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Commit 1aa7b556 authored by 422b3b2bc6aaa112c184ee1926b56a23's avatar 422b3b2bc6aaa112c184ee1926b56a23
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\ No newline at end of file
module2/exo4/journal.ipynb 0 → 100644
View file @ 1aa7b556
{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"myData = open(\"./backup.data\", \"r\").read()\n",
"myDataEval = eval(myData.replace(\"false\", \"False\"))\n",
"myHistory = myDataEval[\"history\"]\n",
"# myAbsHistory : burned calories of my abdominal training app\n",
"# Categories :\n",
"# 0 Corps complet\n",
"# 1 Abdos\n",
"# 2\n",
"# 3\n",
"# 4\n",
"myAbsHistory = [eval(i[\"temp1\"])[\"calories\"] for i in myHistory if i[\"category\"] == 1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[31.080000000000048,\n",
" 33.88000000000006,\n",
" 39.20000000000008,\n",
" 37.800000000000075,\n",
" 43.1200000000001,\n",
" 51.80000000000013,\n",
" 62.440000000000175,\n",
" 49.00000000000012,\n",
" 69.1600000000002,\n",
" 55.160000000000146,\n",
" 72.88000000000031,\n",
" 73.10000000000016,\n",
" 75.14000000000016,\n",
" 82.40000000000035,\n",
" 83.36000000000033,\n",
" 87.10000000000022,\n",
" 15.619999999999987,\n",
" 95.14000000000036,\n",
" 105.58000000000035,\n",
" 102.2200000000004,\n",
" 109.42000000000039,\n",
" 111.48000000000036]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myAbsHistory"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"111.48000000000036\n",
"15.619999999999987\n",
"67.5490909090911\n",
"27.204934530520816\n"
]
}
],
"source": [
"import numpy as np\n",
"absMax = np.amax(myAbsHistory)\n",
"absMin = np.amin(myAbsHistory)\n",
"absMoy = np.mean(myAbsHistory)\n",
"absSd = np.std(myAbsHistory)\n",
"\n",
"print(absMax)\n",
"print(absMin)\n",
"print(absMoy)\n",
"print(absSd)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 25, 0, 120]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.plot(myAbsHistory)\n",
"plt.axis([0, 25, 0, 120])"
]
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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