03-Regresion_lineal_y_regresion_logistica.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/UDEA-Esp-Analitica-y-Ciencia-de-Datos/EACD-04-MACHINE-LEARNING-1/blob/master/03-Regresion_lineal_y_regresion_logistica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "\n",
    "**Recuerda que una vez abierto, Da clic en \"Copiar en Drive\", de lo contrario no podras alamancenar tu progreso**\n",
    "\n",
    "Nota: no olvide ir ejecutando las celdas de código de arriba hacia abajo para que no tenga errores de importación de librerías o por falta de definición de variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'>Modelos básicos de aprendizaje</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget --no-cache -O init.py -q https://raw.githubusercontent.com/UDEA-Esp-Analitica-y-Ciencia-de-Datos/EACD-04-MACHINE-LEARNING-1/master/init.py\n",
    "import init; init.init(force_download=False); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regresión múltiple (problemas de regresión) y regresión logística (problemas de clasificación)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a centrarnos por un momento en el problema de regresión. Es casi seguro que para diferentes problemas hayan tenido que utilizar una regresión lineal, polinomial y/o múltiple, con el propósito de encontrar una función que pudiera explicar el comportamiento de un conjunto de datos. Supongamos que queremos modelar un conjunto de datos empleando una función polinomial de la forma:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "f({\\bf{x}},{\\bf{w}} ) = w_0 + w_1 x +  w_2 x^2 + \\cdots + w_Mx^M = \\sum_{j=0}^{M} w_j x^j\n",
    "$$\n",
    "\n",
    "donde $M$ es el orden del polinomio. ¿Cómo se ajusta ${\\bf{w}}$?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso particular, la función polinomial que hemos escogido corresponde al <mark>**modelo**</mark> que vamos a usar. Necesitamos entonces definir el criterio por el cual vamos a ajustar los parámetros del modelo, en particular los pesos $w_j$ (El valor de $M$ también requiere ser ajustado, sin embargo si cambiamos $M$ se está cambiando el órden del polinomio y por lo tanto el modelo en sí mismo. Más adelante veremos cómo enfrentar el problema de seleccionar el modelo más adecuado, por ahora supondremos que el valor de $M$ está dado de manera a prior). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>El criterio (función de costo):</mark>\n",
    "La función de error más usada es (<b>para un mismo modelo podemos tener diferentes criterios de ajuste</b>):\n",
    "$$\n",
    "E({\\bf{w}}) = \\frac{1}{2N}\\sum_{i=1}^{N} \\left\\lbrace f({\\bf{x}}_i, {\\bf{w}}) - t_i \\right\\rbrace^2 \n",
    "$$\n",
    "Con esta función de error podemos hallar una $f$ para la cual, la distancia perpendicular de los puntos $x_i$ a $f$ sea mínima."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from local.lib.util import plot_model_reg\n",
    "def linear_prediction(t, x):\n",
    "    t0,t1 = t\n",
    "    return t0 + t1*x\n",
    "t0 = np.random.random()*5+10\n",
    "t1 = np.random.random()*4-3\n",
    "\n",
    "plot_model_reg([t0,t1], linear_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo con datos generados artificialmente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "Perror = 0.3\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "x1 = np.linspace(-10,10,100).reshape(100, 1)\n",
    "x2 = x1**2\n",
    "\n",
    "#Polinomio real\n",
    "y = 3*x1 + 7*x2 - 2\n",
    "X1, X2 = np.meshgrid(x1, x2)\n",
    "Y = 3*X1 + 7*X2 - 2\n",
    "\n",
    "#Asumimos que puede haber ruido en la medición\n",
    "y2 = y + Perror*np.std(y)*(np.random.rand(100,1) - 0.5)\n",
    "Y2 = y + Perror*np.std(y)*(np.random.rand(100,100) - 0.5)\n",
    "\n",
    "#plots\n",
    "ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "p=ax.plot_surface(X1, X2, Y, rstride=2, cstride=2, cmap=cm.jet, alpha=0.7)\n",
    "## surface_plot with color grading and color bar\n",
    "ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "p = ax.plot_surface(X1, X2, Y2, rstride=2, cstride=2, cmap=cm.jet, alpha=0.7)\n",
    "cb = fig.colorbar(p, shrink=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>  Usando una estrategia de alto nivel (caja negra): </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression().fit(np.c_[x1,x2], y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ¿Cómo hacemos una predicción?:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[57.30977534]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(np.array([8,5]).reshape(1,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ¿Y los parámetros del modelo?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.6425449  7.10999564]]\n",
      "[-7.38056209]\n"
     ]
    }
   ],
   "source": [
    "print(reg.coef_)\n",
    "print(reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302.05820004239865"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ECM(w,x,y):\n",
    "    w = w.reshape(1,-1)\n",
    "    x = np.c_[x,np.ones((x1.shape[0],1))]\n",
    "    return np.mean((np.dot(w,x.T).T-y)**2)\n",
    "\n",
    "ECM(np.c_[reg.coef_,reg.intercept_], np.c_[x1,x2], y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>  Usando una estrategia de nivel intermedio: </font>\n",
    "\n",
    "Sabemos que el problema que debemos resolver corresponde a un problema de minimización sin restricciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.64254486,  7.10999573, -7.38056821])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "r1 = minimize(lambda w: ECM(w, np.c_[x1,x2], y2), np.random.random(size=3))\n",
    "r1.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302.05820004241536"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ECM(r1.x, np.c_[x1,x2], y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### También podemos hacerla predicción:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57.30976935183637"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(r1.x,np.array([8,5,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>  Usando una estrategia de más bajo nivel: </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un algoritmo simple que nos permite solucionar el problema de optimización es el <mark>**Algoritmo de Gradiente Descendente**</mark>. Un problema de optimización puede tener múltiples formas o algoritmos para resolverse, cuando el problema de optimización es <b>convexo</b> podemos esperar que casi con cualquier método encontremos el valor del parámetro que hace la función óptima, sin embargo, es posible que el problema de optimización no sea convexo, en cuyo caso el algoritmo o método de optimización juega un rol más importante. También es importante tener en cuenta la velocidad de convergencia del algoritmo o método de optimización, el costo computacional y el número de parámetros libres.\n",
    "\n",
    "El algoritmo de gradiente descendente consiste en aplicar iterativamente la siguiente regla:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "w_j(iter) = w_j(iter-1) - \\eta \\frac{\\partial E({\\bf{w}})}{\\partial w_j}\n",
    "$$\n",
    "\n",
    "donde $\\eta$ se conoce como la tasa de aprendizaje."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos hayar un $\\bf{w}$ que minimice el error en el problema de regresión lineal, entonces derivamos la función de error con respecto a cada uno de los parámetros $w_j$:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial E({\\bf{w}})}{\\partial w_j} = \\frac{1}{2N}\\sum_{i=1}^{N}\\frac{\\partial }{\\partial w_j}\\left( f({\\bf{x}}_i,{\\bf{w}}) - y_i\\right)^2\\\\\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para efectos de cálculo podemos asumir que el vector $x_i$ contiene todas las características de la muestra $i$ y una característica adicional con valor de 1, que multiplicará al término independiente $w_0$.\n",
    "\n",
    "$$\n",
    "\\frac{\\partial E({\\bf{w}})}{\\partial w_j} = \\frac{1}{N}\\sum_{i=1}^{N}\\left( f({\\bf{x}}_i,{\\bf{w}}) - y_i\\right) \\frac{\\partial }{\\partial w_j} f({\\bf{x}}_i, {\\bf{w}})\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\frac{\\partial E({\\bf{w}})}{\\partial w_j} = \\frac{1}{N}\\sum_{i=1}^{N}\\left( f({\\bf{x}}_i,{\\bf{w}}) - y_i\\right)x_{ij}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La regla de actualización de los pesos en cada iteración estará entonces dada por:\n",
    "\n",
    "$$\n",
    "w_j(iter) = w_j(iter-1) - \\frac{\\eta}{N} \\sum_{i=1}^{N}\\left( f({\\bf{x}}_i,{\\bf{w}}) - y_i\\right)x_{ij}\n",
    "$$\n",
    "\n",
    "$$\n",
    "w_j(iter) = w_j(iter-1) - \\frac{\\eta}{N} \\sum_{i=1}^{N}\\left( \\sum_{k=0}^{d} w_k x_{ik} - y_i\\right)x_{ij} \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si la solución de un problema requiere que sigamos una estrategia de más bajo nivel, entonces se hacen más importantes conceptos de algebra lineal y cálculo, en muchos casos no sólo para comprender los algoritmos, sino también para realizar implementaciones computacionalmente más eficientes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradiente descendetente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.6425449 ]\n",
      " [ 7.10999564]\n",
      " [-7.38056209]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Inicialización\n",
    "MaxIter = 1000000\n",
    "w = np.ones(3).reshape(3, 1)\n",
    "eta = 0.0001\n",
    "N = len(x1)\n",
    "Error =np.zeros(MaxIter)\n",
    "\n",
    "#Matriz extendida\n",
    "X = np.array([x1,x2,np.ones((100,1))]).reshape(3, 100);\n",
    "\n",
    "#Iteraciones\n",
    "for i in range(MaxIter):\n",
    "    tem = np.dot(X.T,w)\n",
    "    tem2 = tem-np.array(y2)\n",
    "    Error[i] = np.sum(tem2**2)/(2*N)\n",
    "    tem = np.dot(X,tem2)\n",
    "    w = w - eta*tem/N\n",
    "#Gráfica\n",
    "print(w)\n",
    "plt.ylim(0,1000)\n",
    "plt.xlim(0,200)\n",
    "plt.ion()\n",
    "plt.plot(np.linspace(0,MaxIter,MaxIter),Error)\n",
    "plt.xlabel('Iteraciones')\n",
    "plt.ylabel('$E$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302.05820004239854"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ECM(w, np.c_[x1,x2], y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'>  Pensemos ahora en el problema de clasificación </font> \n",
    "\n",
    "### Regresión logística\n",
    "\n",
    "Una forma alternativa de entender el problema de clasificación, es buscar una función que sea capaz de dividir el espacio de características y dejar los conjuntos de muestras de cada clase separados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from local.lib.util import plot_frontera\n",
    "plot_frontera()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando es posible dejar todas las muestras de una clase a un lado de la recta, y las muestras de la otra clase al otro, el problema se conoce como linealmente separable. Sin embargo eso no sucede en la gran mayoría de problemas reales. El problema de clasificación se puede entonces pensar como el problema de encontrar una función polinomial $f$ que pueda dividir los conjuntos de datos de las diferentes clases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observando la figura anterior:\n",
    "\n",
    "Si se pudiese encontrar la función dada por la línea negra en la figura anterior, la cual sería de la forma $f({\\bf{x}}) = w_1 x_1 + w_2 x_2 + w_0$, se podría utilizar como función de clasificación ya que cualquier muestra evaluada en la función obtendrá como resultado un valor positivo si se ubica a un lado y un valor negativo si se ubica al otro (los valores ubicados justo en la función obtendrían un valor de 0). Teniendo en cuenta que los valores de las etiquetas para el problema de clasificación (las variables a predecir $t_i$), solo pueden tomar dos valores ${0,1}$, entonces una forma simple de usar la función $f$ como clasificador sería asignar las muestras a la clase 1 cuando al ser evaluadas en la función $f$ obtengan una valor positivo y asignar 0 cuando suceda lo contrario.\n",
    "\n",
    "$$\\text{Class} = \\left\\{\n",
    "                \\begin{array}{ll}\n",
    "                  1\\;\\;\\;{\\rm{if}}\\;\\;\\;w_0  + w_1 x_1  + w_2 x_2  +  \\cdots  + w_d x_d  \\geq 0\\\\\n",
    "                  0\\;\\;{\\rm{\\text{other wise}}}\n",
    "                \\end{array}\n",
    "              \\right. $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "image/png": {
       "width": 300
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"local/imgs/funcionsigno.png\", width = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El problema con la función $sgn$ es que es discontinua y no puede ser usada como criterio de optimización para ningún algoritmo basado en gradiente. Un alternativa es utilizar alguna función que tenga una comportamiento asintótico similar pero que sea continua y derivable, por lo que podemos usar la función sigmoide dada por:\n",
    "\n",
    "$$\n",
    "g(u) = \\frac{\\exp(u)}{1 + \\exp(u)}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u=np.linspace(-10,10,100)\n",
    "g = np.exp(u)/(1 + np.exp(u))\n",
    "plt.plot(u,g)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método que utiliza la función sigmoidal para encontrar una frontera de separación polinomial se conoce como <b>Regresión Logística</b>. La función objetivo (criterio de entrenamiento) está dada por:\n",
    "\n",
    "$$\n",
    "J({\\bf{w}}) = \\frac{1}{N} \\sum_{i=1}^{N} -t_i \\log(g(f({\\bf{x}}))) - (1-t_i)\\log(1 - g(f({\\bf{x}})))\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si se analiza con detenimiento, la función criterio $J$ minimiza el error de clasificación. Es necesario tener en cuenta que dicha función está definida para $t_i$ que toman valores $\\left\\lbrace 0,1 \\right\\rbrace$. La ventaja del método de regresión logística es que la función para la actualización de los pesos $\\bf{w}$ es muy similar a la función para la regresión lineal. La derivada de $J$ está dada por:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial J({\\bf{w}})}{\\partial w_j} = \\frac{1}{N} \\sum_{i=1}^{N}\\left( g(f({\\bf{x}}_i,{\\bf{w}})) - t_i\\right)x_{ij}\n",
    "$$\n",
    "\n",
    "La única diferencia es la inclusión de la función sigmoidal $g$. Por esa razón podemos usar el mismo algoritmo con solo una pequeña modificación. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoide(u):\n",
    "        g = np.exp(u)/(1 + np.exp(u))\n",
    "        return g\n",
    "def Gradiente(X2,y2,MaxIter = 10000, eta = 0.001):\n",
    "    w = np.ones(3).reshape(3, 1)\n",
    "    N = len(y2)\n",
    "    Error =np.zeros(MaxIter)\n",
    "    Xent = np.concatenate((X2,np.ones((100,1))),axis=1)\n",
    "\n",
    "    for i in range(MaxIter):\n",
    "        tem = np.dot(Xent,w)\n",
    "        tem2 = sigmoide(tem.T)-np.array(y2)\n",
    "        Error[i] = np.sum(abs(tem2))/N\n",
    "        tem = np.dot(Xent.T,tem2.T)\n",
    "        wsig = w - eta*tem/N\n",
    "        w = wsig\n",
    "    return w, Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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('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",
       "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 = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(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 (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // 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",
       "\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAGACAYAAABFmt0fAAADcUlEQVR4nO3BAQ0AAADCoPdPbQ43oAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4M8AgkMAAW+Sm+kAAAAASUVORK5CYII=\" width=\"432\">"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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('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",
       "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 = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(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 (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // 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",
       "\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAGACAYAAABFmt0fAAADcUlEQVR4nO3BAQ0AAADCoPdPbQ43oAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4M8AgkMAAW+Sm+kAAAAASUVORK5CYII=\" width=\"432\">"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "from sklearn import datasets\n",
    "import time\n",
    "fig, ax = plt.subplots(1,1)\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "X2 = X[:100][:,:2]\n",
    "y2 = y[:100]\n",
    "ax.scatter(X2[:,0], X2[:,1], c=y2,cmap=\"Accent\");\n",
    "w,_ = Gradiente(X2,y2,MaxIter = 1)\n",
    "x1 = np.linspace(4,8,20)\n",
    "x2 = -(w[0]/w[1])*x1 - (w[2]/w[1])\n",
    "line1, = ax.plot(x1,x2,'k')\n",
    "\n",
    "for i in range(1,10000,100):\n",
    "    w,_ = Gradiente(X2,y2,MaxIter = i)\n",
    "    x2 = -(w[0]/w[1])*x1 - (w[2]/w[1])\n",
    "    line1.set_ydata(x2)\n",
    "    if i > 1000:\n",
    "        ax.set_ylim([1.8,4.8])\n",
    "    \n",
    "    fig.canvas.draw()\n",
    "    fig.canvas.flush_events()\n",
    "    time.sleep(.300)\n",
    "line1, = ax.plot(x1,x2,'k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Veamos el mismo proceso en el espacio de búsqueda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "def n_grad(X2,t,w):\n",
    "    N = X2.shape[0]\n",
    "    Xent = np.concatenate((X2,np.ones((X2.shape[0],1))),axis=1)\n",
    "    return 2*Xent.T.dot(sigmoide(Xent.dot(w))-t)/N\n",
    "\n",
    "def cross_entropy(X2,t,w):\n",
    "    epsilon=1e-12\n",
    "    Xent = np.concatenate((X2,np.ones((X2.shape[0],1))),axis=1)\n",
    "    predictions = np.clip(sigmoide(np.dot(Xent,w.T)), epsilon, 1. - epsilon)\n",
    "    N = predictions.shape[0]\n",
    "    ce = -(np.sum(t*np.log(predictions+1e-9))+np.sum((1-t)*np.log(1-predictions)))/N\n",
    "    return ce\n",
    "\n",
    "def plot_cost(cost, t0_range, t1_range, vx=None,vy=None, vz=None):\n",
    "    k0,k1 = 60,60\n",
    "\n",
    "    t0 = np.linspace(t0_range[0], t0_range[1], k0)\n",
    "    t1 = np.linspace(t1_range[0], t1_range[1], k1)\n",
    "\n",
    "    p = np.zeros((k0,k1))\n",
    "\n",
    "    for i,j in itertools.product(range(k0), range(k1)):\n",
    "        p[i,j] = np.log(cost(np.r_[t0[i],t1[j], vz]))\n",
    "\n",
    "    plt.contourf(t0, t1, p.T, cmap=plt.cm.hot, levels=np.linspace(np.min(p), np.max(p), 20))\n",
    "    plt.ylabel(r\"$w_2$\")\n",
    "    plt.xlabel(r\"$w_1$\")\n",
    "    plt.title(\"loss\")\n",
    "    plt.colorbar()\n",
    "\n",
    "    if vx is not None:\n",
    "        plt.axvline(vx, color=\"white\")\n",
    "    if vy is not None:\n",
    "        plt.axhline(vy, color=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "g = []\n",
    "loss_history = []\n",
    "def log(xk):\n",
    "    loss_history.append(loss(xk))\n",
    "    g.append(xk)\n",
    "#Estoy graficando una función de tres variables pero sólo dos de los ejes, debo garantizar la misma región del espacio\n",
    "init_t = np.ones(3)#np.random.random(size=3)\n",
    "\n",
    "loss = lambda t: cross_entropy(X2,y2,t)\n",
    "r = minimize(loss, init_t, callback=log, method=\"BFGS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "x1 = np.linspace(4,7,20)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.scatter(X2[:,0], X2[:,1], c=y2,cmap=\"Accent\");\n",
    "x2 = -(r.x[0]/r.x[1])*x1 - (r.x[2]/r.x[1])\n",
    "line1, = ax.plot(x1,x2,'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(121)\n",
    "plot_cost(loss, (10,99), (-10,-99), vx=r.x[0], vy=r.x[1], vz=r.x[2])\n",
    "g = np.r_[g]\n",
    "plt.plot(g[-10:,0], g[-10:,1], color=\"blue\")\n",
    "plt.scatter(g[-10:,0], g[-10:,1], color=\"blue\", s=20)\n",
    "plt.scatter(g[-1,0], g[-1,1], marker=\"x\", color=\"white\", s=200)\n",
    "\n",
    "# plot gradient at some points\n",
    "for _ in range(10):\n",
    "    t = np.random.random()*20+r.x[0], np.random.random()*20+r.x[1], r.x[2]\n",
    "    grad = n_grad(X2,y2,t)\n",
    "    plt.arrow(t[0], t[1], -grad[0], -grad[1], head_width=1, head_length=0.5, fc='green', ec='green')\n",
    "for _ in range(10):\n",
    "    t = -np.random.random()*20+r.x[0], -np.random.random()*20+r.x[1], r.x[2]\n",
    "    grad = n_grad(X2,y2,t)\n",
    "    plt.arrow(t[0], t[1], -grad[0], -grad[1], head_width=1, head_length=0.5, fc='green', ec='green')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(loss_history, marker=\"o\")\n",
    "plt.xlabel(\"iteration number\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### zoom al óptimo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f59046a8090>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(15,6))\n",
    "plot_cost(loss, (r.x[0]-5,r.x[0]+5), (r.x[1]-5,r.x[1]+5), vx=r.x[0], vy=r.x[1], vz=r.x[2])\n",
    "plt.scatter(g[-1,0], g[-1,1], marker=\"x\", color=\"white\", s=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Veamos el efecto del número de iteraciones y de la tasa de aprendizaje:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe1d7a8c4eac47c5b392cb642ed2c025",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='MaxIter', index=4, options=(1, 10, 100, 1000, 10000), value=10000)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def GradientSigmo(MaxIter=10000, eta = 0.001):\n",
    "    plt.clf()\n",
    "    plt.figure(figsize=(30,10))\n",
    "    plt.axis([None, None, 0, 100])\n",
    "    iris = datasets.load_iris()\n",
    "    X, y = iris.data, iris.target\n",
    "    X2 = X[:100][:,:2]\n",
    "    y2 = y[:100]\n",
    "    #Aprendizaje\n",
    "    w = np.ones(3).reshape(3, 1)\n",
    "    N = len(y2)\n",
    "    Error =np.zeros(MaxIter)\n",
    "    Xent = np.concatenate((X2,np.ones((100,1))),axis=1)\n",
    "\n",
    "    for i in range(MaxIter):\n",
    "        tem = np.dot(Xent,w)\n",
    "        tem2 = sigmoide(tem.T)-np.array(y2)\n",
    "        Error[i] = np.sum(np.abs(tem2))/N\n",
    "        tem = np.dot(Xent.T,tem2.T)\n",
    "        wsig = w - eta*tem/N\n",
    "        w = wsig\n",
    "\n",
    "\n",
    "    print(w)\n",
    "    print('Error=',Error[-1])\n",
    "    #Grafica de la frontera encontrada\n",
    "    plt.subplot(1,2,2)\n",
    "    iris = datasets.load_iris()\n",
    "    X, y = iris.data, iris.target\n",
    "    X2 = X[:100][:,:2]\n",
    "    y2 = y[:100]\n",
    "    plt.scatter(X2[:,0], X2[:,1], c=y2,cmap=\"Accent\");\n",
    "    x1 = np.linspace(4,8,20)\n",
    "    x2 = -(w[0]/w[1])*x1 - (w[2]/w[1])\n",
    "    \n",
    "    plt.plot(x1,x2)\n",
    "    plt.show()\n",
    "    print(MaxIter,eta)\n",
    "\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "interact(GradientSigmo,MaxIter=[1,10,100,1000,10000], eta=[0.0001,0.001,0.1,1,10]);    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bibliografy"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[1] Bishop, C.M. Pattern Recognition and Machine Learning. Springer, 2006.\n",
    "[2] Murphy K.P., Machine Learning: A Probabilistic Perspective. The MIT Press, 2012."
   ]
  }
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