final-project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Initialize packages to be used, enable matplotlib inline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import csv\n",
    "import pywt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Haar Wavelet Python Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vnc5S4MACF6dq5G6D3PWDbWD9s+tfERGvnOsLFqVjmA1J4/2EYtWZ3G2Qu36w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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x931ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = pywt.Wavelet('Haar')\n",
    "phi, psi, x = w.wavefun(level=10)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlim(-.02, 1.02)\n",
    "ax.grid(color='grey', linestyle='--')\n",
    "ax.plot(x, psi);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "For the final project a significant amount of data had to be analyzed. The data we will be analyzing was obtained from the console log of the web application and placed into a text file. For ease of integer parsing, the X, Y and time step of each value are not correlated and are simply listed in order. Correlating them together is part of the initial cleaning of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[166, 19, 13217713, 161, 25, 13217729, 154, 38, 13217755, 147, 54, 13217771, 128, 95, 13217793, 110, 132, 13217814, 107, 143, 13217837, 107, 152, 13217845, 113, 189, 13217880]\n"
     ]
    }
   ],
   "source": [
    "# First we start by turning our text file lines into a clean list\n",
    "f = open(\"data/base_trial.csv\")\n",
    "empty_list = []\n",
    "for row in f:\n",
    "    i = int(row.strip())\n",
    "    empty_list.append(i)\n",
    "\n",
    "# Now let's do a test print for verification\n",
    "print(empty_list[0: 27])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[166, 19, 13217713], [161, 25, 13217729], [154, 38, 13217755], [147, 54, 13217771], [128, 95, 13217793], [110, 132, 13217814], [107, 143, 13217837], [107, 152, 13217845], [113, 189, 13217880]]\n"
     ]
    }
   ],
   "source": [
    "# Now that we have our initial list, we need to organize the data\n",
    "# into individual correlated arrays\n",
    "complete_list = [empty_list[x:x+3] for x in range(0, len(empty_list),3)]\n",
    "\n",
    "# Now let's do another small test print for verification\n",
    "print(complete_list[0:9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Very nice! Now that we have the data in correlated arrays, lets combine it\n",
    "# all together in a function so that we don't need to re-do this every time.\n",
    "def dataClean(file):\n",
    "    f = open(file)\n",
    "    empty_list = []\n",
    "    for row in f:\n",
    "        i = int(row.strip())\n",
    "        empty_list.append(i)\n",
    "    complete_list = [empty_list[x:x+3] for x in range(0, len(empty_list),3)]\n",
    "    return complete_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[161, 25, 13217729], [154, 38, 13217755], [147, 54, 13217771], [128, 95, 13217793], [110, 132, 13217814], [107, 143, 13217837], [107, 152, 13217845], [113, 189, 13217880]]\n"
     ]
    }
   ],
   "source": [
    "# Now that the data has been reduced into a function, clean data from the\n",
    "# console output can be produced on the fly!\n",
    "print(dataClean(\"data/base_trial.csv\")[1:9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[161, 25, 13217729], [154, 38, 13217755], [147, 54, 13217771], [128, 95, 13217793], [110, 132, 13217814], [107, 143, 13217837], [107, 152, 13217845], [113, 189, 13217880]]\n"
     ]
    }
   ],
   "source": [
    "# The function can be utilized for both .csv and .txt files without need\n",
    "# to change anything. CSVs could potentially be useful in the future for\n",
    "# removing or changing large pieces of data to introduce outlier behavior.\n",
    "print(dataClean(\"data/base_trial.txt\")[1:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Plotting\n",
    "Now that we have clean/useful data, we can proceed to plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Building plotting function\n",
    "def plot(file):\n",
    "    working_list = dataClean(file)\n",
    "    name = file\n",
    "    x, y, t = [], [], []\n",
    "    for i in working_list:\n",
    "        x.append(i[0])\n",
    "        # y is flipped in the \n",
    "        y.append(800-i[1])\n",
    "        t.append(i[2])\n",
    "    plt.title(file)\n",
    "    plt.xlabel('X-position')\n",
    "    plt.xlim([0,800])\n",
    "    plt.ylim([0,800])\n",
    "    plt.ylabel('Y-position')\n",
    "    return plt.scatter(x,y, s=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gIslwINuA+vRlZnupuDHdY4FZNRjw601E/F22LOmQiHixtCGZjT3FDejFbSFm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x810a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trial2 = plot((\"data/base_trial2.txt\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets compare our data plotting to the true drawing from the web application:\n",
    "![drawing](pictures/base_trial2.PNG)\n",
    "Looks about right!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mljO0rwuoMhZYm3u8DnhjfgZJM4GZ6eE2SUt7qbbuOAB4vK+LaIDr7Fmus+f0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\n9Gt5ptY+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x931f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot some data initially\n",
    "data1 = plot((\"data/left_bottom_top_right.txt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}