Skip to content

Instantly share code, notes, and snippets.

@meli-lewis

meli-lewis/RP_Hajcak_Foti.ipynb

Created March 19, 2015 02:21
Show Gist options
  • Save meli-lewis/23cc1141af754048cc6e to your computer and use it in GitHub Desktop.
Save meli-lewis/23cc1141af754048cc6e to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
RP_Hajcak_Foti.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:bb9dfc17022518d1fbe09ddcb5b5f12826934c3f07676629d9973cc6298d04b8"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import bokeh.charts as bc\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import requests\n",
"import qgrid\n",
"import seaborn as sns\n",
"from IPython.display import Image\n",
"from scipy.stats import pearsonr\n",
"\n",
"%matplotlib inline\n",
"qgrid.nbinstall()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The purpose of this notebook is to document data analysis for a [Reproducibility Study](https://osf.io/ezcuj/wiki/home/?_ga=1.257932747.1792380294.1420926336) conducted in collaboration with the [Center for Open Science](https://cos.io) and fulfillment of my [undergraduate thesis](https://osf.io/3k4uy/) at Reed College.\n",
"\n",
"###The original article was [\"Errors are aversive: defensive motivation and the error-related negativity.\"](http://www.ncbi.nlm.nih.gov/pubmed/18271855)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = pd.read_csv(\"rp.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>participant</th>\n",
" <th>gender</th>\n",
" <th>ERN</th>\n",
" <th>EPS</th>\n",
" <th>errors</th>\n",
" <th>startle_type</th>\n",
" <th>startle</th>\n",
" <th>startle_peak</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>-21.910744</td>\n",
" <td> 26.391241</td>\n",
" <td> 20</td>\n",
" <td> error</td>\n",
" <td> 61.934547</td>\n",
" <td> 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> -1.841113</td>\n",
" <td> 9.538078</td>\n",
" <td> 41</td>\n",
" <td> error</td>\n",
" <td> 44.829327</td>\n",
" <td> 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" <td> -8.708967</td>\n",
" <td> 5.001650</td>\n",
" <td> 82</td>\n",
" <td> error</td>\n",
" <td> 13.093092</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> -8.348466</td>\n",
" <td> -2.151122</td>\n",
" <td> 19</td>\n",
" <td> error</td>\n",
" <td> 7.380994</td>\n",
" <td> 61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 7</td>\n",
" <td> 0</td>\n",
" <td> -2.914750</td>\n",
" <td> 5.765736</td>\n",
" <td> 63</td>\n",
" <td> error</td>\n",
" <td> 37.685368</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> -3.072484</td>\n",
" <td> 12.568234</td>\n",
" <td> 19</td>\n",
" <td> error</td>\n",
" <td> 60.816753</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> -1.936810</td>\n",
" <td> -4.323942</td>\n",
" <td> 33</td>\n",
" <td> error</td>\n",
" <td> 16.031044</td>\n",
" <td> 65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 11</td>\n",
" <td> 0</td>\n",
" <td> -4.286741</td>\n",
" <td> 2.355393</td>\n",
" <td> 51</td>\n",
" <td> error</td>\n",
" <td> 3.954227</td>\n",
" <td> 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 12</td>\n",
" <td> 0</td>\n",
" <td> -7.835635</td>\n",
" <td> -1.885065</td>\n",
" <td> 7</td>\n",
" <td> error</td>\n",
" <td> 5.671492</td>\n",
" <td> 61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 13</td>\n",
" <td> 0</td>\n",
" <td> -2.167574</td>\n",
" <td> 1.310271</td>\n",
" <td> 53</td>\n",
" <td> error</td>\n",
" <td> 5.057071</td>\n",
" <td> 72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> 14</td>\n",
" <td> 1</td>\n",
" <td> -8.543142</td>\n",
" <td> 0.952917</td>\n",
" <td> 51</td>\n",
" <td> error</td>\n",
" <td> 42.697914</td>\n",
" <td> 57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> 15</td>\n",
" <td> 1</td>\n",
" <td> -7.334250</td>\n",
" <td> -0.021293</td>\n",
" <td> 18</td>\n",
" <td> error</td>\n",
" <td> 2.971516</td>\n",
" <td> 66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> 16</td>\n",
" <td> 1</td>\n",
" <td> -1.243219</td>\n",
" <td> 6.085452</td>\n",
" <td> 36</td>\n",
" <td> error</td>\n",
" <td> 10.866097</td>\n",
" <td> 66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> 17</td>\n",
" <td> 1</td>\n",
" <td> -8.005402</td>\n",
" <td> 10.339608</td>\n",
" <td> 26</td>\n",
" <td> error</td>\n",
" <td> 18.022871</td>\n",
" <td> 57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> 19</td>\n",
" <td> 1</td>\n",
" <td>-17.255142</td>\n",
" <td> -0.908369</td>\n",
" <td> 12</td>\n",
" <td> error</td>\n",
" <td> 5.068305</td>\n",
" <td> 60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> -5.665667</td>\n",
" <td> 1.169279</td>\n",
" <td> 63</td>\n",
" <td> error</td>\n",
" <td> 13.973429</td>\n",
" <td> 48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> 22</td>\n",
" <td> 0</td>\n",
" <td>-12.539272</td>\n",
" <td> 12.214434</td>\n",
" <td> 14</td>\n",
" <td> error</td>\n",
" <td> 34.683296</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> 23</td>\n",
" <td> 0</td>\n",
" <td> -2.954746</td>\n",
" <td> 0.895508</td>\n",
" <td> 57</td>\n",
" <td> error</td>\n",
" <td> 7.488783</td>\n",
" <td> 59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> 24</td>\n",
" <td> 1</td>\n",
" <td> -5.850971</td>\n",
" <td> 11.673366</td>\n",
" <td> 51</td>\n",
" <td> error</td>\n",
" <td> 40.177113</td>\n",
" <td> 81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 25</td>\n",
" <td> 1</td>\n",
" <td> -3.949103</td>\n",
" <td> 2.384780</td>\n",
" <td> 29</td>\n",
" <td> error</td>\n",
" <td> 39.782413</td>\n",
" <td> 60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" <td> 4.124257</td>\n",
" <td> 2.746532</td>\n",
" <td> 13</td>\n",
" <td> error</td>\n",
" <td> 16.575405</td>\n",
" <td> 66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> 27</td>\n",
" <td> 1</td>\n",
" <td> -5.184582</td>\n",
" <td> 24.002224</td>\n",
" <td> 31</td>\n",
" <td> error</td>\n",
" <td> 57.307110</td>\n",
" <td> 56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> 29</td>\n",
" <td> 1</td>\n",
" <td> -2.147012</td>\n",
" <td> 11.441307</td>\n",
" <td> 44</td>\n",
" <td> error</td>\n",
" <td> 21.896090</td>\n",
" <td> 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 30</td>\n",
" <td> 1</td>\n",
" <td> -8.587319</td>\n",
" <td> 3.745202</td>\n",
" <td> 20</td>\n",
" <td> error</td>\n",
" <td> 5.890623</td>\n",
" <td> 57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> 31</td>\n",
" <td> 1</td>\n",
" <td>-11.941545</td>\n",
" <td> 3.479194</td>\n",
" <td> 33</td>\n",
" <td> error</td>\n",
" <td> 33.076218</td>\n",
" <td> 67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 32</td>\n",
" <td> 0</td>\n",
" <td>-11.796526</td>\n",
" <td> 6.872339</td>\n",
" <td> 31</td>\n",
" <td> error</td>\n",
" <td> 10.741505</td>\n",
" <td> 66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 33</td>\n",
" <td> 0</td>\n",
" <td> -3.789471</td>\n",
" <td> -1.269627</td>\n",
" <td> 80</td>\n",
" <td> error</td>\n",
" <td> 23.902311</td>\n",
" <td> 61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 34</td>\n",
" <td> 1</td>\n",
" <td> -1.806187</td>\n",
" <td> -0.594354</td>\n",
" <td> 24</td>\n",
" <td> error</td>\n",
" <td> 1.697242</td>\n",
" <td> 66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 35</td>\n",
" <td> 1</td>\n",
" <td> -8.525034</td>\n",
" <td> 3.001086</td>\n",
" <td> 32</td>\n",
" <td> error</td>\n",
" <td> 46.812138</td>\n",
" <td> 59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 36</td>\n",
" <td> 0</td>\n",
" <td> -8.062650</td>\n",
" <td> -2.409631</td>\n",
" <td> 31</td>\n",
" <td> error</td>\n",
" <td> 80.058876</td>\n",
" <td> 65</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",
" </tr>\n",
" <tr>\n",
" <th>96 </th>\n",
" <td> 16</td>\n",
" <td> 1</td>\n",
" <td> -1.243219</td>\n",
" <td> 6.085452</td>\n",
" <td> 36</td>\n",
" <td> unpred</td>\n",
" <td> 4.587745</td>\n",
" <td> 62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97 </th>\n",
" <td> 17</td>\n",
" <td> 1</td>\n",
" <td> -8.005402</td>\n",
" <td> 10.339608</td>\n",
" <td> 26</td>\n",
" <td> unpred</td>\n",
" <td> 8.055993</td>\n",
" <td> 64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98 </th>\n",
" <td> 19</td>\n",
" <td> 1</td>\n",
" <td>-17.255142</td>\n",
" <td> -0.908369</td>\n",
" <td> 12</td>\n",
" <td> unpred</td>\n",
" <td> 6.123931</td>\n",
" <td> 57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99 </th>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> -5.665667</td>\n",
" <td> 1.169279</td>\n",
" <td> 63</td>\n",
" <td> unpred</td>\n",
" <td> NaN</td>\n",
" <td> ???</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td> 22</td>\n",
" <td> 0</td>\n",
" <td>-12.539272</td>\n",
" <td> 12.214434</td>\n",
" <td> 14</td>\n",
" <td> unpred</td>\n",
" <td> 32.635765</td>\n",
" <td> 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td> 23</td>\n",
" <td> 0</td>\n",
" <td> -2.954746</td>\n",
" <td> 0.895508</td>\n",
" <td> 57</td>\n",
" <td> unpred</td>\n",
" <td> 6.668186</td>\n",
" <td> 61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td> 24</td>\n",
" <td> 1</td>\n",
" <td> -5.850971</td>\n",
" <td> 11.673366</td>\n",
" <td> 51</td>\n",
" <td> unpred</td>\n",
" <td> 36.772022</td>\n",
" <td> 83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td> 25</td>\n",
" <td> 1</td>\n",
" <td> -3.949103</td>\n",
" <td> 2.384780</td>\n",
" <td> 29</td>\n",
" <td> unpred</td>\n",
" <td> 39.538456</td>\n",
" <td> 56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" <td> 4.124257</td>\n",
" <td> 2.746532</td>\n",
" <td> 13</td>\n",
" <td> unpred</td>\n",
" <td> 13.691594</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td> 27</td>\n",
" <td> 1</td>\n",
" <td> -5.184582</td>\n",
" <td> 24.002224</td>\n",
" <td> 31</td>\n",
" <td> unpred</td>\n",
" <td> 43.455639</td>\n",
" <td> 60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td> 29</td>\n",
" <td> 1</td>\n",
" <td> -2.147012</td>\n",
" <td> 11.441307</td>\n",
" <td> 44</td>\n",
" <td> unpred</td>\n",
" <td> 8.144090</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td> 30</td>\n",
" <td> 1</td>\n",
" <td> -8.587319</td>\n",
" <td> 3.745202</td>\n",
" <td> 20</td>\n",
" <td> unpred</td>\n",
" <td> 2.501384</td>\n",
" <td> 67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td> 31</td>\n",
" <td> 1</td>\n",
" <td>-11.941545</td>\n",
" <td> 3.479194</td>\n",
" <td> 33</td>\n",
" <td> unpred</td>\n",
" <td> 27.388660</td>\n",
" <td> 70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td> 32</td>\n",
" <td> 0</td>\n",
" <td>-11.796526</td>\n",
" <td> 6.872339</td>\n",
" <td> 31</td>\n",
" <td> unpred</td>\n",
" <td> 4.894676</td>\n",
" <td> 65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td> 33</td>\n",
" <td> 0</td>\n",
" <td> -3.789471</td>\n",
" <td> -1.269627</td>\n",
" <td> 80</td>\n",
" <td> unpred</td>\n",
" <td> 28.796970</td>\n",
" <td> 77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td> 34</td>\n",
" <td> 1</td>\n",
" <td> -1.806187</td>\n",
" <td> -0.594354</td>\n",
" <td> 24</td>\n",
" <td> unpred</td>\n",
" <td> 1.694895</td>\n",
" <td> 69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td> 35</td>\n",
" <td> 1</td>\n",
" <td> -8.525034</td>\n",
" <td> 3.001086</td>\n",
" <td> 32</td>\n",
" <td> unpred</td>\n",
" <td> 43.067608</td>\n",
" <td> 64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td> 36</td>\n",
" <td> 0</td>\n",
" <td> -8.062650</td>\n",
" <td> -2.409631</td>\n",
" <td> 31</td>\n",
" <td> unpred</td>\n",
" <td> 86.637543</td>\n",
" <td> 61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td> 38</td>\n",
" <td> 1</td>\n",
" <td> -6.804902</td>\n",
" <td>-12.884763</td>\n",
" <td> 46</td>\n",
" <td> unpred</td>\n",
" <td> 21.586960</td>\n",
" <td> 64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td> 39</td>\n",
" <td> 1</td>\n",
" <td> -6.923191</td>\n",
" <td> -0.523201</td>\n",
" <td> 42</td>\n",
" <td> unpred</td>\n",
" <td> 21.056841</td>\n",
" <td> 71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> 41</td>\n",
" <td> 1</td>\n",
" <td> -2.584587</td>\n",
" <td>-11.953424</td>\n",
" <td> 36</td>\n",
" <td> unpred</td>\n",
" <td> 20.585722</td>\n",
" <td> 83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td> 42</td>\n",
" <td> 0</td>\n",
" <td> -7.002459</td>\n",
" <td> -1.633260</td>\n",
" <td> 32</td>\n",
" <td> unpred</td>\n",
" <td> 8.519885</td>\n",
" <td> 65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td> 43</td>\n",
" <td> 0</td>\n",
" <td> 0.650213</td>\n",
" <td> 8.476102</td>\n",
" <td> 22</td>\n",
" <td> unpred</td>\n",
" <td> 36.958660</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td> 45</td>\n",
" <td> 0</td>\n",
" <td> -6.900466</td>\n",
" <td> 4.112835</td>\n",
" <td> 17</td>\n",
" <td> unpred</td>\n",
" <td> 10.976958</td>\n",
" <td> 60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td> 46</td>\n",
" <td> 1</td>\n",
" <td> -7.699009</td>\n",
" <td> 9.863975</td>\n",
" <td> 35</td>\n",
" <td> unpred</td>\n",
" <td> 56.319504</td>\n",
" <td> 59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td> 47</td>\n",
" <td> 0</td>\n",
" <td> -8.745496</td>\n",
" <td> -0.842558</td>\n",
" <td> 64</td>\n",
" <td> unpred</td>\n",
" <td> 7.276809</td>\n",
" <td> 65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td> 48</td>\n",
" <td> 0</td>\n",
" <td> -8.689076</td>\n",
" <td> -6.583182</td>\n",
" <td> 18</td>\n",
" <td> unpred</td>\n",
" <td> 8.437429</td>\n",
" <td> 81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td> 49</td>\n",
" <td> 1</td>\n",
" <td> -1.894857</td>\n",
" <td>-12.443699</td>\n",
" <td> 12</td>\n",
" <td> unpred</td>\n",
" <td> 43.247162</td>\n",
" <td> 60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> 50</td>\n",
" <td> 0</td>\n",
" <td>-12.713764</td>\n",
" <td> 6.240077</td>\n",
" <td> 41</td>\n",
" <td> unpred</td>\n",
" <td> 3.885637</td>\n",
" <td> 71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td> 51</td>\n",
" <td> 0</td>\n",
" <td> -6.939233</td>\n",
" <td> 4.418236</td>\n",
" <td> 39</td>\n",
" <td> unpred</td>\n",
" <td> 23.882261</td>\n",
" <td> 69</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>126 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
" participant gender ERN EPS errors startle_type \\\n",
"0 1 1 -21.910744 26.391241 20 error \n",
"1 2 0 -1.841113 9.538078 41 error \n",
"2 5 0 -8.708967 5.001650 82 error \n",
"3 6 1 -8.348466 -2.151122 19 error \n",
"4 7 0 -2.914750 5.765736 63 error \n",
"5 8 1 -3.072484 12.568234 19 error \n",
"6 9 1 -1.936810 -4.323942 33 error \n",
"7 11 0 -4.286741 2.355393 51 error \n",
"8 12 0 -7.835635 -1.885065 7 error \n",
"9 13 0 -2.167574 1.310271 53 error \n",
"10 14 1 -8.543142 0.952917 51 error \n",
"11 15 1 -7.334250 -0.021293 18 error \n",
"12 16 1 -1.243219 6.085452 36 error \n",
"13 17 1 -8.005402 10.339608 26 error \n",
"14 19 1 -17.255142 -0.908369 12 error \n",
"15 21 1 -5.665667 1.169279 63 error \n",
"16 22 0 -12.539272 12.214434 14 error \n",
"17 23 0 -2.954746 0.895508 57 error \n",
"18 24 1 -5.850971 11.673366 51 error \n",
"19 25 1 -3.949103 2.384780 29 error \n",
"20 26 0 4.124257 2.746532 13 error \n",
"21 27 1 -5.184582 24.002224 31 error \n",
"22 29 1 -2.147012 11.441307 44 error \n",
"23 30 1 -8.587319 3.745202 20 error \n",
"24 31 1 -11.941545 3.479194 33 error \n",
"25 32 0 -11.796526 6.872339 31 error \n",
"26 33 0 -3.789471 -1.269627 80 error \n",
"27 34 1 -1.806187 -0.594354 24 error \n",
"28 35 1 -8.525034 3.001086 32 error \n",
"29 36 0 -8.062650 -2.409631 31 error \n",
".. ... ... ... ... ... ... \n",
"96 16 1 -1.243219 6.085452 36 unpred \n",
"97 17 1 -8.005402 10.339608 26 unpred \n",
"98 19 1 -17.255142 -0.908369 12 unpred \n",
"99 21 1 -5.665667 1.169279 63 unpred \n",
"100 22 0 -12.539272 12.214434 14 unpred \n",
"101 23 0 -2.954746 0.895508 57 unpred \n",
"102 24 1 -5.850971 11.673366 51 unpred \n",
"103 25 1 -3.949103 2.384780 29 unpred \n",
"104 26 0 4.124257 2.746532 13 unpred \n",
"105 27 1 -5.184582 24.002224 31 unpred \n",
"106 29 1 -2.147012 11.441307 44 unpred \n",
"107 30 1 -8.587319 3.745202 20 unpred \n",
"108 31 1 -11.941545 3.479194 33 unpred \n",
"109 32 0 -11.796526 6.872339 31 unpred \n",
"110 33 0 -3.789471 -1.269627 80 unpred \n",
"111 34 1 -1.806187 -0.594354 24 unpred \n",
"112 35 1 -8.525034 3.001086 32 unpred \n",
"113 36 0 -8.062650 -2.409631 31 unpred \n",
"114 38 1 -6.804902 -12.884763 46 unpred \n",
"115 39 1 -6.923191 -0.523201 42 unpred \n",
"116 41 1 -2.584587 -11.953424 36 unpred \n",
"117 42 0 -7.002459 -1.633260 32 unpred \n",
"118 43 0 0.650213 8.476102 22 unpred \n",
"119 45 0 -6.900466 4.112835 17 unpred \n",
"120 46 1 -7.699009 9.863975 35 unpred \n",
"121 47 0 -8.745496 -0.842558 64 unpred \n",
"122 48 0 -8.689076 -6.583182 18 unpred \n",
"123 49 1 -1.894857 -12.443699 12 unpred \n",
"124 50 0 -12.713764 6.240077 41 unpred \n",
"125 51 0 -6.939233 4.418236 39 unpred \n",
"\n",
" startle startle_peak \n",
"0 61.934547 63 \n",
"1 44.829327 63 \n",
"2 13.093092 58 \n",
"3 7.380994 61 \n",
"4 37.685368 58 \n",
"5 60.816753 58 \n",
"6 16.031044 65 \n",
"7 3.954227 63 \n",
"8 5.671492 61 \n",
"9 5.057071 72 \n",
"10 42.697914 57 \n",
"11 2.971516 66 \n",
"12 10.866097 66 \n",
"13 18.022871 57 \n",
"14 5.068305 60 \n",
"15 13.973429 48 \n",
"16 34.683296 58 \n",
"17 7.488783 59 \n",
"18 40.177113 81 \n",
"19 39.782413 60 \n",
"20 16.575405 66 \n",
"21 57.307110 56 \n",
"22 21.896090 63 \n",
"23 5.890623 57 \n",
"24 33.076218 67 \n",
"25 10.741505 66 \n",
"26 23.902311 61 \n",
"27 1.697242 66 \n",
"28 46.812138 59 \n",
"29 80.058876 65 \n",
".. ... ... \n",
"96 4.587745 62 \n",
"97 8.055993 64 \n",
"98 6.123931 57 \n",
"99 NaN ??? \n",
"100 32.635765 63 \n",
"101 6.668186 61 \n",
"102 36.772022 83 \n",
"103 39.538456 56 \n",
"104 13.691594 58 \n",
"105 43.455639 60 \n",
"106 8.144090 58 \n",
"107 2.501384 67 \n",
"108 27.388660 70 \n",
"109 4.894676 65 \n",
"110 28.796970 77 \n",
"111 1.694895 69 \n",
"112 43.067608 64 \n",
"113 86.637543 61 \n",
"114 21.586960 64 \n",
"115 21.056841 71 \n",
"116 20.585722 83 \n",
"117 8.519885 65 \n",
"118 36.958660 58 \n",
"119 10.976958 60 \n",
"120 56.319504 59 \n",
"121 7.276809 65 \n",
"122 8.437429 81 \n",
"123 43.247162 60 \n",
"124 3.885637 71 \n",
"125 23.882261 69 \n",
"\n",
"[126 rows x 8 columns]"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#show sortable grid for data\n",
"qgrid.show_grid(data, remote_js=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<script type=\"text/javascript\">\n",
"if ($(\"#dg-css\").length == 0){\n",
" $(\"head\").append([\n",
" \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.css' rel='stylesheet'>\",\n",
" \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick-default-theme.css' rel='stylesheet'>\",\n",
" \"<link href='http://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.10.4/css/jquery-ui.min.css' rel='stylesheet'>\",\n",
" \"<link id='dg-css' href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.css' rel='stylesheet'>\"\n",
" ]);\n",
"}\n",
"</script>\n",
"<div class='q-grid-container'>\n",
"<div id='9d8648c0-cbab-4f51-82f6-c16144437902' class='q-grid'></div>\n",
"</div>"
],
"metadata": {},
"output_type": "display_data"
},
{
"javascript": [
"var path_dictionary = {\n",
" jquery_drag: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/jquery.event.drag-2.2\",\n",
" slick_core: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.core.2.2\",\n",
" slick_data_view: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.dataview.2.2\",\n",
" slick_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.2.2\",\n",
" data_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid\",\n",
" date_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.datefilter\",\n",
" slider_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.sliderfilter\",\n",
" filter_base: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.filterbase\",\n",
" handlebars: \"https://cdnjs.cloudflare.com/ajax/libs/handlebars.js/2.0.0/handlebars.min\"\n",
"};\n",
"\n",
"var existing_config = require.s.contexts._.config;\n",
"if (!existing_config.paths['underscore']){\n",
" path_dictionary['underscore'] = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.7.0/underscore-min\";\n",
"}\n",
"\n",
"if (!existing_config.paths['moment']){\n",
" path_dictionary['moment'] = \"https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.8.3/moment.min\";\n",
"}\n",
"\n",
"if (!existing_config.paths['jqueryui']){\n",
" path_dictionary['jqueryui'] = \"https://ajax.googleapis.com/ajax/libs/jqueryui/1.11.1/jquery-ui.min\";\n",
"}\n",
"\n",
"require.config({\n",
" paths: path_dictionary\n",
"});\n",
"\n",
"if (typeof jQuery === 'function') {\n",
" define('jquery', function() { return jQuery; });\n",
"}\n",
"\n",
"require([\n",
" 'jquery',\n",
" 'jquery_drag',\n",
" 'slick_core',\n",
" 'slick_data_view'\n",
"],\n",
"function($){\n",
" $('#9d8648c0-cbab-4f51-82f6-c16144437902').closest('.rendered_html').removeClass('rendered_html');\n",
" require(['slick_grid'], function(){\n",
" require([\"data_grid\"], function(dgrid){\n",
" var grid = new dgrid.QGrid('#9d8648c0-cbab-4f51-82f6-c16144437902', [{\"null\":0,\"participant\":1,\"gender\":1,\"ERN\":-21.910744,\"EPS\":26.391241,\"errors\":20,\"startle_type\":\"error\",\"startle\":61.934547,\"startle_peak\":\"63\"},{\"null\":1,\"participant\":2,\"gender\":0,\"ERN\":-1.841113,\"EPS\":9.538078,\"errors\":41,\"startle_type\":\"error\",\"startle\":44.829327,\"startle_peak\":\"63\"},{\"null\":2,\"participant\":5,\"gender\":0,\"ERN\":-8.708967,\"EPS\":5.00165,\"errors\":82,\"startle_type\":\"error\",\"startle\":13.093092,\"startle_peak\":\"58\"},{\"null\":3,\"participant\":6,\"gender\":1,\"ERN\":-8.348466,\"EPS\":-2.151122,\"errors\":19,\"startle_type\":\"error\",\"startle\":7.380994,\"startle_peak\":\"61\"},{\"null\":4,\"participant\":7,\"gender\":0,\"ERN\":-2.91475,\"EPS\":5.765736,\"errors\":63,\"startle_type\":\"error\",\"startle\":37.685368,\"startle_peak\":\"58\"},{\"null\":5,\"participant\":8,\"gender\":1,\"ERN\":-3.072484,\"EPS\":12.568234,\"errors\":19,\"startle_type\":\"error\",\"startle\":60.816753,\"startle_peak\":\"58\"},{\"null\":6,\"participant\":9,\"gender\":1,\"ERN\":-1.93681,\"EPS\":-4.323942,\"errors\":33,\"startle_type\":\"error\",\"startle\":16.031044,\"startle_peak\":\"65\"},{\"null\":7,\"participant\":11,\"gender\":0,\"ERN\":-4.286741,\"EPS\":2.355393,\"errors\":51,\"startle_type\":\"error\",\"startle\":3.954227,\"startle_peak\":\"63\"},{\"null\":8,\"participant\":12,\"gender\":0,\"ERN\":-7.835635,\"EPS\":-1.885065,\"errors\":7,\"startle_type\":\"error\",\"startle\":5.671492,\"startle_peak\":\"61\"},{\"null\":9,\"participant\":13,\"gender\":0,\"ERN\":-2.167574,\"EPS\":1.310271,\"errors\":53,\"startle_type\":\"error\",\"startle\":5.057071,\"startle_peak\":\"72\"},{\"null\":10,\"participant\":14,\"gender\":1,\"ERN\":-8.543142,\"EPS\":0.952917,\"errors\":51,\"startle_type\":\"error\",\"startle\":42.697914,\"startle_peak\":\"57\"},{\"null\":11,\"participant\":15,\"gender\":1,\"ERN\":-7.33425,\"EPS\":-0.021293,\"errors\":18,\"startle_type\":\"error\",\"startle\":2.971516,\"startle_peak\":\"66\"},{\"null\":12,\"participant\":16,\"gender\":1,\"ERN\":-1.243219,\"EPS\":6.085452,\"errors\":36,\"startle_type\":\"error\",\"startle\":10.866097,\"startle_peak\":\"66\"},{\"null\":13,\"participant\":17,\"gender\":1,\"ERN\":-8.005402,\"EPS\":10.339608,\"errors\":26,\"startle_type\":\"error\",\"startle\":18.022871,\"startle_peak\":\"57\"},{\"null\":14,\"participant\":19,\"gender\":1,\"ERN\":-17.255142,\"EPS\":-0.908369,\"errors\":12,\"startle_type\":\"error\",\"startle\":5.068305,\"startle_peak\":\"60\"},{\"null\":15,\"participant\":21,\"gender\":1,\"ERN\":-5.665667,\"EPS\":1.169279,\"errors\":63,\"startle_type\":\"error\",\"startle\":13.973429,\"startle_peak\":\"48\"},{\"null\":16,\"participant\":22,\"gender\":0,\"ERN\":-12.539272,\"EPS\":12.214434,\"errors\":14,\"startle_type\":\"error\",\"startle\":34.683296,\"startle_peak\":\"58\"},{\"null\":17,\"participant\":23,\"gender\":0,\"ERN\":-2.954746,\"EPS\":0.895508,\"errors\":57,\"startle_type\":\"error\",\"startle\":7.488783,\"startle_peak\":\"59\"},{\"null\":18,\"participant\":24,\"gender\":1,\"ERN\":-5.850971,\"EPS\":11.673366,\"errors\":51,\"startle_type\":\"error\",\"startle\":40.177113,\"startle_peak\":\"81\"},{\"null\":19,\"participant\":25,\"gender\":1,\"ERN\":-3.949103,\"EPS\":2.38478,\"errors\":29,\"startle_type\":\"error\",\"startle\":39.782413,\"startle_peak\":\"60\"},{\"null\":20,\"participant\":26,\"gender\":0,\"ERN\":4.124257,\"EPS\":2.746532,\"errors\":13,\"startle_type\":\"error\",\"startle\":16.575405,\"startle_peak\":\"66\"},{\"null\":21,\"participant\":27,\"gender\":1,\"ERN\":-5.184582,\"EPS\":24.002224,\"errors\":31,\"startle_type\":\"error\",\"startle\":57.30711,\"startle_peak\":\"56\"},{\"null\":22,\"participant\":29,\"gender\":1,\"ERN\":-2.147012,\"EPS\":11.441307,\"errors\":44,\"startle_type\":\"error\",\"startle\":21.89609,\"startle_peak\":\"63\"},{\"null\":23,\"participant\":30,\"gender\":1,\"ERN\":-8.587319,\"EPS\":3.745202,\"errors\":20,\"startle_type\":\"error\",\"startle\":5.890623,\"startle_peak\":\"57\"},{\"null\":24,\"participant\":31,\"gender\":1,\"ERN\":-11.941545,\"EPS\":3.479194,\"errors\":33,\"startle_type\":\"error\",\"startle\":33.076218,\"startle_peak\":\"67\"},{\"null\":25,\"participant\":32,\"gender\":0,\"ERN\":-11.796526,\"EPS\":6.872339,\"errors\":31,\"startle_type\":\"error\",\"startle\":10.741505,\"startle_peak\":\"66\"},{\"null\":26,\"participant\":33,\"gender\":0,\"ERN\":-3.789471,\"EPS\":-1.269627,\"errors\":80,\"startle_type\":\"error\",\"startle\":23.902311,\"startle_peak\":\"61\"},{\"null\":27,\"participant\":34,\"gender\":1,\"ERN\":-1.806187,\"EPS\":-0.594354,\"errors\":24,\"startle_type\":\"error\",\"startle\":1.697242,\"startle_peak\":\"66\"},{\"null\":28,\"participant\":35,\"gender\":1,\"ERN\":-8.525034,\"EPS\":3.001086,\"errors\":32,\"startle_type\":\"error\",\"startle\":46.812138,\"startle_peak\":\"59\"},{\"null\":29,\"participant\":36,\"gender\":0,\"ERN\":-8.06265,\"EPS\":-2.409631,\"errors\":31,\"startle_type\":\"error\",\"startle\":80.058876,\"startle_peak\":\"65\"},{\"null\":30,\"participant\":38,\"gender\":1,\"ERN\":-6.804902,\"EPS\":-12.884763,\"errors\":46,\"startle_type\":\"error\",\"startle\":22.658545,\"startle_peak\":\"65\"},{\"null\":31,\"participant\":39,\"gender\":1,\"ERN\":-6.923191,\"EPS\":-0.523201,\"errors\":42,\"startle_type\":\"error\",\"startle\":21.289732,\"startle_peak\":\"57\"},{\"null\":32,\"participant\":41,\"gender\":1,\"ERN\":-2.584587,\"EPS\":-11.953424,\"errors\":36,\"startle_type\":\"error\",\"startle\":4.842049,\"startle_peak\":\"63\"},{\"null\":33,\"participant\":42,\"gender\":0,\"ERN\":-7.002459,\"EPS\":-1.63326,\"errors\":32,\"startle_type\":\"error\",\"startle\":4.750764,\"startle_peak\":\"60\"},{\"null\":34,\"participant\":43,\"gender\":0,\"ERN\":0.650213,\"EPS\":8.476102,\"errors\":22,\"startle_type\":\"error\",\"startle\":41.001339,\"startle_peak\":\"57\"},{\"null\":35,\"participant\":45,\"gender\":0,\"ERN\":-6.900466,\"EPS\":4.112835,\"errors\":17,\"startle_type\":\"error\",\"startle\":14.791293,\"startle_peak\":\"57\"},{\"null\":36,\"participant\":46,\"gender\":1,\"ERN\":-7.699009,\"EPS\":9.863975,\"errors\":35,\"startle_type\":\"error\",\"startle\":60.783512,\"startle_peak\":\"54\"},{\"null\":37,\"participant\":47,\"gender\":0,\"ERN\":-8.745496,\"EPS\":-0.842558,\"errors\":64,\"startle_type\":\"error\",\"startle\":6.580106,\"startle_peak\":\"66\"},{\"null\":38,\"participant\":48,\"gender\":0,\"ERN\":-8.689076,\"EPS\":-6.583182,\"errors\":18,\"startle_type\":\"error\",\"startle\":-0.516764,\"startle_peak\":\"80\"},{\"null\":39,\"participant\":49,\"gender\":1,\"ERN\":-1.894857,\"EPS\":-12.443699,\"errors\":12,\"startle_type\":\"error\",\"startle\":35.191723,\"startle_peak\":\"61\"},{\"null\":40,\"participant\":50,\"gender\":0,\"ERN\":-12.713764,\"EPS\":6.240077,\"errors\":41,\"startle_type\":\"error\",\"startle\":9.850739,\"startle_peak\":\"68\"},{\"null\":41,\"participant\":51,\"gender\":0,\"ERN\":-6.939233,\"EPS\":4.418236,\"errors\":39,\"startle_type\":\"error\",\"startle\":25.975296,\"startle_peak\":\"68\"},{\"null\":42,\"participant\":1,\"gender\":1,\"ERN\":-21.910744,\"EPS\":26.391241,\"errors\":20,\"startle_type\":\"predict\",\"startle\":38.58828,\"startle_peak\":\"73\"},{\"null\":43,\"participant\":2,\"gender\":0,\"ERN\":-1.841113,\"EPS\":9.538078,\"errors\":41,\"startle_type\":\"predict\",\"startle\":31.27043,\"startle_peak\":\"66\"},{\"null\":44,\"participant\":5,\"gender\":0,\"ERN\":-8.708967,\"EPS\":5.00165,\"errors\":82,\"startle_type\":\"predict\",\"startle\":7.57419,\"startle_peak\":\"59\"},{\"null\":45,\"participant\":6,\"gender\":1,\"ERN\":-8.348466,\"EPS\":-2.151122,\"errors\":19,\"startle_type\":\"predict\",\"startle\":12.835707,\"startle_peak\":\"59\"},{\"null\":46,\"participant\":7,\"gender\":0,\"ERN\":-2.91475,\"EPS\":5.765736,\"errors\":63,\"startle_type\":\"predict\",\"startle\":27.047993,\"startle_peak\":\"58\"},{\"null\":47,\"participant\":8,\"gender\":1,\"ERN\":-3.072484,\"EPS\":12.568234,\"errors\":19,\"startle_type\":\"predict\",\"startle\":53.481987,\"startle_peak\":\"58\"},{\"null\":48,\"participant\":9,\"gender\":1,\"ERN\":-1.93681,\"EPS\":-4.323942,\"errors\":33,\"startle_type\":\"predict\",\"startle\":20.203735,\"startle_peak\":\"63\"},{\"null\":49,\"participant\":11,\"gender\":0,\"ERN\":-4.286741,\"EPS\":2.355393,\"errors\":51,\"startle_type\":\"predict\",\"startle\":1.695961,\"startle_peak\":\"67\"},{\"null\":50,\"participant\":12,\"gender\":0,\"ERN\":-7.835635,\"EPS\":-1.885065,\"errors\":7,\"startle_type\":\"predict\",\"startle\":5.034391,\"startle_peak\":\"65\"},{\"null\":51,\"participant\":13,\"gender\":0,\"ERN\":-2.167574,\"EPS\":1.310271,\"errors\":53,\"startle_type\":\"predict\",\"startle\":3.861289,\"startle_peak\":\"74\"},{\"null\":52,\"participant\":14,\"gender\":1,\"ERN\":-8.543142,\"EPS\":0.952917,\"errors\":51,\"startle_type\":\"predict\",\"startle\":42.399624,\"startle_peak\":\"58\"},{\"null\":53,\"participant\":15,\"gender\":1,\"ERN\":-7.33425,\"EPS\":-0.021293,\"errors\":18,\"startle_type\":\"predict\",\"startle\":3.230959,\"startle_peak\":\"62\"},{\"null\":54,\"participant\":16,\"gender\":1,\"ERN\":-1.243219,\"EPS\":6.085452,\"errors\":36,\"startle_type\":\"predict\",\"startle\":4.973537,\"startle_peak\":\"59\"},{\"null\":55,\"participant\":17,\"gender\":1,\"ERN\":-8.005402,\"EPS\":10.339608,\"errors\":26,\"startle_type\":\"predict\",\"startle\":7.310519,\"startle_peak\":\"59\"},{\"null\":56,\"participant\":19,\"gender\":1,\"ERN\":-17.255142,\"EPS\":-0.908369,\"errors\":12,\"startle_type\":\"predict\",\"startle\":5.829415,\"startle_peak\":\"65\"},{\"null\":57,\"participant\":21,\"gender\":1,\"ERN\":-5.665667,\"EPS\":1.169279,\"errors\":63,\"startle_type\":\"predict\",\"startle\":12.804151,\"startle_peak\":\"64\"},{\"null\":58,\"participant\":22,\"gender\":0,\"ERN\":-12.539272,\"EPS\":12.214434,\"errors\":14,\"startle_type\":\"predict\",\"startle\":12.301938,\"startle_peak\":\"61\"},{\"null\":59,\"participant\":23,\"gender\":0,\"ERN\":-2.954746,\"EPS\":0.895508,\"errors\":57,\"startle_type\":\"predict\",\"startle\":6.518363,\"startle_peak\":\"60\"},{\"null\":60,\"participant\":24,\"gender\":1,\"ERN\":-5.850971,\"EPS\":11.673366,\"errors\":51,\"startle_type\":\"predict\",\"startle\":20.235477,\"startle_peak\":\"74\"},{\"null\":61,\"participant\":25,\"gender\":1,\"ERN\":-3.949103,\"EPS\":2.38478,\"errors\":29,\"startle_type\":\"predict\",\"startle\":35.256821,\"startle_peak\":\"58\"},{\"null\":62,\"participant\":26,\"gender\":0,\"ERN\":4.124257,\"EPS\":2.746532,\"errors\":13,\"startle_type\":\"predict\",\"startle\":13.966159,\"startle_peak\":\"57\"},{\"null\":63,\"participant\":27,\"gender\":1,\"ERN\":-5.184582,\"EPS\":24.002224,\"errors\":31,\"startle_type\":\"predict\",\"startle\":23.154129,\"startle_peak\":\"61\"},{\"null\":64,\"participant\":29,\"gender\":1,\"ERN\":-2.147012,\"EPS\":11.441307,\"errors\":44,\"startle_type\":\"predict\",\"startle\":12.76548,\"startle_peak\":\"59\"},{\"null\":65,\"participant\":30,\"gender\":1,\"ERN\":-8.587319,\"EPS\":3.745202,\"errors\":20,\"startle_type\":\"predict\",\"startle\":1.789462,\"startle_peak\":\"63\"},{\"null\":66,\"participant\":31,\"gender\":1,\"ERN\":-11.941545,\"EPS\":3.479194,\"errors\":33,\"startle_type\":\"predict\",\"startle\":31.805397,\"startle_peak\":\"64\"},{\"null\":67,\"participant\":32,\"gender\":0,\"ERN\":-11.796526,\"EPS\":6.872339,\"errors\":31,\"startle_type\":\"predict\",\"startle\":2.843659,\"startle_peak\":\"68\"},{\"null\":68,\"participant\":33,\"gender\":0,\"ERN\":-3.789471,\"EPS\":-1.269627,\"errors\":80,\"startle_type\":\"predict\",\"startle\":21.54689,\"startle_peak\":\"60\"},{\"null\":69,\"participant\":34,\"gender\":1,\"ERN\":-1.806187,\"EPS\":-0.594354,\"errors\":24,\"startle_type\":\"predict\",\"startle\":2.931575,\"startle_peak\":\"61\"},{\"null\":70,\"participant\":35,\"gender\":1,\"ERN\":-8.525034,\"EPS\":3.001086,\"errors\":32,\"startle_type\":\"predict\",\"startle\":44.554462,\"startle_peak\":\"63\"},{\"null\":71,\"participant\":36,\"gender\":0,\"ERN\":-8.06265,\"EPS\":-2.409631,\"errors\":31,\"startle_type\":\"predict\",\"startle\":78.299492,\"startle_peak\":\"62\"},{\"null\":72,\"participant\":38,\"gender\":1,\"ERN\":-6.804902,\"EPS\":-12.884763,\"errors\":46,\"startle_type\":\"predict\",\"startle\":16.098455,\"startle_peak\":\"63\"},{\"null\":73,\"participant\":39,\"gender\":1,\"ERN\":-6.923191,\"EPS\":-0.523201,\"errors\":42,\"startle_type\":\"predict\",\"startle\":22.569033,\"startle_peak\":\"66\"},{\"null\":74,\"participant\":41,\"gender\":1,\"ERN\":-2.584587,\"EPS\":-11.953424,\"errors\":36,\"startle_type\":\"predict\",\"startle\":13.005221,\"startle_peak\":\"62\"},{\"null\":75,\"participant\":42,\"gender\":0,\"ERN\":-7.002459,\"EPS\":-1.63326,\"errors\":32,\"startle_type\":\"predict\",\"startle\":4.248161,\"startle_peak\":\"65\"},{\"null\":76,\"participant\":43,\"gender\":0,\"ERN\":0.650213,\"EPS\":8.476102,\"errors\":22,\"startle_type\":\"predict\",\"startle\":28.091797,\"startle_peak\":\"59\"},{\"null\":77,\"participant\":45,\"gender\":0,\"ERN\":-6.900466,\"EPS\":4.112835,\"errors\":17,\"startle_type\":\"predict\",\"startle\":10.379953,\"startle_peak\":\"60\"},{\"null\":78,\"participant\":46,\"gender\":1,\"ERN\":-7.699009,\"EPS\":9.863975,\"errors\":35,\"startle_type\":\"predict\",\"startle\":45.519608,\"startle_peak\":\"61\"},{\"null\":79,\"participant\":47,\"gender\":0,\"ERN\":-8.745496,\"EPS\":-0.842558,\"errors\":64,\"startle_type\":\"predict\",\"startle\":7.56852,\"startle_peak\":\"68\"},{\"null\":80,\"participant\":48,\"gender\":0,\"ERN\":-8.689076,\"EPS\":-6.583182,\"errors\":18,\"startle_type\":\"predict\",\"startle\":3.695404,\"startle_peak\":\"74\"},{\"null\":81,\"participant\":49,\"gender\":1,\"ERN\":-1.894857,\"EPS\":-12.443699,\"errors\":12,\"startle_type\":\"predict\",\"startle\":52.023682,\"startle_peak\":\"57\"},{\"null\":82,\"participant\":50,\"gender\":0,\"ERN\":-12.713764,\"EPS\":6.240077,\"errors\":41,\"startle_type\":\"predict\",\"startle\":3.335688,\"startle_peak\":\"68\"},{\"null\":83,\"participant\":51,\"gender\":0,\"ERN\":-6.939233,\"EPS\":4.418236,\"errors\":39,\"startle_type\":\"predict\",\"startle\":19.231867,\"startle_peak\":\"71\"},{\"null\":84,\"participant\":1,\"gender\":1,\"ERN\":-21.910744,\"EPS\":26.391241,\"errors\":20,\"startle_type\":\"unpred\",\"startle\":32.498344,\"startle_peak\":\"79\"},{\"null\":85,\"participant\":2,\"gender\":0,\"ERN\":-1.841113,\"EPS\":9.538078,\"errors\":41,\"startle_type\":\"unpred\",\"startle\":39.312057,\"startle_peak\":\"66\"},{\"null\":86,\"participant\":5,\"gender\":0,\"ERN\":-8.708967,\"EPS\":5.00165,\"errors\":82,\"startle_type\":\"unpred\",\"startle\":8.608694,\"startle_peak\":\"60\"},{\"null\":87,\"participant\":6,\"gender\":1,\"ERN\":-8.348466,\"EPS\":-2.151122,\"errors\":19,\"startle_type\":\"unpred\",\"startle\":6.228533,\"startle_peak\":\"67\"},{\"null\":88,\"participant\":7,\"gender\":0,\"ERN\":-2.91475,\"EPS\":5.765736,\"errors\":63,\"startle_type\":\"unpred\",\"startle\":36.791275,\"startle_peak\":\"60\"},{\"null\":89,\"participant\":8,\"gender\":1,\"ERN\":-3.072484,\"EPS\":12.568234,\"errors\":19,\"startle_type\":\"unpred\",\"startle\":43.015087,\"startle_peak\":\"59\"},{\"null\":90,\"participant\":9,\"gender\":1,\"ERN\":-1.93681,\"EPS\":-4.323942,\"errors\":33,\"startle_type\":\"unpred\",\"startle\":20.506233,\"startle_peak\":\"61\"},{\"null\":91,\"participant\":11,\"gender\":0,\"ERN\":-4.286741,\"EPS\":2.355393,\"errors\":51,\"startle_type\":\"unpred\",\"startle\":1.501707,\"startle_peak\":\"65\"},{\"null\":92,\"participant\":12,\"gender\":0,\"ERN\":-7.835635,\"EPS\":-1.885065,\"errors\":7,\"startle_type\":\"unpred\",\"startle\":10.078726,\"startle_peak\":\"67\"},{\"null\":93,\"participant\":13,\"gender\":0,\"ERN\":-2.167574,\"EPS\":1.310271,\"errors\":53,\"startle_type\":\"unpred\",\"startle\":3.632312,\"startle_peak\":\"70\"},{\"null\":94,\"participant\":14,\"gender\":1,\"ERN\":-8.543142,\"EPS\":0.952917,\"errors\":51,\"startle_type\":\"unpred\",\"startle\":41.090355,\"startle_peak\":\"57\"},{\"null\":95,\"participant\":15,\"gender\":1,\"ERN\":-7.33425,\"EPS\":-0.021293,\"errors\":18,\"startle_type\":\"unpred\",\"startle\":2.754656,\"startle_peak\":\"63\"},{\"null\":96,\"participant\":16,\"gender\":1,\"ERN\":-1.243219,\"EPS\":6.085452,\"errors\":36,\"startle_type\":\"unpred\",\"startle\":4.587745,\"startle_peak\":\"62\"},{\"null\":97,\"participant\":17,\"gender\":1,\"ERN\":-8.005402,\"EPS\":10.339608,\"errors\":26,\"startle_type\":\"unpred\",\"startle\":8.055993,\"startle_peak\":\"64\"},{\"null\":98,\"participant\":19,\"gender\":1,\"ERN\":-17.255142,\"EPS\":-0.908369,\"errors\":12,\"startle_type\":\"unpred\",\"startle\":6.123931,\"startle_peak\":\"57\"},{\"null\":99,\"participant\":21,\"gender\":1,\"ERN\":-5.665667,\"EPS\":1.169279,\"errors\":63,\"startle_type\":\"unpred\",\"startle\":null,\"startle_peak\":\"???\"},{\"null\":100,\"participant\":22,\"gender\":0,\"ERN\":-12.539272,\"EPS\":12.214434,\"errors\":14,\"startle_type\":\"unpred\",\"startle\":32.635765,\"startle_peak\":\"63\"},{\"null\":101,\"participant\":23,\"gender\":0,\"ERN\":-2.954746,\"EPS\":0.895508,\"errors\":57,\"startle_type\":\"unpred\",\"startle\":6.668186,\"startle_peak\":\"61\"},{\"null\":102,\"participant\":24,\"gender\":1,\"ERN\":-5.850971,\"EPS\":11.673366,\"errors\":51,\"startle_type\":\"unpred\",\"startle\":36.772022,\"startle_peak\":\"83\"},{\"null\":103,\"participant\":25,\"gender\":1,\"ERN\":-3.949103,\"EPS\":2.38478,\"errors\":29,\"startle_type\":\"unpred\",\"startle\":39.538456,\"startle_peak\":\"56\"},{\"null\":104,\"participant\":26,\"gender\":0,\"ERN\":4.124257,\"EPS\":2.746532,\"errors\":13,\"startle_type\":\"unpred\",\"startle\":13.691594,\"startle_peak\":\"58\"},{\"null\":105,\"participant\":27,\"gender\":1,\"ERN\":-5.184582,\"EPS\":24.002224,\"errors\":31,\"startle_type\":\"unpred\",\"startle\":43.455639,\"startle_peak\":\"60\"},{\"null\":106,\"participant\":29,\"gender\":1,\"ERN\":-2.147012,\"EPS\":11.441307,\"errors\":44,\"startle_type\":\"unpred\",\"startle\":8.14409,\"startle_peak\":\"58\"},{\"null\":107,\"participant\":30,\"gender\":1,\"ERN\":-8.587319,\"EPS\":3.745202,\"errors\":20,\"startle_type\":\"unpred\",\"startle\":2.501384,\"startle_peak\":\"67\"},{\"null\":108,\"participant\":31,\"gender\":1,\"ERN\":-11.941545,\"EPS\":3.479194,\"errors\":33,\"startle_type\":\"unpred\",\"startle\":27.38866,\"startle_peak\":\"70\"},{\"null\":109,\"participant\":32,\"gender\":0,\"ERN\":-11.796526,\"EPS\":6.872339,\"errors\":31,\"startle_type\":\"unpred\",\"startle\":4.894676,\"startle_peak\":\"65\"},{\"null\":110,\"participant\":33,\"gender\":0,\"ERN\":-3.789471,\"EPS\":-1.269627,\"errors\":80,\"startle_type\":\"unpred\",\"startle\":28.79697,\"startle_peak\":\"77\"},{\"null\":111,\"participant\":34,\"gender\":1,\"ERN\":-1.806187,\"EPS\":-0.594354,\"errors\":24,\"startle_type\":\"unpred\",\"startle\":1.694895,\"startle_peak\":\"69\"},{\"null\":112,\"participant\":35,\"gender\":1,\"ERN\":-8.525034,\"EPS\":3.001086,\"errors\":32,\"startle_type\":\"unpred\",\"startle\":43.067608,\"startle_peak\":\"64\"},{\"null\":113,\"participant\":36,\"gender\":0,\"ERN\":-8.06265,\"EPS\":-2.409631,\"errors\":31,\"startle_type\":\"unpred\",\"startle\":86.637543,\"startle_peak\":\"61\"},{\"null\":114,\"participant\":38,\"gender\":1,\"ERN\":-6.804902,\"EPS\":-12.884763,\"errors\":46,\"startle_type\":\"unpred\",\"startle\":21.58696,\"startle_peak\":\"64\"},{\"null\":115,\"participant\":39,\"gender\":1,\"ERN\":-6.923191,\"EPS\":-0.523201,\"errors\":42,\"startle_type\":\"unpred\",\"startle\":21.056841,\"startle_peak\":\"71\"},{\"null\":116,\"participant\":41,\"gender\":1,\"ERN\":-2.584587,\"EPS\":-11.953424,\"errors\":36,\"startle_type\":\"unpred\",\"startle\":20.585722,\"startle_peak\":\"83\"},{\"null\":117,\"participant\":42,\"gender\":0,\"ERN\":-7.002459,\"EPS\":-1.63326,\"errors\":32,\"startle_type\":\"unpred\",\"startle\":8.519885,\"startle_peak\":\"65\"},{\"null\":118,\"participant\":43,\"gender\":0,\"ERN\":0.650213,\"EPS\":8.476102,\"errors\":22,\"startle_type\":\"unpred\",\"startle\":36.95866,\"startle_peak\":\"58\"},{\"null\":119,\"participant\":45,\"gender\":0,\"ERN\":-6.900466,\"EPS\":4.112835,\"errors\":17,\"startle_type\":\"unpred\",\"startle\":10.976958,\"startle_peak\":\"60\"},{\"null\":120,\"participant\":46,\"gender\":1,\"ERN\":-7.699009,\"EPS\":9.863975,\"errors\":35,\"startle_type\":\"unpred\",\"startle\":56.319504,\"startle_peak\":\"59\"},{\"null\":121,\"participant\":47,\"gender\":0,\"ERN\":-8.745496,\"EPS\":-0.842558,\"errors\":64,\"startle_type\":\"unpred\",\"startle\":7.276809,\"startle_peak\":\"65\"},{\"null\":122,\"participant\":48,\"gender\":0,\"ERN\":-8.689076,\"EPS\":-6.583182,\"errors\":18,\"startle_type\":\"unpred\",\"startle\":8.437429,\"startle_peak\":\"81\"},{\"null\":123,\"participant\":49,\"gender\":1,\"ERN\":-1.894857,\"EPS\":-12.443699,\"errors\":12,\"startle_type\":\"unpred\",\"startle\":43.247162,\"startle_peak\":\"60\"},{\"null\":124,\"participant\":50,\"gender\":0,\"ERN\":-12.713764,\"EPS\":6.240077,\"errors\":41,\"startle_type\":\"unpred\",\"startle\":3.885637,\"startle_peak\":\"71\"},{\"null\":125,\"participant\":51,\"gender\":0,\"ERN\":-6.939233,\"EPS\":4.418236,\"errors\":39,\"startle_type\":\"unpred\",\"startle\":23.882261,\"startle_peak\":\"69\"}], [{\"field\": null, \"type\": \"Integer\"}, {\"field\": \"participant\", \"type\": \"Integer\"}, {\"field\": \"gender\", \"type\": \"Integer\"}, {\"field\": \"ERN\", \"type\": \"Float\"}, {\"field\": \"EPS\", \"type\": \"Float\"}, {\"field\": \"errors\", \"type\": \"Integer\"}, {\"field\": \"startle_type\"}, {\"field\": \"startle\", \"type\": \"Float\"}, {\"field\": \"startle_peak\"}]);\n",
" grid.initialize_slick_grid();\n",
" });\n",
" });\n",
"});"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# take subset of trials for which participant response was an error\n",
"error_trials = data[data['startle_type'] == 'error']\n",
"pred_trials = data[data['startle_type'] == 'predict']\n",
"unpred_trials = data[data['startle_type'] == 'unpred']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#main finding using one trial type for appropriate DF\n",
"corr_data = data[['ERN','EPS']]\n",
"corr_data.corr(method='pearson', min_periods=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>ERN</th>\n",
" <th>EPS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ERN</th>\n",
" <td> 1.000000</td>\n",
" <td>-0.253099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EPS</th>\n",
" <td>-0.253099</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 66,
"text": [
" ERN EPS\n",
"ERN 1.000000 -0.253099\n",
"EPS -0.253099 1.000000"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"error_trials['errors'].describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"count 42.000000\n",
"mean 35.428571\n",
"std 18.255828\n",
"min 7.000000\n",
"25% 20.000000\n",
"50% 32.500000\n",
"75% 45.500000\n",
"max 82.000000\n",
"dtype: float64"
]
}
],
"prompt_number": 67
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The main finding of the article replicated is A in the following figure."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(url=\"http://www.frontiersin.org/files/Articles/82577/fnhum-08-00064-HTML/image_m/fnhum-08-00064-g001.jpg\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<img src=\"http://www.frontiersin.org/files/Articles/82577/fnhum-08-00064-HTML/image_m/fnhum-08-00064-g001.jpg\"/>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
"<IPython.core.display.Image at 0x10aa5dcd0>"
]
}
],
"prompt_number": 69
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### I failed to replicate this finding:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.jointplot(error_trials['ERN'],error_trials['EPS'],kind=\"reg\",stat_func=pearsonr, color = \"slategray\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 88,
"text": [
"<seaborn.axisgrid.JointGrid at 0x10c7fc3d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAGoCAYAAAD4hcrDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10c7fc310>"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment