{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures - Cours interférences\n",
    "\n",
    "On considère le système des fentes d'Young avec :\n",
    "$$\\delta=\\frac{ax}{f'} \\ ; \\ i=\\frac{\\lambda_o f'}{a} $$\n",
    "\n",
    "L'intensité sur l'écran est donné par la formule de Fresnel :\n",
    "$$I=2I_o \\left(1+\\cos\\left(\\frac{2\\pi}{\\lambda_o}\\delta\\right)\\right)$$\n",
    "Ou bien en utilisant l'interfrange :\n",
    "\n",
    "$$I=2I_o\\left(1+\\cos\\left(\\frac{2\\pi x}{i}\\right)\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b841b00>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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04OLorKY2VMpwQjtZHyca9OD1gYSmNlSKlhp3TjToxVQqh2yuqMnCs82gF407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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1091ed5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "N=500\n",
    "x=np.linspace(-2,2,N)\n",
    "i=1\n",
    "I=2*(1+np.cos(2*np.pi*x/i))\n",
    "\n",
    "plt.plot(x,I)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour faire une représentation 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10bf7dd68>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SBjubRh4HNDMrxy00M6uNzqAr0JsDmpkVV/Hn0Lwup5mVoshPuWX0WE1uRh5JujetILdP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\nDTesZ75xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088cd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "I2D=np.zeros((N,N))\n",
    "for j in range (N):\n",
    "    I2D[j,:]=2*(1+np.cos(2*np.pi*x/i))\n",
    "plt.imshow(I2D,vmin=0,vmax=4)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On somme cette fois 2 systèmes de franges de mêmes interfranges mais dont les franges centrales sont décalées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10ccbc3c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SfYOkhyUdLlO637Vc+tSoIqKRlnr9FoA/sP2EpIuBxyXts314qcSpUUVEfaZqTB+3jDuM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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102eb4470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xo=0.2\n",
    "\n",
    "\n",
    "I2Da=np.zeros((N,N))\n",
    "for j in range (N):\n",
    "    I2Da[j,:]=2*(1+np.cos(2*np.pi*(x-xo)/i)+1+np.cos(2*np.pi*(x+xo)/i))\n",
    "plt.imshow(I2Da,vmin=0,vmax=8)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "On peut regarder ce qui se passe lorsqu'on somme deux intensités correspondant à la même frange centrale mais à des interfranges différents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10d294da0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x102e8afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i1=1\n",
    "i2=1.1\n",
    "X=np.linspace(-10,10,N)\n",
    "\n",
    "\n",
    "I2Db=np.zeros((N,N))\n",
    "for j in range (N):\n",
    "    I2Db[j,:]=2*(1+np.cos(2*np.pi*(X)/i1)+1+np.cos(2*np.pi*(X)/i2))\n",
    "plt.imshow(I2Db,vmin=0,vmax=8)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "colab": {
   "name": "python4tp.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.6.1"
  },
  "toc": {
   "base_numbering": "0",
   "nav_menu": {
    "height": "369px",
    "width": "618.333px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table des Matières",
   "title_sidebar": "Sommaire",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
