckswnSGYOb0p509GqVz3SiYTQ3r8H69YdnRiqPord3DYDXGRxEMloXKJJOSBYU\nXwvIAbAGJSXTaR/aAIObSFZTMKKkZLFnRS6L5SDOnWuC71zizs7zqK4+puv5o6ks0CYxFFtaFyhS\namJ3v5Y47SsXwF1gd5UXg5vSXrCagv+cU0EyFYkjzZNPoE1iKLa0dmEoNbEbjQY8/fRyPPLIYZw4\nYYcgHOCqaj4Y3JT2gtUU5KE+e/YnmJjgRiPJLNAmMZScAhWcxQFq3/Z8Pzt7L1u3ZjC4EyhVlu3T\nu2A1hebmJejufhpDQyYYjefwpS/l49gxbjSSzJQ2ieHfWvIKVHDmbnyBMbhjRE0/m9iktxrAEfT0\nGNHV9Rt0dGzmDSWJtLS84+njHh8XcNNNv0RtLTcaSWZKm8Rs2fKa6qlKDPn4CfZecze+wBjcKoX7\nx6ymn00sQR4BsBG+qwa51+PljSPx5KX+48dz8L//t743hEl18hYUQZhCZ+ck1NbetM5HpvAFe6/d\nBTBxk5H0W2QlGAa3StIPmANdXbskOxD53sjtdqffjUKpn00sURohv6HwxpE85KV+pzMXjY0dvB46\n8uCD7TMbUqirvbGJNn6U3mu73YlHHnkDx4+PAChCVdUQrNbbWVj2weBWSfoBOwKbrRk2m3KwNjV1\n+N0oFi4c9XtNq3XFzKL5q+F7Q+GNI3lYrSvw5z8/A4fjfwAYBXAX+vregMVykC0iOiEWmm+Dew16\ng+EDWK1MoaVgAAAgAElEQVQbAz6fTbTxE3jVtHkA7geQMbO8LSsvvhjcKkk/YHkIFqzi18sAvAjA\ngczMAly5kgOHwym5wRuNBnR0bEZjo7TPtLHxLd44koC7e0QsgP0TxG6NP+Ojj95Hb+82sEUkeQTr\nylq4cARdXfMhbkwhoKrKEbSgpXU+MoUv8Kpps8DKS2AMbpV8P2Dnz5/yqyX7EkN+PoC5ALZgelos\nNXZ1tfgt1+fuj3PfeDZs6EZp6RhWrXoBg4NX8caRAO5r4d3z9xsAfgngRwAycPky4HtT6eyc9CuU\nUXwF6l6y2524cuUSDIYdAIpUzQXmkqrxo/ReL1hwAeK9k5WXQBjcKvl+wMQdiAKXyJubl6CrqwWD\ngwsgCN4bvM12DZYvVx45Lr/x1Nbu9azeRfHT1zeA5cv34/Ll6yHuE1wP4C8AboA3rEfhe1Nhv3fi\nBepeEv+uvJtVcC5w8svImARwCcAzAK6CyXQOVut9CT6q5MLg1sA3xO12JxobpU10LS3vwGZrhrij\nje96u7Nhsz3m2W/WF/u1k8O6da/h8uWfQrqt55cBDMN7LVdh9uyfYGLiFrj7vTs7D7DWnUCB+qX5\nd6U/g4OlAO7xfP2FL7zOvysZBneElJrovDeLuwA8BeALAGYDWIVANw8OiEkODsc1kC5xaoLR+CFu\nvTUPgLf7wuVagPZ2b7+30/kZHn74MPbs+WaiDj0tubs1+vqyYDK1oKioAosWXfK0gvHvSn+UFtAh\nKQZ3hJRK9N4PngHAt5Gb+wwuX96GYDcPDohJDPmgpvnz+zE+7r1p5Ob+HR999H1MTUkXz3E4nPjj\nH/9vTEw8MfPc1Th+fEcCziC9SXd2E3DLLdLWLKt1BXJyWnH69Bz+XemE0gI6U1OJPqrkwuCOkFKJ\nXv7Be+yxjXjmmeA3Dw6ISQx5i8ntt/8cmZktcDiugdF4DgcPbkBhoQEXLox4fsYd9lNTZZDWzovi\nfwJpLlRTuNFowP799ZLrR7EXyepz8nthYWEBr58MgztCSjVlpRDev/8GfviSkPzGPzR0HXp6gg8K\nfPjh3+PIka2Qj2HIzT3Hfu44Y1N4cuIiUrHF4I4Qa8r6puXGf+JEJrxjGFoBTACYjU8//S4aG1/n\n5yGO2MWUnKI1KNBud+Khhw7PtFZysSM3BjelNW03/osQa9oGiOvMt0KcNgaOWo6zQAVn36baiopL\n2LbtNt7w4yhUgVhtUzpr7soY3JTWtLSYVFbmo719H4ACAL0Avj3zCJtqk4X8hn/lCm/48RSqQKw2\nkDmdTxmDmygAd61A3J3I7qkVPPvsamRnd2BgYAqlpQYAr3KVuyTDG35ihSoQq70+HMOgjMFNFIB8\nqpG7VsBxDcmPN/zkpvb6cDqfMgY3UQDyWkFf31zuCqYTvk21FRXj2LYt9A0/kilMFB61Y0s4nU8Z\ng5soAHmtwG4/jd7eZnCgTPLzbRUpLlY3D5gDoeKHrVaRYXATBeCuFYh93A709Zlhs7HfNBnEonbM\nfvHoYgtG7DC4iQJw1wrcNTaL5QB6e9lvmgxiUTtmv3h0sQUjdhjcRCppXeyDNY/oi0XtmIu5RBdb\nMGKHwa1BsBsxb9KpS2u/HGse0ReL2jH7XaOLLRixw+DWINiN2H/hhxeQk5PtNxeY0gdrHtHnWzte\nsOAiXK4JVFcfY2E5ibAFI3YY3BoEuxHLHztxIhNOp/9cYEofrHlEn2/t2GI5iLa2rWCLRnIJ1oLB\nlsnIMLg1CHYjlj8GDIG1rfRltzvhck3AYHgJwEVUVhbAaq1J9GGlFLZoJD95ULtcE2hvZ2FLKwZ3\nGNwfvr6+LJhMLSgqqsCiRZckTUDy5iGXKw/t7axtpaumpg7PDQoQkJ29lzWLKGOLRvKTdyGKBVkW\ntrRicIdBvgTmLbf4lxLlzUMOhxPZ2d65wOznSS9aaoNsRgxPsL5UbguZHOR/B94d9kLvHsbd3fwx\nuMOg5SYsnwtM6UVLbZCj0MPjW1i2251obGSTbLKR/x1UVhYgO1v97mHc3U2KwR0GNslRuLSMrGWf\nrXbyG35W1q/A9zLx/P8OaoLWoPk3EByDOwyc3kDh0jI3mAVE7eQ3/MnJYQRrkqX4CPR3EKhbiH8D\nwWkKbkEQ8Pjjj+PDDz9EdnY2nnzySVx77bWex9966y3s3LkTWVlZWLduHerq6qJ2wInEBRooHlhA\n1M5/VscoZs1qQUHBAlRWTsNqvSPBR5h+go3ZkLeQdHbuQGVlPiYmMjwzMaqqCnndZDQF9x//+Ee4\nXC60trbi5MmTaGlpwc6dOwEAk5OTeOqpp3DgwAHk5OSgvr4et99+OwoLC6N64KmEg5HIFwuI2lmt\nK9DV1QKb7QYA7wF4GFNTRjidHNGfKMHGbMhbSJzOL6K9fQRAPdyFr5ycVl43mUwtP9Td3Y3bbrsN\nAHDTTTeht7fX81hfXx/MZjPy8/Mxe/ZsLFmyBF1dXdE52jiy252wWA6iuvoYLJYDcDicMftd7g92\nT889aGvbjMbGjpj9LqJUZjQa0NHRgNpaJ3JyDACOAHgdwD709c1K8NGlp2D91Wbz5xBbRgB3CwlQ\nIHn+2bP5cTpS/dBU4x4dHUVBQYH3RbKyMD09jczMTL/H8vLyMDKiv9HU8RzZy4EYqYktKYnhbrH4\n6ld/Dpvtfnj3U29J9KGlpWD91e5uoc7OSTiduQDuAvAGfMclLFw4moCjTm6agjs/Px9jY2Oer92h\n7X5sdNT7Ro+NjWHePHVBVFxcEPpJcWKzGeEbpjabMeLjC/TzFRWXJB/siorxpHov1NLjMaul5dwe\neuiwpPCXk9OK/fvro39wUZCK1+7qq78k2T/96qu/lJLnCST39XvxxVo88EArzp7Nx8KFo9i1aw0K\nC8XjLS4uwKFDm2G3O/HAA+04e/b/g8k0joyM3+CTTwpnnr/K83wSaQrum2++GR0dHbjzzjvR09OD\niooKz2Pl5eUYGBjA8PAwcnNz0dXVha1bt6p63WSa52wy2eFb6jOZHJLjC7c25Z7HrfRz27bdhitX\nvIORtm1bnlTvhRqpPE9d67mdPj0HvoW/06fnJOV7lKrX7rrrhvG3v3n/hq+7biQlzzPe18/3HrZg\nwQVkZExicLA0yH1wFn7xC+8yv1NTSvd66XN8FRam5ucT0F7g0hTcK1euxNtvv42NGzcCAFpaWnD4\n8GGMj4+jrq4Ozc3N2LJlCwRBQF1dHUpKSjQdXCKFGtmrtSk90M9xMFLq4ZSWxHL/DXPVwuiSryAJ\n7ANwDxe4iSNNwZ2RkYEnnnhC8r2FCxd6/r9s2TIsW7YsogNLtFAje7X2S7M/O31ondbFvvHo4KqF\nseG/fGmB5/+8n8UHF2DRSGttirWw9KF1WheXPKVk5j9X3l0o4v0sXhjcGmmtTXFxDfKlVLtmqwwl\nM997WGnpRQATGBw8xPtZHDG4NdJam+LiGuRLqXZtNgtslaGkpfUeprULaGjICYvlNXYd+WBwE8WI\n/EbV3LwELS3vBKhdOwG04+jRacyf/zHmzXsOmZmXsGRJFlyueaiuPsabFuma1i6gBx9sZ9eRDIOb\nKEbkN6o339yOiYkyAMvQ0zMf0tp1O4CNGB/PwPi4AKAVwHfw/vstsNkeAW9apHdau4DEldPYdeRL\n05KnRBRaX99c+N5wJia+AmATxJDOQF/fLLhcEzAYXkJm5hkAn3ueC4g3K4fjGvCmRXoSaLlo+fKm\naruAFi4c0fRzqYw1bqIYsds/hO8iPuI6zO5QFmC3D6C3t9nn8X0Qg939XAFG48czNXD2d1Niqe2j\nDtQkrnVg7q5dd0kWqOIAOAY3UcwUFpbBZmsFcAVANsR1mAUYDB/g1ls/w1/+UgLf2rTBcBnXXvsK\nhoZOo7DQjPLyvXjssVps386bFsVOpIEsf63OzkkotRJpHdRWWMgBvXIMbqIYKS+fRG/vZohN4G/C\nYDiAqqosWK0b0djYgeHhWfCtkVdVZWH37jsASPce3r3bHPdjp/ShdtCYmj7qpqYOOJ058P1cs5Uo\n+hjcRDEibRqchNW60lOTEW96yyAOQsuHwfABrNaNiTtYSltqB40pLR4lr6339WUBuBv8XMcWg5so\nRoI1DYo3wfkA6iHWth2qp3lxSVSKJrWrOVqtK3Dlygs4cSITwBBcrjw88sjv0d6+Fe7ausnUAkDb\n55rUY3ATJYC7Nt7XNxd2+2n09ZlhsRxQFcLhzodl0JMS9+fizJk8mEzbUVhYhvLyqYDjKIxGA3Jy\nsuF0ip+99nYBBsNL8K2tFxVV4JZbOCYj1hjcRAngro3fd99v0dtbBputAL29w3C5DmPPnm8G/dlw\n58Ny7XNSIt/l65ZbQn8u/DcYuQjf/uxFiy5JXsM9NYyFxuhicBMl0PHjIwDuh/vGd/z4jpA/E+5G\nNVz7nJRo+VzIP3uVlQUQBGnzucPh9IQzC42xweAmSqgiSGswRSF/Itz5sNyRjpTIPxf9/X+HxRK8\nVuz/2atBY2OHpPk8O9sbziw0xgaDmyiBKiun0N7uW4OZDvkz4c6H5Y50pMT9uejsnITTmQun04K2\nNnEp3kCfL6XPXrBwFgsHDgBHAOTh/PlTcDiWsLk8QgxuogT6yU8qcfJkCxyOa2A0fozHH6+N+u/g\njnSkxP25qK4+hp6eezzfD7dWHKxFx2pdga6uXbDZxBUCbbbVaGxkc3mkGNxEcSQf4e1yTXhuauPj\nArZv38sFVyiuIu1KCdaiYzQaUFKyGDYbm8ujicFNFEfywTry6TS8qVG8RdqVEqhFx11I7e//FFxJ\nLboY3ERxFGo6DW9qFGtK8/pj0XTtLaR+DmAfDIbLM0v+coxFpBjcRHGkNJ0mO5sDxyh+Ak3RivZC\nPd5CqgHAJpSVHcLu3bdH5yTSHINbA65ERVopTacJ9Nnh54xiIdAo8GjPueY0xNhhcGvARQVIK7Uj\nvO12J1as2OsZuKb0OQsW7Ax9CiRQoEZ7zjWnIcYOg1sDLipAsdbU1AGb7QYE+5wFK0CycEmBBArU\naNeQOQ0xdhjcGrAJiGJNDOlRBBu4FqwAycIlBeIbqHa7E42NYstMaekYVq16AYODV7GGnOQY3Bqw\nCYhiTSwcroG4r3EeTKZeWK0NCs9RDnYWLtNLoK4R9/fFXeg+nNkBbNLzuLxlprZ2L44e5QCyZMfg\n1oBNQBRrYuHw9ZkbsRNWa4NfH3WwAiQLl+klUNeIfAcwm60Vvb2bPY+zZUafGNxESUhN4TDYc1i4\nTC+BAth/3YB8yeNsmdEnBjcRUQJFYwZAoACWf989bsL9OFtm9InBTUSUQNGYARAogN3fP3NmLoaG\nTqOw0Izy8r2ex9kyo08MbiKiBIpGP3OgkeLSGvwdUTtmSiwGN1GK4eIr+hLtfuZYzeHn5yp5MLiJ\ndETNzZOLr+hLNPqZfT8X/f2TiMVIcX6ukgeDm0hH1Nw8OcVHX6LRzyyd9vVbxGLHOX6ukgeDm0hH\n1Nw8OcVHfyJthpZ+Lu6GwbADZWXXR3WkOD9XyYPBTaQjam6enOKjP5E2Q0s/F/NRVXV11LfQ5Ocq\neTC4iXTAXSM7cyYPJtP2maUrpzw3Tw4c0rdIm6G1hGq4nxlOHUseDG4iHZAvXXnLLXsl039CbQFK\nyS3SZuhwQtUd2J2dn8Hp/AHcn5krV15ATk42C386wOAm0oFgNTI1W4BScotnM7S3EHgYvp+ZEycy\n4XRqb65nq0/8MLiJdCBQjcxud6KzcxKhtgB1P5c31vgJ5/2OZzO0txA4At/PDDCESAp/nC4WPwxu\nIh0IVCNrauqA05kDYBWCbQHqbU6/AcDozJahr6u6saZ74Gs9/2QNMrEQ6AAwAWAPZs8+h//5P4sA\n5KG9XXtzPaeLxQ+Dm0gHAtXIxJvjMgCvAnAgM7MAN91U7Pc8sTld7AMXa1etqm+s0QogvRQA5Mfp\ncl1Ce/u3Ee75xyPI7HYnHnroME6fnqP6PbVaV6Cra5fn8zAxISA7W1y/PDtbe3M9p4vFD4ObSMfE\nm+V8AHMBbMH0dAba28UbsW+4SEPkcwCfoL+/EPfd9/8iJ2cu+vvnB7zxqwkgLSu6dXbuQFVVSdIF\nuPw4DYYd0BLAWoMsnAKOlkKV0WhAScli2GzSc4q0uZ7TxeKHwU2kY+6b5dGjUxgf996Iz5yZ63mO\n3e7E+fOnANRCDJE3AXwfTmcG2tt/B6Aevjf+p59eLgmO0lJXyADSsqKb0/lFtLX9E7q6dqGkZHHI\nkIpXjd1/D+siaFmJrLl5Cbq6WuBwXAOj8WM89litqp8LJ4y11upjUTvmdLH4YXAT6Zj7ZvnVr/4/\nGB/33oiHhk7DvRuU2Ez+INx94FlZI5icdN/sCyC/8YvBsRrAEfT0GHH11X/FqlUvYHDwKsWalHeA\nnPd1OjsnUV19TBKwyntDH4HN1gybLXRIxavPWH6clZXTik3IoQoSLS3veJqjx8cFbN++F7t3myW/\nS+k1wgljrQHM2rG+MbiJUkBhYRlstlYA+QBGUVjoDQh5EGRknIO3BjkMeW1SfP4BAHMAZOCzz74E\nYBhHjyqvxOUdIOd9HaczFz0990gC1h0WnZ2TcDpzAdwF4M9QG1LBAk0pAAXBXWgxwmSyq66h+4fa\nHYo/F6ogoSaAlV6jtHQMPT2/g1ioGkZpaeAwtlpXICendaaPW30Aa6kd62WMQjpgcBOlgPLySfT2\nboY7OMvL93oeE2tlb8LdJD4x8U8wmVpQUrIYpaXDyMn5zUwft3jjb2x8a2bU8RbP6x0/viPg7/YO\nkBMLDpmZ72J6+sGZR72B5Q4Lh8O9X/SfcP78KdhsqyGvMfqGRGnpIIDZ6O+3I1CTtVIAApAsWqO2\nhh4q1NzHdvToNIB9EAsgBkn3BKCuNqwU7qWlLrivlXjcLwQ91v3763HhwkjI84pUso6ST0cMbqIU\nEKzp02pdgc7OP8DpdAeEESUliz016OLiAsmN32pdgTfeeNOnOd3dz6vMO0BuFYA3kZk5jenp+TOP\nCigtvSh5vm8wOhxL0NioPM3NGxLufvjPAeyDwXAZVVVZknMMXLvVPqo7UA1TvoqdWGDZKOmeAJSv\nifw1lcYPDAxcJTnuwcGrwjruWOF0r+TB4CZKAcFqiUajAVVVs9DWpq4v1Gg0YOXKTMmc3srK6YDP\n9zaBi0toTk6KAQu4AGRDnC8c3nFLQ8LdD28AsAllZYc8G2i4g7C//1P418aFiEZ1i036OQCWzRRM\nxBqm/+C1CQCtmDevGBbLQUnQ+w70a2x8Cy7XBNrbt8Jda1216peorZWGu9jikXzTqjjdK3kwuInS\nQLiDkZ599k7ZgCyxJhmoFrp791pUVx9DT483YIHXAazG4OAhxd8RrM9UGhL+/fBu3tpvoNr43pk+\nbofq/l/lGnW9p4bpP8huNoCNGB5uQVubdL14ALKpZS9BWpsu9Rs7kKwDx5L1uNIRg5soDYQ7GCnQ\n8+X9nC7X88jOnouBgXkzU86qABjhHTXuwPnz76G6Gn7hHKzP1DckxMFZyqPavbVf/9o4AOzevdav\nKyAU/xp1PnwLDO5jO3NmLoaGTqOw0Izy8r04c6bCb2609zXc/16EvBCiVIBJxr5jTvdKHgxuIlJN\nHmrHj4/A6bx/5nu1MJlaUFRU4Qk0u31XwOlewfpM1YZEaemg6hHYaslr1AbDB6iq8tbYpcfm7dO2\nWA7g3XeDN9dXVhb4TS1rbOSgLwoPg5soTblreuFMl/JvJi6Cb/h6B72JgVZdfSxALTRafaazoXYE\ntlr+TcIbVU17ci+4MjRUiIyMT3D69A0oKxvGqlW/xOBg6cxr1WhamY7IF4ObKE359+WGrunJQ83l\nmgq6MUWwcI5Gn6k44jq6I7CVavtq5jB7F1xpBXA/3n8/A++/L6C2dm/AOfAAB31R+DQF95UrV/DD\nH/4QQ0NDyM/Px1NPPQWj0Sh5zpNPPol33nkHeXl5AICdO3ciPz8/8iMmoqjQUtOTh5rD4Qy4MYXd\n7oTLdWlmre8iLFnigsslSFZU09okHHw0efivE2pRkfCWdM1HOO8rB31RuDQF9759+1BRUYGHHnoI\nb775Jnbu3Il/+7d/kzzn1KlTeOGFF2AwcGUdomQUjZpesL7opqYOz65agID332/xLAEaTl+uPFyb\nm5fg619/bWaL0mkAv4bBMMtvbneo17FaV6heVERNIcf7fkr3uQ71vnLQF4VLU3B3d3fDYrEAAJYu\nXYqdO3dKHhcEAQMDA/jxj3+MCxcuYP369Vi3bl3kR0tEUeOu6YU7XUotedg5HNdAS1+uPFy7ulr8\ntigtK5sjGU2u5nXctVw1x6SmkON+P/v6ZsFuFwfpLVp0iTVoirqQwf3KK69gz549ku9dddVVnmbv\nvLw8jI6OSh6/dOkSGhoa8K1vfQuTk5PYvHkzbrzxRlRUVAT9XcXFBeEev67w/PQrFc+tuLgAhw5t\njtnrV1RckoRdUdEnOHfO+3VFxXjA93V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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Xnew = gmm16.sample(400, random_state=42)\n", + "plt.scatter(Xnew[:, 0], Xnew[:, 1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "GMM is convenient as a flexible means of modeling an arbitrary multi-dimensional distribution of data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### How many components?\n", + "\n", + "The fact that GMM is a generative model gives us a natural means of determining the optimal number of components for a given dataset.\n", + "A generative model is inherently a probability distribution for the dataset, and so we can simply evaluate the *likelihood* of the data under the model, using cross-validation to avoid over-fitting.\n", + "Another means of correcting for over-fitting is to adjust the model likelihoods using some analytic criterion such as the [Akaike information criterion (AIC)](https://en.wikipedia.org/wiki/Akaike_information_criterion) or the [Bayesian information criterion (BIC)](https://en.wikipedia.org/wiki/Bayesian_information_criterion).\n", + "Scikit-Learn's ``GMM`` estimator actually includes built-in methods that compute both of these, and so it is very easy to operate on this approach.\n", + "\n", + "Let's look at the AIC and BIC as a function as the number of GMM components for our moon dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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JczLnJffOucn9Gz4FJS28/vlJzhl6iA3z5vs3JhMZrONsi6H/g4dgKH9gDSioy8rKiI6O\n7vv8kUce4Ve/+hUmk4mEhAQWLVrUOzp65UpWrFiBzWZj1apVaDSu1/2bHjKVM63FHG4sYGHMgvO2\n6TzVZE4MYf+Jek5VtJIcF+CgKoUQQlxOZ7eZ97YVsbugDjelgtvmxbM4KxaV2+h7a3lQo77twdn+\nKjT0GHls7zNE6SJ4JPOnF2w/U3WO598+TObEEB6+dYoDKhw58le985J759zk/l2ZwvKzvPbPk7S0\ndxMVrOP7NyUTEzoyjxLs1qIW39BpvJjgn8jJs2do6mghWBt43vakKF8igrx6B5UZe/Dxcr1eBSGE\ncEZdPWY+2FHCl4drUCoU3Dw7jpvnxI3KVvS3je7qRqn0kFQADjcevWDb1zOVWawyU5kQQowWpytb\nWf1qDl8eriEiyItf3pPObfPHjfqQBgnqIUkNnoybwo28iwQ1wOwpYahVSnYeqcXq2CcLQggxpnWb\nLLy7tYjfv5NPc1sXi7NiWH1vBvHh/b+VNFpI1/cQaNVakgPGc7zlJA3GRkK9Qs7b7uWhZsbEEPYe\nr+dkRSuTZVCZEEKMuOKaNl7ZeJKGsx2EBmh54MZkEiN9HV3WoEmLeojSQ3u7vy/Vql4w7avlL/Nl\npjIhhBhJJrOFD74s5rm38mg828G1GdE8eV+mU4Y0SIt6yFKCJqFSqshrLOCG+Gsv2J4Q4UNksBf5\nRc20Gbrx1bk7oEohhBhbyuraeWXjSWqbjQT7eXD/Dcl2WSN6JEmLeog8VR5MDpxIvbGBWkP9BdsV\nCgXZaZFYrDb2yKAyIYSwK7PFyrpdpfz2zTxqm41cNT2Sp+6f4fQhDRLUVyQ9ZCpw6e7vWZND0aiU\n7Doqg8qEEMJeKhv0PPPGITbsK8ffW8Mv7kxj5XUT8NC4RqexBPUVmBI0CY1SzeGGo1xs3hith5oZ\nyaE0neuisPysAyoUQgjXZbZY+WxvGc+8cYiqRgPzU8N5+oGZLjeAV4L6Cri7aZgSlExjZzPVhtqL\n7rNgWu/ylzvzL75dCCHE4FU3GXh2TR7rd5fhrVXzs2Wp3Ls4GU9312hFf5vrfUcjLD0klcONBeQ1\nHCXaO/KC7ePCfYgO0ZFf1Mw5Qzd+MqhMCCGGzGyx8vmBCj7dW47FamPW5DBWXJuEl4fa0aXZjbSo\nr9CkwIm4u2k43Hjx7u+vZyqz2mzsLpBBZUIIMVSVDXp+8+ahvlb0T5dO5cGbJ7l0SIME9RXTuKmZ\nGjSZlq5WKvRVF90na1IYGrWSXTJTmRBCDJrZYuXj3aU888YhKhsMzE0J5zffn0laYpCjSxsREtTD\noG/yk4aLj/7WeqiYmRxKS3sXJ8pkUJkQQgxURb2ep18/xKd7y/Hx0vDzO1K5/8ZktC7eiv42Ceph\nMDFgPJ4qDw43FmC1WS+6T/ZXM5XtkJnKhBCiXyazlXW7SnjmjUNUNxmYnxrBMw/MJGVcYP8HuxgZ\nTDYM1EoVqUFTOFB/iLK2ShL84i7YJy7Mm5hQHUeLW2jVd+PvLYPKhBDiYsrq2nl140lqmo0E+rhz\n7+JkJse71itXgyEt6mEyvZ+5v3sHlUV+NahMXtUSQoh/ZTJb+GBHMb958xA1zUaumhbZ+170GA5p\nkKAeNhP9E/FSa8m/TPd31qRQ3NVuvTOVWWVQmRBCfK2kpo0nX8vl8wOVBPp48B/Lp7Hy+gku+V70\nYElQDxM3pRtpwVNo79FTfK7sovt4uquYOSmUs+3dHC9rGeEKhRBi9OkxWVi7vZhn38qjrqWDa9Kj\nePqBGSTHOv8c3cNFgnoYTQ+5fPc3QPZXM5XtkJnKhBBjXFH1OVa/lsumnEqCfT15ZMU07rp2vMvM\n0T1c5P/GMEryG4e3WseRxmPckXQLbkq3C/aJC/MhNsyboyXNnG3vIsDHwwGVCiGE43SbLKzbWcrW\nQ71zT1yXGc1t88fhrr7wd6aQFvWwclO6MS0kBYPJyJlzJZfcLzstApsNmalMCDHmnK5sZfUrOWw5\nVEVIgJbH7k7nzmuSJKQvQ4J6mH3d/X34EpOfAMxIDsVd0zuozGK9+MAzIYRwJV09Zt7efIbfvZNP\nU1sni2bG8NR9mSRG+Tq6tFFPgnqYJfjF4avx4UjTccxW80X38XRXMWtSKK36bo6VykxlQgjXdrL8\nLL9+JYdth6sJD9Ty+Mp07rgqEY20ogdEgnqYKRVKpodMpcPcyenW4kvutyCtd6aynTJTmRDCRXX3\nWHhr82n+8N4RWtq7uCErlifvyyQhQlrRgyGDyexgemgqX1bvIa/hKJMDJ150n9gwb+LDvSkobZFB\nZUIIl3Om6hyvbjxJ47lOIoK8eODGZOLDfRxdllOSFrUdxPvE4O/uR0HzCUyX6P6G3la1zQa7jsqr\nWkII19BjsvD+9iJ+9/Zhmto6WTwzhtX3ZkhIXwEJajtQKBRMD51Kp7mLky2nL7nfjOQQPGRQmRDC\nRZTWtvPU67l8kVNFiL8nj92dzrKrElGr5Fn0lZCgtpP0AUx+4qFRMWtyGOcMPRQUy0xlQgjnZLb0\nrnT17Jre2cUWpkfx5P0zSIyUZ9HDQZ5R20mMdxRBHgEcay6kx2JC43bxtVMXpEXwZX4NO47UMm18\n8AhXKYQQV6ayQc8/NpykuslAkK8H99+QzESZ/nNYSYvaTnq7v1PptvRwouXUJfeLCfUmMdKXY6Ut\nVDUaRrBCIYQYOovVymd7y/rWi16QFsFT98+QkLYDCWo7Gsjc3wA3zY4F4LO9F1/MQwghRpOaZiO/\nfTOP9bvL8PHS8PM7Uvneoomy0pWdyP9VO4rShROiDeJ480m6zN14qNwvul/KuEDiwrw5dLqJ6iYD\nUcG6Ea5UCCH6Z7Xa2JxbxbpdpZgtVmZPCWPFwiS0Hhd/tCeGh7So7UihUJAekorJauJ4y8nL7ved\nOfEAbNhXPkLVCSHEwDW0dvD8O4dZ+2UxWnc3frIkhe/fNElCegRIUNvZQOb+BkhNDCQ21Jvck43U\nNBtHojQhhOiX1WZjW141q1/Nobi6jcyJITzz/Zky+HUESVDbWYQujHCvUE6cPU2nueuS+/W2quOw\nIa1qIcTo0NzWyR/fO8LbW86gdlPy0C2TefjWKXhrNY4ubUyRoB4B6SGpmK1mCppOXHa/tKQgokN0\n5BQ2UNcirWohhGPYbDZ2Ha3l16/kcLKilbTEIH7z/ZnMSA51dGljkgT1CJge+lX3dz+jv6VVLYRw\ntFZ9N3/+oIDXPz+FQqHggRuT+cntKfjqLj4YVtifjPoeAaHaYKJ0EZw8W0SHqQOtWnvJfaeNDyYq\n2IsDhQ3cPCeesIBL7yuEEMPFZrNx4EQDb285Q0e3mcnxAdy3eKIsGDQKSIt6hKSHpGKxWTjST/e3\nUqHg5jnx2GywUVrVQogRUNts5M8fFPDyhkIsVhv3XD+BVXekSkiPEhLUI2R66FSg/+5vgPQJwUQG\nebH/RAONrR32Lk0IMUbpO3p4a/Npfv1KDsdKW0iO9eepB2aQPS0ShULh6PLEV6Tre4QEeQYS6x3N\n6dZi9D0GvDWXntSkt1Udx4ufnGDDvgruvzF5BCsVQrg6s8XKtrxqPt1bTme3mVB/T+64OpG0xCAJ\n6FFIgnoETQ+dSoW+iiNNx5kXmXXZfTMmhBAeWMa+4/XcPCeOYD/PEapSCOGqbDYbh88088GXxTSe\n68TLQ8Xya5K4anokKjfpYB2tBnRnXnrpJe68805uv/12PvroIyorK1mxYgV33303Tz31VN9+a9eu\n5fbbb+fOO+9kx44d9qrZaaUPcPITAKVSwc2z47DabGzcX27fwoQQLq+iXs/v38nnr+uP0dLexcL0\nKJ774SyuzYyWkB7l+m1R5+TkkJ+fz3vvvUdHRwevvvoqzz33HKtWrSIjI4PVq1ezdetW0tLSWLNm\nDevXr6erq4vly5czZ84c1GqZXu5r/h5+jPONpehcKW3denzdvS+7/4zkUD7ZW87eY/XcNCuOIGlV\nCyEGqVXfzbpdJew7Vo8NSEsMYtlVCYQHejm6NDFA/f4ZtWfPHsaPH8+PfvQjHn74YbKzsyksLCQj\nIwOA+fPns2/fPgoKCkhPT0elUqHT6YiLi+P06dN2/waczfSQVGzYyG8q6Hff3lZ1LBarjX8eqBiB\n6oQQrqLbZOHTPWU89tJ+9h6rJzJYxy/uTOOnS6dKSDuZflvUra2t1NbW8ve//52qqioefvhhrFZr\n33YvLy8MBgNGoxFv729aiFqtFr1eb5+qndi0kBQ+KvqMww1HyY6a0+/+MyeF8unecnYX1HHjrDgC\nfeV1CSHEpVltNg6eaODDnSW06rvx8dKwYuE45qaEo1TKQDFn1G9Q+/n5kZCQgEqlIj4+Hnd3dxoa\nGvq2G41GfHx80Ol0GAyGC77en+Dgy3f/uppgvEkOTqSwqQill5lAbf+LrK+4fiJ/fi+f7Udr+dHt\nqSNQ5cCNtfvnSuTeObeL3b8TpS288ulxiqrOoVYpWXZNEkuvlmUonV2/QZ2ens6aNWu49957aWho\noLOzk6ysLHJycpgxYwa7du0iKyuLlJQUXnjhBXp6euju7qa0tJSkpKR+C2hqGnut7hT/KRQ2FbH1\n5D6ujpnf7/6TY3wJ9vNgy8EKrkmLGDWTEAQHe4/J++cK5N45t3+9f03nOvlgRwmHTjUCMCM5hKXZ\nCQT5emLUd2HUX3pBIDGyhvIHcr9BnZ2dzaFDh1i6dCk2m40nn3ySyMhInnjiCUwmEwkJCSxatAiF\nQsHKlStZsWIFNpuNVatWodHICisXMy0khbVnPiavsWBAQe2mVHLTrDhe+/wUnx+o5K7rxo9AlUKI\n0a6jy8zG/eVsOVSF2WJjXIQPd16TRGKkr6NLE8NIYbPZbI4sYKz+Vf+X/Jc51VrE07MeJdAzoN/9\nzRYrj790gHOGHn730Cz8vR0/Qb60ypyX3DvnFhDgxUfbzvDx7lL0HSYCfNxZuiCBGZNCUcqEJaPa\nUFrU8vKcg3wzpWj/o78BVG5KbpwVi9li5fODMgJciLGqsPws//anHaz54jQ9Jiu3zR/Hsw9mkTU5\nTELaRUlQO0hacApKhZK8Acz9/bU5KeEE+riz80gt5wzddqxOCDHamMxW3t1axH+9d4TKBj3zpobz\n3A+zuHl2HBq1m6PLE3YkQe0gXmotEwOSqNLX0NjRPKBjelvVcZjMVjYdrLRzhUKI0aLhbAfPrslj\ny6EqwgK0/OnfFnDfDcn4yRrRY4IEtQP1TSk6yFa1v7c7O/JraDP22Ks0IcQosf94PU++nktFg565\nU8NZfW8midF+ji5LjCAJagdKDZ6MSuFG3gDm/v6aWtX7rLrHbOULaVUL4bK6esz8Y0MhL28oRAH8\n4DuTuP+GZNw10s091khQO5CnypPkwAnUGuupNzb0f8BX5k2NwN/bne351bR3SKtaCFdTUa/nqddy\n2Xe8nrgwb568L5OsSWGOLks4iAS1g33d/T3YVvXimTH0mKx8kSOtaiFchc1mY0tuFb9dc4iG1k6u\nnxHN4yvTCfHXOro04UAS1A6WEpSMWqkir/Eog3mlfUFaBL46DdvzatBLq1oIp6fv6OEvHx3j3W1F\neLqr+NmyVL57dZIsQSkkqB3NQ+VBavAUGjqaKGkrH/BxapUbN8yMpdtkYXNulf0KFELY3enKVp58\nLZcjxc0kx/rz1P0zmJoQ6OiyxCghQT0KzI2YCcDumv2DOm5BWgQ+Xhq25VVj6DTZozQhhB1ZrFY+\n3l3K79/Np83Qw+0LxvGL76bJa1fiPBLUo0Ci3zhCtSEcaTyGocc44OM0ajcWz4yhq0da1UI4m7Pt\nXfzh3SN8urecAG8PHr1rOjfOipOlKMUFJKhHAYVCwdzImZhtFg7UHxrUsdnTIvHRqtmWV4WxS1rV\nQjiD/KImVr+aw5mqc6RPCObJ+zNJjJKFNMTFSVCPEjPD0lErVeytOYjVZh3wce5qN66fGUNnt4Ut\n0qoWYlQzmS28veUMf/noGD1mK/dcP4Ef3ToFL1kvWlyGBPUo4aXWMj0klcbOZs60lgzq2KunRaHz\nVLPlUDUd0qoWYlSqazHy2zfz2JZXTUSQF7/6XgbZ0yJRyEIaoh8S1KPI3MjeQWV7ag8O6jh3jRuL\nZsbQ2W1bbkc9AAAgAElEQVRma161PUoTQgyRzWZj77E6nn79EJWNBhakRfCr72UQFaxzdGnCSUhQ\njyLxPrFEeIVxtOk4bd2DWyv4qmmReHmo2JJbRWe32U4VCiEGo7PbzMsbCnll40mUSnj41il8b9FE\n3GW1KzEIEtSjSO+gsiysNisH6nIHdaynu4rrZ8Rg7JJWtRCjQXl9O0+9nsuBEw2Mi/DhyftmkDkx\nxNFlCSckQT3KzAibhkapZm/t4AaVAVyTHoWXh4rNOZXSqhbCQbp6zHy0s4TfvplHY2snN2TF8uhd\n0wn283R0acJJSVCPMp4qTzJC02jpauXk2aLBHeuu4trMaIxdZrYflla1ECPJZrNxsLCBX758kI37\nK/DVaVj13VSWZifINKDiishPzyg0NzILgL01BwZ97ML0aLTuKr7IqaKrR1rVQoyEqkYDv38nn79/\negJ9h4mbZ8fx2wezmBIv04CKK6dydAHiQrE+0UR7R3Ks5STnutvwcx/4RAhaj95W9Sd7yvgyv4bF\nM2PtWKkQY5uxy8THu8rYnl+NzQbTkoL47jVJhEg3txhG0qIepeZF9A4q21ebM+hjF2ZE4enuxqaD\nlXT3WOxQnRBjm9VqY+eRGh77+wG2Ha4mxF/Lz+9I5Se3T5WQFsNOgnqUSg9Nw8PNnb21OVisgwtb\nLw81C9Oj0XeY+DK/xk4VCjE2ldS08cybh3hj02lMFivLshN45oEZpIyTbm5hHxLUo5SHyp3MsOmc\n626j8OzpQR9/bWY0Hho3Nh2soNskrWohrlSboZtXNhTy2zV5VNTryZocyrMPZrE4K1YGiwm7kp+u\nUeyb5S8HP6hM56lmYUYU7R0mvjhYOdylCTFmmC1Wvsip5PGXD7D3eD0xIToevWs6P7h5Mv7eshyl\nsD8ZTDaKRXlHEOcTQ2HLaVo6Wwn09B/U8YtnxrL7aB3/PFDBnJRwAn097FSpEK6psPwsb285Q11L\nB14eKlZeN54FaZGyFKUYUdKiHuXmRmZhw8a+Qc7/Db3vVS/NTqDHbOWDHcV2qE4I19Tc1slf1x/j\nv947Qn1LB9lpETz7gyyumh4lIS1GnAT1KJceMhVPlQf76nIHPagMYNaUMOLDfcg52cjpylY7VCiE\n6+gxWfh0TxlPvHyQvNNNJEb68ut7M7ln0US8tRpHlyfGKAnqUU7jpmFmWDrtPXoKmgsHfbxSoWDF\ntUkAvLO1CKvVNtwlCuH0bDYbh8808cQ/DvLxnjI83VV8/6ZkHrt7OrFh3o4uT4xxEtRO4OuZyvYM\nYVAZQEKEL7OnhFHVaGBXQe1wliaE06trMfKntUf533XHaNV3s2hGDM/+IIvZU8JlrWgxKshgMicQ\n7hVKgm88p1qLaOpoIVg7+Pc1l2YnkHemiXU7S8mcGIKXh9oOlQrhPLp6zHy2t5zNuVVYrDYmxwew\nYmES4YFeji5NiPNIi9pJzI3sfVVr7xAGlQH46dy5eXYchk4Tn+wpG87ShHAqNpuNnJO9i2d8frAS\nP507P16Swqo7UiWkxagkQe0kpgWn4KXWsr8uF5N1aIttXJsRTYi/J9vzaqhpNg5zhUKMfnUtRv74\n/hFe/KR38YzvzInjtw/OZPr4YOnmFqOWBLWTULupyQrLwGAycrTp+NDOoVJy59VJWG023tt6BptN\nBpaJsaGrx8wHO4r59Ss5FJa3kjIukGe+P4Nb541Do3ZzdHlCXJY8o3YicyJnsq1qF3tqDpARmjak\nc6QmBjIlPoDjZWc5UtTMtPHBw1ylEKOHzWYj73QT724rolXfTaCPBysWJpGWFCQtaOE0pEXtREK1\nwYz3T6ToXCn1xsYhnUOhUHDnNUm4KRW8t70Ik1nmAReuqa7FyJ/eP8LfPj6OvqOHm2bH8ZsHZzJN\nurmFk5GgdjJfz/891EFlABFBXlw9PYqmc11szq0artKEGBW6eyx8tLOEX7+Sw4nyVqbEB/DMAzNZ\nMn8c7tLNLZyQdH07mdTgyXirdRyoO8TN4xahcRvaa1a3zI3jQGE9G/ZVMHtKuCwuIJze15OWvLut\niLPt3QT6uHPnNeOZPl66uYVzkxa1k1EpVcyKyKTD3El+Y8GQz6P1ULNk/ji6TRY+3FEyjBUKMfLq\nz3bwwtqj/HX9cdqNPdw0O5bfPJhF+gTp5hbOT4LaCc2JmIECBXtqhzZT2dfmTY0gJlTH/hP1lNS0\nDVN1Qoycb7q5D3K87CyT4wN4+oGZLJmfIN3cwmVIUDuhIM9AJgYkUdpWQa2hfsjnUSoVrFg4HoB3\ntp7BKq9rCSfx9WjuJ/5xgI37K/Dx0vD/bpvCqjtSCQvQOro8IYaVBLWTmvf1/N9X2KoeH+3HzEmh\nlNXp2XusbjhKE8KuGs528MIHR/nr+mOcM/Rw46xYfvv9LNInhEg3t3BJMpjMSU0JTMZX48PBusPc\nknAD7m5DX4JvWXYC+UVNfLSzlIwJIXi6y4+FGH26TRY27q9g08EKzBYbk+P8WXHteJn2U7g8aVE7\nKTelG7MjZtBl6SKv4egVnSvAx4MbsmJpN/bw2b7y4SlQiGFisVrJOdnAEy8fZMO+cry1Gn506xRW\nfTdNQlqMCQNqOi1ZsgSdTgdAVFQUDz30EI8++ihKpZKkpCRWr14NwNq1a3n//fdRq9U89NBDZGdn\n261w0TuobFP5NvbUHGB2ROYVnWvRjBj2FNSxJbeK+akR8pxPOFyrvptdR2vZdbSWVn03bkoFN2TF\ncvPsONw1MlBMjB39BnVPTw8Ab775Zt/XHn74YVatWkVGRgarV69m69atpKWlsWbNGtavX09XVxfL\nly9nzpw5qNWynKK9+Hv4MTlwIsdbTlKprybGO2rI59Ko3bjjqkT+9vFx3ttWxM+WpQ5jpUIMjNVm\no7D8LF8eruFocQtWmw0PjRtXT49kYUa0/AEpxqR+g/rUqVN0dHTwwAMPYLFY+PnPf05hYSEZGRkA\nzJ8/n71796JUKklPT0elUqHT6YiLi+P06dNMmTLF7t/EWDYvMovjLSfZU3OQFROHHtQA6ROCmRjj\nR0FJCwUlLUxNGPy610IMRXtHD3uP1bEzv5bGc50AxIZ6c9X0SGYkh+ChkXETYuzq96ffw8ODBx54\ngGXLllFeXs6DDz543qpLXl5eGAwGjEYj3t7efV/XarXo9fp+CwgO9u53H3FpCwIz+KD4E/Iaj/CD\nrDvxVHtc0fn+3x3T+Lc/fskHO4qZnxGDWnX5YQxy/5yXo++dzWajsOwsm/aXs+doLWaLFY3ajYWZ\nMSyeHUdStJ+M4r4MR98/MXL6Deq4uDhiY2P7/tvPz4/CwsK+7UajER8fH3Q6HQaD4YKv96epqf8w\nF5eXFZrJhrIv+PzELuZFzrqic3mpFGRPi2T74Rre23SSRTNjLrlvcLC33D8n5ch719ltZt/xenYc\nqaGmqXdd9PBALdlpkcxOCcPLo/dxWXOz4XKnGdPk357zGsofWP2O+v7oo494/vnnAWhoaMBgMDBn\nzhxycnIA2LVrF+np6aSkpJCXl0dPTw96vZ7S0lKSkpIGXZAYvFkRGSgVSnbXHBiWNaZvnTcOLw8V\nn+0ro83YMwwVCgEV9Xpe//wUq/53L29vOUN9SwczkkP4z+XT+M33Z3JtZnRfSAshvtFvi3rp0qU8\n9thjrFixAqVSyfPPP4+fnx9PPPEEJpOJhIQEFi1ahEKhYOXKlaxYsQKbzcaqVavQaIb+bq8YOD93\nX6YGTeJI03HK26uI9710K3ggdJ5qbps/jrc2n2HdzhLuuyF5mCoVY023yULOyQZ25NdSVtcOQKCP\nBzfNjmXu1Ah8veR3hBD9UdiGowl2BaT7ZnicbDnD/x79B1nhGaxMvuOKz2exWnnqtVxqmoz86t4M\n4sIufIwh3W/Oy973rq7FyJf5New7Vk9HtxkFMDUhkKumRzIlPhClUp49Xwn5t+e8htL1LUMpXcSE\ngESCPALIazjK7Yk3o1V7XtH53JRKli8czx/ezeedLUU8dvd0GdgjLstktpBf1MyO/BpOVZ4DwMdL\nw03pscxPjSDI98p+JoUYqySoXYRSoWRO5Ew+KfmcnPrDZEfPueJzJsf6kz4hmLzTTRwobGDW5LBh\nqFS4EovVysmKVg6eaOBwUROd3Rag92cne1ok05KCULnJBIhCXAkJahcyKzyTDaWb2VN7gAVRs4el\nBfzdqxIpKGnhgy+LmZYUJO+zCmw2GyW17Rw80UDuqQbaO0wABPq4k50Wydyp4TK1pxDDSH7ruhBv\njY604CnkNR6lpK2cRL/4Kz5nkJ8ni2bE8Nm+cjbur+D2BQnDUKlwRtVNBg4WNnCwsIHmti6gd+Dh\nVdMjyZoUSkKkL0p5PCLEsJOgdjFzI2eS13iUPTUHhyWoAW7IimXPsTq+yKliXmoEIX7yrHGsaDrX\nSc7JBg4UNvS98+yucWPW5DCyJoeSHOsvXdtC2JkEtYtJ8ksgRBtEflMBS003o1NfeReku6Z3HvC/\nf3qCtduL+fGSlGGoVIxWbcYeDp1q5EBhPSU1va9UqdwUTEsKImtyGFMTAnFXy6IYQowUCWoXo1Ao\nmBuRxbriDRysy+OamPnDct4ZySFsP1zN4TNNFJafZVJcwLCcV4wOnd1mDp/pHTRYWH4Wmw0Uit5B\nYVmTQkmfEIxWJiMRwiEkqF3QzPB0Pi3dxJ7aA1wdPW9YBpUpFApWLBzP06/n8u7WIp68/8qW1RSO\nZzJbKChp4UBhA0eLWzBbrADEh/uQNSmUzOQQ/HTuDq5SCCFB7YJ0ai+mBU8lt+EwRedKGO+fOCzn\njQ3zZl5qBLuO1vLl4RqWL/YdlvOKkVVW187b24rYV1Db9zpVeKCWrEmhzJgUSqi/LCUpxGgiQe2i\n5kVmkdtwmN01B4YtqAGWLBhH7qlGPt5dxg3zZAS4M6ls0PPx7jKOFDcDEPDV61QzJ4USHaKTCW2E\nGKUkqF3UON9Ywr1COdp0gvYePT6a4VkSz0er4Za58by3rYgX1xVw36IJ8gt+lKtuMvDJ7jLyzjQB\nkBjlyz03TCLC30NepxLCCUhQuyiFQsH8yFm8f+ZjdlXv46Zx1w/bua+eHkne6Ub2HK0lIkB72aUw\nhePUtRj5ZE8ZuScbsQHjIny4dV48k+MCCAnxkbmihXASEtQuLCs8gw1lm9lZvY+FMdl4qIZnYJDK\nTcmPbp3CM2/m8cGOYqJDdUyWUeCjRsPZDj7dW8aBwgZsNogN9ebWefFMTQiU3g8hnJDMVODCNG4a\nFkTOpsPcyf663GE9t6/OncfuzcRNqeDFj4/TdK5zWM8vBq/xXCevbjzJL18+yP4TDUQG6fjxkhR+\nfW8GqYlBEtJCOCkJahc3P2o2aqWabZW7sFgtw3ruibEB3H3dBIxdZv667hjdpuE9vxiYlrYuXv/8\nFL986QB7jtURFqjlR7dO4cn7M5k+PlgCWggnJ13fLs5bo2NWeCa7avZxuLGAzLBpw3r++akRlNW1\ns/NILW9sOsWDN02SYBghrfpuNuwvZ9eRWixWG6EBWm6ZG8eMiaGy3rMQLkSCegy4JmYeu2v2s7Vy\nJxmhacMepCsWjqe60cCBEw3EhflwXWb0sJ5fnK/N0M3GAxXsyK/FbLES7OfBd+bEkzU5FDeldJIJ\n4WokqMeAIM9ApoWkcLixgFOtRSQHjB/W86tVSn50WwpPv57L2u3FxITomBjrP6zXENDe0cPnByr4\n8nANPWYrgT4efGdOHLOmhMnCGEK4MPnXPUYsjFkAwNaKnXY5v7+3Oz+6bQoKBfzfJ8dp+WoZRHHl\nDJ0mPtxRwiP/t58vcqrw8lRzz/UTeO6HWcxLjZCQFsLFSYt6jIj1iWa8fyKnWouo1FcT4x017NdI\nivJjxcIk1mw+w/+uP8Zjd01HI6ssDVlHl4kvcqrYcqiKrh4LvjoNS7MTmJ8ajlol/1+FGCskqMeQ\na2MWcKa1mG2Vu7hv8gq7XCN7WiRldXr2HKtjzRenuf/GZBlcNkg9JgvbDlezcV8FHd1mfLRqbp0b\nT/a0SPnDR4gxSIJ6DEkOGE+kLpzDjQXcPG4RQZ7DP0mJQqFg5fXjqW4ysPd4PXHhPlyTPvytd1dk\ntdrYd7yej/eUcra9G627iqXZCVwzPQp3jQS0EGOVPNwaQxQKBQtjFmC1Wdletdtu11Gr3PjxkhS8\ntWre21bEmapzdruWK7DZbBwtbmb1azm8+s+TtBtNLJ4Zw+8ensUNWbES0kKMcRLUY0x6SCr+7n7s\nq83B0GO023UCfDz40a1TsNngbx8fp1XfbbdrObOSmjZ+904+//1hAbXNRuamhPP8D7NYdlUiXh5q\nR5cnhBgFJKjHGDelG1fHzMNkNbGrZp9drzUhxp/vXp1Iu7GHv64/hslstev1nEldi5G/rj/Gb9fk\ncabqHKkJgTx1/wzuvzGZAB8PR5cnhBhF5Bn1GDQ7fAafl23tW6xD42a/ltvCjCjK69vZf6KBt7ec\n4d7FE+12LWdwztDNp3t7ZxOz2mwkRPiwNDuBCTHy3rkQ4uIkqMcgD5U78yNnsaliOwfqDjE/apbd\nrqVQKLhn0URqmo3sOlpLXLg32WmRdrveaNXZbebzg5Vszq2kx2QlNEDL0gXjZC5uIUS/pOt7jFoQ\nPQeVUsW2yp1YbfbtknZXu/Hj21LQeap5e/MZimva7Hq90cRssbLlUBWPvLifDfvK8dSouOf6CTzz\nwAzSJ4RISAsh+iVBPUb5aLyZGZZOc9dZjjQdt/v1gvw8eeiWyVhtNv66/hjnDK49uMxqs3GgsJ7H\nXzrAu1uLMFus3DYvnud/OIvsaZEym5gQYsDkt8UYdk3MfBQo2FKxA5vNZvfrTYoLYFl2Im2GHv72\n8XHMFtccXHai/CzPvH6Ilz4tpFXfzcKMKJ5/aBY3z4mXV62EEIMmz6jHsFBtMKnBkznSdJyic6WM\n90+w+zWvnxFNeX07OScbeXdbESuvm2D3a46Uino9H+4o5kR5KwBZk0K5df44Qvw8HVyZEMKZSVCP\ncQtjFnCk6ThbKneMSFArFAruW5xMbbORLw/XEBfqzbzUCLtf1x5sNhsWq42Wti4+2VPGgcIGACbH\n+bM0O5HYMG8HVyiEcAUS1GNcvG8sCb7xFLacpsZQR6Qu3O7XdNf0zlz2zBuHWLP5NJHBOsZF+Njt\neiazhbI6PbXNRnrMVswWK6ZvfTT9y+fmr75m7ttmO+/zb3/89gODmFAdy7ITmRw//FOzCiHGLglq\nwbWxCygpKGNr5U6+N+nOEblmiL+WH3xnMn9ee5S/rj/Gr+/NxNdLMyznNnSaKK5po6j6HEXVbZTX\ntWO2DP4ZvAJQqZSo3ZR9H7XuKlRaJWqVErWbArVKiUbtRubEEGZMCkUpo7iFEMNMglowOXAiYV6h\nHGo4wnfGLcLfw29ErpsyLpAlC8bx0c5S/u/j4/z7nWmDHg1ts/V2PRdVfxPMNc3fTI2qUEBMqDdJ\nUb7EhXnjrlZ9K2TdUKkU5wXx1x/VKiVuSoW8PiWEcDgJaoFSoWRhzALeOrmW7VW7uT3p5hG79g1Z\nsZTX68k73cTa7cWsuHb8Zfe3Wm1UNRr6Qrm4pu28ecQ1aiXJsf4kRfmSFO3HuHAfPN3lx1wI4bzk\nN5gAIDM0jc9KNrG39iCL465Bq9aOyHUVCgX335BMXUsHW/OqiQv3ZvaUb56Td/dYKK1r7wvmkpo2\nunosfdt9vDSkTwgmKcqPpChfokN08o6yEMKlSFALAFRKFVdFz+Xjkn+yu+YA18ddPWLX9nRX8ZMl\nKTz9xiHe2HSaHpOV+rMdFFW3Udmgx2L95vlyeKCWxEjf3mCO9iXEz1O6p4UQLk2CWvSZGzmTTeXb\n+bJ6D1dHz0Ntx8U6/lVogJYHb57E/3xYwJtfnAbATakgLsy7r7WcEOWLj3Z4BpwJIYSzkKAWfTxV\nnsyLzGJL5Q5y6g8zJ3LmiF4/LTGIh26ZTGNrZ+/gr3Af3NUyk5cQYmyTh3niPNnRc3BTuLG1yv6L\ndVzMjORQbpodx4QYfwlpIYRAglr8Cz93XzLDptHY0cyx5kJHlyOEEGOeBLW4wMKYBQAjtliHEEKI\nSxtQULe0tJCdnU1ZWRmVlZWsWLGCu+++m6eeeqpvn7Vr13L77bdz5513smPHDnvVK0ZAuFcoKUHJ\nlLVXUtJW7uhyhBBiTOs3qM1mM6tXr8bDwwOA5557jlWrVvHWW29htVrZunUrzc3NrFmzhvfff59/\n/OMf/PGPf8RkMtm9eGE/C2OyAdhaucOhdQghxFjXb1D/7ne/Y/ny5YSEhGCz2SgsLCQjIwOA+fPn\ns2/fPgoKCkhPT0elUqHT6YiLi+P06dN2L17YT4JvHPE+sRxrPkm9scHR5QghxJh12dez1q1bR2Bg\nIHPmzOHFF18EwGr9ZiSwl5cXBoMBo9GIt/c3S/pptVr0ev2ACggOlqUAR6vbUxbxX3v/zp7G/Tw8\nY+VF95H757zk3jk3uX9jR79BrVAo2Lt3L6dPn+aRRx6htbW1b7vRaMTHxwedTofBYLjg6wPR1DSw\nQBcjL1YTT4g2iF3lB1kYcRV+7r7nbQ8O9pb756Tk3jk3uX/Oayh/YF226/utt95izZo1rFmzhokT\nJ/L73/+eefPmkZubC8CuXbtIT08nJSWFvLw8enp60Ov1lJaWkpSUNLTvQowaSoWShdELsNgs7Kja\n6+hyhBBiTBr0zGSPPPIIv/rVrzCZTCQkJLBo0SIUCgUrV65kxYoV2Gw2Vq1ahUYjUz26ghlh0/ms\n7Iu++b89VR6OLkkIIcYUhc3BL8pK983ot6l8O5+VbuK2xBv73rEG6X5zZnLvnJvcP+c17F3fQgDM\nj8xC46Zhe+VuzFazo8sRQogxRYJa9Eur1jI3YiZtPe3kNhxxdDlCCDGmSFCLAbkqei5KhZKtlY5Z\nrEMIIcYqCWoxIAEe/mSEplFvbKCwRSazEUKIkSJBLQasb7EOmVZUCCFGjAS1GLBIXTiTAiZQfK6M\nsrYKR5cjhBBjggS1GJRrY3tb1Vsrdzq4EiGEGBskqMWgJPklEOMdxdGmE9TqZbEOIYSwNwlqMSgK\nhYJrY7OxYeOjE/90dDlCCOHyJKjFoKUFTyFaF8HuihxK28odXY4QQrg0CWoxaEqFkmXjbwXggzOf\nyHvVQghhRxLUYkgS/OKYG5NJpb6G/XW5ji5HCCFclgS1GLK7U5egcdPwackmOkydji5HCCFckgS1\nGLIArR+LYq/GYDLyz/Itji5HCCFckgS1uCJXR88jyDOQndX7qDPK61pCCDHcJKjFFVG7qVmadDNW\nm5UPz3yKg5c3F0IIlyNBLa7YlMBkkgPGc6q1iILmE44uRwghXIoEtbhiCoWCpUnfQalQ8lHRZ/RY\nTI4uSQghXIYEtRgWYV4hXBU1l5auVrZV7nJ0OUII4TIkqMWwWRy/EG+Nji8qttPadc7R5QghhEuQ\noBbDxlPlwS3jFmOymlhfvNHR5QghhEuQoBbDamZ4OrHe0eQ1HqWotdTR5QghhNOToBbDqnce8FsA\n+KDoEyxWi4MrEkII5yZBLYZdvG8MWWEZ1Bjq2Fub4+hyhBDCqUlQC7v4TsJiPNzc2VD6BUZTh6PL\nEUIIpyVBLezC192bxfELMZo72FC62dHlCCGE05KgFnaTHTWHEG0Qu2v2U2Ooc3Q5QgjhlCSohd2o\nlCqWJt2CDRsfnPlE5gEXQoghkKAWdjU5cAJTApMpOldKftMxR5cjhBAOYbVZMfQYh3SsaphrEeIC\ntyfdzKmzZ1hXtIEpgRPRuGkcXZIQQlwRm81Gp7kLvcmAvseAwWTs/dhjQG8ynv/xq+02bKz97v8N\n+loS1MLuQrRBXB0zn80VX7K5Ygc3jbvO0SUJIcRF2Ww2GjubaTA29oWv3mTA0HP+fxtMRiy2/ueJ\n8FR54q32IlgbhLfaa0g1SVCLEXF97NUcrMtja+UOZoVnEOgZ4OiShBCCTnMXFe1VlLVVUNZeSXlb\nJUbzpV8p9XBzR6f2IsY7Ep3GC2+1Dp1Gh7fa66uPX32u8UKn9kKlvPKYlaAWI8JD5c6tiTfwRuF7\nrCveyIMpKx1dkhBijLHarDR2NFHWVklZewVlbZXUGRuw8c1A10CPAJIDxxOli8BH4/2tMPZCp9ah\ncVOPeN0S1GLEZIZOY1f1fo40HePU2SImBiQ5uiQhhAvrNHdS3lZFaXsF5W2VlLVX0mnu7NuuUapJ\n9IsnzieGeN9Y4n1j8NF4O7Dii5OgFiNGoVBwx/hb+P2hv/Bh0ac8lvkz3JRuji5LCOECrDYr9cZG\nyr4K5dL2ShqMjee1loM8A5kSmMw43xjifGOI9Ap3it9BEtRiRMX4RDE7IpO9tTnsqtnPVdFzHV2S\nEMIJGXqMVOirerux2yoob6+iy9LVt13jpiHJb1xfSznOJwZvjc6BFQ+dBLUYcTePW8ThxgI2lm0h\nIzTNaf/xCCFGRpe5myp9DRX6Kiraq6hor6al6+x5+4Rog0j1mUy8bwzxPrGEe4U6RWt5ICSoxYjz\n1ui4Mf46Piz6lM9Kv2DFxNsdXZIQYpQwW83UGOqoaK/uC+b6f+nC9lJrmRQwgVifKOJ8eruxdUN8\n9ckZSFALh5gfOYs9tQfZV5vD3MiZxHhHObokIcQI+3oUdkV7NeXtVVToq6jR12L+1vvJGjcNCX5x\nxHpHE+sTRaxPDIEe/igUCgdWPrIkqIVDuCndWJb0Hf5y5GU+OPMpq6Y/PKb+4Qkx1thsNs52naNC\nX0VlezUV7VVU6qvpsnT37eOmcCNSF0aMTzRx3tHE+kQT5hWCUjG2Z7uWoBYOMzEgidTgKRxtOs6h\nhiNkhk1zdElCiGHSZe7unUikvYKytgoq2qvRmwx92xUoCNUGE+sTTYxPFLHe0UTpwlE74D3l0U6C\nWjjUksSbONFyivXFG0kJmoSHyt3RJQkhBslms9HU2dI3u1dpWzm1hvrzniv7u/uRFpxCnE9vF3a0\ndxx0NlwAABQkSURBVBSeKg8HVu08JKiFQwV5BnBtzAI+L9/GFxXbuSVhsaNLEkL0o9vSQ2V776tR\npe3llLVVYjB9szKUWqlinG8s8b6xjPONJc4nFl/30TeRiLPoN6itVitPPPEEZWVlKJVKnnrqKTQa\nDY8++ihKpZKkpCRWr14NwNq1a3n//fdRq9U89NBDZGdn27t+4QKui72KA3V5bK/cxazwTEK0QY4u\nSQjxFZvNRktXK2VtFZS2VVDWXkGNoQ6rzdq3j7+7H+khqX3BHKkLH5Y5rkWvfv9Pbt++HYVCwbvv\nvktOTg5/+tOfsNlsrFq1ioyMDFavXs3WrVtJS0tjzZo1rF+/nq6uLpYvX86cOXNQq+V5g7g8jZuG\n2xJv4NUT77Cu+DMemnqfo0sSYszqsZio1Ff///buPDaqqmED+DP70mmn09LKUqBQ2tLKIpQgwguC\n1igxgiifW3CDRCCiBEWkooJQNnEjBj4X/BLBP0QiBE3URFDgs6Dwmo+ytFRl69sWSjtdprNv5/tj\n2ktLS1vQztyZPr9kMvfOnOmc6cm9z5w7954TOozdeBHnbBfR5L3627JaocLg+IGh65WbgzlRZ45g\njWNfl0Gdn5+Pu+66CwBQVVUFs9mMw4cPY9y4cQCAKVOmoKioCEqlEnl5eVCr1TCZTEhPT0dZWRlG\njBjRs5+AYsLY1NE4VHkEJ2tLcdpahluTsyNdJaKYFJpH2YV6TyPq3Q2o9zSioXn5suMKKuxVbaZv\nTNSZMSZlpBTKafEDoGFvOay69d9WKpVYvnw59u3bh82bN6OoqEh6Li4uDna7HQ6HA/HxV3+DMBqN\naGpq+udrTDFJoVDgvzJnYsOxzfjv4v9Bv7hbQkP/JQzCEPMgpBpTev0lGkRdCYWwOxS8ngbUuxua\nQ7jx6mOeRngD3g5fr1KokBbfH0MTQsNuDjWnw6JPDPOnoGt1+2vRhg0bYLVaMXv2bHg8V697czgc\nSEhIgMlkgt1ub/d4V1JSeIJBNPsn2y8lJRvPK5/G/nNFOFt3AVWOyyiq+g0AYNQYkJmcjszkochK\nHoJhSekw6WJ3JKJw4LYXna44rDh+qQRWZx2srnpYnQ2wOutDN1c93H7PdV8brzOhf3wqko0WJBss\nofvWN0MiL4+SoS6Deu/evaiursZzzz0HnU4HpVKJESNG4OjRoxg/fjwOHTqECRMmYOTIkXj//ffh\n9Xrh8Xhw7tw5ZGZ2PY1hTQ173dEqJSX+H2+/nLhc5IzMRSAYQKXjkjQ13YXGchRfLkXx5VKp7C3G\nlObp6WJvbN+e1hNtRz3D7ffgz4azKK37AyXWMtS4rB2Wi1MbkaxPgkVnRqI+ERZdIiw6Myx6MxJ1\nZiTqEjufS9kFNLjcANzXL0N/2818QVYIIURnBVwuFwoKClBbWwu/34/58+dj6NCheP311+Hz+ZCR\nkYHCwkIoFArs2rULO3fuhBACCxcuRH5+fpcV4M4ieoV7Z2/3OnDBdjW4L9jK24xqpFVpMTg+DUPM\ng6UAl+PcsnLAoJYvIQSqHJdRYi1DSd0fONdwXhpSU6/SIcsyDDl9h0IbMCBRZ4ZFHwpkrUob4ZpT\nd/RIUPc07iyiV6R39tfOP3veVt5u8P5kfZI0xd2wxCFIM/XnUKWIfNtRW3afA2fq/kSp9Q+U1pWh\n0Xu1bQbGD0BOUhZyk7Ix1DwYKqWK7RfFGNQUVnLcWbj8Lly0VYTmqG0OcIffKT0/0NQfd6ZNQt4t\nt3V+GDDGybHtepNAMIALtv+gtC7Uay63VUhfME2aOOQkZSM3OQvDkzI7PCrE9oteDGoKq2jYWYSG\nNqzF+cZyFNeexoma0xAQiNMYMbHfeEwecAeSDZZIVzPsoqHtYk29uwEldWUosf6Bsvo/4fKHfgtW\nKpQYah6M3KRs5CRnIc3Uv8srHNh+0YtBTWEVjTuLOnc9/rfyVxRV/QaHzwkFFBjVJxd3pk1CliWj\n1xwWj8a2izbegA9/NZyTTgK77LwiPZestyAnORu5SVnIsgy74TGv2X7Ri0FNYRXNOwtfwId/XynG\nwYoi/KepEgDQN+4W3DlgIsb3HRvzk4NEc9vJiS/gg9VdhxqXFbWulvuWW500cIhWqUGmJUPqNaca\n+vytL4Vsv+jFoKawioWdhRAC523lOFhRhP+7chIBEYBepccd/cZhStodSDWmRLqKPSIW2i5cnD6n\nFMA1rjopiGtcVjR6bG1OXmwRpzaijzEZwxKHIDcpGxnm9H/0+mS2X/RiUFNYxdrOotFjwy9Vv+GX\nyl9haz7rNjcpG3emTURucnZMjYwWa233dwRFEI0eW4dBXOuywul3tXuNAgok6szoY0hCiiEZfZpv\nLctGjaFH68z2i14MagqrWN1Z+IN+HK85hYMVh3G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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_components = np.arange(1, 21)\n", + "models = [GMM(n, covariance_type='full', random_state=0).fit(Xmoon)\n", + " for n in n_components]\n", + "\n", + "plt.plot(n_components, [m.bic(Xmoon) for m in models], label='BIC')\n", + "plt.plot(n_components, [m.aic(Xmoon) for m in models], label='AIC')\n", + "plt.legend(loc='best')\n", + "plt.xlabel('n_components');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The optimal number of clusters is the value that minimizes the AIC or BIC, depending on which approximation we wish to use. The AIC tells us that our choice of 16 components above was probably too many: around 8-12 components would have been a better choice.\n", + "As is typical with this sort of problem, the BIC recommends a simpler model.\n", + "\n", + "Notice the important point: this choice of number of components measures how well GMM works *as a density estimator*, not how well it works *as a clustering algorithm*.\n", + "I'd encourage you to think of GMM primarily as a density estimator, and use it for clustering only when warranted within simple datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Example: GMM for Generating New Data\n", + "\n", + "We just saw a simple example of using GMM as a generative model of data in order to create new samples from the distribution defined by the input data.\n", + "Here we will run with this idea and generate *new handwritten digits* from the standard digits corpus that we have used before.\n", + "\n", + "To start with, let's load the digits data using Scikit-Learn's data tools:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "digits.data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Next let's plot the first 100 of these to recall exactly what we're looking at:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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e2fn5eTBY9cLR0FK8dSXGCsa8TOKc6BvO6v7+fgDMT58+2bt37xLTpkql0lwp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_digits(data):\n", + " fig, ax = plt.subplots(10, 10, figsize=(8, 8),\n", + " subplot_kw=dict(xticks=[], yticks=[]))\n", + " fig.subplots_adjust(hspace=0.05, wspace=0.05)\n", + " for i, axi in enumerate(ax.flat):\n", + " im = axi.imshow(data[i].reshape(8, 8), cmap='binary')\n", + " im.set_clim(0, 16)\n", + "plot_digits(digits.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We have nearly 1,800 digits in 64 dimensions, and we can build a GMM on top of these to generate more.\n", + "GMMs can have difficulty converging in such a high dimensional space, so we will start with an invertible dimensionality reduction algorithm on the data.\n", + "Here we will use a straightforward PCA, asking it to preserve 99% of the variance in the projected data:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 41)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "pca = PCA(0.99, whiten=True)\n", + "data = pca.fit_transform(digits.data)\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The result is 41 dimensions, a reduction of nearly 1/3 with almost no information loss.\n", + "Given this projected data, let's use the AIC to get a gauge for the number of GMM components we should use:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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WtBDt6TeD1d+P1Kt6UFVTxxd5pWaXIyLSLOeqanl5eTbvbzlCRHgQT81IUuD7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\n6gXOOmr4ZGcxG3Z9/Xh9d24c2UvH60U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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_components = np.arange(50, 210, 10)\n", + "models = [GMM(n, covariance_type='full', random_state=0)\n", + " for n in n_components]\n", + "aics = [model.fit(data).aic(data) for model in models]\n", + "plt.plot(n_components, aics);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "It appears that around 110 components minimizes the AIC; we will use this model.\n", + "Let's quickly fit this to the data and confirm that it has converged:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "gmm = GMM(110, covariance_type='full', random_state=0)\n", + "gmm.fit(data)\n", + "print(gmm.converged_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now we can draw samples of 100 new points within this 41-dimensional projected space, using the GMM as a generative model:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 41)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_new = gmm.sample(100, random_state=0)\n", + "data_new.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, we can use the inverse transform of the PCA object to construct the new digits:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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xcWKtNxqNXIlN+ixiz2WTJ633HNsP+qxfw1giykhB62EwQV93d3d2eHgY1kGr1QoKGQSu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\nwevBls0zd7H2ztp0eY6IAsmInBRGqSqU9pGh8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "digits_new = pca.inverse_transform(data_new)\n", + "plot_digits(digits_new)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The results for the most part look like plausible digits from the dataset!\n", + "\n", + "Consider what we've done here: given a sampling of handwritten digits, we have modeled the distribution of that data in such a way that we can generate brand new samples of digits from the data: these are \"handwritten digits\" which do not individually appear in the original dataset, but rather capture the general features of the input data as modeled by the mixture model.\n", + "Such a generative model of digits can prove very useful as a component of a Bayesian generative classifier, as we shall see in the next section." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.12-Gaussian-Mixtures.md b/notebooks_v2/05.12-Gaussian-Mixtures.md new file mode 100644 index 00000000..03decece --- /dev/null +++ b/notebooks_v2/05.12-Gaussian-Mixtures.md @@ -0,0 +1,435 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) > + +Open in Colab + + + +# In Depth: Gaussian Mixture Models + + +The *k*-means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application. +In particular, the non-probabilistic nature of *k*-means and its use of simple distance-from-cluster-center to assign cluster membership leads to poor performance for many real-world situations. +In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering. + +We begin with the standard imports: + + +```python deletable=true editable=true +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +import numpy as np +``` + + +## Motivating GMM: Weaknesses of k-Means + +Let's take a look at some of the weaknesses of *k*-means and think about how we might improve the cluster model. +As we saw in the previous section, given simple, well-separated data, *k*-means finds suitable clustering results. + +For example, if we have simple blobs of data, the *k*-means algorithm can quickly label those clusters in a way that closely matches what we might do by eye: + + +```python deletable=true editable=true +# Generate some data +from sklearn.datasets.samples_generator import make_blobs +X, y_true = make_blobs(n_samples=400, centers=4, + cluster_std=0.60, random_state=0) +X = X[:, ::-1] # flip axes for better plotting +``` + +```python deletable=true editable=true +# Plot the data with K Means Labels +from sklearn.cluster import KMeans +kmeans = KMeans(4, random_state=0) +labels = kmeans.fit(X).predict(X) +plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis'); +``` + + +From an intuitive standpoint, we might expect that the clustering assignment for some points is more certain than others: for example, there appears to be a very slight overlap between the two middle clusters, such that we might not have complete confidence in the cluster assigment of points between them. +Unfortunately, the *k*-means model has no intrinsic measure of probability or uncertainty of cluster assignments (although it may be possible to use a bootstrap approach to estimate this uncertainty). +For this, we must think about generalizing the model. + +One way to think about the *k*-means model is that it places a circle (or, in higher dimensions, a hyper-sphere) at the center of each cluster, with a radius defined by the most distant point in the cluster. +This radius acts as a hard cutoff for cluster assignment within the training set: any point outside this circle is not considered a member of the cluster. +We can visualize this cluster model with the following function: + + +```python deletable=true editable=true +from sklearn.cluster import KMeans +from scipy.spatial.distance import cdist + +def plot_kmeans(kmeans, X, n_clusters=4, rseed=0, ax=None): + labels = kmeans.fit_predict(X) + + # plot the input data + ax = ax or plt.gca() + ax.axis('equal') + ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2) + + # plot the representation of the KMeans model + centers = kmeans.cluster_centers_ + radii = [cdist(X[labels == i], [center]).max() + for i, center in enumerate(centers)] + for c, r in zip(centers, radii): + ax.add_patch(plt.Circle(c, r, fc='#CCCCCC', lw=3, alpha=0.5, zorder=1)) +``` + +```python deletable=true editable=true +kmeans = KMeans(n_clusters=4, random_state=0) +plot_kmeans(kmeans, X) +``` + + +An important observation for *k*-means is that these cluster models *must be circular*: *k*-means has no built-in way of accounting for oblong or elliptical clusters. +So, for example, if we take the same data and transform it, the cluster assignments end up becoming muddled: + + +```python deletable=true editable=true +rng = np.random.RandomState(13) +X_stretched = np.dot(X, rng.randn(2, 2)) + +kmeans = KMeans(n_clusters=4, random_state=0) +plot_kmeans(kmeans, X_stretched) +``` + + +By eye, we recognize that these transformed clusters are non-circular, and thus circular clusters would be a poor fit. +Nevertheless, *k*-means is not flexible enough to account for this, and tries to force-fit the data into four circular clusters. +This results in a mixing of cluster assignments where the resulting circles overlap: see especially the bottom-right of this plot. +One might imagine addressing this particular situation by preprocessing the data with PCA (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), but in practice there is no guarantee that such a global operation will circularize the individual data. + +These two disadvantages of *k*-means—its lack of flexibility in cluster shape and lack of probabilistic cluster assignment—mean that for many datasets (especially low-dimensional datasets) it may not perform as well as you might hope. + +You might imagine addressing these weaknesses by generalizing the *k*-means model: for example, you could measure uncertainty in cluster assignment by comparing the distances of each point to *all* cluster centers, rather than focusing on just the closest. +You might also imagine allowing the cluster boundaries to be ellipses rather than circles, so as to account for non-circular clusters. +It turns out these are two essential components of a different type of clustering model, Gaussian mixture models. + + + +## Generalizing E–M: Gaussian Mixture Models + +A Gaussian mixture model (GMM) attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset. +In the simplest case, GMMs can be used for finding clusters in the same manner as *k*-means: + + +```python deletable=true editable=true +from sklearn.mixture import GMM +gmm = GMM(n_components=4).fit(X) +labels = gmm.predict(X) +plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis'); +``` + + +But because GMM contains a probabilistic model under the hood, it is also possible to find probabilistic cluster assignments—in Scikit-Learn this is done using the ``predict_proba`` method. +This returns a matrix of size ``[n_samples, n_clusters]`` which measures the probability that any point belongs to the given cluster: + + +```python deletable=true editable=true +probs = gmm.predict_proba(X) +print(probs[:5].round(3)) +``` + + +We can visualize this uncertainty by, for example, making the size of each point proportional to the certainty of its prediction; looking at the following figure, we can see that it is precisely the points at the boundaries between clusters that reflect this uncertainty of cluster assignment: + + +```python deletable=true editable=true +size = 50 * probs.max(1) ** 2 # square emphasizes differences +plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=size); +``` + + +Under the hood, a Gaussian mixture model is very similar to *k*-means: it uses an expectation–maximization approach which qualitatively does the following: + +1. Choose starting guesses for the location and shape + +2. Repeat until converged: + + 1. *E-step*: for each point, find weights encoding the probability of membership in each cluster + 2. *M-step*: for each cluster, update its location, normalization, and shape based on *all* data points, making use of the weights + +The result of this is that each cluster is associated not with a hard-edged sphere, but with a smooth Gaussian model. +Just as in the *k*-means expectation–maximization approach, this algorithm can sometimes miss the globally optimal solution, and thus in practice multiple random initializations are used. + +Let's create a function that will help us visualize the locations and shapes of the GMM clusters by drawing ellipses based on the GMM output: + + +```python deletable=true editable=true +from matplotlib.patches import Ellipse + +def draw_ellipse(position, covariance, ax=None, **kwargs): + """Draw an ellipse with a given position and covariance""" + ax = ax or plt.gca() + + # Convert covariance to principal axes + if covariance.shape == (2, 2): + U, s, Vt = np.linalg.svd(covariance) + angle = np.degrees(np.arctan2(U[1, 0], U[0, 0])) + width, height = 2 * np.sqrt(s) + else: + angle = 0 + width, height = 2 * np.sqrt(covariance) + + # Draw the Ellipse + for nsig in range(1, 4): + ax.add_patch(Ellipse(position, nsig * width, nsig * height, + angle, **kwargs)) + +def plot_gmm(gmm, X, label=True, ax=None): + ax = ax or plt.gca() + labels = gmm.fit(X).predict(X) + if label: + ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2) + else: + ax.scatter(X[:, 0], X[:, 1], s=40, zorder=2) + ax.axis('equal') + + w_factor = 0.2 / gmm.weights_.max() + for pos, covar, w in zip(gmm.means_, gmm.covars_, gmm.weights_): + draw_ellipse(pos, covar, alpha=w * w_factor) +``` + + +With this in place, we can take a look at what the four-component GMM gives us for our initial data: + + +```python deletable=true editable=true +gmm = GMM(n_components=4, random_state=42) +plot_gmm(gmm, X) +``` + + +Similarly, we can use the GMM approach to fit our stretched dataset; allowing for a full covariance the model will fit even very oblong, stretched-out clusters: + + +```python deletable=true editable=true +gmm = GMM(n_components=4, covariance_type='full', random_state=42) +plot_gmm(gmm, X_stretched) +``` + + +This makes clear that GMM addresses the two main practical issues with *k*-means encountered before. + + + +### Choosing the covariance type + +If you look at the details of the preceding fits, you will see that the ``covariance_type`` option was set differently within each. +This hyperparameter controls the degrees of freedom in the shape of each cluster; it is essential to set this carefully for any given problem. +The default is ``covariance_type="diag"``, which means that the size of the cluster along each dimension can be set independently, with the resulting ellipse constrained to align with the axes. +A slightly simpler and faster model is ``covariance_type="spherical"``, which constrains the shape of the cluster such that all dimensions are equal. The resulting clustering will have similar characteristics to that of *k*-means, though it is not entirely equivalent. +A more complicated and computationally expensive model (especially as the number of dimensions grows) is to use ``covariance_type="full"``, which allows each cluster to be modeled as an ellipse with arbitrary orientation. + +We can see a visual representation of these three choices for a single cluster within the following figure: + + + +![(Covariance Type)](figures/05.12-covariance-type.png) +[figure source in Appendix](06.00-Figure-Code.ipynb#Covariance-Type) + + + +## GMM as *Density Estimation* + +Though GMM is often categorized as a clustering algorithm, fundamentally it is an algorithm for *density estimation*. +That is to say, the result of a GMM fit to some data is technically not a clustering model, but a generative probabilistic model describing the distribution of the data. + +As an example, consider some data generated from Scikit-Learn's ``make_moons`` function, which we saw in [In Depth: K-Means Clustering](05.11-K-Means.ipynb): + + +```python deletable=true editable=true +from sklearn.datasets import make_moons +Xmoon, ymoon = make_moons(200, noise=.05, random_state=0) +plt.scatter(Xmoon[:, 0], Xmoon[:, 1]); +``` + + +If we try to fit this with a two-component GMM viewed as a clustering model, the results are not particularly useful: + + +```python deletable=true editable=true +gmm2 = GMM(n_components=2, covariance_type='full', random_state=0) +plot_gmm(gmm2, Xmoon) +``` + + +But if we instead use many more components and ignore the cluster labels, we find a fit that is much closer to the input data: + + +```python deletable=true editable=true +gmm16 = GMM(n_components=16, covariance_type='full', random_state=0) +plot_gmm(gmm16, Xmoon, label=False) +``` + + +Here the mixture of 16 Gaussians serves not to find separated clusters of data, but rather to model the overall *distribution* of the input data. +This is a generative model of the distribution, meaning that the GMM gives us the recipe to generate new random data distributed similarly to our input. +For example, here are 400 new points drawn from this 16-component GMM fit to our original data: + + +```python deletable=true editable=true +Xnew = gmm16.sample(400, random_state=42) +plt.scatter(Xnew[:, 0], Xnew[:, 1]); +``` + + +GMM is convenient as a flexible means of modeling an arbitrary multi-dimensional distribution of data. + + + +### How many components? + +The fact that GMM is a generative model gives us a natural means of determining the optimal number of components for a given dataset. +A generative model is inherently a probability distribution for the dataset, and so we can simply evaluate the *likelihood* of the data under the model, using cross-validation to avoid over-fitting. +Another means of correcting for over-fitting is to adjust the model likelihoods using some analytic criterion such as the [Akaike information criterion (AIC)](https://en.wikipedia.org/wiki/Akaike_information_criterion) or the [Bayesian information criterion (BIC)](https://en.wikipedia.org/wiki/Bayesian_information_criterion). +Scikit-Learn's ``GMM`` estimator actually includes built-in methods that compute both of these, and so it is very easy to operate on this approach. + +Let's look at the AIC and BIC as a function as the number of GMM components for our moon dataset: + + +```python deletable=true editable=true +n_components = np.arange(1, 21) +models = [GMM(n, covariance_type='full', random_state=0).fit(Xmoon) + for n in n_components] + +plt.plot(n_components, [m.bic(Xmoon) for m in models], label='BIC') +plt.plot(n_components, [m.aic(Xmoon) for m in models], label='AIC') +plt.legend(loc='best') +plt.xlabel('n_components'); +``` + + +The optimal number of clusters is the value that minimizes the AIC or BIC, depending on which approximation we wish to use. The AIC tells us that our choice of 16 components above was probably too many: around 8-12 components would have been a better choice. +As is typical with this sort of problem, the BIC recommends a simpler model. + +Notice the important point: this choice of number of components measures how well GMM works *as a density estimator*, not how well it works *as a clustering algorithm*. +I'd encourage you to think of GMM primarily as a density estimator, and use it for clustering only when warranted within simple datasets. + + + +## Example: GMM for Generating New Data + +We just saw a simple example of using GMM as a generative model of data in order to create new samples from the distribution defined by the input data. +Here we will run with this idea and generate *new handwritten digits* from the standard digits corpus that we have used before. + +To start with, let's load the digits data using Scikit-Learn's data tools: + + +```python deletable=true editable=true +from sklearn.datasets import load_digits +digits = load_digits() +digits.data.shape +``` + + +Next let's plot the first 100 of these to recall exactly what we're looking at: + + +```python deletable=true editable=true +def plot_digits(data): + fig, ax = plt.subplots(10, 10, figsize=(8, 8), + subplot_kw=dict(xticks=[], yticks=[])) + fig.subplots_adjust(hspace=0.05, wspace=0.05) + for i, axi in enumerate(ax.flat): + im = axi.imshow(data[i].reshape(8, 8), cmap='binary') + im.set_clim(0, 16) +plot_digits(digits.data) +``` + + +We have nearly 1,800 digits in 64 dimensions, and we can build a GMM on top of these to generate more. +GMMs can have difficulty converging in such a high dimensional space, so we will start with an invertible dimensionality reduction algorithm on the data. +Here we will use a straightforward PCA, asking it to preserve 99% of the variance in the projected data: + + +```python deletable=true editable=true +from sklearn.decomposition import PCA +pca = PCA(0.99, whiten=True) +data = pca.fit_transform(digits.data) +data.shape +``` + + +The result is 41 dimensions, a reduction of nearly 1/3 with almost no information loss. +Given this projected data, let's use the AIC to get a gauge for the number of GMM components we should use: + + +```python deletable=true editable=true +n_components = np.arange(50, 210, 10) +models = [GMM(n, covariance_type='full', random_state=0) + for n in n_components] +aics = [model.fit(data).aic(data) for model in models] +plt.plot(n_components, aics); +``` + + +It appears that around 110 components minimizes the AIC; we will use this model. +Let's quickly fit this to the data and confirm that it has converged: + + +```python deletable=true editable=true +gmm = GMM(110, covariance_type='full', random_state=0) +gmm.fit(data) +print(gmm.converged_) +``` + + +Now we can draw samples of 100 new points within this 41-dimensional projected space, using the GMM as a generative model: + + +```python deletable=true editable=true +data_new = gmm.sample(100, random_state=0) +data_new.shape +``` + + +Finally, we can use the inverse transform of the PCA object to construct the new digits: + + +```python deletable=true editable=true +digits_new = pca.inverse_transform(data_new) +plot_digits(digits_new) +``` + + +The results for the most part look like plausible digits from the dataset! + +Consider what we've done here: given a sampling of handwritten digits, we have modeled the distribution of that data in such a way that we can generate brand new samples of digits from the data: these are "handwritten digits" which do not individually appear in the original dataset, but rather capture the general features of the input data as modeled by the mixture model. +Such a generative model of digits can prove very useful as a component of a Bayesian generative classifier, as we shall see in the next section. + + + + +< [In Depth: k-Means Clustering](05.11-K-Means.ipynb) | [Contents](Index.ipynb) | [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.13-Kernel-Density-Estimation.ipynb b/notebooks_v2/05.13-Kernel-Density-Estimation.ipynb new file mode 100644 index 00000000..89499c73 --- /dev/null +++ b/notebooks_v2/05.13-Kernel-Density-Estimation.ipynb @@ -0,0 +1,1097 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In-Depth: Kernel Density Estimation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "In the previous section we covered Gaussian mixture models (GMM), which are a kind of hybrid between a clustering estimator and a density estimator.\n", + "Recall that a density estimator is an algorithm which takes a $D$-dimensional dataset and produces an estimate of the $D$-dimensional probability distribution which that data is drawn from.\n", + "The GMM algorithm accomplishes this by representing the density as a weighted sum of Gaussian distributions.\n", + "*Kernel density estimation* (KDE) is in some senses an algorithm which takes the mixture-of-Gaussians idea to its logical extreme: it uses a mixture consisting of one Gaussian component *per point*, resulting in an essentially non-parametric estimator of density.\n", + "In this section, we will explore the motivation and uses of KDE.\n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Motivating KDE: Histograms\n", + "\n", + "As already discussed, a density estimator is an algorithm which seeks to model the probability distribution that generated a dataset.\n", + "For one dimensional data, you are probably already familiar with one simple density estimator: the histogram.\n", + "A histogram divides the data into discrete bins, counts the number of points that fall in each bin, and then visualizes the results in an intuitive manner.\n", + "\n", + "For example, let's create some data that is drawn from two normal distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def make_data(N, f=0.3, rseed=1):\n", + " rand = np.random.RandomState(rseed)\n", + " x = rand.randn(N)\n", + " x[int(f * N):] += 5\n", + " return x\n", + "\n", + "x = make_data(1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We have previously seen that the standard count-based histogram can be created with the ``plt.hist()`` function.\n", + "By specifying the ``normed`` parameter of the histogram, we end up with a normalized histogram where the height of the bins does not reflect counts, but instead reflects probability density:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Gj7UGAADEzzK0XS6XAoFA9Hks4TudGknKz3dbrpPOTB7/yIgr1S3cVV6ea9b/2872/mbK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\nNgAAhiC0AQAwBKENAIAh/h+zvw1TQgOgbwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = plt.hist(x, bins=30, normed=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Notice that for equal binning, this normalization simply changes the scale on the y-axis, leaving the relative heights essentially the same as in a histogram built from counts.\n", + "This normalization is chosen so that the total area under the histogram is equal to 1, as we can confirm by looking at the output of the histogram function:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "density, bins, patches = hist\n", + "widths = bins[1:] - bins[:-1]\n", + "(density * widths).sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "One of the issues with using a histogram as a density estimator is that the choice of bin size and location can lead to representations that have qualitatively different features.\n", + "For example, if we look at a version of this data with only 20 points, the choice of how to draw the bins can lead to an entirely different interpretation of the data!\n", + "Consider this example:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "x = make_data(20)\n", + "bins = np.linspace(-5, 10, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 4),\n", + " sharex=True, sharey=True,\n", + " subplot_kw={'xlim':(-4, 9),\n", + " 'ylim':(-0.02, 0.3)})\n", + "fig.subplots_adjust(wspace=0.05)\n", + "for i, offset in enumerate([0.0, 0.6]):\n", + " ax[i].hist(x, bins=bins + offset, normed=True)\n", + " ax[i].plot(x, np.full_like(x, -0.01), '|k',\n", + " markeredgewidth=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "On the left, the histogram makes clear that this is a bimodal distribution.\n", + "On the right, we see a unimodal distribution with a long tail.\n", + "Without seeing the preceding code, you would probably not guess that these two histograms were built from the same data: with that in mind, how can you trust the intuition that histograms confer?\n", + "And how might we improve on this?\n", + "\n", + "Stepping back, we can think of a histogram as a stack of blocks, where we stack one block within each bin on top of each point in the dataset.\n", + "Let's view this directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.2, 8)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "bins = np.arange(-3, 8)\n", + "ax.plot(x, np.full_like(x, -0.1), '|k',\n", + " markeredgewidth=1)\n", + "for count, edge in zip(*np.histogram(x, bins)):\n", + " for i in range(count):\n", + " ax.add_patch(plt.Rectangle((edge, i), 1, 1,\n", + " alpha=0.5))\n", + "ax.set_xlim(-4, 8)\n", + "ax.set_ylim(-0.2, 8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The problem with our two binnings stems from the fact that the height of the block stack often reflects not on the actual density of points nearby, but on coincidences of how the bins align with the data points.\n", + "This mis-alignment between points and their blocks is a potential cause of the poor histogram results seen here.\n", + "But what if, instead of stacking the blocks aligned with the *bins*, we were to stack the blocks aligned with the *points they represent*?\n", + "If we do this, the blocks won't be aligned, but we can add their contributions at each location along the x-axis to find the result.\n", + "Let's try this:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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G6XQ+bqGeRU1B4NYyNq7vm9SPP1vpM820GVBfBK6x79SjbfU0UrWBy/VIDQ0E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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_d = np.linspace(-4, 8, 2000)\n", + "density = sum((abs(xi - x_d) < 0.5) for xi in x)\n", + "\n", + "plt.fill_between(x_d, density, alpha=0.5)\n", + "plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1)\n", + "\n", + "plt.axis([-4, 8, -0.2, 8]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The result looks a bit messy, but is a much more robust reflection of the actual data characteristics than is the standard histogram.\n", + "Still, the rough edges are not aesthetically pleasing, nor are they reflective of any true properties of the data.\n", + "In order to smooth them out, we might decide to replace the blocks at each location with a smooth function, like a Gaussian.\n", + "Let's use a standard normal curve at each point instead of a block:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats import norm\n", + "x_d = np.linspace(-4, 8, 1000)\n", + "density = sum(norm(xi).pdf(x_d) for xi in x)\n", + "\n", + "plt.fill_between(x_d, density, alpha=0.5)\n", + "plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1)\n", + "\n", + "plt.axis([-4, 8, -0.2, 5]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This smoothed-out plot, with a Gaussian distribution contributed at the location of each input point, gives a much more accurate idea of the shape of the data distribution, and one which has much less variance (i.e., changes much less in response to differences in sampling).\n", + "\n", + "These last two plots are examples of kernel density estimation in one dimension: the first uses a so-called \"tophat\" kernel and the second uses a Gaussian kernel.\n", + "We'll now look at kernel density estimation in more detail." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Kernel Density Estimation in Practice\n", + "\n", + "The free parameters of kernel density estimation are the *kernel*, which specifies the shape of the distribution placed at each point, and the *kernel bandwidth*, which controls the size of the kernel at each point.\n", + "In practice, there are many kernels you might use for a kernel density estimation: in particular, the Scikit-Learn KDE implementation supports one of six kernels, which you can read about in Scikit-Learn's [Density Estimation documentation](http://scikit-learn.org/stable/modules/density.html).\n", + "\n", + "While there are several versions of kernel density estimation implemented in Python (notably in the SciPy and StatsModels packages), I prefer to use Scikit-Learn's version because of its efficiency and flexibility.\n", + "It is implemented in the ``sklearn.neighbors.KernelDensity`` estimator, which handles KDE in multiple dimensions with one of six kernels and one of a couple dozen distance metrics.\n", + "Because KDE can be fairly computationally intensive, the Scikit-Learn estimator uses a tree-based algorithm under the hood and can trade off computation time for accuracy using the ``atol`` (absolute tolerance) and ``rtol`` (relative tolerance) parameters.\n", + "The kernel bandwidth, which is a free parameter, can be determined using Scikit-Learn's standard cross validation tools as we will soon see.\n", + "\n", + "Let's first show a simple example of replicating the above plot using the Scikit-Learn ``KernelDensity`` estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.02, 0.22)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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y+FNLRGVvemYW0bRFdAz6iCRJmJxdQjabFR2lJLCoiajs9fSPQHXWiI5Bd5CVWvj6BkXH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\n+PHj8Pl8sFqtOHH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neighbors import KernelDensity\n", + "\n", + "# instantiate and fit the KDE model\n", + "kde = KernelDensity(bandwidth=1.0, kernel='gaussian')\n", + "kde.fit(x[:, None])\n", + "\n", + "# score_samples returns the log of the probability density\n", + "logprob = kde.score_samples(x_d[:, None])\n", + "\n", + "plt.fill_between(x_d, np.exp(logprob), alpha=0.5)\n", + "plt.plot(x, np.full_like(x, -0.01), '|k', markeredgewidth=1)\n", + "plt.ylim(-0.02, 0.22)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The result here is normalized such that the area under the curve is equal to 1." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Selecting the bandwidth via cross-validation\n", + "\n", + "The choice of bandwidth within KDE is extremely important to finding a suitable density estimate, and is the knob that controls the bias–variance trade-off in the estimate of density: too narrow a bandwidth leads to a high-variance estimate (i.e., over-fitting), where the presence or absence of a single point makes a large difference. Too wide a bandwidth leads to a high-bias estimate (i.e., under-fitting) where the structure in the data is washed out by the wide kernel.\n", + "\n", + "There is a long history in statistics of methods to quickly estimate the best bandwidth based on rather stringent assumptions about the data: if you look up the KDE implementations in the SciPy and StatsModels packages, for example, you will see implementations based on some of these rules.\n", + "\n", + "In machine learning contexts, we've seen that such hyperparameter tuning often is done empirically via a cross-validation approach.\n", + "With this in mind, the ``KernelDensity`` estimator in Scikit-Learn is designed such that it can be used directly within the Scikit-Learn's standard grid search tools.\n", + "Here we will use ``GridSearchCV`` to optimize the bandwidth for the preceding dataset.\n", + "Because we are looking at such a small dataset, we will use leave-one-out cross-validation, which minimizes the reduction in training set size for each cross-validation trial:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.cross_validation import LeaveOneOut\n", + "\n", + "bandwidths = 10 ** np.linspace(-1, 1, 100)\n", + "grid = GridSearchCV(KernelDensity(kernel='gaussian'),\n", + " {'bandwidth': bandwidths},\n", + " cv=LeaveOneOut(len(x)))\n", + "grid.fit(x[:, None]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now we can find the choice of bandwidth which maximizes the score (which in this case defaults to the log-likelihood):" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'bandwidth': 1.1233240329780276}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.best_params_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The optimal bandwidth happens to be very close to what we used in the example plot earlier, where the bandwidth was 1.0 (i.e., the default width of ``scipy.stats.norm``)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Example: KDE on a Sphere\n", + "\n", + "Perhaps the most common use of KDE is in graphically representing distributions of points.\n", + "For example, in the Seaborn visualization library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), KDE is built in and automatically used to help visualize points in one and two dimensions.\n", + "\n", + "Here we will look at a slightly more sophisticated use of KDE for visualization of distributions.\n", + "We will make use of some geographic data that can be loaded with Scikit-Learn: the geographic distributions of recorded observations of two South American mammals, *Bradypus variegatus* (the Brown-throated Sloth) and *Microryzomys minutus* (the Forest Small Rice Rat).\n", + "\n", + "With Scikit-Learn, we can fetch this data as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import fetch_species_distributions\n", + "\n", + "data = fetch_species_distributions()\n", + "\n", + "# Get matrices/arrays of species IDs and locations\n", + "latlon = np.vstack([data.train['dd lat'],\n", + " data.train['dd long']]).T\n", + "species = np.array([d.decode('ascii').startswith('micro')\n", + " for d in data.train['species']], dtype='int')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With this data loaded, we can use the Basemap toolkit (mentioned previously in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)) to plot the observed locations of these two species on the map of South America." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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pXMmYcjXOv7HAmh99HaY2JybwdzyIopotHGINvqTSQSl2yHAmAEde4jNKrE/I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\nk9Vm4/iF6+QXVbFm++cIj04A7kZwEUEVXEuyTjZk4AuxoTbFtDXXYzR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.basemap import Basemap\n", + "from sklearn.datasets.species_distributions import construct_grids\n", + "\n", + "xgrid, ygrid = construct_grids(data)\n", + "\n", + "# plot coastlines with basemap\n", + "m = Basemap(projection='cyl', resolution='c',\n", + " llcrnrlat=ygrid.min(), urcrnrlat=ygrid.max(),\n", + " llcrnrlon=xgrid.min(), urcrnrlon=xgrid.max())\n", + "m.drawmapboundary(fill_color='#DDEEFF')\n", + "m.fillcontinents(color='#FFEEDD')\n", + "m.drawcoastlines(color='gray', zorder=2)\n", + "m.drawcountries(color='gray', zorder=2)\n", + "\n", + "# plot locations\n", + "m.scatter(latlon[:, 1], latlon[:, 0], zorder=3,\n", + " c=species, cmap='rainbow', latlon=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Unfortunately, this doesn't give a very good idea of the density of the species, because points in the species range may overlap one another.\n", + "You may not realize it by looking at this plot, but there are over 1,600 points shown here!\n", + "\n", + "Let's use kernel density estimation to show this distribution in a more interpretable way: as a smooth indication of density on the map.\n", + "Because the coordinate system here lies on a spherical surface rather than a flat plane, we will use the ``haversine`` distance metric, which will correctly represent distances on a curved surface.\n", + "\n", + "There is a bit of boilerplate code here (one of the disadvantages of the Basemap toolkit) but the meaning of each code block should be clear:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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7XPY4yaVzJ4mOiqBr586MGj2G7dt3cOv2LU6edOfx48e4uR7H2tq6wAvfuXOb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\nO11TuRcFiQQBGpCSkoLvJV8AXF1dOX7sL1JTU7lw4Tzu7i2ZO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set up the data grid for the contour plot\n", + "X, Y = np.meshgrid(xgrid[::5], ygrid[::5][::-1])\n", + "land_reference = data.coverages[6][::5, ::5]\n", + "land_mask = (land_reference > -9999).ravel()\n", + "xy = np.vstack([Y.ravel(), X.ravel()]).T\n", + "xy = np.radians(xy[land_mask])\n", + "\n", + "# Create two side-by-side plots\n", + "fig, ax = plt.subplots(1, 2)\n", + "fig.subplots_adjust(left=0.05, right=0.95, wspace=0.05)\n", + "species_names = ['Bradypus Variegatus', 'Microryzomys Minutus']\n", + "cmaps = ['Purples', 'Reds']\n", + "\n", + "for i, axi in enumerate(ax):\n", + " axi.set_title(species_names[i])\n", + " \n", + " # plot coastlines with basemap\n", + " m = Basemap(projection='cyl', llcrnrlat=Y.min(),\n", + " urcrnrlat=Y.max(), llcrnrlon=X.min(),\n", + " urcrnrlon=X.max(), resolution='c', ax=axi)\n", + " m.drawmapboundary(fill_color='#DDEEFF')\n", + " m.drawcoastlines()\n", + " m.drawcountries()\n", + " \n", + " # construct a spherical kernel density estimate of the distribution\n", + " kde = KernelDensity(bandwidth=0.03, metric='haversine')\n", + " kde.fit(np.radians(latlon[species == i]))\n", + "\n", + " # evaluate only on the land: -9999 indicates ocean\n", + " Z = np.full(land_mask.shape[0], -9999.0)\n", + " Z[land_mask] = np.exp(kde.score_samples(xy))\n", + " Z = Z.reshape(X.shape)\n", + "\n", + " # plot contours of the density\n", + " levels = np.linspace(0, Z.max(), 25)\n", + " axi.contourf(X, Y, Z, levels=levels, cmap=cmaps[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Compared to the simple scatter plot we initially used, this visualization paints a much clearer picture of the geographical distribution of observations of these two species." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Example: Not-So-Naive Bayes\n", + "\n", + "This example looks at Bayesian generative classification with KDE, and demonstrates how to use the Scikit-Learn architecture to create a custom estimator.\n", + "\n", + "In [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb), we took a look at naive Bayesian classification, in which we created a simple generative model for each class, and used these models to build a fast classifier.\n", + "For Gaussian naive Bayes, the generative model is a simple axis-aligned Gaussian.\n", + "With a density estimation algorithm like KDE, we can remove the \"naive\" element and perform the same classification with a more sophisticated generative model for each class.\n", + "It's still Bayesian classification, but it's no longer naive.\n", + "\n", + "The general approach for generative classification is this:\n", + "\n", + "1. Split the training data by label.\n", + "\n", + "2. For each set, fit a KDE to obtain a generative model of the data.\n", + " This allows you for any observation $x$ and label $y$ to compute a likelihood $P(x~|~y)$.\n", + " \n", + "3. From the number of examples of each class in the training set, compute the *class prior*, $P(y)$.\n", + "\n", + "4. For an unknown point $x$, the posterior probability for each class is $P(y~|~x) \\propto P(x~|~y)P(y)$.\n", + " The class which maximizes this posterior is the label assigned to the point.\n", + "\n", + "The algorithm is straightforward and intuitive to understand; the more difficult piece is couching it within the Scikit-Learn framework in order to make use of the grid search and cross-validation architecture.\n", + "\n", + "This is the code that implements the algorithm within the Scikit-Learn framework; we will step through it following the code block:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.base import BaseEstimator, ClassifierMixin\n", + "\n", + "\n", + "class KDEClassifier(BaseEstimator, ClassifierMixin):\n", + " \"\"\"Bayesian generative classification based on KDE\n", + " \n", + " Parameters\n", + " ----------\n", + " bandwidth : float\n", + " the kernel bandwidth within each class\n", + " kernel : str\n", + " the kernel name, passed to KernelDensity\n", + " \"\"\"\n", + " def __init__(self, bandwidth=1.0, kernel='gaussian'):\n", + " self.bandwidth = bandwidth\n", + " self.kernel = kernel\n", + " \n", + " def fit(self, X, y):\n", + " self.classes_ = np.sort(np.unique(y))\n", + " training_sets = [X[y == yi] for yi in self.classes_]\n", + " self.models_ = [KernelDensity(bandwidth=self.bandwidth,\n", + " kernel=self.kernel).fit(Xi)\n", + " for Xi in training_sets]\n", + " self.logpriors_ = [np.log(Xi.shape[0] / X.shape[0])\n", + " for Xi in training_sets]\n", + " return self\n", + " \n", + " def predict_proba(self, X):\n", + " logprobs = np.array([model.score_samples(X)\n", + " for model in self.models_]).T\n", + " result = np.exp(logprobs + self.logpriors_)\n", + " return result / result.sum(1, keepdims=True)\n", + " \n", + " def predict(self, X):\n", + " return self.classes_[np.argmax(self.predict_proba(X), 1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### The anatomy of a custom estimator" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Let's step through this code and discuss the essential features:\n", + "\n", + "```python\n", + "from sklearn.base import BaseEstimator, ClassifierMixin\n", + "\n", + "class KDEClassifier(BaseEstimator, ClassifierMixin):\n", + " \"\"\"Bayesian generative classification based on KDE\n", + " \n", + " Parameters\n", + " ----------\n", + " bandwidth : float\n", + " the kernel bandwidth within each class\n", + " kernel : str\n", + " the kernel name, passed to KernelDensity\n", + " \"\"\"\n", + "```\n", + "\n", + "Each estimator in Scikit-Learn is a class, and it is most convenient for this class to inherit from the ``BaseEstimator`` class as well as the appropriate mixin, which provides standard functionality.\n", + "For example, among other things, here the ``BaseEstimator`` contains the logic necessary to clone/copy an estimator for use in a cross-validation procedure, and ``ClassifierMixin`` defines a default ``score()`` method used by such routines.\n", + "We also provide a doc string, which will be captured by IPython's help functionality (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Next comes the class initialization method:\n", + "\n", + "```python\n", + " def __init__(self, bandwidth=1.0, kernel='gaussian'):\n", + " self.bandwidth = bandwidth\n", + " self.kernel = kernel\n", + "```\n", + "\n", + "This is the actual code that is executed when the object is instantiated with ``KDEClassifier()``.\n", + "In Scikit-Learn, it is important that *initialization contains no operations* other than assigning the passed values by name to ``self``.\n", + "This is due to the logic contained in ``BaseEstimator`` required for cloning and modifying estimators for cross-validation, grid search, and other functions.\n", + "Similarly, all arguments to ``__init__`` should be explicit: i.e. ``*args`` or ``**kwargs`` should be avoided, as they will not be correctly handled within cross-validation routines." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Next comes the ``fit()`` method, where we handle training data:\n", + "\n", + "```python \n", + " def fit(self, X, y):\n", + " self.classes_ = np.sort(np.unique(y))\n", + " training_sets = [X[y == yi] for yi in self.classes_]\n", + " self.models_ = [KernelDensity(bandwidth=self.bandwidth,\n", + " kernel=self.kernel).fit(Xi)\n", + " for Xi in training_sets]\n", + " self.logpriors_ = [np.log(Xi.shape[0] / X.shape[0])\n", + " for Xi in training_sets]\n", + " return self\n", + "```\n", + "\n", + "Here we find the unique classes in the training data, train a ``KernelDensity`` model for each class, and compute the class priors based on the number of input samples.\n", + "Finally, ``fit()`` should always return ``self`` so that we can chain commands. For example:\n", + "```python\n", + "label = model.fit(X, y).predict(X)\n", + "```\n", + "Notice that each persistent result of the fit is stored with a trailing underscore (e.g., ``self.logpriors_``).\n", + "This is a convention used in Scikit-Learn so that you can quickly scan the members of an estimator (using IPython's tab completion) and see exactly which members are fit to training data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally, we have the logic for predicting labels on new data:\n", + "```python\n", + " def predict_proba(self, X):\n", + " logprobs = np.vstack([model.score_samples(X)\n", + " for model in self.models_]).T\n", + " result = np.exp(logprobs + self.logpriors_)\n", + " return result / result.sum(1, keepdims=True)\n", + " \n", + " def predict(self, X):\n", + " return self.classes_[np.argmax(self.predict_proba(X), 1)]\n", + "```\n", + "Because this is a probabilistic classifier, we first implement ``predict_proba()`` which returns an array of class probabilities of shape ``[n_samples, n_classes]``.\n", + "Entry ``[i, j]`` of this array is the posterior probability that sample ``i`` is a member of class ``j``, computed by multiplying the likelihood by the class prior and normalizing.\n", + "\n", + "Finally, the ``predict()`` method uses these probabilities and simply returns the class with the largest probability." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Using our custom estimator\n", + "\n", + "Let's try this custom estimator on a problem we have seen before: the classification of hand-written digits.\n", + "Here we will load the digits, and compute the cross-validation score for a range of candidate bandwidths using the ``GridSearchCV`` meta-estimator (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import load_digits\n", + "from sklearn.grid_search import GridSearchCV\n", + "\n", + "digits = load_digits()\n", + "\n", + "bandwidths = 10 ** np.linspace(0, 2, 100)\n", + "grid = GridSearchCV(KDEClassifier(), {'bandwidth': bandwidths})\n", + "grid.fit(digits.data, digits.target)\n", + "\n", + "scores = [val.mean_validation_score for val in grid.grid_scores_]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Next we can plot the cross-validation score as a function of bandwidth:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'bandwidth': 7.0548023107186433}\n", + "accuracy = 0.966611018364\n" + ] + }, + { + "data": { + "image/png": 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/hjz5hE27i7H9wFlcmzkWK65L83Q5NAiebGtrtdnx05km/HiiHkdO1TuvBRASJEVmShSu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\nh7Curncny5sRrlY4R/BhKgXEIlGvx+T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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.semilogx(bandwidths, scores)\n", + "plt.xlabel('bandwidth')\n", + "plt.ylabel('accuracy')\n", + "plt.title('KDE Model Performance')\n", + "print(grid.best_params_)\n", + "print('accuracy =', grid.best_score_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that this not-so-naive Bayesian classifier reaches a cross-validation accuracy of just over 96%; this is compared to around 80% for the naive Bayesian classification:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.81860038035501381" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.cross_validation import cross_val_score\n", + "cross_val_score(GaussianNB(), digits.data, digits.target).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "One benefit of such a generative classifier is interpretability of results: for each unknown sample, we not only get a probabilistic classification, but a *full model* of the distribution of points we are comparing it to!\n", + "If desired, this offers an intuitive window into the reasons for a particular classification that algorithms like SVMs and random forests tend to obscure.\n", + "\n", + "If you would like to take this further, there are some improvements that could be made to our KDE classifier model:\n", + "\n", + "- we could allow the bandwidth in each class to vary independently\n", + "- we could optimize these bandwidths not based on their prediction score, but on the likelihood of the training data under the generative model within each class (i.e. use the scores from ``KernelDensity`` itself rather than the global prediction accuracy)\n", + "\n", + "Finally, if you want some practice building your own estimator, you might tackle building a similar Bayesian classifier using Gaussian Mixture Models instead of KDE." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.13-Kernel-Density-Estimation.md b/notebooks_v2/05.13-Kernel-Density-Estimation.md new file mode 100644 index 00000000..634383e1 --- /dev/null +++ b/notebooks_v2/05.13-Kernel-Density-Estimation.md @@ -0,0 +1,561 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) > + +Open in Colab + + + +# In-Depth: Kernel Density Estimation + + +In the previous section we covered Gaussian mixture models (GMM), which are a kind of hybrid between a clustering estimator and a density estimator. +Recall that a density estimator is an algorithm which takes a $D$-dimensional dataset and produces an estimate of the $D$-dimensional probability distribution which that data is drawn from. +The GMM algorithm accomplishes this by representing the density as a weighted sum of Gaussian distributions. +*Kernel density estimation* (KDE) is in some senses an algorithm which takes the mixture-of-Gaussians idea to its logical extreme: it uses a mixture consisting of one Gaussian component *per point*, resulting in an essentially non-parametric estimator of density. +In this section, we will explore the motivation and uses of KDE. + +We begin with the standard imports: + + +```python deletable=true editable=true +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +import numpy as np +``` + + +## Motivating KDE: Histograms + +As already discussed, a density estimator is an algorithm which seeks to model the probability distribution that generated a dataset. +For one dimensional data, you are probably already familiar with one simple density estimator: the histogram. +A histogram divides the data into discrete bins, counts the number of points that fall in each bin, and then visualizes the results in an intuitive manner. + +For example, let's create some data that is drawn from two normal distributions: + + +```python deletable=true editable=true +def make_data(N, f=0.3, rseed=1): + rand = np.random.RandomState(rseed) + x = rand.randn(N) + x[int(f * N):] += 5 + return x + +x = make_data(1000) +``` + + +We have previously seen that the standard count-based histogram can be created with the ``plt.hist()`` function. +By specifying the ``normed`` parameter of the histogram, we end up with a normalized histogram where the height of the bins does not reflect counts, but instead reflects probability density: + + +```python deletable=true editable=true +hist = plt.hist(x, bins=30, normed=True) +``` + + +Notice that for equal binning, this normalization simply changes the scale on the y-axis, leaving the relative heights essentially the same as in a histogram built from counts. +This normalization is chosen so that the total area under the histogram is equal to 1, as we can confirm by looking at the output of the histogram function: + + +```python deletable=true editable=true +density, bins, patches = hist +widths = bins[1:] - bins[:-1] +(density * widths).sum() +``` + + +One of the issues with using a histogram as a density estimator is that the choice of bin size and location can lead to representations that have qualitatively different features. +For example, if we look at a version of this data with only 20 points, the choice of how to draw the bins can lead to an entirely different interpretation of the data! +Consider this example: + + +```python deletable=true editable=true +x = make_data(20) +bins = np.linspace(-5, 10, 10) +``` + +```python deletable=true editable=true +fig, ax = plt.subplots(1, 2, figsize=(12, 4), + sharex=True, sharey=True, + subplot_kw={'xlim':(-4, 9), + 'ylim':(-0.02, 0.3)}) +fig.subplots_adjust(wspace=0.05) +for i, offset in enumerate([0.0, 0.6]): + ax[i].hist(x, bins=bins + offset, normed=True) + ax[i].plot(x, np.full_like(x, -0.01), '|k', + markeredgewidth=1) +``` + + +On the left, the histogram makes clear that this is a bimodal distribution. +On the right, we see a unimodal distribution with a long tail. +Without seeing the preceding code, you would probably not guess that these two histograms were built from the same data: with that in mind, how can you trust the intuition that histograms confer? +And how might we improve on this? + +Stepping back, we can think of a histogram as a stack of blocks, where we stack one block within each bin on top of each point in the dataset. +Let's view this directly: + + +```python deletable=true editable=true +fig, ax = plt.subplots() +bins = np.arange(-3, 8) +ax.plot(x, np.full_like(x, -0.1), '|k', + markeredgewidth=1) +for count, edge in zip(*np.histogram(x, bins)): + for i in range(count): + ax.add_patch(plt.Rectangle((edge, i), 1, 1, + alpha=0.5)) +ax.set_xlim(-4, 8) +ax.set_ylim(-0.2, 8) +``` + + +The problem with our two binnings stems from the fact that the height of the block stack often reflects not on the actual density of points nearby, but on coincidences of how the bins align with the data points. +This mis-alignment between points and their blocks is a potential cause of the poor histogram results seen here. +But what if, instead of stacking the blocks aligned with the *bins*, we were to stack the blocks aligned with the *points they represent*? +If we do this, the blocks won't be aligned, but we can add their contributions at each location along the x-axis to find the result. +Let's try this: + + +```python deletable=true editable=true +x_d = np.linspace(-4, 8, 2000) +density = sum((abs(xi - x_d) < 0.5) for xi in x) + +plt.fill_between(x_d, density, alpha=0.5) +plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1) + +plt.axis([-4, 8, -0.2, 8]); +``` + + +The result looks a bit messy, but is a much more robust reflection of the actual data characteristics than is the standard histogram. +Still, the rough edges are not aesthetically pleasing, nor are they reflective of any true properties of the data. +In order to smooth them out, we might decide to replace the blocks at each location with a smooth function, like a Gaussian. +Let's use a standard normal curve at each point instead of a block: + + +```python deletable=true editable=true +from scipy.stats import norm +x_d = np.linspace(-4, 8, 1000) +density = sum(norm(xi).pdf(x_d) for xi in x) + +plt.fill_between(x_d, density, alpha=0.5) +plt.plot(x, np.full_like(x, -0.1), '|k', markeredgewidth=1) + +plt.axis([-4, 8, -0.2, 5]); +``` + + +This smoothed-out plot, with a Gaussian distribution contributed at the location of each input point, gives a much more accurate idea of the shape of the data distribution, and one which has much less variance (i.e., changes much less in response to differences in sampling). + +These last two plots are examples of kernel density estimation in one dimension: the first uses a so-called "tophat" kernel and the second uses a Gaussian kernel. +We'll now look at kernel density estimation in more detail. + + + +## Kernel Density Estimation in Practice + +The free parameters of kernel density estimation are the *kernel*, which specifies the shape of the distribution placed at each point, and the *kernel bandwidth*, which controls the size of the kernel at each point. +In practice, there are many kernels you might use for a kernel density estimation: in particular, the Scikit-Learn KDE implementation supports one of six kernels, which you can read about in Scikit-Learn's [Density Estimation documentation](http://scikit-learn.org/stable/modules/density.html). + +While there are several versions of kernel density estimation implemented in Python (notably in the SciPy and StatsModels packages), I prefer to use Scikit-Learn's version because of its efficiency and flexibility. +It is implemented in the ``sklearn.neighbors.KernelDensity`` estimator, which handles KDE in multiple dimensions with one of six kernels and one of a couple dozen distance metrics. +Because KDE can be fairly computationally intensive, the Scikit-Learn estimator uses a tree-based algorithm under the hood and can trade off computation time for accuracy using the ``atol`` (absolute tolerance) and ``rtol`` (relative tolerance) parameters. +The kernel bandwidth, which is a free parameter, can be determined using Scikit-Learn's standard cross validation tools as we will soon see. + +Let's first show a simple example of replicating the above plot using the Scikit-Learn ``KernelDensity`` estimator: + + +```python deletable=true editable=true +from sklearn.neighbors import KernelDensity + +# instantiate and fit the KDE model +kde = KernelDensity(bandwidth=1.0, kernel='gaussian') +kde.fit(x[:, None]) + +# score_samples returns the log of the probability density +logprob = kde.score_samples(x_d[:, None]) + +plt.fill_between(x_d, np.exp(logprob), alpha=0.5) +plt.plot(x, np.full_like(x, -0.01), '|k', markeredgewidth=1) +plt.ylim(-0.02, 0.22) +``` + + +The result here is normalized such that the area under the curve is equal to 1. + + + +### Selecting the bandwidth via cross-validation + +The choice of bandwidth within KDE is extremely important to finding a suitable density estimate, and is the knob that controls the bias–variance trade-off in the estimate of density: too narrow a bandwidth leads to a high-variance estimate (i.e., over-fitting), where the presence or absence of a single point makes a large difference. Too wide a bandwidth leads to a high-bias estimate (i.e., under-fitting) where the structure in the data is washed out by the wide kernel. + +There is a long history in statistics of methods to quickly estimate the best bandwidth based on rather stringent assumptions about the data: if you look up the KDE implementations in the SciPy and StatsModels packages, for example, you will see implementations based on some of these rules. + +In machine learning contexts, we've seen that such hyperparameter tuning often is done empirically via a cross-validation approach. +With this in mind, the ``KernelDensity`` estimator in Scikit-Learn is designed such that it can be used directly within the Scikit-Learn's standard grid search tools. +Here we will use ``GridSearchCV`` to optimize the bandwidth for the preceding dataset. +Because we are looking at such a small dataset, we will use leave-one-out cross-validation, which minimizes the reduction in training set size for each cross-validation trial: + + +```python deletable=true editable=true +from sklearn.grid_search import GridSearchCV +from sklearn.cross_validation import LeaveOneOut + +bandwidths = 10 ** np.linspace(-1, 1, 100) +grid = GridSearchCV(KernelDensity(kernel='gaussian'), + {'bandwidth': bandwidths}, + cv=LeaveOneOut(len(x))) +grid.fit(x[:, None]); +``` + + +Now we can find the choice of bandwidth which maximizes the score (which in this case defaults to the log-likelihood): + + +```python deletable=true editable=true +grid.best_params_ +``` + + +The optimal bandwidth happens to be very close to what we used in the example plot earlier, where the bandwidth was 1.0 (i.e., the default width of ``scipy.stats.norm``). + + + +## Example: KDE on a Sphere + +Perhaps the most common use of KDE is in graphically representing distributions of points. +For example, in the Seaborn visualization library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), KDE is built in and automatically used to help visualize points in one and two dimensions. + +Here we will look at a slightly more sophisticated use of KDE for visualization of distributions. +We will make use of some geographic data that can be loaded with Scikit-Learn: the geographic distributions of recorded observations of two South American mammals, *Bradypus variegatus* (the Brown-throated Sloth) and *Microryzomys minutus* (the Forest Small Rice Rat). + +With Scikit-Learn, we can fetch this data as follows: + + +```python deletable=true editable=true +from sklearn.datasets import fetch_species_distributions + +data = fetch_species_distributions() + +# Get matrices/arrays of species IDs and locations +latlon = np.vstack([data.train['dd lat'], + data.train['dd long']]).T +species = np.array([d.decode('ascii').startswith('micro') + for d in data.train['species']], dtype='int') +``` + + +With this data loaded, we can use the Basemap toolkit (mentioned previously in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)) to plot the observed locations of these two species on the map of South America. + + +```python deletable=true editable=true +from mpl_toolkits.basemap import Basemap +from sklearn.datasets.species_distributions import construct_grids + +xgrid, ygrid = construct_grids(data) + +# plot coastlines with basemap +m = Basemap(projection='cyl', resolution='c', + llcrnrlat=ygrid.min(), urcrnrlat=ygrid.max(), + llcrnrlon=xgrid.min(), urcrnrlon=xgrid.max()) +m.drawmapboundary(fill_color='#DDEEFF') +m.fillcontinents(color='#FFEEDD') +m.drawcoastlines(color='gray', zorder=2) +m.drawcountries(color='gray', zorder=2) + +# plot locations +m.scatter(latlon[:, 1], latlon[:, 0], zorder=3, + c=species, cmap='rainbow', latlon=True); +``` + + +Unfortunately, this doesn't give a very good idea of the density of the species, because points in the species range may overlap one another. +You may not realize it by looking at this plot, but there are over 1,600 points shown here! + +Let's use kernel density estimation to show this distribution in a more interpretable way: as a smooth indication of density on the map. +Because the coordinate system here lies on a spherical surface rather than a flat plane, we will use the ``haversine`` distance metric, which will correctly represent distances on a curved surface. + +There is a bit of boilerplate code here (one of the disadvantages of the Basemap toolkit) but the meaning of each code block should be clear: + + +```python deletable=true editable=true +# Set up the data grid for the contour plot +X, Y = np.meshgrid(xgrid[::5], ygrid[::5][::-1]) +land_reference = data.coverages[6][::5, ::5] +land_mask = (land_reference > -9999).ravel() +xy = np.vstack([Y.ravel(), X.ravel()]).T +xy = np.radians(xy[land_mask]) + +# Create two side-by-side plots +fig, ax = plt.subplots(1, 2) +fig.subplots_adjust(left=0.05, right=0.95, wspace=0.05) +species_names = ['Bradypus Variegatus', 'Microryzomys Minutus'] +cmaps = ['Purples', 'Reds'] + +for i, axi in enumerate(ax): + axi.set_title(species_names[i]) + + # plot coastlines with basemap + m = Basemap(projection='cyl', llcrnrlat=Y.min(), + urcrnrlat=Y.max(), llcrnrlon=X.min(), + urcrnrlon=X.max(), resolution='c', ax=axi) + m.drawmapboundary(fill_color='#DDEEFF') + m.drawcoastlines() + m.drawcountries() + + # construct a spherical kernel density estimate of the distribution + kde = KernelDensity(bandwidth=0.03, metric='haversine') + kde.fit(np.radians(latlon[species == i])) + + # evaluate only on the land: -9999 indicates ocean + Z = np.full(land_mask.shape[0], -9999.0) + Z[land_mask] = np.exp(kde.score_samples(xy)) + Z = Z.reshape(X.shape) + + # plot contours of the density + levels = np.linspace(0, Z.max(), 25) + axi.contourf(X, Y, Z, levels=levels, cmap=cmaps[i]) +``` + + +Compared to the simple scatter plot we initially used, this visualization paints a much clearer picture of the geographical distribution of observations of these two species. + + + +## Example: Not-So-Naive Bayes + +This example looks at Bayesian generative classification with KDE, and demonstrates how to use the Scikit-Learn architecture to create a custom estimator. + +In [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb), we took a look at naive Bayesian classification, in which we created a simple generative model for each class, and used these models to build a fast classifier. +For Gaussian naive Bayes, the generative model is a simple axis-aligned Gaussian. +With a density estimation algorithm like KDE, we can remove the "naive" element and perform the same classification with a more sophisticated generative model for each class. +It's still Bayesian classification, but it's no longer naive. + +The general approach for generative classification is this: + +1. Split the training data by label. + +2. For each set, fit a KDE to obtain a generative model of the data. + This allows you for any observation $x$ and label $y$ to compute a likelihood $P(x~|~y)$. + +3. From the number of examples of each class in the training set, compute the *class prior*, $P(y)$. + +4. For an unknown point $x$, the posterior probability for each class is $P(y~|~x) \propto P(x~|~y)P(y)$. + The class which maximizes this posterior is the label assigned to the point. + +The algorithm is straightforward and intuitive to understand; the more difficult piece is couching it within the Scikit-Learn framework in order to make use of the grid search and cross-validation architecture. + +This is the code that implements the algorithm within the Scikit-Learn framework; we will step through it following the code block: + + +```python deletable=true editable=true +from sklearn.base import BaseEstimator, ClassifierMixin + + +class KDEClassifier(BaseEstimator, ClassifierMixin): + """Bayesian generative classification based on KDE + + Parameters + ---------- + bandwidth : float + the kernel bandwidth within each class + kernel : str + the kernel name, passed to KernelDensity + """ + def __init__(self, bandwidth=1.0, kernel='gaussian'): + self.bandwidth = bandwidth + self.kernel = kernel + + def fit(self, X, y): + self.classes_ = np.sort(np.unique(y)) + training_sets = [X[y == yi] for yi in self.classes_] + self.models_ = [KernelDensity(bandwidth=self.bandwidth, + kernel=self.kernel).fit(Xi) + for Xi in training_sets] + self.logpriors_ = [np.log(Xi.shape[0] / X.shape[0]) + for Xi in training_sets] + return self + + def predict_proba(self, X): + logprobs = np.array([model.score_samples(X) + for model in self.models_]).T + result = np.exp(logprobs + self.logpriors_) + return result / result.sum(1, keepdims=True) + + def predict(self, X): + return self.classes_[np.argmax(self.predict_proba(X), 1)] +``` + + +### The anatomy of a custom estimator + + + +Let's step through this code and discuss the essential features: + +```python +from sklearn.base import BaseEstimator, ClassifierMixin + +class KDEClassifier(BaseEstimator, ClassifierMixin): + """Bayesian generative classification based on KDE + + Parameters + ---------- + bandwidth : float + the kernel bandwidth within each class + kernel : str + the kernel name, passed to KernelDensity + """ +``` + +Each estimator in Scikit-Learn is a class, and it is most convenient for this class to inherit from the ``BaseEstimator`` class as well as the appropriate mixin, which provides standard functionality. +For example, among other things, here the ``BaseEstimator`` contains the logic necessary to clone/copy an estimator for use in a cross-validation procedure, and ``ClassifierMixin`` defines a default ``score()`` method used by such routines. +We also provide a doc string, which will be captured by IPython's help functionality (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)). + + + +Next comes the class initialization method: + +```python + def __init__(self, bandwidth=1.0, kernel='gaussian'): + self.bandwidth = bandwidth + self.kernel = kernel +``` + +This is the actual code that is executed when the object is instantiated with ``KDEClassifier()``. +In Scikit-Learn, it is important that *initialization contains no operations* other than assigning the passed values by name to ``self``. +This is due to the logic contained in ``BaseEstimator`` required for cloning and modifying estimators for cross-validation, grid search, and other functions. +Similarly, all arguments to ``__init__`` should be explicit: i.e. ``*args`` or ``**kwargs`` should be avoided, as they will not be correctly handled within cross-validation routines. + + + +Next comes the ``fit()`` method, where we handle training data: + +```python + def fit(self, X, y): + self.classes_ = np.sort(np.unique(y)) + training_sets = [X[y == yi] for yi in self.classes_] + self.models_ = [KernelDensity(bandwidth=self.bandwidth, + kernel=self.kernel).fit(Xi) + for Xi in training_sets] + self.logpriors_ = [np.log(Xi.shape[0] / X.shape[0]) + for Xi in training_sets] + return self +``` + +Here we find the unique classes in the training data, train a ``KernelDensity`` model for each class, and compute the class priors based on the number of input samples. +Finally, ``fit()`` should always return ``self`` so that we can chain commands. For example: +```python +label = model.fit(X, y).predict(X) +``` +Notice that each persistent result of the fit is stored with a trailing underscore (e.g., ``self.logpriors_``). +This is a convention used in Scikit-Learn so that you can quickly scan the members of an estimator (using IPython's tab completion) and see exactly which members are fit to training data. + + + +Finally, we have the logic for predicting labels on new data: +```python + def predict_proba(self, X): + logprobs = np.vstack([model.score_samples(X) + for model in self.models_]).T + result = np.exp(logprobs + self.logpriors_) + return result / result.sum(1, keepdims=True) + + def predict(self, X): + return self.classes_[np.argmax(self.predict_proba(X), 1)] +``` +Because this is a probabilistic classifier, we first implement ``predict_proba()`` which returns an array of class probabilities of shape ``[n_samples, n_classes]``. +Entry ``[i, j]`` of this array is the posterior probability that sample ``i`` is a member of class ``j``, computed by multiplying the likelihood by the class prior and normalizing. + +Finally, the ``predict()`` method uses these probabilities and simply returns the class with the largest probability. + + + +### Using our custom estimator + +Let's try this custom estimator on a problem we have seen before: the classification of hand-written digits. +Here we will load the digits, and compute the cross-validation score for a range of candidate bandwidths using the ``GridSearchCV`` meta-estimator (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)): + + +```python deletable=true editable=true +from sklearn.datasets import load_digits +from sklearn.grid_search import GridSearchCV + +digits = load_digits() + +bandwidths = 10 ** np.linspace(0, 2, 100) +grid = GridSearchCV(KDEClassifier(), {'bandwidth': bandwidths}) +grid.fit(digits.data, digits.target) + +scores = [val.mean_validation_score for val in grid.grid_scores_] +``` + + +Next we can plot the cross-validation score as a function of bandwidth: + + +```python deletable=true editable=true +plt.semilogx(bandwidths, scores) +plt.xlabel('bandwidth') +plt.ylabel('accuracy') +plt.title('KDE Model Performance') +print(grid.best_params_) +print('accuracy =', grid.best_score_) +``` + + +We see that this not-so-naive Bayesian classifier reaches a cross-validation accuracy of just over 96%; this is compared to around 80% for the naive Bayesian classification: + + +```python deletable=true editable=true +from sklearn.naive_bayes import GaussianNB +from sklearn.cross_validation import cross_val_score +cross_val_score(GaussianNB(), digits.data, digits.target).mean() +``` + + +One benefit of such a generative classifier is interpretability of results: for each unknown sample, we not only get a probabilistic classification, but a *full model* of the distribution of points we are comparing it to! +If desired, this offers an intuitive window into the reasons for a particular classification that algorithms like SVMs and random forests tend to obscure. + +If you would like to take this further, there are some improvements that could be made to our KDE classifier model: + +- we could allow the bandwidth in each class to vary independently +- we could optimize these bandwidths not based on their prediction score, but on the likelihood of the training data under the generative model within each class (i.e. use the scores from ``KernelDensity`` itself rather than the global prediction accuracy) + +Finally, if you want some practice building your own estimator, you might tackle building a similar Bayesian classifier using Gaussian Mixture Models instead of KDE. + + + + +< [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) | [Contents](Index.ipynb) | [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/05.14-Image-Features.ipynb b/notebooks_v2/05.14-Image-Features.ipynb new file mode 100644 index 00000000..c036e786 --- /dev/null +++ b/notebooks_v2/05.14-Image-Features.ipynb @@ -0,0 +1,698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Application: A Face Detection Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This chapter has explored a number of the central concepts and algorithms of machine learning.\n", + "But moving from these concepts to real-world application can be a challenge.\n", + "Real-world datasets are noisy and heterogeneous, may have missing features, and data may be in a form that is difficult to map to a clean ``[n_samples, n_features]`` matrix.\n", + "Before applying any of the methods discussed here, you must first extract these features from your data: there is no formula for how to do this that applies across all domains, and thus this is where you as a data scientist must exercise your own intuition and expertise.\n", + "\n", + "One interesting and compelling application of machine learning is to images, and we have already seen a few examples of this where pixel-level features are used for classification.\n", + "In the real world, data is rarely so uniform and simple pixels will not be suitable: this has led to a large literature on *feature extraction* methods for image data (see [Feature Engineering](05.04-Feature-Engineering.ipynb)).\n", + "\n", + "In this section, we will take a look at one such feature extraction technique, the [Histogram of Oriented Gradients](https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients) (HOG), which transforms image pixels into a vector representation that is sensitive to broadly informative image features regardless of confounding factors like illumination.\n", + "We will use these features to develop a simple face detection pipeline, using machine learning algorithms and concepts we've seen throughout this chapter. \n", + "\n", + "We begin with the standard imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns; sns.set()\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## HOG Features\n", + "\n", + "The Histogram of Gradients is a straightforward feature extraction procedure that was developed in the context of identifying pedestrians within images.\n", + "HOG involves the following steps:\n", + "\n", + "1. Optionally pre-normalize images. This leads to features that resist dependence on variations in illumination.\n", + "2. Convolve the image with two filters that are sensitive to horizontal and vertical brightness gradients. These capture edge, contour, and texture information.\n", + "3. Subdivide the image into cells of a predetermined size, and compute a histogram of the gradient orientations within each cell.\n", + "4. Normalize the histograms in each cell by comparing to the block of neighboring cells. This further suppresses the effect of illumination across the image.\n", + "5. Construct a one-dimensional feature vector from the information in each cell.\n", + "\n", + "A fast HOG extractor is built into the Scikit-Image project, and we can try it out relatively quickly and visualize the oriented gradients within each cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3aysyZQG/lWskn89jZmYGMzMzKBQKIuj5zXqTxX5o0Mt1rAW/vo/8oP9ubss2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NCTJ08sboJjeI0kmYHe7XZ1cnJi8phgRZQW5urV1ZUVXGFfqtVqlkWG8bq5uVG9Xtf+/r6Vs47H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gCov5P5TUMGPtSqKn1ANQCEjVto2OypNgFzApx1Op0QplzHquOucx+4a/iuZaeaN9/g/x67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\nWSu8J1pTlWfVksnvp6enXv+P0lnMPRuOtg2lVGu7AtwjAIGv1M1Em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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from skimage import data, color, feature\n", + "import skimage.data\n", + "\n", + "image = color.rgb2gray(data.chelsea())\n", + "hog_vec, hog_vis = feature.hog(image, visualise=True)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 6),\n", + " subplot_kw=dict(xticks=[], yticks=[]))\n", + "ax[0].imshow(image, cmap='gray')\n", + "ax[0].set_title('input image')\n", + "\n", + "ax[1].imshow(hog_vis)\n", + "ax[1].set_title('visualization of HOG features');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## HOG in Action: A Simple Face Detector\n", + "\n", + "Using these HOG features, we can build up a simple facial detection algorithm with any Scikit-Learn estimator; here we will use a linear support vector machine (refer back to [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) if you need a refresher on this).\n", + "The steps are as follows:\n", + "\n", + "1. Obtain a set of image thumbnails of faces to constitute \"positive\" training samples.\n", + "2. Obtain a set of image thumbnails of non-faces to constitute \"negative\" training samples.\n", + "3. Extract HOG features from these training samples.\n", + "4. Train a linear SVM classifier on these samples.\n", + "5. For an \"unknown\" image, pass a sliding window across the image, using the model to evaluate whether that window contains a face or not.\n", + "6. If detections overlap, combine them into a single window.\n", + "\n", + "Let's go through these steps and try it out:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Obtain a set of positive training samples\n", + "\n", + "Let's start by finding some positive training samples that show a variety of faces.\n", + "We have one easy set of data to work with—the Labeled Faces in the Wild dataset, which can be downloaded by Scikit-Learn:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(13233, 62, 47)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import fetch_lfw_people\n", + "faces = fetch_lfw_people()\n", + "positive_patches = faces.images\n", + "positive_patches.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us a sample of 13,000 face images to use for training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Obtain a set of negative training samples\n", + "\n", + "Next we need a set of similarly sized thumbnails which *do not* have a face in them.\n", + "One way to do this is to take any corpus of input images, and extract thumbnails from them at a variety of scales.\n", + "Here we can use some of the images shipped with Scikit-Image, along with Scikit-Learn's ``PatchExtractor``:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from skimage import data, transform\n", + "\n", + "imgs_to_use = ['camera', 'text', 'coins', 'moon',\n", + " 'page', 'clock', 'immunohistochemistry',\n", + " 'chelsea', 'coffee', 'hubble_deep_field']\n", + "images = [color.rgb2gray(getattr(data, name)())\n", + " for name in imgs_to_use]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(30000, 62, 47)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_extraction.image import PatchExtractor\n", + "\n", + "def extract_patches(img, N, scale=1.0, patch_size=positive_patches[0].shape):\n", + " extracted_patch_size = tuple((scale * np.array(patch_size)).astype(int))\n", + " extractor = PatchExtractor(patch_size=extracted_patch_size,\n", + " max_patches=N, random_state=0)\n", + " patches = extractor.transform(img[np.newaxis])\n", + " if scale != 1:\n", + " patches = np.array([transform.resize(patch, patch_size)\n", + " for patch in patches])\n", + " return patches\n", + "\n", + "negative_patches = np.vstack([extract_patches(im, 1000, scale)\n", + " for im in images for scale in [0.5, 1.0, 2.0]])\n", + "negative_patches.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have 30,000 suitable image patches which do not contain faces.\n", + "Let's take a look at a few of them to get an idea of what they look like:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RzxA4SMKrVqtDpUxEAGlcUvmy2gDAWMY6AGm/qOOYmUxGDGudka7hej4j8gUd\nCMoRKkJ+jgqVkC9h6U6nY7ukjWtAVJQJhcBBkx8+b+qZXq8nVSK6Pnhrawu7u7t45513pLEF57O4\nuIgnn3wSZ8+exblz56TapFKp3DN8dl+UrVaeJC1QrNav9l75HQ1jWpNZtHfL62ghNI51x3uyXMc0\nTelqQihLz4eNFTgXeqehUGgoE1pnX+vOTKZpSpcWn8+HdDotVrgd8nq9WFhYwPr6ujTSzufzkhHN\nBhT0ksrlstQEh0IhyYJmiQKFwezsLLrdLtbW1iQholKpoNPpoNFoIB6PIxQKYWlpCdvb22OtN3Cn\n8NSCXGf8jlIA9Ga1RW2tp+RnmJhibXxglzTKQv51OByYn5/Hb/7mb+LSpUv4r//6r6EkLqvQ1kqN\na6DXw+l0YmVlBTdu3EC/38e1a9ewsbExdqa6zurWSl7DkaPGQeRA79VRe0tfS3vR+nnZVQKtVktq\npMnLRFuoPJlJD+wbFYSZ2ZSAvEBlze/RoKUCp9Klp0myyx9WOWaVSeRhroVO7tEJglRW/Ay/p41M\n4CDvgYbDOPwM7JcPErUisS98NBqVhCHKNY5Be6WcNxu5APt92z0ezx09oPXhIEzysmuQaWSCfQIM\nw5BDD+gkMRnK7/eL55/NZpHJZPDWW28hm83i4sWLKJVKaDabmJ2dxdmzZxGNRvFzP/dzmJmZQTqd\nxtTUlCjr3d1dCTW+F93XBCktQDX0puNc1tgZP0trlT+adNq+Ji3cxvVs+bC0lUiGtkKYTDNnMhIh\naHqHFO6RSETKF7gJCSFTkfOUIrsbJp/PC0RFq5QJFBzX9vY2tre34fV6JRYEYKhbF9ePynRnZwfb\n29vI5/NIJBJyQEG320UsFoPf78fu7q4U2Y9DOpuQa60RD8KYFJi9Xk8atOvyAz4bfo9z0x6uDlPo\nDFY7ZPVGKXTS6TT+8A//EFtbW/i3f/s3UVBWj1bfj89ZK2Ly7srKCj7/+c/jr/7qryTDXCeG2V1j\nrqPeT1TcOlFNK02NJHBs2kiwwvPaM+f/dwvxHIYoRGlAMavf4XBIwpVhGNJPnNnzTCLiGlOYk38o\nfImQ6PprJsJoj8gukV9HrSt5EDioraU80IiWhsn5jLTxwPcI21M+jUvPP/+8tHtMpVKiPMPhMJaX\nl3HmzBk5bKRWq0msk14lW74yWQmAeOuap+jRmuZBX2VmJts1JHXLUGCfJ5lvE4/H0Ww2h7qa1Wo1\nbG9vYzBsD/XvAAAgAElEQVQY4Mc//jGuXbuGa9euSTLaU089hRMnTiCZTOKhhx6S+3BP0uCj4cY1\neC+6b8pWL54VfrAqWw0X6xiMtgY1k1nvpS1JrSDtbhYGvvW9eS16u1boezAYCMwcDAaHLGN6tKlU\nSjL+KADYhYpMyjo6u2MOh8Myf8a3aHHRq+WGZGMNJhXkcjl4vV60Wi3s7Oxgbm5O4s7b29tDfXkZ\noyXiYH2udonroMswNITH58rNSkhOf04rHsZp9PpxvQlpsSxEe2rjEnkkEongC1/4AvL5PJ5//nmB\nw7SxaR2rXgOtbAkvPv3005iZmRlKqNKJHXaIQlo3LtAJYzQarLynkaO7GQwcs/6bwknvlXGMX45R\nJ1xx/OQbj8cj5UCMfdLzpVzhfqRxpishGGekAtbGhYaXD0uUE0SsCEWS15jsRE+Lx3Ey2Yf3DQaD\nKBQKMj56lDSmgYPEKI2qafjeDnEfNxoNbG9vyxpHo1FBxGjwsssSY5bcd6ZpSrxZ91UuFouYnZ2F\n2+1GPp+XDk7MNeG87PZGJu/2+/vNd9ilrNVqSV7N9vY2crkcGo0GLl++jLW1NWxubiISiaDZbOKZ\nZ57BysqKtLdNpVJypCPXwu12DyW90tg4jDF2XxOkrIPRsR/+b/0hk1ohK+256B99T6tlblcwacjJ\n6XQOZfM6nQd1Y1Si8XhcYqFUehSe3PysWdWQgxb2WlBwI9ohNsmIRCJyb8aUaeUzJkym0WVLtOJr\ntRqKxSJmZmaQTCaxu7sLYL+Ol5ZipVIRD51esbV05bDEDc3nZH1efLa6FpGJGKNiSMBwobvOAeBz\n0SGKcSE3jaaEQiH8+q//OrxeL775zW+iVCqJR0sFZg1/6L+5H6yvdzodvPTSS5IhybNheYiHHdKJ\nYFrpkQe1kcK9o2PaHJ8VRdLPbJQBPM7+s5KOL7tcLqmtJYRsGAeZpxod4Z7lOJilSv4gHKvlBOfL\nuB/7iNshygYq+FqthlarNWQEENHSp93oYwI5bzbB4cEl7XZblCvLbmq1Gvx+vyBN1lyHw9KnP/1p\nOdWLZYAsi9KKVveCJ29TnhARAw4MX0L7hP85N7ax5T2IzNkhxk65h9rtNm7fvo29vT1cv34dnU4H\n58+fx+3bt4dCMx/60Idw9OhRHDlyBA899JCUDxmGIbJ/fX1dwg48Xg84kC+9Xu9QKOR9UbaHIR3j\nIeMzLkGGGgWfaBoFq+lr2t3sg8F+MXq/30c8Hpd2kiyHYZzV5XJJ60bCTwz2EzpmaU84HEYkEhF4\nyul0yqkSVqE8TgeY9fV1eL1ePPTQQ1J6lMvlZBNHIhF0u10kEgmUSiXs7OwgHo9jZmYGkUgE6+vr\nAPaP2KMFTWubGaELCwtyjJbH40EymUShUMC1a9dkHnbJ2miC/3MNCf9w4wLDiAkFsEYadMhB8wUF\nKT0F8oXdtaYi4tg+//nP4+GHH8Zf/uVfYm9vb2icVphax3dJo/h6MBjglVdewcmTJ/H7v//7WFpa\nwjvvvINsNotHH33UdrtGJr/oGKKGkUflVfBzVABUQIRceViHvp7+nlbSGm2yQxpm1/yhkRUNZWsk\nQUOYvJb20DlejTpQGfOZjNuBjtemIqXnRmOafXxZ+05oeRQiR+iSfE8ZqROkmIug18wuHTt2TBIU\nqQxdLhey2awoWrZq1JnSnA/HR2ekUqlI/JQZy4ax32qUvMP6/kajIaEdO0SkCoDAvJVKBcViETdu\n3EC73cb169eRy+WkxzSP1ltaWhIUr1qtCh9x/lyLUCgkTYx0preG79+L7puyHWXx6vesxEnSctde\nir6eNRbEzayTTca1qMk0hINisRhSqZT0Eua9NJRDA4GZyYZhSAtKdnYiXMSYMAu7aUlz04zT1Whq\nagr9/v7h3brAe35+Hu12G9lsVhJGHA4HHnnkEWxtbaHZbOLMmTPSQ5ln05ZKJVQqFYmXEY5h03N6\nV/Titra2xla23ISEpPi6XgN+TitdEr9PeNHqBQPDsCEhMY2C2KEnnngCDocDly9flrrer3/969jY\n2BBUhmMclVRkhWmtMC4/UyqVEI/H8eSTT2JjYwP/8R//gVgshgcffNDWeHkfenNUOlxjzc9WRaSJ\nvDk9PS2KwoouaQWrvXZC5XbX2qp89G+SlgN8/W73GWWY6/vwu1Z5Y4dorOt2pk7n/jmw4XBY2v3t\n7e2h1+vJYSUsSeE1KE/29vZQLpdx4sQJiZnmcjm4XC6pXuj1eqIA7zbHw4wbODCmstms8AhDTrrt\nLg0XZvPy2Wtj1ul0CvJFohMVDAbFcGAei11lm8/nsba2hmKxiEKhgM3NTVy5cgXZbBbtdhvxeBwP\nPvgglpeXEQqFcOLECUxNTSEejyOZTKLf70vPfkLDPM83FAoJ+qGdCZ5HzE5V95J79+3UH10or+lu\nXqcOoOuEF12npgvUNVysYy18XUO6hyWHw4FYLCZCmXNhvR6ToXQPzVKpJMcyMZ2fXaUIBzUaDYnN\n0pNgKj8tcJ4AYneDM+a6tbWFdDotiQOZTEYSnnS2pT6ei2fwssmFaZoC73AOvV7vDvhSJ4ZtbW2N\nZVFroUZ+0QpHn+pB0rAiN7uGLPXaacjHGovj5+3yRzqdxptvvimC8Z/+6Z8E/qLnQc/L2qxCKwqd\necr3+ZtHq/E5tVotZLNZzM3N4Ytf/CL29vZsjdmaK8F9RMOShonVYNVjpOW/vb0t8TZmvutnQuNH\nPwdrjsZhSWfpWksAqcAp3Om1W58nx6GzobWC5m+OnfwCjEYd7kWUV9og0WPh9aiQ2GlKQ9aMxeof\nNpro9/tDOR4s6wIOEobGdTTombKHNfck8zZIhO0JIUciEVQqFYnfcjwamqehxxJBespMrKrVarZz\nP1599VWsra1hdXVVPOlIJIInnngCp0+fRjKZxAMPPCD7kXHaSCSC3d1dgd0JzWs4mbKe1RqMOzP3\ngU057iU/7tupPzrDdRTUS9LxM12QTCGg41q6XpD/W4Ws1fu1Qy6XC1NTUyJEer2enIrDnqRMHDAM\nA7lcDoVCAblcDq1WS86fZMym1WqhUqlI16N4PC5Cg6cKmebBYQbjFKXzvNxms4lsNitdVTY2NhAO\nhzE3NyfGB5tbcM2vX7+O3d1dUfSDwUC8cRLbOgIQKJkCiSn7fN8O0Spm5rY1iYYHVdfrdQDDPYUp\n1LXQ0qgErVJ+Txt3Oq5lVwH86Ec/Gorfa08ZOOBrCm7gzmTBUfzKzwEQgfeTn/wEN2/elHroWq2G\nr33ta9jZ2cHTTz996DFzjrr8iQqT+0fHELle2iulcGXMjsaELini87ubwrNr2OhG+3zWOtNXZ5Rb\nm/JrZEOHK3T5jF4bbYDxhwafXaJyIeRK4U2D1TAMaZYDQBQF/2f/dRoofr8f5XJZvCqGr/isCHk3\nm03ba0xiaaAu7QEgDgP7C5imKWVG5Feeasa4OT8PQHiGvMJ58Ozv9fV18YDn5+fxG7/xG4ce87e/\n/W1MT0/DMAz8/M//PI4fP47l5WUcO3YMR44cwebmJqrVqhg/xWJRetXTQWGmOmUGm/tQZ9E4CAQC\nErNmLs9hMqj/T2K2o5QhcKBotWXNHzL8qCzjca23e5HT6UQ8HpcEBAomdn3R6fq0+rLZLPL5/B1K\nk9/lmbgU0PQYmSmsM7DHmRet3WKxKILH7/cL9ORwOMSSS6fTcLlc4o0DBw3leRQaSykoUJndl8vl\nEA6Hpf7z1q1bME0TR44cGUsoaaVojZ3pJCMKKP1ZDZfpmJeOmdJoYwYy11Z3FLK73ho+4rgYE7V6\n1cCBoWmFVoHhPaFfY1iBBz1wH2xubuI73/mONBk5LGmlz7VltqgO1RBl4V6k8tLxUCb3kLcBSHhk\nVMWA9ijtGjbc+/RstbLV9yACwD3J585kQ90Bi0aajtlyHTg+HQaw22Ky19vvS67bQ9LAqVQq0g2M\nMGq9Xpd1tMahGcaJxWIolUpotVpIJBJisDPjlghZs9kcqysacFDGSBlFmJjJXWzFSF7nOHkSWCAQ\nEIVLmJVyMBAISO3+3t4ebt26hd3dXaytrSGXy4nMOnHihK0xP/zww/iFX/gFpNNpHD9+HIlEQmR0\nJpMRuUi5QCOGfMQ4Lx0NjXDoXs7kP+YBUNbo9pl3o/8zZauhP6sAsCY46AQAegk6BvR+Kdt+v49i\nsSiLzt/aKmUWYLPZxO7uLgqFAsrlsoyNCgrY33xMrgKGT5zR5Re0mug52CF607Sea7Wa9GHO5XKS\naVypVCTuUigUpH6WBxnw9BAqtfn5eVy6dAn5fB6zs7PIZrMoFos4cuSIzIGW+zin/owq4aGgMQzj\njqOyrM+cCpV8QUFMYcCNp7NqtSejFcFhiQrIWqerFemoZCGSVr7WfATOyTT3m6k888wz+PGPfyy8\n1+/3Ua/XbfMHSRsA2lvUHinjfwCGvBfOjePj2rndbmnVqbuh6WS1/039p/aKdSxfK1COU5fHABDE\nQceS6XFyDtqoo3LTyWF2DYR2u421tTVJmGQpoEZ//H6/HPpRrVZFgHN+XNtisSghq0KhIGMKBALo\n9XpDiBYRpns1Wbgb0dBi6EzLW91fwLoeOmSnD5HXhhL5i928aJix13IkEkE0GrWdj0AnwuVy4ebN\nmyiXy3A690s3dQ6Hbg5CQ4K8TOen3+9LNQnnxXXQ+5XICPnnXnvxvilbrTyB4bIf/fC0Z6c9Ex1z\n4wOztpQbJSz1Pe3CKt1uF5lMRh4ILTzCnBRITHPnIQDcyEzXZ9q4Fkr9/n7fUdao8XXGSRmXsZso\nQFhnamoK6+vrKJVKEr9hJqPf7xeY2efzoVQqodPpoFgsolKpyLgZbyZcTEOAm3kw2K+bSyQSkmBQ\nqVSGaosPS1r4k3TGLFvvAQfMr2FFvQnIa9YEJB03sj4LwH5Lz2AwKNmUegxWpamRGG1AaEPR6unq\n1+bm5vDQQw/hpZdeGtro44x5lMethaQ2VrSApILTUBnheq0ES6WS5AbQI9PJYXwOdvlah1T0GnMd\nCdfSmKXByu+xakCjGPQA+T4PZGfMVMevXS6XbRSBypZnodIYYdYtZQV5h/dl3T2fLQW52+1GIBAQ\nOcH6YsYagYOYuA4L2CWuL69DD1mjRzrubkVlNP/ovWGV9zRAgAOjj3kuDBcdlniwitvtRjqdxpEj\nRzA9PY3p6WlJaiU6QYhbh0WAfXnCY045FofDIfkIbI9JOcK5cx/9P+HZak9VK1BrNiT/ZsmLngSZ\nh54tN41ViFmJi2EVZIchwkC68NxaosLDBdhRifEubniO1+/3IxgMDp2dSM9VM6PuGaof5GHp4sWL\nwsAUfL1eD7u7u2g2m5iampJUfVrCFAabm5tot9tIJpOSQNBsNtHpdHD79m0cP34cx48fx1tvvSXf\n4fPt9/vI5XJoNpv3PP1iFOmkFiZp6YYmOh6ok2O4PnyNvGZNktKlVky0YcxpHK8FAJaXl9FsNlEs\nFgXB4IYE7sx45f1JWqDxff7WhsXc3JycHsO5+nw+QXrskFaowLC1rkl72lROowwKfpZ7jIawNaFQ\nG07awzgsUchpA0tTv99HpVJBoVBAsViUUA/XUdeu0sDiofaGsR835VFqDPcQBuW9bt68aWvM3W4X\n29vbkmxI2UakCYAgUIPB/glB3Js6QZDyjsqakCyAIZ5jyMjlcgkiMY6y1c+72+0KUqV5kjkW+h7k\nC/Kn5jPGmAnl0rjpdrtDXe/m5+cRj8fx4osv2hrz7/3e72Fzc1O6QlWrVRSLRVy5ckUU5vT0NE6e\nPIlgMIiZmRkpyaQx43A45LlzHRlrN01TwnzAsE5jeafumDVyXW3NaEzSQgQ4aKk3SkDS6ueG4ubV\nm5/XGiXM3ovuZXmM+nylUpFEIY6dxdOEEOhN8n/GXQgx06OMxWKSUs64kmZUbjxa2xTGdkjH0ByO\n/ab1hJTcbjei0agYB/oYw36/L1nUGq7NZrNSCsQ2k1po6JglsC882ADDDmkjjBYxBSw3MHmDfKOh\nWt2MQScm8dpWJcd7aWvbrjHGZ0iUQhtOmjTfa9LwlPU1bSwwu5xexhNPPIEvfelLeOmll/Dd737X\n1pi1wtReoTY29PtayWpv3OrBM/eA8U4+F83jo/bsYUkrHGC4vzNh33w+j5s3b2Jzc1OyaLnmNHKJ\nZnQ6HemCNhgM5GB7bShTeZEXr127ZmvMLMEjLEvBHQqFRLG2222J37KtKuOHAASCTqfTovjZZL/V\namFvb2/oTFt6bP8buJ5HfdL4pUzRiNC9ekZzzTgmJpNyTpFIBK+++ip++MMfIpvNwuFw4KmnnsJT\nTz2FxcVF23LvzJkzOHfuHHK5HAKBALLZLHK5HG7fvo1MJjMk2zgnKlPyIxEHGhdEDPi/dvA4F51o\neC9E774fHg8cdAvSwt7qlusm29ry13EuvsbfoyA86zjsEi0XWqY6HjkYHJQ8MIOP1iDnSe+xVCrJ\ndxh7oQKkQGXKOwXUONnI8/PzYtl5vV7E43EEg0HMzs5KRxRa+OwMUywWUa1WpQUlY8vMdhwMBkgm\nk2g0Gsjn80in05IAEYlEUKvVEAqFcOTIETEsxiXtATFOq3kHOOh8ZIWJNVTMeYz6HPnEqmDtlhvQ\naOGRb6yVthqXo5SMFQq18rOOkTFBz+PxYH5+Hs899xyeffZZGIaBn/3sZ7bGTK9Fx62tiVv6b2tS\nk17PUeO2xr713+MYNCRmhRI+tRojzOan0tFz0zA2ZQtDQfoAdOYcsH+5NR5vlz84BsbZW60WCoWC\nyAXen/3EGbYxDEMSFgll0tjn+dR0RHhth+PguEDdVGcc0vuNyXPAARJDea33KnCwt3QlAfmeKGC3\n28Xrr7+Ofr+Pl19+GfV6HaFQCCsrK3jyySfxyCOPIJVK4cyZM7bGTIMrGo2i2+0ilUphenoaDz74\n4B0Z3rqMTDs1Ok6tUSMihHQuGD5iuVAgEECxWESpVMKRI0fuOsb76tlqRahhY/4Aw4kyfHD8X0PR\nWvjqB6pjQ9YxjAMVAgewF61rHceyMhXnQIvJ5XKJIuWc2JHF4XCINUToRXvy1tq8wxBrdymgnU4n\nTp48iVarhXfffReFQgEejwepVEq8aW5gNtze3d1FNpvF/Pw8/H4/qtUqAoEA3n77bWSzWRw7dkzm\nxWfK8gMmi9mlUciGFq4aqtRKlWuvu7roEIW2ZK3QD6+lY/F2iDC7TjbTRiNplKLVG926L0hEVILB\nIK5evSqdbxYWFiSBw24HKd6H6zAq81Z7unqcmtc1bGwVWvoZ6nndbZ6HISohZkhr44peI9eD3qk2\nIjQCQr7lntNjY6e3YDAo+5bzYGLSYcntdiORSMjpWZ1OB9lsFtVqVbwkh8OBUCiERCKB2dlZabDA\nmCXDBfRmq9Uqpqen5RxsGugu1/7RmCxJ5PvjkEapeGY3a/HpXAD7e163aOWpTMxRcTj2W2MyUfPS\npUvY2dnBhQsXpGTmAx/4AFZWVnDq1Ck5d5vOiB3SxgsPIGDC0+LiooyZSpTrpOPKXEeNUtEj15/j\nwQP02svlsiSqvRfd1zpbLSwZf6VC4Obm53SmIIAhC3UUaQHGe/5vYCsAQ/VYTApqNpvizfAM28Fg\nP21/b29PBDAPUed7/B4VEeN8hJV1PEN78HbJ4XAgn89jZ2cHnU5HYq+svcvlclhYWJA17vV6UhA/\nPT0tyVJ7e3uYn5+Hz+dDNpuVa/NoQW4gdlmpVqsoFAqIRCJjlRzQw9cCms+QYQedsU1hqzcBvR/g\nzmxbvZ6E2bQXqhXlYYkbl8aARiJG8Z1WrlZ+1Z/h94F99GF+fh4//elPh0IQLFGwO2YqWev4rApS\nz0EjTPr7fN3q4VqVMzD6+EA7pI1l3Z2NY2d4I5lMDnm2mldoBDPZKBQKYWpqSso2mGMBQPYujeyd\nnR3cuHHD1pi9Xi+OHTuGnZ0dOa+6Uqlgb29P+pEzSZJIErBveLOvsM/nQzwel1yLYrGIdDqNaDQK\nl8slfZC9Xi82NzeRzWYlbDSO0cv7E8nzer2C2hEJ47OlUuI6UxHzu6ZpYnV1FRcuXEAmk0Eul0O1\nWsXDDz+MeDyO06dPY3Z2FlNTU9JVj7F3u0mWjLt2u10pcSwWi1Kex3p4bSDqmLNOsuOeBg7awzL3\ngCeeUaFT6R4Ghbyv7Ro5SSYBsYE1cPCAtQCiMNEZyu91favlrGNMvIcdYhIFPSS2L8zn86hUKiiX\nyygUCuJFFotFGau+n7Z43G63ZPtWKhWBYtPptJyGoedi1xvn0XxUSv1+fygBQTOG2+2WDcnkAI7V\nNE05IYMbmMxGyI3xJholmUwGkUhkLARBG2H6f87BWrZj9XKBg7gcxwrgDmGrlSuFAl+3yx9EHjwe\njxg1XEOrJ8i/rcpslLLSxEQSehm6eQGfgd111lmkHJveO9YxatiY62/1Zq3rChzwv9V71p85LPEa\nfF68FnBQWxsMBgXl0CUqFJRarrhcLjmBi+ETGs/0Zlgix2Qgu/zh9Xpx4sQJBINB6bbVaDQEFaLx\n0m63sbu7i0ajgXQ6jVAoJGNtt9uo1+v44Ac/iOnpadnfVNjhcFgMesoqNp3Qa2R3rckDzWYT6XQa\n7XZbYprcY1qGEH4vlUoic9566y3cvHkTtVoN6XQaZ86cgcvlwoc//GHJX6EnTOOfGdY0PA5LzBlg\nLJvNPig7dKIT1509BTTCxXnojG/dPIVxXx3CoKd8rzHfN8+WE+LCaqGqBY4VHuTE+b+Gie+2abVH\nSyHKTWiHKEgJSxEiJRRULBbFgySzhEIhhMNhSTVnjIMZcXzwtHJ1rIBJEIwLj+OVe71enDlzBp1O\nB9vb28J8rJ1NpVJD8aB6vY7NzU0YhoGZmRlJCmHpUDgchsfjkQOS5+bmJOYOQCAxQpxzc3O2SyQA\niOFFqFcnR3F9gYN4rYbsKRgoyHRWszVpilAkLV2daWk3KYP3r9VqUgKkezCP8uy0Yhvl2ZI0FL6+\nvj4Em1rvPw6NiqtavXL9P/mYn6PS1iVIGkXQ37V601o5H5Z0MiJjllZjxjAODpInSkQ0hM+DsUzy\nLJOBeG0apZVKBTs7O8hkMuj1enLAuB3yer04fvw4YrEYQqGQ1K2nUilkMhnxvKrVKkqlEm7duoVi\nsShQuGEYyOfz2NjYQDKZRDAYRDKZRCaTwfb2NjweDxYXF6WUL5/PSw9m3RvdLoVCIXEwGMqg8cr1\najQauHHjhnRhorFy5coV1Ot16am+vLyMZ555BrOzsxL+iEajCAQCKBQKEtLx+XzSiY9GsB2inOcP\nPXAaWsAwf+sEKf0e3+drRF2pjHX4yTAMga+JlrwX3dfeyFrR6tikVchp65v/6/pabVloKM3qQWir\n3JqRehjyer1YWlqC0+mUFoz0BP1+vxSg93r7PVkTiQSmpqakSThhKQbVWVhPIUULVsOfowSfHeIR\ne2wjyaOmWODNDcTaWSZwVCoVmKaJUqkkyTg0GtLpNOLxuLRV4yHxS0tLCIfD2NzcRCaTQSAQQCaT\nEQjMDlkFsvY0NS/oZBeujza8OD/9fS10dc0078mNbVfZUiB5PJ6h5Ch9baty5e9gMIgzZ85gdXV1\nKHtbf4cnKt24cWMotk8ecrvdOHnypK0xW2HeURCw9XV9X+BAwWsDYpR3PIqX38tIfi+iB0HvntfV\nz1zPUb9PVIMeL9+jYqXxVa1WBcl58803sb29Lc91c3MTP/3pT22NmcownU4jGAwik8mgVqthenoa\nhUJBjPZarYZCoYCtrS1Uq1Xs7u7KGlNB37hxQwxirjVLgOjB6b7cAIZaUdqh1dXVIWTQ4XBIuIzG\nZS6Xk1wFna/AGv9jx45hbm4Oy8vLSCQSotzomTMZjXKJYQB9wIsd0k1ryKtEBbhHtS5wOBwivykb\nyPvWhim6vJB8VKvVxEjT7W3fi+6LsrV6onqjaStYv6YXmwtBKEkLBF7fKqA1o/B1u7V9Xq8XDzzw\ngEAQ+vABxks0HDw1NYUjR44gFAqhXC5Lkwuv14tUKiXp/YRk2FGG8Ztx26tp4gbg+Hu9HnK5nHjW\nOjbcbrfl3vTUCZMQYmMCCRUIEx4MYz8Zxel0olgsol6vIx6Pj73BdfIbFQmZnAYN4bxRMTkrT1iz\naIGD9nzc+Bpq5n3trjWPLSR/6QxqYPQZzYPBAKlUCl/84hdx/fp1/MM//IN4Tdpb83g8ePDBB/GP\n//iPQ9Csrhe1e8i2FUnSxi7Xkpa8VVnqv6172RqT1YpZG8B2lSxJIxWE66yNLoADQaoNaxo3VLaE\nXOmNmKYpSi6Xy2F9fR0vv/yytA8kGjIOZG8Y+6U6TDZk2IXhgXK5jGaziUQigWAwiJs3b2JnZ0fQ\nJzZp4FFxlUpFru/xeJDNZiWTmd4cs23H3YuvvPKKKNBQKCRVCUxOo2Kbnp4Wg5Brnkgk4Ha7xZt3\nOp2iWIniuVwuKbEhdOxwOKTrEz1IO8RrRiIRaZfrcDgQiUQE7uX9gIPSJPJmNBqV9aMXqyFmHWpi\nmK7b7UpSK1tUvucYbT+J/yXpDcoNbxVy2rvRk9VJT9rCt25wLTytdbx2SG9sdlJiRxn2Bm232wKH\nsA6VjapZMM1zbBn3oTJxOp0SZ9JxOBKhMzu0t7eHer2OSCQyBGkyoSkajWJubk4SEYLBoDTYqNVq\n0tuUB8yzDOidd96B1+vFysqKbJx8Po98Po/V1VX4/X455mucgwiAg8YUwHBvWMLxREQGg4OmF7Q0\n+by4UbieVE5ayRIpsSYF2TV2WPrDUIff70coFBrqsjWKXC4XTpw4gcXFRczOziKRSOCb3/wmVldX\nh5QH10OfF6rrs0OhkO16Sh0ztXqc2sDlvrQaMRqW5/dGlQeRrH/rLGc7REXJygXeV0PaAOQzOvRE\nlICeCZtfMATSbrdRKpWwubmJ3d1dbG5uYnNzU7xGwzAwPT1te63ZzJ58wSYKxWJRYHk2v2ebQo0K\nDO9ABLkAACAASURBVAYDhEIhzMzMIJfLSU9ynci1ubmJaDSKeDyORCIBYN/ITiQSQ4cI2CGdNFav\n13HkyBEpe6RS50EqlCk6wTEYDMI0TZTLZQlLUTExrkonQ3foYgJmv9+33fKVGebtdltOYSNfMvOY\nsoKyROeBMBeC+5ZIrC5xouxptVpD9cx0BO8lP+6bstVWso4zjYILrJb0qGxI4AAusXrLVqWrlbYd\nYrs1ei30Ckk6xb5WqwlMxOxUjoOtHPk6MCyUCAFRwFK4jXNyhy69YW0slS8Vwc7OjiSFbG5uotPp\nCKTNOjWekMFYDNej1WohEolgc3MTpVIJpmni6NGj8Hg8klA1jodurbW2Zg5qga6hZJ2lrq1SbiId\ngqCioIesY4h8DnYolUpJT1d6PpFIBIlEQoywUVmK4XAYH/3oR2Vcp0+fxpe//GW88MILeP311wX6\nikQiuHHjxtAe0Iaoz+dDJBKxNWa9V7Sy1Q0puKa8p34uVoXMZ6C9Vz47jlN7+hpKt0PRaFSyYfUe\nAg6SKwGIQtPlQRwDkYdisSheLBsXMOGRmaZnzpxBKpWSwzoSiYTtDlL9/n7PX4aKdJJnOByWrFka\n5LxXKBTCzs6OKK5kMiklODqmzPAV6+QpL5xOJ2KxmK2xanr22WflHFcaLpSBhE0Z+9an4gCQkhvu\nQXrZVKjMutY9iClv8vk81tfXBTo/evToocdMxVipVDA9PS1ePb1pn88n3u1gMBA5Td5hVjUVMSF5\njYzSKw8Gg9JbgPk2pmnec83vi7KlINUD117qKIVrtaatm/puuD43ufZqtXdsh3jSCvuRhkIh+P1+\n6djS6XQEnjIMA3t7e7h9+zYMw0A8Hsf09LQUPrPsx+FwDCVAEDLRp9g4nU5pWG6XOG9mxxHiIKQM\nDAs8rg/noxO8mDjFTR2NRkV5pFIpsQR1WzMynl2iIKJy1AJVe6laaGs4mV6L5imd4a4bAejvAAfC\n2m5Mn8q10+nI0YnsqcvNCtx5sMDCwgKWlpaGareXlpbwB3/wB0in0/jud7+LRqOB48ePI5PJwOPx\nYGZmRmo+qaTHWWe2FNWQvY5lWaF3bbjq7HrOxQpLW2F963WtCvuwFA6HEQqFhsalr0N+GcUjrBZg\nKIh9iXWWMuuZdSnezMwMpqenRRm+9tprtsbscDjkmi6XS5rKJBIJCcFQEXW7XSSTSYTDYSSTSWxu\nbsI0D1o88rxXeuS6soDyhTHPcrksdarjJNAlEgnJ7mZyKOFjKk2iSvooUD5nKjfdQIV7kHA00QYa\nOW+88QbW1tZQKBSQTqdt84g2YsjjlKvMQeHxlDQSIpGIVA9QxgEHThq9bL7HNWDJFuO+AIYSCO9G\n90XZ6sC1NZnJuhH5W3ui+nP8rS1pTaOuqwWUHer39xurU6jSsmaDcyZWaAuKfU6pnHTMguOhpUi4\ngpYqmZBMqTPpDkszMzMoFosijAOBANLpNLLZLNrttrSly+fzqFarWFxcRKlUwu7urowxnU5jMBhg\nY2NDvOJEIoF0Og2n04nt7W3Mzs5ifn4emUwGt27dQrvdxsLCArxer7TKs0PW+CvHT6WoPUStaIE7\nvTWdhexwHByr1u/3ZSOStHBmhvVhiUKNgponJenECytK43Q68eSTTw5l7nLe4XBYOoAB+0Lv9u3b\nOH36ND772c/ib//2b4eUG69ph1wul8SttLLU8DDXW/PsqP2mDRl+V+9ZPWeNaI1rjBHG1c+W4+da\njxon11dDieFwWJQBv8f4eywWk/pW7mGXy4Vbt27ZGrPT6ZS6aJLX6x3qw1yr1eTA+GAwKJAwIWU2\n4SCftNttJBIJObpOHxHZ6XSQz+eRzWaRSCQkidMu0QjQnrh2XnTGv0YOrQ7QqDAE+Y9KmyWDu7u7\n0n2OitoOkRc9Ho+cQ0v5SciXni9zT1iyRP2gZQedKNbdAhhCf4hw+nw+QT3/n0iQAoZTs/k/cKfV\nSyFqhYb5nVECVr+viZufWdB2FRcAYXZCIKZpygYpl8vY29sT68npdCKdTss8KpWK1GJGIhEEg8Eh\nL4xJCKVSCXt7e0PQEOEVu5vFMAwkEgk597LRaMg6NZtNlMtl2UDsXFMsFpFMJrGwsIDV1VVsbGyI\n9X3kyBHMz88PHbQQDodRLpclI4+MxjjvOEd76eQ3jpe8wedHocfNynloxUxlrbMICSkCGBKw1vCE\nXWVLOGrUyTZWXqNXnUgksLy8PBSnp0BqtVrY2NiQRBSeTXrq1ClMTU0NCTzytl3FZQ3FUAFp9Egn\nd/G3XluiA1Yjx/r3KMWnPVC746Zit5b+cPxW2UGZw3ijjt/zWDutUGhoeL1eUZJa2I5jJAC4A37l\nvci3lIumaYrxxjExTkukyufzyTm2pmmKt0w5xX1669YthMPhsWQevT4qe8LBOjei2WwOlTdqpEgj\nmMBwiIhGKcsfn3/+ebz11luIxWJ46qmn8Nhjj0l5oR0idM3sccartWft8XgkCcvr9cqhA/wueYuy\ngHKfsoRGCHmtXq/LGpumiZs3b+LYsWN3HeN9O/WHQg7AEExojQFp0v/z4Y2KGenPWIWFtmjterYO\nx35PUm5gxlXZh5UQCK3LVCqFWCyGwWCAra0t6bBCWIrJFlQYZAbGkfb29iQGwLZtdsdcKBQwPz+P\nUCiEQqEgDHHq1CkUi0Wsra1JwkA4HJY2dw6HQ8babDaRyWSkUw3b4QGQpC79/Dwej3RdoXdvl6yx\nPC1INXqgk6W0QNcwkDba+OysClrfx4qkHJZ0XFAjL9ZkIz3H5eVlLC4uDpW+cZz1eh3Xr1+HaZry\n/LvdLubn52WsOiloHDjWOn/t3QIYMmrIe3pu2pvRNbaj1o7XtML02ss9LFFZ0WPRil3ve85Pzw04\naBhvGIYoXo6bysQ0TYGYdQiK/GM3G1mPwYoe6PWxhsy4pwAM7U+N1PB7VrSATgWP6hvHQODhC6xE\nYOki10bHcQkHa8ieSVQ0konsUd5QRrzxxhu4ceMGyuUyfud3fgePPfYYlpaW5B52iGE5Jn3SMGMF\nCfNOaOzotp884Uejr5Tp3W5X6n/L5TLi8biEjMg/i4uLuHXrFq5du4Zf/MVfvOsY72tTC60wtVVu\n/Q0MH6xN0sKTf2uhqSFBbmgygFbwdsjhcMh3idFT2RKGIHP5/X7JAsxkMlKX2+v1JJmIypbWFwCB\ng5jF6vP5pDPMOJuFY2ZGnWmaOHbsmGxMtlZsNpuykUqlEnK5nNTE7ezsSF0uW0vSItQbHoC0LCOz\n53I522MGho+c054U32N7T/3ctYIFcEd2oGEYQyEELUQ5J1qydq1pq3GnX7NCvaa5n0189uxZOVrM\nKoQrlQrW19cBQMqu3G43VlZW7lBO2pO2Qxoq1kaB3kdagHOfacWuPXheU895lBLRc9X3szNuxi/5\n/VEomJ6PNrL1EXsaTeM5ybpEyTRNyQDXNdh2USYqacYwY7GYwJxsaKObIQwGA0FJdJ6CPlqPiYz8\nDkuHqBx4gAI9vHFitqFQCKVSCclkcijcxQRJnexlPWeaf+vERp2zw25SyWQS+Xwev/Zrv4Zut4tT\np05Jcw6dX3JYopPCMjyNjmlDT+si7bGyokB3HqNhybALnQnyO8MthUJBmo+8F90XZWttNGCNuWoL\nWStjbvxRcSBewxrU1ptZb7ZxPBcA0oqL9+50OtIxqNfrSeIGE5rInMCB0iuXyzAMQ+AhlhBxvBT6\ntBB1WYtdYsax0+mU7MRarSZx1Ww2K4y+ubkpcVjGd/hdn8+HTCYjhgotQ8aFmEAAQJitVCrJZrdL\njG0zK1BvVq4pPRqd+GSFPrmpOHftlfF56vZrtMxpzdslbfzxf5LVKEylUnjssceGNrzm6UKhIDEl\nwpw86adQKNyh+KwxssOus46xcW31mLTnbP3R89JjGPUecOd5uVZFfViyPmP9TLXhwjFw39PQojer\n5QKVIY1pLZvq9bo01KdcsqsAKL/Ii/F4XDonsQe5w+EQw5o9xjUkSx6IRCIyZpbNAJBYJ3sC8zMM\ndY0DI/v9fknGa7fbYohouJ7en3Xd+L8u0dMyXRsLH/rQh8TQLZfLkgTGigg7h2xoL1vnTBjGwSls\n2kHguo7qH80143W4FuFwWOQjcKDIeUTivZCP+15nyw1i3YCarLEYHZPT16G1ZPWCrfCyhprsjlXH\n+DiOer2OVqslMcqpqSm43W7xFnVTiW63Kw0uOF7W4vLH6XQiFAqJN8qYLZWwHaI3y+YWZLqbN2/K\n/QOBAFKplHzO6/VienpajBeWPxByYXkENzE9dZ4eVCwWJa1+cXFxrAQpbaAAwx4iPV3dN1p7U/y+\n9lA07KYNMP6tlTCTul577TU899xzhx6zla+sfMd70kA4fvw4ZmZmBFbTY+h2u7h+/bqUJVEoh0Ih\nCQkQ0qMw0+tlZ8z8nlaqOmZrVZ7WuYzyqDUUP8rg4N8atbBDeo2t3jJRCmtCGgU+M3Z17J5zpBKl\n4mOstlAoiCKmYLaruJjwBGCodzobILRaLYTDYWmOs7GxgWq1Kp44AFFo8XhcDhygt0iUjdAtDwFx\nOp3IZrOyZ+0SW1Tu7u5KaRlLqlh1wHrUwWAgvQKAOxPTdB92PrtQKCS1ulTmbEphGMZYp51p2Jph\nJ8rSYDA4ZLxznPSiNW8RdqZxYRiGeO90qHhtrovT6cTMzMw9S5Xu6xF7JL3ZRi2qtpr5v67VAzCk\naLmA2oPVEOQor+MwRGuTXmy1WhUPhPFNNoUwTVNqxdiHWEMSVIIcJ4/v4gPUcIyei13ievDUIVrp\nwD5DMqbMrjOsHWQhPGMRnFej0UCpVMLc3JxkTOtNx7M//X4/pqenEQwGx8qA1MkvNDK0MNXxPq0U\ndIKP9vSYyMDv0WPWn+H7kUgEV69exbe//W1bypZkfU6jlFEoFMKHP/xhES40IgFIWOHixYvy2eXl\nZbz88ss4derUHYrJCvPaIY2W6PXVHieVEOdiVZD83KicAh3qsSpnKvXBYIATJ07YGrcVytak9/zd\n3huVgGe9pp47x65LxuwiTc1mUzqyUZkUi0XE43HpKcwyQnp4hGZ1dzPdxY21uTQ+k8mknGoDHHQY\nCwaDKBQK2NzctDVmALhy5QoWFhYwOzuLRqMhylRD97yPNnIJvRPi5mcASPyYJVeEzAl3O537J4kF\nAoEhSP+wxHHp8kadSEenQ5+FrPWHlgnkGRq1VsNeG7p8Dv1+X44ovRvdF2VLS0ULB26Me8UltcWs\nSXss2nPhg9eChItjd7Ps7e3hr//6r4cUn74ePQ02SKAlbM2OtbaJ1Jvc6pHzNc7JbjemcDiMYDCI\nWCyGcrmM3d1dNJtNSWWv1WrIZrOSiTw1NQUAYjhsbW3B4XBgZmYGly9fxs7ODkzTRDwel/EQ+mLG\nLN/r9/v42c9+Zvt4LCtxbTTkqb0Zvq4zzBnPs66rVsy8JnmOG5IQ+oULF8Yeq6ZR/Dw9PY2FhQWx\ntlkDzTIvHr1mGPuJJB/4wAfwve99Dw899NBQQpCe193u/17E9dD7Q0PGVs9VIyuj3rcKKz0m6334\nzJaWlvCNb3zD1rjJV7pTEfeZhos18TMszdDxV46TCTs0EFlTz7wDwshutxsXLlzAl770pUOP+fr1\n6/jMZz4ja8HfyWRS+jDzNSqzUUp91DoCw3kp5Gm9NvTo7VIul5OqhXq9jqNHjwoM73A4hk4V094r\nAKnRL5fL0lVNhxL0WGlg0CtldjMA2611gTtbMFJHcJ9R+RKxdDgcUm5p5V/KGYadyLv6uD5tiBYK\nhXt2vTLMcdynCU1oQhOa0IQmdGiyf9jhhCY0oQlNaEITskUTZTuhCU1oQhOa0PtME2U7oQlNaEIT\nmtD7TBNlO6EJTWhCE5rQ+0wTZTuhCU1oQhOa0PtME2U7oQlNaEITmtD7TBNlO6EJTWhCE5rQ+0wT\nZTuhCU1oQhOa0PtM96WD1I9+9CM5uo0dXdhdiS0R2aGj0WhI/0+eyMDj4Ph9NtnPZrPS7aTRaKDd\nbiOdTuPIkSOIRqNDR2mxT/HnPve5Q4/7v//7vwEcdHxiezJgv1l3uVyW9mQ8UIC9g7PZrLQ29Pv9\nmJubk05FrVYLvV4PDocDrVZL2n0BkAb/nHev18NHPvKRQ4/Z5/MhGo0OddXSXYN0Cz3dvlB3WGKv\nWX3klLVVmT7NwzRNaUfJfsvsjnNY+spXvoK/+Iu/kFOSgOGTfvQpLIFAAJFIRMbOMXI+PF+40WjI\ncYK8nl4Pdv/iHP70T/8Uzz///KHHvLKyIl3D9IEJuv8qSd9Ht3/js2CbTP7vdrulAxC/U61W5ZhG\nXr/f7+P27duHHvMv//Ivy7PTXYl4fz0G3YlHt0y19lfmfDlWXoP9e3XHLN0X+vvf//6hx33s2DHp\nmMQ+xux7zD2kT8thNzGPxyNdp9gHW/e/DQaD+PjHP46jR4+iVquhVqthbm4O0WgUrVZL1nlmZgaN\nRgO/8iu/cugxf+ELX5AORk6nE+fOnZPG/L1eDydPngRw0Phe9xF2OBwoFovSfalYLOIHP/jBHT2o\n2+02zp07B5/Ph0AggM3NTZw4cQIrKyvo9Xp455138Ed/9EeHHjMAfPnLX8aDDz4obWXJ0+zwpLvo\nAcOntmne0KcZWfcgydo6lf9fvnwZX//61w895lQqhc9+9rNyP56EpMnhcGBhYQHpdBrAfrcwr9cL\n0zRx69YtFItFABg6KIad93i0qMvlQrPZxPr6Our1OjweD9LpNLxeL/7mb/4G29vbdx3jfVG2//RP\n/4RTp07hkUceQTKZhGmayOfzwoT6WCa/3z90HB6PdeNBvtzYkUgEsVgMi4uL0mfz7bffxuuvv45/\n+Zd/gc/nw0c+8hGcOXMGyWRSNp4dKpVKck8KddM0EYvF0Gg0ZMM3Gg353NTUFHq9HmZmZlCv19Fs\nNrG2tobV1VV4PB4sLy9jenoawP7m8vl8Q2cnhkIhAAftyuw2+LIyMzeltR2Zbms4qvk/W9vpE5v0\nPfSG16+NSzyEgY3XSWxfp8//pTDlpm+32/B4PHIaCttn6hNXOFZ9OIE2Jqjg7NC//uu/olwuY3V1\nFa+++irOnz+Pra0tUV76MGrOz+v1yng4V5445HQ6xWDhOaYAhsZKpaiPPrND+rnSoNN9dSmgdGtD\nKln9Ptdf98tlU3cqQb1neDiHNnjsEA0SCm3yw9mzZ7GysoJ8Po/z58+jVqvJ2vD8VG0A6Sb1brcb\nv/qrv4pHHnkEP/zhD/Hss8/KEZe5XA4vv/wykskkvvrVr+Ltt98e6whGUiqVgsPhQDweRyaTQTKZ\nRKVSkQNIuG5sH0rDCgC2t7exuLiIUCgkJ4jxGaZSKczOziKbzSKbzcLj8WBxcVGex+nTp22NmTTq\nYBCtZA+z30cpVet37vX/YYmtH8mrPp/vjja+NHx4TCf3prWvNk8B43jIMzz+UBuQ+rjUe439vijb\nz33uc9ja2sLFixflIHV6JwDksF9adjxqjhtCKwE+9E6nI4KHTfEXFxcRDAaxsLAgxx7xRIxxjqxr\nNBriZXKhaWVyXNa+soVCQQQTewifO3dOPJB3330X165dw+LiovQXjsfjIsQoHKiA7Y5Z9yDVpL1T\nfs7q2eoG9GRAa69pYLhZN706rcDH2TDValUOOuB4tRfFsVl7yPIsYD5rzp8KiwKa3hyVse6PTGE8\njmETCoXw6KOP4oknnoBpmrh+/TouXryIN954A9evX0ehUABw0Lu53W4P9VwFINZ1vV6X17iptRLm\nvjAMQw66sNuHmoqV60jS/WC1J6u9X6vQ4vf+P/a+Ozay6zr/m0ZO7zPsbbl9V1quVtKqWDWyqqNm\nGbIT23CsGLZhW3ECJLHhIAaMOP7BSRwgluNEsOMmy5acyFVQi622khaq27m73CWXvQyncoYzLDPz\n+4P4Ds88cVd8lL3IHzwAwTbz5r777j3lO989B1g2ynSUdQSv5+ts6/OdhNeqVCqIRCK44447cOut\nt8LpdKJUKiEajWJxcRH79+/H008/jZdffvltzUDo0FJuvfVWNDc3Y2pqCps2bcLc3BwKhQK8Xi+c\nTieuuuoqdHd3o7e3F7FYzHQ3Kyp+KuZ4PC61xC0WiyAvPp+vRvEnk0lBAjKZDDweD5xOp/SZ1c4K\nOwEFg0EAkL63iUQC1epScxSzovc61wMAcaBWqlO/0jW07tKNRM72mSv9vFqx2WwIBAJSC1o7ddQR\nAGp6/+o6zXo9+/3+ml7evCYRWItlqXMR24mytvY7renzYmwdDge6urrQ0dGB+fl5HDt2DK+++ioC\ngQAuueQSNDQ0iOfv9XproFbdG1FDVoQY7Xa7dN2xWq1oaGhAJBKp8czZI5Kw0GolGAyKV64hU6/X\ni5mZGenYE4/HkclkACw9THpWfE8+n4fdbkcwGMTll1+OSqWC8fFxjIyMSJutWCwmHS8ItVGxmREa\ner1gjYZLR7v6ayXDa+z7yc/QHXXoJb4bY8u2XSzyzc+haAVdKpWQSqVkzXAdUHkBS04LUwuMHIwO\nBTchu4GYjRL3798Pi8WCWCwmn5vL5XDBBRfg3nvvxb59+/CDH/wAJ0+elCLpXMtUwhynnks9hzRe\n2rFh1FwqldYcCXA+7Xa7QNZUOBqx0N+1Q6WRJxpfDf8bRTtuxg5eqxGOr6urC1/96ldFHwwPD4tx\nmJ+fx65du3D55Zfjd7/7Hb773e8imUxKkXvtBLS1teHyyy+Xnq8tLS0YHR3FW2+9hW3btiGfz6Ol\npQV9fX2oVqvYtm0bmpubTY1ZO+Eshs+uN4RlAaBQKCCXy8HhcMg+SiQS0uDD5XIhnU6jo6MDIyMj\nNQ4LG643NzcjlUrVIG1Op9M0WgOcu4vVShGtsZD/2f5mRNjMjOGdRKeTtBibO9Bu0JnUaBlFNy3g\nNfR3oq3sULZa5/G8GFv2a+Qi27VrF3p6etDf349nn30WbW1t6OrqQigUQi6Xk5tlz9VKZanxNlvb\nAUs3zpwbuwoxH8M8n26nxP+bESppDRcvLCwgkUjItdgLlQ+LD4FtsSjz8/PSONlut2Pbtm3Svm9q\nagoDAwNiyD0eDzweD1wu15oWne5yAywbRy4K/t8YwehuGPxd52iM19XGm9GhWSWq51rDvSttaL5G\nt/Pi5y8sLEhkwr/xvui8eTweuR6fJ3PELpfLdIeUUqkk+WFGK9u3b0dvby8OHDiASy65BD09PXjg\ngQewb98+gZ+4HjW8aTRAKzlNhKyotOvq6qRrkxnR19QtyXSEb2zxaFxXdHz5rPTzA1DDudDX1wbd\njNjtdmzevBkPPvggRkZGUCgUMDMzA5/Ph0wmg+npabS0tCAUCsFms+Hmm29GZ2cnPvvZz8o1iG4U\ni0Vcd911qFaXGrG3trZKtHPzzTejXC5j27Ztku46fvw4FhcXMTo6itbW1lWPmXA255LPzeVyIRgM\nYnJyUhCZcDgsTcqDwaC03uOeYlTNOeW8+3w+Qd68Xi8aGhowODgIYEnnMD9pRrTjBOBt6+Bcol+r\noeiVHDj9ev2/tTiQi4uLKBQKsn51Jyfd3pTCMZ3tWoxUde6ZhpXcIu5htkf9PwEj02ixAXNdXR3s\ndjva29sRjUYxNDSEo0ePIh6Po729XVppUbHQy/B4PCgUCrIxmMcBlh8SjTPbO+meimaNLQ03F75e\nMHNzczXej34obN0ELEcJzGdR4bJ1Xn19Pbq6urB9+3akUikcOnQIv/vd7zA7OytR/g033LDqMWtl\nZ4S5VyJHcfEQgqXC13k6bng+N91rU8P/jOTN5rY4Z7r9Hb84Jh1l6/FqYpImvuixV6tViQL15mD6\ngeM3i3zQyKbTafj9fsnxLy4uSmvC5uZmfOUrX8FDDz2E7373u2LQaTg5JpLkeN+a/MUm4OQcWCwW\nxONxfOQjH8Ell1xiaszam9eOKICatWJUgkbvXUdsWtFwrRhbGa4lT6vF6XTiE5/4BEqlEmZmZuB2\nu6W1md/vR2NjY42TODk5iXA4jO3bt+PIkSOiGzi+pqYmNDY2or+/X+D+lpYWTExMCNx/8uRJtLS0\nIBaLwWazSdprtaLvu1AowGJZ6k0bi8WQz+fhcDjEOWRjeeYGA4EACoWCkJLq6uqQTCblutpxrlar\nmJiYQDAYxMjIiBgKEk3NChEjADUOlhlDq3/X4+Q19Rzx2qv9nJVkYWEBqVQKsVgM1WoVs7OzyOVy\nZx0beRQrCXU7AEEb6JxT97DHufE+zyXnxdgyeVwulyX6LBQKsFqt8Pv92LlzJ+bm5jAyMoL//d//\nRVtbG3bt2gWLxSJEjEKhICxmYElZzc3NieLkl9PpFMVEA3DkyBHxIv/0T/901eMmzFgsFgXipUJi\n1Aws54LcbndNXosLlXkjKnMyrplXPHjwIF5//XX09/eLE7J37160tLRIzm+1Qk/amH9l7nKlyFYb\nSRILiESsZAA0JKvJP4y21tLPlixNeqEcl8PhgNvtFkeNSkb38dSLXBtZOmo2m00UWyaTqWkIrT1h\ns8a2UCigu7tbYL5gMCiRLhVeKpVCsVjE7bffjsXFRTz44IOYn5+XZ0PjzHlkdKkdST4Xwuy33HIL\nvvzlLyObzZpiIgNvZ45qA0vRClAbDCN6wfcbFTO/62eljfVa0I+Ghgbs3LlT+stWq1UxWH6/X56h\nw+FANptFKBRCqVTCZZddhpMnTwKAQMncD9wT1WoVnZ2dCAQCeOONN+B2u+FyuXDmzBkMDw8jGo0i\nEolgcHDQFOFIP8dEIgEACIfDgsRx33Ae9TpnHt/lciGVSsHj8WB4eFjunc9tcHAQe/fuxVtvvSW9\neMkPCAQCZzUo7yQ6sDA6XquVszlX7ybddK7PMjKkz2X89PpfyUkwptqM+0ajOKuV82psLRaLRHyE\n7Ug0CofD6Orqwq5duzA5OYne3l7Mzc0hHA5LroQblwuRuSbS/qmMqfSpvNrb29HR0WF64dXX/4CZ\n7wAAIABJREFU14uHSSNls9ngcrkkb6ajJ/0aRsWMBhm59Pf34/Tp05J4dzqd8Hg8uOaaa/DRj360\nBn4ul8tob283NWYNAWrDqpWgEQ4EliMVTXjifGvHgc+B72Fky9d5vV643W5TY6boa+njYRyXjnR1\nE3FtFOhoUHTUzc/g72QvaxjUjOzYsQNTU1NIJBJoaWlBfX09IpEIQqEQRkZGJGIFliKX++67D6Oj\no3jyySflGhq6J3rAqJvIDmHz5uZm3HvvvbjsssuQSqVw5swZyeutVvQRH64TPbfMf3NuuT6owHU6\nwZgL14qJn6OhcqJOGkFZrdTX18PtdiOVSqFcLmNwcBD19fXwer2w2WzIZrNwOp01SIXVakVHRwds\nNpsgCrzvUCiExsZGTE9PI5fLYWJiAh6PBzfeeCNGRkZQV1eHjRs3Cou4WCzi5MmTuPnmm1c9Zp2W\nqFQqeOGFF/D+978fpVIJdXV1wkxmjpU6a3FxEX6/X1IjhESLxWKNEaxUKhgaGsKpU6fQ2toqDhkd\nebKdzUqpVJLTFgwUqNeM/AztqBnTDysZ6ZXyuCuJWcPO8RGB1LpenyihreAapIPNPKwOUrRUKhXk\n83nh5NCp4VpfjeNwXowtGZhzc3PCvOUxCJ6H83q9kq8Nh8Pw+XxIpVIYHx/Hs88+i6amJsRiMfj9\nfiG01NfXw+Px1Gw0GmFNUgoGg3A6naaZeeFwuEaB8KgOjYDP55OHwHupq6tDPp9HXV0dPB4PAoEA\n5ufn8eqrr+KnP/0p+vr68P73vx933XUXLBYLotEoxsfHxfkgZKgVolnR0YheaDQ4wDKjWF+f72H0\nYjRw2rhq0gAXttvtriGnmRFjFE0DMzc3V5Nz0Z67hosJ9XDh6/yujoRp3Nxut0QCPE9oVkjOCgQC\ncjazWq2Kwi8UChKRRKNRuN1ufP7zn0d/fz96e3vFseH922w2UbyaAVqpVHDdddfhi1/8IiwWCyYm\nJvDWW28JF8KM6Dyq9uq1siBkr3N3NPx0LDUHQ0PGVGh0hPkstUNklojGzweWDMH4+Di2bt2Kubk5\n5HI5TE5Owuv1CrJlsViQzWbhcDjg8/lq8sRET44dO4Zdu3YhEAjIcZ+WlhbMz88jm83izJkzaG5u\nRlNTE44ePQoANTn/1Qj1BrknfX19eO2113DjjTeiXC6L3mAul7oik8kgnU4jEAggkUigoaEBv/nN\nb0TRcy44r48//jjuvvtuxONxeTZce5OTk6bnmmfU+ay5v4xomNEg6hSQEXHSa+5sxtf4PzPCfc10\nkd4XZO1bLBa4XC7RHQBk31utVjmCt5IjWC6XMTMzI7o9Go2ivr5e9sH/KWNbV1cHv9+PxcVFeDwe\neZDV6hJJgTkLYPnBxGIxxONxbNmyBYlEAqOjoxgeHkYoFEJbWxscDgdmZ2fhdDrlprnRCctwcfIw\nvBnh2HS+0Gq1CtWeCt3hcCAYDAo07na7kcvlcOLECRw+fFjITxdddBH+/M//HJ2dneJwjIyM1BwR\n0MdYzpVXeKdx87sRYqWR1QtbR306MtFRIpUlDR/fT4XscDgQCAQQj8fXlCcykmt4bcK7jJSYz9d5\nY74HqCX86LnjOLkGjKSxtcxzPB5HZ2cnbDYbJiYmMDMzA7vdjqamJgDA4OAg8vk8XC4XnE4njh8/\njt27d+M973kP+vr6xFHQaQiOi0bWarXi+uuvx+c//3nMz89jfHwcyWQSdXV1En2YEY1K6O864qRx\n0ogR55aIDueQSJIROSHngr/zXvj5ZlmyyWQS09PTiEQiiMViOHr0qDjfqVSq5nw1138mk0E+n5e0\nkoZgn3rqKdxzzz3w+/3CWk8mk3LsZ25uDvX19XImcy1EI37W6OioGKfHH38c+Xwed955JxoaGmCx\nWDA9PQ273Q6fz4fp6WkEg0FYrVYh/Dz11FPo7e2VnD0dBn7G/Pw8nnnmGVx11VWIRqPweDzCjifj\n2YzQUK5k+IwoGf+m/2/8G8e50vWM0e9aoWWOychDMH6OEQ42jlv/T/+ful9zWLQzsho5L8aW3lux\nWITH44HFYqkJ3wmfsOKSrkRTV1cHt9uNtrY2bNy4EalUCi+99BKOHTuGrq4udHd3CzzN13LSueFp\nOAKBgKlxE/7lZOp8Ec89LiwsyBk/n8+HRCKBhx9+WBTrpk2bcMMNN4ijUaksnaPTxsvpdKJQKMhB\n6mw2K3nPtUS2ekNoxvHZFjQXjob+tOHV0aH24vjd5/MhHA6jo6MDbW1tUsDAjHAx66iY80Ulrx00\nwqxGxUBkw6j0Cc0WCgXMzc0hn8/D6XQK+qGNzGrFZrMhl8vJPNlsNni9XnG4FhYWMDMzg2w2i3K5\njFgshomJCVx99dX4yU9+gtnZWZRKJbjdbvHKXS6XRJVOpxOtra34zGc+g0qlgqmpKRSLRWQyGbS0\ntCAQCJg++0l4Xh810vlVoJbowby3MWerkQJNTAKWHSfjc2FE9E75tJUkmUzi9OnT2LVrFxoaGsQQ\n1tXVIRwOw+/3o1QqYWpqSvZTpVLByZMn5Z41UjQ8PIznnnsOV199taQU5ubmBGUKBAKw2WwYGBgQ\nJ9osf0LXDWAqzel04rnnnsPx48dx9913Y8+ePYjFYpicnJRjgBaLBTMzMzh69CjeeustcSY06ZHP\nhejK9PQ0fvWrX2HTpk3Yu3evnLVdixNJclF9fb3sD8KrRDdcLldN6gNYSheSlKTRL6PeMRq830fu\n1mKpPR/L+QTwtsCBETjnjnZGHzk0zlu1WkU4HEYwGITdboff7xe7xWDvnYzueTG29Ia5Iag0LZYl\n8kAymRRFu7CwUMMiJgRtsVgkH3HVVVdhaGgIo6Oj2LdvHzZs2IBwOIxIJFIDH2q4q1wuizFbrRQK\nBSF00TP2eDwSrTC32NvbizfffBOHDx/G5OQk7rnnHtx///1wOp1yvERTxfmgSSri5qlUKlKaUEc3\nZkQbWc4blRw3jYapjd6bXvyMRJibM/6dyjQcDqO7uxttbW1obGwUMogZoTE33ocel97wxgpbxg3L\ne+d3AAIXU3nW19fLF4sImJG+vj4Eg0G4XK6aMoqvvvoqmpqaBAYPh8MS8SYSCbS2tqK7uxtHjx6V\nnD4NAslTXG8f+chH0NHRIWd1s9ksAoEAuru7MTY2ZnrMmrBkFJ2H5f81YUs7X1q4z/h/ssKN+Ttt\nZM2iTLlcDr/4xS9w/fXXS3Uln89Xk35qaWnB4OAghoaGYLVa0dXVhRdeeEH2g0ZOqtUqHnvsMfT0\n9MDhcKCjowO9vb1wOp3I5/OSByWvJJPJYOfOnabGzPlg9TvNCxgcHMT/+3//D42NjbjhhhvQ3NyM\nhYUFTE1Nob+/HwcPHhRWLFNXjKp4DaJu+XxeAphXXnkFr7zyCjZs2CBQu1kplUqSztKkNn7ROeFe\nJPxaLBZrHG2dLlhpvWlOgCYkne315xLqZgY+OmWoUTDtsJB0CyylCOg0kHSro12r1YpAICDOBh1k\n2jadnjmbnBdjy7Ng2rvU0CyjO/1AgeWHQYMLQBi/mzdvRltbGyYnJzE2NoZ0Oo1Tp06hra1NcHlG\nyDqnZ0YeeOAB3H///QgGg7DZbBgfH8f09DSAJcLLoUOHsLCwIFHdpZdeiq6uLjidTiwsLCCdTosS\n1RueJRoZxXFOeL/MBZiBKChcUHoemUfTUYWRMcp51rCkfo2GjjTkW19fj3g8jo6ODoRCIXg8nhrK\n/WqFJBD9WUYjq+9Le9XG/JHO2fJohSbNaIYyo2CjsV+NtLS0iDJjQZa+vj5xxnbs2IGXX35ZIhWP\nxyPlSu+44w4cPnwYlUoF2WxW8v2Ezi0WC3p6enDttddicnJS5p0pmYGBAUlFmBHuPVZHo/Or2dFG\nZ4zPRSMG2hki4qHzuQDetr7038yWTrVYLHjmmWfw6KOP4qabbpIz6tPT06JH4vE43G435ufnEY/H\n8V//9V84deqU8CH0frBYLDhx4gS+8Y1v4GMf+5hUfuvu7kYkEpEiESdOnJATAj6fz9SYuX45L7oA\nD7C0v8bHx/GjH/2oZp1znDzxQP1FfgGfFbkBGs4k4W9gYACnTp0yrfOA5XOmXAsrPQuNnpEjo8mr\nvJ/VGk0j7Gs2d6vTQVx/KxH9ziYMnM42Zp1a084wr78a3XFejO1nPvMZ3HzzzbjmmmvQ2dkJh8Mh\nlVuYW2XkZfSI9YFwliQrl5frW3Z0dKCpqQmFQgHZbBbHjh1DNpvFtm3b0N7eLp4X835m5MCBA7jv\nvvvg8XhQLpfhdrvh9Xpx4YUXYufOnfjUpz6F5uZmjI+PS1RAZcljCfpsMRfQwsKCwMosXEFImflm\nRgpmx6w3ghYNB2sHQC9EvcgIk/NaGj7UhKjm5mZ0dXWhpaUFwHJEaVZIDuOi5XU0UqEjbV17mOuH\n4y4UCqKMCTW63W7xSJlDZf6Xz8yssSWZKZFIIBaL4eDBg+LIpFIpdHR0oKurC729vbjgggvEyCQS\nCVx66aVSB5xnsHWpOZ/Ph927d0tdcELWRFTIezALE8bjcUQiEbz11lvIZDI1kRKwnKflc+ca5vi0\nMtLrh3Oo30sjAOBtBtdsowoSLP/u7/4Obrcbe/fuRSKRwOTkJNra2jA/Py/s7Ewmg/vvvx9HjhwR\n48O1SzIXYfvnn38eR48exec//3k0Nzcjk8kIWlMqlRCJRHD69GksLCzg17/+Nb785S+vesyaJGSx\nWGoKsWhInfNENEujUgBqCHMcO4MJfoaG8hmV6QYnZiSTyUjTlKampprnSSeV8LLL5RLG9tzcnOiz\nTCYjZ4uBlXOl2mEzBgdrETpfnAc6tvl8/h3XWy6Xk3lzOp1vcwYXFxeRyWQwPz8Pp9OJpqYm2Yuz\ns7OrQiHPi7G98847cfToUTzxxBPYsWMHbr/9djmfyIXGTaFD/cXFRWGMGlmMFosFbrdbPLu6ujp4\nvV6Ew2HJ7+Tzefj9fkQikRqYYLXy1a9+FS6XCz6fD1brUhFxl8sFh8MhinFyclIegI5O6SDojWO1\nWuVgNKMqRiuEZug9UXGtpdyaniOK9sD08Q7+TmW0EktZbwpeg9WLdu3ahZ07dyIajSKTydTkuM1I\npVKRYw6cOxoCrdDpzWumNBUXsLQpWLaxUCgIO5E5cEb4rNDF4wJrOfpDI+h0OpFMJtHY2IjGxkY5\nOpHL5bBnzx4sLCwImYmfp58HHRkqKuaXOjs7JR/ncrnEYaivr5dcMCP31Qr31M6dO5HNZoVLwTml\nU8ox6Uo6Oq+tFSMNho7K6CgaoUL93MyIZiP/zd/8DW677TZcdtllKBQKOHLkiByjOXLkCJ566inh\nQBCxMZIAAYizMjU1hb//+7/H1VdfjS1btiAUCuHZZ5+Fw+EQYubLL7+M4eFhU8aWClhHeTT2nFOj\nc0qkRjs0fB50MOmMa5RK5+I1OrYWw8X1y2NoRDI0qsF9xK5mjLw5bhYeOluUCLy9CYoOEswS/4jA\n6PrFDJLe6aQB38vIdiU9QPY4T0eEw2Ehaurz2ueS82JsP/ShD2FhYQETExP43e9+h+985zu45ppr\ncOedd8pD4kZnwtk4+brDgtvtxsmTJzE0NIRMJoORkRGMjIzgfe97Hzo7O+FyudDe3o5cLodUKoX+\n/n6USiXTOZd/+qd/QldXFz70oQ9h27ZtYmRzuVzNotP1bbkhtPECljv88PgAoU86GTyaxFaCVqv5\nWs5ALZNQP3wqcH0GF6gt2K2PDAG1Z+g05E8yWEtLC/bs2YOOjg4p5rAWQ8vPIomJBkgXkdcKhopd\nQ8Ua0uQY9REwo1LTUQc3zFqZ3yyAbrfbpcat1WpFLpfDwMAA+vr60NPTI9E0FS6ZooxodDRZLBYR\nDofFsWHenw7b8PDwmmDkyy67DNVqFV1dXchms7DZbHIdHqtJp9NyXlFHrytFJfydKAPnRKMzei3y\n72aVKY09yU8//elP8dhjj8Hv9wuLf2ZmpqY7GCMczTHQqRtt7MrlMn7729/i2WefrTnOROeD+92M\n6JQLP4dcDTqSRhRAOwI6UtIse84H1/9KzHteYy3HrMhrIAHUmMfnvemxEk6mg6NL5Z4NPePvxuh3\nLQ6C1mlauJ+YpiSaBaBmznRhFjrHdOL5bDTDnkx37Yj8nzC2P/nJT3DttdeiqakJH/nIR3DvvfcK\nZFwsFiVCICSoYUNuyqGhIaTTaYyMjODll18WYtWBAwfQ1NSED37wg9i2bZtM+NzcHKLRKILBIDo7\nO5HP53Hs2DFT4/7Sl76E6elpJJNJDAwMIBKJiLLUBB1ucELNJH/QgaBBZeUYOhiM5mw2G44fP46v\nf/3r8Pl8uOKKK9Dc3Ix4PG6a1MV8C+dTR6l6Y1N0JSyyrbVyoPCeeD2Hw4FIJILNmzfDYrFgbGxM\n5mQtbGTN/NbKXXvTzHcxumOkS4VI5crz17oiz+zsrHiwLL9J54AQtlnSDsdLBarJIx6PBwcOHJCa\nvXQIyDJmKziLxSLRIzcs75eEP5Ls7HY7kskkKpUKjh07hptuusk08sH5ZBsxdpwhYzYcDiORSKBU\nKmF6ehrj4+MSDWvHC1hWlHSS9D3wtdqpI4qyFmNL54lVovjcCaVy3XA+jMQ4KktNPNSkLo5Hl1ml\nsXC73Wt2fLmOmQPl3uNn6zPA2mFciRnO+eU6Mh5n0w4p9c1anN/JyUnkcjnE43F0d3eLEWWlLuoQ\nphh005VoNCpjoY7TeVRg5WM22qk2uzY4Z7Ozs287C221WhEOh+XoluahkIwKQPR2tVpFNpvF9PQ0\nbDYbgsGg6EWfzwe/3498Po/XX38d2WwWPp8PmzdvlnPe55LzYmz379+PI0eO4JZbbsFFF11UAwPS\nw2OxCp10TqVS6O3tlSIA9fX1aGlpwXXXXQen04lf/epXuPvuu3HXXXdhx44d4mkQiqHnWqlUEIvF\nTFfb2bx5M7Zu3SrNAk6dOiWM6IaGBgSDQVQqFaHBa+iMEDfHAEA8Wm4Y7XVGo1F8+tOfxvz8PKam\nprB//36cOnUKHo9HmiKvVjQZiFEbYR8jdEVlqaE+YJmxqaMZXTErHo+jpaUFFosF+Xxe2sOtlC9e\n7ZitVqtUvKGio1ItlUrybC0Wi+RY9GbVhoQVh5xOJ7LZLHK5HNLpNAqFghg8pgS0MjYjc3Nzoog5\ntyTi8PhWKBSqSUMsLi4Kg1Z37eEzYTTl9/thsVgkd1YqlTA2NoZkMolQKISPf/zjGB4eNk2ASafT\nwp5mNOrz+RAMBqVIfiQSwezsLFpbW9HU1IShoSHpKKPPsmpIVnv4VKw0Cho1WSkSW40Y00xMuQDL\ndabpYHIcOqokJK6dAIfDIdwEfXTDeGRF56LNiBHG1UeiNMTMyNuIhvG++PkrQfAcK/UOP1eXgTQr\nlUpFeA9EPTREzLnR6IDmT1gsFjFQqzWca0ktGN9/tjQQHQLj6/X6pf4hYZEoEvkgmqRWKBSQSqXE\nKW1vb19V1bzzYmy/9rWvyXlAwoSsmMJKUoz6EokETpw4gWeffRbHjx/H9u3bceONN+L9738/2tra\nUC6X8cQTT6BSqeAv//Iv0d3dXVP6UMMWPJxOJWa2kDhb4xGfb2pqwuLiIlKpFKanp3Hy5Els3LgR\nzc3NAlcQQmHEw2LizG3oaI0KtVqtSveWRx55BEePHsXMzAw2bNiA3bt3mxozN5iOKHRuUzNP+X+t\nsDV8rxck38dN1dLSIn2DZ2ZmJHfJSlpmhcqBC5rKMZ/PS5k0Fi6oVpf7dPJvNAJAraEGliJtKg/C\no4QetaE1GwWwU4tGOiyWpfN+zOWHw2GJThsaGuRoGwBp08j5JgrCCG5sbAzxeFzO8RUKBezYsQPB\nYFAKPJjN2TY0NKC1tRUDAwOYnJxELBZDOp1GMplEc3MzGhsb4fF4JJLzeDwSVU5MTMDhcCCfz8Pr\n9aJYLEqFJho3o7PFvZDJZGrykGuB7AFIS7lKpSLsbf0MqU8094B7UqM2Wk9oR4fIE9+roXSzldG0\nceS1uc65Xow5XY6XiIuRC6LhShoXXlP/zvtcq2hEycjXYKoGgBxvK5fLNRA9yWCrNfbvxtBSisUi\nEomEGEkaQOZZjXI240w9R51GCJo6gk50tbpEZNSN6s8l58XYMiTnZuDPc3NzmJqawunTpzE5OYls\nNguLZalay2233Yb7778foVBI4OTZ2Vnk83lccMEFaGpqQl1dnShdbnYyTtl6T0N9ZqMA1idlOTfm\nzyKRCDZs2CDNEw4fPiwVgFpbW6Veq8PhQDQarYFxuSnoGFCJffWrX8W+ffvw4Q9/GJ/85CfR0tLy\ntjZ9qxEaRkYYzAsDb2cCGnOz2hBzUwG1xmt+fh5utxuhUAjxeBy5XA6Li4sIhUKSG11LIwJGmJqR\nzN95HpmwIY06o0mtGBcXFwVy5RcZv36/XxweRkBUHITgzAgVvt5oGo612+2YnJyUNUGj6XA48MMf\n/lCiM8LPGkIHgJMnT+Liiy+Wwg09PT3o6+vDwMAAbDabtGw0I2yWXiwW0djYKCQrq9UqRWc4PySM\n+P1+dHV14ciRI+jr64PP55OonoQvnZ4wnq/lcSVGXGS5mp1rrhHmbhmNaueQz1u/TzOpOcc0DjQM\nHLsx16ujdLNzzXkwvs8YjeqImvenj2jRATCmV2jMjQVeNKFwLZEtAwfuHyIDOgdPcp2ui0CDBCwh\nKHo8Wo9ph0z/Xe8js7q6Wq0ikUjg5MmTsNvt6O7uRldXF8rlsjCjjfcYDAbfFpHqqNzhcEjpYD0+\nq9WKHTt2oFgsSvVAox5YSc5bIwImqUulEoaGhnD69GmcPHkSfr8fu3btwpVXXolIJIJKpSIGdmFh\nQaBhMssAyNEhkjs4OcxhaG+SCmMtBAd6xDS43Jzc+HV1ddi6dauMZ2JiQo4JtLW1SY9TYJngoY3f\nwsKCLITrrrsOTU1NsNvt6Ovrw9DQEPx+Py644AJTY+YG4YZjZKF7LupIV3uv9OhogIzMU27cYDCI\nxsZGxGIxDA8Piweo79Ws8Gw0I1C9sTkuEjfIpgaWN6vOXzHC4lwY89bGfPZKEf9qhYxNKh7m4SuV\nCpqbm+HxeHDq1Cls2rRJ5np0dBTPPfdczZlJGgpGlBaLBQMDAyiVSohGo0gkErBYlmppEyWanp7G\n9u3bTY13aGgIY2Njsq55FpzrgudJ6Szy+MTCwgK2b9+ObDYrrQTJrNdOG+9RR4SM2jOZDHw+Hxob\nG/Enf/Inpuea86zRmLq6uho2uV4P2hHT0SvHZcwjMhjg89BMWb7HrNAYclwcO8fL74SJgWWWtIbE\njYaLY2MEyXHr6+ojQ2ZF6yu9/zl+HUXr8q0cH/WwUc5maPX/1yokyREZoiwuLr6tuAftxUpCXUgn\nWKNmwJL98Xg8Nef1V5NmOC/GNp/PI5lMore3F1NTU7Barejs7MQ999yDHTt2wGKxCKOXbEOdc6TX\nTXiRJCVGUVyUmmDABc6cMADTypSbl0qcRlLngWiEvV4vNm7cCK/Xi6NHj+L48eNobW2VY0c62uOD\nJlwUiUTw3ve+F3v27EE6ncbMzIxEvWYhWW1EjHlaykoGV28u3qM2UAAkomhubkYsFnvb2LSyMyv6\nyANhHz5rjo3RJxWmkQTDTUVFq48jUAloEhbnwgh9rlboCBqVNs9RLywsdV5paWmRlIHb7caLL74o\n90NjoeFMPp9Dhw5J675CoQC32y2krnw+j7a2NqTTaVNjTiaTsNvtmJ6eRn19vZwnbW9vx4YNG5BI\nJDA7O4vGxka4XC5MTk4KDGy1WrFnzx784he/QDQalSiecC6fmc6/EVUoFotS4u4LX/gCTp8+bWrc\n2nBznpkK0FEcnzlRFo6LjgxzcTp6pOOh9YXeF2t1xvTRJyPhiScOtNHS+1DnuLmXgdoa4Lw+1w9f\ny/WtjzyZkWAwiHA4jHA4XFMDAah1FmjkWcNZi66joI2u8We977SeNrsfK5Wl6ntsFMCAiPUZiGix\nDja5HZxXrdtpPzgW/m8lB0KjAP8njO1vfvMbgV5vueUW2XRWqxX5fF42CiFAQjmM0Kjk6dlquIKe\nIw2TNpD6Yeo8w2qFD5AsOy44vfjJYiOc5vF4sG3bNlSrSw2dx8bGMD8/j2AwiI6ODiGC8UtHX16v\nV/5PBWFWNARLiEQfXdAeNlBb5Yf3rIlqxryt1+tFa2srfD6fMHz5PuO1zY5be+V0srRXr+GzVCol\nDo++B03h1+PmuHSUwfcDkBZmZqRarWJ6ehr5fF6OdunCJm63G8FgUNrv8dD/a6+9VgNvEk6uVpfP\nRy4uLmJ0dBQDAwO44oor4PV6UV9fj3w+j0wmIxwAs52s6PjRGLlcLmzduhWxWEyU9/T0NNLpNFpa\nWjAzMyNM2rq6Ovh8PnR3d2N4eBjBYFCiSr2G+Ezo9LCcoMvlwpe+9CXYbDZcf/31psYNLDuSJEvR\nmMzNzSEQCGDTpk3o6elBIBCAx+ORXD7z/6Ojozh+/DjGxsZw4sQJuS73L+9Bj1/vmbVGttyThLjp\nIOj0BZEcInr8fCpyYLn6l873Mn3C77FYDD6fT4p8rIVF3d3djdbWVrjdbvh8vpq6zByHRr7oiOkI\nkux+7lkdYZ/N+FLW4vyS2JRMJmUfUv/pM7ETExNIpVKyHvka2pZKpSIOJ//GqoVEcrQwIGBQcC45\nL8b2hhtuwObNm4WwxNyb7ipCpcoFRe+MRyl4U4wcmPNkwXHWMCakyXwOFyyhYDPCc7GaaKWVs46u\nZmdnZfOQndzW1iYsuMnJSRw5cgTVahUNDQ0CGWsWIo0Byz3SeTAjVIxaYRgNt2Y8cmEbYRXjhtDP\nhUXaWZaQi4351rUopUwmg0wmI/NIBV4qld7mnADLkQHHZMyP6WdFdjKVAaNfQkAAZF2aEbJ6Wb3M\n7XZjfHxclD95BboYxdjYGI4fPy6RFo+6EaqiMuZ9/8///A/27t2LYDCIiYkJWK1WxGJQapNlAAAg\nAElEQVQxgdvNdnW54IILMDo6inA4jLq6OmEZk4RWqVQQiUSQz+cxMDAgz9dqXSq60dfXhxtuuAHf\n+973hEConQWuLRo4rmGXy4UvfvGL8Hg8OH36tOlxa+NN4Wds3rwZl19+OWKxGLq7u6UCUkNDAwCI\nsW9ubsZNN92E06dP480338RvfvObmipadPA1oqbJUWaddeaCNStZcyUA1HRM0jnbxcXFGtRD6wLO\nAXUac+o33ngjOjo64PP5kMvlUK1WsX//flNjBiDOmC7Ob3Sk6ZgYDb52INaiB96NGHPWnFc6ikwf\naXjcmObjvRnRv3MFEvq955LzYmy7u7tRrVYFHtUekv6ZXqqeIEYmNEicoJmZGYGsCA/QAJdKJTFY\n9CAZ7psRGncNYfN3UvIJYxO60Md79AZpaGhAPB7H9PQ0pqenhZ3p8Xjg9/sRCoUEJhwcHMTg4KBU\nKrn44otXPWZCaJo0pD12it40WhEYI10aNCofEgJ4to5nj/n6mZmZNZ2zZWlFKjwaHpKyaBwJE+oI\nlvOtSV06KgeW82A6r6uPiK2FTELol2fxGhoa4Ha7xZhns1ls2rQJTU1NyOfzaGhowA9+8AMUCgXM\nz8/D5/PJ2qTzScXMfNCLL76Il156CZdeeqko/Gq1isbGxjUVmSdBKxAIoFQqSanTVCol0F8oFEK5\nXBaWPwsBjIyMyFlDrlWHwyEIh3badBQWDAbR3d2NUCiE/v5+IUuZER316Zzrxo0bhe9ht9uF/EW0\noaWlBSdOnEAsFsOuXbvQ19eHWCyGvXv3orGxET//+c9x5syZt60jOsJ0gs7GaD2XcP3xuWr0hl8N\nDQ24+OKL4fF4kEgkMD09jVOnTsm6B1DjlGseisViwZVXXokrrrhCdOaJEydgtVqxefNmlMtlvOc9\n7zE1Zj3X3IMaaeQYtDNPBKlcLtfUCj9bBGvkgKz0s9m9aLVa5YwveSeDg4OwWpeO3Pn9foHWKXRq\ngVpmssPhkDVfLBaRzWaxuLiIbDYrOXKzZ/KB82Rsc7mcFFonNZ8tyDQMzC+n01kT3gMQ48WHzPxL\npVIRKJcbEVguKAEsw1pmvWldroxGS0dTWrnwSIn2gBhN8T5IcInFYgKDjY2N4dixY1J8w+l0oru7\nG9FoVP5nRuLxOBwOB3K5XE3vVm10jEbXSLrQc6a9ewBSsIGeYiAQkEXJ+VrLQtS5WKYMdFUn/o/z\nz4LnQK3XrfMqXF90jubm5iRnQ+PANbUWIgnLtxH98Hq9iEajOH36NOz2peL1DQ0NOHPmDDo6OvDz\nn/8cv/zlLyXnz3ui0aKRphEncvO1r30N3/72t+WYjWYKm0U+hoeH0draKp228vk8IpEI+vv7hTWc\nSqUQi8VQLpfluJTf70csFhPDEYlEkEgkxEnQLGojurCwsICbbroJU1NTSKVS2LRpkxx1W63wWBTv\nne0Id+zYga6uLony6IRznc/MzCAUCuHKK68UR5Dz1tbWhr/+67/GX/zFX8icA5CCKYSrgeX9YEZo\nUDVqx2ccDofxwQ9+EDfddBOKxaIQ0YaHhzE8PIwXXngBTz75pFxrpT27c+dOXHLJJRJBa2Z7b28v\nYrEYNm3aZGrMAGoiQB2takcHWH6+3O/6tUBttStjftYY4WtZi7G12ZbO/pM5XKlU0N/fLwiSRg75\nGVxHRmHBF7LBgSVdwnO1TL+YTfOdtxZ7+oiAZmEyegSWJogFABgdWa1LFUCAZciFECvhH8KEGhLU\npBpGCWa9aSppi8UiBCetyPX1mLPTuQwWwOB4CUdp54I9edPpNHp7e1Eul4Xtu2PHDmzevNnUmLdu\n3YpisVhTWNsYuWrPUjOUOd/MI+sNQcPACJ9HOFKplDDF6YysxXAZCSj8XDpoerNwDWhhxMDXMjq0\n2+3CYOZ9aMiTSnktypTpCva05Vquq6tDIBBAJBLB2NgY2tracODAAXzrW9+qQT6q1SUSHKNhzh+V\nMxmlo6Oj+NGPfoTPfe5zUsGG92K2oL/P5xMnwe12I5fLoVgsSss6DaEx9UOouVJZOuoUi8WkAQfz\npkajz/mdn59Ha2urHNshS3utDhkRKqfTibvvvlv6+s7NzSEYDNZEWpFIBENDQ2hoaMDIyAiKxSLi\n8bhA5oxg7rvvPnznO9+pSelwv2oOxNVXX21qvMYUDXPOV1xxBT75yU+iq6sLuVwOBw8exO7du+F0\nOhGJROB2u9HZ2Ynrr78e//Zv/4bJyckaFJCO/J133lnjgI6OjgrS0tTUhKampjXxJ7SzokU7U0aD\nyr8bfz/bnPDntYxvJeG6WMnh4zi1Q7jSfRj1IO2HhsupR3gfJFBpO3Y2OS/Glg3jGUVQCdKA6b9x\nc//4xz/G4OAg6uvr0dPTI+3rmB+kJ6crf+jrMPdphBfNCD0ibdQBCBRBz4bOgYaRuVCZT6Znp40E\nX1tXV4fGxkaEQiGMjIxgfHwcBw8eRFNTExobG02Nee/evRgaGsLRo0cBvJ1sYMxFcIFqQ8xNwPvT\nETvvn+QH3feSR17WQuyicaHRBJYr7jAiYHEIvl7nZbgRNGTOe2U5Rq4/fhavvdZGBMy708DPz89j\nbm4Ozc3NkgOOx+N4/vnn8cADD8ixBI6fDoQ2+tzYAOTaxWIRv/71rxGPx/GBD3wA1WpVegZnMhlT\nY/Z6vXLgn/CvzWZDQ0MDvF4vJiYmMD09jWw2i1KphN27d8tZ8GQyiTNnzqCnp0f2H7kSHK9+nvzb\nZZddJoopEolg//79sFgs2Lt376rHTXIZUwjbtm1DKBRCS0uLOA8bN27E0NAQisWiHEvjHmZErrkW\nyWQSi4uL6OjoQHd3NwYHB2vWk1bE8Xjc9DE8rifmX8vlMm6++WZ87nOfg8ViwcTEBEZGRtDU1CQF\nRvh5PD54//3340tf+lLNWrdarfjABz4gzQLcbjfS6bQcR/F4PCgUChgcHERzc7OpMQNAIBBAOBwW\nlEk7onrf6Xwn/6fTOBS+/1zEJ2PUa1bsdjs6OjoEjdAlIsmp4V6j6MCIREAAwvvhOuG9zczMIJfL\nwe12y34JhUK46KKLEAqF8O1vf/vcYzR9V2sQn88nkRyT/jQ8rLvJyMZms8Hv9+PP/uzP0N/fj8HB\nQezbtw///d//je3bt+Oaa67BZZddJoaXZCsew9A5Wk4SANmoZoRkIxofknc4XhqrQqEgZ7tokJnv\nqKurqzluQEPAMfKBW61WOVMZjUZhsVgwNTWFw4cP48orr1z1mHfs2CFkFiMb15jk1x6sNrQ698J7\nN0Zcmi1MQ0Pnx2xui2Ph/MzPz9fkV0ulUk1XDh2R6zHyOrxHGmtdsJ/j1QQsRr5mkY+f/exn0he2\noaEBmzZtwubNmyUnWalU8NBDD+GHP/xhDYzv9Xpr0BsiIrqLiM6xe71ezM7O4sEHH8Ts7Czuvvtu\nxONxzM7OSn/l1QoZxTwb7nK5MD4+LoYrk8lgYWGhBnLnenK5XEgkEmhqapKiBX6/XwoGGAl2lcpS\n2T9yNhobG2sY1WZEQ9UWyxLZqaGhQVIKPK5y6tQp2GxL7QhtNhtaW1uRSCQQCoVQV1eHvr4+NDY2\nyimIUqmEVCqFzs5OjIyMyOcYiU3XX3+9qX0IQDgdRGdcLhf+6q/+CtVqFaOjo3LGmQ53sVgUciVR\nmosuugibNm3CmTNnZO85nU5s3bpVntPo6KgwsIGl0q9PP/003ve+963JcHm9XgQCAQAr1zGm6Khd\n6wajrAY5ercRr91uR3NzM4LBoOxrQvbkIxiFY61Wa1sCxuPxmiOHwHIJS6Yp6uvrEQgEEI/HsWPH\nDjkqd84xmrqjNUo0GhWjYmQJE1ph1xxgaeJcLhe2bduGnp4e3HnnnTh58iSeeuopPP7443jxxRex\nZ88eXHjhhYjFYlJJh2d0dQk8YEmZTU9Po7e3F3fccceqx83ImMQt/o2bULOoqQQY2VFJ6bOHwHJe\nUUdf+pgSHY5qtYqWlhaB0FcrwWAQbW1taGtrQ39/fw2Dl5/P70bYhIaXhor5DELH/J+uKex2u2ty\n2gsLC6ahTX4GiVaMnHl8gHOvq7To9URnRZPqiGbooyma6UznwmazSYs5s+zJnp4eOavHCGNkZATz\n8/Po7+/H008/jQMHDoiC5Jh0CVGgthCBfi68R52j/f73v49Dhw7hxhtvxK5du0xHLqynzOu6XC60\ntbWJwuEZYUZ2JBrRWOzevRs+n0+UF+eeDpmRA+D1etHc3Cz5X8KcZlMNPT09OHr0qBj4eDyOcrmM\n6elpLC4ulbKkkaDjAkCKb2SzWTkdwKidMH0kEhH2rtFYWCxLvW0vvfRS02ea9dlLh8OB2267DS6X\nCyMjIzVEHUa95JnQgbBYLEilUrj11lvx7//+74JCcMw01Ha7vaZs5qFDhwR96O3txZ49e0yN+1wR\nKP8PnB0mXs3fziXvBlqmHuJ8at1svLZ22Jk20MEJx250FLg/GWTkcjnJ859LzouxnZycRHNzc40h\n0tHRzMyM5H005MrFykpN3d3dKBaLOH36NNLpNE6fPi1eLZtp0xjm83mMjo4ik8mgr68P+/fvRy6X\nM2VsgWW2LpWDhvyoBDVePzc3J4e8mcfTSXY6FzrKZSTHn8lU5b2bEYfDgZaWFmzZsgVTU1PI5XIA\nlr3FlcgH2qPkAiS5i14+F6HD4UAsFkMsFkMul5NnxO8spWhW6DVyLjhH9Po1rMd55ZzReHKcGlYm\no5kGTRsCGjRgbWcoiSIsLi4imUzi2LFj2L9/Pw4cOCCRm87Ncm2z6g7nUztqOgqgo0YIlfDi/v37\ncfDgQTQ3N2P79u34oz/6o1WPmXWPdQ1ZYGndDg4OoqWlBd3d3Xj00UcxNTUFh8MBv9+Pqakp2Gw2\nXHjhhQLNA0A2m5V7IOlLE2OYD56amkIoFMKBAwcQj8dNt7u88847cejQIUk/0bFheUl+HpVgKBQS\nx6+1tRXT09NobW2V42msQMeKWHSIeByL816tVnHPPfegoaEBAwMDpsbMsdBJvPzyyzEzMyNRtV6X\nXq+3xtniGuC4tJPJEqYTExNCHNPEUQBySqC1tdXUmAGI86hTHUBtswxjIRfOv5Hbop+L8fXnMsJm\nDS7nh3Axa7ZT3G63cD44zxrxamtrk6Atk8mIQ8xjUBpp0mTLiYkJPPfcc6ivrxddezY5L8a2t7cX\nMzMz2Lhxo4Tn2WxWPAHCaBaLRc7KUunrRQgsQdIXXXSRLDCHwyGU+ampKUxMTGBqakoOOLOLyR//\n8R+jpaXF1Ljp4ehKLJoGz8IFLHzByIUwMw2I9nBpnHldnYCnYqYCWUtB/3K5jHg8jq1bt+Ktt94S\n2rrOYeocLKNAnc8w5m+B2gP0ZCTrRu9UVFSEZiWbzSKdTtc4Azx2wS99BEjn7zguYJllSKdFn+um\ncaPDw4hNe75m5Kabbqox7ByzzvFzjdI4EbngkTEe69CVkHSUq5+bdtJmZ2dx8uRJ05WYAoEA+vr6\nhCBFAh/htomJCdTV1SGfz6O+vl5KN9rtdmk88Oabb2JsbAxAbcsyinE+U6kUZmdna9qYvZNiMgrL\nSNIYMs8ZDAYxMjICi8Uic7F161YcOnRI4EAiMH19ffB6vchms1LRi/fAdcJ0EJ8Nc7p2u900ykTj\nRJ3G8790YsmuBiAG1OfziWMFoKY6Edf+/Pw8pqenEYvFsLi4KCzvRCKBTZs2SREGbfzMCAMJvQ6N\nfI6zOe2a/Gf8v3Yi+Pta9t25hM4LT6sAkLPiRidBp46CwSAaGhpQqVREXzCIYApUF8chQra4uIj+\n/n4AeMdz+ufF2G7fvh3T09N45plnUF9fjw0bNiAUCgkUmUgkBB4i5s4Fz0VHg8foeGpqCoODgxgf\nH0cqlRIvt7GxEVu2bEEkEpHKIcDSAiKL06xoyE+fH9QRF6uLaBhUe4TaWBHu0Ql8Ri18PaNcDWms\nRujVd3R0iEfGRU4xQsk09hpyoUHSr6FxIrRI+jtzFToHb1ZY2pCKXZeZ1MQLGkeOj/Oo2cg610sl\nyvyZrjymYWhez4xoh8XIqmfOT0PzzOVqHoBeD9pz5lrn6/l3DXVRAZuRoaEhud+6ujopBZlOpxGJ\nRJBOp/Haa68hGo2KkUkkEtIMoaGhAd/85jelQg+fj0YLNMzmdDrxxhtvYM+ePQiHw/B4PMjlckgm\nk6bGDSw52pXKUlU39iLN5XJYWFiQtoBs0ci2asPDw4hGo/B4PAgEAqhUllpijo6OAoAYLr0niCjR\nCTl8+DDa2tpMGwaiF9RnTz75JHp6eiRvTbFarSgUCuLY1NXVwe/3I5lMIhaL4Ve/+lUNYrC4uIiH\nH34Y//iP/yiG0WazYXp6GmNjYyiXl854R6NROQrz+xQj2Uk7LWd7/Vo+Y61itVqlghhQCxdrPgpf\ny71I/WyMyqmTXC4XPB6PHPUjoqOj/3PJeWMj2+1L9YOHh4cFDmpvb0c2m8UjjzyCXC6HtrY2XHPN\nNdiwYQMaGxsFruKDnZ2dRTKZxOHDh4XKf+GFF6Knpwd33303otGoQHqEk3VfwrXUGWbOB1j23DRN\nnF4PKxNReTNHByx3r6HipNLlRiE5anFxUZjbOpdrRqrVqnTl0eQmrUx4L0bREYo+NqTJYNXqUhnK\nxsZGxONxRCIROWdLRrbZ88z8PB7HoGjlrWFhYLlOKSNBI2ysm8rzXujQ8PX8nzbmZueaa0BvOJ1z\nNSIUehxU6trb1wQzRsW8T01S4+eZZX4Teclms3IEjSxesnO3b98ujRNSqZQoqEsuuQQnT57Ea6+9\nhqamJplnFjjhetEGN5/P44033kBPTw8GBwfhcDgQCoVM95bm2Ofm5pBIJKRcZLFYxMaNG4WINTo6\niiNHjuDaa68V9vfIyAhKpRK6u7vR3Nxc0/SC+3JwcFD2HCFUOkMHDhzAXXfdZdoAaEewWq3i+eef\nx1133YWuri7pFcsWhlTi2gGsr6+v0Zfcy1arFa+//jp++9vf4rrrrkNdXR0ymYzUMo7FYrBarZic\nnBSi0+9DdITLNU1jpNEwvnal1IwxmtUpB70P3o2xtdvt6OrqQnNzMxYWFnDixAkMDg4KozidTsNi\nWWoGz5QB8/5GhI9VBO12OzZs2CAtL5uamuByubCwsNTMYzXkyvNibMnADAQC8Pl8SKfTSKfTGB0d\nxcLCAm6++WbMz8+jr68PDz/8MCqVCt773vfipptuEritXF6qaDMxMYHW1lbs3r0bTU1NAu3QsOpy\nh3ywFovFdA1ZChUSoUkdkQCQylX0fLixCCXSWSAlXUcrmlSkiVE6wjW76NhknTCrXvz6u94IXOg6\nkiTUpRUGF+vw8DC6urrQ1tZWA5UyqlgLdMWxGZ0MGkV+0VBpBiyND8ep2cs0UoRf9VElPScail6t\neL3et/Xt1A4UHRU+a82S53OhgiIExbWjWeIaPifpzoxHrSUajSKdTsPn80n1snw+j5mZGQQCAczP\nzwuhkTlX5rsikQj+9V//Vco5AhADoQl/vC8AQqYijMdzpGaLWuTzeWSzWXmOL730Enbv3o1gMCiE\npxMnTqBYLGLXrl0YHR3F0NAQnE4nYrEYJiYmsLCwgKNHj9YcAZubm0MsFsP+/fsFRdKEP7t9qQvX\nvn37TBeI0BAsP+trX/savvjFLyIajQobfX5+XvKFgUAAi4tLNXxDoRAefPBB0Ql0DMn9ePDBB2Gz\n2XDBBRdICqa1tRUtLS1SFczs+jiXaF2kTzDoNBnlbBDxOxGvfh+wstW61Cu8paUFpVJJUAwAwuCm\nsTUiXcbIlnuxrq4OXq9XgiWXyyUE2tWS/c6LsaXyYbUdn8+Hjo4O5PN5nDp1CtlsFk1NTdizZw+K\nxSJeeOEFfPvb34bVasWtt94qhqxarUpJPCqobDYr0UOpVJI8C3NMGuIy+yB5LfaLpDEE3n5ImsaV\nG5QOgs5/0XjqKIAQIslWNBiEIc0eR9GlCHXhj5XyJ8bjMzS2/L9mS/O4Vi6Xk5w4HRw+XzobZqFv\nfj5ztIw8jbkRnVum4jVGhBwrlZO+N32MSMPlzLuaNbYazuaGM3roVLgrdYzSDoUmpOi1wdcQKuRZ\nQGOedLXCPeHxeIQVzMYGAGrO2xYKBczNzSEUCuGGG27ACy+8gFOnTmF2dlbW8+zsbE1dZ84hHTaH\nw4H+/n5873vfw44dO5DJZHD77bebdsieeuopMeiVSgVDQ0MIBoPYu3evdHrxeDzo6OhAU1MTDh06\nhFQqJcRMlnR87LHHhH3s8XgwOTmJfD4vUQ35FnSgmMp5+umnsXv3blNj1gaH62JgYADf+MY38N3v\nfhcul0vQMOoLKnG/349/+Zd/wZNPPin5XUZgRK+mpqbwjW98A5/4xCfw4Q9/GFNTUzh06JD0cW1q\nasIDDzxgumSjhqyN6NhKOnSl3O3Z1qe+xko66d2I5m9orgZJUUwhBAIB0TcUtvWsVqs1+oNkKeZy\n+axYMU7rzHeS89bPlspdV5EClinXhw4dEmhy165d2L17tzAG6c3zWIfb7UahUJBr6Oo0jOyoeJkT\nPnz4MOrr6031/2REor0XDVkyt5bJZGQB8ewvX8+IWhtQkowYdWm4G1huH6aJN2bGTMWsN6hR9ILX\nTDsANe+lweOCY1SZz+eRz+cxOzuLQCAg+TCjZ7ha0RA9jYwxr8q5I6GICgxYbquoN4oxyiJTXFdr\n0oiC2XHryFnn4LmJGY2ShMQznBwfHSx6yEbiCOdEHxkiQqLr1JoR3biDHYCYYuH8EhrO5XKIxWLo\n6urCm2++iYceekhqJdMoca1zj2huAo2xzWbDK6+8gscffxw33HAD9u3bh1KphK997WurHvfjjz8u\n65InGk6cOIFoNIrOzk4Ui0XMzs5KtDg2NoZCoYCRkRG4XC7E43HYbDaEQiHMz8+jra0Ng4ODsNvt\nePXVVwWOp4HluiKqdfz4cTzzzDO4/fbbTc23Jj7y++HDh/G3f/u3+NjHPiZs19HRUalVPT09jf/4\nj//AgQMHatiwJPlZrVaZ12w2i3/+53/GM888g1tuuQVNTU144oknUCqV8POf/xyvvvoqHnjgAVNj\n1lCq5kZQVxjXqpFwqVEe/k0bab0vV4KU1xLdVqtLvBmWBo5Go/Ksc7mcnKGNRCJobW1FtbpUq5+N\nHvRxLH3EkOfJ7XY7/H6/pA0bGhoQiUSQzWaRSCRWVQ/+vBjbRx99VAYzNzcnDQlmZ2eRyWSQTCZR\nKpVw+eWX46qrroLFYsGHPvQhab7NHCCVGyeIHgajGiolGioq0bGxMfzyl7/E5OQkPvWpT6163FTK\nHo9HDA49XW5OQsRAbX1fQoYsJUcDYrHUFrIga5bKkwtR1/M0I+FwWOBcnVsGauFi4O1nznTeUsPI\nAKR3aalUEiLG6OioKD+fzycOxVoIUhqa1+iBMQ/KvxPm1JWXiEIwuuc9a9IavV09J9z0Zg0XnzUN\nIhELzqXOZdHg6tKW3NyaZUonjM6AJkXxS1e6MSszMzOyZ0jK8Xq9cuyKzlVDQwM6OjqQyWSQSCTw\nrW99SxSZZmoy5aGdPK5zfT47k8lgy5Yt+MpXvoInnnjCNLOX8Dr3CM/Uv/TSS1hYWMDGjRtht9uR\nz+dx/PhxWCwWtLS0wOl0IpPJSM7O4/HAarViamoK09PTeOmll8Q5MhoMzeJfWFjAj3/8Y3zzm980\nNWbmgAm3co/89re/xb59++R0Bo8k0aniWuE9cyyax6B5FG+88QZeeeWVmmYsfEZmxagzzoWirBSd\n/j7yrmtx2JlyAiBpJAZd3EOEg5lSotARp3BeWfCGtoZ7jlCyDqzeSc6LsQ0EArjwwgvlPCy90Eql\ngnQ6jeeffx4OhwN33HEHWlpaMDk5idHRUSQSCcnx0DBxA5fL5Rq8vVqt7QXKBcJycdFoFCdPnjQ1\nbkYqGnZk/VgaAq2gNJTIaFsbZw2D6lwcI3Yq+/n5eSFxmK3GpJtm6xyEJjAY2bAa+jNGJ1rJc8zT\n09NyJrOjo0OQCmODazOi55Dj1ZC2Hhv/ttKGZNTLNcB0As826vyzvnfNfF+t6HOHHC/HyOtpB4aR\nCAlsLLJB1ECjKPxds021s8e8rdmqaMBSYYtUKoXNmzcjn8/XKBA6tnTYAOChhx5Cf38/2tvbZV6N\nZ6EpNDC878XFRWFqZ7NZ/OQnP8HOnTsxMjJiasw8Ysc9bbPZ5CjRM888g76+Plx99dVobGyU+uos\nLBONRsVglctlDA0Noa+vD6+88orMIZ1djQJxzrXjY0boFPG7PhfOYioMPKgfuHaY4+a+4FpndKv3\nM09q0MgCy+TC1RoCLSvlTjW6p1M+q0WEzvYaHSG/GxiZ+57Xmp+fF0PpdrvR0tIixlZXI6PDyxrl\nDJB4Jp4tMI1BFfPh2WwWuVwO+Xz+HdfHeTG299xzjzx4blbCVouLi9ixYwc8Ho+cqWWDcrIOBwYG\nEA6HEQqFEIvFaiBHGi99/tVisUg0mcvlcPr0aRw8eNB0BRgqS56p5Wfq3q2E1bjwaDCM0LPO71FB\n69yt/hvPdDGCMCPMVXB8/DydR+SzMMJBetw6etdGu1JZatLM88w7duyQRer3+6Un7VrEmCuiaEiV\nv2svX58H1HlmKrfZ2VnMzMzU3CujYG4k3UVotUKjp4llOtLjuiTcys8h0YxEF8KEAKSCEOFiFkWh\nl67zzGtRUJ2dndi8eTPGxsYwPj4Oh8OBpqYmRKNR6VzF1M7g4CD+8z//EzMzM2hra5NavJq4xzXO\nZ0ODxmfJZ+D1elEul/Hwww/j4osvxm233WZq3JVKpeY8vmYMu1wuDA8P45FHHkE0GpWuQrFYDG63\nG5lMBtlsFiMjI5iYmJA8LecRwNsMB58t71PzNVYrfK9mt+ovfTSFn2F0WLlWeY5dI2o64qXTYIR7\n1xLZns3YUl/ZbMu1g8mROFvelveg97WOfPllRPHMzjVPfPC9LGVKZ6utrQ2VSoXz47kAACAASURB\nVAW5XE6Oaer3uN1uOXvNdQMsP5vFxUUprzk3N4ehoSEkEgkUCgVMTk7WNDs5m5wXY+tyucRY6UIE\n3Jh+v1/gSBpjl8uFzZs3I5VKIZlMore3F4FAQCjdXq9XGMI616chuHw+j9dffx1f//rX0d7ejvvu\nu29NY9feps1mkyiJsBQXvCZR6ahRe54aZqEiNkYGNOhrIUgtLi51oUmlUtI0m/egcy8angWWIWbt\nxGhGLBXn4uJSda5kMolkMindhfRGX4vY7XYEAgFBPQqFghgo5kk0+YiKUKcVGO3Mzc0hnU5jamqq\n5llowhiPfFgs5lsv6rk2EqS4OdnKjc+Wr9NGmdAxlRePLel0AxUclbDOCxMiMyOTk5PSjP6CCy6Q\ns80s+jA1NYVwOIynn34aDz/8MNrb28Vx5frXDhyAmn2nkQJg2YDQGZqfn8fLL7+MZ599Fp/4xCdW\nPW4eswCWFb+RlV0qlTAwMICBgYGaNALnneiL1WqtqX1LQ8Y1zmfFv9OomI1sOTe6kIV+XkQuiILR\nwPN/nGfuX65tvYd5D9SnfC4roQ7vVrRxNDrr7/aa71aMjqfmftjtdin0USwWRVcb4XK916jPKBq5\n1JwRfdzznXTfeTG2WlkTijQSj7ghdf5rfn4egUBAqnsUCgUkk0mMjY0hGo2ioaFBDoJbrdaaDcOo\nYPv27fj4xz+OkZER09V26K0Y4Uzm1vQZTl3flBudm4mbhdVpuEGYB6XC0MaAv69lrovFItLptOQv\n6QSs5FECtf0mjblRvWj5vMrlsniOIyMjwmAlW28txksrTf7MudVHgahUdV9SQjjMsxA25nGIcDiM\nxsbGmt61NHAa4kylUqbG7HQ6BQLW+XYqas6hrhSm55fHCLh2AdSsAQ0l02AAy8x3GmMz0tTUhNHR\nUal+lsvlMDExgfn5eUxNTaGvrw9nzpyRM5qMZo0ICLDc/UqX6NPHngjrcU+Wy2VZJ2YVrEaGiEgw\nT0ynhsZXo0fAcoRJQ2s8qqIjIo3uEB1aa5SoHS9en3/TPAONUhgbLnD96FrPWmfocTPtRMdS5zDN\nCudHj0WjNpxLI2FKi1GP6HQW50B/lvF9Zsc7MzODiYmJGmfJZrNJgwYij7QT9fX1Es3qIhgajqcw\n4NIIAu+BpVjfCbI/L8aWFYeM5CVNGNBHXngzOmfFKjB2ux1DQ0MYGxtDLpfDxo0ba9pAabjDarWi\nvb0dd999NzKZjGllSpiN0DVzRcDykQ6tQDVBhhuAEYjO2ep8ATcNFzM9YCprs57pkSNHMDExgcOH\nDyOdTkvSn0pQM3A1UUpDuPp3vVH4v2q1ilQqhVOnTgmk3t7eLlEay5eZEZJwVhqPFm2MKfpnOgP6\nXHQymRQ2IrDMfOYmYXEGXVlmNcJ5Yc7PGGlq46NJUDQS7BJFpUuHiOuB8LJGbugQcV/oe1+N/Oxn\nP4PNtlQ6kIUdZmZmhBHPSExD9RoO1XlN7jvmRrVTyrnQ0D7nvFqtYmpqytS4y+WykH+M+4g/G/Pu\n2knU+5PlULXe4f84p7xv/bvZyFY/S436VKtV+Hw+QYUA1DjeGiXTRpkscRbR0Q6FNqpGCNesDA8P\nC4KxUsRGEqQ2ktqwrvQ3/XdtqPSa0Z9jtg51JpMRFrbFYsEbb7whLH/maQEInwNYRmR4T+80Z9qR\n494gSlWtvnMJUkt1rbjfuqzLuqzLuqzLuqxKzFPV1mVd1mVd1mVd1sWUrBvbdVmXdVmXdVmXP7Cs\nG9t1WZd1WZd1WZc/sKwb23VZl3VZl3VZlz+wrBvbdVmXdVmXdVmXP7CsG9t1WZd1WZd1WZc/sKwb\n23VZl3VZl3VZlz+wrBvbdVmXdVmXdVmXP7CclwpSl112GUqlknTfYE9UVkZh2yur1YpcLgeHwwGf\nzwePx4NSqYRUKiX9Ulmej9U8IpGIlOfTLZKCwaD8XCgUpFrJ4cOHVz3uSCQi49L1UXWnHnYjYhUb\nXW6NFbBYScbYrkrXGDXWKNbVncbGxlY95u985zs11+Y1WP2KZdb4mSyHyNJ6fD66by/LI9rtdqni\n5XQ64XK5pCUc6+eyZOOnP/3pVY8ZAL7//e9Lj1yPx4NAICCVlnhNh8OBUCgk1YLYBcfv96NUKkkt\naGNbrWAwiFQqhTNnzsDn8yEWi0lVG9ZKraurg8/nw0c/+tFVj/mxxx7Dnj17aipecS5ZWcrlckmZ\nP5bSY2UxVl5yu91SsYetwXTDAWOxf1bjqVQqeO2110z1WP3xj3+Mnp4epFIpHD9+HGNjY9LX0+Fw\nIBwOo7OzE21tbWhqapJSjNyz1epSy0nWhK1Wq0in0xgcHMThw4dx4YUXYsuWLTWdU2ZmZvDKK6/g\n4MGDKBaLWFhYwMjICPbt27fqcb/44otSzYjXZYlXXU+a3bLGx8eln6nP55NqYi6Xq6ZuMtutcX+w\nutDc3By8Xq+UgeRz6+zsXPWYv/CFL0jlomKxKBXLWHGMnxkIBFYsk0odxhKeXA8skq+F653dmqrV\npfZwgUAAl1xyyarHDABvvfUWyuUyAoFATRchVqZyOp3SSAaAzBvXKb+zvSBfo2taUxfp+9LPRVfr\nW4288cYb0mdZ1yrWjV10n+tKpSI1+/X+4lgeffRRnDlzBnNzc/jsZz+LYDAo1d64J3Qteb6X/1tJ\nzouxZQFwXRuUG5X1glcqF8japCyArstp8cFFIhHkcjksLCzU9CfUdSqNSmq1YixUrcugcfMByx1e\ndN1TvobXoLHgeHSZvXMV8DdbQ1aXDmTDcl0eTZcn42JhKcFisSi1gvnZLDfH5vasAcqyg1zIFF3e\nz4yw+9P4+DhmZ2fhdruxuLiIN998E6VSCXv37kWhUBClNTs7K+sgk8nIPdB5AyDOFw14V1eXGDM2\nPUgmk1Kqby1doYzrmUJnh0pDl77Ua4hfrLuqyzLy+axUZJ2fabbEpMVigdvthtfrRVtbG3K5HJLJ\npHSvCgaD8Pl8UkqVZQFZSxqAlJ1kkX6v14uuri65ZjgclvXndrvR3d2Nuro6tLe3I5vNolQqmXJ6\ngaU+vOVyGfF4XLpKcR64lh0OB4rFIo4cOSJOJBucsPtLKpWC1+tFoVBAMBgU481a51zzXq9XSifq\nHsNmhE4Wazmz4QD/p2uoa9EOllHokP0hhWVXC4UC5ubmEAgEANT27talaakLGRTRCaJjo3vyGstr\n0lHXXZwslqWubSs5FWeTQqEggRjnmfNPfaEdKq4P3pexBeq9996LXC4nukTrTV2Ck+/VJW3PJufF\n2DY0NGBychKTk5PS0mtiYgJutxs7d+5Eb28vANTUXQWWolNGCQDg8XgQDocxOjqKQqEgja1ZF5Rt\nvIz1YtkH91xex9lERy1UgHyYuk7m/Px8jbHn/WiPztjblKLrmupNZpyP1Qh7N+ZyOYyNjdW099J1\nahcWFkR56sYI/Jn37XQ6kc/npSsNFSyNgy6kTqfpnQpyryTsJkQPsVgsoq6uDlu2bEEikUA6nZYN\nbuwLTOXr8/ngdruRTqeRy+XQ0NCAXC4nRoPjrlQq8lls0E2DaEZ00XcaJABSEJ4FzRnN+Hw+iX5Z\nC1kXPdcdcvil27ydrWC9GWGfT+3YRiIRGT/rvWq0hr+zGwojLa5vl8sFr9eLUChUY6j5eclkEk6n\nU9ox6rW3WqGip3OxkoLL5XL4h3/4BzQ3N6O9vR2PPPIIrr32Wtx4442CgsTjcaTTabhcLrhcLqTT\naXg8Hjz77LMYGRnBnXfeKQaa9avX2pVGryk+R71XdOMMABJUnKsBiQ5YjPPz++ieAwBHjx6F1WrF\nxMQEZmZmcPPNN2NgYADBYBCzs7Pwer3inLHu/fbt23H48GFs2rQJw8PDiEQi2LRpE44cOYJIJIK2\ntjbRQZlMBsPDwxIRbtmyBeVyGWfOnEF3dzfm5uaQSCRw+eWXr3rM2WwW+XweAOTZ2e12pNPpt0Xh\nbOxQrVbFyM/Pz8PpdNY00rBYLHJNYDmQYcTM93MfOxwOtLa2nnWM58XYlkolgYotFot006Ey8fl8\n0icwGo0CQI2Hz44MLpdLoGUA4nXwtUYhnDA3NweXywW/329q3MZolj8TEqbQWOom8Nqw8TW624ix\nSbUWvt9skXlgaaE1NjZi586d+OUvfynKQsPKOjKqr6+H0+mUIto6iiL0QgPIBaq9UxpbjnUtYwaW\n5nZiYgLFYhGhUEg8ULvdLtAdo2l2ZNENG+hkaeeiWCzWtMIizMb7NzaCMDpLqxmzVnC6BZzuIMK5\n0i0LAchrtOPG12jUhE5NqVSSVl/BYPBtqMJqx6yfr1YghN/plNC4EaJn1FJXVwe3213TcUd/160L\niTzQ2dCRkRn53ve+BwC4+OKLcdVVV9XMxf9n702D27yu8/EHBBfsIACCCyhxEXdRmyVZi2lJlnfX\nlhc5Tto4nnbstIldZ2mmbZpOOmmmTabtNNsH161rJ9O0SSeLY8dLYkfyGsuS7MiWrH0lxZ0EQWIn\nSADE/wPzHB7AlM2XTfT7f+CZ4UiiSODivvee5TnPOYeN5o8ePYqLFy9ix44d2LRpE0wmE9ra2hCN\nRtHb24sVK1bg7bffRnV1NaanpzE2Ngar1Yo1a9bgzJkzOHHiBFavXg2v14v6+npp/M+zYXTNegwi\np1TxmWvdQkSJkTg/06Xu0nwOeOGozv+LPPbYY/B6vejq6oLD4cCePXtw4cIFWK1WXLhwAV/4whew\nf/9+3HHHHXjxxRfR1NSE119/HaWlpXjppZdgNpvx9ttvIx6P4+mnn8bY2Bj+8z//U3Ti2bNnEYlE\n8Oqrr2L79u1444035LWi0ShWr179gUZrPvF4PLDZbHn3joGRHn5DuL2srEwcnkIkic+JCKG+43r8\nHqNzPke+96XkssHI9CDoRVB4wJhrs9vtAiFTKfIDUtEySiktLZUJHslk8n1GhUqKm/lhm1EoGirQ\nEzv4f/y3zjXMJ1S2fK1Cj3w+j7QQkl6oJBIJDA8P541+01NbuAZ674yQtLesJ5zQYSBERxiFe1u4\nP9rBMCI8B5xEw7xxOp1GWVkZqqqqxNhPTU1hcnJS4CdeKu2hUnkRQmK0ScPB/LPD4ZAJNEaV1fj4\nuChsDnvnXumcE/dHw1bacaGi5M/x7xrmj8fj6OnpQSwWg91uR1tbW57xWqiUlJTINCHumx7rCLx/\n6gnPLyH4oqIi+cxEdajECO8zsqDwHPPZGoW/169fj4GBAYlgqCTJkwBmJ0fF43EcOnQIgUAA3d3d\naGpqgs1mwz/90z/B5XLBbrejq6sL+/fvh8ViwejoKK6++moMDAzgN7/5DbLZLLq6urB8+XKJWNxu\n9/tSP0aETmk2m81T8Pw795bPW49ZnO+15hubx9/5Xcjq1atx++23o6+vDw6HA6dPn8bu3btRXV2N\nv/3bv0VdXR1sNhsaGhpQWVmJ9vZ2HD16VHTExo0b8Zvf/AbBYBBr165FW1vb+1InTU1NOHToEFpa\nWvDWW2/hpZdewtq1a/Hss8/C5/Nh48aNhtZstVoFZZmamkIikZDzmU6n5Z5ovgr1Fw2xhsSBuelg\nNNxcP/9tFEm4LMaWuUO/34/p6WlcuHABLpcLxcXFGBkZgc/ny/MCefGpJF0ul3j2w8PDOHbsGAKB\nAFatWoWvfe1reOGFF/Doo4+isrJSktTpdBqxWEw2fWpqCqFQyNC66V1SKWriiza2+oswq/Y0+XA+\naLbkfDDQpSCjD5KpqSn09/eju7tbhtYXvkZhHrpwYDUNsYbQTCaTEJH4b/3ZKHzWRiWbzeL8+fNi\nAIuLi+F0OhGLxRCNRsWw0Es1mUxwu915eWntyFB58ZnokY4kRdHAk/BhVJkeOXIEuVxOYEtC0jpf\nyzXo4d6MGqPRqIyOowKYL1+bzWYRiUSwb98+BINBLFu2DF6vV4hVRqSsrEwGaU9NTQnczZyshrF5\nLqxWK2w2m5CbtPNKJ4P3RMO8jApsNhvsdrvM/2We1IhMTU2hpaUFFRUVwivQowfT6TR27NiBn/70\np/jiF78It9uNH/3oR0K2q6mpwac//WlUVlZi//79MJlM+PM//3OYzWb4/X48//zzcLvd+NznPpeH\nLOhh40ZTOtPT0wJfc5wiz4QmK9LJpHEA5vLQFL6/doR0VKVJeFx/4QD0hUprayvOnDmDzZs3Y//+\n/bj++uvx61//GuFwGLfddhsymQw6OjowNTWFjo4OOJ1OtLS0wGw2w+v1oqamBqtWrcLGjRvx9NNP\n4/Tp0+js7BTHoqqqCjabDWvWrBFkYdmyZTh16hRWr16NyspKWK1WQ2vm52Z+nLqNaBgDPCIvvDf6\nvmljSyPM51XID9Bo1UKDi8tibEtLS7F161bs2LEDExMTePTRRwVWoQI0mUzweDwYGBgQyIleNy8+\nFeXk5CQikQgGBgYAzMLJtbW16OnpESX0yCOP4JlnnsEPfvADWK1WpFKpRc8r1ReNxol5gGg0KnM9\ndR6r0BPSeSz9d77efFCEzgMuVKgQ9WxgSiHRhhEJYURgjujDKIbGiPlJTVTT80qpbI0cPi1ML0Qi\nEXR0dEhkRMg4kUhIlMoLQmeCZBzNJuSZIcGFTlAmk4HNZkMkEpHInmzVD5tHWSjvvvuuPN/6+npx\nIAsdMJ3b11EilSIdIo140Eng2SktLUUkEkE4HEZlZSWAuSHeRoVroKGlcD18xkRjNJrDiIznhlwI\nnmOy0vl7NIYkO/LOG83ZHj58GBMTE9i+fTuWLVuWB9kDs0owHo+LEc9ms+L8TExMwOPxwOv1wul0\norm5GW+88Qa++MUvorOzE3feeSd8Ph+CwSB8Ph9CoRDGx8fh9XrFQBQOE1+IxGIxcUC4H3q9ZKoz\nvcVzQMdMIwN0aPj/OneozwCjZbPZnEd2NCLbtm0T/XbjjTeitLQUu3btktedmZnB2rVrMTU1hbVr\n1yKdTsPv94tRs1gsqK6uRlFRET7ykY/I5+YdqK+vR1FREa6//npkMhncdtttKC0txfr165HL5cT5\nNSJ8bd4xjboAc4RWfaYtFksemkOUhHpXM5q5r9Q7JpNJcuw81x8ml6XONpPJwGKxYNmyZVi+fLlE\noPQyqNj1YaJ3wi/tJTKHOzMzg76+PkQiEZSWliKZTEpJQm1tLXw+H2w2m3iURg8e18MHofNvGj7V\ncHNhnldLIeTK72mhB3Wp1/gw4V5Zrda8iI+vzc+koS3uNdeVSqUE+uda6IWTjazzvlR8NL6LWbfZ\nbEZraysaGhokf8iojxE1GYdcBw2SVuK8DFSQ4XAY4XBYeAJFRUVIJBLw+/2w2+1iZHXaYqEyMjKC\n/v5+DA8PIxQKYWxsDBMTE2Ikeb75uoxeuEZNhtE5JmAWbh0ZGcHw8DDC4bBA52S1ksxhdK/1+eJZ\npfFnHlT/X6Ey104DnV/tELAcTMPOLAnjM1iMM/bwww+joaFB0ARNWORnSCaTSKVSiEQiACC6ZWxs\nDNPT00gmk5icnITf78ff/M3f4Gtf+xoSiQRee+01jI6OYnx8HDabDU6nE06nU/K0FotFHGojUlZW\nJkgbCVl8LYfDIXwJnlc6P4WpNi0888yl8zMbjbo/SIqKisS5oK7QAQUJf1wz958OAoMnGi+LxZJH\nCtNVGUxHALPkTg6lN4oyZTIZjIyMyD2MxWIYGBiAyWTCd77zHYTDYYyPjyMUCqGoqAiRSATj4+N5\nhMWZmRkkEglEo1FhzadSKQwODopOisfjeO655+QsE7UguvJBclki25GREcRiMfh8Png8HuzcuRNP\nPfUUYrEYwuEwYrGYKA3mYBOJhMBkVqsV58+fl9dbs2aNRATf+c53Zj9IcTHC4TCKi4ths9mElFFX\nV4ehoaFFedPJZFLgYA2VZTIZuFwuWRuNmr6MhTlM7VEVHiQqDEaIjIwXU25A2K5wPRpiZU6cUYCG\nhAudiELSETBHBONF48+WlJQgkUgsythOTk7CbrfD4/EgHo/D6XTmeZfJZBI1NTUCv6ZSKanBJfqR\nSqUEHqXzlUql0N3djZmZGTQ0NKCmpgZutzsvR0xlZfSCp9NpRCIR9PX14ciRI3C73XA4HKivr0d1\ndXVe2QBRBu4b189oWzOiydYcHByUes/JyUkMDw+LQV4MOQqYI9dQwTGKosKnt8591OvmOSWCkE6n\nEY1GxZk1m83CVKVBMJlmCY5EH/RnNiLf+MY34PV6sXnz5jzWuza2JpMJDodD2OmMwpnPtNvtsNls\n+MUvfoHz58/D7/cDANra2lBRUYEDBw7gW9/6FrZu3YqVK1cCgDhtiymzslqteQ4cjYx2gktKSvJq\nWRnNXsqwa0e5MH9olMB1KaEeZSVCZWUlEokEUqkUPB4PEomEoF1Wq1WCED5XnvGZmRnY7XaYTCZE\no1GcOHEC119/fZ5zXPgZWPVg9C4eOHAAFy9eRH19Pfr6+hAOh9Hc3AyXyyXs5ldffRUjIyPYvXs3\nenp6EAqFcNddd8Htdgtx65lnnoHFYkFNTQ1isRi2bduGixcvIp1OY3R0FH6/H/39/WhsbMSJEydg\nt9uRzWZhtVrR39+Pv/7rv77kGi+LsY1Gozh48CAOHToEs9mMoaEhKaJva2uDy+VCPB7H2bNnAcwq\nAo/Hg2QyiVgshsHBwTzvmt6S0+nE4OCg5GU6OjoQi8Vw/vx5/NVf/RWi0ajQ/Al9GZHCeiqd04zH\n4+LZUYFQ+HOaXagjChoRrSi0MBfGSM6IWK1WyQ/TEBZCHczrtrW1wWKx5OUzaDjpidJT1TVqzJsy\np6vJMLx8RqWlpQUXLlwAkF/iwtpaRiz8fISd+XPRaFTOUSwWg81mw/T0NOLxOGpqasRjjsfj0uyA\nzsTRo0dRV1cHn89naM30kqenp8WZrK6uRmlpqRheGi7uCQ2T/pyMfshJePvtt3H8+HH09fWJop+c\nnMTo6KgQd+x2uzg9RkQ7e+QfaLRCQ4VEQlgJwMiXBCV+Dn4+Gn9+Lp4rPjO+Nw2vEbnrrrtQU1MD\nv9+PkZEROY+8d9lsFi6XCx/72MekznfXrl2oqqpCOp3GvffeK2hAV1eXoF5btmxBc3Mz0uk0br31\nVnE6SktL86J9pimMCJ173m/uO6FW7hEjWV3ydqn0gM7H0kHTZ+p3IT/4wQ9QU1MDu92OM2fOoKOj\nAy+//DI6OjrQ3NyMvXv3Yv369bjjjjswNTWFf/iHf5B9C4fDePjhh/H222/j4sWLuPrqq/HOO+/A\nbDZj27Zt+OY3v4n29na89tprsNvt6OjowIEDB9DR0YHTp09j165duOmmmwyfD6af+vr6sHr1arz6\n6qu45pprcODAAQn26uvrMT09jbfeeguHDx+Gy+VCJBKB2+2GyWRCLBbDqlWrEIlEcOONN+Lf/u3f\ncPLkSTz33HNSuvSnf/qnePzxx/GZz3wG//Vf/4WvfOUrwnO46aabPnCNl8XYArNRQE9PD4qKijA6\nOir5NGA2qmEzAnpEOsLS0BYPFw2KZnHyUpeVlSEWiyEejyOZTAosxEhjoaLzTpqwonOchYdcG9VC\nyFm/roaTNfSqIRnNjFuosA6OkbGGv/UesrZSR9uaoUcDzfXTWGvWI99DN7hYLGOzsrISU1NTGBoa\nQiQSgcfjyYOXWIeby+UkKmdzhampKZw5c0Y6F9E5o2InhEcPenJyUvJkY2NjWL58OcrLyw3vdVHR\nbDcfv98vr+H1euFyuYRsBECgVJ2n5QUFIBB9NBrFqVOncPDgQQwNDcFisaCurg6ZTAbDw8OIRCLy\nLPjMjEZb2mBoR1ATQPgnjRORBZ6XeDwOl8sl6yb8SYY1z49OQ/Cs670wIlVVVTCZTHnOM58BAOET\nrF69GsBsI5OOjg5YrVbEYjE0NTVhenpaEK9169YJpMt7euutt4oRp97hHlgslkWR0Rj1ZLNZIcSV\nlJTk1YSyhESnqS5lbMgEJ+KgnZ7fldx77704dOgQnn/+eWzcuBHpdBqVlZVCJFu9erUgBkQuvvzl\nL+Pll19Ge3u7EFidTifOnj2La6+9FqtXr8a//uu/oqmpCZlMBm1tbeju7sapU6fw0EMP4dSpU7Db\n7aiurn5fvnohQoi+pKQELpcLFRUVkvv1+XxC3rJarWhpaYHdbkdFRYV03GIenHncmZnZDl5DQ0N4\n6KGHcOjQIQwNDcFms8Hr9eL+++/HCy+8gI0bN+Kqq65CMBj80NLBy2JsqRjefPNN5HI56RLEcqDR\n0VGYzWZUVlbi3LlzyOVmW7xVV1fnRViMomiUk8mkXAJ6tsyHUGlPTEygvr4ebrcbHo/H0Lr1RdbR\na0lJiUBsvFBUonQieGF1+dJ8OUFNouF70pNmrtuI6E4oVA40mEB+ZyPmGqgMdS5OOwfMwWlSF5Up\nEQOWY+ko3ogcPHhQ9qm/vx8ulwuVlZVCZCBEnU6nEQ6HYTKZpJ3mzMwMvF4vQqEQQqEQysvLJUpl\nOUJ5eTlMJpNEtMFgEA6HIy+HpAvYFyJutxv19fW44oorsGXLFkktMGrS0CsNI40Snwv3M51OY2Rk\nBEePHpW2kps2bcK6detQXFyMs2fP4vnnn88jC0ajUYyNjRlasyaSaEcVmGvCwty2zWZDUdFsqV0s\nFpPnoM+NTjPoeuFCp4zvoSNmI6LZ4ow8dbtL7gsdVJ5nsl/ZBpb3kmQqloKVl5djZmZGvq/ZvIV3\ndKHCHDsdZ30fU6mUOF9NTU0YHR1FLBZDZWWlQMvzKW8GE3wd5gxpWGh86TQYrR2nFBcX47bbbsOZ\nM2cETenv78e1116L48ePo7OzUzgJzPEyOozH43KHtY5sb2/HuXPnYLFYMDw8LFyM8vJyWCwW7N+/\nH2NjY1i3bh3C4TCcTueC17t161bkcjnU1NTAZrPh9ttvRzabxapVq7Bu3ToAs3qsoaEBLpcLw8PD\nQpbkXra2tgq/wGw24+6770ZpaSlGR0fxB3/wB+JM3nzzzdJcqbS0FBMTVR0qTwAAIABJREFUE1i7\ndu2HrveyGNtAIIDR0VFMTEzkQSCZTAbd3d1SxwbMRjE81L29vbBaraipqUEkEkEmkxHPlIZ3aGhI\nIDtCjrFYDMCsMnS73ejp6cFHP/pRfPzjHze0bjLyCo3HfF4nL5UmdbFPJ39fR4KFxliz4SgfRJS4\nlBBaouLTOUEqDLPZLPvMn+N7U1nSadAwuu44pfO0uuPRYssN6EUytcB+vUNDQ3A6nULKiEQiAuXT\nuy4pKUFVVZXsLR0wu90uEdnMzAycTqcQYfx+PwYHB1FZWYmKigoMDg7m9dNe6F7bbDZ4PB5UVFRI\n9MUzSGeJ+6gNDp8JjWZJSQkGBwdx6tQpKYvYvHkzPB6PnAmPx5PHoh4bG8vjMixE6LUDs2xZp9MJ\nr9crDhXJW3SsaMBI5uG9o0NoMs2yMnVtqCbSaTaujlaMRi50VMh2ppOk2abaISaBjhGjLuPgvWZu\nkMaCZ0TnEHUtr1FjS2RFVyQUOh38bEQBSCK6VCmaXoPWS3RAi4uLBcnR6IkRKSsrw+bNm2EymXDd\nddchlUphw4YNUo6zc+dO2fvi4mJ89rOfRVlZGW666SakUimsWrVKGuZog3bvvfcCQF5KiBD42rVr\n4XA4cOWVV2JyctJQq0YAYlOItvAslpaWylnI5XLioC9fvly+Z7PZJK0JQKJbIoRVVVXipE9PT0sv\n62XLlkkToYWkdC4LGzmXywkNnZezvLxcuvnQUwcg2D9hKmA24iDJxO12S/0sE/aEafQXDRvZqmfP\nnsUvfvELQ+subDagIwB+6X/PV/aiCVY0UDrfpKFczQ4uLAUxsteFObHC7lXAXHSqOxTxGWhiloa0\n+XnmI39RtDI3Iiy7icfjqK2tlcYLfL6MsphuoMJkXo0GlVARL0ZZWRlcLpfkwD0eD5YtWwaXy4Wq\nqiopJaqoqFgUBE64VbMqSdZiPk7vNf9fN/IHIPnlRCIBr9eLxsZG1NfX57Xwo5NBJmsymTQc2Y6M\njGB0dFQ4B3Ryw+Ewent7ceHCBXFoGBnymcZiMUxNTcHlcsn9AiARL8vydFmOTrfofLBRI2AymaQc\nkPAvjYrubcx0AfedXcR0xEfDS91DBawb0/Nu8r0XgzIVisVigd1uf99XLBZDWVmZkLtSqZTAmwuV\neDwuAQ31I5niRiUajUoNNu8H7xqdGu6NTmnwLmhUTacByVgvKytDZWUlbDabOMYulwsbNmyQM7kY\nYihRjcI18SwDEP6Hds5efPHFvPw/kT8N69PZ17qRP88g68PksuVsrVarFIhbrVb4fD5MTU0hHA7n\nkQ/Y2tHj8WBsbEwuOR+Ux+PBmTNnRPnU1tbmlQzRoJFST2N74sQJ9Pf3G5pGwwvJB8YDpmtNCzeZ\nObRCZrLOmfL/tPHTpU/8/mKMFtdMxc+/6xxwodHVRoAHUDOU9Weh4dZwIQ+4hvmMChWxw+EQdnoy\nmZS2atrQMKqjA0cnJZlMoqSkBA6HQz4X62hZesL0BQ0E238yt2ZECKHzvGk2pr6IGrrX0CufB5nT\n3D82nqAB0SUGzEmxCYzR+s/33nsPVqsV9fX1aGxsFFLWiRMnMDQ0hEwmg0AggJqaGlRUVEjnrUgk\ngpGREbhcLlxxxRXyeQgRRqNRgdXY1pGfk84GWfDsaW5E6KDofDuHUhAqpZOjDafJZMLY2JgMHeA+\nksleWlqKsbExHD58GNu3b5fAAJiLPnmWQqGQYRLdQoRGQQcLRvPDdDrogND5W2xJkG5eQkRD7wWQ\n3yaXeXsNMV+8eFEiP93Ag5+ZBk/rbSIrRh2bUCiEF154Ac3NzbjiiitgMplw/vx5nDlzBs3NzTh3\n7hyuuuoq9Pb2Ynh4GDt37kR/fz/KysrwxBNPYNOmTUgmkxgdHZVgkNyEYDCIQCCAiooKYSUDQGNj\nIyYnJwWxnZqa+kAo+bIZW2B2ccCsF8YG8cXFxYhEIjJhgn1LT548KbCA1WqF3++XzSDEQAiJbE1g\nDnrlKDj+LL1KI+L1ehGJRPJKCXTEx4OnPSJG2fR2eIAKoyYNIwFzCpa5YEYzizFc2ivkGnVOVnuB\nXAt/lv/Pny8uLhaYi5+FkYmG5nQOcDGiyW6pVAoWiwVerxe5XA69vb3IZrNoamqC0+lEIpFARUUF\nJiYm8mAqbSwZteiIE4CU0dAZ454wOjIi0WgU0Wg0jyBGD5j7QGWnG57Q8FCh8nyQ7MU2kCSEEaEB\nIOvkOTeqTF9//XVMTExg48aN8Pl8OH/+PPbs2YOLFy9KztpkMsHv92PlypXYunUrSkpKcOrUKbz9\n9tvwer3w+XxwuVxSnnXx4kWcOXMGJ0+eRENDg1QYaDIYnadQKIRz587h+PHjhtatB2H4fD6JTkwm\nkwwsYTSn7xGjYLPZjFQqJagHy1KKi4tx7tw5vPDCC1izZg0CgYDA1NzjkpISjI+P49FHH8Ujjzxi\naN1aLoVU6c5abDVq1IkqLy+X80sUZbFkx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/3qV1i3bh3q6+slQCIBlb2zNRfk5Zdfxo4d\nO4TVTH0yMjKCXC4nPalLS0tx7tw5tLa24n//93/xR3/0R5fsDX0pefzxx7F161ZcddVVYrz7+vrw\nzjvviPN7/PhxmM1mGc966tQpbNmyBWfOnEFZWZk02li7di06OjpgMs0OUHjyyScRCATQ09ODkydP\nor6+Hhs3bsThw4fh9Xpx9uxZ1NbWIpfLYffu3Zdc42UxttXV1VizZg26u7sxPT0tzFZGU2SQkpTA\nS0XPjp15QqEQ/uVf/kUiYOZRZ2ZmEI1G4fV6xcPVxpYKyWjt1unTp5FIJARe5CFnvSSVLCMpfp8G\nVEfDrIsE8i8AlS0PCPOQ9KiMCp0BMkW18N+6pg/IrxdlPknDrIQeZ2ZmpPMUPx8NFH9+sSxqzqyl\nMWGXFjohPDPcd6fTif7+fpw7dw6dnZ3SBYbOFku99N6ylpY1rHRG+NmNNhKgceUeMGKZmZkRNijJ\nT+x0pSEuRiN0ZPh8GAnoiVKZTEaIR/q5GC1X4u+xqQc7D5WVlSEQCKCqqkpK6RKJRB6Jq7KyEu3t\n7Vi/fj3Onj2LiYkJlJaW4qqrrsKqVaskV8vPMR9Rj0Qk1souVNjoJBgMoqmpCR0dHTJkYnR0FHV1\ndXjwwQeRzWbh9XqFn3HLLbfI/d28eTNWrFgBp9OJbdu24YorrkAwGMR1110nKFp7ezu+8IUvoK6u\nTkqhaCSNRltAfmMcPksNIxMy1XeMd5PrZlSoKwAKZbHExPmEpTwTExMAgLq6OoTDYQwODmL58uX4\n5S9/KUa4o6MD+/btw5YtW/DMM8/AbDbjT/7kT/DDH/5QalvZq/6HP/whuru70dDQgMOHD+Phhx+W\nubhf/epX8ZGPfAQvv/wyent7cffddxsiSZnNZhw5cgSdnZ1wOByw2Wx4+umnBa2xWCzw+Xzo6urC\nW2+9BY/Hg1AohD179mB4eBg2mw3xeByf+tSnUFdXJ3pj//79WLNmDWKxGILBIO666y4cO3YMHo8H\n0WgU3/ve91BSUiJdC/+fG1tCZPRwpqamxKDovB8Lzbl5xNQ505Sehs5/aTISjQdzIVSyNIBGJRQK\n5TUb0EpE56W0odHvU5ir1XlEfl//SeVUGC0bEX5WXk7dpQiYg7Vo2EgU0qQdAPKsCiGSQmIXI2Bt\nNBYDXZEwxzIts9mMUCiUdw5opFj643a7RVExz8J9IwyXyWSQSCTgcDjEi7bb7UKMMplMUma2GAic\nz4zohM73FebzuccaUQDmjKt+XtrpIcOaKAIdncUYW8J0Xq8XK1aswOnTp3HhwgXJ5a9cuRIzMzOC\nJBUXF6O2thadnZ1S3rF582acPXtW+ttWVFRg+fLlghIUdiPTCAkhZqOGy2w2I5FIoLKyUqBNl8uF\ndHp2cDmbXdCZZ/90h8MBi8Ui7N6GhgbMzMwgFArJGWLkS2fJ5XIhHA7LWSe3xGif4aqqKoFdU6kU\namtr5XzweyzPYbRLxrbZbMbY2FheMwiWMvr9fmEzk8vAUXKxWEy6rk1MTBgud+Qe/ehHP4Lf75c9\n8Xg8aGtrw4ULF7B792785je/EX4K4fg//MM/xPe+9z0MDQ3hnnvuwauvvgpgTl+2trYik8lgYmIC\nDzzwAFavXi0NZq677jrs2rULzz33HK655hqJihcqgUAAVqsVTqdT7sf27dsxMTEBn8+HgYEBlJWV\n4e2335YJQOFwGLW1taiurkZ7ezv27dsnDVlisZi0Cj5z5owEUqOjo3ltYuvr67F9+3YAwM9+9rMP\nXONlMbYXLlwQhqPJNDsajbBQOBxGKpUSb5cK1uFwYHR0VIrSly1bJgOgqShJiOCFIMzGVpA6ymXb\nNiNy+vRpDA0NyYGlgdGsYc3G1WQLXYsLzN+2kaKJJIy29HsYEa6FcBOZrproRNIGc56JREKgVSpw\n/qz+4vqohHROXUfyizG2jBwIRzHKp/FhFzEdrdK46R6wuqsUyXeM1Lxeb14ka7fbZXABoVIjwn3Q\nULtGCHSuXxsfTZLi5yPcqFtr5nI5GWpfCB9qx8yIcK1OpxMrV65EJBLB0aNHMT4+juHhYfT09Iix\n7e3tRSAQADDLYqajs2rVKsnzlpeXo7q6Gl6vN8/Z5D7onL52xBYDyc7MzORN4qGTR7hY5y31eSVE\nT8dtZmYmr883uQq6TJDIEolvzP8bERL7aFj1WWBDCGC2zlaTnzRkzIiYTjIHrXAQAw1wLpcTJ4Tp\ntampqUWV/thsNqxevVo6IpFU5HK5sGLFCrzyyisyBWjv3r2iY3SJ14svvohz585h9+7dmJqaQigU\nwjvvvCNIz4ULF6RZBD9LX18fTCYTKisrDQ8F2bp1K6qqqvJ019atWxEOh1FWVoZ169bBbDaju7sb\nHR0dsNlsqKmpEbJgIpHAzTff/D79snr1ajn3GzduxPDwMK655hq88847iEQi+OQnP4nDhw9j+fLl\nuP/++z9wjZfF2A4ODsrBI32aykf/ndEWjTDrLNPptAwsWL58OQYGBsSDbmlpEfYtvc9IJCIwtd1u\nX9RcWABSGkEDT+VKIsN8hpCXnTChVi7zEVoKyVKMuviwjQqVqZ7CU5gz03lkRpNsk0mFRWWmUQdg\nLidMxaediMLPYkQ8Ho+07qQhMpvN4iQxR8R+reXl5ZJC0DllOnHj4+PSGMDhcIhyy2QyMkQcmIUn\naYyNGi72Ama6ozBXXchQLCS+0RiTeU2hwtW5VZKoSLahwV4Ma5Noz8qVK8WYdHd3Y3R0VJjbLF2q\nra1FIBCAzWYTh8jr9cLv96Ourg7Nzc1ob28XCJlngUxrfi5GbswZL+aclJWVCWGPo/bIBuWeUV/Q\niaFx5ZnW6R86nJqHwfNEp5EOaC6XM8z7oIHUCJH+P543QpAU/Tl4RtiZjI0k9HMnQ1ijHGTP6u5p\nRvZ569atcs64d1dccQVyuRza29vFSWG5DX/261//OkwmE1paWqTDWFFRESorK/GRj3wEAGTqDteb\nTCaxa9cuBINBfOELX0B9ff2HzoYtlEAgIM+IAQfb+7KRjclkwurVq8VOuFwuqW/W+o56h3qF9bhm\nsxmNjY2w2+3Yu3cvHn74YVRXV2P79u15FRCXkstibKn0gbmcojZ+ulsJoSjtCRL+AyBj1niASWDQ\njDiWgdCYsFuP0QuuDaf+k6JzUlqxzqdkL6XIebn1XvFnFwN/87Do39VwpoaBGf0xx0lnh4oHmJt8\nRKGypNLnc41Go0IMWowwx0aFmMlkpA9yNBpFIpHAwMAA1q9fL+kGGntO2CHBqKioCBUVFZicnERx\ncbGMOmPUrusWo9GoMBGNOmSFkLBOLWgmrhb9PToq5CloZjifEc8gnVD9XOd7/Q8T/j6h0/b2dni9\nXvT392N0dFTgVrfbjUAggMbGRvj9fokoE4kEBgcHkcvlEAgEsH79ejQ2NsLj8chQea5bR/M0Fjry\nNSKJREKgUgBSFlVVVYXx8XFBx3T7UDrErMHPZrMCL8diMXHgdJkenx/PC+v5HQ6H4bvI51vovPL5\nFqYA+P90FBhl8U7SeJJMSqFDAcwSsYgKMWI0KtS11GvZbBZut1tO5+GhAAAgAElEQVQ4EXR+yUHJ\n5XJSNsNnz8CKqaqioqK8Hskej0fum91uR0lJCQKBgOjqjRs3Glrz8ePHheGuyVUMuDRSEQwG89bL\n581qCzo0dIItFotMCuN+79y5U+4oS1c/zLG5LMZWH2YqRDYSIHSSzc42kvf5fFIjx9+ZmpqSekJG\nvHzAY2NjAq9oAhNrH0tLS/PyPEaEl1HDeJrMUsg01p+RRkjDsIU5W35fX3Qagvm84YWIzi/ryDuT\nyaC+vl4GkPMzsL0bIzvupc5X8VBqxyAcDsNutwuznHkmHb0ZEUaf3GPdxpDwb2lpqeTltDOmI3ZG\nNRaLRTxWk8mE8fHxvDw2nQ0abSD/ki5EdFmHhv0Lc/SFThP/j+sgK5mKTP8ejQffS+dEqYCNCPeA\nxt3tdqOyshLNzc3Cbi4uLpZGA3y+JEOFw2EcOnQI0WhU8ng+n08cNKYutNLlGdfR7WIiWzp6yWQS\nb7zxBjweD7Zt2ybpAj2Zhe/BXDoNMTDn4LI0RcP2fI769/QZMyKhUEh+Lx6Pi9PH1yWUTcWvy1VI\nRizM2TKKZ9c0Bibj4+OSZgmHw3noj1EhP4B6j/dEN8Bh7p8pkEwmg/Pnz6OmpiaPbc17y2icDjId\nCk0y5Lnk/TciJ0+elHahHR0dsFgsGBgYgNPpRE9PD8rKynD11Vdjz5496O/vx65duwDMnoV3331X\nnLlnn30Wn/3sZ+Hz+ZDJZPD000/jpptuwoEDB9Dc3IyOjg6Zcev3+0WPvv7661ixYgW8Xu8l13hZ\njC0bFpDoYrfbEY1G5fDS06ioqMDo6KgoFCoTMovZbDyZTEppQiaTgdVqRXl5Ofr7+yXKJVEim83K\nZCCjD/CVV17BwMAAgLkLSnhN175RwRYeIl5UbYwJcRRGJbzkrAumITQaKdI5KFQQ6XQa4+Pjgixw\nVCF/lsqATQEYpeqcKB0GeqbMofOz6QjaqGjWK4C8aUJFRUXSWIHPn8S1QCAgz5zngmsnA5nQMg0r\nS1t0zrSwLGchQnKHyWSS19TQv4YjC5+RyWSS1IfL5cqrQ+Q55bOi0aVDQmW3mL2m4iYcy7vClA2N\nFkvFaBQtFgtisRjOnTuHAwcOCOu3urpaIgKminhmqUBZ807nQhPxFirsHVxdXY1ly5Zh5cqVosRL\nS0sxPj4uBsput0tkQoOk6/t1VQDvKNnjhRNnuOc0EkbkzJkzecafaRKOB+TdGh4eFueSJY2ZTAaV\nlZUSyXL9paWlqK6ulrp0loDlcjl4vV6pI08mk4jFYhKNGZFnnnkGy5Ytw5kzZ9DU1ASLxYJVq1ah\nu7sb9fX1gi4Gg0F4vV7E43F4vV4pofH5fIjFYvB6vQiFQigqKsrLkZPISIPMwErfTaOydu1a9PX1\n4dixY5iamkJbW5tMAWpoaEB/fz9+8pOfYHp6Gj6fT5CsoqIiDA0NYWRkBKlUChcvXsTw8DA8Hg8m\nJydx8uRJALPI3ZtvvolYLIY77rgDb775JhwOB/7u7/4ODocDx44dw49//GP89Kc/veQaL4uxLWS1\nssEAMEcmAvC++YtUNHrIAABhJpvNZvEC9cxbXm5GWjqaMyLsnkNlzt9n7pVrmM/jLTR6zA3x/2h4\n+Xr8fqEhNgoT6nXqMpyysrK8An+S0JgHymQy6Ovrw+TkJJqbm+WCEzLS8B8JGUB+7omfeTGXhVE1\nR+Hxe4R1li9fjosXL8JsNqOyshKTk5M4fvy4jNRj5KpbIrJN56pVqyRyZ603EZXS0lJhHho1ttFo\nVGoM9b7r51eIYgBzzhnPANmcmlDEZ8SfoWPD8xYOh4VUYkR4djU6w+fGO0NlqFMzhOvHxsYwPj6O\njo4OdHZ2SjcrGmu9Zg2HE+pkNGR0rw8ePIj+/n4EAgHccccdeOutt9Dw2wYDL7/8Mrq7u2G1WnHu\n3Dnce++92LBhA/793/8dd999N1paWvD888/D4/Fg586dePfdd3Ho0CEkk0l0dXWhq6sLe/fuRU1N\nDRoaGjA8PIwLFy6gq6sL58+fx5EjR6QWm40QFiKjo6PiJFF5k1BI1AaY7TNAZ5OEvlwuB7/fL0M4\n+GWxWFBbWyuOBqPkQkePYnRQBQCsW7cOBw8eRG1tLQYGBoRkFAqF0NjYKLNz9+zZg1tuuQWnT59G\nT08Pdu7cie7ubnR3d+O9997D3Xffje9///sYHR3Fjh07EA6HccMNNyAcDmP58uVSnkiHFZhL3Rl1\nxtLpNEZGRsQ57O7uRjQahdlsxooVKzA1NYXTp09j8+bNGBwcFEczHo/j9ddfx8qVK1FZWYna2lq0\ntbXBbJ5tUNPU1IT7778fTz75JO655x58+ctfxtmzZ3HDDTcgGo1K/X8gEPjQKUWX1djqMoD5jG3h\noABeTN2hCMg/QHw97e3TeFC4IUbrbOfrKkSDyL9rCFH/jCbtcJ2anKENv4YOC/O/RkVDmBqmASAk\nIe55RUUF4vE4MpmMFNYfPnwY6XQaFRUV4n3zdwnNMXqhktbe+2LgNiB/MgxzLBz2XlJSgmAwCLvd\njmQyiWAwiFwuh7a2try6bQAShadSKXg8Hvh8PgwNDYmTx1Ihkuq4ZzqfuFAZGhrKy+Vw/YWiI7nC\ncxEOh9Hf3y/kJ6ZDaPiYT56amkIymUR1dTXcbjcGBgYwNDSEd999Fw888MCC18w7SHhQO6H6rPLs\ncC00Ei6XCx0dHbjyyiuxZs0aVFRUCIGQuTfNO9Cfn5Gtfq+Fyv3334+jR4/iP/7jP3DDDTeIwRoc\nHMShQ4dw3333oampCf/4j/+Ivr4+dHZ2YmhoSN4/GAwKGrVnzx7U19ejo6MDP/vZz7B582YcO3ZM\nOnh1d3fjf/7nf7B9+3Y89dRTaGhogMViweuvv47Pfe5zC17zXXfdBZNptn7barXiwoULiEQi8pzp\ncGljy9wyzyujYDKw7Xa7NAQhNE4HbD7kbjF6xOFwSEkU65L/+Z//Gd/61rekVOm5556TvvPXXnst\nfvCDHwCA5EX5bJxOJ7q6ulBXV4ennnoKJ0+exPXXXy/pE+34AXODZ4yWtPl8PqxduxZbtmyBy+XC\n6dOncd9990k6KRAIoL6+Hi+88IIgA0xp+v1+9Pb24o477sCxY8fw4osv4qabbpJg5POf/7ykKzwe\nD9auXYtnn30Wfr8fO3fuRElJCTweD7797W/j6quvvuQaLxtBStcn+f1+dHd3A0BeXo2KkkomEomg\npGR2wDcvOwCZi2g2m6W1n55xOz09La9bUlIiJUFGH6B+T2AOFqOiIgSnm19T0TBSK8znUonpSFfn\ngwkXMRpYzF5TmWpCjSY7pNNpMbJmsxlerxepVArBYFBgQTpIultWOBwWQ63zjIzcgTljZ1TKysok\nX0jjzcgTmPN02QmKBpYM2tbWVkEhGF1ls1nxdgkhApDWgYSSPR6PPC8jEgwGEQ6H83r0ageH+17o\nZNJJCQaDOHr0KN544w3pnOZ0OmVEGlMWVKQWiwXr16+X+thjx47h2LFjhtbMqJUGtJA4yH0mbMoz\nOjMzg/LycmzatAnNzc1wOp1wu91IJBIwmUxyHvTras6DZrPzrhuR0dFRIeWw9WtZWRkuXrwIYLY2\nlC3/mHYiOhOJRGSa1JEjR2AymXDTTTchl8vh4MGD2LNnjxCqeK5YyjQ0NITW1lbccMMNH6hI5xOW\nrxCxGxsbQyqVEmeP55SpGJbtEMlg/pOBAmczcw/5J5/rfMHEYnrCu91uXHPNNTh27Bi8Xi9KS0tx\n8803IxAIIJlMoqKiAldffTXq6+sxPDyM1157DZ2dnaioqMDKlSvx2GOPYWZmBi0tLXjvvfdw8uRJ\n4d1UVVXhjTfeQEdHB2pqavI4FtSrrMgwImVlZaitrZVcdldXF4C5lpNNTU0wm8349Kc/LfaFSOtf\n/MVfyBo2bdokaGlRURE+/vGPC7FuZmYG3/zmN1FUVISuri5xdiYnJ7Ft27YP7fd9WefZ0qjG4/E8\n8hOVvslkgtvtlryU/hl6+hwezg0DILVnOjJmBKa71RgdscfItjCy1oqa/6Zo8hHw/gYWWrSB5v/r\nOsxCpvJChIZbi44oaOg5po05PJ/Ph+XLl8Pn88FmswlJitHN9PQ0Tp8+DbvdLvV2dAoIewGQ+bNG\nhQZRk2iofLgGRgiMkuhpMiKIRCKYmJhAQ0NDHvErGo3CYrFI9FpWVibdonjJOLPXiPD5aHKHNrTA\nnHOla1DpsDFKYVtEOkQcpVdcXIy6ujqpT6dyBmbLRcbHxw1PsqKzxH2YjzVNg6mVOc8K6+XnK0/h\n2dJ5Uc1j4GsvBrkpLS2VRhQ07JOTk6itrcXMzGy7TrvdjlgsJikD7ldjYyOeeeYZGdPIu8Ah4ST/\nALMlaMzF53I53HPPPTh48CC+9KUvwev1GjK4dKg5MpINQGjYuRZyS+hA0LEiIkdh7pnPsaSkRJwn\n5nB/F8IzsXLlSlgsFoRCIdx6663CuE2lUtiwYQNyuRzcbjfWrFkjTrnFYsFDDz0kaZGPf/zjglxO\nT0+LfqezoO+LRlqMng/qHVYxUJ9wX3kOiRzx7nJtXE8oFJJ7QaSOOg6AlILxvOi0CQOBS8llHx5v\nMpnkw9PrZx4pm80K+5EGVf8MDykhN47aY42Zhqa4mRqKNurl0RHQnrkmCc13IOjdX+r/5hOtfHgQ\nKEahTe246PVqsg5hd/4/SWgtLS2Ix+Pi1BD6B2b3MxAIyPhDdqqhYqXHvZgIEYCMjyMEnMlkhOjh\n8XjEOL7zzjvo6OgAAInQGUFZrVbpk617z3q9XnEqSKJhXpdGmD9rRBh5E4UgMkEnisaXX4W5UqfT\nKX1WaawikYjkgh0OBzo6OlBdXS2OSENDg8D7xcXFhpsWaNiY0KRm2WunTKMtVFY8X/xZfaY0U5p/\n15+d778YY/ud73wH4XAYV155JSoqKjAzM9vYpqKiAgDwjW98A263G8eOHZP+uOl0GocOHcKJEydw\n+vRp+P1+Oftnz56F3+/H4OAgurq60Nvbi7Nnz+Kqq65CT0+PGI9AIICvfOUrOH78OL761a8aWjP7\nfdOgEtamsqbBJIJFNjL1lM/nyzs/mmzHM8vnRqPyuxCiQJz3XFFRId3agLnBMprkRD0CzJ4rHbkD\ns+eJsDGjTTKadRUDz7PRu0iUhRA270cul8MjjzyCT37yk++b/sPPybOu69tpk4gmsIRKVzPQISM3\n5sPO9GUr/aFnwEvCvJ/T6UQwGAQwh9fncjlpyM4ReyRAFUanNKIfVC/JPIlRz08fXnpAlzKkWqiw\nCHsRbmaumZE6nQHtBOh812IK0qlMeTnpLfL9NKTMg0blx4PFzwnMXepMJiON/qurq0WZ8XX4e4uJ\nxgEIq5K5HLLWSVJjPWomkxHaPZGLnp4emYbDaUDFxXPDBxgVa9SBhpf5s1QqZRj5oLFi3abOZXOf\nNTFKIyF8Rk6nE2vWrEF1dTVMJhMuXryIkydPIh6Po7q6Gps2bUJbW5tEOMXFxVLuZrVaDc/gLcyp\nF5KgNAucZ4dQZjQazTO2JOUUOhb6sxaSwvhlVJnecccdmJmZkeb1XV1dKC8vR21tLR588EGMjIyg\nrKwMwWBQYL/du3djz549WLFiBRobG1FWVob6+nps27YNL7/8MpxOJ1atWoW1a9cikUhgz549+Pa3\nv43BwUFUVFRgYmICP/7xj/Hzn/8cqVQKDQZnB5MUxvvOemred7vdLu0kSexjfp5nlIaZ6AGNq46o\ndOrldyFsDGGz2fJgbeoNBiEA8shNfK68V1wnkQ2eE/4+jS3vA8+VdiqMSF9fH4aGhoRkNjg4CLfb\njbGxMcRiMRkbyZQY239mMhmpdODYTL3/NKjaEdVpIu1MfpBcFmObTqdRV1cHYNbQDAwMyENhbS2N\nMPMcRUVFCAaDeQeLHjYwB4dyY2l09YeOx+NCsuFoMSNCKIoHgIdZdy7Snh2hbiodGjNeCG1UdSSr\nPTt6TYslSFGJz8dqZq6IB4dr44EpJFaxsJ81rZFIRLxdrUT1lBudpzYi9CqJfGiYh4MIAMjg53A4\njMnJSZk6w25CjL7ZFUuXUjFCZl6fZQrM9xs9HzyX2vgQ2tN5b52bB2afOT9PUdFsHTGNKREFOhse\nj0ecNDqS3OdkMomxsTFDa9bKQcPx/AxkuWroVzOUqSBZ01p4jvR94efV54FnzChjvbq6GtlsViZN\ntba2IpvNYnh4GAcOHBD+hslkwsaNGzE9PY2VK1eitbVVnC72E960aZO0mORs7a1bt8oEJ6aqHA4H\ndu/eLU6l0UlFvG9MGUQiEelwx33j+/N56AEWzMHq5hH8P0b3qVRKDN7vythqAp3OL1Nv0PmKx+Oy\nNgZUmrBFx4Lnma/B86Nr6XUUrGFbI2tevnw5fvazn2HlypWyFjL44/E44vE4uru7MTk5icHBQSH8\nTU1N4eabb0Zzc7OkbHiPgTmeAYlbRCS0rmSg90FyWYythg7otVwq/0lvmh+OEY2+xHzNdDotsAON\nG0WTUvieizmM2hvXhotGkoeESqsw90XReTwtGnbVr18YGSxU+Dn5fmTfFvan1Tk0XgBthIG53r/0\nrisrK6XVH/eUhld7fotxFHK5nCgmvQ9kBBLeLiqa6zRGSJgtHWlwfT4fampqkMlkEAwGZaC82WxG\nNBpFJBJBaWmpzCvm0AKje02jQfKdjjy0sdWOCT+r/n3WBPPi6tpfRppESPiM6ZwabTRPA6uNLtdJ\no6rXr5nRwFzbRZ371K9FZUynU0f2/MyLgZHp8Gnlr52cWCwGh8OBP/7jP0YgEJC7zmEFMzNzrPlc\nLoeWlhZh5gNzXYxMJpPkfxnxMAdo1BkDIJEc18yGFFTgPMus+eUec52a4b8YstNiREeW7DvN/WPE\nR71CJIftDedbI5EvrR9Ypsf+zTwv89WrL0Soi9asWSNpLU774ev19PTg9OnT6OzsxBVXXIHOzk5M\nTEygtrYWP//5z2EymYQcx+fGdRPRoV1hKpRnXZdyXUoua852YGBAWn7xkgBzjRyIlVPJcPLG9PS0\nMEb54BjV1NbWStSrW5OxcT3rKBdjaDUUQiXCg09ShcfjQXV1NSYmJjA0NJR3UAvJIPw3D6surNYM\nWi2LiRK5R3xNDZ9q4aEnFKyNvja4NBiFjd61ItWlLYsxtuxSNDMzg/b29ryolDl6k8mEWCwmEYxu\ntFFSUoKRkRHpfMPabNYn8lIkk0khzTmdThkiXoiKLEQ0IsCLyLXSWJFgl81mhVtANjf3qzA61AZO\nG1wKnSedJ1uoMLen0yH6jOvom2unEtXwL583oy3NbeBraXhZ7xmwOJYsI+9sNivP1GQyYefOnXA4\nHALLj46Owm63i/HK5XJSS80OS9FoFH6/X6IvPd6SE6I4fpApLKOlg3RsgLmggueTjsnU1FRe+oL7\nT73HfxfC8L9PoWNAw0fHjvef+Ur2SyBxjnehqKhIokDC5/p88cwwgtakUEaTH2a4CoVtKm+99VYp\nRYtGo/D5fJicnERNTQ1yudk+x36/H6OjowiFQvB6vTh37hw++tGPSj8Gnns9gxiAkKt0L2ieyYWc\n58tWZ9vb24uhoSGUlJRgxYoV0s0nkUjA5/PJz1IJ0XNindT4+LgoM0bJzKkmEgkZvae9Il3WoBmS\nCxVCooTttAFiBJBKpaRImgeTQgNWSJriunWkqXM5wJwSMyqENPRatCHUomE/vT564NoBIAmIF1G3\nY9NENr1PRsTlcgkxaHJyEslkUiB1OlPZ7OxweUKwmiCkGYY0tCyj0Y0hSLxjTtfhcCAQCGBqasow\nwYTGlGxTvfeaOKQjSUY0VLZcF/eMhlk/L7KtaQy4/w6Hw/BcWMKQej3aKBbm+Pk5+Sw0xEeFydek\n0HjrVIo2uvzMRoT7op1VIiGZTEa6n2WzWfj9fin3m4/IRUJdKBQSNjihRjact9lsKC8vh9lszouk\njYhGHZjfJ1uW55UcFWCuiQ/fi/pDR1E8Y79Poc4C5nQE10v+Q+Ez15E364JZcwsgz5nXxDvdn7qo\nqEiiUaNCB5Lvn8vlBBUj/N/w2/GKZvPsQAHu5/Lly+F0OlFVVSXnXufIC1EHIN/h5Rn/ME7QZTG2\nmUwGLpcrr5SHF097MBo+5J98iIVKghtCCFEbKg1hAXNEhcVEiYw2dCTFy0hDoD9PIWRG0ZBboTK7\nlCzGg+XF0IaaipJr0wQGvk/hBdZKSkOMQ0NDwsLTEY0uSzCqlIBZEhsbWLAbFJ0tMjZ1XpiMR136\nQgWezWZlPi0jE7YhdDqdEikyVVFeXo7R0VHD+60vo04r0PHQTof+PxoOKld9dnXuXLNMdQ9nnWc1\nykbWOXqdG+eZYTRFpVSY4tCevnYUCyFuRivcC53W4TMyIiS08exOT09LbSaj2nQ6jZ/+9Ke47rrr\n4Ha784Zr6Fw094FzYPlvdmtip6fp6WnYbDYxCEadX9bU6lacvJ8ke2YyGekgxvcvLKWikb4cUS0w\niwqm02npzTw5OSntdsnMZcSqgwI+a71Gwv/cA55hTTaiU6TTcEYddtbN0hGnwdWBAR0njUQRBeR9\nYjqLd5Nr5b81wqCfUWGgNZ9cFmObSqXQ0dGBkZERTE5OYnR0VDp4sGYOmF9Jk2msIWLCD2zTp8t6\n2EtUe9S6VMiIaCVCCJIwQmVlpWz24OAggPdDHxpyJczB7xdG2ToPyn8bhVIAzPvgqai4lsK8soZ3\nNDRMw1BcPNe55vjx4xgfH8dVV10lCoilKNqYL2bdJSUlqKurk16vPPw0VFSc7N2bTCYxOjoKYLZm\n0mazIZVKYWRkBAAE7rJarXC5XKJEiZCk02lRAi6Xy3AfWX3ptPNS6NjxsjLC1kaNZ6jQuZmvdpX/\nT2a4jhaMrpkOH7/HZ8BokevWd0azSvl7hXl6TdArfH3+roarFyqsoWR073Q6MT4+jpKSEqkxHR8f\nx69+9Su0t7cLAsIyFYfDIfnmSCQCu90u58Dj8SAYDOYRdEwmk+RWeX+NImPcLzoWOi1F56yoqEgI\nXDRa1FVsvgLMkXT4FY/HpeZds5wZhDA6NcqwByDMaEZ0dA65H9wnHWho8hP5FXxvi8UiTgvPs2Yh\n6xI0vobR88HPrzuw0YlkMMF0E3+W76PTOJrIRSicrYI1X4D3EUCe8f4guSzGNhwO48iRIzIvNRKJ\nCDTCw88PoxcPQC4EoSNOs6DC0pEN8wZ6egkNbzKZlPFhCxUNC5SVlYmCm56elgbbOo/Fi6Q73PBy\nMM8IIM+Do/KhgmVHnsV6eJoJqoktOi+rCU06t8af4+swh24ymWCxWGTyEscX8r14MBlpLMbYVldX\nI5VKIRwOy+Xln4SVKQ6HA+FwGFarFZ2dnWJc6Ynz0vFyeDwejI+Pyz7TA+7t7UVtba2M/jJ6PqiQ\ngNnnGA6HRbHzrGvDpi+jvuhUwjz3/L42eox8GKEVGrKFCveAz5Tf4zr5vPW6CwldmhiiFZVGq3gG\nC8+hXoMROX78OPbv349kMolAIIBdu3ahqqpK+vISuvT5fDLYobu7G+Xl5ejo6MCFCxcwNjaG2tpa\nOBwODA4OCou9r69PYE/yQEpLS9HX15c3bWwx8CYNCJDvfPPP4uJieDweKR+jY3UpFI77PDU1lddc\nhDqH50mngYyKxWLJM4CE0+noMlrXaQUSvagP+T1gzpEmIgFASGv8rDTQZvMsibG4uBgtLS0LXjOd\nI74+Dfnk5KQEdIW6UFeCaL4Bo1s+M+pBGnA6kzoqX0ia4bIYWzICgblaLN2EgoumQdXQlIYGeGgZ\nYfKBsl1jbW0t4vG4UOt1pFiYEzUqxORpXC4FS2vCjFaKwNwl0xGJhpf5u/rnjULfNJJA/uXmYdIw\noI6eqIApGsakYS4pKcH1118vRkYfYE34WQyMTMVD6K4wF0iP1263S/Say+WwZcsWWCwWHD16VMqA\n6HHrOb0ul0sYpXyd6upqIdtxepAR0QS0RCKB9957D729vaivr0dzczNqa2sl16PZ6oVGisIzqs8X\n10ToWxOmuEdGhAZUv3chdM2zq2Fi3jetYJg/1rCxfh8K92i+aHqh0tLSIo5GMBjE3r17cffdd+PZ\nZ5+Fy+XCsmXLsG7dOoyOjiIWi+G1117DG2+8gXvvvRfPPfccfv3rX8PpdCKVSuHOO+9ET08PXnzx\nRTT8tnZ28+bNeOedd+Ss3HPPPaioqBCWKZERI8JGLBSyoUkS5ffcbjei0aiQc1KpFIqLi6UkrVC4\nd+SvsNEMoVCWGZrNZsPlSgDy0ix8LTpN1AUaauXPMYrkuSDSxOYvRN1YG049UYgoLCay5Z7opkfU\nb4zQ6UwWVpIU1sZrlKCQZcy10pjrYOn/F8aWM2qp0EtLS0U5aw9Dd/ahEiGpRRNvCK/MzMyx5JxO\nJ1pbW9HX1ydNLIC5yJC5LyOio7RcLie9MBOJxPuILhSdYNcUfp2r0kpXww+MAPR7GjW22sAXQsZc\nC/chlUoJvFMohcqYHncgEEAqlUI8HpfonUZZH26jQiiMF4BKh7lFnhvuTVVVFbq7u3Hq1Ck5A5pk\noZu3U8GZzbOdsTQDlHXaJpMpj6i3EBkZGcG5c+cwNjaG4eFhvP766zh58iTa2toQDAbR1NQkCpOp\nDp4NjZro/M/ExAQGBgYwNjaG3t5eHD16FGNjY3LGSktLMTExgcHBQQSDQUxMTBha8+nTp/NIHTwj\nGurWJC8qWDq9uVxOIkJNxNNkRGDuHBamJXhvjx8/bmjdFosFJ06cQDAYFMeRaMuOHTtQV1eHXG62\nfeB7772HiYkJ7NixAz6fD4899hhuvfVWbNu2DY8++igOHjwoCv/GG29ESUkJ9u3bBwD46le/in37\n9iGVSiGZTMLv98tIx/9r6Y1G61jNUOiQ81xmMpl5ja2G7hmB6cY5fI58rh/WQnA+0XpL6yc+53Q6\nLcNCmAPlmjWpkiRXIkwmk0ny64wudQpR6xyjxpYpoUK+A+Clh+4AACAASURBVP+uywV1yRLPOfUW\n18Se5NRJ7E9AfT8zM/O+stMPc9ZNucWwhpZkSZZkSZZkSZZkwWKcgbMkS7IkS7IkS7IkhmTJ2C7J\nkizJkizJkvyeZcnYLsmSLMmSLMmS/J5lydguyZIsyZIsyZL8nmXJ2C7JkizJkizJkvyeZcnYLsmS\nLMmSLMmS/J5lydguyZIsyZIsyZL8nmXJ2C7JkizJkizJkvye5bJ0kLr99tvx5JNPSntFdprRvXXT\n6TRSqRQmJydl3Fk6nUYkEpEZphy/Njk5KZOA2KuY3T0KpwPxPdPpNF566SVDw7ZXrlyJ73//+4hE\nIsjlctK7NxQKSXeWiooKZDIZDA39f+z9yW/kabbXj79jcNiOeQ6Hpxwqs6q6uquqW9VNdzMJJBZI\nSEhs4O4QCATin0AgIfEPgGDDAokFgh0LhARI995Nc6tuq7u6q7urcnCm5wjHPNlhOyK+C+t1fCI6\nq8qfuLr5u9LPR7KcGY7hE8/nec7wPue8z4m++uor48WdTCY6Pz/X6uqqSqWSTk9PNRgMdH19rWaz\naWw80u1UokKhoNFopH6/r7W1NV1dXemP/uiPAl3z//k//2eOFQj6M9iV0um0crmcMT/BzOXZfhaZ\nf6APZG25X4w5g7N6NBrZ6MM/+IM/CLBDpH/5L//lHE0nQyq4tuFwaCTj8Xjc7j8sMCsrKxqNRsbK\n44dSS/OMRp7TVZJ9Tjwe1z/9p//0ztf8s5/9TGtra0qn08Zu5RmXYNthz45GIw0GA+PuHgwG6vf7\nc+w08N7CYMRc34uLC3scdh7e/+jo6M7XzChK3pP7OBwOJd1O9eE5UNdBvD4ajX6PgYoxcX52K98j\nFAoZz+wiZeq/+Tf/5s7X/c//+T/X3/pbf8s+a319XSsrK6rX6xoOh2o2m/ryyy/185//XI1GQ9vb\n2/r444+1tbWly8tL/eIXv1C321WlUtFHH32kcrlsXL/ZbNYGG/iJP61WSxcXF8rn86pWq/rZz36m\n//gf/+Odr/lf/+t/rR/96Ecaj8fq9/vGPJdKpVQulxWPxzUcDtVut7W3t6fPP//cBhNUKhW9++67\neu+99/T06dO5vXB+fq5ms6nj42MdHR3p8vLS6EfhBucefPrpp/p3/+7f3fmaJemf/JN/YpN9YEWC\nwP/6+lrlclnZbNaocTk/DGtJpVLGuMeozHa7bTOBr6+vjQlsZ2fHuO7he379+rWGw6H+9//+33e+\n5n/0j/6Rzs/P9eTJE1tX6Ya9sFqtam1tzTj6m82mPvnkEz169Ejvv/++dnZ2TF8fHh7q1atXdiZX\nV1eNZx179Id/+If6/PPPlUqlVK1WbRJSs9nU//yf//Nrr/GtGFuvJDC2XpFDq4XygKrP/+YAYJig\nv/PE3Z72DoOwOKkkiFxfX6vb7Rq3Ju/B5oAi7erqSul0Wn/tr/01TSYTtVot7e3tKR6P20i3fr+v\nZrNpvLJsOjZrOp02BTcajWy6RNDrhmNYmp+2wr9RltCoLRpXb/A81aRXrn7tuafcG8/7usx1Q0q+\nurqqfr8vSWY0I5GIOp2OUchB/dbv9+fmWWLsoAGFIxvKNgytXwMMSRBBIYVCISOFl24pQj0HMQbe\njxNbNKDsWcZGQhG4ODOXc8Lzgwife319bRy8KHJGpnn+ZO/QcD/80Ip+v2+8t3xn6ZZujzXi+6GQ\nl6Eh5T2hvfSjNWOxmJLJpFKplHq9nq1tJBJRtVq1EYooxXA4bAPm2SPs5cV5q4lEQqlUKvA4w/X1\ndSPBZ396rl0/6clzEcO5ixPU7Xbn9GKv11Oz2VS9Xler1bLv7znOWfdl9B5nZTqdKp/Pq1Ao2ICA\nbDarTCajRqNhw9crlYr29/ftc+Gq5//RaFTFYtH2QSwWUzabVSwWUyaT0Xg8VrfbtUEew+HQBkDc\nVQh64vG4rXMymdTV1ZUGg4Emk4mGw6H9v9fr2VCS/f19JRIJGwYxHo9ttnE+n1ckEtHl5aVSqZTe\neecdO5uvX7/WwcHB3MSgb5K3YmzxxFEQGE42Nhvde/bewOKpoeAxBih9T27uDQef+02DA75JUDyJ\nREK9Xk+dTkfhcFgffvihefShUEj7+/u6vLzUxsaGOQNPnjxRp9MxJcnG/PDDD/X//t//UyQS0ebm\npn7961/ba/r9vrLZrDY2NnR8fGycwEHXmrXgAEua4yGFf9Vz4/opQz4C9DN8eX8/GQg+VEjWV1dX\nA3NQS7dDETqdjlZXV22cF0YTQv9SqWSKAOM4m81sIDc8yXijzOLEsHi+VCZEwZMbdH/ARcucTz90\nm+gcrto3zSPFUWSfYwDZM97RxED6YQWLs0PvIovn7fz83OYFY5z4TPYAZwiOW39O4Z3195y9zHnk\nnOPQLA7cuIv4/ee5xSUZN3qxWFS5XLaoBuOez+f16NEjRSIR1et1nZ+fq9PpaDKZ2Dxbj9zg8DDq\nUZJNugkifsKSH94A0sQPXPGZTEbRaNSGnsP93m63jZu63++rVqvp+PjYonoMG8Nc2GegQEEF9Ask\n7uLiwhyOeDxuTgkO5vHxsVqtljKZjBKJhEqlkulfzgFDGYbD4e+NtIOzmNGRu7u7+uijjwJfM1Pg\nJpOJyuWy0um02u222u226YNMJmNOLAY1HA5bMMUQheFwqE6nYzo5l8spFotpZWVF5XJZ7777riKR\niNrttl6+fKlOp/Ot3OpvxdjilS1Cv3jZXqn4sUv+tUTE/rXczMUxZhwY/5ygE10kGZTJASC6grif\nWYf9fl+NRkPj8Vj5fF7r6+tqtVrq9XoaDoeazWY29uvs7MwGmTOLk3GAwDHdblftdluFQiHwiCzv\nlfsZqN54+lFqi2O/FpWgn/zC83gvT8rNcxcNyl1lNBrZgUSx8N2vr69tOs/6+rpWV1dNGRJtDAYD\nO9RMfZJuFHGj0Zgj18dBKxQKBusyPSWI+AgPJIDPgQid9UKxsoY4EuxTzgKpEg/x8jxJcw4nBiyI\nMAKSqS3+fYisgH0xpCAbpCQYF4nhRnFirPyEKfaVR0ZQ4EEEZwb9gIPHsAn2XLvdNkWKcmQqEIZf\nkn1/1pcf0KpGo6Fut6tYLGZRU1Dx99lH4QyiQJ+srKwYCoYeYO273a45sqPRaG4IBQNaUqmUzbWF\njB+9uIyx7Xa75rTn83mtrKzo7OzMRtaVSiWl02lbd+Z7RyIRS41Mp1Ol02mb4AX8j4G9uroyw5hM\nJpXNZvXgwQNdXFxoa2tLOzs7ga758vLS9i62gnsbj8e1tramZDKpwWBgk4gmk4na7bZNBGu326rX\n6+bckzbs9/vq9/va3d1VOp02575cLs/NTcZp/zp5K8b2/PzcZsGSP8UgkrsCKsNb5vADnWCQeR6H\nGvHR7qJB9rmHIMI4KPKRjL3iGvFSs9mseaDpdFqhUEi9Xk/9fl9XV1dKpVKqVCq6urrS6empHYhm\ns6nhcGiTKIDqyBFz7UEEWI9I0Y/Q81HW4sxRDDIQLcqL4fEeqsdI+8jYw3zLQFfAf8ViUd1u1+7X\ndDq1jb++vm6Q0NXVlTY2NgwyYsLI1dWV4vG4VldX1W63lc1mlUwm5wwHCo570+12FYlEVCwWA12z\n//5+LBcTffzf/A/GjvmZKAn2LuMBfRTrERveU1puOgq5Sr63R4AYqcYPUJskg7uJUP2MT8ZeAkX3\n+32tr6/PTbHhO7OngghOFkaeNQHFYC3ZH7VabS6PnsvlbA41iJFPWxER+hF1GGYf6QcR7/SjwzzU\niF7iHMZiMXPWrq6u5vLrIAy9Xs+cnVQqpUwmo0KhYJGXH2NI1BxUut2uGZpUKqVHjx5ZPUkul9PO\nzo5arZam06nN+KWeAih4dXVVg8FAmUxG8XhcZ2dn6vV6Nm84Ho9bSgiEKZ1O63vf+56KxeLvjSf8\nNqEGxuuL8/NzW6dYLGb7uVgsanNz08YmUsuyv7+vTz/9VOFw2KD7yWSiXq9nUW+j0dBkMlGj0TD0\nJpPJmKP0TfJWjG2v11Or1ZorqMEL5jFveH3ukM0DtMPrvfH00SuHx3/GYsHPXYWxf41GQ8+ePdPa\n2prdJDYZOZO1tTWdnZ3p9evXkmRKIJ1O6+HDhwqHwzYasNVq2UE8Pj7W2tqayuWyfb/V1VXlcjnV\narXAEQC5ITaHpDklzRr4HC25Uh+tAt37qA1IyBtkDDFe+zKDwaWb4dzRaFSDwcCUuL82ohKUKlEp\nygQFieI6Pz+3nDfeLUYGLxsDw70MUogmyQqyPETMtfvxiUS1wIcgIihzX3DGbFIfabH+3sj4EY1B\nBIOO8yrJjA/OLfA4RgJFv1j/EA6H1W63bY9ghLkv1B7wOT6a73a7ga47Ho9bjpVr9igTZ7VSqdgZ\nCIVu5gw3Gg3lcjmtr6/biEX2K055KBSy/C3nnM8C3QqK2Hhj7utHeJxCPlAM/xoK6iigwhn3RnZ9\nfV25XE6pVMqcR2B6IPRlZHt7W5PJRIeHhzo5OVGr1dLq6qqq1eocXL2ysmJQ82w2s3Gd3oHkuqmV\nWF1dte8nyRC+cDis4XCowWCgYrG4VN0HhjaVSikUCpmxJdi7vLzU2tqaNjY2tLGxYWml6XSq58+f\n65e//KV+8Ytf2HO3traUyWS0srKi4XCo3/3ud6rVatrY2FCr1VKr1TI0kv31TfJWjO3FxYXq9bpt\nOA/x+g2J4fUGGYPBDEheJ90WGKCYUZ5eIUmaiw6CCBtrfX1dDx480OXlpVXglUoli8a4nq2tLfN8\nnjx5okwmY4q+2+2aEqPyNZPJmFEoFosqFAo6PT017zudTs/NPb3rWhPZ4/FfX1/PKXs2mFfgi1W7\nRLu+6OLr1tHDg0TkQcXPoaRoiTwrEcvl5aWSyaShC0Br6+vrpmSSyaR9R4p/GCAv3ULSa2tr5rQx\n1zeo9Ho9uz4MLEYfY8DjQPg4MHj05OWGw6H9AIP5++QVNVXkoBdBhM9kTyAYKOB7IgWUJueLiDsW\nixlaBTSNUyzdRhq9Xm8uwiXC6HQ6ga6bfcEcXYpdEonE3CDwbDZrTkK329VwONTJycncXvZFcuxd\n4Fvp5gwkk0ml02lLMbD/g4hH13wtBbrOzwLGWBG5XlxcKJfLmb6gAyKVSll0SEHY6uqqrq+v1Wq1\nLLKPx+NzqYsgAlK3sbGhTCajvb09ffXVVzo4ONAXX3yhSqWi73znO3rw4IFBzEDapNVwYDY2NpRI\nJEz3sV+BxoHBPYTP34MIqCOV5Zz38XisXq9nkHqxWFSxWLQUAg7DZ599pl/+8pfqdDoKhUIqFotK\nJBLmrOFAojtwFtbW1pRKpQxZ+MZrDHojlpHZbGaHQ5ofRu4rK30hDgZ0sQLTR7t404vv56OCxYgg\niHDz8XTb7bZarZYZcQpyqGxMJBJqtVqWp61UKnO5ltFopNXVVeXzeUkyg7q6uqpMJmPwETD5MsYW\nyA9ICYW8srJi+RVveIlEUVY+D8saAx2iHDgo/J3XRqPRwJE4AoxGZBiNRq0isdvt2rXhZfoiOOC5\nZDKp0Whk10kVM0Uk/BtlTYogmUwuBRM2Gg3baygUoiP2zmLe1T9GZWmn07HcEIYNw+Wr7VnrxZ8g\n4p0rUhg85lvu+NzBYGCV8z5iJEfOe3mnBcOLs8R+xtBQ1RxEiL7X1tYsnUBxF3ohGo1awZMvgmm3\n27bvgZ7D4bDtVZwX9gtoBUaRStagOX0fsWL0+O3TAOgvvic6zxc8+cK+eDyuZDJp0LrvKgCxIBpf\nxtgeHh4qk8kY3JvP5/XBBx/oiy++0OnpqTqdjn7961+rVqtpd3d3Ds736SS+G2ea/Pn6+rpms5ma\nzaYV3vkuEhzkoIJOI723srJixWfxeNycJlADdDutltls1vQf19Hv91Wv13V6eqpMJqN3333XHEWi\naNIbf2GMrTRfIesjTw9PeoiSNgHfZsJzvbL1hQi+IGHRo1ymchNPEWNADjAWi5m3Rm8tcOJ4PNbB\nwYF2d3cVi8XU7XYtWonH49ra2lK9XteXX35p+UNeS2UlyiOocK3kxdg80q0B9oVM3uPH2fH3y7dF\n8H4epkJReWWwTIEUkcP6+rrlSKh4xVMlGuMASzKvHpieIiV/Db4q1BdsEJVeXl4qHo8Hbv2hbxID\n7vOfvrCPPlsKoNinvuoYBcte9m1uKGLvFHlYMeg6S7K1JIpiXTFenDEcKBxB7gnKc3V1da4IkJ54\nX4DE632EH9SxAckgeiLn6usIKGgk0qaFij5c9gqOF+uKjsC4kevlu+IAkTK6q5DjJiL3OX32Hk4w\nxVB0OCQSCftbOBy2nGA2m7UCKhwiHC7ume8gCOqsS9KHH36oXq+n8/NzZTIZu8fRaFTValXX19fW\nfsT9jcViGgwGyuVyisfjFjGenZ3p7OzM6iPQ6dxD2m3W19fNCPqg666ytrZm7Ttecrmc1WxQW0Cf\nO/cjn8/r4cOHSqVSOjk5UbPZVK1Ws7+XSiW7j6BQa2tr1tLJev+FyNkii1GodFux5+EynuOrIvE2\nOABeufmfxTwtr1+maGc2u+n5HI/HymazVnEHlAbkwbURgVECTr9Wq9VSsVg0+JwS9I2NDfX7fYMk\naFGhGvDbqtveJHjrKBZvfHzVqS+i4Lv6394h4odN5VuxWAcfvXHflhHuP0QAQHs+qsW4QvyRz+et\nyps8VyqVsnwj13VxcWEtAuTLqDIPamglaWNjw4qx8JyJKHzECFpxcXGhwWCgTqejRqOh09NT1Wq1\n3zPOb4IfV1dXzdD4iCVo4R9Oo6S5CAIl3+v15pQ8PYUYeN8j6wupcEy9c8B78l68xjtLdxVPhrNY\n3YtihkRjZWXFWsRGo5EpTu4Pip73GI1GViyG0qS6lBQQHQJBBPIdCDj4Dj7/y/4cjUaGlLCfcKaI\nGoFJE4mE6US/FyBgwKlMJBJLRYi9Xk/pdNo6JVhXULiTkxM7jysrK0okEup0Onrx4oVBx9ls1pCe\nUCg019M9Go2UyWRULpeVTCY1m820t7dnxvvhw4eBrzmZTFqulx/WgLNOdwioQDabtcpjSUYMsrq6\nqul0ahEsOXFSPjhROKqpVMrSUt8kby2yxUv3nr+kOaOKovbQsleURGF4cz665RBjkH0EtkwlsiSD\nbCTZDZJuDFG321Wv17PK4Wg0qrOzMyuD39rashJ9PNmLiwu12211u10r5ri6utJwOLTc7dXVlUFD\n5F+DrjWtM3yHUChk3jBrCmRLRM3BRVmhwHxxC04LisJXM3uGmaB5ROnW6Pu+V+BCH3lxL7xiHw6H\nBm0DH89mMzvoKJxEImFFFESjrD/QYhAZDocW/ePYEOHjrPjott/vGwNZo9FQo9FQq9Uy5IMqZV+M\n5OEwilJ81XfQyDYcDhsJCFAq5wtFj3L1Fcf+fHGNRE4YWuk298s68DhICxFP0GrT4XBoeXlgS3o/\nk8mkKUh0AhBfPp9XPp83xQjqw1nwLVqsKeuEcZNukYCg14xzTcqGQMGjS8C/vV7PiB5yudwc0kVx\nF9Eh+86jbDiT0+nUIuBlqpGTyaTlM3FKIpGIGR/uHRW7wO6gAJ1Oxyq8C4WCSqWS5ZlHo5FOTk40\nHo+VSCSslgVEBNSp0WgEumY+Ayc9mUxa9OmhefQUeVbQFoIi7FK/3zfWK0nmxON4cma4ZtCeb5K3\nZmzxBnyBFAaVPCxeNIeVg82XwmuTbkkafG4L5evziYvQcxCJRCIGQyQSCatiBIrkhkAG0Ol0rCpt\na2vLetOy2ayazaZ5yI1GQ+VyWY8ePbL8IXDXdHrTn1YqlfT69evA1wz0gTfH90BB+6gLpMEX+XAv\nJM0ZUd/CwmM+Z+ernZeBkT0UTWvVZDIxWJC9AJwo3fbWcrAxDPTj8pv8JEaRakUgU6/8gkitVjPo\nyx9unxMHUfCRLvuUgiOqMDF4HqL1RnaxIpb7FUQwNLFYzIpuLi4uzGhyD3Bo8fpRKuwFnoNjADqA\ngwE87x07Uhrj8ViZTCbQddO/iaHJZDIGAWYyGfsMv9YrKysqFosKhUJ6/fq1taX0ej2rWOe+YcQw\nijiohULBIOCg14wTzf0E0eOe+tY8n/vPZDIqlUrWO0qUybVKskKvdDptaBufRStLLBZbCtEjGqeF\nJ5vNajQaqVqtWrql0WhYfp6iOohorq5uKERfv36tFy9eqFqtamdnZ+6MnJ2daTKZWF88udxoNPp7\n3SZ3EZieIpEbEhPOCWcK5ywcDhuC4LsXcIpHo5E6nY4VvpI6ow4BxAGnjfWF/vab5K0ZWwyVh8fw\nNji4KFOgFW6kzyHx5bxB5dD74hL/XBTeMgVSNM4DJ/vIOp1Oq16vW7UbB/309FQnJydW2ZbL5cwb\nz+VyFsHT9hONRtXr9ZTL5dTv9y0aoGou6DWz4X21I8bMowJAzKytf5xD6/PobCaULZE4BhxPfJlq\nZJQ1Cqjf7xucR3RAteba2pra7bbK5bJarZYZWDhZUZgedqU1BNie1pTr62tT3EH3xxdffGGOWDqd\ntmhCumVNo50LR6vZbBq6AV8uUSaQIOQEi4Vskub2tRS8zxYI9/Ly0hxIuF0X6ygoHAEdILKhKBCE\n6urqSu1224gC+AwfPfqcGTnMINLr9dTr9UzZUbyTTCaNcWk8Hls1N45mLpdTPp83+A9mIIxcNptV\nNptVqVSy7y3d5m8hQxiPx1Z8dVfpdDpqtVrK5/NzyMyb7ilpESKuYrE4l78FqkT5wziVTqdNn7Lf\neX/ptmAy6FrjdHFPZ7OZisWi6QMK1mBu6/V69lg4HNb29rbq9brxdl9eXtq6kw4aj8eqVqvmgKE7\nHz16pEqlEuiaWRe6R0hvzWYzM/CghhSzsgdSqZRqtZqd2ePjYzUaDXPsMMKkIUjLgURxvr8tzfBW\njC3QgI9AMQi+SIGDK91yuPpk+eLG8dGr92gXZZkIQLq5gdB7UU0s3Ua8m5ubBptMp1Pz9ICWz87O\nlEwm9eDBA7VaLdsIhULB4NZYLGaHGEUKjJvNZgMbWwrDFr8vShTvnUOxCHP5lhRfdU3RCH1y6+vr\ncxGlh++X8aapoozH4wbh0bLDd7m6ujLShGKxqNFoZG1AyWRyrvqVQhjgdAo4yCWh6MjzArMHkaOj\noznHjugfI+Tzr75lTbqF94kIMKxEsRz0xWh70ZEMutZAftz3xZYczhLGGCgURIF2PO41CtYX7fCd\nfSsUn4chDuqQeQhvdXXVjKwncAFix5HHQYMCtd/vG0oGIgPUubW1ZSkJDLV37JcpsGw2mzo5ObGc\nH0EFEZX/XuQYMf4+Z0tE6BEluKBhU/MFdR6eXuYsUgtRKpUsEJBuedVDoZAVbJG2AmZdW1tTsVi0\nqvFEImHwPU47iNPFxYWePXumRCKhSqVi6MnKyopSqVSga/aV+mdnZ3OOK4NhwuGwMpmMWq2WIaPU\nbBweHur09NSKtsh7k+Jhb0PMgQMv3da3fFvv+FuFkT3U6w82yhQl71skOEC+/cF7377NZzGa9VXK\n/D2I+EphvCCIFVZWVpTP5xUOh3VycqLz83Ol02nVajVJUiaTMaOLEvc5rul0aoqVwoZms2lQBYcq\n6AHHcLA+rD/CungCdp6LISVKl2RwHIbEXw+GBdTiz5Ijv76+NvYt3zoDZOMVCTlbql3xrmezmZGP\nY2hRwpFIRJlMZo7RiIjft+sEEaIV9ib72repUSGLE+F/OLC+iMjfK3//Fh1G7kdQAdplH+DMAQXS\nCuHztJJMqUejUSOeQcHzXJQdyhij5wuqMNZBzyJQO1EKLR1+PWKxmCECPEb/Zblctnx5t9s1dAOY\ntlgszpGM+Opw37YVRFqtlo6OjiySovWEXPjV1ZW1fbH/IangO4DI4GRyLlkD7l2327W0ioePl0np\nUEsCqgERjC+Ui0QiFh2iY9nHqVTKnIZMJmMO23Q6VaPRsAKw8/Nz1Wo1zWYz1Wo1xeNxbWxsqFKp\nBC4ObbfbqlQqRgTE/qPvnv1C8Zy3L6PRSH/yJ3+icDisH//4x6rX64b45fN5XVxcGArF9/CRPHD+\nt/Xqv9UCKYwr0JovSvG5ODa3L4DyiofIBI/KH2Y8HJ8L4ZAHPeDj8VivX7/WysqKnj59qna7rdPT\n0zkFnsvl1Ol0bJJEPp+3A399fa1Op6Nnz55ZeXwymZwrIEilUhbFI1dXV2o2m/YeQQTlB7G2h0cX\njS6RB6xV5J690p9Op3NcuD768y0Tnq1qGSPgc1rkxi4uLpTNZq0nFuOIYkep0GQOVEWuE4eGgqh4\nPG6wHpEE1Ym+heKuwuf7IpQ3RRaLTg+P8RuFhSzCuf61vC+OadA9TYSxWONA3s+zYoHk+N5lUhy+\nGI4zR7VmKBQyxeRbiPjtx0veVSCTwTGSZBAne2GxeEySoThAs/F43OosQNN4nr9OqPh4X0mBK6i7\n3a7q9bp1HmDc0X/j8diM/3R6w0IEvSHXhGGCrB+4mO8FrWKj0VC73Taii0UEJOh1T6dTHRwcmKNY\nr9eNwWsyueEUpvOCSDwWi1meF+IQDBzXzj0k9wzi9M477+jx48f6wQ9+MOf43FUikYi++uornZ6e\nKplMWnpGkjlTHgKmW2M2uyFeSSQS2t3dVSaTMQgfp4vhCvTdgnTw/kTwf2FytihSX1HMxvZQmzeM\n3CC8fhSMj4AxRrynL+R5k7EOIpeXlzo6OjKvHg+S3A/sMhwIFAwKPpFIWJWbJPPK19bWNB6P5w49\nUzQuLy+Nd9Qn+u8qi1Ag0Z9vzyHXQG6Qw0D1rodEJc0VEPDbe9mLhW9BIwDeA+VC5LSysqJutztX\nsEO+iKpWfq+vr88dULxOnBg8acby+SkffN+gjo03mIh3Uvg//2ZNvRPkc+Rf996+XmER3QkqKBnp\nljuXAjJQGAyxN0TUVGCoQaqoXPbwOddMnQDfLxaLGdoQlBqz1+uZgud6fGsMCi+RSNh3AhmRZLnQ\ndDptBs6TilB3IWkuvYKxIE8aRC4vL23qDOkMChdZLKwcuAAAIABJREFUL8gVotGo8vm8isWiRbYg\nDZxTD+NjbGlpweGnKhsdukz9xN7enp3zXC5n64ojNh6PzYkolUpzjlm1WrWCsul0apA0/fLvvPOO\njRWtVCoKh28oP9fX1/X+++/ryZMnFgQEkeFwaGNNcZ5BIi8vL63LoVAo2H2l1oOJPe+//74mk4lK\npZKtpzQ/kAadwnuzv2H++iZ5a322vjhH0tyGAIbkty/EwXPlQPtqTgzxYt4MWIvPWWbD8fkoBpQD\nZep4p8xpRIlxkHy0Apw1m93OaoXWjEM3nU5VLBaNG9dXiAaRRCJha8E1cKCHw6ENKsDIYsBwiHBU\npFsnidJ5X7TmjStrjBKfzWb6F//iXwS6bpyV8XhshpMh8Y1GQ4lEwlAEirJQAJKs+IEiGSqNIRvH\n+SAfzPqimEkDBBFv+PweRMl5x8UrvjflXBf3rzfSi1HvshGLJFPWEMGz/yAtoIXNR88UiAAhSzIk\ngX0t3ToIkUhkjuaQyJCIAuMeRPr9vlqtluW4OWdUTKNkeWw2mxnpCIY+nU4rn8+bIqWA6ejoSEdH\nRwY5YuAojoRBa5m+d5A8Wlu8LvN1KbQqra+v22cDCXNO2bsYW4wcRaQIe4gzGlRwpqnk7nQ6SqfT\nNtmM9kyug9w+rUjwCxDBks9kH7XbbU2nU21vb+vRo0fK5/NWcY2uDNofvL+/r4uLC3344YcW2VKg\nGonczOKl8KlYLGpjY8Pys1Qwoz9KpZJNfOK74YxRAIoOZ/3Zj98kbzWylWSeBl6Fj1JRXOS0pNs8\nD8l1324gzeckyHFy+L1XvUwksL6+rqdPn+r09NRuCpVzsByx4WkpoYWj2WwaFB6JROxvr1+/NqiI\n3rl2u22cyiiF2Wym4+PjwPSH9GV6I+o9diDOxTyLb7XyxTDcGw9f8pt7xj3ikC6DIhBJcFBpEp/N\nZiqXy5YzwSCQhuBAA19BwYajQpsJLEZAzjhrKDCKKILIYrS5+NgiFOxft1jA8k35WR7/uhx8EEFZ\nUKSCw0sPJOs4HA5t7CB7in3OtfjzQG7fUw1SWMLzPRy9zIAN+ifZI71ez1p2cJpYW19FD4qWSCS0\nublpjj1RzcHBgSqVijY3N1Uul21CEEWdXgcFESDYyWSier2ulZWbWajZbNbWgxQNRgndRnrJp9Qg\nkACBYjzkdDq1iD6RSMwxoy1Dn4r+8B0k0g06QDqQVMHx8bG2traUzWbtPoAwkS8Hnu/1etrb27MA\nigJHCt34bvA6B5GDgwPTsZ1OR+PxWGdnZxZRX1xc6MWLF1pbW9POzo5VtFMzk8vldH5+rlwup2w2\nq+PjY+sBBp1g8ANOI9OEcDy/jYb0rVUj4w155eyNIT/eQC0qeF9x7P/mq2gx0j6qXFaARJPJpF6+\nfGkN2vTrkUfyhST8myT69fW1kaNjiDc3Ny1Xwdzbq6srG8t3fX2tra2tpYgWuA6iNg4eP4tFOv5v\nrNVifpYo2Ue93Af/ffHulmGQ4j3wLqF+4x7E43FdX19bWf94PFar1bLCI0lGxVYoFMxBoOBHkhV6\nEDVg3GezmeXUg17zm2BkbzB95Ov/vphv9UZ30WGUNHcvFj8riOBskJclz4ajh+EESvXXgGff7/fN\niJIb86kemIPI34Kw+O8V1HD5dBOGqtPpzFVGs1c8MQi6hupYqPf6/b7Ozs50fn6us7MznZ6eKpVK\nGfn+eDxWrVaz0XDhcDhwNM6ZQhFDZgO5ij+rREqexITrHwwGFu2xbz0iyH0lH4lTulgLclcBLgW2\nD4VCqtfr5swAf9PKc3FxoSdPnlhFO73tfB+gV08NSq6ZPl5+GFAfVO91Oh0Vi0Xt7+/baELWCF1d\nr9ct/02rly+iwzCHw2FriwNRIXIHVSWKR2fcJcB4azAyEelivyyHBAXoK3C/TpF4o8DzOej8Rhaj\njCBCgpx+NhQQ1GncSKItX6wlyXKj5H04aNyki4sLnZ6eGhRKL2AoFLIDGdTDW+yXlG5zc776mb4+\nDgSHHYOMwfXODc4SOVk8f9/qQgQZVHx+GeiOw87h5ZDjBVOV3Ol0rKF+e3vblA3RGFEwvbt450RJ\nwPdBc3KLxm7RkC5CxW/KzS4+7+ui2jdByd543VW4f/F43HLeoAgoZqBfFAuOi2eZIsr1OWVfeeyR\nA5w18onJZFInJyeBrluSOaSNRsMi2ZWVFWuN4VqB/XyRlmdUo+oVRSrdtOlks1lVKhXlcjmDSK+u\nrqxtj/cIut5XV7cj8zjjGBPqFHxvtdd/oGh8P2+E2Me8B2cUneSj0iCSzWbVaDQMIaJLYnNzU5lM\nxgrhmBndbrd1dHRk+rJYLNqwFdYM+Pvhw4fq9XoGKUs3TvDOzo7p2GX09ebmpiQZZeRsNrNolSEC\nyWRS1WrVZvNSSLm5uWl5XnQNfOyff/65+v2+IQZ8f+prfDrt2/TeWzO2FEz4yJPWCRaXA8RzvPcH\n3IlgYHmP6XQ6R0GIgkIhLCP0xaZSKX3yySc6ODhQrVbT1taW5TNgUgmFQnMHGhkMBqrVavrkk0+M\nSg0Fl8vl1Gg0DHqTZJ4VGzGoh+cVno+s/P/JYfFcHsNjI+JFKLrCsOLd+sIoognfpxhEfH4eg43D\nwVqgiEhBULjhezAhXvAVm3BO+xYfnA0a7H3h0F3lTYZy0fj69ffP9QVo32SA/WOLv5fJ21IF79MM\nQMtEvb4yFweZfXt1dUMcjzH2cD29k+wVFL50y0+OUgrKxiTd7MOTkxOroSBn7OfOTqdTm/RDVS2R\n0nQ6NeQon8/ro48+MmRpOByqVqvZ/NL19XXj24XQJqjgtGIAKaoEWfBtYZ4pjeic+oPBYGAVyr7v\n3uetKSqiPuHrHLe7CD2v7I9GozGXd+U9ca6fPXumZ8+eGekDTjjXjC6BxALEAwiWolKCGZ9GvKsQ\nlPjIk1QfZw10FaeAQQvoCJCD09NT1et1HR4e6ujoSJFIRJVKxVjXfMsfZwCO9G+8xqA3YllZVPy+\nvcRHpzzXKxKKfLwB8IYbQ0zRgDfgvPcymw7DAw0j0dLFxc0osVwup1arZX1zrVbL+un8dVLow3ty\nvbFYzEgWPGtUKpWyfO0y1cgeHfDfH0fEt3vwfPI0vpiF3BeHlvvAe/BD/ovCrmUiALx9ZltOJhOb\nUwukDMRMgQK5R54ryQ4wuXSKWjxPKpEOxpg9ExRFeJOhXYSP/d/9v/1+9NC8h4sXDax/7uI13FVY\nJ6qPyWODVpA7ZE3ovaboiXvkqRN9OwuQMmgIFcm+cAkjHlSA9UjdcO0oaXJwvmqefU7xE4rcsyzh\n3AKLM1CEqJm5tkHPoi+G4ozwg5Pg6zem06kVdUkyBjJ0Hx0F7BEKKzG0HpnyxWpBBW5m0B4fCeIE\ncJ5WVlZ0enqqRqOhbDararVqeyYejxufM3nz4+NjDYdDhcNhPX/+3Bi60NUeRg8inU5HmUxGhULB\niqOolMeBh2CkWCyacWe6EXry6upKv/71r/Xzn/9ctVrN9DfGdDAYzN0LX3vxbfrj/yeDCPB82bye\nNEG6haAkzeVgaLImavURnFdy0i3rC/mNZaAJD/+1222DGUKhkNHvAZV5CJyNDwevJDs0sBhhpKBb\nA+plSgUMSUEPi28rIM/pC4koTvHUbos520XPcvH7+ZwRHjtKxFPlBb3uaDRqVYAY0ul0qkKhMJcz\n49/k4VDcvkUDbxknwyt3/s0hBKILet2LcK538qTfL3pij78pKvX7ltfw+CJkzO9lYGSiV5AgzqUn\nrSDyJfcFvO8dXNi9fGrFn2nyh+xtHDVmnn5bFLAoKDKUHJ/BKDr2I4rbr5XvTfWGHkiZ70Pk3u12\nzQgw5xTDG1S4DgwnyA9GlgEn9JmT25RuozSue7EHHnie9aQ9hc/z9yOITKc3bYf0plP93ev1TG+A\nfqTTab377ruqVqtWk4JeoB0GdjhSRdA+7uzs6L333tOPf/xjo6PkjAQtVgRtwVCDEPZ6Pa2urqpW\nq9kUsXK5bBPczs7OtL+/r7W1NT19+lThcFj7+/v69a9/rXg8rh/+8IeG7OG0LSKstBH+heizxbNj\nk4Bxs4m8Jybdklp4Jh6vwDxG7o2vzzF6j49/L2NsiUApYmLDHx4e6vDw0LhXiZAoWkin02Y4w+Gw\n6vW62u32HBQL0wkeHe0vvV5vzhMPImdnZ5ZfoDDER6EYXh9FeQSA61084Bxq1pkcXDqdnpscs2wF\n5GAwsP5C7juf12q15vK2wPBEYETjKAcMLcqGfLX3dNfW1tTr9eYi92XlTcVOPqpdNMYeXvaG1z/2\npgj2TcVWQcUbS9aJdeEaJFmhIhXeIA8oWZ939N/TR+aXl5dzFc3SLelK0GuHg5p833A4VKvVUqvV\nMk5qkAoMOf/HmWcN0Sn0vBOJoTe63a7VkNByBqQcRHx//9XVlTnbRKfQLfqRbkTYRN7UU/D9QSbI\n37K/3zQ60LfjBZG9vT298847lvOUbqLdaDSqZrNplewwREmylFi9Xtf+/r6SyaQVk/Z6PaO9zWaz\n5mz89Kc/1fvvv285UmSZa5ZuAzPqg3BWer2eDg8Ptbq6qlKppFAopMPDQyuUW1lZ0cbGhqRb8pRS\nqaSnT5/qnXfe0dHRkU5PT82xx8lkDxGFf5uz/tYiWzhuvYLxisMrFyJfX+VLqTgHH0/QKzYfEXtD\nvGxFMl5kt9u1z7q4uLAqRaJIRq3NZjOdnp7q/Pxc5XLZjFs6nTbezO3t7blSeqKLyWSi4+Nj86Y/\n/fTTpeC2Tqdj1aJEs74oy5NpUySE8fGTT/AwgRPJK8NfOplMLKLFG2eIwjIwcqvV0uXlpR06P5uT\n/UI/nK+A5jrJs7HhF9tRaCk4OzsziAwvlchsmchWmp/96w2lR0b4/6Jx9c/zj/nPWIyMFz8viLBn\nfZ7eFwOl02kbC8n1EHn56mQcWIwn+wEDwL71/bnkd310dlfJ5XJ2poi0JZlDKN066Vw3Thm95544\nnnVm2gw1F/TDNhqNOQYnIrIggmPtq+WJbEOhkF2bd3jQM0RK3tByHaB2fP/FqN3XVyxTIMWknkwm\no6+++sreGxQIh+vq6srYqgaDgXZ2drS7u6uf/exnqtfrSqfT5vBubW1Z7vzRo0d6+vSpPvjgAzvP\n3jFaBv4G6QSZqdfruri4sOEus9lM1WrV2gi/+OILXV/fsNWlUinrrf3qq6/U6XT0wx/+UO+99542\nNjbm6juazebcZ7LWdyHieKswsnSbQ5Ru20fwwNhkvp+W11A4wObhtzekHnLzN2vx/3cVDwEDUdE+\n4udLUpncarXsxgCxhEIh9ft9lUol846pShwOh9rf37f3Pzs7s4IJX5UYdK05uEQADMb27T8cIGAX\n7ssiSoBC9v2V/J9qPCAiX50cVBgKD7MLCohIFhgHUo5w+HY2qFe+0m1Eg2KQbg5Gp9OxYjagTYgx\nMApBZDEyfdPjb4KP35TDZW19Nf1iBPumPG5QowXS40k+IMIn+vLpHdpWvKHH8QJ98vvIox44ouxH\njPAyEXmhUNDm5qbdOyg8ia64r55YBWNL2mYRDaPPHMeO3lKGyZOmYKqQL066i/i8NQ4GECt727fH\ngHBJMiNPqwp7nnX3DgNnEsfRn91l1no6nWpvb8+GMxQKBfX7fUNCEomEFTb1+33rboCGEwdGku2V\nVqtlbFSlUklbW1sKh8NWj8F94fwHvW4+n/VbW1vTZDLR2dmZrq+vtb29rQcPHlhHgyRLN9GxgK55\n9913jRwjEonovffeUywWU6PRULVatQCs2+1qOBxajvsvRIGUVyT8X5qn+eOwEwWwgXxk6qNWNhte\nFu/pq2nZjCiDoMYL+JvfqVTKxqmxoYBA+v2+Xr58ac9ZX183ooBut6vNzU2DjOmha7VaevXqlXl2\nEF6vrKyoUqnMVWbfVVBqrBcFUHiLi5GTf3+fL/EQlEcZMMY4UPzbO0vLHPBOp2N5MnJQHt4kQvVG\nlpm17Af2AgaYQ4XiZUjB9fW1DS8ALuUeLyOLRncR6vUQsn9s8XHp93O2ixHsm4qulhEiJ2B0aC+5\nz35wyNramsGbvvAGmJX8us8RkkqgYAnD679DECkUCtrY2DBjQ7V6Op025iX2oXTL143zSpU0LR4U\niPF8HAUiK4oW4TEn5xx0jUGNyF9TuNfv9w2B8lEq+fJkMmlFOZxRdCXOOTA/n+V7pHF+gjoIklQu\nl/Xy5Uudnp4qFrsZRZnL5YxIhPRZOp3WcDhULHYz8J5gqFgsWsUv6YrBYGAzebe2tpTP5239MeK+\nuI4UxF0FTgOPZqyurmpvb0/T6VSVSkWpVMpavdbX13V2dqZIJGJI1/HxsTKZjMrlsorFok0PomXz\n9PTU0hhcH61mROXfJG+tGlmap53zBSFe+bD535Qk98bWK32fe/TKCm+JIqCgh/zy8tL6zYBL8JKo\nvJVuI99UKmV0gru7u1alDB9rvV6fe+9wOKxqtWqeVS6Xs8KC09NTFYvFwMaWCN4XbuFhs154vr5q\nFFjIw8e+dYoDwL95H38vfeQcVNrttikIck/T6U1PJjkiSTaQIBwO2/pT+MC9ABKF0Ua6UfqQW8BA\nxdB3X6X8Z5U3QcNvikbfFHks5mw9AvSm6Hnxc+4il5eXxjdNNbqfTws0TDsPefjV1VVrpUkkEsbf\nTWoBCM9H55FIRO122wrViIqXWWdmzp6fn1ve0Leo+VoNzjxOtq+2TyaT5nxSwEMRkHS7h0E+qH7m\nTAYR9p/PpeJ0dDodK7giuuY3Ue3i2EIiV/QF+sg7xv7zfJV+EInH4yoWi/ryyy8tSAHx4PxhRP3A\nBqL0Wq2mZrNpiB9IRyaT0Ww2MypGerYpSkNXeST0ruIry3FgxuOxsYZJMrraVCplEPfW1pY2NjY0\nHA716tUrFQoF5fN5NRoNNZtN7ezsKJVKmTOJgzGdTm06GxH9XwgYmc2/WMDkvXuUOe0uHtLkPVDA\neFB8QW9YF6OrxUrbIIK3KEmlUmmOFII8BaX6wGlEBURcVLxKvx9FRqNR89a73a5qtZrG47Hlx5aB\nkn0xk1faPqJlM2JoF4vTeN3ia1FAvDfXBgSG4lsGsu/1euZseNIMoOWrqxvuY/hxiUS4t3wm5Bbe\nYPsIFipMevF8XUDQA8468e9Fo4riWISQPfzn34vH/L89fOvlTY/dRfiOpAJQlsD/ft/Qi03bD4Z4\nMBjMIRq8hpwtcJ4vUPP5fs98dFfJ5XLK5XLW58h96/V65gQQaZPn9FWz0i2BxOXlpUUoGANgYpAn\n9A1OGYxHQcRX9aPfZrOZMSdxDmnZIfXBNCmQHfYJZxYYGQSKv2EYier9dJ0gwn1Lp9NGZ0lKrFKp\nGCEI5P7UO8xmM2v3GQwGKhaLOjk50WAw0PPnz5XNZlUul40mVJKlG6ib8K1ZQQSSCY+6gHLhBMAW\nFovF9MEHH2h3d1eTyUSnp6dqNptqt9vKZDLa3Nw03bBYub6+vq4HDx7MkeTgwH3bWr9VUgsPFXtF\n5BXieDyeM5A+D8tmexN5xNf9fxnF/6Zrp5jGK8jLy0s1m8052IbWioODA7tpl5eX1tuFlwo0m06n\nbZOdnJxYUVU+n1/qoHCA2WwoRJQO60kBiXRbWObbpPCkvfLnPTz0Jd022qPwljECKysrc4Z0PB5b\nvsr3BOfzeQ0GA/NQKb7xUDnKHeMhyRSBJONehgO4WCxab+4ysmhQ/Rq/ych6w7xYNPimKNj/9q9Z\nfPwuQuSPscUYothRWLRX0bJCVEohEcYUowCEB1zLuqOkPd2dTxfdVYhGKLIByms2m0ZIQuommUwq\nl8vZXsTAw0bGPqbAD+cMfl8cNfaUz4cGEYIFjxRMp1NzXFDU6XTaDCz5XYwp94fr8ak3aiak23PP\n+3AWloGRQeA2NjaUTqd1cnKi4+Nj43aWZG1huVzODB3nEK4AdObr16/1m9/8Ru+//76KxeKco0E0\n6J2yu0Cyi7JYC8S9JWVQqVQ0GAxUrVb1+PFjtdttvX792nQjXPbpdFrRaNRQFEbqUcglyQqrYPbi\nPH2b3nurw+PxaH2Y770HNog0D4Oi/Nm8YPz+RvmNhZHFw1m2QAqlsbOzo3A4rLOzMw2HQ1WrVdv0\ng8FA+XxehULBNsrx8bFevHgh6Qb+KhaLKhQKlrBvt9vGt0lOLBwO67333rPxX+TCFnOs3yZvMo4c\nQA+ze2Psvy9rTsTihcPuHSDWn8eWqTSVZKQBFFyQ+242mzbAORKJWB9uOp02pYOTwFoB+RQKBevP\nBhLlmlFoq6urxn2by+UCXfOiceQH47mYu1183iKs7N/X//66z1wmusWg4uyhjLyzQaEbRWOcOfK6\nqVTKjJ00T0PJGaT6mD2OoWUvBoWSgVaJpKbTGzaodrttBYVAx8lk0iBL9ggwKC1h8G/DMgQvL84e\n0R2RrifBuKtQoLiYh8cAU50MuuCNpSRDtzBCvsoYBIgzjcMAzE8Qs8xZPD8/NxapZDKpSqWin//8\n53r69Klms5sBKdBbgoRRP9Hv981JlqR8Pm8G7fvf/74KhYKdC9+eCMzv4fEgfc0YO5zzer2uwWCg\nUqlkPeRwIoNKXl9fq16va2Njw+wSgtND8VO/37duCe7LysqKFcjWajXrOPk6eWvGlskIkiwP6L0z\njKJX3B5yxhCwIX0e1nuwvjeUz14WRo7FYsrlctYfx2fDfLK6umqeHgxQ8XhclUrFIohoNGpTPo6O\njvSb3/xG0m3LAnM1YZBh43LYl7luad4ASjKFx+OLBtNDzqy1z5v75+Fx85jP+3I/ggoFLq1WS5lM\nRuFwWM1m04pcfKEJ0b8vkPOR7MrKio3Ww4DPZjMrmgKO5mCmUilrXQkib6o78Gu5aBgXI1z/OK9b\nVI7eaPu/fZNB/jZBWYO6cN9hggKhWKRk5N+QrcDwxT6A/pP8t3feIpGIFdMsc92kc3wrGykX5pQW\nCoW53CvpHq73/PxcrVbLDASV2PxmDTBw0m3E5avE7yr0eBIc4PTjxJIv91zerBvOPPce9AkniX+z\n7/1ITl7nnb0gQi8s7xONRvX48WNVKhVjYopGo9a6yGQjv64+cEqn0/roo4/0ne98xybw4JSdn59b\nSxPpCMg9ggj3mP3JhJ5SqWQdGTgypPnoayZ9l81mLT9PvUwqldKDBw/06tUry1nDYAaHNsHTtxXQ\n/dkrQu4gvroRT5MCIwyvr0rmh02EofYwoSd88AbJ5w/ZcP7/QQTYqdVq2dSLbDZrXKrg9BwqMPxC\noaCdnR3bRCTj4XSdzWZzwwyYq9lsNs3T45AEvWYfuS7mX4FB+BuK3heW+Mf95/v74o0MjpKPnJfp\n7SsUCtZ+Qb673+8bc83BwYEpV8+ERd6RyAzDDPwDPA4DkiSL9ongJpNJ4GHmyKIC9mvE33w7ijfM\niwVRi3/z92exkJD3C6pMOQuLrRJ8Fw99Ag9KsvXDaZFuDNr19bW13XgEiUgY+NS3VoEyBBHaLNAB\nnB/IFSqVisrlshW7eX7tZDJp7Tv06pOD83qBPCcE9dRMeLQtiABnwqzk0ywYFYwt+3fR+fJnlx/W\nnhoRaCzJX0NBuGxaJJFIGLkD6B4sWjg7rNnh4aEODg5sMAEIJoiJJO3u7iqfz2symdiMWb774rrC\ndx50dvBsNjNkAhj40aNHVkhZr9d1cnKig4MDdTodvXr1SoeHh/baYrGoUqlk9TMQsdAVEolE9PDh\nw7lakVKppJOTE33++eeaTCbKZrPfeI1vLbIlv4MBkOYrjvm7j74wzl5peZyf9/Aeo68mRPj/MobL\nt4/g2VerVYXDNzR1R0dHBl9wPTzfFyJdX1+rWCzqxz/+saQbhUMFHkZ4c3PT8oq0DQX1pvlM3/LD\nurKWHHq//t64cp/4Pjg1vlDDe92+2tk/L4h897vf1bNnzyxXBR81608VYL/ft/GDKP1sNmtGPhqN\nGq0cDoCHkskt8p60DFAVuay8KWfrjaFHCBYLpBYj1jdFt/75vM/ia+96neFw2IpUyLFyT9kX5BlR\n5uS3ff8zCAOG1EeRVHzTyuJztUS8QQRDglGhAtRX/GLcR6ORsbDR+kOKANrVaPRmvm2xWLRcLyxM\nnIvFgrllUCbOoU974YCQyyYSi0aj5mj7vYT+8FCpb40jbUK/u+/SWLZYEUcGCJ57/eTJE00mE+3t\n7anX69l4wnK5bA4z1eK0SE6nU9XrdeVyOdOdkUjEZt2enZ3NEXwQjASRUqmkbDarUCg0R/7x+vVr\ny5VDLbuysqL9/X1dXl7q6dOnxqmNTu/1etrf359D2+jr7na7KhQKikZveJ4PDw8Vj8f15MmTvzg5\nW9+/56OgxWIeDqWHzzAQHoJBgHgoUPJ9tsv21y5eO79pcajX66pUKiqVStYbywYHbg6FQuZNN5tN\nNZtNTac3rSxAS+Fw2IojptPbsU2Xl5fGGLMMJOsNpDeqXtHzOOu8CAn7XBD/9rk2HI9FuJT3Diqx\nWEyPHj3S9fW1jo+PDdblvpJziUajBv2haMkTkZNF8fMaOKJZc3KW5BCbzabW19cDT6JZhOm/7vfi\nc7wyXYSDFw0wz/FnhPu4zDpjQIEwqVilvYciNf9ZrJsvpMPA+kI6jCsRDWcWZedz8kGjLuo7fAHN\nbDYzfmEUNZXD/X5fFxcXVlSFscJwEPViIIhySVvgePggYVk94p3a6fS2VQpCf3KWtMgwZMOfPeB+\nDDKomzQfAVNxjV5cBkYmbUYajXTXcDjUy5cvNZvNtL29rUqlomKxqOPjY9XrdaNllG6cmouLC7s3\n/nq5f/6+UMNzfHysUChk6bm7SrFYNMcfO4Bznc1mNZvdVIHHYjGbXlQqlazLBGIb1vHo6EiHh4d6\n5513tL6+rrW1NbVaLXM8GUY/HA61sbFhNu6b5K0ySPnIFmXvIykUDRdN3gQD6nuofASLsSVH4wum\nPHvLsoeFQ9Dr9cxr6/V6Wl9ft+Q6/bhECkCZw9fTAAAgAElEQVQ7CMPJp9OpbUQiLIo+VldX1e/3\nNRwOre806GF5E8SI8SR68Wvhe5Z5LvfDQ14+Z+WV3ddFYUFlY2NDp6en2t7etjUil0VrxNnZmTY2\nNqwQgceJeqkEx4HBSNNcz4EgsmE/UfATNCLn+3tF6td8ETXgOez7xQKWRWPsxb+Xr21YNtoi2iev\nLWlOkQC94wwDvXY6HbsGoEqKWkAPYOIBVaBQSpJFZEElFApZr2Y+n7cUA4QWRLaSLGInzeAjS+7L\ndDq1Qjwib9IXOOn+LC1TP8E58ZWyRKn87vV6dv7Jg4Nu+VoU9CO6jh5PT8sIvIkTRhFYUMlkMnMF\ncjgEDGCPxWIqFApKJpPK5/N68OCB2u228T5Dd0l+vFqt6unTp2YQuV8gEzhEkoxEpNPpBIKSZ7OZ\n3W/uYbPZ1MXFhUqlkkXSqVRKL1++VDKZ1DvvvGNrhxPG2vX7fWWzWT1+/Fjr6+tqt9v60z/9U2Uy\nGVWrVStipTDs4ODgL0afrYcl/YZlkxPNsQkxpNwMX2RBIt33xfnIFkONd+6LNJYV2IdOT0+NehGC\n7ffee08XFxc2cxIv1HuBDx48UCaTsYIcxm1xk5nYQQVyu922TR00T7QISfriGh8heQILLyhw/zfe\nh9dOJreziP1nLluQId2w1vieNipFs9msMRvl83kjG59Op9rc3DRyACDOSCSiTqdjyhLlBddtJBKx\niEGSQXihUEjtdjvwWr/JuVmEARcfY53f9Lqv++3XdvEeBxEGrxO1EfF7lAOlAasZZwe4nfsxmUzM\nifRDKOhlxahiRKBu5PlBBEML2w/nxPdp0rLjDZTPdS4StaBTMHo4lMlk0tbD656g1cgYef7tnV7O\nEQaRaJVWII/QeT2JeKfAG3MYjSh2azQaga5ZuulprlQq6na7Nj8YQ8X5qdVqevXqlTY2NpRKpVSp\nVGxQBBA0EPNsNtOHH35oEPhirQ01E1SVM/B9a2vrztfs0SocwW63q06nowcPHqjX69mov6urK1Wr\nVW1sbOjg4GBufwAbn5+f66c//an+9t/+20qn0/rss880HA61s7Oj7e1tvX792riya7WapPlZw2+S\nt9Znu6hUfB7WMxPhmZCH8YqcSJWNxoakehDPddGj9MVSywjvR7UjjsNkMtHR0ZE6nY4ikZuB2PV6\nXZPJRI8fP7YogeECbLLNzU29fv1aR0dH9p5A0C9fvtTBwYG2trZs0kYQ4WB6w8rhRgkBN3kF63tx\npVsomnu1uHZ4gVRPsh4cxqDCUGmYnyD7AJq6vr5Wt9s1RikKqXykRD4xlUqZwoGAgX1AlEskLMmK\naYLuj0UD6uXrCl0WIeTFHK5HERaf469vWaSGySXsCSJYnFJgPUnm7fvr9/cXpxcHk/OJUeN9QREw\n1stcOznWRCJhLGsUHrVaLYuMvLOOgTo/P7cBGTgTEGNIt3NyWRd6uvke0nI9++gN7h3nxZ89+lPR\nV1RZE8mj5/guPp8OxO+HK7DfI5GbwS1BHUhJNs2rUCgolUrp8PDQWqwajYbBwqTDqPpNJpOazWbm\nJIfDN9wBzAcGKka3MLgdRjMQB5izggjczUT8pEOY2DQajVQqlSwNgj5mOMnFxYW63a7tz3K5rFKp\npJ2dHeOD3t7eVqFQsLXBwcRB+ja2rrdqbPntK1v5kW4rib1B9QVQHBIONTfQQ8yS5t7DR2RBhRwz\nN8AXlJBPRWnDTMTficCvrq7U6XSMpIIDmM/nzfPlkMBzSlM+OYcgwoblOy++3nuT3tiiDPn7m1od\nMMr8nef7PPCyFZAPHz40hXp0dKRqtWrThIAmQ6GQms2mEomEwuGwut2uQcgcdAp5MBAcKA4G3q2H\nOH2BUFBZNKhvimYXDe+bXusfX4SfF42zf31Qo8WaeM5v34IHysRaYiCBmIHw+V68z8rKir0e1ATy\nAsgxMA6+SDLIdUuy3sZFJihqCsjdgmz4QqTJZGLoEtfvIWRYjDjbi0VvQdMM3tH3Dr8/g773ezab\nGTTLuqNHpFvyGZxMIjh+eE8gfU96EfS6Z7ObPthyuWxI2MXFhbLZrKXLyPsTsaKTGXNJZwD97gwj\nwMGAeYm2K4hqqNwOIsDlwLqHh4fKZDKqVCp2nkARMKy0FkqySDiTyWhra0vvvfeenjx5YjnpcDis\nbDZrLFQ4BxS44WR8k7xVGJncj2/38YqaiNb30HpuU7xScnUYVQSj4auS/UYOKhhbNjtDBcg5ABFh\nONvttl1Dp9PR2dmZTQKid5SbmkgktLGxoZOTE52dnVmrRLlctqIEKuaCiIdIvXjl4dfrTfDkmzY6\nymbR4/SfF/SAeNnc3LT1pliBghbPuDObzYwreXFMINR77Dfu28nJiQ0eGA6Hdu8Y+oAEZa3xCtgj\nMP7/i5HsomH1a7b4Xot/9xHuspGtN7RESuS2qU3wBT0Uj8TjcaNKTCQSxgHuextR9D5axthFIhEr\nulnmLFJJSkSCg9psNtXpdObavtAPGFucAOBsX+TH92dfgZ5kMpk5lMg7UUHXG53k7xe5bq8DQ6GQ\nOp2OOp2OcUrH43GrPwA1wGiAhuHge6ja16wsc81UfYPKZbNZSxNQU4LeazQaOj8/VyaTsZQb1/L4\n8WNDLGkBQ7/3ej0bKNJsNg2tGg6HS3E6w1XQ6XQMLchkMmo2m9ai2e/3lc/nLVWA042+qFarevfd\nd7W7u6tKpaLpdKr9/X2jczw6OrLK58FgoLOzMyvY/Lb98daMLd4n1cjeWBIRUSHo8xXkl6TbBnP4\nbzG+Hib0OV4Pyy0jKLdOp6NMJqPd3V1Vq1UdHx9b5Zr3smezmQ0hHg6HdlPC4bA2Njasp5be0f39\nfYM7MRiwsBAtBD0s3vDhuftIYjGy8l62/zsKxkPJvBdrDBztPf9lofpqtap2u61isWg9zcViUcPh\nUK9fvzblEwqFbMQXUiwW53hkB4OBCoWC5Wwp+KCAgsPF3sEJXLZAatFgLsLGPO57kv0PRsq/59fd\ng8W8eFDDBUownd5O4QGJCYfDqtVqdm99NS4RMEgU1KOgPbwXkD4RG3vEV7NzVoMIbV8oVCILCgop\n9uK6iaLZjxguWInYD5KM8Qgl3e12rX3E50OD6hGf8wWFw7gCb7OnQbiGw6ENPEin00bzCpUkUR8t\nayj6VCplyBlOUygUMng9iPheWJwoqtUxlPRoU0zHMAHIZNgb1WpVtVrNHAKfrgLtAKbF2SXICiLc\nWz+RB0PK/ZRu9uWDBw+s82M8HpuhL5fLevjwoSqViiGocCCwZ4fDobWS+eAP9Oyb5K0YW2Aa6feL\nPxYLEHzRkzegwBoURvkbw/M8fCzdkup/XV7t24SmZgo7/LXzORhOogUiMiqR2Ugoi/F4rPX1dRvJ\nB4kFJfKsBV7vMtftlZqPPH30hZFZjHSleQJ8DAm5ukV4zUPJfxYUYTKZaGdnR+PxWA8ePLB8T6vV\nsp5jFD4wGRXhQEjhcNgqBP1gA16HQvMwI8bAFwYFWWf/b9ZrMTfrUQGMK69ZPA9veuxN0fMyayzJ\nlJ5XkhhM6XYiEhAt68o6SZobEs/38+9F9Mk+5OwT3QFLBhGiKohNfFsfn+uFvmvWjopaFLKfVsMo\nO+kWvYKGz9eELGO4+N7oJK6LPCt/56xzbZ7qEjQBI8TrfU4ZQflT4ZxKpQJfL3SNkqz7YjKZ2DXQ\nqsS4vUwmY0ERNJnT6XQOYcBosw44wrSP+Zw47xdEqITH0LM/cALgaQZyp+J5NBrp9PRUjx49UqFQ\nMEpS/j6dTo0PgeujFc47UXdxDt5aZOtp3bz4x4BR/GPem8Swem/TKyD/4xUWzw1aBv/y5Uv9wR/8\ngabT295faZ5wH8gNxbKYt+MmeUVMLpfGfA9/eyNJlV4Q8V4/338R4vDGlLXxudfF9fdEBD5aWzTI\nfxYjQEU2Q+Rp3cjn8zo9PbWcIF4xpBdUy0JKwVxcYOLFMWXkbCVZMRX9uEELu74pp7f4t8Uo2Btd\nxD938X7wmDfi/hzcVdhrfH/ptlLW53ElmZPI3zAUFN94vnLuO5A0kTwFUt1u1yIzPxLxrvLZZ59Z\nYQpzSn/1q1/Z8I7j42MNh0ObG03+kO+ay+WUzWaVTqeNrY3CRqp2a7WaGZVKpWLVsaQsPvvss0DX\nfH19rVqtZpAuzikIAXqF+bZETVT+SjeVruQ5PZkIhoUxnuwH33c8m820t7cX6Jql255mSYaC0O3h\nHQDGU7ZaLV1dXalSqajdbhtF7drams7OzjSdTtVoNHR8fKxsNmtTmZgtjB4kFcCZDCLsSTgNCN5A\nKE5PT43/GjQAaJg9CrRN/3Oz2bSJR/V6XQcHB5YaIe/MQHkcnG+S0GxZ7Xgv93Iv93Iv93Ivd5K3\nwo18L/dyL/dyL/fy/89yb2zv5V7u5V7u5V7+nOXe2N7LvdzLvdzLvfw5y72xvZd7uZd7uZd7+XOW\ne2N7L/dyL/dyL/fy5yz3xvZe7uVe7uVe7uXPWe6N7b3cy73cy73cy5+zvBVSi7//9/++arWaUqmU\nOp2OisWiNe9vbW1pMBhoNBppa2tL5XJZ3W5Xf/qnf2qTGh49eqR4PK7Dw0O9fPlSa2trevr0qZFC\nQ8/3/vvvS5J+9atf6aOPPtL19bWeP3+uZ8+e6S/9pb+kv/t3/67+3t/7e3e+7v/23/6bXr58qXa7\nrWw2q2q1qu9973sKhW4Gwx8fH+uLL75QJpNRoVCwRm4ICWazmWq1mur1ul6/fq3p9GZ8V61W01df\nfaWNjQ399re/1dnZmYrFog2hHwwG+s53vqN0Oq3V1VX95//8n+98zT/60Y/0D//hP7QJKTC0tNtt\nXV9f28QLP9cVCsRKpaLxeGwk3blczhhUaGKHMADCgrW1NSUSCSWTSWWzWUWjUf37f//v9W//7b8N\ntEf+1b/6V/oH/+AfGEc0jD9MPpFuKSU9qw9EG2dnZ2o0Gmq32+p2u8ZLnUqllMlkjDCDH5i8stms\nstms4vG4/vt//+/6D//hP9z5mv/rf/2vCoVCc4xDcL8eHx/r+PjYCAa2t7f1+eef6/Hjx4pEImo0\nGhqNRur1ekqn0zbOjD2yt7encrms7e1tPXv2zEYudjodbWxsGAtWMpnUf/pP/+nO1/w3/sbfsNfV\najVls1njMi6Xy0okEvrwww+VyWR0cXGhjY0NVatVNZtNPXr0SJlMRmdnZzYfGNITGH/+x//4H/rj\nP/5jY/6q1Wr63e9+p5/85CeqVCr6O3/n79hg8L/8l//yna/7v/yX/6LLy0uVSiUbiXd1daVms2kk\nJ7Bj7e3t2bCP9fV1bW5uGlEETEMwLRUKBRsw7ilHc7mcffbm5qbtNZim7iIrKyvK5XL6x//4H+uv\n/tW/atcBEQhkEb1eT5eXl9rb21OlUtHJyYndl1gspuPjY1UqFaN3/Oijj3R+fm4TsGA0uri4UDQa\n1cuXL429aX19XT/96U/vfM3SDZnJ97//fUkyUh1YozyBix9V6Nms2Avj8Vi5XM5oP2HwYhoa5Cfp\ndFqlUkmVSkXb29v68MMP9c/+2T/Tb3/720BrzSB4P5CGiWqe9hS6YAiJoCuVfp8kaTQaSdLc+EbW\ngO8M+czJyck3ctm/FWMbiUS0tbWlV69eGeVZqVTS8fGxzRlk4PdsNjMydPhVa7WaHjx4oN3dXbXb\nbTWbTdXrdW1tbdlopV6vp7OzM6XTaSWTSb148ULValXZbFY/+MEPtL29HZjdaGNjwzhBofZqt9sq\nFAo2+q1QKOjFixdGeg2hO1Rh5XJZ4/FYDx8+1C9/+UtVKhX98Ic/1AcffKD/9b/+l9rttj0HjtbZ\nbKbhcKjd3d3ArEbpdFqpVMoOAJR1fkYnk0PG4/EclzSUcZC5p1IpcxwwzqPRyPhcYWhJJBLGtiMt\nR9cI8w3cr55AHiYgz9LEAG2Uip8Gxd5hoPV4PFaxWLT7wsxM2HBgr4Hf967y3nvv2eixcrlsa9Tr\n9TQcDvWjH/1I5XJZsVhMv/nNb/TX//pfVywW09HRkbESQWH46NEjm3wymUxsBjK0fv1+X5VKRZub\nm8b4BJ92EOl0Ospms9rf31e5XFYoFDLeaaa8wHvLXFLoA9PptBH6RyKRuVF96+vrOj4+1vb2tuLx\nuJ31L774Qj/5yU+0vr6u999/3xy2oGuNc8HEGfZqNptVLBbT2tqafvvb39o+HI1G2tzc1ObmplZW\nVowNKxaLKZPJSJLx+MLxC9PTcDhUo9Ew5c0aB+XOxsgcHh4aIxpsULwXzmU0GtXTp0+NND8ej6vX\n66lWq+nhw4dqt9vGqQxrGpOMOG+cH8YoMsAgqMBmN5lM5oa+DAaDOUPrWfpwuPL5vFHTwgKFXodP\nG1pK9g1j8Nj/n332WWDmPE+3imH1s6vhZfbfD6MMpST6wTOrEVhcX1/bHgyFQnM8yJ5R8JvkrRjb\nfD6vi4sLVatVm5oQi8VUqVR0eXmpfr+vYrGodDptip2JLnjdv/zlL/Xd735Xm5ubNubr6upKOzs7\nOjo60vn5uZrNpiqVih48eKDf/e53Go1GevLkyRwXbhApFou6uroy6rfpdKrPPvtMjx8/1sOHD7W+\nvq58Pq8//MM/tGkSTIoYjUY6OTlRqVSyGYiXl5c6OTlRKBRSPp/Xd7/7XW1vb5sRxxMNhUIW+QQZ\noCzd0lMyOq5Wq6nT6SidTmttbc0iVbxhPG0m7WB4Ga/GARqNRnaIoL5LJpOmkNmknps6qMCLzeb2\ngyv4bp7O8OLiwriQ8WJZ516vZyPhUGwYJ2YSM1EI0vWgdJ6/+tWvVC6Xtbu7a5NvpBuKu2w2q9Fo\npLOzM0WjUT1+/Nii7nq9rmKxqPPzcz179kzf//73zXl88eKFnj59qsPDQzUaDRu3COd2PB5Xq9VS\nsVg0irogEo1Gtb+/r0ajoXQ6rXw+r+PjY+3s7NiYwmw2a++fSCTU7/f15MkTM7AoSenGeCcSCXW7\nXTMEGDAMBFFNpVIx2s2gpP7X19fmkIN4XV5eqtls2r7NZDKaTCbKZDLK5/M6Pz83575Wqxkagl5h\nFitG1s+RhVoyl8sZaX3QcXXsS6gkQ6GbUX/pdNrOH07eYDBQu93W5eWlMpmMBoOBhsOhrq6u1Gq1\njDAfNAAndJEr+fT01PRPuVxeaggL5219fd2cGxAh1gDjxlAEHPBut2tGOZFI2MAFSTajPBaLKZfL\nGcd8qVQyY46TEDTICIfD9jpPZQq9KAZ3kT5VkhlUggxPQ+v1GU4GziaOw1054d+KsUUhxuNx4x/F\n+LXbbYtmt7a2zJPMZrM6PT3VwcGBCoWC8SZXq1W9fv3a4JNoNGoe63Q61cuXL01ps0my2azxbgYR\nIsCjoyOLAPr9vmq1ms7OzvTJJ58omUzqwYMHOjk50cuXL1UqlWw4Qb/f12Aw0Mcff6zhcKgf/OAH\nyuVy2tvb087Ojj7++GMNBgPjOj05OVE+n9ePf/xjffnll4pGo9rZ2Ql0zb1eT/V63WbsSrLZk8yc\nZIoHisXzMXsPjdf5ucAoByAhDy/58YZBhYiWKMXzIzOLFm5eDqLngSXCIiJnmHO73TYv+fr6WqlU\nykjl4admNF9Qw7W9vW3TWHBWUDrJZFKVSsUg+E6nYxE601j29vb07rvvajabmeP5wQcfGOdtv9+3\nwQBEmkyrASIN6kDi+O7s7CgajSqTydi4RxQo6FA8HrfUAxHfysqKTaLp9/vmvNTrdY1GI1NYH374\nof7kT/5E8XhcL168MON9fX1tqaAgUigU7DM9VBeLxVSv15VMJg1pwcCvrKyo1WqZkWBC12g0UjQa\ntalS+Xxeg8HADEskEjES/1gsZnsuqLHFYWVk3mw2M2iY+cAo+mazqV6vp2q1qul0amhDMplUr9dT\nPB7X1taWOSvAnszbxjFlPwOZB3UgJdn0Ic4/M4qlWz50JmWhHxgq0O125/jYOc/oj1gspkKhYBN0\ngHHZ1/DGLzuBi4gVowmHN06Hh4AlzUHLON3+TC3yxQMb+4Ej8N1/m7wVY8sQgsvLS21vb6vb7erB\ngwc2TNhP1WFRms2mksmkDV7nRm5ubmp7e1u1Wk0nJyfa2dmxKTmFQkFra2tqt9uWe4S8nOg2iHz6\n6acql8vmkefzeX355Zcaj8d6/vy5pBsoEe/45ORE3W7XvhNw2fn5ufL5vDqdjnZ3d827bbfb+r//\n9/+qWq3qk08+0dbWlkHAjx49spFzQYRZk4yBSqVSGgwGGgwGNgeWvAneOoceQ8mGWpyNi6EAgmVc\nmR9jtsz8TEl2fUBqfvTibDYzD3k0GpmnDNyGYcDoe4MM0bmfosSsUqIZDl9QZfrpp5/q4cOHSqfT\nevXqlcLhsAqFgkqlkmazmV69eqXvfe97KpVK6nQ6Ojw81Oeff67333/fYE7WuV6vGyz9i1/8QplM\nRpeXl9rY2NDa2poODg5UKpV0dHRk0dpkMtGTJ08CXXM8Htfm5qYNhI9EItrY2DCn5enTp3ZOGFtX\nLpfVarVsXCQObrvdViqV0ng8tnzpcDjUzs6OUqmUfvGLXygSiajf72s2uxnszrlkjNxdhVqBwWCg\nXC6nfr+vk5MTGyhATpP8ZyQSUafT0fHxsa6vr21GbavVUqlUsigSmB/nXJKdDQ9B+iESdxXgVdaK\niTTn5+dKp9OGBGWzWX311VfmCIVCIWUyGUWjUcsDJhIJTSYT7e/v24g4DB7Gimj3+vpalUpFsVhM\nJycnga5Zkg1A8dPHIpGI0um0pXZ4HoYNhxcHDCO2WOvhpykxXAF0iYDADzsJut5+zCoGHoNO0MBZ\n9znYxRGIfhiIj4L9RDny+3cdBvJWjO2rV6+Uz+dNqRcKBdXrdfMoJRkktLGxYXnA58+fa21tzTyo\neDyug4MDbW1tqdfr6csvv7QRdihZRrE9evRIrVbLYDAigSBycHCgTqejnZ0dVatVJRIJ7e7u6vnz\n53rx4oVBfx9//LFttJWVFU2nU21sbOjq6sognouLC6VSKV1cXOjhw4dzI+ueP3+uZDKpjz/+2G4u\nxUF//Md/rL/5N//mna8ZheAnXnAIyT+jNAeDger1+tyEGWBkvFAg4tFoZJ6nJIuOr66uDBriuwdV\nSpIM0gXaRgECleHxMtSaz2VKSi6XMyXZarV0enqqZrOpfr9vyp75phSEMZoRCRqR5/N51Wo1vXr1\nyqCxy8tLFQoFDYdDbWxsaDKZmEHodrv6K3/lr0iSjo6OVK1W1e129bOf/UxPnjzR6empotGofvKT\nn+iP/uiPLJKTbgzAYDDQD37wAz1//lx7e3v6zne+Ezgnl06nbRRhq9VSvV63PP/u7q7Bp8Vi0aID\nnAjyzBgO9hSFSr1eT6VSSRcXFzo+PlYsFlM6ndb29rYqlYpF/EReQaTb7dqUHuBClDbpgm63a2c9\nHA7riy++MIg8Fotpb2/PUiAPHz60VAJw4XQ6VTb7/7H3Zr1xZtd6/1NkkawqDjWPnElJ1GC11G6p\nT45P2kBsAwlOcHAOEiDIXe7yIfIJAiRfIBcBkutcBEmQATmJkdiJ7W734B7UaklNimOxijWPLBar\nWP8Lnt/ipuy0+fIPC77gBoSW1CK56333XsOznvWsiEajkUKhkGq1ms3X9hqISbIEot/va29vT8fH\nx1bi6fV6ZrsIwoHip6amNDU1pefPnxtKx3MbGxuzIexTU1MWELEgRs7MzKjf7+uLL77QX/3VX3na\nNyTN2dlZu9PsgRnCvV5PoVDIng02A4csXYxmpGQYDAbN7hHskuFy95ga5NWGvOlYQREIHN3vRxDv\njnvEh7w5tpDvyd+7JUk+85vf//+13oqzlWRzECUZ6QNojcjh4ODAam937txRsVhUPp/X6empVlZW\nLsEWuVxO1WpV7XbbWKXM2ZycnFSv11MymVQoFLpEWvKyuNy1Ws1qp0tLS0qn07p3755ev36tSqWi\nlZUVTU9Pm3Fin2Spkiy6phjPWLdIJKJ+v69CoaBQKKTl5WVFIhFJUiQSMVbgVRfZ6unpqUWM4+Pj\nRiYigh8MBqpWq8aYlWTkkUAgYHVaGMcYORaRoBv1USf2OvZN0qWgBAPF30sX8BWQO4hBt9u1geKh\nUMjq1Lu7u9rZ2bF5nJIsiAHWwmixX68GlQASpne9Xle9Xtfp6anm5+c1Pz9vQRMjAWdmZgz2pBb4\n4MEDlUolRSIRpdNpffTRRwoEAsb8bTabWltbUywWs2dP3dnrKLJSqaR+v2/scZfhzcjC2dlZxeNx\njUYjzczMXAqA3Hmr1Lnn5uaMlARyc3h4qFQqJb/fr6WlJS0tLRnrFyPuZQFVFgoF9Xo91et1hcNh\nY/wy4DuXy2l8fFw7OzsaDAbK5XLKZDIaGxvTwcHBpcyLGi7lFD4D54nPyue7ThApyWqZ1Lv5u2Aw\naIYeTgTozvHxsdWLX716ZaS+RCKhk5MTK4XAXXDnB+NIYOV7XWdnZ7YPF/XimRC80+1ArZXPNTs7\nq7OzM7VaLc3MzCgej1u3Bba6VqtZeYjyC7Vy3omX5SJa7n7JULGDbi1Wkp1tdz4zz88NvsmaWZBH\nsfVXycTfirM9Pj42x8McwHA4bK0nRJN+v1+VSsXmHS4vL2swGJhxSiQSRsKgVYWMYWlpSeFw2AgT\n+XxeDx8+NHLJdWDkdrut6elpPX/+XJFIRLlcToPBQKVSSb1ezyBxCF1AOc1m0yLvSCRiFwqyTjQa\nNYP/wQcfWCsFtby5uTmDrbxmAP1+3w5Aq9Uyg0xW4kZisAK5QKenp4rFYkokEorH4xYhggjQ5uOy\n79xf7uH2ulzWM/tidiT1W+Afdxaw3++3jBvGI1l3p9NRs9k0As3k5KRmZ2fN2HM5IYh5ZcjeuXNH\n2WxWPp9P1WpV+/v7arVaWl5etsyxUqmoWq3az/7Vr36lVCqlVCql3d1de99bW1uKx+P6+c9/buWS\ncrmsiYkJvfPOO2o2m6pWq5Z9TU9PKxTs5zMAACAASURBVBqNqlKpeNozRrBYLNrsUHd+Kk7e7/fb\nuXbbSur1ulKplNrttkKhkMG3U1NTunPnjpUgstmsUqmUKpXKpVYu7j/Dya+6ms2mRqORksmkhsOh\nZXoTExN68eKFOSey7Uwmo7m5ObMVpVLJSDn1el2zs7NKp9O230qlokQiYbA4cCz1QxwxTOarLAyw\nG3AD+1JPhiwFDM9n6HQ6CgaDxiOAg5FMJg3RcQNdyiu0QQKjeyUaSboUfOIA3yQvkjgRABMU4yy5\nUzitqakpY1pPT0+rUqmYY3Rng8Ogvo4NcZ8H95qM1y1JAf8SnGAzyLj5/wTf/NlNMPh37uf+o2Aj\nMyQ4HA4bS5QscHx8XDMzMzo9PVUwGNTh4aE++eQTvfvuu/bwx8bGdHh4qEQiYbALBisQCFgWs7a2\nZtnCnTt3lEqljJBAlOZl8bPn5+f17bffam5uziDub775RtJ5XyvGyT2Ybj2ASM8lR0xMTCiVSimR\nSOjevXtWLyL7Oj4+tpqJl8WBIwqlDYWWEb/fb46d6LdSqRjDG4gImj4wdL/fVyAQsN5bmJuSrE7i\nZqFeF8Q4tz5GZkFAAAGEGiwkLxiM1HuBlcnOqYvCfAdRIcPBqXslSEEWI0rOZDKKx+OW3bqw0/T0\n9KX+z+3tbWWzWRWLRSMpffXVV1pfX9d7772njz76yAzX7OysKpWKer2etUjAAgYFueoKh8Pa399X\nIBCwMsHKyoq1zADx9no9O8dv1rclWVY4PT1trOPl5WV9+umn9nkwwu65A372+qy51wx7TyQSajQa\nmpqaMmiYvs1IJGIOdWZmxjghGF2CcUlGGiTYwDl3Oh0jOF2HiSzpEpv19PRUpVJJd+7cuZSFYheo\nVVYqFUUiEZ2cnFhCsrGxYT22jUbD6s+0EPV6PZXLZTUaDY2PjxsRjLKJ1wXUjSNk8fzI/kAMXZvD\nZ+KZ0q6E04fI5ULJwWDQ7jgO7Tp7hiTLzyT4xvm75xcEjkAex+z+1y07cQ/cNkGSK7gqfxTOlloJ\nkBMOYHx8XJVKxR5INBrV/fv3dXR0pGfPnhksmc1m1el0lM/nNTs7q8XFRTuURNu9Xk/dbtcyo2g0\naplwJBLR3Nyc55eI4YecQ3RMnfDbb7+1mhdGkIzDrc00m00tLS0Zc5A6KU5sdnZWw+FQs7Oz1qfZ\naDS0v7+v0Wik999//8p7phUHx4WAAUYKoxMOhxUKhbS0tKS5uTmDjGFlAqVI59kyLEM+GwfMZfJJ\nF5Gt10Vk6PbxnZyc6Pj4WPV63fr2jo+PLxkZkA4uARBXNBrV8vKyQbh89uPjYxWLRfV6PTO0BEle\nz8enn36q27dvmzEFGaC2RZ8idcTp6WnF43EdHBwokUjo5z//ufx+v8rlspUTFhcXdXh4aMa/3++r\nVCoZ2hMIBNRoNBQIBPTs2TPPyEe73Vav11OpVDJnxF7b7bb1C8PElS4YmJ1OR6PRSOVy2aDWo6Mj\nlUolZTIZQwYQESGbHQwGSiQS8vv9Ojg4UD6f9xz4BoNBHR8fX2L8u0zdaDSqfD5vNUR6faempnRw\ncGDCNK6QAZk77H0CUj4vPe+IM3gl7VCThNxZLpet9tdoNJRKpTQ1NWU14k6nY1wCymzNZlOZTMba\naGir4WwBj4MSTk9PW5KA87rOIiOEr0FwdHx8bLY2EokoHo8rFAopn8+r1+spHo9ramrK7A6lMshP\nBO84MJydpEv7vk7NFnuLfcA5unwUnv+bnRNv2jFJl8RaXAi93+9b4uTFdrwVZwu0A7QqnbNmoekT\n3Z2eniqRSOjJkyf6+OOPLz2wVCqlcrmscDhs2QoRxmg0UrvdVrFYVDKZ1MLCgkXWiURC6XRayWTS\n874hEeXzeWUymUs1TIgmsBohCcAcxPhS16hWq5IuMtdoNGpQKSoyrVZLtVpNxWLRDF4ul/P8rMkS\nyT4nJyfNEBI8QHCAJQgMRVQHAuFGsYiN8P2h9YNUAAtdx9kSSbvMSqBj6s9kjHwO4GQuQLvdVqPR\nsFoq7HTY2DhsF56mtODV+EvSgwcPrC5eqVS0trZmGQXta41GwxjD6+vr6vV6yuVyRq47ODjQ9773\nPX3xxRd65513VKlUDHbrdrsqFouG5vR6PaVSKVOckqStrS3PzzmRSKhcLls9rlKpaHZ21u4oJQVa\nfWD4E8CBAkxOTqparWo0GqlUKikcDiubzWp/f18nJyeanp5WOBxWp9NROBzW3NycPvroIwuwvazD\nw0PNzc1peXnZiFb0dwKNoyI0GAysH9xVP5Nk5xgVI6B0gtFIJGJ3A6eHgUa16aorlUqZDaNHGXY0\n9WyCWpjK8A5SqZTtAyKXy9Ql66TeyPeZmppSLBYz5+G1dCbJsjjqqdwvbBjwKUkIdxQUhkyS+4V9\n5s9ksWTALveDn32d7Fa6ED/h68m23Z5aHKybjbIHHLNb6+XrCdLcdiC3g+OPos8W5p8LuZHGA2nS\n01Uul40AMDk5aYV3HHK321UsFruUaZG14hQ4lDCfYch5rcnhKF14ish3ZWVF9XpdCwsLRhZxWYvA\nKa54BFGd+/Lcni96A7PZrIkeeKkR8XNdsgeLehNZtCTbC1Eh/x0bG7MghvoJ+6VfEcIV7Tcu9H2d\niwLrWLog0L0Z4XLBXbo9z7jT6aherxshKpFIKBKJKJlMGvEHWVC3f9BtVPcKFf785z+3DBr+AK0b\n5XLZgq3Dw0O99957Oj4+1pdffmn7mZqa0tOnT7W9va3bt2+bIEetVtPr168Vi8V0dnamg4MDa+PK\nZDI6OzvT5uamisWi9vb2PO2Zfl0MKQEf55X3hxoW5/3169fKZDKXSDiUcNbX182QAhFGIhGVy2UL\nOlAwQyTFq7NNJBIWkEsyZIsaJZk2e0NsYWZmxoQwGo2GqSt1u11DkoCOXcIZULpbnvAakHHvgTVb\nrZYqlYqWlpbsvpDRYifcvlgQE1fkBdSKgBdHODk5abAumS/Pw+sio+31etbrDTeCbE6SOWDqngjn\nuIgeto5zwb8lOCD7d3UTpqenPe8bRzk1NWXCQAQDLmLGu3QhYpaLqlGjfTNocdEKvsYlY33XeivO\nFniBixEOh3VwcKCZmRmLwrrdrur1urEgw+GwGo2GRZhAyrBoEWzY29tTNps1ubhqtWp1YGpKZJ6Z\nTMbTvhERGA6H2t7e1ve//31Vq1XFYjElk0n9+Mc/ttoWEb8kg0yIujFubgYMWQCjPxgMLFPk0tB0\n72Wh9cuhQYmm3+/bIcShkuXS9A8bGXiKg+b2vLosSaJYDuLY2Jixiq+zOMjUdTCEwDlkSS7rECNA\nhO3z+czQIHBBXdM1XBgStybsdd9/9md/pvHxcR0dHVlGkc/nVS6X1Wq1tL6+rnK5rPv379v5ePLk\niba3t+0+AAf6fD59+OGHhppgBFAlc43/0dGRTk5OVCwWLcO96lpYWFC73bYgBOPT6/VMixsWqauq\n5LY8gB7k83mtrq5eMjLUSufn5/XNN98omUwaeXE0GmllZUWJRMIzsWtzc9PESgaDganNQegiy4bI\nBeRHIEjZio4FsnfamgaDwSXyH/26yFfCfPayCLDJ5CHonJ6emuwrWa10Xj8eGxszaVhKBCQeBBFu\nvdFNBNzSDp/t/0+GCCJGbRX7RO/0ycmJAoGACbMQXODkWq2WBf6cJbo2otGoms2msZ4p8eBwr9Nn\nK120JPJ8cPDs783s1eUjSJeJTjhR6UIxi+9L5u62jf2+9VacratS1G637UOUy2WNRiO7OM1m8xK1\nfG5uzvqfeCmwgcfGxpROp1WpVCwLgzBCHyFZgM/nM9jX62q1Wjo7OzNSxf379+0hE10CN5EFU3tw\nFY+A0ome3KydSJWa5ObmplKplA038LK4sPwsLiYEmF6vZ2IGRH/ATxgmt9bhZr4uocMlnfH5uSyu\nbuhVF5eLjIearatTymdwW8DYKwEbAUaz2TQBBDJxnDlBBmSpcrlsZ9HL+vjjjw39ODg4UCaTMaIO\nLUFjY2MmMB+NRrW5uWk1POpA0WhUr169MtnH4XCoRCKho6Mjq38Sse/v76tUKllW6rUmFwgETMSC\nLKXRaNg5QfIUdINsPxaLKRAIqNlsqlAoaHp62pjdz58/17179yRJ2Wz2EtQ8OzurbDZrfa604nnd\nN0E3ma10ripF/WxqasqU5MhmuAfAnG6Pp0u+CwaDSqVSFnASVOCEIed5zcbhStRqNdXrdZVKJbVa\nLVWrVes77nQ6hrZIF0IRJB3wEHCkELe4F9xT2hRBq4DSt7e3Pe1ZOs/WsNPSRaZLDZ59oWsM6xvm\nNk4Mpj2kUMo+ZMsgZpIMteDzXbfNikCd3/M83MADZI+ExO2scBnM/Bc7ytdyV7DlJAR/FJnt6emp\ntre3NTc3p0qlYgaTKNRtNaBOxIQgomwIHDCC6deCSMDkFi5yLpeT3+/X/v6+9Q16pcFDNkCGrtls\nqtlsWrZMkZ3ojyZ2goKzszODgXEE9GeRYWH8XXk5hCeSyaTnaBpFFshi1FAp5HPZiTj7/b4FNbT4\nkAFyAOnr5MK7jpcDhpMjwLjOogaCDCHPyO1lI8ChYZ2aMcLtlCqoc0GgmZ2dVTAYNJGO4XCoWCxm\nYieQ27yswWCgeDyus7Mz07ju9XpaW1vT2NiYCoWCDUMIhULWpnZ8fKxHjx5pb29Po9HI2OLpdFov\nX740EQ6MlSRTA0JpC6fptYWGrBDeAz8Hwg7PaWxszBTQYMB2u10TihkbG1MkEtFnn31mz412p6mp\nKUOfKDNUKhVrcSPA87K4Zy9evFC1WtX6+roFBclk0uq3R0dH1nuLchN1W7d9jDNMCwvGkgEjnKdQ\nKKRSqSSfz2fTiq66FhYWDE5l6AXBAax/3gn3vN1uy+/3q9VqWXuP2yYJ8kVgwb2DRCXJgoy9vT3r\nmvCyEK6A5IRjJMg+Ozsze0CPMBk2DH8EL1zkkqSpXq/bZ+Pfww+h1/g6ztZ1pthfzvib5CccK8Ek\n+3jz379ZqyW4caFj+pz/KNjIpVLJJq+Ad2cyGR0cHKjRaBgLb2JiQl9++aW1IkBgmJiYsN4x4EQ+\naDabNaiw1+vpyy+/1JMnT8wgICnnsmuvuoDpMJLUICRZXQqpMrcVBRgL5wTEiaHhe6An6vP5rBfU\n7/drYWFBkuzrvKxCoaB8Pm8XAkfKBUJlhwyVflUOF0EPkS1ZIVAd74PsELiWgypd9BV6WTAYXRYk\nqIZ0HoWic0vQhDFgjF6z2TSBCZADShEYDYIMakywhIPBoGcdakn66quvNDk5aazker2ug4MDywIC\ngYDW1taUTCb14sULjUYjGzhAGYGA7D//5/+sQqGgdDqtTCZjRK9ms6n9/X3L0tn74uLitaBNMk6G\nD7Tbbc3Pz9vEHtSDxsfHTdULVm+327URmeVy2XgXbs/y2NiYWq2W5ufnNTc3p1arpW63ewnS9Soj\nyFCQfr+vra0t/bt/9+90//59PX361GA9EC0CTdc5EjQAGcL+JoNHxjQajVqADWEPaL3f73uST43F\nYjo6OrrUV7u7u2sCOZxt7Mjx8bHtHx6Li4QhgkFdEzSQfuFarWbv8+TkRPv7+545Hyzgec4XrGzU\n4nBAPBeCX2wIyBl2hUTIVXXC7rgDTCDQekWZ3A4JziIQLw4Tu4RjhcT6ZjaLb3EhY7esxt+7bXBX\nUZ97a5mtC13ywHO5nMF9NFBzaHw+nw4PD3Xnzh0jPLmMYqBLJpUwxSSXy+n999+3g0L05TVrkaRf\n/vKXkmQM5L29Pfl8Pn377bd6//33FYlEtL6+bozBcrlsbUGS7MVzSPn8XAqcMgEIEC59dFx4L6vV\nauno6EhnZ2cGRZOJM3kECBBSEFEaWszArWSEMzMzVs9z94ODBYpBEOA6USmGGBiawGVyctL6tEFD\nqEHTo+e+X8gvw+HQAhicHixfiF3As/ROr66uetozGRSqSaenp4pEIjaWjmieNhRYtLDMf/GLXyid\nTqvdbuu//bf/puHwfLQehqLZbFow4BI90um0QaNea7Zk8aiwBYNBE4AIBAImA+j2ps7OzpqS1cTE\n+ShJ6opLS0uqVquWAYLQTE9PG+pA1smzDofDFlBedbn9sI8ePTKlqPHxcQvKXFIQ/bUuwY/34ff7\n7ayDSqEFzK+TkxNjPrvlIK8LoQp6dmu1mvb29izLJYOen59Xv9+3oB24lgzQrRlyxikZBQIB1et1\nu8tIezYaDa2trXnes5vFkiVjNyYmJsyJuZ8RZNF1SLx73gsOze0GcG0Fz/66pK5sNqt4PK79/X1D\nMFwnSoBAyRFn67KmSSBcaJkk7c02IAICtw78XeutONvBYGDZIbWIWCxmze8HBweX+kGBQRkiQC2Q\n2ms+n1coFLIaGTUB4DxaEAqFgsnDQRjwsujRfPjwoe7du6d6va7f/OY39pna7bY2Nze1trambDar\nly9f6ujoSMlk0rJVHCmZOlT6//t//68eP36sZDJpFw+xC+p90P+9PmtaOPg9f/b5fNb/CFQoyaA0\nCGr0+s3Nzdm4MmAht1eN4IgaOxD5dcgNrrMFNuZi0LrBBQIqdwUu6OekvkKQ4/bUudkz75cIPhAI\neB5nWK1Wbebsyt+MXJyZmbl02cmuVlZWtL+/r263q2w2q//9v/+3pqendXR0pIWFBf35n/+59WJm\nMhnjJVBGuX37to6Pj7W/v28OEKUpL2t+fl7VavVSoML5lGRGnjnRsGkZhDA3N6eJiQmVSiUzyDyD\n09NTIyim02kzZOFwWOVy2QaJ83O8rC+//NLE+9PptNbX103YgbNRKpWsT5yAnlGX7XbbtNghHaKz\nTeDZ6XRs/GcgEFA+n1cymVSr1bIM3ssCmYtGo+ZwSqWSXr9+rVqtpnA4bHOM3TISQTaDCTqdjuLx\nuOlkw54Oh8PK5XLa2dlRo9Ew51IoFLS/v2/9/V4XDkq6ELJwyyzYYhwuv3czXhw2nwfUAaU9AnRK\nbtxRV2zG66IX3eXSuK1XLofFJd26toZF0ECZT9Il++cSqfi+fxQ1W1dnF2dCz1o4HNbR0ZFpHAeD\nQWu8z+VyVnRH1eXk5MTIDEzPCYVCWlxctLoKTuDs7Mx6La+S5r+57t69q06no6+++kqFQsGi6ePj\nY5XLZa2vr9s0F0mXCA/skQK6JMvGpHOI+NmzZ3r06NElIX+kHF1Ch5c1Pj5ukC89pmRFOBwyROB5\nWIc4urGxMZtkFAqFrO7CM3WdoUs+gdl6nUZ69/C+WSvhgoBWgBgwPo+aHHsjcoalDGQeDAat9k/P\nLaInICBe1qNHjxSLxVSpVPT8+XMbUA871GVH7+/vq16va2ZmRsViUQ8fPlS/39fq6qqazaYODw+1\nvb1tJBeeI9rEQIPj4+NmWKnleVmUKWAfY0CoaSKDiANGhQxRB3eYB4FPIBBQsVg0GUhXzjEej+v1\n69eGQtVqNQWDQc9Bwn/8j/9R7777rpLJpBltjB5IBzKWGH8Y51999ZX29/eN8EQ5CLJUvV63TKxc\nLtt7hK1NBuy19Yds3O/36+joyGqSBwcHGh8ft8CHgRbSxcDy4fB8gAWkT8pQkO+oreOU0ZXv9XpW\nSiIY9brIll2HjyKT21fKGo3OJTDhf/C1vAccGnYZ8hl1WnePV1VjenOdnZ2PV93Z2bE7iE0gU3W7\nMIC0XX4I9w2fI10OPEgysIMuVH6VBOOtDSKQpHw+r5OTExtD5rZ0bP/N5JRbt25ZzWVyctIifRjL\nvV7PFKF4KYFAwNieQIYw+vj/V0313ZXL5az1gagYjdp/+2//rf7yL/9SDx8+VD6ft0izWq2qVqsp\nFouZs4VoQnbIRUFsIZVKaTgc2shB2hAgGnhZzOEky+r3+8byBkIliqT1CuRAkrFIcaK0ML1J6pIu\nmHlEpBj+64ha1Go1i4JhTMMkdcU/YJnzdxAuqK1AUKOuJcl0ohm9CGSPqhnSeF6JXW77E9lbLBZT\nNptVNBpVsVjUt99+q2fPnhkUj7MiINvf3zdFpXa7rcePH8vv92tnZ0fSBUkPpTEU01wo0ssajUbW\nE0zgh0HESFGHh+XP+yW4wvh0u129ePFCDx48sDuLQSqXy1r5m+lWlCgikYjVLL2y7JeXl9Vutw0h\nePbsmcHROFl69l0nhyTqJ598olKppFwud0k+0OUIwKjf3Nw0LfPB4FybHWKZl0VgJMnue6fTMXY2\nUCq9s6PRyO6vq0tNQMTXgMgQREJWq1arBjFDtKrVap72LJ2fh2w2q3Q6rXg8rmq1ajrdBBxk2Tgu\nN5nh3nJuXL4HfxcMBtXr9Wx/OFnKDdfps8VmkIlj88mW3bPL+3Hrtb8rm3cZ1px/gn8CB5dU9Z3P\n1dMnuubiUCQSCesrg1EpyQwQog+xWMzYe4hxYyCZPgL8B0zMSDscrasjC6To9bIQBU9OTiqbzer4\n+Fi5XE6//vWv9Ytf/EI//elPNTExoR/+8Ifa29uzi1ytVpVKpWwOL5mOz+czluHs7Kw5WbJMMnCi\nrHq9fm3pQ+pjZLRuNorSCz/PhUFwtkCJwWDQ2KhcBA4yzG/6cN2D6XW5E55w6tRv3donpYdsNmut\nBIx8w/FgsHj/bk+xdDEOCwQFh+61/nlwcKDZ2Vlj4S4uLur09FSffvqpGo2GTaLJZrOKRCJaXFxU\nPp/X1taWbt26pUqloi+//FKLi4tKJBLKZrOWcULa4R6ge8vou+fPn9t78bJwpOFw2LJmWpSI8oH0\nefYI+WcyGZPrKxQK2tnZ0cOHD1Wr1axdIxwOa2dnRxMTEzo6OrIsiDaifD5/Lcj+vffes5aUbrer\nb7/9VoVCQQ8ePNCPfvQjG7ru8/kUj8dNxCQUCml1dVULCwv6+uuv9erVK8vsh8Oh1tbWFIlEDJnB\ndtDrfnh4aBOavM7gJVDHiUjnMD53iD1w/xChGQzOpxWRbUOUGo0udNYp48DGPj4+VqFQuKSQRl3S\n60IMiLYed9KT22pHZgcki8N1a7HwXcLh8KV2GkiYnGuXQ+E1q5V06Z67ThVCGTbQ7a2VLitMuQ5a\nuhi56fbUuvaNP/Pzf+9z9fyprrFSqZRqtdolqI5oHaYjfbDUkYbDob755hvduXNHu7u7ymQyWl5e\ntobzUqmkQqGg1dVVmxpEVOtCRJAkQqGQRWNXXT7f+bgoxMOJNH/4wx9qY2NDxWLR6qJIRLqC/6zx\n8XGryRCl8iLJuiVZ/QhIDCEDLwsDCYOSw0TmiVOingXUSWTJ5XcFDBAOoTUEFiIoA5efz3mdPltm\n0GKgiBxd9jQsdPo7gfdQboK0gMOWZBN+yHRpd6Lfj/eMWIqXNT8/b72vp6en2tnZsXaRjY0Na6mh\ndQ3t3g8++EDtdlulUkmrq6tKJBL6+uuvTREJacTBYGCTdWCfUmeE3XkdghTlAWpswH8uNAj050Jl\nZHvUaql1EgzXajVtbW2p3+9rfX1d9+7dM/b4nTt3rBxEKcPLWl5eNpRlb29P09PT1nbU6/W0t7en\n8fFztSocQzqdNkGQWCym//k//6e++OILm/DDyENJlnFxX7AjdCEAqXtZa2trajQaKhQK1sID7A5S\nw/kkG+Qe0YdNqYBAHQcL4x6nAkmQfnGy0Os4W945tW7sBPVW+DHYsXA4bPVRujDcur/b3ojjcydY\n0TII2fK6JEvpon1QuoB9XSITdgFmN3tykTFXDMNF9dgj38slg/3RtP4A/7nqLUQysInn5+fVaDQs\nmltYWNCzZ8/U7/e1s7Njjf5c+F/+8pdKp9N69913lc1mradrbGzMajCMWKvX66rVap7JAp1OR8vL\ny0YKIcMaDAZaWVlROp1WNptVt9tVOp3WwsKCAoGAsYFhWDNikJoztdlmsymf71xIAEUfoCQOideB\n9/1+34TXifBAAojghsOhKeRAmBofH1cmkzGjA3uZbJfPwzg7jDSHkv1CyPK6Op2OKS9B5MLoVCoV\ni65R8wkEAgYJY2iINOkFdvt2A4GAtdKcnp6aek2v1zMnW6/XPe251+vpv/7X/6qpqSklEglDDcbG\nxmzgwcTEhD7++GMNBgPdvXtX6XTaAoOxsTGbTJVIJPSb3/xGmUxG6+vr+uijj7SysqK7d+8qFArp\n4OBAo9G5BjEZMxma1+eMAhhRPojEm1C120dOGQWhkLm5OZXLZdN3hiBFWWR2dtb0rIfDoTKZjA4P\nD7W+vm6zeL0s7jiBYzabVSaTsSyMvcXjcXMw1DqXl5dNm3llZUU//elP1Wg0NDk5qXa7bfbJ7Rhw\nAw0CSK+ITTweNzIWanSj0cjGh9J6VywWtbi4eEnAv9/vWzbGewDhgDjKOSdoDAaDWl5e1uTkpCqV\nitrt9rWQMXpHKT+5dWRJBgk3m02dnp7Pbi4Wi8ZJSKVS5qSozXKnsf8gm/AGsCOu7fO6Z5eBzNfz\nTIGGSQwIJjkrOErsF8RMN3OlXu3+rNFodCno+a711pzt1NSUtre3jbWJoAUZ38zMjPL5vHZ2dgxu\npjbLvM+JiQk9fvxYi4uL2tzc1A9+8ANtbGxYtOdqeUoXcIbf79fe3p4KhYL+9E//9Mr73tvb0+Hh\nobXO0LZBe4872qvVahnUNjMzY3M+YY4ivEEggDiGW3shY3Dlxmgn8bIgAbXbbY1GI4MNXdZlq9Wy\nfk36VOkhxNHSYyldDGVwp6eQfblwynUuivsz3VnALjuZQQ2NRsMgcPdzTkxMWF0ftIRnCnJCbe/s\n7EwzMzOq1+sWADH43cuanZ3V48ePValUVKvVlMvljHFLm0Q+n9fKyopSqZSCwaBCoZB2d3dNIa3V\nahlpZn19XRsbGyqVSnr33XdN3KBQKCgWi9k7W11dtWzfq9NyiR1kUcgCTk9Pm9yedBH9E/gcHh7a\neRoMBrp165ai0ag++eQTxWIxbW5uKhaLmaxiPp9Xv9/XvXv3rM1lZmbGxg0+evToyvuGV0Cds9vt\nKh6P2/dj6AClJ4JBMsNsNmsB8Pxt3wAAIABJREFUWr/f169+9Svt7OyoVqsZZL+8vPxbAScsd1pC\nvCzQvJmZGfV6PSWTSdM+BjVqNptKJBIqFAqmEsa8X8psblDL3wF9Q6ajhIYqFpnoddqVzs7O7Hm3\nWi0LcnBA9NoDxyIyhKoZBDSQNOwbyCMoAU5bktX8XSUqr4ugwGUPQzylxEDW++YwBOBjtybr9tOS\nweKUXQIn3/v3rbfibH0+n5aWluwFMoYMViwHI5FImEoT0cJgcK669OGHH+revXtmHP7JP/knWllZ\nsYgFdRsciJvBhMNhPXjwwHPNBZgYmDUSiahQKBhLLxAI6JtvvjECwOzsrH70ox+ZyAXM5XA4bLVZ\nDChQL6ILXGjaFvjszWbT054x9G47EXUcqPGuZCPZDXCWG8lT5+UAIvR/enpqZDRXLATj7VVoQboY\n3UWGRAQqySLMZrNpU3Gg7VObox+VC0HNmgsNaQzZRmqtELNOTk4861BD2ENDlz5V0BBaSZLJpE5P\nT/Xq1SsbTYZcH5nOgwcPrA6OQaXfEx4A/YD0okveW2gI7kBYcJ4I+SM6gTa5JGvq5+vZZzabNchW\nkn2PpaUlc+bLy8s6OTnR3t6eFhYWLFPzqo38y1/+Utls1nqOJV0i9c3MzGh1ddWyF6Q9Cb4gQd26\ndctg1kAgoBcvXliN88WLFxoMBpqfn7e7gOO6jgMolUqKx+NWV0V9azAYWBsY7x9jz6zuer1uGtmo\nNuFEKKdg7LnLBDiBQMBaJq8T+BI8I31JKYB6Pv8GohaB1NnZ+RjGSqVizx1pT0YwEtTDpHZ7YLlL\nb/bfXmVRN+b9u8QyiE3Shd4xfod/5zpOSbYvVzPZ7RUmIMPp/tEQpD7//HPdunVLGxsbZriBTPkA\n09PTSiaTevbsmTFS6/W6ksmk7t27Z1lKv9/XD37wA6shSdL29rYdUrIzRn6tr69rampKyWRS6+vr\nnvb9p3/6pxoOzyf8oMp0eHho0RqiFLAA19bWtLm5qcePHxtVn4NEdIp0G3D39PS0qTURURF9HR4e\n6mc/+5nu379/5T3zM2AeA4twEAkKxsbO50YSfTNnGIIUbEafz2cROBkFJC8EEqTLMItXByCd12x5\njhAoJF0y8kDNjUbjEqOUhcFxdZApVVCvJdoFnYAhDqTvZdHOsLKyYv2QtVpNm5ubJraOOInP51Mu\nl7PsABY0Um/ZbFaDwUDpdNoIX+1228h2jNmrVCoaGzsXbQC+9bJ479JFEINhbbVaGg6HVvIZDoea\nmZlRMBhUrVYzqHU0GmljY0Pj4+fCGMDkn376qf7xP/7HNsmGiVuHh4cGd+bzeS0uLnouj1DL29zc\nNAM6MTGhpaUlQxNwSpRNQNBgesPAv3v3rvx+v0GtBL79/vns4GQyabVoyIrS1Ugw7sLhT05OWqAf\nDof1zTffWKKRy+WMMApbnSSBoJ3AiD/jYN/UV+/1ejZRihYhr8G6dEEagjNCVs9d4ZmQMTabTSN7\n0d6DVCqdAmTqnU5HpVLJiLDxePyStCxoltdnjf0BASDodrN0zgefz9Vh5+uxNy4Uze8JAtx2IC9t\njm/F2aIPSzRPVObWEtPptHZ3d00KrlKpGKMzk8no7//9v2/D2yWp0Wjo66+/NqGM5eVlY98isNDt\ndq2ZHr1TLyscDhv54vT0VN1uV7du3TKywmBwPkmIySCNRkPPnj1TKpXS/Py8ZSm9Xk+xWEy7u7vW\na7e1taX333/fDjSwIjATZBCvbFPpguDAhSYqd8UoODD0PxPc0B4D89jvPx/VRx8fF51LT70XJwZM\n6XWVy2XrNaSNirMBFNTv922mLdko7VHNZtNQASaWuEpRrrYzl5Egg8vt1QEgywhch2F3CXCQtiAR\noeedSCQMyQHOgtQGKxMpR1o+QCV4VuFwWPF43NOeO52Owca9Xs9al4AhqeMD109MTJjB5n7x9eg9\nT05Oant7WwsLC5bJ7e7u6tGjR1ZP5B3hTLx2BgSDQW1ubhrhaWdnR6VSyRAydJczmYwSiYRlSH6/\n3/rxOZsTExNaXFzU+++/r9PTU+3v75st4gz1ej0bl4jwArbnqgtRHNqGeF7b29s6OTnRgwcP1O/3\n9eLFC2WzWSWTSaulw/ienp424iSlE6ZM4SyAT93OAWyJV5sn/bY0IwQoUBHXiQ2HQ5PDJahkTzwD\n7LlbFiJR6na71icN0nYVstHvWtghslPQADfLZbmCFDhfPpf7812UziVH8b3fJEx913orzhbYBmgk\nGAyqWq1qOBzaZBs+ILVPWiUSiYQePHigjY0Nfe9739PExIQ6nY6eP3+uVqul5eVlra+vWw0DCJEM\n7OTkRJVKRblcTrFYzNO+y+Wyabpy2MbHx1WpVC4FC9QheJm1Wk3z8/PWgkOjP7WMmZkZBQIB7e/v\nW/RNM3U2mzX2owvfXXXR1zYajcxxc9BQcsHp0M5DyweQLDVN2nr6/b7VlOgBxOHi1PieOHevq9ls\nqlwu28XgXBAYIMzhMomBgcjAIXABIZN94xBRlpFkimI+n8+evdeMnDr42NiY9vf3NT4+blKjGMaz\nszNztLA0ad/BkEuyIAgB/XK5LElKJpPy+/3WT1ytVk2pa2dnxzNMSLkChjawm6RL0B71QUoKQOSI\nXpCNAZF+/fXXevTokWnzjo+P2/xW0AVq6e12W998841+8pOfXHnf8XhciUTCzh/iFtRYQY9AcFyS\nHoGQa3BDoZDu3LmjSqWizz77TIPBwEhHrvoY0DgQtJcFAUqS1Y7RjGY4SrVavaSfvL6+fmmf6BIA\nZUO4JBN2J+p0u12VSiUVi8VLqI3Xhe1w+TTA6u4wBxeaZa8nJyf2c0HXKHsQ4KLZDukUpJDS13VE\nLdxn/madlWAdZ+qSnNi/GyT8rl5c/p+b5XK//6j6bDlobs/Y7Oysqf+4rLTZ2VlVKhUbMPDBBx/Y\ny5+ZmdHR0ZG2trbk8/n04MEDzc/PWw0J50f9jTrjYDBQoVDw7LhgehaLRU1PT2ttbc2MPJcXaBlS\nz/HxsUkyIvqOMefiTk5O6v3339eHH354KXOJRCI6PDw053dVgWt3kQ3z9W5/LUYSpxkOh5XJZMxA\njY+Pm3ykq5bjEs14F/S8MnqMg4wz9LrOzs4uwUfUVaPRqMH1yHnCJHT76ZLJpKEXZLL02rr9ey40\nxPdwWwO8rMXFRTsHmUzGjD01c3p3eZadTsdqWjxvDFI0GlUikbCRb7DuURgCEYHJC8zntbaFIYP0\nxLMmkAEmnJycNIibewSEDCt6bW1N3W5X+/v7Gg6HWlxcVKlUUqfT0crKion/Q1YDqu92u4ZiXHWR\ntRA80XOK7SCwZJAA08Mw7AigUJtjmH00GlUmkzGVKzI2ZDKBgP/e3/t7evr0qac9f/bZZzaVCaO/\nsrKiTCZjZ5jMMRAImI1iCAX3sNvtGukLCUi3Dtnv93V4eGgODdi/Xq9fq6QjyYI7nC3BN5mpW7Ms\nl8sW3GAruJdk39xtgnJ63OGmkARcR9BCutArdh3ncDi08w6SR3DJsyNYZ/9A4+73dclTfA0Iqluz\n/aNo/aGGQ62MCJeMlnqM3+/XkydP9N5771nv0+7uriYnJ7WwsGDD4ZPJpHK5nGZnZ20cGS9xYuJ8\nWDNMOGj1o9FIv/71rz2xkYEm6SWjhy+ZTCqdTptMYKVSMegll8up1Wrp8PDQalNkW6lUyoYBBINB\n/cmf/ImJ1pfLZRuphzj61NSU7ty54+lZQ2fHsfO8CQIw+OPj4wbluPUNjPD4+Lj1v7lOn/oRB44s\nwM0arxNNQ1ihxECQ4BIfRqORFhYWjARGUANhi9mvXGr+iyC+m4FLMkUfDItXglShULA6O4YamNvt\n/WZqT6PR0MLCghYWFkzrOJ/PG1mLMoM7YYbBEvSQUqvGYXuVEJQu1HpAMwhQMawY9UQioXK5bC1Y\nc3Nz6vf72t7etoCuUqmoXC7rT/7kTwySBWZGQ3l1dVW9Xk/7+/tqtVra2NjwjDKBtvh8PqvJgyjR\n/gNK4JYEUCly+1kJLEajkXE5nj17Zg4cuJve4du3b+vp06eehTgePnxoUo2cPerXnAuCXzJFhB6O\nj49NrIJuiKmpKbtr9MvjzOCGECx89dVXOj4+9rxn6YJpiwNzW+ngQuDsadUjiHH/HwgXmSAOm8Af\nwl8wGLTWPu7MdQhSONY3W5QIEN5kEIPi8DPZF/aQ3mD24rZh8TNcBvQfhbN9/PixSqWShsOh9buh\nYHN4eGhMwbm5OSWTSSOZnJ6eam1tzZzl2dmZVldXrY2CqMPv91v0fHJyolKppFevXimRSOjp06e6\nffu2vv76a4t+r7rm5+cNQgqFQqabygUna00kEtYfi1PL5/P64IMPdPv2bZMDTCQSJiU3NTWlfD5/\niUbPSw2FQgYreu0N7vf7Ojo6MuJBOBy2aJ9RcvQBU2d29+CyoXFkZIsQl1yCABkpTGoMoNcVCARM\nUP7s7MwgSOqqXHieP44AQQt3+o/rALksGOBQKHSpn5LWG+aeellAoogjIMk5Gp3PRf3qq6+Me8Al\npz5/cHBgz/Kdd95Ro9GwLJmB69vb2+p0OkaQIlCCQIUR9rqorQOTulkHwZ50Ab9C4OJ+cU87nY7y\n+byWlpYsmGk0GlpfXzeSHc6uUqlof39f9+/f19LSkud9M/s3HA4bEW52dtbsx/z8vJV7ILxQ0oCM\nCTSM4W232wqHw3r33Xf1X/7Lf1GtVjPN7XQ6rVKppHv37ukv//IvLTHwsmg7o6ZP1rqzs6N2u63t\nvxnsTrcCes+tVkupVMruMW1k9P7inIGesU/0oZfLZQv+riMw4/aouqUsJBjZJ33DkDwhhUr6LYgV\n54QjdZ0ZdV1mLPP1XtbY2LkgErV6AgO+N5/JRQSwcyQU7AH7wP7cr3V/77Lcr7Lft+Js/+Iv/kIH\nBwfWNM/EHyJ9cPRwOKxCoaB2u62lpSXlcjnLVg4PD5XL5WyKCjAG5A6gvKOjI4XDYd2/f9/quDS2\ne51XSk1ZOncqCNhz4aXzA0iBH4PIgcnn81pbWzMWIYe3WCwqGo1aROdKlfHvaIL3miW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s8Oe8J/oKXNXYlEIjbn+Ojo6NIsZL7n+Pj4d86XfivO1n2QfABXjo7BzAyX5++ZWuN+H/cB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\nafmZPET1eh37+/tIJBJSpM6/GTUCFotFhhFQ0SwvL2NgY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r6x7Utf+tKXvvTlOUvf2Pal\nL33pS1/68pylb2z70pe+9KUvfXnO0je2felLX/rSl748Z+kb2770pS996UtfnrP0jW1f+tKXvvSl\nL89Z+sa2L33pS1/60pfnLH1j25e+9KUvfenLc5a+se1LX/rSl7705TlL39j2pS996Utf+vKcpW9s\n+9KXvvSlL315ztI3tn3pS1/60pe+PGfpG9u+9KUvfelLX56z9I1tX/rSl770pS/PWfrGti996Utf\n+tKX5yx9Y9uXvvSlL33py3OWvrHtS1/60pe+9OU5S9/Y9qUvfelLX/rynKVvbPvSl770pS99ec7y\n/wB45kixx1m61wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(6, 10)\n", + "for i, axi in enumerate(ax.flat):\n", + " axi.imshow(negative_patches[500 * i], cmap='gray')\n", + " axi.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our hope is that these would sufficiently cover the space of \"non-faces\" that our algorithm is likely to see." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Combine sets and extract HOG features\n", + "\n", + "Now that we have these positive samples and negative samples, we can combine them and compute HOG features.\n", + "This step takes a little while, because the HOG features involve a nontrivial computation for each image:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from itertools import chain\n", + "X_train = np.array([feature.hog(im)\n", + " for im in chain(positive_patches,\n", + " negative_patches)])\n", + "y_train = np.zeros(X_train.shape[0])\n", + "y_train[:positive_patches.shape[0]] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(43233, 1215)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are left with 43,000 training samples in 1,215 dimensions, and we now have our data in a form that we can feed into Scikit-Learn!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Training a support vector machine\n", + "\n", + "Next we use the tools we have been exploring in this chapter to create a classifier of thumbnail patches.\n", + "For such a high-dimensional binary classification task, a Linear support vector machine is a good choice.\n", + "We will use Scikit-Learn's ``LinearSVC``, because in comparison to ``SVC`` it often has better scaling for large number of samples.\n", + "\n", + "First, though, let's use a simple Gaussian naive Bayes to get a quick baseline:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.9408785 , 0.8752342 , 0.93976823])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.cross_validation import cross_val_score\n", + "\n", + "cross_val_score(GaussianNB(), X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that on our training data, even a simple naive Bayes algorithm gets us upwards of 90% accuracy.\n", + "Let's try the support vector machine, with a grid search over a few choices of the C parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.98667684407744083" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.svm import LinearSVC\n", + "from sklearn.grid_search import GridSearchCV\n", + "grid = GridSearchCV(LinearSVC(), {'C': [1.0, 2.0, 4.0, 8.0]})\n", + "grid.fit(X_train, y_train)\n", + "grid.best_score_" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 4.0}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.best_params_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take the best estimator and re-train it on the full dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearSVC(C=4.0, class_weight=None, dual=True, fit_intercept=True,\n", + " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", + " multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n", + " verbose=0)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = grid.best_estimator_\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Find faces in a new image\n", + "\n", + "Now that we have this model in place, let's grab a new image and see how the model does.\n", + "We will use one portion of the astronaut image for simplicity (see discussion of this in [Caveats and Improvements](#Caveats-and-Improvements)), and run a sliding window over it and evaluate each patch:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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GA2m324/srnAciI1O2aKfQQp+9cHmWq2WDIfDYNoBIENgEw8GKm0Dx2YXYGdo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rxkci6Xc4BkfScRIxbxsqQIIPv48aPlcvfr5nSHHLMHHwoll1mVAD3WDURgerECxuLioleUZSnV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\nVqvZH/7wB69UCqNk0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HcRNUgMJStTwzmPKcLx+BnUFxDD3zSuQ2VOEhI1n4nnYuABRMLk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\nubnfLFZBR2toqQkXzX6YQjQveHd+M3FZUZDyPQFmC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_image = skimage.data.astronaut()\n", + "test_image = skimage.color.rgb2gray(test_image)\n", + "test_image = skimage.transform.rescale(test_image, 0.5)\n", + "test_image = test_image[:160, 40:180]\n", + "\n", + "plt.imshow(test_image, cmap='gray')\n", + "plt.axis('off');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's create a window that iterates over patches of this image, and compute HOG features for each patch:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1911, 1215)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sliding_window(img, patch_size=positive_patches[0].shape,\n", + " istep=2, jstep=2, scale=1.0):\n", + " Ni, Nj = (int(scale * s) for s in patch_size)\n", + " for i in range(0, img.shape[0] - Ni, istep):\n", + " for j in range(0, img.shape[1] - Ni, jstep):\n", + " patch = img[i:i + Ni, j:j + Nj]\n", + " if scale != 1:\n", + " patch = transform.resize(patch, patch_size)\n", + " yield (i, j), patch\n", + " \n", + "indices, patches = zip(*sliding_window(test_image))\n", + "patches_hog = np.array([feature.hog(patch) for patch in patches])\n", + "patches_hog.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can take these HOG-featured patches and use our model to evaluate whether each patch contains a face:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "33.0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels = model.predict(patches_hog)\n", + "labels.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that out of nearly 2,000 patches, we have found 30 detections.\n", + "Let's use the information we have about these patches to show where they lie on our test image, drawing them as rectangles:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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i4gInJyc4Pj7GxcUFLi8v0Ww2HaPhRE6SxJlMKAtSRT2fK3ix/GRH3W4XjUajZA7BvqO+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\nIGORNw5r152+wi+V0a2MpKZj3krC7gb3pxlJKgyzLvs17QRPf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.imshow(test_image, cmap='gray')\n", + "ax.axis('off')\n", + "\n", + "Ni, Nj = positive_patches[0].shape\n", + "indices = np.array(indices)\n", + "\n", + "for i, j in indices[labels == 1]:\n", + " ax.add_patch(plt.Rectangle((j, i), Nj, Ni, edgecolor='red',\n", + " alpha=0.3, lw=2, facecolor='none'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All of the detected patches overlap and found the face in the image!\n", + "Not bad for a few lines of Python." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caveats and Improvements\n", + "\n", + "If you dig a bit deeper into the preceding code and examples, you'll see that we still have a bit of work before we can claim a production-ready face detector.\n", + "There are several issues with what we've done, and several improvements that could be made. In particular:\n", + "\n", + "### Our training set, especially for negative features, is not very complete\n", + "\n", + "The central issue is that there are many face-like textures that are not in the training set, and so our current model is very prone to false positives.\n", + "You can see this if you try out the above algorithm on the *full* astronaut image: the current model leads to many false detections in other regions of the image.\n", + "\n", + "We might imagine addressing this by adding a wider variety of images to the negative training set, and this would probably yield some improvement.\n", + "Another way to address this is to use a more directed approach, such as *hard negative mining*.\n", + "In hard negative mining, we take a new set of images that our classifier has not seen, find all the patches representing false positives, and explicitly add them as negative instances in the training set before re-training the classifier.\n", + "\n", + "### Our current pipeline searches only at one scale\n", + "\n", + "As currently written, our algorithm will miss faces that are not approximately 62×47 pixels.\n", + "This can be straightforwardly addressed by using sliding windows of a variety of sizes, and re-sizing each patch using ``skimage.transform.resize`` before feeding it into the model.\n", + "In fact, the ``sliding_window()`` utility used here is already built with this in mind.\n", + "\n", + "### We should combine overlapped detection patches\n", + "\n", + "For a production-ready pipeline, we would prefer not to have 30 detections of the same face, but to somehow reduce overlapping groups of detections down to a single detection.\n", + "This could be done via an unsupervised clustering approach (MeanShift Clustering is one good candidate for this), or via a procedural approach such as *non-maximum suppression*, an algorithm common in machine vision.\n", + "\n", + "### The pipeline should be streamlined\n", + "\n", + "Once we address these issues, it would also be nice to create a more streamlined pipeline for ingesting training images and predicting sliding-window outputs.\n", + "This is where Python as a data science tool really shines: with a bit of work, we could take our prototype code and package it with a well-designed object-oriented API that give the user the ability to use this easily.\n", + "I will leave this as a proverbial \"exercise for the reader\".\n", + "\n", + "### More recent advances: Deep Learning\n", + "\n", + "Finally, I should add that HOG and other procedural feature extraction methods for images are no longer state-of-the-art techniques.\n", + "Instead, many modern object detection pipelines use variants of deep neural networks: one way to think of neural networks is that they are an estimator which determines optimal feature extraction strategies from the data, rather than relying on the intuition of the user.\n", + "An intro to these deep neural net methods is conceptually (and computationally!) beyond the scope of this section, although open tools like Google's [TensorFlow](https://www.tensorflow.org/) have recently made deep learning approaches much more accessible than they once were.\n", + "As of the writing of this book, deep learning in Python is still relatively young, and so I can't yet point to any definitive resource.\n", + "That said, the list of references in the following section should provide a useful place to start!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.14-Image-Features.md b/notebooks_v2/05.14-Image-Features.md new file mode 100644 index 00000000..a44b95f7 --- /dev/null +++ b/notebooks_v2/05.14-Image-Features.md @@ -0,0 +1,322 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + +< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) > + +Open in Colab + + + +# Application: A Face Detection Pipeline + + +This chapter has explored a number of the central concepts and algorithms of machine learning. +But moving from these concepts to real-world application can be a challenge. +Real-world datasets are noisy and heterogeneous, may have missing features, and data may be in a form that is difficult to map to a clean ``[n_samples, n_features]`` matrix. +Before applying any of the methods discussed here, you must first extract these features from your data: there is no formula for how to do this that applies across all domains, and thus this is where you as a data scientist must exercise your own intuition and expertise. + +One interesting and compelling application of machine learning is to images, and we have already seen a few examples of this where pixel-level features are used for classification. +In the real world, data is rarely so uniform and simple pixels will not be suitable: this has led to a large literature on *feature extraction* methods for image data (see [Feature Engineering](05.04-Feature-Engineering.ipynb)). + +In this section, we will take a look at one such feature extraction technique, the [Histogram of Oriented Gradients](https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients) (HOG), which transforms image pixels into a vector representation that is sensitive to broadly informative image features regardless of confounding factors like illumination. +We will use these features to develop a simple face detection pipeline, using machine learning algorithms and concepts we've seen throughout this chapter. + +We begin with the standard imports: + +```python +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn as sns; sns.set() +import numpy as np +``` + +## HOG Features + +The Histogram of Gradients is a straightforward feature extraction procedure that was developed in the context of identifying pedestrians within images. +HOG involves the following steps: + +1. Optionally pre-normalize images. This leads to features that resist dependence on variations in illumination. +2. Convolve the image with two filters that are sensitive to horizontal and vertical brightness gradients. These capture edge, contour, and texture information. +3. Subdivide the image into cells of a predetermined size, and compute a histogram of the gradient orientations within each cell. +4. Normalize the histograms in each cell by comparing to the block of neighboring cells. This further suppresses the effect of illumination across the image. +5. Construct a one-dimensional feature vector from the information in each cell. + +A fast HOG extractor is built into the Scikit-Image project, and we can try it out relatively quickly and visualize the oriented gradients within each cell: + +```python +from skimage import data, color, feature +import skimage.data + +image = color.rgb2gray(data.chelsea()) +hog_vec, hog_vis = feature.hog(image, visualise=True) + +fig, ax = plt.subplots(1, 2, figsize=(12, 6), + subplot_kw=dict(xticks=[], yticks=[])) +ax[0].imshow(image, cmap='gray') +ax[0].set_title('input image') + +ax[1].imshow(hog_vis) +ax[1].set_title('visualization of HOG features'); +``` + +## HOG in Action: A Simple Face Detector + +Using these HOG features, we can build up a simple facial detection algorithm with any Scikit-Learn estimator; here we will use a linear support vector machine (refer back to [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) if you need a refresher on this). +The steps are as follows: + +1. Obtain a set of image thumbnails of faces to constitute "positive" training samples. +2. Obtain a set of image thumbnails of non-faces to constitute "negative" training samples. +3. Extract HOG features from these training samples. +4. Train a linear SVM classifier on these samples. +5. For an "unknown" image, pass a sliding window across the image, using the model to evaluate whether that window contains a face or not. +6. If detections overlap, combine them into a single window. + +Let's go through these steps and try it out: + + +### 1. Obtain a set of positive training samples + +Let's start by finding some positive training samples that show a variety of faces. +We have one easy set of data to work with—the Labeled Faces in the Wild dataset, which can be downloaded by Scikit-Learn: + +```python +from sklearn.datasets import fetch_lfw_people +faces = fetch_lfw_people() +positive_patches = faces.images +positive_patches.shape +``` + +This gives us a sample of 13,000 face images to use for training. + + +### 2. Obtain a set of negative training samples + +Next we need a set of similarly sized thumbnails which *do not* have a face in them. +One way to do this is to take any corpus of input images, and extract thumbnails from them at a variety of scales. +Here we can use some of the images shipped with Scikit-Image, along with Scikit-Learn's ``PatchExtractor``: + +```python +from skimage import data, transform + +imgs_to_use = ['camera', 'text', 'coins', 'moon', + 'page', 'clock', 'immunohistochemistry', + 'chelsea', 'coffee', 'hubble_deep_field'] +images = [color.rgb2gray(getattr(data, name)()) + for name in imgs_to_use] +``` + +```python +from sklearn.feature_extraction.image import PatchExtractor + +def extract_patches(img, N, scale=1.0, patch_size=positive_patches[0].shape): + extracted_patch_size = tuple((scale * np.array(patch_size)).astype(int)) + extractor = PatchExtractor(patch_size=extracted_patch_size, + max_patches=N, random_state=0) + patches = extractor.transform(img[np.newaxis]) + if scale != 1: + patches = np.array([transform.resize(patch, patch_size) + for patch in patches]) + return patches + +negative_patches = np.vstack([extract_patches(im, 1000, scale) + for im in images for scale in [0.5, 1.0, 2.0]]) +negative_patches.shape +``` + +We now have 30,000 suitable image patches which do not contain faces. +Let's take a look at a few of them to get an idea of what they look like: + +```python +fig, ax = plt.subplots(6, 10) +for i, axi in enumerate(ax.flat): + axi.imshow(negative_patches[500 * i], cmap='gray') + axi.axis('off') +``` + +Our hope is that these would sufficiently cover the space of "non-faces" that our algorithm is likely to see. + + +### 3. Combine sets and extract HOG features + +Now that we have these positive samples and negative samples, we can combine them and compute HOG features. +This step takes a little while, because the HOG features involve a nontrivial computation for each image: + +```python +from itertools import chain +X_train = np.array([feature.hog(im) + for im in chain(positive_patches, + negative_patches)]) +y_train = np.zeros(X_train.shape[0]) +y_train[:positive_patches.shape[0]] = 1 +``` + +```python +X_train.shape +``` + +We are left with 43,000 training samples in 1,215 dimensions, and we now have our data in a form that we can feed into Scikit-Learn! + + +### 4. Training a support vector machine + +Next we use the tools we have been exploring in this chapter to create a classifier of thumbnail patches. +For such a high-dimensional binary classification task, a Linear support vector machine is a good choice. +We will use Scikit-Learn's ``LinearSVC``, because in comparison to ``SVC`` it often has better scaling for large number of samples. + +First, though, let's use a simple Gaussian naive Bayes to get a quick baseline: + +```python +from sklearn.naive_bayes import GaussianNB +from sklearn.cross_validation import cross_val_score + +cross_val_score(GaussianNB(), X_train, y_train) +``` + +We see that on our training data, even a simple naive Bayes algorithm gets us upwards of 90% accuracy. +Let's try the support vector machine, with a grid search over a few choices of the C parameter: + +```python +from sklearn.svm import LinearSVC +from sklearn.grid_search import GridSearchCV +grid = GridSearchCV(LinearSVC(), {'C': [1.0, 2.0, 4.0, 8.0]}) +grid.fit(X_train, y_train) +grid.best_score_ +``` + +```python +grid.best_params_ +``` + +Let's take the best estimator and re-train it on the full dataset: + +```python +model = grid.best_estimator_ +model.fit(X_train, y_train) +``` + +### 5. Find faces in a new image + +Now that we have this model in place, let's grab a new image and see how the model does. +We will use one portion of the astronaut image for simplicity (see discussion of this in [Caveats and Improvements](#Caveats-and-Improvements)), and run a sliding window over it and evaluate each patch: + +```python +test_image = skimage.data.astronaut() +test_image = skimage.color.rgb2gray(test_image) +test_image = skimage.transform.rescale(test_image, 0.5) +test_image = test_image[:160, 40:180] + +plt.imshow(test_image, cmap='gray') +plt.axis('off'); +``` + +Next, let's create a window that iterates over patches of this image, and compute HOG features for each patch: + +```python +def sliding_window(img, patch_size=positive_patches[0].shape, + istep=2, jstep=2, scale=1.0): + Ni, Nj = (int(scale * s) for s in patch_size) + for i in range(0, img.shape[0] - Ni, istep): + for j in range(0, img.shape[1] - Ni, jstep): + patch = img[i:i + Ni, j:j + Nj] + if scale != 1: + patch = transform.resize(patch, patch_size) + yield (i, j), patch + +indices, patches = zip(*sliding_window(test_image)) +patches_hog = np.array([feature.hog(patch) for patch in patches]) +patches_hog.shape +``` + +Finally, we can take these HOG-featured patches and use our model to evaluate whether each patch contains a face: + +```python +labels = model.predict(patches_hog) +labels.sum() +``` + +We see that out of nearly 2,000 patches, we have found 30 detections. +Let's use the information we have about these patches to show where they lie on our test image, drawing them as rectangles: + +```python +fig, ax = plt.subplots() +ax.imshow(test_image, cmap='gray') +ax.axis('off') + +Ni, Nj = positive_patches[0].shape +indices = np.array(indices) + +for i, j in indices[labels == 1]: + ax.add_patch(plt.Rectangle((j, i), Nj, Ni, edgecolor='red', + alpha=0.3, lw=2, facecolor='none')) +``` + +All of the detected patches overlap and found the face in the image! +Not bad for a few lines of Python. + + +## Caveats and Improvements + +If you dig a bit deeper into the preceding code and examples, you'll see that we still have a bit of work before we can claim a production-ready face detector. +There are several issues with what we've done, and several improvements that could be made. In particular: + +### Our training set, especially for negative features, is not very complete + +The central issue is that there are many face-like textures that are not in the training set, and so our current model is very prone to false positives. +You can see this if you try out the above algorithm on the *full* astronaut image: the current model leads to many false detections in other regions of the image. + +We might imagine addressing this by adding a wider variety of images to the negative training set, and this would probably yield some improvement. +Another way to address this is to use a more directed approach, such as *hard negative mining*. +In hard negative mining, we take a new set of images that our classifier has not seen, find all the patches representing false positives, and explicitly add them as negative instances in the training set before re-training the classifier. + +### Our current pipeline searches only at one scale + +As currently written, our algorithm will miss faces that are not approximately 62×47 pixels. +This can be straightforwardly addressed by using sliding windows of a variety of sizes, and re-sizing each patch using ``skimage.transform.resize`` before feeding it into the model. +In fact, the ``sliding_window()`` utility used here is already built with this in mind. + +### We should combine overlapped detection patches + +For a production-ready pipeline, we would prefer not to have 30 detections of the same face, but to somehow reduce overlapping groups of detections down to a single detection. +This could be done via an unsupervised clustering approach (MeanShift Clustering is one good candidate for this), or via a procedural approach such as *non-maximum suppression*, an algorithm common in machine vision. + +### The pipeline should be streamlined + +Once we address these issues, it would also be nice to create a more streamlined pipeline for ingesting training images and predicting sliding-window outputs. +This is where Python as a data science tool really shines: with a bit of work, we could take our prototype code and package it with a well-designed object-oriented API that give the user the ability to use this easily. +I will leave this as a proverbial "exercise for the reader". + +### More recent advances: Deep Learning + +Finally, I should add that HOG and other procedural feature extraction methods for images are no longer state-of-the-art techniques. +Instead, many modern object detection pipelines use variants of deep neural networks: one way to think of neural networks is that they are an estimator which determines optimal feature extraction strategies from the data, rather than relying on the intuition of the user. +An intro to these deep neural net methods is conceptually (and computationally!) beyond the scope of this section, although open tools like Google's [TensorFlow](https://www.tensorflow.org/) have recently made deep learning approaches much more accessible than they once were. +As of the writing of this book, deep learning in Python is still relatively young, and so I can't yet point to any definitive resource. +That said, the list of references in the following section should provide a useful place to start! + + + +< [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) | [Contents](Index.ipynb) | [Further Machine Learning Resources](05.15-Learning-More.ipynb) > + +Open in Colab + diff --git a/notebooks_v2/05.15-Learning-More.ipynb b/notebooks_v2/05.15-Learning-More.ipynb new file mode 100644 index 00000000..a16b31a4 --- /dev/null +++ b/notebooks_v2/05.15-Learning-More.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) >\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Further Machine Learning Resources" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This chapter has been a quick tour of machine learning in Python, primarily using the tools within the Scikit-Learn library.\n", + "As long as the chapter is, it is still too short to cover many interesting and important algorithms, approaches, and discussions.\n", + "Here I want to suggest some resources to learn more about machine learning for those who are interested." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Machine Learning in Python\n", + "\n", + "To learn more about machine learning in Python, I'd suggest some of the following resources:\n", + "\n", + "- [The Scikit-Learn website](http://scikit-learn.org): The Scikit-Learn website has an impressive breadth of documentation and examples covering some of the models discussed here, and much, much more. If you want a brief survey of the most important and often-used machine learning algorithms, this website is a good place to start.\n", + "\n", + "- *SciPy, PyCon, and PyData tutorial videos*: Scikit-Learn and other machine learning topics are perennial favorites in the tutorial tracks of many Python-focused conference series, in particular the PyCon, SciPy, and PyData conferences. You can find the most recent ones via a simple web search.\n", + "\n", + "- [*Introduction to Machine Learning with Python*](http://shop.oreilly.com/product/0636920030515.do): Written by Andreas C. Mueller and Sarah Guido, this book includes a fuller treatment of the topics in this chapter. If you're interested in reviewing the fundamentals of Machine Learning and pushing the Scikit-Learn toolkit to its limits, this is a great resource, written by one of the most prolific developers on the Scikit-Learn team.\n", + "\n", + "- [*Python Machine Learning*](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning): Sebastian Raschka's book focuses less on Scikit-learn itself, and more on the breadth of machine learning tools available in Python. In particular, there is some very useful discussion on how to scale Python-based machine learning approaches to large and complex datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## General Machine Learning\n", + "\n", + "Of course, machine learning is much broader than just the Python world. There are many good resources to take your knowledge further, and here I will highlight a few that I have found useful:\n", + "\n", + "- [*Machine Learning*](https://www.coursera.org/learn/machine-learning): Taught by Andrew Ng (Coursera), this is a very clearly-taught free online course which covers the basics of machine learning from an algorithmic perspective. It assumes undergraduate-level understanding of mathematics and programming, and steps through detailed considerations of some of the most important machine learning algorithms. Homework assignments, which are algorithmically graded, have you actually implement some of these models yourself.\n", + "\n", + "- [*Pattern Recognition and Machine Learning*](http://www.springer.com/us/book/9780387310732): Written by Christopher Bishop, this classic technical text covers the concepts of machine learning discussed in this chapter in detail. If you plan to go further in this subject, you should have this book on your shelf.\n", + "\n", + "- [*Machine Learning: a Probabilistic Perspective*](https://mitpress.mit.edu/books/machine-learning-0): Written by Kevin Murphy, this is an excellent graduate-level text that explores nearly all important machine learning algorithms from a ground-up, unified probabilistic perspective.\n", + "\n", + "These resources are more technical than the material presented in this book, but to really understand the fundamentals of these methods requires a deep dive into the mathematics behind them.\n", + "If you're up for the challenge and ready to bring your data science to the next level, don't hesitate to dive-in!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) >\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/05.15-Learning-More.md b/notebooks_v2/05.15-Learning-More.md new file mode 100644 index 00000000..f6e36620 --- /dev/null +++ b/notebooks_v2/05.15-Learning-More.md @@ -0,0 +1,76 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) > + +Open in Colab + + + +# Further Machine Learning Resources + + +This chapter has been a quick tour of machine learning in Python, primarily using the tools within the Scikit-Learn library. +As long as the chapter is, it is still too short to cover many interesting and important algorithms, approaches, and discussions. +Here I want to suggest some resources to learn more about machine learning for those who are interested. + + + +## Machine Learning in Python + +To learn more about machine learning in Python, I'd suggest some of the following resources: + +- [The Scikit-Learn website](http://scikit-learn.org): The Scikit-Learn website has an impressive breadth of documentation and examples covering some of the models discussed here, and much, much more. If you want a brief survey of the most important and often-used machine learning algorithms, this website is a good place to start. + +- *SciPy, PyCon, and PyData tutorial videos*: Scikit-Learn and other machine learning topics are perennial favorites in the tutorial tracks of many Python-focused conference series, in particular the PyCon, SciPy, and PyData conferences. You can find the most recent ones via a simple web search. + +- [*Introduction to Machine Learning with Python*](http://shop.oreilly.com/product/0636920030515.do): Written by Andreas C. Mueller and Sarah Guido, this book includes a fuller treatment of the topics in this chapter. If you're interested in reviewing the fundamentals of Machine Learning and pushing the Scikit-Learn toolkit to its limits, this is a great resource, written by one of the most prolific developers on the Scikit-Learn team. + +- [*Python Machine Learning*](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning): Sebastian Raschka's book focuses less on Scikit-learn itself, and more on the breadth of machine learning tools available in Python. In particular, there is some very useful discussion on how to scale Python-based machine learning approaches to large and complex datasets. + + + +## General Machine Learning + +Of course, machine learning is much broader than just the Python world. There are many good resources to take your knowledge further, and here I will highlight a few that I have found useful: + +- [*Machine Learning*](https://www.coursera.org/learn/machine-learning): Taught by Andrew Ng (Coursera), this is a very clearly-taught free online course which covers the basics of machine learning from an algorithmic perspective. It assumes undergraduate-level understanding of mathematics and programming, and steps through detailed considerations of some of the most important machine learning algorithms. Homework assignments, which are algorithmically graded, have you actually implement some of these models yourself. + +- [*Pattern Recognition and Machine Learning*](http://www.springer.com/us/book/9780387310732): Written by Christopher Bishop, this classic technical text covers the concepts of machine learning discussed in this chapter in detail. If you plan to go further in this subject, you should have this book on your shelf. + +- [*Machine Learning: a Probabilistic Perspective*](https://mitpress.mit.edu/books/machine-learning-0): Written by Kevin Murphy, this is an excellent graduate-level text that explores nearly all important machine learning algorithms from a ground-up, unified probabilistic perspective. + +These resources are more technical than the material presented in this book, but to really understand the fundamentals of these methods requires a deep dive into the mathematics behind them. +If you're up for the challenge and ready to bring your data science to the next level, don't hesitate to dive-in! + + + + +< [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) | [Contents](Index.ipynb) | [Appendix: Figure Code](06.00-Figure-Code.ipynb) > + +Open in Colab + + diff --git a/notebooks_v2/06.00-Figure-Code.ipynb b/notebooks_v2/06.00-Figure-Code.ipynb new file mode 100644 index 00000000..786dc460 --- /dev/null +++ b/notebooks_v2/06.00-Figure-Code.ipynb @@ -0,0 +1,2789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "\n", + "\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) |\n", + "\n", + "\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Appendix: Figure Code" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Many of the figures used throughout this text are created in-place by code that appears in print.\n", + "In a few cases, however, the required code is long enough (or not immediately relevant enough) that we instead put it here for reference." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import os\n", + "if not os.path.exists('figures'):\n", + " os.makedirs('figures')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Broadcasting\n", + "\n", + "[Figure Context](02.05-Computation-on-arrays-broadcasting.ipynb#Introducing-Broadcasting)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Ojg529uxZ1tHRwQoLCynrCbJyc3OZm5sby83NFTSLV9Z+L/OaZY05inZ1cqz/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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Adapted from astroML: see http://www.astroml.org/book_figures/appendix/fig_broadcast_visual.html\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "#------------------------------------------------------------\n", + "# Draw a figure and axis with no boundary\n", + "fig = plt.figure(figsize=(6, 4.5), facecolor='w')\n", + "ax = plt.axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False)\n", + "\n", + "\n", + "def draw_cube(ax, xy, size, depth=0.4,\n", + " edges=None, label=None, label_kwargs=None, **kwargs):\n", + " \"\"\"draw and label a cube. edges is a list of numbers between\n", + " 1 and 12, specifying which of the 12 cube edges to draw\"\"\"\n", + " if edges is None:\n", + " edges = range(1, 13)\n", + "\n", + " x, y = xy\n", + "\n", + " if 1 in edges:\n", + " ax.plot([x, x + size],\n", + " [y + size, y + size], **kwargs)\n", + " if 2 in edges:\n", + " ax.plot([x + size, x + size],\n", + " [y, y + size], **kwargs)\n", + " if 3 in edges:\n", + " ax.plot([x, x + size],\n", + " [y, y], **kwargs)\n", + " if 4 in edges:\n", + " ax.plot([x, x],\n", + " [y, y + size], **kwargs)\n", + "\n", + " if 5 in edges:\n", + " ax.plot([x, x + depth],\n", + " [y + size, y + depth + size], **kwargs)\n", + " if 6 in edges:\n", + " ax.plot([x + size, x + size + depth],\n", + " [y + size, y + depth + size], **kwargs)\n", + " if 7 in edges:\n", + " ax.plot([x + size, x + size + depth],\n", + " [y, y + depth], **kwargs)\n", + " if 8 in edges:\n", + " ax.plot([x, x + depth],\n", + " [y, y + depth], **kwargs)\n", + "\n", + " if 9 in edges:\n", + " ax.plot([x + depth, x + depth + size],\n", + " [y + depth + size, y + depth + size], **kwargs)\n", + " if 10 in edges:\n", + " ax.plot([x + depth + size, x + depth + size],\n", + " [y + depth, y + depth + size], **kwargs)\n", + " if 11 in edges:\n", + " ax.plot([x + depth, x + depth + size],\n", + " [y + depth, y + depth], **kwargs)\n", + " if 12 in edges:\n", + " ax.plot([x + depth, x + depth],\n", + " [y + depth, y + depth + size], **kwargs)\n", + "\n", + " if label:\n", + " if label_kwargs is None:\n", + " label_kwargs = {}\n", + " ax.text(x + 0.5 * size, y + 0.5 * size, label,\n", + " ha='center', va='center', **label_kwargs)\n", + "\n", + "solid = dict(c='black', ls='-', lw=1,\n", + " label_kwargs=dict(color='k'))\n", + "dotted = dict(c='black', ls='-', lw=0.5, alpha=0.5,\n", + " label_kwargs=dict(color='gray'))\n", + "depth = 0.3\n", + "\n", + "#------------------------------------------------------------\n", + "# Draw top operation: vector plus scalar\n", + "draw_cube(ax, (1, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n", + "draw_cube(ax, (2, 10), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n", + "draw_cube(ax, (3, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n", + "\n", + "draw_cube(ax, (6, 10), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '5', **solid)\n", + "draw_cube(ax, (7, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted)\n", + "draw_cube(ax, (8, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted)\n", + "\n", + "draw_cube(ax, (12, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '5', **solid)\n", + "draw_cube(ax, (13, 10), 1, depth, [1, 2, 3, 6, 9], '6', **solid)\n", + "draw_cube(ax, (14, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '7', **solid)\n", + "\n", + "ax.text(5, 10.5, '+', size=12, ha='center', va='center')\n", + "ax.text(10.5, 10.5, '=', size=12, ha='center', va='center')\n", + "ax.text(1, 11.5, r'${\\tt np.arange(3) + 5}$',\n", + " size=12, ha='left', va='bottom')\n", + "\n", + "#------------------------------------------------------------\n", + "# Draw middle operation: matrix plus vector\n", + "\n", + "# first block\n", + "draw_cube(ax, (1, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid)\n", + "draw_cube(ax, (2, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n", + "draw_cube(ax, (3, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '1', **solid)\n", + "\n", + "draw_cube(ax, (1, 6.5), 1, depth, [2, 3, 4], '1', **solid)\n", + "draw_cube(ax, (2, 6.5), 1, depth, [2, 3], '1', **solid)\n", + "draw_cube(ax, (3, 6.5), 1, depth, [2, 3, 7, 10], '1', **solid)\n", + "\n", + "draw_cube(ax, (1, 5.5), 1, depth, [2, 3, 4], '1', **solid)\n", + "draw_cube(ax, (2, 5.5), 1, depth, [2, 3], '1', **solid)\n", + "draw_cube(ax, (3, 5.5), 1, depth, [2, 3, 7, 10], '1', **solid)\n", + "\n", + "# second block\n", + "draw_cube(ax, (6, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n", + "draw_cube(ax, (7, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n", + "draw_cube(ax, (8, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n", + "\n", + "draw_cube(ax, (6, 6.5), 1, depth, range(2, 13), '0', **dotted)\n", + "draw_cube(ax, (7, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted)\n", + "draw_cube(ax, (8, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted)\n", + "\n", + "draw_cube(ax, (6, 5.5), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted)\n", + "draw_cube(ax, (7, 5.5), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n", + "draw_cube(ax, (8, 5.5), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n", + "\n", + "# third block\n", + "draw_cube(ax, (12, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid)\n", + "draw_cube(ax, (13, 7.5), 1, depth, [1, 2, 3, 6, 9], '2', **solid)\n", + "draw_cube(ax, (14, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '3', **solid)\n", + "\n", + "draw_cube(ax, (12, 6.5), 1, depth, [2, 3, 4], '1', **solid)\n", + "draw_cube(ax, (13, 6.5), 1, depth, [2, 3], '2', **solid)\n", + "draw_cube(ax, (14, 6.5), 1, depth, [2, 3, 7, 10], '3', **solid)\n", + "\n", + "draw_cube(ax, (12, 5.5), 1, depth, [2, 3, 4], '1', **solid)\n", + "draw_cube(ax, (13, 5.5), 1, depth, [2, 3], '2', **solid)\n", + "draw_cube(ax, (14, 5.5), 1, depth, [2, 3, 7, 10], '3', **solid)\n", + "\n", + "ax.text(5, 7.0, '+', size=12, ha='center', va='center')\n", + "ax.text(10.5, 7.0, '=', size=12, ha='center', va='center')\n", + "ax.text(1, 9.0, r'${\\tt np.ones((3,\\, 3)) + np.arange(3)}$',\n", + " size=12, ha='left', va='bottom')\n", + "\n", + "#------------------------------------------------------------\n", + "# Draw bottom operation: vector plus vector, double broadcast\n", + "\n", + "# first block\n", + "draw_cube(ax, (1, 3), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '0', **solid)\n", + "draw_cube(ax, (1, 2), 1, depth, [2, 3, 4, 7, 10], '1', **solid)\n", + "draw_cube(ax, (1, 1), 1, depth, [2, 3, 4, 7, 10], '2', **solid)\n", + "\n", + "draw_cube(ax, (2, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted)\n", + "draw_cube(ax, (2, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n", + "draw_cube(ax, (2, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n", + "\n", + "draw_cube(ax, (3, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted)\n", + "draw_cube(ax, (3, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n", + "draw_cube(ax, (3, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n", + "\n", + "# second block\n", + "draw_cube(ax, (6, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n", + "draw_cube(ax, (7, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n", + "draw_cube(ax, (8, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n", + "\n", + "draw_cube(ax, (6, 2), 1, depth, range(2, 13), '0', **dotted)\n", + "draw_cube(ax, (7, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted)\n", + "draw_cube(ax, (8, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted)\n", + "\n", + "draw_cube(ax, (6, 1), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted)\n", + "draw_cube(ax, (7, 1), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)\n", + "draw_cube(ax, (8, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)\n", + "\n", + "# third block\n", + "draw_cube(ax, (12, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)\n", + "draw_cube(ax, (13, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid)\n", + "draw_cube(ax, (14, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)\n", + "\n", + "draw_cube(ax, (12, 2), 1, depth, [2, 3, 4], '1', **solid)\n", + "draw_cube(ax, (13, 2), 1, depth, [2, 3], '2', **solid)\n", + "draw_cube(ax, (14, 2), 1, depth, [2, 3, 7, 10], '3', **solid)\n", + "\n", + "draw_cube(ax, (12, 1), 1, depth, [2, 3, 4], '2', **solid)\n", + "draw_cube(ax, (13, 1), 1, depth, [2, 3], '3', **solid)\n", + "draw_cube(ax, (14, 1), 1, depth, [2, 3, 7, 10], '4', **solid)\n", + "\n", + "ax.text(5, 2.5, '+', size=12, ha='center', va='center')\n", + "ax.text(10.5, 2.5, '=', size=12, ha='center', va='center')\n", + "ax.text(1, 4.5, r'${\\tt np.arange(3).reshape((3,\\, 1)) + np.arange(3)}$',\n", + " ha='left', size=12, va='bottom')\n", + "\n", + "ax.set_xlim(0, 16)\n", + "ax.set_ylim(0.5, 12.5)\n", + "\n", + "fig.savefig('figures/02.05-broadcasting.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Aggregation and Grouping\n", + "\n", + "Figures from the chapter on aggregation and grouping" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Split-Apply-Combine" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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SErZv306rVq0AuOeee0xOJSINMXfuXPbv389vfvMbBgwYoPPBpEnQHjBpdt58\n803Wr1/Piy++iJ+fn9lxROQGaT+YNEVqwKRZycrK4sUXX2TKlClERUWZHUdEGonOB5OmRg2YNBs/\n3vclIreWuv1gb7/9NhkZGWbHEflJ2gMmzUZ9+75E5Nai/WDSVGgGTJoF7fsSaR60H0yaCjVgclW1\ntbVmR2gU2vcl0rxoP5g0BWrADBQaGkpRUZHZMaipqSEkJITz589fdUxSUhKbN282MNXN0VT3fV24\ncIHQ0FBiYmJMzbFz504dzyFNkvaDiatTA2aQEydOUFtbS8+ePc2OwsGDB7FarXh6el51TFJSEn36\n9DEw1c1Rt+/rrbfealL7vnbs2IHNZuPQoUMUFBSYkqGwsJCEhAQcDocp9UVulO4XKa5MDZhBsrKy\nCAkJAS79cg0PD+fw4cPExcUxYcIEIiMjWbZsGQC5ubmX3Y+wtLSUoUOHcvr06QbVPnfuHHPmzGH8\n+PFMnTqVLVu2EBwczJ49e5g4cSKTJk1i/PjxbNmyBYDJkydTXl7O888/T01NDbt3775sXFOZGWvK\n+77effddwsLCCA8PJykpyfD6lZWV/OY3vyEuLs7w2iKNRfvBxJXpU5AGyc7OJiQkhLS0NDIyMkhO\nTmbevHlMmDCB+Ph44FLjs3//fgIDAzl16hQXL16kZcuWLF68mJiYGG6//fYG1Z49ezZDhgxhyZIl\nHD9+nJEjR7JgwQJWrlzJ0qVL6d69O7m5ucTGxhIeHk5UVBTu7u4kJiZit9tZtWrVFeNGjx7dmC9P\no2vK+76OHj1KTk4OK1asoLCwkClTpjB37lw6duxoWIYXXniBRx99FH9/f8Nq3srKy8ux2+3cdddd\npmUoLi4GMD1Du3btDK2p+0WKq1IDZpCsrCxKS0vZtWsX27Zto6SkhMzMTCoqKli9ejVwaaaqoqIC\nd3d3rFYrRUVFlJWVUVBQQEJCQoPq5uXlkZ+fT2JiIgA9evSgdevWDB48mK5du5KSkkJVVRVFRUV0\n7twZgAMHDhAUFARA27Ztefzxx+sd56oqKyuZOXNmk9v3VWfdunUMHToUT09P+vfvj9VqJSUlhWnT\nphlSPzk5GTc3NyIiIpy/tOXG+Pv7k5eXZ2oGoxufq2Uwo6nX/SLFFakBM0BlZSVHjhwhLCyMbt26\nkZaWRu/evRk4cCArV64EoLq6mkOHDjmXKf39/cnPz+eNN95g0aJFWCyWBtU+duwYfn5+zusLCgpw\nc3Nj/fpXfOfeAAAgAElEQVT1nDhxgujoaKxWK4mJiXh7ewOXZutiY2MBSEhIoLi4uN5xrmrmzJl8\n/fXXTfK8r8rKSjZs2ICHhwfDhw/H4XBgt9tJTk4mOjqali1b3vQMGzZs4PvvvyciIoLq6mrnn99+\n++0Gz8I2d59//rnZEZo9nQ8mrkZ7wAxw4MAB+vTpw4wZM5g4cSKpqal4e3uTk5NDWVkZDoeDxYsX\nk5qa6rzG39+fZcuWERwcTEBAQINre3t7U1BQQFVVFdXV1cTHxxMUFERmZiaRkZEEBwdz5swZ0tPT\nCQwMpLa2ltzcXGfNq41zVW+99VaT3fcFsHHjRjp16sQnn3zChx9+yEcffcTOnTux2+1s3brVkAx/\n+ctf2LRpE+np6bz99tu0bt2a9PR0NV/SpGk/mLgazYAZIDs727mk5+fnh4+PD4WFhURHRxMVFUWb\nNm0YMGAACxcudF7j6+tLeXk5c+bMuaHagYGBjBgxgrFjx+Lj40OrVq3o27cv/fr1Iz4+nqSkJDp0\n6IDVasXDw4Pa2lp69OjB9OnTWbt2LbGxsfWOc0XZ2dksWrSoSe77qrNu3TqeeOKJyx7z9PRk8uTJ\nrFmzhjFjxhieqaGzryKuRvvBxJVYHPqM+Q2p29BaWFjYqM87a9YsRo4cSXh4uGkZrpeZOSorKxk+\nfDgeHh6mLz26wn8PZRC5utdee42XX36ZpKQk7QcT02gJ0sXs27ePcePG0atXr2tqvuSSun1fTe28\nLxExns4HE1egJUgXM2TIEJ3afJ3q9n2tWLGiSe77EhFj1e0HGzZsGNOmTeP999835AMuIj+kGTBp\n0m6FfV8iYjzdL1LMpgZMmqymft6XiJhL94sUM2kJUpqsJ598klOnTpm+6V5Emi6dDyZm0QyYNElv\nv/02aWlpTfa8LxFxDTofTMyiBkyanOzsbBYuXKh9XyLSKLQfTMygBkyaFO37EpGbQfvBxGjaAyZN\nysyZM7XvS0RuCu0HEyNpBkyajKZ+n0cRcW3aDyZGUgMmTUJWVpbO+xKRm077wcQouhfkDerYsSN2\nu5077rjDtAzFxcUApmaoy9GuXTvOnj3bqM/rSvd5vBZ6T/wzw814P4gYQfeLlJtNM2Di8prafR79\n/f1p166dqRnatWvnEhn8/f1NzSDSUGbeL/Krr75i/vz5hIaG0r9/f0aOHMnKlSupra29aTVtNhtB\nQUFX/X56ejo2m43f/e53Ny1Dc6NN+DfIy8sLLy8vCgsLTctw1113AZia4Yc5GlNTvM/j559/bnYE\nEblB9d0vskWLFiQmJvLEE0/ctHtHHj9+nIkTJ/Ltt99y55134ufnxxdffMEf/vAHjh07xu9///ub\nUvdf8fb2ZsSIEfTt29eU+rciNWDisrTvS0TMVLcfLCIigrlz51JSUsL69etp3bo1kydPvik1X3jh\nBb799luioqJ49tlnATh8+DATJ04kPT2d6Oho7r777ptS+6cMHjyYwYMHG173VqYlSHFJOu9LRFzB\nkCFDePTRR1m2bBnr1q2jpqaG1NTUm1KrpKSETz/9FA8PD3796187H7fZbMTHx/PnP/+Znj17Ultb\ny4oVKxg+fDj9+/fn4YcfZvv27c7xy5cvx2az8d577xEdHU1QUBCTJk2iuLiY+Ph47rnnHh588EE2\nb958RYYNGzbwwAMPMHDgQF566SXnJ0F/vARZVyM1NZVZs2YRHBzML37xCz755BPnc1VUVPDb3/6W\ngQMHMmjQIObPn8+5c+duymvXFKkBE5fU1PZ9icitx+FwsGTJEtatW8cPP6+2e/duDhw40Oj1cnNz\nAejVqxceHh6XfS88PJyBAwfi5ubGwoULWbZsGZWVldxzzz0UFRXx9NNPs3XrVuDS8inAq6++yrlz\n5+jYsSPZ2dlERESwefNmfH19+frrr3n22Wex2+3OGjU1NSxcuJC7774bh8NBcnIyK1eurDdrXY2E\nhASKioro0qULRUVFPPfcc84xzzzzDBkZGVitVu6++242btzI008/3XgvWBOnBkxcjs77EhFXUFFR\nQWpqKiUlJZc9fv78ed54441Gr3f+/HmAn/wAzddff01qaiodO3bk/fffJykpiRUrVgCwdOnSy8YG\nBQXxl7/8xbmKYLfbSUlJISUlhZ49e/L9999f9gGDuoYzKSmJlStX4nA4ePfdd38ys6+vLxkZGfzf\n//t/adOmDV9//TXffPMNJ06c4IMPPiAkJISMjAzWrVtHeHg4f/vb38jLy2vQ63OrUQMmLkX7vkTE\nVXh6erJz504eeeSRK763fft2Z8PUWNq2bQtcavyuJicnB4fDQWhoKJ06dQLg/vvvp3Pnzpw4cYLy\n8nLn2EGDBgHQtWtXALp164bVagXg9ttvB6C6uto53mKx8G//9m8ADBgwgA4dOnD69GmqqqqummfI\nkCEAtG/f/rLnPHr0KABffPEFNpsNm83mXPI8ePDgNb0etzptwheXoX1fIuJq2rdvT2pqKgsWLGD5\n8uXO5qiwsJA//vGPxMXFNVqt3r17A3Ds2DG+//77y5YhZ86ciYeHBwEBAT/5HHVLgxaLhdatWwPQ\nosWluZYfPl/duB9yOBzU1tY6P+HZokULLBbLVT/xabFYLnvOunEOh4MLFy4A0L179ys+Ofmzn/3s\nJ3+G5kIzYOIytO9LRFyRxWLh97//PW+++eZl94d8//33acyzzO+44w7uueceqqqqeO2115yP79u3\nj127dvHhhx9y7733ArB3717KysoA+OSTTzhz5gw9e/a84eZm27ZtwKWZtm+//Rar1Yqb29Xnaupr\n5AB8fHyASwdTL1u2jD/+8Y8EBgbSv3//nzxvrDnRDJi4hKZ43peINC9RUVHYbDZiYmLIysrif/7n\nf9i0aRMPP/xwo9VYuHAhUVFRJCcn8/HHH9OlSxeysrJwOBzMnj2bvn378vDDD7Np0yZGjx6Nv78/\nWVlZtGjRgrlz5zqfpyGNYZs2bXjuuedITU3l8OHDWCwWHnvssauO/6kad999N/fffz/79u1j1KhR\ndOjQgb///e90796dJ5544rqz3Yo0Ayam074vEWkq7rnnHnbs2MHo0aO5ePEia9eubdTn9/X1JS0t\njfDwcM6ePcuhQ4ew2WwkJCQwdepUAOLj43nqqado3749WVlZ+Pj4sGzZMsLCwpzP8+OZKYvFUu9j\nP/zzHXfcwUsvveS8ldnUqVOZMmXKVZ+jvtmvHz62ZMkSxo0bx7fffkt+fj4PPPAAf/rTn3B3d2/A\nK3Pr0b0gb5ArnELvChkamqOp3edRRATg4sWLzJ49m9TUVP72t7/Rq1cvsyNJE6MZMBdw4cIFQkND\niYmJMTVHXFwcf/rTnwytOW/ePE6dOqV9XyLSpLRs2ZLly5fzwgsvXHH8g8i10B4wF7Bjxw5sNhuH\nDh2ioKDAuXnRKPn5+SxatIicnBzDb578/PPP8/DDD2vfl4g0STNmzLjsMFORa6UZMBfw7rvvEhYW\nRnh4OElJSabUj4yMZOTIkYbX7tKlCz//+c8Nrysi0lh+6uBUkavRDJjJjh49Sk5ODitWrKCwsJAp\nU6Ywd+5cOnbsaFiGultHfPrpp4bVFBFpzgYNGkReXh5eXl6mZag7tNXsDP7+/nz++eemZTCLZsBM\ntm7dOoYOHYqnpyf9+/fHarWSkpJidiwREbmJ8vLyTF+6tNvtLpGhud6aSDNgJqqsrGTDhg14eHgw\nfPhwHA4Hdrud5ORkoqOjr3r6sIiING1eXl54eXk1+0/Q12VojtSAmWjjxo106tSJDz74wPnY+fPn\nGTZsGFu3bmXMmDEmphMREZGbRUuQJlq3bt0VJwJ7enoyefJk1qxZY1IqERERudl0EOsNcqUp3KZ4\nEKuISHPkCn9fKoO5NAMmIiIiYjA1YCIiIiIGUwMmIiIiYjA1YCIiIiIGUwMmIiIiYjA1YCIiIiIG\nUwMmIiIiYjA1YCIiItJoamtrzY7QJKgBExERcWGhoaEUFRWZHYOamhpCQkI4f/78VcckJSWxefNm\nA1M1XWrAREREXNSJEyeora2lZ8+eZkfh4MGDWK1WPD09rzomKSmJPn36GJiq6VIDJiIi4qKysrII\nCQkBYMeOHYSHh3P48GHi4uKYMGECkZGRLFu2DIDc3FxGjRrlvLa0tJShQ4dy+vTpBtU+d+4cc+bM\nYfz48UydOpUtW7YQHBzMnj17mDhxIpMmTWL8+PFs2bIFgMmTJ1NeXs7zzz9PTU0Nu3fvvmycZsYu\n52Z2gKauvLwcu93uvJ+VGYqLiwFMzVCXo127dqZmcAWDBg0iLy8PLy8v0zKUl5cDmJ7B39+fzz//\n3LQMIk1ddnY2ISEhpKWlkZGRQXJyMvPmzWPChAnEx8cDlxqf/fv3ExgYyKlTp7h48SItW7Zk8eLF\nxMTEcPvttzeo9uzZsxkyZAhLlizh+PHjjBw5kgULFrBy5UqWLl1K9+7dyc3NJTY2lvDwcKKionB3\ndycxMRG73c6qVauuGDd69OjGfHmaNDVgIo0sLy8Pu91uavNjt9sBcxswu91OXl6eafVFbgVZWVmU\nlpaya9cutm3bRklJCZmZmVRUVLB69Wrg0kxVRUUF7u7uWK1WioqKKCsro6CggISEhAbVzcvLIz8/\nn8TERAB69OhB69atGTx4MF27diUlJYWqqiqKioro3LkzAAcOHCAoKAiAtm3b8vjjj9c7Ti5RA3aD\nvLy88PLy0t3kMX8GzlXoPXF5BhFpmMrKSo4cOUJYWBjdunUjLS2N3r17M3DgQFauXAlAdXU1hw4d\nci5T+vv7k5+fzxtvvMGiRYuwWCwNqn3s2DH8/Pyc1xcUFODm5sb69es5ceIE0dHRWK1WEhMT8fb2\nBi7N1sXGxgKQkJBAcXFxvePkEu0BExERcUEHDhygT58+zJgxg4kTJ5Kamoq3tzc5OTmUlZXhcDhY\nvHgxqampzmv8/f1ZtmwZwcHBBAQENLi2t7c3BQUFVFVVUV1dTXx8PEFBQWRmZhIZGUlwcDBnzpwh\nPT2dwMBAamtryc3Ndda82jj5J82AiYiIuKDs7Gznkp6fnx8+Pj4UFhYSHR1NVFQUbdq0YcCAASxc\nuNB5ja+vL+Xl5cyZM+eGagcGBjJixAjGjh2Lj48PrVq1om/fvvTr14/4+HiSkpLo0KEDVqsVDw8P\namtr6dGjB9OnT2ft2rXExsbWO07+yeJwOBxmh2jKXGmpx1WWIM3OYTZXeB2UQcS13az/f8yaNYuR\nI0cSHh5uWobr4QoZzKIlSBERkSZu3759jBs3jl69el1T8yXm0xKkiIhIEzdkyBAyMjLMjiHXQTNg\nIiIiIgZTAyYiIiJiMDVgIiIiIgZTAyYiIiJiMDVgInLNamtrzY4gInJLUANmopMnT9K3b18iIiL4\n5S9/ydixY5k4cSJffPGFoTkyMjIYN24cERERPProoxw8eNDQ+s1ZaGgoRUVFZsegpqaGkJAQzp8/\nf9UxSUlJbN682cBUIiK3Lh1DYTIPDw/S09OdX2/dupW4uDi2b99uSP1jx47x2muvsWHDBm677TZ2\n797NU089xa5duwyp35ydOHGC2tpaevbsaXYUDh48iNVqxdPT86pjkpKSWLVqlYGpRERuXZoBczHl\n5eV06dLFsHru7u689NJL3HbbbQAEBARw5swZLly4YFiG5iorK8t5A90dO3YQHh7O4cOHiYuLY8KE\nCURGRrJs2TIAcnNzGTVqlPPa0tJShg4dyunTpxtU+9y5c8yZM4fx48czdepUtmzZQnBwMHv27GHi\nxIlMmjSJ8ePHs2XLFgAmT55MeXk5zz//PDU1NezevfuycZoZExG5PpoBM9n3339PREQEDoeDc+fO\ncfr0aVasWGFYfavVitVqdX4dHx/P8OHDcXPTW+Nmy87OJiQkhLS0NDIyMkhOTmbevHlMmDCB+Ph4\n4FLjs3//fgIDAzl16hQXL16kZcuWLF68mJiYGG6//fYG1Z49ezZDhgxhyZIlHD9+nJEjR7JgwQJW\nrlzJ0qVL6d69O7m5ucTGxhIeHk5UVBTu7u4kJiZit9tZtWrVFeNGjx7dmC+PiMgtTb9lTfbjJcis\nrCxiYmLIyMi4rDG62SorK5k/fz6lpaVaZjJIVlYWpaWl7Nq1i23btlFSUkJmZiYVFRWsXr0auDRT\nVVFRgbu7O1arlaKiIsrKyigoKCAhIaFBdfPy8sjPzycxMRGAHj160Lp1awYPHkzXrl1JSUmhqqqK\noqIiOnfuDMCBAwecNwVu27Ytjz/+eL3jpH6DBg0iLy8PLy8v0zKUl5cDmJ7B39+fzz//3LQMrqK8\nvBy73e68F6IZiouLAUzP0K5dO9Pqm0kNmIsJCQmhV69e5OTkGNaAffXVV0yfPh1fX1/eeecd3N3d\nDanbnFVWVnLkyBHCwsLo1q0baWlp9O7dm4EDB7Jy5UoAqqurOXTokHOZ0t/fn/z8fN544w0WLVqE\nxWJpUO1jx47h5+fnvL6goAA3NzfWr1/PiRMniI6Oxmq1kpiYiLe3N3Bpti42NhaAhIQEiouL6x0n\n9cvLy8Nut5va/NjtdsDcBsxut5OXl2dafRFXogbMZA6H47Kvjx07RlFREX379jWk/tmzZ3nssceI\njIxk5syZhtSUSzNKffr0YcaMGRw5coQpU6awYsUKcnJyKCsrw8vLi8WLF1NRUXFZA7Zs2TIGDRpE\nQEBAg2t7e3tTUFBAVVUVFouF+Ph4goKCyMzMZPr06QQHB5Obm0t6ejpxcXHU1taSm5vrrJmZmcmM\nGTOuGCdX5+XlhZeXF4WFhaZlqJvlcIUMovfEjzM0R2rATFZdXU1ERARwqRlzOBy8+OKLhn0y7r33\n3qOkpISdO3eyY8cOACwWC0lJSXTs2NGQDM1Rdna2c0nPz88PHx8fCgsLiY6OJioqijZt2jBgwAAW\nLlzovMbX15fy8nLmzJlzQ7UDAwMZMWIEY8eOxcfHh1atWtG3b1/69etHfHw8SUlJdOjQAavVioeH\nB7W1tfTo0YPp06ezdu1aYmNj6x0nIiLXzuL48RSMXBdX+heEmRlcKYfZbtbrMGvWLEaOHEl4eLhp\nGa6HK2RwFa7wWiiDa3GF10IZzKVjKERc3L59+xg3bhy9evW6puZLRERcn5YgRVzckCFDyMjIMDuG\niIg0Is2AiYiIiBhMDZiIiIiIwdSAiYiIiBhMDZiIiIiIwdSAiYiIiBhMDZiIiDRYbW2t2RFEmiQ1\nYCIiLiQ0NJSioiKzY1BTU0NISAjnz5+/6pikpCQ2b95sYCqpc+HCBUJDQ4mJiTEtwyuvvMKwYcOI\niIggIiLihu/S0dzoHDARERdx4sQJamtrDbsV2U85ePAgVqsVT0/Pq45JSkpi1apVBqaSOjt27MBm\ns3Ho0CEKCgrw8fExPEN2djZLly4lODjY8Nq3As2AiYi4iKysLOfN13fs2EF4eDiHDx8mLi6OCRMm\nEBkZybJlywDIzc1l1KhRzmtLS0sZOnQop0+fblDtc+fOMWfOHMaPH8/UqVPZsmULwcHB7Nmzh4kT\nJzJp0iTGjx/Pli1bAJg8eTLl5eU8//zz1NTUsHv37svGaWbs5nr33XcJCwsjPDycpKQkw+tXV1fz\n5Zdfsnr1asaNG8esWbM4deqU4TmaMs2A3aDy8nLsdrupd3QvLi4GzL+rfHFxMe3atTM1gyvQe+Kf\nGfR+uD7Z2dmEhISQlpZGRkYGycnJzJs3jwkTJhAfHw9canz2799PYGAgp06d4uLFi7Rs2ZLFixcT\nExPD7bff3qDas2fPZsiQISxZsoTjx48zcuRIFixYwMqVK1m6dCndu3cnNzeX2NhYwsPDiYqKwt3d\nncTEROx2O6tWrbpi3OjRoxvz5ZH/7+jRo+Tk5LBixQoKCwuZMmUKc+fOpWPHjoZlKC0t5b777mPu\n3Ln07NmTxMREZsyYQXp6umEZmjo1YCIiLiIrK4vS0lJ27drFtm3bKCkpITMzk4qKClavXg1cmqmq\nqKjA3d0dq9VKUVERZWVlFBQUkJCQ0KC6eXl55Ofnk5iYCECPHj1o3bo1gwcPpmvXrqSkpFBVVUVR\nURGdO3cG4MCBAwQFBQHQtm1bHn/88XrHSeNbt24dQ4cOxdPTk/79+2O1WklJSWHatGmGZbjjjjt4\n6623nF9HR0ezYsUKTp48idVqNSxHU6YG7AZ5eXnh5eWlu8lj/gycq9B74vIMcm0qKys5cuQIYWFh\ndOvWjbS0NHr37s3AgQNZuXIlcGnZ59ChQ85lSn9/f/Lz83njjTdYtGgRFoulQbWPHTuGn5+f8/qC\nggLc3NxYv349J06cIDo6GqvVSmJiIt7e3sCl2brY2FgAEhISKC4urnecNK7Kyko2bNiAh4cHw4cP\nx+FwYLfbSU5OJjo6mpYtWxqS4x//+AeHDx9m3LhxzsccDgdubmorrpX2gImIuIADBw7Qp08fZsyY\nwcSJE0lNTcXb25ucnBzKyspwOBwsXryY1NRU5zX+/v4sW7aM4OBgAgICGlzb29ubgoICqqqqqK6u\nJj4+nqCgIDIzM4mMjCQ4OJgzZ86Qnp5OYGAgtbW15ObmOmtebZw0vo0bN9KpUyc++eQTPvzwQz76\n6CN27tyJ3W5n69athuVo0aIFv//97zl58iQAycnJ2Gw2unbtaliGpk6tqoiIC8jOznYu6fn5+eHj\n40NhYSHR0dFERUXRpk0bBgwYwMKFC53X+Pr6Ul5efsMf/w8MDGTEiBGMHTsWHx8fWrVqRd++fenX\nrx/x8fEkJSXRoUMHrFYrHh4e1NbW0qNHD6ZPn87atWuJjY2td5w0vnXr1vHEE09c9pinpyeTJ09m\nzZo1jBkzxpAcfn5+PPvss8TGxlJbW0u3bt1YsmSJIbVvFRaHw+EwO0RT5kpLPa6yBGl2DrO5wuug\nDK7lZr0Ws2bNYuTIkYSHh5uW4Xq4QgZX4QqvhTKYS0uQIiJNzL59+xg3bhy9evW6puZLRFyPliBF\nRJqYIUOGkJGRYXYMEbkBmgETERERMZgaMBERERGDqQETERERMZgaMBERERGDqQETERERMZgaMBER\nERGD6RgKE9XW1rJmzRref/99amtrqamp4cEHH2TWrFm4u7sblmPt2rWsW7cOi8VCjx49ePHFF+nU\nqZNh9Y108OBBampqCA4ObvB9826WkydPEhYWRu/evXE4HFy8eJG2bdsyf/58BgwYYGiWjIwMVq9e\nTYsWLfDw8GDBggU3dKsbV7Z//37atm1Lnz59XO49IebYu3cvXbt2xdfX1+wocgvTDJiJXnjhBQ4c\nOMCaNWtIT08nLS2NY8eO8dxzzxmW4dChQ/zpT38iJSWFTZs20aNHD/7P//k/htU32l/+8hfuvfde\n+vfvz6RJk3jppZfIzs7GVW4I4eHhQXp6Ohs2bGDTpk1MnTqVuLg4QzMcO3aM1157jdWrV5Oenk5s\nbCxPPfWUoRmMlJyczIABAwgODiYqKor4+Hi+/PJLl3lPiPFWr15NYGAgAwYMYMqUKSxevJijR4+a\nHUtuMZoBM0lxcTHvv/8+e/fupW3btsClX76LFi0iKyvLsBz9+vXjgw8+oGXLllRVVVFaWsodd9xh\nWH2jWSwWampqOHToEIcOHSIlJYVFixbh7+9PQEAAAQEBjBkzhqCgIJeYDSkvL6dLly6G1nR3d+el\nl17itttuAyAgIIAzZ85w4cIF3Nxuvb8yLBYLVVVV5OTkkJOTA8DChQvp3bu38z0xbtw4zZA1Iy1a\ntKCyspKsrCzn38cvvPACNpuNgIAA+vfvT0REhGbI5IboXpA3qKH3sfrggw9YtWoVqamppmX4oZ07\nd/Lss8/SunVr/vznP9OjRw9DcqSmpvLaa69dd62G+vbbbzly5MhPjmnVqhX+/v74+voyfPjw6579\naeh/jx8vQZ47d47Tp0+zYsUKHnjgAUMy1OfXv/41Fy5c4L//+78NybBy5UpWrlx5XdfciG+++YaC\ngoKfHNO6dWt69+6Nj48P48aNY+rUqddVo2PHjtjtdlP/cVNcXAxgeoZ27dpx9uzZ67rutddea5S/\nK69VaWkpRUVFPzmmTZs22Gw27rrrLqKiooiMjLyuGnpP/DNDQ94Tt4Jb75+zTUSLFi2ora01O4bT\niBEjGDFiBH/5y1/4j//4D3bu3GlI3T59+vCLX/zCkFoAmZmZP9mAde/enX79+hEQEMB9993H6NGj\nDcsG/1yCrJOVlUVMTAwZGRlYrVZDs1RWVjJ//nxKS0tZtWqVYXX79+9v6Hvib3/72082YHfeeadz\nJiw0NNTQbHLJgAEDDP0FvWfPnp9swO666y769etH//79efDBB3nooYcMyya3DjVgJunfvz/5+fl8\n9913ziVIgJKSEp5//nmWL19uyEb848ePc/r0ae655x4AIiMjeeGFFzh79iwdO3a86fX79+9P//79\nb3qdOr/73e/YunWr8+v6Gq4f/vcwW0hICL169SInJ8fQBuyrr75i+vTp+Pr68s477xj6oZDBgwcz\nePBgw+rNmTPnsn9w1NdwtW7d+oZqeHl54eXl1Sgzkg3VmLOiN5rhej300EOGNjkxMTHs2bPH+XV9\nDVerVq1uqIbeE5dnaI7UgJmka9eujB07lmeeeYaXXnqJ9u3bU1FRwcKFC+nUqZNhv/BKS0uZO3cu\nGRkZ/OxnP2Pjxo34+/sb0nyZoU2bNoSFhblsw/XjHQHHjh2jqKiIvn37Gpbh7NmzPPbYY0RGRjJz\n5kzD6pqlTZs2jBo1qlEbLmna2rZty+jRoxu14RL5MTVgJvrd737H66+/zqOPPoqbmxvV1dWMGDHC\n0E+cDRw4kOnTpzN58mTc3Nzo0qULr7/+umH1jTZ//nzmz59vdoyrqq6uJiIiArjUjDkcDl588UV6\n9uxpWIb33nuPkpISdu7cyY4dO4BLG9WTkpJuycb85ZdfNjuCuJhb+ZPg4jrUgJmoRYsWPPXUU6Z/\nxBis6IUAACAASURBVH/SpElMmjTJ1AwCVquVQ4cOmR2D2NhYYmNjzY4hInJL0zlgIiIiIgZTAyYi\nIiJiMDVgIiIiIgZTAyYiIiJiMDVgIiIiIgZTAyYiIiJiMDVgIiIiIgbTOWAiIi7qxzdov3jxIm3b\ntmX+/PkMGDDAsBwbNmwgKSkJi8UCwLlz5ygpKWHPnj106tTJsByi98StRA2YiIgL+/EN2rdu3Upc\nXBzbt283LMMvf/lLfvnLXwJw4cIFHnvsMWJjY/WL1iR6T9watAQpItKElJeX06VLF9Pqv/3229x2\n221MmDDBtAxyOb0nmibNgN2g8vJy7Ha7qXd0Ly4uBsy/q3xxcTHt2rUzNYMr0Hvinxn0frhx33//\nPRERETgcDs6dO8fp06dZsWKFKVnKy8tJSkpiw4YNptSXS/SeuDWoARMRcWE/Xm7KysoiJiaGjIwM\nrFaroVlSU1MZPnw43bt3N7SuXE7viVuDGrAb5OXlhZeXF4WFhaZlqJvlMDPDD3M0d/+vvXsPqrrO\n/zj+EhARUkJNIhwJJo5XFEqny5TZhmYm6yrdXNdqlzLFtLKsNORmo401Wpo2ZpiYNNrioi3Zxcop\nq601QyxRaRFQWAcsTxnHywHP+f3hePZn2W4pfr7fc3g+/go67Ps17Elefr5vvl/eE6dnQOtKSUlR\nfHy8duzYYfyH7caNGzV79myjM/G/8Z7wT+yAAYCNeb3e0z6urq5WbW2t+vbtazTH4cOHtW/fPqWk\npBidi5/jPREYOAEDABtzu90aM2aMpJM/eL1er+bMmaO4uDijOWpra9W9e3cFBwcbnYuf4z0RGChg\nAGBTsbGx2rlzp9UxJElJSUlGb3OAM+M9ETi4BAkAAGAYBQwAAMAwChgAAIBhFDAAAADDKGAAAACG\nUcAAAAAMo4ABAAAYRgEDAAAwjBux2kBLS4uGDh2qPn36aPny5ZbleO+99/T4449r27ZtlmVoy+rr\n6zVs2DD16tVLXq9XJ06cUHh4uB5//HFdfvnlRrM8/fTTeuedd3ThhRdKkuLj47VgwQKjGQAgkFHA\nbGDTpk3q3bu3du7cqb179yohIcF4hpqaGs2fP/9nzxiDWWFhYSopKfF9/NZbb2nmzJnG7za9fft2\nLVy4UMnJyUbnAkBbwSVIG3jttdc0bNgwjRw5UitXrjQ+/+jRo3rsscc0c+ZM47Px3zmdTnXv3t3o\nTLfbrYqKCq1YsUKjR4/WtGnTdODAAaMZACDQcQJmsX/961/asWOHli5dqpqaGt1111165JFHFBkZ\naSxDTk6Oxo0bJ4fDYWwmzuzYsWMaM2aMvF6vDh8+rIMHD2rp0qVGMzQ2Nurqq6/WI488ori4OBUU\nFCgzM/O0kzn8Nk6nUy6XS5deeqllGerq6iTJ8gwRERGWzbcT3hP/ydBW3xOcgFlszZo1uv7669Wp\nUyclJSUpNjZWa9euNTa/qKhIISEhvh/6sNapS5Dr16/XBx98oFWrVunhhx9WfX29sQw9evTQsmXL\nFBcXJ0nKyMjQvn37jGYINA6Hw/IfMhEREbbIwF/0gJM4AbPQ0aNHtX79eoWFhenGG2+U1+uVy+VS\nUVGRMjIyFBwcfN4zrF+/3nfq4na7ff/80ksv6aKLLjrv8/HfpaSkKD4+Xjt27FBsbKyRmXv27NHu\n3bs1evRo3+e8Xq9CQvjj4mxt3brV6giwmaioKEVFRammpsayDKdOvuyQoS3iT1QLvfHGG+rSpYve\nffdd3+d+/PFH3XDDDXrrrbc0atSo857hr3/9q++f6+vrNWrUKC41Weinp5DV1dWqra1V3759jWUI\nCgrS3LlzNWjQIMXGxqqoqEi9e/dWdHS0sQwAEOgoYBZas2aN/vznP5/2uU6dOmnChAkqLCw0UsB+\nql27dsZn4j/cbrfGjBkj6WQZ83q9mjNnju9yoAmJiYnKysrSpEmT5PF4dPHFF3MLCgBoZe28LP6c\nEzsd4VqZwU45rGaH7wMZAHuzw38fZLAWS/gAAACGUcAAAAAMo4ABAAAYRgEDAAAwjAIGAABgGAUM\nAADAMAoYAACAYRQwAAAAw7gTPgAAfsTj8aiwsFClpaXyeDxqbm7W0KFDNW3aNIWGhhrL8eqrr6qo\nqEgdO3ZUQkKCcnJy1LlzZ2Pz/R0nYAAA+JGcnByVl5ersLBQJSUlKi4uVnV1tWbPnm0sw2effaaC\nggKtWrVKJSUlGjJkiLKysozNDwScgAEA4Cfq6upUWlqqTz75ROHh4ZKksLAw5efnq6yszFiOiooK\nXX311erevbskafjw4crKylJLS4tCQqgWvwbfpXPkdDrlcrl8z7OyQl1dnSRZmuFUjoiICEsz2AHv\nif9k4P0AtK6KigolJib6ytcpXbt2VWpqqrEcAwYM0OrVq3XgwAHFxMRo3bp1amlp0ffff69u3boZ\ny+HPuAQJtDKHw2F58YiIiLBFBofDYWkGINAEBQXJ4/FYHUODBg3SlClTNGXKFN16660KDg5WZGSk\n2rdvb3U0v8EJ2DmKiopSVFQUT5OX9SdwdrF161arIwAIUElJSaqqqtKRI0dOOwVraGhQdna2Fi9e\nbGQR3+VyafDgwUpPT5ckfffdd3r++ecVGRl53mcHCk7AAADwE9HR0UpLS9OsWbPU1NQkSWpqalJe\nXp66dOli7LcgGxsbNWHCBF+GpUuXatSoUUZmBwpOwAAA8CO5ublasmSJxo0bp5CQELndbqWmpmrq\n1KnGMsTHx2vixIm6/fbb5fV6dcUVVyg7O9vY/EDQzuv1eq0O4c/scPnPDhnslAMA7M4Of16SwVpc\nggQAADCMAgYAAGAYBQwAAMAwChgAAIBhFDAAAADDKGAAAACGUcAAAAAM40asFqqvr9ewYcPUq1cv\neb1enThxQuHh4Xr88cd1+eWXG8vx9NNP65133tGFF14o6eQN9hYsWGBsPgAAbQ0FzGJhYWEqKSnx\nffzWW29p5syZeuedd4xl2L59uxYuXKjk5GRjMwEAaMsoYDbjdDrVvXt3Y/PcbrcqKiq0YsUK1dbW\nKi4uTjNnzlRMTIyxDAAAtDUUMIsdO3ZMY8aMkdfr1eHDh3Xw4EEtXbrU2PzGxkZdffXVeuSRRxQX\nF6eCggJlZmaedioHAGhdTqdTLpfL9ygeK9TV1UmS5RkiIiIsm28lCpjFfnoJsqysTPfdd582bNig\n2NjY8z6/R48eWrZsme/jjIwMLV26VPX19UbmA0Bb5HA4VFlZaWkGOxSfiIgIORwOq2NYggJmMykp\nKYqPj9eOHTuMFKA9e/Zo9+7dGj16tO9zXq9XISG8NQDgfNm6davVEWAxbkNhMa/Xe9rH1dXVqq2t\nVd++fY3MDwoK0ty5c1VfXy9JKioqUu/evRUdHW1kPgAAbRHHHBZzu90aM2aMpJNlzOv1as6cOYqL\nizMyPzExUVlZWZo0aZI8Ho8uvvhio7egWLFihcrKyrR48WJjMwGgtbS0tOjNN9887SoC8GtQwCwU\nGxurnTt3Wh1DaWlpSktLs2R2hw4dtGrVKvXv31/333+/JRkA4Gw0NDRo3Lhx+sMf/mB1FPghLkHC\nUuPHj9ef/vQn5efna/v27VbHAYBf5R//+IeGDx+uhoYGTZ482eo48EMUMFjuueeek8PhUGZmpo4e\nPWp1HAD4r1555RXdeuut2rFjh0aMGKH27dtbHQl+iAIGy7Vv314vvfSSDhw4oClTplgdBwDOyOPx\naMaMGXrggQf073//W127dtWDDz5odSz4KQoYbCExMVFPPfWU1q1bd9p9yQDADn744Qelp6fr2Wef\n1ZEjRyRJqamp6tmzp8XJ4K8oYLAN9sEA2NFXX32l4cOHa/369b7PhYSE6K677rIwFfwdBQy2wj4Y\nADspLi7W73//e/3zn/887fNXXnmlbr75ZotSIRBQwGAr7IMBsIsff/xRubm5qqmp+dm/S0tLU7t2\n7cyHQsCggMF22AcDYAedOnXSO++8o+HDh5/2+YSEBD3wwAMWpUKgoIDBltgHA2AHl1xyicLDw3XZ\nZZepc+fOkqSbbrrJFg+yhn+jgMG22AcDYLU5c+Zoy5YtevHFF7Vq1Sr17dtXmZmZVsdCAKCAwbbY\nBwNgpffee0/PP/+8HnzwQaWmpmr06NH68ssv1b9/f6ujIQC083q9XqtD+LPIyEi5XC716NHDsgx1\ndXWSZGmGUzkiIiL0ww8/tOr/blFRkTIzMzV//ny/eF7k4MGDVVlZqaioKMsyOJ1OSbI8g8Ph0Nat\nWy3LAJytb7/9VjfccIMuu+wy/e1vf2PhHq2OEzDYnr/tg1VWVsrlclmaweVy2SJDZWWlpRmAs+H1\nenXfffepXbt2Wr58OeUL5wUnYOfo0ksvlaQz/ppyW8pwvnM0Nzdr+PDhOn78uN5//3117Nix1We0\nFjv8/0EG4Ozl5+dr0aJFWrNmjVJTU62OgwDFCRj8AvtgAEz46d4XcL5QwOA3uD8YgPPp22+/1cMP\nP6whQ4YoKyvL6jgIcBQw+BV/2wcD4B/Y+4JpFDD4He4PBqC1nbrf14IFC9StWzer46ANoIDB77AP\nBqA1sfcFK1DA4JfYBwPQGtj7glUoYPBb48eP14QJE9gHA3BW2PuClShg8GsLFy5kHwzAWWHvC1ai\ngMGvsQ8G4Gyw9wWrUcDg99gHM8fj8VgdAThn7H3BDihgFvJ4PHrllVeUnp6uMWPGaNSoUXr22Wfl\ndruN5tizZ48mTJigMWPG6NZbb9XOnTuNzm8N/np/sGuvvVa1tbVWx1Bzc7NSUlL0448//uJrVq5c\nqTfffNNgKqD1ndr7ksTeFyxFAbNQTk6OysvLVVhYqJKSEhUXF6u6ulqzZ882luHYsWPKyMjQxIkT\nVVJSoszMTM2YMcPY/Nbkb/cH279/vzwej+Li4qyOoq+//lqxsbHq1KnTL75m5cqV6tOnj8FUQOub\nM2eOPvroIy1cuJC9L1iKAmaRuro6lZaWau7cubrgggskSWFhYcrPz9ewYcOM5fj4448VFxen6667\nTpL0u9/9Ts8995yx+a3J3/bBysrKlJKSIknatGmTRo4cqd27d2vmzJm67bbblJ6erkWLFkmSdu3a\npZtvvtn3tY2Njbr++ut18ODBs5p9+PBhTZ8+XWPHjtU999yjjRs3Kjk5WR999JHuuOMO3XnnnRo7\ndqw2btwoSZowYYKcTqeys7PV3NysDz/88LTXcTIGf8DeF+wkxOoAbVVFRYUSExMVHh5+2ue7du1q\n9A+Gmpoade3aVU8++aR2796tyMhIPfroo8bmt7ZT+2CZmZm68sordf/991sd6Rdt375dKSkpKi4u\n1oYNG1RUVKQZM2botttu07x58ySdLD5ffPGFBgwYoAMHDujEiRMKDg7WM888o/vuu08XXXTRWc1+\n6KGHdM0112jBggXat2+fRowYoSeffFLLly/XwoULdckll2jXrl2aNGmSRo4cqfHjxys0NFQFBQVy\nuVx6+eWXf/a6W265pTW/PQFl8ODBqqysVFRUlGUZnE6nJFmeweFwaOvWrcZn//+9L5NXGYBfQgGz\nSFBQkC0WmltaWrRlyxatWrVKSUlJev/99zVx4kRt3rxZ7du3tzreWRk/frw+/fRT5efn68orr1Ry\ncrLVkc6orKxMjY2N2rx5s95++201NDRo27Ztampq0ooVKySdPKlqampSaGioYmNjVVtbq0OHDmnv\n3r2aP3/+Wc2trKxUVVWVCgoKJEk9e/ZUhw4ddNVVVyk6Olpr167V8ePHVVtb67tEU15eroEDB0qS\nwsPDdffdd5/xdTizyspKuVwuS8uPy+WSZG0Bc7lcqqysND7X6/Xq3nvvlcTeF+yDAmaRpKQkVVVV\n6ciRI6edgjU0NCg7O1uLFy9WaGjoec/RvXt3xcfHKykpSZJ04403KisrS/v371dCQsJ5n3++PPfc\nc6qoqFBmZqbef/99dezY0epIpzl69Ki++eYbDRs2TBdffLGKi4vVq1cvDRo0SMuXL5ckud1u7dy5\n03eZ0uFwqKqqSi+++KLy8/PP+odIdXW1EhMTfV+/d+9ehYSEaN26ddq/f78yMjIUGxurgoICxcTE\nSDp5Wjdp0iRJ0vz581VXV3fG1+HMoqKiFBUVpZqaGssyXHrppZJkiwymnbrf19q1a/nLAmyDHTCL\nREdHKy0tTbNmzVJTU5MkqampSXl5eerSpYuR8iVJQ4YMUX19vSoqKiRJW7duVVBQkHr06GFk/vli\n932w8vJy9enTR5mZmbrjjjv0+uuvKyYmRjt27NChQ4fk9Xr1zDPP6PXXX/d9jcPh0KJFi5ScnKz+\n/fuf9eyYmBjt3btXx48fl9vt1rx58zRw4EBt27ZN6enpSk5O1rfffquSkhINGDBAHo9Hu3bt8s38\npdcBdrRp0yb2vmBLnIBZKDc3V0uWLNG4ceMUEhIit9ut1NRUTZ061ViGbt26acmSJcrNzdXRo0cV\nGhqqF154wVgBPJ/svA+2fft23yW9xMREJSQkqKamRhkZGRo/frw6duyoyy+/XHl5eb6vueyyy+R0\nOjV9+vRzmj1gwAClpqYqLS1NCQkJat++vfr27at+/fpp3rx5WrlypTp37qzY2FiFhYXJ4/GoZ8+e\nmjx5slavXq1Jkyad8XWA3Rw8eFDTp09n7wu21M7r9XqtDuHP7HSsb2UGO+X4qSlTpmj9+vV68803\njeyDna/vw7Rp0zRixAiNHDnSsgy/hR0y2IUdvhdtLYPX69WYMWNUVVWlzZs3c+kRtsMlSAQ8f7s/\n2E99+umnGj16tOLj439V+QIg5efna8uWLdzvC7ZFAUPAs/s+2P9yzTXXaMOGDXr44YetjgL4hU2b\nNmnRokXsfcHWKGBoE3heJNA2sPcFf0EBQ5vhr8+LBPDr8JxH+BMKGNoUf98HA/DL2PuCP6GAoU3x\n930wAGfG3hf8DQUMbQ77YEBgYe8L/ogChjbpp/tgLS0tmjp1qnbs2GF1NMCvWP1MW/a+4K8oYGiz\nTu2DTZ48WTfddJNeeOEFLV261OpYaOOuvfZa1dbWWh1Dzc3NSklJ0Y8//viLr1m5cqXefPNNg6l+\njr0v+CsKGNqs9u3ba+TIkfr888/1wQcfSJLeffdd37M5AdP2798vj8ejuLg4q6Po66+/VmxsrDp1\n6vSLr1m5cqX69OljMNXp2PuCP6OAoc168MEHlZubq///NK7q6motXrzYwlRoy8rKypSSkiLpZLkY\nOXKkdu/erZkzZ+q2225Tenq6Fi1aJEnatWuXbr75Zt/XNjY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def draw_dataframe(df, loc=None, width=None, ax=None, linestyle=None,\n", + " textstyle=None):\n", + " loc = loc or [0, 0]\n", + " width = width or 1\n", + "\n", + " x, y = loc\n", + "\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " ncols = len(df.columns) + 1\n", + " nrows = len(df.index) + 1\n", + "\n", + " dx = dy = width / ncols\n", + "\n", + " if linestyle is None:\n", + " linestyle = {'color':'black'}\n", + "\n", + " if textstyle is None:\n", + " textstyle = {'size': 12}\n", + "\n", + " textstyle.update({'ha':'center', 'va':'center'})\n", + "\n", + " # draw vertical lines\n", + " for i in range(ncols + 1):\n", + " plt.plot(2 * [x + i * dx], [y, y + dy * nrows], **linestyle)\n", + "\n", + " # draw horizontal lines\n", + " for i in range(nrows + 1):\n", + " plt.plot([x, x + dx * ncols], 2 * [y + i * dy], **linestyle)\n", + "\n", + " # Create index labels\n", + " for i in range(nrows - 1):\n", + " plt.text(x + 0.5 * dx, y + (i + 0.5) * dy,\n", + " str(df.index[::-1][i]), **textstyle)\n", + "\n", + " # Create column labels\n", + " for i in range(ncols - 1):\n", + " plt.text(x + (i + 1.5) * dx, y + (nrows - 0.5) * dy,\n", + " str(df.columns[i]), style='italic', **textstyle)\n", + " \n", + " # Add index label\n", + " if df.index.name:\n", + " plt.text(x + 0.5 * dx, y + (nrows - 0.5) * dy,\n", + " str(df.index.name), style='italic', **textstyle)\n", + "\n", + " # Insert data\n", + " for i in range(nrows - 1):\n", + " for j in range(ncols - 1):\n", + " plt.text(x + (j + 1.5) * dx,\n", + " y + (i + 0.5) * dy,\n", + " str(df.values[::-1][i, j]), **textstyle)\n", + "\n", + "\n", + "#----------------------------------------------------------\n", + "# Draw figure\n", + "\n", + "import pandas as pd\n", + "df = pd.DataFrame({'data': [1, 2, 3, 4, 5, 6]},\n", + " index=['A', 'B', 'C', 'A', 'B', 'C'])\n", + "df.index.name = 'key'\n", + "\n", + "\n", + "fig = plt.figure(figsize=(8, 6), facecolor='white')\n", + "ax = plt.axes([0, 0, 1, 1])\n", + "\n", + "ax.axis('off')\n", + "\n", + "draw_dataframe(df, [0, 0])\n", + "\n", + "for y, ind in zip([3, 1, -1], 'ABC'):\n", + " split = df[df.index == ind]\n", + " draw_dataframe(split, [2, y])\n", + "\n", + " sum = pd.DataFrame(split.sum()).T\n", + " sum.index = [ind]\n", + " sum.index.name = 'key'\n", + " sum.columns = ['data']\n", + " draw_dataframe(sum, [4, y + 0.25])\n", + " \n", + "result = df.groupby(df.index).sum()\n", + "draw_dataframe(result, [6, 0.75])\n", + "\n", + "style = dict(fontsize=14, ha='center', weight='bold')\n", + "plt.text(0.5, 3.6, \"Input\", **style)\n", + "plt.text(2.5, 4.6, \"Split\", **style)\n", + "plt.text(4.5, 4.35, \"Apply (sum)\", **style)\n", + "plt.text(6.5, 2.85, \"Combine\", **style)\n", + "\n", + "arrowprops = dict(facecolor='black', width=1, headwidth=6)\n", + "plt.annotate('', (1.8, 3.6), (1.2, 2.8), arrowprops=arrowprops)\n", + "plt.annotate('', (1.8, 1.75), (1.2, 1.75), arrowprops=arrowprops)\n", + "plt.annotate('', (1.8, -0.1), (1.2, 0.7), arrowprops=arrowprops)\n", + "\n", + "plt.annotate('', (3.8, 3.8), (3.2, 3.8), arrowprops=arrowprops)\n", + "plt.annotate('', (3.8, 1.75), (3.2, 1.75), arrowprops=arrowprops)\n", + "plt.annotate('', (3.8, -0.3), (3.2, -0.3), arrowprops=arrowprops)\n", + "\n", + "plt.annotate('', (5.8, 2.8), (5.2, 3.6), arrowprops=arrowprops)\n", + "plt.annotate('', (5.8, 1.75), (5.2, 1.75), arrowprops=arrowprops)\n", + "plt.annotate('', (5.8, 0.7), (5.2, -0.1), arrowprops=arrowprops)\n", + " \n", + "plt.axis('equal')\n", + "plt.ylim(-1.5, 5);\n", + "\n", + "fig.savefig('figures/03.08-split-apply-combine.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## What Is Machine Learning?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# common plot formatting for below\n", + "def format_plot(ax, title):\n", + " ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + " ax.yaxis.set_major_formatter(plt.NullFormatter())\n", + " ax.set_xlabel('feature 1', color='gray')\n", + " ax.set_ylabel('feature 2', color='gray')\n", + " ax.set_title(title, color='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Classification Example Figures\n", + "\n", + "[Figure context](05.01-What-Is-Machine-Learning.ipynb#Classification:-Predicting-Discrete-Labels)\n", + "\n", + "The following code generates the figures from the Classification section." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "from sklearn.svm import SVC\n", + "\n", + "# create 50 separable points\n", + "X, y = make_blobs(n_samples=50, centers=2,\n", + " random_state=0, cluster_std=0.60)\n", + "\n", + "# fit the support vector classifier model\n", + "clf = SVC(kernel='linear')\n", + "clf.fit(X, y)\n", + "\n", + "# create some new points to predict\n", + "X2, _ = make_blobs(n_samples=80, centers=2,\n", + " random_state=0, cluster_std=0.80)\n", + "X2 = X2[50:]\n", + "\n", + "# predict the labels\n", + "y2 = clf.predict(X2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Classification Example Figure 1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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ffbXPiyEiInI3PQbxM8880/n/VqsVeXl5sFgsDi2KiIjIXfQ4a3rAgAGd/wUG\nBmLChAnIz89XojYiIqJ+r8c94uLi4s7/l2UZNTU1MJvNDi2KiIjIXfQYxNnZ2V1+9vLywoIFCxxV\nDxERkVvpMYjnzJmD0NDQLm1lZWUOK4iIiMiddHuOuKSkBMXFxVizZg2Ki4s7/ysqKuIyiERERH2k\n2z3iK1euoLi4GC0tLV0OT6tUKowaNUqJ2oiIiPq9boN4ypQpAIAzZ87wLlpEREQO0uM54qioKGzd\nuhVGoxHAjZnT9fX1eO655xxeHBERUX/X43XEX331FTw8PFBZWYnw8HC0trbaTN4iIiKie9NjEMuy\njKlTpyIxMRERERF49NFHbRaAICIionvTYxBrtVqYzWYEBQWhoqICGo2GN/QgIiLqIz0G8bBhw/D5\n559j8ODBOHbsGD799FP4+voqURsREVG/1+NkrczMTGRkZECv1+PZZ59FeXk5EhISlKiNiIio3+sx\niC0WC44dO4br16/jgQceQHV1NZKSkpSojYiI+sjh3VtwfscamK6XQtL7wD9lLB5Z/lNotVrRpbm9\nHg9Nf/PNNzAajbh27RpUKhXq6uqwceNGJWojIqI+cHj3FpSueR1pbXkY7tWCDHUlovLX4YN//4no\n0gh3EMTXrl3D9OnToVarodVqsWDBAly7dk2J2oiIumhvb8emz9/DF2/9GutX/RktLS2iS3IJ53d8\niWgPU5c2rVoF//JjuJSXK6gq+l6PQSxJEiwWS+fPbW1tkCTJoUUREf3Q1cKLeO8ni+F74C+ILvwG\ngcc+wKp/XIy8szmiS3N6puvFdttjPK24cOKAwtXQD/UYxGPHjsWqVavQ0tKCbdu2YeXKlcjKylKi\nNiKiTjs++D1Gaaqg19z42NKqJYzQ1WLvqv8SXJnzU3nav9KlxWSFf1C4wtXQD3U7WSs3Nxfp6ekY\nPHgwIiMjUVRUBFmW8fjjjyMsLKzXHYeE8BIoR+MYK4Pj7HiybIC+8gLgY7stqKEA16uvIjVtqPKF\nuYiIkZNhPPEZdOqu+15X9LH4p6VPAOD7WKRugzg7OxtDhgzBxx9/jOXLlyMkJKRPO66pae7T16Ou\nQkJ8OcYK4Dg7XkiIL0pLq6G1GgGobbZ7SBaUllQhODRO8dpcxZwnX8GHJSXwLT2KWC8LWkxWXFIP\nxLTn/wV1dW18Hyukuy873QZxdHQ0fvvb30KWZfzmN7/pbJdlGZIk4bXXXuv7KomI7IiOjkGTfxwg\nl9psq9DIYUTqAAAX7UlEQVRHYXbGSOWLciFarRYvvPZHFF68gPPH98M/OAIvzZwLtdr2iw0pr9sg\nfuihh/DQQw9h9erVeOyxx5SsiYioC0mSkDp7KUrW/zdi9MbO9kqjBrHTF/Na2DuUmDwEiclDRJdB\nP9DjDT0YwkTkDCbNXogc/yCc2/U1LI1VUPkEIWXyfGRNmSW6NKJe6TGIiYicxchxkzBy3CTRZRD1\nqR4vXyIiIiLHYRATEREJxCAmIiISiEFMREQkECdrEVGfslqtWL/qz7h+di+srY3QBEYgeeoiTLx/\nvujSiJwSg5iI+tQn//uviLy8BRFaFeABoK0BFWv/E9kmE6bMXSS6PCKnw0PTRNRnqqsqYc3fA29t\n14+WSL0Zl3Z/CVmWBVVG5LwYxET9RHNzE3KOH0HltQphNZw4sAuJHu12t6nqS9HW1qZwRUTOj4em\niVyc1WrFZ2/9Gzou7EWYtR7nZS8YIjOw5O9+iwEBgYrWEhoZjUqjjCAP2zXLLVov6PV6ReshcgXc\nIyZyYi0tLdi2fg32bNsAk8lk9zFfvfsHRFzahCEeLQjy0iLR24T0huNY/YefKVwtMCprIkq8Btm0\nW6wyPBJGQaNR7rt/dVUlvn7/DXz1zn/hwtkcxfolulvcIyZyUps/exfX9n2BZE0DTFYZf920EsMe\nfhnjp8/tfIzFYkH9ub2I1nT9Ti1JEgKrz+FS/nkkpaQpVrMkSZi5/Jf49k+vIdlSCh+tClXtQEXg\nMDzz418qVse2Lz/Ete3vI8nTAJUk4cLJr3AobhKW/fx3UKm4/0HOhUFM5ISO7N0B074PkK63AFBB\nqwYyUI38Nb+HGSoUn9gNc2M1rDof1FZVAFG2qw9FeFhw+cJZRYMYAAanDMWgP36Ffd9uxLXqcgxK\nG4UFY8Yp1n/J1SJUb38PKV5GADcOkUd5yhhQtgdbvvgQ8x5bplgtRHeCQUzkhC4d+AaD9Rab9mSP\nNmx786eYE+d5o6EV8PSWcbayFcPCvbs8tqxdg6yMUUqUa0OtVmPqAwuF9H1k6xokenbg+xD+nrdW\nhbLzhwAwiMm58BgNkROyGprstkuShECNuUtb/AAdWkwWmK03Lw2yWGU0RQxHfEKSQ+t0RrKxHZJk\nO1kMAKxGg8LVEPWMQUzkhDQBEXbbTRYrVHZCJjXYCzurNShpMuFCmycuhU/E0p/9wdFlOqWwpOFo\n7LDa3aYPS1C4GqKe8dA0kROa8NDT2PvfR5Gs67pnnF3cgokxPjaPN8oS5v/kfxAQHIrg4GD4+fkr\nVarTmTRrPv60ZwOGG3KhUd380nLWFIwHlzzf+XN1VSX2b/kSsFowevqDiI1jSJMYDGIiJxSfmIyW\nH/0Wx9e9B/O1i5BVamij0+GNGnigyObxlT7xeGhMVreHZN2JSqXCC7/5P6x7/49ouZIDyWyGPnIw\n5ix5EZEDYwEAmz59F9ezP0aSZzskAAeOfIGDwx7AE6/8Qmzx5JYYxEROauioLAwdlYWOjg6oVCpo\ntVpcvngB3/7xH5GuroZWLcFilXHB6IesZa8yhG/h4eGBx1f83O62vHOn0Lr3A6R4WfD9hK5BXmbU\nnd+I7K1pmDJHzCQzcl8MYiInd+vdqBKSh+Dp/1qDHV99iPa6a9D4BGLRw88gKDhYYIWu5dSu9Yj3\ntJ2RHqgHLp3YAzCISWEMYiIX4+Pjg4XP/o3oMlzXbWZOyybeC5uUx1nTRA7AVYacl19MCjrMtrOq\nZVmGPjReQEXk7rhHTNSHsr/5GgV718JcVw6Vpx/8U8fjkRf/QdF7LPdXldfKcaUgH4NThiIkNPSe\nX2fmwifxzuFtGCUXdbkU7LQlHEsefaEvSiW6K5Is6Kt7TU2ziG7dRkiIL8dYAbeO857NX6Jh8/8g\n/JY7YnWYrbgcNQXP/7N7XtPbFzw9Jbz587+BR1kOQtUGVFq8YY4fi6U/eb3z/LnBYMC3X32I1rIC\nSDpPDJk0D8Nvc1vNxoZ6bPrgj2i7ehawWuAZnYrpT6zonFXtbvh5oYyQEF+77Qzifop/WMq4dZzf\n/cnjGGq+bPOYy21aTP7Fp4iOjVO4uv5h1e9+gkFl+6C+5Zpgk0VGUexMPPOP/476ujp88uvlGGYp\ngk5942xbuUEF9fgnsfDZV0SV7VL4eaGM7oKY54iJ+oDRaAQayuxuG+RpxKnDexSuqH+oqqoELh/t\nEsIAoFVLMBUcxs4t6/DmT5/FKPlqZwgDQJSnFU0H16Cywv6/CZEzYRAT9QGtVguLzvaOVwBQ3yEj\nbGCcsgX1E2VXLyMI9mcye3XUoezzX8GvvsDuNdSDPTtwcNvXji6RqNcYxER9QJIk+CaPhclie6an\n2GsQMidMUb6ofiB+cApqJD+72663GhHnp8Ntb2NitX/PaSJnwiAm6iOLX/4n5AdnocSgBgA0dMg4\nKcVi9su/5l2v7lFgYBA80ibBaOkaqG1GC0xWGXqNCmarbPdysSsGLcbMWKBUqUT3jNdUEPURvV6P\nF//1LVzKP48LJw8iJDIGP54yiyHcSy+99nu8/SsJbRcPwtfYgDq1P6orynB/wo095bQQLxwoacb4\naN/Oc8nXOwDNiPmIiVPuuuCzOceQd/BbQJaRmDkFo7ImKdY3uTbOmu6nOAtSGRxnx/t+jA0GA2pr\nryMwMAgf/+wxDFNVdj6mxWjBuao2tMhaxIyaisRxs3Df9AcUq/GTN34Dj/PfIMrzxsdpVTtQGzcV\ny37+ny7xRYzvY2V0N2uae8REbmL35q9QfHQ75PYmqAIiMHrO40gfkalI3xaLBevefxN1+Ycgt7dC\nGxyLkXOfxPDM++74NTw9PTFwYDQAIGbiQlTvfgeh+huHrH10aiSH+qIj80k8vOxVh/wO3Tm8dwf8\nzm9CiOfNwA3zADxKdmPX5q8w48HFitZDrkfYHjERKWfVm7+Ddd+HCNTdbLtq9sbol/8D46fOdHj/\n//uzHyOqaCc8NDenpVw1eyHrlT8gc+LUe3rNbV+vxvmda2Gqr4TGPxiDJ87FQ0uf7/mJfewvr/0/\nRBZ+a3dbafQk/Ph37yhcEbkaYXvEPAziWDzUpAxXGOfGxgaU7/kSaZ5d2+M0rdj76f9hcHqWQ/sv\nyL8AzcW98PDqOjc0TtOG3Z+9i/iU0bd9fndjPGrSXIyaNLdLm4h/i/bW7heKaG8zOP37A3CN93F/\nwBt6ELmpQ7u2IknXYnebXH0FBkP3qxH1hdyj2Yj1sl12EADM1UUO7VsJA+KHot3OIhImiwzfmFQB\nFZGrYRAT9XPefv4wWOxfT2tRax2+IIWn3wC7QQUA0Hk7tG8lzFz4BHI9U2Gx3jzLZ5VlnFbHY9aS\nZQIrI1fBICbq5yZMnYUizUCbdlmWoY8ZBq1W69D+pz6wCHmy7WpJJosMv6QxDu1bCTqdDj/6t3dx\nbdjjyPcegjyvFJSlLMKy1/8KLy+vPulDlmUc3L0Vn/z+p/jkP/4em1e/f+O2qtQv8PKlfornfJTh\nKuOccygbxz/6LdJ0jVCrJLSZLDivicfj//I2QsLCHd7/qcN7cWTVfyBVdR16jQqVBqAydCSW/fKN\nzhWUuuMqY+xIq/7nNQRc3IJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "point_style = dict(cmap='Paired', s=50)\n", + "ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)\n", + "\n", + "# format plot\n", + "format_plot(ax, 'Input Data')\n", + "ax.axis([-1, 4, -2, 7])\n", + "\n", + "fig.savefig('figures/05.01-classification-1.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Classification Example Figure 2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Yza24du2qO6m2J9aIiEiMGzdRcnxNzU3s379HkljVavlkmJGRjZUrf9Lt3qj2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\nG6Tvgg3xmQpERNQ5RRLx6tWrUVdXD4P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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get contours describing the model\n", + "xx = np.linspace(-1, 4, 10)\n", + "yy = np.linspace(-2, 7, 10)\n", + "xy1, xy2 = np.meshgrid(xx, yy)\n", + "Z = np.array([clf.decision_function([t])\n", + " for t in zip(xy1.flat, xy2.flat)]).reshape(xy1.shape)\n", + "\n", + "# plot points and model\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "line_style = dict(levels = [-1.0, 0.0, 1.0],\n", + " linestyles = ['dashed', 'solid', 'dashed'],\n", + " colors = 'gray', linewidths=1)\n", + "ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)\n", + "ax.contour(xy1, xy2, Z, **line_style)\n", + "\n", + "# format plot\n", + "format_plot(ax, 'Model Learned from Input Data')\n", + "ax.axis([-1, 4, -2, 7])\n", + "\n", + "fig.savefig('figures/05.01-classification-2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Classification Example Figure 3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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zFkXyipRbVKbOT/aoItvQ3bfdmrT+w2DFxSV65Nt3a8tbb6k7HJWiUS2YUaXL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\ny9GmRucLAuAagghgCFJh6cJgNTXtk2UlnrJsWdk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', **point_style)\n", + "ax[0].axis([-1, 4, -2, 7])\n", + "\n", + "ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, **point_style)\n", + "ax[1].contour(xy1, xy2, Z, **line_style)\n", + "ax[1].axis([-1, 4, -2, 7])\n", + "\n", + "format_plot(ax[0], 'Unknown Data')\n", + "format_plot(ax[1], 'Predicted Labels')\n", + "\n", + "fig.savefig('figures/05.01-classification-3.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Regression Example Figures\n", + "\n", + "[Figure Context](05.01-What-Is-Machine-Learning.ipynb#Regression:-Predicting-Continuous-Labels)\n", + "\n", + "The following code generates the figures from the regression section." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "# Create some data for the regression\n", + "rng = np.random.RandomState(1)\n", + "\n", + "X = rng.randn(200, 2)\n", + "y = np.dot(X, [-2, 1]) + 0.1 * rng.randn(X.shape[0])\n", + "\n", + "# fit the regression model\n", + "model = LinearRegression()\n", + "model.fit(X, y)\n", + "\n", + "# create some new points to predict\n", + "X2 = rng.randn(100, 2)\n", + "\n", + "# predict the labels\n", + "y2 = model.predict(X2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Regression Example Figure 1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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MF8/QXG3jlccfop+LEcTHT5xi3fZfCPD1ZvrE8Wi12np7FkGoS25LxFlZtdse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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot data points\n", + "fig, ax = plt.subplots()\n", + "points = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,\n", + " cmap='viridis')\n", + "\n", + "# format plot\n", + "format_plot(ax, 'Input Data')\n", + "ax.axis([-4, 4, -3, 3])\n", + "\n", + "fig.savefig('figures/05.01-regression-1.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Regression Example Figure 2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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8fMrTq7wwcurewueejOM4WOUqnuuSMHWctTL6gEahUNgwqriT1B3Bw4mwqu8h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86Pi+tdU1HJYk0nEAjQ2tp6ugY4m0VRAEVHsapEU0lsFsh2xc+41rn3Ze78Obz1yj6I2bErVhIp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\nXMqXUzX/HwaHgccrBGvOFY7HOUDpzdNLV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d.art3d import Line3DCollection\n", + "\n", + "points = np.hstack([X, y[:, None]]).reshape(-1, 1, 3)\n", + "segments = np.hstack([points, points])\n", + "segments[:, 0, 2] = -8\n", + "\n", + "# plot points in 3D\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter(X[:, 0], X[:, 1], y, c=y, s=35,\n", + " cmap='viridis')\n", + "ax.add_collection3d(Line3DCollection(segments, colors='gray', alpha=0.2))\n", + "ax.scatter(X[:, 0], X[:, 1], -8 + np.zeros(X.shape[0]), c=y, s=10,\n", + " cmap='viridis')\n", + "\n", + "# format plot\n", + "ax.patch.set_facecolor('white')\n", + "ax.view_init(elev=20, azim=-70)\n", + "ax.set_zlim3d(-8, 8)\n", + "ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + "ax.yaxis.set_major_formatter(plt.NullFormatter())\n", + "ax.zaxis.set_major_formatter(plt.NullFormatter())\n", + "ax.set(xlabel='feature 1', ylabel='feature 2', zlabel='label')\n", + "\n", + "# Hide axes (is there a better way?)\n", + "ax.w_xaxis.line.set_visible(False)\n", + "ax.w_yaxis.line.set_visible(False)\n", + "ax.w_zaxis.line.set_visible(False)\n", + "for tick in ax.w_xaxis.get_ticklines():\n", + " tick.set_visible(False)\n", + "for tick in ax.w_yaxis.get_ticklines():\n", + " tick.set_visible(False)\n", + "for tick in ax.w_zaxis.get_ticklines():\n", + " tick.set_visible(False)\n", + "\n", + "fig.savefig('figures/05.01-regression-2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Regression Example Figure 3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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X7oQdN7x+il3pCYhofoKBAOebj/PDt4e5ePIEui7HhCWSaMg5YolkhXD3Zjut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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.collections import LineCollection\n", + "\n", + "# plot data points\n", + "fig, ax = plt.subplots()\n", + "pts = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,\n", + " cmap='viridis', zorder=2)\n", + "\n", + "# compute and plot model color mesh\n", + "xx, yy = np.meshgrid(np.linspace(-4, 4),\n", + " np.linspace(-3, 3))\n", + "Xfit = np.vstack([xx.ravel(), yy.ravel()]).T\n", + "yfit = model.predict(Xfit)\n", + "zz = yfit.reshape(xx.shape)\n", + "ax.pcolorfast([-4, 4], [-3, 3], zz, alpha=0.5,\n", + " cmap='viridis', norm=pts.norm, zorder=1)\n", + "\n", + "# format plot\n", + "format_plot(ax, 'Input Data with Linear Fit')\n", + "ax.axis([-4, 4, -3, 3])\n", + "\n", + "fig.savefig('figures/05.01-regression-3.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Regression Example Figure 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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w8vIymM2mLhMrERFRV/Te6mU41icNguy3W29BLkd+TjKWrluF26+81ut+RmMV\n3l+7CgZbHeIVKtwydSYye/q+OpTL5cKXG9fj0OnTiFGpcNO0mdAnJ3e6P511qqoaMqX35Eu5xXP1\nCqJwxEQEETXr6E13Z0dRdOVRGKdOFUOlUnp9TaVSoqSkhNMjiIiIzuGwqQpCqucUDJlahQMVp73u\ns/9QAf6yYikqsntCiFZBFEVsXPIOHht/KS4dd1G75zSZanD/qy/hSGIyZFFREG02rHrzNTx88STM\nnHhJp/vUGTFqFUTRAUHwXMAwVu39moMo3HD5TiJq5stNtzctR1EkJiYiLS0VLldjcsEXnd0/mDIz\ns2C3O7y+Zrc7kJHh+5MZIiKiSKT0csPdRC3Ivba/vm4VKntlNo+iEAQBlsweePP7jXC73e2e84Ul\nH+JojwzI/luDQpDJUJeRiTe++xY2m60DvQicm6+4HFHGUo92oc6Ey0YPD0FERNJjIoKImnXkpttk\nahxFoVC0HmClUChgMDSOojiXplEYHd0/2GJj45Camg6n09mq3el0IjU1ndMyiIiCwOVy4cSJQlRW\nVoY6FAqACb0GAJY6j3ahqgZTcz1vvI1GI/IbvE9RKIqLwvZfdrV7zn2Vla2mgjSpTE3Dyo3rfYg6\neFL0ejx85VQk15TAZa2D2+lATE0pbhjUE9MmhXa0BpFUODWDOq0rzuunjvntptveKjFwrpvukydP\ndmrqQneY+jBv3nzk5S1Cebnn1BEiIgqs1Zs/wd7KdRB6GuGyKBBlzMHNlzyEnumZoQ6NOujqS6dj\nT94RbLAZ4dY3FquUl1ditjYFk8aO99je5XLBLRO8Hsstk7X50KQlexujJgSFEpZ6qx/RB8eUiy7C\nxLFjsXHrFtRZrbjluvmwWsVQh0UkGUEURX7jqUPsdjteeukllJSUQC6Xw+VyISMjAw8//HDI5/VT\nx/n7uZpMJvz1r3+FXq/3eK2iogLPPPMM4uLaTiR0dn8pmUwmFBcXIysrq8vEREQUTtZtXYXN9YsQ\nl9X6WVnFxmi8eP/HkMu9D+On7mHbzh34evcOyAQZrrpwPEYOHdbmtrOf+Cv2/zdp0VJGSTm+feY5\nj5GUZ7v1qafxkzbao11rKMPah/+IzJ49/e8AEQVMyEZEVFRYQnXqkNDrY8Kuz2+88QpcLnurG8i6\nujo8++w/sWDBg2HZ5/aES5/nzl0As9mEkpISZGRkIDY2DiZTA4AGj231+jgkJ6d6HUWRnJwKu13W\nznsi6+RUxBNZAAAgAElEQVT+UpJhyJAhqKiwBDWmrjbKKFy+1/6I1D6Hg0j83MKpz9/mr0LcRM/L\nU93oaiz9cilumXNLWPXXF+H0GQ/IGYQBOYOa/91Wv/T6GNx+4WQ8uXUdLD3TmtvVZypx09BRqK5u\nf0TDzRddgoKvVsOU9tv+Yl0tpupToVHFdrn3NJifs8lUg/c/XY4igwkqhQzjhw/A5dOnQRC8jzqR\nSjh9t30VqX32hlMzqEN8WV0hXC5qI1VsbJzPUyI6O3WBUx8adeXVQ4iIpGATqhHrpT0qTgnDoSLJ\n46HQmTBqDN5MTMLH336Dcls9ElVqXDNxBkYPG+HT/qOGDsMLahU+3rQBp+rrES1XYGK/frhx1pVB\njrxrqaioxMPPvopKRU8IssYRInvXFyD/6An85YH7QhwdRTImIqhDfJnX36cPVxMIpK72lLwllUqF\nBQse9BhFEYj9u3K/A63l6iFNnM7G5MSCBQ+GMDIiImloEAfgjEe7zeJATx2H0gdaRWUl1mzbBK1K\njdmXTINWqw11SK30790HT/Xu+M3yeQMG4bkBg1q1iaKI7bt3wlxbi4vGjO1yfQ60vCWfo1KZ0Wr0\ng0wdg61HKnHloUPIHTgwhNFRJGMigjqESxpKpzs9JfdnFEV7+3enfgeCL6OMwj0RQ0Q0JHUSDpxe\ngtgerS9RjT9FY/6Ns0MUVfA5HA7U1dUiNjYOMi8rPQTDK5/9H1ZVH0ddbz1Epwsfvvs07h0yCVdO\nvEyS84fCT7t3YeHatShSR0FUqZC85TtcNXgQ7r72+lCHFjRHS6sgCKke7YIuGRu++4mJCAoZLt9J\nHRLJSxqazSbk5x+QbFnJlk/JExMTkZaWCper8SY9nEVav30ZZUREFO6mTbga2eUzcPo7GWrKrDhz\nyArjxiTcNuHxdosTdkcOhwPPL3kNN3/wF9z81dO47b3H8N6aJQh2LflVW9bjM6EM9X1TIchkkKmU\nqBqUjpd/3YpTpeH5e2M2m/CPVatwKqUHZHHxkGujUJ3WEx+cKMbXWzaHOrygaasOhCiKEATeClLo\nhN9fdJJMpM3rD8UT+mA8Je8OUx0icXQARxkRETW6bvpduLLhd8g/tA8JWcnodXHvUIcUNE999DJ2\nD2yATJUMAUA1gM+rfwW+Woq7Lr85aOfdePwA3Dmetbzq+6bik+/W4ZGb7vbreJVVVfh627eIidJh\n1qRLoVR6T6yH0kdrVqM6tSfOvi13x8Zj3d59mDFpckjiCrZBmXqUFbs9kg5CnQFXTL0lRFERMRFB\nndDZugDdTSjm7/vylNzXqRDdaapDIPvdXfw2yshz9ZBwH2VERHQ2tVqN84eNCXUYQVVaVoq9mjOQ\nqZJatcsSorBpz17c4b4xaNM0LG47ALVHuyAIqHXb/TrWi0vew+qKk7Bkp0KsduCdhT/gj+OnYeqF\nEwIUbWBUW20Q2lj+tbrBc1WwcPH722/EoadeRImYArmi8XpPrDfi8pHZ6JXTK8TRUSTjeBzqtMZ5\n/YPD+kap6Qn92cNCFQoFDIayoE3TCORT8u401SFSRwfMmzcfcrkK5eUGGI1GlJcbIJerwnaUERFR\nJNuV/wtc2d7WCAGMUQ6YTDVBO3eGyvvKZm6bHb1jkn0+zrINX+ETVyVqe6U3TvHQqFHevyee/2kd\njMaqQIUbEBkJCXDbvSdZ0nRREkcjnZiYWCx65nHcMjoFI/UNGJfuwpO3TcGCu24LdWgU4TgigsgH\noXpCH6in5N1tqkOkjg6ItFFGRESRrF9WbyD/eyAr3uO1aKsM0dHBWwb9tokzsXP9+6jpm9LcJooi\neh2uwo0L7vf5OBuP5kPM8IyzpncPfLRhDR644faAxBsIN866HGuefQane2S1atcaK3DdzOkhikoa\narUat954XajDIGqFIyKIfNDWE3qbzYby8nLExXl/ohEIgXhK3h0LIUby6IBIGGVERBTpcvvnovcZ\nlUdhSpfNgWyLFiUlp4J27n45ffDcxddjVKEVMfklSMw/jUuKXHjttoegVntO2WiL2e199KIgk8Hs\n8G+KBwDU1lpQXW30ez9fqNVq/HvuXJxfWw11aTFkZSXoW30Gj04Yj9HDRgTlnETUNo6IIPLB2U/o\nnU4nDh06BK1Wi7S0NCxe/DpSU9Px+ON/Dvi5A/GUPBhTHZqKXo4YMRjByGl2pdEBLQt86vXBe0JF\nRESR5Ylr/oCnl7+Oo3orZOmxMG4uAOwidg7Nxl0730LvDWr8ecbN6J85KODnHjFoCF4fNOS/qyd4\nX1mhPT3VOhzx0u6ut6JfUl+fj3P85Em8tPJTHKyvhUsmYKBOh5tHT8DkC8Z1KK625GRl47U/PYLa\nWgsaGuxISkpqf6cgqKurw7uffI5DZRUAgMEZqXjsAf8KhBJ1d4IY7PWB2lBRYQnFaUNGr49hn7s5\nu92Ot956DUVFhaiursbw4cM9pg3odDrMnbsghFG27Y03XoHL5TnVQS5X+VVs8+yily6XC8nJqV2y\n6GVneSvwmZ2dhVtvnRd2fT2XcPt/2ReR2udwEImfWyT1ORz7+9HXy/Bu8SaYK2ugzkqBbkDPVq/H\n5ldh8ZWPIjlEN83nsv/XfDy0+QuYMvXNbaIoou+hUnz8p6d8WnK1vr4ev3vlOZzuldmqPbr8DF6a\ndjWG5Q4OeNyhZLVaseB/n0eRtgcEWWPxTLfLifNkVXjxL492yRVHgiUc/39uT6T22RtOzSDykUql\nglwuR3x8PGJjY70WriwpKQla4crOCtRUh7OLXur1+i5b9LKzvBX4rKurC8u+EhGR9KxWK1aUbofq\n/BzIdRqPJAQAmAYl4qNNK0IQXfuGDhqMp8fNxIhTJkQfLkLi4VOYUt6A1+9+yKckBAB8vHYVSjPT\nPdpr01LwyfebAx1yyP3ni+U4qU5vTkIAgEyuwAFHPD5fvTqEkRFJi1MziHzUVPBREIDkZO8VpeVy\neZddWjIQUx26W9HLzoikvhIRUWhs2fE9TH2jIQcAuffng4JMgNFVJ2lc/hg3YhTGjRgFl8sFmUzm\n9zSPUosJgsb7LUl5gzUQIXYpR05XQqbQebTLVWocOFkagoiIQoMjIoh81FTwMS4uDtXV1V63cblc\nXX5pyc4UQuyORS87KpL6SkREoaFVaSA6XI3/cLq9biO6RSTJPW9cuxq5XN6hWhNxSjVEt/e+xynC\nb5pCG/kmAIBCxlszihz8thP5qKngo0ajgdVqhdPpbPW60+kM++UWg1H00ldmswn5+Qckm/oSyr4S\nEVFkmDBmPFJPNAAANJlJqD/i+UQ8/lcjfjflaqlDk8wt0y9H/KnTHu0KYzWuGD46qOc+WXQSn61e\niYO/FgT1PC1dMKgv3LZ6j3Z3vRkThuVKFgdRqHFqBpGPWq6cMXDgwOZVMxISElBdXY3evfvh4Ycf\nhsnUEOpQg+bs1UOaOJ1OpKamByUJ461gZGpqetCLY4air0REFFnkcjnmDpuJhQVrIA5KRt2vpajZ\nVgBNlh5yJ9DXrMWfZ93ZJQtVBoo+ORn/M3kmXt28DkXx0RCVCvQwmnB1//MwZdxFQTmnzWbDX19/\nHbtrbXAmJkPY+yty5Svwj3vuhb6N6beBcs3MmdiT/zJ+rjZB0DVeS7hrqzGrTxymTpoU1HMTdSVc\nNUMikVohNdz6fPZNscVSC41Giz/84U9ITk4Oyz6fTepVMwK12kdHcNWMRpHwvT5bpPY5HETi5xZJ\nfQ7X/pYZyrB06yqYXTakqeNxYd9hiI+LR3Z2Ttj2+Wxutxs/7twOS30drr9yBiwW76MSA+GJ11/D\nZrcKgvy3gpGiKGKopRKLHnssaOdtea7vfvoJ2/YehEwAJo4cjtmzpkTE59xSpHy3W4rUPnvDERFE\nfghEwcfu7uz3YPjwQbDbgzPLK9QFI7193n36ZETcDwgREQVXemo6Hr7+3lCHEVIymQwXXXAhAECj\n0QQtEWG1WrHTUAEhLatVuyAIyLe7cfxEIfr06h2Uc7c818Rx4zBx3LignoeoK2ONCKIO6EzBx3DR\n9B7ExQXvPegqBSP5eRMREYUHs9mEWpn3awuHLgaFRUUSR0QUmZiIIKIuiwUjiYiIKJCSkpKhh8vr\nazpLNYafN0TiiIgiExMRRNRl/VYw0nOFEhaMJCIiIn8pFApMHdAPYn1tq3a3w44L9YlBL1ZJRI1Y\nI4KoGzGbTTh1qhiZmVl+3YT7s19HzxEs8+bNR17eIpSXe66aQURERB3jdDrx/upl2F15Cm5RxOC4\nFNx9xfWIiopqd9/aWguWrl+DGpsNQzNzcNmESRAEweu2oihix97dKK+swMTRYxEfnxDorvjt9zfd\nDPlnn2LDoSOocLgQJwPGZ2fg4dvvCXVoRBGDiQiiDpD6Zr2jS1j6s1+olslsDwuEEhFRuDt2shDv\nffcljlkroRTkGBLdAw9cdbtPSYGOcLvdeOCN57E9OxqyDB0AYLfLgu2LnkPe/MfOed7N23/EP7eu\nRVWvHhC0cnxauAefbN+K1+c/Ap1O12rbA4d+xXMrP8PRGA1EXRRezduOaakZeOTWuW0mLqQgCALu\nueFG3O12o7bWAp0uGvIWK2gQUfBxagaRH+x2O9544xW88MKzWLHiU7zwwrN4441XYLfbg3revLxF\ncLnsSEtLRWJiItLSUuFyNSYOArVfR88hFRaMJCKicFRUWoz/983b+LGviDNDklB6Xjy+zqzFA+88\nC7fbHZRzfrl5HXb00ECm/u1BgyCX4/CAFLy3Zlmb+9ntdry09WsY+2Y2L30pxEbjQK9U/Gvp+622\ndTgceGL5EhzLToeQmACZWg1LZg8ss1vwwarlnYq/pqYaJ04UwuHo3MoaMpkMsbFxTEIQhQATEUR+\nCMXNetMSlgpF6wFMCoUCBkPjEpad3a+j5/A1/vz8A506BhERUbj6YPOXqDlP36pNkMtwpL8Gq7es\nC8o5d5UWQoj2HPUgKOQ4aDK0ud+qTetRlpniuZ9Mht01rfdbseFrnOqh99gW0TpsOn7Y/6ABGKuN\nePClf+PqVxfihiVLcP3zzyDv8087dCwiCi1OzSDyUdPNelpaaqt2hUKB8vLGm3W9Pibg5/VlCcvc\nXM9RAv7s19FznEtXnepBRETUlRQ5agDoPNplsVocKD4ZlHPK0Pa0CIXQ9nPKmrpaCG1cL9hcrVei\nKDPVQKbReD+O0/+RpKIo4pE330CBPg1CbAIUAAwA3i85jahVX+J3V17l9zGJKHQ4IoLIR77crAdD\nR5ew9Ge/YCyT2dWnehAREXUFGsH7c0FRFKGVeb/u6KwpA4YBVZ4jFd3WBoxOzW5zv6ljx0NTesbr\na310rR9Y5GZkQzSbvW7bQ+N/7Ysfd+7Ar2qtR20JMSYG6wry/T4eEYUWExFEPgrGzbovOrqEpT/7\nBXqZzGBO9SAiIgonF6b2h1hr82hXHavEnAsvC8o5J10wHrPsMRBaJiMsdRhXYsUtM69uc7/sjCxM\n0SZCrLO2ao8pPYPbx09u1XbpRRcj11gLURRbtasqjbjm/Av8jrngZCEQF+/1tQqb5/tHRF0bExFE\nPgr0zbo/5s2bD7lchfJyA4xGI8rLDZDLVe0uYenPfh09hzehGj1CRETU3fxuxhxMKtVCVmwEAIgu\nN9QFBsxNH4uczLZHJ3SGIAj4+50L8O9+EzGjAphWAfw9ZTgW3v+Xdgs3Pnn3AtwX3QO5JVXIKjLg\nojN1+OfEKzBuxCiPc7zy+z9iorEOsSdPQXmqFH1LDHh44AhMu2ii3zH36ZkB0eJ9hEWiWu338Ygo\ntATx7DSlRCoqLKE4bcjo9TFh2+e2lrIMxz63V/cg2H3u6BKW/uzn7zm89dlsNuGFF571qKcBAOXl\nBvy///d4t139Ihy/1+1hnyNDMGrchEIkfm7h1ufqaiNWb1sBh9iAYTkjMWrob0/Pw7G/TQqO/IpN\n+3+GSqbAtRNnIikpCUB49Nlms8FqrUd8fIJPy3Z667Moirj9mf/FsbSerdvr6zC3Rzruvvb6gMYs\ntXD4nP3FPkeGtq4vWKySOiwSixGqVCosWPBghxMCndW4hKX/5/Nnv46e4+xjNI4esbeaniHF6BEi\nIuq+1m1bjXXlXyBmlBoyuQy/ntqBde+vwl9uecpjul+4ye0/CLn9B4U6jKDQaDTQtFG40leCIOC5\nufPw9Icf4KDDCbs2Csm1Flyak4O5c64LUKREJJXw/otOQdWyGGETp7MxObFgwYMhjCz4AnGzHu7m\nzZuPvLxFKC/3TFQRERGdzWiswteGLxB/gba5TZephVV/Bh+tfQ93XHlPCKPrWhoaGlBVVYnkZH3Y\nPvzxpmd6D7z16GMoKi5C2RkDhgwaDJ3Oc8URIur6mIigDgnVUpZdTVvTUvzdJhyFevQIERF1L2u2\nrUDsSM+5/gqNAsfrC0IQkbRcLhfeWP4f/Gw8AYvbjgxlLK4dMgE3Xj6zeRun04l/Ln0b28wlMGoF\nJFtFTEzIwZ9umNtubYdwkp2Vjeys4NTPICJptJmIKC8vx5dffgmz2YyBAwdi2rRpUP+3EMzixYtx\n7733ShYkdT2+FCPs0yc4q0h0BW1NS3n88T+3u004T13xhqNHiKglXl9QW+yiDTK59zrqDsH7qlVd\nncVihlKp8mlawt8/WIjNGXbI0hIAANUADhduhG6rCmNzG+tk/OPDRVib7oCsZxoEAFUAltlMcH3y\nDh77Hf/fIaLuo81VM9auXYtp06bh/vvvh1wuxwcffAC73S5lbNSFhWopy2Axm03Izz/g87KSLael\nJCYmIi0tFS6XHS+99FK72+TlLQpWN4iIujxeX1BbhmSej7rT3pdhTBbSJI6mc77duQ3z3vk75ix7\nEnM+ehz/7/+ew+ny021uf6LoBLYpKiHTth4RYs+Ixwc7NgFovFb53lYG2VkPgmQaNbaailBfXx/4\njhARBUmbiQiHw4FevXohKioKs2bNQk5ODpYuXQqXyyVlfNRFhXIpy0Cy2+14441X8MILz2LFik/x\nwgvP4o03XjnnRXHTtJSzi2YpFAqUlJTAbDadcxuDocznhAcRUbjh9QW15YLzxyGmIBXOhtbfhbpd\nDlwx6toQReW/Xfm/4MXja3FyqA7i4DTYh6Zh32A5Hln2ChwO7w9xtuzdDkdOstfXTlhrIIoijhQe\nhylJ63Wbilg5yspKA9YHIqJgazMRoVKpcPToUTSt7nnZZZchJiYGn332WZt/RCmyzJs3H3K5CuXl\nBhiNRpSXGyCXq7pVMcKOjFo417QUuVyOkpISn6auUMf5O4KFiLoOXl/QuTx+y9PofXAEnD+oYPsR\niP4xHfOG/QkDenef1SS+2LMJDb3jPdrLhsRi2cZVXvdJjUuCWGv1+lq0TAFBEJDdMxO6Gu8jRuIt\nTqSkeC6ZTb6pra1F3tIleC7vHXy8fDlHaRFJoM0aEZdffjnWrFmD+vp6DBs2DABw1VVX4ZtvvsGx\nY8ckC5C6ru5ejNCXgpve+nOuaSkul6t5Wko4TV3pKlh3g6j74/UFnYtSqcTcq+4LdRidYnBaAHiu\n5CDXqnCy/IzXfaZNmIz33/wWp4dFtWoXnS6MTcoBAOj1eowU4/CD2w1B9tuzRNHlwgWqFMTExAas\nD5Fk1759+MfSZahO6AlBroC7ogyrd/4Dz993N3KyskIdHlHYanNEhF6vx5133tl8kQAAMpkM06dP\nx5/+9CdJgqPuobEY4eBulYQAfCu46c25pqU0JWPCZepKV8O6G0TdH68vKNzFyjxX/gAA0eVGvDLK\n62tyuRx/nnwDUvcZ4KpvHPUglNVgxK/1eOrOBc3bPX3L/Rh3zAZloQGu2npojhtw8Qknnvhd907e\nhIooilj4xUrUJGdDkDc+n5Wp1ChPysKLSz4JcXRE4a1Dy3dGRXn/I0rUnXSm4Oa8efORl7cI5eWt\nn8w//PDDMJkazrlNd5q60pV0dARLR88ViUuuEoUary8oHEzJOR/5FT8C+uhW7boDFfjdjW0nDEbm\nDsMn/XKxass6nKmowcgBEzDm+lHQaDSwWBqvV3Q6HV7+/V9QcroEhwqPYfAFA5Cemh7U/oSzX/bt\nw0lRA28Ln/5aXReQa4u6ujq8+Z8lyD9VAZfbjX7pSbj7hquRnta9CrASBVqHEhFE4eC3UQv2VkUl\nfRm10Na0lMbpAQ3n3IY6xpcRLJ1dJpRTP4iIqLOumDgdp1dW4KsD+1HXLx6i1Y60Ew2Yf8F17V4H\nKJVKzJl6RbvnyOiRgYwenObZWWaLGaLS+++7Q5DDarV16trN6XTiwaf/jSJ5DwjyFEAOGCqB/H8t\nwmuPPwB9svcCpUSRoM2pGUSRoLMFN32ZltJdp650NVIsGcupH0REFAj3zr4VS274O/7QMAxPxE7B\nR/c8i4tGjA11WHSWsaPHIMla4/W1bI0MKSkpPh1HFEUYDAbU1FS3al+++iucEJNb1fQAAGNUJj5Y\n9mXHgiYKE+2OiKipqcHq1atRU1ODO+64A8uXL8fs2bMRH+9ZDZiouwmXUQuRMJWgMyNYWmrrvZJy\n6gcR8fqCwl90dDSumnp5qMPokBUb12HTkXzUu13I0kbjjssuR05m+BVu1Gg0mD1iMD4sKIIY/dvf\nHqWpEjdcMg6CILR7jE3ffY+Pv/kOxRYnZBAxMDkKC268GgP69UNB0WnIVRqPfQRBwMkz3hMgRJGi\n3UTEmjVrMG7cOGzcuBHR0dE477zzsGLFCtx5551SxEckicZRC93vJjNUUwla3szr9TFBO8/ZOlN3\no733SoqpH0T0G15fEHVN//rwHSxz1EBMafx9PwBg+6fv4sXZNyO334CgnPNk0Ul8tnkTbC4XzsvM\nxN03zQnKeby56/rrkLZpM9bt3oMaqx16nRZzrpyCcaNHt7vv3gMH8PLq79EQnQohERAB/OoG/rb4\nQ/zfU3+GWumt+kSjc71GFAnaTUTU19ejT58+2LhxIwRBwMiRI7Fz504pYqMwFAlP7qXUcipBE6ez\n8YZ7wYIHA34+bzfz2dlZuPXWeZLUUOjMCJb23qtzTf2wWGpRW2uB2WySNPFCFM54fUGBYrGYsWXH\nFiTEJGDcqPGQyTjzuKNKTpdidVUpxIzWhRQrc3rgnU1r8VIQEhFL1qzC23v3oiE1HYIgw9rDR7D+\nz4/hpfsfhk7nuQxqMMycMhkzp0z2e79lG7agIdpz+kZVVA8s+XIlZk0chy3vrAJiWm9TbziJap0T\nT7y4CL3Sk3Dj1VcC4PUFRZZ2ExFKpRJms7n538XFxa2GRRP5gkUAAy8UUwm83czX1dUFLfHRFn9H\nsPj6Xp099cPpdKKgoAA6nQ7ffLMGa9askDTxQhTOeH1BgfDOynfxQ/1uCIO1cNU6sOSDL3DnqN9h\n9JD2n2aTp69+2Aprz1R4m5Dwq8kY8PNVVVXhvT17YO+R0XxOWZQO+zVaLFz6ER6/+96AnzOQKixW\nAFqPdplcgXKjGUPPOw/XjjqA5TuOwhWbDkEQYD66C9rYVJRpslFWCWwvr8KWXc/iPy//DQCvLShy\ntJsynjZtGpYsWQKj0Yi33noLX3zxBaZPny5FbBRGWAQw8HyZShBITTfzZ98oKBQKGAyNN/Ndla/v\n1dnFS/fs2YPc3Fz06dOn+XvblHghos7h9QV11uotq/FD8n4oR8ZCoVFCnRwF53gN3vrl/1BbWxvq\n8EJCFEW43e4O769WKIE29lf4UC/BX8s2rEddWg+PdkEmwz7DmYCfr6XOvlcAEK9Vez+22414XWNt\niHm33ITFj9yJK3urcHGSFfHxSVAn/VZgW6ZQwqDKxD8Xvd+pWIi6m3YfPdTW1mLevHmoqqqCKIpI\nTk6GXM45TeQ7FgH0rrPTVKRYRaKlUNZQkOq9ajn149ChQ2ho+MJr4qXl95bTjYg6htcX1Fk/lu6E\ncrTn02hhpA5fbFmO2y+/LQRRhUa5wYAXVn6M/bUVcIgiBuoSMfeiaRh13jC/jnPt1On46M1/w9Sr\nZ6t2URQxLNG3FST80eB0eqwo0cQhigE/HwCcLivDK0s+QUG5ES5RRD99PO6YcRnOHzrU72PNHD8a\ne1dug1uX2KpdZynFzbMfaP53VmYm7r/rdrzz4RKI8SqPESeCIODgiXIAQGlpKb746hvYHC4M6Z+D\naVMmc7oRhaV2ExEbN25E//79fV6+huhsLALYWqCmqTRNJaitNaOurg5qtRoNDQ3Q6XR+rSLhK38S\nH4G6OQ/0e+XrihuxsXHQ6aKgVns/h0qlxIkTJ7Bt2xZONyLqIF5fUGfVoR7eLmVlSjnMdrPnDmGq\noaEB9/9nIU7m9oQgZAIAdgE4tnU5XtdGYUCffj4fKzo6BvcMuwAvbtsAY7kBco0GsmgdkqotmP/E\ncwGPfeqYC/D58i/gSvb8O9CvxQo6O/ftxcebN6PIZEGUQo7RWRm4/+Zb/J7OZbVa8fCri2BIzAZa\nFON8cumXeCk6Gn179/breBPHj0PpmQos/+EXVMrjILidyJRbce+Ns5CUlOSxvcvtBrxOfAFcbhGf\nr1yD/3y9Gy5d4zSOzYcOYM2mH/DC3x+FVuuZdCPqzuRPPvnkk+fa4NixYygsLERtbS0qKipgMBhg\nMBiQlpZ2rt3aVV9v79T+3Y1Op47YPms0Gnz//VZER0d7bGMymTF9+iyo1Z5LG3VHvnzOb731Glwu\nO2JjY6HVahEdHQ2Xy4FfftmDMWN8X2Pcbrdj586fUVpaAp1OhzNnzqCiogJ2ux3x8QkYMWJUQJ8u\nqtUaFBTkw+VytMrMO51OxMTEY8KESbDb7XjrrdewYcPXOHr0V3z//VYUFORj+PDzOxRLe++V2WzC\nsWNHodFo2v0ODR9+Pn75ZQ8MhnLY7XaYTGbExMT/dzqGZ2ztfW9Pny6FILg7/Tn6y58+B0ok//2K\nJDqd9yHGwcLri8CItO9qy/7uyN8BSw/Pvjtr7bgAQzCw10CpwwuK9j7jj9auwPoEN2Rn3ZTbEqJR\nm/eSUmwAACAASURBVH8UU0Zc4PO5amsteGfdapxUCNAOGQwo5HCZzBDPH4Ld277D9BGjoVIF7m+F\nPikZJ/fvxfEGBwTlbw+tUs6U4bE51yExPgHb9+7B31avRXFMEup1sTBpo5FfZ8PhH7/DZePG+3W+\nj5avwLY6OYSzfvft2hiYTx7GhcOG4pNVq7Bu2484UXQSA3r3bjfZMWTQQFw1aRz6RgOzRg7A/Ftu\nQFYbo1JTkxPx1ebvAbVnYcrBegEbdhbCFd2zedlQmUKFamcUqooPYtzokX711R9GYxW2fvcD7A47\n9Hp90M5ztkj7+wVEbp+9aTeNGBUVBaBxmFBLw4b5N9SLIpe/T6PDWSCnqTTWKXChT58+AIDExEQ4\nnU4cOnQIgCsoBSS9LZ/ZVLyxKaaOruJx9iiKc79Xp/Hii8/DZKr2eTSCvytunOt7m5CQhOrqKkmn\nG7HgK4UbXl9QZ80cfBnePPIhFP1/W1lBFEVod7kw667LQxiZtE6YqiBL8v47UNLgX62Mv72/GNvT\nE6CQNT7NV2dmQNWzB+p378XRkcPx9pfL8PAtgV1i96n77kf/VSvwQ2EhrC4XcmJi8KcHFyAmqnG6\nw9LNW1Cf1Pr3VqZUYUetGfsOHsCw84a0eezqaiOOFh5Hr6wc6PV6FFUZIVN6f6+OGypw29+ewZno\nNMiUKrjLyrD652fwv/fchgF9+56zDyqVChMvuqjdvmZmZGDqkJ5Y92sNZNpYAI3f2ThbCfQxPeDQ\nRnlO25DJcbDQ0O6xO8LtduOFl97Czn1lcLgTAffPyEwDHnnoDmRlZQblnERN2k1EzJ49W4o4KMx5\nu4FtuomKJIGapnKum3StVgun04nKyqqA3xB7u5nv0ycDFRWWDidZ2rrBvuiii8/xXqlQU1OF9PT0\n5jZfEx7+rLjRVuJl5MgLsWbN8jZiC850I6mXaiUKNl5fUGeNHjoGFlstvtq+HgZ1NeQOGXqJPXHf\nVQ9F1AosMXIlRNHW/BS9pVi5999Rb4zGKuyyWSDIYlu1CzIZ5PFxcNdbcbAu8FNeBEHALbOvwS0t\n2vT6GFRUWAAAJ01mQBPrsZ+YkIxt+/Z5TUTY7XY8/eZb2FFuRK06GlENa3F+cgwSonQQRdHre3Wq\npBTy/mOaK/nLlGpUJmTh5SWf460nHgtEVwEAD907F4M2bcbWXQdQ3+BEZnIsbp1zP77avAmCzHvi\nyOF0N/fLbm9AdHRglvp8+92P8NPeBsgVPaCQA4AGZdXAM/96B2+99qTX94koUNr9K71w4UKv7X/8\n4x8DHgyFL3+fRoerQBWYPFdCIyEhAWazOaj1N7zdzHc0ydLWDfbGjd+0+V5VVlYiKyurVVswRiO0\nlXg5fryk3c8xkEUsWfCVwhGvLygQJo+ZjMljJsNiMUOpVEGjCY+pnv64efJMfPX/2bvvACfK9A/g\n38kk2c1utmV7pSwdpImogAiIFKUIFpqInq56cDbs/q54trMgx6mgJ3rKWQFFKdKrogLSRJCmtG1Z\ndtmw2c2WJJP5/bFHWZLtyUyy+X7+0pnMzDOb7PLOk/d9nsVzUZZZs/uEprgUwzr0bvB58s1mlIaF\nwtO/5GJMNCSrFaI2rJnRNp6hlqSSy+FApCHG475//Hs+tlTqoYlLgw6AIyIa21wSuubnI9QuoCr2\nkmKcpWdRLovw9Hh/xGpHXl4uUlJSPextmhHXDcGI64bU2Hb9wCvxxXcLIYTFub0+OcaAZ16YjUMn\ni+FwCUiNNWD88H4Ydt2gZsWxY9cxiNpEt+2ni8Px7Xff49qB9c/yIGqqehMR06ZNO//fLpcLBw8e\nhCRJzb5wfLx3MnmBhPdc/f+Zmd7t5uBv6nqf4+Mj0KpVBmw2m9t0/1atMhr8s+nVqyu++MLz76HF\nYkFqaipKS0vRs2dnREX5/nMXHx9RZ0ySJHmMpaSkBEVFBW7rEbVaLc6cKUBychIcDofbz8pqtXoc\nbOr1OpSXW7z+Gbv0c5uZmVbr+5ienobFiz9GTk4ORFGEJElIS0vDzJkzm7yEIi/vWJ1JHl/c86X4\n94u8jeML7wm2e/Z0vy39Z1Df2OK5ghvw2pbVyE03QdBpEX3yNCa17oo/3Dq+wde4os9lSFq1GGc8\n7HOeLoQ+IwP9E1IU+1mfu87ADq3xcUGZWw2MBIsZ9z/zp/PLvM6x2WzYaS6CxlQzcSBoRBywOfDI\n9Vfj4627UBgWD0EUEVFSgGsyYrG8quYXHOc4RB20WpfP7zs+vgcGdFiJb49XQNRdKEwZ6cxHvrkE\nJboOEMKMEADkVQHvfLkNKSmxGDywX5OuJ8syymx2wEMJL1EfgaKiQkXe65b+u+tJMN6zJ/UmIqIv\nqlgLAP3798e7776LgQMHNuvC56ZbBYuLp5gFC96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pPkkQEbU09c55ys/Px3XXXQdRFKHT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the model fit\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', s=50)\n", + "ax[0].axis([-4, 4, -3, 3])\n", + "\n", + "ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, s=50,\n", + " cmap='viridis', norm=pts.norm)\n", + "ax[1].axis([-4, 4, -3, 3])\n", + "\n", + "# format plots\n", + "format_plot(ax[0], 'Unknown Data')\n", + "format_plot(ax[1], 'Predicted Labels')\n", + "\n", + "fig.savefig('figures/05.01-regression-4.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Clustering Example Figures\n", + "\n", + "[Figure context](#Clustering:-Inferring-Labels-on-Unlabeled-Data)\n", + "\n", + "The following code generates the figures from the clustering section." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# create 50 separable points\n", + "X, y = make_blobs(n_samples=100, centers=4,\n", + " random_state=42, cluster_std=1.5)\n", + "\n", + "# Fit the K Means model\n", + "model = KMeans(4, random_state=0)\n", + "y = model.fit_predict(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Clustering Example Figure 1" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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EUI3PCACqQxDDV6ZPn6FYLFbwvlgspnh8uu0xmOQFwEsYI4avRCIRtbV1TBgj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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the input data\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.scatter(X[:, 0], X[:, 1], s=50, color='gray')\n", + "\n", + "# format the plot\n", + "format_plot(ax, 'Input Data')\n", + "\n", + "fig.savefig('figures/05.01-clustering-1.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Clustering Example Figure 2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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KTWvFv4sXj//pqTsac9syqhWv/fu1ig8UQlQ76axVx7WK6kLs5J/YeuyPvPOR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\nhMPhcLt/IvJtDGJq9VI3boEcEOnULqrU+DU33+3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PT0ZO7t+w9Xge7Kb2kK01iHAU4+kp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\nv9AAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the data with cluster labels\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.scatter(X[:, 0], X[:, 1], s=50, c=y, cmap='viridis')\n", + "\n", + "# format the plot\n", + "format_plot(ax, 'Learned Cluster Labels')\n", + "\n", + "fig.savefig('figures/05.01-clustering-2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Dimensionality Reduction Example Figures\n", + "\n", + "[Figure context](05.01-What-Is-Machine-Learning.ipynb#Dimensionality-Reduction:-Inferring-Structure-of-Unlabeled-Data)\n", + "\n", + "The following code generates the figures from the dimensionality reduction section." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Dimensionality Reduction Example Figure 1" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Xi8V8c1IBtSTfTTy1h7T01RKkPT33uL6vsObHpQ9T0UZcBTJ7+EnS66//X8Xj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\nA532xVzn6tbEAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_swiss_roll\n", + "\n", + "# make data\n", + "X, y = make_swiss_roll(200, noise=0.5, random_state=42)\n", + "X = X[:, [0, 2]]\n", + "\n", + "# visualize data\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(X[:, 0], X[:, 1], color='gray', s=30)\n", + "\n", + "# format the plot\n", + "format_plot(ax, 'Input Data')\n", + "\n", + "fig.savefig('figures/05.01-dimesionality-1.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Dimensionality Reduction Example Figure 2" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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JO26muCKb2KBr0SjRRPhGU1GbhyG4jF/97tkmu364O5nOIEQrpOzcgH2g49Je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\nJgCVAwNKYqP4x/ffss5bwRIbjS40hLRmsby7ainqZb7Fvfv9Ak42iUE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.manifold import Isomap\n", + "\n", + "model = Isomap(n_neighbors=8, n_components=1)\n", + "y_fit = model.fit_transform(X).ravel()\n", + "\n", + "# visualize data\n", + "fig, ax = plt.subplots()\n", + "pts = ax.scatter(X[:, 0], X[:, 1], c=y_fit, cmap='viridis', s=30)\n", + "cb = fig.colorbar(pts, ax=ax)\n", + "\n", + "# format the plot\n", + "format_plot(ax, 'Learned Latent Parameter')\n", + "cb.set_ticks([])\n", + "cb.set_label('Latent Variable', color='gray')\n", + "\n", + "fig.savefig('figures/05.01-dimesionality-2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Introducing Scikit-Learn" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Features and Labels Grid\n", + "\n", + "The following is the code generating the diagram showing the features matrix and target array." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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QAAAMEKAAABggQAEAMECAAgBggAAFAMAAAQoAgAECFAAAA5Zt27bTReDiY1mW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\nQAEAMECAAgBg4P8BcxeynoHUQewAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(6, 4))\n", + "ax = fig.add_axes([0, 0, 1, 1])\n", + "ax.axis('off')\n", + "ax.axis('equal')\n", + "\n", + "# Draw features matrix\n", + "ax.vlines(range(6), ymin=0, ymax=9, lw=1)\n", + "ax.hlines(range(10), xmin=0, xmax=5, lw=1)\n", + "font_prop = dict(size=12, family='monospace')\n", + "ax.text(-1, -1, \"Feature Matrix ($X$)\", size=14)\n", + "ax.text(0.1, -0.3, r'n_features $\\longrightarrow$', **font_prop)\n", + "ax.text(-0.1, 0.1, r'$\\longleftarrow$ n_samples', rotation=90,\n", + " va='top', ha='right', **font_prop)\n", + "\n", + "# Draw labels vector\n", + "ax.vlines(range(8, 10), ymin=0, ymax=9, lw=1)\n", + "ax.hlines(range(10), xmin=8, xmax=9, lw=1)\n", + "ax.text(7, -1, \"Target Vector ($y$)\", size=14)\n", + "ax.text(7.9, 0.1, r'$\\longleftarrow$ n_samples', rotation=90,\n", + " va='top', ha='right', **font_prop)\n", + "\n", + "ax.set_ylim(10, -2)\n", + "\n", + "fig.savefig('figures/05.02-samples-features.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Hyperparameters and Model Validation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Cross-Validation Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def draw_rects(N, ax, textprop={}):\n", + " for i in range(N):\n", + " ax.add_patch(plt.Rectangle((0, i), 5, 0.7, fc='white'))\n", + " ax.add_patch(plt.Rectangle((5. * i / N, i), 5. / N, 0.7, fc='lightgray'))\n", + " ax.text(5. * (i + 0.5) / N, i + 0.35,\n", + " \"validation\\nset\", ha='center', va='center', **textprop)\n", + " ax.text(0, i + 0.35, \"trial {0}\".format(N - i),\n", + " ha='right', va='center', rotation=90, **textprop)\n", + " ax.set_xlim(-1, 6)\n", + " ax.set_ylim(-0.2, N + 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### 2-Fold Cross-Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Xr17p1atXtUeBNmPnzp1J4vvmIKpp8dxKEU455ZRs27btX+5BWrt2bevlxx9/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\nnOjGf5sKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_axes([0, 0, 1, 1])\n", + "ax.axis('off')\n", + "draw_rects(2, ax, textprop=dict(size=14))\n", + "\n", + "fig.savefig('figures/05.03-2-fold-CV.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### 5-Fold Cross-Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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UeXm5Vq5cqWXLltmeBGCaefjwob766iu53W45jqPdu3frypUrun//vuLxuNatW6fCwkJ1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\n3gUupAf+CC6XS++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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_axes([0, 0, 1, 1])\n", + "ax.axis('off')\n", + "draw_rects(5, ax, textprop=dict(size=10))\n", + "\n", + "fig.savefig('figures/05.03-5-fold-CV.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Overfitting and Underfitting" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def make_data(N=30, err=0.8, rseed=1):\n", + " # randomly sample the data\n", + " rng = np.random.RandomState(rseed)\n", + " X = rng.rand(N, 1) ** 2\n", + " y = 10 - 1. / (X.ravel() + 0.1)\n", + " if err > 0:\n", + " y += err * rng.randn(N)\n", + " return X, y" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "def PolynomialRegression(degree=2, **kwargs):\n", + " return make_pipeline(PolynomialFeatures(degree),\n", + " LinearRegression(**kwargs))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Bias-Variance Tradeoff" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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goaEB0dHRmDlzJh5//HFs2bIFzzzzDGpqalo974mcoRDMsaFOaNeuXRg/fjxC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y = make_data()\n", + "xfit = np.linspace(-0.1, 1.0, 1000)[:, None]\n", + "model1 = PolynomialRegression(1).fit(X, y)\n", + "model20 = PolynomialRegression(20).fit(X, y)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "ax[0].scatter(X.ravel(), y, s=40)\n", + "ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray')\n", + "ax[0].axis([-0.1, 1.0, -2, 14])\n", + "ax[0].set_title('High-bias model: Underfits the data', size=14)\n", + "\n", + "ax[1].scatter(X.ravel(), y, s=40)\n", + "ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray')\n", + "ax[1].axis([-0.1, 1.0, -2, 14])\n", + "ax[1].set_title('High-variance model: Overfits the data', size=14)\n", + "\n", + "fig.savefig('figures/05.03-bias-variance.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Bias-Variance Tradeoff Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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NyMhI7r7JcvWYkEgk+Q6vlclkBQ691a9LTU2Fn58fLl26BIVCwb2GPj4+cHR0xO7du9G6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\nNQEBAADb6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "X2, y2 = make_data(10, rseed=42)\n", + "\n", + "ax[0].scatter(X.ravel(), y, s=40, c='blue')\n", + "ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray')\n", + "ax[0].axis([-0.1, 1.0, -2, 14])\n", + "ax[0].set_title('High-bias model: Underfits the data', size=14)\n", + "ax[0].scatter(X2.ravel(), y2, s=40, c='red')\n", + "ax[0].text(0.02, 0.98, \"training score: $R^2$ = {0:.2f}\".format(model1.score(X, y)),\n", + " ha='left', va='top', transform=ax[0].transAxes, size=14, color='blue')\n", + "ax[0].text(0.02, 0.91, \"validation score: $R^2$ = {0:.2f}\".format(model1.score(X2, y2)),\n", + " ha='left', va='top', transform=ax[0].transAxes, size=14, color='red')\n", + "\n", + "ax[1].scatter(X.ravel(), y, s=40, c='blue')\n", + "ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray')\n", + "ax[1].axis([-0.1, 1.0, -2, 14])\n", + "ax[1].set_title('High-variance model: Overfits the data', size=14)\n", + "ax[1].scatter(X2.ravel(), y2, s=40, c='red')\n", + "ax[1].text(0.02, 0.98, \"training score: $R^2$ = {0:.2g}\".format(model20.score(X, y)),\n", + " ha='left', va='top', transform=ax[1].transAxes, size=14, color='blue')\n", + "ax[1].text(0.02, 0.91, \"validation score: $R^2$ = {0:.2g}\".format(model20.score(X2, y2)),\n", + " ha='left', va='top', transform=ax[1].transAxes, size=14, color='red')\n", + "\n", + "fig.savefig('figures/05.03-bias-variance-2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Validation Curve" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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pXbo08+b9QGJiEkuXLgYgJKQ633+/mH/+cyA//LCAJk2ecUr5+T6cw8LuUKNG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JDWWIjSjcvaRAQg/Z/vHQwG0dPTg5ycHGVJnGi34rI2ERFRhuHMmYiIKMOwOBMREWUYFmciIqIM\nw+JMRESUYViciYiIMgyLMxERUYZhcSYiIsowLM5EREQZ5i8j/j8f7uiIWgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 1, 1000)\n", + "y1 = -(x - 0.5) ** 2\n", + "y2 = y1 - 0.33 + np.exp(x - 1)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y2, lw=10, alpha=0.5, color='blue')\n", + "ax.plot(x, y1, lw=10, alpha=0.5, color='red')\n", + "\n", + "ax.text(0.15, 0.2, \"training score\", rotation=45, size=16, color='blue')\n", + "ax.text(0.2, -0.05, \"validation score\", rotation=20, size=16, color='red')\n", + "\n", + "ax.text(0.02, 0.1, r'$\\longleftarrow$ High Bias', size=18, rotation=90, va='center')\n", + "ax.text(0.98, 0.1, r'$\\longleftarrow$ High Variance $\\longrightarrow$', size=18, rotation=90, ha='right', va='center')\n", + "ax.text(0.48, -0.12, 'Best$\\\\longrightarrow$\\nModel', size=18, rotation=90, va='center')\n", + "\n", + "ax.set_xlim(0, 1)\n", + "ax.set_ylim(-0.3, 0.5)\n", + "\n", + "ax.set_xlabel(r'model complexity $\\longrightarrow$', size=14)\n", + "ax.set_ylabel(r'model score $\\longrightarrow$', size=14)\n", + "\n", + "ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + "ax.yaxis.set_major_formatter(plt.NullFormatter())\n", + "\n", + "ax.set_title(\"Validation Curve Schematic\", size=16)\n", + "\n", + "fig.savefig('figures/05.03-validation-curve.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Learning Curve" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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JZyGEEMLHSDgLIYQQPkbCWQghhPAxEs5CCCGEj5FwFkIIIXyMzwxC8tZbEBRkIiVFIzpa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\nETkYhjMREZGDYTgTERE5mP8Hlp/kNMiE2u4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = np.linspace(0, 1, 1000)\n", + "y1 = 0.75 + 0.2 * np.exp(-4 * N)\n", + "y2 = 0.7 - 0.6 * np.exp(-4 * N)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y1, lw=10, alpha=0.5, color='blue')\n", + "ax.plot(x, y2, lw=10, alpha=0.5, color='red')\n", + "\n", + "ax.text(0.2, 0.88, \"training score\", rotation=-10, size=16, color='blue')\n", + "ax.text(0.2, 0.5, \"validation score\", rotation=30, size=16, color='red')\n", + "\n", + "ax.text(0.98, 0.45, r'Good Fit $\\longrightarrow$', size=18, rotation=90, ha='right', va='center')\n", + "ax.text(0.02, 0.57, r'$\\longleftarrow$ High Variance $\\longrightarrow$', size=18, rotation=90, va='center')\n", + "\n", + "ax.set_xlim(0, 1)\n", + "ax.set_ylim(0, 1)\n", + "\n", + "ax.set_xlabel(r'training set size $\\longrightarrow$', size=14)\n", + "ax.set_ylabel(r'model score $\\longrightarrow$', size=14)\n", + "\n", + "ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + "ax.yaxis.set_major_formatter(plt.NullFormatter())\n", + "\n", + "ax.set_title(\"Learning Curve Schematic\", size=16)\n", + "\n", + "fig.savefig('figures/05.03-learning-curve.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Gaussian Naive Bayes\n", + "\n", + "### Gaussian Naive Bayes Example\n", + "\n", + "[Figure Context](05.05-Naive-Bayes.ipynb#Gaussian-Naive-Bayes)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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0Tq3fv+PCwHMCFdFdMRUKvLB+o0VnB2zfriLBy/qJdCZLHzJb7PZhfkOZjh2P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\n9HPGrhZOPcuH06lE4MmHhjg0mqn4GAeHozz20O8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Rd/zgZyTNxYmA00UPX7/zR3z8HzdG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nwpV/imzXt4a1E1N195ad+LLAtXRztISaSkJGW1W5y4eDGlxSW8u/U9SmfOYPt77yHCMar/tpEP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\nfZGWY04/jjf/uRv7Q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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_blobs\n", + "X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\n", + "ax.set_title('Naive Bayes Model', size=14)\n", + "\n", + "xlim = (-8, 8)\n", + "ylim = (-15, 5)\n", + "\n", + "xg = np.linspace(xlim[0], xlim[1], 60)\n", + "yg = np.linspace(ylim[0], ylim[1], 40)\n", + "xx, yy = np.meshgrid(xg, yg)\n", + "Xgrid = np.vstack([xx.ravel(), yy.ravel()]).T\n", + "\n", + "for label, color in enumerate(['red', 'blue']):\n", + " mask = (y == label)\n", + " mu, std = X[mask].mean(0), X[mask].std(0)\n", + " P = np.exp(-0.5 * (Xgrid - mu) ** 2 / std ** 2).prod(1)\n", + " Pm = np.ma.masked_array(P, P < 0.03)\n", + " ax.pcolorfast(xg, yg, Pm.reshape(xx.shape), alpha=0.5,\n", + " cmap=color.title() + 's')\n", + " ax.contour(xx, yy, P.reshape(xx.shape),\n", + " levels=[0.01, 0.1, 0.5, 0.9],\n", + " colors=color, alpha=0.2)\n", + " \n", + "ax.set(xlim=xlim, ylim=ylim)\n", + "\n", + "fig.savefig('figures/05.05-gaussian-NB.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Linear Regression\n", + "\n", + "### Gaussian Basis Functions\n", + "\n", + "[Figure Context](05.06-Linear-Regression.ipynb#Gaussian-Basis-Functions)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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KvLzxGA6XT+uQYsLpCxa+OHKRfrkp3DEir1s/2+fz0thYj9ncRCAQ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\nrm0QwiKjyxaJO6+GQimFoihot0dNgElZlpg5vlwbFKVsg223O6jVxo9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "\n", + "class GaussianFeatures(BaseEstimator, TransformerMixin):\n", + " \"\"\"Uniformly-spaced Gaussian Features for 1D input\"\"\"\n", + " \n", + " def __init__(self, N, width_factor=2.0):\n", + " self.N = N\n", + " self.width_factor = width_factor\n", + " \n", + " @staticmethod\n", + " def _gauss_basis(x, y, width, axis=None):\n", + " arg = (x - y) / width\n", + " return np.exp(-0.5 * np.sum(arg ** 2, axis))\n", + " \n", + " def fit(self, X, y=None):\n", + " # create N centers spread along the data range\n", + " self.centers_ = np.linspace(X.min(), X.max(), self.N)\n", + " self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])\n", + " return self\n", + " \n", + " def transform(self, X):\n", + " return self._gauss_basis(X[:, :, np.newaxis], self.centers_,\n", + " self.width_, axis=1)\n", + "\n", + "rng = np.random.RandomState(1)\n", + "x = 10 * rng.rand(50)\n", + "y = np.sin(x) + 0.1 * rng.randn(50)\n", + "xfit = np.linspace(0, 10, 1000)\n", + "\n", + "gauss_model = make_pipeline(GaussianFeatures(10, 1.0),\n", + " LinearRegression())\n", + "gauss_model.fit(x[:, np.newaxis], y)\n", + "yfit = gauss_model.predict(xfit[:, np.newaxis])\n", + "\n", + "gf = gauss_model.named_steps['gaussianfeatures']\n", + "lm = gauss_model.named_steps['linearregression']\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for i in range(10):\n", + " selector = np.zeros(10)\n", + " selector[i] = 1\n", + " Xfit = gf.transform(xfit[:, None]) * selector\n", + " yfit = lm.predict(Xfit)\n", + " ax.fill_between(xfit, yfit.min(), yfit, color='gray', alpha=0.2)\n", + "\n", + "ax.scatter(x, y)\n", + "ax.plot(xfit, gauss_model.predict(xfit[:, np.newaxis]))\n", + "ax.set_xlim(0, 10)\n", + "ax.set_ylim(yfit.min(), 1.5)\n", + "\n", + "fig.savefig('figures/05.06-gaussian-basis.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "## Random Forests" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Helper Code\n", + "\n", + "The following will create a module ``helpers_05_08.py`` which contains some tools used in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting helpers_05_08.py\n" + ] + } + ], + "source": [ + "%%file helpers_05_08.py\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from ipywidgets import interact\n", + "\n", + "\n", + "def visualize_tree(estimator, X, y, boundaries=True,\n", + " xlim=None, ylim=None, ax=None):\n", + " ax = ax or plt.gca()\n", + " \n", + " # Plot the training points\n", + " ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis',\n", + " clim=(y.min(), y.max()), zorder=3)\n", + " ax.axis('tight')\n", + " ax.axis('off')\n", + " if xlim is None:\n", + " xlim = ax.get_xlim()\n", + " if ylim is None:\n", + " ylim = ax.get_ylim()\n", + " \n", + " # fit the estimator\n", + " estimator.fit(X, y)\n", + " xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n", + " np.linspace(*ylim, num=200))\n", + " Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "\n", + " # Put the result into a color plot\n", + " n_classes = len(np.unique(y))\n", + " Z = Z.reshape(xx.shape)\n", + " contours = ax.contourf(xx, yy, Z, alpha=0.3,\n", + " levels=np.arange(n_classes + 1) - 0.5,\n", + " cmap='viridis', clim=(y.min(), y.max()),\n", + " zorder=1)\n", + "\n", + " ax.set(xlim=xlim, ylim=ylim)\n", + " \n", + " # Plot the decision boundaries\n", + " def plot_boundaries(i, xlim, ylim):\n", + " if i >= 0:\n", + " tree = estimator.tree_\n", + " \n", + " if tree.feature[i] == 0:\n", + " ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2)\n", + " plot_boundaries(tree.children_left[i],\n", + " [xlim[0], tree.threshold[i]], ylim)\n", + " plot_boundaries(tree.children_right[i],\n", + " [tree.threshold[i], xlim[1]], ylim)\n", + " \n", + " elif tree.feature[i] == 1:\n", + " ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2)\n", + " plot_boundaries(tree.children_left[i], xlim,\n", + " [ylim[0], tree.threshold[i]])\n", + " plot_boundaries(tree.children_right[i], xlim,\n", + " [tree.threshold[i], ylim[1]])\n", + " \n", + " if boundaries:\n", + " plot_boundaries(0, xlim, ylim)\n", + "\n", + "\n", + "def plot_tree_interactive(X, y):\n", + " def interactive_tree(depth=5):\n", + " clf = DecisionTreeClassifier(max_depth=depth, random_state=0)\n", + " visualize_tree(clf, X, y)\n", + "\n", + " return interact(interactive_tree, depth=[1, 5])\n", + "\n", + "\n", + "def randomized_tree_interactive(X, y):\n", + " N = int(0.75 * X.shape[0])\n", + " \n", + " xlim = (X[:, 0].min(), X[:, 0].max())\n", + " ylim = (X[:, 1].min(), X[:, 1].max())\n", + " \n", + " def fit_randomized_tree(random_state=0):\n", + " clf = DecisionTreeClassifier(max_depth=15)\n", + " i = np.arange(len(y))\n", + " rng = np.random.RandomState(random_state)\n", + " rng.shuffle(i)\n", + " visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,\n", + " xlim=xlim, ylim=ylim)\n", + " \n", + " interact(fit_randomized_tree, random_state=[0, 100]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Decision Tree Example" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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GlTtP6ROQBw8eyBwXxcXF2L9/v3BOKB1/y5Yt0a1bN0gkEixYsECmV/rc3Fxs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\nKETkkpOTq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 4))\n", + "ax = fig.add_axes([0, 0, 0.8, 1], frameon=False, xticks=[], yticks=[])\n", + "ax.set_title('Example Decision Tree: Animal Classification', size=24)\n", + "\n", + "def text(ax, x, y, t, size=20, **kwargs):\n", + " ax.text(x, y, t,\n", + " ha='center', va='center', size=size,\n", + " bbox=dict(boxstyle='round', ec='k', fc='w'), **kwargs)\n", + "\n", + "text(ax, 0.5, 0.9, \"How big is\\nthe animal?\", 20)\n", + "text(ax, 0.3, 0.6, \"Does the animal\\nhave horns?\", 18)\n", + "text(ax, 0.7, 0.6, \"Does the animal\\nhave two legs?\", 18)\n", + "text(ax, 0.12, 0.3, \"Are the horns\\nlonger than 10cm?\", 14)\n", + "text(ax, 0.38, 0.3, \"Is the animal\\nwearing a collar?\", 14)\n", + "text(ax, 0.62, 0.3, \"Does the animal\\nhave wings?\", 14)\n", + "text(ax, 0.88, 0.3, \"Does the animal\\nhave a tail?\", 14)\n", + "\n", + "text(ax, 0.4, 0.75, \"> 1m\", 12, alpha=0.4)\n", + "text(ax, 0.6, 0.75, \"< 1m\", 12, alpha=0.4)\n", + "\n", + "text(ax, 0.21, 0.45, \"yes\", 12, alpha=0.4)\n", + "text(ax, 0.34, 0.45, \"no\", 12, alpha=0.4)\n", + "\n", + "text(ax, 0.66, 0.45, \"yes\", 12, alpha=0.4)\n", + "text(ax, 0.79, 0.45, \"no\", 12, alpha=0.4)\n", + "\n", + "ax.plot([0.3, 0.5, 0.7], [0.6, 0.9, 0.6], '-k')\n", + "ax.plot([0.12, 0.3, 0.38], [0.3, 0.6, 0.3], '-k')\n", + "ax.plot([0.62, 0.7, 0.88], [0.3, 0.6, 0.3], '-k')\n", + "ax.plot([0.0, 0.12, 0.20], [0.0, 0.3, 0.0], '--k')\n", + "ax.plot([0.28, 0.38, 0.48], [0.0, 0.3, 0.0], '--k')\n", + "ax.plot([0.52, 0.62, 0.72], [0.0, 0.3, 0.0], '--k')\n", + "ax.plot([0.8, 0.88, 1.0], [0.0, 0.3, 0.0], '--k')\n", + "ax.axis([0, 1, 0, 1])\n", + "\n", + "fig.savefig('figures/05.08-decision-tree.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Decision Tree Levels" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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AsUahy7F4lrOxUWAnXev3eYShZ9gkclKOfspra252ve19ZlEbHx/YRUTUhEHN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\nNzE1JBv/UXcsZR5g4MTZDhLWvMLR00b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9nCr3oaHZg/VLmwnPFLHOkXnpVSNPrlITGKCi\nvEJE4VN8T4sJhVKJ1TbersjcvZeVbOnDzc35c4nRVkoam1xCjos7QnJLon+wAW9P+/+RLMsMWePv\nm4gjyzIaxcC49ugIFSsXjfDurvdR5uSx6JEXHP37egfx8NSjUinZ8/4feHJ+KV4eCsCds1UyjSNa\nfKzvkRxnRadzrijlr28mf/5fO33fuUvW8P5L+xkXviUwTrSxSpo7FnGO7d2CevQIEUGDHCv1p+mG\nhhfW3MDfV8Q0U+LnLxt4cYMOrVbgWLkKr4hFd3bTboNCqcJidZ6bvYLf3dsapTg0rgpZZPAIne09\nLiHHxR0xSjxG4w3Hb5bVKmMU788hOMBg/zCB3s65RQVBIC5azcpFg2zc+S6Jaf/Mkse+Bdi97fv7\nhvDydkehULDjrV/wrdWVaDRKQElJuY22ISvJ/vvQRktOQokgCPi7tzBr6V84jVe04il2/vHY+MlN\nYFIk+c6q4QEc2LoRb7GMEL8RDh4L4kaHwLce78DDXWR4msRPf2vgb76tQxQF9h53Izhh8R1feyIE\nUcRmcxaHbDZQKO5+raQRx5eN9/UYxjBixNPrsx+iufjsfGWEnMy8HI7vX0FFfQl6zTB9hlCSC+4s\nY/jnRVu3btzCoq/fxvUWC3PyZd46WkrQirG405sfAnPfKWLyx64VHiJgLDtJQWYX1ZfHL1jau9X4\nJo1XbacWP8OZyl8wJcPuMinLMqeqwokIGiY2yr7pMholqpvTWTLTm70fvIKWWkDArMqkeOWTTuOM\nDBsYbX2Hx5eMAgrSEoew7G0nMdY+d1EU+NqTbvznH4KIjEshJCaHouVp93QffXw9OdGZwGxrLUql\nfS5nqxSExN193oro5CmcrTxAXsZY2+FSFVn5M2//IRcubmJ6YTGHd/SguHAKN5WB3tEo8oqfv69z\nau/WAmMbLFmW6emz0dpmZXbeCCXnLzBt9ljOBG+fMc8ZcfQsQf5jz3hSrMzhUwd4ctkwBydYw/QP\nuxHq7uyOKwgCyfmPUXftVRJj7G2SJNPQHo17dS9z8+2nfQNDEl2jmYiiwO73fou74gqSrEbwmMqc\nRc6lwTvaOvEwf0jRAiugIDK0D72qnfAQuyisVAp8d4OOn7wcRkxCAjEp05me+NnDNwFiE6LZ9loU\naYnXHXbvUKmaxKx5d30tn6A0GptPEB0xdm9LzniQuTDnnubo4uGhcMkatm0eQCddRKU002uIYcaS\nb9y3+QiCQFu3GzDmKSRJMt09Njq6rGQldtN4pZX4pChHf18/+wGRzWbDQ7iIl8eYUJGXZqPk1R2s\nfc7Ch7vGj2exaceFqoqiSFjySm50bCY0yP5sWSwyXSOxnK3qYEq6vYR5R7eM1W0Kg/2DnNjzGp7q\nJiySDje/mRQUOYswddW1xHvvJHsGgAJP9w5iQ2T8fe2n7hqNyHc36PjvN2OIiY8lOXsuSVH3JsZO\nmZ7P9je28sLasSSr2w/pyJ179wKRUhtLb/9ZR5g+wMX6QOY84hKMXdwZ81au5+3No3grqxEEiV5T\nAvNWv3Df5uPr78XxBjWzpo7ZGrPZbmt6em2E+rYzODDsKGEuiqLD1vT3DRHtW41GM/Y8zJlq5j9+\nv4fHvwFXJ0gZaJPHHzar1So8Q+fTP7jfIaaPGCR6DAnUNDQ7HAcam0HlU0BXRxdnD7+Bh/oGZpsH\nXuHzyJ0+2+maZ06eYFbiYaLCBUCB1drCzCwFHu72CAl3vciGx3T8xytJRMVGkjltPoHB91YBb9rc\n+Wzecoinlo+FmW0+4MOcR4ru+lpmIQKjscbpkKq1J5hUl4hz3/jKCDlgT94ny8uxWKyo73OFElmW\nEaReXn9/iLnTtWjUAnsPj9Dda6O718buQwaCMwKcPmOxWBEEKD9xnKH+Zm6VfSXTDeqvKZmV78aW\n3SOsWWp/cEYMEjWtWawodL4eQHJ6GudKn+LdvYdRicMMmcOYu+pZRoYH2bhvNyphCLMYxeLHH+Hg\n1jd4ZPZxh2tz38BB9u5UUbT8Ucf1yk+cZNXsEfjohLp/0EZ4qPPpoCAIxMT4M3fl0/d6Gx3MW/td\n3tr1BjrRvhjzjSwkOyvz0z94C3GJcRyuX0j3sSNkJBg4X+uOSbuYtLuMSXfxcFO0/DFk+VGsVptT\nyev7gcViBamfNz8YpGimDptNZueBEZQKaGmzUnbWzJRlzqGNZrMFURQoPXSA0eFObv0pkMyt1NSr\nyU7XcKDEwPw59kVOd69Ejyl33Mk5QN70mZQdHuJC3QkUwihDlmiWr99AW+t1Nu47gEocxaZOYNEj\nq9jz7ks8s/iiQ5htbd/BiYNaZhaPbWIqyo+xvtDCx3awtd1KUpyzXVcoBOITQihaNXm2Zu7KP+f1\n3W+iV93AInkQkrCIyJjIu75OTkE++zbXcK2ljMRoI6crvdCErLnrmHQXDy+CILBo3QYkSUKSpPvu\nuj40OIKSAd7dOkzRTC0DgxLb9w0THami4ZqFE2dsrPi6s2eiyWTfiB3buxONdQBw9saTjG00NmtI\niFVRembUkWPreqsNm65gwsO4WQuWcnS3ES6cRcDMiJTAo994nobqKt7aV4JCMKNwT2feisXs2vif\nbFhZ78ivd/nqJs6XeZFTUOC4XnN9OU/dpNVeu25lxhRnG6fXicTFRzJv1VP3cAfHEEWRKQv/nDf3\nvIdW2Y7R6kN0xnJ8/O7MM/pmZs5fyJZ3r5IYfJGoEBMnL/oRkPDoffPccvHgoVIpWfr4t7DZbMiy\nfN9tTVtrO26qYTbtMFE0U0dbp5Wtu4eZOU1LZa2J0rMizxQ65z41mczYbBIHt28mM2yUW22NabiV\n/gEtYSFKLlaZyEqzf76mQUbjN/FhbtHyx9i5XUJtqQBkjGIqT3/3aS6ePsWFfacQBAl9QB4ziuey\n640f8cKaGx99coDyio3U1QSQmDKWVHio8xJRGTcdwndamZrt/D1CgkSi42IpXvnIZ7t5t6DVakgs\neJE392xGq+xh1BpA8sw1n6kAzdyl63jjnRYyo2sJ9LNy4kIg0TlPTso8XXw2vlJCDtgXPp8k4kiS\nxNE92xCNl7GiJTxpHklp6Xc9js1mo793CB8/T0RR5EZrJxq1Gr8A+yKm5Xo70zO6yU7z4MIlEyaz\nzLOPebJ93wi5mW7kZGh4fdcpYAb9vQOc3P1/+OuvUVUzxLOPqChrG2V0VOeIyTabZZQKK80tZnLS\n3JmW68bmnUO0tGtwD13E0icev+1cc6fPBeY6tfkF+hEZ65x0UyvXOkQcAB8vEdF0CRgTcoJCQ7l+\nAxJj7a8D/BQcOmagIG9sczI6KmFV3FTWZhLQ6dxY9MjknEQWLXuUgf7FXLpyjZSiePR3mUXfhQuw\n25pPEnEsFitHdn6ARm7CLOmJz1pCdPzde4xYLFYGB4Ydp02tzR3o3XWOHDOXzlewZv4wQQEenLlo\nQiHCt57zYtveEablujE1W8Mb+/YRn/QndLS1c/7IK/jprnOxcohvP6dmX+coVqu7Q1QZHpHw9rRR\nfn6Y9es8iY1SsWn7EI1tboQkrWXhuhW3nWtB0WLA2Q04wTOFhJQUx2tJkvBS1TnGAwgLFrBUXgDG\nhBx3L396+mT8fe39kuPVfLhr2FH5D6CrR0LtEX/X9/ST8PLxZPHjk5OQeOHa5+jpWkXV9evkLku6\n56T7Lh5ORFH8xCTqo6Mmju1+F63YhtHqSeq0FYRFhN/1OGazheEhA75+XsiyTHNTO94+Hg5v4fLj\nJWx4FATBndPnjXi4i/z5N73Zvs/AzHwtU7Ik3i/ZwYI162m6cpXLpzfiq2vhXMUwP/imG1v2GJFl\nlUOc6eq2Eh5sY+vuQb7xjDfXW6x8sH2Qa63uJEx9mqJlt/eEm7tkLbDWqS0tJ4e0nDGPt/6+IaID\nrjiJQUmxMucOlANjQo5C7YXRKDlOmfNz7AL2ysVjp8wNjTJ+YXe/VvwkgkKCWPDoi/d8HVEUWfbk\nt+lo66K2rYOZa5Pv+wGDiweTTxP/BvoGKTv0PjpFF6M2X/Jmr8Uv8O4PQo1GE8ZRM94+HthsNpqb\n2gkI9HGsx6vPHuOZdSosFhWnzhkJ9FfwNy/6suvgCCsWupOTbmPPwb0ULlnB5UuXaL70Pt7adiqr\nh/n+N7V8sMMI+WNCRWOzhfQEidfeG+Rbz3pRU2/h/W1DXLvhRebs5ykomj7hPEVRZP6qpwBnATd3\n+gxgzMv5an0j+anN3JzzamqmjbcOHncScmzokSTZEQqZk66hpNTI3Blje6hTFwTi028KyZgEImKi\niYj5/j1fR6VSsvKZH9By/QZX+/qZ93iSSzC+zzx0ln7fpj+yemapw733SNklzgx+nSnT7/yhKT92\nkNGOPYT69XG+zYumFivF04cYtig52ZrIgkdfxNPLnes1GgTBSk7Gx1UUZGwfhXULgoC7qgWA0r0v\n8/yKywwOSWhVEqHBClYu0rN51zAqJYwYZFpuWMnJ1DCnwI2NmwawqWKJTMhl8XMr8LopTOLemCA3\nhOy8eEzJSGXLKwlEhtbj5iYiy9DUEcCb29yYk9fDjQ6RfSe0xCb3Ul1RSWpmxrhrfhnw8vYgK+/u\nPXpcuLhT9rz3Ek8vrHBsDrYduITF8pckpNy58HBi/zYYOkKg9xCnW7zo6rZQXDBI27Cakz0ZLH7s\nT/ELCOBGm5KQIMjPsdsa2005sERRQK9oBuDcoZd5fmUTjc0Wgn0FfLwVrFyo54Mdw2jUMDwi09Ri\nYU6BloJcN375h348g9IJjshg7arlk+JNIgjChPmtbrU1+bNm8cGrB/n62hsolQKCALXXg3h/j8jM\n7H7qm5SUlLsTm9zIlcv1d52M+IvCL8DbIfC7cPF5sO/dn/HCqqsOcfSNDytRzP8ngkPHe+nejsM7\n3kVrOYmPp4HjjV4YDBYK8we4VqOjfSSPhetewMc/kPYumYhQkRlT7bZgxCDhprGPq9GIqOUmZFmm\npuwPPLeykzMXjDy7ToVOJ7K0WM87W262NWYWzNWTla7h3/63n4jEKXgHpvD4uqWf6bT4VkRRQJJF\nbs2nc6v9mVa4iLfeO8Hzq3sQRQG1SuB8fTCyUiI/Y5DKOiWnK72ISbpIs38AEdGTe1A1WQSFBBAU\ncud/cxcu7gZJkjiy5ad8Y13bR7/jDfxm4yUWPv0TPDzuLBeTLMvs2/wqvqpzuGuN7L/iiRILM/MG\nqa12Z0CexbwVT+Cm92Z4RMbDXWR2gd3WtHda8fW2iwZengowXsFstnCj6hXWLxvk4DED33pGg1ot\nUDRTxztbhlAqYdQg09RqYWmxnoRYgX/8aT+JmTPx8E9h/eMLJsX7SKlUjst5BePz6+XOWsZ7e87x\n+JJBBEHA01PB0fMBGKyj5CSPcOaSiop6byLjjuCmXURQSNA9z+3zIDwylPDI0Ps9DReA4kc/+tGP\nvoiBhi1NX8Qwn4jJZGbg6utkJY/9qEeHw74dx2nvNBGb9OmiQ1trO7Ybv2FZoYHIUIHMJBPDQ31k\npahIihPIiOtmx94B0nKncfZsK7HBLY5Fzp5DBnIzNI4qKxX1fkSnFtJV9xYp8Taut1hx14sE+CkQ\nRYG0JA3J8WpOnx9l9WIP8rLc8PZSMDNfx7WmQRQeeSRnpE1aHqD6+k4ifK+idbNf70aHTKuhiKi4\nJKd+sWnT2HvERvVVLReuJjN/3XdIzF3CgZMa+jvrePFZC1lxNxjqOE1VvYbw6HvLW+Hiq4GHOvoL\nGefLYGu6O/vQGd4iLsr5JPj9t/czOKwkMi75Ez5t53JVDSHi6xRNMxEZBtkpJjra+5g5VU18NCSF\nt7HnsIWcgukcOVJPWnQnSqWALMts2jnMvFk6h4h0sSEY7+BMlIPvER0OlTVmkuLUaLUiKpVAerLd\n1hwtHeXr671ITtDg462gcIaOCxV9eIfPJDZxcoQSQRCoqmomKbzFkXyvvhGGVUsJjYxy9BNFkaik\nAvYcMlFzzZ3KpgyWr3+RyNRF7DhgQWW9wjeftJIZ20prwymaOnwJDvtybrBcfLE8TLamrqaOFL8d\nBAWM2Zr0RBuv/WE3VtmLsKiYT71G+YkT5IVtYlq2lagwyEsz0djYS/EsN+KjJEJ9rnP8nI78WTPZ\nse0i2UkDiKLd1ryzZZjlC/QOEel8QziSIpBEn534+4qcrTAyJdsNQRBwcxPJSNGQnKBm7xED3/8T\nH6Ij1fj7KimereVkWS/hiYWER919KONEaNzUlJ+qIzOuy3ECfqFGgei3lsCQYEc/lUpJYGQ+ew4b\nqW30pPpGLquffZGA2GI27xgmxLuJ59aayYxtpvp8Kf3GKPwDAydlji4ebL4IW/NlsDMAp4+fZEFW\nCfqPPPcFQSA13swffrsTpTaEoNBP9wI8tm8Xi7P3kpViIzoc8jNN1F/po3i2loRoGzrhGtWNoeQU\nTGfLpnJyUkYQBAGbTeaDHcOsXKR37HnON8TQ22dmftZJ3NxEKmtMZKfbD7M83EXSkzUkxanYtm+E\nv/ueL2EhKoIClMyfo+VQSR9xWfMJCvnsFXVvxsvHk8MHK8lJ6nfMr+S0isCk9Xj7juUv1eq0uAfk\nsvfIKDXXfLjSM521z30bj5BC3tvSSWpUK08sN5ER08Tp46eRNCl4+bgOglzc3tY8VELOqMHISNsu\nYiOdy6jcaDeREtlKc288/oGffJpx6vBuVsyucxJPYiNVHDphIDFOjUIhUHdNIiqlkLjUXA6eELl0\nWeSNTQaMoyOYzTYiQpRUNSgx6ZYTGhlNY9Uh0uJN+HiLHC0dJS1pzP2+p1dm+34zj65wVrsjQwW6\nms9z4WIXiel3V4L7dsQkpXOgxERNnZGqK140jxQye+GKcUKRQqEgNimV6OQC4lKycNNqEEWRpku7\neHp5p6N/kD+cv9hJTNr8SZmfiwebh2lz1dneg6d0gCB/59OYzi4T/vpmLJopeHh+cnK4i6W7WZDv\n/F2CAhScrzQTFaFCpRKoaRCJTplJbGo+ew5buVSv4rV3h1ArRhkZsREVruLURTWa4HUEBIfQ1nCI\nhGgbgX4KjpwcJSl+7NS7oVHizAUTi+c5hxq6a23II+e5UDlCbPLkeLHFJGWxc/8w9VfNVFzxZUBY\nTP7s8Yn3VGoVsckZRCcXEJucgUqlRKFQ0HZ5G48sGqvUFR4iUX62h5i0ueOu4eLh42GyNdcarpIQ\ncNqxuQK7J0p3jxHBch2vsELUmk/OGVh3bhezstuc2hSiQG+/hL+vAr1OoKJWSUzKNCKTprH74CiX\n6jW88lYvvl5m+gdsREeoOFiqJShpPe6engy1HSEiBHy9RE6dMxEbNTaHU+dl2trMjpP2jxkdHcVH\ndYGKGu5I7L4TIhJy2bG3j4ZGKxUNgVg9VpI1dbwHtpvWjbiULLutSUpFoVCgUinpvrKJFUUjjn6x\nkTZOnOonNnXGuGu4ePh4mIScuqpKcuNqUSjG9gQqJfT1jTLU10x40rxPDAEFuFa5g6mpzpV0e/ps\neHuKuLmJ+HrDmUo1cal5hMRMZfchAxWXNbzydjfhQRb6ByUiwxRsOeBO4rQNyLKA2lSKr7eAKEBT\ni4WQoDEPm92HZQRsTMlyznnV3T1EsL6C2qu6OxK774SQ6Bx27O3iSpPMxfoQ9OGPkpCaOq6f3l1P\nfGoOMSnTiE5IcoTpDza9x8JZFsAukiXFWjhyfIjY1MnZ47l4sLmdrXmoQqs8PPU0d0cCjY62rm4r\nep1AepLAW4cvkJxuf+iMRhOnS44iKpRMmzPHEW/spvdmaFhyeNXAx0Zo7LVF+riCk0jhkpXsfPs3\n/OCFIQL93TGbZX7xikz63K8zZdYUAMzqPLp6DhHgJ5AUp+a194aYnqehtVNHU38OofFmTKbzThnY\nrzRaSIxRou86T2d79x1lNa+vqaOpoZr41Byi46LGvS8IAvNWPHbnN/QWNIrxZekMQz0c2PoeKo2e\n/Lnz0WpdOSJcfPWJig1jb1kYGckdjrZr1y0EByqYlmvl7ZIyQsLsVZpGhg2cPnYErd6DqTNnjMUb\nCzosFueSke2dVgL9b7I1sl3gVamUFK98lC2v/ZR/eHEUL08PDAaJn/5OZM7q75Geaveq6zRkMjRy\nGg+9SFCAkjc3DTM9T01Dszvdlhm4BzYjSQ1OpWxb261kpKjoryjDYHhkwkTHt1JdcYkbTVdIzckn\nNHz8iZdSqWTh2mfu4o46o56gBOZQfzcHtr6LRufNtLnz7nvCexcuvgiypuRwYJMfTy4bq1p3vtJI\nUpyaiDAD+8+UM6PILnD29w5ytvQoXr6B5BXkOw5dLJLbuEqY3b02kuPtz5Asy1gku63R6dyYt/IJ\ntr36Y378lzZ0Og8GBm3860tqlq3/KyKi7d402w+lkJtWRYC/EoXCzLtbR5iarabmmgcGVTGC/hzg\nLB71D9jIy4CG48eQ5ZWf6m0syzIXTp+lp7OVnGmz8Qv0HddHq9Ww6JGv3eVdHUMtDo5r6+tu4+C2\nd9B6BjFt9hxXjggXDwVTZxWy6+BeVs0fdbQdPjHKtFw3RgxdXKlrJCnVHjre1dFNRflJAkIjfQyN\nmgAAIABJREFUycjJcjzLVmn8+mHEIDsiAaxWGUmw76E8vT2ZveQJDrzzz/z07wTUak/aO6386689\nWP38DwkKCSIkLIRtr0YTHd5EYpyaPYdG2LRzlJx0JRV13oi+SzG07gf6nca0WGSSoi3UHDkEfHpV\nSkmSOFt6isH+bqbMKJwwpYWntwdLPmN+PZPJgqduZFx7V1szB7e9g6dfBFNmzLivlZhdfDl5qDxy\nALwCk3n/g0t0tnVSU2/mequVpcV6BoZkmocKiIyJ5WpdHZeO/Acrp58lxr+SXdvL0Pqk4unlSUhE\nFFs2l5ObOubu9/p7Q6xd5o4gCJw4q0If9igBwXa33dbmG/ja3iEpzv7wKRQCuekyF+pDiEmwnzjF\nJKVTclpB1WWJjoEwAhOfYFg1n15jPKk5M8gumMnWD8+QkTCCQiFwqcbEgWMGVCqR/n4jFyr6yJhS\ncNvvDLDznd8R4/4BC6bU037lGKfO9BGXkjWp97a2+ippUdcdm8BT54wI2Fg3r5H4wBp2by/FPSAT\ndw9XmbqHkYfplFwQBNQecWzedJ7e7l6qLpsZGJQonKnjeiuMqosJDgul+sI5msp/xupZFQTpLrB9\nSzmBkXlodW4EhESxb1cZGYlGBEHAbJbZsmeYxfPsG6q9x7SEpK7Hx8++eam6WElO6E5Cg+yCr0ol\nkBIvUdcWT3iUXbiNS8lhf4mNmnqBbkMUoWnP0G2Zw4iUSHpuAYnpU9m/p5y0eCOiKHDi9CiV1WYs\nVujvG+FKo0RC2u1DUCVJYtsb/0NW8HbmZtdx5dIxLtWaiE4Yfyp1L1RX1JIV3z5WIvz4CL5eFlbO\naSTKt4qtm8sJjp6Km0s4fih5mGyNQiFiUYSzfct5+noHqag2IwgwNceNyssCHhGr8PLx4lzpMXrr\nfsGqWVV4iufY+uF5opILUKmUuHuHUHbsNMmx9tPg4RGJY2WjzJqms4dq7vUgbeYGx2/3qWMlLM4t\nweujkrxuGpGwQBvdphwCguwhRzHJU9h9cJTLVxX0m+IIy9hAm6EAkyKJ9NxphESnU3bsDMmxZgRB\nYM/BYVrbbYyMyvR0D9I94EFEzO3ziZnNFra//h/MTNzHjLQ6Lpw+SlOratJDuasqKslO7HW83rl/\nmPhIM0tmNBGku8jmTReISZ1+36v8uLg/PEweORqNmj6DP3t3n6evd5iLVWa8vURSEjWcr9IQmrwG\nN62G0kO7sd74LStm1aAwnGbHzlri0wtQKERQelNXeYbYCHuKi65uK7X1ZrLT3ZAkmY3bfShY/E1H\njqxj+7bzeNE5x0G2u15ErbKh8J6Lh6cHgiAQmZjPjv3D1DeqGLQmEpH1DZoHcpB1qaTnTMHDL5aa\ni+eIi7QiCALvbx9kcEhmYEiio70foxRCcPjtw7KHB0fY/daPKc48ytSEy5SVHKVrwPsTP3O3qFRK\nKs6eJStxTDh+b+sgBdkmiqc04SmeY/OWGpIyp3+q15OLryau0KqP0LvrSZtSTGnpVZbM7ic30w2T\nSWbjrgjmrXwWURQ5e/D3PLW0HZVKQKMWyE4e5eCRbuLS7IYoOHoq//fb/fT3D1NdZyE6UsnFKjPb\nDghETflrEtPSHOPVVFaRG12O1m3swVMqBS5dCSAm2V5dQRAEouITiU6ZSUzqdLo7btBT9xozEk/Q\n01zC6dMtzF31Iq+8eY2ma41U15n4iz/xJT5GTWqShuigDsoq9EREx074nS9dqCAraAsf51gNDQLL\nSDN9lgy8fScv9jI0OoXNH9YhmXsZHpE4eVZi/Rp7bLxSKZCVbGT/4QHiXG6CDyUP0+YKwMvHm5S8\nBRwrqWHtwhHSkjQMDUtsOpxA4bJHEQSBiqO/5bHFvSgUAjqtQE7KMLsP9hOfmodGo8YjMIdXXt5H\nb6+Buqt2l+GKahNb9qtIL/ohUXFjLsEVZ04zN+uy04mNXidwrjaA2CR7tRVBEIhJTCEqZSaxKdO4\nfqUWY+tGChJP0lJ3lOraIabO/xa/f6WK9tZWmlutfHuDN/ExajJTNWiFRupawgkOnTiuvPToERZn\nHyQiVLQvsEJlbjRfR+U9A61u8spu+4UksmVbLUq5n/ZOG5evwpolmo+qFgpkJ4+w+5CBuJTsSRvT\nxYPDw2Zr/AICiM+aT9mJizy+1EhCrIbuXomjldlMnbMAm81GfdmvWTN/GFEU8NALZCcNsvvgKHEp\n9sMVQZfGm6/upbvHSFOLBR9vBVW1Zjbvc2f6yr93SrpZV1HGtNRGpzn4eEFpZRCxiXbvP4VCQWxy\nBlEps4hNzedyRRmK/nfJjyulvrKEljaRlOnP8fuXL9LW2sGwQeaFJ72Ii1aTk65moPMy/ZY0fG7K\nL3EzR/ds48l5p/DzsduauEiJ6qomAmKKJlVU0fvEsntXDW7KAarrJYZGBBbMsW8ytW4C6XED7D8O\nMYkpn3IlF19FHiYhByAoNIzQ+LlcPHuOx5fZiI5Q09ImUdk6g7TcaRhGRumq+Q2L59oFWm8vgeSo\nbg6VqoiOT8THz4+ukUh2bD5Ee6eZji4rGrVATb2ZD/b5sOCJ/4eXj6djvMaaMrITWpzmoNNYqWqO\nJyzSnpNHpVYRn5pNVPIs4lKncrFsPx6WzeRGlVJZXsLgqC+hKY/yxz+epampC51W5Mk1nsRFq8nL\nVFFfXY3G9/ZrlMM73mbD8io89CKiKJAUY+Ps2RYiU+ZNqoeM6BbO4cOX0akHOVFuw9dXSX623SvS\nXS8QG9pD6UUvIqInJxTMxYOFS8i5haSsAo6f1VNZ705tew5FKzegVtt/nNsubyYlzuzUv75RIDLF\n7n7nptVgNCnJibtCQZ6a6AgVgQFKOiwLmDKz0Olzvn5+nD52zHHSBdDUInOlO4/G2tM0NtTjHxzl\nUJ9tNhs1x3/B40sH8PQQiQiRiQ1p48Q5d1Y++SznL/aSFn3DqQSvh17gQjXEpEzslXOp/Ahzs686\ntYUGQckZL2ITJycOHeyKclL2LIbkKfTapqEynSU5TnLqU9fkRlTy7Ekb08WDw8O2uQK7cJKYOYPD\nZSouXfHiSk8B89c8g0KhQJIkuurfd3pGBEGgvknjeEb07nr6+kwU5jaTm6khNkqFh4cKg9tqMvKc\nBVEPLz+qzx4lJmLseuerBHot+VypOknT1WsEh8c4wkQNBiNtF3/J6gUGPNxFosJkfLTXudwaydJH\n1nPo0BWWzO7H33dsU+TvC6crRGKS8yb8vg0Xj5Cf6rzo8vc2c7YuYtISmAJodW4kZc+ly5jDjaEs\non3PER48tqASBIG6JneikicuKeriq83DaGuUSgUxqbM4cFyg6qovLSNzmLf8MQRBoKuzD3fzNqdn\nRBQFLjfqHM+Ip7cX7Td6WT6ng/RkDfHRakSFBlXwUySmpjmNZZPU9LacJPimlIKHS1VYdVNpqDhG\nc2MrIZHRjpCjro5ubG2/Y9FsM+56kbhIG1bDVYbkPOYuXcuunWd54VGrU/h4eIjM0dPibfNyNdUc\nJjuhw6lNwQhtw1Pw85+8AyoPTw/iM4toHcigpTeWgqSL9oo5H6FUClRd8SImecqkjeniweFhE3LA\nvgcKjZvJ/hKJqmsB9LLQkU/zck0DaUGHnJ4RtVqgssHd8YwEBgdy7Woz6xb0k5ygISFWzYhZS0Dy\nN4mMjXYaq7fPhMZyDi/PMdu1/4Q7kiaV+osl3LjRRVhklMNLpaG2nhDxDWbm2XDXiyRGW2m7fgWv\n8AXkzlrM/l0n+ZP1olP4eFyklQMn1cTeRoy9UXeA9DjnvD69PaOofIsm1evXx8+XmLR5NPak0djm\nzYqZVxxJ5AF0WoHztd7EJE9uNIWLBwNXjpxbUCgUzJq/cML3TDY/wDkHg9HqHHs9o3gJJw9CafUZ\nBFlC0qRRvHLduGvp9FqUgWv4cN+HFGQNcvmahvKaUArStzN7qoTFIvPBrhMkz/oBoeFhNF1rIzO+\nAxhLQurtKSIZryIIAkseeZYLO0+OG+dGa9dtv6tvUBxnLu6mrcNEaJCS3EwNlZdFIuMnN9zhY8Kj\n7CXpDlQGcHMMvCzLGKyu0pguHi7UahVzF68Y1y6KIqNWP8B5M3KrrSla/hgH96gQRiuREVF65DJ7\n4bJx1wsI8qdOXMbOI3vJSx2msl7Hudog5ue/S16GjNEo8fb7J5m+7If4+Hlz6fwFZucNAWMLrsgw\nkZOXaxDFApY+9gJdtX/FzTXrZFmmrbV33Ngfo9KHUXXZxNUmCzGRKtKTNVyo0RCfOXmC8c3ExEcS\nGRPGsU2+TMvuc7RbrTIm6ctZttOFi88LrVZD0bK149r9/L0oO+HNNMbc9iVJxmjzc+q3YO0GtmzX\noZbqsMkq3ANnMHX2nHHXS0hJ5PCOBXSdOEpGgoGz1e5UNPixau7rpObZQ7M2vnmSRU/+PVqthovl\npTw1xwKMbUqyU+Gtw2eJT06gcPnTdHb/t1PuQYtFprdnfM6Ij7HiT02diYZGC8nxahJi1dRd9yZt\nfvBtP/NZEQSBhJQ4wqLCuLBvM5HhBsd7wyMSCjdXtTwXDxcennqKV47PqRkVE0nVMXciw02ONqNR\nApXzc7lg3bd5Z8dGdGIjVkmLT0Qh2dnjBYrcgmns21RNVEs5idFGyi56Ud3oydPJrxCVa0/I/u5r\nZax67m9RKBQ01Z3lqVvqJhRNt/BWSSnzl68gd84aBoc24uM9ZmuGRyTM5tt71oxafblUa+TadStZ\nqRoiw1W093mT4HVnJdfvBlEUSU5LxM/fl5PnDlA03ep470aHjGfA7cNNXTycuALtJiA0eQVbD7hh\ns8mYzTLv7nInIXf1uH4zipdQuPYfmLvuHyla/shtXeymzCwkZ+l/Utn/5wRk/ZiI4FFmT7WfmqtU\nAk8uG6KqbCsAgUG+XO9wTqIlSTIWyd7mptVQU29leGTs1P1Y2SiqT8i1NzLUSWe3zPIFevx8Fbz0\nx0FO1eWQkDw55YRvR1jKWjbv1WMySXT1SPxhczD5RY9+rmO6cPEg4RO9lL3H1EiSzOioxOtbfMic\nucapjyAIFC5Zy9y1/0jh2n9g1oLxIs7HzJy/nOTC/+Bi7/cIyflnEiP7ycuwV+lzcxN5fnUP5Ue3\nABAeFcXlRueEwPYFlz2UwU2rpeysGZNpzNbsPmRAq739gsdo6KGrV7CXI1YI/OLlQZqHZ+Ef6Hfb\nz9wrCoUCr8iV7DiswWqVaW2X+MOHkcxcNH5D68LFw4hSqUTlt4iS0wpkWWZwyMYfNgVSMM95XaNQ\nKJi/+inmrP0RRev+P6ZOUEnuY4qWP0Zk/k/stib775mS0kXqR0sKd73I8ytvUHpwBwAhEdE0NDp/\nvrtXQudl39xpdR7sPjSCzTZWUfTD3cN8UjSm0dDP0AisWOjOiEHmFy8PY3Kbd0fJ2D8rOp0bstcS\nDpxQIkkyV6/LvLk7iRnFiz63MV24eJDw8NQzJBZxpsK+TujulXh1WyQzipc69dNo1Cxat4HZa/6J\nonV/S3b+xBEFgiCw6JEX8M/8V/seKu27LCpoJyrcfn1fb5HHF1yl7OgRAHRewXT3OkcCXGkSCImI\nBkBUati0cxhZttsaWZb5cNcwojyxaCzLMkMDfYDI8gV6Wtut/O/LBnTBSz7XXDUBQf50Wos5fsZu\ns6vqZHadyiJvusvL2IUzD61HzieRnJFJf9iPeff4QURRQf6y+ejddZ/+wU9Aq9WQNSULq9WKTtU3\n7n2Nwp5R3d1DR4+5gMbmI0RHCFitMm/v9GHqkpWOvgnxvhw52Y3NBpIEyfFqAgK9JhzXMDKKangP\nS4vtHj7RESqef8KD3RcnL8zhdqRkZWOI/wkflhxB5+7JsmddSbpcuLiZ7PwZdHcm8faRQ6jc9Mx9\ntBg3t3tz1XX30JE9NZv2tm7CAga4Wa8XBAGNaLc1oeEh7DyeTWzEGYL8RUwmiTd3BDP/iSWOvhmp\nnuw+NIQogtUK2eka+usmtoWd7d2E60qYO81ua5IT1Hh4KLnQ9fnHc+cUzGKgL4v3Tpbg4xfIqg1T\nXNUdXLi4iWmFC2ltTuetw8dx0/uw5JkiR5jlZ8Xb15Ns32wqL1SRHD0CjAnDGo2IYLOHI6RlZbDl\ntWQC/arx8lRgMEh8cDCKVc/bvX0UCpFpue5s3TOCUmm3NbPytRy6NHHlufqaOqbGl5OZbLeV2eka\nFEoVA+6ff56agqLF9HTm8/axEwSFRbJmgyvMwYWLm5m7ZB3XGnLZeOg0eu9gVm2Yfc9rf/9AP/wD\n/Tiydx9rpkjc7N3n6y1iGrJ7/+fPms0Hr5Xw7LImdDqRgUEbB8+lsvo5e5EGlUrF7AItm3cOo1YL\nWCywtFjPvoqJbeG5slOsnlNNWLB9XTN9ihajVcQ/If2evs+dULj0EW60zGTj0XKi4hJZsf7z8Wx2\n8WDjEnJug7evJ8XL13x6x7tEqVQyZA4GWh1tkiQzahsLA5i/+mnOlcZTWn8JCXcKli/D09vukSOK\nIsOksnLOGUc8eUOTgHvQxAmEr9RfIztpkJsXWO56EZuxZcL+k41Or6VoyZIvZCwXLh5E/AP9mL9q\n8j3VAoN8KSkJID97LLbbZJIwM5aoeOnj36K05CijFfWg8KX48WVoP4r59vbxoKU/keeX16JQ2BdN\nF2sVBMXMnHC86ovnWTPFOXwiLFikpPoKMGPSv9+tePl4ULzs9t5KLlw87IRFhBIWMT4c4l6JT4rn\n3B4vIsLGQo76BiSU2ijH6xVP/wX7Dx7AOnodQR3MsqcXOzZ3MfFRbDsRw9fXXHcIsMfKVcRnFE44\nXlNDJetveSsjWeStI5UkpCRO6nebCL9AX+YvHx8u68KFCzsx8THExE/+IU5G7lSOlX9I8cyxkKPG\nZhnvIPtzr1AoWPb0D9lxaA+SqR2lNpIVT8939M0rmMqeN8PYsGYsHcX2Q1pypi+YcLyBzgbC0p1F\nqFl5NjafPk/g4onTc0wmoeEhhIav/PSOLh5aXELOfSAy/RHe2fVHls0ZoLcf9pZFMG/t4059cqcX\nABO7Gi5Y+w3e3eaGTriCTVbh5judgqLCCftGx0Zx6Zg7EWG3xqu68ke4cPFVRhRFfGLXsHnvRhbP\nHqGlXeDwuXiWPDUmUAuCwPS5hUDhhNeYt/a7vLnzdfSKRqyyDq/QQnKn5kzYNyE1jbNVCqbnjLk1\nd/dKaL1d+SNcuPgqo9VqEP1WsvPIhxQXjFLXKFJWm8GK9WObI4VCweyFtw9BmrPie7y++w3cVTcw\nSx4ExS0gMX7iSpyhUUnUXd1NYuyYaNzQCCFRn7+I48KFi/uHX4AP1cJiDp7Yw+ypFiouK6hszmPJ\nY2OH2bfLSwj2dVHu/Bd5Y/d76JTtmCUfwlOX4RfoO2F/rXc4XT0SAX5jYs7ZSwrib0kA78LF/UKQ\nPw4U/JxpGzn6RQwzqVw6V07n1QOoxBGGreHMXvwM7p6Tk9zKZDJz5kQpHt7eZORkfq5hAEd3byLW\ncy9TMmW6eyU+OBDJ0qd/6KiUBXBk91mObaoEYPbaDAqXTlyV5lYsFivv/f4ALTXd+Ed68OR3Fk0Y\no952owuzyUJUTOjkfCkXDxwh+rlfyDgPoq05V1pCf2sJSsHIqBxD4YpnnJ7Pe8FgMHL25EkCgkNI\nTv98Qw/2bnqVvOjjpCUK3OiQ2VqSwOrn/8rJrXrXeyc5s7cOhUpk/pO5TJt7Zy7Ko6Mm3v71Prqu\nDRCa5Mvjf7IAtXp86EXL9XYQBMIjXGL1w4rL1tye0kN7MPadRoEFsyKZohVPOKpL3SuDA8OcP1VG\nWFQ08Umfb1LO7Rt/RXHOeWIiRK41Sxw4l83Kp190vC/LMpteOcSlkiY07iqWbyggI+/O8gIODgzz\n9kv76W8bITY7mHUb5o0LDZFlmWtXWtG7awkK/vxygLn4cvNF2JoH0c7IskzJ3q3IwxcAGVmbQeHS\ndZO21+np6qPyXDlxSSlERH9+h0WSJLHl1f9i1Zw6QoMEqupkzjbOZNG6DU59Nr60h4bydvQ+atZ+\naw7xKXeWvqKnq4+3XzrASK+J1OlRLH9i1rh7JMsyV+qa8fH1xC9g8iryuXiwuJ2tcQk5t6Ghtg6x\n8+dMz7GXDZckmT9ujWPFsz+8zzNzRpZlThzch2WwCpusJjK1mMSU8Zu1q/VXuVZTjs4riPxZs50W\nbjvePs7b3y9FMWzPLGjTG3n8Z/msXD++WsWt/O3639C81YRCUCLJEj5zbPzPthdRKsdKHP/L11/h\n6uF+JLNM2Ax3/vrXTxIacfvqVVcuN3Nk23lCY/xYuKZg0haZ94vXf76L4+9VMzpgITLXj+/99BEC\ngydW/7/KuDZXE3Ox/DShwh9IT7J7spjNMm/uy2TZk392n2fmjCRJHN2zDcF0FaukIzFnEZGx412n\nay9Vc+NaJV7+UeQWTHNalGz85R52/ugSSrNd7JV9R/nG7+YzZ/HEXj4fY7PZ+P7qX9JzWEQURGyy\njdBlCv7zne84rt/T1c+Pv/kGLceHQZCJnOPFP7z8LF7eHre97qVzDZw+VENMajCFSx7svDqyLPPb\nf9nMmR1XsYzaiC0I4Ac/ewKPSTp8eJBw2ZqJOXX0IBkB7xDz0b5nxCCx6dgMFj3ytfs7sVuwWKwc\n3bUZpXQdi+xJRsEKgkNDnPrIskzF2fP0tNXjF5JAZl6O0/P70o8+4MT/XEcpfSSIh4zygzdXkZX/\nyV47BoORv1j2KwzlGgRBwCpbSFrvxT/+7gVHn6arbfzsO+/RdsqA6AYJC/z4+98994n5zcqPV1FR\neoWUvCimF32+h3efNzabjf/94btUHmhGliBpdgjf/68nJu3w4UHCJeRMzJFdmylK2UWg/8dJj2X2\nVyxk3orJD+28FwwGI8f3fIBG7MBo82bq3DX4+juvz2VZ5lzZKQa6mwiNSSc53dkb59//7HUqX+lB\n8VEKCzHawD9tfYaoWGebdSvdnX389YrfY6nSIggCFtFE/p+G8Rf//oSjT/X5q/zqL7fSddaI0hPS\nlgfzw18+e9t9kSzLHN9/nvqKVnJnJ5I9LWnCfg8KRqOJ//7B21w+3o6oEMlaHMmL//LoA78v/Czc\ndyEnImryS0J+nmSnWNn6qvPDfPjEKM/8mRGF8svzDxQXaeGNX3oSFmw3IFt2G/ibfzNiNDtHzdls\nErIkoVAqxi0gNJ0+xJicT8WvaS5hChyflPlmrEaJmK4c9HiOtckWan3LULrbX8t9KpKHnDdz9W7n\nsAVMnCFeGlQQPBiHrxyEWTbS6FaNLWD4Exc9VoOMZlSPTbBhcx9Fof7yJFS2jkBsbzY67JtJWZZp\n0F7AFjB8n2f2xdPc1P6FjPOg2Zo5U6288StnW/Pae0P8/U8lRPHLs9hPS7Dw1kveeHvZ7d/LG4f4\n8a+sSJKzPbTZbMgyKCewk7r2ACItzgn7rrhdxBIw6NTW22t/7e5tFyGkEUjtm45aGNsoGeRhLvuV\no/ioipaqV0eyYarDVsiyTI2+HKuPgYkQB9REDCfhhR9GeYSr2mosvre3NbIsIxsEtGYPrIIFi7sB\nUfnlsTUMKUgeyHfcI1mWqdWeweJ3+xLOX1U6mrs+vdMk8KDZmuXzbLz0Ex+ntv98aYCXXv/y2BmA\n/EwLb/zSBzc3+/P1778c4KXX5XFeMVarDRhva2RZxqMtjHBbnFN7ne4skp+zPejvGwLsOcEAbIMC\nKf3TEYWxa/bTRWtwFYqPyoMqutxJMI6Jz5IscdnzFIK3lYmQe1VEjKTgjhcj8hDX9dXga/pEW2Mb\nEdCY9FhFM5KHCcWXyNZI/UqSBqeiEOzrTEmWqHMvB1/zfZ7ZF88Xsa550OwMwFOrJH7yd87eI3//\n7/288eGX5/9YlmWKCqz84b99USoFZFnmb/61n3e2i+OeTavVhiAwTkCQbBL+bXEEyuFO7VX6Mmze\nRqe24X77b/HH6xqhX0368HSnsTqFZm4E1ztsnb7LjzhzhuN9m2zlnPoomsDx6ytZllH26YgxpKET\n3Bmkn+v6aiTv2z+XsiwjDCvRWPRYFEas7kZExZfnb6To05IyMhVRsM/JKlup8TiF7GW5zzP74rnd\nuuYLy5FjNX15xI87QcA2rk2tAqtZRLZ9Ob6LLMssK1Y5RByA1Ut0vLvVREmZwtFnapaZrz+tISJU\nwevvG/lwF5gtyo/el0hKHCbcu5bmKh+EXns4gmhVf+rfzDYqoJM9bs5tilJQgUmFVWX3LtAbPccZ\nRK3Zi36Ts4ED+2LAfygUX9k+B7XgRrwxm6q+MoRbDpVlWYYREZXRjRhbHHrBLiZ1jLbQ4dmEqP5y\nLEzVIzqHiAP2nCTepiDaRkdcFbw+J8zS+Gf3S80E/wYKBVgkGwJfjv9jSZJ4ep3GIeIAfH29B5v3\ndHO+1v5alv9/9s47MKoy68PPnT6T3nsnIL1D6L1IEZCigth17brf6upa1rq66q67uq6uXWFtIIjS\nIfReU6jplfRkkun9fn8MJgyTBFgloOb5L3duee/N3DPnPe85vyMyeqCLuxdqCPCT8OkyE6u3unCK\n7mME0UVSLx2RmhxKj4Ug1YW6t7ukXv8zi9kKIqh83e+1xC71COIAqPFBtMlwnDV/AbZAD1sjCAIa\nawANDivn43I4STAmEoC7JEIl+NDF3Ifj+gOg8U5rlhnkKC0qEpxdUQs+iKLIGXMRVUFlV00wx88c\n6PGMBEHAzxZMtd38i179v5r55fk1Lq9tEkG4yu7DwQO3a5qDOAB/fMCftelaCkvcL7soupg8xs4d\nC91ZxJ98aWbTDjnCWWdfIrHRdUAj/tIcyrPDkZncwSuJQ47tvHs1Gd32wVfjnnRKbHKPIA6AjxiA\n3SRFVLm3+1k9u4RKBAlKSwAma6PX3ThtTpKMXfHFfYyP4Ee8sTv58mykSk/b4XK6kJjkyC1KUlzd\nUQhKRFGk1JRLU1DtVTPB8jEHNgdxwH3/aksQemvdFRxVJ1cTrbm3V1sShVLu4LnH/JHXSp3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8LK7JXIn5XTb9CvN4unM5DzP+IS7V5lBHIZOJ1XRrXf17f1FrtRUanU1v6V9774DrnUglw1hP79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fSBfyO8/uFWnPY8pD8HVewZbDl6gon5Rbxx8AiaxKyY/SWtCVGti0nxBVCY4ujt7SYlOY0+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\npEfz+akqiErD4PTf5KwoC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from helpers_05_08 import visualize_tree\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.datasets import make_blobs\n", + "\n", + " \n", + "fig, ax = plt.subplots(1, 4, figsize=(16, 3))\n", + "fig.subplots_adjust(left=0.02, right=0.98, wspace=0.1)\n", + "\n", + "X, y = make_blobs(n_samples=300, centers=4,\n", + " random_state=0, cluster_std=1.0)\n", + "\n", + "for axi, depth in zip(ax, range(1, 5)):\n", + " model = DecisionTreeClassifier(max_depth=depth)\n", + " visualize_tree(model, X, y, ax=axi)\n", + " axi.set_title('depth = {0}'.format(depth))\n", + "\n", + "fig.savefig('figures/05.08-decision-tree-levels.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Decision Tree Overfitting" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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j1VdgMZslvH0kCau2fUfmZERET+9x9xYc+TAOlBUdw45Mff9rX28RAS5F0HXrx+20ACIi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\nbqSlpSI+2ooHn30Ek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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = DecisionTreeClassifier()\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "visualize_tree(model, X[::2], y[::2], boundaries=False, ax=ax[0])\n", + "visualize_tree(model, X[1::2], y[1::2], boundaries=False, ax=ax[1])\n", + "\n", + "fig.savefig('figures/05.08-decision-tree-overfitting.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Principal Component Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Principal Components Rotation" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def draw_vector(v0, v1, ax=None):\n", + " ax = ax or plt.gca()\n", + " arrowprops=dict(arrowstyle='->',\n", + " linewidth=2,\n", + " shrinkA=0, shrinkB=0)\n", + " ax.annotate('', v1, v0, arrowprops=arrowprops)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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xEQVCuBWMZJ2IwHL9XsxmFQQhHpcu1SI9PYn9TNQGAxtEREQBcv78OWzYsB46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\nNmAZ8c5GJyrD3NeuHwcOtMRc+dC9yiGv23Nd701tbZMikXx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.RandomState(1)\n", + "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n", + "pca = PCA(n_components=2, whiten=True)\n", + "pca.fit(X)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n", + "\n", + "# plot data\n", + "ax[0].scatter(X[:, 0], X[:, 1], alpha=0.2)\n", + "for length, vector in zip(pca.explained_variance_, pca.components_):\n", + " v = vector * 3 * np.sqrt(length)\n", + " draw_vector(pca.mean_, pca.mean_ + v, ax=ax[0])\n", + "ax[0].axis('equal');\n", + "ax[0].set(xlabel='x', ylabel='y', title='input')\n", + "\n", + "# plot principal components\n", + "X_pca = pca.transform(X)\n", + "ax[1].scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.2)\n", + "draw_vector([0, 0], [0, 3], ax=ax[1])\n", + "draw_vector([0, 0], [3, 0], ax=ax[1])\n", + "ax[1].axis('equal')\n", + "ax[1].set(xlabel='component 1', ylabel='component 2',\n", + " title='principal components',\n", + " xlim=(-5, 5), ylim=(-3, 3.1))\n", + "\n", + "fig.savefig('figures/05.09-PCA-rotation.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Digits Pixel Components" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def plot_pca_components(x, coefficients=None, mean=0, components=None,\n", + " imshape=(8, 8), n_components=8, fontsize=12,\n", + " show_mean=True):\n", + " if coefficients is None:\n", + " coefficients = x\n", + " \n", + " if components is None:\n", + " components = np.eye(len(coefficients), len(x))\n", + " \n", + " mean = np.zeros_like(x) + mean\n", + " \n", + "\n", + " fig = plt.figure(figsize=(1.2 * (5 + n_components), 1.2 * 2))\n", + " g = plt.GridSpec(2, 4 + bool(show_mean) + n_components, hspace=0.3)\n", + "\n", + " def show(i, j, x, title=None):\n", + " ax = fig.add_subplot(g[i, j], xticks=[], yticks=[])\n", + " ax.imshow(x.reshape(imshape), interpolation='nearest')\n", + " if title:\n", + " ax.set_title(title, fontsize=fontsize)\n", + "\n", + " show(slice(2), slice(2), x, \"True\")\n", + " \n", + " approx = mean.copy()\n", + " \n", + " counter = 2\n", + " if show_mean:\n", + " show(0, 2, np.zeros_like(x) + mean, r'$\\mu$')\n", + " show(1, 2, approx, r'$1 \\cdot \\mu$')\n", + " counter += 1\n", + "\n", + " for i in range(n_components):\n", + " approx = approx + coefficients[i] * components[i]\n", + " show(0, i + counter, components[i], r'$c_{0}$'.format(i + 1))\n", + " show(1, i + counter, approx,\n", + " r\"${0:.2f} \\cdot c_{1}$\".format(coefficients[i], i + 1))\n", + " if show_mean or i > 0:\n", + " plt.gca().text(0, 1.05, '$+$', ha='right', va='bottom',\n", + " transform=plt.gca().transAxes, fontsize=fontsize)\n", + "\n", + " show(slice(2), slice(-2, None), approx, \"Approx\")\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "\n", + "digits = load_digits()\n", + "sns.set_style('white')\n", + "\n", + "fig = plot_pca_components(digits.data[10],\n", + " show_mean=False)\n", + "\n", + "fig.savefig('figures/05.09-digits-pixel-components.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Digits PCA Components" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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89thjAIDXX38d/fr1g7+/v+b7nd3n3MVV+cjNzcXzzz+P+Ph4VKtWDb169UJU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\n4oCOiIiIiIjIoP4/QFJA2Z7Z0loAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA(n_components=8)\n", + "Xproj = pca.fit_transform(digits.data)\n", + "sns.set_style('white')\n", + "fig = plot_pca_components(digits.data[10], Xproj[10],\n", + " pca.mean_, pca.components_)\n", + "\n", + "fig.savefig('figures/05.09-digits-pca-components.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Manifold Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### LLE vs MDS Linkages" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def make_hello(N=1000, rseed=42):\n", + " # Make a plot with \"HELLO\" text; save as png\n", + " fig, ax = plt.subplots(figsize=(4, 1))\n", + " fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n", + " ax.axis('off')\n", + " ax.text(0.5, 0.4, 'HELLO', va='center', ha='center', weight='bold', size=85)\n", + " fig.savefig('hello.png')\n", + " plt.close(fig)\n", + " \n", + " # Open this PNG and draw random points from it\n", + " from matplotlib.image import imread\n", + " data = imread('hello.png')[::-1, :, 0].T\n", + " rng = np.random.RandomState(rseed)\n", + " X = rng.rand(4 * N, 2)\n", + " i, j = (X * data.shape).astype(int).T\n", + " mask = (data[i, j] < 1)\n", + " X = X[mask]\n", + " X[:, 0] *= (data.shape[0] / data.shape[1])\n", + " X = X[:N]\n", + " return X[np.argsort(X[:, 0])]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def make_hello_s_curve(X):\n", + " t = (X[:, 0] - 2) * 0.75 * np.pi\n", + " x = np.sin(t)\n", + " y = X[:, 1]\n", + " z = np.sign(t) * (np.cos(t) - 1)\n", + " return np.vstack((x, y, z)).T\n", + "\n", + "X = make_hello(1000)\n", + "XS = make_hello_s_curve(X)\n", + "colorize = dict(c=X[:, 0], cmap=plt.cm.get_cmap('rainbow', 5))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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p527nPX1+6V/2VnjM9Caw2WztvgOampqyvxCKyL5P+act5Z/2lH9kT1P+ka5S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iFr88UXsZaW6Lsul5eX6R88iHT2LHsyGdbPnGFPGNKs10nU69TOniX8hV9AH2fgvKtwy6Fj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trp0iFAKICYr5kkngikajRmXU6XQayyKnG+byxTw3gZW7mYuu/yI0JapQWYVY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\nERERUUJsOCIiIiIiIiIiooT+P2wTBDSvVgX3AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d.art3d import Line3DCollection\n", + "from sklearn.neighbors import NearestNeighbors\n", + "\n", + "# construct lines for MDS\n", + "rng = np.random.RandomState(42)\n", + "ind = rng.permutation(len(X))\n", + "lines_MDS = [(XS[i], XS[j]) for i in ind[:100] for j in ind[100:200]]\n", + "\n", + "# construct lines for LLE\n", + "nbrs = NearestNeighbors(n_neighbors=100).fit(XS).kneighbors(XS[ind[:100]])[1]\n", + "lines_LLE = [(XS[ind[i]], XS[j]) for i in range(100) for j in nbrs[i]]\n", + "titles = ['MDS Linkages', 'LLE Linkages (100 NN)']\n", + "\n", + "# plot the results\n", + "fig, ax = plt.subplots(1, 2, figsize=(16, 6),\n", + " subplot_kw=dict(projection='3d', axisbg='none'))\n", + "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0, wspace=0)\n", + "\n", + "for axi, title, lines in zip(ax, titles, [lines_MDS, lines_LLE]):\n", + " axi.scatter3D(XS[:, 0], XS[:, 1], XS[:, 2], **colorize);\n", + " axi.add_collection(Line3DCollection(lines, lw=1, color='black',\n", + " alpha=0.05))\n", + " axi.view_init(elev=10, azim=-80)\n", + " axi.set_title(title, size=18)\n", + "\n", + "fig.savefig('figures/05.10-LLE-vs-MDS.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "## K-Means" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "### Expectation-Maximization\n", + "\n", + "[Figure Context](05.11-K-Means.ipynb#K-Means-Algorithm:-Expectation-Maximization)\n", + "\n", + "The following figure shows a visual depiction of the Expectation-Maximization approach to K Means:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + 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9l1dffZWysjJWrFjBkiVLAA4qn8FNN93EVVddxd13382FF17IihUrWL58eW6/\nKIrccMMNPPjgg/h8Pr7xjW+wdetWFixYwPnnnz9sEsYjyU4F/6OPPmLEiBFDEjBOmTKF5557jqVL\nlzJ+/Hg2bNjAokWLEEXxgO7bbbfdRmdnJ0888QR1dXWDFI6qqiqmTJnCsmXLWLhwISeeeCI7duxg\n4cKFQ9r3eDx88sknzJgxY0gYxiWXXMJ///d/c/XVV3PzzTfjdrv53e9+R19f36DymMcKDc1tSOqu\nVbF0KkU6ncHpctLQ2sn0SQMu8Lqu09MbpLW+joyrGJs8MPR58/LoC7eSMiS0zwwCo0dUUNfWTSwW\np3ZHlHQ6i6rIOFSVcDxFRVEZsZRGFpFYKITkzCJhI5mIk9QjpNIpbB4b2+sNOoJRbKoPweym0Otk\nRNVAgknR7qAn0EtZackXuv5YLDbkpQYDBs09Q48mTpzIWWedxQ033MCtt97KzJkz6e/v5+2332br\n1q25UCqXy0UgEKCtrY3KykouvfRS7r//fn7xi19QVFTE448/zvbt26muriYQCAAwb948fvnLXxKN\nRnn66af5wQ9+8IWu63DR0NqJ4tx1X+KJOIamozqdNLW2M6Z6oJRVOp1G0zQ2btiAs6Q6d7zX5yUW\n6CeR1jAMA1EUGV1ZRu+WZqL9fXT0hsmmU7hdTgzDIG2a2POKSScihKNxIn3dOPIK0bIayUSMbCpN\nNp1kbLGLFR+vJYuNTU2dyOiUFvgo+WyyFEtlDslKmCUvB0dzZy+yMnBfTNMkFosiiiJZUaQ/HMkl\ngI1GYxhahg2bt+Er2+UN4FIdhNMmsfjAqpZkkxhRUsjKdZto2L6FlAamYTC2ZgKaYMO0KeDwkEkm\niQSjRPt6sPuKSOsm8WiYTCZDMBPHP7KIFWvW43C52bijFYcNqkqL8Pl8AEQSX7wijiUrFhYHz4Es\nLAkIR7Ss7VeZiRMnIooijz76KN///vcJBoM8//zz9PX1HXDOBhjIqXDFFVewePFi6uvrueiii3A6\nnXz88cf87ne/44QTTuCaa67JHb/773766afz4osvct9993HBBRewcuXKQbqP3+9n5MiR/OpXvyKR\nSFBWVsbf//53Ojs7cwnYvV4vqVSK5cuXM3Xq1FwZz6uuuoobbriBsrIy3nrrLV566SXuv//+Q3Dn\ndnHddddxxRVXcO+993Leeefx6aef5vI1fF7vBsvYcIiRJOmABpsj4T61NyH54Q9/yP/8z/+wePFi\nLr74Yu550V31AAAgAElEQVS8807S6TTz5s0DYPTo0SxYsIB58+axbt06Lr744r22t/u2k08+maef\nfpqnnnqKv/71r0yZMoX//M//HPRgXHbZZaiqypIlS3j55ZcpKiriiiuu4Mc//vE++z1cOZzP8xDs\n7zu773e73Vx99dW88MILfPrpp7z22muD9l999dX09vaycOFC0uk0I0eO5Gc/+xmvv/4669at2+85\n/vGPfyAIAjfeeOOQY+bNm8d3v/tdmpubWbZsGb/5zW+orKzkyiuvpKGhYZDV9qabbuKpp57i448/\nZsWKFYPO4XK5WLp0KY888ggPPPAAmqYxffp0li5dOmz1ioO5V0eCWDKNKEpkMmnqGlro6Y8g21VU\nu0SJU2L6pPE0trRR1x7A5nCREBRaWlopKsjHl+cD08RBBsXhoi8UweN2gQBCKoxit5NJplE8PjRd\npz+RJZXR2NHaAbqGt6gctaiCZLCLWLAH06biLijB51TJLymmO5HFLttQnE4A+tMaQlsbVZWV6JqG\nw3HgL7698fDDD/Pwww8P2f6jH/0oV6lld37961/z7LPP8uyzz3Lfffdht9uZNWsWL774IiUlA4aP\nc845h5deeomLLrqI5cuXc9ddd/Hoo49y6623kk6nOeGEE1iyZMmguL8LL7yQa6+9FsMw+P73v3/U\nGq4SaQ1kmVg0yvamFsKJLA6HA1UWyZR4GVM9go1b6+gMJbA7XcQ0iY6GRirLSnGoKjabDSndj6+o\njEBfiJKiAlwuFT0WwGa3YyRB8eaTTCcxsJHKGGzasg1BFMgrrsRZVEUi2Eki0I2WV4LT46W4wIfg\n8tKXiFNa6MWhDshLRyiBTeqjoKAAURAOieu4JS8HRyKjoSoQ6O2lobWDRNZEVRw47SJ5doMTpk5m\nzcYthNMmumESyeiEmpqprCzHZpNxOVUivY3IpRVkMhk+XbuWdz5cQ3sc7IXjc96Ca7c0IGlJVNVJ\nJJZEEEX8xeU4CirI9vfQ39NFtqQK1aFSVVJMTHCSSvZTXO7Llcysb+9hoqLgcDiwSZasWFh82QiC\ngMthZ38agJZJke8/NsNWDzUHO6/c3/HV1dU88sgjLFiwgGuuuYbCwkLOPPPMnGEzEAhQVFQ0rA6x\nJ3fccQeTJ0/mpZde4uc//zmJRIKqqiquv/565s6dOyhXwu5tnXbaafzkJz/hxRdf5LXXXmP27Nk8\n/PDDuVw3MFCJ59FHH+Wxxx4jHA4zatQoHnvssVyliQsuuIDXXnuNW265hVtuuYUrrriCJUuW5L4T\ni8UYOXIk8+bNy+lk+7pnu/dvf7rbnDlzeOSRR1i4cCGvvfYaEydO5Pbbb2fevHnD5s87EARzL5px\nW1sb3/jGN1i+fDmVlZXDHWIxDKFQiObm5mFrm+4kmUxSXV19zMbIW1jsycGOF4dqfKmtayCQNPnH\nh6vo121IsoJpGAh6lvI8lVMmj6Q/LaB8psDV1tWjS056erpR0EjpBg6Hi5bWVoJ93ZQVFpCIhimt\nHMmW+kZSNg+SZCej6QR7u1EEE8lXhKwl8RSUgGhDEXXsWoJRI6owTANFNElkDUpKSmlra6WsrBS7\nMhBWoyUiTJswDiET54xZ0/Z1accE7e3tfPOb3+T//u//ho3v3htHSl4+3rCFSFbg/z5YRVZyIcl2\ndF3DZuqMLXEzbVQ5ccGB9FmM5tpN2xEdTnq7u1AkyBoCkk1mR309WipOfp6XVDxKWVU167Y2gOoD\n0UYinSbS14MiidjzirCbGg5fPqIo4ZHBSISpGT+ORCqBKkFWVCjMz6ehoZ4xY8fmXvxSNsGEcaNx\ni1lmTN63MfBY4MuQl0M5d/n76vWEYik+XL8NU3EjSTb0bBZZ0JkxqoiqUj9pyY0gCCTiMba2BjAQ\nifT1IACS3U4mq7F12zY6mnYQUcuRfcV7PV8y0IqsxRk5pgbZ7QMMPDYTm5ZmzJjRxKIRVJuIpLpx\nupy0tbYyarfKQi4hS1VFGSPyHIwbPfILXfuR5vPKCljz10OBdQ8/H3UNTbSGM/tcUHSSYdbUCV9i\nrywsDpx33nmHkSNHMm7cuNy2ZcuWcf/997Nq1aohOXZg/+OFlbPhEOP3+/fp3WCaJpIkWYYGC4tD\nwOiqcmo3bqAxECUYTRDoC9IfiZI1BRJZg0+3NOUMDQAuRQEBFMVOwpCwO1S2N7WSkFzkV4yjT3eQ\nzRtJU28c0xRJayZZXSOZjGNz5ZNVvET7AgR7A6Bn0VIxEuEgXl8eiVSKPFXGFEQSukRzVy+S4iTU\n04XxWT6UVEajYVstTlkk1B8edC2aptHZ1U0w1M+xxNFcoWRPKksKWbFyNYG4TjASI9AXJB6Po5mQ\n0gU21LfmDA0ATocNm802kJDP7kTTDepaOjDdRbjLxxAR3CS9FbQGE2i6RtoAXddIJ5PYfcVkbU5C\nfQGC3e2Iho6WjBMNBvCXlJFMxilwO0lpEE0bNHf2YJMV4v29mMbAPe2PRGlrqEOxSUNCsVKpFO2d\nXcRicY4ljiV5Kcv38OHqT+nPmARDYXqDIZKZNIg2+tMmtQ2dOcOQ6nQhGQMeS9FEGk9+If39ERo7\ng/QEw0RcI/ZpaABQi6rIyB5atq/HzKbJJuOkwn34isvR0gmK/V4iaY3eWJrOnhCmrpFNJeGze9oT\n6CXU1YogkMvBtJNoNEZHVzfpdPrw3KzDwLEkKxYWAGNHjcRhpPcaJqElokweN+qA2opEo9Q3tbCj\nsZlE4vOVZLewOFjee+89rrrqKl5//XXWrFnDSy+9xBNPPMG//Mu/DGtoOBCsMIrDwMSJE6mtrUXX\n9UEeDslkEkmSDrjMSygUIhAI5GJ1i4qKjrkKDRYWhxOHw0FHVxem7AZJAgQ0UyedSOAsK6Sts4Xj\nTDOnEFSUl9K/vZ7Orm7i6SzJjE4woWNmOvE4VbwFxYQCnaSSaYK9fTgLHcQSKURBQJfAtKkYcgZZ\nEohH+impqMArqpTmOdFNyAoSmp5Fy2Zwuj3ohoHXIVOe76Kzu5dIqJcZp84hIcqs2daC3ykxY/IE\ntu5opK0vgig70DUNRWxm2nGjyNtHremjhaMxvGZvFObn0d7di+EsBlECTNK6jpJJYUqF9PaF2L0g\nWWVZMdsa2+kO9GGINsLxFOFEFtmM4PZ4kRUHqViCtq4W0oYNu5ghk8lgAgIipuKBTAZJ9ZBNRMgv\nLqHI5sDnUTG0NBlTJKtlEaQBr5xUNkuB143HbaOts4dsOkXlqGn0ZaB93XaqS3yMqx7Bp7Xb6I1m\nsCkOtOYevIrIjEnjhy2ZdbRxLMmL16USCMcRfG5Eu4JhGCTTGnkOnUgihUPYpVAIgkB5sZ+6tm4C\nkSjR+ma6QhHiyQydPX24qwfnz9HTSdKRIIo3H0nZNU9wFlYQrutEMnTy/D6K7U4K/G7i4T6SmoGu\nadgcNhAENEGissBDWtNpbutElSWKKkfRGs7Q0LmRyaMr8Pu8rK2tI541kewKemMnxV4n0yaOO+p/\ni6O9fxYWeyIIArOnT6K2roGuUARDtCOIAmY2Q4HHwcRpNajqvpPIR6IxNtU1EcuaucWS+q468lSJ\naTVjDknZdwuLvfHTn/6Uxx9/nCeeeIK+vj6Ki4u57LLLuO666z53m5ax4TAgSRJTpkyhv7+fnp6e\nnLHgQEMndF0fZKwQRRHTNGlubqatrY2JEydaJXMsLBiobe/yl5CfMNARMA1wOBQEAUKhMA5pcKya\nzWZj7MhK1m6qI5o1iMTT6JIdlzefvlCI/lgzmCbRZBJcfnQTsNnRENDSKWTFgSQKuL1FxBNRov39\niDJE7TZqG1tx5RXjUB1k4jFi8ThepwOlogRFlskYMH7MqFycn111Es5qvPfRagyHF7tzIJml7bP9\nn9TWc9aJ047qMm8VFRWDssUf7WxvbMFfVIJb9pDOZBFEcCgKhqETSyRRhMErqW6XmwKPg3gqS1pL\n0B9NIaouRLuPzp5enE4XqUScFE4MG8iiDcMmkM2kCG/9BFG2Yxo6ScVBb3sjFeXluMaPItbaQlco\nhjO/EFWWScSCeDwevC4HCc2kUJZBsjNh/Bj4THwdLjctvTG6uj/BUPNwuAYMCzabjQywdvN2Zk+f\nzNHMsSYvG+uaKasow5BdpNJZbJKE3S6TyaTJaDpe++DVy6LCQrZtq0MzRLp7e4lnJYIdTahlu9xR\nDV2j7cNXiDRtJpuIIDu9eKsnUXnKdxClgSmZo2Q09VvWM3biVIoKnXzyyccYNicOdwKbKJAKtZCf\nl4fP6SAYTeByKjicLmpGVQADyZcVt5dNDe0oUjuoXpTPwottNg/BjE5tXQOTxh9dSZh351iTFQuL\nnYiiyOTjxjLRMOgPh9F0Hb/PNyjGf2/E4nE+rq3HprpRdtPQHC4XKeCjdVs49YRJx4Rh2eLYRFVV\n7rnnHu65555D1ubRO4v9CpCXl8f48eOZMGEC48ePP+DQidraWmRZHpL3QVVVZFmmtrb2cHTXwuKY\nozcUoaioAJuZRbHbUdUBQ0MylaKju5ux5QVk4rtKYJqmSXd3L4lUGrvLh2Czowk2+qMJwrE4oUiU\nvlAQXVLQsjqappFJJVDsDmx2BZuRgUwKDUBRSaXihLOwvbkNhysPxeVBsCmIqgtN08gmwoiSxNat\nW8hzyoysGhzLZrPZ2NbSg00eZuJgd9La3nl4b+DXjEg8RVlJEVoqiqoqOD7LTp1KpWluaOCECSPJ\npHbVwzYMg3A0QTKRwOEtxLTJZE2JYDhGPKPT3dNFJJEgresg2Ekl4vTWrSMW6MQ3djreUVPxjZmO\ns3ICnrGz6NVV3v3nKnbs2IHLX4TicGPYHNgUlUQsiqilSaU02lpbKct3U1RYOKj/NrvCttaeYQ1Q\n4bRBPJ4Yst3i82MIEnluN1omhVN1YLfLGIZBKpOlrXEHMyaMRtN2lZ7UdZ2MDol4DLvLj4FIIpUZ\n5LnQ9uEr9NWuIJuIAJBNROirXUHbh6/kjlG8+cTjcbK6TmNflEzWxOH1IztcmLID2a7QF+hClW1E\n43FCgR5GlRfidO4KGQNIagYtPYPDtWBgQaQrFB2y3cLC4tAhiiL5fj/FhYU5Q0MqlWJbfSObtzfQ\n2NI2JNxiS0MrNnXvruqS08v2hpbD2m8Li0ON5dnwJbO/0IhQKIRhGHt1HxQEAV3X6e/vt/I+WHzt\nEUWR0kI/4ViKnmA/sXSW/miMTDoLyTBBrZpUWxsjq8qobWynuTNAfWMzYVQygT5km4xgmCRTKUTF\nRSre/1mpO4GspiFLNoxUCtnIgGDgcMg4ZS82RSEVT6EnojjcHrz5VaSzWYx0jFgqjWmaFOc5qSgs\npNwjU1Y5FsM5fAhUao/Y6p3YbAOJBi0OHZI0IC+prEFPqJ94KkN/NEkmncYlpKjrjuKT+skvKmZL\nQxstXb1sb2omjkqkL4QoSmiGSSaTQbCrA94uNidgkErEiHc34R45CdkxfMZmuycfuyefrq46XH09\nuPL8pLMaGDqjK0ooyXNQXeykvKiSjM055PvZbJbsXqom22Q70VgMl2vo9yw+H4rdTnmRH1MUCYaj\nRBNpIrEk6XSKCp+dtTvaKXTZEGUH25s7aQ8E2dLQTEpyIYsZRIxBmen1dJJI0+ZhzxVp2ox+0rdz\nhglBFJCNDKImUDZqHKlEjGS4j7RuIKEzadwY/KrImPICFNU5qATwTlKpFMJwhkwgqxm58q0WFhaH\nF8Mw+LR2O33RNMpnFWT0eIL6jg1Ul+QzdtQIstkswVgKh2vfHhDd4RiTdgsP3ZNsdsAAeiCeFBYW\nXwaWseFL4kBDIwKBwH7jsVRVpaenZ1hjg5XnweLrRHVFKW19OxhTWYyq2NnW1IokiKiqk6oRJRh2\nN6FMho3vfkjVyFGg+jBkJ1pWIpvRyaYzpJKJAcOCy4+pa2TiGmqeA4e3gGwsCIZJFgFZEnGaGjav\nD5tdocxXjEsuJWuaePP8NLW0IjvdeP1eNE0jlk7R2hXkhFFljB89ko3NAeRh6jznuYZ/3rPZDD53\nweG+hV8rSvxeopqAaRg47BJbG1sRTROXS2XiqLFodg8t0QibV36CM78Ym6cQwxZA02UysdiA10y8\nDx0Bm+rC0DXSyTDOvBISPa24qmqGGBqGi823l45j69ZPmPONC3CIItlshkAkQToe4RszJuJQZLri\nQ43OkiThcQz/2tYzafJ8R3+Oj2OJQq8TUXEhiiKyBNFYAlEU8HtUxk+cSEaSWN/ciks0iOkikisP\nU+7BwE401DeQVyGbybWXjgRzHg17kk1ESEeDOJWBUIg8r5vK6tEko2EcikxHR5T84lJUIJGM094b\nJq3oXHT6THpCYRLD5FJ02GWkvRgzHbJkGRosLL4kVq/fTFJw5AwNMDCeS04PTb0xoIX8PA+Cbf/h\nEVl9IAHs7sYE0zT58OO11LUGSOkmdpvIyNJCKov9jB9dbT3rFkcUy9hwgHxRJX5naMSecVaqqmKa\nJrW1tUyZMgVd1w9oUNB1fcj/q1atIhwOY7fbEUURr9dLIpGw8jxYfGVxuZyMKc2jvjPEuBFl9IWj\nGGICVTYpLSunt7eX3mCY9rDJ9rXbKCgoRHV76Wptx1T9CIKCKivYNZ1oKEA22ofN4SKbiCLa7Kie\nfDKRXqK97XjsMmJJMal0FoehYffYcao+HKqT3v5+7KoLuygQDPZiCBKyJGCk03T29lGQ78fV1kVq\nj5XEbCLGydNqaO6LY3cMDpuS9TRlJUVf9i39SjOyqoJgeBuG140oigQiCWQliddpx+X20tnZTXdv\nkEB/AluwHZ/Xi9PppLerD8mTj2DouHwKmqYR7u3EyCSR7CpaMoKWTeNx7lL29xebr5QfR9u29biL\nq9AFCadig1SS1o5OzjvzFNo/2YTNOdh4IGTinDBhDN2JLDbbrommYRgUeBQrcdghZtL40Xy0diNl\nxfmYpkkolkFNpSnK86AbOu1dATp7+gmGIrjz/DjtEk6Xh1BPH7KnEMHIIom7DEaKNx/Z6R3W4CA7\nvSiefABM08CrKhjpBH6vi0A0hi8/n0wyTjieAEHCJoqEklH6QiHGjqhkzZZm7M5dhi7DMCjxOREE\ngeQeq6DZTIbRxfmH8c5ZWHx16AuFaOvqJaPpyJJEZWkBhflDn594PEFjeyeZjIZNkqgoLaDA76ez\nu4eobsNuH35uLysKDV19FPq9GMZeXNd2xxw8j4hEo/zPG8tJ2DzYFQ/IkDJNVm1tY+32Fgo/3cKs\nKccxdmTFkPBsC4svA8vYsB8ONlnjcEYJ4IBDI/ZVNnN3dj+nruu88cYb2O12lM9WTnf2MZVKUVhY\nSGtrK3PmzLG8HCy+cowdNYLykiI2bqnDSMaoKPLj9ngJBkNEMyZZA0ybgmEKCLKDZCiIIsvEUkkQ\nBExTw8hmMbQMzoJK7A4HhmEOeCcEe3A53SQj3eR5FY4r99PY3o3XnY/H46XMJ4Pqpqu7E0lyIQom\neXn5pDNpCt0qBV6FmOBk7ebtnHT8JLbsaKQ3HEfTDbwuhXETBypOONUu6tt7SGRNRAz8LoVp0yZY\n2dgPA9MnH0dfKMSqTzchaUkqSwtQVSedXd2kBRlEibQGituNIdrRDQ2baJJNxjBMMLIZwEQSRJT8\nCmS7TLS7DbV45KDz7IzN38nO2HyAEaf/GzbVTaCjmYrjpqFl0hR5nZQWVNIR06lvbuXkaRPY2tBC\nKDZQ8izfozJh+iQURcG2o4H23ggZQ0ASDErz3Ew+bjwWhxZJkjht1vF0dPUQ6upAyCSorqhAtttp\nam1HtDsRRBtpU8AtyWiSjJlNI2GiJaPohoFdUUn0deAsKEdSVLzVkwbJxU681ZNyni+ZniYmzqxh\nfEUhadFOc8cWXHkFpHQdvz8fwdTwO2RGVk1hXUM3ZcXFzJwwkh0tHUTiaWySSGmem+PGTMQ0TTZs\n3UFvJIlmCigSVBf7GV1d9WXfTguLYwpd1/l4Qy2RrIDiUAERNAjs6MQptXPStAnYbDZM02Td5u0E\nYmkUpwuwgQHddR04pTYEDOz2fZcMlFU3ff2RQRVu9obPac/pAJlMhlff+YC0w4/9swSzmpaloyuA\nJtgQBZE0IO1ooyMUp9zvZErNuH20bmFx6LGMDfvhYDwS9maU6OjooLq6ep/n2RkaUVRURHNz8z6t\nj8lkclB7K1euxGaz5QwNhmHQ3d2NIAjYbDaCwSBer5eNGzfi9/stLweLrxxOp8rM4yexoaGNjDKw\nGhxNpJBkB7JdRkvGsSkDq77OvHwi8Rg+j494JALiQDUCtWwEyXSadKQf2elGwEDQ0mjxNHl5RRiK\ngq+glAnufMLhMEUFeSgOEb9HpVNViKZT9EUS2BQVv9NOgVehqKgYsilCCY10Or3X7O+V5aVUlpeS\nTqex2WzW83mYKfD7mXPCVHb0xJBUJ4amkcjoyIodQRAQBNA1DUEQMHQTjy8PTVKIhkLITjdaMkpe\nSSXxWIR0IkY2FcNVuUvZP9DY/IwOejJGoVelKM+Fx+vFJmh0BqPUjHUwfdLwBoSasaM5bsxA7ghZ\nli0X2cNMeWkxJ0wcS2ski2y3E4tGMcUBzxLD0HOLBJIooWlZ8gqKyBgCsXCI/IrRtG/7FGdBOQCV\np3wHYFiPFxhYKPCKGUaNm0CBR6Y/msQtG+iJIFndhl3PUFLgo7jAj92uoCgCrV0BTpw2kVlTfMP2\nf/qk4zAMg2w2i91ut4yYFhYHwJqNW0mJKopj8PNidzjImiar1m/hlBlTWF+7nVBW+MzQMPg4Dajd\ntImJkwdXCtJ1nZ6eABldQxQESooKSaZFKgrzaO1PIdmGV8+0TIaxFbs8Huub24hkBCR14HjTMGnr\n7EG0O3MKXiSZpj8qMqKygkDCYNO2eiYfd/RWorH46mEZG/bBwSRrbG1t3atRwm6309jYyNixYwft\ni0QihEIhzM9cHH0+H+PHj6etrS23bU9M00SSpFy+hlAoRCgUGpSFuru7G1mWc9/f6SmRSqVy1Sym\nTJny+W+MhcVRiCRJTBhZwurGIIrqRNMNZBnsigOHpCMrMgYg2+y4nW7S2RQ20cRhF9GQkWQJSXQQ\n093Y7A4cIqgFBaSCHSheHw6Ph1giSXlZCapTJdDTjeC1M3VUOdP/5Qw+XL+NllASv78ASbYNPH+m\nidfpQJRtxBKJ/bowKsPkdLA4PPjzfJS4JHo0nUw2AzvLDjoUFNFEkQfGT7vLg92E3kAPit2G0y6Q\nNGRkh4yuKWRNJ6ng4KohBxqbb7fLzJoyPlfuVNc0/PluUhltr++AnQiCYMnLl0j1iEpcq9ajAelM\nBumzMBZVEnDKIrI08FvJihOfz0drcwOqw4kqmhSXVxNqqcU9YiKiZGPE6f+GftK3SUeDKJ5duTxM\n00QK1HHq6afT3dlOvq2IkyaOoqbCx5q6DlDc2BU70meymk0lKSmvJJXNDtvn3RFF0ZIXC4sDpD8c\nIZI2sKt7n/8ndJHWtna6Iykcrr17LhiySl9fHwUFAzmYWtvaCEQS2BxuRHHAMyKwo5VYnsy/nnMm\n0c3b6UtnhugTyUQcKR0jmVTpDQYpzM+no7eflG7y/7P3pjGSpPl93hN3RN5n3Xf1Nd0997G7s+Ry\nScpLSqIs0vZ+sGHJhCFYggHbkKEPAmzZhu0PMiDIlmH7gyQIMEzKMAyaNC3ZJJfa5R4zu3MfPX1W\nddddmVl5Z0Zm3BH+kFXZlV1V3T3XcnomH2AwqMyIyIjozDfe93/8frLrIooi3a4J8gO/c1GiY3lY\n/R7xRJJSs8sl30c+I6AxZsxnzfib9hAeV6xxfX19ZHH/IJIkIQgCnU6HVCpFEARsbGwgCMLI8RuN\nBteuXePixYvcvn17WCVxRK1Wo91us7CwwJ07dygWi1SrVVzXxTRNYFD1AIwMUrIs0+/3SSaTj3Sz\nGAtMjnmS+YVXXqTW/AFb9RaBbSJEPq7tkI7H6Nh9bNtCVnUUAmRdYaKY4/z8NNt7ewRSjHazjmt6\nRF5ApjiFIApYvoeiqGiKgh8MxNYSiTjx+DJFqc83XxhkLGRZ5vf+7F1k9TDjGfjIoc/c4gKu1SOT\nGov3fdH4N379l/ndP/wTGl6E12kQaRoEIUkFHMvEsm0USSLy+mTTKTKZNBcWZri1do9Ii9HEodTq\nIYTeSHDgcXvzZYFhoMFzXTK6RD6XAac3zj5/wZAkid/61Vf5v/7V60iBS7/dRxQENF1DaTexzSaW\nqKNpCn6/xWQxz2QuRyETY+ugRaOa5fq1N4gvXEXW40iaMRSDBPD6XRL9fX7jr/xFMrkC/W6TX3jh\nMiuLC4Nqxdr3KLvSsUCDzUwuiaIqxMaFLWPGfKbslA5QjYc7+6i6ztvX75CbXnjodvl0ilqzTT6f\nZ2d3l7oVoh7T4xEEAUGSMTITvP3hDV557ir75QN2KjVM24Uoolw+QNV1itOz7JkBG7USuPd459p1\nds0QJZZCiCJarSbxZHokASmKEn5wX6hW1uNs7eyxujza+heGIVs7e9S7PcIIYqrM6sJY52HMp2cc\nbHgIjyvWWKvVWF5ePvP9VCqF4zg0m01SqRQbGxvouj4ymXRdl8nJSRRF4fbt2zz99NO0Wi0ODg7w\nPI+trS0SiQSrq4PSpyiK2NjY4M0330SWZdLp9PA4giDQbDbJZDLDzzheoXGam8XH1aYYM+aLyl/9\nzrfZ2t3ng5trvHZji0IhRy53jkajQbnWxGrWeOn589zd3qU4N0s6ncJxLEwn4ML8JQq7GQ6aHfwo\nwvd80vkiBD6NRoOklEFRagiEiEQsLt53i1hdXuRXOl2ubTcQRZFEKk4hmyUIAyaSxtiG6guIoij8\ntd/8ddY3t3n93Ygb+20KxQIXVlfYL5epNdtEvRYvPHOOmxu7LKwsYBg6xXyGQJB4ZuVF3rt9jwNd\nZKu8RWxyCeCxe/NXJxIkpMHYnM3nSCUTeK7L6uRYvO+LyESxwG//5ndY39zlj197mwMLctkMqyuL\n7OxWysMAACAASURBVO5XaLXaxKOA1aVF7uwccOHcIoIo0un2mTy/zNdeuMof/dH3qFfW8Th8nkYR\nsgipuMEv/ep3sJwQr1ojtHtkkklgUJXwb/6lX+X3/viH2ESIosDE1DS6ruFaPZ6++PDFzpgxYz4e\nrh/wOEskxz3d7eU4kxN5atUKvudRbfdR4w8kHqKIuCJixAzatk2t0WBmaoKZqYmBy8Q715hZWhlZ\njwTAnVKTA0cmCCzi6mEbtaTSsX28oEM6OficMAxQxBDjsM1DFEUcf1SIslZv8N6dTSQ9gSQN5iqW\nA3vv32GxmOTSuZVHXueYMWfxpQk2fB4Z+ccVa3xUQCKRSFCv1wnDkE6nc9gTfD/QEEURoigSjw8G\nguOVB5lMhmvXrrGysnIi01Wr1cjlctTrdWzbHlZJHAnWtFqt4T1wXXfkfjzoZvG42hRjxnzREQSB\npflZZFmi40U0ujaebZM0NOITCYKsjhA4/Iff/Yv89MYmAS7np3PU2yZyLMZMMY/dM3FR0WMRXiDT\n7DkIkUet3aFjDXQVpvMpDkyLdz+6xfNXLiIIAi8+c4VYbIP9ehs/FHH6HaYzCa6OBZm+sEiSxMXV\nZVzPB3WHVt/BtS2K6ThpOSAqxElqAr/9l3+JaztVwsjl0lyBVt9FM3SmcimE0KW0szNy3Ef15gu9\nOn/nP/63kXSDWscmYqDfsDSRZXlh7ud9G8Y8Jrquc/XSOZqdLjd2qvTckMD1mM0nySsBERHnZ/M8\nc36OrXqfIPC4vFDADERUI8Gzzz1Lx3Lxg4hUwqB8UMPxQgxdZW1jG82IoSkyKxMp3rqxzrMXlpma\nKCDLMn/521/no7VNWn2X0PcQ3ICry9PkTqlSHDPmq4DjOKxt7lJtm7h+gCyK5JIxVhemSR0G6z4J\nmiJjOo/eTlUevYwSRZHLK7Nsbd5D1kfPKfR95Mjn3MogYKjqOjul2tDtYnt3H0dQkR9YZ2zuVpC0\nGKl0mna7RRSGCKKIOPhALNdHdxw0TSMKfGancsO1ShRFyMeSh/2+xftrO6jxk5ovejzBbstB29ph\neXEsKjvmk/HEBxs+z4z8cbHGB/UVstksqVQKy7LInWKB8yDz8/NsbGxQLpeHQQUYBAFEUSSbzbK/\nv094aI3n+z4vvfTSmboRpmkShiGJRIJ+v49pmiP9mEfbO46DKIrouk7qWBn3gw4aj6tNcVrrxZgx\nX0RanR4L8wvMhSGObVOuHNAU0xi5GHgOGw2LpCIQz+bRYnHmgoDd3V3iCYEgZ+BJCrF4iv1SGT9U\nKBZnqbc6xHWNbDaL1aoxN3OZpiuwvrHN+ZVFBEHg8vkVLq2GuK47tKEd88WnZ7usLC8SBAGObbO5\nu4+QmUBRVVwh4KDnk1Yi4oVpVFXFdR22N7d5di7NNauJeHmFd7f2kbMDIcCH9uaHIS9Myrzy0gvA\n4DnmeR6apo3bJ54QnBAunV/F9zz6/R73dkrEirNIskzdg4wTklIFkvkZZFmm1+uxs7XNc3Np7uw3\niSVTSIpCu1Ejk82jqQqdnsX01ASiIBCGFnoqx4frOxRyGWRZJhGP8/XnruB5HmEYjjUYxnylMXs9\n3ri2hhxLIuoJjpqSOwG8cX2DqyvTTE98MvvohZkJ9m9sohkxzJ5JuVLHtB3CEBRFJBPXKWQyvHT1\nAjd36ujx+JnH8n2PC4uzZJIJbu036PQcgjBCkQTy2STJeIzdUgXLHWivJESfp1YX0HWd/VoTWRlt\nY+j1LJxwsIDLpVM0UxnMZplEdgpNlbGCEEmS6Vk2siyRFD0W5u+3bLl9k+XLV4Z/r2/tosTO1pxQ\nVJXNSoOlhbnx82nMJ+KJDzZ8nhn5bDbL1tYWa2trwwX7EdVqlUqlwtzcHKurq490kHAch1dffZU7\nd+7Q6/WGQYVCoUCj0aBerw9VoqMoYn9/n2vXriHL8qm6EZ1OZ3jNuq4jiiKWZSGK4nCRI8syrVYL\nwzB47rnnhvs+6GbxuNoUD7ZejBnzRUZVJCLbRxRFPM+n6UTDHkxZGlhZSZMzTOggSBCEAk+9cpXp\nyQl+8OYHOJFEo9Ek8lzmjRgbWzuAjO/ahI7JwtwMzU6X6akJSo0O549VGT44Xoz54qPKMk44CMQ2\nWm18OYZyGJSVRBHjUPNmJW9g2h6iqvLir36dZCJO4a3reEFE/E++x0/WS0iZ6eFxH+zNDwOfGXub\n//G//+/ubyNJ4za1JwxVlokYaG6U603kxKByMAxDFFkjkc3jmm1WCnHapkUhn+Dbz/4qtWaTlQOT\nVqvFfvmA55+5SqfbZWO3TCQqBFYPw1BJZQbHU+NJNrb3OL9yv7963JI1Zgy8d+Mucuz06gU1Fufa\n+i6FbOYT/V5SySRZXWTz4IDdehfViCPH7gf3Wm5IZ2ODX37xEtVWl44fnplYUAKHuekp2t0+s1OT\nzB57r1ypcmOzhKLrIA7m9F3f54fv3eLiXBHbCxEfeDS0u13kw7YJSZZYmJ2mXS0RhDaaBK12G0WL\n0e+1WMlpvPL880MxyCAImM7ER+5JrdNHMh5uzemLKtV6nYlC4XFv4ZgxQ57oYMPPIyMvCAKKopxo\npxBFcSj8mM1mH9tBIpPJDPUVALa2tpAk6YQqrK7rKIrChx9+SLFYHAYnUqkUiURi5LqLxSK7u7tM\nTk7iui7r6+sEQTCs9nj11VdHyqeOu1nA42tTPNh6MWbMF5nl+Vk23/oILZGi1myh6oNgYBj4ZFOD\nLIQsK1i+yyuXR22gEoaGGCnMzhrUTQdRM5iadGhbPhMZg0wmB8fGBD98tDf2mC82C9NF3ru7j6ob\ntHsWojr4jniOzcT8JAB6IoHthTz71GhbjKEIGIkkv/VX/3XCf/k9rq/dpeGrqNn7QYfAc9C7+zy1\nUOBv/Ad/Y0TAa8yTx1QuyU7LQZIkun0XLXFYteLaTBQG/+6iFkNVZJ67fP/7MimJ3Nw+IJ/P4wcR\nB6ZHPJHAshxCYGVxHlGS8KweMJiDuONn75gxI1RrdRxkHhZGUONJ7m7vcmn1bE21h3H53BJv3vhX\niMroQtxzXWIynL9yhXdvrPG1Zy/zzrWbtOxoOM+AQeWyGjq88sxTCILA3FSR0q2tYdKj0WhRaVuD\nQMMhQeBTSMTR40nu7NW5t76OmMgSRiBLIoXMyeCKpqk8tbqArOq0uibz2RjlgyoT88u88tz9CgbH\n6lOIK1y9dHFkfz8IeVSoW1EU+n37cW/dmDEjPNHBhs87I99sNgFYWlqi1+vRbreHi/6JiQni8TiW\nZdFqtbh8+fJIO8cRlmUhSRKXL18GRlszjlohHgw02LZNPp9ne3ubTqeDruskEomBPU61Sr1eH8mC\nCYLA9PQ0hUKBdrvN1atXKZfLJBIJstnsMJDw4LkcXePu7u4weHHUHnIa48zbmCcJRVF45tw8H93d\nwfN9kFU82yaf1Jgs3hd2DIKTgYLzizO8dWMTNRYnritYERi6Sr9vkckM8hKeY1OYHfTW+3aPn775\nLp4gESGQNDTOL82STDw8WzDmi0Mhn2O5Y3Kv3CAMQwgjAtdmtpAmFrs/poenBJaWZya4vd9A1XSe\nefoKV59/gQ/efoONrW1U3UAUBTJJhb/+N/8Wkizj9Rr86KdvEUqDtol80uDCysKJCr0xX1zOLy/S\nu36HcnvgBBX6PpHvsjw7MUwESLKM647aUmqaxmwuwUHPp5jPUWpuIWsGIiGFQgHx8DmbOMyiWlaP\nju3yo581iSQFSRQpZuJcWFkalzSP+cpSqbdQHtFGJAgCrW7/E3/Gxm6Jq1eu0OtZ1JotvCBEFkUK\nswUS8UHAoNMPMXs9Xn72Cs1Wm+3SAY7nI4si0/N5picnhsfLpFPEFTgaEcqNDpKqYvX6dMweYRQR\n2l3Ovfwcvb7F+naFWqvHZGoQ7A6A7VqX0OkRiSrq4VrD7ZsU5xZRFJWJQg5YIIoirGYFPbTpmCa6\novDKpVUyh8lO3/dZ39ym0jS5traBrBkkDY3piSKx+MlAuOe6xONjZ7oxn4wnOtjweWfkjwcz4vH4\niNbCEceDGccdJI6EKpeWlkYCHcerII63QhzpLkRRRL/fx7IsMpkMU1NTHBwckDhctKiqShRFQ40G\nVVWxbZtisThyjouLi1QqFXzfZ2NjA1EUyeVyLC4uIknSiNZFLpejWq2iKArVapWDgwOWl5dHggsP\ntl6MGfMkMDVRYKKQI/7O+2zU+0zMzKKo93MhURSRjp8MWGZSKV56apH1rX2mUgZ3tnaZzmZJqQPR\n2NDzmMjEsWyLD2/cIgRkI0XkO0xmk8zOTPPG9bu8/NQK6dQnF6ka8/Pl3PICi3PT+FaXXqRSyE+O\nPGM816U4dbKMdGF2GlmS2Nw/YCqpslWu8otfe4Erl59CjqXwHJvFySytdpvNnT1ERUPSYoiBx/xU\nHrQYr713nW+99Mw4qPuEIAgCz1+9SK/Xx2zXkWIJ8tkMHFv/O32TucuLJ/a9evEcdze22au1KGgR\ndbPBM+dmafQ8iCICx2J+aZbNrR0ODg5Q4mkiSUYJPVYWZnA7Hu0PrvPKc1d/jlc8ZswXh0G18aOD\nbeGjNd4H24XhYQIwIplMIEkSLdMGxSAeH/x3Gnoszn6lOmi7yKTJZk6KLB7nhcvn+b//9Cc0nYjN\nvRq27yMbCSRRwrdNZqdmuLm5z0GlwvzyKnosiWW2MRKD4zqOQ7drUatsMre4QDKVJqXLKMpooLrX\n7WKoKh3bI9KSWFHE2zfvMZVNsjQ7zZsf3UE0kghanIniBB1fwALubO+zMJk7oUWn4lHM5xkz5pPw\nRAcbTnOLOE3I8Xjbwlmc5mbxSYIZR60SD2N6epo333yTSqVCPB7HNE10XScIAsIwZGJignq9jmma\n5HI5Wq0W1WoVSZIIw4HwnO/7uK5LsVhEVdUT1Qi+79NsNllcXByptDgSzgyCAF3Xh8GOer1OFEXo\nuj601Tx37hxweuvFmDFPCqIo8rUXn8N/5xrBA8rRkd3lwpXTNV2y6TQvPzMYO77jv8D65jbNbp+d\nvTJKTENTJe5tbTMzNUmlYyOrKmgaVdNBq9UoFAqsbe/x0tVLn/s1jvnsUBSFX/7Gy7z2wc2R8T8M\nQxKSz9QZgmNHVmUwUPe+u71Ho9OlVKkSKyTxrA61js1EsUjXlxBEAdDZLNWJxWKoWoL1zW0ufsKS\n3zF/PsTjMb79ted5f31vZO3juS7zhdSZ/eKrywusLg8U6OvNJjulKgeNJo1Wl+RkmmppBz9SyBUn\nsIfF4jprW3s8+9R52o5ArdEYqtaPGfNVImFoVA+rdY9otVoc1JuYlkcURaiKxHxaG87rTyMMQ26s\nbVBpdvEYtEaLkU8haeD5PuJjyD08bkBje6/E7e0yhZl5rJ1tdkv7iEYSudtjeiLH/MIcoiTRbLYJ\n9Azl/f3BefdNdvcr9BwXPZYmlkggpwvsV1toBxW+8fyVkc/pmV2qlRLL5y8+0B6hU7VCfvKHf8LF\ny1eHlVHTEwVam7uIqoFsJNgs1Uglk8iHY5dn25yf+WRCm2PGwBMebDjekhAEARsbGwiCMNJasbe3\nh+d5rK6unjrYPMzNYn9/n+Xl5UcGHB43E3X8s1ZXV7Ftm83NTSRJotfrsbCwQDKZpNFoEIvFaLfb\nrK+vUywW6ff71Ot1NE3DMAxSqcEkplarkclkRgZTy7LY2tri/PnzJ87NMAy63S47Ozsj7RTz8/Ps\n7OwQhuFQqLLT6aAoyonWizFjnjREUeTVF65y+94W9U6fMIzIJQ0uXroy0sYUhiF37m1SbQ+2ySZ0\nLq4soGna0Gf6G88Psolr97Yw0ln2y9VBoOEQWdWotTqDtqbeuMfxScQwdF595hJrm7u0ehaSKDKV\niXN+eXRSZ1k2a5s7NE0bSRQopGNcWFkiFjN4+tK5kW3f+egWhWmFta3dw0DDADWepHxQY2lhjpZp\n/Vyub8xnSzGf40VJ5O5Omb7toikSS1NZFudmRrZrttvc3SnRs1xUWWIqn2Z5YY58Nkv+AavuH731\nIZEW56O1TTgWsBDUGLVajWKxSLnWHAcbxnwlWZyf5W7pA6TYING2t1/ioOugqDpyTBsIQ7sOSirH\nT965xjdfuHqiZTkMQ15/9yM82UCOJUcWRJ0A1ja2WL1w6cR+x/F9j3Ti0e0F+5UDbu/VUOOD81VV\nhWx+gnh6sK/nu4RRhAj0HZcwgo1Kg0xM49zyIl6zRyQbtLomvX6PYi5DMZMgm5lnY6+C73tkkini\nqkhodVg+f/HU8+j3Lfpyir1SifnZQUuooiqszk1xb7dEJKkosSSlSoW52VncvsnqVO7EWDZmzMfh\niQ42HG9J2NjYIAxDer0enU4HGLQ+xGIxcrncma4UD3OzKBQKrK2tcfHiyR/tUQWFbdvMzMzQbDbJ\nZh8+4Dz4WZlMZmiNF0UR29vbLCwsEIYhnU4HURQRRRFZlocVD5Zl4fs+jUaD2dlZXnrpJUzTZHNz\nc9j6kMlkEAThzCBIt9tF0zQ6nc6wIkIURRYXF4faFJqm0Wq1ePnll8cVDWO+FEiSxOXjlhEPEEXR\ncOIhHlpNNT34yXs3+MUXrpwYIxxv4HQhiQJEIRzrn/aCQarjQW/sj8OHH13np2+/jx+EFLNpfvM3\nfn3c0/9zJBYzePby+TPfdxyH1z+4iRxLgRYjAEpmQOO963zjhasn+uk9PwQRJEGEBzJh3mF13LiF\n4skll8mQe8izst5o8u6dHdRYHDQFF7hb7dKz7nL14uqJ7d0gRAFEQeS4Sogky9iOSxRFyOPvy5iv\nKKIocnF+ilt7NWzH5+5elb4f4rgBkQBCGDKZ0jH0eSJR4P2ba7z09FMjx7izsTV43p/xnJ6YnuHu\n5g4Xz41Wm1mWRalSxbRdQrdPTDhPEARoqoLZ6yPLErPTU8Mghed5vPfRbf70tbe4V6pT7/Ro912C\nMEKIAlRJIKZKXLlylUuXLhKEEY2OiRbPELhNGs0G8XSBxOEzJQxDDFVksphlYXaKMJwh6LX4hRcu\nIkkSP3j7+pn3rdE20fRDIcljr8djBk+fX6HebNHp9Qlti+mExOqVk0GaMWM+Lk/8N+jy5cu89tpr\n7O7ukkwmhyWLvu/TarUoFotEUXSqK8Wj3CzS6TS7u7uYpjnUTDheQaFpGrquk0wm2draGgYTWq0W\nYTgQe1pdHUwi7t69S6lUQtM0stks8Xh8GCAxDGPonFGv12k2myQSCWRZRlVVTNPEMIxhVcPR9R0N\nkInEoL9senqaTCbDnTt3HiqcGYYhuq7TbDZPtF8c130Iw3AcaBjzlWF3v4wjqCcCBHIsxdrmLlcu\njAYq4oZGpddjopCnvLaFYtwXVdJkcRAgTJ/tvX0aURTxO//nH/DHP/2AmzWPMJ4fjA3eHv/4//kx\nX7s4x9/6a//WMCMx5s+PtY2dQaDhGKIo0g9kSpXqsKXiCF2VcXzIpxN0Kq1hNUwUReiKhOe6zMyN\ne2K/rKxt7w8CDcdQFJW9hsk52z7xzNYVmQBIJTSa/XBYDeN7Lol0AqdvsnRx3KI15qvL/Ow0kiTx\nv/7+H1H3dDQjhqQGEAakUzqJdIrrd7e4tDxP3XVwHAftmKhkpdFB1M4Wcc5mc5T2Sniui3I4Xtfq\ndbYrTdRYgkiMWFhYZLdu8rMbm7Q6HYrFCWzHI3TfICnDRD7LD15/gx9f30IoLCPKBUgVUFKMOGn0\nAp8fv/0hG/fWKc7Mo6an8F2bXCKJZXtIxv3xQRRFOr1BNd3R376s43oetmkiqg8TzhxEut0gHAre\nDxEgn8uQz2WQfWvc0jfmM+OTp92+IEiShGmazM/PD8UTBUEgnU4zOzuLoijs7OwMhRyP8zhuFpcu\nXaJSqWBZg/LWjY0NdF1HFEWCIGB+fp4gCNjZ2WFtbY0bN24MgwTlcpnf/d3f5c/+7M9ot9vE43Fk\nWaZarfKTn/wEXdeZmpoiCAJ83x9aaSqKwr1796jX6wiCMGyfOM6DWhXHr+9RgpjHbTAfdW/HjPmq\nUG93hz2KD9IyTypaL87NILh9JFlifjKHZ/UhivDtPrl0Ei2weerc4z+swzDkb/+Xf59/+P9d54ad\nJkoU7qvaKxoNfYb/dzPg3/97/4i33/3gk13kmM+M1hktMoqiUm22T7y+ujCD2++RTqfIxVU81wHA\n73fJpRJMJuQTAYoxXx7O+r7o8QT75YMTry/PFPFsm9nJCVQ8Am+gYS/7NoYic3Gu+Mj5y5gxX3Y0\nTUGNp5mfzJPSRIopg6WZyYFQoyAgqAabu/tosQR7x35nvu9j+48WW1hZXSGBg9fv0O122a40EWUV\n0XdYnMzi2DZ7TZMD06Mnxik3OhjJFK6oc7Pu8A/+6e/w410Paeoionx2ZaIoyRizF6mIed5+4w3u\n3fkI17ExTZNe/xRHjSg8XkyJrOm0Ot1BJdQDIhKBH7BbqrC+tcdBrUGr1R6x7j4NQ30MsYoxYx6T\nJ76yodlsEgQBgiDg+z4wmLQf2ZMJgjBsrzgulAiP52YhSRLLy8tMT0+zvr4OMGJ9CbC+vo5pmsRi\nMXzfp9/vDzUXisUi7Xabfr/P9PTAe9vzPAzDoNlsMjk5STabxXEc+v0+3W4XURTJZDIYhoGqqjiO\nQ6lUYnJycthycSTm+OD1HJ3zg4EE0zTpdDqEYYht23ie91Cf97H7xJivGtJDxgJZOvmeKIp8/ZlL\nXLtzj7gisDKVodtuMjOb4fL5ZSbPEBI8i//s7/8P/Nk+iPrZ1RCCIFDXpvm7/9Pv8k/+iwzLSyeV\n7sf8fJBEgbPCuqd9l5KJBM+fn+PW5j7FdJyEKmCZJisXZ3nq3BLpMyyHx3w5OGt8CYIAWTmpdD87\nPYkfBGzsV5mfyGF224Run6eeXubCytI40DBmDHBnY4d6q4MrOkOhRk3tkkkmhhXJphPieR5RNLrY\nf1TCDQbP3Evnlkmlknz/tTeZLWSIxwwSiThBEHBvr0zbCggEBUmAvuPg2Bb1Tp83fvRnOIWnkKTH\nX2rJWoz4uZcorb/F5NQURmqKvf19nEDEiMeJGTpRFJJJGCPClL7voWsquWwGwd+CwwRlqVKl3Oii\n6AYIEloyy972LmLgcHF5fmjheRzHtrh6frR60nEcumYPXVOH93XMmMfliQ82lMtlDg4OMAxjRPW5\n0+nQbreZnJxEVVXa7faJH8hpi/LTONJBSKVSJJNJOp0OtVqNarU6tKyUZXlYlWCaA99tQRAGPd2H\n1Rf2Yalkt9tF13Vc1x22aGiaRr/fJ5vN4rouoihSqVSGLRaiKHLv3j1yuRyKorCwsHAiUHJUiXBc\nODMMwxPCj4ZhsLOzQ7FYPFlGxenuE6e5dTxKo+LT7DdmzM+buakipdvbaMbow9f3PCYKp9tXxmIG\nX3vuCr7vD39jDyMMQ/ZKFYIgYHZ6cjhmvfXOu/zJ9RJialSEKXAsnE4DLZVD0u4vSBraNP/z//Z7\n/IO/959+kksd8xkwkU2y2bBO9LPa/R4Ly6dXtBTyOX4hn8M7zFKf5VRwhOd51Gu7iJJMoTCLKA7a\ncxqNCgC53OQw+Dzuq/1ik0/G6IYnXw+dHnPT506+waB6anFuBsdxkGX5kdWG/X6PTruCqsXJ5SYB\nDjWeyiiKRjZbxPM8JEl6LKetMWO+yPT7Fj+7dpdAVJC02NB5IQQO2n08zyObzaLoOqVSiZfOvTjc\nV5ZlYqr8oHzOCcTQJZVKIooiih5Di3xKB1W8/Qq1Rh0vkjG9COXw+SxrMbZ3S9y5dRM7Nf+xAg1H\nCKJEbPl5brz5E6yrL+O4Dl3bZ1JSB+3gCZ254gLqsTFfCgbWlIIgMJlJUHdCao0WBx17pMVTEAWy\nyRi+I3B3t8KlpVk07f68JfB9JuLKULC20Wyxtr1Ps+ciKipR4GNIsDCVZ2n+7HZO3/e5u7VDrd0j\nCCMUSWC6kGVhdno89nwFeeJnJ+vr6+Tzebrd7sjETVEUoiiiUqkwPT1Nv99nYmK0RPX4ovwsjmf4\nXddla2trxPHCcRxM08TzvGHlAQwqCY7O5yiw0Gq1mJqaot/v0+v1AGg0GszNzQ0rM44moaIokkql\nsG0bQRDY398fWngahkGpVOKpp5469TyPC2fu7OwgSdLIRDSKImZnZxEE4YQApnVoJXTkPvEwt47d\n3V0uX778sV0+HrbfmDF/XmQzaRaLSTYPOuiHvdWubZM1RJYW5h667+Ms9PbLB9zY3ENQY0iSxJ29\n62QNiZ4b8E//9z8gTE4PnfPCwGf3td+ns3kdr99BiaVILV1h7pu/hXg4efnZnT16vd6wwuphtNs1\neu3bCHQAiVCYYGrm6fFv8FOwsjhPo3OTlu2jHj4PrJ7JymSaVPL04NQRjwoyAJRLtxCDDSbyKkEQ\nUtm9Sd/NEVMb5LPQM3t89M4msXiWVKqAF2TwwgyaYgEBgpRnYnKVbreF1W+TSk8Qiz2ehkituoVr\nbSJEPSJUBGWWqelLZ+objXk0Vy8s89P3b+BLOvLh/MTtd3l6de6Rk+8H2ygfJIoi9nbeIaZWmcxq\n2LbH7qaC6ydJ6DVyWZmDgyo76wdkclNoagrHz4GQQJFNQEJWJykU52k0Kvi+Qy4381jf0yiKKJdu\nEXl7CLhEQhzVWKJQHFddjfl8ef/WXeR4imTYo2X7SMeew7Ks0DQdYrqNZujoQjBorTjGbD41CBif\n8T0Pw5DJdGI4f711dxNLTqCoOpEcUe/5mF5ArXrA7HSRRDKDAJjdLtvVFurU6dWNZmmD5vq7BK6F\npOpkz71IYno0QC0pGk68gKbrqPEErXoVu33A/NIqoiiyvbPD08uvAIMAwWwxPRyfr15c5bV3PqRc\nbyFpo8mTwPOYzsbIpqcoVZts7+5zfnVpUBnuWswWUkPnrYNanQ/W91BjcYzE0RikEQHr5Ta24wy3\nPU670+WtG+tIegJRHqyvBoK4JlulD/nGc5fHYtdfMZ7oYEOz2USWZeLxOO12e6jXcIQgCAiCqHG5\nOwAAIABJREFUQK/XO5Gph9FF+WmTqAcz/Nvb2+i6PtzWtm06nc4wQLCzs8Pi4ugD1rIsLMui3+/T\nbrcplUqYpjk8ZjKZpNVq0e/3kWV56CTRarVoNpusrKwgSRLtdhvbtocVAkEQ0Gg0SCaTp1YiXL58\nmT/90z9la2tr2C5xJKAZRdHQCrRSqdDtdonH40iSxNLS0shxHubWEUXRJ3L5eNh+Y8b8eXJxZYnZ\niR5b+xWiKGJqbvozsZazLItrG/vo8ful8ooR50cf3mBlcZ7tahcheb/iZ/e136d+46fDv71+Z/j3\nwre+C4BpTPFf/8P/hf/q7/xHGMbZJdWdTh2//zbTEyowmDBEUZ3d7ddZWP7FT31tX1UEQeDlZy5T\nqzco15oIgsDiyjkSiY8nCnoa9foeSX2TmDH495JliUSsh939Y3KTLxKGYFvrXL2k0O9XEZQc9fp1\nDLlFJvUMqqrRbGzwoz/5xywtFUjEYlR3U0jaU8zMv/TQxW31YJOYcov8hAoMvleet8P+rsPs/HOf\n+tq+qqiqyrdefpbd/TLtnoUsiaxevvpYC/pHUd6/wVS+iSQNvi+6rqBKFSL3HQqFF2i32sTUPa5e\nUmi2dklmnqa0/zM0NWSi8CwCAjvb7/LDd+5y6dIkkqSzt5FHiz/D9OzDba/3dt5nMldDliUG35eA\nXv8mtSrjgMOYz41Ot4vpDcR1hVyO7s4OoRhDFO8H0GVVo9HpkHFMXvmlF04cY2VpgUb3Bh2PEwGH\nMAxRvD5XnhnMU6/fuUsgx1BUjWq9iWm7mG6IiwxGms39GpNZl1wuy73bN1CKJxfhoeey+f1/Tmf7\nJlHgDV+v33qL1MJTLP3Kv4Oo3J8zG1Or7N69yZWXfxFN1zEr2wO9uDAgnUqzX6kxlc8wkVC4uLIE\nDCoK9soVFCGkXtmj74EXhEiiwEQ2xeLcNFOTAx+KQj5Ps7LHfFpFU+NMT54f0XS7cW8XNXZ6y4Si\naWzVTGYmuyPB9TAMefvGOkrsZFugLMsgJ3n7o9u8+sJ4/v9V4okONlSrVSYmJqhWq8zMzLC/v48o\niiO9jEEQsLe3x3e/+91Tj3H58uWRDPwRD2b4m80mmUyGTqeDoihUq9Vh+epRdvDIgvPSpUuYpkmz\nOZiAqqqKoig0Go1hhsL3fXRdx/O8of2lbdtD3QlN0ygUCsM2j2Qyieu61Go10uk0U1NT+L5PrVYj\nlUqNnGe5XGZ9fX1ox3mk5dBqtYjFYly5cmV4zpOTkwiCwIULF07cm0e5dRw5aHxcl4+z9hsz5otA\nIhE/4Tzxabm3U0KPj2a7Dw6qqMks9XYX03bh8O3Asehsnm5d1dm8TvC130DSDERJ5s72Hu/eWOOb\nL5794Dbbd5kuDsRz6/UGvu+QTudJGT1uXn+NTLZILr/4yOzpmNMp5HMU8p8+IHUc19ohVxgN1Hba\nZRYX4jQ7ZWw7YKo4eHzHYgqbO+tMFgIMQ6PZKaHpGczW6/ziKyKVeptkQsZz7+HYPUp76plBgyiK\n8Oy7xFIqQRBQq9WAiHy+iGutsbUhoRsJCsWFcVXMJ0AQBOZnp0cs5z4TgtKJfw/PrTFREDC7Tfq9\nCtMTg8VUJiWyvnGNc0sSgR/R6dQIPBtVfJ+Xn5VwfAtdD+j3KviWQ/UgRnFi6dSPtW0bXS4hyzq2\n7dBqNZAkmUKhwPb6W3iehaImyednxlUxYz5TSgc1NCNGIe2y0xiIxFfKZcx+H0kdVNN6rk3k97l6\n4RJLiyd/dYIg8NLTl7m3uc1erU3fG/Q56bLAVDbBxdWnh2LwpaZJIZPko80ynb5DEIZIIghBiEiI\nnsjQ6poIUYDphQinjI+b3//ntDc+PPF6FHi0Nz5k8/uw8mu/PXJ+PWewJjA0HSGdw2vsk07FySfy\nmM0DXvj6ZfKH8/wPb61T61r03YD3rq/RDuOoushMJoluGASeS9fsMTlxP8FqxBKsnHJv9ssHBLL2\n0EWiHotzb6fEc5fvz222dvYQtIcH3HseNFvtE5UmY768PNHBhiAISCQS1Ot1RFFkfn5+KIR4RCqV\nIpfLkc+fbikmSRJPP/00rVaLg4ODYeXAgxn+arVKPp/HNE22t7dxXRcYtFEEQYAsyxiGQbvdptvt\n4jjOsH3Btm3q9Tq5XI54PI7v+2xubjI5OUksFiORSGDbNvl8nlgshqZplMtlFhYWcBwH2x6oWB99\nfiwWY29vDxgERY4CDdeuXRsGV2KxGJZlHYriRCPikhsbG5w7d79H9Cz3isdx6zhywXjwXn2S/caM\n+bLiBwEPmv+4vocoyQShO/K602ng9Tuchtfv4HQbxLRBr2Sjb/GHP/ghhmjzwvMvA9Dv9/n+9/4Z\nobdHFAV02nVeeOFl0skexTwoisTt2x8giD6TU8+SydrUa+uE0nkmp85/9hc/5mMj4J58TfAO/+8j\nCO7I4s11GhhG4fAvm1ZjjXw2BERqB7vIYot0EsrlNe5ubiIrBSanTrYGeZ6HpthUq21Cf5dCTh5k\nuK69QTppkM5AIp7hYO82evI5stmpz+Pyx3xcIpejKpQjBDwURca0HQQcjsYfQRQhbCAIBWRFwu+Z\nmN27zE0pRGHE2t01FubjFDIh2zsbbGzsEHv1b5/artVuVyhkVPb3NtDkOpN5Favv8P47PyabzVBM\npwmCiL2tW+QmvkbsjCzpmDEflyOthVwuQ6Nr0vdDpqanCcMQs9MmCAJi2Ryiq/KN56+ceRxBEFhd\nXmR1mTO1dHb2Ssh6nLBjsbG9C0YCSVTwvZB6rYyg6OixJK7noShJeo7Pg6F7s7RBZ/vmQ6+ps30T\ns7xJYmpp+FooyOD1kRSNiVySuWwcLZHE8UJcJ+L9WxtMFxrUmh1CLQmyyr2NLRK5AmarB6JEud5m\npiihGzr9wGdze5flwwCDIp8eNG52TWT50VVXPXv0WVVtm0jSw1skVCPGXqU2DjZ8hXiigw1HAo/z\n8/NDEcRE4r4C7ZHQ4vz8o/MImUzmoQvfowV5r9ej0+kM2wEEQaDT6QwDH+l0elih0Ov1EEVxmOkX\nRZF+v48gCExNTWHb9lDwUVXVQ+GvBplMZqgvoWnaMOPoOPd9go+u6UgHYWtri/Pnzw81Ho5EJR/U\nrjh6r9PpkDpUPz8rQ/U4bh3H782n3W/MmC8rCV2lbjsjv7W4YdBo9IhpMilN4ii8oKVyKLHUqQEH\nJZZCSw6y6J7VpViIoyVlPlr/KXJU5vpH3yep/Ixf+UYVXb9fDvn62z/gnb0czz7zTebnisxNWaiq\nQKnRIJtboJDX6Zp3abdzpNOnB2bH/PwIozgwap8ZRRpBYCGIMQZTbZcoDPF9jzAYPMotq0urYeHa\n+2SMgGotJJ3yScZV8DsszopoaoXa3u/guX+B2fkXR4IWrWaJ7Y2PSOl7JFMpIEe1Wufy+Qiz18Zz\nfaSUxNSERPngPYLUd8YVDl8AIiEOD3ijRGj0rT6GkcKxG0BAEAQ4rosoakCE2W1wUCkR08o4FnR7\nDsW8jKb0kYU+CzOQzZbYuP3PKM78GpNT9ysgwzCk3Tpg7+6PyKW7KLE0oNJoHPDcFZnd/ToREaoq\nMzsFe+V3iS196+d5W8Z8iSlk0uzUS6i6zrmFOXYrFRqdPgES8USS0PdJ6DLnF+ZJpx6uoXPEWS1N\nnu9Ta7S4sVVmam6Ber1Bs9MmEhWUeIZuu41pmsQVgSBwCZWTDg/N9XdHWidOIwo8mmvvjAQbhFgG\nXItkKoEqKXRcKGoJNA0ESUKKpXh3bZd2z+XKhST75QOUWBJVEJAaXRAlZM2g1mwxZ0whSTLNXoeF\nw8RqIXW2K90nIQyjB/MqpxKEp6jljvnS8kRLghaLRSzLQhRFFhcXmZiYQBCEYRBgYmKCiYkJDMPg\nzp073Lx5kzt37tBsNj/2Z0mSxM7ODr7vMz09jeu69Ho9wjAknU5jGAb9fp9yuczm5iaNRmOoqyCK\nIoIg0O12iaKIIAiGwYlUKoXjOERRRKfTQdd15ufnTwjORVFEo9EgnU6PVA2IoojneciyzO7uLs1m\nc/j+kRXnUYChf+jVq+v68B5YlnVCOPP4NT/uvfks9hsz5svK8uI8OL2R13K5HJHZJJ3QmM4YhMGg\nXFLSDFJLp2diUktXhq4UYnOT2YVpHLtHLGnw0x/+N3z3L/wL/tIv14eBBhhkbr75ssy/+5sd6uV/\nyes/fYd4XMZxI+Kx+9slEypmZxPXdR/LpWfM50cqe456Y3Rimi/McmvNIwxUyuUm77/3Flv3fsad\n2+8hyzab965j9/aYLCbIZjWCwCbwKgRBBGEHPwhoNC0cV2JpTkQTNqiU7w6Pv7v9Lin9Frlkj3NL\nAsVsl73dTQK/iySJCKKOKHSH208UFMqlu8MqvzF/fmixFcze6L+DYUyyX5Zpt3tsbde4eeMNdjbf\n4N7aDXzPZH/vJla/wdxMEkOTIOxh9w+ICFHkPrbt0+5aIKgUs30U1uh0BvOGIAjY3fwxK3MtskmT\nhZmIVKzO5sY2qmJBFJFKJel2Dobnk0qYNBr1YavomDGfhkI+hyYcBtgEmJua5Jnzy5yfybNYTHF1\nZZbFqQK5VJy3rt3kx+98xOvvXefW3Y1HfgejKBrqrVmWhW1ZbOyUiASJKAoJgFQqja6IGIrERDFH\nLhnHs/u898ZPiTjZMhS41mNd14ntBAHfdZFCH8cNSWXvJ0XjmoIgCLR7NpFisL1bpms5wwByOmkQ\nhoN75AaDhCWArCeoVmt4lsm5xdPFrydzGdzDquqHkY6NVlSpZ1RKHCeKIgzt02vVjHlyeKIrGx4U\neIzH4yOlfkEQsLa2BvCxHBGO2zUeuUa0Wi329vaQZRnLsjAMYyi8CANNhe3tbdrtNpIkoSgKqVQK\nXdexLAvTNIcBg1QqRSwWGw5oMzMzZDIZms3mQKHadYnFYrTbbWRZxvM8bNsmFosNqxFgULkxMTFB\nrVbDMAxc18W27eE2uq7T6XSIomhoyXl0zsf1Js6q6Pi4bh2fdr8xY76siKLIN559io/WNmh0LcIo\nQhcjVmYKfO+Hr9NTMvTu3SS5NNBemPvmbwGc6kYBEPoeczmbqVyDnBZy+2e/w9/9m+Yj+6K//arI\nH//gA17/GczOTeL7d3E9BcOYpG816XRsDKWM42oIyjxT0xcferwxnw+JRJowfInSwS1EWkSItDoi\nyAu8994fMlss4/kW9abG6soi9WaHvXIT18tz4WKMXi9Bvdng4MBjYbaHaQYkjAhZilBVj3Jpja7V\nRNF2Cf2vE0R5cokKmqaRzEzR6ZkYmsvctMS1G1UsS0fTDFzvNrYdYRgFer0yPeseujSL48XRYufI\nFx7u2jLm8yFfmKdeg9LBPSTBJIxkag0d14lTKf0BE5kqPcvFtuKsriyyW+qyuVFlYXGV4mQC0zS4\nvV4iZkgk5CaddkAyIWA74DptWp02RsLH8XfJFl7E9RTmpzxEUUKPT9IxS8QMSCf61BomrZZMIpGk\nZ1/H6ttoWhrT3CWkSWBl8IIMyexlksmxDfaYT87Vcwu8e3sL5ag9R2Ao0Ou5DpW9TZhfGVQOSyGt\nXo+q2WSr3ODFS8sntHbCMOTG2gYHLZODVpdKvYXn+bi2yX7bxUei74XI6mBuGzu0lIwA1/NIxXSM\n5YvcWb934lwl9ez58MO2i+wu83NX8QUZNfTR9MH7oe9TzA3m+o4foGkCrX6fKAiHC7t0KnkoGFkj\nQCBw+hQLeRKJOLZp8sLLl4nFTj+vYiGPtrH7UGtQp9dl9fzoHGFhpsh766WhQ9NpuH2TlStXH34j\nxnypeKKDDfBwgcetrS3OnTuHLMtDLYejdoZkMnnCEeG4XaOmacPWjDAMKZVKIy0RyQeszUxzMNGf\nnZ0liiKy2SypVArTNDFNc1jdcHSOjuPguu7Q3jIWi3FwcMDVq1eRZZl2u02z2SQIAlKpFIIg4Lru\nsEUkiiJEUSQej1OtVoGB2nW9Xh8JSBSLRba3t7Gs+9HSRCIxEALzvKHew2l8XLeOT7vfmDFPOmEY\nUqpWEEWRyXxxpJ3IMHRefuapYaDvh299yDt3tqiHOmI8RiFfp9MooeWmESWZhW99l+Brv4HTbaAl\nc8OKhigMEEuv89J3FtGDDo3de/x7v9VBEEYDp+1OwMa2x/KCQjp1/71f+2WZ/+MPd3j164uYfZlE\nymPj3mskEgkmJ8+TzRyNUZtUyuJYx+FzxPM8yvUqMU0nnx2d+KZSeVKpbxKG4aD6LfxT1m7+kJeu\n9iEKkaU4fqBx0HBR1CS53ASdLrz5TglddQgCDVDZL5VYmE9ROhAIohTxWBNd8ek7GrIU4/Uf/WOc\n3gfEdG8gVOxqyMoU3/j6MyTiAgf1Mt++mESWRcy+RiLZ58bN7zM1OUU29zTxuAGEdLof0WqpZDKn\nV8qN+fT0+z263RrxeJZEYlTtPV+YB+YJw5BW64B8+CPu3v4Rz15yAAFRTGJZKge1AEkWmJ1f4d5m\nn0p1D9d1+OCDGjGthqZ6aKpEzxIJoizfelVAEiLixixZA3L5DteufUg0Oai+UtU0qbSKZZmIis1u\nqc63v5knCEPSgYEsNVhbe4fpmUUyhYnDBI9DpfoGivLtR+o7jRlzFrlshhefErizuUer5yFpKoEf\noAkBZrPB7PIFiODe9h6dvgtHFcO+z+Zemb/+V35lOCcPw5DX3rmGr8Ro9D0qXRc5kUUGGnseUeQg\niCJN0ySZkNCPOa31LQvPcVBEmWQmjxTdOXGu2XMvUL/15kNbKQRJIXv+xZHXYmLA1Mwst+/cYeUw\nQee7LoWERi43mEOLh5UUoSATeNZwYdfpdGibfRLpDKZlIcoCPdvB65t865uXyWUfPgd/9uIKb924\nixI72Ybi2n0uzBVPJBULuRwZvUz3sE3jQTzXZWEi/Vh24WO+PDzx/9pnCTweWUiKosjW1hZhGKKq\n6rDNolar4bouMzMzQ/HI43aNt27dGpYTHy0YjsQej1oSjqoout0upmliGAaKoiBJErZto2na8P9H\n+ySTyaHNpaIoNJvN4XlduHABwzAIgoCZmRmmpqbY2dmh3+/T6/WGtpoPalEcX9DH4/GBQrSuE4Yh\nlUplqPvQ7/cRRXHo3nFWZcdxHtet47Pab8yYJ5XN8i532yXEpA5+xM0721zMzTM7MSqiJwgCWzu7\nXL+3y9ZBBz2Rx+qazJy7inD3BrXtm8TnLiKIIpJmDMUgAbxuA7F2nd/4tsHLKxXSCXj3zdvMTN7/\nHbtuxH/ynx/wL77XY78cMDMl8Rv/Wpx/9N9OoKqDsSIea9Fo2sRTC7iOSzHnsltpcmFq+v9n782C\nJEvP87zn7GvumbVkrV3V1dt0zwoMgAHBwUIABsQFIdq0BVmU7HDYJsMh3unKsi8sRyh047AiHIxw\nBB0SJUoRNEmRJikuoLARA2D2nu6eXqq6qytrr9z3k2f3RXZndXVVz/QAGBIzk89dZZ3KOll1zn/+\n//2/9/1G76NpClGrRByfHifJvw9c21hjz2uiJk2Cto+8v87Ts2dIJY4uIgVB4GDvJv3mGyzMNIfW\nl0hCEgX8YADBgK5Tww9iTCNFKuGimzmy2SWuvX2D2O9T2vKYmTYQhBaDgcvANXnttZeYne7ytc+B\nKD74/3UIwwbffOkWTvAE587mcAYRghBjWFna7RYrpwTWSj2emDo812RCZa9yZyw2vA+EYcju1mvY\nRo18QqXb89iqJpksPn9iv/pa5Tpe+2WW53wUNUaTFCCGuE+lvg2xQKenoasC25ttJDb5b38FVFUB\nDsubXbfNn32zghdmefqpVZLicM6Rywi0Wntks7Po5hTd3hq2laDddlmcz+G6AV6gkEwnqNfKnDkt\nsb4dk3tgnJosqOzXbjM9M97hHPOjk0ml+MRTw6y0dreLKivousZ33rwJMVy/UwJFR9YfWBTLCn6k\n8Xt//i3+3i98CVmWub62TqhaEMWsbe3T90IGfguIadabqHYKqd8kcrr0RIU48AmCkMBzcLptCsUF\nmrV9dE3HkGOihzbb7OlTJOfPn9iN4j7J+fNH8hoAikkFy28ylTZRhBgNn9nJNOnU4dhr6cowUlgQ\nSBgabhQxcByqrT6yNqy+SBgai9MTCKJA0G9T6YY0Wi0yqUeHNKaSCV548iyrG1tUWj2iWCSOQ7K2\nzvmlIhP5k/OdPnbpPFdurHHQ6qCa9qgDXTjosTCZ4czS4ok/N+bDywdebLjPwwGPq6ur6LpOqVQa\ndYV4kPvtKF9++WW++tWvjioW4jjm5s2boxDI++JEr9fD8zwKhQKtVotarUa9XkeSJIIgIJ/Pj6oH\nLMui2+2OukhomobneSiKQrPZHLXntG0bz/MolUosLS3xpS8Nw7buCycAi4uLqKrK2traqDpiYmLi\niF0kk8mMOkCkUin6/f4oFFJRhp6uIAgoFotomoZlWSwsLByr7DiJx+3W8ZP6uTFjPojUmw1u98to\nmQfS1rMK11vbpCwb2xq+vls5YLW6yfdev8wrr6wycFTsdEysyMSKhCGaJDs13Gsv4+sqsWogKAqS\nEJJWY56eE3juhTayFFDe9xDyOtNZBzicSP3G/1zm//43h+GSu/vh6Ovf/BeTAHzxM/D7f9XmK19S\n6XTapBMmhUIK3z+6G6EqLkEQPDI4a8yPxp2dEmXZQb9X/qsaGhgab2zf4sWzz40E7oP9Vfqdm5R3\nvoHff5ue5pGyU6hKhCzF3FqrI+CimxkEQUGIysRxkXTCYe32Oq3WAWLsELgd9gQZ29IxDI3vfK/E\nz38xojh18hRAkgS++LMx66W3+P6ry3z+5748zAeKA9otFzGTPHFXTIj7798f7SPM3s6bzEx2EYRh\nFUAyoZNMeOzsvc7swqeAe1VVO1dwe6tUdv4U/E28XoCmWkh2jNPz2diuI0sgK2liOeDq69t87gWX\npYWTrwNNE/naVwxurrX51neu87nPTRPHEb2+hKZ1ATDNJL14mUZzl0qlSSY7RaVtYegKjaZPp+uj\n6VlSqRNC+uLe8dfGjPkR0HV9VCVze72EZtps7eyDosMJYrkgirRdiR++cYWFmSnWd8qk8pNcubFK\npeujKArivfwBWdXp9AfgRkwXcmxs7eBqJrZlops2gahSr1URAp9MKsG5Cxd5c7WE/lDL2MXPf52N\nbw67TjxY4SBICsn58yx+/utHjo86Vf67r3+ZL3/hc/zwyk3QTu7mMpFLc3e/CcQsLS1w/fY69baD\nrA7D5eP4Xr6DKNBq1LGlkGa3x831LT71zDt3hDBNg6cvnCGOY4IgQJKkdw2AFwSBpy6cwfd9NrZ2\nCKIIXTVZmD39WOHxYz58fGjEhocJw5B+v08URY8s1xEEgVarxWuvvUa1WkXTNFqtFoPB4FhpUDKZ\npN/vc3BwgGmaWJaFpmnYtj0SIvb29lhcXBxlMpRKJTRNG4kdruui6zqVSgVBECiXy6PWlF/4whdG\nk/x0Ok0cx6PcCM/zOH36NK1W68QchGQySblcxnVdJicnmZyc5O2338Z1XVRVPWJnqNfr2LZNqVTC\n8zwSicRjZSe8W7eOn/TPjRnzQWKjtoeWOH5v6imLu+UdLp06S6VR43pnh/WtXcRAQU8kkBIG7VoN\nTbTpVzaQKxFJsljmJJYc0Rw0mCieQsq5nJsvsVTssZjvkUmEOL0utcoAJXnoqmy1Q/7kGydP4P/k\nGz3+eTsklZTQNBHbzpMtPIuZ6BN5N/D7EZJ0dCLgB9I4yPV9YLdXR0mfUD6e0tk+2GN+eoaD/TVM\nZQ1ZX8OYjQgHOrIY4Q7adLsx7bbDylKE78tEcUy1oVBr9JmZHvDWtS0srU026ZNP+5i6QmnLp93u\nsHanxmc+IVKcOrojfpLtZmlBotoo0R9kWDm9CICo7GMa+3QHJ1jkeOeWZ2PeO0EQoIoVBOHhZnpg\nao1RhtTu1uskrS0SSglTFIk8Dc/3kYQBpS0XIpfzpwUGnkivH/DqWw5PXXBYWnj36+DcikK92abb\nXmNxYRpDjbm2KvFCdihOWlYaWbYI5QyzC9NHT1K8g2X16PRPGEeEsYVizE+e4F4oYrM3QNROziRo\nNlvUygc4rkfTg5u7TYSdOlvlGkby6I59IpWmu7eH44W0uz3mls8SuAM0TSUIAtwgQhJ1xFgkDgNm\n5+fZWl+jE4YIDzw/RUVl6cv/iO7+Bo211wk9B0k1yKw8d6yiIY4jli0fa+oU33njBu1mDSsjHsmK\nu086naHQc+j2+iiqwumFIuvfewPRTCIpGnIcoCkat1dXsW2LVHGGg17IxvYquiLz1IUzjyUgvNdN\nB0VRWBlXMYzhA96N4p2QJIl2u31iiSEwymFwHIdOp4MgCDiOQ6vVolKpED3UlsWyLIIgoNvtIkkS\nuVxuZH+AYfXC/S4VQRCQTCbJZrN0Oh12dnY4ODgY2SGSySSJRALbtpmYmGBhYYH19XVqtRphGHL1\n6lVKpdLIwhHHMa1Wi1Kp9MgU3cXFxZG9QpIkUqkU+XyeXq+H4zikUqlha557IomiKFiWxY0bN7h6\n9eq4DeWYMT8GXvzo+8eNh/fsRm2P7sAhcmLSqSSWoUHko1oiTuUAqREQhSGR6+I2KqhuSCbScbb2\nkDdfRalU6W3ewdYidF1kZkphbk6l3z8cq+5u+uzun3wuu/shpa3hbkocxwjiUITVdZP+QMcNbBTl\nUJiNooiQ6fFOxPuAF508jsuyTN8fVsSFbglvUCaTljCMHBEquq7SbId0Oz6ZNLhuSLcfUNr20Q0d\nQVTY3m3QbW8gxG363Sq6JqCoIsunNCYLCtu7HufPHD4XPS/m1/7JARdfLPHcF7e4+GKJX/snB3je\nUMR6/umYy2/85ej4ZHKCRjMg5qjdYzDwUfR3bzM95r3heR6aevI9bRoijtMZCg5qFX+wj20pSEqK\nQSAymTfZ2nWJw5BkArq9gEbDo9YQadQ7fOypx78OXvi4xpUr19na+Ab1ymVkUaLaXmYEQ/D9AAAg\nAElEQVS/arNfTdL1L7J4+ov0+u6RczTMKWq1Pop6dAHXaLokUqd+wn+tMWPA1HWcfp8gPtn+V63W\naPQ8FMNCUs3hpqSi0BqEdL2ITrdz5HhBEMimU/TbTWTNJI4jdF0jl04ymc9iqQLTk3kSqSzNegND\nkfnUC5+me/fNE3+/PbXI3Gd+mcUv/NfMfeaXjwkNAFZrg//mH/w9ZFlGsxLkiwvcXrs96ir3MMWs\nzS98+ilMwcPvdShO5plJ6kzrAVOWyKDdYml5mani0JYpiiKKmaLSj3jlrbfHHajGvK98aCsbCoUC\na2trj+yIcHBwQBRF5PN5oigaZSqYponnedTrdfL5/JGfsW2bZrNJv99H1/VRZQMMAyLn5+fZ3d2l\n3W7j+z5RFJHNZvF9nyAI6HQ6eJ7H1NTUqKIhCAI0TSOVSvHyyy8zNzc3yo14EMMwWFlZYW1tjYWF\nhRNzEH7+53+eTqczsi0kEglmZ2exLIvbt2+Ty+WOea/v20kex1IxZsyHlW6/x+peiVbQRxREcorN\n+fnlx97VtySVJsf7RsdxjC0Pd+/6kUev3UOShtUCs4UMYVAhCsDrO/jVDrZs0mkcIMQSB7UWYizg\nRiFnvxhioaD7OnevDZhcclma9UlaIvXm4STh1LxCcUo6UXAoTkkszA13Jl65LHLp4pMABEFI110h\niETanQGWqdBqBzh+geLseEw4iWqzznp1h17ooYgSk3qKlbnHXziZksZJcoM38MhY+aF1RXYRGFrz\nEok0vjvNQW2HdDJkvdSh7/rksjq9XsSpRZNup0bKHnBw0Of0KQNdlchlRLpdj2ZHJJuCWt1ncfbo\nY/9xbDcKN3AcF8PQ8LyQWvcSpqHQ77uoqky9GRJLi0xNL763P+RHhM2DXbbaZbwoRBdl5lOTx7Jc\nHoWu67Rr6kPSzpBONyY9kaHZLJNOyLQbfUAmmZpGiNvc3d5FkiRcP2avHGLbKnGkkMtCNt3hvdqv\nsukAXYvRTZmYN9jdfZbzFz6BpmkjUXJ3e44g2CSVHI57QShz0H6WXFrE84ZiZ70popqXjoVcjhnz\nk2C2OMXb6zsnfs/3PFp9D1GSSdxr26jpBlJcwfECDN2g1elhmdYRoV2SZWzbxLZ1nFYN2bYJXAlD\nVShmk3TdEKKAfqtGO2GgWGkuPfMxrl25jLXwJMJjivZxHGO2S/z3X/8aiQfC3gVB4NKTlyhv30Uh\nTygMLdJC6JNLGFx85glUVaU4PcVgZYD6xi3Me+uTqzdvMzmXP/a7BCIUVaEbCmzv7TNXnD52zJgx\nPwk+tGJDJpMZ5S08vMC+rwxGUYRtD8NLkskkBwcH6LqOYRgjO8WDScntdptTp04RhiGtVgvbtgnD\nENu2cV2XSqWCLMvs7++zsrIyqiYYDAZ0Oh1kWcYwDPb395mYGIZoadqwNHJ/fx9RFEmn06PAyoeR\nJImFhQXS6TSe552Yg/CgbeG+Utlut4eD0gm+tfs5EGEY0mw2x5aHMR85+k6flzevo2ZtZIZZKPU4\n4Purb7FSmKXZ76LJCgtTM4/c5V+emuMHW2+jpo96Kv1Gj1yuwKtvvMatjVu4ikCzOWAQBURKSCpl\nkEmKbLyxQbW9T6U+QOjIxJFGRIQjdvEVj/W7MTNTk4SBQGVLoF2TCBo6guLR7Qu4boSmiaSSwzDI\nBxcN9/n5L1qjsui1rbPMX/wk+zUPSU6wsLyAKIp0ux0a/TaJTI7cOCX+RMr1KlcaJbSkiYxMDGz7\nXbq3r5OzUvR9l4RuUixMPjJYczEzydudHTTrcLEXxzFqL2RyvjAUokMZP/DotrchHvZOT6ayOG6W\ngbuFaSZQVZOE3UaRPJIJjzhyKPkRzaZDPmsRRTGyAr12hKMKXF/1+PzPHArZj2u7WZ7vcPm6wKnF\nAqqe59wTw0lps1mj7wzITU2O08Ufwe3tDUphEzWlIQMBcLO/h7M1QBQE/DikkMiQTZ/cBlIURWJp\nDs/bQlUP/8ZhGOJF0yiKgmmm6PcDHMdBERqATxTL5AtF1ta7WHoNRBvD1DG1Fmt32jx5/vC9Hvc6\nOLMkcWutyac/mcF1Oty59X+Qtf8Ojqfhudyb15i0evM49yzpVmKGi09miKKIRmOYRTU19+h7Y8yY\nHxdRFDm3MMWt7TJw9DlWb7aRVY1o0CZdXEDwh/bmlGUQVbpYlk2nP6Db7ZBMHuYZeO6AZCpNJpcn\nOZFmZW6KaqvD1l4VJwhxHA/dtPCjgGqzRU7RmV9YxAth9cpryLlZ9Mw7C4xBt86y6fKP/od/gJ1I\n4A4GSLI8GlsFQSCVyfOZZy7gOAOiOCKVTB7bFNF1nZQu4QONRp1QUk9c7NmGiiiKiKLKTrk+FhvG\nvG98qGcHzz//PN/97nePBMcA1Go1AIrFIp7njQIX77eku28zuF/BAMOJYBRFo6+LxSLZbJbV1VXC\nMCSbzeI4Dr7vH2lnORgMiOOYVCqF7/ujioX7LS3vixVxHLO3t8fU1HAwarfbNBqNkVhyv5WmYRh4\nnseZM2fe8bMXCgVKpRKGYdBoNE5sL3X/s8OwcqJcLo/FhjEfOW7vb6Fmj4oEYRhyvbrOrteiOD1F\nGPZZv7XPM8XTZFPHFwWmYfLM5GlulTdphwMQYO2tG1zZvc0/jcr0csNwpqjaw9h3mVULnLtwiYSu\nsPnKdXau3CJRLWAK90LU7s3DzdgGDza+28Lt7PDlF5PYlsGgLdBtBuztx9Q2k/zenzT5+7883AX5\nP//Z8J4+qRsFwPqmwKmljxH6+xRPvXg0MdtOYNsnBLmNGXGntoOWOuqb9TyXb5ZWeeLUCpZts+t1\nuXNzm+dPXTxx7J3OTxJGEeuNPRwhQAghp1o8eXpYSSIIAj0ngRbXUcwBmiYDEr4fc/3lDrGcZXH5\nCcr7r5FJybhOl263R6/voUoCrmfhuCFJW0TXJLpdD00V8bwQTT303T6O7ebJJyQMPaLabTM9858d\nOSadPlkYHzMkiiJKvQpq5uj40nX7/H/bN3n2wiUkSWKruUHiYIuPr1w8UdCcLp5nf08gbm6hyB5+\nIBOLMxRnh+0nLcvm6lqfnNnFMAIkUcK2TDrdDrWmgpQtcOHCRWrl75BOyDRbfRaKh/f9414HpiFg\n6iGNRo0oUnjirIYsrmInTRKJFLVmzHRxnr6zTc87w8Tk0uh9RFEkl3u8ao4xY35cTi3McWlzm8ul\nGqqVRLy3IHe6HSRZZnZuljiOySaHgu/C3AzX1u6CqpFO2nRbTbgnNsRRhBgMsO0EvtPj7BOLFHI5\nqu0u84vDLnHEMXsHZWqqSTY/SbdRI5u0OD+bxZKf46BSo7pzHS8EyUqjJrIgCATdJsKgSUYXObsw\nwy/9wt9lr1Ljzm6FWJQRohBbk5kqZEmlUoiqRqvTYSJ/vFLhyOefneTaxgHdvoOsHLeTh57L5PTh\n+O14J1v7xoz5SfChFhvy+TwrKys4jkOz2Rwt3G3bJpPJjMSF+50dzp07x5UrV9A0jXQ6PQpwDMOQ\nKIooFAr0+33S6TRzc3NsbW0xOXlUod/Y2MC2bRKJBEEQ8MILL7Czs8Pq6iqmaSLLMrqu0+l0CMNw\ntLgPwxBd12m1WvT7fQRBODJJrVQqlMtlTp069Vg7AplMhu3t7dFnfJj7eRAPdrUY5zaM+agQRRGt\nThtFkukEDnA0fG1jdxtxIoF77wEsSRJSzuat3dt8NvmxE+/BMAyxZB0FkX/9R7/LDyaaiOcSQP7w\n3QtJ4vNwtzfg9l/8MefVJdb/8DLp6tQ73tdWlKLyuso3hV1+5nkL3RC5uyHTLutMm1lee8Vlftbj\nM59QUVWB3/wXk/zz9nCRsDB3GPS2sx/x0pvP8qv/8GfwPJdqdZtCYeyzfzeCIKDd7WDqBm1/gPlQ\nEOL6wQ7WXJ72oI9l2yiqAjmFt7bW+MTKcStKHMfEUUxKNUmGEYtTM6STR1PBDUMi8mbY2nPIJDt4\nbgPHjZiYMOn1M+wfHCDhsrdXw9AjtvdcVFXgqScM9sse5WpAOqHS7Q5QZIGtfYGFeZPdA590ang9\nPK7t5qCqUlwIcZw+hnE8oGzMUVzXpef0h3ZJ42ioWhiGbDQOkAo2vu8jSRKaqTPQI25t3eX8wvKx\n9wvDkG6o0w3nkAM4XVw8JmIlExZBMMPG1jqZZJ9er44fSCSTEqnMFNvbJTTZo1ptYugxB9WQQn44\nBXzc66Bci8hkdDqdHrncNK0upFIOXhihKFmIq8TxHKah0mqvE8ePN18ZM+b94PM/80ks6yp39hsE\nUYAkwvxEGtHOEIUhBgEzk0VgKIY9dX6F1c19wsgnqUvE/RaDgYOl68wXJ6nVakxNTDA5UWBzew9B\necCmLQhoisTSwhyThRxM5UhqAssLs3zrpZfxJY10cZGYmM5uCU1qo2s6uYVTpLM55qcnuHnlTW7v\nVFAN60jVmw/c3asx4/skEwnkx7B3Tk8UcF2P7e0tkBKjjhxxFBO4DguTWZKJQxFUHN+nY95HPtRi\nA8CFCxe4fv06qqqOcg7ud2IQRZG5ucOJdiqVYn5+fhQamUgkMAxjJEh0u1183+fcuXN0u91Hdroo\nFouj7AdRFDl37hzr6+uIojgKntQ0jUxmuEN6XwSxLItSqcTFixePPaB1XSeOY+7evcvKysp7+uz3\n227e56TPDoxT58d8JLi7u8nd9gGRIROFERtbG8xJ86P8FYB26KAJNtLDGbpJjb3KAcWH/NaXb1+n\nKrtols7/829/hyunPCTr0RUCsqUjfe0Cl3/rFaZ30sfu9yD26dPFxEYWhveuhsGdN5L0ahFmQuLU\nShJvEOL1NGJ9hb/+dpnVO1V+4UsqE3mZVFLiySeG97TnxfzV90Qi8Um++MUvA6CqCmG3CYzFhkcR\nxzFv311lz28hGCpRNWB9t8TZ5FmUe1VqvW4PXxOQwhBFPrqwbEbDBeeD428Yhnz/1mWClIJsKYDA\nK+U1ltp5Ts8ujo4TaVOcXcF1F7i99iaaZGEnDOayGVbXKiTsDjeuVzi7LNHrh2RSMt1+RODHJCyB\nO5sexAKqErFflpmczGCZfV6/UuHCvcK4x7Xd7FXn+fTPTbJf38cwlo4dO2ZIEAS8efcmdRwkTcFp\ndthr1jh39szoHj+olFHSJv7AQ3pg/iCKImW3xfmH3rPb7/Fy6TpyxkRUh4HRf71xhacmlpjIDnc3\nXdclaQekUxfp9ZZYW32ZdDJNImXw7JTB7n4boiqVcoOZaYVCTubl17tcPDeUQR/3Orh2Ey6cFZnI\nC1RrHdwow7wh4veGoqymhPh+gKoqmMbgxM5eY8b8TSEIAp945hITpS12qi26ro/r9HE8h4mUzdRE\nYVRFCDA7PUm7N8BHYsISyefyyIpCFIVIkszu5iZ2Og3xyZ0ufHdAJpUeis1Ax3GIoohTczOUmh6x\nqCCIEgllkZniNFEUIkQh04UMgijQ7juohsVJyLrJ1kGdC5pEOvXO7Srvszg3w3+eTvJ73/g+oaJD\nDKapML2wiPhQ56mMPb5Px7x/fOjFBkmSuHTpEs1mcxScmM1mcV33WAAkMKpYiOOYmZkZkvcCWhzH\noVgsEkURcRyf2OnifhXB/dY0qqrSarWwLIulpSV2d3eRZRlJkhgMhonjQRAgCAKFQoHNzc0jHS4e\nRhCEI1aMx/3siUSCGzduoKpDf9Z928iDOI7zWC0wx4z5ILNb2Wfdqx2xTWSLk9zc3eDZ0xdG3V8i\nIjzHpZCZPfLzsqIw8L0jr+2U96lpAZqmc/P6Dd6yW8jWUatF0Bvg7jfRptLI1nBHUhAF7F99lurL\nrzK1WwAgiiNu8SYV9vAYoKJTiKc5yzOIgkgqzLNXKfPc9LNUN3263RYTaRtB8hmUUyiTCX7wehtn\n0EKWYgRBw/VlFDXPFz7/JdIZk2rz0BoWf/gfAT8WNzfXqege+n1ria5hTWRY297gwtJwxe77HoIi\nEjs+uZmHbGiyNAx7fEBsuLG1TpwzkB8Y542kxXqrwowzebg4ExQgQtUUpidlMumhKBRFEa6vo6st\nMikdTXZJFwR29qDbDYgIaXdCnr2oEIQyopRnqlik26sThxKa5uJ5Hqo6/P3vZrvpdCPszLO4ro+q\njasa3onX1q/jpWRMYTi+aFMa216D0s4Wi7PzAARhiCiKmLGM8tBmxf2WfQ/y9s4d1NzheCUIAno2\nwdsHdylkhqHPkiTh+8P/pyQJrCzbmMawRLrb9ej1YLFo4TsSuhaRsmWcQYzvxyjK410HjhMRhBKX\nLph0Oh1wdTRFod12iMXh4scLRGT5vsApYr/HVnljxvykEQSBpcV5lhbvdVgKQ7716lU0+/iCXRRF\nLiwvsHrrBrnUHKqmDTPNfA+FkK+++DEOKjVu7zXxI+FIPaTb67AyN0G1czg/iAQR13UxdI1CyiYQ\nZHrOAFGMUYQQy9JIJhIgQHlvj8nJSaIgQHxE/o2kWYSu8546RCUSCS6enqMXP3pt4fa6LF06/djv\nOWbMe+UjM9N8MDgR4OrVqyeGR4qiyPz8PPV6nVQqdSyEMQxDrl+/Tr/fP6LY389meOKJJ6jX66MS\nx/uVDMlkkm63CwzzGAzDGIa9pFKjqoUoikilUieeFzASMjzPO/a9d2JxcZFOp4OiKI98X0mSxnkN\nYz70bLbKqMmj5ceT2RydfoetjU0W7vWElnoBxdQExkOlyk6ry/QDu88AB906qj2cVH/7zR8inz8U\nGiI/ZPM3/5LmK7fxa12UnE36+dPM/9qXEBUJUVPwnzFgd3j8Ld5kh7ujn/cYjL4+z3MIgkDkRmiy\nRrPmQqiye1AmmTWRLIHJwhyG1eLCE6dJJRMoMuwfNHC8DJmsxX45YHJ6uHio1lwy+XHruUcRxzF7\nTgPloZ2m+Yki19Zu0KjWyeSzWLaNd7PEuaXTx8ZXzedYuXvd6yKYx3McjJRNqbLLufl7ZfTSJGG4\njSgKSOJhp5NKLWBuNosgStzdfBNJirD1mIELL3w8ye2SixcoNNs6pi4RRgqK5rMwA6WtkLlpnX//\nhx3+4a8Mz+GdbDdxHPMHf1Hk7//qz1GuScwsTP7Yf9cPK91el47kowtHF9jLk3O8fesGxcIkqqZh\naQaV8h4XF45XKCaVo7uLYRjSjBxMji/aI1ujWq9RyOWRZRkvzAM9fH+A8UAL21ZXZXEhS6cdcedu\nhCwNszt+9lNJ/uBPu/yXXxsKGe90HQD8zh/4fPZnz7B1oOP5SRbnCvi+w9s3q5w5N0UYRkRxdrQQ\n8sL8ODR0zE8VwzBEkSdPz3N1fRfVtPD9gEqtThDGqIpIxlT5la+8iKnr7OyXiaKI3PwcmfRQnMik\nUhSydbZ2dsGLQIgxVJWV03Nomk7/9voDnYZiREHEMHQytkm10ydhaqQzU2QeCIKPoojI7bO0MIet\nibTc4EjV0/1jNAIK+feee/LMhTN8/823CWTjWAWz2+9xfmES2z65omLMmJ8EH9knwX2LQRiGhGFI\nu90miiI8zyOVSvGJT3ziRFvB/WoB13Wp1+sjYaBQKIyqIGq12uj1+w/eZDKJ4zjU63VkWWZhYWH0\nnveFimw2y9zcHPV6nSiKjlQwPGh9+FGyFR78vCe1zbxw4cJ7fs8xYz5ouFGAeMLE/fTsIs5Gmebt\nXWLgE/kV2g8dFvgBQsvl9fgWvcBFk2Qm9TRhHAHDHex1pwIcJjpv/uZfUvmzy6Ov/Vp39PXiP/4K\nAOpX5+h8o4ThGlTYO/G8K+yxEvtDS0Uk0GjUcZ2QVqeJbioETQXyFTbXNE4/PYEf2tSbZZqVHoQq\n1XafrnOTS5e+hCAIHFQ8FOPJE8MLxwwJw5BAjI5dLZIocmnlPM7aAbXmDpok80LhDH3p6OPU6w8Q\n+wHfXXuTQeBjSArzqQniOOJR7tjogXydqelzbJfapBN1wkgnikIqtQBVPwVRGXfgcvGJJQKvSrVR\npTgVUakHeL5AcVJBt6aoNwJcN8IWezQaHRrNDufOpMhl8/zr3z3gV/8LcySQPGi7GX7+mH/3RwW+\n/NVfp1wTSOefG/vv34FWt4NiasdeNzSdc3PLiHtdqkGFpGLyTHKe+KHFhNPsIgzgmzdfxY8ikorO\nfHJi5LV+GFEEPzxc1hSmnmZr5/tk0zq9foRhQLkK6ewKnnMLRY6YX1wmokkQNshlRE4vGfz7/9Dl\nv/qa9cjrII5j/t0fODx1MYei6HSdNKJksL2zjyC6GJrMnbUNNnWRjz3/FI7jUW2YTM0+/ZP4s44Z\n8xNnaiKPpir8xXdfZqvWRVR1ZEnA1lVMGQ4qNc6dXmJp4WSLYS6b5eLpBSL1eKXX4myRm3c2ka0k\nqjC0TGu6RtLSEARo1w+wssP3Df2Adr1CwlCYmZ7EtgzmZqao1hrUWh167rCli67I5BMmUxNFRPG9\nBznKsszPPHeJu6Ut9uodBr6PKIhkbIPlJ04NqyvGjHkf+ciKDfcX2D/84Q+p1+sj+0I+n0dRFK5f\nv86FCxcemWOwvLw8amX5MKdOneLu3bv4vs/8/LB00rZtdnZ2SKfTnD59mna7Ta/Xo9/vjwIl+/0+\niUSCRCJBr9ej1WoRRdEx68OPMuE7yU7ycNvMMWM+7BiiQmMw4KBZxYl8FFEib6Zodtp0m3WyEwUS\ntk1PV/B2GtihzCD2kQUJ2fGJchaxpWHeK6A8CB16O2Usc4JGpYpjiaPowKA3oPnK7RPPo/nKbYLe\nANnS0eazOPYqsRviMTjxeI8BfXokSSMrMhgxTrlHwrSHVVKKiK6n8JpQqejMnc2zeV1DEzWC0MNv\nWnhSipu3B5w++yyqqTPo32V/6xZRrKDo81j2BK7bI5HIjHckGY6ZSizS6naodJv4cYiKxGQqx53N\nDWxJQzcNzHSSHgLxXgshk8SPA3RRwW87MJVAVGRMhs+JO06VQatDMmMycAZ4notl20NrXa/PdPYw\nHFAQBOYWP0G73aDc0ml2Npifn0WWJcrlLnt7B2iaSSAs0uy2eCINvhcTBB61pkBG8JC10/jOgM2t\nfeaLITOTCeyERRCZfPw5ld/5/SqaLvLVz4dY5lAYb7VDvvHXKo3uDBef/XUU+3mi0KfTuEqn7hIL\nFmZiGVHUiKKAVCo7FiGATDKFv7tLPXBoDLqERJiiRiGR5sb6GlOTk8i6hJhLMOgNUPY6iLZOGIfY\nsk6vPSCay6EIAgrDFpk3OruE3QGkE3S7XeIoxk4M23WHnQGTK4XR79c0jfmlz1Gr7bF9EJJPd5iZ\nGwbP7jZ1dvf2sK0pHMem2a5z8ZyOqkqUqyK//bseuWzMl17URvaawSDiL7/t4rgm586e4sIZiWq9\ni2p8HFVukU1LGLpEo9FHM5e5sxlzZytJNjeHojeoHfwA4oBYSJLMnCUIfERRPtJOcMyYvy1ub+4w\nOX+K6UXxWDXxdsslXlvn/Mqj82lmJzKsV7vID+X06LrOudPzbGzuoIoRjtMHYiYtCUtW+eSTn6HZ\nbLK6vkHfi8hk84iSxObGGguLiwRBQD6XIZ+7VyEZM8qViKII23g8G/XDiKLI8qkFlsfFjGP+FvhI\nzyivX79OLpc7MbshjmOuX7/OpUvHk8ThaLeHhydakiSxvLxMrVbDtu3Rwv7Tn/40u7u7+P5QrTQM\ng1QqxWAwwHEc8vk8t27dYnp6ehRAKYoiyWRyJDT8uNkKD9tJxoz5MLJT3mezVcaNfZRYYtDtIVgq\npYNdrtQ3mJ4pkkyn8IAr++vcvX2bp557hqYWUumVUWsxp2cWyEUJzswPJxzfvfUGqnW0EkCSJISs\nya3Lb3Mg9I90fnH3m/i17onn59e6eAct5KV7+QnEmNio6CcKDio6JsMxQE+o5CZzOGUPxBhRE5EF\nmXpZYWoiwOn32L3TwVSGuy6uq5DJTKCrOs29FvITClL0FtMFFRAYDLrsbv8HunWL6el56vsQMEtx\n9uKP+2/4QBDHMXd2Shw4Tfw4QvQjojDEUwSurd9kX+gyPV3EsEwGwLduvEqv2eaJpy8xkELK9S2S\nocLs5DQX0gtk0xl83+fb62+iK0cfsaqh0SHkyltXCLIagiIRN3axPIlL2QUyqeNjczKZIXnxc9Sq\n21Tq67Ram4TuPqBwZlmi3fHxBlO8db1KwpaYmZlgc6fP4tI8fmAQCgu0an0cp8vAldF6ArYt0+2p\n/MJXFnjrZp4//24dRfYQBIFYMPilr30eWZG5dqtD4LXI2NtoaQUQabX2OCh9j2S6SCqZZX9LRtZX\nKEws/k38u/7WCcOQG1vr1LwOxDHCIARZwBEiXnr7Nfy0xsz0NLKq0CXk+69+m4SiYVgFIGJv5w5T\nRoqcZfPC0iVkWaZSr9KWg+M2nIRJY2ufW9d2ETMGgiggVEOSkcYnZ86duBmSy02Ty/0i+3u32K9s\nU6vcQRTaGIbO2WWF7V0Jx5nl2s06hpHg2WdX2C+7zM9N88ff2qDXB3+wja4HPPvUMsUpHVkWKdfa\nGLrO3Y11Pv6UjWEMBVdJSZNMWlw8G7K2XaPX9pmZ7N+r6hSoVNbYXP1PTBXPICs6OyUDO32JVOqd\nW/eNGfN+UWs0aA5iVH0osD583ymKyla1zenFo+G+D3JqfpZW5xbVgYfyUGtJRZJ59vQMT184M6oe\nNs1LlLZ3uVnaY3O/ipWbxgJ8z0UI+nz6kx9ndWOX67dLZBMmjh8SxzG6IjNZyKFpKm6vzfzZC4+0\nWo8Z89PKR1ZsaDQaRFH0jmGMYRjSbDYfuTh/N2vCJz/5yWOTgVwux0svvTQaLIIgGFkwoijizp07\n1Go1FhYWhhO/OKZSqbC1tYVt2ziOg2maxHE86mYxZsyHnTAMubu3RcPtIgki04kc04WT/eN3dzeH\nIZApHQmFt9fXGBgCRVIEKYUpq8h+tUK/28PzfW7dXsWYSNFpd8kVNBRDI9JjSnvbmJnDUL5+7GNh\nEPg+u5UDwjjCVDRaTpfU3ARWGCK9cpinok2lUXL2iYKDkrNRJ4c7fO5mHbOrI2NMAj0AACAASURB\nVAsKhXj6SGbDfQpMIwsKcRwzdWYCURfIzaRpN7oQiCAKdKoG6ztlFjMekSODBp4XMvBssomhqNGv\ne3SaN1iYPZwcVcqrnFoQaLdbyLJEIS/hebsc7GtMTj1e55ufNlzX5fbeJv3QRRVk5nNTJy7kAd5a\nv0lD95FTGoQhb22sgqlwSptEmU5jezKl7U1yiTSdXo9bd9YozE7R7/WRBJHKzj5bAxen1WVy2SSb\nzlBvNZCs4WKs3+tTaQ6tdWkrwXq3zPnTK9S6LXZbVVqDDpIoozQlwlW4NHOahGUfO89cfhbfn0QS\nekwUivj+s2xvfovA3WKqELG6PsGlU9MoisgpLUO10aHTaTM5+yk21q8gxHl0rUPSipGlCBGB0raP\n73X5u794AUEQ6Pd9gjg/SlMXqCHFJTRteP34foDTu8PKkkKjWcMwpjEM6HRv0mxapNOFY+f9QaDd\n7bBR3sGNAwxRZXlq7sSqxTiO+f7qZeKsgWgadLtd1jo7iIHEQmqC3MoctV6LO2u3yRfyVA6q7FT3\nmJmZwXUG6KaBlrYo97qYmsFWeY9TxTmqnRbqvcV7q9Wi0WkhCAJJ3WIrbHHh9Fn2mzW2m2V6/oAa\nEnZVp+33eWbx3ImVSFPTZ2k2syRtD8tcptU8w075BwwGXSZzAlv7s1x6cpK+EzE3P0HfqXL+7Gmm\nF36Oty//K2wLEnaPpAVxHNHrxbgexEEDwxjeS612gG4MWwcqikSvfYeluWVEcTi+NJtNNGmHcys6\njVYVK30ay4o5qLyGaX7hkQu5MWPeTzZ3K6jvYiFUTZs7m9uce4dSgKefOMvWzh7b5Rrt/lCsNTWF\nhYkMi3MzAEeu8cW5GVqdHo3ugIHvIwCZiTTpe3kQaVvj8u0dErbF9NQkCOAFsHltlWDQZak4wXfe\nvIUkxBRSFmcW5zDNcReJMT/9fGTFhkql8q5+ZcMwKJfLjxQbfhRrQqPRwLbtE6sptra2mJ+fp1Qq\nUa/XyWazRFFErVZjMBhQq9X41Kc+RRzHlEoltre339HqMWbMh4EgCHhp9TJkDUR1eK1f7+9RW29y\ncens6LhOr8uNrTv8+eXvoacSXFheQRBFXENANTT2O00CISKVTKFrBhs315hZnCN7Zp44obDvt2lv\ntFFTNm7o4zd7CF2f5+bPDTu5hDGVSpX15h56NoEkSTT7bW5vr/O58x9jIpNjUrKp3Tsf2dJJP3/6\nSGbDfdLPnx51pfD+bJNJb5j3cpZnAI52o2B69Lprdfn1/+V/wjAN/u3/9gfkQwM3GOD7PraZIKEs\nsbNZwtL7FIsWkpwlm0uOfm8YayhSFxhObur1BvlMAMhYlkS31ySVzKGqMmFzE/jgiQ2tTptXd26h\nZiwEQWJAzGvVO6w4kyxOHXYXqTRqvHX7Jt9dv0w+n+P86bNUGzWkjIkoitze20TOmEzYeQxVp1za\nITGRYeLSKXZub3L9rWt09QihmEQQRcKrl/nD732Drz/9c/ziZ7+E23fZq5TZ81qY6QSCILB5cJdG\nv8Uzmk4qCmjgkJwYisa9Zp8wrfHK5g0+d/a5ExPHa9UNJvLDhVwQuGiqialZJAyXMIZrNzro1gLF\n6QlEdQFBN4mVn0FJ+GQyf8pEQaNRGzDwA5LpKbK5NG/dqlOuRiiqga5PkLQOfcj9fkAueyhMVSu7\nTOaH0wZJdEbPvIStsle5+4EUG3bK+1xvb6MnLUBiQMhLm1d5dmqFbOpQ0N/c2+F7b7/GWmePqdwE\nZ1fOsFevoCaHFUerOyWs6QxFU+cgAqfdIzGRIZsV2dva5+rrl/ElCIUYMRbIhRq/8eWvc6o4hybL\nuF6X9a0SHSXEsE0EEW6uX0dVNCzDRO91SBdy5O91e2i1BzgJkVfvXONTZ0/OR3C6m0zlhyKGKEZI\nkkEqbZJJCETA917pUCyeJZW2kbQFJCuLoH0SUbvDzNSrpBM65eoALw6Zm1um1hQ5qDnUWwKgYdkZ\nNG34/oEfEoQyxgNl3v3ePtMT9xdb/dHrE3mFg8ptposPN/ocM+b9x/V9EN953jzMNqph6ToJ23xk\nu8m5mWnmZqZP/N7DRFFEtdOnWDx+vDMY0HZjZiYLlA92cboGoiTTqNdxBi6ZbAbNTmHc64zUCuAH\nV2/x8QunSSaOi9Njxvw08ZEVG8J77afeiXa7Ta1WG02oCoXCidUE78Wa8CiR475tQlVVlpeXaTab\nBEHA1tYWmqZRKBRQVZXBYIBlWRiG8a5WjzFjPgzc3L6LkDOPVCGphsZer8tsu0U6mWKvWua3X/oT\nSkGTdiZEFjxuvLHNoppj8uIpojjmoN+g3WpjpG267S6BFrPfqBLEPn7XR9dtdsMW2X6MmbQJLJVY\nk/g3/+kPEQyFtzZusenW8BQRQRJRZYWCZiOaOi23Tz/weOapp/mz0mWUhSwA87/2JYATu1EARK6P\nvt0njoc+bFEQOc9zrMQ+fXqYWMNQSCAQPZ7+pQt89oufJY5j/ursd+isu5iBgazK6LpOL+rwwot/\nh+2d69iJNMoDZfydbkB66hSyfBgw67l9MonhMWEQIkuHuzAi7vv0H31/uXVQQssenXzpCZPb9V3m\nCtNIksTVtZv8/rVvsxk0CfM6m94u115aZzk9Q+r0NH4QsN2ugK+gKDKNZhNFERi0Grz53ZfwV9Jo\nn5rlyEheSNIA/mXvFX7rf/8j5qaL9NIyXhQiqwq6JJOSLdJTOcrNGj3fQXlgYebHw44TUtqgtLfN\nqZn5Y58tjrzRfXCwdw1VahAiU9oJabclnv/4UxzUNKaKTwxbm5aT5AtFTOtrXP7+98hmfey0RVrS\nkGWVKzcCvvqVX2F7602efCKP8MAzsd6IsFJP4Hkh+v1yYw5/fxyLiOLhPSkKJ+eN/DQTxzGrjW30\nzNEkdi2T4GZ5kxfuiQ1/9fr3+KuNy+zSQcyYrLfvcPVbtzlVnMdKZum7AzabBxhSHyGGRreNFonU\n7u7y1tvXEJay6J8a7nQKDG3YZT/kn37nX/Fb//F3efHFF/nOtVdxcwp+EKKqCqaoYak6U4VJur0e\ntUEH2T4MoPTjYWVmVw1pddqkEkmOMwyTC8OQ8sEVDKWLG+rcrjXoOUk++5kn2a8mmS4uEQQhYmeR\nbHaCheWvcbB7AzshkMzYqKpJHAtUNzQ+/rFzdPo7LMwdLr7iKKLWUkiklo6UeIvCg2F2h9eWIAgI\n8Qfvehnz4UB6l7n/QbnMQa2NocqEWpJgu4YubbI8M8HM9I/ekcd1XfxIOHHhtV+pI2s6sqYzLc9x\ndiaP7/uEvsvUzFAkrzQ6TBYOu1jIRoK3bq7zmY8/+SOf05gxfxN8ZMUGSZKO+KsfJAxD7t69iyAI\nGIaBKIo/sWqCR4kc7Xb7SPcJy7LIZDIEQXBEnGi1WkeCIt/N6jFmzAedxiNaBeqWybW7q1gJm//3\npT+nkoN0MY8z6CArEpGpcuvuHmolRUdw8aQIK50ktmT2Dxp4ns9yNkm93KbrO2wPdohFKLU3sNMp\nlHIfP1mhkQR9oNJTBhx0ugimgZY0USyL/VqXVDtgO1GmmCyg5xIkf+jQyQ/DH0VFYvEff4WgN8A7\naKFOpkYVDXEUs3LN52v/42/wr//l7zB4O0YShkOyLCgkObynB0qfi7+4xD/7v/5XYHjvf+Frn+UH\nf/Eqta06YeAhZBSef/Y5pqanMPIarVYX2ekCITE6op7h2ReeJvBLwNDaYZgJer09LEul01fITxwu\nWCKOJ23/tBNFEXW/j8XxhZeUNHjt+hUiBX77+3+KtJTDVvL0xQDBAt9UePv2Ok8XUxw4LURbQ5Il\nAiFmz2uheDF7V+8QPTuNlnx06apkaTifW+CN/3iV5EoRdTKFaojIdoK9UhW5r3PQqSOIItVuByfy\ngRi15eNMncIwdHreyUKPYU3Q72/S7TTRpA2mJkxAJZeZYm+vydb2FqlUkVarR7uXojg/rIgxTYtC\n8cusld5AEoe1N36YYGnleZJJm+2D5zmo19GUe5YfwaYfTLJ85kXqlZcp3rv9YnSiqIsoigRxAkE4\nfJZF8QevnLfWqBMa8okToWbocH19jbW9Df74zstYy9MkYhVPjsDU/n/23jRGsiw9z3vuvsS+ZuS+\nVFZW1tpdvQ+n2TNNkTOiDEEkvZCAJRsWCNmEYfmPIfifAdnwJtgQYECEAVuyRIGGIVsCSXFfhjPD\nme6eXqu69szKPSMyY9/j7tc/ojqrsjKrq3u6qruqKx6ggY5b90acG3nuiXPe833vR0+3uLZ2izOp\n59i3mpipGL4s0G532Au6hKUWt7c2MN9cOvazRUWCixOstvps/vH/R2ZxilqviZ5L4GsygqbSXa8Q\nTScpNat0rT7dQRM7cIcpFu0Qf2oOPWJSbzePFxvEOEHQYX39KoVUg0hEBQxsO09pb8D+fhlBCul0\nbVq9LFMziwCMjc/SaX6T25srSGKbEAE/KHDxxZdxHJ/b6xlqzRqiMIxWCIUUTjDNiZOvUqt/n2zm\nTunvUAWsYQlwMX3QrOHrUam9EV8N2WSMtWrv2PSjnd0i1Z4HksJ4IYckSUiGQQhc26niet5BisTn\nRRCEodJ4DJ2+jagNx1AhDFFVlUq9iRm/u8HpCRLtTudQ9QgrlKjW62TT6SPvOWLEk8IzKzbkcjk2\nNzePzctcX19H13Vc1yVxT+jUo4gmeJDIcb9/hCAINBqNI1EQQRAcev2wVI8RI76u7JSKWLZNTvfY\nlfso6Qz1fgchCECREGUJOR1hbfU2mfNzaD2PQibHtc3bWIKPLEus7G/hCSDpGi17gGxqBIMQXQM3\nKrBqdsmlcnR9j51SmYEBIh79So1eq0M+kcbtulTaDXACBimJn/nlX+DHv/tntOYjqIXh+CFH9AMz\nSAC/Z3P6JvyT/+IfUtws8e83f4Xf/e0/oFaq49YCFF8jJMCPOCTnY/zy3/2b/Gf/5d87JFQWZsd4\n6bUXkb95eBh3PZcLr5xFUVW2V3dwBi56RGN2aZpUOoVtJ9ne/SvGsj6JRJyNNQXb8YnE7qZMWJaL\nrB2/SHpaWdlcIxlNUCvVsPIakiYgODYBIZImI2sKbkJl9eYKiaUJ9IFM1IxyZWsFN/Dolpu0Cgrm\nfUKD17Ow95poheSBkCSIApHvnKH6l9dIxBX6/T5Wd0A+n6ZTa1FRm8MSxjkTGQ3fcTEzBjdL65ye\nmMeQjvfjSSZzbK0nsHtXmMnfFacHlkSucJ5ur0exqpDKn2Nm4XCusRmbZXHh6IK03rA5fe6v02nv\n4zhFCF1CEuQnltB1Ay/1PKX998hnRXL5cXa3SximRipz16m92XKIJk4cee+nFc/zuL59G/W0wdX2\nDn7WoIuN5IX4gCRLyFEdS2uxub2FWUiSMAwCQWCjtY2Dw9q1G8S/e9hk9bi+IidMnIsFbr19nfjL\ni9jNJh1RIhaNMjGVpVGtUgx0GgzQMjFkJNzuAH0syfX1VRbHZ0imjw/jzuVPUtzeJvDKd4SGId2+\nweTMKbZ36/TsKLH8zzCdvTvXUVWVRPIkE6eO5qtXaiJnn/8O5f1V8KtACGKGqbklJEli0D9LpXqV\nbEYjGhtnr3wVWU6RHbu7QNuv+OQnH+z0P2LE42R2aoL14iWQD4+HtmVRbvVRzSiybxONHBbcVU3n\n1naZ6YnCT7XhqOs6xgNsSrwgOKhkpQghmq5jOR7cswkpSTK24x5uk65TrjVHYsOIJ5pnVmx4UDWJ\ndrt9NwRQFA+iCD7hi0YTPEjk+CR6AsCyLHK5HPV6/cj1x0VF+L5/5NiIEV8XUmqUeugeek5ty2an\nU+Hc5CKtWgPBUBEAQZMQbAhsH1SJUBZRBQlns8r07Aw9a0Cn3YZWn25MQozppCSD9Y1NwqjEoNJG\nM3Ts7gAhqRMoIp1Om57Vpy24KOk4oeMjqBp+ALV+B1mP0dnYp3A6xaDZRRYEvvXmt2ntVbn+4Spl\ncUBYiCKIAnLD5lyY5efnX+PX/sEvI8sy8fNxVj6+zQsvXsTtBnTtFr7okUwmyWcKqGmJv/Mbv3rk\n2Z+dn6GyW8Gqegc7NK7nEi0YTEwNTdty+aPeMJqmMT3/c9Rqu3jdFkrsbzDwGoT9Oq7bp29piOoC\nY4Wnr0aWKIqkZAPnvuO1Wp2e4LGUy7G7XwRRQhAg1CRk28dzPERVRtIU5K6Iu9Vg8uQCtU6LXquD\nMLAobu0Q+2t3c8wD12frN//k2BQZUZEQVRk0CaIqhCKW7bLfqXMikqV6Y5OJsydotHqIASRVk3Q+\nQxiGbN3e4M03HhwWOzX7Gtc+ukqpskUs4hOiYZhjGIaBYZrs1RNMTs4duS43dpad3R8yMRYe9KVO\n18ZlAV3X0fVZYPbIdfF4hkjk56lWtgj8Hhh/C4cy7U4bSXLoWxH06AXi8advsptJpZEq66Brh47v\n7hWJZJJoqooT+viEyJKIF/qovoAT+kiKhJGMY+23iIgqsfks2/slfMuhcXMd/YXpg/d7WF9RMlFC\nUyWUQIzpSIrCoG+x6exxzpykvVPBnM3Qa/dQfIFcJE4kFsPTXKy9Jqn54+cisiwTS71Cv/0xpf0G\nui4TYhJPZFAUhcnxDOulWWKxo/no8fR59ivvkM8qB2Nvre5gxJ9HkiTGJ04Bp45cl83NYtsF9mvr\nEHq40iIIZdrtHr4PlpMgkXlxVF53xFeGKIq8dPYk715dQdSjB+Nhcb+CrOrgDFhcmD72WsWMsr65\nw+LC0bHyszCRSbLVtI70f/mOeOH7Hpn48VGFnudi6EcF4wcEaY8Y8cTwTI/2x1WTaDQaiKKI7/tM\nTx8/2HyRaIIHiRzxeJxKpYKiDB3n4/E4jUbj0LWO45DP54+858ggcsTXmeWpeX688hFByjiYFJT2\nSozFMkQjEYQQlBWfMAiGRn0C5GMp+tYAYRCSCzRShSn2NovUBx0SmsbUC8/x1ttvE3gOPaVDYMq4\nfQu31UPPxrAHPooq4Pku1UYHL6oMF4+iSIiHIIiIcR2vbVFu1DgfP8Fry89RbzQo9eoEukQ2k+XU\n0imMmsu5zAyu5zM/PcP4+NFdyKn5SfS/ZrB+dRtFvDuJ8QOP3GzyWJ8XQRB46fUX2dnapVaqIwgC\nucmJA6Hh0xAEgWx2CrhrmOi6Lq7rUsgZT3VZreXCHO8Vb6IkIwf3Ua5VmM6NIYkihUyeS7e3IG0O\nBSpRYiwSozsYoFoCGV8lnhtj5ePrOCLk4inGp8ZZub166HO2fvNPDpl/urXuweu5v/+LAESWJ+mt\n7BFZHEdOGHh9l61ikV+ZfInzixcolsvUgz5oEo5lo/oic9mJTx3TRVEknT2BqUVRxR10/e5WWbdn\no5lLx/79VFVlYubbVMprhH4TBBkzOkMhmzly7v1IknSf+LSMbdsEQUAy//SlT3yCIAgspabuMYgc\n0mg1OTE33HnPRZOsNYbCvyRLSIhkdYOeNUCsu4zFc+iRBFc+vAwCTE9Osn7lJmru7gL+s/WVCQa7\ndfTJDJIhEugyXgC7G9t84+KbTJ+YY3Nvl77igSJj9y0iqEw/oCrPJ0QiEdLZZYQwQcxsISt3+1a5\n6pDJnT32ulgshaq+yX51FcI+IRrJ9AlM8+HpD5qmMT6xfM+RswwGA0RRJKNpD7xuxIgvi3gsyrdf\nvsD65g6VVhcvCMEdMJ7Mksukh+YqxyCKIl3rfjn7s7M4P0Pz42t0PJDuERwSpkbL8tBDm8mJ4dij\nKtKhQtiqGBK9bwPUdR3SYw8fw0eM+Cp5psWG46pJAOTz+SMRDffzRaIJjhM5otEou7u7eJ7HwsJw\noEmlUgeGkmEYHhtpMRgMmJub+6nbMmLEk44sy3xz6eKh0pcntCxeYfgsRKIRJs00H6xtoE6kwfUx\nJBUdmVRf4tyLL1CkjTKRJKOmKG8UcWs1fDFAUCTCwMNudVAn0ihxA98PCGwLxVfxwgDBcRElDUQB\nSRbxXA+8AIKQYGDj2z4BHkEQkM/lyOdyDAYWQRhgGgZh1OLlpRc+9R4n5sdplbqcOD/H/k4Zu+8g\nKSKxVIRv/eLrD7xOEASmZ6eYnp164DmfFUVRvhal6BKxOK/PXWB1b5u+Z6GKMsuJSYTU0DRyYnyc\n+HWRzZ0SSjqG5IVokozmgu5qnHr+HDXZxhSyRHSF3Vvr2J0GYvLuotrrWTR/snrs5zd/sorXG3p2\naPkErQ83MKYz+GFA0LcJuxaiHyIrCrNTU8yEIb1+H1mS0HUds/vwbSpBHica9RkMNBqtfQTBIQxV\nGp0JTp567YHXSZJEYfzRVBjRviaLxsl8gZgZYaNSxA5cDFHlTG4O9U750fnJGd65fZWK4CBHdTRP\nQJUVlLbDTHKc9MwYXcUjphWQNYXt6ytYcsAnFqWfta/o4yl6q/uoaRuXEEmR8doWjd4AJRDQdI2l\nuQV832dgWWiqiizLGPanV9VSFAXbyzI5ZtBq7uP36wiCRxgauJxmtnD8pgrcEQ0mjxcjPi/HpayO\nGPFVIkkSiwuzLN55rSsSfdRPveaLIggCL184y/rmNrvV1oFwMZUyEctVpufvpqMVcmlu7VRRdQPP\ndZjJHI1Akn2b8bGjm5AjRjxJPNNiwyfcW03i04wj7+WLRBM8qGTmN7/5TYrFIo7jYBgG8XiccrmM\nbdtIknQk0iIMQyRJGvk1jPjaI0kSi1NzB69t2+YHW5fRE1EanRbJqTwLuxbbm/sEErTrDlHL5OUX\nXiQ/lqe52aFcrmBk4khxnZUPr6GNRyFlYvcGKLkYQddCL6Rwym20uAkByFYIvoAUCEimxmCvQeB4\nRCYzCLKC5/ioXoCxlOPP/ur7nFk6xVgmj2EMFwC+71PQjy+ZdS9jhTytUy12bpaYWxpGNlhen/nz\nMyRTo+f786JpGmdnFw9elyr7XB3soekqpXqZ6cUF7PU1KtsNBEmitWexSIJX33gdTdfZW71GpVUn\nnklCVKN4aQ1BuieNZ6+JW+se+9lurYuz3zrw6BARUFNRBEHEbTtEjQidSMhf/OgHLC0sMp4fO9it\ncgY2J+MPj0wpjC+zs9klHvFIZ08Nqw1UA7LjF0eRbj8F8WiMC9G7KQHCxgq1O6lbe506p5eXubl6\nk3qjjqoZtEt9ThvjvPjGC7iuxw+uvktLtogQwVZF/Hu8lT5PX/F7A0RZQU1GEQG70iWZTrHl1Ch9\n+C5zkzOM5fIH/WXQ6DA393CvjPz4RXZ23yaTTGEkx7Btl2pDZXL21S/wrY0Y8fUiFTXptp1PrVTn\neS6Z7MN/0x/G/Ow087PTB+sNQRBottt8eH0NX9ZQFJVoNEbKaFBrt5gZz5FOH54LOP0eF08erVo0\nYsSTxkhsuI9PM478hEcVTXBcycxMJnNIhFhYWKDRaGCa5qEBcDAYIEkSZ86c+cLtGDHiaUPTNOaM\nHJu9OnudOnrM5NSpU0yXckwl82TSaXZ3ipjxCNd2buPoAulkklKjRmV/H0WW0UWVvfUyLj6+AKKp\nMqg0EewANRnDK7aQLZ+oZiCECo3dMqImIakqftsmsLsIHZvsxARBXCUey1PsNqi5XRZSExiKStJT\nObX42YzQls6eZObENLubRQQRpuee/1pEGjwJjOfGKK5UaGBTsdrEknEunD+PXWwwPz5NMpVk9dYK\nfhhwZXcVJRMh7kTYL5Vo1ZpEYzH2S3fT2rRCEiUTPXYRqWSiqGPDyahdbqHGDfx6n6BnI3mQnsgT\nnc5TL1bYc1tUN9ssT8yD4zGjpSlkH75LJQgC03Mv0+222auXkCSNwvTMQ8s5j/hsnJqa50e3PqKj\nBXQll1QmxYvRFxCrFtMTEyQSCW7euEGj02arXSY3M0GtuM72xiZu30K8Z7/is/YVANEbhml7tQ5B\nY4Ch6mSmxkktTLG/vs2u06K21uLU7AJee8DZ9MxnijBRVZXp+TdoNMq06g1ULc7kbOGpTpcaMeJR\nMz87xca7lxHN48rIDhHdAVPjR71KflrufQaT8TjffuU5intlqs02YRjy+tkFRFFgp9Jk0O8hSDKh\n65KMKFw4PUsqcbzw4Xketze36VkuhCGZRJSZqYnRMz/iK2EkNtzHgzwVPuHLiCY4ToS4Pwpibm7u\noW1oNBpUKpWDa3K5HKnU8S7nI0Y8TQRBgCxJyFWP0u1tchN5EkaU5aVzB8ZLkXiES2s3SUznUYBc\nMs3GfhFtOoO3VSUzM4G1B41uG8F2kFQZSVFwWg0GOw0SRoSg0cbXQnpr+6iWgzCVRIiogIDseaSy\nebKJNJlEElEUifdEVEOju9/gZy/8DKnE5xsndF3nxKmRS/ujxnVdoprJ+uoNmnaNMS9HxoySP/fc\n3QW6InGztIGeHU4088k0W5Ui+kKO/nqFhHY3hU2O6CRfWTyUh/8JyVcWDyoN9K/skH71FGFvmM6R\nH8+SjWdQFYVUNsXJWIGOM6C/W+W7F1//3KHm0WicaPTBE+MRPx2O65A24ty6+gFWxMOMpilEU2TO\n3jXBHDg2W90yamz4N8tGEmyX91DmMihX76oNn7Wv9G6VMCcyaAkTt9pGU3WmZ6fJ6kkEYTg3Wc5M\nU203CHdbvHnxtc9tsphK5SE1CrkeMeI4JEniwuIMl1Z3UY/xJnH7HV48vfBYF+yCIDA5Psbk+GEv\nloW5GbrdLpbtEItGjoiMYRhS2i/T7w+o1Oq0nBAtEkcUh5FujUqXlZ2PeH5plmzm6TPzHfF0MxIb\njuE4TwX4aqMJjhMg7ucTccF1XTY3N4lGo4yNjR1Uutjc3GRnZ4czZ86MQm1HPLU4jsNbq5cIkjry\nRIy0ncfTJHTTODT5jsfi9G91SE4PJ9eaoqKIAo4ogiCweekGTlQhDAKQRIKBizDwUDQVydCw2wPG\nlqaIpuO4IuxvFekVK7Rul4lPZZnJjKPHTWaTw2fMtR1iZppkKsVANVGk0fD6JNBst3iveAs1FSFx\nYpxY1cEiIBKJHooEUEOReujzSQa8JijIuorr+tiWhaTKtFdKRE4ODT5nV+jo+AAAIABJREFUfuM7\nw/c/psIADCsQCE0b6/I2iYksk4kCsXiCxdwdfw03wDBNYvE4ltAZ5bQ/IWyXS9xo7qAno6TmCthC\nD2fgEY/HDp9o+wj3mC1qgYSeimB1Bxi6QX+9jDE/HHse1lcA3LUqkViUwfVdMtkcE7ksETXCQmHY\nX1RRxjQMZgwDue2OqjmMGPEYyGczvKZrrG4WqXV6uF6Aqkjk4hEWL5zCNL+6cToajRKNHj2+vrnN\n+l6dUNZod3pslBsIgUs23mV6ajh+yLIMcpxLKzu8oqnEjnujESMeE6Nfq2N4kKfCZ4km+Crwff+Q\nOLK7u4umaXS7XTqdDvPz80iShGEYhGHItWvXOH/+/Ffd7BEjfiqubd9GzA5zmgESsklXD9nt1sjE\nkgdCWtiyODe3xH69hRjTsS2LSDRGY7eIkotiBnH26xUcx0FUFYSugxY3EUQRQZLRNYF0Kk0iFqfZ\nayNFNWTToBCLkkinQBCIhgqFdA4AqeuRPHEnckgQCMLgmNb/dKyvrFPeruI6PtGUycLyHPHEaEf7\ns3Blbw0tPZxY6YaO4YmEKZ2t5j5nI3cnXAlRJxKPUWw2kOMGnXabhB6huL9HZDaHMUhR+eF7qGNJ\nlLiBqEjM/f1fxOtZOPst1LHEwS51GIa0//wqE2dPIAJjE5O49R4FM0ksEiEIAuKCdrBgfJSVy3zf\n59bVFRp7LcIwJJGNsnR+CVV9sPFZGIZsbW1Rq9ZIppLMzMw8k4tZ3/e5Ud9GTw+FhVw2R3GjjpyO\nslXd48T40DcpCALmMuO0HZ+61UOLR+g7A3RHpNexmPzZ81z9gx+hz2QRJPFT+wqAtVnFTMXIT0/g\ntHoUCgXsWpdzC6dQZBnPdcnrd8WO8BH2mH6/z+2ra7RqXSRZJDuR5sTywudKyXlQJOiIEU8jsWiU\ni2eXgCe/b6+ub7FZ6yEbw9+yvXoLVTcAg/rAxd3YYmHurq+DbEa4vVXk+TNLX1GLRzyLPHuzic/B\nZ4kmeBK4du0aiqKgqirdbncYYi7LB1Us1tfXWVwcmqUJgoDv+zSbzafi3kaMuJ+620Phbojj7OQU\ntzbXcFWoNGtkE2mClsVLM8tc3lujMDnO/t4eqzsleu06PdkjsAeIAfiqiGaY9HcbmCjERZ1Op4EZ\nMUmkYkRME1mSGNg20XQC3QpZWjxJf7OCno1TqpTpNNvEQ5XFibslKzUnJB57NGLApXc/prPbR5Ik\nJGQGZYcP9i5z8VvnSSQ/3aiq0+lw89IqrXIbgEQ+zvLzJ4k+I7salmXREz3urVo+X5hmpbiBLYcM\nBhaSIKL2Pb595mV+Ulkhk86wvbPDTqlJJ2jRVQJEe0Dg+6RfPUn1rVtEzk5hTA3LjckR/cDgD8Dv\n21g/WuP0z7+K1gmYnZ3B3msiz2bY2tslq8WIorAwfbeUZEo9vq765yUIAt7+3rsIfQlBkBCATtHi\n7f13+ZnvvHpEQGi32/wf//if8ZM/fJ/dDyuEtkAoB5hTKgsXZ/gPfv1X+NZ33nhmhIft/SJq8u7Y\nIooiC5kJ1msl7DtBDFavT9JTWVg4zYpXo+D7bGxtYu216Hpt+lqA1e2Qef4EpT+6ROa7FxDl4cX3\n9xUAZ7uOXrWZfuMi+r5NqjCLIav0x03WS9uczs2SUSKMTxaA4eInrT6a57ff7/Pun3+Aio6EDC6U\nV+o0qk1eeeOlh16/ubbJ9koRu2uj6ApjszmWzp58ohdnI0Z8Hp7kvuy6Lmt7dbTIUIgcDCzsAD5x\nepIUheagT6/fI3JPWki13fsKWjviWWbkJvWU02g0CILgYEBst9uHdrAEQUAQBNrt9sExwzAol8tf\neltHjHgUBPft6omiyPL8Iifj46Q6IieENG+eepFMMs1cPI/V7bPfbxJbmqBud1HTMVRVwfId5LiJ\nGDExcgmMuSyWbaPGIgiyTL/dQxRFgjCkYXfo2QMs16bcayKoErOZcWbNDMJ+jzPzJw8qUNjtPkuZ\nL16KEobPc32rcSTtSRN1Vq+uA3dyNYt7bKxtYFl3q3Lbts373/sIt+FjKhFMJYLb8HnvLz7Edd1H\n0r4nHd/34b4dWsPQuXBimQkxTrYvcy4ywc8uv0gymSQvROj2ejSxSC+OU+k1MXJxAj/AU0CJm+Te\nvIC/06T1vet03l/H3m/hVDv0rhfhnR3y6w7T37qALQb0cdlv1YnEosymC6RclbSvszR74mDn2Kl3\nWS7MPZL73d7cIegenSDLnsrtG2sH38n25g6/969/n7/9M7/O7/83P6D2zgDdiWIIEUw/Bpsat//N\nHv/df/K/8r/99//kkbTtaeDe39JPSCQSXJhbImspjFkaL2cWefnkeabGJlA6Lu1uBysqIkQ1eqqP\nkY7j2g5KJsLYm+dp/uAGjbduETjeofd1Sk2Cd7bI9xSyLy3RrjexdYGm3SWZSHAiO4W03+d0dprZ\nybsRFdT7nJyceyT3e/vaGiqHxQ9RFLFqDvt7wzmCbdtsrG1Q2i0dqtS1vrLOxkc7SI6CqUZRAo3y\nap2rH1x9JG0bMWLEp7O2tYNq3hUeLctCEg/PFVTDZL9cO3TMC+6MJSNGfEk8G9sVX2MqlQq6fney\ncNxkSdd1Go0G8fjdnVbf97+0No4Y8ShJKAbWMcflQOCb5186lPs+PzHD+vu7yJrKXrFMLBqhXu0h\nRFW0VAyrNyAcWEgRDdfzkJMafq2HWG6TzmWpDdoEjosngt8ekEqlCPsuRiFLuVVjcmGW+YZOvCtg\nhR66IDNfWCIejR3Tws/P/k4Z/QG73p1ah3qtzpW3r4EtIcsyax9tkZ/LcPq5Zf7gX/8RK2+vIyoi\nMydmmJqcRBAEVHRu31xn+dzXP4wyEomgP0BXGYskuXjq3KHx8rkTp1n78Z+imArbxT0SZox+fYAs\nK6AJOLZN2BuQen6esGsjBNC5sk00FqMwOUHipUn67S5Nycav9shPjCP1PcgZtAc9Zk8ucEafQOiG\neASYosbJ+QufqaLAZ6Gx30Q+xitEEATatQ6b61usX96kuFXin/6P/xKhpCEKx+85CIKAXDH4vX/8\np8wuzfArv/ZLj6SNTzKTuQK3Ny+jJw9HDoiiyNL4HEszd81bBUHgtcUL/NaP/i1CXKbd72IGCl7H\nRoloeCKIXkjq7CyiKNJ+6zYhEA5cDFVjfHqS8W+/RqPdwhICxL5HYj6F2LZphTYqKicWFliQMjQ6\nPUIgqZicWFp+ZJ5L7VoX8ZhpoCKrVEtVavs19m5X0WV9mJ6j3Obsq6eRZInf/a3fx2p5ROIRls8u\nEYlEkCWZ/c06S+edT03bGTHiWSIIAvbLFWzHJRGPkXpIROJnxXI8hHvGb13T8IPWkWfa9Q8LC5LA\nqHLRiC+VkdjwJfA4q0L4vn9o0PjEDPJ+7j82Mogc8bRyamyWd3dvoKbuLggcy2FCSR5rsmcmIpyN\nJ9FkFVf0iWOxubOF3/fA9YhNZAgdn6DWJZqK4zU8JiMJknqK2l6HitUkDEIiMQNdlEmaUWRlGBnR\n3qvxynO/SDadeSz3KikiQRAcPzEQBS7/6CqaYBzETRqKyd6tKn/+O79JfaWL7KtAwEdrV9g5scOr\nr7+CKIr0W/0Hfqbv+ziOg6Zpn2tC0um06HaqGGaCZDL7Oe/08bGYmuRGt4gavds3nJ7FydTRMmCC\nIBDPpMilo8NcXV2mHg7YLu7g9X3CMCAxnsPrWEiigpFPkhnIFOQYyfEc21tl2sGAwHVJZtIIfZd0\nKoMgiXQGFsmBxHPPn31saQmS9OC/V6/fZ/2jLTTZ4Hd++98ilI4KHF7o0qeLSRRZGHYqqanzz/+n\n/5tf/tW/dWxIseu6+L5/SPT+LJT3y3Q7PfKF3BOT1qNpGlNamuKgg2rc/X6cZpcLx5S7cxyHyZkZ\ndNPAciyidpPd2j7WwMb1Bki6SmQyR9DoE794CkVXiVQccmIUNRdje3MfV/TxHZtULkvQ7JMbG0OU\nJVr9LqfEJIuz80c+91EhyRKhc/y/7Zf2kW0dQxk+N7IsQyjzh//PH1PdblBbaaMpOnapzV+sfJ8X\nv/08E5MTqKJKrVpnfKJw7Ps6zvADP48YEQQBe8U9HMdhfHL8kYlzI0Y8bm6srrFbbYOiI8ky7l4L\nXdzi5Mw4hfyj/Z00TANVPOoBdP+onY0/mrS9ESM+KyOx4TFyv3Hj46gKIUnSISEhHo9TqVSO/JDf\nO0kcDAbMzc19oc8dMeKrIh6N8Y2Zs6zubdPxLFRRYj6aZ2ps/NjzP9m5VQSJdDyJ3a0T1U0wwXZs\nfMvBrXTJT4wR+gEx2WR56gSnZ0+wV69wpbiGUO/TtH3yc+OIdxZ0vu8zYUUfm9AAMDM/w8aVbQzx\ncBmuMAxxggFmGAfp7rFKpcyH71ymttVkLJ8nvBPAZCgmjY02OzM7TM1MERIcETGCIODj969S32kQ\neCGSJjK+MMaph0RA+L5Pcfsd4pEmhbROv2+zvW6SG38FXf/qKyxM5gsYqsZ6rYgVeGiizJnMLJnk\n8eW/xDtTM0VSSJpRXFfA1EzCKPiOh9MZIDYsYjNjBK0BuViaF2eWyaWzFPQdthtlxJZF2xPJTt8V\nNAbtDi/NXHis/geTCxNc2rqGrh5e+Huehxu6ROU4O7s77F2pod7jZBGEATf5kAolHCxUdHLhOKe4\niCiItK4M+NM//DO+8zd+4eCawWDAlfeu0S53IRBQozJzp2eYnvv0FKJut8tHP/6YoBeiyCqbl7aJ\nF2Jc/MZzT8Ru2+nZE8TLJYqdGm4YEBFVFqfOHMp5/gRJkiAIEEUBXdWIDGRy6Sz9iosfkZFEkUGt\njeEIJKbyWNtVJrMTvDZ9BkESyWsJSuU9JFnAkXSS43fHkt5enTe+8XOP9V4zEyn2b9aOzEP6dh/Z\nlFFk5eCY7djsbO3y1p+9Ty6ZPRgHBUHACKNcfvsq+V/KM7AGKOrRPl6v1bn54Sq95gCASNLg1MVF\n0g8pw1feK3P93ZuIvoIkStz+aIvJk2Msnz8q/owY8STx4ZWbNByQzbuRjqquEwAfr5fwff9IicvP\nQyGTpLJVRblnvl9IJ9iptpHuHAt8n/g9ZrROv8fCmccnYI4YcRwjseExcq9x4708yqoQuVyOzc3N\ngx3daDRKrVY75KBrWRa53NAxPwxDJEkamUOOeKoxDZML859tspkz4mx4LXLxFPVmn0I0hd8ecKW4\njqRKCAEUEnm6+23iksHLz79IwleJx+NEozEGSkBqziCqR7iyeYtu4KAIIgtSjH/3jb/+WO9TlmVO\nvbjIzfdW0WUTQRBwPRc5JjCVm6K1NTR68nyPm5dvEVoi1fUaoq3SbfbwXI9EfPisy4LKhz/5iJ31\nIvFclM0bW5x+5TTL55YQBIGP3rnMoOygy+bBL0PxRpmNlU1SqRSiJJGfyjAzP3OojaWdD5gcGyAI\nwwmNaWqYps/u/vtMzb7+WL+fz0o6mSKd/GzRZBklQisMSetRbCVEbAlYUpSbxV1kXUXxA9LJLO1S\nk4Ic5+LF54nbCrlslkgkQlhWOXVugna/y8r+NoPAxRRVzkRmee3cC4/3PjNpxk/lKd3cO0i/sRyL\nzEwS09ZwGj5/8cffQ+kah7a7bvIhu6wfvHawDl6f5kUUX+P//B/+BctnlpmZmyYMQ977/ofIroqp\n3lmEe3DtxzdYu7mGoRoousrkwgT5sdyhNn7044+RbIVPsj101cSqulz76DrnXjj7+L6cz8FkfpzJ\n/PHi5b3ouk40UAiAuGwgjo8jlyu0XZVWs4ys68SRMGUNr9RiIVbgxPw8MTNGPB5H0mT0iMGLC2e4\ntX6b7XIFL/SJiBqvTz/P7MSj8X55EIvLJ2jV2vTKA1RlGC0wcPosPDfN9krp4LxatcrOSolus0/Y\nEui6AzxcUvEU0p0/pN2yefsH7xCEPpVyhcJcnue+cZ5sLkO/3+fSD6+iSwYR7U5/GcAP/+AtCjN5\nhAD0qMHcqZlD6Z6u63LlresYcuRAVDUVk/JqjUhsm+m56cf6/YwY8dNSrdep9Fy0B5Q0Vg2Tm1sl\nJgr5n9qEciyf49ZmiYC7a4xMOonneRTrHRRNx7e65OdP4vs+vtXluZOzJO4v4ztixGNmJDY8Ju43\nbryfR1UVIpVKsbOzc0hcmJ6eZnt7myAIUBSFMAyJx+MMBgMkSeLMmTM/9eeNGPG0MTc+TXWlRVuX\nmDLS7HSrZNNZ5uotulpAMpcmakZolGSMWASCkGx0mFMpigLGQCSVTxCLx3gj/SowFO30lk808vjD\nvydnJsmOZdlc3cJ1XNL5FOOT4+wV96jcbqAqKhurG3RqveG4EoQoskTEiNJ0G7i+jSKpVOtVuoM2\naTODHNGp3+7xl2s/ovWdJs+9coFmsYOhDidGjutgWRYbNzYRQpHYy0kEwWerVqJebvD8q88Bw6gG\nRawcCA33EjPbdLsdoo/Iv+LL4szMIj9euUQ6EWdQtwl0k2wyzaDVxTVV0rkMqiCjBSLJTAavM6Aw\nOQmAaRiY7YDYdJxEMsn0ncWi53qM++aX4my+fG6JiZkCuxtFwhBOTS2QzqS5/vFN7Hqbfss61A4v\ndKlQOva9KpQ4GbrIgkKr2OWP/q8/4zt/500kWYaBeDCDsG2LTrfL7koJM2Zw8uwiftflWukWnbMd\nTpwaeh1UK1X8boCkHP4cURSp7tQJLz7ZZeaO49z4Au/t3GQ6O856eYdEPEEhkca3bMR4hEwmg+AG\n1Gt1EoUMYt8nXhguqHPJLJXdvaHJ7YmTLHMSGKb5nIlNPva2C4LAS6+/QKVcoVysIskSzy2cwTRN\n6vtNnIZPEASs3djEGwRY1oAgCNB1Dd1IUO/USUSG85f9YhnLG3BqeRmnEnC7tMnGtS3+vd/4JbbX\ndtCl4dgShiGWbVGv1altNrDqDjML0/S6Fh/sXubsN06RuyNQbaxsoktHQ74VWaW0sT8SG0Y8sWwW\nyw8UGj4hlHV2SntMTzxc2HwQL5xZ5J0rtxD16MHYOZbPkk4l2NncYGYuT0z2SccizE0vPhHRYyOe\nPUZiw2PifuPG4/ikKsQXjTI4c+bMkXSN2dlZqtUqrVaL2dlZBEFgbm5uFNEw4plDEAReXjrPfq1C\n2a9zIpnhev0WJy9+k4E1YLdfx1AjTJ8aY21jDa/eJzk13AV3Bjavjp8CQvYaTQJVAscnLZlcPPHl\niXaaprF09uShY4WJAmvxTfY3ynzwV5fwm8OdwO3yFslEknQuRSKexExrCIJAf6fLwskFcvlh2Oaw\nlKbJu3/yIfnJHP1Oj17QZW11i261S7/bRwpktLjKYDDAMAwkSaK526VWrZHJZnBdF1U93tXa0GU6\ndu+pExsUReGN0y+ytbdLOqoSuD6XKh7PvbJAtdWgEQwwYxHGo2nWtzaJRyaRleHq2er0+cXTr1Hp\ntmgEHVAkBNtnQk+xPHfiS7uHeDxO/MLh0qsnTs3z4/V36HTah4736eIca7k6jHDo0yPO8HdD8Q3+\n8nf+ihe+9RztdpvBYMDmyhZWx6Xd6KArGomxKHNLsyiKgqZobF3fZfbEDLIs0+10D3bQ78d3/Ce+\npv1xJGJxvnXyIuulbdKJOQbdHlcjAS+9sMxOYx9bCDGzUTJ6nFJpl1fO332O3WaXv3n2dTYb+3Ql\nD0ESke2AE8lxxrP5L+0ecvkcufzhCJS55Rkuff8Kl9+7wvoH2wSOQHfQoVjZIZ6MoGMQi8UozOTY\n3t5GViTOnD2Logx3WRVZI+wGfO/3v08un6NZb9Nqt9i6tYs/8Gk1WphmBFEFf2YcSZbRJJ2Vy2vk\nfmHYFsd2H9gfXHtkcj3iyWXgeCArn3qOrCh0eoMv9DmRiMkbL55jdXObcqOL6/nIkkghHuFn/503\nR/4mI54IRmLDY+J+48ZPO++LIkkS58+fp9lsUi6XD4woz549+8SKC2EY8nt/9Ke8dekGu9UW7b6N\nKAhk4iZT2QS//N1vceHckxFSO+LrwVgmx1hmOIntBTZ+fDgpHu8XqDRr4MDi0ss4lQ5qx0MWJBZj\n44znhovz075Pr9djv1lj32ryl6sfoAkKs4k802MTX+q9VCs1Nm9t06o3+ckP3qW23UQKZHzXZzw2\nxd7+HlftKywsLGCiIWkSWkw/MG3zA59GrU672WZnd5eNrU3kgUpxp0RcSZLKpGjU2th9G0EFRZJJ\n5dNkx1MUJgvsbe+TyWbQNI2Gfbyo2mr7JPOPz8/icSIIArPjU8zeeV3xexiZGGNjY7TbbRqdFrIo\ncebMN6DeR+146KLC+dwiyXiCOaZxHId+v89Oo0zV6fAXN98jKmksZCfJPsAv4nGxs7lDaWOf4m6R\nZqOJxN2IHJMoKvqxgoOKjskw7D0UAm5du8VGcZ3b19cZ1GwqOzUSZoJUJk1tp44QCBS3QmRRJRqP\nUJjNk0gl2N0uMjs/Q24sx/pH2+jK0R0/Lfb5DEmfJGRZ5uT0MA+60WrSjYtohs7Y+Bi1Wo2ePWA8\nFuOkniXjqEgdj4iocWLuPIZuMFWYwLIsut0uO0GFteYea80SSSXC8sQcpvHlGbqFYcjqjdvUig1u\n3rjFjXdv4TfFO2X1ZMajU1z5+DpzC3Nkcik0XcfyBkwvTNOz+uys3aDX7yILChomez/YZeHEPPX9\nNoOqRTwWx4jqNHbbNKUO+8U9PCvEjOtMnZgEK8S2bTRNI5aKUt9oHetzYsY/nyHpiBFfJp9VMn0U\n0qosyyyfmGf5EbzXiBGPg5HY8Ji437jx0857VCSTySdWXLiX//d3/4B//vs/ZNsxEfUocOe/EDaa\n8H7T5nc/+m3O5hT+wa//KufOnP6qmzzia4YuKPTu/L9hGsyYw3D3IAhYMsZYnJo7co0kSZRaVXbo\noCZ1VIauz7cGZbyix/zEzJFrHgfFnRK33r2NJuvUd9pEwhiaWiO0RRKpYfrHrD5PpVOkWN+hb7SQ\ndJl6rcL1S9cpTBWo7tfoN2069Q624+IbAlvVNcx+giAW0qy26HV7CLaMIksMug65jExtq4EgiiTu\nKQFYrhiUt2+RSBtMz2YRRRHP83GCcRTl03d2nhaMe+L+4/H4QV651Rvw0vJFUomj466qqlzavEk/\nJiJGDETABj6qrfM8fGmCw40rtyivVFFklUHF4fTiWT7YuEw0GPYVWVDIheOHPBs+Icc4sqBgY5GQ\nDHZv7SN4Mta+x+3b6yTDDIEEjUoDq20hhQrxSIxuu0fMjLO7UkJYFpCkoRhnGAb9oEdpbZ94IkYm\nMxSjHNdm7tzXIyQ+FokS7rlg6AiCQDab5RPPeave4dWTzx37u68oClfK60jZKModgacHvLVxldcX\nHl151Ifx3l+9j13zEQQBu+6RjRdY21sjk8gh3RGDBAV6/S5Wp4e92efK7Uu0dwaEbQndMRERsQUL\n2+ihRmUSZpL9jQpxKUWghDSsJnbXJQhDCpkcg94AXTW4fWWd2XNTB9EM+UKOH1XeQrRlUpkksdid\n586zOL28+KV8HyNG/DQkIgY1+9PPcQZ9JuZmP/2kESO+BozEhsfE/caNx/GsVYVwXZf/6h/+I364\n7YCRR/yUjYkwmuXKAP7T//lf8B//3HP8vf/o1768ho742rOQm+Td8gp6/LDDvN/oM790/P6A7/ts\n92toqcM+DaqhsdEoMxdOP/YQ8E6nw0dvXSahDtM8PMej0+5hD1wGTQshFDFMHVkSSSXTeKKLFpgo\nPQ3F0ynfrnPr8hqpZBIxkGg0mjhYpJ0koisjKAL9bh+341KpllEFnbAWUG2VcQcumqFSHzR481e/\niW3b/OQv34e+RK+bY/P6HpfeLrL80gni6ZNMTH19RMKZRJ7VQeVQOcQwDIk50rFCAwx3uFuyiyYe\n/g3QYiZr1d3HLjYMq5NUufXhKrnkMCS/2+kRleK4hgW9u7XeT3ER4HA1CsYPjveNFjNMUS/XCLWA\nTC+NIqggQLfdozNo02l3UESVaqtCubXHYNFCj2i4ms13/8Ofo16rc/lHV4mLSXrCgO1bO2xIG5x7\n9QwLL8wx8zXJv5dlmTElTs1zD+3Ie67LuJ564AbDemkbIX00gkHNRFkpbXFu7uQxVz06fN/n1o0V\n6tst4rEEruvSbfYZdAf4VkDVqpJKJVB1jZgZpxU0MQSNSz+5zGAFouGdKKY7Q6BJFHMQJegHvPvW\ne+SzY8SkkE67S7VewbFdZFGmfalBpbHP7MwcelQjPhtBVVW2NrZZ/WCNyew022s73PhoBT2hcOEb\n5zh//jTJ1JO/sTLi2WVxdpLdD66jR+MPPCeiCCTiD/73ESO+LozEhsfEccaN9/KsVYUIgoDf+K//\nW95rRxEfYppzLwMjz//+vRu43r/kP/+7f/sxtnDEs0QynuA5b45b1R16OBCGJEWDi7MPLkfbaDXB\nOH6n3pGH4qFpPp5w51azxdV3b9Ctdrn9/haKUSSejbGxsUFxs0TggKGZdPsduv0OqWwKGwtTNbHq\nDnoyQiQeYX1/A78X4DsBgghBEBLXU9SLTSzXRtAkPNvDdwISRoper4foK3idgOJ2kcnpKex6H9fx\nuHX5KrKjggyJZAEoDBe4RY9T579eJrSzY5OEpZDNxj6W4CMGkJEjPLd47oHXlFt1tMjxY13HO94j\n4VGxu13k9uV19jbK1Lea7BsVYrkYG+sb1HfbFFIFylaFhD/ccxcFkdO8yMnQpU8PkwiyMOzrHRrI\nskKn0yIIA9JKhvJWhZ7dxUxEqDdqaIJORIti9S0kV8GuOTRSDVIkaTVDPM/jyjvX0QQDFJien2Z6\nfljVwsiqXxuh4RPOz5/i6uYq++0mnhiiBALjRprl2YUHXtNy+ojq8WkkPf8hW6RfkJVrq+yulNhe\nKdKr9ZENiVg2xvr6bdxqiKFEsL0BrXYbsS8SSeuoksyVq1cRdw10HrxzIAoiyc4Y24Mt0ktZ6rU6\nGgayrmB1bARPpL3fpZ8aEIQ+7UYLy7JYeX/toNLJwql5Fk7N43r+jawDAAAgAElEQVQuucks2dzT\nmZ414tlB13XOzU9wbXMf1TxaujoYdHjlua+PID9ixKcxEhseI/cbN37Cs1gV4n/5zX/Key0D8acI\nqw71BL/1g+u8dP4DXn358ZaOG/HskE9nyaezuK6LKIoPTWkyNJ2webzHiuAHjy1lwPd9PvrhZVQM\nTD2CIsu0y102rm4z6PchDOm0OwgexBMJFFmh0ayhRhT6nkW/U2NrdQdN0DHCCB16WP0BalQhncgQ\nBAGdVo+B06ettlBdA1XQcEIXXTSw6CMi02g0Of3yKZLpCW5fXaNb698tewh02h3KuxV6vR5e6HL+\npbNksl+fRcHc+BSzhUlcd7hj/TB/AVVW8P3+sf1KFh9d+tz9tJotVt67jSYbRMwIbanH/kaZ6+/d\nAl/CdwMEV8aIGTR7FRJO9kAQlwXlwAwSoClU8RWPRJhC0EQSZgInsGk32ziBxa6zg2gr+FgIkogu\nmfSDDnJosFfZ58xLp4mkDN798Xtgi4dmHNVylUalRfeDDpIocuLsApFI5P7beSoRBIFzcyc5G4a4\nrouiKA+NelIFiT7Hm61KjzFianN9i9LNMppsoGs6Fg67N0vU3rpKVE6y3y/T6TTQVJ1UPInn+7Q6\nbQQtxCp5JDhc2tsLXfp0MYkeCFYAeXeajzc+YlKbw/IsFFVGkzW6YQctSFBulPjZ598gomh88PaH\nh8aWMAgp7e7RbXRZXb2N812Hk2cWj/VyGDHiSWFyfIxoxGRtu0i9M8APQjRFopCKsnj2/Kj/jnhm\nGPX0x8iDjBuftaoQa+sb/JufrCKaY8f+e7e0TmP1A3xngKTqpBZfJDo+f+gcx8jyj/7Zv+JfvXTx\nqXMrH/Fk81lFgkgkgumKxy4HEoLx2MSGrfUt5EADESRZxhd9evUBoivh2wETswWqtTq2a6EFGqHo\no6oyoSewWdokJiQQPZF+2Mf1PAQCIlocVB83dLDbHq7lEXghru0xcBsYqokiqYRKgB7VSccyOJ5D\no95gbnmG7v/P3nsFy5GmZ3pP+szy3hzv4A5MN9p3zwzH7TiSQzNkkIyhJO4GFRJDUvBGF5JCoQvd\n6Uah0N5sKIKK2F2uyJXIndWulk7cGXJnNKZ72gLd8Dg43pT3WZVWF4VTQOGcA6C7gTbofCIQAWRl\nVWUlqv78/ze/732bPe4+EdVSle2beyiSiugqdPZMLv7oCseenWNy5vFH+H1UCIKAqqoP3hGYyU9w\n8/ouUmq87cbzPHJa/IhnfXjWrm+gyUNxO5lOcuHVd3F7PpKtYPs2iXycarWMJuqkplLstbYZdG0M\nM4qMgotDR2riyz4pLQ0eJKNpBm4PR5TpN228AXiegNlrIXgKhmogOhKe5pBIJIgYUTqDFp7nkp1I\n0yy3SGh3hKeNWxu0djtIkgK2QGu7x2s7b/Lsl54iFn9yyorfz/dlKpVnr7qCFhmvhrHMAUvRDx6N\n9yC2b+6iyMNjzBTSvPvqFWRHAVNEi6gYKZ1qq4JnuRi+hqiJKKrCpWvvkXDzo7YJz/e4ylvjrTj+\nsBVHFEQEQcDpudTtKmEtim1ZKKpEPplHV3TMXgcPl8mZSRqVJkltWHXj+z7X37uB0/URRRHP8aiv\ntvnp1qu8/LUXgwVbwCeaeCzK+dMnPu7DCAj4WAlG6Y+AT4tx4/2o1+uUy+WRYJLNZkkmkw+13//+\nf/4bTCN3wHXXsy1Wf/AntNYv47v2aHv1ys+JzZxi7ivfRVTuTNRu9HT++t//Hd/62lce18cMCLgv\nT08f5+erl/BiGoqqMOhbqF2HpxeOLqf/sJid/thd9HA4hCjX8HwPz/FxbZ/5uVlMepRqO/QcCX8A\n7V6LTrODK/gonooqafTdPp7v0rU7uI5DWILBYIBngabrKIJC22+iKhpd2hTCE0TCUTzPx/MdtJDM\n5MwEYhhs2wF7uBjYXS+hSMPfqiPYJOIJJEli5b01JqYnPpMCoSRJnMvNc6F0Czk+jA3td3skHJUT\ni/MPfoEPyKBnjf4uCAKRaIRKtQ6A73oogsLC8UVagzqNdo10JouXtNkqb2H1bAw/TFLMoEoag0Ef\nH5dGo46neiBJ9MweuAJhPYLoiQwcE1GEvtglE59GVw08zwcJtJhMMpVETYk09jqElBCWZVHfaaHK\nQ/8LLazdjmE1uP7uCs9+7unHdm4+ySTjCRa6GW40dtHjEQRBwGx2mFZTo0Scx8GgNxi2twCKLGOE\ndXrVAZIoYTsOYS3CsVPHKDX3aNk1RFti0B0MWyDu+l1f5a0xk1GL/ujfp3gWgKw/Qc3fQxdDuJJN\nMT2LJMh4noePR2E+iyzLZLIpzF0LVVGpVWrYHW9UIWREhsabkq1y88oKJ84cf2znJiDgs47neZRL\na3jOLvgWCBqyOkEme9CjynVdyqVb+G4VQQDPN0imlzBup+k0m3Usq4dhxD51kdi+P2wHlCTpU5ua\n9HESiA0B98V13bFWEFEU8X2ftbU1Njc3WV4e9rjfb78fX7yJEDs4uV79wZ/QvHXhwHbftWneusDq\nD2DhG/9wtF00YvzNT976WMSGhxVbAp5swqEwXzz1HNvlPTr9HjEjRXHm8S0EAIyoQdVtIkvD4VoU\nRWYWpymXymxV1xk0TWrVBr1uD7fvEzeidN02NCTUgUHba5KW8rTsJqqgoWsKPa+DIYSRVZFu38YI\nh1AkGVcSER0RbDCECB2rTTwVR9M0jIjMieUTuK7LxFwBRVG4/votXNvDG/hICjieTWbijgme3RnG\nhUYikft9xCeWXCrDl+NJ1ve2sfo2uVTxSEPJR4UeVum07nhC6LrOzLEpbq2uUlrbBQsajRadThfF\nVQhpBm1rQNzM0bZbSL6CKEl0nDaqqKNoCh23RVJM4UsuvuATihgIAoSVEGaji+ob2LaD7dlEjAiy\nLJMsTJDJZ+jbfU4tn2E7skN1pUm1VB0JDZY7YHK6MDrWVrn1WM/NJ52FiRkmB3nWS9v4vs/01Nx9\nTaYfBUZEx7sdzeN5HolknFRS5NLFGt2eSbvVptc2abfbRKQYoipjdTrIrjyqanB8mzI7h75+mR2O\n+TayoCALCpY3wHANOq6FJ3gYYQVEKC7OE0vEsIUBL734Cq//6E2cpker3h6NJwPfZHZqERgKac1y\n87Gem4CAzzK9Xoda6WcUsv5d7YA9bPsKG7dWKEy9MqrcajbLdBtvks/evRjvUqtvsXJTIRkTiYX7\nJAwFs2+zXYtgRI+RTN6p2qrXSwzMCgB6KEsikf0IP+3hmGaPevUqgreLqng4DrikCUUXSNw2Xg54\nMIHYEHBfLl26hKIoB0pBDcPA930uXbrE2bNnj9yv1WpRc9UDX7TOzi1a65fv+96t9ct0dleJFOZG\n21a2K1y7du0jW/Q/rNgS8NlBEAQmc4UH7/iImJ2fYePKJnjDX5ERNWh1OtSbdU4tn+Lq29dRujpi\n1wJ88MBuePg2hMQIju/QF3r4nkdf6OGKwwm/YmlIUQlDM9BkjYE5wBd9onKMbr+HjAIDib3SHvFi\nhBfOPo8SU0jORplbnANAVmSuXriOJfaRdZFsLkW+cEd88fE+878PSZKYn/joDBDnTszy+sbb6Ldb\nKfSITrvaw7Ztjh8/zts/uYBuhul3HVBARsZtDMvg03KailOmL3QRPIm+0EUWJRzXwrYcQmGdkC2i\nCAqDQR9cYejTMOjiCT5mp0/f3mbyWIGFkycIp0LMPzVFIpkgkUywGlml3C4xwEQPa0xPTJK8K1VA\nlII7RpqmcWz68VW+3MvUsUluvL6KKqvIioKsSXQaPcKRMMl4induvYti66imjRvyUF0JtyVwd7B3\njw4Wh5ueWvTp0R15gUiCTNfqgijRrDdp9BqcPL/EzNIUgu5z5qXhNfX5X3iWa+9dZ3XdwRb6GFGD\nmZlFjLtMeIXgDmNAwGPB932qe68yVTz4G1MUmekJj83t15ie+zy9Xger8ybF/MFW0oG5x2RiF+QZ\nwuHhvCkakYhGHFrtd6iUbTQ9Sqv2DslYn0RquIbo9dbYXDVIZM4TiQzbDh3HoVy6Dl4HAEFKkssv\nPLZKg06nSbfxKsWMDNwdPdyj1X6Tcukk2dzcY3nvJ41AbAg4knq9jud5R5ZAC4KA67qsrq4eud87\nl64iRDIHttdvvDnWOnEYvmtTv/7GmNiwWa6xtbVFLBYjEok89kX/w4otAQGPC1EUefZL53nv9cu0\nyz3i6ShXr10hm8kSi0VZVTewNBev7RMNRTHtHqIroKDjiDYJP03N3yMupfF8H9EVKEZm6Hot8EVM\nu4fvg6wo+I6HjUdEjWJ6PUKpCKlUknDBYPKZDL/0W19H1+84z+eLefLFPLKoIPQP/v5CSeOx35kN\nGCcajXLmlZNcf2cFszkgno9y48Z1ZmdmsC2bWDhGrdNAFiV0RaNlNpF8Bc/3cQWISDEccUBYDOO4\nFho68XACV7DxXI+u3SKsxBBlCcETkUUFWVPp+k3CmRCpdBw9o3L6K8f4ha99fmxcnlucY2p2ih/9\nu5+gSweTW5KFx+dlEXA4k9MTOLbN2uVNnJ5DJB9ibX2N2YU5dja2SSYS7G6V0DV9GHXabaOi4wl3\nTFtCRFDRDxUcVHRCDM0eLX+ALmsYcoie0iKSCpMqDNtsnv/lp3nmhadH8whRFDl59gTZYoaLP7yC\npmhjr+u4DoWpj070DQj4LFEpb5DPuNxvmZiItmm1anRaaxQzB4WGXs9Ek0vE4jr1xh4+eYS7Gqpj\nUZWVtZ8TCYWYyGtwl9lsKKQRCnmUKj9DEF5hY/UCvvMm2ZSIIChooRy61mBv8wZ69GmSyUc/FjQq\nbzJZOPzzx6Iq1dolTDMfzHEegkBsCDiScrk8trA4DMMwuH79OjMzM4c+3mg0EQ5xXnct86GO4eB+\n4rCHrFymWq0yPT3sG3sci/6HFVsajcan3pMj4JNNOBzmhS8+h2VZuK5LYiLKzTfW2bq1jayI5Ody\nWI6FYA+d2xFFJASQJcDHECKYbhdV0NA0g1gygi7IaIJB12yjiBKCJzDwHWRFQldDCJJHOp0iFAlR\nKGQ4efbEkePB8nMnePuHF3B6HvVKE9/3iBejvPj55z7aExUAQDafJfv1LP3+cPFnxDR2r1dYubyC\naqjkZtPUN1s4lovveoiihOLLeJKP5EjgKcMUElHECOnEYnEG9NDFMB2zi6zI+I6H6fbRNB1ZEpGU\nGOl0Ej2qMzmV5+TZ44cnccgyS08vcO2Nm1hdh3azjSgKZGZTPPvUuY/6VAUAswuzzMzPYJomiqIg\nSSK9Sp9Wp0UkESWNjd3w6PcGOJ6Nquj4lofvDaO9ZUEh6xfHPBv2yVIcpVLUxRJzkSV82SOhx8nk\nMugxjeJUkdNPnTr0WpvOpMktpti9VqHT7NDvDZBUkZnTE8wtzD72cxMQ8FnEsfZQYvdfIkbCGrvV\nTQSvzGHLyUZ9h2JuKCBEQi7dTmtUpbCPKu6gKmng8HbUeNTh9Vf/CctLHomkDriAzWBwnVolQj53\njEbzbdrtF4lGH12Vc71eIhUfAEcb/KZTOjuVGxhTwQ3HBxHUoAUcieseHvN3L47jHPmYrkq4g96B\n7ZL6cErgvfupkjBy+JYkiY2NjbFF/6PkYcWWUqn0SN83IOAoVFXFMAzC8Qhzx2ZZfu4khZkC6UyK\nRDqGqquEIyFUXQHJQ1M0bNUiEU6haMMKncxkmrbZQhJlun4bKSJgaSZdpYkXctBCGmLYZ25qnmQy\nSWEqR24iS79jHXlciWSCWD7KTmmXgWUhSAKiL1It1T7CsxNwL7quo+s6iUycpeUFTj1zgtxUhqnp\nafSohqZrRBNRREXAkxzCWghPdYhGYwjq0KMkmonQ7reQJZWWWyMUUejKTXpqGzUkoeoSWkxhZmqW\nRCpBYSpPtpClUT3af2FiuogYEqlWK8PoWUXEGTjUbxtZBnz0CIJAKBRCURRyk1mOnzvG8rMniWdj\nzM7NIOkikioSi8cQJJGEmqYhlEfPP8F5JplHZXjNVNGZZJ4TnAeGbTqyISEbArFElMnCFOlsismZ\nCTLpDJVS5chjm1mcpud2aDVb2K6DrMqYrT6dTufxnpSAgM8ogvBw8398Z/jnEERhMPq7okg4znjl\nk+O4qHIX3zu4RgDwfY928yqp6AbR2PiiX9MU0gmTamWFZEKl3bjxcMf7kPR7JQzjwUlCgv9o1x1P\nKkFlQ8CRSJKE7/sP3O9+0VPHFxdQfvK3EBqPM0suPUP1ymv3baUQJIXksWfHtqUjd378giDgeR7d\nbpdwOEypVHqkFQau6z5UL9i+KBOYSAZ8VMwdm+G1lTeJJ+Kg+ZS3KmiGTrVUQdfDhMIGe14LQwIt\nrBAyNFoNl77UpmeFSWdS9AcDIloUTdaJZkOkJhPsrZeRzeEC1fUcFE0hEg+RyidRtKN/5xurG3R3\nBiwtLI1tX393i0w+PTzOgI+N4lyBlTfWSOfT9D2TfqWNHtGoNXZIhlNYhkW/79KTusRiEWREPN9m\nYHSx3SjpdJJ+f0AqmqUtNZmfT5Mp5Fi5uEJYjKKqGrZnI2sy4YRBOp9C1Y6Ogr38zhU0W2dxaXF8\n+2tXyX47G7h9f8xkptI01tokswne7V3GN0VkXcRsdcjFi2x1tlAVhY44wDS7GIQRBZFTPMsx36ZH\nlxDhUUWD7/v08jVO5ZaHwqes4EkuyD5aRCGdTxOJHW0i+97PL5MOZ0kfv8swzoN3X7vMy1994XGf\njoCAzxyerwKHiwD7+L4Pgg7C4Yvyu1cPA8tBUcdb5wYDC0MH+7A8cYamk2Hdph82sSznwOK/0ehS\nr+8xsA1EMYnneY/w2jG+9un3B9Rru4ADyCSSeQxDB/yhv0V1C7u/M3xc0AlH54jFgvn/PsEVPeBI\nstkspnn/dgfTNDl27NiR+xmGQS50sDQyUpwnNnPqvq8dmzk15tfg+z756J3Bpt1uU6lUuHz5Mmtr\na9Trj/au2L0lwK1Wi7W1NVZXV1lbW6PVunPn7uLFi6ytreH7/piJ5MWLFx+6QiQg4GEJh8MsPTPP\nhXcukIgmUCMSjmgRmQxRdyrsCVuEUxpSREBVFNp2k7njs5xaOE0xM4VgCMwdn2VqboLCVJZoPsxX\nv/1lnn7lHJbSpT1o4SgDElMRCsfzZAppphcnjzyevY3KoaKjrhpsrGw9zlMR8BBMzUwSm4pw6Z3L\n5HMF0Dw8xcXIa+wMtmhqVfSkihZWQPCxJYeT509xbPYk6UQGKSqxeHqBwmSW6YUpUpNJXvjiMyyd\nn8cUurT7TaQYpGbjTB2fIJwwmF08vLUOoLrTOLRkXvY1NtY2HuepCHgITp07QV/qsnlrh3whhyNY\nKFEJNS2z3r2OHeuhx2SmM/OY8QZN8U4FkywoxITESGiwfQt7rsV/+d/8AVMnC5j+8PuiJESyC2nm\nl2eJZkJH3ijo9/t0Kocvenq1/gPnKAEBAe+fcHSWTndw331q9QHJ9AK+eFQqQwTPGyoJPVMlHBqP\nu5QkiZ7poKiH34zw3DrubQFBlu/Mx2vVJjtbKxjqHkszfcLKJTqtd9hcv/jwH/ABKFoKy3LwfZ/N\nzRt0mxcoZJsUsl0K2SZm+102N67RNUU2V/+OqHqJQqZNIWNSSNcR7J+xsfrq6PN/1gkqGwKOJJlM\nsrm5ie/7h04MfX8YhzM3N8fFixcP3S+ZTHK8EGO3ZiEq46rk3Fe+y+oPhqkTd1c4CJJCbOYUc1/5\n7tj+Qn2Tb/zGy7iuy/b2NqIoouv6KP/26tWrlEollpaWKBQKH7qqIJvNsra2hqqq3Lp1C0EQxtoq\nyuUyGxsb5HI5MplMYCIZ8JFiWxbLp5ep1epMnMxT3iuzvbLLwBowLc0R0gwEBZqdBoZhEJuIUl2v\n0W13CYUMmq0GqVSKmfkZIgWdarfEbmkXParT6XXITk+w/MIpovEQs2enSKVTRx6L5zjA4d4mrhOI\nbZ8IPDj77Bmq5RozZybYWN1ib32XqxdtJmMnEQQJQfVpNpokcwnQPdqdHs7ARQ8ZtFpNEqk4k1MF\n4nMRtmqbdHs9ZEPG8gak54qce/4Mclhk+fkT961482wXDvHzlSQJe3B0W17AR4MoihhaiOXzJ2g0\nmiw8O8utq2tE1g1Wr8B0eh58H0cekGmlUbMSV65colMaptwIvoin2ITyOqfOL/Gl73yB7Ru7uIKP\nr/mImk/xeJazzy0jhUWeeuXo66Nt2+Af4ZuEwGAwCAzaAgIeMfF4mo3VNCGjdWi1gG27DNwpMrpO\nKnOMvfIu+ez4mJ/NTVAu7RGPCijaQfFZ11WuVSOcmzhibuE7qIpMu6NQnBxeMCqVOrpcJZWX2b9f\n7nk2ulJH8f4Db/38ApMzXyCTnflQVQ7p9ATb65fx7BsUMl0kadygNplUkVo1tm6+xosvPM29y+lI\nWCNktNnefIupmfEK7c8igdgQcF+Wl5fHoh/3MU0TSZJYXl6+736KovALL5znyr/5IRVlPM5LVFQW\nvvEP6eyuUr/+Bq5lIqkGyWPPjlU0APiey8mERzaTZmNjA8MwEAQB3/dpNBqYpkk6ncYwDFZWVhgM\nBh86pWJfbFlZWRm9391omoZpmtRqNbLZw/OAAxPJgMdFdbeBqqoUCnlsy6KyViekRcnFiggIKJpM\nx2wTDkcRfOiV+sSicbzwMLve7zucO32a4nSOdr/N7tUqS5PHYXLYK9lqtrn49gX+6//pDx/oXRJO\nRmh1uge2O65DMhtkUX8SaJZbaKrOxGSRWqVGVIrSkNsU4hMICqiKTL1TI5PLYHb6iAOffDFPq9Gi\n227j+xrzT50lP5VnffsWTk3gxPwpmAfXsWk0W1y6/h7/1X//B0ea6u4TSYVxmgdb9EzLpDB18nGd\ngoCHpN/vYzYGhMNhwuEwG7c2yESylP0qhfgEkiEgItFqNMgVcnSaXc4sPE3i2QRrG2uYZo/iYoFX\nvvgSxZkCFy6+TcRNcvp4Fo7DoN+n2Wyw29rht379N+57LJFIBDkk3lvVDICkC8RisYMPBAQEfGgm\nZ55je/MtdHmPVFIbzbkrVQuHaSanzwCg6waR5Its7b5OJumg3W6hE0WBVm+KvuUwN3dwjlyqOCSy\nX6VnbhK63SLR7jSwLRNJksGXsewBrlAEwPM8HKtCJLHfoufT69bxXYGQkSOV9ggbO9jOG2yvXyeR\neeGAIeXD4HkejuPgi3N4zmtIhyQnWZbDblVl+ZhFr9chFDrYBiaKIoZSot/vP3AO9aQTiA0B90WS\nJM6ePUuj0aBUKo38CObm5sYWz/fb7+zZs/Rtl//1L97EjR6Mp4kU5g6IC/cSql7lP/pHv0a73UaS\npNFktlarEQ6HEUWR0O38bUEQsG0bXdePrCrwPI8L77zG6s0fI1IHfDziFKeeZ2HxDPV6Hdd1MU2T\ndruNLMtjlQuWZSGKIuHwMNJr3zfiMPZNJAOxIeBRcveCrrRTRpN1rH4Z3/fQtRCJZAJREajvNoiE\nooiCiCc6pJMZ0uk0XbvF/KlZwqEwl354iaw+cddri8QTcUJOiDd+8iaf+8oro8darRaO45BIJEZ3\nDpZOLfDTjdfQuCM0+r6PGPaZmp36CM5GwAMRhNGCrV5uIkkKdt/C94djVDQWwxVcWtUWYSMCElj2\ngHwhDwUYiD2WTi3i+R57m2Um43eSACRZIZ1O02612N7aZnJq2HKzLwaLokg8fmfSt3h6ngs/uoQm\n3ZmAOY5DeiZBJHJ0737AR8md1X271sVxPZyBiyjKRCJhVFWj75jUyw0MLYQnu0iizInFoVgkJT3m\nj8/RajXoVE1iqTsR2Jquk9MLlK5XRnMFGPofNeoNdEMfXU8FQWD21BRrF7ZR5buuwY7FzNmpwN8j\nIOAxIYoiUzPP0u/32avdQsDFRyGTX0BRxj15IpE44fBXqFa3qXdK4HsIUpSlU1+m12uzU76B4JcR\nBQ/Xk0EqkEwfI2+E2NmWqNVeJ6RWiYQcIlEZz/O4tFpClGJMTJ6k1Vqh3++QTd+5eWj2mniuiSTl\nUI3U7eNQ2SlVmJxMsrP3Krr+lftW2d1Nq1Wl07yJRAVZ9mhVt9AUjc0dl0TURVMFHNejPzCQ1Eli\nkRqRiE29VTpUbABIJTV2a7coTty/bfxJJxAbAh6KRCLxUIvlo/b7pW9+nY3tPf78jU38ePF9vbdW\nucZ/+49+HfCpVqtEo8O+L8uyGAwGRKNR8vk7sTm6rlOv14nFYgeqCizL4i//7f+C0/sBz5y6wbc/\nJ44t2rZ2/jf+9l9naFvP8fwrv0+32yUej9PpdEgkEgiCgCiK5HI5wuEwq6urKIpCs9k8UmyAh0/2\nCAh4WNLFBFvVEpIk4brDvkBJEpEkEc0YlvzpukGjvYahhUGDidkinVoHRdTQQyGqe1XURYlwODJM\nlLoHRVYobw/7sRv1Bu/9/ApWy0YQRATVZ+bUFPNLc2iaxov/4DluvHuTRqWFIIgkC3FOnj0eLAY+\nISRzMbq7wx5c7/Z4JN826gtFhmOXJAm0220MLYSiyhTmcrTKHRRRRdU0ypUyqek4ES166HuElDDr\nN9aZnJpka32LGxdX8U0fH1CiEieeXiKbz5JKp3j6i2e4eWmVbrOHospMTOdYOD5/6OsGfLTouk4o\nacBtOwTPHfZNG4aO2a6jqsPxZTDo4/R9NNUjmU5gRFQGLRsBEckHc2DSl01S4cMr/9y+P6oMvPru\nNbZv7iE4Ip7vEkoZnHvpNKFQiLnFOVRNY/PGFmZ3gBHWWFqcY3J64tDXDQgIeHTouv5Qi2VBEMhk\nJoGh2Nzr9SiXdrGtbWRh6KnmuCqCXCSTOz66gSfLBr4Yom+ZWLaJ74NPlPmFJUqlG7hOHVeYoVz9\nOZnb3dG+7+E6HapNg3S2OFZRLdxOwSjkJPZK1x/q2KuVTUT3XYpZFW4n6uBBMiZRrdpY7hyCryKr\nCqnY8L3M7jBBR+Boo/vhwT7g8c8AgdgQ8FjYT2awbZu1tY0CMswAACAASURBVDUikQjf+eVvUMi/\nyb/4m59RC00jiPdvb3D7XRakCt/+pZfwXYelEycYDAZYloVt27RaLebn5w+9E7afo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X28d1NtxBLMvqM+63jfPV1VVSqRSnTt1+btDtcCd0AnamY4RCIfL5/EAqRafTIZlM4vf7EUWx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BIKhfY4XRzEg2pK5PN5NjY2Hjmo8Sjn3BUqHTHiXmanJtguXcQWAsOFuuu6aE6P6cnD7chE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qmqYRRQfX//7y84R4EhPDo7Tmuqy0q9iZJL7nozQcLgwdO7RfLW+sYmQOTpK047UPzVPy\nNHQ6HW58dIuVuVXufjRPppBmcHSQrcoWq0trFLNFdloVUmGvUouqqJzkItORR5sWcRK7SST9yCOw\nHMJuRNtpkrazLMwtQRCBrVBtVgmdiLiWxOk44GlUl2psxbfJ5fLUwwrxeJyP3/0EZ9snFUuRip3B\ncbpsLTeJjAKvvfXwqZLPm7MTx3n/7jVqmoudiuO0u1jdiAvjh69jXaluYSf2X9wpikLFbR5lc6lW\nqtz+5B63Pr5DY71JMpdkaHSQra0Nqott4mYcp+NCo4mma+TiBZygwx/Wf8PK1hIjTLHEXSL2H4dt\nJU60GWOueY/CdJHNnXVwVGwjhtv2CF2FtfubZJN5nLaPVghQFIUPf3MF3TPJ5/LkL+VptVq02y3K\nJ4aYPrk/x8Tz7OLoCf54/zpuTMWK23TqLXKRxempw/vLttNANQ9YXmIZbDQrRxpsWF5cYeHmEjc+\nvInbCMgW0+T6ciwvL9PZ8khaKdrdNp7nEUvGwAqhG3Hz5nVYtshFxUOr3qWiLP6Gx+3mdS4f+y7L\na0uonoZl2LgdH8cNWWaVfLxAs9lmsFKh0+nwye+uE9PjDAwMUCqVqNfrOG6H02+cpK/Yd2T7QogX\nTb1eB1eBR6xq7Da6BEGAYRgPf+IRyuVzBKqPwcPbEE/Hnqnk05+TYIN4qT1PswWKxSLz8/N7lnN8\n2eMmrBTiMKcnZph2HFa3NrAsk4Hh/odGyRVFPTQIpvB4lQ6eRBAEvPfPH2AEFiYWthpjZ7HG9T/e\noFlr0ml0MMMYRrJBo1ndXU4BvSUVaR7834tc1vVFitEg7bBFJpnHMkzqtSqeH+DYG4Qt0BSdiBBN\n0XH8LpqnsryyxPD0ELliiU+vXKe6WidmPgi+6JpOu+6y/vub5PpzTE1PPpMXA09C0zRemzlDo9lg\nq1Yhkx0gn314Cd6H9YajvBtTr9X56NdXsVQbW4vhKiHLN9f44Dcf0qm5NOtNPNfDi1xMTEI/ou22\nKGWGGVs9TkDAbT4hIsTDxYtcOrSIk9wNWCmKQn+nzCdzH1HSe8kCNV1FQ6cVNEh4cZZWF7n05qtk\nYmmuvH8FOuqeKzFN0+i2XP7wyz+SSMUZHB585u5SPal4LM7bJy+yXdmh3m5SHCyTTCQf+prHuZt3\nFJYXlrnz/n1M3cJSYoSBy6337rHT2MZpujTrbYKOj27oGEaMbrOLbip03Q7OSkAm2huA9SOPNs09\n/UVXDHLtQa7Of0xJH8YNA0xdQUOh7dfIOFmWthe5cOE8fj3k9tU7xPQH76soCqqi0ql7/PrvfsP3\nf/a2zGwQ4jE9zs07AJ6B46+u6+QGM3Q2Ds7VAr1rosGx4jfYqscnwQbxUnueZgvkcjmWlpYOnSL1\nVRNWCnEYy7IYHy4/1nOH+we4eWsJLb9/2nzeSBzZQOn+nXk0zwAVEskkba9Fbb0BbR0Nk+xAhu1r\n26S0HMpgyGZtHaWtkQryD9Y4R20aRgXTMBjWx0jFMgSGQ2QE1KpVul2Xtt/EazlovknSSqErGugq\nVtKkmO2n5TXQDY1MX4qNhQ2ML5Skq1VrLNxYwlBMHN/n/odLrN5Z49UfXCAef3TJrOdFKpk6dNnE\nlw3n+1nZvoOd2Pv9oygibxxdhY57N+5jqb2/Tb6Y4+aHd/BqAdQ1ErEEnWyHSqWCqVioNnS9Fnqo\ncWflNlmvD0OxKEZD3OYTlrjLb/l7fNzeUpyotxRHVVRURcVpOtSMKqlYhsiJUIyATCZDNp6l2amR\nTCToH+xj+d4KudiDi8P1lXXW729h6hbdsM2d9+aZv7XE5e+/+sIEqAAKuTyF3OMNivusFAtBY985\n2Om6HEsd3ayG+RtLmHrvlmcyl+TulavgqDjbPv39A7Rq92jstIkpMQJ8IhPCIOTa9Wv0B6O7UbUw\nCrnJh3uXbn2hv9hKjM36MraVJGEncV0XtJBifoCYGcNvO+iWTrlcZml+mZz1YPbC3O37tDY76LpB\npxXw8f+9RnEix+kLpw76SkKIL0in00R6+MjnWUnzmcizNHv+BO/+8j10f/9UjCAIMPMaTamftQAA\nHJ1JREFUkzPP5rK7F+fsJcQTKBaLuyUvD9PpdOjv/2qJYD5XqVS4desW169f59atW1QqlSd6n8/N\nzs7ied6+Nnc6HTzP+0oJK4V4GlRVZSY3QrfxoNRkFEW4201ODI4f2ec2Ks3dAZiiKGhGbzAShSGB\nF5CwkpTHRzFLCmoKjh07Rm4qzWZ2kWXjLuv6Io7VZcgcIxnkcFyHVrNJo9XAqwe0Ox0iB1JGlqSS\nISIijDzqVNAsKGZ7xwQFhU7QZGRshGQqSagGu/tg8fYyhmqBohBpETE7hhHaXPvg5pHtl2ddNp1h\nkBRO+0FVkCAIYKfNifLRJbdrVR/0T9u2UdQIoghFUfE8n3yqj9JoCXI+Qcwl1ZegY7SoNXYwlN7F\n3eczWwD8z6oBuHRZZo6bfLj7/n3REPWwght1qVMhkUqSjfeCwJEHWkoj35cnlUvh+b33cRxnN9AA\nvSoUhm4QNuD2tTtHtl+edeNDZcyaj+95u9tcxyPv6AweURnMKIpo1x+cYzPZNH7gEUURhmLiui7F\nQoncaAYv0cazu2AErO+sE7b3ztC5yYcsM7dbBeeg/pKOcrTDNt2wRYsmuXyBuBVDUXr9ojReRDcM\n4snY7s2R7c1t2ltddL03S8K0DWzTZvt+lfXV9SPZL0K8SDRNozCcf+gNxyAIKJW/+myBKIq4d+se\nv/uHd/mnv/kVv/ybX/Her99nZWn1idtrmiaX37lEcsCmG7TpdDt0uh08zaXvWI5Lb118ZmfBffuh\nGiG+RUc1WyAIAq5du0YQBMRivTVUURQxPz/P0tISs7OzTzRb4rASnl8nYaUQX9doaYhMPMn85goO\nASndZurY9JGucTQsfU/5t2Q8iTZqsL66wcrcIi2nRbvTxncDus0uoR6hopP2CjSDOkYQI6mlaAQ1\nTMXCNmO0whbJMIHjd3prNE0VTVeIJ2J0qk00xUQLQwxLBz1C13RShQQzJ44TRiFD5SKNWovGcptG\nvQG+AhoEYUC2P737m6+t1440l8Wz7vTEDMXtTVZqWwSE5M0U48dnj3R/mLaB7/YuKqMoIpfL4Voe\nTadObadC2I3oOG1cJ8CpN3F1j9CPerNnPuNHHpscfLG4ySrTkYeuGOiKgY+HHpp4iodp66gGKKrG\nwGiR4kCBrtvl/HdP8+kfr0MXttYeBBq8wKU01LvAVVWV7ZUKnD6yXfNMU1WVN46/wsLaMlvNOgow\nmexnuHx0sxoURUE3H5yfO50Ow+URWvUma9urtDsurXobz3VpN12CVgNN1Wm2OsTC+O6shsftLymy\nrIT36Qv7aOtNTNtANXrXDGPHR+kr5nGjLm98/zL/8n/+gKXEqW7V0T6rWuIEHcrDvUCdaVisLWxQ\nGnwxqpgIcZRmz5/g99vvornmvjFAGIYYWZVjJ6e+0nuGYcgffvVH/Fpv/PB5ImK/FnH7D/eobleZ\nfeXwPDUPY5omZy6dJrgQ0G63UVWVeDz+zAYZPifBBvHSm52d3RMY+NyTlLf83LVr1w4tpxlFEdeu\nXePMmSfP0n9QCU8hvk2ZVJqz31CZS4Dy1Ah/vPcxMbP3m9VtHcvt5V8YLY+xfGeVcEfFb4KqGiT0\nDFs7W+BHFNR+tqINfCzUUMdXPVAi/KiDGqRIpOMQKMSsOJ12hygIMZU4ruOgKzqNehNChdRAklOn\nZ7DiBlZBY2J6ovf7Vq+x+sEKrucQKD6Z/jRjE18oGxrxUgcbAEqFIqXCN7e+tH+0yMLHy+ia0RtM\n2joKKoqhUB4e5eZHt1GaJmG9iWnHiOkJ6jvtPTkD2jR371B/mUuXNq3dXCAqOr7noysm1Z0abtIl\nN5Jh+tRxrJjJ4PF+srksr759gSt/+JTWrRZd10GzVPrG8vT3P9g3YfDoqb4vMkVRGBscYewb/My+\n4Ry1pQcX85Eaolk6yWwcW+3jk7lPURwNpaljJmKoiopbc1B4EKR43P6ioBKGAb7vo4Y629s7xFIx\nBqb6OHZiCjdyOX5xCtu2Off2WW58cItWt0ngRVhxg9HpMokvlBINXvL+IsTj0nWdN/7kMjeu3GRr\naYfQ6QWj9YRGaaLIzOyxrzyQv/bxDYL6wcuvTcNi8+4OS7llRkaHn7jdmqaRSj3e0sVngQQbxEvv\nac8WeFQ5TUVRCIKAarUqAQMhnlA6nWbqwhh3P5rDUmMUB/v4l1v/QjaVIVfIsXh7hYiAKIyIG0na\nfhPFV9Eji1CJSKsZXLrYagIv8ImrccyY3St96Gg4vouK0gsIRBq6GmCYBo2gSipZgGREfMBm4FQ/\nr/30AgNDvTutiqJw+uJppk9P88//+9ek7My+oEIil3gm1oC+TMYmRmnVW6zd2SRmxsn2p/noX64w\nMjKC63kYmkHDb2KoJrqi0uo0MQOT8AuVJ+IkMbEPHECa2MTpDfiiKEJVNXTNoKHUyCbSKKmQbDnJ\n6LlBLv34PNlc79hvWRaX3rrA2MwIH/7TVVKJ/WUgM33Pz0Xli2L2/Czvtz+kudHBNm3sjMn84jwT\nk5MszS8RN+NUmlUSRpwg8vC6EckwS4XN3fd43P7i0CWuJUBV8XUHI5HDyCj0H+tj6nKZ17/36m6O\nl2wuy+vvvEaqL8n6rW1i9t6E0UEQkCtJVQohHpeu65y+cIrwXLi7RPlJZwuEYcjW4jaWengid9Ow\nWJ1b/VrBhueNXO0I8ZmnNVvgcctpbmxsSLBBiK9hbGKU4fIQi/NLRGEfnt5l/eY264u936A1bBJE\nIXqo0XUCVFVDCzU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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "from sklearn.metrics import pairwise_distances_argmin\n", + "\n", + "X, y_true = make_blobs(n_samples=300, centers=4,\n", + " cluster_std=0.60, random_state=0)\n", + "\n", + "rng = np.random.RandomState(42)\n", + "centers = [0, 4] + rng.randn(4, 2)\n", + "\n", + "def draw_points(ax, c, factor=1):\n", + " ax.scatter(X[:, 0], X[:, 1], c=c, cmap='viridis',\n", + " s=50 * factor, alpha=0.3)\n", + " \n", + "def draw_centers(ax, centers, factor=1, alpha=1.0):\n", + " ax.scatter(centers[:, 0], centers[:, 1],\n", + " c=np.arange(4), cmap='viridis', s=200 * factor,\n", + " alpha=alpha)\n", + " ax.scatter(centers[:, 0], centers[:, 1],\n", + " c='black', s=50 * factor, alpha=alpha)\n", + "\n", + "def make_ax(fig, gs):\n", + " ax = fig.add_subplot(gs)\n", + " ax.xaxis.set_major_formatter(plt.NullFormatter())\n", + " ax.yaxis.set_major_formatter(plt.NullFormatter())\n", + " return ax\n", + "\n", + "fig = plt.figure(figsize=(15, 4))\n", + "gs = plt.GridSpec(4, 15, left=0.02, right=0.98, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)\n", + "ax0 = make_ax(fig, gs[:4, :4])\n", + "ax0.text(0.98, 0.98, \"Random Initialization\", transform=ax0.transAxes,\n", + " ha='right', va='top', size=16)\n", + "draw_points(ax0, 'gray', factor=2)\n", + "draw_centers(ax0, centers, factor=2)\n", + "\n", + "for i in range(3):\n", + " ax1 = make_ax(fig, gs[:2, 4 + 2 * i:6 + 2 * i])\n", + " ax2 = make_ax(fig, gs[2:, 5 + 2 * i:7 + 2 * i])\n", + " \n", + " # E-step\n", + " y_pred = pairwise_distances_argmin(X, centers)\n", + " draw_points(ax1, y_pred)\n", + " draw_centers(ax1, centers)\n", + " \n", + " # M-step\n", + " new_centers = np.array([X[y_pred == i].mean(0) for i in range(4)])\n", + " draw_points(ax2, y_pred)\n", + " draw_centers(ax2, centers, alpha=0.3)\n", + " draw_centers(ax2, new_centers)\n", + " for i in range(4):\n", + " ax2.annotate('', new_centers[i], centers[i],\n", + " arrowprops=dict(arrowstyle='->', linewidth=1))\n", + " \n", + " \n", + " # Finish iteration\n", + " centers = new_centers\n", + " ax1.text(0.95, 0.95, \"E-Step\", transform=ax1.transAxes, ha='right', va='top', size=14)\n", + " ax2.text(0.95, 0.95, \"M-Step\", transform=ax2.transAxes, ha='right', va='top', size=14)\n", + "\n", + "\n", + "# Final E-step \n", + "y_pred = pairwise_distances_argmin(X, centers)\n", + "axf = make_ax(fig, gs[:4, -4:])\n", + "draw_points(axf, y_pred, factor=2)\n", + "draw_centers(axf, centers, factor=2)\n", + "axf.text(0.98, 0.98, \"Final Clustering\", transform=axf.transAxes,\n", + " ha='right', va='top', size=16)\n", + "\n", + "\n", + "fig.savefig('figures/05.11-expectation-maximization.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Interactive K-Means\n", + "\n", + "The following script uses IPython's interactive widgets to demonstrate the K-means algorithm interactively.\n", + "Run this within the IPython notebook to explore the expectation maximization algorithm for computing K Means." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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SRxRCr9DwkfqM+HwkgYGBTuvyL4PBwMtvvuL0XJmyZRn0bs4Urn/nMl4+Fub0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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn; seaborn.set() # for plot styling\n", + "import numpy as np\n", + "\n", + "from ipywidgets import interact\n", + "from sklearn.metrics import pairwise_distances_argmin\n", + "from sklearn.datasets.samples_generator import make_blobs\n", + "\n", + "def plot_kmeans_interactive(min_clusters=1, max_clusters=6):\n", + " X, y = make_blobs(n_samples=300, centers=4,\n", + " random_state=0, cluster_std=0.60)\n", + " \n", + " def plot_points(X, labels, n_clusters):\n", + " plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis',\n", + " vmin=0, vmax=n_clusters - 1);\n", + " \n", + " def plot_centers(centers):\n", + " plt.scatter(centers[:, 0], centers[:, 1], marker='o',\n", + " c=np.arange(centers.shape[0]),\n", + " s=200, cmap='viridis')\n", + " plt.scatter(centers[:, 0], centers[:, 1], marker='o',\n", + " c='black', s=50)\n", + " \n", + "\n", + " def _kmeans_step(frame=0, n_clusters=4):\n", + " rng = np.random.RandomState(2)\n", + " labels = np.zeros(X.shape[0])\n", + " centers = rng.randn(n_clusters, 2)\n", + "\n", + " nsteps = frame // 3\n", + "\n", + " for i in range(nsteps + 1):\n", + " old_centers = centers\n", + " if i < nsteps or frame % 3 > 0:\n", + " labels = pairwise_distances_argmin(X, centers)\n", + "\n", + " if i < nsteps or frame % 3 > 1:\n", + " centers = np.array([X[labels == j].mean(0)\n", + " for j in range(n_clusters)])\n", + " nans = np.isnan(centers)\n", + " centers[nans] = old_centers[nans]\n", + "\n", + " # plot the data and cluster centers\n", + " plot_points(X, labels, n_clusters)\n", + " plot_centers(old_centers)\n", + "\n", + " # plot new centers if third frame\n", + " if frame % 3 == 2:\n", + " for i in range(n_clusters):\n", + " plt.annotate('', centers[i], old_centers[i], \n", + " arrowprops=dict(arrowstyle='->', linewidth=1))\n", + " plot_centers(centers)\n", + "\n", + " plt.xlim(-4, 4)\n", + " plt.ylim(-2, 10)\n", + "\n", + " if frame % 3 == 1:\n", + " plt.text(3.8, 9.5, \"1. Reassign points to nearest centroid\",\n", + " ha='right', va='top', size=14)\n", + " elif frame % 3 == 2:\n", + " plt.text(3.8, 9.5, \"2. Update centroids to cluster means\",\n", + " ha='right', va='top', size=14)\n", + " \n", + " return interact(_kmeans_step, frame=[0, 50],\n", + " n_clusters=[min_clusters, max_clusters])\n", + "\n", + "plot_kmeans_interactive();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Gaussian Mixture Models" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Covariance Type\n", + "\n", + "[Figure Context](http://localhost:8888/notebooks/05.12-Gaussian-Mixtures.ipynb#Choosing-the-Covariance-Type)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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BQ/5fqhwo49urqoqqgK6rhEwTwzDQdb38WmKyI1kzu5HW224Uw6NxAq2+h5n4\nfkBsLIlljw+XOkr7qlVdAzTimSKpTJ6uJa2YdTw0zAw3MTAcZ82q5RXdr8QIIarP9/3x+lQRx3HH\n61OlepPv+aWVvf0DdSnf90FRCPyAgFJF3fd9GP//RD1MVZXytYkmqkGaqpbuh/L1iVRVIWTqhEMh\nTNOcd7mP9ByvZoyQZKPOeZ5HwbKwxtfB9/xg/OAGLwhKF9ryAxRFQ9U1NE1HVaf5WtWZbx66dSjc\nRCgy/4RgYrX98hr7E3eO3/BtHy+Tx3c9/MBDVUDTVHRVRVMVFAU0VUHXVSKh0hr8mqbN+/WPFkey\nZnYjrbfdCAqWRSrnYobrtxU9l8sTT+XRzTC6eWwk8LpuAAZDsTQtUYOO9tZaF2lGTmCQSCbpaG+v\n2D4lRghxZDzPwyoWsawiXmAzPJLB9wM838f1J+pUoKgamq6jadrkBtKJ/45XUVSOfD6CP1E2wBm/\nRlGm6OGOpQh8j1g6TSZtlS6UqquETJ2W5qYpPadHeo5XM0ZIslEHbNsmXyhQtF08L8D1A1yvdIGt\nABVdP2iJSmX8Ty0d61r9Nu6VqaqKqpowTVknkhM3gKIDqbyFO5pBwcfQNXRNRVcVNK2U6UcjEQzD\nOCp7R45kzexGWm+7EdTz8CnfDxiNJyh6KrpZv8lQNelmmFzRJzcYY2lnW132cuiGwWgyR3PT1ErB\n4ZIYIcTcfN+nYFkUChaO65fqU56P6wX4gYKm66V6VVHDDoxSnUoDTaufOpWmaeVGVzPUhGooBEAx\ngELBJ5ZKEAQuIUPD0FRCpkZHe9sRnePVjBGSbCwi3/dJZ9LkCw6O5+M4Ho4XABq6aaBNHOUqqCqE\n6uSgX0y6MXntfx+wATzIZj2Gx5IEeJiaQs7KkUlbRCMGTdGmY7I3RFTe0Ej9Dp/K5QrEU7lSb4Z+\n9CXcC6FqKmgRhmJpWqMm7e31t2pVtYZTCSFKdapcLk+uUBxvoC0lFp4Pqq5jGCaKopfrVHXYJnFY\nVFXFDB/4jXKAYtEn3jeKqSuETZ221ijRSP00RkmyUSWO45DJZinaHrbrY7sew8kmMlm3NBRAAdUE\nmaI3f5qmoUUOJBQuYSzfI5tycWOjaGqAqWuYuoppqLQ0Ny9ovKMQBcsina/P4VOJZJpMwTtmezNm\nopthspaHHUvQ3dlBvXV6VmM4lRDHGtu2yWRz2M6BOpXngW6YpQZKBRQDDGPaQRRHPVVVCUWiQKn3\nY99IFiVDP7bWAAAgAElEQVRIEglpLGlvqXniIclGheQLBTLZPEXHo2h7BGgYoRCKooEGhgbhcBMF\nK1froh51dF1H10uH8kQ3o1UMiKWSqIpHyNQwdY2W5ghN0WhtCyvqWj0OnwoCGI0nsF0N3ZDkeTqq\nruEEKvuHYizvbi/Hg3owMZyqpbm5rsolRL2ybZt0ZnJjLeiY4cl1qmMxqZgvMxQCQrjAvpEMhpqi\nvSVCe1trTYahS+Q7DEEQkM3myOaL2K6HZbsoqlH6clUDoz5HYBxTFEUhNH4xGh+wfMjE8vhekrCp\nY+oazdEQzc1NR+X8D7FwiWSSQK2vk9d1XYZGkyh6GPUYHzY1F0VRUIwIAyNJli5pqasrkJvhJoZj\nCY6r0sX+hGhUvu+TzWXJ5R1s16PoeFBurNVRjGNzSHklmaHS71o86xJLDdESMVnWvWRR6z6SbMxT\nsVgkkcpi2S6W46Pr4113mkFIRjU0BMM0AbPc+5FLFtkfyxA2VMKmTntrE+FwfVU2xeIIgoBYqoAR\nqp9eDdt2GIqlZNjUAulmhJGxHJ1tHh3t9fN95iyfYrFIKFQ/SZAQi61YLJLO5ijapcTC8YLStYH0\nUn3KlKmXVaPrOug6BS9g195BlnW20NqyOHPdJNmYQan3IksmVyRvu/j++IQc3SAsn1pdsiybp58a\nIJkyaG9z2Lx5JaHwzMNODp6MbvnQN5RGVZNETZ3mJpOW5hbp9ThGxOIJVL1+Es1i0WZ4LCOJxmHS\nzRDxVJHWtiwH1qpceIyoJDMcYTiW5Pjjli3K6wlRDxzHIZnOYNmlUSC+r40Ph9LRzPIKsnWhlvFh\nMSmKghFuZjhRJJnOs2LpkopfgHTKawZBEMy92bHB8zziiRT5vE2u6KBq4Yp9AZZV5Of/1k8yadDe\n7nD5e1dLC1eF/fTRXQzsP6l0tZwgYOVxu3nf1Scf1r4cx8FzLZrCBk1Rk86ONlnt6ijl+z473h7A\nDDXXrAwHx4emphynb2gj2ly/15BoFI5TpKs9TGtz6butZIw4HLZd4IQV7USjkkSKhcnnLX74w53E\nYiZdXTbXXXcKkUj9NJBMcF2XsWSaQsGhYLu4vkIoFGmIhrtax4daKVpZjlvaRntb9Xo55myjHx3N\nVO3FK6W7u+WwyxkEAYlkinSuiOX4mKEIqjpxmRZ7/O/I/esTe0gm12NZDvFYwMO5XVx+xQkV2Xel\ndbQ3kUjWx0T22VoaDi3nwICPZTmTbs/2PuZuxVApFD1Gk3neejtO2FTHLyTWvqDAeSTH52Lq7j68\nQNMo722mcg6NxCl4Okqhdsf8vz6xh8HBkzFDCn0DoyRyMd797vodqNzaEiGdKdS6GEBpuNn27TGy\nWZ3mZpdzzu7CGF/jsrUlQm9/kiUtBZqbo1WIEQv3xpv7OOGQpXCP5hjRKO+r3sv5T//0G2Kxd5LP\n2wwNBWQyr9bFdVN83yeZSpO3HCzHw/Vg+fIukil3fIuAgpWvaRlnOo+rX4eojOrXyRR+/dYI3R1p\n2tsOv5FrtvhwpBc+bFjZbI59g6O8tWeQRD4APUI40jSeaFReMmWUr0+vKErptpjT008NMDh4Mlbh\nBAYHT+appwZm3La9zWGioy4IAtrbnBm3Xci+S5PNowRamEQedu4dYt/gKJlMff84ibl5nkcqZ9e8\n1S2ZMvADj4KdwDAjZLMyVnO+tm+PEYutomitIBZbxUvbY5Me1w2TsXSRXK5QtRixEI6vkclmj3g/\n4tgSj4fKcUpRFOLx2o2MyBcKDI3E2bNvmLf2DpMsgKuE0M0o4Ui05vH0UPM9j+shPtSKEQ4zmioy\nlkhWZf/HVLJRLBYZHBpl554BhhIWrhIiFGlelOEx7W1OaQ1L5ncQi5JkypgUYGdL0jZvXsmKFbvQ\njZ3E488Ri6v86xN7KFrT904tZN8TNE3DDDfhKiGGkza79g4yODRKsVg8jHcnam14dAwzXPvlkNta\nbQrFMfRQlICA5mZ37icJALJZHWV8XoaCMm2ippsm8VSO8y/ornqMmIthhhgdk4YKsTCdncVJFeHO\nzsX7zQmCgGQqxb7BUXbuHWTfSBbLN6reSFsp8z2PF6MOUc8MM0Q845BMpSq+7/o+QiokmUrR2z/E\nnoEkRUIY4eZJV6leDJs3r2TlcbsJR/awYsUuNm9euaiv36gW0tIQCptcfsUJdHVCZ+d5uM5JFW3F\nOJRuGOihJoqE2DtYOsYSycqfpKI6PM8jU3DrohXutNMjLD8uRjg8SFfXPs45u6vWRWoYzc0uAePn\n8SyJmm5GSKQLXPbeNYsWI2biKyZp6RkVC3DNNWtZu/Z1mpp2sGbNq1xzzdqqvp7jOIyMjrF3/whv\n7hkinvVxlRBGqGn8Gg6NY77n8WLXIeqRYYYYSeTxPK+i+z1q++p932ckliBbsEELoRvRmq7VHAqb\nvO/qk+tmLkSj2Lx5JU89tWvSmMiZTIyffPrpPKrWywlrVqHr5pTWhontYnGIx59j+YpldHX6R5QA\nmuGD17EepCVi0t3ZLpPK69hoPFkXvRpjyTSqEeXd57XU1VyIRnHO2V28tH3fpDkb07Fth+0vJfh5\nfISB/iIrVzroujlti2Q1YsTBdMMgkc4v2rKTovFFImFuuunMqs4tsW2bsWSGfNHBdiEUjqBoOuEG\nX89gvvWIWtYh6okZbmL/UKyiK+cddcmGbduMjqXIFlzMcBRdrgazIJZl89NHBxkY8Oti6beJlob5\nmBg/qWopMpkl7Nm7i5NOXDultWFiO0VR6Ow8ma7OmSfrL3g53YPXse4fpTms0dbWWK1Ax4pMvogR\nrm18yOXyZAteQ10Z3LYdnn4mRiwWTJmQXQuGafDud6+Yc7vt22PE4qsh8MlZSXr39HPKySdN2yJZ\nzRgxoeiUfq9Ms3G+e3H0mbiGWL7o4LgKoUgE1TA43NBYb3UImH89oh7qEPXC9jXSmUzFGkSOmmTD\nKhYZjafIF31CkSihiFTwJsx1sE8sfhwEAVu39pNMrqdQsMlmA/7jyV1ccfmJoCgHjU2sxbuY28T4\nyRNOaGHPnjF8LzPtkLWFjLM8OKgM5gOeemp+q4gpikIoHMUBdvbFsHIFupe0EpGLBtaFVDqNqtX2\nu/A9n3gqXxfX0phtRadDbd8eI5tZS7HoYlkBL23fN6/Kfq2V53YoGqtPiDI6sJtwRJu2pXMxYoQZ\njhBPpFmxTIbMicVVLBYZS2YpFB3c8WuIqYYx6+iP+Vaan35qoLzy5kLOh1rwAx/XdfH9AFVVSCRU\nAgJOWNNE7554TesQtaYbJol0QZKNCa7rsrd/iP1DOYxw+Ji/mncQgOe5uK6L7bi4ns/WrX2MjJyA\nokA86ZP82Q7e/e6V+P74cxi/7JWiMDDqAGMUvdK458GYS/9wsjQuMQgObAuoKqiqgqoo6KqKooCq\nqqgqaJqKaejouo6m6YuSoLS3lYKbrmmcdGIbK1ZEpz2pJ7ZTFGXOcZaVmAAWCkXJFwL6h9JEQ2mW\ndXVU/QI6YnaJdAHNqG2wGI4n6yLRgAMrOikocyYQ2ax+YGW9GSZk16Pm5lJypKCgG2FOO0PnQ1uO\nmzY2LVaMyBYOjPkWopps2yaeyExOMEyD+baxz7fSXKuVNx3HxSpauK5PEIAfBAR+qc7iB+D7AUEQ\n4FO63w+gJR0mm3FKlZoAlFCafCYOKKxY7dPV6dJzZjMjiQyqoqBpKqoCupmgkGhH10JoeqjqdYha\nsRwfz/MqMhy8MX4lpuH7PsMjY6Qtl+XLuzHClZ3MUs98z6dgFbCKLq4f4Lk+PqXPxPdBUVQUTR2v\n5GtkrWa08VZKFbDs1hkrOe0dOtlMGM8rTbps79AxzLl7icpTMv3SX+AEeDmLwPPxAx9NHU9EFAVN\nU9BVhXBIR0Hj2WeHKtLFON9xmQuZB7KQSsdczHAYF3h7X4zWiM6ypZ11v4rH0ci2bYouhGoY/TLZ\nLK6vodXJ1z+fFZ0mNDe7ZDNzT8iuhIX0uMzl0LkdZ29cQyyepLurfcq2ixUjVCNMIplk6VK5gKOo\nPN/3iSeS5AoORYfSEKkFJBgHm2+lub3NIZmszqRpz/WwihZF28PzfFw/oJAv8uJLI2QLIdpa4bd+\na9nUGKEAWvmfslAoQtE+MEfuXe8qLZ19IN4cj37QvsarN5xz7ipe2j5IKgXRiMXJ67oYGk2gqwrR\nsEkkGi03YlSyDrHYDDNMKp1hScfUGLlQDZdsBEFAbCxBIlPECEUbYuzbkXAcl3w+j+P6OF6A43kE\nvoKqG2ja+Nenl5KImeotB7fozVU5OOfsLt749b5J47EPh6Io6LpRPsJs2+HFFydXGvKOx3PbeomN\nrkJVVMYSKvnCTq66ah2GsfBDc77jMhcyD2QhlY75CkWasIKAnXuHWNISpquzoy5WRDpWxBNpQjWc\n8eh7Pom0VTe9GlCbGHGo6RKLhfS4zGW6uR0FxyafL0y5ovdixQhN00hla3vBM3H0SaXTpLMWedvH\nMMOoeuSIG1fmW2nevHklv3px96Q5GwsRBGAVLQqWjef6eEGA6/l4XkDR8Xj1PxPkC+FyjHjl9SzJ\nzKkoKMTHKh8j5rudD9gBWBkXLxkjbGqYhsaFm5bz7DOVrUMsFlVVsYqVubB1QyUb6UyGkbEsih7G\nDDfVujgVly9YWJaN6/m4ro/j+YCGETIADVTQ52gJne4He0GrtWyP4boRmpsLFZ34OVOlwbKbUXWd\ngcERHEdnZMzijLPGMAwVXVMwdA3PdXnpxRj5XDPt7e60vR/VmoS1kErHQpQuFNhMuuiR7Bti2ZJm\nWZlmEQRBQKbgYNawkWKkhsOnZuopqNcYMdHj4nkOA4Mj9PdZwOCU152rB2Smx3XDJJbKsToSOeyh\nnkcaIxxfpVCwDvv5QkDpQnvJVI6c5aDoIXQ9TOgwpqXN9Fs6n6R64rm200x7W3Zev8Oe65HLFyja\n7nijqo+i6aXGyvHuCFUv/b308iBjiROmxAjfc8t1iOGh9ILjw3y3mQ9V11D1CB5QcAPS+RzvOKeN\ntuYo4XDjzSWu1CjPhkg2fN9n/9AolqtihI6eJMNzPdLZHLbjYdkeqmag6TqggQ6H0bg/Y6V+3qu1\nxFYRDptYll3RiZ8zDdNobnbZuXOYQuF4FEXBcaO8+nqy/Lou8MsXY4wMr8L3XcaSpd6P913dM2n/\n9TgJ6+DAaxrTB15N09C0JoYTRZKZPMct65LlcqsolU6jGbWbGJ4vWNi+OmejQbXMFB/matHzPZ9C\nscgLLwwwllhJyNQpFg3yVh9nn7UUAF1XCYfD6PrhHb/TxYiJHpeBwRHy+dVEoyliseYpsWmuHpDZ\nHtf0MIlUmiXtiz+UqRQjhnjisVG6l6hcc81aIhFZRELMj+d5xMZKw6TcQMUMhTGOsCFlpt/S+STV\nE8+NREPE88Upv8NBAIWCRcEq4ng+tuvh+wqGGUJRjDnrPTPFiIPrELYT5aXtsQXFh/lus3AKhhnG\nA0YSeXQlS2dHC6HQ/L6j+dQhGkXdJxvpTIbheBY9FMUwG3+oST5vkS9YFB0P11cwTBMUDaNCCe9C\nxl5X6rnzaRGYaZjGOWd38cbrfbhuFsPwWLmyiWw2O6VcmmagaaV9pgpR9o2kyNlF7IJNNBKecTxp\nLZedmyvwHswwTbwgYHf/CMs7pZejWrJ5G02rXetSIpVF12s3fGq+57htO+QLFq4PrucRBAq6YZK3\no2iGjmLoaAEU7Cju+M+I60I2kQc8dE1FVxVCIZ1IJHLYMWKix6W/zyIaTbFyZdO05Z7rfc32uOO4\nbHumH5yOGXtOq2UiRhiaSz7TySOPvMr115+2KK8tGlcul2cslSVv+5ihw5+HMZ0jmdB86HPHEgqp\nVJqi4+O4Ho4XoE70WiigH7TrI4kR86lDzBX3ZtqmUj0eE8ubD8dzRM08nZ3tc/amLqQOUe/qNtmY\n1JvRwEOmggAymSwF2yWRzZLNemi6gaIbVGNNgoWMvZ7pubCwiZ/zaRGYaZiGYRqcfkaUWCwyY5kP\nfU8tLR6GGULVwthBQCFdBD1O3mpBVU1Mo7k8nnQxezwOTWxicRYUtBVFwQw3MZIs9XKsWt4tE8gr\nLF90MWvUlZ3L5fExqGW/1VzxIZvNUyg6eIFaumaMMn7tmHHRqEvSPhAjotHJz9cNnYmfFRew8z6p\nXJIdr8fJZE5BVdQFxYgDPS6DxGLN844RC3l8+/YYydQpBI5LodBetRgxXcNHuXKmqniOTTzeeMMs\nxOLwfZ/h0Ti9fXG8QMcMHd4wqbkcyYTm1tYiqVSGQIGcZdHVmiHndAIaim5gzlLjPNx6xOHUIaar\n28y0TaV7PHTTxCZg3+AoK5Z2TIqvR1qHqGd1mWykM1mGxzLoZuP2ZuTzFtm8RaHoohkhVNXAMCJo\nenWvDrzQsdfTze04eDz2XGzb4Y3X8+QLhYNaFaYeVrMN05irzOec3cXzL+zhzR024NLeHsGxHaDU\nQqzpOu969/G8tH2EdFohGsrRc/oy8vnCoi47d2hiE48/R2fnycDCVuXQjfFejr5hlnW20NrSXLUy\nH0uy2RyqVrsu6EQmj1bDXg2Y/lxzXY9sNo/leKi6iaqZ6IDjuuzYkSSf14lGXU7raee0nnZ+s2MY\nzw0TjVqc1jP7KiWqpuK5Grt6fXL5YXQVVi7vWPQYMdvzs1kdRVHwFI8Av2oxYrqGj/Y2Sst1GxGy\nmTQndhar8tqicRUsi7Fkhpzl0r20C81sqmqDxUIWPLAsm3//t17iCYg226w/vZ2cvRfPb6ZlSYFz\nzl7FgcXyZ3ak9YgjjQ+z7eNIRovMTEEzowyMJFnR3V5eEKdSdYhK0rTK1MHrLtmIjSUYyziYDTg3\nw3VdkqkcBcclQEPXjfHJ3dUxU/feQuZnTGTrz7/Qi64bZLM6XV0up6+fX1fh9u0xbKcJx2nGdRX2\n70+ycePClsKcqcwHv7/hoQxdXaeh6wbJZKl14eLNYZ59tp83d9j4vk0o7LB0aTd6KEqghhiM5di1\ndzfxYY1oOMzatR1VPVkPTWyWr1hGV+euSZPl5ktRFIxwE8OJAsWiTXfXkmoV+5iRyRVqdqXudCYL\nyuK2Ws8nPmSzebKWg66b470SB+zYkSSZXFZa5SXv8vOf97Kks4NoFM59Vxt2cX5J8I4dSRwvQkAH\nbqAwODJGe0cK6J73ezmcGPH8C700N6cYGnIZGRmlaJmoKqzriUx67t49aWynmeOOa8IqJlm5ojox\nYrqGj2v+v26eeqoUI1oiw1xzzcaqvLZoLEEQkEgmSWWLOF7pmhhmOFTxnu6ZhhnP1rMXBJDNZSlY\nLluf7iM+thZdN8nFHR772W9Ytrx7QXUIOPJ6xGz1nonz/M0dNrbTxHErl5FM6uU6xC9/OUgyqcwY\nI4aHRomPtWKaPitXRCu61LduRhgcTbJ6RReKUtk6RCU4tk13V7Qi+6qrZGP/0AgFR8OsRt9gFRUK\nFqlsgaLtY4TC45O8q+9IuvcOzdbf3FFg2fK1+F7Ai3ssXnrxbU4/o3XO8YnZrM5xK7sYGOzHcXRM\nI8Y5Z5+44Pcy17KXo6PN9PXtpbllJYbhoesKv3x+mNde6yKfa2VkNE8QjNLRXuT009fy0vYBADo6\nzsYqjlDIpxmO/SfXfuCsaV+/EnM7Du1+7ur0ufyKE+hobyKRzC34MwEwzBCpgkNxcITjlnfLErlH\nIF900czaJBuZXBFVX9y4Nlt8cF2PZCqLrxjo+vSfST5/IEbE4mM4ziqi0RB9fQX27tnLmhPCnNbT\nPmkYwEz7WdrdzmhsGNfVMENJek4/jqFYkvaWKOF5TpZcaIwYHMixdu2p7NmTYHBwKZY1RMhcycjI\nHmAIXdeJxVbR3e2yf2CY0ZEB1p2qsOmiU6Z9/SONEdMNT5mo2HW0NzE0FMWs0MpeojH5vs9ILEE6\nX0QzImhGlGoeEvMdZux7PulMFsvxKNqlHlBNM0rX7BqPH4NDI9j2SbS0RBZUh4DK1CNmalyZiBHZ\nbI5EIkQs1k93d3e5DhGLraK/L8OevZ0EQYxoZDm2PYSuxwBobj6NTHYE29bIZHq58srp48NsZZiN\nZoSJx5N0dbVXpQ5xJHzPpilamaXN6yLZ8H2fPfuGCLQIutE4K/FksznSWQs30NANs2KTvA8228F7\nJBO6S9m6gml6rFi+FCjta2Awi213oChdxGJL50xgSuMcdVavOo6AgK4u+7AmT8227CVAvpAnmfTJ\n5ZPY9ij9/TkUJcCxu0CJ4zjLUZUIubzOG2/0sn9/gGnadHcvZ/Wq44DSEpUZK6AwFKe1OUxz84He\ns0rM7ajGNTkAdMPA9jXe7hvkhFXLZLWqw1AsFnE9pSbzJQoFCy/QqhZsZ4oRM8WHg3szZmondVyX\nsXiCZAoMw8exFQzTZzRWwLZbIHBIJtv4zY5hzjxj9h+jaNTFtjWWL1taulBouzM+bEAnmSkSKhTp\naJ97QYTZl8b1GY1lKFo+ydRudL0D244xNPwbslmNoqWg6SEMI0QuH+UX2/bT0tKOog6wcsVSjl+9\nilB4kAsv6qJgFaddEepIY8Rc8cEwwySSKbo6pRfzWGPbNqNjKbIFFzMcxQxXNsOYKVGebZix7/mk\nMlkKRRfHDTBCoUmL2RxajyhaCqGwz8BgbkF1CKhMPWKmxpWJGJHL58jmFAhcMpm3y3UIQ89hFV0c\nZw2qEsb1woyM2LzxuoVtmyjqCCtXLEXTDELhYNZyHU4DsKIo5G0fx3GrVoc4XOHZJtksUM2TDatY\npH8wjh5qQm2QVttcrkAinQPFRNXDVf0QZzt4JyY0+V6pZc40cvzyl3Nn09u3x2hpOZ1MNo9tq2Sz\nv2FdTwvJZEDR8khn+gmCNNlMhpYWh4PXtT+0YnPmGe289vrcc0TmMtuylwoKjqOiKBq2XaRYXINj\n++hGK449RMBSFIZQdIV8fj9W4XggT2vbCvYPDHP86lXlCV+ltbsNklmPZDZGR0uUpqZoReZ2VOua\nHFC6uA6hZt7uH2b1ik7CIZlIuhDxRJpQpDbzJdLZ6g7fmilGHHz+uK5DcmiUnz6aJdqicPrpnbPu\nc8eOJNHoieTyFo6j4vk7Wdl1OoNDDr5nk3fG2Nfv8vbuOJkMtLTAaT3tBOPPnW6ex8H3TdB1vbQs\nZCxJ95I2FHXm34DZl8bNoSjd+MEQgbsB1x3Bc9eTyfgEwQpcrwi8hWWB547geS00t3RRyHcyMNjP\nqlUrx4dHKOQsh+mq+0caI+aKD6qqkq/QBbREY8gXCsQSGSw7wAxHCEWqE9dnSpQPbkl3nCKjo/v5\n/g8KRJsKbNjYTVNzM2ghpmsDPrQe4fmvsnLFufT2Zkhn+oEM/X02gwPpSfWDYPy5h3MtsNnMtry+\nZQW4rgpBCM/L4bhry3UIyxrBc9sIghFUXcGy+sl5SUyzjbb2zmlixMLLMBfdDJHO5Ohc0lY3q015\nnkd7pHJJb02TDatYpG9wDDPcGBNgLavIWDKLi4ZuLE7FZbaDd+IEfeP1NHA83UtXEospc2bTpaVk\nNVavKrUmanoH4DI89Bbx+CiqegYBSygWo3j+r3j55RbeeL2P08+I4roO8dhqBgYGicULPP3UEOdf\n0Mnmi+Y3PnOmVthDEydDT2OGLAqFURKJgHQ6i6J0oygBmtZF4KuEQyEUxcd1Exj6GLqexSr2EBAl\nl1+KH+xh2VKbUHhwUgA7uAyR8CjvPLudpmiWwmGuwLGYy+sa4Wb6Bsc4fsUSSTgWwCpWbpztQvie\nj2V7Ven1nDBTjDj4BzwxOIIZXo0bhEhnlDl7JPL5UoxYvqwJz3MZG4uQSPRTtHI4rko0sp5MxiEI\nIuzflwJFY/euIUIhh1BoNfF4kmTKYvtLu3nHO9o444yuWYdbqXqIkXiSriWteJ4/Z4zYt3+IbGaU\nllbIZnuJxyM4ToCqhAiFNYrFCGZIQ1FShMw8vp/A9wultfxZgeu2kUjsY0mHSuDn6erad6CCoxiM\nxZO8+GJy0jl9uKv0LCQ+FGp0nIrFlUqnSaQL2J6CGQpjVnmE5UyJ8kRLeizms39oH82tp1NwIhRS\nKq+9vo93v3vmHseD6xGe5zA83MTo6K5yHaK1dTnDwy66MYrn+9i2we5dO1lzQvMhdYjXOf+CTt51\n7oqK1yEe+MEoLa0hdr71C5JJncDX0TQdJdDKdYii7aGF8ihKjCBw8LwTUdUV5TrEdDFirjIsdDVQ\n23b4xfP9+M6SRV+ifyaeU2DJyspcZw1qmGw4jkPfYLwhEg3f8xkZS2K7CrpR3Z6MQ8128E5Mispm\ndYrWgaAwVzZ96D7jsQSueybLlytkMiux7Rj5fIBpuPi+SaFwPK6bJRaLMDz0axx3hKHhDnK51RDY\nPPN0PzDEhReuLr+GbTu88MIgb+4oADrrekzede7yGVthS6tF9PKLbaMUrJOwbRVFaSUIXkfXj0PX\n1+N5YVxnBD8YQFW6KVg2mpaivb2F5uYCptHJ4JANLAOgaLUQiQxz2WWTJ6IeWobtr+5jw5kdFF7a\njme309HhL6j78t//rY9fPr8c29YxTRfH6eOqq0+e9/MXygw30T84xpqVnZg1moPQaCzHYz6rolRa\nIpXBqPIctJlixMGTJn/yaBaP8IFhifnZY8TEErcKCiOjcWAlK5Y30dHhsWfPDgzDQlELRMNtJFPD\nRCIn4XkFClaAVdiHba/E9VaSz1v86sURBgf7ee97V5cTjomVrjIZSCUztLW30tIScMpJHv17LOLx\nNVNixJlntPPww6/R22uRTK4gFD6F+JgFwSAopxAON1MoDGDb/YCC7zURkMA0TJYsyaOqPq6rkM2G\n0PUmilYrKCobN4YnNc5ous5/PLWbXPrsSS3BhzvEYUHxQdGxLItwuLHmLYr5SabSxFM5AsVENyKz\nLgVbSdMlyp7rkc0XWP+OFvxAw31KpWgdqI8tpB6xf2AYWMvxK1vKdQjDNNH0PL5vks+vRlEU4mMt\nWJb5JlMAACAASURBVNaeedchtm+PkUxCPJags2sJ7e1BqZc2uXaBdYjjMYwuPC+M5x5ah0jS1NRC\nc3MR02gnky3VHRw3NGOMmK0eczg9NC+8MMTLL7fjFzsXpQ4xF9/36WgOV3SOaE2SDc/z2LN/tCES\njUw2RyJdQDcjky5AM6FSF3yZyXwO3uZml1zWGZ+gpdG5ZBTHbp+xHIfuU9eX4LmlgyocATPUTGub\nTj7fQi5nllZGMrzxioqObWsUChAEOqrqUSi08uaOBOeee/DKMKOk0m1Y1pkoisJrryXR9diMrbCG\naaDrBuHIGgqFCJbVhaq6BMFxOE6BkGmgRzzyeQVFzREJe2QyrwMn0d3VwnHHrSaTeQPXbSGVSuN5\nCpHICJ1dHVPe/3RliDY1cf5FJ+HaBdpbwgtqVXj5ZYtcrvS9OA68/PIe3nPZgdbMlStV3vlbnRVt\nqTDCTewdiHHiapnDMRfHcfADFQgW/bUt28Xxg5rGiLFkmuZWlbExb3yit0p7WwLXbZ6xt+HgoU+G\nXqDj/2fvzd7jOM8sz1/skZErgMRKcBNJiZQoW5a6bFdNTdvdT7uqe2Z6qm7GT81/U/czt9V/wMxN\nj3su2vV0P122yx67uhaXXJZkkSLBHSC2BJB7RMb6LXORAAiQILgIpFS2zh2ZKzIiTpzv/d73nMnx\ngt2yLOr1MvPzAcaaIE1NwMIwDGxbAjAYuGhtUhQKMBFFQH/gc3Opz+XLDZaW+ty7m1KIEmhNlr/J\nKI6YnQ24c2+LsF/sz5Ic5Ihr1/vUG++iuYPmNHkuUCpAihbVqoNtZ9i2g1bLNCYW6LSXyIsLSGkw\nMTFDpRLS75coBwHD4QDLbuM6+shB1GH0KA9lrxL8sm2Sz+KHRr3gT/90LCxczyMaxV8tNn7LsLfI\nwPCw3MNOm69jZ3x/ody38fwhb749ydr2YJxXZduYgO8n3LmzhhAOtl3w7rvHt/Qd5B3XGTE9M158\n72mIs2cCHq5K2u1Huyquq3gRDVGpXGGztU0cv0cYDVhcrLDVusHc3KvTEEoHeO7xHHGcjnmZDI5b\nSzlpcgpDNl6bhjgOIk9ozs+d6Hu+9sXGeBh8C+dLvtBQUrHd6ZMrE9t9esvUq4m4f4TnOXk/eL/J\nD35wkzy/gOsqguAKP/jB2ILuKHHz+Hv+8pebtNsaJTVKSeJ4mUrFR6sQw3BIkxbnzjXQaN667LKy\nvM362gyGUcJ1XUxTAOLQb9Hp1hgM1qlUxhekENYTcxiP79REkY3rSgphoKSmKDS2nWIYAtPSjCvT\ninrN4Z13LrG+PoOQAWfOjK3ZppqTwANMa2Z38P0yjUbrid/ruO9guyUGI0EUd5hrTmBaY9lz/A2h\nQOtHVSMoDvXIbqw7/GJ048R7MR2/woPVFm+cmf8q/O8YhNEI1yuTpK824+ZxCCEoJHz08RfHEcNo\nhFA2b789yU9+8oCiWMS2JFE0x3/5L+tcuFg+0lHKtu39Nqtr1wX9/ngIe3snwbYKhsM7TE87rK7e\nG89SZR0W5quYpkGa9kmSGnluYNs+pilxHE0c2/t2ummWIUSJJFkmCAyEsDAwiGObah06231K3sSh\n6/PRDV5goMlzAIlWMaY1XtjYtsJz4eKFORzHBRaBAWfPBFj2DJZ5j053hrl5yfzcJWbnWkcu/OoN\ni04rxPdqz9Uy9Xn4YTPW/Pgnq/zhH85jGAZ5oZ730H+FLzkGwyHt/tGLjD28juBZpRXvfdAgyQS2\n23hKxdoA6oCF1gX3798jTUtPLZAc5J1f/lLQbo85Yk9DtFpdLl3SxKMu/X4b3xecO9tgeub5NUQY\nbVMU9u5MibVf8NQcryGkNDAMTZ5rTCNFklMqjTni2Rri2RzxMu1SxxemBVppDHitGuIoKKWoBe6J\nO19af/7nf/7nxz0hjk92YG15rYXhlE/0Dyn5Lml6ct7oo1FCqz3EdHxM8/iq8dJSihTjFiYDA6Vi\n3njjaFLxPIcsP/meXMuy2N6GWnWSes1jfT1ifSMlikpsbysKMeDMmfpTXz8z49PrrfFgeR2lFBcv\nXKTbiRnFk5TLk+RFG623OHcu4etfnyJNBdvbmwjRxvd3aE5bvHPVJo69/d9iNMqI4z6OMzNOxnYT\nzp6N+eD9JoNBC6ViGo0+H7zf3K/Mb2x02NxU7OwsI9UIaGGaTSzrNtVKB02KX8pwvTdJ0nUGgw7t\ntkO7k5IXOadPj/jjPzqLaUZUKhaTk8P998/zgl/9apulpRTTzPD9AUKM6HVXMM0S7XbE7IyPZVlj\n4W7a9Ichtmngug4/++kqm5sXkWKCMJyk01nhwsXxoGsYhmzvDIGQUmmH3/sXJmHkI8V4V8V2bNKs\nzzvv1E782BuWS7/fp1GrfO5rqlx+ucGCk+aIk0ZvEOGVghPliOdBfxCiTecL44iiKBiO8v1zutsz\nqJTrRFFOr2cRRjlxbCNFxNzc04s/U5MuUdRhY6ODUppmcwG/1GRlZRnLukRQquC6KUXR5dRizre/\nPUWn0yIMeyjVYnLKZmZ6hno9Io5tlCwTJxlSehRFB9uewHUzyhWHajXkypUGw6iDkgOmpqL9a3h7\ne0g4DNje2SCKOki5hWkOse1tLKuF4+Y0p8Bx5xkOdwiHPQbDgDDsk6ZgGJv86Z+ex3HiJ/gBOMQR\nlpXjuVsYpOxsL2FaZTY3+iwuBtj2k/eEz8MPhmFgmDFvvjkWPFLkNGpfzmLcy3DEl50fYPx3neT3\nHAyHrG91iTMTy/X3i1ZH4dcfjQ6dC0KGT71XvKjWiaIR7W5IlCkwHSzbeep94vbtnEp5iokJh+Eg\nY3Mzf24NMTvjMxi0WF7eQil4663LeO4ES0v3sMwroDsEgY3jrPCvvnuK5Dk1RJ4LHEdQ5BU8b0QQ\nGMwv9AmCAUIOqdd6vP+N5n5G0J6G6PdXybMBWm9hWU1M8wGmuYHj5oc0RBj2CEOPdmfEcKiemyP6\nfeh1VyhXCmq1LkpJbt/O2d4e7uuIx/GrX23Tbi8iRZU4rjEYtFjcnZuN45j2zgCT/LVriMch8xGn\nF2ZeSk8cxw+vdWejtd1BmT7Wl9h1qj8IGY4Ejvd8A+AvOxB00jj4PdqdAimqCLFAUWiWln7D+x8k\nFIVEKY0CtIIsy7l5s0ecWARlzfS8j1JNlK0IU1BmCZyAoF7BdO/zxqUG//CrDQb9Wc5fqPHgQYhS\nm5SCEd2eyYf/GFLkJvWGwZuXagRBSpFfQ2sD18vo95v8+qP2IWerXx9Y6UuhMM1ZKpU6YZhiWYJK\nWVCv/x7l8hpCzLPTLuh22nQ7G5Qr53DdgiKHQX8TKB9Z5c3zgv/0gzt0uns7HqeZnWvt9n++ixQG\n7faTFWfbLdEZphSFONaJ5t/8m3M4zgb9gaRRV3znO+f4xS829ntk0WO7z1cBwzBQls/WTpe5meMd\nhn5XkQvJFxERmhUKzON3814l+sMRtv2I/PfmMPoDQSEmcewRWTbD8vJd3js6fuZQijhIZqansCyb\n7a2EKGpQrZbJFXiexfyCvetO16Zef4u3r0iWl0NGoxZheIt+X7O+IVEKpqcdXGdIMCXw/dtUqxXC\ncAPLqnJzqc/Vq1MUecbDBwm/+Jv+vvPdvbs3qZQv0ev1Ma1JHOc+09P/mjT9hKnJedodidaSPF9n\ncvJtkmQVw6iTJMtUKle4dr311HDAxzliavI+zZqiyH8fURhsbj696vx5+EE/xg9ZIV/ugH+FLxzP\ns5PxOF7WeOBp0HrcthUmOeYLOGYepyFuLV3jD//wydccrtbDqcUAKRaxbYflVpfBoEmj0cQvNfH8\nPrNz43bIfv88ly+fZulmH6keEAQJ3R780z8NKURKraY5c6ZMLh7SaNTo9X5FUXi0+wblhsebb05g\n2xZ5UfC3/7hOPDIJAkGRC7TRZHpmkbXVGMdSVMqCcuVf7nPEQQ1x+swVhGiTxDXQT+eIvb/zs+vx\nfjBgvbFIo7EG2M+1c32c2c+3vjmPwxpF/vo1xEEUacqpmYlXkuf12hYbSZoyjAWu/8XYTz4P2u0+\nsQD7BYZuT8Ky7SSw9z3CoYHtruD6bzOMtlFopBHTCwu83S27PQFx7+6IQswxM1Oi39esrX+GZU1R\n8gUahWkamKa13wJgOzZ5UcYwXVbXBwjVxLIEOz3FveUh2jqL5Q8YxprPln7De++doV41ME2T4fDK\nIVH//vtN/uN/XGJlZRJIaDTKjEY7lMsL+H6KknUMw6ZWn8cv9en1CuK4hJATGCYotQmUaDTGv7dt\nQ5oe3X7w0UdtOt0LSFkjjjWbrVXKlfGpr6RmYzOiKCy2WjEfvF8c2jK1HZcwFWhjmzt36/tDnt/+\n1qOWnKN6uQ8Ok477LZ8cJj2pXl3LsgjjglqcEARf3uvri0IuXn9bilKavBi7UH0RHDGMRmjTPTQS\nvzeHYVkRjl3glyaIRwV5Lrl2vX2oneoojigKn/v3NykF00RRjuvm+9xQFOObPbC7e6FZXg5J0hJS\n2iwvGxTCwnFOIYoumxs7TE6qfaeqm0t9NBdR0qDf19xc2kIKwc3rgn7PQsmEv/vbh3h+E78E9XpA\nntcwjVksyyPPJFtbJYT00RqUWiUIHEr+WdKsjG0b2Lbz1KHXoziiVAooYomUiuWVkCyzWF2N+M53\n8ieu00a9YHUoWHkYkaYmc7MbZOk0nu8+kx8a9YI/+t5F4mR3EXpgSDyOU/7yLx/Q6XhMTWX8yZ+c\nPzID5Ct8sYjjhFa7jzJc7OdcZOzhpLIVlFR0e0PiXGA5/gs7Zh7kKc9dw/C/wXCQISWIIqPID98b\nHy3Qx+3bszM+D5b/CduepBwYpAn4XrrPEXluUvITOl1Nkg64e69HJqvYdpmtrsnd5Qh4A9OGKDFY\nfniHd792imoVqtVJwmhhLNJHmtt3trh8ucFPf7rG5mYdkFSrVZJkm1LJp1SVTM3a5FmJcnkC03AP\ncMQjDeE6HrXaKURQxbZ5KkfstXfFSUJRVNjYXOX04qn95z5LRwD4fsqdOyFCWNi25N130/3HDAP+\n7b+9SOnArNbr1BAwnqWuBtYr0xCvZbGhtWZ968ttcbu51UEaLrb9Yr3vLzsQdJLI8pwkybhwKUBq\nA8Od5eOPE0y7hG0qKpVpbt8ecOFClV/8fIONTYFpzOF6FlBlZycEwDLncZyQonAo+W1830aIkDQL\nITC5dr2N5woePkxI0xJK2SRpDyXPoVQZ02wAy4CF5DS5mOHm3T79XodK+RbIMkpW2GrFCNFiZeUs\nWTaN1rC29gmmWcZ1K3humShcQ+mIKDLxPJt6wyDLV1GqgmULDNNG7877aq2x7YJK5egB4HEfpyJJ\n2CU9i0plXCW4fTtkZ7uMlFAqWfzjh4cdMWDsToPlINQyWk8BBXC8gD0oMJ6W/nmSvbqO77Ox3ePC\n2ZN1kPjnjqIo0Pr1z7NEoxHWrt/t6+aIoiiIU7mbKfMIe3MYUkju3A3o92O0tqhWavT7s9xc2nom\nRwgpgRG23cf3J7DMdfqDFNfJkKKGEIIg2OMIj3jURjO9O5sfYNBFyhGaBnkh6Xab/OQnq0SRB2wB\nAVK5dNopWgsG0SJpFpClHaLRFJMTfTy/jm23kDIhz3tEkUmpJCnECqaq7fNDnpuUA0WSahxHHLur\ndBRH1OoKRMKNm3021msIAeWyzV//9ZNOMd/5zgJ/8RcfkqaL+L6gXv+AX/zi4VOv58cXIJ7n7S82\nDg6J/+VfPmBl5WsYhkEUaX74w0/5sz+78sLnxFd4NcjznNZOj1QYuF75qSGZx+Hz5jPleUF/GJFk\nEsfzsV/SfOIgT/l+yn//mx5SljFNRaU6xa8/anP1aoMf/ucHDIclkrSNYZwFaiQJLN1qEZTO4rp9\nstxFqTu8ceFNtnc+oz+M8dyCMGvgBRYPNwRCNTBMnzgdoUbnUKp6SENkeZMib9Jpw8rKLTzfxzIL\nIKC1mbO2usrGxhxCTKI1bG/fxjBKOE4JKQ0sK8W0U1LRxzI2mZlT9LqHNUSWG+M2reJ4jtjblXAc\niRAGRWHvP1cIwa8+7JAkE5imxp4N+PVH7SM4XwMDwGGsIR7pFcuQhxYa8Po1hCFT5k69uvvUa1ls\nbO10MZ3gdXzUS2G73UMa7j+rIds0TUnSgkxIwMK2bQxrfECvXp1ieXmdNGtg25Lp5jRx3OcXP9+g\n032TPOug9BRJeo9abQYhxrsXjquZm53BcW2kzKhW4e6dIWnaJEk9lpY0b5zv4tgtbMcmSQoM5tFU\nMIwhSu1gGKf2HUaXl9dw3Etg+AzDCobRo1SySQR8dnOAxkPr8apeyhJBUCEIVikKm0p1jVptBqUk\nhpFSKmmazWniUY3BcESWrOP7d0jTOzSnPK68XeWD94/e+lxZHqJUgO91KYTN1OQ2H7x/CYC/+cV1\npDyLZQlc9zy3lu4euV2c5RXOX5pDZhllv8koXv7cx/AkggQPwnQDWltt5uemn/3k3xGE0WicfPua\nkecC0zzZFODnxXCUPrHQOIirV6ew7D5LN3MM02e62dwfzn4WR0xMBMzNTmFak2xv3UdrhW01cV2P\nO3c10OHq1Snu3V1HyAJNCcuaQBQRhiERMgF9CtO0URJWVpbxS4s4jqLT9jCMHkG5TmGViEddtLax\nnAydOJimRRA08fxVXDciz+9hGov4pQwp5xkOKpSDmX1+EMV1KucCms2CmZlpGo21J3aVnsURWqb8\n7Ce/QYg3se0C37/Cxx9f43/+Xw7/pp7vsnh6gWbzzP7/vez1fHBIvNPxDnFEp/NVts6XAUoptrY7\nhKkcJ35/AYaAcZwwiBLyAhzPO9Esn299c45bS/eJkyaOI1iYXyCK2vzwPz9ga/u98f2qt4HSXSYn\nFzEMgzS1adQjZmabOK6JUJPU6wOGcYFfzOP7Hg8evJiG2Gl3AciLU9jOJIN+DvSYavr0BwF5JjDM\nPQ3hUS6X8P0thLCwrA2mpydRSmJZJr5ngRGQxbP7HCHldRYWXJpNeSxHjJPSazi2xvM6eG6bZjPf\ntdvdRMgdQGMYAk2FKEp5HGla4szp+QP/3gRACUmj8nK7lSelIfI05sz8UVGmJ4dXvthIs4xhLHH9\nL+bG+yy0231yaR87wPVlQhiNiNMCjHE4zeMuMnvtD2BhWYLp5hSmZY2rjSvjG5dlSZQAw2jgeS0c\nO8HzCkql8wBIWTDoh1jWBMOhwLIbhGGKlCbxKOLrX6/SaMxx5+46o1GMqW1M0ybLUhw7xPehWmnS\nH9g4doHnS9ARQowQYoBt14niARNNSa+7SpE6eN4209NNTi+OXa+2Wm3m5h59n62tLo69Sq/fIY4l\npnGVLAMDRRjdI4qmDs2DwHjrc6u1QJZX6HTaeO6AP/gfJvjWNy/tP2diwieOJ5DKJAwVtdrRQ4Lj\nflawXIc47TI39/n7J0+6V9c0TcJMU08Sgi8oLfvLhkIoTPP1O3znQsEXIEC00hSF3B+YfBwHZzAq\nlZBSaQHLGlfpnpcjpBS022OOGQ5zHGcCKTVhmPDRxwmW3efcOY8kqbGzE5PlIZalMMwQiDAYUAom\nCQKbXt9CyIxG3cY0xxxR5HewzDpKFThuDwiw7R18/zSeD4uLCzSba0RRjSydR8qC9fVNpFyj118i\njiWW9S6GabO1ndOc2qTT7gGTT3DEhx+2uHatSZ5VieMdpqdzvvF+lQ/eH3NEkQmmp0tE0QxCmvT7\niomJJ4UEnOz1LNS46jk1lRFFj95zaip76ff8Cp8fWmva3R69MMPxAlz/9e8ij0Yx/TBBYmHbJ7vI\ngEcLcLCx7YKF+VlMa9wCemdY2he3liXRYgrfv0teQK2xxtTcO5iOjTY10TDH9cq7FrIvpyF6PYWU\n4LoS1xkCCsMI0SogipLdYoeLVi6u22ei0WButrqbH9an2TwFsBtMOsT1t2lt3CCOzX0N0WrB+XMP\nqVQEUeQfyRH9wSKDwRZgcfZsl//9zy7vP56mJZpTDltbYx3R6Qzw/Sc54mmze1rlVCov11p7EpxT\npClzU5VXHhL8yu/C7e4A90vqGd4fhCTCwHrB1qnXDa01w3BEkgksx8Oyn35S7FlLTkxqtrcTer2V\nfXvLtdVVOl1NqTSJjrfw/TaXL09w5fIcRSH5+c8fEEUeQgyZnj7P6uqIwRCE6OJ5U4BJXlQRUpEk\nD7AsjWMXuK5BUQg8N6JWr/LG+TrLy31gSCFa6NRH6x6el+G4V1DaIAh8SsE6Jd+l1x8i8oz+4FOc\njUmuvF2m0RjPkewFBim1iGUlFIWHViFYNYoiIMs2UOoNlpcDFhcrh4azoshmsxWTZU0qlSaWtY5t\nH+6l9PwC2AFtgyF2//0kDvazBpMpX3vv8/fdn1Sv7kG4nk+nF3612NiFVK8/WwNASMURpkWvHMNo\nhH2M+tjjBwODIJggju8zOTVBEIhjOeLihWlu3eqzvLzGYBBSrZ5jYqLG6tptpBxhmgWWWccwffr9\nWUqlVaTaRBNgGquYloNtGzTqisXFKTrdjG53hJRdiqJKr1dgWuD7Ase9hNIGlYqP76/R7YKWQ2TR\noijmGfQL/tV3z3Ptep801WxsbpOk55icqrK6uoWSBZblM4oc0tRACJ9y+fK+T/9Bjri1lJOmY9co\nvzSFYdx4rP3BwvMSYB30uP2hFBxdkDjJ61mq8c7Gn/zJeX74w08PzWx8hS8GwzBkuxth2D6u//ot\nJ7IsZ3VzRDcssJ9z6PtlsDefMD2jWV8fsbNzm3eu1vjg/Sb37t4h2dZolRFUSljWNc68cZpyBc6e\nvcTf/e06nc6zNQQYNBotXGdIapYwjE20dvDc5JCG0DpCyoQ0ncegx8RkjVGkyPJ5fE9hml1M4yGm\nqSkFNml6n/WNOrVqweJphyR5FEyq9QymmaEx0EruawilOqw8LDOKm5w5XX1iyPvWUk6eT1OpjDsG\nijw9pCEqFYHSZQxjB5SFbW3DEZYkR83uiSKnOfHy4wWfl3OKPKPZcKlVX/2IwytdbEgpGaWS5zR2\neq3I87EtpO1+ORdCe4iThOEow7Y9bMc6VJncEwgHdzfieNxbaJkG83NlHLfO5csVbi71qVZLdLq/\nwfVqNJsF3/3uGUolj0IIfv7zDfqDcTuDac6wujpugXJdnzwbIEREyQ9o1Gusre4w1bzIhbLm9u2Q\nNN3AMM7jeorhYJN793ZwnILmVJV2u0Q8AsOoABHlIMJxYXq6jO83CQLB0lKZLBsHyGSqg9Ixv/+t\nRX790Rr9PoTDFmkWoORZlJpE6RZKJNh2gFImWb7J5mYDGHJwo6dSEeT5eCGptcZ15RPDXzMz08Sx\n3u3Z1MzMHN2C9HjffZ5nZFmO5718yM7n7dV9GuJcIYR4amDb7xKklPCa25mEEF/InAhAOEq5cy95\nJj/A2FhgcmqCb32zQSHEsRzxySdb3LnbQEqLvGhgGDGdboJtn0GpECkB2jTqMxgYrK0WnF58hyQZ\nMRrFKOVjWxVGcZ+1tc/w/QmE2MF1z5MkGqUsguAus7MTCJGM2z+ny/S6Jd64cJbNjXWS4QUcZ0S9\nUeHa9bX9m/fyg4gsvU0UZaTJApouUtaQsodpaeK4T5G3GI0iFub9xzhAPJaBcbhf2/EcZuYmiRNN\nlmk8T7O4ePQN/SSvZyHHi41Syf9qRuMLhhCCja0OmTRxvNe/yBBC0OkNyYTB5GSdz0vrzwoi3ptP\nsEyDM6ereP4k77/f4MNftQjKJtr4OV5lgnpd893vvr2vIf76J6vPrSGyPCcIBOfOvc3tO484wvN8\n4lGL5ZVtlAbD0JjGWeKkIB6VsOz7VGvVfY44Mz1Brxsy1TzL1tYOhjmLZUVUqgG2tUGjMQ4mta2I\nXj8kSxeRmEiVovSehtDE8YBWK8AwhizMz7wQR3zwfpPPrt9ncmqv5ewyadp+4nd/XENorfEMQfA5\nDB8+D+fIQlD1TSYajZf+/BfBK1Uj2+0+rv/lnNXY7gyODet72WTwk0oU10rTHYYU0jhkX3mwMtnP\nx64te+FbAJ6b8fDhCCktLEty6WLGZ9dT7tyx6A8kUKHRSPje987vi5Dxe04wHDqkmULJ3liITGrK\nQRUpMyxLMjkVMN306fW6Y8GiNbalyDMPMMi0xOAUSbKDpka3uwrGLJZVwTAVSvWwHZibLSOEpNvp\n8XDFoz9wKfkawzTBCBhEOcNRwre+Occ/ftiiWrtMtJEgVR3T2ME0XaS8hVJ1bHsd+H2iqMXdOx6t\nzfv8j3/YoFwp7VdiHtlZzlCpHA75azTGbRl7W5tjK7tnw3Y9drpDTs01eXwe+3GHiL104NeRFAvg\n+QE77d5XsxuAkBrjFen+px3POE6PdbQ7KY54HEmScOt2xDBceCo/BIGgE0vanXQ/STxJSvz0pw/Z\nbJUARb0+xZuXNO+9t7j/uuUHGb2uh5SQpB5J0qVUCiiXq/ieREgD0MzMVtBoxj1kBrYlEYWJxiFN\nxy5dSdxgbq5JluZI6WBZdUwLlBpRDhIqVQ8lxzuzw2GCkDFS+mDG+8FeUWTv37zv3b1BXrxD0mth\nWQ2QEVotoVWC1n1s6wpCTBCNRvzd399hdnbA73+7QrlS4q3LJa5de7ifmvzW5cfvCQaNCc0F4/y+\n2GhO3f1cx+mo82avErr32PZWxsUz/a/cp75gtLs9OoMUr1TGecGdypfl+/3X9W1sr8/X3msSVKoc\nM4b1QnhWEPHjrklvvtXnZ3/T4e5dmzCygSmmqtnn0hBBEO/aaR/gCG2TY6MpE4UWfqnOcNBGawPL\nmsZwFFlWMN2MjuSIIjfHOSW7AaFZ7vGt98ZC+r/9tx5ZOjneaTXBNCRK30TJKSxrA9O8QBgWjEY+\nq6vX+N73qsD4/vksjnBch3eu1mi3Z17I3lwVKVPzR3dIvGoNoZTCswVzMzMv/NqXxSsL9dNas9ke\nYDuvPl79RYNuev0hhbaf6tqzZ+l29+4pBgMH05hkGG7tB7Ach4PBLeEw4OOPb7G9PQ6bWVsPaUYY\n3gAAIABJREFUuXEjOTb4BSBNMzqDaCyqleLGjS7Lyzm9XkQYAvpgQFjK4qlHN6NWK6LbHXvNm2bK\n1KRkeTmj3W5SiFmknCRJUnw/Z3LS5caNLjdupLS2uiTxJFKZmKaNZhXTLFOtSc6eqVApbzE3Z1Or\nR9RrBlleZXsnQckqYbSMkJNonaK0i1YpE41pRvE2StawbBfHMXGdIbVqSL2hCIeblErnSdKCeGRS\nFB6uZ+E4KQuncmZmq4SjEQ/uZZTL0/R6LaSsovSQIJjCslJcp4ZpRYhiBFzANMuY5gyd9gO+9rVp\nLMviyuXGkSF/e9gLI1IqplLZQSn9zHCePWSF4hf/3z2ufVqwvtbdD/x6POCr11/lzJnqscFfJ404\nSZmsv1h45m9jqF93EGFazokHf6Zpzn/4i9/w6bU36HY9THOWXm+VCxcbhFGCOmZgY48j8szn9p0R\n16+3ieOIhw+H3Lmbs7HRe+a5dxB7QVOf3Riwtp7iezVM0zySH6YmXW7dWiGObRwno15b4O7dVTY2\nZ8nzBaScJM9ShIi5fHm843HjRpfr13uMRhXywkIrG4M1XG/8GW9cqDM16VIqbVGtQbUaUq8ZrK7Z\nKFVlNOogpYlWKUqbaEwMo4pUQ0QRYFkuGIqgFHL6TIl6fbQfIOi5LlnWJM86GEaA70Gt5tJo9Pf5\neGNDMhpBmvWx7SoYKb6/gOsN0PoMmh5FvgXGBQzDJQgusr52i699bZr5uQBNTL1ucPas4vf+xewT\nv/vsjE2WbJNmXXa2b2FaVTY3ek8N+Dt4jvzsp6v8+qPRsfzQ6axw9eoMaVrsP5bnVUbDc2xt3ebq\n1S9P0eB3JdQvSVNWN3ZIhf1SBhPH8cOz8LOfrrK8Mssos4jiCT69dv+FNcTjOBhYeed2n6A0gWla\nR4aMPnw4YHu7oChCDKfPVNNgdVXR6cyemIa4crnBYBCz8tBEqSpR1EZKB60TpDJRSo41xKiDUhVs\nZ6whLKt/LEeYVv1QQOjszLjYvdNWDPopUgRYZoFWBvWag+NUKMQsWbqCaV3GoIpS4yHzr399FuA5\nOWKsI/J8uBsUHNBuh089RiJPmGvWEUI+F0ecpIaQUmKTcWZh9oVe9zz4QkL9ev3jdw6+KGgNYZwf\n+93GnuszSDm2dNvY7BOUn++nOhjcstnaJs8v0GiUuXNnDceZZH4uODb4JU0zdnoj7t4bEccJ3U6P\nIHgDy7Lo55oovEulOru/gt7ztt9DnDgYRgWtFFEcc+u2JE1SpFLsZsOQpXDjs4Tf/GYby1yg19eI\n4iJaP8RgCs0aQSnANDewrRLVmsnERECWgxQSIQs67fsMh5JqpYbnzZJlCVr1UToD6mAY1GoWSbyG\n0hMIIXHdANvpAJBlJkEA081JtNpmGN6hHJQ4d87hyuVxOJ1tezilLlma8c47Z1nfaNHr9TAYUa9d\nxPV8+v0hkXJxnLGzlWNnDIePju2zbEcPPv7LX26y1ZrbPW4O9+7e4fvfv/TUqvMnn3RpbS1S9pok\nMfu2c084RPTHrz9p96nj4HgB/cGAyYmJV/YZ/xwg5HGy/+XxN7/YoLW1OK6YR7C80qFSHR9PpY+f\nE9njiI3NbZLkDEJEXLvWBepcujRJmuZP5Yc9HNwd2WrtUK2+Q65CCiHY3ukwPzd7JD9oIMv2OCJn\np92m3zNIsz6mWQcs0kzS7Wk++aTF2npEGDbJch+lRkAXwwywbE2j7jMMW/S6AxZPm9TrAXEiWVsN\nqVZLxKP7uO4cpaAgjtPxp6s+cAGpDFy3hJQrmNY0IDFMD89NAYeiMDFMg6mpSTrdLRxbUA5uM9ec\nozHRP+QY02hoFhcrLMz7rG9s7XOE675JnGQUuUapGNv2cJ0My7L3OeJ5bIlt2+GP/niOH/9omSL/\nfdKk4L//7Soffnibb36z8tTq4tNsKY/jgb3HTNNEqeIr96nXDK01ra02UapxPsdcxnH8cBxGo5i1\nVgKOxjZKrK6tv7CG2MNRHGFZFnlRYX1jizOnF5+owgsh6XQLtOljui5JUnD7ztEaYulmzsrKA5J4\ngp1tmyKfQOsHGMw8oSEmJhzeemuBu/dCwtDgJz9ZPcQRll0AGRgCVB/NWQzTJCiXSZJlDGN6V0O4\ndLsdrF3OmJkea4Wd9pgjarXb1Bs1qtWQK5cfifBqVXP+/DztTpc0EpScLpXqRYSYptcd0O1NYJoO\nrmvhOi6j6FFh+Xk4Yu85v/zlJll6heXlbfLcPlJDiDyjOVHBdR1+/KPl5+OIE9IQUkg8q2Bxfu6F\nXncSeGWLjTQTmOar39V4UYRhiHnMgDXsea5L4vhRGM3zpv4edBzIcwvXHffeCuHAbh/34+mRe8jy\nnH6Ucffe6FGr1ABGccrcbBkDY/dCGvchem6KFAb/+GF/vz970B+SpnPEo20KcRboUKk0iUY7mIaP\nkALXdQmjkNFoEtBoPR7QMk0H25Fo7ZDnZSzLoRAGrVaXev0yBgYPH46AiJnpKdqdNVpbfYrdgrHj\n2Cg1BGNIkgTUqlVcd5ssqwI2ppkSxzWKvEkhSvuiaG5+nkuXFJZtE8c2N5f6+73mV69Oc/36Q0Tq\n841vmAhR4aOPZpDSRYoc08yQsoPWGt+ronRGGIb88pebR7anHNfCMh4o3yaOT49tJrvVp/hlPzpP\nbNcjTvpsbhrcuRMDy5QDQXJEOnA5SLh2rXdkMOBJwzRNkvR327lGKYXWr8Ytpj9w8H1BGOpd20dz\n3wlEafatG4/CHkcUxXh31XEkReGwZ1/1NH44iIOtEJ2uwTCMmZ7TzMyU6HV3cNw2QSC4eKHKtevt\n/RkOKQSFKCFEieEoROuxba2jKuT5FlpPYBhQrVa4fUeyteVjWyZKljHMdNzLbY//vG7XwbQmDnHE\n1lZMms4RReso1SCMIpQqMChhGAmWV0OrLYQo8Dybkp9g2QWG4VCvSTZb0ZhrzJg0rdDpbjE7O02j\nobh0aZb7t7tEUeWQY8zBwctvfEPvc0SRg1J9CrGFFEM8r0FQrjDor1IER3PEUfxgGo+OuWEYLK+s\nEUWXyN0Bm5uV50oVl1Lx4YcR/cE6a6sbNBqL2Lb9hIPMHkdkmYFVjPh3/y565rn4FU4GwzBiqzPE\n9so4n9Nl6jh+OApZltPphQgsGlMO7fZYK7yohjiIxzkijGJOL1Y5darMzvYGnr+J7ycIYfBXf7WJ\n40e8+dYkw1FGLs4+U0OA4MEDiVK75RyjDnqI4z6pIaDg7r2Qfn/2EEcUosow7IwzswyJbbNbCHlI\nGNZwnBGlUkZRCI7TELOz01QrOZZdIo4tHp+rGAeatnE9zWQVoMlHH7loVaB0D+hQ5Hcp+fVXqiFE\nkTNR8/bnNPY4QgjJ8kr4SjWELAQlR7Aw9/papw7ilS02skJifAndbsM4x7SP74GtVATzc3NstlbJ\nUhMhlun3F5968h3EwRvf1OS4mgCMXZCcMWk8Xk3I84J/+Ps1lu6OsGwbrSWTk00sy8ZxFEVh7r+u\nWtW8e7W5O5DV2x/Imm76XL/eIk0hSZZJ0hjDsMlyiwowOyuxrTU63YRatU6v51DkTTQ2BhrLXqFW\ns1HKIQpHQB1ROHQ7CtPSpGmMEBZhWFApm+y0uwgxjVIOlqUxjBaViolhljCMKuVyGdOS1Co5zfOL\nSKm4fSei240xzRHNKY/B4JEoksKg057e7yVfW13le987jW3bvPfeAkLkTNXGW6Iry+M5jDTp4nnv\ncu5cn3AoiKJbVEpvcenNd2m37SMrP8f1q44Hyh8JBNdVxxL6WDQarG70SaILVKtdNjfP02zeYH7+\nqHRgBXQBF8h5VjDg58U4g+V3F0IIDPPVWEI16gVnz5zhwYNbbLYUJf8hRbGwO4cgMY5psN7jiK3W\nkLwIWFgos74+AMbH66ie3zwv+PDDFreWckCglGB2dg7LcnBdSZoUmJaDYRpcuOjz7tXGE0Ob002f\nbvc+aHufI0wDKtUATYhtDTHMHrVqnZnpGW7fXibPTiFMF00VgxXqdR/btoljTZZLHPswR3TaEsOI\nSeIY37/IaNRGylO43hauexbT2EDrDtXqZVwPiryK45aYmy0jpeLe/YgoyrBMjeeGaJXSaGztioU+\nna0Z2m2TPDcPVQ0PXudFXrCyfIf7D2xM8xzNqXk8r09eLJEkLp535qkccRQ/vP+Nxv4x34z17s6Q\ngefJY6uLB20pHywPgRppco56fZ5+/0MWTy8c4SAz5gjDcNH7AWBf4VVCa83q+hatbnxiwcN7/LDy\n8C5JbFEUS7Q7b/DjHy0/sRPW6Q4YZQrbGTtMvayG+OijNv2+QafdZao5wfrakOlpsc8Re2Yppmnw\nztWA999v8IP/p8VOdw6vBLOzp7l7b4dqtUSrdYdRDGgwTYdK5UkNoZQmz+ZRuo6Bxra62J6LX3pS\nQzywYmbn6mxtHeYIqeYPa4iysZubMUe5XMayJnGsLZrTn09DjAuXTVxDUK9X9zliecXB4DSnFmYZ\nDHeQ8tVpCFHkNMoO1cqjHbM9jlheCQnDyVemIURRUPF4rTMaj+OVzGxordnqhq9lXgOef2ZDSUU/\nTMeJ0MdgdsZnGG5TqVhotcPc7NcxmCCOawwGrWNnNyzLYnGxyhtvjO1mh+EWSsWcOpVx+nROIUY0\nGv392YG9+ZBPPoU0r6PUHHGcUoicaqWC73vY1kMqVb2/NWiaJjdudHm4WkapCaT0yPKY4TBEyDnQ\nk0SjLYoiwDByHLtGo97j3//7sziOxnbmWF/bRoh5QINhYZqrzM+ZlMs5WV6AvgRGGVG4DIb36PUm\nCYc5UpkYxg62XSGJPVzXol53Mc0Ey04xDEGtOsHcnE+t6jGKOwxDj/WNjCSRmKYkS316/ZR6rcfk\nhM1oZHHjRp+dHYckDrAtj7zQmGa833NpmhZZnuG5DqNRwXAQE40E5bKLZZrYjo8m5urVszi2fWQv\nKsDSUooUB2deHj1ndsbnwf1V0qyM7ycszAdMTA6eerz3+jTXN9v4dsL5c2d2Mwti/tc/OcM779S4\ncLFBpVIiTQs++SSlXnuDmek6U5OTaGLeeaf2zPP2ZZFlGVONynPPbfy2zWzEcUKcK0zLOvGZjcXF\ngF5vja2tiCAo8faVr5Gmc3Q6K0zNupjW00XiHke8e7WOpovWCadOZczOJth2TqXSPTRbtMcRH39c\nYjAoIdVZojCmEBn1eo1yUALzBrUJ55kcEYU9lH4D26oQjbaQooRhSDyvwdxcnytv17GdObQ2WFnZ\nRqpZwMSyTCx7lYV5g1KpIE7yIzkizSRKmijVIyjPonVBvRaAkVCrOhhGm0q1huOUmZ4ukSQh/b5F\nlsHOdoTWIwxmCENJlgsmJroEJZt/+qeQe/didrYlWs2j8UkzhWlGh67PPdFlmgGd9oBarYnnppim\ngWNr6nWHc+cXn8oRR/HDubM+1cDlzJkKnc4K7Z0dTKvE+fM1DMNgutk+sm96cTGg01lByJBBf5Uz\np9/CNC0sy2ZySvL975/iwsUGtv3o/DzIEZONBo6t+OY3vzytkL9tMxtxnLCysYMX1CjEydlk7/FD\nrW6j1QZnTv/B7nn9qM8+y3K22j0K7RzSJC+qIQD+4R/W+c1vbO7eVbTbFfJCoNQ8YdjZ5wjbuk29\noWg0+rz/fpO///uHPFj1Mew5lCqR5TFBoBj0Q9L0PKN4EykaKBXjuo0nNcR6lzSto5UNho1pdlg8\nnTI1mTAMNUKcxbRqCOnS7d7n4UpEt1tBSDCoIOQOpjH5SENYKbV6gVYJvj+16w5ZIkm6hzSEZSkM\nGnS7Q3w/oihStHa5eXNwrIZQImNqsn6II3rdEeVygyAoqNUcsjyn2WxSr3uYhnliGmJu1mOi6jxh\nMbvHEcvLbXx/xLmziyeuIYo8ox6YzO62nL1KvPaZjVEcY9tfvhaqURJjPccCaK9SlucF//f/NaTb\nK8aDywvlZ25dHvU+e6hVSwzDw9te41XyWZShUCKAZItqtYRjRzhum3pDcOXy6SdsTMcDnmrcsmUY\nFIWJbUlmZkrcv7eOwXlMU2HbLkIsU60GXLveJgwNovAuGCmGEWJZDlIWoCVCLtCc8lhbU2R5gmOb\nZPkG6ItoJFJbULSwzALf6+P7Jo4zAYaBYWYYgJA23a4BxMzNl3HcApFESKlxXZBiE7SDbSbESY07\ndxsYBqSpSZIauK5LNErx3D5LN12gvd9SJaTBrz5s0e+fY27OQLPJ1laOKKaQEqQqWFsLOXum/lRH\niKcF6+wdr+9//xK/3t8ifdQb/rSt029/ex6tFJ3WHLbtHhusc9Ihfs+C7XhE0Yjqa/DQ/jJCo8YO\nZ68Ae5aD7c5DlpamuHU7w/NiHNdESZ6rIH1UL/DTOKLTvUBR+EjpQrRKo1HGdYZ4/iZTFcG3/uDc\nE3NoR3FEreYgVTSeazDOYTkGUtrkxTJpau7zQ5qaWLYEEsBCk+JY+pkcARZFsYrtJLhuiOuYZLlB\now5R1EHIEkoJbDtgZydGU8K2doAJhEyoVksk8T2knNjniE8/DXC9KYp8i2gkKbKCSsUiTXb4+CMf\neLTjfLDqWK2B40CRw9ZWgGVb2JZBlj+dI47iB8MyyUVByff5oz8+t+ttv0F/MDi0M3GUU8xee9WP\nf1SwuTk+KZ6XI4CvQvxeIVrbnd3A4ecvyDwv9vghTXP+z/+jx9Z2juclnDtXpT9w6PYGRInEdkvH\ndVw+l4YAuLWUkCQXKYoCKV06nWt8/etn9tulpiqC/2k3qHIUJ3SHI6LMxytxiB+CQCBljVZrc1dD\nGMDRGiLLYixrAcvKELvW154X4Pvn0eohQlgoFWMYW0jxJlqXUXoIYoCw1qmUwbKSfQ3RqGuyfEhe\nVEhTgyAosb0dUyod1hC23SPNUsBCiDKd7ilarS553nyqhrh0ocTc9LggcJAj6g2LPFf0BznxaAKl\nc0ZRlY2NkMXFyoloiK9drTBV87Asmx//aPkJJ6kxRyyzuXn+mdrgRTVEkaVM1VwmJ16Pve1xeCU7\nG/1BhHyNPVTPqlqmac6P/uoB/+n/XeHTa33iOGZ+Lnimk8OvfrXN+rpLlk0jxLgKd/ZszMyMv+/s\n8CKuEJ7nkOWHT96lpZRev6CQPlpbaJ1QqUrefNPm/W9MMjsTYB4QTIUQXPt0h8+ud4jCKkq3cZ2c\nRmOb06d9iqJGkgqkdAAHy3LAKLCtAa2tGfr9EkJU0LqDbQdIlWCgMK0Cz1uk0+ljmNZuaqdFnscY\nxthNwzQCMCSnFuDceY80DYmTLZTcJI4jNG9SKjUQRYiQO1y4ILCsEpXyHEobWFaTPE8xTJM8T4mi\nGFHk2HYJy6qR5ytYpoEQawTBBWzbot0R3LvbpxA5002f+3dHKFljfaNFnhm0Nm9hWiVse0S1doY8\nu8npM+YTlZ89HHSfOuo5B6tKi4vV/ccOuow9vsM1N1ti0F/GNDOmm22+852FQy41e+fnwUrnUc87\naZiWhaGLceX7OfDbtrORpClJrjFN85kcMehH/Ie/+A3/9b8O+OSTZd55p/5cloI/+fFtdtpvjSuD\nWQnLvMnlqxMoxYlyxGDgEI9slLZBj2hMCL72dYd/+S9nWVyskuaCPY/f4zmihONOkSYFSWoDLq5r\no1VGUZjE8SRCVDDNIeWgjhAKqRJMU+B7Bpa18EyOMEzJ6dNQKYekWYYUKyRpSBROYZpNPG8CKR7g\nOjmuE7GwcI5GvYKUFqIQZLlAKg8pe6SJSZLm+F4FxymRpfcBE8N4iONewXFsWq2C69fb5HlGvw9a\n1ZGyYDgc0O2u0u7sYJpQKZ/GsibI0utP5Yij+ME0TXyH3T51sG2LCxcb+1XHvWv4OKeYZ137R3HE\nzNQ63//fLuM8JQ3+i8Bvw85GURSsrG+Tawdn95g+z87ny3DEz366yv0HPkmyQFEEROEOs6c2mVuc\nRL4ERxzFDwAffzQgz2cocoFUNrbdZWFhinPnkn2OAOj2hoxSyY0bfT77rPsEP3zrW7MMw5Ruz0Mp\nByEDLAssWxyhIdpYpodUKQaSctkGY4J2u9jlBxPDKNA6B1wwfEyjjmEWlMseC6dyXCfb1xD5/8/e\nu/3GkZ5pnr+I+OKUGXnOJJMUKVGnKqlKsutgl8tut2302D3bvcC00YtpYLC7fbsL9N3uv7HAAgP0\nXQ8wmKuBF7NrG70z6O6d7nKv4amqdqm2SsfSWeKZzEPkKY5fROxFkhRJkRRJSVWyx8+VBJKZZMYX\nTzzv973v80QDwvAUltVAxn2StEXB6VNvFHdoCNNICCOfMEwZDEckSYyUgny+vKeGWF0Z8fCuh0wk\nkxMWd+5EJLJAksQMh33m5+/jeSG6ISkW54jjh1jmkLNnvefWEM0JwWStSD5vv3B+eJaGiIMR0xNF\nSsVnu6i+KHzpJxtfTWbv/vjHXyzx4UdNBl4TEZlcvfoYIfYf/N3EcCg4MV1naXmeOBYYeot33znD\nJwf07B3VQ99xJI2JPKoe4PZGCG0e2zYZDKpcvTbe1c9gK8iv0+4yGE6gqk2yrEOappTLXX70o1kA\nbt5apd0aoetlwBr3VUYtHj1SMAwP0yzQ63eR0sA0lkgTHSltkCrttoeuq5SKE+jiHnauQhSvkiYX\nkEkImYLQ+ggjZjA8TaOhIJMRZH2GIxffGxFFIZVyg0JhxOVLda5ea+G6GY2GzdraAOgQRefQtGmi\nKCIIW+i6i24UaU5WQJG4LvQHXbpdD5RJLNPED0wW5uepVlKuf34L3z+LboAmziK0jGJplizLKFVy\n/PCHDaIo3tpdsCwfUAgC69i5BttdxnYP5+mGzre/c4KpiYPbHfYL4HmZ+RvPMEb6rcZmENNh8G/+\nzU3mF76LoiiMFjL+6q9+yf/yv37zmT/XnGrg9u4ShjqmGdNsjk/CDurrPQ5HTE/lyNIRrXYfIe6S\nz9u47sTWHFkUxXxx5/Accf9eG8M4BcR4foiMl3AKMwihEyHodgeYpk4i18cckZjIGILwcBxhWTFO\n4QKFgsLy8gB39REZEt/vAQVKpTIXLkrAwHXHD8tG3WJ+4R5wEik1NPUN4mQeqOP7HXL5CSaaZUr5\ndR4/gkG7Q6vVJstOYVl5olhgW1/gBwqtVp9EzjIzU0fKYGwmoY1PHmvlHN/7XpkrV1r84h9dHEdy\n+VKZq9fcjWsC3/9eeeuajI0Gnr2GDnKKOWz41vbvM5SJrYwNzwv4+c8f7EgS/13+xtHRHwxYaQ+P\nlQB+HI5wezqn56Z5+OguvifRrGW+9e03UFWVK1dW9+SI4+TwvH7B4OpVF1VRGAzX0LQ2qysZ5bJB\nHMXEiaTdHXH3vs+9uwHdroYmzpFl/R38IITg4oUyC/PzDAYCTeuhafoODWHbVSIE/b5EVdpEoU+a\nFUgHDrHsoypVyuUJsrSFqnnIuE0cj9upEhmiMEShjZP/OqIktjSE2/Px/YAgGFvnC13h7LlxYbVd\nQ3ijNeK4iapOEkURvtfFtFwUtfqUhlhfaZPE58jlQjzf5t7dO5yYMbkzn9Bq9cmyOUzTQhc241Dg\nPKY5xdvvdHjnnfpzaIiMJA6YapS3NgteND/sxqaO6LoajuXyP/6rr5HPvTo5dy+n2HjFBI7b04ki\nAciNyX+d4fDZgzXj4zLB7MwJMjLq9Qjd0A8Uns8KzNmNd9+pM/Qfki86nDcCFhYtBoNTBMF4oPPm\nrXWArcGn9XWDwF/Ass6iaRWcvEK1xlab1eVLdS5eKPPXf71IEJbxvS5CXKTr9ojjGr3+dcheR9Vs\nkkQQRg+ACpDheQ8Q2hDLbGBZk2iaynvvVbhz5w7tlkKSqBRLKb6fp1DI0FSFJNEYDCJ0/SRSmiQy\nj5RLzM2Nb6TxYOfYPevCBUmvOcXNW+NhKU2TkEmSNKBevIaUBoqikyQjsvQ1kkQgE5tEDsg7Vdxe\nhShcRCZToIwr6EJBHwd3iaUdgTvbr8OdOwtAiZOzhUNdk/3Xwt5Hp8BGsujxsJ9F5ovA8/xev+nI\nMg5dbHQ6+R0Pgk7ncGKkXoOzZ54cf1drt1E4uDg9Dkd8cmWJXF5w2fK5fz+H676G56VMT+X45MoS\nQ3+I25s9NEcMBrC05NHt+thWjkA5SSIn6boLRKEETmGaBaK4QhjdRVEapPSIvQU0tYsQk5RLE0xM\n5Dl5ssJnn91iOMihKB5zczrlUoNNSy63l5CmdYQYC6koWqRcirh44Unx43mCUk6iG5PEcZHbt4fI\nOB776FsecdzCNFuYIiIKi/hBgKLU8b2U8bCkRxDU6fVUSqUSUmaoig0E1Gs6rXZ7B0fsvgY//elV\nSuXLe14TRVE2ggoPxgtvk9x27/785w949OhrKIrCcJjxs599/rtk8SNiZa1N30+OVWjA8TiiXIrx\nPZ0TMxUyFSaayQ7nor044qj8APCt95oIMRbGS4srDEdv4Pk6V68m+MF9Lr89zRe3h9y5W6bTzuEH\nCqrSQjeaqCSUyvYWPwgh+NGPZrdpiNYODRGEmxxxBsPUSZIMWCLOfOQwRWg3EPopVC2jVCpz6lTK\n8tJjHjy4D4rAzgVUq85Wi+umhkiTJkLkkTIgCJeZqftPccSFC5LBYIJHD/MMBhGKAmHgoaqSLL2C\nqtloqkWSDIn8k4SBiZQlZLJAsTQ5jjRIl4FppMwQwkTThpi5PJ6/hKYtUquu8e4753dsKh9FQ6RJ\nikbEicnaRhvak7XwMtuo//EXSywsnEKJEzLr6/zH/3j1leKIZxYbjcbRj2BkGjKKvtyj30p5/xt/\nelrliy8gGWhomoJtp9TrCsXCwe0l3/l2k3//72/S75sUiyHf+RfncfI29bpCa12waTq9/bWktLG2\n7UpLae94n6ff0+b9b8+gaRb/9OsFVldzyFhF1RSEGpJ3BCvLHqtrI9I0j5SSJJkgCLsoispo1EZK\nKBQkb75R59YXLoPBOE1YUUtkGKiahhApQdAiSQw0TaJpFVAGgAXEkJ1EUVMUpYOijhByGhprAAAg\nAElEQVQiQRchpZLDt75V5uYNB9+3GY581lbvMhr1ee21KoYeE4UDNFFACBfLytFoZLz3rWn0DUee\n97/1ZGbgyqcrtFoG6y0FyCF0i3rNwrL65J2zrK36G0Nn99E0gUwMUExUVcG2IIrzTE+ZjDwTRVFQ\nKDAx0Wb6hIbjZLz//gkM3cD3Basrq0SRoNMdUiqWMC19z2tyGPzgBzN8+OHq1m7T++/PYGyb/1Ez\n9cA1eNDXotjBzpk7/n/Q9x8Fti6PdQ8fBS/79Y8LVZOowycFx0GfabMZ8ejxZr5BSrMZPfMaBEGI\nYaqsrl4FJN98r8gf/9FrLKy51OujF8oRf/ij8RDg3//DfebnHeJIQxMKQvMplnQePZS0ugdzRC4X\nIsTYkOHRwxaK2kTTFJz8BEbQwnVbJIkkyyRCr6BpEZoGZCfICCCbA6WFEDMY+hKQEEWPmJgsMNWc\nwS+N+aHTXSYIupyam9loL8hQ1SFJsoymJZTLHn/y4wv78oPb1WnUDdbXQeg2jtPAtlJs22UwmGJt\nOUccG2jaTVRFIc0MLCuHpilkGZw/X8XQR4w8iziWnD5TpFZb3MER/+k/rWzxg2FIYmky2dz7mqRZ\nStnRKDoHr4cf//gcf/t387iuTrk8dpAxjxAIt3u9WcLYurfCsITjPDnJCMPSK3vf7cZX/XumacrD\nxyvoeYfJ4v665Fn3+3E44kd/OMP/9r//A15QoVSK+c53xhoC2FdHHJ0fYDtH/Ot/3afd1pEShDFE\nX4Bcccinn/rIpImqZiRSI85apFkHz+vz+ecB5XJ2KA2RpRJFSdFEBRiiqhppqkM2S4YE8ui6Rz5X\nQ9djCgWb8hsFYJuGWLvLyBtrCMuEwSBGVUckUR+hhUxOpvzJj8/tyxFrqyooJsNhiC7yNBp5oEKW\n2WRZSr9jkcglhKYjZQ5FsdE0hXwOorC4jSNMVNXBsnwajZSvfR3ef//yhoZwj6wh4jikmDOoV5/O\nsXjR/LAbo5FG0cphl6vAq8cRz6wI1tcHR37RTmeEJ7+8YqNSztN1R/t+/ZvfqNFzH/PxJ22EqfP6\nBZs335jac9BqOz78cIVc/iL5/Hhn61e/Glezb75R4pMrD8bCc0Pkb76WED5BEG3bAfdptftcudJC\nShsh/B1HcFEU89GHiwRRnls328RxgySxIYH1dgfT6uIHRcJQJ4oy4jhD0wak6RCyKTRxGrC4fn2V\nRw8f4BTOsbrqIeUlpJwnSVRct0WhUCeKPDR1HQUdXRcE/ubOWQEUEwUPRdXJ52pMTY17CQeDFgBB\nCIOBRyzzKGqTOB6ytupimAGWPUUUFjH0IuVSi1NzBlGYMBqF3LrlMhgo9Nw+pXKBnC2ZnfXpdH1A\nUC5ZVCoTrK159PpDwrCAqgYkaQ3DzAFdksRlOOyjaQVMMaJeuUC88og0NXCcBaZPlAiCGCEkg36A\nbiQsLa7T6781HnyLTAaDVcKgsHVNnnXt98Jbbz0JEguChCB48hpaFtG19l6Dz1qfhj6k7YXbdjyG\nB37/URBoEkM7HKEdl5iOwxFfBtzeiM5oPLPxrGvw539+jr/6q3+k08lTrY748z+/+MxrsBnI1GyO\nr1sc3cXzJf1+8MI5YrOt4sonK0TRNFLmiSUsr6xi22v4gfVMjvj1P93FKUyhKOzghyDokHcMen0V\nXZMkSQSZgpQQRxkoCeBsnCbqCKGQy1WZmiqjGwn9/k5+UJUStlWj596mWqtgmYskyUWyTENVMqrV\nwQ5+GGcGhUCG5wt67i3qdYder4VhTKDry1QqVe7ddxFajiwdW3RDhUKxTBjcI4o0ej0T2/YYDT0a\njRrR0kPyOR/HETiOtYMjtvODH2TAg6euyeb1StMULVFJ5LNPyb773Se7nZ4vN+wqn4291qepxpgb\nNu2m2WNlJdjiiFqt95Xcd8fhiK+SH8IwZH65jWbmUZQQ2Hvg/ln8AEfniDCI+Nn/fZNS9ZuU2akh\ngH054nn4wXEkyytdAv81NHNAqlRZXn5ArX4aKV2GowxVTcmyGLIOiSygqqdJU4Xr19uH0hCKJknT\nIboukBLSTAGUsYZQIM2OpiHm5nQGgyHD4RyWCbYt0MWdAzlC6A+wMxPPCykWSlQqEywtu/S6Q2z9\nBAoqkMeyC8AaMmnR63XRtDKFwhJBENFo1MjW51Ho8/oFAzBotTI++GCBd9+pH1FDZMgooF5x0FR9\n33XxIvlh652zDBmOqORDVgc5RqPwK+OIg/jhpVQEmnq0fumXhZ298IL/+X86SyYOv6O93zHnQYmS\n2z2yN3v7No/jhBA8eOBy/dp93rxU5N136ly50qLfnQUBUTwEimjaCmGoAEsMR2P3ljC4iUxmUZUI\nQz9Jxh2E5qBpAlVTSRLBcGhSKChIqaFqKraRY6pZ4u69OxiGSqnYRVHnGAwekyQ6qrpCLqcS+D0y\n8mhaRrEAuj6+ATIyTCNkedljMNDxgwyh2QiRUalUmJr2SRIb3zNx0yGgoguPixfG7iy3brk7QnxG\n3iqTkycoOItMNRXc3hSKmgIKjhPi9lSyNEEXKom8TxzZlEoBUVwY/32DHnMXLaqVFfKOoF5PGA5L\nuO7pp46ca/Uqg2GPONaYnEzR9QjTWt66JgfhOD2zQju+69HY3ebujpmNF4Esy3iOX+s3HmNhtn+7\n5O5Zmb/4i68faVZmrx5cRQGF7FgcsboyTasVMvLsHRkS29sqwqhPllaQ8gFJoqMo9+kPbOJIIQxv\nQDaHqoZ7ckTomdjJeD5iOz90u4+IY51SMUKIOdI0odO5N251FEMs+xzeaECS5tG0mFLZQNcHWwnl\niUzwPY2RpwFDbDvCME2qtQrvvOOwvFggCD1ApVxSqVbHs02b/LA9LHSqOYlTmMRxFpmYsHB7RcZe\n8gqa6pMmKWmWoihd0uQuJ06U6Pd7eKNpQME0L9DrX2N6usnbb2f84Afn+OCDhafaUrbzg64nzM5O\nU6vtvCabyLJsQ7zsv3Ze5JzVtjfe+uef/Mlpfvazz3fMbPwOB6M/GLLaGaI/R3bGcTliNPJp932C\nuHTgrN9eHHEcDbF9fcMiRu42MrWRMkFVVFZWVvGDEYlsgaZhmAkKOYTukGUmQosOrSGEmENRLHr9\ne6iKh9D6yGRiHNqpqhhmukND7M0RcktDvHnJ4eGDkCAcawjLUimVx6c0+3JEvo6vPqBWNYhliTSV\nKKlHGldRDIFpaEh5gzB0qDc8DKNBvy/odLrkcwnl8kOCwOIbpxTefOPknnO4h9UQUsYYWspM80nb\n1JfCD4zzMwxNcupkk5k/q77SHPFSio1ioUCr18G0j9aq8qKxuxc+iq/xtW80EQcEbm3HXn36u4Xo\nzsHC8SLcTSCbRcviwoAgKCOlR6s1wUcfP+CLWzEjr4pm9igWdfr9ALCwTI1qdZLhYEgUTWKYGomX\ngJKgao/RRUwUDyCpMBxEVKsxjhMSRQHt9gOiyMEwXCYaeaanBE6hSpqUWFtvoxCQy+cge40gyBHm\n7pBlCxh6yKVLDnZOZzDYDMrJsO3TlMstAj8kih6i63WyNME0JMvLPmHUJJcb77gJY4lr11o8fBjS\n6WYUCiDj/MasjIaCwsOHIeXyaUZeZ8MBZ4kf/GCaDz5YYGFRR1Xz1OpfI5EDhqNFdDGHkxekMmJp\ncZk//qPx51ss2Pyf/9fjPcm8XM6YmXG2rl293uf99xuHuu6H7ZndXAu9HjRKkh/90D4WoRx2OOyo\niMIQp/LycjxedYy7E/bvtX/eWZm9enCDIOLjj5cJouK+/LCXyBgnz3pEUYUkyWh3JvjkSot33qlz\n/ZqH5/voekKlYrC42MM06mNraz2H251BGOPTqzQdYBgalr1A4IfIZICiVkiTFNMI0bSEJAlptxcA\n8L0ul78mME2DdmuaVrtDt+tRLpucOTPH6tqI0XCdaqVAEDymUIR8bkBjooJpjsP2rl1r4Tgho5Ek\nlgqJ7BJH40H169cC4iSPbVc2CrM+PbfPr/5Lxp3bbQxDoBspMrZR1PG9ux9HvHmpwKe/vkOWnsLJ\nJ1Rrb+Dkl2i3S8Ac+ZyOlCpx1OaHPxzf64Zu7LlpVC7LHfxQq7n7FodZmiJ2OUIddu28KNFh29Yr\n1X/9qqPV6dIZxBun48fHcThiOPToDEKEbn5pGmKTH6aaNuWqytArEAQ2qpoi5Sqddp1Nw6QMn3xe\nASI8b0iajlvDNPXwGsIbdZiYqDLRmGV1bUS/v0SWLQAqzUmPs+eqWxpiO0cMhxFRFBJFkKVgGpJb\nt+SROaLV7hDHM0xNKqwsr+G2Yt76Wo779+d59KiDqhZoNr+DjLvE8RJSVsnnJlBUlf5gESFifvjD\nxpaV8GE4Yi8NISOfStHeEdR31HVzXI4IA49GyaJaGednvOoc8VKKDV3XUZWXm458GOzeefS8Alki\n4ZDFxkE7DIcZLNwklUcPPaI4IZElsixD1yUKCl/c8oniEnGUp9ONEFaEwj0UpYRhQL0+QxTDcLRA\nFMnx6YOaoVBEynUMo4XntUiTAMPQOXOmxI0bXwBvQiaJwglu37nK229XWV66D2icP6fj+VXSZJIk\nTVlf97GsPBcuGly8MI0QgnzeYjQKAPjoYxchBFPNJrCC656mUDA3WitcSuUCI2+VOFIJQp+V5Yz1\ntQAhziJjn07bwDCW0Y0iQiQbg5YaQgiak+M0S90A2zY3htJWCMICvjdAiDJJsgIYjEYRptBQ2JmK\nvd/g9l7Xbj/sJn/XZd/dqO3YLEpkHNCK6y90sPtFQCHBsv7rdazRNLFhu7g3DnIHOQz2OpH6xS+W\naK2fRDftZ/ID7OSI9XWPYsEhyzQMIxm3TV1pEcV5ojBHpy1JM4skuYlhnMA0Iyy7Bgha7XtILBTG\n1qy+r2z0fY85IgxHTE8VsO151tdCyF4nQ6XXT7lx4yZvvFHBdR8hNI1GLaVSO4mmqkxOFujqXaam\nNXI5ZyvzZjtHhJHF1FSdicmU+/cWCaOT6IZGLlfh4cOHTDSarK0t4/bAjdbJqDIcQhDMEYYmubxO\nHD3GKYwfsPtxxFtfd3h0LyTKFdD1ZJxv05kgSTpEsclgEFIoGsCzszOOwg9pIpEy4YN/WNy61q02\nh1o7z1PQftWdAb+pWF3vMAhSDPP5ue+oHNEfDHGH8Vag8fNqCBhzxOrKOu2OwmDgY5oGtr1LQ8QO\nUZhx9cYdTBs87+E4iK9sEwQ13N4TDaEoGVGYB5axrHU8bx3fCxjuoyHu3LnGn/3ZHB9+eJfh0MRx\nQqaaZWASgMnJAoZuMzXtbBQXTUolZ4sf4AlHoKzQaU8DHigaMG6RmmiUj8QRcaSgEiCocPrk65jW\nMt/9boNvvTfFv/t3j/H8MsNhF8Oo4/nrRGGeKIwpFI0tbt2Oo3JEIiVCTZiZrKJutA9sLxpuf9Fl\nejpGCOOZ6+aoHJHIBCUNOD1d27Lj/k3ASxusMHTtK7fA3Wvn0TJzu+Tq/thvB3K7EO33bcrlg11n\nGhMZi4sjgvA2+fwk01OTGw9UwYnpSa5fv0sqi3iDFeqTXyeRy+jGSdqdVaJwiKHPoWkZw8GQOArR\nNIM0PYOqZtgWxHISKdvcf2DgukMgwTBzKApEUY3Hj3NMNTffc5Ge28ftFdH1seNVtWZw+dLeD9tc\nTuJG45tQSkGlIsbDmy2fL27HxHEbVTlLGEYIMYtMFggCG00LyOVs/KBHLp9QLNymVC5QKKxScHQG\nwyc3di43FgdCCM6es3Bdk8VFkFIllwMpV4hDj2rR2XKb2sRuQrh8qcyHHy7vaWG5H3afZPTcq5TK\nM08VMLuxuRbUTEU9hlh92TD1l5fh8ZsA0zBI5WDfgL3ndQfZ60TK7elbuTjP4gfYyRFra22Go1vU\nakWmmhM4zsqW/fb163dJkiKxXMey3kdVXExrhsD/DIhQuYBlRgRBl9EoQtUshCihKgG2BUlyHgjw\nfYXh8DGKoqBpFooCrlvmwQObqeYkUkoWFm/gh+EWP5w9Z3H50v6hUJscoakqlm1TKGo06jbrLR+3\nG9HtDpDSIk0dDF0SRTP4/kPy+Un8oIcQFk4uZu60QhiNd0P34gghBK+/ZjMc2igo3Ls/wjASarUc\na6vzpFmCbSsHcoRl+UipbFjeHo4fVBV++f+u7BAE7fZ/oVY798y1c9yCNssydPG7YuOoWF5pMYqV\nLbH/vDgKR/T7Q9xRvOO9n1dDwJgjCoU3GQw9sswkjm4yPXVhh4ZYWp5nbW1EogzJizcQIiNNuyhq\nhpQBhj6H0GA4GiDjkEg1kfEceSfDttQDNURGgw8/7OAUzm20WEkePrqBppWPzBFJouMUDAw9QFHg\nzp0E217H9y3cnkWaPJsjvNECBbtLpfwOQogdz2jd0HnzUo5Wy+bBA4hjjVotR8+9RxCWyeXULW7d\njsNzREYcBlSKFsVCacdrbC8awqjEg4fznD939pnr5igcEQU+ZUdnon40N81XAS+t2DB1jeAlHW7s\nPnb68Y/P7fl9e/bCK7DaHiGOWRHuroCLRZ+Mp3fWoyjm+rU+nr+GrktOTE+Qy0+xtLTC1Wur6EJh\nYiIjy8ApTGNaDv1el4G7hKoDPEYXkkpZJZYBq6sRiioQWgFNK5HIVdJUIyMljkeEoSTLCqBIZGxA\nEkEGadpnYd6lte5jWRKnENOon9/RnvB7vze779+73bq2XOqSy1VYb/kEgUMcLSPEG4RymTDKgCHl\nUoluNyRJFLJMQWgZppEwM1t4kgQu5dZrbh6z7n6/disg1mymp+ZYW1nDqilcugTvvrPzJttN5h9+\nuHzkfIPd5F+rVymXn73r6TiS0SDE1JwvJRH8qDBeYmDgq47NXIJ7j0c0Jo09OeJlzMqUSzEdd7yW\nDuIHeJojLlys03M7xHGLL75YYn09j2EGlIr1LY4Y9LukWRfPH6Bqd6nXU3QRsbwSEMghQpQAH00I\noijb2PRJkdJneSXCMkugSOLYIIv8jfCtLktLI1w3QFUjTHMWXR8cih/gYI4YB3hKwmiEZcaomrXx\nYE22+CHLMkwr4dKl+pb95l4cIaXkm9+Y4uq18b1Zq67jOBfHczLKKobu8eal3IEccRx+UFXlKUHQ\nnJqkXnv22jluQZtIieEcL2Tzv1YsrqwRxOKplre98FwaYg+MRv5ThcZ+OIqGuHKlxadXAhR1hemp\nCWZnBZ9/fper1z7foSFmZqYJ4xW8cJN7OkSRR6/nUyrpZGmA20tRVQVVKSBEESljwjDE0DUgZTiM\nkLKAlDEZBsqGhtBEj1u3IizrEZYlyeUsVGXuWBzRbo2IpQUZBIGDZY3wfI3RsEuSgKrszRFpKtG0\nEW9/bZJqpYSMZ/jkyvKez+jNwmF1xSMSeU5Mn+DENAyHN5lsNnCclaee6YfhiDgK0Ei4eXVIvx9S\nLrV2tDxt54jTp4ssLT7Esh8+89lyGI6QiSSNRsw2K9i/od0KLyVBHEBVFbp9D028+HpmdxJj153n\n5Mmnp+D3SnoVQmPk+aDu/L2iKD5Umufu9Mjvf28K113k0cMlRsMuxWLCwsKQv//Pa6ytTaAok8Sy\nhB8sodCj168Cb6CJE2SZjarcRVFGqKpPFMUE3gVUNcTOT1KptJg77SD0CjLOCCOJoviYhkDXJabp\nEYYuadLc6MfMYZp9UNaIoy5C75PIOmk6R0ZKmp7E9xaZmprGcfKUijmcQsbJ2Z19rYYhiGNJLOWW\nE0QuJ3nnnTp+0KW1PkTXQ1TFAKWMYRo4ToKqVmhO5UiShCy9x8hbJwwGhKFgONQIoz7tTsTjxxJd\nT/na5SJTTWdHQrqqqkxO5Dh3LgdZQCqHnDtj8cd/dIJTp0o7rsl+acuJHK8FBYU09ThzZtxPuV8K\n+NpaH88rbpF9tdrn/fennkoR32stdDuPMPTkwDTPw6TTvmjEUUS9nMM4Qnjhb1OC+H/4D3d49Ohr\neKGO50/uyRH7JUHvhSCI+Pv/PM8nV0YsLnSYmcnt+f0zMzmWlu+TEh3ID7dvR3z80Twj7yRxPEEc\nFwnDZUrFgJFXIU3fJIonSdOdHBFGMWF4Dsu0cApT1KqrXLhYouDU6XRckgw0bUSlXCeMhhimT5J0\nUZUZ0jRlkyOkXCZJAlS1Q5pMkaUCVZ3D9wW6GHDq5PS+/ACH5whNtdCNJjAgl5vGNDJ0I8LJ94ii\necIwYzBoMxqprK51mJ11uPWFuydHpElMtVLYSue9eKFMf7BGRsCpUyl/9N9MPZMjjsMPSpbSdz0G\ng+qWIJicaPOH/3zumWvnsIm/uzkijmLqlfyhUue/TLyqCeKPF1cIU/1QhQY8n4bYjTCIWO95pJn6\nwjXE4uIkkMf3JwnCZTxP4I0GWPY7OzRELu+TKS1UtcZwMEQmTUxDp1wu4eQHVGtNqlWDVmsISohp\nCIRQyLI+hpkQRaAoFoqaQ9dD0nSeNHERep80bSDlSTIy0vQkw9E85XKF5mTlUBrixo3O1v383jdr\nqNqAVmvMD416lcEgQdfLOE6Cok7u4Ag/uI/b6tPruISBjmVKTpwoHGjgspngfflSgQyPjIBqtc+P\nfjjL+fOFHc/0w2iIKHJ57YzJZK3EP33UZmXl/J4p4IsLnS2OUBSFCxeG/Is/OfnMZ8uzOCIKRsw2\nHSrFEvpL0NMvEl96gjhAzrbR1d5Lee2njp3co7WvVEt51jrejtONjz9e5upVAylVhFCQcpnvfvfk\nUz+717GoEDqTzbEj0o0b83Q6GoPBkDSNMIwbTDSm0EUfz8tYWQlQlQGFQo4k0ZlsNvj+98p8cqXF\nr38NqrqGbRXQVZdCwd6x0z/VNIFxareTX0dRIVpQ0bSHqGpCGC6SSBXL0om0Dro4Qxj0QFGRcQ/L\nchCauWMXZbOFKZaS69faPHwYIwTMzAhkkvLggUmSKKhqwqNHj2k0GjjOANseD5MGQYYQCfVaFd9/\ngGVVuHhRksg6//TrPFlWJZbQ7axy3etw5uwp0iTj8WOfe3cXOXsuv3XisRtvXy5TLh/NavGg8L39\n3MWO0r+9HYqS8d/+0WvkXsEkXw1JPv/qpId+2Wi3zQ2OUI7FEbvxj79YYn7+JI8eLxIEJteufban\nM41pGXz/e7OE6ZP1vJ0fbt5cGJ9AkvLoESjKOoXCOk6+fgSOWCFnW5hWj1q9yrvv1PjkyhL11S6Z\nUgJUZNJm7tSYIxYXVGRyB0UJiaIcSRIDDpo2JEstVDUAAlTFR2gRljVuQ9qPHyDhtdfzvHa+zPVr\nLe7c1ZFxQhAMuXfH4+z5HOfPqwyGDVZX1wmCjHKpCMoKuvA5e87i4oWT/PVfL+J5TVTVGdv4Lj/m\ngw+WcArn9uYIdWdb0UGOX/vhOPygqcc/BTuu+UOaSXT91WrLfFXxaHEFiXmgoNuN59UQm5BSstrp\nIwybX/7y8QvREDdvLiClxdLSgDgeYppdppoRUg4ZDpcIwhJRtJMfvvENi0CWuXatzZVPfRR17MA5\nMZFD18fty54nOH26hecVSVMPhYAo6mIYZWT8AEXNE4aLZJmBohiomosuzjAc9dA0hUS6mEYB1IxG\nffzMe5aGAAXXnaTV7hCF8Nln9zhzZhLHCbHtaTRNjMN9SWjUq6ytjzlibk7l0sUm//bfDiH7fXQd\nfB8+/uhXWFaO1ZXpDUMNdYdz37M+58NgkyOyVJJEkqmZlObEOLvioJanF80RcRRiipSzsxM0auVX\n1mL+sHipZVK1lKPVk2iH3G04LJ46diofbdfYskzyVkCQPLHn/eKWj++P++3iOOPmjU8RYnnP6vnp\ngWJl6yHVbmcMBiFpeposKxHHHoa5jm1L/OAcquISxUUGgx4Tk+OH3eZsi21JNM3ixIk8qupQKt9D\nxiFkMDFpb2VVFAqSRBYYDE8QBOsEwSRh+IAoKpFleYIwRVV1/GCdLDtNlhkYuo3CgOZURrn8dAvT\n9WstPv00IwiKaCJhZbVFGEqEdhY7Z+C6XaBAEKSUS7MsLN5AFwXgM5qTDUplhYsXZreKho8+dgF1\nM6+IIIQoEqyuemRpRhQXSZIA161y89bqjpkRKUPKjo1lHbyLFkXRtvmM8TU6qHDYT2gch5SyLCOn\nK69koSGlpFb8ap3gvmrUaiHDzZ7/Y3DEbrg9nUePFxkMxhyxslrm7/6fhxi68ZSLiG0auJ0Rn302\ndph59NCjMZGhqQpS6rhuSpr4JMkZsswmjlKM6uq+HGFZAZ9s8E2lHHJytoqmaWRklMvuFn+cnKmw\n1m5Rrdee4oh2S0HTSkSRRxxZKOoKCqeRSR+ooKgxiuoyWc/hOCvoBjv44dYtl9t3HNxuRJKodLtd\n7t/vsroKUXSCOF4jkVWGoz7lSp1CYZnh4C6JFMBnTE1tckRz28aCRpqOTzXjOCVJYHlF4Ww+Y73l\nE0WFLY64cWOZb717ODe57djNEWPnn6Pxg6aqL80xbj9oqvK7AfFDYHllHZmZaEdsGX1eDbGJtZaL\nMMZc+6I0hJQ6S0tt4vgMWVYiCBoMBg85eTLED95kNHxaQ/hBxK27HmFkMtX0yOUKWxyRsyWbUm/m\nRBHICKOMTtvDti8jhEDVVun1fKKoRJoUSYkQmk0sXchmiGMTTTVQVJdTMyrV2vpTGmI7R6SZyqPH\nS5iGQNN8NHEK131EIsftSHNzJxkO7yClgdBAN7poWoHzZ2Le+nqdcqmIEBppsralIaIoodPVuX7N\nI4pGBOHYQnvTue84hcVeGuKtt6r8+qMviPw8EycE3//+mf3XzbaWpxfFEWmakkQek7UixcLxbZtf\nNbzUYqNULLLWXUbb522Oa/m1u4L8wx+dO3Q4yiaqlRKLKy0UfVOUiR0Va7cb7dvbuznUmSaSO3dW\nGfTXKBThxPQkEAMGplkgDPsoioehj6jVK0CBwPdYXLqBH4zI5+HypfNPhkQbksWlVdbXljZ6j0/w\nySfrrK8UaXU6JFmFJG2Rsx3u3ElR1HVq1SKt1jLdjg+UsEyVNFUJI4M0idF1kArEtl0AACAASURB\nVLKDpilYVot/9gcnse0nIj6WkqvXWnxyZcBgcJosE8hRCtkaKAWEsAmjBRI5g6apBIHJfP8xlj3N\niRN1MjJK5dWnBsxzOUm5ZNHtRvhBiqIE5PKCIHDw/R62PT4RUVAYDODqtRaDXkIhL/nOt09ims9e\nBx9+tLrnNTpKvsFxkcYBtanj//zLRCYDKuUXk9XxVWJz7mK7b7h9yOJuM5fg4bxPdUIciyO2Y2xr\na25xhGWl/H+fjpidfeMpFxHLHgfodTqnWV5ZY30d1tbu8+abpxAiBjSSRGCaDlK2AO1AjpCygOvO\noaDgOHUGg+sbvcfbMzqaLK+s4UdlUN2nOKLbWcEPxjwsdBVdd/D9cZCkqoZoQiFJOpw/L7l0aXbH\nSWMsJffuBqyshCTyBCgwGqm47hKalieKfDKmEFqRNHVotTu4bkKtPh4o3Y8j5uZ0ul2f/kAly1QM\nQ0chYG3NJ0m0sZX2Bkf03Iirn7sMR6NDZ9/A0xxx9dreNtawNz8kSYLtfPmtC6r6u0LjWWi1u7QH\nMb/61fJXoiF6vQEpxrYElhejIYSISVOxpSFUNSJjRK1eBQpEUUirdZsw7CE0gZQO164NGAZjjrDt\nMp53n2qtsmFfr9Bu1TfsYk3KpS4/+EGFv/mbHr1+b6MzoUTP7QBlNC0hQ0PoNr4/RIhNDWGSpWv8\nsz84s6eGuHUzYnXVR8azJElGkgoCbR2hl1CUFRI5g6JaBIGk3ekgNJtqdZY0iSFTqZfX+e53z+/4\njGdmJPfvBfh+SpaqFMqCKM7TakscZ7zpZxgJrstTRcOR+cHP+OjDO/zoD07xL//06+xV67+sTKxN\nRIFPMSeYnJr6rdtseOksWs6bDKJ0R1/+Jo5rC7i7gjRN88hEoSgwUSux0u4jdIvXLxhcveoipYYQ\nCQUnv+eROjw5bl9aXsP3T2KaY1eC9fXbnDoVMj+vEQY1Ck5GY2LsjgAwHCgI3aTRuEguN0+1Or21\ny6agoGk6J2dnMK3lLT/n4Uin0w4IRufIlBELborvpyhqgSBwaHdWQVGwbJPBQBKGGWQRQsQYJmha\njnxeUCjGXLpU2UESwFYbxGikE4VdFKVBlhlkOOjCAxLiWEHXYwzD3nBa0CkUx9P/aZJw7+5oxy6H\nEIKLF8okss3Dhy3aHZ9ioUS9Pjt22IpcTNOnUa+RZRnt1jqx/zqG7jAaqnz66f6CYDv2a3vYD8c9\nVt0NGQVMVot7ktFXjSzLKB5z9uJVw89//oBHj76GoigMhxk/+9nnh/YR3/QcX15tEWbGsThiO77/\n/WmuXfuMldUylpUyd8phaWnvI3VVVRgNBcsra3jeLPlcwshrs75+m8uXbe7fX+PRIwtV5qhWauTy\nwwM54t7d2zSb4/cZt1s0tnIkYDOjY/xeWeazvNjC92MU1dniCCEkurAJggFhlBJFfYRWQtU18rk8\nubyOk1/nrbcmn/rbr19r0e1KolAnTVsoSh0QyGQCTfTIUCFLQJFYpkocqwhtXCQkiWS91WFlOQJa\nO1omL12qAW2ufLpOkgjKJYt6fYZebxFdaMSaPeYIMnqdAbp6YU/hdhCOwhF78UMcBtj20VOznxe7\nW8Z+h53o9Qd0hjG/+lXrK9EQcSxxRxG68WTz40VpiMuXbbzRKq47RcGBvFNmqjlPuTzmh1MnKyiK\nAEymp2dw3YylpWvUm8nYREYIqrUK33pvfOLw0cfuRsvz5AZP5fnggwViOU2SFJAyo9VewbJjYqkx\nGmYkaUYU9cfZPGKsIcYckdtXQ7i9jMAfJ7RnmQ6M274VJSSOFHQjRtNyqGpK6MUo1gglUdEVh+XV\nNdZXMoRY3lEo/Hd/eo6f/vQm9+6r6CLjwoVzaJrOoH8FTYswjISp5gTt1k2k3N8+eD9sXgcZB6iZ\nBnGFSnn/bKqXdcIZRyGGlnJqqoxp/nY8v3fjpRcb9VoF9/EKqpl/6mvP63P/vDAMnUYlz3rX41vv\nNRHiybGmlDaum5EmGYtLIwy9w4cfjitmy/K5c2dho7d6gcaEzcnZCqZV5fvfK/PRx8t8cesBIHj9\ngsG77zQBuH5jgTt3BuRyfSYnKsw/nufzz7sILcAp2ExN5VlZCXa81zgESEdRBQolktBjbXmVYlES\nxMsEfoCqWYTBAAUThWVQIlRtnWr1AnH0CMuyKZe6XLzwtGPEw4chYTiLaTiEoQbZMopSQFEEpllH\naF1k0maiUQICZBLjOGvUa28AsLbeBprEUR43yrZaooQQvPXWJG+9NT612EwBnZw0OXc2AmK84QpF\nJ8XWp0jkExu5ZxUNmxgfP+/df/2yIKOQeiX/UtJAXwTicERjsvlV/xovBE/mLsb80G4fnYRVVeHQ\nXtcHwLQM/uIvvs4vtk5i15mctFhZkTx6PCQIVJqTS4RBA9MyyFk+6+sBcaSjaZLGhM2pueqWF/yY\nIx5xGI5YWGyztNyiUtFQVQXTeMIPuqE/4QhFQVFyyDDCdUOcvDZus/SHgEYQXCfNZhHaAhk2inoP\nx5kgn1MQesLc3N6f78OHIUKcxTBdPC+PyiKaVkdVQYgy4JJlZYoFF9M0KJe6TE3ZDIYZ662xyLGs\nIa6b29EyuckRmnjCDxnZ1ozGzVsunudiGh6mOkmWHn5jYRPPyxGqmn0lpwza74qNfTHyPNY6HoZl\nf2UaotMb7Cg0gCNpCMeRjIYx6+s94kjHtvucPn2KXH7MEW+/1eCnP71Lv29TLPr8+MenMQzB9RsL\ntFoZht6h0XiNKPK4dese/YHHervF2fNlum6CLnpcvTbe/MvlJHH85NRF11P6Ax1VHTAadgmjAbal\nUSrrdLt3yaiiqikg0ERMzh5riP04YlND2FZKX22RJEsoShGFAkJXqJSb9AdXKOZzBN4SpuFQK4+Y\nO13FdW0WFpbwvFlyuR6tlrOjUMg7Nv/9//DGDoeojIzv/F4DIeKNz3oF062SyKPyQ4ZlDOiEBUy9\nhKYKyuXWc62LoyKJJUoWMlUr4uSf1si/TXjpxYaiKEzViyy1Rk+F7Dyvz/2LgG1Z1IoZ7X6woxKO\no5hPrixw/ZoH5Gk0XqPVEnxyZQFQgBJCWCRyfESYUcZxJLqh8957U1ukI8STuYDv/f4sQXCf1ZUm\nN248oNOto6oN6nVBz73FcBBTKE7QaLzG6gr85Cc3qdWrSPkAVclj2eB5GXFUg3SWYHAPw3QoVEy6\nnTKqlpHLT6KqHkKM3RzIJ8zNKU+1RjyBhqIo5PM5wqgHSArOkCAsIkSLUsnEtgRJsgZonD+v8/rr\nZ7l7r4XnCXThU6mOd1kVFDzv6fe4eKHMzZurDPoJuXzMW5frFAvOllPSmEiOLgjef3+SDz7Y2faw\nn33li4CMImol65Wc0wCIwoBmrbjnKeJvIrbmLjb4oVYLj/waOctk4B795/bC7l2tMIj4y7/8mCCY\nwbIkpdK7/OIXj/nDfz6HqqoIrU5MhSzLUFjB2Wi/zRifUJyas59ao9/7/VmGw9tcvVrk2vUVhsMK\npvEWiXRZXOxSLKS8+eYFWi3BRx8/QAgd11V2cMTIgySxSLMJEplhGCUKRYPBIBnvBtqvbXBEj3oN\noMfcnMmlfbJ2QEPVNKqVMtADUgpOd4sjqlWDOFrEsio4TsgPfjCNrmvcvLXKynKEZQ1pNOyD+WGX\nza0Q4knbVZrj7m3vhXDEzhyeZ/OD/hXZR4tXzIXqVUGSJCyt9dCtsTD7KjSElJIgStntcrv7ZOwg\nDfHuO3V+8pObwDSaVsYwJlhcWuDtt7ON1xKcPVfZWKfWRor2WEP0Bz6//GXA1asBjx/fIY7eJpf3\niQOXG1evMjUzQ6VyinYL/u7vHlAqF0mSJVTFxjChUbeYX+gDp4E1VPUkmtYGDLLsMfn8aYANjhhS\nqSQczBFjDYGikXdyxHEfyxoy7LcQio+urfCtdyq0W236uk2xuMyPf3wGwxh/FvOPA3K5HtPT+X0L\nhadbHKd23LdH0RBpmpLKkLyl82d/+iY//9k93N5gqy3quO39R8HmXEa9nKNS/s3LzDgOvpRmVCef\npzDw8NOd7VQvu//tsMjnbSBjab3P1WvDHQ+i4dAlDJ4shvGNIDgxnYcso9Ue0u12uHw55vKlBh9+\nuMz1a32i+CQnTuQJAmVHpT4mmesEoY2iZAjh4I1CqrUJhNanOVnl+vVbuL0YVW3y3nsNzp6Z3PKI\n1tQWYThF110lCDJUpUAqNby+S8aA1BlhWiC0KrX6uCVCE6sIIZ6yqbx4oczcnM6duwNkDPl8gqqM\nqNdzKOoi1WqFnjsgl7uwNWymiVVs2+TypfEOx9Vr4+E22OlMkQFJHIOSoguNb7xdJW/bW2mb23Hc\nWQpDN546Kt2+AzIaxvzkJzd39Lcft/CQUUi1aL2yDk9ZlpEz+K0aKNucu9g+s3FU5PM5ZOvFuXjs\nfhA1pxrU608cZzZ3VmVY5Pz5HHfudgkDwWCwzuVL54mimP/jJ3dod85iGCnTUzk+ubK0ax1nQA8p\nU7IMVFWnVJ7FdRMsW3L9+i2C0CZLl3j77W9jGPoOjlCVIX7k4HkdwihCUXSkTJCJRxYHqGqMrito\n6hT1emXrvj6QI+70cHsJlgW21ee11yfptFcplQsbHHFpiyPu3hufXoyLhRaum3vK/W47dhQWuyBl\nTL3s8O47uRfCEbs99DcLtr2LjwxDfPmFe5Ik5Aq/c6LaC4srra1CA74aDdFxh0+damzHzg0vODGT\nI5EzW18fDseFw2SzQS5f4tatLoPBeEPvwoVTz9QQY2xyhImiJqjKmCMGgw61Uokvbt7ED0AVTS69\nWWHmRA3ff7AxxzEApcL9ex38IBq70AmNwSAkDBOicJ68UwP6T3FExrhTYTs/nDoluH27TRSCkgRY\naoeJSokTEwkTE1OUyyClQMo5yuUxD2zOTo3/pmVaLefAQuFZLdCH0RCJlChZTCFnUmzUUZTxZvPu\ntqi//ZuHW6158/2Qv/zLT5iZnX5hhUfojyg7BhO/hXMZB+FLm3xrTta592gZ1Xoihl5U/1sQhPzt\n3zx8rko0n89x+x/mWVmcQDdzW31/jsOeDiV37ozdEPL5DNseIUTG1WsurdYM3v/f3p19x5FfB57/\nxh4ZuS9IJEASJGtnbapFlkruskpeVHL3uFv26SMf9Tz1OTN/WD/10ej0jCXZnraW9qhk2WbJKmoh\ni0UWySqySGJNAAnkFnvMQwIgdmLJRCbI+3nQEYpAIgBk3Pgt93dvd54wLDA93eDc2eyWmfp6kGm2\nYmZnDIIQogh0PSCX7XLjxh1a7TeIwlkipcKNG3Vef21sI0/78uWQ3/zGIpMpEIU+nW5C54FC2rmA\n690kDsvE/mc8f+kiShATE9FeiYgin2tXF1hZmYRE4YsvPO7cfsiFCxbPP9fg7ucerhtj22NEkcaF\ncy5vvFHhw1/puF2F2bk2YaixWHe59FK4sUvywgt5Prn+kE57rXrNC2V0JcQwdOzs7nXid9t9OOpZ\nip1VPdjIk52Zncf3n6VQSG/8Pd96q3LonY/Q71ItZR9bHWuYQr/DhaknI31q3fq5i+NQVZXI9/nb\nv7vN9HR87AfG9nNme3WSrlRUrt5sY9sTWFYvRly91gB61VOiKEe3C9MzDZz01jDsuimmzk2gKA+Z\nnTGI495r25ZHvV4H/h2KAr7vcONmg9dfG9tyluNyJuTKFQfb6hWJcL0Ed14hZY/jB/cAG5IHXLjQ\n+91u3nG4caPBYn2M+qJLEKg8uH+fb3xjkpmZacKwgOuuoGnjrK50+OY3e7ul+8WI3XYtDiIIQ65/\nvIjXUSkV232LEffurjI2FqJpBgoKN290N0qObo8RjeWIWlnjT/8kdaJpk4HvkUkfvvLWk66xsoof\n62zebBrGGMINQvR9yhKvH/5ef0+tNK6SL5zdMYbIZEJu3XKx7dpGjPj7v3tIvvDavmOI3vX2YkRj\neZlW2yJOfJIkIZNpcud2Qrv9NVz3PpqR4fatOs8/X6BQzG6c4/if//NzDHOKlB0ShCnqi8uY5iTZ\nrI/vW8TxLTJpnampRwe2Wy24dnWOleVx4hg+v+3y6fU7PP9cmtdf7vLpzRBLt4EpPC+Nbc3wp3/S\ne77+7GcLxFHC9EyLINCYm+3w9lsBhmkcabHxMGOIMPDR1YRS1iadzu/6OZsXkT69uczkZICum2ul\nzl+lUsltnAn6+nuTR9r58N0uaVvl3FR15PrnnISBNfXbTlEUbFOnsdL/Rn8///lDvvji4q6NVg7j\nt7/1SOJxPL+JquvEcYevvVPa0oDn7bcqTE44XLt2lyiOsO0GZybHSXDxfZUozNJqr+B5NisrM3S6\nHnG0sJY7aeH5IfPzq8RRiaXlO3S7c+jaHd56S6VYsLn2sUsU+ShqmyQu4XldVDXizJlVpqbyFAs6\nv/zldZrNDoaxiK418XwfJ+VTKU9RLKmMVTrkCxOAzsxMgNtZwtBVum2TKMhz45NFlusO7XaHXHoc\nx1mlXu8Q+M8SRw5BkKLZnOalF7PMzy1x7apPY9nG7YBhdFCTDmcnHQwNMimdZ84XeOnFEs9eLJNJ\n90rWmoaxZzrPXs2zDsuyDH75zw+3vNby0j0su4qCQr3ewTQN8jmL9QZei4utQ3zvhMh3mawWMI/Y\ncR4G39TP91wmK1msY1wjPFlN/Tb7f/7mE+pLr+K6uWPFB4CPrrSJwl7JRUVRyOV9xquLOxoynT3r\n8C//fJ1EVXfEiJWVmCDIkcQBKysPiaMuvu8xXrVxHIv7DxbpdHKkbJ2l5dv4/iyadodq1ae+aBBF\nQS+twujQaulEsUKz5XLmzCq1msP9+6vcvLmA212gWFKJ4xa+762letXI5VSKBZdUqsj8QpflRkQS\nz3PxYpbPP/e4fTtkdcUhCLReB1/DRTfStNoJSTIFSo4gsEFpMF51qC+s8MknMasrDr6nYlk+KC7j\nVWejSefZM/bGxwdx/foSSwsldHWyrzFiaTlFs7lIPp8jIaHdWiab6Q1utseIwEsRuFPHer8c1JYY\nEftUinsfUh2mYTX1C4KAh/MrGNZgUlgPOoYIw5DVlr/vGGZ7U7h0JqBUWt0yhtA0jfGqvWMcUa+3\nKeTLtNorBEGOOOrQatdpNXv38LmpNGGYMD+/SnPVwXVdVlc/A77g7JmHvPhikeufeESRj6Z6GNoZ\nOu0QYhPi+zz/XJYwDPn97+o0lhtE4QqGPovntjD0ANsu4ZgpMpk2X3otRaedZna6xeJ8iJLM4thF\niMe4ebPNynIZ140pFS+Sy3VwnBTLywrd7gUUJU+n66Cqi1SrNr/68D43buq0mlk0zULVXBI6Gw32\n1ht17tdEd7ODjCEC38VUY8p5h2Ihu2uD2/X7bnOTx/kFh0ZjjnKpxPRME9s2KZV6Xc3DqMnc7MqW\nhpCPiw++28XSIs6MFynmj5bmnE5bI/+chSE19duN46Qo5z0Wmx6G2b9V4kbj8IfEdsvLK+QDuh2N\ntFWh6y6RKnQwzLFdZ8yvvJqjXq/uWK1w3YTJiSoff/wJcAbTNMhmX+GjK9O8/83eA+TttyrcuX2L\nbPZZyuVeKsXc7MfkC5fI5+u0WjXC4DYwg6bNAWOwVk3/6rUG2dzLGGaBJEnodD7CNNtE0TlarS5j\nTsiLL6XQ9Z25oiuNq6w2C3jdMnGchahNva5RKOTRFQWVIiSgJgo6GarlPGl7hdDvkgRp0EKISnx2\newHPbbFYX6JcKZLJBICC69rYdnfj/++1c3DYKlL72f5a5UqJQqG3SlIuLZDNvgKw8Tc66PcOAx/b\ngImJykhWnVoXBj6lrPHEHy47jtUVG8zDHyLdK0ZszhGvlONdV1Yt2+Ttt0rM1Muoqr4lRkzUaszM\n3mdhYQVdG2OsWqZe76VKvP/N3MZK38fXVimVnuG119I8fDiNH+TJ511arRqqcp8oLqHrv6eXex0A\nSW9FvnGBUqlFp50jiT9G10HTfJKkCqhoWu+g58zMZwRBEcOISaUu8smNOiuNJq57jgSTIATX7dLp\npHY5ZMqm8xcJYbQMJChKRBSb3Lm9TLOpsNJYJZtN0Wx2yRdyZLMJzz2b5fad5o4zGps1V0JM49GA\nrV8x4syZNAvz01j2DJlMSKFg7jhAvv756lou+okXLjGevlXPx3k4W8e0BxfjDjqGaLY6GJvKsu+2\nwr69X0uhkOw6hjBMY8c4IpfrktAbQzyc/oLm6gym9RJj1UnqdYXLl+d4443KxpmPOHmW8+d7C6DN\n5sd0ui9sjCF0/S6uexdVXcSyquRzX+L2rdneN48vkUn1xhC2/Tl+5y5B9wxJkJDOOJRyCe/+4RTf\n//4torBKKuWTz73OYv0TVpsFup00UWyhRTrTMx2ctL6lUEWvJG1Mq6Vz5UqdTOYScIsoium6SziO\nw69/3eXO7et9HUMkSUIUuKRMnWo1v8c51V3+/psKDVy8mGP64V3s1F1q49MUCl/ZeO1CPjhwUQLf\n7ZKyFC5MFo+1WPmkOPEC4qVigTBaYrUb7NiKPOrBnEIhYLF+uENiu5Xd3Zz/OTER8AdfmaTZ7aKb\nNrB1xLnb1p/vh/zgB1dZXU2haS4vXUpjGr1J1W6pVIXCo+C53LBYXV3F92Jc9z5xvEilovPypRcx\nTQfXndl4nTNn0kxPNwgCDV1PqNWmaDRmAY1MZomvfuUlDNOg1VrYct6kXCniul9gmAZqmCGTGcf3\nXTKZcEvZvlSqV8av9zdJMTZm0enU1jqtfoquT3H3LnQ6UzRb90kSE8gzdS7LrVsPgDxnJtPcutXm\n42uf8cqruS0Bw7Zdbt1qbpQIfO0197F/r73sF9gDv8BHV6a3/I0+ulLfs4NwT0Lou5Tz6bWzPKMr\nDAKytkqlVBz2pZyYo/TeqFZiZpZ6q0KHOUT6uBjxuBzx999/lh/9/U3cILdxOPk3v61Tr3+GoYfU\nxmG8VkZbW+lajxHr+cmtlo7n9gbcvqexsuqTxL34EIazFAp3eenFNzDN3jmi9RihoDA5mWZ6epV6\nvUuhPI7jaKw2l4jjJs8/l+PVVyt4fots9lHKQqejky9kse0HuG4RXY+xrDyO0+HSSwUe3L9PYyWN\nYcRUxzM4Ti8tzPNtikUT1y2jKArN5hdoWo3paXDdGrOztzDMF2h3WoyPO/z857f37hKu60RRSCYV\ncfNmt+8xQlUVXnnV2Sgtvn6Id3uM6LQCDDU1lMIlxlOYYrGfZqtFkBgcZMo36DFEksDmscD2lKn1\nw98HTQva/rnvvnuGv/+7qxtVqKamKpA8WrHfHCMeN4bQ9QUKBXdLjFj/+s1jiFZzkVdf/QNu3ZrG\n9SxU9ff85V++uul7PErP3W8Msb6IuriUJe0ojI05ZDKNtUI5BmNjeTqdGu12i657Ac+bw/fHjzSG\n6FXzija6iOdz9/G7NsVcmmy5fOgKcpsXkTRV5StfyfD+t87guWN88MHnW95PH3wwvW9RAplk7O7k\nuxUB1UqJeH6Rtre1u/hR+268/81z/KB9uENiu81Od8v/tCyXf/jxLRpti0JB3XjD73Zg6aMrdfKF\n18hmEz7+eJFr1x4wNlZlcsLZMajdPuD23EWazUmCcBLbBlihVMpims6OPE/XVTh3NktCwtysRa1W\nAXoBzbLNLTfk1oE4FAo5KpXa2nmGBcqled5+q5eXuV5Bq1JReOXl2sZrrK/E+r6GZa5w5sxF7t1z\n1zqlPuoGDL3up6AxPdPGdQuEYYd6vbrtgFvvcBsYrK/IHtV+gX23v9F+n7++m1GrHT5YnbQojEgb\nCeNj5WFfyok6Su+N73znEv/X33xCfSl1qEOkB40Rew1wbNviva+fI1J6D5zLl2doNC5QqymEYcSd\nz35F925346D4fjFifqFBGJTRjV58yKQ7TJ3TMda6F2/fXdVUlbNnMxi6TaUyRoDLmTMFDLPOG2/0\ntvwdJ6ThP4oP64e3L168sNYATKWQn+PSS72zGd/85rm1crQ6hUKXZ5959DqVcoX64hxBoGIaTarV\nSWZm/LW+PBampRCG2toqpEU2q7BQ72zpEv7JjTlevlQkZalrD+nhxYgPL38GYfnEC5fI4fCdFhvt\njff545zkGAJ2X2Hf/p7y/WDPCmjbP/fy5ZmNMcTD6TYPH14nm6tw5kwaVVW2xIjHjSH2jxFbxxCp\nlMPrrz8HgGXrpDO9rznMGMIwDf76r5/noyt1wjCFri9uWeBb361x3TaOs4KmpoiTo40h3n6rwve+\n91s6zRKmnlDJv8On1x/y/reOdm5xr0Wk3eL9Xp8rk4z9DWWyAVCrlnk4O48bKRs5eketmW1Z1qEP\niR20ZN4v/2mW5sqbJKHH7IMm/+Lex7JTW0opXr3WWDt82KFU7lVzabV1XHeOMIzodBb50z99ddsr\nbx1wO2mLTvc+cdxF00Jy+RqGPsPs7E0gpFBIEfjBjgdnoZDas478Xg/ZD3/1gHrdx9BDzl/oHdjf\nHPRy2RSrze6m15glndHJZALCsEijoWAYEUGQYBghSZKw3syg1yE5Wsv37v379jSI9cNtjz6eOdTf\nbrPDNurb7fOjMESlV/VmVMvabhZFEZYWMFGrDvtSTtxRem+k0w5/+e3nWG0f7nsdNEZsH+D89GfX\nMQ2TxoqBnVrlhVeKXL/e4jdXXBS1xfi4zfXrdVZXDFTtU1IpHccJ+Pf/fvuk6VGMMA2VMLyLpoKm\nhThOhXKlRSZzl5s3fNZjxJtvjG00Cn0UH0ySICYMPPKFR/Fhr8Pbn9yoY1oKK41l8oUsn9xo7ChH\nm07btNvuptepk3L0tY7FOZotBV2PCMMEy/Q2uoH3YpRHQkIYbu0S3m6BpSXkMhlctzPUGPEn701R\nKQ/2nMZu5HD4Vp1OFz9SMA84Uhn8GGLrpHf7YHy3Skrbdz92q4CWrH3eb664wCrLDY9OxyKKFFzv\nAc3VFn/478q8885FXHe9cdD+YwjHqVAortBYfrRT8sffuLhRcrbfYwh4VSaBHwAAIABJREFUdK/t\nHEP0XuPNNxPCMEOjkeHBgxadzuHGEFEUkUQ+jqlzbrzGRPnFje99nHTHwxQa2P65vttBT2ImZZKx\nr6FNNgDO1Ko8mJnHC0HTtROtmX3QdIj14GUYNoZhc/PaJ1QnzqGbFq6r8IMfXCVf6HWu9IOIG580\n6HaLeN48Cq+jKgq6foar15aYmChtvK7rpjgzWWF6Zp4gMOh2YkqlFJ7XS1ey7QapVEi+cIk4Crl6\ndY6bN77glVedLSsj29MAtteR3zwZ+uhKnbff6jXcG69dQEGh0di/2+ZetcN1XWGx/vmmfMs6rtvi\ntdd6nYJv3vDx9TSTE+M7Ath6Q6PeyohGubRA4Bf61g/joOI4Jok8itkUmczuVSpGTRRG6IrH2Ykn\nq/LUQR2194Zt6KxyuIZuh40R0Bvg/PY3bc6dexlFUeh2Ev7mf/wvytWvoqjTdDp5btyYY2UlII5f\nQjfyxNEKgX9vx/t/c4xQtRSqukIuX0LTLEyrQaGQAL17+VGMmOaVVx3e+3rvftocH0yrzTPPZreV\nt4XXX0tx+06Tj660NiYdn9xoEEXPEUe9GLG5Gd9220vXhmHIJzfm0DSFlcb02pmNT9fObDT58tuT\n3L4zx2Ld3egSHoUhhYxPodBLGVlvnBqGBroerMWVkxH6HtXqcA5o6xpPZaWavdSXVzGtg6ezDnoM\nkUpZtBouut67Vw+SMrV992O3CmhAb0KiTjM7o9Lp5onjRUhexrYU7BTo+hKmYeK6vUH848YQptWg\nsbyytlMS8nB6ju99b/rUjSEmauMEfpdSvkkhXSGT7p2jzOXn+fDDO/i+iWn6vPPVo6daHlYcxwRe\nh1zK4OzZyoHPhjzNhv4bOjtRZXZ+kaYbDrRm9m6pDgeZyW4PXrqWJWOP4fpNwsSl0TApFNYOH06O\ns7T0ezRtlShqoqoF2p0u1apOo6Hwi3+6T73eG3jbdsCtW3N0u1MoikIma5PJ3MG2rrLeVbjVKhKF\nCtMz83S7U4Rhi3o9teXG3m0bdvMqyubJUG9V5S43b3TodOcxjJDJieqhDl9u/X57D3i/+pWAj67U\nabXqO4Lw+uE23+/1Glg/QH/U0paHtd7UJ+uYFAsHq9c/CgLfI2ur1KpP50QDjt57I5uxmFn09h3I\n9StGwNbJx8pymnIVJieqTM/cp9VaRlUT4riI67YINI8kUfD9gF/8U33XGJF2ElTVIo4+olgobHQd\n/+AXDRT2jhFb79cxXM/nl/9yj2b7fG+g4CcbZyjWP752bZq7d11cbwldjxirlHZtxreXrZOP3dP8\nXnvV4tJLIZ/caNBcnSOfTvjDr01t+oxe49ReakUEnFxnX1NnaIMH56BL+E+BIAjoBmAdYu416L4b\nCiqX/+ULXD934LLt23c/YPfDzQoKkxNVFhZuoaARRd2negzx5pttfntljm7rIdUxnT/+xotbzt8o\nqECJ3jA2BGYPfA1HFYURceRSSFtUnrI+Gcc1EpGtVi1jr6yysNztS83s3Rw1l3N78KpUbOr1BNvM\nkiQZcqmrBF4XTddRNZ0L5w1WV0063RJhmOoNQPBZrC+ha2/R6XS5dWsOVe0yM72AojpYVohhGLRb\nWd58y95YdVjvirleBcYwosdWZtm+irLcsHoH7AINw4iYnekShFOEYYEgSHg4/cVG19J+2i91YbfD\nba2WPtDu39BrEKYSknMsstnRrjK1ne92qRZtCvnTsQMzKEftvVHI5wj8ebTU3hVt+h0jNipWlUJC\n38UwU5w9O0k2s8jde7C8lAEUFAVMy+u995sX6XS6fPrpLKurDZrNEMuyKZf1tYPwJV559VGMWB/I\nHDRG2JZJEuSIfR9F00hQmZlVcFa93sRiLMXduwFBWCOKsr0ymwuzvPRS/2OEqmm8eilLIZfG2FEs\nxGbqXHbTxy1g9+o//YwRURhSyA4nlTIMAsqF0U/jPCkrq03MQ5a67Vffjb384oNpGvVnwVD3aLa3\n087U550V0GDtrJVmUKnkUNUuq6tP2xgiwfc8TE3BNDQqE0We/6u9U4XbnRTPP1fc9PHKwLp/+56H\noUaUsqmnpuN3v43EZAN6gwHTMHg439joENrPN86Rczm3BS/P9bcMLP7qr97k8uU69XqEmWrz7h/W\n+B//9xKTEzU6nTs4ThrL7JWIRXm0wtBuL6KqXcAk8HPUF7rUJhzq9cmNnM5GQ2GlcRXbglB3mJxM\n75kXum63Q2O+emntMHdCGNzgpUuPKlGYRpu335ra8/UGZbdc190qexx/tyMh9H1MHSq5FI5z8nnY\nx+W7bSbHclLedhcHrU6lqiopU9v3mPHgYsSr/PQfP6Pt9VZD//gbF/nv//0L4vhzoFeQoVqt9AYA\nazFifq5GHJcx9AZxZLG66mJZ4zhOi7nZKt//fq9buG2HFAqf46S6+MHBYkTK8bl9J4fv+XT8FVQ9\nIQxTRJHC/HwTQ4+oVlMsLPTiiKF3ufRSf3fTwjAgZarki7vfj3vlwg8mRmySBDh7NP4atDjyyWaf\nroIP+3H9CEU5/hCl3+MI2yrQdhfQrdSBVvT3SiPannq1/t9ee83nxicBmfTYEz+GiOPeWTLL0LBN\njfFCAU0/2FbWbilzR10w2ovndkkZcKaSJZ12jvw6YoQmG9Drw3HhjM69hwuoptPXN06/cjnXBxbr\nAewffrxCIQ//+T+fw7JNojDitUsrzM7ZKNoUqm5QqfRu7FYz2VSrPiKbHafTvUkYZtH1JpMTz6Og\n8Mn1JmFUXstXLnHpUhvbbtBqtQ7QYXP7obE0inKfINAxjBBNNVHVR5UoKhVnx8qg7/t7Vs/ol91y\nXddTQuD4tfUD30dXY2xTZ7yaP3AAGyVxHBP7bS5MjsnBsz0cpjqVY+m0gmTPre9BxohMOsWX3krh\npHuHKV97PcdE/VFX4ULhARBuxIg4VtC0hEymFyOCIKZY9NdSseZxu+dotjr4fopyaZ7vfvfiWl71\nwWOEqhkEbptcMYNp3CdOUhh6gwsXLJothdp4eu3a7C1pRUEYcuU3szQa7NknYy9hGKIpEeX8zt2M\nzfbKhe9nj57t4igmnxnezkLKGqnH8dC5fohuHb8f1yDGEZaex/VXKFcOdw4MHk0+1nfpPvhFY8dz\nVtdnqNfzKBTWntNPzhgi8H0UJcI2dFKOvnEG47B2S5n74Y8WjrRgtFkURcSBSyZlyKHvPhq56GYY\nBs+en2B2bpF6PT72G2fdUXM591oV2SuAabrGf/jzF/jgg2nqi2A7Lb70ahk0g5s3H+DcW8QPHGzL\noOv2ak8nSQ5Q0DSDhITlZR/dmNpYSbhz+yr/x/95sNW77ZWeZmdX1srU9epRFwpzFAp3tzTM2e6X\n//yA3/xGIQxVdF0hDGd4993+7n7slmZ1kMoe+4nCkCQOSJk6pZKDbfevceRJ8z2XrKVQOz8peaH7\nOEx1qmIhx9KDRSx79wOng4wR3U5C98pv+Nof9SYbew2mP77eixGplIppVlBUjUolRy67RL4w2StE\n4Wt0u4vEyQu9n3kpy9VrSwde4d8cI+4bIb5fYGLM4OHMIlHokiQG2cw0nm9tqVK17uNrde7etXE9\nBU1TiMI6b7yx/85HGHiYhkYxa2FZj39475WGedwYsa/YI5cdzhmuKIpI21Lydl0QBESx2pcBylF3\nLHezOUaMOy1eevXxu2B7pf7tt0v3uPhwmsYQmhbjdj7rVcLSVSrlNOYBYsDj7JYyd5wFI991MbSY\nUjZFIV+T526fjdxkA3oBYaJW4ezYfT6908JIOYBypJXGrQMB+PZ/GjvUFupuk4qvvzfJr37Votlq\nYVkRFy5ktwSw3W6CTsfl3DeyXHrB4t8+mqXVtlms31urxFAnDH3u3P4Y0IniBCXy6bQhinoP6sAP\nDrS7sP1h/OJLKe7d/Rjfr2KaEfnca+j6LH/2Z73yirvVAL/+cZtu9+WNQHXzxlXefffAv7IjO0wz\nJOg1aAt8D11NsAwNJ2sNLQWiX+I4JvI7TI7lJW3qAA5TnUrXdQxtZyLVScWIwMsR+i66ae85mP76\nH53jlZfzfPirWW7eWAJCXnwpxSuvnOH//fteCcswrGOakzRXfaIIUk6HRuPgD8bNMWKiVqXV+oSl\nRQtNSVOrfIlWwydb+IK33iyi6QZRFHH1Wn2jRO7nn3dxvfPEcUIYJty9e5s33tj5fcIgACUmZeqU\ny3mUPvSuOWyMOKgoDCnlhpcmEQRdxvLDqYA1isIwRNHUvrzWcXcs90vD8v2AufoKim6hqrtf716T\nikZD4cGDR2chdP3R/fG4+PDRlTqNhjISY4j138P6hMpJuVz/eJFm43VINJLI5M7N3/O///Xgm88e\ndsEojmN8t03a1hmv5UjZcmZqUEZysrHuO9+5xA9+8BlfPHDJFEPee++ZQ7/GYbdQtweW+iI7VkV+\n8cE0np/D9/MEgcLnn9d556tdfvLju3vmhTqOTbGQxtQtpibHWG218YIynh+haSa/+vU847VeQPL8\nFR4+vIWmnkdVYzLZMh9dqe9oFLTbasnOh/EErtvY0gV0c+rBboEQtC0/80m9TQ5SDz8KQ+IowDI1\n0raFc0pTpHbjuy5ZW6F2XqpcHNRhq1OlTB1v23zj5GJEwK8/nKbpZshmoz3TEw3T4N13z22Z4K83\n+yoUFKLoGa5c+SVRNIauJxhGhcX6VbZXdjlIjChnQv7DW8/zwS8aeO76vWdAVKBWztH1XD78cJ6l\npRqJEtNtQ6OxgGkFxDFA0pv0Bz6KkqCrKrqqYBg6djbd9zKuh+2ZcVAaIekhLlQ4prbnYFUcz3Er\nVO0XH0zT4OxEhaXlFdquj27aO+67RoNdU/8W60t0Oo92H+bnb3P5cvLY9OW9qjldvjwzhDFEQuD5\n/OrDBywtXEBRNPymyeLcKpq2NhnRQOFkUpEOWiDA91x0JaaYcSikZBfjJIz0ZCOVsvkv/+VlANrt\nDtMLDeJYP1RQPuwW6vbAsrj4r5TLz21ZFWmsGFy8MMnde7fxPAPLnAHSBx6waLpGsdBbxUoS6Ha6\nNJdDYt8nVmImJkyWlz1yueZaablJWq2tpR/3Wi05bHrS9hzoRgNM06XV+gyIKJV6ZTaHIyEMQpI4\nRNcUTF2jkDVJOXkUBYqFNMuNQ3ZqG0FxHBMFXTkEfgSHrU5VyGe4P7e6pcrNScaIlcaX6IZLeJ59\nqMPNm+9TTdOojNXQ9eW1POpG7/DoNseNEYqq4KRSRGEO2+wNxBMSMpZCY+UB3a6OZfl86ZLOeCmL\n2qeV6JMWhgGV/PDuuziOSZ+CZqKn1XErVD0uPigKlEt5ckHI8kqLX/7rFzRWeymOrpuw0rhKvnB2\nx/O3XCnSbD06C+G55rGKH2yOEWfPZmk0fLLZ/o4hkjjsVWzLr2JQwDRU0oUchGUcu1fcIAx94rhL\nq3UdCBkfr/Dmm8N/f4dhSBJ6pG2DajWLk0oxVs6ysNAc9qU9FUZ6srFZOu3wnJNivr7ESruLYTkH\nmo0edgt1e2CpTYxTKW9dFfngg2m6HYPnnn2GJEmYmHCPnBeqKOCkU0xUbWZmSmurHB7nxj0KxSIo\nCXEYYlst4ri8MdE66EFJ3w8Iw5C52U9ZT8l4+61HgWT7IGOxvky58gZjYyu9/Mz8Hb76lecP9LMc\nR68qhY+qJOiaiqGpGLpKKpPCtKxTVab2oJIk6TUGckzGJ2R15SSkbBtDbWz5bycZIzRVQ8cmiaND\nHW7efp8WCx75wgvbDphvNYgYAZDOaBjmObpd0PUI06qf2okGJFhajDPEwX7gdRmrnGFx8fQvnPTL\nKMXCg8YHw9CpVgoQtiCICAlB7U0qCoWdqX+FApw9O7lxD8/NHq9Ayub7VFUVzk8p5AvVPc837R8f\negt9cdSbXPjdFg8//x2KEvPlL5f45p+9tCVzY/Pv6PO796mUv0xUBNdVqZSv8Wd/9qVD/Sz90mu+\n18WxNIo5m3yu9PgvEgNxaiYb0AtA42Nlxsoxs/OLtNwI094/z/awW6jbA0ulHO9YFdntNT/4YPpY\neaGbX7NWC/jOd97i8uX6xvd4991niZII3w8JoxjbXKW1WkDTdBRN3/Og5JUrdRqNC9RqvYCj6w+2\nbM1u3zLV9RK6rnHubJYoillYsHatlnEcYRgQhyGarmBqKpqqYjkajn3wsnenne92ydgqU+eq0jH4\nhOUzFsudaOP3ftIxwjaztLoLZKoHP9y8/T79429c5Oq1/c8uHPQw9eFjRBVdz+O5AVEUc/PGNK67\nMLDKdYMU+i612nDLzaZtSaHazjRN4igEhl/k49DxoRDS7eZRFIUwCihm7vPOV87gBxGKqqMbvWHX\nzv4bqV37bxzUYWNEJh3QaQUkSUiSQDHfQqeArqnomoqddTBMk5/+5C5e+8s8+0wv3pnG7R1n2jb/\njixzlWeefYEwiAjDiOnpFD/80UJf+148jtftYhmQTRmUa+Nyf40AJem1vN3TKG8x+b7P3GIDO+3Q\n7sQb//04dbV7NfIP/7UH+brNaT/Hrf3tuT4///lDFhcTnHSXd742jm4YxHFCnKyl5iQJP/3xPK4/\ngaKoqKqGnZrnm9/cu1HO5csztJoX8byQL+43gRWmzp1dK3G317ZustZZMyZJIlQVFFVBUxRUVUVV\nQFUVVEVBU8G0TGzLPvZuxWlJo9p8nb7rYhsJtbHRK6k3NpZ9/CftYpRjxLqxsUfb5UmScPXmPS5/\nuDq0GJHJdHnh5QxOZuvvPJdNsdrsAsdvYNer5//4r//ZzxY2ndcAy57Zcvhzu6PFiOHY/PvcLgwD\nyllzqPXzfdflXC3H1LmxU3MfHdZRf67P7s+iGifzt9n+LBnEGCJJes3huq5HEMYEUUwYxcQxKIpG\nFEf89neNfe/Xg8WHtedxEkOckCQxKKAqoKsqmq6iqypRFPBvH9Zpt9MUCuGeP+P3v/8Qt3th42M7\ndZe//usze/78P/nxXRqNl3HdgFu3l4Elnn/u2bUd3uP1vdhP4PsohKQtg1Ihg3WAssmbnwuj7DRd\n515O1c7GdqZpcm6iSjqt89FvP+N/fTBHu5Phwf1p8vm3MQzr0HW1j5rfedivO27tb8s2+daf738Y\nFuBsrcn0TIUkWcu1dDrYekQSJ8RxsrXJWQJffjPHtau3mZtPUOM61erLxIEPKKwuheg82rHRVGVj\nUmGkTAzTQNM01D5UnXnSrHcgPVvN4zi7l14Vg7W5AeDH169SnnhvqDFiZaVJsxuh7rGbd9wGdgc9\nTH3YcrJvv1Xh4+sPqNcTTGOJsbEXgP73vRisXvrUsBt1GVosFXD2kDKNHcUcBmnzBGMQYwhFAcu2\nsLaVZE8SiKIQ3wv4k3fN3mJhkpDECcn68zaBGFATD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.mixture import GMM\n", + "\n", + "from matplotlib.patches import Ellipse\n", + "\n", + "def draw_ellipse(position, covariance, ax=None, **kwargs):\n", + " \"\"\"Draw an ellipse with a given position and covariance\"\"\"\n", + " ax = ax or plt.gca()\n", + " \n", + " # Convert covariance to principal axes\n", + " if covariance.shape == (2, 2):\n", + " U, s, Vt = np.linalg.svd(covariance)\n", + " angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n", + " width, height = 2 * np.sqrt(s)\n", + " else:\n", + " angle = 0\n", + " width, height = 2 * np.sqrt(covariance)\n", + " \n", + " # Draw the Ellipse\n", + " for nsig in range(1, 4):\n", + " ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n", + " angle, **kwargs))\n", + "\n", + "fig, ax = plt.subplots(1, 3, figsize=(14, 4), sharex=True, sharey=True)\n", + "fig.subplots_adjust(wspace=0.05)\n", + "\n", + "rng = np.random.RandomState(5)\n", + "X = np.dot(rng.randn(500, 2), rng.randn(2, 2))\n", + "\n", + "for i, cov_type in enumerate(['diag', 'spherical', 'full']):\n", + " model = GMM(1, covariance_type=cov_type).fit(X)\n", + " ax[i].axis('equal')\n", + " ax[i].scatter(X[:, 0], X[:, 1], alpha=0.5)\n", + " ax[i].set_xlim(-3, 3)\n", + " ax[i].set_title('covariance_type=\"{0}\"'.format(cov_type),\n", + " size=14, family='monospace')\n", + " draw_ellipse(model.means_[0], model.covars_[0], ax[i], alpha=0.2)\n", + " ax[i].xaxis.set_major_formatter(plt.NullFormatter())\n", + " ax[i].yaxis.set_major_formatter(plt.NullFormatter())\n", + "\n", + "fig.savefig('figures/05.12-covariance-type.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "\n", + "< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) |\n", + "\n", + "\"Open\n" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + }, + "widgets": { + "state": { + "a65a11f142ca44eebc913788d256adcb": { + "views": [ + { + "cell_index": 92 + } + ] + } + }, + "version": "1.2.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/06.00-Figure-Code.md b/notebooks_v2/06.00-Figure-Code.md new file mode 100644 index 00000000..bccefebc --- /dev/null +++ b/notebooks_v2/06.00-Figure-Code.md @@ -0,0 +1,1662 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + + + + +*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* + +*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* + + + + +< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) | + +Open in Colab + + + +# Appendix: Figure Code + + +Many of the figures used throughout this text are created in-place by code that appears in print. +In a few cases, however, the required code is long enough (or not immediately relevant enough) that we instead put it here for reference. + + +```python deletable=true editable=true +%matplotlib inline +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +``` + +```python deletable=true editable=true +import os +if not os.path.exists('figures'): + os.makedirs('figures') +``` + + +## Broadcasting + +[Figure Context](02.05-Computation-on-arrays-broadcasting.ipynb#Introducing-Broadcasting) + + +```python deletable=true editable=true +# Adapted from astroML: see http://www.astroml.org/book_figures/appendix/fig_broadcast_visual.html +import numpy as np +from matplotlib import pyplot as plt + +#------------------------------------------------------------ +# Draw a figure and axis with no boundary +fig = plt.figure(figsize=(6, 4.5), facecolor='w') +ax = plt.axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False) + + +def draw_cube(ax, xy, size, depth=0.4, + edges=None, label=None, label_kwargs=None, **kwargs): + """draw and label a cube. edges is a list of numbers between + 1 and 12, specifying which of the 12 cube edges to draw""" + if edges is None: + edges = range(1, 13) + + x, y = xy + + if 1 in edges: + ax.plot([x, x + size], + [y + size, y + size], **kwargs) + if 2 in edges: + ax.plot([x + size, x + size], + [y, y + size], **kwargs) + if 3 in edges: + ax.plot([x, x + size], + [y, y], **kwargs) + if 4 in edges: + ax.plot([x, x], + [y, y + size], **kwargs) + + if 5 in edges: + ax.plot([x, x + depth], + [y + size, y + depth + size], **kwargs) + if 6 in edges: + ax.plot([x + size, x + size + depth], + [y + size, y + depth + size], **kwargs) + if 7 in edges: + ax.plot([x + size, x + size + depth], + [y, y + depth], **kwargs) + if 8 in edges: + ax.plot([x, x + depth], + [y, y + depth], **kwargs) + + if 9 in edges: + ax.plot([x + depth, x + depth + size], + [y + depth + size, y + depth + size], **kwargs) + if 10 in edges: + ax.plot([x + depth + size, x + depth + size], + [y + depth, y + depth + size], **kwargs) + if 11 in edges: + ax.plot([x + depth, x + depth + size], + [y + depth, y + depth], **kwargs) + if 12 in edges: + ax.plot([x + depth, x + depth], + [y + depth, y + depth + size], **kwargs) + + if label: + if label_kwargs is None: + label_kwargs = {} + ax.text(x + 0.5 * size, y + 0.5 * size, label, + ha='center', va='center', **label_kwargs) + +solid = dict(c='black', ls='-', lw=1, + label_kwargs=dict(color='k')) +dotted = dict(c='black', ls='-', lw=0.5, alpha=0.5, + label_kwargs=dict(color='gray')) +depth = 0.3 + +#------------------------------------------------------------ +# Draw top operation: vector plus scalar +draw_cube(ax, (1, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid) +draw_cube(ax, (2, 10), 1, depth, [1, 2, 3, 6, 9], '1', **solid) +draw_cube(ax, (3, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid) + +draw_cube(ax, (6, 10), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '5', **solid) +draw_cube(ax, (7, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted) +draw_cube(ax, (8, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted) + +draw_cube(ax, (12, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '5', **solid) +draw_cube(ax, (13, 10), 1, depth, [1, 2, 3, 6, 9], '6', **solid) +draw_cube(ax, (14, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '7', **solid) + +ax.text(5, 10.5, '+', size=12, ha='center', va='center') +ax.text(10.5, 10.5, '=', size=12, ha='center', va='center') +ax.text(1, 11.5, r'${\tt np.arange(3) + 5}$', + size=12, ha='left', va='bottom') + +#------------------------------------------------------------ +# Draw middle operation: matrix plus vector + +# first block +draw_cube(ax, (1, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid) +draw_cube(ax, (2, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid) +draw_cube(ax, (3, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '1', **solid) + +draw_cube(ax, (1, 6.5), 1, depth, [2, 3, 4], '1', **solid) +draw_cube(ax, (2, 6.5), 1, depth, [2, 3], '1', **solid) +draw_cube(ax, (3, 6.5), 1, depth, [2, 3, 7, 10], '1', **solid) + +draw_cube(ax, (1, 5.5), 1, depth, [2, 3, 4], '1', **solid) +draw_cube(ax, (2, 5.5), 1, depth, [2, 3], '1', **solid) +draw_cube(ax, (3, 5.5), 1, depth, [2, 3, 7, 10], '1', **solid) + +# second block +draw_cube(ax, (6, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid) +draw_cube(ax, (7, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid) +draw_cube(ax, (8, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid) + +draw_cube(ax, (6, 6.5), 1, depth, range(2, 13), '0', **dotted) +draw_cube(ax, (7, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted) +draw_cube(ax, (8, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted) + +draw_cube(ax, (6, 5.5), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted) +draw_cube(ax, (7, 5.5), 1, depth, [2, 3, 7, 10, 11], '1', **dotted) +draw_cube(ax, (8, 5.5), 1, depth, [2, 3, 7, 10, 11], '2', **dotted) + +# third block +draw_cube(ax, (12, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid) +draw_cube(ax, (13, 7.5), 1, depth, [1, 2, 3, 6, 9], '2', **solid) +draw_cube(ax, (14, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '3', **solid) + +draw_cube(ax, (12, 6.5), 1, depth, [2, 3, 4], '1', **solid) +draw_cube(ax, (13, 6.5), 1, depth, [2, 3], '2', **solid) +draw_cube(ax, (14, 6.5), 1, depth, [2, 3, 7, 10], '3', **solid) + +draw_cube(ax, (12, 5.5), 1, depth, [2, 3, 4], '1', **solid) +draw_cube(ax, (13, 5.5), 1, depth, [2, 3], '2', **solid) +draw_cube(ax, (14, 5.5), 1, depth, [2, 3, 7, 10], '3', **solid) + +ax.text(5, 7.0, '+', size=12, ha='center', va='center') +ax.text(10.5, 7.0, '=', size=12, ha='center', va='center') +ax.text(1, 9.0, r'${\tt np.ones((3,\, 3)) + np.arange(3)}$', + size=12, ha='left', va='bottom') + +#------------------------------------------------------------ +# Draw bottom operation: vector plus vector, double broadcast + +# first block +draw_cube(ax, (1, 3), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '0', **solid) +draw_cube(ax, (1, 2), 1, depth, [2, 3, 4, 7, 10], '1', **solid) +draw_cube(ax, (1, 1), 1, depth, [2, 3, 4, 7, 10], '2', **solid) + +draw_cube(ax, (2, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted) +draw_cube(ax, (2, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted) +draw_cube(ax, (2, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted) + +draw_cube(ax, (3, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted) +draw_cube(ax, (3, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted) +draw_cube(ax, (3, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted) + +# second block +draw_cube(ax, (6, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid) +draw_cube(ax, (7, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid) +draw_cube(ax, (8, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid) + +draw_cube(ax, (6, 2), 1, depth, range(2, 13), '0', **dotted) +draw_cube(ax, (7, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted) +draw_cube(ax, (8, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted) + +draw_cube(ax, (6, 1), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted) +draw_cube(ax, (7, 1), 1, depth, [2, 3, 7, 10, 11], '1', **dotted) +draw_cube(ax, (8, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted) + +# third block +draw_cube(ax, (12, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid) +draw_cube(ax, (13, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid) +draw_cube(ax, (14, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid) + +draw_cube(ax, (12, 2), 1, depth, [2, 3, 4], '1', **solid) +draw_cube(ax, (13, 2), 1, depth, [2, 3], '2', **solid) +draw_cube(ax, (14, 2), 1, depth, [2, 3, 7, 10], '3', **solid) + +draw_cube(ax, (12, 1), 1, depth, [2, 3, 4], '2', **solid) +draw_cube(ax, (13, 1), 1, depth, [2, 3], '3', **solid) +draw_cube(ax, (14, 1), 1, depth, [2, 3, 7, 10], '4', **solid) + +ax.text(5, 2.5, '+', size=12, ha='center', va='center') +ax.text(10.5, 2.5, '=', size=12, ha='center', va='center') +ax.text(1, 4.5, r'${\tt np.arange(3).reshape((3,\, 1)) + np.arange(3)}$', + ha='left', size=12, va='bottom') + +ax.set_xlim(0, 16) +ax.set_ylim(0.5, 12.5) + +fig.savefig('figures/02.05-broadcasting.png') +``` + + +## Aggregation and Grouping + +Figures from the chapter on aggregation and grouping + + + +### Split-Apply-Combine + + +```python deletable=true editable=true +def draw_dataframe(df, loc=None, width=None, ax=None, linestyle=None, + textstyle=None): + loc = loc or [0, 0] + width = width or 1 + + x, y = loc + + if ax is None: + ax = plt.gca() + + ncols = len(df.columns) + 1 + nrows = len(df.index) + 1 + + dx = dy = width / ncols + + if linestyle is None: + linestyle = {'color':'black'} + + if textstyle is None: + textstyle = {'size': 12} + + textstyle.update({'ha':'center', 'va':'center'}) + + # draw vertical lines + for i in range(ncols + 1): + plt.plot(2 * [x + i * dx], [y, y + dy * nrows], **linestyle) + + # draw horizontal lines + for i in range(nrows + 1): + plt.plot([x, x + dx * ncols], 2 * [y + i * dy], **linestyle) + + # Create index labels + for i in range(nrows - 1): + plt.text(x + 0.5 * dx, y + (i + 0.5) * dy, + str(df.index[::-1][i]), **textstyle) + + # Create column labels + for i in range(ncols - 1): + plt.text(x + (i + 1.5) * dx, y + (nrows - 0.5) * dy, + str(df.columns[i]), style='italic', **textstyle) + + # Add index label + if df.index.name: + plt.text(x + 0.5 * dx, y + (nrows - 0.5) * dy, + str(df.index.name), style='italic', **textstyle) + + # Insert data + for i in range(nrows - 1): + for j in range(ncols - 1): + plt.text(x + (j + 1.5) * dx, + y + (i + 0.5) * dy, + str(df.values[::-1][i, j]), **textstyle) + + +#---------------------------------------------------------- +# Draw figure + +import pandas as pd +df = pd.DataFrame({'data': [1, 2, 3, 4, 5, 6]}, + index=['A', 'B', 'C', 'A', 'B', 'C']) +df.index.name = 'key' + + +fig = plt.figure(figsize=(8, 6), facecolor='white') +ax = plt.axes([0, 0, 1, 1]) + +ax.axis('off') + +draw_dataframe(df, [0, 0]) + +for y, ind in zip([3, 1, -1], 'ABC'): + split = df[df.index == ind] + draw_dataframe(split, [2, y]) + + sum = pd.DataFrame(split.sum()).T + sum.index = [ind] + sum.index.name = 'key' + sum.columns = ['data'] + draw_dataframe(sum, [4, y + 0.25]) + +result = df.groupby(df.index).sum() +draw_dataframe(result, [6, 0.75]) + +style = dict(fontsize=14, ha='center', weight='bold') +plt.text(0.5, 3.6, "Input", **style) +plt.text(2.5, 4.6, "Split", **style) +plt.text(4.5, 4.35, "Apply (sum)", **style) +plt.text(6.5, 2.85, "Combine", **style) + +arrowprops = dict(facecolor='black', width=1, headwidth=6) +plt.annotate('', (1.8, 3.6), (1.2, 2.8), arrowprops=arrowprops) +plt.annotate('', (1.8, 1.75), (1.2, 1.75), arrowprops=arrowprops) +plt.annotate('', (1.8, -0.1), (1.2, 0.7), arrowprops=arrowprops) + +plt.annotate('', (3.8, 3.8), (3.2, 3.8), arrowprops=arrowprops) +plt.annotate('', (3.8, 1.75), (3.2, 1.75), arrowprops=arrowprops) +plt.annotate('', (3.8, -0.3), (3.2, -0.3), arrowprops=arrowprops) + +plt.annotate('', (5.8, 2.8), (5.2, 3.6), arrowprops=arrowprops) +plt.annotate('', (5.8, 1.75), (5.2, 1.75), arrowprops=arrowprops) +plt.annotate('', (5.8, 0.7), (5.2, -0.1), arrowprops=arrowprops) + +plt.axis('equal') +plt.ylim(-1.5, 5); + +fig.savefig('figures/03.08-split-apply-combine.png') +``` + + +## What Is Machine Learning? + + +```python deletable=true editable=true +# common plot formatting for below +def format_plot(ax, title): + ax.xaxis.set_major_formatter(plt.NullFormatter()) + ax.yaxis.set_major_formatter(plt.NullFormatter()) + ax.set_xlabel('feature 1', color='gray') + ax.set_ylabel('feature 2', color='gray') + ax.set_title(title, color='gray') +``` + + +### Classification Example Figures + +[Figure context](05.01-What-Is-Machine-Learning.ipynb#Classification:-Predicting-Discrete-Labels) + +The following code generates the figures from the Classification section. + + +```python deletable=true editable=true +from sklearn.datasets.samples_generator import make_blobs +from sklearn.svm import SVC + +# create 50 separable points +X, y = make_blobs(n_samples=50, centers=2, + random_state=0, cluster_std=0.60) + +# fit the support vector classifier model +clf = SVC(kernel='linear') +clf.fit(X, y) + +# create some new points to predict +X2, _ = make_blobs(n_samples=80, centers=2, + random_state=0, cluster_std=0.80) +X2 = X2[50:] + +# predict the labels +y2 = clf.predict(X2) +``` + + +#### Classification Example Figure 1 + + +```python deletable=true editable=true +# plot the data +fig, ax = plt.subplots(figsize=(8, 6)) +point_style = dict(cmap='Paired', s=50) +ax.scatter(X[:, 0], X[:, 1], c=y, **point_style) + +# format plot +format_plot(ax, 'Input Data') +ax.axis([-1, 4, -2, 7]) + +fig.savefig('figures/05.01-classification-1.png') +``` + + +#### Classification Example Figure 2 + + +```python deletable=true editable=true +# Get contours describing the model +xx = np.linspace(-1, 4, 10) +yy = np.linspace(-2, 7, 10) +xy1, xy2 = np.meshgrid(xx, yy) +Z = np.array([clf.decision_function([t]) + for t in zip(xy1.flat, xy2.flat)]).reshape(xy1.shape) + +# plot points and model +fig, ax = plt.subplots(figsize=(8, 6)) +line_style = dict(levels = [-1.0, 0.0, 1.0], + linestyles = ['dashed', 'solid', 'dashed'], + colors = 'gray', linewidths=1) +ax.scatter(X[:, 0], X[:, 1], c=y, **point_style) +ax.contour(xy1, xy2, Z, **line_style) + +# format plot +format_plot(ax, 'Model Learned from Input Data') +ax.axis([-1, 4, -2, 7]) + +fig.savefig('figures/05.01-classification-2.png') +``` + + +#### Classification Example Figure 3 + + +```python deletable=true editable=true +# plot the results +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) + +ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', **point_style) +ax[0].axis([-1, 4, -2, 7]) + +ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, **point_style) +ax[1].contour(xy1, xy2, Z, **line_style) +ax[1].axis([-1, 4, -2, 7]) + +format_plot(ax[0], 'Unknown Data') +format_plot(ax[1], 'Predicted Labels') + +fig.savefig('figures/05.01-classification-3.png') +``` + + +### Regression Example Figures + +[Figure Context](05.01-What-Is-Machine-Learning.ipynb#Regression:-Predicting-Continuous-Labels) + +The following code generates the figures from the regression section. + + +```python deletable=true editable=true +from sklearn.linear_model import LinearRegression + +# Create some data for the regression +rng = np.random.RandomState(1) + +X = rng.randn(200, 2) +y = np.dot(X, [-2, 1]) + 0.1 * rng.randn(X.shape[0]) + +# fit the regression model +model = LinearRegression() +model.fit(X, y) + +# create some new points to predict +X2 = rng.randn(100, 2) + +# predict the labels +y2 = model.predict(X2) +``` + + +#### Regression Example Figure 1 + + +```python deletable=true editable=true +# plot data points +fig, ax = plt.subplots() +points = ax.scatter(X[:, 0], X[:, 1], c=y, s=50, + cmap='viridis') + +# format plot +format_plot(ax, 'Input Data') +ax.axis([-4, 4, -3, 3]) + +fig.savefig('figures/05.01-regression-1.png') +``` + + +#### Regression Example Figure 2 + + +```python deletable=true editable=true +from mpl_toolkits.mplot3d.art3d import Line3DCollection + +points = np.hstack([X, y[:, None]]).reshape(-1, 1, 3) +segments = np.hstack([points, points]) +segments[:, 0, 2] = -8 + +# plot points in 3D +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(X[:, 0], X[:, 1], y, c=y, s=35, + cmap='viridis') +ax.add_collection3d(Line3DCollection(segments, colors='gray', alpha=0.2)) +ax.scatter(X[:, 0], X[:, 1], -8 + np.zeros(X.shape[0]), c=y, s=10, + cmap='viridis') + +# format plot +ax.patch.set_facecolor('white') +ax.view_init(elev=20, azim=-70) +ax.set_zlim3d(-8, 8) +ax.xaxis.set_major_formatter(plt.NullFormatter()) +ax.yaxis.set_major_formatter(plt.NullFormatter()) +ax.zaxis.set_major_formatter(plt.NullFormatter()) +ax.set(xlabel='feature 1', ylabel='feature 2', zlabel='label') + +# Hide axes (is there a better way?) +ax.w_xaxis.line.set_visible(False) +ax.w_yaxis.line.set_visible(False) +ax.w_zaxis.line.set_visible(False) +for tick in ax.w_xaxis.get_ticklines(): + tick.set_visible(False) +for tick in ax.w_yaxis.get_ticklines(): + tick.set_visible(False) +for tick in ax.w_zaxis.get_ticklines(): + tick.set_visible(False) + +fig.savefig('figures/05.01-regression-2.png') +``` + + +#### Regression Example Figure 3 + + +```python deletable=true editable=true +from matplotlib.collections import LineCollection + +# plot data points +fig, ax = plt.subplots() +pts = ax.scatter(X[:, 0], X[:, 1], c=y, s=50, + cmap='viridis', zorder=2) + +# compute and plot model color mesh +xx, yy = np.meshgrid(np.linspace(-4, 4), + np.linspace(-3, 3)) +Xfit = np.vstack([xx.ravel(), yy.ravel()]).T +yfit = model.predict(Xfit) +zz = yfit.reshape(xx.shape) +ax.pcolorfast([-4, 4], [-3, 3], zz, alpha=0.5, + cmap='viridis', norm=pts.norm, zorder=1) + +# format plot +format_plot(ax, 'Input Data with Linear Fit') +ax.axis([-4, 4, -3, 3]) + +fig.savefig('figures/05.01-regression-3.png') +``` + + +#### Regression Example Figure 4 + + +```python deletable=true editable=true +# plot the model fit +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) + +ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', s=50) +ax[0].axis([-4, 4, -3, 3]) + +ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, s=50, + cmap='viridis', norm=pts.norm) +ax[1].axis([-4, 4, -3, 3]) + +# format plots +format_plot(ax[0], 'Unknown Data') +format_plot(ax[1], 'Predicted Labels') + +fig.savefig('figures/05.01-regression-4.png') +``` + + +### Clustering Example Figures + +[Figure context](#Clustering:-Inferring-Labels-on-Unlabeled-Data) + +The following code generates the figures from the clustering section. + + +```python deletable=true editable=true +from sklearn.datasets.samples_generator import make_blobs +from sklearn.cluster import KMeans + +# create 50 separable points +X, y = make_blobs(n_samples=100, centers=4, + random_state=42, cluster_std=1.5) + +# Fit the K Means model +model = KMeans(4, random_state=0) +y = model.fit_predict(X) +``` + + +#### Clustering Example Figure 1 + + +```python deletable=true editable=true +# plot the input data +fig, ax = plt.subplots(figsize=(8, 6)) +ax.scatter(X[:, 0], X[:, 1], s=50, color='gray') + +# format the plot +format_plot(ax, 'Input Data') + +fig.savefig('figures/05.01-clustering-1.png') +``` + + +#### Clustering Example Figure 2 + + +```python deletable=true editable=true +# plot the data with cluster labels +fig, ax = plt.subplots(figsize=(8, 6)) +ax.scatter(X[:, 0], X[:, 1], s=50, c=y, cmap='viridis') + +# format the plot +format_plot(ax, 'Learned Cluster Labels') + +fig.savefig('figures/05.01-clustering-2.png') +``` + + +### Dimensionality Reduction Example Figures + +[Figure context](05.01-What-Is-Machine-Learning.ipynb#Dimensionality-Reduction:-Inferring-Structure-of-Unlabeled-Data) + +The following code generates the figures from the dimensionality reduction section. + + + +#### Dimensionality Reduction Example Figure 1 + + +```python deletable=true editable=true +from sklearn.datasets import make_swiss_roll + +# make data +X, y = make_swiss_roll(200, noise=0.5, random_state=42) +X = X[:, [0, 2]] + +# visualize data +fig, ax = plt.subplots() +ax.scatter(X[:, 0], X[:, 1], color='gray', s=30) + +# format the plot +format_plot(ax, 'Input Data') + +fig.savefig('figures/05.01-dimesionality-1.png') +``` + + +#### Dimensionality Reduction Example Figure 2 + + +```python deletable=true editable=true +from sklearn.manifold import Isomap + +model = Isomap(n_neighbors=8, n_components=1) +y_fit = model.fit_transform(X).ravel() + +# visualize data +fig, ax = plt.subplots() +pts = ax.scatter(X[:, 0], X[:, 1], c=y_fit, cmap='viridis', s=30) +cb = fig.colorbar(pts, ax=ax) + +# format the plot +format_plot(ax, 'Learned Latent Parameter') +cb.set_ticks([]) +cb.set_label('Latent Variable', color='gray') + +fig.savefig('figures/05.01-dimesionality-2.png') +``` + + +## Introducing Scikit-Learn + + + +### Features and Labels Grid + +The following is the code generating the diagram showing the features matrix and target array. + + +```python deletable=true editable=true +fig = plt.figure(figsize=(6, 4)) +ax = fig.add_axes([0, 0, 1, 1]) +ax.axis('off') +ax.axis('equal') + +# Draw features matrix +ax.vlines(range(6), ymin=0, ymax=9, lw=1) +ax.hlines(range(10), xmin=0, xmax=5, lw=1) +font_prop = dict(size=12, family='monospace') +ax.text(-1, -1, "Feature Matrix ($X$)", size=14) +ax.text(0.1, -0.3, r'n_features $\longrightarrow$', **font_prop) +ax.text(-0.1, 0.1, r'$\longleftarrow$ n_samples', rotation=90, + va='top', ha='right', **font_prop) + +# Draw labels vector +ax.vlines(range(8, 10), ymin=0, ymax=9, lw=1) +ax.hlines(range(10), xmin=8, xmax=9, lw=1) +ax.text(7, -1, "Target Vector ($y$)", size=14) +ax.text(7.9, 0.1, r'$\longleftarrow$ n_samples', rotation=90, + va='top', ha='right', **font_prop) + +ax.set_ylim(10, -2) + +fig.savefig('figures/05.02-samples-features.png') +``` + + +## Hyperparameters and Model Validation + + + +### Cross-Validation Figures + + +```python deletable=true editable=true +def draw_rects(N, ax, textprop={}): + for i in range(N): + ax.add_patch(plt.Rectangle((0, i), 5, 0.7, fc='white')) + ax.add_patch(plt.Rectangle((5. * i / N, i), 5. / N, 0.7, fc='lightgray')) + ax.text(5. * (i + 0.5) / N, i + 0.35, + "validation\nset", ha='center', va='center', **textprop) + ax.text(0, i + 0.35, "trial {0}".format(N - i), + ha='right', va='center', rotation=90, **textprop) + ax.set_xlim(-1, 6) + ax.set_ylim(-0.2, N + 0.2) +``` + + +#### 2-Fold Cross-Validation + + +```python deletable=true editable=true +fig = plt.figure() +ax = fig.add_axes([0, 0, 1, 1]) +ax.axis('off') +draw_rects(2, ax, textprop=dict(size=14)) + +fig.savefig('figures/05.03-2-fold-CV.png') +``` + + +#### 5-Fold Cross-Validation + + +```python deletable=true editable=true +fig = plt.figure() +ax = fig.add_axes([0, 0, 1, 1]) +ax.axis('off') +draw_rects(5, ax, textprop=dict(size=10)) + +fig.savefig('figures/05.03-5-fold-CV.png') +``` + + +### Overfitting and Underfitting + + +```python deletable=true editable=true +import numpy as np + +def make_data(N=30, err=0.8, rseed=1): + # randomly sample the data + rng = np.random.RandomState(rseed) + X = rng.rand(N, 1) ** 2 + y = 10 - 1. / (X.ravel() + 0.1) + if err > 0: + y += err * rng.randn(N) + return X, y +``` + +```python deletable=true editable=true +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import LinearRegression +from sklearn.pipeline import make_pipeline + +def PolynomialRegression(degree=2, **kwargs): + return make_pipeline(PolynomialFeatures(degree), + LinearRegression(**kwargs)) +``` + + +#### Bias-Variance Tradeoff + + +```python deletable=true editable=true +X, y = make_data() +xfit = np.linspace(-0.1, 1.0, 1000)[:, None] +model1 = PolynomialRegression(1).fit(X, y) +model20 = PolynomialRegression(20).fit(X, y) + +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) + +ax[0].scatter(X.ravel(), y, s=40) +ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray') +ax[0].axis([-0.1, 1.0, -2, 14]) +ax[0].set_title('High-bias model: Underfits the data', size=14) + +ax[1].scatter(X.ravel(), y, s=40) +ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray') +ax[1].axis([-0.1, 1.0, -2, 14]) +ax[1].set_title('High-variance model: Overfits the data', size=14) + +fig.savefig('figures/05.03-bias-variance.png') +``` + + +#### Bias-Variance Tradeoff Metrics + + +```python deletable=true editable=true +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) + +X2, y2 = make_data(10, rseed=42) + +ax[0].scatter(X.ravel(), y, s=40, c='blue') +ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray') +ax[0].axis([-0.1, 1.0, -2, 14]) +ax[0].set_title('High-bias model: Underfits the data', size=14) +ax[0].scatter(X2.ravel(), y2, s=40, c='red') +ax[0].text(0.02, 0.98, "training score: $R^2$ = {0:.2f}".format(model1.score(X, y)), + ha='left', va='top', transform=ax[0].transAxes, size=14, color='blue') +ax[0].text(0.02, 0.91, "validation score: $R^2$ = {0:.2f}".format(model1.score(X2, y2)), + ha='left', va='top', transform=ax[0].transAxes, size=14, color='red') + +ax[1].scatter(X.ravel(), y, s=40, c='blue') +ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray') +ax[1].axis([-0.1, 1.0, -2, 14]) +ax[1].set_title('High-variance model: Overfits the data', size=14) +ax[1].scatter(X2.ravel(), y2, s=40, c='red') +ax[1].text(0.02, 0.98, "training score: $R^2$ = {0:.2g}".format(model20.score(X, y)), + ha='left', va='top', transform=ax[1].transAxes, size=14, color='blue') +ax[1].text(0.02, 0.91, "validation score: $R^2$ = {0:.2g}".format(model20.score(X2, y2)), + ha='left', va='top', transform=ax[1].transAxes, size=14, color='red') + +fig.savefig('figures/05.03-bias-variance-2.png') +``` + + +#### Validation Curve + + +```python deletable=true editable=true +x = np.linspace(0, 1, 1000) +y1 = -(x - 0.5) ** 2 +y2 = y1 - 0.33 + np.exp(x - 1) + +fig, ax = plt.subplots() +ax.plot(x, y2, lw=10, alpha=0.5, color='blue') +ax.plot(x, y1, lw=10, alpha=0.5, color='red') + +ax.text(0.15, 0.2, "training score", rotation=45, size=16, color='blue') +ax.text(0.2, -0.05, "validation score", rotation=20, size=16, color='red') + +ax.text(0.02, 0.1, r'$\longleftarrow$ High Bias', size=18, rotation=90, va='center') +ax.text(0.98, 0.1, r'$\longleftarrow$ High Variance $\longrightarrow$', size=18, rotation=90, ha='right', va='center') +ax.text(0.48, -0.12, 'Best$\\longrightarrow$\nModel', size=18, rotation=90, va='center') + +ax.set_xlim(0, 1) +ax.set_ylim(-0.3, 0.5) + +ax.set_xlabel(r'model complexity $\longrightarrow$', size=14) +ax.set_ylabel(r'model score $\longrightarrow$', size=14) + +ax.xaxis.set_major_formatter(plt.NullFormatter()) +ax.yaxis.set_major_formatter(plt.NullFormatter()) + +ax.set_title("Validation Curve Schematic", size=16) + +fig.savefig('figures/05.03-validation-curve.png') +``` + + +#### Learning Curve + + +```python deletable=true editable=true +N = np.linspace(0, 1, 1000) +y1 = 0.75 + 0.2 * np.exp(-4 * N) +y2 = 0.7 - 0.6 * np.exp(-4 * N) + +fig, ax = plt.subplots() +ax.plot(x, y1, lw=10, alpha=0.5, color='blue') +ax.plot(x, y2, lw=10, alpha=0.5, color='red') + +ax.text(0.2, 0.88, "training score", rotation=-10, size=16, color='blue') +ax.text(0.2, 0.5, "validation score", rotation=30, size=16, color='red') + +ax.text(0.98, 0.45, r'Good Fit $\longrightarrow$', size=18, rotation=90, ha='right', va='center') +ax.text(0.02, 0.57, r'$\longleftarrow$ High Variance $\longrightarrow$', size=18, rotation=90, va='center') + +ax.set_xlim(0, 1) +ax.set_ylim(0, 1) + +ax.set_xlabel(r'training set size $\longrightarrow$', size=14) +ax.set_ylabel(r'model score $\longrightarrow$', size=14) + +ax.xaxis.set_major_formatter(plt.NullFormatter()) +ax.yaxis.set_major_formatter(plt.NullFormatter()) + +ax.set_title("Learning Curve Schematic", size=16) + +fig.savefig('figures/05.03-learning-curve.png') +``` + + +## Gaussian Naive Bayes + +### Gaussian Naive Bayes Example + +[Figure Context](05.05-Naive-Bayes.ipynb#Gaussian-Naive-Bayes) + + +```python deletable=true editable=true +from sklearn.datasets import make_blobs +X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5) + +fig, ax = plt.subplots() + +ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu') +ax.set_title('Naive Bayes Model', size=14) + +xlim = (-8, 8) +ylim = (-15, 5) + +xg = np.linspace(xlim[0], xlim[1], 60) +yg = np.linspace(ylim[0], ylim[1], 40) +xx, yy = np.meshgrid(xg, yg) +Xgrid = np.vstack([xx.ravel(), yy.ravel()]).T + +for label, color in enumerate(['red', 'blue']): + mask = (y == label) + mu, std = X[mask].mean(0), X[mask].std(0) + P = np.exp(-0.5 * (Xgrid - mu) ** 2 / std ** 2).prod(1) + Pm = np.ma.masked_array(P, P < 0.03) + ax.pcolorfast(xg, yg, Pm.reshape(xx.shape), alpha=0.5, + cmap=color.title() + 's') + ax.contour(xx, yy, P.reshape(xx.shape), + levels=[0.01, 0.1, 0.5, 0.9], + colors=color, alpha=0.2) + +ax.set(xlim=xlim, ylim=ylim) + +fig.savefig('figures/05.05-gaussian-NB.png') +``` + + +## Linear Regression + +### Gaussian Basis Functions + +[Figure Context](05.06-Linear-Regression.ipynb#Gaussian-Basis-Functions) + + +```python deletable=true editable=true +from sklearn.pipeline import make_pipeline +from sklearn.linear_model import LinearRegression + +from sklearn.base import BaseEstimator, TransformerMixin + +class GaussianFeatures(BaseEstimator, TransformerMixin): + """Uniformly-spaced Gaussian Features for 1D input""" + + def __init__(self, N, width_factor=2.0): + self.N = N + self.width_factor = width_factor + + @staticmethod + def _gauss_basis(x, y, width, axis=None): + arg = (x - y) / width + return np.exp(-0.5 * np.sum(arg ** 2, axis)) + + def fit(self, X, y=None): + # create N centers spread along the data range + self.centers_ = np.linspace(X.min(), X.max(), self.N) + self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0]) + return self + + def transform(self, X): + return self._gauss_basis(X[:, :, np.newaxis], self.centers_, + self.width_, axis=1) + +rng = np.random.RandomState(1) +x = 10 * rng.rand(50) +y = np.sin(x) + 0.1 * rng.randn(50) +xfit = np.linspace(0, 10, 1000) + +gauss_model = make_pipeline(GaussianFeatures(10, 1.0), + LinearRegression()) +gauss_model.fit(x[:, np.newaxis], y) +yfit = gauss_model.predict(xfit[:, np.newaxis]) + +gf = gauss_model.named_steps['gaussianfeatures'] +lm = gauss_model.named_steps['linearregression'] + +fig, ax = plt.subplots() + +for i in range(10): + selector = np.zeros(10) + selector[i] = 1 + Xfit = gf.transform(xfit[:, None]) * selector + yfit = lm.predict(Xfit) + ax.fill_between(xfit, yfit.min(), yfit, color='gray', alpha=0.2) + +ax.scatter(x, y) +ax.plot(xfit, gauss_model.predict(xfit[:, np.newaxis])) +ax.set_xlim(0, 10) +ax.set_ylim(yfit.min(), 1.5) + +fig.savefig('figures/05.06-gaussian-basis.png') +``` + + +## Random Forests + + + +### Helper Code + +The following will create a module ``helpers_05_08.py`` which contains some tools used in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb). + + +```python deletable=true editable=true +%%file helpers_05_08.py + +import numpy as np +import matplotlib.pyplot as plt +from sklearn.tree import DecisionTreeClassifier +from ipywidgets import interact + + +def visualize_tree(estimator, X, y, boundaries=True, + xlim=None, ylim=None, ax=None): + ax = ax or plt.gca() + + # Plot the training points + ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis', + clim=(y.min(), y.max()), zorder=3) + ax.axis('tight') + ax.axis('off') + if xlim is None: + xlim = ax.get_xlim() + if ylim is None: + ylim = ax.get_ylim() + + # fit the estimator + estimator.fit(X, y) + xx, yy = np.meshgrid(np.linspace(*xlim, num=200), + np.linspace(*ylim, num=200)) + Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()]) + + # Put the result into a color plot + n_classes = len(np.unique(y)) + Z = Z.reshape(xx.shape) + contours = ax.contourf(xx, yy, Z, alpha=0.3, + levels=np.arange(n_classes + 1) - 0.5, + cmap='viridis', clim=(y.min(), y.max()), + zorder=1) + + ax.set(xlim=xlim, ylim=ylim) + + # Plot the decision boundaries + def plot_boundaries(i, xlim, ylim): + if i >= 0: + tree = estimator.tree_ + + if tree.feature[i] == 0: + ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2) + plot_boundaries(tree.children_left[i], + [xlim[0], tree.threshold[i]], ylim) + plot_boundaries(tree.children_right[i], + [tree.threshold[i], xlim[1]], ylim) + + elif tree.feature[i] == 1: + ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2) + plot_boundaries(tree.children_left[i], xlim, + [ylim[0], tree.threshold[i]]) + plot_boundaries(tree.children_right[i], xlim, + [tree.threshold[i], ylim[1]]) + + if boundaries: + plot_boundaries(0, xlim, ylim) + + +def plot_tree_interactive(X, y): + def interactive_tree(depth=5): + clf = DecisionTreeClassifier(max_depth=depth, random_state=0) + visualize_tree(clf, X, y) + + return interact(interactive_tree, depth=[1, 5]) + + +def randomized_tree_interactive(X, y): + N = int(0.75 * X.shape[0]) + + xlim = (X[:, 0].min(), X[:, 0].max()) + ylim = (X[:, 1].min(), X[:, 1].max()) + + def fit_randomized_tree(random_state=0): + clf = DecisionTreeClassifier(max_depth=15) + i = np.arange(len(y)) + rng = np.random.RandomState(random_state) + rng.shuffle(i) + visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False, + xlim=xlim, ylim=ylim) + + interact(fit_randomized_tree, random_state=[0, 100]); +``` + + +### Decision Tree Example + + +```python deletable=true editable=true +fig = plt.figure(figsize=(10, 4)) +ax = fig.add_axes([0, 0, 0.8, 1], frameon=False, xticks=[], yticks=[]) +ax.set_title('Example Decision Tree: Animal Classification', size=24) + +def text(ax, x, y, t, size=20, **kwargs): + ax.text(x, y, t, + ha='center', va='center', size=size, + bbox=dict(boxstyle='round', ec='k', fc='w'), **kwargs) + +text(ax, 0.5, 0.9, "How big is\nthe animal?", 20) +text(ax, 0.3, 0.6, "Does the animal\nhave horns?", 18) +text(ax, 0.7, 0.6, "Does the animal\nhave two legs?", 18) +text(ax, 0.12, 0.3, "Are the horns\nlonger than 10cm?", 14) +text(ax, 0.38, 0.3, "Is the animal\nwearing a collar?", 14) +text(ax, 0.62, 0.3, "Does the animal\nhave wings?", 14) +text(ax, 0.88, 0.3, "Does the animal\nhave a tail?", 14) + +text(ax, 0.4, 0.75, "> 1m", 12, alpha=0.4) +text(ax, 0.6, 0.75, "< 1m", 12, alpha=0.4) + +text(ax, 0.21, 0.45, "yes", 12, alpha=0.4) +text(ax, 0.34, 0.45, "no", 12, alpha=0.4) + +text(ax, 0.66, 0.45, "yes", 12, alpha=0.4) +text(ax, 0.79, 0.45, "no", 12, alpha=0.4) + +ax.plot([0.3, 0.5, 0.7], [0.6, 0.9, 0.6], '-k') +ax.plot([0.12, 0.3, 0.38], [0.3, 0.6, 0.3], '-k') +ax.plot([0.62, 0.7, 0.88], [0.3, 0.6, 0.3], '-k') +ax.plot([0.0, 0.12, 0.20], [0.0, 0.3, 0.0], '--k') +ax.plot([0.28, 0.38, 0.48], [0.0, 0.3, 0.0], '--k') +ax.plot([0.52, 0.62, 0.72], [0.0, 0.3, 0.0], '--k') +ax.plot([0.8, 0.88, 1.0], [0.0, 0.3, 0.0], '--k') +ax.axis([0, 1, 0, 1]) + +fig.savefig('figures/05.08-decision-tree.png') +``` + + +### Decision Tree Levels + + +```python deletable=true editable=true +from helpers_05_08 import visualize_tree +from sklearn.tree import DecisionTreeClassifier +from sklearn.datasets import make_blobs + + +fig, ax = plt.subplots(1, 4, figsize=(16, 3)) +fig.subplots_adjust(left=0.02, right=0.98, wspace=0.1) + +X, y = make_blobs(n_samples=300, centers=4, + random_state=0, cluster_std=1.0) + +for axi, depth in zip(ax, range(1, 5)): + model = DecisionTreeClassifier(max_depth=depth) + visualize_tree(model, X, y, ax=axi) + axi.set_title('depth = {0}'.format(depth)) + +fig.savefig('figures/05.08-decision-tree-levels.png') +``` + + +### Decision Tree Overfitting + + +```python deletable=true editable=true +model = DecisionTreeClassifier() + +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) +visualize_tree(model, X[::2], y[::2], boundaries=False, ax=ax[0]) +visualize_tree(model, X[1::2], y[1::2], boundaries=False, ax=ax[1]) + +fig.savefig('figures/05.08-decision-tree-overfitting.png') +``` + + +## Principal Component Analysis + + + +### Principal Components Rotation + + +```python deletable=true editable=true +from sklearn.decomposition import PCA +``` + +```python deletable=true editable=true +def draw_vector(v0, v1, ax=None): + ax = ax or plt.gca() + arrowprops=dict(arrowstyle='->', + linewidth=2, + shrinkA=0, shrinkB=0) + ax.annotate('', v1, v0, arrowprops=arrowprops) +``` + +```python deletable=true editable=true +rng = np.random.RandomState(1) +X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T +pca = PCA(n_components=2, whiten=True) +pca.fit(X) + +fig, ax = plt.subplots(1, 2, figsize=(16, 6)) +fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1) + +# plot data +ax[0].scatter(X[:, 0], X[:, 1], alpha=0.2) +for length, vector in zip(pca.explained_variance_, pca.components_): + v = vector * 3 * np.sqrt(length) + draw_vector(pca.mean_, pca.mean_ + v, ax=ax[0]) +ax[0].axis('equal'); +ax[0].set(xlabel='x', ylabel='y', title='input') + +# plot principal components +X_pca = pca.transform(X) +ax[1].scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.2) +draw_vector([0, 0], [0, 3], ax=ax[1]) +draw_vector([0, 0], [3, 0], ax=ax[1]) +ax[1].axis('equal') +ax[1].set(xlabel='component 1', ylabel='component 2', + title='principal components', + xlim=(-5, 5), ylim=(-3, 3.1)) + +fig.savefig('figures/05.09-PCA-rotation.png') +``` + + +### Digits Pixel Components + + +```python deletable=true editable=true +def plot_pca_components(x, coefficients=None, mean=0, components=None, + imshape=(8, 8), n_components=8, fontsize=12, + show_mean=True): + if coefficients is None: + coefficients = x + + if components is None: + components = np.eye(len(coefficients), len(x)) + + mean = np.zeros_like(x) + mean + + + fig = plt.figure(figsize=(1.2 * (5 + n_components), 1.2 * 2)) + g = plt.GridSpec(2, 4 + bool(show_mean) + n_components, hspace=0.3) + + def show(i, j, x, title=None): + ax = fig.add_subplot(g[i, j], xticks=[], yticks=[]) + ax.imshow(x.reshape(imshape), interpolation='nearest') + if title: + ax.set_title(title, fontsize=fontsize) + + show(slice(2), slice(2), x, "True") + + approx = mean.copy() + + counter = 2 + if show_mean: + show(0, 2, np.zeros_like(x) + mean, r'$\mu$') + show(1, 2, approx, r'$1 \cdot \mu$') + counter += 1 + + for i in range(n_components): + approx = approx + coefficients[i] * components[i] + show(0, i + counter, components[i], r'$c_{0}$'.format(i + 1)) + show(1, i + counter, approx, + r"${0:.2f} \cdot c_{1}$".format(coefficients[i], i + 1)) + if show_mean or i > 0: + plt.gca().text(0, 1.05, '$+$', ha='right', va='bottom', + transform=plt.gca().transAxes, fontsize=fontsize) + + show(slice(2), slice(-2, None), approx, "Approx") + return fig +``` + +```python deletable=true editable=true +from sklearn.datasets import load_digits + +digits = load_digits() +sns.set_style('white') + +fig = plot_pca_components(digits.data[10], + show_mean=False) + +fig.savefig('figures/05.09-digits-pixel-components.png') +``` + + +### Digits PCA Components + + +```python deletable=true editable=true +pca = PCA(n_components=8) +Xproj = pca.fit_transform(digits.data) +sns.set_style('white') +fig = plot_pca_components(digits.data[10], Xproj[10], + pca.mean_, pca.components_) + +fig.savefig('figures/05.09-digits-pca-components.png') +``` + + +## Manifold Learning + + + +### LLE vs MDS Linkages + + +```python deletable=true editable=true +def make_hello(N=1000, rseed=42): + # Make a plot with "HELLO" text; save as png + fig, ax = plt.subplots(figsize=(4, 1)) + fig.subplots_adjust(left=0, right=1, bottom=0, top=1) + ax.axis('off') + ax.text(0.5, 0.4, 'HELLO', va='center', ha='center', weight='bold', size=85) + fig.savefig('hello.png') + plt.close(fig) + + # Open this PNG and draw random points from it + from matplotlib.image import imread + data = imread('hello.png')[::-1, :, 0].T + rng = np.random.RandomState(rseed) + X = rng.rand(4 * N, 2) + i, j = (X * data.shape).astype(int).T + mask = (data[i, j] < 1) + X = X[mask] + X[:, 0] *= (data.shape[0] / data.shape[1]) + X = X[:N] + return X[np.argsort(X[:, 0])] +``` + +```python deletable=true editable=true +def make_hello_s_curve(X): + t = (X[:, 0] - 2) * 0.75 * np.pi + x = np.sin(t) + y = X[:, 1] + z = np.sign(t) * (np.cos(t) - 1) + return np.vstack((x, y, z)).T + +X = make_hello(1000) +XS = make_hello_s_curve(X) +colorize = dict(c=X[:, 0], cmap=plt.cm.get_cmap('rainbow', 5)) +``` + +```python deletable=true editable=true +from mpl_toolkits.mplot3d.art3d import Line3DCollection +from sklearn.neighbors import NearestNeighbors + +# construct lines for MDS +rng = np.random.RandomState(42) +ind = rng.permutation(len(X)) +lines_MDS = [(XS[i], XS[j]) for i in ind[:100] for j in ind[100:200]] + +# construct lines for LLE +nbrs = NearestNeighbors(n_neighbors=100).fit(XS).kneighbors(XS[ind[:100]])[1] +lines_LLE = [(XS[ind[i]], XS[j]) for i in range(100) for j in nbrs[i]] +titles = ['MDS Linkages', 'LLE Linkages (100 NN)'] + +# plot the results +fig, ax = plt.subplots(1, 2, figsize=(16, 6), + subplot_kw=dict(projection='3d', axisbg='none')) +fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0, wspace=0) + +for axi, title, lines in zip(ax, titles, [lines_MDS, lines_LLE]): + axi.scatter3D(XS[:, 0], XS[:, 1], XS[:, 2], **colorize); + axi.add_collection(Line3DCollection(lines, lw=1, color='black', + alpha=0.05)) + axi.view_init(elev=10, azim=-80) + axi.set_title(title, size=18) + +fig.savefig('figures/05.10-LLE-vs-MDS.png') +``` + + +## K-Means + + + +### Expectation-Maximization + +[Figure Context](05.11-K-Means.ipynb#K-Means-Algorithm:-Expectation-Maximization) + +The following figure shows a visual depiction of the Expectation-Maximization approach to K Means: + + +```python deletable=true editable=true +from sklearn.datasets.samples_generator import make_blobs +from sklearn.metrics import pairwise_distances_argmin + +X, y_true = make_blobs(n_samples=300, centers=4, + cluster_std=0.60, random_state=0) + +rng = np.random.RandomState(42) +centers = [0, 4] + rng.randn(4, 2) + +def draw_points(ax, c, factor=1): + ax.scatter(X[:, 0], X[:, 1], c=c, cmap='viridis', + s=50 * factor, alpha=0.3) + +def draw_centers(ax, centers, factor=1, alpha=1.0): + ax.scatter(centers[:, 0], centers[:, 1], + c=np.arange(4), cmap='viridis', s=200 * factor, + alpha=alpha) + ax.scatter(centers[:, 0], centers[:, 1], + c='black', s=50 * factor, alpha=alpha) + +def make_ax(fig, gs): + ax = fig.add_subplot(gs) + ax.xaxis.set_major_formatter(plt.NullFormatter()) + ax.yaxis.set_major_formatter(plt.NullFormatter()) + return ax + +fig = plt.figure(figsize=(15, 4)) +gs = plt.GridSpec(4, 15, left=0.02, right=0.98, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2) +ax0 = make_ax(fig, gs[:4, :4]) +ax0.text(0.98, 0.98, "Random Initialization", transform=ax0.transAxes, + ha='right', va='top', size=16) +draw_points(ax0, 'gray', factor=2) +draw_centers(ax0, centers, factor=2) + +for i in range(3): + ax1 = make_ax(fig, gs[:2, 4 + 2 * i:6 + 2 * i]) + ax2 = make_ax(fig, gs[2:, 5 + 2 * i:7 + 2 * i]) + + # E-step + y_pred = pairwise_distances_argmin(X, centers) + draw_points(ax1, y_pred) + draw_centers(ax1, centers) + + # M-step + new_centers = np.array([X[y_pred == i].mean(0) for i in range(4)]) + draw_points(ax2, y_pred) + draw_centers(ax2, centers, alpha=0.3) + draw_centers(ax2, new_centers) + for i in range(4): + ax2.annotate('', new_centers[i], centers[i], + arrowprops=dict(arrowstyle='->', linewidth=1)) + + + # Finish iteration + centers = new_centers + ax1.text(0.95, 0.95, "E-Step", transform=ax1.transAxes, ha='right', va='top', size=14) + ax2.text(0.95, 0.95, "M-Step", transform=ax2.transAxes, ha='right', va='top', size=14) + + +# Final E-step +y_pred = pairwise_distances_argmin(X, centers) +axf = make_ax(fig, gs[:4, -4:]) +draw_points(axf, y_pred, factor=2) +draw_centers(axf, centers, factor=2) +axf.text(0.98, 0.98, "Final Clustering", transform=axf.transAxes, + ha='right', va='top', size=16) + + +fig.savefig('figures/05.11-expectation-maximization.png') +``` + + +### Interactive K-Means + +The following script uses IPython's interactive widgets to demonstrate the K-means algorithm interactively. +Run this within the IPython notebook to explore the expectation maximization algorithm for computing K Means. + + +```python deletable=true editable=true +%matplotlib inline +import matplotlib.pyplot as plt +import seaborn; seaborn.set() # for plot styling +import numpy as np + +from ipywidgets import interact +from sklearn.metrics import pairwise_distances_argmin +from sklearn.datasets.samples_generator import make_blobs + +def plot_kmeans_interactive(min_clusters=1, max_clusters=6): + X, y = make_blobs(n_samples=300, centers=4, + random_state=0, cluster_std=0.60) + + def plot_points(X, labels, n_clusters): + plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis', + vmin=0, vmax=n_clusters - 1); + + def plot_centers(centers): + plt.scatter(centers[:, 0], centers[:, 1], marker='o', + c=np.arange(centers.shape[0]), + s=200, cmap='viridis') + plt.scatter(centers[:, 0], centers[:, 1], marker='o', + c='black', s=50) + + + def _kmeans_step(frame=0, n_clusters=4): + rng = np.random.RandomState(2) + labels = np.zeros(X.shape[0]) + centers = rng.randn(n_clusters, 2) + + nsteps = frame // 3 + + for i in range(nsteps + 1): + old_centers = centers + if i < nsteps or frame % 3 > 0: + labels = pairwise_distances_argmin(X, centers) + + if i < nsteps or frame % 3 > 1: + centers = np.array([X[labels == j].mean(0) + for j in range(n_clusters)]) + nans = np.isnan(centers) + centers[nans] = old_centers[nans] + + # plot the data and cluster centers + plot_points(X, labels, n_clusters) + plot_centers(old_centers) + + # plot new centers if third frame + if frame % 3 == 2: + for i in range(n_clusters): + plt.annotate('', centers[i], old_centers[i], + arrowprops=dict(arrowstyle='->', linewidth=1)) + plot_centers(centers) + + plt.xlim(-4, 4) + plt.ylim(-2, 10) + + if frame % 3 == 1: + plt.text(3.8, 9.5, "1. Reassign points to nearest centroid", + ha='right', va='top', size=14) + elif frame % 3 == 2: + plt.text(3.8, 9.5, "2. Update centroids to cluster means", + ha='right', va='top', size=14) + + return interact(_kmeans_step, frame=[0, 50], + n_clusters=[min_clusters, max_clusters]) + +plot_kmeans_interactive(); +``` + + +## Gaussian Mixture Models + + + +### Covariance Type + +[Figure Context](http://localhost:8888/notebooks/05.12-Gaussian-Mixtures.ipynb#Choosing-the-Covariance-Type) + + +```python deletable=true editable=true +from sklearn.mixture import GMM + +from matplotlib.patches import Ellipse + +def draw_ellipse(position, covariance, ax=None, **kwargs): + """Draw an ellipse with a given position and covariance""" + ax = ax or plt.gca() + + # Convert covariance to principal axes + if covariance.shape == (2, 2): + U, s, Vt = np.linalg.svd(covariance) + angle = np.degrees(np.arctan2(U[1, 0], U[0, 0])) + width, height = 2 * np.sqrt(s) + else: + angle = 0 + width, height = 2 * np.sqrt(covariance) + + # Draw the Ellipse + for nsig in range(1, 4): + ax.add_patch(Ellipse(position, nsig * width, nsig * height, + angle, **kwargs)) + +fig, ax = plt.subplots(1, 3, figsize=(14, 4), sharex=True, sharey=True) +fig.subplots_adjust(wspace=0.05) + +rng = np.random.RandomState(5) +X = np.dot(rng.randn(500, 2), rng.randn(2, 2)) + +for i, cov_type in enumerate(['diag', 'spherical', 'full']): + model = GMM(1, covariance_type=cov_type).fit(X) + ax[i].axis('equal') + ax[i].scatter(X[:, 0], X[:, 1], alpha=0.5) + ax[i].set_xlim(-3, 3) + ax[i].set_title('covariance_type="{0}"'.format(cov_type), + size=14, family='monospace') + draw_ellipse(model.means_[0], model.covars_[0], ax[i], alpha=0.2) + ax[i].xaxis.set_major_formatter(plt.NullFormatter()) + ax[i].yaxis.set_major_formatter(plt.NullFormatter()) + +fig.savefig('figures/05.12-covariance-type.png') +``` + + + +< [Further Machine Learning Resources](05.15-Learning-More.ipynb) | [Contents](Index.ipynb) | + +Open in Colab + + diff --git a/notebooks_v2/Index.ipynb b/notebooks_v2/Index.ipynb new file mode 100644 index 00000000..873eed10 --- /dev/null +++ b/notebooks_v2/Index.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Data Science Handbook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Jake VanderPlas*\n", + "\n", + "![Book Cover](figures/PDSH-cover.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the Jupyter notebook version of the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "### [Preface](00.00-Preface.ipynb)\n", + "\n", + "### [1. IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)\n", + "- [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)\n", + "- [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb)\n", + "- [IPython Magic Commands](01.03-Magic-Commands.ipynb)\n", + "- [Input and Output History](01.04-Input-Output-History.ipynb)\n", + "- [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb)\n", + "- [Errors and Debugging](01.06-Errors-and-Debugging.ipynb)\n", + "- [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)\n", + "- [More IPython Resources](01.08-More-IPython-Resources.ipynb)\n", + "\n", + "### [2. Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb)\n", + "- [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)\n", + "- [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb)\n", + "- [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)\n", + "- [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)\n", + "- [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)\n", + "- [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)\n", + "- [Fancy Indexing](02.07-Fancy-Indexing.ipynb)\n", + "- [Sorting Arrays](02.08-Sorting.ipynb)\n", + "- [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb)\n", + "\n", + "### [3. Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb)\n", + "- [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb)\n", + "- [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)\n", + "- [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb)\n", + "- [Handling Missing Data](03.04-Missing-Values.ipynb)\n", + "- [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)\n", + "- [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb)\n", + "- [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb)\n", + "- [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)\n", + "- [Pivot Tables](03.09-Pivot-Tables.ipynb)\n", + "- [Vectorized String Operations](03.10-Working-With-Strings.ipynb)\n", + "- [Working with Time Series](03.11-Working-with-Time-Series.ipynb)\n", + "- [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb)\n", + "- [Further Resources](03.13-Further-Resources.ipynb)\n", + "\n", + "### [4. Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb)\n", + "- [Simple Line Plots](04.01-Simple-Line-Plots.ipynb)\n", + "- [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb)\n", + "- [Visualizing Errors](04.03-Errorbars.ipynb)\n", + "- [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)\n", + "- [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb)\n", + "- [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)\n", + "- [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb)\n", + "- [Multiple Subplots](04.08-Multiple-Subplots.ipynb)\n", + "- [Text and Annotation](04.09-Text-and-Annotation.ipynb)\n", + "- [Customizing Ticks](04.10-Customizing-Ticks.ipynb)\n", + "- [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb)\n", + "- [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb)\n", + "- [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)\n", + "- [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb)\n", + "- [Further Resources](04.15-Further-Resources.ipynb)\n", + "\n", + "### [5. Machine Learning](05.00-Machine-Learning.ipynb)\n", + "- [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb)\n", + "- [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)\n", + "- [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)\n", + "- [Feature Engineering](05.04-Feature-Engineering.ipynb)\n", + "- [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)\n", + "- [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)\n", + "- [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)\n", + "- [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)\n", + "- [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)\n", + "- [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)\n", + "- [In Depth: k-Means Clustering](05.11-K-Means.ipynb)\n", + "- [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)\n", + "- [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb)\n", + "- [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb)\n", + "- [Further Machine Learning Resources](05.15-Learning-More.ipynb)\n", + "\n", + "### [Appendix: Figure Code](06.00-Figure-Code.ipynb)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks_v2/Index.md b/notebooks_v2/Index.md new file mode 100644 index 00000000..0e2e6f0c --- /dev/null +++ b/notebooks_v2/Index.md @@ -0,0 +1,102 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.10.3 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Python Data Science Handbook + + +*Jake VanderPlas* + +![Book Cover](figures/PDSH-cover.png) + + +This is the Jupyter notebook version of the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* +The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)! + + +## Table of Contents + +### [Preface](00.00-Preface.ipynb) + +### [1. IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) +- [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) +- [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) +- [IPython Magic Commands](01.03-Magic-Commands.ipynb) +- [Input and Output History](01.04-Input-Output-History.ipynb) +- [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) +- [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) +- [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) +- [More IPython Resources](01.08-More-IPython-Resources.ipynb) + +### [2. Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) +- [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) +- [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) +- [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) +- [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb) +- [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) +- [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb) +- [Fancy Indexing](02.07-Fancy-Indexing.ipynb) +- [Sorting Arrays](02.08-Sorting.ipynb) +- [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) + +### [3. Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb) +- [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) +- [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb) +- [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) +- [Handling Missing Data](03.04-Missing-Values.ipynb) +- [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) +- [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb) +- [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb) +- [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) +- [Pivot Tables](03.09-Pivot-Tables.ipynb) +- [Vectorized String Operations](03.10-Working-With-Strings.ipynb) +- [Working with Time Series](03.11-Working-with-Time-Series.ipynb) +- [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) +- [Further Resources](03.13-Further-Resources.ipynb) + +### [4. Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) +- [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) +- [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) +- [Visualizing Errors](04.03-Errorbars.ipynb) +- [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) +- [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) +- [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) +- [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) +- [Multiple Subplots](04.08-Multiple-Subplots.ipynb) +- [Text and Annotation](04.09-Text-and-Annotation.ipynb) +- [Customizing Ticks](04.10-Customizing-Ticks.ipynb) +- [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb) +- [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) +- [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) +- [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) +- [Further Resources](04.15-Further-Resources.ipynb) + +### [5. Machine Learning](05.00-Machine-Learning.ipynb) +- [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) +- [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) +- [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) +- [Feature Engineering](05.04-Feature-Engineering.ipynb) +- [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) +- [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) +- [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) +- [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) +- [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) +- [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) +- [In Depth: k-Means Clustering](05.11-K-Means.ipynb) +- [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb) +- [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) +- [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb) +- [Further Machine Learning Resources](05.15-Learning-More.ipynb) + +### [Appendix: Figure Code](06.00-Figure-Code.ipynb) diff --git a/notebooks_v2/data/BicycleWeather.csv b/notebooks_v2/data/BicycleWeather.csv new file mode 100644 index 00000000..41fc503c --- /dev/null +++ b/notebooks_v2/data/BicycleWeather.csv @@ -0,0 +1,1341 @@ +STATION,STATION_NAME,DATE,PRCP,SNWD,SNOW,TMAX,TMIN,AWND,WDF2,WDF5,WSF2,WSF5,FMTM,WT14,WT01,WT17,WT05,WT02,WT22,WT04,WT13,WT16,WT08,WT18,WT03 +GHCND:USW00024233,SEATTLE TACOMA INTERNATIONAL AIRPORT WA US,20120101,0,0,0,128,50,47,100,90,89,112,-9999,1,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999 +GHCND:USW00024233,SEATTLE TACOMA INTERNATIONAL AIRPORT WA US,20120102,109,0,0,106,28,45,180,200,130,179,-9999,-9999,1,-9999,-9999,-9999,-9999,-9999,1,1,-9999,-9999,-9999 +GHCND:USW00024233,SEATTLE TACOMA INTERNATIONAL AIRPORT WA 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Bush,182 +44,Barack Obama,185 diff --git a/notebooks_v2/data/state-abbrevs.csv b/notebooks_v2/data/state-abbrevs.csv new file mode 100644 index 00000000..6d4db366 --- /dev/null +++ b/notebooks_v2/data/state-abbrevs.csv @@ -0,0 +1,52 @@ +"state","abbreviation" +"Alabama","AL" +"Alaska","AK" +"Arizona","AZ" +"Arkansas","AR" +"California","CA" +"Colorado","CO" +"Connecticut","CT" +"Delaware","DE" +"District of Columbia","DC" +"Florida","FL" +"Georgia","GA" +"Hawaii","HI" +"Idaho","ID" +"Illinois","IL" +"Indiana","IN" +"Iowa","IA" +"Kansas","KS" +"Kentucky","KY" +"Louisiana","LA" +"Maine","ME" +"Montana","MT" +"Nebraska","NE" +"Nevada","NV" +"New Hampshire","NH" +"New Jersey","NJ" +"New Mexico","NM" +"New York","NY" +"North Carolina","NC" +"North Dakota","ND" +"Ohio","OH" +"Oklahoma","OK" +"Oregon","OR" +"Maryland","MD" +"Massachusetts","MA" +"Michigan","MI" +"Minnesota","MN" +"Mississippi","MS" +"Missouri","MO" +"Pennsylvania","PA" +"Rhode Island","RI" +"South Carolina","SC" +"South Dakota","SD" +"Tennessee","TN" +"Texas","TX" +"Utah","UT" +"Vermont","VT" +"Virginia","VA" +"Washington","WA" +"West Virginia","WV" +"Wisconsin","WI" +"Wyoming","WY" \ No newline at end of file diff --git a/notebooks_v2/data/state-areas.csv b/notebooks_v2/data/state-areas.csv new file mode 100644 index 00000000..322345c5 --- /dev/null +++ b/notebooks_v2/data/state-areas.csv @@ -0,0 +1,53 @@ +state,area (sq. mi) +Alabama,52423 +Alaska,656425 +Arizona,114006 +Arkansas,53182 +California,163707 +Colorado,104100 +Connecticut,5544 +Delaware,1954 +Florida,65758 +Georgia,59441 +Hawaii,10932 +Idaho,83574 +Illinois,57918 +Indiana,36420 +Iowa,56276 +Kansas,82282 +Kentucky,40411 +Louisiana,51843 +Maine,35387 +Maryland,12407 +Massachusetts,10555 +Michigan,96810 +Minnesota,86943 +Mississippi,48434 +Missouri,69709 +Montana,147046 +Nebraska,77358 +Nevada,110567 +New Hampshire,9351 +New Jersey,8722 +New Mexico,121593 +New York,54475 +North Carolina,53821 +North Dakota,70704 +Ohio,44828 +Oklahoma,69903 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OzNEzDMQeo(g8m=xaQwCa literal 0 HcmV?d00001 diff --git a/notebooks_v2/helpers_05_08.py b/notebooks_v2/helpers_05_08.py new file mode 100644 index 00000000..0f3b15aa --- /dev/null +++ b/notebooks_v2/helpers_05_08.py @@ -0,0 +1,83 @@ + +import numpy as np +import matplotlib.pyplot as plt +from sklearn.tree import DecisionTreeClassifier +from ipywidgets import interact + + +def visualize_tree(estimator, X, y, boundaries=True, + xlim=None, ylim=None, ax=None): + ax = ax or plt.gca() + + # Plot the training points + ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis', + clim=(y.min(), y.max()), zorder=3) + ax.axis('tight') + ax.axis('off') + if xlim is None: + xlim = ax.get_xlim() + if ylim is None: + ylim = ax.get_ylim() + + # fit the estimator + estimator.fit(X, y) + xx, yy = np.meshgrid(np.linspace(*xlim, num=200), + np.linspace(*ylim, num=200)) + Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()]) + + # Put the result into a color plot + n_classes = len(np.unique(y)) + Z = Z.reshape(xx.shape) + contours = ax.contourf(xx, yy, Z, alpha=0.3, + levels=np.arange(n_classes + 1) - 0.5, + cmap='viridis', clim=(y.min(), y.max()), + zorder=1) + + ax.set(xlim=xlim, ylim=ylim) + + # Plot the decision boundaries + def plot_boundaries(i, xlim, ylim): + if i >= 0: + tree = estimator.tree_ + + if tree.feature[i] == 0: + ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2) + plot_boundaries(tree.children_left[i], + [xlim[0], tree.threshold[i]], ylim) + plot_boundaries(tree.children_right[i], + [tree.threshold[i], xlim[1]], ylim) + + elif tree.feature[i] == 1: + ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2) + plot_boundaries(tree.children_left[i], xlim, + [ylim[0], tree.threshold[i]]) + plot_boundaries(tree.children_right[i], xlim, + [tree.threshold[i], ylim[1]]) + + if boundaries: + plot_boundaries(0, xlim, ylim) + + +def plot_tree_interactive(X, y): + def interactive_tree(depth=5): + clf = DecisionTreeClassifier(max_depth=depth, random_state=0) + visualize_tree(clf, X, y) + + return interact(interactive_tree, depth=[1, 5]) + + +def randomized_tree_interactive(X, y): + N = int(0.75 * X.shape[0]) + + xlim = (X[:, 0].min(), X[:, 0].max()) + ylim = (X[:, 1].min(), X[:, 1].max()) + + def fit_randomized_tree(random_state=0): + clf = DecisionTreeClassifier(max_depth=15) + i = np.arange(len(y)) + rng = np.random.RandomState(random_state) + rng.shuffle(i) + visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False, + xlim=xlim, ylim=ylim) + + interact(fit_randomized_tree, random_state=[0, 100]); \ No newline at end of file From 3ee9ce82f5e006e239ad9e936223adc56ab1bf9d Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Mon, 8 Mar 2021 06:49:10 -0800 Subject: [PATCH 02/14] Add pre-commit github action --- .github/workflows/ci-build.yaml | 22 ++++++++++++++++++++++ .pre-commit-config.yaml | 15 +++++++++++++++ 2 files changed, 37 insertions(+) create mode 100644 .github/workflows/ci-build.yaml create mode 100644 .pre-commit-config.yaml diff --git a/.github/workflows/ci-build.yaml b/.github/workflows/ci-build.yaml new file mode 100644 index 00000000..e5a4c0fb --- /dev/null +++ b/.github/workflows/ci-build.yaml @@ -0,0 +1,22 @@ +name: CI + +on: + # Trigger the workflow on push or pull request, + # but only for the master branch + push: + branches: + - v2 + pull_request: + branches: + - v2 + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.9 + uses: actions/setup-python@v2 + with: + python-version: 3.9 + - uses: pre-commit/action@v2.0.0 \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..6d572233 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,15 @@ +# Install the pre-commit hooks below with +# 'pre-commit install' + +# Auto-update the version of the hooks with +# 'pre-commit autoupdate' + +# Run the hooks on all files with +# 'pre-commit run --all' + +repos: +- repo: https://github.com/mwouts/jupytext + rev: v1.10.0 + hooks: + - id: jupytext + args: [--sync] \ No newline at end of file From 795099efd1e5aeaefe18c9083c46ba0f6ce94a83 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Mon, 8 Mar 2021 07:44:03 -0800 Subject: [PATCH 03/14] Update 00.00-Preface --- notebooks_v2/00.00-Preface.ipynb | 13 ------------- notebooks_v2/00.00-Preface.md | 9 --------- 2 files changed, 22 deletions(-) diff --git a/notebooks_v2/00.00-Preface.ipynb b/notebooks_v2/00.00-Preface.ipynb index ce3662ee..cf00448e 100644 --- a/notebooks_v2/00.00-Preface.ipynb +++ b/notebooks_v2/00.00-Preface.ipynb @@ -93,19 +93,6 @@ "This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Python 2 vs Python 3\n", - "\n", - "This book uses the syntax of Python 3, which contains language enhancements that are not compatible with the 2.x series of Python.\n", - "Though Python 3.0 was first released in 2008, adoption has been relatively slow, particularly in the scientific and web development communities.\n", - "This is primarily because it took some time for many of the essential third-party packages and toolkits to be made compatible with the new language internals.\n", - "Since early 2014, however, stable releases of the most important tools in the data science ecosystem have been fully compatible with both Python 2 and 3, and so this book will use the newer Python 3 syntax.\n", - "However, the vast majority of code snippets in this book will also work without modification in Python 2: in cases where a Py2-incompatible syntax is used, I will make every effort to note it explicitly." - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks_v2/00.00-Preface.md b/notebooks_v2/00.00-Preface.md index 44cca9e3..6206407f 100644 --- a/notebooks_v2/00.00-Preface.md +++ b/notebooks_v2/00.00-Preface.md @@ -75,15 +75,6 @@ If you are looking for a guide to the Python language itself, I would suggest th This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages. -### Python 2 vs Python 3 - -This book uses the syntax of Python 3, which contains language enhancements that are not compatible with the 2.x series of Python. -Though Python 3.0 was first released in 2008, adoption has been relatively slow, particularly in the scientific and web development communities. -This is primarily because it took some time for many of the essential third-party packages and toolkits to be made compatible with the new language internals. -Since early 2014, however, stable releases of the most important tools in the data science ecosystem have been fully compatible with both Python 2 and 3, and so this book will use the newer Python 3 syntax. -However, the vast majority of code snippets in this book will also work without modification in Python 2: in cases where a Py2-incompatible syntax is used, I will make every effort to note it explicitly. - - ## Outline of the Book Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story. From 8e6ddff89b3dfe994e7435286fec2738aa79e9f2 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Thu, 11 Mar 2021 06:20:18 -0800 Subject: [PATCH 04/14] Update 01.00-01.04 --- .../01.00-IPython-Beyond-Normal-Python.ipynb | 25 +++-- .../01.00-IPython-Beyond-Normal-Python.md | 25 +++-- .../01.01-Help-And-Documentation.ipynb | 101 +++++++++--------- notebooks_v2/01.01-Help-And-Documentation.md | 99 ++++++++--------- notebooks_v2/01.03-Magic-Commands.ipynb | 8 +- notebooks_v2/01.03-Magic-Commands.md | 8 +- notebooks_v2/01.04-Input-Output-History.ipynb | 16 +-- notebooks_v2/01.04-Input-Output-History.md | 16 +-- 8 files changed, 145 insertions(+), 153 deletions(-) diff --git a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb index 44ec590c..344f9a9e 100644 --- a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb +++ b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb @@ -34,7 +34,7 @@ "metadata": {}, "source": [ "There are many options for development environments for Python, and I'm often asked which one I use in my own work.\n", - "My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or Atom depending on my mood).\n", + "My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or VSCode depending on my mood).\n", "IPython (short for *Interactive Python*) was started in 2001 by Fernando Perez as an enhanced Python interpreter, and has since grown into a project aiming to provide, in Perez's words, \"Tools for the entire life cycle of research computing.\"\n", "If Python is the engine of our data science task, you might think of IPython as the interactive control panel.\n", "\n", @@ -73,11 +73,10 @@ "\n", "Once you do this, you should see a prompt like the following:\n", "```\n", - "IPython 4.0.1 -- An enhanced Interactive Python.\n", - "? -> Introduction and overview of IPython's features.\n", - "%quickref -> Quick reference.\n", - "help -> Python's own help system.\n", - "object? -> Details about 'object', use 'object??' for extra details.\n", + "Python 3.9.2 (v3.9.2:1a79785e3e, Feb 19 2021, 09:06:10) \n", + "Type 'copyright', 'credits' or 'license' for more information\n", + "IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help.\n", + "\n", "In [1]:\n", "```\n", "With that, you're ready to follow along." @@ -97,23 +96,23 @@ "This process (known as a \"kernel\") can be started by running the following command in your system shell:\n", "\n", "```\n", - "$ jupyter notebook\n", + "$ jupyter lab\n", "```\n", "\n", "This command will launch a local web server that will be visible to your browser.\n", "It immediately spits out a log showing what it is doing; that log will look something like this:\n", "\n", "```\n", - "$ jupyter notebook\n", - "[NotebookApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook\n", - "[NotebookApp] 0 active kernels \n", - "[NotebookApp] The IPython Notebook is running at: http://localhost:8888/\n", - "[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", + "$ jupyter lab\n", + "[ServerApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook\n", + "[ServerApp] Jupyter Server 1.4.1 is running at:\n", + "[ServerApp] http://localhost:8888/lab?token=dd852649\n", + "[ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n", "```\n", "\n", "Upon issuing the command, your default browser should automatically open and navigate to the listed local URL;\n", "the exact address will depend on your system.\n", - "If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/* in this example)." + "If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/lab/* in this example)." ] }, { diff --git a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md index 119244dc..84e43149 100644 --- a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md +++ b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md @@ -32,7 +32,7 @@ jupyter: There are many options for development environments for Python, and I'm often asked which one I use in my own work. -My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or Atom depending on my mood). +My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or VSCode depending on my mood). IPython (short for *Interactive Python*) was started in 2001 by Fernando Perez as an enhanced Python interpreter, and has since grown into a project aiming to provide, in Perez's words, "Tools for the entire life cycle of research computing." If Python is the engine of our data science task, you might think of IPython as the interactive control panel. @@ -63,11 +63,10 @@ Start by launching the IPython interpreter by typing **``ipython``** on the comm Once you do this, you should see a prompt like the following: ``` -IPython 4.0.1 -- An enhanced Interactive Python. -? -> Introduction and overview of IPython's features. -%quickref -> Quick reference. -help -> Python's own help system. -object? -> Details about 'object', use 'object??' for extra details. +Python 3.9.2 (v3.9.2:1a79785e3e, Feb 19 2021, 09:06:10) +Type 'copyright', 'credits' or 'license' for more information +IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help. + In [1]: ``` With that, you're ready to follow along. @@ -83,23 +82,23 @@ Though the IPython notebook is viewed and edited through your web browser window This process (known as a "kernel") can be started by running the following command in your system shell: ``` -$ jupyter notebook +$ jupyter lab ``` This command will launch a local web server that will be visible to your browser. It immediately spits out a log showing what it is doing; that log will look something like this: ``` -$ jupyter notebook -[NotebookApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook -[NotebookApp] 0 active kernels -[NotebookApp] The IPython Notebook is running at: http://localhost:8888/ -[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). +$ jupyter lab +[ServerApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook +[ServerApp] Jupyter Server 1.4.1 is running at: +[ServerApp] http://localhost:8888/lab?token=dd852649 +[ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). ``` Upon issuing the command, your default browser should automatically open and navigate to the listed local URL; the exact address will depend on your system. -If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/* in this example). +If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/lab/* in this example). diff --git a/notebooks_v2/01.01-Help-And-Documentation.ipynb b/notebooks_v2/01.01-Help-And-Documentation.ipynb index 00e8bfd5..d0af7f49 100644 --- a/notebooks_v2/01.01-Help-And-Documentation.ipynb +++ b/notebooks_v2/01.01-Help-And-Documentation.ipynb @@ -65,10 +65,8 @@ "In [1]: help(len)\n", "Help on built-in function len in module builtins:\n", "\n", - "len(...)\n", - " len(object) -> integer\n", - " \n", - " Return the number of items of a sequence or mapping.\n", + "len(obj, /)\n", + " Return the number of items in a container.\n", "```\n", "\n", "Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window." @@ -82,13 +80,9 @@ "\n", "```ipython\n", "In [2]: len?\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Namespace: Python builtin\n", - "Docstring:\n", - "len(object) -> integer\n", - "\n", - "Return the number of items of a sequence or mapping.\n", + "Signature: len(obj, /)\n", + "Docstring: Return the number of items in a container.\n", + "Type: builtin_function_or_method\n", "```" ] }, @@ -101,9 +95,9 @@ "```ipython\n", "In [3]: L = [1, 2, 3]\n", "In [4]: L.insert?\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Docstring: L.insert(index, object) -- insert object before index\n", + "Signature: L.insert(index, object, /)\n", + "Docstring: Insert object before index.\n", + "Type: builtin_function_or_method\n", "```\n", "\n", "or even objects themselves, with the documentation from their type:\n", @@ -113,9 +107,11 @@ "Type: list\n", "String form: [1, 2, 3]\n", "Length: 3\n", - "Docstring:\n", - "list() -> new empty list\n", - "list(iterable) -> new list initialized from iterable's items\n", + "Docstring: \n", + "Built-in mutable sequence.\n", + "\n", + "If no argument is given, the constructor creates a new empty list.\n", + "The argument must be an iterable if specified.\n", "```" ] }, @@ -145,10 +141,10 @@ "\n", "```ipython\n", "In [7]: square?\n", - "Type: function\n", - "String form: \n", - "Definition: square(a)\n", - "Docstring: Return the square of a.\n", + "Signature: square(a)\n", + "Docstring: Return the square of a.\n", + "File: \n", + "Type: function\n", "```\n", "\n", "This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write!" @@ -164,13 +160,13 @@ "\n", "```ipython\n", "In [8]: square??\n", - "Type: function\n", - "String form: \n", - "Definition: square(a)\n", - "Source:\n", + "Signature: square(a)\n", + "Source: \n", "def square(a):\n", - " \"Return the square of a\"\n", + " \"\"\"Return the square of a.\"\"\"\n", " return a ** 2\n", + "File: \n", + "Type: function\n", "```\n", "\n", "For simple functions like this, the double question-mark can give quick insight into the under-the-hood details." @@ -186,13 +182,9 @@ "\n", "```ipython\n", "In [9]: len??\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Namespace: Python builtin\n", - "Docstring:\n", - "len(object) -> integer\n", - "\n", - "Return the number of items of a sequence or mapping.\n", + "Signature: len(obj, /)\n", + "Docstring: Return the number of items in a container.\n", + "Type: builtin_function_or_method\n", "```\n", "\n", "Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does." @@ -220,18 +212,20 @@ "\n", "```ipython\n", "In [10]: L.\n", - "L.append L.copy L.extend L.insert L.remove L.sort \n", - "L.clear L.count L.index L.pop L.reverse \n", + " append() count insert reverse \n", + " clear extend pop sort \n", + " copy index remove \n", "```\n", "\n", "To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\n", "\n", "```ipython\n", "In [10]: L.c\n", - "L.clear L.copy L.count \n", + " clear() count()\n", + " copy() \n", "\n", "In [10]: L.co\n", - "L.copy L.count \n", + " copy() count()\n", "```\n", "\n", "If there is only a single option, pressing the Tab key will complete the line for you.\n", @@ -247,11 +241,13 @@ "\n", "```ipython\n", "In [10]: L._\n", - "L.__add__ L.__gt__ L.__reduce__\n", - "L.__class__ L.__hash__ L.__reduce_ex__\n", + " __add__ __delattr__ __eq__ \n", + " __class__ __delitem__ __format__()\n", + " __class_getitem__() __dir__() __ge__ >\n", + " __contains__ __doc__ __getattribute__ \n", "```\n", "\n", - "For brevity, we've only shown the first couple lines of the output.\n", + "For brevity, we've only shown the first few columns of the output.\n", "Most of these are Python's special double-underscore methods (often nicknamed \"dunder\" methods)." ] }, @@ -265,23 +261,22 @@ "Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``:\n", "```\n", "In [10]: from itertools import co\n", - "combinations compress\n", - "combinations_with_replacement count\n", + " combinations() compress()\n", + " combinations_with_replacement() count()\n", "```\n", "Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session):\n", "```\n", "In [10]: import \n", - "Display all 399 possibilities? (y or n)\n", - "Crypto dis py_compile\n", - "Cython distutils pyclbr\n", - "... ... ...\n", - "difflib pwd zmq\n", + " abc anyio \n", + " activate_this appdirs \n", + " aifc appnope >\n", + " antigravity argon2 \n", "\n", "In [10]: import h\n", - "hashlib hmac http \n", - "heapq html husl \n", - "```\n", - "(Note that for brevity, I did not print here all 399 importable packages and modules on my system.)" + " hashlib html \n", + " heapq http \n", + " hmac \n", + "```" ] }, { @@ -311,12 +306,12 @@ "We can search for it this way:\n", "\n", "```ipython\n", - "In [10]: str.*find*?\n", + "In [11]: str.*find*?\n", "str.find\n", "str.rfind\n", "```\n", "\n", - "I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one." + "I find this type of flexible wildcard search can be useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one." ] }, { diff --git a/notebooks_v2/01.01-Help-And-Documentation.md b/notebooks_v2/01.01-Help-And-Documentation.md index 9f493bef..b0eff08d 100644 --- a/notebooks_v2/01.01-Help-And-Documentation.md +++ b/notebooks_v2/01.01-Help-And-Documentation.md @@ -59,10 +59,8 @@ For example, to see the documentation of the built-in ``len`` function, you can In [1]: help(len) Help on built-in function len in module builtins: -len(...) - len(object) -> integer - - Return the number of items of a sequence or mapping. +len(obj, /) + Return the number of items in a container. ``` Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window. @@ -72,13 +70,9 @@ Because finding help on an object is so common and useful, IPython introduces th ```ipython In [2]: len? -Type: builtin_function_or_method -String form: -Namespace: Python builtin -Docstring: -len(object) -> integer - -Return the number of items of a sequence or mapping. +Signature: len(obj, /) +Docstring: Return the number of items in a container. +Type: builtin_function_or_method ``` @@ -87,9 +81,9 @@ This notation works for just about anything, including object methods: ```ipython In [3]: L = [1, 2, 3] In [4]: L.insert? -Type: builtin_function_or_method -String form: -Docstring: L.insert(index, object) -- insert object before index +Signature: L.insert(index, object, /) +Docstring: Insert object before index. +Type: builtin_function_or_method ``` or even objects themselves, with the documentation from their type: @@ -99,9 +93,11 @@ In [5]: L? Type: list String form: [1, 2, 3] Length: 3 -Docstring: -list() -> new empty list -list(iterable) -> new list initialized from iterable's items +Docstring: +Built-in mutable sequence. + +If no argument is given, the constructor creates a new empty list. +The argument must be an iterable if specified. ``` @@ -123,10 +119,10 @@ Now we'll use the ``?`` mark to find this doc string: ```ipython In [7]: square? -Type: function -String form: -Definition: square(a) -Docstring: Return the square of a. +Signature: square(a) +Docstring: Return the square of a. +File: +Type: function ``` This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write! @@ -138,13 +134,13 @@ IPython provides a shortcut to the source code with the double question mark (`` ```ipython In [8]: square?? -Type: function -String form: -Definition: square(a) -Source: +Signature: square(a) +Source: def square(a): - "Return the square of a" + """Return the square of a.""" return a ** 2 +File: +Type: function ``` For simple functions like this, the double question-mark can give quick insight into the under-the-hood details. @@ -156,13 +152,9 @@ You'll find this particularly with many of Python's built-in objects and types, ```ipython In [9]: len?? -Type: builtin_function_or_method -String form: -Namespace: Python builtin -Docstring: -len(object) -> integer - -Return the number of items of a sequence or mapping. +Signature: len(obj, /) +Docstring: Return the number of items in a container. +Type: builtin_function_or_method ``` Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does. @@ -182,18 +174,20 @@ To see a list of all available attributes of an object, you can type the name of ```ipython In [10]: L. -L.append L.copy L.extend L.insert L.remove L.sort -L.clear L.count L.index L.pop L.reverse + append() count insert reverse + clear extend pop sort + copy index remove ``` To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods: ```ipython In [10]: L.c -L.clear L.copy L.count + clear() count() + copy() In [10]: L.co -L.copy L.count + copy() count() ``` If there is only a single option, pressing the Tab key will complete the line for you. @@ -209,11 +203,13 @@ For clarity, these private methods and special methods are omitted from the list ```ipython In [10]: L._ -L.__add__ L.__gt__ L.__reduce__ -L.__class__ L.__hash__ L.__reduce_ex__ + __add__ __delattr__ __eq__ + __class__ __delitem__ __format__() + __class_getitem__() __dir__() __ge__ > + __contains__ __doc__ __getattribute__ ``` -For brevity, we've only shown the first couple lines of the output. +For brevity, we've only shown the first few columns of the output. Most of these are Python's special double-underscore methods (often nicknamed "dunder" methods). @@ -223,23 +219,22 @@ Tab completion is also useful when importing objects from packages. Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``: ``` In [10]: from itertools import co -combinations compress -combinations_with_replacement count + combinations() compress() + combinations_with_replacement() count() ``` Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session): ``` In [10]: import -Display all 399 possibilities? (y or n) -Crypto dis py_compile -Cython distutils pyclbr -... ... ... -difflib pwd zmq + abc anyio + activate_this appdirs + aifc appnope > + antigravity argon2 In [10]: import h -hashlib hmac http -heapq html husl + hashlib html + heapq http + hmac ``` -(Note that for brevity, I did not print here all 399 importable packages and modules on my system.) ### Beyond tab completion: wildcard matching @@ -265,12 +260,12 @@ Similarly, suppose we are looking for a string method that contains the word ``f We can search for it this way: ```ipython -In [10]: str.*find*? +In [11]: str.*find*? str.find str.rfind ``` -I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one. +I find this type of flexible wildcard search can be useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one. diff --git a/notebooks_v2/01.03-Magic-Commands.ipynb b/notebooks_v2/01.03-Magic-Commands.ipynb index eba5ed1c..20c4a6d2 100644 --- a/notebooks_v2/01.03-Magic-Commands.ipynb +++ b/notebooks_v2/01.03-Magic-Commands.ipynb @@ -56,7 +56,7 @@ "... return x\n", "\n", "```\n", - "The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into IPython you get an error:\n", + "The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into older IPython versions, you get an error:\n", "\n", "```ipython\n", "In [2]: >>> def donothing(x):\n", @@ -119,7 +119,7 @@ " return x ** 2\n", "\n", "for N in range(1, 4):\n", - " print(N, \"squared is\", square(N))\n", + " print(f\"{N} squared is {square(N)}\")\n", "```\n", "\n", "You can execute this from your IPython session as follows:\n", @@ -151,7 +151,7 @@ "\n", "```ipython\n", "In [8]: %timeit L = [n ** 2 for n in range(1000)]\n", - "1000 loops, best of 3: 325 µs per loop\n", + "430 µs ± 3.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n", "```\n", "\n", "The benefit of ``%timeit`` is that for short commands it will automatically perform multiple runs in order to attain more robust results.\n", @@ -164,7 +164,7 @@ " ...: for n in range(1000):\n", " ...: L.append(n ** 2)\n", " ...: \n", - "1000 loops, best of 3: 373 µs per loop\n", + "484 µs ± 5.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n", "```\n", "\n", "We can immediately see that list comprehensions are about 10% faster than the equivalent ``for``-loop construction in this case.\n", diff --git a/notebooks_v2/01.03-Magic-Commands.md b/notebooks_v2/01.03-Magic-Commands.md index 6dd3af53..15cebf8e 100644 --- a/notebooks_v2/01.03-Magic-Commands.md +++ b/notebooks_v2/01.03-Magic-Commands.md @@ -50,7 +50,7 @@ Consider the following simple function: ... return x ``` -The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into IPython you get an error: +The code is formatted as it would appear in the Python interpreter, and if you copy and paste this directly into older IPython versions, you get an error: ```ipython In [2]: >>> def donothing(x): @@ -110,7 +110,7 @@ def square(x): return x ** 2 for N in range(1, 4): - print(N, "squared is", square(N)) + print(f"{N} squared is {square(N)}") ``` You can execute this from your IPython session as follows: @@ -138,7 +138,7 @@ For example, we may want to check the performance of a list comprehension: ```ipython In [8]: %timeit L = [n ** 2 for n in range(1000)] -1000 loops, best of 3: 325 µs per loop +430 µs ± 3.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) ``` The benefit of ``%timeit`` is that for short commands it will automatically perform multiple runs in order to attain more robust results. @@ -151,7 +151,7 @@ In [9]: %%timeit ...: for n in range(1000): ...: L.append(n ** 2) ...: -1000 loops, best of 3: 373 µs per loop +484 µs ± 5.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) ``` We can immediately see that list comprehensions are about 10% faster than the equivalent ``for``-loop construction in this case. diff --git a/notebooks_v2/01.04-Input-Output-History.ipynb b/notebooks_v2/01.04-Input-Output-History.ipynb index 7e827a5f..b3f2b58b 100644 --- a/notebooks_v2/01.04-Input-Output-History.ipynb +++ b/notebooks_v2/01.04-Input-Output-History.ipynb @@ -44,7 +44,7 @@ "source": [ "## IPython's ``In`` and ``Out`` Objects\n", "\n", - "By now I imagine you're quite familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython.\n", + "By now I imagine you're becoming familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython.\n", "But it turns out that these are not just pretty decoration: they give a clue as to how you can access previous inputs and outputs in your current session.\n", "Imagine you start a session that looks like this:\n", "\n", @@ -67,11 +67,14 @@ "These inputs and outputs are displayed in the shell with ``In``/``Out`` labels, but there's more–IPython actually creates some Python variables called ``In`` and ``Out`` that are automatically updated to reflect this history:\n", "\n", "```ipython\n", - "In [4]: print(In)\n", - "['', 'import math', 'math.sin(2)', 'math.cos(2)', 'print(In)']\n", + "In [4]: In\n", + "Out[4]: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In']\n", "\n", "In [5]: Out\n", - "Out[5]: {2: 0.9092974268256817, 3: -0.4161468365471424}\n", + "Out[5]:\n", + "{2: 0.9092974268256817,\n", + " 3: -0.4161468365471424,\n", + " 4: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In', 'Out']}\n", "```" ] }, @@ -157,7 +160,7 @@ "In [14]: math.sin(2) + math.cos(2);\n", "```\n", "\n", - "Note that the result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary:\n", + "The result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary:\n", "\n", "```ipython\n", "In [15]: 14 in Out\n", @@ -174,11 +177,10 @@ "Here is how you can print the first four inputs:\n", "\n", "```ipython\n", - "In [16]: %history -n 1-4\n", + "In [16]: %history -n 1-3\n", " 1: import math\n", " 2: math.sin(2)\n", " 3: math.cos(2)\n", - " 4: print(In)\n", "```\n", "\n", "As usual, you can type ``%history?`` for more information and a description of options available.\n", diff --git a/notebooks_v2/01.04-Input-Output-History.md b/notebooks_v2/01.04-Input-Output-History.md index a885909d..3194ab06 100644 --- a/notebooks_v2/01.04-Input-Output-History.md +++ b/notebooks_v2/01.04-Input-Output-History.md @@ -38,7 +38,7 @@ We'll explore those here. ## IPython's ``In`` and ``Out`` Objects -By now I imagine you're quite familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython. +By now I imagine you're becoming familiar with the ``In [1]:``/``Out[1]:`` style prompts used by IPython. But it turns out that these are not just pretty decoration: they give a clue as to how you can access previous inputs and outputs in your current session. Imagine you start a session that looks like this: @@ -57,11 +57,14 @@ We've imported the built-in ``math`` package, then computed the sine and the cos These inputs and outputs are displayed in the shell with ``In``/``Out`` labels, but there's more–IPython actually creates some Python variables called ``In`` and ``Out`` that are automatically updated to reflect this history: ```ipython -In [4]: print(In) -['', 'import math', 'math.sin(2)', 'math.cos(2)', 'print(In)'] +In [4]: In +Out[4]: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In'] In [5]: Out -Out[5]: {2: 0.9092974268256817, 3: -0.4161468365471424} +Out[5]: +{2: 0.9092974268256817, + 3: -0.4161468365471424, + 4: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In', 'Out']} ``` @@ -135,7 +138,7 @@ The easiest way to suppress the output of a command is to add a semicolon to the In [14]: math.sin(2) + math.cos(2); ``` -Note that the result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary: +The result is computed silently, and the output is neither displayed on the screen or stored in the ``Out`` dictionary: ```ipython In [15]: 14 in Out @@ -148,11 +151,10 @@ For accessing a batch of previous inputs at once, the ``%history`` magic command Here is how you can print the first four inputs: ```ipython -In [16]: %history -n 1-4 +In [16]: %history -n 1-3 1: import math 2: math.sin(2) 3: math.cos(2) - 4: print(In) ``` As usual, you can type ``%history?`` for more information and a description of options available. From 75160a70d03618a94a0dcca0866b53f0bac3f5e0 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Thu, 11 Mar 2021 06:37:39 -0800 Subject: [PATCH 05/14] update 01.05 --- .../01.05-IPython-And-Shell-Commands.ipynb | 18 +++++++++--------- .../01.05-IPython-And-Shell-Commands.md | 18 +++++++++--------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb b/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb index cfc3efe6..9e4f8711 100644 --- a/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb +++ b/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb @@ -38,7 +38,7 @@ "The magic happens with the exclamation point: anything appearing after ``!`` on a line will be executed not by the Python kernel, but by the system command-line.\n", "\n", "The following assumes you're on a Unix-like system, such as Linux or Mac OSX.\n", - "Some of the examples that follow will fail on Windows, which uses a different type of shell by default (though with the 2016 announcement of native Bash shells on Windows, soon this may no longer be an issue!).\n", + "Some of the examples that follow will fail on Windows, which uses a different type of shell by default, though if you use the *Windows Subsystem for Linux* the examples here should run correctly.\n", "If you're unfamiliar with shell commands, I'd suggest reviewing the [Shell Tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation." ] }, @@ -52,11 +52,11 @@ "The shell is a way to interact textually with your computer.\n", "Ever since the mid 1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating system through familiar clicking of menus and drag-and-drop movements.\n", "But operating systems existed long before these graphical user interfaces, and were primarily controlled through sequences of text input: at the prompt, the user would type a command, and the computer would do what the user told it to.\n", - "Those early prompt systems are the precursors of the shells and terminals that most active data scientists still use today.\n", + "Those early prompt systems are the precursors of the shells and terminals that most data scientists still use today.\n", "\n", "Someone unfamiliar with the shell might ask why you would bother with this, when many results can be accomplished by simply clicking on icons and menus.\n", "A shell user might reply with another question: why hunt icons and click menus when you can accomplish things much more easily by typing?\n", - "While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can intimidate the average computer user.\n", + "While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can be intimidating.\n", "\n", "As an example, here is a sample of a Linux/OSX shell session where a user explores, creates, and modifies directories and files on their system (``osx:~ $`` is the prompt, and everything after the ``$`` sign is the typed command; text that is preceded by a ``#`` is meant just as description, rather than something you would actually type in):\n", "\n", @@ -90,7 +90,7 @@ "```\n", "\n", "Notice that all of this is just a compact way to do familiar operations (navigating a directory structure, creating a directory, moving a file, etc.) by typing commands rather than clicking icons and menus.\n", - "Note that with just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations.\n", + "With just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations.\n", "It's when you go beyond these basics that the shell approach becomes really powerful." ] }, @@ -100,7 +100,7 @@ "source": [ "## Shell Commands in IPython\n", "\n", - "Any command that works at the command-line can be used in IPython by prefixing it with the ``!`` character.\n", + "Any standard shell command can be used directly in IPython by prefixing it with the ``!`` character.\n", "For example, the ``ls``, ``pwd``, and ``echo`` commands can be run as follows:\n", "\n", "```ipython\n", @@ -136,7 +136,7 @@ "['/Users/jakevdp/notebooks/tmp/myproject']\n", "```\n", "\n", - "Note that these results are not returned as lists, but as a special shell return type defined in IPython:\n", + "These results are not returned as lists, but as a special shell return type defined in IPython:\n", "\n", "```ipython\n", "In [8]: type(directory)\n", @@ -182,7 +182,7 @@ "/home/jake/projects/myproject\n", "```\n", "\n", - "The reason is that shell commands in the notebook are executed in a temporary subshell.\n", + "The reason is that shell commands in the notebook are executed in a temporary subshell that does not maintain state from command to command.\n", "If you'd like to change the working directory in a more enduring way, you can use the ``%cd`` magic command:\n", "\n", "```ipython\n", @@ -197,7 +197,7 @@ "/home/jake/projects/myproject\n", "```\n", "\n", - "This is known as an ``automagic`` function, and this behavior can be toggled with the ``%automagic`` magic function.\n", + "This is known as an ``automagic`` function, and the ability to execute such commands without an explicit `%` can be toggled with the ``%automagic`` magic function.\n", "\n", "Besides ``%cd``, other available shell-like magic functions are ``%cat``, ``%cp``, ``%env``, ``%ls``, ``%man``, ``%mkdir``, ``%more``, ``%mv``, ``%pwd``, ``%rm``, and ``%rmdir``, any of which can be used without the ``%`` sign if ``automagic`` is on.\n", "This makes it so that you can almost treat the IPython prompt as if it's a normal shell:\n", @@ -216,7 +216,7 @@ "In [20]: rm -r tmp\n", "```\n", "\n", - "This access to the shell from within the same terminal window as your Python session means that there is a lot less switching back and forth between interpreter and shell as you write your Python code." + "This access to the shell from within the same terminal window as your Python session lets you more naturally combine Python and the shell in your workflows with fewer context switches." ] }, { diff --git a/notebooks_v2/01.05-IPython-And-Shell-Commands.md b/notebooks_v2/01.05-IPython-And-Shell-Commands.md index 370d26ac..ed1e4bb8 100644 --- a/notebooks_v2/01.05-IPython-And-Shell-Commands.md +++ b/notebooks_v2/01.05-IPython-And-Shell-Commands.md @@ -36,7 +36,7 @@ IPython bridges this gap, and gives you a syntax for executing shell commands di The magic happens with the exclamation point: anything appearing after ``!`` on a line will be executed not by the Python kernel, but by the system command-line. The following assumes you're on a Unix-like system, such as Linux or Mac OSX. -Some of the examples that follow will fail on Windows, which uses a different type of shell by default (though with the 2016 announcement of native Bash shells on Windows, soon this may no longer be an issue!). +Some of the examples that follow will fail on Windows, which uses a different type of shell by default, though if you use the *Windows Subsystem for Linux* the examples here should run correctly. If you're unfamiliar with shell commands, I'd suggest reviewing the [Shell Tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation. @@ -46,11 +46,11 @@ A full intro to using the shell/terminal/command-line is well beyond the scope o The shell is a way to interact textually with your computer. Ever since the mid 1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating system through familiar clicking of menus and drag-and-drop movements. But operating systems existed long before these graphical user interfaces, and were primarily controlled through sequences of text input: at the prompt, the user would type a command, and the computer would do what the user told it to. -Those early prompt systems are the precursors of the shells and terminals that most active data scientists still use today. +Those early prompt systems are the precursors of the shells and terminals that most data scientists still use today. Someone unfamiliar with the shell might ask why you would bother with this, when many results can be accomplished by simply clicking on icons and menus. A shell user might reply with another question: why hunt icons and click menus when you can accomplish things much more easily by typing? -While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can intimidate the average computer user. +While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks, though admittedly the learning curve can be intimidating. As an example, here is a sample of a Linux/OSX shell session where a user explores, creates, and modifies directories and files on their system (``osx:~ $`` is the prompt, and everything after the ``$`` sign is the typed command; text that is preceded by a ``#`` is meant just as description, rather than something you would actually type in): @@ -84,13 +84,13 @@ myproject.txt ``` Notice that all of this is just a compact way to do familiar operations (navigating a directory structure, creating a directory, moving a file, etc.) by typing commands rather than clicking icons and menus. -Note that with just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations. +With just a few commands (``pwd``, ``ls``, ``cd``, ``mkdir``, and ``cp``) you can do many of the most common file operations. It's when you go beyond these basics that the shell approach becomes really powerful. ## Shell Commands in IPython -Any command that works at the command-line can be used in IPython by prefixing it with the ``!`` character. +Any standard shell command can be used directly in IPython by prefixing it with the ``!`` character. For example, the ``ls``, ``pwd``, and ``echo`` commands can be run as follows: ```ipython @@ -122,7 +122,7 @@ In [7]: print(directory) ['/Users/jakevdp/notebooks/tmp/myproject'] ``` -Note that these results are not returned as lists, but as a special shell return type defined in IPython: +These results are not returned as lists, but as a special shell return type defined in IPython: ```ipython In [8]: type(directory) @@ -160,7 +160,7 @@ In [13]: !pwd /home/jake/projects/myproject ``` -The reason is that shell commands in the notebook are executed in a temporary subshell. +The reason is that shell commands in the notebook are executed in a temporary subshell that does not maintain state from command to command. If you'd like to change the working directory in a more enduring way, you can use the ``%cd`` magic command: ```ipython @@ -175,7 +175,7 @@ In [15]: cd myproject /home/jake/projects/myproject ``` -This is known as an ``automagic`` function, and this behavior can be toggled with the ``%automagic`` magic function. +This is known as an ``automagic`` function, and the ability to execute such commands without an explicit `%` can be toggled with the ``%automagic`` magic function. Besides ``%cd``, other available shell-like magic functions are ``%cat``, ``%cp``, ``%env``, ``%ls``, ``%man``, ``%mkdir``, ``%more``, ``%mv``, ``%pwd``, ``%rm``, and ``%rmdir``, any of which can be used without the ``%`` sign if ``automagic`` is on. This makes it so that you can almost treat the IPython prompt as if it's a normal shell: @@ -194,7 +194,7 @@ myproject.txt In [20]: rm -r tmp ``` -This access to the shell from within the same terminal window as your Python session means that there is a lot less switching back and forth between interpreter and shell as you write your Python code. +This access to the shell from within the same terminal window as your Python session lets you more naturally combine Python and the shell in your workflows with fewer context switches. From 153182a6eff8b25f54d3cc035d035f69a931a3e6 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Thu, 11 Mar 2021 06:51:32 -0800 Subject: [PATCH 06/14] Update 01.06 --- notebooks_v2/01.06-Errors-and-Debugging.ipynb | 51 ++++++++++++++----- notebooks_v2/01.06-Errors-and-Debugging.md | 24 ++++----- 2 files changed, 51 insertions(+), 24 deletions(-) diff --git a/notebooks_v2/01.06-Errors-and-Debugging.ipynb b/notebooks_v2/01.06-Errors-and-Debugging.ipynb index 2550e5b1..2da41e7d 100644 --- a/notebooks_v2/01.06-Errors-and-Debugging.ipynb +++ b/notebooks_v2/01.06-Errors-and-Debugging.ipynb @@ -53,7 +53,10 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -70,7 +73,10 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -95,7 +101,7 @@ "metadata": {}, "source": [ "Calling ``func2`` results in an error, and reading the printed trace lets us see exactly what happened.\n", - "By default, this trace includes several lines showing the context of each step that led to the error.\n", + "In the default mode, this trace includes several lines showing the context of each step that led to the error.\n", "Using the ``%xmode`` magic function (short for *Exception mode*), we can change what information is printed.\n", "\n", "``%xmode`` takes a single argument, the mode, and there are three possibilities: ``Plain``, ``Context``, and ``Verbose``.\n", @@ -107,7 +113,10 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -126,7 +135,10 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -157,7 +169,10 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -176,7 +191,10 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -203,7 +221,7 @@ "This extra information can help narrow-in on why the exception is being raised.\n", "So why not use the ``Verbose`` mode all the time?\n", "As code gets complicated, this kind of traceback can get extremely long.\n", - "Depending on the context, sometimes the brevity of ``Default`` mode is easier to work with." + "Depending on the context, sometimes the brevity of ``Plain`` or ``Context`` mode is easier to work with." ] }, { @@ -230,7 +248,10 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -265,7 +286,10 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -316,7 +340,10 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -377,7 +404,7 @@ "\n", "| Command | Description |\n", "|-----------------|-------------------------------------------------------------|\n", - "| ``list`` | Show the current location in the file |\n", + "| ``l(ist)`` | Show the current location in the file |\n", "| ``h(elp)`` | Show a list of commands, or find help on a specific command |\n", "| ``q(uit)`` | Quit the debugger and the program |\n", "| ``c(ontinue)`` | Quit the debugger, continue in the program |\n", diff --git a/notebooks_v2/01.06-Errors-and-Debugging.md b/notebooks_v2/01.06-Errors-and-Debugging.md index 697ca76d..26a9c06e 100644 --- a/notebooks_v2/01.06-Errors-and-Debugging.md +++ b/notebooks_v2/01.06-Errors-and-Debugging.md @@ -42,7 +42,7 @@ When the interpreter hits one of these exceptions, information about the cause o With the ``%xmode`` magic function, IPython allows you to control the amount of information printed when the exception is raised. Consider the following code: -```python +```python jupyter={"outputs_hidden": false} def func1(a, b): return a / b @@ -52,40 +52,40 @@ def func2(x): return func1(a, b) ``` -```python +```python jupyter={"outputs_hidden": false} func2(1) ``` Calling ``func2`` results in an error, and reading the printed trace lets us see exactly what happened. -By default, this trace includes several lines showing the context of each step that led to the error. +In the default mode, this trace includes several lines showing the context of each step that led to the error. Using the ``%xmode`` magic function (short for *Exception mode*), we can change what information is printed. ``%xmode`` takes a single argument, the mode, and there are three possibilities: ``Plain``, ``Context``, and ``Verbose``. The default is ``Context``, and gives output like that just shown before. ``Plain`` is more compact and gives less information: -```python +```python jupyter={"outputs_hidden": false} %xmode Plain ``` -```python +```python jupyter={"outputs_hidden": false} func2(1) ``` The ``Verbose`` mode adds some extra information, including the arguments to any functions that are called: -```python +```python jupyter={"outputs_hidden": false} %xmode Verbose ``` -```python +```python jupyter={"outputs_hidden": false} func2(1) ``` This extra information can help narrow-in on why the exception is being raised. So why not use the ``Verbose`` mode all the time? As code gets complicated, this kind of traceback can get extremely long. -Depending on the context, sometimes the brevity of ``Default`` mode is easier to work with. +Depending on the context, sometimes the brevity of ``Plain`` or ``Context`` mode is easier to work with. ## Debugging: When Reading Tracebacks Is Not Enough @@ -103,13 +103,13 @@ The ``ipdb`` prompt lets you explore the current state of the stack, explore the Let's look at the most recent exception, then do some basic tasks–print the values of ``a`` and ``b``, and type ``quit`` to quit the debugging session: -```python +```python jupyter={"outputs_hidden": false} %debug ``` The interactive debugger allows much more than this, though–we can even step up and down through the stack and explore the values of variables there: -```python +```python jupyter={"outputs_hidden": false} %debug ``` @@ -117,7 +117,7 @@ This allows you to quickly find out not only what caused the error, but what fun If you'd like the debugger to launch automatically whenever an exception is raised, you can use the ``%pdb`` magic function to turn on this automatic behavior: -```python +```python jupyter={"outputs_hidden": false} %xmode Plain %pdb on func2(1) @@ -132,7 +132,7 @@ There are many more available commands for interactive debugging than we've list | Command | Description | |-----------------|-------------------------------------------------------------| -| ``list`` | Show the current location in the file | +| ``l(ist)`` | Show the current location in the file | | ``h(elp)`` | Show a list of commands, or find help on a specific command | | ``q(uit)`` | Quit the debugger and the program | | ``c(ontinue)`` | Quit the debugger, continue in the program | From 4ee690779515b07f3cdf755caff4f4aa1c440a74 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Fri, 19 Mar 2021 06:50:42 -0700 Subject: [PATCH 07/14] Update 01.07 and 01.08 --- notebooks_v2/01.07-Timing-and-Profiling.ipynb | 158 +++++++++--------- notebooks_v2/01.07-Timing-and-Profiling.md | 61 +------ .../01.08-More-IPython-Resources.ipynb | 6 +- notebooks_v2/01.08-More-IPython-Resources.md | 2 +- 4 files changed, 89 insertions(+), 138 deletions(-) diff --git a/notebooks_v2/01.07-Timing-and-Profiling.ipynb b/notebooks_v2/01.07-Timing-and-Profiling.ipynb index 2d716b38..cab56ddd 100644 --- a/notebooks_v2/01.07-Timing-and-Profiling.ipynb +++ b/notebooks_v2/01.07-Timing-and-Profiling.ipynb @@ -37,7 +37,7 @@ "Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, \"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.\"\n", "\n", "But once you have your code working, it can be useful to dig into its efficiency a bit.\n", - "Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to dig into a multiline process and determine where the bottleneck lies in some complicated series of operations.\n", + "Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to examine a multiline process and determine where the bottleneck lies in some complicated series of operations.\n", "IPython provides access to a wide array of functionality for this kind of timing and profiling of code.\n", "Here we'll discuss the following IPython magic commands:\n", "\n", @@ -69,7 +69,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "100000 loops, best of 3: 1.54 µs per loop\n" + "1.53 µs ± 47.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], @@ -94,7 +94,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 loops, best of 3: 407 ms per loop\n" + "536 ms ± 15.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -124,7 +124,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "100 loops, best of 3: 1.9 ms per loop\n" + "1.71 ms ± 334 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], @@ -152,8 +152,8 @@ "output_type": "stream", "text": [ "sorting an unsorted list:\n", - "CPU times: user 40.6 ms, sys: 896 µs, total: 41.5 ms\n", - "Wall time: 41.5 ms\n" + "CPU times: user 31.3 ms, sys: 686 µs, total: 32 ms\n", + "Wall time: 33.3 ms\n" ] } ], @@ -174,8 +174,8 @@ "output_type": "stream", "text": [ "sorting an already sorted list:\n", - "CPU times: user 8.18 ms, sys: 10 µs, total: 8.19 ms\n", - "Wall time: 8.24 ms\n" + "CPU times: user 5.19 ms, sys: 268 µs, total: 5.46 ms\n", + "Wall time: 14.1 ms\n" ] } ], @@ -205,8 +205,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 504 ms, sys: 979 µs, total: 505 ms\n", - "Wall time: 505 ms\n" + "CPU times: user 655 ms, sys: 5.68 ms, total: 661 ms\n", + "Wall time: 710 ms\n" ] } ], @@ -269,6 +269,25 @@ "text": [ " " ] + }, + { + "data": { + "text/plain": [ + " 14 function calls in 0.932 seconds\n", + "\n", + " Ordered by: internal time\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 5 0.808 0.162 0.808 0.162 :4()\n", + " 5 0.066 0.013 0.066 0.013 {built-in method builtins.sum}\n", + " 1 0.044 0.044 0.918 0.918 :1(sum_of_lists)\n", + " 1 0.014 0.014 0.932 0.932 :1()\n", + " 1 0.000 0.000 0.932 0.932 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -279,21 +298,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the notebook, the output is printed to the pager, and looks something like this:\n", - "\n", - "```\n", - "14 function calls in 0.714 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 5 0.599 0.120 0.599 0.120 :4()\n", - " 5 0.064 0.013 0.064 0.013 {built-in method sum}\n", - " 1 0.036 0.036 0.699 0.699 :1(sum_of_lists)\n", - " 1 0.014 0.014 0.714 0.714 :1()\n", - " 1 0.000 0.000 0.714 0.714 {built-in method exec}\n", - "```\n", - "\n", "The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of execution time is in the list comprehension inside ``sum_of_lists``.\n", "From here, we could start thinking about what changes we might make to improve the performance in the algorithm.\n", "\n", @@ -337,7 +341,30 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Timer unit: 1e-06 s\n", + "\n", + "Total time: 0.014803 s\n", + "File: \n", + "Function: sum_of_lists at line 1\n", + "\n", + "Line # Hits Time Per Hit % Time Line Contents\n", + "==============================================================\n", + " 1 def sum_of_lists(N):\n", + " 2 1 6.0 6.0 0.0 total = 0\n", + " 3 6 13.0 2.2 0.1 for i in range(5):\n", + " 4 5 14242.0 2848.4 96.2 L = [j ^ (j >> i) for j in range(N)]\n", + " 5 5 541.0 108.2 3.7 total += sum(L)\n", + " 6 1 1.0 1.0 0.0 return total" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%lprun -f sum_of_lists sum_of_lists(5000)" ] @@ -346,25 +373,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As before, the notebook sends the result to the pager, but it looks something like this:\n", - "\n", - "```\n", - "Timer unit: 1e-06 s\n", - "\n", - "Total time: 0.009382 s\n", - "File: \n", - "Function: sum_of_lists at line 1\n", - "\n", - "Line # Hits Time Per Hit % Time Line Contents\n", - "==============================================================\n", - " 1 def sum_of_lists(N):\n", - " 2 1 2 2.0 0.0 total = 0\n", - " 3 6 8 1.3 0.1 for i in range(5):\n", - " 4 5 9001 1800.2 95.9 L = [j ^ (j >> i) for j in range(N)]\n", - " 5 5 371 74.2 4.0 total += sum(L)\n", - " 6 1 0 0.0 0.0 return total\n", - "```\n", - "\n", "The information at the top gives us the key to reading the results: the time is reported in microseconds and we can see where the program is spending the most time.\n", "At this point, we may be able to use this information to modify aspects of the script and make it perform better for our desired use case.\n", "\n", @@ -390,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -407,14 +415,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "peak memory: 100.08 MiB, increment: 61.36 MiB\n" + "peak memory: 141.70 MiB, increment: 75.65 MiB\n" ] } ], @@ -426,7 +434,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We see that this function uses about 100 MB of memory.\n", + "We see that this function uses about 140 MB of memory.\n", "\n", "For a line-by-line description of memory use, we can use the ``%mprun`` magic.\n", "Unfortunately, this magic works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the ``%%file`` magic to create a simple module called ``mprun_demo.py``, which contains our ``sum_of_lists`` function, with one addition that will make our memory profiling results more clear:" @@ -434,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -465,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -474,6 +482,25 @@ "text": [ "\n" ] + }, + { + "data": { + "text/plain": [ + "Filename: /Users/jakevdp/github/jakevdp/PythonDataScienceHandbook/notebooks_v2/mprun_demo.py\n", + "\n", + "Line # Mem usage Increment Occurences Line Contents\n", + "============================================================\n", + " 1 66.7 MiB 66.7 MiB 1 def sum_of_lists(N):\n", + " 2 66.7 MiB 0.0 MiB 1 total = 0\n", + " 3 75.1 MiB 8.4 MiB 6 for i in range(5):\n", + " 4 105.9 MiB 30.8 MiB 5000015 L = [j ^ (j >> i) for j in range(N)]\n", + " 5 109.8 MiB 3.8 MiB 5 total += sum(L)\n", + " 6 75.1 MiB -34.6 MiB 5 del L # remove reference to L\n", + " 7 66.9 MiB -8.2 MiB 1 return total" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -485,28 +512,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The result, printed to the pager, gives us a summary of the memory use of the function, and looks something like this:\n", - "```\n", - "Filename: ./mprun_demo.py\n", - "\n", - "Line # Mem usage Increment Line Contents\n", - "================================================\n", - " 4 71.9 MiB 0.0 MiB L = [j ^ (j >> i) for j in range(N)]\n", - "\n", - "\n", - "Filename: ./mprun_demo.py\n", - "\n", - "Line # Mem usage Increment Line Contents\n", - "================================================\n", - " 1 39.0 MiB 0.0 MiB def sum_of_lists(N):\n", - " 2 39.0 MiB 0.0 MiB total = 0\n", - " 3 46.5 MiB 7.5 MiB for i in range(5):\n", - " 4 71.9 MiB 25.4 MiB L = [j ^ (j >> i) for j in range(N)]\n", - " 5 71.9 MiB 0.0 MiB total += sum(L)\n", - " 6 46.5 MiB -25.4 MiB del L # remove reference to L\n", - " 7 39.1 MiB -7.4 MiB return total\n", - "```\n", - "Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 25 MB of memory usage.\n", + "Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 30 MB of memory usage.\n", "This is on top of the background memory usage from the Python interpreter itself.\n", "\n", "For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt)." @@ -529,7 +535,7 @@ "formats": "ipynb,md" }, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -543,9 +549,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.07-Timing-and-Profiling.md b/notebooks_v2/01.07-Timing-and-Profiling.md index 3e9a82a0..edf75e43 100644 --- a/notebooks_v2/01.07-Timing-and-Profiling.md +++ b/notebooks_v2/01.07-Timing-and-Profiling.md @@ -8,7 +8,7 @@ jupyter: format_version: '1.3' jupytext_version: 1.10.3 kernelspec: - display_name: Python [default] + display_name: Python 3 language: python name: python3 --- @@ -35,7 +35,7 @@ In the process of developing code and creating data processing pipelines, there Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, "We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil." But once you have your code working, it can be useful to dig into its efficiency a bit. -Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to dig into a multiline process and determine where the bottleneck lies in some complicated series of operations. +Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to examine a multiline process and determine where the bottleneck lies in some complicated series of operations. IPython provides access to a wide array of functionality for this kind of timing and profiling of code. Here we'll discuss the following IPython magic commands: @@ -133,21 +133,6 @@ Now we can call ``%prun`` with a function call to see the profiled results: %prun sum_of_lists(1000000) ``` -In the notebook, the output is printed to the pager, and looks something like this: - -``` -14 function calls in 0.714 seconds - - Ordered by: internal time - - ncalls tottime percall cumtime percall filename:lineno(function) - 5 0.599 0.120 0.599 0.120 :4() - 5 0.064 0.013 0.064 0.013 {built-in method sum} - 1 0.036 0.036 0.699 0.699 :1(sum_of_lists) - 1 0.014 0.014 0.714 0.714 :1() - 1 0.000 0.000 0.714 0.714 {built-in method exec} -``` - The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of execution time is in the list comprehension inside ``sum_of_lists``. From here, we could start thinking about what changes we might make to improve the performance in the algorithm. @@ -176,25 +161,6 @@ Now the ``%lprun`` command will do a line-by-line profiling of any function–in %lprun -f sum_of_lists sum_of_lists(5000) ``` -As before, the notebook sends the result to the pager, but it looks something like this: - -``` -Timer unit: 1e-06 s - -Total time: 0.009382 s -File: -Function: sum_of_lists at line 1 - -Line # Hits Time Per Hit % Time Line Contents -============================================================== - 1 def sum_of_lists(N): - 2 1 2 2.0 0.0 total = 0 - 3 6 8 1.3 0.1 for i in range(5): - 4 5 9001 1800.2 95.9 L = [j ^ (j >> i) for j in range(N)] - 5 5 371 74.2 4.0 total += sum(L) - 6 1 0 0.0 0.0 return total -``` - The information at the top gives us the key to reading the results: the time is reported in microseconds and we can see where the program is spending the most time. At this point, we may be able to use this information to modify aspects of the script and make it perform better for our desired use case. @@ -224,7 +190,7 @@ The ``%memit`` function can be used rather simply: %memit sum_of_lists(1000000) ``` -We see that this function uses about 100 MB of memory. +We see that this function uses about 140 MB of memory. For a line-by-line description of memory use, we can use the ``%mprun`` magic. Unfortunately, this magic works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the ``%%file`` magic to create a simple module called ``mprun_demo.py``, which contains our ``sum_of_lists`` function, with one addition that will make our memory profiling results more clear: @@ -247,27 +213,6 @@ from mprun_demo import sum_of_lists %mprun -f sum_of_lists sum_of_lists(1000000) ``` -The result, printed to the pager, gives us a summary of the memory use of the function, and looks something like this: -``` -Filename: ./mprun_demo.py - -Line # Mem usage Increment Line Contents -================================================ - 4 71.9 MiB 0.0 MiB L = [j ^ (j >> i) for j in range(N)] - - -Filename: ./mprun_demo.py - -Line # Mem usage Increment Line Contents -================================================ - 1 39.0 MiB 0.0 MiB def sum_of_lists(N): - 2 39.0 MiB 0.0 MiB total = 0 - 3 46.5 MiB 7.5 MiB for i in range(5): - 4 71.9 MiB 25.4 MiB L = [j ^ (j >> i) for j in range(N)] - 5 71.9 MiB 0.0 MiB total += sum(L) - 6 46.5 MiB -25.4 MiB del L # remove reference to L - 7 39.1 MiB -7.4 MiB return total -``` Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 25 MB of memory usage. This is on top of the background memory usage from the Python interpreter itself. diff --git a/notebooks_v2/01.08-More-IPython-Resources.ipynb b/notebooks_v2/01.08-More-IPython-Resources.ipynb index 91d083e8..359f6f4e 100644 --- a/notebooks_v2/01.08-More-IPython-Resources.ipynb +++ b/notebooks_v2/01.08-More-IPython-Resources.ipynb @@ -59,7 +59,7 @@ "- [*Learning IPython for Interactive Computing and Data Visualization*](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.\n", "- [*IPython Interactive Computing and Visualization Cookbook*](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython–it also goes into some depth on a broad range of data science topics.\n", "\n", - "Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very useful if you use it well and use it often.\n", + "Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be useful if you use it well and use it often.\n", "As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer." ] }, @@ -94,9 +94,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.08-More-IPython-Resources.md b/notebooks_v2/01.08-More-IPython-Resources.md index 6df81915..3e5a5513 100644 --- a/notebooks_v2/01.08-More-IPython-Resources.md +++ b/notebooks_v2/01.08-More-IPython-Resources.md @@ -49,7 +49,7 @@ Much more information is available both in print and on the Web, and here we'll - [*Learning IPython for Interactive Computing and Data Visualization*](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis. - [*IPython Interactive Computing and Visualization Cookbook*](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython–it also goes into some depth on a broad range of data science topics. -Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very useful if you use it well and use it often. +Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be useful if you use it well and use it often. As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer. From 175a5dc1f3161f49dcbc954f88dea18d0e5d1414 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Fri, 19 Mar 2021 06:58:47 -0700 Subject: [PATCH 08/14] update requirements.txt to most recent versions of packages --- requirements.txt | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/requirements.txt b/requirements.txt index fe9cdd91..b7e0e372 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,12 @@ -numpy==1.11.1 -pandas==0.18.1 -scipy==0.17.1 -scikit-learn==0.17.1 -scikit-image==0.12.3 -pillow==3.4.2 -matplotlib==1.5.1 -seaborn==0.7.0 -jupyter -notebook +numpy==1.20.1 +pandas==1.2.3 +scipy==1.6.1 +scikit-learn==0.24.1 +scikit-image==0.18.1 +pillow==8.1.2 +matplotlib==3.3.4 +seaborn==0.11.1 +jupyterlab line_profiler memory_profiler numexpr From 7d64b946f551a4f41c2ab0c2de089b47637cf331 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Tue, 5 Oct 2021 05:52:15 -0700 Subject: [PATCH 09/14] Update requirements --- requirements.txt | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/requirements.txt b/requirements.txt index b7e0e372..f782b32c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,11 @@ -numpy==1.20.1 -pandas==1.2.3 -scipy==1.6.1 -scikit-learn==0.24.1 -scikit-image==0.18.1 -pillow==8.1.2 -matplotlib==3.3.4 -seaborn==0.11.1 +numpy==1.21.2 +pandas==1.3.3 +scipy==1.7.1 +scikit-learn==1.0.0 +scikit-image==0.18.3 +pillow==8.3.2 +matplotlib==3.4.3 +seaborn==0.11.2 jupyterlab line_profiler memory_profiler From 231dc690f729814480909643b6c3f3cb1084fad3 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Tue, 5 Oct 2021 06:45:46 -0700 Subject: [PATCH 10/14] Update 02.00, 02.02, 02.02 --- .../02.00-Introduction-to-NumPy.ipynb | 16 +- notebooks_v2/02.00-Introduction-to-NumPy.md | 6 +- .../02.01-Understanding-Data-Types.ipynb | 169 ++-- .../02.01-Understanding-Data-Types.md | 60 +- .../02.02-The-Basics-Of-NumPy-Arrays.ipynb | 746 ++++++++++-------- .../02.02-The-Basics-Of-NumPy-Arrays.md | 209 +++-- 6 files changed, 673 insertions(+), 533 deletions(-) diff --git a/notebooks_v2/02.00-Introduction-to-NumPy.ipynb b/notebooks_v2/02.00-Introduction-to-NumPy.ipynb index 40e06ba0..bfd78208 100644 --- a/notebooks_v2/02.00-Introduction-to-NumPy.ipynb +++ b/notebooks_v2/02.00-Introduction-to-NumPy.ipynb @@ -73,13 +73,16 @@ "metadata": { "collapsed": false, "deletable": true, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "'1.11.1'" + "'1.21.2'" ] }, "execution_count": 1, @@ -109,7 +112,10 @@ "metadata": { "collapsed": false, "deletable": true, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -186,9 +192,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/02.00-Introduction-to-NumPy.md b/notebooks_v2/02.00-Introduction-to-NumPy.md index 3e9d767a..b418515c 100644 --- a/notebooks_v2/02.00-Introduction-to-NumPy.md +++ b/notebooks_v2/02.00-Introduction-to-NumPy.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python @@ -57,7 +57,7 @@ If you're more the do-it-yourself type, you can go to http://www.numpy.org/ and Once you do, you can import NumPy and double-check the version: -```python deletable=true editable=true +```python deletable=true editable=true jupyter={"outputs_hidden": false} import numpy numpy.__version__ ``` @@ -67,7 +67,7 @@ For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias: -```python deletable=true editable=true +```python deletable=true editable=true jupyter={"outputs_hidden": false} import numpy as np ``` diff --git a/notebooks_v2/02.01-Understanding-Data-Types.ipynb b/notebooks_v2/02.01-Understanding-Data-Types.ipynb index 22977418..00537574 100644 --- a/notebooks_v2/02.01-Understanding-Data-Types.ipynb +++ b/notebooks_v2/02.01-Understanding-Data-Types.ipynb @@ -57,7 +57,7 @@ " result += i\n", "```\n", "\n", - "Notice the main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:\n", + "Notice one main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:\n", "\n", "```python\n", "# Python code\n", @@ -86,7 +86,7 @@ "\n", "The standard Python implementation is written in C.\n", "This means that every Python object is simply a cleverly-disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as ``x = 10000``, ``x`` is not just a \"raw\" integer. It's actually a pointer to a compound C structure, which contains several values.\n", - "Looking through the Python 3.4 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):\n", + "Looking through the Python 3.10 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):\n", "\n", "```C\n", "struct _longobject {\n", @@ -97,7 +97,7 @@ "};\n", "```\n", "\n", - "A single integer in Python 3.4 actually contains four pieces:\n", + "A single integer in Python 3.10 actually contains four pieces:\n", "\n", "- ``ob_refcnt``, a reference count that helps Python silently handle memory allocation and deallocation\n", "- ``ob_type``, which encodes the type of the variable\n", @@ -141,7 +141,10 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -164,7 +167,10 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -193,7 +199,10 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -216,7 +225,10 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -245,7 +257,10 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -304,7 +319,10 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -342,7 +360,10 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -362,7 +383,10 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -393,13 +417,16 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 3.14, 4. , 2. , 3. ])" + "array([3.14, 4. , 2. , 3. ])" ] }, "execution_count": 9, @@ -422,13 +449,16 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 1., 2., 3., 4.], dtype=float32)" + "array([1., 2., 3., 4.], dtype=float32)" ] }, "execution_count": 10, @@ -437,7 +467,7 @@ } ], "source": [ - "np.array([1, 2, 3, 4], dtype='float32')" + "np.array([1, 2, 3, 4], dtype=np.float32)" ] }, { @@ -451,7 +481,10 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -493,7 +526,10 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -516,15 +552,18 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1., 1., 1., 1., 1.],\n", - " [ 1., 1., 1., 1., 1.],\n", - " [ 1., 1., 1., 1., 1.]])" + "array([[1., 1., 1., 1., 1.],\n", + " [1., 1., 1., 1., 1.],\n", + " [1., 1., 1., 1., 1.]])" ] }, "execution_count": 13, @@ -541,15 +580,18 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 3.14, 3.14, 3.14, 3.14, 3.14],\n", - " [ 3.14, 3.14, 3.14, 3.14, 3.14],\n", - " [ 3.14, 3.14, 3.14, 3.14, 3.14]])" + "array([[3.14, 3.14, 3.14, 3.14, 3.14],\n", + " [3.14, 3.14, 3.14, 3.14, 3.14],\n", + " [3.14, 3.14, 3.14, 3.14, 3.14]])" ] }, "execution_count": 14, @@ -566,7 +608,10 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -591,13 +636,16 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 0. , 0.25, 0.5 , 0.75, 1. ])" + "array([0. , 0.25, 0.5 , 0.75, 1. ])" ] }, "execution_count": 16, @@ -614,15 +662,18 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.99844933, 0.52183819, 0.22421193],\n", - " [ 0.08007488, 0.45429293, 0.20941444],\n", - " [ 0.14360941, 0.96910973, 0.946117 ]])" + "array([[0.09610171, 0.88193001, 0.70548015],\n", + " [0.35885395, 0.91670468, 0.8721031 ],\n", + " [0.73237865, 0.09708562, 0.52506779]])" ] }, "execution_count": 17, @@ -632,7 +683,7 @@ ], "source": [ "# Create a 3x3 array of uniformly distributed\n", - "# random values between 0 and 1\n", + "# pseudo-random values between 0 and 1\n", "np.random.random((3, 3))" ] }, @@ -640,15 +691,18 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1.51772646, 0.39614948, -0.10634696],\n", - " [ 0.25671348, 0.00732722, 0.37783601],\n", - " [ 0.68446945, 0.15926039, -0.70744073]])" + "array([[-0.46652655, -0.59158776, -1.05392451],\n", + " [-1.72634268, 0.03194069, -0.51048869],\n", + " [ 1.41240208, 1.77734462, -0.43820037]])" ] }, "execution_count": 18, @@ -657,8 +711,8 @@ } ], "source": [ - "# Create a 3x3 array of normally distributed random values\n", - "# with mean 0 and standard deviation 1\n", + "# Create a 3x3 array of normally distributed pseudo-random\n", + "# values with mean 0 and standard deviation 1\n", "np.random.normal(0, 1, (3, 3))" ] }, @@ -666,15 +720,18 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[2, 3, 4],\n", - " [5, 7, 8],\n", - " [0, 5, 0]])" + "array([[4, 3, 8],\n", + " [6, 5, 0],\n", + " [1, 1, 4]])" ] }, "execution_count": 19, @@ -683,7 +740,7 @@ } ], "source": [ - "# Create a 3x3 array of random integers in the interval [0, 10)\n", + "# Create a 3x3 array of pseudo-random integers in the interval [0, 10)\n", "np.random.randint(0, 10, (3, 3))" ] }, @@ -691,15 +748,18 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1., 0., 0.],\n", - " [ 0., 1., 0.],\n", - " [ 0., 0., 1.]])" + "array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]])" ] }, "execution_count": 20, @@ -716,13 +776,16 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 1., 1., 1.])" + "array([1., 1., 1.])" ] }, "execution_count": 21, @@ -825,9 +888,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/02.01-Understanding-Data-Types.md b/notebooks_v2/02.01-Understanding-Data-Types.md index c18d8220..0e6e6e77 100644 --- a/notebooks_v2/02.01-Understanding-Data-Types.md +++ b/notebooks_v2/02.01-Understanding-Data-Types.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python @@ -55,7 +55,7 @@ for i in range(100): result += i ``` -Notice the main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable: +Notice one main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable: ```python # Python code @@ -80,7 +80,7 @@ But what this type-flexibility also points to is the fact that Python variables The standard Python implementation is written in C. This means that every Python object is simply a cleverly-disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as ``x = 10000``, ``x`` is not just a "raw" integer. It's actually a pointer to a compound C structure, which contains several values. -Looking through the Python 3.4 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded): +Looking through the Python 3.10 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded): ```C struct _longobject { @@ -91,7 +91,7 @@ struct _longobject { }; ``` -A single integer in Python 3.4 actually contains four pieces: +A single integer in Python 3.10 actually contains four pieces: - ``ob_refcnt``, a reference count that helps Python silently handle memory allocation and deallocation - ``ob_type``, which encodes the type of the variable @@ -118,29 +118,29 @@ Let's consider now what happens when we use a Python data structure that holds m The standard mutable multi-element container in Python is the list. We can create a list of integers as follows: -```python +```python jupyter={"outputs_hidden": false} L = list(range(10)) L ``` -```python +```python jupyter={"outputs_hidden": false} type(L[0]) ``` Or, similarly, a list of strings: -```python +```python jupyter={"outputs_hidden": false} L2 = [str(c) for c in L] L2 ``` -```python +```python jupyter={"outputs_hidden": false} type(L2[0]) ``` Because of Python's dynamic typing, we can even create heterogeneous lists: -```python +```python jupyter={"outputs_hidden": false} L3 = [True, "2", 3.0, 4] [type(item) for item in L3] ``` @@ -164,7 +164,7 @@ Fixed-type NumPy-style arrays lack this flexibility, but are much more efficient Python offers several different options for storing data in efficient, fixed-type data buffers. The built-in ``array`` module (available since Python 3.3) can be used to create dense arrays of a uniform type: -```python +```python jupyter={"outputs_hidden": false} import array L = list(range(10)) A = array.array('i', L) @@ -179,7 +179,7 @@ We will explore these operations in later sections; here we'll demonstrate sever We'll start with the standard NumPy import, under the alias ``np``: -```python +```python jupyter={"outputs_hidden": false} import numpy as np ``` @@ -187,7 +187,7 @@ import numpy as np First, we can use ``np.array`` to create arrays from Python lists: -```python +```python jupyter={"outputs_hidden": false} # integer array: np.array([1, 4, 2, 5, 3]) ``` @@ -195,19 +195,19 @@ np.array([1, 4, 2, 5, 3]) Remember that unlike Python lists, NumPy is constrained to arrays that all contain the same type. If types do not match, NumPy will upcast if possible (here, integers are up-cast to floating point): -```python +```python jupyter={"outputs_hidden": false} np.array([3.14, 4, 2, 3]) ``` If we want to explicitly set the data type of the resulting array, we can use the ``dtype`` keyword: -```python -np.array([1, 2, 3, 4], dtype='float32') +```python jupyter={"outputs_hidden": false} +np.array([1, 2, 3, 4], dtype=np.float32) ``` Finally, unlike Python lists, NumPy arrays can explicitly be multi-dimensional; here's one way of initializing a multidimensional array using a list of lists: -```python +```python jupyter={"outputs_hidden": false} # nested lists result in multi-dimensional arrays np.array([range(i, i + 3) for i in [2, 4, 6]]) ``` @@ -220,56 +220,56 @@ The inner lists are treated as rows of the resulting two-dimensional array. Especially for larger arrays, it is more efficient to create arrays from scratch using routines built into NumPy. Here are several examples: -```python +```python jupyter={"outputs_hidden": false} # Create a length-10 integer array filled with zeros np.zeros(10, dtype=int) ``` -```python +```python jupyter={"outputs_hidden": false} # Create a 3x5 floating-point array filled with ones np.ones((3, 5), dtype=float) ``` -```python +```python jupyter={"outputs_hidden": false} # Create a 3x5 array filled with 3.14 np.full((3, 5), 3.14) ``` -```python +```python jupyter={"outputs_hidden": false} # Create an array filled with a linear sequence # Starting at 0, ending at 20, stepping by 2 # (this is similar to the built-in range() function) np.arange(0, 20, 2) ``` -```python +```python jupyter={"outputs_hidden": false} # Create an array of five values evenly spaced between 0 and 1 np.linspace(0, 1, 5) ``` -```python +```python jupyter={"outputs_hidden": false} # Create a 3x3 array of uniformly distributed -# random values between 0 and 1 +# pseudo-random values between 0 and 1 np.random.random((3, 3)) ``` -```python -# Create a 3x3 array of normally distributed random values -# with mean 0 and standard deviation 1 +```python jupyter={"outputs_hidden": false} +# Create a 3x3 array of normally distributed pseudo-random +# values with mean 0 and standard deviation 1 np.random.normal(0, 1, (3, 3)) ``` -```python -# Create a 3x3 array of random integers in the interval [0, 10) +```python jupyter={"outputs_hidden": false} +# Create a 3x3 array of pseudo-random integers in the interval [0, 10) np.random.randint(0, 10, (3, 3)) ``` -```python +```python jupyter={"outputs_hidden": false} # Create a 3x3 identity matrix np.eye(3) ``` -```python +```python jupyter={"outputs_hidden": false} # Create an uninitialized array of three integers # The values will be whatever happens to already exist at that memory location np.empty(3) diff --git a/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.ipynb b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.ipynb index 2d5222ce..607e3194 100644 --- a/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.ipynb +++ b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.ipynb @@ -59,7 +59,7 @@ "metadata": {}, "source": [ "First let's discuss some useful array attributes.\n", - "We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.\n", + "We'll start by defining random arrays of one, two, and three dimensions.\n", "We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run:" ] }, @@ -67,30 +67,36 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ "import numpy as np\n", - "np.random.seed(0) # seed for reproducibility\n", + "rng = np.random.default_rng(seed=1701) # seed for reproducibility\n", "\n", - "x1 = np.random.randint(10, size=6) # One-dimensional array\n", - "x2 = np.random.randint(10, size=(3, 4)) # Two-dimensional array\n", - "x3 = np.random.randint(10, size=(3, 4, 5)) # Three-dimensional array" + "x1 = rng.integers(10, size=6) # One-dimensional array\n", + "x2 = rng.integers(10, size=(3, 4)) # Two-dimensional array\n", + "x3 = rng.integers(10, size=(3, 4, 5)) # Three-dimensional array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Each array has attributes ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array):" + "Each array has attributes including ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array), and `dtype` (the type of each element);" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -99,75 +105,23 @@ "text": [ "x3 ndim: 3\n", "x3 shape: (3, 4, 5)\n", - "x3 size: 60\n" + "x3 size: 60\n", + "dtype: int64\n" ] } ], "source": [ "print(\"x3 ndim: \", x3.ndim)\n", "print(\"x3 shape:\", x3.shape)\n", - "print(\"x3 size: \", x3.size)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another useful attribute is the ``dtype``, the data type of the array (which we discussed previously in [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)):" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dtype: int64\n" - ] - } - ], - "source": [ - "print(\"dtype:\", x3.dtype)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Other attributes include ``itemsize``, which lists the size (in bytes) of each array element, and ``nbytes``, which lists the total size (in bytes) of the array:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "itemsize: 8 bytes\n", - "nbytes: 480 bytes\n" - ] - } - ], - "source": [ - "print(\"itemsize:\", x3.itemsize, \"bytes\")\n", - "print(\"nbytes:\", x3.nbytes, \"bytes\")" + "print(\"x3 size: \", x3.size)\n", + "print(\"dtype: \", x3.dtype)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In general, we expect that ``nbytes`` is equal to ``itemsize`` times ``size``." + "For more discussion of `dtype`, see [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)):" ] }, { @@ -187,18 +141,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([5, 0, 3, 3, 7, 9])" + "array([9, 4, 0, 3, 8, 6])" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -209,18 +166,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "5" + "9" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -231,18 +191,21 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "7" + "8" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -260,18 +223,21 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "9" + "6" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -282,18 +248,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "7" + "8" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -306,25 +275,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices:" + "In a multi-dimensional array, items can be accessed using a comma-separated `(row, column)` tuple:" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[3, 5, 2, 4],\n", - " [7, 6, 8, 8],\n", - " [1, 6, 7, 7]])" + "array([[3, 1, 3, 7],\n", + " [4, 0, 2, 3],\n", + " [0, 0, 6, 9]])" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -335,9 +307,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -346,7 +321,7 @@ "3" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -357,18 +332,21 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "1" + "0" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -379,18 +357,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "7" + "9" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -408,20 +389,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[12, 5, 2, 4],\n", - " [ 7, 6, 8, 8],\n", - " [ 1, 6, 7, 7]])" + "array([[12, 1, 3, 7],\n", + " [ 4, 0, 2, 3],\n", + " [ 0, 0, 6, 9]])" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -441,18 +425,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([3, 0, 3, 3, 7, 9])" + "array([3, 4, 0, 3, 8, 6])" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -491,135 +478,152 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + "array([3, 4, 0, 3, 8, 6])" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = np.arange(10)\n", - "x" + "x1" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([0, 1, 2, 3, 4])" + "array([3, 4, 0])" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[:5] # first five elements" + "x1[:3] # first three elements" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([5, 6, 7, 8, 9])" + "array([3, 8, 6])" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[5:] # elements after index 5" + "x1[3:] # elements after index 3" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([4, 5, 6])" + "array([4, 0, 3])" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[4:7] # middle sub-array" + "x1[1:4] # middle sub-array" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([0, 2, 4, 6, 8])" + "array([3, 0, 8])" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[::2] # every other element" + "x1[::2] # every other element" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([1, 3, 5, 7, 9])" + "array([4, 3, 6])" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[1::2] # every other element, starting at index 1" + "x1[1::2] # every other element, starting at index 1" ] }, { @@ -633,46 +637,52 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])" + "array([6, 8, 3, 0, 4, 3])" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[::-1] # all elements, reversed" + "x1[::-1] # all elements, reversed" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([5, 3, 1])" + "array([8, 0, 3])" ] }, - "execution_count": 23, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x[5::-2] # reversed every other from index 5" + "x1[4::-2] # reversed every other from index 4" ] }, { @@ -687,20 +697,23 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[12, 5, 2, 4],\n", - " [ 7, 6, 8, 8],\n", - " [ 1, 6, 7, 7]])" + "array([[12, 1, 3, 7],\n", + " [ 4, 0, 2, 3],\n", + " [ 0, 0, 6, 9]])" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -711,80 +724,82 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[12, 5, 2],\n", - " [ 7, 6, 8]])" + "array([[12, 1, 3],\n", + " [ 4, 0, 2]])" ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x2[:2, :3] # two rows, three columns" + "x2[:2, :3] # first two rows & three columns" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[12, 2],\n", - " [ 7, 8],\n", - " [ 1, 7]])" + "array([[12, 3],\n", + " [ 4, 2],\n", + " [ 0, 6]])" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x2[:3, ::2] # all rows, every other column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, subarray dimensions can even be reversed together:" + "x2[:3, ::2] # three rows, every other column" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 7, 7, 6, 1],\n", - " [ 8, 8, 6, 7],\n", - " [ 4, 2, 5, 12]])" + "array([[ 9, 6, 0, 0],\n", + " [ 3, 2, 0, 4],\n", + " [ 7, 3, 1, 12]])" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x2[::-1, ::-1]" + "x2[::-1, ::-1] # all rows & columns, reversed" ] }, { @@ -799,40 +814,52 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[12 7 1]\n" - ] + "data": { + "text/plain": [ + "array([12, 4, 0])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(x2[:, 0]) # first column of x2" + "x2[:, 0] # first column of x2" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[12 5 2 4]\n" - ] + "data": { + "text/plain": [ + "array([12, 1, 3, 7])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(x2[0, :]) # first row of x2" + "x2[0, :] # first row of x2" ] }, { @@ -844,21 +871,27 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[12 5 2 4]\n" - ] + "data": { + "text/plain": [ + "array([12, 1, 3, 7])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(x2[0]) # equivalent to x2[0, :]" + "x2[0] # equivalent to x2[0, :]" ] }, { @@ -867,25 +900,27 @@ "source": [ "### Subarrays as no-copy views\n", "\n", - "One important–and extremely useful–thing to know about array slices is that they return *views* rather than *copies* of the array data.\n", - "This is one area in which NumPy array slicing differs from Python list slicing: in lists, slices will be copies.\n", + "Unlike Python list slices, NumPy array slices are returned as *views* rather than *copies* of the array data.\n", "Consider our two-dimensional array from before:" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[12 5 2 4]\n", - " [ 7 6 8 8]\n", - " [ 1 6 7 7]]\n" + "[[12 1 3 7]\n", + " [ 4 0 2 3]\n", + " [ 0 0 6 9]]\n" ] } ], @@ -902,17 +937,20 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[12 5]\n", - " [ 7 6]]\n" + "[[12 1]\n", + " [ 4 0]]\n" ] } ], @@ -930,17 +968,20 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[99 5]\n", - " [ 7 6]]\n" + "[[99 1]\n", + " [ 4 0]]\n" ] } ], @@ -951,18 +992,21 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[99 5 2 4]\n", - " [ 7 6 8 8]\n", - " [ 1 6 7 7]]\n" + "[[99 1 3 7]\n", + " [ 4 0 2 3]\n", + " [ 0 0 6 9]]\n" ] } ], @@ -974,7 +1018,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer." + "Some users may find this surprising, but it can be advantageous: for example, when working with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer." ] }, { @@ -988,17 +1032,20 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[99 5]\n", - " [ 7 6]]\n" + "[[99 1]\n", + " [ 4 0]]\n" ] } ], @@ -1016,17 +1063,20 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[42 5]\n", - " [ 7 6]]\n" + "[[42 1]\n", + " [ 4 0]]\n" ] } ], @@ -1037,18 +1087,21 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[99 5 2 4]\n", - " [ 7 6 8 8]\n", - " [ 1 6 7 7]]\n" + "[[99 1 3 7]\n", + " [ 4 0 2 3]\n", + " [ 0 0 6 9]]\n" ] } ], @@ -1062,16 +1115,18 @@ "source": [ "## Reshaping of Arrays\n", "\n", - "Another useful type of operation is reshaping of arrays.\n", - "The most flexible way of doing this is with the ``reshape`` method.\n", + "Another useful type of operation is reshaping of arrays, which can be done with the ``reshape`` method.\n", "For example, if you want to put the numbers 1 through 9 in a $3 \\times 3$ grid, you can do the following:" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 36, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1085,7 +1140,7 @@ } ], "source": [ - "grid = np.arange(1, 10).reshape((3, 3))\n", + "grid = np.arange(1, 10).reshape(3, 3)\n", "print(grid)" ] }, @@ -1093,18 +1148,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that for this to work, the size of the initial array must match the size of the reshaped array. \n", - "Where possible, the ``reshape`` method will use a no-copy view of the initial array, but with non-contiguous memory buffers this is not always the case.\n", - "\n", - "Another common reshaping pattern is the conversion of a one-dimensional array into a two-dimensional row or column matrix.\n", - "This can be done with the ``reshape`` method, or more easily done by making use of the ``newaxis`` keyword within a slice operation:" + "Note that for this to work, the size of the initial array must match the size of the reshaped array, and in most cases the ``reshape`` method will return a no-copy view of the initial array." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A common reshaping operation is converting a one-dimensional array into a two-dimensional row or column matrix:" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 37, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1113,71 +1174,78 @@ "array([[1, 2, 3]])" ] }, - "execution_count": 39, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.array([1, 2, 3])\n", - "\n", - "# row vector via reshape\n", - "x.reshape((1, 3))" + "x.reshape((1, 3)) # row vector via reshape" ] }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1, 2, 3]])" + "array([[1],\n", + " [2],\n", + " [3]])" ] }, - "execution_count": 40, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# row vector via newaxis\n", - "x[np.newaxis, :]" + "x.reshape((3, 1)) # column vector via reshape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A convenient shorthand for this is to use `np.newaxis` within a slicing syntax:" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 39, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[1],\n", - " [2],\n", - " [3]])" + "array([[1, 2, 3]])" ] }, - "execution_count": 41, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# column vector via reshape\n", - "x.reshape((3, 1))" + "x[np.newaxis, :] # row vector via newaxis" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 40, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1188,21 +1256,20 @@ " [3]])" ] }, - "execution_count": 42, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# column vector via newaxis\n", - "x[:, np.newaxis]" + "x[:, np.newaxis] # column vector via newaxis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will see this type of transformation often throughout the remainder of the book." + "This is a pattern that we will utilize often through the remainder of the book." ] }, { @@ -1211,7 +1278,7 @@ "source": [ "## Array Concatenation and Splitting\n", "\n", - "All of the preceding routines worked on single arrays. It's also possible to combine multiple arrays into one, and to conversely split a single array into multiple arrays. We'll take a look at those operations here." + "All of the preceding routines worked on single arrays. NumPy also provides tools to combine multiple arrays into one, and to conversely split a single array into multiple arrays." ] }, { @@ -1226,9 +1293,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 41, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1237,7 +1307,7 @@ "array([1, 2, 3, 3, 2, 1])" ] }, - "execution_count": 43, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1257,9 +1327,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 42, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1271,7 +1344,7 @@ } ], "source": [ - "z = [99, 99, 99]\n", + "z = np.array([99, 99, 99])\n", "print(np.concatenate([x, y, z]))" ] }, @@ -1284,9 +1357,12 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 43, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1296,9 +1372,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 44, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1310,7 +1389,7 @@ " [4, 5, 6]])" ] }, - "execution_count": 46, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1322,9 +1401,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 45, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1334,7 +1416,7 @@ " [4, 5, 6, 4, 5, 6]])" ] }, - "execution_count": 47, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1353,48 +1435,50 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", - " [9, 8, 7],\n", - " [6, 5, 4]])" + " [1, 2, 3],\n", + " [4, 5, 6]])" ] }, - "execution_count": 48, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = np.array([1, 2, 3])\n", - "grid = np.array([[9, 8, 7],\n", - " [6, 5, 4]])\n", - "\n", "# vertically stack the arrays\n", "np.vstack([x, grid])" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 9, 8, 7, 99],\n", - " [ 6, 5, 4, 99]])" + "array([[ 1, 2, 3, 99],\n", + " [ 4, 5, 6, 99]])" ] }, - "execution_count": 49, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1410,7 +1494,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similary, ``np.dstack`` will stack arrays along the third axis." + "Similary, for higher-dimensional arrays, ``np.dstack`` will stack arrays along the third axis." ] }, { @@ -1424,9 +1508,12 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 48, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1453,9 +1540,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 49, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1467,7 +1557,7 @@ " [12, 13, 14, 15]])" ] }, - "execution_count": 51, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1479,9 +1569,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1503,9 +1596,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 51, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1533,7 +1629,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similarly, ``np.dsplit`` will split arrays along the third axis." + "Similarly, for higher-dimensional arrays, ``np.dsplit`` will split arrays along the third axis." ] }, { @@ -1567,9 +1663,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.md b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.md index 34f95f6b..651f73bc 100644 --- a/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.md +++ b/notebooks_v2/02.02-The-Basics-Of-NumPy-Arrays.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python @@ -49,40 +49,28 @@ We'll cover a few categories of basic array manipulations here: First let's discuss some useful array attributes. -We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array. +We'll start by defining random arrays of one, two, and three dimensions. We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run: -```python +```python jupyter={"outputs_hidden": false} import numpy as np -np.random.seed(0) # seed for reproducibility +rng = np.random.default_rng(seed=1701) # seed for reproducibility -x1 = np.random.randint(10, size=6) # One-dimensional array -x2 = np.random.randint(10, size=(3, 4)) # Two-dimensional array -x3 = np.random.randint(10, size=(3, 4, 5)) # Three-dimensional array +x1 = rng.integers(10, size=6) # One-dimensional array +x2 = rng.integers(10, size=(3, 4)) # Two-dimensional array +x3 = rng.integers(10, size=(3, 4, 5)) # Three-dimensional array ``` -Each array has attributes ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array): +Each array has attributes including ``ndim`` (the number of dimensions), ``shape`` (the size of each dimension), and ``size`` (the total size of the array), and `dtype` (the type of each element); -```python +```python jupyter={"outputs_hidden": false} print("x3 ndim: ", x3.ndim) print("x3 shape:", x3.shape) print("x3 size: ", x3.size) +print("dtype: ", x3.dtype) ``` -Another useful attribute is the ``dtype``, the data type of the array (which we discussed previously in [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)): - -```python -print("dtype:", x3.dtype) -``` - -Other attributes include ``itemsize``, which lists the size (in bytes) of each array element, and ``nbytes``, which lists the total size (in bytes) of the array: - -```python -print("itemsize:", x3.itemsize, "bytes") -print("nbytes:", x3.nbytes, "bytes") -``` - -In general, we expect that ``nbytes`` is equal to ``itemsize`` times ``size``. +For more discussion of `dtype`, see [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)): ## Array Indexing: Accessing Single Elements @@ -91,49 +79,49 @@ In general, we expect that ``nbytes`` is equal to ``itemsize`` times ``size``. If you are familiar with Python's standard list indexing, indexing in NumPy will feel quite familiar. In a one-dimensional array, the $i^{th}$ value (counting from zero) can be accessed by specifying the desired index in square brackets, just as with Python lists: -```python +```python jupyter={"outputs_hidden": false} x1 ``` -```python +```python jupyter={"outputs_hidden": false} x1[0] ``` -```python +```python jupyter={"outputs_hidden": false} x1[4] ``` To index from the end of the array, you can use negative indices: -```python +```python jupyter={"outputs_hidden": false} x1[-1] ``` -```python +```python jupyter={"outputs_hidden": false} x1[-2] ``` -In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices: +In a multi-dimensional array, items can be accessed using a comma-separated `(row, column)` tuple: -```python +```python jupyter={"outputs_hidden": false} x2 ``` -```python +```python jupyter={"outputs_hidden": false} x2[0, 0] ``` -```python +```python jupyter={"outputs_hidden": false} x2[2, 0] ``` -```python +```python jupyter={"outputs_hidden": false} x2[2, -1] ``` Values can also be modified using any of the above index notation: -```python +```python jupyter={"outputs_hidden": false} x2[0, 0] = 12 x2 ``` @@ -141,7 +129,7 @@ x2 Keep in mind that, unlike Python lists, NumPy arrays have a fixed type. This means, for example, that if you attempt to insert a floating-point value to an integer array, the value will be silently truncated. Don't be caught unaware by this behavior! -```python +```python jupyter={"outputs_hidden": false} x1[0] = 3.14159 # this will be truncated! x1 ``` @@ -160,41 +148,40 @@ We'll take a look at accessing sub-arrays in one dimension and in multiple dimen ### One-dimensional subarrays -```python -x = np.arange(10) -x +```python jupyter={"outputs_hidden": false} +x1 ``` -```python -x[:5] # first five elements +```python jupyter={"outputs_hidden": false} +x1[:3] # first three elements ``` -```python -x[5:] # elements after index 5 +```python jupyter={"outputs_hidden": false} +x1[3:] # elements after index 3 ``` -```python -x[4:7] # middle sub-array +```python jupyter={"outputs_hidden": false} +x1[1:4] # middle sub-array ``` -```python -x[::2] # every other element +```python jupyter={"outputs_hidden": false} +x1[::2] # every other element ``` -```python -x[1::2] # every other element, starting at index 1 +```python jupyter={"outputs_hidden": false} +x1[1::2] # every other element, starting at index 1 ``` A potentially confusing case is when the ``step`` value is negative. In this case, the defaults for ``start`` and ``stop`` are swapped. This becomes a convenient way to reverse an array: -```python -x[::-1] # all elements, reversed +```python jupyter={"outputs_hidden": false} +x1[::-1] # all elements, reversed ``` -```python -x[5::-2] # reversed every other from index 5 +```python jupyter={"outputs_hidden": false} +x1[4::-2] # reversed every other from index 4 ``` ### Multi-dimensional subarrays @@ -202,22 +189,20 @@ x[5::-2] # reversed every other from index 5 Multi-dimensional slices work in the same way, with multiple slices separated by commas. For example: -```python +```python jupyter={"outputs_hidden": false} x2 ``` -```python -x2[:2, :3] # two rows, three columns +```python jupyter={"outputs_hidden": false} +x2[:2, :3] # first two rows & three columns ``` -```python -x2[:3, ::2] # all rows, every other column +```python jupyter={"outputs_hidden": false} +x2[:3, ::2] # three rows, every other column ``` -Finally, subarray dimensions can even be reversed together: - -```python -x2[::-1, ::-1] +```python jupyter={"outputs_hidden": false} +x2[::-1, ::-1] # all rows & columns, reversed ``` #### Accessing array rows and columns @@ -225,116 +210,110 @@ x2[::-1, ::-1] One commonly needed routine is accessing of single rows or columns of an array. This can be done by combining indexing and slicing, using an empty slice marked by a single colon (``:``): -```python -print(x2[:, 0]) # first column of x2 +```python jupyter={"outputs_hidden": false} +x2[:, 0] # first column of x2 ``` -```python -print(x2[0, :]) # first row of x2 +```python jupyter={"outputs_hidden": false} +x2[0, :] # first row of x2 ``` In the case of row access, the empty slice can be omitted for a more compact syntax: -```python -print(x2[0]) # equivalent to x2[0, :] +```python jupyter={"outputs_hidden": false} +x2[0] # equivalent to x2[0, :] ``` ### Subarrays as no-copy views -One important–and extremely useful–thing to know about array slices is that they return *views* rather than *copies* of the array data. -This is one area in which NumPy array slicing differs from Python list slicing: in lists, slices will be copies. +Unlike Python list slices, NumPy array slices are returned as *views* rather than *copies* of the array data. Consider our two-dimensional array from before: -```python +```python jupyter={"outputs_hidden": false} print(x2) ``` Let's extract a $2 \times 2$ subarray from this: -```python +```python jupyter={"outputs_hidden": false} x2_sub = x2[:2, :2] print(x2_sub) ``` Now if we modify this subarray, we'll see that the original array is changed! Observe: -```python +```python jupyter={"outputs_hidden": false} x2_sub[0, 0] = 99 print(x2_sub) ``` -```python +```python jupyter={"outputs_hidden": false} print(x2) ``` -This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer. +Some users may find this surprising, but it can be advantageous: for example, when working with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer. ### Creating copies of arrays Despite the nice features of array views, it is sometimes useful to instead explicitly copy the data within an array or a subarray. This can be most easily done with the ``copy()`` method: -```python +```python jupyter={"outputs_hidden": false} x2_sub_copy = x2[:2, :2].copy() print(x2_sub_copy) ``` If we now modify this subarray, the original array is not touched: -```python +```python jupyter={"outputs_hidden": false} x2_sub_copy[0, 0] = 42 print(x2_sub_copy) ``` -```python +```python jupyter={"outputs_hidden": false} print(x2) ``` ## Reshaping of Arrays -Another useful type of operation is reshaping of arrays. -The most flexible way of doing this is with the ``reshape`` method. +Another useful type of operation is reshaping of arrays, which can be done with the ``reshape`` method. For example, if you want to put the numbers 1 through 9 in a $3 \times 3$ grid, you can do the following: -```python -grid = np.arange(1, 10).reshape((3, 3)) +```python jupyter={"outputs_hidden": false} +grid = np.arange(1, 10).reshape(3, 3) print(grid) ``` -Note that for this to work, the size of the initial array must match the size of the reshaped array. -Where possible, the ``reshape`` method will use a no-copy view of the initial array, but with non-contiguous memory buffers this is not always the case. +Note that for this to work, the size of the initial array must match the size of the reshaped array, and in most cases the ``reshape`` method will return a no-copy view of the initial array. -Another common reshaping pattern is the conversion of a one-dimensional array into a two-dimensional row or column matrix. -This can be done with the ``reshape`` method, or more easily done by making use of the ``newaxis`` keyword within a slice operation: -```python -x = np.array([1, 2, 3]) +A common reshaping operation is converting a one-dimensional array into a two-dimensional row or column matrix: -# row vector via reshape -x.reshape((1, 3)) +```python jupyter={"outputs_hidden": false} +x = np.array([1, 2, 3]) +x.reshape((1, 3)) # row vector via reshape ``` ```python -# row vector via newaxis -x[np.newaxis, :] +x.reshape((3, 1)) # column vector via reshape ``` -```python -# column vector via reshape -x.reshape((3, 1)) +A convenient shorthand for this is to use `np.newaxis` within a slicing syntax: + +```python jupyter={"outputs_hidden": false} +x[np.newaxis, :] # row vector via newaxis ``` -```python -# column vector via newaxis -x[:, np.newaxis] +```python jupyter={"outputs_hidden": false} +x[:, np.newaxis] # column vector via newaxis ``` -We will see this type of transformation often throughout the remainder of the book. +This is a pattern that we will utilize often through the remainder of the book. ## Array Concatenation and Splitting -All of the preceding routines worked on single arrays. It's also possible to combine multiple arrays into one, and to conversely split a single array into multiple arrays. We'll take a look at those operations here. +All of the preceding routines worked on single arrays. NumPy also provides tools to combine multiple arrays into one, and to conversely split a single array into multiple arrays. ### Concatenation of arrays @@ -342,7 +321,7 @@ All of the preceding routines worked on single arrays. It's also possible to com Concatenation, or joining of two arrays in NumPy, is primarily accomplished using the routines ``np.concatenate``, ``np.vstack``, and ``np.hstack``. ``np.concatenate`` takes a tuple or list of arrays as its first argument, as we can see here: -```python +```python jupyter={"outputs_hidden": false} x = np.array([1, 2, 3]) y = np.array([3, 2, 1]) np.concatenate([x, y]) @@ -350,54 +329,50 @@ np.concatenate([x, y]) You can also concatenate more than two arrays at once: -```python -z = [99, 99, 99] +```python jupyter={"outputs_hidden": false} +z = np.array([99, 99, 99]) print(np.concatenate([x, y, z])) ``` It can also be used for two-dimensional arrays: -```python +```python jupyter={"outputs_hidden": false} grid = np.array([[1, 2, 3], [4, 5, 6]]) ``` -```python +```python jupyter={"outputs_hidden": false} # concatenate along the first axis np.concatenate([grid, grid]) ``` -```python +```python jupyter={"outputs_hidden": false} # concatenate along the second axis (zero-indexed) np.concatenate([grid, grid], axis=1) ``` For working with arrays of mixed dimensions, it can be clearer to use the ``np.vstack`` (vertical stack) and ``np.hstack`` (horizontal stack) functions: -```python -x = np.array([1, 2, 3]) -grid = np.array([[9, 8, 7], - [6, 5, 4]]) - +```python jupyter={"outputs_hidden": false} # vertically stack the arrays np.vstack([x, grid]) ``` -```python +```python jupyter={"outputs_hidden": false} # horizontally stack the arrays y = np.array([[99], [99]]) np.hstack([grid, y]) ``` -Similary, ``np.dstack`` will stack arrays along the third axis. +Similary, for higher-dimensional arrays, ``np.dstack`` will stack arrays along the third axis. ### Splitting of arrays The opposite of concatenation is splitting, which is implemented by the functions ``np.split``, ``np.hsplit``, and ``np.vsplit``. For each of these, we can pass a list of indices giving the split points: -```python +```python jupyter={"outputs_hidden": false} x = [1, 2, 3, 99, 99, 3, 2, 1] x1, x2, x3 = np.split(x, [3, 5]) print(x1, x2, x3) @@ -406,24 +381,24 @@ print(x1, x2, x3) Notice that *N* split-points, leads to *N + 1* subarrays. The related functions ``np.hsplit`` and ``np.vsplit`` are similar: -```python +```python jupyter={"outputs_hidden": false} grid = np.arange(16).reshape((4, 4)) grid ``` -```python +```python jupyter={"outputs_hidden": false} upper, lower = np.vsplit(grid, [2]) print(upper) print(lower) ``` -```python +```python jupyter={"outputs_hidden": false} left, right = np.hsplit(grid, [2]) print(left) print(right) ``` -Similarly, ``np.dsplit`` will split arrays along the third axis. +Similarly, for higher-dimensional arrays, ``np.dsplit`` will split arrays along the third axis. From 68ff5a4d78209f34d5b7246565889e0f295d1c2b Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Tue, 5 Oct 2021 06:45:57 -0700 Subject: [PATCH 11/14] Update jupytext version --- notebooks_v2/00.00-Preface.md | 2 +- notebooks_v2/01.00-IPython-Beyond-Normal-Python.md | 2 +- notebooks_v2/01.01-Help-And-Documentation.md | 2 +- notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md | 2 +- notebooks_v2/01.03-Magic-Commands.md | 2 +- notebooks_v2/01.04-Input-Output-History.md | 2 +- notebooks_v2/01.05-IPython-And-Shell-Commands.md | 2 +- notebooks_v2/01.06-Errors-and-Debugging.md | 2 +- notebooks_v2/01.07-Timing-and-Profiling.md | 4 ++-- notebooks_v2/01.08-More-IPython-Resources.md | 2 +- notebooks_v2/02.03-Computation-on-arrays-ufuncs.md | 2 +- notebooks_v2/02.04-Computation-on-arrays-aggregates.md | 2 +- notebooks_v2/02.05-Computation-on-arrays-broadcasting.md | 2 +- notebooks_v2/02.06-Boolean-Arrays-and-Masks.md | 2 +- notebooks_v2/02.07-Fancy-Indexing.md | 2 +- notebooks_v2/02.08-Sorting.md | 2 +- notebooks_v2/02.09-Structured-Data-NumPy.md | 2 +- notebooks_v2/03.00-Introduction-to-Pandas.md | 2 +- notebooks_v2/03.01-Introducing-Pandas-Objects.md | 2 +- notebooks_v2/03.02-Data-Indexing-and-Selection.md | 2 +- notebooks_v2/03.03-Operations-in-Pandas.md | 2 +- notebooks_v2/03.04-Missing-Values.md | 2 +- notebooks_v2/03.05-Hierarchical-Indexing.md | 2 +- notebooks_v2/03.06-Concat-And-Append.md | 2 +- notebooks_v2/03.07-Merge-and-Join.md | 2 +- notebooks_v2/03.08-Aggregation-and-Grouping.md | 2 +- notebooks_v2/03.09-Pivot-Tables.md | 2 +- notebooks_v2/03.10-Working-With-Strings.md | 2 +- notebooks_v2/03.11-Working-with-Time-Series.md | 2 +- notebooks_v2/03.12-Performance-Eval-and-Query.md | 2 +- notebooks_v2/03.13-Further-Resources.md | 2 +- notebooks_v2/04.00-Introduction-To-Matplotlib.md | 2 +- notebooks_v2/04.01-Simple-Line-Plots.md | 2 +- notebooks_v2/04.02-Simple-Scatter-Plots.md | 2 +- notebooks_v2/04.03-Errorbars.md | 2 +- notebooks_v2/04.04-Density-and-Contour-Plots.md | 2 +- notebooks_v2/04.05-Histograms-and-Binnings.md | 2 +- notebooks_v2/04.06-Customizing-Legends.md | 2 +- notebooks_v2/04.07-Customizing-Colorbars.md | 2 +- notebooks_v2/04.08-Multiple-Subplots.md | 2 +- notebooks_v2/04.09-Text-and-Annotation.md | 2 +- notebooks_v2/04.10-Customizing-Ticks.md | 2 +- notebooks_v2/04.11-Settings-and-Stylesheets.md | 2 +- notebooks_v2/04.12-Three-Dimensional-Plotting.md | 2 +- notebooks_v2/04.13-Geographic-Data-With-Basemap.md | 2 +- notebooks_v2/04.14-Visualization-With-Seaborn.md | 2 +- notebooks_v2/04.15-Further-Resources.md | 2 +- notebooks_v2/05.00-Machine-Learning.md | 2 +- notebooks_v2/05.01-What-Is-Machine-Learning.md | 2 +- notebooks_v2/05.02-Introducing-Scikit-Learn.md | 2 +- notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md | 2 +- notebooks_v2/05.04-Feature-Engineering.md | 2 +- notebooks_v2/05.05-Naive-Bayes.md | 2 +- notebooks_v2/05.06-Linear-Regression.md | 2 +- notebooks_v2/05.07-Support-Vector-Machines.md | 2 +- notebooks_v2/05.08-Random-Forests.md | 2 +- notebooks_v2/05.09-Principal-Component-Analysis.md | 2 +- notebooks_v2/05.10-Manifold-Learning.md | 2 +- notebooks_v2/05.11-K-Means.md | 2 +- notebooks_v2/05.12-Gaussian-Mixtures.md | 2 +- notebooks_v2/05.13-Kernel-Density-Estimation.md | 2 +- notebooks_v2/05.14-Image-Features.md | 2 +- notebooks_v2/05.15-Learning-More.md | 2 +- notebooks_v2/06.00-Figure-Code.md | 2 +- notebooks_v2/Index.md | 2 +- 65 files changed, 66 insertions(+), 66 deletions(-) diff --git a/notebooks_v2/00.00-Preface.md b/notebooks_v2/00.00-Preface.md index 6206407f..4c9f2cf1 100644 --- a/notebooks_v2/00.00-Preface.md +++ b/notebooks_v2/00.00-Preface.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md index 84e43149..f737a514 100644 --- a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md +++ b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.01-Help-And-Documentation.md b/notebooks_v2/01.01-Help-And-Documentation.md index b0eff08d..0160caff 100644 --- a/notebooks_v2/01.01-Help-And-Documentation.md +++ b/notebooks_v2/01.01-Help-And-Documentation.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md index af166780..07756386 100644 --- a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md +++ b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.03-Magic-Commands.md b/notebooks_v2/01.03-Magic-Commands.md index 15cebf8e..9e0ab32f 100644 --- a/notebooks_v2/01.03-Magic-Commands.md +++ b/notebooks_v2/01.03-Magic-Commands.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.04-Input-Output-History.md b/notebooks_v2/01.04-Input-Output-History.md index 3194ab06..0266bb04 100644 --- a/notebooks_v2/01.04-Input-Output-History.md +++ b/notebooks_v2/01.04-Input-Output-History.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.05-IPython-And-Shell-Commands.md b/notebooks_v2/01.05-IPython-And-Shell-Commands.md index ed1e4bb8..a2ecf2e5 100644 --- a/notebooks_v2/01.05-IPython-And-Shell-Commands.md +++ b/notebooks_v2/01.05-IPython-And-Shell-Commands.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.06-Errors-and-Debugging.md b/notebooks_v2/01.06-Errors-and-Debugging.md index 26a9c06e..4f7be233 100644 --- a/notebooks_v2/01.06-Errors-and-Debugging.md +++ b/notebooks_v2/01.06-Errors-and-Debugging.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/01.07-Timing-and-Profiling.md b/notebooks_v2/01.07-Timing-and-Profiling.md index edf75e43..f4be2a2b 100644 --- a/notebooks_v2/01.07-Timing-and-Profiling.md +++ b/notebooks_v2/01.07-Timing-and-Profiling.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python @@ -213,7 +213,7 @@ from mprun_demo import sum_of_lists %mprun -f sum_of_lists sum_of_lists(1000000) ``` -Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 25 MB of memory usage. +Here the ``Increment`` column tells us how much each line affects the total memory budget: observe that when we create and delete the list ``L``, we are adding about 30 MB of memory usage. This is on top of the background memory usage from the Python interpreter itself. For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt). diff --git a/notebooks_v2/01.08-More-IPython-Resources.md b/notebooks_v2/01.08-More-IPython-Resources.md index 3e5a5513..a5f7badc 100644 --- a/notebooks_v2/01.08-More-IPython-Resources.md +++ b/notebooks_v2/01.08-More-IPython-Resources.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md index e91bd159..752d8646 100644 --- a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md +++ b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/02.04-Computation-on-arrays-aggregates.md b/notebooks_v2/02.04-Computation-on-arrays-aggregates.md index 4e52167d..1b190189 100644 --- a/notebooks_v2/02.04-Computation-on-arrays-aggregates.md +++ b/notebooks_v2/02.04-Computation-on-arrays-aggregates.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md index b9914ad6..a0fa2fa2 100644 --- a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md +++ b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/02.06-Boolean-Arrays-and-Masks.md b/notebooks_v2/02.06-Boolean-Arrays-and-Masks.md index 1c0448e5..3096b943 100644 --- a/notebooks_v2/02.06-Boolean-Arrays-and-Masks.md +++ b/notebooks_v2/02.06-Boolean-Arrays-and-Masks.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/02.07-Fancy-Indexing.md b/notebooks_v2/02.07-Fancy-Indexing.md index f6d2b832..2b774ddf 100644 --- a/notebooks_v2/02.07-Fancy-Indexing.md +++ b/notebooks_v2/02.07-Fancy-Indexing.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/02.08-Sorting.md b/notebooks_v2/02.08-Sorting.md index 6cc46498..0d29cbd6 100644 --- a/notebooks_v2/02.08-Sorting.md +++ b/notebooks_v2/02.08-Sorting.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/02.09-Structured-Data-NumPy.md b/notebooks_v2/02.09-Structured-Data-NumPy.md index 31f00183..cb30602f 100644 --- a/notebooks_v2/02.09-Structured-Data-NumPy.md +++ b/notebooks_v2/02.09-Structured-Data-NumPy.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.00-Introduction-to-Pandas.md b/notebooks_v2/03.00-Introduction-to-Pandas.md index 013b2e36..edf0d1f0 100644 --- a/notebooks_v2/03.00-Introduction-to-Pandas.md +++ b/notebooks_v2/03.00-Introduction-to-Pandas.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.01-Introducing-Pandas-Objects.md b/notebooks_v2/03.01-Introducing-Pandas-Objects.md index 1d638fc4..4a11a35d 100644 --- a/notebooks_v2/03.01-Introducing-Pandas-Objects.md +++ b/notebooks_v2/03.01-Introducing-Pandas-Objects.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.02-Data-Indexing-and-Selection.md b/notebooks_v2/03.02-Data-Indexing-and-Selection.md index af3d3836..e6cad452 100644 --- a/notebooks_v2/03.02-Data-Indexing-and-Selection.md +++ b/notebooks_v2/03.02-Data-Indexing-and-Selection.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.03-Operations-in-Pandas.md b/notebooks_v2/03.03-Operations-in-Pandas.md index c705da54..e2875065 100644 --- a/notebooks_v2/03.03-Operations-in-Pandas.md +++ b/notebooks_v2/03.03-Operations-in-Pandas.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.04-Missing-Values.md b/notebooks_v2/03.04-Missing-Values.md index 5476fd9d..474239c0 100644 --- a/notebooks_v2/03.04-Missing-Values.md +++ b/notebooks_v2/03.04-Missing-Values.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.05-Hierarchical-Indexing.md b/notebooks_v2/03.05-Hierarchical-Indexing.md index 551c0729..0d02a7ad 100644 --- a/notebooks_v2/03.05-Hierarchical-Indexing.md +++ b/notebooks_v2/03.05-Hierarchical-Indexing.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.06-Concat-And-Append.md b/notebooks_v2/03.06-Concat-And-Append.md index 7083bd20..c1c5dda9 100644 --- a/notebooks_v2/03.06-Concat-And-Append.md +++ b/notebooks_v2/03.06-Concat-And-Append.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.07-Merge-and-Join.md b/notebooks_v2/03.07-Merge-and-Join.md index 44700a62..2447b7bc 100644 --- a/notebooks_v2/03.07-Merge-and-Join.md +++ b/notebooks_v2/03.07-Merge-and-Join.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.08-Aggregation-and-Grouping.md b/notebooks_v2/03.08-Aggregation-and-Grouping.md index bbf03a7a..4040d24a 100644 --- a/notebooks_v2/03.08-Aggregation-and-Grouping.md +++ b/notebooks_v2/03.08-Aggregation-and-Grouping.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.09-Pivot-Tables.md b/notebooks_v2/03.09-Pivot-Tables.md index aba98584..c8d2540f 100644 --- a/notebooks_v2/03.09-Pivot-Tables.md +++ b/notebooks_v2/03.09-Pivot-Tables.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.10-Working-With-Strings.md b/notebooks_v2/03.10-Working-With-Strings.md index 9c09ef73..27bb3770 100644 --- a/notebooks_v2/03.10-Working-With-Strings.md +++ b/notebooks_v2/03.10-Working-With-Strings.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.11-Working-with-Time-Series.md b/notebooks_v2/03.11-Working-with-Time-Series.md index 5c71c3bc..35b2b498 100644 --- a/notebooks_v2/03.11-Working-with-Time-Series.md +++ b/notebooks_v2/03.11-Working-with-Time-Series.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.12-Performance-Eval-and-Query.md b/notebooks_v2/03.12-Performance-Eval-and-Query.md index 861612c4..9b83194d 100644 --- a/notebooks_v2/03.12-Performance-Eval-and-Query.md +++ b/notebooks_v2/03.12-Performance-Eval-and-Query.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/03.13-Further-Resources.md b/notebooks_v2/03.13-Further-Resources.md index 1d684316..3a6e7918 100644 --- a/notebooks_v2/03.13-Further-Resources.md +++ b/notebooks_v2/03.13-Further-Resources.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.00-Introduction-To-Matplotlib.md b/notebooks_v2/04.00-Introduction-To-Matplotlib.md index c5b6796b..238b7c0f 100644 --- a/notebooks_v2/04.00-Introduction-To-Matplotlib.md +++ b/notebooks_v2/04.00-Introduction-To-Matplotlib.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.01-Simple-Line-Plots.md b/notebooks_v2/04.01-Simple-Line-Plots.md index 0024ea23..f4d60373 100644 --- a/notebooks_v2/04.01-Simple-Line-Plots.md +++ b/notebooks_v2/04.01-Simple-Line-Plots.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.02-Simple-Scatter-Plots.md b/notebooks_v2/04.02-Simple-Scatter-Plots.md index 4de8e640..49a0848e 100644 --- a/notebooks_v2/04.02-Simple-Scatter-Plots.md +++ b/notebooks_v2/04.02-Simple-Scatter-Plots.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.03-Errorbars.md b/notebooks_v2/04.03-Errorbars.md index 87a76370..6b667d8f 100644 --- a/notebooks_v2/04.03-Errorbars.md +++ b/notebooks_v2/04.03-Errorbars.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.04-Density-and-Contour-Plots.md b/notebooks_v2/04.04-Density-and-Contour-Plots.md index 6c0f76a3..d764e573 100644 --- a/notebooks_v2/04.04-Density-and-Contour-Plots.md +++ b/notebooks_v2/04.04-Density-and-Contour-Plots.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.05-Histograms-and-Binnings.md b/notebooks_v2/04.05-Histograms-and-Binnings.md index fd37e9f6..935b8c17 100644 --- a/notebooks_v2/04.05-Histograms-and-Binnings.md +++ b/notebooks_v2/04.05-Histograms-and-Binnings.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.06-Customizing-Legends.md b/notebooks_v2/04.06-Customizing-Legends.md index 4a59f415..e93f93ba 100644 --- a/notebooks_v2/04.06-Customizing-Legends.md +++ b/notebooks_v2/04.06-Customizing-Legends.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.07-Customizing-Colorbars.md b/notebooks_v2/04.07-Customizing-Colorbars.md index 62798097..85762077 100644 --- a/notebooks_v2/04.07-Customizing-Colorbars.md +++ b/notebooks_v2/04.07-Customizing-Colorbars.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.08-Multiple-Subplots.md b/notebooks_v2/04.08-Multiple-Subplots.md index bb1d7407..1b98d0db 100644 --- a/notebooks_v2/04.08-Multiple-Subplots.md +++ b/notebooks_v2/04.08-Multiple-Subplots.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.09-Text-and-Annotation.md b/notebooks_v2/04.09-Text-and-Annotation.md index 28d03cec..6db26e76 100644 --- a/notebooks_v2/04.09-Text-and-Annotation.md +++ b/notebooks_v2/04.09-Text-and-Annotation.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.10-Customizing-Ticks.md b/notebooks_v2/04.10-Customizing-Ticks.md index 473a317e..e830b2b1 100644 --- a/notebooks_v2/04.10-Customizing-Ticks.md +++ b/notebooks_v2/04.10-Customizing-Ticks.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.11-Settings-and-Stylesheets.md b/notebooks_v2/04.11-Settings-and-Stylesheets.md index 2faa2bb0..3a895fb0 100644 --- a/notebooks_v2/04.11-Settings-and-Stylesheets.md +++ b/notebooks_v2/04.11-Settings-and-Stylesheets.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.12-Three-Dimensional-Plotting.md b/notebooks_v2/04.12-Three-Dimensional-Plotting.md index 135eebb2..7494d136 100644 --- a/notebooks_v2/04.12-Three-Dimensional-Plotting.md +++ b/notebooks_v2/04.12-Three-Dimensional-Plotting.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.13-Geographic-Data-With-Basemap.md b/notebooks_v2/04.13-Geographic-Data-With-Basemap.md index 1154330c..08a8e4d2 100644 --- a/notebooks_v2/04.13-Geographic-Data-With-Basemap.md +++ b/notebooks_v2/04.13-Geographic-Data-With-Basemap.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.14-Visualization-With-Seaborn.md b/notebooks_v2/04.14-Visualization-With-Seaborn.md index ccab7c17..e16701c3 100644 --- a/notebooks_v2/04.14-Visualization-With-Seaborn.md +++ b/notebooks_v2/04.14-Visualization-With-Seaborn.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/04.15-Further-Resources.md b/notebooks_v2/04.15-Further-Resources.md index 4cacd5b8..75db6451 100644 --- a/notebooks_v2/04.15-Further-Resources.md +++ b/notebooks_v2/04.15-Further-Resources.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.00-Machine-Learning.md b/notebooks_v2/05.00-Machine-Learning.md index a4d616cf..f7f3cef8 100644 --- a/notebooks_v2/05.00-Machine-Learning.md +++ b/notebooks_v2/05.00-Machine-Learning.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.01-What-Is-Machine-Learning.md b/notebooks_v2/05.01-What-Is-Machine-Learning.md index 9d6804b8..92e7d0c4 100644 --- a/notebooks_v2/05.01-What-Is-Machine-Learning.md +++ b/notebooks_v2/05.01-What-Is-Machine-Learning.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.02-Introducing-Scikit-Learn.md b/notebooks_v2/05.02-Introducing-Scikit-Learn.md index 63d06471..900e978a 100644 --- a/notebooks_v2/05.02-Introducing-Scikit-Learn.md +++ b/notebooks_v2/05.02-Introducing-Scikit-Learn.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md b/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md index 8f43d089..ec503a92 100644 --- a/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md +++ b/notebooks_v2/05.03-Hyperparameters-and-Model-Validation.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.04-Feature-Engineering.md b/notebooks_v2/05.04-Feature-Engineering.md index 02e87d46..55838894 100644 --- a/notebooks_v2/05.04-Feature-Engineering.md +++ b/notebooks_v2/05.04-Feature-Engineering.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.05-Naive-Bayes.md b/notebooks_v2/05.05-Naive-Bayes.md index b77dfd03..947a1d9a 100644 --- a/notebooks_v2/05.05-Naive-Bayes.md +++ b/notebooks_v2/05.05-Naive-Bayes.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.06-Linear-Regression.md b/notebooks_v2/05.06-Linear-Regression.md index 3588f27e..cbd9da15 100644 --- a/notebooks_v2/05.06-Linear-Regression.md +++ b/notebooks_v2/05.06-Linear-Regression.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.07-Support-Vector-Machines.md b/notebooks_v2/05.07-Support-Vector-Machines.md index e6b78726..11ba129c 100644 --- a/notebooks_v2/05.07-Support-Vector-Machines.md +++ b/notebooks_v2/05.07-Support-Vector-Machines.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.08-Random-Forests.md b/notebooks_v2/05.08-Random-Forests.md index 163041d3..24da0cd7 100644 --- a/notebooks_v2/05.08-Random-Forests.md +++ b/notebooks_v2/05.08-Random-Forests.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.09-Principal-Component-Analysis.md b/notebooks_v2/05.09-Principal-Component-Analysis.md index 79227e02..ae1c23df 100644 --- a/notebooks_v2/05.09-Principal-Component-Analysis.md +++ b/notebooks_v2/05.09-Principal-Component-Analysis.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.10-Manifold-Learning.md b/notebooks_v2/05.10-Manifold-Learning.md index c492d0b9..72462a50 100644 --- a/notebooks_v2/05.10-Manifold-Learning.md +++ b/notebooks_v2/05.10-Manifold-Learning.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.11-K-Means.md b/notebooks_v2/05.11-K-Means.md index 25085eb4..b7975c6f 100644 --- a/notebooks_v2/05.11-K-Means.md +++ b/notebooks_v2/05.11-K-Means.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.12-Gaussian-Mixtures.md b/notebooks_v2/05.12-Gaussian-Mixtures.md index 03decece..39eafd92 100644 --- a/notebooks_v2/05.12-Gaussian-Mixtures.md +++ b/notebooks_v2/05.12-Gaussian-Mixtures.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.13-Kernel-Density-Estimation.md b/notebooks_v2/05.13-Kernel-Density-Estimation.md index 634383e1..c07df4b5 100644 --- a/notebooks_v2/05.13-Kernel-Density-Estimation.md +++ b/notebooks_v2/05.13-Kernel-Density-Estimation.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.14-Image-Features.md b/notebooks_v2/05.14-Image-Features.md index a44b95f7..621ba3ca 100644 --- a/notebooks_v2/05.14-Image-Features.md +++ b/notebooks_v2/05.14-Image-Features.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/05.15-Learning-More.md b/notebooks_v2/05.15-Learning-More.md index f6e36620..d8dc9c16 100644 --- a/notebooks_v2/05.15-Learning-More.md +++ b/notebooks_v2/05.15-Learning-More.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/06.00-Figure-Code.md b/notebooks_v2/06.00-Figure-Code.md index bccefebc..48c0efc2 100644 --- a/notebooks_v2/06.00-Figure-Code.md +++ b/notebooks_v2/06.00-Figure-Code.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python diff --git a/notebooks_v2/Index.md b/notebooks_v2/Index.md index 0e2e6f0c..3f0de425 100644 --- a/notebooks_v2/Index.md +++ b/notebooks_v2/Index.md @@ -6,7 +6,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.10.3 + jupytext_version: 1.13.0 kernelspec: display_name: Python 3 language: python From e890dafd06e57bc8c73d3bf3da2d15df5df3a1ec Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Wed, 6 Oct 2021 06:25:30 -0700 Subject: [PATCH 12/14] Update 02.03, 02.04 --- .../02.03-Computation-on-arrays-ufuncs.ipynb | 258 ++++++++++++------ .../02.03-Computation-on-arrays-ufuncs.md | 98 +++---- ....04-Computation-on-arrays-aggregates.ipynb | 178 +++++++----- .../02.04-Computation-on-arrays-aggregates.md | 62 ++--- notebooks_v2/data/president_heights.csv | 2 + 5 files changed, 366 insertions(+), 232 deletions(-) diff --git a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.ipynb b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.ipynb index a382c03e..2423e17e 100644 --- a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.ipynb +++ b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.ipynb @@ -62,13 +62,16 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 0.16666667, 1. , 0.25 , 0.25 , 0.125 ])" + "array([0.11111111, 0.25 , 1. , 0.33333333, 0.125 ])" ] }, "execution_count": 1, @@ -78,7 +81,7 @@ ], "source": [ "import numpy as np\n", - "np.random.seed(0)\n", + "rng = np.random.default_rng(seed=1701)\n", "\n", "def compute_reciprocals(values):\n", " output = np.empty(len(values))\n", @@ -86,7 +89,7 @@ " output[i] = 1.0 / values[i]\n", " return output\n", " \n", - "values = np.random.randint(1, 10, size=5)\n", + "values = rng.integers(1, 10, size=5)\n", "compute_reciprocals(values)" ] }, @@ -103,19 +106,22 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1 loop, best of 3: 2.91 s per loop\n" + "2.61 s ± 192 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ - "big_array = np.random.randint(1, 100, size=1000000)\n", + "big_array = rng.integers(1, 100, size=1000000)\n", "%timeit compute_reciprocals(big_array)" ] }, @@ -137,7 +143,7 @@ "## Introducing UFuncs\n", "\n", "For many types of operations, NumPy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a *vectorized* operation.\n", - "This can be accomplished by simply performing an operation on the array, which will then be applied to each element.\n", + "For simple operations like the element-wise division here, vectorization is as simple as using Python arithmetic operators directly on the array object.\n", "This vectorized approach is designed to push the loop into the compiled layer that underlies NumPy, leading to much faster execution.\n", "\n", "Compare the results of the following two:" @@ -147,15 +153,18 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0.16666667 1. 0.25 0.25 0.125 ]\n", - "[ 0.16666667 1. 0.25 0.25 0.125 ]\n" + "[0.11111111 0.25 1. 0.33333333 0.125 ]\n", + "[0.11111111 0.25 1. 0.33333333 0.125 ]\n" ] } ], @@ -175,14 +184,17 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "100 loops, best of 3: 4.6 ms per loop\n" + "2.54 ms ± 383 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -202,13 +214,16 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 0. , 0.5 , 0.66666667, 0.75 , 0.8 ])" + "array([0. , 0.5 , 0.66666667, 0.75 , 0.8 ])" ] }, "execution_count": 5, @@ -231,7 +246,10 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -257,7 +275,7 @@ "metadata": {}, "source": [ "Computations using vectorization through ufuncs are nearly always more efficient than their counterpart implemented using Python loops, especially as the arrays grow in size.\n", - "Any time you see such a loop in a Python script, you should consider whether it can be replaced with a vectorized expression." + "Any time you see such a loop in a NumPy script, you should consider whether it can be replaced with a vectorized expression." ] }, { @@ -284,29 +302,32 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "x = [0 1 2 3]\n", - "x + 5 = [5 6 7 8]\n", - "x - 5 = [-5 -4 -3 -2]\n", - "x * 2 = [0 2 4 6]\n", - "x / 2 = [ 0. 0.5 1. 1.5]\n", + "x = [0 1 2 3]\n", + "x + 5 = [5 6 7 8]\n", + "x - 5 = [-5 -4 -3 -2]\n", + "x * 2 = [0 2 4 6]\n", + "x / 2 = [0. 0.5 1. 1.5]\n", "x // 2 = [0 0 1 1]\n" ] } ], "source": [ "x = np.arange(4)\n", - "print(\"x =\", x)\n", - "print(\"x + 5 =\", x + 5)\n", - "print(\"x - 5 =\", x - 5)\n", - "print(\"x * 2 =\", x * 2)\n", - "print(\"x / 2 =\", x / 2)\n", + "print(\"x =\", x)\n", + "print(\"x + 5 =\", x + 5)\n", + "print(\"x - 5 =\", x - 5)\n", + "print(\"x * 2 =\", x * 2)\n", + "print(\"x / 2 =\", x / 2)\n", "print(\"x // 2 =\", x // 2) # floor division" ] }, @@ -321,7 +342,10 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -351,7 +375,10 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -373,14 +400,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Each of these arithmetic operations are simply convenient wrappers around specific functions built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` function:" + "Each of these arithmetic operations are simply convenient wrappers around specific ufuncs built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` ufunc:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -431,7 +461,10 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -461,7 +494,10 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -483,7 +519,10 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -512,13 +551,16 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 5., 5., 2., 1.])" + "array([5., 5., 2., 1.])" ] }, "execution_count": 14, @@ -545,7 +587,10 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -563,17 +608,20 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "theta = [ 0. 1.57079633 3.14159265]\n", - "sin(theta) = [ 0.00000000e+00 1.00000000e+00 1.22464680e-16]\n", - "cos(theta) = [ 1.00000000e+00 6.12323400e-17 -1.00000000e+00]\n", - "tan(theta) = [ 0.00000000e+00 1.63312394e+16 -1.22464680e-16]\n" + "theta = [0. 1.57079633 3.14159265]\n", + "sin(theta) = [0.0000000e+00 1.0000000e+00 1.2246468e-16]\n", + "cos(theta) = [ 1.000000e+00 6.123234e-17 -1.000000e+00]\n", + "tan(theta) = [ 0.00000000e+00 1.63312394e+16 -1.22464680e-16]\n" ] } ], @@ -596,7 +644,10 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -605,7 +656,7 @@ "text": [ "x = [-1, 0, 1]\n", "arcsin(x) = [-1.57079633 0. 1.57079633]\n", - "arccos(x) = [ 3.14159265 1.57079633 0. ]\n", + "arccos(x) = [3.14159265 1.57079633 0. ]\n", "arctan(x) = [-0.78539816 0. 0.78539816]\n" ] } @@ -631,26 +682,29 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "x = [1, 2, 3]\n", - "e^x = [ 2.71828183 7.3890561 20.08553692]\n", - "2^x = [ 2. 4. 8.]\n", - "3^x = [ 3 9 27]\n" + "x = [1, 2, 3]\n", + "e^x = [ 2.71828183 7.3890561 20.08553692]\n", + "2^x = [2. 4. 8.]\n", + "3^x = [ 3. 9. 27.]\n" ] } ], "source": [ "x = [1, 2, 3]\n", - "print(\"x =\", x)\n", - "print(\"e^x =\", np.exp(x))\n", - "print(\"2^x =\", np.exp2(x))\n", - "print(\"3^x =\", np.power(3, x))" + "print(\"x =\", x)\n", + "print(\"e^x =\", np.exp(x))\n", + "print(\"2^x =\", np.exp2(x))\n", + "print(\"3^x =\", np.power(3., x))" ] }, { @@ -665,7 +719,10 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -673,9 +730,9 @@ "output_type": "stream", "text": [ "x = [1, 2, 4, 10]\n", - "ln(x) = [ 0. 0.69314718 1.38629436 2.30258509]\n", - "log2(x) = [ 0. 1. 2. 3.32192809]\n", - "log10(x) = [ 0. 0.30103 0.60205999 1. ]\n" + "ln(x) = [0. 0.69314718 1.38629436 2.30258509]\n", + "log2(x) = [0. 1. 2. 3.32192809]\n", + "log10(x) = [0. 0.30103 0.60205999 1. ]\n" ] } ], @@ -698,15 +755,18 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "exp(x) - 1 = [ 0. 0.0010005 0.01005017 0.10517092]\n", - "log(1 + x) = [ 0. 0.0009995 0.00995033 0.09531018]\n" + "exp(x) - 1 = [0. 0.0010005 0.01005017 0.10517092]\n", + "log(1 + x) = [0. 0.0009995 0.00995033 0.09531018]\n" ] } ], @@ -741,7 +801,10 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -752,16 +815,19 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "gamma(x) = [ 1.00000000e+00 2.40000000e+01 3.62880000e+05]\n", - "ln|gamma(x)| = [ 0. 3.17805383 12.80182748]\n", - "beta(x, 2) = [ 0.5 0.03333333 0.00909091]\n" + "gamma(x) = [1.0000e+00 2.4000e+01 3.6288e+05]\n", + "ln|gamma(x)| = [ 0. 3.17805383 12.80182748]\n", + "beta(x, 2) = [0.5 0.03333333 0.00909091]\n" ] } ], @@ -777,16 +843,19 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "erf(x) = [ 0. 0.32862676 0.67780119 0.84270079]\n", - "erfc(x) = [ 1. 0.67137324 0.32219881 0.15729921]\n", - "erfinv(x) = [ 0. 0.27246271 0.73286908 inf]\n" + "erf(x) = [0. 0.32862676 0.67780119 0.84270079]\n", + "erfc(x) = [1. 0.67137324 0.32219881 0.15729921]\n", + "erfinv(x) = [0. 0.27246271 0.73286908 inf]\n" ] } ], @@ -832,14 +901,17 @@ "cell_type": "code", "execution_count": 24, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0. 10. 20. 30. 40.]\n" + "[ 0. 10. 20. 30. 40.]\n" ] } ], @@ -861,14 +933,17 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 1. 0. 2. 0. 4. 0. 8. 0. 16. 0.]\n" + "[ 1. 0. 2. 0. 4. 0. 8. 0. 16. 0.]\n" ] } ], @@ -890,9 +965,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Aggregates\n", + "### Aggregations\n", "\n", - "For binary ufuncs, there are some interesting aggregates that can be computed directly from the object.\n", + "For binary ufuncs, there are some interesting aggregations that can be computed directly from the object.\n", "For example, if we'd like to *reduce* an array with a particular operation, we can use the ``reduce`` method of any ufunc.\n", "A reduce repeatedly applies a given operation to the elements of an array until only a single result remains.\n", "\n", @@ -903,7 +978,10 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -933,7 +1011,10 @@ "cell_type": "code", "execution_count": 27, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -962,7 +1043,10 @@ "cell_type": "code", "execution_count": 28, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -984,7 +1068,10 @@ "cell_type": "code", "execution_count": 29, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -1024,6 +1111,9 @@ "execution_count": 30, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "scrolled": true }, "outputs": [ @@ -1051,9 +1141,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ``ufunc.at`` and ``ufunc.reduceat`` methods, which we'll explore in [Fancy Indexing](02.07-Fancy-Indexing.ipynb), are very helpful as well.\n", + "The ``ufunc.at`` and ``ufunc.reduceat`` methods are useful as well, and we will explore them in [Fancy Indexing](02.07-Fancy-Indexing.ipynb).\n", "\n", - "Another extremely useful feature of ufuncs is the ability to operate between arrays of different sizes and shapes, a set of operations known as *broadcasting*.\n", + "We will also encounter the ability of ufuncs to operate between arrays of different shapes and sizes, a set of operations known as *broadcasting*.\n", "This subject is important enough that we will devote a whole section to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb))." ] }, @@ -1104,9 +1194,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md index 752d8646..46227983 100644 --- a/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md +++ b/notebooks_v2/02.03-Computation-on-arrays-ufuncs.md @@ -51,9 +51,9 @@ The relative sluggishness of Python generally manifests itself in situations whe For example, imagine we have an array of values and we'd like to compute the reciprocal of each. A straightforward approach might look like this: -```python +```python jupyter={"outputs_hidden": false} import numpy as np -np.random.seed(0) +rng = np.random.default_rng(seed=1701) def compute_reciprocals(values): output = np.empty(len(values)) @@ -61,7 +61,7 @@ def compute_reciprocals(values): output[i] = 1.0 / values[i] return output -values = np.random.randint(1, 10, size=5) +values = rng.integers(1, 10, size=5) compute_reciprocals(values) ``` @@ -69,8 +69,8 @@ This implementation probably feels fairly natural to someone from, say, a C or J But if we measure the execution time of this code for a large input, we see that this operation is very slow, perhaps surprisingly so! We'll benchmark this with IPython's ``%timeit`` magic (discussed in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)): -```python -big_array = np.random.randint(1, 100, size=1000000) +```python jupyter={"outputs_hidden": false} +big_array = rng.integers(1, 100, size=1000000) %timeit compute_reciprocals(big_array) ``` @@ -84,38 +84,38 @@ If we were working in compiled code instead, this type specification would be kn ## Introducing UFuncs For many types of operations, NumPy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a *vectorized* operation. -This can be accomplished by simply performing an operation on the array, which will then be applied to each element. +For simple operations like the element-wise division here, vectorization is as simple as using Python arithmetic operators directly on the array object. This vectorized approach is designed to push the loop into the compiled layer that underlies NumPy, leading to much faster execution. Compare the results of the following two: -```python +```python jupyter={"outputs_hidden": false} print(compute_reciprocals(values)) print(1.0 / values) ``` Looking at the execution time for our big array, we see that it completes orders of magnitude faster than the Python loop: -```python +```python jupyter={"outputs_hidden": false} %timeit (1.0 / big_array) ``` Vectorized operations in NumPy are implemented via *ufuncs*, whose main purpose is to quickly execute repeated operations on values in NumPy arrays. Ufuncs are extremely flexible – before we saw an operation between a scalar and an array, but we can also operate between two arrays: -```python +```python jupyter={"outputs_hidden": false} np.arange(5) / np.arange(1, 6) ``` And ufunc operations are not limited to one-dimensional arrays–they can also act on multi-dimensional arrays as well: -```python +```python jupyter={"outputs_hidden": false} x = np.arange(9).reshape((3, 3)) 2 ** x ``` Computations using vectorization through ufuncs are nearly always more efficient than their counterpart implemented using Python loops, especially as the arrays grow in size. -Any time you see such a loop in a Python script, you should consider whether it can be replaced with a vectorized expression. +Any time you see such a loop in a NumPy script, you should consider whether it can be replaced with a vectorized expression. ## Exploring NumPy's UFuncs @@ -129,19 +129,19 @@ We'll see examples of both these types of functions here. NumPy's ufuncs feel very natural to use because they make use of Python's native arithmetic operators. The standard addition, subtraction, multiplication, and division can all be used: -```python +```python jupyter={"outputs_hidden": false} x = np.arange(4) -print("x =", x) -print("x + 5 =", x + 5) -print("x - 5 =", x - 5) -print("x * 2 =", x * 2) -print("x / 2 =", x / 2) +print("x =", x) +print("x + 5 =", x + 5) +print("x - 5 =", x - 5) +print("x * 2 =", x * 2) +print("x / 2 =", x / 2) print("x // 2 =", x // 2) # floor division ``` There is also a unary ufunc for negation, and a ``**`` operator for exponentiation, and a ``%`` operator for modulus: -```python +```python jupyter={"outputs_hidden": false} print("-x = ", -x) print("x ** 2 = ", x ** 2) print("x % 2 = ", x % 2) @@ -149,13 +149,13 @@ print("x % 2 = ", x % 2) In addition, these can be strung together however you wish, and the standard order of operations is respected: -```python +```python jupyter={"outputs_hidden": false} -(0.5*x + 1) ** 2 ``` -Each of these arithmetic operations are simply convenient wrappers around specific functions built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` function: +Each of these arithmetic operations are simply convenient wrappers around specific ufuncs built into NumPy; for example, the ``+`` operator is a wrapper for the ``add`` ufunc: -```python +```python jupyter={"outputs_hidden": false} np.add(x, 2) ``` @@ -179,24 +179,24 @@ Additionally there are Boolean/bitwise operators; we will explore these in [Comp Just as NumPy understands Python's built-in arithmetic operators, it also understands Python's built-in absolute value function: -```python +```python jupyter={"outputs_hidden": false} x = np.array([-2, -1, 0, 1, 2]) abs(x) ``` The corresponding NumPy ufunc is ``np.absolute``, which is also available under the alias ``np.abs``: -```python +```python jupyter={"outputs_hidden": false} np.absolute(x) ``` -```python +```python jupyter={"outputs_hidden": false} np.abs(x) ``` This ufunc can also handle complex data, in which the absolute value returns the magnitude: -```python +```python jupyter={"outputs_hidden": false} x = np.array([3 - 4j, 4 - 3j, 2 + 0j, 0 + 1j]) np.abs(x) ``` @@ -206,13 +206,13 @@ np.abs(x) NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions. We'll start by defining an array of angles: -```python +```python jupyter={"outputs_hidden": false} theta = np.linspace(0, np.pi, 3) ``` Now we can compute some trigonometric functions on these values: -```python +```python jupyter={"outputs_hidden": false} print("theta = ", theta) print("sin(theta) = ", np.sin(theta)) print("cos(theta) = ", np.cos(theta)) @@ -222,7 +222,7 @@ print("tan(theta) = ", np.tan(theta)) The values are computed to within machine precision, which is why values that should be zero do not always hit exactly zero. Inverse trigonometric functions are also available: -```python +```python jupyter={"outputs_hidden": false} x = [-1, 0, 1] print("x = ", x) print("arcsin(x) = ", np.arcsin(x)) @@ -234,18 +234,18 @@ print("arctan(x) = ", np.arctan(x)) Another common type of operation available in a NumPy ufunc are the exponentials: -```python +```python jupyter={"outputs_hidden": false} x = [1, 2, 3] -print("x =", x) -print("e^x =", np.exp(x)) -print("2^x =", np.exp2(x)) -print("3^x =", np.power(3, x)) +print("x =", x) +print("e^x =", np.exp(x)) +print("2^x =", np.exp2(x)) +print("3^x =", np.power(3., x)) ``` The inverse of the exponentials, the logarithms, are also available. The basic ``np.log`` gives the natural logarithm; if you prefer to compute the base-2 logarithm or the base-10 logarithm, these are available as well: -```python +```python jupyter={"outputs_hidden": false} x = [1, 2, 4, 10] print("x =", x) print("ln(x) =", np.log(x)) @@ -255,7 +255,7 @@ print("log10(x) =", np.log10(x)) There are also some specialized versions that are useful for maintaining precision with very small input: -```python +```python jupyter={"outputs_hidden": false} x = [0, 0.001, 0.01, 0.1] print("exp(x) - 1 =", np.expm1(x)) print("log(1 + x) =", np.log1p(x)) @@ -273,11 +273,11 @@ Another excellent source for more specialized and obscure ufuncs is the submodul If you want to compute some obscure mathematical function on your data, chances are it is implemented in ``scipy.special``. There are far too many functions to list them all, but the following snippet shows a couple that might come up in a statistics context: -```python +```python jupyter={"outputs_hidden": false} from scipy import special ``` -```python +```python jupyter={"outputs_hidden": false} # Gamma functions (generalized factorials) and related functions x = [1, 5, 10] print("gamma(x) =", special.gamma(x)) @@ -285,7 +285,7 @@ print("ln|gamma(x)| =", special.gammaln(x)) print("beta(x, 2) =", special.beta(x, 2)) ``` -```python +```python jupyter={"outputs_hidden": false} # Error function (integral of Gaussian) # its complement, and its inverse x = np.array([0, 0.3, 0.7, 1.0]) @@ -310,7 +310,7 @@ For large calculations, it is sometimes useful to be able to specify the array w Rather than creating a temporary array, this can be used to write computation results directly to the memory location where you'd like them to be. For all ufuncs, this can be done using the ``out`` argument of the function: -```python +```python jupyter={"outputs_hidden": false} x = np.arange(5) y = np.empty(5) np.multiply(x, 10, out=y) @@ -319,7 +319,7 @@ print(y) This can even be used with array views. For example, we can write the results of a computation to every other element of a specified array: -```python +```python jupyter={"outputs_hidden": false} y = np.zeros(10) np.power(2, x, out=y[::2]) print(y) @@ -329,32 +329,32 @@ If we had instead written ``y[::2] = 2 ** x``, this would have resulted in the c This doesn't make much of a difference for such a small computation, but for very large arrays the memory savings from careful use of the ``out`` argument can be significant. -### Aggregates +### Aggregations -For binary ufuncs, there are some interesting aggregates that can be computed directly from the object. +For binary ufuncs, there are some interesting aggregations that can be computed directly from the object. For example, if we'd like to *reduce* an array with a particular operation, we can use the ``reduce`` method of any ufunc. A reduce repeatedly applies a given operation to the elements of an array until only a single result remains. For example, calling ``reduce`` on the ``add`` ufunc returns the sum of all elements in the array: -```python +```python jupyter={"outputs_hidden": false} x = np.arange(1, 6) np.add.reduce(x) ``` Similarly, calling ``reduce`` on the ``multiply`` ufunc results in the product of all array elements: -```python +```python jupyter={"outputs_hidden": false} np.multiply.reduce(x) ``` If we'd like to store all the intermediate results of the computation, we can instead use ``accumulate``: -```python +```python jupyter={"outputs_hidden": false} np.add.accumulate(x) ``` -```python +```python jupyter={"outputs_hidden": false} np.multiply.accumulate(x) ``` @@ -366,14 +366,14 @@ Note that for these particular cases, there are dedicated NumPy functions to com Finally, any ufunc can compute the output of all pairs of two different inputs using the ``outer`` method. This allows you, in one line, to do things like create a multiplication table: -```python +```python jupyter={"outputs_hidden": false} x = np.arange(1, 6) np.multiply.outer(x, x) ``` -The ``ufunc.at`` and ``ufunc.reduceat`` methods, which we'll explore in [Fancy Indexing](02.07-Fancy-Indexing.ipynb), are very helpful as well. +The ``ufunc.at`` and ``ufunc.reduceat`` methods are useful as well, and we will explore them in [Fancy Indexing](02.07-Fancy-Indexing.ipynb). -Another extremely useful feature of ufuncs is the ability to operate between arrays of different sizes and shapes, a set of operations known as *broadcasting*. +We will also encounter the ability of ufuncs to operate between arrays of different shapes and sizes, a set of operations known as *broadcasting*. This subject is important enough that we will devote a whole section to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)). diff --git a/notebooks_v2/02.04-Computation-on-arrays-aggregates.ipynb b/notebooks_v2/02.04-Computation-on-arrays-aggregates.ipynb index b5eb814d..b401f274 100644 --- a/notebooks_v2/02.04-Computation-on-arrays-aggregates.ipynb +++ b/notebooks_v2/02.04-Computation-on-arrays-aggregates.ipynb @@ -33,8 +33,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question.\n", - "Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the \"typical\" values in a dataset, but other aggregates are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.).\n", + "A first step in exploring any dataset is often to compute various summary statistics.\n", + "Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the \"typical\" values in a dataset, but other aggregations are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.).\n", "\n", "NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and demonstrate some of them here." ] @@ -53,24 +53,31 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ - "import numpy as np" + "import numpy as np\n", + "rng = np.random.default_rng()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "55.61209116604941" + "52.76825337322368" ] }, "execution_count": 2, @@ -79,7 +86,7 @@ } ], "source": [ - "L = np.random.random(100)\n", + "L = rng.random(100)\n", "sum(L)" ] }, @@ -94,13 +101,16 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "55.612091166049424" + "52.76825337322366" ] }, "execution_count": 3, @@ -123,20 +133,23 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10 loops, best of 3: 104 ms per loop\n", - "1000 loops, best of 3: 442 µs per loop\n" + "89.9 ms ± 233 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", + "521 µs ± 8.37 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ - "big_array = np.random.rand(1000000)\n", + "big_array = rng.random(1000000)\n", "%timeit sum(big_array)\n", "%timeit np.sum(big_array)" ] @@ -162,13 +175,16 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "(1.1717128136634614e-06, 0.9999976784968716)" + "(2.0114398036064074e-07, 0.9999997912802653)" ] }, "execution_count": 5, @@ -191,13 +207,16 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "(1.1717128136634614e-06, 0.9999976784968716)" + "(2.0114398036064074e-07, 0.9999997912802653)" ] }, "execution_count": 6, @@ -213,15 +232,18 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10 loops, best of 3: 82.3 ms per loop\n", - "1000 loops, best of 3: 497 µs per loop\n" + "72 ms ± 177 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", + "564 µs ± 3.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], @@ -241,14 +263,17 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1.17171281366e-06 0.999997678497 499911.628197\n" + "2.0114398036064074e-07 0.9999997912802653 499854.0273321711\n" ] } ], @@ -277,21 +302,24 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[ 0.8967576 0.03783739 0.75952519 0.06682827]\n", - " [ 0.8354065 0.99196818 0.19544769 0.43447084]\n", - " [ 0.66859307 0.15038721 0.37911423 0.6687194 ]]\n" + "[[0 3 1 2]\n", + " [1 9 7 0]\n", + " [4 8 3 7]]\n" ] } ], "source": [ - "M = np.random.random((3, 4))\n", + "M = rng.integers(0, 10, (3, 4))\n", "print(M)" ] }, @@ -299,20 +327,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By default, each NumPy aggregation function will return the aggregate over the entire array:" + "Numpy aggregations will apply across all elements of a multi-dimensional array:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "6.0850555667307118" + "45" ] }, "execution_count": 10, @@ -335,13 +366,16 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 0.66859307, 0.03783739, 0.19544769, 0.06682827])" + "array([0, 3, 1, 0])" ] }, "execution_count": 11, @@ -366,13 +400,16 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 0.8967576 , 0.99196818, 0.6687194 ])" + "array([3, 9, 8])" ] }, "execution_count": 12, @@ -390,7 +427,7 @@ "source": [ "The way the axis is specified here can be confusing to users coming from other languages.\n", "The ``axis`` keyword specifies the *dimension of the array that will be collapsed*, rather than the dimension that will be returned.\n", - "So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, this means that values within each column will be aggregated." + "So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, values within each column will be aggregated." ] }, { @@ -399,9 +436,7 @@ "source": [ "### Other aggregation functions\n", "\n", - "NumPy provides many other aggregation functions, but we won't discuss them in detail here.\n", - "Additionally, most aggregates have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (for a fuller discussion of missing data, see [Handling Missing Data](03.04-Missing-Values.ipynb)).\n", - "Some of these ``NaN``-safe functions were not added until NumPy 1.8, so they will not be available in older NumPy versions.\n", + "NumPy provides several other aggregation functions with a similar API, and additionally most have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (see [Handling Missing Data](03.04-Missing-Values.ipynb)).\n", "\n", "The following table provides a list of useful aggregation functions available in NumPy:\n", "\n", @@ -435,7 +470,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Aggregates available in NumPy can be extremely useful for summarizing a set of values.\n", + "Aggregates available in NumPy can act as summary statistics for a set of values.\n", "As a simple example, let's consider the heights of all US presidents.\n", "This data is available in the file *president_heights.csv*, which is a simple comma-separated list of labels and values:" ] @@ -444,17 +479,20 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "order,name,height(cm)\r\n", - "1,George Washington,189\r\n", - "2,John Adams,170\r\n", - "3,Thomas Jefferson,189\r\n" + "order,name,height(cm)\n", + "1,George Washington,189\n", + "2,John Adams,170\n", + "3,Thomas Jefferson,189\n" ] } ], @@ -473,7 +511,10 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -482,7 +523,7 @@ "text": [ "[189 170 189 163 183 171 185 168 173 183 173 173 175 178 183 193 178 173\n", " 174 183 183 168 170 178 182 180 183 178 182 188 175 179 183 193 182 183\n", - " 177 185 188 188 182 185]\n" + " 177 185 188 188 182 185 191 182]\n" ] } ], @@ -504,15 +545,18 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean height: 179.738095238\n", - "Standard deviation: 6.93184344275\n", + "Mean height: 180.04545454545453\n", + "Standard deviation: 6.983599441335736\n", "Minimum height: 163\n", "Maximum height: 193\n" ] @@ -537,16 +581,19 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "25th percentile: 174.25\n", + "25th percentile: 174.75\n", "Median: 182.0\n", - "75th percentile: 183.0\n" + "75th percentile: 183.5\n" ] } ], @@ -569,27 +616,33 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "import seaborn; seaborn.set() # set plot style" + "plt.style.use('seaborn-whitegrid')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "image/png": 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KSkpSYmKievfurbFjx0oyd/5dGftf//rXBjH3u3fv1qBBgzR06FBNmjTJ+R5T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\nRU5ErkJggg==\n", 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" ] }, "metadata": {}, @@ -603,13 +656,6 @@ "plt.ylabel('number');" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These aggregates are some of the fundamental pieces of exploratory data analysis that we'll explore in more depth in later chapters of the book." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -641,9 +687,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/02.04-Computation-on-arrays-aggregates.md b/notebooks_v2/02.04-Computation-on-arrays-aggregates.md index 1b190189..966af3d2 100644 --- a/notebooks_v2/02.04-Computation-on-arrays-aggregates.md +++ b/notebooks_v2/02.04-Computation-on-arrays-aggregates.md @@ -31,8 +31,8 @@ jupyter: # Aggregations: Min, Max, and Everything In Between -Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question. -Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the "typical" values in a dataset, but other aggregates are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.). +A first step in exploring any dataset is often to compute various summary statistics. +Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the "typical" values in a dataset, but other aggregations are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.). NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and demonstrate some of them here. @@ -42,25 +42,26 @@ NumPy has fast built-in aggregation functions for working on arrays; we'll discu As a quick example, consider computing the sum of all values in an array. Python itself can do this using the built-in ``sum`` function: -```python +```python jupyter={"outputs_hidden": false} import numpy as np +rng = np.random.default_rng() ``` -```python -L = np.random.random(100) +```python jupyter={"outputs_hidden": false} +L = rng.random(100) sum(L) ``` The syntax is quite similar to that of NumPy's ``sum`` function, and the result is the same in the simplest case: -```python +```python jupyter={"outputs_hidden": false} np.sum(L) ``` However, because it executes the operation in compiled code, NumPy's version of the operation is computed much more quickly: -```python -big_array = np.random.rand(1000000) +```python jupyter={"outputs_hidden": false} +big_array = rng.random(1000000) %timeit sum(big_array) %timeit np.sum(big_array) ``` @@ -73,24 +74,24 @@ In particular, their optional arguments have different meanings, and ``np.sum`` Similarly, Python has built-in ``min`` and ``max`` functions, used to find the minimum value and maximum value of any given array: -```python +```python jupyter={"outputs_hidden": false} min(big_array), max(big_array) ``` NumPy's corresponding functions have similar syntax, and again operate much more quickly: -```python +```python jupyter={"outputs_hidden": false} np.min(big_array), np.max(big_array) ``` -```python +```python jupyter={"outputs_hidden": false} %timeit min(big_array) %timeit np.min(big_array) ``` For ``min``, ``max``, ``sum``, and several other NumPy aggregates, a shorter syntax is to use methods of the array object itself: -```python +```python jupyter={"outputs_hidden": false} print(big_array.min(), big_array.max(), big_array.sum()) ``` @@ -102,20 +103,20 @@ Whenever possible, make sure that you are using the NumPy version of these aggre One common type of aggregation operation is an aggregate along a row or column. Say you have some data stored in a two-dimensional array: -```python -M = np.random.random((3, 4)) +```python jupyter={"outputs_hidden": false} +M = rng.integers(0, 10, (3, 4)) print(M) ``` -By default, each NumPy aggregation function will return the aggregate over the entire array: +Numpy aggregations will apply across all elements of a multi-dimensional array: -```python +```python jupyter={"outputs_hidden": false} M.sum() ``` Aggregation functions take an additional argument specifying the *axis* along which the aggregate is computed. For example, we can find the minimum value within each column by specifying ``axis=0``: -```python +```python jupyter={"outputs_hidden": false} M.min(axis=0) ``` @@ -123,20 +124,18 @@ The function returns four values, corresponding to the four columns of numbers. Similarly, we can find the maximum value within each row: -```python +```python jupyter={"outputs_hidden": false} M.max(axis=1) ``` The way the axis is specified here can be confusing to users coming from other languages. The ``axis`` keyword specifies the *dimension of the array that will be collapsed*, rather than the dimension that will be returned. -So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, this means that values within each column will be aggregated. +So specifying ``axis=0`` means that the first axis will be collapsed: for two-dimensional arrays, values within each column will be aggregated. ### Other aggregation functions -NumPy provides many other aggregation functions, but we won't discuss them in detail here. -Additionally, most aggregates have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (for a fuller discussion of missing data, see [Handling Missing Data](03.04-Missing-Values.ipynb)). -Some of these ``NaN``-safe functions were not added until NumPy 1.8, so they will not be available in older NumPy versions. +NumPy provides several other aggregation functions with a similar API, and additionally most have a ``NaN``-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point ``NaN`` value (see [Handling Missing Data](03.04-Missing-Values.ipynb)). The following table provides a list of useful aggregation functions available in NumPy: @@ -162,17 +161,17 @@ We will see these aggregates often throughout the rest of the book. ## Example: What is the Average Height of US Presidents? -Aggregates available in NumPy can be extremely useful for summarizing a set of values. +Aggregates available in NumPy can act as summary statistics for a set of values. As a simple example, let's consider the heights of all US presidents. This data is available in the file *president_heights.csv*, which is a simple comma-separated list of labels and values: -```python +```python jupyter={"outputs_hidden": false} !head -4 data/president_heights.csv ``` We'll use the Pandas package, which we'll explore more fully in [Chapter 3](03.00-Introduction-to-Pandas.ipynb), to read the file and extract this information (note that the heights are measured in centimeters). -```python +```python jupyter={"outputs_hidden": false} import pandas as pd data = pd.read_csv('data/president_heights.csv') heights = np.array(data['height(cm)']) @@ -181,7 +180,7 @@ print(heights) Now that we have this data array, we can compute a variety of summary statistics: -```python +```python jupyter={"outputs_hidden": false} print("Mean height: ", heights.mean()) print("Standard deviation:", heights.std()) print("Minimum height: ", heights.min()) @@ -191,7 +190,7 @@ print("Maximum height: ", heights.max()) Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values. We may also wish to compute quantiles: -```python +```python jupyter={"outputs_hidden": false} print("25th percentile: ", np.percentile(heights, 25)) print("Median: ", np.median(heights)) print("75th percentile: ", np.percentile(heights, 75)) @@ -201,22 +200,19 @@ We see that the median height of US presidents is 182 cm, or just shy of six fee Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)). For example, this code generates the following chart: -```python +```python jupyter={"outputs_hidden": false} %matplotlib inline import matplotlib.pyplot as plt -import seaborn; seaborn.set() # set plot style +plt.style.use('seaborn-whitegrid') ``` -```python +```python jupyter={"outputs_hidden": false} plt.hist(heights) plt.title('Height Distribution of US Presidents') plt.xlabel('height (cm)') plt.ylabel('number'); ``` -These aggregates are some of the fundamental pieces of exploratory data analysis that we'll explore in more depth in later chapters of the book. - - < [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) | [Contents](Index.ipynb) | [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb) > diff --git a/notebooks_v2/data/president_heights.csv b/notebooks_v2/data/president_heights.csv index ade149d7..78447473 100644 --- a/notebooks_v2/data/president_heights.csv +++ b/notebooks_v2/data/president_heights.csv @@ -41,3 +41,5 @@ order,name,height(cm) 42,Bill Clinton,188 43,George W. Bush,182 44,Barack Obama,185 +45,Donald Trump,191 +46,Joe Biden,182 From 4d709a9126f68f8e1151d923eba98248adf0f134 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Wed, 6 Oct 2021 06:40:30 -0700 Subject: [PATCH 13/14] Update 02.05 --- ...5-Computation-on-arrays-broadcasting.ipynb | 180 ++++++++++++------ ...2.05-Computation-on-arrays-broadcasting.md | 65 ++++--- 2 files changed, 157 insertions(+), 88 deletions(-) diff --git a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.ipynb b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.ipynb index ee0496c1..7c7d29b3 100644 --- a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.ipynb +++ b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.ipynb @@ -33,9 +33,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We saw in the previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.\n", - "Another means of vectorizing operations is to use NumPy's *broadcasting* functionality.\n", - "Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes." + "We saw in a previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.\n", + "This section discusses *broadcasting*: a set of rules by which NumPy lets you apply binary operations (e.g., addition, subtraction, multiplication, etc.) between arrays of different sizes and shapes." ] }, { @@ -51,7 +50,10 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -62,7 +64,10 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -93,7 +98,10 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -118,22 +126,25 @@ "We can think of this as an operation that stretches or duplicates the value ``5`` into the array ``[5, 5, 5]``, and adds the results.\n", "The advantage of NumPy's broadcasting is that this duplication of values does not actually take place, but it is a useful mental model as we think about broadcasting.\n", "\n", - "We can similarly extend this to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array:" + "We can similarly extend this idea to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1., 1., 1.],\n", - " [ 1., 1., 1.],\n", - " [ 1., 1., 1.]])" + "array([[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]])" ] }, "execution_count": 4, @@ -150,15 +161,18 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1., 2., 3.],\n", - " [ 1., 2., 3.],\n", - " [ 1., 2., 3.]])" + "array([[1., 2., 3.],\n", + " [1., 2., 3.],\n", + " [1., 2., 3.]])" ] }, "execution_count": 5, @@ -183,7 +197,10 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -209,7 +226,10 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -234,7 +254,7 @@ "metadata": {}, "source": [ "Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* ``a`` and ``b`` to match a common shape, and the result is a two-dimensional array!\n", - "The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission)." + "The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the online [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission)." ] }, { @@ -279,7 +299,10 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -313,14 +336,17 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1., 2., 3.],\n", - " [ 1., 2., 3.]])" + "array([[1., 2., 3.],\n", + " [1., 2., 3.]])" ] }, "execution_count": 9, @@ -345,7 +371,10 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -379,7 +408,10 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -412,7 +444,10 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -447,7 +482,10 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -457,7 +495,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,2) (3,) " ] } @@ -480,7 +518,10 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { @@ -502,15 +543,18 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1., 1.],\n", - " [ 2., 2.],\n", - " [ 3., 3.]])" + "array([[1., 1.],\n", + " [2., 2.],\n", + " [3., 3.]])" ] }, "execution_count": 15, @@ -526,7 +570,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Also note that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``.\n", + "Also notice that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``.\n", "For example, here is the ``logaddexp(a, b)`` function, which computes ``log(exp(a) + exp(b))`` with more precision than the naive approach:" ] }, @@ -534,15 +578,18 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([[ 1.31326169, 1.31326169],\n", - " [ 1.69314718, 1.69314718],\n", - " [ 2.31326169, 2.31326169]])" + "array([[1.31326169, 1.31326169],\n", + " [1.69314718, 1.69314718],\n", + " [2.31326169, 2.31326169]])" ] }, "execution_count": 16, @@ -597,11 +644,15 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ - "X = np.random.random((10, 3))" + "rng = np.random.default_rng(seed=1701)\n", + "X = rng.random((10, 3))" ] }, { @@ -615,13 +666,16 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 0.53514715, 0.66567217, 0.44385899])" + "array([0.38503638, 0.36991443, 0.63896043])" ] }, "execution_count": 18, @@ -645,7 +699,10 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -663,13 +720,16 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { "text/plain": [ - "array([ 2.22044605e-17, -7.77156117e-17, -1.66533454e-17])" + "array([ 4.99600361e-17, -4.44089210e-17, 0.00000000e+00])" ] }, "execution_count": 20, @@ -699,7 +759,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "One place that broadcasting is very useful is in displaying images based on two-dimensional functions.\n", + "One place that broadcasting comes in handy is in displaying images based on two-dimensional functions.\n", "If we want to define a function $z = f(x, y)$, broadcasting can be used to compute the function across the grid:" ] }, @@ -707,7 +767,10 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -729,7 +792,10 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -741,23 +807,27 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "image/png": 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LLB6BTSeAm0BtVGgQ2+y9R0HtcXia/X4buD1hWHSqNm/a3V/7CWlmMzTo2HwY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\nHIwtxPGVX5uCmie15H4fjF7PZkv34g0GhNbr2EYra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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "plt.imshow(z, origin='lower', extent=[0, 5, 0, 5],\n", - " cmap='viridis')\n", + "plt.imshow(z, origin='lower', extent=[0, 5, 0, 5])\n", "plt.colorbar();" ] }, @@ -799,9 +869,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md index a0fa2fa2..39438214 100644 --- a/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md +++ b/notebooks_v2/02.05-Computation-on-arrays-broadcasting.md @@ -31,20 +31,19 @@ jupyter: # Computation on Arrays: Broadcasting -We saw in the previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops. -Another means of vectorizing operations is to use NumPy's *broadcasting* functionality. -Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes. +We saw in a previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops. +This section discusses *broadcasting*: a set of rules by which NumPy lets you apply binary operations (e.g., addition, subtraction, multiplication, etc.) between arrays of different sizes and shapes. ## Introducing Broadcasting Recall that for arrays of the same size, binary operations are performed on an element-by-element basis: -```python +```python jupyter={"outputs_hidden": false} import numpy as np ``` -```python +```python jupyter={"outputs_hidden": false} a = np.array([0, 1, 2]) b = np.array([5, 5, 5]) a + b @@ -52,21 +51,21 @@ a + b Broadcasting allows these types of binary operations to be performed on arrays of different sizes–for example, we can just as easily add a scalar (think of it as a zero-dimensional array) to an array: -```python +```python jupyter={"outputs_hidden": false} a + 5 ``` We can think of this as an operation that stretches or duplicates the value ``5`` into the array ``[5, 5, 5]``, and adds the results. The advantage of NumPy's broadcasting is that this duplication of values does not actually take place, but it is a useful mental model as we think about broadcasting. -We can similarly extend this to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array: +We can similarly extend this idea to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array: -```python +```python jupyter={"outputs_hidden": false} M = np.ones((3, 3)) M ``` -```python +```python jupyter={"outputs_hidden": false} M + a ``` @@ -74,7 +73,7 @@ Here the one-dimensional array ``a`` is stretched, or broadcast across the secon While these examples are relatively easy to understand, more complicated cases can involve broadcasting of both arrays. Consider the following example: -```python +```python jupyter={"outputs_hidden": false} a = np.arange(3) b = np.arange(3)[:, np.newaxis] @@ -82,12 +81,12 @@ print(a) print(b) ``` -```python +```python jupyter={"outputs_hidden": false} a + b ``` Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* ``a`` and ``b`` to match a common shape, and the result is a two-dimensional array! -The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission). +The geometry of these examples is visualized in the following figure (Code to produce this plot can be found in the online [appendix](06.00-Figure-Code.ipynb#Broadcasting), and is adapted from source published in the [astroML](http://astroml.org) documentation. Used by permission). ![Broadcasting Visual](figures/02.05-broadcasting.png) @@ -111,7 +110,7 @@ To make these rules clear, let's consider a few examples in detail. Let's look at adding a two-dimensional array to a one-dimensional array: -```python +```python jupyter={"outputs_hidden": false} M = np.ones((2, 3)) a = np.arange(3) ``` @@ -133,7 +132,7 @@ By rule 2, we now see that the first dimension disagrees, so we stretch this dim The shapes match, and we see that the final shape will be ``(2, 3)``: -```python +```python jupyter={"outputs_hidden": false} M + a ``` @@ -141,7 +140,7 @@ M + a Let's take a look at an example where both arrays need to be broadcast: -```python +```python jupyter={"outputs_hidden": false} a = np.arange(3).reshape((3, 1)) b = np.arange(3) ``` @@ -163,7 +162,7 @@ And rule 2 tells us that we upgrade each of these ones to match the correspondin Because the result matches, these shapes are compatible. We can see this here: -```python +```python jupyter={"outputs_hidden": false} a + b ``` @@ -171,7 +170,7 @@ a + b Now let's take a look at an example in which the two arrays are not compatible: -```python +```python jupyter={"outputs_hidden": false} M = np.ones((3, 2)) a = np.arange(3) ``` @@ -194,7 +193,7 @@ By rule 2, the first dimension of ``a`` is stretched to match that of ``M``: Now we hit rule 3–the final shapes do not match, so these two arrays are incompatible, as we can observe by attempting this operation: -```python +```python jupyter={"outputs_hidden": false} M + a ``` @@ -203,18 +202,18 @@ But this is not how the broadcasting rules work! That sort of flexibility might be useful in some cases, but it would lead to potential areas of ambiguity. If right-side padding is what you'd like, you can do this explicitly by reshaping the array (we'll use the ``np.newaxis`` keyword introduced in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb)): -```python +```python jupyter={"outputs_hidden": false} a[:, np.newaxis].shape ``` -```python +```python jupyter={"outputs_hidden": false} M + a[:, np.newaxis] ``` -Also note that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``. +Also notice that while we've been focusing on the ``+`` operator here, these broadcasting rules apply to *any* binary ``ufunc``. For example, here is the ``logaddexp(a, b)`` function, which computes ``log(exp(a) + exp(b))`` with more precision than the naive approach: -```python +```python jupyter={"outputs_hidden": false} np.logaddexp(M, a[:, np.newaxis]) ``` @@ -236,26 +235,27 @@ One commonly seen example is when centering an array of data. Imagine you have an array of 10 observations, each of which consists of 3 values. Using the standard convention (see [Data Representation in Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb#Data-Representation-in-Scikit-Learn)), we'll store this in a $10 \times 3$ array: -```python -X = np.random.random((10, 3)) +```python jupyter={"outputs_hidden": false} +rng = np.random.default_rng(seed=1701) +X = rng.random((10, 3)) ``` We can compute the mean of each feature using the ``mean`` aggregate across the first dimension: -```python +```python jupyter={"outputs_hidden": false} Xmean = X.mean(0) Xmean ``` And now we can center the ``X`` array by subtracting the mean (this is a broadcasting operation): -```python +```python jupyter={"outputs_hidden": false} X_centered = X - Xmean ``` To double-check that we've done this correctly, we can check that the centered array has near zero mean: -```python +```python jupyter={"outputs_hidden": false} X_centered.mean(0) ``` @@ -265,10 +265,10 @@ To within machine precision, the mean is now zero. ### Plotting a two-dimensional function -One place that broadcasting is very useful is in displaying images based on two-dimensional functions. +One place that broadcasting comes in handy is in displaying images based on two-dimensional functions. If we want to define a function $z = f(x, y)$, broadcasting can be used to compute the function across the grid: -```python +```python jupyter={"outputs_hidden": false} # x and y have 50 steps from 0 to 5 x = np.linspace(0, 5, 50) y = np.linspace(0, 5, 50)[:, np.newaxis] @@ -278,14 +278,13 @@ z = np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x) We'll use Matplotlib to plot this two-dimensional array (these tools will be discussed in full in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)): -```python +```python jupyter={"outputs_hidden": false} %matplotlib inline import matplotlib.pyplot as plt ``` -```python -plt.imshow(z, origin='lower', extent=[0, 5, 0, 5], - cmap='viridis') +```python jupyter={"outputs_hidden": false} +plt.imshow(z, origin='lower', extent=[0, 5, 0, 5]) plt.colorbar(); ``` From ce26f2373b1a05adb0ec3d6fbcd74abd25b89907 Mon Sep 17 00:00:00 2001 From: Jake VanderPlas Date: Mon, 18 Oct 2021 19:37:40 -0700 Subject: [PATCH 14/14] remove contents cells from chapters 00 and 01 --- notebooks_v2/00.00-Preface.ipynb | 36 ++----------------- notebooks_v2/00.00-Preface.md | 22 ------------ .../01.00-IPython-Beyond-Normal-Python.ipynb | 36 ++----------------- .../01.00-IPython-Beyond-Normal-Python.md | 22 ------------ .../01.01-Help-And-Documentation.ipynb | 36 ++----------------- notebooks_v2/01.01-Help-And-Documentation.md | 22 ------------ .../01.02-Shell-Keyboard-Shortcuts.ipynb | 36 ++----------------- .../01.02-Shell-Keyboard-Shortcuts.md | 22 ------------ notebooks_v2/01.03-Magic-Commands.ipynb | 36 ++----------------- notebooks_v2/01.03-Magic-Commands.md | 22 ------------ notebooks_v2/01.04-Input-Output-History.ipynb | 36 ++----------------- notebooks_v2/01.04-Input-Output-History.md | 22 ------------ .../01.05-IPython-And-Shell-Commands.ipynb | 36 ++----------------- .../01.05-IPython-And-Shell-Commands.md | 22 ------------ notebooks_v2/01.06-Errors-and-Debugging.ipynb | 36 ++----------------- notebooks_v2/01.06-Errors-and-Debugging.md | 22 ------------ notebooks_v2/01.07-Timing-and-Profiling.ipynb | 32 ----------------- notebooks_v2/01.07-Timing-and-Profiling.md | 22 ------------ .../01.08-More-IPython-Resources.ipynb | 32 ----------------- notebooks_v2/01.08-More-IPython-Resources.md | 22 ------------ 20 files changed, 16 insertions(+), 556 deletions(-) diff --git a/notebooks_v2/00.00-Preface.ipynb b/notebooks_v2/00.00-Preface.ipynb index cf00448e..7a9f48e6 100644 --- a/notebooks_v2/00.00-Preface.ipynb +++ b/notebooks_v2/00.00-Preface.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -153,16 +131,6 @@ "Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **``conda install packagename``**.\n", "For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/)." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -185,9 +153,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/00.00-Preface.md b/notebooks_v2/00.00-Preface.md index 4c9f2cf1..1708aabb 100644 --- a/notebooks_v2/00.00-Preface.md +++ b/notebooks_v2/00.00-Preface.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) > - -Open in Colab - - - # Preface @@ -122,10 +107,3 @@ To get started, download and install the Miniconda package–make sure to choose Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **``conda install packagename``**. For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/). - - - -| [Contents](Index.ipynb) | [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb index 344f9a9e..ee669a6f 100644 --- a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb +++ b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -114,16 +92,6 @@ "the exact address will depend on your system.\n", "If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/lab/* in this example)." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -146,9 +114,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md index f737a514..33e4753f 100644 --- a/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md +++ b/notebooks_v2/01.00-IPython-Beyond-Normal-Python.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) > - -Open in Colab - - - # IPython: Beyond Normal Python @@ -99,10 +84,3 @@ $ jupyter lab Upon issuing the command, your default browser should automatically open and navigate to the listed local URL; the exact address will depend on your system. If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/lab/* in this example). - - - -< [Preface](00.00-Preface.ipynb) | [Contents](Index.ipynb) | [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.01-Help-And-Documentation.ipynb b/notebooks_v2/01.01-Help-And-Documentation.ipynb index d0af7f49..a46f7e12 100644 --- a/notebooks_v2/01.01-Help-And-Documentation.ipynb +++ b/notebooks_v2/01.01-Help-And-Documentation.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -313,16 +291,6 @@ "\n", "I find this type of flexible wildcard search can be useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -345,9 +313,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.01-Help-And-Documentation.md b/notebooks_v2/01.01-Help-And-Documentation.md index 0160caff..7ed20a0b 100644 --- a/notebooks_v2/01.01-Help-And-Documentation.md +++ b/notebooks_v2/01.01-Help-And-Documentation.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) > - -Open in Colab - - - # Help and Documentation in IPython @@ -266,10 +251,3 @@ str.rfind ``` I find this type of flexible wildcard search can be useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one. - - - -< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.ipynb b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.ipynb index 55d4bb55..0c2583af 100644 --- a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.ipynb +++ b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -170,16 +148,6 @@ "While some of the shortcuts discussed here may seem a bit tedious at first, they quickly become automatic with practice.\n", "Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -202,9 +170,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md index 07756386..4a1072eb 100644 --- a/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md +++ b/notebooks_v2/01.02-Shell-Keyboard-Shortcuts.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) > - -Open in Colab - - - # Keyboard Shortcuts in the IPython Shell @@ -143,10 +128,3 @@ The Ctrl-c in particular can be useful when you inadvertently start a very long- While some of the shortcuts discussed here may seem a bit tedious at first, they quickly become automatic with practice. Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts. - - - -< [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) | [Contents](Index.ipynb) | [IPython Magic Commands](01.03-Magic-Commands.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.03-Magic-Commands.ipynb b/notebooks_v2/01.03-Magic-Commands.ipynb index 20c4a6d2..460b9717 100644 --- a/notebooks_v2/01.03-Magic-Commands.ipynb +++ b/notebooks_v2/01.03-Magic-Commands.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -201,16 +179,6 @@ "Finally, I'll mention that it is quite straightforward to define your own magic functions if you wish.\n", "We won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb)." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -233,9 +201,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.03-Magic-Commands.md b/notebooks_v2/01.03-Magic-Commands.md index 9e0ab32f..245468ef 100644 --- a/notebooks_v2/01.03-Magic-Commands.md +++ b/notebooks_v2/01.03-Magic-Commands.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) > - -Open in Colab - - - # IPython Magic Commands @@ -183,10 +168,3 @@ In [12]: %lsmagic Finally, I'll mention that it is quite straightforward to define your own magic functions if you wish. We won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb). - - - -< [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) | [Contents](Index.ipynb) | [Input and Output History](01.04-Input-Output-History.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.04-Input-Output-History.ipynb b/notebooks_v2/01.04-Input-Output-History.ipynb index b3f2b58b..bd4dbe0f 100644 --- a/notebooks_v2/01.04-Input-Output-History.ipynb +++ b/notebooks_v2/01.04-Input-Output-History.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -187,16 +165,6 @@ "Other similar magic commands are ``%rerun`` (which will re-execute some portion of the command history) and ``%save`` (which saves some set of the command history to a file).\n", "For more information, I suggest exploring these using the ``?`` help functionality discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -219,9 +187,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.04-Input-Output-History.md b/notebooks_v2/01.04-Input-Output-History.md index 0266bb04..ef2fef03 100644 --- a/notebooks_v2/01.04-Input-Output-History.md +++ b/notebooks_v2/01.04-Input-Output-History.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) > - -Open in Colab - - - # Input and Output History @@ -160,10 +145,3 @@ In [16]: %history -n 1-3 As usual, you can type ``%history?`` for more information and a description of options available. Other similar magic commands are ``%rerun`` (which will re-execute some portion of the command history) and ``%save`` (which saves some set of the command history to a file). For more information, I suggest exploring these using the ``?`` help functionality discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb). - - - -< [IPython Magic Commands](01.03-Magic-Commands.ipynb) | [Contents](Index.ipynb) | [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb b/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb index 9e4f8711..b8bd7261 100644 --- a/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb +++ b/notebooks_v2/01.05-IPython-And-Shell-Commands.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -218,16 +196,6 @@ "\n", "This access to the shell from within the same terminal window as your Python session lets you more naturally combine Python and the shell in your workflows with fewer context switches." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -250,9 +218,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.05-IPython-And-Shell-Commands.md b/notebooks_v2/01.05-IPython-And-Shell-Commands.md index a2ecf2e5..f9d7735f 100644 --- a/notebooks_v2/01.05-IPython-And-Shell-Commands.md +++ b/notebooks_v2/01.05-IPython-And-Shell-Commands.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) > - -Open in Colab - - - # IPython and Shell Commands @@ -195,10 +180,3 @@ In [20]: rm -r tmp ``` This access to the shell from within the same terminal window as your Python session lets you more naturally combine Python and the shell in your workflows with fewer context switches. - - - -< [Input and Output History](01.04-Input-Output-History.ipynb) | [Contents](Index.ipynb) | [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.06-Errors-and-Debugging.ipynb b/notebooks_v2/01.06-Errors-and-Debugging.ipynb index 2da41e7d..c27b90ba 100644 --- a/notebooks_v2/01.06-Errors-and-Debugging.ipynb +++ b/notebooks_v2/01.06-Errors-and-Debugging.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -416,16 +394,6 @@ "\n", "For more information, use the ``help`` command in the debugger, or take a look at ``ipdb``'s [online documentation](https://github.com/gotcha/ipdb)." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { @@ -448,9 +416,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.9.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/notebooks_v2/01.06-Errors-and-Debugging.md b/notebooks_v2/01.06-Errors-and-Debugging.md index 4f7be233..5cb74310 100644 --- a/notebooks_v2/01.06-Errors-and-Debugging.md +++ b/notebooks_v2/01.06-Errors-and-Debugging.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) > - -Open in Colab - - - # Errors and Debugging @@ -143,10 +128,3 @@ There are many more available commands for interactive debugging than we've list | ``r(eturn)`` | Return out of a subroutine | For more information, use the ``help`` command in the debugger, or take a look at ``ipdb``'s [online documentation](https://github.com/gotcha/ipdb). - - - -< [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb) | [Contents](Index.ipynb) | [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.07-Timing-and-Profiling.ipynb b/notebooks_v2/01.07-Timing-and-Profiling.ipynb index cab56ddd..4de635c1 100644 --- a/notebooks_v2/01.07-Timing-and-Profiling.ipynb +++ b/notebooks_v2/01.07-Timing-and-Profiling.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -517,16 +495,6 @@ "\n", "For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt)." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { diff --git a/notebooks_v2/01.07-Timing-and-Profiling.md b/notebooks_v2/01.07-Timing-and-Profiling.md index f4be2a2b..e4b399e4 100644 --- a/notebooks_v2/01.07-Timing-and-Profiling.md +++ b/notebooks_v2/01.07-Timing-and-Profiling.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) > - -Open in Colab - - - # Profiling and Timing Code @@ -217,10 +202,3 @@ Here the ``Increment`` column tells us how much each line affects the total memo This is on top of the background memory usage from the Python interpreter itself. For more information on ``%memit`` and ``%mprun``, as well as their available options, use the IPython help functionality (i.e., type ``%memit?`` at the IPython prompt). - - - -< [Errors and Debugging](01.06-Errors-and-Debugging.ipynb) | [Contents](Index.ipynb) | [More IPython Resources](01.08-More-IPython-Resources.ipynb) > - -Open in Colab - diff --git a/notebooks_v2/01.08-More-IPython-Resources.ipynb b/notebooks_v2/01.08-More-IPython-Resources.ipynb index 359f6f4e..9d6d71e9 100644 --- a/notebooks_v2/01.08-More-IPython-Resources.ipynb +++ b/notebooks_v2/01.08-More-IPython-Resources.ipynb @@ -1,27 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n", - "\n", - "\"Open\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -62,16 +40,6 @@ "Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be useful if you use it well and use it often.\n", "As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) >\n", - "\n", - "\"Open\n" - ] } ], "metadata": { diff --git a/notebooks_v2/01.08-More-IPython-Resources.md b/notebooks_v2/01.08-More-IPython-Resources.md index a5f7badc..65c88c73 100644 --- a/notebooks_v2/01.08-More-IPython-Resources.md +++ b/notebooks_v2/01.08-More-IPython-Resources.md @@ -13,21 +13,6 @@ jupyter: name: python3 --- - - - -*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).* - -*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!* - - - -< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) > - -Open in Colab - - - # More IPython Resources @@ -51,10 +36,3 @@ Much more information is available both in print and on the Web, and here we'll Finally, a reminder that you can find help on your own: IPython's ``?``-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be useful if you use it well and use it often. As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer. - - - -< [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb) | [Contents](Index.ipynb) | [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) > - -Open in Colab -