Histogramme-incertitude_2nde.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tracer un histogramme de donnnées"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussienne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 20, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# moyenne\n",
    "m = 11.5\n",
    "# écart-type\n",
    "s = 1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 1/(s*math.sqrt(2*math.pi)) * np.exp(-(x-m)**2 / (2*s**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd76a108668>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussienne en histogramme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [0]*8 + [3, 14, 41, 80, 100, 41, 14, 3] + [0]*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zx6cZ3Mh7cEGz24DXZ+AS4Injf0qugKFXWNPqs3nmn0tbgVsXafNZ4OVJ1nfDFC/vlk1MkkuBdwCvqqrvD2mzlOM+idrm39v59SH7nNbjUV4GfLWqDi22ctJ9doKcWLnzbBJ3mSd05/pyBnerHwbe2S37EwYnPcDTGQwDHAD+BbhgBWr6VQZ/Rt0P3Nf9XA68EXhj1+bNwEMMPmFwN/DCFeqvC7p9frnb//E+m19bGPwnMQ8DDwBzK1TbMxgE93PmLZtKnzH4x+Yw8EMGV0/XMri3sxvY372e3rWdAz44b9s3dOfbAeCaFajrAIPx2uPn2vFPmf0scMeJjvsK1PYP3Tl0P4MA27iwtm7+//weT7KubvmHjp9b89quWJ+dICdW7Dzzm7GS1Li1MnQjSRqRQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+B61npxqiWTYjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.bar(x, y, align='edge')\n",
    "plt.show()"
   ]
  }
 ],
 "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
}