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Innov-Plus/filter.ipynb

Created May 12, 2020 15:41
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filter.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# %load_ext autoreload\n",
"# %autoreload 2\n",
"# import drowsy.switching as sw\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import norm\n",
"from scipy import optimize\n",
"from datetime import timedelta\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.dates import DateFormatter\n",
"import warnings\n",
"import os\n",
"\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_path = 'C:/Users/Florent/Desktop/toucango_pybind/Synthese/Synthese/Data/'\n",
"reference = pd.read_csv(data_path + 'reference.txt', delimiter='\\t')\n",
"\n",
"# Charge le fichier bkg.csv\n",
"def load_bkg(exp, raw=True):\n",
" row = reference.loc[reference['Ref'] == exp]\n",
" blink = pd.read_csv(\n",
" data_path + row['ID'].values[0] + '/' +\n",
" row['Expérimentation'].values[0] + '/bkg.csv',\n",
" sep='\\t')\n",
" blink['Debut'] = pd.to_datetime(blink['Debut'])\n",
" blink = blink.set_index('Debut')\n",
" blink.rename(columns={'eye_cli': 'blink'}, inplace=True)\n",
"\n",
" string_windows = '60s'\n",
" int_windows = 60\n",
" blink['Blink_rounded'] = blink.round({'blink': -1})['blink']\n",
" data = blink['Blink_rounded']\n",
"\n",
" # Calcule la moyenne glissange sur 60 seconds\n",
" if not raw:\n",
" data = data.rolling(window=string_windows).mean()\n",
" start = data.first_valid_index()\n",
" row_transition = data.index.get_loc(data.loc[\n",
" data.index >= start + timedelta(seconds=int_windows * 2)].index[0])\n",
" data = data.iloc[row_transition:]\n",
"\n",
" return data\n",
"\n",
"# Charge le fichier status.csv\n",
"def load_status(exp):\n",
" row = reference.loc[reference['Ref'] == exp]\n",
" status = pd.read_csv(\n",
" data_path + row['ID'].values[0] + '/' +\n",
" row['Expérimentation'].values[0] + '/status.csv',\n",
" sep='\\t')\n",
" status['Debut'] = pd.to_datetime(status['Debut'])\n",
" status['Fin'] = pd.to_datetime(status['Fin'])\n",
" return status"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Charge tous les expérimentations et met dans un dataframe res_df\n",
"\n",
"res_dict = {}\n",
"res_dict['exp'] = []\n",
"res_dict['blink'] = []\n",
"res_dict['status'] = []\n",
"\n",
"for exp in reference['Ref']:\n",
" row = reference.loc[reference['Ref'] == exp]\n",
" if os.path.exists(data_path + row['ID'].values[0] + '/' +\n",
" row['Expérimentation'].values[0] + '/bkg.csv'):\n",
" blink = load_bkg(exp, raw=False)\n",
" status = load_status(exp)\n",
" res_dict['exp'].append(exp)\n",
" res_dict['blink'].append(blink)\n",
" res_dict['status'].append(status)\n",
"\n",
"res_df = pd.DataFrame(\n",
" res_dict,\n",
" columns=['exp', 'blink', 'status'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"blink_zeros = []\n",
"blink_not_zeros = []\n",
"for i in res_df.index:\n",
" for j in res_df.loc[i]['status'].index:\n",
" blink = res_df.loc[i]['blink']\n",
" if (res_df.loc[i]['status'].loc[j]['Etat'] >= 2):\n",
" b = blink[blink.index >= res_df.loc[i]['status'].loc[j]['Debut']]\n",
" b = b[b.index <= res_df.loc[i]['status'].loc[j]['Fin']]\n",
" blink_not_zeros += list(b)\n",
" if (res_df.loc[i]['status'].loc[j]['Etat'] == 0):\n",
" b = blink[blink.index >= res_df.loc[i]['status'].loc[j]['Debut']]\n",
" b = b[b.index <= res_df.loc[i]['status'].loc[j]['Fin']]\n",
" blink_zeros += list(b)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0xb6db358>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot\n",
"\n",
"bins = np.arange(0, 500, 1)\n",
"mu0, std0 = norm.fit(blink_zeros)\n",
"d = norm.pdf(bins, mu0, std0)\n",
"\n",
"plt.figure(figsize=(12, 4))\n",
"hist = plt.hist(blink_zeros, bins, normed=True)\n",
"\n",
"plt.plot(bins, d)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0xbc17d30>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bins = np.arange(0, 500, 1)\n",
"mu1, std1 = norm.fit(blink_not_zeros)\n",
"d = norm.pdf(bins, mu1, std1)\n",
"\n",
"plt.figure(figsize=(12, 4))\n",
"hist = plt.hist(blink_not_zeros, bins, normed=True)\n",
"\n",
"plt.plot(bins, d)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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