optimalProbesAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output generated\n",
    "This Jupyter Notebook provides a guide to how you can extract the information from the data generated.\n",
    "First we go through what each output file contains and how to understand the output files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mITER1\u001b[m\u001b[m/                       \u001b[1m\u001b[34mITER6\u001b[m\u001b[m/\r\n",
      "\u001b[1m\u001b[34mITER10\u001b[m\u001b[m/                      \u001b[1m\u001b[34mITER7\u001b[m\u001b[m/\r\n",
      "\u001b[1m\u001b[34mITER2\u001b[m\u001b[m/                       \u001b[1m\u001b[34mITER8\u001b[m\u001b[m/\r\n",
      "\u001b[1m\u001b[34mITER3\u001b[m\u001b[m/                       \u001b[1m\u001b[34mITER9\u001b[m\u001b[m/\r\n",
      "\u001b[1m\u001b[34mITER4\u001b[m\u001b[m/                       optimalProbesAnalysis.ipynb\r\n",
      "\u001b[1m\u001b[34mITER5\u001b[m\u001b[m/\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cp ITER*/*output* ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter_10_output_sets.txt  iter_4_output_sets.txt   iter_8_output_sets.txt\r\n",
      "iter_1_output_sets.txt   iter_5_output_sets.txt   iter_9_output_sets.txt\r\n",
      "iter_2_output_sets.txt   iter_6_output_sets.txt\r\n",
      "iter_3_output_sets.txt   iter_7_output_sets.txt\r\n"
     ]
    }
   ],
   "source": [
    "ls *output*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num_probes</th>\n",
       "      <th>Num_distances</th>\n",
       "      <th>Score</th>\n",
       "      <th>Pair1-resid1</th>\n",
       "      <th>Pair1-resid2</th>\n",
       "      <th>Pair2-resid1</th>\n",
       "      <th>Pair2-resid2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.53996085962209</td>\n",
       "      <td>72</td>\n",
       "      <td>236</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.85319696500996</td>\n",
       "      <td>72</td>\n",
       "      <td>154</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.3796246466103</td>\n",
       "      <td>148</td>\n",
       "      <td>242</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20661676211227</td>\n",
       "      <td>123</td>\n",
       "      <td>274</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.52279315336759</td>\n",
       "      <td>110</td>\n",
       "      <td>281</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.21585011277249</td>\n",
       "      <td>40</td>\n",
       "      <td>202</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.28482225698727</td>\n",
       "      <td>73</td>\n",
       "      <td>175</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.87032892682866</td>\n",
       "      <td>7</td>\n",
       "      <td>155</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.10270204439083</td>\n",
       "      <td>76</td>\n",
       "      <td>175</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.21020113546678</td>\n",
       "      <td>124</td>\n",
       "      <td>276</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.48961986269804</td>\n",
       "      <td>75</td>\n",
       "      <td>150</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.95788025552148</td>\n",
       "      <td>147</td>\n",
       "      <td>240</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.72123547860454</td>\n",
       "      <td>6</td>\n",
       "      <td>153</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.66162172436938</td>\n",
       "      <td>36</td>\n",
       "      <td>112</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.86547659748193</td>\n",
       "      <td>5</td>\n",
       "      <td>158</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.67185539844968</td>\n",
       "      <td>70</td>\n",
       "      <td>237</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.04811824308246</td>\n",
       "      <td>6</td>\n",
       "      <td>237</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.92279316196943</td>\n",
       "      <td>5</td>\n",
       "      <td>150</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.29064102734885</td>\n",
       "      <td>45</td>\n",
       "      <td>281</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.11448945167794</td>\n",
       "      <td>164</td>\n",
       "      <td>234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.77932135445171</td>\n",
       "      <td>36</td>\n",
       "      <td>112</td>\n",
       "      <td>124</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.74632649993624</td>\n",
       "      <td>45</td>\n",
       "      <td>281</td>\n",
       "      <td>70</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.86408111049783</td>\n",
       "      <td>6</td>\n",
       "      <td>237</td>\n",
       "      <td>123</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.30909996124544</td>\n",
       "      <td>76</td>\n",
       "      <td>175</td>\n",
       "      <td>147</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num_probes Num_distances             Score Pair1-resid1 Pair1-resid2  \\\n",
       "0           2             1  2.53996085962209           72          236   \n",
       "1           2             1  2.85319696500996           72          154   \n",
       "2           2             1   2.3796246466103          148          242   \n",
       "3           2             1  1.20661676211227          123          274   \n",
       "4           2             1  1.52279315336759          110          281   \n",
       "5           2             1  2.21585011277249           40          202   \n",
       "6           2             1  2.28482225698727           73          175   \n",
       "7           2             1  2.87032892682866            7          155   \n",
       "8           2             1  2.10270204439083           76          175   \n",
       "9           2             1  1.21020113546678          124          276   \n",
       "10          2             1  2.48961986269804           75          150   \n",
       "11          2             1  2.95788025552148          147          240   \n",
       "12          2             1  2.72123547860454            6          153   \n",
       "13          2             1  1.66162172436938           36          112   \n",
       "14          2             1  2.86547659748193            5          158   \n",
       "15          2             1  2.67185539844968           70          237   \n",
       "16          2             1  3.04811824308246            6          237   \n",
       "17          2             1  2.92279316196943            5          150   \n",
       "18          2             1  1.29064102734885           45          281   \n",
       "19          2             1  3.11448945167794          164          234   \n",
       "20          4             2  1.77932135445171           36          112   \n",
       "21          4             2  2.74632649993624           45          281   \n",
       "22          4             2  2.86408111049783            6          237   \n",
       "23          4             2  3.30909996124544           76          175   \n",
       "\n",
       "   Pair2-resid1 Pair2-resid2  \n",
       "0          None         None  \n",
       "1          None         None  \n",
       "2          None         None  \n",
       "3          None         None  \n",
       "4          None         None  \n",
       "5          None         None  \n",
       "6          None         None  \n",
       "7          None         None  \n",
       "8          None         None  \n",
       "9          None         None  \n",
       "10         None         None  \n",
       "11         None         None  \n",
       "12         None         None  \n",
       "13         None         None  \n",
       "14         None         None  \n",
       "15         None         None  \n",
       "16         None         None  \n",
       "17         None         None  \n",
       "18         None         None  \n",
       "19         None         None  \n",
       "20          124          276  \n",
       "21           70          237  \n",
       "22          123          274  \n",
       "23          147          240  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = open('iter_1_output_sets.txt','rb')\n",
    "file=[]\n",
    "for line in f:\n",
    "    cols=line.strip().split('\\t')\n",
    "    file.append(cols)\n",
    "f.close()\n",
    "\n",
    "import pandas as pd\n",
    "file = pd.DataFrame(file,columns=['Num_probes','Num_distances','Score','Pair1-resid1','Pair1-resid2','Pair2-resid1','Pair2-resid2'])\n",
    "file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "Below are certain plots to understand the output generated from the program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAFkCAYAAACuFXjcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3+VBspUgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106f8a050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "populationSize=24\n",
    "N_ITERATIONS=10\n",
    "\n",
    "scores=[]\n",
    "\n",
    "for i in range(1,N_ITERATIONS+1):\n",
    "        f= open('iter_'+str(i)+'_output_sets.txt','rb')\n",
    "        for line in f:\n",
    "            columns=line.strip('\\n').split('\\t')\n",
    "            scores.append(columns[2])\n",
    "        f.close()\n",
    "        \n",
    "t=[]\n",
    "for i in range(0,N_ITERATIONS):\n",
    "    l=i*populationSize\n",
    "    u=l+populationSize\n",
    "    t.append(l)\n",
    "    x=range(l,u)\n",
    "    plt.plot(x,scores[l:u],'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the scores are increasing as the number of rounds are progressing."
   ]
  }
 ],
 "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}