Dissimilarity_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import ase\n",
    "import ase.io as aio\n",
    "import qml\n",
    "\n",
    "import scipy as sp\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from qml.kernels import gaussian_kernel\n",
    "from qml.math import cho_solve\n",
    "import copy\n",
    "%config Completer.use_jedi = False\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mol</th>\n",
       "      <th>Eafw</th>\n",
       "      <th>Earev</th>\n",
       "      <th>dErxn</th>\n",
       "      <th>ETS</th>\n",
       "      <th>ERt</th>\n",
       "      <th>EPr</th>\n",
       "      <th>enan</th>\n",
       "      <th>HRt</th>\n",
       "      <th>HPr</th>\n",
       "      <th>HTS</th>\n",
       "      <th>Hafw</th>\n",
       "      <th>dH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1abp1</td>\n",
       "      <td>8.674063</td>\n",
       "      <td>17.793659</td>\n",
       "      <td>-9.119595</td>\n",
       "      <td>-1.453883e+06</td>\n",
       "      <td>-1.453892e+06</td>\n",
       "      <td>-1.453901e+06</td>\n",
       "      <td>R</td>\n",
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       "      <td>-1.453672e+06</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1abp2</td>\n",
       "      <td>7.572784</td>\n",
       "      <td>19.457186</td>\n",
       "      <td>-11.884402</td>\n",
       "      <td>-1.453885e+06</td>\n",
       "      <td>-1.453892e+06</td>\n",
       "      <td>-1.453904e+06</td>\n",
       "      <td>R</td>\n",
       "      <td>-1.453681e+06</td>\n",
       "      <td>-1.453691e+06</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1abp3</td>\n",
       "      <td>12.316128</td>\n",
       "      <td>22.034368</td>\n",
       "      <td>-9.718239</td>\n",
       "      <td>-1.453878e+06</td>\n",
       "      <td>-1.453891e+06</td>\n",
       "      <td>-1.453900e+06</td>\n",
       "      <td>R</td>\n",
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       "      <td>-1.453688e+06</td>\n",
       "      <td>-1.453667e+06</td>\n",
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       "      <td>-13.183974</td>\n",
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       "      <td>-1.453889e+06</td>\n",
       "      <td>-1.453902e+06</td>\n",
       "      <td>R</td>\n",
       "      <td>-1.453678e+06</td>\n",
       "      <td>-1.453690e+06</td>\n",
       "      <td>-1.453669e+06</td>\n",
       "      <td>9.181712</td>\n",
       "      <td>-11.817877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1abp5</td>\n",
       "      <td>10.178204</td>\n",
       "      <td>22.937354</td>\n",
       "      <td>-12.759150</td>\n",
       "      <td>-1.453881e+06</td>\n",
       "      <td>-1.453892e+06</td>\n",
       "      <td>-1.453904e+06</td>\n",
       "      <td>R</td>\n",
       "      <td>-1.453680e+06</td>\n",
       "      <td>-1.453692e+06</td>\n",
       "      <td>-1.453670e+06</td>\n",
       "      <td>9.975511</td>\n",
       "      <td>-11.326537</td>\n",
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      "text/plain": [
       "     mol       Eafw      Earev      dErxn           ETS           ERt  \\\n",
       "0  1abp1   8.674063  17.793659  -9.119595 -1.453883e+06 -1.453892e+06   \n",
       "1  1abp2   7.572784  19.457186 -11.884402 -1.453885e+06 -1.453892e+06   \n",
       "2  1abp3  12.316128  22.034368  -9.718239 -1.453878e+06 -1.453891e+06   \n",
       "3  1abp4   9.298435  22.482409 -13.183974 -1.453880e+06 -1.453889e+06   \n",
       "4  1abp5  10.178204  22.937354 -12.759150 -1.453881e+06 -1.453892e+06   \n",
       "\n",
       "            EPr enan           HRt           HPr           HTS       Hafw  \\\n",
       "0 -1.453901e+06    R -1.453681e+06 -1.453689e+06 -1.453672e+06   8.495217   \n",
       "1 -1.453904e+06    R -1.453681e+06 -1.453691e+06 -1.453673e+06   7.375741   \n",
       "2 -1.453900e+06    R -1.453679e+06 -1.453688e+06 -1.453667e+06  12.109041   \n",
       "3 -1.453902e+06    R -1.453678e+06 -1.453690e+06 -1.453669e+06   9.181712   \n",
       "4 -1.453904e+06    R -1.453680e+06 -1.453692e+06 -1.453670e+06   9.975511   \n",
       "\n",
       "          dH  \n",
       "0  -7.957442  \n",
       "1 -10.650710  \n",
       "2  -8.383520  \n",
       "3 -11.817877  \n",
       "4 -11.326537  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data.csv', index_col=0)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['ase_reactants'] = [aio.read(\n",
    "    'data_react_xyz/' + data.mol.values[i] + data.enan.values[i] + '.xyz') for i in range(len(data))]\n",
    "\n",
    "data['ase_products'] = [aio.read(\n",
    "    'data_prod_xyz/' + data.mol.values[i] + data.enan.values[i] + '.xyz') for i in range(len(data))]\n",
    "\n",
    "mbtypes = np.load('./mbtypes.npy', allow_pickle=True)\n",
    "\n",
    "data['SLATM_react'] = [qml.representations.generate_slatm(coordinates=mol.positions, \n",
    "                                                 nuclear_charges=mol.get_atomic_numbers(), \n",
    "                                                mbtypes=mbtypes, local=False) for mol in data['ase_reactants']]\n",
    "\n",
    "data['SLATM_prod'] = [qml.representations.generate_slatm(coordinates=mol.positions, \n",
    "                                                 nuclear_charges=mol.get_atomic_numbers(), \n",
    "                                                mbtypes=mbtypes, local=False) for mol in data['ase_products']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['SLATM_diff'] = [x - y for x,y in zip(data.SLATM_prod.values, data.SLATM_react.values)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target is the Forward activation energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data.Eafw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "slatm2 = np.vstack(data.SLATM_react.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "slatmdiff = np.vstack(data.SLATM_diff.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Representation distance vs target distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we compare the dissimilarity plots of SLATM2 vs SLATMDIFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmat_slatm2 = sk.metrics.pairwise.pairwise_distances(slatm2)\n",
    "\n",
    "\n",
    "dmat_slatmdiff = sk.metrics.pairwise.pairwise_distances(slatmdiff)\n",
    "\n",
    "\n",
    "dmat_target = np.subtract.outer(data.Eafw.values, data.Eafw.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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7ruvuemmmwZ4ye1quu1KYBAAUgYhgwxn0oZzmdFdBhqup6kqfNR5IgcZMlUmk34BT53r3hTPaUIHNNHnpnjv4jVFCYyqBNquAzIAOXSH+gDy03sjgLT4AW0dgEQPSHuflro5EK4rKjJyhxHSJEHENRY9hl7CqVzXMxFYYndagFcjFBiKniyfR2OQoNPeJ324I2vCEGiiz+FKNfYWJRAlUCSBtuuADEAO2bE+gPzPPRFAWvwaRABp+OY0qQSqlFItM8DreAp8xm0VD0iVfmV/VQCk7NqLHiE9vlDw8LXpA5J+QIj1TDmAJ0LIYJVEEzqgiT4HK8XYepRAlEAVCbRdB2QA8srH1geQc/8eAaTKQzHkY2sBCB6M0M+8efNCD7XHba4ekCVLHzFaVoOMs/cZ1a4+QwzwKiAgHwht3Ja1I8dSdqx+8HwAYg1vkZci29XXzu9CDf5Q70eRjH3PgYaR0DGVvZBVIaROe2XnbE7fN2EINNHn5nRP47VGCbRdAm3XARFA2v4E9T6+WgAyNjZmppQkEnNoGzZsqDTKpz3taeb3v/99pXNG+WC+ZC4AyRnqAaE4oXIIAYrQtrL7PKTE8SIAgXch1EPgywUpC72S34cY/CEA4oMcDUb2/+o5qAohoWWN64RkVX1mXNdTp41RP6cJQ6CJPkf9PsXxRwlsShJouw7IAOQVO9X3gJx3d/SAtPihrQUgl112WXZJd911l/ngBz9o3vzmN5tnPetZ9u9XXXWV+fa3v20+9alPmaOPPrrFl9/80PiSPbhkqdlqyy0LB9Qvj0i/AaTME4H++j122acrrKnMMA8dTxU4cN28MgCp4tHxeXDktdbJreG49bllMgoBsCpvWFl/VdoapWObMASa6HOU7kkca5TApi6BtuuADEBevnN9APn5ogggLX6QawGIvJ4XvvCF5phjjjFveMMbcpf53e9+15xxxhnm0ksvbfHlNz80CSAyBKvqztShV9Jv+EC/LiO7rJ86xibbLIMPyqJXCBlUNSsJTPqaXLLMgYfyPFKOIRBSBorWI+HwbNa5V77nMQRa+tlf6HvR5HFNGAJN9NmkjGPfUQKDkkDZXNePfgehE9uuAzIA+T+71AeQ8/8WAaQfD+CA2ugZQLbYYgtzww03mMc97nG5Id56661mv/32M6tWrQoa+vHHHx90HA46/fTTg4/t5cDLL7/cnHbaaebaa681ixcvNuecc4459NBDsyY3btxoTjnlFAtaS5cuNc94xjPMf/7nf5onPelJwd0OE0CqKkoqvbLzysKWgoURcCD7chnx+vSicKWy1f5+wofLQ1MEHr7iAD4QCYGQEPig/HyQ0M9JsAxE+tlXwGPV6CFNGAJN9NmokGPnUQJ9lEDZnNjHrpxN9UM/tl0HZABy8K71AeSCuyKADPph7KH9ngFkr732Mq94xSvM5z73udww3ve+95nzzjvP/OUvfwka3kEHHRR0HHJPLr744qBjez3o/PPPN1deeaU54IADzOGHH94FIJ/5zGfMqaeear71rW+Zxz/+8eYTn/iEAbTgmufOnRvUvQtABmEAVlGYRcqtzDB3eSnKBFFmjOJ8l9ejqK+ylXx6DEL61v27rie0upUr5IrXVnaPeF9cIOKCkDK5h3xfJseQNoqOGcSz3uuY6p7vA8ey9powBJros0wO8fsogVGQQJme5jVUWfTR1x0yL/UKIW3XARmAvGS3+gBy0Z0RQFr8UvUMIL/4xS+scb7HHnuYZz7zmfZSr776avPXv/7V/PjHPzYve9nLWnz54UMD+EgPCLwfO+ywg3nve99rTjjhBNvQ2rVrzWMe8xgDMHnnO98Z1HgogPSqbIpyDcq8AfJCinIiQkOkQva2KDLyXf1oZd91TSK0KMRjkLvmggT7OuAhgUbDx6SjrzHH2NEGrzH7mSao15Wv74FtAkJ6fd6DXr4+H6TfjdBraMIQaKLPPos7Nhcl0IgEygAkBDzYRpmOGGQJ9LbrgAxAXrR7fQD51R0RQBp5S8I67RlA0M3f//5385WvfMXccsstBob5E5/4RPOud73L7LTTTmGjcBz1yCOPmG9+85u2TRj/aPOtb32r2bIkUbt2hyUnagC54447LHRdd911Zv/998/OPuSQQ8z8+fNtEr7rA0jBP37wkkFOSEIvygEpU1Rl112W7Byy4qL7cJWr1SFSRavCoYayDrfKjPaNG03RZCBlFmKsaxkUTSSh4GFhQ206mJNbChw8xgUgaENCiIUPUQ0L49YQgmNC5Vv07NT1ULjuS5XKWr0+72XvQ5u+H4Yh4NM7y5Yt6yr/3SbZxLFECbRJAkXzTdF8UQYtvEaX3hsUhAxD7/Ry73IAMm1q9aYmNhgTAaS63IZ4Rk8Asn79evOSl7zEfO1rX7MhSP36oAzvS1/6UjNr1izz9Kc/3UIN/rZ69Wpz0UUX2ZCoYX80gPz2t781z3nOc8w999xjPSH8vOMd7zB/+9vfzIUXXugc4sknn2zzRvSnaQCxRm1AaWVfvkcReOC7Mi+Ly1j2JWujPSh0GutFxi4Ndx2+ZI32QI9BFdjogjQBHy7wkIDigw/dpssj4gMsnhsCIyH3X47FNVmGAmE24XqeuQgg/dVwPr0TAaS/co6tbdoS8Om3Mq9HCIBUhQ87Z/dQnn9kAOQFexhTF0Au/mv0gLT4lewJQHBd2267rYExrpPQe7nm5z73uWbPPfc0X//61820adNsUxMTE7baFjwPyLMY9scHIPfee6/Zfvvts+G8/e1vN3fffbe54IILnEMM9YC4gKAXZVPmASkzCIsqXbngo0ghu1bri+6nDLmS8IG/Ew7YH9vOcjzGpmTeAxeEaCXO83qBDnktZeFi8r6EAgjaD4EQ37WV3etBvVtVJthenvVBjX9Q7Q7DEIgekEHdvdju5iSBshzIkHlMH1Ok6wbl/cAYhqF3enk2Mg/IQXvWB5BLbo8A0stNGPC5PQMIks3Hx8fNpz/96b4NFZ6PP/zhD2bvvffOtXnzzTcbbFQYWlmrbwMyxoaByRyQuiFYeky+KlguI7EXoywUQEJlptvLQGByY5YwXpSXkUuk9qzi6D7Y3voNk9YDouFDjl2CCOUmvSFlHgO05QudCs2JqAIf6K9XAJHQ6pNvSLne0GeAgFO2ulcl9EpDU5WxjOqxTRgCTfQ5qvcnjjtKgBKo6wGREmxD/sdIAcjzH1cfQC69LQJIi1/fngHkPe95jznrrLOsxwJwMHv27Nzl1imZi0Tus88+24Z3yQ/Cmo466ihz//33D12kviT04447znzgAx+w41m3bp1ZuHBhrST0+x5aks8BUTH+vRhmRTkIdQTpghnAgPZUSKXtyseQBrNvHDLfg1AAQ50AQo9IDj5S2bm8ITjOBSJyLL5QKdcYNcjkJhqRUO7Kl/FdswQRnfvhHIPjWeFxZbkhZWFXvUCv7/pCAa7OszmK5zQBA030OYr3Jo45SsAFDy6plIVh6XbKdGvd/LvQO9Z2HZB5QJ73+PoAcvmtEUBCH4gGjusZQIrK59YtmfvP//zP1tvw2c9+1jz72c+23off/OY35v3vf7+tuPWFL3xhKKJasWKFuf32221fSDQHTOF6FyxYYHbeeWcLGtjt/cwzz7QhaJ/85Cftxot1yvBqAJHAEeItKBJIP70fPpghgFggUInVLjgIgQ8LHNKIT/M+JHwQSvT1u4AnJCzL1Sf+pr0TOrfEdz1VwKPqQx3qWagDIWWTY9Wx+uQziH7qjK3Jc5owBJros0kZx76jBPopgTKvb2hCetXwq37qy7brgAxAnrtXfQC54i8RQPr54Pe5rZ4BpM/jsc3BkwDY+OpXv2pzP/BBmNexxx5rQ71mzJgxiG672gRMuADr6KOPtnt/cCNCJOHLjQj32Wef4PHxJSsCEGm81VFAgwIQX+6H9ErQcC8rJasF5jLc6fnAsRZ4RBUsnQPiAzhAiCsky3XDZKK7Bp3QNvR5VcKsOKaqXhCelwPXNNlbPj9l4Vh1njXfgx+9Hn6V0IQh0ESfwUoxHhglMCISqFMVS58TupAk57R+iKftOiADkOfsXR9ArvxzBJB+PCwDaqOVAMJrRa4H9hOBoY8QL+y6vql9hgkgRavxocZmUfiVDJfSxrs2pnVCOL/3gYc05H3woQ1v3Yf0gOBYGPaucUgPjq/SlpSXyxtSBB5lq2dFz7jvPpWV6S2C2DIQyeTaQ8WVCCARQDY13R2vJ0pASiA0Qd11XGiBjtB5OuTOjAyAPOsJ9QHkqlsigIQ8DA0d0xcA+d3vfmd++MMfmkWLFlnvhfz85Cc/aejSRqPbQQOICxiKDOAiBRcafkX48K30SMNfGsY6dEsb/zLp3JXgLe+4LLPrq4zlgyALD8K7UgYhRR4K6fGQ8siVGC7Y6FBfk/y/Lq3rgiIrX5UT46tC1tVeH0vkRgCJADIaGjmOMkqgvgRC8i1986+ee2MOyKPJvm/PfGJ9ALn65ggg9R/ngZ/ZM4B8//vft4nhSBj/5S9/aX/edttt5r777jOvfvWrbX5Enc+aNWvMjTfeaB544AEzOTmZa+JVr3pVnSZbec6wAKQoL0MLxgUhRfBhDfY0JGrdhkmbL8HjaWgX5V+4VpGk0a+rXbEv9psz0rWxjZCr1JDW4VdlAML2ywAEx7kgxAdQBByOuyhErmz/FPm99vDIcfkgRAIKx+PaM6QfANGPNvSzGjqZt/LlTwfVxEpkE322+R60YWy9eEYHNf5+rrgPaoxtaNd373QuSNk9HiaEtF0HZCFYEUDa8IgPZAw9A8iTn/xk8853vtP80z/9k5k7d6654YYbzG677Wb/hv0xXJvulV0J9tAA1Dz00ENdhyIhfcOGDWVNjMz3RQAijcOqe2do47YKgGijNBQ+fHt0aMMWSlZ7QeR4XbkeEnIkePhK/aIPnxeE/bvkK+FAhpHV8Rq5PB6uELPQ0LiiYgQuEJHXV7Qhowvg5N9CPSM8p8xoqbuyVzZ5F730ZWNqUmE0YQg00WeTMm5T3708x8O+jja/N8OWhe6vav5HyH0PDcXSc3QdWbRdB2QA8vQn1feAXPOn6AGp83AM6ZyeAQRld//0pz+ZXXfd1WyzzTbmkksuMfvuu6+55ZZbzAte8AKzePHiypeCfA/shP6xj33MoCTvpvzhS3bPgw9nZXhdhmbdSliuUKUQReiSuSvpnAY74QN7dDBHg6DgysWQECD70iV2XeBRBlMSPiS4+UKxXNda5L1wHa+NdL2RYSh4FAGVnXRUWFRIAmNR4r0rUb0qkOSOrxC2VVYCeFDvfduMqiYMgSb6HNT9HIV2q+jcKuVc61x7lfeube9KnesdxDmDgI+iBZyie1b3HrVdB2QA8g/71AeQ3/0xAsggXoA+tdkzgOy0007mF7/4hYWOpzzlKeaDH/ygecMb3mCuuuoqc/DBB9ubX/Uzb948uxHhHnvsUfXUkTveBSDa0OwKn6mQDNwvAJHwIduUMCDhQx6jIcBlEOOaXft7yPZdv7vK49LzoT0gvqR0vZpU5rmo+pAVxQXr+yOP9d33UAPCB34Yf0gZ4aqeirqhW6HXU1XuZcdXmbirGJBV2sUYmzAEmuiz7H5sit+HPDcu4Ag5r6q8QhYrdJtVn+WqYxrl4+uGXfkAM2RxqZ8g0nYdkAHI0/atDyC/vykCSItfsp4B5IgjjrAbEB5//PHm1FNPNV/84hfNIYccYvNBDjjgAFMnCf2tb32rec5znmPe9ra3tVh0/RkaX7K/3f+QWTB/y1yjOuyqThhWkYEbegW+crs43xV2hTwQfif7kGDgylfAWGWFK7btAw/XBMAJ04Z4pVWuXKFYRfkfHLPP0yInkDLj2TfZ+O6Lq+0yAA2ZlKT3R8rItermzNXweDaKVu3KgKTIuCmTa+izW3ZcmYHViyFY1jbH1oQh0ESfZfdiU/k+5JkJgY5+eUKc77NaxArRIZvK/enXdYQASNHik28cwwKRtuuADECe+mRjpk6tftsQqn/tjRFAqpWqekQAACAASURBVEtuaGf0DCBLliwxSBjfYYcdbLI4Ng/EpoEIozrxxBPNVlttVfliUH73Na95jdl2222tZwV7gMgPNircVD4SQOD54Scz0MX+DXXCsIoAJNSQDvF+wBOxdmIyAxJZRUoaqa6QKGnwY7w+8JBhUT440LkfGkTQlws++Dc5Ybj6cBkFVYzlolAs3x4hvIaysdvvC0K0dAiabM+1T0vIdVU1XMpK/tZZpe1VF7j6DDEiQ/oNgZAmDIEm+gyR1ygdU+UZKYKJMiNVh3OGyMgF/6H6wdV+yHMcMq5N5ZgQ+MC1uuaTEBmEQIjrfsq5tqyftuuADEAOeEp9ALnuhgggZQ9Cg9/3DCCDGPs3vvEN8653vcvMmjXLbL311nYndH7w+x133DGIbhtpky/ZnYsfNHNTAKGylxBStgruG3wuXGpyoz2sbGVNKr+q3g8cDw8IQUKOywUf2vCX8ME2ZFK6C05yfYiqVxJGspAjx94fenIN8U5oeYcY6xK0+LvONSnLASmCkZBnxDXOkAplVa6vTDZS3j4gaQJEBqUAyoy3JgyBJvoclHyH2W4ZdJTp1lDDtQg6XGGaZTIoKiShn8+Qd73smS4bzyh/H3oPffDhu7dlHmMLHJ7w6xAvl5Z523VABiD77VcfQK6/PgJIi1+2ngHkyCOPNM9//vPNgQceaB7/+Mf35VK32247Ay8H8knGxsb60mZbG+FLdvu9D5j5qHmdfiSEFBnuRdflMqQ1UPB834oZv89K4dJDke5dgT4ICGtT8LAAIkrx6muSIVFy/BqW1qfll4u8Iq7r94Ve6fAsqdBd0CUhSu/n4ZoIQnYs114ODWp6cnNBhYQpXoO85qKJygcHIRBS5R3yGTE+L14VEAkxkKqMteqxoTHcst0ig60JQ6CJPqvKuW3HhxqeHHeV411GaRnsOHVfQX5gkffR9XwO+z0bFahx3RetE6rCh7yXIVUHQ+9X2/ROlXc6A5An718fQG78QwSQKkIf8rE9AwjK7V522WXm1ltvNQAHgAj+AUr23nvvWpezYMECg80NN6ckdABI5gERYVcQoE3aFvkMoQamNG75uwSJIgOJSjC3YV4KHzjPl/uhPSA0uLUHwgUhGjQINtorIvvH73KMMsFdg4gEORmaZeUpvGw658S3D0jofdAvQVmYVxckiL1NfBBV5/nIwNCzT0rd6/NBoe5Pt+8ykKqs+PmUTWioYR1lFRqS1zZDIAJI9btd1fCknmJPXZXxUo90l34I3JhU663s/XJASMjqetX3fdCA0lYgCQVLvQCo56qyJ7AOiHgXfhzPRNt1QAYg+x5QH0Buui4CSNmD1uD3PQMIx46NBy+99FL7j0CycOHCWmV4jzvuOJv/8eEPf7hB0Qyna75kf7nn/m4ASaGjDoBobwIMJZvgXRCG5Vqd1sayNM5l1Sp4Pej5kJsR8ngayDT8p09NPFtykpEAAu+HBhL5fypznwFIEJGgo4HElR/Cuy49OPjdBSF6gpQeEJenQ7ad/a6MECkvaVBoeKLxIcPM5N+kcVK0Oqc9LL4KZf0wBnLPlwA+V0iWz1PigpgQ8NDH9Nt48oXOFRmF/K4JQ6CJPoejUQfTS1X4cIWuut5/DSmho9fPb9f/AyGkyvtUFVBc11L1veuH3gmVaZXjyp4H1yKTb/GvSzepe1cGIlVC6OSxbdcBGYA86an1AeRP10YAqfJgD/nYvgHIypUrbfI5IeS6664zT3ziE2053aofhF+dddZZtqwvNjrUSeinn3561SZbe7wLQDJDsiaA+EKvCCDWuHassmVGrkpkzqDDEXbFqlUEEPxEOV6dq0HjH+ABQx0/tRfEBSAynIttyp3RfdeBtn3eENs3N0N0VMpywZsvQRz3yhd6VZTfoSewstwPKSudB+ILOZMPvWy/KMzLByD9MD4kFMnf6wCIbku/4GWx+L7ziwyespCYIk9L9IC0VgWXDqzsXS0Lt+nSx0r3lj1XcoAhoTcuI7+qIV8WkhsCEiF99qud0pvYxwNC4cMHoWW6KSRMtUuX1qhqNjIA8kQAyLTqd3DDhDE3RwCpLrjhndEzgJxwwgnW44Ed0PfZZx/zvOc9z4Zg4ef8+fNrXclBBx3kPQ9J6BdffHGtdtt4EpXAzXffZz0gckWahmVVD0iR90PDRG5yKyi16vN8EAoIIPBcZN4QsbrPlXoa/+NjCQTwGuWYpQfFByASoiZFP2MyXElAiDbSCUIu490lP8jJBSFFeR9lVbuKJiLp8ciANIUm+X/5uw7D8hnlcgKVRoKGNldlrF7eoX56QAYBICEGE/qtYjBSXhFAenlymju3CD7qgofr+Sla4MDVF+mZECipI8Eq4Tx1qm6Fzj2h72Wda6x6Tgh8SPDIzSViAU/PJ0VVCPVcgHN1lUyrDwWEFIEdjxsZAHnC0+oDyC2/jx6Qqg/5EI/vGUCQJI5wKYRNYf+PJzzhCUMc/uh3JQFkvtgHxBqTDg+INLx8irlXD4hLqjIkSW8YyKpX+LlmYkMuFCszwISnAcb/uMMDQuOOIU++EKys3C9Coxxx1IAQyo/KWgMQvpdjkB6GUADRRgHakFDhqt5lrzEgxrsIQOR3GqD4nTYIpNfIdX8JH5zIfPARsmJZ9FaGrO75jtHtFo2lTMZVjCvf9VQBkQggo6erQ+BDPmc6X06+63IBh5Iog45QiYUUvwhpq8zQ1+9M1/8rhg5po1nOFU4dVWED3pDrrXNM6DOhw505f+piJtk1i2uzHm61kKZlVRdARjIEa69/qA8gf/ldBJA6D/qQzukZQOD5gAcEoVdXXHGFmTp1apaEjkT0CCTFd7JqCFYZgLhW5WjIyxAsjgqKscyo1N4PggLak3kfDL+S+SAcj/R2MPyKACAlxL58CehU5LbilgAQnsdrkRCi80F0/xybvlM6jMy167pr0vRdQ84gcYCTawVLjk2HjfFZkLLVICGfh5yBJCBIQgvHICdB+czp373GuYKsquDhM05kf6HPbRVwKTPCup4RTyJxbpwlhlMTK5FN9DmkOa1v3fh0qdSd+L0IPFzvvA88qgBt3y7S0ZDrHSjyhlYBk37kM1R9R/slq1D44DORLaCp0GXOn116SXq4ZTl5ASR1wMMnr7brgCwH5PFPrw8gt14TAaRfL8AA2ukZQPSYACRf+MIXzHe+8x27MeEG7EYZP14J9NsDEgIgclIMuTVl4Vf4ntBBDwi9ItoQw2QFAJB5GHoMvtAl9oNrlADiyp/oFUDkNUugkGPV5XDldz4vjm/y0Qa39nLwe1f1LkwwhCpZTjc3HmEo62o8+hwffJSteIYYUJwMi0qCaln4ntEyAAl5tl1GQNXziq47xFhqwhBoos+qcm3y+CL4cHk9nMcLCOf3RcUpXNcbsglhmbevrhzl+5V7X1OgDoWSMr1RVv0uJJyo7jWGnhcCH5xXXSXr9f3X7emFn0z/iygIqxc9xTvKZOy6zrbrgAxA9nxGfQC5/X8jgIQ+5A0c1xcAQaI5k8/hBcGDs99++xnkcpx22mkNXNbodFmUA0KFM2NaPmFbh+f4DE0ZNoXf++UBgfJk8jl+pxdEwgcBhMrY54WQihfXoV3UGgTYbh0AkYnvkKEOwdIAQVggTOinqp8Aog3VMgChke4KwZKJ5HrMLmNZ3wPp/uf5VSa4oj7QXpU9P1xv8iDAIxR6ijSLvO4Q8GBbTRgCTfQ5KlrZBRPUBTQy8dNV1SjTVyre36XbtDzqwkaRcRzy/stjfHpIvh86zFPrD/0uSWM60ycF4Vq++a3ue1/lXfQ9o1UAhM9GLpRX7Ivl84Bl84kKIcb4++n5aFLvVNEBGYDs/sz6AHLH1RFAqgh9yMf2DCBbbbWVWbFiha1YhZAr/EMC+rx0V+8hX8/IdacBRCt6GphQQFnpWrVPSM5wFivdMoTIFZOqheVT8GUeEFcCehmAaMPZN1H2CiAz0opXuj9fErqcNGh0SADB93JC80FIFQ9IEXzY50Hdb10+2BWC5Rpj0cvhCv/ywYfPiAh5+com0tBnMqSv0GP6YaCE9uU6rgkYaKLPXmQ0jHNd4KxzN/j/KvkerhLePtgI9WaEhodRh3Xp1x7y0KiTZHioy4vqgpaqoZiud7MuiPieobL33+fhlPfKdT98c66vPb3w4wKxMv1ZZaGo7TogAsgwtF6zffQMIOedd14Ejh7uIV+yPy5abKtguVZBuAIijWip3KsAiIUSz+RTpNgJM0UJ6LIClgtAME6GXvULQFDyl5OsVOAMwXIBiK8Mb06O6YqVhg9tfFcBEDtOtTJatOLonMAdlbAoS18YVkg1K9e9D5noyyZvKdOyUAt5bEjfPbx2OYjspZ1+nNuEIdBEn/2Q1SDaqGJgagDRCxb6HdfwUVYMwnV9VcBIj8eX9CzH6TXMxcKHL/STuodQUhTOqT0hWncUhWVW0TOu6xmEPtHzaAiEVJJ7yV5JlLm2BZy6fJQ3Itz9WcaM1SjDOzlhzB1XRQ/IIJRmn9rsCUAmJibMzJkzzfXXX29L8PbyOf7444NP3xT3ASGAQAgSQqan5WpDS/H6VvD1HiC+GFSX8irygMgKWEUAguuiF4crZ67NCPkQ5NzX6Q7s+BvGgipY6yYnbQUs6eXRYUsMsdL9sn9diYvna6+LrGaVM5IVDGiAwf9dyfQhD7prpVBO9lW8IKGx2nUAINQwqBJ2NQhjQd+3kHswrGOagIEm+hyWPOv0U2TkW6NRbeCqw698oVdSd7jgwwc/mR4s2DNEh9hKXSPHXGT0sv8QLyyOcekdGsJyAUiCiJzPckZzgIFNOYTqhFB9FNpe2bPkg5AyUHUtAuZ0foBsfN6kTQ5Adn12fQC567cRQMoe4ga/7wlAMO499tjD/OQnP7EhWL18ivb+kO1uqvuA3HDXvbl9QJgIDGUCQ70qgHDSodEuK1bpySlT8qL0n8uYRlvaA8IcEFsBK90DZPX6DRkoyHYkCMh8DF1G0lW+Fgpb5n/A8yH3CJHXIL0rZRAiJ9UigHA92zxXTqracNAAIo2BMuOjyyUv9k1BO9IY0PktvpAIO9aATatCJujQyR59tglAXDIo011l90qeX0UuOK8JGGiizzIZN/l9HQBxhkE5Yv1xnIYPX56JTze7QsF0sQ7qevmTMvWFfHVVpVJzgAyzkqGeGkT4TulFIL77OV3pmWdCwof0eHPvXcE+Vr53PkTPFT2XZV4QH6gWtekCi6LqYWX63KeP2q4DshCsXZ5TH0D+dmUEkCYVa0nfPQPImWeeaX74wx/aqlcLFixo8aW2c2h8yQggUlFmK/RTphiZiO6KDc0MX08OiNw7g5MfJ7CiTZB0nGsZgAA+mBPCRHUa6AQQhkDJPAxpmPtK8KJdtIlkdwKJrrYlJ0ldbpfJ2dpYp4KWcijaxVzLn5OxfsJceSAuACmK+9YhDkVGgPwO51WBEBdE+f5W1YCvMnlShr0aBmVvexVIqAIfdUCkCUOgiT7L7klT39eBDwkKZd4PCR/OkC2ls7Uul/rBtzgjoUPq3dCcEtfquyxmIRd1tA6inpALJi5wCdVHXSDiAAvX++uDk1CPQB2dUwYgkI3MBZH63/e8u+b30PC00GtF323XARmA7PyP9QFk0W8igDSlWAP67RlA9t9/f3P77beb9evXm1122cXMnj071+11110XMAz3ITfffLNZtGiRWbduXXYAPCCvfOUra7fZthP5kl135z1mztx5OaMRhi2N5aoAosOINID49rTwGWYSWriiJ70SchPC1RMbbJiUzDexE1gKUizDSwDhBCbzLQgWDDMgfCD0it4P/I39SMNVe1okyPnCsVzX7TIWtIHs8oJQ9r4VShovelWzqDxukQEgvyPcSejLxuwon+mCCd9E7P17wAZhVTwgReDTz/c3FELqAkgoqDVhCDTRZz/vXT/a8t1Xvegijca6oVc6XMoVKst+fIsfhAyX/siqEjr2Rgp9fuX7wHeduXRSn+iFDg0eODbEQyLfcw0nbCNEP5V5TooWQIre0TIg8cFdaLheGYDosYXo0DJZyD7brgMyAHnsc+sDyN+viADSD2U5oDZ6BpBTTjmlcGgnnXRS5aHfcccd5tWvfrW56aabDIBjYxoHi9+t8t2E9hbRAILr48oTw7CYByITqn2K0zWx2ZK1ab4EjHY9wWkPgDbMfPDB8CsoYpbgpQdk1cSGLEeDEw1AANcg9wFhHggfEjlO/k7IkOBh4SMNw5IwRWijrNhXVQjxGQjamJceCv2g+0IipDHiW63MVsFST4Zr0mffRfkgZZO8nOjl+F3GecjKpJaBbqesiktuDCUhFZUVi+OEMggJNd6KxlLWRxOGQBN99uN+9aONMo8HDfxMJ6XzT6/wUQQhRZ4NOx4R2qV1BnPhOG69sMHzq8hOh1JR/+j9lTgPFXlqqWP0MfJcOZ+x7354THz6J9PjSseUvatlMnSF1lUNw3KFYLHfqhBSdD1t1wEZgOz4vPoAcs/lEUDKHtoGv+8ZQAYxdng4sKP617/+dbP77ruba665xjz88MPmfe97n/nsZz9rnvvc5w6i20ba5Ev2uzv+bj0gUvlCecBwDgUQrfxoANMFDO8B/uZKqnaFIUmBuEKvZBgU4ACeDwAI4AP/lyt2nHxwPVtMm5pVw5IAIg126fHABCt3XAeQoC+MyRWChTYpN3iOMuARe3/QoySrcnUBhJj09WqXdpNr45wGg/6ZC8dIDRtcn8tIQJtFGypqr8h4WrBAGw+uyR1/88V/SznUSWAvggg+Z8xpygwBhxelbAWy7IUtS/TMjbPAixMKIeivjpeoCUOgiT7L7tcgv68KHfp9DIEPubAj90mSOkBCgitpXHqVpVcD5xWBhgYUPX6XV6dI3rkQKrEIosNXXfrJ6pt0Az35k3pIhqy6vCcuUKkDI3UM+SJ95JOXfrYo6yLw8HlPcmOukIju8l5Jve8ae9t1QAYg2x9YH0AWXxYBZJCKtce2+wYg1157rbnlllusx+KJT3yiQWhW3c8222xjLr74YvPkJz/ZbLnllhZA9tprL/s3QAg2PtxUPhpAqLzpBeHKPRR/mQekCEBkwnZRVSf070sK15MqjX8knwM8GHoFbwh+1wAivRH0hEjFKQEE3g0LMWKXdXxPr8f6Dcg1SQCEky0nLtkP4M0FIdJDoidJtqdDHvQzJydP/Z0LPBiSpoFD9qONd73y6HoGdEJ/GYD4Jiv07YqjlseHlPT1AYiGthBvSFUIqRPzXmR0hMBHkDFRADhNGAJN9DlMnR0SXkUY4Lj0fQzd60ODh9YfMpzUl99GfWGLhYgKf/JcjtflNeXCUnIMdWJyZa5Vef13l27r6EUUrkgWdfA3qYPkAk6Zd8QHI5zzynJIpH5yLZi59HEvEFLleZXPThms+mTvhQjHvl8uT4hvPvJ5QdquAzIAeczz6wPI/ZcWAsib3/xm89a3vtVuJRE/w5dAzwDywAMPmNe//vV2J/T58+fbcKlly5bZXdC///3vm2233bbyVWFzQwANvB+osvWNb3zDtvfXv/7V7LvvvmbVqlWV22zrCXzJrr79bgtbVMYuAMnyFzwbEYYACD0T6IeKMqfMPIYS2ubEJydFW/1KeT/WiER0adxhMsCENXN8qvXq0AshDQBChgy3gnJnGFbSXwc+5Mof2pEJ7jNTT4uEHXpH5ERKA14+I75wCddEzXvmOl/HZut25WTk8rLo0CsaAZyMy0KzfJN7dl8cq2za6Pc9HxpUfe+YnhjlCmcIhMh2XZ6muu92kbciBDzsvSvZ0K3IAOK4mzAEmuiz7n2qet4w4EOGpfId9nk9cCwWaVwhmdQHNkw2zd+Qi0X0BHPxgn11oAfnJRLyLZi4vB/6ueX1dL2r6Qa4ie6ekoEI9Lcvny7Ea6v1UtE+I/bYtAIg9R7/hp++Mr9aL5eFY7n0uO/Zc733ocBa9jyHglSvENJ2HZAByMKD6gPIA5cUAsjhhx9ufv7zn5uddtrJvOUtbzFHH3202XHHHctuUfy+TxLoGUBe97rXWTA4++yzzROe8AQ7LCSP40buueee5nvf+17loSLECp6OQw891BxxxBFm6dKl5qMf/ag544wzLJj88Y9/rNxmW0+QAGJDsFJFK/NAuKKvAUQqYU5AvE65Ai9L8LoARCp13aZvcpXeD/xuQ6/Wb7C5IPB+6ARxa/CLcCjAAf+WjTn1cLhyPXA9WendNCSLEzXHSNl1ktwRwpaEe2mPh02Ax+SaTqR6cpLAZdsX4VI4j59sVV/FEktvh/aGyPY48bvKZMqwAx36YCdeluX15InoiRvX6At/kPddeyrkxFzmIZH3Qr5zrg022WdohZeq77DPENX3uqqXRd6/kDG55CnPa8IQaKLPEFn145giePQZ3lJv8nfXSjbPz3scOoszGjKYc+crqqH1hDzOVThEejd4nXJM8nd9TT5PiEvmrhCqRK9WgxDXRrC58C7HxqrUN77wLDlfSU+Ia6Ej09OOhTVf1ayqz6DW3V33RW0+y/Z17qLst8wbIvW1XsQp8szLdtuuAzIA2eYF9QHkoYtLQ7AQ3o8qrt/61resbfmiF73IvO1tbzOHHHKIGR8fr/o4xOMrSKBnAMGq/a9+9SvzD//wD7luETb1kpe8xDzyyCMVhpMceuGFF5qVK1eaww47zCAh/RWveIX585//bLbeemvzgx/8wLzgBS+o3GZbT3ABCBUwDHYmVUP5E0CkQahDmOSkw8nQBSBFBq+WlcxbcHk/LIBMCABJYYQ5J1SW9HrMSj0Tco8Orh4WJZqzvC+uS+aEyPHSUHdBCCZVXSELf5OVXqT8dMw1vsME71rVdq3McyKSK5pow5d4Lg2E3OTrAAw5Sctr4DgIsDocq2zy18ZyEYyEvlMSpOT+NtrQKAKRIkNCjyPUe1EUiua7Np/HQ/fpNSBasiNx242P0GfLe59EaVvXMUUg4guncXkhuFBB3aD1o8xTox6QYCE3U5V6gh4Rgg7AA56OEOhwQQjHJ2Xhek9cYVD5hZ0OgOhwLBxH/SsXR3QFR+m1xXhCckI4h8i5jzoua0PsL1LmeawDH6450yfP7B4ID6mUt65CyXa0V7kMRPh9FQhhX9ABC7aaX2qg9/ou1j0/A5CtX1gfQB7+daXrQ3j/f/3Xf9momzlz5pg3vvGN5t3vfrd53OMeV/cy4nkFEugZQObOnWuuuOIKs99+++W6wY088MADba3pfnyWLFliEJrFSlj9aLMNbRQBCJSKTaZO/zFxVxuRmdFcsgcIV+MwAeoJSSpsrZyLcj8IBUw+X7luQiWiT1q3PQ1PJqDPGp+axRRn4xdeDrSn8z2g1F0QIsMHGOYlS/1i1Q6eEMqtLP+DstEhDdIw8Rnq8lrkaqhOKM2voCb3I+kXCZydJ1PKzq5KChjhZKzDsEJKZ3JCl9fhCoNwTfwcnfYkFL1PctwSQlyAEwIiVd9dDY7yurLrKai6VRRqVeZpKTOGmoCBJvqses96Ob4MQl330+UhkF6QIgDRXk4bKpqGXWU6S4SSMr+NOoLgIsNck3yRDniwD6m7NZDY70rgS8vVZeRqnUK4kKXhewnB0npH6rKy3/G9XtTo0meeMOVeninXPCvb64Jah+fDBx6ynRAI0XrTByCUpeu6RwZAFryoPoAs+VUwgCxevNicddZZFkDuueceg/As/O2SSy4x//7v/26OO+64fjw+sQ0hgZ4BBG4qeDkQarXDDjvYpnHzjjzySAsM55xzThR4gQQ0gEhFKgGE4UrSNS4NQdfkib+xBC/3z5B/k8Mqctu6JlcZEiW9HyvWTWShWLJCFScwVsECgOjcCwKGhA+76aAIueJkLif1iTTpchqqQKV5JgQQ9pP0ZbKQLClPVuKiDOSkLiEC8tJ5My65yRVIJpXKlczEgPDHbOv7wntuw8ZEdRlOLlwJlGFa/C6kSo185himJScu1zVWCWOS58vQwhxIlyRa5mRSsTyvCxzlu1PUdhF4lBl7IXJDG03AQBN9DmsiCDXAq0BIpgeEUenblJV6jN4P6ios/PgSzGWOm/SaaN0rxxx6nXy/9TutvaMdPZAkneMj9U1ROBXDUrlIgnOlh0LPW9rTkvXlqJ6l9Ye8Hl/+Rxn0hzyLRfJ1PjsB3o6yeyb1qr623P1T+nKTBpCtXmTMlBqhUBvXG7O0GECwf93PfvYzgw21L7roIlv46JhjjrH2KxbX8UEu87HHHmtTAeKnvxLoGUDuvvtuGyuH2Dkk8sBDgc0DkSz+P//zP+axj31s5RF/6lOfMo95zGNsdQL5AZk++OCD5oQTTqjcZltPoCFw5W2LzJbz0iR0kQdiw5TSXAXXZoSu65KTFsOvmCwu9wSRngMqN200uSZAtsWJlbkfhI+V6xMvCL/XLvyZ49NypXgxWbAfmzuyYTLbV0QDR7bPCNoXZYUpB1l+F6FeEkCkV4THyVU8KUuX98IanOkkk03ewhhGW7rMLsFDx4Dr1UzpbcmMYzUZ5/NAEiNB7hUjw65cXpHsHhd4UTpGSOKG4fOQTYbpmOQYXc9g0aqqhhDXxCr7lu07c1A8QOKDh34YJ/ZZKFlpDgWQpY8sG3ooRASQ5KkqghB+Z70S6T5KfG99Zcm5WCIXaDIASRPNmYfnCrlKvpu0Ff6kTuRYXQtN8v3QoaAuQ1/CQKIn8rpEelqlPijTKThWeiZy77UIj5Lvts8D2z3GRB/psCutJ1x6uR9zfxlwsA9XmJX8W1EYV87zK0I1y0CkLAxL3gcpi5HxgMx/cX0AeeSXhR4QVFydnJw0b3jDG8zb3/72rkgeyAvgccABB5g777yzH49SbENIoGcAYVu//OUvbZ4GqmChDC8Seep+dt11V/Pd737XPPvZz8418b//+7+24labHoSTTz7Z6M0YAU/33Xdf0OVLAJH7gECpcMVJGtUuD4juyAUg8CTY5NNxcQAAIABJREFUFbh0EpQGr14J06vb0hhnaIEEAwkgy9dNGIRhAUZcHhBCAZLQWZ2K4yd8JICDZPZkvBJmWOoXP7OkdLHyRMhA+1uMTzXMN8mHZI1ZMHGVlZQTvQybkiFTHC+Nf63g80ZKx5gIgZCcMcEkcxFbTU9S5znIl8iUoU6uSdwVniWNjLLf8b2M2dYGAMfvMvLleIogRMvTFepVJ4Y7J1tPjf2Ql7YMPDIZKKPLJ6sIICFSr3ZM6D3KDEfH6nVVAJG6FeFXOlxUlxaXelXmfED30UPKNq1eSiFISkIvIrmecQ0hUnfI0FSpO/gOSujg36RXFX/zQYd83l2hlhyH1Ck5r6jQfzxf99XrPkVVnqrQMEwZZsX7IxemQvqU+tMFJq6iHhJEfPKWfY8MgMx7SX0AefSiQgBB8aTXvOY1ZubMmSG3JR7TZwnUApAFCxaYW2+91YAe4aX44he/mLmr+jE+PAzYU2S33XbLNYeEdMDNmjVr+tFNX9oAgPzoRz+yifiZ4TF1anD5YQLI5X/5W7IRoVK68HoUbUbouggJIHT7w1gngMhJTRqMerLJJmexizoUqpxcmfuB0ruPrFlv4P0AfBQByJzpUw28IExKl/3Q+0HoYEUt7flAf4QR6ZWgRwOAQQjpgo80CV7nhlBpsz3mzOgEUE6szNWgJ4LXoWO3Jaxpj5I1LMSGhzkDwgEgsu5+x7OUJIbq+OyiybwoZEJPXtJQ0EaDfH70s6i9IPrZlhDiNFYCdykughG54qiPq+oJqWrURg9IX1RsT42E3jNtXEpjnx4QvZGrzI3TCztyccQurEwkZXbl3kYuL6tL50idYfVFQI6H793D+axkVeQ91QBiIUFU3SsCD5/3k23Kd93lWdULJ/I8n5fAji8gNNO1mFHlASuTvdwTBu1q8OD5ZaGdPk8WZaFzYDLdmnqomS8qZS3liN8jgBiv/YoiSO95z3tsPkj8DE4CtQAE1QFuvPFGu08HdizHan+d/T58l4WKAyeddJKtQCA/oFX8HSDSlg8A5Kc//am5/vrraw2JAHLJn+/KdkKXhhq8BDIRnV4RrUxk5z4AgadCr8JTQbmUPtvkRMlzCSAZFExssF4PAMija9dnAILvqWgJAXOmT7NeidnTp+XK46IP5kusQQgXdjtPx0sIWZX+nfuM2OtJw7A4VshOJ7rPmoYyvEnOiftfYsDnZKjCIDQkyHuU5ZCkyeMyv6MrZ4WhHGI1kzkssn/ksyTGQpLXoj0fuiyvLI/JEAoaCS4QkffdTt6Okr7aYNAgoic3Of7cyqSIV3Z5ZXRYRW7FNhBCfC+fb4XYl+ju8v7VerHTkzSEuNpvwhDYlEOwXPerzHB05Vbgbzr8iuFTsrKgBAkXZNCzm30ndAC+c3laZRgWrkd7P/T1uAxr7dnuvPNJLpw0Wrlwo+cD6oAQz6nUB65FDK0viuCD/er3p2sRoWCDz17e27rn6nArPlcu8Ch7JvU9dcnUlVNHHeuDEMq2Cb1TRa5ZFaw5L63vAVlxYaEHBPYrEs0XLlyYG9pDDz1ktttuOzMxMVFlyPHYihKoBSAvfvGLzf3332+e+tSnmm9/+9sGe4HMmjXL2XUdgvzMZz5jTjvtNPuPJXd//etfmw984AN2f5APfehDFS9zcIcDQDBOlCOeMWOGecYznmE++clPWjhzfdauXWvwjx+8ZMid+fXNd5rZDg8IDE2Zx5CbKApi3zlh0QMCo51VpWRoFMehDVw59iIAkeFXj6xdbx5ds94sSyFEAwjDrwAh+McwLPbF8dm9RFIPh4QQAhS/X5XuN2I9FJMIRUrCkSxkjI3ZECz8Ljc+9EGIXnHiimYZsLn257AGg6jYJUPIEqMmGW/RKlgyiSC8oZNY3x1+lZa9zEK08oYFJxq5Wlbm3aiStM72i94uTqI0NrS8aAT5VvRC+qjzdstwhbp9VMkxcYVE0LAchiHg0zvYNHbevHl1RDiS57iMvqrwkRX2UOGssrIV9Ueo56MDJ91Vr/Tihxyvz4vne899+R7wtEujXy82+CrryXfHB9t6LBI87PmO/BAfwOD4XsMv+eC68jHKdHLZQ+/ypHE+4Lll4OHqw+XRkvod3+vqgvibTk6X8obe2XbBVsFVosquvd/fZwAy++D6ALLyAuf1oW2kC6BQ0m233ZZbQN+wYYM599xzzQc/+EFz77339vuyYntCArUABPDx+c9/3m5A+JOf/MS89KUvtca361OnChYeDNz8L33pS2bdunW2WYRlIfn8xBNPbFUp3vPPP9/uzP74xz/eQtknPvEJmwvzpz/9ye5boj+unBEcc9GfACBzs9VuKGAayzIMqywRXa644PcqAOLzgkBhSoOc4QUMgWLYFT0gy9YkXhB8LytUAUAIHzIMizJipS4JH2gjqYqV5IQw9AowYj0jaWgDlbqVWbrhofaEMOTLQl0WhpUAi1TwErhCAERXn9LnM48F18DkUhqfRZMNPRm85zL8Sv/O/+tKWb7YaZ+BwrwYeV6RB4Xjx/GuyVv2I8v8FnlCdHxzVUAIDW+oAyFlbeuxFoVh8ZkdBoD49M7mDiC9wkcnZKqjI6kv6dEtSjrHsa6QTV+oplNfKA8jjUyfB8SlI1wFLGQ7gwQQDSFFXkO9UFTHmvNBQp22fOe4nqsMQES+UVGfZWFYcvFGLvTocCz04fL4Qu9st82C9gPIrP9TH0BWn++8vrGxsUI7EsWUkNv7kY98pJ+PRWxLSaAWgMg2kKfx+9//3mls9yrtFStW2FwQeFcQluWDnF776ef5iB3cY489rLfm+OOP72ratxJ5wR/vsAAijTQCCFbyQxPR+wUgcuBczS/K/wBwwPMB+KAHBCFTMLi5mr/FeBJ+RQiBZ0cmcqN9eDdosBM+ZOI5Qr0AHytUpS2OlzJD2+gLIWvMB0m+m2IwDg0hVNpy4neFT8lJWRr9nTCsfEUvGT4GWUiDxUKImow0GEgg0CDi8+a49tnwhS64QMC3w7rLsAl9d9gPn29XLgsnSq7m6f4k7BT122VgqMplEhI0hGiACAEOOZbQFWl779NY/mEASPSAuHMnfCEyMvSK76zOoUuKZSS5HXKRRubd8e+ywAaBQ1a6kjpW/+7LG9CAkQtdVLmEmVGa/l3mgXABxVdFj++E1gtF+qDsPcje5dT7EeIV8b1noToo9Liyd97nwfCdV9Xj4Qu9ory1/oKsXWGs2fPhKLiB46F3dtx26/YDyMyX1QeQNb9wXt9ll11mPSCIsPnxj39skNec2RDTp5tddtkl21Yi9LmJx1WXQM8AUr3L8jO+853vdOV/8Kz3v//9NuSpzR+EqO25557mK1/5Sukw6Wb8+Y1/NfO2TEIhZDIxjGcY0TKMSBqNugMfgGCilDkgmBBzCl3uIiviamV8s0w+Z2IlgAHwgepXyP+QAMKJGP3IXdBtHkhWoSrJv2CiN41+ekHwf5n7AYMefa1Y20l0Z219TpAM9UIfc1DyV4RiMR+E/ZftR6ITyKURLAFAegmkQWJhKt0lXhssvlUyaUjQUOczAcMBYVkaPjoenWTTRQlErodQh2Vx/HZ11JEP4ltNLX3A0wM0gMhwMmnI6FAs16Qr74Gr/5xcHauNemJ2QUjodRUZRSHeD5y/dNmyoYdCxByQPPxLI9+X9yFDWOW7TB2BNljpSuoNhIrSayyPdYV5Jjkh+TK8LPmrn0kZdkMdK8M2u6EjLeme5pVxQQb6Vy50+PJB8N74IEQaxdn7GZCf4QIVGf7lyiWTOqHoPS3ylmgjv+x91xAooUKDR8hmg67+fHlzWt8VhaxpENGykrpv+aOPmt2237b9ADLj5fUBZO3PC6/vb3/7m9l5551bFVVT9ixuSt+3EkDmz59vACGveMUrcrLGTpTYFAZJQ239YKURHpB3vOMd5mMf+1jpMCWA2BAsxnKKECx6QOyK/rROUrJL6UsA0ZsQFgGIS1HJtvSuvkwAZ0iUTEAHhCxft96GTGHixYceHFxLHkA64U8Md5KVr6T3A7CzHOCBSlspgOBvBkYmK8MA1tL9PxDmNSvdc4ReEMowg5Q0FEt7QDIQSj0yNAx4QwkBDJWTAIJjuIopw8eyyjgiCdVXnlGuanGFslOMoAMhBA8JdAQQ3wSMsblCrCQU0CBxgYp+7jgh+lb6pGwyQ8ezQou2XZNoKIj44ANjk0aHD0LYv35xi6pp+YAoAkip+hvKAVXzPuz7i7DTtMAF3lu5n5KFD1Fml3pLw0dHRyaeYO1RlQAioYMhW9ITgjHpnAWfx0J7QOWcQV2iFzD47vsq6WmY8VXEct3QMmNfQ4grPNQFHUVVr3y6r8i74HsYfV4yXfGKet/VTpkXxKWb5DVrONHeLupk3iept/Gd3ksE8gGA7LnDwvYDyPgr6gPI+vO6rg8FlPbZZx+DMCz8XvTBxoTxMzgJtBJALrjgArvfB3aofN7znmevHiXRkG+CZPS99957cBKp2PK//uu/mle+8pWWoh944AGbAwL33k033WTdeGWffgKI9n64NiGUK3C+8B85Zhwjd+ZNPBJJ9SlMlDCwk70/EHo1kXlAACQ0uNEeJjwYn4AChECxGpbMv2C4ggQQnftB7weOgedlPQBEenOwgo9+EO4FL0vqBcEkDGOdniS7R4jYJ8QHIPRgyMRxhpRpCOGEoXNACH6yNCeO0dVv9LNCYwFjp8EgQ/FkGJn06Og8Djkxuu65hh32WzfkQl+HnCxDAUQbHL56/y5Doyj+WrZr76NjB/ayd1Z+H1raVxtZ0iCJHpAqEg8/tgp40MDnObLkrgy9csGHTDR3eT50NT8CSxKWtcGqL+pTGbLFHBLqE/seTyYLOqza1xWGJcJxpKdcbmgLL4mrKqBcgEj0YbU9hnhnWOLbdaf0+yr/LxcepBfEByTO9ku8LhpairwkUm/idw0cGkz08a7QuTIQ4TX5QEmDB+U0jmIlnkUdn1cEALLXjo9pP4BMe2V9AJk4t+v6AB6o3IrKV8wFQTiW/iAPBAnp8TM4CbQSQHC58HS8+93vNhdddJGtxYxd1S+55BKb7N2mD0Dp8ssvNyjbhlLEz3zmM83HP/5xu19JyKcMQKqEYJUBSOYBSSs0SaXqGms2Gadxzgy7wmQpS+EiCZ2hV/j58Op1tiwv+uOYoChh9KP8LiBk7vTxDADkCjoNfhl6RdCRuR9Mcp9c3w0gKB9FL4j0dBBCMBnj74QQGtyUgVx5lHuNSMOWxquvKADbkPHgEv60ccFKXp0JqLuiFzdPlBW+LISkIMVwMg0gNiQsVbBMepX3W+8H4MrPKCvT65o4XYZDEYBYw0rtLO9qt8uIUEZHyCQvvTLS2Cl7Z7Unhcf7QMTlBdF9RAApk3r176vAR+aFdOx2TriQYME8NakPZaK51V1p2JULPtgWdQPGKkO0uCmhPU4U2fDlGEjQSOAk0R3y7/hd5pBJbyphRAMI9WLn78VAkvSd1CLvAg3xjsrv5H5EPK+sap9+Gso8LK7xVH2iimDDFY7Fv3XtAyIM3cmA/VwwTp+MqMO4OIX/E0TkvZMLTLa9VMeuWrF8NABk7FX1AWTyZ10AIsOu8HvRJ2QRueqzFI/vSKC1AIIhIocCYVcw7AEfyKvY1D69AIhUrC43sZw8ZSlYuv59scV6BZnHS2PabkCYVqLizudLVq+z8IFcEHhAYLxzxW761CQpfO6MaWbejHEzV+SBuACECewsuctJHaFXOe8HAMSGYE1CUxsD5QovCI1yG44FD0KalJ56QiSESIOb104jgaER+D8+kFl3SdlOuVwJMT4DQ8LHOq6AppCXGdzp3h+EClT0sqWLkc8iSgzze4IWPU1ykmdOiqy2I98jvdLJvWZyBky6uhYaA64NEbk651utk9fuM+ylkcNjXKEYvjhsX4WtIiOmCGj0ea5qMxJ2XPorAkh/tXoRfOhVaeZ7YAQEdUJ6CHxIGEk8GskO6K5iGtp7LBckOvklSaU8+R1z3KyU9EptakzqkCi9Gp7oOHiik/0/ZEVA6gz8jd4RCSOy2AZ0gswd0eFeZfDgCjVyGdi41Ny75AGYTGcE5JsUgYgP7jJ9rmDB52WVYXLZnJzeMwJHWV9dgCXuMa+BUKELocgQOjmvuXLrRgZAzCH1AcT8T2s9PP3VeqPZWl8AZHJy0tx+++02BAm/yw9DqMrE46oYhXOwy/j+++9v8yr4Of3008uaG5nvCSA/u+H2rB4/VzK4shGSA+LyfjCOmbugM7QJkxvhQytDvZpD9z9+cpdyTo5yDxB4PgAg2AtkKX4CQHIbEU6x+RjzZ4xbL8j8meMWSGBA01iWeROEGwII+rbgIXM/1k0YAxcpdv7DBIFJCMoaNe2nTc1BCD0F+Gk9H2k4FpPSpZFIgx0/JURIo7a75nr+kWNYBeRGY0QaNPZaJxJjRXpcpMco826k4yW0wbujq3l1QARGRLIrugQ7XepT9sNwC25ORuOkKoDoCd4XLiAnyZwnwrN66qr7H7Lq6VMCUjb0ZNUFEBcQ6dr7ZV6QCCD9U9d14EODB8Mj5bspgUJ6OKzHVuyx5AIP5sPpDVTp4ZDeFQsbdpOg9N/EZCfHDYasBBB6C/GTuo/7qWIxBjQhvqP3MZf/wfDOsTHrIZH5Zi5Q0Tkk2jviAhJXaJDUFa6QraLQKP1dXf1QtLDg2iNEPqU+iJB/lx6OonnWB1E+T5H0aHSAMAnBKvKAUFeNnAfEHIos0hpKYr0x5qeFAIJ97LbZZhvz8pe/3LaP6qVnnHGGjWD53ve+FxRGX2Ng8ZRUAj0DyNVXX22OOOIIA1eWjqOrEkN30EEHBd0UtHnxxRcHHTsKBxFAfnTdrRZA9EqxDq/BBCB3Q9fXSGDgyh7DpawBn+4szgRKl+s4p2TTVR9MkJ1dypMyuZyEsWv5inUbbOldAAj+AUYAIhZA0jZoTAM84AFhGJYvBwQAAs8KK0ixJC9Cr/B3w3/MAckByNRk8p0xzYKIKxyL4Vezx6flJl5cfwiA4DhXudvs/I2dsAoaKHLfEml0oD9rdMhVNhQjSEPFbOniGUkJ4xAISYwEBUQb87spy1CiZBLrVM/S4Rp8JkPDsPQz6TJAXF4QTpCceH2lg0Pea59xQRjoBkglMNFJaEgXT9FekAggIXesP8foe6VXqvl/XemKXg8JH3xHqS/p9c0gI1fdLtmnSO9hhJw1gAf0FsKyVtvy4Yn+hD6wRTqyf+liSu5vqV6QhTakqDLwkBCSLsRIAIFCIJSkeXJYaOhscgsdkFTP4/ziyzuTAEMDWL7j+D4ERFzwrhcxip4KCS5ZuKfanNcFJxouQmCiytPpC60q8ny4YEPKgtAhdaQMwZLwoe+Jr7LgquXLRyMJ3by6Bw/IOYUAstdee9lIG5Tjveqqq8wLX/hC84UvfMGcd955Ztq0aTbvOH4GJ4GeAWS//fazeRnYtGX77bfvKmeGHcLjxy8BAsj3fv9nM1fshE63OL0E0gtik8rECreGBig6nYCebdyXhgdIz0bR/aExLiu86MpUdg8Qm/ux1jy6dsI8vGqdWbZ2nZ1oqXRxPQghggG9pYUQlMdNjGoaaB3PSgI1rHol4QMVsCbXrDcG/9ZPGIMQLPzjJ6lDacz0BD7sT+yjkoYwUZ4YB8YDbwzL9MpJQIedSQOFk4CcZHP3QMGHNjzsdcqVToAHPDnSeYiVzPEOQNm9U2ZMy8oKMx8EHpyifU04LlcMs5zMJIDAANFhF4QPGZ7lMiBcUOvypNRNRg/RJSEhU/0GEC0L6QWJABJy13o/phf4YKUr6Y3MhVKlsEGPrAWLdJNUwgThA+Gj0F9YKLFFM9ZhY1ZRNIM6yy46TCb6ywciAA+5OIH/08iW8JGGnmYeYP3/JDGkAyHjU61e5CIHoYIeaYJIDkLSfZRkArsM1dIQ4tp1nUa1L7zKByCu0C2pv/C73ixRPlHoz+fVcIFB1TCpkKfX59Vx5b1pnaE9xdSpVo+ltoALPHCeqwIWjh2ZMrxjh9UHkMmfFALIFltsYTeORhEhbHSNCqtnnXWW3Uj6+c9/vnnwwQdDbm08pqYEegaQ2bNnmxtuuKHn/AxZGi3kWvCAgF5BqaP8IYB855pbzBZzkjK80q2K1XsYnzCSXXuB6GuXHhAa9HJVTsYs+5SsDIniiiCOtcngaVlaJrQz/wMeEIDH0jVJ+BWAhMfyepCLseWM6Tb8yuaBzEg2BGR/MrSLu6vbxHOU9F0/ab0s6+H5AHysTSEEHhDmgYjwK0yuFj7gBRmfZsaQcyI2QSSAQKb4XY6jA12deGwdspabAEToEM9lUqlMnKcHKisd7Aqz4A1Fm5jBp4/bUDJU9SKEIB+EK5d4PghQcnd3OWFpCJHPjJy0IAO05wMQrJjq0p++50//3TVJFlVuwfmyektHLFOyajSu977MWzEoD0iHfxNPiisMy2eAxBCs/mhwH4DIvA+X50OW2SWAQBdJfac9GdY7m3o2uEBivbbpBqnwADNcFDlx1mMLvQV9hd+hs5zwITwigA0ZgiXFJEOupqYeXwsYSQ5cDjawIEPvB/SiXaQBgIzlFjnKQrNkrogM1/JBiFwk67z/nQpersp28h0pM8xdIZ4uCHF5QSDKUE9InadTv+sh+Sy+cFSpswhoPiCR4VmuUFO5UIIckJHYiHDq4fUBZMOPCwEElbAuvPBCG+aPf8g5Puqoo8xf//pX85SnPMVgM+z4GZwEegYQuK4QN3fwwQf3NMqpU6fa0mhIOA/5IFzp+uuvN7vvvnvI4a09hgByxm//mHhA0jKKDFmy4UNcvefu3movEHlxGkBg7Frvh5gck+TwThlYKjVdOxx/1wDC1T7kM6AdnYD+0Kq1NhEdAALPCCFHGv9bz5pu5s1ME9EdHpCkrG8ywScriMk/G3pF8Fi9LvkdEzkmdYZg2YkV/6YZM3PcmBn4lwAIS/PSE+PzgNAI4TXK8rmZoSlWnVjiMjP006o2OnyMIJiFXXB102Vk0JOT5rIQQBiSxxCyJJm+4wWhEeHykLmMc2k8wAsyE7vEp/HgekVN7g3iAhz7vIhqWznQUWCtQwZ0mJYPQEJfZN+14vx+54Do6+T/6ankpB8BJPTuVTuuzPPB57IIPrTnA2FXHY9G4o3lhqsSPliNjzqL+xSxKmDmraXesvCRAgjDR7EQAR2An9QJDLlizgdzKwES+DC3Q3o1ABqED/mT8JHmxtljoCOhK+2/FEjSBYh8YnM+NCvTL8g3gEckTVxHSXLqHC7muEIvtTc0mXuSS2JpYf1++uYnl2e1aH8S6UXh3Fb0pJXlgVBHudoo8tjIOYS6gdco/0/vhtYfLvDSpXZzgKJKjXOeH5md0Mf/b30AWf+jQgA58sgjrQcE8IGcj0WLFpmtt97abgHx4Q9/2Pzxj3+spozi0ZUk0DOAnHPOOeajH/2owQ7l++67rxkfzycLhW7kgnrM2LwPLrGQz5e//GVz8803bzIA8uUrbjSz4AGZkih2rELT2ExK1yb/mERdFoLF5GnEHydehAm7Ooef/A6xqph40ZaruhHvA/eroCEuQ7AwGTP/44GVa82D+Ldqrc0Fsd4KTKrpqtzsGeNmyxnjZsGs6WbrLaZnYVjSAyIrayV5JJ0ck0muIgI+8I9hWAxR4KQL6AB82H/T7c/xVH7Mp8g8IKkHQcrTVrNJAUsmkCNsSk4e9BRwwuaKHr0lLgDJhV/Z0KvU6IABgg9DLGBnYGUzDR/TECp3d2dVLLmrO8akPy6PlzQaXDHf2qjwVcGSRqBrwzSMJRc7niZMUnZ6tVRO7tpwCNEPrmN8oQhy0nedV+ZV0edwvDoMKwJI3TvnPq/M42GNzDQclQanTDj3hV25cj468NEJp4LeA3DgPYeXg3sUQfetAXBQT61a29FX+PtaQgj0Y1pAA7qFOWCqkIu9eoIHPR8MsZLQIUGjCoj4ICadi+Sihv1dJKvLfBG+w51k9yl2vxEucoSBSQIirkUCXWFLA05RhT5p4Pf3KUxac+monDfHU8XL593geDVsyOtwQUd2ngc65PkAkO22WdDaKlFcnDXTX1MfQNb9sPD6HnnkEWu/3n333ebYY4/NFtJPOukkM336dPORj3xkEI9LbDOVQM8AAnDQHySKIyG9ShI64u1wfJXPd7/7XZt3MsofvmRfuPT6DEDo6uau4UiUtqv2M6bZEBmZKNhlZKZQQQOaq3iYIO0kujaJXWaOg1R0hB96Ydi2C0B0BSxAB+Bj8Yo15r4Va8xKTL7r1icTLD5YcZsxbraaNd1sO3uGsV6QGeNZ+BAOYZlKjC/ZTV14P+wKIrweE8asSgGEoQ1YScQHz0/aj5k13WT/ACA28T3ZgwTVuCSAYOUfE6U1UqzBAu9OstcJISIJD+vkmsiETbkqSJnJcDKGaLBiTlZWU654WgNE3M0SANF7mvD/eiy4l9orwf/rsKjk3CQZ3RoNaciVhgQaGK5J3Qc5nCw5PjzH2sOivSp1q9tQiq7yvF2x1QEbEVYFEGmQ6HwtDSGQF3TAtgu2GqohQL2zbNmyrPreqOlRV76RDLfC9fj2+NDwYfUhPJdih3ObdJ56eaE3ud+RzXlL4SPTq2kenA0TJXjg58q1xhBA7N/XQtF1dCPDMB0boWVeDuo2wgdcBjbcdGontCoERAgtDMGSoVny/Ow4kbie/o37h/DdTXRDd3lffs9y5zJkS+eXaZCADpIQYvVUGhnAd6vM+6G9+T74r7O4EdqWD0ByoWdK/+iFGKmv9DWFLqi4SoNDjhFARk3jbXrj7RlA4kYuvT0UNAT+/dfXmTlpEnoGIGnoFTwg8BzUBRDp/XABCA1J9Ktd2DTKsxyQtJIWJ2a0bff/WLXO3L9yjVm8fI25b+UaM7lyTTLR0gOCyXLmuJk5EwAy3SycPTOrhkXjX+56jjwSJJwjjjqX+wEIAYBgUmdYAxNaCBIDAAAgAElEQVS4GUpAz8fsGQmEpKFY0pPEPUkY4iYdBqjDzwRTemRkSWHIBJOkhhBOpMwDYaUwZ+UbhlowEZUroR3rOYlNABhh/4807wMApfc00XuDSGBgc6GhURJCuGKJCUxDCL1mEkKKcopwvNzR3T7jY51kd5cHBG374relke97A/sBIC74KMudigDSm04MObsf8CEr0UnwkAshDAMlaDC0CvqJ+x2xCIf1ejA/DeAB4ICewu/QW/ZnWjyD3mF6b6XXA4t6OsHc/j/1hEiQsL8LGEFeB3QGc0HYFnWJyzNi/5bqGradnZ/mlNC7woWRtB1d2jfxdiS6ETqW7zwXzrjY44IQ6puOrsF6UrIYUhVCqJeKkt3lc+aDCq1n9HEaYMqgw/U9/+bK66AO1J4SOa7sfLGAK8fVVU0wPW5kAGTma+t7QNb8d+nCDrwg11xzTdc2ElgQf9Ob3hSijuIxNSXQM4DU7DeelkqAAPLpX11nZs6ekxl6dp8KsXEf8xZgbFrFblee8h4jufpXxQPiAhB5g3zwgcmZ4Vc578eKNcnEi0kWoUUYJzwTCIeaPcM8Zs5Ms+0WM2wYFsvgoj+GhiGsywJIWj0GfWSx1HYiR/siDEt6QFLQseABANliRpIHAghJq14xnE2WtJXwkIWapTkoTB4nWPBYhiPYSl6pgU258Vro9ejK/7Arn6Lspq52QwOAOS3Tptowsmxnd2xIiD1NUjAhSEkjX79kenWYE5cMjer83u0JkdetPQldfaWhffi7rKAl+5KrqXIVVEKNbLcIRsqARCe86knbtxKqZRaiuKRBET0gIRKrfkwZgLg8H9ytvLPpX1IOV4eW2sWHtMIVvLAADQkedp8j/m3N+iTcShbGWAvdtD7RgXaxZE3yf+at2Vy2iSQJ3VF+W+7dkfyeenehC2TuB9S/TEDXMMJCFhJIpHdDnivBJIMQ0T4LfLgS3FnuF4sKaQU9FsTINlJN9ZUEEeSN6PAuhmtJ76hdGBM5d/SGuAx2+Te9wWEVvSL1gXyfZRtFx1i9R++GAwyK2uR3OsTKBR18c8rAiCAj37SVy5ePRgjWFgCQ6dWVxEa8e8UAcu655xrkgaxcudLMnTs3F4UDAFmyZEn1fuMZwRLoC4CcffbZ5qtf/aq58847bS1lbF+PWsq77babOeSQQ4IHszkeSAD5xEXXWgCBAQllAsVtASQrW5skbRNAXKV4OSljgsXvCPWRO4czYRzeC07GzAGhcsMEQiXLlV5pkONc7v3BUAQknT+Uhl/dv3y1McvXJCt+mJRhZGP1DEnhW0y3QDBv7iwbhrVw9gxbBpeJjNLzwCoygI8s+ZwhDQxrwE9M/jYJPd0JHZM0gIPeDwAI+sXfUBXLAyFcgWcSP8MvpPcCxgo/OaNdxETze4Zs4Zq64APjZdKproTDOHACCJNFpyeJ9azmJSd46c2hB4Ox1xyPz5DmdeM8WUVLh2NZwEqhV4Y/aMNfewxoEMAY0Lsv6zAsPneuiV3rhjIYySZmBekybIGrqj54ccksyPshwioigAxGq+t74wq94rvMBRT8X8KH3DTQVvWbzIddrhQLIHaT1TXJJqsoN75k9XqzUoaFQtfJCn1ZGBY8IQ74yBLQ07BOqBa5iaDYQDDzhjD0KvNqcLNBBSL0ZuDZlx4QsQ9IVhVLJ65LD4jH69GV6K7gRJb2tTplWqJbmLdGD7/e8FR7QPj/fIhWPkdEvsOuCn0+Q79sDxFfCJeGHheQaPDoCvt0bLgq25V9u8Ks5BsVGg420gAy+3X1AWTlDwo9INhC4mUve5n55Cc/GZx/PBiNtnm22jOAYBOXj33sY+a9732vOfXUU23VAFSm+ta3vmWwy+Qll1yyeUo28KoJIKdc+Hsze85cG3JC4xbGOXMVks37kr0gtOKWRiaTLjHZ5jbvSxPQi0KwpLHINpGorsvjsvwk4OCRNevM0tXrzQOr1pr7lq8xjwJA4AEhHLA6FQxpCwMzzPi8WWa7OcgDmWH3BKEbn3X30b4EkKz0LsMY6GGxYQ5rjVmfJnLbHJCxxNMCD0jan/V+4P8pgIzNHO8qa0u3P40WGXpFD4hOQud9SvYySeKg8Uk8JVhdTWSH823pXVvtRsAH/8Z9AGweSJoLwlAHekDs2BGOlZTltZ4Q6/1AsYLkOeHu7rIyjXwMkdsiGCoBTezXmG5All1PulqZGAD5MAgfhBQ97nyuWFzBwk7ahy7F65rQ5d98/YQAiQxjkEaBDP+S7XcZua44/fSEXGhFBJBA7VfvMJ/3g3Coq11Jz4cshZ2Vz03zPLjooBdYOuCRbLQKCLF6B2BhPR/Id0vL6zLJXHpFmKvGKn5YfGDlPuaVcW2jDEK0R4QeCe0N0RBiE801qIjQq1zVrDTsijBCwJBJ7jIMjB4QATPYd4mLGtBROiSLXo0OZCDcslNJSy5y6BwRLhh0H9NdSSsEUOS8Jz2jOuld96sXTIrAowxCfPkdcnGky8vhyJn1eXJdYVgjE4I19/X1AWT59wsBBNtI3HTTTSNfzKieJm3+rJ4BBFvWgx4PPfRQ68LCniAAEIAIEssfeuih5q+yxSMggHzsgt9ZAGEoD41J6wGZwd3DuwGECkoaS3ITQlnClpVcsiR0sfO2DK+Rq0MEECZjc1WQ7cL7gQkZAHLPo6vN+kfhAVmdAAjKTcLiZcIkIGDuTGPmzjJbzZlpq2FhTxBOQgQdJp8j7AHGQC75HGENABBCCIwB28/GJI4Znhbs/TFrRicEaxZK8aa5IDDkRVI6AEiHtMnwNZbZzErn8lkSO5XLMALMxTa6aqOCDw0b8v8SSmDkshKWpZrEa6P/6d3d8zukcyLPP/gcl15B07kZOiSLEML7RC+dXLXLGe7CUJdGAr0geiXU5VFxxUqzD9+qX8hrzlVSvYmkhpAq8KEBKYZghdyJeseEhF5Jz4dNLEdVO1tEorMYAH3GxRi7S7koU07dhp9YCEm8HuvMklXrzKPMQeNPCxyp94OhVXavD/zN8ZO6SodfSbilYUnvnfSGuEKyZA5HBhJpTocIj+oLgMh9RizUiER1BTgMt0oWGzp5Ia6cMh1ixcUR/CSYSADwQwgS2POb8yX/d3tOtC7Ti3AEGO1dkfpC/u4qnZtb7FDeD1eOimt/FNciTJ2wK9nOyADIvDfUB5BHv1cIIIcddph5/etfb1772tfWU0jxrJ4k0DOAzJo1y9ZRRtiVBJDbbrvNoATv6tWrexrgpn4yAeQjv7jGzJ6bAki6QkwPCACEiegszUvFqQ0/rv5htR4Tr61Ln5bg5d4c3CBQTuY6KZAQIgGEte45QaM9TNC58CsACP4hT8OGYKU5INbwT/My5s4yY3NnmgUzp1sPCA1bGO1clczBByZyrjoippoAwkRPTPw02lkFi/kfDMUClBBC0pyQmSgLDG+I2ogQsmP5X+u9YN1+W5s/DfVKVwDhjYDHgUY770eW4Iodz7Pa/2rnY7uHCUpypn+X8eBZCFYKH2JPE5uUjl3k6QlJf8q9QeTKH8eUJaKnxg4nMBgHjMfWYViMyWbYBEOxOomi7sp1stIWjQXuG6CBh5N20UQtVwJ70Qk6vrqoohf60TLz9R09IL3clfBzq3g/8A6iJLbNxyKA0NsBAFH7Ddn9PdL9PlgxkInm8HzY8roSPJj3gfeb/+wu5+k+HzYHbkOS72EXGdJNU1n5CrrE6pSS66dXxMKHSFAnAHTt+ZEmpct8D3ovcp6LFFIkVMgQLJ8HJLfZoT/MC6FYiTe249VnhSvqfBrEMt/BFzalPZjUcbaoFwtayFwRJ4hoQHGDiQYOjlfms7lAh9eD76Re8wGL1Edax/lAxLfg4dKRvgR0HDsyALLlEfUBZNl3CwHkm9/8pvm3f/s385a3vMW5jcSrXvWqcOUUj6wsgZ4BBB6QT33qUzbXQwLIl770JRuCde2111YeFKAFZXy5JwgqbWG/EfT1kpe8pHJ7bT7BByBQeDCMaSAjBAsgUgVAUEaW3gQmdnOnXqz4SSORQKMNabmSLytfyaRMJKDfs3y1WQrPx7JVxjy6ypgVaX4GY34AIAyLmjfLmDkzbUWsBECSsosIW8KO59zg0K4sYvKWmw/CAwLAQWInQARhEPQmYMK0u5+nIVeAj6wSVuoBoSE/a7pBKBZkyspSuHZ5vdnmh9ZwEDX7WaHGbng41TDmmZNMlkcyscFM4vq587E0QAgf8NzIv3MllEYFN1TkuNNkeoQ4MCEdz4mMsWYiqJyofIY0J3GZe6QTQ2U+CH6XO6X7vBE+AJEAk/XjmKzlZBqy0pcDceHZc737NBxwzS5PiAY23//5dx984PuYA9Jf7VsEIL6NBmXeByGDZXUzr3C63xA9nvz7o2k5cMCHXRTh/h7ZxoKpp4PvMHUFFk34OxYYrI6SuV/YA2iDMWmV8mwPEF+Yn/SKhFTDYsiVBA+dE+JKTpfVt3T+SJYTIndbT0sCy8R1HCe9Nqq6oit53PW+szIhNyjsMuLTwh/8O8O6Ot78ZGFFAo30tro9Jd2A4ipLzgUYLmCgXQktcoHGByM+0PJV7nIBSZHuof5x6amRApCtjqwPIEv/XyGAuLaRoLyqbCPRXy23+bTWM4CceeaZ5sQTTzSf+9znzNve9jbzjW98w25jDyjB73BvVf0AMuAae9e73mVQIm3vvfe2GxwinOv000+3G8ZsKh8CyId+/r9mC4Rgpcm63AOEEFIFQLjaZz0gYhdxxDLLzQilkUjlKRP/IGObzL5hMtlNXex8jvwPluDFBoQIv3oU4GEBJM0DQZw09wHpAhCUyJ1hS/HK/Av0hfK71mvADbu40sgkdOkBYfiD9oBkVbCQDzIjLcWbekFsUnoSpsXcGiRKUpljvw+AEK7Pej8IEDKBgmEPAIS0YhknNFbLsnkf0ghhSBpzPviTAMXdjyF4GALI/2D4FQEkzQPRFbEIIPjJndm14c5xyXdHTtY6F0R6xQgLaBuVt7iqWQYghCBXHogPQOQ5vsnTF+usk+B10rg0QHzhWD7d4morJ0tV7SaL605XZSUQSqiJ+4CEa/NeAIS5WDLkSu5uTs9uUno32XDQLtwUVbzCO+3ycNgFBvH+Qw9Cf+B4W/EuDbUELMv3XgKILs3bsYw6ZXqphxjm6ko6VzCQVdYigGivhy4BzDAuhljlEtVTAJHnYJw4JpdILzZT5N95HPWd+L+sYKUBgrDSAY9kAQv/5/zlC5mSwMD8Nwkicv7L2mMZ8jQfjoswUgda+FHlgjWEZICk9IErPyTEM9J1nsg9K4MPfL9yxfKh7z8U/qYnHpott9zSmK3eaMxYjSpYk+uMWfqd0jK8VcYUj+2vBHoGEAzn61//uvnEJz5hd5PEZ8cddzQnn3yyBZI6n2222cZcdtll5klPepKFmP/4j/8wf/jDH8yPf/xjm/B+yy231Gm2lecUAQi8HVihh5egCoCwAhZ3QWclKQIIEzGxMigNPK20s1jqyY12Qy4Y49igjxO1TEAHgKxcttKYZauNeWRlmqOxLpmEaUwDCubM7PybPcNuEChDsGxZTBtDrQCEEMK6+tb7kSaCMqGT/dADYqtuOQDEJqUnu6Rjd/YkBCuZxPBZl14jgGs9vTDog4aCjHtOvSDwSMgVOpb3zOCFYRky4VxWwuKu6NkNSSvcsKxwlgfiTkSfM72zqaOtLpVOhtLQxf1ESIr80AOgK1TJPBBOxHgeZRiVNOZlP/pFk8fJRPRBAAj6dhmp8jmnAcOEer3rclVF4fPQ0PshDSbddtyIsJq0Bw0gLLDhAhB4Qaw+kCV3meshvZjM6coqXQE80iIUWbltmyiW6BR8qFv4f3rxtEck5wmRyeIq7IqJ4zkISKFAAoX2VhAkMKaQ3/V47HkKNuQY2K7Mb/H1VdS2/i4dawYYKvwqgYGkUIj04mqdxrlILr5Q/8m/sR9XUQ7oXu0Z0TBCnSgT1yUIWZ0hwsnw/7KyvNqjIhdA5FtGcGli4aPK254ByNZvqg8gD58dDCBr1qwxM2fOrDLEeGyPEugLgHAM8FBMTk6ahQsX9jQshF4hr2TnnXe2yUEAkZNOOskCzl577WVWrVrVU/ttOpkv2QnnXmW2mDsvZ+BZ74cCEF0FS16LrIDFkpLwXAAYuJEW4QEGMnc4l8aZjhnlMbJkJdvAJI0KWNj9HACyBt4PwAf+wRvC8Ch04AjBsnAwc7qB8c6PhQ8bR52GPRFEmOwpS/ACQOglkUno8ErQAwLQkB6Q6Wl4lsgDyXJA0lhrVK/Kwq8Y201jAgOVcdQAkOnTbBgW450JgDb8Ctch25DQgd9pkGjvB/uwgJMm1vPntKnOSljcEwSTncxtkJsQAkCkEcfwAIZZubwgHRhJ9gbRYVhl71NVANETKSdj1yTq6jsUQGTYRF0IyYVAiJK/Id4PjB33Zukjy4Zej39Ud0LvB4AwAZ3eYJkLknlBJjbYsrvSA5IBCPLQshCsNL+DGwtm1e4YdpXu9cHNUqETJIQwB4TFJ6RHJIMRlSTiCHGyWdbaC1IEIUXeCb5UqoR19q75Qhzl8drL4Uqol+AhgQLXwmt0gYarLfk3DVgiEZ/FO6DDAB9zxqdl5YG5oavOUev2hCSbLWLByheCJb0jZXkjEkaKQsRcEJLLm/N4QHw6amQAZNuj6gPIg2cVAsiGDRtsESVsI3H//febW2+91RZRQlTPrrvuWnsRvWxOjN8nEugLgExMTJhLL73Uhl4dccQRNhfk3nvvNfPmzTNz5sypLGskrx9zzDHm1a9+tdlnn33MBRdcYJ71rGfZfJKXv/zl5r777qvcZltP0AACQwjKkSFY/fCA+ABEekAkhGiokRsRytK+tjzlmnUGIViLlq0y65em8GEBBHkaqISV1rkngCAnY26SA2LL5MIbASObE40OWSJg4CcSQOVeIHJjL4Zg0VMAAGHOiSzDy00J0x3S4YHpSkJHGJgMv2KIhfSA2MzHJAcEP5GMbiePKVOSMrwbN5pJhmEQQuTKKNoigNDooMHB8AX0wX1AEDJWAiAsxesKwWL4VT8BRK/YWYPaYZzI4xjClfOwlOSAuAAke16F0VMWfiXb4Zh0GJbvPXDpD5/ng4YC+9OhEvr9igASrp3LAITPIBZg6PFjEjp0l91kMF2U6SykTBhU96N3t+MFSRZuoOeQ/2GT0F05IMzxyCWZi01GmXTOjUf5vtuiEwQSEYpFSLGVsoSHhGLK4CEtq8ukcJujIXZFt38XxrzLoyENfN9t0PqpKGzM14buR4NFFtalrskXHuby4shjZYK+XMxJ9TZAxObOiTLm0ElFO7a7vSNJEntILgiP8YWHET50aXIdNmr1Sqoz+TvnHv4/l0dCWafn8RaNDIAsPLo+gDzw7UIAQQI6cpXx8+1vf3u2jcR///d/m89//vN2X7v4GZwEegYQJIgffPDBZtGiRWbt2rUZQWJfELi0QJZVPz/60Y8syIBOX/jCF5qLLrrINoG8kssvv9ycf/75VZts7fEyBAsbEcoQF5cHJCQJvSwHxNa7T5PQZVx7tvKcKix+l4QTbbD7WugqMQAQeEAAIJMAkCXLjVmyslOKF5OytcimJkAA8EASOiAEMELDGn1yZZCGO70frJ+PyV96QAA4CIfQIVgADgsdaRI6AYTwwRCtNK8CFcaSvTymZDkvNvzKei7WJ1WqpAck0fIpgKSbLCIG2O6bkQAIqodl+35oAKHhQQMDxzLem/umVPCAwCtmNyO0E2onTMoFkjTK8FNWlZEeEO7RIUMP+D08IOiLpTQ54dHwsz8dXhbZFib5KpWwqkAB+y964fWEridzea6GDN1u9IAMV7X2UoaXG4ICQmTuR5LzAQjproIFLwmr/dkyvPTEMgzLLk5AT4h9fggcLFyRAQiAApumWlJP9/xJk9GZI2KPFfBiFypSHSoXKGRCuN1/Y2pHJ2kIscCShkVJzwRunTBOszspw8DkQgnByOrCFJgyWBLhZNRlMtpTLkxk3goxJlf5YMwZcqPEon1IchBmy2IlXpSkRFZHNlw0gp4khNj9lBK9xhLhLOQBTwc3dZWLJhpG9H5J0AuyxC+PL4MQnSeic1LsVIrwskAA8ekntIOd0BdsNT84RGm4b7rIAdnuzfUB5L5vFV7fnnvuab72ta9ZO1MWUUIEDha9ly5dOuzL3qz66xlAuP8HypltvfXW2T4gyOGAFwPleOt84OVYvHixecpTnmJYqeCaa66xXhUkpW8qnyo5IHYjQmEsyxhzGX4lKzkRGLiSx0k2AxDHXiBdhqutpd8NINyga/GK1eZu5H4sWdH5h3AsAAImaHxs0jfK8KYAMmeGMXNmJRWrsHlgmn9hvQLMAWEYFncZhscDbWKndfwkgMgyvJhsuBEhS/Gm3g7bP/rDvzT8y+4JImSKodr9AlDBStb11yFYzDfhhJZOoHC3W/jIYCots8vzc7HgytDgeTQS7A7yCFxOd3G3Y0+S3mUSOp+JuTOm2RU8boyICjD4cANCemZkLkjdHBAJIHxeCB4I25MgwFAnTuAIdZDhYr7kTAk3oe+7ywOTAYyowe/ygIT21+X5UKEPNBJseyUJ6DgmekDC7q7Pw6X3QPLtA8LcN1YClGXFWeGP3g/qTYZhYR8QVv7L5YHQOypDsPiOMwSTsGH/nr7zXHSgMc99QbqgJT0eItIeUiaFw0vKBZHsp9ifQ4dD+cStwYPwA9jg+HIwlepqVxiZHK+rPxkCxvAxeHil15fwwL9zc0VZdph7P/Fv0McaZggy9ie81umi0bSptrw99KUNbU4LbDA8i9EIhBANIPk8kW5PiKws6coLkWFa0kvMfl3hWDoPRIdg8f+u/A9duGNkAGT7t9QHkMVnFgKIbxuJm2++2Tz96U83K1asCFNO8ahaEugZQJAwfuWVV9rcDEmQd911ly2buynla9SScMlJgwYQuQcIJlyACHf8dRmiNJq0UQkAQYgCVxHtJl1r1puHVq01i5evMfci5wMA8hA8IMuNWQoAWZOEGSUWWeKRgOdDekBsCNa0ZIWOK4OsHoPJnd4P/ETOB8r7cjNC1z4g6UaDSfgV/s1MPC3aC2KhpLM5IRO3CSB240GZuwHjQYZgYQJleBQNANaNRCP0gMDgkB4UXxK63AME50vA4TXxpwCQJIwg8X7gdxnHLB89QAg8WDIUK+mmU3YSE+0W48lEnCWIpyGB0gPCHBCZ6C7hg6Aj+8dkytVFu8mmWG3kRI7QA0JA0cqdhZuCUru6BHBuHCo5tSixs+x91x5DCTAhOSAcZwSQYkm77rXMa7LPw8aNNqeNzwafQb0LugzDkl4P/M4d0HNe3tQzAl2XK8WLRRFWyKMXRIdiyZBLhpb6PB3a8yH/rxPUtfFO7wcrYsn/Z6FOSsZMK5G5J4QGq7vSEsEEDoJSllivQGpKvriF7W1jmpeC3zlmHRKW5Wekm8hmHguGtwrvjr4+wgbmFv5OXczFG8qCupoAMn3chs0iB8TmgqQgAmhgJcHQvDgdguUDD52wLr0brj1Dyjy1Wv9UgQ/ckpEBkB3fWh9A7vmvQgB52tOeZhCt88Y3vjFnv55yyinmV7/6lbniiivKpoH4fQ8S6BlAFixYYH7zm99Y2JAAgr8dfvjhNrGnzgc3Hq4x5JUgJAuVtc4++2yz2267mX/8x3+s02QrzxkmgKCcJOras6Rukg+QzERQonpPBE7syW7Cbg9IBiDI+wCAPLzcmIdXJIno3A0dDTExHPBBAEE4lg9A5OZech+QFWmJX1TBQvs2FEJsRAgPAdq03o8UPrLNCBmalXpC6BkRSeS5/A1d4UZ6KDjp6YmPTxk9IPTo5OLD0zALuQEhAYSrkHofEMIHrs8xecqd0KUHBOCBD+GDBhnvbRZ3nCbRl+2KzgR3JNz7Cha4yv3KxHULSg4A0RsS2ufSFSIiNgiUL7VOrtcvfDZheyAkq0iTekpCvClyjLJ9/L2sClYEEL9K9slehoxqz4eGj0RvJcU26NXET+kFxu+2LG9a5Q+ldwkgDM2SeSC2oqDVR+vSnc7TRRK837ocr+9vBBMa99QV2tjHu6vzLSgy5nNIAx66KJczIapR8Tybc5IukBBsinJRZDgYVoimbjBmGjZj3WjM1ElkkRozhv+nHUDdTI4l8LFhLP2H31FoJA2F6vJkiF3b6QHJPBoFAJLpSOUBAmxIANEeEKtDp2UhWNgHilUECR/4WVUXao8Gw6n1zu9ccOkndFDfuHSmr2T5yOSAPPZt9QHk798sBJBzzz3XvOlNbzIf+tCHbB4IwOMvf/mLOeuss8x5551nXvziF7fSbtxUBtUzgLzuda+ztZrPOOMMCyA33nij2Xbbbe3GhKhihX1Cqn5QbhcPxZFHHmmhA+4wVCb48pe/bB+KX/ziF1WbbO3xdQGkaCf0bNffNNkSIQdcvcMEitU+TMw4jh87JzgghMZkUQjW3x9dbe6VAAIvCPJB4KkASOADAxpJ5wSQeVsYM3dmEg6FvS0wYWBy5CpilveRbkRod0IXu6BzLxB4RRB/jckUbVgASXdcZ8lfCSCEjiwfJE3uFpW4unI3GF5BeeXyM+jWTyd/xjdzFdG3Koq2ZJlO5rEwmV7uA6JyVnCNYwwdSD0grObCHBCdEE9jjOF5NNboxcjAwno/krhnV8iB9IS4Sv2yXVnuV4Z5cbd2DSAMw8oZ9J4qPCHJ5p3nurNTuwxXoBHA8DAZU62VhS5d3LEBk7aL6vEXbUQYASQv6RDo4PNlf6awzg0I+Tfp5UMCugxJtUnoIhGdFbG4x1HXd/Aap5sRMgTLCSAyV0zvgM59hGwp7nRDU/nuEzyoM2R+RQYgBZWwJIRANxFEIBC8Q7KkL0O/dFiVrMgH0ABUTN2YAAZBAz8BH/JvAJEMbtJ3DeABCJkggPB36EjCAmGEuRrpuLUHRCfWy5wOGVq9kwAAACAASURBVG7GMC2GxGoASculy72VEH7FsGa9gKPhA0DS0YedaoC+XA+E4nKRRuqaUPggUFAvSV2rFzrqwAfOGRkPyAABBHK48MILbSUsFDlCFdcDDjjAbvewqW163UYjuGcAQbWrgw46yEydOtXme8ClhZ8IzULCeJ2SvPvvv7857rjjzFFHHZXzqlx//fU24X1zrYIlc0CKAAQTuZxIsYIH8CCE2HCDiQ02jAWTc8fV24ljlQ8rPSAIwbIbBaarhrkQLAAIvB+AD/zD/wkgDFdC/gegY/7sJBQLvwMWmAMiAcR6NtJVRla7gseD4IFd1/E7SmJuxF4jWC3EpDYt8X5k8JH2yfwPm3iebkiY7ipu4UiGTzF2W25CaAEBfaR17uHax2SX5mTY3+EVYTI9E8zp+aBRIuPDXVVzJBFiUrWejzRUTF5D6kVwVXLJcirSpHoY0LhvtjzwhkmD6mcyTImTaJajYSfbcgghsMpnRXrVsktJwZariviJyZ8hWToUwQUPuedR7I3ggwMNCQQbGWvNxHid1KmvRxq98juZxK9BROZ+SAiRHp0IIO5QOpeXg3KX0KFhRFd6o+eDAMIKWNCN8AJL74f1iqSbrdrd0EViOhPWu3JA6KWVRSb4nst9jFjJD/oEhj6LbGiPh/SE6Cp5uNhcEjo3+ku9HKLUbLbRYO5BhmeCJYHFviRjEwlUWLhIvRsuwIB+ZZgVvR26fXtDphozmXo/CCETUzuekAmE26bwwRwW6e2QYVXQsfRmyMR6Qkh2bHocw6xkgZBpIq8EujTV2VjAwaLNljMBIeNZAQ8ujOSBoxOSCr0oS5G78jgkfMgFHp3nofPDtG7yJZtTl0md46p65fJ8SP2Dxc+RSELf+Zj6HpBF32htkn0bgWDYY+oZQDDg1atXm+9973vmuuuuywgS3gsk+NT5YB8QeD1Qh1mGdd1xxx021AvVtTaVDz0g//qzq8wWc1AFq7PPAoxLbEA4nxsRzkxKxtKQ0+EpjIPWHhBUqmIZSb0ZIeUoE+rkngjME9Ft2hyQtevNQyvXGnhAHkIOCMCDEIIkdAACK7hID8iWWxijPSAYCIx75n8AQLJKM+sT0GDiOUr8oj/mgEyfSCbHDZj4GH6VeltY7pclf7mjuKyIBQDirsDSc8Fdjmk40APC8paY3NJ9QLLER4YMMYGTe5oQNnh9hBxdmperlZxEZe4Kc1k8YViyigsnH0KmBQ8AZFqeVBp5NMQJBK5SlEzMzOeHJMCah1XATf7tZIUYm+SZVpthInqWayLCohgKVfaOl8GHhhC5ciihh7HZOF72zfZDQrF0+BY9ULgPPi/I5gogRTkdGip4D2V+hz0mNcZluB9LTEvwIHSvn+zkrxE+rPcjzfEgdNifClBwTAYfAAvqJRkmKgtn0Bsi9Ydzt3SxK7oMx3KV55Y5FghvwoeeWFa5kjubS++hLIrBkFCCB6BjPIWQ8Q1JiBVeafRnw6scuR1WV6djwFjwD7rb/oT3QwEIPCIWQkQoFsfeVeUq9YRYuEhDs3wQIr0aMh+PoJGFYhFQoK+Toh4z0/LrSD4HiKCAR5KQDh2V5MF1Q0g9+NBgkXk1RPEKV8I5xOwqEx6a+6EBRNsLIwMgu7y9PoD87esRQMom0ga/7wuA9Hv8e+yxh83/eNGLXpQDEMTlffrTn7Zwsql8CCDH/fS3ZsbsOXblmaVOoRQJIFvNnG53RA8FEKz2cRPCpavXGVRxwU9AA1f7GIIlV2m4wRINKq5ou0rwwrPywKq15r7la8yjj6ACVuoFQS4IQ7AwCeODSk7cAwQAwr1A6IWwk1pacpITexeArOl4QB5Nf59cawwmTkyUmPjWI5wr9YDAwwKviwzBQmK6DWmCJ6SThJ7Vy8c4XGWAGTLBEKlslU2EYOFvnPizUpwbEqiSe4Iw/MqGZMhQrLREZzLzpKt1acUu7mtCLwjC1qZ3KnhZ7xgquYwlRr4GEMAHDKt1aS4PjcDs3qfJlwQExC/rOGjXhIyhAkKYS5Tk2HaqYEnv2kxM7GNjdrKXieh6FVGDg+tdD4UPF4S4ViXhUZQAItuXBjMNYRoHbF96Q3RlGlmO08orLdVMYxs6YLttFgx1ohzWRoQhYVXyeYFMdEgVgYPHSTDkRqlZ5avUqwvdRs8fF0+gD3UJXnpzbR7IeuSDJJ4Rqzvx/7UT1nvctQO6hhC+30w2x3sNEKHBLxcxdOUrGWZlQ6PSKnpW16QAAA+E1ZEp8BNCsADAfBBfqV22JzdBnSagA/oTIAIviAYQ+fKxTwIHvpPQYX8nhKTeEFceiG/PDxlulat0RY+J2ueEFQJFYrn1GEsAYUiW/Nv0BDZsmfuZqfdDeGX/P3tvAmZbVV2NLu6tunV7ehSJYoMNtrGPit2L3YsxdrFDwQa7KIJE1N/f2KBGCQE0ttghUV8kNiEa9SkRDCKKKCL+CrbYPEWku1xuV1W3e9+Ya411xp5nrX32OVV1b9W1zvfVV6fq7P7sPdcca4wxZ4n9YGlyLZPPSQxjnDmBsleacHA+s4aESvoexfgZ+0f5ZUryUB9buL7Go5JEi3FHv84FA0Bu9+LRAcivPtQXV/fdd9+wV8Vb6MeaG2+8cU9JNeflecwYgNzqVrcKj3jEI/LPne50pxmf6CmnnGLNYc4880wzAcHzgX4jkGVBm3fsscfOeB/zZQNMBF7+uW8mABI1+EjWoEvdd8WysM/EuP1GU0KWWkXQq5Xh1S7oGDwBFAhAIMPibJ8CEA2mBCExQYrVk2jijNWvsI3tBmhu2DIVfr9hMuy4aXMI66QMr0mwpvsZEDASxoCgDwhN6Mk/kRPz7U32wxoQSg8QyK/AgoBhWTYdAhkQDIAAIJOofpVAiJnR0ZRwIpYBZgleTeg5ODEoaRUuysDoA+llnM0qWFqX3xIF9gERkKGSq1LCQtNpHFUiADEGJPU08SyOgJAoz0M9+5jkM6FGYgbJlX1/WwFA4nvOHDP5J8PhvSSYEaQfhIMwB+g4YPaeJIIPb0LXfYBdUb01WRc/AzgIhMw2ANFqWD42MPFV8MFlGtKHBD61mAPPy7MgXB/bXrd+fTjkwP0XPADpAjYIuggqCDj4fxaBIPOK31rW2QMN/k32g/c2JYe49wE6eP8r44FYRk8cfR4b0ZBwa2pECrM5QATYC+t+nuIQTei5Sl/qho5n2mSkqdwuE/4S4GA/EBrBLZmXXhoeeOhNqSAEMUurYpGQVGaS0lFKvEBX2KRN+g3QAb8HvR4AI3jxGAg8uF+ADIINZUDUfO6BCGiVEvDQkuPGvIiHhVWvGFttgieBEMhTKYFlg1aCD/6N354lKZjPfTzyAIRxjz45lBBX5ratG3qp8AUuLQ3oHoAQKBCMtJXdVVBBptX/T2+bBcuA3P4lowOQqz7YF1eRW/J1ww03hLe97W3hsY99rPX9wAvNB+ELQTd05JyLr7m7AjMGIJBeoecHOqGjjf0tbnGL8PCHP9wACX4ffvjhIx3961//eutESbnVxMREOPHEE8Nb3/rWkbY3X1ciAHnxZ74RVqxeYzMhCHhM1PYD8Fg+HvAbLEgJgKgumgPwZNIwY5AFUADwiD9TBh7ibHiqgJVmX7y8JiYJqS+G6KMh4yKoQXOu6wEIILliHxCADwASLcNrEqzUiJASLIAR6wMiAIS9PzC4s+uwybGmo+RKPSDTW0JYWQEgO1ITQkqwDHCgCpZ0SCcIQZJP4yYHac/CkAFRiZSWdqQuuSHBEnZjEPjg57kKVipLaf1TEngyCZawNuwLYuV3k6Ey1bNXE3oEHegATRak5wHBdwzAwkZcYFHYo4M9RVia0suwVMPcSCALfUBoXiewNgYk9QPRwVwHYH1mS9KoYZ7pLJGSmUZlXmoApMZ+6L4HgRCdLPCyiIUOQGYqp6oBDoJY+jc80Ch9DrDB4hpcD0wG7n/tgG6gY6rX5RxMByRWiJW5+SjBRUkKyqp8nJyg/MpklclknvtluMaDrBrVxU9hoCQhfCb/+j/GmlxqV2YD6EXD8oxZACLm7xDAkUFHMp5TcuUlXyXJFf0eJTCCz/B/7/dgw8AMnKTzeS7VC7BS8LZo9SzPbJivL/VLwsQM5LHqz6v0/tAGrloYo8aCeAaEE3e1BoMeUPT5xlyfoBoLQqBSk3ry80H9PxakB+QOLwlh6cQw4T4uu30qhF/2AxDdECq1wsPsJ7Xf+973Whne//zP/xx+v4trdL4CMwYguieU3P36179ular+/d//3fwg6GY+6gs9RCC3wnbg/Vi9evWom5q36xGAHPPpC8KKVWuMhkXwQ0IIxoPgAwCEcixq9Dmr6gFI7Fy+w/TNZkCf3Bqu2zwVbtg8HeAHKTEg9AH4ZJAa6l5jrh77ATnXdZumwiYFICjBCyBCjwYGZAxCACAqwSIDAhCARN4G1p1xtlE7n9vMo7AfACAAO9jnqskQVk2FsGxrnMHDgDc1FsKWZSFsxnZXxn2aBCsl8QQdlGXZ/tNMGSvGsAdIQ2axNaIxD0BUe8xBlXdbbi4mXZIpyVC2h1VylAHBsZi/RJomqgcE8jF2kU8yNsoKwGAtgx48aeWnU/EAK0yABovacwRygFSClzKuGgtSZkB6SU/W5ScZDC8DgQrYFAAQABzfs0Q9GTUJQdtD7BNhn+jPJQDBcXkQ0uyv0mMr9xQA0gY8VFJVq1AV82KUAY8Mh2cu4Nlg7FEQwgIKLCdNRo8sH9kO9axFT0dPXoW4ZZ6Oya1hEhMb2tVcGU+bhEgMCJZDbNKYgP/RVE5Gk6VqyShogl/zVZBRQPzSylFWyjaxDUjoPeAoPRCcwMiGdZFwGfhJzEYuoUuvRyql6/t5UG5FdoPHCmmVej94fJ7l0KaAZDdomFcwUiorTPBS84sIqIhMR8/jkXs0aZn01P2c46v1/0A8gi8tTfqVim8oA0IWhCzzsDIsBSR4X2JCCCZ8HKQki5/n9ZOEy29bFRK6Dm+bBVMF67CXjg5AfnFGK7OMnBLFjdARXV8opIRiSIuNCOc2dZ4VAIIvCX0/yIRcdtllBhjAgIDF6PL6+7//e2M3Vq1aFfC+7XX66ad32eSCWIYA5Hln/0+DAUGytnZimRnQAT4OXDVRBSBMNDnYU4JVAyAYeAcxILx4/QAksh9szPVHMhLwfECCxUaE6IwOxgKzg0ymrfLVihD2XRVlWJBgEYBgGRvMwXxgoE9ld637OX6S5wPeDwCQFZtDWAsGZCqE5VvjzB4GxMnxEDZPhLBxeQgb0v7AgrAxIYCISbLSD83o1FKzTKUyMGRDkLhrn47cWTeZHHVgVeOn+kG08k32hUh1HL99ADdKsPSYCT5k5m/J8vEIXseiB4Regyy/2rEj7NBO7PiS0+COplxYD6DXsyBqHleWjIUL9F7hvagJqpdg2axj8oFg5hAmcC1V6WcBSw9ymzHcL9/wZ7QwIKVBuk1+5feTZx+p8RZduPeB6DVbaBIsf+0VePpY1MZwUCZFgEH2QkGFAY1UxY2FFLSqlQINykQBNiAbJeuBOGgTKOrn4MQGy3uT1cjGcsivtvcACM3nWh58r62xghT9E2biThWlWMbWemSIobuU4KtPYutYNGxDSorfat5mjPI3HmMN/k/fCKtWUZKl4IfmcpV51SpbKfAg6FBQxGNig0ECDgMb0iDQezos7iT5GNnnmjeE//f7yGV40350Migb19H1vNcd3VjdFOv4OxffGF+a5as+zvm/lQnh5B3j1rB+EAIJAog2AKIxquYR0QIYWL7GiiyYPiB3+rvRAcjPPtAKQA499FBjP1796lc3nqp//ud/DmBBIP1ffM3dFZgxAHngAx9ovT/ufve7m+zqYQ97WHjoQx8a9tlnn6GOGjTYOeecY+vhfe0F89D5558/1Lbn88IEIEd96ut9DAgYj/1XLDMAYhKsFctMP4+Z7i4eEAIQyq+u3TRlcixIEOgH0GDnKxIxmfDdzwlAwH7cjMpUYCMAPsB+XH9zNKPjbwzySHgxuGEm33qArIwABD8AI0iq2YMDgydmHOH3YGJgAGSqJ78C+Ni2KYS1m0PYe3MIqyf7AQjBx/qVIezAPpMZ3UCIMCGsMMUa8uzGTkkFkhImKvSAqAmdAx5AAgc5ygi0gZg28yLz4c2pNK+y6AyIBWqZwXYANFF+xUpe1DyzmaP0ByHrQACJZCz3N2GVHQKQlCiMw5SZAAilCbgnIJWiF8TPBuqg6RNRPnclAKKzjr5DsDdk6qBbS377QIeYDEuyBwU8fJY8AOkiv9L91gDIniLBGhV4aDNAZTOU5QB4MLnUjh0B8tHm3xFQEGxk0JFkpDSXI06xySpjFn6jw3lkVdPEBliMHF+mevEGkx6IV7nJoIAQgJGlW0OY2BZ/UDmK1aMAOvAeQIM/TPK1fK3eLGreJsgA+LCfpSFMy2+AAPyPkqaYscataY+PYcEHGwgSnNh2UzEPAzOJfVFWBsfCalZaqUqZDpbPbRTqSJUGtcxuBhxJqkUWhOXMPUOiEzxkR7As3wvQiFUJmw0OWVgjSkkx6REnXQhA+DlW7dIHieyHTi5o7KoBka4AQ1lbDzx0G94volUsCUZ0fcYpMCC7uvjFMLkYc6Nw55eNDkB++v5WAHLWWWeFY445xto70ANy8cUXh6985SvhIx/5SHje8543zCEvLjvkFZgxAEEndIACVKyiGX1U38eQx75HLM6H7BmfPM8ACEEAJCoAGgAgB6ycCPuvjB4QMCIxIVw60IROAAKgABCCilUom+sBCC6kn+HhxdUmXhumoZPenhmQDEAACsCAAHxce3MINyQQAgYEs4QYtMYAQGBAXxXCfqtD2G9VBCNIrKHXxWBD6RO9H1ZmN4EPvAfQgbdkYlMI+24MYR8CkOkowcLACAM6Aci6VSHcnCpumRkd4AOSpuU9FoS6YQVBBCAEH5Rd0DjK+vU0QWqFlT4Agtr7yZCuunBWv6LnRbXjcbTolfjNXpXk/2DlsFSON3eTNyCy1Dr8NgCIMh8VBgTrgUGBJIFSLPWC1FiQEjjQEr8EKLy/KHXQcrylhl1+kK497H5fupzqnTFI64DuAQjW8z6QrgBkT/eA6HUosR24dmRfyXgAdGgTQIKGEqORTeKpLC4AA3oOATxALtpjNnp+DgIU9jsi4DAPh++9o7JOP7GBvxlnKLHavi0Wthj0Q+bDfieWwzMcmsgbYEiyKkvk0w8SfDIfAB78IRBRJoSMhFbxGSS3UsbDOpinSSHPzrDSlh1nKqfLjubKyOA9GgpSAkX/W24GGEvdNvokcZKG3g1lRBSIlEz1ABh9/7fOuXHc0B4oBB3yf/ozGmPcWGRd1VBurEZqxDq2RHt/LMneTN0G4kkJhDDmKSOS/5e+t1LMKEmuasCjJCnVmFYyr2v5Xhwbco9Db3HALi1+MUzSlgHIXV4+OgD5yfsGnt93vvOd8O53vztceeWVYefOnabeOe644wIm1xdfc3sFZgxAcHhgQGBChwTrwgsvDEuWLDH5FZiMl770pXN7BvNk6+jSDtruD3/4Q7jb3e4W3vWudxkTNOjFh+yJH/vvsHw1yvDGwGclAifGwoGrlhvzcdDKiVwJCxKZLgCEZnEwH9ejXO7GLeG6zbEnCMAEZhUZGH3nVz+DziTAQM3kVivnawb0TUkSBenVH9eHcG36wd9LUaVqWxx04c2AJwNNCPdfHcL+a3oyLCTSCMxI8DHTSADC5AAJAvaDylcwuO+3MQIQ/ECGtWJaJFgJgKxfEcK61SGABdm2oteYEMl8BiNJhsXB0Y4hGcdpPqUUjHX8lQEhA6GlH1M5V7uwbEaYgQfACJiIVG7YZmZTmV4yLOybYp180ehQqmCpkT4bLtGoMJkvCUiWLgkYTPFCQmieD1bmIfDRpmZSxhL18eE7Un10jQXxTEXJA8JBTwGINSEUKQQTBD+TOCoI8dVedBCn5tqX/lU5gz6zftafFZk0gdDt4/2wVbDmYxle9XHYfSSFBbQnh5dY1UAHJZ/sRQMgQhZ287bIUgB0aD8O69eB/ydmA+zIliSvyoCDYCMD+VTy2kpcI54k9oMSKj7P7CGE3/zfBBiOrZFRha9s2fby7/Ed8f9s4Jf9FNKt3Hsm6Odgl3CCj5zYJxCiAERZEC5vwEDM6X6AUXO7Zzb0OLXqlbE20miQoElBkgIQHBdiRgYViFXpf+yLhDgIEJIq9eXyuCqT8gb1RjUvsCK+wlf6m+DLAEgyqzcM7HvlsrjeW2ZxaCwCCryn/JPJew+sxEasyt6WZFkKQjTp1wId/r3FCOnTosCAEyE14OHBDSdWvAysxIpoPMXyG26+Odzu4AMHJuiDcpi5+jwDkLseOzoAueK98/b85uq6LaTtzgoA0RNGO3to5z75yU+ObEKveUDAtCxfvtwMQ0984hMD2Jf58ILh/qijjgoAIQ95yEOshwnoOxjob3Ob27QeIh+yR3/oK2H56lUW9Cz5S3IYsB8HrZowDwgYEEix2O9Bu6GzCSESBe2CDtP5HzZOGvPx+w1bzDSO0rlgMpAEMChF+rlXclX1/eoDYRMvekCuNlYC7MeGEK5eF8I1N4Xwh3UhbFwfwtrJKE3AC8ZweDL2Xh3CgWtDOGBNBCMAA0imWbWFGmuwD6x8BQkWzeeTG0PYHz8bQtgXUqwEQDC4EuiA9QDwuGlVCAAi61eFsCKxHgY+lkdAAvaFpW05i4bEHImLZ2EIFAAqMuWfkn8CAEoS+I3j8prhu9CBmH0BNEEiOMH6uB4YrCkTU/mYSrD0PSuKgc1IAGRHLgcsvUgIiHQ/Se41zgZdy3APjmd5QqkiltavZ5JakmE1Sv0m4LFKzJ8ZgCxdUq2Jz4F31Oe95APxyUmXSlh+/2oq1wo1qsNeCI0IayyHxhVlN7C8GcKl7wZ7b2RgIZIqNvdjRSotC814pYCDvg0DGqkC31bKCMkWKvjQXjvKghCYZACSKuqRCQEACVNRVgXgsXw6lqgl+wFAwr8JTABATILlPB9atpYMQm7Ml5rwKesBZsPkVQl8IMkHE6IMCP9n66Vt0Aie44xUv9LGgRmMJF0nK3Bpo0HETcuIXcNBNhNkaXOyMDiepanXhoEJBz4MmKTiGR58KAti/jmRT5Gx8IyGAQsHMgg2UvxSqZPGJJ0giYRLBB55mb32snLl+j+LRc67xThRAiBxWYTqXu8llT15UKLAQmMa4wjBCM+Jy5TADY8bx8WJlTZpqU6MYLubN2xYGADk7q8YHYD86D2LAGTUQXMXrDdjAALDOdgP/ID92LBhQ7jXve5lciwwII9//OOHPg2sh67qqKB15zvf2WgxVCVYunRpuMtd7hJ++tOfmuwLxnfQZbv7BaruPve5T/jABz6QDwUytCc96UnhHe94RycA8ogPfjlMrIyNCNGJFQCEvo+DVy8PB66cMClWrRlhCYBAagXPxzUbJw2EACxEADJtMiotw6uz3OwEy2DIkpdMIiCZAIsCZuWaTZNhB+RXYD8APPDz+xtD2HZz9Ghg0KY5HKBgfE0EIGBAAEDQLFABCBIJarQBPJAo0OgOqdeOTSEccHMEIfttiAAE+4AMAoPk1HiUYJEBIQhZmkAHjO/aI4S9QVgKmAAE0i/2HsExAJAAIGCcxngPnTMGVAMAGJCT5ICzcvjWPQBpJE7JeE4Agu1rB2TKGlg2mMxNnw8ELEmqiEVWhPIGHAPLCvukTRkQVpCZGAujABC9wbU8Kv/P2bmovU4VsFI/EA7qJRaEg68m+SV2o+vzr7OFTDJqsgW/zbYqW1oiWBMJJgjzGYDcuO6msHbtWjtdz2CprIqV9VixKkuqrE9Q7wcMh5m/8QPmQtgN9WXgczbHpFkcy4LhsDLh9G4YgBD/UmbzwFSmkre+0af22CFzCW8Z2VXEFfwfzzXAxwoyHyjpXWA9CEoIRvgbIMQzCSxXq8ADoILeiSynSoCiz3S+JMYwLEcGxDwY6CSeZFG5H4c0Jhz0EFBe5U3pNfCRGZu0XxrjAT6U5VDmg0U5WP42M8QEJGldyrDUx8GY5VkRyqz26rEannn1oCMm+RFw+CS/BA6aQCVKNUsgxQOQfha1t0/vw1AgkeNiYkFUeuXZkhoQUfCB9yoz09hWk2ItKAbkHseNDkD+z7sXAcig2LAbP58xABkbG7NyZez9ARM6B7RRzwvyJYCZj33sY3lbYApgFjriiCPCi170onDkkUeGLVu2WMOY3fmanp4OK1euDJ/5zGfCk5/85Hwoxx9/vJV3gyxNX1NTUwE/fOG8bn3rW4eHfuBLsRFh6mTNLq0AHQZAVkUAYgzIsijBYrM5BJMaAInSq8nwu5u3GADBezYjpFyCNDSkMWrK85WUqLtGp2BIJTIAQdldeD8IPvB7x/oIQDBYKwDZKzEgACDwgpABYQlcMiCqzQYbYqV+t4QwDgAC9kMkWApAUAXLAMjKEG5YE8JNKyPzMpH8JjC+G/MhLIj2IjEjfCq3SfM7wAgSFrIZgxgQNgFTCVaevU3JFCUiKhNhIzNLCpwHhGWDFYBggFcfCM+DRk8CEHaY1xLAlJLhumNftp2xsCozIL17AaChxpB5QOABiEqwtPoMGRACXyznzegzBSDqD9FB3s8WcoZTTeRdY0oNfOixKwDR68XjW3fT+jk3g9biThcAAukTAJj5OgRsEJQ0AIj03KAhHPEiezUITMhqQIplZXLh95AmgPRhEUz44g2YDKCc0AMQz4wQfOSSujChT8WCF5BvIn5k+VUFhJQkWWBCrNpV+s2bhtIlLaurPg4CEjAfABaW3BdYEHpAaEInAPHN/7hf9gwp3bzqTfFgBMuziiDeY+JE5VfcP30fnGzJTf6EBSEjQvZDfXL0ypV8IFo9S3sqDQAgvmJeTMyb4MOzC1imjVHoAkAUfPQmMfoBiMqrfHwpSbNqvpHa8WbpGNidJT1PSs2YmBwDAQAAIABJREFUrsezaeOGheEBuefxowOQH/7LIgDpOpjthuVmDECQQM8UcPjzPuSQQ8J///d/97EbP/7xj8NjHvOY8Pvf/94YEry//vrrd8Nl6+3y6quvDjjeiy66KDz4wQ/OH7z97W+3bu5ga/T15je/OZx00kl9x/yQ938xLDMGZInJXsiAUIJ1y9XLhwIg1uV3cmvyfkya/Oq36zeHP2yYtF4gkFCRAeE+AT5Mn59M7jrzzCRDJVjoLWJleAFArksABDKsQQAE4AMsCErxQgqFgYkmdCQclEcQAPA3PCCrkgdkn00R4KAaFuQTkBBggIbUiwDkxlXRB7IZlbAAPFxzQv6tjQiZ6Ogx4D1YGSQ2SOYpEzCJ1LJ4/Jb8p0ovOSGA1yPJrwhANHFSeQjfJ1+OyQ5YBQv78NW7OLvojeicfUwSLDsUsiB2bqmUsPYzMb12s5kh6+MTIGjde84EalJf8wxYIp4GRmNA0j1GoEtwQyN6eTazJzHpwoAo8OBxNZiJZEjX88B7vNo6outDq8CD58jPdZYV+50PAKQWdxSA4PhLLAh7dAwyllN6xe7j9G7Qx6EgJE5mRN8Hql4pCAEDYk0BSyCEjAg7jnvDOQELe+vgc0wesBKWVsBS9gMNTQFCBhnP+blWwDJTd5IwsQM4zduISWAwBgEQeOSM9UgyLIAPMiE0qVOGpf4Se75FgkUGpnGzOnmVl2b50Uh7fVD2paZz7/9g1SsvxzLJlTAm2rhVK1aVZFclf4d4REaRXeVJAQEgg9gPVsQqeUHI7FKGBcbFgxfGkxprg2OiH6TGzjC+lJgS9aAoAOF+2ypiLRgT+p+/cnQA8oN3LQKQ3Zoht+98xgDk9re/ffjud78b9t9//8aebrrpJpMlXXXVVUOfPprDoJkhZFz6gszrCU94gsm8sN0///M/t0oOu/NFAPKtb30rl3HD8fzjP/5j+MQnPhF+8pOfNA6vNhO5KwEIWBForLMEa8leVt6XySHeMzljUkIAgvUwo5n7gFgJ3k0RgMD/AfkVfk+LBAszaqhOBSZi6ero/8gm9ImYwNMAntmHyVidBuyHyrBWb47eDwCQNVtiGV4kDpBCYBZxalkIN8P3ISb07QnoQO6FRJ7Ag2Z0ggcwAWQJrPnhZLNEJ5N31rCnyRIAQWf16Gcx/0UynRPYUELCsruDTOgm82L3dkit0M2dpvNk8lQJFgd56IITxW8+EIIQHlPOlmONfJTgtepX4v1gt3IPPnTg9c8ePQH6fy/BKpnQa+xHKblve95L4KNxLEnfzYo3zSSiOTPq9+NBh9/ufAYgtbizfv16m0DyHhAFlGRKcW3ZAJD+D4ITz4ZoSV2VbhKkEIxohSsyq5BioWeHGdK3bY/dyfG88NlsMByukAMZRQUmKnPMAEQYEGNBAEDgA0keEJVZeVkWTehgPmBEZ5Up3gClBJ6ldtskWJRcmQ8EYCRJsQBOaF4HGNHO494P0oUFabAhBe8HQQ3BB2ZDGN9qvg/63/rM6JCIikGdMq2a/0ON6LnsbrqwrIblzOmIc4hRfvKCkwrq/1C208swm81DEQt6ZnUPQHoxDYxLN+Ch7MYwzEhp8iSDqSFZD24LAAW50yEH7j9vE/RsQr/3CaMDkMveObAM79Of/nRTsSy+dv0VmDEAQcWra665Jhx00EGNo0dXdBiwVW7U9fSe/exnh29/+9vhtNNOC/e///3N73HJJZeEE0880VgGJPZnn312OPXUU8P3vve9rpudk+WGlWD5g+BDVpJgQW51wKoJq4AFBgTVsGZbgoXjQTDCTHSUYIGBiWCEwUpN6EUAAm8GJFgAHvy5eX0ECBjUKcECAFmLKlhJfgUGBN4GZUCQLKgB3fcBWZb6f+ydDOhoRFhiQAyErIxlePdy/g8AEIKR7AFJ7AW7sWPGlE0Q2QWZFapiVtys8MLEn4OjZx18Hw4CECRVapr13gzKrNBAEcADYESaD5rhEwCEZvQ02FMTTACZq2Fpf5J0rGxCGAHImIHRUqdyzxgoQ2b7kQ7otUaEuL9WJXO7Ahzcg1r4QOUJHGy7PsA1EMLjpWlTzaVeslDa1zAARBOE+ewBIQDh+ZaYLMo7sQwBB5bTTuba38PAiHhDADoAWKzyVerdwWpYqH6FanxaOAPvEWfAjACE0BuyZWv0mjRACdkN/9tXwKL8SplNgBHEmrHEflgFLCfBotkczAfiDKtfwXNW8oDgIpGFIAuCHB/AQ1kRJvdkSNj/g8wHwIgyIlxO5V3aQd32K3etByIN0JGWK5UMJvhgpS1IxDy4INOhFa3wPzAhrKhHIEIJFgEM5aHao4Ogo1b1CsfUqJAlgER6g6DwBlkE9XZloOGqXymoIJMAD2YNeOhECpYD8GCcVVmXZ1XwdxvLofFNQZTGELzXCn76GY/Lsx6eieVYz3V3R/W9rjEcy+0KAHLwwQeHTZs2hac97Wkm8VcVyzDHurjsaFdgZADyhS98wfYIozWkRnvvvXc+ApjHzzvvPJNReQlSl8NEZ/UTTjghfPzjHw/bYDREkjw2Fp773OdaZ3V0S4e/Ai+wILv7BRP6fe97X6uCxRfM8ajU1dWEPmoVLAaTtipY8GpAKgX/B2RT8IBggGcVLAQ29X4AjGgCqN2G2Vuk0QkdAARVsK69KQIRGNJv3BDCTqmChVk8GMFzH5DVURZFAGIDaPJf0ABuyUIyg7MS1qYtIRj4mIwAZxX2kSQQrIJlndBXhHBz6oZO/wfZjxIDwlLAOA7KONgdGYmKmbhTRatsnuSA6zqh2yAuvT/M75HK+zZMtFKZigCF0ijKEijvIvOBPibGvCTZF8EIzfDLl1kfECbXvB9VRpO7uWNQQ1nKJbHsM5pz7T2xzLqUs+oVm3SpAVOpfm7fS3c8AMH9xB4gvNewz1IndLunnUGT+/Ggxz/7pWpOed1UvhPbGBfNtJ8V7RpPajIsnd30Eiw+r/i9Kz0g/pw4uHsAkr9P3odOlqXHTZM6/0f/D4CJL8drHpIESjihgXuSwESZEJrSyYBoVS2AD3y+YSqyI5CZYjlrNsgGgnhu2ftDpVfs56NNCK0MLypjwauWWBD6PRR8GABJpXfx3kuwwMBShqXVsCyuJYkUgIhVl0L1q736jelgN7wBvVQFq+QFsZjD5oSltubpm6X/Q8v1+puD4MPM7maoiMZzshcl74eVgoKUFsuqJyTFRmFmI6BJJXQBWlxVK5O4skwtS+4SnOBY+V47pzMmp/8pK9KoYpX6f9Bric+0/4c2IFSAoowHKl5xm6UmhHzGSwbyUmzzDEfJr8b1SjIsBT+lEuCWPxXiKTwgB+637/xnQO7796MzIJee3np+yFW/9KUvBTQkxO/b3e524fnPf77lmre85S27DgWLy414BUYGIGA+8AI7gSpV+hofHw+3ve1tjcH467/+6xEPLQQAEUitsP073OEOAdKs+fhiGd4zzjjDZFgf+tCHwoc//OEAz8qhhx7aesilPiBI0GhCZyWstj4gTGSYELCbcK0RIQZsNvjSwOaTTEss0qw2K9hg4GcndAAZq4IFbwZ9INetD+G6DfFv+EPQ1AsDIwYn+D3QjHDf1bECFgEImwBqDw5tGGYzlUmSBckXgA3AB37AgACAYEYSAyZmDukDgRdkC7qgSxNCMAk1AKLmcdWSm548VdzJHpDUZVcHVmU/tPyt6tG1H4fp2FM1LJbGxf/sS0mDNM2cZDga8qsEQuQzNhNUNkFLpVryx32gAWVquMXmg2Q/1CTuu5/HwTjOAPYS1h2p3+JOS6x91ah4b+0VlqP8brq/0VBT5V0lU6YO2MM8+37/uh3VTXetgtW274VcBasGQPR8B3VAtzixc6c1I9SYwfuAndD5t8q2AFZ0goP+EZbpZRUtsiT8vzEjqU9I7GsUYxreNzqf4/kCGJlCJbtUXMJPcLAiFgEMJxzGwHhsjyZ1AA52QFd5lvYCyY39KncLh8kMPlIjQpa8tYpYBRDiTejsBYLdsKM6d1mTYGXAkeKLycbSSp4JsW2mbQMs9YGKBDD6enqkOE/AYmZymaTR5oNiLm+wGzgkHhclVzHRaIISdktvbLPZ+ZwgpFG9aiz60CD5ZFzDZAurT3qWl/GB/zewsmRJLt+r8aML4ChNrJD1YIzyTQbt/6kyl74nGKkxHyq34nq8TfAZco+FAUBeFcLYxDDhPy67bSqES0/rDLCuvfZaax8BMALpPLqjgxWB7J/57vAHsbhG2xUYGYBwo0CM8IAccMABs3Klt27dauZy9NK4053uNCvb3BUbAftxyimnWCPCu9/97sbUoCLYoBcByNM+fl5uRIgAieC4dmK8Uyf0GgDBAM1SvAAL8Qed0GNzLyYESvtyZqckv8I6aGB481Qc6NFP5LpN02Fyw+YQbtwUwg0bIhOCH/QGAQCB9AEDCWbskfgDdOyD7uQr4/ua/4LGb1agMj9I6gcCwLMy+T+o38bsIwZy6KbBtmwCC7I8si4AHbmKFBr7pb/VA0IZGL0oNLl6eZSa0Dn4cUZPuxPnqj2J5cB10Ko9WpEKPhH8TXkUB1zKFwDePABhCWB8hqpeYEKWL7MqVvH+gSY6NSO0mecIClRCoxpo9JaB8RzsB6R49AQ1Bm/pFKy6ZyadUZLTBCI6YCqroh3RvadEZ/m6Jv5cDscwaB0O5nqv6yxibX3P6uhy2gMkn3NKGhaSBGtQvOJ3rcuVJG/KyNo6uDcSMB1FuqWVtnxpX06omGwL/pHpbRGQJJbEDO3mA0nMCH1l2vCUbKf6RViRj8BlW6FcLwCI9QORbuiIRSWjt0qz7KLAz5HkWSaxQkUsMayz+Z/2AAE4YJzReMMvRO//tg7pFmPScfoeIPhMGRrKsEosCEFGBgJNEGDMiZbYNWY3Vd5TWRX22YX1YNU+/Ma2WS1QWRqZFIrgIjb3RUxchj5bY0t7/Y2s4EqUHZdjHWRdsc8HPq81LvQSLIsByW/G94wL+reCDb+cByv6t1bUKhnNPfjQfXM/CwaA3P/E0QHId0/tDEBwXdAV/cwzzzRVD+RZ8DLvs88+VpHVe5K7xMrFZdqvwIwBiG5+cnLSGgXO9HXggQcGmLrveMc7znRT8359ApAj/5/zw4rVaxqd0JEI7r9imTUgRDNCAJJ9lo8HzByzE7qeoGdAYmnLbVb1Cr0/0BME0imWxbRkdMfOqnmPiQNnHSF54OCObujYJvqK3AyzNozoBCBgPwBAABowiOOFhBkJP8rgwvsB8AFGxDwLrgqWSShSwzAM/pYwJEM6QA1YkOnEgKCGPyQRLCNpDMh4CJuXh7AzsR8efLABoXUWTx4UHB8rR7EXiJb39M37KJGiPprnYIM3wFCqOEUJiGnSEwghuGFFLFs2mdX5hWLUM920Ax/aeFD7kCTwEaVUTQlWb+Z5ewYiTBrJTKwA+EjeD20SWBuU1WPCQ24mmCpIbw7g9BtRBuHNo/kSSLfg0oNc8mTMNgApMSl6LBzo/5QAiP8uBjEkfoKEcaUESJQdwXtlSBivAEDYSZ2lfkuyLQKSOHEChmRrWD8ZGVyLJ4wtyo5QvmWSLvQOAXuC5VMlPI1LJslKXhAyIfib7AJBSJvXgv026O1QEKLAA7SAZwH4RSjooHQO/1MAop3OTVmF0sEVoEQAkj0re4WwrSSvEoaDno7cUBDSrQQ0tMQumQv1e2B/HlQ1JFfSiJDMSYNdSV3Xc3+SJPsaWxrobzMAkib2tAIfAAqrPvaASi9+ghkpgQ/zkSXze6nSlZdV4RTbwIbGPD8Bo0yHLleLOQpkCFQWNAB54KtHByDf+eeBAAR+ZfiKATKguIGtAMzHox71KGv18A//8A/hs5/9bPjNb34z7/PJhXaAMwYgO3bssIpPkB/hi/zZz34WUBnrDW94g8mw8EUO+3rVq14VIOM6+eSTh111wS1PAHLUp74eVqxSAAJTeGRAIMOiFAtJYhcAggEacikABgy4GYBMJQCCLsWpEzoDVqm6keq1MwCZ2hZQWvOGzZFVsW7o8IHA93HDxii/umlzlE2hypMBkKWReQAAAfjAD5oCIqHGwMHEnV3I1X+hXckBdsCAAIgsnQxhxVSUSWQAsjSCjx0TEeDwB0CDJm5jDMCEpN+s0sKEl/IpyrDYCI3SJWqPDSQk4GKzcdRgi/8j9yBIlXxYRtSb0vFdaNJA2QP8HvB9sBKWeT4S48EyvRPjxlrsDfYjgVNqk5ns0fQbkzj89Jp1YTCFLIqsBCphsRBBPwCJA7Lpo0WCxf3k5FI8BAQYXvZAf4maNruCDy43DAjx0oYSA1ILIJQX8XNf01+TgTYPCD/T67Ur+oD48xrkARklkNaAiJ1rwVNS8pCwypZ9luRZKiH0gAQAhOb2tsaHYG5R3pfyUTC4N05Ohx2sikXggd9kQViRj2CExSiULWExCbCbiA+QbTHBR5Us9giBR8RXy2r03tgrmdTpDUnVrowlEUkUmxCq1GoQE8IO6AqMeCw4Pn7uv3SW+2X3di3Jy14dmCQhK8FJE42PlJLqMogb6uMogQ2CEm9MNzAjLIuCDpYmLzA18MVlAJJipLG8KL4CWSj+lyZuCFY4OTMqAPFeDgUBbeyGjsd4X4s1fjkPXGrgg+stGAbkL14zOgC5+JRWAAJ5FXrJQW3zwhe+MBx99NFhv/32azwJqHT6Z3/2ZwG57uJrdq/AjAHIW97yFqOr8BsNAn/0ox8ZAPn0pz9tMiRUsxr29YpXvMIM6Icddli43/3uZ6ZzfZ1++unDbnLeLs9E4Hln/0+DAQEtvHZimTEeowIQk2BNQjK11ZgP9v/YhIZf22JFmZzwafdXo44hm4ifqkabIATbI7C5ZsNkmDQAsjGEdQmA4G8wIKwchYEKTAQAAUEIJFnswYGBhgZtDvyZCUkzkOwHAsDD7uhWtQaDPvptpO7B08t6wIP9P8h25GpRqZwtPRU0MQIEkMFgJR0el1aoYhlJGjJZjYUXlBIsNcM2AE1qpKZsiOmukagA2KTZRQyqPHYAEVa8yl2Go2RKK1gRNORDSYmcGoOR3Gniz1lA7QPjwUcsTtBjMyg74H6yByBdJ+4Dn7PMLpIAbBczjwBJqrnmwDjMw7orAIgHHzy+QTIIns986APir+lcABDdR405qsm1sK52Xq/JtbyficZ234PEe0jYWwQTMpiMYewyILJlOmz1Eiz6ttTEnoFJ6qqOyQVIJ/1EhXZip9RySeqaToZEmxd6zzjDsgcbKuHS9wpQcCH9eur/wHuCITsG/I0iHiLJ0i9Smx7SPM+SwDhO/A/LEFQoOPBgQf/2wMLYHXo9KgDF+z4apYHRbT2V+7X+Iz0WREsIA4j4cuP424MQxikFIWz+q34QxEBfQrzk18AlZRUrjXPeVO5Bhe9knsdrZyovyVb3GADy4NeODkC+9U+tAAQT5AAe8O7WXvAg//a3vx3o5x1m3FpcNl6BGQMQgAT4Nf7yL/8yrFmzJlx++eUGQGDiwZe6bt26oa/1Ix/5yOo6ML2ff/75Q29zvq4wCgBBkoggyMQG56aaa5iMMStICZYNtpunhwYgcbtlAGIG9yTDQnWtTes3hbBucwQgYEEAEhSAkAEBAIEECyAEgIQAxAZONO6DVCnJHli1hrOU5gOBBCv9AITsmOrV4sdACAnWchjNBYSg6zn+JuPBilGazFMuEDOhCIa0ERolVTaSJEkAvR/UOZMRIJOhPQkARKyXQfKEaDUs65iefnojTK8RIY9bJWPpupX6d3gAQukKqw555oumSt+IcqYAJJ9KavzF7dF8DtDjK2oNqnLVVgrXP+Mqx/Lsh4IvLcHLBEAT6DYAohKshuxCKm79KQKQUrwtgRIPSIaRa5Eh4f1dAyPaEJFVswA6bprammWpjWpaFn/S86psiH/vO66zoITGDQUpWB/POcGIgYHEQFhcEUmU/e1mXEuAhD1BaExXU3qDJYHpnGxH2nYNgKgnpAR0fClg3TcAiWdA6NXwDImBkBRLpZyurU9AAgaFYIXLcHKGMiyyIAY6AD4weVMBIQAbacIGEyrqe9MS9L7wBr0k9ICwgWobABnEgNSkVqV4RABTBC/CgGn83GMAyEP+1+gA5KKTWwEIJrqf8YxnhImJpskd7RXQ6gGMyOJr7q7AjAHIihUrDGyg2pMCkCuuuCI84AEPsEpWi6/6FfAABEEpymjqDEgNgGAvnOWGJnq2AQgGeiuNuXW7Dd7wkmA2EQzIzTcDfGyK3g8DIJtjEz8k4XhBOgTGY/VELMVLAIJZfQ4yfQyIVK6xxoBT8YcMCBgR+EMw8FPOQFO27UsrYGmvjCS/IvsBcIT12ECQHcPVv2Fd0CGzEACiBkjqnHGuZFHUQ+JnSbMEKwEP23bSbXMApwldAYh6QMaXWlUpVrBScyWSfQOQImWhDEv7dVB2h4GVvT8GGdApwbIk3sk/cjle0aVzOTAebQyI91MogPFPkCauXeOLn23UCja1DuijeED+VCVYXb8HXa5WNtn3I6lV2PIlfykZ9X1IonE9ethQmIMVs/CevrhsXk8xbuPWbWEHvVuQkvryvr7poe/Krh3YWUmPjIk2KMWzAhaiJJEaZGbXXiCQSFE2hYusz6YxqwJC2lgQW1eKOZAFsUmixLCUmiFi/xkApfK93gdiTEmq8qcsiIELB0YIOErSLsRrAhAyHpkBQeWtOguiMZMABACD0lOVYCkQMdZWqmexIpb2MPJyUsS0Qd6Pkl9E2eU2n5mPkV0BCNZbMGV4HwoAMoK3eNtkCBe2A5ClS5da4SDfx+6GG26w/6FM7+Jr7q7AjAEIJFKvfOUrw3Oe85wGADnppJPC1772tXDhhRfO3dHvAVsexQNSAiCWbKZSmCqZwgA7WwyIAhCW+M0ABKAA4IMgBOwEwAEGZatYkkzoZEDEAwKjoI1t7AOiDAhZEAIQgA6yKwAjMIdilpHAAJ4JgB2rcJUAiDEHbOKHz5P8Sg3oOAYFIOzdgf0SPCAYeQBCLbTO4Gnn8RrwIONBsEMPSB5RUu199vtQ+RWOP/lWYvPIpQFVrLK50jWR5EyxdaeGByRVq8KuUM1KPSC+GWWbB0QTbd5/+lsHR5spTJIra0BYkGC1DdZ9AEQ8BY2EtlIFy4MPrQBWkznYPSnb82CkBJgalW/20CpYcxV2u3hIPEPiq2s1ZIap6hvACMuIW+ne6W0RdGztle5lzxHfAJEd2Q2M0JReYkK01DaBh01gpOIT/E0AgrigzKe9F/mnsSMijeJFV9+I94awQpaa1fXLYlxSwKPeFPpC2kzz9lAkoKHva8eirEXu25EYDgALO9YEVmhgz8BDQAy9JCrjotdjGAACDyVlWKnyH5hYxlH1gqgMi/44z4CAPd5dAMTiN5nWEViQBdOI8GGvGx2AfOMdrQwIyuvCu4zCR/qCkgdKnBtvvHGuwt3idmdDgvVf//Vf4aijjgqve93rzAcC4IHmg6C2vvjFL4ZHP/rRI11oABdIu375y19aBYJDDjnEKhWg7O8RRxwx0jbn40oegCCB8WV4vQdkGADCKljUOkfvxrR5QEoz4TGBal4plHBlozAM5LkSVjK3oxLWOhrRPQAxycFevTK86EBOCdbq5QF9K3C+lrju2Nmr4e/lV2pEp/8DYIS1+7N3YmmUdQFcQHZlFbCS1IvAg0ZufE4jNyqmLNkrgiD1b7CPAJsQ0ohmM3lpgLQuwKlKDS8dGxHSwK5GdgUf2v/De0CwTQIQLcObjnkJwUcyU2I2Lxu7pYIUASkByPT27XaKEYCgPkA0aLIEL9i3NcvGU+lKrQgTe3loB+DSM+XZiRoDosdKxqakk9aBtsR8DGIp9Bh9xa0sZZSZSn9ObZW1aj1AeMzY355WhndXxNFBYGQYIML7n5WyfLleABKW8s39jqQTe1+PEcYc9YSwxDbLdiv7YT6wNJHhJyQ4qUEwYvEnTXSQSdXiFBmIpCCtpWx9hanSF6XbJKNb2o/vG4Jt1YBJjnnSCNGDIfra9HhzSd3E1uQu6QQeBCbJeG6G99ToEICkBEAowcJEVMOM3muOqJM2jJkKQGLJXinfm/qEgPkt+UAIQnxs8cxGFyak5Anx7Gyp+hW/gpIXBJ+V5FgLBoA8/H+PDkAueHsRgNz73ve2HnYAGne7292s0TVfYD1+9atfWR8QeJkXX3N3BWbMgODQUEXg7W9/e7j00kutUsB97nOf8MY3vtH6eYzy+tznPmeg5tnPfraBDsi54CtBrw2Ami9/+cujbHZerjNKGd5BAITN5jh7BwYETAh0z2zahcG4bEKPs+IMcr5RGGcKKVtgKd7rCUDgBVmfTOLGHsAgvqRXdcoqYKUyvKsmct8KfDkEOdkQqqUvG5WwUj8QJAJYBvsgM4EBSI3bkGIReGgSr2AEg1Uqq4jj2KFSMCYZSBwapS05iwe9sZTIJIsSEVXPT0IPCYGM/ZZqWUwC8kiSBl+azXm89jcMl+NWYhIDKEvvsqoLBlDIAwjq4AfCtTUAkrtRRwRSYkAowfI66Fg1KprQdaZ/0INFOQEHdhwbqm4pu0L2Qw2cCjxq+xgWkHQBICpj6ApudB2VpnkAUpKs7Y5qNHNtQh90T3T5vM0zohKtQdW0MiOcuqirJyQ3NEyFOQhA4v8BTBJjAtkW+h9BespKWZgkAcObGY/k8cq9RFIDU3pK6Aeh/FInIlj4AiwI44yW1PUXTBN5NXAjSS+V60Wyr6whmT3dB98jVlk8SnJSBSh5IkakprXeO5yVr5XU9d4OSq1Y2pysRy51XgAgBjiSAZ0SLFY1JDPC9cfHQm3ShgBEZaI19hf/9/1A8FyzNLkWpyh5zRq+MfGKdTGkx5gdgV4XIKLLeyCyacOGcMsD9htYprbLszoXyzBGhUe+fnQA8vV/LJ4fJsrxwm9UXdUm18uWLbMKrk996lMD3i++5u4KzAoAme2JkVH/AAAgAElEQVTDAzo94YQTzACkvpIf/OAHhkqvueaa2d7lbtueByA9D8jSsHZ5LMOLPiD7r4y/fR+QUkJDrT80z5RKkQHBAIpEtB+A9IAHkzStRKMDszEgUor3us1T4Y9gJVAeF9Wv4AeBPAoDNAYzBNlCGd7xlRNWOpaz35R4WSdjyLdYmQZ1+uEnURkW5VeUSHEQxGBDkzmYDzWeW8nf1FcjeyliMl8EIFYyF0mEGMe1EhYHUDIguIs46HLQ1iQD/4MEg+wIfxOI+G1bcy2U3cUgm35b48Y44NYqumiPDnyHBCD4DtFpOiZkUdtKAELQkZsQplKVHiRY/XupmFYCCVr9ioOebscAiMww5m26br89LOYoOXlauySp+nAPC0CGCQz6LCqQqpnQue1169fv8o7ECwGA4Pp0+X5LTQ/ZQwTr9+75HRb3yOLCFwL2A7EM1f3YxLAJQmLpXiyzfmraAAliaEOORRCyLUmuaEIn8CCLarEE5nYpOqETEToJQRDAm4QTLBpjvHQJj4matjXxH1SqVwGJvkecgB2kMWGSGBr1rbWBJZ6DAhKeRwYhroGhzXQkn4f32rHoB7usK+BQ0MHeTlYtq8yAGPObGGT2BOkHHr1qfZzcoflcfWSMLeyIzomXEjNSM6j7ZQkguHxb+W+NxY1YJGy4MiELBoD8X/8wOgA5/22tAAsVXGFCn43+dcOMFYvLxiswawDke9/7XrjyyiuN1jr88MPDfe9735Gv8cqVK431AApVAIImMXe9610DGh7uKa+uAKTWiHAQAGE3dJad1AGXnau1q7U3FtM/oAN3lGD1OqJfu2kqXLNpMuwAAMEPTeIAICrBcmV4ly9fFvZeDgASE0xIvTD4NxqFeSkWGxKWAAgGOM6GWZ+P1PXcN+/L7EfyUiwbC0tsdj8eBxL2WK1KEoosoUrmTAUfXlaAjagPxIMO/s1ZTwIQrEcpGfXQGDyl5G5+P760D4DoQMpz4ffHXgnaD4QDFgZRrYkPKRZlgFoml/KrQQBEn01t0mXldwE8ljQBiGc/aoNnl2fem5dLvg2dlfQSLGUyMgAqJW8tB6PJRU1+pasjBuy37z67dCZyoQAQvU4lw3qJCVFfCO57AhDEFotj27bnCRTKSa05a5KX4nM2LyQoAeNLmVZmQMh8sGKWTliw87qa1wlMGF/o+1CGgbGjdH/50rXqmbAGfa75n8YlbK9W7pdxR/etLIxnROhXY4yzz1PM47b4GbdZ66GgPUFKZXtZ8crLXbUCIcvxMlZKJ/SmDCvKtjhxQ+8cJ19ify2wG012tjQJw0meWglexIBBQETBRSkGKhhhrC4BEJV6NUBIBXhwmQUjwXrUG0YHIF976y6Nq13GqMVleldgxgDkd7/7XXjWs54VLrroImtZjxfa1z/4wQ8On/rUp8Ktb33roa/3He5wB/N/oBOlAhD4StCcEOBkT3nNpgcE1wSDL5NMNiOkEZ29O9iwC4M3QIgmlDprg88hb4BsB6wEfCMchPGb27t202S4ZuNU2Erwgd/wZ4CdQLKNgc8a6qXSuKkLeisAyewHOqKnH8qw2A8EYAQDPCtUcZCix4NekAYAWdrspcFytgJAcL4mw1JjaQmAMAGgudJXnSHD4Y3m1HhzIFcmxBIFlXclAKIygzTL5xmQEgDB5liWmb0RkJBRusSZPHYnV/mVmjDpDfLyKw+A/XPpAYiBkNR9OIOb5L9QYJCTfxlEddsl4M3PPQjxx8T9RElZ8mjwGFx9/S5xxh8LE4KcLDjGyG9zEYB0ucpxmRoIURakBEDIahBQgBnOZvTUpJDyUg8+zAOSmrqaGZ19iny5XfWBNIzqhb4hrIKlrIdKmTSWNCRMYAqS5JOV98gWlDxpSYrZqIill1v36WVZHpjkz5OBjBItggwPWrycjMYzL9lSoNTWO4Rd0PU8PRAxViT1UDIGeWmvWlYCJpSuWhne5KPDpAtL7Xp2Fs93kxVp+uAG9QEZBEQYLzwTXJJv+d5DCmJK4EMZD/0c7xcMAHn0G0MYH6EK1tbJEP77LX0ABI0G0TD7gAMOCPvuu69Nmtdeiyb07rF5lCVnDEDg88CNDCrrzne+sx0DTOgveMELrIHgueeeO/RxnXLKKba9M88800zs8Hz85je/MVkWvCXHHnvs0NucryvU+oCwORKkV5ApHbhqIqydGI8SrKSfR+BjAPIzgUg2MdhqN/QMQFKJXtXPMxHVWRea1AFoKFFgvXwDIKiwNbk1XLt5KsCIvgnMB1kQAhAMOmQmzBje61A+viKem0qwsB8kBpb8k/3Q9/B8mLwr+T+ow8aMGwZqzoKB5fC9P+gPMYCSKmElKROTUUvYIVECCGEJTiYWmiyUwAf7g9DcqX4P+krIepSkWBzIPQApgA9KsGwWD3XtJ8Yii4EKU4llyAl5akRIKRaldbx3CApoRO/KfrSBD5VhEYSYB2RsqWmlu0qwhmUkSo3u+gCIFVpAEtFrIkYQ7gfpEngpxRI9TgUevMal5IAJ9SIA6R6dSwyXN6Tj2eXkik3GbO/FL7IcnEhhOXE1oYPdxWQLWRGa0LFMH/vBUrzafJATF75xofYJYWxQ5oCXgaCbZWnxf5+k26RHindMyJls+54ajEuly6zMRKnRc83fwW15X4myOWRBlPFVHx23gXPh+fmyvZzcqf6fhUAS8CiVRieoQfxP8YfAI5a8X5o7obO0rjZJLUmySg1Za1KrQQBEQQSZldq2ZgJAfLxeMADkMW8aHYCce1IfAEFu+cxnPtN6f5x11lmtAOS5z31u9+C0uOTQV2DGAAR9QL71rW8F+Db09f3vfz885CEPCVu2bBn6oLDC61//euukTrkVbpYTTzwxvPWtbx1pe/N1JQKQF3/mG9YJnR4QgAwklKiABZkSfsMDojM1owAQlp5kuUpeFwMgY3FGmIDAEqSdURYV6+hHUybr6PcYkAhAbgYAMRCyOXo4VIIFYMBGeqk/xxIHQJA0NACIziKSEVEAAiBCY2cGICjrxC7nqRKWla1Ncit6QMAiLB/PpsQ+AAIA5/0fOlgr6+GrYGX/R+p2TtOpVsKqeUFw0bsAEAykUgVLAYgNosLocEYYAEQrnzEp5vLshs5BWRkKbz73mmRsSxv2KcOiAKTEgLR5QDwAGcS4KOiqPfM6s8hjawMgtX36YytVmumTRzhGB9/HIgDpFp09+8F7udRBHfe6sbfJ84S4gv9l03kqwcuGqvicPY5KAKTRqFCrYKnsSvv+KHvaYFLRjDAZzVWmpMk4n381l6tUaVCyzvWwjhrS2y5zqay13qsl/0Zte20G9xKg0W0reNL3ev7sI8IYrPFXWSH6YeBbS/GQMc3Y3VQWXAtiMBZCbjqWSpQrAOmxwjCd9yoE1krxDgIfpUpZJeZDi36Uyobr5yoBxVdUa0j4pwpAukWbxaV2xRWYMQAB64FKVWg6qK9LLrkkHHnkkeEXv/jFyOexefNmk1uhsha8H1qpYOSNzrMVCUBe+tkLw8Sq1ZnqBQAB2wGGgCBktgAIq8D4JNEb6gyApEFcS1dyMM4AJDEgVooXDAjL5LIkJRkQYyUmYmncVIIXVZyQ8HJfpr/etj3KHLSUJWVYGPw3TUZTugIQek0wGLHDOQGPSrDYfBAsyLLxnMTz3HEcmrQYC8LEQhsFegO6luElALHO5zv6jeysiFUyo5cAiJXiTdW9WPWloGXGQMp69TogEUhyVlgTOSzHWT/WwFfpFa+LN59TulR6nHzn8GEZED+AEij5fZXYEd5HbY85r40fyD1YqO3X/u+ARG2QXwQgsxdwa+wHAYhnPrRTOhlhyqtYelfjGgGIeUSSRItNV1kBayvZWDYpNf8HzOXJN1ZrPpp7CaH6Xer5gUujhSfs2SfbIaW9s/cjNeorsSElTxq254EDv45KH53Gt5WZGPTqEJZCt8n3XX1S/nwZ73isZGuUFcF7X2lQQZpJs5IBP8m0CDg4ocZn3sBEAh5k/QkwjH1Yspd51HQyrgFApHiGsiV+P8MCj3gZesysxkACEsajUXwgJSnWggEgj3vz6AzIV97cx4DgvLu+1q5d23XRxeVGuAIzBiCf//znrQTv+973PjOeQ08HQ/orXvGK8NrXvjY86UlPGuGwQjjvvPPs59prrzUAoi9Is/aUlwKQlWBAEACXLrHGSGBACED2TWzBbDAgbQBEQQgTVy5PdqLHgEyHm6e2hRs2T4drNk4GK8XLalgqwcKGlAGxJoETARIsUOGcrSfbYrOVauIkEwL2Az/ZAwKWBc0OU2UpyhIAQCC/Wu4YkMR6mJE7ARF2xY3nHT3zjeNgQkHNNm88Doppxk1N7FjfPCQmu3JGdjYgYyUcBSFMCpQBSSV3e0b0Xglek0oZ6EDd+jErbcvSuTqjz6Sc/SyYyGXjo/WegSwqfhcqwYoDa1PzrL4JTdJL8ifVN2O9kgndyw40wVf2oQY4ho0Fyt7wGtT2WQIhpeOoaa2LoEbAyyID0v3bGwRACDhicY1m9SsFINaMUBgQghE1oROAmIR16zYrX56rX2EyBM+1NSbEewc+KLek2dxiQWJD+bzjtL0xu8RylNiMIgApgBMFEHqZlYVoAyK6Pv0Xyvx6YEQQUQI9pf/541OQo9vS/VimLsxOmvhBwg/gwPGz5/OKsSsDBGmIiv8p4wHzOdfnmOS9H74XCLdLwEE/COOGl1IxzgzqCVKKRz6WdJVj6bZsKE7XHbnHIQfuP29N2rkM7/990ugA5P99U9/5oflgm+8D12jnzp22zGIn9O7xeZQlRwIg3rizadOmsG3bttzMhe/hARnFxIPazGhqiC7rBx98cN/Ncs4554xyrvNynRIAQTKIxHhXAhBqXjWgoimfziJyUMZMYWZBpraG6zdNBVTCuloZkBIAAQOSwAd+L58YTz0s2LOi1wsk+0AafhCAj+QBIQjxAAQzZZBgKQCxUryR8TDgkWRYaIJIBoZJNm6SadGN23FQQkVvB2fpBHxw8GOynz0kvk+AByClUpb0l1j/FFeCVwzoBAqUTGHww3sOrP6GrwGQOMjuZQDE+z84AGPA9KZtnZnTfdUqT2H9mv+Dsj+VH/iBs6v8atCD3mArpAGhAosG8JHZ3UEgqCbDKgEpXqdFCdagbyx+3gWAaOlw+j8ajQilDG+vAhYqXCUJVpJiKQBB+d31k9vCzYg1+AHrQX8aK2Ap88HJBe10XvJB6IQDE27+bmM9dIJCAYr3fliyXjDYlnp/lIzhXF9N79heLv+bWAcFRPa5Y0sakrL0mW07gQk9d8+6pM+Q4OekXoo6aILPGK5gQ9nXEuAo+jvSJKD6PLTvh7HBjikpeTbi5YusRglQ+PimT0EX2aey0IxLJSmp3xaXRdw59BYHzH8A8vi3jA5AvvTGvvO74IILugWcEMLDH/7wzssuLjj8FRgJgMDE0/U1iokHoANGdDQj3NNfBCAv/9w3w8o1axoSLAAQ9gKBAR1sCBkQyGU0sNVM6FaGd3KrzfipgRyDMqUyXqPPJna49gAhHMDjoNwbrDEjCBnWui3TZkT//c1bYiUsSKTwg8GZlU/YnwNgYNVyAwhrlycGJHlP0JuCpXit2gxN4NZJeFtkP/ADlgXyK/xoHxBKvdiI0LqgA3wk4IHf0lkcHgo1IyIwU1duVXNQlpPHkUtPoqpXrwoNKqoAfKiEi7I1FAGw49afNgbELngy07OsJgEIpWOpahcketmrMRZZCwUgOrPPZ8h39OZgRclBBjSNJoFR60xpgtc6155PlfdhGT9oU+ZVMl3mREOSp9qA7JkHHo+XgfH/HnwwSeDnJRAyDAszjA9kEYAMF91LHhBsoVQBi36nUg8QlVixn5FW99PSvPjcGriiASEboxoQSZX5KMXKJvTEfHLSAom9sp2UZ5bYDyb8lB9RWqQz/iX2g/IkZSk8m9ALAvFdqe+H/zr8vuivIPurfUg4aaLrkC1RWVWB5VBw4Z99L0Vi0h4nKnp9jOxv8TGSydAYh+XbSuwCwNRiHSdPbB/02KViFhr72wBHbYKj9BS0TXRoPKqxILyOOa4VJHIbbr453O7gA+c/APnrt44OQL74hnl7fsNFvz1z6ZEAyFxfiv333z/AQ4JyvHv6iwDktf/17bByzVqbMUFAA8BgJSx4QQyAYMY+GY+pUdVApaZMgoZcux6sBRpqTW8LaFCIQVilONlwnGhpXneTEyVzuNdPGxOSOqz/YeNkuGbDZDSiEyAAHBQByERAE0J4WygZQlAl2wK9dZ8ZnSZ0gA6wH9gHAQmSAPbPyI0GAXQSAEE/EMiyXDIPmRu7iKsMzK5dqvyFc9yqnYtxYTjIpooqTKb5XRCAFKVk3oieOwsnY6rbvpWRVON86glCAGKSKankooxFaRDT79yAQdI7e+lVPKfmgKz3nDIW+oyqDEsHP5Uo8JjpK+G11+SiDRDo/oYFIFi3pKHuCkB4jLW4tAhA5jZid6mCpVIsZUG02aDJsFIpccqx6AEhAEE/IkywsInrJICHFdcQAMKu6FmqmeRYWnrbPF/be+Zzb9LWWX/KSCnHUlmW+iA4QWGJv2/iB0+EsBCMKfxqSibxEvjA/8h4lPajoKNUeavQFFG9ESopqr+PIEOBCcGGToZoLKGBPDMhmdHoldNVqRW9HH57WN9Lq3rSrijrakg4OzRSrbERevlLky19Ek+ZnKGcy8em0nb0fwsGgPzN20YHIF/4hz4A8sMf/jDc/e53D5Bi4X3b6573vOfcBrQ/8a3PSwAC7wgM5294wxv2+K+HAORt514alq9abckREzRWwgLwWDUek2VUO1KTnAcguGDW50FAg5WPpOZ5alvugq7JIpNAzkgz4LEPiDWyS53VWUufUqwbtkyH6zdPhT9smIwyLIADk2Bt6+mcWR7XGJCJsC+6oCcJFBkYHDOqbW1Bz5GtsWKNVaLSMrxgVlSGhWQAGmwM8laCcUn0frDkrxrR2U182ZhJ3CL4iP4JDmqsGNU4Bp3NFIYCfTh6iTv0x5SSofRnrB6W+wbQUK9d1WlGVxDSy4Rj/XoCEJYObpFgsYqVr4JVe4iweVZ6IYjqVcCCXyNeF+qaVRfNgdlvm/cUQCtfmnT48pbjSbft5VxtA3VpBtAfR40B4XJtM4dMdhRA+e0P8oF0kV0sMiDDh/dhWBAsO0wjQi3FC+BB1hilem/cPB1uRkzjpAfZEHpBvPlcJxqwDCpflRr4WZKfGNWYQUrSL53NCUR8OdpSCV4yFSp9arvUrfKrJLnK+0/m+Fxtin4MOQ9hYjzg8IxGE3hEsNG3jPTqYZzA7zbTuHpB1NeofjaMoxxvdRkdA0vSKj2+EuPRFz+cDG7Y2OZjjQKO0oRIjS328WzBAJAn/uPoAOTzry96QK655ppw0EEHGQiB1wOej34MvugBGT5CD7fGvAQgxx9/fEDTQaBP/IyPjzfO6vTTTx/uLOfx0gQgp51/WVi9dm2eVWGzNrIg+I0EM3ZrXWqB0ydtOjvIZoRgO1jHnmZLJPmUKDCA5YAuwR6f0czJTtosycttYdCGRAFleI0F2TgZNhGAsPwkKXvrTr7MzOes7AWZGWfTyRzg+MiCGAPRACCJAaEHxCRYADpJUwyfB3uAoOIWAQjL8Ka+H74OvM4wAcCxGzIbmBkLIpptmM5pAidg5GDFGViaWhu9A6wyVqqOReDhJRoqwRjQA8QAA9gymNDTe87oEWCUbn8dyMmaePDRZYDWbTPpJ4jTAQ/708FeJQ2ZTRGddx009bTgWGYQA1JjZLhuYwazIvmqDeg+MejCfmjCsAhAhg/MHoAwRtUaEUJ2WGJB/GRKZERi/49Ss1VMshgIURYEEyFWrQ9m9OQVaxSdSNWxYFQHAMlM6vZm53ADIWAsCkm8Jf4OiGjVJ8SHUmO+kiQK+ylVq9IE2ZveKaNiqVsyLzG4RMAkRnBO5DAW6vjiTdSUUOk4xphTSvy9nIqTJ57p0ImSuJ0IbNTH5tlcZTsYq/QYCCr8/7yZvAsg8HGjNlnRlaltmyhp+2zBAJAnv310AHLO/+4DIOgpd5vb3MaAB963vQ499NDhg9TiGp2vwLwEII985COrJ4Cb5vzzz+98gvN9QQKQ91xweQYgNLhRhgUwgoRZK2CVAIgOxgpAMLiSBWFFKzNJu1lqDf68bhjwe93Qo3RLB2vKEzBAA3zgZx0lWDbzlyqYmQA3ejFWmfwKcrJxM9pnAJIa5mH7VpdfpVi+DwgACH60EaENikuj0VwZEBjSE/uxBCxSKv2r5m09Bl47sCAwp7KRGa8Xvx/vweDAxevF47fqOSwrzCREQYhVxgHASdpsJgGQMOCckuzKzg1MCJoNigcEx6Fd0COgiHXsPUjlQKqJQROAwCgemR3VOpcYMj+QMjm0sqhuRsmYFOkzg2PWhMAnJ0XQVAAIgwCIPROFY/EApE2fXZQyFMy9iwBk7qNtFwDC7xzfu3pBGtJImWSIPY56sU0ZXmu4Ojkd1m3ZGgyEpJ+tZEAIPlgZi40JATroXbPfW6MXROWcjI28bNk/kYCImr99TFBgwG7gltE7OVabITwDH7AcAn54HGQydPvSWVyZVvVBxJgTT0rlU5qc56S7A7vBiRTGMsYiZTmajEesfOULZ3D/ur4HHhoXFHSU3uv59LESBaBXAx7+uih4ozogflW9ggKD/CRtLAjXXTAm9Ke8Y3QA8h+vW/SAzH1YHnkP8xKAjHw2C3BFApD3X/jD3IgQgZ2GYsyy06tgDEihC7qetveBYDDNAGQ6eisAQpgkasdqnfmpARAk4752PgZlAyAbIgC5njIpNvLjbFzyMqwyBgSm+mW5Izb3h+0TgGhZzMyCWB+QBD4sCUgSLA6mGCBpOicDIl3PCeZ86VoN2PTPqBwM//OSNTazogFcGRCAGMivGiyIl1+xVK91R5fmZEwaOMOIBKAAPrz/AyA13h+QTUXjuAKQ0kwbE4coJRsOfPhBjhWI8FUoCNH7imDHGBAnv+rKNHB7OnD7Z4DHkO/jBEK4Dw7seQYzXSsuP1DGsAhAdlu07eoD0YpYpbK8ACecXMDziljJ+IhnnywvJllu2DKVS473mdK1J4ixvizRm+SjAClZggmmJBnVBzUiVAkVpVdtngyLE01WIku6CCp8Qz8pqJG9bd7bQSloYjsoPeWzbAxEmlzgM6kAgzeKn9HXeMQ4RfmTNgIsAQ//v7ZS4VxWf9twwfiYQFDfsTtGtpT0D5JHlR4SjS39rFB/zB4WhHSdMFkwAORvTx4dgHz2fw0EID/96U/De97znnDllVcaK3KXu9zF2kigx93ia26vwKwBEDQc/OUvfxke9rCHBXRHZx3luT38hb/1XQVAkAhD04zfTLBrDeM428MB3PtAWEcf28Lsfhygp8MfN06aDOu6zVNhB2cIlQFBT4vlsfrV3svR46QMQAyEiBckG9IxkFsfkFQNyzoSY8CHpGFnlBdQskQfiLEhqH7VazpI4zlZAxoYMRggUYFswxKT1AEeTAhOA//jIIUBb1mBKeAdqeuyxKeBKPYI8P0/SjpsyhtwTmNLAytu0biv5nOAIO2x4WcH/WDfOw/WyO919i1VeamZMRWo4r3eM/p0eoMnAYhqr3PyX0jueU/id5s0wZcabquGRflVQy5SkqgUGg+WIk+fUVTKb5aSDrtei53Qhw7ibSxIBr+J3aUckEyIdkfH87x1R3zOddJB2RDIstZP9fqAGPhIgASleYser1IPI+0Xwr4gkGWVQAhOgoAhv680ISyxHuoB8d6Rkqm94TtJ5nVneIfkVPtskGWlZyNPdrQk834ChJIqAwaJNVFZVSmGabzg5yVplS7nS/hq/ChNypTiUO355bKeAcH/fdXB0o1e6gfigRKvbS3uldhjf14+FuPvBdMH5Gn/NDoA+cxrWwHIZz/72fCsZz3LWj486EEPsst08cUXh+9+97vh3/7t38LTnva0oePT4grdr8CMAcgNN9wQnvGMZ5gsCujx5z//ebj97W8fjjnmmLDPPvuE0047rfvRyJIXXnhh+OAHP2igBjfJIYccYh3Xb3e724UjjjhipG3Ox5UUgKxGFaw0K0MGhLPa2QMiDIiffWFCQz00m28Zo5AkBpjl42CrAzkTUk30OONPSRKN6GQo8gzh1FbrAwIj+rWbwIJMBZMoMOGOG89SImuqmAzokJUx4GoJXO/DyAO9VqHhPujPQOJKsztlWAAiycCt7AfkXzRtI3HnIKgJC0EIS3mWTPvYhlL6/A6UycnmVnR4pzmV5TlZlrMGQJAIJAmEr3yF/aL5IJkQ+oaUZagBDz8rSE+G+j40AaDsjLNxyqwoCOE1Kt1bNlOaSliqfMPrxUvPqSYAJQO5rjMIhNTYDz9zWHq+2mLIsBKsRQAyekSugRC7pjujbFTjIYExYlgJhDAmIk7ieTc2JPnnUA2LBTdY9Q8ghN46k7cWPSCpDLcxn2miRJsTsq8QYwAOuGCGtRPRRn5ejkXGghJUlWepebzEbFCyJZ8pM9Cf8Pca+3nQ4Z9jxgjfm4JgA6elMtEa0+GPgZ4OsiVaJKNNKqoxq5Sgz/T5L93NPg7W7njGGt8NXcGFvq8V0fDMk49hGqMWDAB5OgDIiuGDxdYtIXy6HYAgV33Oc55jfef09aY3vcnyzauuumr4/S6u0fkKzBiAHH300dat/CMf+Ug4/PDDw+WXX24A5Nxzzw0nnHBC+PGPf9z5YLjg5z73OesB8uxnP9tugiuuuMK2+f73vz988YtfDF/+8peH3uZ8XYEA5IPf/D9hFU3oSS/PvgysgJU9C4nu9sFFpQkcZDGQmhwoya9qAKQ0i8LBvAZAsC12CEY3dAMgm6fMkG4182m+jKjGWAg0HwT4wI8xEGNRKoTAS+YByQD2mY3gqTlYw4yuVWjMzJ36ZxgAid3Ccw+QibGg3g8a0NVTQ7XXz0cAACAASURBVMp/2w5UEItJDAGcya+SF4aSNV89RXunsPoO5WrqZ7FZV0qvyIJoIqLJhsgpIHvoNQmM7Av+pvGc4IPVrErMgoJMAk0mDSy5670fXhqBvxWE+OdKQRqugx9ACUC8/6Ntxo7Hyn21MSBYxgOQ0rPfxn4MAzxqHpTazGrpmV1sRDhadB4EQrBVkwImT1JXEMLJA3pB2JSQsQ6eELCaACZR3rrVqvZlptYqYiUDOoGH9QlJ/2ODQsQsrYxFAKKNAvXSeBBC5iIzHqkKILxj3jzOvwuMScnLQZBQYzhK8k4+U4wzymyUwEZjOZGLloFIz0jOfZeq87VVsPLxz8eV0e7C5lreb1YCIFymxKooaPMx2l8vft5lQkXjDmPWggEgzzglhGUjAJDpLSH8+2taGZCVK1daKd7DDjus8UViIv1e97pX2Lx582zcFovbqFyBGQOQW97yluGrX/2qfVlr1qzJAORXv/pVuMc97hE2btw49MW/973vbeAF4Ea3+YMf/CA87nGPCyihtqe8CEA+9K0fhTVrYhUsJrcAHDbrnUzogwCIAYY02HoAYkkwytomBiQPymmwK81C6zKcFSQroD4QDswAHgAgqBZz4+R0NF5jgE1yAoIAyq8IABgcKX9So3xrSV4zfqK+/o6eBItMC8vWpgZ+vuwuwU+sKgbPTQRCeg0VePFa8L4rJeY0Xppca0cEGpxFJZjiTGsGIhmE0AOSTPuu2aGvuGXsR6qIxnNYs2w8syGexi8xEzzfhi8jSS26Volpew79AEsGBPul/wP71kSglvzXGJBBcaDG8pVmZUsJit9+DXDofZHfF0zziwBk0DfW/XP/3fp7nH93ASGccFBfCMCFByFkQrRZIQtz9FXOy6xIqnxXK8GNGKBMqPrBcDmUFdFGf2QtSlWwfJlcrVqV+hdF31dkUOHhIHvAxN6zpDgUL5uKz0z8zsBmaNLsE+jabH6NOVGmI2/LNRtkTFGG1jMmGl9Kd1epUh7HgZkuX9o271uNBYOYapVqKfjT70iBSONaixRUz2fzxg3hkAP3H+iR6P5Ezu6SzI3CM/95dABy9qtbz++v/uqvTGb1/Oc/v3HwH/vYx8LZZ59tue3ia+6uwIwBCADC97///XDHO96xARagoQNYgERr2BdQKViP2972to1tgg67613vGiYnJ4fd5Lxdng/ZR7/9YwMgDCIcHCi7QcJMjX+pCSFP0AMQAgYOjg0GpFAdqJQgGSORZu69SVN9IPB+gAnJJk0nwaKZnuwHmAiWjLWAL6wDpRDcHxkcAzUAHjR2WlWZVEEK49/YWM8Hkkrvlipf0f8Bdom9KTTJ5bEAhFCWpQmPyg+0SgvPQ6uQaYKCBocKQqwaGRkclWJwZF+6JBceYMld7XpOgMqKadrYUb9LDzg1Se5Jo5o+EKyv8jIsVzNu6gPWNrOX/TZJjuWZt2EASLxEvcowXR9ybzxvY18GAY58HVuq3pSuhz6viwxI12+uf7kaCNEEkpKsNiaEzzsnBmhOZ8NCBRxappdyVEq2vHcNz77FLPYC0SpYWn5bq+D5fiE4GSkBbldBK2SVGhEqAJHPSz4yAyDSkBT3K3tsKIvhAUYby1f6rPas95Lo+P2ygpXGWC/91MQ7S7FEGeAnYErjWn4GZRzscj+V1tP7jZ97/1kN6PBaqdncgz/+jW2XZFqM4TUQYtfVAZEFY0I/8tTRAci/ndgHQL7whS/kQHL11VeHN77xjeHpT396+Iu/+Av7Pzwgn/nMZ8JJJ50UXvrSl44enBbXHHgFZgxAHv/4x4f73Oc+4a1vfauBBdBZqJ38zGc+M+zYscP8G8O+0AEd/o9HPepRDQCC3iAnn3yygZM95UUActbFV4S1a9faaemM9GwAEMzq6UA5yAOi1xYBuQZA0CSQBmtoo2FEX5cqYoEVMXMn5Aap4zaSY3o/0N2dM/cc2GpSL/pXcldyM5+LvlrLWbJii5WujVKs8ST1sn4Z6T3ZF8qYOGvGc6fsamo7OsYnc7UMVH5g9uAFUi70FPC+ment8ODEa8prm0si63mkCjgYXCm9Irgo+T7yMmlGU2cVdWZY3ysDEgewuhGd21MAgmvl5VA60MVkIhpM+X4UAKLXVvfJbQ4bC7qwH12BB/ftj1GPbRGADPsNDbd8l6RxFBDiJ1s2TPUaumrTQh9b8bef+LFnnGwtwIY2JuQERKMqXmquiuXwkpLp9rdWymvIq3yjwGZZXsaT6BuLTVgZT7IPbCnLd4e+anolVkST4xooaT4P8ZRYojeeTi9G5G0UDO0KYiyWSCUrnZgrHWfprirJ+LBcm7zPPk9jAZdTsJE/k/FCS96XjsNP7NRYjobMLXVeJ/jgOr5qVh/wS9d608YN4dBbHDD/GZDnnDY6APnkq4qNCLtEGHiat+NZXXzN2RWYMQABGHjEIx4R7nvf+5oR/W/+5m/M93HjjTeGiy66KABMDPs65ZRTwr/+67+GM888Mzz60Y82zwcaxkCWBbR67LHHDrvJebs8AcgnL7kyrFy9JidsBCE0TrMJoe+C7hNfBk8EQU3oaURn5RcmwBpMfRJFzTSWRYAle8IZfc76mSwBAASNuhIQMQCyDcl7HEA54MH8DfABNqQkfULCj8RdK9GotKGv6owO2t6Mmfpn+J4ZSNYJQFYvW2qDrAcgBAe8jmRBNOGMg1zTSMnzxXkAaOSZ0QTGDPwlFoSzrjozi4HKm0CV+aDpHcfLkrsrx2MvlTb/h/dm+ARZwQfOqzaY1wCIbs/eu5r1pRlL71MpSRL0we2rMjUC+8FkpwGM0kCOz4YFHnp8/vnxA7+/Rlx33U3rw3777rNLEwHGnfXr1+eJj3kbJDsc2CAQUpNj+epYyoTos4sKgg3ZVeqtxNjEuJUlXMnMHid7YGgX5pM9mNQLlkvzphK9ZlZP0lJKsrwUS+VY9It5rwflWUjWJUbohAVjCr0gWsZbGVDPhjaT5PgllUBF6b7ve1YkXrSV62Vs8RMiNdmYB0S1W2nQ/cNxksvVAIiOEwQdHEe5b43Fenx43xVQ1VgQjWuDgMjmDRvC7Q4+cJfGnQ6Pcl4kS7BmGYAMcwyLy87tFZgxAMHhwZPxgQ98IFx66aXGeoARefnLXx4OPvjgkY/+9a9/fXjnO9+Z5VYTExPhxBNPNKZlT3opAFkjndAVgHCWviTBKoEGC5Y7dppsimyHsiD4H4y63teQA6SWsEyeEpUUaYUnbdoF6dVNk1sNhMAjwvK1BkDGYoIM6VOUYsFAHRNnngMH/80oF1wBIbniDMvvUtKQkUGaAZTStfRP6IyfVRdL/o/ScYwKQHrXvglA7JqxAhllXTTHSuM+7FcHdu01Qs+HTxQiuOtV46oZ0P1z00uQoVqLHhhfBctLGboCEAUhfjavV+t/SR9DouvxeGcLeGB7bfKrmYCPfPsNaBZWkowtApDZiehdPCElPwi7peuECydqyL5SigXAQVM6P2tW0OoxnAQjZJAJRooMKMEIgQfjmnrElAWxfiCpZG5fdavEeqT/s4QuxxTEQS1iYZMZiB9JhqUz6ozPGlN673uAQ1nF0jNcYgjbnusSa9iWrLdJtEoAyJfNrZXMLQGOktdImRBlQABCSgx0XywWNmMQCFEQ1gZEuFypaAfiEDqh3/mQW8x/AHLU6aMzIJ/4+3l7frMT9Rb2VmYFgMzVJUAFAjAsADXwfqxevXqudrXbtksA8qnv/SQyICkQjQpALAEWcKHAQQEJQQgTZr0APmCqDCuzIKlHBmRYAAs0ZxKA4G8k3MqA0KcA3wUYCM7YMynTgbxNW21meq0uoyZ0DLqpWy+8H968rZWjcBzKKPA4FHxY8iA+EE00yYCogZ3Xk8wJEhZeM0oz4vfTq7blBzlN+rPsKgEMNqLkLKXK9ZgYKKireyrimajemuCj5P2w5F3kDvi7UXbWsRE1BoQSrNJsqk9cSoCgdj5MKAbV3i8BEG5zEYDstjA4KzseNItdYkF0MoaFO9r8IGRFOKFAiaX6urA+e4uQCVVviQIS21eq+Jcni2o+kVKpbq12JeyHr2xlcSIZzQk0EAu5HOIggUcZbIDt7TXJo0HcyxlHeYZrEsWanItxSBPsmmTJg49GN/FKzx9/M/K+8dX1atJWjr/a7JdAxMd6P3FRkrp28Ya0mdR5DXg9GedgQr/7bQ6etwl6ZkCOfufoAOTjJww8v02bNoULLrgg/Pa3vw3T09ONr/+4446bldi0uJHyFZgxAEG1AAAD37AFJh4AiOc+97mL177lCvAh+/SlPw1gQPBicqb6fwwQ/NsCksyYlAIm2Q2VYdn7xIooSOH6PnnjNrhOiQWBtpn9QAhCOENoMq80aFIyRPkTvRgc7HRfvVnHfiYEJS9zYz8rd+lM6JAcpKZ9q8fHjBnQhn16HAo+8H++NAEZBEDUwO4BjJdhMfHw1x5gRF9a+lLNoTx2SqQ8+NC/kSAQYLQ9gFrVxpe19IOhDvg6sJdmN72cShMErYDF+1gTl9lkPPy5zyUAWZRg7f5QPxMWBOu2gRAyv1rhTp9pZTx0MiXHT/F/ldgRghZMWhCwRAMapVjNOBEor0rAo8eMxopW7A5u75csydUV8XeJRfUTGOWkvt8k3gY6/IRB7RnRbXjpojYS9HFCl9Xj5XL8vAQ8Bkk+9W4usR74vARKsqQ2+SfbgEgz7icfTJJi5XOoeGE8I4Tzaesj4oHIlo0bwr1ue6uBCfrueqozAHneu0YHIGe9svX8LrvssoBKWMhVAUT222+/cP311wcUQjrooIMW+4DM8Zc/YwCCdvVnnHFGeOQjH9k4VCDKF7/4xQFt7vfkFyp1wZ+ir9e+9rVmlu/y4kN2zmU/D6vW9DwgCCZI1HQGnLPfwwAQbbqFQS831RNqGMfpB277385eV3B8joGxJsVSEGLsR/I8eAbElxTmIGmMwM7YbZwAhL/ZEwT7iM2/HAjRPiAYXKV08eqJMZN9MXnHbwIPZWB0YCRw8rOYqt2NXYGjSbMkeVI5Gb0gGfylREcBT2kQVyaCVWm8TKInq0jlNFOjPy+JyPtyZtY8aCffhzITlEop8PDJQYmRKCUZBBra/8NftzZGpcuzNGgZBR86uCuY6sqClECX37+/VrofXXZ3+DF2xz4HfT+z+XkbEMGzWWtUSGkq4qRnQo3tSOBAJ3b0udZy5Yxl9lsawJbW5f9sW65SHn0FamT2PrE8+SAsB8u5M470pI97NeIW11XpVXNioAk6/LPTpRAFv9tBbEcNbGD9kjfEA5B8bImRLXUa98cyCIho3NexsgRKVNqsk2oEt6XvUu/7Grvjz0snID3w0GU9SNHruGAYkBf8y+gA5MzjWwEI/Mt3utOdzEKAxtnoYzc+Pm7NCY8//vjwlKc8ZTbD0uK23BWYMQBZvnx5+MlPfmIlc/X161//2hoTbtmyZY++6DhvdH1/0YtelM8TjFBXuRgTgS9c/osIQBKzwUSNSTKBCAaLmQIQnaGpfTkaXLfuaJaOBbio1cvX6jCsIEWjNo2PZD9wTv0N/KIJXWcD2RdEjaAwuefSvFI9qgQ+sF/t91GSLakPhYmHTxq8eZDshw7gvJ4KYrwMgwxI7dr7QVWTdprDdbbS3yNMNHySrNpkBTxkVDQB0W3owF9iLGqJtw70Okh6iUcpuZjtoNGYAU3JiZ2LK01ZAyBdAIcecy2pKQG23QEGdsc+Z/s77bo9BSMl2Yz6QizBTBLWQV3T7RlPXjvPfkB+CpkoJh+0B5AHMFhf17UJoxRvMSmD9VW24xNKyKr43ILBZCzSCQtOlOiERo5ZfaW2y2CD8aIr2PDPlcYbggwfg/h91kzotrw8u/5vz3bUJFxt2xn2niqBEI6vbKzrgQj2QVakETNSLFI/C4/Vx13GUw82SiwQ46u/PgAgC8IDcsy7RwcgHz2uFYAAdHznO98JmEjH+29/+9uWt+J/UO8gt118zd0VmDEAuc1tbhPe+973WvUrfX3+8583I/rvfve7uTv6ebBlAJBXvvKV9jPKi4nAl374y8yAkEblIEH5Ff+mVEYDOPftB9gSA9IFgOj2vH6Z7IYvMUsWRAdclWDh+Ak+YEKH8Zkz7tgfBl4cG3XWHKzJhFDahXKYqIa1cQoSrShXwH44EBvLMjEWIMHScrtI3rVSlCbCKp9SCRaBUJw1jb4NAqqm5KmnkVaJFWdHYaxng0Kc46Bu3TlhT3I7dmrn9dIEondOvTK6uBY+mfZ16XVg4gBHeQaT89pgqIN4232vSYDe15oMlZIa3aY3jI7ynDUG+iEByEzARyPJqlTt2h1gYHfsc6bf20zWr4EQbJMxapBBXX0eHnAQOKB6X37mEwjxEyoqaWWssWpcqQ8SQQ3/tmMUmaaXaJLpMJY3mclL7KyXWPXiX68IRX5eneyH174ELGr3OJdtk1Dl7RaadmqMKTEn/lhqbEfburV9dLnXdEJKQaKOr1iG363/v9+HB5eliShe60HSM18Bq3SeMKEfdquD5r8E60XvGR2AfPgVred34IEHWrVWsCAAIe9+97vDYx/7WAMeKKa02Am9y5Mw+jIzBiCvec1rwqc//ekAL8jDHvYwOxLIr17wgheEv/3bvw2nnnrq6Ee3ANYEAJmamjLz0q1vfWvzwrz61a8Oy5Yt63T0TAS+8qOrDICohlMBB/0fCDwzBSCcuSsdoM7QMqgaAKmwIL5WvjIgWI8vnosBkNSPg00ImYD2BvHyzCGAB/bHKlsKQHQ/BCCob6/VriiXamNADAhJMqAAhNdDZ5mU/eD/mdSQSVHfDd4TyJCOjwlGD5Tgb/VmeMM5k/cmGCmDj9L36Qf9EvOhZS05eLXNKHKbXq6gA6YH1rxeOlhyO7MNOvRebwzeAxiQYcDHIDla6XMc1+4AA7tjn50C4hwtVJNk8dnjM+ulWXheWSVL44KflMF9X/PIsVAHY4kCGbLE9JUwNsc4EdkQf+w5/ohssicx7cmraixHjBuR6fBMZ0nSxGfYx438t5i5B4GNrrJE/9z5Z6cEProwHm3nUnvW22SZxf4frrqhMiIKVPytXpqs0TGl9t6DjT6WqFKZb8F0Qn/xe0cHIB86thWAPOYxjwnPe97zwpFHHmlNB+EJgfH8E5/4RFi3bp0xIYuvubsCMwYgSLyPOuoo6xw5hi7UMGbt2BGOPvpo84Z0TcT9KZ533nkBP9dee61tT1/oDzJfXigVDKS87777hksuuSS87nWvC0984hPDRz7ykeIhAqzghy8kAgAu5/74VwZAGCB1tgrJYG9mazgAwgpO1CPrLBuTxdqsFgdnnaWjnrnU6XuTeDMyM9HoA7LUGAiCEHhamOiqRlYrbXF/JRbEMy1MpLXkL8CO75fRB0CkDwTP2YMGDiI66Op3pEwOkxlPvTOBUW25ByMe5NSqXTVnM/vL6Np9VJlx9zOCZD9yR2EkKFLogPdkTf7Aa1KaZdZ1S/4PfP96rMMAj9r5lY5HH8bZBCBtxzAoidIYsPfee8/pTGQt7uwpfUAGjQc+iecz6u8Vz4YQANTkWH6SAR4QlafSv+ZjmgczGqc9G+LlOmQ3GQO0Mh5LiuvEiI8VHnhozx//rPNvvb4qodJ46N/XpFT6zNSS/q6xC/vkxIbGkUGTJV2OoasfTK9NBrCuzL1XHfiJmhL74WVXej1Lfjb9rnh+fQU9BIwg97jlAfvNadwZ9Fy2fZ5N6C95XwgTK4bf1NSWED748tbz+973vhc2bNhgHubrrrvOZFff/OY3w2GHHWaT6ve6172G3+/iGp2vwIwBCPf0s5/9zAw8K1asCPe4xz2sG/qor5NOOim85S1vCfe73/2slwg6UurrnHPOGXXTndZ785vfHHAMba/vfve7dnz+9bnPfc6YH1RS2H///fs+r22bAIRBRyUxWYIlddpLM0k6qDLgtQEQLt8WrBWwgAXhLB5lWNowsAZAmMyyGhUASGxEuDT6WVKiTOlBnF2MXhACp8y0bN0e1AvCXiOUYCGfZXdfgg/tdu7Bh8oQcjKyMzISCkK6AJA4AMStxEbHsQa8zmp6eZb/nnQQV6lXSTrWZSZTB6W87YKOGvcAAYiaV7m+n30rAYU2WRnWJwBR5obff1fgMYhl8AN7KfEszRDqdhtm+EqpzkHgpyv4wPXdFWxELe78KQCQ0j2gsdLeS+8j/K1yLGVBVJ6JdcgMa7wgCOEkDMtvE4TkuJJiTFN6hdLlURbG/erx2fOYnt9BpnI+Z1i+FCvamveVfBp+MCuBh1ohiZIMqhSb9Px0f6X1S5Kr2lhWY1C4j6r3a4hmp3qfeSDix+a+a6myUHk/E9BR+n70OiDuHLjfvvMfgLz0/aMDkDNeNm/Pr1MyuocvNGsAZDavE0AHuqGDWdkdL4AH/LS9IL2CAd+/fv/734c/+7M/CxdffHF44AMf2Pf5KAzITABIVw9IG6Dh4KoABIMjjJb0gUR/RqxOhYEXUiwvwVITunUnBwOSTJQM0FohxhvRYwd2VMHabqV/2SsEnYb50gZbBCBs4FcCH6zCpQlwrGQSDaDKWihjxIQ8bhMgaklucodj4TZY2UtZDwISBSMEImBE8BpDlS3XE6Y0q9lFSuEHdT/bhs+RcGgfEA+E+bevN683uII0X9K55v/A92/7d1KotmeviyzKm0MbyYwHYC0yrGGABPcxaOD357YrAMifGgNSAx2aCPJ7KDF33g/iO6ZrXEBcrDEhajrHe0ysaEzwjLQ+Q/59vr8KyaqCjdqzi1hRazjK9X0VpkEJeum5Kj27XZiOQSCFY9Qg4NE2odbHCBQARtfJkEHNC3kdSjItf4303NtAh8VyxqtK3Kpdx1LcWRAA5GUfGB2AvP/vOgEQKG1QsRWT3fCCwBuy+Jr7KzBjALJ9+/Zw1llnVeVS559//tBnAeYAcqY73OEOQ6+7u1f44he/GJ7whCdYaV4Y9Ae91APCPiA+8VQJVokubySBqe9GL6HtzeSrjKCm1ddkVWlj9YFkFqQAQNiUUL0TPB/Kr2BAX44mgGO9EraWGKQZQZYKLkmxAGwAdsC8aLNDNaHjetH7gRK8nj3oAZHom9DZfSYoNrvpkgU/2HI7nNnXwUFnMFXzrdfUAxBNjjiI1I2jNL1313LXgId9P8l0WtIZ839ZL+7kWZrI8Zx8oqf3NN7jvJT96CKJaFz/CjPRMIbKszAbAGRY1sMnjLVYsCsAyO4APYNi31x83gY8/D3JmJPv33S/tJXozU1Jhb0gE9IFhNDrwbLjpXigxznofGqySD5vjEnxGW6azbv2/cE2asxGl2ey9j23sRK18+oCPvy6tWOvAY3acz7ouyj10dJzLxUB4eeDjrE0QeiBVgYuHSdzFgwD8vIzRgcg73tpKwDBNUCxpLPPPjsgl7XnZenS8IxnPCO8733vC5DGLr7m7grMGIAce+yxBkAe//jHF+VS8EgM+0IfDZSxfcMb3jDsqrt0eZRsA9MB/SBuVMiyTjjhBJNmoQpYlxeTD5VgYT1NPGsMSG1WSRNbbaxlfUDY1VskBz6Q6XHrzL0HBMqAgI2gNIp+Dc7090uwloZVy8ZzPw7un0l7z1/SM6NTipUBSGJZuCyPGbP4xniMLw2+43qTAWmatjnIcpDoab/jjKUffChrUF8DE2osS9025WRWTlPkWPodaSJU8uX0Syma4EMN6x5I6MCl4ELNolzHgq9rcql/s4GgzrA27hUHfnle/hh4PgDTPHYmSqVnpgvjUTyOXQRABh3fIOCyCEC6RMr2ZQYlh23SPP1MwQeBAJ9V9njIjeUcCMFyvjqWl2PhOK3HSFo2PiOxB5KXgfm/265AjalrTjrELZCt5XOole5K8WJQrGDcqB3foOfDr1+awW+U0ZYEuwy0ehUA9dnzgKP2XJaOtzRh58dJ/bskR21jQkoMiF6XEkOsTAjPZRgQsmAAyLEfHB2AvPclrQDk6U9/evjBD34Q3vOe94QHPehBxoB861vfsh4g97znPa3A0uJr7q7AjAHIAQccED7+8Y9bN8nZeuHLxzZxA+AHjWH0dfrpp8/Wrma0ne9///vhZS97mZVsg8QBvpdnPvOZAZXB0Emzy4vJx3lXwIS+ti8BxCCBRK1rE0IOmkz+ckOtHdEcqcmxDtqlGRbvV2DlF9U2szkXQIcxEwkY4G+dDWfyTwM6wIF2Ec+DvbAgqpnW5oQbktSL5XpxjkxguU14TMh+0HvimQ8/APP78gBC5VRxP/2DOKVEGBR43qxiw6REwZwmF7VEQxP9no67DD5q8ikOYhysShILn4B4gIF9K9BSUFIahPV7189VtubZj9KgPyhxKSUQfUlcAiGlZ5HMj0+Aah6QYRIW7m8Q+MByiwCkS6SsLzOM3IpxMT/rcn+UwIfGJU0qFYTwfqcsqwZClAnV7tg4/t62YywDIPHHqrG9yxWrg5Je/NL4wOXb4kFJquljTNd7v/jMO0lUDXgw3pcY25KEyT/jXL/LdRy0TIN1lUavBKxYXz0hOs6UrlUJjDCOtJnPhwEh2Afizn777tNJojToGszF59mEftyHRgcg735x6/mtWrUqfPWrXw1HHHFE4xQuvPDC8LjHPc66oy++5u4KzBiA3OpWtwr/8z//Y3WUZ+vlu6rrdoFQR5F1zdaxzfZ2+JB946e/CasTAME+dJaY5WoRYHx51NrxKHjwFVZKTZA0ICOAexMdB09vpPQ+EN/DA4OrMiA4L4ADyq/wt744iHsWRKthKQtCqRcHGIIMABwwIQQ8pV4Z3oSpAwOvH2crNUnAcpqQq48F56qyCp3trHk/aolFGYA0G4UxiVZpnq7XpSym3w//9n4Pv5zeM6VBWP+n684V+PADe/aBOBDSmGF1FdCGBR+DEplFADLbEbN/e8MAEF2W90djZtqZ0dVL5BNKL6GsyS5rXdMVhFgMaAEipaS165XVe5BF0eboyAAAIABJREFUMtSz1kjiXf+PUmxQEMK4q0yLPhPNfTeLyfhno4+hqJSPbbI6cZt6Dr6sd81f1gfQKrLOtuvcx6w51rV0z5S+y9J18lXEulS+GsSA6DkvGAByPABItwndxnc1tTmEf2kHIJDJf+lLX7LCSfr64Q9/aJPqe3ofu64xZK6WmzEAOe2008JVV11lzQh9taq5Oug9absEIBf/4v8zAKLBFMHEwIfVbd8rG7Y12HJ5DmA5uEkZQC/D4gy/v46lSiAqJUIC3QdAUld07QfCuvdMxDkgsZs72ByAA5UW8fh1NpFeEHgxtKNwBCBbrUoWvCBchywBrhnZDwIQ7GvFGOre9/arDIwOADxuAgaCCL1eTPy1YhSDOwclXZ9SLK2Opefsvz9lpLRSl3pWsD+/fyxbYzn0XikN4twPDeNejsHvscSW6X1Xez55vGRU+u7jQgKAazloppT3bVsi6Y/JJ0z4vK361Sjsh17vtpi1yIDMXkQvgZFB4LiN+eBzWWI/fMzqAkA8C6qTQcoc1hiRYa8UAUe8F/sLPuTnwHXh5rOu45G+97IsPj9tMcM/c97P4Z+XxkTBgEaFerxd/RS6jn/P6zxoAsHfb37Sg6CSy5WYEL/v2ah81Qb8eG4LBoCc8OHRAcg7X9TKgHzoQx+yFhJQ3KD4EV7XXHONleN9ylOeEl7ykpcM+8gtLj/EFZgxAHnyk58cvv71r4f99tsv3O1ud+uTS/3Hf/zHEIfzp7cok4/vXvW7HgARQ7CWLrWZnTRjWwrOfnaPibQCEPwPEgG8NGErBW0vRSIL4iUG3gsCMAKgogCEzATAgbIfngHhIM6BWkEIOwqzyhYqYuF/DPK9a7VXQD18dl2PEqx28KHX00vP9DroYFFiHXIiLiV4lYFSOZden1LipFInAxbJvK2JPN73AEqzlK4O6KX7xYOQDAzcPeZlDrXBetDTWzKd+wRo0DZ8klIDzUwO8/chLEgb+OibFW0pw1kCRv74ByUwWH4RgHT51rsv0wZCvESvBj7s/kmTOHjfxn7wXvMAhDEMcVOZUGVIVa6pySuPU43N2g190NUg2OByDSlTpQqcn3EfxGrwcz/hodspMSiMMxpHSpMatYmOWnLtY4Eu13b+Pp51eWZL179v/BU2xHtCSob0EuPhr0GX2NU2QcTjxjK7I+4Mum/18yzBmmUAcu9737sxWf7zn//cJPQsGvTb3/42TExMhDve8Y4BMvvF19xdgRkDkOc///mtR4dmLqO8brrppvDRj340XHnllXazHH744eGYY47Z46oSeADCAMNkrVfrPVUNKgCQYjB0DIgNhqlcpA6muq4PvAyo2UeyPfbl8F3RkWBng7qVygVjETsIc9Bk8yuaxBWQ6DFgEN5RqI+vIIdsC39jHZSvRelabpclf/lbu677ylI6m8ckgH4ZL7NgAlyTJvBccA5MSDyT0psV6+m96wCkv3QmmR49Bu3hocClNriWZiO5Pa7TN/i5xAXLlQBA6X7ktkqm8y6JvA6cfO8ZCy+HKDEi/P5q22h7HoY5ztLx1uLg7kgEdsc+RxkHhl2nJsdqY0E8CME+a2DAT0aUwITFsEr/HwIRxgcFRLotHINnR3gtaufo7+3atavdx/7/JUBSYzhKRnWNQ37izIOQQbFmEOgYBDb64mChUlTXWFa7rrUCBnY/NeR1yeBT2JDGSR1ranG8TXJV/Z5THJ/vMSADkFd9ZHQG5LQX9jEgg3q86dfypje9adgQtLj8EFdgxgBkiH11XhTdKR/72MdaU8MHPOABYefOnQH/27JlSzj33HOt8/ie8lIAsvfaXsm3QQDEByS9Hqpbjsk5gEBMiJlQ12qY++vKAZfejBoLgoFVq2R52RKCITwXBAjo7M7/MegqTQ2w5JN47SqsPUJ0QNZKV70u6Evyvjko1qRKTD4IhBSANJJTAYJM/v13gHUJZNTbwu9BByY/u8kZTN+dXGVruH414OHBhCYnbRVldLnSbJsOam0zjrwWXWZeG9e1Q+OvNqlUTtIK5mLdT9+xO/lXFwlDlxjUZTZ1dyQCu2OfXa7XTJcZBoDwWec+Z2JC57Os8YITPX4So8Z6KChR9kXfYz9t5Vy7Xr8GICsUamgwpsLIc9zx4AL/JxNChpaxpMaC2DqpBLiPOzWgg+U8Wz/IG6Hb5vXpEkN0vTbQ13Yf+ViPZQeNvYPOR2PKsABE153vMSADkBM/OjoAOfWYeWuy7/qs7snLzQoA2bZtmxnRf/nLX4YjjzwyrFmzJlx99dVh7dq1Vk532NdDH/rQcNhhh4UPf/jDYWxszFbHPl74whea3+Qb3/jGsJuct8vzIfv+r36fJVheqkKZjUqwOBCUTqwLACkFQm8C9GUnOZOvLAhlBuyMzsaEWMZLvBQcIHEmINFz4LHncsEuiWfzQe2UroMD2QFKrtjs0CfupcFSExEOHEwKmGDooOq3ocE9A7cEpAjcBkku9FrYuSyJzFeJtVEAVxrkdQBt01vXpBZ+4O4KPkqgw9+vjQSnA+gYNnHg8p4V8c/LIMnVKKwH99EFfGDZ3ZEI7I597qogPNcgRCdKSkChDYQwWfXyS29I92Ak389SZWnY66njgo2psq3ac9IFTGBbGgvxN8ewtrjUpaeHxltutxjXBjQX1XXz89ky6dDl2vr7jNdQgWyNSattfybgQ69LKW75eDTfY0AGIK85c3QAcsoLOgGQSy+9NKtt7nrXuwbItBZfc38FZgxA0HAP5cqgm4OO7mc/+1m4/e1vH175yleGycnJcMYZZwx9FmA+LrvssnCXu9ylse4VV1xhPTY2b9489Dbn6woKQPaRpjekqJmE4vcoAATnTQmVMiC8HgAZHnjotVIQ4lkQygyYXKsUi//TZCyzH+iATnN98rVowqhSBK+dhuyKTAjN6Tp4RqARZUsefCjrwYG1pJf1IIHbR1LgB1s/0FqCIZV0CKSMCRFZhiYibVXJCD4IQHIzQNc4sCR3sGPVSjKFqjI18DFI8qCDHb+7GvCwZV333tL6bc/oMIxFmwdAj0X3VwMLowKQruADx7A7EoHdsc/dEYMH3Qtet89jrMlp1JvBSQZ9lvW9AhE+/15y5dctya4Yd7hs7TrW7tUG2+GrfLm//bY1PiiY6GpC90xsQ4rpZFC1ZXkMJY+ExpFBMWaYGNLlXp1rAOLPh9euxN60Mba1WDTfY0AGIK/92OgA5J+e3wpA0AEdrRMwgb7PPvuY2mb9+vXW2w3NCRc7ond5EkZfZsYA5ElPepIxHvBroIP55ZdfbgDkggsuMMYCBp9hX7e4xS3CJz7xifCYxzymsSrqNR999NHhj3/847CbnLfL8yG7/NdXB3RCbwRoms6T3GdUAEIZFgGEJsl9A46bje75FaKUS0EIPlM/CGRRKsUikODAgiSaXoxcBUsAiM7QcT/KJKj5naUte8e3IzfYImhjlSuyH21yJb0OXjLhK5coeNFBuW0bJe23l3f5AY1sDhkwPX6a90tSCILXRoLdoaQlk3MPygZJrdoafHkpB4+pNJh2fUgHsRZt9/eg+z0f35AlOYcBHP4YdkcisDv22fX7navlagmjv19KM9kZKFSMxQ3gQLmr+MC8HKsEWnjeyrDk/7X0s6k95yXAomCEwKYEUEoJ7SCzuQcDJRlVTXLVti4+GwQ+GLv0t25Tt9H2jA/zHM82ACmxH36CaBjwMehc5nsMyADkf50VwvIRyvBObg7h5Oe1AhB0PIdyB/kmfMZ4YaIbVbCgwvnUpz41V+FocbshhBkDEDQivOiii8Kd73xnAyIEIL/+9a8DqKxR2IrjjjsunHPOOeHUU08ND37wg82E/s1vfjO8+tWvDk996lPDu971rj3my+ND9qPf/sEACF4aiCzB3UUApBawuoIQABCVYgFEaLAnACH48GV4mQgwMVcQ4vtpWGUZdnV3FY7Y30ON7ryOg6QBXr7mpRKlgdKDkNI2KEnz0q7SjGi+ZpReidej1u9Dj6skt2okKYWSlgpkdBD3Az+3U2LN/P3TBxTcjOeops8uAMQnc7Xzb0veugaZQQP9oO3sjkRgd+xz0HXYFZ+3gRDGn3zvpLhCD519nnx0XLZLeV7GMV3Wy6H83/4YZuva+PPv4icpmdF9cl9iUgexHT6W8u9BjEdt3xq3/DL4exADUlqndN0HsWqjSrD8uM/z8ZM1jUmdSnniLucy32MAj2/J6/417DUCANk5uTnseMdzWwHI3nvvHb72ta+F+9///o2v+pJLLrEJcBRDWnzN3RWYMQBB+V2AA4ANBSD4H8DCKGzF9PS0gQ3It+D9AC22bNmy8Hd/93fh5JNPthJpe8pLAQgeBr5yQujARyloF4OkzNSRAcnSJikvqfvL72X2VwdGDKA56XdsCDuiY7AFCPGSA5yPZz+07wT2rQO0gg9N3nNXcZEzea8JmYMuzIdPvAdJJPz18uAD2/PbUOmVsin4f8lj4u+BbEQfi/1gBrEePhmoPSu1pIEMT+ne6AI8dH8lBkQT9mFByDDgo2uMGFVm1TjPIXws/rh2RyKwO/bZ9fvYFcsNSiL1GcbxlECIl2Pl5SS+ejY1yzldw7oSC6HX4f9v7zqgraqxdkSlK6gjKr2IXeyjWAHLwIxIEbvYsTHOqFhHBBtW7B3FBoNi74IOIGAZdRQcENARUZBiAywgouK/vkjun5uXc1LuOTe57+2s5VLfzUl2vuTss7/svZOkHI00rLJY1zodoPtOyO+0KbdDGNa6b5mLp0NuJ01OnY5Jw0a3oaBbL6JPeW5MBxmIZ+REdDV0VQ69siUftljI6yV2HSDkW/uih7wJyM9DjkklILBZcev59ttvX/QqIQVgn3324eGxVPJDoGQCAhcWDGdc6ILJxA2SiJvr0aMHP1fZ9xheDBneE7jHQEDgDqtf38MNlx92mbQsXrIZ8xYVPCBFruucCAiEF0o1LX6U11v9QTUlpYuTqcS59+K+EbQhbs+Vw6LE3RZCeXKDXDo+GPLpQrHUY3pVg1cOXUoy3sXHT447LvrgS7HRqjEiG+5pxn6B8CkhGVVix1cbI3Kst+5YS/m+DzW8QQ1tsDE+1FArQWySjIC0j7zNy6D7sOI5EwlJGkupngcbjGzGVSouIQyBEH26YFmuujZhWWIDR+hMHy+I6hVFW8V3fBQfzZoX4bB5Z9JyAnXzkrQpoNvgCEk6Cu+pJrzSBhcTaUX7pqN41XkXMiWFXyUREJO3WcY56V2KXQcI+eoOHOFNQFZc0TeVgMBOhZcDoVZNmzblUM2fP58dddRRbL311uOROFTyQ6BkAoLJ6tKlC1tzzTV5vgeSxPFvhGbhtKomTZpYSX/22Wezyy+/nDVo0IDhv9PKDTfcYNVmJVSSCUjjxpIHRErahbIR+R86Ba4bp/rBU4/iTcJGZ5QJAoJnQCoKJ7Yod13IXhDhKZGVvpxQXUiqXn07r6gnSIg4cUvdQUy60EvGRfV8JF0YKJMP3QlgwuDg/1ZisNWPa9IJYuJZlYzIbetux5XDEFSyoXpBdOTD5oOquvJlzweeT0sqN71bunwWPkfKuhbtuO5S2nxg02TMmnwU1rmHJySEIRCiT9OaCfF7mlEpbzyoYTWqZ1i8z2m5IOpGRhoBMWGh8zjI75fp/chy/acZw+omB+RSj9HF32yP607Sa2mbGFluYKQRVpvQqzRPitC5YiNIzKf4/hfpT0XP6MZo+gbErgOEfA0uHulNQJZdfnQqAZk3bx7fLJ8+fTpr0aIFD/fHgUrbbrste+aZZ1jz5s1NryL9XgICJRMQ9I37OXBiAI4yW7VqFb+nAwwSp1nZFpw6ALaJkwjw30kFC2T8+PG2zUZfT7xkH87/ouAB0RlqagK6+rHRDVQ23mUCojOo09pLC8MS4UX8aN7VFxXKd17IBqYcGiW8H6ri1HlB5JwQ8YGXQ7yEfLJhLjwu8hG2MuFIIx9CZvX2WhVjoeCTdgyT7gFQ8Zc/SuIjpou7TiIigoAUfbhSYoN149ARHnVXTn5ON2/y7zYERDaSXAmI6eNqevGzNMCS1oVJBvF7CEMgRJ+2eJSzXikExCcES3731fsgkt4ZFQ9diJPQ3zqDX37P8sRW907pyIaQIY10FH0DFWPbh2yIPnV6w6QL1M0nlUii7TTyIV/8a7PZxPW55gCaJEx8CVbsOkDIt+7gf3oTkO8uPcrqGN5XXnmFzZo1i0fbIJ1gv/32y/NVobZXI1ASAfn555958vnzzz/PJ42KOwK+BMSkNMWHTt3FEzvxqhFctBu+epe6QDykeOUkDwjalU/Eknf8xQcQfYjEcCjYtVd7P+SPgnhOZ7wLrwg/jUu6qFAOJUMf4rQWORldRz6SkrWT4rR1hmZaG7oxcNxXe45Ee6ZdMZ1xkRSClURCCh/ghLPv0wiIzoDJgoCo4QW6tydtncdIQnxkCmEIhOjTXTuW54mkXW2TBwS/q5sUSR4QVR+r+jdtpDYhTrJHVq5vCm/MGuG09W/KH9N9g+T2ksZi+hYmyWR6TsZGd0iA+Fvakc1q2LLQ/7pvifBAq54Q8e3SRUHIpETXZtr8xq4DhHzrXTLKm4AsueTIRAKC/OK6deuyqVOnsm222SbrV4Has0CgJAKC9ps1a8ZPERBHmFn0aawCF5hwh6mV8RtyS6pLcQ3BStrh0uGRFoaVhF+SYYm2xK3ecgiWnKMhCIgabiR/HNXwKPXjIH/000iIeqytrIh1eR+FY2tX7y7piANk0YVJJBEEOUwpCTdxV0oR8XMgIDJ5k4lAEqGSsZYxkXEu+tArF3il5X+k7R7axKwXZPM8DUtnMPgY/K5Ex0bXlCJHCEMgRJ82OIaqk0ZCRA6IrBvUvDjIre5uq7pE7sP0vpjWuo5wiBP+IEsp4ZM+c5C2iSK3Z0MGVB2G521OsJL1o+33zXasKgHxIR/qN0Cnz2USIt/5JMKwdNhUdw/IBpc9zGp5nIK1asVy9s2gI1I9IO3atWNPPvkk22677WyXAtXLEIGSCQhOpYLr6t577y3cWl6qfMgnWbhwYZX8kW+++Yb/7ddffy21i2ieV5PQVYKh7n6kudhdP6IqCDpFJntBVAKi5mmAgEAGmZSIPgou5dVHy+rChlTCpB5bKfcnH8GrJm9jHDrvh8BSJh+6D6KOBGEccqiX+HjozrU3kag0w6TKnKg3/Er/r3pBhOFhIiEuBCQrY6ZU8lFYR44JpDrDyMYIclEQpZAP9BOCDITo0wXTEHV1BEEYjbocEOEBEc8lERBZd/D/Ntxmrq4n2/dVl8ic9t7YYmwiS6Z20p6v4t1RNidU8mEiZjbftCQdK//dxiuG+i7eDx0B0ZEQ8e3CfMpH1Rc2h1Zvopnm1qSXYtcBQr4mlz/iTUC+vPjwVAKCQ5Iee+wxNnLkSIYTXamUF4GSCUivXr3YuHHjWMOGDXniDpLI5QJ26Vpq1arFj+9Vb6HEresI9Vq2bJlrk9HWdz0FS0dA1BAmMVhXD4gLAZFJhtgNzJqAYBxJp2KlEZDatWrxHTP51Cjx37KRrsMyjQTpPlw6AiIbGUk5LWqdwpxJxkkVMmogI2mx4eJDl7aDlhSvbdpNFW3aGBpq/64hIi4GiE1omzynLiEZhY+/R9K5uo5CGAIh+oxWCUuCqSREDcMqeHelo3aTYvxdPCA6ksHfWc1t4bJRXuSFlS51TWqvHHNgIljqRk7ROyi9TyavR5pHNm2cJsNcp4u5vk45OjnpmGY1BEtdX+o8pXlAxLyb1koSnjHoHZf1J3TUJkNGexOQhRcdlkpAdthhB/bxxx8zpBO0atWqiv363nvvuYhMdR0RKJmAHH/88alduhzDK06/uvnmm1m/fv2Kjt2F1+Ott97ip23h4sPqUnQERDYWbTwgaWFMwtCVwwiSDMUkA4wTjNUfXDkHRPZI4L/lcCNxCaGaIG4KwZLlzYqAyLtIsudF94EvCrNYHSqlS0ZXw69UsmDy5ugISFqoV5oxogsF03lHkowZ2aARa09+v9IIiLpmbNaWTYhF0vudFwHxIR+qjLaGTQyGABEQ/Qpz9YKI91i9kFAYrGmhl1XWTwLZUHWLHCYpez1kL6O8Fl1JfpbfVpuLDtX+ijZCLD2evmFIprEmeUJ0+tt0UhrakvFQiZg6r4WoAcnjIXtBhOxJ4bW63+Xxxq4DhHzNr3zUm4B8/o9DUwnIJZdcwk++SiqDBw82LRH6vQQESiYgJfRd5VFx+tXEiRNZx44d+eWDouC/W7duzc455xzWvn37LLsN2pZ4ycRN6OoHJS8CIhuKRTsqmpcxiYDI4VYyAZGJSUEJSid7qEfw6giUkK9UD4h6d4Ya+mUKwRIGhHpiDcaVRGbkBaUSEfymEhpB7oTRIj9vCqey8XqgPbGOxH+LPtJOqRFjTPqQpXnMdC9VUhiWTpakl7JKyEaKB8JE6HQ4Z6EMXIlICEMgRJ9ZYFuONtJICPov5WZ0/o6nhGCZvJ66PA9hqKrvucmQLwuWyvHltn26EgqXjQlbGUQ9GxIi624TEVHblfWFOAGrMJcJ+XK2XpA0XRS7DigHAXFdC1Q/WwQyISA4TeDVV1/llwYeeeSR/ELCBQsWsHXXXZeHZrkWeFXgBcHz1b3k6QERSlGOY5ZDCAqGpXQ3g2xsFnkDpMsI5dvQYTyDcCQRELQhlKDwRMgEJIkAlJuAqGFEBZykhHH19lqZHOh29ZNCMNSxyR8v9WOXZJC4EA/+MUtINpd/k9819cSatLh09R3VeUF0JFdt07RT62pk2BKQLLwfKgYuJCSEIRCiz0rS5UkkpGA8rjasTRfPySE4ae+5rHdVvZx2b4aJfLgQ9lLmxxR25du27XvkSlh85NGNUdZ1ujWjklV5DVTRGdLmn/o90Xk+VN3tOtex6wAhX6urH/P2gHx2wSFaDwguuT733HPZ008/zcOvcOzuLbfcwu+vo1I+BEomIMjL6Nq1K7+85aeffmIfffQRa9u2LTvzzDPZihUr2F133eU9mhkzZvB2V65cWdTGQQcd5N1mbA/6nIJlUjS6OGaXECw1p0QoWUEyhCtZzgPh94Cs+p2IpHlA5BAs8d86A1b2PMhkQL5jRHhmkpLQBdFJCsFK8gqoHgk5jKIKaRPntSfsxKshXfIHSOcJ0X2UxHzo8k3Swqp0rvkqcdWK3KUQD5t3yzX8ykQO0gyULAmISz+2RpPAK4QhEKJPm/URU51Sdr7Fey7rEjlXBL8nhSelvaNJd2fYGKy6dWl6v3TzUWpSetZznDYG13fRRjZfIsLXhOEAAplUpHmwTBtCNuOOXQcI+dpe8zirVa++zdQU1Vn143L2yfl9tAQE5OOOO+7g99XhKF7chN6pUyeekE6lfAiUTEB69uzJPR7Dhw9nG2ywAXv//fc5AUEY1UknncRvRXctc+bMYWh32rRpPD4Pl8OgiFi96ngKFi4ibNxIcxO6dAu6uout7oDIOKuGr0xAZCNYNajVNnVeEPUuEEEKdJcRyu3LhAB3gMiJ4aKeKrdIbBcfcuFxcT0FS/SVFIIlf8TUsKk0QzaJxBQMkNUfHFl+MVaXiw5FP6aQLD5/ShidiXTgGdtEc916Ud9vowdkNekpNUTEhRTIMroYYjYfclf9ptYPYQiE6LNUnEI8n0ZCVINSrHudV0TWuXKbutDOpHEmbQ4khTYmvV8m4zUEznn0mce7m/QtUHWezhuCMdrkxLgQD52+tx137DpAyLfptY+zNesVH25ks15+/XEZ+/g8PQHB8btDhgxhhx9+OG/q7bffZnvssQffNEeeMZXyIFAyAYHLCknhuJAQREQQkE8//ZSfWAVXl2vp3r07XwT33HMPJzNYHDiCd8CAAWzo0KFsr732cm0y2vriJft4wZeFm9BlI1N39F5S/Kc8yCQCIn801SQ4rsyUHfGkMCzhBZFDsAQBEX+TlbAIFQARwPnmhQsJFWNZNtR1XhDZwyLuAikyLpFrssYaiadg2RCQJPJQML4NR+GqGOvCuVDHZHyoHg8TCUGbiZd1peRKJJEPbchTSsKe6SWT11ZSSIGpDd0aTXrG1gOi+4irc20jl2+dEIZAiD598Qn9nGnHW37fhaxJZEToliJdrcmTMO2U67ybSd+FchIRW+PX9Z21WQOl9m3qI21O0giIbs5lIqLT2aYohyxCzmLXAUK+za97wpuAfHjuwVoPCHKKsdGNe+xEqVevHo/gwR10VMqDQMkEBGcnv/baa5xsyAQEfzv44IP5cbquBaRm/PjxrEOHDqxRo0acgIDg4G8gIVOmTHFtMtr6KgFRPyJpBMRmB1jdeZdjUnWgmHIyRHsinICTAOnuD9kLoia6o21OQCTvh+xB0JEmlYTI3haZBBUMxtXkQ/QlG+34m46A6JS57AVRPyA6L0QabjIB0X2Mkhan3I8v+UhbI2nhVlU+gBkRD4zVhXyUalTojAaXHeBS+7dRPCEMgRB92mARax0bEpJGRPBb0QlImlCcKkasRQJ30fdCziFQNhyyICHleBdinX/d3LqSSJvQtXIQDyF37DpAyLfV9U96E5AZA3prCQg2uBctWlR01QPs1//+97+sTZs2MS/DaiVbyQTksMMO4yRh2LBhnIBgAnF/R48ePfiN5S7H8Apk11tvPfbuu+9y7wdcZbjkECdkIckdd434eFVinTVXAmLj/ZCVpZrPkEZATIRG5FzgYyx27+WE9IJ3QtyaLn1AhQGtXq4kExAhty4ECn8TpEd3B0mBgEg5GWhbDr1CHZWQiL8lrQ81yVDdedSFxckkQ+f9MO1uymORZZZl1fVrE2ol2s6CfLgaJKZz/fn4MrhXQ51LFW8iIGEuP4xVB9vKZUtCkoxVnQGqC8kxhX3J8mq9lJp3KO3di+19sJ2PUPVsw7BsSIuNJ8Omjg8WlUJAtr3xKW8CMu2sXloCgrvmunXrxurUqVOA7rnnnmNRzri3AAAgAElEQVRdunQpugvE5x47n7moqc+UTEBw2hXIARgl8j123nln/m94MSZNmlTlNnMboBFiBU8H8kBwqtaSJUvYwIEDOckBMZk+fbpNMxVRpxwERDbsbQkIPli6HTsXEqKGGAnvg3o0rjxRshdER0h0OSG6UC8R8qV6PUwGvU6WpIWkkgDxkZdvRBYERB2LzeJMyvdQDXX5A+VyrK5KdGSCo/53kdHjSBJsQgzyIh9CbrFGdAQn6QOft0xCthCGQIg+bdZ87HVcjE917enGZktKkvSSLWE3kX+X98K2z9jn0lc+l1AsmzWQplttCabPWGLXAUK+7W962puATD2zp5aAmO6vE3j6bKD7zEVNfaZkAgLgfvzxR/bII49wcrBq1Sq244478tMFEFPnU8aOHctvO+/duzf75JNP2IEHHshmzZrFk9xHjx7NWWp1KeIlm7Pwq0IOiGz48HPfpbPAfTwgsvGrJkjqjFD1Y6Wea87bk47lVfNBdLv+qudB9Xzg/1Vvg+hHll8OySpcdqjcHi57W3ShS0kYyn93cZfrDGwdCVHJlbqGTR8bk9xp5EP1eOiM66RwDt0aSXr/fI7S9TH000iDzdwVffRTwsvKYWyFMARC9FlddLbJg2laf6U+L3B0TWg2rXkiIXYr1Hf+TM/ZeKKy1Eex6wAh3063PONNQN79W4/UiwjtZpxq5YWAFwEBwRg3bhxDqNRll13GLwesX9/9mDSXQS1evJj3l3ZrpUt7sdSVCUjjxv9/Chbk801AFwa7zoDH32yPf5QxsiUhaniU+tGTPRPCiJSNVt1NsWoOhegj7YZyQTzgbeEGrnSbbJLHQF0Tpg+8ydjm3qbVRE3NKbFZf6YPUpIHRvzdhnQIbAokQzHGTR88EwZq+7pxm/owGU66Nk1GYNJ4i/py9PTYzKmuTghDIESfvvjE+pzJoBRy265F2/ZkPGzbVjE0bXbY1OfvdpnekZjWgI8HRP4mJ43FVt9nhUXsOkDIt8utz7K1PE7B+uXHZeydMw4iApLVgsmhHS8CAs8GwqyaN2/OQ68WLlzoFWqVw3gqrknxkn32xdf84sUkV3lSroHuAyAryLRkamFgp7nn5Q+cKwmRla4svyAh3Luj+YAJ+WUvgmhLPSWL/311ronsKZLv/sDfC2TOcO+FGjZmMgqSPsBqgrxKotIWalKbqtEgYyqPL+1I3cR4YgfiYSIdad4JXwPftk1X4yx0+BXkDWEIhOiz4pSzg8AmPSHrKYdmC1Vt2lfbVQ8BMfWbRibyykMwyRTj7z4ExGb+TAQka8IXuw4Q8u12+3PeBOTf/bsTAYnxJVotkxcB6dixI7/hfM8992SXXnop94Ak3Xg+aNAg5+EjPu/oo4/moVbVzeOhgiFesvlffVN087su3MbmI5CWKOkTAiTk1R0rWSA3SjgWTsbS7fjoiIdul171bLjkVMghVyAhMskx3XWhw850VK7AR7Stk93GC+L68dd5P1TPh9ymdsdTE3qUJIeJdPCPo8VJWT47pjbtJimZtB3iGMgHERDnz0PUD5gMTR+PhalNV0Bc3sFY3hHXMeZVP+scIFVOo87O0ONUKQRkjzue9yYgr59+IBGQvF6GDNr1IiAffvghGzx4MD+V6r333uNH8K611lpVxAF5wO+uBTedv/zyyzznAxfF9O3bl22//fauzVREfR0BSdvp1g1KKC2TctR5RkR7NkaeTEJUMpOUEyLLKzwRkFcY7GkekDRjnhOc1cSnMAblfg5df7a7SGlkREea1HmRZZdDsYTc2nlMMOB1GKlhZC7kwzb8IivSYYt52gtrsz7TnnfZDXYx0LJQMiEMgRB9ZoFVpbThs0uuG5uJfGSxVl3frSz6rJR5lOU0fV995i+NgHC96eCZdsU0dh0g5Nv7rhe8CcikU/9CBMR1YZSxvhcBkeXDcWY4T7lJkyaZir106VL26KOPslGjRrHJkyfze0DgFcGpWK1bt860r5CNmQhI2o6I+iEwKUjTx0w15LmxLCV467wgBSKwOgwK9cVxuUnKlZ+GJZGFIiUvtYO/J5EQWTZdCJacX6Ia564GsQ63JHe5GvImsBAkRMVUh7mMh8440CWb21wmmAXxcDVWTONzefd8+3bqI8MdRtt+QxgCIfq0xaM61bPVuT6eERucsnhnairhUPFNiy5Imgvb+U/Sk7Y622YtqHVi1wFCvs53v+hNQCac8mciID6Lo0zPeBEQOQkdIVjnnnturknon3/+OXv44YfZfffdx3NPfvnllzLBk383MgFZr1FxErpsKNvuhJiUZJpCTMtnEEjYkhCZPIhnZa9HUYiZegKWBQkRxEc26HVkTc39SLv/wnW20z7uMhGRSYgstwvRSCJNLvkeNmsoyetRqiFjm9ciY6IzykqVI2mOQxpZIQyBEH26vl/Vvb6tcWoiJ77vRMg1X4lza9rgK5WEmDzdRd+LDDZKYtcBQr79hr3kTUD+dXI3IiARv2xeBKScSeg///wze+GFF9jIkSP5v3Hz+vz588sC6ZAhQ3ifU6dOZbVr12bwyqhl7ty5rH///vyWduACD83QoUN5fZsiXrJFXy8uygFRDU4b47FAEgy37Nrs6KuyqyFX6nG+aj6ISkDUkCsxHnVccjvqeNSwJpMxr0s8N+WBmObMNpeCh4dJIWKyV0h3ulYplwjqyEmV9WJw5dve1SHwSTNebNeXr+FkmiOf30MaYyEMgRB9+sxLTXvGlpS44hJyfbvKGmt90+aejdxqG665f0kbUTZ9q3Vi1wFCvgPuGcPWrt/AeYg/L1/GXu7XlQiIM3Lle8CLgOSdhI7hT5gwgYdfPfHEE+zXX3/ld4LgbhEkpiPsqxwFeS6NGzdm8MAMHz68CgGBXMhNwc3v119/Pfvmm2/Ysccey2W99dZbrUQUL9lXi5dUISAFY88hWdiWhFgJJ1WyISDqrr/ahxp6ZeVBSPGGqKdkceUsYZXm/XDZbUrCymRA60gIJ03KvSVy+zb5Jao8NmMp8jgpu2emS8p8Pno2H1oTfq5rtNT6oYy0EIZAiD5LnR96nhAIhUAW5MNG9jSdmLV+il0HEAGxWTGVXceLgOSdhI7jfb/++mvWtWtXTjq6d+/O6tatGwzpBx54gJ155plVCMhLL73EL0mcN28ea9q0KZcPFzIed9xx7Msvv0wkFPJA0ghIkjJyUUSq4nR158v1dSRE/pu64y+MbV2CNMamGsVpSl5HblDfdKeJTf6HjyGc5pFKStAX8y48OVmGg8lryuSlkddPUT6JRN5c1ljaiwksfMKvbF52m5BBm3aKsMsgtMG1zxCGQIg+XXGh+oRALAi4fkdt5Lb57mSlh3XyxK4DhHzd7h3r7QF56aQ/kQfEZjEGquNFQIp2bHNIQh82bBg75JBD+MWDMZQkAoIjhp955hn2/vvvF8RcsmQJDxNDSFbnzp2N4ssERJcDktSAi2Ky2b1JUoYmAiJIhtjtF/+vJo9DXjUHpBCGlXIXCG9PeEESEt1VEpLk+UhKHDdOklLBJhxOR0IENkkGr41HyCSrC/lAW/Jll6JtG4+KPC8mmXS/23x8fdd+KWEsLu+Vz7hjMQRiNz6ywpbaIQSyQCBrAmKr//LUR7HrACHfgfe97E1Anj/hACIgWbwAObVRMgHJSq6zzz6bXX755axBgwYM/51Wbrjhhqy6tWoniYCcfPLJ7NNPP+VHBsulTp06DM8cccQRVdr/6aefGP4RBS9ZixYtmBqCZaOgbJVTKQRENTR9vCBirCIES87/sM0l0N05Ihv0ICHq6VC6o34TvUqaMDebJGgb+XV5LUUkxOL+DFHfRibUNZEkW8+HzTpMky2RRDiMWW3DZt37khCbtq2UhkOlchgCSXrn22+/tfLUOgyHqhIC1Q4Bm2+oz6BN+jVPfVQOveODiWwbNWrUiPV44BVvAvLMcfsTASllEnJ+1ouAPPvss6xbt25s7bXXZvjvtII7PWwKvAVPPfUUz7lI8xzgbhF4F3zLJZdcwi9PTCvvvPMO23nnnQtV0gjIZ599xsaOHVvUHBLQH3roIX6HiVqS+nfNARHt2igo290bFy8IN/5XeyT4EbOr8xrUvIciQ1u+p2O1AWqSX5bd5GXhhvfqPmyT3n3XkS3+qvyl9udi0KvYmnI9svIQCRlVsmT62NpiY1ozhf41hzGk9WHbrq2ctvXKYQgk6R0iILazRPVqOgK231EfnMqZ+yHkK4fe8cFCla/3g//yJiBPHrsfEZBSJiHnZ70IiHz3R1pCOMgCErVjKsgtwT9pBfeMyDknWYZg2XpAVPlKyQexVZy2IUA6o1pHQnQYy7kfSbkIakiVjoSIv+luKtcd92vyCOhkNe2iJ8kv2pLHkbaDZjLMk/J2bMZkIh1JRMokU+h32oYsmOavyjsWIP8DMpTDECAPSOgVS/1XOgJ5eUFkXEr5zrviWw694yqTXF/Id8iIcd4E5LG++xIBKWUScn7Wi4DkLBPD0bYISwKBUQt+a9myZd4iFLVvSkLHKVmbbLIJf2b06NH8JKwsktBtFBPqmIwxl114Gy8I+tSFYom/F+WNrPaSyGFXssxpt20nERHVE5JkSCb1qVs8Qo6kpPakBWe6LdxEQrJYyCZPB8fb4SS16kA+BK62JMT0DmUxT0lthDAEQvSZJ4bUNiGQNwJZEBC5DZfDOfLQT7HrACHfYSPHsdr1GzpP78rlP7DRRxMBcQaujA9ESUDWXHNNtnDhwiq3q+OYW9y4Xi6vCsjO4sWLeZjZddddx29kR9l0001Zw4YNuRw4hnejjTbiv6MuTsDq2bNnZsfw2hiDJuWUBQHh5GI1mZAJiPz3pLtBxHpWk85Nxrv8HqTdO1KFgIjwLk2Yl0ufLu+hPE+qxyJLEpI017YXCJrWis16c8El67om+dX+iIDoZyB24yPrdUPtEQJZIJCkT0ynS7r0bePZdmkvpo0PF7mFjjpy1HhvAjLqyC7kAXEBvcx1SyIgq1at4snWTz75JE/GhseiTZs2rE+fPqxv375aD4bN+BDW9cUXX/D7NeSCfIutttqKLVu2zKaZkuuATDz44INV2sEdJZ06deJ/B0k5/fTTq1xEiER0myJessVLlnong5qMsnIREIxXJQpVyMHqMJekJGgdZrJyN5EcPG9LdPI2tnVyi/HZGsZ8PJrQIFvCIfpz2W2zWbe2dUxr07adUuqZsA4tYwgyEKLPUuaQniUEYkLApFMKel7atDPJ7+KlNrVl83vsOkDI1/fhCd4EZMQRnYmA2CyGQHW8Cchvv/3G7+d48cUX2Xbbbce22GILhr/NnDmTTZs2jSH5/Omnn3Yaljj96uabb2b9+vVj9evXLzwPb8Nbb73F4B15/fXXndqNuXJMBMTGILf1ggDzJHezbV6COm9y0nuaIZ+Um2EzPt1acTFQ0/JtksK7kj5mNqFVMuFKWuc6+X2xsH2XXDCzbdO3XpqxEIOcIQyBEH36zh89RwjEjIAtGXEdQ966KXYdIOQ7bvSr3gTkgcM6EQFxXXhlrO9NQO6//37297//nd+DoZ5ahVOqEIZ02223sWOOOcZ6OKKdiRMnMty2jtOkRMF/Izn8nHPOYe3bt7duM/aKpRIQGyVlk4Rua5CaCAjwNuVRuHg/bEiIbo5t+rDBznf9JHmdTNgk9afzeJjmrNxejzzx9J0HlQjL7cQibwhDIESfpcwhPUsIVCICLuSk3Poodh0g5Dvx0YneBGT4ofsQAYn4xfEmIAcccADr0qULu+CCC7TDu/LKKxmIhHpErQ0Wxx9/PIMXZN1117WpXtF1SiEgLgqrFEWYRmBcDW0fQzqJhCSRHd3leqjrgpfLokrL/0gLf7MlI64eo7RxmkiL07gDnRrlImMSAclrLbjKhvohDIEQffpgQ88QAoRAPgjErgOEfCc/PsmbgAzrszcRkHyWTyatehOQjTfemI0ZM4YnYevKlClT+F0hixYtykTQ6tqIKwEp1XDyCUcxeVBMOSbqJYFFu9AeF9LZJPzJhrYtZuIZU/s2RryNp0jGQUdGfMhajOTDRH5t56cUHSBkKEdfrnKGMARC9OmKC9UnBAiB/BCIXQcI+U59fDKr08D9FKyflv3A7uqzFxGQ/JZQyS17ExCERCEpXBw/q0qyYMECnpAu3/ptK+1VV13FT5Y64YQTih6577772FdffcXOP/9826airxdSCcAoszXIXEgI33U2JN/ZGPFpk+fSfrlDkYTcriTEtFhNmJnm0vS8qX/xu6mfwvgtLgK0bctWtkqsF0IHhOizEueGZCYEqisCsesAIV//J1/zJiC3996TCEjEC9ibgCAZHN4N9aQqMVacYtW0aVOvI3OR6zFq1Ci2++67F0GHJHTcLj5nzpyIIXUTLXYlkGZM6kiAabc7DR2TMWrytMht23hAsjLIk8aUREB0BCWpDVsZTdihfdu2TCvY1JftGjC1Y5KjuvweQgeE6LO6zBeNgxCoDgjErgOEfH976nVvAnJLrz2IgES8WL0JCI7KRYhV0nGz8HwgRMvnzg7cQo7TtOBBkcsnn3zCj+FdsWJFxJC6iRa7EpBH43oRk+3pTi6I2ZIQm7PUXQ3yNIPZ5nx4W8PcBQ9OLCxzMVzHK+Swad9nbDbtumJRifVD6IAQfVbi3JDMhEB1RSB2HSDkO+uZN7wJyI09dicCEvEC9iYgSBS3KTgty7XglKvBgwezo48+uujRESNG8L+DiFSXErsSMBEQ8bspJCqr+TKFghWMZiW3pJSjaG0NZRNB8zHS03AzyeVLOFxITWH+LcKt1LGY5M9qzcTeTggdEKLP2OeB5CMEahICsesAIiDVfzV6E5A8obnmmmv4zeL4BydtoYwbN46dd955bMCAAezCCy/Ms/uyth27ElDBMBnRoYiIr6HuaqQnGc15ekBcDHXX8ci4ufSThLdpfSQ9l0XfZX1xM+wshA4I0WeGkFFThAAhUCICsesAId85z77p7QEZelBH8oCUuE7yfDxKAoILDXG87y233MJWrlzJLzisV68eTz6/+OKLvW9YzxNI37ZjVwKuBKSwI+5wA6wvdlk9V4rRbpJBJWQ2BrqrMW4rv2u7prGl/W4zTt3z5ZSxlPFl+WwIHRCizywxo7YIAUKgNARi1wFCvvOf/7c3AbnmwN2IgJS2THJ9OkoCIkb8ww8/8FwQkA+EZSXlm+SKUM6Nx64EfAlIVkRENa7z8LDYGvA+S8GUiK626WqAp8nu2pbP+IiElI5aCB0Qos/SkaIWCAFCICsEYtcBQr4LX3iL1fU4hnfFsh/YVX/ZlQhIVgsmh3aiJiAzZsxgc+fO5V4QuRx00EE5QBGmydiVQKkEBM/7kAYbUuDTrm6WbfryXR1CRluPgCtpMMnu2p7vOLNYJ2gjlLxZjdunnRA6IESfPtjQM4QAIZAPArHrACHfwJfe9iYgV3T7IxGQfJZPJq1GSUCQZN6rVy82bdo0Hm6FECwU/Dc3aH/9NZPBx9BI7EpAxsjWiC43rqUSEZMR7zseHflQZdX17WqE28rv2q7PuEtZI+WQz2dMeT8TQgeE6DNvHKl9QoAQsEcgdh0g5Bs85h1vAnJp112IgNgvibLXjJKAdO/eneGekXvuuYe1bduW4f6PxYsX8wT0oUOHsr322qvsQOXVYexKoBIICCelnjkntsa7z/yrBCRJRpsjg039lzqOUo1/X+JRar8mXCrh9xA6IESflTAXJCMhUFMQiF0HCPkuG/sfbwIy6E87EwGJeEFHSUD+8Ic/sPHjx7MOHTqwRo0asbfffpttvvnm/G8gIVOmTIkYUjfRYlcC6mh8DU03VEqrbUNGSjXYZQlhRKcdwSt+syEgpRjkWY/JZRZc10Up43SRqxLqhtABIfqshLkgGQmBmoJA7DpAyDfklXe9CchF++9EBCTiBR0lAVlvvfXYu+++y70f7dq1Y/feey/r3Lkzmz17Ntt2223Z8uXLI4bUTbTYlYBuNK7Gpq4NWwM0i77cZsS+tjqGpDtKbAmILSZaPJV7T+xH4VbT5zJGtYdSxukmbWXUDqEDQvRZGbNBUhICNQOB2HWAkO/qce95E5AL9t2RCEjEyzlKAoIQK3g6evbsyY488ki2ZMkSNnDgQDZs2DBOTKZPnx4xpG6ixa4E3EaTf+1YCInNfSA2IVjCa1GqUZ6l9yNtFm3kNM2RTRv5r6R4egihA0L0GQ/iJAkhQAjErgOEfNeOe4/Va7iO84T9+MP37DwiIM64lfOBKAnI2LFj2bJly1jv3r35recHHnggmzVrFttggw3Y6NGjC5cTlhOovPqKXQnkNe5S2zUZuaJ9V2NXbbeU501J6DJpkPuR/24TToaxmpLZbfEyzYstHkn92T5vkqM6/R5CB4ToszrNGY2FEKh0BGLXAUK+6ydM8SYgAzrvQB6QiBdqlAREhxeS0BGaJU7CihhTJ9FiVwJOgylzZZNRHcrYleWyOYY3iXwIOG1IiImAoC0TXqV6Pso8/dWmuxA6IESf1WbCaCCEQDVAIHYdIOS76dWp3gTkzE7bEwGJeK1WDAGJGMOSRItdCZQ0uJwfNhnUoQiIauzbEIgkLwZvy/KELxsSUiA1q34/2pr3W+v3462phEEghA4I0WcYdKlXQoAQ0CEQuw4Q8t066X1vAnLG3tsRAYl4+RMBCTw5sSuBwPAYu08jIaENa50nRDcgU/5GHgTECCxVKBsCIXRAiD7LBih1RAgQAkYEYtcBQr47Jv/Xm4CcvlcHIiDGlRCuAhGQcNjznmNXAoHhMXZv8oLIDZSbkCSdiqUOykRAUN+GhCS1U+5xGyeNKhQhEEIHhOiTpp0QIATiQSB2HSDku/v1ad4E5JQ9tiUCEs+SqyIJEZDAkxO7EggMj1X3LiSknITE1gMCmUwkxERATM8TCbFaSkEqhdABIfoMAi51SggQAloEYtcBQr573pjO6nucgrX8h+9Zv923IQIS8fonAhJ4cmJXAoHhserel4CIxvMyzl0ISCkkxEQ+8h6n1SRRpUQEQuiAEH3SEiAECIF4EIhdBxABiWet5CUJEZC8kLVsN3YlYDmMYNVKJR/c8DckYfsezUsEJNiyqKiOQ+iAEH1W1KSQsIRANUcgdh0g5LvvzQ+8PSAndNyaPCARr2MiIIEnJ3YlEBie1O7LQT5KGb8LATF5MUwhWDYeFBuyVcp46Vk/BELogBB9+qFDTxEChEAeCMSuA4R8D741w5uAHLvrVkRA8lg8GbVJBCQjIH2biV0J+I4r7+dKIR8mj0dWsmdFQGzIh5DZRGSIhGQ1u9m1E0IHhOgzO8SoJUKAECgVgdh1gJBv5DszvQnI0btsSQSk1IWS4/NEQHIE16bp2JWAzRjKXceVfJSLcKg4ZHUKlgsB4QRjjfR7PULhUe51Uin9hdABIfqslPkgOQmBmoBA7DpAyPfwf2Z5E5Ajdt6CCEjEi5kISODJiV0JBIYnsXsbEhLa0M6KgAAEWxJiIh/kAYlvRYfQASH6jA95kogQqLkIxK4DhHyPvvehNwE5dMfNiYBEvMSJgASenNiVQGB4jN37JogbG86gQpYExIaEEPnIYNICNBFCB4ToMwC01CUhQAgkIBC7DhDyPf7eR6zBOus4z+Oy779nfXbcjAiIM3Lle4AISPmw1vYUuxIIDE/Fd2+TB2JDHGQgZG+I7bOhvUEVP5E5DiCEDgjRZ44QUtOEACHgiEDsOkDI99TU/3kTkF7btycC4rguylmdCEg50db0FbsSCAxPxXdv8oLYEghfIIh4+CJXvudC6IAQfZYPUeqJECAETAjErgOEfM++/7E3ATlou02JgJgWQsDfiYAEBB9dx64EAsNTLbq3yVfJcqBEOrJEM/+2QuiAEH3mjyT1QAgQArYIxK4DhHwvTJvtTUD+sm07IiC2CyJAPSIgKaAPGTKEvfDCC2zq1Kmsdu3abOnSpVVqr6E5cejOO+9kp556qtV0xq4ErAZBlawQSCMiNqQh6XmbZ60EpEpBEAihA0L0GQRc6pQQIAS0CMSuA4R8Y6Z/4k1Aum7TlghIxOufCEjK5AwePJg1btyYff7552z48OGJBOT+++9nXbt2LbTUqFEjVq9ePatpj10JWA2CKhEChIA3AiF0QIg+vQGiBwkBQiBzBGLXAUK+V2bM8SYg+2/VhghI5isnuwaJgFhg+cADD7AzzzwzkYA89dRTrGfPnhYtVa0SuxLwGhQ9RAgQAtYIhNABIfq0BoQqEgKEQO4IxK4DhHzjZs5hDddZ1xmPH77/ju27JREQZ+DK+AAREAuwTQSkWbNmbMWKFaxNmzbsxBNPZCeffDKrVauWRcuUA2IFElUiBKoxAiEMgRB9VuMppKERAhWHQOw6QMj36qxPvQlIpy1akwck4pVJBMRictIIyBVXXMH23XdfHnI1btw4NmjQIHbhhReygQMHalv+6aefGP4R5dtvv2UtW7Zk8+bNY+uu687yLcSnKhEhEPO9JRHBVKNEwYe2RYsW3MOK8M08CumdPFClNgmBykWgHHqnFHQEAZn80WfeBGSvzVoRASllEnJ+tsYRkEsuuYRdeumlqbC+8847bOeddy7USSMgakPXX389u+yyy/ii1xWb/nOec2qeECAEIkRg9uzZrG3btrlIRnonF1ipUUKg4hHIU++UAo4gIG/8b643Adm9fUsiIKVMQs7P1jgC8vXXXzP8k1Zat27N6tat60VAXn/9dbbnnnuyRYsWsY022qhKN+pOJHY9W7VqxebOnZvb7mdWa0jsmFSCt4ZkzWrWi9upFFwrRU6gK7ygS5Ys4Yde5FEqWe/kgUclrY88xo82azoGNX385dA7paxdIiCloFcZz9Y4AuIzLS4ekNtuu42de+65PJyiTp06xu5ij8OUB0CyGqfTqwLh6gVb6kOEaTqmlYRP9quDcu8EAUHIHwzRmhj+S+/Ad142b90AACAASURBVHzTM9b5F/Pz1ux53h6QXdu1iHZ8eei1SmuTCEjKjMErsXjxYvbss8+y6667jk2ePJnX3nTTTVnDhg3Zc889xz0dHTt25DkgEyZMYAMGDGDHHXccu/nmm63WQiUpQZLVakqdKxGuzpAZHyBMiYCkIVBJ68O42D0r1HQMaPyVQUD+88nn3gRk57bNiYB46odyPEYEJAVlEIkHH3ywSg0QjU6dOrExY8bwhPOPP/6YrVq1isdvn3TSSax///5srbXWspq/SlKCJKvVlDpXIlydITM+QJgSASECQmuA1kAyArHrSCHfe3Pms3U8Duj5/rvv2I5tmhEBMX4tw1UgAhIOe94zYrOvuuoqTmRsQrZCikuy5oM+4Zo9roRpOqaVhE/2q6Oy9G4e46+0b08eGNA7ELftIQjI+58t8CYg27VqSgQkj5cnozaJgGQEJDVDCBAChAAhQAgQAoQAIVA6AoKATJ+70JuAbNNyEyIgpU9Fbi0QAckNWmqYECAECAFCgBAgBAgBQsAVAUFAZn6+yJuAbNl8YyIgrsCXsT4RkDKCTV0RAoQAIUAIEAKEACFACKQjIAjIR/O/8CYgmzXbiAhIxAuNCEjEk0OiEQKEACFACBAChAAhUNMQEARk9oIvvQlIu6ZNiIBEvHCIgJRhcu644w5+jO/ChQvZ1ltvzW666Sa21157JfY8ceJEdvbZZ7MPPviANW3alJ133nns1FNPLYOkjLnI+uqrr7LOnTtXkWvmzJlsiy22yE3eSZMmcTzfffddjulTTz3FevbsmdpfKExdZQ2FKQ5CePLJJ9msWbP4kdK77747u+aaa9jmm28eHa4+sobC9c4772T459NPP+U44v0fNGgQ69atW+7vv8u7DGFCvSN5KQqX8YdaH3mN3VXvVLf5dx1/dZt/Hx0Z2xoQBGTOoq+87qnB82023pAISF5KJoN2iYBkAGJaE6NHj2Z9+/blhv0ee+zB7r77bnbvvfeyGTNmsJYtW1Z5dM6cOWybbbZh/fr1Y6eccgrDzeqnn346e/jhh9nBBx+cq7Susgql/eGHHxYpiA033JCtueaaucn60ksvcVx23HFHjomJgITE1FXWUJh27dqVHX744WyXXXZhv/zyC7vooovYtGnT+Dpt0KCBdi5D4eojayhccVcQ3gXcHYSCY71BnqdMmcLJiFqywtT1Xc6q39xeeseGXccfan04Dsu6uqveqW7z7zr+6jb/PjoytjUgCMjcL772JiAtN/oDERBrrVH+ikRAcsZ811135YYydkFF2XLLLfmOPXYp1HL++efziw/hRRAF3o/333+fvfnmm7lK6yqrUNpLlixhjRs3zlW2pMbXWGMNIwEJiakst42sMWAKmb/66ivWpEkTviu+9957a+GPBVcbWWPBFUCuv/76nISceOKJub3/ru9yLHOZlRJxHX9M6yMrDEQ7Nnqnus1/perdrOdetGejI2NbA4KAzP/qG28C0mzDDYiA5LWoMmiXCEgGICY1sXLlSla/fn322GOPsV69ehWq/f3vf2dTp07lxp1aYOztsMMORTepY4f/0EMPZcuXL2drr712LhL7yCo+2q1bt2YrVqxgW221FRs4cKA2LCsXoRljNh/XUJiqY7aRNQZMITcu12zfvj33gsAjpyux4Gojawy4/vrrr1wXHHvssdwDgvclj/ff512OZS6z0BM+449hfWQxdl0bNnqnOs1/JevdvNaAjY6MbQ0IArLom8XeBGTjDdYnApLXosqgXSIgGYCY1MSCBQtYs2bNeLgQYupFufLKK3koBkKX1LLZZpsx3MD+j3/8o/DTG2+8wcO30N4mm2ySi8Q+skJ+xNrutNNO/ELFESNGsLvuuovhY560a5618DYf11CY+nwIY8D0t99+Yz169GDwbE2ePDlxymLA1VbWkLiCxHXs2JGT9IYNG7JRo0axP//5z1pcs8DU513Oot+s323f9nzGH3J9+I7T9rlK0pG2Y3KpZzP+6jz/tjoyNh0gCMhXi5d4E5AN11+PCIjLy1LmukRAcgRcfAhBIGCAiDJkyBBurCPhV0dAjj/+eH4zuiggMHvuuSdPuN54441zkdhHVp0g3bt3514JhJGVo9h8XKBYQ2DqQ0BiwLR///7shRdeYK+99hpr3rx5KgEJjautrCFxxY783Llz2dKlS9kTTzzBc8Dg/dR5QLJYqz7vchb9luN9t+nDZ/wh14fNmEqpU0k6spRxJj1rM/7qPP+2OjI2HSAIyOKlS70JyPqNGxMByeOlyqhNIiAZAalrxicUIJQb1EdW3ZhBrkaOHFmUw5IjxNUuBCs0pmeccQZ7+umnuWerTZs2qVMXaq0KoVxkDY2r3P9+++3H2rVrxw+kUEsWmPq8y1n0m+d77tK2z/hjWh8uY7Wpa2OAV6f5z2rjp9zfMpu5dK3joiNjWwOCgMybN8+bgLRo0YIIiOuiKWN9IiA5g41kSIQo4RQsUbDziRCXpCR0nJyD04dEOe2003jOSDmS0F1k1UHXp08ftnjxYjZ+/Pickf29eZuPK5LrQmEqg2AjayhM4abHxwr5RgihQ/6HqYTC1UfWULjq+t13330ZPowPPPBAlZ+zwrSS9I5pnfn87jr+mNaHz3jTnrHRO1mtu6xlz6I9m/FXt/n30ZGxrQGErGITbNGiRd7LABEjON2rbt263m3Qg/khQAQkP2x5y+I4SORGIAxr2LBh7J577uF3fLRq1YqHWs2fP5899NBDvL44Cg9H8OIoXpAOnIJVzmN4bWXFfSZIQMdxoth1hOfj6quv5mEmvXv3zg3ZH374gSdJoyBh/4YbbuCJ7zhdCEcbx4Spq6yhMMVRz8hNeOaZZ4ru/mjUqBG/FwQlFlx9ZA2FK3K5cOcHCMf333/PHnnkEf6OjBkzhu2///65YVpJeicPReE6/lDrI4+xo01XvRPyu5MHBq7jr27z76MjY1wDICGwLXxL7dq1iXz4gleG54iAlAFkeD+uvfZansOBE4VuvPHGQpI2Es5xSRl2nUVBfPhZZ51VuIgQOxPlvIjQVlbUA6ECgYKRCiICIzUpwTYrqJMujcLpQthVjglTV1lDYYpdQl25//77OZ4oseDqI2soXHHU7rhx4/i7DzLXoUMHhvcZ5CNvTCtJ72SlG+R2XMYfan3kMW606ap38EzI707WOLiOv7rNv4+OrG5rIOs1Re1ljwARkOwxpRYJAUKAECAECAFCgBAgBAgBQiABASIgtDQIAUKAECAECAFCgBAgBAgBQqBsCBABKRvU1BEhQAgQAoQAIUAIEAKEACFACBABoTVACBAChAAhQAgQAoQAIUAIEAJlQ4AISNmgpo4IAUKAECAECAFCgBAgBAgBQoAICK0BQoAQIAQIAUKAECAECAFCgBAoGwJEQMoGNXVECBAChAAhQAgQAoQAIUAIEAJEQGgNEAKEACFACBAChAAhQAgQAoRA2RAgAlI2qKkjQoAQIAQIAUKAECAECAFCgBAgAkJrIFcEcHv20qVL2dNPP8376dSpE9t+++3ZTTfdlNhv69at2Zlnnsn/ia2o8scsaynYXXLJJXzOpk6dWkoz9CwhEAQB0jtBYC+5U9I7JUNIDRACFYMAEZCKmarsBcVH+sEHH6zS8J/+9Cc2ZsyYTDpUDYHFixeztddem62zzjrVgoB89dVXrEGDBqx+/fpGvCqJrGRhCHz66aesTZs2bMqUKZx0UiEEgADpHfd1oG58kN5JxpD0jvv6oicIgRAIEAEJgXokfcIQ+OKLL9j9999fJFGdOnXYeuutl4mUKgGxaTRmQ93Gg5M0xpjHpcpMBMRmpVIdHwRI77ijRnrHHjMiIPZYUU1CICQCREBCoh+4bxM50ClyhFOBnEyYMIGHU6F88MEH7LzzzmOTJ09mv/32G9/tfuCBB1i7du34bmdaCNaXX37JTjzxRPavf/2LbbzxxuyKK65gF110UVEI1rfffsvOPfdcHhK0YsUKtvPOO7Mbb7yRbbfddrz/2bNns7PPPpv9+9//ZsuWLWNbbrklu+qqq9h+++1XQBjG/8knn8w+/vhj9thjj/ExDBw4kP8tqaCt0047jT355JPcY3POOeew5557riiETCUVMNzvu+8+Tuw22GAD1qdPH3bLLbdwrCZOnFjUFbD65ptv2F//+leOHbxDwOwf//gHO+KIIwp18WyHDh1Y3bp12b333stq167NTj31VIa+RAHGmINnnnmGAa9NN92UXX311ezAAw/kVd544w12wQUXsHfeeYf94Q9/YL169eIYwXujK4KAYPyYE8j5l7/8hd1zzz2scePGhUdAXq+99lo2Z84cBiz+9re/sdNPP53/vsYaaxQ1vc8++7Bbb72VzxvmHXIsWbKE43TwwQfzeUGBXM8++yx78803+f/PmDGDYz9p0iQu7wEHHMDnH8+jAMfrrruO3XXXXWzhwoVss802YxdffDHHHuXVV19lnTt35mvs/PPP5+1hjUL2zTffPPBbWPO6J71Deof0Dumdmqf5aMQqAkRAavCayMIQmD9/PjeOYSRfeOGFbN1112Wvv/4623333blxZyIgf/7zn9m8efPYsGHDuGENAxYhO1deeSUnITAu99prL7b++uuzQYMGsUaNGrG7776bE5yPPvqI//3999/n5AN9wkhHWNn111/PPvzwQ9ayZUs+wzCOv//+e3b55ZdzA/bxxx/nRAfkaYstttCuAhjSIBwgFCBHIAYwZkGYRA6LTEDQJn575JFH2NZbb80WLVrEZevXrx8nFzC8QXjw/yhoE/g9/PDDnCwBuxdeeIGdddZZHMNdd92V1wO2wAQk68gjj+SGOXAdO3Ys23///dmqVavYHnvswccHwxwkBkb2mmuuybp168amTZvGscHYQSIQvgHSA3lU75cAAgRk6NChXAZg+d133/Gx/fGPf2T//Oc/eTWQkcGDB7PbbruN7bDDDlxGjO2GG25gxx57LCc7qA/DH3hgfkH8mjRpwskCSAcI00knncRq1arFSRsKQgDRHggUCAXWF9o95phj2I8//shJxC+//MLGjx/P62MeQRIxJ+3bt+dEBQQN+ID0CAKCsVxzzTVsww035L//+uuvHGcq5UWA9A7pHdI7pHfKq3WotxgRIAIS46yUSSYYAiNHjuRGu1xg4GEH2cYDAqMcBjeMfeR2qCWNgIBAgKSAPAhje9asWdyDAUMaBARGJnbrsWOO0DBRsMOPHf8kDwYMXuzew9AWBAREZsSIEfz/QWxAAC699FJujKrlhx9+4DvzDz30EDvssMP4zyARzZs3533qCAgMb5Cj6dOna7GwDcECSQAGIACCgMBYhpdEFBj2Xbp04Ub6yy+/zInGzJkz+e6/WmC416tXj8smymuvvcaNc3h51PlHHRAQeD6wBjBmFOQFQTaQJmAHcgeDXvbW4JkXX3yRe1ySQiFAPJo2bcq9ISBbIEogjfAQQX6QFHhDunbtyknnW2+9xcmEKJ9//jlr0aIFX3PNmjXjnhCsk44dOxbqgNQsX76cjRo1qsgDsu+++/I6kBFjAaHRjb9Mr2CN7Ib0Dukd0jukd2qk8qNBFyFABKQGLwgYAjAm77zzziIU4FXAPzYEBB4M7CjrktnRaBoBwe43wmQQVgUjVBQYoNhZBwFBaA1Ch2BAywWGI8JyYADjYwYi8fzzz7MFCxbw3XH8PmDAAB4ehALjv3///jyUSxR4AGAMw8hVCzwXCNP57LPPCl4U1MHOPAx3HQGBJweeCJAbGM/Apnv37myttdYqyKCe7gViARIxevRoPhc//fQT/wek69FHH+XPwQMCQnX77bcXxOzRowcnSPDOYIz4DbLqCp5F6JlMECEjDHR4SkB21AICAvL1ySefFH5CaBfCr+BR2GqrrbgnA/MC74UowB5eKngzkggIiAc8XvDM7Ljjjtwzg3HACwTMQRQRmoWwN5CEV155hXtP5II5B4kA+QAZU0M6Vq5cyecK5EV4QEBisVZR4K1B3+r81mB1ULahk94hvUN6p/i7UraXjzoiBCJCgAhIRJNRblFMoRBz585lrVq1Yu+99x435lAQvgPDU+SAwIBv2LChFwFBTschhxzCDW7ZiJUJCAgGDFYYkWqBMQwDFKFS2CGHxwCeERjFIDYw3HVEQbQDY7dnz55FuRTiNxw/izG7EBA8C+IDgxlhR9jFxylQ2NmH8a/zgIA84B/Iue2223JDGiQFpCXt6GLIjfEjFA34YOxJBAQEA6FaCG9TC7wYqnGPOjoCgjAskAuMB54reEHgQRPeK9E2yCTGnURAQDxA/uABA5H5+uuv+TjgNcHfMW4QBxR4dnDCGNaBWjbZZBPubdptt934+oA3RC7wmMFTIggISI3IXxHzK3JXyv3u1eT+SO+Q3iG983vOHBVCoCYjQASkBs++yRCAMQ3jD3kJ2M1HgXGNHApBQOB5gPfDJwQLzyD/AsYmdrFRxN9ECBb6gxGKHfwkhQ3D/dBDD+VhYygIn0LYEMbnS0DQBrxAMLDRNgoMWLSLfIS0dsWSEmN59913+W47wotOOeUU7pkRBR4SELrhw4fzPyGfA4QB/9gSEBAChGMlhWAdddRRPB9l3Lhx1qtdhGCBhCJcCgUkD+tAhGABC4xH4K42Dm8USMF//vMfttNOOxV+hvcFY4Z3Azk4yBWBxwleI+R7gIQIzxXyO5544glONIQnSe4HeS/waiAfpW/fvtrxEQGxnvayVCS9k0xASO/8HvpJeqcsryJ1QggERYAISFD4w3aedBwmDD1xwhDi6rF7j6Rh7FQjhOntt98uEBCcjoTdcIQlIQkdO+TI6QChsElCB7mAoYqQHPSL3X8Y7HIS+t57780TrLELjjZRH+E38ALgRCyEK2G3HYmNOHkJBjGMzhNOOMGbgGBmkEOCfhAetNFGG/FkZ+QaJCWhYxcfIVXwCIC44TnkhSA0C+FSIG7wztxxxx08nwUYIwcCBjbyaOD5QX2EXuHUJlsCAllRH/OD5+EFQi4NsIBR/9///pd7CY4//nhOnuBlAVkBuYP3RFdEEjrmH94VeD+QVwEihaR5FJzIBa8KTq3CPMKTBbIBooaEeYRjIbEeuOFZ5FpgfaCIBHSMH2F2ICXAA2FeOAFLEF7MNTxVWF9Ye6gDMgq8QDrgbcFpZlifSJbfc889uazwpsAzh2R4IiBh9YzaO+mdZAJCeuf3wy9I78T1zpI0hEAeCBAByQPVCmkz6UIwGPkwYFFgqMKQxw41/o6dadkDgjowcGEcIrEZBqE4hrdt27bGU7CwMw/jFCFLMPKx+wUCIedKgHyInXCEgCH0B6QEhi9CbEA+ICOIDwxUJNEj/Em+cV0X/pQWgoVxYTdSPoYXngt4g5LaBWFAPgcwAxGBZwbjEYnPkA8eA3hGYKzD6EZiO2SHdwKkBQnu2P2DIe5CQNAOcmJgvCM/QhzDCy8DCrwMwBAnaKFfnJSF5HocIpBEQNA/5MUY0D5IAUiHfEcMkrxBIBDTDWKDMWPuQAoFSbnsssu41wS5HSKUDidnnXHGGTxvR8gIQon/R18gLqL873//43MKrxtwQ1ggiBXIFkgWxgMiBWKHnBWEWYEoYWxYJ0RA4lJIpHfSCQjpHdI7cb2xJA0hkA8CREDywZVaJQQIAUKAECAECAFCgBAgBAgBDQJEQGhZEAKEACFACBAChAAhQAgQAoRA2RAgAlI2qKkjQoAQIAQIAUKAECAECAFCgBAgAkJrgBAgBAgBQoAQIAQIAUKAECAEyoYAEZCyQU0dEQKEACFACBAChAAhQAgQAoQAERBaAzUWARw1i5OecCldqKI7nSsLWXAkME6jWrp0aRbN5daG6/h1J1rlJhw1TAjkgADpnRxAdWyS9I4jYFSdEMgBASIgOYBKTeaDQNLN2ja94bjWp556it8dIgqOu8SxrrijI1TBscI4vhZH8PoW3cc0LwKC2+XlY4h9ZRbPuY5/5cqV/JheHNmMOY21ZI1TrOOsCXKR3tHPMumd+FY/6Z345oQkSkaACEg1WR24dwIGWa1atTIf0c8//8wvIwxdsjYEQo8nq/5jMwRwLwfWo+7m8qzGHHs7NcUQIL2TvhJ1Gx+xr11b+Ujv2CJVvno1Re+UD1HqKU8EiIDkiW6ObYsd7pEjR7LzzjuPffTRRwwXtjVr1ozfDP3Pf/6Th99ss802/AZxKCYU8Rz+jedw6R0uiMOt3bjUD0WECOCWa1xCB8MfhgZumMaFgwhbWrFiBb+F/MYbb2Tbbbcdfw6XFSLsB7dh48Pbvn17dvfdd/N6KLid+oILLuCX4uHCQFxWh8sE4QFAwQcNF/HhpmtcJIgL7zAW/A1F3fHG7dgIyUF7uHRuypQpDGQJO/SQC5fRiXY/++yzwmzgIjuMSQ2FWLVqFR8vbmXHzvyWW27JLxbEpXcoggDh5nJcfPfWW2/xMeIWbtzci4J+/vrXv/JLGbFbjzHhoj5xs7e6JNSPOMaIG75x4eHYsWP5fOKG74MOOki7mjCvEydOLPoNBEDM8+jRo/mc4DZ23BKO2+I32WSTQn38Py6XnDNnDpcVc3766adr+9JdIIfngAtuYh8zZgy/7BAXU0L2li1b8hvRcQEjLkcEnpjv/fbbr9C+6/jVECybceJGdsjx0EMP8YsycfElLsCUL3tUB2yaR1y8iIsfJ02axNcvLufEmsO6TsIJY630QnqHMdI7jH9PSO+cydL0K+mdStd2JH/eCBAByRvhnNqHIQDDfJddduEGLsKImjdvzv8GgxCGc9OmTXnYEYz4adOmcWNZPAfScMstt7DatWtzgxO71a+//nqBgAwdOpQbrDAYYbThhmsQlfXXX58NGjSINWrUiJMLtAfyg7+D7Oywww7cCMUzyK3YbLPNOEFB/7vvvju7/PLL+c3XMPBhqOM3GMGCKODWc9SBQff444/ztj744AO2xRZbcKLxxz/+kd+avvXWW3PZ0e/48ePZggUL2E477cTbgcGOG7VByNZZZx3eV5MmTXg/IBOQbcMNN6xCQGBAgpRgXBgHSBn+hv6BnSAgkAX44G+QD3KBNAHDAw88kBMPyADDFIYqbvXGjdy6ojPAMY8gBZhbEB3IAYMYY1ULwpGAIea9X79+/GfcFC/mGcYS5hCesaOPPpqPC+QUBURn8ODBDLeS4+8gcGgDN4wfe+yxVfqCwd6tWzc+z7jdHAU4Tp48mROQDh06cFzatm3LbyP//PPPOfnAvNetW5c9+OCDHBfcBA9yIuZcvvUeBCxt/DoCgrGnjXPIkCF8TLjFHSTo5ptvZrjBHTKL2+bVwabN48KFC/lYgdUxxxzDfvzxR35TOwwOrMUknLDuKr2Q3iG9gzVMeuf37y/pnUrXaCR/SASIgIREv4S+YQgcf/zx3MgXHojZs2dzoxiGH8iHKNhxhuF+5ZVXcsMUz8Ew3HXXXXmVWbNmccMMO/qoByMcdefPn88NTBQYVvBYfPnll6xOnTqFtjfddFPuSYEyhqENg1lnvMJQq1evHjfuRYGXAAocu+MwUGGMg+SMGDGCV8FOPozpSy+9lJ166qkFAgBDGV6OpAJvDbwnMDJhSKLoQiFUDwi8Df379+feFFGAB4jA7bffXugfhuyJJ57Iq4BggAzNnDmTkyQYpgcffDA37G2KjoCAMIKEoQAbkKgXX3yx4IlR200KhcA8gxi1a9eOP3LHHXdw4oDdfxSQAHjHjjjiiEKT8AChL3irdEXn4hekAMZ8jx49UocNrE477TROPlFcx68jIKZxYg3BW4F/ULA+QJJAupIISNo8goDjXYGXRxS8c/AgglyBdFfXUAjSO6R3xJonvZOuX0nv2HwBqU5NRoAISIXOPgyBU045hYdCidAkhC0deuihhZAmMTQkWvfu3Zu7i/Ecdm7xnLwjC4P9pptu4uQBhjl2yeFBEAVeFoRPgUTIBbu/MOxgyOI57DaDVID0HHLIIQXjF4YnjGE5lwQEY/ny5dyIBwHCBw0EAGFeooBcwaCH0ZeUAwJShN9Bkr744gtuYKJd7OyLcCITAUF4Gbw6MHAhvyhnnXUWDy1D26L/t99+m5MSlCVLlnDPBMIR4OUAOYGBDeICDCA7jNmkojPAH330UY6dKJALxA4kTleSDAFgCQIjCrxhkAehZsIrhPmU84awi4/+gKOupBEQGOEgcaKgb5BHeKPgoULbWC8DBgzgHh4U1/HrCEjaOOGNgDdGzI+QDe8DcEgiIGnzCA/eK6+8wj1wcsF4Qd7gJarOBIT0zu+zTnqnNQ/vxD+i4PtCemcV94KS3kn87NEPhABHgAhIhS4E3SlHIBhHHXUUDxlSwz0aNmxYCM0BAQEpkQ1PEBCEpsDI1R0TCYIBIxgGoFqgaBH7joJwLOQvvPTSS9zoe+SRR7jnBARj//335zkGasFOPIw5nSENTwdOroJMSQQE+RUwqGHsIr8DHhrkZCA8SnwcbQmIaqjieYSPjRs3Tts/8myA3YQJEwp5Nsi3AAYvv/wyN74RdnTGGWdYkQednMAX5BC5BS4ERD2GF8Y25gLEDwQDO3TIIRKeMNE21k6bNm2cCQjIGGQVBeQPXgKEZcFTBrLTp08fjhPGk0RA1NPK5PEn5YDIxw3L4xSGAHI14F0TReCQREBQL2keQTBwahneCbUgvwahd9WZgKjrivQO6R2ZgJDe+a1AQEjvVKiBRWKXBQEiIGWBOftOdAQExv/mm2/OE2NlY0vuXYRQiHAr/IawEYQPySFY6v0Y2PGF4QUvhm0yLUJ7sCv87LPPcmKE0B8Y8knFRECwi44ddiS5i3wPtIUQJYQX9e3blzcNwxGkBvkbgoCA4Dz88MPcAyCKbQgWvBnwpugIkI6AyOO78MILORlBYrYNefAhIAj5wa40PAu2hgDqIdcCz1188cXWCxS5OVhjIKOiJN3NgbwheORE+zj2GH2Cw7jdWAAABOVJREFUSJWLgEBGEC141QQ+8JAhLA3kNo2AJM0jiC0OIpg+fXriSV86nKxBjrgi6Z3f88xI7zAeakh65//vWZI3PkjvRKzESLRoECACEs1UuAmSdM8DEo2RTI5dd8S4f/311zx8CMYgPAUiiRS/IQkdIVGIx8eu+JtvvsmF0HlA8DtCjJAkjp1fGKEgBAg5gYcCIVYw8rDDjd1zhOMgnAsGP+rDAN9tt914/gk8MNglRt4EiI0wZk0EBCE8yDOBAYiTjJA3gnAhjAW5KvDgiJO6QFKQxyIICD6WCIlCqBY8JPBaqOOEUYzcDZyCBeMUSetIXlaT0OUcFJWAoD8QNfQHjwDCsTAu7BLnRUBg7MK7ABKGscEbpVsf6gcSYUbwSCFJHTLDKwbcIDdOjdIV5Pog7whhYvCqIfwMhBcJ3aoHBF4GkDbgCGIFIgKycsIJJ5SVgCAsEGR0+PDhnGhjvSHPqEuXLvyQBl1Jm0ese6wPhOphzQNvEHN4+5DYDw+SDqc8jsh20xql1ya9Q3pHrCLSO8UXvar6lfRO6fqGWqjeCBABqdD5TTIEcAwtEolx5CiSyHE6FsKREJ4EEiKew8lKMJ5AFHDaFf4f4UtJBAR/B/kQu78IecLOMkgJDFhcDAfCAfKD8B4YZYizR+4IiAIKTovC8yA6IDTYhT7ssMMKSd8mAoI2YDQjkRpjg5cHBi0IAQw+hErB8wHigbwUOT75ueee40Y1DGJ4UUzH8CK+e6utttIew5tGQBBqhfAz4AqyhFO3YPwmXXaoy4FIC0HSLVccKICdSHiyQCLkY3iTQpNEO0jUxxwhDwekEGsEuIE86Aq8bJhn5MUgn0M+hlclIMAYZAPyYT3gpCjkKckXGbqO3zUEC2MAcUUujziGF2vlk08+4UQBXjFdMc0j8qMwHoTeAXO8O5hrEFaQLR1Otp7DmFUS6R3SO2J9kt5JJyCkd2LWZCRbDAgQAYlhFsooQ143ZJdxCNQVIVASAkg+R04SwsPEaWMlNUgPGxEgvWOEiCpUcwRI71TzCabhOSNABMQZssp+gAyByp4/kt4dAdyhggMBEDIFbwXyeRAWBi8OiAiV/BEgvZM/xtRDXAiQ3olrPkia+BAgAhLfnOQqERkCucJLjUeIAA4lOPzww3nSOMLTcJEiLupMuhwywiFUvEikdyp+CmkAjgiQ3nEEjKrXOASIgNS4KacBEwKEACFACBAChAAhQAgQAuEQIAISDnvqmRAgBAgBQoAQIAQIAUKAEKhxCBABqXFTTgMmBAgBQoAQIAQIAUKAECAEwiFABCQc9tQzIUAIEAKEACFACBAChAAhUOMQIAJS46acBkwIEAKEACFACBAChAAhQAiEQ4AISDjsqWdCgBAgBAgBQoAQIAQIAUKgxiFABKTGTTkNmBAgBAgBQoAQIAQIAUKAEAiHABGQcNhTz4QAIUAIEAKEACFACBAChECNQ4AISI2bchowIUAIEAKEACFACBAChAAhEA4BIiDhsKeeCQFCgBAgBAgBQoAQIAQIgRqHABGQGjflNGBCgBAgBAgBQoAQIAQIAUIgHAJEQMJhTz0TAoQAIUAIEAKEACFACBACNQ4BIiA1bsppwIQAIUAIEAKEACFACBAChEA4BIiAhMOeeiYECAFCgBAgBAgBQoAQIARqHAL/B5MVmrdWd5f2AAAAAElFTkSuQmCC\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=[8,3.5], sharey=True)\n",
    "\n",
    "ax = axes[0]\n",
    "hist, xedges, yedges = np.histogram2d(1 / dmat_slatm2.mean() * dmat_slatm2[np.triu_indices_from(dmat_slatm2, k=1)], \n",
    "                                      dmat_target[np.triu_indices_from(dmat_target, k=1)], \n",
    "                                      bins=100, range=[[0,8], [-15,15]])\n",
    "ax.imshow(hist.T, aspect='auto', origin='lower left', \n",
    "          interpolation='gaussian', \n",
    "          extent=[0,10,-20,20],\n",
    "          cmap='ocean_r')\n",
    "\n",
    "\n",
    "ax = axes[1]\n",
    "hist, xedges, yedges = np.histogram2d(1 / dmat_slatmdiff.mean() * dmat_slatmdiff[np.triu_indices_from(dmat_slatmdiff, k=1)], \n",
    "                                      dmat_target[np.triu_indices_from(dmat_target, k=1)], \n",
    "                                      bins=100, range=[[0,8], [-15,15]], density=True)\n",
    "\n",
    "cax = ax.imshow(hist.T, aspect='auto', origin='lower left', \n",
    "          interpolation='gaussian', extent=[0,10,-15,15],\n",
    "         cmap='ocean_r')\n",
    "\n",
    "\n",
    "# axes[0].set_xlim(0,4)\n",
    "axes[0].set_xlim(0,3)\n",
    "axes[1].set_xlim(0,2)\n",
    "\n",
    "axes[1].set_xlabel('Euclidean distance between \\n representations in the training set')\n",
    "axes[0].set_xlabel('Euclidean distance between \\n representations in the training set')\n",
    "\n",
    "axes[0].set_ylabel('Difference between forward \\n activation energies [kcal mol$^{-1}$]')\n",
    "axes[0].set_ylim(-15,15)\n",
    "plt.tight_layout(pad=1)\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.85, 0.16, 0.02, 0.78])\n",
    "cbar = fig.colorbar(cax, cax=cbar_ax)\n",
    "cbar.set_ticks([])\n",
    "cbar.set_label('Probability density')\n",
    "\n",
    "# fig.savefig('sim_plot_LR.png', dpi=300)\n",
    "# fig.savefig('sim_plots.png', dpi=300)\n",
    "# fig.savefig('sim_plots.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we compare the variance of each feature with the importance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "variances = np.var(slatmdiff, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we use the regression coefficients between each feature and the target as importance score\n",
    "lin_regs = [sp.stats.linregress(xs, ys) for xs, ys in zip(slatmdiff.T, [Y for i in range(slatmdiff.shape[1])])]\n",
    "rvalues = np.array([ccc.rvalue for ccc in lin_regs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[8,3])\n",
    "\n",
    "ax.plot(variances, label='Variance', c='black')\n",
    "ax.plot(rvalues ** 2, alpha=0.4, label='r$^2$ coefficient', c='C0')\n",
    "\n",
    "ax.legend(ncol=1, fontsize=10)\n",
    "ax.set_ylim(-0.02, 0.5)\n",
    "ax.set_xlabel('Features')\n",
    "plt.tight_layout()\n",
    "fig.savefig('./feature_regression.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We take the 500 features with the highest r2 value and normalize them with a min max scaler\n",
    "# Here this is done for the whole data to ilustrate the dissimilarity plots, for the training this step will be \n",
    "# done for each train split.\n",
    "\n",
    "num = 500\n",
    "slatmdiffp = sk.preprocessing.MinMaxScaler().fit_transform(slatmdiff[:, np.argsort(rvalues ** 2)[-num:]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmat_slatmdiffp = sk.metrics.pairwise.pairwise_distances(slatmdiffp, n_jobs=-1)"
   ]
  },
  {
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1050\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=[10.5,3.5], sharey=True)\n",
    "\n",
    "ax = axes[0]\n",
    "hist, xedges, yedges = np.histogram2d(1 / dmat_slatm2.mean() * dmat_slatm2[np.triu_indices_from(dmat_slatm2, k=1)], \n",
    "                                      dmat_target[np.triu_indices_from(dmat_target, k=1)], \n",
    "                                      bins=100, range=[[0,8], [-15,15]])\n",
    "ax.imshow(hist.T, aspect='auto', origin='lower left', \n",
    "          interpolation='gaussian', \n",
    "          extent=[0,10,-20,20],\n",
    "          cmap='ocean_r')\n",
    "\n",
    "\n",
    "ax = axes[1]\n",
    "hist, xedges, yedges = np.histogram2d(1 / dmat_slatmdiff.mean() * dmat_slatmdiff[np.triu_indices_from(dmat_slatmdiff, k=1)], \n",
    "                                      dmat_target[np.triu_indices_from(dmat_target, k=1)], \n",
    "                                      bins=100, range=[[0,8], [-15,15]], density=True)\n",
    "\n",
    "cax = ax.imshow(hist.T, aspect='auto', origin='lower left', \n",
    "          interpolation='gaussian', extent=[0,10,-15,15],\n",
    "         cmap='ocean_r')\n",
    "\n",
    "ax = axes[2]\n",
    "hist, xedges, yedges = np.histogram2d(\n",
    "    1 / dmat_slatmdiffp.mean() * dmat_slatmdiffp[np.triu_indices_from(dmat_slatmdiffp, k=1)], \n",
    "    dmat_target[np.triu_indices_from(dmat_target, k=1)], \n",
    "    bins=100, range=[[0,8], [-15,15]], density=True)\n",
    "\n",
    "cax = ax.imshow(hist.T, aspect='auto', origin='lower left', \n",
    "          interpolation='gaussian', extent=[0,10,-15,15],\n",
    "         cmap='ocean_r')\n",
    "\n",
    "\n",
    "axes[0].set_xlim(0,3)\n",
    "axes[1].set_xlim(0,2)\n",
    "axes[2].set_xlim(0,2)\n",
    "\n",
    "axes[0].set_xlabel('Euclidean distance between \\n representations in the training set')\n",
    "axes[1].set_xlabel('Euclidean distance between \\n representations in the training set')\n",
    "axes[2].set_xlabel('Euclidean distance between \\n representations in the training set')\n",
    "\n",
    "axes[0].set_ylabel('Difference between forward \\n activation energies [kcal mol$^{-1}$]')\n",
    "axes[0].set_ylim(-15,15)\n",
    "plt.tight_layout(pad=1)\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.85, 0.16, 0.02, 0.78])\n",
    "cbar = fig.colorbar(cax, cax=cbar_ax)\n",
    "cbar.set_ticks([])\n",
    "cbar.set_label('Probability density')\n",
    "\n",
    "# fig.savefig('sim_plot_LR.png', dpi=300)\n",
    "# fig.savefig('sim_plots.png', dpi=300)\n",
    "# fig.savefig('sim_plots.pdf')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}