ML_Spark_MLlib.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://swan.web.cern.ch/sites/swan.web.cern.ch/files/pictures/logo_swan_letters.png\" alt=\"SWAN\" style=\"float: left; width: 20%; margin-right: 15%; margin-left: 15%; margin-bottom: 2.0em;\">\n",
    "<img src=\"https://spark.apache.org/images/spark-logo-trademark.png\" alt=\"EP-SFT\" style=\"float: left; width: 25%; margin-right: 15%; margin-bottom: 2.0em;\">\n",
    "<p style=\"clear: both;\">\n",
    "<div style=\"text-align:center\"><h1>Demonstration of ML Training using Apache Spark MLlib </h1></div>\n",
    "<div style=\"text-align:center\"><h2><i>Random forest and Gradient-boosted trees</i></h2></div>\n",
    "<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">"
   ]
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   "cell_type": "markdown",
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   "source": [
    "This notebook demonstrates ML using Apache Spark MLlib and has been developed in the context on Hadoop and Spark service at CERN  \n",
    "***Author: Matteo Migliorini***_  \n",
    "***Contact: Matteo Migliorini / Luca Canali / Prasanth Kothuri***  \n",
    "  \n",
    "To run this notebook we used the following configuration:\n",
    "* *Software stack*: Developing bleeding edge (it has spark 2.3.1)\n",
    "* *Platform*: centos7-gcct\n",
    "* *Number of cores*: 4\n",
    "* *Memory*: 10G\n",
    "* *Spark cluster*: Analytix\n",
    "\n",
    "Let's test if the spark session has been created correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
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       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - in-memory</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://swan006.cern.ch:43764\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.3.1</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>yarn</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>RandomForest_swan</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7f4fdc05acd0>"
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     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "spark"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "* [Dataset description](#description)\n",
    "* [Load the dataset](#load)\n",
    "* [Feature preparation](#preparation)\n",
    "* [Random Forest](#rf)\n",
    "    * [Grid Search](#gridSearch)\n",
    "* [Gradient-Boosting Trees](#gbt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=description></a><br>\n",
    "\n",
    "## Dataset description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset used in this example is the same used in [this paper](https://arxiv.org/pdf/1402.4735.pdf) and it has been produced using Monte Carlo simulations. It is a binary classificatin problem where the goal is to train a classifier able to distinguish between a *signal* process, the production of new theoretical Higgs bosons, and a *background* process with identical decay products but distinct kinematic features. <br>\n",
    "\n",
    "Each row of this dataset contains 28 features plus the label:\n",
    "* 21 *low-level features* which represent the basic measure made by the particle detector\n",
    "    * Momentum of the observed paricles\n",
    "    * Missing transverse momentum \n",
    "    * Jets and b-tagging information\n",
    "* 7 *high-level features* computed from the low-level features that encode the knowledge of the different intermediate states of the two processes\n",
    "    * Reconstructed invariant mass of the \n",
    "\n",
    "The dataset can be downloaded [here](https://archive.ics.uci.edu/ml/datasets/HIGGS) (This version doesn't have the header) or found here `hdfs://analytix/user/migliori/benchmarkData/Higgs10M.csv` .  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=load></a><br>\n",
    "\n",
    "## Load the dataset\n",
    "\n",
    "As firts step we will load the sample insto a Spark dataframe infering the schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('There are', 10000000, 'events')\n"
     ]
    }
   ],
   "source": [
    "df = spark.read\\\n",
    "    .format('csv')\\\n",
    "    .option('header', 'true')\\\n",
    "    .option('inferschema', 'true')\\\n",
    "    .load('hdfs://analytix/project/spark/examples/MLHiggs/Higgs10M.csv')\n",
    "\n",
    "total_events = df.count()\n",
    "print('There are',total_events,'events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "text": [
      "root\n",
      " |-- label: double (nullable = true)\n",
      " |-- lepton_pT: double (nullable = true)\n",
      " |-- lepton_eta: double (nullable = true)\n",
      " |-- lepton_phi: double (nullable = true)\n",
      " |-- missing_energy_magnitude: double (nullable = true)\n",
      " |-- missing_energy_phi: double (nullable = true)\n",
      " |-- jet1_pt: double (nullable = true)\n",
      " |-- jet1_eta: double (nullable = true)\n",
      " |-- jet1_phi: double (nullable = true)\n",
      " |-- jet1_b-tag: double (nullable = true)\n",
      " |-- jet2_pt: double (nullable = true)\n",
      " |-- jet2_eta: double (nullable = true)\n",
      " |-- jet2_phi: double (nullable = true)\n",
      " |-- jet2_b-tag: double (nullable = true)\n",
      " |-- jet3_pt: double (nullable = true)\n",
      " |-- jet3_eta: double (nullable = true)\n",
      " |-- jet3_phi: double (nullable = true)\n",
      " |-- jet3_b-tag: double (nullable = true)\n",
      " |-- jet4_pt: double (nullable = true)\n",
      " |-- je4_eta: double (nullable = true)\n",
      " |-- jet4_phi: double (nullable = true)\n",
      " |-- jet4_b-tag: double (nullable = true)\n",
      " |-- m_jj: double (nullable = true)\n",
      " |-- m_jjj: double (nullable = true)\n",
      " |-- m_lv: double (nullable = true)\n",
      " |-- m_jlv: double (nullable = true)\n",
      " |-- m_bb: double (nullable = true)\n",
      " |-- m_wbb: double (nullable = true)\n",
      " |-- m_wwbb: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can count how many events there are foreach class to see if the dataset is unbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+-------+\n",
      "|label|  count|\n",
      "+-----+-------+\n",
      "|  0.0|4700495|\n",
      "|  1.0|5299505|\n",
      "+-----+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.groupBy('label').count().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the dataset seems to be balanced, hence we can split it into `train` and `test` dataframes.<br>\n",
    "The fraction of  events used for the training stage is $0.98$ and the remainig $0.02$ will be used to test our classifier. In this way we will have approximately $200$k events for the testing phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = df.randomSplit([0.98,0.02])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Events for the training 9799800\n",
      "Events for the validation 200200\n"
     ]
    }
   ],
   "source": [
    "print('Events for the training {}'.format(train.count()))\n",
    "print('Events for the validation {}'.format(test.count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=visualization></a><br>\n",
    "\n",
    "## Data visualization\n",
    "\n",
    "We can plot some features to see how data are distributed with respect the two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "from pyspark.sql.functions import col\n",
    "%matplotlib notebook\n",
    "\n",
    "def compute_hist(data, feature, target='label', n_bins=100, x_lim=[0,3]):\n",
    "        \n",
    "    ## Fix the range\n",
    "    data = data.where((col(feature)<=x_lim[1]) &\n",
    "                      (col(feature)>=x_lim[0]))\n",
    "    \n",
    "    sgn = data.where(col(target)==1.0) \n",
    "    bkg = data.where(col(target)==0.0)\n",
    "\n",
    "    ## Compute the histograms\n",
    "    bins_sgn, counts_sgn = sgn.select(feature).rdd.flatMap(lambda x: x).histogram(n_bins)\n",
    "    bins_bkg, counts_bkg = bkg.select(feature).rdd.flatMap(lambda x: x).histogram(n_bins)\n",
    "    \n",
    "    return (bins_sgn, counts_sgn), (bins_bkg, counts_bkg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "    // 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"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "## hist is a touple containing bins and counts foreach bin\n",
    "hist_signal, hist_bkg = compute_hist(data=train, feature='m_bb', target='label', n_bins=50, x_lim=[0,3])\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "ax.hist(hist_signal[0][:-1], bins=hist_signal[0], weights=hist_signal[1], alpha=0.5, label='signal')\n",
    "ax.hist(hist_bkg[0][:-1], bins=hist_bkg[0], weights=hist_bkg[1], alpha=0.5, label='background')\n",
    "ax.set_xlabel('$m_{bb}$')\n",
    "ax.set_ylabel('counts')\n",
    "ax.set_title(\"Distribution of $m_{bb}$\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=preparation></a><br>\n",
    "\n",
    "## Feature preparation\n",
    "\n",
    "In this section we will prepare the input for the classifier. <br>\n",
    "For the random forest we don't need to scale the features and encode the labels, we need only to create a `Dense Vector` containing the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lepton_pT',\n",
       " 'lepton_eta',\n",
       " 'lepton_phi',\n",
       " 'missing_energy_magnitude',\n",
       " 'missing_energy_phi',\n",
       " 'jet1_pt',\n",
       " 'jet1_eta',\n",
       " 'jet1_phi',\n",
       " 'jet1_b-tag',\n",
       " 'jet2_pt',\n",
       " 'jet2_eta',\n",
       " 'jet2_phi',\n",
       " 'jet2_b-tag',\n",
       " 'jet3_pt',\n",
       " 'jet3_eta',\n",
       " 'jet3_phi',\n",
       " 'jet3_b-tag',\n",
       " 'jet4_pt',\n",
       " 'je4_eta',\n",
       " 'jet4_phi',\n",
       " 'jet4_b-tag',\n",
       " 'm_jj',\n",
       " 'm_jjj',\n",
       " 'm_lv',\n",
       " 'm_jlv',\n",
       " 'm_bb',\n",
       " 'm_wbb',\n",
       " 'm_wwbb']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature = train.columns\n",
    "feature.remove('label')\n",
    "feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a vector containing all the features we can use the `VectorAssembler`: we need to instantiate it specifying the column containing the features and the output column that will contain the assembled vector. After this we can *transform* (create the assembled vectors) the tain and test dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "assembler = VectorAssembler(inputCols=feature, outputCol='features')\n",
    "train = assembler.transform(train)\n",
    "test = assembler.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how a single row of the features column looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(features=DenseVector([0.2747, -1.984, 0.7695, 1.4013, 1.496, 0.9565, 0.3327, -0.0321, 0.0, 0.7049, -0.8722, -1.0061, 2.2149, 0.6468, -0.353, -0.6304, 0.0, 1.0686, -0.2303, 1.3877, 3.102, 0.8953, 0.9776, 0.9931, 0.7897, 0.6978, 0.7528, 0.7791]))]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.select('features').take(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the correlation matrix for the features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.stat import Correlation\n",
    "\n",
    "matrix = Correlation.corr(train.select('features'), 'features')\n",
    "matrix_np = matrix.collect()[0][\"pearson({})\".format('features')].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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=\"1200\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "matrix_np = matrix_np.reshape(len(feature),len(feature))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax = sns.heatmap(matrix_np, cmap=\"YlGnBu\")\n",
    "ax.xaxis.set_ticklabels(feature, rotation=270)\n",
    "ax.yaxis.set_ticklabels(feature, rotation=0)\n",
    "ax.set_title(\"Correlation Matrix\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=rf></a><br>\n",
    "\n",
    "## Random Forest\n",
    "\n",
    "**Random forest** are *ensemble learning* methos for classification and regression problems. In a Random forest we are combining  a large number of independent trees trained over random and equally distributed subsets of the data obtained by sampling with replacement the original dataset. More infos about random forest [here](#https://en.wikipedia.org/wiki/Random_forest).\n",
    "\n",
    "As a starting point we will train a simple random forest with $50$ trees, without tuning the hyperparameters. We need to specify the columns containing the features vector and the label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(labelCol='label', featuresCol='features', numTrees=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = rf.fit(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use our trained model to predict the test instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = rf.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----------+--------------------+\n",
      "|label|prediction|         probability|\n",
      "+-----+----------+--------------------+\n",
      "|  0.0|       0.0|[0.61620582497646...|\n",
      "|  0.0|       1.0|[0.45361806258826...|\n",
      "|  0.0|       1.0|[0.49516458841220...|\n",
      "|  0.0|       0.0|[0.51070779550800...|\n",
      "|  0.0|       0.0|[0.52628372242881...|\n",
      "+-----+----------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred.select('label', 'prediction', 'probability').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the test dataset is small we can convert it into a pandas dataframe. This allows us to use `sklearn` and `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pd = pred.select(['label', 'prediction', 'probability']).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>prediction</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.616205824976467, 0.3837941750235329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[0.45361806258826753, 0.5463819374117326]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[0.49516458841220823, 0.5048354115877918]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.5107077955080056, 0.4892922044919945]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.5262837224288193, 0.4737162775711807]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  prediction                                probability\n",
       "0    0.0         0.0    [0.616205824976467, 0.3837941750235329]\n",
       "1    0.0         1.0  [0.45361806258826753, 0.5463819374117326]\n",
       "2    0.0         1.0  [0.49516458841220823, 0.5048354115877918]\n",
       "3    0.0         0.0   [0.5107077955080056, 0.4892922044919945]\n",
       "4    0.0         0.0   [0.5262837224288193, 0.4737162775711807]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_pd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to convert the column `prediction` into a list, because now it is of type `VectorDense`. We will create a new column `encoded_label` that we will use to plot the `roc curve`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "pred_pd['probability'] = pred_pd['probability'].map(lambda x: list(x))\n",
    "pred_pd['encoded_label'] = pred_pd['label'].map(lambda x: np.eye(2)[int(x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.array(pred_pd['probability'].tolist())\n",
    "y_true = np.array(pred_pd['encoded_label'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.749\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import auc, roc_curve\n",
    "\n",
    "fpr, tpr, threshold = roc_curve(y_score=y_pred[:,0], y_true=y_true[:,0])\n",
    "auc = auc(fpr, tpr)\n",
    "\n",
    "print('AUC: {:.3f}'.format(auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
    },
    {
     "data": {
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       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot([0,1], [0,1], 'k--', color='orange')\n",
    "plt.plot(fpr, tpr, label='auc = {:.3f}'.format(auc))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=gridSearch></a><br>\n",
    "### Grid Search\n",
    "\n",
    "We can try to improve the performance of the classifier by tuning the hyperparameter such as the number of trees in the forest and the depth of each tree. <br>\n",
    "\n",
    "First of all we will create a smaller sample of $\\sim 80k$ for the grid search and cache it, because we will use it for different iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will use 81493  events for the grid search.\n"
     ]
    }
   ],
   "source": [
    "subSample = train.sample(fraction=1.0/120, withReplacement=False).cache()\n",
    "print('We will use',subSample.count(),' events for the grid search.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we will perform a small grid search: in a real case a larger grid and more folds for the cross-validation should be used. We will now perform a search on a small grid, ideally you want to test more parameters and use more folds for the cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
    "\n",
    "rf = RandomForestClassifier(labelCol='label', featuresCol='features', \n",
    "                            maxBins=20, subsamplingRate=0.7,\n",
    "                            minInfoGain= 1e-05,\n",
    "                            featureSubsetStrategy='all',\n",
    "                            minInstancesPerNode=2)\n",
    "\n",
    "evaluator=BinaryClassificationEvaluator(metricName='areaUnderROC')\n",
    "\n",
    "paramGrid = ParamGridBuilder()\\\n",
    "  .addGrid(rf.maxDepth, [10,20])\\\n",
    "  .addGrid(rf.numTrees, [50])\\\n",
    "  .build()\n",
    "\n",
    "cv = CrossValidator(estimator=rf, \n",
    "                    evaluator=evaluator,\n",
    "                    estimatorParamMaps=paramGrid,\n",
    "                    numFolds=2, parallelism=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestModel = cv.fit(subSample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possile to see the score obtained (in this case the AUC) for each set of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.7805406505739716,\n",
       "  {Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='maxDepth', doc='Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.'): 10,\n",
       "   Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='numTrees', doc='Number of trees to train (>= 1).'): 50}),\n",
       " (0.780174188868053,\n",
       "  {Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='maxDepth', doc='Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.'): 20,\n",
       "   Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='numTrees', doc='Number of trees to train (>= 1).'): 50})]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip(bestModel.avgMetrics, paramGrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the parameter we are interested in and use them to train a random forest on the full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BestModel:\n",
      "\t-maxDepth = 10\n",
      "\t-numTrees = 50\n"
     ]
    }
   ],
   "source": [
    "print('BestModel:\\n\\t-maxDepth =',bestModel.bestModel._java_obj.getMaxDepth())\n",
    "print('\\t-numTrees =',bestModel.bestModel._java_obj.getNumTrees())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get the list off all the parameters (including the default values) using `extractParamMap()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities'): 'probability',\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name'): 'rawPrediction',\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='seed', doc='random seed'): -4140900678877021401,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='predictionCol', doc='prediction column name'): 'prediction',\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='numTrees', doc='Number of trees to train (>= 1)'): 50,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='subsamplingRate', doc='Fraction of the training data used for learning each decision tree, in range (0, 1].'): 0.7,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='featuresCol', doc='features column name'): 'features',\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='featureSubsetStrategy', doc='The number of features to consider for splits at each tree node. Supported options: auto, all, onethird, sqrt, log2, (0.0-1.0], [1-n].'): 'all',\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='impurity', doc='Criterion used for information gain calculation (case-insensitive). Supported options: entropy, gini'): 'gini',\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='minInstancesPerNode', doc='Minimum number of instances each child must have after split.  If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1.'): 2,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='maxMemoryInMB', doc='Maximum memory in MB allocated to histogram aggregation.'): 256,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='minInfoGain', doc='Minimum information gain for a split to be considered at a tree node.'): 1e-05,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='cacheNodeIds', doc='If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.'): False,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='maxDepth', doc='Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.'): 10,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='maxBins', doc='Max number of bins for discretizing continuous features.  Must be >=2 and >= number of categories for any categorical feature.'): 20,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='checkpointInterval', doc='set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext'): 10,\n",
       " Param(parent=u'RandomForestClassifier_48beaadf31caf56c3373', name='labelCol', doc='label column name'): 'label'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestModel.bestModel.extractParamMap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A benefit of using ensemble methods (like Random Forest and Gradient Boosting) is that it is really easy to extract the importance scores of each attribute. As the name suggests, the importance of a feature indicates how usefull a feature was during the construction of the trees. The more an attribute is used to make decision within a tree, the higher its importance is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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",
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       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
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       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
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       "\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",
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       "\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",
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       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
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       "        event['data'] = 'scroll'\n",
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       "            event.step = -1;\n",
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       "\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"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,10))\n",
    "ax.barh(range(28), bestModel.bestModel.featureImportances.toArray())\n",
    "ax.set_yticks(range(28))\n",
    "ax.set_yticklabels(feature)\n",
    "ax.set_xlabel('Importances')\n",
    "ax.set_title('Feature importance')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one would expect the most important features are the *high-level* ones (because we are putting information inside them) and momentum of lepton, missing energy and leading jet. <br>\n",
    "We can now remove the `subSample` dataframe and train the model on the full dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "subSample = subSample.unpersist()\n",
    "del subSample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "important_features = ['lepton_pT', 'missing_energy_magnitude',\n",
    "                      'jet1_pt', 'jet2_pt',\n",
    "                      'm_jlv', 'm_jj', 'm_jjj',\n",
    "                      'm_bb', 'm_wbb', 'm_wwbb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "assembler = VectorAssembler(inputCols=important_features, outputCol='important_features')\n",
    "train = assembler.transform(train)\n",
    "test = assembler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_all = RandomForestClassifier(labelCol='label', featuresCol='important_features', \n",
    "                            numTrees=100, maxDepth=15,\n",
    "                            maxBins=20, subsamplingRate=0.6,\n",
    "                            minInfoGain= 1e-05,\n",
    "                            featureSubsetStrategy='all',\n",
    "                            minInstancesPerNode=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.6 s, sys: 4.62 s, total: 17.3 s\n",
      "Wall time: 29min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = rf_all.fit(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now save the model. It will be saved as a **parquet** file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.write().overwrite().save(path='hdfs://analytix/user/migliori/benchmarkData/rf_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and it is possible to reload it in the following way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import RandomForestClassificationModel\n",
    "loaded_rf = RandomForestClassificationModel\\\n",
    "        .load('hdfs://analytix/user/migliori/benchmarkData/rf_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_full = loaded_rf.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pd_full = pred_full.select(['label', 'prediction', 'probability']).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pd_full['probability'] = pred_pd_full['probability'].map(lambda x: list(x))\n",
    "pred_pd_full['encoded_label'] = pred_pd_full['label'].map(lambda x: np.eye(2)[int(x)])\n",
    "\n",
    "y_pred = np.array(pred_pd_full['probability'].tolist())\n",
    "y_true = np.array(pred_pd_full['encoded_label'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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 sklearn.metrics import auc, roc_curve\n",
    "fpr, tpr, threshold = roc_curve(y_score=y_pred[:,0], y_true=y_true[:,0])\n",
    "auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot([0,1], [0,1], 'k--', color='orange')\n",
    "plt.plot(fpr, tpr, label='auc = {:.3f}'.format(auc))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>This result is consistent with the results obtained in the paper when using a BDT and a Shallow NN with all the features ($\n",
    "AUC\\approx0.81$). We managed to obtain the same result using only the important feature found with the cross-validation. </b><br>\n",
    "\n",
    "We can now plot the predicted signal and background "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_signal_pred, hist_bkg_pred = compute_hist(data=pred_full,\n",
    "                                               feature='m_bb', target='prediction',\n",
    "                                               n_bins=50, x_lim=[0,3])\n",
    "\n",
    "hist_signal_pred, hist_bkg_pred = compute_hist(data=pred_full,\n",
    "                                               feature='m_bb', target='prediction',\n",
    "                                               n_bins=50, x_lim=[0,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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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Ro99EUAAAQQQQAABBBBAAAEEEAgmwP13MKe0b0UAmPYRTmj/+ABK6MDQLAQQQAABBBBAAAEEEEAAgVQJcP+dquGM3BkCwMh07FiMAB9AxeixLwIIIIAAAggggAACCCCAAALBBLj/DuaU9q0IANM+wgntHx9ACR0YmoUAAggggAACCCCAAAIIIJAqAe6/UzWckTtDABiZjh2LEeADqBg99kUAAQQQQAABBBBAAAEEEEAgmAD338Gc0r4VAWDaRzih/eMDKKEDQ7MQQAABBBBAAAEEEEAAAQRSJcD9d6qGM3JnCAAj07FjMQJ8ABWjx74IIIAAAgggUIkCS5YssXnz5tmcOXNs0aJFtnjx4krsBm1GAAEEEKhjgfr169uKK65ozZs3t0aNGoU+OvffoclSuQMBYCqHNfmd4gMo+WNECxFAAAEEEEAgPoG5c+fa999/b0uXLo2vUmpCAAEEEKg6gZVXXtnatGlj9erVC9x37r8DU6V6QwLAVA9vcjvHB1Byx4aWIYAAAggggEC8AtnCP924aUYHBQEEEEAAgUICf/75Z61NWrVqZS1btiy0W833uf8OTJXqDQkAUz28ye0cH0DJHRtahgACCCCAAALxCeix3y+//LJm5t9KK61kLVq0cI9yhZm9EV+LqAkBBBBAoNIE9MqI2bNn2/Tp02ua3qFDB2vYsGGgrnD/HYgp9RsRAKZ+iJPZQT6AkjkutAoBBBBAAAEE4hXQ+/706K+Kwr+2bdsS/MVLTG0IIIBA1QgoAPzll19cf1u3bu3+oBSkcP8dRCn92xAApn+ME9lDPoASOSw0CgEEEEAAAQRiFtDvPHoEWGXNNde0Jk2axHwEqkMAAQQQqBaBBQsW2JQpU1x39Ueldu3aBeo699+BmFK/EQFg6oc4mR3kAyiZ40KrEEAAAQQQQCBegW+++cYWLlzoZv2tv/76zP6Ll5faEEAAgaoS0EJSX3zxhXuthB7/1WPAQQr330GU0r8NAWD6xziRPeQDKJHDQqMQQAABBBBAIGaB//3f/zW9vL1Bgwa27rrrxlw71SGAAAIIVJtAlOsK99/VdpZk7y8BIOdBWQT4ACoLOwdFAAEEEEAAgToWiHKjVsdN5HAIIIAAAhUkEOW6wv13BQ1wCZtKAFhCXKrOLcAHEGcHAggggAACCFSDQJQbtWpwoY8IIIAAAtEEolxXuP+OZp22vQgA0zaiFdIfPoAqZKBoJgIIIIAAAggUJRDlRq2oA7IzAggggECqBaJcV7j/TvUpEbhzBICBqdgwTgE+gOLUpC4EEEAAAQQQSKpAlBu1pPaFdiGAAAIIlF8gynWF++/yj1sSWkAAmIRRqMI28AFUhYNOlxFAAAEEEKhCgSg3alXIlIgur7XWWjZ16lQ74ogjbNSoUYloUykbUW39LaUldSNQlwJRrivcf9flCCX3WASAyR2bVLeMD6BUDy+dQwABBBBAAIH/E4hyowZeeQSqLRCrtv6W56ziqAjELxDlusL9d/zjUIk1EgBW4qiloM18AKVgEOkCAggggAACCBQUiHKjVrBSNiiJQLUFYtXW35KcNFSKQBkEolxXuP8uw0Al8JAEgAkclGpoEh9A1TDK9BEBBBBAAAEEwt6oXfPcl6lGO33X9VLdv0rqHAFgJY0WbUXg/wuEva5oT+6/OYMkQADIeVAWAT6AysLOQRFAAAEEEECgjgXC3qgRANbxAFXx4QgAq3jw6XpFC4S9rhAAVvRwx9p4AsBYOaksqAABYFAptkMAAQQQQACBShYIe6NGAFjJo11ZbScArKzxorUIeAJhrysEgJw7ngABIOdCWQQIAMvCzkERQAABBBBAoI4Fwt6oEQDGM0A//PCDXX/99fbss8/a119/bb///ru1aNHCVlttNdtkk02sV69e1q9fP2vWrFnNAYMEYn/++aeNGDHC7r77bvv8889tueWWs44dO9ohhxxiJ510kum4a6+9tqvzzjvvtCOPPLJWh/T/o0ePtvbt29u3335rs2fPtuHDh9uDDz7o/n/55Ze3zp0723HHHefqzFV+++03e/zxx+25556zd99916ZMmeL62Lx5c9too41sr732suOPP95WWmmlnHUE6W88o0EtCCAQp0DY6woBYJz6lV0XAWBlj1/Ftp4AsGKHjoYjULECgx4dZJNnTI7U/k1abWIj+46MtC87IYBAdQuEvVEjACz+fHn11VetT58+NmfOnLyVPfbYY247rxQKxFSfgsOJEydmrbdbt252yy232Oabbx4oAHzmmWesd+/eLvjLVhQo3njjjVm/t9NOO9nLL7+ct38KIp988knbYIMNsm5XqL/FjwQ1IIBAKQTCXlcIAEsxCpVZJwFgZY5bxbeaALDih5AOIFBxAlvfvrVN/E/2m7ZCnenetru9efSbhTbj+wgggMAyAmFv1AgAizuJFi5caOuss46bide0aVM74YQTrEePHm7m36JFi9xMuTfeeMPGjx9vN998c6gAcI899rCnnnrKNXDbbbe1U045xc3+mzFjhpsReM8999hWW21lb731VsEAsFWrVm623k8//WSDBw+2XXbZxc3W++CDD2zYsGHuhf0qTz/9tAsdM8t2223nAs6+fftaly5dbI011rClS5fa1KlTXd8eeOABW7Jkia2//vr24YcfWqNGjZapgwCwuHONvREol0DY6woBYLlGKnnHJQBM3phURYsIAKtimOkkAokSIABM1HDQGASqRiDsjRoBYHGnxgsvvGA9e/Z0lWTO8PPXrEd59chs0EeAH3nkEdtnn31cFXp0eOzYse7xX3+5+uqrbejQoTVfyvcIsDZSAPjaa6/ZxhtvXKuer776yjp16mQLFixwAZ+OnVl0Xq277ro5sZ5//nkXHCoEvO222+zoo48mACzu1GJvBBIjEPa6QgCYmKEre0MIAMs+BNXZAALA6hx3eo1AOQUIAMupz7ERqF6BsDdqBIDFnSv33ntvzbvzfv3111oBX6Ga882I23333d1svMaNG7tZdprBl1k0A0+z8d5//333rUIBoN5RqFmE2cpBBx1k9913n62yyio2c+bMQk3P+v19993XHn74YTfLUWFoZmEGYCRWdkKg7AJhrysEgGUfssQ0gAAwMUNRXQ0hAKyu8aa3CCRBgAAwCaNAGxCoPoGwN2oEgMWdIy+++KLtvPPOrpJrr73WPV4btOQKxDRbUDMF58+fbwrVHnrooZxV6pinn356wQCwXr167tHhVVddNWtdV111lZ155pnue7NmzXKzBfMV1aUFRfQItFcUMI4cOdLatWtn//73v5fZnQAw6JnBdggkSyDsdYUAMFnjV87WEACWU7+Kj00AWMWDT9cRKJNA0EVAJk2bZL/98VutVvIOwDINGodFIAUCYW/UCACLG/Q//vjDLXrxzTffuIq6du3qQrsddtjB/XfDhg1zHiBXIPbFF1/ULKTx97//3f7617/mrEOP9G6//fYFA0DNIJw+fXrOeu64446ax3YV3inEyyyvv/66W+lYj/vmmyWoWYt63DmzEAAWd66xNwLlEgh7XSEALNdIJe+4BIDJG5OqaBEBYFUMM51EoE4FJnwzwWbOr/2YVIvGLaznOv99F1TQkm2mIAFgUD22QwCBTIGwN2oEgMWfQ59++qn179/fPvvss1qVKQhTEHj44YfbAQccYPXr16/1/VyBmFb93Xrrrd22WuX32GOPzdlIf1iY7xHg9u3b51z9V5WPGjXKBg4c6I6jhUvUNn+58MIL3WIhQYseT84sBIBB9dgOgWQJhL2uqPXcfydrDMvVGgLAcslX+XH5AKryE4DuI1ACgbiCu7jqKUEXqRIBBCpQIOyNGgFgPIO8ePFi9947/fPKK6+YFtbwly233NKefPJJtzqwV4IEgLfeeqsNGjQoZyPrIgCcMGGCWzVYRSsea+ERrQq85pprupWEvWDz/PPPt4svvthtRwAYz3lFLQgkQSDsdYUAMAmjlow2EAAmYxyqrhUEgFU35HQYgZILxBXcxVVPyTvMARBAoCIEwt6oEQCWZlh//PFHt4jHTTfdZO+99547iFb1HT9+fMEAsBSPABczA/DAAw+0+++/3y0QorZlW5BEnTr55JNdfwkAS3NOUSsC5RIIe10hACzXSCXvuASAyRuTqmgRAWBVDDOdRKBOBeIK7uKqp047z8EQQCCxAmFv1AgASzuUekdg9+7d3Uq9DRo0sDlz5riVfVXyLQLStGlTW7BgQWyLgBQTAHbq1MkmT55s/fr1swcffDAnmPr51ltvEQCW9pSidgTqXCDsdYUAsM6HKLEHJABM7NCku2EEgOkeX3qHQDkE4grusi0WskmrTWxk35Hl6BbHRACBChcIe6NGAFj6AT/jjDPsmmuucQf64YcfrE2bNnkDQH2zV69e9uyzz7qwcOrUqVln3ekx2y5durhwUaVU7wDccMMN7fPPP3dt0qzGbOWDDz6wLbbYouZbPAJc+vOKIyBQVwJhrysEgHU1Msk/DgFg8scolS0kAEzlsNIpBMoqEFcAWNZOcHAEEEidQNgbNQLA4k6BV1991QV6HTt2zFrRokWL3IIeCun0vrxZs2a5mYAq+RbFeOihh2y//fZz22nm3dixY2255ZardYyrr77avY/PK6UKAPv27evebdikSRP78MMPl+nrjBkzrEePHvbJJ58QABZ3OrE3AokUCHtdIQBM5DCWpVEEgGVh56AEgJwDCCAQtwABYNyi1IcAAnEIhL1RIwAsTl2r42rhi+2339723HNP69y5s5utN3/+fPvyyy/t5ptvtrffftsdZPDgwXbttdfWHLDQqrjeLEDtsO2229qpp57qwjcFbnfffbf7p1u3bjX1ayXfI444olaHjjzySBs9erQV8wjwuHHjbP/993f1rrHGGnbOOeeYFjVReeONN2z48OH2008/uUed33zzTfd1ZgAWd16xNwJJEgh7XSEATNLolbctBIDl9a/aoxMAVu3Q03EESiZAAFgyWipGAIEiBMLeqBEAFoFtZgoAhw0bVrCSvffe28aMGVPz/j/tUCgAnD17tnvs1gsQMw+y+eab28iRI91jwCr33XefHXDAAbEHgKrwqKOOco8YZytaBVizETW70bMgACx4SrABAhUjEPa6QgBYMUNb8oYSAJacmANkEyAA5LxAAIG4BQgA4xalPgQQiEMg7I0aAWBx6vPmzXPv6nv++edN78HTO/6mT5/uKl199dXdDL3DDjvM+vTps8yBCgWA2kGLiIwYMcLuuusutwJvvXr1rEOHDi7oO+2009y7+RQEqjz11FPWu3fvkgSAqlQzDm+99Vb3GLAebVb/dthhB7f6r/rpD0MJAIs7r9gbgSQJhL2uEAAmafTK2xYCwPL6V+3RCQCrdujpOAIlEyAALBktFSOAQBECUW7Uijgcu5ZZQKGcAkaVr776yoWDFAQQQCBOgSjXFe6/4xyByq2LALByx66iW84HUEUPH41HIJECBICJHBYahUDVC0S5Uat6tAoG0HsHn3zySffewWnTprkZghQEEEAgToEo1xXuv+McgcqtiwCwcseuolvOB1BFDx+NRyCRAgSAiRwWGoVA1QtEuVGrerSEAnz//ffWokWLWu8N9Df19ttvt2OOOcZ9aciQIXbVVVcltCc0CwEEKlkgynWF++9KHvH42k4AGJ8lNYUQ4AMoBBabIoBAIAECwEBMbIQAAnUsEOVGrY6byOECCmhV37POOssOPPBA22mnndxKvkuWLLGvv/7a7r//fnv44YddTa1bt7bJkydby5YtA9bMZggggEBwgSjXFe6/g/umeUsCwDSPboL7xgdQggeHpiFQoQJxBYATvplgM+fPrKXQonEL67lOzwqVodkIIFBOgSg3auVsL8fOLaAAcODAgXmJ2rRpY0888UTNQiB4IoAAAnELRLmucP8d9yhUZn0EgJU5bhXfaj6AKn4I6QACiROIKwCMq57EAdEgBBAoi0CUG7WyNJSDFhT4+eefbdy4cfbMM96MW5wAACAASURBVM/Yp59+ajNmzLC5c+da8+bNbcMNN7S99trLjj/+eGvatGnButgAAQQQiCoQ5brC/XdU7XTtRwCYrvGsmN7wAVQxQ0VDEagYgbiCu7jqqRg4GooAAiUViHKjVtIGUTkCCCCAQEULRLmucP9d0UMeW+MJAGOjpKIwAnwAhdFiWwQQCCIQV3AXVz1B2sw2CCCQfoEoN2rpV6GHCCCAAAJRBaJcV7j/jqqdrv0IANM1nhXTGz6AKmaoaCgCFSMQV3AXVz0VA0dDEUCgpAJRbtRK2iAqRwABBBCoaIEo1xXuvyt6yGNrPAFgbJRUFEaAD6AwWmyLAAJBBOIK7uKqJ0ib2QYBBNIvEOVGLf0q9BABBBBAIKpAlOsK999RtdO1HwFgusazYnrDB1DFDBUNRaBiBOIK7uKqp2LgaCgCCJRUIMqNWkkbROUIIIAAAhUtEOW6wv13RQ95bI0nAIyNkorCCPABFEaLbRFAIIhAXMFdXPUEaTPbIIBA+gWi3KilX4UeIoAAAghEFYhyXeH+O6p2uvZLdQA4ffp0e/vtt90/77zzjvvnl19+cSN4xBFH2KhRoyKP5u+//26bbLKJTZkyxdXRvn17+/bbbwvWp/1uvPFGGzt2rH399de2cOFCa9eune2555526qmnunqClKlTp9r1119vTzzxhH333Xe2wgorWIcOHWzAgAF20kkn2YorrhikGnvjjTdsxIgR9uqrr9q0adOsefPmtummm9qRRx5pBx10UKA6omzEB1AUNfZBAIF8ApOmTbJ5i+bV2mSlhitZp9adQsERAIbiYmMEECggEOVGDVQEEEAAAQRyCUS5rnD/zfkkgVQHgPXq1cs5ysUGgEOHDrWrr766pv4gAeBXX31le+yxh+kHNltp1qyZ3XPPPdanT5+8Z+djjz1mhx56qM2ZMyfrduutt54LBjt27Ji3ngsvvNAuvvhiW7JkSdbtFEqOGzfOGjVqFPtPCx9AsZNSIQIIxCRAABgTJNUggIATiHKjBh0CCCCAAAIEgJwDcQtUTQCoWXYbbrihPfvss86wmADwgw8+sK5du9ryyy/v/pk7d27BGYDapkuXLvbll1+64w8aNMgOPPBAa9y4sb344ot22WWX2bx589zMvddff90222yzrGOtY2+77bY2f/58W2mllezcc8+1Hj16uP+/7777bOTIkW4/hYDvvvuuNW3aNGs9t9xyix1//PHue5o5eN5551mnTp3shx9+sOuuu861SUWzAO+99964zzsjAIydlAoRQCAmAQLAmCCpBgEEnAABICcCAggggECcAlGuK9x/xzkClVtXqgPACy64wAV1+qd169buEd211167qABw8eLFttVWW9l7771nF110kd1+++2mx3ELzQA8//zz3Ww7lSuuuMLOPPPMWmfNm2++aTvssIP9+eeftuOOO9pLL72U9azS91555RVr0KCB+/fWW29da7srr7zSzjrrLPc19V+z/DLLrFmznMOvv/5qa665putLy5YtazZTH/fdd1/TTEMVhYE77bRTrGc5H0CxclIZAgjEKEAAGCMmVSGAAAEg5wACCCCAQKwCBICxclZVZakOADNHMo4AcPjw4TZkyBBbf/317eOPP3Yz7QoFgH/88Ye1atXKBW6ahTh58mRbbrnlljnRNCNPM/NU9N5CBZf+oq8pfFQ57rjj7Oabb16mDj3Oq3cTfvbZZ+59fnoPomYp+osCyLPPPtt9acyYMW4mYmZRQLfWWmuZwkA9tqxHiuMsBIBxalIXAgjEKUAAGKcmdSGAQJQbNdQQQAABBBDIJRDlusL9N+eTBAgAQ5wHCvo23nhj++233+yFF15wj94qJCsUAOqx4169erkjXX755TXhW+ahJ06cWDOjT4/2XnrppbU20WO6elRYRdt6YWBmPTqG9ld55plnbLfddqu1yTbbbGOacah3Ds6YMcMaNmyYVaF3795ufy0wou1yPU4cgrBmUz6AoqixDwII1IUAAWBdKHMMBKpHIMqNWvXo0FMEEEAAgbACUa4r3H+HVU7n9gSAIcZVi2I8+eSTdthhh9m//vUvt2eQAND/+K+Ct+7du2c9qh7/1aw9BYx6HPjll1+utZ2+ptV6mzRpYrNnz3aPAWcrOoZCPhUde9iwYTWbLVq0yO2vYymUfPrpp3MKKGxU6KjiBZ4huPJuygdQXJLUgwACcQsQAMYtSn0IVLdAlBu16haj9wgggAAC+QSiXFe4/+ackgABYMDzQAtsaEGMVVZZxT7//HNbbbXV3J5BAsD+/fvbgw8+6LbX+/cU8uUqm266qXu0WI8M6/Fdf9HXfv75Z9M2H374Yc46dIwWLVq47++///72wAMP1Gyrx4+12IfK4MGD7dprr81Zz/jx461fv37u+zfddJOdeOKJAbUKb8YHUGEjtkAAgfIIEACWx52jIpBWgSg3amm1oF8IIIAAAsULRLmucP9dvHsaaiAADDCKCtT07r5p06a5d/Qde+yxNXsFCQA14++tt95yM++00m++0qdPn5r37S1YsMA9fqui/9aKwSqaifj444/nrUcrBGsmoY6tGYFe0Yy/3Xff3f2vFgwZOnRoznq0irD3HsJzzjmn5vHjAGRuld985ccff7Ru3bq5Tb777jtr27ZtkGrZBgEEECi5AAFgyYk5AAJVJRDlRq2qgErQWS2C5z0Bs3Tp0hIcofRVHnnkkTZ69OiCCw2WviUcoZCAFm/Uq6FUSrF4YqHj8/3qE4hyXSEArL7zJFuPCQADnAfHHHOMW+1XK+6+/vrrVq9evZq9ggSAem/gp59+6lYi/umnn/Ie8YADDqiZsafZfquuuqrbXu/g82YdahvNSMxXdCzNINSCIJMmTarZdOzYsTZgwAD3///85z9NC4/kKlpIZKONNnLfPvnkk+2GG24IoPXfTfxGhXYiACwkxPcRQCCIQN8xfe39H9+vtekWbbawRw96NMjuNdsQAIbiYmMEECggEOVGDdTiBAgAi/Nj73ACBIDhvNi6eIEo1xUCwOLd01ADAWCBUXzllVdsp512svr169t7771nnTt3rrVHkACwQ4cO9s0331i7du3s3//+d94jHn744XbXXXe5bfzBmP57zTXXdF/3v4MwV2XaVvvo2F999VXNZqpbx1BRqHnUUUflbI/arP1Vjj76aLvtttsCn/MEgIGp2BABBGISiCu4i6uemLpFNQggUOECUW7UKrzLZW8+AWDZh6CqGkAAWFXDnYjORrmuEAAmYujK3ggCwDxDsHDhQve+vS+++MKGDBliV1111TJbBwkAq3EGII8Al/1nmwYgUHUCcQV3cdVTdQNAhxFAIKtA6Bu1Fy9Lt2SPc0vePwLAkhNzAJ8AASCnQ10LhL6umLlXdGlCkgpP4NX1iCXneASAecbCW71XPyh6hFfv1cssQQLAanwHYKFTnA+gQkJ8HwEEwgrEFdzFVU/Y9rM9AgikUyD0jRoBYNEnAgFg0YRUEEKAADAEFpvGIhD6ukIAGIt7GiohAMwzilqAY9GiRTZw4EDbbbfdsm55yimnuJV5W7ZsWfOOPL2rb+edd67ZnlWAl6UjAEzDxwd9QCBZAnEFd5OmTbJ5i2ov2LRSw5WsU+v/rqBOQQABBMIIhL5RIwAMw5t1WwLAogmpIIQAAWAILDaNRSD0dYUAMBb3NFRCAJhnFMO8x85fzY477mi6EHjFm0mo/9eKvJoRmK38+eef1rx5c7d67w477GAvv/xyrc30tVdffdWtJjx79mxr0KBB1np0jG222cZ9T8f2VkHT/yvQXHHFFW3x4sXWq1cv06rAucpll11m5513nvv2Cy+8ULO6VRwnPgFgHIrUgQACfoG4AkBUEUAAgTgFQt+oEQAWzZ8ZAOr35muuucbGjRtnU6dOtYYNG7rX/Bx77LF20EEHZT2efmd+5pln3D9vvfWWe6f2vHnzrFmzZtaxY0fbfffd3SJ5mgRQqKiuUaNG2SOPPGIffvihmzyg3+P1JJEWGdx///3dZAP/vUeQVYCfe+4523fffd29w/rrr2/6f+8RP69Nev+4fqfX7/w//vijtWjRwrp162aDBw92v9sXCku9Nl1wwQVuW90TaCHBiRMnusUN//KXv9i3335bi0D91bvDtfjg5MmT7ddff3XH3WKLLezggw92/yy33HJZ2fTudd0DZd5PZW4ctt3vvPOODR8+3N1LaXFFjZsmbOheZ8MNN8w7hPPnz3fnz/333+/Og0aNGtkGG2xgGiO9J13vjGcV4EI/BXw/ToHQ1xUCwDj5K7ouAsA8wxdXAPjss8+6sE3l8ssvt7PPPjvrUXUh1S8BKueee65deumltbbTBUoXcBVtu9VWW2WtR8fQ/ir6pSVz9qLCQYWE+gVGF0D9EpSt9O7d2+2vmZDarmnTprGd7ASAsVFSEQII/J8AASCnAgIIJFEg9I0aAWDRw+gPh7So3a677mpff/111noHDBhg99xzzzJ/WPcCuHyNWXXVVV2ot+222+bcTIFfv379bMqUKXn7pe8rEPRKoQDwwQcfdEGawjYFawr4WrVqVesYCuv23ntvF1xmFt3n/P3vf3f7e5MFli5dmnU7fVEB4B9//LHM/Un79u1rBYAKWBWOfvbZZzn7u9122zk3hYKZpRQBoJ7OUuCpyRaZRRMjnnrqKTf5IltRyKmgMFd/dI93xhln1Nzrvfjii24BSQoCpRQIfV0hACzlcFRU3QSARQ5XkHcA6sKqC4/++qW/MH3yySe1/sLnNeH444+3W265xf3v22+/bV27dq3VOn3NC/2OO+44u/nmm5dp/ZIlS2yTTTZxFynNJpw+fbotv/zytba74oorakLIMWPG2IEHHrhMPQro1DfNFNxjjz3siSeeKFKq9u4EgLFyUhkCCJgZASCnAQIIJFEg9I0aAWDRw+gPAPX79Hvvvedm++m1PCuvvLJ9/PHH9o9//MO+/PJLd6zTTjvNzfDyl0MPPdT9wVwz7DRjbs0113QhoQKu559/3u644w4Xnil00yw3/a6fWfT7uPb1AjjVpd+711lnHfc7thYa1Ky98ePHuzqCBoCaXad7Af3er+Dqsccec3/Y9xcFn507d3azA9XuE044wfbZZx+3nY515ZVXunecq326x1DJFwCqLrl16tTJTj/9dHe/oZlxCjhPPfVUt7/6qZmVOraKjnfUUUfZGmus4QLQG2+8seYJJ0160Gy8+vXr12p33AGgnrxS/9RuhYD6t9ot8+uuu84Zamz1c5o5KUKBofbX+aOiSRVy1CxLzawcMWKEOxd0jmmGoQoBYNE/vlQQQCD0dYUAMIBqdWxCAFjkOAcJAHUI/2PACuDOPPPMWkfWLxi6gOtCk2/Ku/cYsC7kmm7uzRj0KtPF/KyzznL/603Vz+zizJkz3S8eCiT1Vztd1PQXTK/oFxL9gqJfJlTifvxXdRIAFnnisTsCCCwjQADISYEAAkkUCH2jRgBY9DD6A0BVdu+99y7zqO/cuXNt++23t48++sg9jqp/K9TyimYM6vflXE8ETZo0yb1yR6HX3/72N7v44ouXafeWW25p77//vqtfswyz/dFdO/3yyy/uFT2NGzeuqSPXDED/7/p9+vSxBx54oNZ+XgX6Xf7hhx92/6uwS2Gcv/z+++/usVUv/NP38gWA+n7Pnj3dpAA9HZSt6P7mqquuct/KZqL6DzvsMGehogBNgZq/xB0Aqm5NZpBBZsB3ySWXuHaqPPTQQ+7+x19uuukm95i3igJkb6KGfxs9Aqww2CsEgEX/+FJBAIHQ1xXuvwOoVscmqQ4AX3vtNfeeBq/ofRte8Kap+sccc0ytUdaFNmwJGgDql4wuXbrU/KVRFxH9EqALvS4UetxXv0Do/9944w3bbLPNsjblgw8+cI8Z6C9XWpVYjwXr4q3/v+++++zWW291+6233nr27rvv5nxsVxcwzThU6dChg/31r391fxH74Ycf7Nprr3VtUtF7UfRLU9yFADBuUepDAAECQM4BBBBIokDoGzUCwKKH0R8AKiTz/qidWbH/6ZqTTjrJzVALUzQTTr83KzhUIOgv/lcAZZthWOg42QJAveJHr/pROeSQQ9x7BbO9E1y/z2tWm/6or1mPehdftqLQ03/PkS8AVIipUNQ/S9Ff58KFC2311Vd37ynfeOONXaCaObtP28+ZM8cFqwo9N9poI/dklL/EHQDqfX2atZlthqbuz/QuQM3k1FjqHYH+on5olmTr1q3drEaFtJlF92/qj16XpEIAWOjM5vtxCIS+rhAAxsGeijpSHQAGeXeHfxSzXfQKjXLQAFD1KIzUX6D0A5utaEq+/iKmX1TyFf0So8cSdAHNVhT+6a9zekFxvqIZgvprZa5+q616v4gunHEXAsC4RakPAQQIADkHEEAgiQKhb9QIAIseRn8AqN9l9Q6+XEXhnUIoLaLx+eef59xu1qxZpqdoFixYUPO7s+rWsRSO6ev+1+7osdgbbrjB1adFMvTUTZjiDwAVPmmmnPeHfoWVqjvX7ETdT+heQUXv2uvbt2/OQysAVFinki8A1GxJPX2Uq2gCg/cuRM1SHDp0aM5tTzzxRLeQiIrCyjZt2tRsG3cAuNdee9mjjz5acPz1rkRvxqQ21oIpenRZ5ZRTTrHrr78+Zx2aJajZgioEgGHOcraNKhD6ukIAGJU6dfsRAPqGtNQBoA6l93DoAqG/xCkQ1F+c9B4JhW16L0XQXw70lyy9t0JBn8I0TWlX4KdVxHQRyvYXqmxnry7Wao/ewTFt2jT33kC9u2PgwIE5V0WL46eAADAORepAAAG/AAEg5wMCCCRRIPSNGgFg0cPoDwD1rrbMlXH9B9A76u68804XpinE8z8mqll9ejegFonQYhD5in6P9s8yU2Cmp5E0E0+/t4ctXgCoVXYVrOlRX5Vcjxv76/fPFNTv3KojV9ETUbfffrv7dr4AsNAMSYWTei+hioJC9T9XGT16tFtBV0UzJbVIi1fiDgD19Jdev5SreK9X0kIfEyZMqNnMP4PzX//6l3t0OVfR94844gj3bQLAsGc620cRCH1dIQCMwpzKfVIdAKZyxFLSKQLAlAwk3UAgQQIEgAkaDJqCAAI1AqFv1AgAiz57/AGgQr1c76zTgfxhmUI+Pe6polBMr8vJtnJstgZmzvLTwn+aUagF/CZOnBi6T9meZAq6MJ9/YUE9mpv57jt/Y/z9zxcAFgoe9Wiy6lJRvzWjMld55plnrHfv3u7beoXRAQccULNp3AFgrneiewfMdTy1S69CUtEKy1rtN0h/CABDn+rsEEEg9HWFADCCcjp3IQBM57gmvlcEgIkfIhqIQMUJxBUA9h3T197/8f1a/d+izRb26EG5HyGqOCwajAACdSYQ+kaNALDosfEHgIUCsHPOOcetCKziBYAKsPRubIV/mtWnWWSaIaZX/zRt2rTmUV8t/qBFIFS0yq3//XheAKhVZLXYX9jiBYAKJPWUz+uvv+6q0CIbQ4YMyVtdKQLAQkGaPwDU6sZ6JVGuokBt9913d99OagA4ZswYO/jgg10bFVhqBeAg/SEADHums30UgdDXFQLAKMyp3IcAMJXDmvxOEQAmf4xoIQKVJhBXABhXPZXmR3sRQKA0AqFv1AgAix6IYh8B9kJBLWIxefJk22CDDbK2SWGct8BgZgAY1yPAej2QHkVWAOXNJNRjyVpYJFcpxSPAhQLAuB4BVtCqEK3QOwf9Kw7nm7lYqN25ZgD6ZynyCHDRP5JUELNA6OsKAWDMI1C51REAVu7YVXTLCQArevhoPAKJFIgruIurnkQi0SgEEKhzgdA3agSARY9RmEVANNNPIZ9mrGnmmooWjnj88cdtiy22sPfeey9new488EC7//773fczA0C929tbOKLYRUC0vxb/07vytHKxihYB0Xu/s5W777675p11cS0CUihIC7MIiN4nOGLECNf0zEVAtBiHFu3ItrKyv6/edvpaKQJAtct7d2KhRUD0fW8FaWYAFv3jSwUBBEJfVwgAA6hWxyYEgNUxzonrJQFg4oaEBiFQ8QJxBXdx1VPxoHQAAQRiEQh9o0YAWLS7PwDUCrgKwbKVd99917p27eq+pZVpvZVc9XiqHlPNtzKwVont0KGDzZ8/P2sA+Pzzz9csbqEw8Nprrw3VL/8qwAoAVWbPnm277LJLTSipEE2rA2eW77//3i0+smTJEuvfv79bfDBb0eq/WgXYK8UEaXrUevXVV3dtVHinurU6cmaZO3eurbPOOvbzzz/bRhtt5FZg9hfNbNRCh3pvobbRI9eZRV9X/zz7Ytqd752Dat9nn33m+qWVmBs3brxMW7TAo/ozffp09z0CwFCnORtHFAh9XSEAjCidvt0IANM3phXRIwLAihgmGolARQnEFdxlq6fJ8k2sU+tOgTx2Xmtnu6TnJYG2ZSMEEEi/QOgbNQLAok8KfwCoyjRLb8CAAbXqnTdvnmkF2A8++MAFVfp3586d3Tannnqqm2Gnr7/66qu2zTbb1Nr3999/tz59+riwxyuZMwD19S5duriwTvXcc889phmD2covv/xiK664Yq2AKVsAqH1nzZplPXv2dO3VysW33HKLDRo0aJlqFXw+9thj7uvjx4+3ffbZp9Y2Cs8UfnkzCvXNYoI07e9/LFdjoFmD/qL61S89UquSLcBUW/v16+e+f8kll9h5551Xq44//vjD9t9//1qhbjHtzhcA6hzQuaCioNWbtehv0LHHHmsjR46s+RIBYNE/vlQQQCD0dYUAMIBqdWxCAFgd45y4XhIAJm5IaBACFS9QygAwDE7/jfrb2P2zz7YIUw/bIoBAOgRC36gRABY98P4AUCGcwrLjjjvOzYZr1qyZffzxx27hD++R38xHPN955x3r1q2ba0fz5s1dsLXddttZo0aNXKCnd/BpXLfddtuaxTmyBYCaPaZ6FDaqKNhSCKgZY4sXL7avvvrKnn32WRs3bpx7DNm/iEiuAFD1zJw50y1Koll2CgFvu+02O+qoo2q5qe5NN93UFFY2aNDABVj77ruv67+OdcUVV9inn37qZkCqvyrFBGnaX7P7NKNQs+VU9ttvPxs4cKC1adPGPSKtx2Rfeukl972tt97ahat6z6K/LFq0yD2OPXXqVBecavak3GSv2YJ6rFrj6V9duZh25wsAtQiMxk/HU9HKxVpgpV27dvbdd9+5QFDjp3NMs0lVCACL/vGlggACoa8rBIABVKtjEwLA6hjnxPWSADBxQ0KDEKh4AQLAih9COoBAKgVC36gRABZ9HvgDQIVRmjGnACpbUUillWgVkvnLRRddtMwMNv/3tRKvHnVVwKWSLQDU1xUYKnhTYJSvZO6fLwBUPXoMViGgFghRUKYViY844ohah3juuefcsfWYaraiGXp6TPjiiy92AZv3SK1/WwWMKoXeAejto8eV9Qi1VlLOVRSc6j1/LVq0yLrJa6+95sK2bO1WYDh8+HAXgg4bNsztX6oAUHXrXYBy9sLizAZrcRadC7169XLfIgAs+seXCgIIhL6uEAAGUK2OTQgAq2OcE9dLAsDEDQkNQqDiBf464a/25cwva/VjvRbrhX4cN1uQGAaHGYBhtNgWgfQLhL5RIwAs+qTwB4AKh/TYrFbs1eOlmlm2/PLLu9lxenzzkEMOyXm8J5980r2PTjPkFEatttpqbkaYZoFpQY5Ro0YVDABVuYI1zdJ7+OGH3ew7hVcK3NZee203E+6AAw5wIZO/FAoAte2MGTOsR48ebmacQsDRo0fboYceWqseBXKXX365e6eh3lu4yiqruBlrmvWo0Mp7517r1q3tp59+WsYibACoCjSLT4/F6t2D6q8WMFHYt/nmmzvvgw8+OOv7Af0HV+CmR4AnTJjg+tmyZUv3KPYZZ5zh/p05xpkND9rufDMAvTo1fgodFRR//fXXtsIKK7iVoQ8//HA3s/SVV15x40AAWPSPLhUEFAh9XSEADCib/s0IANM/xonsIQFgIoeFRiGAgB5Lun1rm/ifiZEtCAAj07EjAqkUiHKjlkoIOpVIAS0qopBNjzjrkVwKAggkXyDKdYX77+SPa120kACwLpQ5xjICfABxUiCAQFIFBj06yCbPmBy5eSwCEpmOHRFIpUCUG7VUQtCpxAno8Va9d1ALawwdOtSuvPLKxLWRBiGAwLICUa4r3H9zJkmAAJDzoCwCfACVhZ2DIoAAAggggEAdC0S5UavjJnK4lApoIZCOHTtm7Z0ea917771N7wlU0cIonToFW+0+pVx0C4GKEYhyXeH+u2KGt6QNJQAsKS+V5xLgA4hzAwEE0i4wZtIY+3Hej7W62WalNnZQp4PS3nX6hwACPoEoN2oAIhCHgN5vp3cXDhgwwLbcckv3Hj6t1KsVa7WCrQJClaOPPtq9o5CCAAKVIRDlusL9d2WMbalbSQBYamHqzyrABxAnBgIIpF0grlWJ0+5E/xBIu0CUG7W0m9C/uhHwFrjIdzStEnzPPfdY48aN66ZRHAUBBIoWiHJd4f67aPZUVEAAmIphrLxO8AFUeWNGixFAIJwAAWA4L7ZGIK0CUW7U0mpBv+pW4P3333crH7/wwgum3721mq5WRdZqxt27d3er2O6555512yiOhgACRQtEua5w/100eyoqIABMxTBWXif4AKq8MaPFCCAQToAAMJwXWyOQVoEoN2pptaBfCCCAAALFC0S5rnD/Xbx7GmogAEzDKFZgH/gAqsBBo8kIIBBKgAAwFBcbI5BagSg3aqnFoGMIIIAAAkULRLmucP9dNHsqKiAATMUwVl4n+ACqvDGjxQgkXSBpi24QACb9jKF9CNSNQJQbtbppGUdBAAEEEKhEgSjXFe6/K3Gk428zAWD8ptQYQIAPoABIbIIAAqEEkha4Ja09oTDZGAEEYhOIcqMW28GpCAEEEEAgdQJRrivcf6fuNIjU5iod6QAAIABJREFUIQLASGzsVKwAH0DFCrI/AghkCiQtcEtaezhjEECgPAJRbtTK01KOigACCCBQCQJRrivcf1fCyJa+jQSApTfmCFkE+ADitEAAgbgFkha4Ja09cXtTHwIIBBOIcqMWrGa2QgABBBCoRoEo1xXuv6vxTFm2zwSAnAdlEeADqCzsHBSBVAskLXBLWntSPfh0DoEEC0S5UUtwd2gaAggggECZBaJcV7j/LvOgJeTwBIAJGYhqawYfQNU24vQXgdILJC1wS1p7Sj8CHAEBBLIJRLlRQxIBBBBAAIFcAlGuK9x/cz5JgACQ86AsAnwAlYWdgyKQaoGkBW5Ja0+qB5/OIZBggSg3agnuDk1DAAEEECizQJTrCvffZR60hByeADAhA1FtzeADqNpGnP4iUHqBpAVuSWtP6UeAIyCAQDaBKDdqSCKAAAIIIJBLIMp1hftvzicJEAByHpRFgA+gsrBzUARSLZC0wC1p7Un14NM5BBIsEOVGLcHdoWkIIIAAAmUWiHJd4f67zIOWkMMTACZkIKqtGXwAVduI018ESi+QtMAtae0p/QhwBAQQyCYQ5UYNSQQQQAABBHIJRLmucP/N+SQBAkDOg7II8AFUFnYOikCqBZIWuCWtPakefDqHQIIFotyoJbg7NA0BBBBAoMwCUa4r3H+XedAScngCwIQMRLU1gw+gahtx+otA6QWSFrglrT2lHwGOgAAC2QSi3KghiQACCCCAQC6BKNcV7r85nyRAAMh5UBYBPoDKws5BEUi1QNICt6S1J9WDT+cQSLBAkBu1QY8OsskzJkfuxa19brVOrTvl3b/vmL424/cZkY+RuePOa+1sl/S8JG99YyaNsevfvj7rNpu02sRG9h0ZW3uqpaKXXnrJevTo4br74osv2k477VSr60ceeaSNHj3a2rdvb99++21VsMjg5Zdfth133NHkU5elnMeuy356x6q2/pbDOMgxg1xXMuvh/juIbPq3IQBM/xgnsod8ACVyWGgUAhUtkLTALWntqejBpfEIVLBAkBu1bJ8XYbr8xlFv2Nbtts67S9vhbe37ud+HqTbvtv036m9j9x+bd5vhbw63Ic8OybpN97bd7c2j34ytPdVSEQHgsiNdzlCqnMcuxzlfbf0th3GQYwa5rhAABpGsvm0IAKtvzBPRYwLARAwDjUAgVQJJC9yS1p5UDTadQaCCBILcqBEAVtCAlrmp5QwAkzq7sJyhVDmPXY5Tsdr6Ww7jIMcMcl0hAAwiWX3bEABW35gnoscEgIkYBhqBQKoEkha4Ja09qRpsOoNABQkEuVEjAKygAS1zUwsFgKVsHgFgKXUro24CwGSMU5DrCgFgMsYqaa0gAEzaiFRJewgAq2Sg6SYCdSiQtMBt+m/TbdHiRbUEGtZvaKs1Wa0OVTgUAgiUWyDIjRoBYLlHqXKOTwC47FgRStXd+Yt13VnnO1KQ6woBYDLGKmmtIABM2ohUSXsIAKtkoOkmAnUokLQAsA67zqEQQCDBAkFu1LJ9fjVZvknBhT28bid9EZBJ0ybZb3/8VmuUeAdgtJOWAJAAMNqZE89eBIDxOBZbS5DrCgFgscrp3J8AMJ3jmvheEQAmfohoIAIVJ0AAWHFDRoMRqAqBIDdqaf/8qsv+XXjhhTZs2DB3bi1dutRmz55t11xzjY0bN86mTp1qDRs2tE033dSOPfZYO+igg3Keg2uttZbb/ogjjrBRo0bZe++9ZzfccINbbfaHH36wRYsWufozy/vvv2+33nqrW6H3+++/d9u0bdvWdt55Zzv99NNtvfXWy3vez58/37X3/vvvt6+++soaNWpkG2ywgenx26OPPtpeeeWVWFYBnjt3rmvnE088YZ9++qnNnDnTHatDhw623Xbb2YEHHmjbbruta6vfNF/js3ksWLDAbrvtNhs/frx98skn7jjNmze3zp07O3/5NmjQIK/JxIkT7eqrr7ZXX33VjWebNm1s1113tSFDhtj666/vVkIudhVg1XvTTTfZ448/bp9//rnNmzfPtbNVq1buGLvttpvtt99+ttpqtWfxBz32v/71L7v99tvt448/tj/++MPWXntt23///e20006zZs2aWb169ZzBBRdc4Lz9JfOclumNN95o9957r+nzRWXDDTe0ww8/3I4//vicnjpnn3nmGffPW2+95c4v9VPH79ixo+2+++528sknW8uWLXOOR9D+VsWHexk7GeS6QgBYxgFK8KEJABM8OGluGgFgmkeXviFQHoG6vMEsTw85KgIIVKJAkBu1tH9+1WX//GHJN99844Kir7/+OuupM2DAALvnnnuyBib+ALB79+52yimn2J9//lmrHn/gtWTJEhs6dKhde+21WYNB7aigSyGTwsds5aeffnJB4WeffZb1+7169bIzzjjD9G8VhYwKZPwlyHv6nn/+eRe+/fzzz3l/pLz+RQ0AP/roI9t7771dkJqrdO3a1R577DFr3bp11k0UhspVvpmlSZMm9sADD9gVV1xRVAAo71122cUFu/mKAmAFZP5SKBBT2Keg75FHHsla9brrrmvPPfec6XxTKRQA6hzp3bu3ffjhh1nr22uvvezhhx+25ZZbbpnve+dGvj6uuuqqrq1e+Ju5baH+VuJndCW2Och1JbNf3H9X4kjH32YCwPhNqTGAAB9AAZDYBAEEQglku8Fsv9JmNrjz/aHq8W98+q75Z2pErpgdEUCgagSC3KjVZUBWDvi67J8/rFK4pJl7Ctz69+9vK6+8spuB9Y9//MO+/PJLR6EZWAqZMosXAG600Ub2xRdfWLt27VwQteWWW9rixYvdbLRzzjmnZreTTjrJRowY4f5/hx12cDP21llnHVtxxRVNQZiCQc2AU1HA0rdv31qHVLiooFHtVdGMsxNOOMEd99///rerW8Gd+vTOO++4baIEgNpHdet49evXt8MOO8yFdGuuuaZpZplmAz711FMulNOMMZXp06e7f/72t7+5tq+xxhpuFllm2WSTTWq+pNllXbp0sV9//dXNMJNPt27dXH9++eUXe/TRR+2WW25x7dhqq62c5/LLL1+rSs0a7Nevn/uaxu7ss8+uCTxfeOEFF/xp5pxm5ennbMcddzQ9Ih22qJ1y1/EHDRrkZsKtvvrqLnTUPYtmIKotgwcPDh0AnnjiifbPf/7TNWnjjTd255Cc5syZ4+rU9zSmOoZKoQBwm222sXfffdfN9FPY16JFC3d+XnzxxTXB8c0332zHHXfcMgyHHnqovfnmm7bvvvu6sdCYK5RWQKtz64477nBjrlmPkydPXma2oyokAAx7dpVm+yDXlcwjc/9dmrGotFoJACttxFLSXj6AUjKQdAOBBAn4F90Y+co3rmX16y1vTRuuGrmVBICR6dgRAQT+TyDIjVpdBmTlGJi67F/mbDU9Jpn5qK8ef91+++1dMKeZUvq3P7ySkRcA6r87derkHr3VI6HZimZwKVRT0eOuelQ3syhc23PPPU3BVfv27d3jl/5HXzUz0JtdpsBS4VhmUb0KabwSNgBUG/SIr2a6KZjU47+ZMwi9ur/77jsX1vlLkNmF3vaaQfbGG2/Y5ptvbs8++2zWx0qffvppZ6KgTY8jK3zzioIoPSartir8U3Clx1z9RSGVjqMwTSVKAKhZojJRyTbDzzue9zj5KqusUqsN+QKxDz74wAXG2nfrrbe2CRMmWOPGjWvtr0fTNUPQK4UCQIWU8swcNz1arbB62rRp7vFqndOZRTNhFUp7jxtnfn/SpEmmgFGPBSvsVaiYWQgAs34E1PkXg1xXMhvF/XedD1MiD0gAmMhhSX+j+ABK/xjTQwTKKXDNc/+d2VFsIQAsVpD9EUAgyI3ahG8m2Mz5M2thtWjcwnqu0zMVgOUKAPv06eNmsmUrb7/9tpt5pqLZaXqnmr/4A0CFfwoMc5WePXu6YE/viFOgk6voUVOFNCoKcfR4slc0O0yz7/QorEIpBXSZRcGMApwZM2a4b4UNABWyeTPDNCNRM9rClKABoGbzaRakimZcKkDNVQ444AD3GK+Cp9dff71ms7Fjx5oe0Va56qqr3Pv+shXNAtTMQJUoAaBCSu9xV4VmCs/ClHyBmGZwajaeisK1zJDZO45mOWo2oEqhAFCPgOt9iNnKueeea5dffrn7lt5pqOA0bNF7KnVuqK1qc2YhAAwrWprtg1xXMo/M/XdpxqLSaiUArLQRS0l7+QBKyUDSDQQSKkAAmNCBoVkIVKFAlBu1tDGVKwB88MEHax4hzWaqkEOP5WqRBy384C9eAOg9gptrTDT7TI9h6rHgMWPGuMUz8hU9Xql372mhkvPPP99t+uOPP7rHalX0rsHrr78+ZxWaJajZgiphA0A9dqxAVO/O0yO92ULGfG0PGgCeeeaZLrTL5ppZvzfzUTPbfv/995pZkQoqFVhqtprammthCoWhCk01yy5KADhlyhQXqqoo/Bo+fHioH798gZje76eZnpoFqcVhchW9s0+P5aoUCgD1qPIWW2yRtSr/bELNPtxss83y9mXWrFluURbNDPXe96ifGc2i1cxYfT3zsWwCwFCnR8k2jnJd4f67ZMNRURUTAFbUcKWnsXwApWcs6QkCSRQgAEziqNAmBKpTIMqNWtqkyhUA6t15mY+x+m2POuoou/POO13IpLBDKwR7xQsA9YiqVobNVbT6bK7HaPONo2aHee8M1GxAb2EPrRar9/LlKvq+Vs5VCRsAykK/g2t2ntodtgQNAHv06BHpXXx6fNVbZVczAvXYr8K5XIu4eO3XNgryogSAqkMemrWoohmamsmpMdU7GQuFpLkCMZ1P2lfB2jHHHGMjR47Mye2/LyoUAP72228526RZqJqNqpJr1qpm9emdl3rPoxYUyVf84+FtRwAY9qemNNtHua5w/12asai0WgkAK23EUtJePoBSMpB0A4GECiQhAKzLG96EDgPNQgABM7c4gRY60PveNCOoGktdfh763wGoEGaFFVbISe5/ZFJhiH8lWi8A1MIJd911V846/I+qhhlbhXijRo1yu9x333017ynUe/G8MDBbfVp8Q6vAqoQNAPX+OZnosVsdM2wJGgB6jzOHrV8hnrca7gYbbOAWt1AIpyAwX9H79bSIRtQA8Pvvv3fv4cs8jma/6fgHH3ywW9SlUaNGyzQjVyCm86lNmzZue51nl156ac4uLFy4sKbuQgGgf+XpzAq1AIrC11znxu233+4WD8lczTpXw7799lv3vkp/IQAMe1aXZvso1xXuv0szFpVWKwFgpY1YStrLB1BKBpJuIBCDwKBHB9nkGZNjqOm/Vbx59JtGABgbJxUhgECRAlFu1Io8ZOJ2L1cAqGDFP6svE0ar+GpFYJVcAaA/qMsG6w/vtHCHZq4FKVpM4i9/+YvbVI8OK2RSUcDnLSiSrR4FhFqlViVqAKjHlHXMsCVoAOiFd5tuuqndfffdgQ+jR4a9R07131qpWeGe3tOXryike+uttyIHgF7dWqTjoYcecrMj9T5Gf9imhUKefPJJW2+99Wo1JUgAeN5559kll1ySswta8MQLqksVAOoRd72LUeGfZlnqMe2dd97ZBa5NmzatcdciM94iNv5A1ms8AWDg07mkG0a5rnD/XdIhqZjKCQArZqjS1VA+gNI1nvQGgWIEst0YFlPf0guWEgAWA8i+CCAQq0CUG7VYG5CAysoVAMbxCHChANC/ArBmCmrGYNjin9WXhkeAvRWAFZrpHXhRijerry4eAc7Wvl9++cWef/559x5CPVqronfq6d16/pLvEWBvxd84HwGOOgPQC7vr169vWj1ZIW22onc3KhxUIQCMcubWzT5Rrivcf9fN2CT9KASASR+hlLaPD6CUDizdQiCCAAFgBDR2QQCBihGIcqNWMZ0L2NByBYCFFgHRjCiFIZrVpcdN/cV7BLhQAOhfhEKPV/7zn/8MqPL/N/vhhx9qZgMWWgRE3/dWLA47A3Dvvfe2Rx991L1DTu0u9H67zI4MHDjQPbasx0L1eGiu4l+oRAucrL766qFNjj32WPfePL2fUW1dddVVs9ZR7CIgQRrmuWlbzUr0P8qfb0Zcx44d3fsL41wEJGoAuNdee7l3WWoBES0kkqtoduj9999PABjkxCjjNlGuK9x/l3HAEnRoAsAEDUY1NYUPoGoabfqKQH4BAkDOEAQQSLNAlBu1tHmUKwDUqrePPPJIVs53333Xunbt6r534okn1qys620cNADU9t6CFc2aNXMz3rTSb9iixSc+++wzF5Z988035s0e89ejBSA0I06r4qqEDQBvu+02GzRokNtXC0GcdtppoZqphUtuvvlm10YFe7mKf1ETzTy77LLLQh1HGz/wwAPuXYUqWplXK/RmK1deeaWdddZZ7ltR3wFYqHFalXnw4MFuMz2OrNmJXskXAHorGWtbLb6hVaezlX79+tn48ePdt0r1CLAeG9fj4/lWZtaYatbm/PnzXVuYAVjozCjf96NcV7j/Lt94JenIBIBJGo0qagsfQFU02HQVgQICBICcIgggkGaBKDdqafMoVwAoR81mGjBgQC3SefPmuZVf9Tjncsst5/7duXPnWtuECQD1bjitFqyiehU6Nm/ePOsw6r2ECuL0njX/ohI33HCDnXrqqW4f/wrB/kq8WXHe18IGgFoARLPStOiFZv+p3QrNshX9rt62bdta37roootcQCWz2bNnu3fH5SpbbbWVvf3226ZHTu+9995lxsC/n8IxzSjULDWv6L14GgOFUnpfohboUHjlL3pPn8LXX3/91X05SgD44Ycfun31eG+2ohl3mgH42GOPudmIsvMW99D2+QJAzbRTyKw6FBrqHYOZwa5mqfbv37/m0KUKAHVu6RzT2GnF48x3Vf7+++/Wp08fFyp7hQAwuZ/EUa4r3H8ndzzrsmUEgHWpzbFqBPgA4mRAAAFPIFcA2L1t90hILAISiY2dEECgRAJBbtT8iyFN/M/EmpZE/Rz0d+XAjQ+0wd3/O3spV7lu4nV23yfhV4XNV6c+i70y/bfptmjxolqbN6zf0FZrslrs6v5VgLt06eLCPc3EUsiiGXoff/yxW/jDe+Q31yO3YQJAdUKz6a677jrXH82Q0+PA2223nXt0VTP3NDNQwYsWmZg1a5bNnTvXVlpppZr+a3GGbt261bxjTiv9qo527drZd999ZyNGjDDNrFOfNHtRJWwA6O2jRUZ0PIVzhx12mO27774u7FM4qcUiFAzqUWH9v7/onXi77rqr+5IWLZFdy5YtazZRuOgVPfqq/sycOdN9SeGeZvTp8VkdV7MYNTYK1rSC75AhQ0zvn/MXfzimQPXss892gZsCNa146y3gohmX8o0SAOqRZj3arKBObdQjshq/P/74w82Au/POO03veVRREPjwww/XamOhRTH8swC1OrLer6eZgHPmzHGz/jSuW265pQtLVXT+KgT0F/85HfUR4HfeeceNh4os1Q6dnwqhFVRqRqg+q/T+xtdff91tRwAY+8dTbBUGua5kHoz779j4K7oiAsCKHr7KbTwfQJU7drQcgbgFdOM59deptaptv3L7gjes+drBKsBxjxL1IYBAVIEgN2pxz4T2t/X07qfb8F7D8zb/jGfOsGsmXhO1i1n304JM5Sj+sESP0vbs2dMFGdnKfvvtZ1rFt0GDBst8O2wAqGDm4osvdv8oXMtXmjRp4t5rlzkbTO8C1Mqsme8j9OpScKegrFevXu5LUQJA7adFRw466CAXROYrmWHTkiVLXECkwC5bydxe78uTsd6zWKgMGzbMzj///GU2Uyio4E/HziyaxahHhfUYsFbuLSYALNQ+zZhTKJr5LsJCAaBmMspA79/LVtZee20XMHrh6eWXX+766y9xBICqz5vBmauvOrcUTioQVSEALHRWlO/7Qa4rma3j/rt845WkIxMAJmk0qqgtfABV0WDTVQTKIEAAWAZ0DokAAlkFgtyoEQDGd/JkhiUKuRQiabbV1KlTbfnll7dNN93U9DjtIYcckvPAYQNAryKFJnpPnlaOVQCpx1MVVGkmnxaDUIinGXe5Hp/V+9f0zjsFk5pFt8IKK7gVWw8//HA3k/GVV16xHj16uMNFDQC1rx7h1YIlCqYUOOr/FUwqiNJjzLLRbMPMopmLV1xxhZu5p/ZpdqMX/GWbnbZ48WIX0mk2n2ahKfjU1xSk6ZFezUKTh2be5Sp6797VV19tr732mvPUDD0Fu0OHDrUNN9ww72O4hc4szXLUWCmEU/v0iO+0adNciLvaaqu5dmnmohbH0OOzmaVQAKjt5TJ69Gi7/fbb3bsANbtQC6mo3+qDHi3WY84qGhPN/PSXuAJA1anZnZqpqr5q7NRHzQzUMTW705sRqW0JAAudPeX7fpDrSmbruP8u33gl6cgEgEkajSpqCx9AVTTYdBWBMggQAJYBnUMigEBWgbA3avWG1YtVsppnAOZ7XDJWZCpDoAgBBZvbb7+9q0GPWSvcpCCQTyDsdUV1cf/NOSUBAkDOg7II8AFUFnYOikDVCBAAVs1Q01EEEi8Q9kaNALC4IQ06W6q4o7A3AvEJnHTSSe5dgJqdqncj5lpAJr4jUlOlC4S9rhAAVvqIx9d+AsD4LKkphAABYAgsNkUAgdACBIChydgBAQRKJBD2Rk2PA8dZkrAISJz9KVQXAWAhIb5flwI///yze8dkrlBP72PUCtJ6LFrvChw3blxdNo9jVahA2OsKAWCFDnQJmk0AWAJUqiwsQABY2IgtEEAgugABYHQ79kQAgXgFotyoxduC6qqNALC6xjvpvdVqxVo9eP/997dddtnFOnTo4N4lqPdRalGRu+++24V/WhDmww8/tPXWWy/pXaJ9CRCIcl3h/jsBA5eAJhAAJmAQqrEJfABV46jTZwTqToAAsO6sORICCOQXiHKjhml0AQLA6HbsGb+AAkBv0ZZctTdr1szGjh3rFoihIBBEIMp1hfvvILLp34YAMP1jnMge8gGUyGGhUQikRoAAMDVDSUcQqHiBKDdqFd/pMnaAALCM+Bx6GYF58+a5FZCffvpp++ijj9wqyFpxWaGfVlzu3bu3nXzyydaqVSv0EAgsEOW6wv13YN5Ub0gAmOrhTW7n+ABK7tjQMgTSIEAAmIZRpA8IpEMgyo1aOnpOLxBAAAEESiEQ5brC/XcpRqLy6iQArLwxS0WL+QBKxTDSCQRiEbhu4nU29deptepqv3J7G9x9cOT6CQAj07EjAgjELBDlRi3mJlAdAggggECKBKJcV7j/TtEJUERXCACLwGPX6AJ8AEW3Y08E0iagFS8n/mdirW51b9vd3jz6zchdJQCMTMeOCCAQs0CUG7WYm0B1CCCAAAIpEohyXeH+O0UnQBFdIQAsAo9dowvwARTdjj0RSJsAAWDaRpT+IICAXyDKjRqCCCCAAAII5BKIcl3h/pvzSQIEgJwHZRHgA6gs7BwUgUQKEAAmclhoFAIIxCQQ5UYtpkNTDQIIIIBACgWiXFe4/07hiRChSwSAEdDYpXgBPoCKN6QGBNIikNYAsBTvNkzLmNMPBKpJIMqNWjX50FcEEEAAgXACUa4r3H+HM07r1gSAaR3ZhPeLD6CEDxDNQ6AOBdIaANYhIYdCAIEEC0S5UUtwd2gaAggggECZBaJcV7j/LvOgJeTwBIAJGYhqawYfQNU24vQXgdwCBICcHQggkGaBKDdqafagbwgggAACxQlEua5w/12ceVr2JgBMy0hWWD/4AKqwAaO5CJRQgACwhLhUjQACZReIcqNW9kbTAAQQQACBxApEua5w/53Y4azThhEA1ik3B/ME+ADiXEAAAU+AAJBzAQEE0iwQ5UYtzR70DQEEEECgOIEo1xXuv4szT8veBIBpGckK6wcfQBU2YDQXgRIKEACWEJeqEUCg7AJRbtTK3mgagAACCCCQWIEo1xXuvxM7nHXaMALAOuXmYJ4AH0CcCwgg4AkQAHIuIIBAmgWi3Kil2YO+IYAAAggUJxDlusL9d3HmadmbADAtI1lh/eADqMIGjOYiUEIBAsAS4lI1AgiUXSDKjVrZG00DEEAAAQQSKxDlusL9d2KHs04bRgBYp9wczBPgA4hzAQEEPAECQM4FBBBIs0CgG7VBg8wmTy6O4dZbzTp1yl9H375mM2YUdxz/3jvvbHbJJfnrGzPG7Prrl91mk03MRo6Mry3UhAACCFSJQKDrSoYF999VcnIU6CYBIOdBWQT4ACoLOwdFIJECaQ0Ar5t4nU39dWot8/Yrt7fB3QcnchxoFAIIlEYg0I3a1lubTZxYXAPeeMNM9eQrbduaff99ccfx792/v9nYsfnrGz7cbMiQZbfp3t3szTfja0uV1PTSSy9Zjx49XG9ffPFF22mnnWr1/Mgjj7TRo0db+/bt7dtvv60KFRm8/PLLtuOOO5p8wpZRo0bZwIED3W5TpkyxtdZaK2wVidj+wgsvtGHDhrm2LF26NBFtohHZBfSzufbaa7tv3nnnnaaf2zAl0HUlo0Luv8MIp3dbAsD0jm2ie8YHUKKHh8YhUKcCaQ0AS9GvOh0YDoYAArEIBLpRIwCMxboaKiEAXHaUCQD/a0IAWDmfAASAlTNWaWspAWDaRrRC+kMAWCEDRTMRqAOBUgRl1zz3ZSwtP33X9SLXU4p+RW4MOyKAQNkECACZARjnyVfOADCpswsJAAkA4/wZq4u6CADrQpljZBMgAOS8KIsAAWBZ2DkoAokUKEVQRgCYyKGmUQhUpQABIAFgnCd+oQAwzmNl1kUAWErd4utmBmDxhnVVAwFgXUlznEyBVAeA06dPt7ffftv9884777h/fvnlF2dwxBFHmN73UKj8/vvv9vTTT9tzzz1n7777rn311Vc2b948a9asma233nrWq1cvO/7442311VcvVJX7vuq78cYbbezYsfb111/bwoULrV27drbnnnvaqaee6t7XEaRMnTrVrr+xPIIoAAAgAElEQVT+enviiSfsu+++sxVWWME6dOhgAwYMsJNOOslWXHHFINXYG2+8YSNGjLBXX33Vpk2bZs2bN7dNN93UvYfgoIMOClRHlI0IAKOosQ8C6RQgAEznuNIrBBD4r0DkALBJk8KLeviRk7wIyMEHL3s68A7ASD8iBIDLsjED8L8mBICRfqTKshMBYFnYOaiZpToArFevXs5BDhIAfvzxx7btttu6wC9fURg4cuRIF77lKwoP99hjD/eLYLaieu655x7r06dP3noee+wxO/TQQ23OnDlZt1MwqWCwY8eOeevRReLiiy+2JUuWZN1OoeS4ceOsUaNGsf+wEADGTkqFCFSsAAFgxQ4dDUcAgQACkQPANAVk2d5xmKb+BTgP4tqEAJAAMNe5RAAY109Z6eshACy9MUfILlA1AaBm2W244Yb27LPPOokgAeBrr71m22+/vdteQaCCuS5dutiqq65qM2bMsIceeshuu+02W7x4sdWvX98UzO2+++5ZpefOnev2/fLL/76XatCgQXbggQda48aN3Qpel112mQsaNXPv9ddft8022yxrPR988IFry/z5822llVayc889160Epv+/7777XBCpohBQMxabNm2atZ5bbrnFzVxU0czB8847zzp16mQ//PCDXXfdda5NKpoFeO+998b+80MAGDspFSJQsQIEgBU7dDQcAQQCCBAA2n9XJ85c5bhEAWBmCDJ79my75ppr3B+19QRNw4YN3dMuxx57bN6nXbQSrLb37hnee+89u+GGG9xqs/p9edGiRVlXWn3//fft1ltvdb9Lf//9926btm3b2s4772ynn366+x09X9Hv9Grv/fff75480h/iN9hgA/d0ztFHH22vvPJKLKsA695E7dSkgU8//dRmzpzpjqX7gu22287dp+ieQ8Vvmq/t2VaeXbBggbtfGj9+vH3yySfuOHriqHPnzs5fvg0aNMhrMnHiRLv66qvdE0sazzZt2tiuu+5qQ4YMsfXXX9+thBznKsCqX2OteyA9saV+6T5SbT3uuOPcfV+2okkVCmiffPJJe/PNN+2LL76wX3/91Zo0aeJWFt5ll13cE19rrrlmwU8O1aVzQOetnmLTvaeK7mm7du1q++67r+299962/PLL19QVJAD88MMP3RNselJOT7A988wzbiz8RU/M/eMf/7BHHnnE/v3vf7sn37TNiSee6I5baNXkKD876q+89Y9+hnSerLzyyrbJJpvY/vvvb8ccc4z72c1Wgj6aHrbdGr+rrrrKPQn4448/uvN2m222sbPPPtu66/MrT1E+oPttrcj92WefmSYm6Wfr4IMPtlNOOcXVxyrABX8M2KAEAqkOAC+44AL3Aal/Wrdubf6kPUgAqMdjFYapno022igrvz4Y9UGoC4N+qPVLXraZh+eff76bbadyxRVX2JlnnlmrPl0kdthhB/vzzz/zLmGv5e114deFUv/eWr9Q+cqVV15pZ511lvuK2q0LQWaZNWuW+8DRBUkXIP1C07Jly5rN9IGlPinQVNEvMLqwxlkIAOPUpC4EKluAALCyx4/WI4BAfgECwPIFgN98840LihTiZCt6ekdP32QLoPwhhm72ddOu39P9xR94KcAYOnSoXXvttVmDQe2n49x0000ufMxWfvrpJxcUKjDIVhTcnHHGGS7AyfU7epAw5Pnnn3fh288//5z35PX6FzUA/Oijj1xIpSA1V9F9mu45dK+WrSgMlWu2J5YUrD3wwAPu3iquAFDhkyZq6P4oW9H9mkJTTcTILEGcNNnj7rvvdvdauYruWfV9hXX5SuY9WqEAUAHqXnvt5e4BdX7rPND9q79MmjTJ/czo1VDZis5d3X8OHDjQfXvKlCmuLn8J+7OjsK9v375uEkyuogD2qaeeyvq6rCDnvOoNEwDqvNUTd3p9V2ZRAKzPjQMOOCBrczWpR0/9yTtb2WKLLVworn+r3HnnnS7gD1MCXVcyKuT+O4xwerdNdQCYOWxhA8Cgw96/f3978MEH3ea6WHg/zN7+f/zxh7Vq1cp92OrDa/LkybbccsstU71m5OkvBSp6b6EuiP6ir2211VbuS/rr080337xMHbo46i8l+sVBf6XQX3f8fxnSDrpI6i8XKmPGjHF/4css+oDQh7fCQH2A6UIXZ+EDKE5N6kIAgUwBFgHhnEAAgaQIBLpRmzDBbObM2k1u0cKsZ8+kdKO4dpRpBqB+l9bv5got9Pu6ZhTpFT+a3eQ9lXPaaae5GXeZxQsxNAlAM4E080pB1JZbbul+P9bN/TnnnFOzm97Brfdqqygk0g39Ouus457uURCmYFAz4FQ0gUCBh78oXFTQ6AVPu+22m51wwgnuuJqFpboV2KhPmhGmku2P9IXCEO2junU8BRmHHXaYC+k0KUCz9TQbUEGLQjnNclTR/YT++dvf/ubavsYaa7iZY5lF9yBe0exFPf2k+x/NIJNPt27dXH80w+zRRx919z1qh+5v5Jl5z6JZg/369XNVaux0/+JNSnjhhRfcPY0mXqy22mpuEoYmSmgGXtjiD4Y8X4U7mjCiunWu6Bzx3PfZZx83ozGzyOeOO+5w4Z1CMo2/ZlXqfe3ee9cVDulrChp1X5hZFLzpXlKzTFUUCKsdmgWqvipwU9/1Pnndf/onaeQLADUrUT8DmmGqc1qz2jSO/qLZlRtvvHHNsRWAHXLIIe4+VuOpiTGatKLxeuutt9yu+QLAID87+lnSE3eqV0VjePLJJ7vJKjKQ58MPP+y+p7BSoWhm+FronPf6GDQAlL9+DjQTVLNMdR4rDNc5f/nll7ufE53TMpFNZtH5oZ8TFZ3zmvm77rrrulBVbdDY+X+OCQDD/sSyfTECBIDF6P3fvvpLnj6oVPRXKE1T9hc9duz9pU4fGl74lnloTW/3ZvTp0d5LL7201iZ6TFePCqtoWy8MzKxHx9D+Kvqg0kXeXzR1WR+y+uDSdPJc06l79+7t9tcCI9ou1+PEUQgJAKOosQ8CCAQVIAAMKsV2CCBQaoFAAWCpG1Hu+ssUAKrbeqQwc2E7Pf6q0EHBnP4or3/7wyvt5wWA+m+9JkdP3uiP69mKwhTv923N7NGjuplFoYHer63wRov+KTzwzzz0308osPQmBfjrUb0KRLwSNgBUGxSiKFhRMKk/8Od6ykehlcI6fwkatGgfPT6s0GvzzTd3r2DyP23k1amFFmWiCQx6HFkz77yi8NELgRT+6d4lMzDTpAodx3svehwBoI6vezDvXsprj4JKvQ7KCz5lp0kS/qLJJn/5y1+WCTK9bXT/o5BXj4YrXLvrrruWOU8UeHrhooJq78muzA0VJGqSySqrrFLzrVwBoCZ8KETU9gqkFAbqlVaZRUGVgmoV/Xvw4MG1NlFYt99++9WEW/pmvgAwyM+O/7w//PDDXUCW+TTdX//615r7YnnIJcp5GTQAVN0K+idMmOCCZ3/RzD+Nncrw4cNduOcvOi+89/nr/FAQmDnD+KKLLnJP6nmFAHCZU5EvlFCAADAGXP3w668DKvpLjPeXKq9q/+O/unjlemeALiz6xeK3335zfznUVHZ/0df01zFNd9dfaHK9L0PHUMinomMPGzasphpdTLW/jqVQUhfeXEVho0JHFf2yoncNxlUIAOOSpB4EEMgmQADIeYEAAkkRIAAs3yPAuhH3XmmTeT74n6zR7LQbb7yx1ib+AFDhn/de8GznVc+ePd3vygpH9M62XEVP6HivFVIopkctvaKZV5p1pEdh9eiyArrMotBHs8q898GFDQAVsukpIpVsAU+hn5mgAaDuV3TfoqIZlwpQcxXNtNMECt27+B8B1Swpb4FFvYfNu9fKrMf/ZFMcAaDedad3rmd7Wkv3L/JXkKbg8vHHHy9Etsz3NYtOs041EUP3c/6wSzNNFXJqtplmZXoz34IeJFsAqJmjmqiiOnWeqs5sjy8vXLjQvRNQbdLsNP18ZCuaxaafDYXJKoUCwEI/O/p50M+FZtLpUf1sE04UPCqg//zzz13gqffnaYKKV4Kel2ECQP1RIPPdiDqe905Pheia6ak1AfxF54UCVrVPP8eZsyy1rQJvvYdUAbYKAWDQM5zt4hAgAIxBUR/Qmsauogt35l+n/I8I6/17uf56qP31YaALpT4ENdXeX/Q1vatD2+R7J4SO0UKPjZi52Yi6qHpFHzTeRVh/1fH+ypONwT/tXn+d0Ytf4yoEgHFJUg8CCGQTIADkvEAAgaQIEACWLwDM9od5/3mhUEGP5WohCYUL/uIFgN4juLnOJ80+0+/dCilyvVon2+/z+gO9/lCvokDDCwr0rsHrr78+5+mrMEe/l6uEDQD12LECUU0G0H1GtpAx389N0KBF7zpXaJfNNbN+bwaYHv/V+9a8CQ4KKhVYKiBTW7PNIFRdCkMVmiqYiSMA1GIjes9irqJQWbO8ZKjHm3MtCKL9dW7ocWf1y3ufoiZqeO+AVOClQNEr/kklmgjihahBP8syA8C///3v9j//8z9ud4VVOj/9wZm/XoWvWvxFRSGlFivJVfyPuOYLAAv97ChE04xJlWwhvP/4/qBXM0v978EPel4GDQB1r6z78ULnQOY9uT4DFOxqvPWuRS8fyFaPfj68NQEIAIOe4WwXhwABYJGK+uuA9y6QXB8WmvGn9yToQqG/3OUr3kVF2+gvK96HtP5bKwarBPmLk/6yo5mEOrb3TgXtqxl/3krFWjBE7zLJVbSKsPceQr3jxHv8OAiZAr58Rb/oaAq6ih4x0OpoFAQQQCAuAQLAuCSpBwEEihUgACxfAKh352U+xuofz6OOOsrNvlHI9P/Yuw8wK6r7/+NfioiAjaYoYlBBRVRAhcUGEY2iiMagiP6iP3+IsaOiEGNLoqKxIdYoYjQWUIy9EcUYNFmKArFD1EiMgIBgoShF/s/n+L+bu7u3zL135k6573keH2TvzJlzXmd2lvPZM3P0b+301+KkAsB8/+5WUFPMYnl6v1/qnYHprwv64x//6N7Ll23T53qcU1uhAaAs9G/0TE8aebnOvQYtemqomHfxaXaZ3rmnLfXKIgVk2RZxSdVZ+yiI8iMAzDdjLf3xTb0bUO92S9+04InCHQWtuRY/0TEaH6bGQ/q7+l0LhCgM1Zgx22uasvVVegCoWYapiR7qNz2anius1CPneh+9ttdff71mBehM50oPFnMFgPm+dwq57tO/z+o+Lu71uvQaANadQFPXQI8q6/HtutemrgeF3tp0naTC10yGus50vWojAPRy92EfvwQIAEuQ1FRp/aZEQZk2pfxK++tu6VP6tbpXri01DV77aLZf6v0M+u1W6gei9pk4cWLOcvSbMP22TL/Z1GpOqS19Ov2dd95Zc6PPVFj6Ywr6beOtt97qWSvTSsjZDiYA9MzKjggg4FGAANAjFLshgEDgAgSA4QWA6b9Mz9TRes+b3p2tTf9GT1+JNhUAZntXW6Z/WxdyMSnEUyChTf+uT72nUL+sT707PFN5eged3tOtrdAAUJMJZOJlLJHp3F6DltTYpxAP7ZseJmnRCz0SW3cyQ6YyNRtM70f3IwDUTNBUiJPpXOlBWd13smvxFD35lWnl2ExlKSRNhUD6XJM01P+6DvONGTOVl2kVYo0FU++6zNUf6e+Ql3vnzp2z7p5ukCsAzPe9U8h1rzrpmtBW9536Xq9LrwFg+vdmId8H6e/z12KdqcftM5WR3h4CwELvFOxfigABYAl6elGtfpuiLdeNQi/b1TsA8k2DVjmp3yjo/9ODMf2/VufSpt8O6bd/uTbtq2N0br1kOLXptxU6h7bx48ebfvOZbVOdU0vD66XDqbZ6ISMA9KLEPgggEJQAAWBQspSLAAKFChAAhhcA6pf1uWZR6QmX1IIC2QLAfGFAeoihYCT1Hu5814neZZZ6/FGPZp5wwgnukEwL+KWXlf40T7EB4PHHH+8eBy108xq0pMI7PSKpGW1eNwVvqZWA9f+aUaVwT4985tpST1v5EQDmC78U7Gj2prb0AFCP+u60007uHXp6EktPWSnI1VhKC0mkrkO9K1Lv4tNWt/9SAaDexaenpQrd0gNAvY9Sj8Br8zKRIz0AzDSzMb0uXgPAQr538l33CmZTr9mKagCY/h5+GaUe9c7Uj+ntIQAs9Epn/1IECACL1EtfIEOPyeoGrkd8M22VOAOQR4CLvLA4DAEEfBHwKwAspTJj3xps81fMqVVEVfsqqx5aXUqxHIsAAjETIAAMLwD04xHgfCFG+grA+kV7aoXQQi7T9Fl9SXgEOLUCcN2JCIWYpGb1xeUR4PRgUNfEwQcfnLG5WiRGj5hmCgD9fARYCz9qEZXUQiJ6JHjMmDFZuyCIR4Dzfe/49Qhw6lH+fJNt0lcczjVzMV+9swXh6bP6eAS4kO929i2nAAFgEdrpN0j9hksrXWV7Ma2Kr8R3AOZjZRGQfEJ8jkDlCCxeudjWrF9Tq8FNGjWxts1/eA9QMRsBYDFqHIMAAkEIEACGFwDmWwRE7+/WAnl63FGD9/Qt9QhwvjAgfREKvUNNr9gpdEtfDCHfIiD6PLVicaEzAFMLF2rxD9W70EVATjnlFPfY8vbbb2+ffPJJ1mamL1SimWya0VboptlT48aNc+9nVF1Tr0WqW065FwHR6560+q/stMhH6r16qX7RgjCaDZhtS591Wrf/FNClFiCp+3iwF7+6i4AoBNRMwNRqxZqVqHfAZ9r03r/UStd+LQKS73unkEVAVO+RI0e6qtddBEQLW2rhHK0grD7JtqX20+dBBIDr1q1zsz29LAKixWZS7+JnBqCXq5t9/BIgACxQUtPl9Zs9Ld+tH366WeZbwIJVgOsjEwAWeOGxOwIJFug9vrdN+8+0Wi0sdaYcAWCCLxiahkDMBDwFgMOGmb3zTu2Wde1qNm5czFqbpbq9e+t5ydofVlWZVfs/Izo9BNGqt0899VTGSqUvdnfmmWfWrKyb2tlrAKj9UwtWaAVQvXqnTZs2Bfdbly5dTO/fVlim1/CkFv9LL0gL/GlGnN7zra3QAFCv89ErjLQpbNKssEI2PfqqmW75HlFNn9lV6EKCqfo8+uij7l2F2rQ67vnnn5+xqunBkB+PAHfr1s1mzZrlgse622effeb8FawdfvjhbjXg1JayUTD4zTffWMOGDesdr2BIi4Yo+MrUf3r0VpNLtGKwwtrU7D2vfVQ3ANRxqqtWAH7++eddMaNGjap572V6uXo3pPpVKxvr6bYZM2ZkPK0WatH3hvbXVkqQpuNT172+Z3Td6/HpuptW191jjz3svffeMz06r1A5fTVjLXaSuj6yvcNRDuo79WGp9c71KLyuC70LUvWTTbt27eq1RzmCrrPUe/oJAL1e4eznhwABYAGKWuRDv0VRuq9vZs38S70jL1cxl19+uV155ZVuF70bQDMCM20qd4sttnCr92ZanUtf0zn1qLHeL9G4ceOM5aS/f0Dn/s1vflOzn25++sGkG6neS6H3iGTb0h9z1vsqtKKXXxsBoF+SlINA/AUIAOPfh7QAAQSyC3gKAMsYkIXSV2VsX92FEB555BH3KGT6phVW9e/q2bNnu6BGfypgSN8KCQAVrmjFU20qV6Gj/k2fadN7CRXE6f3aTZs2rdlFi+2de+657u/pKwSnl5GaFZf6WqEBoEIbvadOIYjGA6p3+iIU6efSv9XrTnJIrYArM41FNOMq29arVy8XImmG3MMPP1yvD9KPUxCiGYXpiylqzKI+UNij0Efjm7qLcygQUviq0EqbHwGgytF7IVOzzVL11DhNgbLCHW1a5XfAgAE1zVBIOWLECPd3TRjRexbTN4299Ihv+rsXM/WfxpqPP/64O/S6666ziy66KCOxxosykk1qyxQA6jNdcwoU9ai5tksuucS0km/dTddfatFHhWqaMZe+Kbg65phjaoXqpQaA6Y/laobpvffeW69e6WNp9UvqvZ2pHfX926NHD/dXBdxaJbjulj4rVZ+VUu9cAaCuC10n2nQ9P/HEE/VWX7766qvt0ksvrakiAWDGS5wvBiRAAOgRdsqUKe4Hu26gmoKupcj1bj8vW/pvweq+tDT9+PSVg7Qq2ejRo2sV/6tf/coUymmru+pU+o7pL3HN9ELV9N9Satp8tpcja4UxHa/fYGi/XD/kvTik70MAWKgY+yOQXAECwOT2LS1DAAGzogNA4WX5pXFBrgoi6gzk6x0/dqyWoi2o2Lw7p8/u04y1NbVf9WBNmpi1Lf5VD9nOnx6C7L333i7c02qceiJHM/TeeustFyCkHvnN9shtIQGg6qLZdHp0UptmUulx4P3339+NGxTWaGagfpGvcGf58uVullj6bCcFTD179nT11aZ/h6sMvddMC/vdcccdpjGF2qTZi9oKDQBTx/zkJz9xExoUzimU0gwxhX0a52gGlYJBTXzQ39O3l19+2Q455BD3JS1aIrv01yApXExtH330kWvPsmXL3JcUhmhGn2bA6byaxai2KjDRuEbh2Q033FDrfHqEW/2mTYGqZq/17dvXzZDTI7KpIEizx+TrRwCY8tWqzFo4sW3btu57WAFfalac2iKf9E1jG7VfZgp2FZ7JSo+Evvvuuy5Ye/PNN03vR/zb3/6Wtf80w05hVmqW4EEHHWQKnDQzULMSFZSq37X4jHzkkdqyBYD6XOGvgim9n1Bb3Uki+pr6SuPb1ArEujZOPPFEN6NVvrq+9fit+jVlofroqbj0rZDvHQWjevRYAa82tVczcjt27OjCXwWCqUBUE2/mzJmTcZZg6p2RKkOPHus/2avvFAhqMovGwKkFZYIKAHV+Oeu61qYgXLMTdd3rmtcj9PqlRPr3MQFgrcuHvwQsQADoAVg3Cv2g1A9v/cNBN5C99trLw5E/7KLfzuiHh347pdWL9EMg07Ry/ZDX+wW16aaq6dfpm76mm4g2/UNGU/DrbvrNjJZ71yME+kGpG01qNa3Uvvptkn6Aasv0Gyp9XT/EdPPWTbnuFHfPDc+xIwGgH4qUgUAyBAgAk9GPtAIBBDILlBQA+oGqRydvuil3SRdcoGdC/Tjbf8vYsMHf8jyWlh6C6JFCrbiqwX6mTbOtFKRkeqqmkBBDZSuU0hM/+k/hWq5NT/Pol+t1H/NV6KMApO77CFNlaTyioExP8WgrJgDUcfoFvwIuBZG5NrUpfdM4QwGWArtMW9399UirjPWexXybnlhSKFV3UyiocYvOXXfTLEY9KqzHgDU5w48AUI//anZmKoite061X7MAM02MUJBz6qmnZqyrylEAqhlqqQVCMvWf9tN1qxl7+dzqHp8rAFS5q1evdrMWNZbVlmmhin/84x8uuNT1mWlTGKnATkba6q6era8V+r2j4FGhWSoYzXRejaHlXjdsTO2r4Fr9n3o8vm4Z+r7RGFmzDLUFGQAq3NeKztna0717dzcLOJUnEADmuzvwuZ8CBIB5NPVbBj36qmnu+mGtH5i68Re6pU9dzjSdW7/10CMD+gdDrh9eqceA9Q+VqVOnmn7bkb6lvwfjiiuuMP0gqLvpJqt3ICiQ1E1Uv41Kf7GuQj/9JjD1mwu/H/9VfQgAC72C2B+B5AokNQBM77HzD+mc3A6kZQggkFOAALC8F0jdEEQhl0IkPYo3f/5894vxPffc0/Q4rWY3ZdsKDTFS5ShY0C/p9e9nBTn697aCKs3k08BfIZ7+nZ3tyRqFNJptpmBSs+j0JI5mf2k2miYA6N//qdfyFBsAqq4a22jBEi0QocAxNdbRLDaNN2SjWUp1N4UbGstonKD6aYJEKvirGwDqWI0rFNJpttrMmTNdsKSvaeyhR3o1S1IeqUc4M/WHJmNo0QS9e12emmGpYFeLKCgY0iw4vwJA9Z/K1yOwmqmlNqpdOo/6QI9npxb+yFZXjccU/shUMyR1vSl40qPomrmYq/9SZcrowQcftEmTJrl3Ei5dutQF1bqO9DopBat6Oi29LvkCQJWt9xBqcoe8tOmJMz15lr7pXHqiTLMctZK2rlUtmKPwUsFx+jv3dL2mP8qucor53lHAq0fFH3roIRe+aryqiTc6r2aB6tzZnlpL1V2PtuvxWs1g1exBzQBUyKaZqmqzZt+VIwBUfTSm131Aq3prYo4m/2gGo0JgzRZWcKpZjtoIAMv7M6LSz5boAFA/JDRdObXpZpZ6j4JCPP2GJn3TbzTSN93wNVU49ZsEvSw325LuqeM000//ZfphqR+i+k2YNv2jQ++G0G/+9MNbN1+9j0R/1w85vRg006Ybouqum60eG9Bjwfohor/rHwqpdx5oNTM9HpDtHxfpKxnrZqR3QegGq9886qauOmnTTV43Y783AkC/RSkPgfgKEADGt++oOQII5BfwFACmF5Nh8YH8Z8mxRwXPAMwUSJVkycEIIODG0OPHj3ePjevxdLbyCxT8c4UJOOXvpIieMdEBYOoFnV7t6/4jIf23BF7LyDbrTscrjNRvH/QNm2nTbzn0W4/0F8pm2k+/cdNKxNmWOVf4p1Wp0t/Dkakc1VWPKWT7x5Hqqt/W1f2tjleLXPsRAPqhSBkIJEOAADAZ/UgrEEAgs0DBAzUCwJIuJS+zoEo6AQcjUMECmnTSoUMHNyNRM/M0Q5Gt/AIF/1whACx/J0X0jASAaR0TdACoU2mavFY70s1SgaDeD6ip3Arb9LLYbO81qHv96BEGvYhVQZ/CNE2JVuB37LHHmlY50qMGXjbNNlR99FJivXRW7w1MTVPX7L+gNgLAoGQpF4H4CRAAxq/PqDECCHgXKHigVuf1Lt7PlGXPKCwCUnIjvBdAAOjdij0RqCugJ+D0qqhM76vXY8l699/999/vDqu7EjKa5RMo+OcKAWD5OifiZ0p0ABhx+4quHgFgRXc/jUeglgABIBcEAggkWaCYgVqSPYJuGwFg0MKUn2QBPUGnhSf1qiotPqlXW2nWn1bPHjdunHsfoTa9g1ErCmcKCpPsE5W2FfNzhUMfgFAAACAASURBVPF3VHov3HoQAIbrX7Fn5wZUsV1PwxGoJ0AAyEWBAAJJFihmoJZkj6DbRgAYtDDlJ1nAyyu09D76p556qtYikkk2iWLbivm5wvg7ij1Z/joRAJbfnDMyBZlrAAEE0gQIALkcEEAgyQLFDNSS7BF02wgAgxam/CQLaEVovQP+5Zdftk8++cSt2rx27VoX9mlBS61iq9mBDRs2TDJD5NtWzM8VAsDId2tZKkgAWBZmTlJXgBsQ1wQCCKQECAC5FhBAIMkCxQzUkuxB2xBAAAEEShMo5ucK4+/SzJNyNAFgUnoyZu3gBhSzDqO6CAQokNQA8Js1X9j6DWud3LADd3B/NmnUxNo2bxugJkUjgEDUBIoZqEWtDdQHAQQQQCA6AsX8XGH8HZ3+C7MmBIBh6lfwubkBVXDn03QE6ggkNQAc+9Zgm79iTq3WVrWvsuqh1VwDCCBQQQLFDNQqiIemIoAAAggUKFDMzxXG3wUiJ3R3AsCEdmzUm8UNKOo9RP0QKJ8AAWD5rDkTAgiUX6CYgVr5a8kZEUAAAQTiIlDMzxXG33Hp3WDrSQAYrC+lZxHgBsSlgQACKQECQK4FBBBIskAxA7Uke9A2BBBAAIHSBIr5ucL4uzTzpBxNAJiUnoxZO7gBxazDqC4CAQoQAAaIS9EIIBC6QDEDtdArTQUQQAABBCIrUMzPFcbfke3OslaMALCs3JwsJcANiGsBAQRSAgSAXAsIIJBkgY8//ti+++47a9Cgge28887uTzYEEEAAAQSKEdiwYYPNnTvX9GeTJk1sxx139FQM429PTInfiQAw8V0czQZyA4pmv1ArBMIQIAAMQ51zIoBAuQT0b55vvvnGna5Dhw7WvHnzcp2a8yCAAAIIJEzg22+/tX/961+uVS1atLDtttvOUwsZf3tiSvxOBICJ7+JoNpAbUDT7hVohEIYAAWAY6pwTAQTKJfD111/bZ599VjNYa9++PbMAy4XPeRBAAIGECSxevNi++OIL16qtttrKWrZs6amFjL89MSV+JwLAxHdxNBvIDSia/UKtEAhDYMLbE2zhioW1Tt2uRTsbsvuQoqsz5qV5RR/r14Fj3xps81fMqVVcVfsqqx5a7dcpKAcBBGIg8P3339u8efPc41raNGNDA7ZmzZoRBMag/6giAgggEAWB9evX25dffmkKAFObHv/VY8BeNsbfXpSSvw8BYPL7OJIt5AYUyW6hUggkRoAAMDFdSUMQSISAHgHWLMBUCKhG6V2AjRo1SkT7aAQCCCCAQHAC+tmhADB9a9OmjbVu3drzSRl/e6ZK9I4EgInu3ug2jhtQdPuGmiGQBAECwCT0Im1AIFkCmULAZLWQ1iCAAAIIlENg8803t3bt2hU0i5zxdzl6JvrnIACMfh8lsobcgBLZrTQKgcgIEABGpiuoCAIIpAnoceAVK1aY3gu4Zs2aejM6wEIAAQQQQCCTgGaM69URW2yxhTVt2rRgJMbfBZMl8gACwER2a/QbxQ0o+n1EDRGIswABYJx7j7ojgAACCCCAAAII+CnA+NtPzfiWRQAY376Ldc25AcW6+6g8ApEXIACMfBdRQQQQQAABBBBAAIEyCTD+LhN0xE9DABjxDkpq9bgBJbVnaRcC0RAgAIxGP1ALBBBAAAEEEEAAgfAFGH+H3wdRqAEBYBR6oQLrwA2oAjudJiNQRgECwDJicyoEEEAAAQQQQACBSAsw/o5095StcgSAZaPmROkC3IC4HhBAIEgBAsAgdSkbAQQQQAABBBBAIE4CjL/j1FvB1ZUAMDhbSs4hwA2IywMBBFICl0y5xOYtm1cLpHPLznZ1v6uLRiIALJqOAxFAAAEEEEAAAQQSJsD4O2EdWmRzCACLhOOw0gS4AZXmx9EIJEmg9/jeNu0/02o1qap9lVUPrS66mQSARdNxIAIIIIAAAggggEDCBBh/J6xDi2wOAWCRcBxWmgA3oNL8OBqBJAkQACapN2kLAggggAACCCCAQNQEGH9HrUfCqQ8BYDjuFX9WbkAVfwkAgECNAAEgFwMCCCCAAAIIIIAAAsEJMP4OzjZOJRMAxqm3ElRXbkAJ6kyagkCJAkkNAGcteda+XrvE6fTp3Mb92a5FOxuy+5ASxTgcAQQQQAABBBBAAAHvAoy/vVsleU8CwCT3boTbxg0owp1D1RAos0BSA8B0xvMP6VxmVU6HAAIIIIAAAggggMAPAoy/uRIkQADIdRCKADegUNg5KQKRFCAAjGS3UCkEEEAAAQQQQACBhAgw/k5IR5bYDALAEgE5vDgBbkDFuXEUAkkUIABMYq/SJgQQQAABBBBAAIGoCDD+jkpPhFsPAsBw/Sv27NyAKrbraTgC9QQIALkoEEAAAQQQQAABBBAIToDxd3C2cSqZADBOvZWgunIDSlBn0hQEShQgACwRkMMRQAABBBBAAAEEEMghwPiby0MCBIBcB6EIcAMKhZ2TIhBJAQLASHYLlUIAAQQQQAABBBBIiADj74R0ZInNIAAsEZDDixPgBlScG0chkEQBAsAk9iptQgABBBBAAAEEEIiKAOPvqPREuPUgAAzXv2LPzg2oYruehiNQTyBTALh9i242fI9HEqN1/iGdE9MWGoIAAggggAACCCAQLwHG3/Hqr6BqSwAYlCzl5hTgBsQFggACKYGkBoDPzx9jS779l2tmp602dX92btnZru53NZ2PAAIIIIAAAggggEDZBBh/l4060iciAIx09yS3ctyAktu3tAyBQgWSGgCOfWuwzV8xpxZHVfsqqx5aXSgR+yOAAAIIIIAAAgggULQA4++i6RJ1IAFgorozPo3hBhSfvqKmCAQtQAAYtDDlI4AAAggggAACCFSyAOPvSu79/7adAJDrIBQBbkChsHNSBCIpQAAYyW6hUggggAACCCCAAAIJEWD8nZCOLLEZBIAlAnJ4cQLcgIpz4ygEkihAAJjEXqVNCCCAAAIIIIAAAlERYPwdlZ4Itx4EgOH6V+zZuQFVbNfTcATqCRAAclEggAACCCCAAAIIIBCcAOPv4GzjVDIBYJx6K0F15QaUoM6kKQiUKEAAWCIghyOAAAIIIIAAAgggkEOA8TeXhwQIALkOQhHgBhQKOydFIJICAycMtFkLZ7m6rfhunfuzffMuNnTX30eyvl4rxSrAXqXYDwEEEEAAAQQQQCBIAcbfQerGp2wCwPj0VaJqyg0oUd1JYxDwTWDMS/N8KyvsgggAw+4Bzo8AAggggAACCCAgAcbfXAcSIADkOghFgBtQKOycFIHICxAARr6LqCACCCCAAAIIIIBAzAQYf8eswwKqLgFgQLAUm1uAGxBXCAIIZBIgAOS6QAABBBBAAAEEEEDAXwHG3/56xrU0AsC49lzM680NKOYdSPURCEiAADAgWIpFAAEEEEAAAQQQqFgBxt8V2/W1Gk4AyHUQigA3oFDYOSkCkRcgAIx8F1FBBBBAAAEEEEAAgZgJMP6OWYcFVF0CwIBgKTa3ADcgrhAEEMgkQADIdYEAAggggAACCCCAgL8CjL/99YxraQSAce25mNebG1DMO5DqIxCQAAFgQLAUiwACCCCAAAIIIFCxAoy/K7brazWcAJDrIBQBbkChsHNSBCIvQAAY+S6igggggAACCCCAAAIxE2D8HbMOC6i6BIABwVJsbgFuQFwhCCCQEnj787dtxZoV7q8TZ/7b/dmkYTPbpvnOsUYa+9Zgm79iTq02VLWvsuqh1bFuF5VHAAEEEEAAAQQQiJcA4+949VdQtSUADEqWcnMKcAPiAkEAgZRA7/G9bdp/ptUC2b5FNxu+xyOxRiIAjHX3UXkEEEAAAQQQQCAxAoy/E9OVJTWEALAkPg4uVoAbULFyHIdA8gQIAJPXp7QIAQQQQAABBBBAIDoCjL+j0xdh1oQAMEz9Cj43N6AK7nyajkAdAQJALgkEEEAAAQQQQAABBIITYPwdnG2cSiYAjFNvJaiu3IAS1Jk0BYESBZIaAI5//3T7z8r3nE6LjRu7P3u062FPD3m6RDEORwABBBBAAAEEEEDAuwDjb+9WSd6TADDJvRvhtnEDinDnUDUEyiyQ1AAwnfH8QzqXWZXTIYAAAggggAACCCDwgwDjb64ECRAAch2EIsANKBR2TopAJAUIACPZLVQKAQQQQAABBBBAICECjL8T0pElNoMAsERADi9OgBtQcW4chUASBQgAk9irtAkBBBBAAAEEEEAgKgKMv6PSE+HWgwAwXP+KPTs3oIrtehqOQD0BAkAuCgQQQAABBBBAAAEEghNg/B2cbZxKJgCMU28lqK7cgBLUmTQFgRIFCABLBORwBBBAAAEEEEAAAQRyCDD+5vKQAAEg10EoAtyAQmHnpAhEUoAAMJLdQqUQQAABBBBAAAEEEiLA+DshHVliMwgASwTk8OIEuAEV58ZRCCRRgAAwib1KmxBAAAEEEEAAAQSiIsD4Oyo9EW49CADD9a/Ys3MDqtiup+EI1BMgAOSiQAABBBBAAAEEEEAgOAHG38HZxqlkAsA49VaC6soNKEGdSVMQKFEgqQHggpVzbc33q5zO8ft0cH+2aNLCdt9q9xLFOBwBBBBAAAEEEEAAAe8CjL+9WyV5TwLAJPduhNvGDSjCnUPVECizQFIDwLFvDbb5K+bU0qxqX2XVQ6vLLMzpEEAAAQQQQAABBCpZgPF3Jff+f9tOAMh1EIoAN6BQ2DkpApEUIACMZLdQKQQQQAABBBBAAIGECDD+TkhHltgMAsASATm8OAFuQMW5cRQCSRQgAExir9ImBBBAAAEEEEAAgagIMP6OSk+EWw8CwHD9K/bs3IAqtutpOAL1BAgAuSgQQAABBBBAAAEEEAhOgPF3cLZxKpkAME69laC6cgNKUGfSFARKFCAALBGQwxFAAAEEEEAAAQQQyCHA+JvLQwIEgFwHoQhwAwqFnZMiEEkBAsBIdguVQgABBBBAAAEEEEiIAOPvhHRkic0gACwRkMOLE+AGVJwbRyGQRAECwCT2Km1CAAEEEEAAAQQQiIoA4++o9ES49SAADNe/Ys/ODahiu56GI1BPYMrHU2zZ6mXu68++tcD92azxFtZ5i96x1hr71mCbv2JOrTZUta+y6qHVsW4XlUcAAQQQQAABBBCIlwDj73j1V1C1JQAMSpZycwpwA+ICQQCBTAJjXpqXGBgCwMR0JQ1BAAEEEEAAAQRiLcD4O9bd51vlCQB9o6SgQgS4ARWixb4IVI4AAWDl9DUtRQABBBBAAAEEECiPAOPv8jhH/SwEgFHvoYTWjxtQQjuWZiFQogABYImAHI4AAggggAACCCCAQB0Bxt9cEhJIdAC4ePFimzFjhvtv5syZ7r8vvvjC9fzJJ59s9913X0FXwQsvvGB33323K2fJkiXWpk0b22effey0006z/v37eypr3bp1ds8999hDDz1kH3zwga1YscK22WYbO/jgg+3cc8+13XbbzVM5S5cutVtuucWefPJJ++STT9wxP/rRj+zoo4+24cOHW6tWrTyV884779itt95qL7/8si1YsMBatGhhu+yyi5144ol26qmnWuPGjT2VU+hO3IAKFWN/BCpDgACwMvqZViKAAAIIIIAAAgiUT4Dxd/mso3ymRAeADRo0yGpfSAD4/fffu5Bv/PjxWctTWHbXXXdZw4YNs+6j0O7www93AWKmbeONN7bbbrvNBW+5tunTp7ugb9GiRRl3a9eunQsGe/bsmbOccePG2dlnn21r1qzJuJ+Of+6556x169a+X8PcgHwnpUAEEiFAAJiIbqQRCCCAAAIIIIAAAhESYPwdoc4IsSoVEwBut912tuuuu9qf//xnx11IAHjxxRfbtdde647r3r27jRw50nbccUf76KOP7LrrrrPZs2e7z7Tf6NGjM3bn+vXrrW/fvvb666+7z4855hgbNmyYtWzZ0hToXXXVVaYZiwoQn3322awzCj/99FPba6+93AxEzc674IILbMCAAa5MHXfTTTeZZhm2bdvW3nzzTWvfvn3G+mg2o45TuLnVVlvZJZdcYr169bJly5aZgsHHH3/cHbf//vvbq6++ao0aNfL1MuUG5CsnhSGQGAECwMR0JQ1BAAEEEEAAAQQQiIgA4++IdETI1Uh0AHjFFVe4R3T1n0IuPSrbsWPHggLAefPmucdyFartvffeNnXqVNtkk01qum3VqlXWp08fe+ONN1wg995771mnTp3qdeu9995rQ4cOdV8/88wz7fbbb6+1z4cffuiCva+//tp22mkne//99zM+fnvSSSfZAw884I599NFH7dhjj61VzqRJk+y4447L2ca1a9e6MFQB5mabbWazZs1ygWb6dtZZZ9kdd9zhvvSHP/zB/vd//9fXS5UbkK+cFIZAYgQIABPTlTQEAQQQQAABBBBAICICjL8j0hEhVyPRAWBd22ICQIV1d955pyuqurraqqqq6nXZtGnTrHfv3u7rmcI9fb1Lly4u1Ntyyy1N33zNmjWrV45mGWoWobZM4Z4e+d12223drL1DDz3UXnzxxYyXz2GHHWaTJ092swk/++wz23rrrWvtp7IHDx7svnbNNdfYL3/5y3rlKNjU7MHly5e7ur/77ru+XqrcgHzlpDAEQhEY9vQwe2fJO76d+8jOR9om3w7yrbywCxr71mCbv2JOrWpUta+y6qHVYVeN8yOAAAIIIIAAAghUkADj7wrq7BxNJQDMgbNhwwYXgmlxDC2MoQAv26bP586d6wI6Paab/v5BzSLceeed3aGnn356TaBYtywFfHp/n7YhQ4bYww8/XGsXLUDyi1/8wn1t4sSJNSFe3XL0mY7XpvcS6v2F6dsJJ5xgEyZMcF9auHBhvYAwta/qquO1qW2dO3f27buGG5BvlBSEQGgCvcf3tmn/mebb+btv3d2Wrdjgytu6WScbvNNVvpUdRkEEgGGoc04EEEAAAQQQQACBugKMv7kmJEAAmOM6+Pjjj2sejVXw9vvf/z7r3vpcAZ02HZd61Fh/T3/8V8Hb8ccfn7UcBYUKDDt06GDz58+vtV/647+5gjt9ppWFtemY+++/v1Y5Klshpc6llYizbaqrwsJUG0455RTfvmu4AflGSUEIhCbgdwCY3pDtW3Sz4Xs8Elrb/DgxAaAfipSBAAIIIIAAAgggUKoA4+9SBZNxPAFgjn7UohpHHnmk22PMmDF23nnnZd1bn2tBDm1aOVer/aa2Cy+80G688Ub3Vy0Y0q1bt6zlHHXUUfb000+7GYTffPONNW/evGZfvYNQC3tsvvnm9uWXX+a8ArWP3ieo9x/OmDGjZt8VK1bYpptu6v6uc2m14Gyb6tqjRw/38UUXXeQWPPFr4wbklyTlIBCeQN1HgL9b953NXvTDokilbgSApQpyPAIIIIAAAggggAACPwgw/uZKkAABYI7rQDP+zjjjDLeHFtcYNCj7u6kee+yxmgU5dFzqUV0dqxl/jzzyw0wWrd7bunXrrGc9++yzaxYI0ey81KPDOkDv8vv888/doiTvvJP7vVtdu3Z17+3TMZoRmNpUphYA0aaFPm677basdVm6dKm1adPGfa42pB4b9vKtoxtMrk116tmzp9tFsxGzrVbs5VzsgwAC4Qso/Bs0aZA9O+9ZXypDAOgLI4UggAACCCCAAAIIIEAAyDXgBAgAc1wI119/vY0cOdLt8cILL5gW18i26fPUrL8bbrjBRowYUbPrEUccYc8//7z7++rVq61p06ZZyxk1alTNTDutLKyVgVObZgNqcY5evXqZFh7JtWkfzfxr0aKFm0mY2mbOnFkTvOlcWngk26a6phYrGTBggD3zzDOev23S34GY7yACwHxCfI5A9AVGvzbanpnn/R6RrUULv/zWfZSEdwDO+7LaVq37Ybb2gD1+eC1Dy01aWr8d+kW/Q6khAggggAACCCCAQGIEmAGYmK4sqSEEgDn4rrzySrv88svdHlOmTLGDDjoo696vvPKK9ev3w6BOx1166aU1++rr+lzb+vXr3eq82TadT8dre+2112z//fev2bVRo0ZuBeADDjjApk6dmrPjDzzwQHe8jlm3bl3NvvqaPtN22WWX2W9/+9us5ehcOl6b2vDyyy97vtgIAD1TsSMCCKQJjHlpXiI9zj/Ev0WUEglEoxBAAAEEEEAAAQQCEyAADIw2VgUTAOboLmYAFj8DkEeAY3UfoLIIREaAADAyXUFFEEAAAQQQQAABBBIiQACYkI4ssRkEgDkAeQdg8e8AzHddcgPKJ8TnCFSmAAFgZfY7rUYAAQQQQAABBBAIToDxd3C2cSqZADBHb7EKMKsAx+mbmboikAQBAsAk9CJtQAABBBBAAAEEEIiSAAFglHojvLoQAOaw//jjj23HHXd0e2hVX80IzLbp87vvvtt9rOM6duxYs+u9995rQ4cOdX/XSrpaUTfbplV/582bZx06dLD58+fX2u2kk06yBx54wH1Nq+hqhd9Mmz7bZpsfXjivY+6///5au6lsLbyhc2lV4Gyb6nrCCSe4j9WGU045xbcrlRuQb5QUhECiBAgAE9WdNAYBBBBAAAEEEEAgAgKMvyPQCRGoAgFgjk7YsGGDtW/f3hYsWGC77LKLvf/++1n33nXXXV2Ytu2227pwLX0RDAV6Ctu0nX766XbnnXdmLGfRokXWrl0799mQIUPs4YcfrrWfAkYFjdomTpxogwcPzliOPtPx2u666y477bTTau2nUE/hnrZcQaLqquO1zZ071zp39u8l9tyAIvDdTxUQiKAAAWAEO4UqIYAAAggggAACCMRagPF3rLvPt8oTAOahPPPMM2sCu+rqaquqqqp3xLRp06x3797u69r/9ttvr7dPly5dXIDYsmVLFxA2a9as3j7XXnutXXzxxe7rjz76qB177LG19lFAqIBRq/Meeuih9uKLL2as/WGHHWaTJ092qw1/9tln9WYKquxUeHjNNdfYL3/5y3rlrFq1yoWfy5cvN9X93Xff9e2iU0HcgHzlpDAEQhGY8vEUW7Z6Wa1zt9ykpfXb4YcV0YvZCACLUeMYBBBAAAEEEEAAAQSyCzD+5uqQAAFgnutAs/d22203W7dune299942depU22STTWqOWr16tR144IH2xhtvWOPGje29996zTp061Ss1/THgs846y2677bZa+3z00UfWo0cP+/rrr91jx5pNqPLqbumPAU+aNMkGDRpUaxd97bjjjnNfO/nkk+2+++6rV8batWtNMxZ1zs0228xmzZpV86hzamfV8Y477nB//cMf/mD/+7//6+t3DDcgXzkpDIFQBHqP723T/jOt1rmr2ldZ9dDqoutDAFg0HQcigAACCCCAAAIIIJBRgPE3F4YEEh0Avv766/bhhx/W9PTSpUvtoosucn/fb7/97NRTT611FWQLuTQrT7PztHXv3t1GjRrlAjMFaL/73e9s9uzZ7jPtN3r06IxX1vr1661Pnz72t7/9zX3+s5/9zIYNG2ZbbrmlzZgxw6688kpbvHixm7WnxUf69++fsRzNHtxrr71syZIlLiAcMWKEDRgwwO2r42688UYXVrZp08YFe5rFl2l7/vnn7cgjj3SzCbfaaiu79NJLrWfPnm7G37hx4+xPf/qTO2z//fe3V1991Ro1auTrdww3IF85KQyBUAQIAHOzP/LhpbZo1T/dTu22aOr+7Nqmq40bOC6U/uKkCCCAAAIIIIAAApUpwPi7Mvu9bqsTHQAq0Ku7AEaubtc7/zJtCskU1mkWX7ZNi3zoHX0K8LJtCiAPP/xwmzlzZsZdmjRp4mYG6ly5tunTp9vRRx9teiQ406bFQZ588knr1atXznIU9J199tm2Zs2ajPspEHzuueesdevWvn+3cAPynZQCESi7AAFgbvKxbw22+Svm1Nqp1BmSZe9kTogAAggggAACCCAQewHG37HvQl8aQACYxpgtAEztollzCvkU4CnMUzC2zz77uIU5ss3Yq9tLmp2n4E0LfOidgCtXrnQr9vbr18+GDx/uHjf2sun8Y8eOdUHfJ5984g7RysNHHXWUnXfeedaqVSsvxdg777xjt9xyi02ZMsUtdtK8eXP3ePCJJ57oZkhmegzZU8F5duIG5IciZSAQrgABIAFguFcgZ0cAAQQQQAABBBDwIsD424tS8vdJdACY/O6Lbwu5AcW376g5AikBAkACQL4bEEAAAQQQQAABBKIvwPg7+n1UjhoSAJZDmXPUE+AGxEWBQPwFCAAJAON/FdMCBBBAAAEEEEAg+QKMv5Pfx15aSADoRYl9fBfgBuQ7KQUiUHYBAkACwLJfdJwQAQQQQAABBBBAoGABxt8FkyXyAALARHZr9BvFDSj6fUQNEcgnQABIAJjvGuFzBBBAAAEEEEAAgfAFGH+H3wdRqAEBYBR6oQLrwA2oAjudJidOgACQADBxFzUNQgABBBBAAAEEEijA+DuBnVpEkwgAi0DjkNIFuAGVbkgJCIQtQABIABj2Ncj5EUAAAQQQQAABBPILMP7Ob1QJexAAVkIvR7CN3IAi2ClUCYECBQgACQALvGTYHQEEEEAAAQQQQCAEAcbfIaBH8JQEgBHslEqoEjegSuhl2ph0AQJAAsCkX+O0DwEEEEAAAQQQSIIA4+8k9GLpbSAALN2QEooQ4AZUBBqHIBAxAQJAAsCIXZJUBwEEEEAAAQQQQCCDAONvLgsJEAByHYQiwA0oFHZOioCvAgSABIC+XlAUhgACCCCAAAIIIBCIAOPvQFhjVygBYOy6LBkV5gaUjH6kFZUtQABIAFjZ3wG0HgEEEEAAAQQQiIcA4+949FPQtSQADFqY8jMKcAPiwkAg/gIEgASA8b+KaQECCCCAAAIIIJB8Acbfye9jLy0kAPSixD6+C3AD8p2UAhEouwABIAFg2S86TogAAggggAACCCBQsADj74LJEnkAAWAiuzX6jeIGFP0+ooYI5BMgACQAzHeN8DkCCCCAAAIIIIBA1fLIQQAAIABJREFU+AKMv8PvgyjUgAAwCr1QgXXgBlSBnU6TEydAAEgAmLiLmgYhgAACCCCAAAIJFGD8ncBOLaJJBIBFoHFI6QLcgEo3pAQEwhYgACQADPsa5PwIIIAAAggggAAC+QUYf+c3qoQ9CAAroZcj2EZuQBHsFKqEQIECb3/+tq1Ys6LWUS2atLDdt9q9wJL+u/uYl+YVfWzUDhz71mCbv2JOrWpVta+y6qHVUasq9UEAAQQQQAABBBBIsADj7wR3bgFNIwAsAItd/RPgBuSfJSUhkCSBJAWAj3x4qS1a9U/XPe22aOr+7Nqmq40bOC5JXUZbEEAAAQQQQAABBCIuwPg74h1UpuoRAJYJmtPUFuAGxBWBAAKZBJIUAKa37/xDOtPhCCCAAAIIIIAAAgiEIsD4OxT2yJ2UADByXVIZFeIGVBn9TCsRKFSAALBQMfZHAAEEEEAAAQQQQCC3AONvrhAJEAByHYQiwA0oFHZOikDkBQgAI99FVBABBBBAAAEEEEAgZgKMv2PWYQFVlwAwIFiK5TcQXAMIIFC4AAFg4WYcgQACCCCAAAIIIIBALgECQK4PCRAAch2EIsANKBR2TopA5AUIACPfRVQQAQQQQAABBBBAIGYCjL9j1mEBVZcAMCBYis0twA2IKwQBBDIJEAByXSCAAAIIIIAAAggg4K8A429/PeNaGgFgXHsu5vXmBhTzDqT6CAQkQAAYECzFIoAAAggggAACCFSsAOPviu36Wg0nAOQ6CEWAG1Ao7JwUAV8FBk4YaLMWzqpVZo92PezpIU8XfR4CwKLpOBABBBBAAAEEEEAAgYwCjL+5MCRAAMh1EIoAN6BQ2DkpAr4K9B7f26b9Z1qtMqvaV1n10Oqiz5OkAHDel9W2at2XzmLAHtu4P1tu0tL67dCvaB8ORAABBBBAAAEEEECgUAHG34WKJXN/AsBk9mvkW8UNKPJdRAURyCtAAJibaOxbg23+ijm+BqR5O4UdEEAAAQQQQAABBBCoI8D4m0tCAgSAXAehCHADCoWdkyLgqwABIAGgrxcUhSGAAAIIIIAAAggEIsD4OxDW2BVKABi7LktGhbkBJaMfaUVlCxAAEgBW9ncArUcAAQQQQAABBOIhwPg7Hv0UdC0JAIMWpvyMAtyAuDAQiL8AASABYPyvYlqAAAIIIIAAAggkX4Dxd/L72EsLCQC9KLGP7wLcgHwnpUAEyi5AAEgAWPaLjhMigAACCCCAAAIIFCzA+LtgskQeQACYyG6NfqO4AUW/j6ghAvkECAAJAPNdI3yOAAIIIIAAAgggEL4A4+/w+yAKNSAAjEIvVGAduAFVYKfT5MQJEAASACbuoqZBCCCAAAIIIIBAAgUYfyewU4toEgFgEWgcUroAN6DSDSkBgbAFCAAJAMO+Bjk/AggggAACCCCAQH4Bxt/5jSphDwLASujlCLaRG1AEO4UqIVCgAAEgAWCBlwy7I4AAAggggAACCIQgwPg7BPQInpIAMIKdUglV4gZUCb1MG5MuQABIAJj0a5z2IYAAAggggAACSRBg/J2EXiy9DQSApRtSQhEC3ICKQOMQBCImQABYeADYfKPmtvtWu9c68O4Bd9f7Wt2SB04YaEtWLan5ctc2XW3cwHERuyKoDgIIIIAAAggggEAUBRh/R7FXyl8nAsDym3NGM+MGxGWAQPwFCAALDwAzHfH3//u79d6ud87C2t/U3j775rOafaraV1n10Or4X0S0AAEEEEAAAQQQQCBwAcbfgRPH4gQEgLHopuRVkhtQ8vqUFlWeAAEgAWDlXfW0GAEEEEAAAQQQiJ8A4+/49VkQNSYADEKVMvMKcAPKS8QOCERegACwPAHg6NdG25hpY2zpqqU1J2QGYOS/PaggAggggAACCCAQGQHG35HpilArQgAYKn/lnpwbUOX2PS1PjgABYHkCwCCck3MV0hIEEEAAAQQQQACBfAKMv/MJVcbnBICV0c+RayU3oMh1CRVCoGCBIIKpMS/NK7geUT3gkQ8vtUWr/umq126LplmrmW8RkCCco2pGvRBAAAEEEEAAAQT8F2D87b9pHEskAIxjryWgztyAEtCJNKHiBYIIppIUAKZfIOcf0rno6yUI56Irw4EIIIAAAggggAACsRNg/B27LgukwgSAgbBSaD4BbkD5hPgcgegLBBFMEQDW7/cgnKN/dVFDBBBAAAEEEEAAAb8EGH/7JRnvcggA491/sa09N6DYdh0VR6BG4JIpl9i8ZbUf2e3csrNd3e/qopUIAAkAi754OBABBBBAAAEEEEAgowDjby4MCRAAch2EIsANKBR2TopA5AUIAAkAI3+RUkEEEEAAAQQQQCBmAoy/Y9ZhAVWXADAgWIrNLcANiCsEAQQyCRAAEgDynYEAAggggAACCCDgrwDjb38941oaAWBcey7m9eYGFPMOpPoIBCRAAEgAGNClRbEIIIAAAggggEDFCjD+rtiur9VwAkCug1AEuAGFws5JEYi8AAEgAWDkL1IqiAACCCCAAAIIxEyA8XfMOiyg6hIABgRLsbkFuAFxhSCAQCYBAkACQL4zEEAAAQQQQAABBPwVYPztr2dcSyMAjGvPxbze3IBi3oFUH4E6AkkN7vzq6PMP6Vx0Ub3H97Zp/5lW6/iq9lVWPbS66DI5EAEEEEAAAQQQQKByBBh/V05f52opASDXQSgC3IBCYeekCAQmQACYm5YAMLBLj4IRQAABBBBAAAEE8ggw/uYSkQABINdBKALcgEJh56QIBCZAAEgAGNjFRcEIIIAAAggggAACJQkw/i6JLzEHEwAmpivj1RBuQPHqL2qLQCaBCW9PsIUrFrqP/jpviftzs43aWI82AwCrI8AMQC4JBBBAAAEEEEAAgbAEGH+HJR+t85Y9APzuu+/s9ddft6VLl1rHjh2tZ8+e0RKhNmUR4AZUFmZOgkCgApneTbd9i242fI9HAj1vHAsnAIxjr1FnBBBAAAEEEEAgGQKMv5PRj6W2wtcAcP78+Xb77be7Ov3qV7+yLbbYolb9pk2bZoMGDbKFC3+YMaKte/fu9qc//cm23377UtvC8TES4AYUo86iqghkESAA9H5pEAB6t2JPBBBAAAEEEEAAAX8FGH/76xnX0nwNAG+++Wa74IILrFu3bjZr1qxaJl9//bV17tzZlixZYhs2bKj1WZcuXWz27Nm20UYbxdWRehcowA2oQDB2RyCCAgSA3juFANC7FXsigAACCCCAAAII+CvA+Ntfz7iW5msAeMQRR9iLL75ol19+uV1xxRW1TG644QYbOXKkNWjQwM455xzr16+fTZ482e644w73tbvuustOPfXUuDpS7wIFuAEVCMbuCERQgADQe6eUEgBeMuUSm7dsXq2TdW7Z2a7ud7X3CrAnAggggAACCCCAQMUKMP6u2K6v1XBfA8Bdd93V5s2bZ08//bQpDEzf9K6/N99804455hibNGlSzUeDBw92fz/00EPthRdeoFcqRIAbUIV0NM1MtAABoPfuLSUA9H4W9kQAAQQQQAABBBBAoL4A42+uCgn4GgC2atXKvvzyS5s5c6b16NGjRvirr74yfaZHf+uGg0888YT97Gc/s6233toWLFhAr1SIADegCulomploAQJA791LAOjdij0RQAABBBBAAAEE/BVg/O2vZ1xL8zUAbNKkia1fv9602Mc+++xTY6KZfZoR2LhxY1u+fLk1b9685rM33njDrQSsY7/99tu4OlLvAgW4ARUIxu4IRFCAANB7pxAAerdiTwQQQAABBBBAAAF/BRh/++sZ19J8DQDbtm1rX3zxhT3++ON21FFH1ZiMGjXKrr/+ett7771txowZtaxSAeBmm23mZg+yVYYAN6DK6GdamWwBAkDv/UsA6N2KPRFAAAEEEEAAAQT8FWD87a9nXEvzNQD88Y9/bFOnTrWf//zndt999zkTzQjcaaed7N///reNGDHCrrvuulpWTz31lP30pz+1Tp062dy5c+PqSL0LFOAGVCAYuyMQQQECQO+dQgDo3Yo9EUAAAQQQQAABBPwVYPztr2dcS/M1ALzlllvsvPPOc6v6XnjhhXbAAQfYAw884Bb50NemT5/uZgGmb5dddpldffXVdtBBB9nLL78cV0fqXaAAN6ACwdgdgQgKEAB67xQCQO9W7IkAAggggAACCCDgrwDjb38941qarwHgd9995xb/eP/9913gl9q0+MfAgQPtySefrOfUtWtXt//FF19sV111VVwdqXeBAtyACgRjdwQiKEAA6L1TCAC9W7EnAggggAACCCCAgL8CjL/99Yxrab4GgEJYtGiRnX322fbMM8/Y2rVr3eIegwcPtttuu8023XTTWk56XLhv374uLHz11VfdjEG2yhDgBlQZ/Uwrky1AAOi9fwkAvVuxJwIIIIAAAggggIC/Aoy//fWMa2m+B4ApCM0GXLZsmbVq1cqFgJm2f/3rX+7dgNoU/jVs2DCujtS7QAFuQAWCsTsCERQgAPTeKaUEgBPenmALVyysdbJ2LdrZkN2HeK8AeyKAAAIIIIAAAghUrADj74rt+loNDywAhBeBXALcgLg+EIi/AAGg9z4sJQDM5FzVvsqqh1Z7rwB7IoAAAggggAACCFSsAOPviu364ALA//u//3OP8+pdfu3atfMkvGTJEhs1apQ7bvz48Z6OYaf4C3ADin8f0gIECAC9XwMEgN6t2BMBBBBAAAEEEEDAXwHG3/56xrU0X2cA6hFeBXlvv/22denSxZPJRx99ZJ06dXLHrV+/3tMx7BR/AW5A8e9DWoAAAaD3a4AA0LsVeyKAAAIIIIAAAgj4K8D421/PuJZGABjXnot5vbkBxbwDqT4CZkYA6P0yIAD0bsWeCCCAAAIIIIAAAv4KMP721zOupYUeAL777ru2++67W9OmTW3VqlVxdaTeBQpwAyoQjN0RiKAAAaD3TiEA9G7FnggggAACCCCAAAL+CjD+9tczrqWFHgDefffddvrpp9v2229vWhWYrTIEuAFVRj/TymQLEAB6718CQO9W7IkAAggggAACCCDgrwDjb38941paSQHgb3/721rt/vWvf+3e5XfGGWdY27Ztc5p89913pvf/Pf3006b/HzJkiD344IORdlyzZo398Y9/tEmTJtlbb71ly5Yts4022si23XZb23fffW3YsGHuz3zbCy+8YAo+Z86caVoEpU2bNrbPPvvYaaedZv379893uPt83bp1ds8999hDDz1kH3zwga1YscK22WYbO/jgg+3cc8+13XbbzVM5S5cutVtuucWefPJJ++STT9wxP/rRj+zoo4+24cOHW6tWrTyVU+hO3IAKFWN/BKInQADovU8IAL1bsScCCCCAAAIIIICAvwKMv/31jGtpJQWAqUU/Uo3fsGGD+1+FgF43HaPHf6urq23PPff0eljZ95s/f74dccQRpkeWc23nnHOOjR07NqPB999/70K+XKsdn3rqqXbXXXeZbLNtCu0OP/xwFyBm2jbeeGO77bbbTGXl2qZPn+6CvkWLFmXcTSs5Kxjs2bOn797cgHwnpUAEyi6weOViW7N+jTvvuKkfuz8bNdjINm0SzC8Oyt5AH09IAOgjJkUhgAACCCCAAAIIFCTA+LsgrsTuXHIAmC6TCv5SQWAuNYV+Cpg0Y+7CCy+MdPi3du1a6969e034t8cee9gFF1xgO++8s33zzTf2+uuv24033mgrV650Tb7mmmvsl7/8Zb3mX3zxxXbttde6r6u8kSNH2o477uhmQl533XU2e/Zs95n2Gz16dEY+rZTct29fd05txxxzjJt52LJlS1Ogd9VVV9nixYtdgPjss89mnVH46aef2l577eVmIDZu3Ni1Z8CAAa5MHXfTTTe5WYaayfnmm29a+/btff0m4AbkKyeFIRC6wJiX5oVehyhXgAAwyr1D3RBAAAEEEEAAgWQLMP5Odv96bV1JAWDdk6RmBL799tvWpUsXr3WI/H6PPfaYHXvssa6evXv3ttdee80aNWpUq94KyfSZwsItttjChXB6PDi1zZs3zz2Wq1Bt7733tqlTp9omm2xS87kWQOnTp4+98cYbLpB77733rFOnTvVs7r33Xhs6dKj7+plnnmm33357rX0+/PBDF+x9/fXXttNOO9n777/vyqu7nXTSSfbAAw+4Lz/66KM17Uvtp8ecjzvuOPfXk08+2e677z5f+4kbkK+cFIZA6AIEgLm7gAAw9EuUCiCAAAIIIIAAAhUrwPi7Yru+VsN9DQD17jjNAnzppZdc+JSUTbPjxowZ45qjdxYeeeSRGZum2XhPPPGE+0zvCNTqxqlNYd2dd97p/qrHnauqquqVMW3aNBciassU7unrClYV6m255Zamb+JmzZrVK0ezDDWLUFumcE+P/Oq9hXok+dBDD7UXX3wxY3sOO+wwmzx5sptN+Nlnn9nWW2/tW5dyA/KNkoIQiIQAAWDubiAAjMRlSiUQQAABBBBAAIGKFGD8XZHdXq/RvgaASSU9++yza2bavfPOO1kX2LjooovshhtucAyayaeZeNr0SLQeoV2wYIHtsssuLsDLtunzuXPnuoBOj+mmv09Rswj12LE2rZycChTrlqWAT49Xa9PiKg8//HCtXbQAyS9+8Qv3tYkTJ9rgwYMzVkef6Xhtei+h3l/o18YNyC9JykEgGgIEgLn7gQAwGtcptUAAAQQQQAABBCpRgPF3JfZ6/TYTAHq4Dm699Va3sq42LzMAFdp9+eWXttlmm7ljPv74Y/euP20K3n7/+99nPas+V0CXOq5jx441+6Y//jthwgQ7/vjjs5ajoFCBYYcOHUwLmKRv6Y//Lly4MOvMPn2mlYW16Zj777/fg5a3XbgBeXNiLwTiIkAAmLunCADjciVTTwQQQAABBBBAIHkCjL+T16fFtIgA0IOaVt3VI81fffWV7bfffvbXv/613jsAtYCHHutds2aNnXjiifbggw/WlKxFNVKPDetR4vPOOy/rWfW5HjnW9txzz7nVflObFkvRYiPadL5u3bplLeeoo45yYaXCSC1U0rx585p99Q5CvbNw8803d0Flrk376H2C++yzj82YMcODlrdduAF5c2IvBOIiQABIABiXa5V6IoAAAggggAAClSbA+LvSejxzewMJAPWIq2axabEMzX5TAKX3zeXaFFRpgYyobgrT9DisFuvQCr4K8Tp37mwrVqywv/3tby6YUzt79Ohhzz//vG211VY1TdGMvzPOOMP9XYtrDBo0KGsz0xcc0XGpR3V1gGb8PfLII+5Yrd7bunXrrOWkP7b8wQcf1Dw6rAP0Lr/PP//cPcqsR5pzbV27dnWrH+sYzQj0uukGk2tTWT179nS76FFnv1cZ9lpP9kMAAX8ECABzOzID0J/rjFIQQAABBBBAAAEEChcgACzcLIlH+B4A3nTTTW4BCoV5eved100B4Pr1673uHsp+CtIU9I0fP75e2xT4qd3Dhg2rtzDH9ddfbyNHjnR1fuGFF0yLa2Tb9Hlq1p/eJzhixIiaXY844ggXLmpbvXq1NW3aNGs5o0aNsuuuu859nv4+Qv1dswEVZPbq1cu08EiuTfto5l+LFi1cwOl1S393Yb5jCADzCfE5AtEXIADM3UcEgNG/hqkhAggggAACCCCQVAECwKT2bGHt8jUA1GqyqfBKAZDCIy2E0bJlS7eSbL7tiiuuyLdLaJ/r0d5f//rXds8997jZd5k2PVp72WWX2cCBA2t9fOWVV9rll1/uvjZlyhQ76KCDsrbjlVdesX79+rnPddyll15as6++rs+1KSzNZarz6Xhtmom5//7715TTqFEjNyPzgAMOsKlTp+Y0PfDAA93xOqaQGZoEgKFdqpwYgbIJ9B7f26b9p/YvEbZv0c2G7/HDTGW2/woQAHI1IIAAAggggAACCIQlQAAYlny0zutrAKiZbX/+859tyy23dO+f0/vykrCtXLnS+vfvXxOEaVbeKaecYjvssIN9++23Nn36dPvtb39rr7/+unvnnmbupd7jp/ZX4gxAHgFOwpVPGxDILUAA6P0KIQD0bsWeCCCAAAIIIIAAAv4KEAD66xnX0nwNAPVOuuXLl5seAx4+fHhcTerV+6KLLnKhnrb77rvPTj755Hr7aHbcT37yE/vLX/7iZubNmjXL9txzT7dfJb4DMF/ncwPKJ8TnCERfgADQex8RAHq3Yk8EEEAAAQQQQAABfwUYf/vrGdfSfA0AmzVrZt999517Z5we/U3CpvcYKthctmyZW/Rj7ty5WZulxUBSj9pqkRCt6KuNVYDrk3EDSsJ3B22odAECQO9XAAGgdyv2RAABBBBAAAEEEPBXgPG3v55xLc3XALBTp05u1V89Ctu7d++4mtSq96JFi6xdu3bua4MHD7aJEydmbZceB95kk03c53ocWgt6aJPJjjvu6P5fq/pqRmC2TZ9rBeXUcR07dqzZ9d5777WhQ4e6v0+YMMGtCpxt23nnnW3evHnWoUMHmz9/fq3dTjrpJHvggQfc17Qar1b4zbTps2222cZ9pGPuv/9+3/qUG5BvlBSEQGgCBIDe6UsJABevXGxr1q+pdbImjZpY2+ZtvVeAPRFAAAEEEEAAAQQqVoDxd8V2fa2G+xoA6r13Y8eOtd/97nd24YUXJkJ46dKl1qZNG9eWn/3sZ/bYY49lbZdWyd1ss83c5wMGDLBnnnnG/b9mEbZv394WLFhgu+yyi73//vtZy9h1111Nqw1vu+22ptVx0xfTUKCnYE/b6aefbnfeeWfGctJDyyFDhtjDDz9caz8FjAoatSnQVLCZadNnOl7bXXfdZaeddppvfcoNyDdKCkIgNAECQO/0pQSA3s/CnggggAACCCCAAAII1Bdg/M1VIQFfA0AFXHrvXePGjW327NlZZ5bFiV6r5WpRk6+//trNhtNsOrUv05b+qO8555xjt9xyS81uZ555Zk1gV11dbVVVVfWKmDZtWs3MSe1/++2319unS5cuLkDUysoKCPXYdd3t2muvtYsvvth9+dFHH7Vjjz221i4KCBUwqm2HHnqoafXmTJtmMU6ePNm90/Czzz7ztT+5AcXpu4C6IpBZgADQ+5VBAOjdij0RQAABBBBAAAEE/BVg/O2vZ1xL8zUAFMLf//53O/roo61FixZ222232eGHHx5Xm5p6n3DCCe6RW22//vWv7YorrqjXJi1+ovf/vffee+4zBWdaFCS1afbebrvtZlosZO+997apU6fWPC6sfVavXm0HHnigvfHGGy5gVDl6pLrulv4Y8FlnneWM07ePPvrIevTo4QJLPXas2YSZAsv0x4AnTZpkgwYNqlWOvnbccce5r2nREy1+4ufGDchPTcpCIBwBAkDv7gSA3q3YEwEEEEAAAQQQQMBfAcbf/nrGtTRfA8CDDjrIOWgmoAIvPb66xRZbuCAr00y1dDTtO2XKlEg6KkTToiarVq1y9TvyyCNdKLbDDjuY3vunmXs333yz/fvf/3af9+vXz15++eV6bdGsPM3O09a9e3cbNWqUC+kU2umxac2a1Kb9Ro8endFi/fr11qdPH9OCI9r0WPKwYcPcLEUtvnLllVfa4sWL3aw9zUjs379/xnI0e1BtWrJkiQsIR4wY4R5b1qbjbrzxRhdW6vFnrWisR5j93LgB+alJWQiEI0AA6N2dANC7FXsigAACCCCAAAII+CvA+Ntfz7iW5msAqNAp9c46vffOy6b9ta/+VLgV1U2Bnt6Hp3cC5toUguo9gQrk6m565FZhnWbxZdu0yIfe0SfLbJvqoJmVM2fOzLhLkyZN3MxAnSvXNn36dDdbU48EZ9q0OMiTTz5pvXr18r1buAH5TkqBCJRdgADQOzkBoHcr9kQAAQQQQAABBBDwV4Dxt7+ecS3N1wCwb9++tRatKBTlL3/5S6GHlHX/L774wsaPH+9W93333Xftyy+/dLPnFJTts88+pkeFBw4cmNfg+eefdyGfAjyFea1bt3bHa2GObDP26jZUs/PGjRvnFvjQOwFXrlzp3lGo2YfDhw93jxt72XR+LdyioO+TTz5xh2jl4aOOOsrOO+88a9WqlZdiCt6HG1DBZByAQOQECADL3yUEieU354wIIIAAAggggEDcBRh/x70H/am/rwGgP1WilEoQ4AZUCb1MG5MuQABY/h4mACy/OWdEAAEEEEAAAQTiLsD4O+496E/9CQD9caSUAgW4ARUIxu4IRFCAALD8nUIAWH5zzogAAggggAACCMRdgPF33HvQn/oTAPrjSCkFCnADKhCM3RGIoAABYHk6Zexbg23+ijm1TlbVvsqqh1aXpwKcBQEEEEAAAQQQQCDWAoy/Y919vlWeANA3SgoqRIAbUCFa7ItANAUIAMvTLwSA5XHmLAgggAACCCCAQFIFGH8ntWcLa5evAeDUqVMLO3udvQ888MCSjufg+AhwA4pPX1FTBLIJEACW59ogACyPM2dBAAEEEEAAAQSSKsD4O6k9W1i7fA0AGzZsmHcF3GzVa9CggWllW7bKEOAGVBn9TCuTLUAAWJ7+JQAsjzNnQQABBBBAAAEEkirA+DupPVtYu3wPAAs7/X/3VgC4fv36Yg/nuJgJcAOKWYdRXQQyCBAAlueyIAAsjzNnQQABBBBAAAEEkirA+DupPVtYu3wNAP/617/mPfvKlStt3rx5NnHiRJsxY4btt99+9pvf/MYaNWpkffr0yXs8OyRDgBtQMvqRVlS2AAFgefqfALA8zpwFAQQQQAABBBBIqgDj76T2bGHt8jUALOzUZtdff72NGjXKTjjhBHvwwQcLPZz9YyzADSjGnUfVEfj/AgSA5bkUCADL48xZEEAAAQQQQACBpAow/k5qzxbWrlADQFV10KBB9sQTT9hDDz1kxx9/fGG1Z+/YCnADim3XUXEEagTGThtr87+a7/4+a/5y9+eWG29jB25zMko+ChAA+ohJUQgggAACCCBX3U9FAAAgAElEQVSAQAUKMP6uwE7P0OTQA8Cnn37ajj76aOvbt6+98sor9EqFCHADqpCOppmRFBj29DB7Z8k7RdXt+N2Ot+FVw+sdO+aleUWVx0H5BQgA8xuxBwIIIIAAAggggEB2AcbfXB0SCD0AnDNnjvXo0cNatWplS5YsoVcqRIAbUIV0NM2MpECmR3e9VvT8qvPtpkNvIgD0CubDfgSAPiBSBAIIIIAAAgggUMECjL8ruPPTmh56ADh58mTr37+/NW3a1FatWkWvVIgAN6AK6WiaGUkBAsBIdkvWShEAxqu/qC0CCCCAAAIIIBA1AcbfUeuRcOoTegA4cOBAe/bZZ61z5872wQcfhKPAWcsuwA2o7OScEIEaAQLAeF0MBIDx6i9qiwACCCCAAAIIRE2A8XfUeiSc+oQSAC5fvtzeeOMNGzNmjL344ovWoEEDtxrw6NGjw1HgrGUX4AZUdnJOiAABYEyvAQLAmHYc1UYAAQQQQAABBCIiwPg7Ih0RcjV8DQAbNWpUcHM2bNjgZv9Nnz7dNt9884KP54B4CnADime/UetkCGSaAdh8o+a2+1a7520gi4DkJfJ9BwJA30kpEAEEEEAAAQQQqCgBxt8V1d1ZG+trANiwYcOCVBs3bmyDBg2ym2++2dq2bVvQsewcbwFuQPHuP2ofb4FMAWBV+yqrHlpddMNYBbhourwHEgDmJWIHBBBAAAEEEEAAgRwCjL+5PCTgawD4m9/8Jq+qQsJNN93UOnbsaPvtt5+1bt067zHskDwBbkDJ61NaFB8BAsD49JVqSgAYr/6itggggAACCCCAQNQEGH9HrUfCqY+vAWA4TeCscRTgBhTHXqPOSREgAIxXTxIAxqu/qC0CCCCAAAIIIBA1AcbfUeuRcOpDABiOe8WflRtQxV8CAIQoQAAYIn4RpyYALAKNQxBAAAEEEEAAAQRqBBh/czFIgACQ6yAUAW5AobBzUgScAAFgvC4EAsB49Re1RQABBBBAAAEEoibA+DtqPRJOfQINAD///HN79dVX7Z133rFly5a5FrZs2dK6du1qffv2ta222iqcVnPW0AW4AYXeBVQAAV8FWATEV85ahREABmdLyQgggAACCCCAQCUIMP6uhF7O38ZAAsCFCxfaBRdcYI8//ritW7cuYy0aNWrkVgC+8cYbrV27dvlryh6JEuAGlKjupDEIGAFgcBfB1AX32/LvFrgT9Nh+S/fn9ptvb8Orhgd3UkpGAAEEEEAAAQQQSIwA4+/EdGVJDfE9APzHP/5hBx98sJvxt2HDhpyVa9CggbVq1cqmTJliu+++e0kN4eB4CXADild/UVsE8gkQAOYT8ufz8w/p7E9BlIIAAggggAACCCBQMQKMvyumq3NncBvypXQFOK1cudJ23nlnW7Dgh5kKCgKHDRtmvXr1sq233tp9bdGiRTZjxgy755577M9//rP7Wvv27e2DDz6wZs2aFXA2do2zADegOPcedUegvgABYHmuCgLA8jhzFgQQQAABBBBAIEkCjL+T1JvFt8XXGYC/+93v7OKLL7aGDRvaXXfdZUOHDs1Zs3vvvdcFhNquvfZau+iii4pvCUfGSoAbUKy6i8oikFeAADAvkS87EAD6wkghCCCAAAIIIIBARQkw/q6o7s7aWF8DwH333demT59up5xyipvh52U79dRTTUFgVVWV/f3vf/dyCPskQIAbUAI6kSYgkCZAAFiey4EAsDzOnAUBBBBAAAEEEEiSAOPvJPVm8W3xNQBs3bq1LV++3CZPnuwe//Wy6f1/hxxyiFsdeOnSpV4OYZ8ECHADSkAn0gQECADLfg0QAJadnBMigAACCCCAAAKxF2D8Hfsu9KUBvgaATZs2tbVr19rMmTOtR48enio4a9Ys23vvvW3jjTe21atXezqGneIvwA0o/n1ICxBIF2AGYHmuBwLA8jhzFgQQQAABBBBAIEkCjL+T1JvFt8XXAHC77bZzC4D88Y9/tBNPPNFTrR5++GH7n//5H9t2223t008/9XQMO8VfgBtQ/PuQFsRXYPHKxbZm/ZpaDWjSqIm1bd626EYRABZNV9CBBIAFcbEzAggggAACCCCAgJkx/uYykICvAeCxxx5rf/rTn6x79+7uXYCNGzfOqbxu3Tr37r/Zs2fbMcccY5MmTaJXKkSAG1CFdDTNjKRA7/G9bdp/ptWqW1X7KqseWl10fQkAi6Yr6EACwIK42BkBBBBAAAEEEECAAJBr4P8L+BoAPvPMM3bUUUdZgwYN3DsA//CHP9g222yTEVszBbVKsN4XqP2ffvppO+KII+iYChEgAKyQjqaZkRQgAIxkt2St1NQF99vy7xa4z3tsv6X7c/vNt7fhVcPj1RBqiwACCCCAAAIIIBCKAOPvUNgjd1JfA0C1TjP5nnzySRfqbbTRRvaTn/zEevXqZW3btnVf+/zzz93swJdeesnWrFljGzZscMc89thjkcOhQsEJcAMKzpaSEcgnQACYTyhan499a7DNXzGnVqVKnbEZrRZSGwQQQAABBBBAAIEgBRh/B6kbn7J9DwC/++47O+mkk2oe51Xol2lT8KdNjw3rnYFaBIStcgS4AVVOX9PS6AkQAEavT3LViAAwXv1FbRFAAAEEEEAAgagJMP6OWo+EUx/fA8BUM5577jm744477K9//autWrWqVuuaNWtmffr0sbPOOssOP/zwcFrOWUMV4AYUKj8nr3ABAsB4XQAEgPHqL2qLAAIIIIAAAghETYDxd9R6JJz6BBYAppqzfv16+/jjj23ZsmXuSy1btrQddtjBGjVqFE6LOWskBLgBRaIbqESFChAAxqvjCQDj1V/UFgEEEEAAAQQQiJoA4++o9Ug49Qk8AAynWZw16gLcgKLeQ9QvyQIEgPHqXQLAePUXtUUAAQQQQAABBKImwPg7aj0STn18DQC/+uorGzt2rGvJsGHDrF27djlbtXDhQhs3bpzbZ8SIEda8efNwFDhr2QW4AZWdnBMiUCNAABivi4EAMF79RW0RQAABBBBAAIGoCTD+jlqPhFMfXwNAvfPv7LPPtk6dOtncuXPztkgLgeyyyy724Ycf2t13321Dhw7Neww7JEOAG1Ay+pFWxFOAADBe/UYAGK/+orYIIIAAAggggEDUBBh/R61HwqmPrwHgkUceac8//7z96le/siuvvNJTi6644gq378CBA+3JJ5/0dAw7xV+AG1D8+5AWxFeAADBefUcAGK/+orYIIIAAAggggEDUBBh/R61HwqmPrwHgdtttZwsWLLBnn33W+vfv76lFL774olsJuEOHDvbJJ594Ooad4i/ADSj+fUgL4itAABivviMAjFd/UVsEEEAAAQQQQCBqAoy/o9Yj4dTH1wBw4403tnXr1tmbb75p3bp189SiOXPmWI8ePUzHrl692tMx7BR/AW5A8e9DWhBfAQLAePUdAWC8+ovaIoAAAggggAACURNg/B21HgmnPr4GgJtvvrmtWLHCXnvtNdt33309tejvf/+77b///tasWTN3LFtlCHADqox+ppXRFCAAjGa/ZKsVAWC8+ovaIoAAAggggAACURNg/B21HgmnPr4GgFrQ45///KeNGTPGzj33XE8tuuWWW+y8886zHXbYwS0GwlYZAtyAKqOfaWU0BQgAo9kvBIDx6hdqiwACCCCAAAIIxEWA8XdceirYevoaAA4bNszGjx9vnTt3trfffts22mijnLVfu3at7b777i40/PnPf2733XdfsK2l9MgIcAOKTFdQkQoUIACMV6czAzBe/UVtEUAAAQQQQACBqAkw/o5aj4RTH18DwNTjvA0aNLBjjjnG7r//fvdob6Zt1apVdtJJJ9njjz9u2v+VV16xPn36hKPAWcsuwA2o7OScEIEaAQLAeF0MBIDx6i9qiwACCCCAAAIIRE2A8XfUeiSc+vgaAKoJJ5xwgk2cONGFeu3btzfNCjzggAOsXbt27mtaJXjq1Kl2zz33mC5CbYMGDbJHHnkkHAHOGooAN6BQ2DkpAk6AADBeFwIBYLz6i9oigAACCCCAAAJRE2D8HbUeCac+vgeA3377rQ0cONBefvllF/hl2zZs2OA+OuSQQ+ypp56ypk2bhiPAWUMR4AYUCjsnRYAAMIbXAAFgDDuNKiOAAAIIIIAAAhESYPwdoc4IsSq+B4Bqi8K9W2+91W644YaaWX5127jddtvZRRddZGeddVbOoDBEG04doAA3oABxKRqBPALMAIzXJUIAGK/+orYIIIAAAggggEDUBBh/R61HwqlPIAFgqikKAufMmWOzZ8+2pUuXui+3bt3aevToYXvuuSfBXzh9HomzcgOKRDdQiQoVIACMV8cTAMarv6gtAggggAACCCAQNQHG31HrkXDqE2gAGE6TOGscBLgBxaGXqGNSBSa8PcEWrlhYq3ntWrSzIbsPKbrJY16aV/SxHJhbgACQKwQBBBBAAAEEEECgFAHG36XoJedYAsDk9GWsWsINKFbdRWURyCtAAJiXqOgdCACLpuNABBBAAAEEEEAAATP3aja9hk3bp59+6hZsZas8AQLAyuvzSLSYG1AkuoFKIOCbAAGgb5Q5Czr/kM7lORFnQQABBBBAAAEEEEiMAOPvxHRlSQ0hACyJj4OLFeAGVKwcxyEQTQECwPL0CwFgeZw5CwIIIIAAAgggkCQBxt9J6s3i20IAWLwdR5YgwA2oBDwORSCCAgSA5ekUAsDyOHMWBBBAAAEEEEAgSQKMv5PUm8W3hQCweDuOLEGAG1AJeByKQAQFCADL0ykEgOVx5iwIIIAAAggggECSBBh/J6k3i28LAWDxdhxZggA3oBLwOBSBCAoQAJanUwgAy+PMWRBAAAEEEEAAgSQJMP5OUm8W3xYCwOLtOLIEAW5AJeBxKAI+ChDc+YhZhqIIAMuAzCkQQAABBBBAAIGECTD+TliHFtkcAsAi4TisNAFuQKX5cTQCfgkQAPolWZ5yCADL48xZEEAAAQQQQACBJAkw/k5SbxbfFgLA4u04sgQBbkAl4HEoAiUKXDLlEpu3bJ4r5Z+ff+P+bNO0ox2+/fkllszhQQsQAAYtTPkIIIAAAggggEDyBBh/J69Pi2kRAWAxahxTsgA3oJIJKQCBogV6j+9t0/4zrdbx27foZsP3eKToMjkwOIFv1nxh6zesdScYduAO7s8mjZpY2+ZtgzspJSOAAAIIIIAAAggkRoDxd2K6sqSGEACWxMfBxQpwAypWjuMQKF2AALB0w3KWMPatwTZ/xZxap6xqX2XVQ6vLWQ3OhQACCCCAAAIIIBBTAcbfMe04n6tNAOgzKMV5E+AG5M2JvRAIQoAAMAjV4MokAAzOlpIRQAABBBBAAIFKEGD8XQm9nL+NBID5jdgjAAFuQAGgUiQCHgUIAD1CRWQ3AsCIdATVQAABBBBAAAEEYirA+DumHedztQkAfQalOG8C3IC8ObEXAkEIEAAGoRpcmQSAwdlSMgIIIIAAAgggUAkCjL8roZfzt5EAML8RewQgwA0oAFSKRMCjAAGgR6iI7EYAGJGOoBoIIIAAAggggEBMBRh/x7TjfK42AaDPoBTnTYAbkDcn9kIgCAECwCBUgyuTADA4W0pGAAEEEEAAAQQqQYDxdyX0cv42EgDmN2KPAAS4AQWASpEIeBQgAPQIFZHdCAAj0hFUAwEEEEAAAQQQiKkA4++YdpzP1SYA9BmU4rwJcAPy5sReCAQhQAAYhGpwZRIABmdLyQgggAACCCCAQCUIMP6uhF7O30YCwPxG7BGAADegAFApEgGPAgSAHqEishsBYEQ6gmoggAACCCCAAAIxFWD8HdOO87naBIA+g1KcNwFuQN6c2AuBIAQIAINQDa5MAsDgbCkZAQQQQAABBBCoBAHG35XQy/nbSACY34g9AhDgBhQAKkUi4FGAANAjVER2IwCMSEdQDQQQQAABBBBAIKYCjL9j2nE+V5sA0GdQivMmwA3ImxN7IRCEAAFgEKrBlUkAGJwtJSOAAAIIIIAAApUgwPi7Eno5fxsJAPMbsUcAAtyAAkClSAQ8ChAAeoSKyG4EgBHpCKqBAAIIIIAAAgjEVIDxd0w7zudqEwD6DEpx3gS4AXlzYi8EghAgAAxCNbgyCQCDs6VkBBBAAAEEEECgEgQYf1dCL+dvIwFgfqN6e/z73/+28ePH23PPPWfz58+3b775xtq0aWM/+tGP7Mc//rEdd9xx1rVr16wlv/DCC3b33XfbzJkzbcmSJe7YffbZx0477TTr37+/pxqtW7fO7rnnHnvooYfsgw8+sBUrVtg222xjBx98sJ177rm22267eSpn6dKldsstt9iTTz5pn3zyiTtG7Tj66KNt+PDh1qpVK0/lFLoTN6BCxdgfAf8ECAD9syxHSQSA5VDmHAgggAACCCCAQHIFGH8nt28LaRkBYCFaZnbrrbfaxRdfbCtXrsx6pIKzm2++ud7n33//vQv5FB5m20499VS76667rGHDhln3UWh3+OGHuwAx07bxxhvbbbfdZior1zZ9+nQX9C1atCjjbu3atXPBYM+ePQtUyr87N6D8RuyBQFACBIBByQZTLgFgMK6UigACCCCAAAIIVIoA4+9K6enc7SQALOA6uOqqq+yyyy5zR3Tu3NmGDRvmZu5tvvnm9sUXX9js2bPt8ccft6qqKrvpppvqlazg8Nprr3Vf7969u40cOdJ23HFH++ijj+y6665zx2vTfqNHj85Ys/Xr11vfvn3t9ddfd58fc8wxrh4tW7Y0BXqq4+LFi12A+Oyzz2adUfjpp5/aXnvt5WYgNm7c2C644AIbMGCAK1PHqf6aZdi2bVt78803rX379gVI5d+VG1B+I/ZAICgBAsCgZIMplwAwGFdKRQABBBBAAAEEKkWA8Xel9HTudhIAerwOXnnlFevXr5/b+6STTnKP32600UYZj16zZo01adKk1mfz5s1zj+UqVNt7771t6tSptskmm9Tss2rVKuvTp4+98cYbLpB77733rFOnTvXKv/fee23o0KHu62eeeabdfvvttfb58MMPXbD39ddf20477WTvv/++K6/upjY88MAD7suPPvqoHXvssbV2mTRpknuUWdvJJ59s9913n0cpb7txA/LmxF4IBCFAABiEanBlEgAGZ0vJCCCAAAIIIIBAJQgw/q6EXs7fRgLA/EamR3d32WUX++c//2l77rlnTUjn4dCaXRTW3Xnnne7v1dXVbpZg3W3atGnWu3dv9+VM4Z6+3qVLFxfqbbnllqZv4mbNmtUrR7MMNYtQW6ZwT4/8brvttq5dhx56qL344osZm3LYYYfZ5MmT3WzCzz77zLbeeutCmpxzX25AvlFSEAIFCwycMNBmLZzljlvx3Tr3Z/vmXWzorr8vuCwOCF6AADB4Y86AAAIIIIAAAggkWYDxd5J713vbCAA9WCkgSy3O8fDDD9uQIUM8HPXfXTZs2OAeoV2wYIELEhXgZdv0+dy5c11Ap8d0GzRoULOrZhHuvPPO7u+nn356TaBYtywFfHp/nzbVVXVO37QAyS9+8Qv3pYkTJ9rgwYMzVkefpdqq9xLq/YV+bdyA/JKkHARKExjz0rzSCuDowAUIAAMn5gQIIIAAAggggECiBRh/J7p7PTeOANADlR651aO3CuO++uor23TTTd1Ry5Ytc+/+00q5egdftu3jjz927/rTpuDt97/PPstGnyug06bjOnbsWFNs+uO/EyZMsOOPPz7rORUUKjDs0KGDW6k4fUt//HfhwoVZZ/bpM60srE3H3H///R60vO3CDcibE3shELQAAWDQwqWXP2vJs/b12iWuoD6d27g/27VoZ0N2L+yXUaXXhBIQQAABBBBAAAEE4ijA+DuOveZ/nQkAPZjq3X16J5/COIVymlF3zTXX2DvvvFNzdGpRkHPOOce0Cm/6pkU1jjzySPelMWPG2HnnnZf1rPpcC3Joe+6559xqv6ntwgsvtBtvvNH9VQuGdOvWLWs5Rx11lD399NMutPzmm2+sefPmNfvqHYRa2EOLl3z55Zc5BbSP3ieoxU5mzJjhQcvbLtyAvDmxFwJBCxAABi3sb/nnH9LZ3wIpDQEEEEAAAQQQQCDxAoy/E9/FnhpIAJiHSe/J02If+lMh2L777mtjx47NepQ+V3C3xRZb1OyjGX9nnHGG+7sW1xg0aFDW4x977LGaBTl0XOpRXR2gGX+PPPKIO1ar97Zu3TprOWeffXbNAiEffPBBzaPDOkDv8vv888/doiTpIWamwrp27WrvvvuuO0YzAr1uusHk2lRWz5493S561NnvVYa91pP9EKh0AQLAeF0BBIDx6i9qiwACCCCAAAIIREGAADAKvRB+HQgA8/TB8uXLax7vbdq0qX377bcuDLv++uvtiCOOMH1t5syZNmrUKNMiHtp++tOf2uOPP15TsvYdOXKk+/sLL7xgWlwj26bPU7P+brjhBhsxYkTNrjrf888/7/6+evVqd+5sm+pz3XXXuY+1srBWBk5tmg2oVYd79epVU+ds5Wgfzfxr0aKFm0nodUt/d2G+YwgA8wnxOQLBCRAABmcbRMkEgEGoUiYCCCCAAAIIIJBsAQLAZPev19YRAOaRSv9G0a5adXfWrFm1ZtSlAjmt4PuPf/zDlagwUOGZtiuvvNIuv/xy9/9Tpkyxgw46KOtZX3nlFevXr1/NcZdeemnNvvq6Pte2fv16tzpvtk3n03m1vfbaa7b//vvX7NqoUSM3o/GAAw6wqVOn5hQ48MAD3fE6Zt26H1YL9bIRAHpRYh8EwhcgAAy/DwqpAQFgIVrsiwACCCCAAAIIICABAkCuAwkQAOa5DpYuXWpt2vzw0nVt5557btZHgPXo74ABA9x+559/vt10003u/ytxBiCPAHODQSAeAgSA8einVC0JAOPVX9QWAQQQQAABBBD4f+zdeXxV1b3H/R9CggRQGRIN4ASCCKEKiAa1TlicUKgyiLfVPg8N4tCC2qsX9Ha4KjihxeJFSfWR2gKCdcCZXrXikIAIViIglQgicxBkVAZ5XmtjQk6yzzl7WHv+7H9aOWt8r8Vu99d9zgqDAAFgGFYh+DEQAGZZg++++y7lq7bqQA/1VVyzS309WJ0QrN6UU2/cqTfn1JXE3wDMtrW5AWUT4nME/BEgAPTHWVcvBIC6JGkHAQQQQAABBBBIjgDP38lZ60wzJQC0sA8KCgqMQzfUpb7i+6Mf/ShtrcLCQlm3bp3xFWF1+Ia6OAW4Phc3IAsbjyIIeCSwaP0i2b57u9H69A+/NP4z95A8adP0RI96pFldAgSAuiRpBwEEEEAAAQQQSI4Az9/JWWsCQJdrfd5558k///lPoxX1+3/du3dP22J1WFj7hN3Kykrp0KGDUUed6qveCEx3qc8nT55sfKzqHX/88TVFn3zySRk2bJjxz9OmTTNOBU53qQBy2bJlcswxx8jKlStTil1zzTXy9NNPG3+mTuNVh5qYXeqzNm3aGB+pOlOmTHEpebA6NyBtlDSEgG2B3k/0lvKvDhxaVH0d2+wUGfmjA6eMc4VXgAAwvGvDyBBAAAEEEEAAgbAK8Pwd1pXxd1y8AWjB+3e/+538z//8j1Hy2WeflSuvvNK01tatW+WII46Q/fv3S9++feWNN94wyql/bteunaxZs0Y6d+4sS5YsSdvrSSedZLw52LZtW1Gn49Y+TEMFeirYU9eIESNk0qRJpu2oNxDVm4jqGjp0qEydOjWlnAoYVdCorunTp8uQIUNM21Gfqfrqevzxx2X48OEWtKwV4QZkzYlSCHghQADohap3bb668mHZ+O0XRgcdj2xu/Genlp3knj73eNcpLSOAAAIIIIAAAgjERoDn79gspauJEABa4Pvkk0/k5JNPNkr+x3/8h/z1r381raXekPvFL35hfKZO4K19gu8NN9xQE9iVlZVJcXFxvTbUycHqJGF1qfKPPvpovTJdunQxAsSWLVsaAaE6lbjude+998ro0aONP54xY4YMGjQopYgKCFXAqE4CvvDCC+X11183nc9FF11khJjqtOHVq1enfVPQAmG9ItyAnKhRBwE9AgSAehz9amXCJ0Nk5faPU7orblcsZcPK/BoC/SCAAAIIIIAAAghEWIDn7wgvnsahEwBaxLzkkkvktddeM8Kw2bNnS58+feqFar169TKO187NzTW+vqtCtupLvb2nvhasDgg59dRTZc6cOdKkSZOaz3ft2iVnn322zJ8/Xxo1aiSLFy+Wjh071htd7a8B33jjjTJx4sSUMsuXL5cePXqIehtRfe1YvU2o2qt71f4a8MyZM2XgwIEpRdSfDR482Piza6+9Vp566imLUtaKcQOy5kQpBLwQIAD0QtW7NgkAvbOlZQQQQAABBBBAIAkCPH8nYZWzz5EAMLuRUUIFeKeffrps2bLFOBV41KhRokJBFeLNmzdPxo0bZ4R/6rrvvvvktttuq9eyeitPvZ2nLvU7grfffrsR0qnQTtVZuHCh8ZkqN3bsWNOR7du3T8455xx5//33jc/V15FLSkqkRYsWxjjUm4cbNmwwgkp1+MjFF19s2o56e7Bnz57G4SYqILz11lulX79+RllVb/z48UZYmZ+fb/zuofoKs86LG5BOTdpCwJ4AAaA9r6BLEwAGvQL0jwACCCCAAAIIRFuA5+9or5+u0RMA2pB87733jDfl1q9fb1pL/V7fHXfcYYRwZpf6yq0K69RbfOkudciH+o0+FeClu6qqqozw8cMPPzQtot5AVG8Gqr4yXXPnzpUBAwYYpxabXepwkBdeeMEIPnVf3IB0i9IeAtYFCACtW4WhJAFgGFaBMSCAAAIIIIAAAtEV4Pk7umunc+QEgDY1N23aJH/605+MYOyLL76Q3bt3GwdunHvuufKrX/0q4wnB1V29+uqrRsinAjwV5rVu3VrU14fVwRzp3tirO0z1dl5paalxwIf6TcAdO3YYJ/aqryaPHMvLnC0AACAASURBVDnS+LqxlUv1P2HCBGM+K1asMKqok4f79+9vvOXYqlUrK83YLsMNyDYZFRDQJkAAqI3Sl4YIAH1hphMEEEAAAQQQQCC2Ajx/x3ZpbU2MANAWF4V1CXAD0iVJOwjYFyAAtG8WZA0CwCD16RsBBBBAAAEEEIi+AM/f0V9DHTMgANShSBu2BbgB2SajAgLaBAgAtVH60hABoC/MdIIAAggggAACCMRWgOfv2C6trYkRANriorAuAW5AuiRpBwH7AgSA9s2CrEEAGKQ+fSOAAAIIIIAAAtEX4Pk7+muoYwYEgDoUacO2ADcg22RUQECbAAGgNkpfGiIA9IWZThBAAAEEEEAAgdgK8Pwd26W1NTECQFtcFNYlwA1IlyTtIGBfgADQvlmQNQgAg9SnbwQQQAABBBBAIPoCPH9Hfw11zIAAUIcibdgW4AZkm4wKCGgTIADURulLQwSAvjDTCQIIIIAAAgggEFsBnr9ju7S2JkYAaIuLwroEuAHpkqQdBOwLEADaNwuyBgFgkPr0jQACCCCAAAIIRF+A5+/or6GOGRAA6lCkDdsC3IBsk1EBAW0CBIDaKH1piADQF2Y6QQABBBBAAAEEYivA83dsl9bWxAgAbXFRWJcANyBdkrSDgH0BAkD7ZkHWIAAMUp++EUAAAQQQQACB6Avw/B39NdQxAwJAHYq0YVuAG5BtMiogoE2AAFAbpS8NEQD6wkwnCCCAAAIIIIBAbAV4/o7t0tqaGAGgLS4K6xLgBqRLknYQsC9AAGjfLMgaBIBB6tM3AggggAACCCAQfQGev6O/hjpmQACoQ5E2bAtwA7JNRgUEtAkQAGqj9KUhAkBfmOkEAQQQQAABBBCIrQDP37FdWlsTIwC0xUVhXQLcgHRJ0g4C9gUIAO2bBVmDADBIffpGAAEEEEAAAQSiL8Dzd/TXUMcMCAB1KNKGbQFuQLbJqICANoE3K9+Ur3d9bbT38idrjP/Ma3SEdDqit7Y+aEifAAGgPktaQgABBBBAAAEEkijA83cSV73+nAkA2QeBCHADCoSdThGoJ/DwP5ahEnKBJ5aMkK92LDZG2axxI+M/exT2kFlDZ4V85AwPAQQQQAABBBBAIAwCPH+HYRWCHwMBYPBrkMgRcANK5LIz6RAKEACGcFEyDOnmn3SK1oAZLQIIIIAAAggggEDgAjx/B74EoRgAAWAoliF5g+AGlLw1Z8bhFCAADOe6pBsVAWC01ovRIoAAAggggAACYRDg+TsMqxD8GAgAg1+DRI6AG1Ail51Jh1CAADCEi5JhSASA0VovRosAAggggAACCIRBgOfvMKxC8GMgAAx+DRI5Am5AiVx2Jh1CAQLAEC4KAWC0FoXRIoAAAggggAACIRfg+TvkC+TT8AgAfYKmm1QBbkDsCATCIUAAGI51sDoK3gC0KkU5BBBAAAEEEEAAgWoBnr/ZC0qAAJB9EIgAN6BA2OkUgXoCBIDR2hQEgNFaL0aLAAIIIIAAAgiEQYDn7zCsQvBjIAAMfg0SOQJuQIlcdiYdQgECwBAuSoYhEQBGa70YLQIIIIAAAgggEAYBnr/DsArBj4EAMPg1SOQIuAElctmZdEgESmaVSMXGCmM0a7d8a/znUXkdZcgJd4dkhAwjnQABIHsDAQQQQAABBBBAwK4Az992xeJZngAwnusa+llxAwr9EjHAGAv0fqK3lH9VnjLDY5udIiN/9EyMZx3dqa3Z8Zns/n6nMYGreh1j/Gez3GbS7chu0Z0UI0cAAQQQQAABBBDwTYDnb9+oQ90RAWColye+g+MGFN+1ZWbhFyAADP8a1R7hhE+GyMrtH6cMurhdsZQNK4vWRBgtAggggAACCCCAQCACPH8Hwh66TgkAQ7ckyRgQN6BkrDOzDKcAAWA41yXdqAgAo7VejBYBBBBAAAEEEAibAM/fYVuRYMZDABiMe+J75QaU+C0AQIACBIAB4jvomgDQARpVEEAAAQQQQAABBGoEeP5mMygBAkD2QSAC3IACYadTBAwBAsBobQQCwGitF6NFAAEEEEAAAQTCJsDzd9hWJJjxEAAG4574XrkBJX4LABCgAAFggPgOuiYAdIBGFQQQQAABBBBAAIEaAZ6/2QxKgACQfRCIADegQNjpFAFDgAAwWhvBLABsmtPU0inAd593t/Rp3ydaE2a0CCCAAAIIIIAAAloFeP7WyhnZxggAI7t00R44N6Borx+jj7YAAWC01s8sALQ6gxkDZ8igroOsFqccAggggAACCCCAQAwFeP6O4aI6mBIBoAM0qrgX4Abk3pAWEHAqQADoVC6YegSAwbjTKwIIIIAAAgggEBcBnr/jspLu5kEA6M6P2g4FuAE5hKMaAhoECAA1IPrYBAGgj9h0hQACCCCAAAIIxFCA5+8YLqqDKREAOkCjinsBbkDuDWkBAacCBIBO5YKpRwAYjDu9IoAAAggggAACcRHg+TsuK+luHgSA7vyo7VCAG5BDOKohoEGAAFADoo9NPPP5nbJu57+NHguPONRWzxwCYouLwggggAACCCCAQCwFeP6O5bLanhQBoG0yKugQ4AakQ5E2EHAmQADozC0MtW7+SSfHwyiZVSIVGytS6hflF0np5aWO26QiAggggAACCCCAQPgFeP4O/xr5MUICQD+U6aOeADcgNgUCwQkQAAZn77ZnNwGg2boXtyuWsmFlbodFfQQQQAABBBBAAIEQC/D8HeLF8XFoBIA+YtPVQQFuQOwGBJwJmL3FVbslK1/5bPCHBvU6P7bZKTLyR884GxS1fBMgAPSNmo4QQAABBBBAAIHYCPD8HZuldDURAkBXfFR2KsANyKkc9ZIuYPYWV22Tazr9UU5pfXFGpls+OJEAMKIbiQAwogvHsBFAAAEEEEAAgQAFeP4OED9EXRMAhmgxkjQUbkBJWm3mqlOAAFCnZvTaIgCM3poxYgQQQAABBBBAIGgBnr+DXoFw9E8AGI51SNwouAElbsmZsCYBHQHghE+GyMrtH6eMiK8Aa1ogj5shAPQYmOYRQAABBBBAAIEYCvD8HcNFdTAlAkAHaFRxL8ANyL0hLSRTgAAwmetePWsCwGSvP7NHAAEEEEAAAQScCPD87UQtfnUIAOO3ppGYETegSCwTgwyhgFkAmHtInhTmdTJGe/Exo6TTEb0zjvyZz++UdTv/nVLmqLyOMuSEu0M4Y4ZUW4AAkP2AAAIIIIAAAgggYFeA52+7YvEsTwAYz3UN/ay4AYV+iRhgSAXMAkC+vhvSxfJgWASAHqDSJAIIIIAAAgggEHMBnr9jvsAWp0cAaBGKYnoFuAHp9aS15AgQACZnrc1mSgCY7PVn9ggggAACCCCAgBMBnr+dqMWvDgFg/NY0EjPiBhSJZWKQIRQgAAzhovg4JAJAH7HpCgEEEEAAAQQQiIkAz98xWUiX0yAAdAlIdWcC3ICcuVELAQLAZO8BAsBkrz+zRwABBBBAAAEEnAjw/O1ELX51CADjt6aRmBE3oEgsE4MMoQABoPVFKf5ysvXCGUqWHzNcSzs6GiEA1KFIGwgggAACCCCAQLIEeP5O1nqnmy0BIPsgEAFuQIGw02kMBAgArS8iAWCqldneKW5XLGXDyqyjUhIBBBBAAAEEEEAgcgI8f0duyTwZMAGgJ6w0mk2AG1A2IT5HwFwgCQGgruBO1x7iDUBdkrSDAAIIIIAAAgggEIQAz99BqIevTwLA8K1JIkbEDSgRy8wkPRAgAPQANUuTBID+m9MjAggggAACCCCAgD4Bnr/1WUa5JQLAKK9ehMfODSjCi8fQAxUgAPSfnwDQf3N6RAABBBBAAAEEENAnwPO3Pssot0QAGOXVi/DYuQFFePEYeqACBID+8xMA+m9OjwgggAACCCCAAAL6BHj+1mcZ5ZYIAKO8ehEeOzegCC8eQw9UgADQf/4wBYBuZj/hkyGycvvHKU1wCIgbUeoigAACCCCAAALREOD5Oxrr5PUoCQC9FqZ9UwFuQGwMBJwJhDkADNvhHc6E69ciANQlSTsIIIAAAggggAACQQjw/B2Eevj6JAAM35okYkTcgBKxzEyyWqCkRKSiwr5Hfr7IrFkp9RatXyTbd2+X6R9+WfPnuYfkSZumJ9pvX3MNAkDNoJqbe+bzO2Xdzn/XtFp4xKFSlF8kpZeXau6J5hBAAAEEEEAAAQTCJMDzd5hWI7ixEAAGZ5/onrkBJXr5kzf53r1Fysvtz7ttW5GvvjKt9/A/ltlvL02NuAZ3uoDi8gZgXY+bf9JJFxHtIIAAAggggAACCIRYgOfvEC+Oj0MjAPQRm64OCnADYjdEQsDszb1f/1pk6NDMw7/jDpG33jpYxkn4p2oTAIZimxAAhmIZGAQCCCCAAAIIIICAQwGevx3CxawaAWDMFjQq0+EGFJWVSvg4zd7cGz9e5JZbMsMMGiTy7LPu8QgA3RtqaIEAUAMiTSCAAAIIIIAAAggEJsDzd2D0oeqYADBUy5GcwXADSs5aR3qmBICRXr6wDT5sQSJfAQ7bDmE8CCCAAAIIIICANwI8f3vjGrVWCQCjtmIxGS83oJgsZNynYRYAtmolUlWVeeZW3wAsLs7cTt1DQBYtEtm+3agzfd6BQ0B2N8mTTce7OwCE3wD0ZyMTAPrjTC8IIIAAAggggAACqQI8f7MjlAABIPsgEAFuQIGw06ldgXSHd+zf7z4AVOFfWZm9EaUZz5qTTrHXzg+lP/jFKFnVvbcQADris12JANA2GRUQQAABBBBAAAEENAjw/K0BMQZNEADGYBGjOAVuQFFctQSO2WkAWPcQEDO6oiKR0lJ7qE5PE07Ty8t3/lH+ffbFBID2VsFxaQJAx3RURAABBBBAAAEEEHAhwPO3C7wYVSUAjNFiRmkq3ICitFoJHqtZ4ObkzT1dhASAuiQDaYcAMBB2OkUAAQQQQAABBBIvwPN34reAAUAAyD4IRIAbUCDsdGpXgADQrhjlMwgEHQAu21ImO/duqRlhvx+1kZZNWkqf9n1YNwQQQAABBBBAAIEYC/D8HePFtTE1AkAbWBTVJ8ANSJ8lLXkoEPMA8LMR/eTrU90dIOKhfuyaDjoAnPDJEFm5/eMU1+J2xVI2zOZvUcZuZZgQAggggAACCCAQbwGev+O9vlZnRwBoVYpyWgW4AWnlpDGvBMIWAJaUyKK3psv23QdOAq6+mjTIkQ45rW0rfPnTM2XrScfarkcFZwIEgM7cqIUAAggggAACCCDgToDnb3d+calNABiXlYzYPLgBRWzBkjrcsAWAItL7id5S/lV5yop0zS2U0oKrk7pKkZk3AWBkloqBIoAAAggggAACsRLg+TtWy+l4MgSAjumo6EaAG5AbPer6JkAA6Bt1EjoiAEzCKjNHBBBAAAEEEEAgfAI8f4dvTYIYEQFgEOr0KdyA2ASRECAAjMQyRWWQBIBRWSnGiQACCCCAAAIIxEuA5+94rafT2RAAOpWjnisBbkCu+KjslwABoF/SieiHADARy8wkEUAAAQQQQACB0Anw/B26JQlkQASAgbDTKTcg9kAkBAgAI7FMURkkAWBUVopxIoAAAggggAAC8RLg+Tte6+l0NgSATuWo50qAG5ArPir7JRDTALD9lNmSt7oqRXFn29ZSeW1fv2QT2Q8BYCKXnUkjgAACCCCAAAKBC/D8HfgShGIABIChWIbkDYIbUPLWPJIzjmkAWDR2qjSvXJuyJNvaF0rFGE4SjsI+dRokTvhkiKzc/nHKFIvbFUvZsLIoTJsxIoAAAggggAACCDgU4PnbIVzMqhEAxmxBozIdbkBRWamEj5MAMOEbIJzTJwAM57owKgQQQAABBBBAIKwCPH+HdWX8HRcBoAvv2267TR544IGaFt5++20599xzM7b42muvyeTJk+XDDz+UjRs3Sn5+vvTq1UuGDx8uF198saXR7N27V/785z/L3/72N1m6dKls375d2rRpIxdccIH8+te/lq5du1pqp6qqSh555BF54YUXZMWKFUad4447TgYMGCAjR46UVq1aWWrHSSFuQE7UqOO7gFcB4NvjHE+l98JJUr5tVUr9rrmFUlpg/e093gB0zB+KigSAoVgGBoEAAggggAACCERGgOfvyCyVpwMlAHTI+69//UtOPfVUUWFc9ZUpAPz++++NkO+JJ55I2+Mvf/lLefzxx+WQQw5JW0aFdpdccokRIJpdjRs3lokTJ4pqK9M1d+5cI+hbt26dabHCwkIjGDzttNMcCmWuxg3IE1Ya1S1AAKhblPY0CBAAakCkCQQQQAABBBBAIEECPH8naLEzTJUA0ME+UGFecXGxEcIVFBTIhg0bjFYyBYCjR4+We++91yjXvXt3UW8PdujQQZYvXy7333+/LFy40PhMlRs7dqzpqPbt22e8Yfjee+8Zn19xxRVSUlIiLVu2FBXo3X333cZYVID48ssvp32jcNWqVdKzZ0/jDcRGjRrJLbfcIv369TPaVPUeeughI9hUc/voo4+kXbt2DpQIALWj0aD/AgSA/pvTY1YBAsCsRBRAAAEEEEAAAQQQqCVAAMh2UAIEgA72wR//+Ee5+eabpXPnzvLTn/5Uxo078HW+dAHgsmXLjK/lqlBNvTU4Z84cadKkSU3PO3fulHPOOUfmz59vBHKLFy+Wjh071hvZk08+KcOGDTP+/IYbbpBHH300pcznn39uBHtbt26VE044QZYsWWK0V/e65ppr5Omnnzb+eMaMGTJo0KCUIjNnzpTBgwcbf3bttdfKU0895UApcxVuQNpJadALARXu796d2nJurkhBgbve+AqwO7+E1yYATPgGYPoIIIAAAggggIBNAZ6/bYLFtDgBoM2FVW/PdenSxfjdPRX4/fOf/5Q//OEPRivpAkAV1k2aNMkoU1ZWZrw9WPcqLy+X3uptozThnvpz1a8K9Vq0aCHqL3BeXl69dtRbhuotQnWZhXvqK79t27YV9RbjhRdeKK+//rqpwEUXXSRvvPGG8Tbh6tWr5aijjrIplbk4NyCtnDQWNQECwKitWKjGSwAYquVgMAgggAACCCCAQOgFeP4O/RL5MkACQJvMl112mfE12eo3437/+99nDAD3799vfIV2zZo1xhuDKsBLd6nPP/vsMyOgU0FjgwYNaoqqtwhPPPFE459HjBhREyjWbUsFfOr3+9Q1dOhQmTp1akoRdQDJddddZ/zZ9OnTZciQIabDUZ+p+upSv0uofr9Q58UNSKcmbUVOgAAwcksWpgETAIZpNRgLAggggAACCCAQfgGev8O/Rn6MkADQhrJ6o04FZuo399Tpu+oE32wBYGVlpfFbf+pSwdtjjz2Wtkf1uQro1KXqHX/88TVla3/9d9q0aXLVVVelbUcFhSowPOaYY2TlypUp5Wp//Xft2rVp3+xTn6mThdWl6kyZMsWGVPai3ICyG1EixgIEgDFeXO+nRgDovTE9IIAAAggggAACcRLg+TtOq+l8LgSAFu22bNkiJ510knFqbmlpac0pu9kCQPW2oHprUF0PP/ywjBo1Km2P6nN1IIe6XnnlFeO03+rrN7/5jYwfP974R3VgyCmnnJK2nf79+8usWbOMNwi3bdsmTZs2rSmrfoNQHexx+OGHi5pTpkuVUb8n2KtXL5k3b55FKWvFuAFZc6JUTAUIAGO6sP5MiwDQH2d6QQABBBBAAAEE4iLA83dcVtLdPAgALfqpr8Cq4O+MM84wTuGt/nputgBQvfF3/fXXG72owzUGDhyYtsdnn3225kAOVa/6q7qqgnrj75lnnjHqqtN7W7dunbadm266qeaAEPWmYvVXh1UF9Vt+69evNw4lqaioyDj7oqIi+fTTT4066o1AO5e6wWS6VHunnXaaUUR93dmLk4btjJeyCPgqQADoK3fcOiMAjNuKMh8EEEAAAQQQQMBbAQJAb32j0joBoIWVUoHf2WefLQ0bNpQFCxZIt27damplCwAfeOABue2224zyr732mqjDNdJd6vPqt/4efPBBufXWW2uKXnrppfLqq68a/7xr1y459NBD07Zz++23y/333298rk4WVicDV1/qbUB16vDpp58u6uCRTJcqo978a9asmfEmoZ2r9u8XZqtHAJhNiM9tC5SUiGQJuG23WVZmu0raCi4CwDu+mC3LdlXJph0HTyc+ulELGXH4WZbHVzR2qjSvTA31t7UvlIoxV1tug4LREyjZMFU+3Z267sXtiqVsmMa9HT0WRowAAggggAACCMRegAAw9ktsaYIEgFmYdu/ebXzdVh3e8Z//+Z81wVp1tWwB4F133SW//e1vjeJvvvmmnH/++Wl7fOutt6RPnz7G56renXfeWVNW/bn6XF379u0zTudNd6n+VH11vfvuu3LWWQeDARViqhOAf/zjH8ucOXMyzl6Fnqq+qrN3715LG6q6EAGgLS4K6xZQJ2pnCbhtd7l/v+0qaSu4CACr2yyr3OR4PASAjukiXXH5no2y8/s9NXPodtmvpFluM+l25MF/qRXpCTJ4BBBAAAEEEEAAAVMBAkA2hhIgAMyyD6oDPnWgxuLFi1N+T09VzRYAJvUNQL4CzA0mUAECwIz8BICB7s7QdN572IOhGQsDQQABBBBAAAEEEPBOgADQO9sotUwAmGG11O/nnXzyyaLeAnzxxRfl8ssvr1c6WwCY1N8AzPaXgBtQNiE+dyVAAEgA6GoDJaMyAWAy1plZIoAAAggggAACPH+zB5QAAWCGfaAO4Zg8ebK0b99e7rnnHtOS6uCOv//978Zn//3f/y1dunQx/rs6+Vf93h6nAJsDcwPiBuSpAAEgAaCnGywejRMAxmMdmQUCCCCAAAIIIJBNgOfvbELJ+JwAMMM6/+IXv5ApU6Y42glffPGFHHfccVJZWSkdOnQw2lCBonojMN1VHTiqz1W9448/vqbok08+KcOGDTP+edq0acapwOkudervsmXLRH1teeXKlSnFrrnmGnn66aeNP1Mn8aoTfs0u9VmbNm2Mj1Qdpw7pxsgNyNG2opJVgQkTRKr3/sMPH6xVXGy1hfrlQnIISPXA+A1A50tJzQMCBIDsBAQQQAABBBBAIBkCPH8nY52zzZIAMIOQjgBw//790q5dO1mzZo107tzZOEwk3XXSSSeJ+tpx27ZtRZ2MW/sgDRXoqWBPXSNGjJBJkyaZNrNu3TopLCw0Phs6dKhMnTo1pZx6o1EFjeqaPn26DBkyxLQd9Zmqr67HH39chg8fnm0v2fqcG5AtLgrHTYBDQOK2opGcDwFgJJeNQSOAAAIIIIAAArYFeP62TRbLCgSALpc1228AquZvuOGGmsCurKxMik3eQiovL5fe6muLP5R/9NFH641Mfb1YBYgtW7Y0AsK8vLx6Ze69914ZPXq08eczZsyQQYMGpZRRAaEKGNVJwBdeeKG8/vrrpgIXXXSRvPHGG8Zpw6tXr077pqBTPm5ATuWoF6iAhuBO1/h5A1CXZHLbIQBM7tozcwQQQAABBBBIlgDP38la73SzJQB0uQ+sBIDq7b2uXbvK3r175dRTT5U5c+ZIkyZNanretWuXnH322TJ//nxp1KiRcdpwx44d642s9teAb7zxRpk4cWJKmeXLl0uPHj1k69atxteO1duEqr26V+2vAc+cOVMGDhyYUkT92eDBg40/u/baa+Wpp55yqVS/Ojcg7aQ06IdATALA9lNmS97qqhSxnW1bS+W1ff1QpI+QCBAAhmQhGAYCCCCAAAIIIOCxAM/fHgNHpHkCQJcLZSUAVF2ot/LU23nq6t69u9x+++1GSKdCu/vuu08WLlxofKbKjR071nRU+/btk3POOUfef/994/Mrr7xSSkpKpEWLFjJv3jy56667ZMOGDcZbe+rwkYsvvti0HfX2YM+ePWXjxo1GQHjrrbdKv379jLKq3vjx442wMj8/XxYsWGB8hVn3xQ1Ityjt+SIQggBw2oZ/ydrd22TFph01U27VsKn0zTvJFwI6iY8AAWB81pKZIIAAAggggAACmQR4/mZ/KAECQJf7wGoAqL5yq8I69RZfuksd8qF+o08FeOmuqqoqueSSS+TDDz80LZKbm2u8Gaj6ynTNnTtXBgwYIOorwWaXOhzkhRdekNNPP92lkHl1bkCesNKo1wIhCAB7L5wk5dtWpcy0a26hlBZc7fXsaT/iArdVPS+f7dlQM4vcvMOlR2EPmTV0VsRnxvARQAABBBBAAAEEMgnw/M3+UAIEgC73gdUAsLqbV1991Qj5VICnwrzWrVtLr169jIM50r2xV3eI6u280tJS44AP9ZuAO3bsME7s7dOnj4wcOdL4urGVS/U/YcIEI+hbsWKFUUWdPNy/f38ZNWqUtGrVykozjspwA3LERqWgBTQEgG5+u09Nv2TDVPl091oCwKD3QgT7N9s7xe2KpWxYWQRnw5ARQAABBBBAAAEErArw/G1VKt7lCADjvb6hnR03oNAuDQPLJEAAyP6IsAABYIQXj6EjgAACCCCAAAIuBHj+doEXo6oEgDFazChNhRtQlFaLsdYIEACyGSIsQAAY4cVj6AgggAACCCCAgAsBnr9d4MWoKgFgjBYzSlPhBhSl1YrgWCdMEFm5MnXgxx4rMnKku8kQALrzo7ZjAbOTm+02tnxPlezav0c25on0/+EnI/kKsF1FyiOAAAIIIIAAAtET4Pk7emvmxYgJAL1Qpc2sAtyAshJRwI1A794i5eWpLRQXi5S5/K0zAkA3q0JdFwJFY6dK88rU33502txXzUWOvvVAbQJAp4rUQwABBBBAAAEEoiPA83d01srLkRIAeqlL22kFuAGxOTwVIAC0zNv2lbnS4l/LLZfPVnBn29ZSeW3fbMX43KYAAaBNMIojgAACCCCAAAII1Ajw/M1mUAIEgOyDQAS4AQXCnpxOCQAzrvVhS1ZKox3fGmUO2bNPOj7xmra9sa19oVSM+eH7pdpapSHPAsDmR0tZ9+vdAZ832l19aiOAAAIIWPpWFAAAIABJREFUIIAAAgh4KsDzt6e8kWmcADAySxWvgXIDitd6hm42BIAZl0RnmFS3IwJAb/426FyzlK8A6w4AS0pEKircIRQViZSWumuD2ggggAACCCCAAAI1Ajx/sxmUAAEg+yAQAW5AgbAnp1MCQALAmO32dAGgClytXqaHgOgOAM3+7lkdYHU5Hb/XabdPyiOAAAIIIIAAAjEW4Pk7xotrY2oEgDawKKpPgBuQPktaMhEgAPQtANzT7FBZNuJy+T6nodHn941zZGe7fLalZoGjn3tPmqzfnNLqriNbyKorzrLcU8mGqfLp7tSDRIoJAC37URABBBBAAAEEEIiqAM/fUV05veMmANTrSWsWBbgBWYSimDMBAsCMbu2nzJa81VXObE1qbT65g6y+9HRt7dGQNwJmAWDTQ3KlW9Mjazo8/4gOcs/xmQ9xmbbhX/LI6g8ODvLwtlKUXySll5eK8AagN4tHqwgggAACCCCAgAsBnr9d4MWoKgFgjBYzSlPhBhSl1YrgWAkAI7hoDNlrAbMAsG6fA1sXycwumQ9xeeir9+TWyldTqha3K5ayYWUEgF4vIu0jgAACCCCAAAIOBHj+doAWwyoEgDFc1ChMiRtQFFYpgDHWPkDgxRdFCgoyD0IFfWbXokUiO3akfqLjd8XeHucapaxyk6s2zEKcrrmFUlrAybuuYBNQ2ZcA0O4hIF79XU3AejJFBBBAAAEEEEDAqgDP31al4l2OADDe6xva2XEDCu3SBDuw2m/urVol0q5d5vE0aGB9vASA1q0oGUsBFQA2WLVWmu0+ML0x74pctkxke47Ioh++BdyqURPplNc64/w/3LZa9u7/3ijzUieRcWeL1LwBaFfOq7d17Y6D8ggggAACCCCAQIwFeP6O8eLamBoBoA0siuoT4AakzzJWLREAZl1O3gDMSkSBNAJq75T+71rp/ZUeIhX+DRwssrsRAaAeUVpBAAEEEEAAAQS8EeD52xvXqLVKABi1FYvJeLkBxWQhdU+jbgCYny8yfrzImDHmPfEGoOHCV4B1b8R4tjdu82z5n0eWyimr9rie4I4ckev6iXzX6EBTnVp1lHsGThLp08de22ZfGS4qEikttdcOpRFAAAEEEEAAAQTSCvD8zeZQAgSA7INABLgBBcIe/k5rB4Cffy4yapRIVZVIWVnkA8CSZc9JxY71su27vZbWId1v+lW/Adgl5yhZvGcdAaAlTQpVCxSNnSrNK9d6A6Lja/bejIxWEUAAAQQQQACBRAvw/J3o5a+ZPAEg+yAQAW5AgbCHv9PaAeBpp4nMmyeSKVRIdwiI2Ux1vFXk4hCQ3gsnSfm2VZbX4IN2t5qWVQHgEYc0kZFHnCeD1z1BAGhZlIJKgACQfYAAAggggAACCCRPgOfv5K252YwJANkHgQhwAwqEPfydenUggIvgTheargBwyta5MrR5T9myb5cMWDc50ADwxD89L02/3JBCtOOYAvnsVz/VxUY7Pwi0mrtEcr9JPdl69+FNZdPpJ9kyaj9ltuStrjLqND/0h+/v2mpBZNGO9bJ93w8nifxQt1luM+l2/lV8ddemJcURQAABBBBAAAE/BHj+9kM5/H0QAIZ/jWI5Qm5AsVxW95MiAKwxTPcGYHWBDXu3BR4Amr1Ntq19oVSMudr9XqCFFAEvrHu3b+VI2SzMdnwKsKMRUAkBBBBAAAEEEEDAjgDP33a04luWADC+axvqmXEDCvXyBDc4AkACwOB2X6h7JgAM9fIwOAQQQAABBBBAINQCPH+Henl8GxwBoG/UdFRbgBsQ+8FUIGEBYJMGOdI+p7UpRbpDQKoLf71vp9y+6QXjH1Ubo1v09X1TeRFK+T6JiHTohTVvAEZk8RkmAggggAACCCDgUoDnb5eAMalOABiThYzaNLgBRW3FfBpvwgLArrmFki3o80neUTdehFKOBhLBSo227pTOEw8EuFYu9bt9Db/bk1I0qK9bV59EXXswxc2PlrLu11uZSvoy5412V5/aCCCAAAIIIIAAAqYCPH+zMZQAASD7IBABbkCBsIe/UwLA8K9RrRESADpfrtyvt0nP2w4c4uL0IgB0Kkc9BBBAAAEEEEAgWQI8fydrvdPNlgCQfRCIADegQNjD3ykBYPjXiABQyxoRAP7AuOBzka27DvxD0Q+nR7dsKdKnjxZnGkEAAQQQQAABBBAQ4fmbXaAECADZB4EIcAMKhD38nRIAhn+NCAC1rBEB4A+MN00SWbyqvmlxsTPnyZNFunVzVpdaCCCAAAIIIIBATAV4/o7pwtqcFgGgTTCK6xHgBqTHMXatEABGakn5CrDz5SIAzBIAOqX94AMRdR/hQgABBBBAAAEEEKgR4PmbzaAECADZB4EIcAMKhD38nRIAhn+Nao2QAND5ctk9BMSsp51tW0vltf6f/qz1EJB0bwA6pSUAdCpHPQQQQAABBBCIsQDP3zFeXBtTIwC0gUVRfQLcgPRZxqolAsBILadZALivcY6oYKr29U3nY2TVFWdlnFuruUuk8M2FKWWCCrgitQgBDJYAMAB0ukQAAQQQQAABBFwI8PztAi9GVQkAY7SYUZoKN6AorZaPYyUA9BHbfVdmAaBZq5t6dpJl11+WscPC2fPluBnvpJQJ6pRb9zLxboEAMN7ry+wQQAABBBBAIH4CPH/Hb02dzIgA0IkadVwLcANyTRjPBmIcAE5Y/b6s/HaLrPnm25q1O6rhYTKkeY/IriUBYPali+PXpLUGgA8+J7Ji/QHIw9pmB81WgkNAsgnxOQIIIIAAAggkUIDn7wQuusmUCQDZB4EIcAMKhD38ncY4AKzGL6vcFP51sDhCnQHgqaMelZztB8NRNYQ4vAEYxwDw6307Ze/+fTW7pOexLSS3QUMpyG1mceekKXbeaHf1qY0AAggggAACCCBgKsDzNxtDCRAAsg8CEeAGFAh7+DslAAz/GtUaoc4AsPcvx9ebOwFgNLZD7/at9AyUAFCPI60ggAACCCCAAAJ1BHj+ZksQALIHAhPgBhQYfbg7JgAM9/rUGV37KbMlb3VV1jFbOQSEADArY2gLEACGdmkYGAIIIIAAAgggYAjw/M1GIABkDwQmwA0oMPpwd0wAGO718XB0cfyqrOKK67xqbwUCQA//YtA0AggggAACCCCgQYDnbw2IMWiCrwDHYBGjOAVuQFFcNR/GPG2ayNq1qR0VFooMHequ87fHuauvsXacfgNQI0tsgzICQBu7RNdXgMeOFXnpJRsdZylaVCRSWqqvPVpCAAEEEEAAAQR8FuD522fwkHZHABjShYn7sLgBxX2FQzY/AsCQLUj94cQ1KIvrvGqvYCjeALz8cpEFCw4M67vvRKqyfzXd8l+K4mKRsjLLxSmIAAIIIIAAAgiETYDn77CtSDDjIQAMxj3xvXIDSvwW8BeAANBfbwe9mQVl+xrnyM62rU1bW3rTANl7WJ6DnvytQgBow9vNG4BmPx9go+uMRQkAdUnSDgIIIIAAAggEJMDzd0DwIeuWADBkC5KU4XADSspKh2SeBIAhWYj0w7B6onB1Cx/dP1x2t2yecV5tX5krLf61XNvcVRhZeW1fW+0RANrgCksAeNllIo0bHxx4p04i99xjYyIURQABBBBAAAEEwiXA83e41iOo0RAABiWf8H65AcVsA5SUiFRUuJ/U+eenPmiHKLhzO7kJq9+Xld9ukTXffFvT1FEND5MhzXu4bToW9b0IABvs2SudJr0kLT+p1GK0rX2hVIy52rSto/5vgbSet7TeZ+qU5Ibf7Un580ztaBmoz42E4ivAOt8A7NdP5NlnU0NAn03pDgEEEEAAAQQQ0CnA87dOzei2RQAY3bWL9Mi5AUV6+eoPXtfD98CBIjNnHmw/RgFg74WTpHzbqhS7rrmFUlpgHijFbIdknY6uANBuO1kHJiLrzjtZcjdtk5ztu9IGgMc+809p84+PrDQnUQ8ASzZMlU93px7WU9z8aCnrfr2l+act5OYNQF3/EqJ6cOotwDFj3M2H2ggggAACCCCAQEgEeP4OyUIEPAwCwIAXIKndcwOK2coTAGZdUALAzER2g7t0XwG2207WhRORNT/pKV9ecZa0mf2RrL70dNMqBIAaAkAri2GljJsg0Ur7lEEAAQQQQAABBCImwPN3xBbMo+ESAHoES7OZBbgBxWyHJCAALKvc5GrRzN6a4g3Ag6Ttp8wW9XVZq1e6Q0C8CgBXDjk349AIAAkAre5dyiGAAAIIIIAAAn4L8Pztt3g4+yMADOe6xH5U3IBitsQEgFkXlAAwK5GWAnaDxHSdNq88+BVX9QYgAeBBKc++AqxlB4gIbwDqkqQdBBBAAAEEEIiJAM/fMVlIl9MgAHQJSHVnAtyAnLmFtpZZANi0qUi3bvaGHOJDQHgD0N5SRr107SCx6rTOsu6CzIe1pDsExMzByWnCYfIkAAzTajAWBBBAAAEEEEAguwDP39mNklCCADAJqxzCOXIDCuGiuBmSWQBYXCxSVuamVZEQHQJCAOhuKakdHwECwPisJTNBAAEEEEAAgWQI8PydjHXONksCwGxCfO6JADcgT1iDa/Tyy0UWLEjtv0cPkVmz3I2JANCdH7UR8ECAANADVJpEAAEEEEAAAQQ8FOD520PcCDVNABihxYrTULkBxWk1PZwLAaCHuDSNgDOBWAeA06aJrD34+4+GUGGhyNChzrCohQACCCCAAAIIhECA5+8QLEIIhkAAGIJFSOIQuAElcdUdzJkA0AEaVRDwViDWAaBXP2fg7ZLQOgIIIIAAAgggkFGA5282iBIgAGQfBCLADSgQ9uh1SgAYvTVjxLEXIACM/RIzQQQQQAABBBCImQDP3zFbUIfTIQB0CEc1dwLcgNz5JaY2AWBilpqJRkeAADA6a8VIEUAAAQQQQAABJcDzN/tACRAAsg8CEeAGFAh79DolAIzemjHi2AskLgBs2lSkWzfr6/riiyIFBdbLUxIBBBBAAAEEEPBYgOdvj4Ej0jwBYEQWKm7D5AYUtxX1aD4EgB7B0iwCzgVCHwA6n5rITZNEFq9y04LIqlUi7dq5a4PaCCCAAAIIIICARgGevzViRrgpAsAIL16Uh84NKMqr5+PYCQB9xKYrBKwJEABmcSIAtLaRKIUAAggggAACvgnw/O0bdag7IgAM9fLEd3DcgOK7tlpnRgColZPGENAhQABIAKhjH9EGAggggAACCPgnwPO3f9Zh7okAMMyrE+OxcQOK2eIuWiSyfXvqpJo1s/e7WWYkBIAx2yhMJw4CBIAEgHHYx8wBAQQQQACBJAnw/J2k1U4/VwJA9kEgAtyAAmH3rtPevUXKy1PbLy4WKStz1ycBoDs/aiPggUCsA8AHnxNZsf6A2mFtnenVPQRE3R91XkVFIqWlOlukLQQQQAABBBCIuQDP3zFfYIvTIwC0CEUxvQLcgPR6Bt4aAWDWJTALTbrmFkppwdVZ61IAgTAJxDoArA193mg97A0a6GmnuhUd/3JF74hoDQEEEEAAAQRCLsDzd8gXyKfhEQD6BE03qQLcgGK2IwgAsy4oAWBWIgpERMCrvdy7fatwCRAAhms9GA0CCCCAAAIIOBbg+dsxXawqEgDGajmjMxluQNFZK0sjJQDMyuRVaJK1YwogoFnAq71MAGhxoXgD0CIUxRBAAAEEEECgWoDnb/aCEiAAZB8EIsANKBB27zolAPTOlpYRCJnAM9sWyLp9W1NGdVTDw2RI8x6uRkoAaJGPANAiFMUQQAABBBBAgACQPVBbgACQ/RCIAAFgIOzedUoA6J0tLSOQEIHYBoBuDgGpfbjSzTcf2AnHHisycmRCdgXTRAABBBBAAAEdAjx/61CMfhsEgNFfw0jOgBtQJJct/aAJAGO2oEwHAf8FYhsA+k9JjwgggAACCCCAQIoAz99sCCVAAMg+CESAG1Ag7N51SgDonS0tI5AQgdAFgLrcdR0moms8tIMAAggggAACiRPg+TtxS246YQJA9kEgAtyAAmH3rlOzALDL0SITr/euT59bLqvc5HOPdIdAsgQIAJO13swWAQQQQAABBPwT4PnbP+sw90QAGObVifHYuAHFbHEJAGO2oEwHAesC6lCQ/9u11HqFNCWbN24kRU2PlNJOV7huK1QN6H4DcMIEkenT3U+xqEiktNR9O7SAAAIIIIAAAqEX4Pk79EvkywAJAH1hppO6AtyAYrYnCABjtqBMBwHrAhO2/FOe2f6R9QoZShY3P1rKusfnzWFjqroDwFtuEXn4YffenCbs3pAWEEAAAQQQiIgAz98RWSiPh0kA6DEwzZsLcAOK2c4gAIzZgjIdBKwLEABmsSIAtL6ZKIkAAggggAACngjw/O0Ja+QaJQCM3JLFY8DcgOKxjjWzIADMuqBf79spe/fvSynXqEFDadkwL2tdCiAQZgECQALAMO9PxoYAAggggAACIjx/swuUAAEg+yAQAW5AgbB71ykBYFbbkg1T5dPda1PKdc0tlNKCq7PWpQACYRYgACQADPP+ZGwIIIAAAgggQADIHjggQADITghEgAAwEHbvOk1QADh75xKZuX2hbcvKPVWya/8eAkDbclQIu4DTQ0DM/k7wG4AWVtvJISCLFons2JHaOL8BaAGbIggggAACCMRDgOfveKyj21kQALoVpL4jAW5Ajtj0VyopEamoyNyulZMiGzSo30aXo0UmBv9j/mWVm7S6Tds2X/70zTta2uQNQC2MNBJRAbO3YgkAPVpMs39JQwDoETbNIoAAAgggED4Bnr/DtyZBjIgAMAh1+uQ3CMKyB8weCuuOzcpDIgGgoxUlAHTERqWYCvRu3yqeM9N9CIgTJQJAJ2rUQQABBBBAIDYCBICxWUpXEyEAdMVHZacC3ICcymmupysA7N5d5OOPUwfHG4BZF4sAMCsRBRIkENsAUNcahiFI1DUX2kEAAQQQQAABXwV4/vaVO7SdEQCGdmniPTBuQCFZX10B4MyZIoMHEwDaXFYCQJtgFI+1AAFgluUlAIz1/mdyCCCAAAIIeCnA87eXutFpmwAwOmsVq5FyAwrJchIA2l4Ip4eAmHXUPqe1jG7R1/YYqIBAHAUIAAkA47ivmRMCCCCAAAJhEOD5OwyrEPwYCAAtrMH8+fPl1Vdflffee08WL14sGzdulJycHGnTpo2ceeaZMmzYMDnrrLMstHSgyGuvvSaTJ0+WDz/80GgrPz9fevXqJcOHD5eLL77YUjt79+6VP//5z/K3v/1Nli5dKtu3bzfGc8EFF8ivf/1r6dq1q6V2qqqq5JFHHpEXXnhBVqxYYdQ57rjjZMCAATJy5Ehp1cqb32TiBmRpebwvZBYANm0q0q3bwb6tHALy5psid955sM7W1SLHHSnymyu8n0OWHnQfAhL4hBgAAjEVIAAkAIzp1mZaCCCAAAIIBC7A83fgSxCKARAAZlmGs88+W959992si/Xzn//cCORyc3PTlv3++++NkO+JJ55IW+aXv/ylPP7443LIIYekLaNCu0suucQIEM2uxo0by8SJE0W1lemaO3euEfStW7fOtFhhYaERDJ522mlZ52+3ADcgu2Ielffqh+HfHufRgO03SwBo34waCAQhQABIABjEvqNPBBBAAAEEkiDA83cSVjn7HAkAsxidcMIJsnz5cuPtukGDBsmPf/xjOeaYY2Tfvn1SVlYm48ePl9WrVxutDB06VKZOnZq2xdGjR8u9995rfN69e3e57bbbpEOHDkb7999/vyxcuND4TJUbO3asaTuq33PPPdd4G1FdV1xxhZSUlEjLli1FBXp33323bNiwwQgQX3755bRvFK5atUp69uxpvIHYqFEjueWWW6Rfv35Gm6reQw89JOotw4KCAvnoo4+kXbt22XeTjRLcgGxgeVmUANBLXdpGAIEABGIbJPIbgAHsJrpEAAEEEEAgHgI8f8djHd3OggAwi6AKxa655hq58sorpWHDhvVKq7fx1NeAly1bZnz2zjvviHprsO6lPldfy1Wh2qmnnipz5syRJk2a1BTbuXOnnHPOOaK+bqwCOfVV444dO9Zr58knnzS+cqyuG264QR599NGUMp9//rkR7G3dulVUeLlkyRKjvbqXmtPTTz9t/PGMGTOMcLP2NXPmTBn8w6EO1157rTz11FNu91pKfW5AWjmdN0YA6NyOmgggEEoBAsAsy6L+BeNLL+lbOys/E6GvN1pCAAEEEEAAAQcCPH87QIthFQJADYuq3pi77LLLjJZ+9atfGb+pV/dSYd2kSZOMP1ZvDhYXF9crU15eLr1VIJMm3FN/3qVLFyPUa9Gihai/xHl5efXaUW8ZqrcI1WUW7qmv/LZt21bUV5IvvPBCef31100VLrroInnjjTeMtwnVW45HHXWUBq0DTXAD0kbpriECwKx+j33znqzauzml3NGNWsiIw63/7mfWTiiAQAIFvt63U/bu35cy80YNGkrLhvX/d80ODwFgFq3vvhMZOFC97m+HNX1Z9f9nysr0tEUrCCCAAAIIIOCJAM/fnrBGrlECQA1Lpg7gaN68udGS+m2+V155JaXV/fv3G1+hXbNmjXTu3NkI8NJd6vPPPvvMCOjU13QbNGhQU1S9RXjiiSca/zxixIiaQLFuWyrgU7/fpy6zryWrA0iuu+464/Pp06fLkCFDTIejPlP11aV+l1D9fqGuixuQLkmX7RAAZgUs2TBVPt29NqVc19xCKS24OmtdCiCAQHoBr/5uxTYAdLOZNm8X2ftD2HrGTSIqBLz6apF589y0eqAuAaB7Q1pAAAEEEEDAYwGevz0GjkjzBIAaFurrr7+uOS1XvQk4a9aslFYrKyuN3/pTlwreHnvssbS9qs9VQKcuVe/444+vKVv767/Tpk2Tq666Km07KihUgaH6vcKVK1emlKv99d+1a9emfbNPfaZ++1Bdqs6UKVM0aB1oghuQNkp3DREAZvXzKqTI2jEFEIi5gFd/twgATTbOTZNEFq/yZkcRAHrjSqsIIIAAAghoFOD5WyNmhJsiANSweM8//7xxGIe61MEe9913X0qrtb8i/PDDD8uoUaPS9qo+VwdyqEu9SajeKKy+fvOb3xiHjqhLHRhyyimnpG2nf//+RhCp3iDctm2bNG3atKas+g1CdbDH4YcfLlu2bMkooMqo3xPs1auXzNPxpsAPvXED0rDxdDRBAJhV0auQImvHFEAg5gJe/d0iADTZOASAMf/bxPQQQAABBBDILMDzNztECRAAutwH6nf01O/2VYdj6hAPdQhH7Uu98Xf99dcbf6QO1xiofnsnzfXss8/WHMih6lV/VVcVV2/8PfPMM0ZNdXpv69at07Zz00031RwQsnTp0pqvDqsK6rf81q9fbxxKUlFRkVGgqKhIPv30U6OOeiPQ6qVuMJku1dZpp51mFFFfddZ9yrDVcSa+HAFg1i3gVUiRtWMKIBBzAa/+bhEAmmycB58TWbH+wAeHtXW+s8rLD9Rt1UpkzJgD/1395MgPPxfivGFqIoAAAggggICXAgSAXupGp20CQJdrpd7IU2/mqUu9Bfj3v/+9XosPPPCA8Wagul577TVRh2uku9Tn1W/9Pfjgg3LrrbfWFL300kvl1VdfNf55165dcuihh6Zt5/bbb5f777/f+LxuKKneBlSnDp9++umiDh7JdKkyKtxs1qyZ8Sah1av2bxdmq0MAmE3Iw88JALPiehVSZO2YAgjEXMCrv1sEgFk2znkHDgnjQgABBBBAAIHkCBAAJmetM82UANDFPnjnnXfkggsukL1790pBQYEsWrTI+M+611133SW//e1vjT9+88035fzzz0/b61tvvSV9+vQxPlf17rzzzpqy6s/V5+rat2+fcTpvukv1p+qr691335Wzzjp4YmnDhg2NE4B//OMfy5w5czIKnH322UZ9VUfN0+pFAGhVKuByBIBZF8CrkCJrxxRAIOYCZn+3mjTIkfY56d9uryYZ1Ky79M07yVSIADDLxiEAjPnfLKaHAAIIIIBAfQECQHaFEiAAdLgP1NdiVYC2efNm4028N954Q1RYZnYl8Q1AvgLscGP5Xa2kRKTu18CLikRKS92N5O1x7uprrF1WuclVawSArviojEBaAbO/W1a5fnX4OTK0+akEgFbBapcjAHSiRh0EEEAAAQQiLUAAGOnl0zZ4AkAHlF988YXxRt2aNWuMN+PU137VoRvpriT+BmA2Vm5A2YQi/jkBYMQXkOEj4L0AAaD3xqY9EAAGBE+3CCCAAAIIBCfA83dw9mHqmQDQ5mqo0E+9+VdZWWmcsPvUU0/JNddck7EVTgGuz8MNyObGi1pxAsCorRjjRcB3Aa8CQF0Tie1XiXUGgHfcIfLDT5O4dtfx9rnrQdAAAggggAAC8RTg+Tue62p3VgSANsSqqqrknHPOkcWLFxu1Jk6cKDfeeGPWFlRY2KFDB6OcOtVXvRGY7lKfT5482fhY1Tv++ONrij755JMybNgw45+nTZtmnAqc7jrxxBNl2bJlcswxx8jKlStTiqnA8umnnzb+TJ3Gq074NbvUZ23atDE+UnWmTJmSda5WC3ADsirlc7kYBXe65PgKsC5J2kEgVYAAMKAdoTMAHDRI5Nln9UykuFikrExPW7SCAAIIIIAAAikCPH+zIZQAAaDFffDNN98Yh3csWLDAqHHvvfeKOmnXyrV//35p166d8ZXhzp07y5IlS9JWO+mkk2Tp0qXStm1bUafj1j5MQwV6KthT14gRI2TSpEmm7axbt04KCwuNz4YOHSpTp05NKacCRhU0qmv69OkyZMgQ03bUZ6q+uh5//HEZPny4lelaKsMNyBKT/4UIAOuZEwD6vw3pMRkC4zbPlso9VY4mm+kQEEcNmlTiDUALkgSAFpAoggACCCCAQPACPH8HvwZhGAEBoIVV2Llzp/Tt21fef/99o/Qdd9whd999t4WaB4vccMMNNYFdWVmZFKt/013nKi9Y63YNAAAgAElEQVQvl97qVFYRUeUfffTRemW6dOliBIgtW7Y0AsK8vLx6ZVQ4OXr0aOPPZ8yYIYPU/0GvdamAUAWM6iTgCy+8UF5//XXTuVx00UXG4SbqtOHVq1enfVPQFsQPhbkBOVHzoQ4BYD1kAkAf9h1dIBBCAQJAC4viVwC4aJGI1X8JyVeJLSwcRRBAAAEEkibA83fSVtx8vgSAWfbB7t275bLLLpPZs2cbJUeOHCl//OMfbe8e9fZe165dZe/evXLqqafKnDlzpEmTJjXt7Nq1yzhFeP78+dKoUSPja8YdO3as10/trwGrrx+rryHXvpYvXy49evSQrVu3Gl87Vm8TqvbqXrW/Bjxz5kwZOHBgShH1Z4MHDzb+7NprrzV+61DnxQ1Ip6bGtmIUAGZ6u+i/WvxEOuTkZ4S7rep52fz9LuMNpV3796SU7ZpbKKUFV2uEpykEEHAi8Ng378mqvZtTqh7dqIWMOPwsJ82l1CEAtECoIwBs3VpE/UvRqqr0XwFWXw0+4wwLA5IDbfFVYmtWlEIAAQQQSIwAz9+JWeqMEyUAzLIPrrzySnnuueeMUuorwCr8q/213LrVc3NzpVOnTqatqrfy1Nt56urevbvxFWIV0qnQ7r777pOFCxcan6lyY8eONW1j3759xu8QVr+NqMZXUlIiLVq0kHnz5sldd90lGzZsMN7aU4ePXHzxxabtqLcHe/bsKRs3bjQCwltvvVX69etnlFX1xo8fb4SV+fn5xtee1VeYdV7cgHRqamwrRgFgpt8Xezx/qHRrfOD3LdNd/dc+Lhv3bTf9mABQ456jKQRcCHj5hi4BoIWFcXoISHn5wcbbthVZvlxk/HiRMWPMOyUAtLAYFEEAAQQQQCC9AM/f7A4lQACYZR9kCvvMqh577LGyYsUK01bVV25VWKfe4kt3qUM+1G/0qQAv3aUOI7nkkkvkww8/NC2iQkj1ZqDqK9M1d+5cGTBggKivBJtd6nCQF154QU4//XTtf1u4AWkn1dMgAWCNIwGgni1FKwh4KUAA6KVulrbdHCaifu6kOgRUAeBXX2XujAAwwIWmawQQQACBOAjw/B2HVXQ/BwJAHwPA6q5effVVI+RTAZ4K81q3bi29evUyDuZI98Ze3WGqt/NKS0uNAz7UbwLu2LHDOLG3T58+xteU1deNrVyq/wkTJhhBX3VwqU4e7t+/v4waNUpatWplpRnbZbgB2SbzpsKbb4p8/fXBtiueFzmsiUiPE7zpz0arZZWbbJSuX5Q3AF3xURmBSAgQAAa4TASAAeLTNQIIIIAAAvYEeP625xXX0gSAcV3ZkM+LG1BIFqj2WxjVQ+pytMjE6wMfIAFg4EvAABAIvQABYIBL5CYAVN9QqKg4MPj8fJFZszJPJN0hIOrPd+xIrctvAAa4KegaAQQQQCCsAjx/h3Vl/B0XAaC/3vT2gwA3oJBshYQFgE0a5Ej7nNZi5xAQs5VSbYxu0Tcki8gwEEiuAAFggGvvJgDUNWyz/w0jANSlSzsIIIAAAjES4Pk7RovpYioEgC7wqOpcgBuQczutNRMWAHJ4h9bdQ2MIBC5gFgBWB/2ZBtfikCZyf+ufZhx/bA8B0bVqBIC6JGkHAQQQQAABzwV4/vacOBIdEABGYpniN0huQCFZUwLAkCwEw0AAAScCmX7rM1N7+Q2byYuF1xEAOkGvrkMA6EaPuggggAACCPgqwPO3r9yh7YwAMLRLE++BcQMKyfp6EAC6/e0+XTJefjVQ1xhpBwEE3AkQALrzc1WbANAVH5URQAABBBDwU4Dnbz+1w9sXAWB41ybWI+MGFJLlJQAMyUIwDAQQcCJAAOhETVMdAkBNkDSDAAIIIICA9wI8f3tvHIUeCACjsEoxHCM3oJAsKgFgSBaCYSCAgBMBLwNAJ+Mxq8NvCWaRdBMkOjkEpPYJxJmGVlQkUlqqaxvQDgIIIIAAAoEK8PwdKH9oOicADM1SJGsg3IB8Wu9Fi0SGD0/fmfp8x47Uz7scLTLxescD5CvAjumoiAACNgXGbZ4tlXuqbNYSqXsIyPI9G2Xn93tS2sk7JEc65OTbbrtuBQLAkAWAZqGh2RA5Tdj13qcBBBBAAIHwCPD8HZ61CHIkBIBB6ie4b25APi1+WZnIGWfY64wA0J4XpRFAIPICXv5mKAGgjwHg00+LfPmlyJgx6TslAIz831cmgAACCCBgX4Dnb/tmcaxBABjHVY3AnLgB+bRIBIAp0F1zC6W04Gqf8OkGAQSiIkAAGOBK6fwK8IwZIoMGZZ4MAWCAi03XCCCAAAJBCfD8HZR8uPolAAzXeiRmNNyAfFpqAkACQJ+2Gt0gEGUBAsAAV48AMEB8ukYAAQQQSIoAz99JWenM8yQAZB8EIsANyCX75ZeLLFiQ2kiPHiKzZqX+GQEgAaDLrUZ1BJIgQAAY4Cq7CQDrHuhx990iffpknozZISBmv4fLbwAGuCnoGgEEEEBAtwDP37pFo9keAWA01y3yo+YG5HIJrZ58mO0QkLrD2Lpa5LgjRX5zheMBcgiIYzoqIoBAQAJmAWCTBjnSPqe1pRFdd9iZcuqhx5qW5TcAsxC6CQAtrY6FQlb/N9VCUxRBAAEEEEAgjAI8f4dxVfwfEwGg/+b0KCLcgFxuA68eVt4e53JgIgSArglpAAEEfBYwCwDtDOHulv3k/LwTCQDtoOku6yZI9Op/U3XPkfYQQAABBBBwKMDzt0O4mFUjAIzZgkZlOtyAXK6UVw8rBIAuF4bqCCAQRQECwCiuWp0x6w4AmzYV6dZNpKhIpLQ0M9Cbb4rcead5GSv1Y8DPFBBAAAEEwi3A83e418ev0REA+iVNPykC3IBcbggCwKyA879dKVu//zal3GGHHJr2a3pZG6QAAgjEVoAAMAZLqzsArCax8luAM2eKDB5sjmilfgz4mQICCCCAQLgFeP4O9/r4NToCQL+k6YcAUOceiHEAOG7zbKncU+VIS/1e1+gWfR3VpRICCCRXgAAwBmtPABiDRWQKCCCAAAJeCRAAeiUbrXYJAKO1XrEZLTcgl0sZ4wDQzYN419xCKS242iUu1RFAIGkCbv7Fg7LKdAiILksOE8kiSQCoa6vRDgIIIIBADAV4/o7hojqYEgGgAzSquBfgBuTSkADQFJAA0OW+ojoCCIRWgACQADC0m5OBIYAAAgiEXoDn79AvkS8DJAD0hZlO6gpwA3K5JwgACQBdbiGqI4CAfgGzNwl1/TQBAaCHAWBJiUhFhXkHVg7xqD4EZNEikR07UtvhNwD1/0WjRQQQQAAB2wI8f9smi2UFAsBYLmv4J8UNyOUamQWAXY4WmXi9y4bdVy+r3OSqEb4C7IqPygggEKCA2f1L15vJBIAeBoC69oxX/3JO1/hoBwEEEEAgsQI8fyd26VMmTgDIPghEgBuQS3YPAkC3wZ3LGdVUN3uAbtIgR9RbNNkuXW/aZOuHzxFAAAEzAQLAAPeFm98A1DVss/9tbtpUpFs38x4mT07/ma4x0Q4CCCCAAAIiwvM320AJEACyDwIR4Abkkj1hAaCuN2hcqlMdAQQQyChAABiDDeImSDT73+ZMJB98IKLqcCGAAAIIIOCxAM/fHgNHpHkCwIgsVNyGyQ3I5YoSALoEpDoCCCCgX4AAUL+p7y0SAPpOTocIIIAAAt4L8PztvXEUeiAAjMIqxXCM3IBcLmoIA0CzH7/PNMvrDjtTTj302HpFvHyAdqlOdQQQQCCjgJf3L34D0KfNF4YA8PLLRRYsSJ1wjx4is2b5hEA3CCCAAAJxE+D5O24r6mw+BIDO3KjlUoAbkEvAEAaAdg/vuLtlPzk/70QCQJdbgeoIIBAeAQLA8KyF45GEIQDkMBHHy0dFBBBAAAFzAZ6/2RlKgACQfRCIADcgl+wxDgDN3iTkcA+X+4XqCCDgi4CTQ4ys3t94A9CXJRRxEwCWlIhUVFgfaLpDQAgArRtSEgEEEEDAkgDP35aYYl+IADD2SxzOCXIDcrkuMQ4AXcpQHQEEEAhMwO6b0GqgVg85IgD0aVndBIC6hmj3NGGzfs8/X+See3SNiHYQQAABBCIuwPN3xBdQ0/AJADVB0ow9AW5A9rzqlSYAdAlIdQQQQEC/gJcBoK7REiTqkszSjpsg0e5pwmZDGThQZOZMnyZLNwgggAACYRfg+TvsK+TP+AgA/XGmlzoC3IBcbomIBIBNGuSI+nqb2ZXuEBCXMlRHAAEEAhMgAAyMPnwdEwCGb00YEQIIIJBgAZ6/E7z4taZOAMg+CESAG5BL9ogEgFa/2uZSg+oIIIBAKAR0BYDzv10pW7//NmVOhx1yqOnJ6XYnzhuAdsUclicAdAhHNQQQQAABLwR4/vZCNXptEgBGb81iMWJuQC6X8Y47RJYtO9jIhqUiR7cWGdbXccNllZsc11UVvTz90tXAqIwAAgj4JGB2iFG2rs0OAfHyfkoAmG1FNH1OAKgJkmYQQAABBHQI8PytQzH6bRAARn8NIzkDbkCal+3tca4bJAB0TUgDCCCAgBYBAkAtjNFt5MHnRFasPzD+w9o6mweHgDhzoxYCCCAQUwGev2O6sDanRQBoE4ziegS4AelxrGmFAFAzKM0hgAACwQkQAAZnH7qe3bxJGLrJMCAEEEAAgaAEeP4OSj5c/RIAhms9EjMabkCal9pFAFiy7Dmp2LFetn23N+OgWhzSRO5v/dO0Zbx8YNWsRXMIIIBAqAWicD/lq8Q+bSFdAeDYsSIvvaRv0EVFIqWl+tqjJQQQQAABTwV4/vaUNzKNEwBGZqniNVBuQJrX00UA2HvhJCnftirrgPIbNpMXC6/LGAB+tXezfFPrh+s5BCQrKwUQQACBegIEgGyKGgE3AeC0aSJr1x5o6rvvRMaMcQ972mki8+aJFBeLlJWZt6cOKrN6ESRalaIcAggg4EqA529XfLGpTAAYm6WM1kS4AWlerxAEgFO2zpVuuW3kpqoZNZMjANS8zjSHAAKJECAATMQyW5ukmwBQBXHl5db6sVrq889FRo0SqapKHwA2aGC1tcxBovVWKIkAAgggkEWA52+2iBIgAGQfBCLADUgf+8P/WCbFX0523KDZg6ZZY9neAFR1Fn23Rq7bOI0A0PFqUBEBBBCIxqnqfAU4Ajv1pkkii7O/4W9rJqtWieTni4wfn/6NQgJAW6QURgABBPwQ4PnbD+Xw90EAGP41iuUIuQHpW1YCQH2WtIQAAgiEQYA3AMOwCjEYg1cBYLt2mXEIAGOweZgCAgjETYDn77itqLP5EAA6c6OWSwFuQC4Ba1X3IgBs0iBH2ue0ThlktkNAVOHlezbKvZv/UVNPtTG6RV99k6UlBBBAIAECZgGg2X25muK/WvxEOuTk+yrDG4C+cjvr7MHnRFasP1D3sLbO2lC1an+NWL0BqDMAbNpUpFu31LFddZXIyJGZxzthgsj06QfL8FuCzteXmgggkAgBnr8TscxZJ0kAmJWIAl4IcANyqVrrh73f+WyjHLulTHYf3lQ2nX6S7Yaj8KaJ7UlRAQEEEIiwgNWfZqie4uP5Q6Vb4zaRnDFBok/L5ua3BEtKRCoqDgz0xRdFCgoyDzrdISCLFons2JF9wjffLPLQQ5nL3XKLyMMPHyyT6VCS7D1SAgEEEIi9AM/fsV9iSxMkALTERCHdAtyARGTDBpH+/Z3Rmvyo97b2hVIx5mrb7REA2iajAgIIIOCpAAGgp7zJbNxNAKhLzOqhJASAusRpBwEEEKgR4PmbzaAECADZB4EIcAMSka++Ejn6aG3+BIDaKGkIAQQQCFRAVwB4W9Xz8tmeDSlzOTGnQO5v/dNA51e7c94A9Gkp4hQAjh0rsnq1yP/+70E83gD0aSPRDQIIRFWA5++orpzecRMA6vWkNYsC3IAIAC1uFYohgAACiRPQFQBG4Q1vAkCftnecAkCzNwkJAH3aSHSDAAJRFeD5O6orp3fcBIB6PWnNogA3IAJAi1uFYggggEDiBMZtni2Ve6oszzvdISB2DxNJ12HPxsfIiMPPsjweOwUJAO1ouSgbhgCw9m8JZppKtkNAzAJAs8NEMvVRVuYCk6oIIIBA9AR4/o7emnkxYgJAL1RpM6sANyD9AeDHR+fIz65PPblXLcSgZt2lb176w0Gi8IZI1g1FAQQQQACBegJ23yRMR3hek05yT6vLPBEmAPSE1dtGgw4Trf6WYCaF/fu9NaJ1BBBAIGQCPH+HbEECGg4BYEDwSe+WG5DLQ0BEZNGGCtm+e3vNVqooEBl+ef2d9avDz5GhzU9Nu+UIAJP+t5H5I4BAXAX8CABn71wiM7cvdEzYu2VbKe10heP6VAxAgAAwAHS6RAABBNwJ8Pztzi8utQkA47KSEZsHNyD3C9b7id5S/lV51oayBYBTts6VT3evkfe+raxpq2tuoZQW2D9ROOtgKIAAAggg4JuAHwHgtG3z5U/fvON4TmH73xveSLSwlASAFpAoggACCIRLgOfvcK1HUKMhAAxKPuH9cgNyvwF0BYBqJHdsekne3rWMAND9stACAgggEBoBAkD7S0EAaN/McQ2nQSJfAXZMTkUEEEiuAM/fyV372jMnAGQfBCLADcg9OwGge0NaQAABBOIsYPcwkXQWmQ4B4Q3AOO8gj+fmNAC0ephIpuFzCIjHi0vzCCAQNgGev8O2IsGMhwAwGPfE98oNyP0WMAsAmzTIkfY5qQeBZDsERI3ksW/ek4+++7JmUKqN0S36uh8kLSCAAAIIxFqAADDWy+vt5JwGgF6MauxYkZde0tdyUZFIaam+9mgJAQQQcCnA87dLwJhUJwCMyUJGbRrcgNyvmFkAGLbfUnI/S1pAAAEEEAizgJ1DQCr3VMmu/XtSphO2/93iK8A+7ragA8ANG0R27z4w4e++Exk4UOTjj/UAFBeL8JahHktaQQABLQI8f2thjHwjBICRX8JoTiCxNyCz363pcrTIxOttL2TvhZOkfNuqUD9I2Z4UFRBAAAEEYisQhVPnCQAjuP2cBok6fkswHVe6APCOO0Teess6Mm8SWreiJAIIZBRI7PM3+yJFgACQDRGIQGJvQASAgew3OkUAAQQQCF6AADD4NYjlCKIUAA4aJPLss9aXgTcJrVtREgEECADZA1kFCACzElHACwECwFqqvAHoxRajTQQQQACBkAkQAIZsQeIynLAEgOp3BBs3PqBaWCgydGh9YQLAuOw65oFA5AQS+/wduZXydsAEgN760noagcTegHgDkL8TCCCAAAIJFYhCAKhrafgqsS5JD9t58DmRFesPdnBYW3edXXaZyJgxmduwGwCq1tRbgLWv/HyRWbMy97Nokcjw4QfL8FVid2tLbQRiIJDY5+8YrJ3OKRAA6tSkLcsCib0BEQBa3iMURAABBBCIl8DX+3bK3v37UibVqEFDadkwL14TFRECwAguqdM3Ce1M1UkAWLf9tm1Fvvoqc6/qAJIzzjhYhq8S21klyiIQS4HEPn/HcjWdT4oA0LkdNV0IJPYGZBIAbmtfKBVjrratmaQ3KWzjUAEBBBBAAIEABQgAA8R32rUfAWCmQ0DKy62NnADQmhOlEEAgRSCxz9/sgxQBAkA2RCACib0BEQAGst/oFAEEEEAgPgLjNs+Wyj1VphO6oElnGdK8R8bJPrNtgfzfrqWmZdrntJbRLfq6xiIAdE3ofwN+BICZZmX1VOJsAaD6LUL19t955x3sjTcA/d9P9IhAyAQS+/wdsnUIejgEgEGvQEL7T+wNiAAwoTueaSOAAAIIpBNQb7TbuT7dvTZt8SHNesrII87N2NyELf+UZ7Z/ZFqma26hlBbYfyu/bmMEgHZWNCRl4xIAqv+v+e9/i2zaRAAYkq3FMBAIg0Bin7/DgB+iMRAAhmgxkjSUxN6ACACTtM2ZKwIIIICABYEzvhpvoZS1ImEJAK2NNnspgsTsRqEr4TRILCkRqajIPp1sh4CYvUnYtKlIt27Z264ucffdIn36WC9PSQQQCL1AYp+/Q78y/g6QANBfb3r7QSCxNyCNAWCSfkydvzgIIIAAAvEVIABMv7YEgBHc904DQF1TtfpV4kz9zZghog4s4UIAgdgIJPb5OzYrqGciBIB6HGnFpkBib0Am/6fs46Nz5GfXt7YsqOOrSZY7oyACCCCAAAIeCxAAEgB6vMWi2bzTIJEAMJrrzagR8Fggsc/fHrtGrXkCwKitWEzGm9gbkMn/KStrJ3LGL60v7AftbrVemJIIIIAAAgiEXEBXAKh+v8/OISDqIJFd+/ek6DRpkCPqIJBMl5//Io43AEO+ecM4vJsmiSxe5W5k6d4AtPo1Zau9FxWJlJZmLv3mmyJ33pm+jJU2rI6HcgjEWCCxz98xXlMnUyMAdKJGHdcCib0BEQC63js0gAACCCAQLwG7h4CYzd7J6b2q30wHiqRTzvQv4tTPc9y+6QVtC9S7ZVsp7XSFtvZoKAECXgaAOt4urL0EVk4nnjlTZPDgzAun2rF6TZ5s7/cQrbZLOQRCLpDY5++Qr4vfwyMA9Fuc/gyBxN6ACAD5G4AAAggggEAoBLwIADfs3SYD1k3WNj9OJdZGmZyGHnxOZMX6g/M9rK39uac7BCSsAaCdGX7wgYiaBxcCCRNI7PN3wtY523QJALMJ8bknAom9AREAerKfaBQBBBBAAAG7AgSAdsUoH0kBp78laDZZAsBIbgEGjYASSOzzN8ufIkAAyIYIRCCxN6A0AWCfkuy/OVS9UH7+9lAgm4NOEUAAAQQQ8EEgSQGgLk5+k1CXZATbUUFinAPAyy8X2bjR/cLwm4TuDWnBE4HEPn97ohndRgkAo7t2kR55Ym9AaQLAkhsKhWAv0luawSOAAAIIRExg3ObZog4CsXtl+t9rv74C/Ng378lH331paehOfh8xXcMEgJbI41uo7teLncy09leSrYRltQ8BKS930mNqnXRfAW7XTmT1avftW/ldQ/e90AICtgUS+/xtWyreFQgA472+oZ1dYm9ABICh3ZMMDAEEEEAAAbcCbg4BMTuQJN1vAN6x6SV5e9cyS8PV9TuCqjMCQEvkFPJKoHYA6eS3DdW40h0CoisAVH3s359ZYNo0kUceyVzGSjjqlTPtxlIgsc/fsVxN55MiAHRuR00XAom9AREAutg1VEUAAQQQQCC+ArN3LpFN+3akTLBVw6bSN++kepO2EwCqyioENLtaHNJE7m/9U8uoBICWqSgYFYHq30j0MwB86CGRW2/NLMSbhFHZQZEZZ2KfvyOzQv4MlADQH2d6qSOQ2BsQASB/FxBAAAEEEEDApYDdADBdd/kNm8mLhde5HI396gSJ9s2o4ZFAWANANV0VAma7XnxRpKAgcykdpx7zRmK2lQj954l9/g79yvg7QAJAf73p7QeBxN6AJkyQBeXPy9bvtsmGrd9K0z1VUtUiVxae21GGNO/B/kAAAQQQQAABBLIKRD0AzDpBiwUIEi1CUSy7wB1/Efkm9Q3crJUWr6pfZOBAkZkzM1e18gZg1s5/KLBqlYh6ezHT1aCB1dYyl1OB5FVXiYwcmbnchAki06eblyFI1LMWDlpJ7PO3A6s4VyEAjPPqhnhu3IBEHv7HMin+cnKIV4mhIYAAAggggEAYBdIdAmL2O4KZxp/uDcDlezbKzu/3pFTNOyRHOuTkh4qDADBUy5G8wdw0SaRuCBjnAFCt8M03i6gAM9N1yy0iDz+cvoSVNxtr1y4ry9zfhg0i/fs7238JCiR5/na2ReJWiwAwbivqYD4rV66URx55RF555RVZtWqVNG7cWDp06CCDBw+WG2+8UfLy8hy0mrkKNyACQO2bigYRQAABBBBIuEDJhqliJwRMFwCataPzMBFdy0QAqEuSdhwJmJ2K3L2DyLC+mZt7818iz39wsIzZm4RWB+TnG4C6AkCrc6sul+1Qla++Ejn6aLutHiifoN9a5Pnb2RaJWy0CwLitqM35vPTSS/Kzn/1Mtm7dalqzU6dORjB4wgkn2Gw5ZgFgSYlIRYU7gzqvzPMGoDtOaiOAAAIIIIBAqsC4zbOlck+VZZZ0h4BEJQC0PFGfChJI+gQdt27M3iS0OsdnbhfJP7x+6erfNlSf6PoKcBwDwOoQsFrw178WGTo0s/4dd4i89VZqmQi8SUgAaPUvVbzLEQDGe30zzm7hwoVy5plnyq5du6RZs2YyevRoOe+884x/nj59upSWlhr1VQg4f/58ad68uTatyN2ATA7vsI1R55V5AkDbglRAAAEEEEAAAR8ECACdIRMAOnNLfC2zNwmtotz1c5EWzTKXVgGjnSvTG4kDzxS54dLMrf3vKyLPvm+nx8xlvXwDsG7P48eLqK8wZ7oGDRJ59tnUEhF4kzByz9/6dhAt1RIgAEzwdjjnnHNkzpw50qhRI+M/e9c5IeqBBx6Q2267zRD63e9+J7///e+1aUXuBkQAqG3taQgBBBBAAAEEwi1AABjs+hAkBuuf+N4zBZLn/UjkyjMzE/39fZG3P6n/+4hOYd8am7nmxm9EhtzntPXUegSAehxpJbQCBIChXRpvBzZv3jw5/fTTjU6uu+46eeyxx+p1+P3330tRUZEsWbJEjjjiCNmwYYPk5ORoGVgSA8BpPzlKHhlyXI3fUY3Ol9v2fqfFk0YQQAABBBBAAAFdAgSAuiSDbYcgMVj/xPfu5s3G2ngTr89MuXm7yH8/nZ3bym8tWgkAzb5SzRuA2f0pEQoBAsBQLIP/gxgzZoyMGzfO6Li8vLwmDKw7knvvvdf4arC63njjDenbN8uP2lqcShIDwIeKRW696CDQkGY9ZeQR51oUoxgCCCCAAAIIIOCPgFkA2KRBjrTPaZ11ANcddqaceuixGcvZ/a3CbJ2qcY1ukfn/o87/dqU8vrX+1xKt1M3Wf1g/JwAM68owrkAErPzW4vWXiAw6K/Pwzh9T/3MCwECWlE7tCxAA2jeLRY2zzz5b3n33XWnatOlWLAkAACAASURBVKls2bLF+Bqw2VVWViZnnHGG8dFvf/tb+cMf/qBl/nEIALfniCw60jrH9CKRR4oJAK2LURIBBBBAAAEEghCwe5pw7THe3bKfnJ93YsZhu2nfrGErJxS/tfMzufPrl03Hperbvf6rxU+kQ05+xmq3VT0vm7/fZbfptOV7Nj5GRhyeOZyYvXOJzNy+UFufKiCd1TPLoQjaeqMhBDwUsPJG4k/PEOlzcuZBEAB6uEg07bUAAaDXwiFtPz8/X6qqquTkk0+Wjz/+OO0oN2/eLC1btjQ+HzRokMyYMUPLjOIQAJa1Eznjl845eAPQuR01EUAAAQQQQMA7ATcBXRQDQCeSj+cPlW6N22Ss2n/t47Jx33YnzZvWOa9JJ7mn1WUZ25u2bb786Zt3tPXZpuHh8mxh5v/Dq/aLzuuCJp1lSPMeGZt8ZtsC+b9dS11127xx6gsQZd0zf9V0w+7t0v9TC181tTGqX7c9Q4YWZA6c7vhitry1ZbmNVjMXzc9pKrOKrslYaNGOdTJ82fPa+lQN3X3cT6RPixMytlmy7Dmp2LFeW79FTY+U0k5XZGzvzc2fy50r/pG1z5r9YfYmIW8AZvWjQDgECADDsQ6+juLbb7+VJk2aGH1eeuml8vLL5v82tHpQ6oTgHTt2SHFxsag3Aq1cKuDLdK1atarmzUL1e4SFhfb/zauVcWgrc/nlIgtT/23qR0eJDHDxL0Qva9pNhh3WW9sQaQgBBBBAAAEEENAhcNumF2TZ7g2OmvrPI/rImU06ZKzrpn2zhjvlFsj9rQZk7PP9XcvlgS1vOpqTWaV7W/WXzrmZvwry/274q3y9b6e2Pnsferzc3uInGdt7cccn8v9tLdfWZ6uGefJEwc8ytjdg7WRt/amGrPx/5Ce2lslLOxZp7feFwuEZ26vat11+qTns/H8OK5b+TX+Usd/7Nv9Dyr79Qttcj8ptJh/2uDFjex9tWy0DPv2rtj5VQ5M69pd+rTpnbPPyir/Iwu1rtfXbvVlh1rDz5U1L5fp/v5i1z1XFtx8oc+dfRP5dZ4zdu4vMmpW1jSALrF27Vk477TRjCF988YUcd9zB36YPclz07a8AAaC/3qHobePGjVJQUGCMZciQITJ9+vSM4zryyCONA0DUgSCLFln7H9oGZj+OGorZMwgEEEAAAQQQQAABBBBAAAEEkimgXsDp1atXMief8FkTACZwA6i374455hhj5j//+c/lL3/5S0YFVVbV6dChg3z++eeWxAgALTFRCAEEEEAAAQQQQAABBBBAAAHfBAgAfaMOXUcEgKFbEu8H5McbgNm+Aqy+hrx06VJRbxeq3yNMdwiJG43arzlH4mvGbiZL3cAE2GeB0SeqY/ZZopY7kMmyxwJhT1yn7LPELbnvE2aP+U6eyA6juM/27t0rKgdQV7du3eTQQw9N5NolfdIEgAncAX78BmAYWCN30EgY0BiDbQH2mW0yKjgQYJ85QKOKLQH2mC0uCjsUYJ85hKOaZQH2mGUqCroQYJ+5wKNqoAIEgIHyB9d50KcA+zFzbsx+KNMH+4w94IcA+8wP5WT3wR5L9vr7NXv2mV/Sye2HPZbctfdz5uwzP7XpS6cAAaBOzf+/vXsBrqq4Hzj+wwJCIJS3ykNQAQviRBQBeUcE5aG08lAUEQoFFRgsKi+VR1UCUgpSHyAPtdRIkWIHDIhFkICUChiBqQiKCkWsAoWCQgUa//Pbvzdzk9xLzknuOffuud+dcdTcPWd3P7uzyfndPbsW3at9+/ayceNGqVChghw/fjzqK7h66m/r1q1NyyZOnChTpkyxppVMzNZ0ldUVZZxZ3X3WVJ5xZk1XWVtRxpi1XWdVxRlnVnWXlZVljFnZbdZVmnFmXZdR4R8FCAAm6VCYMGGCZGRkmNZv2bJFWrZsGVFi2rRpMn78ePPZmjVrpEuXLtaIMTFb01VWV5RxZnX3WVN5xpk1XWVtRRlj1nadVRVnnFnVXVZWljFmZbdZV2nGmXVdRoUJACb3GNBDMUJBv2HDhsncuXMLgeTm5krTpk1l9+7dUrlyZfnmm2+kTJky1sAxMVvTVVZXlHFmdfdZU3nGmTVdZW1FGWPWdp1VFWecWdVdVlaWMWZlt1lXacaZdV1GhQkAMgZCrwHrCbzZ2dlyww035EOZMWOGjBkzxvxs0qRJMnnyZKvQmJit6i5rK8s4s7brrKo448yq7rKysowxK7vNukozzqzrMusqzBizrsusrDDjzMpuo9IiwivASTwMcnJypE2bNnL69GmpWLGi6GvB6enp5v+XLFkiL774otFp1KiRbNu2TVJTU63SYmK2qrusrSzjzNqus6rijDOrusvKyjLGrOw26yrNOLOuy6yrMGPMui6zssKMMyu7jUoTAGQMrFy5Uvr37y8nTpyIiKHBv6ysLGnQoIF1WEzM1nWZlRVmnFnZbdZVmnFmXZdZV2HGmHVdZmWFGWdWdptVlWaMWdVd1laWcWZt1yV9xVkBmPRDQGT//v3yzDPPmECfTmZly5Y1Ab8+ffrIiBEjJCUlBSUEEEAAAQQQQAABBBBAAAEEEEAAAUsFCABa2nFUGwEEEEAAAQQQQAABBBBAAAEEEEAAAScCBACdKJEHAQQQQAABBBBAAAEEEEAAAQQQQAABSwUIAFracVQbAQQQQAABBBBAAAEEEEAAAQQQQAABJwIEAJ0okQcBBBBAAAEEEEAAAQQQQAABBBBAAAFLBQgAWtpxVBsBBBBAAAEEEEAAAQQQQAABBBBAAAEnAgQAnSiRBwEEEEAAAQQQQAABBBBAAAEEEEAAAUsFCABa2nFUGwEEEEAAAQQQQAABBBBAAAEEEEAAAScCBACdKJEHAQQQQAABBBBAAAEEEEAAAQQQQAABSwUIAFracVQbAQQQQAABBBBAAAEEEEAAAQQQQAABJwIEAJ0okQcBBBBAAAEEEEAAAQQQQAABBBBAAAFLBQgAWtpxyVTt/fv3y5w5cyQrK0v++c9/yoUXXihXXHGF9O3bV4YPHy4pKSkx4Vi9erW8+OKLsnXrVjl8+LDUqFFDrr/+ehk6dKh07do1JmVwk8QV8HKcvfzyyzJo0CBHjX/ppZdk4MCBjvKSKfEFvvnmG3n//ffNPzq36D9Hjx41Fb/33ntFx0as02uvvSY6jnbu3CnHjx+Xiy66SNq1a2fmyxtuuCHWxXG/BBDwa5xNnjxZpkyZ4qjF69evl44dOzrKSyY7BLZt2yarVq2STZs2yUcffWT+VipTpozUqlVL2rRpI4MHD5a2bdvGtDHMZzHlTPib+TXGmMsSfih4VsETJ06YeUz/HtPx9uWXX5q57PTp01K5cmVp0qSJdOvWzcxn1apVi0k9eMaMCSM3iZEAAcAYQXIbbwRWrlwp/fv3F52sI6VGjRqZwGCDBg2KXYHc3FwT5Fu4cGHUewwZMkTmzZsnF1xwQbHL4cLEFfB6nBEATNy+97pmpUqVilpErAOA+sdr7969zR+2kZLOXxMnTpRJkyZ53Wzu77OAX+OMh2afOzaBimvfvr1s3LixyBrdc889smDBAilbtmyRec+XgfmsRHxWXuznGGMus3KIxKTSa9eulc6dOxd5r+rVq8sf//hHufnmm4vMGy0Dz5jFpuNCDwUIAHqIy61LJpCTk2O+UdY/AitWrCjjx4+X9PR08/9LliyR+fPnmwI0CKjf4KSmpharQL3vtGnTzLXNmjWTMWPGmBWG+/btk6efflq0Hpo039SpU4tVBhclroAf4yw8ALhmzRqzWiJaqlOnjvkGkhQMgfDATN26daVx48by9ttvm8bFOgDYr18/Mzdq0rly1KhRZqzt2rXLzF06p2nSLzP0Sw9ScAT8GmfhD806rs6XLrvsMqlQoUJwkJO8JfpFq84hOqf06dPHrCq+9NJL5X//+5/87W9/k5kzZ5qVNJp0LsrMzCyRGPNZifisvNjPMcZcZuUQiUmlNQD4y1/+0vyddN1114n+bXbJJZeIBusOHjwoy5Ytk+XLl5u5Tb/I0Dc40tLSilU2z5jFYuMijwUIAHoMzO2LL9ChQwfJzs6W0qVLm38XfHVtxowZJlinSVe06C9zt2nv3r1y1VVXyblz56R58+amnPLly+fd5tSpU6L10ACj1kNfeWnYsKHbYsifwAJ+jLPwAODnn38u9evXT2ARqhZLAZ2bdCsB/Udfxf3iiy9EAyOaYhkA1Nctb7zxRnPfW2+9Vd544w35yU9+kteUI0eOmD90Dxw4YALMn332mVSpUiWWTeVecRTwa5yFPzT/8MMPcWwxRfst0KNHDxkwYID06tUr39wSqofOMfqlrf5dpWnDhg2iK7qKk5jPiqNm/zV+jjHmMvvHS3FboIG98L+PIt3nL3/5i/ziF78wH+m/NSDoNvGM6VaM/H4JEAD0S5pyXAnoty0tW7Y01wwbNkzmzp1b6Hr9pqZp06aye/du80CreyDpXjRu0gMPPCAvvPCCuUS/wW7VqlWhy7ds2ZIXfNT8zz33nJsiyJvAAn6NMwKACTwIfK6aVwFA3a9G95jRP2q1DF1JWjDp6kBdVaNJVzc/8sgjPree4vwS8Gqc8dDsVw/aWc6bb75pvoDQNHLkSLN/c3ES81lx1JLjmliNMeay5BgvJWnlz372M9mzZ4/oq8C6R6DbxDOmWzHy+yVAANAvacpxJTBhwgTJyMgw12gALhQMLHgTfXVXl1dr0lcru3Tp4rgcXb2gD8mHDh0SneQ1kBgthX4J1K5d2xxEcr79lhxXgIxxF/BjnGkjCQDGvasTpgJeBGZOnjxp/kA9c+aM3HLLLSYQGCnp53q4ke6pqiuqN2/enDAuVCS2Al6MM60hD82x7aeg3e3bb7/N245Fg3i6R7PbxHzmViy58sdijDGXJdeYKW5r9c0NfQNMt6HSeclN4hnTjRZ5/RYgAOi3OOU5EghtBKz7B+kplvr6baSkq/Zat25tPtLN7Z2eTqj59RU43etPU7RVhqEy9XM9ITh0XegVPkeNIVPCCvgxzrTxBAATdgj4XjEvAjPr1q2TTp06mbboFyfjxo2L2i7dzFr3INQ59bvvvivxRv2+A1KgIwEvxpkWTADQEX/SZvr3v/+dd2qmrgRcsWKFawvmM9dkSXVBLMYYc1lSDZliNVZX/ulbZqEtovTEYDeJZ0w3WuT1W4AAoN/ilOdIQFep6H4yuunqhx9+GPWaY8eOSdWqVc3nuin10qVLHd1fM4W/RjBr1ix58MEHo16rn48ePdp8rt9o6zfbJPsF/BhnqhQeAOzYsaN5pUDHd6VKlcwJ1jfddJPcf//9oitMScEW8CIw8+yzz5rX7TTp3n8///nPoyLqwSCh1/L+8Y9/SJMmTYINnqSt82KcFXxo1lMU9fezfkmn23DoWNIVqPqFGftLJufA0/nn9ttvN43XPZqnT5/uGoL5zDVZUl0QizHGXJZUQ8ZxY3Xfdz3IaOXKlWablK+//tpcqycB33333Y7vwzOmKyoyx0GAAGAc0Cny/AL//e9/8w7i6N69uwnUnS/p0mxdyaL79+mKQKdJ9xXUoIum119/XXr37h31Uj0RSgOMmvQ6fcAh2S3g1zhTpfAAYDS1cuXKyezZsxlbdg+rImvvRWBGV/yFHrT1W2o90Cha+u1vf5u3999bb70luiKQFDwBL8aZKoWvAIympsFAnfN69uwZPFhaFFVA92XWrQV0b11N+uqcHjzkNjGfuRVLnvyxGmPMZckzZopqaVF/n+t8NHXqVNdbP/GMWZQ8n8dTgABgPPUpO6KAbrRas2ZN89kdd9whunH9+ZKerKkHgOhS7V27djlWDT9FWPfM0pUL0ZJ+Hlr1pw/QDz30kONyyJiYAn6NM229/oHxxBNPmJUR+oBUt25dg6KvCPz5z38WDTCHTtScN2+eDB06NDHRqFWJBbwIzAwfPlyef/55Uzfdy1T3LI2W9NAj3Zhak447PdGTFDwBL8aZKmkAUE9D1FWmLVq0kFq1asnZs2fNquZXX33VvF6uSQ+j0VUUXbt2DR4uLYooMHPmTHn44YfNZ/q7Tn+3FScxnxVHLTmuidUYYy5LjvHipJXRAoDXXHON2fpJ9wEsTuIZszhqXOOXAAFAv6Qpx7GAHrJx6aWXmvz33HOP/OEPfzjvtZpXr9H9/D799FPH5WhARvcN1PTOO+/IjTfeGPXa8D1p9LrHHnvMcTlkTEwBv8aZtv4///mPed032uExuspVH5j0QTolJUX27dsnF198cWLCUasSCXgRmBk8eLAsWrTI1EvHzuWXXx61jppP82tavHix9O/fv0Tt4eLEFPBinGlLQ6/7Rmu1foFx3333mY81OKi/k8uXL5+YSNQqZgIbNmwwW1nofln6Ba5+GRv6ItdtIcxnbsWSI38sxxhzWXKMGSet1N9pBw8eNFlPnz5t/obS7aT0VXN9rtQ3c3r06OHkVvny8IzpmowLfBQgAOgjNkU5E/BrZRbfzjjrj6Dm8mucOfV78skn5fHHHzfZ9b8fffRRp5eSzyIBLwIzrJixaAD4VFUvxpnTqg8ZMkQWLlxoshdn7ySn5ZAvMQR0L9F27dqJ7smsW1msWbNG9ICt4ibms+LKBfe6WI8xp1LMZU6lgpdPvyC99957zRf3+vts4MCBrhrJM6YrLjL7LEAA0GdwiitawK+92difoei+CHIOv8aZU0N9jV1X/emrwLq5fuhVOqfXk88OAS8CM+yZZUff+1lLL8aZ0/rrPpT6erCmX/3qV+Y1KlIwBT7//HNp27atHDp0yLz2ra/9lnTvR+azYI6V4rbKizHmtC7MZU6lgplPt6HS1YAVKlSQAwcO5B066aS1PGM6USJPvAQIAMZLnnLPK+DH6aycAswg9GOcuVEO1UdP09RvvEnBE/AiMMOpmcEbJyVtkRfjzGmd9FAuPZxLk+6dm5WV5fRS8lkkoEE/Xfmne9nqKhndS2vAgAElbgHzWYkJA3MDr8aYUyDmMqdSwcyXmZmZd/qv7nF71113OW4oz5iOqcgYBwECgHFAp8iiBfT1kY0bN5pvXXR/htKlS0e8SE/9bd26tflM9/ObMmVK0Tf/MYf+0ar7O2jSU33125poST8PrWLQ6y677DLH5ZAxcQX8GGduWq97JumryQQA3ajZldeLwEz4HqUZGRmiK2iiJT31V1eX6pyqDzdly5a1C5DaOhLwYpw5KlhETp06ZX53ayIA6FTNrnxHjhyRDh06yEcffWQqrkE7fXU3Fon5LBaK9t/DyzHmVIe5zKlUMPP99a9/lS5dupjG6UnA48ePd9xQnjEdU5ExDgIEAOOATpFFC0yYMEH0QVbTli1bpGXLlhEvmjZtWt6ErPvOhCbqoksQ86plnTp1zKsremqmnp4ZLTVu3Fg+/vhjqV27tjlwJNphDk7KJU/iCPgxzpy2VgN/eqK1jkvdTF3/8CAFT8CLwMzJkyelevXqcubMGXOauZ5aHinp57rK9MSJE+Y06s2bNwcPmBYZAS/GmVPabdu25Z2cqHtozZ8/3+ml5LNAQA+10kPTPvjgA1Nb/Tts7NixMas581nMKK29kddjzCkMc5lTqWDmCz8heM6cOTJy5EjHDeUZ0zEVGeMgQAAwDugUWbTA+++/nxf0i7Y6Lzc3V5o2bWoCd5UrVxbdQ61MmTJF3zwsxwMPPCAvvPCC+YmuJmzVqlWh6zUAqQ/LmjT/c88956oMMieugF/jzInAU089lXe6NCdNOxGzM49XgRldaaWBP13Zp3sm6ZcbBdOSJUukX79+5sdPP/20PPLII3YiUusiBbwaZ0UW/OO+fwsWLDBZOWnaiZg9eXRFlH7R+t5775lK62FVemhVrBPzWaxF7bmfX2PMiYjuYcpc5kQqmHm6d+8uq1atMo1bv369dOzY0VVDecZ0xUVmHwUIAPqITVHuBEKvZ+oDbXZ2dl4QLnSX8BOWJk2aJJMnT85XwLvvvivp6enmZ3qSk36TUzDt3btXrrrqKjl37pw0b97clFO+fPm8bHokvNZDvwXUeujrLg0bNnTXEHIntIDX40wfxPV0xGbNmkV10L1CevXqZVZw6SmKn376qVltSgqeQHECM+HfQkea61Qp/LW52267TZYvX2425Q8lfZ3quuuuMxtZ6xcm+npKlSpVggdMi4yAF+Ns165d5vdjgwYNoirrVhn6pZ0mPdRI57LQ68B0jd0C+vvp1ltvzTugatSoUTJ79mzXjWI+c02WNBf4NcaYy5JmSEVsqM5Bd955p/l7O1qaNWuWjB492nys2z598skn+f6m4hkzuceQ7a0nAGh7Dwa4/jk5OdKmTRvRIJxuKK6va2pAT/9fV7KE9uRr1KiRCdClpqbm03AyOesFuqeDvsKiSYM0+iqL7g24b98+mT59umg9Qvl0DwhSsAS8HmehcairSPXh6ZprrhHd609fD9AgzLJly8w/+v+aYrmXUrB6ys7WbNq0yQRBQkkDcaGVdzq/6SuS4WngwIGFGurkgVkv0tV9Ojdq0rnywQcflFq1aok+7OgKU53TNOl+p6EgjZ2q1LqggB/jTMehjlcdW127dpWrr75aqlWrZr5A0y0ydJP00OnlGnzWILQGo0nBENAvqbRPNekrwBr8O992KLq/qP59VjAxnwVjPHjRCr/GGHOZF71nzz3r168vutWAjjc9xVyf+fQ5U3+mfy/p77LQKmedx/QgK92aJzzxjGlPf1PTwgIEABkVCS2wcuVK6d+/v9mzKlLSPy51Yo60IsHp5KyvEusy/0WLFkW1GDx4sAk4XnDBBQntReWKJ+DlOAsfh+erXUpKiug3jkOHDi1eI7gqIQU0oPfKK684rlsoEBx+gdMHZv1ypHfv3nmvrBQsVOevxx9/vNBqaceVI2PCCvgxzsLH4fkgNCi4cOFC6dmzZ8J6UTH3Am73Pq5Xr55ZiVowMZ+5t0+WK/waY8xlyTKiIrdTA4D79+8vEkG3UtFnw86dOxfKyzNmkXxkSGABAoAJ3DlU7f8FdJJ+5plnTKDv4MGD5tRKDfj16dNHRowYIRo4iZScTs6ha3WfBw3ybd26VXSVjm6qf/3115uVMrragRRsAa/GmX6juGLFCrPHpK5U/eqrr8z40lUz+gqmvoLeqVMns7JGVwaSgiXgR2CmoFhmZqbZ8mDHjh3mFHU9XKZdu3ZmvgztZxosZVrjxzjTfXZ1uwKdy3Tl9Ndffy1Hjx41q5erVq0qaWlp5hAarUulSpXolIAJ+BWcYT4L2MBx0Ry/xhhzmYtOCWDWPXv2mGdKXeWnb2iEfpfpFhf6d7i+qdOjRw/p27cvz5gB7H+aJEIAkFGAAAIIIIAAAggggAACCCCAAAIIIIBAgAUIAAa4c2kaAggggAACCCCAAAIIIIAAAggggAACBAAZAwgggAACCCCAAAIIIIAAAggggAACCARYgABggDuXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkDGAAAIIIIAAAggggAACCCCAAAIIIIBAgAUIAAa4c2kaAggggAACCCCAAAIIIIAAAggggAACBAAZAwgggAACCCCAAAIIIIAAAggggAACCARYgABggDuXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkDGAAAIIIIAAAggggAACCCCAAAIIIIBAgAUIAAa4c2kaAggggAACCCCAAAIIIIAAAggggAACBAAZAwgggAACCCCAAAIIIIAAAggggAACCARYgABggDuXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkDGAAAIIIIAAAggggAACCCCAAAIIIIBAgAUIAAa4c2kaAggggAACCCCAAAIIIIAAAggggAACBAAZAwgggAACCCCAAAIIIIAAAggggAACCARYgABggDuXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkDGAAAIIIIAAAggggAACCCCAAAIIIIBAgAUIAAa4c2kaAggggAACCCCAAAIIIIAAAggggAACBAAZAwgggAACCCCAAAIIIIAAAggggAACCARYgABggDuXpiGAAAIIIIAAAggggAACCCCAAAIIIEAAkDGAAAIIIIAAAggggAACCCCAAAIIIIBAgAUIAAa4c2kaAggggAACCCCAAAIIIIAAAggggAACBAAZAwgggAACCCCAQEAFPv74Y2ncuLFpXWZmpvTr10+WLl0qixYtkpycHPnuu+/kyiuvlFGjRsmAAQPyFL7//ntZvHixvPzyy6L3OHXqlKSlpcljjz0m3bt3D6gWzUIAAQQQQAABBIIrQAAwuH1LyxBAAAEEEEAgyQX+9Kc/yZ133mkUsrOz5amnnpI1a9ZEVMnIyJBx48bJnj17pG/fvrJz585C+UqVKiVvvPGG9OzZM8llaT4CCCCAAAIIIGCXAAFAu/qL2iKAAAIIIIAAAo4FJkyYIBrY09SiRQvZsWOH3HfffXLbbbdJSkqKCQZqUPDs2bNSvnx52b59u6Snp8u5c+fk17/+tXTs2NH892uvvSbz5s0z92natKns2rXLcR3IiAACCCCAAAIIIBB/AQKA8e8DaoAAAggggAACCHgi0K1bN1m9erW5d5UqVWTt2rVy7bXX5itr/PjxMm3aNPOzypUry8UXX2zy1a5dO1++Hj16SFZWlvnZsWPHTF4SAggggAACCCCAgB0CBADt6CdqiQACCCCAAAIIuBbQIN6hQ4fMdcuWLZNevXoVusc777wjN910k/l5uXLlZOvWrWaVX8H0u9/9Th566CHz4y+++ELq1avnuj5cgAACCCCAAAIIIBAfAQKA8XGnVAQQQAABBBBAwFOBI0eOSI0aNUwZN998s7z11lsRy1u3bp106tTJfDZ27Ni81YAFM8+cOVMefvhh8+NIKwAnTZokv/nNb2TDhg3Svn37ItvmNn+RNyQDAggggAACCCCAQFQBAoAMDgQQQAABBBBAIIAC4Sv7Fv73bQAAB9dJREFU9NTfQYMGRWzl3Llz5f777zefffDBB9KsWbOI+YYPHy7PP/+8VK1aVY4ePVooj+4r+Oabb8rx48elUqVKRYq6zV/kDcmAAAIIIIAAAgggQACQMYAAAggggAACCCSTQPiKvX/9619y0UUXRWy+Bv80CKh7/3311VdRidq1ayebNm0yB4OsX7++UL46deqYg0Q++eQTR8xu8zu6KZkQQAABBBBAAAEEIgqwApCBgQACCCCAAAIIBFBgwIABsnjxYqlVq5Z8+eWXUVvYqlUr+fvf/y5du3aVVatWRcz3ww8/mEM/Tpw4YU4H1v0Aw9Phw4elZs2a0qdPH1m6dGmRmm7zF3lDMiCAAAIIIIAAAgicV4AAIAMEAQQQQAABBBAIoEBaWprs3LlTunfvbl7NjZRyc3MlNTVVTp06JXoa8NSpUyPm++yzz+SKK64wn73yyiuiwcXw9Pbbb5t9BjMyMswrxHqqsB4mUrZsWdFXfWfNmmVOIQ4lt/kD2D00CQEEEEAAAQQQ8FWAAKCv3BSGAAIIIIAAAgh4L3DmzBmpWLGinD17Vh599FF58sknIxa6e/duadKkiflMV+7pCr5Iafny5XknCGtQ8eqrr86XTQN+GkDs1q2bZGdny+233y7VqlWTrKws2bt3b6FDSNzm916MEhBAAAEEEEAAgWALEAAMdv/SOgQQQAABBBBIQoEPP/ww7zCPZcuW5QXvClJkZmbK3XffbX6sgbqGDRtG1Jo4caI88cQTcuGFF8q3334rpUuXzpfvjjvuMAHE+vXrm/0B9d+adGXhtddeK3v27DErAps3b25+7jZ/EnYhTUYAAQQQQAABBGIqQAAwppzcDAEEEEAAAQQQiL+AvqY7cOBAU5F9+/bJ5ZdfHrFSY8aMkRkzZpjVgrq/X6lSpSLm69mzp6xYscIE87Zv314oT6NGjczhH3pISJs2bfJ9Pn36dBk3bpy89NJLeXVymz/+otQAAQQQQAABBBCwW4AAoN39R+0RQAABBBBAAIFCAqNHjzb77v30pz+VY8eORQ3sde7cWdauXWuCdhq8i5bq1asnBw4ckMGDB8uCBQvyZTt58qQpp23btub134IpFIzUk4aHDRsmbvPTvQgggAACCCCAAAIlFyAAWHJD7oAAAggggAACCCSUQKdOnWTdunXSoUMHeffdd6PWrUaNGnLkyBEZPny4PPvssxHzaQCxatWq5rPf//73MmLEiHz5Nm7cKO3btzcHiOg+gAWTvjqsrxCvXr1abrnlFnGbP6FgqQwCCCCAAAIIIGCpAAFASzuOaiOAAAIIIIAAAtEEqlevLkePHpVRo0bJ7NmzI2Y7ePCg1K1b13w2f/58GTJkSMR8GkBMT083n2nwTlf6hac5c+aYcl599VW56667Ct2jdevWkpOTYwKNFSpUELf56WUEEEAAAQQQQACBkgsQACy5IXdAAAEEEEAAAQSSVkD3GtTXfCOtDty8ebN5vVhf/dVXgDW5zZ+0sDQcAQQQQAABBBCIoQABwBhicisEEEAAAQQQQCDZBNLS0mTnzp3SokULee+99/JOCNY9A3XloO75t2PHDrnkkksMjdv8yeZJexFAAAEEEEAAAS8ECAB6oco9EUAAAQQQQACBJBD4/vvvJTU1VVq1amVOG65Zs6bZ5+/w4cPy+uuvS25urmRlZZk9AjW5zZ8EhDQRAQQQQAABBBDwRYAAoC/MFIIAAggggAACCARPYPv27dK8eXMZO3asDBo0SEaOHGlWAZYrV070hOEpU6bIlVdemddwt/mDJ0aLEEAAAQQQQACB+AgQAIyPO6UigAACCCCAAAIIIIAAAggggAACCCDgiwABQF+YKQQBBBBAAAEEEEAAAQQQQAABBBBAAIH4CBAAjI87pSKAAAIIIIAAAggggAACCCCAAAIIIOCLAAFAX5gpBAEEEEAAAQQQQAABBBBAAAEEEEAAgfgIEACMjzulIoAAAggggAACCCCAAAIIIIAAAggg4IsAAUBfmCkEAQQQQAABBBBAAAEEEEAAAQQQQACB+AgQAIyPO6UigAACCCCAAAIIIIAAAggggAACCCDgiwABQF+YKQQBBBBAAAEEEEAAAQQQQAABBBBAAIH4CBAAjI87pSKAAAIIIIAAAggggAACCCCAAAIIIOCLAAFAX5gpBAEEEEAAAQQQQAABBBBAAAEEEEAAgfgIEACMjzulIoAAAggggAACCCCAAAIIIIAAAggg4IsAAUBfmCkEAQQQQAABBBBAAAEEEEAAAQQQQACB+AgQAIyPO6UigAACCCCAAAIIIIAAAggggAACCCDgiwABQF+YKQQBBBBAAAEEEEAAAQQQQAABBBBAAIH4CBAAjI87pSKAAAIIIIAAAggggAACCCCAAAIIIOCLAAFAX5gpBAEEEEAAAQQQQAABBBBAAAEEEEAAgfgIEACMjzulIoAAAggggAACCCCAAAIIIIAAAggg4IsAAUBfmCkEAQQQQAABBBBAAAEEEEAAAQQQQACB+AgQAIyPO6UigAACCCCAAAIIIIAAAggggAACCCDgiwABQF+YKQQBBBBAAAEEEEAAAQQQQAABBBBAAIH4CBAAjI87pSKAAAIIIIAAAggggAACCCCAAAIIIOCLAAFAX5gpBAEEEEAAAQQQQAABBBBAAAEEEEAAgfgI/B/nb4EQY8o2WAAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "ax.hist(hist_signal[0][:-1], bins=hist_signal[0], weights=hist_signal[1],\n",
    "        alpha=0.5, label='signal')\n",
    "ax.hist(hist_bkg[0][:-1], bins=hist_bkg[0], weights=hist_bkg[1],\n",
    "        alpha=0.5, label='background')\n",
    "\n",
    "ax.hist(hist_signal_pred[0][:-1], bins=hist_signal_pred[0], weights=hist_signal_pred[1],\n",
    "        label='predicted signal', histtype='step',\n",
    "        linestyle='--', color='green', linewidth=2)\n",
    "ax.hist(hist_bkg_pred[0][:-1], bins=hist_bkg_pred[0], weights=hist_bkg_pred[1],\n",
    "        label='predicted background', histtype='step',\n",
    "        linestyle='--', color='red', linewidth=2)\n",
    "\n",
    "ax.set_xlabel('$m_{bb}$')\n",
    "ax.set_ylabel('counts')\n",
    "ax.legend()\n",
    "ax.set_title(\"Distribution of $m_{bb}$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=gbt></a><br>\n",
    "## Gradient-Boosting Trees\n",
    "\n",
    "Like Random Forest, Gradient Boosting Trees is a machine learning technique which produces a prediction model in the form of an ensemble of of weak prediction trees. Unlike random forest, GBT optimize a cost function over a function space by iteratively choosing a function that points in the negative gradient direction. More about GBT [here](#https://en.wikipedia.org/wiki/Gradient_boosting).\n",
    "\n",
    "The pipeline in spark is the same of the random forest, it is sufficient to substitute `RandomForestClassifier` with `GBTClassifier`.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import GBTClassifier\n",
    "\n",
    "gbt = GBTClassifier(labelCol='label', featuresCol='features', maxIter=50, maxDepth=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 21s, sys: 1min 44s, total: 4min 6s\n",
      "Wall time: 19min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "gbt_model = gbt.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_gbt = gbt_model.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pd_gbt = pred_gbt.select(['label', 'prediction', 'probability']).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pd_gbt['probability'] = pred_pd_gbt['probability'].map(lambda x: list(x))\n",
    "pred_pd_gbt['encoded_label'] = pred_pd_gbt['label'].map(lambda x: np.eye(2)[int(x)])\n",
    "\n",
    "y_pred_gbt = np.array(pred_pd_gbt['probability'].tolist())\n",
    "y_true_gbt = np.array(pred_pd_gbt['encoded_label'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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"
    }
   ],
   "source": [
    "from sklearn.metrics import auc, roc_curve\n",
    "fpr_gbt, tpr_gbt, threshold_gbt = roc_curve(y_score=y_pred_gbt[:,0], y_true=y_true_gbt[:,0])\n",
    "auc_gbt = auc(fpr_gbt, tpr_gbt)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot([0,1], [0,1], 'k--', color='orange')\n",
    "plt.plot(fpr_gbt, tpr_gbt, label='auc = {:.3f}'.format(auc_gbt))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  },
  "sparkconnect": {
   "bundled_options": [],
   "list_of_options": [
    {
     "name": "spark.executor.instances",
     "value": "15"
    },
    {
     "name": "spark.executor.cores",
     "value": "2"
    },
    {
     "name": "spark.executor.memory",
     "value": "6G"
    },
    {
     "name": "spark.dynamicAllocation.enabled",
     "value": "false"
    },
    {
     "name": "spark.driver.memory",
     "value": "8g"
    },
    {
     "name": "spark.driver.maxResultSize",
     "value": "7g"
    },
    {
     "name": "spark.driver.cores",
     "value": "4"
    },
    {
     "name": "spark.app.name",
     "value": "RandomForest"
    }
   ]
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}