Stroop Data Processing to numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from os import path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 17] File exists: 'Homework Sample Data/Homework 3'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['StroopData.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = path.join(\"Homework Sample Data\", \"Homework 3\")\n",
    "try:\n",
    "    os.makedirs(data_dir)\n",
    "except OSError as err:\n",
    "    print(err)\n",
    "\n",
    "data_filename = 'StroopData.csv'\n",
    "os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "Data Downloaded from http://opl.apa.org/Instructors/DownloadData.aspx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClassID</th>\n",
       "      <th>UserID</th>\n",
       "      <th>NumTrial</th>\n",
       "      <th>Condition</th>\n",
       "      <th>ColorORWord</th>\n",
       "      <th>WordDisplayed</th>\n",
       "      <th>ColorOfStimulus</th>\n",
       "      <th>ColorOfResponse</th>\n",
       "      <th>ReactionTime</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>1</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>YELLOW</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1389</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>2</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>2043</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>3</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>RED</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1389</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>4</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>YELLOW</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1092</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>5</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>1310</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClassID  UserID  NumTrial Condition ColorORWord WordDisplayed  \\\n",
       "0     6110  156772         1      ConW           C        YELLOW   \n",
       "1     6110  156772         2      ConW           C         GREEN   \n",
       "2     6110  156772         3      ConW           C           RED   \n",
       "3     6110  156772         4      ConW           C        YELLOW   \n",
       "4     6110  156772         5      ConW           C         GREEN   \n",
       "\n",
       "  ColorOfStimulus ColorOfResponse  ReactionTime Gender  Age  \n",
       "0               Y               Y          1389      M   26  \n",
       "1               G               G          2043      M   26  \n",
       "2               R               R          1389      M   26  \n",
       "3               Y               Y          1092      M   26  \n",
       "4               G               G          1310      M   26  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(path.join(data_dir, data_filename))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all trials, all users had to respond with the color of the stimulus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify whether the color and meaning of the word matched or not\n",
    "\n",
    "The Stroop Effect expects that trials where they don't match will have a longer reactoin time than the ones in which they do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Correct'] = df['ColorOfResponse'] == df['ColorOfStimulus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fa686de0d10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fa686d30990>], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Ohg4dqrlz52rt2rXq37+/XnvtNbN6BQAAF2nzSFySvvzyS82aNUs5OTkaO3as6urqFBcX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\niyLEAQCwqP8P5obptPRU3z4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa686e04490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='ReactionTime', by='Correct', bins=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter out any responses above 600 msecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fa686bac450>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fa686348310>], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AwGIodwAALIZyBwDAYih3AAAshnIHAMBiKHcAACyGcgcAwGIodwAALIZyBwDA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\nWhUrUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa686c255d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filt = df.loc[df['ReactionTime'] < 6000]\n",
    "df_filt.hist(column='ReactionTime', by='Condition', bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter out any trials where the color and word did not match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fa685d16690>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fa685bc2ed0>], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vwp3rYikpiRG/TztmsEsOu2Swo08++UQzZsxQcXGxxo8fr+bmZn+R5+XladmyZRo+fPgV\nzQMp/DMhkCvd9lcyazpz29F4XuzyOiDDlzmCFfBo+U2bNmn58uWSpOPHj8vj8SgnJ0e1tbWSpB07\ndigzM1MZGRnau3evWlpa1NzcrPr6eqWns48IMEljY6OmTZum2bNn66677pIk/fCHP9Tbb78tSdq9\ne7duuukm5gEQZQHfuU+aNEkLFizQ5MmT5XA4tHz5cvXq1Uvl5eVqbW1VWlqa8vPz5XA4VFJSoqKi\nIlmWpbKyMsXFxUXiMQCIkLVr1+r06dNas2aNVq9eLYfDoQULFujRRx+Vy+VSSkqKlixZooSEBOYB\nEEUByz02NlYrVqzosLy6urrDsoKCAhUUFIQmGQDbWbhwoRYuXNhh+bp16zosYx4A0cNJbAAAMAzl\nDgCAYSh3AAAMQ7kDAGAYyh0AAMNQ7gAAGIZyBwDAMJQ7AACGodwBADAM5Q4AgGEodwAADEO5AwBg\nGModAADDUO4AABiGcgcAwDCUOwAAhqHcAQAwDOUOAIBhKHcAAAxDuQMAYBjKHQAAw1DuAAAYhnIH\nAMAwlDsAAIah3AEAMAzlDgCAYSh3AAAMQ7kDAGAYyh0AAMNQ7gAAGIZyBwDAMJQ7AACGodwBADAM\n5Q4AgGFig1nps88+08SJE/Xss88qJiZG8+bNk9PpVHp6uioqKiRJGzdu1IYNG+RyuVRaWqpRo0aF\nMzeAKGhra9OCBQt07Ngxtba2qrS0VH/3d3/HTABsJmC5t7W1qaKiQj179pQkVVVVqaysTFlZWaqo\nqFBNTY2GDRum6upqbd68WWfPnlVhYaFycnLkcrnC/gAARM6rr76q3r17a8WKFTp9+rS+//3v68Yb\nb2QmADYT8GP5xx57TIWFherXr58sy9LBgweVlZUlScrNzdWuXbu0f/9+ZWZmKjY2Vm63W6mpqaqr\nqwt7eACRNW7cOM2cOVOS1N7erpiYGGYCYEOXLfdNmzapb9++ysnJkWVZkiSfz+e/PiEhQR6PR16v\nV4mJif7l8fHxam5uDlNkANFyzTXXKD4+Xh6PRzNnztTDDz/snw0SMwGwi8t+LL9p0yY5HA7t3LlT\ndXV1mjt3rpqamvzXe71eJSUlye12y+PxdFgejJSUxMArXYWmJnen/02fPu6w57qUaNynHTNI9shh\nhwx29Mknn2jGjBkqLi7W+PHj9fjjj/uv6wozIVwZrmTWBCtaM0nqutvDxAydcdlyf+GFF/x/nzJl\niiorK7VixQrt2bNHt956q3bs2KHs7GxlZGToySefVEtLi86dO6f6+nqlp6cHFeDEifD+NH/ypCfw\nSpf4N+HOdbGUlMSI36cdM9glh10y2E1jY6OmTZumn/3sZ8rOzpYkDR48uEvNhECudNtfyazpzG1H\n43mxy+uADF/mCFZQR8t/1dy5c7Vo0SK1trYqLS1N+fn5cjgcKikpUVFRkSzLUllZmeLi4jp70wBs\nbu3atTp9+rTWrFmj1atXy+FwaOHChVq2bBkzAbCRoMv9+eef9/+9urq6w/UFBQUqKCgITSoAtrRw\n4UItXLiww3JmAmAvnMQGAADDUO4AABiGcgcAwDCUOwAAhqHcAQAwDOUOAIBhOv177qazfD41NHwQ\n9PqpqTcoJiYmjIkAAOgcyv0inzef0BMbGhWf/EnAdc+c+lQrZ9+ptLTgzrwFAEAkUO6XEJ/cT+7e\n/aMdAwCAK8I+dwAADEO5AwBgGModAADDUO4AABiGcgcAwDCUOwAAhqHcAQAwDOUOAIBhKHcAAAxD\nuQMAYBjKHQAAw1DuAAAYhnIHAMAwlDsAAIbhK18B4Gu0t7fr6NH6oNZtaPggzGmA4FHuAPA1jh6t\n18zHX1V8cr+A63720bvqO2BwBFIBgVHuAHAZ8cn95O7dP+B6Z04dj0AaIDjscwcAwDCUOwAAhqHc\nAQAwDOUOAIBhKHcAAAxDuQMAYBjKHQAAw1DuAAAYJmC5+3w+LViwQIWFhZo8ebLef/99NTQ0qKio\nSMXFxaqsrPSvu3HjRk2cOFH33nuvtm/fHs7cAKJo3759KikpkSS9++67ys3N1ZQpUzRlyhT913/9\nlyTmARBNAc9Qt3XrVjkcDq1bt061tbX6xS9+IcuyVFZWpqysLFVUVKimpkbDhg1TdXW1Nm/erLNn\nz6qwsFA5OTlyuVyReBwAIuSZZ57RK6+8ooSEBEnSO++8ox/84AeaOnWqf53GxkbmARBFAd+55+Xl\naenSpZKkjz/+WMnJyTp48KCysrIkSbm5udq1a5f279+vzMxMxcbGyu12KzU1VXV1deFNDyDiBg4c\nqNWrV/svHzhwQNu3b1dxcbHKy8vl9XqZB0CUBbXP3el0av78+Vq2bJluv/12WZblvy4hIUEej0de\nr1eJiYn+5fHx8Wpubg59YgBRNXbsWMXExPgv33zzzZozZ45eeOEFXXfddVq1apU8Hg/zAIiioL84\npqqqSrNmzdKkSZN07tw5/3Kv16ukpCS53W55PJ4OywNJSUkMuM7VaGpyh/X2+/Rxh+wxhPu56CoZ\nJHvksEOGriAvL89f5Hl5eVq2bJmGDx9+RfNAssfzfj5DuOdHsEI5ZzrLTtuju2fojIDl/vLLL+v4\n8eOaPn26evToIafTqaFDh6q2tlbDhw/Xjh07lJ2drYyMDD355JNqaWnRuXPnVF9fr/T09IABTpwI\n70/zJ096Aq90lbcfiseQkpIY9ueiK2SwSw67ZOgKfvjDH6q8vFwZGRnavXu3brrppiueB1L4Z0Ig\nX9324Z4fwQrVnOksu7wOyPBljmAFLPf8/HzNmzdPxcXFamtrU3l5uW644QaVl5ertbVVaWlpys/P\nl8PhUElJiYqKivwH3MXFxV3VAwFgf5WVlaqsrJTL5VJKSoqWLFmihIQE5gEQRQHLvWfPnnrqqac6\nLK+uru6wrKCgQAUFBaFJBsC2+vfvr/Xr10uSbrzxRq1bt67DOswDIHo4iQ0AAIYJ+oA6AEDkWT6f\nGho+CHr91NQbLvhtBnRPlDsA2NjnzSf0xIZGxSd/EnDdM6c+1crZdyotLbiDF2Euyh0AbC4+uZ/c\nvftHOwa6EPa5AwBgGModAADDUO4AABiGcgcAwDCUOwAAhqHcAQAwDOUOAIBhKHcAAAxDuQMAYBjO\nUAcAhuA89DiPcgcAQ3AeepxHuQOAQTgPPST2uQMAYBzKHQAAw1DuAAAYhnIHAMAwlDsAAIah3AEA\nMAzlDgCAYSh3AAAMQ7kDAGAYyh0AAMNQ7gAAGIZyBwDAMJQ7AACGodwBADAM5Q4AgGEodwAADEO5\nAwBgGModAADDUO4AABjmsuXe1tamOXPmaPLkybr77ru1detWNTQ0qKioSMXFxaqsrPSvu3HjRk2c\nOFH33nuvtm/fHu7cAKJo3759KikpkSRmAmBDsZe78tVXX1Xv3r21YsUKnT59Wt///vd14403qqys\nTFlZWaqoqFBNTY2GDRum6upqbd68WWfPnlVhYaFycnLkcrki9TgARMgzzzyjV155RQkJCZKkqqoq\nZgJgM5d95z5u3DjNnDlTktTe3q6YmBgdPHhQWVlZkqTc3Fzt2rVL+/fvV2ZmpmJjY+V2u5Wamqq6\nurrwpwcQcQMHDtTq1av9lw8cOMBMAGzmsu/cr7nmGkmSx+PRzJkz9fDDD+uxxx7zX5+QkCCPxyOv\n16vExET/8vj4eDU3NwcVICUlMfBKV6GpyR3W2+/Txx2yxxDu56KrZJDskcMOGexo7NixOnbsmP+y\nZVn+v3eFmdCZDOGeH9EWzPyy0/bo7hk647LlLkmffPKJZsyYoeLiYo0fP16PP/64/zqv16ukpCS5\n3W55PJ4Oy4Nx4kRwL/grdfKkJ/BKV3n7oXgMKSmJYX8uukIGu+SwS4auwOn88gPArjATAvnqtg/3\n/Ii2QPPLLq8DMnyZI1iX/Vi+sbFR06ZN0+zZs3XXXXdJkgYPHqw9e/ZIknbs2KHMzExlZGRo7969\namlpUXNzs+rr65Wenn4VDwFAVzFkyBBmAmAzl33nvnbtWp0+fVpr1qzR6tWr5XA4tHDhQi1btkyt\nra1KS0tTfn6+HA6HSkpKVFRUJMuyVFZWpri4uEg9BgBRNHfuXC1atIiZANjIZct94cKFWrhwYYfl\n1dXVHZYVFBSooKAgdMkA2Fb//v21fv16SVJqaiozAbAZTmIDAIBhKHcAAAxDuQMAYBjKHQAAw1Du\nAAAYhnIHAMAwlDsAAIah3AEAMAzlDgCAYSh3AAAMQ7kDAGAYyh0AAMNQ7gAAGIZyBwDAMJQ7AACG\nuez3uQMAzGT5fGpo+OCy6zQ1uXXypEeSlJp6g2JiYiIRDSFAuQNAN/R58wk9saFR8cmfBFz3zKlP\ntXL2nUpLS49AMoQC5Q4A3VR8cj+5e/ePdgyEAfvcAQAwDOUOAIBhKHcAAAxDuQMAYBjKHQAAw1Du\nAAAYhl+FAwBcVjAnvPkqTngTfZT7VeA/PIDugBPedD2U+1XgPzyA7oIT3nQtlPtV4j88AMBuOKAO\nAADDUO4AABiGcgcAwDCUOwAAhqHcAQAwDOUOAIBhgvpVuH379unnP/+5qqur1dDQoHnz5snpdCo9\nPV0VFRWSpI0bN2rDhg1yuVwqLS3VqFGjwpkbgM1MmDBBbrdbkjRgwACVlpZeclYACL+A5f7MM8/o\nlVdeUUJCgiSpqqpKZWVlysrKUkVFhWpqajRs2DBVV1dr8+bNOnv2rAoLC5WTkyOXyxX2BwAg+lpa\nWiRJzz//vH/ZAw880GFW5OXlRSsi0K0E/Fh+4MCBWr16tf/ygQMHlJWVJUnKzc3Vrl27tH//fmVm\nZio2NlZut1upqamqq6sLX2oAtnLo0CGdOXNG06ZN09SpU7Vv3z4dPHjwglmxe/fuKKcEuo+A79zH\njh2rY8eO+S9bluX/e0JCgjwej7xerxITE/3L4+Pj1dzcHOKoAOyqZ8+emjZtmgoKCnT06FHdf//9\nHWYFMwGInE6fftbp/PLNvtfrVVJSktxutzweT4flwUhJSQy80kXa29t1+PDhoNY9depEp28/XPr0\ncV/28V7JcxFqdsgg2SOHHTJ0FampqRo4cKD/77169dLBgwf914d7JoTa+QxNTe4oJ+maAs26zrLT\n/4muotPlPmTIEO3Zs0e33nqrduzYoezsbGVkZOjJJ59US0uLzp07p/r6eqWnB/cFKSdOdP6n+cOH\n39PMx19VfHK/gOt+9tG76jtgcKfvIxxOnvR87eNNSUm8oucilOyQwS457JKhq9i0aZPq6upUUVGh\n48ePy+PxKCcnR7W1tRo+fLh/VgTDDs/7+QwnT3oCrI1Ludysu1h7e7uOHq3/2uv79HFfsB2i8e2a\ndpgH53MEq9PlPnfuXC1atEitra1KS0tTfn6+HA6HSkpKVFRUJMuyVFZWpri4uM7edKcE+4UtZ04d\nD2sOANKkSZO0YMECTZ48WQ6HQ8uXL1evXr1UXl5+wawALnb0aH3Qb9b4ds3gBVXu/fv31/r16yV9\n8ZFbdXV1h3UKCgpUUFAQ2nQAuoTY2FitWLGiw/JLzQrgYny7ZuhxEhsAAAxDuQMAYJhO73PHlbF8\nPjU0fPC11zc1Rf+gEQCAGSj3CPm8+YSe2NCo+ORPAq7LQSMAgKtBuUcQB40AACKBfe4AABiGd+4A\nAAQp0El3Lhat46codwC29NofX9fRhsDHqEjSN1N66/Z8vnEO4ddVTrpDuQOwpdf3vqcPWgYGte43\nPjys2zkBHiKkKxw/xT53AAAMQ7kDAGAYyh0AAMNQ7gAAGIZyBwDAMJQ7AACGodwBADAM5Q4AgGEo\ndwAADMMZ6mwo0He/X4zvfgeCF+jc4E1Nbp086ZGkTr0OATuh3G2I734Hwqcz5wb/7KN31XfA4Aik\nAkKLcreprnDuYqCrCvb1debU8QikAUKPcgcAhExndyuy6yM8KHcAQMh0ZreiZI9dHyYeh0G5AwBC\nqjO7Fe2w68PE4zAodwBAt2facRiUO4Auz+fz6fDh94Jat6t8rApcDcodQJfXfOoz4z5WBa4G5Q7A\nCKZ9rIqOOMFX8Ch3AECXwAm+gmercv/Dlq1qPNkUcL1PP/2rpKTwBwIA2Aon+AqOvcp99190Om5Q\nwPU8TWcXUzjXAAAKS0lEQVQikAYAgK7JVuUO+wh0UoeLded9WwBgN5Q7LqkzJ3Xo7vu2AMBuKHd8\nLfZtdQ6fdgD20Zkj60089wHlDoQIn3YA9tGZI+tNPPdBSMvdsiwtXrxYdXV1iouL0yOPPKLrrrsu\nlHcB2BqfdnyJeYBo687nPghpudfU1KilpUXr16/Xvn37VFVVpTVr1oTyLnCRznz01N7eLsmhmBjn\nJa/vit98BPtiHqC7i+ZJd0Ja7nv37tWIESMkSTfffLPeeeedUN78Bc6c+jSo9T5vPinJEfTtdmZ9\nO6x78uM6Lfv/Dqqnu0/AdU8dr1ePhF5Br9vr2sC/lih9sS1C+cPAV3/IiJYrydDQ8EHQ/y+DXa8r\ni+Q8kMIzE1g3vOvaJYcd5vNZz0n9eukPQ7arLqTl7vF4lJiY+OWNx8bK5/PJ6bz0O0VJSkn5cv0X\n/9/sUMYBIio7+zu6++67oh3DNq5kHkhfzoRVVT/uxL19/0oiAsa6/Kusk9xut7xer/9yMC9kAGZi\nHgDRE9JX2ne+8x398Y9/lCS99dZbGjQouI91AZiHeQBEj8OyLCtUN/bVo2MlqaqqStdff32obh5A\nF8I8AKInpOUOAACijx1gAAAYhnIHAMAwlDsAAIah3AEAMExUyt3n80XjbgHYEPMACL2IfSvchx9+\nqKqqKr3zzjv+M1UNGjRI8+fP59djgG6GeQCEV8R+FW7KlCn66U9/qptvvtm/7K233tLy5cu1fv36\nSESwjaamJq1Zs0a7d+/2n6IzKytLM2bMUN++fckQwQx2yWGHDJHEPPiSXba9HXKQIYQ5rAi55557\nOrU8XE6ePGktW7bMGj9+vDVy5Ejr9ttvtxYvXmw1NjZGLMOPfvQj6z/+4z+s5uZmy+fzWc3Nzdbv\nf/9767777iNDhDPYJYcdMkSSXeaBZUV/Jthl29shBxlClyNi+9y/9a1vaf78+frP//xP/elPf9If\n/vAHzZ8/X9/61rciFUGSNG/ePN1yyy1av369tm3bpnXr1ikrK0s//elPI5bB4/Hoe9/7ntxutxwO\nh9xut8aPH6+WlhYyRDiDXXLYIUMk2WUeSNGfCXbZ9nbIQYbQ5YjYPvfFixerpqZGe/fulcfjkdvt\n1ujRozV27NhIRZD05RN23vkn7MUXX4xYhr59+2rVqlXKzc31f7nGH//4R6WkpJAhwhnsksMOGSLJ\nLvNAiv5MsMu2t0MOMoQuR7c7/eyPf/xjDRo0qMMT9t5772nlypURyXDu3DmtW7dOe/fuldfrldvt\n1i233KLCwkL17NmTDBHMYJccdsjQXUV7Jthl29shBxlCl6PblbtdNlxra6sOHTokj8ejpKQkpaen\nKy4uLmL3Twb75bBDhu7IDjPBLtveDjnIEJoc3a7cpehvuO3bt+uJJ55Qamqq4uPj5fV6VV9fr7Ky\nMuXl5ZEhghnsksMOGbqzaM4Eu2x7O+QgQwhzhOtIP7vatm2bdfvtt1szZsyw5syZYz344IPWuHHj\nrC1btkQswz333GM1NzdfsOz06dPWhAkTyBDhDHbJYYcM3VW0Z4Jdtr0dcpAhdDkidkCdXfzqV7/S\nunXr5Ha7/cuam5s1derUiP1U1tra2uHjvh49esjhcETk/slgvxx2yNBdRXsm2GXb2yEHGUKXo9uV\nux023D333KO77rpLmZmZSkxMlMfj0d69e1VSUkKGCGewSw47ZOiuoj0T7LLt7ZCDDKHL0e32uW/c\nuFHV1dWXfMIKCgoilqOxsVH79+/3H8CTkZGhb3zjGxG7fzLYL4cdMnRHdpgJdtn2dshBhtDk6Hbv\n3O+++26NGTPmgifswQcfjPiGe+utt7Rr1y7/ATxnz55Vfn5+RD9BIIO9ctghQ3dkh5lgl21vhxxk\nCE2ObvfOXZJqamoueMIyMzMjuuEqKyvl8/mUm5urhIQEeb1e7dixQ21tbXrkkUfIEMEMdslhhwzd\nWTRngl22vR1ykCF0ObrdO/eve8Jef/31iG249957Ty+88MIFy2677Tbde++9Ebl/Mtgvhx0ydFfR\nngl22fZ2yEGG0OWIyve5R9N7772nyspK3XbbbcrOztZtt92myspKHT58OGIZfD6f3nzzzQuW7dmz\nRy6XK6oZamtro54h0s/D1+Www3MR6QzdVbRnAq+Dy2fojrMxFDm63cfyRUVFKisrU1ZWln/Znj17\n9PTTT6u6ujoiGRoaGlRVVaWDBw/Ksiw5nU4NHjxYP/nJTyL2xRkXZzh37pyGDh2qn/3sZxo4cGDE\nM/h8PjU1NWnEiBGaO3euUlNTI5Lh4hyWZemzzz5TTk6OFi5cGLXnwuPxKDs7W/PmzYtYhu4q2jPB\nDvPgUjm660ywwzy4OMeVzIRu9859+fLl+s1vfqPc3FyNGDFCI0eO1L/+679q6dKlEcvw/vvv69Ch\nQ3K5XJo7d662b9+uX/7ylxHdn9PS0iKHw6F/+Id/UFVVleLj43X06FHV19dHLEN7e7vmzJmjZ599\nVs8995wGDx6sOXPmKNI/b/7qV7/SL3/5Sz399NPq0aOHhgwZog8//FD/93//F7EMf/7znzV48GCt\nXr1aCQkJ+tu//VsdPnxYx44di1iG7iraM8EO80BiJpxnh3kghWAmhOZcOuiMgoIC69SpU9bJkyet\nkpISa9OmTZZlWVZxcXHEMhQVFVlvvPGGtXnzZiszM9NqbGy0mpubI/p92iNHjrS++93vWiUlJVZx\ncbF16623WiUlJVZJSUnEMliW5b+/++67zzpy5IhlWZb117/+1Zo8eXLEMkycONHyer3WlClTrPr6\nen8GzlBnPjvMA8tiJpxnh3lgWVc/E7rdAXUlJSVqbW295HXr16+PSAaXy6WkpCRJ0po1a3Tffffp\n2muvjeivWfh8Pg0fPlyS9MYbb6hv376SpNjYyP2XeOmll1RRUaHCwkLl5OSopKREzz//fMTu/2Ix\nMTH+j/6++c1vRvTdQmxsrOLj45WQkKDrrrvOn4Ffgwu/aM8EO8wDiZlwsWjOA+nqZ0K3K/dZs2ap\nvLxcq1evVkxMTFQy9O/fX1VVVZo5c6bcbrdWrVqladOm6fTp0xHLcP3112vhwoVaunSpqqqqJElr\n166N6O/29u3bV0899ZQee+wxvf322xG734t5PB5NmDBBZ86c0W9/+1vdeeedWr58ufr37x+xDGPG\njNEDDzygQYMGafr06RoxYoT+9Kc/KTs7O2IZuqtozwQ7zAOJmXCeHeaBdPUzIWbx4sWLwxvRXv7m\nb/5GZ86cUVtbm4YNG6akpCT/n0gZPXq0PvvsM6Wnp8vlcikxMVHf/e53derUKeXm5kYsgySlpaX5\nl3300UcqLS2N6E/qTqdTubm5amho0LvvvqsJEyZE7L7PO3+ax1tuuUUDBgxQ7969deLECT300ENy\nOiNzWEpmZqb69eunhoYGuVwuWZal/Px8FRUVReT+u7NozwQ7zIPzOSRmgh3mgXT1M6HbHS0PAIDp\nut3R8gAAmI5yBwDAMJQ7AACGodwBADDM/w/hfx6o07MtgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa686026850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filt.hist(column='ReactionTime', by='Condition', bins=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa685a92510>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X3CKGvt1ux+v1hr/vDfzeNo/HE27zer04nc5++5hMpnC/3n0zMjJIT0/ni1/8\nIqmpqaSmpjJ9+nQOHToUMfQbG93XP8o4k5PjiNvxnb/gJRQKcrK+jvz8kTQ3e/F4uwjSGd7HYU/D\n7em8xqv08Hp9GI0BUtMj73ujfUdPuAd+8y/s3r6Fuc9MAaD2ZCtFwx20e7toanLj8934Otd4fu+u\nh8YX3zS++BWtDzMR/6pNmzaNTZs2AbBnz54+YVxUVERdXR0ulwufz8fOnTuZMmUKU6dO7bdPcXEx\nO3bsAGDz5s3cc889HDt2jPnz5xMKheju7mbXrl1MmjQpKoOT26/N68Mc9NLZ2XlHLuLrNWzEaJyD\nsqmp3onFDIOdaZxradcqfhFJaBGP9GfPns3WrVspLy8HehbjrV+/no6ODubNm8eSJUtYsGABoVCI\nsrIycnNz++0DsHjxYpYuXUp3dzdFRUXMmTMHg8HA5z73OZ5++mlSUlL4/Oc/T1FR0QAOWQZSm8eH\nubsZgIKCwtgWcw0Gg4EJk+/nw82/5cTRA4zIGc6Ftk5OX2gnV7ODIpKgIoa+wWBg+fLlfbaNGjUq\n/O+zZs1i1qxZEfsAFBYWsnr16iu2f/nLX+bLX/7ydZYsdyp/IIinoxtL5wXgzrxc73IT776PDzf/\nlkP7P2T67C+y9+gFTp73kOvQ1SMikph0cx6JGnd7NwA+73kACgoKYllORAVFxaRb7Rzav51BDgvp\nqWZONXr7LDgVEUkkCn2JmjZvzy1421vPYTAYGDHizrtG/3Imk5lxk0ppa2ni7KnjjMix0dUdoNnd\nHevSREQGhEJfoqb3xjxtzWcYMmQoaWk3fqnd7Tbx7ks36hmR2zOtf6b5xq8WEBGJBwp9iZo2r49A\ndwded+sdvYjvcmMmTMWcYuHQ/u0MzbJiNBoU+iKSsBT6EjVtXh/d3kYARo68s8/n97KkpjF63N2c\nO12Hu+Ucw7KstHn9tLh1dz4RSTwKfYkal8eHz9Mb+oWxLeYGTLz7fgAO7t/O8Jyehz4danDFsiQR\nkQGh0JeoafN2hUM/Xqb3AcbfVYrBYODQ/u3kZV8M/Xo9gEdEEo9CX6KmZ3q/93K9wtgWcwPsjkzy\nR02g/tghjMF2bGkmak668F98joCISKJQ6EvUtHl9+NubcDgyyMwcFOtybsjEu+8nFApSU7WToYNS\n6fQFOXZaU/wiklgU+hI1re4OfJ4mRo4swGAwxLqcG9J7Xv/A3m0MGdRzqeH+YxdiWZKISNQp9CUq\nOn1+PK2NU3tYAAAgAElEQVTnCYWCcTW13ysreyjDRozmWM0+MlK7MRkNVB1rjnVZIiJRpdCXqHB5\nL1+5Hx+X633cpCkPEgj4OXF4H0XD7dSdc9Pm1aV7IpI4FPoSFX2v0S+MbTE3qXjKdAAOVW1nQn4G\nANXHNcUvIolDoS9R0ebx4fPEx4N2riY7N48hwws4fqSKwpwUAE3xi0hCUehLVLR5ffi8jZhMZoYN\ny4t1OTetuOQBAv5u6o/uZZAjlarjzQSDeuqeiCQGhb5ERauni25PI7lDh2M2m2Ndzk2bNOVBAN5/\n/z+ZPHowno5uXbonIglDoS9Rce78eYL+TvLz43Nqv1fusHwG5w5n164dTMzveerenqNNMa5KRCQ6\nFPoSFadPNgAwetSoGFdy68ZPKqWrqwvX2YOkmI3sVeiLSIJQ6EtUnD+bQKE/+T4Atm75D4oLBnGq\nyUtja0eMqxIRuXUKfYmK5qbTABQUxH/o5w7NZ9iw4WzbtoXiAgegKX4RSQwKfbllwVAIT+tZAEaO\nHBnjam6dwWDgoYdm0tHRga/5CICm+EUkISj05Za1d/rxuRtJs2Vis9ljXU5UzJw5C4AP399IwVAH\nNfWtdHT5Y1uUiMgtUujLLTvX1Iq/s5Ws7OGxLiVqRo0aTUHBKN5/fysTR1gJBENUHdeNekQkvin0\n5ZYdqT0OQM7QETGuJHoMBgOPPjobn6+L7uYaAPYc0RS/iMQ3hb7csuMnekI/b0R8X6P/cY8++ikA\n9uzYxCBHKvtqm/AHgjGuSkTk5in05ZY11NcD8XvP/avJzx/J+PET2LlzO5Py0/F2+jlwQlP8IhK/\nIoZ+KBRi2bJllJeX8/zzz9PQ0NCnfcOGDZSVlVFeXs7bb799zT719fXMnz+f5557juXLl1/xc776\n1a+ybt26aI1NbpOzZ3re3zGjR8e4kuh79NHHCQQCBJsPAPDhgfMxrkhE5OZFDP3Kykp8Ph9r165l\n0aJFVFRUhNv8fj8rVqxg1apVrF69mnXr1tHc3HzVPhUVFSxcuJA1a9YQDAaprKwMv9aPfvQj3G73\nAAxRBlrTuZMYTBYK8xNnIV+vRx55DIPBwN5d/8ngjDR2H2mk2x+IdVkiIjclYujv2rWLGTNmAFBS\nUkJVVVW4rba2loKCAux2OykpKZSWlrJ9+/Yr+lRXVwNQXV1NaWkpADNnzmTbtm0A/P73v8doNPLw\nww9Hd3Qy4AKBAK6Ws1hsOTjtqbEuJ+pyc4dw991T2LdvD8XDTXT6Auyr1RS/iMSniI9D83g8OByO\nSx3MZoLBIEaj8Yo2q9WK2+3G6/X22W4ymQgEAoRClx5RarPZcLvdHDlyhPXr1/O///f/5ic/+cl1\nF56T44i8UxyLl/E1NDQQDPhJdw4hb3jmFe0WSxC7rRmbPa3PdsfHvu9Ph9eC0ZhyXftGs68RH9nZ\nDpzOnvfgs599kr17d2PwHAby2He8mTkPX/1URry8dzdL44tvGl9yixj6drsdr9cb/r438HvbPB5P\nuM3r9eJ0OvvtYzKZwv16983IyOCdd97h/PnzPP/885w6dQqLxUJeXl7Eo/7GxsQ9FZCT44ib8e3e\n3TOLk5E1rN+aXS43Hm8XQTrD2xz2NNyeziv2/Tiv14fRGCA1PfK+0ezb7u2iqcmNz9fz+1pa+hAm\nk4mtm95jSOmf82H1GU6eaiXVYrqibzy9dzdD44tvGl/8itaHmYjT+9OmTWPTpk0A7Nmzh3HjxoXb\nioqKqKurw+Vy4fP52LlzJ1OmTGHq1Kn99ikuLmbHjh0AbN68mXvuuYdvfOMbrFu3jtWrV/P5z3+e\nr3zlK5rmjyMnLl6ul52beOfze2VmDqK09H5qag4xJtuPrzvI3lpdsy8i8Sfikf7s2bPZunUr5eXl\nQM9ivPXr19PR0cG8efNYsmQJCxYsIBQKUVZWRm5ubr99ABYvXszSpUvp7u6mqKiIOXPmDODQ5Hao\nPdYT+kOHx/8996/l8cef4MMP36elfidQwocHznHfxCGxLktE5IZEDH2DwXDF5XWjLnt86qxZs5g1\na1bEPgCFhYWsXr36qj/rL/7iLyKVI3eYuvoTgIG8vMS5G19/Zsz4BDabjW1b/sikJx5gX+0FXO0+\nMqyWWJcmInLddHMeuSWnTtaTYs0iy2mLdSkDKjU1jUcemU1j43ny0psIBEN8UHU21mWJiNwQhb7c\ntLa2VjzuNlLsOThtiXe53sfNmfNpAE4d/gCT0cB/7j/T54oUEZE7nUJfblp9fR0AFlsuTnviT3Pf\nddfd5OXls+39Tdw10sapRi8nzibmSmERSUwKfblpdXUnALDYc3DaEj/0DQYDc+bMpauri7SOowBs\n2XcmxlWJiFw/hb7ctN4j/RRbcoQ+wKc+9QQA+z/aRKbdwgcHzuHr1m15RSQ+KPTlptXXnwAg1ZGD\n3ZoS22KiKBQK4Xa7cLnarviy2WxMnlzCvr27GZsToKPLz9Z99eH2trY2necXkTtWxEv2RK6mrq4O\nc6qNrEGDMBkT5/NjR7uXTR81k5k1uN/2/PH3sX//Xo7u3wL2B/jd9lN0dHYBYDQ08OBd+WRkOG9n\nySIi1yVx/lLLbeXz+Thz5hQptlwyE/BBO2npVqw2R79fU+//JBZLGkeqtpGbmUpjm4+AIbWn3ZrY\nly6KSHxT6MtNOXWqgWAwSIotJyFD/1pSU9O5a9pDtDafxxY4BcCRhrYYVyUiEplCX25KXd2lRXyD\nHMkV+gD3PPgpAE4e3ILFbOToqTaCQZ3LF5E7m0JfbkrvIj6LPYfMJLhG/+NGFIxlaF4hNVU7GD4o\nRKcvwMlGT+SOIiIxpNCXm3L5NfrJNr0PPdfslz74KYLBAB1ndgNwWFP8InKHU+jLTamvP4HJnII5\nfVBSTu8D3F06kxRLKgd2bWRwhoXTTV48Hd2xLktE5KoU+nLDQqEQ9fV1OLOGYjAYk/JIHyAt3cbk\naQ/TcuEc9sBpAI6c1BS/iNy5FPpywxobz9PR0YHV2fM8+cwkPdKHSwv6TtVsIcVk5Mgptxb0icgd\nS6EvNyz8oB17DmaTEVta8t7jqXdB3+GqHQzPDNLeGeBgvSvWZYmI9EuhLzesdxEfqdlk2i0YDIaY\n1hNLly/oa7+4oG/bgcYYVyUi0j+Fvtyw3iN9v3lQUk/t97q7dCYWSxoHdv2RLLuJA3VttLi7Yl2W\niMgVFPpyw3qv0Tdbs5N2Ed/l0tJtTLlvFm0tTaR21BIMwZb9euSuiNx5FPpyw+rr6xicnYvRbGGQ\nQh+A+z/xaQBOVG3EYjbyn3tPE9TT9kTkDqPQlxvi9XpobDxPzpARAGQ6ku9ufP3JGTKCMROm0nC8\nhgKHi6a2Tg6eaIl1WSIifSj05YbU19cD4Bw8DEDT+5eZPqvnaP/C8a2ApvhF5M6j0Jcb0ns+39Z7\njb5CP2zMhKlk5w7jo+1byLIG+OhwI+2dukOfiNw5FPpyQy49aCcXIGlvwdsfo9HI9Jlz8Pu7sbTt\npdsfZPuh87EuS0QkTKEvN6T3kbqkDQbAadM5/ctNve8TWK1Wqnf9EUJ+tmqKX0TuIAp9uSH19Sew\n2+10BNJIs5hIT03eu/H1JzUtndmzn6Cl+QLO7uPUnnJx5oI31mWJiADXEfqhUIhly5ZRXl7O888/\nT0NDQ5/2DRs2UFZWRnl5OW+//fY1+9TX1zN//nyee+45li9fHn6Nn//855SVlfH000/z7rvvRnN8\nEkV+v5+TJxsYObKQNq9P5/Ov4sknP4vRaOT0oQ2EQiHerzob65JERIDrCP3Kykp8Ph9r165l0aJF\nVFRUhNv8fj8rVqxg1apVrF69mnXr1tHc3HzVPhUVFSxcuJA1a9YQDAaprKykpaWFtWvX8tZbb/H6\n66/z6quvDtxo5ZacOXMav9/PiPyReDq6dT7/KoYOHcYjjzzGmZPHCbQd5f2qs3oIj4jcESKG/q5d\nu5gxYwYAJSUlVFVVhdtqa2spKCjAbreTkpJCaWkp27dvv6JPdXU1ANXV1ZSWlgIwc+ZMtm3bxqBB\ng3jnnXcwGo00NjaSmqoguVP13nM/t/cafbvO51/Ns88+D0Dnya20uLs4UNcc44pERK4j9D0eDw6H\nI/y92WwmGAz222a1WnG73Xi93j7bTSYTgUCA0GV3KLPZbLjd7p4ijEZ+/vOfU15ezp/8yZ/c+qhk\nQPSu3M8cPLznn5rev6qxY8dx//0PcLb+EJ0t9Wzdryl+EYm9iKuw7HY7Xu+lhUjBYBCj0Rhu83g8\n4Tav14vT6ey3j8lkCvfr3TcjIyP8/Re+8AWeeeYZXnzxRbZv38599913zbpychzXbI93d+L4zp8/\nDcCQEQVQc44RwzIi1mmxBLHbmrHZ0/psd3zs+/50eC0YjSnXte+d0tfr8ZGd7cDpdPC1r/1XPvxw\nGx0nt7D7cCFWexq29JQbfs07zZ34uxlNGl98S/Tx3aqIoT9t2jQ2btzInDlz2LNnD+PGjQu3FRUV\nUVdXh8vlIi0tjZ07d/LCCy8A9NunuLiYHTt2cO+997J582amT5/O8ePH+eEPf8g//MM/YDKZsFgs\nfT4cXE1jo/tmx3zHy8lx3JHjq6k5jMlkoiNoBSCFyO+Dy+XG4+0iSGd4m8OehtvTeY1ePbxeH0Zj\ngNT0yPveKX2NQFOTG5/PSGHhBCZNuovq6n3YRp3l3S21fGJK3g2/5p3kTv3djBaNL74l8vii9WEm\nYujPnj2brVu3Ul5eDvQsxlu/fj0dHR3MmzePJUuWsGDBAkKhEGVlZeTm5vbbB2Dx4sUsXbqU7u5u\nioqKmDNnDgaDgYkTJ/LMM89gMBiYOXNm+Ly/3DlCoRAnThwnP38k7vYAoOn9SAwGA/PnP893vvMt\nWo9tYuv+sXEf+iIS3yKGvsFg6HN5HcCoUaPC/z5r1ixmzZoVsQ9AYWEhq1evvmL717/+db7+9a9f\nb80SA42N5/F6vdx77yhaPD3PitdCvsgeemgmI0cW0nByNweP1nG2eSJDs6yxLktEkpRuziPX5cSJ\n4wAUFo6mqa0To8HAoAwd6X9cKBTC7XbhcrXhcrXh8bh56ql5hIJBWmv/g407T4Tb+vsK6XG8IjKA\ndDs1uS6XQn8U1Qc7GeRIxXQday+STXu7h00fucnMGhzeljK4mMysXFobdrBh+yfJsBowGAxX9O1o\n9zL7/jFkZDhvZ8kikkT0V1uuS11dT+iPyC+k1d1FtvPGV7Yni7R0K1abI/xlz8jkkbnlEApw5uAG\n2jpNfdp7v9KttliXLiIJTqEv1+XEieOYTCaszhxCQHamQv9GTL5nJpmDh+E6uYP9B4/FuhwRSVIK\nfYkoFApx/Pgx8vJG0Nbec2OmbGd6jKuKLyaTiUfnPgOhIPs/+A2+7kCsSxKRJKTQl4guXLiAx+Om\nsHA0F9p6rl3X9P6Nm3zPw2RkDcN1chf7DtbGuhwRSUIKfYmorq5nOrqwcBRNbR2AQv9mGI0mPjmn\n52h/2x9/GetyRCQJKfQlostX7jddPNIfrNC/KVPvm4E1cxiNx3dw7JjO7YvI7aXQl4iOH7/8SP/i\nNfp6rO5NMRqNPPDo00CI371z5Y2qREQGkkJfIjpx4jhGo5H8/JFcaOskK0PX6N+Khx6eQXpWIWeP\n7+H4kepYlyMiSUR/ueWaeu65f4xhw/Iwmiy6Rj8KzCYTUz/xNAC//dXrugufiNw2Cn25ptbWFlwu\nF6NGjaLZ3UkInc+PhnunTcU+dDLnT9VSvef9WJcjIklCoS/X9PF77oOu0Y+GrIxURk17Egwm3vv1\navz+7liXJCJJQKEv13T5Ij5dox89BoOBSROKcBbcT8uFc+zc+vtYlyQiSUAP3JFr6j3Sz87OoeZ8\nKwDp5gAuV9t19Xe7XaBT1v0aPTyDwWMfxXPqIza+u46xxffEuiQRSXAKfbmmurrjGAwGDp/tpuZM\nT9DXnm7lbLPnuvo3N53DasvAancMZJlxKc1ipnDEENqKHuHCoX9n8x9+wez7l8S6LBFJYAp9uaYT\nJ46TmzuEDOcgOupbMBhg8KBMjMYrHw3bn3bv9X04SFZFeU7qCh+i69xH7N6+kaNHy5g2rTTWZYlI\ngtI5fbmq1tZWWlqaGTmyEABvRze2tJTrDnyJLC/bRnqahawJT0IoxE9/+g8Eg8FYlyUiCUqhL1d1\n4kTPIr6RI0cSCIZo7/JjS9fkUDQZjQaK8pxYBhWRP3Yahw4d4A9/eDfWZYlIglLoy1XV1fUs4hs5\nsoCOrp5HwdrTU2JZUkIan5+JARg0bi6pqan89Kc/xuPRaRERiT6FvlxV7wNh8vML8HYq9AeK3ZpC\nXq6d9pCdT80to7m5mX/+53+KdVkikoAU+nJVtbVHMBqNjBxZQHuXH1DoD5QJIzMBSB/xICNG5POr\nX73FwYMHYlyViCQahb70KxQKcezYUUaMyCctLQ3PxSN9m0J/QAwbbMWRbmbvcQ9f+8tvEgwGefXV\n7+L3+2NdmogkEIW+9OvcubN4PB6KisYC4G7vCR+nzRLLshKWwWBg9DArgWCINoby5JP/hWPHjvLm\nm2/EujQRSSAKfelXbe1RAIqKxgDgau8mNcVEmsUUy7ISWuEQKxazkY27T/HVP/06gwdn8//+32vU\n1Z2IdWkikiAU+tKv2tojABQVjaXbH8TTEcBpt2Aw6Br9gZJiNjK9OJtmVxf7T3hZuPBbdHd38/3v\nv6Jr90UkKiKGfigUYtmyZZSXl/P888/T0NDQp33Dhg2UlZVRXl7O22+/fc0+9fX1zJ8/n+eee47l\ny5eHX2PVqlU8/fTTPPPMM/zkJz+J5vjkJl1+pH++tedBO5l2Te0PtEemDMFsMvDbD+p46KFPMGvW\nI+zbt5d//ddfxLo0EUkAEUO/srISn8/H2rVrWbRoERUVFeE2v9/PihUrWLVqFatXr2bdunU0Nzdf\ntU9FRQULFy5kzZo1BINBKisraWhoYP369bz11lusW7eOLVu2cPjw4YEbsVyX2toj2O12hgwZytmW\nntB32lNjXFXiy7RbeGjyMM63dLD90Dn+6q++QUZGBj/96Y+pr6+LdXkiEucihv6uXbuYMWMGACUl\nJVRVVYXbamtrKSgowG63k5KSQmlpKdu3b7+iT3V1NQDV1dWUlvbcV3zmzJls27aN4cOH87Of/Sz8\nmn6/n9RUhUssdXZ2cvJkA6NHj8FgMHCuuQPQkf7t8sT0AowGA7/dVsegrMF84xtL6Orq4rvf/Rut\n5heRWxIx9D0eDw7HpSekmc3m8PnFj7dZrVbcbjder7fPdpPJRCAQIBS69IxVm82G2+3GZDKRmdlz\njfKrr75KcXExBQUFtz4yuWknThwjGAyGV+6Hj/Rt+jB2O+RmpnN/8RBONXrZc6SJWbMe5fHH53Lo\n0EHeeOOfY12eiMSxiDdSt9vteL3e8PfBYBCj0Rhuu/x2oV6vF6fT2W8fk8kU7te7b0ZGBgA+n48l\nS5bgcDh4+eWXr6vwnJzEflRrLMe3aVPPGowpU+4iJ8dBY1sXlhQjuYNtN7yQr8NrwWhMwWFP67P9\n49/fSN9b+bkD3xdstrSb6mvER3a2A6fTwRc/XcwHB87y2w/qeOyBUfzt377M/v17WL36dR5//FGm\nTJlyw68fLfp/L75pfMktYuhPmzaNjRs3MmfOHPbs2cO4cePCbUVFRdTV1eFyuUhLS2Pnzp288MIL\nAP32KS4uZseOHdx7771s3ryZ6dOnA/Dnf/7nPPDAA7z44ovXXXhjo/uGBhpPcnIcMR3fRx/tA2Do\n0AJOn2njXHMHgxwWPN6uG34tr9eH0RggNb0zvM1hT8Pt6bxGr6v3vZWfezv69vTvvKm+7d4umprc\n+HxG0owwvXgo26rP8m8bDjOzZDgvvfQ3/NVf/Tnf+MY3ee21NVit1puq71bE+ndzoGl88S2Rxxet\nDzMRQ3/27Nls3bqV8vJyoGcx3vr16+no6GDevHksWbKEBQsWEAqFKCsrIzc3t98+AIsXL2bp0qV0\nd3dTVFTEnDlzqKysZOfOnXR3d7Np0yYMBgOLFi2ipKQkKgOUG3f48CFMJhOjRxdxrqWdYAgyrHq6\n3u1WNquIjw438qtNtdw7IZcpU6ZRXv4c//Ivq/n7v3+Vb3/7ZV1CKSI3JOJfcoPB0OfyOoBRo0aF\n/33WrFnMmjUrYh+AwsJCVq9e3WfbY489xt69e2+kZhlAgUCAo0cPU1g4itTUVE43tQIK/VgY5Ehl\n7gMF/OvmY/zm/RM8/ckxvPjif2XPno/4/e/fpaRkGp/5zGdjXaaIxBHdnEf6aGioo6uri3HjJgBw\nuqlnbYZCPzYevzefwRlpvLejgXPN7aSkpLB8+fdwODL40Y9+EL6JkojI9VDoSx+HD9cA9BP6etBO\nLFhSTDz9yBgCwRA/f+8woVCIoUOH8Z3vLMPn62Lp0pfwej2RX0hEBIW+fMzhw4eAS6F/qslLmsVI\nmkW/KrFSOj6HSaOyqDrezH/uOwPAgw/O4Nlnn+PkyQa+//2KPpfDiohcjf6SSx+HD9dgMBgYM2Ys\n/kCQ8y0dDB2UrgVjMWQwGPjKExNITzWx9o9HaGrruVnSV7/6NSZPLmHDhvd46603Y1yliMQDhb6E\nBYNBjhypYeTIQtLT0znX0kEgGGJo1o1fcy7RlZWRxrOPjqPTF+D1fz9EMBTCbDazfPn3GDw4m//z\nf37Mhx9ui3WZInKHU+hL2OnTp/B6vYwf3/d8/pBB6bEsSy56aPJQSooGc7CuhY0fnQIgOzuHV175\n/sUPAN/R/flF5JoU+hJWU3MQgHHjxgNw/IwLgLxshf6dwGAw8KUnJmBPT2HdhqOcPN+zgK+4eBLf\n+tZ38Hg8LFmyCLc7MW9OIiK3TqEvYQcO9DxMaeLESQAcPdWG0WCgYIgtlmXJZTLtqSyYOxF/IMhP\nf11NV3cAgE996gmeffY5GhrqWb78O3owj4j0S6EvYQcOVGMymRg3bjzd/iAnzrjJz7WTmmKKdWlJ\nIRQK4Xa7cLnarvk1ekgKMybncLrJyxvvVoe3f/WrX2P69IfYvv0DfvjDFVrRLyJX0B1XBIDu7m6O\nHKmhqGgsqalpHD3Zhj8QZEyeM9alJY2Odi+bPmomM2twxH2zM8w4bWa2HWgiFAww2BZk9v1jePnl\nV/hv/+2/8tvf/prc3CF85StfvQ2Vi0i80JG+AHD06BF8Ph/FxT1T+0dO9dx+d8wIhf7tlJZuxWpz\nRPxyODKYNXUEZpOBXUfbCBp7HntstVp59dX/ybBhw3n99f/L+vX/FuMRicidRKEvQD/n80+2ATBW\noX/HctpTuXfiELr9QbbXtBAI9kznDx6czQ9+8L9wOp388Ievsm3blhhXKiJ3CoW+AHDwYDUAxcV3\nEQqFOHqqjayMVLIydI3+nWxMXgaFQx1ccHXz+x2nw9vz8wuoqPghJpOZpUuXsHv3rhhWKSJ3CoW+\nAD1H+na7nfz8kZxr6cDd3q3z+XHAYDAwfdIQrKkm3tt1lkN1LeG2u+66m+9+91WCwQAvvbSQqqp9\nMaxURO4ECn2hra2VkycbmDBhEkajkSMne87njx2RGePK5HpYUkzcP2EQBgP87LcHaO/sDrdNn/4g\nL7/8Cj6fj29+86/C92IQkeSk0Jfw+fxJk+4CoPZUz/l8HenHj8EZFj5VOoxmVxdr3jvcp23mzE/y\nne+8THt7O4sW/SW1tUdjVKWIxJpCX9i7dw8Ad989BYAjJ9tITTExIlc35Ykns+8ZxujhGXxQfY4P\nDpzt0/bYY4+zePH/wOVy8dd//efU1ByKUZUiEksKfWHv3t2YTCYmTZqMp6ObMxfaGT08A5NRvx7x\nxGQ08NXPFJOaYmL17w/T7Ors0z537pPh4P/v//1rOscvkoT0Vz3JdXZ2UlNzkLFjx2O1Wjnc0Hs+\nX1P78WhIlpXyR8fQ0eXnZ+sPEPzYXfk+/ek/YenS/4+Ojg4WLfpLreoXSTIK/SR34EAVfr+fkpKp\nAOyqaQRgclHku8LJnWlmyXCmjMnmUH0rf9jecEX7Y489zvLl36O7u5tvfvOvdR2/SBJR6Ce5ffsu\nnc/3B4LsOdpEVkYqo4dlxLgyuVkGg4Evz51Ahs3CrzbX0nDxaXyXmznzk1RU/BCDAb797W/qzn0i\nSUKhn+T27t0NwN13l3DgRAsdXX6mjcvBYDDEuDK5FRlWC195YgL+QIh/+k01votP47vc/fc/wI9+\ntBK73cHf/d33+Od//ic9pEckwSn0k5jf76e6ej+jRo3G6cxkV815AErH58a4MomGkjHZfHJqHqca\nvaz+Q02/gT5p0mRWrvwZeXkjWLXqZ6xY8bd0d3f382oikggU+kns4MEDdHZ2UlIylUAwyO4jTTjt\nFj1kJ4GUPzqGgqEOtu4/y6a9p/vdJz9/JCtX/owJE4p59931LFz4F7S2tvS7r4jEN4V+Etux4wMA\nSkvvo6a+FU9HN9PG5WDU1H7CSDGb+Prn7sKWZubN9w5z7LSr3/0GDcrif/2v/8OsWY+wd+9uvvrV\nL3HkyOF+9xWR+KXQT2I7dnyIyWRi6tRSdl5cta+p/cST7Uznzz47iUAgxE/+dT9NbR397peens7y\n5RW88MKfce7cWb7+9RfZuLHyNlcrIgMpYuiHQiGWLVtGeXk5zz//PA0NfS8B2rBhA2VlZZSXl/P2\n229fs099fT3z58/nueeeY/ny5X1ep7m5mccffxyfzxetsck1uN1uDh6sZuLESdhsdj463Ig9PYVx\n+Zraj0ehUAi324XL1dbv18jBZp58II8Wdxd/9/OPaDjT2Ke993y/wWDgS196gVde+TsMBiPLln2b\nH//4f+o8v0iCiBj6lZWV+Hw+1q5dy6JFi6ioqAi3+f1+VqxYwapVq1i9ejXr1q2jubn5qn0qKipY\nuHAha9asIRgMUlnZcxSxZcsWXnjhBS5cuDBAw5SP2717J8FgkNLS+6g63ozL62PauBzdhS9Odfz/\n7aqhuqQAACAASURBVN13fFRlvvjxz/RkZlImjTRSaQmEEopAABF1IYoFgUXuRXTlp8JVr9eCoKur\nrgh4F3V10Suoiwr+LuAKKhakSZUSAoEQILSQ3hOSaZl67h8DA4FAQIW05/16zWtmzjnPnOebk5nv\nKc95HouZLfsK2J5detmHWinRo7OeqnobC1YeYUNmEduzS1m/+wRGY+PT/sOHj+TDDz8hJiaOL79c\nzpNPPkZZWWkLRScIwu+l2V/4zMxMhg8fDkCfPn04dOiQd97JkyeJjY1Fr9ejUqkYMGAAe/bsuaRM\nTo5nrPacnBwGDBgAwIgRI9i5cycACoWCTz/9lIAAcZR5o2Rk7AZg4MDBrN2dD8Co1KiWrJLwG/n4\natHq/K74GJgcSXKcAaPVybacWpxo8NU2PcZCfHwiixd/yu23j+Hw4UNMm/YAmzZtusFRCYLwe2o2\n6ZtMJvz8/LzvlUolbre7yXlarRaj0YjZbG40XaFQ4HK5Gt0ypNPpMBqNAAwZMoSAgABxj/ANlJGx\nG51OhzaoM0cLzpAcZyCmk1/zBYU2TSaT0b97KEmxBupMdr7fmU9JddPX+MHznX7ppdd4/vkXsdls\nPP744/ztb/OwWCw3sNaCIPxelM0toNfrMZvN3vdutxv52VPAer0ek+l8b19ms5mAgIAmyygUCm+5\nc8v6+zfu9e1aOoQJDW3fCep6xpeXl0dJSTG33norW7M99+ZP+kOPy65TrXaj19Wg0/tc87qsZjVy\nuQq/i8pe/P5ayv6W9V7/sqDT+bT6Oo8aGENUmJ7N+4r45XAtAf7lPHRXJ7Q+qiaX/9OfHiAt7Sae\nf/551qxZzYEDmcyfP59+/fpdc11bO/Hb0ra19/h+q2aTfmpqKj///DNjxowhKyuLbt26eeclJiaS\nn59PfX09Pj4+7N27l2nTpgE0WSY5OZmMjAwGDhzI1q1bGTx4cKN1XcuRfmWl8aqXbWtCQ/2ua3zf\nfPM9AEm9BvHTgWI6h+mJNvhcdp319UZMZhtuGpqcfyVmsx253IXG93xZP70PRlPzn9VU2d+y3htR\n1lO+oU3UOTJYS/rgGH7OLOLHXUXsPFTBpFFdGZQU1uQOuMEQwcqVK5k/fwHLly9jypQpTJ78AA89\n9P/QaDTXXOfW6Hp/91qaiK/t+r12ZppN+rfffjs7duzg/vvvBzyN8b777jusVisTJ07khRde4OGH\nH0aSJCZMmEBYWFiTZQBmzZrFyy+/jMPhIDExkTFjxjRal+j69fo517obYPPmTcjlcqqlcCTJys0p\nIZc05LqQ0VgP4spLu2Tw8+G21FCsdhkb95ez6NscNuwt5J7h8fSMC7rkO6lWq5kx40mGDElj7tzX\n+OKLz9i69WdmznyRvn1TWygKQRCulkxqoxfS2+veHFyfvdX6+jrW7z6By+lg4bz/JDquB7pef0Kt\nkpM+IAy5/PI7XDVV5Wh1/gSFXPs9/FUVpcjlikZlr/ZIv6myv2W9N6Ks1VyN1epqU3U2m+rpG6/H\n5tbw7c4iDp7yDK8cH65jzMBIukX7eZN/SIgfVVWe/02r1cqyZZ/y7berkSSJu+4ax/TpTzRqz9PW\ntOcjRRDxtWU37EhfaD98tToOZ3numPDt1AuXW6JPlxD0flceUc9ivnSUNqH98NzuV0NgUDDdorSE\nBqg4UmAkr8zM/6w5TrC/muQYP8IC1fjpfTCZbd6ycb1vZ8GIkSxc+C5r1qxm27bNPPbY46Snj23U\nhkcQhNZBfCs7mCPZnlv17L4JhAb60FX0sy/Q+Ha/qE5B3DYwljuHxBIdqqO63s62Q9Vsya6lrM6N\nj1bvXdZXq6N79yQ+/vhzHn30cRoarLz55hxmzJjGkSOHWzosQRAuIo70OxCrxcTJ3IP4BkSi1gUx\nuGe4aEchXFZwgA+j+kdTXdfAwZPVFFaYWLszH3+tii6dA0mI8G/UVuTuu+9l6NA0/vnPxWzdupnp\n0//EzTePYsqUBwkPj2hyHX5+/uJ/UBBuIJH0O5AjB3fjdjnRRvShZ1wQBr/20eJauL6CA3y4JTWK\nMyYbx4vqyc2vZV9uJftzKwnyU5B1rILoToEY/FQoFXKGpj9MVPchbPzuCzZv3sjWbZvpN2gUQ0fd\ng05//lKS1WLm9pu64O8vzjYJwo0ikn4Hsj9jGyCjU8JAencJbunqCG1MoF7DqAGd6Z0QxOkyI6dK\n6qg800C1EU5WeLrQ9lEr0Poo0Woi6X/XbGoK9nN419dk7lxP9r5tDL3lHtJG3YPGx7eFoxGEjkkk\n/Q4i6/BJKktOoQ3pwm1DeqBUiOYcwq+jUSvoHhNI95hAioqLqbNIWF1qaow2LA1O6s12aurPNvaT\nJxI2+Ck0BRnUntjI5rUr2LnlB4aMHEvfgSNaNhBB6IBE0u8A6sx2Pvh0FQADhowiJEAcZQm/Dx+V\nHK2h8a2CkiThcLoxNzgxNzg8OwHRo6noPpiCQ5s4k7eNzT/+L9s2rOLkHXfx8EMPExwc0oJRCELH\nIZJ+O1dnsvH2iv1U5O1FoVQzfMTIlq6S0M7JZDLUKgVqlcLTbiT03JwI6gbGk5t/F5k7fqLi2BbW\nfPMvfvjuG8akj+WBKVOJjBSDPgnC9SSSfjtWXGXm7ysPUHB8H05rDX0G3oxGI47yhZYToNcwqGdn\nBiRN49Dx0ezZuYWSIz/z/Xer+eH7rxk8OI3x4ycyYMBN4j5/QbgORNJvp3Lyavjg60NYbU7UdfsB\n6D/0Dy1cK0HwkMtldIkO5N//8ig7j0zkiy+/oerUdnbu9DyiojozbtwE0tPHtuke/gShtRFJvw0p\nK6+gzmi+4jI2h5vN2TVknTIil8GQOAdLf8ime/dkwsI736CaCsLVUSnljE1LYFDyY3z+00j2ZWVj\nKtxFWekBFi58h48//h9uueU20tPH0rt3X+/R/4X9A/waon8AoaMSSb8NOV1cTYO86S5zJUmisMLE\nniMVWBqcBOrVDE2J4Je1SwBISU27kVUVhGsSZtDy7KS+/NIznOUb46ivT0dZl82Z/F38+ON3/Pjj\nd4SHRzBmzJ2MHn0Hfn5+rN99Al+t7prXJfoHEDoykfTbgTMmGxlHKiittiCTQe/EYFISgzGeqWL/\nrk0YgjvRrXsv3C1dUUG4AplMRlpKBCkJwfzvxuPsPqzDEHYTIzpZMBXvY+vWTXz66cd8+unH9OyZ\nQmRiP/oOvBm/gKCWrnq7Jc6otD8i6bdhDqebAyeqOJJfiyRBRLCWgUlhBOo9Pe1tWfclLpeTkWP+\niFyhEElfaBP8dWoeu7snQ3p2YulPuRwok9Ep5A/MfuMBrBWH+emn79m/P5OcnGw2rFlKTEISPfsO\nJbnPYPwDG3c65ZYkrDYnZqsTS4MDpUKOXHJgaXDif+VxpgQ8w2qLMyrti0j6bVRRpYndOeWYG5z4\naVUM7BFGVKjOu1ddU1nK/l2bCAmLoveAm3GZylq4xoLQWHNHkXGhKp6flMT3u0vYnl3B0g2nCdAF\nMPyep7jzvjp27trFiaP7KTh1hPyTh/nhq4+Jiu1GZHwf/MKTsCmCqa5vwOm6dPTwDfsriQv3Iy0l\ngpuSO6H3VV3PUNs0X60OrU40pmwvRNJvY5wuNxlHKjheVIdMBr0SguidGHxJD3sbf/hf3G4Xt9xx\nPwqFAlcL1VcQLufCIX2vJCxAyegBYRwvNnO63MIPe0oAkMtSCBswgMCeddQWHqSqIIvi/OMU5x8D\nQOkTQGBkElEJfYiO74m/vx6ny0290YJCoeBEsYnTZcdYvvE4t6RGcXda/PkuBQShnRJJvw2pMztZ\nd7CAWqMNg5+GYb0jmhw052TuAbIztxEZ04WefYe2QE0F4eqcG9K3OVodhAYbGOBwUVRpoqCkmjNm\nN0aLHZlMiz5mMMGJaehVDmw1J6kpPkTxqWyqTu2i6tQusuUKouO6kdAthcjOXZiYnoZS48/OnHJ+\n3l/Ehr1F7DxUxr+PSWJgt2AUoo8A7A4Xp0pNHCsygdyGXC5DIZdh8NMQHqRFLhfX6tsikfTbiIMn\nq1m1qwqHS6JrdAADk8Ka7D/f4bCzZuUiZDI5d0+aLjo4EdoVtUpBQmQA/koLcnnj7n/P6w7cgcvl\noij/GMdyMjl17CCFebkUnDoCwFefa0hJ6UufPn0Z168n5Q1RrNtXxuKvs/kp3I8/pfcgplPHO6Vt\naXCwN7eSnYfKOFFch8t96aURALVKTnSonh6xBkICfG5wLYXfQiT9NmBzVjHLfjqGTCaRlhJOYtTl\nG8b8/MNyaipLGTLyLiI7J97AWgpC66JQKIhNSCI2IQnwXE7IP5lDbk4mVcXH2Lt3N3v37gZALpcT\nG5eAjyGW7OJgXs7L5+5Rfbk7LR6VUtGSYVx3kiRxvKiOn/cXk5lbidPlafIbH+FH51BfbDYHAf56\n3GfHVCirsVBYbuJUST2nSupJiPSnX9cQdKJdRJsgkn4rJkkSq7ae4vud+eh9Vdya4k9A0OUTfu6h\nDLZvXE1QSDij7ph8A2sqCK2fr1ZHj5RBxCQkMSwlArfbzaFD2Rw6dJCcnGyOHDmM/dQJ7/L/2KHj\ns+Bohgzoy6DU3nTr1o2oqM7t5uyZ1eZkZ04ZP+8rprjK0+lXRLCWob3CGZwcTnCAD/X1dWzPLkWr\n03rLxYb7MSgpjNJqC5m5lZwqqSe/zEifLsEkxwchF7fotWoi6bdSDqebJT8cYdfhcsIMvjz9xz7k\n5eXTcJnla6vLWbXsPZQqNZMefl6MVy4IzQgMNDBs2AiGDfMM8etwOKiuLmbbtl0cOHiA/QcOcqY0\nlx/X5PLjmhUA+Ppq6dKlK127diMxsStxcfHExcXj59d27v8rrDDx8/5iduaUYbO7UMhlDEoK45Z+\nUXTrHHhV99XLZDIiQ3SEB2s5VVzPvmOV7DtWRUG5ibSUCAL06hsQifBriKTfCpksdt5ekUVu4RkS\no/z5z/G98dOqyctrenljXQ2fffAaVouJeyY/TkR0/I2tsCC0AyqVipSUFMLD45g48X4ADhwtYvGX\nmygpzMNlLkVpryAnJ5vs7AONygYFBXt3AOLi4omNjScqKpqQkNAWPzMgSRLVtWfIOlFLxrEajhd5\nbpMM1Ku4tW8nBieH4K/1nJq/+BZKo7Eemr6sD4BcJqNLdADRYTr2HK7gdJmR7345TUpiMPFh4nR/\naySSfitTVmPhf5bsobDcRP/uoTwyNhm16vLXFE3GM3z2wavUVJYy4g8T6D/kthtYW0Foe67UP4Ba\n7aa+3uh9Hx/px+tP3MW27ErWZ5ZisbmI8ZXRK9JFsMpISXEBp0/nkZ+fx759e9m3b+9Fn6chIiKS\nqKgoIiOjL3iOJjw8ArX6+h4Rl1abWbc7j19yKnGc7a8g3KAhIUJLeJAPchkcPFl12fI1VeVodf5o\n9Vdu1OijVjKibySxZUb2HCkn63gVJ4uURIb40V90ztOqiKTfiuw/XsnH3x3GanPxh4Gd+eOoLle8\nPlZWfJr//9FcztRUMmTkXdx657/dwNoKQtt0pf4B9LoaTGbbJdNVCritXyjZp2oornHwy0k5GpWB\ngd27MOX2IOI66bDZGigqKqSgIJ/CwgLKykopLS2htLSU/PxLT9PJZDJCQkIJCwsjLKwToaGe5wtf\nBwUFo1BcW0NCh9PNvmOVbN5fTG7hGQA0KjkpsQb6du+ETLr6vjktZtM1rTs23I+IYC37j1eRW3CG\n9785RvzuYm7u04mU+EAU13Cbn+jC9/oQSb8VcDjdfLsjj+935qNWynnm31LpFRN42eXdbjf7dm5g\n7eol2O0NjLrz37j5DxPEF0QQrtLl+gfQ6X1wX6bljBZIirYQEwQ1DRpOlJjZfqiS7Ycq0WoURIX4\nEG7QEBLck8TQXlx474zVYqKsJJ+YIAW1tTWUlBRTXFxEeXkZR48eISfnUJPrVCgUBAeHEBQUTFBQ\nEAZD0NnnYAyGIAwGA0FBQQQEBlFplNhzpII9RyowWR0AJMUauKmHgXqTFb2fP346NUbT5VoG/T7U\nKgU3JXci2NdBbrGVvDIzeWWn8FHJCQlQE+yvJkCnQqWUo1bKUMplyOQy5DLP5QKZDBqsFtGF73Ui\nkn4LO5Jfy9KfcimrsRAS4MMT96XQv1cklZXGJpcvPH2M9d9+zukTOWh8tNw/7XmS+wy5wbUWhI7L\nT68jNi6MPt0kSqst5JXWU1Bu5HixmePFZhRyGSGBPoQE+BAS4EugXoMhWI+vVs+wlIhLEpnL5aK2\ntoaKigoqKsqprPQ8X/j65MnjHD3quHLFZHIUaj1qHz1BhkA6R4aCIoisSg3VZgn/wBCCggwg16DV\n+eGr9dRJqVJflwOGQJ2CIT0CUPgEcrSglvwyI0VVDRRVNb/TIZPB9qMHCTNoCQnwpXOYnoRIf2LD\n/dBc4XKn0Lxmk74kSbz66qvk5uaiVqt544036Nz5/LjsmzZt4oMPPkCpVDJ+/HgmTpx42TIFBQXM\nnj0buVxO165deeWVVwBYuXIlK1asQKVSMX36dEaOHHndAm4NJEniZEk96zIK2Xu0ApkMbu0fzbjh\nCWh9Lt0kDoeDX37ZztJlSzmW6zki6JEyiLETH71kgBFBEG4MuVxGVKiOqFAdTlcnKmqtlFSZKaky\nU15jpbzGCtQCniSm81FwMM9ISKCOAL0aX7USjVqBj0qBRq1Ao+5EWFwEITES3dwSLreE++yzxeag\nsuoMZZVVlJSWU1peibH+DC67CZfNBE4zGlkDbocZq/kMxadLKD59tXEo0Pj4ovHRnn34ep8lSUKj\n0RJgCEbjo8Xn7DyVRoNKpUGt8UGl0qBSa1CrPc9KlbpR48UAvZqbkjsxKCkMk9VB5Rkr9WYHdqcL\nu8ON0+XGLYHklnBLnpgdTicSMk4U13G8qI6dOZ7PUshlxIX70T3GQI/YQLpGBaJRi52Aa9Fs0t+w\nYQN2u53ly5dz4MAB5s2bxwcffACA0+lk/vz5rFq1Co1Gw+TJk7n11lvJzMxsssy8efN45plnGDBg\nAK+88gobNmygb9++LF26lNWrV9PQ0MDkyZNJS0tDpWpfLT8lSaKkyszh/Fp+yS4jv9xzJB8X7scD\no7sTH3H+lh+LxUJOTjYnThxj3769ZGTswWQ6u3yXntySfj/xXXu1SByCIFxKqZATGaIjMsQzGp3D\n6aa6roGq+gbqTDbqzXbqTHaOFRk5VtT0WbyrF0pYXBR9gnyJ7aSje2d/okMad4vrdDoxmYwYjUYq\nKsrIPlmF2+3C7WygtrYWq8XkeZhN2Bos2GwWbA1W6morsDVYkaQrNNm/CkqVGqVShUql9uwknN0h\nUKk1qM7OUyhVKFWq86+VSlRKNQqlCrfbRfeYYPRx/tgcMmrNTqrqHJTX2TmcYyfnsAKZXIlSqaJz\nuD+JUQbiIwLpmxyJ3C2hVCq9j5a+e6K1aTbpZ2ZmMnz4cAD69OnDoUPnrz2dPHmS2NhY9Ho9AAMG\nDGDPnj1kZWU1KpOT49lNy8nJYcCAAQCMGDGCHTt2IJfL6d+/P0qlEr1eT1xcHLm5ufTq1fqSmtFY\nj8PhQJIk78Ptlqg32zA3OHA6ndidbqwNDiw2B0aznZr6BqrrrBRXmTCZrbhddnA5iA3zoXu0Hn+f\nStavyaCmpprq6iqKioooKSlq9KULD4/gjjvGEhXXE0N0zxb8CwiCcDVUSjnhwVrCg893amM21dMz\nRotb5ku9xYHN4TnStTncntdONw6nG4Vc5u3nXi7zPLtdNorKTfj7a9FqFKiU5xNZQXk9BeWXG61Q\nRU29mui4HgSFhOGn92n2mr4kSdjtDdgbrJSVFOCw29H4+Hh2Ds4+7HYbjgsfDht2m+f53DSrxYzT\nacdqNVF/phqHw3ZNOxMbr3K5PGDrlRaQyVAoFCgUSu+zUul5rVQqUavO7yBcOP+mm4YyZcqDV13f\ntqLZpG8ymfDzO9/gRalU4na7kcvll8zTarUYjUbMZnOj6QqFApfL1WiD63Q6TCbTJcue+4zW5ttv\nV7Ngwbzf7fNKgJ1NTPf392fgwIHExCTQpUtXkpN7ERsbh0wmY+feA9TWVfyq9SlcNqx2868q22A1\nI5crsZivfbs0VVaOHUsTLaSv93pvSFmLhYYGV9uq8zWUvXjbtYU6X0vZ5v43f8t6a6sr2FhiIyDQ\ncNll5IDkAhc0GhmztqYKP50/Abpfdwa0wWrBYjZe9XcPQKFU4avVo9Mrmx0JsSk1VeXI5efLSpKE\ny+nAecHD5XTiOvvsdJ19djqwmk0kxwejVKqw2+3Y7XYcDjsOh+PsawcOhx273YHd4aC23kq92Yql\nwYHJ3IDN7sDtdoHkQnK7kSQ3uF24JDcuyYXN5kaSnCDZUClArfScHXE6nbhcLpxOJ1qtFuiASV+v\n12M2n08W5xL+uXkm0/lbOsxmMwEBAU2WUSgUjU6zmM1m/P39m/wMf//me7cKDb2xg2FMmzaVadOm\n3tB1Xuzu9GEtun5BEAShbWv2YkdqaipbtmwBICsri27dunnnJSYmkp+fT319PXa7nb1799K3b1/6\n9evXZJnk5GQyMjIA2Lp1K/379yclJcXbBsBoNHLq1Cm6du36uwcqCIIgCB2dTGrmIsuFLfEB5s2b\nR05ODlarlYkTJ7J582YWLlyIJElMmDCByZMnN1kmPj6e06dP8/LLL+NwOEhMTGTOnDnIZDK+/PJL\nVqxYgSRJzJgxg9tuE73KCYIgCMLvrdmkLwiCIAhC+yDuZRAEQRCEDkIkfUEQBEHoIETSFwRBEIQO\nQiR9QRAEQeggWu2AO+vXr2ft2rW89dZbABw4cIA33ngDpVLJ0KFDeeKJJwBYuHAhW7ZsQalU8sIL\nL9C7d29qa2t57rnnsNlshIWFMW/ePDQaTUuG06zmxjhoKw4cOMCCBQtYunTpNY21YLPZmDlzJtXV\n1ej1eubPn4/BcPlOTG40p9PJiy++SHFxMQ6Hg+nTp9OlS5d2EZ/b7eall14iLy8PuVzOa6+9hlqt\nbhexXai6uprx48ezZMkSFApFu4rvvvvu8/aMGh0dzfTp09tVfIsXL2bTpk04nU6mTJlCampqu4lv\n9erVrFq1CplMhs1m4+jRo3zxxRfMnTv3+sQntUJz5syR0tPTpWeeecY77Z577pEKCwslSZKkRx55\nRDpy5IiUk5MjPfjgg5IkSVJJSYk0fvx4SZIk6fXXX5dWr14tSZIkLVq0SFqyZMkNrf+vsW7dOmn2\n7NmSJElSVlaWNGPGjBau0bX76KOPpLFjx0qTJk2SJEmSpk+fLmVkZEiSJEl/+ctfpPXr10uVlZXS\n2LFjJYfDIRmNRmns2LGS3W6XlixZIv3jH/+QJEmSvv/+e2nOnDktFkdTvvrqK2nu3LmSJElSXV2d\nNHLkyHYT3/r166UXX3xRkiRJ2r17tzRjxox2E9s5DodDevzxx6XRo0dLp06dalfx2Ww2ady4cY2m\ntaf4du/eLU2fPl2SJEkym83Su+++267iu9Brr70mrVy58rrG1ypP76empvLqq69635tMJhwOB9HR\n0QAMGzaMHTt2kJmZSVpaGgARERG43W5qamrYt2+ft+//ESNGsGvXrhsew7W60hgHbUVsbCzvv/++\n9/3FYy388ssvHDx48JKxFo4ePUpmZiYjRozwLrtzZ1OdFLec9PR0nnrqKcAzFKpCoeDw4cPtIr7b\nbruN119/HYCSkhICAgLaTWznvPnmm0yePJmwsDAkSWpX8R09ehSLxcK0adN46KGHOHDgQLuKb/v2\n7XTr1o3/+I//YMaMGYwaNapdxXdOdnY2J06cYOLEidf1t7NFT+//61//4rPPPms0bd68eaSnp7Nn\nzx7vNLPZ7D11BZ5++wsLC/Hx8SEwMLDR9Iv789fpdK2yL/+LXWmMg7bi9ttvp7i42PteusqxFs5N\nP7eNzy3bmvj6+gKe7fTUU0/x9NNP8+abb3rnt/X45HI5L7zwAuvXr+fdd99lx44d3nltPbZVq1YR\nHBxMWloaH374IeC5pHFOW4/Px8eHadOmMXHiRE6fPs0jjzzSrr57tbW1lJSUsGjRIgoLC5kxY0a7\n2n7nLF68mCeffPKS6b93fC2a9CdMmMCECROaXe7iQM718a9SqRr18W8ymfD39/cuHxQUdMkfqrW6\n0hgHbdW1jLVwYfytdZuVlpbyxBNPMGXKFO68807+9re/eee1h/jmzZvHc889x4QJE7DZzg/K0tZj\nO3e9dMeOHeTm5jJr1ixqa2u989t6fHFxccTGxnpfBwYGcvjwYe/8th5fYGAgiYmJKJVK4uPj0Wg0\nlJeXe+e39fgAjEYjp0+fZuDAgcD1/e1sE1lFr9ejVqspLCxEkiS2b99O//796devH9u3b/eMVV9S\ngiRJBAYGkpqaytatnsEWt27d6j1N0ppdaYyDtupaxlq4cLyGLVu2tLptVlVVxbRp05g5cybjxo0D\nICkpqV3E9/XXX7No0SIANBoNcrmcXr16ec+2teXYAJYtW8bSpUtZunQpPXr04L//+78ZPnx4u9h2\n4NmpmT9/PgDl5eWYTCbS0tLazfbr378/27ZtAzzxWa1WBg8e3G7iA8jIyGDw4MHe99fzt6XVdsO7\nZ88eVqxY4W29f/DgQd544w3cbjdpaWn813/9F+Bpvb9161YkSeKFF14gNTWV6upqZs2ahcViwWAw\n8NZbb+Hj49OS4TRLusx4BW1NcXExzz77LMuXL7+msRYaGhqYNWsWlZWVqNVq3nrrLYKDr304z+vl\njTfe4McffyQhIQFJkpDJZPz5z39mzpw5bT6+hoYGZs+eTVVVFU6nk8cee4yEhAReeumlNh/bxaZO\nncprr72GTCZrN/+bF95ZIpPJmDlzJoGBge1q+y1YsIBdu3YhSRLPPvssUVFR7Sq+Tz75BJVKxdSp\nnpFcr+dvZ6tN+oIgCIIg/L7axOl9QRAEQRB+O5H0BUEQBKGDEElfEARBEDoIkfQFQRAEoYMQa5fe\n/gAABM9JREFUSV8QBEEQOgiR9AVBEAShg2i1o+wJgtBYcXExo0ePpmvXrkiShNvtxmw2c++99zbZ\nfeevcfDgQdatW8dzzz3Hpk2byMnJuebPPnPmDA899BAymYzKykoAQkJCkMlkLFmyhFmzZjFnzhxC\nQ0N/lzoLgnD1xH36gtBGFBcXM3XqVDZu3OidVlFRwejRo/nqq69ISEj4zetYvXo1e/bsYd68eb/5\ns8DTeRbgHQpbEISWJY70BaENq6ioADzjUyxevJi1a9fidrsZNmwYzz33HADvvPMOu3btoq6uDoPB\nwMKFCwkODmbNmjV8+OGH3m53Z86cyXvvvYfFYmHRokWEhYV5dwCysrKYO3cudrsdg8HAX//6Vzp3\n7swDDzxA7969yczMpLa2lpdeesk7WuTljBo1imXLlrF79242b95MeXk5FRUVTJ06lZKSEnbt2oXB\nYOCjjz5CrVbz9ddf8/nnnyNJEj179uQvf/kLarX6uv9tBaE9Etf0BaENKS8vZ9y4caSnpzN48GDe\ne+89Fi5cyLFjx8jJyeGrr75i9erVlJWVsWbNGgoKCsjLy2PFihWsXbuWmJgY1qxZQ3l5OfPnz2fJ\nkiWsWbMGt9vN/v37eeqppxg1ahSPPfaYd50Oh4NnnnmGV155ha+//ppJkybx9NNPe+c7nU6WL1/O\n7Nmzeeedd5qNQSaTeV9nZ2fzz3/+k2XLljF//nxGjhzJt99+6x1j48SJE3z55ZcsX76c1atXExQU\nxCeffPL7/lEFoQMRR/qC0IZ06tSJ1atXAzB//nxyc3MZPHgwCxYsIDs7m/vuuw9JkrDZbERFRXHX\nXXcxa9YsVq5cSV5eHllZWcTExJCVlUX//v0JCwsD8A4TfO6zL3T69GkCAwPp2bMnAGPGjOGVV17x\njvh17si+a9eu1NfXNxvDhVcUU1NT0Wq1aLVaZDKZd9CRqKgo6uvr2b17N/n5+UyaNAlJknA6nSQn\nJ//aP58gdHgi6QtCGzVz5kzuvfdePvnkEyRJYurUqTz00EMA1NfXo1QqycnJ4ZlnnuHhhx9mzJgx\nyOVyJElCqVQ2Sr41NTWXXY/b7ebipj/nGhKCZ2Q+8BzBX00ToQuP9FUqVaN5Fw8n7XK5SE9P589/\n/jMAFosFl8vV7DoEQWiaOL0vCG3IhUlVoVDw/PPP8+GHH5KUlMQ333yDxWLB6XTyxBNPsG7dOjIy\nMrjpppuYNGkSCQkJ7NixA7fbTUpKCgcPHqS6uhqAuXPnsmnTJhQKBU6ns9E64+Pjqaur49ChQwD8\n8MMPREZG4u/vf8X6XU0MzU0fNGgQGzZsoKamxjsS5WeffdbsOgRBaJo40heENuTCo2TwnFrv168f\ne/fuZfTo0fzxj3/E7XYzYsQI7r33XsrLy3nyyScZN24cBoOBm2++maKiIsLCwnjxxRd5+OGHcbvd\n9OvXj/Hjx5Ofn8/777/P22+/7b0bQK1W8/bbb/PXv/4Vq9VKYGAgf//735usz8XvryaGK03v0aMH\njz/+OA8++CCSJJGUlMSjjz56VX8rQRAuJW7ZEwRBEIQOQpzeFwRBEIQOQiR9QRAEQeggRNIXBEEQ\nhA5CJH1BEARB6CBE0hcEQRCEDkIkfUEQBEHoIETSFwRBEIQO4v8AsWLe8ZQYb3QAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa685d54ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "sns.distplot(df_filt['ReactionTime'], fit=stats.exponnorm, bins=21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert DataFrames to 1D arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arrays = {}\n",
    "arrays['trialnum'] = df_filt['NumTrial'].values\n",
    "arrays['subject'] = df_filt['UserID'].values\n",
    "arrays['stimword'] = np.array([x[0] for x in df_filt['WordDisplayed']])\n",
    "arrays['stimcolor'] = df_filt['ColorOfStimulus'].values.astype(bytes)\n",
    "arrays['respcolor'] = df_filt['ColorOfResponse'].values.astype(bytes)\n",
    "arrays['reaction_time'] = df_filt['ReactionTime'].values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save 1D arrays as txt files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for varname in arrays:\n",
    "    np.savetxt(path.join(data_dir, varname + '.txt'), \n",
    "               X=arrays[varname],\n",
    "              fmt=\"%s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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