Principal Component Analysis/Principal Component Analysis ( Simple Example with Detailed explanation).ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "Lets take a simple random variable data and compute a PCA on them.\n",
    "In this code we try to convert 3D space into 2D by using PCA where information of 3D space is preserved as much as possible in 2D space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing some useful libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we create a random data set of 3D space and try to plot on 3D graph so we can visualize how our data look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   First  Second  Third\n",
       "0      2       2      2\n",
       "1      3       5      4\n",
       "2      5       4      4\n",
       "3      5       6      5\n",
       "4      7       6      5\n",
       "5      7       8      8\n",
       "6      8      99      7\n",
       "7      9       9      8"
      ]
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    }
   ],
   "source": [
    "x = [2,3,5,5,7,7,8,9]\n",
    "y = [2,5,4,6,6,8,99,9]\n",
    "z = [2,4,4,5,5,8,7,8]\n",
    "# we have 3 columns and 8 row so this can be represent in matrices as\n",
    "original_data = np.matrix([x,y,z]).T\n",
    "df = pd.DataFrame(original_data,columns=['First','Second','Third'])\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
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       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter3D(x,y,z,color = 'red', marker ='*')\n",
    "ax.set_xlabel('X axis')\n",
    "ax.set_ylabel('Y axis')\n",
    "ax.set_zlabel('Z axis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we try to reduce the dimention of this 3D space to 2D space. How? by using Principal component analysis ( PCA )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps of PCA\n",
    "1. Normalize the data\n",
    "2. Find Covarience matrix of normalized data\n",
    "3. Compute eign values and eign vectors of covarience matrics\n",
    "4. Identify the Principal Component (Axis which capture most of the variation)\n",
    "5. Choosing components and forming a feature vector\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Normalize the data\n",
    "we know that our data is lies between 2 to 10 integer number but we need to convert this data into standard normalization form or standard distribution so that is mean is 'zero' with 'one' standard deviation.\n",
    "\n",
    "Why we need to convert our data into standard distribution??\n",
    "\n",
    "because PCA calculates a new projection of our data set. And the new axis are based on the standard deviation of our variables. So a variable with a high standard deviation will have a higher weight for the calculation of axis than a variable with a low standard deviation. If we normalize our data, all variables have the same standard deviation, thus all variables have the same weight and our PCA calculates relevant axis.\n",
    "\n",
    "here axis is a Principal component which capture most of the variation\n",
    "\n",
    "But in our data set we dont need normalize because all variables have same unit and lies between 2 - 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\begin{array}{l}{z=\\frac{x-\\mu}{\\sigma}} \\\\ {\\mu=\\text { Mean }} \\\\ {\\sigma=\\text { Standard Deviation }}\\end{array}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can calculate Z-Score value of each variable by using above formula\n",
    "where,\n",
    "x = each variable\n",
    "mue = mean of each column\n",
    "sigma = standard deviation of each column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.6464639 , -0.4972671 , -1.69080559],\n",
       "       [-1.20740686, -0.40023937, -0.68884672],\n",
       "       [-0.32929278, -0.43258195, -0.68884672],\n",
       "       [-0.32929278, -0.3678968 , -0.18786729],\n",
       "       [ 0.5488213 , -0.3678968 , -0.18786729],\n",
       "       [ 0.5488213 , -0.30321165,  1.31507101],\n",
       "       [ 0.98787834,  2.63996274,  0.81409158],\n",
       "       [ 1.42693538, -0.27086907,  1.31507101]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_data = np.array(np.empty(list(original_data.shape)))\n",
    "for i in range(original_data.shape[1]):\n",
    "    normalize_data[:,i] = ((original_data[:,i] - np.mean(original_data[:,i])).reshape(original_data.shape[0])) \n",
    "    normalize_data[:,i] /= np.std(original_data[:,i])\n",
    "normalize_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, 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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter3D(normalize_data[:,0],normalize_data[:,1],normalize_data[:,2],color = ['red'], marker ='*')\n",
    "ax.set_xlabel('X axis')\n",
    "ax.set_ylabel('Y axis')\n",
    "ax.set_zlabel('Z axis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph is similar to the graph of original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Covarience matrix of normalized data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.14285714, 0.48838667, 1.02909177],\n",
       "       [0.48838667, 1.14285714, 0.42098776],\n",
       "       [1.02909177, 0.42098776, 1.14285714]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covarience = np.cov(normalize_data , rowvar=False)  # rowvar set to false so it take covarience column wise\n",
    "covarience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if any diagonal value is tends to zero which means that that columns is getting by the combinations of other columns. In simple words that column space is lie in either first or second column space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Compute Eign value and Eign vectors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eign values : \n",
      "  [2.48113541 0.11103279 0.83640323]\n",
      "Eign vectors : \n",
      " [[-0.64341034 -0.71846191  0.26426429]\n",
      " [-0.43332342  0.05723388 -0.89941931]\n",
      " [-0.63107365  0.69320759  0.34815124]]\n"
     ]
    }
   ],
   "source": [
    "eign_val , eign_vct = np.linalg.eig(covarience)\n",
    "print(\"Eign values : \\n \",eign_val)\n",
    "print(\"Eign vectors : \\n\",eign_vct) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These three eign vectors are perpendicular to each other or these are orthogonal vectors i.e we can make a full 3D space by using the linear combination of these 3 vectors. Therefore we can calculate eign vectors to find only those values which are perpendicular to each other and use to form 3D space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Finding Principal Component\n",
    "\n",
    "Principal component is a axis which contains the projection of eign vectors on it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorted_eign_val : \n",
      " [2.48113541 0.83640323 0.11103279]\n",
      "sorted_eign_vct : \n",
      " [[-0.64341034  0.26426429 -0.71846191]\n",
      " [-0.43332342 -0.89941931  0.05723388]\n",
      " [-0.63107365  0.34815124  0.69320759]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2.48113541, 0.83640323, 0.11103279])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting eignvectors as well as eign values\n",
    "sort_index = eign_val.argsort()[::-1]\n",
    "sorted_eign_val = eign_val[sort_index]\n",
    "sorted_eign_vct = eign_vct[:,sort_index]\n",
    "print(\"sorted_eign_val : \\n\",sorted_eign_val)\n",
    "print(\"sorted_eign_vct : \\n\",sorted_eign_vct)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we find which principal axis is useful to capture most varied data\n",
    "lets check which eign value is greater\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.20503854188085"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eign_val_1D_space = eign_val[sort_index[0]]\n",
    "eign_vct_1D_space = eign_vct[:,sort_index[0]]\n",
    "(sorted_eign_val[0]/(sorted_eign_val.sum()))*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what is this value?? this value shows us that if we try to plot our whole data in 1D space then we cover 72% of data i.e\n",
    "only 18% of data accuracy is lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.95436332706889"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eign_val_2D_space = eign_val[sort_index[0:2]]\n",
    "eign_vct_2D_space = eign_vct[:,sort_index[0:2]]\n",
    "((sorted_eign_val[0]+sorted_eign_val[1])/(sorted_eign_val.sum()))*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we try to put data in 2D space then accuracy is 97 %"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Choosing component and forming vector\n",
    "\n",
    "Time to convert our original data into new 2D space and 1D space data so we can plot them and visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "eign_vct_2D_space[:,1] = -eign_vct_2D_space[:,1]\n",
    "forming_vector_2D = normalize_data.dot(eign_vct_2D_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Principal Component 1  Principal Component 2\n",
      "0              15.562530              -4.432286\n",
      "1              12.485298              -2.476382\n",
      "2              13.421150              -0.943429\n",
      "3              11.398768              -0.358615\n",
      "4              11.335409               1.134743\n",
      "5               9.265107               3.047568\n",
      "6             -81.670800              -0.472929\n",
      "7               8.202537               4.501330\n"
     ]
    }
   ],
   "source": [
    "reduced_data_2D = pd.DataFrame(forming_vector_2D,columns = ['Principal Component 1','Principal Component 2'])\n",
    "print(reduced_data_2D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, 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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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AQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBDwLbBhk7RmnbR1q1SqpFSpolSsmO9Zgo9HvWbfIhoQu6G3CCS0N0oCIYAAAggggIAvgXUbpQXfSmvX7xqxWFHp4AOlWlWlokV9zRZ8HOo1+xbRgNgNvUUgob1REggBBBBAAAEEfAi4puOLr6Vt2/Ycbf8K0h8PLzRNCPWaPbFoQOyG3iKQ0N4oCYQAAggggAACVgHXdHz0hbT5t4Ij1TxEqnFIweOyYAT1mn0TaUDsht4ikNDeKAmEAAIIIIAAAlaBH1dJcxYmFqVkcemEhoXiKgj1WmIpsbdRNCB2Q28RSGhvlARCAAEEEEAAAavAl/Ok1WsTj9KwruS+jpXlB/WafYNpQOyG3iKQ0N4oCYQAAggggAACVoH/zJbWbUg8Sv1aUuU/JD4+Q0dSr9k3jgbEbugtAgntjZJACCCAAAIIIGAVmDl3+2N3Ez3cjeh/qJjo6IwdR71m3zoaELuhtwgktDdKAiGAAAIIIICAVWDJD9KCJYlFcY/hPamRVDz73wtCvZZYSuxtFA2I3dBbBBLaGyWBEEAAAQQQQMAqsOU36cOZ0rbcgiMdcqBUp0bB47JgBPWafRNpQOyG3iKQ0N4oCYQAAggggAACPgR+WCnNXbT3SKVLSY2PkEqU8DFj8DGo1+xbRANiN/QWgYT2RkkgBBBAAAEEEPAl4B7HO/9b6betu0fcr7zkbj4vVdLXbMHHoV6zbxENiN3QWwQS2hslgRBAAAEEEEDAp8DWbdKK1dtvSncvKCxZQqpSSSpfxucsGRGLes2+TTQgdkNvEUhob5QEQgABBBBAAAEEYhGgXrOz0oDYDb1FIKG9URIIAQQQQAABBBCIRYB6zc5KA2I39BaBhPZGSSAEEEAAAQQQQCAWAeo1OysNiN3QWwQS2hslgRBAAAEEEEAAgVgEqNfsrDQgdkNvEUhob5QEQgABBBBAAAEEYhGgXrOz0oDYDb1FIKG9URIIAQQQQAABBBCIRYB6zc5KA2I39BaBhPZGSSAEEEAAAQQQQCAWAeo1OysNiN3QWwQS2hslgRBAAAEEEEAAgVgEqNfsrDQgdkNvEUhob5QEQgABBBBAAAEEYhGgXrOz0oDYDb1FIKG9URIIAQQQQAABBBCIRYB6zc5KA2I39BaBhPZGSSAEEEAAAQQQQCAWAeo1O2vwDci3336rAw88UDk5OfmudtOmTVqxYoWqV69u10hzBBI6zRvA9AgggAACCCCAQAEC1Gv2FAm+ASlWrJj+9a9/qUOHDvmudvz48dHvbd261a6R5ggkdJo3gOkRQAABBBBAAAEakNhzIPgGpGjRoho7duweG5Bx48apU6dO+u2332LHinsCGpC4hYmPAAIIIIAAAgjYBKjXbH7u0xnRgLgm46KLLtpttWvXrlW3bt00ZcoULVu2zK6R5ggkdJo3gOkRQAABBBBAAAGugMSeA0E2IIMGDdLgwYMTXnzXrl01atSohMeHOpAGJNSd4bwQQAABBBBAAIHtAtRr9kwIsgH597//rRdeeEG5ubl66qmndPLJJ6tWrVq7rLZIkSIqW7asjj322OgrWMWLF7drpDkCCZ3mDWB6BBBAAAEEEECAKyCx50CQDcjOqz711FN18803q1WrVrFjpHsCGpB07wDzI4AAAggggAACexegXrNnSPANiH2JmROBhM6cveJMEUAAAQQQQKBwClCv2fc9YxqQ9evXa/HixVq9enX01azfH3/605/sGmmOQEKneQOYHgEEEEAAAQQQKECAes2eIsE3IGvWrFHPnj319NNP5/uuD9eMuPtBeA+IPRmIgAACCCCAAAIIILB3ARoQe4YE34C0b99ezz//vLp3767mzZtr//33z3fV7vcy/SChM30HOX8EEEAAAQQQyHYB6jX7DgffgFSoUEGdO3fWvffea19t4BFI6MA3iNNDAAEEEEAAgUIvQL1mT4HgG5ADDjhAQ4cO1ZVXXmlfbeARSOjAN4jTQwABBBBAAIFCL0C9Zk+B4BuQv/3tb9q4caOeffZZ+2oDj0BCB75BnB4CCCCAAAIIFHoB6jV7CgTfgKxYsUJnn3129DLCbt26qWbNmipWrNhuKy9atKhdI80RSOg0bwDTI4AAAggggAACBQhQr9lTJPgGxDUW7ilXezvc7//22292jTRHIKHTvAFMjwACCCCAAAII0IDEngPBNyDuK1gFNSBO6YknnogdK+4JaEDiFiY+AggggAACCCBgE6Bes/m5TwffgNiXmDkRSOjM2SvOFAEEEEAAAQQKpwD1mn3faUDsht4ikNDeKAmEAAIIIIAAAgjEIkC9ZmfNiAZky5Yt+uc//6k333xTy5cv1913363GjRvrp59+0r///W+ddtppOvTQQ+0aaY5AQqd5A5geAQQQQAABBBAoQIB6zZ4iwTcga9euVatWreQ2u2zZstEjeSdPnqyWLVtq27ZtqlGjhjp27Bi9KyTTDxI603eQ80cAAQQQQACBbBegXrPvcPANyDXXXKPHHntMzz33nI4//nhVrlw5uhLiGhB39OjRQ++++64+++wzu0aaI5DQad4ApkcAAQQQQAABBLgCEnsOBN+AVKtWTe3bt9fw4cO1atUqHXjggbs0IPfff78GDhwY/V6mHzQgmb6DnD8CCCCAAAIIZLsA9Zp9h4NvQEqVKqVRo0apS5cu+TYgDz74oPr06RN9NSvTDxI603eQ80cAAQQQQACBbBegXrPvcPANSK1atXTRRRdpyJAh+TYgnTt31vTp0zV79my7RpojkNBp3gCmRwABBBBAAAEEChCgXrOnSPANSK9evTR27Fh9+umnKl++/C5fwZo2bZrOOOMM9e3bV7fffrtdI80RSOg0bwDTI4AAAggggAACNCCx50DwDcjq1at14oknasWKFWrTpo0mTJig8847T5s2bdKkSZNUr149ffjhh1FzksrDNUSuMZo6daoWLlwYPaHryCOPVP/+/dW6det9OhUakH1i40MIIIAAAggggEDKBKjX7NTBNyBuiWvWrNFNN92kZ599dsfN5vvtt58uvPDC6PG7+++/v10iyQgXXHCB3n77bZ1//vnRO0nWr1+vJ554QrNmzZK7L+Wqq65KMqKiRw03adJEM2bMiGJyIIAAAggggAACCIQlQL1m34+MaEB2Xqa7EuLe/+GehlW0aFG7wD5GeP/993XsscfK3SSfd7irMkcffXR0tca9MLF48eJJRSehk+JiMAIIIIAAAgggkHIB6jU7ecY1IPYlxxvhuuuu0z333KNvv/026bezk9Dx7g3REUAAAQQQQAABqwD1mlVQypgGZM6cOVqwYIHcPSG5ubm7rfzSSy+1a3iI4J7Y5V6a6L425u4LSeYgoZPRYiwCCCCAAAIIIJB6Aeo1u3nwDYi7knDJJZfovffey7fxcARFihTR1q1b7RrGCO5RwO4rWG3bttXzzz+/12jLli2T+2vnw32+Y8eO3ANi3Ac+jgACCCCAAAIIxCVAA2KXDb4BOeuss6Kbvd17QJo3b77HG85r1Khh1zBEWLt2rU466aSoqZg5c6aqV6++12ju7e2DBg3Kdww3oRs2go8igAACCCCAAAIxCtCA2HGDb0Dc15jcfRWDBw+2rzamCO7mc/eI4E8++UQTJ06MGqWCDq6AFCTE7yOAAAIIIIAAAuEJ0IDY9yT4BqRKlSq69dZbdfXVV9tXG0OEzZs365xzzoneB/LCCy9EX7/a14OE3lc5PocAAggggAACCKRGgHrN7hx8A9K7d2/97//+b/TSwdCO3377Te59IC+//LLGjRun9u3bm06RhDbx8WEEEEAAAQQQQCB2Aeo1O3HwDYh7sd9ll12mqlWrRldBatasqWLFiu228tq1a9s1kojg3kXSoUMHjR8/Xo8++qguv/zyJD6d/1AS2kxIAAQQQAABBBBAIFYB6jU7b/ANiHvZoHvKlXv0rvv7no5UPwXr2muv1b333hvd79GlS5fdTuu0006T+/pYMgcJnYwWYxFAAAEEEEAAgdQLUK/ZzYNvQNzTovbWeOQRuPtEUnm0aNEiejrXno5p06bJjUnmIKGT0WIsAggggAACCCCQegHqNbt58A2IfYmZE4GEzpy94kwRQAABBBBAoHAKUK/Z950GxG7oLQIJ7Y2SQAgggAACCCCAQCwC1Gt21oxoQNz9HY8//rhefPFFLVy4MPpKVq1atXTeeefpb3/7W743pdtpUh+BhE69OTMigAACCCCAAALJCFCvJaOV/9jgG5D169dHL/n78MMPlZOTEzUe7li0aJE2btwYvX38jTfekHthYaYfJHSm7yDnjwACCCCAAALZLkC9Zt/h4BuQPn366J577tGwYcPUo0cPlS5dOlr1r7/+qpEjR+qGG26QeyLVXXfdZddIcwQSOs0bwPQIIIAAAggggEABAtRr9hQJvgFx7/1o3bq1Ro8ene9qO3furDfffFOLFy+2a6Q5Agmd5g1gegQQQAABBBBAgAYk9hwIvgEpVaqU7rvvPnXt2jVfjIcfflg9e/aMrohk+kEDkuk7yPkjgAACCCCAQLYLUK/Zdzj4BsRdAXEv9fvv//7vfFfrXgI4efJkroDYc4EICCCAAAIIIIAAAlwBiT0Hgm9A3P0d7grInXfeqWuuuUbuiog7Nm/erAceeEDXX399dAXk7rvvjh0r7gnoqOMWJj4CCCCAAAIIIGAToF6z+blPB9+ArFu3Tq1atdKnn36qcuXK7XgKlnscr3tC1nHHHacpU6ZEv5fpBwmd6TvI+SOAAAIIIIBAtgtQr9l3OPgGxC1xy5Yt0U3oL7/8cvQeEHe4x/Gee+65uuyyy1SiRAm7RAARSOgANoFTQAABBBBAAAEE9iJAvWZPj4xoQOzLzIwIJHRm7BNniQACCCCAAAKFV4B6zb73GdeArFixIlr1gQceaF99YBFI6MA2hNNBAAEEEEAAAQR+J0C9Zk+JjGhAFixYoJtvvlmvvfZadN+HO9w9H2eddZYGDx6sOnXq2CUCiEBCB7AJnAICCCCAAAIIILAXAeo1e3oE34B89tlnatmypfJuRq9Xr1606rlz5+64+XzatGk65phj7BppjkBCp3kDmB4BBBBAAAEEEChAgHrNniLBNyDNmzfX7Nmzo7edN2zYcJcVz5w5M3pL+pFHHqm33nrLrpHmCCR0mjeA6RFAAAEEEEAAARqQ2HMg+AakTJkyuuGGGzRgwIB8MQYNGhS9I2Tjxo2xY8U9AQ1I3MLERwABBBBAANPlUhgAACAASURBVAEEbALUazY/9+ngG5CqVauqb9++6tWrV76rHTFihIYPH64lS5bYNdIcgYRO8wYwPQIIIIAAAgggwBWQ2HMg+Aakf//+mjhxoqZPn66cnJxdQNxVj5NPPllnn3227rjjjtix4p6ABiRuYeIjgAACCCCAAAI2Aeo1m19GXAFxzYdrQtzTry6//HLVrVtXRYoU0Zw5c6KXE5YvX15Dhw7d7WWE7sb1TDtI6EzbMc4XAQQQQAABBAqbAPWafceDvwJStGjRXVbpmg935Obm7vj1vF/L+3X371u3brXrpDgCCZ1icKZDAAEEEEAAAQSSFKBeSxIsn+HBNyBjxozZp1V26tRpnz6Xzg+R0OnUZ24EEEAAAQQQQKBgAeq1go0KGhF8A1LQArLp90nobNpN1oIAAggggAAC2ShAvWbfVRoQu6G3CCS0N0oCIYAAAggggAACsQhQr9lZM6IBcfd7TJ06VfPnz9fq1at3uf/DEbh7PtyN6pl+kNCZvoOcPwIIIIAAAghkuwD1mn2Hg29AZs2apfPPPz9qPna+8XznpWfqTee/3z4S2p7QREAAAQQQQAABBOIUoF6z6wbfgJx00kn68ssvNXjwYDVt2lQVK1bMd9X16tWza6Q5Agmd5g1gegQQQAABBBBAoAAB6jV7igTfgLiXD95444265ZZb7KsNPAIJHfgGcXoIIIAAAgggkJDA+vXSuHHStGnSxo1StWrSJZdIJ57ovjqfUIhgB1Gv2bcm+AakTp066t69u3r27GlfbeARSOjAN4jTQwABBBBAAIECBR5/XOrVS1q3bvehrgF59lnp0EMLDBPsAOo1+9YE34Dcf//9evzxx/XRRx+pZMmS9hUHHIGEDnhzODUEEEAAAQQQKFDgv/9buuKK7Vc5dnpn9C6fq1lT+vBDqUqVAsMFOYB6zb4twTcgbon/+Mc/9Oijj+rSSy/VoYceqmLFiu22cvd7mX6Q0Jm+g5w/AggggAAChVdg1SqpalVp8+Y9Nx95Oq5JeeSRzLSiXrPvW/ANyHfffae//OUvcpu9p4OnYNkTgQgIIIAAAggggIBFYPhwqW/fxCLk5EjLlkn77ZfY+JBG0YDYdyP4BuSMM86I3gHSo0cPuSdi7ekpWK1atbJrpDkCCZ3mDWB6BBBAAAEEENhngdatpSlTEv/4yy9LbdsmPj6UkdRr9p0IvgEpV65c1HwMGTLEvtrAI5DQgW8Qp4cAAggggAACexRwN5h/9FHiQE8/LbVvn/j4UEZSr9l3IvgGxN3z0a9fv+hJWNl+kNDZvsOsDwEEEEAAgewVOPdc6aWXEl/f1KnSqacmPj6UkdRr9p0IvgEZMGBA9BWsd955R0WLFrWvOOAIJHTAm8OpIYAAAggggMBeBZ55RrroooKR3BOyDj5YWrxYKl684PGhjaBes+9I8A3IG2+8Eb2IsESJErr88stVvXr1fJ+C1bJlS7tGmiOQ0GneAKZHAAEEEEAAgX0WcE+/qlVL+v77gkO4b9b371/wuBBHUK/ZdyX4BuT3Vz3cE692PnJzc8VTsOyJQAQEEEAAAQQQQMAq8MknkvuZsHsT+u+PvHeD/PnP0gsvZObVD7cmGhBrlkjBNyBjxoxJaJWdOnVKaFzIg0jokHeHc0MAAQQQQACBRARmzZKuu06aNGnX0fvvL3XrJg0YIJUokUikMMdQr9n3JfgGxL7EzIlAQmfOXnGmCCCAAAIIILB3gfnzpbfekjZt2v6CwrPOkkqXznw16jX7HtKA2A29RSChvVESCAEEEEAAAQQQiEWAes3OmhENyM8//6w777xTL774ohYuXBjd81GrVi2dd9556tu3rypUqGCXCCACCR3AJnAKCCCAAAIIIIDAXgSo1+zpEXwDsnz5cjVr1kzz589X7dq1Vb9+/WjVc+bM0YIFC3T44YfrvffeU+XKle0aaY5AQqd5A5geAQQQQAABBBAoQIB6zZ4iwTcg7tG7Tz75pP71r3+pXbt2u6x4woQJ6tixo9wN6I8++qhdI80RSOg0bwDTI4AAAggggAACNCCx50DwDcghhxyiCy+8UPfee2++GL169dKzzz6r7xN56HTsnLYJaEBsfnwaAQQQQAABBBCIW4B6zS4cfANSunRpjRgxQl27ds13tQ899JB69+6tX375xa6R5ggkdJo3gOkRQAABBBBAAAGugMSeA8E3IHXr1lWjRo3kvm6V3/HXv/5VM2fO1Ndffx07VtwT0IDELUx8BBBAAAEEEEDAJkC9ZvNznw6+ARk0aJDcX1dffbX69++vqu5B0pKWLl0aPRlr1KhRGjhwoG655Ra7RpojkNBp3gCmRwABBBBAAAEEuAISew4E34Bs2bJF7irHSy+9FD1+t1y5ctHf161bp9zcXJ177rnR1ZHixYvHjhX3BDQgcQsTHwEEEEAAAQQQsAlQr9n8MuIKSN4SX3/9db388svRe0Dc4d4D4pqPNm3a2BUCiUBCB7IRnAYCCCCAAAIIILAHAeo1e2oEfwXEvsTMiUBCZ85ecaYIIIAAAgggUDgFqNfs+x58A7JhwwatWrVK1atXz3e13377rSpVqqQyZcrYNdIcgYRO8wYwPQIIIIAAAgggUIAA9Zo9RYJvQK688kp98MEH+uKLL/Jd7dFHH62mTZtGN6Nn+kFCZ/oOcv4IIIAAAgggkO0C1Gv2HQ6+ATnssMN0ySWXRE+6yu9wT8gaO3as5s2bZ9dIcwQSOs0bwPQIIIAAAggggABXQGLPgeAbkJycHN1///3q0qVLvhijR49Wjx49tHHjxtix4p6ABiRuYeIjgAACCCCAAAI2Aeo1m5/7dPANSJUqVdS5c2cNGTIk39W6d4O4JmTFihV2jTRHIKHTvAFMjwACCCCAAAIIcAUk9hwIvgFp166dpk6dqs8//1zVqlXbBWTJkiVy94C0aNFCzz33XOxYcU9AAxK3MPERQAABBBBAAAGbAPWazS8jroDMmjVLJ5xwgkqXLq3u3burYcOG0apnzpwZ3Xj+66+/RjepH3XUUXaNNEcgodO8AUyPAAIIIIAAAghwBST2HAj+CogTePfdd6OvYc2fPz96C7o73FvQ69atG339qlmzZrFDpWICGpBUKDMHAggggAACCCCw7wLUa/tul/fJjGhA8hoO9zWsvKdd1alTJ/r6VV5DYqdIfwQSOv17wBkggAACCCCAAAJ7E6Bes+dHxjQg9qWGH4GEDn+POEMEEEAAAQQQKNwC1Gv2/acBsRt6i0BCe6MkEAIIIIAAAgggEIsA9ZqdlQbEbugtAgntjZJACCCAAAIIIIBALALUa3ZWGhC7obcIJLQ3SgIhgAACCCCAAAKxCFCv2VlpQOyG3iKQ0N4oCYQAAggggAACCMQiQL1mZ6UBsRt6i0BCe6MkEAIIIIAAAgggEIsA9ZqdNaMakA0bNmjlypU66KCDVKpUKfvqA4tAQge2IZwOAggggAACCCDwOwHqNXtKZEQDMn36dPXt21cfffRR9ALCyZMnq2XLllqxYoUuuOAC3XjjjWrTpo1dI80RSOg0bwDTI4AAAggggAACBQhQr9lTJPgG5MMPP1SLFi108MEHq3Xr1nr88cd3NCBu+c2bN9chhxyip59+2q6R5ggkdJo3gOkRQAABBBBAAAEakNhzIPgG5PTTT9fSpUv1ySefaOPGjapcubLefPPN6AqIO2699VaNGzdO8+fPjx0r7gloQOIWJj4CCCCAAAIIIGAToF6z+blPB9+AVKhQIWoyrrvuOq1atUoHHnjgLg3I6NGj1bNnT7n7QzL9IKEzfQc5fwQQQAABBBDIdgHqNfsOB9+AlCtXTnfddZeuuuqqfBuQYcOG6c4779RPP/1k10hzBBI6zRvA9AgggAACCCCAQAEC1Gv2FAm+ATnhhBNUs2ZNjR8/Pt8G5KSTTlJOTo6mTp1q10hzBBI6zRvA9AgggAACCCCAAA1I7DkQfAMyZswYXXbZZRo+fLjatWunQw89VFOmTNGRRx4ZPf3qiSee0LPPPqvzzz8/dqy4J6ABiVuY+AgggAACCCCAgE2Aes3m5z4dfAPiTrJPnz665557VLJkSW3evFlly5aNbkh3j+S9/vrr5b6GlQ0HCZ0Nu8gaEEAAAQQQQCCbBajX7LubEQ2IW6Z7CpZ71O7cuXO1bds21alTRx06dNCJJ55oVwgkAgkdyEZwGggggAACCCCAwB4EqNfsqZExDYh9qeFHIKHD3yPOEAEEEEAAAQQKtwD1mn3/aUDsht4ikNDeKAmEAAIIIIAAAgjEIkC9ZmcNrgFxN5wnexQpUkSPPfZYsh8LbjwJHdyWcEIIIIAAAggggMAuAtRr9oQIrgFxj9x1DUUyhxv/zTffJPORIMeS0EFuCyeFAAIIIIAAAgjsEKBesydDcA2IfUmZG4GEzty948wRQAABBBBAoHAIUK/Z95kGxGC4devW6C3to0eP1nfffRe9o6RLly7q27evihUrlnRkEjppMj6AAAIIIIAAAgikVIB6zc6dMQ3ImjVr9Oabb2rhwoXRqmvVqqXWrVurYsWKdoV9jHD11VfroYce0t///nedfPLJmj59evRiRPfro0aNSjoqCZ00GR9AAAEEEEAAAQRSKkC9ZufOiAbEXWUYNGiQNm3aFL18MO/IycnRgAED1K9fP7tEkhG+/PJLNWrUSNdcc43uu+++HZ/u2bOn7r//fs2cOVNHHXVUUlFJ6KS4GIwAAggggAACCKRcgHrNTh58A+KK+969e+uEE05Qjx49VK9evWjVc+bM0ciRI6MXFLq3pLvCP5XHTTfdpCFDhkQ3v7urMXmHu0JTu3Zt3XjjjbrjjjuSOiUSOikuBiOAAAIIIIAAAikXoF6zkwffgBx22GGqVq2apk6dutt9Fb/99ptOPfVULV26NOVPwWrTpk10leOHH37YbReqVKmiY445RhMnTkxqh0jopLgYjAACCCCAAAIIpFyAes1OHnwD4r5m5b6C1b1793xX+8ADD+j666/Xxo0b7RpJRHBfrypZsqRmzJix26caN26sLVu2yH1Na0/HsmXL5P7a+Zg9e7Y6duwYxXQxOBBAAAEEEEAAAQTCEqABse9H8A1IkyZNdPrpp2vo0KH5rrZ///6aNGlSvo2AnWfPEdyVGXelw914/vvD3ZC+fPlyzZ8/f48BBg4cGN3Xkt9BAxLnzhEbAQQQQAABBBDYdwEakH23y/tk8A2I+xrTRRddpPHjx0eNyM5H3u8988wzcl+JSuXBFZBUajMXAggggAACCCAQhgANiH0fgm9ALr30Un322Wf66quvohvQ69atG70pfe7cudFfRx55pI4++uhdJNzvjxkzxq6zlwjcAxIrL8ERQAABBBBAAIEgBWhA7NsSfANStGjRpFfpGhD3ksA4D/eUK/e1MJ6CFacysRFAAAEEEEAAgbAEaEDs+xF8A2JfYjwR3BOw3JOu9vQekM8//1wNGzZManISOikuBiOAAAIIIIAAAikXoF6zk9OAGAy7du2qRx55JHoTetOmTfX+++9Hb0K/8sor9fDDDycdmYROmowPIIAAAggggAACKRWgXrNz04AYDN17SP7xj39o9OjRWrJkSfS+ki5dukSPBS5evHjSkUnopMn4AAIIIIAAAgggkFIB6jU7d3ANiHuxoLvv47XXXlOpUqXUsmXLAlfp7vmYMmVKgeNCH0BCh75DnB8CCCCAAAIIFHYB6jV7BgTXgLRo0SJ6ypV7xK5rQPL+vaClTps2raAhwf8+CR38FnGCCCCAAAIIIFDIBajX7AkQXANiX1LmRiChM3fvOHMEEEAAAQQQKBwC1Gv2faYBsRt6i0BCe6MkEAIIIIAAAgikW2DWLOmpp6Qff5TKlpXcS6PPOEMqVizdZ2aan3rNxBd9OPgGZPr06Xr77bfVv3//fFc7bNgwNW/eXCeddJJdI80RSOg0bwDTI4AAAggggIBd4PvvpUsvlfK7P7dmTWn0aKlVK/s8aYpAvWaHD74BcW8cL1++vJ577rl8V9uuXTutX78+umk90w8SOtN3kPNHAAEEEECgkAu4qx0nnigtWpQ/RJEi26+AvPqqdPrpGYlFvWbftuAbkCpVqqhfv3669tpr813tiBEj5K6C/PDDD3aNNEcgodO8AUyPAAIIIIAAAjYBd+XjX//aewzXhBx4oPTdd1LJkrb50vBp6jU7evANSE5Oju6++25dffXV+a521KhRuu666/TLL7/YNdIcgYRO8wYwPQIIIIAAAgjsu8CKFVK1atLmzYnFePppqX37xMYGNIp6zb4ZwTcg9evXV6NGjfTMM8/ku9r27dvLJcLXX39t10hzhJQm9IYN0pdfSr/+KlWvLtWqlebVMz0CCCCAAAIIZLTAuHFSx46JL8E1H64JybAjpfVahtkkerrBNyADBgzQHXfcoZEjR6pbt267rOvBBx/UNddcE92gfvvttye65mDHpSSh3Y1hw4ZJ//yntG7d/1k0by716SO1bRusDyeGAAIIIIAAAgELPPig9Ltaba9ne+aZUgbew5uSei3gbfZxasE3IBs3boxeRjhjxgxVr15df/zjH6N1z5o1S4sXL9axxx6rt956S2XKlPHhkdYYsSe0u0p06qmSa0L2dAwZIu3hiWNpxWFyBBBAAAEEEAhbYPz45L5S5a6WFHS/SIArjr1eC3DNvk8p+AbELdjd3zF8+HBNmDBB8+bNiwzq1KmjCy+8MLr/w70xPRuOWBN6yxapQQNp/vyCqV58UTrnnILHMQIBBBBAAAEEEMgTWLtWOuQQaePGxExeekn6858TGxvQqFjrtYDWGeepZEQDEidASLFjTegJE6R27RJbbrNm0rvvJjaWUQgggAACCCCAQJ6Ae2jQQw8V7OHeB+J+KJqBLyWMtV4rWC4rRtCABLSNsSa0+57lxImJr3buXKlu3cTHMxIBBBBAAAEEEPj5Z6lFC+mzz/Zs4d6KPm2adNxxGekVa72WkSLJn3RGNCDbtm3TpEmTtGDBAq1evVq5ubm7rLRIkSK65ZZbkl99YJ+INaHr15fmzEl8xW+8kbEvCEp8kYxEAAEEEEAAAe8C7qtYPXpsf8KV+wr4zsdJJ0mjRknHHON92lQFjLVeS9Ui0jxP8A3IF198ob/85S9atGjRbo1Hnp1rQLZu3ZpmSvv0sSZ0o0bSF18kfpLuJxPuJxgcCCCAAAIIIIDAvgi4t6I/95zk/u6uerg3n2dw45FHEGu9ti/OGfiZ4BuQZs2aac6cOXrsscfUvHlzVaxYMQOZEzvlWBO6a1fpkUcSO5HSpaVly6Qstk4MglEIIIAAAggggMCuArHWa4UEO/gGxL0JfeDAgerXr1/Wb0msCe2ufrirIIkcnTtLo0cnMpIxCCCAAAIIIIBAoRKItV4rJJLBNyDu3R/uUbs9e/bM+i2JPaEvv7zgxqJSJemTTyT3dAoOBBBAAAEEEEAAgV0EYq/XCoF38A2Ie8P5yy+/rOnTp6tYBj6qLZkcij2h3Y1g7qtYjz+e/2lVqya9+qrUsGEyp81YBBBAAAEEEECg0AjEXq8VAsngGxD39Kubb75Z7klYXbt2VY0aNfJtRFq2bJnx25WyhP7Pf7Y/o9u96+PXX6UaNaS//337e0JycjLekQUggAACCCCAAAJxCaSsXotrAQHEDb4BKVq06C5M7olXOx/ukbw8BSuATOIUEEAAAQQQQACBQiBAA2Lf5OAbkDFjxiS0yk6dOiU0LuRBJHTIu8O5IYAAAggggAACEvWaPQuCb0DsS8ycCCR05uwVZ4oAAggggAAChVOAes2+7zQgdkNvEUhob5QEQgABBBBAAAEEYhGgXrOzBteAPPnkk9GqOnbsKHf/R96/F7TUSy+9tKAhwf8+CR38FnGCCCCAAAIIIFDIBajX7AkQXAPimg53U/m6detUpkyZqAkp6OAm9IKE+H0EEEAAAQQQQAABHwI0IHbF4BqQxYsXR6tyj9t1R96/F7TUvPEFjQv590nokHeHc0MAAQQQQAABBLgJ3UcOBNeA7LyoLVu2aPbs2TrggANUzb0kL8sPGpAs32CWhwACCCCAAAIZL0C9Zt/C4BsQ9zWsu+66S7169bKvNvAIJHTgG8TpIYAAAggggEChF6Bes6dA0A2IW1716tXVu3fv6K9sP0jobN9h1ocAAggggAACexTIzZV+3Sxt3SaVKC6VLBEkFvWafVuCb0BuuukmvfHGG/rggw9UokSYiWjfhu0RSGhfksRBAAEEEEAAgYwR2LZNWrZS+n65tPGX/zvtiuWlqlWkShWDWgr1mn07gm9AXn/9dfXr10+5ubm64oorVLt2beXk5Oy28pYtW9o10hyBhE7zBjA9AggggAACCKRWYOtW6ct50tr1e563WhWpdjWpSJHUntseZqNes29D8A3I7x/D6x65u/PhGhMew2tPBCIggAACCCCAAAIpF/hqgbTip4KnrVNdOqRyweNSMIIGxI4cfAMyZsyYhFbZqVOnhMaFPIiEDnl3ODcEEEAAAQQQ8Crgvm71yazEQpYqKZ1wVBBXQajXEtuyvY0KvgGxLzFzIpDQmbNXnCkCCCCAAAIIGAW+WSJ990PiQY6qIx2wX+LjYxpJvWaHpQGxG3qLQEJ7oyQQAggggAACCIQuMGu+tGpN4md5+KHbb0pP80G9Zt+AjGhA1qxZozvvvFMvv/yyFi5cGK26Vq1a+vOf/6y+fftGLyrMhoOEzoZdZA0IIIAAAgggkJDA/y6QViZw/0desEDuA6FeS2h39zoo+Abkm2++UYsWLbRkyRLVqVNH9erVixY0d+5czZs3T1WrVtXbb78dPR0r0w8SOtN3kPNHAAEEEEAAgYQFvl0mLVya8HAdU1+qUDbx8TGNpF6zwwbfgLirHFOnTtX48ePVtm3bXVbsroi0b99e7hG87p8z/SChM30HOX8EEEAAAQQQSFhg8xbpwy8k9wLCgo5yZaQmDQoalZLfp16zMwffgJQrV07XXHONhg4dmu9qb7jhBo0aNUrr1q2za6Q5Agmd5g1gegQQQAABBBBIrYC7AuKuhBR0BHIDujtN6rWCNqvg3w++AalcubIGDBig7t2757uaBx54QIMHD9by5csLXm3gI0jowDeI00MAAQQQQAABvwLu6seC76Sle6jj3PvfjqglVQ7nfl/qNXsKBN+AdO3aVXPmzIm+hvX7lxJu3bo1+vpVgwYN9NBDD9k10hyBhE7zBjA9AggggAACCKRH4Of10vcrpNVrpa3bpJLFpcp/kA6uJJUulZ5z2sOs1Gv27Qi+AZk5c6b+9re/qXz58tFVkLp160ZvPndNibv6sWHDBj3xxBPR7+98ZOJN6SS0PaGJgAACCCCAAAIIxClAvWbXDb4BcVc9XMORm5sb/X3nw/2aO37/6+7X3NWRTDtI6EzbMc4XAQQQQAABBAqbAPWafceDb0AGDhyYb4NR0NJvvfXWgoYE9/skdHBbwgkhgAACCCCAAAK7CFCv2RMi+AbEvsTMiUBCZ85ecaYIIIAAAgggUDgFqNfs+04DYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7MG14C0bNky6VW5N6FPmTIl6c+F9gESOrQd4XwQQAABBBBAAIFdBajX7BkRXAPSokWL3d58vmTJEi1YsEAVKlRQ7dq1lZubq4ULF+rnn3/W4YcfrmrVqmnq1Kl2jTRHIKHTvAFMjwACCCCAAAIIFCBAvWZPkeAakN8v6dNPP9Xpp5+u2267TVdccYVKlCgRDdmyZYsefvhhDRw4UJMmTVKTJk3sGmmOQEKneQOYHgEEEEAAAQQQoAGJPQeCb0DcFZEGDRrowQcfzBfjqquu0pw5czRt2rTYseKegAYkbmHiI4AAAggggAACNgHqNZuf+3TwDUjZsmV19913q2vXrvmu1l0F6dOnj9avX2/XSHMEEjrNG8D0CCCAAAIIIIAAV0Biz4HgG5CDDjpIrVq10rhx4/LFuOiii6KrHz/88EPsWHFPQAMStzDxEUAAAQQQQAABmwD1ms0vI66A9O7dWyNHjlSvXr2iKx0HH3xwtOply5bprrvu0n333acePXro3nvvtWukOQIJneYNYHoEEEAAAQQQQIArILHnQPBXQDZt2qR27drp1VdfjZ6OVa5cuejv69ati56GdeaZZ+r5559X6dKlY8eKewIakLiFiY8AAggggAACCNgEqNdsfhlxBSRviRMnTtSLL76oRYsWRY2HexzvOeecozPOOMOuEEgEEjqQjeA0EEAAAQQQQACBPQhQr9lTI/grIPYlZk4EEjpz9oozRQABBBBAAIHCKUC9Zt93GhC7obcIJLQ3SgIhgAACCCCAAAKxCFCv2VkzogGZP3++Ro8eHb0NffXq1dFXsHY+3D0hU6ZMsWukOQIJneYNYHoEEEAAAQQQQKAAAeo1e4oE34BMmDBBHTp0ULFixXTEEUdo//33z3fVvIjQngxEQAABBBBAAAEEENi7AA2IPUOCb0Dq1aunnJwcTZo0SZUrV7avOOAIJHTAm8OpIYAAAggggAACkqjX7GkQfAPimo/hw4erW7du9tUGHoGEDnyDOD0EEEAAAQQQKPQC1Gv2FAi+AWnYsKEuvPBC3XTTTfbVBh6BhA58gzg9BBBAAAEEECj0AtRr9hQIvgF54YUXojedf/TRR6patap9xQFHIKED3hxODQEEEEAAAQQQ4CtYXnIg+AZkwIABev311zVnzhyde+65jVlf1gAAIABJREFUqlGjRnRD+s6HewrWoEGDvICkMwgNSDr1mRsBBBBAAAEEEChYgHqtYKOCRgTfgBQtWrSgNcg1IFu3bi1wXOgDSOjQd4jzQwABBBBAAIHCLkC9Zs+A4BuQxYsXJ7RKd2Uk0w8SOtN3kPNHAAEEEEAAgWwXoF6z73DwDYh9iZkTgYTOnL3iTBFAAAEEEECgcApQr9n3PaMakJUrV2rhwoXRV65q1qypSpUq2QUCikBCB7QZnAoCCCCAAAIIIJCPAPWaPS0yogH59NNP1bNnT3344Ye7rPjkk0/WiBEj1KRJE7tEABFI6AA2gVNAAAEEEEAAAQT2IkC9Zk+P4BuQzz77TM2aNYtW2r59ezVo0CD656+++krjx4+P/vm9997T0UcfbddIcwQSOs0bwPQIIIAAAggggEABAtRr9hQJvgE5++yz9fnnn+v999+Pvna18+FuUD/ppJPUuHFjvfLKK3aNNEcgodO8AUyPAAIIIIAAAgjQgMSeA8E3IBUrVtR1112nW265JV+M2267TXfffbfWrFkTO1bcE9CAxC1MfAQQQAABBBBAwCZAvWbzc58OvgEpU6aMhg4dGt0Dkt9x3333qX///tq4caNdI8EIbq4nn3xSL730kr788kutWrUqujrTtm1b3XjjjXJN074cJPS+qPEZBBBAAAEEEEAgdQLUa3br4BuQ448/Plqlu8+jZMmSu6x48+bNO+4P+fjjj+0aCUaYNWuWGjZsqFNOOUVt2rRR5cqVNWPGDI0ePTpqRNw/V6hQIcFo/zeMhE6ajA8ggAACCCCAAAIpFaBes3MH34A8/fTTuvjii6MnXfXu3Vv169ePVu1uQndPwHJJMG7cuOgG9VQd7nHA33//fdSE7Hw8/vjj6ty5c/SVsGuvvTbp0yGhkybjAwgggAACCCCAQEoFqNfs3ME3IG6J9957r26++Wb98ssvO1acm5urnJwc3XHHHerVq5ddwkOEn3/+Wfvtt58uu+wyPfbYY0lHJKGTJuMDCCCAAAIIIIBASgWo1+zcGdGAuGX+9NNPmjx5cvQiQnfUqlVLp512mvbff3+7gqcIc+fO1RFHHKEbbrghum8l2YOETlaM8QgggAACCCCAQGoFqNfs3hnTgNiXGn+ESy+9VGPHjpV7d0mjRo32OuGyZcvk/tr5mD17tjp27BjdQ+IeLcyBAAIIIIAAAgggEJYADYh9P4JvQFxR7gpyV5jnd7iC/9hjj42uPOzLsWXLFi1YsCChj7onclWvXj3fsY8++qiuvPLK6N4Pdw9IQcfAgQM1aNCgfIfRgBSkx+8jgAACCCCAAALpEaABsbsH34D813/9V/SI3YkTJ+a72rPOOktly5bVhAkT9klj0aJF0de5EjmaN2+ut956a7eh//M//6MLLrhA7lxeeOEFFS9evMBwXAEpkIgBCCCAAAIIIIBAcAI0IPYtCb4BOeSQQ6J3gPTr1y/f1Q4fPjx6GtaSJUv2SWPTpk16++23E/rsAQccoLzHAud9YNKkSTrnnHN08skn67XXXlPp0qUTipXfIBJ6n+n4IAIIIIAAAgggkBIB6jU7c/ANiCvo3csG3deb8jseeeSRqEHZ+QlZdpbEIrjG5cwzz9RRRx2lKVOmqFy5col9cA+jSGgTHx9GAAEEEEAAAQRiF6BesxMH34DUrl1brVu3lrvHIr/j8ssvl7sKsXjxYrtGEhE++uij6Lzc17dcI+LjaVwkdBIbwFAEEEAAAQQQQCANAtRrdvTgGxB3deOhhx6K7q1o27btLit+9dVX9Ze//CW6OnL//ffbNRKM4JqdY445RuvWrdOwYcNUpUqVXT7p/t09IjjZg4ROVozxCCCAAAIIIIBAagWo1+zewTcgq1at0nHHHRdd4WjatOmOx9vOnDlT77//vmrWrKmPP/5Yf/jDH+waCUZwN6Kfeuqpexy9p5vVCwpPQhckxO8jgAACCCCAAALpFaBes/sH34C4Ja5cuTJ6ud/zzz+vtWvXRquuWLFi9OSpIUOGqFKlSnaJACKQ0AFsAqeAAAIIIIAAAgjsRYB6zZ4eGdGA5C0zNzdXK1asiP71wAMPVJEiRewCAUUgoQPaDE4FAQQQQAABBBDIR4B6zZ4WGdWA2JcbdgQSOuz94ewQQAABBBBAAAHqNXsOZEQDsnr16ujt4m+++aaWL1+ucePGRe/dcF/NGjlypNq3b68GDRrYNdIcgYRO8wYwPQIIIIAAAgggUIAA9Zo9RYJvQJYuXRrdfO7+XqdOHc2dO1eTJ09Wy5Yto9XXq1dPZ5xxRvSukEw/SOhM30HOHwEEEEAAAQSyXYB6zb7DwTcgnTp10ssvvyz35Cn3VvTKlStHV0LyGpDrr78+egP5rFmz7BppjkBCp3kDmB4BBBBAAAEEEOAKSOw5EHwD4t6p4d7zMXjwYLlH8rqbz3duQNw7Qvr37681a9bEjhX3BDQgcQsTHwEEEEAAAQQQsAlQr9n83KeDb0BKly6tBx54QF26dMm3AXH3gLgGZMOGDXaNNEcgodO8AUyPAAIIIIAAAghwBST2HAi+ATniiCOiezxGjBiRbwPSrl07zZs3T5999lnsWHFPQAMStzDxEUAAAQQQQAABmwD1ms0vI66A3HrrrdETsKZOnarDDjss+grWlClTojeRjxkzRpdddpmGDRumvn372jXSHIGETvMGMD0CCCCAAAIIpERg5Upp/nypaFGpbl33gumUTOtlEuo1O2PwV0A2bdqk1q1b69NPP1WTJk300Ucf6aSTTpJ7NK97IlazZs2ie0JKlChh10hzBBI6zRvA9AgggAACCCAQq8CMGdKdd0r//rf022/bpypVSurQQerXzz3dNNbpvQSnXrMzBt+AuCVu2bIl+grWM888ozlz5mjbtm3RI3k7dOiga6+9ViVLlrRLBBCBhA5gEzgFBBBAAAEEEIhFwDUdF17o6rr8w5crJ732mnTKKbFM7y0o9ZqdMiMaEPsyMyMCCZ0Z+8RZIoAAAggggEByArNnS0cfvb35yM3N/7NFikj77SfNmSNVqZJc/FSOpl6za9OA2A29RSChvVESCAEEEEAAAQQCEujaVXrkkcRO6LbbpJtvTmxsOkZRr9nVg2xAfvzxx+j+jmOOOUbly5ffscqtW7dq6NCh0c3n7s3oDRo00O233x49JSsbDhI6G3aRNSCAAAIIIIDAzgK//iodcIC0cWPBLu4qSPXq0qJFBY9N1wjqNbt8kA2Iu69j9OjR+uGHH1SmTJkdq+zZs2f0TpAKFSpET8SaPXt2dH/Iu+++qxNOOMGukeYIJHSaN4DpEUAAAQQQQMC7wJIl0qGHJhfW3aBerFhyn0nVaOo1u3SQDchxxx2nevXqaezYsTtWuGLFClWtWlU1a9bUhx9+qAMOOEDffPNN9ESsFi1aaPz48XaNNEcgodO8AUyPAAIIIIAAAt4FfvxROuigxMMWLy5t3iy5qyEhHtRr9l0JsgGpXLmy+vTpo+uvv37HCp966il17NhRDz30kK688sodv+7e/+GejvXdd9/ZNdIcgYRO8wYwPQIIIIAAAgh4F3A3nR9+uPTNN4mFdk/BeuedxMamYxT1ml09yAYkJycn+qpV586dd6ywR48eGjVqVHRvyOEui///4b6q1a1bN/3qvmCY4QcJneEbyOkjgAACCCCAQL4Cw4dLib4z+umnpfbtw4WkXrPvTZANiHvHR7t27XTHHXfsWGHjxo21ePFirVq1apdVP/zww7rpppt2+3U7TeojkNCpN2dGBBBAAAEEEIhf4OefpeOPl+bO3ftcTZtK06ZJIb9fmnrNni9BNiCXXHKJpk6dGr39/OCDD9YHH3ygpk2bqn379nJfxdr56N69u9555x198cUXdo00RyCh07wBTI8AAggggAACsQm4m9HPPlvaU8nWvPn2N6Tvv39sp+AlMPWanTHIBiTvEbzFixePvm7l3n7uHsHrGhF3JSTvcG9Er1Gjhtq2bRvdG5LpBwmd6TvI+SOAAAIIIIDA3gTciwhfekl69FHJvZywaNHtLyi86irptNO2/3voB/WafYeCbEDcstyTrm677bboSVe1atXSjTfeqGbNmu2y4ilTpqhXr1665557dJrL2gw/SOgM30BOHwEEEEAAAQSyXoB6zb7FwTYg9qVlXgQSOvP2jDNGAAEEEEAAgcIlQL1m328aELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAAIIxCJAvWZnpQGxG3qLQEJ7oyQQAggggAACCCAQiwD1mp2VBsRu6C0CCe2NkkAIIIAAAggggEAsAtRrdlYaELuhtwgktDdKAiGAAAIIIIAAArEIUK/ZWWlA7IZRhObNm+udd97RxRdfrLFjx+5TVBJ6n9j4EAIIIIAAAgggkDIB6jU7NQ2I3VBPPvmkrr76am3YsIEGxIMnIRBAAAEEEEAAgVAFaEDsO0MDYjRcs2aN6tWrp969e6t///40IEZPPo4AAggggAACCIQsQANi3x0aEKNht27dNHnyZM2aNUulSpWiATF68nEEEEAAAQQQQCBkARoQ++7QgBgMZ8yYoeOPP16vvPKKzjzzTBUpUoQGxODJRxFAAAEEEEAAgdAFaEDsO0QDso+G27Zt04knnqiDDz5YL774YhQlmQZk2bJlcn/tfMyePVsdO3aUa2waN268j2fGxxBAAAEEEEAAAQTiEqABscsW+gZky5YtWrBgQUKSZcqUUfXq1aOxDz/8cHTfx1dffaVatWol3YAMHDhQgwYNyndeGpCEtoNBCCCAAAIIIIBAygVoQOzkhb4BWbRo0Y4GoiBO96jdt956SytWrIhuPO/evbsGDx6842NcASlIkN9HAAEEEEAAAQQyW4AGxL5/hb4B2bRpk95+++2EJA844IDono9rrrlGTz31VNSM5OTk7PhsnTp1dO6552r48OGqVKmSKlasmFDcvEEkdFJcDEYAAQQQQAABBFIuQL1mJy/0Dci+EJ533nk77vvY0+fvuusu9enTJ6nwJHRSXAxGAAEEEEAAAQRSLkC9ZienAdkHww8++EBLly7d7ZN//etfdcopp6hHjx5q2LCh6tatm1R0EjopLgYjgAACCCCAAAIpF6Bes5PTgNgNd0RI5h6Q/KYloT1uBqEQQAABBBBAAIEYBKjX7Kg0IHZDGhCPhoRCAAEEEEAAAQRCFqABse8ODYjd0FsEEtobJYEQQAABBBBAAIFYBKjX7Kw0IHZDbxFIaG+UBEIAAQQQQAABBGIRoF6zs9KA2A29RSChvVESCAEEEEAAAQQQiEWAes3OSgNiN/QWgYT2RkkgBBBAAAEEEEAgFgHqNTsrDYjd0FsEEtobJYEQQAABBBBAAIFYBKjX7Kw0IHZDbxFIaG+UBEIAAQQQQAABBGIRoF6zs9KA2A29RSChvVESCAEEEEAAAQQQiEWAes3OSgNiN/QWgYT2RkkgBBBAAAEEEMh2gV9+kV57TVq8WCpVSjrlFOmoo2JfNfWanZgGxG7oLQIJ7Y2SQAgggAACCCCQrQJbt0pDh0ojRkirVu26ymbNpOHDpRNOiG311Gt2WhoQu6G3CCS0N0oCIYAAAggggEA2CmzbJnXoII0fLxUpIuXm7r7KkiW3Xxlp1SoWAeo1OysNiN3QWwQS2hslgRBAAAEEEEAgGwUefFDq1m3vK3ONyX77Sd9+K5Uv712Bes1OSgNiN/QWgYT2RkkgBBBAAAEEEMg2AXe144gjpK+/Tmxlrlm56qrExiYxinotCaw9DKUBsRt6i0BCe6MkEAIIIIAAAghkm8B//iM1aZL4qtxN6e+8k/j4BEdSryUItZdhNCB2Q28RSGhvlARCAAEEEEAAgWwTmDhROvPMxFdVt640d27i4xMcSb2WIBQNiB0qFRFI6FQoMwcCCCCAAAIIZKTAe+9tf9Ruooe7WvLpp4mOTngc9VrCVHscyBUQu6G3CCS0N0oCIYAAAggggEC2Cbj3flSrJq1enf/Tr36/3ltukQYP9q5AvWYnpQGxG3qLQEJ7oyQQAggggAACCGSjQP/+0rBhe1+ZewpW0aLSokXbGxbPB/WaHZQGxG7oLQIJ7Y2SQAgggAACCCCQjQJr10ruZYOzZuW/urx3g9x9t3TttbEIUK/ZWWlA7IbeIpDQ3igJhAACCCCAAALZKrBihdSxozRp0u4rLFtWuvPOgt8VYrChXjPg/f+P0oDYDb1FIKG9URIIAQQQQAABBLJdYOZM6Z//lBYvlkqV2n6D+iWXxPLywZ0pqdfsiUUDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28RSGhvlARCAAEEEEAAAQRiEaBes7PSgNgNvUUgob1REggBBBBAAAEEEIhFgHrNzkoDYjf0FoGE9kZJIAQQQAABBBBAIBYB6jU7Kw2I3dBbBBLaGyWBEEAAAQQQQACBWASo1+ysNCB2Q28R3n//fTVr1kxjx45V/fr1vcUlEAIIIIAAAggggIAfgdmzZ6tjx45677331LRpUz9BC1kUGpCANnzcuHFRQnMggAACCCCAAAIIhC3gfmB88cUXh32SgZ4dDUhAG7Ny5Uq98cYbqlmzpnJycoI5s7xOnyszwWyJ6UTYTxNfcB9mP4PbEtMJsZ8mvuA+zH4GtyWmE8rbz8cee0ylSpVSmzZtVKlSJVPMwvphGpDCuvNJrJvvOiaBlQFD2c8M2KQkTpH9TAIrA4aynxmwSUmcIvuZBFYGDGU//W0SDYg/y6yNxB+47Npa9pP9zC6B7FoNfz7Zz+wSyK7V8OfT337SgPizzNpI/IHLrq1lP9nP7BLIrtXw55P9zC6B7FoNfz797ScNiD/LrI3EH7js2lr2k/3MLoHsWg1/PtnP7BLIrtXw59PfftKA+LPM2kjLli3TI488oiuvvFIHH3xw1q6zsCyM/cyunWY/2c/sEsiu1fDnk/3MLgF/q6EB8WdJJAQQQAABBBBAAAEEEChAgAaEFEEAAQQQQAABBBBAAIGUCdCApIyaiRBAAAEEEEAAAQQQQIAGhBxAAAEEEEAAAQQQQACBlAnQgKSMmokQQAABBBBAAAEEEECABoQcQAABBBBAAAEEEEAAgZQJ0ICkjDpzJxo3bpzuv/9+zZ07V7m5uTr88MN1xRVXqEuXLipatOguC5s1a5auv/56vffee9GvN2vWTP/4xz/0xz/+MXMBsuzMn3nmGT3wwAP64osvov087LDDdNVVV0WPWd75+Pbbb6O9nDRpkn799Vcde+yxuv3223XKKadkmUh2LKd58+Z65513dPHFF2vs2LG7Lerdd9/VTTfdpBkzZqhUqVI6/fTToz+b1atXzw6ADF7Fxo0b9eSTT+qll17Sl19+qVWrVqlmzZpq27atbrzxRlWsWHG31fHf2nA3fOvWrbrrrrs0evRofffddzr00EOj/1/27dtXxYoVC/fEC/mZffrpp9F/O6dOnaqFCxeqbNmyOvLII9W/f3+1bt16Fx322J4sNCB2w6yOcMcdd+jmm29WmzZtdM4550QF6/PPP69p06bpuuuu0/Dhw3esf968eTruuON0wAEH6Jprrol+feTIkVqzZo0+/vhj1alTJ6utMmFx1157re677z61a9dOrmB1++n2rXTp0hoyZMiOJaxcuVJNmjTRpk2b1Lt3b1WoUEGPPvqo5syZE/3HuWnTppmw3EJzjq54vfrqq7Vhw4Z8G5D3339fp556qho0aKDLL79ca9eu1b333qsyZcpEDUmlSpUKjVWIC3XNRMOGDaPm3v23tnLlytG+uALWNSLun92fwbyD/9aGuIv/d07uz+JDDz2kv//97zr55JM1ffp0PfHEE9Gf0VGjRoV98oX47C644AK9/fbbOv/889W4cWOtX78+2jf35/PBBx+MflCXd7DH9kShAbEbZnUE9z/CGjVqRA1EkSJForVu27Yt+sO5aNGiqLnIO9wf3okTJ2r27NnRT3zc4X76U79+fZ155pmaMGFCVluFvrhXX301+onqU089pYsuumivp9unTx/dc889+uSTT6JGxB0///xz9NOggw46KPp1jjAE3J/BevXqRY2i+0ldfldA3NWrH3/8UV999ZXKly8fnbgrat0PDNwPEtxPaznSJ+Aa/u+//z5qQnY+Hn/8cXXu3Fl333233A8P+G9t+vYo0ZndFaxGjRpFP4RzP+zJO3r27Bl9k2DmzJk66qijEg3HuBQKuB/UuP9WuivEeYf7IdzRRx+tFStWaPny5SpevHh0lZI9tm8MDYjdMKsj5OTkqFWrVnrllVd2Waf7KZ37Q+j+p+kO95OCP/zhD2rfvr3GjBmzy9hOnTpp/Pjxcv+TLVeuXFZ7hby4P/3pT3Jf9XCXmd2VD7dnecXo78+7atWq0Vft3E+Ddj5uvfVWDR48WF9//TVXtALZ7G7dumny5MnRT+nc/zh/34C4n5bXrVtXAwYM0KBBg3Y5a3cVbP78+Vq6dGkgq+E0dhZwTf9+++2nyy67TI899hj/rc2A9HBfc3RXk7/55hvVqlVrxxm7r/TUrl07+kqd+2YBR+YIuB/SuB/Iua8lux+ussd+9o4GxI9j1kZxPzF//fXXo69anXvuuVHh6q5kuD+A7j6CvEuSH3zwQXSp2V127tq16y4e7tfc5Uo35sQTT8xaq5AX5poNV8i4/XJft3Ffjfvpp5+0//77Rz9hdf/DLFGiRLSEZcuW6ZBDDlG/fv00bNiwXZblcuGss87S008/HTWbHOkVcFcx/l97ZwKt1dTG8ceUeZZEZJZZpiQx6NXFAAAVTUlEQVRDRWSIRAupzJYhGUssEUUyz0syRFgkKRFRZFxlKFPmECHzIlMDvvXb39rXfk/nvPd973ve6d7/s5b1re/ec/fZ57fP+/b89zPsnXfe2W0QEGUkShkVIKxVt27d3Oe4Y8eOGRNmjakDYc2JbMkqiwB1dy1atLB+/frZ4MGD3eT0XVtZaxSdDZtzRDnmzJmzyESbNGliLVu2dJkCsuohQMbAqFGjXMYHdSFa43TWTgIkHY71dhS+RHv06GETJ06seUbqBYYNG2bdu3ev+Rl1IaRgUUTZqVOnDB78DPHCB5jcSlnpCbz55pvuHz7EByl0/fv3t2bNmjkhMXr06AynFaeWMDQixdfy+BlTuE7oGUHKrpCsfARYRwR906ZNbezYsW4icQKE9B1S6li7aOoHa0xqCFExn2pXvifSnaMEevbs6Ypip0+f7j53mL5rK/s94TPWqFEjl+IYNVKXFyxY4LIHZNVBgJRyUrDYjOWzh2mN01k7CZB0OFb8KHzpzZw5M6d5UpjqO+OwS84u6fz5893ON+NQ8EohMs4rogMbMWKE8Y/lhAkTXHed0OiixI4B14SiJafJ6KJFCNRlLelK5rtX0Skp7GRFih3rOWPGDFekTLck0rWGDh3qup2FRuoV9QYDBw50zQlkhROoy3py19tuu83VfVDX4VM94gQIa0X6FbvppGKFRmMBup+x5nSskxVOoK7rGb2zXxtqPxCR3vRdW/gaFXMEugoS6aDwPGpkCVBHQNqjrPIJ0KyjdevWLkJMVMv7RVrjdNZOAiQdjhU/CgXjYT5qtgmTFz558mSjzRzpHXzYRo4cWfMnpGHhrODQUGROnYh25Ur3CtRlLX1Ug4465CKHNnz4cNetxXf5UASkdGvJneqynhREIgR79erlanK8KQJS2rWLu1td1jM6zpgxY9zmDps+RCgpfPWm79ryr3G2GWh3vLLXJ9fZUXzOxikNV0iZwy/ypjXOlWL26yRA0uFY8aPwYYoWFCdNmja6CA9a7bZv394VkNO2NTSf1sGHk3Qd5SWX7hWoy1qSSkeqTqtWrWzKlCkZk+XLlfoBCiMpkFQNSOnWkjvVZT1JjaObGRsFbAB4o9U16Y6kyJFux/kRqgGp/PUMZ0jEmJbn7JaPHz/etcgOTd+1pV3PfO+m+oB8iVXe9WR88BkkM4ANANKvQtMap7NmEiDpcKyXo3jHJa5t65AhQ1xhpC8sr60LFoffcbiWumCV71UJWyOHs+CsAc6GoK6Hw7IwdcEq3zrlcufOnTvX1H0kXU9rXWo/fNqcumDlQra817BJxGYAO6yTJk2K/b7Ud21516i2u7OJQ8MAdcGqjVRl/n7hwoUu+jhu3DjjEOa4Zita43TWTgIkHY71chQKHymao3MOHXS88QEl6kEKFqkgXlRQYE4NCIfVUeCM+XNA2DHwBVz1ElYVPJTveMQXq9/RIc2OnVbSrshLJkULo8Ccg+ooTuYdwObOnetqRPwhaVXwyPV2igj/uNa5Xbt2dfU9vXv3dmdK+JoPCszJPY87B4Q6krDGoN5Cq/AHmzp1qjttmVRZhAgd6pJM37WVu5jUCtDwI+kcEBqCRM97qdynaVgzo7EHHQPJ+qAGi425ONMap/NeSICkw7HejoKjygF25D926dLFEB++KwtnQgwYMKDm2REkpG5xHggOEEaXHSIfHGRIzrqsfARoKIBwJMWKzkdEOajtoQA5bPPJDBGWCI958+a5A9D8Seg4sOzMhkXs5Xsi3TlKIK4GhGtoPIBzi4CksQDnS9DXnnNDpk2bZo0bNxbMMhKYNWuWc1oR+bS+pog5NP5/hw4d9F1bxjXK59a0oqeJB7V1bdq0MQ6440RtGj7QPEJWmQT4t46NN/wdnw0QzpTPoP9sao0LX0MJkMIZ1usRcEApTqbzFSFlciNxYjhPIu4DSqvPvn37ui9cjGJ10rW041MZrwnig9OyyS2nwweHDVLI7M9zCWeJU9SnTx93yB3vAbvogwYNyijGq4yn0iw8gSQBwu/ZVadzGdEuhAf/mJKm1bx5cwEsMwFqedq1a5c4C98YJLxA37VlXrQst2ejjvN1SG+dPXu2ywjg30v+bQwbClTuEzTMmbVt2zZrrSx1sVyDaY0Lf0ckQApnqBFEQAREQAREQAREQAREQARyJCABkiMoXSYCIiACIiACIiACIiACIlA4AQmQwhlqBBEQAREQAREQAREQAREQgRwJSIDkCEqXiYAIiIAIiIAIiIAIiIAIFE5AAqRwhhpBBERABERABERABERABEQgRwISIDmC0mUiIAIiIAIiIAIiIAIiIAKFE5AAKZyhRhABERABERABERABERABEciRgARIjqB0mQiIgAiIgAiIgAiIgAiIQOEEJEAKZ6gRREAEREAEREAEREAEREAEciQgAZIjKF0mAiIgAiIgAiIgAiIgAiJQOAEJkMIZagQREAEREAEREAEREAEREIEcCUiA5AhKl4mACFQPgTvuuMNOPPFE+/LLL61Zs2bVM/GEmXbv3t2mTJlin3zySdGeZeLEidahQwd78cUXbbfddsv7PvWNed4A9AciIAIiIAI5E5AAyRmVLhQBESg3gcmTJ1u7du1ip7H88svbb7/95n5XDGcY5/++++6zLl262DbbbFNSFBIgJcXdYG/2ww8/2M0332zt27e3PfbYo8Fy0IOLgAgUn4AESPEZ6w4iIAIpEfAC5Pjjj7e2bdtmjLrUUkvZ4Ycf7n62cOFC++uvvwxRsthii6Vy96eeesr2228/GzFihCEISmnz5s2zv//+25Zbbrmi3Zbx//zzT3ePxRdfPO/7FEP05T0J/UFBBD744APbfPPNbeDAgXbhhRcWNJb+WAREQASyEZAA0fshAiJQNQS8ABk2bJidcMIJBc977ty5tuKKK+Y0TjkECBGdFVZYIaf5lfsiCZByr0Dh95cAKZyhRhABEciNgARIbpx0lQiIQAUQyFWAxDnDRC3uv/9++/rrr+2ss86yp59+2pZYYgn7/vvv7ffff7dBgwbZww8/bLNnz3aRkw033NCItJx88sk1KV1RBNl2ionCEJU5+uij7bDDDrP+/fsbDt7aa69tZ555pp1++ukZw1GrsvHGG9sVV1xhffv2tTfeeMNFXEaNGuUiLtEaEH/9DTfc4MabOnWqrbzyynbSSSfZgAEDFon8vPDCCzZ48GA3DhGV9dZbzw488EC7+uqr3TziakDYBb/sssvsrbfesptuuskeffRRFyUhRefGG2+0DTbYoOYZkgTIrFmz3HwQcD/++KOryTnqqKMcj0aNGtX6VsHxuuuuc5Gnjz/+2EVottxyS+vTp4916tSp5u9fffVVu/jii+2VV16xBQsW2FZbbeU4wj40z43nPvvss+3111+3xo0b2/nnn+/WmlQ7eMJr6aWXtlNPPdUuueSSxLU655xzbPr06bbqqqs6UXzRRRe59yq0CRMmOI6sKdGlHXbYwc01TCcM35eePXtav3797O2337YmTZrYeeed5+YRtccee8ytH/cngtWyZUsXueC98cbzbLLJJu5+W2yxhV166aWO4/rrr2+XX365HXrooRnrH70HnwHWViYCIiACaRKQAEmTpsYSAREoKgEvQHBIo2lQRAqWWWYZd/9sAgTHFIeMgmsiIDipjDVy5Ejn5PF7Ig8zZsywP/74w4mWmTNn2tChQ+2qq66yU045xXbddVd3n+22285dH2feoeT3X3zxhRu7adOm9tBDDzknGccR59UbjjEO+c8//2w9evSwrbfe2jnbOOtJAoTnZa5HHnmkbbbZZvbEE0/Y448/bsOHD3fCx9sDDzzgxuQeOLf8L880evTomsL2bAJk2223tVVWWcU5qxT2I0Zw2nGQ+XkSc5zf1q1bu3XBOef5EQp33323Ez9jx47N+r78888/TmSMHz/eOnbsaPvss48TVq+99pqtttpqbh7YSy+9ZHvvvbf7GSKCd4F6HRzzW2+91a1ZyNlzgxFCjPkgDu655x7nwB988MHOWWetnn/+efcOdOvWLWMMxMlPP/3k1qZFixaO/ZNPPmm9evWqmRd/wHt1xBFH2KabbmrHHnus8Ux33nmnff755zZmzBjHAfPvC/VF33zzjWuisM466xhr9/LLLxvv/p577lkzh+uvv94JaZh4wcG1CCru6YWXFyCIHsaFBUKV9xlB/OGHH9pGG21kc+bMcSLPizYYYIjiXXbZpaifaw0uAiLQ8AhIgDS8NdcTi0DVEshWhI4zivOX5Az7CAiO3e23357BYKWVVrJjjjnG7eonWb4pWN6hZDyiLQgebP78+damTRt77733XDQGZxBDFHz11VeLOLv8LkmAcD1OrHcW//33X1cgT1oZIgf75ZdfnJPNTjrOvxcM/I7rfY1MNgGy++6727PPPmtLLrmkG5NICMX4F1xwgdvZT2K+77772kcffWTTpk1zEQJv3nnmnnvttVcic4QUTjvRjiuvvDLjunDuONc40jDlWTEiNa1atbLPPvvMRbWinBFqBxxwgLv2u+++c/xZMxxz3hE/Bj9HRCJEvPm14j3y1zKfQw45xIhK4NgjOIjEMB8iYaFYIxJEFAchhBAkYuLfFyIkCKztt9++Zg6MwfuDwMAQtAgDRC0svTEGYoGoHgKHtfUChKgejBA1GO8eUZBzzz3XRUIwpWAlvor6hQiIQMoEJEBSBqrhREAEikfACxBSZ8I0E+7ILrRvuZstAvLOO+8sErVYd9113e486U7egY0+RV0FCDvpRFNCu/fee12E4pFHHnGOPMbcESfsREeLwJMECA4mEYnQTjvtNPcc3377rfsxu+EU57PLj8hKsmwChDG6du2a8afsmuPU4lhjUeY42URJEA/8FxoOPw44aUakhSXZ/vvv7xx/rudecYYIgx2pZ4iH0O666y6XRhflDF+c+NB4fxArpON5ocXviay8//77Thx6435cx7wQF95I2yJKcc0117j0LiIXtDTGwSfFKzRStUjhI/KC2PAChOtphRwaQokOVaTZYddee62LniHseHdDQ0QzLmKCqJgXILxDRDhCYw0QV0R6MAmQxFdRvxABEUiZgARIykA1nAiIQPEIpFEDQlrVsssumzFJaj9wztk1xyFjV54UFiIV3uoqQNgVJ9UpNBxJdqrDNCycWnanvZMZXp8kQKjBiDqrpBANGTLE7b5jRCj4Gc4qNQJJVlsNSLT1MAKQ1CfS2LCoACECE/KLu+9xxx3n0pGSjFQ50tCoQUky5kCEhkjAGWeckXEZ9S6kgEU5x3FjPRBtiJDQeA9Ir+Ld8MZarbnmmo5paPz9WmutVZOGRRoYqW9hlMpf/+CDD7rUOcQiqW1egMQJBX7Ge0HtBobYohFDNkMMwcULEN4BhEloiB0+C88884z7sQRIVqT6pQiIQIoEJEBShKmhREAEiksgDQFCqkyckbZCWg73IGWKSETv3r2NIm+srgKECAc78KElCRDSarh/1LIVoUevx9GkkB2HFktDgBDloCYltNoEiBcGRGQ6d+4cy5yCfCJESZaWAPERCe7ji9Cj3BAgRBmihz0iQHgvaOvsjTFIaSN6EVpdBIiPzoRF6KSehRZdf+ppEG7U0CS1ZiYtjbS3sAidZgChIUBIA0N8YhIgxf3+0ugiIAL/EZAA0dsgAiJQNQSKKUBCCDiDpC0RuaCLE2lZdDKiEDrXc0C8Q0may7vvvpvBOCkFqxgCpFwpWAg40toQIBxuVxfzKViIwyRHO1sKFmlnRFlYRyJRaQqQQlOw6EpFRyofmcpHgBDhIn2NWpEdd9wxK9p8BAg1IqSi6RyQuryt+hsREIF8CEiA5ENL14qACJSVQDEECI4fzqQvUvYPyG4x7VfppES3K7+jT459tIVuHJSkInRSo+iiRcE0zrMvCk/amWfsQiIgv/76a0YRevicuRahcyo2Rei+vawvQqeuwRcwx9XdkMpGGhRONvUIoRFRgEW2c1joSkVqHG1oieqEFs4dJxznmVoNXwfE+EQ1KPKOFqHHCb18IyCsXVIROvPgeX0ROt3NiCJ59nQ6I/JDJ61PP/3U1fzkI0D4G8anPmXcuHEZNSswojaFFDEsHwECJ2pKqF8haiQTAREQgWIRkAApFlmNKwIikDqBYggQ0m6aN2/u0oRoN7v66qs7R/aWW25xnYwQIDiIdJMi7YbOQRQA4zhTF5GUQhTXhpeUI3L/qY9gF5uWp96KJUAY37fhxbmk+J17UetA/QFdqrB82/CuscYarjaD1rdYnAChZoE6BOpEKAYnGkTbYMQC9yYyET3RPnxpwja8pHzRchYRRKtZnHnftcy34WXtaDNLwTr1FwifuDa8aQgQ34aXGg/EAK2C+Y/7c09vFHj7Nsl09EI4wQr+cW14WZ/aUrAYm2en5gWmROuINiGKEHysKcIrXwHC3KhD4n9J5YMnzQZ22mmn1D/LGlAERKBhE5AAadjrr6cXgaoiUAwBwqF8dCTCAWdnmZ1zHHRa27LDjxPmDfFA2gy7yuxu53sQIcIGR5GD7qIF08UUIMz/ueeec9EK6k8QR4iugw46yAmh2gSIP4gQwQAfDtDDAeawRm9JBxHS7pU6FAq5fdth/o7OTrRN9gIm6UWEM0XkpL7hVHPGB/UoiDdStLzxXHEHEUa7d6VVA+IPjfTdqKi3QGQxh7CLFvOjfggGCCI6l/mDCDnQ0Vs+ERD/NxSPE6mgvTJRPArgidZxZgmiJF8BwvWTJk1yXcuI0PHZ0EGEVfUVqcmKQNUQkACpmqXSREVABKqJQDaHslqew5+Ezm49kR/Z/wlkE4tiJAIiIAIiUDsBCZDaGekKERABEcibgARI3siq5g8kQKpmqTRRERCBCiUgAVKhC6NpiYAIVDcBCZDqXr9ss5cAqb9rqycTAREoDQEJkNJw1l1EQAQaGAEJkPq74BIg9Xdt9WQiIAKlISABUhrOuosIiIAIiIAIiIAIiIAIiICZSYDoNRABERABERABERABERABESgZAQmQkqHWjURABERABERABERABERABCRA9A6IgAiIgAiIgAiIgAiIgAiUjIAESMlQ60YiIAIiIAIiIAIiIAIiIAISIHoHREAEREAEREAEREAEREAESkZAAqRkqHUjERABERABERABERABERABCRC9AyIgAiIgAiIgAiIgAiIgAiUjIAFSMtS6kQiIgAiIgAiIgAiIgAiIgASI3gEREAEREAEREAEREAEREIGSEZAAKRlq3UgEREAEREAEREAEREAEROB/JXoZfQ6AfO0AAAAASUVORK5CYII=\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(forming_vector_2D[:,0],forming_vector_2D[:,1],c=['red','blue','pink'])\n",
    "plt.xlabel('First principle component')\n",
    "plt.ylabel('Second principle component')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BOOM!!! BONUS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "original_data_2 = original_data.copy()\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(original_data_2) # it calculate mean and standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.6464639 , -0.4972671 , -1.69080559],\n",
       "       [-1.20740686, -0.40023937, -0.68884672],\n",
       "       [-0.32929278, -0.43258195, -0.68884672],\n",
       "       [-0.32929278, -0.3678968 , -0.18786729],\n",
       "       [ 0.5488213 , -0.3678968 , -0.18786729],\n",
       "       [ 0.5488213 , -0.30321165,  1.31507101],\n",
       "       [ 0.98787834,  2.63996274,  0.81409158],\n",
       "       [ 1.42693538, -0.27086907,  1.31507101]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_data_2 = scaler.fit_transform(original_data_2)\n",
    "normalize_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.34185223,  0.57650605],\n",
       "       [ 1.38500417,  0.19891434],\n",
       "       [ 0.83403128, -0.0622294 ],\n",
       "       [ 0.48984677, -0.17846693],\n",
       "       [-0.07514091, -0.41052113],\n",
       "       [-1.05163525, -0.87559189],\n",
       "       [-2.29332056,  1.82994551],\n",
       "       [-1.63063773, -1.07855655]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(normalize_data_2)\n",
    "reduced_data_2D_2 = pca.transform(normalize_data_2)\n",
    "reduced_data_2D_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, 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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Second principle component')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(reduced_data_2D_2[:,0],reduced_data_2D_2[:,1],c=['red','blue','pink'])\n",
    "plt.xlabel('First principle component')\n",
    "plt.ylabel('Second principle component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.64341034, -0.43332342, -0.63107365],\n",
       "       [-0.26426429,  0.89941931, -0.34815124]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_ # return eign vectors of PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA on correlation is much more informative and reveals some structure in the data and relationships between variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-37-b1c7b6e9362a>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-37-b1c7b6e9362a>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    [2.48113541 0.11103279 0.83640323]\u001b[0m\n\u001b[1;37m                         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "[2.48113541 0.11103279 0.83640323]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.48113541, 0.83640323])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  }
 ],
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