{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Matplotlib Tutorial\n",
      "=================\n",
      "\n",
      "Advanced Python School in Zurich, September 2013\n",
      "-------------------------------------------"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Basic plotting\n",
      "==============\n",
      "Anscombe dataset\n",
      "----------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\n",
      "y = np.array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\n",
      "\n",
      "plt.plot(x, y, 'o')\n",
      "plt.xlabel('x')\n",
      "plt.ylabel('y')\n",
      "plt.title('Anscombe dataset')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<matplotlib.text.Text at 0x104c7c2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5pqKioqwsAehQWDyF9mDZVE95eblef/11lZaWauvWrWpoaNCyZcus6h7okFg8hfZg2R5/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\nHznlgVpYGWAuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104c659d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Linear regression\n",
      "---------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a, b = np.polyfit(x, y, 1)\n",
      "plt.plot(x, y, 'o')\n",
      "plt.plot(x, a * x + b, '-')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[<matplotlib.lines.Line2D at 0x104c78b10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wsDDBVYnRsWNH7Nu3D+Xl5ZAkCfn5+ejcubPosoR6+umnsWbNGgDAmjVrMHLk\nSMEViZOdnY2lS5di69at0Gq1ossRJjw8HJcvX0ZxcTGKi4sREBCAoqKiR/8FL9mI0WiUhg8fbqvd\nq8Lhw4elyMhIqWvXrtKoUaOk0tJS0SUJ88Ybb0idO3eWunTpIk2aNEm6e/eu6JLs5vnnn5fatm0r\neXh4SAEBAVJWVpZ07do1KSoqSnrqqackvV4v3bhxQ3SZdlH7WHzwwQfSk08+KQUFBUndu3eXunfv\nLr3wwguiy7SLn49F48aN5d8XvxQSEiJdu3btkftpUMcmERGJ5dq3kBARqRxDnIhIxRjiREQqxhAn\nIlIxhjgRkYoxxImIVOz/AEJajdD+IFHoAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10508fb10>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Multiple datasets\n",
      "-----------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xvec =  np.array([[10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5],\n",
      "               [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5],\n",
      "               [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5],\n",
      "               [8,8,8,8,8,8,8,19,8,8,8]]).T\n",
      "yvec = np.array([[8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68],\n",
      "              [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74],\n",
      "              [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73],\n",
      "              [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]]).T\n",
      "\n",
      "plt.plot(xvec, yvec, 'o');"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RIflq3MjtgEQDleFCHFCmo0DufaAvXPkCOHUKqKsD4uOBzk5g2jScv3RV8pxGbgckGqgM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\nIiId+//UfdnMeUzPSAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1050bd250>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Visual encodings \n",
      "==============\n",
      "Length (`plt.bar`)\n",
      "----------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.title('length')\n",
      "plt.bar(x, y, 1)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<Container object of 11 artists>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0kR544AEnewIAjJLPsixrrC8aHBzUxRdfrIaGBqWnp6uoqEh/+9vflJ2dfWpQn0/SmIeN\nAfWoNx5rUc8L9WxEpP1qvrHXs7USf+eddzRr1izNnDlTKSkpuvHGG7Vjxw47QwEAYpBs50Wff/65\nMjIyol8Hg0H94x//+N6zfLH0ZQP1qDcea1HP9HqndhbGL1shPtKkEvnfDwCYyGxtp6Snp6u9vT36\ndXt7u4LBoGNNAQBGx1aIL1iwQJ988okikYj6+/v1wgsvaOnSpU73BgAYga3tlOTkZD3++OO6+uqr\nNTg4qJtvvjl6ZAoAIHFsHyd+7bXX6sCBA/r000+1bt26Id8bHBxUYWGhSktLY25wvOnp6VFFRYWy\ns7OVk5OjpqYmt1tyVHV1tXJzc5WXl6cVK1boq6++crulmKxevVqBQEB5eXnRx7q6ulRcXKzZs2er\npKREPT09LnZo33Bzu+eee5Sdna2CggKVl5fr+PHjLnYYm+Hm960//elPSkpKUldXlwudOeN08/vz\nn/+s7OxszZ07V7///e9HHCcuZ2xu2bJFOTk54/5dXTvWrl2rJUuW6KOPPtL777/vqf+BRCIRPfnk\nk2pubtb+/fs1ODio7du3u91WTKqqqlRfXz/ksZqaGhUXF6u1tVWLFy9WTU2NS93FZri5lZSU6MMP\nP1RLS4tmz56t6upql7qL3XDzk069B/f666/rwgsvdKEr5ww3vz179qiurk7vv/++PvjgA919990j\njuN4iB8+fFi7du3SmjVrPHeUyvHjx/XWW29p9erVkk5tK02ePNnlrpzj9/uVkpKivr4+DQwMqK+v\nT+np6W63FZPLLrtMqampQx6rq6vTqlWrJEmrVq3Sq6++6kZrMRtubsXFxUpKOvVjvXDhQh0+fNiN\n1hwx3Pwk6Xe/+50efPBBFzpy1nDze+KJJ7Ru3TqlpKRIkqZOnTriOI6H+J133qna2troPyQvaWtr\n09SpU1VVVaVLLrlEt9xyi/r6+txuyzFpaWm66667lJmZqQsuuEDnn3++rrrqKrfbclxnZ6cCgYAk\nKRAIqLOz0+WO4mPr1q1asmSJ2204aseOHQoGg8rPz3e7lbj45JNP9Oabb+rnP/+5QqGQ9u3bN+Jr\nHE3anTt3atq0aSosLPTcKlySBgYG1NzcrNtvv13Nzc368Y9/bOx/xYdz8OBBPfroo4pEIjpy5Ih6\ne3v1/PPPu91WXPl8Pk9u+91///2aNGmSVqxY4XYrjunr69PmzZu1cePG6GNey5mBgQF1d3erqalJ\ntbW1Wr58+YivcTTEGxsbVVdXp6ysLFVWVmr37t1auXKlkyVcFQwGFQwGVVRUJEmqqKhQc3Ozy105\nZ9++fVq0aJGmTJmi5ORklZeXq7Gx0e22HBcIBNTR0SFJOnr0qKZNm+ZyR8565plntGvXLs/9Aj54\n8KAikYgKCgqUlZWlw4cPa/78+frXv/7ldmuOCQaDKi8vlyQVFRUpKSlJx44dO+NrHA3xzZs3q729\nXW1tbdq+fbuuvPJKPfvss06WcNX06dOVkZGh1tZWSVJDQ4Nyc3Nd7so5c+bMUVNTk06ePCnLstTQ\n0KCcnBy323Lc0qVLtW3bNknStm3bVFZW5nJHzqmvr1dtba127Nihc845x+12HJWXl6fOzk61tbWp\nra1NwWBQzc3NnvolXFZWpt27d0uSWltb1d/frylTppz5RVachMNhq7S0NF7Du+a9996zFixYYOXn\n51vLli2zenp63G7JUQ888ICVk5NjzZ0711q5cqXV39/vdksxufHGG60ZM2ZYKSkpVjAYtLZu3Wod\nO3bMWrx4sXXRRRdZxcXFVnd3t9tt2vL9uT311FPWrFmzrMzMTGvevHnWvHnzrNtuu83tNm37dn6T\nJk2K/t19V1ZWlnXs2DGXuovdcPPr7++3brrpJmvu3LnWJZdcYu3Zs2fEcWxdihYAMD547xASAJhA\nCHEAMBghDgAGI8QBwGCEOAAYjBAHAIP9D/rcpv0pyOV7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1050a2850>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Position and color (`plt.scatter`)\n",
      "-------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.title('position and color')\n",
      "plt.scatter(x, y, c=y, s=100)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.collections.PathCollection at 0x105279a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1052c8690>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Position and area (`plt.scatter`)\n",
      "---------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.title('position and size')\n",
      "plt.scatter(x, y, s=y*20, facecolor='none')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.collections.PathCollection at 0x10527c210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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E4Pnz57CyssJnn32GoUOHYvTo0UhJSYGDgwP27NnzxkR63tgkIm1JS0tD27ZtkZycrPJi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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1052cbcd0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Length and angle (`plt.polar`)\n",
      "------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "theta = (x - x.min())*1./(x.max()-x.min())*2*np.pi\n",
      "\n",
      "theta = np.vstack([theta, theta])\n",
      "radius = np.vstack([np.zeros(len(y)), y])\n",
      "\n",
      "plt.polar(theta, radius, 'b');"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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onlFYlTPZsmULpkyZwmzXxcHBAR999BETW5r07t3b4GsfPAACAoDwcMOunzZt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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105471950>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Creating visual style\n",
      "==================="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Using style arguments\n",
      "-------------------\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, y, 'o', markersize=10, mfc='k')\n",
      "plt.xlabel('x', size=16)\n",
      "plt.ylabel('y', size=16)\n",
      "plt.title('Anscombe dataset', size=16, weight='bold')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0x1054c8f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wiIhqh88EQendsIiIqGb5TBAAvBsWEVFt8Kkg4N2wiIhqns8EAe+GRURUO3wm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\nEWkcg4CISOP+Bzt1+dcAdimmAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1054752d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Customising globals\n",
      "-------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rc_defaults = matplotlib.rcParams.copy()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib\n",
      "matplotlib.rcParams.update({'xtick.labelsize' : 16,\n",
      "                            'ytick.labelsize' : 16,\n",
      "                            'axes.labelsize' : 16,\n",
      "                            'axes.titlesize' : 18,\n",
      "                            'lines.markersize' : 10,\n",
      "                            'lines.linestyle' : 'None',\n",
      "                            'lines.marker' : 'o',\n",
      "                            'lines.markerfacecolor' : 'k'})\n",
      "\n",
      "plt.plot(x, y)\n",
      "plt.xlabel('x')\n",
      "plt.ylabel('y')\n",
      "plt.title('Anscombe dataset')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.text.Text at 0x10526fc10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CgoLEoEGDCtySvXTpUhEUFCQqVqwo624usq1Sn7hnMBjQs2dPtGrVCjk5OdixYwdSU1PN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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1054c8210>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matplotlib.rcParams.update(rc_defaults)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Using object-oriented API\n",
      "-------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = plt.subplot(111)\n",
      "line, = plt.plot(x, y, 'o')\n",
      "line.set_markersize(10)\n",
      "line.set_mfc('k')\n",
      "\n",
      "ticklabels = ax.get_xticklabels()\n",
      "ticklabels[0].set_size(15)\n",
      "ticklabels[-1].set_size(15)\n",
      "\n",
      "plt.setp(ticklabels[1:-1], visible=False)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[None, None, None, None]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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J5XJKpVI+VQTAD66+FL300kv19ddfe10LPJJIJAbNzP8rn88rkUioo6PDp6oAVBudogbK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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10548a910>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Adding grid\n",
      "-----------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, y, 'ko')\n",
      "plt.grid(axis='y', which='major', ls='-', lw=0.5)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MUWVl5QXXsjg7B4DMGPRyys6dO7V161aNHz9eS5cu1aeffqrly5c7kQ0AcBG2llPO2bFj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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105a1f410>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Configuring spines\n",
      "------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = plt.subplot(111)\n",
      "plt.plot(x, y, 'ko', ms=10, clip_on=False)\n",
      "\n",
      "ax.spines['top'].set_visible(False)\n",
      "ax.spines['right'].set_visible(False)\n",
      "ax.spines['bottom'].set_position(('outward', 20))\n",
      "ax.spines['left'].set_position('center')\n",
      "\n",
      "ax.xaxis.set_ticks_position('bottom')\n",
      "ax.yaxis.set_ticks_position('left')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JM1w8Hp82Q36/bDareDyu9vb2MlVVPnV1dXr55Ze9LgMoG2bKhkun07M6DoDZCGXDRSKR\nWR0HwGyEsuEcx5FlWQXHWJYlx3HKVBGAUqIlznCu6yoWiymVSl1zTDQa1cjISOD7lWdCSxyChgd9\nhqusrFRLS4skFexTnouBDAQRM2WfmMs7+gphpoygIZTha4QygoYHfQBgEEIZAAxCKAOAQQhlADAI\noQwABiGUAcAg19w8Ql8sAJTfjH3KvOkCfkGfMoJm2kz5vW+6eL8rb7q4Mo4ZMwDMrmlrynP9TRcA\n4KVpofxB3nQBAJhd00KZN10AgHemhTJvugAA70wLZd50AQDemdYSx5su4Ce0xCFoprXE8aYLAPDO\nNQ+5Z0cfTDE5Oan7779fNTU1+t3vfjflZ8yUETS8eQTG++lPf6o///nPevvtt9XX1zflZ4QygoYD\niWC08fFxHT58WNu2bSN8MSfwNmsY7bHHHtPu3bv11ltvXXNMd3d3/mvbtmXbdukLA0qEUIaxDh06\npKqqKjU3N+vIkSMzjmH2jKBh+QLGGhwcVF9fn+rq6tTZ2akXX3xRW7Zs8bosoKR40AdfOHr0qH78\n4x9P674AgoaZMnwjFAp5XQJQcsyUAcAgzJQBwCCEMgAYhFAGAIMQygBgEEIZAAxCKAOAQQhlADAI\noQwABiGUAcAghDIAGKSoUL7WsYlzEffiKu7FVdyLq7gXV93IvSCUPyDuxVXci6u4F1dxL64qWSgD\nAMqDUAYAgxR1dCfn2ALAjfmgpyMX9Y4+jlwGgPJg+QIADEIoA4BBCGUAMEhRoTw5Oanm5matX7++\n1PUY7dy5c3IcRw0NDWpsbNTQ0JDXJXlm586disViampq0ubNm/Xuu+96XVLZbN26VeFwWE1NTflr\nZ8+eVVtbm+6++26tXbtW586d87DC8pnpXnz3u99VQ0OD7r33Xm3atEnnz5/3sMLymeleXPGTn/xE\n8+bN09mzZ6/7OUWF8p49e9TY2DjnuzAeffRRrVu3Tn//+9/1l7/8RQ0NDV6X5IlUKqWnn35aw8PD\nOnHihCYnJ3Xw4EGvyyqbrq4u9ff3T7m2a9cutbW16dSpU1qzZo127drlUXXlNdO9WLt2rUZGRnT8\n+HHdfffd2rlzp0fVlddM90KSXnvtNf3xj3/Uxz72saI+57qhPD4+rsOHD2vbtm1zugvj/Pnzeuml\nl7R161ZJ0vz587Vo0SKPq/LGwoULZVmWXNfVpUuX5LquqqurvS6rbFavXq2PfOQjU6719fXpoYce\nkiQ99NBDeu6557worexmuhdtbW2aN+//0bJy5UqNj497UVrZzXQvJOk73/mOnnrqqaI/57qh/Nhj\nj2n37t35mzxXjY2NacmSJerq6tJ9992nRx55RK7rel2WJxYvXqzt27crEonoox/9qG677TY98MAD\nXpflqYmJCYXDYUlSOBzWxMSExxWZYf/+/Vq3bp3XZXjm+eefV01Nje65556if6dg0h46dEhVVVVq\nbm6e07NkSbp06ZKGh4f1zW9+U8PDw7r11lvnzP+ivt/o6Kh+9rOfKZVK6Z///Kf+85//6Nlnn/W6\nLGOEQqE5v9QnST/84Q91yy23aPPmzV6X4gnXdfXkk0/q8ccfz18rJkcLhvLg4KD6+vpUV1enzs5O\nvfjii9qyZcvNV+tDNTU1qqmp0YoVKyRJjuNoeHjY46q8cezYMa1atUq333675s+fr02bNmlwcNDr\nsjwVDof1+uuvS5JOnz6tqqoqjyvy1i9/+UsdPnx4Tv9jPTo6qlQqpXvvvVd1dXUaHx/X8uXL9cYb\nbxT8vYKh/OSTT+q1117T2NiYDh48qM9+9rN65plnZrVwv7jjjjtUW1urU6dOSZIGBgYUi8U8rsob\n9fX1Ghoa0jvvvKNcLqeBgQE1NjZ6XZanNmzYoJ6eHklST0+PNm7c6HFF3unv79fu3bv1/PPPa8GC\nBV6X45mmpiZNTExobGxMY2Njqqmp0fDw8PX/wc4V6ciRI7n169cXOzyQXn755dz999+fu+eee3Jf\n/vKXc+fOnfO6JM/86Ec/yjU2NuaWLl2a27JlS+7ixYtel1Q2Dz74YO7OO+/MWZaVq6mpye3fvz93\n5syZ3Jo1a3Kf/OQnc21tbbl///vfXpdZFu+/F/v27ct94hOfyEUikdyyZctyy5Yty33jG9/wusyy\nuHIvbrnllvzfi/eqq6vLnTlz5rqfU9SBRACA8pjbLRUAYBhCGQAMQigDgEEIZQAwCKEMAAYhlAHA\nIP8DLgXR3OUQdV8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105a24f50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Annotating plots\n",
      "================"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Adding legend\n",
      "-------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1, = plt.plot(xvec[:,0], yvec[:, 0], 'bo', label='dataset 1')\n",
      "p2, = plt.plot(xvec[:,1], yvec[:, 1], 'ro', label='dataset 2')\n",
      "\n",
      "#inset legend\n",
      "l1 = plt.legend(frameon=False, loc='lower right')\n",
      "\n",
      "#external legend\n",
      "l2 = plt.legend((p1, p2), ('new label 1', 'new label 2'),\n",
      "                ncol=2, numpoints=1, loc='upper left', \n",
      "                bbox_to_anchor=(0, -0.1))\n",
      "plt.gca().add_artist(l1) # re-insert inset legend"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.legend.Legend at 0x105a2b990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ud0myefbZZ8XQoUPFtddeK+bPny9aWlrkLilo5syZI3r37i1iYmJESkqKWLdu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\nxhAnIlIwhjgRkYIxxImIFIwhTkSkYP8f2hMHKr3pTM4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1054cda50>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Placing text\n",
      "------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = plt.subplot(111)\n",
      "\n",
      "plt.plot(x, y, 'bo')\n",
      "\n",
      "plt.text(5, 9, 'Text in\\ndata coords', \n",
      "         transform=ax.transData, # data coordinates (default)\n",
      "         size=15)\n",
      "\n",
      "plt.text(0.8, 0.1, 'Text in\\naxes coords', \n",
      "         transform=ax.transAxes, # axes coordinates\n",
      "         size=15, ma='right', ha='right')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.text.Text at 0x10849ce10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y0Wq1smXLFgkICBA3NzdZtWqVyd/NHpmSnSal7Y8//iihoaESGhoqwcHB8sIL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\niBMRWTGGOBGRFWOIExFZMYY4EZEV+z9TTAiz1LC4SAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108424050>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Arrows\n",
      "------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = plt.subplot(111)\n",
      "\n",
      "plt.plot(xvec[:,2], yvec[:,2], 'ko')\n",
      "\n",
      "ax.annotate(\"Outlier\",\n",
      "                xy=(13, 12.74), xycoords='data',\n",
      "                xytext=(10, 11.5), textcoords='data',\n",
      "                fontsize=12,\n",
      "                arrowprops=dict(arrowstyle=\"->\",\n",
      "                                color='r',\n",
      "                                shrinkA=2, shrinkB=5,\n",
      "                                connectionstyle='angle,angleA=180,angleB=45,rad=0'\n",
      "                                )\n",
      "            )"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.text.Annotation at 0x10889ba50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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goVsSgUCIA0FCtyQCgRAHgoRuSQQCIQ4ECd2SCAQubAJBQrckAoGOTQDoJejYBIAwQ4gD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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108473990>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Color\n",
      "====="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Heatmaps and colorbars\n",
      "----------------------\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(yvec.T, interpolation='nearest', cmap='gray')\n",
      "plt.colorbar(orientation='horizontal')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x1071cb488>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1088b87d0>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Color scales\n",
      "------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(nrows=3, figsize=(6,2))\n",
      "fig.subplots_adjust(hspace=0.4)\n",
      "c = np.linspace(0, 1, 50)[None, :]\n",
      "\n",
      "cmaps = [('Sequential','PuBu'),\n",
      "         ('Diverging', 'RdBu'),\n",
      "         ('Qualitative', 'Accent')]\n",
      "\n",
      "for ax, (cname, cmap) in zip(axes, cmaps):\n",
      "    ax.imshow(c, interpolation='nearest', aspect='auto', cmap=cmap)\n",
      "    ax.set_ylabel(cname, rotation=0)\n",
      "    ax.set_xticks([])\n",
      "    ax.set_yticks([])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAB+CAYAAADoWlzzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nttcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105896bd0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Custom color maps\n",
      "-----------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import colors\n",
      "\n",
      "cmap1 = colors.LinearSegmentedColormap.from_list('smooth', ['r', 'y', 'b'])\n",
      "cmap2 = colors.LinearSegmentedColormap.from_list('segmented', ['r', 'y', 'b'], N=6)\n",
      "\n",
      "fig, axes = plt.subplots(nrows=2, figsize=(6,1))\n",
      "for ax, cmap in zip(axes, [cmap1, cmap2]):\n",
      "    ax.imshow(c, interpolation='nearest', aspect='auto', cmap=cmap)\n",
      "    ax.set_xticks([]), ax.set_yticks([])\n",
      "    ax.set_ylabel(cmap.name, rotation=0)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAABGCAYAAADxeIK+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105896810>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Discretise color maps\n",
      "---------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cmap_discretise(cmap, n_levels):\n",
      "    original_cmap = plt.get_cmap(cmap)\n",
      "    colors_i = np.concatenate((np.linspace(0, 1., n_levels), (0.,0.,0.,0.)))\n",
      "    colors_rgba = original_cmap(colors_i)\n",
      "    indices = np.linspace(0, 1., n_levels+1)\n",
      "    cdict = {}\n",
      "    for ki,key in enumerate(('red','green','blue')):\n",
      "        cdict[key] = [(indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) \n",
      "                      for i in xrange(n_levels+1)] \n",
      "    discrete_cmap = colors.LinearSegmentedColormap('discrete', cdict, 1024)\n",
      "    return discrete_cmap\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n",
      "cmap = cmap_discretise('PuBu', 4)\n",
      "img1 = ax1.imshow(yvec.T, interpolation='nearest', cmap='PuBu')\n",
      "img2 = ax2.imshow(yvec.T, interpolation='nearest',  cmap=cmap)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10698bb90>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Figure layout\n",
      "=============\n",
      "Subplots\n",
      "--------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(8,4))\n",
      "\n",
      "ax1.plot(xvec[:, 1], yvec[:, 1], 'ko')\n",
      "ax2.plot(xvec[:, 2], yvec[:, 2], 'ko')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10711c150>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAeEAAAD9CAYAAACLKc9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIQwAgCGEMAAAhhDCAAAY8n/wVKWKCOgPTgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106c5d7d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Gridplot\n",
      "--------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import gridspec\n",
      "gs = gridspec.GridSpec(4,4)\n",
      "\n",
      "for i in range(4):\n",
      "    for j in range(4):\n",
      "        if i == j:\n",
      "            continue\n",
      "        ax = plt.subplot(gs[i, j])\n",
      "        ax.plot(yvec[:, i], yvec[:, j], '.')\n",
      "        plt.setp(ax.get_xticklabels(), visible=False)\n",
      "        plt.setp(ax.get_yticklabels(), visible=False)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PHyYL3swlYdCnq4f4+LRYLGSz2Tz/d1/3wpjJ9tzyx/FV2na7zXs3YM9IunqIj09Dj0vJ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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107100290>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Complex layouts\n",
      "---------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs = gridspec.GridSpec(2, 2)\n",
      "\n",
      "gs_topright = gridspec.GridSpecFromSubplotSpec(2, 2, gs[0,0], \n",
      "                                               wspace=0.4, hspace=0.4)\n",
      "\n",
      "for i in range(3):\n",
      "    plt.subplot(gs[i+1])\n",
      "for i in range(4):\n",
      "    plt.subplot(gs_topright[i])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OHKc2VVVVeO211wAAVqsVc+fORWNjo88dIjIa65qCgeLgnpSUhKamJty5cwd9\nfX0oKipCZmamU5u4uDicP38eANDe3o7GxkbMmzfPuB4TacS6pmCg+Fhm8uTJ+PLLL7F27VoMDAxg\n27ZtWLBgAb7++msATw4S3rt3L7Kzs2G32/H48WN89tlnCA0N9UvniXzBuqZgwAOyadzjAdlkVjwg\nm4iInHBwJyIyIQ7uREQmxMGdiMiEOLgTEZkQB3ciIhPi4E5EZEIc3ImITIiDOxGRCXFwJyIyIQ7u\nREQmpPkMVeDJBvOLFy9GQkICDyqgcYF1TaYnCvr7+8VqtUpzc7P09fWJ3W6X+vp6pzZdXV0SHx8v\nLS0tIiLS0dEx4ut4CEOkibf1pVdd+xKbyBta6kvzGaoFBQXYuHHj0GEHYWFhxvwUItIJ65qCgeYz\nVJuamuBwOJCWloYHDx5g165d2Lx584ivxXMmSS/+OENVbV0DrG3Sz5g6Q9XhcKC2thbl5eXo7e1F\namoqli5dipiYGKd2PGeS9OKPM1TV1jXA2ib96HmGquLgruasyaioKISFhSEkJAQhISFYvnw56urq\n3H4TEI0FrGsKBprPUH311Vfxyy+/YGBgAL29vbhy5Qri4+MN7TSRFqxrCgaaz1CNi4tDeno6bDYb\nJk6ciJycHH4T0JjGuqZgwDNUadzjGapkVjxDlYiInHBwJyIyIQ7uREQmxMGdiMiEOLgTEZkQB3ci\nIhPi4E5EZEIc3ImITIiDOxGRCXFwJyIyIQ7uREQmpMsZqgBw9epVTJ48Gd9//72uHSQyAuuazE5x\ncB8YGMCOHTtQVlaG+vp6FBYWoqGhwW27PXv2ID09nZso0ZjHuqZgoPkMVQA4dOgQNm3ahFmzZhnW\nUSK9sK4pGGg+Q7WtrQ3FxcW4cOECrl69OuoRZjxnkvTijzNU1dY1wNom/YypM1R3796N/Pz8oX2H\nR/vnK8+ZJL344wxVtXUNsLZJP2PqDNWamhpkZWUBADo7O1FaWgqLxTLi2DKisYJ1TcFA8SSm/v5+\nxMbGory8HBEREUhOTkZhYSEWLFjgtn12djZeeeUVbNiwwTkIT6shA3lbX3rVtS+xibyhpb40n6FK\nNN6wrikY8AxVGvd4hiqZFc9QJSIiJxzciYhMiIM7EZEJcXAnIjIhDu5ERCbEwZ2IyIQ4uBMRmRAH\ndyIiE+LgTkRkQhzciYhMiIM7EZEJcXAnIjIhzQdknz59Gna7HTabDS+88AKuX79uSEeJ9MbaJlMT\nBf39/WJRRS6tAAAGnUlEQVS1WqW5uVn6+vrEbrdLfX29U5uqqirp7u4WEZHS0lJJSUkZ8XU8hCHS\nxJf6Ym3TeKClvjQfkJ2amopp06YBAFJSUtDa2mrMTyEiHbG2yew0H5A93LFjx5CRkeH2czxEmPSi\nxyHCrG0ai8bUAdmDKioq8M033+DixYtuP89DhEkvehwizNqmsWhMHZANANevX0dOTg7KysowY8YM\nnztD5C+sbTI9pQfyDodD5s2bJ83NzfLo0SO3L53u3r0rVqtVLl26ZMhLASJPfKkv1jaNB1rqS/MB\n2R9//DG6urqwfft2AIDFYkF1dbWxP5GINGJtk9nxgGwa93hANpkVD8gmIiInHNyJiEyIgzsRkQlx\ncCciMiEO7kREJsTBnYjIhDi4ExGZEAd3IiIT4uBORGRCHNyJiEzI1IO7Xvsij6fYwXjPwYj1Zf64\nWmk+QxUAdu7ciZiYGNjtdly7dk33TvqKhRg8sb01nusaYH0FQ1ytFAf3gYEB7NixA2VlZaivr0dh\nYSEaGhqc2pSUlODmzZtoamrCkSNHhnbQIxqrWNcUDDSfoXru3Dls2bIFwJNzJru7u9He3m5cj4k0\nYl1TUFDa7P3bb7+Vd955Z+jPJ0+elB07dji1Wb9+vVy8eHHoz6tXr5Zff/11xIbz/OCHkR/e0Kuu\nWdv88MeHr3Q5Q1Vc9ht2vc7180SBpFddu2tDNFYoPpZRc86ka5vW1lZERkbq3E0i/bCuKRgoDu5J\nSUloamrCnTt30NfXh6KiImRmZjq1yczMxIkTJwAAly9fxvTp0zF79mzjekykEeuagoHmM1QzMjJQ\nUlKC6OhoTJkyBcePH/dLx4l8xbqmoODz03o3SktLJTY2VqKjoyU/P99tm/fff1+io6PFZrNJbW2t\n32KfOnVKbDabLFq0SJ5//nmpq6vzS9xB1dXVMmnSJDl79qzf4lZUVEhiYqIsXLhQVqxYoUtcNbE7\nOjpk7dq1YrfbZeHChXL8+HFd4mZnZ8vTTz8tCQkJo7YJVH0ZFTtQda0m9iDWtjZG1bVug3t/f79Y\nrVZpbm6Wvr4+sdvtUl9f79Tmxx9/lHXr1omIyOXLlyUlJcVvsauqqqS7u1tEnvwF6hFbTdzBdmlp\nafLyyy/Ld99955e4XV1dEh8fLy0tLSLypCj1oCb2/v375aOPPhqKGxoaKg6HQ3Psn3/+WWpra0f9\nJghkfRkRO1B1rTb2YDvWtrbaNqquddt+IJBzh9XETk1NxbRp04Zit7a2+iUuABw6dAibNm3CrFmz\nNMdUG7egoAAbN24celEYFhbmt9jh4eHo6ekBAPT09GDmzJmYPFnxCaAqy5Ytw4wZM0b9fCDry4jY\ngaprtbEB1rYetW1UXes2uLe1tSEqKmroz3PmzEFbW5vHNnoUo5rYwx07dgwZGRl+idvW1obi4uKh\nFY5qp+FpjdvU1IQ//vgDaWlpSEpKwsmTJzXHVRs7JycHN27cQEREBOx2Ow4ePKhLbF/65q/6MiJ2\noOpabWzWtn9q29fa0v7r1P/oOXfYqNgAUFFRgW+++QYXL170S9zdu3cjPz8fEyZMgDx5DOaXuA6H\nA7W1tSgvL0dvby9SU1OxdOlSxMTEGB47Ly8PiYmJqKysxK1bt7BmzRrU1dVh6tSpmmKrEcj60jt2\noOpabWzWtv9q25fa0m1wD+TcYTWxAeD69evIyclBWVmZ4j+D9IxbU1ODrKwsAEBnZydKS0thsVhG\nTL3TO25UVBTCwsIQEhKCkJAQLF++HHV1dZq/AdTErqqqwr59+wAAVqsVc+fORWNjI5KSkjTF9rZv\n/qwvI2IHqq7VxmZt+6e2fa4tTW8ChnE4HDJv3jxpbm6WR48eeXzpdOnSJd1e/qiJfffuXbFarXLp\n0iVdYqqNO9zWrVt1mVGgJm5DQ4OsXr1a+vv75eHDh5KQkCA3btzwS+wPPvhAcnNzRUTk3r17EhkZ\nKb///rvm2CIizc3Nql48+bu+jIgdqLpWG3s41rY2RtS1rlMhS0pKZP78+WK1WiUvL09ERA4fPiyH\nDx8eavPee++J1WoVm80mNTU1fou9bds2CQ0NlcTERElMTJQlS5b4Je5wen0DqI37+eefS3x8vCQk\nJMjBgwd1iasmdkdHh6xfv15sNpskJCTI6dOndYmblZUl4eHhYrFYZM6cOXLs2LExU19GxQ5UXauJ\nPRxr23dG1fUEEW6OQURkNqY+iYmIKFhxcCciMiEO7kREJsTBnYjIhDi4ExGZEAd3IiIT+n+RBR2Z\njHByEQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1092b7c90>"
       ]
      }
     ],
     "prompt_number": 42
    }
   ],
   "metadata": {}
  }
 ]
}