Python_Introduction_ipynb_txt.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tengal/Python-Introduction/blob/main/Python_Introduction_ipynb_txt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8lPCmtDDkjw"
      },
      "source": [
        "### Introduction to Python\n",
        "You will need to have a version of Python installed (hopefully a new version like 3.9 and above). You will also need to have some of the standard Python libraries installed, either via 'pip install ...' or via a conda environment. Libraries that you will need for this class include: numpy, scipy, matplotlib. \n",
        "\n",
        "For folks who are used to matlab, note that Python starts with the zero index while Matlab starts with index 1. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_VfVUKfDkj1",
        "outputId": "1c43250f-dd78-4225-849e-ba21efcdf6cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n",
            "4\n",
            "1.0\n",
            "-12\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "# Basic Operations \n",
        "# -- similar to many basic programming languages (C, etc.)\n",
        "\n",
        "# use the python function 'print' to print the output\n",
        "print(2 + 2)\n",
        "print(2 * 2)\n",
        "print(2 / 2)\n",
        "print(2 - 2 * 7)  # Order of operations matters\n",
        "print((2 - 2) * 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oELIoPWlDkj4"
      },
      "source": [
        "#### Notice a big difference with C or Matlab -- no semicolons needed at the end of statements! However, we will need colon symbols for indicating an indented block (see the loops below)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kECS5l_IDkj4",
        "outputId": "ea791290-23e1-4a11-d394-5f9a38b7c360"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4\n"
          ]
        }
      ],
      "source": [
        "# Assign variables using '=' symbol\n",
        "a = 2 + 2\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aDfwcpGDDkj5",
        "outputId": "1daf0750-eca4-4f75-ea34-6c31cd864dc0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "45\n"
          ]
        }
      ],
      "source": [
        "# Loops\n",
        "count = 0  # Initialize the variable \"count\"\n",
        "\n",
        "# Add all the numbers 1-10\n",
        "for ii in range(10):  # note that range(10) = [0, 2, ..., 9]\n",
        "    count = ii + count  # indicate a code block to loop over by indenting the code lines\n",
        "    \n",
        "print(count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fm6jOZGkDkj6",
        "outputId": "8d76a95e-fa76-4f9e-bd06-2ea9a4a50e5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10\n"
          ]
        }
      ],
      "source": [
        "counter = count  # Assign count to counter\n",
        "\n",
        "# Subtract numbers until count is not greater than 10\n",
        "while (counter > 10):\n",
        "    counter = counter - 1\n",
        "print(counter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6If1VEFXDkj6",
        "outputId": "9c33d129-0219-436f-8815-8319a35fc093"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3\n",
            "3\n"
          ]
        }
      ],
      "source": [
        "# Create a vector (this is also called an 'array')\n",
        "A_Vector = [1, 2, 3, 4]\n",
        "\n",
        "# Access a particular element of the vector\n",
        "print(A_Vector[2])  # prints 3 because A_vector[0] is the start of the vector!\n",
        "print(A_Vector[-2]) # prints 3 because A_vector[-1] is the end of the vector, i.e. we are counting backwards from the end\n",
        "\n",
        "# best way to work with vectors and multi-dimensional 'arrays'\n",
        "# is to use the NumPy library, which is imported as follows:\n",
        "import numpy as np  # call the numpy library np for shorthand\n",
        "\n",
        "B_Vector = np.ones(4)  # same as B_Vector = [1, 1, 1, 1]\n",
        "A_Vector = np.array([1, 2, 3, 4])\n",
        "\n",
        "# np.zeros(N)  -- make a vector of N elements, all initialized to 0\n",
        "# np.random.randn(N)  -- make a vector of N elements, all randomly initialized between (0, 1)\n",
        "# np.eye(N)  -- make the identity matrix of size N x N\n",
        "\n",
        "# Create a numpy-style Matrix\n",
        "\n",
        "A_Matrix = np.array([[1, 2, 3, 4],\n",
        "                        [5, 6, 7, 8]])\n",
        "\n",
        "# Matrices are like stacks of vectors so [], zeros, ones, eye.. all work to\n",
        "# create matrices as well.\n",
        "\n",
        "# np.zeros((N, N, N)) -- Make a N x N x N tensor with elements all initialized to 0 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xoya44HeDkj8",
        "outputId": "ad66cef3-5f87-4cb4-cee2-08582d7628b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[30 70] (2, 4) (4, 2)\n",
            "A =  [[1 2 3 4]\n",
            " [5 6 7 8]]\n",
            "A^2 =  [[ 1  4  9 16]\n",
            " [25 36 49 64]]\n"
          ]
        }
      ],
      "source": [
        "# Operations on Vectors/Matrices\n",
        "\n",
        "# matrices and vectors have to have one matching \n",
        "# dimension to be multiplied together\n",
        "mat_vec_product = A_Matrix @ A_Vector \n",
        "\n",
        "# The dimension of numpy objects can be accessed by .shape and\n",
        "# the .T operator takes the transpose (NxM to MxN)\n",
        "print(mat_vec_product, A_Matrix.shape, A_Matrix.T.shape)\n",
        "\n",
        "# square every element in the matrix A. Note that this is different than doing A @ A\n",
        "elementwise_square = A_Matrix ** 2 \n",
        "\n",
        "# can print multiple things by separating with a comma in the print function\n",
        "print('A = ', A_Matrix)\n",
        "print('A^2 = ', elementwise_square)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztFHR_MfDkj9"
      },
      "outputs": [],
      "source": [
        "# Create a function in Python \n",
        "# Time, and fuctions of time f(t) are represented by vectors \n",
        "\n",
        "# numpy.linspace(-100, 100, 200) generates a 200-dimensional vector by splitting up the\n",
        "# interval (-100, 100) into a uniform grid. Default is to omit the last point (t=100)\n",
        "the_time = np.linspace(-100, 100, 200) \n",
        "\n",
        "F1 = (the_time ** 2) * np.sin(the_time)  # defining t^2 * sin(t)\n",
        "F2 = (the_time ** 2)  # defining t^2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "76mSJNq1Dkj9"
      },
      "outputs": [],
      "source": [
        "# For plotting, we need to import another standard python library\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nVY-JPi4Dkj-",
        "outputId": "3c3c87a3-c270-4bdc-8f80-1a14aefbe1ba"
      },
      "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": [
        "plt.figure()\n",
        "plt.plot(the_time, F1, 'r-', linewidth=2)   # Plots F1(t) in a red solid line\n",
        "plt.plot(the_time, F2, 'k--', linewidth=2)  # Plots F2(t) in a black dashed line\n",
        "plt.xlabel('Time')           # Adds the label \"time\" to the x-axis\n",
        "plt.ylabel('Functions')      # Adds the label \"functions\" to the y-axis\n",
        "plt.legend(['F1','F2'])      # Creates a legend with entries \"F1\", \"F2\"\n",
        "plt.grid(True)               # Turns on the grid lines \n",
        "plt.title('Plots of F1 and F2')  # Gives the plot a title\n",
        "plt.show()  # actually show the plot (more important when not using a Jupyter notebook)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Z9nqtpIDkj-",
        "outputId": "b32412e5-6733-41dc-bd37-f603fc9329a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ax - b =  [-1.11022302e-16  1.33226763e-15]\n",
            "sum(AAxx - bb) =  8.780823290699402e-14\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-5-f32bcafc0812>:14: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  x, _, _, _ = np.linalg.lstsq(A, b)\n",
            "<ipython-input-5-f32bcafc0812>:22: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  xx, _, _, _ = np.linalg.lstsq(AA, bb)\n"
          ]
        }
      ],
      "source": [
        "# Linear System Solving (2 equations + 2 unknowns) \n",
        "\n",
        "# Imagine we have the following 2 algebraic equations\n",
        "# z  + 2y = 1\n",
        "# 4z + 5y = 1\n",
        "\n",
        "A = np.array([[1, 2], [4, 5]])\n",
        "b = np.array([1, 1])\n",
        "\n",
        "# The following will solve Ax = b in a least-squares sense (if an exact solution doesn't exist)\n",
        "# Note that this is usually what A\\b does in matlab\n",
        "\n",
        "#  if function returns a bunch of stuff, and you don't care about that stuff, use a _\n",
        "x, _, _, _ = np.linalg.lstsq(A, b) \n",
        "print('Ax - b = ', A @ x - b)\n",
        "\n",
        "# Even for very large system\n",
        "\n",
        "AA = np.random.randn(100, 100)\n",
        "bb = np.random.randn(100)\n",
        "\n",
        "xx, _, _, _ = np.linalg.lstsq(AA, bb)  \n",
        "print('sum(AAxx - bb) = ', np.sum(AA @ xx - bb))  # get like 1e-14, pretty close to zero\n",
        "\n",
        "# You might get a FutureWarning here -- this is letting you know something\n",
        "# might be off in the lstsq function -- in this case it is fine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F6G_iFsSDkj_",
        "outputId": "e889cab3-7ecb-483b-d465-1dc4a08ee226"
      },
      "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"
        },
        {
          "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": [
        "# Ordinary Differential Equations (ODE)\n",
        "from scipy.integrate import solve_ivp  # import the standard ODE solver in Python\n",
        "\n",
        "TSpan = [0, 10]     # Initial Time and Final Time\n",
        "t = np.linspace(0, 10, 1000)\n",
        "X_initial = [1, 0]  # Initial Conditions\n",
        "\n",
        "# solve_ivp wants a true function with arguments -- good time to show how to do that:\n",
        "# define a function that takes t and X0 as arguments, and returns \n",
        "def ode_test(t, X0): \n",
        "    X = np.zeros(2)\n",
        "    lam = 2\n",
        "    zeta = 1\n",
        "    X[0] = X0[1]\n",
        "    X[1] = - lam * X0[0] - zeta * X0[1]\n",
        "    return X\n",
        "\n",
        "# Now that we have defined a function that we want to call, solve the ODE\n",
        "# Calls the function ode_test at each time step and solves for new state\n",
        "# given initial state then repeats at every time step.\n",
        "ode_test_solution = solve_ivp(ode_test, TSpan, X_initial, t_eval=t)\n",
        "T_out = t\n",
        "Y_out = ode_test_solution.y.T  # transpose the solution for indexing later\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(T_out, Y_out)\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Solutions')\n",
        "plt.legend(['Position', 'Velocity'])\n",
        "plt.title('Position and Velocity')\n",
        "plt.grid(True)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(T_out, Y_out[:, 0])  # The \":\" here takes all elements in the rows \n",
        "                              # associated with the first column. This is called\n",
        "                              # index slicing and it is incredibly useful.\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Solutions')\n",
        "plt.legend('Position')\n",
        "plt.title('Just Position')\n",
        "plt.grid(True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3hlkB0vyDkkA"
      },
      "outputs": [],
      "source": [
        "# Animate your plots (if time)\n",
        "import matplotlib.animation as animation  # import the standard animation library\n",
        "\n",
        "# Enable an interactive plot in a Jupyter notebook\n",
        "%matplotlib notebook\n",
        "\n",
        "# Define a function to update your animation\n",
        "def update_line(num, t, data, line):\n",
        "    line.set_data(t[:num], data[:num])\n",
        "    return line,"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y3t3lEixDkkA",
        "outputId": "5651e05f-f314-4c9f-d575-df3be227ad1d"
      },
      "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,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\" width=\"640\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "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,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\" width=\"640\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "l, = plt.plot([], [], 'b')  # this code looks like nonsense but stick with it\n",
        "plt.xlim(0, 10)\n",
        "plt.ylim(-1, 1)\n",
        "plt.grid(True)\n",
        "easy_anim = animation.FuncAnimation(fig, update_line, range(0, 1000, 50), fargs=(T_out, Y_out[:, 0], l))\n",
        "\n",
        "# repeat for v\n",
        "fig = plt.figure()\n",
        "l, = plt.plot([], [], 'r')  # this code looks like nonsense but stick with it\n",
        "plt.xlim(0, 10)\n",
        "plt.ylim(-1, 1)\n",
        "plt.grid(True)\n",
        "easy_anim = animation.FuncAnimation(fig, update_line, range(0, 1000, 50), fargs=(T_out, Y_out[:, 1], l))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dyv-v7IZDkkA"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}