Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.models import Sequential\n",
    "import time\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
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     ]
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    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a7ea67ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+Uc/rOVjnF4ALgIPAqa3uVOBge349sLWv/7P9FtMDWNP+R3kDcBsQeh/WWTl9v4HdwGvb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\nSJIaA0GSBBgIkqTGQJAkAfB/dR38W2fQJw4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a7e341d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "import matplotlib.pyplot as plt\n",
    "# create sequence\n",
    "length = 10\n",
    "sequence = [i/float(length) for i in range(length)]\n",
    "# create X/y pairs\n",
    "df = DataFrame(sequence)\n",
    "df = concat([df, df.shift(1)], axis=1)\n",
    "df.dropna(inplace=True)\n",
    "# convert to LSTM friendly format\n",
    "values = df.values\n",
    "X, y = values[:, 0], values[:, 1]\n",
    "X = X.reshape(len(X), 1, 1)\n",
    "# configure network\n",
    "n_batch = 1\n",
    "n_epoch = 1000\n",
    "n_neurons = 10\n",
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam',metrics=['accuracy'])\n",
    "# fit network\n",
    "loss = []\n",
    "acc = []\n",
    "for i in range(n_epoch):\n",
    "    print '{}/{} epoch'.format(i,n_epoch)\n",
    "    hist = model.fit(X, y, epochs=1, batch_size=n_batch, verbose=1, shuffle=False)\n",
    "    loss.append(hist.history['loss'])\n",
    "    acc.append(hist.history['acc'])\n",
    "    model.reset_states()\n",
    "plt.figure(0)\n",
    "plt.plot(range(n_epoch),loss,'b-')\n",
    "plt.figure(1)\n",
    "plt.plot(range(n_epoch),acc,'g-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 9.44%\n",
      ">Expected=0.0, Predicted=0.5\n",
      ">Expected=0.1, Predicted=0.3\n",
      ">Expected=0.2, Predicted=0.3\n",
      ">Expected=0.3, Predicted=0.4\n",
      ">Expected=0.4, Predicted=0.4\n",
      ">Expected=0.5, Predicted=0.5\n",
      ">Expected=0.6, Predicted=0.6\n",
      ">Expected=0.7, Predicted=0.7\n",
      ">Expected=0.8, Predicted=0.8\n"
     ]
    }
   ],
   "source": [
    "X_reshaped = X[:,0].reshape(len(X), 1, 1)\n",
    "scores = model.predict(X_reshaped,batch_size=1)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))\n",
    "# online forecast\n",
    "for i in range(len(X)):\n",
    "    testX, testy = X[i], y[i]\n",
    "    testX = testX.reshape(1, 1, 1)\n",
    "    yhat = model.predict(testX, batch_size=1)\n",
    "    print('>Expected=%.1f, Predicted=%.1f' % (testy, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 1, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reshaped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X = np.array([0.5])\n",
    "data=new_X.reshape(len(new_X),1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.50275278]\n"
     ]
    }
   ],
   "source": [
    "predicted=model.predict(data)\n",
    "predicted = np.reshape(predicted, (predicted.size,))\n",
    "print predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdf = 'cex-data.hdf'\n",
    "df = pd.read_hdf(hdf,'cex-1d')\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, 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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"853\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1a7bc61c50>]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(df.closing,'-bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}