notebooks/wave_predictors/sine_wave_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sine Wave Predictor\n",
    "\n",
    "This notebook is a very simple recurrent neural net using tensorflow to predict the next value/values of a sine wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import dependancies\n",
    "import tensorflow as tf                    # our machine learning library\n",
    "import numpy as np                         # our python library for dealing with arrays\n",
    "import matplotlib.pyplot as plt, pylab     # our graphing library\n",
    "import time                                # for pauses when updating graphs\n",
    "import os\n",
    "\n",
    "# this is to present our graph interactively \n",
    "% matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow version = 1.2.0\n"
     ]
    }
   ],
   "source": [
    "required_tf_version = '1.2.0'\n",
    "# install tensorflow version 1.2 if not already installed\n",
    "if tf.__version__ == required_tf_version:\n",
    "    print 'tensorflow version = ' + tf.__version__\n",
    "else:\n",
    "    print 'current version of tensorflow =', tf.__version__\n",
    "    time.sleep(0.1)\n",
    "    updateq = bool(input('update to %s?' % required_tf_version))\n",
    "    if updateq == True:\n",
    "        ! pip install tensorflow==1.2.0\n",
    "        print 'updated to ' + tf.__version__\n",
    "        time.sleep(0.1)\n",
    "        os._exit(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set the size of the state\n",
    "state_size = 8\n",
    "# set the number of lines to learn in parallel\n",
    "# in this case we are just looking at one sine wave \n",
    "batch_size = 1\n",
    "\n",
    "# training parameters\n",
    "iterations = 2000\n",
    "learning_rate = 0.1\n",
    "\n",
    "# number of steps to predict into the future\n",
    "steps_to_predict = 30\n",
    "\n",
    "# number of previous points to be inputted, must be >= steps_to_predict\n",
    "truncated_backprop_length = 30\n",
    "\n",
    "# total size of training data series\n",
    "# this calculation will return an array the exact size required\n",
    "total_series_length = iterations+truncated_backprop_length+steps_to_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set our line function\n",
    "def line_function(x):\n",
    "    # use numpy's sin function\n",
    "    return(np.sin(x))\n",
    "\n",
    "# function to generate training data\n",
    "def generate_data(length):\n",
    "    \n",
    "    # create x data for line with max value of length\n",
    "    # it increases by 0.1 each step\n",
    "    x = np.linspace(0,length, length*10+1)\n",
    "    \n",
    "    # create y data with our function using the x data\n",
    "    # we reshape so that each time step is in a new layer/page\n",
    "    # the rows are the batch size and the columns are the features\n",
    "    y = line_function(x).reshape(-1,batch_size,1)\n",
    "    \n",
    "    # return the y data\n",
    "    return(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# function to update plot\n",
    "# input X and Y data for real line and predicted line.\n",
    "# also the time to sleep inbetween graph updates and which axes/figure to update\n",
    "def plot_data(X_data,X2_data,Y_data, pred_data, sleep_time, ax, fig):\n",
    "  \n",
    "    # plot real line\n",
    "    ax.plot(X_data,Y_data, 'g')\n",
    "    # plot predicted line\n",
    "    ax.plot(X2_data,pred_data, '--r')\n",
    "    \n",
    "    # draw to choosen figure\n",
    "    fig.canvas.draw()\n",
    "    # pause for specified amount of time\n",
    "    time.sleep(sleep_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# function to update plot during training\n",
    "def training_plot(pred_training, save):\n",
    "\n",
    "    # set the X data for both the prediciton and label's line\n",
    "    X_training = np.linspace(0, pred_training.size, pred_training.size)\n",
    "    \n",
    "    # update the real line's data by using the training data up to the current time step\n",
    "    Y_training = x_data[0:pred_training.size].reshape(-1)\n",
    "        \n",
    "    # set max and min for the X axis so it moves along the graph with the prediction line\n",
    "    if X_training.size > 400:\n",
    "        xmin = X_training[-400]\n",
    "    else:\n",
    "        xmin= 0\n",
    "    xmax = X_training[-1]\n",
    "    training_pred_ax.set_xlim(xmin, xmax)\n",
    "    # set the y axis limits\n",
    "    training_pred_ax.set_ylim(-1.2,1.2)\n",
    "    \n",
    "    # update the plot on the graph\n",
    "    plot_data(X_training,X_training,Y_training, pred_training, 0, training_pred_ax, training_pred_fig)\n",
    "            \n",
    "    # plot the loss on the second figure\n",
    "    training_loss_ax.grid(True)\n",
    "    training_loss_ax.plot(training_loss)\n",
    "    training_loss_fig.canvas.draw()\n",
    "        \n",
    "    # save the images of the graphs if requested to\n",
    "    if save == True:\n",
    "        training_pred_fig.savefig('training_graphs/training_graph_step_%s.png' % start_index )        # prediction graphs\n",
    "        training_loss_fig.savefig('training_graphs/training_loss_graph_step_%s.png' % start_index )   # loss graphs\n",
    "\n",
    "    # return the updated prediction array\n",
    "    return(pred_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "\n",
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       "\n",
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       "\n",
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       "    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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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": {
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DxzydLrbLxuq16emj/UIkIAmAW19lPkwQqgLaF6aaOW6fHy4do/+pT1jUrJ+ytvOfDsfaDDcpHEhyNpTjJc/yQY/1Z7cOwF3XDk8JH4gGAYmCDqasYgHwWOGrIvEX+vU8gtL/vPhTvA3ZfwClNsvT8OgXmIITLO444chl3+3G59v2K9vYPC3mhcL8AMhZCddw4ORgCYBLUSMh7KA5lfA/nflTr2mZ5eEGPz8imFI7h08uc6+BEjThaCyxq1XFNmQfQSJ7aPxq6tHCLNg+fIs4dbGrEU83HAT5XmJv5aZ2KwewaKPs/XDzhPTemDOVAWxMSdKb4rwMZs7voHx0scnUlT4G86fXZ4m1POKgLmZPpCAJgFtZjwZthWqAprvejz7bqZe13nU4K6YP32I0BU2jBJ46kLAbzX8YuN+LF21S0/juHlaKiYOSzJqnvpZiIDZi7iFrpLpZhAg/k4HhX//8Btc3/s2DzFREZh7kYwzh/UULn6s4I4fknxx5U7s2FOM3t3icdeMEejq4D0CwoFuokMkoElAmxhOxk2FooDemluk56bx12szJw/GReP7hmzx+5YWgu15x/DCB1ngl7HwV4rXTErWcxDpIw4CVizi4jxd6HtC/J3MMc93XvK5iu8zC/X0sbuuGSFsvWSruKv3evH217v0i7k6xEXpbwEH9wmtt54izGwS0CSgRYhDhJqA5l9cb36lISrChZ9dkYbRKd2EwNkqJ1pbCAqPVeIf720Hr2HKXyvyKh2NNxha5Q/Z9R0BqxZx3z2gloEgQPz9Dz2ePvbP5duRs++4XgmIi+eO7cU9pG01d99sOYA3v8yFywXcfMkQTEyjt4CBzLVT+5KAJgFtZjwZthUqAprXQ162apd+mCMhLgp3zRwZFiei21oIqmrc+k40f63Iy0bdc90ooW5ZNBy4IdCxLe5C4BFD+hGIvxP0Hi6pwjPLMvUf6mPkbrjt0qHCl3SzgzueF/2vFTv0t4CXnjkAV50zMGTfhNo90UlAk4C2O+aaHS8UBDS/oe+lj3Zik1qk10P+zcyR6BYmuWe+LAS8EskbX2pYtfWgXm7pd9eNQpdEur3Q6QnoC3dO+0jjt4wA8QfsPVSOv7+zDRXVbkw7ox9mTE4OigpHdnHHS909+24Gio7XYPLo3pgzVQ4KfESf9ySgSUALEaPBLqBr6+r1Lyj+6lDu2xG/mjHckZutnCLT14WAH+7hB3v4AR9ekeR3s0brYpo+ziHgK3fOeUgjt4ZAuPO3u6AUT72TgZpaj36b6+RRvYMmYOzkrqyqDk+9vQ37jlTgzGFJuPmSVKrTH2CkkIAmAR1gCJnTPZgFdE2dB88szYB2oBRj5W56ndGoSJc5wASJFX8Xgo9/yNeFdEJ8NH573ShHbwMLEogtc9Nf7ixzhAwbQiCc+dP2H8fTyzLgdntx66VDMCHIcnzt5o7niPMfG7w6VHpqd9x22VBERoTXWmVokrXQiQQ0CWgz48mwrWAV0Dyv7JllGcg9UIpxqd31W6DC8QvJyELAD7i8/oWGuJhI3H3dSCT3olPihidQAB2NcBfAcNTVZATClb/s/GI8+14m6uuZXu+Yf/8G28cJ7via9eyyExs+vLTqL67kGz4RwQadEP6SgCYBLUQgBqOA5l9ETy/NwK6DpRg/5MSveV7zOBw/RheCH3Yc0i864Dv2fCeaSi3ZHz1GubPfUxqxOQTCkb8decfw3PLtvHqTftvpqJSuQRkcTnHHUw6fX56JrPwSpA3sjDuvHq7Xy6aPfwiQgCYB7V/EWNQ62AR0VQ0Xz9uwu6AME4b2wC2XDglb8cxDIpCFYLNahH+v2KGfmL9v9mi9/BR97EMgEO7s85JGagmBcOMvY9dR/PP97XolCS78hg/qErTB4SR3bk89/vX+DmTsPoYh/TvhNzNH0E60n5FEApoEtJ8hY03zYBLQfOeZn/jOKyjTr4e9ZfrQsL8cJNCF4Medh/DihzsRHxuF398wBr27xlsTaGT1NAQC5Y4gdRaBcOIvZ28Jnlqaodc1/vWMERg6oLOz4Ac4utPc8Utn+ObF1tyjGJ3SFXdcNSysN4L8pZMENAlof2PGkvbBIqD5r3aetsGrbfCTzPMvGRL24jnQHejGgPouowCvfJqDju2j8Yc5Y9E9TEoAWjKh/DDq9CLuh6vUtBkEwoW//ENlePLNrfrNrrxEKE89CPaPCNzxNY3Xz87eW6JftMLfprokui3Wl9giAU0C2pc4sbxNMAhoXsf43x/sAE854NU2fnHlMBLPDZFh1kLwxcb9ePvrXHRNbIc/3DAGnROoTrTVk88s7qz2k+w3j0A48MdvM/3L61tQWe3Gz68cpleQCIWPKNzxSlJ/e/vEW9Xzx/bB9Rek0GUrPgQYCWgS0D6EifVNRBfQ/LDKa19oWL31IFL7dcTd146kfLEmYWHmQvDh2j1YsWYPkjrH6SKal7qjj3UImMmddV6S5ZYQCHX+istq8Njrm1FcVoubLlYwKYjqPLcVtSJxxy+heeLNLThYVInLzxqAK88Z1Jb7Yf93EtAkoIWYBKIL6BVr8vDh2ny9XvHvrx+DuHaRQuAmihNmLgT8x8qy1bvx2fp96NejvS6i20UT3lZxbSZ3VvlIdltGIJT5K6+qw+NvbEHhsSrMmDQI0ycOCKlQEI274xW1+Mvrm/UbC2edn4Kp6X1DCm+zH4YENAlos2PKkD2RBXRjveJuHdvh/jljkdg+xtAzhnInsxcCLqJf/SwH32UUYkRyF/1mx3AtEWh13JjNndX+kv2TEQhV/vhh7b++tRX5h8px8fh+mDklOeTSCkTkruh4tb7jX1pRp99rEGyX09j5/UACmgS0nfHW4liiCuiNOUfwwood6BAfjfvnjEH3TnTtdHMkWrEQ8BPi/3g3Ezv2FGPKmN6Yc6EccguoCJPPCu5EeK5w8SEU+ePnTf7xXiYydx/D2SN64uZpqSE590Xl7mBRBR57fQv4AcN7Z49GSp+O4TKd/HpOEtAkoP0KGKsaiyigdx8sxRNvbkVkhKSnbVB9YvtfI/NdKP5K8UBRJa47bzAuGt/PqhAMW7uiLuJhS4ifDx6K/L31VS6+3LRfr7TB6xOH6tsnkbnLyi/G0+9k6OmKf7xxLG0eNTMvSUCTgPbz69qa5qIJ6GOlNXh0ySZUVLnxm2tHYNjA4C3Wbw1j9r1G5oeI/rRkk/5KkVc+CcYre+3gwOgYIi/iRp8pnPqFGn+rthzQD2z36hqvp8yF8nkT0bn7dttBvPqZqh/ofuDGsYhvFxVOU6vNZyUBTQK6zSCxo4FIApqX9HnstS04UFSBGy6U9bI+9GkdAasXgr2HyvXDRF7GcN/1o5HcK5EoMQkBq7kzyU0y0wICocQfv6Kb1ySOj+W7nuPQLcRrwQcDd0tX7dIPdPPqU/dcNwqRES6aiw0IkIAmAS3EZBBFQPPcu+eXb8e2XUcxZXRvzJlKebe+BIgdC0Hm7qN49t1MtI+NCovF1RfczWhjB3dm+Ek2mkcgVPg7kXe7GW7PiR/Jg3uH/o/kYOCOb1rwK7+3aEU4e3hP3HxJaOajG/l+IQFNAtpI3JjeRxQBvWzVLny6fh+GDuik33ZFv7Z9o9quhaDx9W6fbu3xwNyxiImO8M1BatUiAnZxRxRYg0Ao8FdWWaenaR0trcHPLk/DGUN7WAOWYFaDhbtadz2eeGOLXhElFMsJGg0LEtAkoI3Gjqn9RBDQ32cW4uVPstGjc5x+aILyvXyn2M6F4LXPVazaelC/jeznV6SF5Ol835EPvKWd3AXuLVk4FYFg549X2+FXdO86WIorzx6Iy88eGDYkBxN3vEY0/5FTUlaLX18zAiMHdw0bnlp6UBLQJKCFmAROC+jcA8f1L/F20RF6egAX0fTxHQE7FwK+4PL6sLkHSjFzcjKmTejvu6PU8jQE7OSO4DcfgWDnr/EH8fgh3fXdZ0mSzAdJUIvBxh0/i8LTbPib2QU30TpJApoEtN9fLYqiXMwYewaAS5KkRaqqPtHUiCzLN0mS9FcAB/h/lyTp+ZycnJdbG8hJAc1/WT+8eCPKq9z47XUjMWRAZ78xCfcOdi8EpRW1eOTVTTheXovfXDsSwwdRlRSjMWg3d0b9pH7NIxDM/K3JLMDiT3Kgp2TdOBYxUeGVkhWM3K3dXohFH2ejd9d4nbNwviWWBDQJaH/XJZeiKFpERMT5cXFxBeXl5RsBzFJVNafRUIOAHquq6q99Ne6UgOa7mX97ayu0A6VUZ9hXsppp58RCsKewDH95fQuiI114cN449KBLbgwx6AR3hhylTs0iEKz85R8q06sd8fm74OZ0dA/xihvNkRes3L3xhYavtxzAOKWbXlo0nN4aNOWRBDQJaL+WpZSUlAmSJC3UNG0a76goyh8AsKa70A0Cepyqqr/y1bhTArqxYD+vLfwLyqf1la7T2jm1EDTmrfPdkPvnjkVsTKThZwjXjk5xF654m/3cwchfWVUdHn1lI4rLanHXzJEYkRyeb5CCkTsev003nsI5jY4ENAlov77PU1JSZrhcrotUVb29QUDPATC+6W4zF9AAHpMkqYgxpjHG7snNzdXTOVr6OCGg1+88jP98mIWeXfihwXEkvvyKhJMbO7kQvPGlhq83H8BYpRvuCOPdEKP0OcmdUZ+p3/8QCDb+6r1ePPVOBrL3luCqcwbisrPC59DgqXEbbNw19Z+n0T38ykaUVtbhnmtH6bdGhtuHBDQJaL9iPjU19RrG2NRTBHS6qqp3NRoaPnx4p5iYmIpNmza5ZVn+GYBrNU07XyQBzS9J4SeK+asnfhiiZ5d4v3CgxuIIaL4b8ve3t0Hdfxyzzk/B1PS+RI8fCATzIu7HY4Zs02Djr/FijlGDu+LOGcP5QZqQ5aatBws27k59nt0HS/HEm1v03PUF89JD/uKbU5+fBDQJ6Lbm+El/b0jheEjTtIv5H5pL4TjFIM+ZLlZVtWNbAtovRwJoXFntxj3PfIuCo5X4w03pOGtErwCsUVcREODXfd/11GqUV9bh8TvPRmr/8NsNEYEH8oEQaA2BtZkFePzVjejdLR5/v2sS4mPpauhgj5jPf8zH88sykNK3I5648xxERYbPTYVSuCZ/+xi04fvTuGWAIhRFUfkhQkmSCj0ezwbG2GxN07Ibu6SlpSVlZWUd4v9fluWrJEm6V1XVM9sS0EVF5T7SYrwZYyduGtyaexTTzuiHmVMGGzdGPX9CQISdFP5K+G9vb0WnDjF46Obx+o2F9GkbARG4a9tLatESAsHC35Hj1Xh48QbUexkevHEcendrH/akBgt3bRG16KOdWLvjEC4Y1wfXXyC31Txk/k470K1TSQK6GXwaytg9K0kS/6nJy9g9Lsvyw4yxjbm5uR8pivIYgMsBuBljxREREb/Izs7WRBDQX2zcj7e/zkVqv4747axRiHCFz69lK7+1RFkIPly7ByvW7NEPJfFi/+H8ethXvkXhzld/qd3JCAQDf26PV68fzOsI3zJ9CM4a3pNoBBAM3PlCVG1dPR55dSMKj1Xhl1cN18+jhMOHBDQJaCHi3I5DhLzs2WOvbUZ8u0g8NH88OraPEeLZQ8EJURYCL2N4emkGsvYU45rJybiELllpM7xE4a5NR6lBswgEA39vfqnhq80HcNbwJNwyfSgx2YBAMHDnK1kHiyrw6KubEBHhwkM3h0c+NAloEtC+zg9L21ktoKtqPHj4lQ0oOl6De64biWEDw7NsklUkirQQ8BJZ/GKc0oo63Hf9aMh9W02/twqSoLErEndBA5pAjorO32a1CP98fzt6dY3XUzdiosPrspTWQkV07vwN88ayogN7dsD/zRmr31gYyh8S0CSghYhvKwU0z3t+4YMsbMw5gukT+2PGpGQhnjmUnBBtIdD2n7iaPSE+Ss+HToiPDiW4TX0W0bgz9eHCwJjI/B09Xo2HFm/U6wY/eBPlPZ8ajiJzZ3TqhFM+NAloEtBG54mp/awU0Ku3HcSSz1QM7pOI318/mpR8Gp8AACAASURBVPKeTWXuhDERF4JPf9yLZat3Y9igzvjNzJGUD90C7yJyZ0GIhqxJUfnjopnfFMpT526+JBXnULWj02JQVO4CmSzhlA9NApoEdCBzxbS+VgnoA0cq8OiSTfp1sQ/PH4/OCe1M85kM/Q8BERcCng/9zLIM7MgrxqzzBmPq+H5EWTMIiMgdEeU7AqLyxw9r80PbE9OScOulQ8L2uufWmBSVO9+jr/mWTfOhH56fjq6JsYGaFLI/CWgS0EIEphUCuukv4V/NGI7RKeFxMtgJQkVdCPgtWQsXrUdljUe/bbJ/Ugcn4BF6TFG5Exo0gZwTkb/M3cf0H69JneOwYN44tIuOFAgxcVwRkTuz0FmTUYDFn+aE9JtfEtAkoM2aLwHZsUJAL/4kG2syC3HhuL6YfUFKQP5R59YREHkh2J53TK/MwRfzhfPS6RDTKVSKzB3Nu7YREI2/xh+tVbUnfrT260E/WltiUTTu2o4231vws0f//iALm3KO4MqzB+Lys0PvynYS0CSgfZ8RFrY0W0BvVo/gn+/vQL8e7fHA3HFhdTuShTS1aFr0haDxdfK5I3ti3rQhTkAk7JiicycscII4JhJ/XDQ9+24m+A70decNxkWUNtVqlIjEnRXhXFnjxsKXN+B4eR3+cMMYfTc6lD4koElACxHPZgrokvJaLFi0Hrx4/4J56Xr5JPpYi4DoCwGPhT+/tgn7DlfgjiuHYVxqd2sBCSLronMXRFA64qpI/H21aT/e/CoXaQM74+5r6eBuWwEhEndt+Wr07+q+Ejz51lZ0SWinV0SKaxc66TwkoElAG50XpvYzS0Dzg2N/f3sb+LXOcy9SMGV0b1P9JGPNIxAMC0HhsUo8/MpGRLpOHCjtkkgHSjmbwcAdzbuWERCFP35g+5FXN6FddAQeuYUuqvIlZkXhzhdfA2mz/Ls8fLQuHxPSeuD2y9ICMSVUXxLQJKCFCEizBPRn6/dh6apdGDW4K/jBQUmi29jtIDhYFoLvMgrwyqc5kPsk4r7rx8DlovgIFu7siONgHEME/twefpXzJhwsqtS/d+nAtm+RJAJ3vnkaWCte0vDxN7Ygr6AMt106FBOHJQVmUJDeJKBJQAsRimYI6H2Hy/WrRONjo/QdkIQ4ujzDLnKDZSHQD7as2IFNahFmTBqE6RMH2AWRsOMEC3fCAuiwYyLw13hVN3/jx9/80cc3BETgzjdPA291hF+q8/IG3dBD88eje8fgL21HApoEdOAzwwQLgQroWnc9HnllIwqPVem5d8MH0VXdJtDis4lgWggqqt14cNF6VFS5qbQdpXD4HOOiNnR67jWWrOvZhZesS0dMFF3V7WusOM2dr36a1W7djkK89FE2knsn6IcKI1zBfdU3CWgS0GbNjYDsBCqgX/9CxTdbDuKCsX1w/YVyQL5QZ/8RCLaFYMeeY3jqnQz9gOmCm8YhOowX/WDjzv/oDO0eTvJXXlWHBxdtQCX/UXoTlazzN9Kc5M5fX81oz98A/ufDLGzIPoKrzx2ES88M7jeAJKBJQJsxLwK2EYiAbtwB6d01Xv8SD2cxFDARBg0E40Lwxpcavt58IOzrhAcjdwbDNCS7OcUfF0P/WrEDm9UizJycjGkT+ockvlY+lFPcWflMbdnmbwB5lazyEHgDSAKaBHRb8W7L340K6Kav42kHxBaqmh0kGBeCpmk/v501CmkDOjsHoIMjByN3DsIl3NBO8df4Op4O5BoPCae4M+6xOT135B3DU0tPvAFcOI/f0xCcaT8koElAmzMjArRiVEC/8MEO/XUQHQgLkIAAuwfrQrCnsAyPvbYZCfHR+sHT+HZRASIRfN2DlbvgQ9oaj53g71hpDRa8vB5eBjwyfzy6hcCBMGvYad2qE9w58ZzNjdmYdjk1vS9mnR+cNwWTgCYBLcR8MiKg1+88rOdTDe6dqB9IoJJkzlEZzAvByrV78P6aPThjaA/87PLQqVHqazQEM3e+PmMot7ObP15r/29vbUXOvuOYNy0V547sFcrwWvpsdnNn6cP4aZy/AXxo8UYcLq7CvbNHY0j/Tn5acL45CWgS0M5HIQB/BXTjbYOeeoaH5qejR6c4IZ4jXJ0I5oWg3uvF469vwe6CMtx++VBMGBoaNUp9jcVg5s7XZwzldnbz98XG/Xj761yqtW9CUNnNnQkum2qC14XmbwA7dojGI/PPCLpbCklAk4A2dUIYNeaPgOaHV55emoEde4px40UKJtNtg0ZhN61fsC8Eh0uq8NDLGxEZIeGRW85Apw4xpmEjuqFg5050fK32z07+Dh6txMOLNyI2ht82eAYS46nWfiD82sldIH5a2XfFmjx8uDYfE9OScNtlQ60cynTbJKBJQJseVEYM+iOgV205gNe+0DBsUGfcPXMk3TZoBHCT+4TCQrBq60G89rmKEcldcNc1I8ImrkKBO5PDOajM2cUfv03uz0s2Y+/hctx59XCMkbsFFU4iOmsXdyI+e6NPPK74LnT+oXL88qphGKt0F9ndk3wjAU0CWohg9VVA83yphYs3ICrCFXY7hUIQ1YITobAQ8DcbT72zDVn5JWGV2xkK3Ik8N6z2zS7+GncKzxqehFumB9dOodUcGLVvF3dG/bOrX+GxSj0ful10BB699YyguUWYBDQJaLvmSKvj+CKgvV6Gx9/Ygl0HS/HzK9IwfkgPIXwnJ4BQWQiKy2r0iyH4QalH549H1zCoLhAq3IXrPLSDv72HyvGnJZuQ2D44c1VFjQ07uBP12U/164sN+/D2N7swVu6GO64aFhRvAElAk4AWYn75IqA/Xb8Xy1btxvgh3fHzK4YJ4Tc5cQKBUFoI1m4vxKKPs5HaryN+N3s0XJIU0jSHEnchTZRDb3/cHi8eeWUjeP7zb68bhbSB4Vkv3YrYorn3P1T5psWTb2yBdqAUt182FBPSxD/MTQKaBLQV3wt+22xLQDceXolrF4k/3XoG2seGX71ev0G1sUMoLQQ8leP55duxNfcorr8gBReM62sjkvYPFUrc2Y+e8yNazd+7q3fjkx/36oe1+aFt+piHgNXcmeepPZaOlFRhwcvBk6JJApoEtD0zo41RWhPQvMwYP7zCDxn86urhGE2HV4TgrKkTobYQlFbW4cGX1qOO1yqdPx5JnUO3TGKocSfc5LDYISv5232wFI+9vhldEtrpFw21i460+GnCy7yV3AUrko1FAoLhMDcJaBLQQsyz1gT0ynX5eP+7vKAscyMEuDY4EYoLwaacI/jXih1I7pWA/5szNmQv6glF7mwIeWGGsIq/xosujhRX4b7rR0PpF3wXXQhDUguOWMWd6M/dmn/8DeDf39mGnfkluHlaKs4R+KIeEtAkoIWYay0J6H2Hy/Hoq5vQIS5KP50bjlctC0FQG06E6kLAb7rkN17OnJyMaRP6BwMVfvsYqtz5DUSQdrCKv7e+ysWXm/bjwnF9MfuC4LxqWXRKreJO9Oduy7/Gq+IZAx695Qx0SWzXVhdH/k4CmgS0I4F36qDNCWheH5KL5/1HKnD3tSMxfFAXIXwlJ05HIFQXgopqN/740npU1Xiw8OZ09O4aH3L0hyp3IUeUjbuY6r4SPPnmVnTvHIeHb05HdFREuMBp63PS3GsZ7jWZBVj8SQ6GDuikH16VBDzMTQKaBLStXxgtDdacgOZpGzx949yRPTFv2hAh/CQnmkcglBeCrVoRnlu+HQN7dsD9c8ciwuUKqTAIZe5CiiibBHRtXT0WvLweR0trcP+csUjunRgOMDryjDT3Woadp3I8+24mMncfE/bGYRLQJKAd+eJoawc6/1AZ/vTqZnTqEK1fmBIbQ4dXhCDKpkVctGd9cWUWfsg6jBmTBmH6xAGiuReQP7SIBwSf453N5u+NLzR8veUApp3RDzOnDHb8+ULZAbO5CzWsSspr9cPc9YLW5ScBTQJaiDnXdAdarzv66kYcLKrEb2eNQtoAqjsqBEmtOBHqCwFP5eBf5JU1biyYl44+3dqLTonP/oU6dz4DEaQNzeQvZ28JnnxrK3p2icNDN6cjKpJSN6wMCzO5s9JPJ22v21GIlz7KxpD+nXQ9IFJdfhLQJKCdnBs/jd1UQC//bjc+Wkd1R4UgxkcnwmEh2JZ7FP94LxP9kzrggbljERkRGqkc4cCdj2EclM3M4q+mzoMFizbgWFkNHpg7DoN6JQQlHsHktFncBdMz++srT+V47r3t2LbrKOZOlTFlTB9/TVjWngQ0CWjLgssfw40Cek9hmV7zuVOHGL3uKKVu+IOic23DZSF46aOdWLfjEK46dxAuOzM0UjnChTvnZoe1I5vF32tfqFi15SCmTeiHmZMpdcNa1k5YN4s7O3x1cozjFSdSOTz1TNcF3TrGOunOT2OTgCYB7XcgKopyMWPsGYC/TZEWqar6RFMjaWlp0W63e4kkSWMBHHW73dfl5eXta20gLqALCkt/ujL23lmjMIRSN/zmxqkO4bIQ8BQO/kVeXuXGQp7K0T34UznChTun5obV45rBX3Z+Mf769jb06hqPhfPGUeqG1aQ12DeDO5tcdXyYH3Ycwosf7URqv4743ezRQqRykIAmAe3vxHApiqJFREScHxcXV1BeXr4RwCxVVXMaDSmK8gvG2HBN0+5QFOU6xthVmqbNaktAv/DuNnz8w15MGd0bc+nKWH95cbR9OC0EmbuP4pllmejXoz3+eOO4oE/lCCfuHJ0kFg0eKH+NqRvFZbV44MaxGNiTUjcsouo0s4FyZ5efIozDUzmeX74dW3OPYs5UGecJkMpBApoEtF9zIyUlZYIkSQs1TZvGOyqK8gcArOkutCzLnwHgbdYDiJBl+ZCmad1aG0jbV8x+9+wadE44kbpBV8b6RYvjjcNtIVj08U6s3X4IV50zEJedNdBx/ANxINy4CwQrEfsGyt9rn6tYtfUgpk/sjxmTkkV8xJD1KVDuQhaYFh6stKJWr8vPUzkevmU8ujucykECmgS0X3MwJSVlhsvlukhV1dsbBPQcAONVVf11oyFZlrfzNjk5OQUNbXIjIyPPyMrKKm5psDue/JrtO1QOSt3wiw5hGofbQlDFUzkWbUBZZZ1elaNvEKdyhBt3wkwakxwJhL/G1A1+QRCP46jI0DgYaxK0lpsJhDvLnRN0gB+zDuG/K3dC6dsR917vbCoHCWgS0H5Nk9TU1GsYY1NPEdDpqqre1WhIUZQdkiRNbSKgd0VHR6dv3769pKXBLr1nBaPUDb+oEKpxOC4EvMD/M8sygj6VIxy5E2ryBOiMUf6qa09U3eC1dil1I0ASDHY3yp3B4UKiW9NUjhsulHH+WOeqcpCAJgHt16RqSOF4SNO0ixt2l5tL4fgUAG+jp3AoilKoqmr31ga6/bEv2TP3TKaqG36xQY2dRuDZt7fiq437cMPFqZh1oeK0OzQ+IeAzAv96NwOf/pCPmeen4MZLhvrcjxoSAk4jUFJWg1/+9RvUebx4/ndTkNQl3hGXJBHvF3cEieYHlQTyRRRXuCBW+SFCSZIKPR7PBsbYbE3TspvsQN/BGBvGDxHKsswPD17Z1iHC6ho3Ky+rFuUZyQ8/EQjXnZRQSOUIV+78DHFhmxvhb2d+Mf729jZQ6oaztBrhzlmPxRldhFQO2oGmHWi/Z0RDGbtnJUniCXO8jN3jsiw/zBjbmJub+1FKSkqMJEmvSZI0GsAxSZJm5eTk5Lc2UNOLVPx2iDo4jkA4LwTBnsoRztw5PnFMcMBf/ih1wwTQTTLhL3cmDRsSZkRI5SABTQJaiMlEAloIGgw7Ee4LwcsfZ+P77YW48pyBuDzIqnKEO3eGg16Qjv7yt+RzFaup6oYQ7PnLnRBOC+REY1UOd70Xj8wfj+6d4mz1jgQ0CWhbA66lwUhAC0GDYSfCfSEI5lSOcOfOcNAL0tEf/n5K3egWjwU3UdUNpyn0hzunfRV1/MZUDrlvR9xnc1UOEtAkoIWYFySghaDBsBO0EAA/pXJ0b48/3hQ8F6wQd4bDXoiOvvLXNHXjjzeNxYAkujDFaQJ95c5pP0Uev2kqx/UXpOCCcX1tc5cENAlo24KttYFIQAtBg2EnaCE4Ad3Ln2Tj+8xCXHH2QP2fYPgQd8HAUss++srfks9ysHpbAS49sz+uPpcuTBGBdV+5E8FXkX34KZXD49UvWOlhUyoHCWgS0ELMCxLQQtBg2AlaCE5AV1XjwYOL1usXrDx40zj069HBMKZ2dSTu7ELamnF84S9rTzH+/s429KbUDWtIMGjVF+4Mmg67bj/uPIT/frgTKX0S8fsbxsAlWV9EjQQ0CWghJhoJaCFoMOwELQT/g25H3jE8tTQDfbq1x4J54qdyEHeGw16Ijm3xx1M3+I+64+UnftT1TxL/R50QwNrgRFvc2eBCyAzBUzn+9f4ObNaKMOv8FExNtz6VgwQ0CWghJhAJaCFoMOwELQQnQ/fKpzn4LqMAl505AFedO8gwrnZ0JO7sQNm6Mdrib/En2ViTWYjLzxqAK88ROxatQ0lMy21xJ6bX4nrF3/z98aX1qHXX4+H545HU2dqqHCSgSUALMRtIQAtBg2EnaCE4GboTB7bWoyQIdv2IO8NhL0TH1vgL1oOtQgBrgxM098wHeWPOEfx7xQ4k907A/90wFi6XdakcJKBJQJsfwQYskoA2AJpAXWghOJ2MrPxi/J3f9iZ43ilxJ9BEMuBKS/xV1rjx4EvrUV7lDpp8fAOPH9RdaO5ZQ98LH+zAhuwjuHbKYFx8Rj9rBgFAApoEtGXB5Y9hEtD+oCVeW1oImuckGC6tIO7Em0/+eNQSfy99tBPrdhzCVecMxGVBdrmPP88fzG1p7lnDXnlVnf7jsaq2Hg/dnI5eXeMtGYgENAloSwLLX6MkoP1FTKz2tBA0zwdP5Vj48gYcK6vB/XPHIrlXoljEASDuhKPEL4ea429rbhGee2+7fmDwgbljERnh8ssmNbYHAZp71uG8WS3CP9/fjoE9E3D/3DGIcJk/B0hAk4C2LoL9sEwC2g+wBGxKC0HLpOTsLcGTb23VD7Tw3ZDoqAihGCTuhKLDb2dO5a+i+kTqBk/hWDgvHb27tffbJnWwBwGae9bi/N+VWfgx6zBmTBqE6RMHmD4YCWgS0KYHlRGDJKCNoCZOH1oIWufiza80fLXpAC4c1xezL0gRhzjagRaKCyPOnDr3GvM/r5mcjEsm9DdikvrYhAB9b1oLtP5jctF6VFh0DoAENAloayPYR+skoH0EStBmtBC0Tkydux4PLd6IQ8VVuG/2aKT27yQMk8SdMFQYcqQpfxuyD+OFD7KQ3CsBf5hjzWtrQ05Sp2YRoLlnfWA0VqLhdfl5HfSoSPNSOUhAk4C2PoJ9GIEEtA8gCdyEFoK2ydldUIrHXtuMzh3a4ZFbxiM2JrLtTja0IO5sANnCIRr5O15Rq6duuOu9ePjm8ehhcQ1cCx8pbEzT3LOH6lc/y8G32wowfWJ/zJhk3jX2JKBJQNsTwW2MQgJaCBoMO0ELgW/QLf9uNz5atxfnjuyJedOG+NbJ4lbEncUAW2ye83fkSBmefTcTfLfthgtlnD+2j8WjknkzEKC5ZwaKbdtoepib14Ye3Mecw9wkoElAtx19NrQgAW0DyBYOQQuBb+B66r149NVN2H+kAnddMwIjB3f1raOFrYg7C8G1wTTn790vc/DqZyrSBnTC3deNgkuy7vIIGx4pbIaguWcf1dr+43jijS3o1ilWf0MTEx34YW4S0CSg7YvgVkYiAS0EDYadoIXAd+i4eH7klY1oHxuFR289Q/+3kx/izkn0Ax+73uXCnX9bpYvmR28Zj84J7QI3ShZsQYDmni0w/zTIO9/k4vMN+3HemN6YM1UJeHAS0CSgAw4iMwyQgDYDReds0ELgH/Yf/5CP977NQ3pqd/z8ijRIDu4YEnf+cSdSa6+X4el3M5GVdwy3XToUE4clieQe+dIGAjT37A0Rt6ceD7+yCQVHK/Hb60YhbWDngBwgAU0COqAAMqszCWizkHTGDi0E/uFe7/Xi8Te2YPfBMtx22VBMTHNO+BB3/nEnUuvP1u/D0lW7MFbuhjuuGuboDzGRcAkWX2ju2c/U3kPl+NOSTUiIj9YPc8e3M/4GkAQ0CWj7I7iZEUlAC0GDYSdoIfAfuiMlVVi4eKP+6v3h+enomhjrvxETehB3JoDogIkDPBXo1U16CtBD89OREBftgBc0ZCAI0NwLBD3jfT9cuwcr1uzBGUN74GeXpxk2RAKaBLTh4DGzIwloM9G03xYtBMYw/y6jAK98mgOlb0fcO3s0XC77D38Rd8a4c7IXfxXND6MeKKrEg/PPwMDu8U66Q2MbRIDmnkHgAuymvwF8fQt2F5Th9suGYoLBN4AkoElABxiK5nQnAW0Ojk5ZoYXAGPKMMTy/fDu25h7FzCnJmHaG/TfHEXfGuHOy19tf5+KLjfsxeVQv/HZuOoqKyp10h8Y2iADNPYPAmdBNfwP48kZ908LoG0AS0CSgTQjFwE2QgA4cQyct0EJgHP2yqjosWLQBlfza2ZvGoV+PDsaNGehJ3BkAzcEuWfnF+Pvb2/SLUh6al44+vTuSgHaQj0CGprkXCHqB912TUYDFAbwBJAFNAjrwKDTBAgloE0B00AQtBIGBn7n7KJ5ZloneXeOxYB6/bjbwGqW+ekTc+YqU8+0qqt1YsGg9yqvceODGsRiQlADiz3lejHpA3BlFzpx+/A3gv97fgc1aEa6ZnIxLJvj3BpAENAlocyIxQCskoAME0OHutBAETsBrn6tYtfUgpqb3xazzUwI36KMF4s5HoBxupi/2K3Zgs1qEGZMGYfrEAbpHxJ/DxAQwPHEXAHgmdS3nbwBf3oCKKjf+eOM49E/y/Q0gCWgS0CaFYWBmSEAHhp/TvWkhCJyBWnc9Hlq8EYeLq3DPdSMxbGCXwI36YIG48wEkAZp8n1mIlz/JhtwnEfddP+anA6fEnwDkGHSBuDMInMndduQdw1NLM9CzSxwWzEtHTJRvbwBJQJOANjkUjZkjAW0MN1F60UJgDhP5h8rw5yWbER8bhUfmj9drlVr9Ie6sRjhw+/8reQg8PH/8SSUPib/A8XXKAnHnFPKnj/vmlxq+2nwAU8b0xlwfbykkAU0CWogIJgEtBA2GnaCFwDB0p3VsvBxj2KDO+M3MkXqdaCs/xJ2V6AZu21N/4tKdvILmL90h/gLH2CkLxJ1TyJ8+bp37RGnIg0crcefVwzFG7tamcySgSUC3GSR2NCABbQfK1o1BC4F52HoZwzNLM7BjTzFmnTcYU8f3M894M5aIO0vhDdj4stW78OmP+zAhrYd+Xfep174TfwFD7JgB4s4x6Jsd+GBRhS6ioyJd+puezgntWnWQBDQJaCEimAS0EDQYdoIWAsPQNduxtLIOC18+UdrO34Mt/npC3PmLmH3tG3Mzu3eKxcJ56YiNiTxtcOLPPj7MHom4MxvRwO2t3nYQSz5TkaKfNRiNCJerRaMkoElABx5xJlggAW0CiA6aoIXAfPB37DmGp97JQI9OsfrBlubEkxmjEndmoGi+jeMVtfqPqKoaT6s/oog/87G3yyJxZxfSvo/Dq938+4MsbMo5gsvPGoArzxlEAtp3+E5qaW3yoUGnQrEbCejgZpUWAmv4W7pqF3hO9FnDknDLpUMtGYS4swTWgIzyNB5+WUr23hLMPj8FF6b3bdEe8RcQ1I52Ju4chb/Fwatq3PothcVlNbh39mik9u/UbFvagaYdaCEimAS0EDQYdoIWAsPQtdqRHyB77LXNyD9UjtsuG4qJaUmmD0TcmQ5pwAY//iEf732bh5HJXfDra0aclvfcdADiL2C4HTNA3DkGfZsD7zpYisdf34KE+Cg9H7pD3OkVkUhAk4BuM5AaGwwfPrxTXV3dO4yx/pIk5bvd7mvz8vJKTzWgKEo9gAwAEmNsr6ZpV7Y1CAnothAS+++0EFjHz+GSKr0+NP8suGkcenaJN3Uw4s5UOAM2tutAqV51I7F9NB66Ob3ZhZsEdMAwC2GA5p4QNLToRFs/ZElAk4D2OYJlWX5CkqRjqqo+qSjK7xljnTRN+8OpBmRZLtM0LcFnwwBIQPuDlnhtaSGwlpMN2YfxwgdZ+lXf/FBhTLRvhf598Yq48wUle9rwq7ofXrwBxeW1uG/2aCj9mn91TALaHj6sHoXmntUIB2afp1I99c427MwvwbVTBuPiM06uiEQCmgS0zxEmy3KOx+OZlJeXdzgtLS3J7Xav1jQttRkBXa5pmu/3YZKA9pkDURvSQmA9M298oeHrLQf0NI5bLx3S6mt9f7wh7vxBy7q2fLH+x7uZyNx9DFecPVD/x5cP8ecLSmK2Ie7E5KWpV6UVtXjolY0or3Tj3tmjTvpRSwKaBLTPESzLcrGmaZ0bO8iyfEzTtNPuG5ZluQ7ANkmSPACeUFX1g7YGoR3othAS+++0EFjPj9tz4kKNPYVluOliBZNG9TZlUOLOFBgDNvLh2j1YsWYP0gZ2xt38Ah2Xb2fYib+AoXfMAHHnGPR+DaztP44n39yKDnFRelpVYvsYvT8JaBLQJyGgKMqXjLEeTf4j/xZnLpfrj16v9xVfBDTfnc7KyjqkKMpAxtg3kiSdp6rqntagJgHt13wWrjEtBPZQcrS0Gg8v3ohatxcPzB2L/kl+vehp1knizh7uWhuFlyx8+p0MdE6I0UsWNndgqaX+xJ/z/Bn1gLgzipz9/RpviFX6dsTvZo/S60OTgCYB7XMkyrKc7fF4JjdJ4ViladqQ1gwoirKYMbZS07TlbQlonx2hhoRAGCOwKfswHn7pRyR1icPTd09G+9ioMEYj+B/9SEkVfvPUt6iu9eCJO8+G7EPec/A/NT0BIRBcCPD60H95dSN+2F6IGVMGY96laTyNzrfXRMH1qKZ5S+A0gbLhEGGxqqpPtHSIcMCAAR3j4+OrsrKy6hRF6coYWytJ0hWqzaQrUwAAFtdJREFUqua0JaCLispNI44M2YsA7aTYi/d73+7Gxz/sxeiUrrjz6uEB5UMTd/Zy13S0E2k5m7GnsBxzL1IwZbT/aTnEn3P8BToycRcogvb25z9yH3llIw6XVONXVw/HRWcnk0ZshQICpwk4aWlpnd1u91IAfSVJ2ldbWzszPz//eGpq6ljG2M9UVb198ODBE10u138kSapnjPE7MJ/WNO2VtsKcUjjaQkjsv9NCYC8/9V6vftFGzr7jmDFpEKZPHGDYAeLOMHQBd3ztcxWrth7EmfyinOnGDoYSfwHT4JgB4s4x6A0PfOBIBf60ZBMiIlxY9pdLSSOSgDYcS6Z1JAFtGpSOGKKFwH7YSyvr9N2Q4+W1uGvmCIxI7mrICeLOEGwBd1q3oxAvfZSNPt3i8QAvTRhlrDQh8RcwFY4ZIO4cgz6ggRvn7kdPXUkCmgR0QLFkSmcS0KbA6JgRWgicgZ5X5OCVOSIjJL0+tJFLVog7+7lrytuCm9LRo3OcYSeIP8PQOd6RuHOcAsMOfLFhH+ZcOowENAlowzFkWkcS0KZB6YghWggcgV0f9IesQ3hx5U5dhD1441jEtfPvUCFxZy93JeW1eOTVjSirqAvozUGj18SfvfyZORpxZyaa9tuiKhytY06/LmyKSRLQNgFt0TC0EFgErI9ml67aBV5maURyF/x6xgifawhz88SdjyCb0KzOXa+/Mcg/VN7szWZGhiD+jKAmRh/iTgwejHpBApoEtNHYMbUfCWhT4bTdGC0EtkN+0oBeL8Mz72ZgR14xLpnQH9dMTvbZIeLOZ6gCasjLYP3nwyxsyD6Cs4YnYf4lxg4NnuoE8RcQLY52Ju4chT/gwUlAk4AOOIjMMEAC2gwUnbNBC4Fz2DeOXFnjxp9e3aSXWLr98qGYMDTJJ6eIO59gCrjRR+vysfy7PAzunYh7Z49GVCQvUhT4h/gLHEOnLBB3TiFvzrgkoElAmxNJAVohAR0ggA53p4XAYQIahi84WqmXWKr3Mtw7azQG90ls0zHirk2IAm6wWS3CP9/frt80+OBN6UiMjw7YZqMB4s80KG03RNzZDrmpA5KAJgFtakAZNUYC2ihyYvSjhUAMHrgXO/KO4ZllmYhrF4n7545FUhsVHog7a7nbd7gcf3l9CxgY7p8zFv16BH79elOPiT9r+bPSOnFnJbrW2yYBTQLa+ijzYQQS0D6AJHATWgjEImdNRgEWf5qDbh3b4YG545DQyo4ncWcdd0ePV+PPr21GWWUd7rhqGMYq3U0fjPgzHVLbDBJ3tkFtyUAkoElAWxJY/holAe0vYmK1p4VALD64NyvW5OHDtfkY2LMD7ps9BjHRzV/UQdxZw115VR0ee30LDhdXYfYFKbhwXF9LBiL+LIHVFqPEnS0wWzYICWgS0JYFlz+GSUD7g5Z4bWkhEI8TXvXh5U+ysXb7IYwa3BW/vHoYIlynH1wj7sznrrauHn99eyvyCsowbUI/zJw82PxBGiwSf5ZBa7lh4s5yiC0dgAQ0CWhLA8xX4ySgfUVKzHa0EIjJi6fei2eXZSArvwRTRvfGnKkyJOnk8vbEnbnc1Xu9eO697cjcfQxnDkvCLdPNKVfXkpfEn7n82WmNuLMTbfPHIgFNAtr8qDJgkQS0AdAE6kILgUBknOJKda1Hv7xj/5EKXHnOQFx+1sCTWhB35nHHd/157vn3mYUYNrAzfn3NCERGmFOujgS0eTyJYonmnihMGPODBDQJaGORY3IvEtAmA2qzOVoIbAbcz+H49dF/eX0zjpbWnHYDHnHnJ5itNF/+3W58tG4vBiR1wH3Xj0a76EjzjLdgifizHGLLBiDuLIPWFsMkoElA2xJobQ1CArothMT+Oy0EYvPDvSs6Xq3vRHMxfcOFMs4f20d3mrgzh7uVa/fg/TV70L1jrF4+sLXKJ+aMeMIK8WcmmvbaIu7sxdvs0UhAk4A2O6YM2SMBbQg2YTrRQiAMFa06cqi4ShfRvKzavGmpOHdkLxJgJlDXeMtg18R2+s5z18RYE6z6ZoLmnm84idiKuBORFd99IgFNAtr3aLGwJQloC8G1wTQtBDaAbNIQB4oq8OSbW1FZ7catlw3F5ZNTUFRUbpL18DPz8Q/5eO/bPHRJiMHvrx+Drh3tE8+0Ax3c8Ubfm8HNHwloEtBCRDAJaCFoMOwELQSGoXOk495D5fjrW1tRXefB7+emQ+5l7u14jjyUA4N+un4vlq3arV/RzcVzN5vFMwloB0g3cUj63jQRTAdMkYAmAe1A2J0+JAloIWgw7AQtBIahc6wjr1H8t7e3wu3xYv70IZiYluSYL8E48Gfr92Hpql3o1CEGv79hjJ777MSH5p4TqJszJnFnDo5OWSEBTQLaqdg7aVwS0ELQYNgJWggMQ+dox10HSvHse5l6OoeVt+U5+pAmD85L1X3y4149bYOLZ57z3KNTnMmj+G6O5p7vWInWkrgTjRH//CEBTQLav4ixqDUJaIuAtcksLQQ2AW3BMBVuLx58YR1KK+tw+VkDcMXZA0+7bMWCYYPSpNfL8NZXufh6y4ET4nn2aPTo7Jx45iDS3AvKUNKdJu6ClzvuOQloEtBCRDAJaCFoMOwELQSGoXO8I+cuK/cI/v72VhQdr8GUMb31MneuU24sdNxRhx1we+rx35U7sVktQu9u8bh75kh0TmjnsFckwhwnIAAH6HszAPAE6EoCmgS0AGEIkIAWggbDTtBCYBg6xzs2cne8ohZPvZMBXqVj/JDuuPXSoZbfouf4w/voQGWNG8+9mwntQCmUvh3xqxnDEdcuysfe1jajuWctvlZaJ+6sRNd62ySgSUBbH2U+jEAC2geQBG5CC4HA5LThWlPuqmrcePbdTOQeKEVqv474+ZXDkBAXHbwPZ4Lnx0pr8PSyDBQcrcS41O647dIhiIqMMMGyOSZo7pmDoxNWiDsnUDdvTBLQJKDNi6YALJGADgA8AbrSQiAACQZdOJW7Wnc9Xly5E1u0Ir228Z1Xj0D/pPAsc7f7YCn++f52HK+owwXj+mDW+SnCpbbQ3DMY+AJ0I+4EICEAF0hAk4AOIHzM60oC2jwsnbBEC4ETqJszZnPceRnDx+vysWLNHkRGujDv4lRMHBY+Ze54pY2vNh/A0m92gWMxc/JgXDS+r5CHK2numTMPnLBC3DmBunljkoAmAW1eNAVgiQR0AOAJ0JUWAgFIMOhCa9xl7DqK/67MQnVtPS4c1xczpySHfF50da0Hr3yag405R9AhLgo/uzwNQwd0Noiu9d1o7lmPsVUjEHdWIWuPXRLQJKDtibQ2RiEBLQQNhp2ghcAwdI53bIu7Q8VVeO69TBQeq9LzovnhQhGqT1gB3MGiCvzz/R3gzzy4TyJ+ccUwvVydyJ+2+BPZ93D3jbgL7gggAU0CWogIJgEtBA2GnaCFwDB0jnf0hTu+K7vo42w9Lzo2JhKzz0/BWcOThExpMAIoT9n4PrMQb3yloc7t1dM1ZkwKjt12X/gzggn1sR4B4s56jK0cgQQ0CWgr48tn2ySgfYZKyIa0EAhJi09O+codF5nfZhTgnW92obauHiOSu+Cmi1OF36FtC4TDJVVY8pmK7L0liI2JwPxLhmCs0r2tbsL83Vf+hHGYHPkJAeIuuIOBBDQJaCEimAS0EDQYdoIWAsPQOd7RX+6OllZj8Sc5uuCM47vRF6TgzGHBtxvtqffi8w378OHafLg9XoxM7oI5UxV0SXT+chR/gsJf/vyxTW2tRYC4sxZfq62TgCYBbXWM+WSfBLRPMAnbiBYCYalp0zEj3PHd6NXbCrB01Ynd6LSBnTFzcjL69QiOcnd5BWX6QUF+aUxCfDSuvyAF6andgzIlxQh/bQYFNbAFAeLOFpgtG4QENAloy4LLH8MkoP1BS7y2tBCIx4mvHgXC3dHj1Xj1cxVZe4r14c4Y2gNXnjMQPTrF+Tq8re34IcGV6/KxMfsIGIBzR/bEzCmDES/IrYJGwAiEPyPjUR/zECDuzMPSCUskoElAOxF3p41JAloIGgw7QQuBYegc7xgod3w3emd+Cd5dvRt7D5cjwiXh3JG9cNlZA9CxvRgVLPhO88q1+diUc0I49+vRXj8IqfTr5Dj+gToQKH+Bjk/9jSNA3BnHToSeJKBJQPsch6mpqdcwxh4CMKS+vj59165dW5rrrCjKxYyxZwB+aZe0SFXVJ9oahAR0WwiJ/XdaCMTmpzXvzOKOXziyWS3C8m9343BJNaIjXRg/tIcuppN7JdieHsGFff6hcnz6415sUot0CPiNilecNRAjB3ex3R+rIsQs/qzyj+y2jABxF9zRQQKaBLTPEZyamqq4XC6vx+P5j9fr/V0LAtqlKIoWERFxflxcXEF5eflGALNUVc1pbSAS0D7TIGRDWgiEpMUnp8zmjh/O+357IT5etxfHymp0H3p2idOFNL/NMCEu2ie/jDY6UlKFH3cexvqdh/Xa1fwzgAvnswfqlUMkSTJqWsh+ZvMn5EOGqFPEXXATSwKaBLTfESzL8iqv1/vb5gR0SkrKBEmSFmqaNo0bVhTlDwBYW7vQJKD9pkGoDrQQCEWHX85YxR3fkc7OL8GazAK9frSnnunpHcMGdkZq/06Q+3bUUykiXC6//D21cb3Xi8KjVcjeV6KLZn5AkH+iIl0YObgrzhnRUx8z1IRzIw5W8RcQKdTZJwSIO59gErYRCWgS0H4HZxsCeobL5bpIVdXbGwT0HADjVVX9dWsDkYD2mwahOtBCIBQdfjljB3cV1W78sOMQvssswMGiyp/8i4mOQErvRKT07YikznFIjI/W/+GVMdpFR/wkerlIrqmrR3WNB1W1Hhw8Won8wnLsOVSGfYfL9ctP+IdvLvNrtycM7YExcjf90pdQ/9jBX6hj6NTzEXdOIW/OuCSgSUCfhICiKF8yxno0+Y/8fSfXtw/k5uau5P+9NQHdkCc99RQBna6q6l0koM2ZtCJaoYVARFZ888lu7o6V1kA7cBza/uNQ9x3Xr81u7hMd5UJsdCSq6zw/CeRT23HB3LtrPAb0TMCgXgkYndJNF+Dh9LGbv3DC1upnJe6sRtha+ySgSUD7HWE+pHA8pGnaxdywrykcfjtBHQgBQoAQIAQIAUKAECAEhEQgtE6bmAQxF9Aul+t3OTk5m5sxGaEoisoPEUqSVOjxeDYwxmZrmpZt0vBkhhAgBAgBQoAQIAQIAUJAYARIQDchR1GUKwE8B6ArY+w4gG38sOCQIUN6er3eF1VVvbRh15mXsXtWkiR+OoiXsXtcYI7JNUKAECAECAFCgBAgBAgBExEgAW0imGSKECAECAFCgBAgBAgBQiD0ESABHfoc0xMSAoQAIUAIEAKEACFACJiIAAloE8EkU4QAIUAIEAKEACFACBACoY8ACejQ55iekBAgBAgBQoAQIAQIAULARARIQJsIZkumFEXhhw6fAeCSJIkfOnzChmFpCAMIpKSk9JEkaYkkSUmMsXoAL2qa9o/hw4d3qqure4cx1l+SpHy3231tXl5eqYEhqIv1CLgURdkE4ICqqpenpqYOYIy9zRjrJEnSlp49e85dvXq1x3o3aAR/ERg0aFBiZGTkSwCGSZLkZYzNj4mJ0Wju+YukM+0VRbkbwC2MMa8kSdsjIyNvrq+v70Xzzxk+2hpVluVFkiRdyhg7rGnaCN6+tbVOUZR/AOC3MFcyxuZpmratrTFC+e8koK1nly/mGi97FxcXV1BeXr4RwCxVVXOsH5pG8BeBtLS0JLfbncS/GNLS0tq73e7NkiRdwRi7WZKkY6qqPqkoyu+5GNM0jV/jTh/BEOCLOGNsrCRJCVxAy7L8jiRJ76qqukxRlH8zxnh1nf8I5ja5c6Ku/iter/fb3NzcxZMnT47ct29ffGRk5P0098QPj9TU1F5er/f7qKio1KysrDo+7wB8AuASmn9i8qcoytmMsQoASxoFtCzLTzQ33xRF4cL5TlVVp8uyfIYkSc+qqjpBzCezxysS0BbjnJKSMkGSpIW8HB4fii5esRhwk83LsrwCwPP8H4/HMykvL+9wg8herWlaqsnDkbkAEeBvEFwu12JJkv7MGLunQUAXaZrGbx/1NszHny5CCnA46m4iAqmpqR34jxtVVZObmpVlOYfmnolAW2SKC2jG2A/R0dGjunTpUl5YWLjc6/U+53K53lBVNYnmn0XAB2h26NCh/T0ez8omAvrU+bZK07QhiqK8AGCVqqr8hxG/sTnb4/FM5mtigC4EbXcS0BZTl5KSMsPlcl10ytXf41VV/bXFQ5P5ABHgr/69Xu/qqKioYR6PZ7+qqp0aTcqyfEzTtC4BDkHdTUZAluVlALh4TnS5XL91uVz8FfIPmqbJfKiGFJ1PGhcLk4cncwEgIMvySEmS/gtgJ4CRADaVl5f/pkOHDgdp7gUArI1dZVnm69qfJUmqYox9ERER8RuafzYSYGCoZgR0saZpnU9d6xRFWSlJ0l9ycnLWNQjor7xe7327du3aYmDYkOhCAtpiGlNTU69hjE09RUCnq6p6l8VDk/kAEGhI31gtSdKjqqp+IMtys18qAQxBXU1GQJbl6Tw/T9O0O1NTUyfzHWgA8wH8oKpqShMB/bGmaVyg0UcgBFJTU8d6vd4fXS7XxJycnE2yLD8NoJy/Nm5uQRfIdXIFwIABAzpGR0e/FxUVNTMrK6tUURT+Y3Y5Y2zBKT9gaf4JFDF+COiPJEl6rKmAZozdm5ubu1Wgx7HVFRLQFsN96itjSuGwGHATzPPcy4KCgo9cLtenOTk5zzb82v7pdVVDCof+WsuE4ciESQgoivIYgDmMMX5AMBZABwArJEniP2CbvkL+KaXKpKHJjAkIDBo0qEdkZCR/WzCIm+P5mQD+wBhLbnxVTHPPBKAtMsE3i7xe70Wapt3WwN9cxthESZKuoflnEegmmG1GQDe71jWTwvFTqocJbgSlCRLQ1tMWoSiKyg8RSpJU6PF4NjDGZmualm390DSCEQRkWV4C4KimaXwHU/80HKwo5hVU6BChEVTt7ZOSkjKJp3A0OUS4nOfu8UOEXq83Izc3l+fz0UcwBBRF+dblct2WnZ2tKYqykDEWx12UJInmnmBcneqOLMvjASyqq6tLz8/Pr1UUZTGAjYyxcyVJovknKH8NqYo8B3p4M2sd/wHbkR+Yl2WZHwb9JT9EyDcGXS7XM3SIUFBSQ8mthjJ2z0qS5OJfMKqqPh5KzxdKz6IoylkAvgOwHQBr+Of+yMjIDW63eymAvpIk7autrZ2Zn59/PJSePZSepamAVhRlIAC9jB2ArQkJCXM2bdrkDqXnDZVn4XnQAF6SJCkKQJ7b7b45NjY2guZecDDc8KNnFgC3JElbO3TocGt5eXkfmn9i8qcoypuMsckAukiSdNjr9S5s167ditraWp5+c9paJ8vy85Ik8bK8lV6v9+Zwzn/Wf9iLSSt5RQgQAoQAIUAIEAKEACFACIiJAAloMXkhrwgBQoAQIAQIAUKAECAEBEWABLSgxJBbhAAhQAgQAoQAIUAIEAJiIkACWkxeyCtCgBAgBAgBQoAQIAQIAUERIAEtKDHkFiFACBAChAAhQAgQAoSAmAiQgBaTF/KKECAECAFCgBAgBAgBQkBQBEhAC0oMuUUIEAKEACFACBAChAAhICYCJKDF5IW8IgQIAUKAECAECAFCgBAQFAES0IISQ24RAoQAIUAIEAKEACFACIiJAAloMXkhrwgBQoAQIAQIAUKAECAEBEWABLSgxJBbhAAhQAgQAoQAIUAIEAJiIkACWkxeyCtCgBAgBAgBQoAQIAQIAUER+H8fDPQT9AC+cgAAAABJRU5ErkJggg==\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot example data\n",
    "# generate data going up to 10, in 101 steps\n",
    "line_data = generate_data(10)\n",
    "test_fig, test_ax = plt.subplots(figsize=(10,3))\n",
    "# plot data but reshape to a 1d array\n",
    "test_ax.plot(line_data.reshape(-1))\n",
    "test_ax.grid(True)\n",
    "plt.title('sample data')\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create placeholders for input data and labels\n",
    "# we are inputting a set of points with an unknown time steps size, but with fixed batch size and features \n",
    "# the labels are the values next in the series with size of steps_to_predict\n",
    "batchX_placeholder = tf.placeholder(tf.float32, [None, batch_size, 1])\n",
    "batchY_placeholder = tf.placeholder(tf.float32, [batch_size, steps_to_predict])\n",
    "\n",
    "# create an initial state placeholder\n",
    "init_state = tf.placeholder(dtype=tf.float32, shape=[batch_size, state_size])\n",
    "\n",
    "# create Weights and a bias for the 2nd layer from the output of the RNN cell\n",
    "W2 = tf.Variable(np.random.rand(state_size, 1),dtype=tf.float32)\n",
    "b2 = tf.Variable(np.zeros((1)), dtype=tf.float32)\n",
    "\n",
    "# split the labels into different arrays for each time step\n",
    "labels_series = tf.split(axis=1, num_or_size_splits=steps_to_predict, value=batchY_placeholder)\n",
    "\n",
    "# set the inputs series to the input array placeholder\n",
    "inputs_series = batchX_placeholder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create RNN cell using tensorflows BasicRNNCell function with number of units as state_size\n",
    "# we are using tanh as our activation function\n",
    "cell = tf.contrib.rnn.BasicRNNCell(num_units=state_size, activation=tf.nn.tanh, reuse=None)\n",
    "\n",
    "# we are using the dynamic rnn function to call the RNN cell\n",
    "# we set the new output series and and the updated current state\n",
    "# we input the RNN cell from above, the input series data adn  use the initial state which is the current state for this prediction\n",
    "# time_major = True means that the input data will be in shape [time_steps, batch_size, features] instead of [batch_size, time_steps, features]\n",
    "states_series, current_state = tf.nn.dynamic_rnn(cell, inputs_series, initial_state=init_state, time_major=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# initialize the logits series as an empty array\n",
    "logits_series = []\n",
    "\n",
    "# for each outputed set of values, we apply the second weights and biases\n",
    "for i in range(steps_to_predict):\n",
    "  \n",
    "    # first, we apply the weights and biases for the 2nd layer\n",
    "    # we then append the predicted value for this time step to the logits_series list\n",
    "    logits_series.append(tf.reshape((tf.matmul(states_series[-(steps_to_predict-i)], W2) + b2), shape=[1]))\n",
    "\n",
    "# calculate loss using l2 loss as it is using continuous data and not classes\n",
    "# again repeat this for each value in the logits series\n",
    "losses = [tf.nn.l2_loss(label - logit, name=None) for logit, label in zip(logits_series, labels_series)]\n",
    "\n",
    "# average the loss for all values predicted\n",
    "total_loss = tf.reduce_mean(losses)\n",
    "\n",
    "# we are using Adagrad optimizer, we can use adam, but for one function it's unnecessary\n",
    "train_step = tf.train.AdagradOptimizer(learning_rate).minimize(total_loss)\n",
    "\n",
    "# generate training data\n",
    "x_data = generate_data(total_series_length)\n",
    "\n",
    "# initialize a current state with zeros\n",
    "_current_state = np.zeros(dtype=np.float32, shape=[batch_size, state_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "save images?False\n",
      "Red dashed line = predictions\n",
      "Green solid line = real future data (Labels)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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, fig.canvas.height);\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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"
    },
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\">"
      ],
      "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",
       "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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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, fig.canvas.height);\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 0 Loss = 0.31012\n",
      "Step 100 Loss = 0.00347613\n",
      "Step 200 Loss = 0.00266503\n",
      "Step 300 Loss = 0.00214356\n",
      "Step 400 Loss = 0.00333881\n",
      "Step 500 Loss = 0.00370353\n",
      "Step 600 Loss = 0.00522161\n",
      "Step 700 Loss = 0.00229546\n",
      "Step 800 Loss = 0.00266726\n",
      "Step 900 Loss = 0.000940664\n",
      "Step 1000 Loss = 0.00311747\n",
      "Step 1100 Loss = 0.00372401\n",
      "Step 1200 Loss = 0.00390367\n",
      "Step 1300 Loss = 0.001169\n",
      "Step 1400 Loss = 0.000918382\n",
      "Step 1500 Loss = 0.0016813\n",
      "Step 1600 Loss = 0.00243989\n",
      "Step 1700 Loss = 0.00370945\n",
      "Step 1800 Loss = 0.00227627\n",
      "Step 1900 Loss = 0.00114124\n",
      "Done Training\n"
     ]
    }
   ],
   "source": [
    "# start session and initialize variables\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# ask to save plot or not\n",
    "save_images = bool(input('save images?'))\n",
    "\n",
    "print('Red dashed line = predictions\\nGreen solid line = real future data (Labels)')\n",
    "\n",
    "# set up variables for plotting during training\n",
    "\n",
    "# initiate real line and X data for predicted line as empty arrays\n",
    "Y_training = np.array([])\n",
    "X_training = np.array([])\n",
    "# the predictions will not predict the first points in the sequence as there is no previous data\n",
    "# so we set the first points to the real lines data\n",
    "pred_training = x_data[0:truncated_backprop_length]\n",
    "# initialize the loss array to plot\n",
    "training_loss = np.array([])\n",
    "\n",
    "# setup subplots which we'll update during the training loop\n",
    "training_pred_fig,training_pred_ax = plt.subplots(1,1, figsize=(12,8))\n",
    "training_pred_ax.set_title('Predictions during training')\n",
    "training_pred_ax.grid(True)\n",
    "\n",
    "training_loss_fig,training_loss_ax = plt.subplots(1,1, figsize=(9,4))\n",
    "training_loss_ax.set_title('Loss during training')\n",
    "training_loss_ax.set_ylabel('Loss'), training_loss_ax.set_yscale('log')\n",
    "training_loss_ax.grid(True)\n",
    "\n",
    "# start training loop\n",
    "# start_index is going to be the index for the first value of the input data in the current iteration\n",
    "for start_index in range(iterations):\n",
    "\n",
    "    # set the end index to the index of the last value in the input data of this iteration\n",
    "    end_index = start_index + truncated_backprop_length\n",
    "\n",
    "    # select input data and labels for current batch\n",
    "    \n",
    "    # the input data is selected using the start and end index\n",
    "    batchX = x_data[start_index:end_index,:,:]\n",
    "    # the labels data is selected from the end of the input data to steps_to_predict number of time steps ahead\n",
    "    batchY = x_data[end_index:end_index+steps_to_predict,:,:].reshape(1,-1)\n",
    "\n",
    "    # run the training step feeding in the selected data and the current state\n",
    "    # return the total_loss to print\n",
    "    # return the train_step to run the optimizer\n",
    "    # return the current state to update it to the next state\n",
    "    # return the logits_series to plot on our graph\n",
    "    _total_loss, _train_step, _current_state, _new_values = sess.run([total_loss, train_step, current_state, logits_series],\n",
    "        feed_dict={\n",
    "            batchX_placeholder:batchX,\n",
    "            batchY_placeholder:batchY,\n",
    "            init_state:_current_state\n",
    "        })\n",
    "    \n",
    "    # print the index and loss 20 times in total\n",
    "    if (start_index)%(iterations/20) == 0:\n",
    "        print 'Step',start_index, 'Loss =', _total_loss\n",
    "    \n",
    "    # update loss array to plot\n",
    "    training_loss = np.append(training_loss, _total_loss)\n",
    "\n",
    "    # plot the new data every time it reaches the end of the previous prediction\n",
    "    if (start_index)%(steps_to_predict) == 0:\n",
    "        # append new predicted data to prediction array to plot\n",
    "        pred_training = np.append(pred_training, np.array(_new_values))\n",
    "        \n",
    "        # plot data\n",
    "        training_plot(pred_training, save_images)\n",
    "        \n",
    "# final plot of data after training has finished\n",
    "pred_training = training_plot(pred_training, save_images)\n",
    "\n",
    "# print when training has finished\n",
    "print 'Done Training'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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, fig.canvas.height);\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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>"
      ]
     },
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     "output_type": "display_data"
    },
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\">"
      ],
      "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",
       "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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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, fig.canvas.height);\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test our model\n",
    "\n",
    "# initialize pred, Y and X arrays as empty\n",
    "pred = Y = X = np.array([])\n",
    "\n",
    "# initialize current state for testing\n",
    "_current_state = np.zeros(dtype=np.float32, shape=[batch_size, state_size])\n",
    "\n",
    "# this will be our x values \n",
    "n = 0\n",
    "\n",
    "# setup subplot which we'll update during the loop\n",
    "testing_fig,testing_ax = plt.subplots(1,1, figsize=(13,6))\n",
    "testing_ax.grid(True)\n",
    "testing_ax.set_xlabel('Time (1 step = 0.1)')\n",
    "plt.title('Inference Live Plot')\n",
    "\n",
    "# loop for 300 steps\n",
    "for i in range(300):\n",
    "    # Y will be the correct line so we will use our line function to get that data\n",
    "    Y = np.append(Y, line_function(n)).reshape(1,-1)\n",
    "    \n",
    "    # increase the steps by 0.1\n",
    "    n += 0.1\n",
    "    # append X array with current step\n",
    "    X = np.append(X, n)\n",
    "    \n",
    "    \n",
    "    # if we haven't got enough points to use as input data\n",
    "    # dont run the prediction and instead use the real points as the prediction line instead\n",
    "    if i < truncated_backprop_length:\n",
    "        # append the real line data point/points\n",
    "        pred = np.append(pred, Y[:,-1])\n",
    "        \n",
    "        # update the graph at the same rate as if it was predicting\n",
    "        if i % steps_to_predict == 0:\n",
    "            \n",
    "            # plot the graph using X as our prediction X data\n",
    "            plot_data(X,X,Y.reshape(-1),pred,0.1, testing_ax, testing_fig)\n",
    "            \n",
    "            \n",
    "    # plot a blue dot where it starts making predictions\n",
    "    if i == (truncated_backprop_length-1):\n",
    "        testing_ax.plot(X[-1],pred[-1], 'bo')\n",
    "        testing_fig.canvas.draw()\n",
    "        time.sleep(0.1)\n",
    "\n",
    "        \n",
    "    # when we have enough previous points to use as input data, run the prediction\n",
    "    # run only when the real data has almost caught up with the predicted data\n",
    "    elif i >= truncated_backprop_length and (i-truncated_backprop_length) % steps_to_predict == 0:\n",
    "        \n",
    "        # set previous, our input data, to the last truncated_backprop_length number of points from the real line\n",
    "        previous = np.array(Y[:,-(truncated_backprop_length+1):-1]).reshape(-1,1,1)\n",
    "        \n",
    "        # predict the new values and set the new current state\n",
    "        # it runs the  output series and only picks the points that have been predicted\n",
    "        # it ignores the values which were also in the input\n",
    "        new_values, _current_state = sess.run(\n",
    "                [logits_series[-(steps_to_predict):truncated_backprop_length], current_state],\n",
    "                feed_dict={\n",
    "                    batchX_placeholder:previous,   # input previous\n",
    "                    init_state:_current_state      # use current state as state\n",
    "                })\n",
    "\n",
    "        # append to pred with the predicted value/values\n",
    "        pred = np.append(pred, new_values)\n",
    "        \n",
    "        # X2 is our X values but the same size as pred so we can plot pred before the Y values reach it \n",
    "        X2 = np.linspace(0, pred.size/10., pred.size)\n",
    "        \n",
    "        # plot the graph using X2 as our prediction X data\n",
    "        plot_data(X,X2,Y.reshape(-1),pred, 1, testing_ax, testing_fig)\n",
    "        \n",
    "\n",
    "# set up final plot\n",
    "final_fig,final_ax = plt.subplots(1,1, figsize=(13,6))\n",
    "final_ax.grid(True)\n",
    "final_ax.set_xlabel('Time (1 step = 0.1)')\n",
    "plt.title('Steps to predict = %i' % steps_to_predict)\n",
    "\n",
    "# sometimes, the predictions go beyond the real line so we cut them off\n",
    "pred = pred[0:Y.size]\n",
    "\n",
    "# plot the correct line, Y\n",
    "final_ax.plot(X,Y[0], 'g', label='correct line')\n",
    "# plot the predicted line\n",
    "final_ax.plot(X,pred, 'r', label='predicted line')\n",
    "# plot the point where the prediction starts\n",
    "final_ax.plot(X[truncated_backprop_length-1], pred[truncated_backprop_length-1], 'bo', label='prediction starts here')\n",
    "\n",
    "# show the labels\n",
    "plt.legend(loc='center', bbox_to_anchor=(0.5, 0.5))\n",
    "# set the axis size to 110% the max \n",
    "pylab.ylim([-1.1*np.max(np.abs(np.vstack((Y,pred)))),1.1*np.max(np.abs(np.vstack((Y,pred))))])\n",
    "final_ax.grid(True)\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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