wmfilt_112015

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['norm', 'mat']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from scipy.io import loadmat\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "from __future__ import division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from scipy import stats as st\n",
    "from scipy.stats import norm\n",
    "import statsmodels.formula.api as sm\n",
    "import datetime\n",
    "import re\n",
    "import shutil\n",
    "import string\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\", context=\"talk\")\n",
    "\n",
    "now = datetime.datetime.now()\n",
    "date = now.strftime(\"%Y-%m-%d_%H:%M\")\n",
    "month = now.strftime(\"%B\")\n",
    "\n",
    "pd.set_option('display.max_rows', 200)\n",
    "pd.set_option('display.max_columns', 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working Memory- FILTER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Loading saw .mat files\n",
    "\n",
    "WM = pd.DataFrame()\n",
    "wm_all = glob.glob('../../../Language/fMRI_tasks/wmfilt/data/gates_lap/*run*.mat')\n",
    "\n",
    "for mat in wm_all:\n",
    "    wm = loadmat(mat, squeeze_me=True)\n",
    "    wm_df = pd.DataFrame(wm['runData'])\n",
    "    WM = WM.append(wm_df)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Organizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>subject</th>\n",
       "      <th>Stim_version</th>\n",
       "      <th>Instruction_version</th>\n",
       "      <th>run</th>\n",
       "      <th>Trial</th>\n",
       "      <th>Image_number</th>\n",
       "      <th>Trial_type</th>\n",
       "      <th>Correct_response</th>\n",
       "      <th>actual_response</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>Reaction_Time</th>\n",
       "      <th>Fixation_1_onset</th>\n",
       "      <th>Instruction_onset</th>\n",
       "      <th>Stimulus_onset</th>\n",
       "      <th>Fixation_2_onset</th>\n",
       "      <th>Probe_onset</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Reaction_Time_onset</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>noresp</th>\n",
       "      <th>Filter_trial</th>\n",
       "      <th>probe_type</th>\n",
       "      <th>probe_type_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.505534</td>\n",
       "      <td>0.01544825</td>\n",
       "      <td>3.021292</td>\n",
       "      <td>6.027135</td>\n",
       "      <td>7.045776</td>\n",
       "      <td>9.049678</td>\n",
       "      <td>1</td>\n",
       "      <td>10.55521</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>DRY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.441893</td>\n",
       "      <td>11.10365</td>\n",
       "      <td>15.11146</td>\n",
       "      <td>18.11729</td>\n",
       "      <td>19.13596</td>\n",
       "      <td>21.13984</td>\n",
       "      <td>0</td>\n",
       "      <td>22.58174</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>101</td>\n",
       "      <td>2R_nr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.16273</td>\n",
       "      <td>27.16838</td>\n",
       "      <td>30.17419</td>\n",
       "      <td>31.19293</td>\n",
       "      <td>34.19872</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2R</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.907076</td>\n",
       "      <td>36.21931</td>\n",
       "      <td>39.22521</td>\n",
       "      <td>42.23101</td>\n",
       "      <td>43.24961</td>\n",
       "      <td>46.25548</td>\n",
       "      <td>1</td>\n",
       "      <td>48.16256</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>102</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8881939</td>\n",
       "      <td>48.2767</td>\n",
       "      <td>51.28191</td>\n",
       "      <td>56.29168</td>\n",
       "      <td>57.31036</td>\n",
       "      <td>60.31619</td>\n",
       "      <td>1</td>\n",
       "      <td>61.20438</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject Stim_version Instruction_version run Trial Image_number Trial_type  \\\n",
       "0     387            2                   2   3     1           21     4RY_cr   \n",
       "1     387            2                   2   3     2           61     DRY_fa   \n",
       "2     387            2                   2   3     3          101      2R_nr   \n",
       "3     387            2                   2   3     4           62      DRY_h   \n",
       "4     387            2                   2   3     5          102       2R_h   \n",
       "\n",
       "  Correct_response actual_response    H    M   CR   FA Reaction_Time  \\\n",
       "0         [32, 33]              33    0    0    1    0      1.505534   \n",
       "1         [32, 33]              30    0    0    0    1      1.441893   \n",
       "2         [32, 33]             NaN  NaN  NaN  NaN  NaN           NaN   \n",
       "3         [30, 31]              30    1    0    0    0      1.907076   \n",
       "4         [30, 31]              30    1    0    0    0     0.8881939   \n",
       "\n",
       "  Fixation_1_onset Instruction_onset Stimulus_onset Fixation_2_onset  \\\n",
       "0       0.01544825          3.021292       6.027135         7.045776   \n",
       "1         11.10365          15.11146       18.11729         19.13596   \n",
       "2         23.16273          27.16838       30.17419         31.19293   \n",
       "3         36.21931          39.22521       42.23101         43.24961   \n",
       "4          48.2767          51.28191       56.29168         57.31036   \n",
       "\n",
       "  Probe_onset Accuracy Reaction_Time_onset LOAD  noresp Filter_trial  \\\n",
       "0    9.049678        1            10.55521  4RY     NaN            0   \n",
       "1    21.13984        0            22.58174  DRY     NaN            1   \n",
       "2    34.19872        0                 NaN   2R       1            0   \n",
       "3    46.25548        1            48.16256  DRY     NaN            1   \n",
       "4    60.31619        1            61.20438   2R     NaN            0   \n",
       "\n",
       "  probe_type probe_type_label  \n",
       "0       None             None  \n",
       "1          1        in_yellow  \n",
       "2       None             None  \n",
       "3          0           in_red  \n",
       "4       None             None  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WM.columns = ['subject','Stim_version',\t'Instruction_version',\t'run', 'Trial',\t'Image_number',\t'Trial_type',\t\n",
    "              'Correct_response',\t'actual_response',\t'H',\t'M',\t'CR',\t'FA',\t'Reaction_Time',\t'Fixation_1_onset',\t\n",
    "              'Instruction_onset',\t'Stimulus_onset',\t'Fixation_2_onset',\t'Probe_onset',\t'Accuracy',\t'Reaction_Time_onset']\n",
    "\n",
    "WM['LOAD'] = WM.apply(lambda row: ('4RY' if row['Trial_type']=='4RY_h' else '4RY' if row['Trial_type']=='4RY_fa'\n",
    "        else '4RY' if row['Trial_type']=='4RY_m' else '4RY' if row['Trial_type']=='4RY_cr' else '4RY' if row['Trial_type']=='4RY_nr' \n",
    "        else '2R' if row['Trial_type']=='2R_h' else '2R' if row['Trial_type']=='2R_fa' else '2R' if row['Trial_type']=='2R_nr'\n",
    "        else '2R' if row['Trial_type']=='2R_m' else '2R' if row['Trial_type']=='2R_cr' \n",
    "        else 'DRY' if row['Trial_type']=='DRY_h' else 'DRY' if row['Trial_type']=='DRY_fa' else 'DRY' if row['Trial_type']=='DRY_nr'\n",
    "        else 'DRY' if row['Trial_type']=='DRY_m' else 'DRY' if row['Trial_type']=='DRY_cr' \n",
    "        else None ), axis=1)\n",
    "\n",
    "WM['noresp'] = WM.apply(lambda row: (1 if row['Trial_type']=='4RY_nr' \n",
    "                                else 1 if row['Trial_type']=='2R_nr'\n",
    "                                else 1 if row['Trial_type']=='DRY_nr'\n",
    "                                else None ), axis=1)\n",
    "\n",
    "\n",
    "WM['Filter_trial'] = WM.apply(lambda row: ('1' if row['LOAD']=='DRY' else '0'), axis=1)\n",
    "\n",
    "#for Lure\n",
    "WM['probe_type'] = WM.apply(lambda row: ('0' if row['Image_number']==42 else '0' if row['Image_number']==44 \n",
    "                                               else '0' if row['Image_number']==45\n",
    "                                               else '0' if row['Image_number']==46 else '0' if row['Image_number']==51\n",
    "                                               else '0' if row['Image_number']==52 else '0' if row['Image_number']==53 \n",
    "                                               else '0' if row['Image_number']==54 else '0' if row['Image_number']==56 \n",
    "                                               else '0' if row['Image_number']==62 else '0' if row['Image_number']==63\n",
    "                                               else '0' if row['Image_number']==64 else '0' if row['Image_number']==65 \n",
    "                                               else '0' if row['Image_number']==67 else '0' if row['Image_number']==69\n",
    "                                               else '0' if row['Image_number']==72 else '0' if row['Image_number']==73 \n",
    "                                               else '0' if row['Image_number']==75 else '0' if row['Image_number']==78 \n",
    "                                               else '0' if row['Image_number']==79 else '1' if row['Image_number']==48 \n",
    "                                               else '1' if row['Image_number']==49 else '1' if row['Image_number']==50 \n",
    "                                               else '1' if row['Image_number']==58 else '1' if row['Image_number']==59 \n",
    "                                               else '1' if row['Image_number']==61 else '1' if row['Image_number']==66 \n",
    "                                               else '1' if row['Image_number']==68 else '1' if row['Image_number']==70 \n",
    "                                               else '1' if row['Image_number']==74 else '1' if row['Image_number']==77 \n",
    "                                               else '1' if row['Image_number']==80 else '2' if row['Image_number']==41 \n",
    "                                               else '2' if row['Image_number']==43 \n",
    "                                               else '2' if row['Image_number']==47 else '2' if row['Image_number']==55\n",
    "                                               else '2' if row['Image_number']==57 else '2' if row['Image_number']==60 else None), axis=1)\n",
    "\n",
    "WM['probe_type_label'] = WM.apply(lambda row: ('in_red' if row['probe_type']=='0' else\n",
    "                                               'in_yellow' if row['probe_type']=='1' else \n",
    "                                               '1_away_red' if row['probe_type']=='2' else None), axis=1)\n",
    "                                               \n",
    "WM.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#correcting for erronious trial (image 117)   \n",
    "WM.loc[(WM.Image_number==117)&(WM.Trial_type=='2R_m'), 'H']=1\n",
    "WM.loc[(WM.Image_number==117)&(WM.Trial_type=='2R_m'), 'M']=0\n",
    "WM.loc[(WM.Image_number==117)&(WM.Trial_type=='2R_m'), 'Accuracy']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "WM['subject1'] = WM['subject']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "WM['subject1'] = WM['subject1'].map(lambda x: x.strip('lap').rstrip('aAbBcC'))\n",
    "#WM['subject1'] = WM['subject1'].map(lambda x: x.strip('1ap').rstrip('aAbBcC'))\n",
    "WM['subject1'] = WM['subject1'].map(lambda x: x.strip('gates').rstrip('aAbBcC'))\n",
    "WM['subject1'] = WM['subject1'].map(lambda x: x.strip('GATES').rstrip('aAbBcC'))\n",
    "WM['subject1'] = WM['subject1'].map(lambda x: x.strip('_').rstrip('aAbBcC'))\n",
    "WM = WM[WM.subject1 != '326_practice']\n",
    "WM = WM[WM.subject1 != '327_practice']\n",
    "WM = WM[WM.subject1 != '328_practice']\n",
    "WM['subject1'].replace('359_attempt2','359', inplace=True)\n",
    "WM = WM[WM.subject1 != '308']\n",
    "WM['subject1'].replace('308_3','308', inplace=True)\n",
    "WM.subject1 = WM.subject1.convert_objects(convert_numeric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "WM['subject1'] = WM.apply(lambda row: ((row['subject1']+1000) if (row['subject1'] > 1000) & (row['subject1'] < 1999) else row['subject1']), axis=1) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2047    120\n",
       "366     120\n",
       "346     120\n",
       "345     120\n",
       "344     120\n",
       "343     120\n",
       "342     120\n",
       "341     120\n",
       "340     120\n",
       "339     120\n",
       "338     120\n",
       "337     120\n",
       "336     120\n",
       "335     120\n",
       "334     120\n",
       "333     120\n",
       "332     120\n",
       "347     120\n",
       "348     120\n",
       "349     120\n",
       "358     120\n",
       "364     120\n",
       "363     120\n",
       "362     120\n",
       "361     120\n",
       "360     120\n",
       "359     120\n",
       "357     120\n",
       "350     120\n",
       "356     120\n",
       "355     120\n",
       "354     120\n",
       "353     120\n",
       "352     120\n",
       "351     120\n",
       "330     120\n",
       "329     120\n",
       "328     120\n",
       "2064    120\n",
       "306     120\n",
       "305     120\n",
       "304     120\n",
       "303     120\n",
       "302     120\n",
       "2065    120\n",
       "2061    120\n",
       "309     120\n",
       "2057    120\n",
       "2056    120\n",
       "2055    120\n",
       "2054    120\n",
       "2053    120\n",
       "2052    120\n",
       "307     120\n",
       "310     120\n",
       "327     120\n",
       "319     120\n",
       "326     120\n",
       "325     120\n",
       "323     120\n",
       "322     120\n",
       "321     120\n",
       "320     120\n",
       "318     120\n",
       "311     120\n",
       "317     120\n",
       "316     120\n",
       "315     120\n",
       "314     120\n",
       "313     120\n",
       "312     120\n",
       "2046    120\n",
       "365     120\n",
       "367     120\n",
       "2014    120\n",
       "410     120\n",
       "368     120\n",
       "412     120\n",
       "413     120\n",
       "414     120\n",
       "415     120\n",
       "416     120\n",
       "601     120\n",
       "603     120\n",
       "604     120\n",
       "2004    120\n",
       "2007    120\n",
       "2010    120\n",
       "2013    120\n",
       "2015    120\n",
       "408     120\n",
       "2016    120\n",
       "2019    120\n",
       "2021    120\n",
       "2022    120\n",
       "2024    120\n",
       "2027    120\n",
       "2028    120\n",
       "2029    120\n",
       "2030    120\n",
       "2032    120\n",
       "2037    120\n",
       "2039    120\n",
       "2041    120\n",
       "2045    120\n",
       "409     120\n",
       "411     120\n",
       "407     120\n",
       "383     120\n",
       "369     120\n",
       "370     120\n",
       "371     120\n",
       "372     120\n",
       "373     120\n",
       "374     120\n",
       "375     120\n",
       "376     120\n",
       "377     120\n",
       "378     120\n",
       "379     120\n",
       "406     120\n",
       "381     120\n",
       "382     120\n",
       "380     120\n",
       "384     120\n",
       "390     120\n",
       "405     120\n",
       "404     120\n",
       "403     120\n",
       "402     120\n",
       "401     120\n",
       "393     120\n",
       "392     120\n",
       "385     120\n",
       "391     120\n",
       "389     120\n",
       "388     120\n",
       "387     120\n",
       "386     120\n",
       "2049    120\n",
       "2023     99\n",
       "308      90\n",
       "2026     60\n",
       "2058     60\n",
       "2042     60\n",
       "2050     60\n",
       "2025     30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WM['subject1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#how many noreponses?\n",
    "noresp = WM[WM.noresp > 0]\n",
    "noresp_piv = pd.pivot_table(noresp, values=['noresp'], index=['subject1'], columns=['run'], aggfunc=np.sum, margins=True)\n",
    "#noresp_piv.columns = ['run','2R', '4RY', 'DRY', 'TOTAL']\n",
    "#noresp_piv.sort([(u'noresp', u'All')], ascending=False)\n",
    "\n",
    "#filter out the subjects with noresponses\n",
    "#WM = WM[WM.noresp !=1]\n",
    "#WM.to_csv('/home/jminas/Documents/WMFILT_ALL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#saving the data to a csv, and reopening so pandas can aggregate the data (this is a weird bug, i need to be a better coder!)\n",
    "#WM.to_csv('/home/jenni/Documents/tmp/WMFILT_ALL_%s.csv' % date)\n",
    "#WM = pd.read_csv('/home/jenni/Documents/tmp/WMFILT_ALL_%s.csv' % date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>subject</th>\n",
       "      <th>Stim_version</th>\n",
       "      <th>Instruction_version</th>\n",
       "      <th>run</th>\n",
       "      <th>Trial</th>\n",
       "      <th>Image_number</th>\n",
       "      <th>Trial_type</th>\n",
       "      <th>Correct_response</th>\n",
       "      <th>actual_response</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>Reaction_Time</th>\n",
       "      <th>Fixation_1_onset</th>\n",
       "      <th>Instruction_onset</th>\n",
       "      <th>Stimulus_onset</th>\n",
       "      <th>Fixation_2_onset</th>\n",
       "      <th>Probe_onset</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Reaction_Time_onset</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>noresp</th>\n",
       "      <th>Filter_trial</th>\n",
       "      <th>probe_type</th>\n",
       "      <th>probe_type_label</th>\n",
       "      <th>subject1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.505534</td>\n",
       "      <td>0.01544825</td>\n",
       "      <td>3.021292</td>\n",
       "      <td>6.027135</td>\n",
       "      <td>7.045776</td>\n",
       "      <td>9.049678</td>\n",
       "      <td>1</td>\n",
       "      <td>10.55521</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>DRY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.441893</td>\n",
       "      <td>11.10365</td>\n",
       "      <td>15.11146</td>\n",
       "      <td>18.11729</td>\n",
       "      <td>19.13596</td>\n",
       "      <td>21.13984</td>\n",
       "      <td>0</td>\n",
       "      <td>22.58174</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>101</td>\n",
       "      <td>2R_nr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.16273</td>\n",
       "      <td>27.16838</td>\n",
       "      <td>30.17419</td>\n",
       "      <td>31.19293</td>\n",
       "      <td>34.19872</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2R</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.907076</td>\n",
       "      <td>36.21931</td>\n",
       "      <td>39.22521</td>\n",
       "      <td>42.23101</td>\n",
       "      <td>43.24961</td>\n",
       "      <td>46.25548</td>\n",
       "      <td>1</td>\n",
       "      <td>48.16256</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>102</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8881939</td>\n",
       "      <td>48.2767</td>\n",
       "      <td>51.28191</td>\n",
       "      <td>56.29168</td>\n",
       "      <td>57.31036</td>\n",
       "      <td>60.31619</td>\n",
       "      <td>1</td>\n",
       "      <td>61.20438</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject Stim_version Instruction_version run Trial Image_number Trial_type  \\\n",
       "0     387            2                   2   3     1           21     4RY_cr   \n",
       "1     387            2                   2   3     2           61     DRY_fa   \n",
       "2     387            2                   2   3     3          101      2R_nr   \n",
       "3     387            2                   2   3     4           62      DRY_h   \n",
       "4     387            2                   2   3     5          102       2R_h   \n",
       "\n",
       "  Correct_response actual_response    H    M   CR   FA Reaction_Time  \\\n",
       "0         [32, 33]              33    0    0    1    0      1.505534   \n",
       "1         [32, 33]              30    0    0    0    1      1.441893   \n",
       "2         [32, 33]             NaN  NaN  NaN  NaN  NaN           NaN   \n",
       "3         [30, 31]              30    1    0    0    0      1.907076   \n",
       "4         [30, 31]              30    1    0    0    0     0.8881939   \n",
       "\n",
       "  Fixation_1_onset Instruction_onset Stimulus_onset Fixation_2_onset  \\\n",
       "0       0.01544825          3.021292       6.027135         7.045776   \n",
       "1         11.10365          15.11146       18.11729         19.13596   \n",
       "2         23.16273          27.16838       30.17419         31.19293   \n",
       "3         36.21931          39.22521       42.23101         43.24961   \n",
       "4          48.2767          51.28191       56.29168         57.31036   \n",
       "\n",
       "  Probe_onset Accuracy Reaction_Time_onset LOAD  noresp Filter_trial  \\\n",
       "0    9.049678        1            10.55521  4RY     NaN            0   \n",
       "1    21.13984        0            22.58174  DRY     NaN            1   \n",
       "2    34.19872        0                 NaN   2R       1            0   \n",
       "3    46.25548        1            48.16256  DRY     NaN            1   \n",
       "4    60.31619        1            61.20438   2R     NaN            0   \n",
       "\n",
       "  probe_type probe_type_label  subject1  \n",
       "0       None             None       387  \n",
       "1          1        in_yellow       387  \n",
       "2       None             None       387  \n",
       "3          0           in_red       387  \n",
       "4       None             None       387  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#appending a column for group pased on subject #s\n",
    "# 0 = kids -rich\n",
    "# 1 = kids -poor\n",
    "# 2 = adults\n",
    "\n",
    "WM['group'] = WM.apply(lambda row:('0' if row['subject1']==311 else '0' if row['subject']==311 else '0' if row['subject']==315 \n",
    "                                   else '0' if row['subject']==332 else '0' if row['subject']==333 else '0' if row['subject']==335 \n",
    "                                   else '0' if row['subject']==336 else '0' if row['subject']==337 else '0' if row['subject']==338 \n",
    "                                   else '0' if row['subject']==339 else '0' if row['subject']==340 else '0' if row['subject']==341 \n",
    "                                   else '0' if row['subject']==342 else '0' if row['subject']==344 else '0' if row['subject']==348 \n",
    "                                   else '0' if row['subject']==349 else '0' if row['subject']==350 else '0' if row['subject']==353 \n",
    "                                   else '0' if row['subject']==354 else '0' if row['subject']==356 else '0' if row['subject']==357 \n",
    "                                   else '0' if row['subject']==360 else '0' if row['subject']==362 else '0' if row['subject']==363 \n",
    "                                   else '0' if row['subject']==367 else '0' if row['subject']==368 else '0' if row['subject']==371 \n",
    "                                   else '0' if row['subject']==372 else '0' if row['subject']==373 else '0' if row['subject']==375 \n",
    "                                   else '0' if row['subject']==376 else '0' if row['subject']==377 else '0' if row['subject']==381 \n",
    "                                   else '0' if row['subject']==382 else '0' if row['subject']==383 else '0' if row['subject']==384 \n",
    "                                   else '0' if row['subject']==385 else '0' if row['subject']==386 else '0' if row['subject']==387 \n",
    "                                   else '0' if row['subject']==388 else '1' if row['subject']==302 else '1' if row['subject']==303 \n",
    "                                   else '1' if row['subject']==304 else '1' if row['subject']==305 else '1' if row['subject']==306 \n",
    "                                   else '1' if row['subject']==307 else '1' if row['subject']==308 else '1' if row['subject']==309 \n",
    "                                   else '1' if row['subject']==310 else '1' if row['subject']==313 else '1' if row['subject']==314 \n",
    "                                   else '1' if row['subject']==316 else '1' if row['subject']==317 else '1' if row['subject']==318 \n",
    "                                   else '1' if row['subject']==319 else '1' if row['subject']==320 else '1' if row['subject']==321 \n",
    "                                   else '1' if row['subject']==322 else '1' if row['subject']==323 else '1' if row['subject']==325 \n",
    "                                   else '1' if row['subject']==326 else '1' if row['subject']==327 else '1' if row['subject']==328 \n",
    "                                   else '1' if row['subject']==329 else '1' if row['subject']==330 else '1' if row['subject']==334 \n",
    "                                   else '1' if row['subject']==343 else '1' if row['subject']==345 else '1' if row['subject']==346 \n",
    "                                   else '1' if row['subject']==347 else '1' if row['subject']==351 else '1' if row['subject']==352 \n",
    "                                   else '1' if row['subject']==355 else '1' if row['subject']==358 else '1' if row['subject']==359 \n",
    "                                   else '1' if row['subject']==361 else '1' if row['subject']==364 else '1' if row['subject']==365 \n",
    "                                   else '1' if row['subject']==366 else '1' if row['subject']==369 else '1' if row['subject']==370 \n",
    "                                   else '1' if row['subject']==374 else '1' if row['subject']==378 else '1' if row['subject']==379 \n",
    "                                   else '1' if row['subject']==380 else '1' if row['subject']==389 else '1' if row['subject']==390 \n",
    "                                   else '1' if row['subject']==392 else '1' if row['subject']==393 else  '2' if row['subject']>400 \n",
    "                                   else None), axis=1)\n",
    "\n",
    "WM['new_group'] = WM.apply(lambda row:('0' if row['subject1']==302 else '0' if row['subject1']==308 else '0' if row['subject1']==315 \n",
    "                                       else '0' if row['subject1']==318 else '0' if row['subject1']==332 else '0' if row['subject1']==333 \n",
    "                                       else '0' if row['subject1']==335 else '0' if row['subject1']==337 else '0' if row['subject1']==339 \n",
    "                                       else '0' if row['subject1']==341 else '0' if row['subject1']==350 else '0' if row['subject1']==351 \n",
    "                                       else '0' if row['subject1']==354 else '0' if row['subject1']==356 else '0' if row['subject1']==357 \n",
    "                                       else '0' if row['subject1']==360 else '0' if row['subject1']==362 else '0' if row['subject1']==365 \n",
    "                                       else '0' if row['subject1']==367 else '0' if row['subject1']==371 else '0' if row['subject1']==372 \n",
    "                                       else '0' if row['subject1']==373 else '0' if row['subject1']==375 else '0' if row['subject1']==377 \n",
    "                                       else '0' if row['subject1']==380 else '0' if row['subject1']==381 else '0' if row['subject1']==382 \n",
    "                                       else '0' if row['subject1']==383 else '0' if row['subject1']==385 else '0' if row['subject1']==386 \n",
    "                                       else '0' if row['subject1']==388 else '2' if row['subject1']==401 else '2' if row['subject1']==403 \n",
    "                                       else '2' if row['subject1']==405 else '2' if row['subject1']==409 else '2' if row['subject1']==415 \n",
    "                                       else '2' if row['subject1']==416 else '2' if row['subject1']==2004 else '2' if row['subject1']==2007 \n",
    "                                       else '2' if row['subject1']==2010 else '2' if row['subject1']==2014 else '2' if row['subject1']==2015 \n",
    "                                       else '2' if row['subject1']==2016 else '2' if row['subject1']==2019 else '2' if row['subject1']==2021 \n",
    "                                       else '2' if row['subject1']==2022 else '2' if row['subject1']==2027 else '2' if row['subject1']==2028 \n",
    "                                       else '2' if row['subject1']==2029 else '2' if row['subject1']==2030 else '2' if row['subject1']==2032 \n",
    "                                       else '2' if row['subject1']==2035 else '2' if row['subject1']==2039 else '2' if row['subject1']==2041 \n",
    "                                       else '2' if row['subject1']==2047 else '2' if row['subject1']==2049 else '2' if row['subject1']==2052 \n",
    "                                       else '2' if row['subject1']==2053 else '2' if row['subject1']==2054 else '2' if row['subject1']==2056 \n",
    "                                       else '2' if row['subject1']==2057 else '2' if row['subject1']==2061 else '2' if row['subject1']==2065\n",
    "                                       else '2' if row['subject1']==601 else '2' if row['subject1']==603 else '2' if row['subject1']==604\n",
    "                                       else None), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>subject</th>\n",
       "      <th>Stim_version</th>\n",
       "      <th>Instruction_version</th>\n",
       "      <th>run</th>\n",
       "      <th>Trial</th>\n",
       "      <th>Image_number</th>\n",
       "      <th>Trial_type</th>\n",
       "      <th>Correct_response</th>\n",
       "      <th>actual_response</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>Reaction_Time</th>\n",
       "      <th>Fixation_1_onset</th>\n",
       "      <th>Instruction_onset</th>\n",
       "      <th>Stimulus_onset</th>\n",
       "      <th>Fixation_2_onset</th>\n",
       "      <th>Probe_onset</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Reaction_Time_onset</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>noresp</th>\n",
       "      <th>Filter_trial</th>\n",
       "      <th>probe_type</th>\n",
       "      <th>probe_type_label</th>\n",
       "      <th>subject1</th>\n",
       "      <th>group</th>\n",
       "      <th>new_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.505534</td>\n",
       "      <td>0.01544825</td>\n",
       "      <td>3.021292</td>\n",
       "      <td>6.027135</td>\n",
       "      <td>7.045776</td>\n",
       "      <td>9.049678</td>\n",
       "      <td>1</td>\n",
       "      <td>10.55521</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>DRY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.441893</td>\n",
       "      <td>11.10365</td>\n",
       "      <td>15.11146</td>\n",
       "      <td>18.11729</td>\n",
       "      <td>19.13596</td>\n",
       "      <td>21.13984</td>\n",
       "      <td>0</td>\n",
       "      <td>22.58174</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>101</td>\n",
       "      <td>2R_nr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.16273</td>\n",
       "      <td>27.16838</td>\n",
       "      <td>30.17419</td>\n",
       "      <td>31.19293</td>\n",
       "      <td>34.19872</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2R</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.907076</td>\n",
       "      <td>36.21931</td>\n",
       "      <td>39.22521</td>\n",
       "      <td>42.23101</td>\n",
       "      <td>43.24961</td>\n",
       "      <td>46.25548</td>\n",
       "      <td>1</td>\n",
       "      <td>48.16256</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>102</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.888194</td>\n",
       "      <td>48.2767</td>\n",
       "      <td>51.28191</td>\n",
       "      <td>56.29168</td>\n",
       "      <td>57.31036</td>\n",
       "      <td>60.31619</td>\n",
       "      <td>1</td>\n",
       "      <td>61.20438</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.009868</td>\n",
       "      <td>62.33673</td>\n",
       "      <td>65.34255</td>\n",
       "      <td>69.35039</td>\n",
       "      <td>70.36907</td>\n",
       "      <td>72.37291</td>\n",
       "      <td>1</td>\n",
       "      <td>73.38278</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.437497</td>\n",
       "      <td>74.39346</td>\n",
       "      <td>78.40135</td>\n",
       "      <td>82.40911</td>\n",
       "      <td>83.4278</td>\n",
       "      <td>86.43362</td>\n",
       "      <td>1</td>\n",
       "      <td>87.87112</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.337378</td>\n",
       "      <td>88.45414</td>\n",
       "      <td>92.46202</td>\n",
       "      <td>96.46975</td>\n",
       "      <td>97.48839</td>\n",
       "      <td>101.4962</td>\n",
       "      <td>1</td>\n",
       "      <td>102.8336</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>103</td>\n",
       "      <td>2R_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.330023</td>\n",
       "      <td>103.5174</td>\n",
       "      <td>107.5246</td>\n",
       "      <td>110.5305</td>\n",
       "      <td>111.5491</td>\n",
       "      <td>113.553</td>\n",
       "      <td>1</td>\n",
       "      <td>114.883</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.616321</td>\n",
       "      <td>115.5735</td>\n",
       "      <td>118.5794</td>\n",
       "      <td>123.5892</td>\n",
       "      <td>124.6078</td>\n",
       "      <td>127.6303</td>\n",
       "      <td>1</td>\n",
       "      <td>129.2467</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>104</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.825802</td>\n",
       "      <td>129.6509</td>\n",
       "      <td>133.6587</td>\n",
       "      <td>137.6665</td>\n",
       "      <td>138.6852</td>\n",
       "      <td>140.7058</td>\n",
       "      <td>1</td>\n",
       "      <td>142.5316</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>64</td>\n",
       "      <td>DRY_nr</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.7263</td>\n",
       "      <td>146.7342</td>\n",
       "      <td>150.7419</td>\n",
       "      <td>151.7606</td>\n",
       "      <td>155.7684</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DRY</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.063527</td>\n",
       "      <td>157.7889</td>\n",
       "      <td>160.7948</td>\n",
       "      <td>163.8174</td>\n",
       "      <td>164.836</td>\n",
       "      <td>166.8566</td>\n",
       "      <td>1</td>\n",
       "      <td>167.9201</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>105</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.927171</td>\n",
       "      <td>168.8772</td>\n",
       "      <td>171.883</td>\n",
       "      <td>175.8908</td>\n",
       "      <td>176.9095</td>\n",
       "      <td>180.9173</td>\n",
       "      <td>1</td>\n",
       "      <td>182.8445</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>4RY_nr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>183.0047</td>\n",
       "      <td>187.0125</td>\n",
       "      <td>190.035</td>\n",
       "      <td>191.0536</td>\n",
       "      <td>195.0614</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4RY</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>106</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996409</td>\n",
       "      <td>197.0823</td>\n",
       "      <td>201.0898</td>\n",
       "      <td>206.0995</td>\n",
       "      <td>207.1182</td>\n",
       "      <td>211.126</td>\n",
       "      <td>0</td>\n",
       "      <td>212.1224</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "      <td>DRY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.797224</td>\n",
       "      <td>213.1479</td>\n",
       "      <td>217.1544</td>\n",
       "      <td>221.1622</td>\n",
       "      <td>222.1808</td>\n",
       "      <td>225.2034</td>\n",
       "      <td>0</td>\n",
       "      <td>227.0006</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>4RY_nr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.2239</td>\n",
       "      <td>230.2298</td>\n",
       "      <td>233.2524</td>\n",
       "      <td>234.271</td>\n",
       "      <td>236.2916</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4RY</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>67</td>\n",
       "      <td>DRY_m</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.974987</td>\n",
       "      <td>238.3122</td>\n",
       "      <td>241.318</td>\n",
       "      <td>246.3278</td>\n",
       "      <td>247.3464</td>\n",
       "      <td>251.3543</td>\n",
       "      <td>0</td>\n",
       "      <td>253.3292</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>107</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.038450</td>\n",
       "      <td>253.4749</td>\n",
       "      <td>256.4809</td>\n",
       "      <td>261.4905</td>\n",
       "      <td>262.5092</td>\n",
       "      <td>266.517</td>\n",
       "      <td>0</td>\n",
       "      <td>267.5555</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.525381</td>\n",
       "      <td>268.5376</td>\n",
       "      <td>272.5454</td>\n",
       "      <td>277.5551</td>\n",
       "      <td>278.5738</td>\n",
       "      <td>281.5963</td>\n",
       "      <td>1</td>\n",
       "      <td>283.1216</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>4RY_nr</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>283.6169</td>\n",
       "      <td>286.6228</td>\n",
       "      <td>291.6325</td>\n",
       "      <td>292.6512</td>\n",
       "      <td>294.6718</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4RY</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>108</td>\n",
       "      <td>2R_nr</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296.6923</td>\n",
       "      <td>300.7001</td>\n",
       "      <td>304.708</td>\n",
       "      <td>305.7265</td>\n",
       "      <td>308.7491</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2R</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>68</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.256838</td>\n",
       "      <td>310.7697</td>\n",
       "      <td>314.7775</td>\n",
       "      <td>319.7872</td>\n",
       "      <td>320.8059</td>\n",
       "      <td>324.8137</td>\n",
       "      <td>1</td>\n",
       "      <td>326.0705</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>69</td>\n",
       "      <td>DRY_nr</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>326.8342</td>\n",
       "      <td>329.8401</td>\n",
       "      <td>333.8479</td>\n",
       "      <td>334.8665</td>\n",
       "      <td>336.8871</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DRY</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>4RY_m</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.169069</td>\n",
       "      <td>338.9077</td>\n",
       "      <td>342.9155</td>\n",
       "      <td>345.9381</td>\n",
       "      <td>346.9567</td>\n",
       "      <td>349.9792</td>\n",
       "      <td>0</td>\n",
       "      <td>351.1483</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>109</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.645604</td>\n",
       "      <td>352.0003</td>\n",
       "      <td>355.0057</td>\n",
       "      <td>360.0154</td>\n",
       "      <td>361.0341</td>\n",
       "      <td>363.0547</td>\n",
       "      <td>1</td>\n",
       "      <td>364.7003</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.151748</td>\n",
       "      <td>365.0754</td>\n",
       "      <td>369.083</td>\n",
       "      <td>373.0909</td>\n",
       "      <td>374.1095</td>\n",
       "      <td>378.1173</td>\n",
       "      <td>1</td>\n",
       "      <td>379.2691</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>110</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.227699</td>\n",
       "      <td>380.1379</td>\n",
       "      <td>383.1438</td>\n",
       "      <td>386.1663</td>\n",
       "      <td>387.1849</td>\n",
       "      <td>390.2074</td>\n",
       "      <td>0</td>\n",
       "      <td>391.4351</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "      <td>DRY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.924822</td>\n",
       "      <td>392.2281</td>\n",
       "      <td>395.2339</td>\n",
       "      <td>400.2436</td>\n",
       "      <td>401.2622</td>\n",
       "      <td>405.27</td>\n",
       "      <td>0</td>\n",
       "      <td>406.1949</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4RY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.117776</td>\n",
       "      <td>0.008135829</td>\n",
       "      <td>3.013975</td>\n",
       "      <td>6.019843</td>\n",
       "      <td>7.021778</td>\n",
       "      <td>9.025676</td>\n",
       "      <td>0</td>\n",
       "      <td>10.14345</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.948287</td>\n",
       "      <td>11.04625</td>\n",
       "      <td>15.05406</td>\n",
       "      <td>18.05991</td>\n",
       "      <td>19.06183</td>\n",
       "      <td>21.06575</td>\n",
       "      <td>1</td>\n",
       "      <td>22.01404</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>101</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.823790</td>\n",
       "      <td>23.08633</td>\n",
       "      <td>27.09413</td>\n",
       "      <td>30.09998</td>\n",
       "      <td>31.10191</td>\n",
       "      <td>34.10775</td>\n",
       "      <td>0</td>\n",
       "      <td>35.93154</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.080590</td>\n",
       "      <td>36.12835</td>\n",
       "      <td>39.13419</td>\n",
       "      <td>42.14003</td>\n",
       "      <td>43.142</td>\n",
       "      <td>46.14784</td>\n",
       "      <td>1</td>\n",
       "      <td>47.22843</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>102</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.938214</td>\n",
       "      <td>48.16842</td>\n",
       "      <td>51.17426</td>\n",
       "      <td>56.184</td>\n",
       "      <td>57.18596</td>\n",
       "      <td>60.19181</td>\n",
       "      <td>1</td>\n",
       "      <td>61.13002</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>4RY_m</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.371856</td>\n",
       "      <td>62.21239</td>\n",
       "      <td>65.21824</td>\n",
       "      <td>69.22604</td>\n",
       "      <td>70.22797</td>\n",
       "      <td>72.24856</td>\n",
       "      <td>0</td>\n",
       "      <td>73.62042</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.882713</td>\n",
       "      <td>74.26916</td>\n",
       "      <td>78.27695</td>\n",
       "      <td>82.28475</td>\n",
       "      <td>83.28668</td>\n",
       "      <td>86.29254</td>\n",
       "      <td>1</td>\n",
       "      <td>87.17526</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.766151</td>\n",
       "      <td>88.31313</td>\n",
       "      <td>92.32093</td>\n",
       "      <td>96.32873</td>\n",
       "      <td>97.33065</td>\n",
       "      <td>101.3385</td>\n",
       "      <td>1</td>\n",
       "      <td>102.1046</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>103</td>\n",
       "      <td>2R_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.845051</td>\n",
       "      <td>103.359</td>\n",
       "      <td>107.3668</td>\n",
       "      <td>110.3727</td>\n",
       "      <td>111.3747</td>\n",
       "      <td>113.3785</td>\n",
       "      <td>1</td>\n",
       "      <td>114.2236</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.993722</td>\n",
       "      <td>115.3991</td>\n",
       "      <td>118.405</td>\n",
       "      <td>123.4147</td>\n",
       "      <td>124.4166</td>\n",
       "      <td>127.4225</td>\n",
       "      <td>1</td>\n",
       "      <td>128.4162</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>104</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.002776</td>\n",
       "      <td>129.4431</td>\n",
       "      <td>133.4509</td>\n",
       "      <td>137.4587</td>\n",
       "      <td>138.4606</td>\n",
       "      <td>140.4645</td>\n",
       "      <td>1</td>\n",
       "      <td>141.4673</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>64</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.620668</td>\n",
       "      <td>142.4851</td>\n",
       "      <td>146.4929</td>\n",
       "      <td>150.5007</td>\n",
       "      <td>151.5026</td>\n",
       "      <td>155.5104</td>\n",
       "      <td>1</td>\n",
       "      <td>157.1311</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.730245</td>\n",
       "      <td>157.531</td>\n",
       "      <td>160.5369</td>\n",
       "      <td>163.5427</td>\n",
       "      <td>164.5446</td>\n",
       "      <td>166.5653</td>\n",
       "      <td>1</td>\n",
       "      <td>168.2955</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>105</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.746806</td>\n",
       "      <td>168.5858</td>\n",
       "      <td>171.5917</td>\n",
       "      <td>175.5995</td>\n",
       "      <td>176.6014</td>\n",
       "      <td>180.6092</td>\n",
       "      <td>1</td>\n",
       "      <td>181.356</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.955376</td>\n",
       "      <td>182.6298</td>\n",
       "      <td>186.6376</td>\n",
       "      <td>189.6434</td>\n",
       "      <td>190.6454</td>\n",
       "      <td>194.6532</td>\n",
       "      <td>1</td>\n",
       "      <td>196.6086</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>106</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.817384</td>\n",
       "      <td>196.6738</td>\n",
       "      <td>200.6816</td>\n",
       "      <td>205.6913</td>\n",
       "      <td>206.6933</td>\n",
       "      <td>210.701</td>\n",
       "      <td>0</td>\n",
       "      <td>211.5184</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "      <td>DRY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997236</td>\n",
       "      <td>212.7216</td>\n",
       "      <td>216.7294</td>\n",
       "      <td>220.7372</td>\n",
       "      <td>221.7392</td>\n",
       "      <td>224.745</td>\n",
       "      <td>0</td>\n",
       "      <td>225.7423</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>4RY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.756118</td>\n",
       "      <td>226.7656</td>\n",
       "      <td>229.7715</td>\n",
       "      <td>232.7773</td>\n",
       "      <td>233.7792</td>\n",
       "      <td>235.7998</td>\n",
       "      <td>0</td>\n",
       "      <td>236.5559</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>67</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.001783</td>\n",
       "      <td>237.8204</td>\n",
       "      <td>240.8263</td>\n",
       "      <td>245.836</td>\n",
       "      <td>246.838</td>\n",
       "      <td>250.8458</td>\n",
       "      <td>1</td>\n",
       "      <td>251.8475</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>107</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.115058</td>\n",
       "      <td>252.8663</td>\n",
       "      <td>255.8722</td>\n",
       "      <td>260.8819</td>\n",
       "      <td>261.8839</td>\n",
       "      <td>265.8917</td>\n",
       "      <td>0</td>\n",
       "      <td>267.0067</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.033333</td>\n",
       "      <td>267.9123</td>\n",
       "      <td>271.92</td>\n",
       "      <td>276.9298</td>\n",
       "      <td>277.9317</td>\n",
       "      <td>280.9376</td>\n",
       "      <td>1</td>\n",
       "      <td>281.9709</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>4RY_nr</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>282.9582</td>\n",
       "      <td>285.964</td>\n",
       "      <td>290.9737</td>\n",
       "      <td>291.9757</td>\n",
       "      <td>293.9963</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4RY</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>108</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.941840</td>\n",
       "      <td>296.0169</td>\n",
       "      <td>300.0247</td>\n",
       "      <td>304.0325</td>\n",
       "      <td>305.0344</td>\n",
       "      <td>308.0403</td>\n",
       "      <td>1</td>\n",
       "      <td>308.9821</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>68</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.951321</td>\n",
       "      <td>310.0609</td>\n",
       "      <td>314.0687</td>\n",
       "      <td>319.0784</td>\n",
       "      <td>320.0803</td>\n",
       "      <td>324.0881</td>\n",
       "      <td>1</td>\n",
       "      <td>326.0394</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>69</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.078593</td>\n",
       "      <td>326.1087</td>\n",
       "      <td>329.1146</td>\n",
       "      <td>333.1223</td>\n",
       "      <td>334.1243</td>\n",
       "      <td>336.1449</td>\n",
       "      <td>1</td>\n",
       "      <td>337.2235</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.457351</td>\n",
       "      <td>338.1655</td>\n",
       "      <td>342.1733</td>\n",
       "      <td>345.1791</td>\n",
       "      <td>346.1811</td>\n",
       "      <td>349.1869</td>\n",
       "      <td>1</td>\n",
       "      <td>349.6443</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>109</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.949789</td>\n",
       "      <td>351.2075</td>\n",
       "      <td>354.2134</td>\n",
       "      <td>359.2231</td>\n",
       "      <td>360.225</td>\n",
       "      <td>362.2457</td>\n",
       "      <td>1</td>\n",
       "      <td>363.1955</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.673085</td>\n",
       "      <td>364.2662</td>\n",
       "      <td>368.274</td>\n",
       "      <td>372.2818</td>\n",
       "      <td>373.2837</td>\n",
       "      <td>377.2915</td>\n",
       "      <td>1</td>\n",
       "      <td>377.9646</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>110</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.766338</td>\n",
       "      <td>379.3121</td>\n",
       "      <td>382.318</td>\n",
       "      <td>385.3238</td>\n",
       "      <td>386.3258</td>\n",
       "      <td>389.3316</td>\n",
       "      <td>0</td>\n",
       "      <td>390.0979</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.348722</td>\n",
       "      <td>391.3522</td>\n",
       "      <td>394.3581</td>\n",
       "      <td>399.3678</td>\n",
       "      <td>400.3697</td>\n",
       "      <td>404.3775</td>\n",
       "      <td>1</td>\n",
       "      <td>405.7262</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.723439</td>\n",
       "      <td>0.03058879</td>\n",
       "      <td>3.06702</td>\n",
       "      <td>6.09349</td>\n",
       "      <td>7.117556</td>\n",
       "      <td>9.149328</td>\n",
       "      <td>1</td>\n",
       "      <td>9.872767</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.786289</td>\n",
       "      <td>11.18455</td>\n",
       "      <td>15.21183</td>\n",
       "      <td>18.23556</td>\n",
       "      <td>19.26252</td>\n",
       "      <td>21.29008</td>\n",
       "      <td>1</td>\n",
       "      <td>22.07637</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>101</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.754978</td>\n",
       "      <td>23.31867</td>\n",
       "      <td>27.33955</td>\n",
       "      <td>30.37277</td>\n",
       "      <td>31.39335</td>\n",
       "      <td>34.42112</td>\n",
       "      <td>0</td>\n",
       "      <td>35.1761</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.906544</td>\n",
       "      <td>36.4564</td>\n",
       "      <td>39.48756</td>\n",
       "      <td>42.50962</td>\n",
       "      <td>43.54751</td>\n",
       "      <td>46.58745</td>\n",
       "      <td>1</td>\n",
       "      <td>47.49399</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>102</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000497</td>\n",
       "      <td>48.62384</td>\n",
       "      <td>51.65162</td>\n",
       "      <td>56.6748</td>\n",
       "      <td>57.70192</td>\n",
       "      <td>60.74116</td>\n",
       "      <td>1</td>\n",
       "      <td>61.74166</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.004657</td>\n",
       "      <td>62.7763</td>\n",
       "      <td>65.81003</td>\n",
       "      <td>69.84385</td>\n",
       "      <td>70.87055</td>\n",
       "      <td>72.89422</td>\n",
       "      <td>1</td>\n",
       "      <td>73.89887</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.926891</td>\n",
       "      <td>74.92435</td>\n",
       "      <td>78.96153</td>\n",
       "      <td>82.99582</td>\n",
       "      <td>84.02725</td>\n",
       "      <td>87.0633</td>\n",
       "      <td>1</td>\n",
       "      <td>87.99019</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.965163</td>\n",
       "      <td>89.09277</td>\n",
       "      <td>93.1143</td>\n",
       "      <td>97.14162</td>\n",
       "      <td>98.18207</td>\n",
       "      <td>102.216</td>\n",
       "      <td>1</td>\n",
       "      <td>103.1812</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>103</td>\n",
       "      <td>2R_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.700934</td>\n",
       "      <td>104.2467</td>\n",
       "      <td>108.2829</td>\n",
       "      <td>111.3105</td>\n",
       "      <td>112.3507</td>\n",
       "      <td>114.3763</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0773</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.352825</td>\n",
       "      <td>116.4158</td>\n",
       "      <td>119.4415</td>\n",
       "      <td>124.4689</td>\n",
       "      <td>125.5027</td>\n",
       "      <td>128.5264</td>\n",
       "      <td>1</td>\n",
       "      <td>129.8792</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>104</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.918488</td>\n",
       "      <td>130.5597</td>\n",
       "      <td>134.573</td>\n",
       "      <td>138.5874</td>\n",
       "      <td>139.6136</td>\n",
       "      <td>141.6362</td>\n",
       "      <td>1</td>\n",
       "      <td>142.5547</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>64</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.755091</td>\n",
       "      <td>143.6718</td>\n",
       "      <td>147.6967</td>\n",
       "      <td>151.7287</td>\n",
       "      <td>152.7568</td>\n",
       "      <td>156.7783</td>\n",
       "      <td>1</td>\n",
       "      <td>157.5334</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.005144</td>\n",
       "      <td>158.8051</td>\n",
       "      <td>161.8258</td>\n",
       "      <td>164.8436</td>\n",
       "      <td>165.8565</td>\n",
       "      <td>167.8714</td>\n",
       "      <td>1</td>\n",
       "      <td>168.8766</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>105</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.138305</td>\n",
       "      <td>169.9085</td>\n",
       "      <td>172.9339</td>\n",
       "      <td>176.95</td>\n",
       "      <td>177.9682</td>\n",
       "      <td>181.9835</td>\n",
       "      <td>1</td>\n",
       "      <td>183.1219</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>4RY_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.995366</td>\n",
       "      <td>184.0014</td>\n",
       "      <td>188.0196</td>\n",
       "      <td>191.0446</td>\n",
       "      <td>192.0608</td>\n",
       "      <td>196.0706</td>\n",
       "      <td>0</td>\n",
       "      <td>197.066</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>106</td>\n",
       "      <td>2R_fa</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.620518</td>\n",
       "      <td>198.113</td>\n",
       "      <td>202.1235</td>\n",
       "      <td>207.1584</td>\n",
       "      <td>208.1984</td>\n",
       "      <td>212.2328</td>\n",
       "      <td>0</td>\n",
       "      <td>212.8533</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.655253</td>\n",
       "      <td>214.2769</td>\n",
       "      <td>218.2901</td>\n",
       "      <td>222.3154</td>\n",
       "      <td>223.3505</td>\n",
       "      <td>226.3825</td>\n",
       "      <td>1</td>\n",
       "      <td>227.0378</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.002141</td>\n",
       "      <td>228.4092</td>\n",
       "      <td>231.4343</td>\n",
       "      <td>234.4597</td>\n",
       "      <td>235.4878</td>\n",
       "      <td>237.5198</td>\n",
       "      <td>1</td>\n",
       "      <td>238.522</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>67</td>\n",
       "      <td>DRY_m</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999354</td>\n",
       "      <td>239.5485</td>\n",
       "      <td>242.5774</td>\n",
       "      <td>247.5915</td>\n",
       "      <td>248.6217</td>\n",
       "      <td>252.6412</td>\n",
       "      <td>0</td>\n",
       "      <td>253.6405</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>107</td>\n",
       "      <td>2R_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.929673</td>\n",
       "      <td>254.6677</td>\n",
       "      <td>257.6931</td>\n",
       "      <td>262.72</td>\n",
       "      <td>263.7548</td>\n",
       "      <td>267.7902</td>\n",
       "      <td>1</td>\n",
       "      <td>268.7198</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.250934</td>\n",
       "      <td>269.8175</td>\n",
       "      <td>273.8324</td>\n",
       "      <td>278.8501</td>\n",
       "      <td>279.8663</td>\n",
       "      <td>282.8838</td>\n",
       "      <td>1</td>\n",
       "      <td>284.1347</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.013393</td>\n",
       "      <td>284.919</td>\n",
       "      <td>287.9488</td>\n",
       "      <td>292.9778</td>\n",
       "      <td>294.0093</td>\n",
       "      <td>296.0371</td>\n",
       "      <td>1</td>\n",
       "      <td>297.0505</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>108</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.339115</td>\n",
       "      <td>298.0765</td>\n",
       "      <td>302.1037</td>\n",
       "      <td>306.1312</td>\n",
       "      <td>307.1634</td>\n",
       "      <td>310.1904</td>\n",
       "      <td>1</td>\n",
       "      <td>311.5295</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>68</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.877718</td>\n",
       "      <td>312.2295</td>\n",
       "      <td>316.2557</td>\n",
       "      <td>321.2812</td>\n",
       "      <td>322.3055</td>\n",
       "      <td>326.3357</td>\n",
       "      <td>1</td>\n",
       "      <td>327.2135</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>69</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.627372</td>\n",
       "      <td>328.3745</td>\n",
       "      <td>331.4003</td>\n",
       "      <td>335.4258</td>\n",
       "      <td>336.4491</td>\n",
       "      <td>338.4752</td>\n",
       "      <td>1</td>\n",
       "      <td>339.1026</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.870358</td>\n",
       "      <td>340.5141</td>\n",
       "      <td>344.5421</td>\n",
       "      <td>347.5555</td>\n",
       "      <td>348.5693</td>\n",
       "      <td>351.5909</td>\n",
       "      <td>1</td>\n",
       "      <td>352.4612</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>109</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.665853</td>\n",
       "      <td>353.6144</td>\n",
       "      <td>356.6283</td>\n",
       "      <td>361.6586</td>\n",
       "      <td>362.6905</td>\n",
       "      <td>364.7231</td>\n",
       "      <td>1</td>\n",
       "      <td>365.389</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.881652</td>\n",
       "      <td>366.7559</td>\n",
       "      <td>370.7804</td>\n",
       "      <td>374.7974</td>\n",
       "      <td>375.8126</td>\n",
       "      <td>379.8469</td>\n",
       "      <td>1</td>\n",
       "      <td>380.7285</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>110</td>\n",
       "      <td>2R_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670513</td>\n",
       "      <td>381.8872</td>\n",
       "      <td>384.9162</td>\n",
       "      <td>387.9277</td>\n",
       "      <td>388.9506</td>\n",
       "      <td>391.9688</td>\n",
       "      <td>1</td>\n",
       "      <td>392.6393</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>lap1055</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720374</td>\n",
       "      <td>394.0011</td>\n",
       "      <td>397.0182</td>\n",
       "      <td>402.0453</td>\n",
       "      <td>403.0778</td>\n",
       "      <td>407.1051</td>\n",
       "      <td>1</td>\n",
       "      <td>407.8254</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>in_yellow</td>\n",
       "      <td>2055</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>DRY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.744086</td>\n",
       "      <td>0.007418269</td>\n",
       "      <td>3.023426</td>\n",
       "      <td>6.03927</td>\n",
       "      <td>7.071727</td>\n",
       "      <td>10.08742</td>\n",
       "      <td>1</td>\n",
       "      <td>10.8315</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>111</td>\n",
       "      <td>2R_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.764478</td>\n",
       "      <td>12.11979</td>\n",
       "      <td>15.13617</td>\n",
       "      <td>18.15208</td>\n",
       "      <td>19.16843</td>\n",
       "      <td>21.1844</td>\n",
       "      <td>1</td>\n",
       "      <td>21.94888</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>4RY_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.715719</td>\n",
       "      <td>23.19978</td>\n",
       "      <td>26.23252</td>\n",
       "      <td>31.24774</td>\n",
       "      <td>32.26386</td>\n",
       "      <td>34.28006</td>\n",
       "      <td>1</td>\n",
       "      <td>34.99578</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.840001</td>\n",
       "      <td>36.31277</td>\n",
       "      <td>40.32769</td>\n",
       "      <td>44.34326</td>\n",
       "      <td>45.35941</td>\n",
       "      <td>47.37501</td>\n",
       "      <td>1</td>\n",
       "      <td>48.21501</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>112</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.740229</td>\n",
       "      <td>49.39179</td>\n",
       "      <td>52.40709</td>\n",
       "      <td>56.42284</td>\n",
       "      <td>57.43835</td>\n",
       "      <td>59.45514</td>\n",
       "      <td>1</td>\n",
       "      <td>60.19537</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>72</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.911924</td>\n",
       "      <td>61.48711</td>\n",
       "      <td>64.50293</td>\n",
       "      <td>69.51847</td>\n",
       "      <td>70.53462</td>\n",
       "      <td>73.5505</td>\n",
       "      <td>1</td>\n",
       "      <td>74.46243</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>73</td>\n",
       "      <td>DRY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.716914</td>\n",
       "      <td>75.56627</td>\n",
       "      <td>79.59743</td>\n",
       "      <td>82.61343</td>\n",
       "      <td>83.62925</td>\n",
       "      <td>85.64537</td>\n",
       "      <td>1</td>\n",
       "      <td>86.36229</td>\n",
       "      <td>DRY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>in_red</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>113</td>\n",
       "      <td>2R_cr</td>\n",
       "      <td>[32, 33]</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.742776</td>\n",
       "      <td>87.66103</td>\n",
       "      <td>91.67706</td>\n",
       "      <td>95.69233</td>\n",
       "      <td>96.70835</td>\n",
       "      <td>98.74068</td>\n",
       "      <td>1</td>\n",
       "      <td>99.48346</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>4RY_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.586821</td>\n",
       "      <td>100.757</td>\n",
       "      <td>103.7727</td>\n",
       "      <td>108.788</td>\n",
       "      <td>109.8039</td>\n",
       "      <td>112.8367</td>\n",
       "      <td>1</td>\n",
       "      <td>113.4236</td>\n",
       "      <td>4RY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lap1030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>114</td>\n",
       "      <td>2R_h</td>\n",
       "      <td>[30, 31]</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.737338</td>\n",
       "      <td>114.8692</td>\n",
       "      <td>118.8845</td>\n",
       "      <td>123.8998</td>\n",
       "      <td>124.9326</td>\n",
       "      <td>127.9484</td>\n",
       "      <td>1</td>\n",
       "      <td>128.6857</td>\n",
       "      <td>2R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2030</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject Stim_version Instruction_version run Trial Image_number  \\\n",
       "0       387            2                   2   3     1           21   \n",
       "1       387            2                   2   3     2           61   \n",
       "2       387            2                   2   3     3          101   \n",
       "3       387            2                   2   3     4           62   \n",
       "4       387            2                   2   3     5          102   \n",
       "5       387            2                   2   3     6           22   \n",
       "6       387            2                   2   3     7           63   \n",
       "7       387            2                   2   3     8           23   \n",
       "8       387            2                   2   3     9          103   \n",
       "9       387            2                   2   3    10           24   \n",
       "10      387            2                   2   3    11          104   \n",
       "11      387            2                   2   3    12           64   \n",
       "12      387            2                   2   3    13           65   \n",
       "13      387            2                   2   3    14          105   \n",
       "14      387            2                   2   3    15           25   \n",
       "15      387            2                   2   3    16          106   \n",
       "16      387            2                   2   3    17           66   \n",
       "17      387            2                   2   3    18           26   \n",
       "18      387            2                   2   3    19           67   \n",
       "19      387            2                   2   3    20          107   \n",
       "20      387            2                   2   3    21           27   \n",
       "21      387            2                   2   3    22           28   \n",
       "22      387            2                   2   3    23          108   \n",
       "23      387            2                   2   3    24           68   \n",
       "24      387            2                   2   3    25           69   \n",
       "25      387            2                   2   3    26           29   \n",
       "26      387            2                   2   3    27          109   \n",
       "27      387            2                   2   3    28           30   \n",
       "28      387            2                   2   3    29          110   \n",
       "29      387            2                   2   3    30           70   \n",
       "0       351            1                   2   3     1           21   \n",
       "1       351            1                   2   3     2           61   \n",
       "2       351            1                   2   3     3          101   \n",
       "3       351            1                   2   3     4           62   \n",
       "4       351            1                   2   3     5          102   \n",
       "5       351            1                   2   3     6           22   \n",
       "6       351            1                   2   3     7           63   \n",
       "7       351            1                   2   3     8           23   \n",
       "8       351            1                   2   3     9          103   \n",
       "9       351            1                   2   3    10           24   \n",
       "10      351            1                   2   3    11          104   \n",
       "11      351            1                   2   3    12           64   \n",
       "12      351            1                   2   3    13           65   \n",
       "13      351            1                   2   3    14          105   \n",
       "14      351            1                   2   3    15           25   \n",
       "15      351            1                   2   3    16          106   \n",
       "16      351            1                   2   3    17           66   \n",
       "17      351            1                   2   3    18           26   \n",
       "18      351            1                   2   3    19           67   \n",
       "19      351            1                   2   3    20          107   \n",
       "20      351            1                   2   3    21           27   \n",
       "21      351            1                   2   3    22           28   \n",
       "22      351            1                   2   3    23          108   \n",
       "23      351            1                   2   3    24           68   \n",
       "24      351            1                   2   3    25           69   \n",
       "25      351            1                   2   3    26           29   \n",
       "26      351            1                   2   3    27          109   \n",
       "27      351            1                   2   3    28           30   \n",
       "28      351            1                   2   3    29          110   \n",
       "29      351            1                   2   3    30           70   \n",
       "0   lap1055            2                   1   3     1           21   \n",
       "1   lap1055            2                   1   3     2           61   \n",
       "2   lap1055            2                   1   3     3          101   \n",
       "3   lap1055            2                   1   3     4           62   \n",
       "4   lap1055            2                   1   3     5          102   \n",
       "5   lap1055            2                   1   3     6           22   \n",
       "6   lap1055            2                   1   3     7           63   \n",
       "7   lap1055            2                   1   3     8           23   \n",
       "8   lap1055            2                   1   3     9          103   \n",
       "9   lap1055            2                   1   3    10           24   \n",
       "10  lap1055            2                   1   3    11          104   \n",
       "11  lap1055            2                   1   3    12           64   \n",
       "12  lap1055            2                   1   3    13           65   \n",
       "13  lap1055            2                   1   3    14          105   \n",
       "14  lap1055            2                   1   3    15           25   \n",
       "15  lap1055            2                   1   3    16          106   \n",
       "16  lap1055            2                   1   3    17           66   \n",
       "17  lap1055            2                   1   3    18           26   \n",
       "18  lap1055            2                   1   3    19           67   \n",
       "19  lap1055            2                   1   3    20          107   \n",
       "20  lap1055            2                   1   3    21           27   \n",
       "21  lap1055            2                   1   3    22           28   \n",
       "22  lap1055            2                   1   3    23          108   \n",
       "23  lap1055            2                   1   3    24           68   \n",
       "24  lap1055            2                   1   3    25           69   \n",
       "25  lap1055            2                   1   3    26           29   \n",
       "26  lap1055            2                   1   3    27          109   \n",
       "27  lap1055            2                   1   3    28           30   \n",
       "28  lap1055            2                   1   3    29          110   \n",
       "29  lap1055            2                   1   3    30           70   \n",
       "0   lap1030            2                   2   4     1           71   \n",
       "1   lap1030            2                   2   4     2          111   \n",
       "2   lap1030            2                   2   4     3           31   \n",
       "3   lap1030            2                   2   4     4           32   \n",
       "4   lap1030            2                   2   4     5          112   \n",
       "5   lap1030            2                   2   4     6           72   \n",
       "6   lap1030            2                   2   4     7           73   \n",
       "7   lap1030            2                   2   4     8          113   \n",
       "8   lap1030            2                   2   4     9           33   \n",
       "9   lap1030            2                   2   4    10          114   \n",
       "\n",
       "   Trial_type Correct_response actual_response    H    M   CR   FA  \\\n",
       "0      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "1      DRY_fa         [32, 33]              30    0    0    0    1   \n",
       "2       2R_nr         [32, 33]             NaN  NaN  NaN  NaN  NaN   \n",
       "3       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "4        2R_h         [30, 31]              30    1    0    0    0   \n",
       "5       4RY_h         [30, 31]              30    1    0    0    0   \n",
       "6       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "7      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "8       2R_cr         [32, 33]              33    0    0    1    0   \n",
       "9      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "10       2R_h         [30, 31]              30    1    0    0    0   \n",
       "11     DRY_nr         [30, 31]             NaN  NaN  NaN  NaN  NaN   \n",
       "12      DRY_h         [30, 31]              31    1    0    0    0   \n",
       "13       2R_h         [30, 31]              30    1    0    0    0   \n",
       "14     4RY_nr         [32, 33]             NaN  NaN  NaN  NaN  NaN   \n",
       "15      2R_fa         [32, 33]              30    0    0    0    1   \n",
       "16     DRY_fa         [32, 33]              30    0    0    0    1   \n",
       "17     4RY_nr         [32, 33]             NaN  NaN  NaN  NaN  NaN   \n",
       "18      DRY_m         [30, 31]              33    0    1    0    0   \n",
       "19      2R_fa         [32, 33]              30    0    0    0    1   \n",
       "20     4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "21     4RY_nr         [30, 31]             NaN  NaN  NaN  NaN  NaN   \n",
       "22      2R_nr         [30, 31]             NaN  NaN  NaN  NaN  NaN   \n",
       "23     DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "24     DRY_nr         [30, 31]             NaN  NaN  NaN  NaN  NaN   \n",
       "25      4RY_m         [30, 31]              33    0    1    0    0   \n",
       "26       2R_h         [30, 31]              30    1    0    0    0   \n",
       "27      4RY_h         [30, 31]              30    1    0    0    0   \n",
       "28      2R_fa         [32, 33]              30    0    0    0    1   \n",
       "29     DRY_fa         [32, 33]              30    0    0    0    1   \n",
       "0      4RY_fa         [32, 33]              30    0    0    0    1   \n",
       "1      DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "2       2R_fa         [32, 33]              30    0    0    0    1   \n",
       "3       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "4        2R_h         [30, 31]              30    1    0    0    0   \n",
       "5       4RY_m         [30, 31]              33    0    1    0    0   \n",
       "6       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "7      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "8       2R_cr         [32, 33]              33    0    0    1    0   \n",
       "9      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "10       2R_h         [30, 31]              30    1    0    0    0   \n",
       "11      DRY_h         [30, 31]              30    1    0    0    0   \n",
       "12      DRY_h         [30, 31]              30    1    0    0    0   \n",
       "13       2R_h         [30, 31]              30    1    0    0    0   \n",
       "14     4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "15      2R_fa         [32, 33]              30    0    0    0    1   \n",
       "16     DRY_fa         [32, 33]              30    0    0    0    1   \n",
       "17     4RY_fa         [32, 33]              30    0    0    0    1   \n",
       "18      DRY_h         [30, 31]              30    1    0    0    0   \n",
       "19      2R_fa         [32, 33]              30    0    0    0    1   \n",
       "20     4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "21     4RY_nr         [30, 31]             NaN  NaN  NaN  NaN  NaN   \n",
       "22       2R_h         [30, 31]              30    1    0    0    0   \n",
       "23     DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "24      DRY_h         [30, 31]              30    1    0    0    0   \n",
       "25      4RY_h         [30, 31]              30    1    0    0    0   \n",
       "26       2R_h         [30, 31]              30    1    0    0    0   \n",
       "27      4RY_h         [30, 31]              30    1    0    0    0   \n",
       "28      2R_fa         [32, 33]              30    0    0    0    1   \n",
       "29     DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "0      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "1      DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "2       2R_fa         [32, 33]              30    0    0    0    1   \n",
       "3       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "4        2R_h         [30, 31]              30    1    0    0    0   \n",
       "5       4RY_h         [30, 31]              30    1    0    0    0   \n",
       "6       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "7      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "8       2R_cr         [32, 33]              33    0    0    1    0   \n",
       "9      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "10       2R_h         [30, 31]              30    1    0    0    0   \n",
       "11      DRY_h         [30, 31]              30    1    0    0    0   \n",
       "12      DRY_h         [30, 31]              30    1    0    0    0   \n",
       "13       2R_h         [30, 31]              30    1    0    0    0   \n",
       "14     4RY_fa         [32, 33]              30    0    0    0    1   \n",
       "15      2R_fa         [32, 33]              30    0    0    0    1   \n",
       "16     DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "17     4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "18      DRY_m         [30, 31]              33    0    1    0    0   \n",
       "19      2R_cr         [32, 33]              33    0    0    1    0   \n",
       "20     4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "21      4RY_h         [30, 31]              30    1    0    0    0   \n",
       "22       2R_h         [30, 31]              30    1    0    0    0   \n",
       "23     DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "24      DRY_h         [30, 31]              30    1    0    0    0   \n",
       "25      4RY_h         [30, 31]              30    1    0    0    0   \n",
       "26       2R_h         [30, 31]              30    1    0    0    0   \n",
       "27      4RY_h         [30, 31]              30    1    0    0    0   \n",
       "28      2R_cr         [32, 33]              33    0    0    1    0   \n",
       "29     DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "0      DRY_cr         [32, 33]              33    0    0    1    0   \n",
       "1       2R_cr         [32, 33]              33    0    0    1    0   \n",
       "2      4RY_cr         [32, 33]              33    0    0    1    0   \n",
       "3       4RY_h         [30, 31]              30    1    0    0    0   \n",
       "4        2R_h         [30, 31]              30    1    0    0    0   \n",
       "5       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "6       DRY_h         [30, 31]              30    1    0    0    0   \n",
       "7       2R_cr         [32, 33]              33    0    0    1    0   \n",
       "8       4RY_h         [30, 31]              30    1    0    0    0   \n",
       "9        2R_h         [30, 31]              30    1    0    0    0   \n",
       "\n",
       "    Reaction_Time Fixation_1_onset Instruction_onset Stimulus_onset  \\\n",
       "0        1.505534       0.01544825          3.021292       6.027135   \n",
       "1        1.441893         11.10365          15.11146       18.11729   \n",
       "2             NaN         23.16273          27.16838       30.17419   \n",
       "3        1.907076         36.21931          39.22521       42.23101   \n",
       "4        0.888194          48.2767          51.28191       56.29168   \n",
       "5        1.009868         62.33673          65.34255       69.35039   \n",
       "6        1.437497         74.39346          78.40135       82.40911   \n",
       "7        1.337378         88.45414          92.46202       96.46975   \n",
       "8        1.330023         103.5174          107.5246       110.5305   \n",
       "9        1.616321         115.5735          118.5794       123.5892   \n",
       "10       1.825802         129.6509          133.6587       137.6665   \n",
       "11            NaN         142.7263          146.7342       150.7419   \n",
       "12       1.063527         157.7889          160.7948       163.8174   \n",
       "13       1.927171         168.8772           171.883       175.8908   \n",
       "14            NaN         183.0047          187.0125        190.035   \n",
       "15       0.996409         197.0823          201.0898       206.0995   \n",
       "16       1.797224         213.1479          217.1544       221.1622   \n",
       "17            NaN         227.2239          230.2298       233.2524   \n",
       "18       1.974987         238.3122           241.318       246.3278   \n",
       "19       1.038450         253.4749          256.4809       261.4905   \n",
       "20       1.525381         268.5376          272.5454       277.5551   \n",
       "21            NaN         283.6169          286.6228       291.6325   \n",
       "22            NaN         296.6923          300.7001        304.708   \n",
       "23       1.256838         310.7697          314.7775       319.7872   \n",
       "24            NaN         326.8342          329.8401       333.8479   \n",
       "25       1.169069         338.9077          342.9155       345.9381   \n",
       "26       1.645604         352.0003          355.0057       360.0154   \n",
       "27       1.151748         365.0754           369.083       373.0909   \n",
       "28       1.227699         380.1379          383.1438       386.1663   \n",
       "29       0.924822         392.2281          395.2339       400.2436   \n",
       "0        1.117776      0.008135829          3.013975       6.019843   \n",
       "1        0.948287         11.04625          15.05406       18.05991   \n",
       "2        1.823790         23.08633          27.09413       30.09998   \n",
       "3        1.080590         36.12835          39.13419       42.14003   \n",
       "4        0.938214         48.16842          51.17426         56.184   \n",
       "5        1.371856         62.21239          65.21824       69.22604   \n",
       "6        0.882713         74.26916          78.27695       82.28475   \n",
       "7        0.766151         88.31313          92.32093       96.32873   \n",
       "8        0.845051          103.359          107.3668       110.3727   \n",
       "9        0.993722         115.3991           118.405       123.4147   \n",
       "10       1.002776         129.4431          133.4509       137.4587   \n",
       "11       1.620668         142.4851          146.4929       150.5007   \n",
       "12       1.730245          157.531          160.5369       163.5427   \n",
       "13       0.746806         168.5858          171.5917       175.5995   \n",
       "14       1.955376         182.6298          186.6376       189.6434   \n",
       "15       0.817384         196.6738          200.6816       205.6913   \n",
       "16       0.997236         212.7216          216.7294       220.7372   \n",
       "17       0.756118         226.7656          229.7715       232.7773   \n",
       "18       1.001783         237.8204          240.8263        245.836   \n",
       "19       1.115058         252.8663          255.8722       260.8819   \n",
       "20       1.033333         267.9123            271.92       276.9298   \n",
       "21            NaN         282.9582           285.964       290.9737   \n",
       "22       0.941840         296.0169          300.0247       304.0325   \n",
       "23       1.951321         310.0609          314.0687       319.0784   \n",
       "24       1.078593         326.1087          329.1146       333.1223   \n",
       "25       0.457351         338.1655          342.1733       345.1791   \n",
       "26       0.949789         351.2075          354.2134       359.2231   \n",
       "27       0.673085         364.2662           368.274       372.2818   \n",
       "28       0.766338         379.3121           382.318       385.3238   \n",
       "29       1.348722         391.3522          394.3581       399.3678   \n",
       "0        0.723439       0.03058879           3.06702        6.09349   \n",
       "1        0.786289         11.18455          15.21183       18.23556   \n",
       "2        0.754978         23.31867          27.33955       30.37277   \n",
       "3        0.906544          36.4564          39.48756       42.50962   \n",
       "4        1.000497         48.62384          51.65162        56.6748   \n",
       "5        1.004657          62.7763          65.81003       69.84385   \n",
       "6        0.926891         74.92435          78.96153       82.99582   \n",
       "7        0.965163         89.09277           93.1143       97.14162   \n",
       "8        0.700934         104.2467          108.2829       111.3105   \n",
       "9        1.352825         116.4158          119.4415       124.4689   \n",
       "10       0.918488         130.5597           134.573       138.5874   \n",
       "11       0.755091         143.6718          147.6967       151.7287   \n",
       "12       1.005144         158.8051          161.8258       164.8436   \n",
       "13       1.138305         169.9085          172.9339         176.95   \n",
       "14       0.995366         184.0014          188.0196       191.0446   \n",
       "15       0.620518          198.113          202.1235       207.1584   \n",
       "16       0.655253         214.2769          218.2901       222.3154   \n",
       "17       1.002141         228.4092          231.4343       234.4597   \n",
       "18       0.999354         239.5485          242.5774       247.5915   \n",
       "19       0.929673         254.6677          257.6931         262.72   \n",
       "20       1.250934         269.8175          273.8324       278.8501   \n",
       "21       1.013393          284.919          287.9488       292.9778   \n",
       "22       1.339115         298.0765          302.1037       306.1312   \n",
       "23       0.877718         312.2295          316.2557       321.2812   \n",
       "24       0.627372         328.3745          331.4003       335.4258   \n",
       "25       0.870358         340.5141          344.5421       347.5555   \n",
       "26       0.665853         353.6144          356.6283       361.6586   \n",
       "27       0.881652         366.7559          370.7804       374.7974   \n",
       "28       0.670513         381.8872          384.9162       387.9277   \n",
       "29       0.720374         394.0011          397.0182       402.0453   \n",
       "0        0.744086      0.007418269          3.023426        6.03927   \n",
       "1        0.764478         12.11979          15.13617       18.15208   \n",
       "2        0.715719         23.19978          26.23252       31.24774   \n",
       "3        0.840001         36.31277          40.32769       44.34326   \n",
       "4        0.740229         49.39179          52.40709       56.42284   \n",
       "5        0.911924         61.48711          64.50293       69.51847   \n",
       "6        0.716914         75.56627          79.59743       82.61343   \n",
       "7        0.742776         87.66103          91.67706       95.69233   \n",
       "8        0.586821          100.757          103.7727        108.788   \n",
       "9        0.737338         114.8692          118.8845       123.8998   \n",
       "\n",
       "   Fixation_2_onset Probe_onset  Accuracy Reaction_Time_onset LOAD  noresp  \\\n",
       "0          7.045776    9.049678         1            10.55521  4RY     NaN   \n",
       "1          19.13596    21.13984         0            22.58174  DRY     NaN   \n",
       "2          31.19293    34.19872         0                 NaN   2R       1   \n",
       "3          43.24961    46.25548         1            48.16256  DRY     NaN   \n",
       "4          57.31036    60.31619         1            61.20438   2R     NaN   \n",
       "5          70.36907    72.37291         1            73.38278  4RY     NaN   \n",
       "6           83.4278    86.43362         1            87.87112  DRY     NaN   \n",
       "7          97.48839    101.4962         1            102.8336  4RY     NaN   \n",
       "8          111.5491     113.553         1             114.883   2R     NaN   \n",
       "9          124.6078    127.6303         1            129.2467  4RY     NaN   \n",
       "10         138.6852    140.7058         1            142.5316   2R     NaN   \n",
       "11         151.7606    155.7684         0                 NaN  DRY       1   \n",
       "12          164.836    166.8566         1            167.9201  DRY     NaN   \n",
       "13         176.9095    180.9173         1            182.8445   2R     NaN   \n",
       "14         191.0536    195.0614         0                 NaN  4RY       1   \n",
       "15         207.1182     211.126         0            212.1224   2R     NaN   \n",
       "16         222.1808    225.2034         0            227.0006  DRY     NaN   \n",
       "17          234.271    236.2916         0                 NaN  4RY       1   \n",
       "18         247.3464    251.3543         0            253.3292  DRY     NaN   \n",
       "19         262.5092     266.517         0            267.5555   2R     NaN   \n",
       "20         278.5738    281.5963         1            283.1216  4RY     NaN   \n",
       "21         292.6512    294.6718         0                 NaN  4RY       1   \n",
       "22         305.7265    308.7491         0                 NaN   2R       1   \n",
       "23         320.8059    324.8137         1            326.0705  DRY     NaN   \n",
       "24         334.8665    336.8871         0                 NaN  DRY       1   \n",
       "25         346.9567    349.9792         0            351.1483  4RY     NaN   \n",
       "26         361.0341    363.0547         1            364.7003   2R     NaN   \n",
       "27         374.1095    378.1173         1            379.2691  4RY     NaN   \n",
       "28         387.1849    390.2074         0            391.4351   2R     NaN   \n",
       "29         401.2622      405.27         0            406.1949  DRY     NaN   \n",
       "0          7.021778    9.025676         0            10.14345  4RY     NaN   \n",
       "1          19.06183    21.06575         1            22.01404  DRY     NaN   \n",
       "2          31.10191    34.10775         0            35.93154   2R     NaN   \n",
       "3            43.142    46.14784         1            47.22843  DRY     NaN   \n",
       "4          57.18596    60.19181         1            61.13002   2R     NaN   \n",
       "5          70.22797    72.24856         0            73.62042  4RY     NaN   \n",
       "6          83.28668    86.29254         1            87.17526  DRY     NaN   \n",
       "7          97.33065    101.3385         1            102.1046  4RY     NaN   \n",
       "8          111.3747    113.3785         1            114.2236   2R     NaN   \n",
       "9          124.4166    127.4225         1            128.4162  4RY     NaN   \n",
       "10         138.4606    140.4645         1            141.4673   2R     NaN   \n",
       "11         151.5026    155.5104         1            157.1311  DRY     NaN   \n",
       "12         164.5446    166.5653         1            168.2955  DRY     NaN   \n",
       "13         176.6014    180.6092         1             181.356   2R     NaN   \n",
       "14         190.6454    194.6532         1            196.6086  4RY     NaN   \n",
       "15         206.6933     210.701         0            211.5184   2R     NaN   \n",
       "16         221.7392     224.745         0            225.7423  DRY     NaN   \n",
       "17         233.7792    235.7998         0            236.5559  4RY     NaN   \n",
       "18          246.838    250.8458         1            251.8475  DRY     NaN   \n",
       "19         261.8839    265.8917         0            267.0067   2R     NaN   \n",
       "20         277.9317    280.9376         1            281.9709  4RY     NaN   \n",
       "21         291.9757    293.9963         0                 NaN  4RY       1   \n",
       "22         305.0344    308.0403         1            308.9821   2R     NaN   \n",
       "23         320.0803    324.0881         1            326.0394  DRY     NaN   \n",
       "24         334.1243    336.1449         1            337.2235  DRY     NaN   \n",
       "25         346.1811    349.1869         1            349.6443  4RY     NaN   \n",
       "26          360.225    362.2457         1            363.1955   2R     NaN   \n",
       "27         373.2837    377.2915         1            377.9646  4RY     NaN   \n",
       "28         386.3258    389.3316         0            390.0979   2R     NaN   \n",
       "29         400.3697    404.3775         1            405.7262  DRY     NaN   \n",
       "0          7.117556    9.149328         1            9.872767  4RY     NaN   \n",
       "1          19.26252    21.29008         1            22.07637  DRY     NaN   \n",
       "2          31.39335    34.42112         0             35.1761   2R     NaN   \n",
       "3          43.54751    46.58745         1            47.49399  DRY     NaN   \n",
       "4          57.70192    60.74116         1            61.74166   2R     NaN   \n",
       "5          70.87055    72.89422         1            73.89887  4RY     NaN   \n",
       "6          84.02725     87.0633         1            87.99019  DRY     NaN   \n",
       "7          98.18207     102.216         1            103.1812  4RY     NaN   \n",
       "8          112.3507    114.3763         1            115.0773   2R     NaN   \n",
       "9          125.5027    128.5264         1            129.8792  4RY     NaN   \n",
       "10         139.6136    141.6362         1            142.5547   2R     NaN   \n",
       "11         152.7568    156.7783         1            157.5334  DRY     NaN   \n",
       "12         165.8565    167.8714         1            168.8766  DRY     NaN   \n",
       "13         177.9682    181.9835         1            183.1219   2R     NaN   \n",
       "14         192.0608    196.0706         0             197.066  4RY     NaN   \n",
       "15         208.1984    212.2328         0            212.8533   2R     NaN   \n",
       "16         223.3505    226.3825         1            227.0378  DRY     NaN   \n",
       "17         235.4878    237.5198         1             238.522  4RY     NaN   \n",
       "18         248.6217    252.6412         0            253.6405  DRY     NaN   \n",
       "19         263.7548    267.7902         1            268.7198   2R     NaN   \n",
       "20         279.8663    282.8838         1            284.1347  4RY     NaN   \n",
       "21         294.0093    296.0371         1            297.0505  4RY     NaN   \n",
       "22         307.1634    310.1904         1            311.5295   2R     NaN   \n",
       "23         322.3055    326.3357         1            327.2135  DRY     NaN   \n",
       "24         336.4491    338.4752         1            339.1026  DRY     NaN   \n",
       "25         348.5693    351.5909         1            352.4612  4RY     NaN   \n",
       "26         362.6905    364.7231         1             365.389   2R     NaN   \n",
       "27         375.8126    379.8469         1            380.7285  4RY     NaN   \n",
       "28         388.9506    391.9688         1            392.6393   2R     NaN   \n",
       "29         403.0778    407.1051         1            407.8254  DRY     NaN   \n",
       "0          7.071727    10.08742         1             10.8315  DRY     NaN   \n",
       "1          19.16843     21.1844         1            21.94888   2R     NaN   \n",
       "2          32.26386    34.28006         1            34.99578  4RY     NaN   \n",
       "3          45.35941    47.37501         1            48.21501  4RY     NaN   \n",
       "4          57.43835    59.45514         1            60.19537   2R     NaN   \n",
       "5          70.53462     73.5505         1            74.46243  DRY     NaN   \n",
       "6          83.62925    85.64537         1            86.36229  DRY     NaN   \n",
       "7          96.70835    98.74068         1            99.48346   2R     NaN   \n",
       "8          109.8039    112.8367         1            113.4236  4RY     NaN   \n",
       "9          124.9326    127.9484         1            128.6857   2R     NaN   \n",
       "\n",
       "   Filter_trial probe_type probe_type_label  subject1 group new_group  \n",
       "0             0       None             None       387     2      None  \n",
       "1             1          1        in_yellow       387     2      None  \n",
       "2             0       None             None       387     2      None  \n",
       "3             1          0           in_red       387     2      None  \n",
       "4             0       None             None       387     2      None  \n",
       "5             0       None             None       387     2      None  \n",
       "6             1          0           in_red       387     2      None  \n",
       "7             0       None             None       387     2      None  \n",
       "8             0       None             None       387     2      None  \n",
       "9             0       None             None       387     2      None  \n",
       "10            0       None             None       387     2      None  \n",
       "11            1          0           in_red       387     2      None  \n",
       "12            1          0           in_red       387     2      None  \n",
       "13            0       None             None       387     2      None  \n",
       "14            0       None             None       387     2      None  \n",
       "15            0       None             None       387     2      None  \n",
       "16            1          1        in_yellow       387     2      None  \n",
       "17            0       None             None       387     2      None  \n",
       "18            1          0           in_red       387     2      None  \n",
       "19            0       None             None       387     2      None  \n",
       "20            0       None             None       387     2      None  \n",
       "21            0       None             None       387     2      None  \n",
       "22            0       None             None       387     2      None  \n",
       "23            1          1        in_yellow       387     2      None  \n",
       "24            1          0           in_red       387     2      None  \n",
       "25            0       None             None       387     2      None  \n",
       "26            0       None             None       387     2      None  \n",
       "27            0       None             None       387     2      None  \n",
       "28            0       None             None       387     2      None  \n",
       "29            1          1        in_yellow       387     2      None  \n",
       "0             0       None             None       351     2         0  \n",
       "1             1          1        in_yellow       351     2         0  \n",
       "2             0       None             None       351     2         0  \n",
       "3             1          0           in_red       351     2         0  \n",
       "4             0       None             None       351     2         0  \n",
       "5             0       None             None       351     2         0  \n",
       "6             1          0           in_red       351     2         0  \n",
       "7             0       None             None       351     2         0  \n",
       "8             0       None             None       351     2         0  \n",
       "9             0       None             None       351     2         0  \n",
       "10            0       None             None       351     2         0  \n",
       "11            1          0           in_red       351     2         0  \n",
       "12            1          0           in_red       351     2         0  \n",
       "13            0       None             None       351     2         0  \n",
       "14            0       None             None       351     2         0  \n",
       "15            0       None             None       351     2         0  \n",
       "16            1          1        in_yellow       351     2         0  \n",
       "17            0       None             None       351     2         0  \n",
       "18            1          0           in_red       351     2         0  \n",
       "19            0       None             None       351     2         0  \n",
       "20            0       None             None       351     2         0  \n",
       "21            0       None             None       351     2         0  \n",
       "22            0       None             None       351     2         0  \n",
       "23            1          1        in_yellow       351     2         0  \n",
       "24            1          0           in_red       351     2         0  \n",
       "25            0       None             None       351     2         0  \n",
       "26            0       None             None       351     2         0  \n",
       "27            0       None             None       351     2         0  \n",
       "28            0       None             None       351     2         0  \n",
       "29            1          1        in_yellow       351     2         0  \n",
       "0             0       None             None      2055     2      None  \n",
       "1             1          1        in_yellow      2055     2      None  \n",
       "2             0       None             None      2055     2      None  \n",
       "3             1          0           in_red      2055     2      None  \n",
       "4             0       None             None      2055     2      None  \n",
       "5             0       None             None      2055     2      None  \n",
       "6             1          0           in_red      2055     2      None  \n",
       "7             0       None             None      2055     2      None  \n",
       "8             0       None             None      2055     2      None  \n",
       "9             0       None             None      2055     2      None  \n",
       "10            0       None             None      2055     2      None  \n",
       "11            1          0           in_red      2055     2      None  \n",
       "12            1          0           in_red      2055     2      None  \n",
       "13            0       None             None      2055     2      None  \n",
       "14            0       None             None      2055     2      None  \n",
       "15            0       None             None      2055     2      None  \n",
       "16            1          1        in_yellow      2055     2      None  \n",
       "17            0       None             None      2055     2      None  \n",
       "18            1          0           in_red      2055     2      None  \n",
       "19            0       None             None      2055     2      None  \n",
       "20            0       None             None      2055     2      None  \n",
       "21            0       None             None      2055     2      None  \n",
       "22            0       None             None      2055     2      None  \n",
       "23            1          1        in_yellow      2055     2      None  \n",
       "24            1          0           in_red      2055     2      None  \n",
       "25            0       None             None      2055     2      None  \n",
       "26            0       None             None      2055     2      None  \n",
       "27            0       None             None      2055     2      None  \n",
       "28            0       None             None      2055     2      None  \n",
       "29            1          1        in_yellow      2055     2      None  \n",
       "0             1       None             None      2030     2         2  \n",
       "1             0       None             None      2030     2         2  \n",
       "2             0       None             None      2030     2         2  \n",
       "3             0       None             None      2030     2         2  \n",
       "4             0       None             None      2030     2         2  \n",
       "5             1          0           in_red      2030     2         2  \n",
       "6             1          0           in_red      2030     2         2  \n",
       "7             0       None             None      2030     2         2  \n",
       "8             0       None             None      2030     2         2  \n",
       "9             0       None             None      2030     2         2  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WM.head(100)\n",
    "#WM.to_csv('/home/jenni/Dropbox/GATES_8thgrade_fMRI/All_development/wmfilt/RAWALL_%s.csv' % date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#excluding the subjects who we do not have brain data for\n",
    "#exclude_subs = [338]#352, 317, 348, 319, 1026, 359, 390, 353, \n",
    "                #1013, 312, 1037, 1007, 2013, 1024, 1025, 1026, \n",
    "                #1035, 1037, 1042, 1050, 1056, 1058, 1064, 1065, 340,336,349,368,411,1055]\n",
    "#for x in exclude_subs:\n",
    " #   WM = WM[WM.subject != x]\n",
    "WM.Accuracy = WM.Accuracy.convert_objects(convert_numeric=True)\n",
    "WM.Reaction_Time = WM.Reaction_Time.convert_objects(convert_numeric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject1</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">302</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>0.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">303</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>0.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy\n",
       "subject1 LOAD group          \n",
       "302      2R   2         0.725\n",
       "         4RY  2         0.600\n",
       "         DRY  2         0.700\n",
       "303      2R   2         0.450\n",
       "         4RY  2         0.500"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets look at accuracy!\n",
    "acc = pd.pivot_table(WM, values=['Accuracy'], index=['subject1','LOAD', 'group'], aggfunc=np.mean, margins=True)\n",
    "#acc.to_csv('/home/jenni/Dropbox/GATES_8thgrade_fMRI/All_development/wmfilt_acc_%s.csv' % date)\n",
    "#acc = acc.reset_index()\n",
    "acc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Reaction_Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject1</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">302</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>1.129071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>1.303166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>1.198379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">303</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>1.237365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>1.147570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Reaction_Time\n",
       "subject1 LOAD group               \n",
       "302      2R   2           1.129071\n",
       "         4RY  2           1.303166\n",
       "         DRY  2           1.198379\n",
       "303      2R   2           1.237365\n",
       "         4RY  2           1.147570"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets look at RT!\n",
    "rt = pd.pivot_table(WM, values=['Reaction_Time'], index=['subject1','LOAD', 'group'], aggfunc=np.mean, margins=True)\n",
    "#acc.to_csv('/home/jenni/Dropbox/GATES_8thgrade_fMRI/All_development/wmfilt_acc_%s.csv' % date)\n",
    "#rt = rt.reset_index()\n",
    "rt.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Exlude subjects with bad acc\n",
    "exclude_subs = [322, 338, 317, 406, 1013, 1037]#307, 320, 374, 406, 321, 322, 344, 345, 303, 313]\n",
    "for x in exclude_subs:\n",
    "    WM = WM[WM.subject != x]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject1</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">302</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">303</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">304</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">305</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">306</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">307</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">308</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">309</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">310</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">311</th>\n",
       "      <th>2R</th>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">312</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">313</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">314</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">315</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">316</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">317</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">318</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">319</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">320</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">321</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">322</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">323</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">325</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">326</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">327</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">328</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">329</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">330</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">332</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">333</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">334</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">335</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">336</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2016</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2019</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2021</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2022</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2023</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2024</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2025</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2026</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2027</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2028</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2029</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2030</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2032</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2037</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2039</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2041</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2042</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2045</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2046</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2047</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2049</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2050</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2052</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2053</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2054</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2055</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2056</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2057</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2058</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2061</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2064</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2065</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>441 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CR  FA   H   M\n",
       "subject1 LOAD group                \n",
       "302      2R   2      13   3  16   1\n",
       "         4RY  2      15   4   9   9\n",
       "         DRY  2      11   6  17   2\n",
       "303      2R   2       5  14  13   2\n",
       "         4RY  2       6  10  14   3\n",
       "         DRY  2       2  14  17   1\n",
       "304      2R   2      10  10  18   0\n",
       "         4RY  2      12   7  16   4\n",
       "         DRY  2      13   3  12   7\n",
       "305      2R   2      15   5  19   0\n",
       "         4RY  2      16   4  20   0\n",
       "         DRY  2      15   4  18   1\n",
       "306      2R   2      17   3  16   3\n",
       "         4RY  2       9  11  15   5\n",
       "         DRY  2      13   6  17   3\n",
       "307      2R   2       8   6   5  10\n",
       "         4RY  2      12   4   6  12\n",
       "         DRY  2      10   6   3  11\n",
       "308      2R   2      12   3  12   1\n",
       "         4RY  2      13   4  12   1\n",
       "         DRY  2      12   2  11   3\n",
       "309      2R   2      12   5  15   3\n",
       "         4RY  2      16   2   8   9\n",
       "         DRY  2      17   2  13   5\n",
       "310      2R   2      10   9  13   6\n",
       "         4RY  2      16   4  10   8\n",
       "         DRY  2      19   0  18   1\n",
       "311      2R   0      19   0  16   3\n",
       "         4RY  0      17   0   8   7\n",
       "         DRY  0      15   4  15   3\n",
       "312      2R   2      11   8   6  13\n",
       "         4RY  2      14   4   8  11\n",
       "         DRY  2      11   9   7  13\n",
       "313      2R   2      11   5   5  13\n",
       "         4RY  2      10   3   6  12\n",
       "         DRY  2      10   3   3  14\n",
       "314      2R   2      15   5  13   6\n",
       "         4RY  2      16   4   9  11\n",
       "         DRY  2      13   7   6  13\n",
       "315      2R   2      11   7  16   4\n",
       "         4RY  2      16   4  11   7\n",
       "         DRY  2      16   4   9  11\n",
       "316      2R   2      13   4  18   2\n",
       "         4RY  2      14   3  18   2\n",
       "         DRY  2      13   5  19   1\n",
       "317      2R   2       6   6   7   6\n",
       "         4RY  2       9   0   5   4\n",
       "         DRY  2       3   4   8   7\n",
       "318      2R   2      18   1  17   2\n",
       "         4RY  2      17   2  13   7\n",
       "         DRY  2      17   3  16   3\n",
       "319      2R   2      15   1  15   3\n",
       "         4RY  2      15   0   8   2\n",
       "         DRY  2      10   8  13   3\n",
       "320      2R   2       0   7  17   0\n",
       "         4RY  2       0   8  15   0\n",
       "         DRY  2       0   7  16   0\n",
       "321      2R   2       6  10  15   1\n",
       "         4RY  2       5   6  15   1\n",
       "         DRY  2       3  10  14   2\n",
       "322      2R   2       7   3   6   2\n",
       "         4RY  2       8   2   3   4\n",
       "         DRY  2       6   3   5   5\n",
       "323      2R   2       9   7   8   9\n",
       "         4RY  2       6  10   7   3\n",
       "         DRY  2       7   7   3   7\n",
       "325      2R   2      13   4  16   1\n",
       "         4RY  2      12   3  11   4\n",
       "         DRY  2      10   6  11   6\n",
       "326      2R   2      11   7   7   9\n",
       "         4RY  2       9   8   9   8\n",
       "         DRY  2      12   6   9   8\n",
       "327      2R   2      12   8  18   1\n",
       "         4RY  2      10  10  15   2\n",
       "         DRY  2      16   2  19   1\n",
       "328      2R   2      11   6  12   2\n",
       "         4RY  2      12   3  11   6\n",
       "         DRY  2      12   5  14   4\n",
       "329      2R   2      10  10  19   1\n",
       "         4RY  2      14   6  18   2\n",
       "         DRY  2      15   4  19   1\n",
       "330      2R   2      16   4  17   3\n",
       "         4RY  2      12   6   8  11\n",
       "         DRY  2      15   5  16   4\n",
       "332      2R   2      17   2  20   0\n",
       "         4RY  2      14   3  17   3\n",
       "         DRY  2      17   2  20   0\n",
       "333      2R   2      14   6  19   0\n",
       "         4RY  2      11   9  18   1\n",
       "         DRY  2       8  11  14   6\n",
       "334      2R   2      13   5  14   5\n",
       "         4RY  2      14   5  15   2\n",
       "         DRY  2      13   6  18   1\n",
       "335      2R   2      18   2  20   0\n",
       "         4RY  2      15   3  14   5\n",
       "         DRY  2      14   5  17   2\n",
       "336      2R   2      12   4  17   0\n",
       "         4RY  2      10   6  16   3\n",
       "         DRY  2      10   6  17   1\n",
       "337      2R   2      12   8  18   2\n",
       "...                  ..  ..  ..  ..\n",
       "2014     DRY  2      17   3  20   0\n",
       "2015     2R   2      14   6  19   1\n",
       "         4RY  2      10   8  18   2\n",
       "         DRY  2      18   2  20   0\n",
       "2016     2R   2      16   4  20   0\n",
       "         4RY  2      17   3  15   4\n",
       "         DRY  2      17   3  20   0\n",
       "2019     2R   2      18   1  20   0\n",
       "         4RY  2      15   5  18   2\n",
       "         DRY  2      17   3  20   0\n",
       "2021     2R   2      20   0  19   1\n",
       "         4RY  2      19   1  16   3\n",
       "         DRY  2      18   2  20   0\n",
       "2022     2R   2      19   0  19   0\n",
       "         4RY  2      17   2  15   2\n",
       "         DRY  2      18   1  19   0\n",
       "2023     2R   2      15   1  15   2\n",
       "         4RY  2      17   0  10   3\n",
       "         DRY  2      14   1  14   2\n",
       "2024     2R   2      15   3  17   2\n",
       "         4RY  2      14   3  15   3\n",
       "         DRY  2      18   1  13   3\n",
       "2025     2R   2       5   0   5   0\n",
       "         4RY  2       4   0   5   0\n",
       "         DRY  2       6   0   4   0\n",
       "2026     2R   2       2   2   6   3\n",
       "         4RY  2       5   3   4   3\n",
       "         DRY  2       2   5   3   3\n",
       "2027     2R   2      16   1  18   2\n",
       "         4RY  2      10   5  17   1\n",
       "         DRY  2      14   5  20   0\n",
       "2028     2R   2      13   7  20   0\n",
       "         4RY  2      13   7  16   4\n",
       "         DRY  2      11   7  18   2\n",
       "2029     2R   2      17   2  20   0\n",
       "         4RY  2      14   4  15   4\n",
       "         DRY  2      15   3  16   3\n",
       "2030     2R   2      17   3  19   1\n",
       "         4RY  2      17   2  18   2\n",
       "         DRY  2      18   2  17   2\n",
       "2032     2R   2      20   0  20   0\n",
       "         4RY  2      17   3  18   1\n",
       "         DRY  2      18   1  19   1\n",
       "2037     2R   2       9   4   9   2\n",
       "         4RY  2      10   2   7   5\n",
       "         DRY  2      12   1  11   1\n",
       "2039     2R   2      20   0  18   2\n",
       "         4RY  2      15   4  14   4\n",
       "         DRY  2      19   1  19   1\n",
       "2041     2R   2      18   1  17   2\n",
       "         4RY  2      16   4  11   9\n",
       "         DRY  2      20   0  16   3\n",
       "2042     2R   2       9   0   9   1\n",
       "         4RY  2       9   1   7   0\n",
       "         DRY  2       8   3   9   0\n",
       "2045     2R   2      16   4  15   3\n",
       "         4RY  2      15   5  13   5\n",
       "         DRY  2      14   4  10   7\n",
       "2046     2R   2      15   5  16   2\n",
       "         4RY  2      14   6  12   8\n",
       "         DRY  2      14   6  15   5\n",
       "2047     2R   2      19   1  19   1\n",
       "         4RY  2      18   2  17   3\n",
       "         DRY  2      18   2  20   0\n",
       "2049     2R   2      18   2  20   0\n",
       "         4RY  2      16   4  19   1\n",
       "         DRY  2      20   0  17   3\n",
       "2050     2R   2       9   1  10   0\n",
       "         4RY  2       9   1   8   1\n",
       "         DRY  2      11   0   9   0\n",
       "2052     2R   2      15   5  19   1\n",
       "         4RY  2      16   3  15   5\n",
       "         DRY  2      18   2  19   1\n",
       "2053     2R   2      18   2  19   0\n",
       "         4RY  2      17   1  18   1\n",
       "         DRY  2      18   1  19   0\n",
       "2054     2R   2      17   1  20   0\n",
       "         4RY  2      15   5  16   3\n",
       "         DRY  2      20   0  19   0\n",
       "2055     2R   2      17   3  19   1\n",
       "         4RY  2      16   3  20   0\n",
       "         DRY  2      19   1  18   2\n",
       "2056     2R   2      18   2  20   0\n",
       "         4RY  2      13   7  20   0\n",
       "         DRY  2      13   4  17   2\n",
       "2057     2R   2      17   3  20   0\n",
       "         4RY  2      14   5  19   1\n",
       "         DRY  2      17   3  20   0\n",
       "2058     2R   2       9   1   9   0\n",
       "         4RY  2       7   2   7   1\n",
       "         DRY  2       8   1   8   0\n",
       "2061     2R   2      14   6  20   0\n",
       "         4RY  2      15   5  14   4\n",
       "         DRY  2      18   2  17   2\n",
       "2064     2R   2      17   1  13   2\n",
       "         4RY  2      12   7  12   1\n",
       "         DRY  2      19   0  15   0\n",
       "2065     2R   2      20   0  17   0\n",
       "         4RY  2      20   0  15   4\n",
       "         DRY  2      20   0  17   1\n",
       "\n",
       "[441 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets calculate dprime!\n",
    "\n",
    "dp = pd.pivot_table(WM, values=['H', 'M', 'FA', 'CR'], index=['subject1', 'LOAD', 'group'], aggfunc=np.sum)\n",
    "#dp.to_csv('/home/jenni/Documents/tmp/dp_%s.csv' % date)\n",
    "#dp.to_csv('/home/jenni/Dropbox/GATES_8thgrade_fMRI/All_development/wmfilt/dprime.csv')\n",
    "dp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# START HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dp=pd.read_csv('/home/jenni/Documents/tmp/dp_%s.csv' % date)\n",
    "#dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>HminusM</th>\n",
       "      <th>HR</th>\n",
       "      <th>FAR</th>\n",
       "      <th>CRR</th>\n",
       "      <th>MR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject1</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">302</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">303</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CR  FA   H  M  HminusM        HR       FAR       CRR  \\\n",
       "subject1 LOAD group                                                         \n",
       "302      2R   2      13   3  16  1       15  0.941176  0.187500  0.812500   \n",
       "         4RY  2      15   4   9  9        0  0.500000  0.210526  0.789474   \n",
       "         DRY  2      11   6  17  2       15  0.894737  0.352941  0.647059   \n",
       "303      2R   2       5  14  13  2       11  0.866667  0.736842  0.263158   \n",
       "         4RY  2       6  10  14  3       11  0.823529  0.625000  0.375000   \n",
       "\n",
       "                           MR  \n",
       "subject1 LOAD group            \n",
       "302      2R   2      0.058824  \n",
       "         4RY  2      0.500000  \n",
       "         DRY  2      0.105263  \n",
       "303      2R   2      0.133333  \n",
       "         4RY  2      0.176471  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp['HminusM'] = dp.apply(lambda row: (row['H']-row['M']), axis = 1)\n",
    "dp['HR'] = dp.apply(lambda row: (row['H']/ (row['H']+row['M'])), axis = 1)\n",
    "dp['FAR'] = dp.apply(lambda row: (row['FA']/ (row['FA']+row['CR'])),axis = 1) \n",
    "dp['CRR'] = dp.apply(lambda row: (row['CR']/ (row['CR']+row['FA'])),axis = 1)\n",
    "dp['MR'] = dp.apply(lambda row:  (row['M']/ (row['M']+row['H'])),axis = 1)\n",
    "dp.head()  #sanity check (just to make sure everything is being calculated properly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fourth approach involves adjusting only the extreme\n",
    "rates themselves. Rates of 0 are replaced with 0.5 / n, and\n",
    "rates of 1 are replaced with (n - 0.5)  n, where n is the\n",
    "number of signal or noise trials (Macmillan & Kaplan,\n",
    "1985). This approach yields biased measures of sensitivity\n",
    "(Miller, 1996) and may be less satisfactory than the\n",
    "loglinear approach (Hautus, 1995). However, it is the most\n",
    "common remedy for extreme values and is utilized in several\n",
    "computer programs that calculate SDT measures (see,\n",
    "e.g., Dorfman, 1982). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>HminusM</th>\n",
       "      <th>HR</th>\n",
       "      <th>FAR</th>\n",
       "      <th>CRR</th>\n",
       "      <th>MR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject1</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">302</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">303</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CR  FA   H  M  HminusM        HR       FAR       CRR  \\\n",
       "subject1 LOAD group                                                         \n",
       "302      2R   2      13   3  16  1       15  0.941176  0.187500  0.812500   \n",
       "         4RY  2      15   4   9  9        0  0.500000  0.210526  0.789474   \n",
       "         DRY  2      11   6  17  2       15  0.894737  0.352941  0.647059   \n",
       "303      2R   2       5  14  13  2       11  0.866667  0.736842  0.263158   \n",
       "         4RY  2       6  10  14  3       11  0.823529  0.625000  0.375000   \n",
       "\n",
       "                           MR  \n",
       "subject1 LOAD group            \n",
       "302      2R   2      0.058824  \n",
       "         4RY  2      0.500000  \n",
       "         DRY  2      0.105263  \n",
       "303      2R   2      0.133333  \n",
       "         4RY  2      0.176471  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Rates of 0 are replaced with 0.5 / n = .025, and rates of 1 are replaced with() (n - 0.5)/n) =.975, \n",
    "#where n is the number of signal or noise trials (20 for hits, and 20 for fa)\n",
    "\n",
    "dp['HR'].replace(1.000000,0.975, inplace=True)\n",
    "dp['HR'].replace(0.000000,0.025, inplace=True)\n",
    "dp['FAR'].replace(1.000000,0.975, inplace=True)\n",
    "dp['FAR'].replace(0.000000,0.025, inplace=True)\n",
    "dp['CRR'].replace(1.000000,0.975, inplace=True)\n",
    "dp['CRR'].replace(0.000000,0.025, inplace=True)\n",
    "dp['MR'].replace(1.000000,0.975, inplace=True)\n",
    "dp['MR'].replace(0.000000,0.025, inplace=True)\n",
    "dp.head()  #sanity check (just to make sure everything is being calculated properly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "dp['HR'].replace(1.000000,0.999999, inplace=True)\n",
    "dp['HR'].replace(0.000000,0.000001, inplace=True)\n",
    "dp['FAR'].replace(1.000000,0.999999, inplace=True)\n",
    "dp['FAR'].replace(0.000000,0.000001, inplace=True)\n",
    "dp['CRR'].replace(1.000000,0.999999, inplace=True)\n",
    "dp['CRR'].replace(0.000000,0.000001, inplace=True)\n",
    "dp['MR'].replace(1.000000,0.999999, inplace=True)\n",
    "dp['MR'].replace(0.000000,0.000001, inplace=True)\n",
    "dp.head()  #sanity check (just to make sure everything is being calculated properly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>HminusM</th>\n",
       "      <th>HR</th>\n",
       "      <th>FAR</th>\n",
       "      <th>CRR</th>\n",
       "      <th>MR</th>\n",
       "      <th>zHR</th>\n",
       "      <th>zFAR</th>\n",
       "      <th>zCRR</th>\n",
       "      <th>zMR</th>\n",
       "      <th>dPrime</th>\n",
       "      <th>h-fa</th>\n",
       "      <th>acc</th>\n",
       "      <th>rt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject1</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">302</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>1.564726</td>\n",
       "      <td>-0.887147</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>-1.564726</td>\n",
       "      <td>2.451873</td>\n",
       "      <td>0.753676</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.129071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.303166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.377392</td>\n",
       "      <td>0.377392</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.629511</td>\n",
       "      <td>0.541796</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.198379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">303</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1.110772</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>-1.110772</td>\n",
       "      <td>0.477132</td>\n",
       "      <td>0.129825</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1.237365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>-0.928899</td>\n",
       "      <td>0.610260</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.147570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>1.150349</td>\n",
       "      <td>-1.150349</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>0.442869</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>1.182943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">304</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.141441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.336038</td>\n",
       "      <td>0.336038</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>1.177659</td>\n",
       "      <td>0.431579</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.090706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.336038</td>\n",
       "      <td>-0.887147</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>-0.336038</td>\n",
       "      <td>1.223185</td>\n",
       "      <td>0.444079</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.125127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">305</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.634454</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.894252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.801585</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.935852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>2.424453</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.866452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">306</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>2.039581</td>\n",
       "      <td>0.692105</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.001260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>-0.125661</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.548828</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.214478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-0.479506</td>\n",
       "      <td>0.479506</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.515939</td>\n",
       "      <td>0.534211</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.112539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">307</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>-5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>-0.180012</td>\n",
       "      <td>0.180012</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>-0.250715</td>\n",
       "      <td>-0.095238</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.996453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>-6</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>0.243762</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1.013209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>-8</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>-0.791639</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>0.791639</td>\n",
       "      <td>-0.472999</td>\n",
       "      <td>-0.160714</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.901769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">308</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>1.426077</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-1.426077</td>\n",
       "      <td>2.267698</td>\n",
       "      <td>0.723077</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.011286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>1.426077</td>\n",
       "      <td>-0.721522</td>\n",
       "      <td>0.721522</td>\n",
       "      <td>-1.426077</td>\n",
       "      <td>2.147599</td>\n",
       "      <td>0.687783</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.153857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.791639</td>\n",
       "      <td>-1.067571</td>\n",
       "      <td>1.067571</td>\n",
       "      <td>-0.791639</td>\n",
       "      <td>1.859209</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>1.109112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">309</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-0.541395</td>\n",
       "      <td>0.541395</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>1.508817</td>\n",
       "      <td>0.539216</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>1.162743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>-0.073791</td>\n",
       "      <td>-1.220640</td>\n",
       "      <td>1.220640</td>\n",
       "      <td>0.073791</td>\n",
       "      <td>1.146849</td>\n",
       "      <td>0.359477</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.160756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.589456</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.589456</td>\n",
       "      <td>1.841575</td>\n",
       "      <td>0.616959</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.186306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">310</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.479506</td>\n",
       "      <td>-0.066012</td>\n",
       "      <td>0.066012</td>\n",
       "      <td>-0.479506</td>\n",
       "      <td>0.545517</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.077528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.139710</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.139710</td>\n",
       "      <td>0.981332</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.204119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>3.579820</td>\n",
       "      <td>0.922368</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.083786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">311</th>\n",
       "      <th>2R</th>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>2.963112</td>\n",
       "      <td>0.817105</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.288347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.083652</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.083652</td>\n",
       "      <td>2.043616</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.306288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>1.772018</td>\n",
       "      <td>0.622807</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.208412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">312</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>-7</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>-0.479506</td>\n",
       "      <td>-0.199201</td>\n",
       "      <td>0.199201</td>\n",
       "      <td>0.479506</td>\n",
       "      <td>-0.280304</td>\n",
       "      <td>-0.105263</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.879743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>-3</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>-0.199201</td>\n",
       "      <td>-0.764710</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>0.199201</td>\n",
       "      <td>0.565508</td>\n",
       "      <td>0.198830</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.753255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>-6</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>-0.385320</td>\n",
       "      <td>-0.125661</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>0.385320</td>\n",
       "      <td>-0.259659</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.756469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">313</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>-8</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>-0.589456</td>\n",
       "      <td>-0.488776</td>\n",
       "      <td>0.488776</td>\n",
       "      <td>0.589456</td>\n",
       "      <td>-0.100679</td>\n",
       "      <td>-0.034722</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.116486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>-6</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>-0.736316</td>\n",
       "      <td>0.736316</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>0.305589</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.115577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>-11</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>-0.928899</td>\n",
       "      <td>-0.736316</td>\n",
       "      <td>0.736316</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>-0.192584</td>\n",
       "      <td>-0.054299</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>1.180637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">314</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.479506</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-0.479506</td>\n",
       "      <td>1.153995</td>\n",
       "      <td>0.434211</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.906225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>-0.125661</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>0.715960</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.935458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>-7</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>-0.479506</td>\n",
       "      <td>-0.385320</td>\n",
       "      <td>0.385320</td>\n",
       "      <td>0.479506</td>\n",
       "      <td>-0.094185</td>\n",
       "      <td>-0.034211</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.992983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">315</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.282216</td>\n",
       "      <td>0.282216</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>1.123837</td>\n",
       "      <td>0.411111</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>1.100594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.282216</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.282216</td>\n",
       "      <td>1.123837</td>\n",
       "      <td>0.411111</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>1.110974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>-0.125661</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>0.715960</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.093819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">316</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-0.721522</td>\n",
       "      <td>0.721522</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>2.003074</td>\n",
       "      <td>0.664706</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.962597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-0.928899</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>2.210451</td>\n",
       "      <td>0.723529</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.958164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-0.589456</td>\n",
       "      <td>0.589456</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.234309</td>\n",
       "      <td>0.672222</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.815449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">317</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.096559</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.096559</td>\n",
       "      <td>0.096559</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.962830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.139710</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.139710</td>\n",
       "      <td>2.099674</td>\n",
       "      <td>0.530556</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.089879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.083652</td>\n",
       "      <td>0.180012</td>\n",
       "      <td>-0.180012</td>\n",
       "      <td>-0.083652</td>\n",
       "      <td>-0.096361</td>\n",
       "      <td>-0.038095</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>1.093388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">318</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>2.871976</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.330328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.385320</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.385320</td>\n",
       "      <td>1.637440</td>\n",
       "      <td>0.544737</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.260772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>2.039581</td>\n",
       "      <td>0.692105</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.137250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">319</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-1.534121</td>\n",
       "      <td>1.534121</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>2.501542</td>\n",
       "      <td>0.770833</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.298713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>2.801585</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.437365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>-0.139710</td>\n",
       "      <td>0.139710</td>\n",
       "      <td>-0.887147</td>\n",
       "      <td>1.026857</td>\n",
       "      <td>0.368056</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.378290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">320</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>1.058328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.218222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.074796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">321</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>1.534121</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>-1.534121</td>\n",
       "      <td>1.215481</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.964384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>1.534121</td>\n",
       "      <td>0.114185</td>\n",
       "      <td>-0.114185</td>\n",
       "      <td>-1.534121</td>\n",
       "      <td>1.419935</td>\n",
       "      <td>0.392045</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.957942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.150349</td>\n",
       "      <td>0.736316</td>\n",
       "      <td>-0.736316</td>\n",
       "      <td>-1.150349</td>\n",
       "      <td>0.414033</td>\n",
       "      <td>0.105769</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>1.097171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">322</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>1.198890</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.368645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>-0.180012</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>0.180012</td>\n",
       "      <td>0.661609</td>\n",
       "      <td>0.228571</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.523230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>1.244956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">323</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>-0.073791</td>\n",
       "      <td>-0.157311</td>\n",
       "      <td>0.157311</td>\n",
       "      <td>0.073791</td>\n",
       "      <td>0.083519</td>\n",
       "      <td>0.033088</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>1.128628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.205761</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>1.262163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-4</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>1.202795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">325</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>1.564726</td>\n",
       "      <td>-0.721522</td>\n",
       "      <td>0.721522</td>\n",
       "      <td>-1.564726</td>\n",
       "      <td>2.286249</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.095705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.622926</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.622926</td>\n",
       "      <td>1.464547</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.217599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.377392</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-0.377392</td>\n",
       "      <td>0.696031</td>\n",
       "      <td>0.272059</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>1.091845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">326</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>-0.157311</td>\n",
       "      <td>-0.282216</td>\n",
       "      <td>0.282216</td>\n",
       "      <td>0.157311</td>\n",
       "      <td>0.124905</td>\n",
       "      <td>0.048611</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1.518895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.073791</td>\n",
       "      <td>-0.073791</td>\n",
       "      <td>0.073791</td>\n",
       "      <td>-0.073791</td>\n",
       "      <td>0.147583</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1.612189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.073791</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>-0.073791</td>\n",
       "      <td>0.504519</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>1.469163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">327</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-0.253347</td>\n",
       "      <td>0.253347</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.873203</td>\n",
       "      <td>0.547368</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.044168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>1.186831</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.186831</td>\n",
       "      <td>1.186831</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.226857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.220640</td>\n",
       "      <td>1.220640</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.865494</td>\n",
       "      <td>0.838889</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.954255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">328</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.067571</td>\n",
       "      <td>-0.377392</td>\n",
       "      <td>0.377392</td>\n",
       "      <td>-1.067571</td>\n",
       "      <td>1.444962</td>\n",
       "      <td>0.504202</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.174238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.377392</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.377392</td>\n",
       "      <td>1.219013</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.181539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>-0.541395</td>\n",
       "      <td>0.541395</td>\n",
       "      <td>-0.764710</td>\n",
       "      <td>1.306105</td>\n",
       "      <td>0.483660</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.085849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">329</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.957105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.805952</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.955316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.449450</td>\n",
       "      <td>0.739474</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.951408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">330</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.878055</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.074378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>-3</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>-0.199201</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>0.199201</td>\n",
       "      <td>0.231526</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.138395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>1.516111</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.066673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">332</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.212084</td>\n",
       "      <td>0.869737</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.034103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-0.928899</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.965333</td>\n",
       "      <td>0.673529</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.234199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.212084</td>\n",
       "      <td>0.869737</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.923791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">333</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.484364</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.066969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-0.125661</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.745518</td>\n",
       "      <td>0.497368</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.131421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>0.199201</td>\n",
       "      <td>-0.199201</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.325199</td>\n",
       "      <td>0.121053</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>1.144766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">334</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-0.589456</td>\n",
       "      <td>0.589456</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>1.223096</td>\n",
       "      <td>0.459064</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>1.057895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>1.186831</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-1.186831</td>\n",
       "      <td>1.820471</td>\n",
       "      <td>0.619195</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.039029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-0.479506</td>\n",
       "      <td>0.479506</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>2.099362</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.942358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">335</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.120333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>1.601062</td>\n",
       "      <td>0.570175</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.164997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.885760</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.113435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">336</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.634454</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.076398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>1.321787</td>\n",
       "      <td>0.467105</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.137632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>1.911858</td>\n",
       "      <td>0.569444</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>1.131663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-0.253347</td>\n",
       "      <td>0.253347</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.534899</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.205502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.996397</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.162556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.169254</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.075007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-0.139710</td>\n",
       "      <td>0.139710</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.421262</td>\n",
       "      <td>0.455556</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.100531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.067367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2016</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.801585</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.031201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>1.841030</td>\n",
       "      <td>0.639474</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.094017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.996397</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.969094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2019</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.579820</td>\n",
       "      <td>0.922368</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.033023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.956041</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.198491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.996397</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.966688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2021</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>3.604818</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>1.105006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>2.648002</td>\n",
       "      <td>0.792105</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.154674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.072580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2022</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.067435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>1.186831</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.186831</td>\n",
       "      <td>2.438951</td>\n",
       "      <td>0.777090</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.267468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.579820</td>\n",
       "      <td>0.922368</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.112209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2023</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>1.186831</td>\n",
       "      <td>-1.534121</td>\n",
       "      <td>1.534121</td>\n",
       "      <td>-1.186831</td>\n",
       "      <td>2.720952</td>\n",
       "      <td>0.819853</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.095897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.736316</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.736316</td>\n",
       "      <td>2.696280</td>\n",
       "      <td>0.744231</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.292786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.150349</td>\n",
       "      <td>-1.501086</td>\n",
       "      <td>1.501086</td>\n",
       "      <td>-1.150349</td>\n",
       "      <td>2.651435</td>\n",
       "      <td>0.808333</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>1.196138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2024</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>2.219541</td>\n",
       "      <td>0.728070</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.293746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-0.928899</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>1.896321</td>\n",
       "      <td>0.656863</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.337464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-0.887147</td>\n",
       "      <td>2.507003</td>\n",
       "      <td>0.759868</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.215300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2025</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.002935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.255024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.130465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2026</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.083065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.180012</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-0.180012</td>\n",
       "      <td>0.498652</td>\n",
       "      <td>0.196429</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1.395655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.565949</td>\n",
       "      <td>-0.565949</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.565949</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.343549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2027</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.564726</td>\n",
       "      <td>1.564726</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>2.846278</td>\n",
       "      <td>0.841176</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.209337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>-0.430727</td>\n",
       "      <td>0.430727</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>2.023946</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>1.204662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.593604</td>\n",
       "      <td>0.711842</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.122745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2028</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.385320</td>\n",
       "      <td>0.385320</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.345284</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.980580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.385320</td>\n",
       "      <td>0.385320</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>1.226942</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.062114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-0.282216</td>\n",
       "      <td>0.282216</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.563768</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.015694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2029</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.212084</td>\n",
       "      <td>0.869737</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.195332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-0.764710</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>1.569306</td>\n",
       "      <td>0.567251</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.246651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>1.970570</td>\n",
       "      <td>0.675439</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.144939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2030</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.681287</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.952320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>2.533671</td>\n",
       "      <td>0.794737</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.012347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>2.533671</td>\n",
       "      <td>0.794737</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.922873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2032</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>2.656290</td>\n",
       "      <td>0.797368</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.104411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>3.264710</td>\n",
       "      <td>0.897368</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.932353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2037</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.908458</td>\n",
       "      <td>-0.502402</td>\n",
       "      <td>0.502402</td>\n",
       "      <td>-0.908458</td>\n",
       "      <td>1.410860</td>\n",
       "      <td>0.510490</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1.102056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.210428</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-0.210428</td>\n",
       "      <td>1.177850</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>1.264208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.382994</td>\n",
       "      <td>-1.426077</td>\n",
       "      <td>1.426077</td>\n",
       "      <td>-1.382994</td>\n",
       "      <td>2.809071</td>\n",
       "      <td>0.839744</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.069018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2039</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.074784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-0.764710</td>\n",
       "      <td>1.569306</td>\n",
       "      <td>0.567251</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.289957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>3.289707</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.102229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2041</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>2.871976</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.015721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.125661</td>\n",
       "      <td>0.967283</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>1.119880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>2.963112</td>\n",
       "      <td>0.817105</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.092176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2042</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.089445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.329111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.604585</td>\n",
       "      <td>0.604585</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.564549</td>\n",
       "      <td>0.702273</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.332543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2045</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-0.967422</td>\n",
       "      <td>1.809043</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.138197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.589456</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-0.589456</td>\n",
       "      <td>1.263946</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.281201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.223008</td>\n",
       "      <td>-0.764710</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>-0.223008</td>\n",
       "      <td>0.987718</td>\n",
       "      <td>0.366013</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.214302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2046</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.220640</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-1.220640</td>\n",
       "      <td>1.895130</td>\n",
       "      <td>0.638889</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.119196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.253347</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-0.253347</td>\n",
       "      <td>0.777748</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.070505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>1.198890</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.154894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2047</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>3.289707</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.898389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>2.317985</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.071519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.928928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2049</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.848893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.486475</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.971369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>2.996397</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.861433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2050</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.018243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.220640</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.220640</td>\n",
       "      <td>2.502192</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.226243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.007829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2052</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.319343</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.007749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>1.677638</td>\n",
       "      <td>0.592105</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.025571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.926405</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.998048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2053</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.826927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>3.213075</td>\n",
       "      <td>0.891813</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.971905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.579820</td>\n",
       "      <td>0.922368</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.871323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2054</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.553183</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.122837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>1.677638</td>\n",
       "      <td>0.592105</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>1.273445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>1.135556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2055</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.681287</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.878404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.963112</td>\n",
       "      <td>0.817105</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.141168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>2.926405</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.899031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2056</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.062940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.385320</td>\n",
       "      <td>0.385320</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.345284</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.093809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.721522</td>\n",
       "      <td>0.721522</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.973642</td>\n",
       "      <td>0.659443</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.978580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2057</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.996397</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.042839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.644854</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-1.644854</td>\n",
       "      <td>2.278494</td>\n",
       "      <td>0.686842</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>1.044143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.996397</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.966188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2058</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.241516</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.113349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.150349</td>\n",
       "      <td>-0.764710</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>-1.150349</td>\n",
       "      <td>1.915059</td>\n",
       "      <td>0.652778</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.320602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.220640</td>\n",
       "      <td>1.220640</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.180604</td>\n",
       "      <td>0.863889</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.115278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2061</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.524401</td>\n",
       "      <td>0.524401</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>2.484364</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.930488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>-0.764710</td>\n",
       "      <td>1.439199</td>\n",
       "      <td>0.527778</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>1.126001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-1.281552</td>\n",
       "      <td>1.281552</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>2.533671</td>\n",
       "      <td>0.794737</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.965636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2064</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1.110772</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>-1.110772</td>\n",
       "      <td>2.703990</td>\n",
       "      <td>0.811111</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.039243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>1.426077</td>\n",
       "      <td>-0.336038</td>\n",
       "      <td>0.336038</td>\n",
       "      <td>-1.426077</td>\n",
       "      <td>1.762115</td>\n",
       "      <td>0.554656</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.308731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.021772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2065</th>\n",
       "      <th>2R</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>3.919928</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.919526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4RY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>2.764560</td>\n",
       "      <td>0.764474</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.072043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRY</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>3.553183</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.939810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>441 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CR  FA   H   M  HminusM        HR       FAR       CRR  \\\n",
       "subject1 LOAD group                                                          \n",
       "302      2R   2      13   3  16   1       15  0.941176  0.187500  0.812500   \n",
       "         4RY  2      15   4   9   9        0  0.500000  0.210526  0.789474   \n",
       "         DRY  2      11   6  17   2       15  0.894737  0.352941  0.647059   \n",
       "303      2R   2       5  14  13   2       11  0.866667  0.736842  0.263158   \n",
       "         4RY  2       6  10  14   3       11  0.823529  0.625000  0.375000   \n",
       "         DRY  2       2  14  17   1       16  0.944444  0.875000  0.125000   \n",
       "304      2R   2      10  10  18   0       18  0.975000  0.500000  0.500000   \n",
       "         4RY  2      12   7  16   4       12  0.800000  0.368421  0.631579   \n",
       "         DRY  2      13   3  12   7        5  0.631579  0.187500  0.812500   \n",
       "305      2R   2      15   5  19   0       19  0.975000  0.250000  0.750000   \n",
       "         4RY  2      16   4  20   0       20  0.975000  0.200000  0.800000   \n",
       "         DRY  2      15   4  18   1       17  0.947368  0.210526  0.789474   \n",
       "306      2R   2      17   3  16   3       13  0.842105  0.150000  0.850000   \n",
       "         4RY  2       9  11  15   5       10  0.750000  0.550000  0.450000   \n",
       "         DRY  2      13   6  17   3       14  0.850000  0.315789  0.684211   \n",
       "307      2R   2       8   6   5  10       -5  0.333333  0.428571  0.571429   \n",
       "         4RY  2      12   4   6  12       -6  0.333333  0.250000  0.750000   \n",
       "         DRY  2      10   6   3  11       -8  0.214286  0.375000  0.625000   \n",
       "308      2R   2      12   3  12   1       11  0.923077  0.200000  0.800000   \n",
       "         4RY  2      13   4  12   1       11  0.923077  0.235294  0.764706   \n",
       "         DRY  2      12   2  11   3        8  0.785714  0.142857  0.857143   \n",
       "309      2R   2      12   5  15   3       12  0.833333  0.294118  0.705882   \n",
       "         4RY  2      16   2   8   9       -1  0.470588  0.111111  0.888889   \n",
       "         DRY  2      17   2  13   5        8  0.722222  0.105263  0.894737   \n",
       "310      2R   2      10   9  13   6        7  0.684211  0.473684  0.526316   \n",
       "         4RY  2      16   4  10   8        2  0.555556  0.200000  0.800000   \n",
       "         DRY  2      19   0  18   1       17  0.947368  0.025000  0.975000   \n",
       "311      2R   0      19   0  16   3       13  0.842105  0.025000  0.975000   \n",
       "         4RY  0      17   0   8   7        1  0.533333  0.025000  0.975000   \n",
       "         DRY  0      15   4  15   3       12  0.833333  0.210526  0.789474   \n",
       "312      2R   2      11   8   6  13       -7  0.315789  0.421053  0.578947   \n",
       "         4RY  2      14   4   8  11       -3  0.421053  0.222222  0.777778   \n",
       "         DRY  2      11   9   7  13       -6  0.350000  0.450000  0.550000   \n",
       "313      2R   2      11   5   5  13       -8  0.277778  0.312500  0.687500   \n",
       "         4RY  2      10   3   6  12       -6  0.333333  0.230769  0.769231   \n",
       "         DRY  2      10   3   3  14      -11  0.176471  0.230769  0.769231   \n",
       "314      2R   2      15   5  13   6        7  0.684211  0.250000  0.750000   \n",
       "         4RY  2      16   4   9  11       -2  0.450000  0.200000  0.800000   \n",
       "         DRY  2      13   7   6  13       -7  0.315789  0.350000  0.650000   \n",
       "315      2R   2      11   7  16   4       12  0.800000  0.388889  0.611111   \n",
       "         4RY  2      16   4  11   7        4  0.611111  0.200000  0.800000   \n",
       "         DRY  2      16   4   9  11       -2  0.450000  0.200000  0.800000   \n",
       "316      2R   2      13   4  18   2       16  0.900000  0.235294  0.764706   \n",
       "         4RY  2      14   3  18   2       16  0.900000  0.176471  0.823529   \n",
       "         DRY  2      13   5  19   1       18  0.950000  0.277778  0.722222   \n",
       "317      2R   2       6   6   7   6        1  0.538462  0.500000  0.500000   \n",
       "         4RY  2       9   0   5   4        1  0.555556  0.025000  0.975000   \n",
       "         DRY  2       3   4   8   7        1  0.533333  0.571429  0.428571   \n",
       "318      2R   2      18   1  17   2       15  0.894737  0.052632  0.947368   \n",
       "         4RY  2      17   2  13   7        6  0.650000  0.105263  0.894737   \n",
       "         DRY  2      17   3  16   3       13  0.842105  0.150000  0.850000   \n",
       "319      2R   2      15   1  15   3       12  0.833333  0.062500  0.937500   \n",
       "         4RY  2      15   0   8   2        6  0.800000  0.025000  0.975000   \n",
       "         DRY  2      10   8  13   3       10  0.812500  0.444444  0.555556   \n",
       "320      2R   2       0   7  17   0       17  0.975000  0.975000  0.025000   \n",
       "         4RY  2       0   8  15   0       15  0.975000  0.975000  0.025000   \n",
       "         DRY  2       0   7  16   0       16  0.975000  0.975000  0.025000   \n",
       "321      2R   2       6  10  15   1       14  0.937500  0.625000  0.375000   \n",
       "         4RY  2       5   6  15   1       14  0.937500  0.545455  0.454545   \n",
       "         DRY  2       3  10  14   2       12  0.875000  0.769231  0.230769   \n",
       "322      2R   2       7   3   6   2        4  0.750000  0.300000  0.700000   \n",
       "         4RY  2       8   2   3   4       -1  0.428571  0.200000  0.800000   \n",
       "         DRY  2       6   3   5   5        0  0.500000  0.333333  0.666667   \n",
       "323      2R   2       9   7   8   9       -1  0.470588  0.437500  0.562500   \n",
       "         4RY  2       6  10   7   3        4  0.700000  0.625000  0.375000   \n",
       "         DRY  2       7   7   3   7       -4  0.300000  0.500000  0.500000   \n",
       "325      2R   2      13   4  16   1       15  0.941176  0.235294  0.764706   \n",
       "         4RY  2      12   3  11   4        7  0.733333  0.200000  0.800000   \n",
       "         DRY  2      10   6  11   6        5  0.647059  0.375000  0.625000   \n",
       "326      2R   2      11   7   7   9       -2  0.437500  0.388889  0.611111   \n",
       "         4RY  2       9   8   9   8        1  0.529412  0.470588  0.529412   \n",
       "         DRY  2      12   6   9   8        1  0.529412  0.333333  0.666667   \n",
       "327      2R   2      12   8  18   1       17  0.947368  0.400000  0.600000   \n",
       "         4RY  2      10  10  15   2       13  0.882353  0.500000  0.500000   \n",
       "         DRY  2      16   2  19   1       18  0.950000  0.111111  0.888889   \n",
       "328      2R   2      11   6  12   2       10  0.857143  0.352941  0.647059   \n",
       "         4RY  2      12   3  11   6        5  0.647059  0.200000  0.800000   \n",
       "         DRY  2      12   5  14   4       10  0.777778  0.294118  0.705882   \n",
       "329      2R   2      10  10  19   1       18  0.950000  0.500000  0.500000   \n",
       "         4RY  2      14   6  18   2       16  0.900000  0.300000  0.700000   \n",
       "         DRY  2      15   4  19   1       18  0.950000  0.210526  0.789474   \n",
       "330      2R   2      16   4  17   3       14  0.850000  0.200000  0.800000   \n",
       "         4RY  2      12   6   8  11       -3  0.421053  0.333333  0.666667   \n",
       "         DRY  2      15   5  16   4       12  0.800000  0.250000  0.750000   \n",
       "332      2R   2      17   2  20   0       20  0.975000  0.105263  0.894737   \n",
       "         4RY  2      14   3  17   3       14  0.850000  0.176471  0.823529   \n",
       "         DRY  2      17   2  20   0       20  0.975000  0.105263  0.894737   \n",
       "333      2R   2      14   6  19   0       19  0.975000  0.300000  0.700000   \n",
       "         4RY  2      11   9  18   1       17  0.947368  0.450000  0.550000   \n",
       "         DRY  2       8  11  14   6        8  0.700000  0.578947  0.421053   \n",
       "334      2R   2      13   5  14   5        9  0.736842  0.277778  0.722222   \n",
       "         4RY  2      14   5  15   2       13  0.882353  0.263158  0.736842   \n",
       "         DRY  2      13   6  18   1       17  0.947368  0.315789  0.684211   \n",
       "335      2R   2      18   2  20   0       20  0.975000  0.100000  0.900000   \n",
       "         4RY  2      15   3  14   5        9  0.736842  0.166667  0.833333   \n",
       "         DRY  2      14   5  17   2       15  0.894737  0.263158  0.736842   \n",
       "336      2R   2      12   4  17   0       17  0.975000  0.250000  0.750000   \n",
       "         4RY  2      10   6  16   3       13  0.842105  0.375000  0.625000   \n",
       "         DRY  2      10   6  17   1       16  0.944444  0.375000  0.625000   \n",
       "337      2R   2      12   8  18   2       16  0.900000  0.400000  0.600000   \n",
       "...                  ..  ..  ..  ..      ...       ...       ...       ...   \n",
       "2014     DRY  2      17   3  20   0       20  0.975000  0.150000  0.850000   \n",
       "2015     2R   2      14   6  19   1       18  0.950000  0.300000  0.700000   \n",
       "         4RY  2      10   8  18   2       16  0.900000  0.444444  0.555556   \n",
       "         DRY  2      18   2  20   0       20  0.975000  0.100000  0.900000   \n",
       "2016     2R   2      16   4  20   0       20  0.975000  0.200000  0.800000   \n",
       "         4RY  2      17   3  15   4       11  0.789474  0.150000  0.850000   \n",
       "         DRY  2      17   3  20   0       20  0.975000  0.150000  0.850000   \n",
       "2019     2R   2      18   1  20   0       20  0.975000  0.052632  0.947368   \n",
       "         4RY  2      15   5  18   2       16  0.900000  0.250000  0.750000   \n",
       "         DRY  2      17   3  20   0       20  0.975000  0.150000  0.850000   \n",
       "2021     2R   2      20   0  19   1       18  0.950000  0.025000  0.975000   \n",
       "         4RY  2      19   1  16   3       13  0.842105  0.050000  0.950000   \n",
       "         DRY  2      18   2  20   0       20  0.975000  0.100000  0.900000   \n",
       "2022     2R   2      19   0  19   0       19  0.975000  0.025000  0.975000   \n",
       "         4RY  2      17   2  15   2       13  0.882353  0.105263  0.894737   \n",
       "         DRY  2      18   1  19   0       19  0.975000  0.052632  0.947368   \n",
       "2023     2R   2      15   1  15   2       13  0.882353  0.062500  0.937500   \n",
       "         4RY  2      17   0  10   3        7  0.769231  0.025000  0.975000   \n",
       "         DRY  2      14   1  14   2       12  0.875000  0.066667  0.933333   \n",
       "2024     2R   2      15   3  17   2       15  0.894737  0.166667  0.833333   \n",
       "         4RY  2      14   3  15   3       12  0.833333  0.176471  0.823529   \n",
       "         DRY  2      18   1  13   3       10  0.812500  0.052632  0.947368   \n",
       "2025     2R   2       5   0   5   0        5  0.975000  0.025000  0.975000   \n",
       "         4RY  2       4   0   5   0        5  0.975000  0.025000  0.975000   \n",
       "         DRY  2       6   0   4   0        4  0.975000  0.025000  0.975000   \n",
       "2026     2R   2       2   2   6   3        3  0.666667  0.500000  0.500000   \n",
       "         4RY  2       5   3   4   3        1  0.571429  0.375000  0.625000   \n",
       "         DRY  2       2   5   3   3        0  0.500000  0.714286  0.285714   \n",
       "2027     2R   2      16   1  18   2       16  0.900000  0.058824  0.941176   \n",
       "         4RY  2      10   5  17   1       16  0.944444  0.333333  0.666667   \n",
       "         DRY  2      14   5  20   0       20  0.975000  0.263158  0.736842   \n",
       "2028     2R   2      13   7  20   0       20  0.975000  0.350000  0.650000   \n",
       "         4RY  2      13   7  16   4       12  0.800000  0.350000  0.650000   \n",
       "         DRY  2      11   7  18   2       16  0.900000  0.388889  0.611111   \n",
       "2029     2R   2      17   2  20   0       20  0.975000  0.105263  0.894737   \n",
       "         4RY  2      14   4  15   4       11  0.789474  0.222222  0.777778   \n",
       "         DRY  2      15   3  16   3       13  0.842105  0.166667  0.833333   \n",
       "2030     2R   2      17   3  19   1       18  0.950000  0.150000  0.850000   \n",
       "         4RY  2      17   2  18   2       16  0.900000  0.105263  0.894737   \n",
       "         DRY  2      18   2  17   2       15  0.894737  0.100000  0.900000   \n",
       "2032     2R   2      20   0  20   0       20  0.975000  0.025000  0.975000   \n",
       "         4RY  2      17   3  18   1       17  0.947368  0.150000  0.850000   \n",
       "         DRY  2      18   1  19   1       18  0.950000  0.052632  0.947368   \n",
       "2037     2R   2       9   4   9   2        7  0.818182  0.307692  0.692308   \n",
       "         4RY  2      10   2   7   5        2  0.583333  0.166667  0.833333   \n",
       "         DRY  2      12   1  11   1       10  0.916667  0.076923  0.923077   \n",
       "2039     2R   2      20   0  18   2       16  0.900000  0.025000  0.975000   \n",
       "         4RY  2      15   4  14   4       10  0.777778  0.210526  0.789474   \n",
       "         DRY  2      19   1  19   1       18  0.950000  0.050000  0.950000   \n",
       "2041     2R   2      18   1  17   2       15  0.894737  0.052632  0.947368   \n",
       "         4RY  2      16   4  11   9        2  0.550000  0.200000  0.800000   \n",
       "         DRY  2      20   0  16   3       13  0.842105  0.025000  0.975000   \n",
       "2042     2R   2       9   0   9   1        8  0.900000  0.025000  0.975000   \n",
       "         4RY  2       9   1   7   0        7  0.975000  0.100000  0.900000   \n",
       "         DRY  2       8   3   9   0        9  0.975000  0.272727  0.727273   \n",
       "2045     2R   2      16   4  15   3       12  0.833333  0.200000  0.800000   \n",
       "         4RY  2      15   5  13   5        8  0.722222  0.250000  0.750000   \n",
       "         DRY  2      14   4  10   7        3  0.588235  0.222222  0.777778   \n",
       "2046     2R   2      15   5  16   2       14  0.888889  0.250000  0.750000   \n",
       "         4RY  2      14   6  12   8        4  0.600000  0.300000  0.700000   \n",
       "         DRY  2      14   6  15   5       10  0.750000  0.300000  0.700000   \n",
       "2047     2R   2      19   1  19   1       18  0.950000  0.050000  0.950000   \n",
       "         4RY  2      18   2  17   3       14  0.850000  0.100000  0.900000   \n",
       "         DRY  2      18   2  20   0       20  0.975000  0.100000  0.900000   \n",
       "2049     2R   2      18   2  20   0       20  0.975000  0.100000  0.900000   \n",
       "         4RY  2      16   4  19   1       18  0.950000  0.200000  0.800000   \n",
       "         DRY  2      20   0  17   3       14  0.850000  0.025000  0.975000   \n",
       "2050     2R   2       9   1  10   0       10  0.975000  0.100000  0.900000   \n",
       "         4RY  2       9   1   8   1        7  0.888889  0.100000  0.900000   \n",
       "         DRY  2      11   0   9   0        9  0.975000  0.025000  0.975000   \n",
       "2052     2R   2      15   5  19   1       18  0.950000  0.250000  0.750000   \n",
       "         4RY  2      16   3  15   5       10  0.750000  0.157895  0.842105   \n",
       "         DRY  2      18   2  19   1       18  0.950000  0.100000  0.900000   \n",
       "2053     2R   2      18   2  19   0       19  0.975000  0.100000  0.900000   \n",
       "         4RY  2      17   1  18   1       17  0.947368  0.055556  0.944444   \n",
       "         DRY  2      18   1  19   0       19  0.975000  0.052632  0.947368   \n",
       "2054     2R   2      17   1  20   0       20  0.975000  0.055556  0.944444   \n",
       "         4RY  2      15   5  16   3       13  0.842105  0.250000  0.750000   \n",
       "         DRY  2      20   0  19   0       19  0.975000  0.025000  0.975000   \n",
       "2055     2R   2      17   3  19   1       18  0.950000  0.150000  0.850000   \n",
       "         4RY  2      16   3  20   0       20  0.975000  0.157895  0.842105   \n",
       "         DRY  2      19   1  18   2       16  0.900000  0.050000  0.950000   \n",
       "2056     2R   2      18   2  20   0       20  0.975000  0.100000  0.900000   \n",
       "         4RY  2      13   7  20   0       20  0.975000  0.350000  0.650000   \n",
       "         DRY  2      13   4  17   2       15  0.894737  0.235294  0.764706   \n",
       "2057     2R   2      17   3  20   0       20  0.975000  0.150000  0.850000   \n",
       "         4RY  2      14   5  19   1       18  0.950000  0.263158  0.736842   \n",
       "         DRY  2      17   3  20   0       20  0.975000  0.150000  0.850000   \n",
       "2058     2R   2       9   1   9   0        9  0.975000  0.100000  0.900000   \n",
       "         4RY  2       7   2   7   1        6  0.875000  0.222222  0.777778   \n",
       "         DRY  2       8   1   8   0        8  0.975000  0.111111  0.888889   \n",
       "2061     2R   2      14   6  20   0       20  0.975000  0.300000  0.700000   \n",
       "         4RY  2      15   5  14   4       10  0.777778  0.250000  0.750000   \n",
       "         DRY  2      18   2  17   2       15  0.894737  0.100000  0.900000   \n",
       "2064     2R   2      17   1  13   2       11  0.866667  0.055556  0.944444   \n",
       "         4RY  2      12   7  12   1       11  0.923077  0.368421  0.631579   \n",
       "         DRY  2      19   0  15   0       15  0.975000  0.025000  0.975000   \n",
       "2065     2R   2      20   0  17   0       17  0.975000  0.025000  0.975000   \n",
       "         4RY  2      20   0  15   4       11  0.789474  0.025000  0.975000   \n",
       "         DRY  2      20   0  17   1       16  0.944444  0.025000  0.975000   \n",
       "\n",
       "                           MR       zHR      zFAR      zCRR       zMR  \\\n",
       "subject1 LOAD group                                                     \n",
       "302      2R   2      0.058824  1.564726 -0.887147  0.887147 -1.564726   \n",
       "         4RY  2      0.500000  0.000000 -0.804596  0.804596  0.000000   \n",
       "         DRY  2      0.105263  1.252120 -0.377392  0.377392 -1.252120   \n",
       "303      2R   2      0.133333  1.110772  0.633640 -0.633640 -1.110772   \n",
       "         4RY  2      0.176471  0.928899  0.318639 -0.318639 -0.928899   \n",
       "         DRY  2      0.055556  1.593219  1.150349 -1.150349 -1.593219   \n",
       "304      2R   2      0.025000  1.959964  0.000000  0.000000 -1.959964   \n",
       "         4RY  2      0.200000  0.841621 -0.336038  0.336038 -0.841621   \n",
       "         DRY  2      0.368421  0.336038 -0.887147  0.887147 -0.336038   \n",
       "305      2R   2      0.025000  1.959964 -0.674490  0.674490 -1.959964   \n",
       "         4RY  2      0.025000  1.959964 -0.841621  0.841621 -1.959964   \n",
       "         DRY  2      0.052632  1.619856 -0.804596  0.804596 -1.619856   \n",
       "306      2R   2      0.157895  1.003148 -1.036433  1.036433 -1.003148   \n",
       "         4RY  2      0.250000  0.674490  0.125661 -0.125661 -0.674490   \n",
       "         DRY  2      0.150000  1.036433 -0.479506  0.479506 -1.036433   \n",
       "307      2R   2      0.666667 -0.430727 -0.180012  0.180012  0.430727   \n",
       "         4RY  2      0.666667 -0.430727 -0.674490  0.674490  0.430727   \n",
       "         DRY  2      0.785714 -0.791639 -0.318639  0.318639  0.791639   \n",
       "308      2R   2      0.076923  1.426077 -0.841621  0.841621 -1.426077   \n",
       "         4RY  2      0.076923  1.426077 -0.721522  0.721522 -1.426077   \n",
       "         DRY  2      0.214286  0.791639 -1.067571  1.067571 -0.791639   \n",
       "309      2R   2      0.166667  0.967422 -0.541395  0.541395 -0.967422   \n",
       "         4RY  2      0.529412 -0.073791 -1.220640  1.220640  0.073791   \n",
       "         DRY  2      0.277778  0.589456 -1.252120  1.252120 -0.589456   \n",
       "310      2R   2      0.315789  0.479506 -0.066012  0.066012 -0.479506   \n",
       "         4RY  2      0.444444  0.139710 -0.841621  0.841621 -0.139710   \n",
       "         DRY  2      0.052632  1.619856 -1.959964  1.959964 -1.619856   \n",
       "311      2R   0      0.157895  1.003148 -1.959964  1.959964 -1.003148   \n",
       "         4RY  0      0.466667  0.083652 -1.959964  1.959964 -0.083652   \n",
       "         DRY  0      0.166667  0.967422 -0.804596  0.804596 -0.967422   \n",
       "312      2R   2      0.684211 -0.479506 -0.199201  0.199201  0.479506   \n",
       "         4RY  2      0.578947 -0.199201 -0.764710  0.764710  0.199201   \n",
       "         DRY  2      0.650000 -0.385320 -0.125661  0.125661  0.385320   \n",
       "313      2R   2      0.722222 -0.589456 -0.488776  0.488776  0.589456   \n",
       "         4RY  2      0.666667 -0.430727 -0.736316  0.736316  0.430727   \n",
       "         DRY  2      0.823529 -0.928899 -0.736316  0.736316  0.928899   \n",
       "314      2R   2      0.315789  0.479506 -0.674490  0.674490 -0.479506   \n",
       "         4RY  2      0.550000 -0.125661 -0.841621  0.841621  0.125661   \n",
       "         DRY  2      0.684211 -0.479506 -0.385320  0.385320  0.479506   \n",
       "315      2R   2      0.200000  0.841621 -0.282216  0.282216 -0.841621   \n",
       "         4RY  2      0.388889  0.282216 -0.841621  0.841621 -0.282216   \n",
       "         DRY  2      0.550000 -0.125661 -0.841621  0.841621  0.125661   \n",
       "316      2R   2      0.100000  1.281552 -0.721522  0.721522 -1.281552   \n",
       "         4RY  2      0.100000  1.281552 -0.928899  0.928899 -1.281552   \n",
       "         DRY  2      0.050000  1.644854 -0.589456  0.589456 -1.644854   \n",
       "317      2R   2      0.461538  0.096559  0.000000  0.000000 -0.096559   \n",
       "         4RY  2      0.444444  0.139710 -1.959964  1.959964 -0.139710   \n",
       "         DRY  2      0.466667  0.083652  0.180012 -0.180012 -0.083652   \n",
       "318      2R   2      0.105263  1.252120 -1.619856  1.619856 -1.252120   \n",
       "         4RY  2      0.350000  0.385320 -1.252120  1.252120 -0.385320   \n",
       "         DRY  2      0.157895  1.003148 -1.036433  1.036433 -1.003148   \n",
       "319      2R   2      0.166667  0.967422 -1.534121  1.534121 -0.967422   \n",
       "         4RY  2      0.200000  0.841621 -1.959964  1.959964 -0.841621   \n",
       "         DRY  2      0.187500  0.887147 -0.139710  0.139710 -0.887147   \n",
       "320      2R   2      0.025000  1.959964  1.959964 -1.959964 -1.959964   \n",
       "         4RY  2      0.025000  1.959964  1.959964 -1.959964 -1.959964   \n",
       "         DRY  2      0.025000  1.959964  1.959964 -1.959964 -1.959964   \n",
       "321      2R   2      0.062500  1.534121  0.318639 -0.318639 -1.534121   \n",
       "         4RY  2      0.062500  1.534121  0.114185 -0.114185 -1.534121   \n",
       "         DRY  2      0.125000  1.150349  0.736316 -0.736316 -1.150349   \n",
       "322      2R   2      0.250000  0.674490 -0.524401  0.524401 -0.674490   \n",
       "         4RY  2      0.571429 -0.180012 -0.841621  0.841621  0.180012   \n",
       "         DRY  2      0.500000  0.000000 -0.430727  0.430727  0.000000   \n",
       "323      2R   2      0.529412 -0.073791 -0.157311  0.157311  0.073791   \n",
       "         4RY  2      0.300000  0.524401  0.318639 -0.318639 -0.524401   \n",
       "         DRY  2      0.700000 -0.524401  0.000000  0.000000  0.524401   \n",
       "325      2R   2      0.058824  1.564726 -0.721522  0.721522 -1.564726   \n",
       "         4RY  2      0.266667  0.622926 -0.841621  0.841621 -0.622926   \n",
       "         DRY  2      0.352941  0.377392 -0.318639  0.318639 -0.377392   \n",
       "326      2R   2      0.562500 -0.157311 -0.282216  0.282216  0.157311   \n",
       "         4RY  2      0.470588  0.073791 -0.073791  0.073791 -0.073791   \n",
       "         DRY  2      0.470588  0.073791 -0.430727  0.430727 -0.073791   \n",
       "327      2R   2      0.052632  1.619856 -0.253347  0.253347 -1.619856   \n",
       "         4RY  2      0.117647  1.186831  0.000000  0.000000 -1.186831   \n",
       "         DRY  2      0.050000  1.644854 -1.220640  1.220640 -1.644854   \n",
       "328      2R   2      0.142857  1.067571 -0.377392  0.377392 -1.067571   \n",
       "         4RY  2      0.352941  0.377392 -0.841621  0.841621 -0.377392   \n",
       "         DRY  2      0.222222  0.764710 -0.541395  0.541395 -0.764710   \n",
       "329      2R   2      0.050000  1.644854  0.000000  0.000000 -1.644854   \n",
       "         4RY  2      0.100000  1.281552 -0.524401  0.524401 -1.281552   \n",
       "         DRY  2      0.050000  1.644854 -0.804596  0.804596 -1.644854   \n",
       "330      2R   2      0.150000  1.036433 -0.841621  0.841621 -1.036433   \n",
       "         4RY  2      0.578947 -0.199201 -0.430727  0.430727  0.199201   \n",
       "         DRY  2      0.200000  0.841621 -0.674490  0.674490 -0.841621   \n",
       "332      2R   2      0.025000  1.959964 -1.252120  1.252120 -1.959964   \n",
       "         4RY  2      0.150000  1.036433 -0.928899  0.928899 -1.036433   \n",
       "         DRY  2      0.025000  1.959964 -1.252120  1.252120 -1.959964   \n",
       "333      2R   2      0.025000  1.959964 -0.524401  0.524401 -1.959964   \n",
       "         4RY  2      0.052632  1.619856 -0.125661  0.125661 -1.619856   \n",
       "         DRY  2      0.300000  0.524401  0.199201 -0.199201 -0.524401   \n",
       "334      2R   2      0.263158  0.633640 -0.589456  0.589456 -0.633640   \n",
       "         4RY  2      0.117647  1.186831 -0.633640  0.633640 -1.186831   \n",
       "         DRY  2      0.052632  1.619856 -0.479506  0.479506 -1.619856   \n",
       "335      2R   2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "         4RY  2      0.263158  0.633640 -0.967422  0.967422 -0.633640   \n",
       "         DRY  2      0.105263  1.252120 -0.633640  0.633640 -1.252120   \n",
       "336      2R   2      0.025000  1.959964 -0.674490  0.674490 -1.959964   \n",
       "         4RY  2      0.157895  1.003148 -0.318639  0.318639 -1.003148   \n",
       "         DRY  2      0.055556  1.593219 -0.318639  0.318639 -1.593219   \n",
       "337      2R   2      0.100000  1.281552 -0.253347  0.253347 -1.281552   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2014     DRY  2      0.025000  1.959964 -1.036433  1.036433 -1.959964   \n",
       "2015     2R   2      0.050000  1.644854 -0.524401  0.524401 -1.644854   \n",
       "         4RY  2      0.100000  1.281552 -0.139710  0.139710 -1.281552   \n",
       "         DRY  2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "2016     2R   2      0.025000  1.959964 -0.841621  0.841621 -1.959964   \n",
       "         4RY  2      0.210526  0.804596 -1.036433  1.036433 -0.804596   \n",
       "         DRY  2      0.025000  1.959964 -1.036433  1.036433 -1.959964   \n",
       "2019     2R   2      0.025000  1.959964 -1.619856  1.619856 -1.959964   \n",
       "         4RY  2      0.100000  1.281552 -0.674490  0.674490 -1.281552   \n",
       "         DRY  2      0.025000  1.959964 -1.036433  1.036433 -1.959964   \n",
       "2021     2R   2      0.050000  1.644854 -1.959964  1.959964 -1.644854   \n",
       "         4RY  2      0.157895  1.003148 -1.644854  1.644854 -1.003148   \n",
       "         DRY  2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "2022     2R   2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "         4RY  2      0.117647  1.186831 -1.252120  1.252120 -1.186831   \n",
       "         DRY  2      0.025000  1.959964 -1.619856  1.619856 -1.959964   \n",
       "2023     2R   2      0.117647  1.186831 -1.534121  1.534121 -1.186831   \n",
       "         4RY  2      0.230769  0.736316 -1.959964  1.959964 -0.736316   \n",
       "         DRY  2      0.125000  1.150349 -1.501086  1.501086 -1.150349   \n",
       "2024     2R   2      0.105263  1.252120 -0.967422  0.967422 -1.252120   \n",
       "         4RY  2      0.166667  0.967422 -0.928899  0.928899 -0.967422   \n",
       "         DRY  2      0.187500  0.887147 -1.619856  1.619856 -0.887147   \n",
       "2025     2R   2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "         4RY  2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "         DRY  2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "2026     2R   2      0.333333  0.430727  0.000000  0.000000 -0.430727   \n",
       "         4RY  2      0.428571  0.180012 -0.318639  0.318639 -0.180012   \n",
       "         DRY  2      0.500000  0.000000  0.565949 -0.565949  0.000000   \n",
       "2027     2R   2      0.100000  1.281552 -1.564726  1.564726 -1.281552   \n",
       "         4RY  2      0.055556  1.593219 -0.430727  0.430727 -1.593219   \n",
       "         DRY  2      0.025000  1.959964 -0.633640  0.633640 -1.959964   \n",
       "2028     2R   2      0.025000  1.959964 -0.385320  0.385320 -1.959964   \n",
       "         4RY  2      0.200000  0.841621 -0.385320  0.385320 -0.841621   \n",
       "         DRY  2      0.100000  1.281552 -0.282216  0.282216 -1.281552   \n",
       "2029     2R   2      0.025000  1.959964 -1.252120  1.252120 -1.959964   \n",
       "         4RY  2      0.210526  0.804596 -0.764710  0.764710 -0.804596   \n",
       "         DRY  2      0.157895  1.003148 -0.967422  0.967422 -1.003148   \n",
       "2030     2R   2      0.050000  1.644854 -1.036433  1.036433 -1.644854   \n",
       "         4RY  2      0.100000  1.281552 -1.252120  1.252120 -1.281552   \n",
       "         DRY  2      0.105263  1.252120 -1.281552  1.281552 -1.252120   \n",
       "2032     2R   2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "         4RY  2      0.052632  1.619856 -1.036433  1.036433 -1.619856   \n",
       "         DRY  2      0.050000  1.644854 -1.619856  1.619856 -1.644854   \n",
       "2037     2R   2      0.181818  0.908458 -0.502402  0.502402 -0.908458   \n",
       "         4RY  2      0.416667  0.210428 -0.967422  0.967422 -0.210428   \n",
       "         DRY  2      0.083333  1.382994 -1.426077  1.426077 -1.382994   \n",
       "2039     2R   2      0.100000  1.281552 -1.959964  1.959964 -1.281552   \n",
       "         4RY  2      0.222222  0.764710 -0.804596  0.804596 -0.764710   \n",
       "         DRY  2      0.050000  1.644854 -1.644854  1.644854 -1.644854   \n",
       "2041     2R   2      0.105263  1.252120 -1.619856  1.619856 -1.252120   \n",
       "         4RY  2      0.450000  0.125661 -0.841621  0.841621 -0.125661   \n",
       "         DRY  2      0.157895  1.003148 -1.959964  1.959964 -1.003148   \n",
       "2042     2R   2      0.100000  1.281552 -1.959964  1.959964 -1.281552   \n",
       "         4RY  2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "         DRY  2      0.025000  1.959964 -0.604585  0.604585 -1.959964   \n",
       "2045     2R   2      0.166667  0.967422 -0.841621  0.841621 -0.967422   \n",
       "         4RY  2      0.277778  0.589456 -0.674490  0.674490 -0.589456   \n",
       "         DRY  2      0.411765  0.223008 -0.764710  0.764710 -0.223008   \n",
       "2046     2R   2      0.111111  1.220640 -0.674490  0.674490 -1.220640   \n",
       "         4RY  2      0.400000  0.253347 -0.524401  0.524401 -0.253347   \n",
       "         DRY  2      0.250000  0.674490 -0.524401  0.524401 -0.674490   \n",
       "2047     2R   2      0.050000  1.644854 -1.644854  1.644854 -1.644854   \n",
       "         4RY  2      0.150000  1.036433 -1.281552  1.281552 -1.036433   \n",
       "         DRY  2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "2049     2R   2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "         4RY  2      0.050000  1.644854 -0.841621  0.841621 -1.644854   \n",
       "         DRY  2      0.150000  1.036433 -1.959964  1.959964 -1.036433   \n",
       "2050     2R   2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "         4RY  2      0.111111  1.220640 -1.281552  1.281552 -1.220640   \n",
       "         DRY  2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "2052     2R   2      0.050000  1.644854 -0.674490  0.674490 -1.644854   \n",
       "         4RY  2      0.250000  0.674490 -1.003148  1.003148 -0.674490   \n",
       "         DRY  2      0.050000  1.644854 -1.281552  1.281552 -1.644854   \n",
       "2053     2R   2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "         4RY  2      0.052632  1.619856 -1.593219  1.593219 -1.619856   \n",
       "         DRY  2      0.025000  1.959964 -1.619856  1.619856 -1.959964   \n",
       "2054     2R   2      0.025000  1.959964 -1.593219  1.593219 -1.959964   \n",
       "         4RY  2      0.157895  1.003148 -0.674490  0.674490 -1.003148   \n",
       "         DRY  2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "2055     2R   2      0.050000  1.644854 -1.036433  1.036433 -1.644854   \n",
       "         4RY  2      0.025000  1.959964 -1.003148  1.003148 -1.959964   \n",
       "         DRY  2      0.100000  1.281552 -1.644854  1.644854 -1.281552   \n",
       "2056     2R   2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "         4RY  2      0.025000  1.959964 -0.385320  0.385320 -1.959964   \n",
       "         DRY  2      0.105263  1.252120 -0.721522  0.721522 -1.252120   \n",
       "2057     2R   2      0.025000  1.959964 -1.036433  1.036433 -1.959964   \n",
       "         4RY  2      0.050000  1.644854 -0.633640  0.633640 -1.644854   \n",
       "         DRY  2      0.025000  1.959964 -1.036433  1.036433 -1.959964   \n",
       "2058     2R   2      0.025000  1.959964 -1.281552  1.281552 -1.959964   \n",
       "         4RY  2      0.125000  1.150349 -0.764710  0.764710 -1.150349   \n",
       "         DRY  2      0.025000  1.959964 -1.220640  1.220640 -1.959964   \n",
       "2061     2R   2      0.025000  1.959964 -0.524401  0.524401 -1.959964   \n",
       "         4RY  2      0.222222  0.764710 -0.674490  0.674490 -0.764710   \n",
       "         DRY  2      0.105263  1.252120 -1.281552  1.281552 -1.252120   \n",
       "2064     2R   2      0.133333  1.110772 -1.593219  1.593219 -1.110772   \n",
       "         4RY  2      0.076923  1.426077 -0.336038  0.336038 -1.426077   \n",
       "         DRY  2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "2065     2R   2      0.025000  1.959964 -1.959964  1.959964 -1.959964   \n",
       "         4RY  2      0.210526  0.804596 -1.959964  1.959964 -0.804596   \n",
       "         DRY  2      0.055556  1.593219 -1.959964  1.959964 -1.593219   \n",
       "\n",
       "                       dPrime      h-fa       acc        rt  \n",
       "subject1 LOAD group                                          \n",
       "302      2R   2      2.451873  0.753676  0.725000  1.129071  \n",
       "         4RY  2      0.804596  0.289474  0.600000  1.303166  \n",
       "         DRY  2      1.629511  0.541796  0.700000  1.198379  \n",
       "303      2R   2      0.477132  0.129825  0.450000  1.237365  \n",
       "         4RY  2      0.610260  0.198529  0.500000  1.147570  \n",
       "         DRY  2      0.442869  0.069444  0.475000  1.182943  \n",
       "304      2R   2      1.959964  0.475000  0.700000  1.141441  \n",
       "         4RY  2      1.177659  0.431579  0.700000  1.090706  \n",
       "         DRY  2      1.223185  0.444079  0.625000  1.125127  \n",
       "305      2R   2      2.634454  0.725000  0.850000  0.894252  \n",
       "         4RY  2      2.801585  0.775000  0.900000  0.935852  \n",
       "         DRY  2      2.424453  0.736842  0.825000  0.866452  \n",
       "306      2R   2      2.039581  0.692105  0.825000  1.001260  \n",
       "         4RY  2      0.548828  0.200000  0.600000  1.214478  \n",
       "         DRY  2      1.515939  0.534211  0.750000  1.112539  \n",
       "307      2R   2     -0.250715 -0.095238  0.325000  0.996453  \n",
       "         4RY  2      0.243762  0.083333  0.450000  1.013209  \n",
       "         DRY  2     -0.472999 -0.160714  0.325000  0.901769  \n",
       "308      2R   2      2.267698  0.723077  0.800000  1.011286  \n",
       "         4RY  2      2.147599  0.687783  0.833333  1.153857  \n",
       "         DRY  2      1.859209  0.642857  0.766667  1.109112  \n",
       "309      2R   2      1.508817  0.539216  0.675000  1.162743  \n",
       "         4RY  2      1.146849  0.359477  0.600000  1.160756  \n",
       "         DRY  2      1.841575  0.616959  0.750000  1.186306  \n",
       "310      2R   2      0.545517  0.210526  0.575000  1.077528  \n",
       "         4RY  2      0.981332  0.355556  0.650000  1.204119  \n",
       "         DRY  2      3.579820  0.922368  0.925000  1.083786  \n",
       "311      2R   0      2.963112  0.817105  0.875000  1.288347  \n",
       "         4RY  0      2.043616  0.508333  0.625000  1.306288  \n",
       "         DRY  0      1.772018  0.622807  0.750000  1.208412  \n",
       "312      2R   2     -0.280304 -0.105263  0.425000  0.879743  \n",
       "         4RY  2      0.565508  0.198830  0.550000  0.753255  \n",
       "         DRY  2     -0.259659 -0.100000  0.450000  0.756469  \n",
       "313      2R   2     -0.100679 -0.034722  0.400000  1.116486  \n",
       "         4RY  2      0.305589  0.102564  0.400000  1.115577  \n",
       "         DRY  2     -0.192584 -0.054299  0.325000  1.180637  \n",
       "314      2R   2      1.153995  0.434211  0.700000  0.906225  \n",
       "         4RY  2      0.715960  0.250000  0.625000  0.935458  \n",
       "         DRY  2     -0.094185 -0.034211  0.475000  0.992983  \n",
       "315      2R   2      1.123837  0.411111  0.675000  1.100594  \n",
       "         4RY  2      1.123837  0.411111  0.675000  1.110974  \n",
       "         DRY  2      0.715960  0.250000  0.625000  1.093819  \n",
       "316      2R   2      2.003074  0.664706  0.775000  0.962597  \n",
       "         4RY  2      2.210451  0.723529  0.800000  0.958164  \n",
       "         DRY  2      2.234309  0.672222  0.800000  0.815449  \n",
       "317      2R   2      0.096559  0.038462  0.325000  0.962830  \n",
       "         4RY  2      2.099674  0.530556  0.350000  1.089879  \n",
       "         DRY  2     -0.096361 -0.038095  0.275000  1.093388  \n",
       "318      2R   2      2.871976  0.842105  0.875000  1.330328  \n",
       "         4RY  2      1.637440  0.544737  0.750000  1.260772  \n",
       "         DRY  2      2.039581  0.692105  0.825000  1.137250  \n",
       "319      2R   2      2.501542  0.770833  0.750000  1.298713  \n",
       "         4RY  2      2.801585  0.775000  0.575000  1.437365  \n",
       "         DRY  2      1.026857  0.368056  0.575000  1.378290  \n",
       "320      2R   2      0.000000  0.000000  0.425000  1.058328  \n",
       "         4RY  2      0.000000  0.000000  0.375000  1.218222  \n",
       "         DRY  2      0.000000  0.000000  0.400000  1.074796  \n",
       "321      2R   2      1.215481  0.312500  0.525000  0.964384  \n",
       "         4RY  2      1.419935  0.392045  0.500000  0.957942  \n",
       "         DRY  2      0.414033  0.105769  0.425000  1.097171  \n",
       "322      2R   2      1.198890  0.450000  0.500000  1.368645  \n",
       "         4RY  2      0.661609  0.228571  0.375000  1.523230  \n",
       "         DRY  2      0.430727  0.166667  0.475000  1.244956  \n",
       "323      2R   2      0.083519  0.033088  0.550000  1.128628  \n",
       "         4RY  2      0.205761  0.075000  0.525000  1.262163  \n",
       "         DRY  2     -0.524401 -0.200000  0.525000  1.202795  \n",
       "325      2R   2      2.286249  0.705882  0.725000  1.095705  \n",
       "         4RY  2      1.464547  0.533333  0.575000  1.217599  \n",
       "         DRY  2      0.696031  0.272059  0.525000  1.091845  \n",
       "326      2R   2      0.124905  0.048611  0.450000  1.518895  \n",
       "         4RY  2      0.147583  0.058824  0.450000  1.612189  \n",
       "         DRY  2      0.504519  0.196078  0.525000  1.469163  \n",
       "327      2R   2      1.873203  0.547368  0.750000  1.044168  \n",
       "         4RY  2      1.186831  0.382353  0.625000  1.226857  \n",
       "         DRY  2      2.865494  0.838889  0.875000  0.954255  \n",
       "328      2R   2      1.444962  0.504202  0.575000  1.174238  \n",
       "         4RY  2      1.219013  0.447059  0.575000  1.181539  \n",
       "         DRY  2      1.306105  0.483660  0.650000  1.085849  \n",
       "329      2R   2      1.644854  0.450000  0.725000  0.957105  \n",
       "         4RY  2      1.805952  0.600000  0.800000  0.955316  \n",
       "         DRY  2      2.449450  0.739474  0.850000  0.951408  \n",
       "330      2R   2      1.878055  0.650000  0.825000  1.074378  \n",
       "         4RY  2      0.231526  0.087719  0.500000  1.138395  \n",
       "         DRY  2      1.516111  0.550000  0.775000  1.066673  \n",
       "332      2R   2      3.212084  0.869737  0.925000  1.034103  \n",
       "         4RY  2      1.965333  0.673529  0.775000  1.234199  \n",
       "         DRY  2      3.212084  0.869737  0.925000  0.923791  \n",
       "333      2R   2      2.484364  0.675000  0.825000  1.066969  \n",
       "         4RY  2      1.745518  0.497368  0.725000  1.131421  \n",
       "         DRY  2      0.325199  0.121053  0.550000  1.144766  \n",
       "334      2R   2      1.223096  0.459064  0.675000  1.057895  \n",
       "         4RY  2      1.820471  0.619195  0.725000  1.039029  \n",
       "         DRY  2      2.099362  0.631579  0.775000  0.942358  \n",
       "335      2R   2      3.241516  0.875000  0.950000  1.120333  \n",
       "         4RY  2      1.601062  0.570175  0.725000  1.164997  \n",
       "         DRY  2      1.885760  0.631579  0.775000  1.113435  \n",
       "336      2R   2      2.634454  0.725000  0.725000  1.076398  \n",
       "         4RY  2      1.321787  0.467105  0.650000  1.137632  \n",
       "         DRY  2      1.911858  0.569444  0.675000  1.131663  \n",
       "337      2R   2      1.534899  0.500000  0.750000  1.205502  \n",
       "...                       ...       ...       ...       ...  \n",
       "2014     DRY  2      2.996397  0.825000  0.925000  1.162556  \n",
       "2015     2R   2      2.169254  0.650000  0.825000  1.075007  \n",
       "         4RY  2      1.421262  0.455556  0.700000  1.100531  \n",
       "         DRY  2      3.241516  0.875000  0.950000  1.067367  \n",
       "2016     2R   2      2.801585  0.775000  0.900000  1.031201  \n",
       "         4RY  2      1.841030  0.639474  0.800000  1.094017  \n",
       "         DRY  2      2.996397  0.825000  0.925000  0.969094  \n",
       "2019     2R   2      3.579820  0.922368  0.950000  1.033023  \n",
       "         4RY  2      1.956041  0.650000  0.825000  1.198491  \n",
       "         DRY  2      2.996397  0.825000  0.925000  0.966688  \n",
       "2021     2R   2      3.604818  0.925000  0.975000  1.105006  \n",
       "         4RY  2      2.648002  0.792105  0.875000  1.154674  \n",
       "         DRY  2      3.241516  0.875000  0.950000  1.072580  \n",
       "2022     2R   2      3.919928  0.950000  0.950000  1.067435  \n",
       "         4RY  2      2.438951  0.777090  0.800000  1.267468  \n",
       "         DRY  2      3.579820  0.922368  0.925000  1.112209  \n",
       "2023     2R   2      2.720952  0.819853  0.909091  1.095897  \n",
       "         4RY  2      2.696280  0.744231  0.818182  1.292786  \n",
       "         DRY  2      2.651435  0.808333  0.848485  1.196138  \n",
       "2024     2R   2      2.219541  0.728070  0.800000  1.293746  \n",
       "         4RY  2      1.896321  0.656863  0.725000  1.337464  \n",
       "         DRY  2      2.507003  0.759868  0.775000  1.215300  \n",
       "2025     2R   2      3.919928  0.950000  1.000000  1.002935  \n",
       "         4RY  2      3.919928  0.950000  0.900000  1.255024  \n",
       "         DRY  2      3.919928  0.950000  1.000000  1.130465  \n",
       "2026     2R   2      0.430727  0.166667  0.400000  1.083065  \n",
       "         4RY  2      0.498652  0.196429  0.450000  1.395655  \n",
       "         DRY  2     -0.565949 -0.214286  0.250000  1.343549  \n",
       "2027     2R   2      2.846278  0.841176  0.850000  1.209337  \n",
       "         4RY  2      2.023946  0.611111  0.675000  1.204662  \n",
       "         DRY  2      2.593604  0.711842  0.850000  1.122745  \n",
       "2028     2R   2      2.345284  0.625000  0.825000  0.980580  \n",
       "         4RY  2      1.226942  0.450000  0.725000  1.062114  \n",
       "         DRY  2      1.563768  0.511111  0.725000  1.015694  \n",
       "2029     2R   2      3.212084  0.869737  0.925000  1.195332  \n",
       "         4RY  2      1.569306  0.567251  0.725000  1.246651  \n",
       "         DRY  2      1.970570  0.675439  0.775000  1.144939  \n",
       "2030     2R   2      2.681287  0.800000  0.900000  0.952320  \n",
       "         4RY  2      2.533671  0.794737  0.875000  1.012347  \n",
       "         DRY  2      2.533671  0.794737  0.875000  0.922873  \n",
       "2032     2R   2      3.919928  0.950000  1.000000  0.984487  \n",
       "         4RY  2      2.656290  0.797368  0.875000  1.104411  \n",
       "         DRY  2      3.264710  0.897368  0.925000  0.932353  \n",
       "2037     2R   2      1.410860  0.510490  0.450000  1.102056  \n",
       "         4RY  2      1.177850  0.416667  0.425000  1.264208  \n",
       "         DRY  2      2.809071  0.839744  0.575000  1.069018  \n",
       "2039     2R   2      3.241516  0.875000  0.950000  1.074784  \n",
       "         4RY  2      1.569306  0.567251  0.725000  1.289957  \n",
       "         DRY  2      3.289707  0.900000  0.950000  1.102229  \n",
       "2041     2R   2      2.871976  0.842105  0.875000  1.015721  \n",
       "         4RY  2      0.967283  0.350000  0.675000  1.119880  \n",
       "         DRY  2      2.963112  0.817105  0.900000  1.092176  \n",
       "2042     2R   2      3.241516  0.875000  0.900000  1.089445  \n",
       "         4RY  2      3.241516  0.875000  0.800000  1.329111  \n",
       "         DRY  2      2.564549  0.702273  0.850000  1.332543  \n",
       "2045     2R   2      1.809043  0.633333  0.775000  1.138197  \n",
       "         4RY  2      1.263946  0.472222  0.700000  1.281201  \n",
       "         DRY  2      0.987718  0.366013  0.600000  1.214302  \n",
       "2046     2R   2      1.895130  0.638889  0.775000  1.119196  \n",
       "         4RY  2      0.777748  0.300000  0.650000  1.070505  \n",
       "         DRY  2      1.198890  0.450000  0.725000  1.154894  \n",
       "2047     2R   2      3.289707  0.900000  0.950000  0.898389  \n",
       "         4RY  2      2.317985  0.750000  0.875000  1.071519  \n",
       "         DRY  2      3.241516  0.875000  0.950000  0.928928  \n",
       "2049     2R   2      3.241516  0.875000  0.950000  0.848893  \n",
       "         4RY  2      2.486475  0.750000  0.875000  0.971369  \n",
       "         DRY  2      2.996397  0.825000  0.925000  0.861433  \n",
       "2050     2R   2      3.241516  0.875000  0.950000  1.018243  \n",
       "         4RY  2      2.502192  0.788889  0.850000  1.226243  \n",
       "         DRY  2      3.919928  0.950000  1.000000  1.007829  \n",
       "2052     2R   2      2.319343  0.700000  0.850000  1.007749  \n",
       "         4RY  2      1.677638  0.592105  0.775000  1.025571  \n",
       "         DRY  2      2.926405  0.850000  0.925000  0.998048  \n",
       "2053     2R   2      3.241516  0.875000  0.925000  0.826927  \n",
       "         4RY  2      3.213075  0.891813  0.875000  0.971905  \n",
       "         DRY  2      3.579820  0.922368  0.925000  0.871323  \n",
       "2054     2R   2      3.553183  0.919444  0.925000  1.122837  \n",
       "         4RY  2      1.677638  0.592105  0.775000  1.273445  \n",
       "         DRY  2      3.919928  0.950000  0.975000  1.135556  \n",
       "2055     2R   2      2.681287  0.800000  0.900000  0.878404  \n",
       "         4RY  2      2.963112  0.817105  0.900000  1.141168  \n",
       "         DRY  2      2.926405  0.850000  0.925000  0.899031  \n",
       "2056     2R   2      3.241516  0.875000  0.950000  1.062940  \n",
       "         4RY  2      2.345284  0.625000  0.825000  1.093809  \n",
       "         DRY  2      1.973642  0.659443  0.750000  0.978580  \n",
       "2057     2R   2      2.996397  0.825000  0.925000  1.042839  \n",
       "         4RY  2      2.278494  0.686842  0.825000  1.044143  \n",
       "         DRY  2      2.996397  0.825000  0.925000  0.966188  \n",
       "2058     2R   2      3.241516  0.875000  0.900000  1.113349  \n",
       "         4RY  2      1.915059  0.652778  0.700000  1.320602  \n",
       "         DRY  2      3.180604  0.863889  0.800000  1.115278  \n",
       "2061     2R   2      2.484364  0.675000  0.850000  0.930488  \n",
       "         4RY  2      1.439199  0.527778  0.725000  1.126001  \n",
       "         DRY  2      2.533671  0.794737  0.875000  0.965636  \n",
       "2064     2R   2      2.703990  0.811111  0.750000  1.039243  \n",
       "         4RY  2      1.762115  0.554656  0.600000  1.308731  \n",
       "         DRY  2      3.919928  0.950000  0.850000  1.021772  \n",
       "2065     2R   2      3.919928  0.950000  0.925000  0.919526  \n",
       "         4RY  2      2.764560  0.764474  0.875000  1.072043  \n",
       "         DRY  2      3.553183  0.919444  0.925000  0.939810  \n",
       "\n",
       "[441 rows x 17 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp['zHR'] = dp.apply(lambda row: norm.ppf(row['HR']),axis = 1) \n",
    "dp['zFAR'] = dp.apply(lambda row: norm.ppf(row['FAR']),axis = 1)    \n",
    "dp['zCRR'] = dp.apply(lambda row: norm.ppf(row['CRR']),axis = 1) \n",
    "dp['zMR'] = dp.apply(lambda row: norm.ppf(row['MR']),axis = 1) \n",
    "dp['dPrime'] = dp.apply(lambda row: (row['zHR']-row['zFAR']), axis = 1)  \n",
    "\n",
    "dp['h-fa'] = dp.apply(lambda row: (row['HR']-row['FAR']), axis = 1)  \n",
    "dp['acc'] = acc['Accuracy']\n",
    "dp['rt'] = rt['Reaction_Time']\n",
    "dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>subject1</th>\n",
       "      <th>LOAD</th>\n",
       "      <th>group</th>\n",
       "      <th>CR</th>\n",
       "      <th>FA</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>HminusM</th>\n",
       "      <th>HR</th>\n",
       "      <th>FAR</th>\n",
       "      <th>CRR</th>\n",
       "      <th>MR</th>\n",
       "      <th>zHR</th>\n",
       "      <th>zFAR</th>\n",
       "      <th>zCRR</th>\n",
       "      <th>zMR</th>\n",
       "      <th>dPrime</th>\n",
       "      <th>h-fa</th>\n",
       "      <th>acc</th>\n",
       "      <th>rt</th>\n",
       "      <th>LOAD_no</th>\n",
       "      <th>K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>302</td>\n",
       "      <td>2R</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>1.564726</td>\n",
       "      <td>-0.887147</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>-1.564726</td>\n",
       "      <td>2.451873</td>\n",
       "      <td>0.753676</td>\n",
       "      <td>0.725</td>\n",
       "      <td>1.129071</td>\n",
       "      <td>2</td>\n",
       "      <td>1.507353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>302</td>\n",
       "      <td>4RY</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.303166</td>\n",
       "      <td>4</td>\n",
       "      <td>1.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>302</td>\n",
       "      <td>DRY</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.377392</td>\n",
       "      <td>0.377392</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>1.629511</td>\n",
       "      <td>0.541796</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1.198379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>303</td>\n",
       "      <td>2R</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1.110772</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>-1.110772</td>\n",
       "      <td>0.477132</td>\n",
       "      <td>0.129825</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1.237365</td>\n",
       "      <td>2</td>\n",
       "      <td>0.259649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303</td>\n",
       "      <td>4RY</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>-0.928899</td>\n",
       "      <td>0.610260</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.147570</td>\n",
       "      <td>4</td>\n",
       "      <td>0.794118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject1 LOAD group  CR  FA   H  M  HminusM        HR       FAR       CRR  \\\n",
       "0       302   2R     2  13   3  16  1       15  0.941176  0.187500  0.812500   \n",
       "1       302  4RY     2  15   4   9  9        0  0.500000  0.210526  0.789474   \n",
       "2       302  DRY     2  11   6  17  2       15  0.894737  0.352941  0.647059   \n",
       "3       303   2R     2   5  14  13  2       11  0.866667  0.736842  0.263158   \n",
       "4       303  4RY     2   6  10  14  3       11  0.823529  0.625000  0.375000   \n",
       "\n",
       "         MR       zHR      zFAR      zCRR       zMR    dPrime      h-fa  \\\n",
       "0  0.058824  1.564726 -0.887147  0.887147 -1.564726  2.451873  0.753676   \n",
       "1  0.500000  0.000000 -0.804596  0.804596  0.000000  0.804596  0.289474   \n",
       "2  0.105263  1.252120 -0.377392  0.377392 -1.252120  1.629511  0.541796   \n",
       "3  0.133333  1.110772  0.633640 -0.633640 -1.110772  0.477132  0.129825   \n",
       "4  0.176471  0.928899  0.318639 -0.318639 -0.928899  0.610260  0.198529   \n",
       "\n",
       "     acc        rt  LOAD_no         K  \n",
       "0  0.725  1.129071        2  1.507353  \n",
       "1  0.600  1.303166        4  1.157895  \n",
       "2  0.700  1.198379      NaN       NaN  \n",
       "3  0.450  1.237365        2  0.259649  \n",
       "4  0.500  1.147570        4  0.794118  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp = dp.reset_index()\n",
    "dp['LOAD_no'] = dp.apply(lambda row: (4 if row['LOAD']=='4RY' \n",
    "                                else 2 if row['LOAD']=='2R'\n",
    "                                else None ), axis=1)\n",
    "dp['K'] = dp.apply(lambda x: (x['LOAD_no']*(x['HR']+x['CRR']-1)), axis = 1)  \n",
    "                                      #K : so load x (Hit rate + (correct rejection rate -1))\n",
    "\n",
    "dp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lure = pd.pivot_table(WM, values=['Accuracy', 'Reaction_Time'], index=['subject1','group'], columns='probe_type_label', aggfunc=np.mean)\n",
    "\n",
    "\n",
    "#lure.columns = lure.columns.droplevel()\n",
    "#lure.reset_index().head()\n",
    "lure.reset_index(0).reset_index(drop=True)\n",
    "lure.columns = ['_'.join(col).strip() for col in lure.columns.values]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'subject1', u'group', u'Accuracy_1_away_red', u'Accuracy_in_red',\n",
       "       u'Accuracy_in_yellow', u'Reaction_Time_1_away_red',\n",
       "       u'Reaction_Time_in_red', u'Reaction_Time_in_yellow'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lure = lure.reset_index()\n",
    "lure.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "piv = pd.pivot_table(dp, values=[ u'CR', u'FA', u'H', u'M', u'HminusM', u'HR', u'FAR',\n",
    "                                 u'CRR', u'MR', u'zHR', u'zFAR', u'dPrime', u'LOAD_no', u'K', u'h-fa', \n",
    "                                 u'zCRR', u'zMR', u'acc', 'rt'], index=['subject1', 'group'], columns='LOAD', aggfunc=np.mean)\n",
    "\n",
    "piv.columns = ['_'.join(col).strip() for col in piv.columns.values]\n",
    "\n",
    "piv['L'] = piv.apply(lambda x: (4*(x['HR_DRY']+x['CRR_DRY']-1)), axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>subject1</th>\n",
       "      <th>group</th>\n",
       "      <th>CR_2R</th>\n",
       "      <th>CR_4RY</th>\n",
       "      <th>CR_DRY</th>\n",
       "      <th>FA_2R</th>\n",
       "      <th>FA_4RY</th>\n",
       "      <th>FA_DRY</th>\n",
       "      <th>H_2R</th>\n",
       "      <th>H_4RY</th>\n",
       "      <th>H_DRY</th>\n",
       "      <th>M_2R</th>\n",
       "      <th>M_4RY</th>\n",
       "      <th>M_DRY</th>\n",
       "      <th>HminusM_2R</th>\n",
       "      <th>HminusM_4RY</th>\n",
       "      <th>HminusM_DRY</th>\n",
       "      <th>HR_2R</th>\n",
       "      <th>HR_4RY</th>\n",
       "      <th>HR_DRY</th>\n",
       "      <th>FAR_2R</th>\n",
       "      <th>FAR_4RY</th>\n",
       "      <th>FAR_DRY</th>\n",
       "      <th>CRR_2R</th>\n",
       "      <th>CRR_4RY</th>\n",
       "      <th>CRR_DRY</th>\n",
       "      <th>MR_2R</th>\n",
       "      <th>MR_4RY</th>\n",
       "      <th>MR_DRY</th>\n",
       "      <th>zHR_2R</th>\n",
       "      <th>zHR_4RY</th>\n",
       "      <th>zHR_DRY</th>\n",
       "      <th>zFAR_2R</th>\n",
       "      <th>zFAR_4RY</th>\n",
       "      <th>zFAR_DRY</th>\n",
       "      <th>zCRR_2R</th>\n",
       "      <th>zCRR_4RY</th>\n",
       "      <th>zCRR_DRY</th>\n",
       "      <th>zMR_2R</th>\n",
       "      <th>zMR_4RY</th>\n",
       "      <th>zMR_DRY</th>\n",
       "      <th>dPrime_2R</th>\n",
       "      <th>dPrime_4RY</th>\n",
       "      <th>dPrime_DRY</th>\n",
       "      <th>h-fa_2R</th>\n",
       "      <th>h-fa_4RY</th>\n",
       "      <th>h-fa_DRY</th>\n",
       "      <th>acc_2R</th>\n",
       "      <th>acc_4RY</th>\n",
       "      <th>acc_DRY</th>\n",
       "      <th>rt_2R</th>\n",
       "      <th>rt_4RY</th>\n",
       "      <th>rt_DRY</th>\n",
       "      <th>LOAD_no_2R</th>\n",
       "      <th>LOAD_no_4RY</th>\n",
       "      <th>LOAD_no_DRY</th>\n",
       "      <th>K_2R</th>\n",
       "      <th>K_4RY</th>\n",
       "      <th>K_DRY</th>\n",
       "      <th>L</th>\n",
       "      <th>Accuracy_1_away_red</th>\n",
       "      <th>Accuracy_in_red</th>\n",
       "      <th>Accuracy_in_yellow</th>\n",
       "      <th>Reaction_Time_1_away_red</th>\n",
       "      <th>Reaction_Time_in_red</th>\n",
       "      <th>Reaction_Time_in_yellow</th>\n",
       "      <th>four-filt_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>302</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.564726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.252120</td>\n",
       "      <td>-0.887147</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>-0.377392</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>0.377392</td>\n",
       "      <td>-1.564726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.252120</td>\n",
       "      <td>2.451873</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>1.629511</td>\n",
       "      <td>0.753676</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.541796</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1.129071</td>\n",
       "      <td>1.303166</td>\n",
       "      <td>1.198379</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.507353</td>\n",
       "      <td>1.157895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.167183</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.614694</td>\n",
       "      <td>1.117499</td>\n",
       "      <td>1.104783</td>\n",
       "      <td>-0.824915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>303</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.110772</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>1.593219</td>\n",
       "      <td>0.633640</td>\n",
       "      <td>0.318639</td>\n",
       "      <td>1.150349</td>\n",
       "      <td>-0.633640</td>\n",
       "      <td>-0.318639</td>\n",
       "      <td>-1.150349</td>\n",
       "      <td>-1.110772</td>\n",
       "      <td>-0.928899</td>\n",
       "      <td>-1.593219</td>\n",
       "      <td>0.477132</td>\n",
       "      <td>0.610260</td>\n",
       "      <td>0.442869</td>\n",
       "      <td>0.129825</td>\n",
       "      <td>0.198529</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.475</td>\n",
       "      <td>1.237365</td>\n",
       "      <td>1.147570</td>\n",
       "      <td>1.182943</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.259649</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.247208</td>\n",
       "      <td>1.168162</td>\n",
       "      <td>1.117852</td>\n",
       "      <td>0.167391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>304</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>0.336038</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.336038</td>\n",
       "      <td>-0.887147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.336038</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>-0.336038</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>1.177659</td>\n",
       "      <td>1.223185</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.431579</td>\n",
       "      <td>0.444079</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.625</td>\n",
       "      <td>1.141441</td>\n",
       "      <td>1.090706</td>\n",
       "      <td>1.125127</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.726316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.776316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.090766</td>\n",
       "      <td>1.216951</td>\n",
       "      <td>0.971560</td>\n",
       "      <td>-0.045525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>1.959964</td>\n",
       "      <td>1.619856</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>-0.841621</td>\n",
       "      <td>-0.804596</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>0.841621</td>\n",
       "      <td>0.804596</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>-1.959964</td>\n",
       "      <td>-1.619856</td>\n",
       "      <td>2.634454</td>\n",
       "      <td>2.801585</td>\n",
       "      <td>2.424453</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.894252</td>\n",
       "      <td>0.935852</td>\n",
       "      <td>0.866452</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.947368</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.083195</td>\n",
       "      <td>0.794040</td>\n",
       "      <td>0.853225</td>\n",
       "      <td>0.377133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>306</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>1.003148</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>0.125661</td>\n",
       "      <td>-0.479506</td>\n",
       "      <td>1.036433</td>\n",
       "      <td>-0.125661</td>\n",
       "      <td>0.479506</td>\n",
       "      <td>-1.003148</td>\n",
       "      <td>-0.674490</td>\n",
       "      <td>-1.036433</td>\n",
       "      <td>2.039581</td>\n",
       "      <td>0.548828</td>\n",
       "      <td>1.515939</td>\n",
       "      <td>0.692105</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.534211</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.001260</td>\n",
       "      <td>1.214478</td>\n",
       "      <td>1.112539</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.384211</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.136842</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.925198</td>\n",
       "      <td>1.108506</td>\n",
       "      <td>1.185694</td>\n",
       "      <td>-0.967111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject1 group  CR_2R  CR_4RY  CR_DRY  FA_2R  FA_4RY  FA_DRY  H_2R  H_4RY  \\\n",
       "0       302     2     13      15      11      3       4       6    16      9   \n",
       "1       303     2      5       6       2     14      10      14    13     14   \n",
       "2       304     2     10      12      13     10       7       3    18     16   \n",
       "3       305     2     15      16      15      5       4       4    19     20   \n",
       "4       306     2     17       9      13      3      11       6    16     15   \n",
       "\n",
       "   H_DRY  M_2R  M_4RY  M_DRY  HminusM_2R  HminusM_4RY  HminusM_DRY     HR_2R  \\\n",
       "0     17     1      9      2          15            0           15  0.941176   \n",
       "1     17     2      3      1          11           11           16  0.866667   \n",
       "2     12     0      4      7          18           12            5  0.975000   \n",
       "3     18     0      0      1          19           20           17  0.975000   \n",
       "4     17     3      5      3          13           10           14  0.842105   \n",
       "\n",
       "     HR_4RY    HR_DRY    FAR_2R   FAR_4RY   FAR_DRY    CRR_2R   CRR_4RY  \\\n",
       "0  0.500000  0.894737  0.187500  0.210526  0.352941  0.812500  0.789474   \n",
       "1  0.823529  0.944444  0.736842  0.625000  0.875000  0.263158  0.375000   \n",
       "2  0.800000  0.631579  0.500000  0.368421  0.187500  0.500000  0.631579   \n",
       "3  0.975000  0.947368  0.250000  0.200000  0.210526  0.750000  0.800000   \n",
       "4  0.750000  0.850000  0.150000  0.550000  0.315789  0.850000  0.450000   \n",
       "\n",
       "    CRR_DRY     MR_2R    MR_4RY    MR_DRY    zHR_2R   zHR_4RY   zHR_DRY  \\\n",
       "0  0.647059  0.058824  0.500000  0.105263  1.564726  0.000000  1.252120   \n",
       "1  0.125000  0.133333  0.176471  0.055556  1.110772  0.928899  1.593219   \n",
       "2  0.812500  0.025000  0.200000  0.368421  1.959964  0.841621  0.336038   \n",
       "3  0.789474  0.025000  0.025000  0.052632  1.959964  1.959964  1.619856   \n",
       "4  0.684211  0.157895  0.250000  0.150000  1.003148  0.674490  1.036433   \n",
       "\n",
       "    zFAR_2R  zFAR_4RY  zFAR_DRY   zCRR_2R  zCRR_4RY  zCRR_DRY    zMR_2R  \\\n",
       "0 -0.887147 -0.804596 -0.377392  0.887147  0.804596  0.377392 -1.564726   \n",
       "1  0.633640  0.318639  1.150349 -0.633640 -0.318639 -1.150349 -1.110772   \n",
       "2  0.000000 -0.336038 -0.887147  0.000000  0.336038  0.887147 -1.959964   \n",
       "3 -0.674490 -0.841621 -0.804596  0.674490  0.841621  0.804596 -1.959964   \n",
       "4 -1.036433  0.125661 -0.479506  1.036433 -0.125661  0.479506 -1.003148   \n",
       "\n",
       "    zMR_4RY   zMR_DRY  dPrime_2R  dPrime_4RY  dPrime_DRY   h-fa_2R  h-fa_4RY  \\\n",
       "0  0.000000 -1.252120   2.451873    0.804596    1.629511  0.753676  0.289474   \n",
       "1 -0.928899 -1.593219   0.477132    0.610260    0.442869  0.129825  0.198529   \n",
       "2 -0.841621 -0.336038   1.959964    1.177659    1.223185  0.475000  0.431579   \n",
       "3 -1.959964 -1.619856   2.634454    2.801585    2.424453  0.725000  0.775000   \n",
       "4 -0.674490 -1.036433   2.039581    0.548828    1.515939  0.692105  0.200000   \n",
       "\n",
       "   h-fa_DRY  acc_2R  acc_4RY  acc_DRY     rt_2R    rt_4RY    rt_DRY  \\\n",
       "0  0.541796   0.725      0.6    0.700  1.129071  1.303166  1.198379   \n",
       "1  0.069444   0.450      0.5    0.475  1.237365  1.147570  1.182943   \n",
       "2  0.444079   0.700      0.7    0.625  1.141441  1.090706  1.125127   \n",
       "3  0.736842   0.850      0.9    0.825  0.894252  0.935852  0.866452   \n",
       "4  0.534211   0.825      0.6    0.750  1.001260  1.214478  1.112539   \n",
       "\n",
       "   LOAD_no_2R  LOAD_no_4RY  LOAD_no_DRY      K_2R     K_4RY  K_DRY         L  \\\n",
       "0           2            4          NaN  1.507353  1.157895    NaN  2.167183   \n",
       "1           2            4          NaN  0.259649  0.794118    NaN  0.277778   \n",
       "2           2            4          NaN  0.950000  1.726316    NaN  1.776316   \n",
       "3           2            4          NaN  1.450000  3.100000    NaN  2.947368   \n",
       "4           2            4          NaN  1.384211  0.800000    NaN  2.136842   \n",
       "\n",
       "   Accuracy_1_away_red  Accuracy_in_red  Accuracy_in_yellow  \\\n",
       "0             0.666667             0.85            0.500000   \n",
       "1             0.166667             0.85            0.000000   \n",
       "2             1.000000             0.60            0.500000   \n",
       "3             0.500000             0.90            0.916667   \n",
       "4             0.666667             0.85            0.666667   \n",
       "\n",
       "   Reaction_Time_1_away_red  Reaction_Time_in_red  Reaction_Time_in_yellow  \\\n",
       "0                  1.614694              1.117499                 1.104783   \n",
       "1                  1.247208              1.168162                 1.117852   \n",
       "2                  1.090766              1.216951                 0.971560   \n",
       "3                  1.083195              0.794040                 0.853225   \n",
       "4                  0.925198              1.108506                 1.185694   \n",
       "\n",
       "   four-filt_diff  \n",
       "0       -0.824915  \n",
       "1        0.167391  \n",
       "2       -0.045525  \n",
       "3        0.377133  \n",
       "4       -0.967111  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv = piv.reset_index()\n",
    "piv = piv.join(lure[['Accuracy_1_away_red', u'Accuracy_in_red', u'Accuracy_in_yellow', u'Reaction_Time_1_away_red', u'Reaction_Time_in_red', u'Reaction_Time_in_yellow']])\n",
    "piv['four-filt_diff'] = piv.apply(lambda x: (x['dPrime_4RY'] - x['dPrime_DRY']), axis = 1)  \n",
    "piv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#a = pd.read_csv('/home/jenni/Dropbox/GATES_8thgrade_fMRI/All_development/wmfilt/master/dprime.csv')\n",
    "b = pd.read_csv('../../All_development/Master/WMFILT_dev_MASTER_5-15-2015_newdp_2015-05-15_15_51.csv')\n",
    "bb = b[['subject', 'group_iq_match', 'art_r1','art_r2','art_r3','art_r4','art_total','fdrms','wmfilt_include_dev_paper','TONI_std', 'wmfilt_reason_all']]\n",
    "bb.columns = ['subject1', 'group_iq_match', 'art_r1','art_r2','art_r3','art_r4','art_total','fdrms','wmfilt_include_dev_paper','TONI_std', 'wmfilt_reason_all']\n",
    "#b.columns = ['subject', 'wmfilt_include_dev_paper', 'wmfilt_reason_all','TONI_scaled']\n",
    "#c = a.merge(b, how='left')\n",
    "#c.to_csv('/home/jenni/Dropbox/GATES_8thgrade_fMRI/All_development/wmfilt/master/MASTER_ALL.csv')\n",
    "piv = piv.merge(bb, how='left') #.to_csv('../Master/WMFILT_dev_MASTER_5-15-2015.csv')\n",
    "piv['new_group'] = piv['group_iq_match']\n",
    "piv.to_csv('../Master/WMFILT_dev_MASTER_%s.csv' % date)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "kk = piv[['new_group', 'K_2R', 'K_4RY', 'L']]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "dp['new_group'] = dp.apply(lambda row:('0' if row['subject1']==302 else '0' if row['subject1']==308 else '0' if row['subject1']==315 \n",
    "                                       else '0' if row['subject1']==318 else '0' if row['subject1']==332 else '0' if row['subject1']==333 \n",
    "                                       else '0' if row['subject1']==335 else '0' if row['subject1']==337 else '0' if row['subject1']==339 \n",
    "                                       else '0' if row['subject1']==341 else '0' if row['subject1']==350 else '0' if row['subject1']==351 \n",
    "                                       else '0' if row['subject1']==354 else '0' if row['subject1']==356 else '0' if row['subject1']==357 \n",
    "                                       else '0' if row['subject1']==360 else '0' if row['subject1']==362 else '0' if row['subject1']==365 \n",
    "                                       else '0' if row['subject1']==367 else '0' if row['subject1']==371 else '0' if row['subject1']==372 \n",
    "                                       else '0' if row['subject1']==373 else '0' if row['subject1']==375 else '0' if row['subject1']==377 \n",
    "                                       else '0' if row['subject1']==380 else '0' if row['subject1']==381 else '0' if row['subject1']==382 \n",
    "                                       else '0' if row['subject1']==383 else '0' if row['subject1']==385 else '0' if row['subject1']==386 \n",
    "                                       else '0' if row['subject1']==388 else '2' if row['subject1']==401 else '2' if row['subject1']==403 \n",
    "                                       else '2' if row['subject1']==405 else '2' if row['subject1']==409 else '2' if row['subject1']==415 \n",
    "                                       else '2' if row['subject1']==416 else '2' if row['subject1']==2004 else '2' if row['subject1']==2007 \n",
    "                                       else '2' if row['subject1']==2010 else '2' if row['subject1']==2014 else '2' if row['subject1']==2015 \n",
    "                                       else '2' if row['subject1']==2016 else '2' if row['subject1']==2019 else '2' if row['subject1']==2021 \n",
    "                                       else '2' if row['subject1']==2022 else '2' if row['subject1']==2027 else '2' if row['subject1']==2028 \n",
    "                                       else '2' if row['subject1']==2029 else '2' if row['subject1']==2030 else '2' if row['subject1']==2032 \n",
    "                                       else '2' if row['subject1']==2035 else '2' if row['subject1']==2039 else '2' if row['subject1']==2041 \n",
    "                                       else '2' if row['subject1']==2047 else '2' if row['subject1']==2049 else '2' if row['subject1']==2052 \n",
    "                                       else '2' if row['subject1']==2053 else '2' if row['subject1']==2054 else '2' if row['subject1']==2056 \n",
    "                                       else '2' if row['subject1']==2057 else '2' if row['subject1']==2061 else '2' if row['subject1']==2065\n",
    "                                       else '2' if row['subject1']==601 else '2' if row['subject1']==603 else '2' if row['subject1']==604\n",
    "                                       else None), axis=1)\n",
    "                                       \n",
    "dp.new_group = dp.new_group.convert_objects(convert_numeric=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "dp_grp = pd.pivot_table( dp, values='dPrime', index=['subject1'], columns=['LOAD','new_group'], aggfunc=np.mean)\n",
    "dp_grp2 = pd.pivot_table( dp, values='dPrime', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "dp_grp3 = pd.pivot_table( dp, values='HR', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "dp_grp4 = pd.pivot_table( dp, values='FAR', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "dp_grp5 = pd.pivot_table( dp, values='CRR', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "dp_grp6 = pd.pivot_table( dp, values='MR', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "k_grp2 = pd.pivot_table( dp, values='K', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "dp_grp7 = pd.pivot_table( dp, values='h-fa', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "dp2 = pd.pivot_table( dp, values='dPrime', index=['subject1', 'LOAD','new_group'], aggfunc=np.mean)\n",
    "\n",
    "acc_grp2 = pd.pivot_table(WM, values='Accuracy', index=['subject1'], columns=['new_group','LOAD'], aggfunc=np.mean)\n",
    "acc_lure_grp2 = pd.pivot_table(WM, values='Accuracy', index=['subject1'], columns=['new_group','probe_type'], aggfunc=np.mean)\n",
    "dp3 = pd.pivot_table( dp, values=['dPrime', 'acccc'], index=['subject1', 'new_group'], columns=['LOAD' ], aggfunc=np.mean)\n",
    "#acc_grp2.head()\n",
    "#acc_grp2['0']\n",
    "#dp3.to_csv('/home/jenni/Documents/tmp/dprime_wmfilt_dev.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figsize=(10, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdfda2ee950>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqQAAADzCAYAAABQUgTPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfda2ee9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = WM.pivot_table(values = [ 'Accuracy', 'Reaction_Time'], index=['subject1','new_group', 'LOAD'], aggfunc=np.mean)#, margins=True)\n",
    "j = pd.DataFrame(j.stack())\n",
    "\n",
    "j = j.reset_index()\n",
    "j.columns = ['subject1', 'new_group', 'LOAD', 't', 'values']\n",
    "# Draw a nested barplot to show survival for class and sex\n",
    "ax1 = sns.factorplot(x=\"LOAD\", y='values', hue='new_group', col='t', data=j, kind=\"bar\", palette=\"muted\"\n",
    "                    ,size=3.5, aspect=1.2, ci=68,sharey=False )\n",
    "ax1.set_ylabels(\"Rates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdfd96e2810>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD+CAYAAADxhFR7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nv50AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfdadd7e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", context=\"poster\")\n",
    "\n",
    "\n",
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = sns.factorplot(x=\"LOAD\", y=\"dPrime\", hue=\"new_group\", data=dp,\n",
    "                kind=\"bar\", palette=\"muted\", size=3.5, aspect=1.2, ci=68)\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"dPrime\")\n",
    "plt.title('dPrime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdfdb5ea650>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9UAAAHvCAYAAABaNPz9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfdb5ea510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#j = dp[['subject1', 'HR', 'FAR', 'CRR', 'MR', 'LOAD', 'new_group']]\n",
    "\n",
    "j = dp.pivot_table(values = ['HR', 'FAR', 'CRR', 'MR', 'h-fa'], index=['subject1','new_group', 'LOAD'], aggfunc=np.mean)#, margins=True)\n",
    "j = pd.DataFrame(j.stack())\n",
    "\n",
    "j = j.reset_index()\n",
    "j.columns = ['subject1', 'new_group', 'LOAD', 'ratetype', 'values']\n",
    "# Draw a nested barplot to show survival for class and sex\n",
    "ax1 = sns.factorplot(x=\"LOAD\", y='values', hue='new_group', col='ratetype', col_wrap=3, data=j, kind=\"bar\", palette=\"muted\"\n",
    "                    ,size=3.5, aspect=1.2, ci=68,sharey=False )\n",
    "ax1.set_ylabels(\"Rates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqQAAADzCAYAAABQUgTPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlclNX+B/DPICAgiLibDEvKddyQVUHxVu5LKpqZmqS4\nlIlKoVguN3PFJVyHFNQuhpSaQCpkmlGogUCYomnuiGKBqKlAgMD8/uDO83MEhIEZHgY+79frvq6c\neZbvnODMd85zFolCoVCAiIiIiEgkemIHQEREREQNGxNSIiIiIhIVE1IiIiIiEhUTUiIiIiISFRNS\nIiIiIhIVE1IiIiIiEpW+2AEQpaamYty4cWjcuDFOnToFMzMzsUMiIhFs3boVQUFBZcr19fXRrl07\nvPbaa5g1axaaNWsmQnQV8/LyAgCEhYWJFoNMJqv0GIlEguPHj6N///5Ys2YNPD09ayEyoqphQkrl\nOnr0KNauXYvY2Fit3ysiIgLt2rVDZmYmYmJiMH78eK3fk4jUU5ttwvbt29GqVSvh59zcXKSkpGDH\njh2Ij49HZGQkDA0NtR5HeR48eIDevXsjNjYWL730EgBgxYoVosTyrIiICOHfCoUC77//Plq3bo3l\ny5erHNe6dWtERESgffv2tR0i0QsxIaVyJScn18p9CgoKcOTIEbz55ps4d+4coqKi6lxC+vTpUxgY\nGIgdBpGoaqtNAIB//etfQrKn1LNnT7Ro0QKffPIJfvjhBwwfPrzW4nlWSkoKgNKkT8nGxkaUWJ7V\ntWtXlZ8NDAzQpEmTMuXlHUtUF3AMKZXh5eWFPXv24O7du5DJZFi4cKHW7nXs2DE8fvwYgwcPxtCh\nQ3Hu3DncuHGjzHFFRUXYtm0bBg4ciB49emD48OEIDw9XOeaff/7B2rVr8dprr8HR0RGjR4/Gd999\nJ7weGRkJmUxW5oN169atkMlkuHv3rspxCQkJeOONN+Dg4CAcm5iYiMmTJ8PJyQlOTk4YM2YMoqOj\ny8SbmJiISZMmwdHREX379sXChQtx//59AMC0adPg4eGBkpISlXMKCwvh7OyMefPmqVmLRNpVm23C\niyj/FpV/qwBQUlKCHTt2YPDgwejWrRt69+6t8vemdPv2bcyfPx9ubm6wt7fH4MGDIZfLUVhYqHJc\nRkYGPvjgA/Tq1Quurq6YOnUqfv/9dwClbcWcOXMAAP3790f//v0BlNaP8rG90vfff4+xY8fCwcEB\njo6OmDBhAuLi4lSOee211zBv3jwkJycLbU3//v3xxRdfaKC2ynfnzh3IZDJERUUBKK0XmUyGffv2\nYdu2bejTpw+cnJzg5+eH/Px8HD16FEOGDIGjoyPGjRuHy5cvq1zv6tWreP/99+Hq6ooePXpgzJgx\niImJ0Vr8VH+xh5TKWL58OebNm4fs7Gxs27YNFhYW5R4XGRmJRYsWvfBaYWFhcHV1rfD1yMhIWFtb\nw97eHpaWlggICEBUVFSZpCwgIAD79+/HvHnz0K1bN5w+fRorVqxAQUEBpk6dCgD48MMPkZKSggUL\nFsDGxgZHjhyBn58fGjVqhMGDB6tZC6UfPmPHjkWnTp0AAJcvX8b06dPh6uqKoKAg6OnpITw8HPPn\nz0fTpk3x73//GwBw7tw5TJ06FQMHDoRcLsfjx4+xfv16zJgxA/v374enpyf8/f2RkJCAPn36CPdL\nSEhAbm4uRo0apXasRNpUm23CiyiTISsrK6FszZo1CA8Px8yZM+Hu7o7bt29j8+bNmDx5svBov7Cw\nEFOmTIFEIsGKFSvQokULxMfHQy6XIz8/H/PnzwcAPH78GG+//TaaNWuGFStWwNTUFNu2bcPkyZMR\nFRWFt956C/fu3cP+/fuxfft2lcfeEolE+PehQ4ewYMECjBgxAr6+vigpKcE333yD999/HyEhIfDw\n8BDOSU9PR0BAAN599100a9YMO3fuxLp16/Dyyy/j1VdfrVY9VYUyXuX/Hz58GFKpFBs2bEBycjLk\ncjmKi4uRkZGBhQsXorCwEMuXL4e/vz8OHToEoDR5nzhxIqRSKQICAtCkSRPExMRg3rx5KC4uxsiR\nI7UWP9U/TEipDFtbWzRp0gSPHj164aOd/v3749tvv33htZ794Hje3bt3kZiYCB8fHwBA8+bN4eHh\ngYMHD+LDDz+Enl5pB/69e/fw9ddfw9fXF1OmTAEAuLi44OrVqzh48CCmTp2K1NRU/Pzzz/jss8/w\n+uuvAwBcXV1x4cIFREVFVSshffnllzFhwgTh5zt37sDd3R1Lly4VPojs7e0RGxuL7777TkhIg4KC\nYG1tjU2bNgnnGhoaYtGiRTh79iwGDhyIJk2aIDo6WiUh/fHHH9GiRQvhw4qorqitNkHp2cfhAJCT\nk4Pk5GSsX78eVlZW6NevHwAgMzMT4eHhmDp1qtBz6eLiAktLS3h5eSEmJgajR4/G3bt30blzZ3h6\nemLAgAEAACcnJ5w6dQrfffedkJDu378fWVlZ2LdvH9q0aQMA6Ny5MwYNGoQjR47g3XffFca2ljes\nQGnz5s3o0qUL1q9fL5T17t0bAwcOxPbt21X+xi9evIjo6GjY2toCANq1a4fBgwcjMTFRqwnp8x4+\nfIg9e/YAAHr16oUDBw7gxx9/xLFjx4T3eenSJXz++efIycmBqakpQkJCAAA7d+5E8+bNAQDu7u5I\nT0/H5s2bmZCSWpiQUrWZm5vD3Ny82udHRkaipKREpdEaOXIkfv75Z/zyyy/o27cvAOD06dMoKSmB\nu7u7yvmbN28W/h0fHw8AZY7Zv39/teN7/lrPPqJTMjY2RosWLfDXX38BKP0gjY+Px9ixY8ucm5iY\nKPw8aNAgHD9+HMuWLYOhoSFKSkoQGxuLoUOHCok4ka6paZug9PzfGVD6pa5fv35YtGiRMKY7ISEB\nxcXFGDRokMqxLi4uMDU1xdmzZzF69GjY2NhALpeXuaZUKsW5c+eEn+Pj42FjYyMkowBgYWGh1vjZ\njIwMZGRkYPTo0SrlBgYGcHd3x+HDh1FcXIxGjRoBKE3QlckoACH5e/z4cZXvqQlubm4qP7dt2xZN\nmjRRSbrbtm0LAEJC+ssvv8DZ2VlIRpX69euHNWvW4P79+2jRooX2g6d6gQkpiUKhUCAqKgpdunSB\nqakpHjx4AABwdHSEkZERoqKihIQ0KysLACp8TPjsMc83jDXx/LWKiooQFhaGgwcP4vbt28jNzRVe\ns7a2BlDay1BUVFRpHJ6enoiKisLPP/+MQYMG4ezZs8jOzsaIESM0Fj+RrgoJCUHr1q2Fn2fPng0z\nMzOVpw7A///dv/nmm+VeR/k6ABw/fhxhYWG4fPky/v77b6H82UftWVlZNW5DlPd8NqlVat26NYqK\nivDw4UO0bNkSAIT/V1Im28+PMde259tXAwODMnWhr1+aMihjy8zMFMakPk8ikSAzM5MJKVUZE1Kq\nkaKiohe+rmzAnpeYmCj0JDzfEwmUPr5+8uQJzMzMhB7Dp0+fVnifZ49RdzmY5x8PVhT72rVrERYW\nhjFjxmDBggVCYz1jxoxy43iRXr164aWXXkJ0dLTQW2plZYUePXqoFTtRXVPdNuFZHTt2VOmZ+/DD\nDzFv3jx888035SafcrkclpaWZcqbNGkCoDQZnT17NlxcXLBy5Uq0a9cOjRo1wubNm/HTTz8Jx0sk\nkkr/divzbIL7PGVb8+xTkBcdL7bKYpNIJOjTpw/8/f3Lfb0qwzOIlJiQUrXVZAJDREQEDAwMEBQU\nVGZJpZs3b2L58uXCmqTKx0T37t3Dyy+/LBxXWFiI/Px8NG3aVDgmKytL5YOpoKAAhYWFKont8x+Y\n9+7dq9L7PXToEBwcHLB69WqhrKioCI8ePRJ+btasGYyNjcu95uPHj2FkZCQkzCNGjMCXX36J/Px8\nHDt2jJOZSOdpa1LT8OHD8cUXX2DTpk0YOnQoTE1NAfz/I2QTE5MXLgx/8OBB6OvrIyQkBCYmJkJ5\nfn6+StLVrl07XL16tcz5OTk5aNSoEYyNjSuNVRlTZmZmmdcyMzNhZGRU5xb2r6527dohLy+vSovy\nE1WGCSlVqLJHRtWdwJCTk4MffvgB/fr1EyYCPcvd3R07d+4U1iR1cHAQdhjp1auXcNySJUsQFxeH\n06dPw9HREUBpT4hy4hMATJ06FTk5OTh48CCaNm0KoHRyklJhYSFOnTpVpV6KoqIilceIALBv3z4U\nFhaq1JWjoyNOnjyJwsJCIflMSEiAt7c35HK5MKnC09MTwcHB2L59O+7cucPH9VTnaatNqAp/f39M\nmTIFn3/+ORYsWACgdNxjo0aNcPjwYfTu3Vs49smTJwgICIC3tzfs7Ozw9OlTNGnSRCUZ/eOPP5CU\nlASgtOdSIpHAyckJJ06cwKVLl9C5c2cApYvye3h4YNy4cVi0aJHQVhQXF5cbZ9u2bWFrayv0yioV\nFhYiPj4eLi4u9WacuLu7O/bv34/bt29DKpUK5d988w2ePHkirIBCVBWiJqTFxcXYunUrtm/fjtmz\nZ6v88Zbn/PnzWLt2Lc6fPw9jY2MMGTIEH3/8MYyMjGop4oajdevWSElJwb59+2BpaakyG1ypuhMY\nYmJikJ+fjzFjxlR4zKhRo7Bt2zbcuHEDL7/8MsaNG4fw8HC0atUKTk5OSEpKQnR0NPz8/CCRSODs\n7Iy+ffti48aNMDExwcsvv4yjR48iJSUFGzZsAFA66145M7R58+YwMDDAl19+CUtLS/z555+Vxu3q\n6oq4uDhERkbCysoKcXFxOHv2LFxdXXH58mWcPn0arq6u8PHxweTJkzFr1izMmDEDDx8+xGeffYbO\nnTvjlVdeEa5na2sLe3t77Ny5E926dasTi2vXV+q2NfHx8diyZQuuXr0KU1NT9O7dG/Pnz2/Q4+G0\n2SZUhZubG/79738jLCwM48ePh5WVFVq3bo1JkyZh9+7dMDMzw6BBg/DgwQOEhIQgIyMDvr6+AEqH\nyPz8888IDAzEq6++iitXriA0NBRjx47Fvn37EBERgYEDB2LChAn46quvMHfuXCxcuBBGRkbYsWMH\nGjVqJKwzqvxSunv3bjg7OwsreDw79OfDDz+Er68vFixYgFGjRqGgoAB79+7F33//jblz52qlfrSh\nouFMSjNmzMB3332HqVOnYsGCBbCwsMDp06exfft2JqOkNtG+pmVnZ8Pb2xs//PBDlcYUZWVlwdvb\nG1KpFAcOHMDGjRsRHx+PJUuW1EK0Dc+0adNgaWmJFStWYN++fRq9dlRUFFq1aiVMWiqPcoaqsrfl\nk08+waxZs7B3715MmTIFkZGRWLx4MaZPny6cs3nzZowbNw5bt27FlClTEB8fj40bN2LYsGEAADMz\nM2zYsAHGxsbw8/PDihUrMGjQIIwYMQISiUSl8S2vx/TTTz+Fm5sbVq1ahTlz5uD+/fvYtm0bvL29\nIZFIMH/+fGFx+x07diAnJwfvvfcelixZAmdnZ+zatavM8ARPT08UFRWxd1SL1G1rzpw5gxkzZsDB\nwQERERFYt24dzpw5gw8++KAWoq27tNkmKEkkkhc+rfD390dxcTHWrFkjlH388cfw9/fHyZMnMWXK\nFCxZsgSWlpb4+uuvhYlFb7/9Nt5++21ERkbi3XffxfHjxxEUFITp06fD2toaAQEBuHz5MszNzREe\nHo5OnTrho48+wsyZM1FUVIQ9e/YIPYDDhg2Di4sL9u7dizVr1gg9pc/GPWjQIGzduhU3btzArFmz\nMG/ePDx9+hS7d++Gvb29NqpOK8r7b/FsWfv27fHVV1/Bzs4OixcvxpQpU3DkyBEsXLgQfn5+tRkq\n1QMSRWVfgbQkNDQUZ86cQUBAANzd3fHuu+++sNdiw4YNiIiIQFxcnPCh8uOPP8LHxwc//PCDyuMC\nIl2xb98+rFy5EnFxcRpdIYD+n7ptja+vL27fvo3IyEihTLnY988//yyMESQiIs0R7ZH90KFDVcb6\nVSYhIQE9e/ZU6eFwd3eHRCLB6dOnmZCSzsnPz8cXX3yBIUOGMBnVInXbmjVr1iA/P1+lTPnf5+HD\nh0xIiYi0QLSEtLw12l4kPT1dZUILUDqzskWLFkhLS9NgZETalZmZifPnz2P37t3Iysrioy0tU7et\nMTY2LjOb+qeffoKZmRk6dOigydCIXigzM1NlLdWKmJqaqiyuT6SLdGaWfW5ubrlLbpiYmCAnJ0eE\niIiqJzExER999BGsra3xxRdfoF27dmKHRC+QkJCAPXv2wM/PT+01bolqYv/+/QgKCqr0uJ49e+LL\nL7+shYiItEdnElJNS0lJUVkChKi22NnZqYxPvHTpkojR6CblkjzaFh8fj1mzZmHQoEEqE+jUoa22\nZuvWrbh8+bLwc6dOnYT93GvD33//jbCwMNy8eRO2trbw8vKqN+tr1hUDBgwQlomrDNsR7aittoZ0\nKCE1MzMrtydUuZtPdfAXjYgqEhsbiw8++ADDhg1T2QyhOjTd1hQXF+P69esqZdevX8e//vUvYY90\nbfP39xcS4suXLyMyMhLr16+vlXsTUf2jM6vzWltbIz09XaXs0aNHePjwIcd1EZFGJScnw9fXFxMm\nTMCaNWvq3ELmCoWizI5jRUVFla4bqSnFxcVITU1VKUtNTa1wsXgiosrUrVb2Bfr27Yvk5GQUFBQI\nZXFxcdDT04OHh4eIkRFRfZKVlYXZs2fjjTfewMKFC8UOp04SOyEmovpHtEf2ly5dwuPHjwGUbkeX\nkZGBxMREAKVbL27duhUXL17Erl27AJQubLxnzx4sWrQIc+fOxV9//YXAwECMHz8erVq1EuttEFEd\np25bs2XLFhgaGuK9997DvXv3VK7VtGlTNG7cWGOxPXr0qExPY1VU1BN56tQptR/Z29vba213JSKi\nqhItIV29ejWSk5MBlO78EBUVhaioKGHP8uzsbJU9x5s1a4bQ0FCsXLkSI0eOhKmpKUaOHIl58+aJ\n9RaISAeo29YkJCQgOzsbr732WplrrVmzBp6enhqLLTU1FR+u/gbN2qg37EihKH9P+RX//Q0SSdUf\nfN2/cxHTPLujR48eat2fCTERaZpoOzWJLSUlBc7OzmKHQUT13IvampMnT2LZrjNobaXedpKKkmJk\nJQSUKW/tvhASvaonhJeTo9D8XxfQqoN6T5kUJQpkHb5f9v4jWkCiV/HWn8+7d/0ePhm77IXbCBNR\nw6Azs+yJiEjzWnVoBam9pVrnlBSXn5BadreEXqOqJ6REREo6M6mJiIiIiOonJqRERLpGIgGeHysq\n0SstJyLSQUxIicrRQIdWk46QSPRg2NRKpcywqZVaE5pqdn+UGSsq0ZMwHyaiamNCSvSM7Oxs+Pv7\n4/XXX4e/vz+ys7PFDomoXE3tRsHQ3AYSPQMYmtugqd2oWru3RE8CU+smKmWm1k3UmtBE9QO/vJOm\nMCElesbatWtx5swZ5Ofn48yZM1i7dq3YIRGVq1FjM1h0m4RWbgtg0W0SGjWu3hbK1WU7yhpmtqbQ\nM9CDma0pbEdZ1+r9SVz88k6axln2RP/zou0Qa2t/cCJ1SUR6Tm5oZoBOkzpCoVCIFoMm6Hr8YlF+\neQcgfHlfv369yFGRLmMPKdH/cDtEIvXpajLHHr7qe9GXd6LqYkJKRBrHJJ7qOg7PqT5+eSdt4CN7\nqne4P7h4srOzsXbtWly8eBFdunTBRx99hJYtW4odFpEKDs8hqnuYkFK9k5qaiuUHllZrO8TyBJ/9\nXP3tENEwt0PkuDLSBezhK8Uv71SXMCGleonbIdY+9jpRbWNCVTOpqan4cPU3aNamg1rnKRQl5Zav\n+O9vaq2F+3fmdWxchAb55Z3KYkJKRBrBXieqbUyoaq5Zmw5obWWv1jmKkmJk3YkuU95a2h0SPX75\npOphQlpP6fpSJroePxHVDiZUDRs/K+oPzrKvZ3R9KRMx4+d2iPVXcXExNm3aBJlMBrlcXunx58+f\nx6RJk9CjRw+4ubnh008/RX5+fi1ESkRVoeufdVQWe0jrGV2fVCJm/MrtEJ/czBHKGuJ2iPVtXF52\ndjb8/Pxw//596OtX3uRlZWXB29sbAwcOxNKlS5GdnY2lS5diyZIl+OyzzzQSE4ms9Nsn8Oyje4ke\n+O2ziupA/en6Zx2VxYS0HtH1SSV1IX7bUda4efAWcu/koYmlSYPcDrG+rVIQHR2NZs2aYdu2bXB3\nd6/0+D179qBx48ZYsWIF9PX1YWdnh48++gg+Pj7w9fWFVCrVSFwkHolED4ZNrVD4KE0oM2xqpdb4\n0YZM7PqrC58VpHlMSOsRXZ9UUhfiry/bIdZUfVqlYOjQoZgyZUqVj09ISEDPnj1VelPd3d0hkUhw\n+vRpJqT1RFO7UXh89SCePsmAgVl7NLUbJUocutrWiFl/deGzgjSPCSlpnNiPfM+dO6f2vZ+nix8Q\nVL42bdqodXx6ejp69eqlUmZiYoIWLVogLS1Ng5GRmBo1NoNFt0miJYS6vomEJuqvqPCfarXXdXV4\nENWMqAlpaGgowsLCkJWVBalUCh8fHwwfPrzC4xMSErB161ZcvXoVJSUlcHNzw4IFC2Bt3fAeq9Zl\nYi/FcvvSCTi/ze9aVD25ubkwNjYuU25iYoKcnJxyziBdJtaXz/oyBrIm9ffk4V0cvHoB8SUn1Tqv\nrg4PopoR7VM7PDwcGzZswPLly+Hg4IC4uDj4+/vD3NwcHh4eZY6/cOECpk+fDi8vL6xevRp5eXlY\nu3YtvL29ER0dDRMTExHehXaI3cMI1Pxbo5hLsTzMvA4gU617E2nTpUuXyi1nj2tpHVS3Z7Au1F91\n4i8pKSnTM3ju3Dn8/vvv0NOrvXGsdaH+xB4eVNl/v86dO6t1Pao+URJShUKB4OBgTJgwAZ6engAA\nGxsbJCcnIzg4uNyENCYmBmZmZvj444+FsoULF8LT0xMpKSn16htOfZtUQg2DctmsZ38PdXHZLDMz\ns3J7Qp88eQIzMzMRIqL6RqFQlOlAKC4u5hhIatBESUhv3LiBrKws9OnTR6Xc3d0dq1atQmFhIQwN\nDVVe09PTK/NowMDAAED9HO8n9rdG0m1ijIurL8tmWVtbIz09XaXs0aNHePjwITp0UG8YilJFvSyl\nayc+qNY16wsbG5tq90LVhfqrTvzPT8hRkslkVVqaTFPqQv1Vhya//Nbk9480S5SE9NatWwCA9u3b\nq5RLpVKUlJTg9u3bZRr+MWPG4KuvvsKuXbswadIklJSU4PPPP4eNjQ3c3NxqLXbSojqwtp2uE3ui\nRH1YNqtv377YvXs3CgoK0LhxYwBAXFwc9PT0yn16Qw0XJ+WIo758+SVVoiSkubm5AFBm3Kfy5/Ie\nl3Xo0AFyuRy+vr4IDAwEUPrNZufOnbX6jZK0R+y17eoDsSdK1MVlsy5duoTHjx8DKB27l5GRgcTE\nRACAo6Mjtm7diosXL2LXrl0AgLfffht79uzBokWLMHfuXPz1118IDAzE+PHj0aqVesNoqH7jpBzx\n1Icvv6RKZzK5K1euwM/PD6NHj8aoUaOQl5eHkJAQzJw5E3v37oWpqana16xoooHY6sJAc7EmGtSV\ntQFrqib1V12amiihid+/miajmpxosHr1aiQnJwtxRUVFISoqChKJBMePH0d2djbu3LkjHN+sWTOE\nhoZi5cqVGDlyJExNTTFy5EjMmzev+m+I6i0OrxJHXfzySzUjSkKqnBjwfE+o8ufykku5XA4rKyss\nXrxYKOvSpQs8PDwQERGByZMnazFiqi1irw2oyzhRonxhYWEvfD0gIKBMWadOnSo9j4jEx8+J+kOU\nhFS5bmh6ejrs7OyE8rS0NOjr68PKyqrMOdevX0e3bt1UykxNTdG8efMyExCqqq4OZM7OzgYeqn9e\nXRnorYmB8rreyIgxUF5TEyWq+/unSZxoQPVZfVmRgkiTRBmcZ2trC6lUihMnTqiUx8XFoXfv3sLs\n+We1bdsWN2/eVCl78uQJ7t27h7Zt22o1Xl2hHOj9LA70JiKqW9hWE5Ul2hhSHx8fLFmyBI6OjnB1\ndUVMTAySkpIQHh4OAAgMDFSZaODl5YWZM2di06ZNGDFiBAoLCyGXy6Gvr48hQ4aI9TbqHA701n1i\nb4ygia1XiejF2FYTqRItIfX09EReXh7kcjkyMzNha2uLoKAgODg4AECZiQavvvoq5HI55HI5du3a\nBQMDA/To0QO7d++GVCoV621USKwxkBzorfu49SpR/ce2mkiVqJ86EydOxMSJE8t9rbyJBgMGDMCA\nAQO0HVaNiL0OpBIbON3GrVeJGga21USluMCjhinXgczPzxfWgSQiIiKiijEh1aDi4uIyY/9SU1Mr\nHNtHRERERExINUqhUJRZeqeoqKjBrwNJRERE9CJMSInqi9LFDZ8r0wMXNyQiorqOCSlRPSGR6MGw\nqeqmEoZNrdSaYU9ERCQGru1SDq4DSbqqqd0oPL56EE+fZMDArD2a2o0SOyQiIqJKMSEtB9eBJF3V\nqLEZLLpN4tqGRESkU5j1VIDrQJIuYzJKRES6hIPLiIiIiEhUTEiJqEEIDQ1F//790b17dwwbNgwx\nMTEvPD4hIQETJ06Eq6srnJ2d4ePjg1u3btVStEREDQsTUiKq98LDw7FhwwbMmTMHhw8fxltvvQV/\nf3+cOnWq3OMvXLiA6dOnw97eHt988w3CwsKQk5MDb29v5OXl1XL0RET1H8eQapJyHchnJzdxHUgi\nUSkUCgQHB2PChAnw9PQEANjY2CA5ORnBwcHw8PAoc05MTAzMzMzw8ccfC2ULFy6Ep6cnUlJS0Ldv\n31qLn4ioIWAPqQZxHUiiuufGjRvIyspCnz59VMrd3d2RkpKCwsLCMufo6emVmRhmYGAAgBPGiIi0\ngZmShjW1GwVDcxtI9AxgaG7DdSCJRKYc99m+fXuVcqlUipKSEty+fbvMOWPGjEF+fj527dqFgoIC\n/PPPP/j8889hY2MDNze3WombiKgh4SN7DeM6kER1S25uLgDAxMREpVz5c05OTplzOnToALlcDl9f\nXwQGBgIofcy/c+dO6Ouz2SQi0jS2rFrCZJRId125cgV+fn4YPXo0Ro0ahby8PISEhGDmzJnYu3cv\nTE1N1brENF+sAAAgAElEQVTepUuXyi1PS0vTQLS6LS0tDS1btqz2uQ0d669mKqu/zp0712I0DRsT\nUiKq18zMzACU7QlV/lxecimXy2FlZYXFixcLZV26dIGHhwciIiIwefJkLUZMRNTwMCElonrN2toa\nAJCeng47OzuhPC0tDfr6+rCysipzzvXr19GtWzeVMlNTUzRv3hzp6elqx1BRL0t2djaAB2pfrz6x\nsbGpdi8U64/1V1M1qT/SLE5qIqJ6zdbWFlKpFCdOnFApj4uLQ+/evYXZ889q27Ytbt68qVL25MkT\n3Lt3D23bttVqvEREDRETUiKq93x8fBAREYFvv/0WGRkZCAkJQVJSEmbNmgUACAwMxLRp04Tjvby8\nkJqaik2bNuH69eu4dOkSPv74Y+jr62PIkCFivQ0ionpL9Ef2oaGhCAsLQ1ZWFqRSKXx8fDB8+PAK\nj3/y5AnWrVuHY8eO4enTp3BycsLSpUshlUprMWoi0iWenp7Iy8uDXC5HZmYmbG1tERQUBAcHBwCl\njy7v3LkjHP/qq69CLpdDLpdj165dMDAwQI8ePbB79262NUREWiBqQqrczm/58uVwcHBAXFwc/P39\nYW5uXu7uKQAwa9YsSCQS7N69GwCwbNkyzJw5E9HR0ZzZTkQVmjhxIiZOnFjuawEBAWXKBgwYgAED\nBmg7LCIigogJaXW28zt58iTOnz+Pn376CRYWFgCA9evX4+LFi3j69CkMDQ1r9T0QERERUc2JNoa0\nOtv5xcbGws3NTUhGAcDS0hKDBg1iMkpERESko0RLSKuznd/Vq1dhbW2NkJAQDB48GO7u7vDz88OD\nBw172QoiIiIiXSZaQlqd7fzu37+P77//HlevXsWGDRuwevVqnDt3Dl5eXiguLtZ+0ERERESkcaLP\nsldHUVERjIyMsG7dOkgkEnTt2hVGRkbw9vbGqVOn8Morr6h1PW7nVzFuR1czrL+a4XZ+REQNi2g9\npNXZzs/U1BSdOnVSmU3v5OQEiUSCK1euaDFaIiIiItIWtXpIk5KScPbsWWRlZWH69Olo27Yt/vzz\nT1hYWMDIyEitG1dnOz9ra+sy40VLSkqgUCjKTWArw+38Ksbt6GqG9Vcz3M6PiKhhqVIPaW5uLqZO\nnYp33nkHGzZsQHh4OP7++28AwLZt2zBy5EhkZWWpdePqbOfXt29fnDt3Dg8fPhTKfvvtNwBAp06d\n1Lo/EREREdUNVUpIN2/ejAsXLmDNmjU4ffo0FAqF8NqMGTMgkUiwdetWtW+u7nZ+I0aMQLt27TB3\n7lxcu3YNiYmJWLZsGZydneHk5KT2/YmIiIhIfFVKSI8ePQpfX194enqiWbNmKq9JpVLMnj0bP/74\no9o39/T0xKJFiyCXyzFkyBBER0e/cDs/Q0NDhIaGwtzcHOPGjYOPjw+cnJwQHBys9r2JiIiIGhLl\nMMe6qEoJ6f3791/4SLx9+/Z4/PhxtQKYOHEijh8/jvPnz+PQoUN49dVXhdcCAgJw9OhRlePbtm0L\nuVyOM2fO4Ndff0VAQEC1xo8SERERVYdMJkN0dDTmzp0LFxcXeHh4qHSOhYeHY8SIEXB0dMQrr7yC\nzz77DMXFxbhz5w5kMhlu3rwpHOvr6wt7e3uVDYHGjBmD7du3VymW1NRUjB49Gj169ICnpyd+/fVX\nODg44NtvvwUAfPzxx5g7dy4WLlwIJycnoaNv7969QoweHh5YtWqVEENiYiJkMpnKmvDx8fGQyWS4\ne/cuAKBfv37YunUr5s+fD2dnZ7i6umLdunXVTnirlJC2bt0a58+fr/D1xMREtGnTploBEBEREema\nrVu3YvLkyUhOToavry82btyIq1ev4sCBA9iyZQuWLVuG3377DcHBwfjuu++wfft2WFpaCtukA6Xb\nqCcmJsLa2hpnz54FADx69Ah//PFHlZayVCgU+PDDD2FpaYlffvkFcrkcW7duRX5+vspxZ86cQefO\nnXHmzBlIpVJERkZi3bp1WLRoEVJSUrBr1y78+OOPCAgIUKsO9uzZgyFDhiApKQlyuRxfffUVDhw4\noNY1lKqUkA4bNgxbtmzB3r17hVnuhYWFuHXrFuRyOeRyOV5//fVqBUBERESkawYOHAhnZ2dIJBIh\nB7p8+TL27NmD8ePHC3NbZDIZvL298c033wAAPDw8kJiYCAD4448/0LRpU7z22mtCWXJyMpo3b16l\nlUbOnz+PjIwM+Pj4wNTUFJaWlnj33XfLHKdQKPDOO+9AT6807duzZw/GjBkDd3d36OnpoVOnTvDy\n8sLBgwfVqgMnJycMGDAAjRo1Qq9eveDh4YFjx46pdQ2lKi37NGfOHNy8eROffvopPv30UwDAuHHj\nhNcHDBgAHx+fagVAREREpGuUy1cCgLGxMQDgn3/+wc2bN3Ht2jX897//FV5XPsYuKiqCh4cHPvnk\nEwBAQkICXF1d4ezsjB07dmDOnDk4ffo0+vbtW6UY/vzzTwCl83mUevToUea457dpv337tkoeBwAv\nv/wy8vLy/rf0YNV06NBB5WdlT211VCkhbdy4MYKCgnDu3DmcOnUKmZmZAIB27dqhT58+sLe3r9bN\niYhqS2hoKMLCwpCVlQWpVAofHx8MHz68wuOfPHmCdevW4dixY3j69CmcnJywdOlSlYafiBquZzfp\neZaRkRFmzZqFyZMnl/t6z5498fDhQ9y6dQsJCQkYMWIEnJ2d4evri4KCApw+fbrKnXwlJSUAoLJU\nZnlx6eurpnsFBQVlxnoqf67ofZW3RfvzZQqFosLzK1NpQlpcXIzDhw+jT58+6NGjR7mZNxFRXRYe\nHo4NGzZg+fLlcHBwQFxcHPz9/WFubg4PD49yz5k1axYkEgl2794NAFi2bBlmzpyJ6Ojoaje4RFT/\n2djY4OLFiypl9+/fh5GREZo0aQITExM4Ozvjl19+QUpKClatWgVTU1O8/PLLOHr0KNLS0ipsl57X\nqlUrAKU9nsreSuVY1Gc932bZ2Njgjz/+UCm7cuUKzM3N0aJFC2Hi0z///CO8np6eXua6z291nZ6e\njpdeeqlKsT+v0jGkjRo1wrJly1SWXyIi0hUKhQLBwcGYMGECPD09YWNjg8mTJ6Nfv34VLhl38uRJ\nnD9/Hps3b4ZMJoNMJsP69evh6+uLp0+f1vI7ICJdIZFIMHnyZHz33Xf4/vvv8fTpU9y+fRvvvvsu\n1q5dKxzn4eGBr776Cq1bt0br1q0BlPac7tixA/b29sL26pVxdHREy5Yt8fnnnyMvLw937tzBrl27\nyhz3fG/ohAkTcPDgQSQkJKC4uBgXLlzAnj178OabbwIoHQKgr6+PmJgYFBcX49q1a4iMjCxz3TNn\nziA2NhZPnz5FQkIC4uPjMXTo0CrX17OqNKlp7Nix+O9//6uyJAERkS64ceMGsrKy0KdPH5Vyd3d3\npKSklNuuxcbGws3NDRYWFkKZpaUlBg0aBENDQ63HTES6a9iwYViwYAE2btwIZ2dneHl5wcnJCUuW\nLBGO8fDwwLVr1+Dm5iaUubq64urVq/j3v/9d5Xs1atQI69evx++//w53d3f4+flhzpw5AP6/V1Qi\nkZTpIZ0wYQLmzJmDlStXwsXFBfPnz8ekSZPg5+cHAGjevDkWLVqEqKgouLi4YNWqVZg7d26Z64wZ\nMwZHjx6Fu7s75s6diylTpmDUqFHqVdj/VGkMqZGREbKysuDu7g4HBwc0b968zHgEAGovF0BEpG23\nbt0CUHZQv1QqRUlJicqjLqWrV6+ia9euCAkJQUREBB4/fgx3d3csWbIEzZs3r7XYiahuev5x9/Nl\nXl5e8PLyqvB8mUxW5hr9+/cv97qV6dWrF6Kjo4W8TPlEu127dgAqzs2mTZumshvm8yZOnIiJEyeq\nlF26dEnlZyMjI5We35qoUkK6Y8cO4d8vmj3FhJSI6prc3FwAgImJiUq58uecnJwy59y/fx/ff/89\nevbsiQ0bNiArKwsrV66El5cXDh06hEaNGmk/cCKiKhg1ahQ6duyIlStXAgDkcjnatGmjcxPOq5SQ\nVidjJyLSVUVFRTAyMsK6desgkUjQtWtXGBkZwdvbG6dOnarSgtXPer5XQen5CQENUVpaGlq2bFnt\ncxs61l/NVFZ/VVkLVFuWLl2KqKioCl+XSCSIiYnBxo0bsWrVKrzyyito1KgRunTpguDgYBgZGdVi\ntDVXpYSUiEhXKScHPN8Tqvy5vK2HTU1NIZVKVcZLOTk5QSKR4MqVK2onpERE6lq2bBmWLVtWpWOf\nXfO0tsTGxmr0elVKSOVyeZUuNnv27BoFQ0SkacrFq9PT02FnZyeUp6WlQV9fH1ZWVuWeo9yVTqmk\npAQKhaLcBLYyFfWylC5A/aDc1xoKGxubavdCsf5YfzVVk/ojzWJCSkT1mq2tLaRSKU6cOIH+/fsL\n5XFxcejdu7fKgtJKffv2xfLly/Hw4UNhpv1vv/0GAOjUqVPtBE5E1IBUKSE9fvx4mTKFQoF79+4h\nNjYWFy9exGeffabx4IiINMHHxwdLliyBo6MjXF1dERMTg6SkJISHhwMAAgMDcfHiRWH9vhEjRmDH\njh2YO3culi5divv372PZsmVwdnYW9qcmIiLNqVJCamlpWW65VCqFk5MTgoKCsHbtWo1N/Sci0iRP\nT0/k5eVBLpcjMzMTtra2CAoKgoODA4DSR5fPbv5haGiI0NBQrFy5EuPGjYOenh4GDhyIxYsXi/UW\niIjqNY1MaurZs6ewvR4RUV1U3pp6SuUtWde2bdsqD1ciIqKaqdJOTZU5d+4c93YmIiIiomqpUg/p\nwoULyy0vKipCRkYGfvvtN5XJAkREREQN1aNHj5CamipqDPb29jA3Nxc1BnVUKSGtaGFWiUSCpk2b\nYuTIkfD399doYERERES6KDU1FR+u/gbN2nSo/GAt+DvzOjYuKl0xpDpCQ0MRFhaGrKwsSKVS+Pj4\nYPjw4RqOUpVGdmrKzMzEzZs3q71bBBEREVF90qxNB7S20q3tOwEgPDwcGzZswPLly+Hg4IC4uDj4\n+/vD3NwcHh4eWruvRsaQ/vDDD3jnnXfUPi80NBT9+/dH9+7dMWzYMMTExFT53BUrVkAmkyE5OVnt\n+xIRERGRKoVCgeDgYEyYMAGenp6wsbHB5MmT0a9fPwQHB2v13hpJSIHSN6EOZQY+Z84cHD58GG+9\n9Rb8/f1x6tSpSs9NTU3F/v37OZGKiIiISENu3LiBrKws9OnTR6Xc3d0dKSkpKCws1Nq9NZaQqqMm\nGXhxcTGWLl2K0aNHq50EExEREVH5bt26BQBo3769SrlUKkVJSQlu376ttXuLkpDWJAMPCwtDfn4+\nvL29tR0mERERUYORm5sLADAxMVEpV/6ck5OjtXuLkpBWNwP/66+/sHXrVnz66afl7j9NRERERLqn\nwln2d+/erfJFHj16pNZNq5uBr1y5EgMGDECvXr1UtvmrrkuXLpVbnpaWVuNr67q0tLRqr5rA+mP9\n1VRl9de5c+dajIaIqGEwMzMDUDYPU/5samqqtXtXmJD269dPazetjtjYWCQnJ+PIkSNih0JERERU\n71hbWwMA0tPTYWdnJ5SnpaVBX18fVlZWWrt3hQmpj4+PWhdSZ8a7uhl4Xl4eVqxYAX9/fzRv3lzl\ntZpMbKqolyU7OxvAg2pftz6wsbGpdi8U64/1V1M1qT8iIqoeW1tbSKVSnDhxQmUHzri4OPTu3Vur\nwyUrTEjnzJmjtZuqm4FfuHABf/75J5YuXYqlS5eqvDZlyhRIpVIcPXpUa/ESERERqePvzOsi39up\nWuf6+PhgyZIlcHR0hKurK2JiYpCUlITw8HDNBvmcKu3UpGnqZuDdu3dHdHS0SllmZiamTZuGVatW\nwcmpepVOREREpGn29vbYuEjMCJxgb1+9XaI8PT2Rl5cHuVyOzMxM2NraIigoCA4ODhqOUZUoCSlQ\neQYeGBiIixcvYteuXTA2NkbHjh1VzjcyMgIAWFpaCj2uRERERGIzNzev9j7ydcHEiRMxceLEWr2n\nKMs+AaUZ+KJFiyCXyzFkyBBER0erZODZ2dmVzqTnTk1EVBXcppiIqG4TrYcUeHEGHhAQ8MJzLS0t\nK1y2iYhISblN8fLly+Hg4IC4uDj4+/vD3NwcHh4eLzyX2xQTEdUO0XpIiYi0jdsUExHpBiakRFRv\ncZtiIiLdwISUiOotblNMRKQbRB1DSkSkTdymuO7jNrs1w/qrGW5TXHcwISUiega3KSYiqn1MSImo\n3uI2xXUft9mtGdZfzXCb4rqDY0iJqN56dpviZ1Vlm+KuXbuia9euGDx4MIDSbYqV/yYiIs1iDykR\n1VvcppiIxPDo0SOkpqaKGoO9vT3Mzc1FjUEdTEiJqF7jNsVEVNtSU1Ox/MBStOrQSpT737t+D59g\nWbW2Ly0sLERISAgOHz6MrKwstG/fvla2EmVCSkT1mqenJ/Ly8iCXy5GZmQlbW1tuU0xEWteqQytI\n7S3FDkNtq1evxpEjR7B8+XJ06dIFP/30E1asWIHGjRvjjTfe0Np9mZASUb3HbYqJiCr35MkTHDhw\nAAsWLBDGzL/zzjuIi4vDoUOHmJASERERkXaZmZnh5MmTMDY2Vilv0aIFLl++rNV7c5Y9EREREQEA\nLCwshLHzAPDPP//g9OnT6NGjh1bvy4SUiIiIiMq1fPly5OTkYMaMGVq9Dx/ZExEREZEKhUKBTz/9\nFIcPH8amTZsglUq1ej8mpEREREQkKC4uxsKFC3Hs2DFs2bIF/fr10/o9mZASERERkWD58uWIjY3F\nzp074eLiUiv3ZEJKRERERACAffv2ITIyEl988UWtJaMAE1IiIiIijbt3/Z6493ZU/7zc3FwEBgZi\n7NixsLW1xb17qu+hVSvt7TwlekIaGhqKsLAwZGVlQSqVwsfHB8OHD6/w+Pj4eGzZsgVXr16Fqakp\nevfujfnz56NFixa1GDURERFR+ezt7fEJlokXgGNpDOr6/fff8fjxY3z99df4+uuvVV6TSCRa3SRE\n1IQ0PDwcGzZswPLly+Hg4IC4uDj4+/vD3NwcHh4eZY4/c+YMZsyYAS8vL6xZswaZmZn45JNP8MEH\nHyAsLEyEd0BERESkytzcvFr7yIutZ8+e+OOPP0S5t2gJqUKhQHBwMCZMmABPT08AgI2NDZKTkxEc\nHFxuQrp792506tQJH3/8sXD83LlzMW/ePPz1119o27Ztrb4HIiIiIqo50RbGv3HjBrKystCnTx+V\ncnd3d6SkpKCwsLDMOWvWrMGuXbtUypo3bw4AePjwofaCJSIiIiKtES0hvXXrFgCgffv2KuVSqRQl\nJSW4fft2mXOMjY1hYWGhUvbTTz/BzMwMHTp00F6wRERERKQ1oiWkubm5AAATExOVcuXPOTk5lV4j\nISEBe/bswXvvvQdDQ0PNB0lEREREWif6LPvqio+Px6xZszBo0CBMnz69WteoaLZYWlpaDSKrH9LS\n0tCyZctqn9vQsf5qprL669y5s9rX5IoeRER1l2g9pGZmZgDK9oQqfzY1Na3w3NjYWMycORNDhgzB\nhg0btBckEdULyhU95syZg8OHD+Ott96Cv78/Tp06Ve7xyhU9HBwcEBERgXXr1uHMmTP44IMPajly\nIqKGQbQeUmtrawBAeno67OzshPK0tDTo6+vDysqq3POSk5Ph6+uLiRMnYuHChTWKoaJeluzsbAAP\nanRtXWdjY1OtXiiA9Qew/mqqJvX3PK7oQURU94nWQ2prawupVIoTJ06olMfFxaF3794wMDAoc05W\nVhZmz56NN954o8bJKBE1DFzRg4io7hMtIQUAHx8fRERE4Ntvv0VGRgZCQkKQlJSEWbNmAQACAwMx\nbdo04fgtW7bA0NAQ7733Hu7du6fyv4KCArHeBhHVYVzRg4io7hN1UpOnpyfy8vIgl8uRmZkJW1tb\nBAUFwcHBAUDpo8s7d+4IxyckJCA7OxuvvfZamWutWbNGeBxHRKSkyRU9/Pz8qrWiBydQVowTAGuG\n9Vcz2phASdUj+iz7iRMnYuLEieW+FhAQoPLzjz/+WBshEREJNLGiBxERvZjoCSkRkTbVdEWPDz74\nAMOGDcPq1aurHQMnUFaMEwBrhvVXM5qcQEk1I+oYUiIibXt2RY9nVXVFjwkTJmDNmjXQ02NzSUSk\nLWxhiahe44oeRER1Hx/ZE1G95+PjgyVLlsDR0RGurq6IiYlBUlISwsPDAZSu6HHx4kVhqafnV/R4\nVtOmTdG4ceNafw9ERPUZE1Iiqve4ogcRUd3GhJSIGgSu6EFEVHdxDCkRERERiYoJKRERERGJigkp\nEREREYmKCSkRERERiYoJKRERERGJigkpEREREYmKCSkRERERiYoJKRERERGJigkpEREREYmKCSkR\nERERiYoJKRERERGJigkpEREREYmKCSkRERERiYoJKRERERGJStSENDQ0FP3790f37t0xbNgwxMTE\nvPD48+fPY9KkSejRowfc3Nzw6aefIj8/v5aiJSJdxvaGiKjuEi0hDQ8Px4YNGzBnzhwcPnwYb731\nFvz9/XHq1Klyj8/KyoK3tzekUikOHDiAjRs3Ij4+HkuWLKnlyIlI17C9ISKq20RJSBUKBYKDgzFh\nwgR4enrCxsYGkydPRr9+/RAcHFzuOXv27EHjxo2xYsUK2NnZwd3dHR999BGio6Nx+/btWn4HRKQr\n2N4QEdV9oiSkN27cQFZWFvr06aNS7u7ujpSUFBQWFpY5JyEhAT179oS+vr7K8RKJBKdPn9Z6zESk\nm9jeEBHVfaIkpLdu3QIAtG/fXqVcKpWipKSk3B6I9PT0MsebmJigRYsWSEtL01qsRKTb2N4QEdV9\noiSkubm5AEob+Gcpf87JySn3HGNj4zLlJiYm5R5PRASwvSEi0gX6lR9Sf126dKnc8rS0NPydeb2W\no/l/OQ8ycO/6Q9Huf+/6PaRZpKFly5bVOp/1x/qriarUX+fOnWsxoppjW1M+/q3UDOuvZupjW6PL\nRElIzczMAJTtmVD+bGpqWu455fVMPHnyRLieuvLy8sot79KlC4JXdqnWNTXDQ8R7A/jfULuK6qcy\nrL/S/2P9VVMV6i8lJQXOzs5VulxdaG/Y1lSAfys1w/qrGQ23NVQzoiSk1tbWAErHadnZ2QnlaWlp\n0NfXh5WVVbnnpKenq5Q9evQIDx8+RIcOHdSOgb9gRA2D2O0N2xoiosqJMobU1tYWUqkUJ06cUCmP\ni4tD7969YWBgUOacvn37Ijk5GQUFBSrH6+npwcND5G9ZRFRnsb0hIqr7RFsY38fHBxEREfj222+R\nkZGBkJAQJCUlYdasWQCAwMBATJs2TTj+7bffRqNGjbBo0SLcunULiYmJCAwMxPjx49GqVSux3gYR\n6QC2N0REdZtok5o8PT2Rl5cHuVyOzMxM2NraIigoCA4ODgCA7Oxs3LlzRzi+WbNmCA0NxcqVKzFy\n5EiYmppi5MiRmDdvnlhvgYh0BNsbIqK6TaJQKBRiB0FEREREDZdoj+yJiIiIiAAmpEREREQkMiak\nRERERCQqJqREREREJCompEREREQkKiakRERERCSqBpeQFhcXY9OmTZDJZJDL5WKHo5MSExMhk8lw\n5swZjVyvX79++M9//qORa2mDl5cXvL29a+1+dbE+IiMjIZPJkJmZCaD260QXsa2pObY12lcX64Tt\nTcMk2sL4YsjOzoafnx/u378Pff0G9dbrtIiICBgaGoodxgtJJJJau5cu1AdQu3Wia9jW1E268LdV\n239XulAnANubhqBB9ZBGR0ejWbNm2L9/P/T0GtRbV6FQKFBSUiJ2GAILCws0adJEa9cvKirS2rW1\nQdv1QdrHtqYU25q6j+0N1RUNqqUcOnQotmzZUqM/vsuXL+Pdd9+Fs7MzHBwc4OnpiWPHjgEAbt68\nCZlMhl9//VU4PiYmBjKZDF999ZVQduPGDchkMly4cAGFhYVYu3YtXnnlFXTr1g2vvvoqVq9ejYKC\nAgCAr68vRowYUSaOFStWoF+/flWO28vLC/7+/li1ahUcHR0RHx8PAEhJScE777yDXr16wdXVFR98\n8AGysrJUzg0MDISbmxscHR0xa9Ys3L9/v+oVVgX9+vXDkiVLhMdzly5dwty5c+Hs7AwPDw+sXr0a\n6mwoprzO8ePHMXjwYEyaNAkAUFhYiHXr1mHw4MGwt7fH0KFDERERoXLuH3/8gTfffBP29vYYMGAA\nIiMjNfpeq0KT9XHt2jXIZDIcPXpUpfz+/fvo0qWL8P6vXbuG9957D3369IGjoyOmTZuGGzduVDnm\nO3fuYM6cOejZsye6d++OkSNH4vDhwwCAU6dOqTx+A4CQkBDIZDLExcUJZSdPnkTnzp3x4MGDKt+3\nrmJbw7ZGF9oagO1NfWhv6osGlZC2adOmRueXlJTg/fffx9OnT7Fv3z7ExMRgwIAB8PPzw7Vr12Br\na4t27dqpjHdKSkpCu3btkJKSIpQlJyejWbNm6NatGz7//HPs27cPK1euxI8//oiAgAAcPnxYGHP2\n1ltv4erVq7hw4YJKHEePHoWnp6da8Ss/lI4cOQIXFxdcv34dU6dORbNmzRAeHo4dO3bg9u3bmD59\nutCr8fXXX2PXrl2YPXs2Dh06hEGDBmHjxo01qcZySSQS4ZHM0qVLMXjwYBw8eBBTpkzBl19+WaaB\nq4pdu3Zh1apVQl0uXboUBw4cwNy5cxEdHY1x48bhP//5D44cOQKg9EPk/fffBwDs27cPcrkcP/zw\nA65fv66hd1l1mqqPjh07wtHREVFRUSrl33//PRo3boyhQ4fiwYMH8PLyQl5eHkJCQvD1119DIpFg\n8uTJyMnJqfQe//zzDyZPnowHDx5g586diImJQf/+/eHv74+ffvoJLi4uMDQ0VPkbSEpKwksvvaTy\nt5KcnAyZTIbmzZtX6b3VZWxr2NboSlsDsL2huqFBJaQ1JZFIsGfPHmzatAkdO3ZE+/btMXPmTCgU\nCgla3KYAABRBSURBVJw+fRoA4ObmVuaXfty4cWU+JNzc3AAAU6ZMweHDh9G3b1+0adMG7u7ueOWV\nV/DLL78AANzd3dG+fXuVP/Dk5GRkZ2dj9OjRasWflZWFxYsXo127djAyMsKXX34JMzMzBAYGomPH\njnBwcMCaNWtw5coVnDx5EgBw8OBB9OnTB5MmTYJUKoWnpyeGDh1avQqsov79+2P48OGwtLTEtGnT\nYGxsrPIhWVWvvvoqXFxc0LJlS2RmZuLgwYOYPXs2hg8fDisrK3h7e2PAgAHYuXMngNJG688//8R/\n/vMfdO7cGTKZDOvXr69SI6lNNa2PN998E6dOnUJ2drZQ9v3332Pw4MEwMTHBgQMHkJOTg82bN6Nr\n167C+378+DEOHjxY6fWPHz+OjIwMBAQEwN7eHlZWVvD19YWDgwPCw8NhZGQEBwcH4W+gqKgIv/32\nG8aNG6fSw5ecnIzevXurUTP1F9satjViYXtDYmFCqgaJRIL79+9j8eLFeO211+Dk5ARXV1cUFxfj\n77//BlD6IfHbb78BKJ3YkJ6ejvHjx+Phw4e4e/cugNJHV3369AEA6OvrY+/evRg6dChcXV3h6OiI\n6OhoPHr0SLjn2LFjER0dLYxP+u677+Dq6gqpVKpW/B07dlQZvJ6amgonJycYGBgIZXZ2djA3N8el\nS5cAlD5a6dy5s8p17O3t1bqvurp37y78WyKRwMLCAo8fP1b7Ol26dBH+feHCBZSUlKBXr14qx/Ts\n2ROXL18GUPpeAUAmkwmvm5qaokOHDmrfW5NqWh9Dhw6FkZERDh06BKA0WUhJSRGSjNTUVNjZ2an0\nFFhYWKBDhw7C78GLXLhwARYWFrCysioT98WLFwGUJjvK5OnixYswMjLCqFGjcP78eTx9+hQFBQW4\ncOGC8HfR0LGtKcW2pvaxvSGxcPqnGjIyMuDl5YWuXbti9erVeOmllyCRSDB8+HDhGHd3dzx69AjX\nrl3D1atXIZPJYGFhge7duyM5ORnOzs74888/hW9m8+bNQ3JyMpYsWQJ7e3sYGhpi8+bNwgcNAIwZ\nMwZyuRw//fQT+vXrhx9++AH+/v5qx//8eLacnBzExsbC0dFRpbygoED4dpuXlwcjI6MXXkfTnr8f\nALXGdSk9G6ey52H8+PEqxxQXF6O4uBgPHz5Ebm4uJBJJmRmnxsbGat9bk2paH8bGxnj99dcRFRWF\nqVOn4ujRo3jppZeED8ycnBz88ccfZX4PCgsL0bp160qvn5OTU+7vRJMmTYR6d3d3h1wuR25uLpKS\nkuDi4oJ27dqhRYsWOHfuHIqLiyGRSODi4lLl91Wfsa0p/zqaxramLLY3JBYmpGqIjY1Ffn4+Nm3a\nhFatWgEAHj16hKdPnwrHtG7dGh07dsSZM2dw6dIl4Rfe2dkZKSkpUCgUsLKyQvv27fHkyROcOHEC\nH374IcaMGSNcIy8vT2WJizZt2qBv3744fPgwjI2N8c8//2DIkCE1fj9mZmbo0KEDFi9eXOY15R+8\nsbEx8vPzVV6rTg+C2MzMzAAAQUFB5fb2NG3aFCYmJlAoFCgsLFT5oHjy5Em5jbQuefPNN7F3715c\nuXIFMTExKmMCmzZtCplMhs2bN5c5r3HjxpVe28zMDE+ePClTnpOTg6ZNmwIo7b0wNjbG2bNnkZSU\nhL59+wIAnJyccObMGRQWFgpjv4htjRLbGt3E9oaqg4/s1aD8MGjWrJlQppzZ9yzl44Jff/1V+JBw\ncnLCr7/+qvIIraioCAqFAhYWFsK52dnZSEhIKPONdNy4cThx4gT279+PIUOGaOSbtL29PdLS0iCV\nSlX+V1BQIDxOsbW1RWpqqsp5ylmzuqRbt27Q09PD/fv3Vd6roaEhzM3N0ahRI9ja2gL/1969x0Rx\nvX0A/65caiOkBRQQRROt7lpYQRe0gE3Z9daUUvCWpmwXGhUbIhcjseLaEAWtwoIFbQ1Es1a0hBQr\nXkIURCmmlsuKtwp4Y8NVLoragitFcH5/8O68DgvK4uLK+nz+O8czM88M5pmzc86ZATjn29LSYrSF\nBobk4uKCDz/8EOnp6bh69SpnTqBQKER9fT3Gjh3LuTZdXV2ws7N76b5dXV3xzz//6KySvXz5Mlxd\nXQEAZmZmmDNnDsrKynD58mV4enoC6O08qVQqXLp0ieZzPYdyTS/KNSMT5RsyFG9Vh7SqqgqlpaUo\nLS3Fs2fP0NjYyJa7urpeur2bmxsAYN++fWhoaEBWVhbOnz8PZ2dnVFRUsK8o8fLyQnFxMaqrqyES\niQD03iRqampw4cIF9iahnQdz5MgRqNVqlJeXIywsDAsXLsT9+/dx+/Zt9PT0AOidNG9tbY38/HzO\nEw599L3xyGQy3L17F7Gxsbh58ybUajWSkpKwZMkSNjF+/vnnKCsrQ2ZmJmpra5Gdnc0uqhhJ7O3t\n4e/vj8TERBQUFKChoQEXLlxAcHAwtm3bBqB3Tt7YsWOhUChQVVWFyspKyOVy2NnZDWkY702zfPly\n5ObmwtPTExMmTGDrly1bBjMzM0RHR6OiogJ1dXVQKpUICAhAaWnpgPvTXpNFixZh8uTJ2LhxI65d\nuwa1Wo2EhARUVVVxvq7i5eXFLlrQzp0TiUS4cuUKrl27ZlLzuSjXUK55m3MNQPmG6O+t6pD+8MMP\nCAkJQUhICHp6epCTk4OQkBB88803nBWBAxGJRIiMjERmZiYCAgLw119/QaFQQCqVori4GPHx8QB6\nJ6+3tbVhypQp7BMJa2trfPDBB2hpaWFXvQKAQqFAZ2cnlixZgri4OKxfvx5r166Fra0tpFIpHj58\nCAAYNWoUxGIxJk2axP7a01ffL11MnToVBw4cgFqtxpdffoklS5bg6tWrUCqV7OT64OBgyGQypKam\nIjAwEOfOncP3338PHo83LInTUF/j6G8/8fHx8Pf3R3x8PBYvXoyYmBjMnz+fvUmMHj0ae/fuxdOn\nT7FixQpERkbCz89v2BdWvIghv06yYMECAL03hOfZ2tri8OHD6O7uhkwmg5+fH3Jzc/Hjjz9yniL0\njUVbtrS0xIEDB+Do6IiVK1ciMDAQKpUKe/fu5Szs8Pb2RnNzM2bPns3WTZ8+HTweDxYWFjoLWkYy\nyjWUa0ZargEo3xDj4jGm8nPMxHV2dmLBggUIDQ1FSEiIscMhI9DBgwexb98+FBYWclY7E/I8yjXE\nECjfEH3Roib0rvQczOR57eKC16mjowN1dXVITU3FmDFjOCs3nz17Nqgvmbz//vsmkRDa29t1Fj30\nZWFhwZl3Z8oGez3a29tRXl6OlJQUyOVyk/i/MFJRrhkZKNfoonxDhht1SNH7yT25XP7CNjweb1Dv\nSDO0c+fOQS6XY+bMmUhLS+OsQrx79y47LDIQHo+HjIyMIQ+9vUm2b9+OY8eOvbDNnDlzkJGR8Zoi\nMq7BXA9PT0+0traio6MDq1evxooVK15TdKQ/lGtGBso1uijfkOFGQ/aEEEIIIcSo3qpFTYQQQggh\n5M1DHVJCCCGEEGJU1CElhBBCCCFGRR1SQgghhBBiVNQhJXo5evQoBAIBVCqVsUN5Y0kkEmzatEmv\nbUpLSyEQCJCTk2OQGGQyGWQymUH2RYgxUK55Oco1xJRQh5SMCEql8q1Ieob8Uooh90XI24JyjXH3\nRd5e1CEl7Des32T0lISQkY9yDSFkINQhNTFisRhhYWEoKiqCv78/hEIhJBIJfv31V7aNQCBAcnIy\ntm/fjlmzZuHkyZMAAI1Gg4SEBIjFYri6usLHxwcbN25ES0uLznE0Gg3kcjnmzp0Ld3d3fPvtt2hu\nbua0uX37NsLCwuDp6Qk3NzcsXboUubm5ep+TRCJBYWEhVCoVG7uHhwciIyN12l65cgUCgQDZ2dko\nKSmBQCDAqVOnkJiYCG9vb7i5uUEqleLmzZuc7ZqamhAdHQ0vLy8IhUL4+fnh0KFDesfan8ePHyM5\nORm+vr5wdXWFr68vYmJicO/ePZ22T58+RWJiIubNm4eZM2dCKpXi1q1bry1WQgaLcg3lGkIMib7U\nZGJ4PB7UajVSU1MRHh4OGxsbpKenIz4+Hg4ODuzXVlQqFezt7aFUKjFx4kQAQEREBC5duoR169bB\nxcUFDQ0NSElJQXBwMI4fP47Ro0ezx0lKSsK8efOQmpqKuro6JCYmIjw8HEeOHAEANDY2IigoCM7O\nztixYwfGjBmD3NxcREdHo6enB1988cWgzyktLQ2rVq2Cvb094uLiMG7cODx69AgnTpxAR0cHrKys\n2LZnzpyBpaUlPv30U1RWVrLbC4VC7Nq1Cw8fPsSWLVsQGhqK/Px8jB49Gu3t7ZBKpTA3N8fmzZvh\n4OCAoqIi7NixAx0dHQgLC3ulv0lsbCzy8/Mhl8sxY8YMVFdXIyEhATU1NcjKyuK0PXjwIIRCIRIS\nEvDgwQMkJSVhzZo1yMvLwzvvvDPssRIyWJRrKNcQYlAMMSlisZgRCATMzZs32TqNRsO4u7szoaGh\nDMMwDJ/PZ2bPns38999/bJuysjKGz+czGRkZnP0VFRUxfD6fOXLkCMMwDPP7778zfD6fiYiI4LRL\nT09n+Hw+U1FRwTAMw8TGxjIeHh5MW1sbp51MJmMkEsmQzksmk7FllUrF8Pl8Jicnh9Nu0aJFTHh4\nOMMwDFNSUsLw+Xxm6dKlnDbac8jNzWVjnzFjBnPnzh1Ou5iYGMbd3Z3p7OzUO9aYmBiGYRimp6eH\niY6OZvbs2cNps2vXLobP5zMNDQ2cWJctW8Zpl5uby/D5fCYvL0+vWL/++mvO9SLE0CjXUK5hGMo1\nxHBoyN4EjR8/HtOnT2fL7777LoRCIaqrq9k6d3d3WFpasuXS0lIAwPz58zn78vb2hrm5OcrLyzn1\nEomEU/bw8AAAVFRUAAAuXLgAkUgEW1tbne0aGxvR1tY21NNjjzdx4kR2CBAA7ty5g9raWvj7+3Pa\nisViTvmjjz4CAKjVajbWqVOnYurUqTqxPnnyBDdu3BhynKNGjUJSUhLCw8M59donRU1NTZx6X19f\nTlkkEgHgXtcXxdp3eJCQ4US5hnINIYZCQ/YmyMHBQafOzs4Of//9N1vum7xbW1v73dbc3Bw2Njbs\nvw90DDs7OwDAw4cPAQAtLS1oaGiAQCDQiYXH46GlpYXdZqgCAgKQlpaGBw8ewNbWFgUFBbC2ttZJ\ntIOJtaamZsBY+567vm7cuIF9+/ahrKwMDx484CzsYBjGoLH2NwePkOFCucZX71gp1xDSP+qQmqBR\no3QffDMMw6k3Nx/8n77vtgO16cvHxwcbNmzot/2kSZMGffyBBAYG4ueff8apU6cglUpRUFCARYsW\ncZ7GALqvJNHG+nz9tGnToFAo+j3O+PHjhxxjU1MTgoKC8N5772HdunWYNm0aLC0tcfbsWezZs0en\nvTFjJURflGso1xBiKNQhNUH379/XqWtra9N5UvE8R0dHAEBzczMmTJjA1nd1deHRo0c6v6b7HkNb\n1v7KdnJygkaj6ffXtaE4Oztj1qxZyMvLg1gsxvXr1/u9KfVdYaodwtNej/Hjx6O6unpYYi0oKIBG\no8Hu3bsxb948tr6wsLDf9gPFqr2uwxkrIfqiXMNFuYaQoaM5pCaorq4OtbW1bFmj0eDatWvg8/kD\nbuPj4wOgd+Xo8/788090d3fDy8uLU//HH39wytp39wmFQgCAl5cXrl69ivr6ek677OxsKJVK/U7o\n/zx79kynLjAwEOXl5cjKyoKDgwPmzp2r0+b8+fOccklJCQCw18Pb2xutra0oKyvjtCsoKEBKSsor\nvTuxu7sbADBu3Di2TqPRsCuE++67qKiIU9bGpL2uwxkrIfqiXMNFuYaQoaMOqQlydnZGVFQUTp8+\njbKyMkRGRqKrqwtSqXTAbdzc3CAWi5GamopDhw5BpVIhOzsbsbGxcHFxweLFizntta8TKSkpwW+/\n/Yb9+/dDJBKxCxxCQ0NhZWWFlStX4syZM7h48SJ++uknbN26FY8ePdL7nOzt7VFZWYkTJ05wFj18\n9tlnMDMzg1KphJ+fX7/bPn78GJs2bUJxcTH7nsBJkybhk08+AQB89dVXcHJyQlRUFI4fP46LFy/i\nl19+wXfffYf6+nqYmZnpHa+Wp6cnACA5ORkXL15EXl4egoKC2Fjz8/PR2NjItn/y5AliYmJQXFyM\nkydPIikpCc7Ozvj444/1jrW/oU1CDIlyDRflGkKGjobsTZCjoyPWrFkDhUIBtVoNBwcHbNu2TefJ\nQ18pKSlISUmBUqnEvXv3YGNjg4ULF2L9+vVs8mEYBjweD7GxsTh27BiioqLQ1dUFLy8vxMXFsfua\nMGECMjMzsWvXLmzevBkajQaTJ0/Gpk2bXnizGsjatWshl8uxefNmBAUFsStCra2tIZFIcPr06QHf\nNxgcHIyamhps2LAB7e3tcHNzQ1xcHDu3zcrKCpmZmUhOTsbOnTvR3t4OR0dHrF69GqGhoXrH+jxX\nV1ds2bIF+/fvx6pVqzBlyhRERETA19cX169fx9GjR+Ho6Ag3NzfweDyEhYWhsrISGzZswL///gt3\nd3ds3bp1SLHS5/zIcKNcw0W5hpCh4zH008akSCQSTJw4ERkZGcYO5bWJjo6GWq1GTk4Op760tBQh\nISHYuXMnAgMDjRQdIaaJcs3/o1xDyKujIXsyolVXV7NDU4QQMlwo1xAyvGjInhjF48eP2ZdFv4x2\nkv3zbt26hcrKSuzevRvTpk3D8uXLDR0i6+nTp4N+YbVAIICFhcWwxUII0Q/lGkJGBuqQEqO4fv06\nQkJCXtqOx+OhqqpKp/7w4cM4evQoPDw8oFAoBpzDZIi5TS0tLVixYsWgYj179iycnJxe+ZiEEMOg\nXEPIyEBzSAkhhBBCiFHRHFJCCCGEEGJU1CElhBBCCCFGRR1SQgghhBBiVNQhJYQQQgghRkUdUkII\nIYQQYlT/A4cY+hzFxaskAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfd8dab810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lure\n",
    "l = WM.pivot_table(values = [ 'Accuracy', 'Reaction_Time'], index=['subject1','new_group', 'probe_type_label'], aggfunc=np.mean)#, margins=True)\n",
    "l = pd.DataFrame(l.stack())\n",
    "\n",
    "l = l.reset_index()\n",
    "l.columns = ['subject1', 'new_group', 'probe_type_label', 't', 'values']\n",
    "# Draw a nested barplot to show survival for class and sex\n",
    "ax1 = sns.factorplot(x=\"probe_type_label\", y='values', hue='new_group', col='t', data=l, kind=\"bar\", palette=\"muted\"\n",
    "                    ,size=3.5, aspect=1.2, ci=68,sharey=False)\n",
    "ax1.set_ylabels(\"Lure\")\n",
    "plt.title=('Lure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdfd98729d0>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD0CAYAAABQH3cdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiPCpE1EZET+H5GHjRV83cw7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdfd9872bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "kk\n",
    "\n",
    "k = piv.pivot_table(values = [ 'K_2R','K_4RY','L'], index=['subject1','new_group'], aggfunc=np.mean)#, margins=True)\n",
    "k = pd.DataFrame(k.stack())\n",
    "\n",
    "k = k.reset_index()\n",
    "k.columns = ['subject', 'new_group', 't', 'values']\n",
    "# Draw a nested barplot to show survival for class and sex\n",
    "ax1 = sns.factorplot(x=\"t\", y='values', hue='new_group', data=k, kind=\"bar\", palette=\"muted\"\n",
    "                    ,size=3.5, aspect=1.2, ci=68,sharey=False )\n",
    "ax1.set_ylabels(\"K Score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from statsmodels.graphics.api import interaction_plot, abline_plot\n",
    "from statsmodels.stats.anova import anova_lm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>group_age</th>\n",
       "      <th>free_lunc_adultscodedhigh</th>\n",
       "      <th>group</th>\n",
       "      <th>LOAD_no_2R</th>\n",
       "      <th>LOAD_no_4RY</th>\n",
       "      <th>motion_keep</th>\n",
       "      <th>beh_keep</th>\n",
       "      <th>num_runs_keep</th>\n",
       "      <th>subject_2</th>\n",
       "      <th>verbal_iq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>   76.000000</td>\n",
       "      <td> 76.000000</td>\n",
       "      <td> 76.000000</td>\n",
       "      <td> 76.000000</td>\n",
       "      <td> 76</td>\n",
       "      <td> 76</td>\n",
       "      <td> 76</td>\n",
       "      <td> 76.000000</td>\n",
       "      <td> 76.000000</td>\n",
       "      <td>   76.000000</td>\n",
       "      <td>  76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td> 1218.750000</td>\n",
       "      <td>  1.513158</td>\n",
       "      <td>  0.513158</td>\n",
       "      <td>  1.026316</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0.934211</td>\n",
       "      <td>  0.894737</td>\n",
       "      <td> 1218.750000</td>\n",
       "      <td> 119.592105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  844.496886</td>\n",
       "      <td>  0.503148</td>\n",
       "      <td>  0.503148</td>\n",
       "      <td>  1.006296</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0.249561</td>\n",
       "      <td>  0.308931</td>\n",
       "      <td>  844.496886</td>\n",
       "      <td>  14.698007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>  311.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  0.000000</td>\n",
       "      <td>  0.000000</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0.000000</td>\n",
       "      <td>  0.000000</td>\n",
       "      <td>  311.000000</td>\n",
       "      <td>  87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>  359.250000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  0.000000</td>\n",
       "      <td>  0.000000</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  359.250000</td>\n",
       "      <td> 108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td> 2005.500000</td>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td> 2005.500000</td>\n",
       "      <td> 119.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td> 2035.500000</td>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td> 2035.500000</td>\n",
       "      <td> 132.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 2065.000000</td>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td> 2065.000000</td>\n",
       "      <td> 148.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           subject  group_age  free_lunc_adultscodedhigh      group  \\\n",
       "count    76.000000  76.000000                  76.000000  76.000000   \n",
       "mean   1218.750000   1.513158                   0.513158   1.026316   \n",
       "std     844.496886   0.503148                   0.503148   1.006296   \n",
       "min     311.000000   1.000000                   0.000000   0.000000   \n",
       "25%     359.250000   1.000000                   0.000000   0.000000   \n",
       "50%    2005.500000   2.000000                   1.000000   2.000000   \n",
       "75%    2035.500000   2.000000                   1.000000   2.000000   \n",
       "max    2065.000000   2.000000                   1.000000   2.000000   \n",
       "\n",
       "       LOAD_no_2R  LOAD_no_4RY  motion_keep   beh_keep  num_runs_keep  \\\n",
       "count          76           76           76  76.000000      76.000000   \n",
       "mean            2            4            1   0.934211       0.894737   \n",
       "std             0            0            0   0.249561       0.308931   \n",
       "min             2            4            1   0.000000       0.000000   \n",
       "25%             2            4            1   1.000000       1.000000   \n",
       "50%             2            4            1   1.000000       1.000000   \n",
       "75%             2            4            1   1.000000       1.000000   \n",
       "max             2            4            1   1.000000       1.000000   \n",
       "\n",
       "         subject_2   verbal_iq  \n",
       "count    76.000000   76.000000  \n",
       "mean   1218.750000  119.592105  \n",
       "std     844.496886   14.698007  \n",
       "min     311.000000   87.000000  \n",
       "25%     359.250000  108.000000  \n",
       "50%    2005.500000  119.500000  \n",
       "75%    2035.500000  132.500000  \n",
       "max    2065.000000  148.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#import rpy2.interactive as r\n",
    "#import rpy2.interactive.packages # this can take few seconds\n",
    "#%load_ext rmagic\n",
    "\n",
    "#d = pd.read_csv('../Master/WMFILT_dev_MASTER_5-15-2015_newdp_2015-05-15_15_51.csv')\n",
    "d = pd.read_csv('../Master/master_real_15-5-20_spssexport1.csv')\n",
    "d.head()\n",
    "\n",
    "gmotion = d.loc[(d['motion_keep'] == 1 )]\n",
    "#adults = group.loc[(group.group_age == 2)]\n",
    "\n",
    "#kids = group.loc[(group.group_age == 1)]\n",
    "gmotion.verbal_iq = gmotion.verbal_iq.convert_objects(convert_numeric=True)\n",
    "gmotion = gmotion[np.isfinite(gmotion['verbal_iq'])]\n",
    "\n",
    "\n",
    "adults_new = gmotion.loc[(gmotion.group == 2 )]\n",
    "\n",
    "kids_new = gmotion.loc[(gmotion.group == 0)]\n",
    "\n",
    "group_new = adults_new.append(kids_new)\n",
    "#group_new.to_csv(\"testtttt.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adults_new_dprime.dPrime_2R = adults_new_dprime.dPrime_2R.convert_objects(convert_numeric=True)\n",
      "kids_new_dprime.dPrime_2R = kids_new_dprime.dPrime_2R.convert_objects(convert_numeric=True)\n",
      "adults_new_dprime.dPrime_4RY = adults_new_dprime.dPrime_4RY.convert_objects(convert_numeric=True)\n",
      "kids_new_dprime.dPrime_4RY = kids_new_dprime.dPrime_4RY.convert_objects(convert_numeric=True)\n",
      "adults_new_dprime.dPrime_DRY = adults_new_dprime.dPrime_DRY.convert_objects(convert_numeric=True)\n",
      "kids_new_dprime.dPrime_DRY = kids_new_dprime.dPrime_DRY.convert_objects(convert_numeric=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dPrime_2R</th>\n",
       "      <th>dPrime_4RY</th>\n",
       "      <th>dPrime_DRY</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td> 2.963112</td>\n",
       "      <td> 2.043616</td>\n",
       "      <td> 1.772018</td>\n",
       "      <td> 311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td> 1.123837</td>\n",
       "      <td> 1.123837</td>\n",
       "      <td> 0.715960</td>\n",
       "      <td> 315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td> 3.212084</td>\n",
       "      <td> 1.965333</td>\n",
       "      <td> 3.212084</td>\n",
       "      <td> 332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td> 2.484364</td>\n",
       "      <td> 1.745518</td>\n",
       "      <td> 0.325199</td>\n",
       "      <td> 333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 3.241516</td>\n",
       "      <td> 1.601062</td>\n",
       "      <td> 1.885760</td>\n",
       "      <td> 335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td> 2.634454</td>\n",
       "      <td> 1.321787</td>\n",
       "      <td> 1.911858</td>\n",
       "      <td> 336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td> 1.534899</td>\n",
       "      <td> 0.379008</td>\n",
       "      <td> 2.123173</td>\n",
       "      <td> 337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td> 2.656290</td>\n",
       "      <td> 1.710923</td>\n",
       "      <td> 3.241516</td>\n",
       "      <td> 339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td> 0.065919</td>\n",
       "      <td> 0.049282</td>\n",
       "      <td> 0.379008</td>\n",
       "      <td> 340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td> 2.484364</td>\n",
       "      <td> 1.328997</td>\n",
       "      <td> 2.072867</td>\n",
       "      <td> 341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td> 1.809043</td>\n",
       "      <td> 1.391830</td>\n",
       "      <td> 0.584522</td>\n",
       "      <td> 342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td> 0.600400</td>\n",
       "      <td> 0.149217</td>\n",
       "      <td> 0.364715</td>\n",
       "      <td> 344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td> 3.524690</td>\n",
       "      <td> 0.884588</td>\n",
       "      <td> 2.966320</td>\n",
       "      <td> 348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td> 1.636788</td>\n",
       "      <td> 2.294346</td>\n",
       "      <td> 3.212084</td>\n",
       "      <td> 349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td> 3.919928</td>\n",
       "      <td> 3.180604</td>\n",
       "      <td> 2.926405</td>\n",
       "      <td> 350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td> 3.579820</td>\n",
       "      <td> 2.072867</td>\n",
       "      <td> 2.801585</td>\n",
       "      <td> 354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td> 1.926609</td>\n",
       "      <td> 1.263946</td>\n",
       "      <td> 1.048801</td>\n",
       "      <td> 356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td> 2.896973</td>\n",
       "      <td> 1.285364</td>\n",
       "      <td> 2.648002</td>\n",
       "      <td> 357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td> 2.996397</td>\n",
       "      <td> 1.683242</td>\n",
       "      <td> 2.926405</td>\n",
       "      <td> 360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td> 2.209312</td>\n",
       "      <td> 1.915192</td>\n",
       "      <td> 2.502192</td>\n",
       "      <td> 362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td> 2.634454</td>\n",
       "      <td> 2.888863</td>\n",
       "      <td> 2.288553</td>\n",
       "      <td> 367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td> 1.956041</td>\n",
       "      <td> 2.764560</td>\n",
       "      <td> 2.865494</td>\n",
       "      <td> 368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td> 1.421262</td>\n",
       "      <td> 1.731625</td>\n",
       "      <td> 1.527207</td>\n",
       "      <td> 371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td> 2.039581</td>\n",
       "      <td> 1.801143</td>\n",
       "      <td> 1.809043</td>\n",
       "      <td> 372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td> 2.963112</td>\n",
       "      <td> 1.932047</td>\n",
       "      <td> 2.865494</td>\n",
       "      <td> 373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td> 3.604818</td>\n",
       "      <td> 2.648002</td>\n",
       "      <td> 2.123173</td>\n",
       "      <td> 375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td> 2.696280</td>\n",
       "      <td> 2.960197</td>\n",
       "      <td> 1.754522</td>\n",
       "      <td> 376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td> 2.025237</td>\n",
       "      <td> 0.997633</td>\n",
       "      <td> 2.106122</td>\n",
       "      <td> 377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td> 2.486475</td>\n",
       "      <td> 0.402050</td>\n",
       "      <td> 0.860439</td>\n",
       "      <td> 381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td> 3.241516</td>\n",
       "      <td> 1.666872</td>\n",
       "      <td> 3.264710</td>\n",
       "      <td> 382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td> 2.996397</td>\n",
       "      <td> 1.948976</td>\n",
       "      <td> 2.537269</td>\n",
       "      <td> 383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td> 1.560834</td>\n",
       "      <td> 1.805952</td>\n",
       "      <td> 1.439199</td>\n",
       "      <td> 384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td> 2.504239</td>\n",
       "      <td> 0.721522</td>\n",
       "      <td> 2.560640</td>\n",
       "      <td> 385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td> 3.919928</td>\n",
       "      <td> 1.844769</td>\n",
       "      <td> 3.241516</td>\n",
       "      <td> 386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td> 2.337356</td>\n",
       "      <td> 2.147599</td>\n",
       "      <td> 0.327138</td>\n",
       "      <td> 387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td> 3.241516</td>\n",
       "      <td> 2.845338</td>\n",
       "      <td> 2.681287</td>\n",
       "      <td> 388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dPrime_2R  dPrime_4RY  dPrime_DRY  subject\n",
       "9    2.963112    2.043616    1.772018      311\n",
       "12   1.123837    1.123837    0.715960      315\n",
       "27   3.212084    1.965333    3.212084      332\n",
       "28   2.484364    1.745518    0.325199      333\n",
       "30   3.241516    1.601062    1.885760      335\n",
       "31   2.634454    1.321787    1.911858      336\n",
       "32   1.534899    0.379008    2.123173      337\n",
       "34   2.656290    1.710923    3.241516      339\n",
       "35   0.065919    0.049282    0.379008      340\n",
       "36   2.484364    1.328997    2.072867      341\n",
       "37   1.809043    1.391830    0.584522      342\n",
       "39   0.600400    0.149217    0.364715      344\n",
       "43   3.524690    0.884588    2.966320      348\n",
       "44   1.636788    2.294346    3.212084      349\n",
       "45   3.919928    3.180604    2.926405      350\n",
       "49   3.579820    2.072867    2.801585      354\n",
       "51   1.926609    1.263946    1.048801      356\n",
       "52   2.896973    1.285364    2.648002      357\n",
       "55   2.996397    1.683242    2.926405      360\n",
       "57   2.209312    1.915192    2.502192      362\n",
       "62   2.634454    2.888863    2.288553      367\n",
       "63   1.956041    2.764560    2.865494      368\n",
       "66   1.421262    1.731625    1.527207      371\n",
       "67   2.039581    1.801143    1.809043      372\n",
       "68   2.963112    1.932047    2.865494      373\n",
       "70   3.604818    2.648002    2.123173      375\n",
       "71   2.696280    2.960197    1.754522      376\n",
       "72   2.025237    0.997633    2.106122      377\n",
       "76   2.486475    0.402050    0.860439      381\n",
       "77   3.241516    1.666872    3.264710      382\n",
       "78   2.996397    1.948976    2.537269      383\n",
       "79   1.560834    1.805952    1.439199      384\n",
       "80   2.504239    0.721522    2.560640      385\n",
       "81   3.919928    1.844769    3.241516      386\n",
       "82   2.337356    2.147599    0.327138      387\n",
       "83   3.241516    2.845338    2.681287      388"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plt.plot(kids['dPrime_2R'],'o')\n",
    "\n",
    "adults_new.dPrime_2R = adults_new.dPrime_2R.convert_objects(convert_numeric=True)\n",
    "kids_new.dPrime_2R = kids_new.dPrime_2R.convert_objects(convert_numeric=True)\n",
    "adults_new.dPrime_4RY = adults_new.dPrime_4RY.convert_objects(convert_numeric=True)\n",
    "kids_new.dPrime_4RY = kids_new.dPrime_4RY.convert_objects(convert_numeric=True)\n",
    "adults_new.dPrime_DRY = adults_new.dPrime_DRY.convert_objects(convert_numeric=True)\n",
    "kids_new.dPrime_DRY = kids_new.dPrime_DRY.convert_objects(convert_numeric=True)\n",
    "\n",
    "kids_new_dprime = kids_new[[u'dPrime_2R', u'dPrime_4RY', u'dPrime_DRY', \"subject\"]]\n",
    "kids_new_dprime = kids_new_dprime.dropna()\n",
    "adults_new_dprime = adults_new[[u'dPrime_2R', u'dPrime_4RY', u'dPrime_DRY']]\n",
    "adults_new_dprime = adults_new_dprime.dropna()\n",
    "\n",
    "\n",
    "kids_new_k = kids_new[[ u'K_2R', u'K_4RY', u'K_DRY']]\n",
    "kids_new_acc = kids_new[[u'acc_2R', u'acc_4RY', u'acc_DRY']]\n",
    "kids_new_rt = kids_new[['rt_2R', u'rt_4RY', u'rt_DRY']]\n",
    "kids_new_lure_acc = kids_new[[u'Accuracy_1_away_red', u'Accuracy_in_red', u'Accuracy_in_yellow']]\n",
    "kids_new_lure_rt = kids_new[[u'Reaction_Time_1_away_red', u'Reaction_Time_in_red', u'Reaction_Time_in_yellow']]\n",
    "adults_new_k = adults_new[[ u'K_2R', u'K_4RY', u'K_DRY']]\n",
    "adults_new_acc = adults_new[[u'acc_2R', u'acc_4RY', u'acc_DRY']]\n",
    "adults_new_rt = adults_new[['rt_2R', u'rt_4RY', u'rt_DRY']]\n",
    "adults_new_lure_acc = adults_new[[u'Accuracy_1_away_red', u'Accuracy_in_red', u'Accuracy_in_yellow']]\n",
    "adults_new_lure_rt = adults_new[[u'Reaction_Time_1_away_red', u'Reaction_Time_in_red', u'Reaction_Time_in_yellow']]\n",
    "\n",
    "kids_viq = kids_new[['vci_scaled']]\n",
    "adults_viq = adults_new[['KBIT_verbalstd']]\n",
    "dprime = [u'dPrime_2R', u'dPrime_4RY', u'dPrime_DRY']\n",
    "groups = ['adults', 'kids']\n",
    "\n",
    "for col in dprime:\n",
    "    print('adults_new_dprime.%s = adults_new_dprime.%s.convert_objects(convert_numeric=True)' % (col, col))\n",
    "    print('kids_new_dprime.%s = kids_new_dprime.%s.convert_objects(convert_numeric=True)' % (col, col))\n",
    "\n",
    "\n",
    "\n",
    "kids_new_dprime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     2.963112\n",
       "12    1.123837\n",
       "27    3.212084\n",
       "28    2.484364\n",
       "30    3.241516\n",
       "31    2.634454\n",
       "32    1.534899\n",
       "34    2.656290\n",
       "35    0.065919\n",
       "36    2.484364\n",
       "37    1.809043\n",
       "39    0.600400\n",
       "43    3.524690\n",
       "44    1.636788\n",
       "45    3.919928\n",
       "49    3.579820\n",
       "51    1.926609\n",
       "52    2.896973\n",
       "55    2.996397\n",
       "57    2.209312\n",
       "62    2.634454\n",
       "63    1.956041\n",
       "66    1.421262\n",
       "67    2.039581\n",
       "68    2.963112\n",
       "70    3.604818\n",
       "71    2.696280\n",
       "72    2.025237\n",
       "76    2.486475\n",
       "77    3.241516\n",
       "78    2.996397\n",
       "79    1.560834\n",
       "80    2.504239\n",
       "81    3.919928\n",
       "82    2.337356\n",
       "83    3.241516\n",
       "Name: dPrime_2R, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kids_new.dPrime_2R.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-2.5592159726412751, 0.012562243204227368)\n",
      "(-2.729031915377945, 0.0079544413988759041)\n",
      "(-3.7969744415344051, 0.00030032359741466187)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "print(ttest_ind(kids_new.dPrime_2R.dropna(), adults_new.dPrime_2R.dropna()))\n",
    "print(ttest_ind(kids_new.dPrime_4RY.dropna(), adults_new.dPrime_4RY.dropna()))\n",
    "print(ttest_ind(kids_new.dPrime_DRY.dropna(), adults_new.dPrime_DRY.dropna()))\n",
    "#piv = pd.pivot_table(group_dprime, values=['dPrime_2R', u'dPrime_4RY', u'dPrime_DRY'], index= 'group_age', aggfunc=np.mean)\n",
    "#sns.boxplot(group_dprime.dPrime_2R, group_dprime.group_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-a55fe72b8ed3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mscatter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madults_new_dprime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msharex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'col'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msharey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'row'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madults_new_dprime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkids_new\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'.b'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madults_new\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'.g'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%s '\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1373\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1374\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1375\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1376\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36madd_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   1484\u001b[0m             \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1485\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1486\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1487\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1488\u001b[0m             \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_line%d'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_update_line_limits\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   1495\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1496\u001b[0m         \"\"\"\n\u001b[1;32m-> 1497\u001b[1;33m         \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1498\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1499\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/matplotlib/lines.pyc\u001b[0m in \u001b[0;36mget_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \"\"\"\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/matplotlib/lines.pyc\u001b[0m in \u001b[0;36mrecache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m    579\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    580\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 581\u001b[1;33m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    582\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    583\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \"\"\"\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: "
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAADXCAYAAACu5TisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f5fcb9490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "scatter, axes = plt.subplots(1,len(adults_new_dprime.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(adults_new_dprime.columns, axes):\n",
    "    ax.plot(kids_new[col],'.b')\n",
    "    ax.plot(adults_new[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5UAAADjCAYAAAAVOzCSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcHEd9//9X70q7WhFDOBIgOCBzTAWSYHJAOCTZxpYg\nJNxHyM8ECDEBzJdw2bF3VrtCqz0UbI4QsMMRAg6EJIQrkGAsG6GDECBAEo7wgYAN5jDk4NZKK+/0\n74+a0fbOds90z3TP9My+n4/HPFaao7u6p7e2Pl1VnwrCMERERERERESkEyP9LoCIiIiIiIgMLgWV\nIiIiIiIi0jEFlSIiIiIiItIxBZUiIiIiIiLSMQWVIiIiIiIi0jEFlSIiIiIiItKxTf0ugGxMzrm3\nAE8HzjWzIzGvPwe4GvgWcJ6ZfbmDffwO8ELg/vgbKJ8D/tTM3h55zzbgqwmbOAH8N3AMeFknZRCR\ncupFHdS0vV8B/gW4xsyeHXl+GxnrIOfcQeB8YMrMFhP2twn4JHA28Jtm9qFuyi8ixSu6XnLO3QTc\nvenpZeD7wKeB15vZ+2I+V0vY5DLwf8AngDkz+9f6+98EPAt4o5k9p0V53g/8FvBcM3tDlmOR8lFP\npZSOc+4ifKV5M3BOhwHlc4F3AGPANDCJv97/yjm3J+Yj/wk8relxCb4R+LvAPzvnfi770YjIoMmj\nDmra3gTwdmAzkLQ4dJY66CLgx8CMc66SsL3L8AHl6xVQigy+HOulkLX1zPOBPwfuCbzHOfeqhM/9\nN+vrqD8C/hEfGB5xzp1df+9LgG8CFznnzkk4ngvrn7tWAeVwUE+llIpz7veBNwBfw9+Fu6mDbdwW\neCX+rtuDzKxWf/61wEeBaefcG83sO5GPfcfM/jpmc1c55z4H7MM38F6StTwiMjjyqINiXAmc1eY9\nqesgM/uac+4y4HXAG4E1jTbn3P3wN9O+Cry028KLSH/lXS/F1TXOuUXg3cALnXP/ZmZvbXrLTxLq\nqDc4544AbwX2Ak8wsx865/4QH3C+0Tl3fzM7EdnXzwJ/CnwP+INujkXKQz2VUhrOuacDb8I3hHZ2\nUWluB7YAb24ElABmtgL8Db634DcybK9xB21Hh+URkQGQYx0U3eZvAc8DprrYzLo6yMyuBg4DO5xz\nz4vsbwT4C2AUeKaZHe9ivyLSZ0XUS3HM7CR+6O2P8cFhFm/DD9eP1lEfBP4KuDf+pljUa4E7AC8w\ns293WmYpFwWVUgr1YRB/CXwFP6zj5i4292HgfvgAstmd6z9XMmxPjTKRIZdzHdTY5s8Cb8bfwX9X\nF5tKqoP+oP7aAefcXerPvQB/0+zVZnasi32KSJ8VUS+1Ymb/C/wDsM05d/8MnwuBpZiXXgTcAryk\nsT3n3GOBJwHvSuj5lAGloFL6LXDOPRXf6PohvtL8ZjcbNLMTZvZFM/u/6PPOuZ/Gz0U6Cfxzhk0+\ntv7zX7spl4iUUu51UMRfAD/BB3pBF9uJrYPM7Kv4HtAzgCucc3cD5oAv0F3PqIj0V5H1Ujufrf/8\n1bQfcM5tB27P+jrqe/iRGqPAa51zP4XvpfwO8NxcSiuloTmV0m9PxlcsAXA74BHAW/LeST0T4tuB\nOwIvr1d0UZudc3dkteEXAD+Nz7C4iK/U5/Iul4j0XSF1UH1I6m/iszj+2Dl3pzYf6bQOek39GC4E\nfgGfnOzpZrbc7TGISN/0pG2U4H/rP3+m6fmRpjoK/A2thwAvx2eCXZcI0cze55z7W+B38CPJ7gY8\nrt4rKkNEPZXSbxfjl/p4CP6O/p855+6T5w6cc+PA3+EbeB8hptIDHobPbPbd+uM7gOHvqP0LsL2H\ndwlFpHdyr4Occ7+AT85zRYYhqB3VQfVhZ8/Cz2f6NWDBzD7dTflFpO8Kbxu1sLn+szlT9d1ZW0d9\nFz8s9234BEIXmNmnErb5gvpnfx14q5n9Q96Flv5TUCn99h/A+Wb2CXxm1dsA73DObW79sXScc7cH\nrgMeh09q8dtmdmvMW/8duKD+eAx+yAn4eZlPNrPP5VEeESmdXOug+uf+GvgG8Brn3J3qvZS3r79l\ni3Pujs65saaPdlwHmdmX8OvEhfj5VyIy2AptG7XR6KH8btPzt7BaR/0WPnvrrcBBfMbXxBtoZvY/\nwAfr/1UdNaQ0/FX67cWNIRBm9sb6BO5H4Yd7XdLNhp1z9wCuBRzwHuB3WwwJ+56ZfTjy/w84574A\n/Al+wvr59cxoIjJc8q6D7gY8oP7vuNENjfXdnglcE3k+jzqom3mbIlIehbWNUmjMpWzOI3GiqY76\noHPun4G/BQ475x6mIa0bm3oqpd+as7D+AX48/4udc7s73Wg9oDyCDyhfa2ZPzDrHyMyuwK/Z9FD8\nHTkRGT5510HfZvVufvTxtPrr/1T//3XtNqQ6SGTDKqRt1E59VMUjgC+a2Rfavd/M3olfF7yCXz5E\nNjAFlVIqZvYd4Dn4O+5vrafkz6Q+rOw9wM8Dc2b2R10U6dn4RuIfOud+s4vtiMgA6LYOMrOTZvbh\n5gerGae/VX/ulpSbzFIHNc+BEpEhkEfbqJ360No3AuOsX1eylUn8cN1HRtfMlY1noIJK59y4c+4/\nnXMajz3EzOzd+Dted2Z1XlEWz8APP/sW8CXn3NNiHttSluV7+EYdwJvqczRFZIjlUAflWZYsdZCG\nv4oMqRzrpcA5d2GkPXSRc24WHxg+FniVmf1thnKdwg/nPwW83Dl3ry7KJgNsoIJKYC9+OKPuxg6+\nkNbf4wuArwO7nXMvzrjtC+rbvit+zlLz46344WSpmNk/4SeW3wV4XcayiEg5FVkH5SplHdTueESk\n/Iqulxrb/ytW20RX4TNIfxGfzPClWTdqZv+GX/LoNsBbnHNxN7hURw25IAwH4/t1zv0Kfo7cEvAB\nM3tWn4skIiIiIiKy4Q1ET2V94fo34xdX1VqBIiIiIiIiJTEoS4pchi/rAeCJfS6L9Jhz7p6kH676\nIzN7X5HlEZGNRXWQiJSN6iUpm9IHlc65+wJV4OFmdso51+8iSe/txPdUp3EToIpTRPKkOkhEykb1\nkpRKqedUOudGgKPAv5rZC+vPfQb4TJY5lc65ceCB+LTszWv/iMhgGsUnY/pkikXhS0l1k8jQUv0k\nImVUWN1U9p7KF+DXGnxUfV4l+JTpI865UTNLW8k9EB+cisjw2QEc63chOqS6SWS4qX4SkTLKvW4q\ne1D5OOBM4HtNz98feLpzbpuZfT3Fdr4N8Pa3v5273OUuORdRRPrhlltu4cILL4T67/eAUt0kMoRU\nP4lIGRVZN5U9qHwO8FOR/wfA2wED9pH+hKwA3OUud+HMM8/MtYAi0neDPCxrKOqmxjSKIIhbmmxw\n9yWSA9VPIlJGuddNpV5SxMy+ZGafjjw+BZwA/rf+/1P9LqOIyEYVhiELRxaYmJ9gYn6ChSMLFDVP\nv5f7EhERkWzK3lMZR60IEZESWDy6yNShqdP/b/y7urM60PsSERGRbErdUxnHzH4lS+ZXERHJRxiG\np3sHwzBk9sjsuvfMHpnNvQexl/sSERGR7AYuqBQRkd7S0FMRERFpRUGliMiQifYopnm+ncbQ05Mr\nJzm5cpKpQ1McOHaAmZ0z6947s3OGIAg63lecIAha7ktERET6S0GliMiQSOpR7KansdXQ08u3X878\nefOMj44zPjrO/HnzXL798kJ6NSd3TK7b1+SOya632w95BtwiIiJlMIiJekREJEZSMpukf3eb5CYI\nAqo7q6eDuyAIWDiy0LN9DZowDFk8ung6SJ/ZOcPkjsmBPBYREZEo9VSKiAyBpB7FfYf3dZXkJs3Q\n0yAITg95LTqhTmNfvZZH72LcMOLFo4s5lVBERKR/FFSKiEhLwzT0NKu8khTVaspgKyIiw0tBpYjI\nEEjqUdx7zt6uk9w0hp4uTS2xNLVEdWc19rPDmFCn297FMISFBZjYCidPFFjQAReG/iEiIoNJQaWI\nyJBI6lHMq6cxzdDTYerVzGM47+IiTE3B8skADg9XwJ2H00H3hH8sLCi4FBEZRErUIyIyRBpBJKxN\nZtOrJDfDkFAnL2EIs9GY9Fg9uD5nlvEtq4l6NrJG0N3Q+He1u7xOmTUC2Q18uYqIdEU9lSIiA655\n3l/S8MxeJrnpV0KdPOU/nDeAY1XGrlzieDV5GPFGsS7orpud7V1vpXpKRUTyoaBSRKQEuskuqqyi\nxelmOG8QwMz6mJS9MwEjIxs3mCyTRk/pyZP+MTXlnxMRkWwUVIqI9NlVn7iq4+yivVjGYyNLm6Qo\nyeQkzM/D+Lh/zM/75+LksWzJIEkKumdmejMMtQw9pSIiw0JBpYhIn13xsSvUy1hynQ7nDQI/P3Bp\nyT+q1fUBU17LlgyiLEG3iIiUl4JKEZGSydLLOAzLePSih66xj371BgZBcu9b2YcvF7XcR2Ob7YLu\novS7p1REZJgoqBQRGXCDuoxHL3roGvvYMreFTbOb2Lx/c097A9sFsWUevlxUEpu47UJ/Ajn1lIqI\n5KP0S4o458aAGeD3gDsCHwcuMbPP9LVgIiIFifYyplnqoFfLeOS97EKjh65h6tAUYRjmmhW1eR+E\nsLKycvq56s5i1q4Iw5DFo4unA8bG8iGD0nsMxS33UZZlRGB1eHIjkBygr0dEpFQGoafyVcALgAXg\nscBx4JBz7u59LZWISE4ufcil63oZO+klKmoZjyJ6rJJ66PZ8ZE9uPYlJ+2gosjcw7ZDWPIcv5zm0\nNymJzb59UKvlv91+J8dpNTxZRETaK3VQ6Zy7HXARsNfMXm9mNwBPBjbjey5FRAbexQ+6eF120TIt\nddDrspRxXmEWWYe0djt8uZeJfpaXYetWrecoIiJrlTqoBH4MPAh4S+S5W4EQGOtHgUREihDtZSxT\nb06WsmTpKUvqoVuzjy57EtvtI89kRtFjz1rmbpctKSLRT1ISG+juxoKS44iIDKdSB5VmtmJm/25m\n33fOBc65ewJvBmrA2/pcPBERofOeskYP3dhI/D3CPIZzRvcxwgijwWiuyYyix75lbgu7r9nN1oWt\nnFo5te697YLYToYvF5nop5HEZizhFm6nNzmUHEdEZPiUOqhsMgP8F/A04E/M7Mt9Lo+ISCHK1JuT\npiyd9pQ1euhO7DnB3Hlz615fCVfYurC1q+Gc0X3cOnMrp6ZPddQbmCR67Mu1ZQ7eeJCTKyep4Sce\n5h3E9lJ0jc3x8WK22+tlREREpBiDFFS+GzgH2Afsdc4lZ18QERlwverNWTtsM77nqVVZ8ugpC4KA\n6o7q6XmFI/U/TSvhSo7DOYM1jzTarc/YLhEQ+KDyePV4rhlto3qxTunISDE3OZQcR0RkeAxMUGlm\nnzWzo2a2D3gNcKlzbrTf5RIRKULRvTnNQ1Z3zy6wZSKMze7ai56lRo/i8epxNo9uXvd6L9dtzDPb\nbVEZeaN6sU6phqyKiEgrpQ4qnXN3ds79vnPup5pe+jdgHL9upYjI0CqqN6d5yOrBcIrlBy62TMIS\nV5a8e8rKsI5j2my3aZIN5dljmKTbRD/p9qEhqyIikqzUQSVwe+AvgCc1Pb8b+I6Zfbf3RRIR6b18\n1yBMGLZ5ziw+uXb7JCzR8uTZU9aL4ZytZM28Gz32sZExdp21a815uHz7ZOZeznbDbpP0oldUQ1ZF\nRCROqYNKM/si8C7gFc65P3TO7XLOXY1P1nN5f0snIlKM5uUpWmVW7TQA6aZszeUBcu0p68VwzrxE\newlP7DnBdU+/jqWpJY5Xl+BYla1bg9RDaOOG3dZq+d1MaLXfjbrm5EY+dhGRPJU6qKx7OvBGYBJ4\nP37dyieZ2Vv7WioRkZzFBWwLRxdiM6t2M+8vcdjm4RnAB4NJSVhaZXrNq6esF8M5k/fdWVKa6LEH\nQcCBA0GqIbRRa4fdhkx9cIGx/dmWackiz7mjg2YjH7uISBE29bsA7ZjZEr5XUj2TIjLUGgFbw9Sh\nKUaD9fnIZo/MEh6dZM+e1Shnqv6xajXdvho9f41hsDtrMxz+5CTBuA+g4pKwtMr0OrljsoB5fEF9\nv43/57r5RI1jbwyDTTofSVoNoZ2cjD+OdZ/ZvggXTLECrKxw+rqo7kz5BafQCGIbsl5DZZXmeunk\n2Ht9HYqIDJJB6KkUERl6SQHbSrgS+/7Z/THPZViMvrk38LqZKieWglIlYWn0Jm2ZCNkyEfasN6n/\nSWnC+vzWtfLMgJt17mi3++rF95a29zHrsatXU0SkPQWVIiIlNhJTTU/vnCEgnyhn7bDN9kM880qi\nkybx0MKCHwK6fMkEy5dMMPXBBRYWetea7zQpTSdDaJM+E5VnsqZe6HUwljZrb1m2KyIyTBRUioiU\nQFLANnvu7LqkNdUdk4UsRh+VFMB0m0SnVqsxf2Q+MfHQ6v5h5no/BJTNJ/3jgilmrl8ciF6iTtZ1\nXP1MwMhH13/BK+EKWxe25jK/stO5o1n0MhjL2vs4Pb3+ubhj72WProjIICv9nEoRkY2ieZ7jzM4Z\nLt9+OUEQnH6t0SPY7by/JGEYsnh0cU0ZovMlG8Nmm8uTdrvTh6apUTv9fNJcwTAMqT1sfWu+9rBZ\nwjD/+Zt5awyhbXwnaYob/UwYTnLgmL8WTq2cokaNlXCFlZWVjudXNs8JLOoaauwr67zSooWhD2ob\nAeGuXXDkiH+tk3mzIiKySj2VIiIlEZ3neLx6HICtC1uZmJ84nWF19b3p5v1lHTLZKrtrc1mzBHaN\n7UYDyoa4uYJBAKOb129ndHM55num1dkQ2tB/vzurHK8eZ3PMicgyvzJpGGr/5452Jm6OZpqe12jP\n6fIyHDzoeyxbHXvSdldW/PYUXIqIeAoqRURKJggCDhw7kDK4Sw4mW61vGadVdtduhlsmbbeVIAjY\nd9761vy+81bnb3abAKZscxST1gDtVrthqHHXULtz2+r1Ro/gqVPrX8syvLZ5H+3maLYacpzUc7o/\nJuFVs8Z2RyItppUVza0UEYlSUCkiUjJ5BHetehzLFkwlJfqpxszfrO6YXBdczM+H1Grpj6eTgLsX\n4r6zA8cOdJUcKe9Mp2mS7zSC2FqkU3p0NN280lb7SBMcF9HzGgS+3Jtjes41t1JExFNQKSIyZJKC\n0n2H97VMkpNndteopO2OBqPrEv1Ee6ealz2p7qwSBEEkuAg5+cAF9ixNMLY/bW8sLKQc4ttLrW4k\nXL798szJkTrtxW0XuLV7PSmIHR1NP5cybh8LC+mD40bP69prqfjERCIiG5mCShGRkikquLu1dit7\nDu1pGUx1m901SfN2586dY3nP8ulAsVUPWHT+5pqgZftqdtgVWgeH0TUv91xX7BqQ8fuP7x1O02uc\nFFzH72fteVxczC/TaTeZULMMeU3aR1pJ11InGXmj5VdQKiKSTEGliEgJdRPcJQWlcYFIczCVJYDJ\nonm7U+dMMRKZpJZ9+YkQzkkfHDa2v3yy60OJL01Cz2DSUNvm5xePLjK9c330F72RkCY5Utx5hM6D\nqU6kDWKzCIL02026lrodHttNUCoiMuwUVIqIlFC3wV1cz+CmkfSrSGXN7trNdrP0gCX1GMVpBG9r\ntx/A4da9wFnmnLabY5g0tzXueUK66iVulYxmcrJ9MNWuN67V67B6HmZn/XIdefcIVqvtg7o011Jn\nGXkHN1uuiEgvaJ1KEZES6zSwi1tPMgiC02scNnQ7pLYffCARMH3dDLXz1h7PdL2HduHIwuk5itM7\nZwiZBOrHeaweiZwzy/iW1bU4263RGafRK9bQ+He12npua9w29x/dz9LUUuY1QNNKs7l2a1cmvd58\nHg4ehLm5zgKvpH10svZnEQbs10VEpCfUUyki0mdXfeKqjufztetVi/YMFjVfsltZ56s1got9F0zC\n9fNwatw/rp+Ho5PregH3HJrinGp0LG0Ax6rMTTQlAMqYwKebOYatZOklbv7+44aIxj2XvO/WvXFx\nr0Pny3V0WoakJVA091FEpD8UVIqI9NkVH7sic/bRTpbF6GRIba2WbbmOTmWdrxaGMDfng0Pml/zj\nWJXZ/cT2Dh4ZmWVuLlyz/Wo1mgAo/zU6k+a23vrhvZy6obtETM3f/+7ZBbZMhGuGno6N+X/v35+8\n/Edy2VsHYZ0OIc0izT7ihh9ffrnmPoqI9JqCShGREsgavGTtVYtK0xNWq4Xsnl1gdGaC0RkftHQa\nXKZZ3qK7+WoBp4e2tpD3fLg0vWLR3uFRfG9q7SOT1D7ie1lH6azXuPn7PxhOsfzARZaX/dDT6Wlf\njoMHsyQ/6mwpkn72DsYl5TlwQHMfRUR6rfRBpXNuxDn3Eufcfzrnfuyc+7xz7vn9LpeISL8U0avW\n7BFzixwM/XIdbPZByyPnsvamtk5iEydtD1hSILN3pvVyLEnb73QZl3Y9rI3e4ePVJUYXfW/q6SD4\nWJXRxSWOV7MlYkr6/n02XH+CZ2fjh58mDc3t5LuKKjozalyw2274cS96U0VExCt9UAnMAPPANcCj\ngb8DXu2cu7SvpRIRyVFZEub4YaUh1y+vb60fXJ7N1FuZfZmQbJICmU7njnbyubQ9rEEQEMT0pgZk\ny7LbGPZ6ciXftVG6/a6KyozabbArIiK9Ueqg0jk3CrwYeLmZLZrZITPbB7weuKS/pRMRycelD7k0\n09DHLL1qWYYzhmHIwkKYKbFL8raKSWITlRTIdLocSzfLuKSZg5jHENHFo4vs+cie+BcPz9AYBrx3\nb/r95fld5d072CrYVVIeEZHyKHVQCZwBvBV4d9PzXwJ+xjk30fsiiYjk6+IHXZy5l7Jdr1qWHp5o\n0pc9SxOwfREOr48sd43NMDLS39Z6XJDcakhrJ72/Ra3R2e0Q0cRhr8AFwRxjn5xc22Nb8JDUoqUJ\ndgf9GEVEhkWpg0oz+76Z/ZGZ/XvTS48GbjazpX6US0Sk39r1qmUZzhhN+sLmk3BBfcHByHIdFwTz\nXLsnS29qvr1IRQ+D7CRBTVZFDREdGxnjuukqJ5aCph7btENzB7fHr6hzKiIi2ZQ6qIzjnLsIOB94\neb/LIiLSb3G9almGMyYnfdkPxyZhfon9W5Y4OFPN3EuZZy9SUfMz+zFnr9MhoknDnvees7dNEqLk\ndR0bytjjlyXYVVIeEZH+Gqig0jl3IXA18E4ze12v9ttucfF+lqGTsqX5TLfH3K9z1ovehl6t2zfo\nevFdSLHGxmF+PmBqqrPWel69SEXOz+wkWO1n/Xb59s6SEEW3ERdEl7XHr4zBroiIrDcwQaVz7iX4\nDLDvBy7sxT6zLC7erpGRpRESfW9SGdKWLbqtWq3G/JH5lp/pZEH1PD/ffvudpcOPO/9Zg5481+3r\nlX4EdsrW2H/Zenjie7/mds9wYinIbT3HXgUo2RITZQtWi67fWpWz8Tu1datfiuR4NXsyIWgfRJet\nx6+swW5ZOOdu2+8yiIjAgASVzrkF4Ep8UPkkM7u1021lCe7SLC7erpGRNTBtfu/C0YXYMrQrW3Rb\nW+a2sPua3Wzev5k9h/a0PJ5uFlTP4/NJ4gKVWm214ZfUUIo7p7Va2FHQ88gc1u3rlX4GdkUvIyHp\nZOnhiUv6U90xmVvjvduevTRBci+u+aLqt7b7jf2dqq91mUEvMvIWpWzBbol80Tn3lH4XQkQk6Pew\nznaccy8EXgW82sxe0uE2tgE3PnHxibzyC68EfOr9yR2TiXd4wzBkYn5i3VpgYyNjLE0tMTIy4gOW\nowvsObQ2vfv8efNUd1YBWDjig8Kk16Pi3jsajLISrqwrQxAE68o2PjrO0tQSQRDEbitO9DNJxxx9\nTyvdfr6VhQXfkGos7A0Bo6OwaRNMT/tFvk82Lds2Pg7T1y2wp+k87ArmObh37fmfn/d3wJPUaiGj\nMxM+oIw6Nc7K7FLfM2I2Wz1fq9odYx7C0Dfo476LpaV8G4Xf+MY3OP/88wHOMrOb8tvyWs65Td3c\nyGqz7W3AjTfccANnnnlmEbs4HSykOfeNvwd5ZT4Nw5DFo4un52wm1btpyhiGPrhqBEUzMz5Ibnwm\nro5Ic83H/a7Mza3vESuyfmsl6XcK6nXctD8PIyluERf1+5nlGtsoelg/fQu4C/BB4GIz+1qO295G\nwfWTiPRWkXVTqXsqnXN3Bf4E+Czwt865Bzc9RrNs74qPXdH1Hebl2jIT8xPsvmY3W+a2rAsoAfYd\n3nf6znxcAozZI7Oxw07j3tscUKbRKu1868+1fz1pSFhe8zqThqnumw1h+wJMTfjH9gVWVmqcPBmy\nZ0/IqVvXb6tGjf0x5+Hg8iy+4dl4rN6p77RHpUzzB4vsjej2OMt0njL4lHPuN/pdiE5l6eHJeymN\n9iMq0vcuthoGmVRH7JsN215v0R7dsTHYtcvfpBqEodsnT8KePb7caco6aBl5JRUHvBLYBXzeOffH\nWdtGIiJ5KHVQCTwCGAN+CfgY8M+Rx0eB23Wz8Wjw1xxIBEHAdMw8I/CB5cEbD7JcW058feFocXNt\n9p6zN/XC5+347QQsLPi5OqduWL/d6Z0zLC4G6xoOzUNLF48uMr1z/dp20ztWn+t0juitD1n0yxxs\njix5ML0ZpjfB9GZql/tG5OlAcfsCK5PrexXqpVjX+KxRY+Fo8v5HRgJ2ja0/NxeMzXDgwPpzM2zS\nNh6TGq3T076XaUDP072BjzrnrurV/KUBDb7XSHNTrZOh0klBclwdcetD2t84jAarMzNw8GB8eZLm\nnnZS72aR9DsVtbKSfpj5IGTk7bdB+v0zsx+Z2SXA2fh20gHg0865B/e3ZCKy0ZR++GseGkM4vvqY\nr3LrT60dxXbBWRdw9OtHgfVDs+bnQ/Zcuwjn7INNy1mnrzB/3jxAquGvYUh9KG3ykNXx0fHTZQRa\nDitrN/x1NBhl9txZJndMsrgYRIZ/hbB9kdELZtk06rcbHp1kz56A5mFlbF+/j7lz5wiC4HTD8Zx7\nnMORrx85XcaQcF3v7ty5cxCQOIw4DEM2z06wQlyAuNbIR/w5r52bfOz3Yhdf4WDb5+bOm6e6o3q6\nAVurhX5e5bI/57vGZtg5Msn09NoLoxfDTNvJe/hrlu3FDVMMQ9+jkld5oKfDy+6O7wl4AvBt4MVm\n9nc5bXtjG+aKAAAgAElEQVQbkeFl7YZ4DpJ2w0UhyG0oZlIdMco4p2bSDU2t1UImtsLyybXvjZYn\n7XDevDWui337YDn+Xua6sqbZJnQ35LVXQ917Jc/fv17VT82cc4/HL7l2T+ANwDvi3mdmR1Jsaxsa\n/ioyVIqsmzZ8UNlsNZCJ/sGswdTW9fPp2hgfHed49TgHjh1IbIRE/4iFhOycXOB61g+pbZ7LCZye\nB9n4d1S08dMc3E3vmGZyxyQjIyOJDamx8ZCl4367WyZClh+4COfU/9IenmHzJy9nZM/WFg1GYueb\nxs0RbXX+GtuKa5zGSZpv2tje9I5p9h/dn2pbnBpn7Mol9s4EaxoWqxlfA7ZuLbZRlW0+3Op782wc\nJTUex8bgxInkbUarliIan71utDnnLgBeA/wCcC1+/lJX+21utPVjLmyR8+FazSnPMyjpZr5jtK48\neQI4POPX56zfRYwrT95zT9NqjBhovkHT0MuAbhiDyjx///oVVMLpjLCH8b2XcUIzaztEVkGlyPDZ\nsHMq83bJgy9p+564+Y4w4hsarazE189BEFDdWWVpKj79e3T40PLJgOtfNsUFwdy67TQWt24eItrY\nR6v9nthzguueft3pMkydM3V6W1sXJli+JDp0tP55VudWZRlWtroUCuw/sn/9aepgjmjSsLMsxkbG\nOF49nmk9N4Dl+pCuhYVwTUOyMeQ1LnlGFsnzVNPPVYp7L6RPw9/pUK/l5fZz4AaxYZnEzK7HN9Je\nCjwY+Jxz7jLn3N3jHlm33+vMnL2YDxeXVbbxO5jn/L5uhqZG532eHlq/fbV+S1qGpdcBpd+v/12e\nn49PzNPp3MhOy5Ln/Mx+G+TMuA3OuRHn3HOA/8TXVdcCz4p5/EHfCikiQyu3oNI5N+qcax+19dHF\nD7qY8dHxVO9d9wfz2CRcP88o44yNjLHrrF2Mj44zyjhcPw+H9q3bRrRBE9cISfojdmShylxMQ6yT\ndPbR/Ub/nb4hFRKcu76QI+fuj50/uRKu8Ij9i2zZGvq7/s2fy3DJRc9ftHHaahut5pseOHaArQtb\nObVyKl0BGjcSti+wZymyzMtCyNRU8jC0NI2qdg36LHOVWr23VWDX7TxJ8D0m0XLFBajD1Pg0s1Nm\n9irgPsCXgUXgppjHjf0oXxZ5zodLujHR7qZanvP7WgWwyeVOSGp2zixj42FX5SlKI7A8dcpnqc3j\n3HUqz+9PuuOcOwf4DHA1cAp4gpk9yszeEvfoa2FFZCi1HP7qnLs9cBHwG/ixQJ8BXmtm3296368B\nbwTOTjOkoteiQziu+eo1LecaRuc7xg0hvPzysN5QD5qGjvq5iI0honO7Z+rrvLUadpU8rNAPH1o7\nzDXt8K52Q7OSthU33LPVfhtDe6cPTVOjtnZb1/u5jVwQP+dy3+F9iYmOonNH44b1hmHIgWMH2Hd4\nH7fWbiUIAjaNbGo53zRuLucII+vLDb7D9oY5OFb132nTMYx8ZJ7aR9aPhxofTz/MtNUwqyzDyroZ\ngpZ1nmTSsLvxcTh+HA4cSB5uW8RcwT7OWXoEPph8AD4z9Xti3haa2fo7Teu3tY0+DH/Na+hiN99r\n83Dtxr+7lWVoauv6rXxLBcUpcvjyIJUhD4M4/LU+KuJK4EnAMvAKYM7MlnLY9jY0/FVkqPRlTqVz\n7j7AR4C7Nr30XeBBZvZ159wYsAC8CN/r+XYz+708C5iHaMV4t7vdLXGuYdY11OIbZiFj43BiKUj1\nBzbuj1hj/cVoAy1NUJl+TbhsDanW86KSA1Tmj8P2A3DOLONb1pYnDEN271/g+nBtlHIBc1w3U03d\nIIyKO86GuDJuDjYzMjKy7vkRxqi9rN7NOhW/NiXzPtFIQ+NGQNJacdFrqFaj5VxMKCaobG7Et/pc\nQ7TB36pc09PpEvHk2fjsw5zK++DXzH0U8CNgL/BnZpZ9TPfqNrfRh0Q9eQWVnTTCy5aMKMtawjLc\nBjFRj3PuJ8AEcBB4gZl9Kcdtb0NBpchQ6decygX8grqX1X/eFnhK/TOvc879NH4i+Evww70eUcaA\nMuqqqwDWzzU8Xl3ieHUp0kPZHKzE/1GJH9YXsHcmXUAJa4cPNQKSlZXO0tmnHR6btK3pHdOx5Y4b\nVnb59skU80wCOFZl7MrV87sa+AUcWaj6Hs1T4/5x/TxHFqukTbPbGM6bNL+p3bynkZGR2CG8s+ft\nZX4+YCxhpPTo5vXP7d0bH1BGh5hu2QK7dycvZN4YQphluGia93Y6PzNa3okJfy1Orz9dTE/7df2a\nxc1FGsR5ls65M5xzLwc+hw8o3wE4M3t1NwFlnMbQxjRzYbvdT7dDkjudg1a2ZSg6GTYrw6lXv385\n+z/gKWb2iDwDShGRzKLrNEYflUrlW5VK5c0xz19YqVROViqVD1QqlZVKpXJlpVLZkrSdMjwqlcq2\nSqUSbtp0czg/H55Wq4Xh/HwYjo+H4dh4Ldy1bz4c3z8eju8fD+cPz4e1Wi1sJ7qN8XH/7xQfW2dl\nxX9+NbTwj/Hx1e3VarVw/nB8GVdWauH4/vGQl7HmMb5/PPY4otsamx0Ld711V9tjr9Vq4cpKbd3x\nzh2eX7dfts+fPoboOY+et9XjrdUfa483T/MxZWwcZ9I5rdXij23u8Hzq73x+fv13mvQYHV3d3spK\n+uuq3TUYV4b5+fjnd+1qXca5ufX7SnPtplWrpf/MzTffHFYqlbBSqWwLi60/vl2pVGqVSuVzlUrl\n3Jy3va1SqYQ333xzthOVoHH+0pzHbuuutb/D6b73Tj7TK7VaLVWdL5JGD+un22R8/xmVSuW1Kd+b\na/0kIv1XZN3UqjI5WalULop5flu9gXW8Uqk8Ku8CFfGIBpXRxsuaRvX2+KAjrSyN4dXP1NYEMGkb\nW82fm5/3QTFT6YPK6LbmDs+lPva4QGRubm1gtmvffDg2Xuso4IoLQLNIahi2Ch47/Vy77zzpO21+\njIwkn4c0+1i96RB3rSRfV82B69xc+/I2rsfmfXX7XXYS4PSw0fajSqVySaVS2VTAtnNptJ2uB8b8\n9RS9QZEmuOw0lsr6vZc5qIyjQFM61av6KfT1yL0qlcrrKpXKv1YqlU9VKpU3VSqVe8e87/GVSuUb\nlUplJeV2FVSKDJl+BZW1SqXy/8U8f6f6a/vyLkxRj7igcl1PWQcBWaeSApVOGubdBsa1WnIP58pK\nrem9rRuEzcFuXIATfS6vXt7GcbQKGqPvS/udrj2eLJ+Lu8biH5s3d9bITnvuksowNuaDyqzlTSpX\nt99l/M2K1tvoYVB5t4zvH61UKpekfG8ujbZWPeLd3qhppZPvvYibSXlLW58kf76cQbL0Tg/rpwfW\nb3zVKpXKTyqVyv/U//3jSqVy//p77lCpVP6+/vzJSqWyP+W2FVSKDJki66ZulhQ5ltsY3B7KMmfo\n1lxnS61aSJj7mDU9exg2zWmqL3vCqe7nBp08ARNbs61dt3b5kvZz+vKcv5JlPmm7JEBhGKZeD3Tt\n59YeZ9IcxKi9e1u/niTtvLSkuXMrKz5ZUGNNy8b3lbR0SEPS70833+W667huz57i1k/Mwsy+6Zy7\nvXPuUufc3zvn3uWc21OfV75GPRP2J4E/6VX5ks5fQ5Hr7HXyvQ/CMhSdLN8EyXWdSIH2AZuB3zGz\n25jZnYAHAbcAr3HO/TzwceAJwEeBXzGzNn+ZRESy6yaoLCjkKs6OF1/F5Zf7v/BrG9DB6pqEEeFH\nZkibNCaNMIT5+ZA9161vAe47vI8wrHURZNVbLomJcZIETMck7eHwDMsng3VrHnaS3CMuAIo2tuKS\ntzTuejT/O04Yxq8118jwm0a03yQu6F84uti2cRh3nLDagB4bg1271jamq9X4czo93Xp9ySwJUtIm\ng2p+b1x52zX+807EU4ZELvWsr5/DB4pPAB4PzAJfrKfzxzk35py7Et94ewDw130qbl9k+d7Lngyl\nm/qkbEmIyqhRz0pufh14vZm9s/GEmf0rPoniduDvgLsBf2RmO8zsC/0ppogMu3ZB5X2dczujD+Ch\n9dfObn6t/nppHQ2v4MCx1b/wjQb02BhrevkamUg3fSzf2+cLCyF7rl2ATetTfy7Xltm6sLXeKxam\namQFAUxPh7B9wS99MTUB2xeYmabt+mrRO+qzuyfZFfjsh41j59jqsUeDldPnbDxMtTh41l6oaC/h\nlrkt7L5m95oew7RBYlqN87Bli1/GZdPm+KB/z3WzbJkIE3seko5z/35/fpaW4MQJuO669Y3p6HU4\nMuKXlNm/P79ejkYj/vhx2ByTuTb6/UYb/EnlzapdIzJND2mRvW0plDoTdrvzl2V0Ri8NYibgVrLe\n7Nlo1ItbmNsD/xbz/GfwdVQFeJiZvbanpRKRDaddUDmFX6sy+nhv/bVXxLx2KN/i5S96t7nRgD5x\nAubm/PIXzC/5x7FqpqVB2glDmLl+ES7Yk9j5mWWY1Wk7FuGCKb+W4uaT/t872n8+ekd9+WTAwb1V\n9rDE2JX+2JN7aH0QG+yZINjjg9jTvaQZxd3Jjw47W64tc/DGgx0vkRJdbiVJ4zwsL/v1I2st+t+X\nu+h5iDagmxvTjetwZsaXIakXMfr+TnqMs1zLrcqbVpZG5JobPOXzMOCtZnaFmX3XzH5sZn8PvBjY\nDbwNP9zslcAvmdnBXhcw7sZEWYeWll3W+qTdSArx0vbiqiczs1EgZpEqGisOL5rZZ3pYHhHZoFoF\nlc/q4PEHRRa2KI1GvR/2FzA+HuTeGAvDkNrDYm5jx/zxTDtsMwxD9scM09rf5vNJd9Tn5gJmptc3\nmqLBSta5Rll6oZKGna15b8yxhSFcvj37WnPx5yF+KLR/LlhT3jWf6nLdvzBMv9YjdDYvLY+1CbPI\nMhRw7Q2e3pUxpTsC/xzz/Efxc5keDjzazC4xsxM9LVld9PzdeiucOlXOoaWDIs3alc1zrxePLviR\nI026vXaHIchK04urnszCxPViiojkblPSC2b2lh6WIzPn3GOAt5nZbbN8Luluc6NR1miY590QCwIY\n3RwzEbVkDb7JSV/WRgNgZmb1nLSaazS5YzKxV7Dx+X37fI9gXsLQBym+rAEzM1WOVyfrPWtdnNjG\n0N9z6sd6eGbNcOAkjeOMO3d56/R67VUZWzUiG9dYnMZxJV2DfbIZOB7z/I/rP68ws3/qZgdXXeVv\nDHRb7yiAzEcQBFR3Vk8HknH1SeMGW8PUoSnmzoP5+Wou1+7a+m11W8P6HTduQjU0/l2t9qc8Q6TW\n7wKIyMbQNlGPc+7Ozrnfcs49wjl3hxbvu7tz7in5Fi9xXw/FDznL5NKHXNq296qoeT5BELDvvPXd\nRBectWvdc2mGbTa22W6YVtxd7lY9ViMj+SfRSNsLlXQ8a94bOba4nrADB9pnd42WK74X1Q+FnptY\nYm5i/XDgIjKgdjOkNeuw1ry/37x7UsqeyCVG15mwr7hCCV3KKClbdNINtv1HZpmcDPPJaD1ESX/a\n1W+aj9q1Hc65p0cfwO/UX9vd/Fr9dRGRXLUMKp1zLwduBt4PfBD4tnPudc6528S8fSfwjvyLuKY8\nY865PwY+DJzK+vmLH3Rxdz1YdNeArsYMqfrQ067NPGwzKmmYVruhRO2GT8YFK93MXWxsc3WYcfx+\no8czNjLGrrN2xZ6bvBohreaiVasB1WrQ0TDTTi6zXi61kMfNk1bXWB5DbQcokUsumbDVgB4e3V67\nwxhk9XspmWEYRtzCc4C3ND3+rP7aH8e89pc9LJuIbBBB0tw759yL8cl4/hp4J3An4LnArwGfBx5p\nZt+MvP9pwDVm1s0yJS055x4HvBmYqZfnpWZ2RorPbQNuvOGGGzjzzDM72neeQ5FWEwUFLZ/rZpsL\nC2uHEsHqEhZrP0f9c+n3s3h08fRd+pmdMy2HviaVsd1+17437nz5QOZkU3qC8XHfQ5D1NMb15sa9\n3osgp5f76ka7a6zo4Xvf+MY3OP/88wHOMrOb8tnqes65GjAPNCfg+Wl84rKXAp9q/pyZHUmx7W3A\njV/96g2Mjp7Z0bUr/bF7doGD4dpfgF3BPNfNdD9eM+/6rUyS6re0f7M62V8/hhH3sH46t5PPmdlH\nUmx7G122nUSkXIqsmxLnVOLvfL3XzJ7WeMI592agCuwHjjrnzjGzm/MsUBufALaZ2Q+dcy/r4X5z\nne8RP6ezu79wzQFX3F3uffvg8stX1yr0n8u+n+a5Ro07wK0CxKyBaPS1pDmwMzPrGyGdJsVo95lO\nv55OAsRBaDCmmTNZ9DzlHpuqP+K8Iua5EJ+VMbVeJiMalBsXZRWGcHhhEh7ImrnXhz85Sdhijdm0\n8q7fyiSp/EXN9x72uZppgkMRkV5oFVSehU+Rf5qZhcC8c+47wBuAQ865nWb2rQLLGN1/bvvJ0qjq\nNOlI2Swvw9at+dypbQSTCwvt7wDHJbQAqO7s7q96LxPjZLXRkmy0MgTH/Kyid3Dppb25dnVd5ieo\nz71eTeIVEIznt/0y129FKOIm1LD87c7COXcn4Gx81uoQ+G/gP8zs//paMBEZeq2Cyv8D7hP3gpm9\nyTk3jh+z/2Hn3HlFFK4IWRpVq0ODB+svT9JdblhN+ADp7tS2Cr7T3AHuNGNsGmXuCSvi7nj0uyii\npynLNoe5J6VZLzJhX3xxb85b0b02G6UHdO313z6JV6f7KGv9VqSNcpx5qydKvAz4lZiXa865fwGu\nNLP3xrwuItK1VvMf/wH4f0kZXc3sdcDlQAW/httD8i9e/tJk1Mtr/bGiFsVOk3Cg3WLy+/a13ka7\nRD9lSiRRtoQueZ+b6HexZQvs3p3vWm6drg/X78QbZeGcm3DOzdTnH5VWkb+zG22NwTD0Uwl6cf2X\nrX4bJHkkDBsEzrnXA38DbAOuwQeXfwg8H5+D4n3AA4B3O+de26diisiQaxVUTgKfAf7GOXfcOfeL\nzW8ws5cDzwPuVv9Z6mZE2kZVY7jmyZWTnFw56Ydr7lhM3YBoDkoXjizkElxmabhFl2UYjxmStbzc\n+vN5pbPvNmOsrP0ulpfh4MF8lxno9LsewKU/irIVeBlwzz6Xo2+GafmLVqJ18Nat/rnjxzf89V9q\nw37zyzn3DODZ+GXW7mFmzzSzK8zsTWZ2tZnNmdkTgbvis75e7Jz73X6WWUSGU2JQWR9/vx34//B3\nvr6d8L7XA7+O79n8YQFl7Kk81h9biAlKF44sdn3nvpOG28hI0lqMsGdP/OfTBN9Z7gAnLXsyrPK8\nO570XUR109OUR+/VsPekOOcOOec+nPTA9wIAvKLp+Vx1uyRCUb02ZRq1ULT4tXGH+/ofdBvg5tez\ngY8DzzCzHye9ycx+BFyET3j4Bz0qm4hsIC2X/zCzmpn9jZk9t9UkbzP7DzN7nJndvvGcc+7B9Wyx\npdGLtfPCMGTvofUtrD0HZ9kyEXY8LKybhtvkJMzNxb/WTcMv7R3gRsbYpakllqaWqO6sDn0v5bDf\nHd9g7gycCzwUf7f/55oed66/7w6R5+6adSdJQWOeQ0uH7bosaopB/L42TvA8jIb45tcvAu+pJ1Js\nqf6e9xI/71JEpCuFrSkJ3Bt4ZoHbD+lguG27RlW3wzXDEFZOxb+23KdhYY07tXHDYJPenyb4znoH\nOAiCvgeTvVoAO6+740nfRVQ3PU0bZc5Rlx4AzOEzsvwbcI6Z/ULjwep88mdFnr9vlh1cdVVy0Jjn\n0NIiem36cQ0VNcVAZACdAXwnw/tvwa+xKyKSqyKDykKZ2T4zu23Wz6VpVHUzXDMIAkY+GtPCOjxD\nI0tgJ3e2u224Zf18lh6NQbgD3K9EInmcm+h3MTYGu3bl29M0bL1XeTOzZTObAX4VuAfwBefcs2Pe\n2vEVdcUV8UFjUb1jef/O9voaipv3vni02Lt1ugEjJTUCJNzKjnUrg5bSXkQGwsAGld1q1ajqZrhm\nEMDsBZNw/TycGveP6+cja5l1rtuGW9ZAcdDnoUR7JQc5kUj0uzhxAq67Lv+epkH/rnvBzD6Pn2f+\nMuBK59wR59wvUECCskEbUtnLa6jVMkVF91bqBoyIiEi8VutUbnidDtWsVgOCoMq+2UlOnYKwtnY7\nnSZs8dvufN2yTtY9G8Tgonkt0ulp2L9//fsGbQHs5qHHRW5f4plZDfgz59x7gauAfwf+vKj9Ddp6\noGUsU5426tqRZbVR1kVN4QnOuXunfO/ZlDxTv4gMpg3bU1mkRsPjxFLAraeCru5sxw3bbOyjm/IN\n0h/hrPMgm3sl9+yBW28trnyy8ZjZzWb2aOAZwFPz3n40aFTv2FpZ5r0XNYd60OrQYbPR1kVN4Qn4\nERRpHo9Hw19FpAAKKgsUBH5Jj26GhQ3ysM1uddJwSJqDFve5svb2yOAws78BfgF4OPDpuPekyYR9\n6aWtkodpeHKzdvPeFXQMt438dzHGPTt8iIjkSsNfe6STRmCrJB2DNGyzU42GQ0Pj39Vq9m1t2uSD\nyMYw2JmZjd3bI/kxs+8BH2nxlkYm7GclveHii30wCa3mendWvmHUmPfeCCSbeyjzrDukXDb638Vm\nZnZTN593zj0Y+EMzS6yfRETSUE+llFKnWS+TMjTu3esblurtkbLSkMrs4pYp0nqSIpkUvfybiGwQ\niUGlc+5OWTfmnPvt7oojURsxhX0ec6BazUFTw11EZHBtxL+LIiKDoFVP5VHn3JlpNuKcu71z7q+A\n90We/grw1m4KV3ZFJYGI2ihJOprnQC0u+qytzdI0HDQHTSReL+qsflPQMfw2yt9FEZFB0iqorADH\nnHP3abUB59xjgM8DFwJfbjxvZh8zs9/PpZQl08skEBslQIpLvBCG3TUc1Csp4m20xDUKOobbRvm7\nKCIySFoFlU8G7oLvsTy7+UXn3B2cc28H3gvcEZjHr3809PqReW6YA6SkOVDT0/6148fVcBDpxqBm\ny+y0Z1VBx8YwzH8XRUQGTWJQaWbvBh4BbAEOOece1njNOfc4fO/k7wL/AvyqmU2b2cmCy9t3Gy0J\nRBiGhH08sD174MABNRyG0UYYilmULOeuX3VWN99vXj2rCjpERER6o2X2VzM7DOwAloAPOeee6px7\nB/BuYAL4f8DDzOzzhZdUeioMQxaOLDAxP8HE/AQLRxYKCy6T5kA1DGvAvlFttKGYeRqEc5dHGQe1\nZ1VERGSjarukiJl9Fngo8A3gr4HfwSfkuZ+ZXWVmJWvStNZt78hGSQKxeHSRqUNTnFw5ycmVk0wd\nmmLxaHGtuslJmJsrbPNSIoMWMJQpE3Yn567XdVa7Mrarg8s6GkQ96yIiIslSrVNpZl8DtgOfBELg\nA2b2rSILVoSrrsrnDv+wJ4EIw5DZI+tbdbNHZgvtrZyaig8shy1g38jKGjC0UYpM2N2cu17VWa3K\nWKuVv5c1ziD0DsvGVaabXiKysaUKKgHM7H+A84APAW9wzl1WWKkKcsUV+fSOKAlEcarV4Q7YZSAN\nfCbsMtRZaXtZyzYaZNB61mXDKcVNLxGRxKDSOXejc+6r0QfwOeCXgQBYdM7dFHn9xvp7cuece7Zz\n7svOuePOuX92zj04j+122zsyrEkggiBgZuf6Vt3MzhmCgg+4DI1fKU7ZAoaUSpEJO49zV3SdlVTG\n6WnYv3/980l1cFlGgwxoz7psLAN/00tEhkOrnsqvAV+PeXwFOFJ/3BR5/mv1R66cc88ArgauAZ4A\nfB+fNGhb3vsaFnnM/ZncMcn8efOMj44zPjrO/HnzTO7oXatuWAN2KU/AkFaZMmEPwrnLo4y6uSSS\nWilueomIbEp6wczO7WE5YjnnAmAf8Hoz219/7nrAgBcDL+xm+/3uHWkEft2UIbqNMPTDshp31mdm\nfGOuk+0HQUB1Z/V0INlND2UexynDoxEwlC0YasXMDjvndgDX4m9qXQQ8Fp+47If4TNhXF524rPnc\nlfF3KqmMMzN+6GhUuzq46ONrVzc1el6zllukV8zs3c65R+CHtB5yzj3azD4Kp296XQ3cGX/T69nK\n1i8iRUk9p7JP7g3cHfiHxhNmdivwj8Ajs27s0kvLcYc/j8QPcdtYWMh/7k8QBB0HlEpwIe0sLg7O\n9VGmTNiD0JPfXMYy9bJmqZvKVG6ROFr+TUTKoOxBZaX+87+anr8RuFe9JzO1iy8ux3CqtIkfWg1j\njdvG3r3r39fPuT9lSnCx0ZYDGITjLdP1kdawZMLOQ9ZrrExDWrNce0WXexB+V6X8ynTTS0Q2prIH\nlbet//xR0/M/wpf9Nlk32O87/GkSP7S7i560jZWVYsrcibIkuNhovaWDcrxluT46MQyZsLvR7TU2\nCHVwnLzLPSi/qzI4dNNLRPopcU5lSTT+hCf9qa31qiBp5TF/sHEXvaHx72q19edGRvxacFEbfe5P\np+dyUG204+0F59yNxNdBm1nNhP08VuujAAjN7J49KmJP6RrLh86jFMHM/sc5dx7w9/ibXncysz/p\nd7lEZPiVvafyB/WfZzQ9fwawYmbHe1yeRGnvOrdbFiDNXfSkbczOlmfuTxmWjhjk3rBODNLxluH6\nyKAUmbDLYJCusSRluPaG4TxKOZRp+TcR2djK3lPZWEvpnkC0ErwnPgNsaWS569wI9JqztGaRtI0g\nKE9myDyOU4bXoFwfZciELfkalGtPJIVWN7C+kvC8bl2ISO4GIai8GXg8cD2Ac24z8FvA+4veedqh\nrK3uOsct6dFqWYC0KezbbaMM+r38wUZbDmDQjrff18cgC+uVUzdL/XRi0K6xJP2+9oblPEr/6aaX\niJRFqYe/1rOVHQCe65ybc849Cp/N7A7Aq4rab68SKCQlfsiSwr7fSS/S6GcZN9pyAIN4vINwDZdF\nGIYsHFlgYn6CifkJFo4snA4we2UQr7EkqptERETyUeqgEsDMrgYuBX4PeCc+I+wjzOymovaZdamD\nvOfolCn1/qDbaOdyox3vRrN4dJGpQ1OcXDnJyZWTTB2aYvFob9dh0TWWD51HEREZJqUPKgHM7JVm\ndln01xgAABY5SURBVA8zu42ZbTezjxe1r04TKBRx11k9OPnZaOdyox3vRhCGIbNH1ldOs0dme95b\nCbrG8qLzKCIiw6DscyoHRr/n6IiIiIiIiPTDQPRU9lK3Q1l111lEihAEATM711dOMztnep6wR0RE\nRCRKPZUxlG5eRMpocoeviBrDYGd2zpx+TkRERKRfFFTG0FBWESmjIAio7qyeDiTVQykiIiJloKCy\nBbXXRKSMFEyKiIhImWhOpYiIiIiIiHRMQaWIiIiIiIh0TEGliIiIiIiIdExBpYiIiIiIiHRMQaWI\niIiIiIh0TEGliIiIiIiIdExBpYiIiIiIiHRMQaWIiIiIiIh0TEGliIiIiIiIdExBpYiIiIiIiHRs\n4IJK59wZzrmvOeee2O+yiIiIiIiIbHQDFVQ6584A3gf8PBD2uTgiIiIiIiIb3sAElc65c4BPAGf3\nuywiIiIiIiLiDUxQCbwH+Hfgkf0uiIiIiIiIiHiDFFRuN7OnAv/d74KIiIiIiIiIt6nfBXDObQLu\n3eItt5jZ983sC70qk4iIiIiIiKTT96ASOBNoFTC+CHhNj8oiIiIiAkBYTwkYBP0th4hI2fU9qDSz\nmxisYbgiIiIyxMIQFhdhdtb/f2YGJicVXIqIJOl7UCkiIiJSJouLMDW1+v/Gv6vV/pRHRKTs1EMo\nIiIiUheGqz2UUbOzq8NhRURkLQWVIiIiIiIi0jEFlSIiIiJ1QeDnUDabmdGcShGRJAM3p1KJfURE\nRKRIk5P+Z3OiHhERiTdwQaWIiIhIkYLAJ+VpBJLqoRQRaU1BpYiIiEgMBZMiIuloGKmIiIiIiIh0\nTEGliIiIiIiIdExBpYiIiIiIiHRMQaWIiIiIiIh0TEGliIiIiIiIdExBpYiIiIiIiHRMQaWIiIiI\niIh0TEGliIhICYShf4iIiAwaBZUiIiJ9FIawsAATE/6xsKDgUkREBsumfhdARERkI1tchKmp1f83\n/l2t9qc8IiIiWamnUkREpE/CEGZn1z8/O6veShERGRwKKkVERERERKRjCipFRET6JAhgZmb98zMz\n/jUREZFBUPo5lc65hwLzwAOA48D1wKVm9t2+FkxERCQHk5P+Z2MY7MzM6nMiIiKDoNQ9lc65+wI3\nAD8AngpcAjwM+JBzrvQBsYiISDtB4JPyLC35R7WqXkoRERksZQ/M/h/wTeCJZrYC4Jz7MvAJYBfw\nwT6WTUREJDcKJEVEZFCVPaj8HPC5RkBZ96X6z229L46IiIiIiIhElTqoNLOrY55+dP3nF3tZFhER\nEREREVmvb0FlfU7kvVu85RYz+37TZ34euBL4pJkdKrJ8IiIiIiIi0l4/eyrPBL7Q4vUXAa9p/Kce\nUN5Q/+9TCyyXiIiIiIiIpNS3oNLMbiJl9lnn3C/hk/KMArvM7MaMuxsFuOWWWzJ+TETKKvL7PNrP\ncnRJdZPIEFL9JCJlVGTdVOo5lQDOud8ArgW+B5xrZl/pYDN3BbjwwgvzLJqIlMNdgU7qhTJQ3SQy\n3FQ/iUgZ5V43lTqodM6dhe+h/BZwvpl1ervsk8AO4NvASpv3ishgGMVXip/sd0G6oLpJZDipfhKR\nMiqsbgrCMMx7m7lxzr0PeBTwe8BNTS/f1EWQKSIiIiIiIjkobVDpnNsM/AQfUcctCX2Jmb2yt6US\nERERERGRqNIGlSIiIiIiIlJ+qbKvioiIiIiIiMRRUCkiIiIiIiIdU1ApIiIiIiIiHVNQKSIiIiIi\nIh1TUCkiIiIiIiIdU1ApIiIiIiIiHdvU7wIUzTn3bOCPgbsB/wa8xMz+Jed9PAZ4m5ndtun5KeA5\nwB2BjwIvMDPrYPsjwIuAZwM/D3wNuMrMXpf3vurbGgNmgN+rb+/j+HVBP1PE/iLbHMd/R/9iZr9f\nxL6cc3cE/jvmpb83s6c45wKgmuP+zgcWgF8Gvgu8BZg1s1r99a6PzTl3LvDhFm+5B/AN8j2uAH9N\nPg+4K/B5YNLMDkXek9f1vxVYBH4HuA3+erzczP417331WtH1U9F1U31bPaufVDcNVt1U38659LB+\nUt2UjnPul4A/BR4E/B/wOjN7eZvPjAMHgKfij/dDwB+Z2bcj79kETAO/jz/mz+HPf6troKdlbHr/\nufjr81wzO1KWMjrnfr7+nnOBCeBTwB9H67o2+8j0tyXNcTjndgBXAr8EfBNYNLO/TFOeHpbxt/HX\n332B/wX+AZgysx+XpYxN7/9L/LV3ViflK6qMzrl7Aq8EHg6cAK4FXmpmcX+jThvqnkrn3DOAq4Fr\ngCcA3wc+5JzbluM+Hgq8Leb5vcAU8HJ8xXE74Abn3G2b35vCDDCPP45HA38HvNo5d2kB+wJ4FfAC\nfIPjscBx4JBz7u4F7a9hL+CA04unFrCvs+s/dwEPjjwm68/P5LU/59zDgA/iGzWPAl4LXAbsqb+e\n17F9qulYHgych69QP4RvsOV2XHUvqm/rzfhr5CvAtc65B+R8bADvAi7CV3CPA74AHHbO/WoB++qZ\nouunHtVN0Nv6SXXTYNVN0Pv6SXVTG865nwWuB1aAJwNvAOadcy9t89E/x9/QuQwfNJ4N/FP9xlLD\na4AX4+uEx+KDjw8451yJytjYxwTwJiK/12UoY71c19WffyFwYb2MR9L8fcj6tyXNcTjn7osPLL4C\nPB74APAXzrkntitPD8v4cHwQ+dn6Nufwv3d/U5YyNr1/N/AMOrz+iiqjc+72wFHgZ/A3zF6Ev7nx\nt+3KE4Rhx8dSavW7lTcC/2hmz68/twkw4ANm9sIutz+GP9GzwE+AzY3eAOfcGcC38Hd9r6g/99P4\nO/gvM7NXZdjPKP5OwqvNbG/k+dfiL4h7Ad/OY1/1z94Of9f6MjN7df25LfgGwDzwZ3kdW9N+fwU4\nAizhv59n5XkeI/t5Ef5u38/FvJbr/pxzR4HvmdljIs8tAr8BPIYcv7eYfb8a+F3gfsBynsdV//xn\ngU+Z2TPr/x/B/779A77HIa/r/9eATwLPM7PXR55/N3BbfEOusPNYlCLrp17VTfXP9qx+Ut00HHVT\nfXuF1U+qm9pzzu3D9+Te3cxO1J+bBS4G7mJmt8Z85l74+ul3zeyd9efuXX/uSWb2HufcfYAvAk82\ns3fX37MZ+Hfgz8zs6n6Xsen9rwCegu/hydxTWeB5fDK+AX9vM/tq/T0T+GvnL83sshZlyvy3pc1x\n3NnMVpxzbwV+1cx+OfK5a4Czzezs5m22UmAZ/xH4KTM7J/K5J+FvdP6imf1nH8u45npwzv0Uvhd/\nFDhlZvdMW7YCy9g4j/uBZwEVM/tJ/T2/jb/5+CAz+25SmYa5p/LewN3xf0gAqH+h/wg8MoftPwq4\nHLgE35gJIq89GD+kIbrv7wOHO9j3GcBbgXc3Pf8l/F2Eh+e4L4Af47vE3xJ57lb8nZRx8j024PQv\nwZvxd3O/GXkp930B9wf+I+G13PbnnPsZ4KH4u0CnmdmkmT0ceEhe+4rZ9/2A5wN7zOx/KeY83hb4\nUWR7NeCHwO1z3l+l/vPapuc/CpyDv3tWyHksWJH1U6/qJuht/aS6acDrpvr+i66fVDe1dwFwQ6NR\nWfc+4A7Aryd85uH1nx9oPGFm/4Xv7W4cz2OB/20ElPX3nDKz+2UJKAsuIwDOud/AD0tu16vYjzJ+\nD3+j7quR9yzhe/W3tSlTJ39bWh3HAyPv+UDT594H/LJz7i5tytSrMn4MeF3T575U/7mtz2Vsvh4O\nAP8F/D1r/0b3s4yN8/h44B2NgLK+3Q+Y2bZWASUM95zKRoX/X03P3wjcyzkXmFk33bSfALaZ2Q+d\ncy9L2PdXYvb9GDKo/xH6o5iXHg3cDJyZ177q+1vB31Vs3AU5C3gZUMMPpdud5/7qLsNfiweA6FCK\n3M5jxP2BJefcR4FfBf4H+FMzuzLn/f0yvqI47px7P/4X+YfAVfgepCKOrWEeMDN7Y/3/RezrbcDz\nnXPvwQ9veya+12Ey5/3dXP95D/xd2oaz8Hf4Gnf3ijiPRSqyfupJ3QS9rZ9UN+W2v37WTVB8/aS6\nqb37sH6eayOAqQBx87EqwLfrwU3UjfXtgb+Grd47tB/f6P0c8MKsvYA5l/GrrH73jdEcf0H9WsxY\nrsLLaGbX44conuacOwv4ReD9bcrUyd+WlsdR7/2/a8w2o8d6S5tyFVpG/Hz3uZh9Pbr+84sZyldY\nGQGcn5v6TPzvS9zfz76V0Tn3afxUjz93zr0GeBr+pu17gefX/+YnGuagsjFn4UdNz/8I30N7G/yd\n746Y2bfa7PtkzNCHH0XK1THn3EXA+fi5RbcrcF8z+LlEANNm9uX6H4vc9uf8OP0q8HAzO+XWTrvI\n9TzWh+rdt/75S/ENgd8GDtSHltya4/5+pv7zGuDt+Mnt5+LnLC3hGx25f2/OT65+ND5pSkMR1+MM\nvkKM/uGbMrMPOOcmc9zfx/F/DK52zv0+vgHwKPwcE4CtOe6rlwqrn/pZN0HP6ifVTZ3vry91E/Ss\nftrQdVO9d/3eLd7yHZp6cyPlguSy3Zb4OulH+OGj4K+t++DnPk/ih6tfAnzQOXc/M/tan8r4Y3wS\nsYY9wCn8CIRfjnl/GcoYLUsjCF7Cz8dspZO/Le2Oo9U2o/tMq4gyruOcOxt/Hb7LzG4sQxnrUzbe\nBOwzs6+6bFONe1HG2+P/BlTxw/ufgr8u/wT4a3wdl2iYg8pGd3LS3f5awfsuZL/OuQvxlco7zex1\nzrlqUfvCD2n7MH64xl7nM5Yt5bW/+lyXNwFvMrOP15+Objvv8xgCvwl83cxuqj93pD62/TL8Xcu8\n9re5/vNaW53/cNg5dyf8H7QDOe4r6iL8HLdogpYirse34YfJPQ/4T3xykZc5536Q5/7qjfkn4Bu/\njbu+n8bfBb8SGMtrXz3Wr/qpsLoJelo/qW7qfH/9qpugN/XTRq+bzsQnDIoTAi+hs/OQ5jObgZ8F\ndprZMQDn3DF8b+0f44c996uMK/Xy3B8f6O40P38s4e39K2NUvW77W2A7fs5lbBbbpu03ypi2XO2O\nI++/V0WUcY3693wdfkTBH2YsX2N/ZNkn6cr4MnwQ94oOyhS3P1Lss/kzrd7fiAt/ADzeVrOB/xB4\np3PugWb2yaQCDXNQ+YP6zzNYm6b9DGDFzI4XvO9x59xofchWdN8tu45bcc69BLgCP/65cTe0kH0B\nmNln6/886nyiiEvxDZy89vcC/B2QR9XvCoK/4Efq/8/12Oq/HHFDcD4EPBef1CSv/TXuDjXPt7ke\n/4f1+znuK+pxwHvN7FTkuVzPo3Pu1/EZwZ5sZu+qP32k/p29HH+HK8/v7YvArznnfg4YN7MbnXMX\nR95SyPVfsH7VT4XVF72sn1Q3dbW/ftVNUHD9pLoJ6jclWubLcH6ZkzOanm78/wfE+0HMZxqfa3zm\nx8BPGgFlvTzHnXMfI9Ij2K8y1m8W/QXwRuAz9etitP6eTdHvqs/nsbH92/H/t3cuoVbUcRz/UBAV\nBbXQMoiizU8yKtCKoNfCEhJNVFwUmtdFVmQmhaEW3EUPgl6uMspFtbCQQtKCHndRkCFFWWH1SxdR\nZAWRPXbd4Lb4zuQ093g89zRzrl6+HzgcztyZ+f3+c2d+/8f8HrKnVwK3ZubrHY7rdP7yfL32LZ10\nqrbjj9q2TvtMhDZ0/JdQmZgdKFHW3Mw8NEH9WtExlNxrLXAN6ktOoJgYdrATk6Ijh/uHkXJCWVB6\nflyE3mB2ZCon6tlffNczKl3A//Oh71V2GfPTiOyIeAStfr6IVqtKl5pGZUXEWRExVKyQV9mL/KoP\nNShvEVoNPIQyAP6F3JZWVH432bYZEXFbsSJf5ZTiu8m2lT7uJ9W2l28JRhuUBUCorMJMxidNafp+\nLONn6vEiHyCXr7Gm5EXEyRFxS0TMyMyDFReWS1DilA+bkjVgJss+NW6bYDD2ybbp+LVNMDD7ZNvU\nG/tRZuYqpS06km77gbOLN2f148pjDqDJWX1seRITf5vVho7nArPRotEoeo7LAfK7wDvHgI4AFLbg\nfZQ8ZUlmbpuATlU9Op6/wzFHbEeqxuOP3fbpUbfWdCw3hGozl6VPrs7MH+iPNnRcgPqqPRzuR9ag\nuOzRiFgx2Tpm5u8oo3r9/iz7h665Hqb6pPJ7lMUI+De19XxgpGXZu1Gx0KrsM1FGuAnLjoi1KJvj\n05k5VFs9aFQW8qfeCiytbb8BxRDsaFDeapQRq/xchjJ17Sx+v9ygLNAAbQsKPK6yBD2ArzUobx8a\nWCyrbZ9fbG+6baDMmDB+QNX0PVIGdV9V234F6qibvI5/oxpMN1fONR2Vq9hJ820bFJNlnxq/XgO0\nT7ZNx69tgsHYJ9um3hgB5kbEqZVti1ByqL1djjmRSpKhUAmRCzncnrfQYLS6zxko2/DuY0DHg+hZ\nrj7bpVfF6uIz2TqWfcEbKGPpvMysZ13tRj99Sy/tGAEW1BYMFgFfZOYvE9CvNR0j4nJUPmQPcG0f\nerWt47P8996bA2xDE/Y5jM+uOxk6gtyGbwzF85fML767PsdTtk4lQETcgeqqPIouxF3IuF1aiVtp\nQs4wcG9mnl7Z9hh6zb0J/eM3oexZszKzHiTb7dwzUCanRH7h9dTDH6FC4P9bVkXmdhSrtKGQvRgZ\n26HMfKGpth1B9l7gk8xcVfxuVFZEbEMJMDZR1NNC9XhuKhI5NCYvIpajcgtbUJHsuSiu5PbMfK6F\ntg0Dd2bm9A5/a1rW22jF9wF0Ha9DE4vNmbm+4ev4BMqUtg6toD0EnA3MzsyDbd6PbTII+9SmbSrO\nNVD7ZNt0fNqmQuYwA7BPtk1HJ1QC4iuUTflx9HZ1GNWAfbLY53SUbfRAOTiPiFeAeSgm8Tdku/5E\n7R0r9nkHZS9ejwbLG1ASqlmZ+fOxoGNNzqUoFrafOpWt6BgR61DM3WPI/bXKr5n5DV04Wt8SqpU5\nLTPLbKS9tONiZM93oXjz61E95KVZKSHTKy3p+BnyDFjGeBfznKgbbBs6dpDxNLLxdY+GSdMxImai\n//XH6B48D8Xav5mZt9CFqRxTSWY+U8y01yKj/yla9fm2YVFjjH8lvBG5e9wHnIbcb5b30ZHMQ64j\nFyF3mrrcaQ3KKlmBMituQB3gPv5rOJqWV6Wt61iyCmUHvAe17UtgcWUlsDF5mflSRIwW5xwCvgNW\nZ+bzLbVtGnKT60TTshaiAdI64Bzk9rQmM8vad03K21h8P4pc2N4DluXhLKdt3o+tMSD71KZtgsHb\nJ9um49M2weDsk23TUcjMnyJiLrAZ2I7KQWysDXxno2RYK5FbO+heeQoNNE9A7qJ31yZri9D1eBi1\neTdKitPzhHIAOtbp6+1KizouLHS6v/hU2cVRStL00Lc8CCyniCftpR2Z+XlELCh0fg1lqF7Zz4Sy\nDR0j4nwUtzsGvFkTN4YW6SakaxvXsQOd+uhJ1TEzv46Ia1Ec+qsopnYr6ne7MqXfVBpjjDHGGGOM\naZepHFNpjDHGGGOMMaZlPKk0xhhjjDHGGNM3nlQaY4wxxhhjjOkbTyqNMcYYY4wxxvSNJ5XGGGOM\nMcYYY/rGk0pjjDHGGGOMMX3jSaUxxhhjjDHGmL7xpNIYY4wxxhhjTN94UmmMMcYYY4wxpm/+AY0c\n9rauGPfAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3f11d7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter, axes = plt.subplots(1,len(kids_k.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(kids_k.columns, axes):\n",
    "    ax.plot(kids[col],'.b')\n",
    "    ax.plot(adults[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAADjCAYAAAAPB9AcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQwIAAAAAQf9fe8IIAADAJszY5qlWJpbcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3e161710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter, axes = plt.subplots(1,len(kids_new_k.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(kids_new_k.columns, axes):\n",
    "    ax.plot(kids_new[col],'.b')\n",
    "    ax.plot(adults_new[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAADjCAYAAAD35yvrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERmW/wfb5dvEWWPJeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3f379f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter, axes = plt.subplots(1,len(kids_acc.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(kids_acc.columns, axes):\n",
    "    ax.plot(kids[col],'.b')\n",
    "    ax.plot(adults[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5sAAADjCAYAAAAL8gAhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9z880wBpoqO/ygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3e3b64d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter, axes = plt.subplots(1,len(kids_new_acc.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(kids_new_acc.columns, axes):\n",
    "    ax.plot(kids_new[col],'.b')\n",
    "    ax.plot(adults_new[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAADjCAYAAAD35yvrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4bDATQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3cef3250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter, axes = plt.subplots(1,len(kids_rt.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(kids_rt.columns, axes):\n",
    "    ax.plot(kids[col],'.b')\n",
    "    ax.plot(adults[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAADjCAYAAAD35yvrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3ecd0650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter, axes = plt.subplots(1,len(kids_new_rt.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(kids_new_rt.columns, axes):\n",
    "    ax.plot(kids_new[col],'.b')\n",
    "    ax.plot(adults_new[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAADjCAYAAAD35yvrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQhaLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3f1156d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter, axes = plt.subplots(1,len(kids_lure_acc.columns), figsize= (15,3),sharex='col', sharey='row')\n",
    "for col, ax in zip(kids_lure_acc.columns, axes):\n",
    "    ax.plot(kids[col],'.b')\n",
    "    ax.plot(adults[col], '.g')\n",
    "    ax.set_title('%s ' % col)\n",
    "    ax.set_ylabel('%s' % col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAADjCAYAAAD35yvrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrgRmR3W6JvAScJuq