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Foucl/FACET_Performance_Tests.ipynb

Created October 10, 2016 14:11
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FACET_Performance_Tests.ipynb
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FACET_Performance_Tests.ipynb
{
"cells": [
{
"metadata": {
"init_cell": true,
"collapsed": false,
"ExecuteTime": {
"start_time": "2016-10-10T15:18:10.541000",
"end_time": "2016-10-10T15:18:11.060000"
},
"trusted": true
},
"cell_type": "code",
"source": "import os\nimport glob\nimport pandas as pd\nimport numpy as np\nimport importlib\nimport IPython\nimport sys\nimport time\nfrom collections import defaultdict\nfrom matplotlib import pyplot as plt\nimport matplotlib\n%matplotlib inline",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"init_cell": true,
"collapsed": false,
"ExecuteTime": {
"start_time": "2016-10-10T15:18:11.061000",
"end_time": "2016-10-10T15:18:11.099000"
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"trusted": true
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"cell_type": "code",
"source": "class Vividict(dict):\n def __missing__(self, key):\n value = self[key] = type(self)()\n return value\n\ndef pd_read_csv(filename, sep=\"\\t\", ignore_com=False, no_head=True):\n head = []\n if not ignore_com:\n with open(filename) as myfile:\n head = [next(myfile) for _ in xrange(6)]\n df = pd.read_csv(filename, sep=sep, skip_blank_lines=True, comment='#', low_memory=False)\n df.columns = [c.replace(' ', '_') for c in df.columns]\n df.columns = [c.decode('unicode_escape').encode('ascii', 'ignore') for c in df.columns]\n if no_head:\n return df\n else:\n return df, head\n \ndef series_from_df(df, channel, res, level):\n ser = pd.Series(df[channel].values, df['FrameNo'].values, name='_'.join([level, channel, res]))\n return ser\n\ndef read_experiment(prefix, datadir= '.', skip_bl=True):\n \"\"\"\n prefix like: 'Christopher_DGJO'\n creates dict: data = {'Online'}\n \"\"\"\n \n all_dirs = os.listdir(datadir)\n dirs = [x for x in all_dirs if x.startswith(prefix)]\n factors = [os.path.basename(x)[len(prefix)+1:] for x in dirs]\n tuples = zip(dirs, factors)\n d = Vividict()\n for directory, level in tuples:\n this_files = os.listdir(directory)\n resolutions = [os.path.splitext(x)[0].split('_')[-1] for x in this_files]\n dfs = [pd_read_csv(os.path.join(directory, x), no_head=True) for x in this_files]\n if skip_bl:\n dfs = [df.dropna(subset=['MediaTime']) for df in dfs]\n this_res_map = dict(zip(resolutions, dfs))\n d[level]['data_frames'] = this_res_map\n for resolution in d[level]['data_frames']:\n channels = ['Disgust_Evidence', 'Joy_Evidence', 'Neutral_Evidence']\n for chan in channels:\n d[level][chan][resolution] = series_from_df(d[level]['data_frames'][resolution], chan, resolution, level)\n #d[level]['disgust'] = [pd.Series(df.Disgust_Evidence, df.FrameNo) for df in dfs]\n return d\n \n ",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2016-10-10T15:18:28.874000",
"end_time": "2016-10-10T15:18:29.036000"
},
"trusted": true
},
"cell_type": "code",
"source": "data = read_experiment('Christopher_DGJO')",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"scrolled": false,
"ExecuteTime": {
"start_time": "2016-10-10T16:05:51.707000",
"end_time": "2016-10-10T16:05:53.323000"
},
"trusted": true
},
"cell_type": "code",
"source": "#%matplotlib inline\nimport mpld3\nmpld3.enable_notebook()\n\n#print \"PP: postprocessed (noow: not overwriting online frame classifications)\"\nresolutions = ['320x240', '640x480', '352x288']\nchans = ['Disgust_Evidence', 'Joy_Evidence']\n\nlocs = [1,3,5,2,4,6]\nn_cols = 2\nn_rows = 3\n\nfig, axes = plt.subplots(nrows=n_rows, ncols=n_cols)\nfig.set_size_inches(n_cols*6,n_rows*6)\nfig.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.95, wspace=0.2, hspace=0.2) \n\n\ncol_no = 0\nfor chan in chans:\n row_no = 0\n for res in resolutions:\n ax = axes[row_no, col_no]\n for level in data:\n data[level][chan][res].plot(title='_'.join([chan, res]), label=level, legend=True, ax=ax)\n row_no +=1\n col_no += 1\n ",
"execution_count": 72,
"outputs": [
{
"output_type": "display_data",
"data": {
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mpld3.draw_figure(\"fig_el151525439260328321599193\", {\"axes\": [{\"xlim\": [0.0, 891.0], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"Disgust_Evidence_320x240\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5, 1.0154320987654319], \"rotation\": -0.0, \"id\": \"el15152567959168\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"PP_noow\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.79733072916666659, 0.93888888888888911], \"rotation\": -0.0, \"id\": \"el15152569969688\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"PP_ow\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.79733072916666659, 0.88425925925925908], \"rotation\": -0.0, \"id\": 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