{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis of behavioral changes\n",
"\n",
"We categorize the behaviors into three types : \n",
" - In the first category (*hand*: red curve) are mouvements of the arm that did not grab any stick and thus not moved the out-of-reached object.\n",
" - The second category (*stick*: green curve) are mouvements that did grab one of the two sticks but did not touch the object with it.\n",
" - The third category (*object*: blue curve) contains the mouvements where both a stick was grabbed and the object was moved by the stick.\n",
" \n",
"\n",
"We define *Overlapping Waves* as patterns of behaviors that follow all of the three following criterions.\n",
"\n",
"###Criterions:\n",
" - *stick* increases quikly from 0 to more than 10 (potentially after an initial phase with a steady low value), and is followed by a plateau (steady curve with small slope) and no abrupt changes. \n",
" - *object* increases quikly from 0 to more than 10 (potentially after an initial phase with a steady low value), and is followed by a plateau (steady curve with small slope) and no abrupt changes. \n",
" - *object* starts to raise at least 1000 iterations after *stick* started to raise."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"img_path = \"../data/2016-01-26_14-37-37-Tools-cogsci-xp1/img/\""
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"analysis_results = {}"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import Image, HTML, display\n",
"from glob import glob\n",
"\n",
"def analysis(config):\n",
" im_list = []\n",
" imagesList=''.join( [\"
\" % str(s) \n",
" for s in [img_path + '{}-log{}-events-100000.png'.format(config, trial) for trial in range(1, 101)]])\n",
" #print imagesList\n",
" display(HTML(imagesList))"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis(config)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"analysis_results[\"F-RmB\"] = 0"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis(\"F-RGB\")"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"analysis_results[\"F-RGB\"] = 0"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis(\"H-RGB-RMB\")"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trials_criterion_ok = [\n",
" 1,0,1,1,1,0,1,1,1,0,\n",
" 0,0,1,1,0,1,0,1,0,0,\n",
" 0,1,0,0,1,0,0,0,1,0,\n",
" 1,1,0,0,1,1,1,0,1,1,\n",
" 0,0,0,0,1,0,1,1,0,1,\n",
" 1,1,1,1,0,1,0,1,1,1,\n",
" 1,0,1,1,1,0,1,0,0,1,\n",
" 1,0,1,1,0,1,1,0,0,1,\n",
" 0,1,1,1,1,0,0,1,0,1,\n",
" 1,1,1,1,1,1,1,1,0,1,\n",
"]\n",
"\n",
"analysis_results[\"H-RGB-RMB\"] = sum(trials_criterion_ok)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis(\"H-RGB-P-AMB\")"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trials_criterion_ok = [\n",
" 1,0,1,1,0,0,1,0,1,1,\n",
" 1,1,1,0,1,1,1,0,1,0,\n",
" 1,1,1,1,1,1,1,1,0,0,\n",
" 0,1,0,0,0,0,1,1,1,0,\n",
" 1,1,0,1,1,0,1,1,0,1,\n",
" 0,1,1,1,1,1,1,1,1,1,\n",
" 1,0,1,1,1,1,0,0,0,1,\n",
" 1,0,1,1,0,1,1,1,1,1,\n",
" 1,0,1,1,1,1,0,1,0,1,\n",
" 1,1,1,0,0,1,1,1,1,1,\n",
"]\n",
"\n",
"analysis_results[\"H-RGB-P-AMB\"] = sum(trials_criterion_ok)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis(\"H-RGB-GR-AMB\")"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trials_criterion_ok = [\n",
" 0,1,0,0,0,0,0,0,0,0,\n",
" 0,0,0,0,0,0,0,0,0,0,\n",
" 0,0,0,0,0,0,0,0,0,0,\n",
" 0,0,0,0,0,0,0,0,0,0,\n",
" 0,0,0,0,0,1,0,0,0,0,\n",
" 0,0,1,0,0,0,0,0,0,0,\n",
" 0,0,0,0,1,0,0,0,0,0,\n",
" 1,0,0,0,0,0,0,0,0,0,\n",
" 0,1,0,0,0,0,0,0,0,0,\n",
" 0,0,0,0,0,0,1,0,0,0,\n",
"]\n",
"\n",
"analysis_results[\"H-RGB-GR-AMB\"] = sum(trials_criterion_ok)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis(\"H-RGB-P-AMB-PGITC\")"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trials_criterion_ok = [\n",
" 1,1,1,1,1,0,0,0,0,1,\n",
" 1,1,1,1,1,1,1,0,1,1,\n",
" 1,1,1,1,1,1,0,0,1,0,\n",
" 1,1,1,1,1,1,0,0,1,0,\n",
" 1,0,1,1,1,1,1,0,1,0,\n",
" 1,1,0,1,1,0,1,1,1,1,\n",
" 1,1,1,1,1,1,1,0,1,1,\n",
" 1,1,1,1,0,1,1,1,1,1,\n",
" 1,1,1,1,1,1,1,0,0,1,\n",
" 1,1,1,1,1,1,0,1,1,1,\n",
"]\n",
"\n",
"analysis_results[\"H-RGB-P-AMB-PGITC\"] = sum(trials_criterion_ok)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'F-RmB': 0, 'H-RGB-P-AMB-PGITC': 79, 'H-RGB-GR-AMB': 7, 'H-RGB-RMB': 60, 'F-RGB': 0, 'H-RGB-P-AMB': 70}\n"
]
}
],
"source": [
"print analysis_results"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}