Thesis Paper.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Workflow to get the trial and fixation tables\n",
    "1. Run ProcessMonkeyGameSingleSubject in Matlab on the TrialSubject Folder from the Experiment\n",
    "2. Run the Replay app on the file in the ProcessedData Folder\n",
    "3. Run the Replay script in Matlab on the Replayer generated file\n",
    "4. Save FixationDetailsForAnalysis and TrialData as structs\n",
    "5. Load the two .mat files into Python using the custom loadmat script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes/References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://stackoverflow.com/questions/15512560/access-mat-file-containing-matlab-classes-in-python\n",
    "\n",
    "http://stackoverflow.com/questions/17071871/select-rows-from-a-dataframe-based-on-values-in-a-column-in-pandas\n",
    "\n",
    "http://nikgrozev.com/2015/07/01/reshaping-in-pandas-pivot-pivot-table-stack-and-unstack-explained-with-pictures/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import pandas as pd\n",
    "import tables\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Functions for loading matlab structs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# http://stackoverflow.com/questions/7008608/scipy-io-loadmat-nested-structures-i-e-dictionaries\n",
    "def loadmat(filename):\n",
    "    '''\n",
    "    this function should be called instead of direct spio.loadmat\n",
    "    as it cures the problem of not properly recovering python dictionaries\n",
    "    from mat files. It calls the function check keys to cure all entries\n",
    "    which are still mat-objects\n",
    "    '''\n",
    "    data = sio.loadmat(filename, struct_as_record=False, squeeze_me=True)\n",
    "    return _check_keys(data)\n",
    "\n",
    "def _check_keys(dict):\n",
    "    '''\n",
    "    checks if entries in dictionary are mat-objects. If yes\n",
    "    todict is called to change them to nested dictionaries\n",
    "    '''\n",
    "    for key in dict:\n",
    "        if isinstance(dict[key], sio.matlab.mio5_params.mat_struct):\n",
    "            dict[key] = _todict(dict[key])\n",
    "    return dict        \n",
    "\n",
    "def _todict(matobj):\n",
    "    '''\n",
    "    A recursive function which constructs from matobjects nested dictionaries\n",
    "    '''\n",
    "    dict = {}\n",
    "    for strg in matobj._fieldnames:\n",
    "        elem = matobj.__dict__[strg]\n",
    "        if isinstance(elem, sio.matlab.mio5_params.mat_struct):\n",
    "            dict[strg] = _todict(elem)\n",
    "        else:\n",
    "            dict[strg] = elem\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Jupyter Notebook settings and Misc Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pd.set_option(\"display.max_rows\", 101)\n",
    "pd.set_option(\"display.max_columns\", 50)\n",
    "directory = './ProcessedStructsForPython/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "Mostly going through the matlab files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "em_list = []\n",
    "em_copy = []\n",
    "for _folder in os.listdir(directory): # go through all the files in the directory\n",
    "    if os.path.isdir(directory+_folder): # if the file is a directory do this\n",
    "        em_sub_list = []\n",
    "        for _file in os.listdir(directory+_folder): # go through all the files in the current subject directory\n",
    "            if _file.find('EM') + 1:\n",
    "                e = loadmat(directory+_folder+'/'+_file)\n",
    "                mdata = e['e']\n",
    "                e = []\n",
    "                for i in mdata['data']:\n",
    "                    s = pd.Series(i)\n",
    "                    e.append(s.to_frame())\n",
    "                    \n",
    "                em_df = pd.concat(e, axis=1)\n",
    "                em_df.columns = mdata['varnames']\n",
    "                \n",
    "                em_sub_list.append(em_df)\n",
    "                \n",
    "                em_copy.append(em_df)\n",
    "                \n",
    "        em = pd.concat(em_sub_list).reset_index()\n",
    "        em_list.append(em)\n",
    "for i in range(0, len(em_list)):  \n",
    "    em_list[i]['block'] = em_list[i]['block'].apply(int)\n",
    "#         if len(em_sub_list) > 0:\n",
    "#             em = pd.concat(em_sub_list).reset_index()\n",
    "#         else:\n",
    "#             em = pd.DataFrame([])\n",
    "#         em_list.append(em)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "trials_list = []\n",
    "for _folder in os.listdir(directory): # go through all the files in the directory\n",
    "    if os.path.isdir(directory+_folder): # if the file is a directory do this\n",
    "        for _file in os.listdir(directory+_folder): # go through all the files in the current subject directory\n",
    "             if _file.find('Trials') + 1:\n",
    "                t = loadmat(directory+_folder+'/'+_file)\n",
    "                mdata = t['t']\n",
    "                l = []\n",
    "                for i in mdata['data']:\n",
    "                    s = pd.Series(i)\n",
    "                    l.append(s.to_frame())\n",
    "                    \n",
    "                trials_df = pd.concat(l, axis=1)\n",
    "                trials_df.columns = mdata['varnames']\n",
    "               \n",
    "                trials_list.append(trials_df)\n",
    "for j in range(len(trials_list)):\n",
    "    trials_list[j]['Acc EM'] = em_list[j]['pmode1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chensteven/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chensteven/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chensteven/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chensteven/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chensteven/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/chensteven/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "fixations_list = []\n",
    "matrix_list = []\n",
    "for _folder in os.listdir(directory): # go through all the files in the directory\n",
    "    if os.path.isdir(directory+_folder): # if the file is a directory do this\n",
    "        for _file in os.listdir(directory+_folder): # go through all the files in the current subject directory\n",
    "            if _file.find('Fixation') + 1:\n",
    "                f = loadmat(directory+_folder+'/'+_file)\n",
    "                mdata = f['f']\n",
    "                l = []\n",
    "                for i in mdata['data']:\n",
    "                    s = pd.Series(i)\n",
    "                    l.append(s.to_frame())\n",
    "                fixations_df = pd.concat(l, axis=1)\n",
    "                fixations_df.columns = mdata['varnames']\n",
    "                fixations_list.append(fixations_df)\n",
    "                \n",
    "for i in range(len(fixations_list)):\n",
    "    fixations_list[i] = fixations_list[i][pd.notnull(fixations_list[i]['Block'])]\n",
    "    fixations_list[i]['Block'] = fixations_list[i]['Block'].astype(int)\n",
    "    fixations_list[i]['TrialInBlock'] = fixations_list[i]['TrialInBlock'].astype(int)\n",
    "    fixations_list[i]['TrialInExperiment'] = fixations_list[i]['TrialInExperiment'].astype(int)\n",
    "    fixations_list[i]['FixCategory'] = fixations_list[i]['FixCategory'].astype(int)\n",
    "    fixations_list[i]['PrePickup'] = fixations_list[i]['PrePickup'].astype(int)\n",
    "    fixations_list[i]['Epoch'] = fixations_list[i]['Epoch'].astype(int)\n",
    "    \n",
    "    fixations_list[i] = fixations_list[i].loc[fixations_list[i]['PrePickup'] == 1] \n",
    "    fixations_list[i] = fixations_list[i].loc[fixations_list[i]['Epoch'] == 2] # 2 is from fixation door opens to reward door\n",
    "\n",
    "    df = fixations_list[i]\n",
    "    fixation_count = df['Duration'].groupby([df['Block'], df['TrialInBlock'], df['FixCategory']]).count().unstack()\n",
    "    fixation_count.reset_index(inplace=True)\n",
    "    fixation_dur = df['Duration'].groupby([df['Block'], df['TrialInBlock'], df['FixCategory']]).mean().unstack()\n",
    "    fixation_dur.reset_index(inplace=True)\n",
    "    \n",
    "    columns = []\n",
    "    for i in fixation_count.columns:\n",
    "        if type(i) == int:\n",
    "            if len(str(i)) == 1:\n",
    "                name = 'FixCat ' + '0' + str(i) + ' Count'\n",
    "                if '01' in name:\n",
    "                    name = 'Target Count'\n",
    "                if '02' in name:\n",
    "                    name = 'Distractor Count'\n",
    "                columns.append(name)\n",
    "            else:\n",
    "                columns.append('FixCat ' + str(i) + ' Count')\n",
    "        elif i == 'TrialInBlock':\n",
    "            columns.append(\"Block's Trial\")\n",
    "        else:\n",
    "            columns.append(i)\n",
    "    fixation_count.columns = columns\n",
    "    \n",
    "    columns = []\n",
    "    for i in fixation_dur.columns:\n",
    "        if type(i) == int:\n",
    "            if len(str(i)) == 1:\n",
    "                name = 'FixCat ' + '0' + str(i) + ' Duration'\n",
    "                if '01' in name:\n",
    "                    name = 'Target Duration'\n",
    "                if '02' in name:\n",
    "                    name = 'Distractor Duration'\n",
    "                columns.append(name)\n",
    "            else:\n",
    "                columns.append('FixCat ' + str(i) + ' Duration')\n",
    "        elif i == 'TrialInBlock':\n",
    "            columns.append(\"Block's Trial\")\n",
    "        else:\n",
    "            columns.append(i)\n",
    "    fixation_dur.columns = columns\n",
    "    \n",
    "    matrix = fixation_dur.add(fixation_count[list(np.arange(2,  len(fixation_count.columns)))], axis=1, fill_value=0)\n",
    "    matrix = matrix[['Block', \"Block's Trial\", 'Target Count', 'Target Duration', 'Distractor Count', 'Distractor Duration']]\n",
    "    matrix_list.append(matrix)\n",
    "    \n",
    "data = pd.DataFrame({\"Block\": 5.0, \"Block's Trial\": 12.0, 'Target Count': np.nan, 'Target Duration': np.nan, 'Distractor Count': np.nan, 'Distractor Duration': np.nan}, index=[100])\n",
    "matrix_list[1] = pd.concat([matrix_list[1].ix[:99], data, matrix_list[1].ix[100:]])\n",
    "data = pd.DataFrame({\"Block\": 7.0, \"Block's Trial\": 80.0, 'Target Count': np.nan, 'Target Duration': np.nan, 'Distractor Count': np.nan, 'Distractor Duration': np.nan}, index=[194])\n",
    "matrix_list[1] = pd.concat([matrix_list[1].ix[:193], data, matrix_list[1].ix[194:]])\n",
    "trials_list[6] = trials_list[6].ix[:323]\n",
    "matrix_list[6] = matrix_list[6].ix[:323]\n",
    "data = pd.DataFrame({\"Block\": 9.0, \"Block's Trial\": 18.0, 'Target Count': np.nan, 'Target Duration': np.nan, 'Distractor Count': np.nan, 'Distractor Duration': np.nan}, index=[156])\n",
    "matrix_list[7] = pd.concat([matrix_list[7].ix[:155], data, matrix_list[7].ix[156:]])\n",
    "data = pd.DataFrame({\"Block\": 11.0, \"Block's Trial\": 49.0, 'Target Count': np.nan, 'Target Duration': np.nan, 'Distractor Count': np.nan, 'Distractor Duration': np.nan}, index=[205])\n",
    "matrix_list[7] = pd.concat([matrix_list[7].ix[:204], data, matrix_list[7].ix[205:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subject1\n",
      "\n",
      "('block 1', 22)\n",
      "('Block 1', 22)\n",
      "('block 3', 25)\n",
      "('Block 3', 25)\n",
      "('block 5', 24)\n",
      "('Block 5', 24)\n",
      "('block 7', 29)\n",
      "('Block 7', 29)\n",
      "('block 9', 20)\n",
      "('Block 9', 20)\n",
      "('block 11', 17)\n",
      "('Block 11', 17)\n",
      "('block 13', 17)\n",
      "('Block 13', 17)\n",
      "('block 15', 30)\n",
      "('Block 15', 30)\n",
      "('block 17', 35)\n",
      "('Block 17', 35)\n",
      "Subject2\n",
      "\n",
      "('block 1', 34)\n",
      "('Block 1', 34)\n",
      "('block 3', 56)\n",
      "('Block 3', 56)\n",
      "('block 5', 26)\n",
      "('Block 5', 26)\n",
      "('block 7', 85)\n",
      "('Block 7', 85)\n",
      "Subject3\n",
      "\n",
      "('block 1', 17)\n",
      "('Block 1', 17)\n",
      "('block 3', 17)\n",
      "('Block 3', 17)\n",
      "('block 5', 24)\n",
      "('Block 5', 24)\n",
      "('block 7', 27)\n",
      "('Block 7', 27)\n",
      "('block 9', 29)\n",
      "('Block 9', 29)\n",
      "('block 11', 47)\n",
      "('Block 11', 47)\n",
      "('block 13', 17)\n",
      "('Block 13', 17)\n",
      "('block 15', 17)\n",
      "('Block 15', 17)\n",
      "('block 17', 39)\n",
      "('Block 17', 39)\n",
      "Subject4\n",
      "\n",
      "('block 1', 27)\n",
      "('Block 1', 27)\n",
      "('block 3', 37)\n",
      "('Block 3', 37)\n",
      "('block 5', 30)\n",
      "('Block 5', 30)\n",
      "('block 7', 17)\n",
      "('Block 7', 17)\n",
      "('block 9', 17)\n",
      "('Block 9', 17)\n",
      "('block 11', 22)\n",
      "('Block 11', 22)\n",
      "('block 13', 28)\n",
      "('Block 13', 28)\n",
      "Subject5\n",
      "\n",
      "('block 1', 28)\n",
      "('Block 1', 28)\n",
      "('block 3', 37)\n",
      "('Block 3', 37)\n",
      "('block 5', 28)\n",
      "('Block 5', 28)\n",
      "('block 7', 41)\n",
      "('Block 7', 41)\n",
      "('block 9', 36)\n",
      "('Block 9', 36)\n",
      "('block 11', 28)\n",
      "('Block 11', 28)\n",
      "Subject6\n",
      "\n",
      "('block 1', 71)\n",
      "('Block 1', 71)\n",
      "('block 3', 27)\n",
      "('Block 3', 27)\n",
      "('block 5', 17)\n",
      "('Block 5', 17)\n",
      "('block 7', 30)\n",
      "('Block 7', 30)\n",
      "('block 9', 19)\n",
      "('Block 9', 19)\n",
      "('block 11', 33)\n",
      "('Block 11', 33)\n",
      "('block 13', 17)\n",
      "('Block 13', 17)\n",
      "('block 15', 22)\n",
      "('Block 15', 22)\n",
      "('block 17', 5)\n",
      "('Block 17', 5)\n",
      "Subject7\n",
      "\n",
      "('block 1', 33)\n",
      "('Block 1', 33)\n",
      "('block 3', 28)\n",
      "('Block 3', 28)\n",
      "('block 5', 30)\n",
      "('Block 5', 30)\n",
      "('block 7', 28)\n",
      "('Block 7', 28)\n",
      "('block 9', 28)\n",
      "('Block 9', 28)\n",
      "('block 11', 28)\n",
      "('Block 11', 28)\n",
      "('block 13', 28)\n",
      "('Block 13', 28)\n",
      "('block 15', 28)\n",
      "('Block 15', 28)\n",
      "('block 17', 35)\n",
      "('Block 17', 35)\n",
      "('block 19', 30)\n",
      "('Block 19', 30)\n",
      "('block 21', 28)\n",
      "('Block 21', 28)\n",
      "Subject8\n",
      "\n",
      "('block 1', 36)\n",
      "('Block 1', 36)\n",
      "('block 3', 19)\n",
      "('Block 3', 19)\n",
      "('block 5', 35)\n",
      "('Block 5', 35)\n",
      "('block 7', 49)\n",
      "('Block 7', 49)\n",
      "('block 9', 19)\n",
      "('Block 9', 19)\n",
      "('block 11', 56)\n",
      "('Block 11', 56)\n"
     ]
    }
   ],
   "source": [
    "# trials_list[7].loc[trials_list[7]['Block'] == 11]\n",
    "# matrix_list[7].loc[matrix_list[7]['Block'] == 11]\n",
    "for k in range(0, len(trials_list)):\n",
    "    print \"Subject\" + str(k+1) + \"\\n\"\n",
    "    l = max(em_list[k]['block'].unique())\n",
    "    for i in range(1, int(l)+2, 2):\n",
    "        print (\"block \"+ str(i), len(em_list[k].loc[em_list[k]['block'] == i]))\n",
    "        print (\"Block \" + str(i), len(trials_list[k].loc[trials_list[k]['Block'] == i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for k in range(0, len(trials_list)):\n",
    "    print 'Subject' + str(k+1)\n",
    "    print len(em_list[k])\n",
    "    print len(trials_list[k])\n",
    "    print len(matrix_list[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sa_df = []\n",
    "csa_df = []\n",
    "\n",
    "for i in range(len(trials_list)):\n",
    "    smoothAcc = trials_list[i].groupby('TrialInBlock').mean().head(18)['SmoothAcc']\n",
    "    smoothAcc = smoothAcc.reset_index().astype(float)\n",
    "    del smoothAcc['TrialInBlock']\n",
    "    sa_df.append(smoothAcc)\n",
    "    \n",
    "for i in range(len(trials_list)):\n",
    "    trials_list[i]['CombinedSmoothAcc'] = trials_list[i][['SmoothAccContext1', 'SmoothAccContext2']].sum(axis=1)\n",
    "    smoothAcc = trials_list[i].groupby('TrialInBlock').mean().head(18)['CombinedSmoothAcc']\n",
    "    smoothAcc = smoothAcc.reset_index().astype(float)\n",
    "    del smoothAcc['TrialInBlock']\n",
    "    csa_df.append(smoothAcc)\n",
    "    \n",
    "for i in range(len(em_copy)):\n",
    "    em_copy[i] = em_copy[i].head(18).reset_index()\n",
    "\n",
    "em = pd.concat(em_copy)\n",
    "em_average = []\n",
    "em_std = []\n",
    "em_var = []\n",
    "\n",
    "for i in em['subject'].unique():\n",
    "    em_average.append(em[em['subject'] == i].groupby('index').mean())\n",
    "    em_std.append(em[em['subject'] == i].groupby('index').std()/ math.sqrt(len(matrix_list)))\n",
    "\n",
    "for i in range(len(em_average)):\n",
    "    em_average[i]['smooth'] = sa_df[i]['SmoothAcc']\n",
    "\n",
    "# overall\n",
    "sa_overall = pd.concat(sa_df)\n",
    "csa_overall = pd.concat(csa_df)\n",
    "em_overall = pd.concat(em_average)\n",
    "\n",
    "csa_final = csa_overall.reset_index().groupby('index').mean()\n",
    "csa_error = csa_overall.reset_index().groupby('index').std() / math.sqrt(len(matrix_list))\n",
    "sa_final = sa_overall.reset_index().groupby('index').mean()\n",
    "sa_error = sa_overall.reset_index().groupby('index').std() / math.sqrt(len(matrix_list))\n",
    "em_final = em_overall.reset_index().groupby('index').mean()\n",
    "em_error = em_overall.reset_index().groupby('index').std() / math.sqrt(len(matrix_list))\n",
    "\n",
    "fm_list = []\n",
    "\n",
    "for i in range(0, len(matrix_list)):\n",
    "    matrix_list[i]['Acc Smoothed'] = trials_list[i]['SmoothAcc']\n",
    "    matrix_list[i]['Acc Smoothed Combined'] = trials_list[i]['CombinedSmoothAcc']\n",
    "    if len(em_list[i]) > 0:\n",
    "        matrix_list[i]['Acc EM'] = em_list[i]['pmode1']\n",
    "    matrix_list[i]['Beta 0 (Intercept)'] = [0.5] * len(matrix_list[i])\n",
    "    matrix = matrix_list[i]\n",
    "    matrix = matrix.reindex_axis(sorted(matrix.columns), axis=1)\n",
    "    matrix = matrix[list(matrix.columns[:10])]\n",
    "    #matrix = matrix.dropna()\n",
    "    fm_list.append(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EM Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RFTL2ZBMRERERFQB7somIiIiIdIhFdjFiLD1M+sBsxDEXacxGHHORxmzEMRdp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\niIiIiJOpyRYRERERcTI12SIiIiIiTvb/4BXaA7Csj0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a9914d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    trials = range(1, len(csa_final)+1)\n",
    "    #errorfill(trials, csa_final['CombinedSmoothAcc'], yerr=csa_error['CombinedSmoothAcc'], ax=ax)\n",
    "    ax.errorbar(trials, csa_final['CombinedSmoothAcc'], yerr=csa_error['CombinedSmoothAcc'], capthick=2)\n",
    "    #ax.errorbar(trials, csa_final['CombinedSmoothAcc'], yerr=csa_var['CombinedSmoothAcc'], capthick=2)\n",
    "    #ax.plot(trials, csa_final['CombinedSmoothAcc'])\n",
    "    ax.set_xlim(0, len(csa_final)+1)\n",
    "    ax.set_xticks(np.arange(0, len(csa_final)+1, 2))\n",
    "    ax.set_ylim(0.4, 0.9)\n",
    "    ax.set_yticks(np.arange(0.4, 0.95, 0.05))\n",
    "    ax.axhline(y=0.5, color='gray', lw='1')\n",
    "    ax.set_title('Combined SmoothAcc Learning Curve (All Subjects)', fontsize=16)\n",
    "    ax.set_xlabel('Trials', fontsize=14)\n",
    "    ax.set_ylabel('Probability of of a Correct Response', fontsize=14)\n",
    "    # plt.tight_layout()\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(handles, labels, loc=4)\n",
    "    ax.grid(linestyle='dotted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QERGBgIAAAEBwcDBCQ0ORlZWFIUOGYOjQoRg+fDhMTU1Rp04dDBgwAEqlEtOm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rlyJSorKx3LfX19iIyMBABs3boVXV1dQkUTJefxvHDhAtauXQvg+0UrmUwmVDRRch5Ps9mMixcv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\nkTpUP1ZTleYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1177a8890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    trials = range(1, len(sa_final)+1)\n",
    "    ax.errorbar(trials, sa_final['SmoothAcc'], yerr=sa_error['SmoothAcc'], capthick=2)\n",
    "    #ax.plot(trials, sa_final['SmoothAcc'])\n",
    "    ax.set_xlim(0, len(sa_final)+1)\n",
    "    ax.set_xticks(np.arange(0, len(sa_final)+1, 2))\n",
    "    ax.set_ylim(0.4, 0.9)\n",
    "    ax.set_yticks(np.arange(0.4, 0.95, 0.05))\n",
    "    ax.axhline(y=0.5, color='gray', lw='1')\n",
    "    ax.set_title('Smooth Acc Learning Curve (All Subjects)', fontsize=16)\n",
    "    ax.set_xlabel('Trials', fontsize=14)\n",
    "    ax.set_ylabel('Proportion of Correct Response', fontsize=14)\n",
    "    plt.tight_layout()\n",
    "    #handles, labels = ax.get_legend_handles_labels()\n",
    "    #ax.legend(handles, labels, loc=4)\n",
    "    ax.grid(linestyle='dotted')\n",
    "    plt.show()\n",
    "    fig.savefig('Smooth Acc Learning Curve (All Subjects).png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ctt+0aw+WfPpfhAUH2Yx+xcfFIiYyvMlyugMWWUREREQerNRggKGsHPqyMhgM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\nEYM0yBIRERERETFIgywRERERERGD/j9dmaKj6M/Y4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1216c2610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    trials = range(1, len(em_final)+1)\n",
    "    ax.plot(trials, em_final['p95'], ls='dashed', color=sns.color_palette('GnBu_d')[0])\n",
    "    ax.plot(trials, em_final['pmode1'])\n",
    "    #ax.errorbar(trials, em_final['pmode1'], yerr=em_error['pmode1'], barsabove=True, capthick=2)\n",
    "    ax.plot(trials, em_final['p05'], ls='dashed', color=sns.color_palette('GnBu_d')[1])\n",
    "    ax.set_xlim(0, len(em_final)+1)\n",
    "    ax.set_xticks(np.arange(0, len(em_final)+1, 1))\n",
    "    ax.set_ylim(0.3, 1.0)\n",
    "    ax.set_yticks(np.arange(0.3, 1.05, 0.05))\n",
    "    ax.axhline(y=0.5, color='gray', lw='1')\n",
    "    ax.set_title('EM Learning Curve (All Subjects)', fontsize=16)\n",
    "    ax.set_xlabel('Trials', fontsize=14)\n",
    "    ax.set_ylabel('Probability of of a Correct Response', fontsize=14)\n",
    "    plt.tight_layout()\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(handles, labels, loc=4)\n",
    "    ax.grid(linestyle='dotted')\n",
    "    plt.show()\n",
    "    fig.savefig('EM Learning Curve (All Subjects).png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dVR1FCCHEZa7Se7J27NjByJEjfbZ/9913DB06lNtvv51PP/20smMJIYS4jKWm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aXFOk5EjRs3xurVq3HHHXcgOzs71nFC7NixAwcPHgz8sWrv3r1xmRPwjVxp164dAN+X8F27dsU4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BQUcrbgaVQQ1TX0gNQXSucWPs0WSo2/vp03Y71UaDUqz/5Juvr9lPy0TfdP0jXed0mrbtJW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\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IiIjI6cCrQVZhYSGJiYluz4eHh1NVVWV5VHNj0tVRTGlVp7VM6QRzWtVpLVM6wZxWdVrL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7GT58OI8//jg7duw4lW21xvGnzgxkprSq01qmdII5req0limdYE6rOq1lSieY06pOa5nSCWa1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121cd95d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(len(em_average), figsize=(12,38))\n",
    "    for i in range(len(em_average)):\n",
    "        t = range(1, len(em_average[i]['p05'])+1)\n",
    "        ax[i].plot(t, em_average[i]['p95'], color=sns.color_palette(\"GnBu_d\")[0], ls='dashed', lw='1')\n",
    "        ax[i].plot(t, em_average[i]['pmode1'], lw=\"2\")\n",
    "        #ax[i].errorbar(t, em_average[i]['pmode1'], yerr=em_std[i]['pmode1'], capthick=2)\n",
    "        ax[i].plot(t, em_average[i]['p05'], color=sns.color_palette(\"GnBu_d\")[1], ls='dashed', lw='1')\n",
    "        if len(em_average[i][pd.notnull(em_average[i]['cback'])]) == 0:\n",
    "            cback = \"N/A\" \n",
    "        else:\n",
    "            cback = int(em_average[i][pd.notnull(em_average[i]['cback'])]['cback'].values[0])\n",
    "    #     ax[i].set_title('Subject %s with learning trial = %s' % (i+1, cback), fontsize=14)\n",
    "        ax[i].set_title('Subject %s' % (i+1), fontsize=14)\n",
    "        ax[i].set_xlabel('Trial', fontsize=14)\n",
    "        ax[i].set_ylabel('Proportion of Correct Response', fontsize=14)\n",
    "        ax[i].set_ylim(0, 1)\n",
    "        ax[i].set_yticks(np.arange(0, 1.1, 0.1))\n",
    "        ax[i].set_xlim(0, len(em_average[i])+1)\n",
    "        ax[i].set_xticks(np.arange(0, len(em_average[i])+1, 1))\n",
    "        ax[i].axhline(y=0.5, color=\"gray\", lw=\"1\")\n",
    "        fig.suptitle('EM Learning Curves (Individual Subjects)', y=1.01, x=0.525, fontsize=16)\n",
    "        plt.tight_layout()\n",
    "        handles, labels = ax[i].get_legend_handles_labels()\n",
    "        ax[i].legend(handles, labels, loc=4)\n",
    "        ax[i].grid(linestyle='dotted')\n",
    "    fig.savefig('EM Learning Curves (Individual Subjects)', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fm_list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12720bc10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MGjrd/JQTo9HI4Pem8/PS9VhbWRFYtTIHD0cyYPDn5jn0f28/xKLFf5KSkkqt\nGg/x05L17D94Mof+yPq35efqZnwiC35YS3x8Ep4VymFvV9D3dxSv2Jges2ZvPga4u5Vh67ZD9B84\nxTx/G2Di5IWcjDiDt7c7jo72TJy8IOMJIrnvxw4fOcWs75ZzLS4efz9Pdu89Zv5xsGoZbZ8dtoJr\ncTfx9nLHNiMWlk8m2b7zCIOHTefQ4VMEBlTCysqKRYvXMnrcnNt6PXvM85qekV+GgtzYXQh4uceH\nR3O6TChFX706VYk9dznHy7xStCl2xZviV7wVpfgFPORF/XpBnDl7kT/X7QZMI/PPP9ecjyfOY/Uf\nO+5zDYuWexk7xabgeVV0Y+6sYVWBfP8SiaZYiIiIPCBSUtN4tVsbDAZ4tu2jYISQar6kpKSanwgg\n94diU7QoQRYREXlARESe5ZNJ83mh4+MEBlTCaIRjx6P59rvfC/3X5SRvik3RogRZRETkAbJyzfYC\n+zEFKViKTdGhm/RERERERCwoQRYRERERsaAEWURERETEghJkERERERELSpBFRERERCwoQRYRERER\nsaAEWURERETEghJkEREREREL9/qHQqyqV/OjvEfZe1ysFISqAd64u7kofsWQYle8KX7Fm+JXfCl2\nxZtbuTLwDweDDUajsWBrk7dA4PC9LFBEREREHlhBwJH8rnSvp1ik3+PyREREROTB9Y9yT81BFhER\nERGxoARZRERERMSCEmQREREREQtKkEVERERELChBFhERERGxoARZRERERMSCEmQREREREQtKkEVE\nRERELChBFhERERGxoAS5GEhOTqZt27Zs2bIl27KTJ09So0aNO27j119/5YknnqBWrVr07duXK1eu\nFEZVJQfDhg2jW7du5r8PHz5M165dqVOnDh06dGDjxo15rr98+XJCQ0OpVasWffr04fLly4VdZRER\nkQeaEuQiLjk5mQEDBnDixIlsy2JiYujduzcpKSl5bmPPnj0MHTqUfv36sWjRIq5du8aQIUMKq8pi\nYcuWLfz444/mvy9fvkz37t2pVKkSP/74Iy+99BL9+vXj4MGDOa6/b98+hgwZQp8+fVi0aBE3btxg\n0KBB96r6IiIiDyQlyEVYeHg4L7zwAqdPn862bOXKlXTs2BEHB4c7bmfu3Lm0adOGZ555hoCAAMaN\nG8dff/1FTExMYVRbMiQkJDB8+HDq1q1rfm3JkiWULFmSDz/8ED8/Pzp06MAzzzzDrFmzctzGvHnz\naNWqFe2HIw95AAAGSklEQVTatSMgIIDx48ezceNGoqOj71UzREREHjhKkIuwbdu20bhxYxYuXIjR\naMyybN26dQwcOJDBgwffcTt79+6lfv365r+9vLzw8PBg7969BV5nuWXSpEk0bNgwS9+fPn2akJAQ\nrK2tza8FBgayZ8+eHLexZ8+eLOuXL18eT09Pdu/eXXgVlyxiYmIIDAxk1apVhIaGUqNGDV5//XWu\nXr0KwOLFi2nTpg0hISE0atSIESNGkJ6eDsCQIUMYM2YMAwcOpHbt2jRr1oyff/75fjbngaP4FW+K\nX/FV3GOnBLkI69y5M4MHD8bOzi7bsrFjx9KxY8e72s6FCxdwd3fP8pqrqyuxsbEFUk/Jbvfu3axa\ntSrbCUxO/R4TE5PrnPCcYleuXDnF7j74+uuvmThxImFhYRw8eJCZM2eyc+dORo0axYABA1i9ejWj\nRo3ip59+YtWqVeb1FixYQLVq1Vi2bBmtWrVi5MiRxMXF3ceWPJgUv+JN8Su+imvslCA/AJKSkrC1\ntc3ymq2tLcnJyfepRv/bkpOTGTZsGEOHDsXJySnLsieffJJDhw4xb948UlNT2bNnDz/99FOu88gT\nExMVuyKiX79+VK9enRo1atC2bVv279+Pg4MDY8eOpWXLllSoUIHQ0FCCg4Oz3DMQEBBAjx498PLy\non///iQmJnLs2LH72JIHk+JXvCl+xVdxjV2Je1aS3Dc2NjbZEqrk5GTs7e3vU43+t33++ef4+PgQ\nGhqabZm/vz/jxo1j1KhRjB07Fm9vb1555RVmz56d47bs7OxyjN3dzD2XguXl5WX+f6lSpUhNTSU4\nOBg7OzumTZvG8ePHOXbsGFFRUTRu3Nj83kqVKmVZDyA1NfXeVVwAxa+4U/yKr+IaO40gPwA8PDy4\nePFiltcuXryY7dK9FIxff/2VjRs3Urt2bWrXrs2MGTPYsWMHderUAaBt27Zs376ddevWsWLFCpyc\nnKhYsWKO23J3d+fChQtZXrt48SJubm6F3g65xWAwZBvJNxqNbNy4kfbt23PhwgWaNm3KtGnTqF27\ndpb32djYZNve7fcUSOFS/Io3xa/4Ks6x0wjyA6BmzZrs3LmTdu3aARAdHc2FCxeoWbPmfa7Z/6aw\nsLAsZ7nffvstBw8eZMKECWzbto2wsDCmTp1KuXLlAFi7di0NGzbMcVu1atVi586d5vnmZ8+e5ezZ\ns9SqVavwGyJ5MhqNLFq0iPbt2zNy5EgA0tLSiIqKynJjpRRNil/xpvgVX8UldkqQ/welpKRw7do1\nXF1dMRgMdO7cmR49elCzZk1CQkIYM2YMjz/+eK6jlvLvVKhQIcvfzs7O2NnZ4e3tjb29PRs3bmTu\n3Lk0b96cxYsXs2/fPsaMGQPcil3ZsmWxsrKic+fOvPzyy9SpU4caNWowduxYmjVrluXSkxS+3EYt\nXFxc2L17N0ePHsXKyoqvvvqKixcvao54EaP4FW+KX/FVnGOnKRbFhMFguOv37tixgyZNmnD+/HkA\n6tWrx4gRI/j888/p0qULLi4ujB07trCqKnlwc3Nj6tSpLFy4kLZt27Jt2zZmz56Nh4cHYHr6RZMm\nTcxPqahVqxajR4/myy+/pHPnzpQuXZpx48bdzyY8kHL6/hkMBvr164ebmxudOnWiR48e2NnZ0bVr\nVw4fPpyvbUnhUvyKN8Wv+CrOsTPc47k4AcDRe1mgiIiIiDywqgL5fvyFRpBFRERERCwoQRYRERER\nsaAEWURERETEghJkERERERELSpBFRERERCwoQRYRERERsaAEWURERETEghJkERERERELSpBFRERE\nRCwoQRYRERERsaAEWURERETEghJkERERERELBqPReC/Lswb872WBUqAyT6jS72st5J9Q7Io3xa94\nU/yKL8Wu+AsH0vK70r1OkEVEREREijRNsRARERERsaAEWURERETEghJkERERERELSpBFRERERCwo\nQRYRERERsaAEWURERETEghJkERERERELSpBFRERERCwoQRYRERERsaAEWURERETEghJkEREREREL\nSpBFRERERCwoQRYRERERsaAEWURERETEghJkERERERELSpBFRERERCwoQRYRERERsaAEWURERETE\nghJkERERERELSpBFRERERCwoQRYRERERsfD/aJ+Af8DNt9MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12720bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.externals import six\n",
    "df = matrix.head(10)\n",
    "def render_mpl_table(df, col_width=3.0, row_height=0.625, font_size=14,\n",
    "                     header_color='#40466e', row_colors=['#f1f1f2', 'w'], edge_color='w',\n",
    "                     bbox=[0, 0, 1, 1], header_columns=0,\n",
    "                     ax=None, **kwargs):\n",
    "    if ax is None:\n",
    "        size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array([col_width, row_height])\n",
    "        fig, ax = plt.subplots(figsize=size)\n",
    "        ax.axis('off')\n",
    "\n",
    "    mpl_table = ax.table(cellText=data.values, bbox=bbox, colLabels=data.columns, **kwargs)\n",
    "\n",
    "    mpl_table.auto_set_font_size(False)\n",
    "    mpl_table.set_fontsize(font_size)\n",
    "\n",
    "    for k, cell in six.iteritems(mpl_table._cells):\n",
    "        cell.set_edgecolor(edge_color)\n",
    "        if k[0] == 0 or k[1] < header_columns:\n",
    "            cell.set_text_props(weight='bold', color='w')\n",
    "            cell.set_facecolor(header_color)\n",
    "        else:\n",
    "            cell.set_facecolor(row_colors[k[0]%len(row_colors) ])\n",
    "    return ax\n",
    "\n",
    "render_mpl_table(df, header_columns=0, col_width=2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PP4/c3FzlPKt3U5hbq03GbrebIjMzE4MGDcJXX30FoGnV1pUrV9DY2GjweLdc\nB9+7d29MmjQJQUFBCA8Pv3s7Rxaxm0LdnJ2dERkZia1bt2LFihVYsmSJMj+rd1OYWqvdkRtga9rT\nTREUFAQXFxeEhYUhIyMDgwcPNlp23rwO/siRIygtLcWhQ4dw6NAhZGRk4Lvvvrvr+0mmsZtC3by8\nvJTyLS8vL7i5uaGkpASABN0UptZqt3x2IEO3203Rv39/nD59GsOGDUNKSgomTJig/MFn0KBByq9B\nR44cgb+/P7p16wZnZ2c4ODjA0dEROp2OT44SYTeFuu3evRvr1q0DAFy5cgW1tbXo2bMnAAm6KVpq\nXqtNrWtPN0VFRQXeffddfPTRR+jWrRvi4uIANHVTvPnmm6irq4OPjw8mTJgAjUaD48ePY+bMmdBq\ntfD398fw4cOttr9kiN0U6jZ9+nRER0cjNDQUWq0Wa9asweeff85uCuocnJ96cXbqxm4KIiLJsZvi\nHsT5qRdnp27spiADnJ96cXa2i90U9yDOT704O5vQ/m6KW9dqjxo1qgO3y7YIM90EpaWlWLRoETQa\nDYQQyM/Px5IlSxAcHAzAsP/D29sbZ8+eRUxMDADA09MTcXFxypeR7t27F1qtFhEREZg4caLV9pcM\nmZs/wG4KNdi8eTMOHjyIuro6hIaGIigoSDnPqt0UptZqc3186yx1EzT79ttvxZw5c0RjY6MQwrj/\nQwghXn75ZXHixAkhhBBRUVHiwIEDory8XAQEBIiGhgZRU1MjRo0addvbyPl1HnZTqFtmZqaYN2+e\nEEKI2tpa8f777yvnWb2bwtxabTJmqZug2erVq7Fy5Urls9um+j8++OAD+Pv7Q6/Xo6SkBDqdDu7u\n7khNTYVWq0VJSQmcnJw6f6eozdhNoW5Hjx7FwIED8fLLL2P+/PnKt7MDEnRTmFurTcYsdRMATb/q\nDBw4EJ6engBa7//QaDS4dOkSpkyZgsrKSvj6+gKA8lbFrFmzlKWbJAd2U6hbRUUFvv/+e7z33ntY\nsWIFXnvtNeU8q3dTmFqrXVVV1WEbYGssdRMAQFpaGmbOnKn8bKr/o6ysDADwwAMPID09HcHBwcq3\nRgNAWFgYjh49iuzsbGRlZXXyXlFbsZtC3dzc3DBixAjY29vD29sbTk5OKC8vByBBN4WptdoduQG2\nxlw3QbPvv/8efn5+ys+39n/Ex8fDw8MD8+fPx/nz5wE0PQtrtVoUFRVhwYIFAAA7Ozs4OjqyRlMi\n7KZQN3+BJk/GAAAK4ElEQVR/f3z99dcAmvLu119/hbu7OwAJuilMrdW+cuVKh22ArbHUTVBeXm62\naKn5kxYA8NJLLyEqKgqOjo7o0qUL3nrrLdx3333w9fVFcHAwNBoNRo4cqXQek/Wxm0LdRo8ejRMn\nTmD69OkQQiAmJgafffYZuymoc3B+6sXZqRu7KYiIJMduinsQ56denJ26sZuCDHB+6sXZ2S52U9yD\nOD/14uxsQvu7KW5dq93cU0HGRDu6KRwdHbFnzx5oNBrcvHkT+fn5OHbsGC5fvmyym+Lw4cNISEgA\nAAwePFi5DFmfufkD7KaQ3d69e00ei82ZZ9VuClNrtbk+vnXt7aZotnLlSvH3v/9dCGG6m6KmpkYE\nBASIiooKIYQQW7ZsEeXl5be1jZxf52E3he1oeSwKIUE3hbm12mSsvd0UAPDdd9/h7NmzyrOtqW6K\nb7/9FgMHDsS6desQFhYGDw8P5UPpZH3sprANtx6LQOd3U1h8m6KiogKXLl3Cpk2b8PPPP2P+/PlY\ntGhRh22ArWmtm6DlKrlbuymabd68Ga+++qryc3M3RUREBHQ6HXx9fXHkyBFkZmYiLS0Nzs7OCAsL\ng5+fn9FtkXVYmv/kyZMRFhYGV1dXvPLKKzh8+DAraSV067EISNBNYWqtNr8avnXt6aYAgOrqapw7\ndw7/9m//ZnD6rd0U7u7u+N3vfocePXqga9euGDJkCH744YfO2yG6LeymUL/WjkWrd1OYWqvNP+C1\nrj3dFACQnZ2Nxx57zOA0U90UgwYNwo8//ojKykrU19cjNzcX/fv376S9odvFbgr1M3UsAhJ0U9y6\nVjs2NpavjM1obzdFUVGRwV/dAdPdFD169MDixYsxd+5caDQaTJo0iWEsEXZTqN+tx2LL+bGbgjoU\n56denJ26sZuCiEhy7Ka4B3F+6sXZqRu7KcgA56denJ3tYjfFPYjzUy/Ozia0r5vC1FrtVatWdfzm\n2QjRjm6KoKAgLF26FMXFxbC3t8fq1avh7e2NxYsXo7S0FEIIFBcXw8/PDxs3bkRKSgr27t0LrVaL\niIgITJw40cp7Tc3MzR9gN4Xs6uvrTR6LzazaTWFqrTbXx7euPd0UGRkZ4o9//KMQQohjx46JBQsW\nGFz22rVr4tlnnxWlpaWivLxcBAQEiIaGBlFTUyNGjRp129vI+XUedlOom7lj0erdFM1MrdUmY+3p\npvDy8kJDQwOEEKiuroaDg4PBZd977z0899xzSg9FamoqtFotSkpK4OTk1On7RG3Hbgp1M3csWr2b\nopmptdpkrD3dFC4uLrh48SImTJiAyspKbNq0SblseXk5MjMz8cYbbyinabVapKSk4P3330d4ePhd\n2CtqK3ZTqJu5Y9Hq3RRA62u1yVh7uik++eQTjBgxAunp6UhLS8PSpUuh1+sBAPv370dAQIBBuxsA\nhIWF4ejRo8jOzkZWVlYn7hHdDnZTqJu5Y9Hq3RRA62u1yVh7uim6d++u9H3odDrU19ejsbHpD67H\njx83WDJbVFSEBQsWAADs7Ozg6OhoFPZkPeymUDdzx6LVuykA070JZFp7uinmzJmDZcuWISwsDPX1\n9Xjttdfg7OwMADh37pzBY+/t7Q1fX18EBwdDo9Fg5MiRGDJkyN3bQTKL3RTqduuxuHjxYmRkZLCb\ngjoH56denJ26sZuCiEhy7Ka4B3F+6sXZqRu7KcgA56denJ3tYjfFPYjzUy/Ozia0r5vC1Fptap3o\nwG6K8vJyLF++HNXV1WhoaMD69evRt29fxMXF4eTJk3BxcQEAJCQk8KuwJGFu/gC7KWSn1+sRHR2N\nixcvwtXVFbGxsejXr59yvlW7KUyt1eb6+NZ1ZDdFVFSU+OKLL4QQQnzzzTfiq6++EkIIERISIioq\nKtq9jZxf52E3hbpt27ZNvPnmm0IIIQoLC8XcuXOV86zeTWGpN4EMdWQ3xcmTJ3H58mVERERg3759\nGDp0KIQQOH/+PGJiYhASEoLdu3fftX0jy9hNoW5nz55VPvvt7e2NwsJC5bzO7qawGMYt12rHxMSw\nC8GC1roJWjLXTdHyMS4uLoabmxsSExNx//33Y/Pmzbh+/TrCw8OxYcMGbNmyBZ9++inOnDH6yCJZ\niaX5T548GStXrkRSUhJycnKU1Xokh4cffhhfffUVgKYVlFevXm1+p8D63RSm1mrX19d32AbYmjvt\npkhNTVXWw7u5ueHJJ58EAIwZMwZ5eXno2rWrsoLLxcUFjz32GPLz8+/OzpFF7KZQt6CgILi4uCAs\nLAz/+7//i8GDByu9MFbvpjC3VpuM3Wk3Rbdu3ZTH2N/fX3mWzs7ORv/+/VFYWIiQkBAIIVBXV4ec\nnBz2G0iE3RTq9t1332HYsGFISUnB+PHjDf74avVuClO9CXZ2dh22AbamI7spli5diuXLl2PHjh3Q\n6XTYuHEjdDodnn32WcyYMQMODg4IDAyEj4+PNXaVTGA3hbp5enri3XffxUcffYRu3bohLi7OYH7s\npqAOxfmpF2enbuymICKSHLsp7kGcn3pxdurGbgoywPmpF2dnu9hNcQ/i/NSLs7MJ7eumMLVWm1on\nLHQTpKWl4ZNPPoGdnR2mTZuGkJAQ1NfXY9myZSguLkZdXR3mzZuHMWPGKNdZu3YtHnzwQQQHBwMA\nUlJSsHfvXmi1WkRERGDixIl3fT/JNEvzZzeFXHJzc/H2228jOTkZFy5cQFRUFLRaLQYMGGCUdZZm\ne8dMrZEWLbopTK3V5vr41lnqJnj88cdFVVWV0Ov1Yty4caKqqkrs3r1brFmzRgghRGVlpRg9erQQ\nQoiysjLxwgsviHHjxokdO3YIIYQoLy8XAQEBoqGhQdTU1IhRo0bd9jZyfp2H3RTq8de//lUEBASI\n4OBgIYQQ8+bNE9nZ2UIIIWJiYsSBAwcMLt/W3hlz7qibwtxabTJmqZvA19cX165dw82bNwEAGo0G\nEydOxMKFCwE0rdiyt2/6heX69etYsGABpk6dqlzf3d0dqamp0Gq1KCkpgZOT093YLWojdlOoh6en\nJz788EPl57y8POX7JEeOHInjx48bXL6tvTPtZTGMza3VJmOWugkGDBiAoKAgTJkyBaNHj4arqyu6\ndOmCrl27oqamBgsXLsSiRYsANP0B9fe//73RfWi1WqSkpGDWrFkGQU3Wx24K9Rg3bpzBAraWuWaq\nd6ItvTN3wmIYm1urTcbMdRMUFBTgq6++wsGDB3Hw4EGUlZUhPT0dAPDLL79gzpw5CAwMxKRJkyze\nT1hYGI4ePYrs7GxkZWV1zs7QbWM3hXq1nJOp3om29M7c0f1buoC5tdpkzFw3gU6nQ5cuXeDo6AiN\nRoMePXqgqqoKZWVliIyMxOuvv47AwECzt19UVIQFCxYAAOzs7ODo6Nih/yDozrCbQr0GDRqkVGIe\nOXIE/v7+Bue3pXfmTlj8NIWptdonT57s0I2wJZa6CWbOnInQ0FA4OjqiX79+CAwMxPr161FVVYWE\nhAR8+OGH0Gg02LJlCxwdHY1u39vbG76+vggODoZGo8HIkSOV97nI+thNoV5Lly7Fm2++ibq6Ovj4\n+GDChAnK6YsWLTI5247Ebop7EOenXpydurGbgohIcmbfpmht/fv/ra8mleL81IuzUzdznSLm3qaw\nA9BaUW7zK2q2zKsT56denJ36/SSEaLj1xFbDmIiI7h6+Z0xEJAGGMRGRBBjGREQSYBgTEUmAYUxE\nJIH/ByV1+ZqMc8QjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12799c350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import table\n",
    "\n",
    "ax = plt.subplot(111) # no visible frame\n",
    "ax.xaxis.set_visible(False)  # hide the x axis\n",
    "ax.yaxis.set_visible(False)  # hide the y axis\n",
    "\n",
    "table(ax, matrix.head(10))  # where df is your data frame\n",
    "\n",
    "plt.savefig('mytable.png', dpi =300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Block Trial only Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Acc EM</th>\n",
       "      <th>Beta 0 (Intercept)</th>\n",
       "      <th>Block's Trial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5138</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5536</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6063</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6908</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.7433</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7736</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7873</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.7865</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.8213</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Acc EM  Beta 0 (Intercept)  Block's Trial\n",
       "0  0.5138                 0.5            1.0\n",
       "1  0.5536                 0.5            2.0\n",
       "2  0.5590                 0.5            3.0\n",
       "3  0.6063                 0.5            4.0\n",
       "4  0.6908                 0.5            5.0\n",
       "5  0.7433                 0.5            6.0\n",
       "6  0.7736                 0.5            7.0\n",
       "7  0.7873                 0.5            8.0\n",
       "8  0.7865                 0.5            9.0\n",
       "9  0.8213                 0.5           10.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = pd.concat(fm_list, axis=0, ignore_index=False)\n",
    "matrix = matrix[['Acc EM','Beta 0 (Intercept)', \"Block's Trial\"]]\n",
    "matrix = matrix.reset_index(drop=True)\n",
    "matrix.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = matrix[matrix.columns[0:1]]\n",
    "X = matrix[matrix.columns[1:]]\n",
    "# https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear/249897\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Acc EM   R-squared:                       0.036\n",
      "Model:                            OLS   Adj. R-squared:                  0.035\n",
      "Method:                 Least Squares   F-statistic:                     54.14\n",
      "Date:                Sun, 30 Apr 2017   Prob (F-statistic):           3.13e-13\n",
      "Time:                        02:20:52   Log-Likelihood:                 490.48\n",
      "No. Observations:                1447   AIC:                            -977.0\n",
      "Df Residuals:                    1445   BIC:                            -966.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------------\n",
      "Beta 0 (Intercept)     1.5160      0.015    101.027      0.000         1.487     1.545\n",
      "Block's Trial          0.0024      0.000      7.358      0.000         0.002     0.003\n",
      "==============================================================================\n",
      "Omnibus:                      129.724   Durbin-Watson:                   2.044\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              164.384\n",
      "Skew:                          -0.818   Prob(JB):                     2.02e-36\n",
      "Kurtosis:                       3.221   Cond. No.                         74.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = results.predict(X_test)\n",
    "print \"Accuracy %f\" % (np.mean(predicted == np.array(y_test)))\n",
    "\n",
    "# the actual value is within 2 sd of the predicted\n",
    "\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    ax.scatter(y_test, predicted)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count only Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Acc EM</th>\n",
       "      <th>Acc Smoothed</th>\n",
       "      <th>Acc Smoothed Combined</th>\n",
       "      <th>Beta 0 (Intercept)</th>\n",
       "      <th>Distractor Count</th>\n",
       "      <th>Target Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5536</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6063</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6908</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.7433</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7736</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7873</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.7865</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.8213</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Acc EM  Acc Smoothed  Acc Smoothed Combined  Beta 0 (Intercept)  \\\n",
       "0  0.5138      0.000000               0.000000                 0.5   \n",
       "1  0.5536      0.500000               0.500000                 0.5   \n",
       "2  0.5590      0.333333               0.000000                 0.5   \n",
       "3  0.6063      0.250000               0.333333                 0.5   \n",
       "4  0.6908      0.400000               0.500000                 0.5   \n",
       "5  0.7433      0.600000               0.500000                 0.5   \n",
       "6  0.7736      0.600000               0.666667                 0.5   \n",
       "7  0.7873      0.800000               0.750000                 0.5   \n",
       "8  0.7865      0.800000               0.400000                 0.5   \n",
       "9  0.8213      0.800000               0.600000                 0.5   \n",
       "\n",
       "   Distractor Count  Target Count  \n",
       "0               2.0           2.0  \n",
       "1               2.0           0.0  \n",
       "2               1.0           2.0  \n",
       "3               2.0           1.0  \n",
       "4               1.0           0.0  \n",
       "5               2.0           4.0  \n",
       "6               3.0           2.0  \n",
       "7               2.0           1.0  \n",
       "8               3.0           0.0  \n",
       "9               0.0           1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = pd.concat(fm_list, axis=0, ignore_index=False)\n",
    "matrix = matrix.drop(['Target Duration', 'Distractor Duration', 'Block', \"Block's Trial\"], axis=1)\n",
    "matrix = matrix.reset_index(drop=True)\n",
    "matrix = matrix.fillna(0)\n",
    "matrix.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EM Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Acc EM   R-squared:                       0.097\n",
      "Model:                            OLS   Adj. R-squared:                  0.096\n",
      "Method:                 Least Squares   F-statistic:                     77.35\n",
      "Date:                Sun, 30 Apr 2017   Prob (F-statistic):           1.22e-32\n",
      "Time:                        02:25:11   Log-Likelihood:                 537.51\n",
      "No. Observations:                1447   AIC:                            -1069.\n",
      "Df Residuals:                    1444   BIC:                            -1053.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------------\n",
      "Beta 0 (Intercept)     1.6593      0.012    133.832      0.000         1.635     1.684\n",
      "Distractor Count      -0.0235      0.002    -11.486      0.000        -0.028    -0.020\n",
      "Target Count           0.0094      0.002      4.613      0.000         0.005     0.013\n",
      "==============================================================================\n",
      "Omnibus:                      162.701   Durbin-Watson:                   2.032\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              219.718\n",
      "Skew:                          -0.893   Prob(JB):                     1.94e-48\n",
      "Kurtosis:                       3.673   Cond. No.                         15.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "y = matrix[matrix.columns[0:1]]\n",
    "X = matrix[matrix.columns[3:]]\n",
    "# https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear/249897\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = results.predict(X_test)\n",
    "print \"Accuracy %f\" % (np.mean(predicted == np.array(y_test)))\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    ax.scatter(y_test, predicted)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smooth Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = matrix[matrix.columns[1:2]]\n",
    "X = matrix[matrix.columns[3:]]\n",
    "# https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear/249897\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = results.predict(X_test)\n",
    "print \"Accuracy %f\" % (np.mean(predicted == np.array(y_test)))\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    ax.scatter(y_test, predicted)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combined Smooth Acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = matrix[matrix.columns[2:3]]\n",
    "X = matrix[matrix.columns[3:]]\n",
    "# https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear/249897\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = results.predict(X_test)\n",
    "print \"Accuracy %f\" % (np.mean(predicted == np.array(y_test)))\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    ax.scatter(y_test, predicted)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count and Duration Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>Acc EM</th>\n",
       "      <th>Acc Smoothed</th>\n",
       "      <th>Acc Smoothed Combined</th>\n",
       "      <th>Beta 0 (Intercept)</th>\n",
       "      <th>Distractor Count</th>\n",
       "      <th>Distractor Duration</th>\n",
       "      <th>Target Count</th>\n",
       "      <th>Target Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.349836</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.135036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.196646</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.204968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6063</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.208260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.159977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.7433</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.104936</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.229158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7736</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.194450</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.203300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7873</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.198316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.273279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.8891</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.305970</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.179975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.9406</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.073323</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.316674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.9506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.319940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.459933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.9719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.459923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.319963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Acc EM  Acc Smoothed  Acc Smoothed Combined  Beta 0 (Intercept)  \\\n",
       "0   0.5138      0.000000               0.000000                 0.5   \n",
       "2   0.5590      0.333333               0.000000                 0.5   \n",
       "3   0.6063      0.250000               0.333333                 0.5   \n",
       "5   0.7433      0.600000               0.500000                 0.5   \n",
       "6   0.7736      0.600000               0.666667                 0.5   \n",
       "7   0.7873      0.800000               0.750000                 0.5   \n",
       "13  0.8891      0.800000               0.600000                 0.5   \n",
       "16  0.9406      0.800000               0.600000                 0.5   \n",
       "17  0.9506      1.000000               1.000000                 0.5   \n",
       "20  0.9719      1.000000               0.800000                 0.5   \n",
       "\n",
       "    Distractor Count  Distractor Duration  Target Count  Target Duration  \n",
       "0                2.0             0.349836           2.0         0.135036  \n",
       "2                1.0             0.196646           2.0         0.204968  \n",
       "3                2.0             0.208260           1.0         0.159977  \n",
       "5                2.0             0.104936           4.0         0.229158  \n",
       "6                3.0             0.194450           2.0         0.203300  \n",
       "7                2.0             0.198316           1.0         0.273279  \n",
       "13               5.0             0.305970           2.0         0.179975  \n",
       "16               2.0             0.073323           2.0         0.316674  \n",
       "17               1.0             0.319940           1.0         0.459933  \n",
       "20               1.0             0.459923           1.0         0.319963  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = pd.concat(fm_list, axis=0, ignore_index=False)\n",
    "matrix = matrix.drop(['Block', \"Block's Trial\"], axis=1)\n",
    "matrix = matrix.reset_index(drop=True)\n",
    "matrix = matrix.dropna()\n",
    "matrix.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EM Acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cor_X.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cor_X.corr?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.052972972310689335, 0.062527406376950326)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Target Count'], cor_X['Target Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.082082771208224409, 0.0038660261328538597)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Distractor Duration'], cor_X['Target Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.061774448667541844, 0.029814324318487333)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Distractor Duration'], cor_X['Target Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.71795860664734468, 1.5421093293162181e-196)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Distractor Count'], cor_X['Target Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.014165185872125183, 0.6186777424327059)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Distractor Count'], cor_X['Target Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.083010681634324388, 0.0034816963100198751)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Distractor Count'], cor_X['Distractor Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1213900411099814, 1.8611053694586777e-05)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy as scipy\n",
    "scipy.stats.pearsonr(cor_X['Acc EM'], cor_X['Target Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.031188775629472105, 0.27303570254187587)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Acc EM'], cor_X['Target Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.26805853353724018, 8.4339734136674586e-22)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Acc EM'], cor_X['Distractor Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.17965022473055067, 1.9664905467105042e-10)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(cor_X['Acc EM'], cor_X['Distractor Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSVJF/FvqkiRJ6q0WzuG0pS5JkqSessIpSZJUEyuckiRJ0shY4ZQkSapIp4Vvi2TglCRJ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\nGTglSZLUUwZOSZIk9dT/BwXFNzfuAiJiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1221b7b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.heatmap(cor_X.corr())\n",
    "plt.tight_layout()\n",
    "plt.savefig('Heat Map of Correlation Matrix.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Distractor Count</th>\n",
       "      <th>Distractor Duration</th>\n",
       "      <th>Target Count</th>\n",
       "      <th>Target Duration</th>\n",
       "      <th>Acc EM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Distractor Count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083011</td>\n",
       "      <td>0.717959</td>\n",
       "      <td>-0.014165</td>\n",
       "      <td>-0.268059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distractor Duration</th>\n",
       "      <td>0.083011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061774</td>\n",
       "      <td>0.082083</td>\n",
       "      <td>-0.179650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Count</th>\n",
       "      <td>0.717959</td>\n",
       "      <td>0.061774</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.052973</td>\n",
       "      <td>-0.121390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Duration</th>\n",
       "      <td>-0.014165</td>\n",
       "      <td>0.082083</td>\n",
       "      <td>-0.052973</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acc EM</th>\n",
       "      <td>-0.268059</td>\n",
       "      <td>-0.179650</td>\n",
       "      <td>-0.121390</td>\n",
       "      <td>-0.031189</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Distractor Count  Distractor Duration  Target Count  \\\n",
       "Distractor Count             1.000000             0.083011      0.717959   \n",
       "Distractor Duration          0.083011             1.000000      0.061774   \n",
       "Target Count                 0.717959             0.061774      1.000000   \n",
       "Target Duration             -0.014165             0.082083     -0.052973   \n",
       "Acc EM                      -0.268059            -0.179650     -0.121390   \n",
       "\n",
       "                     Target Duration    Acc EM  \n",
       "Distractor Count           -0.014165 -0.268059  \n",
       "Distractor Duration         0.082083 -0.179650  \n",
       "Target Count               -0.052973 -0.121390  \n",
       "Target Duration             1.000000 -0.031189  \n",
       "Acc EM                     -0.031189  1.000000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_X.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x128253410>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD9CAYAAACV6WqFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121b95390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor_X = X[X.columns[1:]]\n",
    "cor_X['Acc EM'] = y\n",
    "\n",
    "plt.matshow(cor_X.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Acc EM   R-squared:                       0.106\n",
      "Model:                            OLS   Adj. R-squared:                  0.102\n",
      "Method:                 Least Squares   F-statistic:                     29.18\n",
      "Date:                Sun, 30 Apr 2017   Prob (F-statistic):           5.98e-23\n",
      "Time:                        02:25:22   Log-Likelihood:                 361.04\n",
      "No. Observations:                 989   AIC:                            -712.1\n",
      "Df Residuals:                     984   BIC:                            -687.6\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "---------------------------------------------------------------------------------------\n",
      "Beta 0 (Intercept)      1.7455      0.034     52.085      0.000         1.680     1.811\n",
      "Distractor Count       -0.0193      0.002     -8.125      0.000        -0.024    -0.015\n",
      "Distractor Duration    -0.2394      0.046     -5.245      0.000        -0.329    -0.150\n",
      "Target Count            0.0083      0.002      3.434      0.001         0.004     0.013\n",
      "Target Duration         0.0100      0.042      0.237      0.813        -0.073     0.093\n",
      "==============================================================================\n",
      "Omnibus:                      109.879   Durbin-Watson:                   1.973\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              147.004\n",
      "Skew:                          -0.873   Prob(JB):                     1.20e-32\n",
      "Kurtosis:                       3.720   Cond. No.                         65.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "y = matrix[matrix.columns[0:1]]\n",
    "X = matrix[matrix.columns[3:]]\n",
    "# https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear/249897\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "predicted = results.predict(X_test)\n",
    "print \"Accuracy %f\" % (np.mean(predicted == np.array(y_test)))\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    ax.scatter(y_test, predicted)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QUEREREREREREpJFTSCgiIiIiIiIiItLIKSQUERERERERERFp5BQSioiIiIiI\niIiINHIKCUVERERERERERBo5hYQiIiIiIiIiIiKNnEJCERERERERERGRRk4hoYiIiIiIiIiISCOn\nkFBERERERERERKSRU0goIiIiIiIiIiLSyCkkFBERERERERERaeQUEoqIiIiIiIiIiDRyCglFRERE\nREREREQaOYWEIiIiIiIiIiIijZxCQhERERERERERkUZOIaGIiIiIiIiIiEgjp5BQRERERERERESk\nkVNIKCIiIiIiIiIi0sg1eEj49ddfM3ny5Crvr1+/nvHjxzNx4kSWLFnS0N0QERERkXNMdaCIiIjI\nhaNpQ578rbfeYvny5TRv3tzr/VOnTvHMM8+wdOlSAgMDufXWWxk2bBjh4eEN2R0REREROUdUB4qI\niIhcWBp0JGHHjh159dVXq7y/f/9+OnbsSEhICP7+/vTp04ft27c3ZFdERERE5BxSHSgiIiJyYWnQ\nkYTDhw8nMzOzyvtFRUW0aNHC9bp58+YUFhY2ZFfkNGy2DsAkoCuwB/g7hnHcUtuEhMmsWxfjajty\n5BHS0xedtt2SJenccsunjnZ7WbZsCDfddIOla3788WfccMNq7PbL8PPbx9q1o4mPH3gGbd+vU9sv\nvviGoUPfp7S0E0FBB9i8+Rb69Ol12nb79h0mPj6FvLxowsMz2bhxEp07d7R0zfMlN7eA++5LZ9cu\n6NFjD8nJowgPDz3f3RIRkQuE6sALi/Pf/e++q2T//j3Y7Z2oqPgeuz2SJk3yiYz0JyqqJ126lDB7\n9gBmztzKwYMhxMYWkpw8CsPA0b6Y/fsPAZcRGnqA2NhmbNtWjmFEAvvx82uJ3X4SaAP8CHQEfgJy\ngCigCIjBzy+fsLB8wJ/c3OZAJPA9cAl+fvl07dqJiIijbN1a6qoH339/MAsXHmP9+pMUFu7FMEKA\nDsABoDVw0nH+KGA/NtsSDMOsQ6GYpk3tVFb6YxjhQAT+/t9jt7eksjIC+AFoQtOmHRg+vAnvvjvB\n9ZkPHgwhOjqX/PwcNm9ujmHkEhh4nC1bJtOpUwfuuy+dvXshJ+cQkZFxdOhwHDhFVlabKvfP955O\nm7acjRsrOXkym+DgSgYMCCUwsBWZmeGu4zzrM+f30fM89a3fGuKcDcnZX8973qVLyTnr94V2v0RE\nPDVoSFiTkJAQioqKXK9PnjxJy5Ytz0dXxOVWYC5gA4w6tTQDQnfbNWtmWGpnBoTudjffPAPDsBYS\njhy5EsNbOXZ/AAAgAElEQVQw29rtBgkJM7DbrQV9ZkA429V2+PCZVFZaazt06PuUlDwJ2CgpMRg0\n6HGKi08fEsbHp5CR8Qhgo7jYYOjQeRw58qila56vQuO++9JJS5sI2PjuOwNIZfHiWxv8uiIicnFT\nHfjz5P53PxV4CndNmEplZVt+/PFWfvzRxldfGWzdOs9V12zfbrB16zP06RPJ8uVTMWs7s31JySKy\ns8GsM83z2e2PO455G2gBFGAGeX5AGeCu0XJzn8EMEW/16M887PY57N79JFCKs5a02w1+9asZjtep\nmCHko16fAyYCC4EAoADD8Pycf+HUqWZe16uoWORz7RROnZpEerpBz55zAIOsrJmOr+cDfwfaAS0p\nKytm0KD3GTv2co/7+igZGeY9hBRgLNu3G5SWvs1//nOUrKweQCHbt99AeflyAgIC+PDDKa7rV1Sk\nkJ5+GHjA695//fV0V23oWb9t33526reGOGdD8v5Z9rzn56bfF9r9EhHxdE5CQsPwDp06d+7M4cOH\nOXHiBEFBQWzfvp2pU6daOteOHTsaoosXnbrfp26YBQiO37vV4Rxdfdp2tdj2Mp92l1m+pmF08mpr\nGB0tt7Xbva9rt1u/bmmp93VLSztZapuT09arXU5OW8vXnDHjc9auvQ9noZGf/xpz5/7SUtv62LUL\nPPu8a5f+/tVG98Ya3SdrdJ+s0X26MKgOPPfO5D65/90Pwbs+C/H4s/m7b12TkdGd48e/c7zn+bUW\nVdpCJ9yhWjQwFXcINwczPBsFhAIxjnMcB9IdfTkFvAU0Afo72hY4vt7V0d7maOv7OZzXqQBifb4e\nC2RjBo+e/fc8xv15srIuB054fH0N3qFkEqWlnWq5r+5zrV//I0VFj3m0XciKFUfw929XTZs2Ve79\nxInvuWrDhqjfLrSasKZ7frb7XVBQyLx535KZGU509DZmzOhBq1YtLrj7dS7pPlij+2SN7lPDOCch\noc1m/kdy5cqVlJSUMGHCBGbMmMHdd9+NYRhMmDCBSy65xNK5+vTp05BdvSjs2LHjDO7TEsyixFmc\nfE+fPndbbJvm03avxbbVXXOaxWuu9Gn7k+XP7Of3Pna7u62fn/XPGhS0jJISd9ugoEOW2kZEfExG\nhrtdRMSP9Olzp6VrFhRk4VloFBR0OCd/D7p0+a9jBKHZ565dy/X3rwZn9neu8dF9skb3yRrdJ2t+\nDgW06sBz60z/bvToscfx734h3jVWEWD3ei8iIpuMjHzMYKw58C02WyvHMbuBRZiB1i7MUYLe9Z75\newRVQ7hewBjco/4yMcPA1bhH9I0BnsE9NdnADAgnelzjr5ijEvOBMI/PYWBOa47BnGLs26/ZmCGj\n8/0TPsc4p8UbwE7Hed4DRjvug+dniSAo6AA9esTVcF/3ON67gbKyCJ+2pVRWzqSyMoWq1z/q895J\nr9rQ/X00v96jR/3+3uzYsYMePYyzes6GVtPP8tnud2JiiutB/nffGYSHp7J48dCz/j24WOjfbWt0\nn6zRfbLmTOrABg8JY2JiSE1NBWDMmDGu94cOHcrQoUMb+vJiWT7wF8ynqAeBvDq03Q3MwLm2oPna\niiDgccwnygeBQMtXtNlKMYwUzOKyEJut1HLbtWtHM3z4TMf6Nd+zdu0oy203b76FQYMed6xJeIjN\nm8dbardx4ySGDp3nWJMwi40brU85iI0tdExVMAuN2Nii0zVxqd9U5VOYhbJ5jw2jwvJ1RUREQHXg\nhSQ5eRSQynff2dm//3Hs9o5UVOzHbm+NWSc+DfSgXbu9bNw4iZ49n6Ok5GHMoLA75eVfcOONC1i+\n3IY70BuIOfU4GzMUtGEGjjMwp/z6UzWQtAElQBLQDPgv5ujEVMyRYYWYU4IBbnC8X4F3yHYFZpg4\nE7POPAK0xwwXQzEDxJGOr1+GGdg1xXwIXQ4swPng21yLMBQ4jBkEvgZkAVdhjjq8AXgZc71DHJ/h\nBmy23axYMYaXX95NWFgSlZXNCAmZS3FxWwoKgoBpQCvatZtHUZEfBQW+YaANc0TlQvz8SrHbczBH\nEU7y+Mz+wEhiY9dU+T6atV8RycnWlvKpTXXnrK3GPN9r8jn7u3evjZyceURGdqNLl9Kzci88HTzo\nPVLRfN0w3wMRkXPlvKxJKD9HfpiFWFPH73XZ+DoO7/UMra1JCMXAPI92j9Thmqcwi7UYzKfM1gOs\n3r17MH78EceGHD244ooeltt26tSBsWMvd/yjH0RsbAdL7Tp37mh5DUJf9Sk06rMmSlZWG2Csx+sV\ndeq3iIiIXDjCw0OrrRHy8gqYPj3dVYfMnn0rjz22FeiMGRCadUZp6VhstoX4+V2K3e4MTsKAnoDn\nrIsVmCMGn8EMAw85jmsO3IhZEwYD/+tx/C68RwrOwwwik4BLMUM+z5Atz/HnfpgPov0wN0fxnNKb\n4ujbrcAsvNdhTAH+S2CgH+XlbQgKOsiAAe355JP7MAO66R7HLsQMHd21cHDw42RkPMr06emOdRrN\n90eONOu57dvd9VVUVHeio39k+fIkzOV/igDnqMxQYArjx6cCLUhLc4avHWjXbh5RUd2JjV3jqg0b\nKpyr7mcjMTGlxhrzfK/JV9PP8tlW04P8c3V9EZGGoJBQHC4FKj1ed6pDW9/1UmIstuvo0876br9N\nmgRy6pR73ZcmTWZZblufDTmmTVvuWkB6+3aD8vKFLFt2h+Vrn4n6FBo1PeG0oj4jGEVEROTi4FuH\nuMOhFHyn2H76qYHdHoJ3YOc7PbYI2II5Is9zevBxYBPwLXCv45zO6cuheNeMXYGNmKMbbcCbeM5+\nMB8e52OGi60xwzd8zlEO3OS4hu/6hKU0adKEsrI5gI2Skny+/fYFwsKSyM8P8znWAPp6vWezXUZ4\neGi1dVh19VVy8gR6904mI2MMzvUa3SGg+wFxeflCPv3UnDLdp09r3n57kFcIaDWcOxthYm01Zn3q\nzwuJ80G+OfAAjRgUkYuCQkJxyMF8olvX0YAAP1K1GLR6Tc92OZavGBISS0GBu/gICbnUctv6FC5m\nYeZdDP+c1Sfomz17AFu3ziMnpy0REdnMmTOpwfopIiIiFwZ3HTUKeBVz1oFZZxjGT8BvcE8N/ga4\n3eP1TsxA7hTeQVs7zMBuDeZD4+cxpwz/B/NBdjCeNWO7dnvJygr1GLEYAdzs0cu3gReAJ3DXmc/g\nXXf+CGx29Kk9vrVs8+aBnDhxHPgA+IHsbOe53vM59gjm1Gn3e4axj7y8ghoCwaozRMLDQ/n66+lM\nn57K3r1B5OTsISKiI7GxhcyePcA1kjM7O4v8/HuBMJYvN5g+3TsE9K1x164tIy+voEoAeLow0TNE\nbNXqMIsXd65yjtpqTCv15/meknw2OAN0rY0mIhcThYTiEIV3sda2Dm3teD+9tVtsV+bTrtzyFfv1\ns5Oe7i4++ve3es36jpD7iTMNNs9UfYqo+kxVnjlzKxkZjwA2MjIMHnsslcWLrY/2FBERkQtXTfWH\nu44KBS7Bs5YLDi6koKAV5hReA/fGJR3xXmvPWUd5buLxkePKAcDDjj//n+N1Mea03nBCQ3fz9df3\n0rbtcx4b0e32Od/3mGsNeta2XTDX3+6IWc9NcvTtG+AA5qYlrQkNLWLIkEjKy4tJT0/GrJE7eZxr\nNP7+c6iocG680hb32ojNgf9QUjKV6dPTawwEqxvd53w/MTGFnTsfISPDxs6dBlu3znPVY+Yaj68D\n3YFC9u61eZ3Dt8bNzw9g+vT0Ktc73QNzzxARqoaRUHuNWdvXnD9Xa9dmkp//B87XlGQREameQkJx\n8B0N+GMd2vquB2h1fUDf46yHhAEBNjyLUn9/y03rNTVg8OBQli93X3fw4IZ/4jllSgqrV7cCYPt2\ng6KiRaxadZ+ltudrqrKIiIhc2GoabeYZAH3/vT/5+e6ZBq1bL8Bmm8vRo23x8zvE4MHN2LFjPgUF\ncZjTjM22NttBDKMMyAVCueGGaFq0CGDt2gzy8/8Xs/5YhHsNwXzMcCyMsrITAAQHt6WiwjlCsRXu\n0YpFmCMTs/Gubb/EDAKdr5OAaMzNQH4NvIC/vx9DhkTy9ts3MXXqcuABqo4ebMXw4W3YutWPgoJJ\nQAHm7soljrvwMBDKvn1fYXhNOLE2+8Rdf5nnzcy8BHc9tgZzDW/zM+TkzPNqm5w8irVrk8jPd65t\nOIqDBzdXucbpHphbqQFrqzFr+5r752rlaa9xJqw8XL8YRjGKiDQUhYTi4Duqr6wObVth7vwWjbmJ\nSAuL7fIwnxA7d1TOt3zF+myqUZ+pAW+/PcEx5QNiY20kJ1vb3bg+1q7Nw1ybxyzk1q6d0+DXBIiK\nOopZoLcAThAdXXKaFiIiInKx8A2rVq6sIDFxEcnJo1wBUGLiItLS3GFTQcFPZGXNAGxUVhq0aLGQ\nkJAsCgrGuI4JCjpCaal7iZvo6Nm8++5vCQ8PJS+vgN69nyEjozvmw+Oq4VhJyVimT09lwAB/0tOd\no93ew3tjkxTMkYIpjvMEYk4p9hxZGO74fTxmyPd7KirWsHx5c3r3TiYiopPH8QMxpy53BvZiGJcw\nYkSE47OHAhPx85uJ3e4OIX/6aQ/33Vda5w083AFeOjARw0jBHVB6rwEZGdnNq214eCjDh0eTlua+\n31FRx0hMTPEKxE4306Qh16V2/1wV4hninq1rWFmX8XxvrCIi8nOmkFAcemAWU051CYSOA1dhhklN\ngK8stovEeyc56+sg1qd4cT49NEcS7qnT08Pc3ONs3XqYvLxosrMzyc8/bqltfZ5Y2u0ReBaEdnuk\npXb15efnjzldyLzHNtvCc3JdEREROf98w6riYpsjFHMHKr5h07593cjI8F672VxDz5yK267ddxQV\ndaa01H1MSUk7V03kXpsvnbVrfyI/v/pwbO/eII4e3Y05MtDAnBI8B/eD5/sxw7tJmLsjj8Hf/3Eq\nKnzX0H7U4/zunZozMsbivYbhFjzXN/zss6eIj2/mmHYcDfhht8diroWYD0RQVGSwdy8+/aZKYOdb\nDzrv6cqVFRQXO9d+TKVZswrCwzMdfTP70aVLaZXvm+/3pLy8abWB2OLFt7rq05EjN3v1x/McoaE/\nkJx8W80/KHXk/rkaBaQQFlbO8OGBZ23TDyujIDVbRkSkZgoJxWEP3lMy9tahbQs8wyRzHZjTs9k6\nYhieO8FZX++uPmvt1Wd34/j4FNe6MMXFBkOHzuPIkUfrdM26PrFs0+YYWVnu702bNscstYP6hZOZ\nmc4n7AA2x2sRERFpDKqGVeAbqFTd+XgRX33lrlmKig4An2HWh4OJjKzk5MnvqW19Z+cU3Y4d21Fa\n+hdKSnoDX+O5QUpOzh7HRiKpeI4gjI6eC9jJymrlPBuwh7Ztv8RuLyc3dy42W2siIn4kICCIY8f+\nTGlpB+CY49jjOHdSjoyMo39/s9bcvr0Az5qosDCS5cunenyGVMC59s3dgI2CAoOcHO/NUnJyDrNz\np1lHbt9uUFr6NkFBzarUaebahIu8RiqOGZNKcrK5uUlt9a/v96Rv3xXUFIjVVJ96nmPHjh1ndSqu\ndw1vIzn5prN6fisDCRpypKSIyIVOIaE4FGAu5lz3qb/mjnKe0zciLLUKCTlOYaH7H+iQkBOWr3gm\na7w41efpYV5etFdb83XDXnPTpikMHTqPvLxowsOz2LhxsuW29QknVUCJiIg0XlXDqtPXA54BUHb2\nt2RkPASE4ZwC/NNPPzB4cOta13f23TTDDOD+gHM03YgRBuvXV2CuaVeGZ30VE9OTNWsG0bmz5zqI\n08jNfY6KirmuczZtOo9u3UL44Yc/eVwnBViNOfrQoGPHEyxefAcATZrM9tgkxcCskz1r3xBgIP7+\naVRUeE4HdgeN1Y203Lw5n/x8M1T0rdPqsulJbWqr587HiLr6rJdthZWBBPUZbCAicrFTSCgOl2Hu\nStwUaAZcWoe2vgtDW9v0JDg4n8LCZ4AYIJPg4OOWr1if8Csi4giea+1FRlq/bnh4JsXF7s8aHp5l\nqV19ArfOnTtaGq1YnfoUf/XZ4EVEREQuDnUJVDwDoL59ISMjzPEVG1BORERH3n77Btf6zlFRJdhs\n/vTtu4KoqKP4+fnz8cfOzelGYY6ia47naDqAEyf+TNUNRcz6Kjw8lJCQU17rIFZUeK9HmJvblszM\nQLyDvhaYG6msAAoxjArXjAw/v3Ds9meATrhHHLqvGxa2h+HDiygrC2T5cvf7Bw/u4aefDCIj4ygr\nKyMzcyfmkj7mtU6caEJNddrZCtNq+/7V94Hwz3EDECv3raGDShGRC5lCQnHIA9xPWOuyPiCcxHvT\nE2sFRlGRzee19RGB9Qm/vv46D5iO87Pu3DnbctuNGyf5jOqzVmCcryeW9Sn+6rPBi4iIiFwczjRQ\n8a1BIICuXb3Pl5iY4jFqcBHey9eYU4nbtfuOqCibq34aOXIz7hpwNDbbXwkKak/r1tnMmWOurx0R\n0YmMDPduxzbbAQwjH3PdweaUle0lMvISvB9yF2JucGJujJeVtaKaUY2zCAwsp6zsdpzrLPr772Dw\n4Hau2m769FTWri0jPz+AgoIICgqmkpFhc0zDnuv1GSsrvacjV1enOYO4vXshJ+cQkZFxdOlSYjmQ\nq+37V9/6VBuAiIhcfBQSikMMZrETglkkWZtGa+qO96YnxZZaFRc3xb1gtEFx8SzLV6xP+FVQ0AHP\ngNF8bU1oaCv69+/oKKaaEhbW6vSNqN/06Po8pa1P8VefDV5ERETk4mWlNklOHkV5+UI+/dRcd3Dw\n4FCSk8d7HeP90LcFnvVZkyYljB27kAUL7vU6t3cN2ArD6ExJySQyMgweeyyVxYs70qHDSXbubOZo\nYSc4OJji4ueBJzE3gRsLzOemmxaycWMlBQWZmFOXO2AuwdOKzMxvOHXqF3iPNuxL69a7KCn5kqKi\nTCoqLqGi4iGWL2/F9OnuDUH69l3B9u1jMUclerZv5/M6Dmf9HRa2h+TkaVXutTuISwUe9Qgcqw/k\n6lI31ndEnTYAERG5+CgkFIcM4Lec2UjCvXg/id1nsV0nvAulTpavWJ/w60ynDANMm7acDz+cgvOJ\naXn5QpYtu+O07erzpLU+betT/NVngxcRERG5eFmpTcLDQ71qpNzcAsc0Y3d45R34ncCznqysDCIg\nwFbj7r8HD4bw/fd7yM93BmvukMrPzx/PEXt2+yygL+668zhffllJXFw4ISG7KCj4Le61E5OAGLKy\n7iMvLwm4CXeNe5KsrB7AONyjHc3+eQZk7s9ViHeN7Fszl+BcA3HIkLxqwzx3EGctkDuXo/u0frWI\nyMVHIaE4+D7ZjKlD2+N4b3pSYKmVOfXDXVjYbIcsXzE39zhbtx4mLy+a7OxM8vOPWx7l5pwynJPT\nloiIHy1PGQYcT8Pd98l8fXr1edJ6vp7S6umwiIiIVGfv3iA8a4R9+4KqPc5zVFt29i4yMu4FwlwP\nWg2jgrCwJCCCfv2K2bp1HgUF3TGXrhnF2rUfeq1XmJkZTnR0LmVlhXz/fSZFRS2ADzGDvFaukCoz\nM9yrfzZbF8c5nXXcagoLH2H7dhswBjPsc4aK3XBOOS4tbUtw8F8oKbkGc3mdkZhTlnEc29zxZ++A\nzBlk7t1rIydnHmFhl7Fv39eUlk7DOU05MPBzyspi8VwDsTo1BY41BXLnsn7TBiAiIhcfhYTikIn3\nk83MOrTtA/yPx+s3LLW66io7O3bMxNw05Xv69LFbvuLAgckcO2ZO9S0uhv79kzl69BlLbZ0bgZhr\n7d1p+Zqmn/C+TzmWWtXnSWt0dK7XNWNi8urY5zOjp8MiIiJSnZyc3XjWJj/9tKfa47zX9PMO4z79\n1CA/f6rrHCEhqYwYEUpamnvDkfz8AMe0Xd/1Cp/Bc8masLAkBg0Ko7y8KX37riA7e5fjeubXW7fO\ncgSUZkBnsx3HMDwfjpfinGZshnFg7mD8I4YRQ7t23xIZGcfRo6+RlXWf4+sGQUGf4+dXQHh4pms9\nRPBcZiYIMDh8+BilpS0xRx2aD6ebNCkAprjaZGWtAKpOF54zZwCegWNkZDe6dCmtMZA7l/WbNgAR\nEbn4KCQUB+dOcs7NR2y1H+7lAN7B2UFLrb79tinwtKvdrl3W1yQ8dswfz+Lw2LG/1KG/Z+6Xvwwk\nPd29I/Mvf9n8dE2A+j5pPYXn96amJ81nm3Y3FhERaVysrmfnuzFIRETHas/nO6rNc+Sd+aDVe8Tb\nmjWDqH4qcQuf88R4vb7ssm4EBRV5BJIDadduHlFR3YmJyaO8vBVHj6ZRUXECCMMwgvGuXQMJDX2T\ngoJcoDPm6L5dwNOUltrIyDDo3z+VDh3asHx5Os6arLQ0FphCcfEhevZcgM12GeHhmfTqFcnq1VM9\nzp+KuWleCs7pxb7L3zjDvPpOF67v6D7fn4F77qnLOuUiInKhU0goDu3w3nzk1Tq0LcZcw7Ar5lor\n1p5YlpVdimeBZ762qiPexWL1xWl16rMhR2BgK+ABnAVdYOBCS+3q86Q1K6sNzmkv5usVZ3SeutLu\nxiIiIo2L1YCqa1eDnTvdI/u6dk2t9ny+o9o8dyouK2vF8uXeIZn3zseFpKU5N4jzXq/QdwZMbGyR\nTyAZRkREN6KijrFq1Q9UVDyBd2B3A35+c7Dbe2HWrQOAFJo27cSpU8eAbMB7yvLHH5fQqVNznCMB\nTc6aLJWSEnNTlOJig6NH38C7TnX2rRR4mqCgEsLCrgKeISKiE127uh/G1ne6cHh4KK+9doMr6Js+\nfTXJyaMwDCwFwL4/A/n5rxEfP7ROfRARkQuXQkJxOIp38XWsDm2DgbkebR+11Mow9nhd0zD2Wr5i\nYOABysrcbQMDD1luW58NOXzXuDFfN6zzNe1XuxuLiIg0LlYDKquj1WbPHsDWrfPIy4smPDyTjRsn\n0bmz+WA3L6+A6dPd55g9uz+JiSlVptkePBhCdHQJNttCDh1qybFj33LiRDFlZXNo3jyKoUObkJx8\nI9Onr/aql3Jy9rBzZwfMZXE8A7sywCAy8hRHjzqnJC+ioOAR3LVsCn5+u7Db3ecrKAgiJ+cQ3vWy\nc2pytNc1KitzfI5zrocYDHSmtPRW/vtf82v9+6fy2ms3uDZ1MadKD8S5kcqZ1H3Vhb2ApQDY92fA\nSq1blx2VRUTk500hoTicwFzfxZxGa762qgvexVcXi+1aAfMwC6ssoKXlKzZpEgw8jrkj8kGaNAm0\n3HbvXjCLpRCgkL17rU+tPh+B3flaFFq7G4uIiDQuVuuc6mZIVBcUzZy5lYwMM3wrLjZ47LFUFi/u\nWO05EhNTThtiJSamsHPnTFf/Rowwj8nNLaCsrNi1CcqQITYOHepIRoZZ63kHdgG0a/c6L7/ch5QU\n59TmcvLzPWvZFgQGdqWkJAlzIxNzI5XIyA3072+2iYnJwzAqyMpawa5deykpcV/Dbs8D5mPWtkcx\ng8kFwHhgM75BrO/ajc6p0tWFp1YCuJrD3tMHwN4/A/nk5u6jb98VdRp9qJpRROTCpZBQHALwXOMP\nHqlDW981CQ9YauXvb1BR4X5q6+8/x/IVy8qKgN6Ya8IEUlb2X8ttjx07ADzmuu6xY3Mttz0fgd35\nWhS6PmGqiIiIXHjqU+dMm7acDz+cgjMoKi9fWGUGRm1TZ32DrbVry7jyyhRycg4RGRlHly4ljlrE\necxx1q7NdG1U4tw5GQz+859n6NMnip077YD5mcz1EL8D7iUqajPt20exePEYABITF5GW5j1CsKIi\nA3N6sHO0oEGXLqXV1mT79/di6FBzxKTdvofS0jigLeZD9ybAVMz1CEPxnTpddaq0jaio7nzxxVhH\n304fnvqqPuw1LAXAnj8D2dnfkpExi6NH6zb6sCF3VBYRkYalkFAcvNcHNF9blQv8BYjFDAhzLbVK\nSAgjPd29Icfw4WGWr+jn15LKSvdaOH5+1gPG4uIoPD9rcXFby20b0y5u5pQad3CckzPv/HZIRERE\nGlR96pxPP3UGUODcvXj4cOszMKKijmLuYtwCOEF+fgX5+YHAvWRkrOGrr0IIDv4Cc9fhMGA1+fl/\nYPv2qjsnZ2R0p0+fY9x44yk2bVpAUVELKioM4F6gVZV+JCeP4rPPZpOV1Q5zRs0lnDr1W8xZL6nA\nRKKj55KcfB/V6dy5I0eOmMvthIfPp7R0Gu7AMcnx53LgI6Ki9nLttWaA6gxifadKe/bvTAK4msPe\n0wfAnj8DfftCRkZdRx+eu6Vxfk405VpELhYKCcVhL96jAffVoW0MMMuj7dOWWj355EA2bnyf0tJO\nBAUd5KmnJli+YnBwWyoq3EVLs2ZRltvabLl4flabLc9y2wtNfQqWyMg4r8IwMrJbw3XUYd++w8TH\np1S7dpGIiIj8POTmFjBt2nJHMPgTgweHUln5E95rTf9EcvJvqC6Y8q1PZs8ewBdf/Aj0why5Nwr4\nAHM2wxrAHElXUjKWoKBZBAREc/JkUyora9o5+SRZWW1co/HM9Q/TOXhws6sfX321k5tvfsf1GSor\n7cBNwAbgZqAAMyCsAFK45JLYWmso52c6ccJ3J+YIwCA09CdCQn4kIiIOu/0kMTF5HDwYzvTpq73W\nX/QN8HwDuKioY67px1FRR/Hz83cEju46r6awt64BsNXw73wtjdPQ6lJHa8q1iFwsFBKKwym81ySs\nqEPb1ngXQ60ttRozZhElJa2AppSUNGPUqEUcPdrLUtvg4AxOnHAXLcHBRyz3dvDgUJYvd49gHDz4\n4n3KV5+CpUuXEr76yn2Pu3QpbcCemuLjU7zWLho6dJ7rybyIiIice9UFJffdl+6aWgwGy5enEBi4\nD3NKrVlfBQe7dyt2nmPkyM3ExhZSVFTM6tVTcdYnn302lx9/dK81aIZzlcCPQBs868zS0r6UljrD\nKned4uf3OXb7ccw6tivZ2bvIyxtUJTTLzS3grrs+YNWqw1RWPulxzRRgNXDY8TodZzhpZUaFu+Z6\nx3UfTswAACAASURBVKtfoaE/MmJEKmVlYSxfPpWMDBs7dy7COerxdPWZbwBXXt7UY/1C6+c5E8nJ\no8jPf42Cgg6WRx+eTed7dF5d6mhNuRaRi4VCQnFoCXTELOyaYC7QbNWPeI9CPGqp1bFjdq9rHjv2\nteUrnjxZjmeoWVRUZrnt/Pkj2LEjhZyctkREZJOUNMly2wtNfQoWZ1Fq7m7MOXkqnJfnvTug+VpE\nRETOl+qCEt/6Alrg5/cLr/fatImr8Rz+/m94HXv0aFuf8zXHDNh+oKzsB0pKxuKuM09i7hJ8HeYG\neL8ATmK3/5Hg4OcpKTGDv4wMg86d5xMQkEt+fgB2eyfatMmmd+8WpKe3Aq6u8hnCwnIpKQmltDQJ\niPT6enUzKjxDrO+/zwCOu/pls11CTMxR16yIvn1XeJzPe7ThRx8dp337ua61Fz3DMN8ArrbznO1g\nKjw8lLlzf0mfPn3O6nmtOt+j8+pSR2vKtYhcLBQSikMAzieRdZkybCrC88mxe4Hn0wnyueYey1cs\nK2uG53p5ZWXW1yT03GkvI8N7p72LTX0KFmdRumPHjnNWHIaHZ1Jc7O5veHjWObmuiIiIVJWbW8Da\ntWX4BiW+9YVZ++0HHsa5eUjXrqmu8/iGLXZ7Dt6jAA9RWel5PnODkS5dNrNmzSDatfsLJSXXYAaE\nI4G/Y04J/gUwznHeAsrLg4CVOKcsFxREYs6OmQHYyMoy+OmnJzAfjhfh/ZC7kOHDAyktPcVHH1Vg\n/m9S7TMqfHclNuthG/AIhuFdZ3rfM+/NS0pLfyQjoysZGU356iuDsrL3+fDDadV+T2o7T13qvPM9\nSs+K8z06ry519MU65VpEGh+FhOJwZlOGTR082gK0t9jO96mx9Q1EgoNDqahw77wbHNzKctvzXXCc\nSxdawbJx4yTX7oDh4Vls3Ki1XERERM6X++5LJz/fH98gKjl5FOXlC1m1qpSKikIglJKSh2nX7nWi\norqfdl29Sy4pIzvb/YA5ISGCwEDn+VrgucFIeHgo3br9gp07T+Jeo7AVsAL4FnCOMlxNZeUM3KFf\nCuZDcO96s6KiA1CGuZHIQsxQczs33tiR5OTxTJ263HH9D4AkmjZtwZgxQSQn3+j6PM6AbeXKCq9z\nh4WVU1bmR3Fx1TrTsyaLji7BZlvIxx/bKC72xxw56X5wvmlTUo3fk+rO47kJilXne5SeFed7dF5d\n6ujGtLmhiFzcFBKKww947igH1tf4s9mOYRi/x70RyExL7fz88rHbPZ8i51u+ZrNm+Zw4cZ+rbbNm\nsy23rU/BcSE8dfV0oRUsnrsDysXD+ffGnLq+52f/90ZERExmwDUIc43AEMLC9pCcPI3w8FCWLbuD\nvn1XsH37WNfxUVHdSU8f5LX+YHLyqCphy5w5U3nssa0cPAixsTaSk6cQHh5a7QYjAF27Guzc6Q7R\n2rWbR1QUREdHukKynTsLvTa1M0PAmzB3F/YcMXgMc2ShAcwBjhEYWMbhw82ZPn01hw+3xAwOzZF8\nV165gmXL3J8RPAO2FK9zDx8eCBikpVW/0UhsrMGaNYNc/wYmJi7yWMfQd7MTU3W159mo7S6Eh+bn\n+2H32aijL7T/dxARUUgoDna8p/7+yXJLmy0Cw3CP6rPZ/j97bx8fVX3nfb8nzyEBMkN4yCQ8xPBg\nBR9aW9ra8iCvK11EWHHbGqX33W7VvSR0r/t1d7WtwrbWdlFo67X3a1/Xmnpty125X5WErS+IUMI2\nWkFcbI2sWBElRAM6SQwkmSFPk8kkOfcfvzlzzpmZJGcyCUnI9/2Pnsx8z+/MScj55PP7Prhsxc2a\n1cHly8Yu8qxZ9s267m7rrrA6tkcivfYmw66rIEw0zP9uzp6VfzeCIAiTBbWxOhOlETWyss7EeN26\n8TqYVor8vR/Z6mUoM0XPXFSTiFu49dZZ/OIXN7Fjx8nw+8+fb8PnM5uB6aiMw16UKbgUh+NDNO3v\nQis6gBuBvyYQ0Dh9upzTp++joGAXsUp4rf0H9RJspSmnTQuycWMqO3fexve+dwyn8znU1GcnDkfq\noNpR16R/+EOT5drXrDEqdEZTe5o/Q1PTGeDL6OXhE7GH3mTb7I6F/O0gCMJkQ0xCIcR1WHcwr7Md\nqWltqB40Stho2j/aiktKSo44TrK9pt//EWYB5/fbz3xMpNfeZNh1FYSJhvy7EQRBmJyUlW3g5Mnd\neDxqOIjHs5XS0qook8uc6bV+/Qkif+fbyaYaykxxuXJIS0vD61WvV1ZqnDq1O9xjuqZGY86cx1BD\n7fKAOmA5KgPyMeBVoIucnJl4vQtCK+pDUAhdr3pWZWdnk5n5OD09i8jI+JDvf/+eqOuD34bic4B7\n2bhRXWtJyT7L1Of09OghL+ZnoK5JVQal+T4apc2j+QyN7KGoMjKjy8OnClcjy080kCAIkw0xCYUQ\n9VhLMS7YjtQ060RadTw87e1LgS2m4+dsr5mZOc/Uk7CTzMy5tmMTKX0c794ogjAZkX83giAIkxOX\nK4e8vOV4PEa5rdnk0DTzu9VBPNmFZiLNlOrqAG1tvrBGq60FvewZOmhpmWl5f2/vLOARDBNPN8N0\nMzBAf38WbveTdHfn0dHRSH//HMCHyjhUg0wuXrwcnpDs92ts3rybjz++KeL67sTpfJrFi5dZDLZY\nhpCdZ6BuFuoa1VyqPZrP0Mjry8tbzhtvbBoq5JrmamT5iQYSBGGyISahEGIehvDqBOybbmCdUAet\ntqI07bwlTtPqbK/4uc9pvPyyIf4+//ky27GJlD6Od2+UeJE+KMJEIJESf0EQBGF8GcrkiGWy2Mku\nrK4O8OlPH6Cl5X1ycxexdKmG292LWRd6vWmWrMWWlgvAo8AV4AiBACgz8E5gJg7HbMwmnuo3eCPG\nROQq2tv/lvZ2fajJ3wNHSEqqID3dQ1HRQm64oZxDh4os19rW5o5xH2ZSXJzPM8+s4v77X2Dx4l8B\nuWRmNqCmHFuHvNjVjnbv50gZC8NqMmvNq5HlN95/O0zm748gCOODmIRCiA7guxhG30/jiO1ClXfk\nAw2hcw1PSkoOsBtwA42kpNifUHz27CWUwFP9DM+cabYdm4ggGI/eKIk83KUPysRlKom2REr8BUEQ\nhPFlKJMjlqaKpZUizSmvtwmvdzOwGY9nN6dP30Be3pvk5PwCn+961Ib1BurrT4TPMXv29Xg8DqAK\nuA9NU+dyOp+muDifQAAqKw0TDwqALqZNC5Ke/mu83gfD16n0ozrPwIDKGFy8eC89Pd309Fira1yu\nRlpbffT2Bi29BsvKvkZpaRUvvpgJ3A848Hq9USW8drSjdVryPlSvw5xB7+dIGQvDKlJrVlc/TXGx\ne1LomquR5TfefRXlbwFBEOJFTEIhRDtm000d28PhAE17FGO68Q9sxaWkBFE7wiouJWW37TW93jwM\nUQper/3BJXl5zZgnObvdftux40EiD3fpgzJxEdEmCIIgTAaGMjnsmixlZRt47bWnaGxcgdpc3opu\n0sENwF/T1LSJgoJd+HwbY55vwYIrvPWWht47UOFg8eJlVFRsoq3Nx80378bjmYsaWnIXMJOvfGUv\naWlu9u/XN6M1lNadbjnP4cM99PV1AA8De1Ga9mNuvHEWDz5YGdVr0OXKMekq/TxO2yW85s3Choa/\n0Ni4DX2IiFr/m6NuWo2FYRWpNb3eZezfv5HJoGvGO8vvaiB/CwiCEC9iEgoh5mIdXDLHdqSmLbTE\nquPhSU6+BPwIKAQ+JDm5a5gIA4ejAXgIw5h83HZsUlIq5knODsde27Ej5fz5i6xbt4+2NjcuVwPH\njm2hqMjefYrswVNb6xg6wIT0QZm4iGgTBEEQJjt2TRaXKwe/3w38temr2UQOD8nNXcRttw12vj7U\nhnZ0SS+o/ohdXSmoYSLvo0zIBjQtJzQIRJ03P78NTQvyxz9epKPDOE9f33RUdYsTSAPuRdMcVFVp\noQxC45n93nupzJ+/i6YmJ9CGeUpwU9MZ2tpW4XLlDFk1YDUeN6I08Q0oberl618vHzXTKtZ1aBqW\nr+3c+SXLtGi7mYCRWlNlgY6+rhmLCozxzvK7GsjfAoIgxIuYhEIIL/A9jAf8jjhim7D2JGyyt6I3\nFfgUaic3Ha/3bdsrLlq0mPfeM4yzRYuKbMc2NLgwCz11PLasW7cvPIGvu1tj7drdfPzxo7ZijR48\n6v62tNjPuJwKO6STFRFtgiAIwmQnPpPlMma9mJT0Dunpb+D3/0PodY2lSxn0fI2Nc4FNqEEj5Uyb\nFuS//bcgvb0prFx5iKamM3i9WzGy8fYB+TQ2xi7Z/eMfj/Hss0oj1dWdC5UjHwnFWjfyNO2S5drP\nnz9NT89PMbTvD4HFgAOP5z5uvvmXoYEvb9PUNBfIpqZGo6Pj/2P69Fzq67M5fdqD6q+YEzrP51CG\n6X2kpPyYior/2+Z9HZ5Y1QuA5WtqivUPLO+x873dufNLnDy5m6YmJ/3901Gl0qOvawarwJhK7VtG\ngvwtIAhCvIhJKISYjXVwSW4csc0YGYH1wCVbUQMDABfRexkODGhDB5jo6GjEbJx1dNg3ztzuVsxC\nLz+/zXbsSFFNr6ObYNvB6MGjYmfPXmY7dirskE5WRLQJgiAIU4nVq3OorDRa22Rn93P77UXAC1y8\nmEVLy0XOn19GScnzMY0eY3MtB7iXjRvLgdSwcaSy8coxqkV6gbsoLDwa83pmzpxORcVaAEpKOkLl\nyBtQ5uIHmLMVMzO78PmMaw8EcjHrOofjOjTtb0PHamNYabeNwP8C3gXcVFU1oIzORaHX9gFbMDIq\nlTmZlbVw1Myv1lYf1dUNwGFUmfWGGGXSjiitajcTcMeOkyFzUQ2UcToPUlycPuq6ZrAKDGnfMjTy\nt4AgCPEiJqEQ4iLWwSXx7F7OBX6CdTfVDmmYjT77cYkZZ0a5ihJ6mhaMI3ZkuFwNdHdbm2DbZckS\nf6gHj4pdsqRnjK5SuJqIaBMEQRCmEnv2fJ3S0iqqq1vxetNob/8fVFZqFBQ8S1eXD69XGWtK8xhG\nT2urj/vvf4FXX20jNfV/k5XVxpo1sykr+1rU1GTICv2/htPZQnHx0bBZFWm6PfSQsWFbVraB3t69\nHD+u0d/fgN/fSn//UyQnz6K4OJnGxutpajKe2SkpOwkGDW2WkVGP36+ZrsF8TS3AjzH07m4M/dsB\nvIgxgfkooLF2bfKomV/btlXh9T5sWn8fhYXq/80VDZFa1W4moGHe5QBbWLz4EBUVw/dkjJfBKjCk\nfYsgCMLoIiahYMI8uCQeFmEVQ4tsxi2IiJtve8VEjLMPPsgwrasfjy3Hjm1h7drdoZ6EjRw7Zl/k\nJZJxNh4lGFL2IQiCIAhTCzvPfn1zbOXKQ9TU6CaS3o7lMIMZPdu26ROEHwEc+HzG4JBI46ig4Cx5\neQ7y8i6RlDSb+vpsSkuPUFa2Icp083qfYd26teFrO3DgWwDMn7+L9vZ/BhwMDHh55ZV/JiPjOuC3\nwJ3ATHJyumhp+SGaVkhGRj2HD68Ply43Nb2Lx7MKZfhlEa13dXNSw+2+jN/fgaa5mDatDJdrLj7f\nbi5eXMaFC3WYy5HN9yQerRVpojmdvZSVbQ4dG/ryySe3sH17/HrzarVPGUwPT9b2LaKXBUGYqIhJ\nKIT4FKrcQeeK7ci0tDp6e42Hc1panc3ID7H2Mrxoe81EjLO6uvcBo49MXd2PbMeOlKKihbZ7EEaS\nSMbZeJRgSNmHIAiCIEwthnv2mw2RpqYzGIM+9Ky7DsyasK7uHHfd1UxSUip/+IMj9N5oEzFaD27F\n5cqhpGRf1PVEmmWD9aS2lt0exe9/Ar9fXZfT+TRZWX14PIXAg4CDnh6NZ581Pm9b2ypuvvmX4f5+\n8JTlsyUlvc/NNx9gyZIeysr+3mIMqevWS5X1voqqHNlsfsWjtSJNtOLi9KihJbpBVVFhb6iemavV\nPmUwPTxZ27eIXhYEYaIiJqEQohmrYXfZduSRIxv5yld2MDCwmKSkOqqqNtqMdGHtg2h/9ywR46y3\ndxFmkdjba18QTbZdv/EowZCyD0EQBEG49hhKAw337DcbIrCRgoLd5OUtD2XdbULvBZiS0kFfXxte\n7yJefPECsBU1pbgds07VDbPB9GDk9VRXB4AA5mzAwXpSG2W3V0IxxnkWL1btbTweBv28LlcOubkL\n8Xh0jZsLfB+4EWhkYOC/s2TJ67au2+nsZfHiQ1HmVzxaK5aJVlo6egbVeLdPGe/1R4roZUEQJipi\nEgohfMAu9CEi6tge//Iv5xgYWAFMZ2AgnX/5l/dZt+7Lw8alpLTT16cLRo2UFPs9CRMx6zTtAmah\nqY7tMdJdv/EyF8ejBGOyln0IgiAIghCb1lYft9zySzyeG4AOamruAKrCGmi4Z3+kIZKXt5w33thE\nW9sqSkt1A8vB0aOXaG//R4xN63KUgfg7kpOfYsYMN2vWOCgru2vI6428Hq83DT0jz+l8muLifB56\naHnM2AMH7mT16sfx+9NRLXQiP5fey8/4elPTu6xcSVjjtbRcwNx3OzX1xwSD3wyvUVv7OiUl+6J0\nYaysv1j9/eLRWrFMtKthUE22jfWrjehlQRAmKmISCiYWonoSJmN3QjHAK694UNON1UPuj3/8ia24\nadPctLcbmYTTptmf+Hv//S+E+tNATY1GIPA7Dh580FZsVlaQzk7DEM3K6rO97khF1XiVFIxHCcZk\nLfsQBEEQBCG2ubNtW5WpfNZcvquIfPbv3HmbxQRzu3uxkwnocv0GlcFXhdKHntArD/DVr9rXTubr\nqas7h9era0SVDVhRsYlTp07FjP35z9/F738C1SfxJtSgETcORy1nzy6iqEhjw4Ygr7/+NJBLZqYH\nj+c7eDzOsMaLHLCXlbUAn8+L3qfw3LmznD6thv7V1GicPKkyK93uLjZv3ktDg2tIDTWc1hrOoLsa\nBpWU0w6N6GVBECYqYhIKIZzAfRji7wPbkR0dXcAOYDFwno6OXltxX/pSKlVVRibhl7/8a9trHjvW\nBFwXPn7llSbbsdOnp9PZqRuiKUyffs52rNvdilnkDlaqEsl4lRRomuXoqqw5Wcs+BEEQBEGIbe5E\n6hjIshhLkc/+yJ6Ad931a+65Z3hDZPVqqKw8gqFJN5KTs5vsbDh/fhklJc+HDa9YRpi1157G0aOr\nKC3tYP/+maEVNBoa/sLmzc2cP5/FkiXvAH00Ns4Nn8P4rB3AfwLKHNU0jTNnyjlzZj0FBc+yePFy\nCgs7qK0torHRGb439fXZLFnSYRmwt3ZtCm+++WzYaPX79fuo/uvx3BAqu9a4555y3nhjE62tPkpL\nYxt9w2mt4Qy6WAbVaGf+xaN9p2LWoehlQRAmKmISCiFmYxV/s+OITQJ2YhiMj9mKSktzYJ6onJpq\nf0XVQNowNf3+J23H5ubeRFPT10zHv7O/MH2Yr1nTgraixqukQHZxBUEQBEGIh1jmTqwpwmVlW2PG\nt7b6qK5uQGXidQAbaGycyxtvRJfNRrJnz2YWLz6I12us73DMw+P5Fh6Pg7fe0qiufpriYjednd0c\nOfIAusbp7d1LWlpalO4pK9vAyZO7Q6XSXTQ2bqOysgq4j7Nn9eEgm8LvLyzUy4k3AAex6uNs4Cge\njxouUlOjUVCwi8gsyWgT7i7Wrz9hyi7stMRAl+V+Q2IabjiDLpZBFWvYSyKaMR7tK3pVEARh4iAm\noRDiPFaxYndCMcBSrAJqqa2oixdnAXeHjz/66IDtFTMy5hIMGmtmZMy1HdvS8g7QizL62mlpqQW+\nNnRQiMbGucAm0/EhW3HjVVIgTZEFQRAEQYiHWObOYFOEY7FtWxVe78MYmnIfhYWOmO+NxOXKobg4\njf37zZq0BbOW8XqXsX//RhyOX5q+foXf/95Daup8lOm3Acihvj4bTYOurhTT+RwoDUjE/1/h6NEP\nUD0En6S/P5eBgRas+rgTYyKzip89+3puu82q8SJNuNZWX2iqs36O28jLe4Kenmw6OrLp67scuu4B\n3G4/kJiGG8nm9Ghrxni0r+hVQRCEiYOYhEIIfSdVZcjBQByx57AKqFpbUS0t71viLl+2X/YbDF6w\nxKpje/j9mVizEHfbjh1pRuB4lRSMtDwajNKPM2dgxYpzU6L0QxAEQRCmOrHMnXh0TKwJvWVlm0e8\nfiAwk8rKSKPOgaaZs/GOEAxuD20ga8Be4JsUFnbGNC0NNJTuBThCe/t1WNvv/Ao1PCWdzMzTpKbO\no739AmrDWL1nyZKe8L3RS4RrazNoaXmf3NxFLF2q0dsbxOMxBpkUFOzms58t5ODBb5rWKge24HDs\nBRKrQtm580ucPLmbtjY3LlcDTz65Zcj3R5uYdyRc9RLPz4wM8RAEQZg4iEkohPgUauqbTjwP5wzg\ncdQEuAtAuq2o7OxsU9yHZGfPsb2iprlRYkoNPVHHdpmDNfPR/rqTr8nwyMqjwVr6ocpxpPRDEARB\nEK51dHNH3yxcv/5EXH3iYk3otRNn7Uun+gm6XDm0tfkoLS2nujoQmlK8AWVmOTG04BXM2i45OcBX\nv1pOWdkdrF9/wvJaTk4Pa9YMcP78PpYu7UXTgjQ2HqKurhevdxbmwSmpqZdYvjybBQu8JCUV8Yc/\nOIBUlAnpwuk8R1mZMTjPrJ1Aw+Mp5/Tp+0hO/jfLNeTlLaehAax6VJmrDQ0uwKo58/PbCASCrFx5\naMjvhX4Pq6sb8HpV/8Pubo3t28upqFgY4z6bB9NYpzEHAgW0tfmG/b6NBhNVX0/FXomCIAhiEgoh\nVHmFsZv5oe3IpKReBgaeDMcmJe2wFXfx4iXgJ+G4ixcfj2PNRqDUtKb92IwMD+bPqo7tMdKMwPES\nGSMtjwYp/RAEQRCEqYjVaFIZeHb6xOlx589nUlCwK5RFhy3Dp7XVxy23/DLUN7CDmpo7gCoqKu4L\nay9lFlZRX3+CpqYzeDxbUUahhsPxT2iaoe1mzGinouK/A9GmZXZ2M3v2lFJf/wG33npr+BpKSp5n\n//52wBicEgxu5OLFp2lpCVrWU+bkRoqLOy16LnrAizru779C9HRnzXJdep/CWJOfVb/Ab4a+F15O\nnvwleXnLozSlYVIeZjANZ2cwTTD4WSorN1JaWs73v2+vjVAiTNQhHtIrURCEqYiYhEIIF+bMPCWC\n7LFs2fW8954Ru2zZMpuRRViF1HVDvDcisuh6zpwx1iwqsi9gurutA1PU8diSiMhIxGBMpHxDSj8E\nM7KbLgiCMDWwYzQNHad0w2232dc6KpPtBxiG2S5qaxdZ3mM2kurqbuL2258Nl9MuWzaDl182tN3q\n1cbzKXJwicezldLSqijzq6xsA4HA7zh8eID+fmsPRK93I0onK/Nw2rQgGzeWR00FViW7NwKvo3oX\nvhs6DgDPkZzcwMaNcykr03thl3P+fAaXL58jN3chS5eWxzRVrSaedXCKWVNaJzPH1nB2BtPoJd1T\nfYNYNswFQZiKiEkohOgADGEHP7Qd6Xa38N57vUA+0EB+vr0xxQMD1mEpAwP2h6XccEMSZ84Y13vD\nDeW2YyEPc2m1w/GrOGJHRm0tGCZsB7W19o3JRAzGRMo39FjVk9BeJkCiiBE1cZHddEEQhKmBHaNp\n6DiI11CJzsBbTkvL2UHfv2PHybCp2N3tZWDgX3E6ZwLvs3q1kz17jIF0LlcOeXnL8XiMyorq6gAP\nPdQRPtb1R2PjXPLyzuDxRBtmyvQD0HC5Gti5cwvf/vYLVFV9RDD4Y1SZcgcqE3EmsBpVzfEj9MqZ\n/n6N9PTysLYZWc8+fXCKD6jihRf8uFw/Z/XqHNxuQte8AdiH09lLcXG6RcMNNZgmsqR7qm8Qy4a5\nIAhTETEJhRAzsWYS2jdmXn31E+DTqN3bZI4fP20rLhBIxuhJWE8gYN84S8T8CgQ+xCx6e3rqbceO\n1MRqabkAGL1eWlrsD0tJRHQnUr6hx546dcpSjjOWiBE1cZHddEEQhKmBYYwMbjQNHTe0oRJLS1lj\nvcBZWlvnMX/+U+HhH2bNFZlZ19i4I7yu2YQb7Nq83jR27XqXdevWApFZkF+moGA3XV3zLIZZZuab\n+P2gZyPefvuzeDy5QGEorgrr0BM981B/HUb6/DTr3qamd0OGZxVwL/39DrxejcrKfWzeHOSee3R9\n7KCsbHPUvYg11CRWSbeur+vrP4j7eq8VJmqvREEQhLFETEIBAIejBU17BF3YOByP2Y4NBnMwiyL7\nk4b9GOZiOvC27TU1zXJkOw4gEJgO7AbcQAOBgH2xNlITa/bs6/F4DIE4e7bdkuyptYspRtTEZSr9\nHAqCIExlrMZIbKMpFnYn6sbSUqokeBcez3LgLPAD/H4HHo8x/MOsuWJn1sFg2mHnzi9x4MCTBIM3\noTbDN9DQUBV+3ao/nOTlLef552/i9tv30dp6EPiAgYG8cCzk0NbmBi6hKlQ09P6D+nWoYw1oxrw5\nnZ/fRknJvrg2nM2bvm1tqygtLefw4aCpZY4DmM4f/uBl40Zj6EssrFmY1qEmsTaX6+3vpU9IEqlS\nmai9EgVBEMYSMQkFADStA9iFXjKsae1xRGdgLqW1O90Y0iKO7f843n//C7z4YiYANTUagcDvOHjw\nwWGiFA5HF5r2UwxD1H5p9UhNrAULrvDWW4ZAXLjQ/v2dSruYYkRNXKbSz6EgCMJUZqTGyFDmk5na\n2gzM5bKHDvUCRzh2bAvbt5/k8GF3hPmltJdZc8XOrIutHVpbfdx++z6CQTfQDtwJzCQ/vy38nshM\nxqamd1m5shWvdz5qU9sYtKc07724XA10d88Lna8c8AAbTe87hzIV76OgYHdo0EgngUDQNIQk/qoJ\n/fujBq2Yy6I76O5OZf/+e+np2UNGxrSYxthU25CVKhVBEIT4EJNQCJEVcTzNdmRa2mV6e/8BXaSk\npdnLQkxJmUZfn5GBmJLypO01jx1rBb4Xjn3llZ/bjp0+fRHt7YY4mjFjke3YkZpYwWA35mEppeO/\nCwAAIABJREFUvb1dttecSruYYkRNXKbSz6EgCIIQP3bNp5aW91GmliqX9fsdIbOrfBDzyzr1F2Jn\n1g2mHaKHojxNamqAixfnUlLyPGVlG8L6o7YWzp17D4/nCVSPwSPAAErDbQAgObmeGTOe48YbcwkG\nz9PcPBNVUePF7X6K/Pwbyc9vQ9NyaGzMprDwdcrKtoZNupUrD9m6T8NRVraB3t69HD+u0d7eSH//\nHOBrgIMTJ7x4vfcTyxjLy2sGnkdp0nbcbv+I1p8sTDVTVBAEIVHEJBRCpGLumQc77Eem5tPba2QS\npqa6bcWlp8+ir894aKenz7K9ZkfHdMwPfHVsj9tvT6Gy0hCft99ub9AKjNzEOnkyA/OwlJMn99he\ncyohRpQgCIIgTE6G20jVyz5bW+ehqldyMGu58+czAKNsubU1D6ijqGghN9wQe+ovDK8dooeiLCUY\n7KS2dgu1tYaBpgzKfZw+/VkG7zGo0d//GF6vg6oqjc2b95KWZtaF29A0QuWtrpjlraNl0rlcORw4\n8C2AkLFqvtZc02e+QnV1AytXHsLtbuXPf64H/in8Xodj74jWT5SrNaxOqlQEQRDiY0xNQk3T+PGP\nf8y5c+dIS0tj586dzJ8/P/z6iy++yG9+8xuSk5P5m7/5G+67T8yB8eM6rALqOtuR3d3NwN+jP3y7\nu+2V73Z11WPu0dLVdcH2mpr2iSVW05ptx/7iF7dz6tRuWlrmkZvbxC9+EbtnTixGamJ1dTVh/axN\ncZ9jJMi0YEEQBGE8EA049RhuI9U6HERDGYWGNrp8+RxgLVsGjRtuiC4PjUffRJpEqamnCAa/G3rV\nyCw7f/4iBw60h64pVo/BK6jSY+NrFy/O4L/+627LeiUl+4Ysb01KSsVsPo6GSRd57wMBTBviR/B6\nH6amxsikNH+GhgZXwuuPhKtVBixVKoIgCPExpibhSy+9RG9vL+Xl5bz99ts89dRTPPPMM+HXf/az\nn1FVVUVGRgZ33nknGzduZPp0+xlhwmhiNezggu1ITZuLWWyoYzvMxzpRucD2mtCFuXxXHdvDLD49\nnsF75sTi/PmLrFu3L9yU+9ixLRQVDR+bmdlPMGhcb2bmgO3rTQTpwyIIgiCMB6IBr31iGXUVFfeF\nv75+/QmLgRed0TcbNUhuGdBDbq7SU9b3GVlw5nPZ0Tf6ddTWOigo2MXs2dezcGE7f/6zRlPTqyjt\neUc4s2zdun0Eg/OBO1AGZgrWHoMzgAbMevm992q4++52GhqMrMHhyluVKTe6Jl3kJraaUlxOdXUA\nrxes9z3X8hlU/0Wu+mby1SoDlioVQRCE+BhTk/DUqVOsWrUKgJtvvpkzZ85YXr/++uu5cuUKDod6\nQOj/FcaDS6gS48XAeaA1jtiLWA3Gi7aiHI4LaNp3MHZSvx/HmrOAj9AnFIPTdmQiomTt2t/S2PgY\nelPu1aufoqFh+7Bxt9+eR2Wl9fhqIH1YBEEQhPFANOC1i26+VVc34PU+TKRRN5iBF5nRB5cxt7pZ\nsGAPJSX7qKsLAL9FDQTRs+CuUFNzhOrqgxQXp1Fb62A4fROZufjZz+7lzTebaGp6An1ASWbmP/Pe\nezcxf/5TeDwpQB+gC7YPUWW5t2BMNe7FvMHd05PEwYPWISSFhdqQ5a2R96Gu7hwlJaNr0OnG2MqV\nh6ip6cCq05tQJugyMjNP4/F8F4/HedU3k6UMWBAEYWIypiZhZ2enZVc4JSWFgYEBkpKSAFiyZAlf\n/epXmTZtGsXFxWRni4ExfmQDK1CZbunAm3HE5mHNCLRngM2alUpLi5FdN2uW/R/H1FQ/waAu8jRS\nUx+3HTtr1gXgKfRJzrm5kUNbBqe5eR5mUaqOh2fPnq9TWlpFfT0UFjooK/ua7TUTYbIJMCmPFgRB\nuDYQDXjtYphvh4ll1A22QVlWtoHq6qfxepeh9OIWUlOfZMWKG5g/v40332ymsbEUOApkkZr6P8nM\nnBcaNqf6A3q9ashJQYG1VFnXN2YdocxG4zqOH9fwem8wfe0ofv8TvPOObp79CHgEw0zbgWq/Y84m\nbMLaw/u5qM969Ogqenr2cOKEF8glEFCZfbqe0QeO/P73PQSDHXi9M9m//w6galCDbqT6SOnAO1A6\nPQCkAaXATAoKdjN79qd56y19o/3qbiZLGbAgCMLEZExNwuzsbLq6jDJQszg8d+4cx44d449//CPT\npk3jkUce4T/+4z/4q7/6qyHPeerUqbG85GuG+O/TLKzNmevjOEcrhjE4ALTaiu3sXIZ5mEdn529s\nr5ma6iIYNA9LcdqOffPNVuBx9M9aU/NEHJ/1QyLLsu3Gfv/7S8P/X1//AfX1NpdMgIcecuP1PkND\ng4v8/DYeemi57ev1+TrYvftdGhpcuN2v89hjK5g5c2xLwR577E9UV29D35H3ep/hqae+MKZrjhbx\n/puz3t/Wq3J/JwLyO9wecp/sIfdp4jIWGhDke26X0b5P5mfWxx9fQPXns2ao5eR8xKlTp5g582LM\nrwPcemsGL71kmG633ZZOWlojr7ySTkdHHsogVNl/weAmnM6f0N4e3R/w8uW5zJnzE5zOxSxY4OOh\nh5bz8svHuPfeY7S0fCZ0bb2W6+jr+wRlkulfy7KcEwojjq9HZTOWAxnAKVJT5xEM/iNqY13H+lnr\n6z/A7+8JZ1lWVmrce69Vz3R1dRIMbsPQk+WcORP9fdPv+5/+lE5HRyawipqambb1kdKBz4e+b310\ndPxt+LUZMxYya5a1fNr8vTIzVv/uxkMbjxXyu8kecp/sIffJHnKfxoYxNQk/85nP8Morr7B+/XpO\nnz7N0qXGg2D69OlkZmaSlpaGw+HA5XLR3t4+7DlvvfXWsbzka4JTp06N4D79CaswcsZxjgqsBuMP\nbMVmZf07PT3GdLesrPe59da/tbViTs5RuruNEpKcnKdsX29397uYP2t393W2Y9eurebll3+EEpIf\nsnZttq3Y8cyQW7du7YjiSkr2hQ27s2c1XK5yKipGdi67+HyNmL83Pt+CSfFvfiT/5sbj/o43I/vd\nNPWQ+2QPuU/2GC8BPRYaEEQH2mEs/m2Yn1lK6+1Dld/uw+nspbg4nbKyb4TKXIsoLTVniH0jrHki\nX+vtnR0u11UlxlkoA7IKyKa3N5O77vo1r77qxes1zMVAIJNLl37E2rXlPPPMN3jwwUp+/3sPweDj\npmvcS2rqk9xyy02hYR6zqKzUs+qySE//M4HAptB6R4Ag8BtUyfEcoDZ0nvtQ04h/SjBo/vxbAC9u\n91PMmfMpWlrOcfnyQn72s3O0tLgZSs9E6h3IYsWKrqjvW/R9Lwfuw+dbwKJFRba05bp1a0Pnep79\n+w1DcMUKKCv7Pwb9XulM1N+1Rs/JDFpa3ic3dxFLl2rjUoUyUe/RREPukz3kPtlD7pM9RqIDx9Qk\nLC4u5j//8z+59957AXjqqac4fPgwfr+fr3/969xzzz1s2bKFtLQ0FixYwN133z3MGYWx4y+YS3Dj\nGVwCC7EKHXtDQNraBjCbi21tP7C94qxZy2hsNNacNWvpkO8343I10N1tiCSXq9F27PTpeYAuZjWm\nT7c3kW4yDhAZj36Gk608OhGkX6QgCNcyogGvLSKfWU5nLwsXVtHS8hGzZy+jp6eVBx6otAzwiGXU\nRA6R+PSnD5jOeyfwP1GlyEof+nwbOXVqNwsXLiQraxetrXn4/emo4SLq2bltW1XIaLSWP4OT7OzL\nvPHGJkCV/D7wQCXHj2tAA9dfn4bT+UuqqhrQtJWhOFX2C38NbEINVrkBh+MKmmac2+G4Qmbm3tAQ\nu2+wfftJTp/+AR7PFU6fPkJq6iWMvoozycu7REnJPurrs3G7W2lo+BhzGXNBwVnKyrZG3a/aWjBa\n+nRg1kfxastY5b2TeahHZM9Jj6ec06fvYzJobEEQhInMmJqEDoeDJ554wvK1wsLC8P/fe++9YfEo\njDc5WHusPBpHrAe1w6oyAtXx8Gja9VinIl9ve0Wv9zzm8givt8527IEDd7J69eP09CwiI6Oegwe/\nbjt2pBPpxssQSiSDcTwMu6nUn2YqGaKCIEw9RANeW0Q+s4qL0wFCxpiDt956Ht3Yi2cztKXlfQw9\nNxNYgGGGATjweJbi8QSA5TgcfwK+F3rteerqeqmru0ys8md4l9WrZ1u0UFNTI17vVsDJ669rFBTs\nRtN+aoopR+lZtXZy8hw2bfJy7FgHPp9+bi+a9gnd3Z+luzuZhx+uprFxbug11TtRzzh0Op+muDif\n3t4U9u9fjyqndqHa1+wFnBQUnOXtt7fG1GctLRcw6/PU1B9z993llJXdwfr1J4jUlkPpvslsCMYi\nelq2OpZNV0EQhMQYU5NQmEwswfqgXRJHrF6KoQus4af9Kt7Gai6+Y3vFnJz5eDzGsJScnALbsT//\n+bv4/Wroid+v8bOflVNRcZOtWLe7FbMAzc9vsxU3XoZQIhmMumF35gyhkpSxN+yuNQE7FFPJEBUE\nQRAmN7GeWVaTajrxbIbqZlZrax7wQ0AfKPJVMjOfxu83m321wEPAUTTtC8DTpKSk0df3Q7ze6PJn\n1au6hQ0b5rJnz9f49rdf4MUXM0MrLwBeAB4EHLS1WcuCVblzR+hY46tfzaSi4j42b/43Kiv1YXtn\ngB+Hr+/48d185SvTQjrLalwtXryMiopNrFx5CHO/RZWluBfooq3NTWnpkZgbubNnX4/HY5xvxYpb\nqKhQWbextOVgus9sHublNZOUlDps1qed7994DpqLnpbdybW26ToR7rMgCFMPMQmFEHVYd18/jCN2\nPlaBZc+wS07OpL/fMBeTk+2bhK2t54FloaOB0LE9EsnqCwQ6gF3oZdmBgL3JyDt3fomTJ3fT1ubG\n5WrgySe3DB80CiTyWXXDTvo9jA1TyRAVBEEQJidWk0Lj6NFVYZPCatK0E2va8GBElorqZb3wLH7/\ngzgcP8ThKAA+YmBgPvBLjIy6TQwM/G+s2rOH1NQKsrM7WL06hz17vhO+TjVl+H7TWk+H4rSoFjQZ\nGW+wbp2by5cPWTbw9uz5OqWlVdTXw5tv5lhKj7u6nGETtbraY+mdqN8Hda8iM9+6gFS6u53s399O\nIPA7Dh580HKflizx89ZbxvUtWdITfm1449ZBba3e1zCA15sKrEL1X4w/63Ow7994tdHRP//58xlc\nvnyO3NyFLF1afk1tuk6E+ywIwtRDTEIhRDZKoLmBRmCa7UiHox5NMwSMw3HBVpymLcVabmy/r6Df\nn4k5e9Hv3207Ni+vGXMGo9vttx37pz91YS77eP11e+vu2HESj+cHqEEpGtu3l1NRYa93YyJISasg\nCIIgCCMllknxzDN3hAZGOCgo2MXs2dezYIEfh2NvKDtt+Oz4yE1Mh2MOmtYFbAVy0LTPo2kdwM7Q\n+17EqhkvYd3cvsQttywP9x+0kovVnMsmNXUnn/tcEnv3bmH7dsNo27nz79ix4ySXLxM6r8Llygl/\n7lOn2iy6t7+/NZwJCEQMAlH3oaxsA6+++i988skm0zU3AqXh4yNHnmTlykOWjLGhqg5ibTZG6r6W\nloucPv0D05p6OXViLXAmQl/lqbDZOhHusyAIUw8xCYUQl4HPo4RDMvCG7cjk5B76+ozsuuRku6ab\nPjXOXE5iD4djHlZxOc92bFJSKmaD0eGwN3wEoKvLaVlXHQ/PeD3kEylp1bMHVLnxOSlxEARBEIQp\nRiz9EpkFeNtt5VRUWDPgWlt94UEdkWWSra0+mprOYJSI3oGmNQFJqN6CM0NfN5tZnZg14/TpGu3t\nRtsZyKep6V1WriRqvdWrobLSrDczCQa388EHP6WoaKFl07akZN+gmVvG576CXtYM0xkY2Mb+/TPD\n741lXLlcOTgc/aG46UAHDkeeJSMxGMynpmaTZd14jTCz7svPb+PYsTlEGqTxZn3GQjahrw5ynwVB\nGA/EJBRCpA1zPDj9/XMwZ9f19//UVlxS0nQGBh4HFgH1JCXZN86+8IUgVVXGQ/OLX+yzHXvhwgzM\ngunixRm2YzMzfQSDxrqZmVdsxSWSvZgIieyymv8IOHtWShwEQRAEYaoRy6Sws/E5VJnktm1VeDzm\nYXm7gO8AM3E4foim3YDqL/h7DDPrDgoKdpOXt5zCwk4CgTwqK83lyo/g8dyKxwM1NZqldHfPns2U\nlpbz7//uR9MygbsAB15vbpSRGeuz6Zumhw8HQ6/lAFuAPaH/KvT7MFgfuStXiizvdzh+YslIVIP/\nDgEd1NY6GAnmjMfqag2frxnwAk7UIJVzrF6dE876zM9vIxAIRmUwDsdU6Ks8EfoBToX7LAjCxENM\nQiHEDKzDR+z3JNS0bFT5QjbQgabZ69OXkuKnr8+YKJeS8kPbawaD7Zh7A/b2ptqOtU7S07h8+Zzt\n2KysLtrbjV3grKwuW3GJZC+OFyPNfhwvUTURxJwgCIIgXEvEMilKS48Mm90UqSEOHw5SUvJ8TCMO\nVqCMNx8OxzRmzuyhq+tfSU/vYsaMncydu5wlS3ooKzMmALe1+SgtLQ/12ksDZmPWWa+++nT4WvQN\n05MnnzKZk16CQQ/796tyX93ILCzUBhkGsh7VF/FF9OxHpZWj78NgBmlk/8O5c/v5/Of3cvy4htdb\nC+jTvzWamwfXpsPpndj9HpfjdJ6jru5By3tV5uQ3o651OKZCqe9E6Ac4Fe6zIAgTDzEJhRAurILN\nZTsyOdlHf/93MQaQPG4zbjZmczE5Odf2mseP96CmKOuT5Z60HZubu8gyGTk3135vwFmzFtDU9BGq\nd2MDs2bNtxXX0GC9v+p4YjPSSc7jJaomgpgTBEEQhGuJWCaFneymyAzE7u5L7N8/k9de+1/4/emA\nMdzDmCZ8hIGBx/D51Nfvvnvw57ieMbdkya9Qg+ys7WBUH0KFbqo5nUtobf0RmubG4WjC7/8CkZuh\nR4+uoqPj17z0Uj8DA62cONFDTk4R8CywHMMgfBY1Idkw4MrKVObiYJusx45tYe1afYhdI8eO3c/2\n7Sfxeu9FDVN5MHxP/P5fWK7dbAgOp3eiTdjlwEaKizujNk+l593gyL0RBGGqIiahEOIjrP0BP44j\ndi5WMTLXVlR3dzPwf2EIyB22VwwGraZbMDjLduzChQFOn/5WeN1Fi+xn9fl8lzGXVvt89gaXJNJT\nZPwy5Pow987RtKCtqPESVSLmBEEQBGHsGSy7yaxX3O5e7rrr1xw+PEB/fzbKAPsdjY3XoZ7Vu8jJ\nmctttw3w1lsf4/M9R09Pt6VH3+9+187mzf/Gnj1fj6l7tm2rwut9GNUj8F8x69jMzMZwCW0g0E1l\n5QOh177KPfeUU19fQE1NhyXm/Pn3KS3t4PTpjwkGfww4aGrSaG7egTFARR/+sRzVLmce8GXWrGkL\nTT/ODvVbNEzQpqYztLWtoqhoIR9//KjlM9TX/wXD1DT32p4d/oyRhuBweidSczqd5ygutmfmSs87\nA7k3wtUi8m+9hx5yj/clCVMcMQmFENbyXSW47NHffxZzv73+/vdsxWnaIswiRx3b5QJWU/NCHLEj\nM78AcnLmm7IQO8jJKbAVl0hPkfHKkGtsnAtsMh0fshU3XqJKxJwgCIIgjA12Niwjy1zvuaecGTMC\neL16Hz4f8ED49YGBJ/jLXzJCvfpq0TQ3Zm03MJBMZeVlFi8+SHFxmmXN8+cvcuDAh8Bh4AzwDZR5\nl0Vm5ps0Nn6XxkYnNTUaTufTRJpqSjPcEY6BM/h8c9i/PxvoR22enwSyGRhYYIlX79cHqaSRmvr/\n8Oc/azQ1PRF6/ctkZj6O3/9ZoAuPZyulpVUxtZuhXbB89jVr1HqxDMHh9E6k5ty58+vs2HGS9etP\nRH3vpOfd4MRzb3y+jkEH9QjCcET+ref1PsO6dWvH96KEKY2YhEIIF7AQY7pxx9Bvt5CNtZ/hX2xF\nORz1lobNDscF+ytm59PZaZQMT59uf8dlpOYXgM/3MSPJJEykp0htbQZmgXj+fMaIzhMvIx22Ml6C\nU4SuIAiCIIwNkX/Enjy5i7ffLrUYIbEMrdWrA6bJwrMsr3d0JNPe/gMM/bgTVXa7DGXCpQKP4vU6\n2L/fukm6bt0+gkG97cxGlNmnXnM4fKGv7wOy6ewMYB7eoTTCBqqqfklHx3LgHDAHw8DchNo41/Xe\nbzEbeA7Hn9C0G0NrbiAYnE5Tk/46gBOHYzGwGqgCXqO62kNbm89yv1pbffT2BnE6n6O//yOys839\nF++KOQVamX5f4uRJvWy5gSefNIahQLTmHGpis6aZIy0HU554tPvu3e9SXb0NaXkjjITI352ToS2V\ncG0jJqEwCizDusO6zGbcdFQvFzfQiDL87DFt2mU6O/8HRlmJvT6IkFjG2ezZ1+PxGJ919my7n3Xk\nJDJoJRFGOmxlvJosJ7KuDD0ZW/T7e+YMrFhxbsLfX/l5EARBsBL5R6zHs9ySHWcYWkaZrTLj1GTh\n+vpsGho8NDaazbY5lvJiWID600Q/x4uWNc2bpG1tblTVSxVKP3pQmYozCQbrgCPoGiYY3GiZjFxW\ndgcuVw6f/3w2L720EbUxHtnHb7bp+E6czqdZvHhZaLJyYcRk5TaUjgXdzFNDSoxr8Ho3UlpqNY62\nbavi4MFvhs+zfn05FRV3h18vKdkXGrRyBThCaup+AoF0vve9Y3g8ylzt7tbYvr2ciorB+2sPNbG5\nurohVLI9uPkrDE9k73FpeSPEQ+Tfpnb7wAvCWCEmoRAicvcwnt3Es1hLf9+3FTVzpgOfz9hBnjnT\nXlYewKVLVzCXR1+65LMdq2ecKcOCuDLOlizx89ZbxmddsqTHduxISWTQSiJMxmErI0WGnowt5vt7\n9uzEv7/y8yAIgmAl8o9YOMvhw+7wxOJt26rweLail+8WFJwNTyPWf3+2ta0KG4aFhZ289toli2mY\nkXGetLQ82tt/CNwK/Bcqqy96k1SZcJWAbrJtJCXlJ/T1pREMLkAZf4aGyctbzhtvGFUkAI89tgKX\nq5zaWgfnztXg9xtrwWUMbTuT4uJ8Kio2hT5H5GTlICrrUJl5KSn76evrw+FoQ9P2ARuAnCjjyGre\nXaG6uiHcR9E6BboKuI9g0EFlpYbT+RzxGFKxNseN59xhy7kizd+hkA01A7e7NaRvpOWNED+R1VAP\nPbR8vC9JmOKISSiEGIg4tm8SZmZ68ft/BBQC9WRmttqK++IXs6iqMoy+L34xy/aaaofXKPuFf7Id\nmUhpxXiUtC5dqnH6tJHRt3Rp+ZivCVOrx58MPRlbJtv9nWzXC/LHmiAIY0tZ2QZOntyFx7MctTn8\nEN3dznAZsPo96UQv+c3Lc0SV1kb+jvJ6r0RM+32QoqKFlJQ8z/79m4EeYC9Kq7XQ3t5LW5sPTYNP\nfWomHk8Q8+/qvr45wFZilQjH0jAzZ06nomItYBh/9fXZvPvuq3R3pwP/L3Ae8NLZeZPFwKuouI+2\nNh/f/vYL/P73A/T3G2ZeX5+DTz7RB5zcG/6vfg36vair82BkTR7B632Ymhpjc6qwUAv9f2SWo9nA\nHF6fxdKu69efCMVbh7dAV8xn3kimLE8ldMNZWt4IIyGyGurUqVPjeDWCICahEGYa1r6CP7Ud6ffP\nAD6FKh9Ox++3ZyY5HKmY+yA6HPaHpVjLQByoXjL2uP/+F3jxxUxgOmfPthMI/I6DBx+0FTsevVvG\nu8ffSDIuJxtTyRAdDybb/Z1s1wuS/SgIwtjicuXw9tullJZWcfiwm+5uZ+gVe8M0Bvsddfr01pD5\ntJzt2/+TsrKZYf1RXe3B681H16ft7RqlpWqjtLq6FNVzUEPP4IOk0Nc2AHeSmvokt9xy05DayWp+\naRw9uorc3NdQOljXxI9y5Ehp1LXrJmh/f2vofZFmnjqeNi3Ixo3l4Wsw7sUV4NekpjYzMJBHf781\n6/Do0VWm+2CUca9e7SQ93b4udLlyeOaZO8Kfs7T0CG53V+iaN4TuWS+QDqynsPBo1DlGMmV5KmE2\nnAVBECY7YhIKIXKxCpvcOGJdWA3GD21Fvf56F8aOr8brrz9te8V585pDu7Qqdt68ZtuxJ054gfvD\nsa++an/d8fhDfLx7/J06dYpbb731qq9/NZGhJ2PLZDOcJ+PPg/yxJgjCaNDa6uP++18IaaVcVq+G\nPXs243LlhHWByvSzGoLD/d4c7HdUpK7q6dlDRsa00NCT2bzyio/29li/2xwog6scqAcew9ChKnPv\nzjsLOHDAWmIcSSxdp2nXYdXE1mPz71d1n7YCe4CLmHsy6hOQN25Mteg4671oMQ1gMa69sLAzfL/N\nWY7q3n4t7kzxyM+5efNe7rlHnTM/P4im9dHYOIvCwqMxn3nxTFmWzHZBEITJjZiEQoh6rOUGF+KI\njTQYZ48wzr4x+dprfxtRovIt27GJrCt/iF+bjJcRO1WYbIbzZPx5mIzZj4IgTDy2basKVVuozdTK\nSi1q4EYsQ3Co35uDTemFaF114oQXr9fYyC0o2EV7e+TvNr0MNwdVzvtvlnMYmXt3Dft5Y+k6h6MO\nTTNr4ksMXt6biyqzngZ8F70nY2bmmyxb9imWLi2PMt2M39dVwA2DXPsdFrMtL6+L/Pxe6utdlJYe\nidt4izU9tapqVej8LgoLOzh6dNWg54z1jBnMGJbMdkEQhMmNmIRCiAUoYaOGY8D8OGJHZjB+4QtB\nqqqMuC9+sc/2ikVFC/n440fjuEaD1auhstJYd80ax3AhYUb6h7jsqgqCMJZMxuxHQRAmHtZMPfXf\nyA3ReDdS1FATo490QcFuysq2AmZdpUqG29utQ9Nmz76e226z/m5ra7vCa6/tpLl5DklJl5k1q89S\nXRKZuTcUsXTdjBkzePnlfah2OB1AB273TvLzbw5fg67rNE03EK09GVescEQNStHRf18fOhTA79cw\na2jztZeU7AubbfA8etWO2Xizqy+HHl4yvJkXjzE8kTbURX9ffeSeC8LkR0xCIYQH+Hv0GbAZAAAg\nAElEQVQMo++xOGL7MU8aVlPehictzYHqg6JEWGqq/RUTeQDt2bOZ0lJz6ePwO806I/1DXHZVJzYi\naITJzmTMfhQEYeJhmEmjl5kcaRrl5S0PP2ON/oMNeL0Ph/ryGWsvWdIT1U+vtzdIY+MO4Ar9/Ufo\n7vZRULCL2bOvZ8mSnrg2SSJ13c6dt/HII9Xk5DTQ1TWDrCwva9YsZc8ea4mvYeBdAfaRmlpPMGiU\nGg91z/Tf1/PnP2WZCJ2Z+SZlZf8wyH2bTizjza6+HHp4ifWcQ12zHSZSZrvo76uP3HNBmPyISSiE\nGAB2AIuBOsB+Vp9qFK0PIEkB2mxFNTbOBTaZjg/ZXjGRB1AipY8j/UN8vHZVxfyyhwgaQRAEQVBm\nUiDwu1C/5lzWrHHEtZkaiVFqHNtA03XVypWHqKkx+gyqsttUysruoLTU+ox2Op8LnUtNE25vV0NN\nbrtt8Gd3LD1kXl/n7rufo7LygfC1rl27lwMHolvaGLouB9jC8uX7WLo0vk3k3NxFeDxH0at4li37\nlEWjWc22dmIZt3b1ZSz9Gq+ZZ1dTTqTM9omU1ThVkHsuCJMfMQmFEEnATkaWSdgBvAcUokqPO2xF\nud36NDi1Zn6+PXMRoLY2A/MD6Pz5DNuxushRmYTnropxNl67qsYkZ6ip0eKa5Dwe9ykREjFERdAI\ngiAIgjKT7OoEO6hSYyNbrqDgbLjU2Iyhk1SfwY0bDcMv8hkNl4k1Tbi+PjumFtA0uOWWX+Lx/ADd\naOzt3UtXVyc+X6NFMxw/rlnOeehQPyUlz0dpikhdt2hRLz09fdTVNVBXl8v99x8MD3wZjKVLNU6f\nvgM4CmTR0vIubW2+qCzL+vps3G4/DsdeGhpcFuMt8jqams7Q1jZ4b0Ez8Zp5djdUh9tQv5ob2BMp\nq3GqIPdcECY/YhIKIZZgFWCLbUempGTR1/cT9IdBSsojNiP7MJcba5q9MmWAlpb3MRuMly+fsx1r\nFjlnz16drLHx2lUdrUnOV+s+JUIi2YAiaARBEARh9FEGn9Grr6urjfXrT0SZQ0PppMhn9OrVTtLT\ny6mu9uD1WjMUY2kBAI/HOiDk+HENr3cb0ZpBNyDVOfv7M0PnG3p4SyAQ5MUXpzHUwJdIyso2cPKk\nYV56PJvCMdFGWuyJxuocu0OfrwuPZyulpVXhdYcy5OKtjhmtDdWrWb0xkbIapwpyzwVh8iMmoRCi\nDuvwkQ9tR/b1LcYsGtTx8Fy8OAu4O3z80UcHbK+ZnT0L2A24gQays122YxMROSPd/Ry/fmFTZ5Jz\nItcrgkYQBEEQRp9Ig8/rTaOmZlNcwzein9HKMGtr81FaarfPXgdWndsS9b7WVh+ZmQG83udQPbbn\nAl8jlqaI1HUrVx4Knyt6bUWsz5mXtxyPZ2S9BvXztbW5UUMHNwA5lnVH05AbrQ1VQ6/5gCoOHw7G\nzNYcDaRf79VH7rkgTH7EJBRCXMbak/BSHLEtWIVXq62o5ubTgB+VSdjOJ5/UYjYNh+LChY+An4bX\nvHDhR7avNi+vGTUlTq3rdvttxz74YCUHD34Tc7lKrF41E4XxmOQ8XiRyvSJoBEEQBGH0MRt8dXXn\n8Hr1UmbDEBtKW5mNNbe7lUCgz5KJaM6YKy2toq7OQ3T/Q42amjswlzyvWDGNo0etWnDbtqrQQBRd\nz+5DlT8PrSmMvosLGGrgSyzDrrBQi6ld7Gx8ms+n1i0H7rWsO5obvqO1oWrotSrgXrq7HezfP/Er\nVgRBEKYKYhIKACQn59Pf/wS60EhOftx2bFpaO729RtlwWlq7rbiurlRU+Ylas6trp+01e3sXYRY9\nvb0LbccmJVnXdTj22o49dqzfsq46nrjok5wNQRf/JGdjCvTEzq6TbEBBEARBuLpEmnjQR2PjXEtG\noG783HVXMy++eASzMdfa6uP3v+8hshRYJ9oI2wdsisqKM96npg07nb0UF6ebtECVSR9s5YEHKonU\ngpGGmtPZy+LFh4bVFEbfxReAp0lNnc6dd2ZEaa5Yht3Ro6uIpV3sbHxGnk8NeykfslQ7kQ3f0dpQ\n1fXa4cNBursnT8WKIAjCVEFMQgGAgYE8zEJDHdtj6dKbOXPGiF269CZbcd3dsy1rqmN7JCdfZGDA\nED3JyR/bjm1ocFnWVcf26Oq6gHmXuKvrou3Y8SARQZfIFOjxQLIBBUEQBOHqMpSJ19Hxa955p4W2\nNjcuVwPXX58FGJODHY69bNtWRTCYTXQpsCJ6aMn08P+bTaXIacOLFx+iomJT+PVIfRBLC0YaasXF\n6ZZzDIbRd1FlSd5yyyEOHIiOi2XYDaZd7Gx8Rp5v48bUqHNNxA1U/TOXlDwfyiBMzMC8moNQBEEQ\npgJiEgoAaJq1ZFjT7JUMAxQV9XHmzDfCsYsX283Mu4RVFF62vebq1Vm8/PKP0Ccqr16dZTs2kV3V\nzMx5BIPlqIl6nWRmzrUdKwiCIAiCcC0xlIn30kv9BINqKEd3t0Zz85PE3qRV2XRKW51j9erBpwir\n3oI+4Ah1db3hXnbxajvj/VfC53K7g2zeHD1BeDhUBqWxdn5+W8z3xWPY2dn4tHO+sdhAHS1TbrQM\nzKs5CGUqIearIExdxCQUQvgwTxoGr+3IQKAD2AXkAw0EAvYMO03rwTrduMf2munpuYASnqCRnv5r\n27GJlNHefvs0KiuNHfN16+yXKguCIAiCIEx2zOaB6sVn9ABUGhJAY2CgFWuVSi7RPfs0ampmokp/\nNQoK3mXPnq+F1zIbSfn5bWhakFdf/RVe78N4vUYvO7uGk37ttbUO5sz5Cb290/D5HsHrVROJk5Of\nIi+vkX37tsRhiPRh1bPBmO8abcNusPONtbkzWqbcaN2PyTZob7Ig5qsgTF3EJBRCBIGL6EafOrbH\nn/7UBTyKLvpef323rThN6x/yeCheew3MgkAd20PTLEf2Axl5jz/ZjRMEQRAE4VrAWmL8ZQoKdpOX\ntzxs4jU2qj5+J0700NRkmIJz517my1+2GnltbVc4eXJ3uCT52DGrORfLSFq58hA1NVZTyK5hFgh0\nU1lplDw7nc9h1pP9/Tfi8WzkxhsfZ8WKz5GX10xSUmq4HDmWfmtsnAtsMh0fYqSMhl6MNHd6evaQ\nkTFt1DToeJpyse7PZBu0Z2Yi/30g5qsgTF3EJBRCLAO+Yzr+V9uRXV1OzA8RdTw8Doc25PFQtLef\nx7wb3d5eZzvWLJ7Ono1vZ2yku56yGycIgiAIwmTFbGbU1QUwdJ+TvLzlvPFGdA++Dz64yNq1ugHY\nyLFj/ydFRdZBc6WlVXg8Rkny9u3lVFQMPYwuHlMoUn85nU9j1qyq1Y25nLkTcOD3f5aamk3A8+gD\nTgbTb+p6vMBRIIumpndpa1s1IrNnNPRipLlz4oQXr/f+hM5pZjxNuVj3ZyL2XbTLRP77YDKbr4Ig\nJIaYhEKISJHUMvTbTWRmNhMMPo8+rS4zs9lmXC6dnR8BbqCBzEz7A0QgFWt5tP0f5dpaMHrfdFBb\n6xg6YBSQ3ThBEARBECYr1uzB3xJdNhxNUdFCPv740SHPG6mPDh8OhvsMDmayxTKFBsvIiu6ZmItq\nqaMMvdTUVtzup0LZgJeALaHP1hWKmc5w+m3nzi9x6NA/4/d/FujE49lKaWnViMye0dCL0X0cc+M+\np/l+zpx5kYqKovD3YzxNuVj3ZzIPrpvIfx9MZvNVEITEEJNQCNGB1XSzv1uUnNwx5PFg9PVdAX6K\nLmL6+n5ke82UlPn09W0xHT9rO7a5+X1U5iSARnPzOduxIy0LkN04QRAEQRAmK1Yz406czqdZvHhZ\nwuZBpD7q7k5l//57OXlyF2+/XRpTY8UyhUpK9sXMyLKe30tmZiOdnftDE5VXc+lSJ3qmIKiehGlp\nfvz+fwiduZ1IQzRSC/b2BvH7n8Aw5ZSxEvm+nTu/xI4dJ2NqSP29dXUezD0eR6IXI82dQAAqK+PT\noJFTq0tLjQy3yPvf2uqjpGTfVSmZvdb09ET+PJPZfBUEITHEJBRCdAHvoaYFf0g8JqHXmxZxnGor\nzuFYjHn3zOEosr3m2rV9vPSS8VC9/Xb7/Qz9/mzMgtDv/4Xt2JGWBezc+SVLz50nn9wybIwgCIIg\nCMJEwGpmzKS4OJ+KiugS43jRDa3Dh4N0d6cCy4HdeDxzKSj4Be+883dRJcoQvWmrqkKiM7LKyjbQ\n07OHEye8dHYGaGzcjtnMi8wU/MxnbuTo0VWUlqpzu91+HA7rxOPS0sgSZmtfQ8iisLAzSjO+9tpT\nNDY+Fj7u7d3LgQPfAsz68gqwD6ezl+Li9BEZsJHmTlubL6Kf9vDnjCfD7WqWzF5r2W3X2ucRBOHa\nQExCIcQ84HEM4fREHLEZmE03+CebcXVYS5w/tL1iVlY65szHadPShokwcDhmYzUnZ9uOHWlZwI4d\nJ+PuuSMIgiAIgjARGCszQze0SkqeDxlNuwGll/x+jbVrd8csWY40pgoKdhGrBNrlyiEjY1qoJ99h\nrGZeNrEyBYfLoIouYba27CkoOEtZ2VbWrz9heV9z8zzL8fHjWoxz5gBbWLz40KiYsDCyjLB4Mtyu\nZsnstZbddq19HkEQrg3EJBQAmDFjPu3txgN+5swFcURbB5eo4+EpKlrEmTO7UT0JGykqsr/mxYtZ\nQx4PxerV1rKLNWvs9yQcaVnARO45MtpM5EltgiAIgiDEz1ibGWVlGzh5chcez1zMeqmtzR3z/ZG6\navbs67nttth9Cg8fDobe24HZzJs+/QzZ2T34/btxOOaxZo2DsrK7hr3WSC24erWT9HTz2ltxuXKi\n3peUVE9/v3GsaZ+Ez5mX14wakqL6e7vd/jjv4OhiNoVzcj6irOwbg753IpfMXguIrhYE4WojJqEA\nwM03ezlxwhAnt9zSFkf0RawZgR/ZirruOjhzZkFozWSKiuwLokT6Cu7Zs5nS0nLOnIEVK7AlCHVG\nWjY8lQTURJ7UJgiCIAjCxMPlyuHtt0spKPgFfr+hl1yuxpjvj9RVS5b08Mwzd7BtWxW1tRmsWPHP\nXL7cT1/fZ4B3UQZcL/ArnM5UiovTaW5O4fjxH4fPMTCwJ1xmPJQZE51V+bWo97W2+ggEukPTlHNZ\ns8bByZO9XLpkVMFkZhq6NykpFXNVjsOxN5HbmTBmU/jUqVNDmlJXo2R2qhll5s/b1HQGj+dRRFcL\ngnC1EJNQAKCmpg14GF2c/PnPO+KIzsOYFtyJKl0enr4+MAuiYPDXtldMpK+gplmObMfByMuGp1JP\nwqmYNakM53PXvGgVBEEQhLHC5crhnXf+jrVrdb3UyLFjsc2QWMaUuVcg+DF04t0onfotMjMfZ+HC\nTwEaTU3WSpgTJ7yhsuShzRizgdba6otpLG7bVkVl5QPoOjUtrZz8/C9w6dLd4fPMm3cg/P8NDS7L\ntahjKxPVKLsaJbNTbQM6cnDMVNHVgiBMDMQkFAAIBK7D/ABSx3a5AnyXePsZvv56imVNdWyPRPoK\nmh+8Z8/GJzSkJ+HwJJI1ef78Rdat2xc2U48d2xKzYflEIZGfJUEQBEEQrBQVLYzZgzCSWMaUVaNZ\nB5KojWwHfv9nOX36rzl9WmPOnJ9gGDBeOjq6geeAjwEHhw4VUFLy/JBm3GDmVaRePHw4iMt1AfCi\n2vKo7EcdO9ppKKNsohqIo8VU2oCGyM/bSax+m4IgCGOFmIRCiPNYS4br4ohVk9j08gl1bAdro2do\nsb1iIn0FExEa0pNweBLJmly3bp/FTB2sYflEYSp9XwVBEAQhkvE2pyLLMmEj6rlsHUhiGC3eUKQD\np3Mxa9eWh2LfxeP5Yej9zwP34fc72L9/6A3AWDqgtdUXuhZ93Tvo7k6lu/tRCgp2k5e3PKos107J\n7lCa41rPtJtKbXsg8vPeMejPjSAIwlggJqEAgKbNRE2UcwMNaNp027FJScsYGNhiOrZnEn7hC1lU\nVRnm4he/OM32mnpfQUNM2e8rmIjQGGnflUTWHG8BHi+JZE2qBuXDNyyfKEw10SoIgiAIZkbLnDJr\nnby8ZpKSUmlocA2qe/T3V1cH8HpTgVXAlyko2E1n5xx8vn5gL9AHXAIWoTa0e0Nn0FiwwEdFxXcA\nWLkSPJ7YWYi1tVBSsi+mDoulA7Ztqwr3kFNG4W5gK+AgL285b7wRPbXYTsnuUJrjamxajqcevRp9\nDycS0Z9364TW/oIgXFuISSiE6AZ+iiFofmg7cmDgE8y7tQMDzbbi0tLSLMepqWmDvDOaRPqf6A9e\nY3CJfaEx0n6GjzyygkOHHqenZxEZGR/y/e/fYzt2su0OJyJUXa4GuruHb1g+UUjkZ0kQBEEQJjuj\nZU5Ze7CpTL6hdE90z7Zy4D7y8pZz9OiqcJ/AurpWvN7HwnFO529YvPgQhYWdPPTQ8vDXrQacNQvx\n0qV6Tp9+DLjy/7N37/FR1nfe/98TAgQJkgxBTDhoioAt7CLiomuXgFR6I0qlu9ogXe1dSxfDtns/\nVvurp189bBchVe/HY9v+GLuPlu6tuyTQA0SosI0HlJXummYBjdwS1IhMEoEkE5OQSAKZ3x9XJnNg\nkrlmrjldmdfzL4bkO9d3rgmZN5/vSTU1L+n3v/+tvvzlHLlcK+VyrdRnn23VG2+06ezZS/X737ep\nv39i0D2RviApT1YHE4crlMU6aBlN4S+VeTQZ+x6mk0x7vQDSC0VCDChUcKCJZgZXk6THJBVL+lDG\niG1kJ06MH/Zxovg+eGtra7Vw4cKo2sYakP7yL19ST8+TMvbD8Wr16nKdPPmnpq5ptyWtVmbX7d+/\n1tSG5enCys8SAAB2Fm5ZbaxFsOH2EwyXe0KzkbHnoJE5AgsspaXbBpYMG/sOjh//iaRJCh3odblW\nqrf3eb3+uld9fcd1/vwP5HDM0qRJTerouHSg/V5Jd6m9PXgZck7OJWpvNw48aW/3StqswCLjtGlH\nVVjosDwDbrjCUawz7aLJtXbLowCA2FAkhCTp8svP6JNP/IHm8svPRNF6sqR/kH8019zJyC0tH0ny\nL8doaSmPpsspEWtAsrKM1m5LWq0sCTG7YTkAAEit0GW106aVy+W6L6bnGm4mX7jcE5qN8vOPafny\nizNH4D7JXu9xud3/ILfbKIh5PFu0bNlSSUYBbufOb0gylhb7Cmdut1ejRz8+0J/wGfDiguXVys9/\nVlddNSdpS0XNnLgcTjS51m55FAAQmyGLhFdffbUcDv9hEN6AdZYOh0P/9//+38T2DEm1e/dfqqTE\ntxy2QXv23BlF6+CTkY3HkU2YENzu0kuLTV8xVfuixBqQ8vJOBi2jzctzm76mlaJbKu4TSyQAwP7I\ngYgktMBUWDg35ozhW7Z74IBHFy5cotzcpzRlyhc0a9ZnYXNPYPEvL++k5s+foIaGXJWVvRSUdQL3\nSZZeDOpvY6PzoudtbW3X73/fE/R948bNUF9fhaRG+Q9G8Wrq1DaVllbo/ffPSfo3SbdKmiipR8uX\nT9X27f69B48fP6FlyyoGD3Xbv3+tZs40t19ztEJnB1ZXP6vly4vCZsBocm2m7QsIAJlqyCLh3Xff\nrT/+8Y+65pprtHLlSl133XVBYREjy9NPvxu0HPZHP6rU9u3mlsMaJyFHfzLyRx/VSfraYLuGhndN\n93fduirt2nWPfAGot/f5wRHgRIo1IM2fP0FNTZslTZXUqPnzzS/RiHUfRMl++xlaYbcDXgAgnZED\nEUk8Z5Y5nXnKyblEHo+xbLejw6u/+IuhM0voIWlNTRWSVl2UdYxC5qeSfiPphKRVCizyhdqwYa/a\n28cqMNfm5n6iFStmqr5+mlpayjV58hzNmvWZzp3r044d9wx+n8Pxj8rKGqWCgm51dl6mRYt2D+aR\nZcsqgvq7dGl5wlZOhBZvPZ452rHjNoXLgNHkWgaBASAzDFkkfPTRR+X1elVbW6uXXnpJmzZt0nXX\nXadbb71V8+fPT2YfkQT19ZKx6XOupE7V10fzH4E8+U9Gbhp4HFl/f2HANbvU33+56Su+/rovvEmS\nY+Bx4rW2fqqDB0+ora1Izc2N8ng+NVWIamkplhFMfY93m76mlUJfJu0fk0kFUQBINHIgIon3zDKz\nmaW1tV3V1eeCvlcaJ+P04lxVV7vV1tYupzNvoJD50sDX/15G7hyvadOO6qGHbhiiD4vlz6fHdNll\nM8PmiUWLdgf1weudrwsXvqJTp7zauze4aBm67Uxz82WDfYy30OKtsV9k+PsZ6zJlMxi8BQB7GnZP\nQofDoeuuu07XXXed+vv79V//9V/atGmTTp8+rVdffTVZfUQSnDr1nqQ5A4+8OnXqWBStPTI2afaF\nkYeH//ZBJySVxdBOks4oePZiSxRtYxfrSLCV0XYrhb5U7B+TqlCYSQVRKwjtAMwiB2I48ZxZFs0h\nKBs27JXHM1rBOfCwpCdkzJy7TWVlxkChy7VS1dW75PFMkpQv49RkqbDQoYkTJ1z03EZumjjwfV5J\nR/Tuux/K6fwXlZRIW7euHvzMLCpqDenD2wN/7lRoHnE664K2nblw4ZTKyvZGvH+xfGb7irfV1efk\n8YyRtFJmMmC8B1sZvAUAezJ1cEldXZ3+/d//XS+//LKKioq0YcOGRPcLSXb27HgZgcgIL2fPPh1F\n6/MyioTGUlqpz1Srvj7JGPWdIKlTfX3mZwOWlOSpqsrftqQkOUWOWA8gCdw7x+ls1FNPrTV9TSuF\nvlTsH5OqUMiG2uYQ2gFEixyIRIvmEJRws/2kqxVamPMV2IyB5eCi4lAZIfCU466uBvX1TVNf3zp5\nPA5VVXkHi4+G8wrMsdIUGatGfCcca/BaFRVr9fnPP6G+vpkyVt2sVUPD26buS7Sf2b7ibVubb2bg\nAVMZMJbB1qGKmOFmezJ4CwD2MGSR8MiRI9q3b59effVVTZs2TbfccosqKiqUl8eMk5Goo6NNxqnE\nV0k6ro6OC6bbZmXNVn//+oDHPzPVrr9/tqS1AY97TF/zmWe+rNpa/wbQzz5rvuhmhdPZGDQS7HQ2\nmWoXunfOI49Uavt2cxtWWykwWtnPMFb19TkKDIXHj+ck5bpsqG0OMy4BmEEORKKEKywNdQhK4Pca\nM/fO6/33syX9Tv6DQrpkZJzgIuC99/5GL744Tka2PaTs7B9qwoQZWrLEIZfrdjU0fHBR3wJPOV6w\noEKHDw9d6Gpq8hUFfXYPfl9W1mRNnPi0rr8+R729l+quu97WlCk5crtXyZjR6FVx8cGI98rKZ/Zw\nMz3DvQexDLYOVcQMN9uTwVsAsIchi4SlpaUqLCzUsmXLlJ+fr08++UT/+q//Ovj173znO0npIJJp\no2JZ+ltQ0KjTp/0hoKDAXOFs9OiGgdmDRrvRo0+Yvubf/V11UNHt7/7uF/rd79aZbh+r/fvXaulS\nX8GuSfv3m5uBZWXPRysFRiuzxnwBsq5OmjfvmOllqS0t7ykwFJ45E83S9dixobY5zLgEYAY5EPEU\nWJRqbq4bnDXoyyaFhWclbZMxK69DRUXGwHFgjjGyRYWMAWavRo9+QlKR+vrGSrpJUoXy83u1fPlY\nuVy36Kqrfi7p3oG2qzVhwrNqa/ufg306dKhTpaUVQy7lbWn5SNIMDVXounjvv86Br3jV3z9eHs+3\nVFdXLrf7OwPfc5umTStXYeFcFRd3aePGG4e9frhrxOszO1w+jGWwdagiZuhsz/z8Y3K5Ep/TAQDW\nDVkk/Nu//VtOscsosxW8AfRs0y0djgsKXG7hcJw31e7mmydr717/MuWbby4wfc3q6r6g/v7+9+aW\nOFs1c+YVMZ1GZwRN/zKalpZy022tjCJbaRsYII8eNV9gLCi4Um63/0CaggJzBU0kBzMuAZhBDkQ8\nXVzsC84mU6f2KnDbG4fjeUkX5xgjaxp/7utbKOkrkrzKz39Wy5dPlcu1OqDQVhDSNjhn/vCHh/X6\n6/9LvkLZG2/8g2644Uo1NjpVXNyp/PxZcrtvlq/QlZf3nlyubw+2D/w8nTq1TV5vn6qrn1d392hJ\nt8i/LU3wDMm33jJmH5aWVkQcyDXzmR3LvoXh8mEsg61DFTFD93ZcvryL/Y8BwCaGLBJ+97vfHbJR\nVxczT0ae4NlfUr3plp2dcxS4bLiz83lT7Z588ibt3/9rffbZeeXknNUPf3ir6WuePx98cMn58+YP\nLol1hpwVkydfLbfbH8YmT54z7PcHsjKKnIoDU2bP9urwYX/Qnz270vQ1kXjMuARgBjkQ8RS8ouJd\nGct0/dmkocGpwMzR2OiUNPxsPens4PdfddUcbd8euPRXKimRqqr8bZcsCS56//d/B+ecTz65XLt2\n3SNf0W7atM2S/kq+QldJyemLTv8N/Ty9/faf68UXsyUdkNShiRPfD9qmJjCHmclZ4T6zQ4uCvb19\nQf02M6g7XD4Mff7164fef3uoIiYDkgBgX0MWCdetW6ef//znkqSf/exnWr/ev+fc3XffrZ07dya+\nd0iiLkmPSSqW1CCpw3TLiRNPBAWgvLyPTbVbvXq3enqelORQT49XX/nKRjU2/qnJq55T8GbR50z3\nN9YZclLsJ8POmtWjQ4f892jWrM9M99dK0LLSNtYCI8EQAOyPHIh4Cl5R8RcaN+5xzZv3Z4PLbm+6\naZuk2xSaOVyulTp4cLPc7rmSPJJ6NWrUP2vMmJPq6bl/4Nm9am5+V4sWKSibbd26Wt/85lYdOOCR\nVCCvV2praw/IbcEDzlKrAot2BQVX6sYb/TMF33qrWU1Nj2q4YlxW1mgFzohcsOAXWrw4fCaKNWeF\nLhXOz/8/Qf3es6dPpaXbhs2ow2W10Of3eLZo2bKlYZ9nqIFHBiQBwL6GLBK2tPhnZu3bty8oHHqD\nT0PAiDBPUuBphVtMt1ywYJKam/0Fu2uucZpqd+rUZAWGmlOnLjN9Tak75PHZsN8VjpX9AWPd489K\n4cxK0LLS1ndgSkvL5SooaDZ9YArBEADsjxyIeApeUZGvq69eELTs1u2+T0Y2GztLmFgAACAASURB\nVK9p044OnmzsdObpyJEylZXtVXW1Vx7PeF24sFI9PV5Nm/YzFRbOVXPzu3K718vtzg/KZk5nnnJy\nLpHHY+xLGHo68YIFo/TGG4EDzp8psGg4Y0a3pEskSX/8Y5Oamr6g4Wb+tba26/XXg5dSnzkzRb/7\nXfAMR5/AbFhYeFq9vdlatGh3xEHoi5dgBxc7u7tHD2TVoTPqcFkt9Pl9szoBAJlhyCJh4D40oWGQ\nPWpGnqysk+rv9weMrCxzswEl6cMP+2UsV75S0of68ENzxb6srDO6cCHwmmei6PFoSSfk28/QeGzO\n6dMfyDjJ2bju6dMbTbeNtcBox8JZ4IEpbnd0B6ZkilQsXQ+8brQzWgHALHIg4vlZE25Fhe/59+zp\nk3Hir5GTCgsdQdfxZahFi3arpsZfcPPt77dokeR25w/8bXDxLrTgVV+vwcNCLrlkjFaubNcf/tAr\nqUU33FCgsWOfH9iTsEu9vdkB+yjeJmmzAotxU6e26atf/T8DhcEzGjfunDye4ou+Z6j7GJgNzexP\n6BM6A7GkJF9jx1Zqz56+oP0Qo9mHerjnnzq1Lar2ZBQAI1km/I4bskgYiDA48o0a1af+fv9o6qhR\n5g4fkaT6+iYFnoxcX/+oqXYlJbl65RX/NZcsmRCpSUB/i3ThwhQZxbpsjRqVY7ptd3fwJtLGY3Os\nHEBiN1YOPckUVpaux+u60Z5aDQDRIgdmpnh+1oRbUVFW5nv+Cg11gnCg4Q/J8Er6VNJLev/93sHl\ntqFtWlpO6PDhBwcfT5tWrquumq7i4jEX/Udv0aLdCp6xd4UCZzt6vQWqqvrW4HN5PL+UdKv8g8nv\nqbd3vK655rnBQdeh7mM0mevie3mHnM48lZZuCzocJtaTkEOff/36uVG1J6MAGMky4XfckEXCs2fP\n6o9//KP6+/vV3d2tmpqawa91d4cu9QzP6/XqiSee0LFjxzRmzBht3LhR06dPH/z622+/rfJyo8hS\nUFCgp59+WmPGjIn1tcCCvr6rFXj4SF+fufdYkrzeqxQYbIzHkeXmjg96PH78JaavOWlSh06f/l/y\nBaGCgh+abutwtCowjDoc5kdIrRxAYjdWDj3JFKkqpFLABZBoVnMgGdD+hvqsiWUWRbgVFf7nN4pS\nl1zSp9tuGz3kliyRDsmorm6Ux/OAPB6Hduww/uMW2ub48TlBOc7tvlxut1RT49WBA/9bdXX3D76W\n0Bw0bdrHKiycO3Dt+7RixQEFFxE/kuQ/0Vd6V3/4wyXyeKaFvY+BoslcQ61Oidee0KHPX1tbG1V7\nMgqAkSwTfscNWSScMmWK/umf/kmSdNlll+nHP/7x4Ncuu8zcctKXX35Zvb29qqys1JEjR7Rp0yZt\n2eLf6+6xxx7TT37yE02fPl2//vWv1dTUpCuvvDLGlwJr3pG0Tcasvg5JdaZber01kk7Jt/TX6z0p\n6d6I7U6cGD/s4+F89lnwbMCeHvOzAa+/fqz27dssX39vuMH8dadPb9WhQ/77NGNGj+m2duMLm8ZS\nWnEASRipKqRSwB2ZUrV8HQjHag4kA9rfUJ81Q82iiLZ46H/+PElrdPPNWyWN1ooVB8K2j3RIhrEc\nOfg/bqFtSku3BS17Nvbzc8s4/KRQf/InP9E773xXTmdemKLbfUH9KSw8peDsfKmkrTIOWCmQ5NWF\nCw2SxijSTMl4FPgSvbVN6Pu7ceMX9eijBy96v8koAEayTPgdN2SR8IUXXrD85LW1tVq8eLEkaf78\n+aqr8xeeGhoalJeXp1/+8pc6fvy4li5dSjhMqXEKPI3NKBqaNVGBS3Clh021srJ0t7v7IwVv0vyR\n6bY5ORMlfXew7dixz5tue/68FHif+vp+YbptrFK174EvbNbW1mrhwoUJv54dpaqQygnSI1Oqlq8D\n4VjNgWRA+xvqs2aoWRTRLsEKff7APQCHaz9ULhruP26+NvX1Dk2btlldXVPU3p4j6dsycmyFpLVq\navIfbhKu6BZ47cbGU5IeGbxeTs4PBgax7x38u97eR2XsEXjxoSyB7LB3dej7e/Bgedhl1GQUACNZ\nJvyOM7UnYay6uro0YYJ/n7ns7Gz19/crKytLHo9Hhw8f1uOPP67p06dr/fr1mjdvnq6//vpEdglD\nyMqarf5+f+DLypodRevZCl5uYa7thAmfC2p36aXFpq9o7KHu388wmoMWP/ro0qDrnjhxqem2f/hD\ndlBb43FirVtXpV277pEvhPX2Pq+dO7+R8OsislQVUu3wnwlELxOWLyBzkAHtb6jPmqGKcdH+Dgt9\n/tA9AIdqP1QxMvA/bpMmndB//EeHxo9/Xnl5J9Xff16ffPLYYJ/z85+V9L2AZ/X9rIa/rq846FvS\n7B8Y9/f3859fqLffPqMLF/x/53DM0Ve+8lsdOGDMLly48PJh70k6C31/29qCV/X47hsZBcBIlgm/\n4xJa4cjNzdXZs2cHH/vCoSTl5eVpxowZKi42CkOLFy9WXV1dxIAY7b4YmSra+5Sf/5FaW/2BLz//\nhOnncDjqB04+9O3xV2+q7Ycfvi3pa4PtPvjgHdXWmtvP0FgqvDbgsct0f5ub35a0evC6TU3vqLZ2\nhqm25883K3AG4/nzn5i6bnt7p8rL31Vjo1NFRa16+OF5mjjR3EEtr7zSp8AQ9sorfUn/d8C/u8i4\nR+Zwn4Y2ceIJBf5+ycv7mPsVAfcnfSUiA0q852Yl8j6tX18kj2eLGhudmjq1TevXz1Vtba3l32FD\ntQ/NUCdP5iswF9XV+V/v979vDFTfeushnTr1AxkH1Hkl/XNQm/Pn84KuJXUOfM2rSy75QF/+8v8X\nlNk2b35X1dUbJO0JeJ6uoOe49NITmjTpU50+Hfx3PT2TBguLVVVerVmzRZs23RDFHU+9cO9vbm7D\nwL3lM0vid5NZ3CdzuE/mcJ8Sw1SR8Pz588rOztb58+fV29urSy4xd8DEtddeq9dee00rVqzQ4cOH\nNXu2f4bZ9OnT1d3drZMnT2r69Omqra3VHXfcEfE5WfYYWSyzmq666tdqbX1YxizAes2aZf5ejxv3\nO3V3Py7pSkkNGjdunKm2Dsdb8p8A1yWHY6rpa44f/6I6Ovz7wIwf/4nptpMnH9apU+WSiiQ1avLk\nItNtly37b1VV+WcwLltWYKptaWnFQLg0lhE6nZXavn2pqWtmZ7+qwFCWnd2e1H8HLDceWvAecl72\nkIuAn6Xhbd8+U2VlgcvXv87P0zD4eTInHgE6lhyYiAwokQPNSMa/jWXLll70d77fYf4lWNH9Dhuq\nfWiGmjZtswJzUUfHRyouvkVOZ97g5/Lp04GHhXwq6aSkF2UU9m7Rl740Wq+8Uq7OzjmS3pXk1ahR\n/6xVq8bK681VVdVfSdqno0dn6L33/qCCgisHnq8z4Nq3SNosaZ6kTuXl5ergwa9q6dJytbUVyels\n0v7939Bdd72twALlm29O0I9+FN2+s6naekby/zyFvj9PPfU/9cgjsb/fdjTU+8DnkTncJ3O4T+Zw\nn8yJJQdGLBK+9NJLcrlc2r17t5qamvTXf/3Xeuyxx3TzzTdHfPLly5frzTff1Jo1ayRJmzZt0p49\ne9TT06M777xTGzdu1P333y9JWrBggZYsWRL1C0B8vPWWQ9JT8gWu//qvR0237es7L+nJwbZ9fY+b\napef/4mamycOPOpXfv4npq+ZkyN1dPj3BszJecJ029bWjyXNkVHoG6XW1nrTbbduvVNlZXvV0CAV\nFzvkcpn7T42VZYQ33DBWe/fGdtBKJklFgGYPOcQT+4AiHcWaA8mAmcfqEqyh2odmKKNgVy63+wuS\nzsrtvk9lZXu1fftdA5/LKyQ9K38x7yVJP5R/xcsP5PVeoQULsvTGG6slfVWSV3/1V5WDB6BI+yQZ\nn+9u9yoZxUCvjJOYK5Sf3yupRR5PmYyDV6Smpt2aOfMKnTz5UFD/i4vfVE2NZ+A5veruPq4dOxbo\n4EGXjhwpGzKrBOaa5uY6ud3GPt5m9nu0IjRPrV9vHA4Y+v60trYHtIpi3x8bi3bfTQCwo4hFQpfL\npV/+8peSpBkzZmjnzp269957TRUJHQ6HnnzyyaC/8y0tkaTrr79ev/rVr6LtMxLA650q/6y+Tnm9\n5k8L9nqnKDC8eb3m9ltZsGC6mpv9hb4FC8wfAtLWdnnQNY3H5vT05Crw8JGenmdMtw3e+9B8ILJy\nCtLYsbEftJJJUhHc2EMOwEgXaw4kA2I40QzshWao2bOl0aPnyu1eLGmvpP9QdbVbbW3tA5/D+ySt\nk2SsGnE4zsrrDcyp16uq6jYtWfJP+trXLt583rhe8Of75MlX68Ybfd/rkMu1WmVlL2nHDt9g99DZ\nzuVaqYMHnxs45KNS0j/IKD76D0kJdz8Cc03o/ofD5Q2rg6ahecrj2RJ25mgmFszSOfelcrYpgJEl\nYpGwr69PBQUFg48nTZo0sP8cRpaTkoylHNGcUGwI3qdPajLVqqFhtAILk8Zjc86fbwm6pvHYHIdj\nsgI/4I3H5sQaiKycgtTY6Azqr/HYnEwKDKkIblaKvwBgB+RAJMK99/5GL744TpJUU+PVuXO/1q5d\n68J+b7gMVVb2kmpqXpJv0NfjuU1lZZUqLvYOFPiulGTM6MvLe1oeT2BO7ZLk0JkzU7R//8UZzijq\nuQZmEBptPvroA82aVaR9+xYP5iiz2c7rlbq6RsnYz9AtY/lzniKdDB2ca4L3P2xurlNb2+Kwmc5q\n8S40Tw2VO+OZu+ySV9M592Vi0RZAYkQsEi5cuFD333+/Vq1aJclYdnLNNdckvGNItskKLNhJBcN/\newCvd5IC9xY0Hkd27NgxSRvl+6A9dsz8EudRo87pwgX/3oCjRp0z3bakRKqq8n/AL1niiNRkUH29\nFHif6uvNtbWyBMdKILFbYLASElMR3Hz/QfDvIWe++AsAdkAORCIYp/3eK99n9htvPDv4tXBZIDS7\nuFwrVV29Sx5PcJFq377FFxX4SkryNXZspaqrz8njGSNjybBXU6e2he2b05mnI0fKVFbmb+PxrBuY\nNejPUcNlu9Clwu3tDw305zZJFTIO3xv+ZOjgXHOLxo17XD091yl0iXXo9d5/P3wh0qzQPDXUfYpn\n7rJLXrUy6J9o6TzLEYC9RCwSPv7443rhhRe0fft2ZWdn68/+7M90113p90sbVrVKul/+UVbzBbsJ\nE/rU3u5fDjFhgrnlu17v5xS8TPlzpq/55S8Xae9e/+P/8T/ML49+5pmbVFtbrpaWy1VQ0Kxnnlkb\nudGAlpaPZIxMG6+1paXcVLvjx09o2bKKgY2sG7V//1rNnHmFqbZWAkkqAoOVQp+VkJiK4MYecgBG\nOnIg4q21tV1dXedkzKzrlFG08w9Oh2aBgwc3X7R3n9OZp+XLx2jHjuAiVWCBz58H7pDTmae2tnZ9\n85u/0YEDP5dUoLNne7R69c/V1DTlorzi+3xftGi3ampWDV7XTI5qbW3XNdc8N7BnYqeMfbD9WUzq\n1Lhx/6JVq8aGLHEOfi2huaa+/vM6fPgrYfsSvDQ5fCHSrNDrrl8/19T3WclddilwWd13M5HSeZYj\nAHsxtdw4JydHzz33nE6dOqXKykpduHAhGX1DEmVnz9D58/4P5+zsGabbLlmSH3Ti75Il+abajRnz\nvs6d83+YjRnzgelr/uu/rg05QMR8oe/RRw8O7Atj7AfzyCOV2r7dXMFu8uSr5XYH7lEzx1S7Zcsq\nBq/Z3e3V0qXlF21sPZRY90GUUhMYrBT6rITEdA5uAGBX5EDE24YNe9XX94j8A9MVQas6QrOA2z03\naNacz1BFqqHygNOZp6ysbHk8D0hy6OBB49rSqiHzSiw5asOGvYOZz3h9wacxSxO0apUj6FrhXkvo\n6ygt3abDh8P3JfSejRr1mS699GmVlOQPHrJndhA39LpDnYwZz9xFgcu6dJ7lCMBeIhYJH3jgAc2Z\nYxRCxo8fr/7+fn3/+9/XT37yk4R3DsmTm9uh9nb/h3NubqfptrGe+HvppQ6dOeMvLl56qfn+Wimc\nWSlEzZrVo0OH/Pdp1qzPTLVraysKuqbx2By7za6zcn9TFRLtshcOACQbORDxFpoT8vN79YtfrB78\nemgWkOpVXe1VW1v7RbMJoy1Svf568AEgRgY1/hwur0Sbo1pb21Vd3ajgWZJXqKhok3p6iiS1qKQk\n76KsbOa1DNeX0Ht24cI4eTzf09ixlYP3LJ2X9FLgso7BcgDxErFI2NTUpOeee06SlJubq7//+7/X\n7bffnvCOIblinQ0oSa2tn+rgwRNqaytSc3OjPJ5PTRVYzp79vIylEL7H5k/tjWbD61BWClEbN35R\nBw+WDy4bfuopczMYJ078QN3d22Tc3w5NnGh+1mSs+yBKqQkMVu5vqkJiOgdnAEglciDiLTQnLF8+\nNig3GgeHbJbbXShpjKR18ngmDp4EbMbQg39nFDyrzzco7lVd3Vv64IM/1cyZV4S09wYdWDLcNTZs\n2Ds4U9E3S3LatI915MgGy4OPw2U6X37as6dP3d2jJd2i0MKnlTyZaBS4ACB9RCwSOhwOHTt2bHAU\n+YMPPlB2dsRmsJlYZwNK0pIlv1Rz82xJE9TdPUqLF29VU9OTEdtNnHhC3d3+oJaX97Hpaw634XUk\nVg6bCFyq3N1tfqnyggXT1dx812B/Fyz4helrxroPolW+8Gvcp2OmZ9dZKfSlKiTaZS8cAEg2ciDi\nLVJO8O0reNVVu+Tx+Adjo/lsDjf4t2XLLcrJ6ZSxxHiCpDZJb8somp1VT8/9Wrr0Zzp58iFTg4eR\nTyQ2ZkkeOXJfwlcn+PLT7bf/XC++mC3pgKQOFRX1DH5PqvKkXbCqBAAMEVPegw8+qHvvvVdTpkyR\nJHk8Hj399NMJ7xiSK9bZgJL0ySf9wz4eyty5l6i5ebOkqZIa9YUv5ETR4wIFLxcxfxqzlcMmYi0m\nNTVNCmrX3GzuBGgp9n0QrQoMv0ePmp9dZ8fR4FhnP8ZaSAVgL5n8n0dyIOLNTE4Y6mASs0LzWnX1\nOX3uc/+mjo7Rkj6T1Kj8/FZ99tmfqKfHfxiIbzuYcO0XLdod9O8/8onEF8+SDBXt75ZI35+VNVqS\nf1Da4fCv0klVnvRJ99+jrCoBAEPEIuGNN96o1157Te+9957eeOMNHThwQN/+9rd16NChZPQPSfLF\nLz6nM2f+RL7ZgH/+5y6dPr3ZZOvgQCL9o6lWtbVeBY5o1taanw1YUiJVVflDWOCG15FYKezEWkxq\naXlPgctbzpw5Zrq/se6DaFUmza6LdfZjrIVUAPaSyf95JAciVlaLQlZWJoTmNY/HOFVY+n8H/66j\n4x9VWNgst9v/fU5n0xDtx6imxjjg5LPPtion5xK9//45Sf8m6VZJE8OeSBypz9H+bon0/Y2NTgVm\nt9df9w4WN2fM6E1JnjTb91TLpNwLAMOJWCQ8efKktm/frt/+9rfq6OjQfffdJ5fLlYy+IYnOnBmr\nwELfmTNPmG6blVWgCxf8H6pZWeZm9V24cIkC90YxHpvzzDM3qbbWvzfgM8+YP93YSmEn1sCalzdZ\nbne5pCJJjcrLm2y6v6napy+TTpqLdfYjgRLIDJn8b50ciFj589anqql5SdXVu7R8+RjTxcJoP5sD\ni5JFRWe1evXzamx06v33j8njyZP0BQX+O75woUD796/U0qW+PNmk/fuN6wVmL6P9usF2Bw545PH4\nt7zJz39Wy5dP1fe+N1fz5z838Fx1qqhYG/F1mv3d4ntte/b0XfT9ga+7ublO0m0KV9y8/fZf6Gtf\nS93hIOn+ezSTci8ADGfIImF1dbUqKyv17rvvavny5Xr66af1gx/8QN/5zneS2T8kzRQFL9+93HTL\nCxdOKHCW3IUL5vYWzM31qKOjbLBdbu4m09e8774X5XbPkG/m4333Vam6+u9MtbUSUmItJrW3n1Hg\nrMn2dvP7wKRq+a7vkJaWlstVUNBs+pCWTEKgBDJDJv5bJwfCqvr6HBn/ZvZKuksej2Ng+XBiZpAF\nDgJLXn3ta5V6661VKi3t1I4duZK6FHxoiVt33fW2brxxxkWFy8DsZbSfOPAVr0K3vLnqqjnavn2V\npk/fHLRv9dKl5Tp58qFh+xz6u6Ww8LRKSysGCp2tks6rqWmKmpvr5HbfJ+NeBv8uCn7df6Fp08pV\nWDj3ouJmU9MUvfXWKgt32Jrhfo/6Cp319cbeiZMnX61Zs3qSuiSZE5YBwDBkkfC73/2uVqxYoe3b\nt+uKK4yDGRyO9DkFC/HlcJyR1+v/4HY4WqJoPVn+GYFdMrs/4KRJc9TU5A9ZkybNNn3FV19tl/Sd\nwf6+8oq5Jc6SBkKX/7VOndpmum2sUrUPjJWlPoGHtLjd5g9pySRWDsEBYB+Z+J9HciCs8m+1kpwZ\nZP6ipHGd48eNva6N05JdA0W2SknjJdVI+p5qavIjLn0N/PdfWHhaf/zjJ/J4Li52GfsZ+q/f2HiZ\nSku3DZu9Qn+39PZmBxU6jUNWVsmYHVgpyfj+UaN6tGrVKLlct2vFigMB181XYeHcgOKov7iZ6sGN\n4X6P+gudlZIektvtGFganbwlyXbcUxsAEmHIIuGLL76onTt3au3atZo6dapuvfVWXbhwIZl9QxIt\nWzZRr7ziO+2tU1/60sRITQJ0SLpf/kAT+WRjSfJ4jiuwWOfxvG/6il7vZAUGMa/3sij6e17+k+06\n5fX2RdE2NqnaV9DK/i/pviwkHVg5BAeAfWTifx7JgbCqoOBKud2VktwKXAKbqGLVUPs/+05L/ta3\nqvT6615JjfJ6i9Xenj/QcviMEzyrsEJNTRvkKzZOm3ZULtd9A9/XqO5u//W93tPaseNBDZe9Qn+3\nLFq0W8EreyYE/Hm8pDxJa3ThQoXGjHHI6cwbcoZeug1uDPd71J85yZ4AkGpDFglnz56tBx98UN/7\n3vf02muvaefOnWppadHf/M3f6Otf/7qWLFmSzH4iwXbs+IbKyvaqoUEqLnbI5brHdNuCgi61tPhP\nKS4oMBf+/OHRmIFYUBDNLLUPFbxkpMF0y6amKTJGZX2Pd0dx3dikKqhZKfRl4vI6AICBHAirZs/2\n6vDhuyR9KqlC+fm9Wr58bMIy0HC50unM086d3xh8XFq6bciTk4dbhWHkqHwZ+3hLhYWOwa/t379W\nS5eWq7HxMnm9p+Xb69tK9jIOW5Ekr0aPrlVfn0PGqp2Vamg4IGnojGmnwQ3/6+5U6HJqAEByRTy4\nZNSoUbr55pt18803q62tTVVVVXr22WcJhyOMlSBxzTVX6uWXJ8kY7Ryla64Zb6qdPzwaQWD27ErT\n1/zSl/L1yiuPSSqW1KAvfcn8fiWpKH6lKqhZea0spQUAkAMRq+DilUMu1+qE7i8XTa50uVbK49mi\n9vYZwyx9vXgVxnC5aubMK3Ty5EMDBcgHw35PJIH3bOrUNnm9fWpq2q3i4i6dOzdNVVUXz8i0UzFw\nKL7XXV/vUEtLuSZPnqPp09vU25s9eDpzMvcnBIBMFrFIGMjpdOqb3/ymvvnNbyaqP7Ch2tpzCjwZ\nubb2WVPtrMyue+65tbrppgq1tWXL6bxEP/uZ+XCUScUvK/c41qW0VvZBBACkL3IgomGleBVLlvAX\nmqSWlhM6fnzOkHsCOp152rTphrD5ZrhVGGZyVTyyVzhtbe0qK0uf5cPxFO51l5ZWxLxlDgAgdlEV\nCYHwgk95M3twidcb9CiqKwYeqtHdHd2hGlb2kbNbASwVo8tW9kEEMpnv94sxgHEs7X+/AMhsicxE\nsWQJX+YpLa3Q4cMPDh5+cfDgZh05Uma6b8PNFjSTqxKVvew+YzDanxf2xgaA1KBICMtuuKFPe/f6\nw9Sf//l5U+3WravSrl33yBcAe3ufD9ovZjipCg4UwCIj1AGxCfz9cvQov18ApLdEZiIrWSK0rds9\nV2Vle033LV0O/LDbwHQk0f68pMve2CPtfQCASCgSQpK1D8AxYxwKPC149Ghz1zROmPOHOOOxOakK\nDhTAIkuXUAfYDb9fANhJIn9nWckSFx/+cVb19cby1cCcO5R0mbE30gamo/15SZdi7Uh7HwAgEoqE\nkGTtA/DEiUmSvjr4+OOPd5q86hkFn1DcYrq/Gzd+UQcPlqutrUhOZ6Oeemqt6bZWFBaekrRNRkG0\nQ0VFPUm5rp2kS6gD7IYCOwA7SeTvrMAsUVh4OqoDLFyulTp4cLPc7rmSzkpaoZaWn+nwYWObGl/O\n/f73Z8etv1L0A+6Rvn+kDRxF+/OSLsXakfY+AEAkFAkhSaqvz1HgB+Dx4zmm27a0vKfAYt+ZM8dM\ntSspyVNVlX8GYkmJ+an7VvYktCIra7QCD2lxOJ5P+DXtJl1CHWA3mXSoEgD7S+SgYGCWiPYAC69X\nWrhwss6erZdUoCVLXtSJE3Pkdie20BPtgHuk7x9pA0d2HUQeae8DAERCkRCSYi/0SVJe3nS53ZWS\nciV1KS9vmql2W7feqbKyvWpokIqLHXK57jB9zVSN6jU2OoOuazwG7IO9ddKXlUOVACDZkjUoGG3m\n27Bhr6qqviVfph0zplKzZvXo0KHEFnqi7Wek7w9XVIv0GZ7On/F2HUS2a3ETAGJFkRCSpIKCK4MK\nfQUF5mfltbeflPSQfMGrvb3cVDsrpxtbGdWzcoIoo4mwO/bWAQDYSbTZK1zxbd++xQot9DQ0fGC6\nD2aKb2b6Gfg8zc11kv5CUn7Y7w9XVIs0q5LP+Piza3ETAGJFkRCSpNmzvTp82L+MdvbsStNt8/Iu\nk9tdLqlIUqPy8iabamclyFgZ1bNygiijibA79tYBANhJtNkrXLEuXKGnoSF8+3AFQTOZ1Uw/A59H\nuk3TppWrsHCu6UwZ6TOcz3gAgFUUCSHJWvGrtfVjSXNk7C04Sq2t9abaWQkyVkb1UnVdIB0wGxYA\nYCfRZi+rA7rhCoJmsqOZfoY+T2HhXL311irTfYv0GW71Mz5cgRQAkFkoHgmOuwAAIABJREFUEkKS\ntaW/PT25CjzMo6fnGVPtUlWsoEiCTMZsWABAqiRjzzyrA7rhCoLxyo5WnyfSZ3giCqTxPgUaAJDe\nKBJCkrWlvw7HZAWGKeNxZKkqVnCCKDIZs2EBAKlihz3zwhXy4pVZrT5PpM/wRBRIAQCZhSIhJEn1\n9TkKDAXHj+eYbltSIlVV+cPUkiWOSE0kWQsyVkairZwgms6nxgEAACRCvPJPOhShIh1gF66QF68B\ntnQfqEv0ahtyNACkP4qEkCSdOvWupNXyhQLj8VdNtd26dbXKygLD1O0J7KkhVSPRdhgBBwAAiKd4\n5Z902PIl0gF26V7IS6RwBdJwp0DHWuwjRwNA+qNICElSZ2ePpM2SpkpqVEdHt+m2qQhT9fWSVCkp\nV1Kn6uvNzV60Kh1GwAEAAJIpXvknHfbFjea1ZNrMN1+m973uFSsOaOLEE9q+fWbQ64612EeOBoD0\nR5EQkqRz5y6R9JB8I7vnzj2V4h4Nr6XlIwX2t6WlPCnXTYcRcAAAgGSKV/5Jh1l60byWTJ35Fvi6\nJa/KyoJfd6zFPnI0AKQ/ioSQJOXkTFFfn//DPidnSkr7E8nkyVfL7fb3d/LkOabbRtqLZjjpMAIO\nAACQTOmYf2Kd5RfNAXaxFMPSYfah1T5Eet2xFvvS8ecIABCMIiEkSX19H0ryf9gbj9PX9OmtOnRo\nm6QJkjo0Y0aP6baR9qIZjtcb9Mh8h1MkHYIqAACwt3SYARgqlll+gbmoqOiEXK6/HjYXRSqGhctZ\n/n59qpqal1RdvUvLl49JagazOgMy0uuOtdiXjj9HAIBgFAkhServL5BULqlIUqP6+ycl/JpWClhZ\nWaMl3SVfeHE4njd9XSv7odht2Ynd+gsAAEa+eAxixpLnhlpGO1R/IhXDwuUsf7/2SrpLHo9DO3Yk\nN4NZ3fsv8HXn5X0sl+vrQV+n2AcAIxdFQkiSRo3ySPoH+ULTqFGPJ/yaVgpYjY1OBYYf47E5VvZD\nsduGy3brLwAAGPniMYgZS54bKhcN1Z9IxbBwz+fvV+oymNW9/wJfd21tLatQACCDUCSEJOmqq+br\nnXf8Qeaqq/7UdNtYR4OtnFBcVNSqwOXRU6e2mW4bzV40oey24bLd+gsAAEa21tZ2VVc3StojqVPS\nypgKaLEseR0qF8XzIA5fv6qr3fJ4brvoWslg5t5k0pY0mfRaAcAqioSQJH3+87165x1/yPn85/tM\nt123rkq7dt0j3+hrb+/z2rnzGxHbnT79oaRHBq95+vSmKHp8XlKFjD0JO+X1mu+vb3S0trZWCxcu\njOKa9ttw2W79BQAAI9uGDXvl8TwgX/6TKlRcbH6g2CeWJa/hltG2trarublOUvQFvXA5y9evtrZ2\nlZWlJoOZuTeZtCVNJr1WJAeFZ4xkFAkhyVoxaf/+CwocfTUeR9bdXRjUrrv7ctPXbGqaImlVwOPd\npttaYbc9WOzWXwAAMLKFztrLz++Vy7U6KdcOt4y2tLRCbvd9Mla3jNe0aUflct0X9fNF87V0kClb\n0sRr5ioQiMIzRjKKhJBkLcicPfuRApf+nj17wlQ7hyN4ybDDYX7JcCYto2WkCgAAjBShGW758rEp\nzTVGwShfxoF4UmGhIyH9Sbc8lylZOl4zV4FAmVJkR2aiSAjLxo27XH19vr0FuzRu3BRT7UpK8lRV\n5V8yXFJiPihZmfnoC2nGnoTHUh7SImGkCgAAjBTpthVKvIplkYqA6Zbn0u19SJRUzlzFyJUpRXZk\nJoqEkGRtdPOmmy5RVZUReiSvli173lS7rVvvVFnZXjU0SMXFDrlcd5jur5WZj4F7KB49an4PRSk1\no8CMVAEAgJEi3ZbhxqtYFqkI6M9z7ZL2as+ePpWWbgubJZORN828D62t7Xr44f9Ue3tTWsx+jEW6\nzVzFyJApRXZkJoqEkGRtdHPr1tUhGzPfbqpda+unOnjwhNraitTc3CiP59OkfGi//rovKEiSY+Cx\nOakYBWakCgAApJN0WzprRbyKlpEGdf15bq+kNerudmjHjvBZ0mzeTPT7sGHDXlVXb4jYj3RGMQeJ\nkG6DHUA8USSEJGuz1WL9JblsWYXc7gdlHFri1dKl5Tp58qGonyd6ZxS4F6LUYrplKmb1EW4AAEA6\nSbels+kg0qCuL8/t2dOn7u7hs6TZvJno92EkrGahmAMA0aFICEmpma3W1lakwOBhPE48K3shpuI+\nEW4AAEA6GQnFo3iLNKjry3OlpdsGZhAOnSXN5s1Evw+sZgGAzEOREJJSM1tt4sQT6u72B4+8vI8T\nfk3JvxdiXV2X5s2Lbi9EZvUBAIBMly7Fo3Ra9mx2UNdMljSbNxP9PrhcK+XxbFF7+wxyLwBkCIqE\nkJSa2WoLFkxSc7N/Rt811ziTcl3fa62trdXChQtjamsX6RSeAQDAyJAug6Z2XPZsJkv6vseX41as\nOBA2xyX6fXA687Rp0w1R52UAgH1RJISk1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UVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12877dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    row = 1\n",
    "    col = 2\n",
    "    k = 1\n",
    "    fig, ax = plt.subplots(ncols=col, figsize=(18, 6))\n",
    "    fig.suptitle(\"Distractor Predictors versus Acc EM\", fontsize=18, y=1.05, x=0.505)\n",
    "    for j in range(col):\n",
    "        ax[j].scatter(matrix[X.columns[k]], matrix[y.columns[0]])\n",
    "        ax[j].set_xlabel(X.columns[k])\n",
    "        ax[j].set_ylabel(y.columns[0])\n",
    "        k = k + 1\n",
    "    fig.tight_layout()\n",
    "    fig.savefig('Distractor Predictor vs Acc EM.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6nOqfQZTv/oGfZ+Dvkf9ztfZrffFa8O/DkSOxzJ2bVutsupp+f4L7yW6MaL2X\n9T1773Gs8Wddf9dO59j2ZM5q1Pd3/dRHdVP/1K+xceApO604eMJi9+7dOXDgAPn5+ZSXl5OamsrA\ngQNPUesEvscfUBjADzbLpQWV29uAOncFlf3WZrk9QeXSGlDnt40uGxKy11I2JGSfrXIJCQWWcgkJ\nhXW93CI2NsNSNjY201a56OjvMEdJ1wBveq7tmTp1PatW3cyuXZNYtWoSSUnrbZdtrGPpo6Zi/35r\nQG5ei4icGRQHnjlq+76x811d02u8cYVhHALyscZhBZ7/Ah/bj7mUOPCxwoDnvI+3xJrk6gtkBtzX\nTj0GsB4IC3puL/AeZizlCqrXe7/gNoYD+RhGmKe897n8Gt53YQ2P7Q263letL0NCgtuxL+j6CGay\nc5nn+k3PtUFlZT4uV1gtbTHvn5OTxw03vEZs7KuMGrWRMWMWcMMNr7Fvnzd+tvbh3r3fMnHiMnJz\n86hJ8O9DdvYBVq26mdTUcTXGmMGv79Qpl6ysb6r1gx3We1nfs/ceJzL+PJ1jW+/fydo+BxFpGk7Z\nzEGHw/zyXrt2LSUlJUyYMIFp06YxefJkDMNgwoQJtG/f/lQ1TyjCHHHthBns2P0Ci8BcltENM3AK\nbUCd2ZjLQ7z7uxy2WS48oM4fMAM6exITS/j22xmYI8D7SEwssl02JeVaRo+e4dlzcB8pKWNslXvw\nwX6sWfMEpaXdiIz8nocfrn+5ttcnn9zCyJFzPHvLZPLJJ5NslRs0qAtZWf7lE4MGvWy7zlORhEpO\nHgOs8IxgFpKcfM0Jr/NMk5BQQGqqgfczPZ2CTBGR+igOPHPU9n1T03d18L5uFRXgdL4GHGH4cCfJ\nyTdy+eUfe+51C7AEmA10xPxniRszyfc4cDZmXNcD+C9wP3Ap5kDuWZhLbIuBPwPtgYPAOF87zVi2\nIzAd6AI86Sm3H8gBOgCLMOPNnwgNjaWyMhnIBW7DXDIbCfwf/pmC3hmJt2IuK44HsjCTZIWe9+Kd\n4Xc98DFwFBjleU9DgBLMQfj5QCxmArM54eGZlJfv87yXDOAuTxvKMGPdO4HlOJ3ljBjhoKzMTURE\nBW730573ecDzXpZ67lsA/AQsBh4IaP98T7tjgGuBv/naEhm5j8TEc+nVawXJydeQlLSed9/9la/s\n+vXPAL/3vKflhIYeorLyGU9fRpKX92tWrYoGVrByZfUYNfh3Zu/e3qSn1x5jBr++rMxNevoUT7+0\npHPnXSRWUZR3AAAgAElEQVQnTwHqn/0WeK/4+BIcjqWePQf9ceaJjD9P59hWA84iAqcoOdipUyff\nPjNjx471PT5y5EhGjhx5KpokNeqKOe2+GWZwYUcl1j1YZjSgvs74940xMIMoO8qx7o84zXaNhw4N\nBv4n4PpV22UHDOjHjTce9HzJRzJwYD9b5caPX0tJibmnTUmJwXXXPU1GRn9bZbt378rBg4/abqPX\nkSMdsC7j6GC7bGOTUMeyRCE2NqbGoFL8TucgU0SkLooDzyy1fd9YJ3+aF94ZSDXt65aaOhuA7Gzv\nrLOumAm7lzGTWoH76y3FTLo9hTWp9YuAOsMxB6XHea7zMGPASzATg1djzgK8FVgHtMC/fLg5ZtLQ\nn/CrrAzeL/AWz8/fYCbqvM/9gMOxAsOoBJxAJB067OLQoUsw4+Z8zARgtOdPh+e9PojTuRhoi8uV\n4Gm/k+bNf2DkyNasXz8P/x5+az3t7Ia56sP7HrvSo8cawsMLWbXqV5hJumTgjoD2zcFMDjoIDe1M\nREQbiov9MaCZBAW40dNG776P0Lz5q4SFRWEmFqsnjfyJzxjgFiIillJc/KugNlJrcik2NoaFC6/x\nxYdHjnhnAdYcYwbHg0OGrPH0uflYXJzDF1sG/u6Zcas1QWkntjyR8efpHNtqwFlE4DTac1BON2dh\nDQbOquO1geIwAxtvYNGxAXV2C6qzm81y5ZhBXCtPnWUNqPMIgUGJOXvRnsmT3+a995oDkJpqUFb2\nFu++e1e95Q4d6kjg+zSvT6xj+dL3/qPA3HMQ20mo+oI0OTanc5ApIiI/H7V939T0PW9NJln3dcvM\n7ESPHu9SWenGTP61Br7AnBHowEwSZgFtMOOzNljjwpYExmwhIV8QHe3E5RrreSyayy+PYcuWbZSW\ndgP+F8P4Pd5ElrksuAj4FWbyra79/cqBVzGTj8HP9ccwxmLOEvwG6MGhQxFYD/X4s+c/N3Cfp2w0\no0d38s3I8ydb7+fqqzdj3cPPuwTVgX8JrHnvrKyd7Nzp3QMxBugX1L4+mIk6Fx06JFNScojiYn/5\nceNCCQ93sH//ZrKyvvHMxAMwcLnCSU0d5/s84+PLsMbJBy3XsbEZnnsXUFeSL5A1gTyMzp3nEBfX\n19ZAZ13xbFpapKUf9u6NrPNe4qcBZxEBJQelVgVYT3CbabNcOvC7gHL2Z/HBd1gDkO9tluuBGfR5\nuWzXeOGFEWzY4F8+fdFFLW2X3bzZBUzG295PP51vq1xIyPdUVgYGtz/YrrOxjuVL3/uPgoZu/qol\nCiIiIj9fwd/zKSllDB8emEyyJrUgHJfLOxtvOXADMJLOnV8kLq4vWVnZpKc/FvD6P1rKN2uWQUWF\ndwC6kBYtOnLxxS3YutW/bHnevEnMmLGF/fujyMo6RHp64F56O/En+gqC2rY36DrCU884zCRh4HOF\n+BOK12MehGI97MNcxvwT0IOwsL/Qr99AunTJpbDQoEePdz3tjSE5eQKxsTEBSS9vu8bgXUI8fHiF\nbwlsVtZO0tPvxpwRWXM/d+68i7g4h+e10/AuAXY6yxk9OoLk5Ot9s+1ycy/1JSr37duDyzXB89lE\nkZKSzkUXRRA4AN++fQHnn/8yW7c2A7I599xoBg9+me++g+++ewLoTps2mcyeHRiX1/V74yQuri+f\nfz6OnJy8gKRpzStOaotnc3Ly2LPnC2C8rx+OHNlTaxvESgPOIgJKDkqtDmHdc/CQzXLBwVGnBtSZ\nG1Rnjs1y2ViDNrvlIDIyGnPvFLNsRMTSBrS3Ldb32tZWKafTzeHD/vfpdJY3oM7GqWnpz4mmJQoi\nIiI/X8Hf8y5XOFDMTTeZyZv09D1kZc3EXFKbCUz1lHTgdJbTo8caT4JnCrGxMQwaVGHZfw664nDM\nIDKyN23aZNG7dwwffugfuC4sfIZ16wJjuBXMmLElYFbaWMLCngTicbsLgCTM5cX+5FtYmHmQiNt9\nJ2YM2hfrkmQwE4CPYyb8WnnKeg8cWYR/5YsR9GdfYCw33LCClStvYOLE5QFtM1i9ejlJSetZuXKS\nZT9qh2M6vXp1o3//1iQnj7ckyMw+cnrasILmzcu58kp30P55Zn8OGYKnP70zJ18lOfkay/0Ck0IT\nJxawatW/8c6AdLnG8n//N5/AAfiuXVsRFVWIy2W+j/XrDW66aQV9+sA335hLwNPTDaZPX8HKlV1t\n/d5440M7K07qmsVaUnIO/tVLhbRtW3P9IiJSMyUHpRYx+PccDMUcdbTjANbg6EAD6myHuTdLBeYG\n0/aSbeYeM4HLiu3PHMzIiCUwwWde2zN8OKxe7X+vI0Y46isCQKdOF3H48A0B1+/YrrOx7rprtW9D\n6dRUg/Lypbzzzu22ynr3DjSXFe+xvXegliiIiIj8fCUnjyElZT4uV2/M2XRjyMzczOefm3vP5eZe\nyrnn/i+ZmbdjxmnRnpJmzPTOO+Ms9/PvReiNIY8wYcK5rFw5iZycPM4993ngaczDNLzbz3hjrx95\n551vqajoSmBc53afCxThcFRhGO8Dw4DltG6dR+vW+bRr15tvvsnGjHuTgPWEhpYwbtx7lJcXsWXL\nEo4e3YdhRAI3YS593ux7v/ApsB3oRGTk45SWDsFMLl6H0/kyo0f745/q+/e1IiUlh9zcPH7xi3W+\n/agNw6CwcA4rV95drc/9fRQD3EybNnN4992a96KuKXnrTUbWxPw838Xl8rexsLCV5TNJSCisY2WI\nvdUitcWHwfdNS4OJE5fb2rvaLGtd9dSr14pa2yAiItUpOSi1aI5175Q/2SznPcXNO/uvynaNoaFF\nVFZegJnkiyA0dJvNkm2BHzGDxQzsJxUhPj6HwKCnU6dc22XnzRvF9u3ek4MzmDev9iUUgXr2LOHL\nL/119uxZarvOxtq0yVsfgMNzbU/gSO6uXfb3DtQSBRERkTNfbQeMxcbGMHp0PKtWeff8s64SiI2N\noVOnAWRmOvDOdDP34GuGYbir1dO2bTfS0/0zv1q3jvYlju66azVZWU/ij0tXYMaY3vhmGW73k5hJ\nyMAE43+BpzAM89rpnM/o0Z0oL2/Ju+8meWbWjcd/AMnNtG49n3feedDXriFD1pCaeinmTMIqYCzm\noPk6zL0JWwITuOyy5URFFZGWFkl29ovExLRjy5YfuOKKj+nZs4T4+CLMAewNnjI7cbnakpS0ntxc\nb8ITwOG5ri64j+qaHVc9eTuUlJR/MGTImmrJNu9nHLwXt9sd6dsTMCbmR5KTbyUpaV0NM/8M26tF\naosPg5OZ2dkH2LHjEezsXW2WvYaaTjEWERF7lByUWrTDOrrZzma5aKynzdlNKkJVVSyBCcmqKrt7\nDhZgjiQ3fJ/DgoLDmHvaJAD7KSiwv+dgUtL7pKebQUtxsUFSUjIbN06tt9ypmVHX+INXtHegiIhI\n01XXcs/6Yhp/wsec6WYmbyaRmbmmWj29ehns2OGPA1u3nsPVV28mIaGAjz4qwhqXuomL+44LLzSX\n037xRQcqKwOTkCWYA91xQeXakpx8TcABIN7HSzH3FswmMjKf3Nw8X+LMfA/RmDGqCzNujAYeIDBZ\nuXVrC/btMw8b2bHjEU8S71HS0x18+aXB+PFLiY9PJjNzmqfcOGAp+/dHERv7jeXQELf7O8aPfxWo\nIDOzgy+ZF9xHdc2Oq568XYbL9QCpqebnuHHjHKKiDNq1S+TIkW89B5M4MBOl5Zj7Ll5PXJw5G3T7\n9u3ExsbU8ZkfW2wbfN+9e3tblpnXPxsx8JCXKbZWuYiIiJ+Sg1KLH2jc8uDgk+Xa2K7RMKx7+JnX\ndgQHfnG269y0KQcYgHe24ief/Nd22Q8/dFjq/eADe8uKT8WMuuHDY1i92r/0evhw+wFTY/cOrG2m\ngYiIiJw56hokrC+m8SZ8UlLKPHsSmvv11RRLBCaHvAdvpKc7SU01aNbscWAZZhyTT2joLr755lFi\nY2PIycmjS5f5lJS8hzlD7hrgeeB8HI6vMYzqS2sTEqwz3SIivqes7E+Ag6wsgzvuWEJkZAv2748i\nPr6c1q3/TH5+P2AP5rY7bqyxZ0tcLoM771wdsFrD2m8ZGbF06hTrmUnpLeckIaGQ5ctvYeTIOWRm\ntqWq6gBud29Wr3Z46vGfHtyQAeacnDzKyopxOucDbTGMKvLy/HXn5XUkL+92TwLOOxtzEv6Tna+z\nfFZ5eQUBy3wNNmy41BLXHWtsG/y7NHHiMstKm8bMRjwVFP+KyJlKyUGpRQHW5cH5NssF7zn4YwPq\n3B9U9gdbpSIiDlNWFhjgHbFdY2VlawJnK1ZW2qsTwDCsB6EYhr2DUE5F0LBkyQTPCXCQkOAgOflG\n22W9gai55yC2R4PtbCwtIiIip7djOWDMm7TJzfWeRLu51qRWYILHPEzD6XnGgXngnT9e69Bhli92\nMg+jmIk/fpwGTAG6YRiXEhb2BG732UA6AGvWdObKK92MH+8/xGPHjm6kpfkTZ5s3u3C5JvvuGRb2\nBIEn4TZv/jglJYEx6y5gCps2vYvLFeZ5zHoisnX5rbksOSysgLKyCJzOaA4efJTY2Hm4XIHvZb6v\nTeYMQ/tJsKlT17N69Z2+e3Xu/Ax5ecGrSKwJTpP/xOPAz2rOnJ2kpEzlZMV1Z+Le1Xl5BQwcuMi3\nskjxr4icSZQclBo1a3YuFRVJAdfJNku2Bebg3//P/gEf5vKFJzA3mP4eu7+e5nJkf51VVQ1JtDXH\nf7JZARDZgLK5WA9CsZccPBVJs2MZUfWW3b59O4MHD7ZdLi0tksCgc+/ehvStiIiInA6OR5LGsGx1\n7L+obcA0OCEZFVVgmfXWpk0iEycuJy0Ndu7Mx5rk6oMZSwI4MQe6bweWAM0pKWnF6tX5XH99ie/w\nlNGj/5e0tMABX+uBdW53f2A5Tmc5o0dH8PDDNzF+/ByyspxUVrbCTEZGYybc7sKMLR2EhT1Jv34D\n6dmzlOTka8jNPcqWLXPIyoqlsrIlbvdEVq+OJinJGwtaV9H499GuOSlb14Bz8IzPtm27ccklgbM4\nowlMXloTgtWX5QYf4nc8tpmpq/2n02xAu+bM2Ul6eh+0HY+InImUHJQatWiRT36+P2Bo0aLAZslc\nGrv/n7kUxH8gCdg7kMTtPmKp0+22X2dERA5lZff5ykZEPGa7bLNmZ1FR4f/yb9bsLFvljiVpdiYt\nVQg+dfDIkT2nuEUiIiLSUMcjSVPbwGhtjwcnJMvKYli9OnB58D5WrXoE/wBv4Iy4jBquj2LuF/gL\nzANBWrFu3R5yc/MwDHC7K3A6XwOOMHy4k9TUQ0Gz7P6LGaN+S3LyrzEMuOSSrqSlQXb2D7Rr9zFd\nu+bzn//kY55gbADXcMMNsHLlDb5+SEpa75tVFriU15tAGj4cy/uMjEwjJGQpsbEZzJ5d/eC7mvpv\n4cJrmDp1Pfv2lQFvAtcC0fTqhWUWZ1paFNnZc2jXrrcneVn3Pn3x8Tmeg+kaPoO0Jjk5eT+7WXZm\nAtWcIXq8+klE5GRRclBqFBmZT36+f1Zc8+ZHbZVzOLp4ToQDcOBwdGlArW2wnpC832a5s7GOsp5t\nu8ZevQbx9df+sr16DbJdtqrqMPAH/AeozLBV7vDhXQQuTTl0aDdwQ92FPM6kpboNOVFPREREfr5q\n27ewtseDE5JmQsufLExL6+rZKy8KuBQzyRZJWNgOevfuwnffPUFJyUCgDLf7PsyThdtiJgbNOMrt\nHkdSknmgx6ZN9+IfKF5B+/Znk5npj2EgERiHyzWWDh0ep127ELKyvIPL6ygqOsqRI1mex5yYM/Hm\nVDsxN/j9ehObCQmF5OTkARW+PQKbN08nM/MhwElxscH06StYubJrnffbvz/KEisGntDsnfHZ2GTv\ntGn9iI09fst8p05d/7ObZWcmUG/leJ6afCZNDBCRM5uSg1Kj0lLrbLaSEnuz20JDs6mo8I+WhYba\nW2prcmINmOwtSY6M/JHS0sBR1oO2azz77EK+/tpftnv3Ittlw8K6UFbmDxzDwjrbKldY6MQ/0l1I\nYaH9L/i0NAhcBu3fH+f005AT9UREROTnq7Z9C+3uZxic0Lr++sXs2LEM81Td9/HOjrvhhlJWrpzE\nkCFrSE0d53u901mOYWSRl9eXmpNR1sd69SqwxDBm7GU+X1HRg6yscMxEYzHQHJerjWevweeB+4EY\n4uL6VkviBL/fmJhvadHiazZsiCEu7jnc7qcC6pyPGRsHt7Xufg1OGPbo0ZuVK8dVK9tQ0dGtWLly\n5DHfx8tsZ037Mp65zATqhuN6avKZNDFARM5sSg5KjUJDu2CeVua9ftVWudatC8jN9R9k0rq13YNM\noPphJj/YKjV0aBQffvhHIAHYz9ChLesr4lNUlEPgwSuFhWG2y7Zrd5T09CRfe9u1m2OrXGhoEeAv\nFxo6v54SftnZPwCP+spmZ9ur81Q4EzeSFhERkeOvtpigrlihrhlTFRVuz6vaAPm0br2Aq6/u6Ssf\nnDQbPTqC5OQkBgxIJj19HNZklFEtwZacPIYtW+aQnt4L+AJ4wFOf9yCP3viXLHsPLnEBacC7QBjx\n8SXV3kd8fJHlIJSyMmfAoSHvUX2/QX+7srJ2MmQIlr6oqf/uuOMtAk929rbjdGN+RtfQ2Fl2p+OM\nuuOdQIW6TwsXETmelByUGl10UTHr1/sDi4svLrZVLj+/NYHJq/z8mQ2otR2BM+rMazt19gXGBVyv\nsV3jpk2lwHS87d20abbtsp98cgsjR84hNzee2NhMPvnE3ijehRdGsGGDPyF50UX2k5kxMV0CluoW\nEBNjb7biqQigzsSNpEVEROT4qy0mqCtWqGvG1Gef5WAeqFEB/EhBQWvKysqYPPktDhxoyeHD3xMT\n8woORw7DhztJTr4Rw4B+/aI4dOhJqqq60qFDFrNn/xKnM5pDh/7Kf/8bA2RTVhYNQFxcX9LTC4Gz\ngPX4D6CLBgro3PlHz4nK3sTNBuBJvDGlw7G02vsAg5tuWuE7CGXIkDUB5a171Y0Y4SA83Ez8ZWXt\nJD39btLTHaSmriMl5V1Gjw4nOXlMtf4LCQkjcJsebztON2Zic32jZ9k1lRl1x3JauIhIQyg5KDVy\nuyEwsHC77Z1WXFHRhsDRrYoKewk+kwt4EP/MQXuHg7RqtRv4HHPm4Pe0bh1FYLKwLpWV1vaa1/bE\nxERzySVdPUFNM5zOaFvlIiOjgd/j39vGftCWl3eQwORrXp69mYNNJYASERGRn4e6ZkyVlDgIjFMN\nYwWrV98MeGOqGfjjrBXExsYwceJyNmxojrk3tUFmZji9ey9k7NgegBuX63bAwerVBklJK0hIMEhN\njQIuwTzYoyPwPVFRVVx++dksWTKF7t2TAw4uaWlpr3k4Rd3vw5r4uQZ4BqczzjPT8XpfsmzQoELS\n0zcAZUAYLtd4Vq2KprR0CZGRLSyDv8GnCmdkxB63QeLG3Ke2Msc6iGx3Rt3pOMOwIbQSR0ROFiUH\npUabNoUS+IX7ySehNktmYl0anNmAWksA/yEo5j4u9fv3v/OBP/nq/Oyzx23XGBb2I2Vl/vaGhdnf\nr7CxCbcffggncN9A89qedu0SPRtwAzho1663rXLHsiTBG1R98w3067fnjAuqRERE5PQXnMSJjy+n\ntv3oWraMIy8v+GAPb+xZ1+EnGZhLhM3XVlYuZ/VqCA09ZCmTklJGauootmxZ5pk9OA1/rDgfaMbk\nyW9RVRUNzMZc3uzCHJy2tjc+PsfyPjp1yvW91717mxMfP5vCQiehocVcdFEUERFmm5OS1vlirsOH\nvyMw4ek95XjzZhcul7ms2RuLmklNa78dr0HixtznRA1Q251Rd6YPkGsljoicLEoOSo0qKqxJPvPa\nDuthG9CQJFJfAvc5tJscLCuznlZsXtsTERFvOVQkIiLOdtnGJtyOZd/Anj1L+PJL/+fSs2eprXLH\nsiQhMKjatevMC6pERETk9BecxLn++pcZP34pmzaZ+/yVlUWTm5tHbGwMI0aEsHp14GC0d0luNhBO\nTUlFMxayrhgxB6ShsrKTpYzLFc706Vv46qskevR4F5fLX8bl6s3q1WMxB7Tvwtw/+iUqK5PwxsBO\n5x6Sk+/ylKkgcPDbMNzcdddq3n33V576xjN+/FLeeWcqEycurzGRVVwcH9TuKE972xIci27YYJ7e\nHDjT7OqrN1d7XWP4Y988YD1r17qZOHFZnQPHJ2rPPLsz6rRnX/3O9NmVInJ8KDkoNTKMfKyBjN2D\nRY4SOCILTzSg1q8I3EAZvrZZLg3rbMW0BtSZB/w2oOws2yXj4g7RmA2fGzv7D2DWrKFs2eLd5zCD\n2bNvqb/QMZQDBVUiIiJy4gXHG5mZHUhIKMTlMpNl3uW+K1dOYsmS8SQlrWDjxmLy8g4D3YDlxMUV\n0KGDg+zsObRr15uePUsth5989tnfyMwMjBkLPPVlAPOAROAw0Iy3365kw4a/UV5ehnlYSCHm0t9C\nAhOL4KB166O4XAbm6clHyMsrISFhAaNGxXHgQBTmEmiAPD79dDH5+bGYcfYYIMaTAK095nI4cjBn\nJm7AXL78OddffwhoZkmSJiQU1jjT7HjtW+e/z3rgZoqLHaxaVffA8YnaM8/ujDrt2Ve/M312pYgc\nH0oOSi3aAN9iBlvfY38GYCHWpcEFdb/cIoLA/WPgS1ulmjePpqRkDhAPZNK8uf2RrtLSo5b2lpba\nP125sRs+N3b2H8CMGVtIT38EcFBcbDB9+gpWrux6wsqBgioRERE58WqKN4KTZWlpMHHics8MJ4Nt\n265g+vQt7N8fRadOuRhGFzIzo7jkkrOYNes8ZszYwtVXb/bNhvr6699z551L+eSTSo4ezcAwAOIw\nB4oXAWMxZ//dTGWlg/z8ZVhj02eAJKyJRYPIyELCwv6C2/0k4MAwDPLzzSXLnTv/4Hn9USAZl+vR\ngPuZdZkzHmuPuYYPj2H16kX4V56MIyJiBcnJ15CUVP/sueO1b533PmvXuikutjdwfKr3zDsZ9Z/p\nW/A01YkAmjEpYqXkoNSiEHgaf/AyzVap8PC+lJffEnBdZLvGkJBEqqr8X0whIYm2yhlGAeaMP++m\n1DNs11lRcQ6BS5krKuwn6mra8NmOYwlS0tIgcL/CtDRH3QU8juVL39teM+BBGyHX4EwPCkVEROpy\nMv4RXVN8lJS0zpIsy84+wI4d5mBn8Ayn669/mffeawFAaqrB5s1LyMp6stpr33nndnJy8ujZ811c\nrv/x1R8ZGUPbtnPIyYnzHHgC5uCxP35yOuPo2nU9hw/vp7i4Iw7Hs0RGFpKV9QfgM8trvTMLY2K6\nAHPIyoqlsrJj0GvcwHIiIwsYNOgdDh36lpiYOTgcHRkxwkFy8vUALFkyodry5pSUMk/i02DDhktr\n/TyO52fnna03ceIyz4zB+geOT/WeeSej/jN9C56mOhFAMyZFrJQclFr0wBq89LBVqrzcusS3vHyv\n7RqrqvZaylZV7bNVzgy0/PsGmtf2hIbup6rKX2do6AHbZRv7RXosQUpj9yts7BJowDOq7ruyXa4p\nOdODQhERkbqcjH9E1xQfJSePobR0CZ9+eoSiIieZmU4C49Ovvw6hS5dnyM2Np7j4OwIHiw8d+jO1\nDYxOnboelyuMwLjzuutiWLkyKSjxlW95DWSTne0mM9N/QInTOd/z838xZx5alyzn5aX7Vm+Ypx4H\nHoT3HRBPVlYCWVmjgPF4ZxOGh6/wJfEMA1q2zPIsXfbvi5iaOo7UVKPGU4u9Ze18dg1NIJ7q2YBe\np8vMrzN95t3p8nmebGf65yZyvCk5KLXYQ+P28WuH9UCSdg2oMwTrkmR7s+Lc7u8xN2QGqMLt3m+7\nxuHDW/Lhh38EEoD9DB/e0nbZxn6RHksg43T2tOxX6HTaS9o2dgk0KPFlh4ILERH5OTtV33OxsTFE\nRrYgL6/m5NqePV9RVeVNCL5qaaNhtLW8Ni7uMBMnLictLZKdO3cDvTCXCXejVasDJCdPAcz4rrzc\nPAilsvIgUVGzKC6OJy8vEpfrLlyuj4LqcQLrgKmYMbC5JyCU0aZNBYWF8cBazNh2GObpxl0ICfkW\nt/s3wFZPmWTM1SwZwFpSUtJ9B7BMnbqe9PQpvvuHhX2N2/1bXxtqOrXYG6tVX5odGbA024xDG5r8\nPdWzAb1Ol5lfZ/rMu9Pl8zzZzvTPTeR4U3JQahGGNVFn91elBHPvFG9S8c8NqDPwBDkAe8t0zb0K\nA4XbrrFVqzjAe1qcQatW9pNmjf0iPZZAxuWyzq50uezNrmzsEmhQ4ssOBRciIvJzdjy+54IHR2fN\nGsqMGVtqvfYOnlrjkGuB+UB7IIyqqsBTfI8QGCNFReUTHe0/jK2iop0v/vLP0HsUeIbzzmvmG6iN\njY3hnXdut7R9yJA1pKaO81xZB9BbtDiEwxGPy+XEf/CIAxhLfv6TuN0P4o+Ll2MOSN9CVdV7mIlB\nb5suBZ4DLgAKcLnuIilpPStXTvL0gf/+UVF7cLmiPXUZGIY1ztu7N9LX9uDPLjt7T7Wl2fXFejk5\neUyb9n/k5WXaGtjeu/cAl122nOzsGByODHr0GMA555Qf95l9p0uMqi14zkxNdcakSG2UHJRadCJw\nLz54wWY56ynH5rVd2VhPOra3z6E52hq4WfR3tms8lqRZY6WlRVJbAFeftm27kZ7un5nZtu2JP1RE\nia/6KSgUEZGfMzv/iK5rZUROTh4DBy7yLa9NTTXYsmVOndfewVNrHBKNeQCdA7iZ5s0fp6Qk8Dl/\nDBoVVUx6+pN4D2MrKnoN65Y53sRSX9zuvdVm0wUmsaxt6ErgKpn27RPo1cuw7MHnPdG4qqqrpc7Q\n0EIqK2/yXBcGtAHMk4hnBtxjhS/ZFRyLDR/uJCLC/3l89lkGeXn+548c2VPrZ5eW1tWyCsX7nuuK\n9aZOXU9KytRqn01tLrtsueezXAHczddfO/j66+M/s+90iVG9Ewa2b9/O4MGDT0kbpOGa6oxJkdoo\nOXtAsgoAACAASURBVCi1+I7APerME4vtCBzBdWCeAGfX2UFlz7ZZrl1QOftLmdu2PUjg+2zX7qjt\nso116NBOzBFr7544O4EbbJXt1ctgxw5/IrRXrxW2yh3LyFhjE1+nyz4wJ4OCQhER+Tmz84/omlZG\nLFx4jSexVIbL1YHAeC03N77Oa29iLHCZr2EcoXnzQjp0SKRXrxX85jdXM27cE5SWdiM8fB/Dh3cj\nL68VCQkO0tISycoKjA+tMwvN5JwBFPH119Fs2XI18B6pqQbvvPMCY8a0Z8mSCcTGxljiqKysA6Sn\n+/d/7tVrha+Na9eWUlHRChiDuafg91RW+uvs0OEwmZnrMOPOciIj/4/S0nGe51tijWdb+pJd1eO4\nGy0x1aBBy8nMrHnwOPizu/76xezY4Y19fyIj4xBud3c6d36Gdu0S6dmztFqs19AZev7P8sTO7NPM\nLxGR40fJQalFFNbZeHvqfrlH+/YuDh++z1euffsnbNfocOzDMPwBlMNhbwZgWNgB3G5/ufBw+4eK\nfPFFJuAPrrZvz7BdtrGKirzLSsyR7cJC+4d8NDYIOpZDRRqb+Dpd9oERERGRE6+mBFJgLBC8X6Db\nvc9yHRubQXFx9VlghgHh4eH06BFFQkK4ZbBx4sTllJQ8BTgoKzPYvXsOcXEABl27lrNjR/XZdhs3\nlpCXtx84B3Nm29UUFz8HvA3cCThwuw1Wr17KnXeuJjw83BN3macCw6UkJVljMe9S5NzcPAYMWER6\n+qdAEaWld9G58xzi4vqSkFBIYWEsmZn++Pqyy5KJivImHXeSnj7O91znzrt8+yAGJ/hycvIsMx27\ndi1ix467CExY1sa6D/UyMjNnkJlplrvkkppjtYbO0PN/lgWWz9hb7ngNIGvml4jI8aPkoNQijsbM\nAKyqKsPc3LkTkEFVVWkD6iy3lIUyW6Xc7ihgDuasxQzKy+0fKpKd3YLAJGh29tMNaG/jhIZ2IXDJ\ndmjoq7bLnop9DhvrdNkHRkRERE68mhJINe8X2BYIx+2+E1iO01nO6NERzJ59C9OnVx8ArSuGCY41\n0tP7kJ4+jtRUF/HxL+B0vgYcYfhwJ0uWmLPtBg1azo4dPfBvf/M2lZX3A6uwxr4GmzaBy1W97tpi\nqNjYGOLi+nqSfKbc3HguucRMgF199WZLHUeOdOH998d5XhecdJxSLWHmTaqZMzHDgEtJTY1m/Pil\n3HSTvcFj65Y6rbATqyUnj8HlWkhe3lm2Bqc/+eQWRo6cQ3Z2NA7HE/To0Z9zznHb+kxFROTUUHJQ\namEdzbW7j5/LFYO5ubP3wAz7yTaHoz+GMTngeonNkvmYyUFvWx9tQJ2Bh6A4PNcn1vDhsHq1v29H\njLB3KvOxOBWJutNlHxgRERE58Wpa3ZCUtM6yX6DTab7W5fIOknalR481LFx4aa0zyawxzFFSUjIY\nMmQNCQkFxMeXY41Xi4A84EUyM2f4Ho+IWOG7X3b2DwTGqrAEc7+/4GXH2RQW5gNHMVeZmPFTfbPe\nguOf4uIwTyJsBQkJRq2xUUOXbnv3JYRJZGTE8vnn4+osW3P78qlpZl+w2NgYnn76ItsrSLp378rB\ng7XH48c7Lm1KW9mIiJwoSg5KjRwOMAz/0leHw95SVIfDesCHeW1PVdU24BD+WYcHgcl1FwLMU98C\nR3q72a4zNjaDw4cDl7Rk2i7bWEuWjA8aGb7edtnGBj+nIlGnfWBERESajpqSW9VjgbuYPPldyyBp\np065dc4ks8Yw63C5HiA19Sipqeto3bqQ5s3/CHQHvqOk5H5gPdCHwNgwJaWMQYOWk539Azk5cZjb\nu4zBTPq5MOPNxVgP1cvG7T4b+Afwa8AgK2snPXvmWGbtedvqjdH27m1O587PkJ0dQ2lpDHAN3gSY\nuSy58bFRcFLN3AaoYXFd4GcSH1+Cw7GUjIzYkxqrHe+4VDMRRUSOnZKDUiPD6IY/+ADDsHcqrrlP\nYOC+gXYPMgFojnkCXCsgFPP0YjuyCR7ptau0tCOBJ86VlnZoQHsbJyfnKFu2HCA3N56srAxcrqO2\nRzcbG/ycikSd9oERERFp2mqOBSoITMIZhpv9+62DyykpZeTm5lU7DGTfvnJcLgdmAnAS+fmBM+ju\np3PnF8nNjae42HvYiHclSzguF1hnDK4AbiYkpBVVVQ5ggue+hzFj0keBaMLCZjNw4BrPnoB3A04C\nZ+15Z70Fz+rr3PkZ0tOnEJgAa2xs5E087tuXDoz13dPp3MPo0dXjuroGk+204f+z9/ZxUd133vd7\nQJ4EBUaI4cEgEdRE26Rr123SBo1XzarRxmzTkLh7t6+Xce8Ed/e6r72SbR6806QPmtjW++Ha1zrt\nbuNuvdqA3u2lqBW3JF0TG7KNNdFUraCJmgzgAzDI0wADnPuPM2fOOcMM82OGR/m+X6+85MB8z+/M\ngXA+fH7fh1DxI8lI61JpZTPyjHc25nivLwhTETEHhTDUA3+LKaCeV4rq60vGKvj6+pKGsWYm9iEo\nF5WiZs3qpLnZXHPWrE7lFePjO4GywJrx8TuUY6N9aK1YUY7b/SzgoKtLY/ny7UOWXliJVvzEMpBk\nqiAiRBAEQRBGn4aG2cA6y/HBQZlkHk8iZWVV7NnzuM3MKi19nb17NYKn4BrHnZ3TSEq6SlfXJnTz\nLpWEhBP4fH8PHAuK0ftkx8f3MzCgoWcRPgZsA7YEri81NYecnGu8//4AUOn/7HqCs/aCNVp29kLu\nvXf4BlhzcysbN/6SY8c8QBYlJQB9VFY+gV7ibPZpdLk2hdQqsWbShYr/5jfnR7zuzZurqKvTS7f1\nycfekHpqpDeQpZXNyDPe2Zjjvb4gTEXEHBTCEFyqW6gYNx34BGM4iL7zqkpm0JpqJcma1gNcxihH\n1o/V+MIXUqmqMo3Fe+6Zrhy7aVMl+/d/HeOh1du7m337vhExTi9nMd+nfqxGbm4z1p3wvLwWpbjx\neMDGYraNh1EnIkQQBEEQRh+7keOhsfEMPl8BCQnb8Pk+C3QAa6iu3s/SpQf92qePhobZ5OZ2sn79\nbt566xoej5lBp8foJce6gXbYb6BBW9ssjhw5jK5LrTFXgOfw+UzDDZrweIzMQP1106c3cODA7egb\n5UZsOTNnfszMmWc4f34BpaWvD+p/WFzcHdARzc2tlJWp6ZrNm6s4cCAFvdTZQWWlRmbmDv95M4AN\n5OW9Rk3NZebMOYDTWc/RoxuYN68goJ8OHfIBP0XP0pzNvn0f87nPlTN/vhZy7WDddf58CkNtRofS\naaaOqgCew+128MEHY6OnpJXNyDPe2Zjjvb4gTEXEHBTCcBF7qa5aFl9iYic9Pd8NxCUmvjiMNa8E\nrXlFKaqlZQ76NOU0YBotLYnKK2paP3ZjUX3S8dGj/VgfWvpxZAYGTgOvoxuSbQwMnFVeM1Qpjgrj\n8YCNxWyT6cqCIAiCcHNiNXL0Ut1ncbsdwM+xmnceTyLHj6/DMOP0bEONRx+t4MKFv6asrILz55O5\nfr2WrKwCLl82So51A62o6CB79qzj4Yd/il6ZopuA06a109c3A72Vjf31hYWJ7N27Gt3gSiI5+QMa\nG2cDvVg1QmZmL/fccyuHDz8RMMHWrHGFnRgcStfs3Lk65EaoqT+sG+ZZWDXyRx9dxuv9NsFVKIMH\nluj3zedby8mTuzh5MoXq6v2sXJloMwmDry8//1XbesGZeKHej6mjxl5PSSubkWe8szHHe31BmIqI\nOSiEJDk5nu7uLUARcIHk5DiluLi4YqyCQD9WpRV4FcOo049VaAL+HlMIbRn65RZ++9urgGkIHjum\nZkgCdHY2YhVO+nFkEhPT6Okxy6cTEy8orxmqFEeF8XjAxmK2yXRlQRAEQbg5sRo5S5fiNwYBHiQz\ncwdFRQu4cKEWj2eT//MO9E1R/eOLF9MC5zh//jIrVtRSV+dD0y4Al9AH05nP8fp6o5+hbgIuXlzO\n/PlQXe3G47E/912uNXR36yW97e0z6O5eCDwI/Aqr5lu5MonqauPa9H/ffXc6LS2hDapQuibcRqip\nR8z1li1zkJhoGo8HD86zna+5+VZKS8v9GYPWYSvmfTMGr3g8Dn9ptrnxGnx9WVlzB5VEX7z40ZDv\nx7zudsIZi9LCZfIwFtmYQ/08SDaoIIw9Yg4KIUlPj6O7+w50UZFEevo5pbjU1Dq8XlMQpKbWDmNV\no/mzYfI9qxg3C3OoSDuq5cgAnZ3Y1uzsVM90TEnpx+czs/hSUgaU4vQhKKag0o/ViNbAGo8HbCxm\nm0xXFgRBEISbH/vzPp2VK/PYs2cdpaXt7N2b7n+VYTjpH1s1gbWPM2ikpLzE4sV/anuO5+RcxVqx\nUVDgZefOR9i0qZK33vopcJ3PfhZcrr/G6cwgOXk6Hs9GTD1agW4QbiM+Ppt165JwuR6iqOhfUBmI\n19zcSmPjaczy59U0Np6mr28hoTZCXa419PT8grff3gFksWyZg9dee8hmos2Z8wput3Xtj9m799tB\n1/yY7b7p2Yf29cxBJz3omZsPAunMn8+gTLyLliIi/fvmAY4AqTQ2nqG8fANQQV2dg6am7WRnL6C4\nuDtiBqV10rOYhhOHscjGHKpSSLJBBWHsEXNQCInXOx3rcBCv91WluKamLqxlr01NXuU1HY4CNM0U\nLQ6H6oRkD5r23wPX6nCoG3wOx9ygNecqx95/fw6VlfZjFVJTb9DaajVQ25TXjNbAGo8HbCxm29at\nX6SmZjstLbk4nfVs27Zh9C7Uj4gQQRAEQRhbwmkF6+fz8lrQNJ9/cIldT7S05GI1vByOIqqq7mPz\n5ipWrTpGYWE7XV03/F9vAGbxu9/V88QTv/AP+DCqOHaiaYTJvksDdKNy7dq4QH/pkpIMKisNzXuV\nlJRuli49OMjc2ry5Crfbuvn9Em73QpqbTwDLMaYfG6an05nB/v1G1mRojh7dwPLlhk5qICNjPqdP\nm/dh+nQfDzywG03z8ckn+7h+vZaODs2mPwsLOwaVIWdm7mDlyryIms3lWkNNzY8CxqzbvY4XXojc\nAiZcZYj0fZ6aSEsfQZhYiDkohKSryz4cRD9W4U7AauSoZ3w5HG40zRQtDke9YlxekMGXp7zmtGm1\n9Paau8nTptUpx+7a9TV/c2koLHTgcj2iFLdsWaZFTLazbJnqvY3ewBqPHdlYzLYtW2psE511walm\nFguCIAiCMDkIpxVUNYTTWU9Xl6kdnc6GIKPJg8PxA3RT7mnAwZUrmj8rz9SO9fXOEP36jOy7WvQM\nvFt4660mSktfx+VaY9OB9fXXaGjYQkPD4CF1wQYIfB74Cl6vRm7uNrzeJDTNyW9/Wz/k0BAr8+YV\n8OmnzwWOS0tf5/Rp8z6sXZsQ6Gk4bdo07r33NrZt+yIvvGA3Yletsk9wLipawJ4960KsGHzfM8jJ\nWWQpCVczdsJVhkx0k0gyG0cHaekjCBMLMQeFkPT3X8VaKtHff00x8mPsJRaXlNdMSJhDT49RHtxB\nQkK+Ulxi4qd0d5trJiZ+qrxmUlImvb1mhmRS0mXlWE2zHSnHRWsqxsJk25GtqwNrqXhdnWPoAEEQ\nBEEQxozRMEuiOWdwBt3Ro4/z+OMfYhpNR9C0rcAh7AadfcBHXl4LdXX2LMS4OC8DA9/zv64L+Bqt\nrcfYu3ctho4ySyCbbbFvvWXqwmADBDoDr/N68/B4vgE4aG310NDwY06evJOaGhenTpUp39NQGZhl\nZZG131i3gAmXKTrRTaLJpqMnC9LSRxAmFmIOCiHRtB7sU3G7FSNTgO1ALvpQkWTlNbOzb+B2l2EI\ng+zs7Upx+fm3cOHCS+gNqD8mPz9bec3u7lnY+//NUo6NViiMR/nqRN+RDaap6RLWXpBNTWo/C4Ig\nCIIgjD6jYZZEc87gDDqAwsJ3LEZTqv9f+5CMZcscwG6/iddEZ6ePc+fqgfWB1wwMJAMvYmYRpqNX\nxITSUdexb46b/QddrjX09u7m6NF+btz4GE172v8V43Wmkan329bLdMvK1O5pOFNVRfvFYs5EExtO\nA0d7HaOd0WecP7jUfKLr6MmCtPQRhImFmINCGLqBy5iTg3sU4zrRhZQhjp4b+uUW9u17kJKSl+ju\nnkty8kX27/+aUpzb3QR8N7Cm2z2coSIN+HxmWXFKSoNybLSG23iUJkz0HdlgsrMX2kpVsrMXjOv1\nCIIgCIJgMhqbjqrnjKSjrEZTY+MZ3O516KZOOQkJ7Tz4YDKvvfYQZWVVeDy6GVlTowHfw6xaqAXM\nacnx8V76+w1zaLCOsvcfbOeee6ZTWloeuMbeXh+trU8AN4DDxMW1k5vbwmc+k05VVbCROfT7D74X\nd9/9I9zuO4F2jh9fTW9vJYmJiVy44AbWMpT2C2fOhLrHqrHREO25RjujL1yp+UTX0YIgCNEg5qAQ\nhjjsk4O/qRh3O/ayjULlFb/73eN4vQuBGXi9SXz3u++xf/9nI8b19trX1I/VSEzsxzp4JTFR3ViM\n1nAbj9KE8RjwEQvFxV4++MC8t8XFqpmrgiAIgiCMNiO16Wg1odzu36Nn+Okbtrm5oYfaBeuo6uod\nrFyZGzAJrUZTS8t9lJUZGWkOXK5SNA3KyoxsMKtmvQVYhT4gpA1jCIk+PK6JmTP7ycqqYv58BmW2\nWVvG5OR4+f3vW2hoyMcw7BIS9vrXyADWMDDwE9zuRQwMnOKhh17jk09mce7c+3i964Z1T/VhJ+a0\nZqjgrbc0v+l5AygnM7OXZcsc9PT4Qg5MUbnHUME3vzk/4vWMNaNdGRN8/unTfaxdWyHlr4Ig3JSI\nOSiEIVqT7xL2sopPlFd8++3rGOUUoPHWW2qlpJpm73OoH6vR3JyH9X3qx2pEa7iNR4nvZBvwIT1I\nBEEQBGHiMlLPaXtmVgvWDVuHY3fImGAd5fEssPUBtGI1CpubWykrq6K6ugePJwG9KsaqWWcAP0Yf\nrvc+0Of/fCJtbf87bW3p3HuvvkZzc6stM9DlWhNYp7S0nIaGLZbzvkpfX7dlrcMYw1EaGtaRlFTB\n++8/TEvL/RYjU+2eDh52kope8WMYkRsoKjpIYmIHe/d+HdWN6cnSjma0K2OCz792bYKUwQqCcNMi\n5qAQhuDeKdcV46YTbc/Bjo4MrEJEP1ahG3iV4ZdAg6Y1YzcWW5RjozXccnKuAmYpc7id8VBEW5I8\nWUSegfQgEQRBEISJy0g9p+36xIlVq9TXOwFd+2zc+EuOHfMAWaSk1ANfQs/w0wjfB9DO4BLR3cAO\nYIH/HGuA/cBZHI470LQE9F7aDwdd79BVIIMNuwVo2n8CP0XX06HLh5ubb1BTc5mWllwaG+vxeG5E\n1HjB5lV+/lmWLJlFZaXxOQ+NjWc4cyaX4fTMG0nTbaTb6VjPl5vbyfr1u6mvd47KZrJsVguCMJUQ\nc1AIQwvWgST6sQrt6Obg8HsO6hORrROS1QzJuLhk+vsL/Nc6jbi4zkghAZKSbtDTY77PpKQ25dho\nDbe4uARUdsZDEW1JciwizxBhp0/D4sW1Y9IjURAEQRCEm5vm5lYaG09jGny9WHWgoVU2b67iwIEU\nYCN6pqBGfv52OjtvxeNJJFwfwGAGm3ZO/5pGKfOvgIvAy2iamfUX6pqG0oCDpxPXYdfGoc+5YkW5\nbdN5+fLtg4atBDPYvHoKIJCBqPdctJcdq/TMC2WKXbz40ZAx4YilnU4oYzHY5H300Qree29dVNcW\nCdmsFoSJR2tr+6DMbfnbdGQQc1AIwwLAWibbqhSVnJxPd7cplpKT1ct0NU3vjWJOSFZbMyVlgI4O\nc3hKSsqA8prvvPO/cd99v/APQbnEsWN/qRwbreF26dJMrILy8uWZymtGa0jGsvNpFWFnz45Nj0RB\nEARBECY2wcbN1q1fZMuWGuU/2PR+eWZ/65ycl/mzPxucBaZrnT6s+ic7ewGnTt1PWVkVdXX/QVNT\nLXV1BZSWvh7yOjQNvxFpDuiYOfMkHR2dDAzci24QrgHO29ZJTs4gK2s72dkLKC7uDlyTrgE96BOG\nU2lsPENLy304nRm4XGuoqXkVt3sR+qC+27CbkgvIz99OTs6iwPvUjVJ75mRLS27E70E488r43NKl\n2Aa8ORxe8vJejdgKJ9R5L16MeDkhiaV6JZSxONmqYQRBGFm2bz9DdfVmxrJ//1RBzEEhDG7sZcX1\nSlHd3fW2uO5u9em/kBThODQzZ06no8MUlzNnblVeccmSz9LV9VlOnDjBkiUbleMgesOtqekc1nt0\n/Xqt8prRliRrmu1IeT2Aujowp/e1U1fnGDpAEARBEISbnmDjpqZmeyBL7fhxDzU1P/IbYKGNwmCT\nJz//8+zbNzgDTDfiLhOsnZzOh9mz53FKS8s5efJZ3G4HJ08GX4f+h2N3dxdudxZ6GfEscnP1vnxt\nba9gat0KoMC2zle+ksGePWWDrkk3AH8UWMftXkdZmf4HqtOZwalTZf4BJWk0Np7G7baW/tZx6tRT\nOJ0Ztj6I/f3NgAejXNrpHI6GDk3wRrampeB2PzemvaeDr6Gx8XTASI1EKA06f/7o9hkUBGFio7ec\nkA2C0UDMQSEMTsyHcQe6UFEhC3vPQafyijNm3E57+wbL8U+U4mbPXkRDg/kLYvbsRcprxkK0hltG\nxhzcbvPeZmTkK8dGW5IcS0lHU9MlrJOrm5rUBsWMFyPd20YQBEEQhMEEm3t6pptxfAS3WzfswukO\n1QoMl2sNR468TlubqZ2yskxja+jr0P9wvHChHmMICGh4vTvo6bnF9jq9h7WXzMwdFBTMs2UjBmsJ\npzODnJxFtqy8s2e7mDPn1cCguqNHNzBvXoFtYnJeXgualsWqVccoLGynt9fH/v1fxzQotwOLSEj4\nkKNH/0rp+zAUxkb2oUM+uroSgNWM9R/TupG6Hbf7TqATt/spysqqlHRoKA365ptPIX0ABWHqkpvb\n7K9mkw2CkUbMQSEMnYC1afN3FOOuY++r8qzyijNmNNLebv6PPmPGFaW42bPdwCsYZcW33pqqvGYs\n/fSiNdyamy8B5hS7lhb1TMfgnRKjWXckYinByM5eaBO/2dkLlGPHg1iMUEEQBEEQ1Ag295zOerq6\njGP70I1Dh3yDTDbVCgynM4NVqzJsfebmz69QvA6NEyfeZfr0dOxGYBbp6Zdtr9MrVuJITb3B3Llt\ntmxEFXPzzJmP0LStBPcMtG4m//73DYFS6uPHNTIzfxp0XYuAtTz8cAfz5sWe2WeUB5eWvm67f6P1\nx3S4DVrdSDWzQlV1aLAGbW7OoazssGz8CsIU5vnnF+N0ygbBaCDmoBCS+Pgm+vvN/n/x8aoDSeZg\nFzlzlNecOTOThoaXgLnAx8yceYtS3Pvve4AXMQTP++9/V3nNWPrpRWu4dXZqWHsrdnSoZx1G2+cw\nloEkxcVePvjAjC0u7laOHQ+kF40gCIIgjD7B5t62bRt44QXrIIx1GNqhqyvBr7dMnTWcYQ9DGYlb\nt36RmprtgYy9/fsfZOnSLQwMfAHoZGDgH+jq+gHWcuFlyxz09s6isXEHMA+oRS8pduN2r+Pq1YPA\nIaAZ6OPQocSw5mZ1dQ8eTyKatoBQPQMHT0m26uTrtuvKyDhDWtoZzp9fEDJjMVrGaupuqA3anTtX\nBw2eWa2sQ4M1qNebOOjnSBCEqUV6+gz27Fk+3pdxUyLmoBCS4uL5nDt3Dt2ou0hx8TzFyCbsvQqb\nlNe8ePEK8N1A7MWLLyrFXbuWg1VoXb2aM4w1ozeScnObsb7XvDw1AzU+fg7WYS/x8f+mvGa04i4W\nUWjE6tmVTPjdmViMUEEQBEEQ1Ahl7hl97IxS2pEqZx3KSNyypcY25ff7369A04qArwReo2lFPPpo\nBXV1yTQ11XLpUgGXL2tABuBFr3QxS3t9vhcsx+V0dW1g717T7DKz4zQKCpLweB5Hr2KxZjDqPQP1\n93wDqAJ6gJ8DDwLplJRkkpRk6rOeniwqK5/A7Xb4TbHQJthwW6iEun/hzhHu8yoTQkPp6uDBM/n5\n2wNTlSMxEcqiVYilEkkQBGGiIOagEJKOjg7g2xgP8o4O1T5z7Viz4vT/1OjpmYtVUOjHKpzHbkhe\nUF4zNiOpD/t0ZZ9S1Be+4KOqylzznnv6lFcczi77SMRZY/WhLUuiOsdYMla744IgCIIghGYsy1lD\nGVLJyWfwek2tlZx8iT17vmMbXqKbdKuBSuzZfDlBxzNs5w7OjsvN3Yo+LG4O8C3i4grIzW3i6FFd\nd+la8zDWntGZmTtYuTIPl+sRm4m0dOnBQe8lFMHXUF29g5Urc0ekPU64z+sTQjcARzh+PI2aGhen\nTpXZ1gulq4O/Pzk5i5SvcazLoqMllkokQRCEiYKYg0JI7EMz2ocxNGMe1qw4feqaKpewm3yXFeP6\ngFcxeg6CmkkH8Mwzizl48CW6u+eSnPwx3/zmo8qxDQ2zgXWW44NKcYmJDqymYkKC8pKCArEYoYIg\nCIIgjBzD3bBrbm5l48ZfcuyYh/7+6aSleZg9+06Ki71hja9gQyon5xozZ6bxm99sQdOKcDgucO+9\nM2hpaQ0yqh4kM/MndHT04PNZ9ecfsetRY6M7tNnV1ZWL1fj7yld285OfPBV4H5qWSUJCIz7fDfRM\nRQdFRQvYsyfcZObIm9bB1+DxLGDv3rWMRHuccJ/X+1wfwehJbp3QbBDq+11Wdjjmio6JvvErLW0E\nQbgZEHNQCElLyyWiG5pxBbuguqq85qxZHTQ3fwsoBC4ya5aqeMjEOskMvqe85l/8xWG8Xj1D0uvV\nWL9+O59++lml2GizDj/+OAN4xHL8C+XrjRaZ4CsIgiAIwlgTbsMunC7ZvLmKAwdSgI1ABW1tZTQ0\nDF1iG9xzsK8vmzfffBaoAB5D027w5puHmTfvf+H1/hE4jZ4dCCUlGVy+nMrJk9uBO9E3tXNISNhG\nWloe99zTR0ICXL68LzC9uKnpNLAWQ/+1t18heFic/X2Y5cn6BrquGUPdA1UTLLi1jd7Lb3immFKF\nDQAAIABJREFUVDgdG+7z+oTQ2xjKBAv1/R4JY2+ib/xKSxtBEG4Gxtwc1DSNl19+mdraWhITE9m6\ndStz5phDKw4cOMC//du/ER8fz1/8xV/w+OMT90FwM6PvggbviqrQhr2s+IbymosX385bb/0dxoN1\n8eJ/VIycZbtW/ViN5mZ76Yh+rEawGN22bUPkIOD8+feBr2K8T/34kaGDYkQm+AqCIAjjjWhAwSCc\nLjHNJgd69UrkbKzgnoOdnT8Niq8CHqe11YFe/mtm+Tkcu7nttk5Onpzm/1wD8BQ+XyYej8abbz5L\nSko2nZ39+Hy34navxuib19l5Kx5PIv392ViNOjO7sM92/RkZ3RQXH6SwsIOtW+/l7rt/hNt9J9DO\n8eOrgSr27Hk8oM+am1spKwu3sWu0tukFEoE1DNeUCmXaNTe30tvr809Rvk5JSSYul65Rn39+MefO\nvWsbNKOy3kQ39kaCydafWxAEIRRjbg6+8cYb9Pb2UlFRwalTp3jllVfYuXNn4Ovf//73qaqqIjk5\nmQcffJC1a9cyY8aMIc4ojAYDA/bpaQMDqoNFFmIvK1Y3B995xxBmAA7/sQpXse+eXlNec2CgDvv7\nPK8cGyxGX3ihItCIeyh6euKxGqj68egi5Q6CIAjCeCMaUDAIp0vMDCyjnHf4JbbmBGAjPtny9Rm2\n116+PJOCgjbgaf/n9dJcw0Ds6bmFnh7ja1rgazk5iwA4fnwd0ApUMH26j7VrEyyltJdt15+Wdo33\n3tsIQGlpeUBDGucN1mabNlWyf//XMQzUjo4fkZaWwcWLaVy44EHfWL4BlONw/C/y8q7aNqojVY0Y\npp3xulWrjtHYeNo2PCQpqSIQk54+g1Onyigrm7jlvePFZOvPLQiCEIoxNwdPnDjBfffdB8Bdd93F\n6dOnbV9fuHAhN27cwOHQH9zGv8LY4vVew2pg6ccqfIq+KzsDPYvwU+U1+/o+wSqi9OPIZGV10tRk\n9hzMylLfNe3paQWMUuaP6elR75FYVwe6SNT7MtbVqf2sTpuWj8+3wXL8Y+U1o0XKHQRBEITxRjSg\nYBBOl7hca+jp+QVvv73D33PwFWbPvoOCgjY6OjpJT9+J13uFlJTbSEu7yi233E5T0yXgS+htZjRK\nSjLp6fkn3nzzGj7fLvTBdcv9X2/DqjWvX69l2rRFmNOE0wA3uuGXDmRhNx7TLNer+d9DBvAYa9ea\nVRku1xqOHHmdtjZDJ3bQ1JROaenruFxrQhiaqYO02VtvabbXVFe34PM9hWlilvs/fhZNc+B22zeq\nVatGrK8z742+ptWwbG1tHyKTURAEQZjsjLk52NHRYdsFnjZtGgMDA8TFxQFQXFzMV7/6VaZPn87K\nlStJS5MMp/HA53MCnwC5QD0+n+rDP3g68XBMqFswzbYO/3Fkli5dyOHDT2CImqVLXxvGmrcC/yem\nIFLvV6iLUXN3talJbaLzl78cZ5tWvHLl6GcORlsCPV5Ij0RBEISbD9GAgkG4PnROZwb7928a9PrS\n0nJ/Fl0FUIbP56CtTaOhoQJ4iuTkH+LzFRAXd53e3kw+/LANn0/vKa3ru5eAPwW8wHZgEXCOrKyC\nENOEvwT83+gauAm9D6FuPMbH/4GcnNNs27YBTYNjx77NtWuJxMXNor09jpaWVpzODJzODFatyrCZ\nbt3dFf7jCnJze7CalLm5H+Jy/W3g/TY3t9LR4ba9pr/fblRmZvbS0xNHV1doM0+1asT+ug7CZWvq\n04o3Iy1qBEEQbk7G3BxMS0ujs7MzcGwVhbW1tRw9epTf/OY3TJ8+nWeeeYZ///d/58///M+HPOeJ\nEydG9ZpvBoZ/j1qBz6FnAMYDJxXPkYW1lwtcGMba7cDfW2K/qxR76VIqVvFz6VLqMNYc3K9QNTY1\n1d6UOTV1jlLsM88U09e3k/p6J3l5LTz99CLlNVtb29m+/Qz19U5yc5t5/vnFpKdHLrl6+uljthLo\nJ574f9mx4z6lNa0M5+co2msFeP75/7QJUI9nJ6+88oVhX+94IL+PIiP3KDJyjyIj92jyMRoaEORn\nQYWJeI+++c35gY8/+ODkkJpBTzId3IcQeoDX6O7+HuCgv1+jqqqcuLjkoNcVAeswS4PXAu1kZ7fw\n2GMF/PKXJ+nvP4SuRX3Ayxh6ND7+WzgcefT1zaS//y9xu6tZsOB/4nDMoq8vHiijvz+TqiqNxx4z\n9cqTT+bi8ezk2LFUuruTgNWAg9OnITv7OtYKneLiJC5e/IiLF3X99Jd/+TY+Xx7wKjAX6EXTPsJq\n3H3+851omsYbb5ify8j4JPC9Tk+3lzVbv2bF/rrVzJ79XWbNKiYvr4UnnzQ1qj6t2Lynp04N8MAD\n/xSVzruZmYj/r0005B5FRu7R0Mj9GR3G3Bz8kz/5E/7jP/6DVatWcfLkSebPN4XBjBkzSElJITEx\nEYfDgdPppK2tLeI5pbfD0ETX/+IAdpOvVvEcv8MuxrKHsfYe7MNMOpViZ816A3gFs6zYwZIlajuZ\nDsdeNM0UTg7HJyxZslkp9q67aqmtNWPvuitO+b2uWLFc6XXBPPzwTwOm2dmzGmlpu9m37xsR4z78\n8A9Yvy8ffpgx7J+J4f4clZaW267V6axgz57lSrGtrQ22621tvW1S/H8uvWYiI/coMnKPIiP3aGgm\nqmgeDQ0IogMjMZH/f2lubmXTpkp+9Ss3Pt8LBGuG8+cvs2JFOY2NmcDP0Y07a5/pRGABdu05g/j4\nPzIwYL4uJeUCCxfu4+rVP9LRkUF8/A5KSjLYtesvKSuror9/i+WcP7Wdb8aMQnp7p9HXtwFdp04P\nev2rQBmQMUivrFixnNLS1y0ZhB7a2i5z6ZIx6O8+IIMPPtjJ9753mvp6J273Ka5ezUGvoEkGLgB9\naNrz6MZmKvn5Z6moeAogqAfgXwYqLfbsmUdZWQV1ddDUdJmmpgV8//u1g6oxjNeZ5/ivIas1cnPf\n5exZ8552dn5KdfWzg75nI8lkqySZyP+vTRTkHkVG7tHQyP2JTLQ6cMzNwZUrV/LOO+/w2GOPAfDK\nK69w6NAhvF4vX/va13j00UfZsGEDiYmJ3HbbbTz88MNjfYkCoIut7RhlxZCgGFeLvedgrfKKcXFt\nDAz8EaP/X1yc2h8F77zjQTcHdbHy298+r7ympjmxvk9NUxcc4UpiRpPg/jP6sQr2ATN6mczoEssQ\nFOmRKAiCcPMhGlAIZuPGX3LgQAowB914WwNkBDTDihXBgztexOHYQlpaAV7vVfr6ctEn9v4ceND/\nmtP092eQnPwiDkcRs2Zd4ejRJ5g3r4Dz5/+E5ct/xtWr2Rw+fJW/+qvXuXIlx3/+VvS+g92W86XT\n2noVKPCf2zpN2fh3kT/usZB6xdCLdXXJ1NZ+gNttLXeuAB6jre0G+/eXYe8naGQ67kCvzMlE37iH\nnByHxQQMvSFuDMkoLS3n5MlncbsdfPDB4HJg1WnCzz+/GKfT1L11dQW43SMz7C6cCajaN1EQBEGI\nnTE3Bx0OB9/+9rdtnyssLAx8/NhjjwVEozCedADbMMXLFsW4FOwZh39QXnFgIBmYjv5jmcrAgJoh\nNDAwH6tI04/VcDhuoGlbA9frcKi+T3UxNZJo2jWsJp+mXVeKKynJoLLSzMosKRn9XddYDL7xMF4F\nQRCE0UU0oBDMsWMeYCPBZpmhGVpacrEbcX+Gpq1l9eoKampacLufsMR+D+gDXmZgwEF3t8ajj1aw\nZ89zgG5A3XXXa3i9ujlnlCDn55/zx+sGn3m+V9DLlkHPWHzRf3w39g3XTuLjvaxbtxuX6yHb+7Oa\nXk1Np/F652AdZqcPQqkAFga9zxmWj1PJzf2UhgZ1TWVd98IFt3+dDGIx8dLTZ9gyA0tLX+fkSfOa\ncnKuUVpaHlWWX/Bk5t5evTImlo1mQRAEYXiMuTkoTBbmYRcptyvG5WEXPblDv9xGGtYBH7oIU6EW\nu0irU14xNfU2OjqsfQNvU44dD1JSOmhtNU2+lBQ1w23Xrq/5J8xBYaEDl+uRUb1OiM3gGw/jVRAE\nQRCE0SU4Q6y/PwW7bmwhP387LpdeMut01tPVZdV4HRgmUXb2QlvmWkpKPl6vg3Bm0ubNVXi9nyfY\nhGtrS2fGjO20t2cHfe0z/msyN72Tk1/kz//cy+HDL+HzfR7oBFbx1a8eYefO1YOm+donAa8FvgV8\nx/J+tqFPRb6BXcsaA/401q+fzmuv/V1Q6e/QmmrwuuXABkayGiNY5/X2Thsyy2+oEuFwlTFSSSII\ngjB2iDkohMHe9Bg+VoyrB/7WEqde4qs3XLaKsrmKcZmYpcEN6Dujavh8n2B9nz7fp8qx48Hs2XfQ\n2GgcOZg9e6FSnGarPlYtRY4NMfgEQRAEQbASXCaanPwioPcnNsy3U6eeCZhG+/Y9SEnJS3i9eegb\no2swTSLNXyZrVFN8BNyBVdfl5bUE1taNwnbs+vZD9NaWmehaN9iInIFVmzocxezf/w1aWlotRuAR\nXC7dGAw2x4Iz3/TBKNbjzwJrycl5mT/7s92BYXWa5qOh4WDg41WrjlFYqHHkyH1K2XjB6yYktJOQ\nsBuns55t2zZEjFchWOctXXrQtmZwlt/QJcKh299IJYkgCMLYIeagEIbz6MbefPTMvI8U4/Kwi568\nYa5pFQYXlKISEjrx+cxd2ISEl5VX7OnJwtyx7qCnZ5Zy7Hg0SS4o6OHkyW9gvNe5c3crxY1Hz5bJ\n1kRaEARBEIThY33e5+Y2A300NMwO+ewPNq0cDrtZdscdS2yv/8EPzvjLgG8Ah0lI2MODDyZbyndN\n4+jIkVvp7n4QU9edQ9MyA+fSs9BW+7/eg95f+2/QM/fK0ftrV6D3HExCNyJ/RbA2bWlpDbkBGvze\nDh3y4XSeRs/cM8zPWrq7rb25vYCD/PzPs2/fukH3trS0nL17zXJbVf0WnHHn883A59tAV5fGM8+o\nDbMbLpGy/EKVCBs/O/39qeiDXRYC3kD7G9loFsIhf2cIwsgj5qAQhjnou5kz0AVSr2JcC3YR1TL0\ny22kAC+hZwxe9K8bmS9/OYuqqlcxphV/+cvqBp/D0YKm/X3geh0O1VJmaxNtOH5co6fnF+zfv0k5\nPjr6sE501jSfUtR49GyRJtKCIAiCcPNjL2HVMIZphHr2BxtIs2Y14nabx8XF3bZzm/olA9iAz3eA\nxMTOkMM4nM4foBt9j2Nk/jU0mAbU+fMp5Of/iKysuVy+DB6PNYNuhv+/dUArCQn/xOLF/0FOzg1+\n85stdHcvBBrwep+grKyKnTtXDzIm9PfmAY4AqXR11dPV9Tj5+dvJyVlEYWEHHR35HD5s7c29m6HK\nZaPVb9aMu5MnP8Tn+5vAOdSH2Q2PSFl+oczD4J+dzMwdrFyZNybtb4TJjfydIQgjj5iDQhiysA8W\nuaQYdxW9n0ohusHXrLyiw9GNpplThx2OF5Ti/sf/WM/995fT0gJOJ/zjPz4UOciPpqVin1Y8XTn2\n7bdbgGcC1/vWWz9Ujo2WhobZ6MLVOD6oFBdLzxZDVJ8+DYsX1yrvzEkT6cjIrqcgCIIw2RlcOmsO\n0wh+9gcbSNu2beCFF9QNJeikro6Qgy/04Wsv+9fPAny43ecoLq7F43k6cI57761g/vxE9u4N1eMP\nIJ2HH76dPXseprm5leLin9DdnYn+Z5M+STmUMeFyraGm5keW6crrgApychbx3nu6dvvc5/bZ7lVK\nisa6dRVhy2X1TMzQZdJDYc24czrP4fGk+7+il+yOlP4YznlCmYerVh3Dej+KihawZ8/gDMpoEI11\ncyN/ZwjCyCPmoBCGJOwNotWy+HRBZm20/JzyirfdVsjly+aaBQUFSnH/8A9HA0JsuOUSCQldQSXJ\nLylfb2fnTKwPJf14dMnJuQqY5Si5uV6luFh6tlgF8Nmz0Ze0RGNI3uyCTnY9BUEQhMnOYAPPHKYR\n/OwPVSa6Z094vacbbq/idi/CGP7R1PRjTp7Udd/x4xrV1TtYuTKXHTse4Pjx/0lDg2kENjYa1RZ2\nE+HIkfvo7d3Nr37Vjc/XDhibwz8G3Lz99jQyM3fR0fEpfX396JUbGvD/UViYFtKYcDozyMlZZBuS\nAqm2e9DUZExGNjInrwSmKYfGXjHS29s55ETgUPpJN03Nc5SUZIyY/hjOeUJ974N/di5cqKW0VO19\nRdKForFubmRYjSCMPGIOCiFxOJrQNFNcORyqg0Vuw24qzlFe89NPLwNbA2t+8skWpbijR/uxCjT9\nWI3k5Dn4fGZscnK+cmxSUj0+n2nUJSXVK8dGS1xcAtaMTodDredgLD1bRqKkJRZD8mYWdLLrKQiC\nIEx2rM976zCN4Tz7DfOnrg6ami6Rnb2Q4mIvLtcaTp0qsw3/OH9+gc2A83gWsHfvWqCCvLy7aGiw\nmnO96FUsdhPB6cxg3z59sMhnPvNPNDTcDpwEvg1UcOVKcJn0w+ibyNtwub5GWdlhmzGRl9dCaWk5\nFy70AD8HHgTSyc8/i8v1VOD9NTVlYO11nZU19EZ4cMXIu+/+Gx5PeH0USj/t2vU1//2DwkIHLtcj\ngzL2QumPYEPuySdzB71GRccMZewZPzvV1T14PIl4PJvYuzdd6X1F0oWisW5uZFiNIIw8Yg4KIdG0\nHKwPVP1YhQvAnxpnQX2QCQwM3G5bUz+OTGfnJayir7PzsvKaPT32yXT6sRp9fX1Yjbq+PjUzMxYu\nXUrEar7qx5GJJRMv2mzF8TAkJxuy6ykIgiBMVuzaYvAk3eZm60TfobWHaf7sAgpwu6fxwQcax479\nX5w+/d9teqK09HXblGJ9qrAjsI49izER6ALKyczsZdkyBz09PpYuPRi4pj/84W/Izf0hPT1L/XHh\ny6Q1LYtVq46Rm9vJ+vX6dOHCwg56enyBwSFG77xly7Lp7k6lqOgndHTMwOdLQ//TyzQe58+vCHtf\n6+qSqa09gZmJuRp9im94fRSsn379ay9PPFHpv07ze6CiP4INOY9nJytWLLe9JprzWI09QysuXXqQ\n48dNEzTS+1LRhaKxbm5kWI0gjDxiDgphcGMfLKKaFTcDe6/C2mGsWRe0Zp1SVFJSFj6fuQubnKw+\nkKS3twuzR+LH/mM1HI75WIWKfjy6NDVdQi/V1u9RU9N2pbhYMvGizVaMxZCcKoJOdj0FQRCEyUqk\nwWybNlWyf785abe3N3zbF9P88QAbMcuCd1BWVmXTLC7XGvbtexmfby7mVGGNnJxr9PZOIz7+X+jv\nvwFkAo8wffoB1q514HKtp6ysKuT0X31q8jX0zdBerNl/1jLpvr4mjh8vBdJ59NGKQC/BpUsPEtw7\nLzGxg/37Z2HXxbuBXcTHX2XmzFx6eghMPzawD+lYj74p/BjwLbq6ugnOgrTqrcbG08BngHeBVFpb\nP2b//qeBTI4f16ip0YejBJubofRHsCFXX+8c9BoVHaNi7EXSfcFfz8m5NmR5teq1RWKqtLkRBEEA\nMQeFsCRg7XEC8Ypxs7Hvts5WXjEuLo6BAXPNuLg4pbi+vmvA32EIBp9PfeIwzMTeI1E9NiPjU7xe\nU6hkZrqHsW50ZGcvtJXSZGcvUIqLJRNPF4NDi8NQxGJIbt36RWpqttPSkovTWc+2bRsiB01CZNdz\nYiJ/DAiCIETm2DG7kff22ztsX9en4pr6Yagpuab5k4VdR2aFHECSllaIx7MeqAKOkZDwIXFx+QEz\nUtd0FUA6Dzyg0d3dRVHRT2hrmwH8FN10ywjooZkzL9PdPRerkTdt2jb6+nzADSAZ6AE2+9d83Kal\nQplb5tet78cJtNPf/zwej4PKSo0nnthNYmJi4P2dP58SFGNouKX09HyJlJSXWLz4TwOGl254Gmbi\nWhyOLWia0aZnnf8+6O/L7b4Tt3sdoNnMzaG/J+GHoajomHDGn/VZm5vby0MPvUZDw+zA+xrq6729\n0yJqzJHQWFOlzY0gCAKIOSiEZQ5gNWRcinEN2LP/GpVXnDlzPq2tGyzH3UpxesmzmTmoXgINMBe7\nAJurHPm5z82yNLpu5+671UyzWCgu9tpKaYqL1e5RLJl40cbGYkhu2VJjGzLzwgsVQzYsn6yICTUx\nkT8GBEEQVBhs5Nm5jl0TNoU9k8u1ht7e3Rw6dJm+PmuMg6amy7YBJFBBSQlUVqajm14aDz7YMmgz\nc/p0H2vXVtDR0cnhwxlYjUwjG8/QND/+8RIefbTe1od6xow8PJ5EdH35FcvVphGsh4wstbq6ZJqa\naqmrK6Cp6TSQY7sHmZm1QBYej900tfYRzM9/Nei+dVj+zcThKLKZesF6Ky6uiP5++1AUHX3as/H5\nSLosOPPuyScXDfl61fMYGXz2DMnBZmVpaXnYrwdnao5W+5mp0uZGEAQBxBwUwnIOeAXIQy8pvqoY\nlwZsB3L9cdOHfrmFJUu8vPmmKYb+9E/VjK/e3svoO7l6XG+v+oTkadMu2UTotGnq/QobGjKHPB4N\noi2RiKW0wog9fRoWL0Y5NhZDcqqIMTGhJiax/PyJ4SsIwlRBN+jM5/yyZY6grw+ekhsOpzODxMRE\n+vr+K3rprd7SJienl+zsu21VE/q04dWUlVl1zUOWISE3gMP09HRTU1NPa2sC+qb3YOPQyFDbufMy\naWldeDzBZuYC9Aoa8/Px8X8gJ+e0rapB06C3t5czZ9rw+ebgdq8G0omPf5G4uJdITZ3L8uXx/OAH\nX+P++18PsY55bVlZc7n33grOn0/m+vVaGhuT6O8vxyifTk//2JZJmZvbabu+2bMbaWgwj5OT36O7\nG+AM8BTQChzmwoVeSktfD/ucsmbeNTe38vjjP+e559qH/WwLl8EX6Vk71NfHqv3MVGlzIwiCAGIO\nCmFJwtrbDp5VjGsA7kEXgvHAfyqv+Ic/fAq8imFInjp1QylO0+KwlkBrmiNChMnKldlUVZlrrlyZ\nrRx75Uoder9C/R5dufId5dhoibZEIpbSCiP2xIkTLFmyRDkuFkNyqoixqWKCTjZi+fkTw1cQhKnC\nrl3rBxl09q8PnpIbjubmVqqr64Hfore2WQMcIz9f/51srZowpg0H/241dMe+fR/j871Af78Dt1vD\n4dgCtGE10Hy+j6iuLmTjxv04HA6qqzejm4rlJCS08+CDyWhaOpWVbf5rqUDPwDtOf//TuN0ay5f/\n2N+/r5n33rtIY+MS9Oy+1Rilx/39f0Z//1oeeEB/FpSWluN2PxU4X37+WZYsmWUzWefPx/befv/7\nDykp+QXd3d0kJ19k8eIc23Nm/frdPPqo+X3Ytu2veOEF87iubhEnT34FKAGqiI+/GChr3rtXo6bm\nVU6dKhvS7Nu8ucp/j0bu2TbcPoOhMjVHu2ez9IYWJiOyUS1Ei5iDQhjswzb0YxWysTdeVp9WfO1a\nAlCAYSxeu/ZHxcjbsJdAtyqv+bOffYOysip/VtzcYT30r11Lxzo5+Nq1mcqxY814PCRiMSSnihib\nKiboZCOWnz8xfAVBmCqEes5Hqzc2b67C43kaUz+WA4apOPh3crh19ux5nNTU3bbyYIejCE3rAnag\na8wWfL7/hseTSWWlRmbmT/3rZgAb8PkOkJjYicv1EIWF/0hb22voJdO9wJ3+15Xjdj/rz2g0rvcr\nmCXLaQRPUgbjGZGJrpUhJ8fBrl33BUzWvLyWQdOUf/CDM3i93wYceL0av/+9cb36+6uvdw7qHbhn\nT0HgHl2+3IM5YOUxZs78qa2s2e1eNGjoSzCj8WyL9Kwd6utj1bNZekMLk5FYNqrFWJzaiDkohOFj\n7P1OLinG3YLdVLxlGGvGYTcWX1aKmjatOag0eHDD5HBott7Y4Rtlh2JgoAn4PwLrDgx8a1jxY8lk\ny2aaKmJsqpigk41Yfv7E8BUEYSoTrd4INp/i4ztYuzYOl+uRkL+Trf3ojHV27lzN5s1V+HyfYtWw\nt9xSj8PRz5UrA2iaF12bVmIMJRncG7GTixfTcDozWLVqHnv3WrVpuf8KUrHr3RmWj1OBD9HLkfVS\nYONZEOoZ4XRmBK69ulrD45kGXOX48VuoqXGRnb0waC379YZ7zgT39IuPf4WcHAef+UwWVVWD3+9Q\nhLruWE2ESM/aqaIFBWGkicXMn2x/Mwoji5iDQhg8WEt8oVkx7gJ2gaWeORjtpOOVK2dRVWWWFT/w\ngPpgkE2bKgOT7c6e1ejt3c2+fd9Qip02LY++PvN6p03LVV53rJls2UxTZddKhO/Nhxi+giBMZaLV\nG8Hm01e/OsPW727jxl/6pyNnUVICn3ySPmgd84/aG+h9C7tISGihpaWN3t7P+V9rNfr0oSQlJZm8\n++53uHbtc+gDO1bR2PgjWlrus/1Oz8trQdN8NDQcpLHxjH/qr3Gudv/5NRISTvDlL88iKclHff0x\n27NAdTiHkYmor2EfUFJSkklSUuTnTPD3or+/ELdbo62tlZSUb+H1fg59AvMqCguPDPn9cbnW4PHs\npLX1tpBTkqMxEaaK1hOEsUb6vgvRIuagEIZi9Alraei9X5IU4zIxB5I0oO/IqtKE3VhUMyR/9rMN\nQT1tNkQO8vPWW8Z6YEyMU2X58gHeeMO83vvvH17mYTREK6Ryc5ux3tu8PPXsyvFAdq2EyYoYvoIg\nTGWi/aN0qI2VzZurOHAgBWPacGXl4Im+hYUdlj9qM4BE4Ov+8uID2Def9X/j472sW7eb1157hJMn\nT/KNb/wnbvedwBHc7qcCpbahfqe3tNhLgQ3TUL/2vw+rzcJVrAT/Qa7/Vw6k0dbmY/363dTXO/3n\nf8R2/ubmVtuAEkMbBn8v4DLwLG1t+nF+/nZychZRWHgk4kaW05nBK698wdZ7OlYTQbSeIIwO0vdd\niBYxB4UwdADWHczvKcb1Ay9EEQfZ2V1cv25mAGZndynFxVIaPLiUpEk5MjU1CesglOnTE4e59vCJ\nXkj1YR/a4hvNy4wZ2bUSBEEQhMlHtH+UDrWxYmqAwRN9reuY04qN0l7j9R2Y+tCanZiFjtq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NwecnM7uXhxNvff/3pgMIdVkxjmjj6YDlyup3A6M/yZh+sD17F2bUKIHnxWo+lBYAewAGinoWE2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\nrzz7I9bX/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m9xsloOxxVyOBbL2Jf77sGveQ5927hxjxwO716HY8Y8oo4O/+Llvn09amtz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\ndNhvz8Gf//wZvfSSuT0H3W6/R6b7mm57F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90douQ6y5GDg4NQUVEh1NXVCS4u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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b9a1c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    row = 1\n",
    "    col = 2\n",
    "    k = 3\n",
    "    fig, ax = plt.subplots(ncols=col, figsize=(18, 6))\n",
    "    fig.suptitle(\"Target Predictors versus Acc EM\", fontsize=16)\n",
    "    for j in range(col):\n",
    "        ax[j].scatter(matrix[X.columns[k]], matrix[y.columns[0]])\n",
    "        ax[j].set_xlabel(X.columns[k], fontsize=16)\n",
    "        ax[j].set_ylabel(y.columns[0], fontsize=16)\n",
    "        k = k + 1\n",
    "    fig.tight_layout()\n",
    "    fig.savefig('Target Predictors vs Acc EM.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smooth Acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = matrix[matrix.columns[1:2]]\n",
    "X = matrix[matrix.columns[3:]]\n",
    "# https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear/249897\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "## Smoothed Accuracy\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = results.predict(X_test)\n",
    "print \"Accuracy %f\" % (np.mean(predicted == np.array(y_test)))\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    ax.scatter(y_test, predicted)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combined Smooth Acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = matrix[matrix.columns[2:3]]\n",
    "X = matrix[matrix.columns[3:]]\n",
    "# https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear/249897\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "## Combined Smoothed Accuracy\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = results.predict(X_test)\n",
    "print \"Accuracy %f\" % (np.mean(predicted == np.array(y_test)))\n",
    "with sns.axes_style('whitegrid'):\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    ax.scatter(y_test, predicted)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing only predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matrix = pd.concat(fm_list, axis=0, ignore_index=False)\n",
    "matrix = matrix.drop(['Block', \"Block's Trial\", 'Beta 0 (Intercept)'], axis=1)\n",
    "matrix = matrix.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "normalized_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preprocessing.normalize?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "normalized_matrix = preprocessing.normalize(matrix[matrix.columns[3:]])\n",
    "normalized_df = pd.DataFrame(normalized_matrix, columns = matrix[matrix.columns[3:]].columns)\n",
    "normalized_df['Beta 0 (Intercept)'] = [0.5] * len(normalized_df)\n",
    "\n",
    "normalized_df = normalized_df.reindex_axis(sorted(normalized_df.columns), axis=1)\n",
    "X = normalized_df\n",
    "y = matrix[matrix.columns[0:1]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    row = 2\n",
    "    col = 2\n",
    "    k = 1\n",
    "    fig, ax = plt.subplots(row, col, figsize=(15, 8))\n",
    "    fig.suptitle('Normalized Predictors vs Accuracy EM', fontsize=18)\n",
    "    for i in range(row):\n",
    "        for j in range(col):\n",
    "            ax[i][j].scatter(X[X.columns[k]], y)\n",
    "            ax[i][j].set_xlabel(X.columns[k])\n",
    "            ax[i][j].set_ylabel(y.columns[0])\n",
    "            k = k + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "normalized_matrix = preprocessing.normalize(matrix)\n",
    "normalized_df = pd.DataFrame(normalized_matrix, columns = matrix.columns)\n",
    "normalized_df['Beta 0 (Intercept)'] = [0.5] * len(normalized_df)\n",
    "\n",
    "normalized_df = normalized_df.reindex_axis(sorted(normalized_df.columns), axis=1)\n",
    "\n",
    "X = normalized_df[normalized_df.columns[3:]]\n",
    "y = normalized_df[normalized_df.columns[0:1]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    row = 2\n",
    "    col = 2\n",
    "    k = 1\n",
    "    fig, ax = plt.subplots(row, col, figsize=(15, 8))\n",
    "    fig.suptitle('Normalized Predictors vs Accuracy EM', fontsize=18)\n",
    "    for i in range(row):\n",
    "        for j in range(col):\n",
    "            ax[i][j].scatter(X[X.columns[k]], y)\n",
    "            ax[i][j].set_xlabel(X.columns[k])\n",
    "            ax[i][j].set_ylabel(y.columns[0])\n",
    "            k = k + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling just predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "scaled_matrix = preprocessing.scale(matrix[matrix.columns[3:]])\n",
    "scaled_df = pd.DataFrame(scaled_matrix, columns = matrix[matrix.columns[3:]].columns)\n",
    "scaled_df['Beta 0 (Intercept)'] = [0.5] * len(scaled_df)\n",
    "\n",
    "scaled_df = scaled_df.reindex_axis(sorted(scaled_df.columns), axis=1)\n",
    "\n",
    "X = scaled_df\n",
    "y = matrix[matrix.columns[0:1]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    row = 2\n",
    "    col = 2\n",
    "    k = 1\n",
    "    fig, ax = plt.subplots(row, col, figsize=(15, 8))\n",
    "    fig.suptitle('Standardized Predictors vs Acc EM', fontsize=18)\n",
    "    for i in range(row):\n",
    "        for j in range(col):\n",
    "            ax[i][j].scatter(X[X.columns[k]], y)\n",
    "            ax[i][j].set_xlabel(X.columns[k])\n",
    "            ax[i][j].set_ylabel(y.columns[0])\n",
    "            k = k + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scaled_matrix = preprocessing.scale(matrix)\n",
    "scaled_df = pd.DataFrame(scaled_matrix, columns = matrix.columns)\n",
    "scaled_df['Beta 0 (Intercept)'] = [0.5] * len(scaled_df)\n",
    "\n",
    "scaled_df = scaled_df.reindex_axis(sorted(scaled_df.columns), axis=1)\n",
    "\n",
    "X = scaled_df[scaled_df.columns[3:]]\n",
    "y = scaled_df[scaled_df.columns[0:1]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with sns.axes_style('whitegrid'):\n",
    "    row = 2\n",
    "    col = 2\n",
    "    k = 1\n",
    "    fig, ax = plt.subplots(row, col, figsize=(15, 8))\n",
    "    fig.suptitle('Standardized Predictors vs Acc EM', fontsize=18)\n",
    "    for i in range(row):\n",
    "        for j in range(col):\n",
    "            ax[i][j].scatter(X[X.columns[k]], y)\n",
    "            ax[i][j].set_xlabel(X.columns[k])\n",
    "            ax[i][j].set_ylabel(y.columns[0])\n",
    "            k = k + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coefficients of Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean = []\n",
    "for i in range(len(results.params)):\n",
    "    mean.append((results.params.index[i], results.params[i]))\n",
    "mean_df = pd.DataFrame(mean, columns=['Feature Type', 'Coefficient Value'])\n",
    "\n",
    "results_list = []\n",
    "d = []\n",
    "for i in fm_list:\n",
    "    X = i[i.columns[1:]]\n",
    "    y = i[i.columns[0:1]]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "    model = sm.OLS(y_test, X_test)\n",
    "    results = model.fit()\n",
    "    #print(results.params)\n",
    "    results_list.append(results.params)\n",
    "for i in results_list:\n",
    "    for j in range(len(i)):\n",
    "        d.append((i.index[j], i[j]))\n",
    "d_df = pd.DataFrame(d, columns=['Feature Type', 'Coefficient Value'])\n",
    "\n",
    "flatui = [\"yellow\", \"yellow\", 'yellow', \"yellow\"]\n",
    "sns.color_palette(flatui)\n",
    "fig = plt.figure(figsize=(20, 8))\n",
    "sns.stripplot(x=\"Feature Type\", y=\"Coefficient Value\", data=d_df, size=12, marker='o', split=True,linewidth=2,edgecolor=\"gray\")\n",
    "sns.stripplot(x=\"Feature Type\", y=\"Coefficient Value\", data=mean_df, size=15, palette=sns.color_palette(flatui), split=True,linewidth=2,edgecolor=\"gray\", marker='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# https://stats.stackexchange.com/questions/146804/difference-between-statsmodel-ols-and-scikit-linear-regression\n",
    "predictions = results.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# compare in\n",
    "lr = LinearRegression()\n",
    "# cross_val_predict returns an array of the same size as `y` where each entry\n",
    "# is a prediction obtained by cross validation:\n",
    "predicted = cross_val_predict(lr, X, y, cv=359)\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.scatter(y, predicted)\n",
    "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Measured')\n",
    "ax.set_ylabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scikit Learn's Linear Regression (With Intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression(fit_intercept=True)\n",
    "print str(lr) + \"\\n\"\n",
    "lr.fit(X_train, y_train)\n",
    "print \"Linear Regression Score: %f\" % (lr.score(X_test, y_test))\n",
    "print \"Linear Regression Coefficients: %s\" % (lr.coef_)"
   ]
  }
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