01 - Scientific modules and IPython.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scientific modules and IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nikolay Koldunov\n",
    "\n",
    "\n",
    "koldunovn@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is part of [**Python for Geosciences**](https://github.com/koldunovn/python_for_geosciences) notes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Core scientific packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When people say that they do their scientific computations in Python it's only half true. Python is a construction set, similar to MITgcm or other models. Without packages it's only a core, that although very powerful, does not seems to be able to do much by itself.\n",
    "\n",
    "There is a set of packages, that almost every scientist would need:\n",
    "\n",
    "<img  height=\"200\" src=\"files/core.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to talk about all exept Sympy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installing python and all necessary packages considered being a hard task in the past. However with introduction of [conda](http://conda.pydata.org/), even complete beginner now can install all necessary packages without a lot of hassle. It works more or less similarly for all major systems (Windows, Mac, Linux). \n",
    "\n",
    "Here are the instructions that will set you up for the rest of the notes.\n",
    "\n",
    "- First download [miniconda](http://conda.pydata.org/miniconda.html) and follow the instructions for your system to install it.\n",
    "\n",
    "- Install necessary packages by executing the following command:\n",
    "\n",
    "    \n",
    "    conda install scipy matplotlib ipython-notebook pandas netcdf4 basemap \n",
    "    \n",
    "    \n",
    "- There is one more package that we have to install with a bit more special command:\n",
    "\n",
    "\n",
    "    conda install -c http://conda.anaconda.org/ioos iris\n",
    "    \n",
    "\n",
    "If everything worked fine - you are ready to follow examples in this notebook series.\n",
    "\n",
    "### For ZMAW users\n",
    "\n",
    "In order to use latest version of python on zmaw computers one has to load appropriate module:\n",
    "\n",
    "    module load python/2.7-ve2\n",
    "\n",
    "### Ubuntu\n",
    "\n",
    "It is strongly recomended to use conda, but if you whant to stick to your Linux repositories, the following command should set you up on Ubuntu or other Debian-based distributions:\n",
    "\n",
    "    sudo apt-get install ipython-notebook python-matplotlib python-scipy python-pandas python-sympy python-mpltoolkits.basemap netcdf-bin cdo\n",
    "    \n",
    "However packages like iris and netcdf4 will not work.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPython and pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to be productive you need comfortable environment, and this is what IPython provide. It was started as enhanced python interactive shell, but with time become architecture for interactive computing.\n",
    "\n",
    "Starting IPython with *--pylab* option loads some necessary modules (NumPy for data array support and Matplotlib for plotting support), that make it similar to Matlab console. However now this considered to be bad practice, espetially for begginers, since it's not always clear where things that we use came from. For the rest of the series we are going to use IPython without `--pylab` option.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IPython notebook "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the 0.12 release, IPython provides a new rich text web interface - IPython notebook. Here you can combine:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I love Python\n"
     ]
    }
   ],
   "source": [
    "print('I love Python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Text (Markdown)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython [website](http://ipython.org/).\n",
    "\n",
    "List:\n",
    "\n",
    "* [Python on Codeacademy](http://www.codecademy.com/tracks/python)\n",
    "* [Google's Python Class](https://developers.google.com/edu/python/)\n",
    "\n",
    "Code:\n",
    "\n",
    "    print('hello world')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### $\\LaTeX$ equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\int_0^\\infty e^{-x^2} dx=\\frac{\\sqrt{\\pi}}{2}$$\n",
    "$$\n",
    "F(x,y)=0 ~~\\mbox{and}~~\n",
    "\\left| \\begin{array}{ccc}\n",
    "  F''_{xx} & F''_{xy} &  F'_x \\\\\n",
    "  F''_{yx} & F''_{yy} &  F'_y \\\\\n",
    "  F'_x     & F'_y     & 0 \n",
    "  \\end{array}\\right| = 0\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10604f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3,4,5]\n",
    "plt.plot(x);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rich media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/F4rFuIb1Ie4\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x106165090>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('F4rFuIb1Ie4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [IPython website](http://ipython.org/)\n",
    "* [Notebook gallery](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to start IPython notebook you have to type:\n",
    "\n",
    "    ipython notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can download and run this lectures:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Web version can be accesed from the [github repository](https://github.com/koldunovn/python_for_geosciences)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download them as .zip file:\n",
    "\n",
    "    wget https://github.com/koldunovn/python_for_geosciences/archive/master.zip\n",
    "\n",
    "Unzip:\n",
    "    \n",
    "    unzip master.zip\n",
    "\n",
    "And run:\n",
    "\n",
    "    cd python_for_geosciences-master/\n",
    "    ipython notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main IPyhton features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use question mark in order to get help. To execute cell you have to press *Shift+Enter*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question mark after a function will open pager with documentation. Double question mark will show you source code of the function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Press TAB after opening bracket in order to get help for the function (list of arguments, doc string)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sum("
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing the underlying operating system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access system functions by typing exclamation mark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/koldunov/PYTHON/test/python_for_geosciences\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you already have some netCDF file in the directory and *ncdump* is installed, you can for example look at its header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf test_netcdf {\r\n",
      "dimensions:\r\n",
      "\tTIME = 1464 ;\r\n",
      "\tLATITUDE = 73 ;\r\n",
      "\tLONGITUDE = 144 ;\r\n",
      "variables:\r\n",
      "\tfloat TIME(TIME) ;\r\n",
      "\t\tTIME:units = \"hours since 1-1-1 00:00:0.0\" ;\r\n",
      "\tfloat LATITUDE(LATITUDE) ;\r\n",
      "\tfloat LONGITUDE(LONGITUDE) ;\r\n",
      "\tfloat New_air(TIME, LATITUDE, LONGITUDE) ;\r\n",
      "\t\tNew_air:missing_value = -9999.f ;\r\n",
      "}\r\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h test_netcdf.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of [cdo](https://code.zmaw.de/projects/cdo) usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\r\n",
      "cdo nyear: Processed 1 variable over 1464 timesteps ( 0.01s )\r\n"
     ]
    }
   ],
   "source": [
    "!cdo nyear test_netcdf.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get information from OS output to the python variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cdo nmon: Processed 1 variable over 1464 timesteps ( 0.01s )', '13']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmon = !cdo nmon test_netcdf.nc\n",
    "nmon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return information from Pyhton variable to the SHELL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\r\n"
     ]
    }
   ],
   "source": [
    "!echo {nmon[1]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magic functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The magic function system provides a series of functions which allow you to\n",
    "control the behavior of IPython itself, plus a lot of system-type\n",
    "features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create some set of numbers using [range](http://docs.python.org/2/library/functions.html#range) command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And find out how long does it take to run it with *%timeit* magic function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 8.60 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "1000000 loops, best of 3: 249 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit range(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print all interactive variables (similar to Matlab function):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type      Data/Info\n",
      "----------------------------------\n",
      "YouTubeVideo   type      <class 'IPython.lib.display.YouTubeVideo'>\n",
      "nmon           SList     ['cdo nmon: Processed 1 v<...>mesteps ( 0.01s )', '13']\n",
      "plt            module    <module 'matplotlib.pylab<...>es/matplotlib/pylab.pyc'>\n",
      "x              list      n=5\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cell-oriented magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Receive as argument both the current line where they are declared and the whole body of the cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000000 loops, best of 3: 940 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "range(10)\n",
    "range(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thre are several cell-oriented magic functions that allow you to run code in other languages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My shell is: /bin/bash\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "echo \"My shell is:\" $SHELL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variable has the value of 1\n"
     ]
    }
   ],
   "source": [
    "%%perl\n",
    "\n",
    "$variable = 1;\n",
    "print \"The variable has the value of $variable\\n\";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can write content of the cell to a file with *%%writefile* (or *%%file* for ipython < 1.0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing hello.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile hello.py\n",
    "#if you use ipython < 1.0, use %%file comand\n",
    "#%%file \n",
    "a = 'hello world!'\n",
    "print(a)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world!\n"
     ]
    }
   ],
   "source": [
    "%run hello.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *%run* magic will run your python script and load all variables into your interactive namespace for further use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type      Data/Info\n",
      "----------------------------------\n",
      "YouTubeVideo   type      <class 'IPython.lib.display.YouTubeVideo'>\n",
      "a              str       hello world!\n",
      "nmon           SList     ['cdo nmon: Processed 1 v<...>mesteps ( 0.01s )', '13']\n",
      "plt            module    <module 'matplotlib.pylab<...>es/matplotlib/pylab.pyc'>\n",
      "x              list      n=5\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get information about all magic functions type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Links:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The cell magics in IPython](http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Cell%20Magics.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}