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jminas/sart

Created July 6, 2015 18:20
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"source": [
"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",
"from __future__ import division, print_function\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)\n",
"import sys\n",
"%pylab inline\n",
"sns.set(style=\"whitegrid\")\n",
"sns.set_context(\"poster\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>date</th>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th>Trial</th>\n",
" <th>Stim</th>\n",
" <th>Response</th>\n",
" <th>RT</th>\n",
" <th>Hit</th>\n",
" <th>CorrRej</th>\n",
" <th>Commission</th>\n",
" <th>Omission</th>\n",
" <th>onset_time</th>\n",
" <th>condition</th>\n",
" <th>regressor</th>\n",
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" <td> p3</td>\n",
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" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
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" <td> 8</td>\n",
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" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
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" <td> 1</td>\n",
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" <th>13</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
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" <td> 3</td>\n",
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" <td> 0</td>\n",
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" <td> 73.341607</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
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" <td> 75.342872</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 16</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.697356</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 77.344183</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 17</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.697141</td>\n",
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" <td> None</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 18</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.730804</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
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" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 19</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.763589</td>\n",
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" <td> 0</td>\n",
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" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
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" <tr>\n",
" <th>19</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 20</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.898024</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 85.349447</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 21</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.796590</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 87.350679</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 22</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.864630</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 89.352045</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 23</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.814444</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 91.353282</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 24</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.730760</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 93.354806</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 25</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.697410</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 95.355934</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 26</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.764334</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 97.357197</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 27</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 99.358579</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 28</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.714419</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 101.359817</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 29</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.881319</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 103.361259</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 30</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.629981</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 105.362471</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 31</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.697276</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 107.363786</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 32</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.664056</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 109.365028</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 33</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.679165</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 111.366365</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 34</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.261399</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 117.069746</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 35</td>\n",
" <td> probe2</td>\n",
" <td> 1</td>\n",
" <td> 0.630576</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 122.773585</td>\n",
" <td> probe2</td>\n",
" <td> Aware</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 36</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.881414</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 124.774853</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 37</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.781239</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 126.776122</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 38</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.764707</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 128.777490</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 39</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.846662</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 130.778736</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 40</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.714815</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 132.780106</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 41</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 134.781343</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 42</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.714883</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 136.782725</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 43</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1.031331</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 138.784018</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 44</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.747828</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 140.785352</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 45</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.714628</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 142.786576</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 46</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.713174</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 144.787956</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 47</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.764616</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 146.789459</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 48</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.697909</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 148.790523</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 49</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.731256</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 150.791806</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 50</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.130440</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 156.495714</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date participant schedule Trial Stim Response RT \\\n",
"0 2014_Nov_11_1015 p3 1 1 6 1 0.793817 \n",
"1 2014_Nov_11_1015 p3 1 2 1 1 0.930410 \n",
"2 2014_Nov_11_1015 p3 1 3 5 1 0.679607 \n",
"3 2014_Nov_11_1015 p3 1 4 5 1 0.680868 \n",
"4 2014_Nov_11_1015 p3 1 5 5 1 0.680668 \n",
"5 2014_Nov_11_1015 p3 1 6 3 None NaN \n",
"6 2014_Nov_11_1015 p3 1 7 4 1 0.696534 \n",
"7 2014_Nov_11_1015 p3 1 8 1 1 0.764287 \n",
"8 2014_Nov_11_1015 p3 1 9 6 1 0.714437 \n",
"9 2014_Nov_11_1015 p3 1 10 4 1 0.647609 \n",
"10 2014_Nov_11_1015 p3 1 11 7 1 0.629115 \n",
"11 2014_Nov_11_1015 p3 1 12 8 1 0.729447 \n",
"12 2014_Nov_11_1015 p3 1 13 1 1 0.747627 \n",
"13 2014_Nov_11_1015 p3 1 14 3 None NaN \n",
"14 2014_Nov_11_1015 p3 1 15 0 1 0.679941 \n",
"15 2014_Nov_11_1015 p3 1 16 8 1 0.697356 \n",
"16 2014_Nov_11_1015 p3 1 17 6 1 0.697141 \n",
"17 2014_Nov_11_1015 p3 1 18 5 1 0.730804 \n",
"18 2014_Nov_11_1015 p3 1 19 2 1 0.763589 \n",
"19 2014_Nov_11_1015 p3 1 20 2 1 0.898024 \n",
"20 2014_Nov_11_1015 p3 1 21 9 1 0.796590 \n",
"21 2014_Nov_11_1015 p3 1 22 1 1 0.864630 \n",
"22 2014_Nov_11_1015 p3 1 23 6 1 0.814444 \n",
"23 2014_Nov_11_1015 p3 1 24 7 1 0.730760 \n",
"24 2014_Nov_11_1015 p3 1 25 9 1 0.697410 \n",
"25 2014_Nov_11_1015 p3 1 26 8 1 0.764334 \n",
"26 2014_Nov_11_1015 p3 1 27 3 None NaN \n",
"27 2014_Nov_11_1015 p3 1 28 9 1 0.714419 \n",
"28 2014_Nov_11_1015 p3 1 29 9 1 0.881319 \n",
"29 2014_Nov_11_1015 p3 1 30 7 1 0.629981 \n",
"30 2014_Nov_11_1015 p3 1 31 8 1 0.697276 \n",
"31 2014_Nov_11_1015 p3 1 32 9 1 0.664056 \n",
"32 2014_Nov_11_1015 p3 1 33 1 1 0.679165 \n",
"33 2014_Nov_11_1015 p3 1 34 probe 2 1.261399 \n",
"34 2014_Nov_11_1015 p3 1 35 probe2 1 0.630576 \n",
"35 2014_Nov_11_1015 p3 1 36 6 1 0.881414 \n",
"36 2014_Nov_11_1015 p3 1 37 4 1 0.781239 \n",
"37 2014_Nov_11_1015 p3 1 38 1 1 0.764707 \n",
"38 2014_Nov_11_1015 p3 1 39 0 1 0.846662 \n",
"39 2014_Nov_11_1015 p3 1 40 1 1 0.714815 \n",
"40 2014_Nov_11_1015 p3 1 41 3 None NaN \n",
"41 2014_Nov_11_1015 p3 1 42 1 1 0.714883 \n",
"42 2014_Nov_11_1015 p3 1 43 1 1 1.031331 \n",
"43 2014_Nov_11_1015 p3 1 44 8 1 0.747828 \n",
"44 2014_Nov_11_1015 p3 1 45 8 1 0.714628 \n",
"45 2014_Nov_11_1015 p3 1 46 7 1 0.713174 \n",
"46 2014_Nov_11_1015 p3 1 47 7 1 0.764616 \n",
"47 2014_Nov_11_1015 p3 1 48 4 1 0.697909 \n",
"48 2014_Nov_11_1015 p3 1 49 8 1 0.731256 \n",
"49 2014_Nov_11_1015 p3 1 50 probe 1 1.130440 \n",
"\n",
" Hit CorrRej Commission Omission onset_time condition regressor \\\n",
"0 1 0 0 0 47.324640 nontarget None \n",
"1 1 0 0 0 49.325874 nontarget None \n",
"2 1 0 0 0 51.327181 nontarget None \n",
"3 1 0 0 0 53.328492 nontarget None \n",
"4 1 0 0 0 55.329802 nontarget None \n",
"5 0 1 0 0 57.331190 target None \n",
"6 1 0 0 0 59.332398 nontarget None \n",
"7 1 0 0 0 61.333699 nontarget None \n",
"8 1 0 0 0 63.335042 nontarget None \n",
"9 1 0 0 0 65.336596 nontarget None \n",
"10 1 0 0 0 67.337623 nontarget None \n",
"11 1 0 0 0 69.338985 nontarget None \n",
"12 1 0 0 0 71.340236 nontarget None \n",
"13 0 1 0 0 73.341607 target None \n",
"14 1 0 0 0 75.342872 nontarget None \n",
"15 1 0 0 0 77.344183 nontarget None \n",
"16 1 0 0 0 79.345482 nontarget None \n",
"17 1 0 0 0 81.346818 nontarget None \n",
"18 1 0 0 0 83.348122 nontarget None \n",
"19 1 0 0 0 85.349447 nontarget None \n",
"20 1 0 0 0 87.350679 nontarget None \n",
"21 1 0 0 0 89.352045 nontarget None \n",
"22 1 0 0 0 91.353282 nontarget None \n",
"23 1 0 0 0 93.354806 nontarget None \n",
"24 1 0 0 0 95.355934 nontarget None \n",
"25 1 0 0 0 97.357197 nontarget None \n",
"26 0 1 0 0 99.358579 target None \n",
"27 1 0 0 0 101.359817 nontarget None \n",
"28 1 0 0 0 103.361259 nontarget None \n",
"29 1 0 0 0 105.362471 nontarget None \n",
"30 1 0 0 0 107.363786 nontarget None \n",
"31 1 0 0 0 109.365028 nontarget None \n",
"32 1 0 0 0 111.366365 nontarget None \n",
"33 0 0 0 0 117.069746 probe Off \n",
"34 0 0 0 0 122.773585 probe2 Aware \n",
"35 1 0 0 0 124.774853 nontarget None \n",
"36 1 0 0 0 126.776122 nontarget None \n",
"37 1 0 0 0 128.777490 nontarget None \n",
"38 1 0 0 0 130.778736 nontarget None \n",
"39 1 0 0 0 132.780106 nontarget None \n",
"40 0 1 0 0 134.781343 target None \n",
"41 1 0 0 0 136.782725 nontarget None \n",
"42 1 0 0 0 138.784018 nontarget None \n",
"43 1 0 0 0 140.785352 nontarget None \n",
"44 1 0 0 0 142.786576 nontarget None \n",
"45 1 0 0 0 144.787956 nontarget None \n",
"46 1 0 0 0 146.789459 nontarget None \n",
"47 1 0 0 0 148.790523 nontarget None \n",
"48 1 0 0 0 150.791806 nontarget None \n",
"49 0 0 0 0 156.495714 probe On \n",
"\n",
" targ \n",
"0 corr_nontarg \n",
"1 corr_nontarg \n",
"2 corr_nontarg \n",
"3 corr_nontarg \n",
"4 corr_nontarg \n",
"5 corr_targ \n",
"6 corr_nontarg \n",
"7 corr_nontarg \n",
"8 corr_nontarg \n",
"9 corr_nontarg \n",
"10 corr_nontarg \n",
"11 corr_nontarg \n",
"12 corr_nontarg \n",
"13 corr_targ \n",
"14 corr_nontarg \n",
"15 corr_nontarg \n",
"16 corr_nontarg \n",
"17 corr_nontarg \n",
"18 corr_nontarg \n",
"19 corr_nontarg \n",
"20 corr_nontarg \n",
"21 corr_nontarg \n",
"22 corr_nontarg \n",
"23 corr_nontarg \n",
"24 corr_nontarg \n",
"25 corr_nontarg \n",
"26 corr_targ \n",
"27 corr_nontarg \n",
"28 corr_nontarg \n",
"29 corr_nontarg \n",
"30 corr_nontarg \n",
"31 corr_nontarg \n",
"32 corr_nontarg \n",
"33 NaN \n",
"34 NaN \n",
"35 corr_nontarg \n",
"36 corr_nontarg \n",
"37 corr_nontarg \n",
"38 corr_nontarg \n",
"39 corr_nontarg \n",
"40 corr_targ \n",
"41 corr_nontarg \n",
"42 corr_nontarg \n",
"43 corr_nontarg \n",
"44 corr_nontarg \n",
"45 corr_nontarg \n",
"46 corr_nontarg \n",
"47 corr_nontarg \n",
"48 corr_nontarg \n",
"49 NaN "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(dtype=float)\n",
"df1 = pd.DataFrame(dtype=float)\n",
"runs = [1,2,3,4]\n",
"subjects = ['p3', 'p5', 'p6', 'p7', 'p8', 'p9']\n",
"for run in runs:\n",
" for sub in subjects:\n",
" df1 = df1.append(pd.read_csv('../data/%s_schedule%d.csv' % (sub, run)), ignore_index=True)\n",
" df = df.append(pd.read_csv('../data/%s_schedule%d.txt' % (sub, run), delimiter='\\t'), ignore_index=True)\n",
"df['condition'] = df.apply(lambda row:( 'probe' if row['Stim']=='probe' else 'probe2' if row['Stim']=='probe2' else \n",
" 'target' if row['Stim'] == '3' else 'nontarget'), axis=1)\n",
"\n",
"df['RT'] = df1['key_resp.rt']\n",
"df.Commission = df.Commission.convert_objects(convert_numeric=True)\n",
"df.Omission = df.Omission.convert_objects(convert_numeric=True)\n",
"df.Hit = df.Hit.convert_objects(convert_numeric=True)\n",
"df.CorrRej = df.CorrRej.convert_objects(convert_numeric=True)\n",
"df.onset_time = df.onset_time.convert_objects(convert_numeric=True)\n",
"df['regressor'] = df.apply(lambda row:('On' if (row['condition']=='probe' and row['Response']== '1') else \n",
" 'Off' if (row['condition']=='probe' and row['Response']== '2') else\n",
" 'Aware' if (row['condition']=='probe2' and row['Response']== '1') else\n",
" 'Unware' if (row['condition']=='probe2' and row['Response']== '2') else None), axis=1)\n",
"\n",
"df['targ'] = df.apply(lambda row:('corr_targ' if (row['condition']=='target' and row['CorrRej']== 1) else \n",
" 'incorr_targ' if (row['condition']=='target' and row['Commission']== 1) else\n",
" 'corr_nontarg' if (row['condition']=='nontarget' and row['Hit']== 1) else NaN), axis=1)\n",
"\n",
"\n",
"#df[df['condition'].isin(['target'])]\n",
"df.head(50)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>date</th>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th>Trial</th>\n",
" <th>Stim</th>\n",
" <th>Response</th>\n",
" <th>RT</th>\n",
" <th>Hit</th>\n",
" <th>CorrRej</th>\n",
" <th>Commission</th>\n",
" <th>Omission</th>\n",
" <th>onset_time</th>\n",
" <th>condition</th>\n",
" <th>regressor</th>\n",
" <th>targ</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>607 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 250</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 737.929284</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>749 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 34</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 229.122536</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1054</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 339</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 947.204337</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1182</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 109</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 378.507595</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1251</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 178</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 524.280609</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1379</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 306</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 825.470929</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1396</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 866.980445</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1412</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 339</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 913.892343</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1609</th>\n",
" <td> 2015_May_13_1441</td>\n",
" <td> p8</td>\n",
" <td> 1</td>\n",
" <td> 178</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 428.655176</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1754</th>\n",
" <td> 2015_May_13_1441</td>\n",
" <td> p8</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 772.007364</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1788</th>\n",
" <td> 2015_May_13_1441</td>\n",
" <td> p8</td>\n",
" <td> 1</td>\n",
" <td> 357</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 862.545017</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1967</th>\n",
" <td> 2015_May_14_1732</td>\n",
" <td> p9</td>\n",
" <td> 1</td>\n",
" <td> 178</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 498.497241</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2039</th>\n",
" <td> 2015_May_14_1732</td>\n",
" <td> p9</td>\n",
" <td> 1</td>\n",
" <td> 250</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 672.556592</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2128</th>\n",
" <td> 2015_May_14_1732</td>\n",
" <td> p9</td>\n",
" <td> 1</td>\n",
" <td> 339</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 888.125392</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2715</th>\n",
" <td> 2015_May_09_1149</td>\n",
" <td> p5</td>\n",
" <td> 2</td>\n",
" <td> 210</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 613.184985</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2749</th>\n",
" <td> 2015_May_09_1149</td>\n",
" <td> p5</td>\n",
" <td> 2</td>\n",
" <td> 244</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 703.875456</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2827</th>\n",
" <td> 2015_May_09_1149</td>\n",
" <td> p5</td>\n",
" <td> 2</td>\n",
" <td> 322</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 890.619271</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2899</th>\n",
" <td> 2015_May_05_1656</td>\n",
" <td> p6</td>\n",
" <td> 2</td>\n",
" <td> 36</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 287.218607</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2984</th>\n",
" <td> 2015_May_05_1656</td>\n",
" <td> p6</td>\n",
" <td> 2</td>\n",
" <td> 121</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 480.672993</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2994</th>\n",
" <td> 2015_May_05_1656</td>\n",
" <td> p6</td>\n",
" <td> 2</td>\n",
" <td> 131</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 508.217094</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3080</th>\n",
" <td> 2015_May_05_1656</td>\n",
" <td> p6</td>\n",
" <td> 2</td>\n",
" <td> 217</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 696.292597</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3431</th>\n",
" <td> 2015_May_07_1639</td>\n",
" <td> p7</td>\n",
" <td> 2</td>\n",
" <td> 210</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 613.643945</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3438</th>\n",
" <td> 2015_May_07_1639</td>\n",
" <td> p7</td>\n",
" <td> 2</td>\n",
" <td> 217</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 635.102918</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3465</th>\n",
" <td> 2015_May_07_1639</td>\n",
" <td> p7</td>\n",
" <td> 2</td>\n",
" <td> 244</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 704.086904</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3520</th>\n",
" <td> 2015_May_07_1639</td>\n",
" <td> p7</td>\n",
" <td> 2</td>\n",
" <td> 299</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 829.196166</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3578</th>\n",
" <td> 2015_May_07_1639</td>\n",
" <td> p7</td>\n",
" <td> 2</td>\n",
" <td> 357</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 975.184715</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3823</th>\n",
" <td> 2015_May_13_1529</td>\n",
" <td> p8</td>\n",
" <td> 2</td>\n",
" <td> 244</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 655.359688</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3892</th>\n",
" <td> 2015_May_13_1529</td>\n",
" <td> p8</td>\n",
" <td> 2</td>\n",
" <td> 313</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 816.304707</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3901</th>\n",
" <td> 2015_May_13_1529</td>\n",
" <td> p8</td>\n",
" <td> 2</td>\n",
" <td> 322</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 841.790277</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4250</th>\n",
" <td> 2015_May_14_1702</td>\n",
" <td> p9</td>\n",
" <td> 2</td>\n",
" <td> 313</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 773.849100</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4401</th>\n",
" <td> 2014_Nov_11_1046</td>\n",
" <td> p3</td>\n",
" <td> 3</td>\n",
" <td> 106</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 280.887815</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4770</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 117</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 362.860705</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4811</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 158</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 452.868232</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4855</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 202</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 556.314679</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4882</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 229</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 618.112701</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5169</th>\n",
" <td> 2015_May_05_1732</td>\n",
" <td> p6</td>\n",
" <td> 3</td>\n",
" <td> 158</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 584.291407</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5213</th>\n",
" <td> 2015_May_05_1732</td>\n",
" <td> p6</td>\n",
" <td> 3</td>\n",
" <td> 202</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 687.738183</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5240</th>\n",
" <td> 2015_May_05_1732</td>\n",
" <td> p6</td>\n",
" <td> 3</td>\n",
" <td> 229</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 749.535963</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5382</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 13</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 154.984658</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5460</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 91</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 333.650508</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5486</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 117</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 400.629591</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5563</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 194</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 569.866960</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5571</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 202</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 593.330871</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5612</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 243</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 697.809792</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5818</th>\n",
" <td> 2015_May_13_1513</td>\n",
" <td> p8</td>\n",
" <td> 3</td>\n",
" <td> 91</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 285.355565</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5929</th>\n",
" <td> 2015_May_13_1513</td>\n",
" <td> p8</td>\n",
" <td> 3</td>\n",
" <td> 202</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 545.550649</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5956</th>\n",
" <td> 2015_May_13_1513</td>\n",
" <td> p8</td>\n",
" <td> 3</td>\n",
" <td> 229</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 607.245625</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5963</th>\n",
" <td> 2015_May_13_1513</td>\n",
" <td> p8</td>\n",
" <td> 3</td>\n",
" <td> 236</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 628.707675</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6046</th>\n",
" <td> 2015_May_13_1513</td>\n",
" <td> p8</td>\n",
" <td> 3</td>\n",
" <td> 319</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 825.196238</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6470</th>\n",
" <td> 2014_Nov_11_0958</td>\n",
" <td> p3</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 244.229222</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6583</th>\n",
" <td> 2014_Nov_11_0958</td>\n",
" <td> p3</td>\n",
" <td> 4</td>\n",
" <td> 140</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 522.227101</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7035</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 717.794467</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7075</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 274</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 820.574957</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7195</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 36</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 281.412532</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7282</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 123</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 493.684849</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7544</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 151.555154</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7640</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 123</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 388.583470</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7657</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 140</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 430.093028</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7811</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 294</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 783.415089</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7902</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 119.489185</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7954</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 79</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 246.236183</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7998</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 123</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 356.890012</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8048</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 173</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 472.233617</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8128</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 253</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 647.926592</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date participant schedule Trial Stim Response RT Hit \\\n",
"607 2015_May_09_1223 p5 1 250 probe None NaN 0 \n",
"749 2015_May_05_1751 p6 1 34 probe None NaN 0 \n",
"1054 2015_May_05_1751 p6 1 339 probe None NaN 0 \n",
"1182 2015_May_07_1656 p7 1 109 probe None NaN 0 \n",
"1251 2015_May_07_1656 p7 1 178 probe None NaN 0 \n",
"1379 2015_May_07_1656 p7 1 306 probe None NaN 0 \n",
"1396 2015_May_07_1656 p7 1 323 probe None NaN 0 \n",
"1412 2015_May_07_1656 p7 1 339 probe None NaN 0 \n",
"1609 2015_May_13_1441 p8 1 178 probe None NaN 0 \n",
"1754 2015_May_13_1441 p8 1 323 probe None NaN 0 \n",
"1788 2015_May_13_1441 p8 1 357 probe None NaN 0 \n",
"1967 2015_May_14_1732 p9 1 178 probe None NaN 0 \n",
"2039 2015_May_14_1732 p9 1 250 probe None NaN 0 \n",
"2128 2015_May_14_1732 p9 1 339 probe None NaN 0 \n",
"2715 2015_May_09_1149 p5 2 210 probe None NaN 0 \n",
"2749 2015_May_09_1149 p5 2 244 probe None NaN 0 \n",
"2827 2015_May_09_1149 p5 2 322 probe None NaN 0 \n",
"2899 2015_May_05_1656 p6 2 36 probe None NaN 0 \n",
"2984 2015_May_05_1656 p6 2 121 probe None NaN 0 \n",
"2994 2015_May_05_1656 p6 2 131 probe None NaN 0 \n",
"3080 2015_May_05_1656 p6 2 217 probe None NaN 0 \n",
"3431 2015_May_07_1639 p7 2 210 probe None NaN 0 \n",
"3438 2015_May_07_1639 p7 2 217 probe None NaN 0 \n",
"3465 2015_May_07_1639 p7 2 244 probe None NaN 0 \n",
"3520 2015_May_07_1639 p7 2 299 probe None NaN 0 \n",
"3578 2015_May_07_1639 p7 2 357 probe None NaN 0 \n",
"3823 2015_May_13_1529 p8 2 244 probe None NaN 0 \n",
"3892 2015_May_13_1529 p8 2 313 probe None NaN 0 \n",
"3901 2015_May_13_1529 p8 2 322 probe None NaN 0 \n",
"4250 2015_May_14_1702 p9 2 313 probe None NaN 0 \n",
"4401 2014_Nov_11_1046 p3 3 106 probe None NaN 0 \n",
"4770 2015_May_09_1133 p5 3 117 probe None NaN 0 \n",
"4811 2015_May_09_1133 p5 3 158 probe None NaN 0 \n",
"4855 2015_May_09_1133 p5 3 202 probe None NaN 0 \n",
"4882 2015_May_09_1133 p5 3 229 probe None NaN 0 \n",
"5169 2015_May_05_1732 p6 3 158 probe None NaN 0 \n",
"5213 2015_May_05_1732 p6 3 202 probe None NaN 0 \n",
"5240 2015_May_05_1732 p6 3 229 probe None NaN 0 \n",
"5382 2015_May_07_1729 p7 3 13 probe None NaN 0 \n",
"5460 2015_May_07_1729 p7 3 91 probe None NaN 0 \n",
"5486 2015_May_07_1729 p7 3 117 probe None NaN 0 \n",
"5563 2015_May_07_1729 p7 3 194 probe None NaN 0 \n",
"5571 2015_May_07_1729 p7 3 202 probe None NaN 0 \n",
"5612 2015_May_07_1729 p7 3 243 probe None NaN 0 \n",
"5818 2015_May_13_1513 p8 3 91 probe None NaN 0 \n",
"5929 2015_May_13_1513 p8 3 202 probe None NaN 0 \n",
"5956 2015_May_13_1513 p8 3 229 probe None NaN 0 \n",
"5963 2015_May_13_1513 p8 3 236 probe None NaN 0 \n",
"6046 2015_May_13_1513 p8 3 319 probe None NaN 0 \n",
"6470 2014_Nov_11_0958 p3 4 27 probe None NaN 0 \n",
"6583 2014_Nov_11_0958 p3 4 140 probe None NaN 0 \n",
"7035 2015_May_09_1206 p5 4 234 probe None NaN 0 \n",
"7075 2015_May_09_1206 p5 4 274 probe None NaN 0 \n",
"7195 2015_May_05_1714 p6 4 36 probe None NaN 0 \n",
"7282 2015_May_05_1714 p6 4 123 probe None NaN 0 \n",
"7544 2015_May_07_1713 p7 4 27 probe None NaN 0 \n",
"7640 2015_May_07_1713 p7 4 123 probe None NaN 0 \n",
"7657 2015_May_07_1713 p7 4 140 probe None NaN 0 \n",
"7811 2015_May_07_1713 p7 4 294 probe None NaN 0 \n",
"7902 2015_May_13_1457 p8 4 27 probe None NaN 0 \n",
"7954 2015_May_13_1457 p8 4 79 probe None NaN 0 \n",
"7998 2015_May_13_1457 p8 4 123 probe None NaN 0 \n",
"8048 2015_May_13_1457 p8 4 173 probe None NaN 0 \n",
"8128 2015_May_13_1457 p8 4 253 probe None NaN 0 \n",
"\n",
" CorrRej Commission Omission onset_time condition regressor targ \n",
"607 0 0 0 737.929284 probe None NaN \n",
"749 0 0 0 229.122536 probe None NaN \n",
"1054 0 0 0 947.204337 probe None NaN \n",
"1182 0 0 0 378.507595 probe None NaN \n",
"1251 0 0 0 524.280609 probe None NaN \n",
"1379 0 0 0 825.470929 probe None NaN \n",
"1396 0 0 0 866.980445 probe None NaN \n",
"1412 0 0 0 913.892343 probe None NaN \n",
"1609 0 0 0 428.655176 probe None NaN \n",
"1754 0 0 0 772.007364 probe None NaN \n",
"1788 0 0 0 862.545017 probe None NaN \n",
"1967 0 0 0 498.497241 probe None NaN \n",
"2039 0 0 0 672.556592 probe None NaN \n",
"2128 0 0 0 888.125392 probe None NaN \n",
"2715 0 0 0 613.184985 probe None NaN \n",
"2749 0 0 0 703.875456 probe None NaN \n",
"2827 0 0 0 890.619271 probe None NaN \n",
"2899 0 0 0 287.218607 probe None NaN \n",
"2984 0 0 0 480.672993 probe None NaN \n",
"2994 0 0 0 508.217094 probe None NaN \n",
"3080 0 0 0 696.292597 probe None NaN \n",
"3431 0 0 0 613.643945 probe None NaN \n",
"3438 0 0 0 635.102918 probe None NaN \n",
"3465 0 0 0 704.086904 probe None NaN \n",
"3520 0 0 0 829.196166 probe None NaN \n",
"3578 0 0 0 975.184715 probe None NaN \n",
"3823 0 0 0 655.359688 probe None NaN \n",
"3892 0 0 0 816.304707 probe None NaN \n",
"3901 0 0 0 841.790277 probe None NaN \n",
"4250 0 0 0 773.849100 probe None NaN \n",
"4401 0 0 0 280.887815 probe None NaN \n",
"4770 0 0 0 362.860705 probe None NaN \n",
"4811 0 0 0 452.868232 probe None NaN \n",
"4855 0 0 0 556.314679 probe None NaN \n",
"4882 0 0 0 618.112701 probe None NaN \n",
"5169 0 0 0 584.291407 probe None NaN \n",
"5213 0 0 0 687.738183 probe None NaN \n",
"5240 0 0 0 749.535963 probe None NaN \n",
"5382 0 0 0 154.984658 probe None NaN \n",
"5460 0 0 0 333.650508 probe None NaN \n",
"5486 0 0 0 400.629591 probe None NaN \n",
"5563 0 0 0 569.866960 probe None NaN \n",
"5571 0 0 0 593.330871 probe None NaN \n",
"5612 0 0 0 697.809792 probe None NaN \n",
"5818 0 0 0 285.355565 probe None NaN \n",
"5929 0 0 0 545.550649 probe None NaN \n",
"5956 0 0 0 607.245625 probe None NaN \n",
"5963 0 0 0 628.707675 probe None NaN \n",
"6046 0 0 0 825.196238 probe None NaN \n",
"6470 0 0 0 244.229222 probe None NaN \n",
"6583 0 0 0 522.227101 probe None NaN \n",
"7035 0 0 0 717.794467 probe None NaN \n",
"7075 0 0 0 820.574957 probe None NaN \n",
"7195 0 0 0 281.412532 probe None NaN \n",
"7282 0 0 0 493.684849 probe None NaN \n",
"7544 0 0 0 151.555154 probe None NaN \n",
"7640 0 0 0 388.583470 probe None NaN \n",
"7657 0 0 0 430.093028 probe None NaN \n",
"7811 0 0 0 783.415089 probe None NaN \n",
"7902 0 0 0 119.489185 probe None NaN \n",
"7954 0 0 0 246.236183 probe None NaN \n",
"7998 0 0 0 356.890012 probe None NaN \n",
"8048 0 0 0 472.233617 probe None NaN \n",
"8128 0 0 0 647.926592 probe None NaN "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p1 = df[df['Stim'].isin(['probe'])]\n",
"p2 = df[df['Stim'].isin(['probe2'])]\n",
"\n",
"noresP1 = p1[p1.Response == 'None']\n",
"noresP1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th>Trial</th>\n",
" <th>Stim</th>\n",
" <th>Response</th>\n",
" <th>RT</th>\n",
" <th>Hit</th>\n",
" <th>CorrRej</th>\n",
" <th>Commission</th>\n",
" <th>Omission</th>\n",
" <th>onset_time</th>\n",
" <th>condition</th>\n",
" <th>regressor</th>\n",
" <th>targ</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>443 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 86</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
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" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1517</th>\n",
" <td> 2015_May_13_1441</td>\n",
" <td> p8</td>\n",
" <td> 1</td>\n",
" <td> 86</td>\n",
" <td> probe2</td>\n",
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" <tr>\n",
" <th>1534</th>\n",
" <td> 2015_May_13_1441</td>\n",
" <td> p8</td>\n",
" <td> 1</td>\n",
" <td> 103</td>\n",
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" <tr>\n",
" <th>1682</th>\n",
" <td> 2015_May_13_1441</td>\n",
" <td> p8</td>\n",
" <td> 1</td>\n",
" <td> 251</td>\n",
" <td> probe2</td>\n",
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" <td> None</td>\n",
" <td> NaN</td>\n",
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" <tr>\n",
" <th>2542</th>\n",
" <td> 2015_May_09_1149</td>\n",
" <td> p5</td>\n",
" <td> 2</td>\n",
" <td> 37</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
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" <td> 231.321797</td>\n",
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" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3298</th>\n",
" <td> 2015_May_07_1639</td>\n",
" <td> p7</td>\n",
" <td> 2</td>\n",
" <td> 77</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 328.411311</td>\n",
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" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3466</th>\n",
" <td> 2015_May_07_1639</td>\n",
" <td> p7</td>\n",
" <td> 2</td>\n",
" <td> 245</td>\n",
" <td> probe2</td>\n",
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" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3902</th>\n",
" <td> 2015_May_13_1529</td>\n",
" <td> p8</td>\n",
" <td> 2</td>\n",
" <td> 323</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
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" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 847.492164</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3928</th>\n",
" <td> 2015_May_13_1529</td>\n",
" <td> p8</td>\n",
" <td> 2</td>\n",
" <td> 349</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
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" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4251</th>\n",
" <td> 2015_May_14_1702</td>\n",
" <td> p9</td>\n",
" <td> 2</td>\n",
" <td> 314</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
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" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4260</th>\n",
" <td> 2015_May_14_1702</td>\n",
" <td> p9</td>\n",
" <td> 2</td>\n",
" <td> 323</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 805.035369</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4667</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 14</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 122.045725</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4771</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 118</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 368.572568</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4890</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 237</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 645.323342</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4915</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 262</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 710.485490</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4956</th>\n",
" <td> 2015_May_09_1133</td>\n",
" <td> p5</td>\n",
" <td> 3</td>\n",
" <td> 303</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 800.493374</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5383</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 14</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 160.701484</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5564</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 195</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 575.583774</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5613</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 244</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 703.526639</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5689</th>\n",
" <td> 2015_May_07_1729</td>\n",
" <td> p7</td>\n",
" <td> 3</td>\n",
" <td> 320</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 878.182345</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6030</th>\n",
" <td> 2015_May_13_1513</td>\n",
" <td> p8</td>\n",
" <td> 3</td>\n",
" <td> 303</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 789.319592</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6430</th>\n",
" <td> 2015_May_14_1718</td>\n",
" <td> p9</td>\n",
" <td> 3</td>\n",
" <td> 345</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 852.167379</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6738</th>\n",
" <td> 2014_Nov_11_0958</td>\n",
" <td> p3</td>\n",
" <td> 4</td>\n",
" <td> 295</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 880.560476</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6829</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 28</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 239.861675</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7036</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 235</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 723.506254</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7267</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 108</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 459.763296</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7752</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 235</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 639.133260</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7792</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 275</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 741.607033</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7866</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 349</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 912.252747</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8016</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 141</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 404.170637</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8143</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 268</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 689.171971</td>\n",
" <td> probe2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date participant schedule Trial Stim Response RT Hit \\\n",
"443 2015_May_09_1223 p5 1 86 probe2 None NaN 0 \n",
"1517 2015_May_13_1441 p8 1 86 probe2 None NaN 0 \n",
"1534 2015_May_13_1441 p8 1 103 probe2 None NaN 0 \n",
"1682 2015_May_13_1441 p8 1 251 probe2 None NaN 0 \n",
"2542 2015_May_09_1149 p5 2 37 probe2 None NaN 0 \n",
"3298 2015_May_07_1639 p7 2 77 probe2 None NaN 0 \n",
"3466 2015_May_07_1639 p7 2 245 probe2 None NaN 0 \n",
"3902 2015_May_13_1529 p8 2 323 probe2 None NaN 0 \n",
"3928 2015_May_13_1529 p8 2 349 probe2 None NaN 0 \n",
"4251 2015_May_14_1702 p9 2 314 probe2 None NaN 0 \n",
"4260 2015_May_14_1702 p9 2 323 probe2 None NaN 0 \n",
"4667 2015_May_09_1133 p5 3 14 probe2 None NaN 0 \n",
"4771 2015_May_09_1133 p5 3 118 probe2 None NaN 0 \n",
"4890 2015_May_09_1133 p5 3 237 probe2 None NaN 0 \n",
"4915 2015_May_09_1133 p5 3 262 probe2 None NaN 0 \n",
"4956 2015_May_09_1133 p5 3 303 probe2 None NaN 0 \n",
"5383 2015_May_07_1729 p7 3 14 probe2 None NaN 0 \n",
"5564 2015_May_07_1729 p7 3 195 probe2 None NaN 0 \n",
"5613 2015_May_07_1729 p7 3 244 probe2 None NaN 0 \n",
"5689 2015_May_07_1729 p7 3 320 probe2 None NaN 0 \n",
"6030 2015_May_13_1513 p8 3 303 probe2 None NaN 0 \n",
"6430 2015_May_14_1718 p9 3 345 probe2 None NaN 0 \n",
"6738 2014_Nov_11_0958 p3 4 295 probe2 None NaN 0 \n",
"6829 2015_May_09_1206 p5 4 28 probe2 None NaN 0 \n",
"7036 2015_May_09_1206 p5 4 235 probe2 None NaN 0 \n",
"7267 2015_May_05_1714 p6 4 108 probe2 None NaN 0 \n",
"7752 2015_May_07_1713 p7 4 235 probe2 None NaN 0 \n",
"7792 2015_May_07_1713 p7 4 275 probe2 None NaN 0 \n",
"7866 2015_May_07_1713 p7 4 349 probe2 None NaN 0 \n",
"8016 2015_May_13_1457 p8 4 141 probe2 None NaN 0 \n",
"8143 2015_May_13_1457 p8 4 268 probe2 None NaN 0 \n",
"\n",
" CorrRej Commission Omission onset_time condition regressor targ \n",
"443 0 0 0 359.413355 probe2 None NaN \n",
"1517 0 0 0 225.132406 probe2 None NaN \n",
"1534 0 0 0 266.710971 probe2 None NaN \n",
"1682 0 0 0 608.717696 probe2 None NaN \n",
"2542 0 0 0 231.321797 probe2 None NaN \n",
"3298 0 0 0 328.411311 probe2 None NaN \n",
"3466 0 0 0 709.787714 probe2 None NaN \n",
"3902 0 0 0 847.492164 probe2 None NaN \n",
"3928 0 0 0 907.175298 probe2 None NaN \n",
"4251 0 0 0 779.566322 probe2 None NaN \n",
"4260 0 0 0 805.035369 probe2 None NaN \n",
"4667 0 0 0 122.045725 probe2 None NaN \n",
"4771 0 0 0 368.572568 probe2 None NaN \n",
"4890 0 0 0 645.323342 probe2 None NaN \n",
"4915 0 0 0 710.485490 probe2 None NaN \n",
"4956 0 0 0 800.493374 probe2 None NaN \n",
"5383 0 0 0 160.701484 probe2 None NaN \n",
"5564 0 0 0 575.583774 probe2 None NaN \n",
"5613 0 0 0 703.526639 probe2 None NaN \n",
"5689 0 0 0 878.182345 probe2 None NaN \n",
"6030 0 0 0 789.319592 probe2 None NaN \n",
"6430 0 0 0 852.167379 probe2 None NaN \n",
"6738 0 0 0 880.560476 probe2 None NaN \n",
"6829 0 0 0 239.861675 probe2 None NaN \n",
"7036 0 0 0 723.506254 probe2 None NaN \n",
"7267 0 0 0 459.763296 probe2 None NaN \n",
"7752 0 0 0 639.133260 probe2 None NaN \n",
"7792 0 0 0 741.607033 probe2 None NaN \n",
"7866 0 0 0 912.252747 probe2 None NaN \n",
"8016 0 0 0 404.170637 probe2 None NaN \n",
"8143 0 0 0 689.171971 probe2 None NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"noresP2 = p2[p2.Response == 'None']\n",
"noresP2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Target Accuracy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jenni/miniconda/lib/python2.7/site-packages/pandas/util/decorators.py:81: FutureWarning: the 'rows' keyword is deprecated, use 'index' instead\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb0cefe9590>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pIQZ8SZIkKSEGfEmSJCkhBnxJkiQpIQZ8SZIkKSEGfEmSJCkhBnxJkiQpIQZ8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\nOWSzE40XAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a5aca250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#calculate target accuracy (no go 3s)\n",
"target = df[df.condition == 'target']\n",
"df['CorrRej'] = df['CorrRej'].astype(float)\n",
"piv_target = pd.pivot_table(target, values = ['CorrRej', 'Commission', 'RT'], rows = ['participant','schedule'], aggfunc=np.mean)#, margins=True)\n",
"#piv_target = piv_target.reset_index()\n",
"piv_target.columns = ['target_missed', 'target_acc' ,'target_missed_rt']\n",
"piv_target[['target_missed', 'target_acc']].plot(kind='bar')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Non-Target Accuracy"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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></th>\n",
" <th>nontarget_acc</th>\n",
" <th>nontarget_miss</th>\n",
" <th>nontarg_hit_rt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p3</th>\n",
" <th>1</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.760528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.996774</td>\n",
" <td> 0.003226</td>\n",
" <td> 0.658671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.996774</td>\n",
" <td> 0.003226</td>\n",
" <td> 0.583747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.823446</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p5</th>\n",
" <th>1</th>\n",
" <td> 0.961290</td>\n",
" <td> 0.038710</td>\n",
" <td> 0.382208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.929032</td>\n",
" <td> 0.067742</td>\n",
" <td> 0.409314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.977419</td>\n",
" <td> 0.019355</td>\n",
" <td> 0.420275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0.796774</td>\n",
" <td> 0.203226</td>\n",
" <td> 0.401415</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p6</th>\n",
" <th>1</th>\n",
" <td> 0.990323</td>\n",
" <td> 0.009677</td>\n",
" <td> 0.456141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.987097</td>\n",
" <td> 0.012903</td>\n",
" <td> 0.592598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.996774</td>\n",
" <td> 0.003226</td>\n",
" <td> 0.535965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.589557</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p7</th>\n",
" <th>1</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.425159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.490383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.396823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.444637</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p8</th>\n",
" <th>1</th>\n",
" <td> 0.983871</td>\n",
" <td> 0.016129</td>\n",
" <td> 0.387411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.887097</td>\n",
" <td> 0.112903</td>\n",
" <td> 0.420165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.829032</td>\n",
" <td> 0.167742</td>\n",
" <td> 0.446633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0.883871</td>\n",
" <td> 0.116129</td>\n",
" <td> 0.425132</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p9</th>\n",
" <th>1</th>\n",
" <td> 0.945161</td>\n",
" <td> 0.051613</td>\n",
" <td> 0.386981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.987097</td>\n",
" <td> 0.012903</td>\n",
" <td> 0.368167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.987097</td>\n",
" <td> 0.012903</td>\n",
" <td> 0.387200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.391914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 0.364348</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nontarget_acc nontarget_miss nontarg_hit_rt\n",
"participant schedule \n",
"p3 1 1.000000 0.000000 0.760528\n",
" 2 0.996774 0.003226 0.658671\n",
" 3 0.996774 0.003226 0.583747\n",
" 4 1.000000 0.000000 0.823446\n",
"p5 1 0.961290 0.038710 0.382208\n",
" 2 0.929032 0.067742 0.409314\n",
" 3 0.977419 0.019355 0.420275\n",
" 4 0.796774 0.203226 0.401415\n",
"p6 1 0.990323 0.009677 0.456141\n",
" 2 0.987097 0.012903 0.592598\n",
" 3 0.996774 0.003226 0.535965\n",
" 4 1.000000 0.000000 0.589557\n",
"p7 1 1.000000 0.000000 0.425159\n",
" 2 1.000000 0.000000 0.490383\n",
" 3 1.000000 0.000000 0.396823\n",
" 4 1.000000 0.000000 0.444637\n",
"p8 1 0.983871 0.016129 0.387411\n",
" 2 0.887097 0.112903 0.420165\n",
" 3 0.829032 0.167742 0.446633\n",
" 4 0.883871 0.116129 0.425132\n",
"p9 1 0.945161 0.051613 0.386981\n",
" 2 0.987097 0.012903 0.368167\n",
" 3 0.987097 0.012903 0.387200\n",
" 4 1.000000 0.000000 0.391914\n",
"participant schedule NaN NaN 0.364348"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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3wOwBHBtjLMnOchUwt9L8D5A5wz8SeBiYCjwDfDbGuL4Z+y5JkiSpmlyX6AAQ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\nJ2Aymaeu3QX8HzJPeRsTY1zYzLWS3Y/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ILzS5S7zEsR+tjv+eiHgYOGuMfU4E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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a592f990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#calculate non-target accuracy (go)\n",
"nontarget = df[df.condition == 'nontarget']\n",
"piv_nontarget = pd.pivot_table(nontarget, values = ['Hit', 'Omission', 'RT'], rows = ['participant', 'schedule'], aggfunc=np.mean)#, margins=True)\n",
"\n",
"piv_nontarget.columns = ['nontarget_acc', 'nontarget_miss', 'nontarg_hit_rt']\n",
"piv_nontarget[['nontarget_acc', 'nontarget_miss']].plot(kind='bar')\n",
"piv_nontarget"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Cannot convert NA to integer",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-7-9d08848a6bd9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mdp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mdp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34mu'participant'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'schedule'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'condition'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'FA'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'CR'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'H'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'M'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'H'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'H'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[0mdp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'M'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'M'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mdp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'FA'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'FA'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\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/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, raise_on_error)\u001b[0m\n\u001b[0;32m 2212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2213\u001b[0m mgr = self._data.astype(\n\u001b[1;32m-> 2214\u001b[1;33m dtype=dtype, copy=copy, raise_on_error=raise_on_error)\n\u001b[0m\u001b[0;32m 2215\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2216\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m 2500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2501\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\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-> 2502\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'astype'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\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[0m\n\u001b[0m\u001b[0;32m 2503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2504\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, axes, filter, do_integrity_check, **kwargs)\u001b[0m\n\u001b[0;32m 2455\u001b[0m copy=align_copy)\n\u001b[0;32m 2456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2457\u001b[1;33m \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\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[0m\n\u001b[0m\u001b[0;32m 2458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2459\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\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/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, raise_on_error, values)\u001b[0m\n\u001b[0;32m 369\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[0mraise_on_error\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\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[0;32m 370\u001b[0m return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,\n\u001b[1;32m--> 371\u001b[1;33m values=values)\n\u001b[0m\u001b[0;32m 372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 373\u001b[0m def _astype(self, dtype, copy=False, raise_on_error=True, values=None,\n",
"\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_astype\u001b[1;34m(self, dtype, copy, raise_on_error, values, klass)\u001b[0m\n\u001b[0;32m 399\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 400\u001b[0m \u001b[1;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 401\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_astype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\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[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 402\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 403\u001b[0m newb = make_block(values,\n",
"\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/common.pyc\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy)\u001b[0m\n\u001b[0;32m 2616\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2617\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2618\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot convert NA to integer'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2619\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobject_\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2620\u001b[0m \u001b[1;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Cannot convert NA to integer"
]
}
],
"source": [
"#dPrime\n",
"dp = pd.pivot_table(df, values = ['Hit', 'Omission','CorrRej', 'Commission'], rows = ['participant','schedule', 'condition'], aggfunc=np.sum)#, margins=True)\n",
"dp = dp.reset_index()\n",
"dp.columns = [u'participant', u'schedule', u'condition', u'FA', u'CR', u'H', u'M']\n",
"dp['H'] = dp['H'].astype(int)\n",
"dp['M'] = dp['M'].astype(int)\n",
"dp['FA'] = dp['FA'].astype(int)\n",
"dp['CR'] = dp['CR'].astype(int)\n",
"\n",
"#'H']/ (row['H']+row['M']))\n",
"\n",
"dp['HR'] = dp['H']/ (dp['H']+ dp['M'])\n",
"dp['FAR'] = dp['FA']/ (dp['FA']+ dp['CR'])\n",
"dp['CRR'] = dp['CR']/ (dp['CR']+dp['FA'])\n",
"dp['MR'] = dp['M']/ (dp['M']+dp['H'])\n",
"\n",
"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",
"\n",
"\n",
"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['dPrime'] = dp.apply(lambda row: (row['zHR']-row['zFAR']), axis = 1)\n",
"dp.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pre-Correct RT and Pre-Error AVG RT"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th>Trial</th>\n",
" <th>Stim</th>\n",
" <th>Response</th>\n",
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" <th>Hit</th>\n",
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" <th>condition</th>\n",
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" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
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" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.681049</td>\n",
" <td> 1</td>\n",
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" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 635.707975</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>258 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 259</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.681024</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 637.709703</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>366 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.262697</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 182.078497</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.262046</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 184.093450</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 11</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.313374</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 186.108396</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>369 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 12</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.264062</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 188.123336</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 13</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.297389</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 190.138306</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 22</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.263261</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 208.273571</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 23</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.262792</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 210.288811</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 24</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.330501</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 212.303661</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>382 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 25</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.214012</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 214.318240</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>383 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 26</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.279475</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 216.333160</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 36</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.280476</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 243.877237</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 37</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.263697</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 245.892012</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 38</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.380707</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 247.906710</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 39</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.363805</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 249.921509</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 40</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.313924</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 251.936443</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 62</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.246200</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 303.660199</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>420 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 63</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.346780</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 305.675144</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>421 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 64</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.430348</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 307.690194</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>422 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 65</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.463402</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 309.705381</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>423 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 66</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.363706</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 311.720607</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>448 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 91</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.263968</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 369.488186</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>449 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 92</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.396921</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 371.503213</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>450 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 93</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.330309</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 373.518162</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>451 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 94</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.346492</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 375.533270</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>452 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 95</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.363350</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 377.548328</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>469 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 112</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.413280</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 426.590524</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>470 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 113</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.313249</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 428.605388</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>471 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 114</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.246741</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 430.620414</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>472 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 115</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.230401</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 432.635450</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>473 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 116</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.263416</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 434.650357</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>476 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 119</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.463241</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 440.695380</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>477 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 120</td>\n",
" <td> 4</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 442.710262</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>478 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 121</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.330563</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 444.725207</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>479 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 122</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.313839</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 446.740523</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>480 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 123</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.263795</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 448.755468</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>486 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 129</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.613999</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 460.844721</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>487 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 130</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.330600</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 462.859745</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>488 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 131</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.263943</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 464.874761</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>489 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 132</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.513734</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 466.889672</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>490 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 133</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1.046889</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 468.904604</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>499 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 142</td>\n",
" <td> 7</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 487.039493</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>500 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 143</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.330870</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 489.054573</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>501 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 144</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.263804</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 491.069549</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>502 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 145</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.312363</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 493.084552</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>503 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 146</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.796468</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 495.099482</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>517 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 160</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.246072</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 523.309342</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>518 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 161</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.513221</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 525.324225</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>519 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 162</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.380391</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 527.339188</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>520 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 163</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.380455</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 529.354216</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>521 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 164</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.546634</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 531.369269</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>612 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 255</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.280744</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 751.701040</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 256</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.264340</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 753.716478</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 257</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 1.462992</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 755.731300</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>615 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 258</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.347393</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 757.746447</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>616 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 259</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.297068</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 759.760896</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>639 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 282</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.296645</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 813.499589</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>640 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 283</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.396701</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 815.514611</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>641 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 284</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.346447</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 817.529435</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>642 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 285</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.313402</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 819.544499</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>643 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 286</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.463692</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 821.559899</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>724 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.612216</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 175.051139</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>725 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.480322</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 177.066042</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>726 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 11</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.529020</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 179.080974</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>727 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 12</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.547139</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 181.096036</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>728 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 13</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.530412</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 183.111110</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>751 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 36</td>\n",
" <td> 8</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 236.849438</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>752 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 37</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.945709</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 238.864646</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>753 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 38</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.430704</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 240.879573</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>754 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 39</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.397391</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 242.894627</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>755 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 40</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.411679</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 244.909077</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>777 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 62</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.329989</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 296.632882</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 63</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.396554</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 298.647794</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 64</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.311779</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 300.662853</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>780 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 65</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.296665</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 302.677745</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>781 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 66</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.313420</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 304.692833</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>834 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 119</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.430310</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 433.667852</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>835 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 120</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.530279</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 435.682780</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>836 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 121</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.430043</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 437.697746</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>837 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 122</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.346823</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 439.712668</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>838 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 123</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.263833</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 441.727965</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 160</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.313843</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 516.281994</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 161</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.480069</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 518.296981</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 162</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.446592</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 520.311963</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 163</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.313540</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 522.326988</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 164</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.446857</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 524.341948</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>926 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 211</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.280669</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 641.227284</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>927 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 212</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.346867</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 643.242241</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>928 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 213</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.363538</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 645.257514</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>929 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 214</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.380209</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 647.272797</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>930 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 215</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.463375</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 649.287644</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 282</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.363353</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 806.472164</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 283</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.663666</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 808.487069</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 284</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.630250</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 810.502026</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 285</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.513624</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 812.517007</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1001</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 286</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.463691</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 814.531912</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7823</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 306</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.428229</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 811.187546</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7824</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 307</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.510699</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 813.192649</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7825</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 308</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.378407</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 815.197768</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7826</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 309</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.362108</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 817.202788</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7827</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 310</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.542393</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 819.207870</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7854</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 337</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.279406</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 880.767972</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7855</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 338</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.246202</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 882.773181</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7856</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 339</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.362215</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 884.778147</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7857</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 340</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.329050</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 886.783173</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7858</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 341</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.212632</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 888.788312</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7870</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 353</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.345041</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 920.272980</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7871</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 354</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.461157</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 922.278014</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7872</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 355</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.411262</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 924.283144</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7873</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 356</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.442810</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 926.288175</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7874</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 357</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.345208</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 928.293237</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7878</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 3</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.302452</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 67.519403</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7879</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.329196</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 69.531026</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7880</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 5</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.302227</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 71.543273</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7881</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.302830</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 73.554411</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7882</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 7</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.395751</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 75.566044</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7941</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 66</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.290835</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 216.394625</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7942</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 67</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.397450</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 218.406392</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7943</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 68</td>\n",
" <td> 0</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 220.417846</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7944</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 69</td>\n",
" <td> 1</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 222.429623</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7945</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 70</td>\n",
" <td> 2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 224.441455</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8018</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 143</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.596591</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 408.194116</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8019</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 144</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.343749</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 410.205569</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8020</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 145</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.382336</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 412.217210</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8021</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 146</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.330870</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 414.228925</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8022</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 147</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.370339</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 416.240943</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8036</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 161</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.756532</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 444.403726</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8037</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 162</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.356796</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 446.415380</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8038</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 163</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.796292</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 448.426985</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8039</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 164</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.370265</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 450.438673</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8040</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 165</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.276782</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 452.450381</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8155</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 280</td>\n",
" <td> 4</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 720.691932</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8156</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 281</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.330754</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 722.703668</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8157</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 282</td>\n",
" <td> 1</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 724.715720</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8158</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 283</td>\n",
" <td> 8</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 726.727011</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8159</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 284</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.370558</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 728.738572</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8212</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 337</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.343452</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 850.117096</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8213</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 338</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.317072</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 852.128819</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8214</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 339</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 1.209657</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 854.140439</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8215</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 340</td>\n",
" <td> 2</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 856.152104</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8216</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 341</td>\n",
" <td> 0</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 858.163778</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8228</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 353</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.357297</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 889.683774</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8229</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 354</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.357181</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 891.695356</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8230</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 355</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.424063</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 893.707167</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8231</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 356</td>\n",
" <td> 9</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 895.718711</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8232</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 357</td>\n",
" <td> 5</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 897.730391</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8247</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 13</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.279250</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 115.592963</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8248</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 14</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.294809</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 117.597997</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8249</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 15</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.328454</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 119.603031</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8250</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 16</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.295791</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 121.608066</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8251</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 17</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.328911</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 123.613168</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8377</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 143</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.278785</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 435.640462</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8378</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 144</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.328331</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 437.645501</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8379</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 145</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.560495</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 439.650567</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8380</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 146</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.328498</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 441.655594</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8381</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 147</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.345184</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 443.660720</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8395</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 161</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.295229</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 471.731610</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8396</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 162</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.328270</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 473.736678</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8397</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 163</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.345343</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 475.741678</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8398</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 164</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.393441</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 477.746790</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8399</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 165</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.394532</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 479.751794</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8413</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 179</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.395274</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 515.246151</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8414</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 180</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.362222</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 517.251155</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8415</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 181</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.345690</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 519.256276</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8416</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 182</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 0.494488</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 521.261361</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8417</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 183</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.312394</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 523.266445</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8419</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 185</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.345545</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 527.276520</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8420</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 186</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.295549</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 529.281694</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8421</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 187</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.279578</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 531.286692</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8422</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 188</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.362361</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 533.291688</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8423</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 189</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.361436</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 535.296868</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8444</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 210</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.328608</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 577.403166</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8445</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 211</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.411644</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 579.408190</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8446</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 212</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.361605</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 581.413482</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8447</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 213</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.345402</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 583.418354</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8448</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 214</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.345794</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 585.423365</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8457</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 223</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.361757</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 603.468935</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8458</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 224</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.411363</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 605.474005</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8459</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 225</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.526979</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 607.479057</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8460</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 226</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.411230</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 609.484196</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8461</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 227</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.378602</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 611.489183</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8514</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 280</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.295295</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 747.452321</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8515</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 281</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.328253</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 749.457387</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8516</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 282</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.295515</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 751.462416</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8517</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 283</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 0.344966</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 753.467444</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8518</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 284</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.311872</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 755.472568</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8540</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 306</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.428255</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 807.007370</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8541</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 307</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.378520</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 809.012446</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8542</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 308</td>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 0.445408</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 811.017466</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8543</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 309</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0.362144</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 813.022518</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8544</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 310</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 0.345613</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 815.027560</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8587</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 353</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.345594</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 916.092742</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8588</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 354</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.345284</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 918.097794</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8589</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 355</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.395294</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 920.102816</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8590</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 356</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 0.378740</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 922.107902</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8591</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 357</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 0.378745</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 924.113009</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>755 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" date participant schedule Trial Stim Response RT \\\n",
"254 2014_Nov_11_1015 p3 1 255 4 1 0.397530 \n",
"255 2014_Nov_11_1015 p3 1 256 9 1 0.781564 \n",
"256 2014_Nov_11_1015 p3 1 257 6 1 0.696266 \n",
"257 2014_Nov_11_1015 p3 1 258 8 1 0.681049 \n",
"258 2014_Nov_11_1015 p3 1 259 6 1 0.681024 \n",
"366 2015_May_09_1223 p5 1 9 9 1 0.262697 \n",
"367 2015_May_09_1223 p5 1 10 5 1 0.262046 \n",
"368 2015_May_09_1223 p5 1 11 1 1 0.313374 \n",
"369 2015_May_09_1223 p5 1 12 4 1 0.264062 \n",
"370 2015_May_09_1223 p5 1 13 5 1 0.297389 \n",
"379 2015_May_09_1223 p5 1 22 2 1 0.263261 \n",
"380 2015_May_09_1223 p5 1 23 0 1 0.262792 \n",
"381 2015_May_09_1223 p5 1 24 7 1 0.330501 \n",
"382 2015_May_09_1223 p5 1 25 4 1 0.214012 \n",
"383 2015_May_09_1223 p5 1 26 5 1 0.279475 \n",
"393 2015_May_09_1223 p5 1 36 9 1 0.280476 \n",
"394 2015_May_09_1223 p5 1 37 2 1 0.263697 \n",
"395 2015_May_09_1223 p5 1 38 6 1 0.380707 \n",
"396 2015_May_09_1223 p5 1 39 8 1 0.363805 \n",
"397 2015_May_09_1223 p5 1 40 4 1 0.313924 \n",
"419 2015_May_09_1223 p5 1 62 4 1 0.246200 \n",
"420 2015_May_09_1223 p5 1 63 1 1 0.346780 \n",
"421 2015_May_09_1223 p5 1 64 2 1 0.430348 \n",
"422 2015_May_09_1223 p5 1 65 5 1 0.463402 \n",
"423 2015_May_09_1223 p5 1 66 2 1 0.363706 \n",
"448 2015_May_09_1223 p5 1 91 5 1 0.263968 \n",
"449 2015_May_09_1223 p5 1 92 6 1 0.396921 \n",
"450 2015_May_09_1223 p5 1 93 6 1 0.330309 \n",
"451 2015_May_09_1223 p5 1 94 5 1 0.346492 \n",
"452 2015_May_09_1223 p5 1 95 0 1 0.363350 \n",
"469 2015_May_09_1223 p5 1 112 5 1 0.413280 \n",
"470 2015_May_09_1223 p5 1 113 9 1 0.313249 \n",
"471 2015_May_09_1223 p5 1 114 9 1 0.246741 \n",
"472 2015_May_09_1223 p5 1 115 5 1 0.230401 \n",
"473 2015_May_09_1223 p5 1 116 8 1 0.263416 \n",
"476 2015_May_09_1223 p5 1 119 4 1 0.463241 \n",
"477 2015_May_09_1223 p5 1 120 4 None NaN \n",
"478 2015_May_09_1223 p5 1 121 8 1 0.330563 \n",
"479 2015_May_09_1223 p5 1 122 4 1 0.313839 \n",
"480 2015_May_09_1223 p5 1 123 6 1 0.263795 \n",
"486 2015_May_09_1223 p5 1 129 7 1 0.613999 \n",
"487 2015_May_09_1223 p5 1 130 0 1 0.330600 \n",
"488 2015_May_09_1223 p5 1 131 0 1 0.263943 \n",
"489 2015_May_09_1223 p5 1 132 5 1 0.513734 \n",
"490 2015_May_09_1223 p5 1 133 1 1 1.046889 \n",
"499 2015_May_09_1223 p5 1 142 7 None NaN \n",
"500 2015_May_09_1223 p5 1 143 8 1 0.330870 \n",
"501 2015_May_09_1223 p5 1 144 9 1 0.263804 \n",
"502 2015_May_09_1223 p5 1 145 4 1 0.312363 \n",
"503 2015_May_09_1223 p5 1 146 5 1 0.796468 \n",
"517 2015_May_09_1223 p5 1 160 4 1 0.246072 \n",
"518 2015_May_09_1223 p5 1 161 1 1 0.513221 \n",
"519 2015_May_09_1223 p5 1 162 1 1 0.380391 \n",
"520 2015_May_09_1223 p5 1 163 2 1 0.380455 \n",
"521 2015_May_09_1223 p5 1 164 4 1 0.546634 \n",
"612 2015_May_09_1223 p5 1 255 0 1 0.280744 \n",
"613 2015_May_09_1223 p5 1 256 2 1 0.264340 \n",
"614 2015_May_09_1223 p5 1 257 7 1 1.462992 \n",
"615 2015_May_09_1223 p5 1 258 2 1 0.347393 \n",
"616 2015_May_09_1223 p5 1 259 2 1 0.297068 \n",
"639 2015_May_09_1223 p5 1 282 6 1 0.296645 \n",
"640 2015_May_09_1223 p5 1 283 8 1 0.396701 \n",
"641 2015_May_09_1223 p5 1 284 1 1 0.346447 \n",
"642 2015_May_09_1223 p5 1 285 0 1 0.313402 \n",
"643 2015_May_09_1223 p5 1 286 7 1 0.463692 \n",
"724 2015_May_05_1751 p6 1 9 1 1 0.612216 \n",
"725 2015_May_05_1751 p6 1 10 2 1 0.480322 \n",
"726 2015_May_05_1751 p6 1 11 6 1 0.529020 \n",
"727 2015_May_05_1751 p6 1 12 2 1 0.547139 \n",
"728 2015_May_05_1751 p6 1 13 7 1 0.530412 \n",
"751 2015_May_05_1751 p6 1 36 8 None NaN \n",
"752 2015_May_05_1751 p6 1 37 4 1 0.945709 \n",
"753 2015_May_05_1751 p6 1 38 5 1 0.430704 \n",
"754 2015_May_05_1751 p6 1 39 9 1 0.397391 \n",
"755 2015_May_05_1751 p6 1 40 4 1 0.411679 \n",
"777 2015_May_05_1751 p6 1 62 4 1 0.329989 \n",
"778 2015_May_05_1751 p6 1 63 9 1 0.396554 \n",
"779 2015_May_05_1751 p6 1 64 0 1 0.311779 \n",
"780 2015_May_05_1751 p6 1 65 8 1 0.296665 \n",
"781 2015_May_05_1751 p6 1 66 6 1 0.313420 \n",
"834 2015_May_05_1751 p6 1 119 1 1 0.430310 \n",
"835 2015_May_05_1751 p6 1 120 8 1 0.530279 \n",
"836 2015_May_05_1751 p6 1 121 0 1 0.430043 \n",
"837 2015_May_05_1751 p6 1 122 8 1 0.346823 \n",
"838 2015_May_05_1751 p6 1 123 2 1 0.263833 \n",
"875 2015_May_05_1751 p6 1 160 4 1 0.313843 \n",
"876 2015_May_05_1751 p6 1 161 6 1 0.480069 \n",
"877 2015_May_05_1751 p6 1 162 4 1 0.446592 \n",
"878 2015_May_05_1751 p6 1 163 8 1 0.313540 \n",
"879 2015_May_05_1751 p6 1 164 4 1 0.446857 \n",
"926 2015_May_05_1751 p6 1 211 8 1 0.280669 \n",
"927 2015_May_05_1751 p6 1 212 9 1 0.346867 \n",
"928 2015_May_05_1751 p6 1 213 4 1 0.363538 \n",
"929 2015_May_05_1751 p6 1 214 6 1 0.380209 \n",
"930 2015_May_05_1751 p6 1 215 2 1 0.463375 \n",
"997 2015_May_05_1751 p6 1 282 5 1 0.363353 \n",
"998 2015_May_05_1751 p6 1 283 2 1 0.663666 \n",
"999 2015_May_05_1751 p6 1 284 2 1 0.630250 \n",
"1000 2015_May_05_1751 p6 1 285 9 1 0.513624 \n",
"1001 2015_May_05_1751 p6 1 286 6 1 0.463691 \n",
"... ... ... ... ... ... ... ... \n",
"7823 2015_May_07_1713 p7 4 306 9 1 0.428229 \n",
"7824 2015_May_07_1713 p7 4 307 9 1 0.510699 \n",
"7825 2015_May_07_1713 p7 4 308 1 1 0.378407 \n",
"7826 2015_May_07_1713 p7 4 309 7 1 0.362108 \n",
"7827 2015_May_07_1713 p7 4 310 9 1 0.542393 \n",
"7854 2015_May_07_1713 p7 4 337 7 1 0.279406 \n",
"7855 2015_May_07_1713 p7 4 338 5 1 0.246202 \n",
"7856 2015_May_07_1713 p7 4 339 8 1 0.362215 \n",
"7857 2015_May_07_1713 p7 4 340 1 1 0.329050 \n",
"7858 2015_May_07_1713 p7 4 341 2 1 0.212632 \n",
"7870 2015_May_07_1713 p7 4 353 9 1 0.345041 \n",
"7871 2015_May_07_1713 p7 4 354 6 1 0.461157 \n",
"7872 2015_May_07_1713 p7 4 355 2 1 0.411262 \n",
"7873 2015_May_07_1713 p7 4 356 7 1 0.442810 \n",
"7874 2015_May_07_1713 p7 4 357 9 1 0.345208 \n",
"7878 2015_May_13_1457 p8 4 3 6 1 0.302452 \n",
"7879 2015_May_13_1457 p8 4 4 1 1 0.329196 \n",
"7880 2015_May_13_1457 p8 4 5 8 1 0.302227 \n",
"7881 2015_May_13_1457 p8 4 6 7 1 0.302830 \n",
"7882 2015_May_13_1457 p8 4 7 0 1 0.395751 \n",
"7941 2015_May_13_1457 p8 4 66 7 1 0.290835 \n",
"7942 2015_May_13_1457 p8 4 67 4 1 0.397450 \n",
"7943 2015_May_13_1457 p8 4 68 0 None NaN \n",
"7944 2015_May_13_1457 p8 4 69 1 None NaN \n",
"7945 2015_May_13_1457 p8 4 70 2 None NaN \n",
"8018 2015_May_13_1457 p8 4 143 2 1 0.596591 \n",
"8019 2015_May_13_1457 p8 4 144 4 1 0.343749 \n",
"8020 2015_May_13_1457 p8 4 145 9 1 0.382336 \n",
"8021 2015_May_13_1457 p8 4 146 8 1 0.330870 \n",
"8022 2015_May_13_1457 p8 4 147 5 1 0.370339 \n",
"8036 2015_May_13_1457 p8 4 161 2 1 0.756532 \n",
"8037 2015_May_13_1457 p8 4 162 7 1 0.356796 \n",
"8038 2015_May_13_1457 p8 4 163 9 1 0.796292 \n",
"8039 2015_May_13_1457 p8 4 164 0 1 0.370265 \n",
"8040 2015_May_13_1457 p8 4 165 1 1 0.276782 \n",
"8155 2015_May_13_1457 p8 4 280 4 None NaN \n",
"8156 2015_May_13_1457 p8 4 281 8 1 0.330754 \n",
"8157 2015_May_13_1457 p8 4 282 1 None NaN \n",
"8158 2015_May_13_1457 p8 4 283 8 None NaN \n",
"8159 2015_May_13_1457 p8 4 284 9 1 0.370558 \n",
"8212 2015_May_13_1457 p8 4 337 6 1 0.343452 \n",
"8213 2015_May_13_1457 p8 4 338 5 1 0.317072 \n",
"8214 2015_May_13_1457 p8 4 339 9 1 1.209657 \n",
"8215 2015_May_13_1457 p8 4 340 2 None NaN \n",
"8216 2015_May_13_1457 p8 4 341 0 None NaN \n",
"8228 2015_May_13_1457 p8 4 353 9 1 0.357297 \n",
"8229 2015_May_13_1457 p8 4 354 6 1 0.357181 \n",
"8230 2015_May_13_1457 p8 4 355 4 1 0.424063 \n",
"8231 2015_May_13_1457 p8 4 356 9 None NaN \n",
"8232 2015_May_13_1457 p8 4 357 5 None NaN \n",
"8247 2015_May_14_1645 p9 4 13 5 1 0.279250 \n",
"8248 2015_May_14_1645 p9 4 14 2 1 0.294809 \n",
"8249 2015_May_14_1645 p9 4 15 9 1 0.328454 \n",
"8250 2015_May_14_1645 p9 4 16 4 1 0.295791 \n",
"8251 2015_May_14_1645 p9 4 17 8 1 0.328911 \n",
"8377 2015_May_14_1645 p9 4 143 6 1 0.278785 \n",
"8378 2015_May_14_1645 p9 4 144 7 1 0.328331 \n",
"8379 2015_May_14_1645 p9 4 145 0 1 0.560495 \n",
"8380 2015_May_14_1645 p9 4 146 1 1 0.328498 \n",
"8381 2015_May_14_1645 p9 4 147 7 1 0.345184 \n",
"8395 2015_May_14_1645 p9 4 161 6 1 0.295229 \n",
"8396 2015_May_14_1645 p9 4 162 0 1 0.328270 \n",
"8397 2015_May_14_1645 p9 4 163 6 1 0.345343 \n",
"8398 2015_May_14_1645 p9 4 164 7 1 0.393441 \n",
"8399 2015_May_14_1645 p9 4 165 5 1 0.394532 \n",
"8413 2015_May_14_1645 p9 4 179 9 1 0.395274 \n",
"8414 2015_May_14_1645 p9 4 180 4 1 0.362222 \n",
"8415 2015_May_14_1645 p9 4 181 6 1 0.345690 \n",
"8416 2015_May_14_1645 p9 4 182 2 1 0.494488 \n",
"8417 2015_May_14_1645 p9 4 183 6 1 0.312394 \n",
"8419 2015_May_14_1645 p9 4 185 9 1 0.345545 \n",
"8420 2015_May_14_1645 p9 4 186 9 1 0.295549 \n",
"8421 2015_May_14_1645 p9 4 187 9 1 0.279578 \n",
"8422 2015_May_14_1645 p9 4 188 7 1 0.362361 \n",
"8423 2015_May_14_1645 p9 4 189 6 1 0.361436 \n",
"8444 2015_May_14_1645 p9 4 210 8 1 0.328608 \n",
"8445 2015_May_14_1645 p9 4 211 5 1 0.411644 \n",
"8446 2015_May_14_1645 p9 4 212 8 1 0.361605 \n",
"8447 2015_May_14_1645 p9 4 213 4 1 0.345402 \n",
"8448 2015_May_14_1645 p9 4 214 5 1 0.345794 \n",
"8457 2015_May_14_1645 p9 4 223 7 1 0.361757 \n",
"8458 2015_May_14_1645 p9 4 224 8 1 0.411363 \n",
"8459 2015_May_14_1645 p9 4 225 4 1 0.526979 \n",
"8460 2015_May_14_1645 p9 4 226 1 1 0.411230 \n",
"8461 2015_May_14_1645 p9 4 227 8 1 0.378602 \n",
"8514 2015_May_14_1645 p9 4 280 0 1 0.295295 \n",
"8515 2015_May_14_1645 p9 4 281 8 1 0.328253 \n",
"8516 2015_May_14_1645 p9 4 282 5 1 0.295515 \n",
"8517 2015_May_14_1645 p9 4 283 9 1 0.344966 \n",
"8518 2015_May_14_1645 p9 4 284 1 1 0.311872 \n",
"8540 2015_May_14_1645 p9 4 306 6 1 0.428255 \n",
"8541 2015_May_14_1645 p9 4 307 1 1 0.378520 \n",
"8542 2015_May_14_1645 p9 4 308 4 1 0.445408 \n",
"8543 2015_May_14_1645 p9 4 309 0 1 0.362144 \n",
"8544 2015_May_14_1645 p9 4 310 7 1 0.345613 \n",
"8587 2015_May_14_1645 p9 4 353 6 1 0.345594 \n",
"8588 2015_May_14_1645 p9 4 354 8 1 0.345284 \n",
"8589 2015_May_14_1645 p9 4 355 1 1 0.395294 \n",
"8590 2015_May_14_1645 p9 4 356 8 1 0.378740 \n",
"8591 2015_May_14_1645 p9 4 357 5 1 0.378745 \n",
"\n",
" Hit CorrRej Commission Omission onset_time condition regressor \\\n",
"254 1 0 0 0 629.704092 nontarget None \n",
"255 1 0 0 0 631.705449 nontarget None \n",
"256 1 0 0 0 633.706766 nontarget None \n",
"257 1 0 0 0 635.707975 nontarget None \n",
"258 1 0 0 0 637.709703 nontarget None \n",
"366 1 0 0 0 182.078497 nontarget None \n",
"367 1 0 0 0 184.093450 nontarget None \n",
"368 1 0 0 0 186.108396 nontarget None \n",
"369 1 0 0 0 188.123336 nontarget None \n",
"370 1 0 0 0 190.138306 nontarget None \n",
"379 1 0 0 0 208.273571 nontarget None \n",
"380 1 0 0 0 210.288811 nontarget None \n",
"381 1 0 0 0 212.303661 nontarget None \n",
"382 1 0 0 0 214.318240 nontarget None \n",
"383 1 0 0 0 216.333160 nontarget None \n",
"393 1 0 0 0 243.877237 nontarget None \n",
"394 1 0 0 0 245.892012 nontarget None \n",
"395 1 0 0 0 247.906710 nontarget None \n",
"396 1 0 0 0 249.921509 nontarget None \n",
"397 1 0 0 0 251.936443 nontarget None \n",
"419 1 0 0 0 303.660199 nontarget None \n",
"420 1 0 0 0 305.675144 nontarget None \n",
"421 1 0 0 0 307.690194 nontarget None \n",
"422 1 0 0 0 309.705381 nontarget None \n",
"423 1 0 0 0 311.720607 nontarget None \n",
"448 1 0 0 0 369.488186 nontarget None \n",
"449 1 0 0 0 371.503213 nontarget None \n",
"450 1 0 0 0 373.518162 nontarget None \n",
"451 1 0 0 0 375.533270 nontarget None \n",
"452 1 0 0 0 377.548328 nontarget None \n",
"469 1 0 0 0 426.590524 nontarget None \n",
"470 1 0 0 0 428.605388 nontarget None \n",
"471 1 0 0 0 430.620414 nontarget None \n",
"472 1 0 0 0 432.635450 nontarget None \n",
"473 1 0 0 0 434.650357 nontarget None \n",
"476 1 0 0 0 440.695380 nontarget None \n",
"477 0 0 0 1 442.710262 nontarget None \n",
"478 1 0 0 0 444.725207 nontarget None \n",
"479 1 0 0 0 446.740523 nontarget None \n",
"480 1 0 0 0 448.755468 nontarget None \n",
"486 1 0 0 0 460.844721 nontarget None \n",
"487 1 0 0 0 462.859745 nontarget None \n",
"488 1 0 0 0 464.874761 nontarget None \n",
"489 1 0 0 0 466.889672 nontarget None \n",
"490 1 0 0 0 468.904604 nontarget None \n",
"499 0 0 0 1 487.039493 nontarget None \n",
"500 1 0 0 0 489.054573 nontarget None \n",
"501 1 0 0 0 491.069549 nontarget None \n",
"502 1 0 0 0 493.084552 nontarget None \n",
"503 1 0 0 0 495.099482 nontarget None \n",
"517 1 0 0 0 523.309342 nontarget None \n",
"518 1 0 0 0 525.324225 nontarget None \n",
"519 1 0 0 0 527.339188 nontarget None \n",
"520 1 0 0 0 529.354216 nontarget None \n",
"521 1 0 0 0 531.369269 nontarget None \n",
"612 1 0 0 0 751.701040 nontarget None \n",
"613 1 0 0 0 753.716478 nontarget None \n",
"614 1 0 0 0 755.731300 nontarget None \n",
"615 1 0 0 0 757.746447 nontarget None \n",
"616 1 0 0 0 759.760896 nontarget None \n",
"639 1 0 0 0 813.499589 nontarget None \n",
"640 1 0 0 0 815.514611 nontarget None \n",
"641 1 0 0 0 817.529435 nontarget None \n",
"642 1 0 0 0 819.544499 nontarget None \n",
"643 1 0 0 0 821.559899 nontarget None \n",
"724 1 0 0 0 175.051139 nontarget None \n",
"725 1 0 0 0 177.066042 nontarget None \n",
"726 1 0 0 0 179.080974 nontarget None \n",
"727 1 0 0 0 181.096036 nontarget None \n",
"728 1 0 0 0 183.111110 nontarget None \n",
"751 0 0 0 1 236.849438 nontarget None \n",
"752 1 0 0 0 238.864646 nontarget None \n",
"753 1 0 0 0 240.879573 nontarget None \n",
"754 1 0 0 0 242.894627 nontarget None \n",
"755 1 0 0 0 244.909077 nontarget None \n",
"777 1 0 0 0 296.632882 nontarget None \n",
"778 1 0 0 0 298.647794 nontarget None \n",
"779 1 0 0 0 300.662853 nontarget None \n",
"780 1 0 0 0 302.677745 nontarget None \n",
"781 1 0 0 0 304.692833 nontarget None \n",
"834 1 0 0 0 433.667852 nontarget None \n",
"835 1 0 0 0 435.682780 nontarget None \n",
"836 1 0 0 0 437.697746 nontarget None \n",
"837 1 0 0 0 439.712668 nontarget None \n",
"838 1 0 0 0 441.727965 nontarget None \n",
"875 1 0 0 0 516.281994 nontarget None \n",
"876 1 0 0 0 518.296981 nontarget None \n",
"877 1 0 0 0 520.311963 nontarget None \n",
"878 1 0 0 0 522.326988 nontarget None \n",
"879 1 0 0 0 524.341948 nontarget None \n",
"926 1 0 0 0 641.227284 nontarget None \n",
"927 1 0 0 0 643.242241 nontarget None \n",
"928 1 0 0 0 645.257514 nontarget None \n",
"929 1 0 0 0 647.272797 nontarget None \n",
"930 1 0 0 0 649.287644 nontarget None \n",
"997 1 0 0 0 806.472164 nontarget None \n",
"998 1 0 0 0 808.487069 nontarget None \n",
"999 1 0 0 0 810.502026 nontarget None \n",
"1000 1 0 0 0 812.517007 nontarget None \n",
"1001 1 0 0 0 814.531912 nontarget None \n",
"... ... ... ... ... ... ... ... \n",
"7823 1 0 0 0 811.187546 nontarget None \n",
"7824 1 0 0 0 813.192649 nontarget None \n",
"7825 1 0 0 0 815.197768 nontarget None \n",
"7826 1 0 0 0 817.202788 nontarget None \n",
"7827 1 0 0 0 819.207870 nontarget None \n",
"7854 1 0 0 0 880.767972 nontarget None \n",
"7855 1 0 0 0 882.773181 nontarget None \n",
"7856 1 0 0 0 884.778147 nontarget None \n",
"7857 1 0 0 0 886.783173 nontarget None \n",
"7858 1 0 0 0 888.788312 nontarget None \n",
"7870 1 0 0 0 920.272980 nontarget None \n",
"7871 1 0 0 0 922.278014 nontarget None \n",
"7872 1 0 0 0 924.283144 nontarget None \n",
"7873 1 0 0 0 926.288175 nontarget None \n",
"7874 1 0 0 0 928.293237 nontarget None \n",
"7878 1 0 0 0 67.519403 nontarget None \n",
"7879 1 0 0 0 69.531026 nontarget None \n",
"7880 1 0 0 0 71.543273 nontarget None \n",
"7881 1 0 0 0 73.554411 nontarget None \n",
"7882 1 0 0 0 75.566044 nontarget None \n",
"7941 1 0 0 0 216.394625 nontarget None \n",
"7942 1 0 0 0 218.406392 nontarget None \n",
"7943 0 0 0 1 220.417846 nontarget None \n",
"7944 0 0 0 1 222.429623 nontarget None \n",
"7945 0 0 0 1 224.441455 nontarget None \n",
"8018 1 0 0 0 408.194116 nontarget None \n",
"8019 1 0 0 0 410.205569 nontarget None \n",
"8020 1 0 0 0 412.217210 nontarget None \n",
"8021 1 0 0 0 414.228925 nontarget None \n",
"8022 1 0 0 0 416.240943 nontarget None \n",
"8036 1 0 0 0 444.403726 nontarget None \n",
"8037 1 0 0 0 446.415380 nontarget None \n",
"8038 1 0 0 0 448.426985 nontarget None \n",
"8039 1 0 0 0 450.438673 nontarget None \n",
"8040 1 0 0 0 452.450381 nontarget None \n",
"8155 0 0 0 1 720.691932 nontarget None \n",
"8156 1 0 0 0 722.703668 nontarget None \n",
"8157 0 0 0 1 724.715720 nontarget None \n",
"8158 0 0 0 1 726.727011 nontarget None \n",
"8159 1 0 0 0 728.738572 nontarget None \n",
"8212 1 0 0 0 850.117096 nontarget None \n",
"8213 1 0 0 0 852.128819 nontarget None \n",
"8214 1 0 0 0 854.140439 nontarget None \n",
"8215 0 0 0 1 856.152104 nontarget None \n",
"8216 0 0 0 1 858.163778 nontarget None \n",
"8228 1 0 0 0 889.683774 nontarget None \n",
"8229 1 0 0 0 891.695356 nontarget None \n",
"8230 1 0 0 0 893.707167 nontarget None \n",
"8231 0 0 0 1 895.718711 nontarget None \n",
"8232 0 0 0 1 897.730391 nontarget None \n",
"8247 1 0 0 0 115.592963 nontarget None \n",
"8248 1 0 0 0 117.597997 nontarget None \n",
"8249 1 0 0 0 119.603031 nontarget None \n",
"8250 1 0 0 0 121.608066 nontarget None \n",
"8251 1 0 0 0 123.613168 nontarget None \n",
"8377 1 0 0 0 435.640462 nontarget None \n",
"8378 1 0 0 0 437.645501 nontarget None \n",
"8379 1 0 0 0 439.650567 nontarget None \n",
"8380 1 0 0 0 441.655594 nontarget None \n",
"8381 1 0 0 0 443.660720 nontarget None \n",
"8395 1 0 0 0 471.731610 nontarget None \n",
"8396 1 0 0 0 473.736678 nontarget None \n",
"8397 1 0 0 0 475.741678 nontarget None \n",
"8398 1 0 0 0 477.746790 nontarget None \n",
"8399 1 0 0 0 479.751794 nontarget None \n",
"8413 1 0 0 0 515.246151 nontarget None \n",
"8414 1 0 0 0 517.251155 nontarget None \n",
"8415 1 0 0 0 519.256276 nontarget None \n",
"8416 1 0 0 0 521.261361 nontarget None \n",
"8417 1 0 0 0 523.266445 nontarget None \n",
"8419 1 0 0 0 527.276520 nontarget None \n",
"8420 1 0 0 0 529.281694 nontarget None \n",
"8421 1 0 0 0 531.286692 nontarget None \n",
"8422 1 0 0 0 533.291688 nontarget None \n",
"8423 1 0 0 0 535.296868 nontarget None \n",
"8444 1 0 0 0 577.403166 nontarget None \n",
"8445 1 0 0 0 579.408190 nontarget None \n",
"8446 1 0 0 0 581.413482 nontarget None \n",
"8447 1 0 0 0 583.418354 nontarget None \n",
"8448 1 0 0 0 585.423365 nontarget None \n",
"8457 1 0 0 0 603.468935 nontarget None \n",
"8458 1 0 0 0 605.474005 nontarget None \n",
"8459 1 0 0 0 607.479057 nontarget None \n",
"8460 1 0 0 0 609.484196 nontarget None \n",
"8461 1 0 0 0 611.489183 nontarget None \n",
"8514 1 0 0 0 747.452321 nontarget None \n",
"8515 1 0 0 0 749.457387 nontarget None \n",
"8516 1 0 0 0 751.462416 nontarget None \n",
"8517 1 0 0 0 753.467444 nontarget None \n",
"8518 1 0 0 0 755.472568 nontarget None \n",
"8540 1 0 0 0 807.007370 nontarget None \n",
"8541 1 0 0 0 809.012446 nontarget None \n",
"8542 1 0 0 0 811.017466 nontarget None \n",
"8543 1 0 0 0 813.022518 nontarget None \n",
"8544 1 0 0 0 815.027560 nontarget None \n",
"8587 1 0 0 0 916.092742 nontarget None \n",
"8588 1 0 0 0 918.097794 nontarget None \n",
"8589 1 0 0 0 920.102816 nontarget None \n",
"8590 1 0 0 0 922.107902 nontarget None \n",
"8591 1 0 0 0 924.113009 nontarget None \n",
"\n",
" targ \n",
"254 corr_nontarg \n",
"255 corr_nontarg \n",
"256 corr_nontarg \n",
"257 corr_nontarg \n",
"258 corr_nontarg \n",
"366 corr_nontarg \n",
"367 corr_nontarg \n",
"368 corr_nontarg \n",
"369 corr_nontarg \n",
"370 corr_nontarg \n",
"379 corr_nontarg \n",
"380 corr_nontarg \n",
"381 corr_nontarg \n",
"382 corr_nontarg \n",
"383 corr_nontarg \n",
"393 corr_nontarg \n",
"394 corr_nontarg \n",
"395 corr_nontarg \n",
"396 corr_nontarg \n",
"397 corr_nontarg \n",
"419 corr_nontarg \n",
"420 corr_nontarg \n",
"421 corr_nontarg \n",
"422 corr_nontarg \n",
"423 corr_nontarg \n",
"448 corr_nontarg \n",
"449 corr_nontarg \n",
"450 corr_nontarg \n",
"451 corr_nontarg \n",
"452 corr_nontarg \n",
"469 corr_nontarg \n",
"470 corr_nontarg \n",
"471 corr_nontarg \n",
"472 corr_nontarg \n",
"473 corr_nontarg \n",
"476 corr_nontarg \n",
"477 NaN \n",
"478 corr_nontarg \n",
"479 corr_nontarg \n",
"480 corr_nontarg \n",
"486 corr_nontarg \n",
"487 corr_nontarg \n",
"488 corr_nontarg \n",
"489 corr_nontarg \n",
"490 corr_nontarg \n",
"499 NaN \n",
"500 corr_nontarg \n",
"501 corr_nontarg \n",
"502 corr_nontarg \n",
"503 corr_nontarg \n",
"517 corr_nontarg \n",
"518 corr_nontarg \n",
"519 corr_nontarg \n",
"520 corr_nontarg \n",
"521 corr_nontarg \n",
"612 corr_nontarg \n",
"613 corr_nontarg \n",
"614 corr_nontarg \n",
"615 corr_nontarg \n",
"616 corr_nontarg \n",
"639 corr_nontarg \n",
"640 corr_nontarg \n",
"641 corr_nontarg \n",
"642 corr_nontarg \n",
"643 corr_nontarg \n",
"724 corr_nontarg \n",
"725 corr_nontarg \n",
"726 corr_nontarg \n",
"727 corr_nontarg \n",
"728 corr_nontarg \n",
"751 NaN \n",
"752 corr_nontarg \n",
"753 corr_nontarg \n",
"754 corr_nontarg \n",
"755 corr_nontarg \n",
"777 corr_nontarg \n",
"778 corr_nontarg \n",
"779 corr_nontarg \n",
"780 corr_nontarg \n",
"781 corr_nontarg \n",
"834 corr_nontarg \n",
"835 corr_nontarg \n",
"836 corr_nontarg \n",
"837 corr_nontarg \n",
"838 corr_nontarg \n",
"875 corr_nontarg \n",
"876 corr_nontarg \n",
"877 corr_nontarg \n",
"878 corr_nontarg \n",
"879 corr_nontarg \n",
"926 corr_nontarg \n",
"927 corr_nontarg \n",
"928 corr_nontarg \n",
"929 corr_nontarg \n",
"930 corr_nontarg \n",
"997 corr_nontarg \n",
"998 corr_nontarg \n",
"999 corr_nontarg \n",
"1000 corr_nontarg \n",
"1001 corr_nontarg \n",
"... ... \n",
"7823 corr_nontarg \n",
"7824 corr_nontarg \n",
"7825 corr_nontarg \n",
"7826 corr_nontarg \n",
"7827 corr_nontarg \n",
"7854 corr_nontarg \n",
"7855 corr_nontarg \n",
"7856 corr_nontarg \n",
"7857 corr_nontarg \n",
"7858 corr_nontarg \n",
"7870 corr_nontarg \n",
"7871 corr_nontarg \n",
"7872 corr_nontarg \n",
"7873 corr_nontarg \n",
"7874 corr_nontarg \n",
"7878 corr_nontarg \n",
"7879 corr_nontarg \n",
"7880 corr_nontarg \n",
"7881 corr_nontarg \n",
"7882 corr_nontarg \n",
"7941 corr_nontarg \n",
"7942 corr_nontarg \n",
"7943 NaN \n",
"7944 NaN \n",
"7945 NaN \n",
"8018 corr_nontarg \n",
"8019 corr_nontarg \n",
"8020 corr_nontarg \n",
"8021 corr_nontarg \n",
"8022 corr_nontarg \n",
"8036 corr_nontarg \n",
"8037 corr_nontarg \n",
"8038 corr_nontarg \n",
"8039 corr_nontarg \n",
"8040 corr_nontarg \n",
"8155 NaN \n",
"8156 corr_nontarg \n",
"8157 NaN \n",
"8158 NaN \n",
"8159 corr_nontarg \n",
"8212 corr_nontarg \n",
"8213 corr_nontarg \n",
"8214 corr_nontarg \n",
"8215 NaN \n",
"8216 NaN \n",
"8228 corr_nontarg \n",
"8229 corr_nontarg \n",
"8230 corr_nontarg \n",
"8231 NaN \n",
"8232 NaN \n",
"8247 corr_nontarg \n",
"8248 corr_nontarg \n",
"8249 corr_nontarg \n",
"8250 corr_nontarg \n",
"8251 corr_nontarg \n",
"8377 corr_nontarg \n",
"8378 corr_nontarg \n",
"8379 corr_nontarg \n",
"8380 corr_nontarg \n",
"8381 corr_nontarg \n",
"8395 corr_nontarg \n",
"8396 corr_nontarg \n",
"8397 corr_nontarg \n",
"8398 corr_nontarg \n",
"8399 corr_nontarg \n",
"8413 corr_nontarg \n",
"8414 corr_nontarg \n",
"8415 corr_nontarg \n",
"8416 corr_nontarg \n",
"8417 corr_nontarg \n",
"8419 corr_nontarg \n",
"8420 corr_nontarg \n",
"8421 corr_nontarg \n",
"8422 corr_nontarg \n",
"8423 corr_nontarg \n",
"8444 corr_nontarg \n",
"8445 corr_nontarg \n",
"8446 corr_nontarg \n",
"8447 corr_nontarg \n",
"8448 corr_nontarg \n",
"8457 corr_nontarg \n",
"8458 corr_nontarg \n",
"8459 corr_nontarg \n",
"8460 corr_nontarg \n",
"8461 corr_nontarg \n",
"8514 corr_nontarg \n",
"8515 corr_nontarg \n",
"8516 corr_nontarg \n",
"8517 corr_nontarg \n",
"8518 corr_nontarg \n",
"8540 corr_nontarg \n",
"8541 corr_nontarg \n",
"8542 corr_nontarg \n",
"8543 corr_nontarg \n",
"8544 corr_nontarg \n",
"8587 corr_nontarg \n",
"8588 corr_nontarg \n",
"8589 corr_nontarg \n",
"8590 corr_nontarg \n",
"8591 corr_nontarg \n",
"\n",
"[755 rows x 15 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#indexlist = df.Stim[df.condition == 'target'].index.tolist()\n",
"indexlist_corr = df[df['CorrRej'].isin([1])].index.tolist() #index list for correct targets\n",
"indexlist_incorr = df[df['Commission'].isin([1])].index.tolist() #index list for incorrect targets\n",
"\n",
"pre_target_corr = pd.DataFrame()\n",
"pre_target_incorr = pd.DataFrame()\n",
"\n",
"for idx in indexlist_corr:\n",
" for x in range(idx-5,idx,):\n",
" pre_target_corr = pre_target_corr.append(df.iloc[[x],:])\n",
" \n",
"for idx in indexlist_incorr:\n",
" for x in range(idx-5,idx,):\n",
" pre_target_incorr = pre_target_incorr.append(df.iloc[[x],:])\n",
" #print(pre_target_incorr)\n",
" \n",
"pre_target_incorr"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fb0a51c8610>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ELfjGmP64afROA77APZEz5JPmjDE9cMlKE9x8z7Py0aogEm16427TTzPG/B2XnP+Ga4G8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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a512acd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"piv_pretarget_corr = pd.pivot_table(pre_target_corr, values = ['RT'], rows = ['participant', 'schedule'], aggfunc=np.mean, margins=True)\n",
"piv_pretarget_corr.reindex()\n",
"piv_pretarget_corr.columns = ['preTarget_correct_RT']\n",
"\n",
"piv_pretarget_incorr = pd.pivot_table(pre_target_incorr, values = ['RT'], rows = ['participant', 'schedule'], aggfunc=np.mean, margins=True)\n",
"piv_pretarget_incorr.reindex()\n",
"piv_pretarget_incorr.columns = ['preTarget_incorrect_RT']\n",
"\n",
"d = pd.concat([piv_pretarget_corr[['preTarget_correct_RT']],piv_pretarget_incorr[['preTarget_incorrect_RT']]],axis=1)\n",
"d.plot(kind='bar')\n",
"plt.title('Correct/Incorrect Pre-target Reaction Time')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fb0a567e990>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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uADRr1owbb7wxaD15mb/eoUMHfx5/y5Yt2blzJwsWLOC6667zl+nduzdt2rQB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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a5014f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"avg1_piv_pretarget_corr = pd.pivot_table(pre_target_corr, values = ['RT'], rows = ['participant'], aggfunc=np.mean, margins=True)\n",
"avg1_piv_pretarget_corr.reindex()\n",
"avg1_piv_pretarget_corr.columns = ['avg1_preTarget_correct_RT']\n",
"\n",
"avg1_piv_pretarget_incorr = pd.pivot_table(pre_target_incorr, values = ['RT'], rows = ['participant'], aggfunc=np.mean, margins=True)\n",
"avg1_piv_pretarget_incorr.reindex()\n",
"avg1_piv_pretarget_incorr.columns = ['avg1_preTarget_incorrect_RT']\n",
"\n",
"d1 = pd.concat([avg1_piv_pretarget_corr[['avg1_preTarget_correct_RT']],avg1_piv_pretarget_incorr[['avg1_preTarget_incorrect_RT']]],axis=1)\n",
"d1.plot(kind='bar')\n",
"plt.title('avg1 Correct/Incorrect pre-target Reaction Time')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Post-Correct RT and Post-Error AVG RT"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "positional indexers are out-of-bounds",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-11-80c3bd636fda>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mindexlist_incorr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mpost_target_incorr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpost_target_incorr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1198\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1199\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1200\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1201\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1202\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 1400\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1401\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1402\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1403\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1404\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\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/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Too many indexers'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\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 132\u001b[0m raise ValueError(\"Location based indexing can only have [%s] \"\n\u001b[0;32m 133\u001b[0m \"types\" % self._valid_types)\n",
"\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_has_valid_type\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1371\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1372\u001b[0m \u001b[1;32melif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0m_is_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1373\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1374\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1375\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jenni/miniconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_is_valid_list_like\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1394\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1395\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1396\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional indexers are out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1398\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
]
}
],
"source": [
"indexlist_corr = [5, 13, 26, 40, 66, 95, 116, 123, 164, 215, 269, 295, 363, 371, 384, 398, 424, 453, 474, 481, 491, 504, 522, 573, 627, 644, 653, 759, 766, 862, 875, 915, 984, 1008, 1091, 1117, 1124, 1160, 1178, 1214, 1220, 1233, 1242, 1255, 1265, 1273, 1326, 1342, 1366, 1406, 1478, 1507, 1514, 1529, 1571, 1683, 1703, 1760, 1812, 1819, 1836, 1865, 1872, 1887, 1913, 1929, 1966, 2041, 2061, 2069, 2082, 2118, 2147, 2165, 2337, 2344, 2354, 2362, 2375, 2432, 2458, 2489, 2513, 2523, 2548, 2576, 2671, 2689, 2695, 2702, 2712, 2720, 2733, 2790, 2816, 2847] #index list for correct targets\n",
"indexlist_incorr = df[df['Commission'].isin([1])].index.tolist() #index list for incorrect targets\n",
"\n",
"post_target_corr = pd.DataFrame()\n",
"post_target_incorr = pd.DataFrame()\n",
"\n",
"for idx in indexlist_corr:\n",
" for x in (range(idx+1, idx+3,1)):\n",
" post_target_corr = post_target_corr.append(df.iloc[[x],:])\n",
" \n",
"for idx in indexlist_incorr:\n",
" for x in (range(idx+1, idx+3,1)):\n",
" post_target_incorr = post_target_incorr.append(df.iloc[[x],:])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fb0a4e01310>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pEuDm9j9w4ACxsbGZtqlcuTLp6ekcOnSIxMREpk+fzosvvsisWbOYPn06NWrU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\nwJckSZJq5P8DaU+YTIJv1RwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a56430d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"piv_posttarget_corr = pd.pivot_table(post_target_corr, values = ['RT'], rows = ['participant', 'schedule'], aggfunc=np.mean)\n",
"piv_posttarget_corr.reindex()\n",
"piv_posttarget_corr.columns = ['postTarget_correct_RT']\n",
"\n",
"piv_posttarget_incorr = pd.pivot_table(post_target_incorr, values = ['RT'], rows = ['participant', 'schedule'], aggfunc=np.mean)\n",
"piv_posttarget_incorr.reindex()\n",
"piv_posttarget_incorr.columns = ['postTarget_incorrect_RT']\n",
"\n",
"d = pd.concat([piv_posttarget_corr[['postTarget_correct_RT']],piv_posttarget_incorr[['postTarget_incorrect_RT']]],axis=1)\n",
"d.plot(kind='bar')\n",
"plt.title('Correct/Incorrect post-target Reaction Time')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fb09ef029d0>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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06dNp164dCQkJvPrqqwAcP36ce+65h6VLl9K3b1+mTZvGBRdcQHx8POvWrQMg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a4029250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"avg_piv_posttarget_corr = pd.pivot_table(post_target_corr, values = ['RT'], rows = ['participant'], aggfunc=np.mean)\n",
"avg_piv_posttarget_corr.reindex()\n",
"avg_piv_posttarget_corr.columns = ['avg_postTarget_correct_RT']\n",
"\n",
"avg_piv_posttarget_incorr = pd.pivot_table(post_target_incorr, values = ['RT'], rows = ['participant'], aggfunc=np.mean)\n",
"avg_piv_posttarget_incorr.reindex()\n",
"avg_piv_posttarget_incorr.columns = ['avg_postTarget_incorrect_RT']\n",
"\n",
"d = pd.concat([avg_piv_posttarget_corr[['avg_postTarget_correct_RT']],avg_piv_posttarget_incorr[['avg_postTarget_incorrect_RT']]],axis=1)\n",
"d.plot(kind='bar')\n",
"plt.title('AVG Correct/Incorrect post-target Reaction Time')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AVG Reaction Time across Pre/Post Target Intervals"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fb09ee2c250>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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aPxzP2a1bN2rVqkVQUBDTpk3j5s2bd9XP1MiXL1+WnOdeIwq+IAiCIAiZYtas\nWezbt4+RI0cSFRVF79692bBhAxEREbRt25aTJ0+itbbX//333/nxxx9p164dAGPHjmXdunUMGTKE\nmTNncurUKZYvX+7kYlGjRg2++OILunfvftfyjh49mrp167Jw4UIqV65MSEgIu3fvdqrz7rvvMnny\nZMaNG0epUqVYt24dI0eOpF69eixZsoT27dszdepUIiMj093u1KlTWblyJa+++ipRUVEMGzaMb775\nhilTpjjVi4qKolmzZsybN4/g4GC01vTt2xdvb2/GjBnD888/z7Bhw0hISLCP0fnz5+natStnzpwh\nPDycN998k++++45XXnmFW7du0aRJEwYMGABAZGQknTp1Srfcw4YN48MPP6Rfv35ERERQtGhR+vbt\ny2+//UZCQgJ9+vRh/fr19O/fnwULFlCiRAn69evHrl27UhzT0qVLJ9vXtPoBJl6ld+/eFCxYkDlz\n5jBkyBA++ugjJk+efFf9BGP1v337NvHx8cTHx3P58mW2b9/OsmXLqFGjBhUqVMjQ+XIacdERBEEQ\nBCHD3Lhxg/j4eAYMGEDr1q0BqFu3LgcPHmT//v2EhoZStGhRNm3ahFIKgJiYGHx8fGjYsCEnTpxg\n48aNTJs2jfbt2wNQv359mjZt6tROVvqvt2zZ0u7H36hRI06cOMHixYtp0KCBvU737t1p0qQJAAkJ\nCcyaNYtnn32W8ePHA8ZX283NjYiICLp27UrevHnTbDc2NpbRo0fToUMHAOrUqcPx48fZsGGDU72K\nFSvSr18/+/sRI0ZQsmRJwsLCcHd3p2rVqri7uzN9+nR7nejoaG7dusWyZcsoXLgwYLIitWjRgo0b\nN9K+fXu7C1H16tXtddLi559/ZseOHYSHh/Pss8/a5e7QoQMHDx7k119/5dChQ0RGRtKwYUMAGjdu\nTJcuXZg1axaNGjVKdkxT6uvMmTPT7MeSJUsoU6YMERER9gnOjRs3WL9+PYULF85UP23ExMQQExPj\nVJY/f36aNm1KWFhYhs51PyAWfEEQBEEQMkyePHmYMGECtWrV4vTp0+zatYuoqCiOHTvGzZs3cXd3\np2XLlmzatMl+TExMDC1atMDd3Z39+/cD0KxZM/vnnp6eBAUFZZuveNu2bZ3eN2vWLElGHluQJcCJ\nEyc4d+4cQUFBdstufHw8jRs35urVq+lObzp79mw6dOjAX3/9xTfffMPKlSs5ePBgkngCx7YB9u3b\nR5MmTXDCujaXAAAgAElEQVR3v6OutWjRwqnO3r178ff3x8vLyy7fY489Rvny5dmzZ0+65EsO27g4\nukvlypWLTz/9lPbt27N//34KFChgV+5ttGrViiNHjnDt2rUU+5VcWWr92Lt3LwCHDh0iKCjIaYWn\nW7dufPjhh05jlBkaNWrEjBkz+Oijjxg3bhx58uThhRdeIDw8HB8fn7s6d04gFnxBEARBEDLFvn37\niIyM5OzZs3h7e+Pr60vevHlJSEgAjEK9YsUKjh49Su7cuTly5IjdEh4bG4uHhwcFChRwOmeRIkWy\nTd5ixYo5vffx8SE+Pt5JGXVs/++//waMJX3EiBFOx7q5uXH+/Pl0tXvw4EEmTJjAL7/8gpeXF1Wr\nVsXT09M+Tsm1bWvfVbksWrRokjqHDx+mevWkWZhc+5sR4uLikr0+Ni5dupTstSpatCiJiYlcvXrV\nXpZcveT6mlY/UmozKyhYsCAVKlSgatWq+Pr6UqBAAcaMGUP+/PkZOnRotrSZnYiCLwiCIAhChjl5\n8iTh4eE0bdqUcePG2V1pQkJCOH78OAA1a9akdOnSbN68mVy5clGyZEkCAgIA43oTHx/PlStXnJTI\nixcvZpvMNoXdxvnz5/H09EwxkNLLywuAN954I0mmlMTERLs/eWpcvnyZ/v37U6dOHRYuXGh3IwkP\nD+fIkSOpHlu8eHEuXLjgVOY6Pl5eXgQFBSVRQhMTE8mfP3+a8qWEzZLuen0OHTpEoUKFKFSoULIT\nnHPnzgFkeOO09PSjQIECScYjLi6O//f//p/9vsoqnn/+eTZs2MCSJUt45plnnLLrPAiIi44gCIIg\nCBnmp59+4vbt23To0MGu3F+7do0DBw441WvTpg07duzg888/p1WrVvbyWrVq4e7uztatW+1lN2/e\nZOfOndmWr3379u1O77du3Ur9+vVTrF++fHkKFy7MmTNnqF69uv0vLi6O+fPnc+XKlTTbPH78OJcu\nXeKll16yK/cJCQlJgnuTo06dOnz55ZdOLkvbtm1zqhMQEMCxY8eoVKmSXb5KlSoRERFhd7PJjPtK\n7dq1Aecxu3nzJiEhIXz88cfUqVOHq1evJgmojYmJwdfXl9y5c2eovdT6cejQIcDcM1999ZXTeGzY\nsIH+/fuTkJBw1246rowdOxY3N7ckwdAPAmLBFwRBEIQc5tK5kzncdsZ3sa1WrRru7u5ER0eTJ08e\nYmNjWbZsWRKXl3bt2rFkyRLc3NyYNGmSvfyJJ56gXbt2TJ48mevXr1OyZEnee+89zp8/T6lSpbKg\nZ0mJiooiX758VKtWjTVr1nDs2DEnmVzx8PBgyJAh9p1J69evz+nTp5k5cyblypVL1oLvGj9QoUIF\n8ufPz8KFC7l9+zbXr19n1apVnDlzhhs3bqQqb9++fWnfvj3Tp0+nefPm7N+/n3nz5gHYJ0Evv/wy\nH3/8MX379qVnz554eHgQFRXF999/b7eGFyxYEIAtW7YQGBjolLc/JapVq0aTJk2YOHEiV65c4fHH\nH2fVqlXcuHGDLl26ULx4cfz9/QkNDWX48OE89thjrF27lh9++IFFixaleX5X0tOP/v37061bN4YO\nHUqnTp34888/mTt3Lt27dydfvnyZ6mdqVKhQgU6dOvHf//6XzZs3J4l/gPt3bwFR8AVBEAQhB/Hz\n82PRmz1yUILGmdqop2zZsoSEhLB69Wr69etHmTJl6NGjBz4+Prz22mucO3eORx99lIoVK6KU4tat\nW1SpUsXpHBMmTMDT05M5c+Zw+/Zt2rRpQ8uWLfn111+TbdPNze2urPujRo1i7dq1REREULVqVaKi\novD19XU6vyvdunXD09OT5cuX2zO8tG7dmuHDh6cooyMFChRg/vz5hIeHM2DAAIoWLcoLL7zA0KFD\n6dKlC4cPH05x/CtUqMDixYuZOHEiU6dOpVy5cowZM4Zx48bZ3VZKlCjBqlWrePvttwkNDcXNzQ1f\nX1+ioqLs492gQQMaNWrExIkT6dy5M6+//nq6xmvOnDnMnDmThQsXcvXqVfz8/IiOjqZEiRKASX/5\n9ttvM3v2bK5fv07VqlVZunSpUwad9F6v9PTD39+fyMhIZs+ezeDBgylatCg9e/a0p8fMbD9TY8iQ\nIXz66afMmDGD4OBgcuXK5dS3+3V3YLf7deYBcODAgcSs9qnKTmy+dA+an9aDhIzxvUHGOfuRMc5+\nZIyzn7sZ49jYWHbt2kVwcLCTr3iXLl0oVqyY3VKdFZw+fZpmzZoRFRVFYGBglp03u9m9ezcFChSw\nK5VVq1Zl165d9OnTh08++YTKlSvnsIQPDw/q98WBAwcICAhIMssQC74gCIIgCPecPHny8NZbb7F5\n82ZefPFFPDw8iImJ4fDhw/adTdPi4sWL/Pbbb2nWc80886Dw/fffExkZSc+ePSlZsiRaa+bNm0fd\nunUzrdynd8xq1qyZqfPfL/xf6WdKiIIvCIIgCMI9J1++fERGRjJnzhxGjBjBrVu3UEqxaNGiVANf\nHdmxYwdjx45NtY6bm5tTIO+DRL9+/bh58yZr1qzh4sWLFC5cmObNm/Paa69l+pzpHbO0Mvzc7/xf\n6WdKiItOFvKgLu88SMgY3xtknLMfGePsR8Y4+5ExvjfIOGc/D+oYp+SiI2kyBUEQBEEQBOEhQhR8\nQRAEQRAEQXiIEAVfEARBEARBEB4iRMEXBEEQBEEQhIcIUfAFQRAEQRAE4SFC0mQKDy1xcXEcPnw4\nQ8f4+flRqFChbJJIEARBEAQh+xEFX3hoOXz4MNPeWMFjj5ZLV/0z504Q9mZ3GjdunM2SCYIg3CEz\nxoisRowbgvBwIQq+8FDz2KPleKJ09ZwWQxAEIUUOHz5MyLKxFHqiSI60H3fqAnN7T3kgjBvbtm0j\nNDSUgwcP5rQodsLCwli/fn2qdQYPHszgwYPvkURJOXPmDOPGjWPGjBl4e3un65jTp0/TrFkz5s2b\nR/PmzbNZwpxjwYIFeHt7U7t27XQfM3/+fBYuXOhU5ubmRv78+alYsSIDBgwgKCjIPoZp8fPPP2dY\n7rQQBV8QBEEQcphCTxTh0aolclqM+5qDBw8SGhqa02IkYdCgQXTt2hWAxMRERo0aRbly5Rg4cKC9\nTvHixXNKPAB2797N119/jZtbkv2QUqRYsWJ88MEHPPHEE9koWc6zYMECRo8eneHjPD09ee+99+zv\nb9++zR9//MHSpUsZNGgQH330EeXLl+eDDz6w19m3bx8zZsxgwYIFFCtWLEvkTwlR8AVBEARBuG+5\nefMm0dHRzJs3j3z58nHr1q2cFsmJMmXKUKZMGfv7vHnz4u3tjZ+fXw5KlTyJiYnprps7d+77sg/Z\nQUbGxYabm1uS8alVqxZ+fn40b96cTz/9lNDQUKc6Z8+eBaBatWqULFny7oROA8miIwiCIAhCprh2\n7RrvvvsuwcHB+Pr6EhgYSFhYGJcvX2bMmDG0bNkyyTEdO3Zk1KhRANy4cYNJkyYRGBhIQEAA48eP\nZ9asWQQHB9vrf/XVV7zzzjuMHj2a7t27Z0oZO336NFWqVGH79u107twZf39/2rVrx5YtW+x19u7d\nS5UqVVi9ejUNGzakXr16/PHHHwBs2LCBdu3aUaNGDZ555hlWrFiRofZv3brFvHnzaNGiBTVq1ODJ\nJ59kyJAhnDlzxl4nODiYmTNn2uVbtmwZAHv27CE0NJTOnTvTtm1bdu7cSbVq1Vi3bp392FOnTjFw\n4EBq165N3bp1GTVqFLGxsQCsXbuWsWPHAhAYGMiCBQsyNGa2MQoLC2Po0KFER0fz9NNP4+/vT8+e\nPTl27JjTcVu2bKFDhw7UrFmTpk2bsmTJEqfPP//8czp27EitWrVo0qQJc+fO5fbt2ymOQ2RkJPPn\nz6dDhw5MmTKF2rVr8/zzzwMQHx/P3LlzadKkCX5+fnTs2JFvvvnGqb2///6bcePG0bBhQwICAnjl\nlVf45ZdfAKhSpQoA4eHhvPrqq+kal7TIly8fQIZWS7IDUfAFQRAEQcgUs2bNYt++fYwcOZKoqCh6\n9+7Nhg0biIiIoG3btpw8eRKttb3+77//zo8//ki7du0AGDt2LOvWrWPIkCHMnDmTU6dOsXz5cifl\nqEaNGnzxxRd07979ruUdPXo0devWZeHChVSuXJmQkBB2797tVOfdd99l8uTJjBs3jlKlSrFu3TpG\njhxJvXr1WLJkCe3bt2fq1KlERkamu92pU6eycuVKXn31VaKiohg2bBjffPMNU6ZMcaoXFRVl93sP\nDg5Ga03fvn3x9vZmzJgxPP/88wwbNoyEhAT7GJ0/f56uXbty5swZwsPDefPNN/nuu+945ZVXuHXr\nFk2aNGHAgAEAREZG0qlTp0yP3zfffMPHH3/M+PHjefvttzl16hRjxoyxf75582aGDh1KlSpVWLhw\nIT169GD+/PksXboUgNWrVzNkyBBq1qzJwoUL6d69O8uWLSMsLCzVcQD45Zdf+OWXX4iIiGDYsGEA\nvP766yxfvpxevXoRERFB+fLl6du3L4cOHQLMBODll19m586djBgxgrlz5/LPP//Qu3dvLl26xOrV\nqwHo0aNHEhnSw+3bt4mPjyc+Pp4bN27w66+/MmbMGDw8PGjTpk3GBzgLERcdQRAEQRAyzI0bN4iP\nj2fAgAG0bt0agLp163Lw4EH2799PaGgoRYsWZdOmTSilAIiJicHHx4eGDRty4sQJNm7cyLRp02jf\nvj0A9evXp2nTpk7tZKX/esuWLe1+/I0aNeLEiRMsXryYBg0a2Ot0796dJk2aAJCQkMCsWbN49tln\nGT9+PAANGjTAzc2NiIgIunbtSt68edNsNzY2ltGjR9OhQwcA6tSpw/Hjx9mwYYNTvYoVK9KvXz/7\n+xEjRlCyZEnCwsJwd3enatWquLu7M336dHud6Ohobt26xbJlyyhcuDBgsiK1aNGCjRs30r59e7sL\nUfXq1e11MsPVq1dZunQpRYsWBeCvv/5i8uTJxMXFUahQIRYtWkRgYKB94tKwYUMuXLjAd999R0JC\nAnPmzKFNmza8/vrrgBlLLy8v3njjDfr27UvlypWTHQcwynpYWJjd6n7s2DHWrVvHpEmTeOGFFwBz\nTc+dO8ecOXOIjo5mx44dHDlyhJUrVxIQEAAY95hOnTrx448/EhgYCEDJkiUpVy59GfdsXL9+nerV\nnZN4uLu7U716dd555x2qVq2aofNlNWLBFwRBEAQhw+TJk4cJEyZQq1YtTp8+za5du4iKiuLYsWPc\nvHkTd3d3WrZsyaZNm+zHxMTE0KJFC9zd3dm/fz+AU5YRT09PgoKCMuWGkx7atm3r9L5Zs2ZJMvI4\nKnonTpzg3LlzBAUF2S218fHxNG7cmKtXr6Y7vens2bPp0KEDf/31F9988w0rV67k4MGDSeIJXJXM\nffv20aRJE9zd76hrLVq0cKqzd+9e/P398fLyssv32GOPUb58efbs2ZMu+dJLqVKl7Mo93Jl8Xb9+\nnX/++Yeff/6Zp59+2umYESNGEBERwbFjx4iNjaVVq1ZOn9smh7b7AZKOg42yZcva/9+3bx8ATz31\nlNO1eeqppzhw4AC3bt3i0KFDFCxY0K7cA/j4+LBt2za7cp9ZPD09WbNmDWvWrGHJkiVUqVKFSpUq\nMXfu3Ls+d1YgFnxBEARBEDLFvn37iIyM5OzZs3h7e+Pr60vevHlJSEgAjEK9YsUKjh49Su7cuTly\n5IjdEh4bG4uHhwcFChRwOmeRItmXLtQ1c4mPjw/x8fFcu3Yt2fb//vtvwCipI0aMcDrWzc2N8+fP\np6vdgwcPMmHCBH755Re8vLyoWrUqnp6e9nFKrm1b+z4+Pk5ljgq2rc7hw4eTWJMhaX/vFk9PT6f3\ntolHQkICcXFxQMrXL6XPvby8yJ07N1evXrWXJXeOvHnzOrVvuzZPPfVUkrpubm7ExsYSFxeX7rSg\nGcXNzc1pzGvUqEHbtm3p06cPa9asSdfKTnYiCr4gCIIgCBnm5MmThIeH07RpU8aNG2e35oaEhHD8\n+HEAatasSenSpdm8eTO5cuWiZMmSdmtq8eLFiY+P58qVK05K/sWLF7NNZptSaOP8+fN4enraAyNd\n8fLyAuCNN95IkjElMTGR0qVLp9nm5cuX6d+/P3Xq1GHhwoV2d5nw8HCOHDmS6rHFixfnwoULTmWu\n4+Pl5UVQUBBDhw5NIl/+/PnTlC+rsLXlKt9ff/3FqVOn7BMV1/5cunSJmzdvZth1yMvLCzc3N1av\nXs0jjzxiL7et/nh7e+Pl5WUPNnZkz549lClThlKlSmWozdTw8fFhzJgxhIaGMm/evEyl3sxK0uWi\no5Tqq5Q6qpS6ppTarZSqn0b9ukqpL5VScUqpY0qpfyulZDIhCIIgCA8JP/30E7dv36ZDhw525f7a\ntWscOHDAqV6bNm3YsWMHn3/+uZN7Rq1atXB3d2fr1q32sps3b7Jz585sy0Cyfft2p/dbt26lfv2U\nVZry5ctTuHBhzpw5Q/Xq1e1/cXFxzJ8/nytXrqTZ5vHjx7l06RIvvfSSXblPSEhIEtybHHXq1OHL\nL790clnatm2bU52AgACOHTtGpUqV7PJVqlSJiIgIu/uRo4tPdlGgQAEqV66cZIyXL1/OyJEjKV++\nPN7e3sTExDh9/tlnnwFkaKMpMGOTmJjI5cuXna7N3r17ee+99/Dw8KBWrVpcunTJHnQLZpLXt29f\n+/hn5di0a9eO2rVr85///IcTJ05k2XkzQ5pKt1LqJWAR8CawHxgKbFZK+WutTyZT/3FgG7AL6AhU\nAaYDXsD9t0OFIAiCIOQwcacupF3pPmu7WrVquLu7Ex0dTZ48eYiNjWXZsmVJXF7atWvHkiVLcHNz\nY9KkSfbyJ554gnbt2jF58mSuX79OyZIlee+99zh//nyWWlYdiYqKIl++fFSrVo01a9Zw7NgxJ5lc\n8fDwYMiQIUydOhUwQcCnT59m5syZlCtXLlkLvmv8QIUKFcifPz8LFy7k9u3bXL9+nVWrVnHmzBlu\n3LiRqrx9+/alffv2TJ8+nebNm7N//37mzZsH3EnD+PLLL/Pxxx/Tt29fevbsiYeHB1FRUXz//fd2\nq37BggUBk8IyMDDQKW9/VjJo0CBCQkL497//TYsWLfj5559ZsWKFPUh48ODBTJw4kUKFCtmzBC1Y\nsIBWrVpRsWLFDLVVpUoVmjdvTmhoKIMHD6Z8+fLs27ePJUuW0KdPH9zc3AgODqZatWoMHz6c4cOH\nU7hwYZYsWcJjjz1mn2x6eXnx7bff4u3tbQ8GvxvCwsLo3Lkz06dPZ/HixXd9vsySqoKvlHLDKPZL\ntNYTrbKtgAaGAyHJHNbJOm9HrfV1YKtSqgQwGFHwBUEQBMEJPz8/5vaeknbFbJYho5QtW5aQkBBW\nr15Nv379KFOmDD169MDHx4fXXnuNc+fO8eijj1KxYkWUUty6dcueAcXGhAkT8PT0ZM6cOdy+fZs2\nbdrQsmVLfv3112TbdHNzuyvr/qhRo1i7di0RERFUrVqVqKgofH19nc7vSrdu3fD09GT58uX2TDWt\nW7dm+PDhKcroSIECBZg/fz7h4eEMGDCAokWL8sILLzB06FC6dOnC4cOHUxz/ChUqsHjxYiZOnMjU\nqVMpV64cY8aMYdy4cXaXmBIlSrBq1SrefvttQkNDcXNzw9fXl6ioKPt4N2jQgEaNGjFx4kQ6d+5s\nz2KTEVIad8fyFi1aMGfOHCIiIli3bp09A1C3bt2AO2O5bNkyPvzwQ4oVK0bv3r2ddv1NqY3k2p8x\nYwbz5s1j6dKlXLhwgVKlSjFixAh69+4NmAlaZGQk4eHhTJkyhYSEBOrWrUt4eLjdLWzIkCHMmTOH\nvXv3Eh0dne6xSGk8/Pz8aNOmDZ999hm7d+92ytDkOl7ZiVtqkepKqUoYZb6V1nqzQ/k8oIXWOslU\nRyn1FkbxL6y1TrTKRgDhQF6t9c30CnfgwIFEx8jn+x2bL11Op0Z6mMnIGO/cuZPlC77midJJA4+S\n49TpH+k1uCGNGze+KxkfBuRezn5kjLMfGePs527GODY2ll27dhEcHOzkK96lSxeKFStmt1RnBadP\nn6ZZs2ZERUXdFxlO0svu3bspUKAAuXLlAsw479q1iz59+vDJJ5/Y00oKd8+D+n1x4MABAgICkswa\n0nLRsd05rlPpE0AFpZSbTYl34EOMpX6qUmo6UBEYBqzNiHIvCIIgCMLDS548eXjrrbfYvHkzL774\nIh4eHsTExHD48GH7Lq5pcfHiRX777bc067lmnnlQ+P7774mMjKRnz56ULFkSrTXz5s2jbt26mVbu\n0ztmNWvWzNT5H2S01mm6TT3++ONJMhvdj6Sl4Be0Xi+7lF/GBOjmB5wiTLTWPyil+gJRwCir+ADQ\n++5EFQRBEAThYSFfvnxERkYyZ84cRowYwa1bt1BKsWjRolQDXx3ZsWMHY8eOTbWOm5ubUyDvg0S/\nfv24efMma9as4eLFixQuXJjmzZvz2muvZfqc6R2ztDL8PIyktZutm5sbU6dOtW/Mdj+TlotOV2AF\nUFxrfc6hvA+wFCigtb7mckxbYC0QCawGSgFvAX8AzTLqopNS6qr7kevXrwPkeO7Th5mMjPG3337L\njg1/ZMhFp0nbUtSpU+euZHwYkHs5+5Exzn5kjLMfGeN7g4xz9vOgjvG1a9cy5aITZ716Aeccyr2A\n267KvcU0YLPWeoCtQCn1LXAE6Iax7AuCIAiCIAiCkA2kpeAftV7LA8cdystjgm+ToyLwX8cCrbVW\nSl0AMhy58CAFOzyoARoPEhkZY7PD4B8ZOn/ZsmXl+iH38r1Axjj7kTHOfmSM7w0yztnPgzrGrvtO\n2Egru/9R4HfgeVuBUioX0AaT6z45TgANHQuUUhWBItZngiAIgiAIgiBkE6la8LXWiUqpacACpVQs\nsBuTz94HmA2glKoAPKq13mMdNgn4j1LqHeB94DFgAka5fy87OiEIgiAIgiAIgiHN/Xm11oswaS97\nYFJgFsTkwD9pVXkd+Nqh/kqMhb86Jth2CrADqKe1vpqFsguCIAiCIAiC4EJaPvgAaK1nAbNS+KwX\n0MulLAaIuUvZBEEQBEEQBEHIIOlS8AVBEARByB7i4uI4fPhwjsrg5+dHoUKFclQGQRCyDlHwBUEQ\nBCEHOXz4MOsGDaFiwZxRsH+9FAcL59O4ceMcaf9BYsGCBXh7e9OtWzfWrl2b5oZRTz75JO+9l3Ph\nhzdv3iQ8PJz69evTrFmzHJMju8lsP8PCwli/fr1Tmbu7O15eXlSrVo1hw4bh7+/P3r17eemll1I9\nV6lSpdi2LaX8M/ceUfAFQRAEIYepWLAQ/kWK5rQYQhosWLCA0aNHA9CkSRM++OAD+2fLly9n//79\nLFy40F6WP3/+ey6jI2fPnmXFihU8+eSTOSpHdnM3/Xz88ceZMWMGJ06YRI9lypTh+PHjRERE8Mor\nrxATE0P16tWdrvXGjRuJjo52KsudO/fddyQLEQVfEARBEAQhnSQmJgLg4+ODj4+PvbxIkSLkypUL\nPz+/nBItRWwyP+xkpp958uTBz8+PXLlyASYPfu3atSlVqhQvv/wyn3/+OV27dnW6rgcPHgS4L6+1\njTSz6AiCIAiCICTHtWvXePfddwkODsbX15fAwEDCwsK4fPkyY8aMoWXLlkmO6dixI6NGjQLgxo0b\nTJo0icDAQAICAhg/fjyzZs0iODg4Q3JUqVKF9evX07t3b/z9/XnmmWd4//33nepcvXqV6dOnExwc\njL+/P506deLrr792qrNu3TratGmDn58fQUFBTJ06lZs3b9rbAAgPD6dp06bplu2TTz6hY8eO1KxZ\nk5o1a9KlSxe+/fZb++dhYWEMGjSIESNGUKtWLQYOHAjA6dOnGTBgAAEBAfTu3Zv169fTq1cvxowZ\nYz/22rVrTJw4kYYNG+Lv70+PHj3sGzadPn3a7q4SEhJCz5490y0zwOrVq2nTpg3+/v60atWKDz/8\n0OnzDz74gHbt2uHv70+LFi2Ijo52+rxKlSosWbKENm3aUKtWLT777DPCwsIYOHBgkr6m1g8bf/zx\nByEhIdSrV4969eoxdOhQ/vzzz7vuZ0rky5cPADc3tyw5371GFHxBEARBEDLFrFmz2LdvHyNHjiQq\nKorevXuzYcMGIiIiaNu2LSdPnkTrOxvf//777/z444+0a9cOgLFjx7Ju3TqGDBnCzJkzOXXqFMuX\nL8+UUjV58mQee+wxFi5cSFBQEBMmTOCjjz4CICEhgT59+rB+/Xr69+/PggULKFGiBP369WPXrl0A\n7N+/n3HjxvHss8+ybNky+vfvz/vvv8+CBQsAo/AC9OjRw8kNJzU2bdrE6NGjefrpp3nnnXeYMmUK\nly9fZtiwYcTHx9vrffnllwAsWrSIXr168c8//9CrVy9OnTrFtGnTeOmll9iwYYPdcgzGWj1gwAA+\n++wzhg0bxty5c8mTJw89evTg999/p1ixYnbZX3vtNd544410j2VUVBQTJkzgqaeeYvHixbRs2ZLX\nX3+dzz77DICZM2fy5ptv0qxZMxYtWkTLli2ZPn06c+bMcTqPrT/Tp0+nXr16AHz11VdOnwGp9gPg\nypUrdO3alaNHj/LGG28wbdo0jh8/Tt++fe+qnzZu375t/7t+/To//PADEydOxMvLK8OTzfsFcdER\nBEEQBCHD3Lhxg/j4eAYMGEDr1q0BqFu3LgcPHmT//v2EhoZStGhRNm3ahFIKgJiYGHx8fGjYsCEn\nTpxg48aNTJs2jfbt2wNQv379DFnHHfHz82PKlCkANGrUiLNnz7J48WJeeOEFduzYwaFDh4iMjKRh\nw4YANG7cmC5dujBr1iwaNWrEoUOHyJs3Ly+//DK5c+emTp065M6dGw8Poyr5+/sDULJkSbs1Py1+\n++03unXrxuDBg+1luXLlYsiQIZw8eZKKFSsCRsGcMGECXl5egLGO//nnn2zatIkyZcpQunRpSpcu\nzUQu5i4AACAASURBVMiRI+3n2bVrF3v37iUqKorAwEB7n9q0acOiRYuYMmWKXc6yZctSoUKFdMmc\nkJDA4sWL6dixoz3eIDAwkNOnT3PgwAECAwOJioqiT58+hISEANCgQQMSExOJjIykV69eFC5cGICG\nDRvSqVMnp/O79nXnzp1p9mPNmjVcuHCBVatWUapUKQBKlCjB4MGD+f333zPVTxtHjx6levXqTmUe\nHh4EBAQQHR1N8eLFM3S++wVR8AVBEARByDB58uRhwoQJgHEHOXnyJEePHuXYsWN4enri7u5Oy5Yt\n2bRpk10RjImJoUWLFri7u7N//34Ap6wnnp6eBAUFsWfPngzL07ZtW6f3TZs2ZcuWLfz111/s37+f\nAgUK2JV7G61atWLatGlcu3aNgIAArl27xnPPPUerVq1o0qQJHTt2zLAcjvTr1w+AS5cucfz4cU6c\nOMEXX3wBYHf9AePPb1N4Afbu3UvlypUpU6aMvaxChQqULl3aqU7evHmpW7eu02pAw4YN2b59e6Zl\nPnHiBHFxcTz99NNO5W+//TYAO3bsID4+Pon7VevWrVm6dCnff/89QUFBAJQrVy7J+ZPra1r9OHTo\nEJUqVbIr92BcgLZu3QqY+y+zPP7448yePZsTJ07w119/sWrVKkqXLs38+fMpWLBgps+b04iCLwiC\nIAhCpti3bx+RkZGcPXsWb29vfH19yZs3LwkJCYBRulesWMHRo0fJnTs3R44cYfz48QDExsbi4eFB\ngQIFnM5ZpEiRTMlSrFgxp/e2ANi4uDguXbqU7HmLFi1KYmIiV69eJSAggIiICKKioli6dCkRERGU\nLl2aCRMm0KhRo0zJdO7cOcaNG8fOnTvJlSuXk5LqGBDqGKwL8Pfffycps8nrWOf69ev4+vomqWcL\nGM0Mf//9N5DydYiLi0v2c9v7K1euJClzJLm+ptWPuLi4ZMcjK8iTJw/Vq1fH3d2dChUq0KRJEzp0\n6MDgwYOJjo5+YH3wRcEXBEEQBCHDnDx50h5wOm7cOLsrQ0hICMePHwegZs2alC5dms2bN5MrVy5K\nlixJQEAAAMWLFyc+Pp4rV644KfkXL17MlDw2xdTGhQsXAKNkFipUiPPnzyc55ty5cwD2Tb6efvpp\nnn76aa5cucJXX33FokWLGD58OLt3786U0jxixAjOnj3L6tWr8fX1xd3dnS+//JItW7akelzx4sX5\n6aefkpRfuHDBbhX38vKiSJEiLF261KnO3WbMsVnXXa/DyZMniY2NtbvfXLhwwWlSZRtf2+cZaS+t\nfnh5edn98R358ssvk50Y3A0VKlRgwIABzJkzh5UrV9K9e/csPf+9QoJsBUEQBEHIMD/99BO3b9+m\nQ4cOduX+2rVrHDhwwKlemzZt2LFjB59//jmtWrWyl9eqVQt3d3e7mwUYt5WdO3dmympqc32x8fnn\nn1OpUiWKFClCQEAAV69etQfU2oiJicHX15fcuXMze/ZsOnfuDECBAgVo3bo1vXv35vLly3artLt7\nxtSm77//3p6Vx3bszp07gdQV8Tp16nD06FEn15NTp045vQ8ICODixYvkzZuX6tWr2/82btzIp59+\nCsAjjzySIXkBypcvT6FChZK4+cyaNYvw8HD8/Pzw8PAgJibG6fPPPvsMDw+PDKeOTE8/ateuzdGj\nR/nf//5nP+7o0aO8+uqraK0z1c//z96dx1dV3fv/fyUyqAiUyWsVNAp1iUPQghWlfFVqW5Cidbi3\n/KpcwVbUiqWoUIf6FRwqlStwnQAVFMT+SttLHQsOtYMjQoqgLS6hEi/aWoliCIMyfv84yWkISc5J\nyCFk83o+Hn0cs87aJ5+zshveZ2XttWtz8cUXp5fpVP3g2FQ4gy9JUiNbsba0Ub/3cfU47uijjyY/\nP5+ZM2fSsmVL1qxZw4wZM9iyZQsbNmxI9xs0aBDTpk0jLy+PW2+9Nd1+2GGHMWjQIG677TY2btzI\nwQcfzKxZsygpKdlhrXW25s2bx4EHHkifPn144YUXeOGFF7j77ruB1Mx8jx49GD16NKNGjeKggw5i\n7ty5vPnmm0yZMgVIXUh6//33c+ONN3LmmWdSWlrK1KlT6dWrF+3atQNSM8mLFi3ihBNO4Pjjj89Y\n03HHHcfcuXM58sgjadOmDc899xzz588HYOPGjTUed9ZZZzF16lQuu+wyfvjDH/Lee+/x6KOPkpeX\nl/6g0K9fP4477jiGDx/OiBEjOOigg3j22Wf5+c9/zs0335yuF+Dll1+mS5cudO/ePWPNzZo147LL\nLmPChAm0a9eO3r17s2DBAp577jnuvfde2rVrx5AhQ5g+fTr77LMPvXr1YuHChcyYMYNhw4btsL4+\nG9m8j/POO4+HH36YSy+9lCuvvJL8/HwmT55Mjx4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QfPXVV/PRRx8xZ84cjj32WPLz8/nj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\nkiRJCWLAlyRJkhLEgC9JkiQliAFfkiRJShADviRJkpQgBnxJkiQpQQz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UAAAE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\nERE1kgQ/IiIiIqJG/gGygHPkEylZEwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a448e690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"avg1_piv_pretarget_corr\n",
"s = pd.concat([avg1_piv_pretarget_corr[['avg1_preTarget_correct_RT']],avg1_piv_pretarget_incorr[['avg1_preTarget_incorrect_RT']], avg_piv_posttarget_corr[['avg_postTarget_correct_RT']], avg_piv_posttarget_incorr[['avg_postTarget_incorrect_RT']]] ,axis=1)\n",
"s = s.reset_index()\n",
"s.plot(kind='bar')\n",
"plt.title('AVG Reaction Time across Pre/Post Target Intervals')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb0a40290d0>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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P3U81AwAAi9jjDH5VbUhyRpI3tdbOmrR9IElLckqSkxe47JQkf99a+4XJ6w9W1ZYkv5zk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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a4f07fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s.mean().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb09ec202d0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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StiU5e8g15yb58GTc96+5O8nXHOKsAADACKNu0Tmjf7xvYP2BJGv6O/yDXpSkllLeXEr5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\nxPO2JPfUWr/jSP0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"text/plain": [
"<matplotlib.figure.Figure at 0x7fb09ec28210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(piv_nontarget[['nontarg_hit_rt']])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jenni/miniconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\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></th>\n",
" <th>Response</th>\n",
" <th>perc_ontask</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p3</th>\n",
" <th>1</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p5</th>\n",
" <th>1</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p6</th>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2</td>\n",
" <td> 0.1250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p7</th>\n",
" <th>1</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p8</th>\n",
" <th>1</th>\n",
" <td> 13</td>\n",
" <td> 0.8125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 13</td>\n",
" <td> 0.8125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p9</th>\n",
" <th>1</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Response perc_ontask\n",
"participant schedule \n",
"p3 1 7 0.4375\n",
" 2 4 0.2500\n",
" 3 7 0.4375\n",
" 4 7 0.4375\n",
"p5 1 8 0.5000\n",
" 2 5 0.3125\n",
" 3 5 0.3125\n",
" 4 8 0.5000\n",
"p6 1 1 0.0625\n",
" 2 2 0.1250\n",
" 3 4 0.2500\n",
" 4 5 0.3125\n",
"p7 1 6 0.3750\n",
" 2 7 0.4375\n",
" 3 6 0.3750\n",
" 4 8 0.5000\n",
"p8 1 13 0.8125\n",
" 2 13 0.8125\n",
" 3 7 0.4375\n",
" 4 10 0.6250\n",
"p9 1 6 0.3750\n",
" 2 10 0.6250\n",
" 3 9 0.5625\n",
" 4 10 0.6250"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''#======================\n",
"Calculate Percentage of Thought Probes\n",
"#======================\n",
"Percent OnTask\n",
"Percent OffTask\n",
"Percent Aware\n",
"Percent Unaware\n",
"Percent Missed TP:Task\n",
"Percent Missed TP:Aware'''\n",
"\n",
"p1 = df[df['Stim'].isin(['probe'])]\n",
"p2 = df[df['Stim'].isin(['probe2'])]\n",
"\n",
"ontask = p1[p1['Response'].isin(['1'])]\n",
"ontask['Response'] = ontask['Response'].astype(int)\n",
"offtask = p1[p1. Response == '2']\n",
"offtask['Response'].replace('2', 1, inplace=True)\n",
"#offtask['Response'] = ontask['Response'].astype(int)\n",
"noresP1 = p1[p1.Response == 'None']\n",
"noresP1['Response'].replace('None', 1, inplace=True)\n",
"\n",
"ont_piv = pd.pivot_table(ontask, values='Response', rows = ['participant', 'schedule'], aggfunc=np.sum)#, margins=True)\n",
"ont_piv = pd.DataFrame(ont_piv)\n",
"ont_piv.reset_index()\n",
"#ot_piv.columns = ['participant', 'schedule', 'ontask']\n",
"ont_piv['perc_ontask'] = ont_piv.apply(lambda row: (row['Response']/16), axis = 1) \n",
"ont_piv"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th></th>\n",
" <th>Response</th>\n",
" <th>perc_offtask</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p3</th>\n",
" <th>1</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 12</td>\n",
" <td> 0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p5</th>\n",
" <th>1</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p6</th>\n",
" <th>1</th>\n",
" <td> 13</td>\n",
" <td> 0.8125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p7</th>\n",
" <th>1</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">p8</th>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p9</th>\n",
" <th>1</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Response perc_offtask\n",
"participant schedule \n",
"p3 1 9 0.5625\n",
" 2 12 0.7500\n",
" 3 8 0.5000\n",
" 4 7 0.4375\n",
"p5 1 7 0.4375\n",
" 2 8 0.5000\n",
" 3 7 0.4375\n",
" 4 6 0.3750\n",
"p6 1 13 0.8125\n",
" 2 10 0.6250\n",
" 3 9 0.5625\n",
" 4 9 0.5625\n",
"p7 1 5 0.3125\n",
" 2 4 0.2500\n",
" 3 4 0.2500\n",
" 4 4 0.2500\n",
"p8 3 4 0.2500\n",
" 4 1 0.0625\n",
"p9 1 7 0.4375\n",
" 2 5 0.3125\n",
" 3 7 0.4375\n",
" 4 6 0.3750"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"offt_piv = pd.pivot_table(offtask, values='Response', rows = ['participant', 'schedule'], aggfunc=np.sum)#, margins=True)\n",
"offt_piv = pd.DataFrame(offt_piv)\n",
"offt_piv.reset_index()\n",
"#ot_piv.columns = ['participant', 'schedule', 'ontask']\n",
"offt_piv['perc_offtask'] = offt_piv.apply(lambda row: (row['Response']/16), axis = 1) \n",
"offt_piv"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>Response</th>\n",
" <th>perc_offt1ask</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>p3</th>\n",
" <td> 36</td>\n",
" <td> 0.562500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p5</th>\n",
" <td> 28</td>\n",
" <td> 0.437500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p6</th>\n",
" <td> 41</td>\n",
" <td> 0.640625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p7</th>\n",
" <td> 17</td>\n",
" <td> 0.265625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p8</th>\n",
" <td> 5</td>\n",
" <td> 0.078125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p9</th>\n",
" <td> 25</td>\n",
" <td> 0.390625</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Response perc_offt1ask\n",
"participant \n",
"p3 36 0.562500\n",
"p5 28 0.437500\n",
"p6 41 0.640625\n",
"p7 17 0.265625\n",
"p8 5 0.078125\n",
"p9 25 0.390625"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"offt1_piv = pd.pivot_table(offtask, values='Response', rows = ['participant'], aggfunc=np.sum) #, margins=True)\n",
"offt1_piv = pd.DataFrame(offt1_piv)\n",
"offt1_piv.reset_index()\n",
"#ot_piv.columns = ['participant', 'schedule', 'ontask']\n",
"offt1_piv['perc_offt1ask'] = offt1_piv.apply(lambda row: (row['Response']/64), axis = 1) \n",
"offt1_piv"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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></th>\n",
" <th></th>\n",
" <th>Response</th>\n",
" <th>perc_noresp on/off task</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th>Trial</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">p3</th>\n",
" <th>3</th>\n",
" <th>106</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4</th>\n",
" <th>27 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"10\" valign=\"top\">p5</th>\n",
" <th>1</th>\n",
" <th>250</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">2</th>\n",
" <th>210</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>244</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>322</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">3</th>\n",
" <th>117</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4</th>\n",
" <th>234</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"11\" valign=\"top\">p6</th>\n",
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
" <th>34 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>339</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">2</th>\n",
" <th>36 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>217</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">3</th>\n",
" <th>158</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4</th>\n",
" <th>36 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"20\" valign=\"top\">p7</th>\n",
" <th rowspan=\"5\" valign=\"top\">1</th>\n",
" <th>109</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>323</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>339</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2</th>\n",
" <th>210</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>217</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>244</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>299</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>357</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">3</th>\n",
" <th>13 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">4</th>\n",
" <th>27 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>294</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"16\" valign=\"top\">p8</th>\n",
" <th rowspan=\"3\" valign=\"top\">1</th>\n",
" <th>178</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>323</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>357</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">2</th>\n",
" <th>244</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>313</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>322</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">3</th>\n",
" <th>91 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>236</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>319</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">4</th>\n",
" <th>27 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79 </th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>253</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p9</th>\n",
" <th rowspan=\"3\" valign=\"top\">1</th>\n",
" <th>178</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>250</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>339</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>313</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Response perc_noresp on/off task\n",
"participant schedule Trial \n",
"p3 3 106 1 0.0625\n",
" 4 27 1 0.0625\n",
" 140 1 0.0625\n",
"p5 1 250 1 0.0625\n",
" 2 210 1 0.0625\n",
" 244 1 0.0625\n",
" 322 1 0.0625\n",
" 3 117 1 0.0625\n",
" 158 1 0.0625\n",
" 202 1 0.0625\n",
" 229 1 0.0625\n",
" 4 234 1 0.0625\n",
" 274 1 0.0625\n",
"p6 1 34 1 0.0625\n",
" 339 1 0.0625\n",
" 2 36 1 0.0625\n",
" 121 1 0.0625\n",
" 131 1 0.0625\n",
" 217 1 0.0625\n",
" 3 158 1 0.0625\n",
" 202 1 0.0625\n",
" 229 1 0.0625\n",
" 4 36 1 0.0625\n",
" 123 1 0.0625\n",
"p7 1 109 1 0.0625\n",
" 178 1 0.0625\n",
" 306 1 0.0625\n",
" 323 1 0.0625\n",
" 339 1 0.0625\n",
" 2 210 1 0.0625\n",
" 217 1 0.0625\n",
" 244 1 0.0625\n",
" 299 1 0.0625\n",
" 357 1 0.0625\n",
" 3 13 1 0.0625\n",
" 91 1 0.0625\n",
" 117 1 0.0625\n",
" 194 1 0.0625\n",
" 202 1 0.0625\n",
" 243 1 0.0625\n",
" 4 27 1 0.0625\n",
" 123 1 0.0625\n",
" 140 1 0.0625\n",
" 294 1 0.0625\n",
"p8 1 178 1 0.0625\n",
" 323 1 0.0625\n",
" 357 1 0.0625\n",
" 2 244 1 0.0625\n",
" 313 1 0.0625\n",
" 322 1 0.0625\n",
" 3 91 1 0.0625\n",
" 202 1 0.0625\n",
" 229 1 0.0625\n",
" 236 1 0.0625\n",
" 319 1 0.0625\n",
" 4 27 1 0.0625\n",
" 79 1 0.0625\n",
" 123 1 0.0625\n",
" 173 1 0.0625\n",
" 253 1 0.0625\n",
"p9 1 178 1 0.0625\n",
" 250 1 0.0625\n",
" 339 1 0.0625\n",
" 2 313 1 0.0625"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"noresP1_piv = pd.pivot_table(noresP1, values='Response', rows = ['participant', 'schedule', 'Trial'], aggfunc=np.sum)\n",
"noresP1_piv = pd.DataFrame(noresP1_piv)\n",
"noresP1_piv.reset_index()\n",
"#ot_piv.columns = ['participant', 'schedule', 'ontask']\n",
"noresP1_piv['perc_noresp on/off task'] = noresP1_piv.apply(lambda row: (row['Response']/16), axis = 1) \n",
"noresP1_piv"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>noresp_on/off task</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>p3</th>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p5</th>\n",
" <td> 10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p6</th>\n",
" <td> 11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p7</th>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p8</th>\n",
" <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p9</th>\n",
" <td> 4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" noresp_on/off task\n",
"participant \n",
"p3 3\n",
"p5 10\n",
"p6 11\n",
"p7 20\n",
"p8 16\n",
"p9 4"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"noresP1_piv = pd.pivot_table(noresP1, values='Response', rows = ['participant'], aggfunc=np.sum)\n",
"noresP1_piv = pd.DataFrame(noresP1_piv)\n",
"noresP1_piv.reset_index()\n",
"noresP1_piv.columns = ['noresp_on/off task']\n",
"\n",
"noresP1_piv"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jenni/miniconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\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></th>\n",
" <th>Response</th>\n",
" <th>perc_aware</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p3</th>\n",
" <th>1</th>\n",
" <td> 14</td>\n",
" <td> 0.8750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 14</td>\n",
" <td> 0.8750</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p5</th>\n",
" <th>1</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 2</td>\n",
" <td> 0.1250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p6</th>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> 0.1875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p7</th>\n",
" <th>1</th>\n",
" <td> 11</td>\n",
" <td> 0.6875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p8</th>\n",
" <th>1</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 11</td>\n",
" <td> 0.6875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p9</th>\n",
" <th>1</th>\n",
" <td> 12</td>\n",
" <td> 0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 11</td>\n",
" <td> 0.6875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 14</td>\n",
" <td> 0.8750</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Response perc_aware\n",
"participant schedule \n",
"p3 1 14 0.8750\n",
" 2 10 0.6250\n",
" 3 10 0.6250\n",
" 4 14 0.8750\n",
"p5 1 9 0.5625\n",
" 2 4 0.2500\n",
" 3 2 0.1250\n",
" 4 4 0.2500\n",
"p6 1 1 0.0625\n",
" 2 3 0.1875\n",
" 3 4 0.2500\n",
" 4 7 0.4375\n",
"p7 1 11 0.6875\n",
" 2 10 0.6250\n",
" 3 8 0.5000\n",
" 4 10 0.6250\n",
"p8 1 8 0.5000\n",
" 2 11 0.6875\n",
" 3 6 0.3750\n",
" 4 7 0.4375\n",
"p9 1 12 0.7500\n",
" 2 9 0.5625\n",
" 3 11 0.6875\n",
" 4 14 0.8750"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p2 = df[df['Stim'].isin(['probe2'])]\n",
"\n",
"aware = p2[p2['Response'].isin(['1'])]\n",
"aware['Response'] = aware['Response'].astype(int)\n",
"unaware = p2[p2. Response == '2']\n",
"unaware['Response'].replace('2', 1, inplace=True)\n",
"#unaware['Response'] = aware['Response'].astype(int)\n",
"noresp2 = p2[p2.Response == 'None']\n",
"noresp2['Response'].replace('None', 1, inplace=True)\n",
"\n",
"aware_piv = pd.pivot_table(aware, values='Response', rows = ['participant', 'schedule'], aggfunc=np.sum)#, margins=True)\n",
"aware_piv = pd.DataFrame(aware_piv)\n",
"aware_piv.reset_index()\n",
"#ot_piv.columns = ['participant', 'schedule', 'aware']\n",
"aware_piv['perc_aware'] = aware_piv.apply(lambda row: (row['Response']/16), axis = 1) \n",
"aware_piv"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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></th>\n",
" <th>Response</th>\n",
" <th>perc_unaware</th>\n",
" <th>perc_offtask</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p3</th>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> 0.1250</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" <td> 0.7500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p5</th>\n",
" <th>1</th>\n",
" <td> 6</td>\n",
" <td> 0.3750</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 11</td>\n",
" <td> 0.6875</td>\n",
" <td> 0.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 10</td>\n",
" <td> 0.6250</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p6</th>\n",
" <th>1</th>\n",
" <td> 15</td>\n",
" <td> 0.9375</td>\n",
" <td> 0.8125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 13</td>\n",
" <td> 0.8125</td>\n",
" <td> 0.6250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 12</td>\n",
" <td> 0.7500</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 8</td>\n",
" <td> 0.5000</td>\n",
" <td> 0.5625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p7</th>\n",
" <th>1</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" <td> 0.3125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 3</td>\n",
" <td> 0.1875</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p8</th>\n",
" <th>1</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> 0.1875</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 9</td>\n",
" <td> 0.5625</td>\n",
" <td> 0.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7</td>\n",
" <td> 0.4375</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p9</th>\n",
" <th>1</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 5</td>\n",
" <td> 0.3125</td>\n",
" <td> 0.3125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 0.2500</td>\n",
" <td> 0.4375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 2</td>\n",
" <td> 0.1250</td>\n",
" <td> 0.3750</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Response perc_unaware perc_offtask\n",
"participant schedule \n",
"p3 1 2 0.1250 0.5625\n",
" 2 6 0.3750 0.7500\n",
" 3 6 0.3750 0.5000\n",
" 4 1 0.0625 0.4375\n",
"p5 1 6 0.3750 0.4375\n",
" 2 11 0.6875 0.5000\n",
" 3 9 0.5625 0.4375\n",
" 4 10 0.6250 0.3750\n",
"p6 1 15 0.9375 0.8125\n",
" 2 13 0.8125 0.6250\n",
" 3 12 0.7500 0.5625\n",
" 4 8 0.5000 0.5625\n",
"p7 1 5 0.3125 0.3125\n",
" 2 4 0.2500 0.2500\n",
" 3 4 0.2500 0.2500\n",
" 4 3 0.1875 0.2500\n",
"p8 1 5 0.3125 NaN\n",
" 2 3 0.1875 NaN\n",
" 3 9 0.5625 0.2500\n",
" 4 7 0.4375 0.0625\n",
"p9 1 4 0.2500 0.4375\n",
" 2 5 0.3125 0.3125\n",
" 3 4 0.2500 0.4375\n",
" 4 2 0.1250 0.3750"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unaware_piv = pd.pivot_table(unaware, values='Response', rows = ['participant', 'schedule'], aggfunc=np.sum)#, margins=True)\n",
"unaware_piv = pd.DataFrame(unaware_piv)\n",
"unaware_piv.reset_index()\n",
"#ot_piv.columns = ['participant', 'schedule', 'ontask']\n",
"unaware_piv['perc_unaware'] = unaware_piv.apply(lambda row: (row['Response']/16), axis = 1) \n",
"offtt = offt_piv[['perc_offtask']]\n",
"unaware_piv.join(offtt)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>Response</th>\n",
" <th>perc_unaware</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>p3</th>\n",
" <td> 15</td>\n",
" <td> 0.234375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p5</th>\n",
" <td> 36</td>\n",
" <td> 0.562500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p6</th>\n",
" <td> 48</td>\n",
" <td> 0.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p7</th>\n",
" <td> 16</td>\n",
" <td> 0.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p8</th>\n",
" <td> 24</td>\n",
" <td> 0.375000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p9</th>\n",
" <td> 15</td>\n",
" <td> 0.234375</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Response perc_unaware\n",
"participant \n",
"p3 15 0.234375\n",
"p5 36 0.562500\n",
"p6 48 0.750000\n",
"p7 16 0.250000\n",
"p8 24 0.375000\n",
"p9 15 0.234375"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unaware_piv1 = pd.pivot_table(unaware, values='Response', rows = ['participant'], aggfunc=np.sum)# margins=True)#, margins=True)\n",
"unaware_piv1 = pd.DataFrame(unaware_piv1)\n",
"unaware_piv1.reset_index()\n",
"unaware_piv1['perc_unaware'] = unaware_piv1.apply(lambda row: (row['Response']/64), axis = 1) \n",
"unaware_piv1"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((0, 1), <matplotlib.text.Text at 0x7fb09ead3350>)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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l3LhxDB06lKOOOoq77767xjoffvghP/vZzzj88MMpKSlhzJgxPPnkk9XLFy1a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\nJEmS1CL/C+UjRGDVTpvvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0a43aa290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#oort = pd.concat([ont_piv[['perc_ontask']],offt_piv[['perc_offtask']],aware_piv[['perc_aware']], unaware_piv[['perc_unaware']] ],axis=1)\n",
"oort = pd.concat([offt_piv[['perc_offtask']], unaware_piv[['perc_unaware']] ],axis=1)\n",
"oort.plot(kind='bar')\n",
"plt.ylim([0,1]), plt.title('Percentage Offtask & Unaware')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((0, 1), <matplotlib.text.Text at 0x7fb09e88fa10>)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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EgC9JkiQlxIAvSZIkJcSAL0mSJCXEgC9JkiQlxIAvSZIkJcSAL0mS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\nxKaZeX9TrP2KOzOfjIgJA4z9J8ATlA8EawD7Ays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"text/plain": [
"<matplotlib.figure.Figure at 0x7fb09eaa3450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"oort1 = pd.concat([offt1_piv[['perc_offt1ask']], unaware_piv1[['perc_unaware']] ],axis=1)\n",
"oort1.plot(kind='bar')\n",
"plt.ylim([0,1]), plt.title('Percentage Offtask & Unaware')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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></th>\n",
" <th>Response</th>\n",
" <th>perc_noresp aware/unaware</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>p3</th>\n",
" <th>4</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p5</th>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 0.0625</td>\n",
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" <tr>\n",
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" <td> 0.0625</td>\n",
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" <tr>\n",
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" <td> 0.3125</td>\n",
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" <th>4</th>\n",
" <td> 2</td>\n",
" <td> 0.1250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p6</th>\n",
" <th>4</th>\n",
" <td> 1</td>\n",
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" <td> 3</td>\n",
" <td> 0.1875</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">p8</th>\n",
" <th>1</th>\n",
" <td> 3</td>\n",
" <td> 0.1875</td>\n",
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" <td> 2</td>\n",
" <td> 0.1250</td>\n",
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" <td> 1</td>\n",
" <td> 0.0625</td>\n",
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" <td> 0.1250</td>\n",
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" <td> 0.1250</td>\n",
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" <tr>\n",
" <th>3</th>\n",
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" <td> 0.0625</td>\n",
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],
"text/plain": [
" Response perc_noresp aware/unaware\n",
"participant schedule \n",
"p3 4 1 0.0625\n",
"p5 1 1 0.0625\n",
" 2 1 0.0625\n",
" 3 5 0.3125\n",
" 4 2 0.1250\n",
"p6 4 1 0.0625\n",
"p7 2 2 0.1250\n",
" 3 4 0.2500\n",
" 4 3 0.1875\n",
"p8 1 3 0.1875\n",
" 2 2 0.1250\n",
" 3 1 0.0625\n",
" 4 2 0.1250\n",
"p9 2 2 0.1250\n",
" 3 1 0.0625"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"noresp2_piv = pd.pivot_table(noresp2, values='Response', rows = ['participant', 'schedule'], aggfunc=np.sum)\n",
"noresp2_piv = pd.DataFrame(noresp2_piv)\n",
"noresp2_piv.reset_index()\n",
"#ot_piv.columns = ['participant', 'schedule', 'ontask']\n",
"noresp2_piv['perc_noresp aware/unaware'] = noresp2_piv.apply(lambda row: (row['Response']/16), axis = 1) \n",
"noresp2_piv\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>noresp_awareness</th>\n",
" <th>noresp_on/off task</th>\n",
" </tr>\n",
" <tr>\n",
" <th>participant</th>\n",
" <th></th>\n",
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" <th>p3</th>\n",
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"text/plain": [
" noresp_awareness noresp_on/off task\n",
"participant \n",
"p3 1 3\n",
"p5 9 10\n",
"p6 1 11\n",
"p7 9 20\n",
"p8 8 16\n",
"p9 3 4"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"noresp2_piv = pd.pivot_table(noresp2, values='Response', rows = ['participant'], aggfunc=np.sum)\n",
"noresp2_piv = pd.DataFrame(noresp2_piv)\n",
"noresp2_piv.reset_index()\n",
"noresp2_piv.columns = ['noresp_awareness']\n",
"#noresp2_piv['perc_noresp aware/unaware'] = noresp2_piv.apply(lambda row: (row['Response']/64), axis = 1) \n",
"nn = noresP1_piv['noresp_on/off task']\n",
"noresp2_piv.join(nn)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>249 </th>\n",
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" <tr>\n",
" <th>295 </th>\n",
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" </tr>\n",
" <tr>\n",
" <th>305 </th>\n",
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" </tr>\n",
" <tr>\n",
" <th>322 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.846442</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 784.304629</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>322 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.846442</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 784.304629</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>329 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.930867</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 805.718938</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>329 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.930867</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 805.718938</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>338 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 339</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.364123</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 831.135318</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>356 </th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 357</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.430703</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 874.563747</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>384 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 27</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.281115</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 218.348220</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 34</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.307391</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 236.149755</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 41</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.230561</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 253.951476</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 50</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.328712</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 275.783868</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>442 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 85</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.295170</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 353.701560</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>453 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 96</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.363489</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 379.563821</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>459 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 102</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.543608</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 395.349894</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>459 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 102</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.543608</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 395.349894</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>466 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 109</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.161403</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 416.848849</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>535 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 178</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.127243</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 563.275797</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 195</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.477188</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 604.924042</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 195</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.477188</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 604.924042</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 205</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.245040</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 632.467858</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>591 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.460565</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 698.295912</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>607 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 250</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 737.929284</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>627 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 270</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 781.925711</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>635 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 278</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.227253</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 801.742319</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>653 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 296</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 841.708855</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>663 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 306</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.227883</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 865.555742</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>680 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.260995</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 907.204156</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>680 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.260995</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 907.204156</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>687 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.027484</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 928.702694</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>687 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.027484</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 928.702694</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>696 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 339</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.541340</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 954.231595</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>714 </th>\n",
" <td> 2015_May_09_1223</td>\n",
" <td> p5</td>\n",
" <td> 1</td>\n",
" <td> 357</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.726671</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 997.894817</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>742 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 27</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 211.320855</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>749 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 34</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 229.122536</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>756 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 41</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.363179</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 246.924144</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 50</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.809579</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 268.756247</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 85</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.909460</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 346.674175</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>811 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 96</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 372.535945</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>817 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 102</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.242952</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 388.322531</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>817 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 102</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.242952</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 388.322531</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>824 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 109</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.210339</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 409.821362</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>893 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 178</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.909666</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 556.248413</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>910 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 195</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.177057</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 597.896791</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>910 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 195</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.177057</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 597.896791</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 205</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.757522</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 625.440587</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>949 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.076702</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 691.268731</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>965 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 250</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.377055</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 730.902000</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 270</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 774.898469</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993 </th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 278</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.176839</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 794.715127</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1011</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 296</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.314051</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 834.681647</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1021</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 306</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.759830</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 858.528320</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1038</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.175991</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 900.177073</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1038</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.175991</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 900.177073</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1045</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.127233</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 921.675948</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1045</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.127233</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 921.675948</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1054</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 339</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 947.204337</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1072</th>\n",
" <td> 2015_May_05_1751</td>\n",
" <td> p6</td>\n",
" <td> 1</td>\n",
" <td> 357</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.457500</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 990.868216</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1100</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 27</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 180.702501</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1107</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 34</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.933019</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 198.449751</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1114</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 41</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 216.196796</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1123</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 50</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.957670</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 237.938181</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1158</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 85</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.090253</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 315.538723</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1169</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 96</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.361714</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 341.306176</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1175</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 102</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.674207</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 357.048508</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1175</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 102</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.674207</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 357.048508</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1182</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 109</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 378.507595</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1251</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 178</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 524.280609</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1268</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 195</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.703635</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 565.790560</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1268</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 195</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.703635</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 565.790560</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1278</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 205</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.918809</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 593.264731</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.670194</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 658.835485</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1323</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 250</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.742244</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 698.339974</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1343</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 270</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.378743</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 742.153029</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1351</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 278</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.935301</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 761.905184</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1369</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 296</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 801.708565</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1379</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 306</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 825.470929</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1396</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 866.980445</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1396</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 866.980445</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1403</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.221753</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 888.423265</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1403</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 330</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.221753</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 888.423265</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1412</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 339</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 913.892343</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1430</th>\n",
" <td> 2015_May_07_1656</td>\n",
" <td> p7</td>\n",
" <td> 1</td>\n",
" <td> 357</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.592652</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 957.407456</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7095</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 294</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.744222</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 868.268885</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7129</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 328</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.076222</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 944.171715</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7143</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 342</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.496642</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 976.078125</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7149</th>\n",
" <td> 2015_May_09_1206</td>\n",
" <td> p5</td>\n",
" <td> 4</td>\n",
" <td> 348</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.676743</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 991.864803</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7177</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 18</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 234.052561</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7186</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.174015</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 255.884178</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7186</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.174015</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 255.884178</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7195</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 36</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 281.412532</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7220</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 61</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.093622</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 339.181179</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7230</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 71</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 363.027812</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7238</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 79</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.326432</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 382.844480</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7255</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 96</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.526172</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 424.492661</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7266</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 107</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.826804</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 454.051570</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7282</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 123</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 493.684849</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7299</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 140</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.243549</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 535.333656</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7325</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 166</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.546759</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 591.419526</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7332</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 173</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.010154</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 609.221169</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7387</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 228</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.446462</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 723.742285</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7393</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.791671</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 739.529363</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7412</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 253</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.426865</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 785.207097</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7426</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.193801</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 820.811037</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7426</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.193801</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 820.811037</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7433</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 274</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.326754</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 842.309890</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7444</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 285</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.264375</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 868.170966</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7453</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 294</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.609928</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 890.002862</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7487</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 328</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.576855</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 965.906200</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7501</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 342</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 997.812780</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7507</th>\n",
" <td> 2015_May_05_1714</td>\n",
" <td> p6</td>\n",
" <td> 4</td>\n",
" <td> 348</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.476554</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1013.599766</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7535</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 18</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 129.797872</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7544</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 151.555154</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7544</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 151.555154</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7553</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 36</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.156551</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 177.024202</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7578</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 61</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.502411</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 234.574668</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7588</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 71</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.361952</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 258.337093</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7596</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 79</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.819094</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 278.089316</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7613</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 96</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.071820</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 319.599371</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7624</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 107</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.355526</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 349.078499</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7640</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 123</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 388.583470</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7657</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 140</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 430.093028</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7683</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 166</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.362191</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 485.936436</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7690</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 173</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.974011</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 503.684029</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7745</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 228</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.461153</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 617.674318</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7751</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.769309</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 633.416432</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7770</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 253</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.559977</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 678.936130</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7784</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.195401</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 714.431020</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7784</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.195401</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 714.431020</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7791</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 274</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.719825</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 735.889870</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7802</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 285</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.378578</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 761.657423</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7811</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 294</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 783.415089</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7845</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 328</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.206647</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 859.010669</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7859</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 342</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.229481</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 890.793312</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7865</th>\n",
" <td> 2015_May_07_1713</td>\n",
" <td> p7</td>\n",
" <td> 4</td>\n",
" <td> 348</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.852159</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 906.535875</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7893</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 18</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 97.694205</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7902</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 119.489185</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7902</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 119.489185</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7911</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 36</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.033865</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 144.974223</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7936</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 61</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.502331</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 202.646259</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7946</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 71</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.330246</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 226.452913</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7954</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 79</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 246.236183</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7971</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 96</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.368674</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 287.814454</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7982</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 107</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.955660</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 317.323479</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7998</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 123</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 356.890012</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8015</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 140</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.689138</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 398.468923</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8041</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 166</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.290699</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 454.461981</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8048</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 173</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 472.233617</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8103</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 228</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 586.564554</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8109</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.914630</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 602.325250</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8128</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 253</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 647.926592</td>\n",
" <td> probe</td>\n",
" <td> None</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8142</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.422218</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 683.469970</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8142</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.422218</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 683.469970</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8149</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 274</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.115739</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 704.931909</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8160</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 285</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.422437</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 730.750204</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8169</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 294</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.754997</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 752.545221</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8203</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 328</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.315499</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 828.322152</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8217</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 342</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.329828</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 860.175373</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8223</th>\n",
" <td> 2015_May_13_1457</td>\n",
" <td> p8</td>\n",
" <td> 4</td>\n",
" <td> 348</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 1.688597</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 875.935426</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8252</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 18</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.376842</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 125.618168</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8261</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.328005</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 147.375450</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8261</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 27</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.328005</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 147.375450</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8270</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 36</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.426879</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 172.845006</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8295</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 61</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.460734</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 230.394995</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8305</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 71</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> 0.346170</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 254.157225</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8313</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 79</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.708947</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 273.909937</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8330</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 96</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.394278</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 315.419475</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8341</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 107</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.775213</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 344.898879</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8357</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 123</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.724884</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 384.403509</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8374</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 140</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.560348</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 425.913134</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8400</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 166</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.345020</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 481.756963</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8407</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 173</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.393731</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 499.504005</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8462</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 228</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.560675</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 613.494302</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8468</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 234</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.460590</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 629.236334</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8487</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 253</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.825498</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 674.756237</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8501</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.394185</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 710.250856</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8501</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 267</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.394185</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 710.250856</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8508</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 274</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.891125</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 731.709971</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8519</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 285</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.295507</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 757.477587</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> incorr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8528</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 294</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 0.460164</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 779.234882</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8562</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 328</td>\n",
" <td> probe</td>\n",
" <td> 2</td>\n",
" <td> 1.701698</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 854.830477</td>\n",
" <td> probe</td>\n",
" <td> Off</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8576</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 342</td>\n",
" <td> 3</td>\n",
" <td> None</td>\n",
" <td> 0.494250</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 886.613417</td>\n",
" <td> target</td>\n",
" <td> None</td>\n",
" <td> corr_targ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8582</th>\n",
" <td> 2015_May_14_1645</td>\n",
" <td> p9</td>\n",
" <td> 4</td>\n",
" <td> 348</td>\n",
" <td> probe</td>\n",
" <td> 1</td>\n",
" <td> 0.460232</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 902.355616</td>\n",
" <td> probe</td>\n",
" <td> On</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>588 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" date participant schedule Trial Stim Response RT \\\n",
"26 2014_Nov_11_1015 p3 1 27 3 None NaN \n",
"33 2014_Nov_11_1015 p3 1 34 probe 2 1.261399 \n",
"40 2014_Nov_11_1015 p3 1 41 3 None NaN \n",
"49 2014_Nov_11_1015 p3 1 50 probe 1 1.130440 \n",
"84 2014_Nov_11_1015 p3 1 85 probe 1 1.213826 \n",
"95 2014_Nov_11_1015 p3 1 96 3 None NaN \n",
"101 2014_Nov_11_1015 p3 1 102 probe 2 1.714118 \n",
"101 2014_Nov_11_1015 p3 1 102 probe 2 1.714118 \n",
"108 2014_Nov_11_1015 p3 1 109 probe 2 1.263998 \n",
"177 2014_Nov_11_1015 p3 1 178 probe 2 1.080429 \n",
"194 2014_Nov_11_1015 p3 1 195 probe 2 1.047107 \n",
"194 2014_Nov_11_1015 p3 1 195 probe 2 1.047107 \n",
"204 2014_Nov_11_1015 p3 1 205 probe 2 1.280573 \n",
"233 2014_Nov_11_1015 p3 1 234 probe 2 0.811709 \n",
"249 2014_Nov_11_1015 p3 1 250 probe 1 1.347381 \n",
"269 2014_Nov_11_1015 p3 1 270 3 None NaN \n",
"277 2014_Nov_11_1015 p3 1 278 probe 1 1.664020 \n",
"295 2014_Nov_11_1015 p3 1 296 3 None NaN \n",
"305 2014_Nov_11_1015 p3 1 306 probe 1 1.213774 \n",
"322 2014_Nov_11_1015 p3 1 323 probe 1 0.846442 \n",
"322 2014_Nov_11_1015 p3 1 323 probe 1 0.846442 \n",
"329 2014_Nov_11_1015 p3 1 330 probe 2 0.930867 \n",
"329 2014_Nov_11_1015 p3 1 330 probe 2 0.930867 \n",
"338 2014_Nov_11_1015 p3 1 339 probe 1 1.364123 \n",
"356 2014_Nov_11_1015 p3 1 357 probe 2 1.430703 \n",
"384 2015_May_09_1223 p5 1 27 3 1 0.281115 \n",
"391 2015_May_09_1223 p5 1 34 probe 1 1.307391 \n",
"398 2015_May_09_1223 p5 1 41 3 1 0.230561 \n",
"407 2015_May_09_1223 p5 1 50 probe 1 0.328712 \n",
"442 2015_May_09_1223 p5 1 85 probe 1 0.295170 \n",
"453 2015_May_09_1223 p5 1 96 3 1 0.363489 \n",
"459 2015_May_09_1223 p5 1 102 probe 2 1.543608 \n",
"459 2015_May_09_1223 p5 1 102 probe 2 1.543608 \n",
"466 2015_May_09_1223 p5 1 109 probe 2 1.161403 \n",
"535 2015_May_09_1223 p5 1 178 probe 2 1.127243 \n",
"552 2015_May_09_1223 p5 1 195 probe 2 1.477188 \n",
"552 2015_May_09_1223 p5 1 195 probe 2 1.477188 \n",
"562 2015_May_09_1223 p5 1 205 probe 1 0.245040 \n",
"591 2015_May_09_1223 p5 1 234 probe 1 1.460565 \n",
"607 2015_May_09_1223 p5 1 250 probe None NaN \n",
"627 2015_May_09_1223 p5 1 270 3 None NaN \n",
"635 2015_May_09_1223 p5 1 278 probe 1 1.227253 \n",
"653 2015_May_09_1223 p5 1 296 3 None NaN \n",
"663 2015_May_09_1223 p5 1 306 probe 1 0.227883 \n",
"680 2015_May_09_1223 p5 1 323 probe 1 0.260995 \n",
"680 2015_May_09_1223 p5 1 323 probe 1 0.260995 \n",
"687 2015_May_09_1223 p5 1 330 probe 2 1.027484 \n",
"687 2015_May_09_1223 p5 1 330 probe 2 1.027484 \n",
"696 2015_May_09_1223 p5 1 339 probe 2 1.541340 \n",
"714 2015_May_09_1223 p5 1 357 probe 2 1.726671 \n",
"742 2015_May_05_1751 p6 1 27 3 None NaN \n",
"749 2015_May_05_1751 p6 1 34 probe None NaN \n",
"756 2015_May_05_1751 p6 1 41 3 1 0.363179 \n",
"765 2015_May_05_1751 p6 1 50 probe 2 1.809579 \n",
"800 2015_May_05_1751 p6 1 85 probe 2 1.909460 \n",
"811 2015_May_05_1751 p6 1 96 3 None NaN \n",
"817 2015_May_05_1751 p6 1 102 probe 2 1.242952 \n",
"817 2015_May_05_1751 p6 1 102 probe 2 1.242952 \n",
"824 2015_May_05_1751 p6 1 109 probe 2 1.210339 \n",
"893 2015_May_05_1751 p6 1 178 probe 2 1.909666 \n",
"910 2015_May_05_1751 p6 1 195 probe 2 1.177057 \n",
"910 2015_May_05_1751 p6 1 195 probe 2 1.177057 \n",
"920 2015_May_05_1751 p6 1 205 probe 2 1.757522 \n",
"949 2015_May_05_1751 p6 1 234 probe 2 1.076702 \n",
"965 2015_May_05_1751 p6 1 250 probe 1 0.377055 \n",
"985 2015_May_05_1751 p6 1 270 3 None NaN \n",
"993 2015_May_05_1751 p6 1 278 probe 2 1.176839 \n",
"1011 2015_May_05_1751 p6 1 296 3 1 0.314051 \n",
"1021 2015_May_05_1751 p6 1 306 probe 2 1.759830 \n",
"1038 2015_May_05_1751 p6 1 323 probe 2 1.175991 \n",
"1038 2015_May_05_1751 p6 1 323 probe 2 1.175991 \n",
"1045 2015_May_05_1751 p6 1 330 probe 2 1.127233 \n",
"1045 2015_May_05_1751 p6 1 330 probe 2 1.127233 \n",
"1054 2015_May_05_1751 p6 1 339 probe None NaN \n",
"1072 2015_May_05_1751 p6 1 357 probe 2 1.457500 \n",
"1100 2015_May_07_1656 p7 1 27 3 None NaN \n",
"1107 2015_May_07_1656 p7 1 34 probe 2 1.933019 \n",
"1114 2015_May_07_1656 p7 1 41 3 None NaN \n",
"1123 2015_May_07_1656 p7 1 50 probe 1 0.957670 \n",
"1158 2015_May_07_1656 p7 1 85 probe 2 1.090253 \n",
"1169 2015_May_07_1656 p7 1 96 3 1 0.361714 \n",
"1175 2015_May_07_1656 p7 1 102 probe 1 0.674207 \n",
"1175 2015_May_07_1656 p7 1 102 probe 1 0.674207 \n",
"1182 2015_May_07_1656 p7 1 109 probe None NaN \n",
"1251 2015_May_07_1656 p7 1 178 probe None NaN \n",
"1268 2015_May_07_1656 p7 1 195 probe 1 1.703635 \n",
"1268 2015_May_07_1656 p7 1 195 probe 1 1.703635 \n",
"1278 2015_May_07_1656 p7 1 205 probe 2 1.918809 \n",
"1307 2015_May_07_1656 p7 1 234 probe 2 1.670194 \n",
"1323 2015_May_07_1656 p7 1 250 probe 1 0.742244 \n",
"1343 2015_May_07_1656 p7 1 270 3 1 0.378743 \n",
"1351 2015_May_07_1656 p7 1 278 probe 2 1.935301 \n",
"1369 2015_May_07_1656 p7 1 296 3 None NaN \n",
"1379 2015_May_07_1656 p7 1 306 probe None NaN \n",
"1396 2015_May_07_1656 p7 1 323 probe None NaN \n",
"1396 2015_May_07_1656 p7 1 323 probe None NaN \n",
"1403 2015_May_07_1656 p7 1 330 probe 1 1.221753 \n",
"1403 2015_May_07_1656 p7 1 330 probe 1 1.221753 \n",
"1412 2015_May_07_1656 p7 1 339 probe None NaN \n",
"1430 2015_May_07_1656 p7 1 357 probe 1 0.592652 \n",
"... ... ... ... ... ... ... ... \n",
"7095 2015_May_09_1206 p5 4 294 probe 2 0.744222 \n",
"7129 2015_May_09_1206 p5 4 328 probe 1 1.076222 \n",
"7143 2015_May_09_1206 p5 4 342 3 1 0.496642 \n",
"7149 2015_May_09_1206 p5 4 348 probe 2 0.676743 \n",
"7177 2015_May_05_1714 p6 4 18 3 None NaN \n",
"7186 2015_May_05_1714 p6 4 27 probe 1 1.174015 \n",
"7186 2015_May_05_1714 p6 4 27 probe 1 1.174015 \n",
"7195 2015_May_05_1714 p6 4 36 probe None NaN \n",
"7220 2015_May_05_1714 p6 4 61 probe 1 1.093622 \n",
"7230 2015_May_05_1714 p6 4 71 3 None NaN \n",
"7238 2015_May_05_1714 p6 4 79 probe 2 1.326432 \n",
"7255 2015_May_05_1714 p6 4 96 probe 2 1.526172 \n",
"7266 2015_May_05_1714 p6 4 107 probe 2 1.826804 \n",
"7282 2015_May_05_1714 p6 4 123 probe None NaN \n",
"7299 2015_May_05_1714 p6 4 140 probe 2 1.243549 \n",
"7325 2015_May_05_1714 p6 4 166 3 1 0.546759 \n",
"7332 2015_May_05_1714 p6 4 173 probe 2 1.010154 \n",
"7387 2015_May_05_1714 p6 4 228 3 1 0.446462 \n",
"7393 2015_May_05_1714 p6 4 234 probe 2 1.791671 \n",
"7412 2015_May_05_1714 p6 4 253 probe 2 1.426865 \n",
"7426 2015_May_05_1714 p6 4 267 probe 2 1.193801 \n",
"7426 2015_May_05_1714 p6 4 267 probe 2 1.193801 \n",
"7433 2015_May_05_1714 p6 4 274 probe 2 1.326754 \n",
"7444 2015_May_05_1714 p6 4 285 3 1 0.264375 \n",
"7453 2015_May_05_1714 p6 4 294 probe 1 1.609928 \n",
"7487 2015_May_05_1714 p6 4 328 probe 1 1.576855 \n",
"7501 2015_May_05_1714 p6 4 342 3 None NaN \n",
"7507 2015_May_05_1714 p6 4 348 probe 1 1.476554 \n",
"7535 2015_May_07_1713 p7 4 18 3 None NaN \n",
"7544 2015_May_07_1713 p7 4 27 probe None NaN \n",
"7544 2015_May_07_1713 p7 4 27 probe None NaN \n",
"7553 2015_May_07_1713 p7 4 36 probe 1 1.156551 \n",
"7578 2015_May_07_1713 p7 4 61 probe 1 1.502411 \n",
"7588 2015_May_07_1713 p7 4 71 3 1 0.361952 \n",
"7596 2015_May_07_1713 p7 4 79 probe 2 1.819094 \n",
"7613 2015_May_07_1713 p7 4 96 probe 1 1.071820 \n",
"7624 2015_May_07_1713 p7 4 107 probe 1 1.355526 \n",
"7640 2015_May_07_1713 p7 4 123 probe None NaN \n",
"7657 2015_May_07_1713 p7 4 140 probe None NaN \n",
"7683 2015_May_07_1713 p7 4 166 3 1 0.362191 \n",
"7690 2015_May_07_1713 p7 4 173 probe 1 0.974011 \n",
"7745 2015_May_07_1713 p7 4 228 3 1 0.461153 \n",
"7751 2015_May_07_1713 p7 4 234 probe 2 1.769309 \n",
"7770 2015_May_07_1713 p7 4 253 probe 1 0.559977 \n",
"7784 2015_May_07_1713 p7 4 267 probe 1 0.195401 \n",
"7784 2015_May_07_1713 p7 4 267 probe 1 0.195401 \n",
"7791 2015_May_07_1713 p7 4 274 probe 2 1.719825 \n",
"7802 2015_May_07_1713 p7 4 285 3 1 0.378578 \n",
"7811 2015_May_07_1713 p7 4 294 probe None NaN \n",
"7845 2015_May_07_1713 p7 4 328 probe 1 1.206647 \n",
"7859 2015_May_07_1713 p7 4 342 3 1 0.229481 \n",
"7865 2015_May_07_1713 p7 4 348 probe 2 1.852159 \n",
"7893 2015_May_13_1457 p8 4 18 3 None NaN \n",
"7902 2015_May_13_1457 p8 4 27 probe None NaN \n",
"7902 2015_May_13_1457 p8 4 27 probe None NaN \n",
"7911 2015_May_13_1457 p8 4 36 probe 1 1.033865 \n",
"7936 2015_May_13_1457 p8 4 61 probe 1 1.502331 \n",
"7946 2015_May_13_1457 p8 4 71 3 1 0.330246 \n",
"7954 2015_May_13_1457 p8 4 79 probe None NaN \n",
"7971 2015_May_13_1457 p8 4 96 probe 1 1.368674 \n",
"7982 2015_May_13_1457 p8 4 107 probe 1 0.955660 \n",
"7998 2015_May_13_1457 p8 4 123 probe None NaN \n",
"8015 2015_May_13_1457 p8 4 140 probe 1 1.689138 \n",
"8041 2015_May_13_1457 p8 4 166 3 1 0.290699 \n",
"8048 2015_May_13_1457 p8 4 173 probe None NaN \n",
"8103 2015_May_13_1457 p8 4 228 3 None NaN \n",
"8109 2015_May_13_1457 p8 4 234 probe 2 1.914630 \n",
"8128 2015_May_13_1457 p8 4 253 probe None NaN \n",
"8142 2015_May_13_1457 p8 4 267 probe 1 1.422218 \n",
"8142 2015_May_13_1457 p8 4 267 probe 1 1.422218 \n",
"8149 2015_May_13_1457 p8 4 274 probe 1 1.115739 \n",
"8160 2015_May_13_1457 p8 4 285 3 1 0.422437 \n",
"8169 2015_May_13_1457 p8 4 294 probe 1 1.754997 \n",
"8203 2015_May_13_1457 p8 4 328 probe 1 1.315499 \n",
"8217 2015_May_13_1457 p8 4 342 3 1 0.329828 \n",
"8223 2015_May_13_1457 p8 4 348 probe 1 1.688597 \n",
"8252 2015_May_14_1645 p9 4 18 3 1 0.376842 \n",
"8261 2015_May_14_1645 p9 4 27 probe 1 0.328005 \n",
"8261 2015_May_14_1645 p9 4 27 probe 1 0.328005 \n",
"8270 2015_May_14_1645 p9 4 36 probe 2 0.426879 \n",
"8295 2015_May_14_1645 p9 4 61 probe 1 0.460734 \n",
"8305 2015_May_14_1645 p9 4 71 3 None 0.346170 \n",
"8313 2015_May_14_1645 p9 4 79 probe 1 0.708947 \n",
"8330 2015_May_14_1645 p9 4 96 probe 2 0.394278 \n",
"8341 2015_May_14_1645 p9 4 107 probe 1 0.775213 \n",
"8357 2015_May_14_1645 p9 4 123 probe 1 0.724884 \n",
"8374 2015_May_14_1645 p9 4 140 probe 1 0.560348 \n",
"8400 2015_May_14_1645 p9 4 166 3 1 0.345020 \n",
"8407 2015_May_14_1645 p9 4 173 probe 2 0.393731 \n",
"8462 2015_May_14_1645 p9 4 228 3 1 0.560675 \n",
"8468 2015_May_14_1645 p9 4 234 probe 2 0.460590 \n",
"8487 2015_May_14_1645 p9 4 253 probe 1 0.825498 \n",
"8501 2015_May_14_1645 p9 4 267 probe 1 0.394185 \n",
"8501 2015_May_14_1645 p9 4 267 probe 1 0.394185 \n",
"8508 2015_May_14_1645 p9 4 274 probe 1 0.891125 \n",
"8519 2015_May_14_1645 p9 4 285 3 1 0.295507 \n",
"8528 2015_May_14_1645 p9 4 294 probe 2 0.460164 \n",
"8562 2015_May_14_1645 p9 4 328 probe 2 1.701698 \n",
"8576 2015_May_14_1645 p9 4 342 3 None 0.494250 \n",
"8582 2015_May_14_1645 p9 4 348 probe 1 0.460232 \n",
"\n",
" Hit CorrRej Commission Omission onset_time condition regressor \\\n",
"26 0 1 0 0 99.358579 target None \n",
"33 0 0 0 0 117.069746 probe Off \n",
"40 0 1 0 0 134.781343 target None \n",
"49 0 0 0 0 156.495714 probe On \n",
"84 0 0 0 0 233.945939 probe On \n",
"95 0 1 0 0 259.662911 target None \n",
"101 0 0 0 0 275.373307 probe Off \n",
"101 0 0 0 0 275.373307 probe Off \n",
"108 0 0 0 0 296.787266 probe Off \n",
"177 0 0 0 0 442.281807 probe Off \n",
"194 0 0 0 0 483.708966 probe Off \n",
"194 0 0 0 0 483.708966 probe Off \n",
"204 0 0 0 0 511.126637 probe Off \n",
"233 0 0 0 0 576.569563 probe Off \n",
"249 0 0 0 0 615.994981 probe On \n",
"269 0 1 0 0 659.723695 target None \n",
"277 0 0 0 0 679.436557 probe On \n",
"295 0 1 0 0 719.162479 target None \n",
"305 0 0 0 0 742.877791 probe On \n",
"322 0 0 0 0 784.304629 probe On \n",
"322 0 0 0 0 784.304629 probe On \n",
"329 0 0 0 0 805.718938 probe Off \n",
"329 0 0 0 0 805.718938 probe Off \n",
"338 0 0 0 0 831.135318 probe On \n",
"356 0 0 0 0 874.563747 probe Off \n",
"384 0 0 1 0 218.348220 target None \n",
"391 0 0 0 0 236.149755 probe On \n",
"398 0 0 1 0 253.951476 target None \n",
"407 0 0 0 0 275.783868 probe On \n",
"442 0 0 0 0 353.701560 probe On \n",
"453 0 0 1 0 379.563821 target None \n",
"459 0 0 0 0 395.349894 probe Off \n",
"459 0 0 0 0 395.349894 probe Off \n",
"466 0 0 0 0 416.848849 probe Off \n",
"535 0 0 0 0 563.275797 probe Off \n",
"552 0 0 0 0 604.924042 probe Off \n",
"552 0 0 0 0 604.924042 probe Off \n",
"562 0 0 0 0 632.467858 probe On \n",
"591 0 0 0 0 698.295912 probe On \n",
"607 0 0 0 0 737.929284 probe None \n",
"627 0 1 0 0 781.925711 target None \n",
"635 0 0 0 0 801.742319 probe On \n",
"653 0 1 0 0 841.708855 target None \n",
"663 0 0 0 0 865.555742 probe On \n",
"680 0 0 0 0 907.204156 probe On \n",
"680 0 0 0 0 907.204156 probe On \n",
"687 0 0 0 0 928.702694 probe Off \n",
"687 0 0 0 0 928.702694 probe Off \n",
"696 0 0 0 0 954.231595 probe Off \n",
"714 0 0 0 0 997.894817 probe Off \n",
"742 0 1 0 0 211.320855 target None \n",
"749 0 0 0 0 229.122536 probe None \n",
"756 0 0 1 0 246.924144 target None \n",
"765 0 0 0 0 268.756247 probe Off \n",
"800 0 0 0 0 346.674175 probe Off \n",
"811 0 1 0 0 372.535945 target None \n",
"817 0 0 0 0 388.322531 probe Off \n",
"817 0 0 0 0 388.322531 probe Off \n",
"824 0 0 0 0 409.821362 probe Off \n",
"893 0 0 0 0 556.248413 probe Off \n",
"910 0 0 0 0 597.896791 probe Off \n",
"910 0 0 0 0 597.896791 probe Off \n",
"920 0 0 0 0 625.440587 probe Off \n",
"949 0 0 0 0 691.268731 probe Off \n",
"965 0 0 0 0 730.902000 probe On \n",
"985 0 1 0 0 774.898469 target None \n",
"993 0 0 0 0 794.715127 probe Off \n",
"1011 0 0 1 0 834.681647 target None \n",
"1021 0 0 0 0 858.528320 probe Off \n",
"1038 0 0 0 0 900.177073 probe Off \n",
"1038 0 0 0 0 900.177073 probe Off \n",
"1045 0 0 0 0 921.675948 probe Off \n",
"1045 0 0 0 0 921.675948 probe Off \n",
"1054 0 0 0 0 947.204337 probe None \n",
"1072 0 0 0 0 990.868216 probe Off \n",
"1100 0 1 0 0 180.702501 target None \n",
"1107 0 0 0 0 198.449751 probe Off \n",
"1114 0 1 0 0 216.196796 target None \n",
"1123 0 0 0 0 237.938181 probe On \n",
"1158 0 0 0 0 315.538723 probe Off \n",
"1169 0 0 1 0 341.306176 target None \n",
"1175 0 0 0 0 357.048508 probe On \n",
"1175 0 0 0 0 357.048508 probe On \n",
"1182 0 0 0 0 378.507595 probe None \n",
"1251 0 0 0 0 524.280609 probe None \n",
"1268 0 0 0 0 565.790560 probe On \n",
"1268 0 0 0 0 565.790560 probe On \n",
"1278 0 0 0 0 593.264731 probe Off \n",
"1307 0 0 0 0 658.835485 probe Off \n",
"1323 0 0 0 0 698.339974 probe On \n",
"1343 0 0 1 0 742.153029 target None \n",
"1351 0 0 0 0 761.905184 probe Off \n",
"1369 0 1 0 0 801.708565 target None \n",
"1379 0 0 0 0 825.470929 probe None \n",
"1396 0 0 0 0 866.980445 probe None \n",
"1396 0 0 0 0 866.980445 probe None \n",
"1403 0 0 0 0 888.423265 probe On \n",
"1403 0 0 0 0 888.423265 probe On \n",
"1412 0 0 0 0 913.892343 probe None \n",
"1430 0 0 0 0 957.407456 probe On \n",
"... ... ... ... ... ... ... ... \n",
"7095 0 0 0 0 868.268885 probe Off \n",
"7129 0 0 0 0 944.171715 probe On \n",
"7143 0 0 1 0 976.078125 target None \n",
"7149 0 0 0 0 991.864803 probe Off \n",
"7177 0 1 0 0 234.052561 target None \n",
"7186 0 0 0 0 255.884178 probe On \n",
"7186 0 0 0 0 255.884178 probe On \n",
"7195 0 0 0 0 281.412532 probe None \n",
"7220 0 0 0 0 339.181179 probe On \n",
"7230 0 1 0 0 363.027812 target None \n",
"7238 0 0 0 0 382.844480 probe Off \n",
"7255 0 0 0 0 424.492661 probe Off \n",
"7266 0 0 0 0 454.051570 probe Off \n",
"7282 0 0 0 0 493.684849 probe None \n",
"7299 0 0 0 0 535.333656 probe Off \n",
"7325 0 0 1 0 591.419526 target None \n",
"7332 0 0 0 0 609.221169 probe Off \n",
"7387 0 0 1 0 723.742285 target None \n",
"7393 0 0 0 0 739.529363 probe Off \n",
"7412 0 0 0 0 785.207097 probe Off \n",
"7426 0 0 0 0 820.811037 probe Off \n",
"7426 0 0 0 0 820.811037 probe Off \n",
"7433 0 0 0 0 842.309890 probe Off \n",
"7444 0 0 1 0 868.170966 target None \n",
"7453 0 0 0 0 890.002862 probe On \n",
"7487 0 0 0 0 965.906200 probe On \n",
"7501 0 1 0 0 997.812780 target None \n",
"7507 0 0 0 0 1013.599766 probe On \n",
"7535 0 1 0 0 129.797872 target None \n",
"7544 0 0 0 0 151.555154 probe None \n",
"7544 0 0 0 0 151.555154 probe None \n",
"7553 0 0 0 0 177.024202 probe On \n",
"7578 0 0 0 0 234.574668 probe On \n",
"7588 0 0 1 0 258.337093 target None \n",
"7596 0 0 0 0 278.089316 probe Off \n",
"7613 0 0 0 0 319.599371 probe On \n",
"7624 0 0 0 0 349.078499 probe On \n",
"7640 0 0 0 0 388.583470 probe None \n",
"7657 0 0 0 0 430.093028 probe None \n",
"7683 0 0 1 0 485.936436 target None \n",
"7690 0 0 0 0 503.684029 probe On \n",
"7745 0 0 1 0 617.674318 target None \n",
"7751 0 0 0 0 633.416432 probe Off \n",
"7770 0 0 0 0 678.936130 probe On \n",
"7784 0 0 0 0 714.431020 probe On \n",
"7784 0 0 0 0 714.431020 probe On \n",
"7791 0 0 0 0 735.889870 probe Off \n",
"7802 0 0 1 0 761.657423 target None \n",
"7811 0 0 0 0 783.415089 probe None \n",
"7845 0 0 0 0 859.010669 probe On \n",
"7859 0 0 1 0 890.793312 target None \n",
"7865 0 0 0 0 906.535875 probe Off \n",
"7893 0 1 0 0 97.694205 target None \n",
"7902 0 0 0 0 119.489185 probe None \n",
"7902 0 0 0 0 119.489185 probe None \n",
"7911 0 0 0 0 144.974223 probe On \n",
"7936 0 0 0 0 202.646259 probe On \n",
"7946 0 0 1 0 226.452913 target None \n",
"7954 0 0 0 0 246.236183 probe None \n",
"7971 0 0 0 0 287.814454 probe On \n",
"7982 0 0 0 0 317.323479 probe On \n",
"7998 0 0 0 0 356.890012 probe None \n",
"8015 0 0 0 0 398.468923 probe On \n",
"8041 0 0 1 0 454.461981 target None \n",
"8048 0 0 0 0 472.233617 probe None \n",
"8103 0 1 0 0 586.564554 target None \n",
"8109 0 0 0 0 602.325250 probe Off \n",
"8128 0 0 0 0 647.926592 probe None \n",
"8142 0 0 0 0 683.469970 probe On \n",
"8142 0 0 0 0 683.469970 probe On \n",
"8149 0 0 0 0 704.931909 probe On \n",
"8160 0 0 1 0 730.750204 target None \n",
"8169 0 0 0 0 752.545221 probe On \n",
"8203 0 0 0 0 828.322152 probe On \n",
"8217 0 0 1 0 860.175373 target None \n",
"8223 0 0 0 0 875.935426 probe On \n",
"8252 0 0 1 0 125.618168 target None \n",
"8261 0 0 0 0 147.375450 probe On \n",
"8261 0 0 0 0 147.375450 probe On \n",
"8270 0 0 0 0 172.845006 probe Off \n",
"8295 0 0 0 0 230.394995 probe On \n",
"8305 0 1 0 0 254.157225 target None \n",
"8313 0 0 0 0 273.909937 probe On \n",
"8330 0 0 0 0 315.419475 probe Off \n",
"8341 0 0 0 0 344.898879 probe On \n",
"8357 0 0 0 0 384.403509 probe On \n",
"8374 0 0 0 0 425.913134 probe On \n",
"8400 0 0 1 0 481.756963 target None \n",
"8407 0 0 0 0 499.504005 probe Off \n",
"8462 0 0 1 0 613.494302 target None \n",
"8468 0 0 0 0 629.236334 probe Off \n",
"8487 0 0 0 0 674.756237 probe On \n",
"8501 0 0 0 0 710.250856 probe On \n",
"8501 0 0 0 0 710.250856 probe On \n",
"8508 0 0 0 0 731.709971 probe On \n",
"8519 0 0 1 0 757.477587 target None \n",
"8528 0 0 0 0 779.234882 probe Off \n",
"8562 0 0 0 0 854.830477 probe Off \n",
"8576 0 1 0 0 886.613417 target None \n",
"8582 0 0 0 0 902.355616 probe On \n",
"\n",
" targ \n",
"26 corr_targ \n",
"33 NaN \n",
"40 corr_targ \n",
"49 NaN \n",
"84 NaN \n",
"95 corr_targ \n",
"101 NaN \n",
"101 NaN \n",
"108 NaN \n",
"177 NaN \n",
"194 NaN \n",
"194 NaN \n",
"204 NaN \n",
"233 NaN \n",
"249 NaN \n",
"269 corr_targ \n",
"277 NaN \n",
"295 corr_targ \n",
"305 NaN \n",
"322 NaN \n",
"322 NaN \n",
"329 NaN \n",
"329 NaN \n",
"338 NaN \n",
"356 NaN \n",
"384 incorr_targ \n",
"391 NaN \n",
"398 incorr_targ \n",
"407 NaN \n",
"442 NaN \n",
"453 incorr_targ \n",
"459 NaN \n",
"459 NaN \n",
"466 NaN \n",
"535 NaN \n",
"552 NaN \n",
"552 NaN \n",
"562 NaN \n",
"591 NaN \n",
"607 NaN \n",
"627 corr_targ \n",
"635 NaN \n",
"653 corr_targ \n",
"663 NaN \n",
"680 NaN \n",
"680 NaN \n",
"687 NaN \n",
"687 NaN \n",
"696 NaN \n",
"714 NaN \n",
"742 corr_targ \n",
"749 NaN \n",
"756 incorr_targ \n",
"765 NaN \n",
"800 NaN \n",
"811 corr_targ \n",
"817 NaN \n",
"817 NaN \n",
"824 NaN \n",
"893 NaN \n",
"910 NaN \n",
"910 NaN \n",
"920 NaN \n",
"949 NaN \n",
"965 NaN \n",
"985 corr_targ \n",
"993 NaN \n",
"1011 incorr_targ \n",
"1021 NaN \n",
"1038 NaN \n",
"1038 NaN \n",
"1045 NaN \n",
"1045 NaN \n",
"1054 NaN \n",
"1072 NaN \n",
"1100 corr_targ \n",
"1107 NaN \n",
"1114 corr_targ \n",
"1123 NaN \n",
"1158 NaN \n",
"1169 incorr_targ \n",
"1175 NaN \n",
"1175 NaN \n",
"1182 NaN \n",
"1251 NaN \n",
"1268 NaN \n",
"1268 NaN \n",
"1278 NaN \n",
"1307 NaN \n",
"1323 NaN \n",
"1343 incorr_targ \n",
"1351 NaN \n",
"1369 corr_targ \n",
"1379 NaN \n",
"1396 NaN \n",
"1396 NaN \n",
"1403 NaN \n",
"1403 NaN \n",
"1412 NaN \n",
"1430 NaN \n",
"... ... \n",
"7095 NaN \n",
"7129 NaN \n",
"7143 incorr_targ \n",
"7149 NaN \n",
"7177 corr_targ \n",
"7186 NaN \n",
"7186 NaN \n",
"7195 NaN \n",
"7220 NaN \n",
"7230 corr_targ \n",
"7238 NaN \n",
"7255 NaN \n",
"7266 NaN \n",
"7282 NaN \n",
"7299 NaN \n",
"7325 incorr_targ \n",
"7332 NaN \n",
"7387 incorr_targ \n",
"7393 NaN \n",
"7412 NaN \n",
"7426 NaN \n",
"7426 NaN \n",
"7433 NaN \n",
"7444 incorr_targ \n",
"7453 NaN \n",
"7487 NaN \n",
"7501 corr_targ \n",
"7507 NaN \n",
"7535 corr_targ \n",
"7544 NaN \n",
"7544 NaN \n",
"7553 NaN \n",
"7578 NaN \n",
"7588 incorr_targ \n",
"7596 NaN \n",
"7613 NaN \n",
"7624 NaN \n",
"7640 NaN \n",
"7657 NaN \n",
"7683 incorr_targ \n",
"7690 NaN \n",
"7745 incorr_targ \n",
"7751 NaN \n",
"7770 NaN \n",
"7784 NaN \n",
"7784 NaN \n",
"7791 NaN \n",
"7802 incorr_targ \n",
"7811 NaN \n",
"7845 NaN \n",
"7859 incorr_targ \n",
"7865 NaN \n",
"7893 corr_targ \n",
"7902 NaN \n",
"7902 NaN \n",
"7911 NaN \n",
"7936 NaN \n",
"7946 incorr_targ \n",
"7954 NaN \n",
"7971 NaN \n",
"7982 NaN \n",
"7998 NaN \n",
"8015 NaN \n",
"8041 incorr_targ \n",
"8048 NaN \n",
"8103 corr_targ \n",
"8109 NaN \n",
"8128 NaN \n",
"8142 NaN \n",
"8142 NaN \n",
"8149 NaN \n",
"8160 incorr_targ \n",
"8169 NaN \n",
"8203 NaN \n",
"8217 incorr_targ \n",
"8223 NaN \n",
"8252 incorr_targ \n",
"8261 NaN \n",
"8261 NaN \n",
"8270 NaN \n",
"8295 NaN \n",
"8305 corr_targ \n",
"8313 NaN \n",
"8330 NaN \n",
"8341 NaN \n",
"8357 NaN \n",
"8374 NaN \n",
"8400 incorr_targ \n",
"8407 NaN \n",
"8462 incorr_targ \n",
"8468 NaN \n",
"8487 NaN \n",
"8501 NaN \n",
"8501 NaN \n",
"8508 NaN \n",
"8519 incorr_targ \n",
"8528 NaN \n",
"8562 NaN \n",
"8576 corr_targ \n",
"8582 NaN \n",
"\n",
"[588 rows x 15 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Pre-Intervals AVG: Thought Probes\n",
"#Pre-RT Avg\n",
"#Pre-Thought Probe TASK AVG RT\n",
"#Percent Missed NonTargets: Pre-Thought\n",
"#Percent Missed Targets: Pre-Thought OFF\n",
"\n",
"# 1. How many targets occured within 10 trials of 'OFF probe'?\n",
"# 2. How many of those targets were MISSED?\n",
"# 3. \n",
" \n",
"indexlistp = df.Stim[df.condition == 'probe2'].index.tolist()\n",
"pre_probe = pd.DataFrame()\n",
"for idx in indexlistp:\n",
" for x in range(idx-11,idx,):\n",
" pre_probe = pre_probe.append(df.iloc[[x],:])\n",
"\n",
"#pre_probe\n",
"\n",
"targ_probe = pre_probe[pre_probe['condition'].isin(['target', 'probe'])]\n",
"targ_probe[targ_probe['condition'].isin(['target', 'probe'])]\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb09e8e7e90>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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/+l+llOVJUlXVg0l+nq6HdK/ZnAIXLly4Od1hq9Pe3t7oErao9vb2jBgxotFlMMBWbePm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\nAADHiAIfAACOkf8CC/tpwLsr5RAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb09e7e68d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#pre_probe = pre_probe[pre_probe.condition != 'probe2'] \n",
"piv_preprobe = pd.pivot_table(pre_probe, values = ['Hit','RT'], rows = ['participant'], aggfunc=np.mean)\n",
"piv_preprobe.reindex()\n",
"piv_preprobe.columns = ['Hit', 'preProbe_RT']\n",
"piv_preprobe[['preProbe_RT']].T.plot(kind = 'bar')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>date</th>\n",
" <th>participant</th>\n",
" <th>schedule</th>\n",
" <th>Trial</th>\n",
" <th>Stim</th>\n",
" <th>Response</th>\n",
" <th>RT</th>\n",
" <th>Hit</th>\n",
" <th>CorrRej</th>\n",
" <th>Commission</th>\n",
" <th>Omission</th>\n",
" <th>onset_time</th>\n",
" <th>condition</th>\n",
" <th>regressor</th>\n",
" <th>targ</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 0.793817</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 47.324640</td>\n",
" <td> nontarget</td>\n",
" <td> None</td>\n",
" <td> corr_nontarg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2014_Nov_11_1015</td>\n",
" <td> p3</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.930410</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
"
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