SimpleModel_AllData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/Users/dorisv/Documents/imaging_behavior\")\n",
    "sys.path.append(\"C:\\Users\\dorisv\\Desktop\\Shawn's data\")\n",
    "\n",
    "try:\n",
    "    import aibs\n",
    "except:\n",
    "    import aibs\n",
    "    \n",
    "from aibs.dev.shawndev import FileTools\n",
    "from aibs.dev.shawndev.BehaviorEvents import BehaviorEvents\n",
    "from aibs.Analysis.InDevelopment import ForageSession\n",
    "from aibs.dev.shawndev import Forage, Psychometrics, ForageTools, FileTools\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn\n",
    "pd.set_option('display.notebook_repr_html',True)\n",
    "pd.set_option('display.width', 200)\n",
    "pd.set_option('display.max_rows', 30)\n",
    "pd.set_option('display.notebook_repr_html',True)\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import os\n",
    "from scipy.misc import comb\n",
    "import scipy.stats\n",
    "\n",
    "import imaging_behavior as im\n",
    "import imaging_behavior.plotting.plotting_functions as pf\n",
    "import imaging_behavior.plotting.utilities as pu\n",
    "from aibs.Analysis.Core import smooth\n",
    "\n",
    "from scipy.optimize import minimize\n",
    "from scipy.optimize import basinhopping\n",
    "import math\n",
    "from math import log\n",
    "from numpy import linalg as LA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\IPython\\nbformat\\current.py:19: UserWarning: IPython.nbformat.current is deprecated.\n",
      "\n",
      "- use IPython.nbformat for read/write/validate public API\n",
      "- use IPython.nbformat.vX directly to composing notebooks of a particular version\n",
      "\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "#load data\n",
    "import io\n",
    "from IPython.nbformat import current\n",
    "\n",
    "def execute_notebook(nbfile):\n",
    "    \n",
    "    with io.open(nbfile) as f:\n",
    "        nb = current.read(f, 'json')\n",
    "    \n",
    "    ip = get_ipython()\n",
    "    \n",
    "    for cell in nb.worksheets[0].cells:\n",
    "        if cell.cell_type != 'code':\n",
    "            continue\n",
    "        ip.run_cell(cell.input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Ori</th>\n",
       "      <th>PosY</th>\n",
       "      <th>Size</th>\n",
       "      <th>abs_reward_rate</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>duration</th>\n",
       "      <th>end_frame</th>\n",
       "      <th>end_time</th>\n",
       "      <th>first_lick_latency_0</th>\n",
       "      <th>...</th>\n",
       "      <th>lick_times_1</th>\n",
       "      <th>mouse_id</th>\n",
       "      <th>number_of_licks_0</th>\n",
       "      <th>number_of_licks_1</th>\n",
       "      <th>number_of_rewards</th>\n",
       "      <th>reward_times</th>\n",
       "      <th>start_date_time</th>\n",
       "      <th>start_frame</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_date_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0   </th>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 1.408580</td>\n",
       "      <td> 54.905943</td>\n",
       "      <td> 1.742769</td>\n",
       "      <td>    104</td>\n",
       "      <td>    1.742769</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>      0</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1   </th>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.408580</td>\n",
       "      <td> 74.060865</td>\n",
       "      <td> 1.182906</td>\n",
       "      <td>    175</td>\n",
       "      <td>    2.925675</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>    105</td>\n",
       "      <td>    1.742769</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2   </th>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 1.373647</td>\n",
       "      <td> 75.229013</td>\n",
       "      <td> 1.250163</td>\n",
       "      <td>    250</td>\n",
       "      <td>    4.175839</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>    176</td>\n",
       "      <td>    2.925675</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3   </th>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 350</td>\n",
       "      <td> 1.317835</td>\n",
       "      <td> 45.388544</td>\n",
       "      <td> 3.349858</td>\n",
       "      <td>    451</td>\n",
       "      <td>    7.525696</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>    251</td>\n",
       "      <td>    4.175839</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4   </th>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 1.261059</td>\n",
       "      <td> 25.822834</td>\n",
       "      <td> 5.015883</td>\n",
       "      <td>    752</td>\n",
       "      <td>   12.541580</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>    452</td>\n",
       "      <td>    7.525696</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5   </th>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 450</td>\n",
       "      <td> 1.214683</td>\n",
       "      <td> 84.197297</td>\n",
       "      <td> 1.483320</td>\n",
       "      <td>    841</td>\n",
       "      <td>   14.024900</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>    753</td>\n",
       "      <td>   12.541580</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6   </th>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 1.167418</td>\n",
       "      <td> 70.252978</td>\n",
       "      <td> 2.033290</td>\n",
       "      <td>    963</td>\n",
       "      <td>   16.058190</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>    842</td>\n",
       "      <td>   14.024900</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7   </th>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 600</td>\n",
       "      <td> 1.084758</td>\n",
       "      <td> 63.358064</td>\n",
       "      <td> 2.100066</td>\n",
       "      <td>   1089</td>\n",
       "      <td>   18.158257</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>    964</td>\n",
       "      <td>   16.058190</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8   </th>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.052723</td>\n",
       "      <td> 36.910872</td>\n",
       "      <td> 4.915870</td>\n",
       "      <td>   1384</td>\n",
       "      <td>   23.074127</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>   1090</td>\n",
       "      <td>   18.158257</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9   </th>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.015602</td>\n",
       "      <td> 51.740277</td>\n",
       "      <td> 2.800250</td>\n",
       "      <td>   1552</td>\n",
       "      <td>   25.874377</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>   1385</td>\n",
       "      <td>   23.074127</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10  </th>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 0.980743</td>\n",
       "      <td> 24.475602</td>\n",
       "      <td> 4.299668</td>\n",
       "      <td>   1810</td>\n",
       "      <td>   30.174045</td>\n",
       "      <td> 2.364923</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  8</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> [27.6072616137]</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>   1553</td>\n",
       "      <td>   25.874377</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11  </th>\n",
       "      <td>     bird</td>\n",
       "      <td>-45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 500</td>\n",
       "      <td> 1.815128</td>\n",
       "      <td> 23.271427</td>\n",
       "      <td> 6.065864</td>\n",
       "      <td>   2174</td>\n",
       "      <td>   36.239909</td>\n",
       "      <td> 4.015518</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  7</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>   1811</td>\n",
       "      <td>   30.174045</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12  </th>\n",
       "      <td>     bird</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.777927</td>\n",
       "      <td> 15.914975</td>\n",
       "      <td> 8.116695</td>\n",
       "      <td>   2661</td>\n",
       "      <td>   44.356604</td>\n",
       "      <td> 1.500413</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  2</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>   2175</td>\n",
       "      <td>   36.239909</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13  </th>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 600</td>\n",
       "      <td> 1.719752</td>\n",
       "      <td> 81.630678</td>\n",
       "      <td> 1.082641</td>\n",
       "      <td>   2726</td>\n",
       "      <td>   45.439246</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>   2662</td>\n",
       "      <td>   44.356604</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14  </th>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.688292</td>\n",
       "      <td> 75.701888</td>\n",
       "      <td> 1.850354</td>\n",
       "      <td>   2837</td>\n",
       "      <td>   47.289600</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td>   2727</td>\n",
       "      <td>   45.439246</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 2.837113</td>\n",
       "      <td> 26.418836</td>\n",
       "      <td> 4.366235</td>\n",
       "      <td> 284423</td>\n",
       "      <td> 4740.058698</td>\n",
       "      <td> 1.983293</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 11</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> [4737.39287935]</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 284162</td>\n",
       "      <td> 4735.692463</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 300</td>\n",
       "      <td> 2.901708</td>\n",
       "      <td> 63.765922</td>\n",
       "      <td> 2.100456</td>\n",
       "      <td> 284549</td>\n",
       "      <td> 4742.159154</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 284424</td>\n",
       "      <td> 4740.058698</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 3.511092</td>\n",
       "      <td> 30.668452</td>\n",
       "      <td> 4.549667</td>\n",
       "      <td> 284822</td>\n",
       "      <td> 4746.708821</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 284550</td>\n",
       "      <td> 4742.159154</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 3.967242</td>\n",
       "      <td> 59.913858</td>\n",
       "      <td> 1.882753</td>\n",
       "      <td> 284935</td>\n",
       "      <td> 4748.591574</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 284823</td>\n",
       "      <td> 4746.708821</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>     bird</td>\n",
       "      <td>-30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 3.939057</td>\n",
       "      <td> 55.006771</td>\n",
       "      <td> 1.716564</td>\n",
       "      <td> 285038</td>\n",
       "      <td> 4750.308138</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 284936</td>\n",
       "      <td> 4748.591574</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 450</td>\n",
       "      <td> 4.146193</td>\n",
       "      <td> 51.320867</td>\n",
       "      <td> 1.916542</td>\n",
       "      <td> 285153</td>\n",
       "      <td> 4752.224680</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 285039</td>\n",
       "      <td> 4750.308138</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 500</td>\n",
       "      <td> 4.313428</td>\n",
       "      <td> 46.464892</td>\n",
       "      <td> 1.916420</td>\n",
       "      <td> 285268</td>\n",
       "      <td> 4754.141100</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 285154</td>\n",
       "      <td> 4752.224680</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>     bird</td>\n",
       "      <td>-45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 300</td>\n",
       "      <td> 3.828474</td>\n",
       "      <td> 53.133577</td>\n",
       "      <td> 1.816478</td>\n",
       "      <td> 285377</td>\n",
       "      <td> 4755.957578</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 285269</td>\n",
       "      <td> 4754.141100</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 300</td>\n",
       "      <td> 3.925290</td>\n",
       "      <td> 45.073362</td>\n",
       "      <td> 3.700263</td>\n",
       "      <td> 285599</td>\n",
       "      <td> 4759.657841</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 285378</td>\n",
       "      <td> 4755.957578</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 4.079184</td>\n",
       "      <td> 28.017221</td>\n",
       "      <td> 5.632619</td>\n",
       "      <td> 285937</td>\n",
       "      <td> 4765.290459</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 285600</td>\n",
       "      <td> 4759.657841</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td> mushroom</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 500</td>\n",
       "      <td> 4.614564</td>\n",
       "      <td> 32.340502</td>\n",
       "      <td> 4.966122</td>\n",
       "      <td> 286235</td>\n",
       "      <td> 4770.256581</td>\n",
       "      <td> 1.915501</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 11</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> [4766.90598388]</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 285938</td>\n",
       "      <td> 4765.290459</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td> mushroom</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 300</td>\n",
       "      <td> 4.956338</td>\n",
       "      <td> 53.609111</td>\n",
       "      <td> 1.750334</td>\n",
       "      <td> 286340</td>\n",
       "      <td> 4772.006915</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 286236</td>\n",
       "      <td> 4770.256581</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td> mushroom</td>\n",
       "      <td>-30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 350</td>\n",
       "      <td> 5.159482</td>\n",
       "      <td> 51.750383</td>\n",
       "      <td> 1.716273</td>\n",
       "      <td> 286443</td>\n",
       "      <td> 4773.723188</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 286341</td>\n",
       "      <td> 4772.006915</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 450</td>\n",
       "      <td> 4.462697</td>\n",
       "      <td> 28.700524</td>\n",
       "      <td> 6.949890</td>\n",
       "      <td> 286860</td>\n",
       "      <td> 4780.673079</td>\n",
       "      <td> 1.933304</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> [4775.35651121]</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 286444</td>\n",
       "      <td> 4773.723188</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td> mushroom</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 500</td>\n",
       "      <td> 4.644082</td>\n",
       "      <td> 21.416369</td>\n",
       "      <td> 7.215661</td>\n",
       "      <td> 287293</td>\n",
       "      <td> 4787.888739</td>\n",
       "      <td> 4.349974</td>\n",
       "      <td>...</td>\n",
       "      <td> []</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> [4784.73796704]</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "      <td> 286861</td>\n",
       "      <td> 4780.673079</td>\n",
       "      <td> 2014-10-03 14:49:45.315000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1290 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Image  Ori  PosY  Size  abs_reward_rate  average_speed  duration  end_frame     end_time  first_lick_latency_0          ...          lick_times_1 mouse_id number_of_licks_0  \\\n",
       "0         bird  -15     0   550         1.408580      54.905943  1.742769        104     1.742769                   NaN          ...                    []  M130587                 0   \n",
       "1         bird    0     0   400         1.408580      74.060865  1.182906        175     2.925675                   NaN          ...                    []  M130587                 0   \n",
       "2         bird  -15     0   550         1.373647      75.229013  1.250163        250     4.175839                   NaN          ...                    []  M130587                 0   \n",
       "3         bird    0     0   350         1.317835      45.388544  3.349858        451     7.525696                   NaN          ...                    []  M130587                 0   \n",
       "4         bird   45     0   550         1.261059      25.822834  5.015883        752    12.541580                   NaN          ...                    []  M130587                 0   \n",
       "5         bird    0     0   450         1.214683      84.197297  1.483320        841    14.024900                   NaN          ...                    []  M130587                 0   \n",
       "6         bird   45     0   550         1.167418      70.252978  2.033290        963    16.058190                   NaN          ...                    []  M130587                 0   \n",
       "7         bird  -15     0   600         1.084758      63.358064  2.100066       1089    18.158257                   NaN          ...                    []  M130587                 0   \n",
       "8         bird   45     0   400         1.052723      36.910872  4.915870       1384    23.074127                   NaN          ...                    []  M130587                 0   \n",
       "9         bird  -15     0   400         1.015602      51.740277  2.800250       1552    25.874377                   NaN          ...                    []  M130587                 0   \n",
       "10    mushroom   15     0   400         0.980743      24.475602  4.299668       1810    30.174045              2.364923          ...                    []  M130587                 8   \n",
       "11        bird  -45     0   500         1.815128      23.271427  6.065864       2174    36.239909              4.015518          ...                    []  M130587                 7   \n",
       "12        bird   15     0   400         1.777927      15.914975  8.116695       2661    44.356604              1.500413          ...                    []  M130587                 2   \n",
       "13    mushroom   15     0   600         1.719752      81.630678  1.082641       2726    45.439246                   NaN          ...                    []  M130587                 0   \n",
       "14        bird   30     0   400         1.688292      75.701888  1.850354       2837    47.289600                   NaN          ...                    []  M130587                 0   \n",
       "...        ...  ...   ...   ...              ...            ...       ...        ...          ...                   ...          ...                   ...      ...               ...   \n",
       "1275  mushroom   15     0   400         2.837113      26.418836  4.366235     284423  4740.058698              1.983293          ...                    []  M130587                11   \n",
       "1276      bird  -15     0   300         2.901708      63.765922  2.100456     284549  4742.159154                   NaN          ...                    []  M130587                 0   \n",
       "1277      bird   30     0   550         3.511092      30.668452  4.549667     284822  4746.708821                   NaN          ...                    []  M130587                 0   \n",
       "1278      bird   30     0   550         3.967242      59.913858  1.882753     284935  4748.591574                   NaN          ...                    []  M130587                 0   \n",
       "1279      bird  -30     0   400         3.939057      55.006771  1.716564     285038  4750.308138                   NaN          ...                    []  M130587                 0   \n",
       "1280      bird    0     0   450         4.146193      51.320867  1.916542     285153  4752.224680                   NaN          ...                    []  M130587                 0   \n",
       "1281      bird   30     0   500         4.313428      46.464892  1.916420     285268  4754.141100                   NaN          ...                    []  M130587                 0   \n",
       "1282      bird  -45     0   300         3.828474      53.133577  1.816478     285377  4755.957578                   NaN          ...                    []  M130587                 0   \n",
       "1283      bird  -15     0   300         3.925290      45.073362  3.700263     285599  4759.657841                   NaN          ...                    []  M130587                 0   \n",
       "1284      bird    0     0   400         4.079184      28.017221  5.632619     285937  4765.290459                   NaN          ...                    []  M130587                 0   \n",
       "1285  mushroom    0     0   500         4.614564      32.340502  4.966122     286235  4770.256581              1.915501          ...                    []  M130587                11   \n",
       "1286  mushroom   45     0   300         4.956338      53.609111  1.750334     286340  4772.006915                   NaN          ...                    []  M130587                 0   \n",
       "1287  mushroom  -30     0   350         5.159482      51.750383  1.716273     286443  4773.723188                   NaN          ...                    []  M130587                 0   \n",
       "1288  mushroom   15     0   450         4.462697      28.700524  6.949890     286860  4780.673079              1.933304          ...                    []  M130587                10   \n",
       "1289  mushroom  -15     0   500         4.644082      21.416369  7.215661     287293  4787.888739              4.349974          ...                    []  M130587                10   \n",
       "\n",
       "     number_of_licks_1  number_of_rewards     reward_times             start_date_time start_frame   start_time              stop_date_time  \n",
       "0                    0                  0               []  2014-10-03 13:29:50.625000           0     0.000000  2014-10-03 14:49:45.315000  \n",
       "1                    0                  0               []  2014-10-03 13:29:50.625000         105     1.742769  2014-10-03 14:49:45.315000  \n",
       "2                    0                  0               []  2014-10-03 13:29:50.625000         176     2.925675  2014-10-03 14:49:45.315000  \n",
       "3                    0                  0               []  2014-10-03 13:29:50.625000         251     4.175839  2014-10-03 14:49:45.315000  \n",
       "4                    0                  0               []  2014-10-03 13:29:50.625000         452     7.525696  2014-10-03 14:49:45.315000  \n",
       "5                    0                  0               []  2014-10-03 13:29:50.625000         753    12.541580  2014-10-03 14:49:45.315000  \n",
       "6                    0                  0               []  2014-10-03 13:29:50.625000         842    14.024900  2014-10-03 14:49:45.315000  \n",
       "7                    0                  0               []  2014-10-03 13:29:50.625000         964    16.058190  2014-10-03 14:49:45.315000  \n",
       "8                    0                  0               []  2014-10-03 13:29:50.625000        1090    18.158257  2014-10-03 14:49:45.315000  \n",
       "9                    0                  0               []  2014-10-03 13:29:50.625000        1385    23.074127  2014-10-03 14:49:45.315000  \n",
       "10                   0                  1  [27.6072616137]  2014-10-03 13:29:50.625000        1553    25.874377  2014-10-03 14:49:45.315000  \n",
       "11                   0                  0               []  2014-10-03 13:29:50.625000        1811    30.174045  2014-10-03 14:49:45.315000  \n",
       "12                   0                  0               []  2014-10-03 13:29:50.625000        2175    36.239909  2014-10-03 14:49:45.315000  \n",
       "13                   0                  0               []  2014-10-03 13:29:50.625000        2662    44.356604  2014-10-03 14:49:45.315000  \n",
       "14                   0                  0               []  2014-10-03 13:29:50.625000        2727    45.439246  2014-10-03 14:49:45.315000  \n",
       "...                ...                ...              ...                         ...         ...          ...                         ...  \n",
       "1275                 0                  1  [4737.39287935]  2014-10-03 13:29:50.625000      284162  4735.692463  2014-10-03 14:49:45.315000  \n",
       "1276                 0                  0               []  2014-10-03 13:29:50.625000      284424  4740.058698  2014-10-03 14:49:45.315000  \n",
       "1277                 0                  0               []  2014-10-03 13:29:50.625000      284550  4742.159154  2014-10-03 14:49:45.315000  \n",
       "1278                 0                  0               []  2014-10-03 13:29:50.625000      284823  4746.708821  2014-10-03 14:49:45.315000  \n",
       "1279                 0                  0               []  2014-10-03 13:29:50.625000      284936  4748.591574  2014-10-03 14:49:45.315000  \n",
       "1280                 0                  0               []  2014-10-03 13:29:50.625000      285039  4750.308138  2014-10-03 14:49:45.315000  \n",
       "1281                 0                  0               []  2014-10-03 13:29:50.625000      285154  4752.224680  2014-10-03 14:49:45.315000  \n",
       "1282                 0                  0               []  2014-10-03 13:29:50.625000      285269  4754.141100  2014-10-03 14:49:45.315000  \n",
       "1283                 0                  0               []  2014-10-03 13:29:50.625000      285378  4755.957578  2014-10-03 14:49:45.315000  \n",
       "1284                 0                  0               []  2014-10-03 13:29:50.625000      285600  4759.657841  2014-10-03 14:49:45.315000  \n",
       "1285                 0                  1  [4766.90598388]  2014-10-03 13:29:50.625000      285938  4765.290459  2014-10-03 14:49:45.315000  \n",
       "1286                 0                  0               []  2014-10-03 13:29:50.625000      286236  4770.256581  2014-10-03 14:49:45.315000  \n",
       "1287                 0                  0               []  2014-10-03 13:29:50.625000      286341  4772.006915  2014-10-03 14:49:45.315000  \n",
       "1288                 0                  1  [4775.35651121]  2014-10-03 13:29:50.625000      286444  4773.723188  2014-10-03 14:49:45.315000  \n",
       "1289                 0                  1  [4784.73796704]  2014-10-03 13:29:50.625000      286861  4780.673079  2014-10-03 14:49:45.315000  \n",
       "\n",
       "[1290 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>Image</th>\n",
       "      <th>Ori</th>\n",
       "      <th>PosY</th>\n",
       "      <th>trial</th>\n",
       "      <th>time</th>\n",
       "      <th>terraincode</th>\n",
       "      <th>pausetime</th>\n",
       "      <th>choice</th>\n",
       "      <th>selectiontime</th>\n",
       "      <th>istarget</th>\n",
       "      <th>speed</th>\n",
       "      <th>lapdistance</th>\n",
       "      <th>timebeforezone</th>\n",
       "      <th>slowing_f</th>\n",
       "      <th>mouse</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0   </th>\n",
       "      <td> 400</td>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td>    1</td>\n",
       "      <td>    1.840244</td>\n",
       "      <td> 53</td>\n",
       "      <td> 0.267095</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 2152.301946</td>\n",
       "      <td> 3937</td>\n",
       "      <td> 0.099917</td>\n",
       "      <td>  1.151120</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1   </th>\n",
       "      <td> 550</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td>    2</td>\n",
       "      <td>    3.024151</td>\n",
       "      <td> 36</td>\n",
       "      <td> 0.268031</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 2155.681332</td>\n",
       "      <td> 2455</td>\n",
       "      <td> 0.100434</td>\n",
       "      <td>  0.995480</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2   </th>\n",
       "      <td> 350</td>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td>    3</td>\n",
       "      <td>    4.273621</td>\n",
       "      <td> 54</td>\n",
       "      <td> 0.399960</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1231.647159</td>\n",
       "      <td> 2661</td>\n",
       "      <td> 0.115117</td>\n",
       "      <td>  1.148952</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3   </th>\n",
       "      <td> 550</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td>    4</td>\n",
       "      <td>    7.623390</td>\n",
       "      <td> 92</td>\n",
       "      <td> 0.667990</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1416.589407</td>\n",
       "      <td> 4376</td>\n",
       "      <td> 0.200129</td>\n",
       "      <td> 11.667522</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4   </th>\n",
       "      <td> 450</td>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td>    5</td>\n",
       "      <td>   12.638539</td>\n",
       "      <td> 52</td>\n",
       "      <td> 0.232075</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 2709.459253</td>\n",
       "      <td> 3349</td>\n",
       "      <td> 0.115528</td>\n",
       "      <td>  0.995757</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5   </th>\n",
       "      <td> 550</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td>    6</td>\n",
       "      <td>   14.122473</td>\n",
       "      <td> 92</td>\n",
       "      <td> 0.249965</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1959.213561</td>\n",
       "      <td> 3544</td>\n",
       "      <td> 0.083261</td>\n",
       "      <td>  1.200958</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6   </th>\n",
       "      <td> 600</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td>    7</td>\n",
       "      <td>   16.155746</td>\n",
       "      <td> 35</td>\n",
       "      <td> 0.314834</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1996.971224</td>\n",
       "      <td> 4097</td>\n",
       "      <td> 0.150395</td>\n",
       "      <td>  0.997373</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7   </th>\n",
       "      <td> 400</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td>    8</td>\n",
       "      <td>   18.256180</td>\n",
       "      <td> 95</td>\n",
       "      <td> 0.832834</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1103.000564</td>\n",
       "      <td> 3796</td>\n",
       "      <td> 0.132865</td>\n",
       "      <td>  1.263148</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8   </th>\n",
       "      <td> 400</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td>    9</td>\n",
       "      <td>   23.172326</td>\n",
       "      <td> 39</td>\n",
       "      <td> 0.384844</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1537.117166</td>\n",
       "      <td> 5221</td>\n",
       "      <td> 0.150351</td>\n",
       "      <td>  0.998283</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9   </th>\n",
       "      <td> 400</td>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td>   10</td>\n",
       "      <td>   25.971682</td>\n",
       "      <td> 60</td>\n",
       "      <td> 2.282713</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1009.442921</td>\n",
       "      <td> 4172</td>\n",
       "      <td> 0.165107</td>\n",
       "      <td>  1.319022</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10  </th>\n",
       "      <td> 500</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-45</td>\n",
       "      <td> 0</td>\n",
       "      <td>   11</td>\n",
       "      <td>   30.271798</td>\n",
       "      <td>  9</td>\n",
       "      <td> 0.384991</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td>  479.653339</td>\n",
       "      <td> 2654</td>\n",
       "      <td> 0.132996</td>\n",
       "      <td>  0.751802</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11  </th>\n",
       "      <td> 400</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td>   12</td>\n",
       "      <td>   36.338231</td>\n",
       "      <td> 67</td>\n",
       "      <td> 5.199634</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1134.592683</td>\n",
       "      <td> 3835</td>\n",
       "      <td> 0.200371</td>\n",
       "      <td>  0.997547</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12  </th>\n",
       "      <td> 600</td>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td>   13</td>\n",
       "      <td>   44.454317</td>\n",
       "      <td> 56</td>\n",
       "      <td> 0.232885</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 2296.542044</td>\n",
       "      <td> 2994</td>\n",
       "      <td> 0.100001</td>\n",
       "      <td>  0.828894</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13  </th>\n",
       "      <td> 400</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td>   14</td>\n",
       "      <td>   45.537133</td>\n",
       "      <td> 81</td>\n",
       "      <td> 0.249979</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 2189.160042</td>\n",
       "      <td> 2485</td>\n",
       "      <td> 0.100368</td>\n",
       "      <td>  0.996217</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14  </th>\n",
       "      <td> 600</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td>   15</td>\n",
       "      <td>   47.387035</td>\n",
       "      <td> 77</td>\n",
       "      <td> 0.349928</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1816.723874</td>\n",
       "      <td> 4015</td>\n",
       "      <td> 0.133068</td>\n",
       "      <td>  1.127178</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td> 400</td>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1275</td>\n",
       "      <td> 4735.792264</td>\n",
       "      <td> 60</td>\n",
       "      <td> 2.617672</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1418.312466</td>\n",
       "      <td> 3336</td>\n",
       "      <td> 0.165583</td>\n",
       "      <td>  1.310952</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td> 300</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1276</td>\n",
       "      <td> 4740.156945</td>\n",
       "      <td> 41</td>\n",
       "      <td> 0.266927</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1791.990335</td>\n",
       "      <td> 2845</td>\n",
       "      <td> 0.115663</td>\n",
       "      <td>  0.995913</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td> 550</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1277</td>\n",
       "      <td> 4742.257880</td>\n",
       "      <td> 78</td>\n",
       "      <td> 0.532857</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1885.810632</td>\n",
       "      <td> 3853</td>\n",
       "      <td> 0.150081</td>\n",
       "      <td> 15.657505</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td> 550</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1278</td>\n",
       "      <td> 4746.807329</td>\n",
       "      <td> 78</td>\n",
       "      <td> 0.317938</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1803.310858</td>\n",
       "      <td> 3617</td>\n",
       "      <td> 0.133047</td>\n",
       "      <td>  1.127365</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td> 400</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1279</td>\n",
       "      <td> 4748.691669</td>\n",
       "      <td> 25</td>\n",
       "      <td> 0.349984</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1615.194855</td>\n",
       "      <td> 3214</td>\n",
       "      <td> 0.133229</td>\n",
       "      <td>  1.125503</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td> 450</td>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1280</td>\n",
       "      <td> 4750.407205</td>\n",
       "      <td> 52</td>\n",
       "      <td> 0.417001</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1596.888169</td>\n",
       "      <td> 2681</td>\n",
       "      <td> 0.150383</td>\n",
       "      <td>  1.097408</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td> 500</td>\n",
       "      <td>     bird</td>\n",
       "      <td> 30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1281</td>\n",
       "      <td> 4752.323583</td>\n",
       "      <td> 79</td>\n",
       "      <td> 0.517064</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1555.536869</td>\n",
       "      <td> 2800</td>\n",
       "      <td> 0.150368</td>\n",
       "      <td>  1.329957</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td> 300</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1282</td>\n",
       "      <td> 4754.241048</td>\n",
       "      <td> 13</td>\n",
       "      <td> 0.367688</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1502.693105</td>\n",
       "      <td> 2517</td>\n",
       "      <td> 0.133231</td>\n",
       "      <td>  1.011983</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td> 300</td>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1283</td>\n",
       "      <td> 4756.056101</td>\n",
       "      <td> 41</td>\n",
       "      <td> 0.399956</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1324.786870</td>\n",
       "      <td> 2757</td>\n",
       "      <td> 0.165626</td>\n",
       "      <td>  1.116007</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td> 400</td>\n",
       "      <td>     bird</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1284</td>\n",
       "      <td> 4759.755876</td>\n",
       "      <td> 53</td>\n",
       "      <td> 1.682798</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1</td>\n",
       "      <td> False</td>\n",
       "      <td> 1213.378869</td>\n",
       "      <td> 4832</td>\n",
       "      <td> 0.415119</td>\n",
       "      <td>  0.522918</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td> 500</td>\n",
       "      <td> mushroom</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1285</td>\n",
       "      <td> 4765.390659</td>\n",
       "      <td> 44</td>\n",
       "      <td> 2.749807</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1716.132505</td>\n",
       "      <td> 4039</td>\n",
       "      <td> 0.133261</td>\n",
       "      <td>  1.500831</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td> 300</td>\n",
       "      <td> mushroom</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1286</td>\n",
       "      <td> 4770.355938</td>\n",
       "      <td> 90</td>\n",
       "      <td> 0.332929</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1459.365217</td>\n",
       "      <td> 4128</td>\n",
       "      <td> 0.133304</td>\n",
       "      <td>  0.997450</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td> 350</td>\n",
       "      <td> mushroom</td>\n",
       "      <td>-30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1287</td>\n",
       "      <td> 4772.105389</td>\n",
       "      <td> 19</td>\n",
       "      <td> 0.367001</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1554.079078</td>\n",
       "      <td> 2669</td>\n",
       "      <td> 0.150007</td>\n",
       "      <td>  1.101244</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td> 450</td>\n",
       "      <td> mushroom</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1288</td>\n",
       "      <td> 4773.822187</td>\n",
       "      <td> 59</td>\n",
       "      <td> 3.964735</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1656.725038</td>\n",
       "      <td> 2517</td>\n",
       "      <td> 0.133237</td>\n",
       "      <td>  1.500442</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td> 500</td>\n",
       "      <td> mushroom</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1289</td>\n",
       "      <td> 4780.771671</td>\n",
       "      <td> 30</td>\n",
       "      <td> 2.481823</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1</td>\n",
       "      <td>  True</td>\n",
       "      <td> 1461.607078</td>\n",
       "      <td> 5273</td>\n",
       "      <td> 0.182853</td>\n",
       "      <td> 13.752887</td>\n",
       "      <td> M130587</td>\n",
       "      <td> 2014-10-03 13:29:50.625000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1289 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Size     Image  Ori  PosY  trial         time  terraincode  pausetime choice  selectiontime istarget        speed  lapdistance  timebeforezone  slowing_f    mouse                        date\n",
       "0      400      bird    0     0      1     1.840244           53   0.267095  False              1    False  2152.301946         3937        0.099917   1.151120  M130587  2014-10-03 13:29:50.625000\n",
       "1      550      bird  -15     0      2     3.024151           36   0.268031  False              1    False  2155.681332         2455        0.100434   0.995480  M130587  2014-10-03 13:29:50.625000\n",
       "2      350      bird    0     0      3     4.273621           54   0.399960  False              1    False  1231.647159         2661        0.115117   1.148952  M130587  2014-10-03 13:29:50.625000\n",
       "3      550      bird   45     0      4     7.623390           92   0.667990  False              1    False  1416.589407         4376        0.200129  11.667522  M130587  2014-10-03 13:29:50.625000\n",
       "4      450      bird    0     0      5    12.638539           52   0.232075  False              1    False  2709.459253         3349        0.115528   0.995757  M130587  2014-10-03 13:29:50.625000\n",
       "5      550      bird   45     0      6    14.122473           92   0.249965  False              1    False  1959.213561         3544        0.083261   1.200958  M130587  2014-10-03 13:29:50.625000\n",
       "6      600      bird  -15     0      7    16.155746           35   0.314834  False              1    False  1996.971224         4097        0.150395   0.997373  M130587  2014-10-03 13:29:50.625000\n",
       "7      400      bird   45     0      8    18.256180           95   0.832834  False              1    False  1103.000564         3796        0.132865   1.263148  M130587  2014-10-03 13:29:50.625000\n",
       "8      400      bird  -15     0      9    23.172326           39   0.384844  False              1    False  1537.117166         5221        0.150351   0.998283  M130587  2014-10-03 13:29:50.625000\n",
       "9      400  mushroom   15     0     10    25.971682           60   2.282713   True              1     True  1009.442921         4172        0.165107   1.319022  M130587  2014-10-03 13:29:50.625000\n",
       "10     500      bird  -45     0     11    30.271798            9   0.384991  False              1    False   479.653339         2654        0.132996   0.751802  M130587  2014-10-03 13:29:50.625000\n",
       "11     400      bird   15     0     12    36.338231           67   5.199634   True              1    False  1134.592683         3835        0.200371   0.997547  M130587  2014-10-03 13:29:50.625000\n",
       "12     600  mushroom   15     0     13    44.454317           56   0.232885  False              1     True  2296.542044         2994        0.100001   0.828894  M130587  2014-10-03 13:29:50.625000\n",
       "13     400      bird   30     0     14    45.537133           81   0.249979  False              1    False  2189.160042         2485        0.100368   0.996217  M130587  2014-10-03 13:29:50.625000\n",
       "14     600      bird   30     0     15    47.387035           77   0.349928  False              1    False  1816.723874         4015        0.133068   1.127178  M130587  2014-10-03 13:29:50.625000\n",
       "...    ...       ...  ...   ...    ...          ...          ...        ...    ...            ...      ...          ...          ...             ...        ...      ...                         ...\n",
       "1274   400  mushroom   15     0   1275  4735.792264           60   2.617672   True              1     True  1418.312466         3336        0.165583   1.310952  M130587  2014-10-03 13:29:50.625000\n",
       "1275   300      bird  -15     0   1276  4740.156945           41   0.266927  False              1    False  1791.990335         2845        0.115663   0.995913  M130587  2014-10-03 13:29:50.625000\n",
       "1276   550      bird   30     0   1277  4742.257880           78   0.532857  False              1    False  1885.810632         3853        0.150081  15.657505  M130587  2014-10-03 13:29:50.625000\n",
       "1277   550      bird   30     0   1278  4746.807329           78   0.317938  False              1    False  1803.310858         3617        0.133047   1.127365  M130587  2014-10-03 13:29:50.625000\n",
       "1278   400      bird  -30     0   1279  4748.691669           25   0.349984  False              1    False  1615.194855         3214        0.133229   1.125503  M130587  2014-10-03 13:29:50.625000\n",
       "1279   450      bird    0     0   1280  4750.407205           52   0.417001  False              1    False  1596.888169         2681        0.150383   1.097408  M130587  2014-10-03 13:29:50.625000\n",
       "1280   500      bird   30     0   1281  4752.323583           79   0.517064  False              1    False  1555.536869         2800        0.150368   1.329957  M130587  2014-10-03 13:29:50.625000\n",
       "1281   300      bird  -45     0   1282  4754.241048           13   0.367688  False              1    False  1502.693105         2517        0.133231   1.011983  M130587  2014-10-03 13:29:50.625000\n",
       "1282   300      bird  -15     0   1283  4756.056101           41   0.399956  False              1    False  1324.786870         2757        0.165626   1.116007  M130587  2014-10-03 13:29:50.625000\n",
       "1283   400      bird    0     0   1284  4759.755876           53   1.682798   True              1    False  1213.378869         4832        0.415119   0.522918  M130587  2014-10-03 13:29:50.625000\n",
       "1284   500  mushroom    0     0   1285  4765.390659           44   2.749807   True              1     True  1716.132505         4039        0.133261   1.500831  M130587  2014-10-03 13:29:50.625000\n",
       "1285   300  mushroom   45     0   1286  4770.355938           90   0.332929  False              1     True  1459.365217         4128        0.133304   0.997450  M130587  2014-10-03 13:29:50.625000\n",
       "1286   350  mushroom  -30     0   1287  4772.105389           19   0.367001  False              1     True  1554.079078         2669        0.150007   1.101244  M130587  2014-10-03 13:29:50.625000\n",
       "1287   450  mushroom   15     0   1288  4773.822187           59   3.964735   True              1     True  1656.725038         2517        0.133237   1.500442  M130587  2014-10-03 13:29:50.625000\n",
       "1288   500  mushroom  -15     0   1289  4780.771671           30   2.481823   True              1     True  1461.607078         5273        0.182853  13.752887  M130587  2014-10-03 13:29:50.625000\n",
       "\n",
       "[1289 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-10, 90)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%capture capt\n",
    "execute_notebook(\"C:\\Users\\dorisv\\Desktop\\Shawn's data\\Object recognition - Running speed from behavioral log file.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Image', u'Ori', u'PosY', u'Size', u'abs_reward_rate', u'duration', u'end_frame', u'end_time', u'first_lick_latency_0', u'first_lick_latency_1', u'image', u'jcam_end_ind_exclusive', u'jcam_start_ind', u'lap_number', u'lick_duration_0', u'lick_duration_1', u'lick_times_0', u'lick_times_1', u'mouse_id', u'number_of_licks_0', u'number_of_licks_1', u'number_of_rewards', u'reward_times', u'start_date_time', u'start_frame', u'start_time', u'stop_date_time', u'choice', u'total_trial_ind'], dtype='object')\n",
      "['M143454']\n",
      "747\n",
      "282 465\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Ori</th>\n",
       "      <th>PosY</th>\n",
       "      <th>Size</th>\n",
       "      <th>abs_reward_rate</th>\n",
       "      <th>duration</th>\n",
       "      <th>end_frame</th>\n",
       "      <th>end_time</th>\n",
       "      <th>first_lick_latency_0</th>\n",
       "      <th>first_lick_latency_1</th>\n",
       "      <th>...</th>\n",
       "      <th>number_of_licks_0</th>\n",
       "      <th>number_of_licks_1</th>\n",
       "      <th>number_of_rewards</th>\n",
       "      <th>reward_times</th>\n",
       "      <th>start_date_time</th>\n",
       "      <th>start_frame</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_date_time</th>\n",
       "      <th>choice</th>\n",
       "      <th>total_trial_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>     bird</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 500</td>\n",
       "      <td> 1.786181</td>\n",
       "      <td> 3.449961</td>\n",
       "      <td>  390</td>\n",
       "      <td>  6.549960</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> None</td>\n",
       "      <td>...</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-27 12:54:41.393000</td>\n",
       "      <td>  185</td>\n",
       "      <td>  3.099999</td>\n",
       "      <td> 2014-10-27 14:05:33.684000</td>\n",
       "      <td>  True</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> mushroom</td>\n",
       "      <td>-30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.600021</td>\n",
       "      <td> 6.633207</td>\n",
       "      <td>  788</td>\n",
       "      <td> 13.183167</td>\n",
       "      <td> 1.649916</td>\n",
       "      <td> None</td>\n",
       "      <td>...</td>\n",
       "      <td>  5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> [8.74988234113]</td>\n",
       "      <td> 2014-10-27 12:54:41.393000</td>\n",
       "      <td>  391</td>\n",
       "      <td>  6.549960</td>\n",
       "      <td> 2014-10-27 14:05:33.684000</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.552413</td>\n",
       "      <td> 3.216621</td>\n",
       "      <td>  980</td>\n",
       "      <td> 16.399788</td>\n",
       "      <td>      NaN</td>\n",
       "      <td> None</td>\n",
       "      <td>...</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-27 12:54:41.393000</td>\n",
       "      <td>  789</td>\n",
       "      <td> 13.183167</td>\n",
       "      <td> 2014-10-27 14:05:33.684000</td>\n",
       "      <td>  True</td>\n",
       "      <td> 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> mushroom</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 2.169573</td>\n",
       "      <td> 6.249905</td>\n",
       "      <td> 1355</td>\n",
       "      <td> 22.649692</td>\n",
       "      <td> 1.016686</td>\n",
       "      <td> None</td>\n",
       "      <td>...</td>\n",
       "      <td> 24</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> [18.4498032981]</td>\n",
       "      <td> 2014-10-27 12:54:41.393000</td>\n",
       "      <td>  981</td>\n",
       "      <td> 16.399788</td>\n",
       "      <td> 2014-10-27 14:05:33.684000</td>\n",
       "      <td>  True</td>\n",
       "      <td> 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>     bird</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 500</td>\n",
       "      <td> 2.720092</td>\n",
       "      <td> 8.333357</td>\n",
       "      <td> 1854</td>\n",
       "      <td> 30.983049</td>\n",
       "      <td> 1.200000</td>\n",
       "      <td> None</td>\n",
       "      <td>...</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>              []</td>\n",
       "      <td> 2014-10-27 12:54:41.393000</td>\n",
       "      <td> 1356</td>\n",
       "      <td> 22.649692</td>\n",
       "      <td> 2014-10-27 14:05:33.684000</td>\n",
       "      <td> False</td>\n",
       "      <td> 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Image  Ori  PosY  Size  abs_reward_rate  duration  end_frame   end_time  first_lick_latency_0 first_lick_latency_1         ...          number_of_licks_0 number_of_licks_1 number_of_rewards  \\\n",
       "1      bird  -15     0   500         1.786181  3.449961        390   6.549960                   NaN                 None         ...                          0                 0                 0   \n",
       "2  mushroom  -30     0   400         1.600021  6.633207        788  13.183167              1.649916                 None         ...                          5                 0                 1   \n",
       "3      bird   45     0   400         1.552413  3.216621        980  16.399788                   NaN                 None         ...                          0                 0                 0   \n",
       "4  mushroom    0     0   550         2.169573  6.249905       1355  22.649692              1.016686                 None         ...                         24                 0                 1   \n",
       "5      bird   45     0   500         2.720092  8.333357       1854  30.983049              1.200000                 None         ...                         15                 0                 0   \n",
       "\n",
       "      reward_times             start_date_time start_frame start_time              stop_date_time choice  total_trial_ind  \n",
       "1               []  2014-10-27 12:54:41.393000         185   3.099999  2014-10-27 14:05:33.684000   True                0  \n",
       "2  [8.74988234113]  2014-10-27 12:54:41.393000         391   6.549960  2014-10-27 14:05:33.684000  False                1  \n",
       "3               []  2014-10-27 12:54:41.393000         789  13.183167  2014-10-27 14:05:33.684000   True                2  \n",
       "4  [18.4498032981]  2014-10-27 12:54:41.393000         981  16.399788  2014-10-27 14:05:33.684000   True                3  \n",
       "5               []  2014-10-27 12:54:41.393000        1356  22.649692  2014-10-27 14:05:33.684000  False                4  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizes = np.sort(dataFrame.Size.unique())\n",
    "orientations = np.sort(dataFrame.Ori.unique())\n",
    "\n",
    "#for the purposes of the experiment, should be m=n=7\n",
    "m = len(sizes)\n",
    "n = len(orientations)\n",
    "\n",
    "evenTest1 = isinstance((m/float(2)), (int, long))\n",
    "evenTest2 = isinstance((n/float(2)), (int, long))\n",
    "    \n",
    "if evenTest1:\n",
    "    s_T = sizes[m/2-1]\n",
    "else:\n",
    "    s_T = sizes[m/2]\n",
    "        \n",
    "if evenTest2:\n",
    "    o_T = orientations[n/2-1]    \n",
    "else:\n",
    "    o_T = orientations[n/2]\n",
    "                \n",
    "s_max = sizes[m-1]\n",
    "\n",
    "print dataFrame.columns\n",
    "print dataFrame.mouse_id.unique()\n",
    "#dataFrame[dataFrame.Image==]\n",
    "\n",
    "dataFrame.Image[dataFrame.Image == \"CoxImage1.png\"] = \"mushroom\"\n",
    "dataFrame.Image[dataFrame.Image == \"CoxImage2.png\"] = \"bird\"\n",
    "\n",
    "N = len(dataFrame)\n",
    "print N\n",
    "\n",
    "N_m = len(dataFrame.Image[dataFrame.Image==\"mushroom\"])\n",
    "N_b = len(dataFrame.Image[dataFrame.Image==\"bird\"])\n",
    "print N_m, N_b\n",
    "\n",
    "dataFrame[\"total_trial_ind\"] = range(0, len(dataFrame))\n",
    "dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def p_real(j):\n",
    "    \n",
    "    dataFrame = dataFrame_list[j]\n",
    "            \n",
    "    p_m_real = np.zeros((len(orientations),len(sizes)))\n",
    "    p_b_real = np.zeros((len(orientations),len(sizes)))\n",
    "    trials_tot = np.zeros((len(orientations),len(sizes)))\n",
    "    trials_stop = np.zeros((len(orientations),len(sizes)))\n",
    "    trials_m = np.zeros((len(orientations),len(sizes)))\n",
    "    trials_b = np.zeros((len(orientations),len(sizes)))\n",
    "    trials_stop_m = np.zeros((len(orientations),len(sizes)))\n",
    "    trials_stop_b = np.zeros((len(orientations),len(sizes)))\n",
    "    correct_m = np.zeros((len(orientations), len(sizes)))\n",
    "    correct_b = np.zeros((len(orientations), len(sizes)))\n",
    "\n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):\n",
    "            \n",
    "            trials_tot[ind2,ind1] = len(dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)].total_trial_ind)\n",
    "            #print ind1, ind2, trials_tot[ind2,ind1]\n",
    "            trials_stop[ind2,ind1] = len(dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.choice==1)].total_trial_ind)\n",
    "\n",
    "            trials_m[ind2,ind1] = len(dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='mushroom')].total_trial_ind)\n",
    "            trials_b[ind2,ind1] = len(dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='bird')].total_trial_ind)   \n",
    "            \n",
    "            trials_stop_m[ind2,ind1] = len(dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='mushroom')&(dataFrame.choice==1)].total_trial_ind)\n",
    "            trials_stop_b[ind2,ind1] = len(dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='bird')&(dataFrame.choice==1)].total_trial_ind)   \n",
    "            \n",
    "            p_m_real[ind2, ind1] = trials_m[ind2,ind1]/trials_tot[ind2,ind1]\n",
    "            p_b_real[ind2, ind1] = trials_b[ind2,ind1]/trials_tot[ind2,ind1]   \n",
    "            \n",
    "            correct_m[ind2, ind1] = len(dataFrame.total_trial_ind[(dataFrame.Size==s) & (dataFrame.Ori==o) & (dataFrame.Image==\"mushroom\") & (dataFrame.choice == 1)])/trials_m[ind2,ind1]\n",
    "            correct_b[ind2, ind1] = len(dataFrame.total_trial_ind[(dataFrame.Size==s) & (dataFrame.Ori==o) & (dataFrame.Image==\"bird\") & (dataFrame.choice == 0)])/trials_b[ind2,ind1]\n",
    "          \n",
    "    return p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, correct_m, correct_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(good_ones)\n",
    "good_ones[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5         0.46153846  0.33333333  0.35714286  0.44444444  0.73333333\n",
      "   0.125     ]\n",
      " [ 0.5         0.6         0.3         0.46666667  0.25        0.38461538\n",
      "   0.54545455]\n",
      " [ 0.16666667  0.3         0.25        0.14285714  0.27272727  0.22222222\n",
      "   0.33333333]\n",
      " [ 0.25        0.36363636  0.4         0.5         0.125       0.28571429\n",
      "   0.26666667]\n",
      " [ 0.61111111  0.33333333  0.42857143  0.16666667  0.25        0.14285714\n",
      "   0.5       ]\n",
      " [ 0.1         0.61538462  0.375       0.33333333  0.16666667  0.125\n",
      "   0.58333333]\n",
      " [ 0.35714286  0.4         0.33333333  0.28571429  0.21428571  0.\n",
      "   0.35714286]]\n",
      "[[ 0.8         0.72727273  0.81818182  0.46666667  0.57142857  0.48        0.4       ]\n",
      " [ 0.70588235  0.66666667  0.35714286  0.64285714  0.46153846  0.63636364\n",
      "   1.        ]\n",
      " [ 0.56521739  0.5         0.44444444  0.46153846  0.57142857  0.4         0.4       ]\n",
      " [ 0.66666667  0.71428571  0.46153846  0.54166667  0.44444444  0.61111111\n",
      "   0.46666667]\n",
      " [ 0.6875      0.52631579  0.58823529  0.61111111  0.66666667  0.61538462\n",
      "   0.55555556]\n",
      " [ 0.55555556  0.42857143  0.58823529  0.73333333  0.73333333  0.64285714\n",
      "   0.875     ]\n",
      " [ 0.75        0.68421053  0.71428571  0.61111111  0.88235294  0.6875\n",
      "   0.77777778]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAHcCAYAAAD4E4EqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOr/pKLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e19b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAHcCAYAAAD4E4EqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIhI1FKiIiIiIlHr/wGK6rPOpl0UhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232b02e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mushroom 0.337943462433 0.156343684069\n",
      "bird 0.610161279427 0.140883467857\n",
      "mushroom 0.486111111111 0.139172046958\n",
      "bird 0.491134036032 0.119476916815\n",
      "mushroom 0.373242630385 0.130118429056\n",
      "bird 0.592905733722 0.114589499268\n",
      "mushroom 0.402696793003 0.15563571354\n",
      "bird 0.482745825603 0.139672229107\n",
      "mushroom 0.662779397473 0.185824326297\n",
      "bird 0.403896103896 0.143163779527\n",
      "mushroom 0.342970521542 0.132749930714\n",
      "bird 0.553244374673 0.125596191779\n",
      "mushroom 0.847424684159 0.148489421886\n",
      "bird 0.18556998557 0.128906477702\n",
      "mushroom 0.271036899608 0.162722121341\n",
      "bird 0.602818270165 0.100311506892\n",
      "mushroom 0.855685131195 0.141343467903\n",
      "bird 0.184353741497 0.13142854013\n",
      "mushroom 0.448412698413 0.18082789508\n",
      "bird 0.565184815185 0.128781663134\n",
      "mushroom 0.759475218659 0.174516308468\n",
      "bird 0.331292517007 0.152623376432\n",
      "mushroom 0.684766763848 0.174686271277\n",
      "bird 0.360379303236 0.179661070139\n",
      "mushroom 0.544946550049 0.191048782617\n",
      "bird 0.423933209647 0.139429554884\n",
      "mushroom 0.321145124717 0.175987313447\n",
      "bird 0.606750392465 0.131744390332\n",
      "mushroom 0.515022675737 0.220351335372\n",
      "bird 0.463855872019 0.175534952929\n",
      "mushroom 0.433673469388 0.130077028408\n",
      "bird 0.490074211503 0.151596781111\n",
      "mushroom 0.614795918367 0.197106569103\n",
      "bird 0.379035250464 0.165656506656\n",
      "mushroom 0.409620991254 0.162762625508\n",
      "bird 0.542517006803 0.136893565295\n",
      "mushroom 0.583090379009 0.197696160267\n",
      "bird 0.317254174397 0.148782033298\n",
      "mushroom 0.662585034014 0.207635719412\n",
      "bird 0.431405895692 0.177607809269\n",
      "mushroom 0.620019436346 0.182279872097\n",
      "bird 0.337641723356 0.172232335692\n",
      "mushroom 0.451895043732 0.214231166576\n",
      "bird 0.532137703566 0.152726235665\n",
      "mushroom 0.707288629738 0.17425884662\n",
      "bird 0.447845804989 0.15649544497\n",
      "mushroom 0.597667638484 0.136430805739\n",
      "bird 0.405936920223 0.166279934948\n",
      "mushroom 0.443148688047 0.192939389446\n",
      "bird 0.480272108844 0.167357078026\n",
      "mushroom 0.595238095238 0.198739607592\n",
      "bird 0.435657596372 0.154065738795\n",
      "mushroom 0.64139941691 0.175452725075\n",
      "bird 0.312152133581 0.140225494789\n",
      "mushroom 0.593002915452 0.204113607565\n",
      "bird 0.395547309833 0.147402568213\n",
      "mushroom 0.474246841594 0.193060501471\n",
      "bird 0.402154195011 0.132512668697\n",
      "mushroom 0.58527696793 0.162916771878\n",
      "bird 0.375324675325 0.13166294999\n"
     ]
    }
   ],
   "source": [
    "p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, correct_m, correct_b = p_real(1)\n",
    "\n",
    "#print trials_stop_m/trials_m, trials_stop_b/trials_b\n",
    "#print sum(sum(trials_stop_m/trials_m > trials_stop_b/trials_b))\n",
    "\n",
    "print correct_m\n",
    "print correct_b\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.imshow(correct_m, interpolation = 'none')\n",
    "plt.jet()\n",
    "plt.colorbar()\n",
    "\n",
    "for y in range(correct_m.shape[0]):\n",
    "    for x in range(correct_m.shape[1]):\n",
    "        plt.text(x, y, '%.4f' % correct_m[y, x],\n",
    "                 horizontalalignment='center',\n",
    "                 verticalalignment='center',\n",
    "                 )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.imshow(correct_b, interpolation = 'none')\n",
    "plt.colorbar()\n",
    "\n",
    "for y in range(correct_b.shape[0]):\n",
    "    for x in range(correct_b.shape[1]):\n",
    "        plt.text(x, y, '%.4f' % correct_b[y, x],\n",
    "                 horizontalalignment='center',\n",
    "                 verticalalignment='center',\n",
    "                 )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "for j in good_ones:\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, correct_m, correct_b = p_real(j)\n",
    "    print \"mushroom\", np.mean(correct_m), np.std(correct_m)\n",
    "    print \"bird\", np.mean(correct_b), np.std(correct_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum 23\n",
      "sum 27\n",
      "sum 19\n",
      "sum 14\n",
      "sum 28\n",
      "sum 12\n",
      "sum 28\n",
      "sum 11\n",
      "sum 28\n",
      "sum 27\n",
      "sum 33\n",
      "sum 28\n",
      "sum 23\n",
      "sum 17\n",
      "sum 23\n",
      "sum 19\n",
      "sum 23\n",
      "sum 21\n",
      "sum 19\n",
      "sum 32\n",
      "sum 21\n",
      "sum 26\n",
      "sum 35\n",
      "sum 24\n",
      "sum 18\n",
      "sum 30\n",
      "sum 24\n",
      "sum 26\n",
      "sum 13\n",
      "sum 21\n",
      "[[ 5.  4.  6.  4.  6.  4.  2.]\n",
      " [ 3.  4.  3.  5.  5.  5.  5.]\n",
      " [ 3.  5.  4.  5.  5.  4.  4.]\n",
      " [ 5.  2.  4.  6.  6.  6.  6.]\n",
      " [ 5.  5.  6.  3.  4.  3.  2.]\n",
      " [ 6.  4.  7.  4.  5.  3.  2.]\n",
      " [ 5.  5.  7.  4.  6.  5.  5.]] [[ 5.  7.  5.  9.  5.  5.  4.]\n",
      " [ 6.  6.  7.  6.  5.  8.  3.]\n",
      " [ 8.  9.  6.  6.  7.  6.  7.]\n",
      " [ 8.  7.  7.  8.  5.  9.  7.]\n",
      " [ 7.  9.  5.  6.  8.  4.  7.]\n",
      " [ 5.  7.  6.  8.  6.  5.  7.]\n",
      " [ 6.  6.  8.  6.  7.  4.  6.]]\n",
      "[[ 0.625       0.5         0.75        0.5         0.75        0.5         0.25      ]\n",
      " [ 0.375       0.5         0.375       0.625       0.71428571  0.71428571\n",
      "   0.625     ]\n",
      " [ 0.375       0.625       0.5         0.625       0.71428571  0.5\n",
      "   0.57142857]\n",
      " [ 0.625       0.25        0.57142857  0.75        0.75        0.75        0.75      ]\n",
      " [ 0.625       0.71428571  0.75        0.375       0.5         0.42857143\n",
      "   0.25      ]\n",
      " [ 0.85714286  0.5         0.875       0.5         0.625       0.42857143\n",
      "   0.28571429]\n",
      " [ 0.71428571  0.71428571  0.875       0.5         0.75        0.625       0.625     ]]\n",
      "[[ 0.625       0.5         0.75        0.5         0.75        0.5         0.25      ]\n",
      " [ 0.375       0.5         0.375       0.625       0.71428571  0.71428571\n",
      "   0.625     ]\n",
      " [ 0.375       0.625       0.5         0.625       0.71428571  0.5\n",
      "   0.57142857]\n",
      " [ 0.625       0.25        0.57142857  0.75        0.75        0.75        0.75      ]\n",
      " [ 0.625       0.71428571  0.75        0.375       0.5         0.42857143\n",
      "   0.25      ]\n",
      " [ 0.85714286  0.5         0.875       0.5         0.625       0.42857143\n",
      "   0.28571429]\n",
      " [ 0.71428571  0.71428571  0.875       0.5         0.75        0.625       0.625     ]]\n",
      "sum 21\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x00000000236C7848>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAHcCAYAAAD4E4EqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFUmSVFsWKpIkqbb+P8m3l5A6b04FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e655f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAHcCAYAAAD4E4EqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23612940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j in good_ones:\n",
    "    \n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j)\n",
    "    print \"sum\", np.sum(np.sum(trials_stop_m/trials_m > trials_stop_b/trials_b))\n",
    "    \n",
    "#print p_m_real, p_b_real\n",
    "#print trials_tot, trials_stop\n",
    "#print trials_m, trials_b\n",
    "print trials_stop_m, trials_stop_b\n",
    "\n",
    "print trials_stop_m/trials_m\n",
    "print trials_stop_b/trials_b\n",
    "\n",
    "print \"sum\", np.sum(np.sum(trials_stop_m/trials_m > trials_stop_b/trials_b))\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.imshow(trials_stop_m/trials_m, interpolation = 'none')\n",
    "plt.colorbar()\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.imshow(trials_stop_b/trials_b, interpolation = 'none')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 23.  10.  10.  12.  15.  11.  11.]\n",
      " [ 14.  12.  10.  14.  11.  13.  10.]\n",
      " [ 11.  13.  10.  13.  11.  15.  11.]\n",
      " [  8.  12.  12.  16.  10.   7.  16.]\n",
      " [  8.  12.   9.  14.   9.  11.  15.]\n",
      " [ 20.  15.  14.  10.  10.  13.  20.]\n",
      " [  6.  16.   6.  13.  13.  12.  17.]] [[ 19.  13.  18.  22.  19.  13.  15.]\n",
      " [ 19.  23.  16.  18.  17.  19.  22.]\n",
      " [ 19.  16.  23.  16.  19.  25.  18.]\n",
      " [ 20.  14.   8.  18.  14.  19.  18.]\n",
      " [ 31.  13.  14.  16.  14.  21.  19.]\n",
      " [ 19.  26.  21.  15.  24.  12.  19.]\n",
      " [ 14.  22.  19.  11.  17.  17.  19.]]\n",
      "6\n",
      "604.0\n",
      "883.0\n",
      "1487\n",
      "0\n",
      "sum of choices nan\n",
      "1289 1 1288\n",
      "1     False\n",
      "2     False\n",
      "3     False\n",
      "4     False\n",
      "5     False\n",
      "6     False\n",
      "7     False\n",
      "8     False\n",
      "9      True\n",
      "10    False\n",
      "11     True\n",
      "12    False\n",
      "13    False\n",
      "...\n",
      "1277    False\n",
      "1278    False\n",
      "1279    False\n",
      "1280    False\n",
      "1281    False\n",
      "1282    False\n",
      "1283     True\n",
      "1284     True\n",
      "1285    False\n",
      "1286    False\n",
      "1287     True\n",
      "1288     True\n",
      "1289      NaN\n",
      "Name: choice, Length: 1289, dtype: object\n"
     ]
    }
   ],
   "source": [
    "p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(0)\n",
    "print trials_m, trials_b\n",
    "print sum(sum((trials_m/trials_b>1)==True))\n",
    "print sum(sum(trials_m))\n",
    "print sum(sum(trials_b))\n",
    "\n",
    "print len(dataFrame_list[0])\n",
    "print len(dataFrame_list[0][dataFrame_list[0].choice==np.nan].total_trial_ind)\n",
    "#print sum(np.isnan(dataFrame_list[0].choice))\n",
    "print \"sum of choices\", sum(dataFrame_list[0].choice)\n",
    "\n",
    "nan_number = 0\n",
    "start_index = 0\n",
    "j = 1\n",
    "for i in range(0, len(dataFrame_list[j])):\n",
    "    #print dataFrame_list[0].choice.iloc[i]\n",
    "    if np.isnan(dataFrame_list[j].choice.iloc[i]):\n",
    "        nan_number = nan_number + 1\n",
    "        if nan_number == 1:\n",
    "            start_index = i\n",
    "        \n",
    "print len(dataFrame_list[j]), nan_number, start_index\n",
    "\n",
    "print dataFrame_list[j].choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def p_stop_real_function(j):\n",
    "            \n",
    "    p_stop_real = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_toFit = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_toFit2 = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenM2 = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenB2 = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenM_toFit = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenB_toFit = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenM_toFit2 = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_real_givenB_toFit2 = np.zeros((len(orientations),len(sizes)))\n",
    "    \n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b  = p_real(j)\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):\n",
    "            \n",
    "            p_stop_real[ind2,ind1] = trials_stop[ind2, ind1]/trials_tot[ind2,ind1]\n",
    "            p_stop_real_givenM[ind2,ind1] = trials_stop_m[ind2, ind1]/trials_m[ind2,ind1]\n",
    "            p_stop_real_givenB[ind2,ind1] = trials_stop_b[ind2,ind1]/trials_b[ind2,ind1]\n",
    "        \n",
    "            p_stop_real_toFit[ind2,ind1]  = (p_stop_real[ind2,ind1]-p_m_real[ind2,ind1])/(p_b_real[ind2,ind1]-p_m_real[ind2,ind1])            \n",
    "            p_stop_real_givenM_toFit[ind2,ind1]  = 1-p_stop_real_givenM[ind2,ind1]\n",
    "            p_stop_real_givenB_toFit[ind2,ind1] = p_stop_real_givenB[ind2,ind1]\n",
    "            \n",
    "    return p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.  4.  6.  4.  6.  4.  2.]\n",
      " [ 3.  4.  3.  5.  5.  5.  5.]\n",
      " [ 3.  5.  4.  5.  5.  4.  4.]\n",
      " [ 5.  2.  4.  6.  6.  6.  6.]\n",
      " [ 5.  5.  6.  3.  4.  3.  2.]\n",
      " [ 6.  4.  7.  4.  5.  3.  2.]\n",
      " [ 5.  5.  7.  4.  6.  5.  5.]]\n",
      "[[ 5.  7.  5.  9.  5.  5.  4.]\n",
      " [ 6.  6.  7.  6.  5.  8.  3.]\n",
      " [ 8.  9.  6.  6.  7.  6.  7.]\n",
      " [ 8.  7.  7.  8.  5.  9.  7.]\n",
      " [ 7.  9.  5.  6.  8.  4.  7.]\n",
      " [ 5.  7.  6.  8.  6.  5.  7.]\n",
      " [ 6.  6.  8.  6.  7.  4.  6.]]\n",
      "27\n"
     ]
    }
   ],
   "source": [
    "p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(2)\n",
    "#rint p_stop_real_givenB_toFit\n",
    "#rint p_stop_real_givenM\n",
    "#sum(sum((p_stop_real_givenB_toFit>p_stop_real_givenM)==True))\n",
    "print trials_stop_m\n",
    "print trials_stop_b\n",
    "print sum(sum((p_stop_real_givenM > p_stop_real_givenB)==True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def p_stop_ProbabilityMatching(x,j):\n",
    "    \n",
    "    dataFrame = dataFrame_list[j]\n",
    "        \n",
    "    p_stop_model1 = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_givenM_model = np.zeros((len(orientations),len(sizes)))\n",
    "    p_stop_givenB_model = np.zeros((len(orientations),len(sizes)))\n",
    "    \n",
    "    prob_misID = probability_misidentify(x)\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j)\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):\n",
    "                \n",
    "            p_stop_model1[ind2,ind1] = (1-prob_misID[ind2,ind1])*p_m_real[ind2,ind1]+prob_misID[ind2,ind1]*p_b_real[ind2,ind1]\n",
    "            p_stop_givenM_model[ind2,ind1] = 1-prob_misID[ind2,ind1]\n",
    "            p_stop_givenB_model[ind2,ind1] = prob_misID[ind2,ind1]\n",
    "            \n",
    "    return p_stop_model1, p_stop_givenM_model, p_stop_givenB_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def probability_misidentify(x):\n",
    "    \n",
    "    #parameters of the model\n",
    "    #k_s = x[0]\n",
    "    #k_o = x[1]\n",
    "    #k_d = x[2]    \n",
    "    #alpha = x[3]\n",
    "    alpha = x[0]\n",
    "    k_o = x[1]\n",
    "    k_s = x[2]\n",
    "    k_d = x[3]\n",
    "    x_0 = 1\n",
    "    \n",
    "    print alpha, k_o, k_s, k_d\n",
    "    \n",
    "    #probabilities of misidentification, from which we infer p-stop. The prob_misID depend on s,o, s_T,o_T\n",
    "    prob_misID = np.zeros((len(orientations), len(sizes)))\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):               \n",
    "                    #our model for prob_msID is a hill function\n",
    "            \n",
    "            if s!=0:\n",
    "                prob_misID[ind2,ind1] = 0.5*1/(1+x_0/(k_s*math.fabs(s-s_T)+k_o*math.fabs(o-o_T)+k_d*(s_max-s)+alpha))\n",
    "                #prob_misID[ind2,ind1] = k_s*math.fabs(s-s_T)+k_o*math.fabs(o-o_T)+k_d*(s_max-s)+alpha\n",
    "            else:\n",
    "                prob_misID[ind2,ind1] = 0.5\n",
    "                 #another version of the model   \n",
    "                #prob_misID[i,ind1,ind2] = 0.5*1/(1+x_0/(k_s*math.fabs(s-s_T)+k_o*math.fabs(o-o_T)+k_d*math.fabs*(s_max-s)+alpha))\n",
    "    \n",
    "    return prob_misID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fitting_probMatching(x):\n",
    "        \n",
    "        p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "        p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j) \n",
    "        \n",
    "        p_base, p_stop_givenM_model, p_stop_givenB_model = p_stop_ProbabilityMatching(x,j)\n",
    "        mle = 0\n",
    "        \n",
    "        #print \"1\", p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit\n",
    "        #print \"2\", p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b\n",
    "        #print \"3\", p_base, p_stop_givenM_model, p_stop_givenB_model\n",
    "        \n",
    "        #print \"x\", x\n",
    "        \n",
    "        for ind2,o in enumerate(orientations):\n",
    "            for ind1,s in enumerate(sizes): \n",
    "                if (p_stop_givenM_model[ind2,ind1]>0) and (p_stop_givenB_model[ind2,ind1]>0) and (p_stop_givenM_model[ind2,ind1] < 1) and (p_stop_givenB_model[ind2,ind1]< 1):                    \n",
    "                    mle = mle + trials_stop_m[ind2,ind1]*math.log(p_stop_givenM_model[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*math.log(1-p_stop_givenM_model[ind2,ind1])+trials_stop_b[ind2,ind1]*math.log(p_stop_givenB_model[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*math.log(1-p_stop_givenB_model[ind2,ind1])\n",
    "                    #print \"legit\", mle, p_stop_givenM_model[ind2,ind1], p_stop_givenB_model[ind2,ind1]\n",
    "                else:\n",
    "                    mle = np.inf\n",
    "                    #print \"non-legit\", mle\n",
    "                    \n",
    "        #norm = LA.norm(  - p_base, 'fro')\n",
    "        norm = LA.norm(p_stop_real_givenM-p_stop_givenM_model, 'fro') + LA.norm(p_stop_real_givenB-p_stop_givenB_model, 'fro')\n",
    "        \n",
    "        #print norm, -mle\n",
    "        return -mle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'results' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-1730bb64d1d9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mp_stop_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_givenM_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_givenB_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_stop_ProbabilityMatching\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mp_stop_givenM_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'results' is not defined"
     ]
    }
   ],
   "source": [
    "p_stop_model, p_stop_givenM_model, p_stop_givenB_model = p_stop_ProbabilityMatching([results.x[0], results.x[1], results.x[2], results.x[3]], j)\n",
    "print results.x\n",
    "print p_stop_givenM_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fitting_SimpleModel(j):   \n",
    "    \n",
    "    print \"j\", j\n",
    "    \n",
    "    # fitting in the case of probability matching\n",
    "    p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j) \n",
    "    x_initial = [0,0,0,0]\n",
    "\n",
    "    #print \"HERE1\", p_stop_real, p_m_real, trials_stop_m\n",
    "    results = minimize(fitting_probMatching, x_initial, method = 'Nelder-Mead')\n",
    "    \n",
    "    p_stop_model, p_stop_givenM_model, p_stop_givenB_model = p_stop_ProbabilityMatching([results.x[0], results.x[1], results.x[2], results.x[3]], j)\n",
    "    mle=0\n",
    "    \n",
    "    #print \"HERE2\", p_stop_model, p_stop_givenM_model, p_stop_givenB_model\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):\n",
    "                #mle = mle + trials_stop[ind2,ind1]*log(p_stop_model[ind2,ind1])+(trials_tot[ind2,ind1]-trials_stop[ind2,ind1])*log(1-p_stop_model[ind2,ind1])\n",
    "                if (p_stop_givenM_model[ind2,ind1]>0) and (p_stop_givenB_model[ind2,ind1]>0) and (p_stop_givenM_model[ind2,ind1] < 1) and (p_stop_givenB_model[ind2,ind1]< 1):\n",
    "                    mle = mle + trials_stop_m[ind2,ind1]*math.log(p_stop_givenM_model[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*math.log(1-p_stop_givenM_model[ind2,ind1])+trials_stop_b[ind2,ind1]*math.log(p_stop_givenB_model[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*math.log(1-p_stop_givenB_model[ind2,ind1])\n",
    "                else:\n",
    "                    mle = -np.inf\n",
    "                    \n",
    "    #print LA.norm(p_stop_real-p_stop_model, 'fro')\n",
    "    print \"which data frame is analyzed\", j\n",
    "    print LA.norm(p_stop_real - p_m_real*p_stop_givenM_model - p_b_real*p_stop_givenB_model, 'fro')\n",
    "    print mle\n",
    "    print \"real\", p_stop_real\n",
    "    print \"model\", p_stop_model\n",
    "    print \"1\", p_stop_givenM_model, p_stop_real_givenM\n",
    "    print \"2\", p_stop_givenB_model, p_stop_real_givenB\n",
    "    \n",
    "    return mle, results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mle' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-6374fb871350>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmle\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtrials_stop_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_stop_givenM_model\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrials_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mtrials_stop_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mp_stop_givenM_model\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mtrials_stop_b\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_stop_givenB_model\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrials_b\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mtrials_stop_b\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mp_stop_givenB_model\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'mle' is not defined"
     ]
    }
   ],
   "source": [
    "mle = mle + trials_stop_m[ind2,ind1]*math.log(p_stop_givenM_model[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*math.log(1-p_stop_givenM_model[ind2,ind1])+trials_stop_b[ind2,ind1]*math.log(p_stop_givenB_model[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*math.log(1-p_stop_givenB_model[ind2,ind1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "851\n",
      "[ 0.00025  0.       0.       0.     ]\n"
     ]
    }
   ],
   "source": [
    "print len(dataFrame_list[5])\n",
    "print results.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "j 5\n",
      "0.0 0.0 0.0 0.0\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]] [[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "x [ 0.  0.  0.  0.]\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "7.22718880542 -inf\n",
      "0.00025 0.0 0.0 0.0\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.33337499  0.37503124  0.37503124  0.40002499  0.40002499  0.40002499\n",
      "   0.33337499]\n",
      " [ 0.37503124  0.40002499  0.37503124  0.40002499  0.37503124  0.40002499\n",
      "   0.35717856]\n",
      " [ 0.35717856  0.37503124  0.35717856  0.40002499  0.37503124  0.35717856\n",
      "   0.37503124]\n",
      " [ 0.37503124  0.37503124  0.37503124  0.37503124  0.40002499  0.37503124\n",
      "   0.40002499]\n",
      " [ 0.37503124  0.37503124  0.35717856  0.35717856  0.40002499  0.40002499\n",
      "   0.35717856]\n",
      " [ 0.37503124  0.40002499  0.37503124  0.40002499  0.33337499  0.40002499\n",
      "   0.40002499]\n",
      " [ 0.33337499  0.40002499  0.37503124  0.40002499  0.33337499  0.40002499\n",
      "   0.37503124]] [[ 0.99987503  0.99987503  0.99987503  0.99987503  0.99987503  0.99987503\n",
      "   0.99987503]\n",
      " [ 0.99987503  0.99987503  0.99987503  0.99987503  0.99987503  0.99987503\n",
      "   0.99987503]\n",
      " [ 0.99987503  0.99987503  0.99987503  0.99987503  0.99987503  0.99987503\n",
      "   0.99987503]\n",
      " [ 0.99987503  0.99987503  0.99987503  0.99987503  0.99987503  0.99987503\n",
      "   0.99987503]\n",
      " [ 0.99987503  0.99987503  0.99987503  0.99987503  0.99987503  0.99987503\n",
      "   0.99987503]\n",
      " [ 0.99987503  0.99987503  0.99987503  0.99987503  0.99987503  0.99987503\n",
      "   0.99987503]\n",
      " [ 0.99987503  0.99987503  0.99987503  0.99987503  0.99987503  0.99987503\n",
      "   0.99987503]] [[ 0.00012497  0.00012497  0.00012497  0.00012497  0.00012497  0.00012497\n",
      "   0.00012497]\n",
      " [ 0.00012497  0.00012497  0.00012497  0.00012497  0.00012497  0.00012497\n",
      "   0.00012497]\n",
      " [ 0.00012497  0.00012497  0.00012497  0.00012497  0.00012497  0.00012497\n",
      "   0.00012497]\n",
      " [ 0.00012497  0.00012497  0.00012497  0.00012497  0.00012497  0.00012497\n",
      "   0.00012497]\n",
      " [ 0.00012497  0.00012497  0.00012497  0.00012497  0.00012497  0.00012497\n",
      "   0.00012497]\n",
      " [ 0.00012497  0.00012497  0.00012497  0.00012497  0.00012497  0.00012497\n",
      "   0.00012497]\n",
      " [ 0.00012497  0.00012497  0.00012497  0.00012497  0.00012497  0.00012497\n",
      "   0.00012497]]\n",
      "x [ 0.00025  0.       0.       0.     ]\n",
      "legit -107.849736403 0.999875031242 0.000124968757811\n",
      "legit -179.750310531 0.999875031242 0.000124968757811\n",
      "legit -242.663562846 0.999875031242 0.000124968757811\n",
      "legit -296.589368371 0.999875031242 0.000124968757811\n",
      "legit -368.489817523 0.999875031242 0.000124968757811\n",
      "legit -422.415623048 0.999875031242 0.000124968757811\n",
      "legit -494.316072199 0.999875031242 0.000124968757811\n",
      "legit -557.229324514 0.999875031242 0.000124968757811\n",
      "legit -629.129773666 0.999875031242 0.000124968757811\n",
      "legit -710.017669606 0.999875031242 0.000124968757811\n",
      "legit -772.930796945 0.999875031242 0.000124968757811\n",
      "legit -835.84404926 0.999875031242 0.000124968757811\n",
      "legit -889.769854786 0.999875031242 0.000124968757811\n",
      "legit -952.682857147 0.999875031242 0.000124968757811\n",
      "legit -1024.58318132 0.999875031242 0.000124968757811\n",
      "legit -1105.47107726 0.999875031242 0.000124968757811\n",
      "legit -1168.38407962 0.999875031242 0.000124968757811\n",
      "legit -1231.29720696 0.999875031242 0.000124968757811\n",
      "legit -1339.14706834 0.999875031242 0.000124968757811\n",
      "legit -1402.0600707 0.999875031242 0.000124968757811\n",
      "legit -1473.96064483 0.999875031242 0.000124968757811\n",
      "legit -1545.86121896 0.999875031242 0.000124968757811\n",
      "legit -1635.73643671 0.999875031242 0.000124968757811\n",
      "legit -1743.58629809 0.999875031242 0.000124968757811\n",
      "legit -1797.5122286 0.999875031242 0.000124968757811\n",
      "legit -1869.41267775 0.999875031242 0.000124968757811\n",
      "legit -1932.32593006 0.999875031242 0.000124968757811\n",
      "legit -2004.22637921 0.999875031242 0.000124968757811\n",
      "legit -2085.11427515 0.999875031242 0.000124968757811\n",
      "legit -2174.98949291 0.999875031242 0.000124968757811\n",
      "legit -2264.86446071 0.999875031242 0.000124968757811\n",
      "legit -2300.81549763 0.999875031242 0.000124968757811\n",
      "legit -2372.71594678 0.999875031242 0.000124968757811\n",
      "legit -2426.64175231 0.999875031242 0.000124968757811\n",
      "legit -2471.58011104 0.999875031242 0.000124968757811\n",
      "legit -2579.42997242 0.999875031242 0.000124968757811\n",
      "legit -2660.31774339 0.999875031242 0.000124968757811\n",
      "legit -2741.20563933 0.999875031242 0.000124968757811\n",
      "legit -2813.10608848 0.999875031242 0.000124968757811\n",
      "legit -2902.98118126 0.999875031242 0.000124968757811\n",
      "legit -2965.89430859 0.999875031242 0.000124968757811\n",
      "legit -3046.78207956 0.999875031242 0.000124968757811\n",
      "legit -3109.6952069 0.999875031242 0.000124968757811\n",
      "legit -3172.60833424 0.999875031242 0.000124968757811\n",
      "legit -3244.50890836 0.999875031242 0.000124968757811\n",
      "legit -3325.39667933 0.999875031242 0.000124968757811\n",
      "legit -3379.32248485 0.999875031242 0.000124968757811\n",
      "legit -3460.21025582 0.999875031242 0.000124968757811\n",
      "legit -3514.13618632 0.999875031242 0.000124968757811\n",
      "7.22554735648 3514.13618632\n",
      "0.0 0.00025 0.0 0.0\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.33518747  0.37639061  0.37639061  0.40111248  0.40111248  0.40111248\n",
      "   0.33518747]\n",
      " [ 0.37593052  0.40074442  0.37593052  0.40074442  0.37593052  0.40074442\n",
      "   0.35820631]\n",
      " [ 0.35767657  0.375467    0.35767657  0.4003736   0.375467    0.35767657\n",
      "   0.375467  ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375467    0.375467    0.35767657  0.35767657  0.4003736   0.4003736\n",
      "   0.35767657]\n",
      " [ 0.37593052  0.40074442  0.37593052  0.40074442  0.33457403  0.40074442\n",
      "   0.40074442]\n",
      " [ 0.33518747  0.40111248  0.37639061  0.40111248  0.33518747  0.40111248\n",
      "   0.37639061]] [[ 0.99443758  0.99443758  0.99443758  0.99443758  0.99443758  0.99443758\n",
      "   0.99443758]\n",
      " [ 0.99627792  0.99627792  0.99627792  0.99627792  0.99627792  0.99627792\n",
      "   0.99627792]\n",
      " [ 0.998132    0.998132    0.998132    0.998132    0.998132    0.998132\n",
      "   0.998132  ]\n",
      " [ 1.          1.          1.          1.          1.          1.          1.        ]\n",
      " [ 0.998132    0.998132    0.998132    0.998132    0.998132    0.998132\n",
      "   0.998132  ]\n",
      " [ 0.99627792  0.99627792  0.99627792  0.99627792  0.99627792  0.99627792\n",
      "   0.99627792]\n",
      " [ 0.99443758  0.99443758  0.99443758  0.99443758  0.99443758  0.99443758\n",
      "   0.99443758]] [[ 0.00556242  0.00556242  0.00556242  0.00556242  0.00556242  0.00556242\n",
      "   0.00556242]\n",
      " [ 0.00372208  0.00372208  0.00372208  0.00372208  0.00372208  0.00372208\n",
      "   0.00372208]\n",
      " [ 0.001868    0.001868    0.001868    0.001868    0.001868    0.001868\n",
      "   0.001868  ]\n",
      " [ 0.          0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.001868    0.001868    0.001868    0.001868    0.001868    0.001868\n",
      "   0.001868  ]\n",
      " [ 0.00372208  0.00372208  0.00372208  0.00372208  0.00372208  0.00372208\n",
      "   0.00372208]\n",
      " [ 0.00556242  0.00556242  0.00556242  0.00556242  0.00556242  0.00556242\n",
      "   0.00556242]]\n",
      "x [ 0.       0.00025  0.       0.     ]\n",
      "legit -62.3173920953 0.994437577256 0.00556242274413\n",
      "legit -103.895787863 0.994437577256 0.00556242274413\n",
      "legit -140.28804006 0.994437577256 0.00556242274413\n",
      "legit -171.488570737 0.994437577256 0.00556242274413\n",
      "legit -213.061388554 0.994437577256 0.00556242274413\n",
      "legit -244.261919231 0.994437577256 0.00556242274413\n",
      "legit -285.834737048 0.994437577256 0.00556242274413\n",
      "legit -325.022598482 0.996277915633 0.00372208436725\n",
      "legit -369.796473313 0.996277915633 0.00372208436725\n",
      "legit -420.163819597 0.996277915633 0.00372208436725\n",
      "legit -459.347952002 0.996277915633 0.00372208436725\n",
      "legit -498.535813436 0.996277915633 0.00372208436725\n",
      "legit -532.130203416 0.996277915633 0.00372208436725\n",
      "legit -571.310606793 0.996277915633 0.00372208436725\n",
      "legit -621.584942091 0.998132004981 0.00186799501868\n",
      "legit -678.144036737 0.998132004981 0.00186799501868\n",
      "legit -722.137352171 0.998132004981 0.00186799501868\n",
      "legit -766.132537347 0.998132004981 0.00186799501868\n",
      "legit -841.534691585 0.998132004981 0.00186799501868\n",
      "legit -885.528007019 0.998132004981 0.00186799501868\n",
      "legit -935.806081801 0.998132004981 0.00186799501868\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.996277915633 0.00372208436725\n",
      "legit inf 0.996277915633 0.00372208436725\n",
      "legit inf 0.996277915633 0.00372208436725\n",
      "legit inf 0.996277915633 0.00372208436725\n",
      "legit inf 0.996277915633 0.00372208436725\n",
      "legit inf 0.996277915633 0.00372208436725\n",
      "legit inf 0.996277915633 0.00372208436725\n",
      "legit inf 0.994437577256 0.00556242274413\n",
      "legit inf 0.994437577256 0.00556242274413\n",
      "legit inf 0.994437577256 0.00556242274413\n",
      "legit inf 0.994437577256 0.00556242274413\n",
      "legit inf 0.994437577256 0.00556242274413\n",
      "legit inf 0.994437577256 0.00556242274413\n",
      "legit inf 0.994437577256 0.00556242274413\n",
      "7.18583497381 -inf\n",
      "0.0 0.0 0.00025 0.0\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.33935743  0.37804878  0.37654321  0.4         0.40123457  0.40243902\n",
      "   0.33935743]\n",
      " [ 0.37951807  0.40243902  0.37654321  0.4         0.37654321  0.40243902\n",
      "   0.36230637]\n",
      " [ 0.36230637  0.37804878  0.35890653  0.4         0.37654321  0.36062718\n",
      "   0.37951807]\n",
      " [ 0.37951807  0.37804878  0.37654321  0.375       0.40123457  0.37804878\n",
      "   0.40361446]\n",
      " [ 0.37951807  0.37804878  0.35890653  0.35714286  0.40123457  0.40243902\n",
      "   0.36230637]\n",
      " [ 0.37951807  0.40243902  0.37654321  0.4         0.33539095  0.40243902\n",
      "   0.40361446]\n",
      " [ 0.33935743  0.40243902  0.37654321  0.4         0.33539095  0.40243902\n",
      "   0.37951807]] [[ 0.98192771  0.98780488  0.99382716  1.          0.99382716  0.98780488\n",
      "   0.98192771]\n",
      " [ 0.98192771  0.98780488  0.99382716  1.          0.99382716  0.98780488\n",
      "   0.98192771]\n",
      " [ 0.98192771  0.98780488  0.99382716  1.          0.99382716  0.98780488\n",
      "   0.98192771]\n",
      " [ 0.98192771  0.98780488  0.99382716  1.          0.99382716  0.98780488\n",
      "   0.98192771]\n",
      " [ 0.98192771  0.98780488  0.99382716  1.          0.99382716  0.98780488\n",
      "   0.98192771]\n",
      " [ 0.98192771  0.98780488  0.99382716  1.          0.99382716  0.98780488\n",
      "   0.98192771]\n",
      " [ 0.98192771  0.98780488  0.99382716  1.          0.99382716  0.98780488\n",
      "   0.98192771]] [[ 0.01807229  0.01219512  0.00617284  0.          0.00617284  0.01219512\n",
      "   0.01807229]\n",
      " [ 0.01807229  0.01219512  0.00617284  0.          0.00617284  0.01219512\n",
      "   0.01807229]\n",
      " [ 0.01807229  0.01219512  0.00617284  0.          0.00617284  0.01219512\n",
      "   0.01807229]\n",
      " [ 0.01807229  0.01219512  0.00617284  0.          0.00617284  0.01219512\n",
      "   0.01807229]\n",
      " [ 0.01807229  0.01219512  0.00617284  0.          0.00617284  0.01219512\n",
      "   0.01807229]\n",
      " [ 0.01807229  0.01219512  0.00617284  0.          0.00617284  0.01219512\n",
      "   0.01807229]\n",
      " [ 0.01807229  0.01219512  0.00617284  0.          0.00617284  0.01219512\n",
      "   0.01807229]]\n",
      "x [ 0.       0.       0.00025  0.     ]\n",
      "legit -48.2152187589 0.981927710843 0.0180722891566\n",
      "legit -83.5671334778 0.987804878049 0.0121951219512\n",
      "legit -119.236035557 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "7.08788615624 -inf\n",
      "0.0 0.0 0.0 0.00025\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.34496124  0.38235294  0.38095238  0.40361446  0.40243902  0.40123457\n",
      "   0.33333333]\n",
      " [ 0.38372093  0.40588235  0.38095238  0.40361446  0.37804878  0.40123457\n",
      "   0.35714286]\n",
      " [ 0.36710963  0.38235294  0.36394558  0.40361446  0.37804878  0.35890653\n",
      "   0.375     ]\n",
      " [ 0.38372093  0.38235294  0.38095238  0.37951807  0.40243902  0.37654321\n",
      "   0.4       ]\n",
      " [ 0.38372093  0.38235294  0.36394558  0.36230637  0.40243902  0.40123457\n",
      "   0.35714286]\n",
      " [ 0.38372093  0.40588235  0.38095238  0.40361446  0.33739837  0.40123457\n",
      "   0.4       ]\n",
      " [ 0.34496124  0.40588235  0.38095238  0.40361446  0.33739837  0.40123457\n",
      "   0.375     ]] [[ 0.96511628  0.97058824  0.97619048  0.98192771  0.98780488  0.99382716\n",
      "   1.        ]\n",
      " [ 0.96511628  0.97058824  0.97619048  0.98192771  0.98780488  0.99382716\n",
      "   1.        ]\n",
      " [ 0.96511628  0.97058824  0.97619048  0.98192771  0.98780488  0.99382716\n",
      "   1.        ]\n",
      " [ 0.96511628  0.97058824  0.97619048  0.98192771  0.98780488  0.99382716\n",
      "   1.        ]\n",
      " [ 0.96511628  0.97058824  0.97619048  0.98192771  0.98780488  0.99382716\n",
      "   1.        ]\n",
      " [ 0.96511628  0.97058824  0.97619048  0.98192771  0.98780488  0.99382716\n",
      "   1.        ]\n",
      " [ 0.96511628  0.97058824  0.97619048  0.98192771  0.98780488  0.99382716\n",
      "   1.        ]] [[ 0.03488372  0.02941176  0.02380952  0.01807229  0.01219512  0.00617284\n",
      "   0.        ]\n",
      " [ 0.03488372  0.02941176  0.02380952  0.01807229  0.01219512  0.00617284\n",
      "   0.        ]\n",
      " [ 0.03488372  0.02941176  0.02380952  0.01807229  0.01219512  0.00617284\n",
      "   0.        ]\n",
      " [ 0.03488372  0.02941176  0.02380952  0.01807229  0.01219512  0.00617284\n",
      "   0.        ]\n",
      " [ 0.03488372  0.02941176  0.02380952  0.01807229  0.01219512  0.00617284\n",
      "   0.        ]\n",
      " [ 0.03488372  0.02941176  0.02380952  0.01807229  0.01219512  0.00617284\n",
      "   0.        ]\n",
      " [ 0.03488372  0.02941176  0.02380952  0.01807229  0.01219512  0.00617284\n",
      "   0.        ]]\n",
      "x [ 0.       0.       0.       0.00025]\n",
      "legit -40.3753401564 0.96511627907 0.0348837209302\n",
      "legit -68.8250480585 0.970588235294 0.0294117647059\n",
      "legit -95.2056133507 0.97619047619 0.0238095238095\n",
      "legit -119.450004637 0.981927710843 0.0180722891566\n",
      "legit -154.789649263 0.987804878049 0.0121951219512\n",
      "legit -185.370955007 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.96511627907 0.0348837209302\n",
      "legit inf 0.970588235294 0.0294117647059\n",
      "legit inf 0.97619047619 0.0238095238095\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.96511627907 0.0348837209302\n",
      "legit inf 0.970588235294 0.0294117647059\n",
      "legit inf 0.97619047619 0.0238095238095\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.96511627907 0.0348837209302\n",
      "legit inf 0.970588235294 0.0294117647059\n",
      "legit inf 0.97619047619 0.0238095238095\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.96511627907 0.0348837209302\n",
      "legit inf 0.970588235294 0.0294117647059\n",
      "legit inf 0.97619047619 0.0238095238095\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.96511627907 0.0348837209302\n",
      "legit inf 0.970588235294 0.0294117647059\n",
      "legit inf 0.97619047619 0.0238095238095\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "legit inf 0.96511627907 0.0348837209302\n",
      "legit inf 0.970588235294 0.0294117647059\n",
      "legit inf 0.97619047619 0.0238095238095\n",
      "legit inf 0.981927710843 0.0180722891566\n",
      "legit inf 0.987804878049 0.0121951219512\n",
      "legit inf 0.993827160494 0.00617283950617\n",
      "non-legit inf\n",
      "6.98051342699 -inf\n",
      "-0.00025 0.000125 0.000125 0.000125\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.34300797  0.38085309  0.37941638  0.40235567  0.40235567  0.40235567\n",
      "   0.33725945]\n",
      " [ 0.38204765  0.40451182  0.37919787  0.40217657  0.37772071  0.40217657\n",
      "   0.36025224]\n",
      " [ 0.36495839  0.38042568  0.3616898   0.40199681  0.37749602  0.35999545\n",
      "   0.37749602]\n",
      " [ 0.38162879  0.38021083  0.37875849  0.3772705   0.4018164   0.3772705\n",
      "   0.4018164 ]\n",
      " [ 0.38183859  0.38042568  0.3616898   0.35999545  0.40199681  0.40199681\n",
      "   0.35999545]\n",
      " [ 0.38204765  0.40451182  0.37919787  0.40217657  0.33696095  0.40217657\n",
      "   0.40217657]\n",
      " [ 0.34300797  0.40468247  0.37941638  0.40235567  0.33725945  0.40235567\n",
      "   0.37794459]] [[ 0.9709761   0.97658763  0.9823345   0.98822165  0.98822165  0.98822165\n",
      "   0.98822165]\n",
      " [ 0.97180939  0.97744092  0.9832085   0.98911714  0.98911714  0.98911714\n",
      "   0.98911714]\n",
      " [ 0.97264563  0.97829726  0.98408568  0.99001593  0.99001593  0.99001593\n",
      "   0.99001593]\n",
      " [ 0.97348485  0.97915668  0.98496605  0.99091802  0.99091802  0.99091802\n",
      "   0.99091802]\n",
      " [ 0.97264563  0.97829726  0.98408568  0.99001593  0.99001593  0.99001593\n",
      "   0.99001593]\n",
      " [ 0.97180939  0.97744092  0.9832085   0.98911714  0.98911714  0.98911714\n",
      "   0.98911714]\n",
      " [ 0.9709761   0.97658763  0.9823345   0.98822165  0.98822165  0.98822165\n",
      "   0.98822165]] [[ 0.0290239   0.02341237  0.0176655   0.01177835  0.01177835  0.01177835\n",
      "   0.01177835]\n",
      " [ 0.02819061  0.02255908  0.0167915   0.01088286  0.01088286  0.01088286\n",
      "   0.01088286]\n",
      " [ 0.02735437  0.02170274  0.01591432  0.00998407  0.00998407  0.00998407\n",
      "   0.00998407]\n",
      " [ 0.02651515  0.02084332  0.01503395  0.00908198  0.00908198  0.00908198\n",
      "   0.00908198]\n",
      " [ 0.02735437  0.02170274  0.01591432  0.00998407  0.00998407  0.00998407\n",
      "   0.00998407]\n",
      " [ 0.02819061  0.02255908  0.0167915   0.01088286  0.01088286  0.01088286\n",
      "   0.01088286]\n",
      " [ 0.0290239   0.02341237  0.0176655   0.01177835  0.01177835  0.01177835\n",
      "   0.01177835]]\n",
      "x [-0.00025   0.000125  0.000125  0.000125]\n",
      "legit -42.5639872442 0.970976097963 0.029023902037\n",
      "legit -72.7894405562 0.976587632551 0.0234123674491\n",
      "legit -101.202842593 0.982334498975 0.017665501025\n",
      "legit -127.958431358 0.98822165263 0.0117783473697\n",
      "legit -163.573308404 0.98822165263 0.0117783473697\n",
      "legit -190.32889717 0.98822165263 0.0117783473697\n",
      "legit -225.943774215 0.98822165263 0.0117783473697\n",
      "legit -251.182498676 0.971809389007 0.0281906109932\n",
      "legit -281.675160593 0.977440916687 0.0225590833134\n",
      "legit -318.575643742 0.98320850447 0.0167914955303\n",
      "legit -350.307149524 0.989117143556 0.0108828564441\n",
      "legit -382.049597813 0.989117143556 0.0108828564441\n",
      "legit -409.271479572 0.989117143556 0.0108828564441\n",
      "legit -440.992042845 0.989117143556 0.0108828564441\n",
      "legit -469.94948863 0.972645633936 0.027354366064\n",
      "legit -504.575932133 0.978297261748 0.0217027382518\n",
      "legit -533.671981314 0.984085683166 0.0159143168341\n",
      "legit -565.999603361 0.990015925518 0.00998407448242\n",
      "legit -621.320908446 0.990015925518 0.00998407448242\n",
      "legit -653.638496243 0.990015925518 0.00998407448242\n",
      "legit -690.572882298 0.990015925518 0.00998407448242\n",
      "legit -719.828178077 0.973484848485 0.0265151515152\n",
      "legit -758.661779391 0.979156684236 0.0208433157643\n",
      "legit -809.091705324 0.984966052376 0.0150339476237\n",
      "legit -837.391716188 0.990918016691 0.00908198330879\n",
      "legit -875.067281993 0.990918016691 0.00908198330879\n",
      "legit -908.059632065 0.990918016691 0.00908198330879\n",
      "legit -945.73519787 0.990918016691 0.00908198330879\n",
      "legit -978.319258243 0.972645633936 0.027354366064\n",
      "legit -1016.75407688 0.978297261748 0.0217027382518\n",
      "legit -1058.22360757 0.984085683166 0.0159143168341\n",
      "legit -1076.75100609 0.990015925518 0.00998407448242\n",
      "legit -1113.6753579 0.990015925518 0.00998407448242\n",
      "legit -1141.40625019 0.990015925518 0.00998407448242\n",
      "legit -1164.53037847 0.990015925518 0.00998407448242\n",
      "legit -1207.46995645 0.971809389007 0.0281906109932\n",
      "legit -1241.73141842 0.977440916687 0.0225590833134\n",
      "legit -1278.63190157 0.98320850447 0.0167914955303\n",
      "legit -1314.87303138 0.989117143556 0.0108828564441\n",
      "legit -1360.13340923 0.989117143556 0.0108828564441\n",
      "legit -1391.86491501 0.989117143556 0.0108828564441\n",
      "legit -1432.61566884 0.989117143556 0.0108828564441\n",
      "legit -1457.62874532 0.970976097963 0.029023902037\n",
      "legit -1484.09970775 0.976587632551 0.0234123674491\n",
      "legit -1516.53142803 0.982334498975 0.017665501025\n",
      "legit -1556.57594921 0.98822165263 0.0117783473697\n",
      "legit -1583.33153798 0.98822165263 0.0117783473697\n",
      "legit -1623.37605917 0.98822165263 0.0117783473697\n",
      "legit -1650.14349619 0.98822165263 0.0117783473697\n",
      "7.0140022833 1650.14349619\n",
      "-0.000125 9.375e-05 9.375e-05 9.375e-05\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.34070567  0.37944908  0.3783492   0.40178325  0.40178325  0.40178325\n",
      "   0.33630541]\n",
      " [ 0.38036847  0.4034283   0.37818248  0.40164741  0.37705926  0.40164741\n",
      "   0.35949629]\n",
      " [ 0.36309399  0.37912121  0.36058892  0.40151119  0.37688898  0.3593017\n",
      "   0.37688898]\n",
      " [ 0.38004558  0.37895661  0.37784768  0.37671824  0.40137459  0.37671824\n",
      "   0.40137459]\n",
      " [ 0.38020724  0.37912121  0.36058892  0.3593017   0.40151119  0.40151119\n",
      "   0.3593017 ]\n",
      " [ 0.38036847  0.4034283   0.37818248  0.40164741  0.33607901  0.40164741\n",
      "   0.40164741]\n",
      " [ 0.34070567  0.40355927  0.3783492   0.40178325  0.33630541  0.40178325\n",
      "   0.37722906]] [[ 0.97788298  0.98220367  0.98660321  0.99108376  0.99108376  0.99108376\n",
      "   0.99108376]\n",
      " [ 0.97852614  0.98285852  0.98727007  0.99176297  0.99176297  0.99176297\n",
      "   0.99176297]\n",
      " [ 0.97917103  0.98351516  0.98793876  0.99244406  0.99244406  0.99244406\n",
      "   0.99244406]\n",
      " [ 0.97981767  0.98417358  0.9886093   0.99312704  0.99312704  0.99312704\n",
      "   0.99312704]\n",
      " [ 0.97917103  0.98351516  0.98793876  0.99244406  0.99244406  0.99244406\n",
      "   0.99244406]\n",
      " [ 0.97852614  0.98285852  0.98727007  0.99176297  0.99176297  0.99176297\n",
      "   0.99176297]\n",
      " [ 0.97788298  0.98220367  0.98660321  0.99108376  0.99108376  0.99108376\n",
      "   0.99108376]] [[ 0.02211702  0.01779633  0.01339679  0.00891624  0.00891624  0.00891624\n",
      "   0.00891624]\n",
      " [ 0.02147386  0.01714148  0.01272993  0.00823703  0.00823703  0.00823703\n",
      "   0.00823703]\n",
      " [ 0.02082897  0.01648484  0.01206124  0.00755594  0.00755594  0.00755594\n",
      "   0.00755594]\n",
      " [ 0.02018233  0.01582642  0.0113907   0.00687296  0.00687296  0.00687296\n",
      "   0.00687296]\n",
      " [ 0.02082897  0.01648484  0.01206124  0.00755594  0.00755594  0.00755594\n",
      "   0.00755594]\n",
      " [ 0.02147386  0.01714148  0.01272993  0.00823703  0.00823703  0.00823703\n",
      "   0.00823703]\n",
      " [ 0.02211702  0.01779633  0.01339679  0.00891624  0.00891624  0.00891624\n",
      "   0.00891624]]\n",
      "x [ -1.25000000e-04   9.37500000e-05   9.37500000e-05   9.37500000e-05]\n",
      "legit -45.8039889186 0.977882978406 0.0221170215943\n",
      "legit -78.1777461536 0.982203670775 0.0177963292246\n",
      "legit -108.488310943 0.986603205499 0.0133967945014\n",
      "legit -136.888202981 0.991083760474 0.0089162395261\n",
      "legit -174.709944563 0.991083760474 0.0089162395261\n",
      "legit -203.109836601 0.991083760474 0.0089162395261\n",
      "legit -240.931578183 0.991083760474 0.0089162395261\n",
      "legit -268.013380307 0.97852613949 0.0214738605096\n",
      "legit -300.66444428 0.982858522453 0.0171414775471\n",
      "legit -340.028319722 0.987270069436 0.012729930564\n",
      "legit -373.688297123 0.991762970248 0.00823702975166\n",
      "legit -407.356545667 0.991762970248 0.00823702975166\n",
      "legit -436.225678743 0.991762970248 0.00823702975166\n",
      "legit -469.877385003 0.991762970248 0.00823702975166\n",
      "legit -500.974964058 0.979171034111 0.020828965889\n",
      "legit -538.039144023 0.983515155178 0.0164848448219\n",
      "legit -569.04839602 0.987938763685 0.0120612363148\n",
      "legit -603.307023272 0.992444061432 0.0075559385676\n",
      "legit -661.962419305 0.992444061432 0.0075559385676\n",
      "legit -696.213461928 0.992444061432 0.0075559385676\n",
      "legit -735.35751064 0.992444061432 0.0075559385676\n",
      "legit -766.744203046 0.979817669286 0.0201823307143\n",
      "legit -808.300664491 0.984173576227 0.0158264237729\n",
      "legit -862.04598091 0.988609295792 0.0113907042081\n",
      "legit -891.995911827 0.993127041854 0.00687295814584\n",
      "legit -931.885474039 0.993127041854 0.00687295814584\n",
      "legit -966.808668945 0.993127041854 0.00687295814584\n",
      "legit -1006.69823116 0.993127041854 0.00687295814584\n",
      "legit -1041.68826983 0.979171034111 0.020828965889\n",
      "legit -1082.84114138 0.983515155178 0.0164848448219\n",
      "legit -1127.06726543 0.987938763685 0.0120612363148\n",
      "legit -1146.68479756 0.992444061432 0.0075559385676\n",
      "legit -1185.82126164 0.992444061432 0.0075559385676\n",
      "legit -1215.20205206 0.992444061432 0.0075559385676\n",
      "legit -1239.69742102 0.992444061432 0.0075559385676\n",
      "legit -1285.87527862 0.97852613949 0.0214738605096\n",
      "legit -1322.57530667 0.982858522453 0.0171414775471\n",
      "legit -1361.93918211 0.987270069436 0.012729930564\n",
      "legit -1400.39000384 0.991762970248 0.00823702975166\n",
      "legit -1448.42251421 0.991762970248 0.00823702975166\n",
      "legit -1482.08249162 0.991762970248 0.00823702975166\n",
      "legit -1525.32415767 0.991762970248 0.00823702975166\n",
      "legit -1552.18293414 0.977882978406 0.0221170215943\n",
      "legit -1580.52792831 0.982203670775 0.0177963292246\n",
      "legit -1615.13774558 0.986603205499 0.0133967945014\n",
      "legit -1657.67041193 0.991083760474 0.0089162395261\n",
      "legit -1686.07030397 0.991083760474 0.0089162395261\n",
      "legit -1728.60297032 0.991083760474 0.0089162395261\n",
      "legit -1757.01181859 0.991083760474 0.0089162395261\n",
      "7.06521764807 1757.01181859\n",
      "0.0 0.000125 0.0 0.0\n",
      "1 [[ 0.6         0.75        0.5625      0.53333333  0.53333333  0.53333333\n",
      "   0.73333333]\n",
      " [ 0.5625      0.4         0.4375      0.73333333  0.6875      0.53333333\n",
      "   0.71428571]\n",
      " [ 0.64285714  0.5625      0.71428571  0.6         0.75        0.71428571\n",
      "   0.625     ]\n",
      " [ 0.5         0.75        0.75        0.75        0.66666667  0.6875      0.4       ]\n",
      " [ 0.6875      0.625       0.64285714  0.64285714  0.53333333  0.53333333\n",
      "   0.57142857]\n",
      " [ 0.625       0.73333333  0.5625      0.66666667  0.73333333  0.73333333\n",
      "   0.73333333]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.6         0.86666667\n",
      "   0.5       ]] [[ 0.8         1.5         0.75        0.66666667  0.66666667  0.66666667\n",
      "   1.2       ]\n",
      " [ 0.75        0.          0.25        1.66666667  1.25        0.66666667\n",
      "   1.25      ]\n",
      " [ 1.          0.75        1.25        1.          1.5         1.25        1.        ]\n",
      " [ 0.5         1.5         1.5         1.5         1.33333333  1.25        0.        ]\n",
      " [ 1.25        1.          1.          1.          0.66666667  0.66666667\n",
      "   0.75      ]\n",
      " [ 1.          1.66666667  0.75        1.33333333  1.2         1.66666667\n",
      "   1.66666667]\n",
      " [ 1.          1.          1.          1.          0.8         2.33333333\n",
      "   0.5       ]] [[ 0.2         0.83333333  0.66666667  0.66666667  0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.66666667  0.33333333  0.33333333  0.83333333  0.83333333  0.66666667\n",
      "   0.8       ]\n",
      " [ 0.6         0.5         0.8         0.66666667  0.5         0.8\n",
      "   0.66666667]\n",
      " [ 0.5         0.66666667  0.5         1.          0.66666667  0.83333333\n",
      "   0.33333333]\n",
      " [ 0.66666667  0.5         0.4         1.          0.5         0.66666667\n",
      "   0.8       ]\n",
      " [ 0.33333333  0.66666667  0.5         0.66666667  0.6         0.83333333\n",
      "   0.66666667]\n",
      " [ 0.8         0.66666667  0.66666667  0.5         0.8         0.83333333\n",
      "   0.66666667]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]] [[ 0.8         0.16666667  0.33333333  0.33333333  0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.33333333  0.66666667  0.66666667  0.16666667  0.16666667  0.33333333\n",
      "   0.2       ]\n",
      " [ 0.4         0.5         0.2         0.33333333  0.5         0.2\n",
      "   0.33333333]\n",
      " [ 0.5         0.33333333  0.5         0.          0.33333333  0.16666667\n",
      "   0.66666667]\n",
      " [ 0.33333333  0.5         0.6         0.          0.5         0.33333333\n",
      "   0.2       ]\n",
      " [ 0.66666667  0.33333333  0.5         0.33333333  0.4         0.16666667\n",
      "   0.33333333]\n",
      " [ 0.2         0.33333333  0.33333333  0.5         0.2         0.16666667\n",
      "   0.33333333]] [[ 0.8         0.7         0.5         0.44444444  0.55555556  0.44444444\n",
      "   0.7       ]\n",
      " [ 0.5         0.44444444  0.5         0.66666667  0.6         0.44444444\n",
      "   0.66666667]\n",
      " [ 0.66666667  0.6         0.66666667  0.55555556  0.9         0.66666667\n",
      "   0.6       ]\n",
      " [ 0.5         0.8         0.9         0.6         0.66666667  0.6\n",
      "   0.44444444]\n",
      " [ 0.7         0.7         0.77777778  0.44444444  0.55555556  0.44444444\n",
      "   0.44444444]\n",
      " [ 0.8         0.77777778  0.6         0.66666667  0.8         0.66666667\n",
      "   0.77777778]\n",
      " [ 0.6         0.55555556  0.6         0.66666667  0.5         0.88888889\n",
      "   0.4       ]]\n",
      "2 [[ 0.33333333  0.375       0.375       0.4         0.4         0.4\n",
      "   0.33333333]\n",
      " [ 0.375       0.4         0.375       0.4         0.375       0.4\n",
      "   0.35714286]\n",
      " [ 0.35714286  0.375       0.35714286  0.4         0.375       0.35714286\n",
      "   0.375     ]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.375       0.375       0.35714286  0.35714286  0.4         0.4\n",
      "   0.35714286]\n",
      " [ 0.375       0.4         0.375       0.4         0.33333333  0.4         0.4       ]\n",
      " [ 0.33333333  0.4         0.375       0.4         0.33333333  0.4         0.375     ]] [[ 0.66666667  0.625       0.625       0.6         0.6         0.6\n",
      "   0.66666667]\n",
      " [ 0.625       0.6         0.625       0.6         0.625       0.6\n",
      "   0.64285714]\n",
      " [ 0.64285714  0.625       0.64285714  0.6         0.625       0.64285714\n",
      "   0.625     ]\n",
      " [ 0.625       0.625       0.625       0.625       0.6         0.625       0.6       ]\n",
      " [ 0.625       0.625       0.64285714  0.64285714  0.6         0.6\n",
      "   0.64285714]\n",
      " [ 0.625       0.6         0.625       0.6         0.66666667  0.6         0.6       ]\n",
      " [ 0.66666667  0.6         0.625       0.6         0.66666667  0.6         0.625     ]] [[ 15.  16.  16.  15.  15.  15.  15.]\n",
      " [ 16.  15.  16.  15.  16.  15.  14.]\n",
      " [ 14.  16.  14.  15.  16.  14.  16.]\n",
      " [ 16.  16.  16.  16.  15.  16.  15.]\n",
      " [ 16.  16.  14.  14.  15.  15.  14.]\n",
      " [ 16.  15.  16.  15.  15.  15.  15.]\n",
      " [ 15.  15.  16.  15.  15.  15.  16.]] [[  9.  12.   9.   8.   8.   8.  11.]\n",
      " [  9.   6.   7.  11.  11.   8.  10.]\n",
      " [  9.   9.  10.   9.  12.  10.  10.]\n",
      " [  8.  12.  12.  12.  10.  11.   6.]\n",
      " [ 11.  10.   9.   9.   8.   8.   8.]\n",
      " [ 10.  11.   9.  10.  11.  11.  11.]\n",
      " [ 10.   9.  10.   9.   9.  13.   8.]] [[ 5.  6.  6.  6.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  6.  6.  5.]\n",
      " [ 5.  6.  5.  6.  6.  5.  6.]\n",
      " [ 6.  6.  6.  6.  6.  6.  6.]\n",
      " [ 6.  6.  5.  5.  6.  6.  5.]\n",
      " [ 6.  6.  6.  6.  5.  6.  6.]\n",
      " [ 5.  6.  6.  6.  5.  6.  6.]] [[ 10.  10.  10.   9.   9.   9.  10.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [  9.  10.   9.   9.  10.   9.  10.]\n",
      " [ 10.  10.  10.  10.   9.  10.   9.]\n",
      " [ 10.  10.   9.   9.   9.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.   9.]\n",
      " [ 10.   9.  10.   9.  10.   9.  10.]] [[ 1.  5.  4.  4.  3.  4.  4.]\n",
      " [ 4.  2.  2.  5.  5.  4.  4.]\n",
      " [ 3.  3.  4.  4.  3.  4.  4.]\n",
      " [ 3.  4.  3.  6.  4.  5.  2.]\n",
      " [ 4.  3.  2.  5.  3.  4.  4.]\n",
      " [ 2.  4.  3.  4.  3.  5.  4.]\n",
      " [ 4.  4.  4.  3.  4.  5.  4.]] [[ 8.  7.  5.  4.  5.  4.  7.]\n",
      " [ 5.  4.  5.  6.  6.  4.  6.]\n",
      " [ 6.  6.  6.  5.  9.  6.  6.]\n",
      " [ 5.  8.  9.  6.  6.  6.  4.]\n",
      " [ 7.  7.  7.  4.  5.  4.  4.]\n",
      " [ 8.  7.  6.  6.  8.  6.  7.]\n",
      " [ 6.  5.  6.  6.  5.  8.  4.]]\n",
      "3 [[ 0.33426559  0.37569919  0.37569919  0.40055935  0.40055935  0.40055935\n",
      "   0.33426559]\n",
      " [ 0.375467    0.4003736   0.375467    0.4003736   0.375467    0.4003736\n",
      "   0.35767657]\n",
      " [ 0.35741021  0.37523394  0.35741021  0.40018715  0.37523394  0.35741021\n",
      "   0.37523394]\n",
      " [ 0.375       0.375       0.375       0.375       0.4         0.375       0.4       ]\n",
      " [ 0.37523394  0.37523394  0.35741021  0.35741021  0.40018715  0.40018715\n",
      "   0.35741021]\n",
      " [ 0.375467    0.4003736   0.375467    0.4003736   0.333956    0.4003736\n",
      "   0.4003736 ]\n",
      " [ 0.33426559  0.40055935  0.37569919  0.40055935  0.33426559  0.40055935\n",
      "   0.37569919]] [[ 0.99720323  0.99720323  0.99720323  0.99720323  0.99720323  0.99720323\n",
      "   0.99720323]\n",
      " [ 0.998132    0.998132    0.998132    0.998132    0.998132    0.998132\n",
      "   0.998132  ]\n",
      " [ 0.99906425  0.99906425  0.99906425  0.99906425  0.99906425  0.99906425\n",
      "   0.99906425]\n",
      " [ 1.          1.          1.          1.          1.          1.          1.        ]\n",
      " [ 0.99906425  0.99906425  0.99906425  0.99906425  0.99906425  0.99906425\n",
      "   0.99906425]\n",
      " [ 0.998132    0.998132    0.998132    0.998132    0.998132    0.998132\n",
      "   0.998132  ]\n",
      " [ 0.99720323  0.99720323  0.99720323  0.99720323  0.99720323  0.99720323\n",
      "   0.99720323]] [[ 0.00279677  0.00279677  0.00279677  0.00279677  0.00279677  0.00279677\n",
      "   0.00279677]\n",
      " [ 0.001868    0.001868    0.001868    0.001868    0.001868    0.001868\n",
      "   0.001868  ]\n",
      " [ 0.00093575  0.00093575  0.00093575  0.00093575  0.00093575  0.00093575\n",
      "   0.00093575]\n",
      " [ 0.          0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.00093575  0.00093575  0.00093575  0.00093575  0.00093575  0.00093575\n",
      "   0.00093575]\n",
      " [ 0.001868    0.001868    0.001868    0.001868    0.001868    0.001868\n",
      "   0.001868  ]\n",
      " [ 0.00279677  0.00279677  0.00279677  0.00279677  0.00279677  0.00279677\n",
      "   0.00279677]]\n",
      "x [ 0.        0.000125  0.        0.      ]\n",
      "legit -70.5598910619 0.997203231821 0.00279676817899\n",
      "legit -117.616622555 0.997203231821 0.00279676817899\n",
      "legit -158.796863985 0.997203231821 0.00279676817899\n",
      "legit -194.097814664 0.997203231821 0.00279676817899\n",
      "legit -241.151745471 0.997203231821 0.00279676817899\n",
      "legit -276.45269615 0.997203231821 0.00279676817899\n",
      "legit -323.506626957 0.997203231821 0.00279676817899\n",
      "legit -367.503681875 0.998132004981 0.00186799501868\n",
      "legit -417.779886915 0.998132004981 0.00186799501868\n",
      "legit -474.338981561 0.998132004981 0.00186799501868\n",
      "legit -518.334166737 0.998132004981 0.00186799501868\n",
      "legit -562.331221655 0.998132004981 0.00186799501868\n",
      "legit -600.045386967 0.998132004981 0.00186799501868\n",
      "legit -644.038702401 0.998132004981 0.00186799501868\n",
      "legit -699.837655858 0.999064254523 0.00093574547723\n",
      "legit -762.611712544 0.999064254523 0.00093574547723\n",
      "legit -811.43743514 0.999064254523 0.00093574547723\n",
      "legit -860.26409392 0.999064254523 0.00093574547723\n",
      "legit -943.957843188 0.999064254523 0.00093574547723\n",
      "legit -992.783565785 0.999064254523 0.00093574547723\n",
      "legit -1048.58439161 0.999064254523 0.00093574547723\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "non-legit inf\n",
      "legit inf 0.999064254523 0.00093574547723\n",
      "legit inf 0.999064254523 0.00093574547723\n",
      "legit inf 0.999064254523 0.00093574547723\n",
      "legit inf 0.999064254523 0.00093574547723\n",
      "legit inf 0.999064254523 0.00093574547723\n",
      "legit inf 0.999064254523 0.00093574547723\n",
      "legit inf 0.999064254523 0.00093574547723\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.998132004981 0.00186799501868\n",
      "legit inf 0.997203231821 0.00279676817899\n",
      "legit inf 0.997203231821 0.00279676817899\n",
      "legit inf 0.997203231821 0.00279676817899\n",
      "legit inf 0.997203231821 0.00279676817899\n",
      "legit inf 0.997203231821 0.00279676817899\n",
      "legit inf 0.997203231821 0.00279676817899\n",
      "legit inf 0.997203231821 0.00279676817899\n",
      "7.20640393794 -inf\n",
      "0.0 0.0 0.000125 0.0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-131-8a25db98ead0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfitting_SimpleModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-130-298440489122>\u001b[0m in \u001b[0;36mfitting_SimpleModel\u001b[1;34m(j)\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;31m#print \"HERE1\", p_stop_real, p_m_real, trials_stop_m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfitting_probMatching\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_initial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Nelder-Mead'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mp_stop_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_givenM_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_givenB_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_stop_ProbabilityMatching\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\scipy\\optimize\\_minimize.pyc\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m    411\u001b[0m                       callback=callback, **options)\n\u001b[0;32m    412\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'nelder-mead'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 413\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_minimize_neldermead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    414\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'powell'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    415\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_minimize_powell\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\scipy\\optimize\\optimize.pyc\u001b[0m in \u001b[0;36m_minimize_neldermead\u001b[1;34m(func, x0, args, callback, xtol, ftol, maxiter, maxfev, disp, return_all, **unknown_options)\u001b[0m\n\u001b[0;32m    506\u001b[0m                     \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mone2np1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    507\u001b[0m                         \u001b[0msim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msigma\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0msim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m                         \u001b[0mfsim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfsim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\scipy\\optimize\\optimize.pyc\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[1;34m(*wrapper_args)\u001b[0m\n\u001b[0;32m    279\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    280\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 281\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    282\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-128-67a9cbd888a5>\u001b[0m in \u001b[0;36mfitting_probMatching\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mp_m_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_b_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_tot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_m\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_b\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop_m\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop_b\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_real\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[0mp_base\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_givenM_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_givenB_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_stop_ProbabilityMatching\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m         \u001b[0mmle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-54-d48ed4fa56b1>\u001b[0m in \u001b[0;36mp_stop_ProbabilityMatching\u001b[1;34m(x, j)\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mprob_misID\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprobability_misidentify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[0mp_m_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_b_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_tot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_m\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_b\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop_m\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop_b\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_real\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morientations\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-103-3e0f4f071fd3>\u001b[0m in \u001b[0;36mp_real\u001b[1;34m(j)\u001b[0m\n\u001b[0;32m     23\u001b[0m             \u001b[0mtrials_b\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOri\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'bird'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal_trial_ind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m             \u001b[0mtrials_stop_m\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOri\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'mushroom'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal_trial_ind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m             \u001b[0mtrials_stop_b\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mind1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOri\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'bird'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m&\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal_trial_ind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\ops.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    553\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 555\u001b[1;33m             \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    556\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36misnull\u001b[1;34m(obj)\u001b[0m\n\u001b[0;32m    127\u001b[0m     \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mboolean\u001b[0m \u001b[0minverse\u001b[0m \u001b[0mof\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    128\u001b[0m     \"\"\"\n\u001b[1;32m--> 129\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_isnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    130\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36m_isnull_new\u001b[1;34m(obj)\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"isnull is not defined for MultiIndex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mABCSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_isnull_ndarraylike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCGeneric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36m_isnull_ndarraylike\u001b[1;34m(obj)\u001b[0m\n\u001b[0;32m    225\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 227\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    229\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    229\u001b[0m                                        raise_cast_failure=True)\n\u001b[0;32m    230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 231\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m         \u001b[0mgeneric\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, block, axis, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m   3028\u001b[0m             block = make_block(block,\n\u001b[0;32m   3029\u001b[0m                                \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3030\u001b[1;33m                                ndim=1, fastpath=True)\n\u001b[0m\u001b[0;32m   3031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3032\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mmake_block\u001b[1;34m(values, placement, klass, ndim, dtype, fastpath)\u001b[0m\n\u001b[0;32m   1835\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1836\u001b[0m     return klass(values, ndim=ndim, fastpath=fastpath,\n\u001b[1;32m-> 1837\u001b[1;33m                  placement=placement)\n\u001b[0m\u001b[0;32m   1838\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, values, placement, ndim, fastpath)\u001b[0m\n\u001b[0;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 68\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmgr_locs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     69\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mmgr_locs\u001b[1;34m(self, new_mgr_locs)\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mnew_mgr_locs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBlockPlacement\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_mgr_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 115\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr_locs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_mgr_locs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    116\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__unicode__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "[a, b] = fitting_SimpleModel(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  status: 1\n",
       "    nfev: 803\n",
       " success: False\n",
       "     fun: -inf\n",
       "       x: array([ 0.,  0.,  0.,  0.])\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "     nit: 134"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "j 1\n",
      "0.0 0.0 0.0 0.0\n",
      "0.00025 0.0 0.0 0.0\n",
      "0.0 0.00025 0.0 0.0\n",
      "0.0 0.0 0.00025 0.0\n",
      "0.0 0.0 0.0 0.00025\n",
      "-0.00025 0.000125 0.000125 0.000125\n",
      "-0.000125 9.375e-05 9.375e-05 9.375e-05\n",
      "0.0 0.000125 0.0 0.0\n",
      "0.0 0.0 0.000125 0.0\n",
      "0.0 0.0 0.0 0.000125\n",
      "0.000125 0.0 0.0 0.0\n",
      "-0.000125 6.25e-05 6.25e-05 6.25e-05\n",
      "-6.25e-05 4.6875e-05 4.6875e-05 4.6875e-05\n",
      "0.0 6.25e-05 0.0 0.0\n",
      "0.0 0.0 6.25e-05 0.0\n",
      "0.0 0.0 0.0 6.25e-05\n",
      "6.25e-05 0.0 0.0 0.0\n",
      "-6.25e-05 3.125e-05 3.125e-05 3.125e-05\n",
      "-3.125e-05 2.34375e-05 2.34375e-05 2.34375e-05\n",
      "0.0 3.125e-05 0.0 0.0\n",
      "0.0 0.0 3.125e-05 0.0\n",
      "0.0 0.0 0.0 3.125e-05\n",
      "3.125e-05 0.0 0.0 0.0\n",
      "-3.125e-05 1.5625e-05 1.5625e-05 1.5625e-05\n",
      "-1.5625e-05 1.171875e-05 1.171875e-05 1.171875e-05\n",
      "0.0 1.5625e-05 0.0 0.0\n",
      "0.0 0.0 1.5625e-05 0.0\n",
      "0.0 0.0 0.0 1.5625e-05\n",
      "1.5625e-05 0.0 0.0 0.0\n",
      "-1.5625e-05 7.8125e-06 7.8125e-06 7.8125e-06\n",
      "-7.8125e-06 5.859375e-06 5.859375e-06 5.859375e-06\n",
      "0.0 7.8125e-06 0.0 0.0\n",
      "0.0 0.0 7.8125e-06 0.0\n",
      "0.0 0.0 0.0 7.8125e-06\n",
      "7.8125e-06 0.0 0.0 0.0\n",
      "-7.8125e-06 3.90625e-06 3.90625e-06 3.90625e-06\n",
      "-3.90625e-06 2.9296875e-06 2.9296875e-06 2.9296875e-06\n",
      "0.0 3.90625e-06 0.0 0.0\n",
      "0.0 0.0 3.90625e-06 0.0\n",
      "0.0 0.0 0.0 3.90625e-06\n",
      "3.90625e-06 0.0 0.0 0.0\n",
      "-3.90625e-06 1.953125e-06 1.953125e-06 1.953125e-06\n",
      "-1.953125e-06 1.46484375e-06 1.46484375e-06 1.46484375e-06\n",
      "0.0 1.953125e-06 0.0 0.0\n",
      "0.0 0.0 1.953125e-06 0.0\n",
      "0.0 0.0 0.0 1.953125e-06\n",
      "1.953125e-06 0.0 0.0 0.0\n",
      "-1.953125e-06 9.765625e-07 9.765625e-07 9.765625e-07\n",
      "-9.765625e-07 7.32421875e-07 7.32421875e-07 7.32421875e-07\n",
      "0.0 9.765625e-07 0.0 0.0\n",
      "0.0 0.0 9.765625e-07 0.0\n",
      "0.0 0.0 0.0 9.765625e-07\n",
      "9.765625e-07 0.0 0.0 0.0\n",
      "-9.765625e-07 4.8828125e-07 4.8828125e-07 4.8828125e-07\n",
      "-4.8828125e-07 3.662109375e-07 3.662109375e-07 3.662109375e-07\n",
      "0.0 4.8828125e-07 0.0 0.0\n",
      "0.0 0.0 4.8828125e-07 0.0\n",
      "0.0 0.0 0.0 4.8828125e-07\n",
      "4.8828125e-07 0.0 0.0 0.0\n",
      "-4.8828125e-07 2.44140625e-07 2.44140625e-07 2.44140625e-07\n",
      "-2.44140625e-07 1.8310546875e-07 1.8310546875e-07 1.8310546875e-07\n",
      "0.0 2.44140625e-07 0.0 0.0\n",
      "0.0 0.0 2.44140625e-07 0.0\n",
      "0.0 0.0 0.0 2.44140625e-07\n",
      "2.44140625e-07 0.0 0.0 0.0\n",
      "-2.44140625e-07 1.220703125e-07 1.220703125e-07 1.220703125e-07\n",
      "-1.220703125e-07 9.1552734375e-08 9.1552734375e-08 9.1552734375e-08\n",
      "0.0 1.220703125e-07 0.0 0.0\n",
      "0.0 0.0 1.220703125e-07 0.0\n",
      "0.0 0.0 0.0 1.220703125e-07\n",
      "1.220703125e-07 0.0 0.0 0.0\n",
      "-1.220703125e-07 6.103515625e-08 6.103515625e-08 6.103515625e-08\n",
      "-6.103515625e-08 4.57763671875e-08 4.57763671875e-08 4.57763671875e-08\n",
      "0.0 6.103515625e-08 0.0 0.0\n",
      "0.0 0.0 6.103515625e-08 0.0\n",
      "0.0 0.0 0.0 6.103515625e-08\n",
      "6.103515625e-08 0.0 0.0 0.0\n",
      "-6.103515625e-08 3.0517578125e-08 3.0517578125e-08 3.0517578125e-08\n",
      "-3.0517578125e-08 2.28881835938e-08 2.28881835938e-08 2.28881835938e-08\n",
      "0.0 3.0517578125e-08 0.0 0.0\n",
      "0.0 0.0 3.0517578125e-08 0.0\n",
      "0.0 0.0 0.0 3.0517578125e-08\n",
      "3.0517578125e-08 0.0 0.0 0.0\n",
      "-3.0517578125e-08 1.52587890625e-08 1.52587890625e-08 1.52587890625e-08\n",
      "-1.52587890625e-08 1.14440917969e-08 1.14440917969e-08 1.14440917969e-08\n",
      "0.0 1.52587890625e-08 0.0 0.0\n",
      "0.0 0.0 1.52587890625e-08 0.0\n",
      "0.0 0.0 0.0 1.52587890625e-08\n",
      "1.52587890625e-08 0.0 0.0 0.0\n",
      "-1.52587890625e-08 7.62939453125e-09 7.62939453125e-09 7.62939453125e-09\n",
      "-7.62939453125e-09 5.72204589844e-09 5.72204589844e-09 5.72204589844e-09\n",
      "0.0 7.62939453125e-09 0.0 0.0\n",
      "0.0 0.0 7.62939453125e-09 0.0\n",
      "0.0 0.0 0.0 7.62939453125e-09\n",
      "7.62939453125e-09 0.0 0.0 0.0\n",
      "-7.62939453125e-09 3.81469726563e-09 3.81469726563e-09 3.81469726563e-09\n",
      "-3.81469726563e-09 2.86102294922e-09 2.86102294922e-09 2.86102294922e-09\n",
      "0.0 3.81469726563e-09 0.0 0.0\n",
      "0.0 0.0 3.81469726563e-09 0.0\n",
      "0.0 0.0 0.0 3.81469726563e-09\n",
      "3.81469726563e-09 0.0 0.0 0.0\n",
      "-3.81469726563e-09 1.90734863281e-09 1.90734863281e-09 1.90734863281e-09\n",
      "-1.90734863281e-09 1.43051147461e-09 1.43051147461e-09 1.43051147461e-09\n",
      "0.0 1.90734863281e-09 0.0 0.0\n",
      "0.0 0.0 1.90734863281e-09 0.0\n",
      "0.0 0.0 0.0 1.90734863281e-09\n",
      "1.90734863281e-09 0.0 0.0 0.0\n",
      "-1.90734863281e-09 9.53674316406e-10 9.53674316406e-10 9.53674316406e-10\n",
      "-9.53674316406e-10 7.15255737305e-10 7.15255737305e-10 7.15255737305e-10\n",
      "0.0 9.53674316406e-10 0.0 0.0\n",
      "0.0 0.0 9.53674316406e-10 0.0\n",
      "0.0 0.0 0.0 9.53674316406e-10\n",
      "9.53674316406e-10 0.0 0.0 0.0\n",
      "-9.53674316406e-10 4.76837158203e-10 4.76837158203e-10 4.76837158203e-10\n",
      "-4.76837158203e-10 3.57627868652e-10 3.57627868652e-10 3.57627868652e-10\n",
      "0.0 4.76837158203e-10 0.0 0.0\n",
      "0.0 0.0 4.76837158203e-10 0.0\n",
      "0.0 0.0 0.0 4.76837158203e-10\n",
      "4.76837158203e-10 0.0 0.0 0.0\n",
      "-4.76837158203e-10 2.38418579102e-10 2.38418579102e-10 2.38418579102e-10\n",
      "-2.38418579102e-10 1.78813934326e-10 1.78813934326e-10 1.78813934326e-10\n",
      "0.0 2.38418579102e-10 0.0 0.0\n",
      "0.0 0.0 2.38418579102e-10 0.0\n",
      "0.0 0.0 0.0 2.38418579102e-10\n",
      "2.38418579102e-10 0.0 0.0 0.0\n",
      "-2.38418579102e-10 1.19209289551e-10 1.19209289551e-10 1.19209289551e-10\n",
      "-1.19209289551e-10 8.94069671631e-11 8.94069671631e-11 8.94069671631e-11\n",
      "0.0 1.19209289551e-10 0.0 0.0\n",
      "0.0 0.0 1.19209289551e-10 0.0\n",
      "0.0 0.0 0.0 1.19209289551e-10\n",
      "1.19209289551e-10 0.0 0.0 0.0\n",
      "-1.19209289551e-10 5.96046447754e-11 5.96046447754e-11 5.96046447754e-11\n",
      "-5.96046447754e-11 4.47034835815e-11 4.47034835815e-11 4.47034835815e-11\n",
      "0.0 5.96046447754e-11 0.0 0.0\n",
      "0.0 0.0 5.96046447754e-11 0.0\n",
      "0.0 0.0 0.0 5.96046447754e-11\n",
      "5.96046447754e-11 0.0 0.0 0.0\n",
      "-5.96046447754e-11 2.98023223877e-11 2.98023223877e-11 2.98023223877e-11\n",
      "-2.98023223877e-11 2.23517417908e-11 2.23517417908e-11 2.23517417908e-11\n",
      "0.0 2.98023223877e-11 0.0 0.0\n",
      "0.0 0.0 2.98023223877e-11 0.0\n",
      "0.0 0.0 0.0 2.98023223877e-11\n",
      "2.98023223877e-11 0.0 0.0 0.0\n",
      "-2.98023223877e-11 1.49011611938e-11 1.49011611938e-11 1.49011611938e-11\n",
      "-1.49011611938e-11 1.11758708954e-11 1.11758708954e-11 1.11758708954e-11\n",
      "0.0 1.49011611938e-11 0.0 0.0\n",
      "0.0 0.0 1.49011611938e-11 0.0\n",
      "0.0 0.0 0.0 1.49011611938e-11\n",
      "1.49011611938e-11 0.0 0.0 0.0\n",
      "-1.49011611938e-11 7.45058059692e-12 7.45058059692e-12 7.45058059692e-12\n",
      "-7.45058059692e-12 5.58793544769e-12 5.58793544769e-12 5.58793544769e-12\n",
      "0.0 7.45058059692e-12 0.0 0.0\n",
      "0.0 0.0 7.45058059692e-12 0.0\n",
      "0.0 0.0 0.0 7.45058059692e-12\n",
      "7.45058059692e-12 0.0 0.0 0.0\n",
      "-7.45058059692e-12 3.72529029846e-12 3.72529029846e-12 3.72529029846e-12\n",
      "-3.72529029846e-12 2.79396772385e-12 2.79396772385e-12 2.79396772385e-12\n",
      "0.0 3.72529029846e-12 0.0 0.0\n",
      "0.0 0.0 3.72529029846e-12 0.0\n",
      "0.0 0.0 0.0 3.72529029846e-12\n",
      "3.72529029846e-12 0.0 0.0 0.0\n",
      "-3.72529029846e-12 1.86264514923e-12 1.86264514923e-12 1.86264514923e-12\n",
      "-1.86264514923e-12 1.39698386192e-12 1.39698386192e-12 1.39698386192e-12\n",
      "0.0 1.86264514923e-12 0.0 0.0\n",
      "0.0 0.0 1.86264514923e-12 0.0\n",
      "0.0 0.0 0.0 1.86264514923e-12\n",
      "1.86264514923e-12 0.0 0.0 0.0\n",
      "-1.86264514923e-12 9.31322574615e-13 9.31322574615e-13 9.31322574615e-13\n",
      "-9.31322574615e-13 6.98491930962e-13 6.98491930962e-13 6.98491930962e-13\n",
      "0.0 9.31322574615e-13 0.0 0.0\n",
      "0.0 0.0 9.31322574615e-13 0.0\n",
      "0.0 0.0 0.0 9.31322574615e-13\n",
      "9.31322574615e-13 0.0 0.0 0.0\n",
      "-9.31322574615e-13 4.65661287308e-13 4.65661287308e-13 4.65661287308e-13\n",
      "-4.65661287308e-13 3.49245965481e-13 3.49245965481e-13 3.49245965481e-13\n",
      "0.0 4.65661287308e-13 0.0 0.0\n",
      "0.0 0.0 4.65661287308e-13 0.0\n",
      "0.0 0.0 0.0 4.65661287308e-13\n",
      "4.65661287308e-13 0.0 0.0 0.0\n",
      "-4.65661287308e-13 2.32830643654e-13 2.32830643654e-13 2.32830643654e-13\n",
      "-2.32830643654e-13 1.7462298274e-13 1.7462298274e-13 1.7462298274e-13\n",
      "0.0 2.32830643654e-13 0.0 0.0\n",
      "0.0 0.0 2.32830643654e-13 0.0\n",
      "0.0 0.0 0.0 2.32830643654e-13\n",
      "2.32830643654e-13 0.0 0.0 0.0\n",
      "-2.32830643654e-13 1.16415321827e-13 1.16415321827e-13 1.16415321827e-13\n",
      "-1.16415321827e-13 8.73114913702e-14 8.73114913702e-14 8.73114913702e-14\n",
      "0.0 1.16415321827e-13 0.0 0.0\n",
      "0.0 0.0 1.16415321827e-13 0.0\n",
      "0.0 0.0 0.0 1.16415321827e-13\n",
      "1.16415321827e-13 0.0 0.0 0.0\n",
      "-1.16415321827e-13 5.82076609135e-14 5.82076609135e-14 5.82076609135e-14\n",
      "-5.82076609135e-14 4.36557456851e-14 4.36557456851e-14 4.36557456851e-14\n",
      "0.0 5.82076609135e-14 0.0 0.0\n",
      "0.0 0.0 5.82076609135e-14 0.0\n",
      "0.0 0.0 0.0 5.82076609135e-14\n",
      "5.82076609135e-14 0.0 0.0 0.0\n",
      "-5.82076609135e-14 2.91038304567e-14 2.91038304567e-14 2.91038304567e-14\n",
      "-2.91038304567e-14 2.18278728426e-14 2.18278728426e-14 2.18278728426e-14\n",
      "0.0 2.91038304567e-14 0.0 0.0\n",
      "0.0 0.0 2.91038304567e-14 0.0\n",
      "0.0 0.0 0.0 2.91038304567e-14\n",
      "2.91038304567e-14 0.0 0.0 0.0\n",
      "-2.91038304567e-14 1.45519152284e-14 1.45519152284e-14 1.45519152284e-14\n",
      "-1.45519152284e-14 1.09139364213e-14 1.09139364213e-14 1.09139364213e-14\n",
      "0.0 1.45519152284e-14 0.0 0.0\n",
      "0.0 0.0 1.45519152284e-14 0.0\n",
      "0.0 0.0 0.0 1.45519152284e-14\n",
      "1.45519152284e-14 0.0 0.0 0.0\n",
      "-1.45519152284e-14 7.27595761418e-15 7.27595761418e-15 7.27595761418e-15\n",
      "-7.27595761418e-15 5.45696821064e-15 5.45696821064e-15 5.45696821064e-15\n",
      "0.0 7.27595761418e-15 0.0 0.0\n",
      "0.0 0.0 7.27595761418e-15 0.0\n",
      "0.0 0.0 0.0 7.27595761418e-15\n",
      "7.27595761418e-15 0.0 0.0 0.0\n",
      "-7.27595761418e-15 3.63797880709e-15 3.63797880709e-15 3.63797880709e-15\n",
      "-3.63797880709e-15 2.72848410532e-15 2.72848410532e-15 2.72848410532e-15\n",
      "0.0 3.63797880709e-15 0.0 0.0\n",
      "0.0 0.0 3.63797880709e-15 0.0\n",
      "0.0 0.0 0.0 3.63797880709e-15\n",
      "3.63797880709e-15 0.0 0.0 0.0\n",
      "-3.63797880709e-15 1.81898940355e-15 1.81898940355e-15 1.81898940355e-15\n",
      "-1.81898940355e-15 1.36424205266e-15 1.36424205266e-15 1.36424205266e-15\n",
      "0.0 1.81898940355e-15 0.0 0.0\n",
      "0.0 0.0 1.81898940355e-15 0.0\n",
      "0.0 0.0 0.0 1.81898940355e-15\n",
      "1.81898940355e-15 0.0 0.0 0.0\n",
      "-1.81898940355e-15 9.09494701773e-16 9.09494701773e-16 9.09494701773e-16\n",
      "-9.09494701773e-16 6.8212102633e-16 6.8212102633e-16 6.8212102633e-16\n",
      "0.0 9.09494701773e-16 0.0 0.0\n",
      "0.0 0.0 9.09494701773e-16 0.0\n",
      "0.0 0.0 0.0 9.09494701773e-16\n",
      "9.09494701773e-16 0.0 0.0 0.0\n",
      "-9.09494701773e-16 4.54747350886e-16 4.54747350886e-16 4.54747350886e-16\n",
      "-4.54747350886e-16 3.41060513165e-16 3.41060513165e-16 3.41060513165e-16\n",
      "0.0 4.54747350886e-16 0.0 0.0\n",
      "0.0 0.0 4.54747350886e-16 0.0\n",
      "0.0 0.0 0.0 4.54747350886e-16\n",
      "4.54747350886e-16 0.0 0.0 0.0\n",
      "-4.54747350886e-16 2.27373675443e-16 2.27373675443e-16 2.27373675443e-16\n",
      "-2.27373675443e-16 1.70530256582e-16 1.70530256582e-16 1.70530256582e-16\n",
      "0.0 2.27373675443e-16 0.0 0.0\n",
      "0.0 0.0 2.27373675443e-16 0.0\n",
      "0.0 0.0 0.0 2.27373675443e-16\n",
      "2.27373675443e-16 0.0 0.0 0.0\n",
      "-2.27373675443e-16 1.13686837722e-16 1.13686837722e-16 1.13686837722e-16\n",
      "-1.13686837722e-16 8.52651282912e-17 8.52651282912e-17 8.52651282912e-17\n",
      "0.0 1.13686837722e-16 0.0 0.0\n",
      "0.0 0.0 1.13686837722e-16 0.0\n",
      "0.0 0.0 0.0 1.13686837722e-16\n",
      "1.13686837722e-16 0.0 0.0 0.0\n",
      "-1.13686837722e-16 5.68434188608e-17 5.68434188608e-17 5.68434188608e-17\n",
      "-5.68434188608e-17 4.26325641456e-17 4.26325641456e-17 4.26325641456e-17\n",
      "0.0 5.68434188608e-17 0.0 0.0\n",
      "0.0 0.0 5.68434188608e-17 0.0\n",
      "0.0 0.0 0.0 5.68434188608e-17\n",
      "5.68434188608e-17 0.0 0.0 0.0\n",
      "-5.68434188608e-17 2.84217094304e-17 2.84217094304e-17 2.84217094304e-17\n",
      "2.84217094304e-17 7.1054273576e-18 7.1054273576e-18 7.1054273576e-18\n",
      "0.0 2.84217094304e-17 0.0 0.0\n",
      "0.0 0.0 2.84217094304e-17 0.0\n",
      "0.0 0.0 0.0 2.84217094304e-17\n",
      "2.84217094304e-17 0.0 0.0 0.0\n",
      "-2.84217094304e-17 1.42108547152e-17 1.42108547152e-17 1.42108547152e-17\n",
      "1.42108547152e-17 3.5527136788e-18 3.5527136788e-18 3.5527136788e-18\n",
      "0.0 1.42108547152e-17 0.0 0.0\n",
      "0.0 0.0 1.42108547152e-17 0.0\n",
      "0.0 0.0 0.0 1.42108547152e-17\n",
      "1.42108547152e-17 0.0 0.0 0.0\n",
      "-1.42108547152e-17 7.1054273576e-18 7.1054273576e-18 7.1054273576e-18\n",
      "7.1054273576e-18 1.7763568394e-18 1.7763568394e-18 1.7763568394e-18\n",
      "0.0 7.1054273576e-18 0.0 0.0\n",
      "0.0 0.0 7.1054273576e-18 0.0\n",
      "0.0 0.0 0.0 7.1054273576e-18\n",
      "7.1054273576e-18 0.0 0.0 0.0\n",
      "-7.1054273576e-18 3.5527136788e-18 3.5527136788e-18 3.5527136788e-18\n",
      "3.5527136788e-18 8.881784197e-19 8.881784197e-19 8.881784197e-19\n",
      "0.0 3.5527136788e-18 0.0 0.0\n",
      "0.0 0.0 3.5527136788e-18 0.0\n",
      "0.0 0.0 0.0 3.5527136788e-18\n",
      "3.5527136788e-18 0.0 0.0 0.0\n",
      "-3.5527136788e-18 1.7763568394e-18 1.7763568394e-18 1.7763568394e-18\n",
      "1.7763568394e-18 4.4408920985e-19 4.4408920985e-19 4.4408920985e-19\n",
      "0.0 1.7763568394e-18 0.0 0.0\n",
      "0.0 0.0 1.7763568394e-18 0.0\n",
      "0.0 0.0 0.0 1.7763568394e-18\n",
      "1.7763568394e-18 0.0 0.0 0.0\n",
      "-1.7763568394e-18 8.881784197e-19 8.881784197e-19 8.881784197e-19\n",
      "8.881784197e-19 2.22044604925e-19 2.22044604925e-19 2.22044604925e-19\n",
      "0.0 8.881784197e-19 0.0 0.0\n",
      "0.0 0.0 8.881784197e-19 0.0\n",
      "0.0 0.0 0.0 8.881784197e-19\n",
      "8.881784197e-19 0.0 0.0 0.0\n",
      "-8.881784197e-19 4.4408920985e-19 4.4408920985e-19 4.4408920985e-19\n",
      "4.4408920985e-19 1.11022302463e-19 1.11022302463e-19 1.11022302463e-19\n",
      "0.0 4.4408920985e-19 0.0 0.0\n",
      "0.0 0.0 4.4408920985e-19 0.0\n",
      "0.0 0.0 0.0 4.4408920985e-19\n",
      "4.4408920985e-19 0.0 0.0 0.0\n",
      "-4.4408920985e-19 2.22044604925e-19 2.22044604925e-19 2.22044604925e-19\n",
      "2.22044604925e-19 5.55111512313e-20 5.55111512313e-20 5.55111512313e-20\n",
      "0.0 2.22044604925e-19 0.0 0.0\n",
      "0.0 0.0 2.22044604925e-19 0.0\n",
      "0.0 0.0 0.0 2.22044604925e-19\n",
      "2.22044604925e-19 0.0 0.0 0.0\n",
      "-2.22044604925e-19 1.11022302463e-19 1.11022302463e-19 1.11022302463e-19\n",
      "1.11022302463e-19 2.77555756156e-20 2.77555756156e-20 2.77555756156e-20\n",
      "0.0 1.11022302463e-19 0.0 0.0\n",
      "0.0 0.0 1.11022302463e-19 0.0\n",
      "0.0 0.0 0.0 1.11022302463e-19\n",
      "1.11022302463e-19 0.0 0.0 0.0\n",
      "-1.11022302463e-19 5.55111512313e-20 5.55111512313e-20 5.55111512313e-20\n",
      "5.55111512313e-20 1.38777878078e-20 1.38777878078e-20 1.38777878078e-20\n",
      "0.0 5.55111512313e-20 0.0 0.0\n",
      "0.0 0.0 5.55111512313e-20 0.0\n",
      "0.0 0.0 0.0 5.55111512313e-20\n",
      "5.55111512313e-20 0.0 0.0 0.0\n",
      "-5.55111512313e-20 2.77555756156e-20 2.77555756156e-20 2.77555756156e-20\n",
      "2.77555756156e-20 6.93889390391e-21 6.93889390391e-21 6.93889390391e-21\n",
      "0.0 2.77555756156e-20 0.0 0.0\n",
      "0.0 0.0 2.77555756156e-20 0.0\n",
      "0.0 0.0 0.0 2.77555756156e-20\n",
      "2.77555756156e-20 0.0 0.0 0.0\n",
      "-2.77555756156e-20 1.38777878078e-20 1.38777878078e-20 1.38777878078e-20\n",
      "1.38777878078e-20 3.46944695195e-21 3.46944695195e-21 3.46944695195e-21\n",
      "0.0 1.38777878078e-20 0.0 0.0\n",
      "0.0 0.0 1.38777878078e-20 0.0\n",
      "0.0 0.0 0.0 1.38777878078e-20\n",
      "1.38777878078e-20 0.0 0.0 0.0\n",
      "-1.38777878078e-20 6.93889390391e-21 6.93889390391e-21 6.93889390391e-21\n",
      "6.93889390391e-21 1.73472347598e-21 1.73472347598e-21 1.73472347598e-21\n",
      "0.0 6.93889390391e-21 0.0 0.0\n",
      "0.0 0.0 6.93889390391e-21 0.0\n",
      "0.0 0.0 0.0 6.93889390391e-21\n",
      "6.93889390391e-21 0.0 0.0 0.0\n",
      "-6.93889390391e-21 3.46944695195e-21 3.46944695195e-21 3.46944695195e-21\n",
      "3.46944695195e-21 8.67361737988e-22 8.67361737988e-22 8.67361737988e-22\n",
      "0.0 3.46944695195e-21 0.0 0.0\n",
      "0.0 0.0 3.46944695195e-21 0.0\n",
      "0.0 0.0 0.0 3.46944695195e-21\n",
      "3.46944695195e-21 0.0 0.0 0.0\n",
      "-3.46944695195e-21 1.73472347598e-21 1.73472347598e-21 1.73472347598e-21\n",
      "1.73472347598e-21 4.33680868994e-22 4.33680868994e-22 4.33680868994e-22\n",
      "0.0 1.73472347598e-21 0.0 0.0\n",
      "0.0 0.0 1.73472347598e-21 0.0\n",
      "0.0 0.0 0.0 1.73472347598e-21\n",
      "1.73472347598e-21 0.0 0.0 0.0\n",
      "-1.73472347598e-21 8.67361737988e-22 8.67361737988e-22 8.67361737988e-22\n",
      "8.67361737988e-22 2.16840434497e-22 2.16840434497e-22 2.16840434497e-22\n",
      "0.0 8.67361737988e-22 0.0 0.0\n",
      "0.0 0.0 8.67361737988e-22 0.0\n",
      "0.0 0.0 0.0 8.67361737988e-22\n",
      "8.67361737988e-22 0.0 0.0 0.0\n",
      "-8.67361737988e-22 4.33680868994e-22 4.33680868994e-22 4.33680868994e-22\n",
      "4.33680868994e-22 1.08420217249e-22 1.08420217249e-22 1.08420217249e-22\n",
      "0.0 4.33680868994e-22 0.0 0.0\n",
      "0.0 0.0 4.33680868994e-22 0.0\n",
      "0.0 0.0 0.0 4.33680868994e-22\n",
      "4.33680868994e-22 0.0 0.0 0.0\n",
      "-4.33680868994e-22 2.16840434497e-22 2.16840434497e-22 2.16840434497e-22\n",
      "2.16840434497e-22 5.42101086243e-23 5.42101086243e-23 5.42101086243e-23\n",
      "0.0 2.16840434497e-22 0.0 0.0\n",
      "0.0 0.0 2.16840434497e-22 0.0\n",
      "0.0 0.0 0.0 2.16840434497e-22\n",
      "2.16840434497e-22 0.0 0.0 0.0\n",
      "-2.16840434497e-22 1.08420217249e-22 1.08420217249e-22 1.08420217249e-22\n",
      "1.08420217249e-22 2.71050543121e-23 2.71050543121e-23 2.71050543121e-23\n",
      "0.0 1.08420217249e-22 0.0 0.0\n",
      "0.0 0.0 1.08420217249e-22 0.0\n",
      "0.0 0.0 0.0 1.08420217249e-22\n",
      "1.08420217249e-22 0.0 0.0 0.0\n",
      "-1.08420217249e-22 5.42101086243e-23 5.42101086243e-23 5.42101086243e-23\n",
      "5.42101086243e-23 1.35525271561e-23 1.35525271561e-23 1.35525271561e-23\n",
      "0.0 5.42101086243e-23 0.0 0.0\n",
      "0.0 0.0 5.42101086243e-23 0.0\n",
      "0.0 0.0 0.0 5.42101086243e-23\n",
      "5.42101086243e-23 0.0 0.0 0.0\n",
      "-5.42101086243e-23 2.71050543121e-23 2.71050543121e-23 2.71050543121e-23\n",
      "2.71050543121e-23 6.77626357803e-24 6.77626357803e-24 6.77626357803e-24\n",
      "0.0 2.71050543121e-23 0.0 0.0\n",
      "0.0 0.0 2.71050543121e-23 0.0\n",
      "0.0 0.0 0.0 2.71050543121e-23\n",
      "2.71050543121e-23 0.0 0.0 0.0\n",
      "-2.71050543121e-23 1.35525271561e-23 1.35525271561e-23 1.35525271561e-23\n",
      "1.35525271561e-23 3.38813178902e-24 3.38813178902e-24 3.38813178902e-24\n",
      "0.0 1.35525271561e-23 0.0 0.0\n",
      "0.0 0.0 1.35525271561e-23 0.0\n",
      "0.0 0.0 0.0 1.35525271561e-23\n",
      "1.35525271561e-23 0.0 0.0 0.0\n",
      "-1.35525271561e-23 6.77626357803e-24 6.77626357803e-24 6.77626357803e-24\n",
      "6.77626357803e-24 1.69406589451e-24 1.69406589451e-24 1.69406589451e-24\n",
      "0.0 6.77626357803e-24 0.0 0.0\n",
      "0.0 0.0 6.77626357803e-24 0.0\n",
      "0.0 0.0 0.0 6.77626357803e-24\n",
      "6.77626357803e-24 0.0 0.0 0.0\n",
      "-6.77626357803e-24 3.38813178902e-24 3.38813178902e-24 3.38813178902e-24\n",
      "3.38813178902e-24 8.47032947254e-25 8.47032947254e-25 8.47032947254e-25\n",
      "0.0 3.38813178902e-24 0.0 0.0\n",
      "0.0 0.0 3.38813178902e-24 0.0\n",
      "0.0 0.0 0.0 3.38813178902e-24\n",
      "3.38813178902e-24 0.0 0.0 0.0\n",
      "-3.38813178902e-24 1.69406589451e-24 1.69406589451e-24 1.69406589451e-24\n",
      "1.69406589451e-24 4.23516473627e-25 4.23516473627e-25 4.23516473627e-25\n",
      "0.0 1.69406589451e-24 0.0 0.0\n",
      "0.0 0.0 1.69406589451e-24 0.0\n",
      "0.0 0.0 0.0 1.69406589451e-24\n",
      "1.69406589451e-24 0.0 0.0 0.0\n",
      "-1.69406589451e-24 8.47032947254e-25 8.47032947254e-25 8.47032947254e-25\n",
      "8.47032947254e-25 2.11758236814e-25 2.11758236814e-25 2.11758236814e-25\n",
      "0.0 8.47032947254e-25 0.0 0.0\n",
      "0.0 0.0 8.47032947254e-25 0.0\n",
      "0.0 0.0 0.0 8.47032947254e-25\n",
      "8.47032947254e-25 0.0 0.0 0.0\n",
      "-8.47032947254e-25 4.23516473627e-25 4.23516473627e-25 4.23516473627e-25\n",
      "4.23516473627e-25 1.05879118407e-25 1.05879118407e-25 1.05879118407e-25\n",
      "0.0 4.23516473627e-25 0.0 0.0\n",
      "0.0 0.0 4.23516473627e-25 0.0\n",
      "0.0 0.0 0.0 4.23516473627e-25\n",
      "4.23516473627e-25 0.0 0.0 0.0\n",
      "-4.23516473627e-25 2.11758236814e-25 2.11758236814e-25 2.11758236814e-25\n",
      "2.11758236814e-25 5.29395592034e-26 5.29395592034e-26 5.29395592034e-26\n",
      "0.0 2.11758236814e-25 0.0 0.0\n",
      "0.0 0.0 2.11758236814e-25 0.0\n",
      "0.0 0.0 0.0 2.11758236814e-25\n",
      "2.11758236814e-25 0.0 0.0 0.0\n",
      "-2.11758236814e-25 1.05879118407e-25 1.05879118407e-25 1.05879118407e-25\n",
      "1.05879118407e-25 2.64697796017e-26 2.64697796017e-26 2.64697796017e-26\n",
      "0.0 1.05879118407e-25 0.0 0.0\n",
      "0.0 0.0 1.05879118407e-25 0.0\n",
      "0.0 0.0 0.0 1.05879118407e-25\n",
      "1.05879118407e-25 0.0 0.0 0.0\n",
      "-1.05879118407e-25 5.29395592034e-26 5.29395592034e-26 5.29395592034e-26\n",
      "5.29395592034e-26 1.32348898008e-26 1.32348898008e-26 1.32348898008e-26\n",
      "0.0 5.29395592034e-26 0.0 0.0\n",
      "0.0 0.0 5.29395592034e-26 0.0\n",
      "0.0 0.0 0.0 5.29395592034e-26\n",
      "5.29395592034e-26 0.0 0.0 0.0\n",
      "-5.29395592034e-26 2.64697796017e-26 2.64697796017e-26 2.64697796017e-26\n",
      "2.64697796017e-26 6.61744490042e-27 6.61744490042e-27 6.61744490042e-27\n",
      "0.0 2.64697796017e-26 0.0 0.0\n",
      "0.0 0.0 2.64697796017e-26 0.0\n",
      "0.0 0.0 0.0 2.64697796017e-26\n",
      "2.64697796017e-26 0.0 0.0 0.0\n",
      "-2.64697796017e-26 1.32348898008e-26 1.32348898008e-26 1.32348898008e-26\n",
      "1.32348898008e-26 3.30872245021e-27 3.30872245021e-27 3.30872245021e-27\n",
      "0.0 1.32348898008e-26 0.0 0.0\n",
      "0.0 0.0 1.32348898008e-26 0.0\n",
      "0.0 0.0 0.0 1.32348898008e-26\n",
      "1.32348898008e-26 0.0 0.0 0.0\n",
      "-1.32348898008e-26 6.61744490042e-27 6.61744490042e-27 6.61744490042e-27\n",
      "6.61744490042e-27 1.65436122511e-27 1.65436122511e-27 1.65436122511e-27\n",
      "0.0 6.61744490042e-27 0.0 0.0\n",
      "0.0 0.0 6.61744490042e-27 0.0\n",
      "0.0 0.0 0.0 6.61744490042e-27\n",
      "6.61744490042e-27 0.0 0.0 0.0\n",
      "-6.61744490042e-27 3.30872245021e-27 3.30872245021e-27 3.30872245021e-27\n",
      "3.30872245021e-27 8.27180612553e-28 8.27180612553e-28 8.27180612553e-28\n",
      "0.0 3.30872245021e-27 0.0 0.0\n",
      "0.0 0.0 3.30872245021e-27 0.0\n",
      "0.0 0.0 0.0 3.30872245021e-27\n",
      "3.30872245021e-27 0.0 0.0 0.0\n",
      "-3.30872245021e-27 1.65436122511e-27 1.65436122511e-27 1.65436122511e-27\n",
      "1.65436122511e-27 4.13590306277e-28 4.13590306277e-28 4.13590306277e-28\n",
      "0.0 1.65436122511e-27 0.0 0.0\n",
      "0.0 0.0 1.65436122511e-27 0.0\n",
      "0.0 0.0 0.0 1.65436122511e-27\n",
      "1.65436122511e-27 0.0 0.0 0.0\n",
      "-1.65436122511e-27 8.27180612553e-28 8.27180612553e-28 8.27180612553e-28\n",
      "8.27180612553e-28 2.06795153138e-28 2.06795153138e-28 2.06795153138e-28\n",
      "0.0 8.27180612553e-28 0.0 0.0\n",
      "0.0 0.0 8.27180612553e-28 0.0\n",
      "0.0 0.0 0.0 8.27180612553e-28\n",
      "8.27180612553e-28 0.0 0.0 0.0\n",
      "-8.27180612553e-28 4.13590306277e-28 4.13590306277e-28 4.13590306277e-28\n",
      "4.13590306277e-28 1.03397576569e-28 1.03397576569e-28 1.03397576569e-28\n",
      "0.0 4.13590306277e-28 0.0 0.0\n",
      "0.0 0.0 4.13590306277e-28 0.0\n",
      "0.0 0.0 0.0 4.13590306277e-28\n",
      "4.13590306277e-28 0.0 0.0 0.0\n",
      "-4.13590306277e-28 2.06795153138e-28 2.06795153138e-28 2.06795153138e-28\n",
      "2.06795153138e-28 5.16987882846e-29 5.16987882846e-29 5.16987882846e-29\n",
      "0.0 2.06795153138e-28 0.0 0.0\n",
      "0.0 0.0 2.06795153138e-28 0.0\n",
      "0.0 0.0 0.0 2.06795153138e-28\n",
      "2.06795153138e-28 0.0 0.0 0.0\n",
      "-2.06795153138e-28 1.03397576569e-28 1.03397576569e-28 1.03397576569e-28\n",
      "1.03397576569e-28 2.58493941423e-29 2.58493941423e-29 2.58493941423e-29\n",
      "0.0 1.03397576569e-28 0.0 0.0\n",
      "0.0 0.0 1.03397576569e-28 0.0\n",
      "0.0 0.0 0.0 1.03397576569e-28\n",
      "1.03397576569e-28 0.0 0.0 0.0\n",
      "-1.03397576569e-28 5.16987882846e-29 5.16987882846e-29 5.16987882846e-29\n",
      "5.16987882846e-29 1.29246970711e-29 1.29246970711e-29 1.29246970711e-29\n",
      "0.0 5.16987882846e-29 0.0 0.0\n",
      "0.0 0.0 5.16987882846e-29 0.0\n",
      "0.0 0.0 0.0 5.16987882846e-29\n",
      "5.16987882846e-29 0.0 0.0 0.0\n",
      "-5.16987882846e-29 2.58493941423e-29 2.58493941423e-29 2.58493941423e-29\n",
      "2.58493941423e-29 6.46234853557e-30 6.46234853557e-30 6.46234853557e-30\n",
      "0.0 2.58493941423e-29 0.0 0.0\n",
      "0.0 0.0 2.58493941423e-29 0.0\n",
      "0.0 0.0 0.0 2.58493941423e-29\n",
      "2.58493941423e-29 0.0 0.0 0.0\n",
      "-2.58493941423e-29 1.29246970711e-29 1.29246970711e-29 1.29246970711e-29\n",
      "1.29246970711e-29 3.23117426779e-30 3.23117426779e-30 3.23117426779e-30\n",
      "0.0 1.29246970711e-29 0.0 0.0\n",
      "0.0 0.0 1.29246970711e-29 0.0\n",
      "0.0 0.0 0.0 1.29246970711e-29\n",
      "1.29246970711e-29 0.0 0.0 0.0\n",
      "-1.29246970711e-29 6.46234853557e-30 6.46234853557e-30 6.46234853557e-30\n",
      "6.46234853557e-30 1.61558713389e-30 1.61558713389e-30 1.61558713389e-30\n",
      "0.0 6.46234853557e-30 0.0 0.0\n",
      "0.0 0.0 6.46234853557e-30 0.0\n",
      "0.0 0.0 0.0 6.46234853557e-30\n",
      "6.46234853557e-30 0.0 0.0 0.0\n",
      "-6.46234853557e-30 3.23117426779e-30 3.23117426779e-30 3.23117426779e-30\n",
      "3.23117426779e-30 8.07793566946e-31 8.07793566946e-31 8.07793566946e-31\n",
      "0.0 3.23117426779e-30 0.0 0.0\n",
      "0.0 0.0 3.23117426779e-30 0.0\n",
      "0.0 0.0 0.0 3.23117426779e-30\n",
      "3.23117426779e-30 0.0 0.0 0.0\n",
      "-3.23117426779e-30 1.61558713389e-30 1.61558713389e-30 1.61558713389e-30\n",
      "1.61558713389e-30 4.03896783473e-31 4.03896783473e-31 4.03896783473e-31\n",
      "0.0 1.61558713389e-30 0.0 0.0\n",
      "0.0 0.0 1.61558713389e-30 0.0\n",
      "0.0 0.0 0.0 1.61558713389e-30\n",
      "1.61558713389e-30 0.0 0.0 0.0\n",
      "-1.61558713389e-30 8.07793566946e-31 8.07793566946e-31 8.07793566946e-31\n",
      "8.07793566946e-31 2.01948391737e-31 2.01948391737e-31 2.01948391737e-31\n",
      "0.0 8.07793566946e-31 0.0 0.0\n",
      "0.0 0.0 8.07793566946e-31 0.0\n",
      "0.0 0.0 0.0 8.07793566946e-31\n",
      "8.07793566946e-31 0.0 0.0 0.0\n",
      "-8.07793566946e-31 4.03896783473e-31 4.03896783473e-31 4.03896783473e-31\n",
      "4.03896783473e-31 1.00974195868e-31 1.00974195868e-31 1.00974195868e-31\n",
      "0.0 4.03896783473e-31 0.0 0.0\n",
      "0.0 0.0 4.03896783473e-31 0.0\n",
      "0.0 0.0 0.0 4.03896783473e-31\n",
      "4.03896783473e-31 0.0 0.0 0.0\n",
      "-4.03896783473e-31 2.01948391737e-31 2.01948391737e-31 2.01948391737e-31\n",
      "2.01948391737e-31 5.04870979341e-32 5.04870979341e-32 5.04870979341e-32\n",
      "0.0 2.01948391737e-31 0.0 0.0\n",
      "0.0 0.0 2.01948391737e-31 0.0\n",
      "0.0 0.0 0.0 2.01948391737e-31\n",
      "2.01948391737e-31 0.0 0.0 0.0\n",
      "-2.01948391737e-31 1.00974195868e-31 1.00974195868e-31 1.00974195868e-31\n",
      "1.00974195868e-31 2.52435489671e-32 2.52435489671e-32 2.52435489671e-32\n",
      "0.0 1.00974195868e-31 0.0 0.0\n",
      "0.0 0.0 1.00974195868e-31 0.0\n",
      "0.0 0.0 0.0 1.00974195868e-31\n",
      "1.00974195868e-31 0.0 0.0 0.0\n",
      "-1.00974195868e-31 5.04870979341e-32 5.04870979341e-32 5.04870979341e-32\n",
      "5.04870979341e-32 1.26217744835e-32 1.26217744835e-32 1.26217744835e-32\n",
      "0.0 5.04870979341e-32 0.0 0.0\n",
      "0.0 0.0 5.04870979341e-32 0.0\n",
      "0.0 0.0 0.0 5.04870979341e-32\n",
      "5.04870979341e-32 0.0 0.0 0.0\n",
      "-5.04870979341e-32 2.52435489671e-32 2.52435489671e-32 2.52435489671e-32\n",
      "2.52435489671e-32 6.31088724177e-33 6.31088724177e-33 6.31088724177e-33\n",
      "0.0 2.52435489671e-32 0.0 0.0\n",
      "0.0 0.0 2.52435489671e-32 0.0\n",
      "0.0 0.0 0.0 2.52435489671e-32\n",
      "2.52435489671e-32 0.0 0.0 0.0\n",
      "-2.52435489671e-32 1.26217744835e-32 1.26217744835e-32 1.26217744835e-32\n",
      "1.26217744835e-32 3.15544362088e-33 3.15544362088e-33 3.15544362088e-33\n",
      "0.0 1.26217744835e-32 0.0 0.0\n",
      "0.0 0.0 1.26217744835e-32 0.0\n",
      "0.0 0.0 0.0 1.26217744835e-32\n",
      "1.26217744835e-32 0.0 0.0 0.0\n",
      "-1.26217744835e-32 6.31088724177e-33 6.31088724177e-33 6.31088724177e-33\n",
      "6.31088724177e-33 1.57772181044e-33 1.57772181044e-33 1.57772181044e-33\n",
      "0.0 6.31088724177e-33 0.0 0.0\n",
      "0.0 0.0 6.31088724177e-33 0.0\n",
      "0.0 0.0 0.0 6.31088724177e-33\n",
      "6.31088724177e-33 0.0 0.0 0.0\n",
      "-6.31088724177e-33 3.15544362088e-33 3.15544362088e-33 3.15544362088e-33\n",
      "3.15544362088e-33 7.88860905221e-34 7.88860905221e-34 7.88860905221e-34\n",
      "0.0 3.15544362088e-33 0.0 0.0\n",
      "0.0 0.0 3.15544362088e-33 0.0\n",
      "0.0 0.0 0.0 3.15544362088e-33\n",
      "3.15544362088e-33 0.0 0.0 0.0\n",
      "-3.15544362088e-33 1.57772181044e-33 1.57772181044e-33 1.57772181044e-33\n",
      "1.57772181044e-33 3.94430452611e-34 3.94430452611e-34 3.94430452611e-34\n",
      "0.0 1.57772181044e-33 0.0 0.0\n",
      "0.0 0.0 1.57772181044e-33 0.0\n",
      "0.0 0.0 0.0 1.57772181044e-33\n",
      "1.57772181044e-33 0.0 0.0 0.0\n",
      "-1.57772181044e-33 7.88860905221e-34 7.88860905221e-34 7.88860905221e-34\n",
      "7.88860905221e-34 1.97215226305e-34 1.97215226305e-34 1.97215226305e-34\n",
      "0.0 7.88860905221e-34 0.0 0.0\n",
      "0.0 0.0 7.88860905221e-34 0.0\n",
      "0.0 0.0 0.0 7.88860905221e-34\n",
      "7.88860905221e-34 0.0 0.0 0.0\n",
      "-7.88860905221e-34 3.94430452611e-34 3.94430452611e-34 3.94430452611e-34\n",
      "3.94430452611e-34 9.86076131526e-35 9.86076131526e-35 9.86076131526e-35\n",
      "0.0 3.94430452611e-34 0.0 0.0\n",
      "0.0 0.0 3.94430452611e-34 0.0\n",
      "0.0 0.0 0.0 3.94430452611e-34\n",
      "3.94430452611e-34 0.0 0.0 0.0\n",
      "-3.94430452611e-34 1.97215226305e-34 1.97215226305e-34 1.97215226305e-34\n",
      "1.97215226305e-34 4.93038065763e-35 4.93038065763e-35 4.93038065763e-35\n",
      "0.0 1.97215226305e-34 0.0 0.0\n",
      "0.0 0.0 1.97215226305e-34 0.0\n",
      "0.0 0.0 0.0 1.97215226305e-34\n",
      "1.97215226305e-34 0.0 0.0 0.0\n",
      "-1.97215226305e-34 9.86076131526e-35 9.86076131526e-35 9.86076131526e-35\n",
      "9.86076131526e-35 2.46519032882e-35 2.46519032882e-35 2.46519032882e-35\n",
      "0.0 9.86076131526e-35 0.0 0.0\n",
      "0.0 0.0 9.86076131526e-35 0.0\n",
      "0.0 0.0 0.0 9.86076131526e-35\n",
      "9.86076131526e-35 0.0 0.0 0.0\n",
      "-9.86076131526e-35 4.93038065763e-35 4.93038065763e-35 4.93038065763e-35\n",
      "4.93038065763e-35 1.23259516441e-35 1.23259516441e-35 1.23259516441e-35\n",
      "0.0 4.93038065763e-35 0.0 0.0\n",
      "0.0 0.0 4.93038065763e-35 0.0\n",
      "0.0 0.0 0.0 4.93038065763e-35\n",
      "4.93038065763e-35 0.0 0.0 0.0\n",
      "-4.93038065763e-35 2.46519032882e-35 2.46519032882e-35 2.46519032882e-35\n",
      "2.46519032882e-35 6.16297582204e-36 6.16297582204e-36 6.16297582204e-36\n",
      "0.0 2.46519032882e-35 0.0 0.0\n",
      "0.0 0.0 2.46519032882e-35 0.0\n",
      "0.0 0.0 0.0 2.46519032882e-35\n",
      "2.46519032882e-35 0.0 0.0 0.0\n",
      "-2.46519032882e-35 1.23259516441e-35 1.23259516441e-35 1.23259516441e-35\n",
      "1.23259516441e-35 3.08148791102e-36 3.08148791102e-36 3.08148791102e-36\n",
      "0.0 1.23259516441e-35 0.0 0.0\n",
      "0.0 0.0 1.23259516441e-35 0.0\n",
      "0.0 0.0 0.0 1.23259516441e-35\n",
      "1.23259516441e-35 0.0 0.0 0.0\n",
      "-1.23259516441e-35 6.16297582204e-36 6.16297582204e-36 6.16297582204e-36\n",
      "6.16297582204e-36 1.54074395551e-36 1.54074395551e-36 1.54074395551e-36\n",
      "0.0 6.16297582204e-36 0.0 0.0\n",
      "0.0 0.0 6.16297582204e-36 0.0\n",
      "0.0 0.0 0.0 6.16297582204e-36\n",
      "6.16297582204e-36 0.0 0.0 0.0\n",
      "-6.16297582204e-36 3.08148791102e-36 3.08148791102e-36 3.08148791102e-36\n",
      "3.08148791102e-36 7.70371977755e-37 7.70371977755e-37 7.70371977755e-37\n",
      "0.0 3.08148791102e-36 0.0 0.0\n",
      "0.0 0.0 3.08148791102e-36 0.0\n",
      "0.0 0.0 0.0 3.08148791102e-36\n",
      "3.08148791102e-36 0.0 0.0 0.0\n",
      "-3.08148791102e-36 1.54074395551e-36 1.54074395551e-36 1.54074395551e-36\n",
      "1.54074395551e-36 3.85185988877e-37 3.85185988877e-37 3.85185988877e-37\n",
      "0.0 1.54074395551e-36 0.0 0.0\n",
      "0.0 0.0 1.54074395551e-36 0.0\n",
      "0.0 0.0 0.0 1.54074395551e-36\n",
      "1.54074395551e-36 0.0 0.0 0.0\n",
      "-1.54074395551e-36 7.70371977755e-37 7.70371977755e-37 7.70371977755e-37\n",
      "7.70371977755e-37 1.92592994439e-37 1.92592994439e-37 1.92592994439e-37\n",
      "0.0 7.70371977755e-37 0.0 0.0\n",
      "0.0 0.0 7.70371977755e-37 0.0\n",
      "0.0 0.0 0.0 7.70371977755e-37\n",
      "7.70371977755e-37 0.0 0.0 0.0\n",
      "-7.70371977755e-37 3.85185988877e-37 3.85185988877e-37 3.85185988877e-37\n",
      "3.85185988877e-37 9.62964972194e-38 9.62964972194e-38 9.62964972194e-38\n",
      "0.0 3.85185988877e-37 0.0 0.0\n",
      "0.0 0.0 3.85185988877e-37 0.0\n",
      "0.0 0.0 0.0 3.85185988877e-37\n",
      "3.85185988877e-37 0.0 0.0 0.0\n",
      "-3.85185988877e-37 1.92592994439e-37 1.92592994439e-37 1.92592994439e-37\n",
      "1.92592994439e-37 4.81482486097e-38 4.81482486097e-38 4.81482486097e-38\n",
      "0.0 1.92592994439e-37 0.0 0.0\n",
      "0.0 0.0 1.92592994439e-37 0.0\n",
      "0.0 0.0 0.0 1.92592994439e-37\n",
      "1.92592994439e-37 0.0 0.0 0.0\n",
      "-1.92592994439e-37 9.62964972194e-38 9.62964972194e-38 9.62964972194e-38\n",
      "9.62964972194e-38 2.40741243048e-38 2.40741243048e-38 2.40741243048e-38\n",
      "0.0 9.62964972194e-38 0.0 0.0\n",
      "0.0 0.0 9.62964972194e-38 0.0\n",
      "0.0 0.0 0.0 9.62964972194e-38\n",
      "9.62964972194e-38 0.0 0.0 0.0\n",
      "-9.62964972194e-38 4.81482486097e-38 4.81482486097e-38 4.81482486097e-38\n",
      "4.81482486097e-38 1.20370621524e-38 1.20370621524e-38 1.20370621524e-38\n",
      "0.0 4.81482486097e-38 0.0 0.0\n",
      "0.0 0.0 4.81482486097e-38 0.0\n",
      "0.0 0.0 0.0 4.81482486097e-38\n",
      "4.81482486097e-38 0.0 0.0 0.0\n",
      "-4.81482486097e-38 2.40741243048e-38 2.40741243048e-38 2.40741243048e-38\n",
      "2.40741243048e-38 6.01853107621e-39 6.01853107621e-39 6.01853107621e-39\n",
      "0.0 2.40741243048e-38 0.0 0.0\n",
      "0.0 0.0 2.40741243048e-38 0.0\n",
      "0.0 0.0 0.0 2.40741243048e-38\n",
      "2.40741243048e-38 0.0 0.0 0.0\n",
      "-2.40741243048e-38 1.20370621524e-38 1.20370621524e-38 1.20370621524e-38\n",
      "1.20370621524e-38 3.00926553811e-39 3.00926553811e-39 3.00926553811e-39\n",
      "0.0 1.20370621524e-38 0.0 0.0\n",
      "0.0 0.0 1.20370621524e-38 0.0\n",
      "0.0 0.0 0.0 1.20370621524e-38\n",
      "1.20370621524e-38 0.0 0.0 0.0\n",
      "-1.20370621524e-38 6.01853107621e-39 6.01853107621e-39 6.01853107621e-39\n",
      "6.01853107621e-39 1.50463276905e-39 1.50463276905e-39 1.50463276905e-39\n",
      "0.0 6.01853107621e-39 0.0 0.0\n",
      "0.0 0.0 6.01853107621e-39 0.0\n",
      "0.0 0.0 0.0 6.01853107621e-39\n",
      "6.01853107621e-39 0.0 0.0 0.0\n",
      "-6.01853107621e-39 3.00926553811e-39 3.00926553811e-39 3.00926553811e-39\n",
      "3.00926553811e-39 7.52316384526e-40 7.52316384526e-40 7.52316384526e-40\n",
      "0.0 3.00926553811e-39 0.0 0.0\n",
      "0.0 0.0 3.00926553811e-39 0.0\n",
      "0.0 0.0 0.0 3.00926553811e-39\n",
      "3.00926553811e-39 0.0 0.0 0.0\n",
      "-3.00926553811e-39 1.50463276905e-39 1.50463276905e-39 1.50463276905e-39\n",
      "1.50463276905e-39 3.76158192263e-40 3.76158192263e-40 3.76158192263e-40\n",
      "0.0 1.50463276905e-39 0.0 0.0\n",
      "0.0 0.0 1.50463276905e-39 0.0\n",
      "0.0 0.0 0.0 1.50463276905e-39\n",
      "1.50463276905e-39 0.0 0.0 0.0\n",
      "-1.50463276905e-39 7.52316384526e-40 7.52316384526e-40 7.52316384526e-40\n",
      "7.52316384526e-40 1.88079096132e-40 1.88079096132e-40 1.88079096132e-40\n",
      "0.0 7.52316384526e-40 0.0 0.0\n",
      "0.0 0.0 7.52316384526e-40 0.0\n",
      "0.0 0.0 0.0 7.52316384526e-40\n",
      "7.52316384526e-40 0.0 0.0 0.0\n",
      "-7.52316384526e-40 3.76158192263e-40 3.76158192263e-40 3.76158192263e-40\n",
      "3.76158192263e-40 9.40395480658e-41 9.40395480658e-41 9.40395480658e-41\n",
      "0.0 3.76158192263e-40 0.0 0.0\n",
      "0.0 0.0 3.76158192263e-40 0.0\n",
      "0.0 0.0 0.0 3.76158192263e-40\n",
      "3.76158192263e-40 0.0 0.0 0.0\n",
      "-3.76158192263e-40 1.88079096132e-40 1.88079096132e-40 1.88079096132e-40\n",
      "1.88079096132e-40 4.70197740329e-41 4.70197740329e-41 4.70197740329e-41\n",
      "0.0 1.88079096132e-40 0.0 0.0\n",
      "0.0 0.0 1.88079096132e-40 0.0\n",
      "0.0 0.0 0.0 1.88079096132e-40\n",
      "1.88079096132e-40 0.0 0.0 0.0\n",
      "-1.88079096132e-40 9.40395480658e-41 9.40395480658e-41 9.40395480658e-41\n",
      "9.40395480658e-41 2.35098870164e-41 2.35098870164e-41 2.35098870164e-41\n",
      "0.0 9.40395480658e-41 0.0 0.0\n",
      "0.0 0.0 9.40395480658e-41 0.0\n",
      "0.0 0.0 0.0 9.40395480658e-41\n",
      "9.40395480658e-41 0.0 0.0 0.0\n",
      "-9.40395480658e-41 4.70197740329e-41 4.70197740329e-41 4.70197740329e-41\n",
      "4.70197740329e-41 1.17549435082e-41 1.17549435082e-41 1.17549435082e-41\n",
      "0.0 4.70197740329e-41 0.0 0.0\n",
      "0.0 0.0 4.70197740329e-41 0.0\n",
      "0.0 0.0 0.0 4.70197740329e-41\n",
      "4.70197740329e-41 0.0 0.0 0.0\n",
      "-4.70197740329e-41 2.35098870164e-41 2.35098870164e-41 2.35098870164e-41\n",
      "2.35098870164e-41 5.87747175411e-42 5.87747175411e-42 5.87747175411e-42\n",
      "0.0 2.35098870164e-41 0.0 0.0\n",
      "0.0 0.0 2.35098870164e-41 0.0\n",
      "0.0 0.0 0.0 2.35098870164e-41\n",
      "2.35098870164e-41 0.0 0.0 0.0\n",
      "-2.35098870164e-41 1.17549435082e-41 1.17549435082e-41 1.17549435082e-41\n",
      "1.17549435082e-41 2.93873587706e-42 2.93873587706e-42 2.93873587706e-42\n",
      "0.0 1.17549435082e-41 0.0 0.0\n",
      "0.0 0.0 1.17549435082e-41 0.0\n",
      "0.0 0.0 0.0 1.17549435082e-41\n",
      "1.17549435082e-41 0.0 0.0 0.0\n",
      "-1.17549435082e-41 5.87747175411e-42 5.87747175411e-42 5.87747175411e-42\n",
      "5.87747175411e-42 1.46936793853e-42 1.46936793853e-42 1.46936793853e-42\n",
      "0.0 5.87747175411e-42 0.0 0.0\n",
      "0.0 0.0 5.87747175411e-42 0.0\n",
      "0.0 0.0 0.0 5.87747175411e-42\n",
      "5.87747175411e-42 0.0 0.0 0.0\n",
      "-5.87747175411e-42 2.93873587706e-42 2.93873587706e-42 2.93873587706e-42\n",
      "2.93873587706e-42 7.34683969264e-43 7.34683969264e-43 7.34683969264e-43\n",
      "0.0 2.93873587706e-42 0.0 0.0\n",
      "0.0 0.0 2.93873587706e-42 0.0\n",
      "0.0 0.0 0.0 2.93873587706e-42\n",
      "2.93873587706e-42 0.0 0.0 0.0\n",
      "-2.93873587706e-42 1.46936793853e-42 1.46936793853e-42 1.46936793853e-42\n",
      "1.46936793853e-42 3.67341984632e-43 3.67341984632e-43 3.67341984632e-43\n",
      "0.0 1.46936793853e-42 0.0 0.0\n",
      "0.0 0.0 1.46936793853e-42 0.0\n",
      "0.0 0.0 0.0 1.46936793853e-42\n",
      "1.46936793853e-42 0.0 0.0 0.0\n",
      "-1.46936793853e-42 7.34683969264e-43 7.34683969264e-43 7.34683969264e-43\n",
      "7.34683969264e-43 1.83670992316e-43 1.83670992316e-43 1.83670992316e-43\n",
      "0.0 7.34683969264e-43 0.0 0.0\n",
      "0.0 0.0 7.34683969264e-43 0.0\n",
      "0.0 0.0 0.0 7.34683969264e-43\n",
      "7.34683969264e-43 0.0 0.0 0.0\n",
      "-7.34683969264e-43 3.67341984632e-43 3.67341984632e-43 3.67341984632e-43\n",
      "3.67341984632e-43 9.1835496158e-44 9.1835496158e-44 9.1835496158e-44\n",
      "0.0 3.67341984632e-43 0.0 0.0\n",
      "0.0 0.0 3.67341984632e-43 0.0\n",
      "0.0 0.0 0.0 3.67341984632e-43\n",
      "3.67341984632e-43 0.0 0.0 0.0\n",
      "-3.67341984632e-43 1.83670992316e-43 1.83670992316e-43 1.83670992316e-43\n",
      "1.83670992316e-43 4.5917748079e-44 4.5917748079e-44 4.5917748079e-44\n",
      "0.0 1.83670992316e-43 0.0 0.0\n",
      "0.0 0.0 1.83670992316e-43 0.0\n",
      "0.0 0.0 0.0 1.83670992316e-43\n",
      "1.83670992316e-43 0.0 0.0 0.0\n",
      "-1.83670992316e-43 9.1835496158e-44 9.1835496158e-44 9.1835496158e-44\n",
      "9.1835496158e-44 2.29588740395e-44 2.29588740395e-44 2.29588740395e-44\n",
      "0.0 9.1835496158e-44 0.0 0.0\n",
      "0.0 0.0 9.1835496158e-44 0.0\n",
      "0.0 0.0 0.0 9.1835496158e-44\n",
      "9.1835496158e-44 0.0 0.0 0.0\n",
      "-9.1835496158e-44 4.5917748079e-44 4.5917748079e-44 4.5917748079e-44\n",
      "4.5917748079e-44 1.14794370197e-44 1.14794370197e-44 1.14794370197e-44\n",
      "0.0 4.5917748079e-44 0.0 0.0\n",
      "0.0 0.0 4.5917748079e-44 0.0\n",
      "0.0 0.0 0.0 4.5917748079e-44\n",
      "4.5917748079e-44 0.0 0.0 0.0\n",
      "-4.5917748079e-44 2.29588740395e-44 2.29588740395e-44 2.29588740395e-44\n",
      "2.29588740395e-44 5.73971850987e-45 5.73971850987e-45 5.73971850987e-45\n",
      "0.0 2.29588740395e-44 0.0 0.0\n",
      "0.0 0.0 2.29588740395e-44 0.0\n",
      "0.0 0.0 0.0 2.29588740395e-44\n",
      "2.29588740395e-44 0.0 0.0 0.0\n",
      "0.0 0.0 0.0 0.0\n",
      "which data frame is analyzed 1\n",
      "0.996267937991\n",
      "-inf\n",
      "real [[ 0.35483871  0.375       0.26086957  0.44827586  0.43478261  0.6\n",
      "   0.38888889]\n",
      " [ 0.38709677  0.4         0.5         0.4137931   0.42857143  0.375       0.375     ]\n",
      " [ 0.34285714  0.43333333  0.43333333  0.4         0.375       0.42105263\n",
      "   0.5       ]\n",
      " [ 0.2962963   0.32        0.47826087  0.47058824  0.42307692  0.34375     0.4       ]\n",
      " [ 0.47058824  0.41935484  0.41935484  0.3         0.2962963   0.3\n",
      "   0.46428571]\n",
      " [ 0.26315789  0.59259259  0.4         0.28571429  0.22222222  0.27272727\n",
      "   0.4       ]\n",
      " [ 0.3         0.35294118  0.30769231  0.36        0.16129032  0.27777778\n",
      "   0.30434783]]\n",
      "model [[ 0.51612903  0.54166667  0.52173913  0.48275862  0.39130435  0.375\n",
      "   0.44444444]\n",
      " [ 0.4516129   0.25        0.41666667  0.51724138  0.38095238  0.54166667\n",
      "   0.6875    ]\n",
      " [ 0.34285714  0.33333333  0.4         0.35        0.34375     0.47368421\n",
      "   0.375     ]\n",
      " [ 0.44444444  0.44        0.43478261  0.29411765  0.30769231  0.4375      0.5       ]\n",
      " [ 0.52941176  0.38709677  0.4516129   0.4         0.44444444  0.35\n",
      "   0.35714286]\n",
      " [ 0.52631579  0.48148148  0.32        0.28571429  0.44444444  0.36363636\n",
      "   0.6       ]\n",
      " [ 0.46666667  0.44117647  0.46153846  0.28        0.4516129   0.11111111\n",
      "   0.60869565]]\n",
      "1 [[ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]] [[ 0.5         0.46153846  0.33333333  0.35714286  0.44444444  0.73333333\n",
      "   0.125     ]\n",
      " [ 0.5         0.6         0.3         0.46666667  0.25        0.38461538\n",
      "   0.54545455]\n",
      " [ 0.16666667  0.3         0.25        0.14285714  0.27272727  0.22222222\n",
      "   0.33333333]\n",
      " [ 0.25        0.36363636  0.4         0.5         0.125       0.28571429\n",
      "   0.26666667]\n",
      " [ 0.61111111  0.33333333  0.42857143  0.16666667  0.25        0.14285714\n",
      "   0.5       ]\n",
      " [ 0.1         0.61538462  0.375       0.33333333  0.16666667  0.125\n",
      "   0.58333333]\n",
      " [ 0.35714286  0.4         0.33333333  0.28571429  0.21428571  0.\n",
      "   0.35714286]]\n",
      "2 [[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]] [[ 0.2         0.27272727  0.18181818  0.53333333  0.42857143  0.52        0.6       ]\n",
      " [ 0.29411765  0.33333333  0.64285714  0.35714286  0.53846154  0.36363636\n",
      "   0.        ]\n",
      " [ 0.43478261  0.5         0.55555556  0.53846154  0.42857143  0.6         0.6       ]\n",
      " [ 0.33333333  0.28571429  0.53846154  0.45833333  0.55555556  0.38888889\n",
      "   0.53333333]\n",
      " [ 0.3125      0.47368421  0.41176471  0.38888889  0.33333333  0.38461538\n",
      "   0.44444444]\n",
      " [ 0.44444444  0.57142857  0.41176471  0.26666667  0.26666667  0.35714286\n",
      "   0.125     ]\n",
      " [ 0.25        0.31578947  0.28571429  0.38888889  0.11764706  0.3125\n",
      "   0.22222222]]\n"
     ]
    }
   ],
   "source": [
    "result_matrix = np.zeros((len(dataFrame_list), 4))\n",
    "mle_matrix = np.zeros(len(dataFrame_list))\n",
    "\n",
    "for j in good_ones[0:1]:\n",
    "     \n",
    "    [mle_matrix[j], results] = fitting_SimpleModel(j)\n",
    "    result_matrix[j, :] = results.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'p_stop_real_function' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-f41c2cfb752e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# fitting in the case of probability matching\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mp_stop_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_real_toFit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_real_givenM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_real_givenB\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_real_givenM_toFit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_stop_real_givenB_toFit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_stop_real_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mp_m_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_b_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_tot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_m\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_b\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop_m\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials_stop_b\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_real\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mx_initial\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'p_stop_real_function' is not defined"
     ]
    }
   ],
   "source": [
    "    # fitting in the case of probability matching\n",
    "    p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j) \n",
    "    x_initial = [0,0,0,0]\n",
    "\n",
    "    results = minimize(fitting_probMatching, x_initial, method = 'Nelder-Mead')\n",
    "    \n",
    "    p_stop_model, p_stop_givenM_model, p_stop_givenB_model = p_stop_ProbabilityMatching([results.x[0], results.x[1], results.x[2], results.x[3]], j)\n",
    "    mle=0\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):\n",
    "                 #mle = mle + trials_stop[ind2,ind1]*log(p_stop_model[ind2,ind1])+(trials_tot[ind2,ind1]-trials_stop[ind2,ind1])*log(1-p_stop_model[ind2,ind1])\n",
    "                if (p_stop_givenM_model[ind2,ind1]>0) and (p_stop_givenB_model[ind2,ind1]>0) and (p_stop_givenM_model[ind2,ind1] < 1) and (p_stop_givenB_model[ind2,ind1]< 1):\n",
    "                    mle = mle + trials_stop_m[ind2,ind1]*math.log(p_stop_givenM_model[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*math.log(1-p_stop_givenM_model[ind2,ind1])+trials_stop_b[ind2,ind1]*math.log(p_stop_givenB_model[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*math.log(1-p_stop_givenB_model[ind2,ind1])\n",
    "                else:\n",
    "                    print 'Im here'   \n",
    "                    mle = -np.inf\n",
    "                    \n",
    "    #print LA.norm(p_stop_real-p_stop_model, 'fro')\n",
    "    print LA.norm(p_stop_real - p_m_real*p_stop_givenM_model - p_b_real*p_stop_givenB_model, 'fro')\n",
    "    print mle\n",
    "    print \"real\", p_stop_real\n",
    "    print \"model\", p_stop_model\n",
    "    print \"1\", p_stop_givenM_model, p_stop_real_givenM\n",
    "    print \"2\", p_stop_givenB_model, p_stop_real_givenB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compute average T1, T0 for each combination (s,o)\n",
    "def pause_and_laptimes(j):\n",
    "    \n",
    "    pausetimes = np.zeros((len(orientations),len(sizes)))\n",
    "    pausetimes_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "    pausetimes_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "    \n",
    "    laptimes = np.zeros((len(orientations),len(sizes)))\n",
    "    laptimes_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "    laptimes_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "    \n",
    "    p_Correct_GivenStop = np.zeros((len(orientations),len(sizes)))\n",
    "\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j)\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):\n",
    "            \n",
    "                pausetimes[ind2,ind1] = df[(df.Size==s)&(df.Ori==o)&(df.Image=='mushroom')].pausetime.mean()*p_m_real[ind2,ind1]+df[(df.Size==s)&(df.Ori==o)&(df.Image=='bird')].pausetime.mean()*p_b_real[ind2, ind1]\n",
    "                laptimes[ind2,ind1] = dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='mushroom')].duration.mean()*p_m_real[ind2, ind1]+dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='bird')].duration.mean()*p_b_real[ind2, ind1]\n",
    "\n",
    "                pausetimes_givenM[ind2,ind1] = df[(df.Size==s)&(df.Ori==o)&(df.Image=='mushroom')].pausetime.mean()\n",
    "                pausetimes_givenB[ind2,ind1] = df[(df.Size==s)&(df.Ori==o)&(df.Image=='bird')].pausetime.mean()\n",
    "                \n",
    "                laptimes_givenM[ind2,ind1] = dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='mushroom')].duration.mean()\n",
    "                laptimes_givenB[ind2,ind1] = dataFrame[(dataFrame.Size==s)&(dataFrame.Ori==o)&(dataFrame.Image=='bird')].duration.mean()\n",
    "                \n",
    "                p_Correct_GivenStop[ind2,ind1] = len(df[(df.Size==s)&(df.Ori==o)&(df.Image=='mushroom')&(df.choice==True)].index)/(float)(len(df[(df.Size==s)&(df.Ori==o)&(df.choice==True)].index))\n",
    "   \n",
    "    return pausetimes, laptimes, pausetimes_givenM, laptimes_givenM, pausetimes_givenB, laptimes_givenB, p_Correct_GivenStop  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_mu_sigma_delta(j):\n",
    "\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j) \n",
    "    \n",
    "    #parameters = fitting_function(1)  \n",
    "    #x = [parameters[0], parameters[1], parameters[2], parameters[3]]\n",
    "    x = [results.x[0], results.x[1], results.x[2], results.x[3]]\n",
    "\n",
    "    percept, percept_givenM, percept_givenB = p_stop_ProbabilityMatching(x,j)\n",
    "\n",
    "    sigma = 1\n",
    "    tau =0.1\n",
    "\n",
    "    delta = sigma*math.sqrt(tau)\n",
    "    mu = (2*percept-1)*sigma/(math.sqrt(tau))\n",
    "\n",
    "    mu_givenM = (2*percept_givenM-1)*sigma/(math.sqrt(tau))\n",
    "    mu_givenB = (2*percept_givenB-1)*sigma/(math.sqrt(tau))\n",
    "    \n",
    "    return percept, mu, mu_givenM, mu_givenB, sigma, delta, tau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#B - 1(discrete)1-+1,0 step, with complicated formula   \n",
    "def fit_SimpleAcc_B(j):\n",
    "\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j) \n",
    "    p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "    percept, mu, mu_givenM, mu_givenB, sigma, delta, tau = find_mu_sigma_delta()\n",
    "    pausetimes, laptimes, pausetimes_givenM, laptimes_givenM, pausetimes_givenB, laptimes_givenB, p_Correct_GivenStop = pause_and_laptimes()\n",
    "    \n",
    "    #x = [results.x[0], results.x[1], results.x[2], results.x[3]]\n",
    "    x = results.x\n",
    "    \n",
    "    percept, percept_givenM, percept_givenB = p_stop_ProbabilityMatching(x,j)\n",
    "        \n",
    "    start_N = delta\n",
    "    end_N = delta*10\n",
    "    no_N = (end_N-start_N+delta)/delta\n",
    "    array_N = np.linspace(start_N,end_N,no_N,endpoint=False)\n",
    "    exp1N = (int)(start_N/delta)\n",
    "    exp2N = (int)(end_N/delta)\n",
    "    \n",
    "    start_M = tau\n",
    "    end_M = tau*15\n",
    "    no_M = (end_M-start_M+tau)/tau\n",
    "    array_M = np.linspace(start_M,end_M,no_M, endpoint =False)\n",
    "    exp1M = (int)(start_M/tau)\n",
    "    exp2M = (int)(end_M/tau)\n",
    "    \n",
    "    norm = np.zeros((no_N, no_M))\n",
    "    mle = np.zeros((no_N, no_M))\n",
    "    \n",
    "    total_len = len(df)\n",
    "    avg_reward = np.zeros((no_N,no_M))\n",
    "    avg_reward_numerator = np.zeros((no_N,no_M))\n",
    "    avg_reward_denominator = np.zeros((no_N,no_M))\n",
    "    \n",
    "    for exp1 in range(exp1N, exp2N):\n",
    "        for exp2 in range(exp1,exp2M):       \n",
    "            \n",
    "            p_modeled_stopping = np.zeros((len(orientations),len(sizes)))\n",
    "            p_modeled_stopping_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "            p_modeled_stopping_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "            p_modeled_stopping2 = np.zeros((len(orientations),len(sizes)))\n",
    "            \n",
    "            for ind2,o in enumerate(orientations):\n",
    "                for ind1,s in enumerate(sizes):\n",
    "\n",
    "                    for k in range(0,exp2-exp1+1):\n",
    "                        p_modeled_stopping[ind2,ind1] = p_modeled_stopping[ind2,ind1] + ((percept[ind2,ind1])**exp1)*((1-percept[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                        p_modeled_stopping_givenM[ind2,ind1] = p_modeled_stopping_givenM[ind2,ind1] + ((percept_givenM[ind2,ind1])**exp1)*((1-percept_givenM[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                        p_modeled_stopping_givenB[ind2,ind1] = p_modeled_stopping_givenB[ind2,ind1] + ((percept_givenB[ind2,ind1])**exp1)*((1-percept_givenB[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                        \n",
    "                    p_modeled_stopping2[ind2,ind1] = p_modeled_stopping_givenM[ind2,ind1]*p_m_real[ind2,ind1] + p_modeled_stopping_givenB[ind2,ind1]*p_b_real[ind2,ind1]\n",
    "                        \n",
    "                    #mle[exp1,exp2] = mle[exp1,exp2] + trials_stop[ind2,ind1]*log(p_modeled_stopping2[ind2,ind1])+(trials_tot[ind2,ind1]-trials_stop[ind2,ind1])*log(1-p_modeled_stopping2[ind2,ind1])            \n",
    "                    if (p_modeled_stopping_givenM[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1) or (p_modeled_stopping_givenB[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1):\n",
    "                        mle[exp1,exp2] = -np.inf\n",
    "                    else:\n",
    "                        mle[exp1,exp2] = mle[exp1,exp2] + trials_stop_m[ind2,ind1]*log(p_modeled_stopping_givenM[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*log(1-p_modeled_stopping_givenM[ind2,ind1])+trials_stop_b[ind2,ind1]*log(p_modeled_stopping_givenB[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*log(1-p_modeled_stopping_givenB[ind2,ind1])            \n",
    "                    \n",
    "                    if np.isnan(mle[exp1,exp2]):\n",
    "                        mle[exp1,exp2] = -np.inf\n",
    "                        \n",
    "                    if o<o_T or s<s_T:\n",
    "                        avg_reward_numerator[exp1,exp2] = avg_reward_numerator[exp1,exp2] + p_modeled_stopping_givenM[ind2,ind1]*p_m_real[ind2,ind1]*trials_tot[ind2,ind1]/total_len\n",
    "                    avg_reward_denominator[exp1,exp2] = avg_reward_denominator[exp1,exp2] + (laptimes[ind2,ind1]+p_modeled_stopping2[ind2,ind1]*pausetimes[ind2,ind1])*trials_tot[ind2,ind1]/total_len\n",
    "            \n",
    "            norm[exp1,exp2] = math.sqrt(LA.norm(p_stop_real_givenM - p_modeled_stopping_givenM, 'fro')**2 + LA.norm(p_stop_real_givenM - p_modeled_stopping_givenM,'fro')**2)\n",
    "            avg_reward[exp1,exp2] = avg_reward_numerator[exp1,exp2]/avg_reward_denominator[exp1,exp2]\n",
    "            \n",
    "    i_min=0\n",
    "    j_min=0\n",
    "    \n",
    "    minimum = np.inf\n",
    "    maximum =-np.inf\n",
    "    maximum_reward = -np.inf\n",
    "    \n",
    "    N_rew = np.nan\n",
    "    M_rew = np.nan\n",
    "    \n",
    "    for exp1 in range(exp1N, exp2N):\n",
    "        for exp2 in range(exp1,exp2M):  \n",
    "        \n",
    "            if norm[exp1,exp2]<=minimum:\n",
    "                minimum = norm[exp1,exp2]\n",
    "                i_min = exp1\n",
    "                j_min = exp2     \n",
    "            \n",
    "            if mle[exp1,exp2]>=maximum:\n",
    "                maximum = mle[exp1,exp2]\n",
    "                i_max_mle = exp1\n",
    "                j_max_mle = exp2\n",
    "                \n",
    "            if avg_reward[exp1,exp2]>=maximum_reward:\n",
    "                maximum_reward = avg_reward[exp1,exp2]\n",
    "                N_rew = exp1\n",
    "                M_rew = exp2 \n",
    "                \n",
    "    print minimum, i_min, j_min\n",
    "    print minimum, i_min, j_min, array_N[i_min], array_M[j_min]\n",
    "    print maximum, i_max_mle, j_max_mle, array_N[i_max_mle], array_M[j_max_mle]    \n",
    "    \n",
    "    a,b = norm.shape\n",
    "    print \"a,b\", a,b\n",
    "    print \"norm\", norm[1,1]\n",
    "    print \"winning norm\", minimum\n",
    "    \n",
    "    aic = 4 - 2*mle[i_max_mle,j_max_mle]\n",
    "    print \"aic =\", aic\n",
    "    print \"log mle =\", mle[i_max_mle,j_max_mle]\n",
    "    \n",
    "    print \"avg reward rate for best fit is\", avg_reward[i_max_mle,j_max_mle], \"or (if minimizing norm)\", avg_reward[i_min,j_min]\n",
    "    print \"maximum reward rate is\",  maximum_reward, \"at N,M\", N_rew, M_rew\n",
    "    \n",
    "    return mle, avg_reward, N_rew, M_rew, exp2N, exp2M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0 0.0 0.0\n",
      "0.0 0.0 0.0 0.0\n",
      "6.74278210752 9 14\n",
      "6.74278210752 9 14 2.87767267075 1.40666666667\n",
      "-inf 9 14 2.87767267075 1.40666666667\n",
      "a,b 10 15\n",
      "norm 6.74278210752\n",
      "winning norm 6.74278210752\n",
      "aic = inf\n",
      "log mle = -inf\n",
      "avg reward rate for best fit is nan or (if minimizing norm) nan\n",
      "maximum reward rate is -inf at N,M nan nan\n"
     ]
    }
   ],
   "source": [
    "[mle, avg_reward, N_rew, M_rew, exp2N, exp2M] = fit_SimpleAcc_B(0)\n",
    "\n",
    "mle_B = np.zeros((len(dataFrame_list), exp2N, exp2M))\n",
    "avg_reward_B = np.zeros((len(dataFrame_list), exp2N, exp2M))\n",
    "\n",
    "#for j in range(0, len(dataFrame_list)):\n",
    "#for j in good_ones[0:1]:\n",
    "#[mle[j, :, :], avg_reward[j, :, :], N_rew[j], M_rew[j]] = fit_SimpleAcc_B(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0 0.0 0.0\n",
      "starting point [0.0, 0.0, 0.0, 0.0]\n",
      "exp1,exp2 v1 1 1\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 1\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 2\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 2\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 3\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 3\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 4\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 4\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 5\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 5\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 6\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 6\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 7\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 7\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 1 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  1 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 2\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 2\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 3\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 3\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 4\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 4\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 5\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 5\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 6\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 6\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 7\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 7\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 2 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  2 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 3\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 3\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 4\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 4\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 5\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 5\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 6\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 6\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 7\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 7\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 3 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  3 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 4\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 4\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 5\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 5\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 6\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 6\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 7\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 7\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 4 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  4 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 5\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 5\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 6\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 6\n",
      "0.0 0.0 0.0 0.0\n",
      "wait, seriously?! [[ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]]\n",
      "1.0\n",
      "0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "here we go:\n",
      "[[ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.]]\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "5.57625723194\n",
      "-inf\n",
      "mle, [[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.]\n",
      " [  0. -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
      "  -inf]\n",
      " [  0.   0. -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
      "  -inf]\n",
      " [  0.   0.   0. -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
      "  -inf]\n",
      " [  0.   0.   0.   0. -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
      "  -inf]\n",
      " [  0.   0.   0.   0.   0. -inf -inf   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.]]\n",
      "exp1,exp2 v1 5 7\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 7\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 5 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  5 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 6\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 6\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 7\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 7\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 6 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  6 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 7\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 7\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 7 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  7 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 8 8\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  8 8\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 8 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  8 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 8 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  8 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 8 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  8 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 8 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  8 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 8 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  8 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 8 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  8 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 9 9\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  9 9\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 9 10\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  9 10\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 9 11\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  9 11\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 9 12\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  9 12\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 9 13\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  9 13\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "exp1,exp2 v1 9 14\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "1.49011611938e-08 0.0 0.0 0.0\n",
      "31.0946439487\n",
      "5.57625716307 -12127.3030855\n",
      "0.0 1.49011611938e-08 0.0 0.0\n",
      "31.0946257802\n",
      "5.57625553397 -inf\n",
      "0.0 0.0 1.49011611938e-08 0.0\n",
      "31.0945808118\n",
      "5.57625150184 -inf\n",
      "0.0 0.0 0.0 1.49011611938e-08\n",
      "31.0945318465\n",
      "5.57624711132 -inf\n",
      "0.0 0.0 0.0 0.0\n",
      "31.0946447168\n",
      "5.57625723194 -inf\n",
      "actual results found [ 0.  0.  0.  0.] for  9 14\n",
      "0.0 0.0 0.0 0.0\n",
      "norm 5.57625723194 mle -inf\n",
      "5.57625723194 9 14\n",
      "-inf 9 14\n",
      "a,b 10 15\n",
      "norm [[ 0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.          0.\n",
      "   0.        ]\n",
      " [ 0.          5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723]\n",
      " [ 0.          0.          5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723]\n",
      " [ 0.          0.          0.          5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723]\n",
      " [ 0.          0.          0.          0.          5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723]\n",
      " [ 0.          0.          0.          0.          0.          5.57625723\n",
      "   5.57625723  5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   5.57625723  5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723  5.57625723]\n",
      " [ 0.          0.          0.          0.          0.          0.          0.\n",
      "   5.57625723  5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723]\n",
      " [ 0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          5.57625723  5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723]\n",
      " [ 0.          0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          5.57625723  5.57625723  5.57625723  5.57625723\n",
      "   5.57625723  5.57625723]]\n",
      "aic = inf\n",
      "avg reward rate for best fit is II nan or (if minimizing norm) nan\n",
      "maximum reward rate is -inf at N,M"
     ]
    },
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'N_rew' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-130-4e37a7bbebbe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgood_ones\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m         \u001b[1;33m[\u001b[0m\u001b[0mmle_C_v1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mavg_rewarde_C_v1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN_rew\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mM_rew\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_Accumulator_C_v1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-129-9ccb02a71d8c>\u001b[0m in \u001b[0;36mfit_Accumulator_C_v1\u001b[1;34m(j, results)\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"avg reward rate for best fit is II\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mavg_reward\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi_max_mle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj_max_mle\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"or (if minimizing norm)\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mavg_reward\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi_min\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj_min\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"maximum reward rate is\"\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mmaximum_reward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"at N,M\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN_rew\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mM_rew\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mavg_reward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN_rew\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mM_rew\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'N_rew' referenced before assignment"
     ]
    }
   ],
   "source": [
    "N = 10\n",
    "M = 15\n",
    "\n",
    "mle_C_v1 = np.zeros((len(dataFrame_list), 10,15))\n",
    "avg_reward_C_v1 = np.zeros((len(dataFrame_list), 10,15))\n",
    "\n",
    "for j in good_ones[0:1]:\n",
    "        [mle_C_v1[j, :, :], avg_rewarde_C_v1[j, :, :], N_rew[j], M_rew[j]] = fit_Accumulator_C_v1(j, results.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fit_Accumulator_C_v1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-70-7e639e3972d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataFrame_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m         \u001b[1;33m[\u001b[0m\u001b[0mmle_C_v2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mavg_reward\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN_rew\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mM_rew\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_Accumulator_C_v1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'fit_Accumulator_C_v1' is not defined"
     ]
    }
   ],
   "source": [
    "N = 10\n",
    "M = 15\n",
    "\n",
    "mle_C_v2 = np.zeros((len(dataFrame_list), 10,15))\n",
    "avg_reward_C_v2 = np.zeros((len(dataFrame_list), 10,15))\n",
    "\n",
    "for j in range(0, len(dataFrame_list)):\n",
    "        [mle_C_v2[j, :, :], avg_reward_C_v2[j, :, :], N_rew[j], M_rew[j]] = fit_Accumulator_C_v2(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Ca -1(discrete)1 - long formula for steps +1,0 -- looking for M,N by brute force\n",
    "def fit_Accumulator_C_v1(j, results):\n",
    "\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j) \n",
    "    p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "    percept, mu, mu_givenM, mu_givenB, sigma, delta, tau = find_mu_sigma_delta(j)  \n",
    "    total_len = len(df)\n",
    "    pausetimes, laptimes,  pausetimes_givenM, laptimes_givenM, pausetimes_givenB, laptimes_givenB, p_Correct_GivenStop = pause_and_laptimes(j)\n",
    "    \n",
    "    x_initial = [results[0], results[1], results[2], results[3]]\n",
    "    print \"starting point\", x_initial\n",
    "    \n",
    "    start_N = delta\n",
    "    end_N = delta*10\n",
    "    no_N = (end_N-start_N+delta)/delta\n",
    "    array_N = np.linspace(start_N,end_N,no_N,endpoint=False)\n",
    "    exp1N = (int)(start_N/delta)\n",
    "    exp2N = (int)(end_N/delta)\n",
    "    \n",
    "    start_M = tau\n",
    "    end_M = tau*15\n",
    "    no_M = (end_M-start_M+tau)/tau\n",
    "    array_M = np.linspace(start_M,end_M,no_M, endpoint =False)\n",
    "    exp1M = (int)(start_M/tau)\n",
    "    exp2M = (int)(end_M/tau)\n",
    "    \n",
    "    norm = np.zeros((no_N, no_M))\n",
    "    mle = np.zeros((no_N, no_M))\n",
    "    xopt = np.zeros((no_N*no_M, 4))\n",
    "    avg_reward = np.zeros((no_N, no_M))\n",
    "    avg_reward_numerator = np.zeros((no_N,no_M))\n",
    "    avg_reward_denominator = np.zeros((no_N,no_M))\n",
    "    \n",
    "    increment = 0\n",
    "    \n",
    "    for exp1 in range(exp1N, exp2N):\n",
    "        for exp2 in range(exp1,exp2M):            \n",
    "             \n",
    "            p_modeled_stopping = np.zeros((len(orientations),len(sizes)))\n",
    "            p_modeled_stopping_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "            p_modeled_stopping_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "            p_modeled_stopping2 = np.zeros((len(orientations),len(sizes)))    \n",
    "            \n",
    "            print \"exp1,exp2 v1\", exp1, exp2\n",
    "            results = minimize(fitting_accumulator1, x_initial)\n",
    "            xopt[increment,:] = [results.x[0], results.x[1], results.x[2], results.x[3]]\n",
    "            print \"actual results found\", xopt[increment,: ], \"for \", exp1, exp2\n",
    "            \n",
    "            percept, percept_givenM, percept_givenB = p_stop_ProbabilityMatching(xopt[increment,:],j)\n",
    "            \n",
    "            for ind2,o in enumerate(orientations):\n",
    "                for ind1,s in enumerate(sizes):\n",
    "                    \n",
    "                    if (ind2==2)&(ind1==4)&(exp1==5)&(exp2==6):\n",
    "                            print \"wait, seriously?!\", percept_givenM\n",
    "                            for k in range(0,exp2-exp1+1):\n",
    "                                print ((percept_givenM[ind2,ind1])**exp1)*((1-percept_givenM[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                                \n",
    "                    for k in range(0,exp2-exp1+1):\n",
    "                        p_modeled_stopping[ind2,ind1] = p_modeled_stopping[ind2,ind1] + ((percept[ind2,ind1])**exp1)*((1-percept[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                        p_modeled_stopping_givenM[ind2,ind1] = p_modeled_stopping_givenM[ind2,ind1] + ((percept_givenM[ind2,ind1])**exp1)*((1-percept_givenM[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                        p_modeled_stopping_givenB[ind2,ind1] = p_modeled_stopping_givenB[ind2,ind1] + ((percept_givenB[ind2,ind1])**exp1)*((1-percept_givenB[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                        \n",
    "                    p_modeled_stopping2[ind2,ind1] = p_modeled_stopping_givenM[ind2,ind1]*p_m_real[ind2,ind1] + p_modeled_stopping_givenB[ind2,ind1]*p_b_real[ind2,ind1]\n",
    "                    if (p_modeled_stopping_givenM[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1) or (p_modeled_stopping_givenB[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1):\n",
    "                        mle[exp1,exp2] = -np.inf\n",
    "                    else:\n",
    "                        mle[exp1,exp2] = mle[exp1,exp2] + trials_stop_m[ind2,ind1]*log(p_modeled_stopping_givenM[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*log(1-p_modeled_stopping_givenM[ind2,ind1])+trials_stop_b[ind2,ind1]*log(p_modeled_stopping_givenB[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*log(1-p_modeled_stopping_givenB[ind2,ind1])            \n",
    "            \n",
    "                    if o<o_T or s<s_T:\n",
    "                       avg_reward_numerator[exp1,exp2] = p_modeled_stopping_givenM[ind2,ind1]*p_m_real[ind2,ind1]*trials_tot[ind2,ind1]/total_len\n",
    "                    avg_reward_denominator[exp1,exp2] = (laptimes[ind2,ind1]+p_modeled_stopping2[ind2,ind1]*pausetimes[ind2,ind1])*trials_tot[ind2,ind1]/total_len \n",
    "                    avg_reward[exp1,exp2] = avg_reward_numerator[exp1,exp2]/avg_reward_denominator[exp1,exp2]\n",
    "                        \n",
    "            norm[exp1,exp2] = math.sqrt(LA.norm(p_stop_real_givenM - p_modeled_stopping_givenM, 'fro')**2 + LA.norm(p_stop_real_givenB - p_modeled_stopping_givenB, 'fro')**2)\n",
    "            print \"norm\", norm[exp1, exp2], \"mle\", mle[exp1, exp2]\n",
    "            \n",
    "            if (exp1==5) & (exp2==6):\n",
    "                print \"here we go:\"\n",
    "                print p_modeled_stopping_givenM\n",
    "                print p_modeled_stopping_givenB\n",
    "                print math.sqrt(LA.norm(p_modeled_stopping_givenM - p_stop_real_givenM,'fro')**2 + LA.norm(p_modeled_stopping_givenB - p_stop_real_givenB,'fro')**2)       \n",
    "                print mle[5,6]\n",
    "                print \"mle,\", mle\n",
    "            \n",
    "            increment = increment+1\n",
    "        \n",
    "    minimum = np.inf\n",
    "    maximum =-np.inf\n",
    "    maximum_reward = -np.inf\n",
    "    \n",
    "    for exp1 in range(exp1N,exp2N):\n",
    "        for exp2 in range(exp1, exp2M):\n",
    "        \n",
    "            if norm[exp1,exp2]<=minimum:\n",
    "                minimum = norm[exp1,exp2]\n",
    "                i_min = exp1\n",
    "                j_min = exp2     \n",
    "            \n",
    "            if mle[exp1,exp2]>=maximum:\n",
    "                maximum = mle[exp1,exp2]\n",
    "                i_max_mle = exp1\n",
    "                j_max_mle = exp2\n",
    "                \n",
    "            if avg_reward[exp1,exp2]>=maximum_reward:\n",
    "                maximum_reward = avg_reward[exp1,exp2]\n",
    "                N_rew = exp1\n",
    "                M_rew = exp2  \n",
    "            \n",
    "    print minimum, i_min, j_min\n",
    "    print maximum, i_max_mle, j_max_mle\n",
    "        \n",
    "    a,b = norm.shape\n",
    "    print \"a,b\", a,b\n",
    "    print \"norm\", norm\n",
    "    \n",
    "    aic = 4 - 2*mle[i_max_mle,j_max_mle]\n",
    "    print \"aic =\", aic\n",
    "    \n",
    "    print \"avg reward rate for best fit is II\", avg_reward[i_max_mle,j_max_mle], \"or (if minimizing norm)\", avg_reward[i_min,j_min]\n",
    "    print \"maximum reward rate is\",  maximum_reward, \"at N,M\", N_rew, M_rew\n",
    "    \n",
    "    return mle, avg_reward, N_rew, M_rew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fitting_accumulator1(x):\n",
    "    \n",
    "    mle = 0\n",
    "    p_modeled_stopping = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping2 = np.zeros((len(orientations),len(sizes)))\n",
    "    \n",
    "    percept, percept_givenM, percept_givenB = p_stop_ProbabilityMatching(x,j)\n",
    "    \n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j)\n",
    "    p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "        for ind1,s in enumerate(sizes):\n",
    "            \n",
    "            for k in range(0,exp2-exp1+1):\n",
    "                p_modeled_stopping[ind2,ind1] = p_modeled_stopping[ind2,ind1] + ((percept[ind2,ind1])**exp1)*((1-percept[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                p_modeled_stopping_givenM[ind2,ind1] = p_modeled_stopping_givenM[ind2,ind1] + ((percept_givenM[ind2,ind1])**exp1)*((1-percept_givenM[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                p_modeled_stopping_givenB[ind2,ind1] = p_modeled_stopping_givenB[ind2,ind1] + ((percept_givenB[ind2,ind1])**exp1)*((1-percept_givenB[ind2,ind1])**k)*comb(exp1+k-1, exp1-1)\n",
    "                        \n",
    "            p_modeled_stopping2[ind2,ind1] = p_modeled_stopping_givenM[ind2,ind1]*p_m_real[ind2,ind1] + p_modeled_stopping_givenB[ind2,ind1]*p_b_real[ind2,ind1]\n",
    "            if (p_modeled_stopping_givenM[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1) or (p_modeled_stopping_givenB[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1):\n",
    "                    mle = -np.inf\n",
    "            else:\n",
    "                    mle = mle + trials_stop_m[ind2,ind1]*log(p_modeled_stopping_givenM[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*log(1-p_modeled_stopping_givenM[ind2,ind1])+trials_stop_b[ind2,ind1]*log(p_modeled_stopping_givenB[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*log(1-p_modeled_stopping_givenB[ind2,ind1])            \n",
    "                \n",
    "    print LA.norm(p_stop_real_givenM - p_modeled_stopping_givenM, 'fro')**2 + LA.norm(p_stop_real_givenB - p_modeled_stopping_givenB, 'fro')**2\n",
    "    norm = math.sqrt(LA.norm(p_stop_real_givenM - p_modeled_stopping_givenM, 'fro')**2 + LA.norm(p_stop_real_givenB - p_modeled_stopping_givenB, 'fro')**2) \n",
    "    \n",
    "    if np.isnan(mle):\n",
    "        mle = -np.inf\n",
    "    \n",
    "    print norm, mle\n",
    "    return -mle\n",
    "    #return norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Ca -1(discrete)1 - long formula for start point and threshold -- looking for M,N by brute force\n",
    "def fit_Accumulator_C_v2(j):\n",
    "\n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j) \n",
    "    p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "    percept, mu, mu_givenM, mu_givenB, sigma, delta, tau = find_mu_sigma_delta(j)  \n",
    "    pausetimes, laptimes,  pausetimes_givenM, laptimes_givenM, pausetimes_givenB, laptimes_givenB, p_Correct_GivenStop = pause_and_laptimes(j)\n",
    "    total_len = len(df)\n",
    "    \n",
    "    x_initial = [results.x[0], results.x[1], results.x[2], results.x[3]]\n",
    "    print \"starting points\", x_initial\n",
    "    \n",
    "    p_modeled_stopping = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping2 = np.zeros((len(orientations),len(sizes)))\n",
    "    \n",
    "    start_N = delta\n",
    "    end_N = delta*10\n",
    "    no_N = (end_N-start_N+delta)/delta\n",
    "    array_N = np.linspace(start_N,end_N,no_N,endpoint=False)\n",
    "    exp1N = (int)(start_N/delta)\n",
    "    exp2N = (int)(end_N/delta)\n",
    "    \n",
    "    norm = np.zeros((no_N, 2*no_N-1))\n",
    "    mle = np.zeros((no_N, 2*no_N-1))\n",
    "    xopt = np.zeros((no_N*(2*no_N-1), 4))\n",
    "    avg_reward = np.zeros((no_N, 2*no_N-1))\n",
    "    avg_reward2 = np.zeros((no_N, 2*no_N-1))\n",
    "    avg_reward_modified = np.zeros((no_N, 2*no_N-1))\n",
    "    avg_reward_numerator = np.zeros((no_N, 2*no_N-1))\n",
    "    avg_reward_denominator = np.zeros((no_N, 2*no_N-1)) \n",
    "        \n",
    "    increment = 0\n",
    "    \n",
    "    for exp1 in range(exp1N, exp2N):\n",
    "        for exp2 in range(-exp1+1, exp1):            \n",
    "            \n",
    "            i=exp1\n",
    "            j=exp2+exp1-1\n",
    "            \n",
    "            print \"exp1,exp2 v1\", exp1, exp2,i,j\n",
    "            results = minimize(fitting_accumulator2, x_initial)\n",
    "            xopt[increment,:] = [results.x[0], results.x[1], results.x[2], results.x[3]]\n",
    "            #print \"actual results\", xopt[increment, :] \n",
    "            \n",
    "            var = xopt[2,:]\n",
    "            percept, percept_givenM, percept_givenB = p_stop_ProbabilityMatching(x,j)\n",
    "            \n",
    "            for ind2,o in enumerate(orientations):\n",
    "                for ind1,s in enumerate(sizes):\n",
    "\n",
    "                    if percept[ind2,ind1]!=0.5:\n",
    "                        factor = percept[ind2, ind1]/(1-percept[ind2,ind1])\n",
    "                        p_modeled_stopping[ind2,ind1] = (factor**(2*exp1)- factor**(exp1-exp2))/(factor**(2*exp1)-1)\n",
    "                    else:\n",
    "                        p_modeled_stopping[ind2,ind1] = (exp1+exp2)/(2*exp1)\n",
    "                        \n",
    "                    if percept_givenM[ind2,ind1]!=0.5:        \n",
    "                        factor_givenM = percept_givenM[ind2,ind1]/(1-percept_givenM[ind2,ind1])\n",
    "                        p_modeled_stopping_givenM[ind2,ind1] = (factor_givenM**(2*exp1)- factor_givenM**(exp1-exp2))/(factor_givenM**(2*exp1)-1)    \n",
    "                    else:        \n",
    "                        p_modeled_stopping_givenM[ind2,ind1] = (exp1+exp2)/(2*exp1)\n",
    "                    \n",
    "                    if percept_givenB[ind2,ind1]!=0.5:     \n",
    "                        factor_givenB = percept_givenB[ind2,ind1]/(1-percept_givenB[ind2,ind1])\n",
    "                        p_modeled_stopping_givenB[ind2,ind1] = (factor_givenB**(2*exp1)- factor_givenB**(exp1-exp2))/(factor_givenB**(2*exp1)-1)\n",
    "                    else:\n",
    "                        p_modeled_stopping_givenB[ind2,ind1] = (exp1+exp2)/(2*exp1)\n",
    "                    \n",
    "                    p_modeled_stopping2[ind2,ind1] = p_modeled_stopping_givenM[ind2,ind1]*p_m_real[ind2,ind1] + p_modeled_stopping_givenB[ind2,ind1]*p_b_real[ind2,ind1]\n",
    "                    \n",
    "                    if (p_modeled_stopping_givenM[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1) or (p_modeled_stopping_givenB[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1):\n",
    "                        mle[i,j] = -np.inf\n",
    "                    else:\n",
    "                        mle[i,j] = mle[i,j] + trials_stop_m[ind2,ind1]*log(p_modeled_stopping_givenM[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*log(1-p_modeled_stopping_givenM[ind2,ind1])+trials_stop_b[ind2,ind1]*log(p_modeled_stopping_givenB[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*log(1-p_modeled_stopping_givenB[ind2,ind1])                        \n",
    "               \n",
    "                    if o<o_T or s<s_T:\n",
    "                         avg_reward_numerator[i,j] = avg_reward_numerator[i,j] + p_modeled_stopping_givenM[ind2,ind1]*p_m_real[ind2,ind1]*trials_tot[ind2,ind1]/total_len\n",
    "                    avg_reward_denominator[i,j] = avg_reward_denominator[i,j] + (laptimes[ind2,ind1]+p_modeled_stopping2[ind2,ind1]*pausetimes[ind2,ind1])*trials_tot[ind2,ind1]/total_len    \n",
    "                  \n",
    "            norm[i,j] = math.sqrt(LA.norm(p_modeled_stopping_givenM - p_stop_real_givenM,'fro')**2 + LA.norm(p_modeled_stopping_givenB - p_stop_real_givenB,'fro')**2)       \n",
    "            avg_reward[i,j] = avg_reward_numerator[i,j]/avg_reward_denominator[i,j] \n",
    "            increment = increment+1\n",
    "        \n",
    "    minimum = np.inf\n",
    "    maximum =-np.inf\n",
    "    maximum_reward = -np.inf\n",
    "    \n",
    "    for exp1 in range(exp1N, exp2N):\n",
    "        for exp2 in range(-exp1+1, exp1):            \n",
    "            \n",
    "            i=exp1\n",
    "            j=exp2+exp1-1    \n",
    "            \n",
    "            if norm[i,j]<minimum:\n",
    "                minimum = norm2[i,j]\n",
    "                i_min = [i,exp1]\n",
    "                j_min = [j,exp2]        \n",
    "            \n",
    "            if mle[i,j]>maximum:\n",
    "                maximum = mle[i,j]\n",
    "                i_max_mle = [i,exp1]\n",
    "                j_max_mle = [j,exp2]\n",
    "                \n",
    "            if avg_reward[i,j]>maximum_reward:\n",
    "                maximum_reward = avg_reward3[i,j]\n",
    "                N_rew = [i,exp1]\n",
    "                M_rew = [j,exp2]     \n",
    "            \n",
    "    print \"norm\", minimum, i_min, j_min\n",
    "    print \"mle\", maximum, i_max_mle, j_max_mle\n",
    "        \n",
    "    a,b = norm.shape\n",
    "    print \"a,b\", a,b\n",
    "    print \"norm\", norm\n",
    "    \n",
    "    aic = 4 - 2*mle[i_max_mle,j_max_mle]\n",
    "    print \"aic =\", aic\n",
    "    \n",
    "    print \"avg reward rate for best fit is II\", avg_reward[i_max_mle[0], j_max_mle[0]], \"or (if minimizing norm)\", avg_reward[i_min[0],j_min[0]]\n",
    "    print \"maximum reward rate is\", maximum_reward, \"at N,M\", N_rew[1], M_rew[1]\n",
    "    \n",
    "    return mle, avg_reward, N_rew, M_rew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fitting_accumulator2(x):    \n",
    "    \n",
    "    mle = 0\n",
    "    p_modeled_stopping = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping_givenM = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping_givenB = np.zeros((len(orientations),len(sizes)))\n",
    "    p_modeled_stopping2 = np.zeros((len(orientations),len(sizes)))\n",
    "    \n",
    "    percept, percept_givenM, percept_givenB = p_stop_ProbabilityMatching(x,j)\n",
    "    \n",
    "    p_m_real, p_b_real, trials_tot, trials_stop, trials_m, trials_b, trials_stop_m, trials_stop_b, choice_m, choice_b = p_real(j)\n",
    "    p_stop_real, p_stop_real_toFit, p_stop_real_givenM, p_stop_real_givenB, p_stop_real_givenM_toFit, p_stop_real_givenB_toFit = p_stop_real_function(j)\n",
    "    \n",
    "    for ind2,o in enumerate(orientations):\n",
    "            for ind1,s in enumerate(sizes):\n",
    "\n",
    "                if percept[ind2,ind1]!=0.5:\n",
    "                     factor = percept[ind2, ind1]/(1-percept[ind2,ind1])\n",
    "                     p_modeled_stopping[ind2,ind1] = (factor**(2*exp1)- factor**(exp1+exp2))/(factor**(2*exp1)-1)\n",
    "                else:\n",
    "                     p_modeled_stopping[ind2,ind1] = (exp1+exp2)/(2*exp1)\n",
    "                        \n",
    "                if percept_givenM[ind2,ind1]!=0.5:        \n",
    "                     factor_givenM = percept_givenM[ind2,ind1]/(1-percept_givenM[ind2,ind1])\n",
    "                     p_modeled_stopping_givenM[ind2,ind1] = (factor_givenM**(2*exp1)- factor_givenM**(exp1+exp2))/(factor_givenM**(2*exp1)-1)    \n",
    "                else:        \n",
    "                     p_modeled_stopping_givenM[ind2,ind1] = (exp1+exp2)/(2*exp1)\n",
    "                    \n",
    "                if percept_givenB[ind2,ind1]!=0.5:     \n",
    "                    factor_givenB = percept_givenB[ind2,ind1]/(1-percept_givenB[ind2,ind1])\n",
    "                    p_modeled_stopping_givenB[ind2,ind1] = (factor_givenB**(2*exp1)- factor_givenB**(exp1+exp2))/(factor_givenB**(2*exp1)-1)\n",
    "                else:\n",
    "                    p_modeled_stopping_givenB[ind2,ind1] = (exp1+exp2)/(2*exp1)\n",
    "                \n",
    "                if (p_modeled_stopping_givenM[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1) or (p_modeled_stopping_givenB[ind2, ind1]==0) or (p_modeled_stopping_givenM[ind2, ind1]==1):\n",
    "                        mle = -np.inf\n",
    "                else:\n",
    "                        mle = mle + trials_stop_m[ind2,ind1]*log(p_modeled_stopping_givenM[ind2,ind1])+(trials_m[ind2,ind1]-trials_stop_m[ind2,ind1])*log(1-p_modeled_stopping_givenM[ind2,ind1])+trials_stop_b[ind2,ind1]*log(p_modeled_stopping_givenB[ind2,ind1])+(trials_b[ind2,ind1]-trials_stop_b[ind2,ind1])*log(1-p_modeled_stopping_givenB[ind2,ind1])                        \n",
    "                        \n",
    "    if np.isnan(mle):\n",
    "        mle = -np.inf\n",
    "        \n",
    "    print mle\n",
    "    return -mle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Image', u'Ori', u'PosY', u'Size', u'abs_reward_rate', u'average_speed', u'duration', u'end_frame', u'end_time', u'first_lick_latency_0', u'first_lick_latency_1', u'image', u'jcam_end_ind_exclusive', u'jcam_start_ind', u'lap_number', u'lick_duration_0', u'lick_duration_1', u'lick_times_0', u'lick_times_1', u'mouse_id', u'number_of_licks_0', u'number_of_licks_1', u'number_of_rewards', u'reward_times', u'start_date_time', u'start_frame', u'start_time', u'stop_date_time', u'choice', u'total_trial_ind'], dtype='object')\n",
      "Index([u'Size', u'Image', u'Ori', u'PosY', u'trial', u'time', u'terraincode', u'pausetime', u'choice', u'selectiontime', u'istarget', u'speed', u'lapdistance', u'timebeforezone', u'slowing_f', u'mouse', u'date'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Ori</th>\n",
       "      <th>PosY</th>\n",
       "      <th>Size</th>\n",
       "      <th>abs_reward_rate</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>duration</th>\n",
       "      <th>end_frame</th>\n",
       "      <th>end_time</th>\n",
       "      <th>first_lick_latency_0</th>\n",
       "      <th>...</th>\n",
       "      <th>number_of_licks_0</th>\n",
       "      <th>number_of_licks_1</th>\n",
       "      <th>number_of_rewards</th>\n",
       "      <th>reward_times</th>\n",
       "      <th>start_date_time</th>\n",
       "      <th>start_frame</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_date_time</th>\n",
       "      <th>choice</th>\n",
       "      <th>total_trial_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> CoxImage2.png</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 49.338896</td>\n",
       "      <td>  3.333263</td>\n",
       "      <td>  393</td>\n",
       "      <td>  6.599877</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> []</td>\n",
       "      <td> 2014-10-09 14:00:44.798000</td>\n",
       "      <td>  195</td>\n",
       "      <td>  3.266614</td>\n",
       "      <td> 2014-10-09 14:59:03.898000</td>\n",
       "      <td>  True</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> CoxImage1.png</td>\n",
       "      <td> 45</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 19.995724</td>\n",
       "      <td>  6.866585</td>\n",
       "      <td>  804</td>\n",
       "      <td> 13.466461</td>\n",
       "      <td> 2.066640</td>\n",
       "      <td>...</td>\n",
       "      <td>  5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> []</td>\n",
       "      <td> 2014-10-09 14:00:44.798000</td>\n",
       "      <td>  394</td>\n",
       "      <td>  6.599877</td>\n",
       "      <td> 2014-10-09 14:59:03.898000</td>\n",
       "      <td> False</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> CoxImage1.png</td>\n",
       "      <td>-15</td>\n",
       "      <td> 0</td>\n",
       "      <td> 450</td>\n",
       "      <td> 1.012102</td>\n",
       "      <td> 48.922026</td>\n",
       "      <td>  3.499956</td>\n",
       "      <td> 1013</td>\n",
       "      <td> 16.966418</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> []</td>\n",
       "      <td> 2014-10-09 14:00:44.798000</td>\n",
       "      <td>  805</td>\n",
       "      <td> 13.466461</td>\n",
       "      <td> 2014-10-09 14:59:03.898000</td>\n",
       "      <td> False</td>\n",
       "      <td> 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> CoxImage1.png</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 400</td>\n",
       "      <td> 1.715533</td>\n",
       "      <td> 43.407370</td>\n",
       "      <td>  2.416671</td>\n",
       "      <td> 1157</td>\n",
       "      <td> 19.383088</td>\n",
       "      <td>      NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> []</td>\n",
       "      <td> 2014-10-09 14:00:44.798000</td>\n",
       "      <td> 1014</td>\n",
       "      <td> 16.966418</td>\n",
       "      <td> 2014-10-09 14:59:03.898000</td>\n",
       "      <td>  True</td>\n",
       "      <td> 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> CoxImage2.png</td>\n",
       "      <td>-30</td>\n",
       "      <td> 0</td>\n",
       "      <td> 550</td>\n",
       "      <td> 1.675219</td>\n",
       "      <td> 11.240568</td>\n",
       "      <td> 15.916443</td>\n",
       "      <td> 2111</td>\n",
       "      <td> 35.299532</td>\n",
       "      <td> 1.099909</td>\n",
       "      <td>...</td>\n",
       "      <td> 10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> []</td>\n",
       "      <td> 2014-10-09 14:00:44.798000</td>\n",
       "      <td> 1158</td>\n",
       "      <td> 19.383088</td>\n",
       "      <td> 2014-10-09 14:59:03.898000</td>\n",
       "      <td> False</td>\n",
       "      <td> 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Image  Ori  PosY  Size  abs_reward_rate  average_speed   duration  end_frame   end_time  first_lick_latency_0          ...          number_of_licks_0 number_of_licks_1 number_of_rewards  \\\n",
       "1  CoxImage2.png    0     0   550         0.000000      49.338896   3.333263        393   6.599877                   NaN          ...                          0                 0                 0   \n",
       "2  CoxImage1.png   45     0   400         0.000000      19.995724   6.866585        804  13.466461              2.066640          ...                          5                 0                 0   \n",
       "3  CoxImage1.png  -15     0   450         1.012102      48.922026   3.499956       1013  16.966418                   NaN          ...                          0                 0                 0   \n",
       "4  CoxImage1.png    0     0   400         1.715533      43.407370   2.416671       1157  19.383088                   NaN          ...                          0                 0                 0   \n",
       "5  CoxImage2.png  -30     0   550         1.675219      11.240568  15.916443       2111  35.299532              1.099909          ...                         10                 0                 0   \n",
       "\n",
       "  reward_times             start_date_time start_frame start_time              stop_date_time choice total_trial_ind  \n",
       "1           []  2014-10-09 14:00:44.798000         195   3.266614  2014-10-09 14:59:03.898000   True               0  \n",
       "2           []  2014-10-09 14:00:44.798000         394   6.599877  2014-10-09 14:59:03.898000  False               1  \n",
       "3           []  2014-10-09 14:00:44.798000         805  13.466461  2014-10-09 14:59:03.898000  False               2  \n",
       "4           []  2014-10-09 14:00:44.798000        1014  16.966418  2014-10-09 14:59:03.898000   True               3  \n",
       "5           []  2014-10-09 14:00:44.798000        1158  19.383088  2014-10-09 14:59:03.898000  False               4  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print dataFrame.columns\n",
    "print df.columns\n",
    "dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          3.44610214  1.9402436  ...,  7.98044729  9.0686903\n",
      "  7.91046619]\n",
      "[ 16.63725363  16.6860894   16.64867485 ...,  16.66285301  16.66048996\n",
      "  16.66718523]\n",
      "[ 0.20713167  0.11627911  0.15267248 ...,  0.47893643  0.54432315\n",
      "  0.4746132 ]\n",
      "frames_acc [ 0.99823522  1.00116536  0.99892049 ...,  0.99977118  0.9996294\n",
      "  1.00003111]\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851]\n",
      "frames_count [ 0.99823522  1.99940058  2.99832107 ...,  0.          0.          0.        ]\n"
     ]
    }
   ],
   "source": [
    "print d[\"dx\"]\n",
    "print d[\"vsyncintervals\"]\n",
    "dx_distances = d[\"dx\"][1:len(d[\"dx\"])]\n",
    "instantaneous_speed = dx_distances/d[\"vsyncintervals\"]\n",
    "print instantaneous_speed\n",
    "\n",
    "frames_acc = 60*d[\"vsyncintervals\"]/1000\n",
    "print \"frames_acc\", frames_acc\n",
    "frames_count = np.zeros(len(frames_acc))\n",
    "frames_count[0] = frames_acc[0]\n",
    "\n",
    "frameCut_indeces = []\n",
    "expind = 0\n",
    "i = 0\n",
    "\n",
    "while expind < len(dataFrame):\n",
    "    frames_count[i] = frames_acc[i] + frames_count[i-1]\n",
    "    #print dataFrame[\"end_frame\"][expind]\n",
    "    if dataFrame[\"end_frame\"].index[expind] <= frames_count[i]:\n",
    "        frameCut_indeces.append(i)\n",
    "        expind = expind+1\n",
    "        \n",
    "    #print i, expind, dataFrame[\"end_frame\"][expind], frames_count[i]\n",
    "    i=i+1\n",
    "    \n",
    "print frameCut_indeces\n",
    "print \"frames_count\", frames_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "208370\n",
      "209049 209048\n",
      "851\n",
      "851\n",
      "[ 16.63725363  16.6860894   16.64867485 ...,  16.66285301  16.66048996\n",
      "  16.66718523]\n",
      "[ 0.          3.44610214  1.9402436  ...,  7.98044729  9.0686903\n",
      "  7.91046619]\n",
      "209048\n",
      "[ 3.44610214  1.9402436   2.54179454 ...,  7.98044729  9.0686903\n",
      "  7.91046619]\n"
     ]
    }
   ],
   "source": [
    "print dataFrame[\"end_frame\"][len(dataFrame)-1]\n",
    "print len(d[\"dx\"]), len(d[\"vsyncintervals\"])\n",
    "print len(frameCut_indeces)\n",
    "print len(dataFrame)\n",
    "\n",
    "print d[\"vsyncintervals\"]\n",
    "lengths_dx = d[\"dx\"]\n",
    "dt_intervals = d[\"vsyncintervals\"]\n",
    "lengths_dx = np.delete(lengths_dx, [0])\n",
    "print d[\"dx\"]\n",
    "print len(lengths_dx)\n",
    "print lengths_dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:19: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_index,col_indexer] = value instead\n",
      "C:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:20: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_index,col_indexer] = value instead\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "851\n",
      "mean_v [ 0.20713167  0.11627911  0.15267248  0.15251374  0.11472615  0.15357348\n",
      "  0.11563416  0.17529503  0.0773329   0.10647258]\n",
      "mean_v2 [ 0.20713167  0.11627911  0.15267248  0.15251374  0.11472615  0.15357348\n",
      "  0.11563416  0.17529503  0.0773329   0.10647258]\n",
      "total l [ 3.44610214  1.9402436   2.54179454  2.54179454  1.91130352  2.55927467\n",
      "  1.9272505   2.922158    1.28849542  1.77468204]\n",
      "total t [ 16.63725363  16.6860894   16.64867485  16.66600374  16.65970229\n",
      "  16.66482218  16.6667914   16.66994207  16.66167151  16.6679729 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:21: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_index,col_indexer] = value instead\n"
     ]
    }
   ],
   "source": [
    "velocity_array = []\n",
    "start_frame = 0\n",
    "\n",
    "total_length = np.zeros(len(dataFrame))\n",
    "total_time = np.zeros(len(dataFrame))\n",
    "for i in range(0,len(frameCut_indeces)):\n",
    "    end_frame = frameCut_indeces[i]\n",
    "    velocity_array.append(lengths_dx[start_frame:end_frame]/dt_intervals[start_frame:end_frame])\n",
    "    total_length[i] = np.sum(lengths_dx[start_frame:end_frame])\n",
    "    total_time[i] = np.sum(dt_intervals[start_frame:end_frame]) \n",
    "    start_frame = frameCut_indeces[i]\n",
    "\n",
    "mean_velocities = np.zeros(len(dataFrame))\n",
    "mean_velocities2 = np.zeros(len(dataFrame))\n",
    "for i in range(0,len(frameCut_indeces)):\n",
    "    mean_velocities[i] = np.mean(velocity_array[i])\n",
    "    mean_velocities2[i] = total_length[i]/total_time[i]\n",
    "\n",
    "dataFrame[\"v_average\"] = mean_velocities\n",
    "dataFrame[\"total_length\"] = total_length\n",
    "dataFrame[\"total_time\"] = total_time\n",
    "\n",
    "print len(mean_velocities)\n",
    "print \"mean_v\", mean_velocities[0:10]\n",
    "print \"mean_v2\", mean_velocities2[0:10]\n",
    "print \"total l\", total_length[0:10]\n",
    "print \"total t\", total_time[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x223e4e48>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29747320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwQ1AAAMENQAADBAUAMAwID/Dw3wgP2uWCRbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bed7240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHrZJREFUeJzt3X+Q7Xdd3/HXDfkBleU2M9kYdZQpTnwTWoKOlMEEbmIl\nYMBoaOu0gBYiIfwWiLYFBHQsPxyROGT4IYYbG8SRTgoIMRpQhzbhFghj2yQM5E1DGVtpBm8oufcW\nSciP7R/nu3qy3HuTez7J7h7zePxz95zP+e757L7Z8Dzf/e7ujrW1tQAAAIs5aqs3AAAAy0xQAwDA\nAEENAAADBDUAAAwQ1AAAMEBQAwDAgKO3egMA96aqzkzywu5+1tx9v5bkC9192f38XP8oyfHdfc0h\n1h+X5Ce7+98dYv24JD/T3bvvz30d5Hl+Mckzkvz9JN+d5PPT0u8m2dvdV9wPz/GoJH+U5FPdfd4R\nHntDdz92w33PTfJ/D7e36ePae3/PFeCBJKiBZXCwX5j/QP0S/X+e5OYkBw3q7r4uyXWHOf67kpyf\n5AEN6u7+jSS/UVVnJHnR/IuN+9GTkvxhd//i/fHO7mMk++MIwNIR1MAy2HGohSkoX53k9iSPSvKB\n7n5zVZ2c5L1Jjkny10n+ZZKHJXnP9O83k1yQ2X8Hr0hyS5JPJHluktur6r8meWSSl0zvYy3JM5M8\nNtPZ8qr6H0k+maSSfDXJP0vyS0keU1WvT/K0JBd09+er6uwkP9HdL532fUKSq7v7MdPtdyT508zO\nNv+rJHcn+Wx3v+JIPjdV9SuZvSC4Mclrk9yW5HuT/FaSf5LkcUne3t2/NX3u3pjkriRfmj6uO6f3\n831JXpPk71XVTUk+k+Ti6bG3JXlBkofMfe7+qLvfOreVnVX1oSQnJvlv3f3yDXv79Wlmv53k/yV5\nfZKvTZ/n36uq1ST/Yfr4HprZi4bDvZAB2DKuoQaW2frZzO9L8k+TPDHJv5nu+40kb+ru05K8PckP\nJXlrkou7+0eTvC3Jr03v4zuTnNXdv5rk3ye5qLs/m+TkJM/o7idndknF03LPM6j/IMnrpudYTfKP\nMwvUz0+XhLw3s0BPkp9Lcsn6gd19S5Lrq+rJ02UiZ2YWp89L8tLpfX6hqh6y4OckSb5n+ry8OMnr\nkvxMkrOTvHBavyTJM7v7zCRfmZ57fX//a/r8/F53/9b02JdOj31Xkotyz8/dfEwnyUpmLyaelOTE\nqjpnw96O6+5dST4wva+ndPdTM4vzHZl9Lm+Z9vvSJN9xhJ8HgE0jqIFl8NdJjttw38MzO8ucJDd0\n993d/ddz9/1Akk8lSXdf0d1/ktnZ5ddW1ScyOyN64vTYL6+fmZ2sn/Xdm+Syqro0yamZnamed0t3\nf2V6+39Pe5w/Y3x5kp+czrZ+T3f/9w3HX5JZcP9kko90911Jzkvysqr6T5mdIT/k2fn74HPT+9yX\n5EvTx3hrkodOezopyeXT5+Opmb0w2Wj9+b+ru6+f3r4myT+c3t74uVv3helFQzKbQ21Y7+nfE5Ps\n6+6vT7evnv794yR7knwkya9mdsYeYFsS1MAyuDHJD1XVSUlSVQ9NsivJn2cWfAe77vYLSZ4wPf5Z\nVfXS6b5/O52hfllmlxQk94y1u5McVVU7k/xKkn+R2eUN38y3x+3BnveuTP9t7e5vZHYZydsz+2HB\ne+juP8vszPnPZXY2O9NzvWg6E/xDSX7kIM9xXx3ueuRbkvxlZj9g+aOZnY3+0w2Pmf94/09Vrf+Q\n4Rn52yA+VOieXFXHV9WOJE9Ocv2G9fXj/iqzy0PWX9w8cfr3zCQ3d/fTkrwpyZsP87EAbClBDWx7\n3b0/yYVJrqyqa5L858wu3fifmUXjfDiuv/2vk7xmOvv6nCS/N933y9PZ391JPrfhmGQW6S/LLGb3\nZHZ29cOZBeR3bXj8wYL1r5IcW1VvmW5fkuSnpuc/mP+Y5Jju/vJ0+4Yk11TVn2V2Xfa1VfWj0zXZ\nB7Px4z/U/u7xdnevJXlFkj+qqj2ZXU/++dzT/Pt+QZJ3VNXVSV6e5FU59IuZZHZ2/3cy+xx+sbs/\nfrC9TWe3Xzzt40+THD+tXZfk/Gl+vx5BDWxjO9bW/EA1wAOlqh6f5GXd/byt3gsADwy/5QPgAVJV\nL8vsco6f3uq9APDAcYYaAAAGuIYaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICg\nBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABgg\nqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAG\nCGoAABggqAEAYICgBgCAAUcvclBVHZXkXUlOTXJ7kvO7+0tz6+ckeX2SO5Nc2t3vne5/TZJzkhyT\n5B3dfdnY9gEAYGsteob63CTHdvdpSV6d5G3rC1V1TJKLkpyV5IwkF1TViVV1ZpIfmY45M8mjBvYN\nAADbwqJBfXqSq5Kkuz+T5PFza6ckuam793X3HUk+mWRXkqcmuaGq/iDJFUk+uvCuAQBgm1joko8k\nj0iyf+72XVV1VHffPa3tm1s7kGRnkhOSPDLJMzI7O/3RJI8+3JOsra2t7dixY8EtAgDAfTIUnIsG\n9f4kK3O312M6mcX0/NpKkluTfC3Jjd19Z5IvVtVtVXVCd99yqCfZsWNH9u49sOAW2Uqrqytmt8TM\nb3mZ3XIzv+VmfstrdXXl3h90GIte8rEnydOTpKqemOT6ubUbk5xcVcdX1bGZXe7xXzK79OPHp2O+\nO8l3ZBbZAACwtBY9Q/3hJGdV1Z7p9nlV9awkD+/uS6rqwiQfyyzYd3f3zUmurKpdVXXtdP9Luntt\n9AMAAICttGNtbVs37ZpvnSwn3/Zabua3vMxuuZnfcjO/5bW6ujJ0DbU/7AIAAAMENQAADBDUAAAw\nQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAA\nDBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0A\nAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFAD\nAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDU\nAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAME\nNQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADDh6kYOq6qgk70py\napLbk5zf3V+aWz8nyeuT3Jnk0u5+79zaiUn+PMmPdfcXB/YOAABbbtEz1OcmOba7T0vy6iRvW1+o\nqmOSXJTkrCRnJLlgiuj1tfck+cbIpgEAYLtYNKhPT3JVknT3Z5I8fm7tlCQ3dfe+7r4jySeT7JrW\n3prk3UluXvB5AQBgW1k0qB+RZP/c7bumy0DW1/bNrR1IsrOqnpdkb3d/fLp/x4LPDQAA28ZC11Bn\nFtMrc7eP6u67p7f3bVhbSXJrkp9PslZVT0nyg0kuq6qf6u6vHu6JVldXDrfMNmZ2y838lpfZLTfz\nW27m9+C0aFDvSXJOksur6olJrp9buzHJyVV1fGbXSu9K8tbu/uD6A6rqE0leeG8xnSR79x5YcIts\npdXVFbNbYua3vMxuuZnfcjO/5TX6QmjRoP5wkrOqas90+7yqelaSh3f3JVV1YZKPZXZJye7uds00\nAAB/J+1YW1vb6j0czppXesvJq/TlZn7Ly+yWm/ktN/NbXqurK0M/2+cPuwAAwABBDQAAAwQ1AAAM\nENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAA\nAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMA\nwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQA\nADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1\nAAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABB\nDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMOHqRg6rq\nqCTvSnJqktuTnN/dX5pbPyfJ65PcmeTS7n5vVR2T5NIkj0xyXJI3dvcVg/sHAIAttegZ6nOTHNvd\npyV5dZK3rS9M4XxRkrOSnJHkgqo6Mclzkuzt7l1JfjzJO0Y2DgAA28GiQX16kquSpLs/k+Txc2un\nJLmpu/d19x1JPplkV5LLk7xh7nnvXPC5AQBg21joko8kj0iyf+72XVV1VHffPa3tm1s7kGRnd38j\nSapqJbO4/qX78kSrqysLbpGtZnbLzfyWl9ktN/Nbbub34LRoUO9PMv+/mPWYTmYxPb+2kuTrSVJV\n35vkQ0ne2d0fuC9PtHfvgQW3yFZaXV0xuyVmfsvL7Jab+S0381teoy+EFg3qPUnOSXJ5VT0xyfVz\nazcmObmqjk/yjcwu93hrVX1nko8neUl3f2JgzwAAsG0sGtQfTnJWVe2Zbp9XVc9K8vDuvqSqLkzy\nscyuld7d3TdX1duT7Ezyhqpav5b67O6+beQDAACArbRjbW1tq/dwOGu+dbKcfNtruZnf8jK75WZ+\ny838ltfq6sqOkeP9YRcAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoA\nABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIa\nAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICg\nBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABgg\nqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAG\nCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCA\nAYIaAAAGCGoAABggqAEAYMDRixxUVUcleVeSU5PcnuT87v7S3Po5SV6f5M4kl3b3e+/tGAAAWEaL\nnqE+N8mx3X1aklcnedv6QlUdk+SiJGclOSPJBVV14nTMcQc7BgAAltWiQX16kquSpLs/k+Txc2un\nJLmpu/d19x1JPplk13TMHx/iGAAAWEqLBvUjkuyfu33XdEnH+tq+ubUDSXbeyzEAALCUFrqGOrMw\nXpm7fVR33z29vW/D2kqSW+/lmENaXV25t4ewTZndcjO/5WV2y838lpv5PTgtGtR7kpyT5PKqemKS\n6+fWbkxyclUdn+QbmV3u8dYka4c55pD27j2w4BbZSqurK2a3xMxveZndcjO/5WZ+y2v0hdCiQf3h\nJGdV1Z7p9nlV9awkD+/uS6rqwiQfy+ySkt3dfXNVfdsxQzsHAIBtYMfa2tpW7+Fw1rzSW05epS83\n81teZrfczG+5md/yWl1d2TFyvB8KBACAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABgg\nqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAG\nCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCA\nAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEA\nYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoA\nABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIa\nAAAGCGoAABggqAEAYICgBgCAAYIaAAAGHH2kB1TVw5K8P8lqkgNJntvdt2x4zAuSXJDkziRv7O4r\nq2rndNxKkmOTXNjdnx7cPwAAbKlFzlC/OMl13b0ryfuSvG5+sapOSvLyJKcleVqSt1TVsUleleRP\nuvvMJM9L8s7Ftw0AANvDIkF9epKrprevSvKUDetPSLKnu+/o7v1JbkpyapLfTPLb02OOSfLNBZ4b\nAAC2lcNe8lFVz0/yyg13fzXJ/untA0l2blhfSbJv7vaBJDu7e9/0Pk9K8rtJXrHgngEAYNs4bFB3\n9+4ku+fvq6oPZhbNmf69dcNh++fW1x/z9enYxyb5/SS/0N3X3JcNrq6u3PuD2JbMbrmZ3/Iyu+Vm\nfsvN/B6cjviHEpPsSfL0JJ9NcnaSqzesX5vkTVV1XJKHJjklyeeq6jFJLk/y0919w319sr17Dyyw\nRbba6uqK2S0x81teZrfczG+5md/yGn0htEhQvzvJZVV1TZLbkzw7SarqVUlu6u4rquriJNdkdo32\na7v7W1X15sx+u8fFVZUkt3b3M4d2DwAAW2zH2traVu/hcNa80ltOXqUvN/NbXma33MxvuZnf8lpd\nXdkxcrw/7AIAAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAA\nDBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0A\nAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFAD\nAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDU\nAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAME\nNQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAA\nQQ0AAAMENQAADBDUAAAw4OgjPaCqHpbk/UlWkxxI8tzuvmXDY16Q5IIkdyZ5Y3dfObf26CSfTnJi\nd39rYO8AALDlFjlD/eIk13X3riTvS/K6+cWqOinJy5OcluRpSd5SVcdOa49I8rYkt41sGgAAtotF\ngvr0JFdNb1+V5Ckb1p+QZE9339Hd+5PclOTUqtqR5D1JXpPkmwvuFwAAtpXDXvJRVc9P8soNd381\nyf7p7QNJdm5YX0myb+72+mN+OcmV3X19VSXJjvuywdXVlfvyMLYhs1tu5re8zG65md9yM78Hp8MG\ndXfvTrJ7/r6q+mBm0Zzp31s3HLZ/bn3+Mc9J8pdTpJ+U5GNJzry3De7de+DeHsI2tLq6YnZLzPyW\nl9ktN/Nbbua3vEZfCB3xDyUm2ZPk6Uk+m+TsJFdvWL82yZuq6rgkD01ySpIbuvvk9QdU1ZeTPHWh\nHQMAwDaySFC/O8llVXVNktuTPDtJqupVSW7q7iuq6uIk12R2jfZrD/LbPNYG9gwAANvGjrW1bd22\na751spx822u5md/yMrvlZn7LzfyW1+rqyn362b5D8YddAABggKAGAIABghoAAAYIagAAGCCoAQBg\ngKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAA\nGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoA\nAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAG\nAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCo\nAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABghoAAAYI\nagAAGCCoAQBggKAGAIABghoAAAYIagAAGCCoAQBggKAGAIABRx/pAVX1sCTvT7Ka5ECS53b3LRse\n84IkFyS5M8kbu/vKqnpIkouS/HCSY5O8obuvGtw/AABsqUXOUL84yXXdvSvJ+5K8bn6xqk5K8vIk\npyV5WpK3VNWxSX42ydHd/aQk5yY5ZWTjAACwHSwS1KcnWT+zfFWSp2xYf0KSPd19R3fvT3JTklOT\nPDXJV6rqD5NckuQji20ZAAC2j8Ne8lFVz0/yyg13fzXJ/untA0l2blhfSbJv7vb6Y05I8v3d/RNV\ntSvJ7yQ5Y8F9AwDAtnDYoO7u3Ul2z99XVR/MLJoz/XvrhsP2z63PP+ZrSa6c3u/VVfUD92F/O1ZX\nV+79UWxLZrfczG95md1yM7/lZn4PTotc8rEnydOnt89OcvWG9WuTPLmqjquqnZldK31Dkk+uH1dV\nj0vyFwvtGAAAtpEj/i0fSd6d5LKquibJ7UmenSRV9aokN3X3FVV1cZJrMgv213b3t6rqkiTvrqpP\nTe/nRePbBwCArbVjbW1tq/cAAABLyx92AQCAAYIaAAAGCGoAABggqAEAYMAiv+XjflNVD0vy/iSr\nmf0BmOd29y0bHvOCJBckuTPJG7v7yqp6SJKLkvxwkmOTvKG7rwqbatH5za09Osmnk5zY3d/atI2T\nZOjrb+d03EpmX38XdvenN3XzD1JVdVSSd2X212dvT3J+d39pbv2cJK/PbF6Xdvd77+0YNs+C8zsm\nyaVJHpnkuMy+Dq/Y9M0/yC0yu7m1E5P8eZIf6+4vburGSbL4/KrqNUnOSXJMknd092WHeo6tPkP9\n4iTXdfeuJO9L8rr5xao6KcnLk5yW5GlJ3lJVxyb52SRHd/eTkpyb2e+6ZvMtOr9U1SOSvC3JbZu6\nY+YtOr9XJfmT7j4zyfOSvHMT9/xgd26SY7v7tCSvzuxrKEkyhddFSc7K7K/QXjD9H/m5SY472DFs\nukXm95wke6ev0x9P8o5N3zXJYrNbX3tPkm9s+o6Zd8Tzq6ozk/zIdMyZSR51uCfY6qA+Pcn6meWr\nkjxlw/oTkuzp7ju6e3+SmzJ7dfHUJF+pqj9MckmSj2zSfrmnheZXVTsy+w/Ma5J8c7M2y7dZ9Ovv\nN5P89vSYY2KGm+lvZtbdn0ny+Lm1UzL7WwD7uvuOzP6Y1q7pmD8+xDFsrkXmd3mSN0yPOSqzM2hs\nvkVmlyRvzezvd9y8iXvl2y0yv6cmuaGq/iDJFUk+ergn2LRLPqrq+UleueHur2b2p8qT2becd25Y\nX0myb+72+mNOSPL93f0TVbUrye9k9qqCB8j9PL9fTnJld19fVUmy437fMPdwf86vu/dN7/OkJL+b\n5BX3+4Y5lEfkb2eWJHdV1VHdffe0drCvt8Mdw+Y64vl19zeSpKpWMovrX9qszXIPRzy7qnpeZt9d\n+Ph06YD/r9s6i/y384TMLrV6RmZnpz+a5NGHeoJNC+ru3p1k9/x9VfXBzP5PO9O/t244bP/c+vxj\nvpbkyun9Xl1VP/BA7Jm/dT/P7zlJ/nKKvJOSfCyzb6fwALkf5/f16djHJvn9JL/Q3dc8EHvmoDbO\nZD6M9+XgX2+HO4bNdaTzW/96+94kH0ryzu7+wGZslG+zyNfezydZq6qnJPnBzP7K9E9191c3Y8Pc\nwyLz+1qSG7v7ziRfrKrbquqEjT9rtG5LfygxyZ4kT0/y2SRnJ7l6w/q1Sd5UVccleWhmp+VvyOx0\n/NOTfKiqHpfkLzZtx8xbaH7dffL6A6rqy5l9W4XNt8j8PldVj8nsTNlPd/cNm7hfZjM7J8nlVfXE\nJNfPrd2Y5OSqOj6z6zV3Zfbt5rXDHMPmOuL5VdV3Jvl4kpd09yc2e8P8jSOeXXd/cP0BVfWJJC8U\n01tmkf923pbZd2AvqqrvTvIdmUX2QW11UL87s1ds12T2U5fPTpKqelVm17NcUVUXJ7kms2vHXtvd\n36qqS5K8u6o+Nb2fF23B3llwfhvex9pmbph7WPTr782Z/XaPi6dLdm7t7mduyUfw4PPhJGdV1Z7p\n9nlV9awkD+/uS6rqwsy+43NUkt3dfXNVfdsxm79tJovM7+2Zffv5DVW1fi312d3tB7o31xHPbqs2\nykEtMr8rq2pXVV073f+S7j5ks+xYW9MzAACwqK3+LR8AALDUBDUAAAwQ1AAAMEBQAwDAAEENAAAD\nBDUAAAwQ1AAAMOD/A5R/Gy4oEPElAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21899cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x219fe588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29e29438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAwQFADAMAAQQ0AAAMENQAADPh/RJoi5GJccLwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc65358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XuUZXdZ5+Fvh1xgpMhCKQw6LFSEFxi5zBIRE2iiJtwk\nElzjjAEVGCACDrc4F1BAR1FUJCwjFzEEBRlRM4gQIgEvaJKWm4okXvJiEHVgEBs03T1ZJiRQ88fe\nJYeiu5OcX6jqY55nLVZXnb33Ob+ql+p8zj77dO3a2NgIAACwnKN2egEAALDKBDUAAAwQ1AAAMEBQ\nAwDAAEENAAADBDUAAAw4eqcXAPzrUFVfneRnknxpkmOSfDDJ/+ju/7dlv/sm+fbu/rGbeP8PTnJV\nd19+iO3HJfnu7j6vqh6f5B+7+4IlvpQviqo6KtP35+uSHJfk6iTf390f+SI81uXdfe8bsd9jkvxU\nknO6++U39zpujKr6qiRv7O5v2onHB7g5CGpgWFXdJslbkjypu98/3/a9Sd6Y5LTFfbv7g5li+6Z6\n0nx/Bw3qJHdK8uQk53X365a4/y+2hye5U3c/NEmq6tFJXpbk9B1c02lJzurut+3gGgBW3i6/2AUY\nVVX/Icnu7n7mltvfneSMJD+S6cz1lyV5SZL/1N1nVNV3JnlOks8kubS7n1dVP5Lkq5LcMcld5u2f\nTHJhkk9kisBHJ3lMki+Ztz0mySuT/MdMZ4GPSvL33f3qqnppkpPmJf1Kd59TVb+U5Jr5ce6U5And\n/YGq+sUkd01ymyQ/291vWPhanpHk9t39o/PZ8D9N8oAk/yvJ7ZL8myQ/1N2/fYjv0f0zPSH4oSS/\n192frKqju/v6qvrDJJ3kbkn2JnlskuuT/HySr52/nud39x9U1UOSvGj+nn04yfclOTbJG5LcYb7t\nG7v7XlX19CTfm+SzSd7f3c9aWM+3J3l1kn+Y7+OuSZ6V5Nokf5XkzCTfneQ/J9mV5Ie7+/fmY09O\n8rz5e3jneZ3fkuS+8/ft56vqb5Lcvbs/XVU/meQvk/xWkl+b7+/WSZ6aZF+S38n0ROlOSS7r7jPn\nGX3p/L9HJXnBQeb4VUlem+RWSTaSPLO7L6uqK5PsSXL3JL+b5Ph5Vt3d31tV35Hkvye5Lsn/TfJd\n3e0/hsDSXEMN3By+OslfH+T2j2SK4o0kv9vdJyW5Kkmq6vaZQvtbuvvBSb6yqk6Z972mux+ZKfCe\n091/kuSiTBH00UyRdUp3PzDTK23fkCky/2LxUpKqelSSr5r3e1CSx1bV182P8Tfd/fAkP5fkzKq6\nbZIHZ4rzh2cK1kW/nCnYk+Tbk1yQKSa/LFPkn5HDvOrX3X+U5CmZzkj/WVX9UZIT581fnuTs7n5Q\npiB+aqaz7Xu7+yHzMa+Y9z03yWO6++QkH0vyhHn/P+/u3Ul+MlNgZ972/d19YpK/rKpbLaznrfP3\n9L9lCugfSfLN8yyuyhTZG5kunXnwZkwv+Mok35HkaUmenym+HzEfl/nYbPn4GzI9AXpEku/P9IQo\nmZ6QPCHJNyX51qpaz+f+P/OgTLM72Bx/JsnL5u/Rs5KcN9/fXTI9cXlwkmcmeUV3f2OSB1XV8Um+\nK8lPz1/r2+bHB1iaoAZuDh/LdLZ3q69N8nfzx32QbetJ3l5V70pyr0xnSZPp7G8yxfOtFw+azyRe\nl+SNVfWaJP82U8juOsjj3yPJJfNx1yd5z/w4SfKBxceYr/V+dqZg/bVM1zkvPu5VST5QVQ9K8vgk\nr+nuv8h0lveNmc6QH/Lv1Kq6d5Iruvux3X1CpjO8vz5v/oeFa8MvzXRm9euSPHL+3vzvJLeaQ/OE\nJOfPtz80UzxWkj+a19mZznInyROT/Jeq+v15v4N9j3ZlekL059199XzbxUn+3eaXfogv6c+6+zOZ\nzjB/eP7+XpUt81p4jCR5e6Yzx29J8qOZzpwnyV939755tv+Q6Wz/4mMfao73mNe6eSnRnef9P9Xd\nH533vbq7r5hv35dprmdlCvffz/SkZnMdAEsR1MDN4S1JTq2qb9i8oao2z7Buvulu60vqH0nyfzKd\naf7mTEH67kPsm0zRc6uquk+SR3f3d2U6+3hUpmD7bL7w77S/zHRGM1V1TKZ4+quDfQFVdUKSr+/u\n78h0icFPz28kXHRupktQbt3dH5rPkq5196MynWH9uYPd9+yUJD9WVZtx+RdJNt+weYf58oVkuqzh\n8iRXZHqz3jdnusTl1zOd3f1opjd1fnOms9G/M9/XSfPXcddMl34k0xnxp85ns/99pjPAW21kmsW9\nqmozZE/O52L2ULF5Q5dIXJPkK+av934L9/vx7n5Ykh9P8hPz/RzqvjZvP9Qc/zLJ7vn2+yX5+I1Y\n265Ml7P8yPx92ZXpVQmApQlqYNh8ZvO0JM+vqkur6j2ZXt4/Y2G3jYU/N7r7k0nOTnLxvP+p+Vzs\nHuxygfcmeXGma4uvrqqLM103/CdJviLT9dXHztfrZn6MC5N8ZL5G+d1Jzu/uD2y53831/H2SE6pq\nT5J3JnlJd39eTHb35pnbX5pv+qskJ1fVH2QK3hckSVX91OKTi9k5mQL6T6vqkiS/kuR75m3XJ3lx\nVV2a6fKPX8h05vse81nU30/yd/MZ3Gcl+a15nWcm+fNM1zB/5Xz8/0zyj/P9Xp7kkqr63fn7894c\nRHd/KskPJ3nXfN37l873ufh9WrQ1gg/28U9numb6wnk9G5nejPrk+ez6T2cK6kMd/y8fH2aO/zXJ\nM+bv/yszvXH1hu5vI8n7krytqn4n0/f7iPnXYIDV5E2JADvsxv4zdwAcmZyhBth5zmwArDBnqAEA\nYIAz1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEEN\nAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQ\nAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAw4\nepmDquqoJK9Mcp8k1yZ5cnd/eGH7aUlekOT6JK/t7tfMtz8vyWlJjkny8u5+3djyAQBgZy17hvr0\nJMd294lJnpvkpZsbquqYJGcnOTXJQ5KcWVV3rKqTk3zTfMzJSb5mYN0AAHBEWDaoT0pyUZJ093uT\n3H9h2z2TXNnd+7r7uiSXJtmd5KFJLq+q30xyQZK3Lr1qAAA4Qix1yUeS2yXZv/D5Z6rqqO7+7Lxt\n38K2A0mOT3KHJHdJ8m2Zzk6/Nck9DvcgGxsbG7t27VpyiQAAcKMMBeeyQb0/ydrC55sxnUwxvbht\nLclVST6V5Iruvj7Jh6rqmqq6Q3d/8lAPsmvXruzde2DJJbKT1tfXzG6Fmd/qMrvVZn6rzfxW1/r6\n2g3vdBjLXvKxJ8kjk6SqHpjksoVtVyS5W1XdvqqOzXS5xx9muvTj4fMxX5HkSzJFNgAArKxlz1C/\nOcmpVbVn/vyJVXVGktt297lVdVaSd2QK9vO6++NJLqyq3VX1vvn2p3f3xugXAAAAO2nXxsYR3bQb\nXjpZTV72Wm3mt7rMbrWZ32ozv9W1vr42dA21X+wCAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUA\nAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEEN\nAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQ\nAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ\n1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAAD\nBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAwQ1AAAMEBQAwDA\nAEENAAADBDUAAAwQ1AAAMEBQAwDAAEENAAADBDUAAAw4epmDquqoJK9Mcp8k1yZ5cnd/eGH7aUle\nkOT6JK/t7tcsbLtjkj9O8q3d/aGBtQMAwI5b9gz16UmO7e4Tkzw3yUs3N1TVMUnOTnJqkockOXOO\n6M1tr05y9ciiAQDgSLFsUJ+U5KIk6e73Jrn/wrZ7Jrmyu/d193VJLk2ye972kiSvSvLxJR8XAACO\nKMsG9e2S7F/4/DPzZSCb2/YtbDuQ5PiqekKSvd39zvn2XUs+NgAAHDGWuoY6U0yvLXx+VHd/dv54\n35Zta0muSvLMJBtVdUqS+yV5XVU9urs/cbgHWl9fO9xmjmBmt9rMb3WZ3Wozv9VmfrdMywb1niSn\nJTm/qh6Y5LKFbVckuVtV3T7TtdK7k7yku9+0uUNVvSvJ991QTCfJ3r0HllwiO2l9fc3sVpj5rS6z\nW23mt9rMb3WNPhFaNqjfnOTUqtozf/7EqjojyW27+9yqOivJOzJdUnJed7tmGgCAf5V2bWxs7PQa\nDmfDM73V5Fn6ajO/1WV2q838Vpv5ra719bWh9/b5xS4AADBAUAMAwABBDQAAAwQ1AAAMENQAADBA\nUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAM\nENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAA\nAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMA\nwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQA\nADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAAwQ1\nAAAMENQAADBAUAMAwABBDQAAAwQ1AAAMENQAADBAUAMAwABBDQAAA45e5qCqOirJK5PcJ8m1SZ7c\n3R9e2H5akhckuT7Ja7v7NVV1TJLXJrlLkuOSvKi7LxhcPwAA7Khlz1CfnuTY7j4xyXOTvHRzwxzO\nZyc5NclDkpxZVXdM8rgke7t7d5KHJ3n5yMIBAOBIsGxQn5TkoiTp7vcmuf/CtnsmubK793X3dUku\nTbI7yflJXrjwuNcv+dgAAHDEWOqSjyS3S7J/4fPPVNVR3f3Zedu+hW0Hkhzf3VcnSVWtZYrrH7ox\nD7S+vrbkEtlpZrfazG91md1qM7/VZn63TMsG9f4ki/+P2YzpZIrpxW1rSf4pSarqzkl+I8kruvtX\nb8wD7d17YMklspPW19fMboWZ3+oyu9VmfqvN/FbX6BOhZYN6T5LTkpxfVQ9MctnCtiuS3K2qbp/k\n6kyXe7ykqr48yTuTPL273zWwZgAAOGIsG9RvTnJqVe2ZP39iVZ2R5LbdfW5VnZXkHZmulT6vuz9e\nVT+b5PgkL6yqzWupH9Hd14x8AQAAsJN2bWxs7PQaDmfDSyerycteq838VpfZrTbzW23mt7rW19d2\njRzvF7sAAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAME\nNQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAA\nQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAw\nQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAA\nDBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0A\nAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFADAMAAQQ0AAAMENQAADBDUAAAwQFAD\nAMAAQQ0AAAOOXuagqjoqySuT3CfJtUme3N0fXth+WpIXJLk+yWu7+zU3dAwAAKyiZc9Qn57k2O4+\nMclzk7x0c0NVHZPk7CSnJnlIkjOr6o7zMccd7BgAAFhVywb1SUkuSpLufm+S+y9su2eSK7t7X3df\nl+TSJLvnY95+iGMAAGAlLRvUt0uyf+Hzz8yXdGxu27ew7UCS42/gGAAAWElLXUOdKYzXFj4/qrs/\nO3+8b8u2tSRX3cAxh7S+vnZDu3CEMrvVZn6ry+xWm/mtNvO7ZVo2qPckOS3J+VX1wCSXLWy7Isnd\nqur2Sa7OdLnHS5JsHOaYQ9q798CSS2Qnra+vmd0KM7/VZXarzfxWm/mtrtEnQssG9ZuTnFpVe+bP\nn1hVZyS5bXefW1VnJXlHpktKzuvuj1fVFxwztHIAADgC7NrY2NjpNRzOhmd6q8mz9NVmfqvL7Fab\n+a0281td6+tru0aO96ZAAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghq\nAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGC\nGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCA\noAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAY\nIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAA\nBghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYA\ngAGCGgAABghqAAAYIKgBAGDA0Tf1gKq6TZI3JFlPciDJ47v7k1v2eUqSM5Ncn+RF3X1hVR0/H7eW\n5NgkZ3X3ewbXDwAAO2qZM9RPS/LB7t6d5PVJnr+4sapOSPKMJCcmeViSF1fVsUmek+S3u/vkJE9I\n8orllw0AAEeGZYL6pCQXzR9flOSULdsfkGRPd1/X3fuTXJnkPkleluQX5n2OSfLPSzw2AAAcUQ57\nyUdVPSnJs7fc/Ikk++ePDyQ5fsv2tST7Fj4/kOT47t433+cJSX45ybOWXDMAABwxDhvU3X1ekvMW\nb6uqN2WK5sx/XrXlsP0L2zf3+af52HsneWOSH+juS27MAtfX1254J45IZrfazG91md1qM7/VZn63\nTDf5TYlJ9iR5ZJL3J3lEkou3bH9fkh+vquOS3DrJPZP8WVXdK8n5Sb6zuy+/sQ+2d++BJZbITltf\nXzO7FWZ+q8vsVpv5rTbzW12jT4SWCepXJXldVV2S5Nokj02SqnpOkiu7+4KqOifJJZmu0f7B7v50\nVf1Epn/d45yqSpKruvsxQ6sHAIAdtmtjY2On13A4G57prSbP0leb+a0us1tt5rfazG91ra+v7Ro5\n3i92AQCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoA\nABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIa\nAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICg\nBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABgg\nqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAG\nCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCAAYIaAAAGCGoAABggqAEAYICgBgCA\nAYIaAAAGCGoAABhw9E09oKpuk+QNSdaTHEjy+O7+5JZ9npLkzCTXJ3lRd1+4sO0eSd6T5I7d/emB\ntQMAwI5b5gz105J8sLt3J3l9kucvbqyqE5I8I8mJSR6W5MVVdey87XZJXprkmpFFAwDAkWKZoD4p\nyUXzxxclOWXL9gck2dPd13X3/iRXJrlPVe1K8uokz0vyz0uuFwAAjiiHveSjqp6U5Nlbbv5Ekv3z\nxweSHL9l+1qSfQufb+7zw0ku7O7LqipJdt2YBa6vr92Y3TgCmd1qM7/VZXarzfxWm/ndMh02qLv7\nvCTnLd5WVW/KFM2Z/7xqy2H7F7Yv7vO4JB+dI/2EJO9IcvINLXDv3gM3tAtHoPX1NbNbYea3usxu\ntZnfajO/1TX6ROgmvykxyZ4kj0zy/iSPSHLxlu3vS/LjVXVcklsnuWeSy7v7bps7VNVHkjx0qRUD\nAMARZJmgflWS11XVJUmuTfLYJKmq5yS5srsvqKpzklyS6RrtHzzIv+axMbBmAAA4Yuza2Dii23bD\nSyerycteq838VpfZrTbzW23mt7rW19du1Hv7DsUvdgEAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGC\nGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCA\noAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAY\nIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAA\nBghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYA\ngAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABghqAAAYIKgB\nAGCAoAYAgAGCGgAABghqAAAYIKgBAGCAoAYAgAGCGgAABhx9Uw+oqtskeUOS9SQHkjy+uz+5ZZ+n\nJDkzyfVJXtTdF1bVrZKcneTrkxyb5IXdfdHg+gEAYEctc4b6aUk+2N27k7w+yfMXN1bVCUmekeTE\nJA9L8uKqOjbJ9yQ5ursflOT0JPccWTgAABwJlgnqk5Jsnlm+KMkpW7Y/IMme7r6uu/cnuTLJfZI8\nNMnHquptSc5N8pbllgwAAEeOw17yUVVPSvLsLTd/Isn++eMDSY7fsn0tyb6Fzzf3uUOSu3b3o6pq\nd5JfTPKQJdcNAABHhMMGdXefl+S8xduq6k2Zojnzn1dtOWz/wvbFfT6V5ML5fi+uqrvfiPXtWl9f\nu+G9OCKZ3Wozv9VldqvN/Fab+d0yLXPJx54kj5w/fkSSi7dsf1+SB1fVcVV1fKZrpS9PcunmcVV1\n3yR/u9SKAQDgCHKT/5WPJK9K8rqquiTJtUkemyRV9ZwkV3b3BVV1TpJLMgX7D3b3p6vq3CSvqqp3\nz/fz1PHlAwDAztq1sbGx02sAAICV5Re7AADAAEENAAADBDUAAAwQ1AAAMGCZf+XjZlNVt0nyhiTr\nmX4BzOO7+5Nb9nlKkjOTXJ/kRd19YVXdKsnZSb4+ybFJXtjdF4Vttez8FrbdI8l7ktyxuz+9bQsn\nydDP3/HzcWuZfv7O6u73bOvib6Gq6qgkr8z022evTfLk7v7wwvbTkrwg07xe292vuaFj2D5Lzu+Y\nJK9Ncpckx2X6Obxg2xd/C7fM7Ba23THJHyf51u7+0LYunCTLz6+qnpfktCTHJHl5d7/uUI+x02eo\nn5bkg929O8nrkzx/cWNVnZDkGUlOTPKwJC+uqmOTfE+So7v7QUlOz/RvXbP9lp1fqup2SV6a5Jpt\nXTGLlp3fc5L8dnefnOQJSV6xjWu+pTs9ybHdfWKS52b6GUqSzOF1dpJTM/0W2jPn/5CfnuS4gx3D\ntltmfo9Lsnf+OX14kpdv+6pJlpvd5rZXJ7l621fMops8v6o6Ock3zcecnORrDvcAOx3UJyXZPLN8\nUZJTtmx/QJI93X1dd+9PcmWmZxcPTfKxqnpbknOTvGWb1svnW2p+VbUr018wz0vyz9u1WL7Asj9/\nL0vyC/M+x8QMt9O/zKy735vk/gvb7pnpdwHs6+7rMv0yrd3zMW8/xDFsr2Xmd36SF877HJXpDBrb\nb5nZJclLMv3+jo9v41r5QsvM76FJLq+q30xyQZK3Hu4Btu2Sj6p6UpJnb7n5E5l+VXkyveR8/Jbt\na0n2LXy+uc8dkty1ux9VVbuT/GKmZxV8kdzM8/vhJBd292VVlSS7bvYF83luzvl19775Pk9I8stJ\nnnWzL5hDuV0+N7Mk+UxVHdXdn523Hezn7XDHsL1u8vy6++okqaq1THH9Q9u1WD7PTZ5dVT0h06sL\n75wvHfDfup2zzN+dd8h0qdW3ZTo7/dYk9zjUA2xbUHf3eUnOW7ytqt6U6T/amf+8asth+xe2L+7z\nqSQXzvd7cVXd/YuxZj7nZp7f45J8dI68E5K8I9PLKXyR3Izz+6f52HsneWOSH+juS74Ya+agts5k\nMYz35eA/b4c7hu11U+e3+fN25yS/keQV3f2r27FQvsAyP3vPTLJRVackuV+m3zL96O7+xHYsmM+z\nzPw+leSK7r4+yYeq6pqqusPW9xpt2tE3JSbZk+SRSd6f5BFJLt6y/X1Jfryqjkty60yn5S/PdDr+\nkUl+o6rum+Rvt23FLFpqft19t80dquojmV5WYfstM78/q6p7ZTpT9p3dffk2rpdpZqclOb+qHpjk\nsoVtVyS5W1XdPtP1mrszvdy8cZhj2F43eX5V9eVJ3pnk6d39ru1eMP/iJs+uu9+0uUNVvSvJ94np\nHbPM353XZHoF9uyq+ookX5Ipsg9qp4P6VZmesV2S6V2Xj02SqnpOputZLqiqc5JckunasR/s7k9X\n1blJXlWRTPDfAAAAtUlEQVRV757v56k7sHaWnN+W+9jYzgXzeZb9+fuJTP+6xznzJTtXdfdjduQr\nuOV5c5JTq2rP/PkTq+qMJLft7nOr6qxMr/gcleS87v54VX3BMdu/bGbLzO9nM738/MKq2ryW+hHd\n7Q3d2+smz26nFspBLTO/C6tqd1W9b7796d19yGbZtbGhZwAAYFk7/a98AADAShPUAAAwQFADAMAA\nQQ0AAAMENQAADBDUAAAwQFADAMCA/w//cezOTN/BegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1beb0780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7e4160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2250ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAwIKgBAGBAUAMAwMD/B7jPzW4PyUfFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29814c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQQ0AAAPMv7AFAAAAC0lEQVQENQAADPi/aFaSF6jbisgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222193c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIKCAYAAAADGdivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCGoAABjw/wEtMx/0SEet+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223e4860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(m_uncertainties, m_times, 'bo')\n",
    "plt.suptitle('Uncertainty vs. Time for mushrooms')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(m_uncertainties, m_speeds, 'bo')\n",
    "plt.suptitle('Uncertainty vs. speed for mushrooms')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(b_uncertainties, b_times, 'ro')\n",
    "plt.suptitle('Uncertainty vs. Time for birds')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(b_uncertainties, b_speeds, 'ro')\n",
    "plt.suptitle('Uncertainty vs. speeds for birds')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(m_oris, m_times, 'ro')\n",
    "plt.suptitle('Orientation vs. Time for mushrooms')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(m_sizes, m_times, 'ro')\n",
    "plt.suptitle('Sizes vs. Time for mushrooms')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(m_oris, m_speeds, 'ro')\n",
    "plt.suptitle('Orientations vs. Speeds for mushrooms')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(m_sizes, m_speeds, 'ro')\n",
    "plt.suptitle('Sizes vs. Speeds for mushrooms')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(b_oris, b_times, 'ro')\n",
    "plt.suptitle('Orientations vs. Time for birds')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(b_sizes, b_times, 'ro')\n",
    "plt.suptitle('Sizes vs. Time for birds')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(b_oris, b_speeds, 'ro')\n",
    "plt.suptitle('Orientations vs. Speeds for birds')\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.plot(b_sizes, b_speeds, 'ro')\n",
    "plt.suptitle('Sizes vs. Speeds for birds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print \"uncertainty vs time, mushroom\", pearsonr(m_uncertainties, m_times)\n",
    "print \"uncertainty vs. speed, mushroom\", pearsonr(m_uncertainties, m_speeds)\n",
    "print \"uncertainty vs time, bird\", pearsonr(b_uncertainties, b_times)\n",
    "print \"uncertainty vs. speed, bird\", pearsonr(b_uncertainties, b_speeds)\n",
    "\n",
    "print \"\", pearsonr(m_oris, m_times)\n",
    "print \"\", pearsonr(m_oris, m_speeds)\n",
    "print \"\", pearsonr(m_sizes, m_times)\n",
    "print \"\", pearsonr(m_sizes, m_speeds)\n",
    "\n",
    "print \"\", pearsonr(b_oris, b_times)\n",
    "print \"\", pearsonr(b_oris, b_speeds)\n",
    "print \"\", pearsonr(b_sizes, b_times)\n",
    "print \"\", pearsonr(b_sizes, b_speeds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0., -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0., -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0.,   0., -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0.,   0.,   0., -inf, -inf, -inf, -inf, -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0.,   0.,   0.,   0., -inf, -inf, -inf, -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0., -inf, -inf, -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0., -inf, -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., -inf, -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., -inf, -inf,\n",
       "        -inf, -inf, -inf, -inf]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAD2CAYAAAAH6zaYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2234b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mle)\n",
    "plt.jet()\n",
    "plt.colorbar()\n",
    "mle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}