{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogramme et évaluation de l'incertitude-type de type A sur une série de mesures"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ":download:`Télécharger le pdf <./histogramme.pdf>`\n",
    "\n",
    ":download:`Télécharger le notebook <./histogramme.ipynb>`\n",
    "\n",
    ":download:`Lancer le notebook sur binder (lent) <https://mybinder.org/v2/gl/pyspc%2Fpyspc/master?filepath=07-activites%2F00-terminale%2Fhistogramme%2Fhistogramme.ipynb>`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Vbecher=[50.65,48.26,47.83,47.76,50.26,47.23,43.88,43.92,48.69,48.66,43.67,47.53,49.55,50.64,43.8,48.53]\n",
    "Veprouvette =[49.61,49.55,50.91,50.87,48.03,50.29,48.58,48.06,50.06,50.72,48.95,49.4,49.21,49.31,49.78,48.77]\n",
    "Vfiole=[49.74,49.77,49.71,49.75,49.52,49.8,49.61,49.56,49.65,49.65,49.52,49.64,49.74,49.81,49.5,49.59]\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.hist(Vbecher,bins=80,range=(43,51),align=\"left\",rwidth=0.3,color=\"r\",label=\"becher\")\n",
    "plt.hist(Veprouvette,bins=80,range=(43,51),align=\"mid\",rwidth=0.3,color=\"b\",label=\"éprouvette\")\n",
    "plt.hist(Vfiole,bins=80,range=(43,51),align=\"right\",rwidth=0.3,color=\"g\",label=\"fiole\")\n",
    "plt.title(\"Volume d'eau contenu dans un bécher, une éprouvette graduée ou une fiole jaugée pour 50mL mesuré\")\n",
    "plt.xlabel(\"Volume (mL)\")\n",
    "plt.ylabel(\"Fréquence\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def statistique(x):\n",
    "    moy=np.mean(x)\n",
    "    ecarttype=np.std(x)\n",
    "    effectif=len(x)\n",
    "    incertitudetype=ecarttype/np.sqrt(effectif)\n",
    "    return (moy,ecarttype,effectif,incertitudetype)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47.553749999999994, 2.378105535399974, 16, 0.5945263838499935)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistique(Vbecher)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49.506249999999994, 0.8814042418209694, 16, 0.22035106045524236)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistique(Veprouvette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49.66, 0.10012492197250353, 16, 0.025031230493125882)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistique(Vfiole)"
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}