docs/tutorials/multi_resolution_gfmm_general_use.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-resolution Hierarchical Granular Representation based Classifier using GFMM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to use the multi-resolution hierarchical granular representation based classifier using general fuzzy min-max neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Execute directly from the python file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the path to the this jupyter notebook file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\docs\\\\tutorials'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_notebook_dir = os.path.dirname(os.path.abspath(\"__file__\"))\n",
    "this_notebook_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the home folder of the Hyperbox-Brain project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/hyperbox-brain')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "project_dir = Path(this_notebook_dir).parent.parent\n",
    "project_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the path to the Python file containing the implementation of the multi-resolution hierarchical granular representation based classifier using the general fuzzy min-max neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\hbbrain\\\\numerical_data\\\\multigranular_learner\\\\multi_resolution_gfmm.py'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_resolution_gfmm_file_path = os.path.join(project_dir, Path(\"hbbrain/numerical_data/multigranular_learner/multi_resolution_gfmm.py\"))\n",
    "multi_resolution_gfmm_file_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run the found file by showing the execution directions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: multi_resolution_gfmm.py [-h] -training_file TRAINING_FILE\n",
      "                                -testing_file TESTING_FILE\n",
      "                                [--val_file VAL_FILE]\n",
      "                                [--n_partitions N_PARTITIONS]\n",
      "                                [--granular_theta GRANULAR_THETA]\n",
      "                                [--gamma GAMMA]\n",
      "                                [--min_membership_aggregation MIN_MEMBERSHIP_AGGREGATION]\n",
      "\n",
      "The description of parameters\n",
      "\n",
      "required arguments:\n",
      "  -training_file TRAINING_FILE\n",
      "                        A required argument for the path to training data file\n",
      "                        (including file name)\n",
      "  -testing_file TESTING_FILE\n",
      "                        A required argument for the path to testing data file\n",
      "                        (including file name)\n",
      "\n",
      "optional arguments:\n",
      "  --val_file VAL_FILE   The path to validation data file (including file name)\n",
      "  --n_partitions N_PARTITIONS\n",
      "                        Number of disjoint partitions to train base learners\n",
      "                        (default: 4)\n",
      "  --granular_theta GRANULAR_THETA\n",
      "                        Granular maximum hyperbox sizes (default: [0.1, 0.2,\n",
      "                        0.3, 0.4, 0.5])\n",
      "  --gamma GAMMA         A sensitivity parameter describing the speed of\n",
      "                        decreasing of the membership function in each\n",
      "                        dimension (larger than 0) (default: 1)\n",
      "  --min_membership_aggregation MIN_MEMBERSHIP_AGGREGATION\n",
      "                        Minimum membership value for hyperbox aggregration at\n",
      "                        higher granular levels (in the range of [0, 1])\n",
      "                        (default: 0)\n"
     ]
    }
   ],
   "source": [
    "!python \"{multi_resolution_gfmm_file_path}\" -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the path to training and testing datasets stored in the dataset folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\dataset\\\\syn_num_train.csv'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_file = os.path.join(project_dir, Path(\"dataset/syn_num_train.csv\"))\n",
    "training_data_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\dataset\\\\syn_num_test.csv'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data_file = os.path.join(project_dir, Path(\"dataset/syn_num_test.csv\"))\n",
    "testing_data_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run a demo program"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If the argument 'validation_file' gets the value of validation file path, the pruning procedure will be used after merging all hyperboxes from base learners. Otherwise, the pruning procedure will not be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 3.847 (s)\n",
      "Testing accuracy (using voting from all granularity levels) =  86.70%\n",
      "Prediction of each base learner at a given partition:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   4 | elapsed:    3.6s remaining:    3.6s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    3.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partition 0 - Testing accuracy =  84.00% - No boxes = 27\n",
      "Partition 1 - Testing accuracy =  87.80% - No boxes = 29\n",
      "Partition 2 - Testing accuracy =  85.40% - No boxes = 27\n",
      "Partition 3 - Testing accuracy =  87.10% - No boxes = 26\n",
      "Prediction for each granularity level:\n",
      "Level 1 - Testing accuracy =  85.10% - No boxes = 101\n",
      "Level 2 - Testing accuracy =  88.20% - No boxes = 38\n",
      "Level 3 - Testing accuracy =  87.10% - No boxes = 27\n",
      "Level 4 - Testing accuracy =  86.10% - No boxes = 20\n",
      "Level 5 - Testing accuracy =  86.20% - No boxes = 14\n",
      "Level 6 - Testing accuracy =  82.60% - No boxes = 10\n"
     ]
    }
   ],
   "source": [
    "!python \"{multi_resolution_gfmm_file_path}\" -training_file \"{training_data_file}\" -testing_file \"{testing_data_file}\" --n_partitions 4 --granular_theta \"[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]\" --gamma 1 --min_membership_aggregation 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Using the multi-resolution hierarchical granular representation based classifier using general fuzzy min-max neural network through init, fit, and predict functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hbbrain.numerical_data.multigranular_learner.multi_resolution_gfmm import MultiGranularGFMM\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create training and testing data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(training_data_file, header=None)\n",
    "df_test = pd.read_csv(testing_data_file, header=None)\n",
    "\n",
    "Xy_train = df_train.to_numpy()\n",
    "Xy_test = df_test.to_numpy()\n",
    "\n",
    "Xtr = Xy_train[:, :-1]\n",
    "ytr = Xy_train[:, -1]\n",
    "\n",
    "Xtest = Xy_test[:, :-1]\n",
    "ytest = Xy_test[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initializing parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of disjoint partitions to build base learners\n",
    "n_partitions = 4\n",
    "# a list of maximum hyperbox sizes for granularity levels\n",
    "granular_theta = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]\n",
    "# minimum membership values between two hyperboxes aggregated at higher abstraction levels\n",
    "min_membership_aggregation = 0.1\n",
    "# the speed of decreasing of membership values\n",
    "gamma = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   4 | elapsed:    2.9s remaining:    2.9s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    2.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MultiGranularGFMM(granular_theta=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6],\n",
       "                  min_membership_aggregation=0.1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hbbrain.constants import HETEROGENEOUS_CLASS_LEARNING\n",
    "multi_granular_gfmm_clf = MultiGranularGFMM(n_partitions=n_partitions, granular_theta=granular_theta, gamma=gamma, min_membership_aggregation=min_membership_aggregation)\n",
    "# Training using the heterogeneous model for class labels.\n",
    "multi_granular_gfmm_clf.fit(Xtr, ytr, learning_type=HETEROGENEOUS_CLASS_LEARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The code below shows how to display decision boundaries among classes at a given granularity level if input data are 2-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "        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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xnDs+fjHP+7Ug/fyHj64/YrJeL8hSwY4bh+mPZ5MS1GZzOfoUwsXLnREo9t27sm2/Jn9gWNdouDapN8mCij+7//+zxHpFBasJychWGdv4pjDsYvtwECHfdptD/qld3zM5DRssmW7sy8z0Q85RrjMyPGuu+5KeZorffnss8+OjSlse44hHH+ZOKHA5wPH7/jECRgKKvqymz/r5R2jKOIoSN2UKjjLpD+m6xv0OU4ouc87lo/9m23GROF9/fXXO/X0Jm+f/drXvuZM/PGe+PGFzxQyjm/7/WDF/cJb7v7+fnzjG99w+hzHW7afO35xQotCPZHgzbRu6ZixqH7FZC76a1hjzYwZM5xJK5c7hQ1jBvcQmlRiMpvnbCbPkHT+lGmswWcQJ/LdOMM78cM83/Wud+E73/lOuuydv6cTk5zspb/wv+446I5XbgaMXf73f/83lh/HIfZNjrOJYjz3Qj6nuHDBfk4WfJa4KZs4WGLSV9NDB/Ak4RSWmGTAwRl1Bhqc8aVIik+cEWaAxRUnzua7Dw1voMcHGIONH//4x84KJzsMgyoKTK7McOXlkUce2c8+D4zgbAxXTDiA8X0sloGBPFeFuNL01FNPJVzZc4N09yHJcnJgYeDP1TUOtBSy7gDCIIQPWwYXXE1jEMV8eKQ+gy4GPiwPZ5q8iQEJZ6V4L4UKBxI+4PkwZRDDAJfsONPN2cP4hzZnEhkMcXWIKxlcgXQfhocffrhj5wc/+IFjJ2jKtm4M8rmiy1U9Pri4UkRuHBi5Es1ZbT7AOFjHB9lHHHGEI745ScAZawYcvJeDKz+TwRUo1o0i15sYuNEeHwoMFNlmbCfy5SolAyuKNdplsOsVsbTJ4JSJItdtRwpRrt7RX9jODBYzEZNcPaQP0n/Z1l//+tdx0EEHOWUgBwbbFJusg7uqz7KkEpNcgaGvcRWE19E/+aCnX7H+DBQ5WcMVdf7NTZzBZIDPvCmOP/KRj8RWE+nfXIn49a9/DQaSvJ+J3PnAIndy/PznPx/7GwNAripw9Z4rk2xvN6V7yKabAU8V/HGcYKDPNuFKFAUj+w8nfrhqygcw60cmXL1n//Q+gN2TqNmvGBjz+pkzZzqXcIVz7NixvroN+xivp79xsoJBMduZ5aM/sh+QNX0uXpi49eM99AOKIPKlmOXP9EOOX0w/+clPHL7eZNpvUwUUflb+vvrVrzp9nf2QYzgDKo7z9CNOGLCfc8KOM/ZBEhlRrNF/OAZyIo/BGdudTLliyWcHfTPRtiqKEfZpPhNOPvlkRzC1tbU5k04ca7/0pS/hlltuQWdnJ7jTxjv2UkByDOFqDvvfN7/5TWclk+MDx3oGjFx94LOH45+bkrHMpD/SZqq+wUkklomJ4xpZ0d85hvLZzMlHCn2OQX/729/2maTwjv3kwd1DfNawrhSj7njIcZVt6/qn3/Zzy+22D8cx2ueqLn2e/YU/sw+xjSh6vCmbuoUlJnPVX8MYa+jLFBn0ce6aInf6LfujO8maalzO9Dmb6TMknV9lE2vY2ObK5xPHfT4PuABAH2Qsy3GQ4xMndenvbBOOS4wZ3fTf//3fzljGGIgTN4mSO8ay7TgB7N3tlU0cLDGZzvP2/l1iMgknr5iMn6mJv4UPGj5QmeI/DcIHDR/EfLgnEzQM/BiEMjBg0JQo0KPQ/Mc//uGIEW/iA54rOHw4cmbTKywoxBi4MzjgltNEW2G5jZLbabhVKX41yLviw2DNfSjH19+73YsCg8FH/CobA0AG60wUfN6tmcccc4wT8DPooJiIP8mS5eKgw2CYAQsHfm8if+ZLoUQBwrryYe9ugeUL0AxqMknZ1i1dnpdffrkj2iiUKXa8iRwo4rgizNVjP4kTCvQniiYOuq6w9t7L4Imc2Pbx4oJign6WrB25HYhbUZiCikm2H4UJB3quEjKA9q7Cpapfpu9M0jcY8HLbJUUqt9y5if2FooXbUt2VnXSMuYJOscR6cEuh32RSTNJ/6EcMdpOd9MogmuML+wRFQqIxhg9h+k/QFRa/DBgsc1Ik0S4N73ib7F0SbnXiRBQnWTiR5k0m/Zb5ZCsm+ToCt+ByYiJ+ddgvv/jruOWLbcZ+lUzMcJcLJzeY4rnymcX7OcaQJyfe4hNtc6KPEyvxK4zuqxl8NnGMpsjyk5KxzKQ/Mr9Uwsj1C443DJjjV7S5JZuTUEy///3vY5/n4c/esT/ZVj9OJlHk8dnlrrb4YeAtN/8/Wd9luzJI5sQQRYg7qcV7sqlbWGIykV+59c9lf/U71sTHXPFtl0xMZvOczfQZ4tevkl2XKtawISYZm/I1JgpJPm/p0/GJ8QjjNU4icgHBvYaxjBsD8bmbaIKTcSz/xpVlTqy5Kds4WGLSn+dJTCbhFL+Nwh/OxN+Z5IwvVwYY1PKh7E3eTsL3aDij7ybvyiTfWeRDJVHirC9XUOLtu7Mx7MR8UCdLFImc5eQqkbu9jNe6YpIPLQ6eyZL3ocughMFefOIMPAMXChjvAM7ZKs7Wc/aVQoXbHxMlBvAchBMx5PVcnaWo5lYNrtZyux8FMAMcCtRM3mmh3Wzq5sdnGMi54tl9R829j4EDB1QKYQ6wfhJnsDmrR4FBXskSVzC4AkVhyBk/Jgouig2mVO3IVXCuoAcVk7RJ4cbE1Sl39ctPvTIVk7RNn6b/c2WaEzpu4ioQf8fZUpYn1XtJ7j1czee2M267YfCY6r1Tb71MiUmucHNGnf2L7594V3O9+XOl1X3XhDPv7gSZd4xJNFHjp238XkMxRVHFMY5jnTe54y1nqSlyEglaCmUG9ZxQc7fz2vBb5pGtmHRFHfsdV5zCSLTD2XtOHLFN498pZx5cKXafKfFikqvFXF3hxB4n+JIlTnByAo/vMrENmTj5yDGVk6VB/SYZy0z6I8uSTBh5xzMGr+7YE19Prnaw/vGTjt6xn32dgjE+cTXT3U5HJvEToana2RtjJLPPFUpOBvM5wfam/8T7fCZ1C0tMRrW/+hlrOOHN52v8BL23zZKJyWyes5k+Q7IdM1LFGqbFJOM7Pn/4vKK443MgUXJfj6LfMzZm33QTBST7SbxY5N/Zf91r48VmtnGwxKQ/z5OYTMIprG2uNM+AjStPDPgoJr3vNrrviCQKzL2BHldxOKOTKLmrfpzF4Yqce5gOg0wGZtxGk+rQGW4x4HbU+L3qrpikwKPQS5bchy7z5xaFRO928F53Jpvv3fFdRyauYroz4ty3zy2EiRLf/6NIjK+j91rvh9/5ez4s+O4a88s0ZVM3N0+uBFLEULBzJZkC2n1fx1uueIFF7twSx8Ca/88ZdAZ+qQIW/p0rs7wnldBhOzH44aowt54ycZsJ/TFdO3J1j+/XBRWTPLSH/YrBER/iQVI6McktguwHfABxwoJ1i0/cWuh9d5jbuzlRQt/nrCfrzj7mbvtMVD623ZgxY5x+xXspxjl5woA92SFKtGNKTLqzrswj3Q4KV4B5DwPzjjFBVsCTtR2Zcps6+x37s/fdVfce+qX3/VL+3h1vk00W8RoezHXkkUc6/untP6b9lnlnKyY52cdJP/oIJ8b4j6tlqQ6TSNc/+I45V9u5VZuiJlnizhhO/sSLSXdFm2I0kRB17TEIZXDH99W5a4GJ4zefD0zxk6Dpyp2MZSb90es78eORX79w36vnJBlX/9zk9llOdnIyJlHibhKOB0wcd9IdhOe14fo8JzzdD5knysM9VITtzVdGgozVyeoWlpjMZX/Ndqzxs7skmZjM5jmb6TMkXb/i3zONNUyLSU508nA9Jsal8TvXvHXj4ZKMlbkNnQdkuYmrjdwGy1iAbe993rqT44m2wWYbB0tM+vE8bXNNSilMMclM3G1O3tURPqC5+sTAl8EGD8rxJm+gl+qBzS2iFAVM7qoDgy2/WwjdPOO38rhiMtFMkLec7kM3nUj48pe/7JyGx+0IFFVMPHjGrTeD+mRCNFEdEzUe3xlzt7RyRYqrrYkSt1vwX6LkHprBv2VTN97PlQGKZQYabqIYZEDtDqhukB//0jgfOpwtZ2DuJq6ecQWWwogCMz544c9c4fSbvAEYWXELnt92DComOVlBUZ3qvYdk5U4lJt2VRy8jBseu/7vCmf7sZcnrKYopCL3ihg8fHvTC1SQK+HiRSFHDv/Gh5yZO2HDSgg8/CoX405FNiUlONiRbzU/G0ns4kHeMYWDMSa9ME7cosS+7B+7QDtuBE1nuu77uIQ/xH8bOJrg17besR7Zikja4Csj3zt2DVfg77+m/3nds/bQBVwq5KsV38t2trInu4zZVTiDEi0m+s8wJBL/Je0CQexYA76XYDHJCdiqWmfTHZL7j1y84JnFsYh3cQ+28Y3+qg5HiT0N2T+71w9QtN9vnzjvvTHoL25e8vSvD2dYtm/7GgmZzv9+yu/FCoudMGGMNJ3M4CZkqJROT2TxnmV8mz5B0PpVNrGFaTHp3JaWrh/v3+FfGvAfscPKMkyxMjHUZR3OCl69R8P1KN4URB0tM+msxrUwm4RS2mHTfzeBBB1yVoZhwH2LxB++4RfI+qJLtE+e1iYQWhZl72AgFGwPcoMnPwRLehy63/qUSMmGJSYqvRFtTyItCyz0VNNl7Mt6HYSIm3tM7XRGQSd1o2w3YGGRwOxhXvryfjOA7fe57AfEngLpl48EU9B++E8pVRzdYZ/tyNc47CUEhSD7xW5b9tL37kPdb10zFZLrgKVFZk4lJ70OKDxEGhVxB8W5Zdd87SlZebr3h+1Lkz+DaPZmS5eADi6uZ8Ss3nADi+3vcMcB7vO9LccWSQb5X6JsSk+4YQl/wnkDrp715TTbBsDcPrtxwVwEnyOjjnEHme87eA4/Iyt0Cb0JMmvJb1jMMMUk7FOz0NQaxFBCcMHITt6xyi7rf5IpJ9n9ORiZLycSk+3vvVne/eZsSk8w/aH9MJybT+UWyPpSuz2bbf9xyc1dEKlGfSkxmWrdsxKD3+ZnqGZBMoGT7nLEx1rj9IJmYzOY569oO+gxJ1zeziTVMi0m/iwHp6sgJW46dPH+DE6lMbmzNyWPG1t74Kow4WGIyXavs/bvEZBJOYYtJzkZz+xy3s3DLJrfUcSaaJ07yJf5Eq2TZbnN1jxhP9b5lKjcJKibTbY9Mt8011cpIum2uPCiCgT+3OXL7CrcrUXRl+2HYbLa5UpS43zdk4JjoO3/cfsZtaEzJxKS3jdxTeLlazEObOJtOP3G3OFJM8x1RbqnjabFBkt9tYYm+ieonH/c4dJbV/eSFn/t4TTIx6W4T53uMfGAlStweTf/xK365PZPXcxU92UmK8fmwHbmqQn9jG8WfWpwuMM30NFee1My8mJK9d5WKcVhiktstuQ2P79FxFT7RVmzycQ9AClNMmvZb8gtLTHrbgs8EvutDP2M7MvETEO6BMOn6ht9trtxGyTaJHwt5qi/zYxDKCasgyfuuYFjbXJPln64/JhNGfv0i3TZX0yuTudjm6p58mWqXCH3DPfk5Sv3Vxljj+mKycTmb52wyP0/3DEnVP7ONNUyLSR765p7gnew8Bj/jjxsHcicQYwjGP9wNxAm6ZN8azzYOlpj00zISk0kphS0mmZE7gHPGnoeeuNuakj2MvYFeqq2m7jts8e8vcCsk34ejuOK20lT71BOBCComaYPbNxKdCsiHEQUBxXT8ATxcmWVgleoAHnd2lsy4OhefKJh5UiKDWP6dL7lzWxlXqDhQJvrGoJ8u4j2EIWjdONvMQI0p2RZevgPDwYrJj5h0y+x9V4entLkfO+dR93xnjQMoHzBBTub0HliRrK7eg5T8ijO3zO47b/w5rAN4eKgGH05c9eYhV/GJfsethHzHImh53X7Fdz0o3P0kNzAld/cbj7zPlJjkKdGcJWc9+U4J8w+SwhKT7Hvsg6lWnfkBdnJgCjM4Ne23LG+qgIIrsey7XI3le8FBE3cncFWXYyNPoeRWWD/JewAPdyMk+uwUJxjckw/jxSR3SvCzCBwjKdgSfXM2WTm4Cs6JA54iHtYBPOnqnKw/ZnsAj7tCyzGUY6limyGbAAAgAElEQVSb0vVZXpdN//HGGMkmUjmGcPIt0wN4ktWN2wHpa9weSBGTKLkHB0atv9oYa1weycRkNs/ZdH6e7BmS6r5sYw3v5BCf8ane/09VjmR9hpP9jPO4cyXVN9fTseH97A+Mp3jAIJ//fP7xZ+4UcidWvXayjYMlJtO1yt6/a2UyCScTYpIzKZyFdFfRuFyf6D0ut0jeBxUPaqAgjD+wgdtfeQInV+E4w83gwE3emcV02x655YLveXnfs8xETPJdM4qGeOHqFWXeA0BYVvd90mSfBuFKG0/+JDeu4HIl15sYyHH7XLwg5WdReEAJVwc5M5bJia7ecgetm/el8/g6s/wMFDgB4H4SxCsmWVcyTDYB4F3R9H7eg/WkTQbr6bavuR+U9wpOngZKP0tWV3dmkOUPKs4YNDOwpbAL69Mg7iEi3oOEvL7BSRv3HYr48rLPJHtHlzbcFU3v5zTS3XPOOec4gs79cLvfwDTTlUnad0+r41ZcivRU34XkFkLvNqBsgmEvZzc4pW36Zvw7dPRLroJQfIQdnNKeSb+l/VQBhbvCx+/EJvu4dzq/4SEuHAeCbHUlZ07QcaxItgPD3UXAOsRfw+cRRSyFCgMuPi+SnWjMsZWBHCep3MR37HnCblifBknHKFF/ZFn8fBqEu1Y4vsbXj5OtrDuTd1KOP9sUk8k+PeLWjbt+2Fe9W+fdT4NkUjf3m56sZ6JdM5ykon33k2dhTv5k219tjDWujycbl7N5zqbz82TPkFQhfTaxBu16Vw75jMgkVkrXZ9wTcOnL7IupJvjjn1PeursLC4zv+O1Yjgt87nCLa6JzQrKNgyUmU3neW3+TmEzCyYSYZFbeA2L4c6KDd9wieQM9BqfcJsvj03mSGAd3voPEvePc0kmRys93xL/bxQNcKDaYKC7Yod1gk4Edvw/GGR2+e8cBxd1yyeuDikmuClKgME+KPtrijBG3pTDQ4v+z87tbutx6sgwMNHkvBxiurPH7iAxguHWRgwXfxeThICyjV/zw/Ylk35PkASn8GweZ+Flnf93jrYAik7p5V2P5YOYqpLsazQc4Z4bZdu5JgfEHo3CFl6s5DHbIw323kisxDDq54sp3b7mi4RUI7ofMXX/jihE58MFIQUc/4WdT+M4B/c87qLvfeeK9fM/W244Mrrjay/fg+K5XUDFJmzyynfUhG+bL1UR+MJyimcEqt31TsHJW3PuZi2TbXL3fL+U2WtadTFg++hFXixgAk3F8eelXfL+W9WRA5r6Hy8N4uEWYjNlHeCgRZ8OZmAfbjgfwUHC7/YUBAh9aPAyHNuPfY0sXmGYjJinOuduBdeThQZw4or9zzGBiQMiZZ9aJk0beb2qGJSa5AsaDtdhn2f+5asbAl/zY3+nr/Jvr62EHp6b9NlVA4a4kcDKMfTLRiZ7cFsf3fciG47d70jLHNfYBfqaHKdVnHhKNWfRRsmb/4c4XcuYKJcc+rpIy8KYf0CcTCU53dZO26c9c3eZqFkUX24iTlRRcbp/0fqeVW2e5dY3+xecPxwp+ZorjA/sD7+XYz+/eUtS6KRnLTPojbaYSk3zf2f0+MkU/vwnJ5yifNVzZ4EQTxx2OQewjXrGZrs8y72z6j1tut334jOT73Zww5kQjV6j5M/tNokmGbOpGm5xI4POHJ1FzbOCziW1ODuTC1W4G9UxR6q82xhrXV1ONy5k+ZzN9hqSKWbKJNWjXu9OAn9Hip4AyWZ1M1WfoS3xOcRcExz9y4LjAZxYTxyg+W3mYGHeXUSAnSu4J5oyHGFdxsSH+SwTx92UTB2crJnnoHOMuN3Gyn7vG+Mk+7+IPJ2CD7CTzG8Pauk5iMglpU2LSewhFsoN33CJ5H1Q8RIcPWz5k6HB8GLgHbjBY5la/RN/t4zXcVsD73cSOzBkcdl7vyYLxR5sHFZPcJsUOwmOaObhxdouBuXuMPwUN6896xyce6MB3gNyVC4pi/j8FKBODFQZaPFzFm9xVgWTfk+QqKbdCsDwU4mQYJHkHx0zqxgc+V464esDkvkvGduH/U9S5h5IkO2WT9zHIYdBBni4jtiHfQ+MEhTdx4OLDwHuYBwcqtjvb3C0L7+EMtbsV17XhBsfuz2xH+p27ou6KwEzEJG1yNYOiyz1IiKuDrkB18+QEAwNwNyUTk/Qtim73VD4+AN13JOjbFK4MeBlox5fXfUi4ebBf8QHlPRSFdeWEhhv8e8cF3sdy8x8fGG7ARR/lJ2+4/cZN6QLTbMQk8yAvPjA5ZjC5HMjHe1ItfY1jRaIxxnvwVJA+4l7rHrDl/kx/pZ+zDAzg2QZ8l5cp7OCUNk36baqAgsEtBRPHKoo6Bkfuyiz7FyccXP9124ZsyMX7CRtO9FHsBEnMk2KJ45w7TnDspA+TMU865nvk/JRHstVLblPlhIkb8HBcoSClyPJ+goWCw20/t4yc/OE7nu5WSY5T3vx53QUXXOBMQrkpGctM+iNtpjtMhvlzN4vrcxwf6JfuOMqJOvZxbvn0pnR9lteGISY5LnEylUE8/ccdv9z2YJ/lISOJTszNtG4sO5+n9B23jfk84phJn+JKOYN993C3qPVX02ON6wepxuVMn7OZPkPSjQuZxhquXS5McHKWib7ASQ3Wn/FFspPv48uUrs/wGcN4iALQTfR3d+eD+ztOzHkPtfPmwziEY6r3W8PJzqRw78smDs5WTHrH/lRtmGx3Qrp2j8rfJSaTtIQpMen9eGuyg3fcIsU/qPjw4xYICjLOPDNo4RZRrr54VxQTVYkz5jyQgCeCcqXO3cPO1R+eCsgOHv9R5kzEJMvMYJVBEbfcMZBlIMmVHAoc7+mO8eXkNi8OWgyMKGy57ZZlYtkYjMSvurrbXdJ9T9L9dIT7PmW8IE3VGeMHx0zqxoGO7Ub2HNQoMiiAWC7v9wm9YpIPeAY4/B3vJw9uPaLY4eDEVQTOVrvfOEtUBwoMnlTIQJL3U7xRGHJlmt/roxCnwE+UOPvHNuQMIX2Fq8IMJDkby7pw1SNTMekGYdwSyHdbOTvOfsGTCRnYcVWN21e9W1BTfRqEwQ9X4zijSf9jO3OVjIMzRSuDokTl5QwpV13ImFt7ue2P/spVXnJhIMUJDu9qBVeSyIb38D1K9iUKdPomZ0lZdn7+Jz7wS/eQzVZMkim3K7KPcyWQD2sKXAoDrpRxNYxBP8cL77t12QTDifyGK1FcZSMb1okTTOy/nIGlP9JvmUwEp7Rrym/TBRQUbFxh5HZ2rr66EzauQOff6etc9eHv6Gu8hj7PlUD6abLvCKcLFmiHq/A8Sp+rgew3FLdcpWQ/8jOOc+ylDT5b6BMUufRpjr9sM44VLGei1QpONPFe+h3f/+cYx/eaOGbQ5zhueE/fTsYyk/5INunEJK+h6KXw4ljIIJTPIT4HeHiH+1mQoIGxO46536UNOhkTX25OqPKZxr7DsZrPBu5M4ft5ybYfZ1o3t670V07yuM8mvhLCMYw7Q/hN7Cj3V5Njjcsn3bjM64I+ZzN9hqQbB/j3TGIN1y59js9Rbvfmbhd3sSKIyEn3nGNeHK/4rObOAPoYdzbQvzkWcgKZExz8l+obvO6WWdpjDMTPovhJmcTB6cb+dPlKTKYjpL9nRICdx11BTHcKXtiBXkYF1k0iIAIiIAIiIAIiIAIiIAIikICAViYtu4U7y8FZaM4Cp0oSk5YbR9mJgAiIgAiIgAiIgAiIgAj4JiAx6RtV9hdySx23/XCvPf+fJ1BKTGbPVRZEQAREQAREQAREQAREQATsE5CYNMyc76rxEA/uP+d7jkw80pgv1KdLWplMR0h/FwEREAEREAEREAEREAERyBUBickE5PleIw9aWbVqlXOgAl+Y5qEG7smiQRrLKwjd49Z5ahxPXuShIN7vdsXblZgMQlrXioAIiIAIiIAIiIAIiIAI2CQgMZmAtvu5Ce+fMhWTPNnvwAMPdI455nedePgOv5/Dfzwpj6f8JfpUhk0nUF4iIAIiIAIiIAIiIAIiIAIiEJSAxGQCYuedd56zLZXH6fMfP+WQqZjkpwV4hDVXInn0t/vheX6gmB8l5t/5MXslERABERABERABERABERABEcgnAhKTPlqL3xrKREzyW2L8xhu3t65Zs8b59pab+E2frq4uvPLKK84Hn71/81EkXSICIiACIiACIiACIiACIiACOSUgMekDf6Zikh+R/vCHP+x8oP6mm27aL6cTTjjB+cg4r+MnQ5REQAREQAREQAREQAREQAREIF8ISEz6aKlMxeRnPvMZfOc738EXv/hFfOMb39gvp+9973v41Kc+BV53wQUX+CiJLhEBERABERABERABERABERCBaBCQmPTRDpmKSb4neeWVVzqCku9Ixqerr74aPOyH111xxRU+SqJLREAEREAEREAEREAEREAERCAaBCQmfbRDpmJywYIFzidGfvzjH+MjH/nIfjlx6+uRRx4JXnfDDTekLcnEiRMTXrN69WpUVVWhu7s7rQ1dIAIiIAIiIAIiIAIiIAKFSuC5555DTU0NeHaJknkCEpM+GGcqJikUKRgvueQS8P3I+EShSSGZrZh87LHHUFZWhlGjRvmojS4RAREQAREQAREQAREQgcIk8OSTTzpx8RtvvFGYFYxYrSQmfTRIpmLS1jZXd8WS365UCpfAwMAA+M9NxcXF4WYgayIgAiIgAiIgAiIgAqERUFwcGkpfhiQmfWDKVEzaOoBHncZHI2ZwybYduzDv5BV4dXcR5s7qxvFva8fsnno0DykDfUJJBERABERABERABEQgWgQUF9ttD4lJH7wzFZN+Pw3Cz4N86EMf8lGSxJeo02SMLumNe/YMYPE5N+LPDzzvXFPXOBSzZ/bgHdM7cPDIRnQ1VEpQho9dFkVABERABERABEQgKwKKi7PCF/hmiUkfyDIVk2vXrkVnZydKS0uxZs0atLS0xHLbvn07urq68PLLL+OFF15AW1ubj5JITGYMKeCNT659HTM++wds37E7dmfNkGpMn9aDpbOGY/6oJoxqqUZJsVYoA6LV5SIgAiIgAiIgAiJgjIDEpDG0CQ1LTPrgnU5MXnzxxeC/JUuW4Nxzz93H4nvf+1786le/wrJly/Db3/7WEZZMJ554Ii666CLw77/4xS98lCL5Jeo0WeFLevNja17FYaeswMZN22PXVFRVYOLkLiye1YkFY1owoaMWZSUSlGZaQFZFQAREQAREQAREIBgBxcXBeGV7tcRkAoIrVqzA2WefHfvL3Xff7Wxp7Ovri/3u1FNPxcKFC52fzzjjDJx55pn4wAc+gEsvvXQfixs2bMDs2bPBk6V42urMmTPBg3IeeeQR5+e77roLTU1NWbWjOk1W+FLevH7jVkdQPrX2tdh1peWlGD1uON7e34m3j2vFlK6hqCwvMVcIWRYBERABERABERABEfBFQHGxL0yhXSQxmQAlBWG6dxj5PuQHP/jBtGKSF7z66qs4/fTTcdVVV2HdunVobW3F8ccf7wjQhoaGrBtTnSZrhCkNbN2+C8efcyNWPrI2dl1xSTG6RrbhqNldWDy+DTN7hmFI1d5VZyUREAEREAEREAEREIHcEFBcbJe7xKRd3kZyU6cxgnUfozyQ55M/uB2X3rQ69nuuVrd2NmJ+fy+WTm7D7N56NNaWmy+MchABERABERABERABEUhIQHGxXceQmLTL20hu6jRGsCY0+u2r/o5Tfr5qn781tAzDnL5eLJvSirkjmtBRX6GTXu01iXISAREQAREQAREQgRgBxcV2nUFi0i5vI7mp0xjBmtToH+9+Fu/+1i3YvXtP7JraYbWYNb0by2YOx2GjmtDbVIVifYvSbsMoNxEQAREQAREQgUFPQHGxXReQmLTL20hu6jRGsKY0ev8TG7DgtGuxedvO2HWVNZWYPLkbS/qG48jRzRjXXotSfTrEfuMoRxEQAREQAREQgUFLQHGx3aaXmLTL20hu6jRGsKY1umbDJhx60jVY+/Lm2LVlFWUYN6ETx/V34ZgxTXhb51BUlOmk17QwdYEIiIAIiIAIiIAIhEBAcXEIEAOYkJgMACuql6rT5K5lXt+yA0edfh0efHJDrBAlpSXoGd2Ghf3dWDShFdO761BToZNec9dKylkEREAEREAERGCwEFBcbLelJSbt8jaSmzqNEay+je7ctQfvv+BWXHXnM7F7ioqL0NHdjMP7e7B0Uhv6RtSjvrrMt01dKAIiIAIiIAIiIAIiEJyA4uLgzLK5Q2IyG3oRuVedJvcNMTAwgDN+cz++cfmD+xSmqb0Bh/T1YNmUdhzUW4/WOp30mvvWUglEQAREQAREQAQKlYDiYrstKzFpl7eR3NRpjGDNyOgv//IvfPy7K7FnYCB2f13DUPTN7Mby6R04ZGQjehqr9OmQjOjqJhEQAREQAREQARFITUBxsV0PkZi0y9tIbuo0RrBmbPS2f6zF28+6Adt37I7ZqB5SjalTu7F05nAcProJY1prUKKTXjNmrBtFQAREQAREQAREIBEBxcV2/UJi0i5vI7mp0xjBmpXRJ158DfNOvgYvv74tZqe8shzjJ3VicX8Xjh7bggntNSgv1UmvWYHWzSIgAiIgAiIgAiLgIaC42K47SEza5W0kN3UaI1izNvryG9sw/5QVePz5jTFbpWWlGDm2HQv7u7DogFZM7a5DVbkEZdawZUAEREAEREAEREAEACgutusGEpN2eRvJTZ3GCNZQjG7bsQvLvn4TbnnwhZi94pJidPa2YEF/DxZPbMOs3mEYWqVPh4QCXEZEQAREQAREQAQGNQHFxXabX2LSLm8juanTGMEamtE9ewbw2Z/ciR9d98+YzaKiIrR0NOLQ/h4sn9yG2SMa0FRbpoN5QqMuQyIgAiIgAiIgAoORgOJiu60uMWmXt5Hc1GmMYA3d6PdWPIov/vQueA56RX1zHQ6a1YNlU9tx8MhGDK+vlKAMnbwMioAIiIAIiIAIDBYCiovttrTEpF3eRnJTpzGC1YjR6+9bg3ecdxN27doTs19bV4MZ03uwbHoH5o1qxqiWKhTrpFcj/GVUBERABERABESgsAkoLrbbvhKTdnkbyU2dxghWY0YffvplHHHqtXhjy45YHpXVFZg4uRtLZnXiqDHNOKC9FqUlRcbKIMMiIAIiIAIiIAIiUIgEFBfbbVWJSbu8jeSmTmMEq1GjL76yGfNOXoE169+I5VNWXoYx4ztwXH83jhnXjMmdQ1FZppNejTaEjIuACIiACIiACBQUAcXFdptTYtIubyO5qdMYwWrc6KatO7HwrBuwavW6WF4lJSXoHtWKYw7swXHjWzGjuw61lTrp1XhjKAMREAEREAEREIGCIKC42G4zSkza5W0kN3UaI1itGN29ew9O+O5K/N/fnojlx5Ne27qbMb+/G8sntaGvtwENtWVWyqNMREAEREAEREAERCCfCSguttt6EpN2eRvJTZ3GCFZrRgcGBvD1yx/CWb+5b588G1vrcXBfD5ZPbcdBIxrQVlehk16ttYoyEgEREAEREAERyEcCiovttprEpF3eRnJTpzGC1brR3932JD504V/B71K6aUj9EPTN6MGyGe04dGQTepuqUFykg3msN44yFAEREAEREAERyAsCiovtNpPEpF3eRnJTpzGCNSdG7169HseccR22bt8Vy7+qpgpTp3VhyczhOGJ0M8a21aJEnw7JSfsoUxEQAREQAREQgWgTUFxst30kJu3yNpKbOo0RrDkz+sy613HoySuw/tUtsTKUV5Rj3KROHN/XiWPGtmBiRy0qdNJrztpIGYuACIiACIiACESTgOJiu+0iMWmXt5Hc1GmMYM2p0Vc3bceRp12LR595JVaOktISjBzbjmP6u52TXqd116G6XJ8OyWlDKXMREAEREAEREIFIEVBcbLc5JCbt8jaSmzqNEaw5N7pj5278x7duwXX3PBcrS3FxMTp6WnDk7B4s4UmvPcNQV61Ph+S8sVQAERABERABERCBSBBQXGy3GSQm7fI2kps6jRGskTDKk16/fNk9uOiPf3+rPEVAc3sj5vX3YunkNuek1+YhZTrpNRItpkKIgAiIgAiIgAjkkoDiYrv0JSbt8jaSmzqNEayRMvrTP6/Gp35wGwbeOugVdY1DceDMHiyf3oGDRzaiq6FSgjJSrabCiIAIiIAIiIAI2CaguNgucYlJu7yN5KZOYwRr5Ize8vCLWPLVG7Fj1+5Y2WqGVmP61B4snTUc80c1YVRLtU56jVzLqUAiIAIiIAIiIAK2CCgutkV6bz4Sk3Z5G8lNncYI1kgafWzNqzjslBXYuGl7rHwVVRWYOLkLi2d1YsGYFkzoqEVZib5FGckGVKFEQAREQAREQASMElBcbBTvfsYlJu3yNpKbOo0RrJE1un7jVkdQPrX2tVgZS8tLMXrccCzs78Sica2Y0jUUlTrpNbJtqIKJgAiIgAiIgAiYIaC42AzXZFYlJu3yNpKbOo0RrJE2umX7Liw+50asfGRtrJzFJcXoGtmGo2Z3YfH4NszsGYYhVTrpNdINqcKJgAiIgAiIgAiESkBxcag40xqTmEyLKPoXqNNEv41MlHDPngF88ge349KbVsfMFxUVobWzEYf192LZ5DbM7q1HY225iexlUwREQAREQAREQAQiR0Bxsd0mkZi0y9tIbuo0RrDmjdHzr3oYX/n5PfuUt6FlGObM6sWyqa2YO6IJHfUVOuk1b1pUBRUBERABERABEciUgOLiTMlldp/EZGbcInWXOk2kmiMnhbn6rmfwnvNvxe7de2L51w6rxazp3Vg2swOHjWpGb1MViot0ME9OGkiZioAIiIAIiIAIWCGguNgK5lgmEpN2eRvJTZ3GCNa8M3r/Exuw4PRrsXnrzljZK2sqMXlyN5b0DceRo5sxrr0WpcUSlHnXuCqwCIiACIiACIiALwKKi31hCu0iicnQUObOkDpN7thHLec1Gzbh0JOuwdqXN8eKVlZRhnETOnF8fxeOHtOEt3UORUVZSdSKrvKIgAiIgAiIgAiIQNYEFBdnjTCQAYnJQLiiebE6TTTbJVelen3LDhx9xvV44ImXYkUoKS1Bz+h2LOzvwqIJrZjeXYeaCp30mqs2Ur4iIAIiIAIiIAJmCCguNsM1mVWJSbu8jeSmTmMEa14b3blrD95/wa246s5nYvUoKi5CR3czDu/vwdJJbegbUY/66rK8rqcKLwIiIAIiIAIiIAJeAoqL7fqDxKRd3kZyU6cxgjXvjQ4MDOD0X9+Hb17x0D51aWpvwCF9PVg2pR0H9dajtU4nveZ9Y6sCCQls3gzU1u7906ZNQE2NQImACIiACBQ6AcXFdltYYtIubyO5qdMYwVowRn9x6+P4xMW3Yc/AQKxOdQ1D0TezG8und+CQkY3oaazSp0MKpsVVEZeAxKR8QQREQAQGHwHFxXbbXGLSLm8juanTGMFaUEZXProWi86+Adt37I7Vq3pINaZO7cbSmcNx+OgmjGmtQYlOei2odh/slZGYHOweoPqLgAgMRgKKi+22usSkXd5GclOnMYK14Iw+8eJrmHfyNXj59W2xupVXlmPCpL0nvR4ztgXj22tQXqqTXguu8QdphSQmB2nDq9oiIAKDmoDiYrvNLzFpl7eR3NRpjGAtSKMvv7EN809Zgcef3xirX2lZKUaOffOk1wNaMbW7DlXlEpQF6QAFXimKR2/iz62te3+zbt3+70zqHcoCdwhVTwREYFASUFxst9klJu3yNpKbOo0RrAVrdNuOXVj+9Ztx84PPx+pYXFKMzt4WLOjvweKJbZjVOwxDq/TpkIJ1ggKtWFFRsIp5XiMOdqOuFgEREAERiCwBxcV2m0Zi0i5vI7mp0xjBWtBG9+wZwGd/cid+dN0/Y/UsKipCc0cj5vX3YPnkNswe0YCm2jIdzFPQnlBYlZOYLKz2VG1EQAREIBMCioszoZb5PRKTmbOLzJ3qNJFpirwryPdWPIov/OSufcpd31yHg2b1YNnUdhw8shHD6yslKPOuZQdngbXNdXC2u2otAiIgAl4Ciovt+oPEpF3eRnJTpzGCddAYvf6+NXjHeTdh1649sTrX1tVgxvQeLJvRgXkjmzGqpQrFOul10PhEoVRUB/AUSkuqHiIgAiLgn4DiYv+swrhSYjIMijm2oU6T4wYogOwffvplHHHqtXhjy45YbSqrKzBpcjeW9HViwehmHNBei9KSgC+lFQAbVSF/CUhM5m/bqeQiIAIikCkBxcWZksvsPonJzLhF6i51mkg1R94WZu0rW3Doyddgzfo3YnUoKy/DmPEdOK6/G8eMa8bkzqGoLNNJr3nbyIOs4BKTg6zBVV0REAERAKC42K4bSEza5W0kN3UaI1gHpdFNW3di4Vk3YNXqdbH6l5SUoHtUK46Z3Y3jJ7Rhencdait10uugdJA8q7TEZJ41mIorAiIgAiEQUFwcAsQAJiQmA8CK6qXqNFFtmfws1+7de/Dh7/4Nv/vbk7EK8KTXtu5mHN7fjWWT2tDX24CG2rL8rKBKLQIiIAIiIAIiULAEFBfbbVqJSbu8jeSmTmME66A2OjAwgHMvfxBn/+b+fTg0ttbj4L4eLJ/ajoNGNKCtrkInvQ5qT1HlRUAEREAERCBaBBQX220PiUm7vI3kpk5jBKuMAvjdbU/iQxf+FfwupZuG1A9B3wye9NqOeSOb0NNUheKgH/gTXREQAREQAREQAREwQEBxsQGoKUxKTNrlbSQ3dRojWGX0TQJ3r16PY864Dlu374oxqaqpwtRpXVgycziOGN2MsW21KNGnQ+QzIiACIiACIiACOSaguNhuA0hM2uVtJDd1GiNYZdRD4Jl1r+PQk1dg/atbYr8tryjHuEmdOL6vE8eMbcHEjlpU6KRX+Y0IiIAIiIAIiEAOCSgutgtfYtIubyO5qdMYwSqjcQRe3bQdR556LR599pXYX0pKSzBybDuO7e/GovGtmIUbhUQAACAASURBVNZdh+pyfTpEziMCIiACIiACIpAbAoqL7XKXmLTL20hu6jRGsMpoAgI7du7Gu755C66/97nYX4uLi9HR04IjZ/dgCU967RmGump9OkQOJAIiIAIiIAIiYJ+A4mK7zCUm7fI2kps6jRGsMpqEAE96/fJlq3DRHx9564oioLm9EfP6e7F0chvmjGhA05AynfQqLxIBERABERABEbBKQHGxVdyQmLTL20hu6jRGsMpoGgI/uXE1Pv3D2zDw1kGvqGscigNn9mD59A4cPLIRXQ2VEpTyJBEQAREQAREQAWsEFBdbQ+1kJDFpl7eR3NRpjGCVUR8Ebn7oBSw958/YsWt37OqaodWYPrUHS2cNx/xRTRjdUo1infTqg6YuEQEREAEREAERyJaA4uJsCQa7X2IyGK9IXq1OE8lmGTSF+ueaVzH/lBXYuGl7rM4VVRWYOLkLi2d14qgxLRjfUYuykqJBw0QVFQEREAEREAERyA0BxcV2uUtM2uVtJDd1GiNYZTQAgfUbt+KwU1bgqbWvxe4qLS/F6HHDsbC/E4sOaMWUzqGo1EmvAajqUhEQAREQAREQgaAEFBcHJZbd9RKT2fGLxN3qNJFohkFfiC3bd+H4r96I2x5dG2NRXFKMrhFtOOrALiwe34aZPcMwpEonvQ56ZxEAERABERABETBEQHGxIbBJzEpM2uVtJDd1GiNYZTQDAnv2DOC/v38bLrv58djdRUVFaO1sxPw3T3qd3VuPxtryDKzrlkIlwEOctmyJTu2qq4Ei7cqOToOoJCIgAiIQgIDi4gCwQrhUYjIEiLk2oU6T6xZQ/vEEzr/qYXzl5/fs8+uGlmGY09eLZVNaMXdEEzrqK3TSq1zHOQ147lzgjjuiA2POHGDlSgnK6LSISiICIiAC/gkoLvbPKowrJSbDoJhjG+o0OW4AZZ+QwNV3PYP3nH8rdu/eE/v7kGG1mDm9G8tmDsdho5rQ21SFYi0BDWoP2rwZqK2NHoJNm4CamuiVSyUSAREQARFITUBxsV0PkZi0y9tIbuo0RrDKaAgE7nviJRx12nXYvG1nzFplTSUmT+7Gkr7hOGJ0Mw5or0WpPh0SAu38NOEVk+vW5VbAsSytrXs5Skzmpz+p1CIgAiKguNiuD0hM2uVtJDd1GiNYZTQkAms2bMKhJ12DtS9vjlksqyjDuAmdOL6/C0ePacLbOoeioqwkpBxlJp8IeMVkrgVclMqST22osoqACIhAlAgoLrbbGhKTdnkbyU2dxghWGQ2RwOtbduDo06/HA0++FLNaUlqCntFtWNjfjUUTWjG9uw41FTrpNUTseWEqSgIuSmXJi8ZTIUVABEQgggQUF9ttFIlJu7yN5KZOYwSrjIZMYOeuPXj/BbfiqjufiVkuKi5CR3czDu/vwdJJbegbUY/66rKQc5a5KBOIkoCLUlmi3GYqmwiIgAhEmYDiYrutIzFpl7eR3NRpjGCVUQMEBgYGcMav78c3rnhwH+tN7Q04pK8Hy6e048DeerTW6aRXA/gjaTJKAi5KZYlkY6lQIiACIpAHBBQX220kiUm7vI3kpk5jBKuMGiTwi1sfxycuvg17+F2IN1Ndw1D0zejG8hkdOGRkI3oaq/TpEINtEBXTURJwUSpLVNpH5RABERCBfCOguNhui0lM2uVtJDd1GiNYZdQwgZWPrsWis2/A9h27YzlVD6nG1KndWDpzOA4f3YQxrTUo0Umvhlsit+ajJOCiVJbctopyFwEREIH8JaC42G7bSUza5W0kN3UaI1hl1AKBJ158DfNOXoGXX98ay628shwTJu096fWYsS0Y316D8lKd9GqhOXKSRZQEXJTKkpPGUKYiIAIiUAAEFBfbbUSJSbu8jeSmTmMEq4xaIvDyG9sw/5QVePz5jbEcS8tKMXJsOxb2d2HRAa2Y2l2HqnIJSktNYjWbKAm4KJXFaiMoMxEQAREoIAKKi+02psSkXd5GclOnMYJVRi0S2LZjF5Z9/Sbc8uALsVyLS4rR2duCBf09WDKpDTN7hmFo1eD4dAhfJd2yJfsGqK4Gioqyt2PSQpQEXJTKYpK5bIuACIhAIRNQXGy3dSUm7fI2kps6jRGsMmqZwJ49A/jsT+7Ej677ZyznoqIiNHc0Yl5/D5ZPbsPsEQ1oqi0r6IN5KCTnzgXuuCP7BpgzB1i5MtqCMkoCLpdlyWXe2XuaLIiACIhAdAgoLrbbFhKTdnkbyU2dxghWGc0Rge+teBRf/Old8Bz0ivrmOhw0qwfLprbj4JGNGF5fWbCC0isqwmiCTZuAmpowLJmxESURlcuy5DJvMy0rqyIgAiKQGwKKi+1yl5i0y9tIbuo0RrDKaA4JXH/fGrzzvJuwc9eeWClqhtZg5oweLJvRgXmjmjGquQrFBXjSq1dUrFuXmRCkjdbWveiiLiZZviFD9pY10/qG5aq55CYxGVYryo4IiMBgJ6C42K4HSEza5W0kN3UaI1hlNMcEHn76ZRxx6rV4Y8uOWEkqqyswaXI3lvR1YsHoZhzQXovSkoi/FBiQYxiiIgwbAYud8eXr178lfDM2YuBG2yI8n9rMAG6ZFAEREIHQCCguDg2lL0MSk74wRfsidZpot49KlzmBF1/Z7Hw6ZM36N2JGysrLMGZ8B47r78ax45rxts6hqCwrnJNewxAVYdjIvNWC3RlFMWnjXVO2kTd5V0UTrdBGeatysBbX1SIgAiJgloDiYrN8461LTNrlbSQ3dRojWGU0IgQ2bd2JhWfdgFWr18VKVFJSgu5RrThmdjeOn9CG6d11qK0sjJNewxCCYdiw1fx8N/all/bmFpXTZ22UI+gpu953iG21jfIRAREQgXwkoLjYbqtJTNrlbSQ3dRojWGU0QgR2796DE767Ev/3tydipeJJr23dzTi8vxtLJ7Whv7cBDbVlESp1ZkUJQwiGYSOz0usuvwQkJv2S0nUiIAIiEIyA4uJgvLK9WmIyW4IRuF+dJgKNoCIYJzAwMICvX/4QzvrNffvk1dhaj4P7erB8ajsOGtGAtrqKrE96zaUYCyPvMGwYb9BBnoG2uQ5yB1D1RUAEjBFQXGwMbULDEpN2eRvJTZ3GCFYZjSiB39/2FD544V/A71K6aUj9EPQ5J722Y97IJvQ0VaE46NKPp765FGNh5B2GjYg2f8EWS21WsE2riomACFgmoLjYLnCJSbu8jeSmTmMEq4xGmMDdq9fjmDOuw9btu2KlrKqpwtRpXVgycziOGN2MsW21KMnw0yG5DOzDyDsMGxFu/oIsmtqsIJtVlRIBEcgBAcXFdqFLTNrlbSQ3dRojWGU04gSe/vfrmHfKCqx/dUuspOUV5ThgUieO6+vEMWNbMLGjFhUZnPSay8A+jLzDsBHx5i+44qnNCq5JVSEREIEcEVBcbBe8xKRd3kZyU6cxglVG84DAq5u248jTrsWjz7wSK21JaQlGjm3HMf3dOG58K6Z116G6PPWnQ6L0/loYoiIMG3nQ/AVVRLVZQTWnKiMCIpBDAoqL7cKXmLTL20hu6jRGsMponhDYsXM33vXNW3D9vc/FSlxcXIyOnhYcObvHOel1Vs8w1FUn/3RI0NcrTX6mIQxREYaNPGl+FVMEREAEREAE9iGguNiuQ0hM2uVtJDd1GiNYZTSPCPCk1y9ftgoX/fGRt0pdBDS3N2Jefy+WTm5zTnptHlKW8KRXick8amwVVQREQAREQARSEFBcbNc9JCbt8jaSmzqNEawymocELrnxMfzPD2+Hd+WwrnEoDpzZg+XTO3DwyEZ0NVTuJyi1zTUPG1tFFoEIEOBYs+Wt17YjUKLMi1BdDQSdWMs8N90pAuYIKC42xzaRZYlJu7yN5KZOYwSrjOYpgZsfegFLz/kzduzaHavBsOY6HDJ7BN45rR1zRjQ636JMlXK5TTSMvMOwkafNr2KLgDUCFJJz5wJ33GEtS6MZzZkDrFwpQWkUsoxbIaC42ArmWCYSk3Z5G8lNncYIVhnNYwL/XPMqZnzmD7EVysrqSsyYNQLLZ3Zg/qgWjG2rlpjM4/ZV0UUgCgS8kzZRKE8YZdi0CaipCcOSbIhA7ggoLrbLXmLSLm8juanTGMEqo3lO4NRf3oNv/eFhpxYSk3nemCq+CESQgFdMrluXvyKM9Wht3QtYYjKCjqYiBSaguDgwsqxukJjMCl80blaniUY7qBTRIvDtqx7GKT+/R2JSKw3RckyVpmAIFMp28kKpR8E4liqSNQHFxVkjDGRAYjIQrmherE4TzXZRqXJLIBsxmcuShxHYhWEjlwyUtwjkA4FC6WeFUo988BkbZVR7AoqLbXjaW3lITNrlbSQ3dRojWGU0zwlITO5tQG1by3NHVvEjS6BQgvZCqUdkHcVywdSeEpOWXQ4Sk7aJG8hPYtIAVJnMewISkxKTee/EqkCkCRRK0F4o9Yi0s1gsnNpTYtKiuzlZSUzaJm4gP4lJA1BlMhIENmMzalG7d4UNm1AD/8cMSkxKTEbCiVWIgiVQKEF7odSjYB0tYMXUnhKTAV0m68slJrNGmHsDEpO5bwOVwAwBicnMTohUMGHGH2VVBLwECqWfFUo9Bqt3sv3i/dI9nTfRKcOD4dMviovt9gaJSbu8jeSmTmMEq4xGgIDEpMRkBNxQRRCBhAQKRYQVSj0Gq5sWFQWr+cBAsOvz8WrFxXZbTWLSLm8juanTGMEqoxEgIDEpMRkBN1QRREBiUj4QWQISk/s3jeJiu+4qMWmXt5Hc1GmMYJXRHBCgePQm/tyKvV/TXod1+70zmeodSr0zuZekTnPNgSMry0FBoFBW9AqlHoPC6RJUUttcJSZz7fsSk7lugRDyl5gMAaJMRIJAEYLt1xlA8v06XjFZUVWB6bNGYPH0dswf2YKJw2tQFHQ61xKhMAK7MGxYqq6yEYG8JVAo/axQ6pG3jhRywdWeOoAnZJdKa05iMi2i6F8gMRn9NlIJ/REIU0x+90+P4Es/uzuWcV3jUMye2YPl0zpwyKhGdDVURlJQhhEIhGHDX4vpKhEYvAQKpZ8VSj0GryfuW3O1p8Sk7b4gMWmbuIH8JCYNQJXJnBAIc5vrM+vewLQTr8C2HbtjdakZUo3p03qwdNZwzB/VhFEt1SgpDrYaahpMGIFAGDZM11P2RSDfCRRKPyuUeuS7P4VVfrWnxGRYvuTXjsRkElLbtm3Dueeei9/85jd47rnn0NDQgKOPPhpnnXUWOjs7/fJ1rrv++uvxne98B/fccw9ee+011NfXo6+vD5/97Gdx+OGHB7KV6GKJyawRykBECWRzAA+rtPr5jZh38jXYuGl7rIbc8jpxchcWz+rEgjEtmNBRg7KS4sgQCCMQCMNGZICoICIQUQKF0s8KpR4RdRPrxVJ7SkzadjqJyQTEKSQp8u644w60t7fj4IMPxjPPPINVq1ahubkZd955J0aNGuWrrb797W/j85//vLOdbs6cORg+fDieeuopR1gyff/738cnPvEJX7aSXSQxmRU+3RxhAtmKSVZt/catOOyUFXhq7WuxmpaWlWL0AcOxsL8Ti8a1YkrXUFSWl0SCRBiBQBg2IgFDhRCBCBMolH5WKPWIsKuoaJYJKC62C1xiMgHv0047DWeffTYOPPBA3HjjjaitrXWucoXhIYccgr/+9a9pW+qll16KrWLefPPNmDt3buyeK664Au94xztQVVWFdevWxfJIazTBBeo0mVDTPflAIAwxyXpu3b4Lx59zI1Y+sjZW7eKSYnSNaMNRB3Zh8fg2zOwZhiFVpTnHEkZgF4aNnINQAUQg4gQKpZ8VSj0i7i4qnkUCiostwgYgMRnHe+fOnWhpacHGjRtx//33Y9q0aftcMWXKFDz88MO49957MWPGjJStdc0112DRokXO9tjrrrtuv2unTp2Khx56CHfffbez7TXTpE6TKTndF3UCYYlJ1nPPngF86ge342c3rY5VmzsGWjsbcVh/L5ZNbkN/bz2aastziiWMwC4MGzmFoMxFIA8IFEo/K5R65IHLqIiWCCgutgT6zWwkJuN433rrrZg/f76zjfWJJ57YrzW4YsmVy9NPPx1nnHFGytb685//jAULFqQVk//6178wevTojFtenSZjdLpxEBK44Oq/4+TLVu1T8/qWYZgzqxfLp7Zi7ogmdNRX5Oyk1zACuzBsDELXUJVFIBCBQulnhVKPQI2niwuagOJiu80rMRnH+8ILL3QOxuEW1N/97nf7tcaKFSvw9re/HYsXL8aVV16ZsrW4utnb24utW7cifpvrH/7wByxfvtzZSnv77bdn1erqNFnh082DkMCf7n4W7/7WLdi1e0+s9rXDajFrejeWzezAYaOa0dtUheIcfIsyjMAuDBuD0C1UZREIRKBQ+lmh1CNQ4+nigiaguNhu80pMxvH+3Oc+hwsuuMARlHxHMj5xWyq3p06fPh333Xdf2ta6/PLL8Z73vAfcPusewPP00087B/Bw++ull17qbKvNJqnTZENP9w5WAg88uQFHnnYtNm/dGUNQWVOJyZO7sKSvE0eMbsYB7bUotfzpkDACuzBsDFa/UL1FwC+BQulnhVIPv+2m6wqfgOJiu20sMRnH+2Mf+xh+/OMf45RTTsFXv/rV/VqDW1/HjBmDsWPHYvXqt969StVst9xyC971rndhw4YNsctaW1vxhS98wRGtJSX+TpF0O0d8Xk8++aSzLffRRx+16z3KTQTynMDzGzbj0JP+hBdf3hyrSVlFGcZN6MRx/V04ekwTJncORUWZvz4aBo4wArswbIRRF9kQAZsEbPu97fxMsSyUepjiI7v5R0Bi0m6bSUzG8f7oRz+KSy65BF/5ylecE13jE99vpJD0KybPP/98fOlLX3K2xfIdy5EjRzqfBuF7l1dddRWWLVsGrl76SRKTfijpGhEIRuD1LTtw9BnX44EnXordWFJagp7RbVjY341FE1oxvbsONRV2TnoNI7ALw0YwirpaBHJPwLbf287PFOFCqYcpPrKbfwQkJu22mcRkHO8wt7ny8yHz5s1ztsRyW2tx8VsfRt+9ezdmzZqFBx54ADfccINzUE+mSZ0mU3K6TwT2Eti5aw8+cOFfcOUdT8eQFBUXYXhPCxYc2IulE1sxs6ceddXmBWUYgV0YNuQbIpBvBGz7ve38TLVHodTDFB/ZzT8CiovttpnEZBzvMA/gOeGEE/DTn/7U2S7LbbPxib8/9dRTcdJJJ+FrX/taxi2vTpMxOt0oAjECAwMDOPO39+O83z8Y+x23vE6eNgJL+4fjqFEtmDC8xvgpr2EEdmHYkGuIQL4RsO33tvMz1R6FUg9TfGQ3/wgoLrbbZhKTcbz9fhqE21TPPPPMlK111FFH4cYbb8RFF12ET3/60/tdy9+feOKJ+PjHP44f/OAHGbe8Ok3G6HSjCOxH4NM/vB2X3PCY83tud504dQSWzu7EsWNaMLGz1vgJr2EEdmHYkGuIQNQJ0M+9iT+3tu79zbp1QE3Nvn+P/znb+hVKPyuUemTbnrq/cAgoLrbblhKTcbx37NjhnK762muv4f7778e0adP2uWLKlCl4+OGHsWrVKmebaqr0gQ98AD//+c/x/ve/H5dddtl+l77vfe/DL3/5S5x77rn48pe/nHHLq9NkjE43isB+BG568HksOusGiUn5hghEnEDQL/cMDIRboUIRYYVSj3BbV9bymYDiYrutJzGZgDcP3znnnHNw0EEHOSuLNW9OZ/JTIZ///Ocxd+5crFy5MnbnxRdfDP5bsmSJIwzdxO9QLl261Dmtlf+/aNGi2N+uvvpq529M//jHPzBu3LiMW16dJmN0ulEEJCblAyKQpwQkJsNpOInJcDjKSnQIKC622xYSkwl4b9u2zTk45+6770Z7ezsOPvhgPPvss87PjY2NuOuuuzB69OjYnTyllVteuRLJ70a6ie9g8ZMgv//9751fzZw5EyNGjAC/M3nvvfc6v6NoPfnkk7NqdXWarPDpZhHYh4BWJuUQIpAfBLTNNZx2GgxicjDUMRxvKAwriovttqPEZBLeW7dudVYZf/3rX2PNmjWor6/H0Ucf7XwupKura5+7kolJXkRB+bOf/czZ5srtsZs2bcKwYcPQ19fnvEdJm9kmdZpsCep+EXiLgMSkvEEE8pOAbcFgOz9TrVIo9UjFZzDU0ZR/5KNdxcV2W01i0i5vI7mp0xjBKqODlIDE5CBteFU77wnYFgy28zPVQIVSD4lJUx6Sf3YVF9ttM4lJu7yN5KZOYwSrjA5SAhKTg7ThVe28J2BbFNnOz1QDRbEeYZcpbHum2kJ2wyGguDgcjn6tSEz6JRXh69RpItw4KlreEZCYNN9kPFVzyxbz+eQih+pqIOjBMLkoZyHmaVsw2M7PVJtFsR7ZlinX79OaaivZ9UdAcbE/TmFdJTEZFskc2lGnySF8ZV1wBCQmzTYpheTcucAdd5jNJ1fW58wBeNi3BGWuWsBevtkKHnslTZ1TFOuRbZmC9r+wPxsTlbYdrOVQXGy35SUm7fI2kps6jRGsMjpICUhMmm14b5BoNqfcWd+0CXjzi1K5K4RyNk4gW8FjvIA+M4hiPbItk8Skz8Yv0MsUF9ttWIlJu7yN5KZOYwSrjA5SAhKTZhveGySuW1c4oov1am3dy05i0qwPRcV6toJH9XiLQNjbUsO2F5W2Ujn8EVBc7I9TWFdJTIZFMod21GlyCF9ZFxQBfsrnHV+/CSvuec6pV0lpCSZOHYGlsztx7JgWTOysRXHQKe+AhMIIUMOwEbDYvi+3UbZcvJPpFZMUyc3N2urq2yny9EIbvmwDTRTqEXRYDbotNQp1tNGWymMvAcXFdj1BYtIubyO5qdMYwSqjg4zArt178KHv/BWX3/ZUrOblleWYOn0ElvV14MjRLTigvQZFQaOegBzDCHrCsBGw2L4vN122qLyTqXcnfbtE3l5o2pdtgYlCPYIOqxKTtrwjP/NRXGy33SQm7fI2kps6jRGsMlrgBP796hY8+uyrTi03b9+JM39zP/7x3N6fmbgq2TO6Hcf2d+H4Ca2Y3l2H6opS41TCCOzCsGGqoqbLFqV3MrXd1ZQXRcOuaV+2Vcso1MP0ttQo1NFWeyofrUza9gGJSdvEDeQnMWkAqkwWLAFuZT3rt/fjvMsfRLLZ7bLyMoyZMBzH9XXh2HHNmNw5FBVlJVaYhBH0hGHDVGVNly1X72R6t7m67CQmTXlRNOya9mVbtYxiPcIuU9j2bLWN8smMgOLizLhlepfEZKbkInSfOk2EGkNFiTQBbmX9wIV/xR9uf2sra3yBK6srMWlKF5bO6sSRo5txQHstSkuKrNUrjKAnDBumKmy6bKbtJ+OSaEVUYtKUF0XDbq58LezaR7EeUSxT2NxlzxwBxcXm2CayLDFpl7eR3NRpjGCV0QIj8PqWHTj6jOvxwBMvxWpWVFyE4uJi52e+C1k9pArTp3Zh2fQOHDaqGSNaqowfuBOPmQJkyJC9v830tFPvKlnUBI3pING0fYnJAhsYsqhOrnwtiyInvDWK9YhimcLmLnvmCCguNsdWYtIuW2u5qdNYQ62M8pTA8xs249CT/4QXN2yO1aCsogyjxnagtrbC+V1JSTF6Wmpw9AFNOHhkE4bXVxg/bCcRzvXr3/rERBi4JSbDoJjehlYm0zMqtCsKRfBEsR5RLFOh+W8h10dxsd3W1cqkXd5GclOnMYJVRguEwANPbsCRp12LzVt3xmrErayTp3Th2OntaKypADexlhQVoXtoJSZ31aGxpiwnQpIFDFNMRvFEUdNBomn7ybqFxGSBDBgBqhHGLoIA2Rm7NMo7GYxVWoYLmoDiYrvNKzFpl7eR3NRpjGCV0QIg8Me7n8V7vnUL+K6km2qH1WDW9B4sm9mBA7sbUFdV7vyJu13rq8tQWbZ322uuEg8FeunNnbjV1dl9qzDb+00wMC32TNuXmDThFflpM8yJn6gQiNpOhqhwUTnyi4DiYrvtJTFpl7eR3NRpjGCV0TwncOEf/46TLl21Ty3qW4Zh7qxeLJvairkjmtCRo62seY42q+KbFnum7UtMZtX8BXVzoYnJKO5kKCiHUWWsEVBcbA21k5HEpF3eRnJTpzGCVUbzlMCePQP45A9ux6U3rY7VgIfrtHY24rD+Xiyb3IbZvfVorN27Iqlkl4BpsWfavsSkXX+Jcm5h7iKIQj2juJMhClxUhvwjoLjYbptJTNrlbSQ3dRojWGU0Dwls3b4Lx59zI1Y+sjZW+uKSYnSNaMVRs7uxeEIbZvYMw5Cq0jysXWEU2bTYM21fYrIw/FC1EAERKFwCiovttq3EpF3eRnJTpzGCVUbzjMD6jVsx/ysr8OSLr8VKXlpWitEHDMfC/k4sGteKKV1DUVlekmc1K6zimhZ7pu1LTBaWP6o2IiAChUdAcbHdNpWYtMvbSG7qNEawymgeEVj9/EbMO/kabNy0PVbqiqoKTJzchcWzOrFgTAsmdNSgrCS3h+vkEVJjRTUt9kzbl5g05hoyLAIiIAKhEFBcHApG30YkJn2jiu6F6jTRbRuVLHMCA3whyUe69e9rseScG7Fj5+7Y1dVDqjF9Wg+WzRqO+aOaMKqlGiXF/ACIUq4JmBZ7pu1LTObag5S/CIiACKQmoLjYrodITNrlbSQ3dRojWGU0RwSe/vfrePf5t+LBJzdkVIK6xqGYPbMH75jWgYNHNaKroTJn34zMqAIFfpNpsWfavsRkgTtoCNXLlQ+GUHSZEIGCIKC42G4zSkza5W0kN3UaI1hlNAcE7npsHY4983rwIJ1MUnNHIw7t78Gyye04aEQDmoeUSUhmAtLgPaYDbdP2JSYNOkeBmM6VDxYIPlVDBLImoLg4a4SBDEhMBsIVzYvVaaLZLipVMAK/u+1JfOjCv4Kf9giaSkpL0N7VhCNm92DJpDb099SjrlontgblaON604G2afsSkza8JL/zyJUP5jc1lV4EcQ8O/AAAIABJREFUwiOguDg8ln4sSUz6oRTxa9RpIt5AKl5KAnw38mu/fxBf/e39+1zX0FKPuvoaX/Qa6qswZ3wzFo1vwbTuYajRia2+uOXiItOBtmn7EpO58Jr8yjNXPphflFRaETBHQHGxObaJLEtM2uVtJDd1GiNYZdQCgV279+CE7/4Nv/vbk7HcioqK0NbdjPl93ehprPZVirbaCswYXoeJHbWoKNOnP3xBy9FFpgNt0/YlJnPkOBHOlj7nTfy5tXXvb9atA2ri5sTif45w1VQ0EchLAoqL7TabxKRd3kZyU6cxglVGDRPYtHUnFp51A1atXhfLqaSkBN2jWnHMgT1YdEALWoZUws8ZrEMqS9E+rEInthpuszDMmxZ7pu1LTIbhBYVlo8jPIOWpss+DqgsLkmojAhYJKC62CBuAxKRd3kZyU6cxglVGDRJ48ZXNmHfyCqxZ/0Ysl7LyMowZ34Hj+rtx7LhmvK1zKCq1ymiwFXJj2rTYM21fYjI3fhPlXCUmo9w6KttgJKC42G6rS0za5W0kN3UaI1hl1BCBh59+GUecei3e2LIjlkNldQUmTe7G0r5OHDm6GQe016K0JOB0v6Hyymy4BEyLPdP2JSbD9YdCsKZtroXQiqpDIRFQXGy3NSUm7fI2kps6jRGsMmqAwHX3Pod3nncz+K6km2rrajBjeg+WzejAYaOaMbK5CsXFEpIG8EfCpGmxt2kTMGTI3qomel/NFATve3JuHiyL3o8zRTy6dk37eHRrrpKJQDQIKC622w4Sk3Z5G8lNncYIVhkNmcDF1zyKL/70rn2s1jfXYc6sXiyd2oaDRzZheH2FvgsZMveomTMdaK9f/9bhJ7muu8RkrlsgN/mb9vHc1Eq5ikD+EFBcbLetJCbt8jaSmzqNEawyGhIBfjfyxB/fiUtu+GfMIk9sbeloxGH9PVg6pR0H9tajsbZMQjIk5lE2YzrQjoqYnDMHWLkSCPo+XZTbTmXzR8C0j/srha4SgcFLQHGx3baXmLTL20hu6jRGsMpoCAS27diFpefehFsfeiFmrbikGJ0jWrCgrwdLJrVhZs8wDK0qDSE3mcgHAqYDbZ6U+dJLe0lUV9sTc/Gfg2hutpd3PrT7YCqjaR8fTCxVVxHIhIDi4kyoZX6PxGTm7CJzpzpNZJpCBfEQ2PD6Nhz+lRV4/PmNsd+WlpVi5LgOLOzrxKLxrZjaVYeqcn0XcjA5TqEG2oVar8Hkm6qrCIhAYRBQXGy3HSUm7fI2kps6jRGsMpoFgX+9sBHzTlmBV17fFrNSXlmOCZO6sLi/E0ePbcH49hqUl0pIZoE5L28tVNFVqPXKSydToUVABAY1AcXFdptfYtIubyO5qdMYwSqjGRJY+ehaLDr7BmzfsTtmoXpINaZO68aymcMxf1QTxrTWoEQntmZIOL9vK1TRVaj1ym9vU+mzIcAt41u2ZGOhMO61uV2+MIjlvhaKi+22gcSkXd5GclOnMYJVRjMg8ItbH8cnLr4NexiFvJnqGoaib2Y3lk/vwCEjG9HTWKWDdjJgm+tbwgos498tjMKnM8IIFiUmc+2hyj9MAuzvc+cCd9wRptX8tKXDtPKv3RQX220ziUm7vI3kpk5jBKuMBiAwMDCA0399H755xUP73NXU3oBD+nqw/M0TW1vr9OmPAFgjc2mhB5ZhBIsSk5FxVxUkBAJefw7BXN6b0Gd+8qsJFRfbbS+JSbu8jeSmTmMEq4z6JLBz1x6874JbcfWdz8TuKCouQkd3M46Y3YslE1vRN6IB9dU6sdUn0shdNhgCy2yDRYnJyLmtCpQFAa8/r1sHRGEHQRbVyehW7y6KbMeHjAqgmzImoLg4Y3QZ3SgxmRG2aN2kThOt9hhMpdm+c7dzYut9/3rzWwwASkpL0DO6HQv7u3DchFZM665DTYWEZD77RZiBJYOytra9NJ56KrdBKus1cuTesqQLmNNt8w1iK2q+EMY236jVSeXJjoAmRwAxyM6Hcnm34mK79CUm7fI2kps6jRGsMuqDwGU3P45PfG9l7MqyijIcMKELx/HE1jFNeFvnUFSU6cRWHygjfUmYQdX69UBra6SrO+gKF8Y230EHrcArHGafz1dUYpCvLQcoLrbbdhKTdnkbyU2dxghWGfVB4NtXPYxTfn6Pc2VZeRkmTOnGcbOGY8GoZkztqUOpTmxNSTHdapePJrBySViH5nAF7KWXJCatNFrATLSNLyCwAr9cQkork/ns4oqL7baexKRd3kZyU6cxglVGfRDwisnK6krMmDUCy2Z2YP7IZoxrr/FhYfBeUuiH2iRqWa6A/e1vwIYNe/+a6+2VfkWy97p0W3NzXacgPSrq74TxYK9N23eDfWVIZYlOgQ7SuFleKzEpMZmlC+X0dsXFdvFLTNrlbSQ3dRojWGXUBwGvmCwuKUbXiDYsmN2F48a3YXbvMNRW6V3JZBgHw6E2ieqezQqY35Vcv4LOb8Ds9zofXSZSl0S5Xvy80NPrt+Kx9W+AonJs8xCMaq0uuO/T+vVp247jd6LFVLn89mFT+dNulPuHyXoXgm3FxXZbUWLSLm8juanTGMEqoz4I3PLQC1h45vWxK4uKitDa2Yj5/b1YOrkNB/bWo6G23IelwXdJmIfamKaXbWAZxgpYkJVcv+8A+g0W/V5nuh3Cth/Veu3aPYDH1m7Cn594Cbc/8Qr2YAD9I+px9NgWjG+vQXlpYbyHHcSnw277qNvz24dN1iOq/cNknQvFtuJiuy0pMWmXt5Hc1GmMYJVRnwS+deXDOPUXe9+bdFNDyzDM6evFsimtmDuyCR3D9H3JeJz5FKhkW9Zs7ye7oCu5flZA/ZbL73U+u0xkLotivXhC9MPPv45rV7+EP65ag2eeXO+sTHb3tmBh33C8fXwrRrfUJnwfu4hbpytKUF5aHBnGqQoS1KfzolIhFtJPHw4xu/1MRbF/mKxvIdlWXGy3NSUm7fI2kps6jRGsMhqAwFV3PoP3nn8Ldu8ZiN01ZFgtZs7oxrIZw3HYqCb0NlWhuIjhnlK8OMp10JSuRbINqrK9P55Xss94BF0B9Vsuv9el4xi1v0etXpu278L9z76Gq/+5Dtff9RyeffLf2L1rt4ON2+g7R7RiQX83DuqtR0mCsYTnfbUNqcSE9iFoqCmLGu6UYiXdp2lsVybb3QiZljdoH840Hz/3Ra1/+CmzrtlLQHGxXU+QmLTL20hu6jRGsMpoQAL3PfESjjrtOmzetjN2Z2VNJSZP7saSvuE4cjQP5Um8ohAwq4K4PJ8ClWzLmu39fsV30Hz8Xu/3unxzzCjV65XNO7HqmVdw5d//jZvufg5rn3vJWZH0Jm6jbxneiAPGtgDYf2KquLgIXY1VWDCuCQf1NqCtLto7IqLEP953c1W2XOWbqO9GqSz5NrbkuryKi+22gMSkXd5GclOnMYJVRjMgsGbDJhx60jVY+/Lm2N369mRikPkUqGRb1mzvl5jMoDP6uCWMdvGRTcpLKBj//dp23PHMK7j8wbVYuepZvPzvV1PeU15ZnvRk15ohVZg+uRPLZrZj3ogmdEd4R0QU+CcDnauy5Spficlse3K07ldcbLc9JCbt8jaSmzqNEawymiGB1zbvwFGnX4eHnnrz+w8ASkpL0DumHcf2deG4ia2Y1lWHmorBfdJrlIKmdE2dbVmzvV9iMl0LZfb3MNols5z33sUTW5/bsBW3Pr0BV9y7Fvfc9yxef/WNmEmuNP70xEMxpLIM7/rGzdi1e4+v7KpqqzB1ag+WzOrAEaObMLa1NpKnwOaafyqYuSpbrvKVmPTVtfLmIsXFdptKYtIubyO5qdMYwSqjWRDYuWsP3vftW3H1Xc/ErBT9f/auA77N6tr/bctLku14yntm72nHJIEsshlJKG3pa2kLhe4+Sl8XZRQKlPd47aOlr33QFjqAFhKgDRlkNAmBDGcnhGwndhzv2HFs2Zbn+13JkiVZsr559Uk6X3/9EVv3nnPu/9zz+fx17z03PAyZualYPDsfayamY1Z+IhL1oUsotZQ0+XK1XFvl9icy6ctD0j5Xwi/SNMN6vvpcXRu2X2jEOwercexYBTraOhziYqN12Pj4MpSONVl/d7z8GpY8vgk3zF2CVLLVy3ETs3HHrGxrFdgJWUbNVYH1J/6+QPSXbf7SS2TS14wIrM8pL+brLyKTfPFWRRsFjSqwklCZCLDta4+/fhj/tf64i6SUjCTcUpyHtVMyrFeHmDR+rkkmDF67aylp8jVGubbK7U9k0peHpH2uhF+kaO7u7cPHV1ux5VwD/lF2Bac/rkJX5yBJTB0Ri93PrEJBeryL+BvtXdh4sBLsv+5PR1cvXtx4ClcbB7fYR0RGoHBUJlaW5OC2cSaMzYgTVOk1IgyIiQz3upVWypgDjaz4a274S2+g+UepORiscigv5utZIpN88VZFGwWNKrCSUIUQ+MvOc/jqix9at7TZn4SkeJTMzMXa6Zm4uTAZecmxqiduCg1HMTFKJU1yLj0XejG4XFvl9icyqdi0cxGkhF+kWHa+rh27yhuw7lA1DpZdQod5cEVyfF4Stj+1AonGaNGi2bUid//nDmw9fMXRNzw8HFn5aVgyOw8LRyZDH+n7jkod20URH4tR6QZEMmap0iMFfznxLmYYzDaTbVEYPCvNitEr9P0lZtzObaX4R6ou6qcsApQXK4unL2lEJn0hFACfU9AEgJNC3MQ9p2pw21Pvw9JlK/PPHn2cHlOn5WLtzCwsKkrFSJNek+ea1HKdEomK3EvPhV4MLtdWuf2JTKozC5XwixTLPq5qw/sX6rH+QBU+PnoZ3V22CtCF6fE48sIaRAsgfN70sh0R33vlAP73vVODTcKAtMxkTJuYhcgo32QySheGsSYjloxMwZTseMQI6CMFB7H4y413KTZquY/Q95fUMYj1j1Q91E95BCgvVh7T4SQSmeSLtyraKGhUgZWEKozA+avXMf+RjWi60emQzM41jZ+YgztKsrF8dBrGZRg0d65JYRgc4pRIVJxlSLVTyB2Xcm2V25/IpFTvDt9PCb9Iscwbmfzr9xZi7U0FUkQO6fPSljP495c/gvPtIrGGWLCz274edqdlWvoILJ6VhTvHpWNm3gjExSp/vlss/krEu6+xB9rnQt5fUsck1j9S9VA/5RGgvFh5TIlM8sWUuzYKGu6Qk0KJCDTe6MTCRzaCEUv7o4vUoXBMJlYWZ1vPNU3NSUCsSisBEs1WpZsSiYqzDDFb0Zy3kglJxuTaKrc/kUlVpiCU8IsUy7yRyde+txBrFCKTzK6tx6pw1zPbwAqCiX3YnZam7GQsLMnHmsnpmJ2fiGRjlFgxw7YXi7/UeJditJjtplLke+sjRK/Y95dU+8T6R6oe6qc8ApQXK48pkUm+mHLXRkHDHXJSKAOBzq4erHl2O3Yev+qQwlYCsgtMWFKci9UTbSsB8SqsBMgwW/GuSiQqUmWI7Se2vTtYcvsTmVR8+lkFKuEXKZbxIpPMtk8qm7DwJ5vQ0maRYiqS0kZgTnE+1k4xYW5BCjIToxU73y0Wf7HtJQ14oBNPXc52CtErpI2csdv78tKjhK0kwxUByov5zgja5soXb1W0UdCoAisJVRGBvr5+/Pvv9+HlLacdWthKADvXtKAkD2sGKr0mGyMVS9xUHI4k0UokKlJliO0ntj2RSUlTgnsnuX6VajBPMslsvNbaiT/tOIeK+jafJtc0t+O9sgqX7bHGEUbMmm47372gKAX5KbEID/O9XdaXMrH4i23vS/9wn/PURWRSjqeorycEKC/mOy+ITPLFWxVtFDSqwEpCOSDw4nun8B9/3O+iKTE1AXNm5WPN1HTMK0xGVmJMUBJKJZI1qTLE9hPbnsgkh+BRQIVcv0o1gTeZFGvnkQuN1jstzR22wkDsidHHYPKUXKwuzsLikakYm2EEq/oq5xGLv9j2PG2To4vIpFLokRw7ApQX850LRCb54q2KNgoaVWAloZwQ2HyoEnc/twM9vYPnmowJBsyYkWe9OmRhUSoKUmMRLjNx4zQcwWqUSAylyhDbT2x7IpOCp4FfG8r1q1TjtU4m2biqGs245UcbUH1t8N7KyOhIjBmfjTtKcrBsVAomZcfLqjzLzivHxdlQFHLmmfmL13UdPHW5k0n7GL2d5+Y1b3npkRpH1M87ApQX850dRCb54q2KNgoaVWAloRwROHHpGhY/ugmtTpeRx+ijMXFyLtYUZ+NW+0qAine+cRyuVZUSiYpUGWL7iW1PZJL3bJKmT65fpWkFAoFMsrHdaO/C0sc349jFRsdQI3QRyBuZgZUlObhtvAnTcxNgiJZW6bW+fpAcSsUymPsRmQxm76o7NsqL1cXXXTqRSb54q6KNgkYVWEkoZwSqm8yY/6P3cKVh8FxTZFQkRo3LxO0luVgxJhWTZa4EcB7SsOqUSOSlyhDbT2x7IpNammnebZHrV6mjDBQyycbHKsF+4Zc78e6+y47hsutFMnNTsYid756YjuKCRCTqI0XDQWTSO2TD3SHJa97y0iN64lAHnwhQXuwTIkUbEJlUFE7/CKOg8Q/upFV5BNo6urHiyS04eLbeZSUgt9CE5aV5uGOcCdPzEmCUuBKgvMXSJSqRqEiVIbaf2PZEJqXPC5495fpVqq2BRCbZGPv7+/HE60fwn+uPuQw5JSMJNxfnYe2UDNyUnwhTgrhKr+wOzIYGm0i9HnCu6cM+a293RZj5q7DQ9rvycsBgkOoB3/2Y/vR0WzshW3B9SxTXwh0P59685i0vPeKQodZCEKC8WAhKyrUhMqkcln6TREHjN+hJsQoIsLOT9/36A7z5wUWHdFbpNT03FYtLcrFmUjqK85OQZBC/EqCCuZJFKpGoSJUhtp/Y9u6gyO3P5AmRIaSNlKRUrFzJk4JzR3+NK9DIpN0tf911Hg/+eg/6GNMaeBKS4lE8Mxd3Tc/EzYXJyEuOlV0wjImfOxfYu5fzhHBSV1oK7Ntn+4WQu2h5Wspr3vLSwxO7UNFFeTFfTxOZ5Iu3KtooaFSBlYT6EQG2EvDMW8fws78dcbEiOT0R84rzcBdbCShIQrrIlQA/DmmIaiUSFakyxPYT257IpJZmmndb5PpV6igDlUyy8X74SQ1WPfk+LF29juHHJ8VhzuxC3D0tA3MLkpGdFCMVGms/Z7/IEqRQZyKT2iPUCrk2aMVQXszXtUQm+eKtijYKGlVgJaEaQODNDy/iS/+zG+xeSvsTnxiHWazS64wMzC9MQZ5Cd77xHq7YSo6e7GNJp5TqjmL7iW3viUz6qtDoC38hxEdIG2c9QtsLbedrDFr73F/jCmQyyXx4oboFM/79bXT12CpQsyqvU2YUYO2sLNw6Mg0TsoyyXO3sF+ctpuydYd96qtY2V+ettPZB+GOb63AAOr+P1CS6/ooPWZOHOlsRoLyY70QgMskXb1W0UdCoAisJ1QgC+87UYeVPt6DD0uOwKNYYi6lTc7F6VhYWF6VgdLoREQF2dUioFt+QmvwJSeyEtCEyOYiAWLyUeiUEOplkOPzvxlN4+A+2O3KtZHJ6gfUOyqUKk0nneAnVd8Zw807q+0TIXPZXfAixjdoMjwDlxXxnCJFJvniroo2CRhVYSaiGELhUewO3/Pg9NFzvcFjFLhGfbl0NyMCiolSMSjcg3LmChR/t91Q8w90clhjai2n40VSuqoer0OjLECGJnZA2RCaJTPqaa0I+/8ErB/CrDR8TmRQClkptZs8Gtm1zLVykpCpeK6BK2kyybAhQXsx3JhCZ5Iu3KtooaFSBlYRqDIHmNgvm/WADLta0OCwbPTHPet/b7WPTMC03AdGR4X63WgvFM3iDwIp1bN3qO6kbrkKjL5uFEEUhbaSQSSW2JPsanz8+91eyHOgrk6/vvoD7XtjtcFlUTBSmTi/A2pIs3FqUinGZym1zdV55G676q1Lzx3lOKCUzGOSouQIaDPhobQyUF/P1CJFJvniroo2CRhVYSagGEahsaMOYB//usGzkhFysmp2L28ekYVpeAmI0QCa1VjyDlxvVTraEEEUhbaSQyVDYXqi2/5xxD1QyyQqDPfm3o/j5W0ddwiotKxmLSguwdnIGZucnItkor9K02HmsZIyH6vtrOAzl7KhQ0jckSzgClBcLx0qJlkQmlUDRzzIoaPzsAFLPDYHa5nYU3PeGQ1/huGysLMnF8jFpmJU/AsboCG62eFPkrXiGGoZJLYwjtp+39jxXtoQk2ELaEJkcOhN5J8uBSiYf/eshPP/2cQeAYeFhyMxJxaKSPKyeZEJJfhISFbiySOw8VvLd4qzbXuRHzo4CJW3zl6xQH7+/cJejl/JiOeiJ70tkUjxmmutBQaM5l5BBKiHAtrpmfuGvDumRUZEYNS4Tq4pzsWJMKqbkxCMm0r+EkmciKFWX2H7e2ouVI2daCNElpI0UMslje6EcbOT25Z0sByqZzP3y645z2xG6COQVpWPZ7FzcMc6EGXkJMETr5LrC2l/sPFZE6YAQf+pWchwkK7QRoLyYr/+JTPLFWxVtFDSqwEpCNYrA2me3YdPBSod1ERERyC0yYWlJLu4Yn46ZeQkwxiiT1EmBgGcyJlWX2H7ezgyKXeGUgqe9jxBdQtq4k0m5V5bIGVOo9g1UMsm+yGJfaLHHlJ2CxTcV4I7xaSgtSEJKXJRi7hQbn4op9jORVXIcJCu0EaC8mK//iUzyxVsVbRQ0qsBKQjWKQG9vHx743w/x+s7zDgvDwsKQnpOKhSW5WDspHSX5iUgyKpfciYGCZyIoVZfYfnRmUMwMoLa+EPBGJn/+xWJ85/ZJvrr77XNnMpmek4Zbb8rHp9j7piAR8bHKfYElNj6VBMSfupUcB8kKbQQoL+brfyKTfPFWRRsFjSqwklCNI/Dc+uN44rVDLlYmmxIxtzgPa6akY25hMjISosGIJs+HZzImVZfYfsFOJnmfGeQ5H7Wo61RVG96/WI+3D1zFiaOX0G3ptpoZHh6GXc/chlmjU1U32wwzjLBVXW1DGwww+NRJZNInRNSAENAEApQX83UDkUm+eKuijYJGFVhJaAAgsP6jctz7y13o7et3WBs3Ig6zZubirumZuKUoBfkpsVzvnxRL1OTALFWX2H7ezgyK3VYqd6z2Lal1dYDBQ+4v1R7eZwbl4BAMfWuuW/DRpWt462gt9hy4hOaG645hRYSH4dWH5uOuOYWqDpXIpGd4xb4bVHUSCScEJCJAebFE4CR2IzIpETgtdaOg0ZI3yBbeCBw814Clj29Ch6XHoTrWEIMpU3OxemYWbh2VitHpRrAklcfDMxmTqktqP3f8lJIjxC9CdAlpI0QXtVEXAXbFRm0LI5RNWHesBh+WVeBaXbOL0sfvmYEf3jVVNUPkk8lULL4pH3dOMA1cB6Lctnq17jUV8qUJxZBqU44Ec0SA8mKOYAMgMskXb1W0UdCoAisJDSAEKupbMf/H76G2qd1hdVR0FMaMz8YdJdlYNjoVE7PiEM2h0ivPZEyqLqn9iEwGUFAEgKlNbd04cLkJ60/W4l8HKlF7pQGMaNqfz84fiZe/MQ8REeGKjMaZQNahDiaYrHKFbnPN+eJraLzRae2ji9ShaEwmVhRn47axJkzNTUBslPxK0mz4paXAgQOKDNlFiJDt3Eq9G5S3niQSAsIRoLxYOFZKtCQyqQSKfpZBQeNnB5B6TSBw3WzBksc24+Slaw57WPn+glEZWF6Si9vHp2Fa7ggYFEj4hhswz2RMqi6p/YhMamKqB5URbZ09OFTRgg2na7F5fyUqL9aht7fXMcbZY01477GlMMREyho3I5Lsf3YCWY5yFMK2lZYRS/czk57OUH77pb14ectphx3hEeHILrBVkl49IR0z8kbILsTjHJuyBuylM1v19LRF3N5cqXeDGraTTEJAKAKUFwtFSpl2RCaVwdGvUiho/Ao/KdcQAl3dvfjcf+/Ee2UVgwlfeDgy8lJx6+w8rJ6YjuK8RIzQK1d5UesEy5N7lEoYlZIjZAoJ0SWkjRBd1IYvAp3dvThRdQObzjZgw4FKnD9dje4uW1Ee9hRmJODA83fAGCudUIZB3Db3fgyukNrt6Ovrx3de3offvz9IKFmBr7SsZCywFv7KQGl+IpKNkZILf7mTSW/ng8V4yPksMZFJMchR20BFgPJivp4jMskXb1W0UdCoAisJDVAE2Da5R/5yCL9894TLCFIzk3FLcR7WTsnATQVJSI2TnvANBw1PQiNVl9R+WifOSo0rQKd+QJvd09uPMzVt2HKuAe8eqsKpE5XobLfd6ciel745D/+2YJRkkqYEmbTb8usNp/D9V/a74J2YmoA5xfnWStLzClOQlSitkrQ7mfRF/oQ4XUxciGkrRDe1IQT8gQDlxXxRJzLJF29VtFHQqAIrCQ1wBF7Zfhbf+O1HLmewEpLjMXtmHj41LRPzipKQkxQrOTn1Bg/PZEyqLqn9tEImla7mKmeqCylqIkd+KPVlK3/Hr7Ri28V6/OPgVZw8WuFYofzlV0rx4LJxouKVbWu1P/ZtrvatrR/jY0zEROvHbMsr29bqvLXV11Uhmw9V4u7ndqCnt8+hw5hgwIwZedZK0guKUlGQJr6SNJHJUJrxNFa1EKC8WC1kPcslMskXb1W0UdCoAisJDQIEdp6oxp1PbwXb/mp/9HF6TJ+Wh7WzsrCwKAVFaXpFK70qRdSEwC9Vl9R+WiGTQrDh1UZIURNetgSDnjM1Zmy/0ID1B6/i6OFLsHTYVicZmfzq8vGihqjkaqQnxcfLr+HWxzahtb3L8XGMPhoTJ+diTXE2bh2ZirEZRugihm6xZYV22gfrhQ2SXjNgvwKH/VLpba6+5DlvifXV1hkT+lJF1NSkxiojQHmxygC7iScyyRdvVbRR0KgCKwkNEgTOVl3Hgkc2ornVVoWRPdEx0ZgwiVV6zcHSUWkYl6FHlE5+JUYmWymiJgR+qbqk9iMy6dmIwKH9AAAgAElEQVQrSmxFFOLvUGgTSGSS+aO6yYz5P3oPVxraHO6JjIrEqPFZuL04ByvGpGJydrxLJWlGJOfOBfbuDR6P0pcqwePLYBgJ5cV8vUhkki/eqmijoFEFVhIaRAg0tHRg4SMbcaG6xTEqVtp/5NgsrCzOxipW2j8nHjEKVHpViqgJgV+qLqn9tEQmy8uHVqVk4yocuOve0+dCMBXaxlkXkUmhqPlupySZdN7mav2ix0c1V19bW71Z39bRjRVPbsHBs/WOJqySdG6RCctn5+GOcSZMz0uAMdpW+Evtiq2+UVanBcWBOriSVPEIUF4sHjM5PYhMykFPI30paDTiCDJD0wh0WHpw5zPb8MHJaoedrLR/ToEJS2bnYvV45Uv7q51cSSWFUvtpiUxqabKp7WctjVVtW5Qkk0PmK8wwwmj9tZR7JocbOzs7+aUXdmPdh+WOZmHhYcjIScWiklysmZSO4vwkJBkiXcik+1ZS522mVjvrhr/KQ4g/xGxdFdPWTozt23IpDoR4g9rwQIDyYh4oD+ogMskXb1W0UdCoAisJDUIEWIGPb/7fR3hl29nBhC8sDCZW2r/EVtp/Nivtb5Be6VUpoiYEfqm6pPYjMjnUK7S9T8hMFd6GF5lsQ9uQuyWFW+m5Jask/fSbx/D034+gP9oCyxvfsDbM+PfXcMvUMbhragZuyk9CnC4acXG2c5TuBCzQCvAo9S6Riz31JwScEaC8mO98IDLJF29VtFHQqAIrCQ1iBF7450n88NUylxEmpo6wlvZfO8VkLe2fqUBpf7W/qZeayEntpxUyybawpqYCYW51TcSuqigxxanwiBIoDsoIZDJpH8Xf91zEl363FR2vfd36q+jP/gYJ+hQUz8jD2pkZKE5LwYQCPZFJZacOSSMEHAhQXsx3MhCZ5Iu3KtooaFSBlYQGOQIbDlTgnuf/NaS0/0yW8M0YKO2fKq+0P5FJ5SaREAIspI1yFpEkNRAIBjLJcPnX2XIsGlPkIJNhlmjEGmMxfXoeVk3IxkNL8ohMqjGBSCYhAIDyYr7TgMgkX7xV0UZBowqsJDQEEDhW3ohbH90EVkDD/sToYzB5Sg7uZKX9i1IxNtMIXfjQ0v7e4OFJaKTqktrPfcxKyREy1YToEtJGiC5q4z8E1CSTPEfFiv3Yz2eylUlGJtmTnJ6EeTOK8Lf7JxOZ5OkQ0hVSCFBezNfdRCb54q2KNgoaVWAloSGCQFWjGfN//B6uNjqV9o+OxKhxWbhjdg6Wjxpa2n84aHgSGqm6pPYjMhkiQeHHYQYqmRyucmz0l34BdNrIZEJSHEqnj8I/7p9NZNKP84xUBzcClBfz9S+RSb54q6KNgkYVWEloCCHQ2tGF5U9sweHzDY5Rs9L+eUXpWD47F3eMN2F6bgIMA6X9iUzaEFCKlAqZakJ0CWkjRBe18R8CgUomwyB89wLMesBoJjLpv2lGmoMcAcqL+TqYyCRfvFXRRkGjCqwkNMQQYKX9v/jCbqx3K+2faS3tn4fVk0woyU9CoiFyWGR4EhqpuqT2cx+4UnKETDUhuoS0EaKL2vgPASKTgNF2e4lHsinFM2LiQkxb3l8oSRk79QlNBCgv5ut3IpN88VZFGwWNKrCS0BBEgJX2f/JvR/Dzt465jD4lPRHzSvJx15QMlOYnIj0hGmHupUQHeohNxuTALFWX1H5EJuV4K7j7KjWnApVMCt3maspOwdzJ+Xj9C8UeySJdDRLccUKj44MA5cV8cLZrITLJF29VtFHQqAIrCQ1hBF7ffQFf+fUHYPdS2p/4pDhraf9PzcjAzQUpyE3xXOlVqaRaCPxSdUntR2RSiFdCs41ScypQyaS713+19TC+s2Sm9df2AjwjUhJQOisPq8Zm4r6bs4hMhmao0Kg5IEB5MQeQnVQQmeSLtyraKGhUgZWEDiDgXJVQjYu+tQr0R5/UYtWT76Ozq8dhot4YiylT87B2ViYWjkzBaJMREW6VXpVKqoXgIlWX1H5EJoV4JTTbKDWnAp1Mst0ND/9xP/53+1FY3viGjUze8xuYkjNxSzHb3ZCOyWlJKMiIIjIZmqFCo+aAAOXFHEAmMskXZLW1UdCojXBoyw9VMsm8frGmBfN/vBGNLR2OSRAVE4VxE7NxZ3EOlo5OxYRMI6J0EY7PlUqqhcw6qbqk9iMyKcQrodlGqTkVyGTS0t2LTz23A9uOXEF/tMVBJot+vA7Lpo/BnRPSMSt/BHR9Ose5SPe7aGmba2jGD41aWQQoL1YWT1/SaGXSF0IB8DkFTQA4KYBNDGUyydzW1GrBokc34kxls8OLukgdCkZlYOXsHNw21oRpuQmIjbIRSqWSaiFTRqouqf20QiZbWwFPR1bZuEwmm5V1dYDB4BlFvd5zfyGYUxsbAgxr58cX9t584Y6nNzL5i/tL8bUV4zULv/t7wplMfnfzIawdVeB4TwwXf0QmNetiMiyAEKC8mK+ziEzyxVsVbRQ0qsBKQgcQCHUyyWBwXnGwT4zw8HBk5adhyew8x4pDQqyOyKRKkcNWcOLibMJnzQIOHpSuaM4cYM8eIpTSERSPXf/g8eNh1Xojk4Xp8Tj+67XQRYTLMVuVvuU1LbjFyw6GO4pzsMxtBwORSVXcQEIJAQcClBfznQxEJvnirYo2ChpVYA1ZocNVJaxDHQxwXe5x/zlYgbOfhfrtxk8GhxgGpGWmYH5JHtZOTkdpQRJiwyIRF2e7c859C5vS2EhdYZTaz91+peQIwaW+fnDVUUh7X23U9o0v/YH+uZdixl6HJZRMlte3Y3d5I948XIOyA+Vob2t3yCweY8LGx5bCGDv89Tw8sd17uhYrf+p6tjrWGIupU/OwZlYmFnk4W01kkqeHSFcoIkB5MV+vE5nki7cq2ihoVIE1ZIWKunwbQD8ELjkECaL/t/k0Hvr9Xjgnx/YqjXdNy8DMtGSMyYslMqmwv8WQydmzgW3bhq6eOW/FJDIpz0FqbXNluwBOXm3FprP1+OeBKpz7pArdXd0OY3PS4rDrmZXITPKyh1nesET1fuODi7j/V7u9V30uTEFu8tCqz0QmRcFMjQkB0QhQXiwaMlkdiEzKgk8bnSlotOGHYLGCyKRvT249egV3Pbsd3T19jsaGeANmzMjDqnFZ+PatuVzIpPPWz+HOCLqPSClSxXNlkpH3hgbbWb3CQtuIvI3Z25lInvb6nkXB1UJJbHt6+3Gmpg1bLzTgnbIqfHyiEp3tFgdgcfoobH9qBSYXJPsFRLZL4am/H8Wzbx510Z+cnoibi/OwdmombhrmPloik35xGykNIQQoL+brbCKTfPFWRRsFjSqwhqxQ2uYqzPWnKpqw8CcbccPc5egQrY/GuDH52P/YXOvvrrf0IyHetuVVjUfMap03/XJW6JQkEELxkaNTTl+h9oVqO6WxZXe8XmzowK6LDVh/uBqHj1SgrWWw6o9OF443v78Iy2favrjh9fT09uGLL+zG+g/LHSrDwsOQkZOKxSV5WD3JhJL8JCQavG/FJTLJy1ukJ1QRoLyYr+eJTPLFWxVtFDSqwEpCBxDwVwEetn22HYPnpbTokLrmdix7Ygsq61sd5unCYmF+40vWnzefuopZo+IQEzl4dYiQceihh5AVYrlkUm4hGqUJhBBs5OiU01eIbaHcRg1s2Qrg1eZO7Cm/hreP1eKjg5fR3NDiAvO3b5+IT80ZWKoW4YDUhBjkpQ1UdBLYr7WjC8uf2ILD5xscPSJ0EcgrSsfy2bm4fZwJM/ISYIjWDSuRyKRAwKkZISARAcqLJQInsRuRSYnAaakbBY2WvBF8tviDTDIiORdzsRd7Aw9Qsx4wDqygtBkAg3hCPAdzsAd7fBJK+9ZPBpLXrZ0ww2QwWnGsM7e5FFCSe0WGGgTCl8Pl6JTT15ddof65WtgyQnmtrRv7Ljdj/fEa7DxQgYbqa2C/l/ssnpZtXd2M9UH+mJ6r18yY/6P3UNXY5lAbGRWJUeOzcHtxDlaMScXk7HhEC/jiiMikXM9Rf0JgeAQoL+Y7Q4hM8sVbFW0UNKrASkIHEPAHmXTWGXCOUIBMsjG3wZX4ScVBTf+pRSCGG6scnXL6SsWf+imDwI2OHhyquI53Pq7F1rIKVF2qR1/v4JllqVqKMhPwr5+tRNoIW9EsT8+x8kbc+ugmtHUMFgKK0cdg0pQcrC7Oxq1FqRibYYQuQtiWdilkknHndvHfS1mH43xG2tfZajFtxcr25SO5X275kk+fhw4ClBfz9TWRSS94d3Z24tlnn8Ubb7yByspKJCUlYdmyZXjyySeRnZ0t2ksXLlzAc889h23btqG2thZxcXEYNWoUVq9ejf/4j/8QLc+5AwWNLPiosw8E1CQj3lQ76/R0HYkWnVZ3vQPjv/Ym+nt06Fr3gNXEb759HEtHpmByTjxiIn3fj8fGXYgCa9861A+5hsV93N6SL+fEk8n0tjIpF0exiadcfXKTV4aL/a5KOWdFlRgHyRCPQEdXL45VtmDDmTpsPHAFFeX16LYMEjyhEtmqpvPKZrw+Cn/8zs0Ym504RMSO41fx8B/2g52VtD/GBANmzsjD2umZWFCUioK0oRVbh7NFLJlkcT53LrA3ADdqCPUJayd3270YXdQ2uBGgvJivf4lMesCbEclFixZh7969yMjIwLx583D58mWUlZUhNTUV+/btQ1FRkWBPvfPOO7jnnntgsVgwbdo0jB49GteuXcPJkydhMBjAiKach4JGDnrUV4sI+IPAysWBkcn8L79uJZOWDfdaxWWO6UD1We8rHnJ1ekq+GGFySTz1ZsBs2+YKQxvQ7v8rFeSOW2p/dmXI/v223kQmpaLo335dPb04XWPG5nP1+ODsNTTf6BRtUG9vP8rP1cDcKn6pLzF1BOYU52PtFBPmFqYgKzEaYSIv3RRDJtlKIntMJtHDDMgOFJcB6TbNGU15MV+XEJn0gPdjjz2Gp556CqWlpdi6dSuMRlsi9otf/AIPP/wwbr75ZuzevVuQp44fP47i4mLrSuS7776LuSzLG3j6+vpw5MgRzJw5U5Asb40oaGTBR501iECwkEke0LonX/VmM0xpTpoNZqB+IBNNqwPMbmQyRMklJa08Zqc6Onr7+nG+zoyPa2+goX2wmrJQbZaePuw604j9hyrQcu0GwsPDwf4eD/cwwmjKSsaCkjysmZKB2fmJSDZEiiaSTIcYMil0TGq087Ul1j4WO9EV0t6TnUpdVaQGBiQzMBGgvJiv34hMuuHd3d2NtLQ0XL9+3Ur02Eqi8zNlyhScOHEChw4dwowZM3x6ixHPPXv2YMOGDVi1apXP9lIaUNBIQY36aBmBYCOTFVV9SB4hbJurCTYmONw21+GSLyFVYJ1932aWXsgkULa5OttpHzuRSS2/AXzbxrapXm/vwY3OHt+N3Vp0dffjVN0NrD9eiz0HK2A0RuPi2ate5URGRyIzJwVLSnKxekI6ZuSNQHzs8BVbhzMqEMik0C2nSpxDVkKG6ElAHYIaAcqL+bqXyKQb3jt37sTChQut21g9bT9lK5Zs5fLxxx/HE088May3Tp8+jfHjx1u3tZ49e1Y1z1LQqAYtCfYTAsFGJhubhZNJI2w7IYYrwDNc8iWWTLLKuVIffySBUnQ69yEyKdXbwdWvpb0HZRXXseF0LQyR4dh+fGA/qYdhxhujUDoqGavGmjCVnX+OEnfVj7tIoWSyvBxITbUV3pGz+iflSx+hxXCkxKMYPIJr1tFoeCFAeTEvpG16iEy64f0///M/eOihh/CpT30Kb7755hBvbNy40brCeOedd4KdhRzuefHFF/Gtb30L3/jGN/D888/j73//u3VFk22XmTx5Mu6++27Ex8fL9jgFjWwISYDGECAyKZ1MOm9zratn5yTNMMG2zdVTMSMDpJ+hVCKRFDv1pOgkMikW5dBob+7qxcdXWqHTheHDiiavg06OjcSk9ASMy9AjSiePSDIlQsmkffVcypx3Hozc/sPNBiVkKyEjNGYsjVIoApQXC0VKmXZEJt1w/O53v4tf/vKXVkLJzki6P+wM5NSpUzF9+nQcPnx4WC987Wtfw+9+9zt873vfs25zdV+dTElJwfr1661nMOU8FDRy0KO+WkSAyKR0MumemDEyKWS1U8o88EcSKEVnIJJJOVdBSPGllD5CV6+kyObVp7u3D+wMZnWz97OXxphwJBujEBEu7OoPX7YTmXRFSEpM+8KYPg9tBCgv5ut/IpNueD/wwAN4+eWX8cgjj+BnP/vZEG+wra/sSg8hW1c/85nPWFcjdTodEhMT8X//93/WLbR1dXX46U9/itdff936+1OnTlmrxvp67MHh3u7ixYvWbblMDj2EQDAgQGSSyKS3eSwl8Qw0MjmkIq9Gg1rouTqNmu83s4hMEpn02+QLEcVEJvk6msikG95f+cpX8Pvf/x4/+clPrBVd3Z/z589biaQQMsm2yq5bt84q4v3338eSJUtcxLEqrwcPHvRKXN11E5nkGxykzX8IEJkkMhnKZNIT+fVfNA6vmQoZifcMkUkik+JnDfUQgwCRSTFoyW9LZNINQyW3uX75y1/GK6+8gqysLFRVVQ3x1m9/+1t8/etfx5w5c/Dhhx9K9iYFjWToqKNGEVCbTLKiM+0Qf8fccHCxeybHf+1N6z2TXesecGla0dwqqJorsysOcda+Ugvw0DbXoV4KtJVJZ3ulXregZmg7F3QhMikeaSKTRCbFzxrqIQYByovFoCW/LZFJNwyVLMDz5JNPWqu+3nTTTfjoo4+GeGvz5s1YsWKFddvsuXPnJHuTgkYydNRRowioSSYZYZuLudiLveqM3qwHjGZX2W0GwOCbvJaiFPuwT1EyaZBeX8cnPlK2nPoU6qOBFJ2BTCa1SNak+ECu34OpP5FJIpPBNJ+1OBbKi/l6hcikG95CrwZh14Owc4/DPazozu23346xY8eCXRPi/vz1r3/F5z//eetdluxOS6kPBY1U5KifVhFQk0w6y1Zl/DLIpLM9Sq1MEpl0rZ5px1iLJM1um9bJmtbtUyWuFRRKZJLIpILTiUR5QIDyYr7TgsikG95dXV1IS0tDS0uLleAxouf8TJkyBSdOnEBZWRlmzZo1rLfa29utsphMViQnJyfHpf3999+PP/zhD7jvvvus5zSlPhQ0UpGjflpFgBeZ9HRVhlRM5GxzZeO1X9/hIDtog7drO8Qmo1LH5KufP0iFFJ20MunLk+I+l+IDcRqCu7XY+JWLt9z+w3lDCdlKyAjuGUOjE4sA5cViEZPXnsikB/xY8Z2nn37auj1169atMAx8tc+uCnn44Ycxd+5c7Nmzx9GT3SfJ/r969Wo8++yzLhJ/9KMf4ec//zlWrlxprexql7Vlyxbcdttt6O3txf79+8GK8Uh9KGikIkf9tIoALzI53OqfWGwYmcz/8uvWM5OWDfe6dC871wbjMNtN28PMmJmR7tIn0FYmeZ3tcz6vJ1Sncx87yEL7ip0Hzu2lXp2h9eRaSfs6u3tRc70L7L/2Jzw8DKlxUUjU66z3Mgfbw1bF42xHo+E+Dz3Nbylz3hkzuf19kUmT7RpbSF3tV3I+BdtcofFIQ4DyYmm4Se1FZNIDcp2dnZg/fz4OHDhgvbJj3rx5qKiosP6cnJxsJX8jR4509HziiSesW17vvfdevPrqqy4SmazFixdbz0wyWSUlJaivr7fK6Ovrs5LWH//4x1L9Z+1HQSMLPuqsQQQCkUw2tVqQde9fPZLJH/z9IqJi+r0i3RXegecWzHb5vA71w65M2hM4IcmoWi5myWO6KwdWS1VAypV6dYbWk2ul7Gvr7MHhyhacqG1Bi2WQTOrCw1CYqEdJ3gjkJsUGHaGsrwfs8RuQE9uL0UQmg8mbgT0Wyov5+o/IpBe8Ozo6rKuM7C7IK1euWO+DXLZsmfW6EPftqsORSSaebXN9/vnnwc5IlpeXIyYmBjNnzsRDDz1kXbGU+1DQyEWQ+msNgUAkkwzDpY9twu7DzbBs+pzWICV7/ISAlARbKbKm1pCVsK+prQsHLjdj/ce1OH6hGWazxWGuTheOnIx4rJxswqKiVIw06RERHjwrlMFIJqV+ccKcrsR8Umuuk9zARIDyYr5+IzLJF29VtFHQqAIrCfUjAoFKJru6e3Hn43uw+dn5fkTPP6prawGjUX3dUrbs8d7m6qwvWMikc0zWmdtgGti3LXZ8/f39qG2x4KNLTVh/rAZ7yirQ1HAd/X2uK/fR+miMG5+NO0uysWx0GsZlGBCli1B/gnHQ0N8PNDTYFLlvhQ60ba52uKRu6SYyyWHChaAKyov5Op3IJF+8VdFGQaMKrCTUjwgEKplkkPX19ePbvzmKl/9Rjq4dd6mKYmkpsHUr4HysTArZkmqkXNIkRa+UVQzeBXik2OiMhdz+UnD11UcJMtnX34/LjR3YXd6I9YerUXa4Eq3NrV5V6yJ1KByTiVXF2Vg1zoSpOQmIjQoOQult0J58L3c+yO3va27I/dwf9vlDp1ycqL9wBCgvFo6VEi2JTCqBop9lUND42QGkXnEEAplM2sF4ZftZ/PClE2gxdwERPcNi1B9tQderD7m0KXzidSydOtKaRE/LHYH4WN0QGZ5WA3gmSTx12QcvRSeRSfkhKpdM9vb141xtG7ZfaMDbh67i+NFKdJg7fRoWHhGO7AITlpTkYvWEdMzM8xwLPgUFSAMik4Ca1xnJeY8EyBQiM6mWCPc5QGSSO+TKKyQyqTymJNG/CAQDmWQIsi19bKXS19Pab0aiLt6lWcw9/4v0lEwsKMnDmikZmJ2fiGRDpM9iJFLIli/7vH3OU5ecJJDIpFQPD/aTQyYt3b04Vd2GzWfr8Y+yKpw9VYUuS5dDeFqiHjufXom8VCNYuDz0h334w/tnHJ+ziq5pWclYUGyLhVIWC0bfsSB/1PwlEJkkMsl/1gWfRsqL+fqUyCRfvFXRRkGjCqwk1I8IBAuZFAqh83jtfaI/+xuEWaKRmDoCc4rzsXaKCfMKU5CZGD0soeRJ8HjqIjIpdDYp047NSefH+S7UcnMdCtNsd93U1duSf293orZ39eJoZQv+eboOmw9UovxcDXp7Biu3TsxPxranlmOEIdpF3683nML3X9nv8rvE1ARrLKyZkm6NhSwfsaAMEnylEJkkMsl3xgWnNsqL+fqVyCRfvFXRRkGjCqwk1I8IEJkE7GSSucGYYMDMGXm4a3om5heloiAtFuFe7t/jSfB46iIyyTcgwyCuemo/hq7At7T3oKziOt75uBbb9leguqLeeiWW/VkxKxdvfG8hoiI9n4PcfKgSdz+3Az29g31YLMyYkYe10zOx0EcssEI37e18cZOrTWwBHiGFb/wRp2Jw4GEf0+Hy5Yh58HoWT3fO8thqKwYjaisOAcqLxeEltzWRSbkIaqA/BY0GnEAmKIpAG9oQB9ut3nWo87rqIUWp8wqL0rKl2MP6ONtkl5H85Zdgvh7uEBmjj8GkKTlYXZyNW4tSMTbDCF3E0ISfR2Imh9hJxUiOTtrmKh51OWSSbe9uaO3G3ktNePtELXYduIyGmmtw5pvfuWMSnv3CLJ/bto+XX8PixzahrX1wW2yMPhoTJ+diDYuFkZ5jgRHJuXOBvXvFjz2Qegi5koPnO0EKdjzs8/Ldm1dz2fyhJ3ARoLyYr++ITPLFWxVtFDSqwEpC/YhAPephgsmPFvhf9blrdVjxo52oamxzGBMZFYlR47Nwe0kOVoxOxeTseES7rerwSMzkEDu5yEoZH5FJ8ahL3ebKiOSVpk7sKb+Gt45UY/+hCrRcu+EwgCX1L351Lr586xjBRlU3mTH/R+/hSoOHWCjOwYoxQ2PBk88FKwywhr6uZ5ESMzwh4GEfkUmeHvW/LsqL+fqAyCRfvFXRRkGjCqwk1I8IhDqZnIM52IM9aOvoxvIntuDw+YFL6QBE6CKQV5SO5bNzccd4E6bnJsAQPVjplUdiRmRy+OCQ6wO5/dUIXSEFeFjF1ov17fjXxUa8ffAqDh+tQHvr4D5Ttp31nUeWYOHkTNEmslhY8dMtOHiu3tGXxUJukQkrZufh9nEmTM9LgHEgFpwx9LSNUbQBnDoI3ebq3I7IpG/n0DZX3xgFUwvKi/l6k8gkX7xV0UZBowqsJNSPCLDzVw2wESg99BC75W4407W+zZVtvU1FqmPM7LzYF1/YjfUflg+u7oSHISs3DUtK87F6ggmz8hORoLcRSp5EhKcuOQSWViblB7MvMtnT148zNW3Ydr4ebx+sxsljFbB0WByKRxijrRVbx+YkSjaGxcKXf/UB3tpz0SUWMnJSsZhdHTIpHcX5SUgyRHKNA8kD8tBRaAEeMbEnpq2SYxEqyx/2+UOnUDyonXwEKC+Wj6EYCUQmxaCl0bYUNBp1DJmlSQTULO4jdcC+bGJbB5/821H8/K2jDhVsy+vk6QVYU5KFpUVpGJ9lsJ4/45kk8dRFZFLq7FKmny8y2dDahf2XmvC347XY+dFFNNdfdyguzEiwEsm0EbGyjWGx8Mxbx/Czvx1xkZVsSsS8kjzcNSUDNxUkIU4Xjbg425liXyt3so1SUACRSarmquB0CllRlBfzdT2RSb54q6KNgkYVWElokCLgi7j5Y9hCbfrOS3vx0pbTVhPZFr8JUwuwZnY2VoxKw4Rso7XCK0+Cx1NXoJNJIZVF7ZU5/YGrr3nvi0zWXLdg3+Um/P1YDXZ9eBE3mgbPSTa9cS9inbZi+9Il5PO/77mIL7+w2+Ue1/jEOBSzSq8zM1CcloIJBXoik5x3KwjxnXsbf8x3f+iUgg31kYYA5cXScJPai8ikVOQ01I+CRkPOIFM0j4BQ4sZzIEJt2na0Crc/9T6RSaPNO0JXnPy9zZWRRCGVRe2VOdl1FkaRY+Q6X81D7fNGJhdMzsSmJ5arYt7e07VY+eT76LT0OOTHGmMxdWoeVkzMwn8szbf+vry6y3oXpvPDKiEnxBHxliEAACAASURBVOoQES7uChRVBuIklM3pOFsha9jPego9R+nNNjHnK9Uenyf5/iB2/tDpD2xDVSflxXw9T2SSL96qaKOgUQVWEhqkCAglbjyHL8Smzq4eFD/8Ls5fbSEyKZJo+ZtMMofZyaGvecXIhHN7oYTZl1wlP/eUiPuDTFqJYk0L5j+yEQ3XOxxDjIqJwqjRuTj801usv3t591XExLre9RATEY78JD0mZsUhxss9l0piJlRWff3g/YdC+4hpFyjzScyYqC0h4I4A5cV85wSRSb54q6KNgkYVWElokCIghLjxHrovm661dmLhIxtxrmrwHFqMIQYzZxVg7cwsLCpKxci0WDoz6cVxWiKTniqLuq8cEZkUF4HNbRYsfnQTPqlocnQMD4tG+9v/Zv35lp/vQ0R0n4vQ6CgdJmTHY+WYVMzMGwFjzGBFZHHalW2tJpkUcielsqMRJo1WCYXhRK2EI0B5sXCslGhJZFIJFP0sg4LGzw4g9QGFgC/i5o/BDGdTRX0r5vxgA661uK68jJuYjTtLcrBsdBrGZxgQpYuwms4zMeOpy+4XKTq1RCY9rQy5jylYyCSrynzzjGTrdSBqP5buXnzhl7uw/WiVVVV/jw5d6x6w/tvw2VcQFjm4FZb9LjwiDElpCbhlRg7uHpeP2fnJSDJGqm2mT/nsbG3DwE1AzmdoTQPX7g639dWXcLs8X+14fy4lpnnbSPoCCwHKi/n6i8gkX7xV0UZBowqsJDRIEQg0Mrnk0U3Yc6rG4Q19nB5Tp+ZizcxMLBqZilEmg8u5L56JGU9dRCa1E5BCtrm2NLWg65mfo3/s4DUeXEdg1gNGs01lmwEwDN536W5HbvN0rG/ZgSk58YiMCOdqphBlQiu8CpGlxTb+eI9oEQeySTkEKC9WDkshkohMCkFJ420oaDTuIDJPUwgEGpkc//W3cKnWVhnTmGDArJn5uGNaOhYWpmKkSW/d2ur88EzMeOoiMqmdMBJEJs0NsLzxDf8ZLYJMMiO3X7yCObmZiIkkMsnbaf54j/AeI+njiwDlxXzxJjLJF29VtFHQqAIrCQ1SBNrQhjjYyiXWoQ4GuJV59MO4GcE1wbaPjdnnbJMzmUzJSMLCmwrx6SnpKC1IQqJh6LY8nokZT11EJv0wMb2oFEsm/RFnl6raMSkn1TqC32w+iX0XqzAqIwHxhij88NUy28hiLLC88l3rP4lM+m9++eM94r/RkmYeCFBezAPlQR1EJvnirYo2ChpVYCWhQYpAPeodxE2LQyQyObxXpCSedGZS2Zkulky6z2llrfEszdnGsV95G5cbm60NH14zGf/99gnrv/ujLY7VUyKTPLzi21darDbrP2RIs1QEKC+Wipy0fkQmpeGmqV4UNJpyBxmjcQS0TCbnYA72YA/CMLh1lVYmXScUkUn/B1igkcno2/6EMJ1rAR4ik/6fR3YLpMS0dqwnS7SIAOXFfL1CZJIv3qpoo6BRBVYSGqQIsAqTDbCVS9RD70Lc/D1kT/YQmSQyafD/TmwXJwQCmXxvfzVuK8202k1k0t9vtuH1E5nUtn8C0TrKi/l6jcgkX7xV0UZBowqsJJQQ0AQCRCZDj0yy6yHibMd64eleSn9PTPd7MWNi+3Hqahs2Xb6MH82bbDUv6ou/QNertvOIvLe5tpi7kPuFN3Fj4J5JRiYTUmPR3taBnu7BFUra5urvmWTTT2RSG34IJisoL+brTSKTfPFWRRsFjSqwklBCQBMIEJkMPTLpTNY0MQmHMaK5pQ8VLWZsPV+P9UfP4cDDK/xOJk9VNGHGtzbAsuFeqy1ZD2zAzaVZqKhrxakTV2DpsNhGFNuFzte+bv3nBxVXMTs7na4G8cOEIzLpB9CDXCXlxXwdTGSSL96qaKOgUQVWEkoIaAIBIpOBTyZ9rTS6r/QFCpksvakf//2n69h4tg4by6pw/lI5zH9+UHNkcvIju7Dm5kykxkbhncPVOPFxFbo6uzEiOwqXf3631d7KtmbkGEdoIubdjaB7JjXpFjJKwwhQXszXOUQm+eKtijYKGlVgJaGEgCYQIDIZ+GRSDDlk1Sz1eqDBdqzX+m+3q0Q1MS9bO3rwScN1/ON0Ld7fX4krl+rQq+twVEe95bnXsfsH91ht5b3N1X1l0k4mlxSmorzZjK1nG3Gt1YKiXB1+tXKmX2wU40Qik2LQoraEAEB5Md9ZQGSSL96qaKOgUQVWEkoIaAIBIpOhQybnzAH27NEmeXT2QmNbFw5cbsbfTl3CB4cqUH+1Cf1s+dXp3saLfeUoCi+0drPfM8nrTldvZHLlaBMyR0TjTE0rrlu6kZrYj5vzsoOSTAbS1tFAslUTfxTICJ8IUF7sEyJFGxCZVBRO/wijoPEP7qSVEOCBAJHJwCeTQlcatboKafcAI4zVzRbsKW/E+uN1WHefbVVP6MMqKfN4hiOTU3Li0NsHdHb3IjzGgoRwW6Uj3qunYnCQsjIZSAQtkGwV4zdq6z8EKC/miz2RSb54q6KNgkYVWEkoIeB3BBpvdCLni6857EjJSMLCmwrx6SnpKC1IQqIhcoiNPBMznrrsA5Wi07mPXY6al6NLsdHvk82HAX39/bjc2IGdFxqw7nA1Dh2uQOMrnxVltlbIZGREuNVuM8wwwkhkUpQXlW8cjPGiPEokUQwClBeLQUt+WyKT8jH0uwQKGr+7gAwgBBRH4PzV65j/yEY03eh0yE7PScXim/LxqUnpKClIQkKsjsikgDsYiUzKm549ff04U92G7Rcb8E5ZFU4cv4LO9k6wqzXYY4yNxIZHl6CoIBYmmKy/K0c5CqG9ba5sZZLIpLz5oHRvIpNKI0ryKC/mOweITPLFWxVtFDSqwBpSQtmKQTvaNTFmPfQIQxh3W+qud6Cto5u7Xk8KD5yrx9d/+yEsXb2Oj/VxesyYkYe1M7KwcGQKitL0CPdQmYVnYsZTlx0ItqIo9g5GTwVwaGVS2FS3dPfiRNUNbD7fgA37q3D2dBW6LYNxkpVixK5nViE7xeCy0sfOSdqJJe8tpL62uQYamfQ0553nNLuL1P1hnxfauDzKywGD25cu7j8Lmw3qtHKvZqwl29QZMUlVGwHKi9VG2FU+kUm+eKuijYJGFVhDRigjknMxF3uxVxNjnoM52IM93Ahla0cX7vr5DnxwsloT4/dkREJSPIpn5uKu6Zm4uTAZecmxCPNS4pMnweOpy45LfT1gsi1+yXqITPqGz2zpwZHKFvzjkzps3l+Jiou16O0Z/IJj+shUbPnpMsTFRlmFOW8bJTLpG1+hLZSa80L1+bOdmnHpz3GRbr4IUF7MF28ik3zxVkUbBY0qsIaMUOcEUCuD5rWSUdVoxvwfv4erjW1aGfoQO9g5yZuL83DXlAyU5ifClBDtlUhaE3ozYLQdA4PaiRlPXUqSSbWrpvoDF6UncLO5G2WXm/H2yVrsOFCB6isN6O8bLKCzdk4hXv33W6AbOH9IZFJpDwzKCxUyqXZcquchkqw1BCgv5usRIpN88VZFGwWNKrCGjFD31QRe5fvdAWZ28NwWd/RiI259bBPMGtna6o6HLkoHU2YSFs3Ox5oJJhQXJCJRP7TgzhAcg5xMshsoxN7B6L4lMDVV3es3AplMsoqttS0W7LvcjHXHa7CnrAKNNU0u0+wHn5qKxz8zfciXGrQyqc6fDU9zPpi2udpR03o1Y3W8S1LVQIDyYjVQ9S6TyCRfvFXRRkGjCqwhI1QrFQ152rHhQAXuef5f6GF3BAw8xgQD8grTEB5mq/To78egj8SsokTcNt6E6bkJMEQPLbbjyUaeRIanLjn+4G0nb31ysHHuy4hkxbUOfFDeiLcO16DscAVuNLU6mrAzui99ax4+N3+UT5U849ndmGA7MyklzgN1DvqcWNSAEBCAAOXFAkBSsAmRSQXB9JcoChp/IR8cev2Z9DkjyMuOF/55Ej98tczFeYmpIzCnOB+zi0ZAF64NMjkiWocJpjhMyopDdGSE4MnGM4nkqUswAB4a8raTtz452Nj79vb143ydGTsuNGD9oWocP1qB9rYOh+iYKB02PLYEc8dnCFLHK549GUNkku92d0ETghoRAhwRoLyYI9gAiEzyxVsVbRQ0qsAaMkL9mfTxJJN9ff341ksf4Y9bzzrUsiI2pqxkLCjJw5rJGRiVanRcG+DvCaCPCkdKXBR04eIq2/IkMjx1yfEHbzt565ODDevb1dOLT6rN2HKuHu+WXcHpj6vQ1dnlEJuSEIudT6/EyMwEwar8+V4hMklkUvBEpYZBiQDlxXzdSmSSL96qaKOgUQXWkBHqz6SPJ5n8xm8/wh+3nXGoDI8IR3aBCctKcnHnhHTMyBuBeA/3NgbaROBJZHjqkuMH3nby1icHm46uXhyrbMGGM3XYeOAKys/VoKe7xyFybE4itv9sBZLjYkSp8ed7hcgkkUlRk5UaBx0ClBfzdSmRSb54q6KNgkYVWENGqD+TPp5kMvMLf0Vzm+2SdV2kDiPHZmFFcTZWjTVhWk48YqKEbyXV8uTgSWR46pKDOW87eeuTis2Njh4cvHwd756qxdb9Fai6XI++vsFzxLdOy8ZbP1wsapu13RZ/vleITEqdEdSPEAgOBCgv5utHIpN88VZFGwWNKrCGjFB/Jn08yaTp3/6CG+22rXvpuWlYclM+7hifhtkFSUgy+K6S6mtCaIVAeLrg3JftUj93ryip1cvGeV+KrpW54M2vrNBOY1s39l1qwvoTtdh1oAL11Y3A4M0feHD5ePzy/tnDXkMz3Lzx53uFyKTUiKZ+hEBwIEB5MV8/Epnki7cq2ihoVIE1ZIT6M+nzF5k0ZadgUWk+7pxgQmlBkvVsotxHKwQiVO6kk+ovte/eZHZpZS7YMWJbWauaOtHRbVt17Ovvxyd1N7D+WA32HqzA9cYWB5xhYcDz95Xi6yvGS4XY2s+f7xUik7JcR50JgYBHgPJivi4kMskXb1W0UdCoAmvICPVn0seTTGZ/8TVcu9FpVcnOS+YUmLBkdi7uHJ+OWXkjECfzvKRWCASRSe+hy+tSdK3MBYZEy8BW1mM1Lbje2W0jkwAOX2zG4cMVMN8wOwCL1IVbt7UunZ4j+/3nz/cKkUnZ7iMBhEBAI0B5MV/3EZnki7cq2ihoVIE1ZIS2oQ1xiLOOtw51MMDgl7Gz5NMEk1U3s0lpO5558yie+tsRx9islVyzk7GgOA9rp2SgJD8RyYZI6dv6zIDRaBPPY/XLm5M8XXCulkMDZZurffy8LkXXApm0bmVt7ca+y01Yd7wWJy9eg9lsOzOM/n7U1zbD0j7wM4B4QxR2/GwlJuYlKTJd/Ple+aSyCXMf3oyudQ9YxzLxiS24fW46lo1Mw6TsOEfFZrXfOYoA6UWIFuaYmuMj2YSAHAQoL5aDnvi+RCbFY6a5HhQ0mnNJQBlUj3oHidOK4WqQSTY2T3dMpmYkY35pPu6azLa8JiNV4JZXlsw5P76IlVbPE8rxOSW0ntFTC5c2Sw9qr3ehu3ewSI43/3X39OFk3Q2sO1KN/Ycrcf1ai8uZSOd+uaY47Hp6FTKS9HKmg0tfv79XzHrAOBCkbQbA0D7s2NR65ygGqJsgteaYWvaSXEKAJwKUF/NEm+6Z5Iu2StooaFQCNkTE+j3pc8N5DuZgD/YgDOLuVxTqrg0HKnD3c9sdzaNiojB1RgHWFmfh1qJUjMscWF70IZCdLRPzsBXDYHsooeVHJq+1daHscjM+aWxznH0cbj719vXjo9ONOHKMbWX1TqRKxpqw8bGlMMTIL0LlbI/f3ysiyKTa7xyxcc/eFe3Dc1/ruVyTbSMH6uoAuV9W8Vq1F4sFtScEpCBAebEU1KT3oZVJ6dhppicFjWZcEZCG9KMfDWiw2q6HXjUSJxQcHjY8+peDeP6dE1aTIqMjMYWRyVlZuHVkGiZkEZkU6isik+qTSbZdtbrZgg8vNeKdE3U4W3EdFsvgPZDefNXf34eaK9dg6RjcysraRg9cgRMTGYEvLByNZ78wCxER4UJdLridP94rZWfrsObZ7eiw9KC/R+fY5rrwVx/gU7NNmJefjLyUmCFb2WP79ehoF/ntkGAkxDVkRHLxYuDAAXH95LbmdZ5Yrp3UnxAQggDlxUJQUq4NkUnlsPSbJAoav0FPigMUgdd3ncd9v/pAFpmkba62s6FxtuO2iqyOBOh0GmK2UleRsKqrlxs7sPNiA9YfqsahI5VobWnzul11OPwK0uOx85lVMI2IDRaYXcbxwcfVWPHEFrAVWfYwMmnZcK/137/deQVLJzAiGYtwty0FjLzNnQvs3RuUsIgalD/PeosylBoTAj4QoLyY7xQhMskXb1W0UdCoAisJDWIElCCT7vCE4iodVY71HSRSE/Sevn6cqWnD9gsNeKesCieOX0Fnu60asdhnzoR0/PMnS6GP1ontGjDtb3tyC7Yfu+qwNzEpBTV/uMP684WqThRmRnssruUct4EyWLatlT1KbHNV6ouPQMGO7AwNBCgv5utnIpN88VZFGwWNKrCS0CBGgMikMs4lMjk8jlK3Dlq6e3Gi6gY2n2/EhgNXcPaTKnRbbNd6iHlioiJw35KxeO7eYlW2soqxRe22ix7ZiL2na61q4hLjUDpjFP75jWnWn4cj9M5kUomzh3LH6e0spDvpY3qUqB4dil+CyfUR9dc+ApQX8/URkUm+eKuijYJGFVhJaBAjQGRSGefyvIZEGYv5SpFS1MRs6cGRyhb885M6bDpQiYoLtejt6XUYPm1kKjY9vhTxsVE+B8N2dLIrcELhcSaTCcnxmDtrNNY/OEUUmRSzisy20za2dcHc6buyLjOCuSFRr0OCXjesT7yRO/ffE5kMhVlNY5SKAOXFUpGT1o/IpDTcNNWLgkZT7iBjAgABIpMB4KQQNLG5vRtll5rx9sla7CirQHVlA/oHzgAyOO4szcefH1qASJ3yBXMCCW5W3KcdruVOb3/qfXz0iW1lMiIiAllZGTj/n7dbf77Y2AJTsuctvtYVvzTb6OvqhVVF7erpxZkaM07Vt6KpQ9iKcXgYkBkXgykZ8cj1cHbTjr+zPa31ehgNti8DiEwG0gwlW/2NAOXFfD1AZJIv3qpoo6BRBVYSGsQIqEEmgxguGprKCLCKrXUtFuy93Iz1x2vwQVkFGmuaXLR+f+1UPHHP9JBZafQGOSOSczEXe+GjYo6Iq0FUdq9k8aW9c/BRhO2aJCKTkmGkjiGIAOXFfJ1OZJIv3qpoo6BRBVYSGsQIEJkMYucG2NAYkbzabMGe8mt462gN9h28jOuNLY5RsOqjL31rHj43f1SAjUwdc80wwwgB1/cEAZlkCLahDQYYiEyqM51IapAiQHkxX8cSmeSLtyraKGhUgZWEBjECRCaD2LkBNjRLdx+OsjOSZ+rx3oFKnP+40jGCqMgIbHx8KeaOzwiwUalnrjOZrEOdlWg5P2/vvYQHXvwAfd2D90xOevJ9rJ6TjmWj2D2ycYhge04HHjHbXC/UteODS41490gNDh+5hE6z6x2eQkadmJqAW0oLsXaiCcUFiUgxup59tdpTYAbqTVZxRCaFoEptCAFXBCgv5jsjiEzyxVsVbRQ0qsBKQoMYASKTQezcABtaR1cvDle04N3Tddi8vxLlZ6ocI9j7X3dgWlFKgI1IXXOdyaSdaLlr/NOOc3jwhX2OeyYnP7ILa27OxMrRJkzJiUNkxOCZUzHVTNk5SXZVy/qDV3H08CVYOsSTSVYcaP7ckfjs1HTMzk+GKWEomTSmmQGzbfWVyKS684mkBycClBfz9SuRSb54q6KNgkYVWCUJ9VQYQpIgjXXSQ289txMsjycyuWZWFhYUpCI1zneVTDE4xMVEgP0/VKpqisGG2gLDkcn61z6POAFVW0MJRyFk8lRFE2Z8awM3MvnNVRPw1eXjvbph+RObcaWhzfo5kclQmq00Vn8hQHkxX+SJTPLFWxVtFDSqwCpaqODCEKIl+7/DHMzBHtgKQQTD88buC/jyC7utQ2EkLy0rGQtK8jC7IBE6py1wSozVZIjC+PR45A9TwVEJPSQjMBEgMinOb1okk7/8SumwZNL92hKtrky2ttquMAmGR8q1PMEwbvsYxKy4B9O47WOhvJivV4lM8sVbFW0UNKrAKlqo4MIQoiVro4O3LWXasE6cFWerrmP6d95GH7soceBJTB2BopFpiiZTLC9LHhGDBaOTsXhkKkanG13Oa4mzmloHIwJEJsV5lcgkwO7DZI9xoA6RmPsx3dF2Jh2zZwP794vzh1Zbz5kD7NkTPORYLM5EJidYITt16pRY6Ki9BASITEoATWtdiExqwyO+CkOoYaXa22rZmApRaDXdU7ELNcYkRqac7bfvlVXgs//1L/T0Dl46HqGLEKNeUNuomCiMHJWBO0qysXxMGiZkGhEdKVxPd28fbnT0gl2S7ulhKwlxMTrERIb23YOCnKHBRoxMHrp8Hf84Uz/kzCRtcx3qMH+SydPVbdhxkZ2ZrHY5MyllZfLTU03WM5MZCdEug7SSAAFnJtkXYfFxtmXE8uouGFzrEAme6UxfYaayW/sFK1e5oRySrbJpqosnMklkUvVJ5qSAyCRPtFXSRWRSJWBFihWS5IgUOWzzYN5WKxQnudtvj5U34tZHN6FN4MXjQu1yb8dIasGoDKwoycVt49MwLXcEDFG+CWVbZw9OXm1FZUsHupxIr7N8tis3wxiNSVkJSDFG0tlMqU7yQz92LUh5Qzt2lzfinaO1KDtUgRvNrQ5LiExqh0y2dPRgf3kT/vlJPbYdqEDV5Tr0D3zBw8jkg8vHoR3tHmfRyie2YP/ZOutnkdGRGDM+C8tnZGLJyDRMzDIiyulLLGs113wz0GCr5mr/Es+56mx1bT8uNpgxr8jW5sU95xEd6/nLJl/T2tIRhm8uzQRY9w4bI/3vrRcRHTv4JZsvGfbPWWGjUXFJmJAZB0O0Tmg3RdtZcbLBYl3BlUqyFTXKD8KITBKZ5DntiEzyRFslXUQmVQJWpFjeZDLYt9UKhV/u9tur18xY/OgmXK69IVSlpHbh4eHIzEvD4pJcrJmUjuK8RCTovSdcTeZulF1uwnunG3ChthXdPZ6Tu7CwcGQnx2Lp2BTMK0xGVmIMEUpJHuLbia00n68zYwerDnqoGsePVqC9rcNhREaSARdf/jT50s0tQt6zShbgYYS/sa0b+y41Yf2JWuw6UIH66kYb+Rp4/vXsSnxvzFrsxV5+k0jpezQVkpfROA2v1G9BSX4SRnh4v7EvQRvQ4BOnFKQgHOJ3W4QqiWLjdn6cSXVd3VBSHewkm/JinyGmaAMik4rC6R9hagSNkD/Y/hmtdrXyxozHtlqmwwTXb8i14AFnu+SSSTaevr5+HLrQgBvtXYoNjx3H/PPO81j3YbmLzNTMZNxSnIe1UzIwI2cEjNFDVyhvdPTg0JVmrDteg48OXUFLU6tjFWSIgWGA3hiLqZOzsWZ6JhYUpaIoLRbhChcSUgwYEoSunl58UmPGlnP1ePfAFZz+uApdnYNzLyUhFjufXomRmQmEloJkcsWoNOQl6xEZ4XrPZI4p0qrlSl33kJWsZnM39lc24a0j1dh/qBLXG1scFrEt5r+4vxSfX54HIwYOMPLymELkz2GugvI+8/pefGZCAUoLkpAW77qFth71jr8pvqDqRa9oQhmqZFJs4SSncgG+3BCQn6uRFwckEJyMJjLJCWg11agRNLyJkZr48JLNGzMe+njokOIfrdrlaSx/3H4W3/ztR2ArHPaHXQ8we2YeVkxKw4iYoeeVrnVYsPF4PQ4edt32OBxWMfpoTJyciztnZWPpqFSMzTBC55Q0S8HZuU+oJmlycXPvz85IHqtswYYzddh44ArKz9Wgp7vH0WxMTiJ2/GwFkuNiBKtW++y0YEM4NBTyRZL7ymTWAxuwYG4OFoxOgjHSdTdAZ0cY7p2bYbX8Tx/WIMZtu2h9mwUbjtXg8JFKmG8MLv9E6sKx7keLsWRaDoR8scfi/yd/PYTfbfrEgRLb/j5+Sh5un5GJW0emYnxWHJg9vra5znv6II4fqcCNv9xrlRV110sI0w3OITFu6O/RoWvT51y2uUqSF2OB5ZXvWlWPf/IfuH36aNw+1oRpeQku57mJTIrxjvC2RCZdsVIjLxbujdBrSWQyCHyuRtAEUrKuFRfyxoyHPh46pPhPq3Z5G8vOE9W48+mt6OrudTTRx+kxYVI29DG2VRHnp63dgk8+vooOp22PQnCKjIrEqHGZuL0kFyvGpGJSdjxiRBT7GU4HkUkhHhi+DVtxPnj5Ot49VYut7NzdpXr09Q1uX148LRvrfrhYVIGmUD473YpWjyuCLeYu5H7+Tdx459+sDole8RoSUmMxbmwGdG7x0GsJx65HZlrbzX/6ECKiXbeTt7R24tzpq7B0DK4cxxuisPGxpRifm2TtZyO4adZ/16EeBnivhvP4a4fw4nu2CpPsWqLRk3KxqjgLS0emYXJ2HLo6wwfIpKs8522LzM6jRyvQsm6tbXy3/UkWmbRssJFS+yNFXn+0BZY3vmEVMfKR9Vg+YwzWTDBhVn4iDE67L2ibq/z3iCcJtM3VFRU18mJ1PBccUolMBoEf1QiaQEvWteBG3pjx0MdDhxTfadWu4cbCriNZ8MhGNLd2OprpInWIiBi6zbW3t9dltWqEMRqPfnoaUhNiXVRYunvx3++exJkrzY7fM3m5RSYsL83DHeNMmJ6XAKMCxTCITEqZqbY+9nN3+wfO3e08UIGG6msuq9UPLh+PX94/W/QZyVA+O12OcqQNkDh377yzqw6fX2CrRB3sjzP5++nnZqDAFC94yGwl9AtLClza/3nrpSErtL4Efv43WwSRSV9y5H5O7ykbgqGOgxp5sdy5Gcz9iUwGgXeVCBqWkDg/vs7KDffNaxBAKmkI7OxeHOKsfXlco+HLR5IG4dZJyJYyJfSIlRGIZJKNzJtiuQAAIABJREFUsaGlw0ooL1YPnrvyNfYJeUnY9tQKJBpdrxCw9+vt7cP9L+7B33ZfcIjSRekwaVoB1gyseEzINiJc7D4o97nArixQ4F47X+MNts8Zkbza3Ik95dew/lgNPiqrGHLu7vn7SvH1FeMlDV3IFktJgjXa6TIuYyIm+rauPhUw1ftuF+AtIlLroZuzwXE/bky0DpseX4bSsQMlTX2Mz5l02JtKqYKqv+e36Hj961YRw61Mqg13qJMoO76hjoMSebHaczWY5BOZDAJvKhE0YRgsSCAEErZVhR5XBMScBQlE7JQodKPUuAOVTLLxd1h6cOcz2/DByWqfcCydkYs3v78QUQK2qj74mz34845zVpls+9zYKflYPTsHK0anYXJOHFjZfjEPbZsSg5bntqyw08X6Duy82IC3j1Tj0OGKIefu3vrhYiydniNZWSDHgpRBX8Ilx923w/Znf6IaUm1N9O1AWOD9zSruLcWOiG1gf5+dt7nW1PajqaMDeyquYcPH1Th4tAI3mgavlGHFt/7w7VvwmZuLfEJMZNInRAHZgMgkXQ3Cc+ISmeSJtkq6iEyqBKxIscFMJuXe5ygSSp/NAz2BZiTjo9O1OFN13etYJ+QmoXRsmuBtjycvN6H4u+8oRibFLmRqoTpgV08funq1QRqYjy83dmDr+Qa8e6gKp05UorPd4vA3O3e342crMTHPdu5O6hPosSB23M47QGpRK6iKKjur/K8T1ahptu3AOV5+DX/ZaVvJdxSgYYVsVrzm8ewhu9Yncpit4v1RFrT+7ptWeXFffRFhXZ53ETiPlclMz0rCrTOzsWIsu3vWth3d+T7J1no9jAbbF73u5ECv70dtiwV7LzdZV7w/KKvAtdrB7e6sz4/vno6ffHrqsO8QIpNiZ2BgtCcySWSS50wlMskTbZV0KUEmaZurfOc4FxbQQ2/9NlnNh8c2V7v9PMYjBqtQS6CFYBPKZJJtJWWJ9cWGdrR1SatqKQRjMW26entx+Gor3iurxPnT1eju6nZ0z02Lw65nViEjSS9GpMe2oRYLcsfLSH7Ol15H08DZZUYm7QVoFj1fBp1bAR4hDurRdWDHVxdamy763b+g63E92+xJhj5Gh6k58Vg2OhWTnQpleSMB3n5vv4/2nZN12H6gAjWVDS5nce+aW4hXvnMLdF52JhCZFOJhahNoCCiRFwfamP1pL5FJf6KvkG41gkbuH2yFhkZihkEglH0UymP3NiWUJpOBss21r78fFY0d2HWpEfsuXUeTWbm7QmW9gPqBT843oPJiHVhBJftTMtZkrQRq8FDFV4q+QIsFudeYyP0SjZ0xzr//DbR32r50sK5MrnvA+u+nt51CdorvVUV3P1nC2/HA5CnWX7904jii+4Z+SRDdF+vyBaM+Mhz5SQaMTXe9wkcsmWQ62yw9OFLRgn+ersPm/ZWouFiL3p7BOTdzdBo2P74MxtihlaOJTEqJOuqjdQTUyIu1PmZ/2kdk0p/oK6RbjaAJtARFISgDSkwo+yiUx86LTLrr0eK2qd6+fpyra8P2C41452A1zpythaVTO2Syw9zhskr02VtG4uVvzkOEyPOrw72YAikWNHmNiVkPGAcK0LUZAEO7Kn8HSnpvwqbOnQ5CqQsPgyEmYkhhLClkkhnMKjufqLqBTWcb8M+yKzj/yVWX1fCcVCN2PbsKmUmu15YQmVTF3STUzwiokRf7eUiaVk9kUtPuEWacGkETSAmKMJSCrxUvH8ldSVADebmrE2Jt0to2X0/2K70yGQhksuJaJz681IA3D9dg7/5LaGtpE+tabu0fv2cmfniXbfVKySeQ3gPOcaskBrJkmWOBtAabCFYB1tAhS9xwnYUUMZNKJpnent5+nKlpw7YLDXj7YBU+Pu56Tteoj8L2J1dgSmGyw0wik6q5mwT7EQE18mI/DkfzqolMat5Fvg1UI2h4JSi+R0ctvCHAw0eaXEnww5TQWgEiIpM2BM5Um7H9Yj3WHazGscOXYOkYLHDjh2niUWVEeBhefWg+7pqjzp2H9B7wn6dLUYqt2Ip2tMME21Uc7tdCib1eSQ6ZZPrZtu9LAxWE1w9UEG5rGbz6i52d/Pv3F2HFrFyrvUQm/Td/SLN6CKiRF6tnbeBLJjIZ+D4EBU0QOFHCEHgkkc46JJgYVF2ErCr4c8ChuDLpjUzqo3X4wqLR/nSHVXeCPgr/Nn8kRmYmqGYLvQdUg1aQ4Ja+VrAvDIywXcLq/p4Q6x+5ZJLZYLvb1II95Y14+3gdPiq7hOYG17ttn/tSCb5920Qik4K8TI0CDQHKi/l6jMgkX7xV0UZBowqsmhcqNkmRMiBnHe7fuEuRp1QfXttcxa4qKDU+KXLUJpNSbFK7jzcy+cIDN+GBZePUVq8J+YH0HlAibpWQ4ey4H79yBL/65nTrr4xr3sTkmVlYMTUDk9LioIvwXJG7M7wda0eOt/bZdbkK+cl65MfZrnjRApm0E8pr5m7su9SMt4/XYGdZBeqvXnM5w3vf0rF49p6bEB/vOs62NsDgerTS51zX3/NbdLz+dWu7kY+sx/IZY7Bmggmz8hNhiI7w2V+pBlo8263U2EiOcAQoLxaOlRItiUwqgaKfZVDQ+NkBflLPO4nU0socj7Ezt/LSo8QUIjI5uM2VyKQSM2pQhlJxoIQcMTKEnPf+rzc/xk+/NNE6WHbPZExcOMZNyMao7BFeQeyJ6MDf7rJdBfLgpg8wNTMOX5s6zfrzcNtchXwh53zPZF39IKnz9ntfnr7R0YMjldfx3uk67NjbgKuX6tHX2+foNndsLrY/e6uLGCKTvlClz7WOAOXFfD1EZJIv3qpoo6BRBVbNCxWTVEkdDA8dUmzjZRcvPVIwcO9DZJLIpFpf+CgVB0rIESqDznsPfatkN01H0v88i/KzNejpHrwaxX7Ppr0HkUkl3sgkw58IUF7MF30ik3zxVkUbBY0qsGpeqNCkSs5AeOiQYh8vu3jpkYIBkUnvBXhoZVKJGTUoQ6k4UEKOUBl03tvzHHhy53Fs3t+EUyevoKuzy3rPJpFJZeOFpPkfAcqL+fqAyCRfvFXRRkGjCqyaFyo0qZIzEB46pNjHyy5eeqRgQGSSyCSbAzzmqFI6lJAjVIZzu+G2lzpvH/3if+7HGx+e9RmO/dEWdL36XWu7qC/+ApFRkTC/9C3rz7trLmJGhq2yq90/3iq9elKk9DZXdxuO1tVh7/l2vH3oKo4erYS5uYvIpE+PU4NAQ4DyYr4eIzLJF29VtFHQqAKr5oUKTarkDISHDl/2eTr3xOwSk6D50uHtc6X1qHlfJW1zpW2uSm5zZRVBw8JshVmUeg8oIUeoDMHtzIDRVogVbHvnjpOX8ccd52Dp7vX62uiJ7MT2R9ZYP4/+7G8QGR2JtlcfsP5cVl2LWZmuZNJbpVdvZNLZHnshHDmFZZyxaO1vxbWmcHxw8RrWHa3B/r1VqP+zbSz253cbzuPBVaNEvTapAI8ouKixyghQXqwywG7iiUzyxVsVbRQ0qsCqeaFCkyU5A+GhYzj7gu3ck/t9lUriS2SSyKSSZNI5LpWap0rIESpDcLv/Z+9MoOMo7vz/1UijYzS6j9E5I8vyLWRZtiTf+MIYHxjbELIh2cBuQjbZf7JLyObA3Eec3SSQTcgmJLCBZBcWwhl8YcCG2PjGwQaD70M+JV+ydd//1yNrNBIadXVPVXX3zK/fy3vBXfU7PlXVU191dVU/Mcmyi6m/bauJSaWPOLocqL7cgi3Ha/HitrN48RulfR7DMYuewyv3zcSCq2dRsvyGkJhkoURlZBGgebEs0t1+SEzK5S3EGw0aIVhNb5R1shRMIjJ8DBZfKH735D/h58mXxCSJSRKTvU8T1rGl542f1cVkPLrP/ahtbMf7ey/hpoqMz4nJr84txFP/bxrzzweJSWZUVFACAZoXS4Ds54LEpFzeQrzRoBGC1fRGWSdLwSQiwwermPT/7on38tNAMfDy42+HxGQwPbJvXTpnkt8SVNZxGIxg5fE8YbXBXC4M30z2iEmlzfcea0LxkLi+YnL+/+ILM9z4zbfYxWT6Hb9H8x/+1Wun8MHnMXfcMNw4KhPjPfLPmXRdXWWsZ1dafk8nsmQkAZoXy6VPYlIubyHeaNAIwWp6o6yTpWASkeFDzyRWVly8/ASyw8u+wpDeTNKbyWCEnp5xqPXZwqO/s9pgLsdBTEbZo9Dwx294cWw6fgqT8rNhs+n73jTQm1I9b1B72mcwFvuqmjHKE6u1KS1RnsSkJZpJSJA0LxaCNaBREpNyeQvxRoNGCFbTG2WdLAWTiAwfeiaxsuLi5cffjpY3rP5vENTakcQkiUkSk72jhHXs6hFp/Ze5Kl5bXvhnr/MVf/0YCws9GJntRFRkhObNi0hMqj3p2O5PmQJs3Ahc3UOKrRKVChkCNC+W25QkJuXyFuKNBo0QrKY3yjpZCiYRGT7CTUxqaQ9lAyLWi8QkiUkSk8aLybEr/oIl40Zg/ogMlOQlQtn51Uy7ufbvI/5vJpOX/QVNTXVAZDu+MK3Qkstce3qAw0FCkvW3IxTL0bxYbquSmJTLW4g3GjRCsJreqDIpSECCN87BzlELJhFe3wzqjSGQ/0DfIOr1E6geLzHtb0dLjHrFJCKA4cUFWDwpH/OKMlDmSUKs3abFtSXK0jeT2r6Z1PMWTukIIsaBXuHLGgtzuSCXuU6+/yV8ePCc782k4yu/xYhhRbixMh8LhmeiKD8SqVGJ3vHEkrORbyZTb/szGhuveGP90owiPPOdawM+B7q6gMbG3tvebyZf6j4epfD7r2Nuae83k47oyD5lBzLKQ/zxsGGJBx8FqUqA5sWqiLgWIDHJFacxxmjQGMPdaK81qPGdtWh0LEb6Z5mg6Y2PdUKqZl/GMtcDp2ox9tuv+EKJjonGyOI8LK7Iw7zhmSjOdSI6KlItVG739QoXLQGQmNQm9PS2iYhxoHfcssbCXC5IMVnbcQX/9F+b8H/fnu/tuspRIZmp2Zg1pRC3lmTjmsIY5DuSQ0pMKkJy6lRg82a/0epoABquHtgZXw80du8YK/Oipa0yaZvbF82L5bYPiUm5vIV4o0EjBKvpjZKYBPqf28i70VgnpGp+ZWzA09nZhVn3rsK2fdW+cCKjIlE4PAfzK/OxaJQLZe4kxEXLEZR6hYsaS//7JCZJTAb6rph17Orpp/1td3V1ISGie5WIIibTUxQxOQRfHJuNkiExyI9PCSkx6c/MNx5NICaVWGjTHS1P0NAtS/NiuW1LYlIubyHeaNAIwWp6o8oSyHM4543TAQcilLWNnC+zLnPtSVNU3j32WSekathliEklhta2Dtz60/VYu7PKF5LNZkOuJxNzJnqwtDgL5QXJSIqLUgs56Pt6JulanZKY7F46qbbcffeR8/i3P2zHoZMNqHnpFi9mz5dfxT/OK8JdN12DCJVdSng9B3jYYV3izjp29fTTz4lJdPnaIOaOx5GalIVrJxbglmtcKC6IwYj4PC9zls8RlHhcmd0joboGiL/6gi/Qv7OMmcG47z/RjPEju3dzTfniq2hsrPP+/wlFGXjrke63rcrV2NKOHz23HWs/PIG2VpuvH2UufQ2Iakdtex1a//Qdb1n/Za4j01NQ6O7+A9aRI735+J6xDUBh4dV8qz9/3z+/7jy64VSjBj1/SPCyoeNAWLpC2JShebHcpiYxKZe3EG80aIRgJaMcv5XSC5N1QqjXvlo9Xv5liUklH+UtyQ+e3Y5fvflJb3oRQEZ2GmZUFmBZSRYmD0lFeoJdVUSo8Rnsvp5JulZ/JCaBcF6hMNhSWdaxq6ef9rftL9a09uFQLD/s3lcxf/wILBnjwqj0ZLjSusXkQG8NtfAP+BzVsVQ5FLlTTr0EaF4stzeQmJTLW4g3GjRCsJJREpPCNx5hnfDq6YxPr9uH7zz1AZTvm3qupLRETJrgwc1lOZhemIa81FhuglKZFPpf/m8Lqgd449DzxkVPbj11SEyGr5hUW+LOOra0iJmefkdiMvCojdw/HJPe/D1uLs/DnKGZyE+KR1Ji96oZEpPBPO2orhYCNC/WQiv4siQmg2douAUaNIY3QcgGwDohEwUgVPwblce7u09hyWNvo629w9dEzmQnKssLvILy2sI0FKTHcRGUWs9z8xe5evsPiUlgoOXuyvezdz29Bc/cNVkT2o8PX8aQrKTP1eGxPFUxysuOYkttiTvrmOMhJpVYRv/wv3G0+grQEo3UzFRcW1mAxcWZyM2wYXpW9zrOzdVHEIfBN6ZpagQmF3eXkbHM1X/57Nf+8Cne330SJ49Xo7Ojc8C+E++MQ7wzESd+N897P//OtbDZu58vUVFR8GRmYvG4HMwZloHhWU60NEXAeXVfHhKTmoYjFQ6CAM2Lg4CnoyqJSR3QzFaFBo3ZWiR04mGdkInKOFT8G5nHZycuYfoP30R9U5uvmYZf48GiiflYNCITpe4kxEQFf2wIiUlRo0CbXeXbtsWPrsOmvWfQ1d79bawt0oY8jwsTx+TiT3eO8f7blAe3Y+++o2hqaPY5iHMAqx64HpNHZfVxyqv/8rLDQoTVFw8xqXy7V/qdV7D/ZK03NGXjK3ehC9dXupGR2onl15Z4//2x9/cgutMxaPitTRFYvqBbfFadbUW+K9r7//XE2eNoMBb+djfsuYR3T1Rj5baTOLT/FNpb2/vEmpyWiMoJHnhSEvHLW0d6733nxX2Iju1e/hATFYGRGU5MLkj1/pHKFhGhGvdgeSlx+1+B/hjhFcTObgFOG/CwjI7QL0PzYrltTGJSLm8h3mjQCMFKRmmZq/BlrrI62cfHLqLiu6/53BWNcWPBxHzcOMLF7QzKcFvmqrwRbITfQXs6G1PtDZsWszW1TZi5fBWOnLnsqxZlj0LRyFwsrMzD5Ox0zC9L9977zYYTWPXpaWzfdRxXLnZvuqJcNlsEnv7Otfi76UN9/8YqzNRi5WVHzY9yn9WXIj4SujdixUDLsQfy5RU18d2v26ob6r0bwTz++h489uLffMWVTY0yc9PgTIvApz/s3vRo9E/+DFtb92Y3ga7OVhs+fWKG9/ZTb5/BzJEpyE6OQWNjhO5NZljF5KXLXTh+uR7rDtbg9R0n8eknJ5GZnYKL564gLj7W+831Tde4kBHtwPQx3W+wN+69DOWPEMoVZbMhKykamYnRvtUOaiJ4sPvMm8o1OABnt/AkMckyOkK/DM2L5bYxiUm5vIV4o0EjBCsZ1TAhEwWLdUJodv9G51Fd24SCf3jeh0mEmOzfBmqTSB5tZtQyV0VITsVUbIb/QXv6MlL79o/V6r4Tl7xCsra+xVclJi4GY0rycVN5HuYOy4QnKR4pSd1voQ+dbMZH5y7ilY/O4K/bj+PC2Ut9XP3yzsn4+rxR3n/j1X952WFhwuqrpqZ3J1AWu94yBh+DoVUwsYpJxW5sXBcO1zRi/eHzWLnnLMa6k7DvTB1cSXHdu0F7khHZGYmEhO7vIOvqunw7zir/3X9nYLXnAIlJ5l5HBTUQoHmxBlgcipKY5ADRaBM0aIxugdD1zzohE0UgVPwbnQeJSb491L89eVgebFdSFvvr95zGksfWeY+G6bniExwoG+fB0vJczBqajqJMB5r6fb/W2NmKD45cwIsfncEHO6pw7vQFX/20xFicfPY2EpMDNYCBYnLKFGDjRkW0sfSM7jJaxKSyMZayI/SJi83YcaIWQ1IcOHW5CamOaN85tWoC0T8ytbK0zJW9HakkOwGaF7Oz4lGSxCQPigbboEFjcAOEsHujRRDLGXoi8fPaMMTfTrDCQU++JCb1UAtcx39csJwdOJAlXn3imXX78e2nNgXctXdaYRryr+7a6z9xP3exAwcuXMabn1Vj1bYTOHrwDNrber+RW1TpwUs/mKMqRrSQlfk8YfWlbAR1rvu4XjgcbCJtoGWu/hy276/GHf/5V5y91IiumBa0PnuX93b07U8goiVmUGTKd66ta7pF/Mh/2YAbpmRj/ggXxrmTEGu3McfYR9ChAU50L8vt//wJJOYUQVnb2I7EuCjUt3Qgzh6B6KjuIz7UBCIvMdkfVKA21RKPlv5KZa1LgObFctuOxKRc3kK80aARgpWMclzephdmKJ6hR2JSb2/4fD2jlrmyCpXBMg3WhjLZ/+Fz2/HLvwx8nujSkixMCXCe6OWmduw4VovX957Fuq3HcepYDTo7e3fvnDs+Hy99fzZi7FfFwyBiREtrBpuzWXyx5KG0z8X6Fu9bQU9C93eqx+vOe7+vDHRduNKMkm++gZY3v+otMvrf3sWiaTlYNCoL4z3dYlLPpfXNpJoPLeJNrazafRZRrMWGWm50PzQI0LxYbjuSmJTLW4g3GjRCsJJREpPc+wCv7+O0BmbEm0mtMeopH65iUlnO+sWfrseanVU+bDabDTmeTFw30YMlxVmo8CQjydG9m2vPpQic8/Vt2HL0Il7efRbvbz+GGmVpq99ZpN9aOAY/u6Oyz7dvLOKJpf142THal5Y8tJQ9f6UZeV9+kcRkgAamN5MsPZ/KKARoXiy3H5CYlMtbiDcaNEKwklETiMmBztCT2TC8lrn2xMxz504tHEhMaqGlXlaLQAhkTa+NS/UtuO7+1dh77KLPtHIUReHwbNxQ6caNo1ze417io7vfKvoLyZMXm/HXoxfw8q7T2LqzCrXne3d9Vb7Be+Jrk/GNG7o33fG/9MYqyo56C/HbNGggX1p4aClLYhJ9NvNh7T/0ZpJlRIRXGZoXy21vEpNyeQvxRoNGCFYyagIxaXQjaJkIGh3rYP5JTPJtHR79Qo+NMxcbMfn7b+Dsxd4jSaJjojFiTB4WV+Zh/vBMjM5x+pan9mTd2ans0NmE9YfP4dWdp/Dh36rQcKX3DD97lA0v/2gO5o7LHxCUnliDFWHBthivmIPNQ0scgcSk0q55qQ7d58E2ogHDU9K8qbB+MzkYfy3iTa2s2n2WfsDDBosfKmMdAjQvlttWJCbl8hbijQaNEKxklMQktyMRjO5MJCb5toAWgRDIsx4b3316C36z+lOfybj4OIwdl4+lE3IxpygDw7OciLT13eazvaMLn52px1sHlLMDT+GTPVVoaeo9PiQxPhobHluA0e7UgJD0xBqsCAu2xXjFHGweWuK4UNeM3Nt6l7mmf/lllE/MxeziTCTH2HUjabE14p8rut84H627CI8zmfkcyAHzbwCc3fv5qJ7rqCb01O6zJM3DBosfKmMdAjQvlttWJCbl8hbijQaNEKxklMQkiUmTj4Jw+2byyz9fj1c+OOptldj4WEyYMAQ3lmVhxpAMjMl1wtbvvIjmtg7sPnEFq/fX4M1tJ3Bw3ym0t/bu2OpxJeK9Hy9AVsrVU+cDtLcWQTRYl+Flh6VbivSlxbaWsso3rUP/4RUcffZmb4ox8/8XsYmRGDo8G7Exfb9/ZWHQU6bD3oRt/3ad9z9/88EBzHLnIScl1isoFSHmcrEJwx57WsSbWlm1+yx58rDB4ofKWIcAzYvlthWJSbm8hXijQSMEKxklMUli0uSjIJzFpDPZiWmTi3BzqQvj81KR3G+znfbOThw8W4/XP6vGW9uqUHX4LDo7endsnTQqC2/eNxfxsepvvLQIIhKTfQloZbfr0waMHxN4x1ddQ5LxXMzDp1s/982i8qLbGROJOL9vcLWIN7WyavdZ8uVhg8UPlbEOAZoXy20rEpNyeQvxRoNGCFYySmKSxKTJR4FRYpLH+af+mzuxHhfj/2Yyyh6FYSNzMWd8Lka54hFl63tsRFtnJzYfuYR3tx5H9cnz3oPoe64vzxyG335rKiIj2Y6a0CqIAnUbXnZYuqVIX1psaymr5FVT0/umkCVPpjIMYnLY2Bbc+7tz6PdyG8qiaZczGmOyEpGdHON7m0nLXJnIUyGDCNC8WC54EpNyeQvxRoNGCFYySmKSxKTJR4FRYpL3+aesYvJXb36C7/9hm69VbJE2ZOVnIC83GVGRfb+V7OwCDhyoxsXqS31a8cHbJuAHy8ZqalmtgojEZC8BrewUzX/uHNDW3ol//cNGvLntmKa2GqhwV0wLWv/8De+t6FueQnSXEwXDspCWHNfbl6I7Pycke25mpTowZ3garh2ajoL0ODQ1RtA3k0G3ChkQSYDmxSLpft42ickAvJubm7FixQq88MILqKqqQmpqKubNm4eHH34YeXl5ulvp4MGDKCkpgWL/+uuvx9q1a3Xb6qlIgyZohJY2oByf0Yje3R15JsP7aAyesSm2RB+1oXUiyDs/XvZoAx5eJLvt8BSTWs4e7ejoxDd/8wH+tP5An4QUURkZ2e8oEHT1+T5S2Zjnj9+diaWTh2iGEWgc7Dh4Du/uPgVlt1iWqzWyCQ8tm+It+sArHyC6o1fMsNTXUsbfF6tYZ7Wv5bmgpexA/g+fuYKj1VdYQxuw3IXWK/hixRjvvZi/+zUiWmKgnEuqHCmjekUAsXExKL4m17vR0+yiDOQ645GU2P3Hi/p6leM8VDbr4bFElYcNVQ5UwFIEaF4st7lITA7AWxF6s2fPxubNm5GdnY1p06bh2LFj2L59OzIyMrBlyxYMHTpUV0vNmjUL7733nnfJEYlJXQipkh8BRUhOxVRsxuaw5KJlIq4HULATQT0+RdT54bPb8Z9/+dhnumiMGwsm5uPGES6UeZIQa2db7igitmBsGvVmkuf5p3r+IPLTV/fg/v/ZwYzOEWvHWw/dgAnDMpjr+BfsPw4cXQ587w/b8F8r92qyp7wha3nhn/uIGk0GNBT297Xv/FmMSL+6y4wGG4GKankuaCnLIbQBTfjH0CMmtfpKTk/CjCmFuLU0GyWZaRiaE01iUitEKi+NAIlJaai9jkhMDsD7/vvvxyOPPIJJkyZh3bp1cF79OODxxx/H3XffjenTp+P999/X3FLPPPMMvva1r+HOO+/E7373OxKTmglShf4E/CcJ4UqH91uHwSbw0YnQAAAgAElEQVTR8eC8MYbgRlPeZH391xvxwnuHfJ6UtxGjx3qwpDIPNwxzoTg/AVH9jpMQHBY380aJSW4JBGHo9S3H8Kf3DuJvxy7hzLm6gJZy0uOx4bGFcGdcPctBh0//58yFtsv4h//Yhrc+rNJsySgxmXrH77Bu+U0YX6RPTA/23FV7/phNTD702mb85E+fgu1dcm/mziQnpk0ZilvGZaMsMw0j3bEkJjWPAKogiwCJSVmku/2QmOzHu62tDZmZmaitrcWuXbswbty4PiXGjh2LPXv2YOfOnRg/fjxza9XU1GDkyJHeOsuXL8fMmTNJTDLTo4KBCPhPVKpRDd5ix6zLXPVsXqKnF5lhIqgnbqVOU0s7Fjz8FrZ8drZXSEZGwj3UhXkT3Vg8Ogvj3Ulwxuo/ckBvbLzqhbOY7GG46dOzOHNx4GXuKc5oXFucA3tUcG+e/cfB2B88j/0HG+Hdz6cjyrtU0s54bEVXTDOuPHuHN/TE2/+AiJZuQSLi6rQ3oe6P/+A1rXwnGNkWi9tnD8fIvCTN7iaNSceE4em+zwm0PBe1lNUcGGOF/jFcOt+F7QfOqdb+f7/9AC31kYhABEhMquKiAiYiQGJSbmOQmOzHe8OGDVCWoirLWA8d6v1rfk8x5Y2l8ubygQcewIMPPsjcWl/84hfxxhtv4OOPP8bJkydJTDKTo4KDERAtdkTb19u6suKS5Ucvh8HqPfTCh/jJnz/yFbFH2zFsVA4WVboxf0QGSvISEWtn+GZKRHCcbJKY5ARSxUz/ZZJojkHrXxei66LGpaMMu4pyy4inr9QzyPh4Ls7lfMItPKsYivisCNHLf4CEpATfm8lxGWkY5en+Q0B1tfo3kz3nWA5UVs85l/3Z0TeTVulN8uIkMSmPteKJxGQ/3r/4xS9w11134ZZbbsFLL730udZYtWoVFi5ciJtuugmvvfYaU2utXr0aCxYs8G7ec99993m/maQ3k0zoqJCGSZ7acis9MM0qpmTFJcuPnrZRq3Pbz9bj1c3dB9xHx0ZjTIkbi8pzMHdoJsbmJ35u9081e2a8T2JSTqt8Tkw2xKPlza9qd85T4Kl55+nL35aa3xC8r3xnmRCb5hOTI51pKBvJ/62y2mY+gdCSmAzBThdkSiQmgwSosTqJyX7Avvvd7+KJJ57wCkrlG8n+1+7du1FaWoqysjJ8+OGHqrgbGhqgdOrY2Fjv8tjo6GgSk6rUqAArAdFiR7R91jz7l5MVlyw/ejkMVs9fTCoH3E+fXIRbyrIwtSANean8J4IiclCzSWJSjRCf+4OJyWfXV6OsIBHpCXZVZ41oQFF6srfcofO1cAj8Bvli+xUUZ6V5fQ29/0Wcr2pCZ2enaoz+Bbrao1D/xlJAEZPnut/CFiz/FV783ixUpnTvjnoERwb9vEBhV4hCprKagtNQ2D8G1s8h/JfG9heTI+LTMH4U32fIlCnAxo0IeDzJYOmSmNTQGcKkKIlJuQ1NYrIfb2VznN///vfe7xofffTRz7WGsvR12LBhGD58OPbv36/aWoooVd52rl+/3vs2Urn0vpnsGRz9nR4+fNi7LHfvXm0766kGTwVMT0C02BFtXy9gWXHJ8qOXw2D1BhKTXyjLwhQSkyJwB2XT7P1sMDH5f1vOYsboVLgSu3f3HHTSjwY40b0RkIiVFP6+eR7fopaXle6zcu/f5jERCSgZ58Gi8TmYU5iB7NhEKMfNOByDC0D/ZayDLYlVs0Ni0kq9zPhYSUzKbQMSk/14f/3rX8fTTz+Ne++917uja/9LOSdSEZIsYlLZpGfixIm47bbb8Nxzz/lMkZiU28lD2ZvoSaho+3rbRlZcsvzo5UBisgHvHK7ByztO46MPj6KlqcWL5D/vnIw7540SgVWITbP3MxKTQppdulEtRyn1b3PlbEp7jB0jR+fjRmUn6OEZKM5NQIzKd9cy3hrK8CG9schhUARITAaFT3NlEpP9kPFa5tre3o7y8nJUVVVh37593vMpey69YjJQ69Kg0dzvQ6aC6EmoaPt6G0JWXLL86OWgR0yOz01BTkqMCJefs6kcax5jt8EW0X3AOe8rVJa5mr2fWVFM+p8FGtsVh4bmTnR0ajsQ4+yFdpSPiuuzzDX69seRlhCLM7/6lrc7qy0b9V8uqlaW9/job0/LmaYDiUnFnrJ7b8GwbMyvdOPG0ZkYl5+E+EF285Uh9GT4EN02ZJ8vAZoX8+WpZo3EZD9CvDbgOXbsGIYMGYKsrCyMGDGijxfl2BHl28uUlBSUlJR4z7FcuXKlWlsFvE+DRjc6y1cUPQkVbV9vA8iKS5YfvRxYxWSMIwZl44dg0bgsjMqQd66koiHTHDEYkRWPuGi+O8e2tndg1/E6rDlYg79sq8LBvSfR0d7hRUJvJvn2KCuKSR4ETp9vR25GVLeYbOhenqt8P5ieGItTT/2j97/Vlo1a9RniH/eSx9/Emk3VPqQRtgjkeDJxXaUHS67JQoUnBcmOgY8YkiH0ZPjg0Z/IhjwCNC+Wx1rxRGKyH2/Wo0GU40EeeuihgK3VIyZZmjMpKcl7rqXeiwaNXnLWryd6oiLavt4WkBWXLD96OQxW7/t/2IZfvdl7lEFMXAzGlORjSI72c/b0xmeLAPJSYjG7MB3lBclIjONzpmVTawd2VV3Gm59VY/W2Khw5cMYnJJVY/+/7s7F4YoHesIXXU/qV/6X29or3+bFaEyQxGd5isq6rDiv+5zM8/tqePl0nIzsN0yvdWDY2B5MKUrzfzUb0W4UgQ+jJ8KF1zFB5YwnQvFgufxKT/Xi3trYiMzMTly9fxq5duzBu3Lg+JcaOHevdlXX79u3eZax6Llrmqoca1RmIgGixI9q+3laVFZcsP3o5DFbvYl0Lpv3wLzhy5oqvWFR0FBRRKetSJpbJqU5MLs3FspIsTBqSinSn/XMTTi3xXG5sx47jtXj1k7N4Z9txnDpW02eXzvnlbvz5B3NgU5SsSS/lEHgtl7Jk08iLxGR4i8met6/PvnsA3/qvTejq6u2PSamJqCz34JZx2Zg2NA3u1Lg+41uG0JPhw8jxR761EyAxqZ1ZMDVITA5AT9l857HHHsPkyZOxbt06xMfHe0spR4XcfffdmDp1KjYqe1hfvZ588kko/1uyZAlWrFih2h4kJlURUQFGAqLFjmj7jGl+rpisuGT50ctBrV5jSzsWPfIWNn96Vq2o0PvJ6UmYXO7BsnHZmD4kDbmpsZoFpTKBPV/Xhi1HL+LlPWfx/rZjqDlzAf4669uLivHvt1doti00+QGMGyUm9fZnEpMkJnvejr//yWksfmQdWtq6l5QrV0pmCmZMHoIvjM3GpIJUuJJ6d/aVIfRk+JD9jCB/wREgMRkcP621SUwOQKy5uRkzZszAtm3bkJ2djWnTpuH48ePe/05LS8PWrVtRVFTkq/nggw96l7x+9atfxbPPPqvaBiQmVRFRAUYCeieHjOYh2j5rHP3LyYpLlh+9HFjqdXR04p9+swn/s/4gS3FhZeIT4zF+vAfLynIwc2gGhmbGMb89VITkyYvN+OuRC3h512ls2Xkcly/0vnFVVtb98htT8LW5I4XFz9OwUctc9fZns4pJ5Y1tIxp5Nk0fW2fOt2OY5+o3k1fPmdS6AY8SYwISvHbVvq8UlogOw4P1lQOnajH226/4rDoSHKisKMQt47NxbWE6CjMdvnsyhJ4MHzoQUhUDCZCYlAufxGQA3k1NTd63jM8//zxOnDjh3Sxn3rx53uNC8vPz+9QiMSm305K3XgJ6J4esDEXbZ42DxKReUr311uyswp8/OIq2Dm0Ht+v13NzagbUfnkC7nz9lI6Dia9y4qTwX84ZnYmS2E1GRgy/57OzswqGaRqw/fB6v7jiFXR8dR8OVXgFhj4rEq/dchzmluXpDNbyerHGm148ZxaQi0qZiKjZjs+HtN1gAkzAJW7AlpMSkksy//m4znlr7mTcvh9OBykoSk6buiGEWHIlJuQ1OYlIubyHeaNAIwWoJo3onh6zJibbPGgeJSb2kjK139lIjrr1nJaqq63yB2KPtGDYyB4smujF/RAZK8hIRG+CsuvaOLnx6uh7rDtbgtR0nsXfPCd9ZkorBJGcMNjy2AKPyU4xNNEjvssaZXj/KG7Wet2sxdzyOrvp4tK6+zZv1sxuqMW1k9+YrapfaRkNq9f3v+9vSUs/IsqHyZlJheO+fduDnVzfkITFpZK8i3wMRoHmx3H5BYlIubyHeaNAIwWoJo3onh6zJibbPGgeJSb2kjK/X0NyGhQ+vw9Z9vd9tRkZGwj3UhXnes+qyMMGTBGds351em9s6sPvEFazcV42V207i0P5TaG9t9yU0JDsRGx5bCFdynPFJBhmBqHHGazltDWrggivILEOzeqCzIwcSuyQmgatbUHDtDLTMlSvOkDBG82K5zUhiUi5vId5o0AjBagmjoiahPcmLtq8Xsqy4ZPnRy8Eq9ZTvNr/+64144b1DvpCVnV6z8tMxq9KDZddkodyTgpR4u/d+Q3MHPqyqxRufncXarSdw4shZdPotl508Ogtv3nc9HIMclm4VNt580QAnus8x5Ck4eG30Q2Jy4N40BVOwERsxEGf/Nu2pzbNtRfdvtT5JbyZFtwDZD4YAzYuDoae9LolJ7cxMV4MGjemaRFpAaj/4wQYi2r7e+GTFJcuPXg5Wq/eTl3fjoed39gk7zZWCqRUe3FSShYLk7o07ztY34829NVi/7TiqT57vcxTBV2YNx2++OQWRkTarpS89Xl5iUvk+8UxHNYZ/40W0N0ahq81uqmWugd4OBgs80AY8GTFJ2P/UrXDAMaCQ7P8HAhKT3QTq6+nNZLB9kuqzEaB5MRsnXqVITPIiaaAdGjQGwjfYtWixI9q+Xnyy4pLlRy8HK9Z7+YMj+OoT70HZWKfnSkhJQPl4N4Zmde96efpik3fH1os1tX1SfOQr5fjekhIrpm1IzLyWudY3tWHBw29h+/5qbx5d7VFoefOr3v//0pZqzBiTgowEtm8meb2BlTE2T59vR27G1d1cG7rfHMf83a+RGZuMqj98adA2pTeT3XhkLEGV4cOQAUxOdROgebFudLoqkpjUhc1clWjQmKs9ZEYjekIl2r5eVrLikuVHLwer1ttx4BzmPbgGjc1tvhTi4mPhTOw+07exoRkNVxp895S3kP/z3Zm4aVKBVVM2Rdx6+vPpiw2Y8aOVOHGu3pdDlC0O9a90i6kPPruC8mFO2BneFOvxHwgcT1uBfJCYDLz0mpa5mmJIUxABCNC8WG7XIDEpl7cQbzRohGC1hFHREyrR9vVClhWXLD96OVi53vGaOsy4ZyXOXhz8nMD4WDveevgGjC/KsHK6pohda3/ec/QC5ty3GnWNrb74Yx0xGDWyAFvum+r9t9rLXUhKHPx4l57KWv0PBo2nLRKTnyegxpfEpCmGNAVBYtIUfYDEpCmaIbggSEwGx8/KtdV+8IPNTbR9vfHJikuWH70crF6vtqEFc+9fg4+PXhgwley0eLy/YiHy07vfkNAVHAEt/Vk5l/QL//5un3NCnUnxGF/mwcLRufj2HLc3GC3fwfkfMRLsd45KLj07zAZrKxBVtW8m1cRu/x1waQMe+mYyuBFMtVkJ0LyYlRSfciQm+XA01AoNGkPxG+pcy+RQT6Ci7euJSakjKy5ZfvRyCIV6rW0dePSlv+G1LcfQ4fcd5YxrsrHi7yuQFK/+LV4ocJCRA2t/fnLlXvzbf2/tE1JKRhIml3uwrDQb411pGOHuPpJFi5gM911hSUySmJQxzskHQPNiub2AxKRc3kK80aARgtUSRlknh3qT4fkmQW8MA9WT8VaiR7T2vF2w0kSQJ2uyFT4ElE2R/vXpLfj92s98SStHuGTmpmFGhQfLxmZjUkEKYiPsSEjoXtpKYpKtfwx2jAibBbml1H5baJmr3PYgb9oI0LxYG69gS5OYDJagCerToDFBIxgUgtoPfrBhhfubBH9+JCaD7U1U38wEmlvbsXTFO9iw+5QvTFukDXlDXJhb4caS4ixM8CQjMS5K9w6dyhEj53DOa3+wYzVYOMn4gxKvZa7KMtwMZAQ8RoQlX9ll1H5bSEzKbhHyp4UAzYu10Aq+LInJ4BkaboEGjeFNYFgAaj/4wQZGYrKboNXeKgTb7lQ/vAicv9KM2feuwoGTvUexRNmjUDgiBwsq8rBopAul7iTERUd6wZjhKAYZqyZ4iUkr/iFK7beFxGR4PSOsli3Ni+W2GIlJubyFeKNBIwSrJYyq/eAHmwTPNwnBxuJfX8ZbCX9/wb5F4Zk72SICPAkcPFWLmctX4cKVZp/Z6NhojC7Ox+LKPNwwPBOjsuMRHdUtJJVLWdqa0H0kKKqrxXwHp5aj8oeuwniXWjFT3Ccx2dtv4rtP/+F6meGPG1wTImNBE6B5cdAINRkgMakJlzkL06AxZ7vIiEq0mJSRgx4f4Zq3HlZUhwgEIrBx7xkseuQttLR2+Io4EhwoLXVjWXkuZg1NxzBXPCJtfY/+qKkBXEbruIwaoMboINT7llVXNag9Y+nNpHrbUwnjCNC8WC57EpNyeQvxRoNGCFZLGFX7wbdEEjqCDNe8daCiKkRgQAJ/XH8A3/z1JnR2dfnuJ6UmomKCGzeX5WB6YRo8aXFQNuDpf5lCTKILyOj+/hKNDqCL7axLXd0h7yiw/xpv1ejbH8cPFlTgB7eUMpmy6qoGtWcsiUmm5qdCBhGgebFc8CQm5fIW4o0GjRCsljCq9oNviSR0BBmueetARVWIQB8CXV1duP9/P8TPXt3d59/Ts1MxvcKDm6/u2OpKihlQSCqVFP157qqOcziAAfSmJakrbM5ebsGW45fwxp6z2LyzChdratHlaEDr//2zN6eHX9+C+26aaMn8tASt9owlMamFJpWVTYDmxXKJk5iUy1uINxo0QrBawqjaD74lktARZLjmrQMVVSECPgJt7Z348uMb8Jetx3z/FmGLQI47A3MmFmDJGBcqhqQgxWEPO2rKG9pj55vw/uHzeGXXaWzfWYW62jovh66YFrS80C0mrfj9o57GVHvGkpjUQ5XqyCJA82JZpLv9kJiUy1uINxo0QrBawqjaD74lktARZLjmrQMVVSECXgJXGlsx9/412H3kvI9IZFQkPEXZWFCZj0WjXShzJyE+JirsiHV0duHA2Xq8ffAcXvvwFHb/rQpNDb0bEsUmdqD22W+QmPTrGSQmw26YWCphmhfLbS4Sk3J5C/FGg0YIVksYDVdRFa55W6JTUpCmI3DifD2u/dFKnLnQ4IvNHmPHyNH5uLEyD/OGpeOavETE2Ht3bDVdEoICamnrwN7T9VizvwZvbDuJ/Z+eRGtLq8+bK8WBNStmYnRmNolJHWJSxs6/ym6uPRtCKf5E7BgrqPuRWUEEaF4sCGwAsyQm5fIW4o0GjRCsljAarqIqXPO2RKekIE1FYNeh85h7/2o0NLf54oqNj0XJWDeWlOfiuqIMjMh2Iqrfjq2mSkJQMA2tHfhbVS3e/LQGq7dV4ejBM+ho793ZtnhIGt5++AbY49vhhJPEpA4xKXuzJhKTggaLxczSvFhug5GYlMtbiDcaNEKwWsJouIqqcM3bEp2SgjQNgTe2HsNtP9+Ajo5OX0wJyU5MKHNj2YRczByajoL0ONhCZQcdDeQvN7Zj2/FLeO2Ts3hn63GcPn4OnZ29nBZUePD83TMRbY9EOD5v1HJmXeYqU0xOmQJs3Bg6G0Jp6M5UtB8BmhfL7RIkJuXyFuKNBo0QrJYwqvaDb4kkdAQZrnnrQEVVwpTAz1/fg3v/uKNP9qmZyZhSUYBlY12YWpiOnOTAO7aGKjZlx9aaK63YcuwSXtl9Bu9vP45zpy/0Sfe7N5Xg0a9M8O1mG47PG7WcWcWkzJ1/Q2ln4VAdf7LyonmxLNLdfkhMyuUtxBsNGiFYLWFU7QffEknoCDJc89aBiqqEGYHOzi588zeb8Md3D/gyV86KdOWlYVZlAZaWZGFiQQrSnNFhRkY50qQLJy42YePhi/jz305j687juHzhSh9Ov/7mFNwxZ0QfNuH4vFHLmVVMhl0no4RNQYDmxXKbgcSkXN5CvNGgEYLVEkYD/eB3oQuNaDQkBxm+lbwLUejN7wiOIB7xAXMVdWi4KLuGNBo5DQkCjS3tWPzoOmzae8aXjy3ShvzCLMybmI/Fo7IwwZOMhLjw3LH1cE0j1itHf+w4hV1/O47Gut5npLKc9fXlczGzJOdzfUFNWIVE5+mXhFrOJCZDsdVDJyeaF8ttSxKTcnkL8UaDRghWSxgd6AdfEXNTMRWbsdkSOVg1yImYiLfxNiIQYeoUSPSaunm4BVdd24RZy1fhyJnLPptR0VEoGpGLhZV5WDjShbF5iYiNDr8dW9s6OvHp6QasO1iD17efwN6PT6KlqcXHKSUhFhseW4AReckDtoeasOLWiCYypJbzQGJy6fhsTMpPRfLVc0rjY2xIiovyLRc2UXoUSogToHmx3AYmMSmXtxBvNGiEYLWEUeUA7QQkeGOtRrX3DZ0yCXDBZYn4KUjxBKZgCjZio+lFr3gSoevhsxOXMHP5Klyu7xVIMXExGFOSjyXleZg7LBOjcpywR5r7Dx8iWqi5tQMfnbiClfuqsWr7SRzadwrtbe0+V0NzkrxCMiMpLqB7NWElIm6jbarlfN//7MDPXt3jDVN5+503xIXrK924JifBt6FTapwdozITMMwVj8gw3C3Y6DYMZ/80L5bb+iQm5fIW4o0GjRCsljBag5qwF46Blrn6L4XtEdr9GzVchLfyR4fBlgJborNTkAMSeOejU1j247fR6nekRXyiA2WlHiwtz8WsoekoynTAFoaT+bqmduw8XovXPz2Lt7ZW4cTRanT67Ww7/ZocvH7PdYiLGXzZr5qwCsWuqZbz6p1V3n7Xc3m/y81NQ15+qu/fUhJiMHlYKm4YnolR2fGIjgq/t+Kh2DeskBPNi+W2EolJubyFeKNBIwSrJYyGu5gc7K2b2mRIaWD/MoEE52AClLWOEZ3JXyiTmDSiBcT7fHrdPnznqQ+g7JjZcyWlJWLSBA9uLsvB9MI05KXGhswyQ2W56pWmDrR3+iUcAHNzWyc+OXMZr+w5iw3bjqH65AXvBjw91x3XjcCT35jCJLJZniXiW1uuB5ac//MvH+OHz27vE1ikn2C0x9jhHpKJBRV5WDTShYL0eCbeIjJVzlFNjIuEPdImwjzZNBkBmhfLbRASk3J5C/FGg0YIVksYVb6PPIdz3lh7vo3zFxGyxY5s34N9D8gyGWIpM5CYtMIB5npys0SnpyC9ougHz27Hr978pJdGBJCRnYYZV3dsnTIkFekJ9pARkvUt7fj4ZB2qLjeh1e/tYqDu0Njahnf2XcQH24/h0rnaPsV+cnsF/uXGa5h7UjiOJdac39x2HF/62Xq0B2iTniWwcyvdmJCfZNiZptGRNniSHLgmz4l4lTfRzB2DCpqWAM2L5TYNiUm5vIV4o0EjBKtljbJOAkQkaKRvPaJPT7x66ohgrWbTKnGq5UH3+xJobevArT9dj7U7q3w3bDYbcj2ZmDPRg6XFWSj3JCPJETo7tl5saMP2Yxex8rNzOHS2Dm3tnardoq2tA599ehr1lxt8ZaMibXj+e7OwqNKjWt+/QDiOJS05f3TkPG54cC1q/b7Z9eenLIHNzElDYWE6Igz6bNceZcOw7AQsHJmJiiEpSLm6SZCmjkCFLUOA5sVym4rEpFzeQrzRoBGC1bJGtUwCeCdppG8Sk30JmKktePezcLV3qb4F1923GnuPX/QhiLRHonBYNuZXurFolAtl7iTEhciOrcob2LOXW7D52EW8/NEZfLDzBC5frEMXwzLXzs5OdPh9R+qMs+PtR+ajtDBdc/cJx7GkNWdF4G/bX42Wtm6h/+aOKjy15tM+rO3Rds3seVWIsEUgOS0RUyfk4+ax2ZhUkAJXUkzIvLnnxSlU7NC8WG5LkpiUy1uINxo0QrBa1qjWSQDPRI30TWKSxCTPvmw2W0fPXsG196zEudomX2jRMdEYWZyHGyvyMH94JsbkOkNmk5POri5UnW/Ce0fP4+WdZ7Bj13FcuVinq1ly0uPx/o8XIS898Hm0gxk203NNFwAdlQbKWSuH1TuqcOt/vBtwCayOsIKukpSaiIoJbt83xZ60OBKUQVM1nwGaF8ttExKTcnkL8UaDRghWyxrV+oPPM1EjfZOYJDHJsy+bydbWfdWY/9BaNLX0HmkRFx+H0nFuLCnPxZyidAx3OUPm+IWOzi4cqK7HO4fO47Udp/HRR8fRVN8rorW0zYRhGVj1wDwkOqK1VOtT1kzPNd1JaKzIQ0wqLncfuYB5D64JuARWY1hcijsSHCgtdWPphFzMLkqno0u4UDWXEZoXy20PEpNyeQvxRoNGCFbLGjVy4mOkbxKTJCYtO2gHCfzVzUfxlcc3oNNvaWdCSgIqxnuwbHw2ZhSmw5MeZ9jGJryZK0ec7D1Vj7UHavD6jpPY98lJtDa3+tykOGNw763jkJkc+FzInsLKm8jK4ZlBv3ky03ONN+9A9niJScW+8keQ9z4+jQa/P4bIykPxU32pEY+8+BEuN/SewxodG43RxXlYXJlPR5fIbAxJvmheLAn0VTckJuXyFuKNBo0QrJY1auTEx0jfJCZJTFp20AYI/HJDK9x3/C9a/TacSXOlYEqFB7eMzcaUwlRkhdB3X02tHdhVdRkrP6vGqm0ncOTgaXS0dfjojPak4p1H5kMRlDIvMz3XZOXNU0zKinkwPxfqmjFr+SocONm7s2+UPQqFI3J8R5eUhtD3xmZgbmQMNC+WS5/EpFzeQrzRoBGC1bJGjZz4GOmbxCSJScsOWo1icnK5B7eUdovJ7BARk5eb2rHjWC1e++Qs3t52HKeO1UDZRKfnun68Gy99fxai7fIPvjfTc01WH/fPueeIKeXfXHB5Q1ga684AACAASURBVBjo2Kl46PsmVVZOza3tWLbiHazffcrnsvvokkzMrfBgSXEWJniSkRgXOjshy2JrNj80L5bbIiQm5fIW4o0GjRCsljVq5MTHSN8kJklMWnbQDhL461uO4cs/Xw/lO8KeKyHZiYoJHiwry8EkTypyU2KDXsppGLsu4EpzG3ZW1eLl3Wfx/vZjqDl9AehNF/+8cAx+ekelYTma6bkmq538c2b1qZx7bPZLWS5+1zNb8Ls1n/lC7Tm6ZEalB8tKsjBROaPVGTpntJq9TUTER/NiEVQD2yQxKZe3EG80aIRgtaxRIyc+RvomMUli0rKDViXwnQfP4foH1qCxuc1XMi4+FmPHubFgXDZGpicg0mbd7KvrmvHG7mps/bAKtecv+03ygV98fQrunDfS0OTM9FyTBSJUxWQPv1+v2ovvPbO1D86UjCQob/2XlWZjWmGatf9II6ujmNQPzYvlNgyJSbm8hXijQSMEq2WNGjnxMdI3iUkSk5YdtAyBH6+pw4x7VuLsxUZfaXuM3fvNV0FOEqIiDToNniF2tSKX6lqw+6MqNFzxyy3KhlfuuQ7XleapVRd+30zPNeHJXnUQistc+7Nb++EJfOHf34FyRmbP5UyKx/gyZXOrHMwYmoGhGXGw2aw7tmT1F7P5oXmx3BYhMSmXtxBvNGiEYLWsUSMnPkb6JjFJYtKyg5Yx8NqGFsy9fw0+PnqhT42YuBhERsn/lpAxbNVi7a3taG3p3bE1yRmD9Y/Ox2h3qmpdGQXM9FyTka/iI9Q24AnE7eNjFzH73lWoa+ztf7GOGBSXKMfu5GHusAyMzHZa+o81svqMmfzQvFhua5CYlMtbiDcaNEKwWtaokRMfI32TmCQxadlBqyHw1rYOfOnnG7Bq+3ENtaxTtCArEe/9eCFcDEd/yMrKTM81JWfl28RG9L7FFcFhoM121DbgERGHmk0HHIhAcG8OT19swIx7VuFETZ3PnT3ajmGjcnBjpRvzR2TgmrxExBqw+ZNa/nR/YAI0L5bbM0hMyuUtxBsNGiFYLWvUyImPkb5JTJKYtOyg1Rh4V1cX7v3TTryy5Shqm9px+UqTRgvmLD55dBbevO96OGLE7qapVYwFElGDCRmtPlhbRLE7B3OwDdtYq4R0uSmYgo3YGLSgrG9qw4KH38L2/dU+XpGRkXAPdeGGiW4sHp2FMk8SnIL7Zkg3lsTkaF4sETYAEpNyeQvxRoNGCFbLGjVS0Bnpm8QkiUnLDlqdgbe0deCP6w9g38neTWt0mjK82uSRmbhpYgEiBe8kpIixqZiKzdgcdM6BhAxPH0EHGQYG6lEPHseSdHR04h9/tREv/vWQj5qy02tWfgZmT3RjaXEWKgtSkeq0hwFVa6dI82K57UdiUi5vId5o0AjBalmjRgo6I32TmCQxadlBS4FLI6Bnl9LBghtIyPD2IQ2On6MjOIJCFHr/ZaAzJY2Iyd+n/9tiXmJSsa+89V/x8m488sKHfVJMc6VgWoUHN5dmY/KQVGSFyPmuRrejKP80LxZFdmC7JCbl8hbijQaNEKyWNWqkoDPSN4lJEpOWHbQUuDQCA+1SquZcEStZyPIWU0SWcg0mtPyFjlKex1uznhgV2z2+Rdvu8cNTrKmxZr0v+rfmz5uO4PZfvAflXMqeKyElARXjlZ1eszGzMB3u9DjYIoL7XpM1XyqnjQDNi7XxCrY0iclgCZqgPg0aEzSCiUIQ/SM7WKpG+iYxSWLSRMOQQjEpAT3PqBrUwAWXSTMSH1Y4ikmF6tZ91Zj/0Fo0tbT7IMfFx6F0XD6WTMjFnGEZGO5yIpKODhHfCTV6oHmxRmBBFicxGSRAM1SnQWOGVjBPDHomS7yiN9I3iUkSk7z6MdkJXQJ6nlHhLCZ5bXDDu0fpaUc9MRw9ewUzlq9CzaXe3XOjY6IxsjgPN1bkYf7wTIzOcSKGdnrVg1dYHZoXC0M7oGESk3J5C/FGg0YIVssalfUjOxAgI32TmCQxadlBS4FLI6DnGaVsqHMO57wxKju4Ksdy9LypHOh7QpFHaMi2zePoDRGNq6cd9cZxqb4F192/GnuPXfSZUM51LRyejRsq3bhxlAvj3ElwRFv3rFe9bMxaj+bFcluGxKRc3kK80aARgtWyRmX+yOoRcLLAsnBgKWPmHAdjqSc3WW1DfoiAUQR4jAs1G2r3g8ndqraDydkMf7hUzne99afrsXZnlS8cm82GHE8mrpvowZLiLFR4kpHkEHusDW+OoWqP5sVyW5bEpFzeQrzRoBGC1bJGRU421KAY6VuP6NMTr546atxE3LdKnCJyJ5tEIBABHuNCzYba/WBax6q2g8nZDGJSiUHZ6fWHz+3AL//ycW9IEUBGdhpmVBZgaUmWd6fXjAQ7lCNF6DKOAM2L5bInMSmXtxBvNGiEYLWsUZGTDTUoRvomMdmXgJnaQq3f0H0iIIsAj3GhZkPtfjC5WtV2MDmbRUz2xPH0un34zlMfoKt3o1ckpydi4ngPbi7LwfTCNOSlxpKg5N3oGuzRvFgDLA5FSUxygGi0CRo0RreAufyLnGyoZWqkbxKTJCbV+ifdJwI8nlFqNtTuB9MKVrUdTM5mE5NKPO/uPoWlj72N1vYOX3jxiQ6UlXqwtDwXs4amY2img3Z65d3wjPZoXswIilMxEpOcQBpphgaNkfTN51vkZEMtWyN9k5gkManWP+k+EeDxjFKzoXY/mFawqu1gcjajmFRi+uzEJcxavgq19S2+EGPiYjCmJB83lefh+mGZGJXjhD2Slrzybn81ezQvViPE9z6JSb48DbFGg8YQ7KZ1KnKyoZa0kb5JTJKYVOufdJ8I8HhGqdlQux9MK1jVdjA5m1VMKnHV1DZh5vJVOHLmsi/MqOgoFI3IxcLKPCwc4cLY/ETE0k6vvLvAoPZoXiwVN0hMyuUtxBsNGiFYLWtU5GRDDYqRvklMkphU6590nwjweEap2VC7H0wrWNV2MDmbWUwqsTW2tGPxo+uwae8ZX6i2SBvyC7Mwb2I+Fo/OwgR3MhLiaKdX3v0gkD2aF8si3e2HxKRc3kK80aARgtWyRkVONtSgGOmbxCSJSbX+SfeJQD3qkYAEL4iBzohkIaQ853rOmVTsxSO+TzWRz0Gr2mbhqqWMSA5a4ugp29nZhW/9ZhOee/eAr7qyo6srLw0zKwuwrCQLEwtSkOaM1mOe6mgkQPNijcCCLE5iMkiAZqhOg8YMrWCeGIz8kTXSN4lJEpPmGYUUiVkJ1KDGJwR5xEhikgdF7TbM9FvjH/3PX9+De/+4o09CqZnJmFJegGWlLkwtTEdOcgzt9Kq9yTXVoHmxJlxBFyYxGTRC4w3QoDG+DcwUgZE/skb6JjFJYtJM45BiYScg87nBU0xOwRRsxEZEoO8GKyLzsapt9t7AVlIkB7YIApd6Y+sx3PbzDejo6PQVciY7UV7mxrIJuZg5NB0F6XGw0VmUwaIOWJ/mxcLQDmiYxKRc3kK80aARgtWyRo38keWxhIwXeP+laIGWs7GUGUikDrbEjVf8wdoxsh8EGzvVDy8CMvtqF7pwDue8gB1wfE4IaiEfqL7IfKxqWwtXlrIiObD4Vyuz69B5zL1/NRqa23xFY+NjUVLixpKKXFxXlIER2U5E2WinVzWWeu7TvFgPNf11SEzqZ2eamjRoTNMUpgjEyB9Znn/1NwVMlSAGWuJmlriN7AdmYUBxWINAqPVVkflY1TbvniiSA69YT5yvx7U/WokzFxp8Ju0xdowYnYfFlfmYNywd1+QlIsYeycsl2blKgObFcrsCiUm5vIV4o0EjBKtljRr5IxtOYjLQEjezdBwj+4FZGFAc1iAQan1VZD5Wtc27J4rkwDPWK42tmPfAWvztcPfbcOWKjIqEpygbCyrzsWi0C2XuJMTH0E6vPLnTvJgnTXVbJCbVGZm+BA0a0zcRlKVVjWhUDTTYZVeKAyN/ZHkuIVOFpVKAZQkrS5lAbni0VbA5DlbfyH4gMi+ybX0CSt/0v9TGYf/dUs1OQOTYs6pt3m0mkgPvWNvaO/H3T2zA61uO+UxH2CKQ487A7EoPlhZnoWJIKlIcJCh5sad5MS+SbHZITLJxMnUpGjSmbh6vkJyKqdiMzaqB8njbxfojyypwVYPWUECmAGPhwFJGQ3qmKhrKuZkKNAWjmUD/DWvUDCjPKitdIseeVW3zbj+RHHjHqtjr6urCA8/vwk9f+aiP+fTsVEyv8ODmsdmYVJACVxLt9MqDP82LeVBkt0Fikp2VaUvSoDFt03gD8//RY4k02O/wWH5ktQhclphZy/AQy6y+WDiwlGH1Z7ZyoZyb2VhTPNoIkJjUxsu/tMhxLdK2/owHrmmlWP0z+NOGA/inJzehs6v3DyRJqYmomODGzWU5mF6YBk9aHB0dEmSHoXlxkAA1VicxqRGYGYvToDFjq/TG5P+jx7KrqAwxqVXg8iQcbH6ssbBMNljKsPozW7lQzs1srEM1HlGrFwZa5lqIQi/GIziC/staZS1z5bVyQuTYs6pt3mNEJAfesfa3t3HvGSx65C20tHb4bjkSHCgtdWPphFzMLkrHMFc8ImmnV91NQfNi3eh0VSQxqQubuSrRoDFXe/SPhuVHj6UMa5YstlgELqs/lnL+30SRmGQhFnwZln4QvBeyEKoEjFq9YCRPXisnRI49q9rm3a4iOfCOdSB7h05fxox7VuLClWbf7ejYaIwuzsfiyjzcMDwTo7LjER1FO73qaQ+aF+uhpr8OiUn97ExTkwaNaZpiwEBYfvRYyrBmyWKLpQyrP5Zysv0pMbH4ZCnDkp8Zy4RybmbkHWoxGbl6wUiWPP7YJXLsWdU27zYVyYF3rIHsXahrxqzlq3DgZK2vSJQ9CoXDr+70OtKFUncS4qJJUGptE5oXayUWXHkSk8HxM0VtGjSmaIaAQbD86LGUYc2SxRZLGVZ/LOVk+yMxySamWdqOyoQnAZmrF/xXLgT6FEBkK/BeOSHyeWdV27zbTyQH3rEOZq+5tR3LfvIO1n90ylfMFmlD3pBMzK3wYElxFiZ4kpEYRzu9amkXmhdroRV8WRKTwTM03AINGsObYNAAWH70WMqwZslii6UMqz+WcrL9kZgkMcnSL6lMYAIyx6xMXwNlzNs/b3v+MVvVNu+xJpID71jV7HV2duGuZ7bgd2s+8xWNiIhAZk4aZlR6sKwkGxOHpCDdaaeNedRgXr1P82JGUJyKkZjkBNJIMzRojKSv7pvlR4+ljLqn7hIstljKsPpjKSfbn1k5sLDiVcYI5rxiJzvGEwin/sM7V972SEx+fjyIZGzU6Pv1qr34t//eCr+NXpGSkYTJ5QVYVpqFaYVpyE2JJUHJ0EA0L2aAxLEIiUmOMI0yRYPGKPJsfll+9FjKsHkjMdnDiYUpSxlW7mYrF8q5mY11KMYTTv2Hd6687ZGYDA8xqWS59sMTuOXf30F7e6cvaWdSPMaXebCsLAczijIwNCMONtrpddDHLs2L5f4qkZiUy1uINxo0QrByM8oysWApwxoQiy2WMqz+WMrJ9qfExOKTpQxLfmYsE8q5mZF3qMUUTv2Hd6687ZGYDB8xqWS65+gFzLlvNeoaW32JxzpiUFzixpKKPMwtysDIbCeiIiNC7bHDLR+aF3NDyWSIxCQTJnMXokFj7vZhmViwlGHNksUWSxlWfyzlZPsjMckmplnajsqEJwEjxqxRpHnnytseicnwEpNKtqcvNmDGPatwoqbOl7w92o5ho3JwY6Ub80dk4Jq8RMTaaafXgZ4bNC+W+zQlMSmXtxBvNGiEYOVmlGViwVKGNSAWWyxlWP2xlJPtj8QkiUmWfkllAhMwYswa1R68c+Vtj8Rk+IlJJeP6pjYsePgtbN9f7QMQGRkJ91AXbpjoxuLRWSjzJMEZQzu99u8hNC+W+zQlMSmXtxBvNGiEYOVmlGViwVKGNSAWWyxlWP2xlJPtj0VMKoeyn8M5uODypjDQkQQOOBABay4lMoI5S1+gMtYgEE79h3euvO2RmAxPMalk3dHRiX/81Ua8+NdDPgjKTq9Z7gzMqXRjaXEWKoakIjXebo0Hi6QoaV4sCfRVNyQm5fIW4o0GjRCs3IyyTCxYyrAGpBy6nYCEgAKpR2gNJqJYfbGWU/KT6a9/jv0PIleE5FRMxWZsHjSFiZiIt/G2JQWlP3MeB7GztjWVCw0CPJ9JZifCO1fe9khMhq+YVDLv6urCipd345EXPuwDIs2VgmkVHtxcmo3JQ1KRlRRDO71eJUTzYrlPXRKTcnkL8UaDRghWbkZZJhYsZVgDqkGNT7ix1gn1cv3FlD/vUM9dyY/EZDi0Mt8ceT6T+EbG3xrvXHnbIzEZ3mKyJ/uXNh3GHb94H8q5lD1XYkoCysd7sGx8NmYUpsOTHgdbhDVX0/Ac2TQv5klT3RaJSXVGpi9Bg8bcTcQysWApw5olicm+pKZgCjZiY5+3i+EkJgfKn7UvUbnwJcDzmWR2irxz5W2PxCSJyR4C2/bX4IYH16Cppd0HJS4+DqXj8rFkQi7mDMvAcJcTkWF+dAjNi+U+dUlMyuUtxBsNGiFYuRllmViwlGENqOdbQKV8oG/+ZC87le3Pn9VADPx5H8ERxCO+Dysj42VtZ9ZyVv7ukzVHKsefAM9nEv/o+FrknStveyQmSUz6EzhWXYdr71mJmkuNvn+OjonGyOI83FiRh/nDMzE6x4mYMN7plebFfJ+RatZITKoRssB9GjTmbiSWiQVLGZ5Zhro/NVZq+avdV7NP94mA1QmE0xjgnStveyQmSUz2J3CpvgVz71+DT45d8N2KjIpE4fBs3FDpxo2jXBjnToIjOjyPDqF5sdxfIBKTcnkL8UaDRghWbkZZJhYsZbgFBPnHRsjOT42VWjxq99Xs030iYHUC4TQGeOfK254eMamsUGlE75srlv6oZUWG0SseRDJmYWWGMq1tHfjiT9djzc4qXzg2mw25nkzMmejBEmWnV08ykhzhd3QIzYvl9lASk3J5C/FGg0YIVm5GWX70WMpwC4jEJNR4q93n2RZkiwiYkUA4jQHeufK2p1VMsu5WHUy/M/pbbJGMg+Eiu66y0+sPn9uBX/7l417XEUBGdhpmVBZgaUmWd6fXjAR7WO30SvNiuT2RxKRc3kK80aARgpWbUZYfPZYy3AIiMUlikmdnIlshSUD2M8lIiLxz5W1Pq5j09y+Sq5G7RItkLJKZKNvPrNuHbz/1Abp6N3pFUloiJk3w4OayHEwvTENeamzYCEqaF4vqaQPbJTEpl7cQbzRohGDlZpTlR4+lDLeASEySmOTZmchWSBJgOa82VBL3X97JQyCJfJ6z2PYvU41q7wZjLBfLMlferFjiGqgMCwe9tq1a793dp7D0sbfR2t7hSyE+wYGycR4sLc/FrKHpGJrpCIudXmleLLcXk5iUy1uINxo0QrByM8ryo8dShltAJCZJTPLsTGQrJAmE6xFDoSYmteTD8jvEUkbGgDBLHDJy1eLjsxOXMGv5KtTWt/iqxcTFYExJPm4qz8P1wzIxKscJe2Ron0VJ82ItvSb4siQmg2douAUaNIY3waABsPzosZThmWWo+1NjpZa/2n01+3SfCFidQDiKSV7fAYp8frDYZimj922fXtu8x4NZ4uCdFw97NbVNmLl8FY6cuewzFxUdhaIRuVhYmYeFI10Ym5eI2BDe6ZXmxTx6ErsNEpPsrExbkgaNaZvGGxjLj57eZUl6M2dZzqTXdqBJigsu762eZVdG7gao1iZq93myIVtEwIwEWM6rNWPcwcTE65kk8vnBYpulDInJYHqK+es2trRj8aPrsGnvGV+wtkgb8guzMG9iPhaPykK5JxnOuNDc6ZXmxXL7KIlJubyFeKNBIwQrN6MsP+z+Zbg5NrkhXm8B9KSp1iZq9/X4pDpEgAiEBwGRzw8W2yxlSEyGfl/s7OzCt36zCc+9e8CXbEREBFx5aZhZWYBlJVmYWJCCNGd0yMGgebHcJiUxKZe3EG80aIRg5WaUZSML/zeF3BxbwJCW73l4pqM22VK7zzMWskUEiEBoERD5/GCxzVKGxGRo9bnBsvn563tw7x939CmSmpmMKRUFWDbWhalD0pGTEhNSO73SvFhu/yYxKZe3EG80aIRg5WZUz7dHWnbg0xOokctcj+AIClHoDZvEpJ7WozpEgAiYmYBeMceSE4ttljIkJlloh06ZN7Yew20/34COjk5fUgnJTkwoc2PZhFzMHJqOgvQ42CJCY2MemhfL7bskJuXyFuKNBo0QrNyM6hGTokWW3smGXij9vwnt+X5SdJ6B4lXLX+2+Xg5UjwgQgdAnIPL5wWKbpQyJydDvh/0z3HXoPObevxoNzW2+W7HxsSgZm4+l5XmYU5SBEdlORNmsLyhpXiy3f5OYlMtbiDcaNEKwcjPKupGF/9tC0SJL72RDLxQSk3rJUT0iQASsRkDk85XFNksZEpNW61V84j1xvh7X/mglzlxo8Bm0x9gxYnQeFlfmY96wdFyTl4gYeyQfhwZZoXmxXPAkJuXyFuKNBo0QrNKN6p0A6AlUpi8lPhKTelqJ6hABImBFAiKfryy2WcqQmLRiz+IT85XGVlz/wBp8dPi8z2BkVCQ8RdlYUJmPRaNdKHMnIT7Guju90ryYT19htUJikpWUicvRoDFx42gITe8EQIMLX1GZvkhM6mkhqkMEiIBVCYh8vmrd0E3L9/cs39KzlJHRbjJX8sjIR7aPtvZO/P0TG/D6lmM+1xG2COS4MzCn0oMlxVmoKEhBSrxddmhc/NG8mAtGZiMkJplRmbcgDRrzto2WyEROQPrHIdMXiUktvYDKEgEiYHUCIp+ver7BtzpPtfhFfxai5t+q97u6uvDg87vwH6981CeF9OxUTK/w4Oax2ZhUkAJXkvV2eqV5sdxeSWJSLm8h3mjQCMEq3ajICQiJyb4E1Fir3ZfeOcghESACliEg8vlBYrJvNzDyvGLLdEiVQP+04QD+6clN6Ozq8pVMSk1ExQQ3bi7LwfTCNHjS4ix1dAjNi+X2ThKTcnkL8UaDRghW6UZFTkBITJKYlN6hySERCFMCIp/lLBu66V2KylKPpYzMZnfAgQhYf/dRmcwG8rVx7xkseuQttLR2+G47EhwoLXVj6YRczC5KxzBXPCItstMrzYvl9igSk3J5C/FGg0YIVulGWb6F4RWUyAmBMtlpRGOfUBV/PWdLfoJPUIxi730t3/Pwyl2xo5Y/fY/DkzbZIgLhRUCkmGQhqdc/Sz2WMiwxUhnzETh0+jJm3LMSF640+4KLjo3G6OJ8LK7Mww3DMzEqOx7RUebf6ZXmxXL7F4lJubyFeKNBIwSrdKO0fEk6ciaH9D0OEyYqRASIwFUCRgsuvf5Z6rGUoY5gXQIX6poxa/kqHDhZ60siyh6FwuFXd3od6UKpOwlx0eYWlDQvltsHSUzK5S3EGw0aIVilGyUxKR05k0MSk0yYqBARIAJXCchcZTIQdLWVF4EaimVFBonJ0O/mza3tWLbiHazffcqXrC3ShrwhmZhb4cFNY7JQXpCMxDjzHh1C82K5/ZTEpFzeQrzRoBGCVbpRlm9heAWlCNeeZae8bLLaOYIjiEc8jPzWZbDJlv89JScSk6wtS+WIABFQCITCHwYDPfdITIZHH+/s7MK/Pr0Fv1/7mS/hiIgIZOakYUalB8tKsjFpSArSnHZTbsxD82K5/ZTEpFzeQrzRoBGCNaSNGjXZMcvOe4NNiPzvkZgM6WFAyREBIQSMer7ySmaw5zSJSV6UrWHnyVV78W/PbO0TbEpGEiaXe7CsNBvTCtOQmxJrOkFJ82K5/YvEpFzeQrzRoBGCNaSNinoLqra8ysi3kf4NSmIypLs3JUcEDCUg6vnKmpTyVjELWd7iPStBWOsq5QZ7TrMshdXii8qan8CanVX4wr+/i/aOTl+wzqR4jC/zYNn4HMwozMDQzDjYTLTTK82L5fYrEpNyeQvxRoNGCFYyqoOAVf5qTWJSR+NSFSJABCxBQNabUfoEwBLdgUuQe45ewJz7VqOusdVnL9YRg+ISN5ZU5GFuUQZGZjsRFWmOY1poXsyl2ZmNkJhkRmXegjRozNs24RYZiclwa3HKlwgQAbMRkCEmzfLJgtnYh3I8py82YOY9q1BVU+dL0x5tx7BRObix0o35IzJwTV4iYu3G7/RK82K5PZHEpFzeQrzRoBGClYzqIEBiUgc0qkIEiAAR4EhAxjJbs3yywBEbmWIgUN/UhvkPr8WO/TW+0pGRkXAPdeGGSR4sHuVCmTsJzlhjd3qleTFDY3IsQmKSI0yjTNGgMYo8+e1PwCpicrCt+2k3V+rXRIAIEAEiQAQGJqB8O/m1X/0VL/71sK+AstNrljsDsyvdWFachYohqUiNtxuGkObFctGTmAzAu7m5GStWrMALL7yAqqoqpKamYt68eXj44YeRl5fH1Eq1tbVYvXo1Vq5ciY8++gjHjx+HzWbD6NGj8aUvfQnf+ta3YLcHP9ho0DA1BxWSQMAqYlLLMjD6LkhCxyEXRIAIEAEiYBkCXV1d+PGfP8Kj/7erT8xprhRMq/Dg5tJsTB6SiqykGEN2eqV5sdyuRGJyAN6KkJw9ezY2b96M7OxsTJs2DceOHcP27duRkZGBLVu2YOjQoaotde+99+Kxxx7zCshx48ahqKgI586dwwcffICWlhZMnToVb731FhwOh6qtwQrQoAkKH1XmSCDUxORETMRmbEYEzLGpAMemIlNEgAgQASJABIIi8NKmw7jjF+9DOZey50pISUDFeGWn12zMKEyHJz0Otgi5v6E0Lw6qWTVXJjE5ALL7778fjzzyCCZNmoR169bB6XR6Sz3++OO4++67MX36dLz//vuqsH/yk5+grq7O+wYyNzfXV/7gwYOYM2eO943nj370I/z4xz9WtUViMihEVFkSAauIycG+KfJf5lqHOjjRm9aRvAAAIABJREFUPf7pIgJEgAgQASJABPoS2La/Bjc8uAZNLe2+G3HOOJSW5mPJhFzMKcrA8CwnIiUeHUJiUm4vJTHZj3dbWxsyMzOhLFHdtWuX942i/zV27Fjs2bMHO3fuxPjx43W3lrJ8VlnqWlBQgKNHj+q2o1SkQRMUPqrMkYBVxORgKYdCDhyblEwRASJABIgAERiUwNGzVzBj+SrUXGr0lYuOicbI4jzcWJGHG4ZnojjXiegoOTu90rxYboclMdmP94YNGzBr1izvMtZDhw59rjWUN5bKm8sHHngADz74oO7W+vTTT70iMDo62rvkNZiLBk0w9KguTwKhIMRCIQeebUq2iAARIAJEgAioEbhU34Lr7luNvccv+opGRkWicHg25le6sWiUC+PcSXBEixeUNC9Way2+90lM9uP5i1/8AnfddRduueUWvPTSS5+jvWrVKixcuBA33XQTXnvtNd2toWzKs2jRIrjdbu/GPMFcNGiCoUd1eRIIBSEWCjnwbFOyRQSIABEgAkSAhUBrWwdu/el6rN1Z5Suu7BuS68nEnIkeLC3OQrknGUkOsUeH0LyYpbX4lSEx2Y/ld7/7XTzxxBNeQal8I9n/2r17N0pLS1FWVoYPP/xQd0tcd911eOedd/Dtb38bv/zlL3XbUSrSoAkKH1XmSCAUhFgo5MCxSckUESACRIAIEAFmAspOrz94djt+9eYnvXUigIzsNMyoLMDSkixMGZKK9AS7sJ1eaV7M3FxcCpKY7IfxzjvvxO9//3ssX74cjz766OcgK0tfhw0bhuHDh2P//v26GuG3v/0tvvnNbyI5ORl79+5FTk4Ok52ewdG/8OHDh73LchVbdBEBIwmEghALhRyM7APkmwgQASJABIjA0+v24TtPfYCu3o1ekZSWiEkTPLi5LAfTCtOQnxorRFCSmJTb/0hM9uP99a9/HU8//TSUYz2U7yP7X8pOrIqQ1CsmlV1g586dC2Wjn1deeQVLlixhbnESk8yoqKBBBEJBiIVCDgY1P7klAkSACBABIuAj8O7uU1j62Ntobe/w/Vt8ogNlpR4sLc/FrKHpGJrp4L7TK4lJuZ2QxGQ/3iKXuSq7wF577bXenWKVpa3KElceFw0aHhTJBg8CoSDEQiEHHm1JNogAESACRIAIBEvgsxOXMGv5KtTW9242GRMXgzEl+bipPA9zh2VidI4T9kh+Z1HSvDjYVtNWn8RkP16iNuBRlqJOnToVZ8+e9e4Cq+wGy+uiQcOLJNkJlkAoCLFQyCHYdqT6RIAIEAEiQAR4EaipbcLM5atw5Mxln8mo6CgUjcjFwso8LBzpwti8RMRy2umV5sW8Wo7NDonJfpxYjwZRjgd56KGHmCifPn3aKySV8yT/5V/+BYpg5XnRoOFJk2wFQyAUhJiMHGT4CKYdqS4RIAJEgAgQAZ4EmlraceOj67Bp7xmfWVukDflDsnD9pHzcNKp7p1dnXPA7vdK8mGfLqdsiMdmPUWtrKzIzM3H58mXs2rUL48aN61Ni7NixUJarbt++HeXl5aqEL126hOnTp+OTTz7BHXfcgWeeeYb7x8Y0aFSbgQpIIlCPeiQgweutGtWIR7wkz/zcKELPBZfXoJKPiBxITPJrL7JEBIgAESAC1iDQ2dmFb/5mE/747gFfwBEREXDlpWHW1Z1eJxakIM0ZHVRCNC8OCp/myiQmB0CmbL7z2GOPYfLkyVi3bh3i47snxMpRIXfffbf3LePGjRt9NZ988kko/1M201mxYoXv3xsbGzF79mxs3boVX/jCF/D8888jMpL/Ya00aDT3e6ogiEANanxCTJALqWZJTErFTc6IABEgAkQgDAj87LU9uO9PO/pkmpqZjCkVBVg21oWpQ9KRkxKj++ULzYvldiISkwPwbm5uxowZM7Bt2zZkZ2dj2rRpOH78uPe/09LSvOKwqKjIV1P5BlJZ8vrVr34Vzz77rO/flbMqlSWtioC89dZbYbfbB2xd/zp6mp8GjR5qVEcEgVASk1MwBRuxERHgtylAD3N6Mymi95FNIkAEiAARsAqB17ccw5cf34COjk5fyM5kJ8rL3Fg2IRczh6ajID0Otgjtv8E0L5bbC0hMBuDd1NTkfcuovE08ceIEUlJSMG/ePO9xIfn5+X1qBRKTt99+O5577jnVFlUOeA3mokETDD2qy5NAF7pwDue8Jh1wCBFiPOMdzBbP+BXx6H/5L6UdaDmwiKW1sriRHyJABIgAESACLAR2HTqPufevRkNzm694bHwsSsa6saQ8F9cVZWBEthNRNm2CkubFLPT5lSExyY+lYZZo0BiGnhwTASYCWt9uKqKcLiJABIgAESACoU7gxPl6XPujlThzofePrvYYO0aOzseNlXmYNywd1+QlIsbO/pkYzYvl9hoSk3J5C/FGg0YIVjJKBLgRIDHJDSUZIgJEgAgQgRAjcKWxFfMeWIu/He5e2aRckVGR8BRlY0FlPhaNdqHMnYT4GLadXmleLLeDkJiUy1uINxo0QrCSUSLAjQAtc+WGkgwRASJABIhACBJoa+/EV57YgDe2HPNlF2GLQI47A7MrPVhanIWKISlIcQy8/4g/EpoXy+0gJCbl8hbijQaNEKxklAgII0Ab8AhDS4aJABEgAkTAogSUPUQeeP5D/PSV3X0ySM9OxfQKD24em41JBSlwJQ2+0yvNi+V2ABKTcnkL8UaDRghWMkoEhBEgMSkMLRnWSID6okZgVJwIEAHhBP604QD+6clN6PTboDIpNREVE9y4uSwH0wvT4EmLC3h0CM2LhTdRHwckJuXyFuKNBo0QrGSUCAgjQBN4YWjJsEYC1Bc1AqPiRIAISCGwce8ZLHrkLbS0dvj8ORIcKC11Y+mEXMwuSscwVzwiB9jplebFUprI54TEpFzeQrzRoBGClYwSAWEEaAIvDC0Z1kiA+qJGYFScCBABaQQOnb6MGfesxIUrzT6f0bHRGF2ch8WV+bhheCZGZccjOqrvTq80L5bWRF5HJCbl8hbijQaNEKxklAgQASIQ8gRITIZ8E1OCRMDSBC7UNWPW8lU4cLLWl0eUPQqFI3KwoCIPi0a5UJqfhLjoXkFJ82K5TU5iUi5vId5o0AjBSkaJABEgAiFHgHYWDrkmpYSIQMgTaG5tx7IV72D97lO+XG2RNuQNycTcCg+WFGdhgicZiXHdR4fQvFhulyAxKZe3EG80aIRgJaNEgAgQgZAjQGeehlyTUkJEICwIdHZ24a5ntuB3az7z5RsREYHMnDTMqPTgC6U5mF+cSWLSgN5AYtIA6LxdkpjkTZTsEQEiQARCkwCJydBsV8qKCIQLgV+v2ovvPbO1T7opGUmYOWkI/vf28SQmDegIJCYNgP7/27sTILuKMlDAHQiKBhUJEJDEUgjIU8uwBAUkiOylIJuoT5Ed3JAtYqkgO4IUslMu7EZWRaAUhYAihUKIgIIEQUCWGCBIWIotLJJX/3k1VDJJhpk7pztzzv1OVUpDzv379Pd3z/R/z1Z3k4rJukXFI0CAQDsFXObazrzqFYFuErj6tulpxx9el1577fU3ur3CqKXTgz/eQTG5CAaCYnIRoNfdpGKyblHxCBAg0B0CHsDTHXnWSwJtE7jzwVlp0+//Lj334itV1w79v2un7+64hmJyESRaMbkI0OtuUjFZt6h4BAgQ6A4BxWR35FkvCbRR4NGnXkif/N5Vab3/Myqdu++GVRfjPkrr4rLZVkyW9c7SmkmThVVQAgQItF5AMdn6FOsggVYLvDD71bTkEounxRdfLM2ZM6fq64c//OHqf6dNm9bqvg+Vzikmh0omBnEcislB4PkoAQIEulhAMdnFydd1Ai0VsC4um1jFZFnvLK2ZNFlYBSVAgAABAgQIEGiYgHVx2YQpJst6Z2nNpMnCKigBAgQIECBAgEDDBKyLyyZMMVnWO0trJk0WVkEJECBAgAABAgQaJmBdXDZhismy3llaM2mysApKgAABAgQIECDQMAHr4rIJU0yW9c7SmkmThVVQAgQIECBAgACBhglYF5dNmGKyrHeW1kyaLKyCEiBAgAABAgQINEzAurhswhSTZb2ztGbSZGEVlAABAgQIECBAoGEC1sVlE6aYLOudpTWTJguroAQIECBAgAABAg0TsC4umzDFZFnvLK2ZNFlYBSVAgAABAgQIEGiYgHVx2YQpJst6Z2nNpMnCmi3oC+mFtFRaqor/fHo+jUgjsrUlMAECBAgQIECgmwSsi8tmWzFZ1jtLayZNFtZsQRWT2WgFJkCAAAECBLpcwLq47ABQTJb1ztKaSZOFNVtQxWQ2WoEJECBAgACBLhewLi47ABSTZb2ztGbSZGHNFlQxmY1WYAIECBAgQKDLBayLyw4AxWRZ7yytmTRZWGsLGsXj3Fv8fVQaVf2nmWnmfPdMuoeyNnqBCBAgQIAAgS4TsC4um3DFZFnvLK2ZNFlYaws6LA0bUKw5ac6A9rczAQIECBAgQIDA/xewLi47EhSTZb2ztGbSZGGtLahisjZKgQgQIECAAAECfQpYF5cdIIrJst5ZWjNpsrDWFtRlrrVRCkSAAAECBAgQUEwOoTGgmBxCyej0UBSTncotms95AM+icdcqAQIECBAg0H4B6+KyOVZMlvXO0ppJk4U1W1DFZDZagQkQIECAAIEuF7AuLjsAFJNlvbO0ZtJkYc0WVDGZjVZgAgQIECBAoMsFrIvLDgDFZFnvLK2ZNFlYswVVTGajFZgAAQIECBDocgHr4rIDQDFZ1jtLayZNFlZBCRAgQIAAAQIEGiZgXVw2YYrJst5ZWjNpsrAKSoAAAQIECBAg0DAB6+KyCVNMlvXO0ppJk4VVUAIECBAgQIAAgYYJWBeXTZhisqx3ltZMmiysghIgQIAAAQIECDRMwLq4bMIUk2W9s7Rm0mRhFbQfAh4m1A8kuxAgQIAAAQLFBKyLi1FXDSkmy3pnac2kycIqaD8EFJP9QLILAQIECBAgUEzAurgYtWKyLHW+1kyafLYi9y2gmDRCCBAgQIAAgaEkYF1cNhvOTJb1ztKaSZOFVdB+CCgm+4FkFwIECBAgQKCYgHVxMWpnJstS52vNpMlnK/K8AlE8zr3F30elUdV/mplmphFpxDz/3vvvPAkQIECAAAECOQWsi3Pqzh/bmcmy3llaM2mysAq6AIFhadiAXOakOQPa384ECBAgQIAAgcEIWBcPRm/gn1VMDtxsyH3CpBlyKWntASkmW5taHSNAgAABAq0QsC4um0bFZFnvLK2ZNFlYBV2AgMtcDQsCBAgQIEBgKAtYF5fNjmKyrHeW1kyaLKyC9kPAA3j6gWQXAgQIECBAoJiAdXEx6qohxWRZ7yytmTRZWAXth4Bish9IdiFAgAABAgSKCVgXF6NWTJalzteaSZPPVuS+BRSTRggBAgQIECAwlASsi8tmw5nJst5ZWjNpsrAK2g8BxWQ/kOxCgAABAgQIFBOwLi5G7cxkWep8rZk0+WxFJkCAAAECBAgQaI6AdXHZXDkzWdY7S2smTRZWQQkQIECAAAECBBomYF1cNmGKybLeWVozabKwCtpyAZfotjzBukeAAAECXSlgXVw27YrJst5ZWjNpsrAK2nIBxWTLE6x7BAgQINCVAtbFZdOumCzrnaU1kyYLq6AtF1BMtjzBukeAAAECXSlgXVw27YrJst5ZWjNpsrAK2nIBxWTLE6x7BAgQINCVAtbFZdOumCzrnaU1kyYLq6AtE4jice4t/j4qjar+08w0M41II+b5995/bxmH7hAgQIAAgVYKWBeXTatisqx3ltZMmiysgrZMYFgaNqAezUlzBrS/nQkQIECAAIFFL2BdXDYHismy3llaM2mysAraMgHFZMsSqjsECBAgQGABAtbFZYeFYrKsd5bWTJosrIK2TMBlri1LqO4QIECAAAHF5CIfA4rJRZ6CwR+AYnLwhiJ0n4AH8HRfzvWYAAECBNovYF1cNseKybLeWVozabKwCtpyAcVkyxOsewQIECDQlQLWxWXTrpgs652lNZMmC6ugLRdQTLY8wbpHgAABAl0pYF1cNu2KybLeWVozabKwCtpyAcVkyxOsewQIECDQlQLWxWXTrpgs652lNZMmC6ugBAgQIECAAAECDROwLi6bMMVkWe8srZk0WVgFJUCAAAECBAgQaJiAdXHZhCkmy3pnac2kycIqKAECBAgQIECAQMMErIvLJkwxWdY7S2smTRZWQQkQIECAAAECBBomYF1cNmGKybLeWVozabKwCkqAAAECBAgQINAwAevisglTTJb1ztKaSZOFVVACBAgQIECAAIGGCVgXl02YYrKsd5bWTJosrIISIECAAAECBAg0TMC6uGzCFJNlvbO0ZtJkYRWUAAECBAgQIECgYQLWxWUTppgs652lNZMmC6ugBAgQIECAAAECDROwLi6bMMVkWe8srZk0WVgFJUCAAAECBAgQaJiAdXHZhCkmy3pnac2kycIqKAECBAgQIECAQMMErIvLJkwxWdY7S2smTRZWQQkQIECAAAECBBomYF1cNmGKybLeWVozabKwCkqAAAECBAgQINAwAevisglTTJb1ztKaSZOFVVACBAgQIECAAIGGCVgXl02YYrKsd5bWTJosrIISIECAAAECBAg0TMC6uGzCFJNlvbO0ZtJkYRWUAAECBAgQIECgYQLWxWUTppgs652lNZMmC6ugBAgQIECAAAECDROwLi6bMMVkWe8srZk0WVgFJUCAAAECBAgQaJiAdXHZhCkmy3pnac2kycIqKAECBAgQIECAQMMErIvLJkwxWdY7S2smTRZWQQkQIECAAAECBBomYF1cNmGKybLeWVozabKwCkqAAAECBAgQINAwAevisglTTJb1ztKaSZOFVVACBAgQIECAAIGGCVgXl02YYrKsd5bWTJosrIISIECAAAECBAg0TMC6uGzCFJNlvbO0ZtJkYW110BfSC2mptFTVx+fT82lEGtHq/uocAQIECBAg0B0C1sVl86yYLOudpTWTJgtrq4MqJludXp0jQIAAAQJdK2BdXDb1ismy3llaM2mysLY6qGKy1enVOQIECBAg0LUC1sVlU6+YLOudpTWTJgtrq4MqJludXp0jQIAAAQJdK2BdXDb1ismy3llaM2mysLYqaBSPc2/x91FpVPWfZqaZ890z6R7KVqVfZwgQIECAQNcIWBeXTbVisqx3ltZMmiysrQo6LA0bUH/mpDkD2t/OBAgQIECAAIGhIGBdXDYLismy3llaM2mysLYqqGKyVenUGQIECBAgQGAhAtbFZYeGYrKsd5bWTJosrK0K6jLXVqVTZwgQIECAAAHF5JAYA4rJIZGGwR2EYnJwft34aQ/g6cas6zMBAgQIEGi/gHVx2RwrJst6Z2nNpMnC2uqgislWp1fnCBAgQIBA1wpYF5dNvWKyrHeW1kyaLKytDqqYbHV6dY4AAQIECHStgHVx2dQrJst6Z2nNpMnC2uqgislWp1fnCBAgQIBA1wpYF5dNvWKyrHeW1kyaLKyCEiBAgAABAgQINEzAurhswhSTZb2ztGbSZGEVlAABAgQIECBAoGEC1sVlE6aYLOudpTWTJguroAQIECBAgAABAg0TsC4umzDFZFnvLK2ZNFlYBSVAgAABAgQIEGiYgHVx2YQpJhfiPXv27HTsscemiy66KD3yyCNpmWWWSVtuuWU68sgj0+jRoweUpWeeeSYdfvjh6fLLL0+PP/54WmGFFdK2226bjjjiiLT00ksPKNaCdjZpBk0oAAECBAgQIECAQAsErIvLJlExuQDvKCQ32WSTdNNNN6UVV1wxTZgwIT300ENp6tSpabnllks333xzWmWVVfqVqVmzZqX11lsv3XfffWnllVdO48ePT9OmTav+jB07Nk2ZMiWNHDmyX7EWtpNJMyg+HyZAgAABAgQIEGiJgHVx2UQqJhfgfeihh6ajjjqqKgInT56cllpqqWqvE088MU2cODFtuOGG6YYbbuhXpnbeeec0adKktP3226dLLrkkDR8+vPrcvvvum0477bQU/37++ef3K5ZiclBMPkyAAAECBAgQINByAcVk2QQrJnt5v/rqq2n55ZdPcWnq7bffntZcc8159hg3bly6884706233prWXnvtPrMVl7SutNJKafHFF0/Tp09Po0aNemP/l19+OY0ZMyY99dRTacaMGfP820CHgEkzUDH7EyBAgAABAgQItFHAurhsVhWTvbyvv/76tPHGG1eXsd5///3zZSPOWMaZy8MOO6y6D7Kv7dxzz0277757dcnsddddN9+ue+yxRzrnnHNS7Lfrrrt2nHmTpmM6HyRAgAABAgQIEGiRgHVx2WQqJnt5n3zyyemAAw5IO+64Y7r00kvny8ZVV12Vttpqq+oBOvFAnb62/fffP51yyinpoIMOSscff/x8u55xxhlpn332SbHfSSed1HHmTZqO6XyQAAECBAgQIECgRQLWxWWTqZjs5X3ggQdWhV0UlHGPZO/tjjvuSGussUZaa6210m233dZntuI+ySg4o6CMeyR7b1deeWVVlMZ+l112WceZN2k6pvNBAgQIECBAgACBFglYF5dNpmKyl/fee++dzjzzzHTwwQeno48+er5sxKWvq666alpttdXSvffe22e2Nt9883TttddW8fbcc8/59o1LXzfbbLMU+11zzTVvmvmeydF7x3vuuSctscQS/X7C7Js2ZAcCBAgQIECAAAECDRR44IEHqnXxc88918Cjb94hKyZ75WyvvfZKZ511VjrkkEOqJ7r23uIVH1FI9qeYjEIxCsaIF/dH9t6i0IxCcrDF5N13350WW2yxtPrqqzdvBDriRSYQP2xj6+9rbhbZgWp4SAkYN0MqHY05GOOmMakaUgdq3AypdDTmYOIky7Bhw9Jrr73WmGNu8oEqJntlz2WuTR7Ojn0gAi4DGYiWfXsEjBtjoRMB46YTNZ8xboyBTgSMm07UOv+MYrKXnQfwdD6YfLJZAn7YNitfQ+VojZuhkolmHYdx06x8DZWjNW6GSiaadRzGTdl8KSZ7eff31SDxepAjjjiiz2z199Ug8XqQ3XbbrePMmzQd03X1B42brk5/x503bjqm6+oPGjddnf6OO2/cdEzX1R80bsqmXzHZy/uVV15Jyy+/fHr22WfT7bffntZcc8159hg3bly6884709SpU9M666zTZ7Yee+yxNHr06DR8+PA0ffr0Km7P9vLLL6cxY8akWbNmpRkzZqQVVlih48ybNB3TdfUHjZuuTn/HnTduOqbr6g8aN12d/o47b9x0TNfVHzRuyqZfMbkA73j4zjHHHJPWX3/9NHny5DRixIhqr3hVyMSJE9MGG2yQbrzxxjc+efrpp6f4s91226Vjjz12nog77bRTuuCCC9IOO+yQLr744qqwjG2//fZLp556aop/nzRp0qCybtIMiq9rP2zcdG3qB9Vx42ZQfF37YeOma1M/qI4bN4Pi69oPGzdlU6+YXID37Nmz00YbbZRuueWWtOKKK6YJEyakhx9+uPr7yJEj05QpU9LYsWPf+OThhx9eXfK6yy67pPPOO2+eiE8++WRad911UzyRLJ6aOX78+DRt2rR01113VX+PWMsuu+ygsm7SDIqvaz9s3HRt6gfVceNmUHxd+2HjpmtTP6iOGzeD4uvaDxs3ZVOvmFyI90svvVSdZbzwwgurS1Tf/e53py233LJ6XUhcnjr31lcxGfs9/fTT6bDDDktXXHFFmjlzZho1alTaZpttqgJ0mWWWKZtxrREgQIAAAQIECBAgQKAGAcVkDYhCECBAgAABAgQIECBAoNsEFJPdlnH9JUCAAAECBAgQIECAQA0CiskaEIUgQIAAAQIECBAgQIBAtwkoJrst4/pLgAABAgQIECBAgACBGgQUkzUgCkGAAAECBAgQIECAAIFuE1BMdlvG9ZcAAQIECBAgQIAAAQI1CCgma0AUggABAgQIECBAgAABAt0moJgcghmfPXt29Y7Liy66KD3yyCPVuyjjHZdHHnlkGj169ICO+JlnnknxHszLL788Pf7442mFFVZI2267bfWOy6WXXnpAsew8tAXqGDcxXn73u9+l3/72t+nvf/97evjhh9Niiy2WPvjBD6YvfvGL6etf/3paYoklhjaEoxuQQB3jZkEN3nfffekjH/lIivhbbLFFuvrqqwd0XHYe2gJ1j5v7778//fCHP0zXXntt9bvqHe94R1p11VXTdtttlw466KChjeHo+iVQ55iJnyennHJK+utf/5qeffbZ6l3gH/3oR9MBBxyQNtlkk34dj52GvsBtt91W/UyYOnVquuWWW9Kjjz6a3vrWt1a/VzrZrIk7UXvzzygm39yo6B4xQeIH4U033ZRWXHHFNGHChPTQQw9VE2m55ZZLN998c1pllVX6dUyzZs1K6623XopF3corr5zGjx+fpk2bVv0ZO3ZsmjJlSho5cmS/YtlpaAvUNW4OOeSQdMwxx1QF5JprrlmNk//+97/pL3/5S3r55ZfTBhtskK655pr09re/fWiDOLp+CdQ1bhbU2MYbb5z+9Kc/pTlz5igm+5WN5uxU97iJLzvjy6r4GRM/d1ZbbbUUv7/+8Y9/pBEjRqQoNG3NFqhzzJx44olp4sSJadiwYenjH/94WmmlldK///3vqrCM7cc//nH66le/2mwwR18JxMmPK6+8ch6NTotJa+J8g0oxmc+2o8iHHnpoOuqoo6oicPLkyWmppZaq4vT88Nxwww3TDTfc0K/YO++8c5o0aVLafvvt0yWXXJKGDx9efW7fffdNp512Wop/P//88/sVy05DW6CucXPcccel5557rjoDGb+ge7b4QmLTTTetzpR/97vfTT/4wQ+GNoij65dAXeOmd2Nnn3122nPPPdPee++dfvaznykm+5WN5uxU57i54447qjNKcSbyiiuuqL6w6tlef/31dPvtt1fjz60VAAAOQklEQVRfhNqaLVDXmIkvN3uu0PrDH/4wz3i57LLL0o477pje9ra3pZkzZ76xfmq2XHcffVyt8OKLL6Z11lmn+hNX13VaTFoT5xtLisl8tgOO/Oqrr6bll18+xWn4+AUa39DOvY0bNy7deeed6dZbb01rr712n/HjMqEoBhZffPE0ffr0NGrUqDf2j29/x4wZk5566qk0Y8aMef5twAftA4tcoM5x01dn4rLrOHvwvve9Lz344IOLvN8OYHACucbNE088kVZfffXqZ9TBBx+cPvnJTyomB5eqIfXpusdNfEF64403pt/85jdpq622GlJ9dTD1CNQ5ZuIWjK233rq69ef3v//9fAe4xhprpPiCIi6JjC8pbO0SiLPRnRST1sR5x4FiMq/vgKJff/31KS4Ni8tYF3RZT5yxjG/3DjvssOo+yL62c889N+2+++7VJbPXXXfdfLvuscce6Zxzzkmx36677jqg47Tz0BKoc9z01bO77747fehDH0pvectbqsvRbM0WyDVuvvCFL1SXJcUliv/5z38Uk80eJvMdfZ3j5p///Gd1P3Zc1nrvvfe2TEp3egTqHDNx/9zmm2/+psVkXE0Tt2nY2iXQaTFpTZx3HCgm8/oOKPrJJ59c3Twel2lceuml8332qquuqr65jWvI4x6Tvrb999+/ujk9Hlxw/PHHz7frGWeckfbZZ58U+5100kkDOk47Dy2BOsdNXz3r+Ub4ve99b/VgHluzBXKMm3h406c//enqYWHf//73q3smnZls9jjpffR1jpvTTz89ffOb30zf+MY30gknnFDdjhFX3sSCMR7e9LnPfS69853vbBdgF/amzjETV27F1TEvvfRS6n2Z669//ev02c9+trpNKO7zt7VPoNNi0po471hQTOb1HVD0Aw88sCrsoqCMeyR7b3HpRlzCsdZaa6V4wlVfW9wnGQVnFJRxj2TvLc4cRFEa+8V9BrbmCtQ5bvpS2Gyzzaqz3LH4O/XUU5sL5sgrgbrHzQsvvFCduV5yySWry/HjDLZisn2Drc5x87WvfS395Cc/Sd/61reqy1x7n51cdtllq99PcSmsrbkCdY6ZUPjVr36VvvSlL6W4fLbnATxx60U8gCcufz3vvPOqW4Zs7RPotJi0Js47FhSTeX0HFD0eVnHmmWdW9xkdffTR8302Ln2NR6X355KguAwkLgeJePEgjN5bFAVRHMR+8XROW3MF6hw3C1OIBV8s/OJ1MvE04Pe85z3NBXPklUDd4ya+BIszEH/84x+rs5GxKSbbN9jqHDdxSXTPw+Hi1Q4//elPq1s94uEp8fqqCy+8sHrlQ/zMiaeb25opUOeY6RGInzOf//zn05NPPvkGSjwbIr6YiJ9F8bwIW/sEOi0mrYnzjgXFZF7fAUXfa6+90llnnZXi9Qxxf2TvLe4BiEKyP8Vkz1mkiBf3R/beeu47UEwOKEVDcuc6x82COhhPD45xEt8Cx1mCeO+brfkCdY6buDRx3XXXrc4WzP2EaMVk88dJ7x7UOW7ilo44yxRbfKkZP2fm3uIBKnG2aWFfsLZPt509qnPMhNCPfvSj9O1vf7u6uiqeHxGvPotXg8QzJeKJwDvssMMb46qdot3bq06LSWvivGNGMZnXd0DR67wUxCn9AdE3euc6x01viLhc8ROf+ET1hOG4tDUucbW1Q6CucfPaa69Vj2yP18bcc8891ftwezbFZDvGyty9qGvcRMx4SFw8GCOePB4Pa+q9xfsC4zVFcSnjn//85/ZhdkmP6hwz8eXmRhttVN3uE180xDuRe7b//e9/1c+iv/3tbwv8cqJLuFvdzU6LSWvivMNCMZnXd0DR67xJ3c3GA6Jv9M51jpu5IR544IHqHV7xSO349jeeImxrj0Bd4+ahhx5K73//+6v3f33gAx+YByi+hIh7veNSxXigSrw3Nx7kZGuuQF3jJgTiQU3xc2X99ddf4ANT4tUPn/rUp6rbO/71r381F63Lj7zOMdPzJPq4FSjOWPfe4r/Hw7+8D7mdg67TYtKaOO94UEzm9R1Q9P4+Pjsu5Yj7Sfra+vsY5Hg9yG677Tag47Tz0BKoc9z09OzRRx+tCsl4qMF+++1X3Qtna5dAXeOmp5jsj8673vWu6iy3rbkCdY2bEIiH7nzmM5+p3ksarwnpvf3iF79IX/7yl6t3Lse7l23NFKhzzGyxxRZp8uTJC71SJq6gid9ZX/nKV6qHO9naJdBpMWlNnHccKCbz+g4o+iuvvFI9gezZZ5+tfnHGL9C5t3HjxlVPSZw6dWp1KUdf22OPPZZGjx6dhg8fnqZPnz7Pk83iHYFjxoxJs2bNSjNmzKjOKNiaK1DnuAmFp59+unp64l133VV90XD22WdXj+q3tUug7nGzIB2XubZrzERv6hw3L774YvW7KWLGlRDxe2nuLR4eFz9/4mxU3P9va6ZAnWNml112ST//+c/TzjvvPM/92T0y8eVDfAlx7LHHpu985zvNBHPUCxXotJi0Js47qBSTeX0HHD0evnPMMcdUl/3Et28jRoyoYsSrQiZOnFidLbrxxhvfiBvv6Yo/8VCU+OE597bTTjulCy64oLoZ/eKLL64Ky9jiW7v49i7+fdKkSQM+Rh8YegJ1jZtY3G2yySZpypQp1Tve4mmKnoo39PJd1xHVNW4WdjyKyboyNbTi1Dlu4nLE4447rno/aTzZted33tVXX5223nrrFPfBxc+jeBiPrbkCdY2ZeOVZ3P8Wv5fi/8cY6dnilWfxb7Hdfffd811231w9R94j8GbFpDXxohkrislF477QVmfPnl3dXH7LLbdUj0KfMGFC9YL4+PvIkSOrX6pjx4594/NxL1tc8hrf1sW7lebe4pHZ8YTF+MZ3lVVWSePHj68esR5nnOLvESve42VrvkBd46bn9Q7xizoeu77EEkssEKf3WGu+YHf2oK5xo5jsrvFT57iJWJtuuml1z2T8zvvYxz6Wnnjiier30+uvv159ufq9732vu4Bb2Nu6xsycOXOq302//OUvK6VY18Q923FLRjxVOjZjpj0D6Kqrrprn7QaxFo6Ccu4vl+Ie2fgyKjZr4kWTe8XkonHvs9WXXnqpOssYZ4XiEtV4eEW8iDdeF9L7MqC+Jk40EpcsxgMO4nHZ8e6ueA/TNttsUxWgyyyzzBDsvUPqVKCOcbPrrrsu8NKh3scUv9Bt7RCoY9woJtsxFgbSizrHTVwGecIJJ1SXJ8YrHpZccsmqSIgvt3oWiQM5NvsOTYG6xkz8/ol74OI1RHHrz/PPP1+9AzkKjHjieKyXbO0QiC+u3+y5HjEWYu3yZsWkNXG+MaGYzGcrMgECBAgQIECAAAECBForoJhsbWp1jAABAgQIECBAgAABAvkEFJP5bEUmQIAAAQIECBAgQIBAawUUk61NrY4RIECAAAECBAgQIEAgn4BiMp+tyAQIECBAgAABAgQIEGitgGKytanVMQIECBAgQIAAAQIECOQTUEzmsxWZAAECBAgQIECAAAECrRVQTLY2tTpGgAABAgQIECBAgACBfAKKyXy2IhMgQIAAAQIECBAgQKC1AorJ1qZWxwgQIECAAAECBAgQIJBPQDGZz1ZkAgQIECBAgAABAgQItFZAMdna1OoYAQIECBAgQIAAAQIE8gkoJvPZikyAAAECBAgQIECAAIHWCigmW5taHSNAgAABAgQIECBAgEA+AcVkPluRCRAgQIAAAQIECBAg0FoBxWRrU6tjBAgQIECAAAECBAgQyCegmMxnKzIBAgQIECBAgAABAgRaK6CYbG1qdYwAAQIECBAgQIAAAQL5BBST+WxFJkCAAAECBAgQIECAQGsFFJOtTa2OESBAgAABAgQIECBAIJ+AYjKfrcgECBAgQIAAAQIECBBorYBisrWp1TECBAgQIECAAAECBAjkE1BM5rMVmQABAgQIECBAgAABAq0VUEy2NrU6RoAAAQIECBAgQIAAgXwCisl8tiITIECAAAECBAgQIECgtQKKydamVscIECBAgAABAgQIECCQT0Axmc9WZAIECBAgQIAAAQIECLRWQDHZ2tTqGAECBAgQIECAAAECBPIJKCbz2YpMgAABAgQIECBAgACB1gooJlubWh0jQIAAAQIECBAgQIBAPgHFZD5bkQkQIECAAAECBAgQINBaAcVka1OrYwQIECBAgAABAgQIEMgnoJjMZysyAQIECBAgQIAAAQIEWiugmGxtanWMAAECBAgQIECAAAEC+QQUk/lsRSZAgAABAgQIECBAgEBrBRSTrU2tjhEgQIAAAQIECBAgQCCfgGIyn63IBAgQIECAAAECBAgQaK2AYrK1qdUxAgQIECBAgAABAgQI5BNQTOazFZkAAQIECBAgQIAAAQKtFVBMtja1OkaAAAECBAgQIECAAIF8AorJfLYiEyBAgAABAgQIECBAoLUCisnWplbHCBAgQIAAAQIECBAgkE9AMZnPVmQCBAgQIECAAAECBAi0VkAx2drU6hgBAgQIECBAgAABAgTyCSgm89mKTIAAAQIECBAgQIAAgdYKKCZbm1odI0CAAAECBAgQIECAQD4BxWQ+W5EJECBAgAABAgQIECDQWgHFZGtTq2MECBAgQIAAAQIECBDIJ6CYzGcrMgECBAgQIECAAAECBForoJhsbWp1jAABAgQIECBAgAABAvkEFJP5bEUmQIAAAQIECBAgQIBAawUUk61NrY4RIECAAAECBAgQIEAgn4BiMp+tyAQIECBAgAABAgQIEGitgGKytanVMQIECBAgQIAAAQIECOQTUEzmsxWZAAECBAgQIECAAAECrRVQTLY2tTpGgAABAgQIECBAgACBfAKKyXy2IhMgQIAAAQIECBAgQKC1Av8PEhwtz4CICKwAAAAASUVORK5CYII=\" width=\"610\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing hyperboxes and boundaries at the first granularity level (level 1). Note that the order of the level starts from 0.\n",
    "multi_granular_gfmm_clf.draw_2D_hyperbox_and_boundary_granular_level(window_name=\"Hyperbox-based classifier and its decision boundaries at level 1\", level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing hyperboxes and boundaries at the last granularity level (level 6)\n",
    "multi_granular_gfmm_clf.draw_2D_hyperbox_and_boundary_granular_level(window_name=\"Hyperbox-based classifier and its decision boundaries at level 6\", level = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of hyperboxes at all granularity levels = 210\n"
     ]
    }
   ],
   "source": [
    "# Get total number of hyperboxes at all granularity levels\n",
    "print(\"Total number of hyperboxes at all granularity levels = %d\"%multi_granular_gfmm_clf.get_n_hyperboxes(level=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of hyperboxes at the first granularity levels = 101\n",
      "Total number of hyperboxes at the last granularity levels = 10\n"
     ]
    }
   ],
   "source": [
    "# Get number of hyperboxes at a given granularity level\n",
    "print(\"Total number of hyperboxes at the first granularity levels = %d\"%multi_granular_gfmm_clf.get_n_hyperboxes(level=0))\n",
    "print(\"Total number of hyperboxes at the last granularity levels = %d\"%multi_granular_gfmm_clf.get_n_hyperboxes(level=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using all GFMM models from all granularity level to make the final prediction using majority voting, in which each granularity level contributes one predicted result for each input pattern and the final predicted result is the class getting most of votes from the models at all granularity levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (majority voting) =  86.70%\n"
     ]
    }
   ],
   "source": [
    "y_pred = multi_granular_gfmm_clf.predict(Xtest, level=-1)\n",
    "acc = accuracy_score(ytest, y_pred)\n",
    "print(f'Accuracy (majority voting) = {acc * 100: .2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use a certain granularity level to make prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction for each granularity level:\n",
      "Level 1 - Testing accuracy =  85.10% - No hyperboxes = 101\n",
      "Level 2 - Testing accuracy =  88.20% - No hyperboxes = 38\n",
      "Level 3 - Testing accuracy =  87.10% - No hyperboxes = 27\n",
      "Level 4 - Testing accuracy =  86.10% - No hyperboxes = 20\n",
      "Level 5 - Testing accuracy =  86.20% - No hyperboxes = 14\n",
      "Level 6 - Testing accuracy =  82.60% - No hyperboxes = 10\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction for each granularity level:\")\n",
    "for i in range(len(granular_theta)):\n",
    "    y_pred_lv = multi_granular_gfmm_clf.predict(Xtest, level=i)\n",
    "    acc_lv = accuracy_score(ytest, y_pred_lv)\n",
    "    n_boxes = multi_granular_gfmm_clf.get_n_hyperboxes(i)\n",
    "    print(f'Level {i + 1} - Testing accuracy = {acc_lv * 100: .2f}% - No hyperboxes = {n_boxes}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaining the predicted result for the input sample by showing membership values and hyperboxes for each class using the model at a given granularity level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_need_explain = 0\n",
    "# Using the trained model at the sixth granularity level to make prediction and explanation. Note that the value for the level parameter starts from 0. \n",
    "level_explain = 5\n",
    "y_pred_input_0, mem_val_classes, min_points_classes, max_points_classes = multi_granular_gfmm_clf.get_sample_explanation_granular_level(Xtest[sample_need_explain], Xtest[sample_need_explain], level_explain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class for sample X = [0.752930, 0.385920] is 1 and real class is 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted class for sample X = [%f, %f] is %d and real class is %d\" % (Xtest[sample_need_explain, 0], Xtest[sample_need_explain, 1], y_pred_input_0, ytest[sample_need_explain]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Membership values:\n",
      "Class 1 has the maximum membership value = 0.989125\n",
      "Class 2 has the maximum membership value = 0.915132\n",
      "Class 1 has the representative hyperbox: V = [0.763805 0.369765] and W = [0.91185 0.48598]\n",
      "Class 2 has the representative hyperbox: V = [0.563065 0.17003 ] and W = [0.6680625 0.65662  ]\n"
     ]
    }
   ],
   "source": [
    "print(\"Membership values:\")\n",
    "for key, val in mem_val_classes.items():\n",
    "    print(\"Class %d has the maximum membership value = %f\" % (key, val))\n",
    "    \n",
    "for key in min_points_classes:\n",
    "    print(\"Class %d has the representative hyperbox: V = %s and W = %s\" % (key, min_points_classes[key], max_points_classes[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show input sample and hyperboxes belonging to each class. In 2D, we can show rectangles or use parallel coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using rectangles to show explanations"
   ]
  },
  {
   "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": [
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\" width=\"608\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_granular_gfmm_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, \"2D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using parallel coordinates to show explanations. This method can be used for any number of dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a parallel coordinates graph\n",
    "multi_granular_gfmm_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, file_path=\"par_cord/multi_resolution_gfmm_lv6_par_cord.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"820\"\n",
       "            height=\"520\"\n",
       "            src=\"https://uts-caslab.github.io/hyperbox-brain/docs/tutorials/par_cord/multi_resolution_gfmm_lv6_par_cord.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1e8371cf588>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load parallel coordinates to display on the notebook\n",
    "from IPython.display import IFrame\n",
    "# We load the parallel coordinates from GitHub here for demostration in readthedocs\n",
    "# On the local notebook, we only need to load from the graph storing at 'par_cord/multi_resolution_gfmm_lv6_par_cord.html'\n",
    "IFrame('https://uts-caslab.github.io/hyperbox-brain/docs/tutorials/par_cord/multi_resolution_gfmm_lv6_par_cord.html', width=820, height=520)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}