bruits uniformes concentriques.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\programdata\\anaconda3\\lib\\site-packages (5.8.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from plotly) (8.0.1)\n",
      "Requirement already up-to-date: pymoo in c:\\programdata\\anaconda3\\lib\\site-packages (0.5.0)\n",
      "Requirement already satisfied, skipping upgrade: cma==2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (2.7.0)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (1.5.2)\n",
      "Requirement already satisfied, skipping upgrade: matplotlib>=3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (3.3.2)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.15 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (1.19.2)\n",
      "Requirement already satisfied, skipping upgrade: autograd>=1.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (1.4)\n",
      "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (2.4.7)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (1.3.0)\n",
      "Requirement already satisfied, skipping upgrade: pillow>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (8.0.1)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2020.06.20 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (2020.6.20)\n",
      "Requirement already satisfied, skipping upgrade: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (0.10.0)\n",
      "Requirement already satisfied, skipping upgrade: future>=0.15.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from autograd>=1.3->pymoo) (0.18.2)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.1->matplotlib>=3->pymoo) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly\n",
    "!pip install -U pymoo   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import random as rand\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import math\n",
    "from matplotlib import cm\n",
    "from scipy.special import betainc\n",
    "from scipy.special import gamma\n",
    "import matplotlib.pyplot as plt\n",
    "from statistics import NormalDist\n",
    "import numpy as np\n",
    "import colorsys\n",
    "import random\n",
    "from matplotlib.tri import Triangulation\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "from pymoo.core.problem import ElementwiseProblem\n",
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "from pymoo.factory import get_sampling, get_crossover, get_mutation\n",
    "from pymoo.factory import get_termination\n",
    "from pymoo.optimize import minimize\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nsphere_volume_m_20(r, dim):\n",
    "    return(((np.pi)**(dim/2))*(r**dim)/(gamma(dim/2 + 1)))\n",
    "\n",
    "def diff_cone_uniforme_opti(rayon, epsilon, c, theta, iterations, dim):\n",
    "    list_points = []\n",
    "    count_in_cone = 0\n",
    "    points_in_cone = []\n",
    "    count_in_cone_ptrue = 0\n",
    "    count_not_in_cone_ptrue = 0\n",
    "    points_in_cone_ptrue = []\n",
    "    \n",
    "    for i in range(iterations):\n",
    "        u = np.random.normal(0,1,dim)  # an array of d normally distributed random variables\n",
    "        norm= np.sum(u**2) **(0.5)\n",
    "        r = np.random.random()**(1.0/dim)\n",
    "        point = r*u/norm\n",
    "        point = point*rayon\n",
    "        list_points.append(point)\n",
    "        z = point[0]\n",
    "        S = 0\n",
    "        for i in range(len(point)-1):\n",
    "            S += point[i + 1]**2\n",
    "        rho = np.sqrt(S) \n",
    "        if (z > c) and (rho <= z*np.tan(theta)):\n",
    "            count_in_cone +=1\n",
    "            points_in_cone.append(point)\n",
    "        \n",
    "        point[0] = point[0] + epsilon\n",
    "        z = point[0]\n",
    "        S = 0\n",
    "        for i in range(len(point)-1):\n",
    "            S += point[i + 1]**2\n",
    "        rho = np.sqrt(S) \n",
    "        if (z > c) and (rho <= z*np.tan(theta)):\n",
    "            count_in_cone_ptrue +=1\n",
    "            points_in_cone_ptrue.append(point)\n",
    "        else:\n",
    "            count_not_in_cone_ptrue += 1\n",
    "            \n",
    "    vol_boule = nsphere_volume_m_20(rayon, dim)\n",
    "    \n",
    "    p1 = 1 - count_in_cone/iterations\n",
    "    \n",
    "    a = (dim + 1)/2\n",
    "    b = 1/2\n",
    "    x = 1 - (epsilon/(2*rayon))**2\n",
    "    Vcap = (1/2) * vol_boule * betainc(a, b, x) #On utilise la fonction beta incomplete de la librairie scipy.\n",
    "    \n",
    "    Psn = p1 - 1 + 2*(Vcap/vol_boule)\n",
    "    \n",
    "\n",
    "        \n",
    "           \n",
    "            \n",
    "    vol_deplace = vol_boule*(count_not_in_cone_ptrue/iterations)\n",
    "    \n",
    "    p_true = vol_deplace/vol_boule\n",
    "    \n",
    "    return(p_true - Psn, p_true, Psn)\n",
    "\n",
    "def diff_par_uniforme_opti(a_, epsilon, rayon, iterations, dim):\n",
    "    \n",
    "    list_points = []\n",
    "    count_in_par = 0\n",
    "    points_in_par = []\n",
    "    count_in_par_ptrue = 0\n",
    "    count_not_in_par_ptrue = 0\n",
    "    points_in_par_ptrue = []\n",
    "    \n",
    "    for i in range(iterations):\n",
    "        u = np.random.normal(0,1,dim)  # an array of d normally distributed random variables\n",
    "        norm= np.sum(u**2) **(0.5)\n",
    "        r = np.random.random()**(1.0/dim)\n",
    "        point = r*u/norm\n",
    "        point = point*rayon\n",
    "        list_points.append(point)\n",
    "        \n",
    "        z = point[0]\n",
    "        S = 0\n",
    "        for i in range(len(point)-1):\n",
    "            S += point[i + 1]**2\n",
    "        rho = np.sqrt(S) \n",
    "        if z > 0 and rho**2 < a_*z:\n",
    "            count_in_par +=1\n",
    "            points_in_par.append(point)\n",
    "        \n",
    "        point[0] = point[0] + epsilon\n",
    "        z = point[0]\n",
    "        S = 0\n",
    "        for i in range(len(point)-1):\n",
    "            S += point[i + 1]**2\n",
    "        rho = np.sqrt(S) \n",
    "        if z > 0 and rho**2 < a_*z:\n",
    "            count_in_par_ptrue +=1\n",
    "            points_in_par_ptrue.append(point)\n",
    "        else:\n",
    "            count_not_in_par_ptrue += 1\n",
    "        \n",
    "    vol_boule = nsphere_volume_m_20(rayon, dim)\n",
    "            \n",
    "    \n",
    "    p1 = 1 - count_in_par/iterations\n",
    "    \n",
    "    a = (dim + 1)/2\n",
    "    b = 1/2\n",
    "    x = 1 - (epsilon/(2*rayon))**2\n",
    "    Vcap = (1/2) * vol_boule * betainc(a, b, x) #On utilise la fonction beta incomplete de la librairie scipy.\n",
    "    \n",
    "    Psn = p1 - 1 + 2*(Vcap/vol_boule)\n",
    "    \n",
    "        \n",
    "            \n",
    "    vol_deplace = vol_boule*(count_not_in_par_ptrue/iterations)\n",
    "    \n",
    "    p_true = vol_deplace/vol_boule\n",
    "    \n",
    "    return(p_true - Psn, p_true, Psn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def appartient_S(point, r1, r2, k1, k2, epsilon, dim, S, count_S):\n",
    "    boule_r1 = 0 \n",
    "    boule_r2 = 0\n",
    "    boule_r1_e = 0\n",
    "    t = False\n",
    "    \n",
    "    z = point[0]\n",
    "    rho = np.linalg.norm(point[1:])\n",
    "    norme = np.linalg.norm(point)\n",
    "                           \n",
    "    if (norme <= r1):\n",
    "        boule_r1 = 1\n",
    "        \n",
    "    if (norme <= r2):\n",
    "        boule_r2 = 1\n",
    "        \n",
    "    point_e = [0]*len(point)\n",
    "    point_e[0] += epsilon\n",
    "    diff = np.array(point_e) - np.array(point)\n",
    "    dist = np.sum(np.power(diff, 2))\n",
    "    if dist < r1**2:\n",
    "        boule_r1_e = 1\n",
    "    \n",
    "    \n",
    "    vol_r1 = nsphere_volume_m_20(r1, dim)\n",
    "    vol_r2 = nsphere_volume_m_20(r2, dim)\n",
    "    \n",
    "    if (boule_r2 == 1 and boule_r1 == 0):\n",
    "        print(str(boule_r1_e) + ' et' + str(k1*(boule_r1) + k2*(boule_r2*vol_r1/vol_r2)))\n",
    "                  \n",
    "    if (boule_r1_e <= k1*(boule_r1) + k2*(boule_r2*vol_r1/vol_r2)):\n",
    "        S.append(point)\n",
    "        count_S += 1\n",
    "        t = True\n",
    "              \n",
    "    return(S, count_S, t)\n",
    "\n",
    "def p_rayon_cone(r, theta, iterations, dim):\n",
    "    \n",
    "    point_cone = []\n",
    "    count_in_cone = 0\n",
    "    for count_loops in range(iterations):\n",
    "        u = np.random.normal(0,1,dim)\n",
    "        norm= np.sum(u**2) **(0.5)\n",
    "        r = np.random.random()**(1.0/dim)\n",
    "        point = r*u/norm\n",
    "        point = point*r\n",
    "        \n",
    "        z = point[0]\n",
    "        rho = np.linalg.norm(point[1:])\n",
    "        if z > 0 and rho <= z*np.tan(theta):\n",
    "            point_cone.append(point)\n",
    "            count_in_cone +=1\n",
    "    p = 1 - count_in_cone/iterations\n",
    "    \n",
    "    return p\n",
    "\n",
    "def p_rayon_par(r, a_, iterations, dim):\n",
    "    \n",
    "    point_par = []\n",
    "    count_in_par = 0\n",
    "    for count_loops in range(iterations):\n",
    "        u = np.random.normal(0,1,dim)\n",
    "        norm= np.sum(u**2) **(0.5)\n",
    "        r = np.random.random()**(1.0/dim)\n",
    "        point = r*u/norm\n",
    "        point = point*r\n",
    "        \n",
    "        z = point[0]\n",
    "        rho = np.linalg.norm(point[1:])\n",
    "        if z > 0 and rho**2 <= z*a_:\n",
    "            point_cone.append(point)\n",
    "            count_in_cone +=1\n",
    "    p = 1 - count_in_cone/iterations\n",
    "    \n",
    "    return p\n",
    "    \n",
    "def vol_par_n_dim_3(r1, iterations, epsilon):\n",
    "    count_in_par = 0\n",
    "    points_in_par = []\n",
    "    for count_loops in range(iterations):\n",
    "        \n",
    "        x = np.random.uniform(-10, 10)\n",
    "        y = np.random.uniform(-10, 10)\n",
    "        z = np.random.uniform(-10, 10)\n",
    "        \n",
    "        point = [x, y, z]\n",
    "        point_e = [0, 0, 0]\n",
    "        point_e[0] += epsilon\n",
    "        diff = np.array(point_e) - np.array(point)\n",
    "        dist = np.sum(np.power(diff, 2))\n",
    "        \n",
    "        if dist < r1**2:\n",
    "            count_in_par += 1\n",
    "            points_in_par.append(point)\n",
    "            \n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.gca(projection='3d')\n",
    "    for point in points_in_par:\n",
    "        ax.scatter(point[0], point[1], point[2], color = 'orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def probs_p1_p2(r1, r2, k1, k2, epsilon, dim, iterations, draw = False):\n",
    "    if draw and dim ==2:\n",
    "        fig, ax = plt.subplots()\n",
    "    liste_r1 = []\n",
    "    liste_r2 = []\n",
    "    S_r1 = []\n",
    "    S_r2 = []\n",
    "    count_in_s_r1 = 0\n",
    "    count_in_s_r2 = 0\n",
    "\n",
    "    #boule r1\n",
    "    for i in range(iterations):\n",
    "        \n",
    "        u = np.random.normal(0,1,dim)  \n",
    "        norm= np.sum(u**2) **(0.5)\n",
    "        r = np.random.random()**(1.0/dim)\n",
    "        point = r*u/norm\n",
    "        point = point*r1\n",
    "        liste_r1.append(point)\n",
    "        \n",
    "        S_r1, count_in_s_r1, t = appartient_S(point, r1, r2, k1, k2, epsilon, dim, S_r1, count_in_s_r1)\n",
    "        if t and draw and dim == 2:\n",
    "            ax.scatter(point[0], point[1], c = 'orange')\n",
    "        elif draw and dim == 2:\n",
    "            ax.scatter(point[0], point[1], c = 'red')\n",
    "        \n",
    "    p1 = count_in_s_r1 / iterations\n",
    "    \n",
    "    \n",
    "    #boule r2\n",
    "    for i in range(iterations):\n",
    "        \n",
    "        u = np.random.normal(0,1,dim)  \n",
    "        norm= np.sum(u**2) **(0.5)\n",
    "        r = np.random.random()**(1.0/dim)\n",
    "        point = r*u/norm\n",
    "        point = point*r2\n",
    "        liste_r2.append(point)\n",
    "        if draw and dim ==2 :\n",
    "            ax.scatter(point[0], point[1], c = 'yellow')\n",
    "            \n",
    "        S_r2, count_in_s_r2, t = appartient_S(point, r1, r2, k1, k2, epsilon, dim, S_r2, count_in_s_r2)\n",
    "        if t and draw and dim == 2:\n",
    "            ax.scatter(point[0], point[1], c = 'blue')\n",
    "        elif draw and dim == 2:\n",
    "            ax.scatter(point[0], point[1], c = 'green')\n",
    "            \n",
    "    p2 = count_in_s_r2 / iterations\n",
    "   \n",
    "    return p1, p2\n",
    "\n",
    "\n",
    "def plot_3d_k1_k2_var(min_var, max_var, nbre_var, r1, r2, epsilon, theta, dim, iterations):\n",
    "    \n",
    "    pas = (max_var - min_var) / nbre_var\n",
    "    grid_2D = np.mgrid[min_var:max_var:pas, min_var:max_var:pas]\n",
    "    ks = grid_2D.reshape(2, -1).T\n",
    "    t = np.linspace(min_var, max_var - pas, nbre_var)\n",
    "    k1, k2 = np.meshgrid(t, t)\n",
    "    list_res_1 = []\n",
    "    list_res_2 = []\n",
    "    for i in range(len(ks)):\n",
    "        res = probs_p1_p2_cone(r1, r2, ks[i][0], ks[i][1], epsilon, theta, dim, iterations)\n",
    "        list_res_1.append(res[0])\n",
    "        list_res_2.append(res[1])\n",
    "        print(\"calcul fait \" + str(i))\n",
    "    true_list_1 = []\n",
    "    true_list_2 = []\n",
    "    for i in range(0, nbre_var):\n",
    "        liste_1 = []\n",
    "        liste_2 = []\n",
    "        for j in range(0, nbre_var):\n",
    "            liste_1.append(list_res_1[i*nbre_var + j])\n",
    "            liste_2.append(list_res_2[i*nbre_var + j])\n",
    "        true_list_1.append(liste_1[:])\n",
    "        true_list_2.append(liste_2[:])\n",
    "    \n",
    "    fig_1 = plt.figure(figsize=(10, 10))\n",
    "    ax_1 = fig_1.gca(projection='3d')\n",
    "    ax_1.contour3D(k1, k2, true_list_1, 50, cmap = 'autumn')\n",
    "    ax_1.set_xlabel('variation de k1')\n",
    "    ax_1.set_ylabel('variation de k2')\n",
    "    ax_1.set_zlabel('P(B(0, r1) { S)')\n",
    "    ax_1.set_title('R1 = ' + str(r1) + ', R2 = ' + str(r2) + ', epsilon = '+ str(epsilon))\n",
    "    \n",
    "    fig_2 = plt.figure(figsize=(10, 10))\n",
    "    ax_2 = fig_2.gca(projection='3d')\n",
    "    ax_2.contour3D(k1, k2, true_list_2, 50, cmap = 'autumn')\n",
    "    ax_2.set_xlabel('variation de k1')\n",
    "    ax_2.set_ylabel('variation de k2')\n",
    "    ax_2.set_zlabel('P(B(0, r2) { S)')\n",
    "    ax_2.set_title('R1 = ' + str(r1) + ', R2 = ' + str(r2) + ', epsilon = '+ str(epsilon))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3d_k1_k2_var(0, 20, 20, 1, 2, 0.5, np.pi/4, 3, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: pymoo in c:\\programdata\\anaconda3\\lib\\site-packages (0.5.0)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.15 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (1.19.2)\n",
      "Requirement already satisfied, skipping upgrade: matplotlib>=3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (3.3.2)\n",
      "Requirement already satisfied, skipping upgrade: autograd>=1.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (1.4)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (1.5.2)\n",
      "Requirement already satisfied, skipping upgrade: cma==2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from pymoo) (2.7.0)\n",
      "Requirement already satisfied, skipping upgrade: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (0.10.0)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2020.06.20 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (2020.6.20)\n",
      "Requirement already satisfied, skipping upgrade: pillow>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (8.0.1)\n",
      "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (1.3.0)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3->pymoo) (2.4.7)\n",
      "Requirement already satisfied, skipping upgrade: future>=0.15.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from autograd>=1.3->pymoo) (0.18.2)\n",
      "Requirement already satisfied, skipping upgrade: six in c:\\programdata\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib>=3->pymoo) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install -U pymoo   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiled Extensions:  True\n"
     ]
    }
   ],
   "source": [
    "!python -c \"from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.core.problem import ElementwiseProblem\n",
    "\n",
    "class MyProblem(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__(n_var=2,\n",
    "                         n_obj=2,\n",
    "                         n_constr=2,\n",
    "                         xl=np.array([0,0]),\n",
    "                         xu=np.array([100,100]))\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        f1 = p_rayon_cone(1, np.pi/4, 100000, 3) - probs_p1_p2(1, 2, x[0], x[1], 0.5, 3, 100000)[0]\n",
    "        f2 = p_rayon_cone(2, np.pi/4, 100000, 3) - probs_p1_p2(1, 2, x[0], x[1], 0.5, 3, 100000)[1]\n",
    "\n",
    "        g1 = -x[0]\n",
    "        g2 = -x[1]\n",
    "\n",
    "        out[\"F\"] = [f1, f2]\n",
    "        out[\"G\"] = [g1, g2]\n",
    "\n",
    "\n",
    "problem = MyProblem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "from pymoo.factory import get_sampling, get_crossover, get_mutation\n",
    "\n",
    "algorithm = NSGA2(\n",
    "    pop_size=40,\n",
    "    n_offsprings=10,\n",
    "    sampling=get_sampling(\"real_random\"),\n",
    "    crossover=get_crossover(\"real_sbx\", prob=0.9, eta=15),\n",
    "    mutation=get_mutation(\"real_pm\", eta=20),\n",
    "    eliminate_duplicates=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.factory import get_termination\n",
    "\n",
    "termination = get_termination(\"n_gen\", 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=====================================================================================\n",
      "n_gen |  n_eval |   cv (min)   |   cv (avg)   |  n_nds  |     eps      |  indicator  \n",
      "=====================================================================================\n",
      "    1 |      40 |  0.00000E+00 |  0.00000E+00 |       1 |            - |            -\n",
      "    2 |      50 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "    3 |      60 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "    4 |      70 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "    5 |      80 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "    6 |      90 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "    7 |     100 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "    8 |     110 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "    9 |     120 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "   10 |     130 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "   11 |     140 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "   12 |     150 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "   13 |     160 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "   14 |     170 |  0.00000E+00 |  0.00000E+00 |       1 |  0.00000E+00 |            f\n",
      "   15 |     180 |  0.00000E+00 |  0.00000E+00 |       2 |  0.00000E+00 |            f\n",
      "   16 |     190 |  0.00000E+00 |  0.00000E+00 |       3 |  0.00000E+00 |            f\n",
      "   17 |     200 |  0.00000E+00 |  0.00000E+00 |       3 |  0.00000E+00 |            f\n",
      "   18 |     210 |  0.00000E+00 |  0.00000E+00 |       3 |  0.00000E+00 |            f\n",
      "   19 |     220 |  0.00000E+00 |  0.00000E+00 |       3 |  0.00000E+00 |            f\n",
      "   20 |     230 |  0.00000E+00 |  0.00000E+00 |       4 |  0.00000E+00 |            f\n",
      "   21 |     240 |  0.00000E+00 |  0.00000E+00 |       4 |  0.00000E+00 |            f\n",
      "   22 |     250 |  0.00000E+00 |  0.00000E+00 |       4 |  0.00000E+00 |            f\n",
      "   23 |     260 |  0.00000E+00 |  0.00000E+00 |       4 |  0.00000E+00 |            f\n",
      "   24 |     270 |  0.00000E+00 |  0.00000E+00 |       4 |  0.00000E+00 |            f\n",
      "   25 |     280 |  0.00000E+00 |  0.00000E+00 |       4 |  0.00000E+00 |            f\n",
      "   26 |     290 |  0.00000E+00 |  0.00000E+00 |       4 |  0.00000E+00 |            f\n",
      "   27 |     300 |  0.00000E+00 |  0.00000E+00 |       5 |  0.00000E+00 |            f\n",
      "   28 |     310 |  0.00000E+00 |  0.00000E+00 |       5 |  0.00000E+00 |            f\n",
      "   29 |     320 |  0.00000E+00 |  0.00000E+00 |       5 |  0.00000E+00 |            f\n",
      "   30 |     330 |  0.00000E+00 |  0.00000E+00 |       5 |  0.00000E+00 |            f\n",
      "   31 |     340 |  0.00000E+00 |  0.00000E+00 |       5 |  0.00000E+00 |            f\n",
      "   32 |     350 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   33 |     360 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   34 |     370 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   35 |     380 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   36 |     390 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   37 |     400 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   38 |     410 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   39 |     420 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   40 |     430 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   41 |     440 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   42 |     450 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   43 |     460 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   44 |     470 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   45 |     480 |  0.00000E+00 |  0.00000E+00 |       6 |  0.00000E+00 |            f\n",
      "   46 |     490 |  0.00000E+00 |  0.00000E+00 |       7 |  0.00000E+00 |            f\n",
      "   47 |     500 |  0.00000E+00 |  0.00000E+00 |       7 |  0.00000E+00 |            f\n",
      "   48 |     510 |  0.00000E+00 |  0.00000E+00 |       7 |  0.00000E+00 |            f\n",
      "   49 |     520 |  0.00000E+00 |  0.00000E+00 |       7 |  0.00000E+00 |            f\n",
      "   50 |     530 |  0.00000E+00 |  0.00000E+00 |       7 |  0.00000E+00 |            f\n",
      "   51 |     540 |  0.00000E+00 |  0.00000E+00 |       8 |  0.00000E+00 |            f\n",
      "   52 |     550 |  0.00000E+00 |  0.00000E+00 |       8 |  0.00000E+00 |            f\n",
      "   53 |     560 |  0.00000E+00 |  0.00000E+00 |       9 |  0.00000E+00 |            f\n",
      "   54 |     570 |  0.00000E+00 |  0.00000E+00 |       9 |  0.00000E+00 |            f\n",
      "   55 |     580 |  0.00000E+00 |  0.00000E+00 |      10 |  0.00000E+00 |            f\n",
      "   56 |     590 |  0.00000E+00 |  0.00000E+00 |      10 |  0.00000E+00 |            f\n",
      "   57 |     600 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   58 |     610 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   59 |     620 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   60 |     630 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   61 |     640 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   62 |     650 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   63 |     660 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   64 |     670 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   65 |     680 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   66 |     690 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   67 |     700 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   68 |     710 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   69 |     720 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   70 |     730 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   71 |     740 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   72 |     750 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   73 |     760 |  0.00000E+00 |  0.00000E+00 |      11 |  0.00000E+00 |            f\n",
      "   74 |     770 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   75 |     780 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   76 |     790 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   77 |     800 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   78 |     810 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   79 |     820 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   80 |     830 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   81 |     840 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   82 |     850 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   83 |     860 |  0.00000E+00 |  0.00000E+00 |      12 |  0.00000E+00 |            f\n",
      "   84 |     870 |  0.00000E+00 |  0.00000E+00 |      13 |  0.00000E+00 |            f\n",
      "   85 |     880 |  0.00000E+00 |  0.00000E+00 |      13 |  0.00000E+00 |            f\n",
      "   86 |     890 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   87 |     900 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   88 |     910 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   89 |     920 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   90 |     930 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   91 |     940 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   92 |     950 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   93 |     960 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   94 |     970 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   95 |     980 |  0.00000E+00 |  0.00000E+00 |      14 |  0.00000E+00 |            f\n",
      "   96 |     990 |  0.00000E+00 |  0.00000E+00 |      15 |  0.00000E+00 |            f\n",
      "   97 |    1000 |  0.00000E+00 |  0.00000E+00 |      15 |  0.00000E+00 |            f\n",
      "   98 |    1010 |  0.00000E+00 |  0.00000E+00 |      15 |  0.00000E+00 |            f\n",
      "   99 |    1020 |  0.00000E+00 |  0.00000E+00 |      15 |  0.00000E+00 |            f\n",
      "  100 |    1030 |  0.00000E+00 |  0.00000E+00 |      15 |  0.00000E+00 |            f\n"
     ]
    }
   ],
   "source": [
    "from pymoo.optimize import minimize\n",
    "\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "\n",
    "X = res.X\n",
    "F = res.F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[69.44001577, 41.41792695],\n",
       "       [99.52690822, 69.86148893],\n",
       "       [17.08830887, 78.62267805],\n",
       "       [75.07624506, 99.96779459],\n",
       "       [72.4387341 , 69.86148893],\n",
       "       [29.76490111, 41.80476411],\n",
       "       [19.08850615, 78.62267805],\n",
       "       [73.59840747, 41.41792695],\n",
       "       [77.21535678, 64.45713839],\n",
       "       [ 4.52122843, 91.26597762],\n",
       "       [14.96429108, 98.54591366],\n",
       "       [ 4.66671881, 96.77347754],\n",
       "       [74.69258579, 78.68886546],\n",
       "       [32.74506169, 80.01995687],\n",
       "       [12.44601949,  8.57257006]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05],\n",
       "       [-1.e-05, -1.e-05]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-9.99999999995449e-06 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-9.99999999995449e-06 0.0\n",
      "0.0 0.0\n",
      "-9.99999999995449e-06 0.0\n",
      "-9.99999999995449e-06 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "for sol in X:\n",
    "    res = probs_p1_p2_cone(1, 2, sol[0], sol[1], 0.5, np.pi/4, 3, 100000)\n",
    "    print(p_rayon_cone(1, np.pi/4, 100000, 3) - res[0], p_rayon_cone(2, np.pi/4, 100000, 3) - res[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0016299999999999093\n",
      "0.0012599999999999278\n",
      "0.00022999999999995246\n",
      "-0.0021400000000000308\n",
      "0.0012999999999999678\n",
      "0.0011700000000000044\n",
      "0.0016700000000000603\n",
      "-0.0006099999999999994\n",
      "4.0000000000040004e-05\n",
      "-0.0018799999999999928\n",
      "0.0013799999999999368\n",
      "0.0016500000000000403\n"
     ]
    }
   ],
   "source": [
    "for i in range(12):\n",
    "    print(p_rayon_cone(0.05, np.pi/4, 100000, 3) - p_rayon_cone(2, np.pi/4, 100000, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# En plus clean avec pymoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Opti_S_cone(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, rayons, theta, iterations, epsilon, dimension):\n",
    "        super().__init__(n_var=2,\n",
    "                         n_obj=2,\n",
    "                         n_constr=0,\n",
    "                         xl=np.array([0,0]),\n",
    "                         xu=np.array([20,20]))\n",
    "        self.r1 = rayons[0]\n",
    "        self.r2 = rayons[1]\n",
    "        self.theta = theta\n",
    "        self.iterations = iterations\n",
    "        self.epsilon = epsilon\n",
    "        self.dimension = dimension\n",
    "        \n",
    "        p1 = 0\n",
    "        p2 = 0\n",
    "        for i in range(20):\n",
    "            p1 += p_rayon_cone(self.r1, self.theta, self.iterations, self.dimension)\n",
    "            p2 += p_rayon_cone(self.r2, self.theta, self.iterations, self.dimension)\n",
    "        self.p1 = p1/20\n",
    "        self.p2 = p2/20\n",
    "        print(\"Pour ce calcul, on aura p1 = \" + str(self.p1) + ', et p2 = ' + str(self.p2) + '.')\n",
    "        print('')\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        res = probs_p1_p2(self.r1, self.r2, x[0], x[1], self.epsilon, self.dimension, self.iterations)\n",
    "        \n",
    "        \n",
    "        f1 = self.p1 - res[0]\n",
    "        f2 = self.p2 - res[1]\n",
    "        \n",
    "        out[\"F\"] = [f1, f2]\n",
    "        \n",
    "class Opti_S_par(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, rayons, a_, iterations, epsilon, dimension):\n",
    "        super().__init__(n_var=2,\n",
    "                         n_obj=2,\n",
    "                         n_constr=0,\n",
    "                         xl=np.array([0,0]),\n",
    "                         xu=np.array([20,20]))\n",
    "        self.r1 = rayons[0]\n",
    "        self.r2 = rayons[1]\n",
    "        self.a_ = a_\n",
    "        self.iterations = iterations\n",
    "        self.epsilon = epsilon\n",
    "        self.dimension = dimension\n",
    "        \n",
    "        p1 = 0\n",
    "        p2 = 0\n",
    "        for i in range(20):\n",
    "            p1 += p_rayon_par(self.r1, self.theta, self.iterations, self.dimension)\n",
    "            p2 += p_rayon_par(self.r2, self.theta, self.iterations, self.dimension)\n",
    "        self.p1 = p1/20\n",
    "        self.p2 = p2/20\n",
    "        \n",
    "        print(\"Pour ce calcul, on aura p1 = \" + str(self.p1) + ', et p2 = ' + str(self.p2) + '.')\n",
    "        print('')\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        res = probs_p1_p2(self.r1, self.r2, x[0], x[1], self.epsilon, self.dimension, self.iterations)\n",
    "        \n",
    "        f1 = self.p1 - res[0]\n",
    "        f2 = self.p2 - res[1]\n",
    "        \n",
    "        out[\"F\"] = [f1, f2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optim_k1_k2_cone_uniforme_S(r1, r2, epsilon, theta, dim, iterations, gen, verbose_time = False):\n",
    "    problem = Opti_S_cone([r1, r2], theta, iterations, epsilon, dim)\n",
    "    \n",
    "    algorithm = NSGA2(\n",
    "    pop_size=40,\n",
    "    n_offsprings=10,\n",
    "    sampling=get_sampling(\"real_random\"),\n",
    "    crossover=get_crossover(\"real_sbx\", prob=0.9, eta=15),\n",
    "    mutation=get_mutation(\"real_pm\", eta=20),\n",
    "    eliminate_duplicates=True\n",
    "    )\n",
    "    \n",
    "    termination = get_termination(\"n_gen\", gen)\n",
    "    \n",
    "    time_1 = time.time()\n",
    "    res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=5,\n",
    "               save_history=True,\n",
    "                return_least_infeasible=True,\n",
    "               verbose=True)\n",
    "    time_2 = time.time()\n",
    "    if verbose_time:\n",
    "        print(\"Temps de calcul total: \" + str(time_2-time_1))\n",
    "    X = res.X\n",
    "    F = res.F\n",
    "    \n",
    "    \n",
    "    return X, F\n",
    "\n",
    "def optim_k1_k2_par_uniforme_S(r1, r2, epsilon, a_, dim, iterations, gen, verbose_time = False):\n",
    "    problem = Opti_S_par([r1, r2], a_, iterations, epsilon, dim)\n",
    "    \n",
    "    algorithm = NSGA2(\n",
    "    pop_size=40,\n",
    "    n_offsprings=10,\n",
    "    sampling=get_sampling(\"real_random\"),\n",
    "    crossover=get_crossover(\"real_sbx\", prob=0.9, eta=15),\n",
    "    mutation=get_mutation(\"real_pm\", eta=20),\n",
    "    eliminate_duplicates=True\n",
    "    )\n",
    "    \n",
    "    termination = get_termination(\"n_gen\", gen)\n",
    "    \n",
    "    time_1 = time.time()\n",
    "    res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=5,\n",
    "               save_history=True,\n",
    "                return_least_infeasible=True,\n",
    "               verbose=True)\n",
    "    time_2 = time.time()\n",
    "    if verbose_time:\n",
    "        print(\"Temps de calcul total: \" + str(time_2-time_1))\n",
    "        \n",
    "    X = res.X\n",
    "    F = res.F\n",
    "    \n",
    "    \n",
    "    return X, F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pour ce calcul, on aura p1 = 0.8538834999999999, et p2 = 0.853411.\n",
      "\n",
      "=======================================================\n",
      "n_gen |  n_eval |  n_nds  |     eps      |  indicator  \n",
      "=======================================================\n",
      "    1 |      40 |      23 |            - |            -\n",
      "    2 |      50 |      32 |  0.00000E+00 |            f\n",
      "    3 |      60 |      40 |  0.00000E+00 |            f\n",
      "    4 |      70 |      40 |  0.00000E+00 |            f\n",
      "    5 |      80 |      40 |  0.00000E+00 |            f\n",
      "Temps de calcul total: 773.7150340080261\n"
     ]
    }
   ],
   "source": [
    "res = optim_k1_k2_cone_uniforme_S(1, 2, 0.5, np.pi/4, 3, 100000, 5, verbose_time = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[12.79590352, 19.71248806],\n",
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       "        [11.17913906,  9.83108709],\n",
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       "        [ 0.3529436 ,  9.21920774],\n",
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       "        [17.1448721 , 13.77043314],\n",
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       "        [-0.1461165, -0.146589 ],\n",
       "        [-0.1461165, -0.146589 ],\n",
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       "        [-0.1461165, -0.146589 ],\n",
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       "        [-0.1461165, -0.146589 ],\n",
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       "        [-0.1461165, -0.146589 ],\n",
       "        [-0.1461165, -0.146589 ]]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs_p1_p2(1, 2, res[0][0][0], res[0][0][1], 0.5, 3, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.36444, 0.99901)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k1 = k2 = 0\n",
    "#Proportion des boules r1 et r2 dans la boule B(e, r1)\n",
    "probs_p1_p2(1, 3, 0, 0, 0.5, 3, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7777777777777777\n"
     ]
    },
    {
     "data": {
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       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
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       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
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       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
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       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
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       "\n",
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       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
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       "\n",
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       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
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       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
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       "\n",
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       "        'mousedown',\n",
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       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "1 et1.0\n",
      "0 et1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k1 = 1, k2 = 0\n",
    "vol_r1 = nsphere_volume_m_20(1.5, 2)\n",
    "vol_r2 = nsphere_volume_m_20(2, 2)\n",
    "print(1/(vol_r1/vol_r2))\n",
    "probs_p1_p2(1.5, 2, 1, vol_r2/vol_r1, 0.5, 2, 1000, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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