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mwegrzynmwegrzyn
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rerun everything with virualenv instead of anaconda
1 parent bbfda4a commit 9993dbe

11 files changed

+598
-504638
lines changed

‎figures/afasThreshCorrelationAll.png

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‎figures/afasThreshCorrelationGroups.png

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‎figures/fittedFuncViolenceChildM.png

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‎figures/fittedFuncViolenceGenPop.png

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‎figures/thresholdPic.png

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‎notebooks/001_questionnaires.ipynb

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‎notebooks/002_basicExpressions.ipynb

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‎notebooks/003_gettingMorphData.ipynb

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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_logfile(whichfolder, whichexperiment):\n",
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"loglist = get_logfile('../experiment/data/','*facesParametric*.csv')\n",
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [
@@ -166,36 +158,36 @@
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" <tr>\n",
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" <th>89</th>\n",
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" <td>./stim/MorphM9_07_orig.jpg</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>3.044200</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>90</th>\n",
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" <td>./stim/MorphM10_05_orig.jpg</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.925446</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>91</th>\n",
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" <td>./stim/MorphM2_07_orig.jpg</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.942962</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>92</th>\n",
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" <td>./stim/MorphF5_06_orig.jpg</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.305991</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>93</th>\n",
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" <td>./stim/MorphM3_09_orig.jpg</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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" <td>2.845542</td>\n",
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" </tr>\n",
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" </tbody>\n",
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],
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"text/plain": [
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" img mouseResp.leftButton mouseResp.rightButton \\\n",
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"89 ./stim/MorphM9_07_orig.jpg 1 0 \n",
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"90 ./stim/MorphM10_05_orig.jpg 0 1 \n",
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"91 ./stim/MorphM2_07_orig.jpg 0 1 \n",
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"92 ./stim/MorphF5_06_orig.jpg 0 1 \n",
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"93 ./stim/MorphM3_09_orig.jpg 0 1 \n",
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"89 ./stim/MorphM9_07_orig.jpg 1.0 0.0 \n",
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"90 ./stim/MorphM10_05_orig.jpg 0.0 1.0 \n",
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"91 ./stim/MorphM2_07_orig.jpg 0.0 1.0 \n",
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"92 ./stim/MorphF5_06_orig.jpg 0.0 1.0 \n",
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"93 ./stim/MorphM3_09_orig.jpg 0.0 1.0 \n",
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"\n",
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" mouseResp.time \n",
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"89 3.044200 \n",
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [
@@ -342,36 +332,36 @@
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" <th rowspan=\"5\" valign=\"top\">F</th>\n",
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" <th>F10_1</th>\n",
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" <td>./stim/MorphF10_00_orig.jpg</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>2.763731</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>F2_1</th>\n",
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" <td>./stim/MorphF2_00_orig.jpg</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>3.099604</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>F3_1</th>\n",
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" <td>./stim/MorphF3_00_orig.jpg</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.701015</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>F9_1</th>\n",
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" <td>./stim/MorphF9_00_orig.jpg</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>2.060379</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>F6_1</th>\n",
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" <td>./stim/MorphF6_00_orig.jpg</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>1.154952</td>\n",
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" </tr>\n",
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" </tbody>\n",
@@ -381,19 +371,19 @@
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"text/plain": [
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" img mouseResp.leftButton \\\n",
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"grades gender idents \n",
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"00 F F10_1 ./stim/MorphF10_00_orig.jpg 1 \n",
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" F2_1 ./stim/MorphF2_00_orig.jpg 1 \n",
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" F3_1 ./stim/MorphF3_00_orig.jpg 1 \n",
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" F9_1 ./stim/MorphF9_00_orig.jpg 1 \n",
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" F6_1 ./stim/MorphF6_00_orig.jpg 1 \n",
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"00 F F10_1 ./stim/MorphF10_00_orig.jpg 1.0 \n",
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" F2_1 ./stim/MorphF2_00_orig.jpg 1.0 \n",
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" F3_1 ./stim/MorphF3_00_orig.jpg 1.0 \n",
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" F9_1 ./stim/MorphF9_00_orig.jpg 1.0 \n",
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" F6_1 ./stim/MorphF6_00_orig.jpg 1.0 \n",
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"\n",
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" mouseResp.rightButton mouseResp.time \n",
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"grades gender idents \n",
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"00 F F10_1 0 2.763731 \n",
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" F2_1 0 3.099604 \n",
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" F3_1 0 0.701015 \n",
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" F9_1 0 2.060379 \n",
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" F6_1 0 1.154952 "
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"00 F F10_1 0.0 2.763731 \n",
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" F2_1 0.0 3.099604 \n",
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" F3_1 0.0 0.701015 \n",
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" F9_1 0.0 2.060379 \n",
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" F6_1 0.0 1.154952 "
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]
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},
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"execution_count": 9,
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"def cleanAvg(fileName):\n",
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <td>0.80</td>\n",
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" <td>0.85</td>\n",
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" <td>0.95</td>\n",
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" <td>1</td>\n",
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" <td>1.0</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>0.85</td>\n",
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" <td>0.95</td>\n",
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" <td>1.00</td>\n",
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" <td>1</td>\n",
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" <td>1.0</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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"grades 00 01 02 03 04 05 06 07 08 09 10 \\\n",
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"A A010 F 0.05 0.05 0.05 0.10 0.25 0.50 0.60 0.80 0.85 0.95 1 \n",
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" M 0.05 0.05 0.05 0.25 0.40 0.35 0.75 0.85 0.95 1.00 1 \n",
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"grades 00 01 02 03 04 05 06 07 08 09 10 \\\n",
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"A A010 F 0.05 0.05 0.05 0.10 0.25 0.50 0.60 0.80 0.85 0.95 1.0 \n",
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" M 0.05 0.05 0.05 0.25 0.40 0.35 0.75 0.85 0.95 1.00 1.0 \n",
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"\n",
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"grades group \n",
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"A A010 F 2 \n",
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"scrolled": true
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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"data": {
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"scrolled": true
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"outputs": [],
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"cell_type": "code",
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"execution_count": 17,
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"bigJasp = bigDfUnstacked.copy()\n",
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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"data": {
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"outputs": [],
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"outputs": [
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{
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"meanDf.to_csv('../outputs/meanMorphsTable.csv')"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.11"
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"version": "2.7.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎notebooks/004_basicPlotting.ipynb

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‎notebooks/005_fittingFunctions.ipynb

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‎outputs/paramsTable.csv

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,,F,F,F,F,M,M,M,M
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,,guess,lapse,slope,threshold,guess,lapse,slope,threshold
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,,,,,,,,,
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A,A001,0.106095834253,0.045968349839,30.0230746092,0.538618614895,0.0602139650767,-0.0329203480027,8.43788791285,0.527474470251
54
A,A002,0.128144372492,0.0505334237738,11.4042482653,0.51550149395,0.141568778062,-0.0564995447936,6.74435815777,0.556186596137
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A,A003,-0.041610748914,0.187949539624,5.70027912436,0.485626760874,-0.0371886145286,-0.202455876346,5.24682856967,0.693391972873

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