2019-10-08 11:40:35 +02:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Chargement des données"
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]
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},
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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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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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2019-10-14 18:03:24 +02:00
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"from mpl_toolkits.mplot3d import Axes3D\n",
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2019-10-08 11:40:35 +02:00
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"%matplotlib widget\n",
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"import pickle\n",
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"import scipy.signal\n",
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"\n",
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"\n",
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"my_data = np.genfromtxt('../Supelec_2012_SIR_Spectral_Analysis_EA_v001.csv', delimiter=',')\n",
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"# c'est plus rapide de charger des pickle que des csv\n",
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"a=\"\"\"\n",
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"with open(\"data.pickle\", \"wb\") as file:\n",
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" pickle.dump(my_data, file)\n",
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"\n",
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"with open(\"data.pickle\", \"rb\") as file:\n",
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" my_data = pickle.load(file)\n",
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"\"\"\""
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]
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},
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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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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.figure()\n",
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"plt.imshow(np.abs(my_data), aspect=\"auto\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Constantes"
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]
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},
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{
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"cell_type": "code",
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2019-10-14 18:03:24 +02:00
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"execution_count": 3,
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2019-10-08 11:40:35 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"# paramètres du signal\n",
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"Te = 10**(-6)\n",
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"Fe = 1/Te\n",
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"\n",
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"def make_cos_window(te, Tcut, length):\n",
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" dT=Tcut/16\n",
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" len1 = int((Tcut-dT)/te)\n",
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" len2 = int(dT/te)\n",
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" len3 = length - len1 - 2*len2\n",
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" cospart = np.cos(np.linspace(0, np.pi, len2))*0.5 + 0.5\n",
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" return np.concatenate([\n",
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" cospart[::-1],\n",
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" np.ones(len1),\n",
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" cospart,\n",
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" np.zeros(len3),\n",
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" ])\n",
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"\n",
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"# fenêtre cosinus\n",
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"cos_window = make_cos_window(Te, 0.6e-3, 4096)\n",
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"# coefficients pour filtre de chebychev\n",
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"chebyfilt = scipy.signal.cheby2(2, 40, [2e3, 2e5], fs=Fe, btype='bandpass', output='sos')\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Aide à l'affichage"
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]
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},
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{
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"cell_type": "code",
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2019-10-14 18:03:24 +02:00
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"execution_count": 4,
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2019-10-08 11:40:35 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_fft(sig, te, absolute=True):\n",
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" N = len(sig)//2\n",
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" signal_f = np.fft.fft(sig)[:N]\n",
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" if absolute:\n",
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" signal_f = abs(signal_f)\n",
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" freqs = np.arange(0, N)*0.5/te\n",
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" return freqs, signal_f\n",
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"\n",
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"def plot_fft(sig, te, absolute=True):\n",
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" plt.plot(*get_fft(sig, te, absolute))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Analyse d'un signal"
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]
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},
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{
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"cell_type": "code",
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2019-10-14 18:03:24 +02:00
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"execution_count": 5,
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2019-10-08 11:40:35 +02:00
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"metadata": {},
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2019-10-14 18:03:24 +02:00
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"outputs": [
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{
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"data": {
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"image/png": "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
|
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|
"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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|
]
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|
},
|
|
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|
|
"metadata": {
|
|
|
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|
"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
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|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
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|
|
|
]
|
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|
|
},
|
|
|
|
|
"metadata": {
|
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|
"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
|
2019-10-08 11:40:35 +02:00
|
|
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"source": [
|
|
|
|
|
"def sig_simple(signal):\n",
|
|
|
|
|
" time = np.arange(0, len(signal))*Te\n",
|
|
|
|
|
" signal_win = cos_window * signal\n",
|
|
|
|
|
" signal_filt = scipy.signal.sosfilt(chebyfilt, signal_win)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" freqs, sig_tf = get_fft(signal_filt, Te)\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" energy_f = np.multiply(sig_tf, sig_tf)\n",
|
|
|
|
|
" bary_f = sum(freqs * energy_f) / sum(energy_f)\n",
|
|
|
|
|
" var_f = np.sqrt( sum((freqs - bary_f)**2 * energy_f) / sum(energy_f) )\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" plt.figure()\n",
|
|
|
|
|
" plt.plot(time, signal, linewidth=1)\n",
|
|
|
|
|
" plt.plot(time, signal_win, linewidth=1)\n",
|
|
|
|
|
" plt.plot(time, signal_filt, linewidth=1)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" plt.figure()\n",
|
|
|
|
|
" plot_fft(signal, Te)\n",
|
|
|
|
|
" plot_fft(signal_win, Te)\n",
|
|
|
|
|
" plot_fft(signal_filt, Te)\n",
|
|
|
|
|
" plt.vlines([bary_f-var_f, bary_f, bary_f+var_f], 0, max(sig_tf), colors=['r', 'g', 'r'])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"signal = my_data[:,101]\n",
|
|
|
|
|
"sig_simple(signal) \n"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-10-14 18:03:24 +02:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"def weighted_quantile(values, quantiles, sample_weight=None, \n",
|
|
|
|
|
" values_sorted=False, old_style=False):\n",
|
|
|
|
|
" \"\"\" Very close to numpy.percentile, but supports weights.\n",
|
|
|
|
|
" NOTE: quantiles should be in [0, 1]!\n",
|
|
|
|
|
" :param values: numpy.array with data\n",
|
|
|
|
|
" :param quantiles: array-like with many quantiles needed\n",
|
|
|
|
|
" :param sample_weight: array-like of the same length as `array`\n",
|
|
|
|
|
" :param values_sorted: bool, if True, then will avoid sorting of\n",
|
|
|
|
|
" initial array\n",
|
|
|
|
|
" :param old_style: if True, will correct output to be consistent\n",
|
|
|
|
|
" with numpy.percentile.\n",
|
|
|
|
|
" :return: numpy.array with computed quantiles.\n",
|
|
|
|
|
" \"\"\"\n",
|
|
|
|
|
" values = np.array(values)\n",
|
|
|
|
|
" quantiles = np.array(quantiles)\n",
|
|
|
|
|
" if sample_weight is None:\n",
|
|
|
|
|
" sample_weight = np.ones(len(values))\n",
|
|
|
|
|
" sample_weight = np.array(sample_weight)\n",
|
|
|
|
|
" assert np.all(quantiles >= 0) and np.all(quantiles <= 1), \\\n",
|
|
|
|
|
" 'quantiles should be in [0, 1]'\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" if not values_sorted:\n",
|
|
|
|
|
" sorter = np.argsort(values)\n",
|
|
|
|
|
" values = values[sorter]\n",
|
|
|
|
|
" sample_weight = sample_weight[sorter]\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight\n",
|
|
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|
|
" if old_style:\n",
|
|
|
|
|
" # To be convenient with numpy.percentile\n",
|
|
|
|
|
" weighted_quantiles -= weighted_quantiles[0]\n",
|
|
|
|
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" weighted_quantiles /= weighted_quantiles[-1]\n",
|
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|
|
" else:\n",
|
|
|
|
|
" weighted_quantiles /= np.sum(sample_weight)\n",
|
|
|
|
|
" return np.interp(quantiles, weighted_quantiles, values)"
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|
]
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},
|
2019-10-08 11:40:35 +02:00
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Analyse de tous les signaux, pour extraire les paramètres significatifs"
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|
]
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},
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{
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"cell_type": "code",
|
2019-10-14 18:03:24 +02:00
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"execution_count": 7,
|
2019-10-08 11:40:35 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
|
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|
|
|
"def extract_params(signal):\n",
|
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|
|
|
" # fenetrage\n",
|
|
|
|
|
" signal_win = cos_window * signal\n",
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" \n",
|
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|
|
|
" # filtrage\n",
|
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|
|
|
" signal_filt = scipy.signal.sosfilt(chebyfilt, signal_win)\n",
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"\n",
|
|
|
|
|
" # fft\n",
|
|
|
|
|
" freqs, sig_tf = get_fft(signal_filt, Te)\n",
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" \n",
|
2019-10-14 18:03:24 +02:00
|
|
|
" # moyenne\n",
|
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|
|
|
" energy_f = sig_tf**2\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
" bary_f = sum(freqs * energy_f) / sum(energy_f)\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
" # variance\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
" var_f = sum((freqs - bary_f)**2 * energy_f) / sum(energy_f)\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
" # moments (3 à 10)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
" moments = [sum((freqs - bary_f)**n * energy_f) / sum(energy_f) for n in range(3, 11)]\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
" # fréquence max\n",
|
|
|
|
|
" max_f = freqs[np.argmax(sig_tf)]\n",
|
|
|
|
|
" # max par pondération suffisemment forte\n",
|
|
|
|
|
" #max_f = sum(freqs * (sig_tf**20)) / sum(sig_tf**20)\n",
|
|
|
|
|
" # quantiles\n",
|
|
|
|
|
" q1, median, q3 = weighted_quantile(freqs, [0.25, 0.5, 0.75], sample_weight=sig_tf**2)\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" return [bary_f, var_f] + moments + [max_f, q1, median, q3]\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"barys_vars_f = []\n",
|
|
|
|
|
"for i in range(my_data.shape[1]):\n",
|
|
|
|
|
" barys_vars_f.append(extract_params(my_data[:, i]))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"color = np.arange(len(barys_vars_f))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"vars_f, barys_f, mom3, mom4, mom5, mom6, mom7, mom8, mom9, mom10, maxs_f, q1_f, meds_f, q3_f = zip(*barys_vars_f)"
|
2019-10-08 11:40:35 +02:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-10-14 18:03:24 +02:00
|
|
|
"execution_count": 14,
|
2019-10-08 11:40:35 +02:00
|
|
|
"metadata": {},
|
2019-10-14 18:03:24 +02:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.colorbar.Colorbar at 0x7fa23b9ff6a0>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 14,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 2 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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2019-10-08 11:40:35 +02:00
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"source": [
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"plt.figure()\n",
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2019-10-14 18:03:24 +02:00
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"plt.scatter(np.log(vars_f), barys_f, marker='.', c=color)\n",
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"plt.xlabel(\"Variance fréquentielle\")\n",
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"plt.ylabel(\"Moyenne fréquentielle\")\n",
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"plt.colorbar()\n",
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"\n",
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"plt.figure()\n",
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"plt.scatter(np.log(vars_f), mom3, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
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|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
2019-10-14 18:03:24 +02:00
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"plt.ylabel(\"Moment d'ordre 3\")\n",
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"plt.colorbar()\n",
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"\n",
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"plt.figure()\n",
|
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"plt.scatter(mom3, barys_f, marker='.', c=color)\n",
|
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"plt.xlabel(\"Moment d'ordre 3\")\n",
|
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"plt.ylabel(\"Moyenne fréquentielle\")\n",
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"plt.colorbar()\n",
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"\n",
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"plt.figure()\n",
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"plt.scatter(maxs_f, barys_f, marker='.', c=color)\n",
|
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"plt.xlabel(\"Fréquence maximale\")\n",
|
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|
"plt.ylabel(\"Moyenne fréquentielle\")\n",
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"plt.colorbar()\n",
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"\n",
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|
"plt.figure()\n",
|
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|
"plt.scatter(q1_f, barys_f, marker='.', c=color)\n",
|
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|
|
"plt.xlabel(\"1er quartile fréquentiel\")\n",
|
|
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|
|
"plt.ylabel(\"Moyenne fréquentielle\")\n",
|
|
|
|
|
"plt.colorbar()\n",
|
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|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
|
|
|
|
"plt.scatter(meds_f, mom3, marker='.', c=color)\n",
|
|
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|
|
"plt.xlabel(\"Mediane fréquentielle\")\n",
|
|
|
|
|
"plt.ylabel(\"Moment d'ordre 3\")\n",
|
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"plt.colorbar()"
|
2019-10-08 11:40:35 +02:00
|
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|
]
|
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|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-10-14 18:03:24 +02:00
|
|
|
"execution_count": 9,
|
2019-10-08 11:40:35 +02:00
|
|
|
"metadata": {},
|
2019-10-14 18:03:24 +02:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fa23bd59da0>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 9,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig = plt.figure()\n",
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"ax = fig.add_subplot(111, projection='3d')\n",
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"ax.scatter(vars_f, barys_f, mom3, marker='.')\n",
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"\n",
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"fig = plt.figure()\n",
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"ax = fig.add_subplot(111, projection='3d')\n",
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"ax.scatter(vars_f, barys_f, maxs_f, marker='.')"
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]
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},
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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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"outputs": [
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{
|
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"data": {
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"text/plain": [
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"Text(0, 0.5, \"Moment d'ordre 10\")"
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]
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},
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"execution_count": 10,
|
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"metadata": {},
|
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"output_type": "execute_result"
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},
|
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|
{
|
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|
|
"data": {
|
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|
|
"image/png": "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
|
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|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
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"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
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|
]
|
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|
|
},
|
|
|
|
|
"metadata": {
|
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|
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|
"needs_background": "light"
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},
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|
"output_type": "display_data"
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}
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],
|
2019-10-08 11:40:35 +02:00
|
|
|
"source": [
|
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|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.scatter(vars_f, mom4, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
|
|
|
|
"plt.ylabel(\"Moment d'ordre 4\")\n",
|
|
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|
|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.scatter(vars_f, mom5, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
|
|
|
|
"plt.ylabel(\"Moment d'ordre 5\")\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.scatter(vars_f, mom6, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
|
|
|
|
"plt.ylabel(\"Moment d'ordre 6\")\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.scatter(vars_f, mom7, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
|
|
|
|
"plt.ylabel(\"Moment d'ordre 7\")\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.scatter(vars_f, mom8, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
|
|
|
|
"plt.ylabel(\"Moment d'ordre 8\")\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.scatter(vars_f, mom9, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
|
|
|
|
"plt.ylabel(\"Moment d'ordre 9\")\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.scatter(vars_f, mom10, marker='.', c=color)\n",
|
2019-10-08 11:40:35 +02:00
|
|
|
"plt.xlabel(\"Variance fréquentielle\")\n",
|
2019-10-14 18:03:24 +02:00
|
|
|
"plt.ylabel(\"Moment d'ordre 10\")\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 11,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.colorbar.Colorbar at 0x7fa228c1a898>"
|
|
|
|
|
]
|
|
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|
|
},
|
|
|
|
|
"execution_count": 11,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
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|
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAESCAYAAAAcxXWZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3xddf348df77oxmp3sk3S0dUCqUvfeUoewhQ5nKEMGBioqg/vgCimjZqGxBKhRKBcos0E13SfdI0+ydu87798e5CWlJk5M0yb1JPk8fx+ae+b4t+dxz3+fzeX9EVTEMwzB6N1e8AzAMwzC6nmnsDcMw+gDT2BuGYfQBprE3DMPoA0xjbxiG0QeYxt4wDKMP6LGNvYg8KSK7RGSFg32PFJHFIhIRkfP22BYVkaWxZVbXRWwYhhE/PbaxB54GTna47xbgCuC5FrbVq+r+seXMTorNMAwjofTYxl5VPwTKmq8TkVEi8raILBKRj0RkfGzfTar6JWDFI1bDMIx467GN/V7MBG5S1QOB24G/OjgmICILReQzETm7a8MzDMOID0+8A+gsIpIKHAq8LCKNq/0ODh2hqttFZCTwnogsV9X1XRWnYRhGPPSaxh77W0qFqu7fnoNUdXvszw0iMg84ADCNvWEYvUqvSeOoahWwUUTOBxDb1NaOEZFMEfHHfs4BDgNWdXmwhmEY3Ux6atVLEXkeOBrIAYqAXwLvAY8CgwAv8IKq3iMi3wJeAzKBBmCnqu4nIocCf8d+cOsCHlTVJ7r7vRiGYXS1HtvYG4ZhGM71mjSOYRiGsXemsTcMw+gDemRvnJycHM3Ly4t3GIZh9ACLFi0qUdXcjh5/0jEpWloWdXatL4NzVNXpyP5u1SMb+7y8PBYuXBjvMAzD6AFEZPO+HF9SFuXzOUMd7esdtD5nX67VlXpkY28YhtF9lKj2/EorJmdvGIbRCgUs1NHSltaq9YrIbSKisTE/jWOFHhaRAhH5UkSmNdv3chH5KrZc7uR9mMbeMAyjDZbD/znwNC1U6xWRYcCJ2BV6G50CjIkt12KPIUJEsrDHFR0MHAT8UkQy27qwaewNwzBaoShhtRwtbZ6rhWq9Mf8H3AG7fT04C3hWbZ8BGSIyCDgJmKuqZapaDszFQbl3k7M3DMNohQJRBymajhKRs4DtqrqsWRFHgCHA1mavt8XW7W19q0xjbxhGtwiHIhQs3UT24Ez6D82Odzjt4iQfH5MjIs27Cs5U1Zl721lEkoGfYqdwupRp7A3D6HLhUIQfHvUrdmzYhRW1uPv5m5l+wpR4h+WIAlHnZWVKVHV6O04/CsgHGu/qhwKLReQgYDswrNm+Q2PrtmPXBWu+fl5bFzI5e8Mwuty6RRvYsWEX9TUNBOtDvPCnN+IdUrtYDpf2UtXlqtpfVfNUNQ87JTNNVXcCs4DLYr1yZgCVqloIzAFOjFXtzcT+VjCnrWuZxt4wjE6zc1MxNx7xKy4a8yPe+dfHTeuzB2cSjdrNodfvYdi4QfEKsd0UJepwaUusWu98YJyIbBORq1rZfTawASgAHgOuB1DVMuA3wILYck9sXatMGscwjE7zwPVPsH75FtRS/nLLs0w/fjJZA9IZOCKXn/3jBl76f28wfNxgvv/7i+IdqmOqEO6k57OqemEb2/Oa/azADXvZ70ngyfZc2zT2hmF0mprKetT6umUM1gWbfp5xygHMOOWAeIS1j4Qo0vZuCc6kcQzD6DQ/uP9CAsl+3F43x194GAPzOlx/LGEoYKmzJZGZO3vDMDrNlMPH89LmPxOqD5OakRzvcDpNb7izN429YRidyuf34vN7O/WclmXxn7/9jzULNnDiJYcz/bhJnXr+1tiDqkxjbxiG0eVmzXyPZ373H4J1IT6f8yUP/e9n5E1sc9Bop1AgrD0/493z34FhGL3e2sUbCdaFAHC5XWxZV9ht11aEKC5HSyJL7OgMwzCAky45HH+Sj6TUAD6/hymHj+vW61sqjpZEZtI4hmEkvP2PnMBf5v2CzWt3MPnQsaRn9+u2a5ucvWEYRjcaNnYQw8bGY+StEDU5+9a1NitLbPvFsRlYlovIpyIytSvjMQzDaC97piqXoyWRdXV0T9N6Uf2NwFGqOhm71sNeS4EahmHEg6oQUrejJZF1aRpHVT8UkbxWtn/a7OVn2KU6DcMwEoplcvad6irgrb1tFJFrsedhZPjw4d0Vk2EYfZz9gDaxUzROJERjLyLHYDf2h+9tn9hsLzMBpk+fnuBVKAzD6D16xwPauDf2IjIFeBw4RVVL4x2PYRhGc40PaHu6uDb2IjIceBW4VFXXxTMWwzCMvYkm+IApJ7q0sY/NynI09iS824BfAl4AVf0bcDeQDfw1Nv9ipJ3zNxqGYXQpRQhr3JMg+6yre+O0NSvL1cDVXRmDYRjGvjAPaA3DMPoARUwaxzAMoy8wD2gNwzB6OVV6RdfLnv8ODMMwupD9gNbtaGlLS/XCROSPIrImVifsNRHJaLbtLhEpEJG1InJSs/Unx9YViMidTt6HaewNwzDa0ImTlzzNN+uFzQUmqeoUYB1wF4CITAQuAPaLHfNXEXGLiBt4BDgFmAhcGNu3VaaxNwzDaIXibOISJ5OXqOqHQNke695R1UjsZfMaYWcBL6hqUFU3AgXAQbGlQFU3qGoIeCG2b6tMzt4wDKMN3dj18nvAi7Gfh2A3/o22xdYBbN1j/cFtndg09oZhGK1QwHL+gDZHRBY2ez0zVterTSLyMyAC/Kt9ETpjGnvDMIxWSXumJSzpSBUAEbkCOB04TlUbCz1uB4Y1221obB2trN8rk7M3DKNdKspqCYUibe/YSyh0Wm+clojIycAdwJmqWtds0yzgAhHxi0g+MAb4AlgAjBGRfBHxYT/EndXWdcydvWEYjqgq9972AvPnrcbr83DfY1cybvKwtg/s4VSlPWmcVu2lXthdgB+YG6sR9pmq/kBVV4rIS8Aq7PTODaoajZ3nRmAO4AaeVNWVbV3bNPaGYTiyfk0hCz5eSyQcJRKO8sQDc/jDU32jtFVnDaraS72wJ1rZ/3fA71pYPxuY3Z5rm8beMAxHklP8WJadTna7XfRLT4pzRN3Drmff82vjmJy9YRiODB6ezTW3nUJWbj/GTxnG9T87I94hdRN7pionSyIzd/aGYTh2xoUzOOPCGfEOo1vZXS97/p29aewNwzBa0Vgbp6czjb1hGEYbTIljwzCMXs4ucWzSOIZhGL2eydkbhmH0cnbVS5PGMQzD6NXscgmmsTcMw+jlesedfZe+g5am4Npju4jIw7Gptb4UkWldGY9hGEZHWIijJZF19cfV03xzCq7mTsGu5DYGuBZ4tIvjMQyjE9VU1PLQDU9wz3cfZOPyLfEOp0s09sZxsiQyR2kcERmL3RAPUNVJIjIFuxznb1s7TlU/FJG8VnY5C3g2Vr/5MxHJEJFBqlroLHzDMOLp3kv+zNJ5K4mGoyx9fwUvbPkrvoAv3mF1ur6UxnkMuwxnGEBVv8SuobyvhvDN6bWGtLSjiFwrIgtFZGFxcXEnXNowjH21aeU2IqEoqhCsD1FdVhvvkDpdZ85BG09OG/tkVf1ij3XdOnuBqs5U1emqOj03N7c7L20Yxl6cfeNJ+JP9JKUGmDhjLFmDMuIdUqdTIKIuR0sic9obp0RERmG/b0TkPKAzUi2tTbtlGEaC+85tZ7D/MftRU17L1KMmEpt8o9fpDWkcp439DcBMYLyIbAc2Apd0wvVnATeKyAvYs6NXmny9YfQsY6eNjHcIXasHpGiccNTYq+oG4HgRSQFcqlrt5Li9TMHljZ3zb9gzrZwKFAB1wJXtfQOGYRhdqbdMXtJqYy8it+5lPQCq+kBrx+9lCq7m2xX7W4NhGEbC6gt39v26JQrDMIwE1ScmL1HVX3dXIIZhGIlIESJWL39AKyIPt7ZdVW/u3HAMwzAST2fl7EXkSeB0YJeqToqtywJeBPKATcB3VLVc7Hz5Q9jPNeuAK1R1ceyYy4Gfx077W1V9pq1rt5XGWdTud2MYhtGbaKemcZ4G/gI822zdncC7qnq
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"text/plain": [
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"<Figure size 432x288 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
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|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 2 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"plt.figure()\n",
|
|
|
|
|
"plt.scatter(q3_f, barys_f, marker='.', c=color)\n",
|
|
|
|
|
"plt.xlabel(\"3eme quartile fréquentiel\")\n",
|
|
|
|
|
"plt.ylabel(\"Moyenne fréquentielle\")\n",
|
|
|
|
|
"plt.colorbar()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.figure()\n",
|
|
|
|
|
"plt.scatter(maxs_f, vars_f, marker='.', c=color)\n",
|
|
|
|
|
"plt.xlabel(\"Fréquence maximale\")\n",
|
|
|
|
|
"plt.ylabel(\"Variance fréquentielle\")\n",
|
|
|
|
|
"plt.colorbar()"
|
2019-10-08 11:40:35 +02:00
|
|
|
]
|
|
|
|
|
},
|
2019-10-14 18:03:24 +02:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
2019-10-08 11:40:35 +02:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"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": "3.6.8"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 2
|
|
|
|
|
}
|
