1b_2D_triaxial_MARE2DEM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OUTDATED, the examples moved to the gallery\n",
    "## See https://empymod.github.io/emg3d-gallery\n",
    "\n",
    "----\n",
    "\n",
    "# 2D with tri-axial anisotropy comparison between `emg3d` and `MARE2DEM`\n",
    "\n",
    "`MARE2DEM` is an open-source, finite element 2.5D code CSEM and MT code, see https://mare2dem.ucsd.edu. The `MARE2DEM` input- and output-files are located in the data-directory.\n",
    "\n",
    "\n",
    "#### Requires\n",
    "- **emg3d >= 0.9.0**\n",
    "- ``discretize``\n",
    "- ``numpy``, ``scipy``, ``numba``, ``matplotlib``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import emg3d\n",
    "import discretize\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate as sint\n",
    "from matplotlib.colors import LogNorm, SymLogNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Style adjustments\n",
    "%matplotlib notebook\n",
    "#%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `emg3d`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = [50, 0, -1950, 0, 0]  # Source location [x, y, z, azimuth, dip]\n",
    "freq = 0.5                  # Frequency (Hz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "  <tr>\n",
       "    <td style='font-weight: bold; font-size: 1.2em; text-align: center;' colspan='3'>TensorMesh</td>\n",
       "    <td style='font-size: 1.2em; text-align: center;'colspan='4'>1,048,576 cells</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th></th>\n",
       "    <th></th>\n",
       "    <th colspan='2' style='padding: 5px 20px 5px 20px;'>MESH EXTENT</th>\n",
       "    <th colspan='2' style='padding: 5px 20px 5px 20px;'>CELL WIDTH</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>FACTOR</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>dir</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>nC</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>min</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>max</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>min</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>max</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>max</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>x</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>128</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>-3,275.02</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>12,049.53</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>100.00</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>342.59</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1.08</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>y</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>64</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>-6,710.68</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>6,710.68</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>50.00</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>543.38</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1.08</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>z</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>128</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>-7,000.00</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>11,754.06</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>100.00</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>413.23</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1.03</td>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "\n",
       "  TensorMesh: 1,048,576 cells\n",
       "\n",
       "                      MESH EXTENT             CELL WIDTH      FACTOR\n",
       "  dir    nC        min           max         min       max      max\n",
       "  ---   ---  ---------------------------  ------------------  ------\n",
       "   x    128     -3,275.02     12,049.53    100.00    342.59    1.08\n",
       "   y     64     -6,710.68      6,710.68     50.00    543.38    1.08\n",
       "   z    128     -7,000.00     11,754.06    100.00    413.23    1.03\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create stretched grid\n",
    "pgrid = discretize.TensorMesh(\n",
    "    [[(100, 16, -1.08), (100, 100), (100, 12, 1.08)],\n",
    "     [(50, 31, -1.08), (50, 2), (50, 31, 1.08)],\n",
    "     [(100, 80), (100, 48, 1.03)]],\n",
    "    x0=(-3275.0225685, 'C', -7000))\n",
    "\n",
    "pgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = (pgrid.gridCC[:, 0] > 0)*(pgrid.gridCC[:, 0] <= 6000)\n",
    "zz = (pgrid.gridCC[:, 2] > -4200)*(pgrid.gridCC[:, 2] < -4000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "\n",
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       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
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       "\n",
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       "    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",
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       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
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       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
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       "\n",
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       "\n",
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       "            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",
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       "\n",
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       "\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",
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       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
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       "\n",
       "    // Add hover states to the ui-buttons\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    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",
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       "}\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",
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       "\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Background\n",
    "res_x_full =  2*np.ones(pgrid.nC)\n",
    "res_y_full =  1*np.ones(pgrid.nC)\n",
    "res_z_full =  3*np.ones(pgrid.nC)\n",
    "\n",
    "# Water - isotropic\n",
    "res_x_full[pgrid.gridCC[:, 2] >= -2000] = 0.3\n",
    "res_y_full[pgrid.gridCC[:, 2] >= -2000] = 0.3\n",
    "res_z_full[pgrid.gridCC[:, 2] >= -2000] = 0.3\n",
    "\n",
    "# Air - isotropic\n",
    "res_x_full[pgrid.gridCC[:, 2] >= 0] = 1e12\n",
    "res_y_full[pgrid.gridCC[:, 2] >= 0] = 1e12\n",
    "res_z_full[pgrid.gridCC[:, 2] >= 0] = 1e12\n",
    "\n",
    "\n",
    "# Target\n",
    "res_x_full_tg = res_x_full.copy()\n",
    "res_y_full_tg = res_y_full.copy()\n",
    "res_z_full_tg = res_z_full.copy()\n",
    "res_x_full_tg[xx*zz] = 200\n",
    "res_y_full_tg[xx*zz] = 100\n",
    "res_z_full_tg[xx*zz] = 300\n",
    "\n",
    "pmodel = emg3d.utils.Model(pgrid, res_x_full, res_y_full, res_z_full)\n",
    "pmodel_tg = emg3d.utils.Model(pgrid, res_x_full_tg, res_y_full_tg, res_z_full_tg)\n",
    "\n",
    "pgrid.plot_3d_slicer(pmodel_tg.res_x, clim=[0.3, 300], zlim=[-6000, 500], pcolorOpts={'norm': LogNorm()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ":: emg3d START :: 20:54:31 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : False\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 3\n",
      "   Original grid  : 128 x  64 x 128     => 1,048,576 cells\n",
      "   Coarsest grid  :   2 x   2 x   2     => 8 cells\n",
      "   Coarsest level :   6 ;   5 ;   6   \n",
      "\n",
      "   [hh:mm:ss]  rel. error                  [abs. error, last/prev]   l s\n",
      "\n",
      "       h_\n",
      "      2h_ \\                                        /\n",
      "      4h_  \\                            /\\        / \n",
      "      8h_   \\                  /\\      /  \\      /  \n",
      "     16h_    \\          /\\    /  \\    /    \\    /   \n",
      "     32h_     \\    /\\  /  \\  /    \\  /      \\  /    \n",
      "     64h_      \\/\\/  \\/    \\/      \\/        \\/     \n",
      "\n",
      "   [20:54:53]   2.504e-03  after   1 F-cycles   [6.991e-09, 0.003]   4 1\n",
      "   [20:55:11]   2.389e-04  after   2 F-cycles   [6.669e-10, 0.095]   5 2\n",
      "   [20:55:25]   1.317e-05  after   3 F-cycles   [3.675e-11, 0.055]   6 3\n",
      "   [20:55:41]   1.016e-06  after   4 F-cycles   [2.835e-12, 0.077]   4 1\n",
      "   [20:55:57]   5.840e-08  after   5 F-cycles   [1.630e-13, 0.058]   5 2\n",
      "\n",
      "   > CONVERGED\n",
      "   > MG cycles        : 5\n",
      "   > Final rel. error : 5.840e-08\n",
      "\n",
      ":: emg3d END   :: 20:55:57 :: runtime = 0:01:25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sfield = emg3d.utils.get_source_field(pgrid, src, freq, 0)\n",
    "pfield = emg3d.solver.solver(pgrid, pmodel, sfield, verb=3, semicoarsening=True, linerelaxation=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ":: emg3d START :: 20:55:57 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : False\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 3\n",
      "   Original grid  : 128 x  64 x 128     => 1,048,576 cells\n",
      "   Coarsest grid  :   2 x   2 x   2     => 8 cells\n",
      "   Coarsest level :   6 ;   5 ;   6   \n",
      "\n",
      "   [hh:mm:ss]  rel. error                  [abs. error, last/prev]   l s\n",
      "\n",
      "       h_\n",
      "      2h_ \\                                        /\n",
      "      4h_  \\                            /\\        / \n",
      "      8h_   \\                  /\\      /  \\      /  \n",
      "     16h_    \\          /\\    /  \\    /    \\    /   \n",
      "     32h_     \\    /\\  /  \\  /    \\  /      \\  /    \n",
      "     64h_      \\/\\/  \\/    \\/      \\/        \\/     \n",
      "\n",
      "   [20:56:13]   2.503e-03  after   1 F-cycles   [6.986e-09, 0.003]   4 1\n",
      "   [20:56:28]   2.378e-04  after   2 F-cycles   [6.637e-10, 0.095]   5 2\n",
      "   [20:56:42]   2.563e-05  after   3 F-cycles   [7.154e-11, 0.108]   6 3\n",
      "   [20:56:58]   3.412e-06  after   4 F-cycles   [9.524e-12, 0.133]   4 1\n",
      "   [20:57:14]   1.729e-06  after   5 F-cycles   [4.826e-12, 0.507]   5 2\n",
      "   [20:57:28]   1.169e-06  after   6 F-cycles   [3.262e-12, 0.676]   6 3\n",
      "   [20:57:43]   1.640e-07  after   7 F-cycles   [4.578e-13, 0.140]   4 1\n",
      "\n",
      "   > CONVERGED\n",
      "   > MG cycles        : 7\n",
      "   > Final rel. error : 1.640e-07\n",
      "\n",
      ":: emg3d END   :: 20:57:43 :: runtime = 0:01:47\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sfield_tg = emg3d.utils.get_source_field(pgrid, src, freq, 0)\n",
    "pfield_tg = emg3d.solver.solver(pgrid, pmodel_tg, sfield_tg, verb=3, semicoarsening=True, linerelaxation=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load `MARE2DEM` result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = np.loadtxt('./data/MARE2DEM/triaxial.0.resp', skiprows=93, usecols=6)\n",
    "mare = dat[::2] + 1j*dat[1::2]\n",
    "\n",
    "bgdat = np.loadtxt('./data/MARE2DEM/triaxial-BG.0.resp', skiprows=93, usecols=6)\n",
    "bgmare = bgdat[::2] + 1j*bgdat[1::2]\n",
    "\n",
    "x = np.arange(80)/10+2.05\n",
    "\n",
    "# Get corresponding emg3d offsets and responses\n",
    "xx = pgrid.vectorCCx[36:-12]/1e3\n",
    "if not np.allclose(x, xx):\n",
    "    print(\"\\n\\n\\n ========= ¡ Watch out, offsets are not the same ! ========= \\n\\n\\n\")\n",
    "\n",
    "em3_bg = pfield.fx[36:-12, 32, 50]\n",
    "em3_tg = pfield_tg.fx[36:-12, 32, 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differences\n",
    "- In `emg3d`, the source is a cell of 100x50x100 meters, with center at (50, 0, -1950); center is same as `MARE2DEM` source location.\n",
    "- In `MARE2DEM` the receivers are at -1999.9 m depth, 10 m above the sea-surface. In `emg3d`, we take the edges with are at -2000 m, hence the seafloor itself; the edges are 100 m long (but the response is normalized)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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FgDQ//t//w+9evXiu0/OmDEDK1euxLRp0xAQEADGmGDxds2kLk5OTggPD0dERAT69esHa2trjB8/3ihAWJs8//LLLzywTZgwAVFRUVi8eDG/ZlXlCXPa/r0OYUBAAL8O4aJFi2q0DuFzzz2H+fPnY8WKFejSpQsYY+jTp4/OdQhrA4QuLi5o2bIlxo0bh+XLlyMiIoL/O3v06CE4v1KpRKdOnfiurXPmzEFUVBQmT57M56nyg5eAkEQimYOqAkIAWLJkCRhjWLlypWD75s2bYWVlBXt7e4wfPx4rVqzAvHnzMHz4cDg4OCA4OFiQXlcdvWfPHr6enzdvHhYvXoyXX34ZdnZ2GDVqlE4gnDhxIhhjeP311xEdHY0NGzbwHxSrA8LDhw+DMQYbGxutniwVVZc6X58uXLgAJycnWFlZYfDgwViyZAmWLVuGcePGoVWrVpBIJIL09QVCQLgOYWhoKFatWsWvEenh4aF3HUICQpJGBIQki1RdgRAAtm3bBsYY3njjDcH2//73v9i+fTsCAwPh7OwMW1tbeHt7IygoCFu2bBHMvgaox/G98sorcHR0hIuLC4YMGYL09HQ+D4YGwtrk+enTp/jwww8xbNgweHt7QyKRwM3NDa+88gp2796tF9Z+/fVXfrF4W1tbvPjii4iNjdVqadQoPj4er776KhwdHSGRSNC5c2e8//77ePLkiVba2gLh7t27MWbMGLRr1w52dnZo1qwZunXrhg8//BC//fab1jl+++03bNy4ET169ICDgwOkUil8fHwwfPhw7N27V2sCHAJCEolkDqoOCL/77jvY29vD3t4e3333nWBfRkYGQkJC4OnpCRsbG7i5uaFr165YtmwZrl+/Lkirr46Oi4tD165dYW9vjxYtWmDMmDG4c+eO3q7+33//PSZNmoTnnnuOnxBMA0fVAeG//vUvODg4gDGGLl26VBmX2tb5Vam0tBQLFizgJ7ZxcnLCCy+8gClTpmiNYWwIIASAa9euYcyYMXBzc4ONjQ28vb3x1ltv6WzRJSAkVRYBIckipQ8ISQ2j2sJrYxABIYlEIpFIJHMUASHJIkVAaFgREJJIJBKJRCKZhwgISRYpAkLDioCQRCKRSCQSyTxEQEiySBEQGlYEhCQSiUQikUjmIQJCkkXq4sWLiI6OFix0TiLVR3FxcdiyZYvY2SCRSCQSiUSqlQgISSQSiUQikUgkEslCRUBIIpFIJBKJRCKRSBYqAkISiUQikUgkEolEslAREJJIJBKJRCKRSCSShYqA0ED6+eef8cMPP9TKAGp9DJliSbE0H1MsxY/lzz//LMITofGoLs82uvdN494nUywplubhusSyvs82AsIGUnJyMpYuXYqPP/4YgPqH5Diuxv7mm28AAN98802tjiNTLCmW5mGKpWnEUvOwJdVNtX220b1vOvc+mWJJsTR91zWW9X22ERAaSASE5leYyBRLiqV5mICw9vrxxx8RHR2NpUuXYvny5cjKyqrTeQgIzffeJ1MsKZambwLCRiYCQvMrTGSKJcXSPExAWHv9/PPPKC0tBQD8+uuvmDt3Lp48eVLr8xAQmu+9T6ZYUixN3wSEjUwEhOZXmMgUS4qleZiAsP5avnw5fvzxx1ofR0Bovvc+mWJJsTR9ExA2MhEQml9hIlMsKZbmYUsEwrt372LTpk2YM2cOQkJCcPXqVa00KSkpmD9/PiZPnoyoqCjcu3dP57mKi4sRERFRp3wQEJrvvU+mWFIsTd8EhI1MBITmV5jIz1xWVobCwkIUFhaipKSE/39y/UyxNE4sy8rK9N7b5gqEt27dQnx8PHJycnQC4ZUrVzBx4kRcuHABSqUScXFxmDJlilYr4G+//YalS5fi/v37dcpHVc+2ivUG3fvi3Pum4qrKoKmY3hUolqZoAkIzF80yajqmWNbPZWVluHfvHpRKJTiOq1OLAFm3KZaGj6VSqcS9e/f0vpCaKxBWlC4gXLVqFfbt2yfYtnTpUhw9epT/99OnT7Fu3Tqkp6dXe42nT5/i999/5/3HH38AUD/bvvnmGy1Xrjfo3jf+vW8qrlgGdd0rpuJvv/0WAPDtt9+KnhdzN8VS/FgSEJqoCAjFM8Wyfi4sLBS81Jn6y4c5mWJpnFgqlUoUFhbqPc7cVRkI//zzT0yYMEELEg8dOoR169YBAP766y9s2bIFx44dq9E1jh07hpCQEN5RUVFVpi8tLcV3330n+vpdZNPwd999x09iRCKRTF8EhAbSDz8QEIplimX9XPlFurb3Mlm/KZbGi6UlAeFPP/2EkJAQrW6giYmJWLx4MQCgsLAQb775JiIjI3mXlZXpvUZtWwj1xZvufePf+6biwsJC0VtbDNESQ6ZYmmIs6/tsIyA0kAgIxTPFsn4mIDScKZbGi6UlAuGDBw8E6RITE7FkyZIGvba+uBMQms69byqu7p4Q2/SuYPxYqvLzoFIkQ5WfJ3qeTdV1vS8JCE1U9QFClUol+g1pzqZKvn4mIDScKZbGi6UlAWFNuow2lAgIxbe5xJKA0HJck1gqE+JQPsIf5cNfRvkIfygT4kTPtymagLCRqa5A+PDC1/h41YcoK8gX/aY0V1MlXz8TEBrOFEvjxdKSgBBQTyqzf/9+wbbKk8rURTWdMI2A0HTufVMxAaHluLpYqvLznsGgxiMDqKWwDrHUZwJCE1VdgPB///sfQjYkYMNnH+L0F3OgUpaLfmOao6mSr5/NFQhDQkLAGMOUKVO09k2bNg2MMYSEhGjtS0pKgrW1NQYMGKC1LycnB4wx3k5OTujevTvi4oRfNmUymSCdxhKJhE+zcuVKdOvWDQ4ODmjRogWCg4ORnp4uOI+Xlxd/rFQqhZeXF0aOHIljx45Vma+KPn36tCBPHTt21Pq7du/eDcYYvLy89MZTc43U1FTRf9vKMVq/fr3FAeGTJ09QWlqK0tJShISE4MyZMygtLeWXldAsO6FQKATLTjT039vYgDAwMBDh4eGi50PjiIgIdO7c2SxjWdt7QmzTu4LxYqlSJAth8G+r0lJEz7upmYCwkamuLYSnkvdgm3wQtssHQfHpO6LfmOZoquTrZ3MGwlatWsHZ2RnFxcX89pKSEri4uKB169Y6gXDSpEkIDw+Hvb09rl27JtingaKEhATk5uYiPT0dM2fOhI2NDRQKBZ9OJpPByckJubm5At++fZtPM2DAAGzbtg1paWmQy+UYNGgQWrdujaKiIj6Nl5cXIiMjkZubi2vXriExMRGhoaGwsrJCVFSU3nxV9OPHj/k82dvbw9XVFV999ZXg7xowYABat25tNCDU5KkhbKlAWFBQIJj1U+MdO3bwaTQL00+aNAlRUVG4e/dug+eDgFDbpaWlDZYfAkLjmd4VjBdLaiFsuFjqMwGhiaih1iFUcUrsTx4LmTwIh+XDkHf+C9FvTnMzVfL1szkDYXBwMHx9fbF9+3Z++44dO+Dr64vg4GAtICwqKoKjoyPS09Px+uuvIzIyUrBfFxQ9ePAAjDFs2LCB3yaTyeDs7FxtHivG8s6dO2CMITExkd+mgZ3Kxy1btgzW1tZ8i2JNYE2TpxkzZmDy5Mn89uvXr0MikWDhwoVVAmHllsfAwEBwHIdz586hb9++aNasGZycnNCrVy+kpKRoHbtp0yYMHToUdnZ2WLZsGTiOQ1xcHHx8fCCVShEYGIgtW7aAMYZ79+7xxyYlJeGVV16BVCqFp6cnZs6cyUNzYGCgVr5qeh9X/A1IdZcxgNBYE09oehVUtKal1dvbG1KpFO3bt8e7776rs65ZuXIlWrZsyZejW7duISgoCFKpFN7e3tixY4dWmS4sLERoaChatGgBR0dH9O7dG3K5nC+zlfMjk8nqFUsxTUBoOa7RGMKdHwqAkMYQ1j2WukxAaKKqz6QyhSW3sCN1CGTyIBw/Pw6ld3JFv0HNyVTJ18+VH+Lff/89VI9KxHEtJljSvKRFR0ejT58+/PY+ffpg/fr1OoHwk08+QdeuXcFxHA4fPgxvb2/BNSuD1+PHj7Fu3ToeeDTp6gKEmZmZYIwJWhr1AWFBQQGsrKywZs0anfnSZU2eUlNT4ejoyLearlixAsHBwVi/fn2VQHju3DlBK2RBQQE4jsOxY8ewbds2XLp0CZcuXcKkSZPg7u6OBw8e8McyxuDm5oZPPvkEWVlZuHr1KnJycmBjY4O33noL6enp2LVrFzw8PARAeOHCBTg4OGD9+vXIyMhAUlISunTpgjfffJOPg6enJyIjI5Gfn4/cXP11IwFhw6iuYwhVKpWgLH+vLK9RmVd+vl848cTn+w1WbxQWFuLll19GaGgo38L+6NEjLFu2DOfOnUN2dja2b98OOzs77N69W1DXODg4YNy4cUhLS+PLcN++feHn54czZ84gJSUFgYGBkEqlfJlWqVQICAjAkCFDcP78eWRkZGDu3Llo1qwZCgoKUFxcjLlz5+KFF17g81Oxt4OuesSUTUBoOa5JLFUpp58BYcgAqCqsd0yuXSx1mYDQRFXfZScuXvsMMnkQZPIgfHU8HKpyGk9o6MJEVlsLCJXlOvv+G8OqRyU1zrcGCPPz8yGRSJCTk4OrV69CKpUiPz9fJxD6+/vzX//LysrQvHlzxMfH8/s14CWVSmFvbw9ra2swxuDt7c0DEsc9+7Jvb28vcL9+/QTX09QLKpUKQ4YMQc+ePQX79QEhx3Fwd3fHtGnTdOarosv/risqQqqfnx+2bt0KlUoFHx8fxMXFVQuENe0yWl5eDkdHRxw+fJjfxhjDrFmzBOkWLFiAF198UbBt8eLFAiAcN24cQkNDBWlOnToFa2trlJSUCGJkaV1GTUU1BsJHJWZRb9Sky2hYWBiGDx8uqGvc3d0FXUXT09PBGMP58+f5bZqPPpoyfezYMTg5OeHRo0eC8/v4+ODDDz8Ex1GXUWOa3hWMG0vl8U+F5fRqpuj5NkUTEDYyNcQ6hAnyCMjkQdguH4SvP6PxhIYuTGS1zR0IOY7D8OHDsXz5ckRERPAvcpWBMD09HU2bNhWM85s+fTrGjBnD/1sDRXFxccjIyMCRI0fg4+ODhIQEwbVlMhkcHR2RmZkp8I0bNwTpNPVCWFgYvLy8cP36dcH+qoDQzc0NYWFhWvmqfM2KedIA4YYNGxAYGIjjx4/D3d0dZWVldQbCvLw8TJkyBe3atYOTkxPs7e1hZWWFjRs38mkYY4JuuxzHYdiwYZgwYYJgW1xcnAAIn3/+edja2goA187ODowxXLp0SRAjAkJxZAlAuGnTJrz00kto3rw57O3tYWNjg27dugnqmr59+wqOOXToEJo2bQplpVYPV1dXvky/8847sLa21vqIY21tjfnz54PjCAiNaXpXMG4slYe2C8qp8vAu0fNtiiYgbGRqCCBUqpTYn/wGZPIgxJ4Kw+1Uueg3qjmYKvn62dy7jHIchyNHjsDb2xve3t749NNPwXHaQDhv3jwwxtCkSRPe1tbWkEgkuHv3LjhONxSdOHECzZo1Q17es/FNtekyOmPGDHh6eiI7O1trvz4gzM/Ph5WVFdauXas3X5VdMU8FBQX8uL158+aB47g6A2FQUBC6du2KTz/9FGlpacjMzETz5s0F+WaM4eDBg4LjgoODMXHiRMG2Q4cOCYCwY8eOmDlzphbkZmZm8q0xBITiypBdRpU3r+mceEJ585rB6o3KQLh7925IpVJs3LgRqampyMzMRGhoqADSKtY1Gh88eFAnELq4uPBlY/Xq1fDw8NB5f+fnq5eaIiA0nuldwbixVG59X12mx/RW//ftuaLn2xRNQNjI1BBAyHEcCktyseHoMgTEfI0Zaw+h/MF90W9WUzdV8vWzuU8qw3HqboweHh7w8PDgu1BWBMKysjK4u7tj3bp1UCgUArdv3x7vv/8+OE4/FFV+iawJEKpUKsycORMeHh7IyMjQmUYfEC5duhRNmjThWwBrC4Qcx2HUqFFgjPET01QHhDdv3gRjDMnJyYLtDg4OiI2N5f997do1Qbc4jtMNhAsWLICvr//CiigAACAASURBVK9g25IlSwRAOHbsWLz66qtVxrFdu3ZYu3YtAaFIMvSkMsqEOJSPDHgGgwaeeKJfv36YMWMG/+8ZM2Zo3YN9+vSpFgg1XUYrlpfKXUbj4+PRpEkT5OTk6M3P22+/rdW1uq6xFNsEhJbjGgHhhhXqlsH1EeryPfZVqMoabgbqxmICwkamhgJCjuNw9fY9vLo5Bf4xCmzfEFurr5+WaKrk6+fGAIQcx+H+/fu4f//ZB5SKQHjw4EHY2trqfGFZtGgR/Pz8wHH6wSsuLg4SiYTvEqpv2Ync3Fy+xWDatGlwdnbGiRMnBPsrThpR1bITq1ev5tNVteyEZqxdZSAsLi7mWyE4rnogLCsrg1QqRVRUFG7fvs3Hys/PD/369cOlS5dw5swZfkbQ6oAwOzsbNjY2mD9/Pi5fvozdu3fD09MTjDH+3F9//TWkUinCwsKQmpqKjIwMxMXFCV7Y+/Xrh6FDhyIvL0/w91R3H1e8n0k1lxgL06vy86BKSzHKlPShoaHo1q0bcnJykJ+fj3fffRdOTk44evQoLl++jCVLlsDJyalaIOQ49aQy//jHP3D27Fmkpqaid+/ekEql/DhllUqFnj17onPnzjh69ChycnKQlJSExYsX82MPd+zYAXt7e6SmpiI/P19rvGFtYymmCQgtxzWJZXnUHDUQHjuM8jcHqrt3X/pa9LybmgkIG5kaEgg5jsOhxDS8sf04Tp6fjOxjsbU6t6WZKvn6ubEAYWVXBMLBgwcjKChIZ7qUlBQwxpCSkqIXCFUqFTp06MBP8qJvYXrGGD8Tpr79FaeVr7gwva2tLVq3bo1Ro0bhyy+/FFy/qoXpd+3axeepqlbL6oCQ4zh89NFHaNWqFaytrfllJ1JTU9G1a1dIJBK0a9cOe/fu1WrZ1AWEHPds2QmJRILAwEBs2rQJjDEeYjlOPbtpv3794ODgAHt7e/j6+uLtt9/m958+fRq+vr6QSCS07IQIamzrEF6+fBk9evSAVCrlW9DffPNNODs7w8XFBdOmTcPChQtrBISaZSckEgm8vLywc+dOuLm5YfPmzXyaBw8e8D0FbGxs0KpVK7zxxhv8GqiPHj3C8OHD4eLiQstOGNj0rmDcWJbPm6CGwOQkKKOXquFw90ei593UTEDYyNTQQKhSqXD4/HjI5EE4ljIaJTQ7U4MXJrLa5gqE5mCKpdBRUVHw9PQ0SCwJCA2jxgaEhvT169f5lvyGPK+5xJKA0HJcIyCcEqwGwsxL6q7hw19G+eIpoufd1ExA2MjU0EDIcRxy7iRji2YpihPTaSmKBi5MZLUJCA1nS4/lxo0bce7cOWRlZSE2NhbOzs5YvHixQWJJQGgYERDq97FjxxAXF4fs7GwkJSUhICAA3t7eePy4YcdJmUssCQgtxzVp1Ch/vZcaCO/kQnX7phoIR/WEqqRI9PybkgkIG5kMAYQcx+H4hdWQyYOwQz4I6UffE/3GNUVTJV8/ExAazpYey1mzZqFly5Z8d9PIyEiUlZUZJJYEhIYRAaF+Hz16FC+88AKkUinc3NwwbNgwXL16tcGvYy6xJCC0HFcLhMVFFZaFUY+LLQ8bwXchFTv/pmQCwkYmQwGhUqXE3uTRkMmD8EXqSBRlXRL95jU1UyVfPxMQGs4US+PFkoCwYSTGpDLk+t37pmICQstxtUCYe/3vmUX78NuUm1ert72zyCgTSJmLCQjNXDV9aDbEDXCr8DK2pg5Sdx1NDKOuow1UmMhqExAazhRL48WSgNAwIiAU3+YSSwJCy3G1QJh+QQ1/YSP4beWb1zxbb3SEv8GXmDEXExA2MhkSCDmOwwnFOsjkQdiZGozkPbtEv4FNyVTJ188EhIYzxdJ4sSQgNIwICMW3ucSSgNBwzit6jOSbD5FXZBrr+FULhGcT1eC3cLL63/l5KB/h/wwI/153lFoKCQgbnQwNhEqVEjtOzsPArccx4t0vUZyTJfpNbCo250reFExAaDhTLI0XSwJCw4iAUHybSywJCA3jw+l3ERCjgH+MAgExChxOvyt6nqqLpTL+kBr6Vs0Dx3FQKZKFMKgZX5iWIvrfIrYJCBuZDA2EHMfhYWkZhm06Df8YBaLfiYVKSV1H61OYyGoTEBrOFEvjxZKA0DAiIBTf5hJLAsKGd17RYx4GNe75kUL0lsJqgXDfFvW6gx+sBMdRC2F9YqnPBIQmKmMAIcdxOHPpBnp/JMfOL1fhSvwm0W9kU7A5VvKmZAJCw5liabxYEhAaRgSE4ttcYklA2HDWdBHdl1YggEGNU249NOlYKj9ZrwbC7c/eU9VrEWqgkMYQ1jSW+kxAaKIyFhByHIfPv5oNmTwIX6aMRtntG6LfzGLbnCp5UzQBoeFMsTReLAkIG0Y0y6jp2VxiSUDYMK7YRVSXzaKFMHqpGgiPCOe8KN+vbjksXx4uepxNxQSEjUzGBMKc/FR+wfrz8XNFv5nFtrlU8qZqAkLDmWJpvFgSEBpGjQ0IAwMDER5uuJfRkJAQBAcHG/XeN4a9vLywfv36KtMQENbfurqIVrY5jCEsj5ihBsKTXwi2q+Rn1UAYPkb0v8FUTEDYyGRMIOQ4Dp8lL4BMHoT98iG489Uh0W9ocyxMZLXNFQhDQkLAGMOUKVO09k2bNg2MMYSEhGjtS0pKgrW1NQYMGKC1LycnB4wx3k5OTujevTvi4oRdW2QymSCdxhKJhE+zcuVKdOvWDQ4ODmjRogWCg4ORnp4uOI+Xlxd/rFQqhZeXF0aOHIljx45Vma+KPn36tCBPHTt21Pq7du/eDcYYvLy8qoxpxfM2adIErVq1wuzZs/Ho74WFNS4tLcU777yDLl26wM7ODk5OTvD19cXixYtx8+ZNg/zeBITiiICw9vUSAaFp2hzeFZJvPtQJgRtO34R/jAIT9l8RPY81iWX57DfUk8Z8fU6wXXX7phoIX+8FlVIp+t9hCiYgbGQyNhCWlhVhZ0owZPIgnDobAlWxuP3JzbEwkdU2ZyBs1aoVnJ2dUVxczG8vKSmBi4sLWrdurRMIJ02ahPDwcNjb2+PatWuCfRrwSkhIQG5uLtLT0zFz5kzY2NhAoVDw6WQyGZycnJCbmyvw7du3+TQDBgzAtm3bkJaWBrlcjkGDBqF169YoKiri03h5eSEyMhK5ubm4du0aEhMTERoaCisrK0RFRenNV0U/fvyYz5O9vT1cXV3x1VdfCf6uAQMGoHXr1jUCQplMxufn8OHDaN68OZYsWcKnefToEXr16gUXFxe89957SE5ORmZmJo4ePYqpU6di4cKFBvm9CQjFEQFh7eulhgZClUoletwICI1jfZPInLx6H/4xCry2I130PNYkluUTgtRAeDVTsF1VXo7yUT3V+2hCmRrFUp8JCE1UxgZCjuOQmh0HmTwIW+VBSPtsueg3tbkVJrLa5gyEwcHB8PX1xfbt2/ntO3bsgK+vL4KDg7WAsKioCI6OjkhPT8frr7+OyMhIwX4NeKWmpvLbHjx4AMYYNmzYwG+TyWRwdnauNo8VY3nnzh0wxpCYmMhv0/eStWzZMlhbW/MtirryVdmaPM2YMQOTJ0/mt1+/fh0SiQQLFy6sERAePHhQsG3ixIkICgri/71q1SpYW1vrzYuhXl4JCMWRMYDQmGusBQYGYvr06Zg+fTqcnZ3h6uqKxYsX8/fttm3b8NJLL8HBwQHu7u4YM2YM8vKEL65paWkICgqCo6MjHBwc0LNnT1y5cgUcpw2EycnJaNGiheADz4oVK9CiRQs4ODhg0qRJWLBgATp37szv15xj5cqVaNmyJby9vcFxHO7evYtx48bBxcUFUqkUAwcOREZGBn9cRESE4Dwcx2H9+vWCcq8599q1a/Hcc8/B1dUVYWFh/IcljuOQl5eHwYMHQyqVwtvbG9u3bycgNKJ3X8gXwODh9Lu4U/QY/jEKvPKRAuVK8T8QVBVLlVKJ8hEBaui7V6C1v3z6SFpyooaxrMoEhCYqMYCQ4zgcOD8JMnkQPpUPR1HmJdFvbHMqTGS1Kz/Ev//+e5SUKUVxbWBC82ITHR2NPn368Nv79OmD9evX6wTCTz75BF27dgXHcTh8+DC8vb0F16wMXo8fP8a6devAGMOmTc9mS6sLEGZmZoIxJmhp1PeSVVBQACsrK6xZs0ZnvnRZk6fU1FQ4OjryraYrVqxAcHCw1ouhLlcGwsuXL6NNmzYCcPb19dXZ3dbQJiAURzUFQpVKJSjL5dx3NSrz+y8WCNZY23+xwKD1RmBgIBwcHBAeHo709HRs374ddnZ2iImJ4euIzz77DFlZWTh9+jR69Ogh+CBy48YNuLq6Yvjw4Th//jwuX74MmUzGf7ypCITHjx+Hs7MzPvjgA/747du3QyqVQiaT4fLly4iMjISTk5MWEDo4OGDcuHFIS0vjzz106FB06tQJJ0+ehFwux4ABA+Dj48PDXE2B0MnJCVOnTkV6ejoOHz4s+Ps5jkNQUBBefPFFfPXVV0hOToa/vz+kUikBoZGckV8C/xgF+m+5yH8kKVeq0PMjdTm5YwKL01cJhPcLn60zWFamtb98xSz1+EKaZbTaWFZlAkITlVhAWFB0C9uSR2B23CZ8uFZmEl1LzKUwkdWu/BAv576rckC7IV1SVvMxBZoXr/z8fEgkEuTk5ODq1auQSqXIz8/XCYT+/v549913wXEcysrK0Lx5c8THx/P7NeAllUphb28Pa2trMMbg7e2NgoJnXzo14/Xs7e0F7tevn+B6mnpBpVJhyJAh6Nmzp2B/VV/d3d3dMW3aNJ35qujy8nI+TxpI9fPzw9atW6FSqeDj44O4uLgaA6HmGhKJBIwxDB48WNB6IJVKtbrcDRs2jM/Pyy+/bJD7lIBQHNUUCEvKlGZRbwQGBqJTp06CZ+WCBQvQqVMnnenPnTsHxhgePlQPy1i4cCHatGkjKBO66qVDhw7B0dERO3fuFOzv3r07pk+fLtgWEBCgBYTu7u4oLS3l7+GMjAwwxpCUlMSny8/Ph1QqxZ49e8BxNQdCLy8vvt7gOA4jR47E66+/Do5TfwRijOHMmTP8/vT0dDDGCAiN5K+uPYB/jAIh+4XdLYftSId/jAIX84pFyVdNY6m6nq0GwhDdHw6VH639e0mKDwyaR3MxAWEjk1hAyHEcjqVmwT9Ggd6bUnAz8aToN7e5FCay2uYOhBzHYfjw4Vi+fDkiIiIwfPhwcBynBYTp6elo2rSpYJzf9OnTMWbMs9nONOAVFxeHjIwMHDlyBD4+PkhISBBcWyaTwdHREZmZmQLfuCFcBkZTL4SFhcHLywvXr18X7K8KCN3c3BAWFqaVr8rXrJgnDRBu2LABgYGBOH78ONzd3VFWVlZjINy0aRMyMzORnp6OI0eOoH379vzLIsfpBsJbt24hMzMTM2fO1HohbSgTEIqjxgiEEyZMEGw7dOgQmjZtivLycqSmpmLo0KFo3bo1HBwcYGdnB8YYLl68CI5Tt56NHz++ynrpueeeQ5MmTXDgwAGt/S4uLti6datg2+zZs7WAsG/fvoJ7OC4ujs9jxWP9/Pz4FvyaAmHFFk+O4xAeHo7evXtrxaJyvgkIjeO49Lvwj1Fg7uc5gu2hh9Tveiey74uexyqBMC1FDYQzR+s8Vnl4lxoI1y4W/e8wBRMQiqCYmBhMnz6dX19Jox9//BHR0dFYunQpli9fjqysrFqfW0wgVKlUmP7xKfjHKPD+Bx9D9fdXRUuxuVTypmpz7zLKcRyOHDkCb29veHt749NPPwXHaQPhvHnz+NkzNba2toZEIsHdu+ppvHV1zTxx4gSaNWsmGEdUmy6jM2bMgKenJ7Kzs7X26wPC/Px8WFlZYe3atXrzVdkV81RQUACpVIrAwEDMmzcPHKf9YqjLusYQ7tq1C4wxHj6r6jKq64W0oUxAaBzVdR3CunQZvVb4SOcEGtcKHxm0y6g+ICwqKkLz5s0xduxYnDx5Eunp6fjiiy8EZW/48OHVAmFAQABeeOEFDB48mG/l09jFxQWxsbGCbbNmzdI5hrDiPawP1Dp37swDYWRkJHx9fQX733nnHZ1jCCumCQ8PR2BgIDiOw8GDB9G0aVMoK80ASUBoPH+SnAf/GAVWnxR+YFyScA3+MQrsUeSLnseqYqlMOqYGwqXTdB6rOn9Kvf8t7UnfLNEEhCIoPz8f169f1wLCn3/+GaWlpQCAX3/9FXPnzsWTJ09qdW4xgZDjOGTcKMCRU/NxQD4El49WXWk3NptLJW+qNvdJZTiOQ3l5OTw8PODh4cG/MFUEwrKyMri7u2PdunVQKBQCt2/fHu+//z44Tj94VZ6ZsCZAqFKpMHPmTHh4eAgmfqhofUC4dOlSNGnShIew2gIhx3EYNWoUGGP8+KO6AuGePXvAGMOFCxfAcerlNPRNKkNA2Phk6EllDqff5cdGaSbQMGS9oekyWnHbwoUL0alTJyQnJ4MxJph9eNu2bYKyt3z58hp1Gc3Ly0OnTp0wbNgwQdru3btjxowZgmN69uxZLRBW1WV079694DgOH3zwAdzc3ASAPHbs2FoBoaZ76NmzZ/n91GXUuF5zUr3ExMfJtwXb3zt9C/4xCmw8kyt6HqsEws/2qoFv7SKdx/JdSsf1tchhTrWJZVUmIKynCgoKtICwspYvX44ff/yxVucVGwhVKhX2nR8HmTwIx1JfR9mdW6Lf5KZemMhqNwYg5DgO9+/fx/37z7rSVATCgwcPwtbWVucLy6JFi+Dn5weO0w9ecXFxkEgkfJdQfctO5Obm8l/Wp02bBmdnZ5w4cUKwv+ISGVUtO7F69Wo+XVXLTpSUlPB5qgiExcXFyM9/9iW5pkCoWXbi5s2bOHHiBF588UW0b98eZX9PDlBSUoKAgAC4uLjg3XffRUpKCrKzs3H06FF0794d//jHPwzyexMQiiNjzTKacst4s4w6ODhg9uzZSE9Px86dO2Fvb4/Nmzfjzp07sLW1xbx585CVlYW4uDi0b99eUCfk5+ejWbNm/KQyGRkZiI2N1TmpTG5uLjp27Ijhw4fz5UczqcyWLVuQkZGBFStWwMnJia+DdNVtmlgGBwfj+eefx6lTpyCXyzFw4EDBpDKXLl3i644rV65g48aNcHV1rRUQchyHgQMHwtfXF2fOnEFycjJ69uxJk8oY0W99ngP/GAXiKn0c2fX37KMRx66Lkq+axlK5K0bdJTRmrc5jVY9LUT7CXz3pzP17ov8tYpuAsJLu3r2LTZs2Yc6cOQgJCcHVq1e10qSkpGD+/PmYPHkyoqKicO/evVpfpzogLC4uRkRERK3PKzYQchyHawVp2CIPgkwehOT4BaLf5KZemMhqNxYgrOyKQDh48GCtcTMap6SkgDGGlJQUvUCoUqnQoUMHfpIXfQvTM8aQm6v+eqtvv0wm489bcWF6W1tbtG7dGqNGjcKXX34puH5VC9Pv2rWLz1NVrZY1BUKNrays0LJlS7z++uvIysoSpHv06BFWr16Nzp07QyqVQiKRoGPHjpg9e7bW2o4NZQJCcdQY1yEMCwvD1KlT4eTkBFdXVyxcuJBvqdi5cye8vb0hkUjw8ssvIy4uTqtO+Prrr9G/f3/Y2dnB0dERr7zyCl9GKtdLt27dQvv27TFq1Ci+90JkZCSaN28OBwcHTJw4EeHh4ejRowd/jD4g1Cw74ezsDKlUigEDBmj1Pti0aRNatWoFe3t7jB8/Hm+//XatgTA3NxeDBg2CRCJB69atERsbS8tOGNFv7s+Ef4wCX117INh+LKsQ/jEKTIvLEiVfNY2lcvMaNRDu/ljv8eWhwWogvKwwWB7NxQSElXTr1i3Ex8cjJydHJxBeuXIFEydOxIULF6BUKhEXF4cpU6YIWvKioqIQERGh5Z9++olPUxUQ/vbbb1i6dCnu379f6/ybAhByHIcj5+dAJg9CnDwYDy+dF/1GN+XCRFbbXIHQHEyxNF4sCQgNo8YGhKbovn374o033jD7WBIQNowHb7sE/xgFMvJLBNsv3C6Cf4wCw3deFj2PVcWyfM0CNRAe3a/3+PJl09VpEj8X/W8R2wSEVUgXEK5atQr79u0TbFu6dCmOHj1aq3PrA8KnT59i3bp1SE9Pr/L4p0+f4vfff+f9xx9/AFA/NL/55psa+9tvvwUAfPvtt7U6rjoXlxZie+oQyORBOH1iRoOe21RtqFhaigkIDWeKpfFiWVhYqPP+JiCsnwgIG9bFxcVYt24dv77gsmXLwBgTLH9jrrEkIKy/y5QqfqKluyXCNfxuP1QvTt/rYwWUIo+9qyqW5YunqFv/Tn+p93jlplXVtiJaiut6X1okEP7555+YMGGCFiQeOnQI69atq9W5dQHhX3/9hS1btuDYsWPVHn/s2DGEhITwjoqKqtX1jaFTl9ZDJg/CPvkQqK59LXZ2SCaukpIS/PDDD2SyWbukpETsotQo9cMPBIQN6eLiYvTp0weurq6ws7NDly5dsH+//pYUc4olAWH9XRX0CWCxWHvBd1OJZfmM19VAeFH/JGjKA7FqIHxvuegxF9sEhFWoMhD+9NNPCAkJ0erKmZiYiMWLF9f4vO+//z5mzpyJ0NBQzJ07F0VFRQCAwsJCvPnmm4iMjORdVlam8xym3kL4zTffoExZip2pwZDJg7D3wFL+hmusphbC+plaCA1niqXxYkkthIYRAaH4NpdYEhDW32l5xfCPUeC1Hek69w/drl6cPv1OiVHzVZtYlo/vpwbCG1f1Hq86/aV6ptFFoaLHXGzX9b60aCB88OCBIF1iYiKWLFli7OzpVG0rbENXTKfS9mL0roPotTkVN0+fEf2GN8XCRFabgNBwplgaL5Y0htAwIiAU3+YSSwLC+vtE9n34xygQeuiKzv2TDl6Bf4wCJ3PEXZxeXyxVZY/VoDf8Zage6s+jKvuyOt0E3ZO9WZIJCKuQIbuMNpRqunhvQ98AtXH4R4nwj1FgxTs7oaq0yGxjsjlU8qZsAkLDmWJpvFgSEBpGBITi21xiSUBYf+9LK4B/jAKL4nXP1rwo/ir8YxTYl1ZgkrFUFdxRg96onlW+d6qKi56BY3GR6HE3xVhWZ4sEQkA9qcz+/fsF2+oyqYyhZIpAeDE7DwExF7D6UCyuJ+4U/aY3tcJEVpuA0HCmWBovlgSEDaOafuy8d6/q9cPo3jfevW8qru6eENvm8K6w6Wwu/GMUiP5K91rS679SL06/6ay4i9PrBUJNy9+kIdWeo3xCkBoIs8WfNdUUY1mdGy0QPnnyBKWlpSgtLUVISAjOnDmD0tJSflkJzbITCoVCsOyEqTzsTREIOY7DZyfnIVYehJNnQ6D6ew2kxmZzqORN2QSEhjPF0nixJCA0jPTF/eHDh3j48CG/fh/d++Ld+2JbpVLx94PYeanK5vCusOL4dfjHKBArv6Nz//av78A/RoHlX4q7OL1eIEw9owbCOeOrPUf5otBqZyO1BBMQVlJBQYFg9k6Nd+zYwafRLEw/adIkREVF4e7duyLmWChTBcKvs49AJg9CrDwIWcdjRL/xTakwkdUmIDScKZbGiyUBoWFUVdwfPXqEwsJCnS4pKdG7j1w7m0MsHz16JHodUZ3N4V1h5qfZ8I9R4PMM3a2t8VfUi9NPPyzu4vT6Yqk88bkaCCPDqz2H8r3l6plGD8SKHndTjGV1brRAaG4yhzGEHKf+crfv/BjI5EE4cXY8VMrG10poDpW8KbuwkIDQUKZYGi+Wle/jiseR6q663MNUJzecKZaWFcsxezLgH6PA+Zu6W1vluerF6UfuErebpV4gjNuhhrz1EdWeQ7n7Y3XaTatEj7spxrI6ExCaqEwVCDmOgyLnc8jkQdgmH4RrpxrfWEJzqORN2QSEhnPFWObk5IAxhtRU/WszkWsWS10mIDSMCAjFNcXSsmLZf8tF+McocPWe7hbXWw9K4R+jQODHaXq7a4sZS2XsRjXkyd6t9hzKxL9bE5dNFz3uYpqAsJHJlIFQpVJhb/IoyORBSDw9UdRKxJQKE1ltAsLaOyIiAh06dICdnR1cXFzQp08fnDmjvbxLbYHQy8sL69evF/3vq+iQkBAEBweLng8CQnFEQCiuKZaWE8uSMiX8/154vuix7t5cj8ufpSksEW9xer1AuDFKDYT7t1Z7DtVlhRoIp4j/fBHTBISNTKYMhBzHIfnybsjkQdghH4Rb5w+LXgBMoTCR1SYgrL137NiB+Ph4ZGVlIS0tDZMmTYKTkxPu3BFOBCAWEJaWljbY30pAaNkiIBTXFEvLieX1QnXrXx/ZxSrTDd52Cf4xCmTki7c4vb5Ylr89Vw2ECXHVnkN1/x6/9IQy94bo8Te1WFZnAkITkbmMIeQLnkqJPSnDsTV1EGL3RoleAEyhMJHVNlcgVKlUWLNmDdq0aQOpVApfX1/s3buX33/8+HEwxnD06FH4+flBKpWid+/eyMvLw2effYaOHTvC0dERo0ePRnFxMX/cgwcPMHbsWNjZ2eG5555DdHQ0AgMDER6uf5D8/fv3wRhDQkICv+3s2bPo0qULJBIJXnrpJRw4cKBKIAwMDARjTGCO45Cfn4/Ro0fDw8MDUqkUL774Inbu3Kl17PTp0zF79mw0a9YMvXr1AsdxSE9PR0BAACQSCTp16oT4+HgwxnDw4EH+2Bs3bmDUqFFwcXGBq6srhg4dipycHHCcuiW0cp6OHz8uyu9NQCiOCAjFNcXScmKZcush/GMUeH13RpXpJhxQL06fdPWBycWyfP5E9cyh509Vew5lQhwPhOUj/GsEkY3RBISNTKYOhBzH4czFL9FHloRem1ORd0EheiEQuzCR1dYLhMrSKvxYeJ6GSFvLfC9atAgdO3bE0aNHkZWVBZlMBolEghMnToDjngFhjx49kJSUhNTUVPj4+CAwMBD9+/dHamoqTp48iWbNmmH16tX8eSdPngwvLy8kJCRAoVDgtddeg6Ojo14gLC0txdq1a+Hs7Iz8/HxwwJL2TwAAIABJREFUHIeioiK0aNECY8aMQVpaGg4fPoy2bdtWCYQFBQXw9PREZGQkcnNzkZurXmfqxo0bWLt2LVJTU5GVlYUNGzagSZMmgi6qgYGBcHBwwLx585Ceno709HQolUp06NAB/fr1g1wux6lTp9C9e3cBEBYXF6Ndu3aYOHEiLly4gEuXLmHs2LHo0KEDSktL8fDhQ4waNQoDBw7k89SQrY+1MQGhOCIgFNcUS8uJ5ReZ99QziB7JrjLdgi/Ui9MfuCje4vR6gXDqa2ogzEir8nhVfh7KR/g/A8LhL6N8ZABU+Xmi/w6mEsvqTEBoojIHIOQ4DtM2H4d/jALr39sjeiEQuzCR1dYHhH+mT9Prf1/fKDjm6eVwvWl/vxotSPvfjHk609Umz0VFRZBKpfjqq68E2ydNmoQxY8aA454BYcVWu1WrVoExhqysZ1N2T5kyBQMGDADHqVsHbWxsBC2NhYWFsLOz0wLCw4cPw97eHlZWVvDw8MC5c+f4fR9++CFcXV3x+PEzGN60aVODdRkNCgrC3Llz+X8HBgbCz89PkObzzz9H06ZNebDkOE6rhfCTTz5Bhw4dBOOKS0tLIZVK8cUXX4DjqMuopYuAUFxTLC0nljtquMbguqSb8I9RYPO52yYVS5VKhfLRvdRdQNMvVHm8SpEshMG/rUpLEf13MIVY1sQEhCYqcwHCM/Ir6P9JMrZ/sQ730pNFLwhiFiay2uYIhOfOnQNjDPb29gLb2Nige/fu4LhnQFhxXJ9MJoOdnZ3gXMuWLUOXLl3AcRzkcjkYY7h27ZogTZcuXbSAsKioCJmZmTh9+jQmTpwIb29v5OWpv27OmjULvXv3FtQLmnPXFgjLy8sRFRUFX19fuLq6wt7eHk2bNsXIkSP5NIGBgZg8ebLguHfffRdt2rQRbNN0bdUAYVhYGJo0aaIVRysrK3zwwQfgOAJCSxcBobimWFpOLN89fQv+MQpsPJNbZbpYuRocVxwXb3F6XbFUfr6vxl1AqYWw6ljWxASEJiJzG0PIF0KVCkfPj4NMHoRTJ2aLXhDELExktc2xy+iZM2fAGMOJEyeQmZkpsAbmNEB4796zBX5lMhmcnZ0F54qIiEDnzp3BcRxSU1N1AqGfn1+VYwg5joOPjw9WrlwJjuMQHh7eYEC4Zs0aNGvWDNu2bYNcLkdmZiYGDRokgDRdYxzXr1+Ptm3bCrZVBsKpU6eie/fuWjHMzMzk7wsCQssWAaG4plhaTiyXJFyDf4wCexT5VaY7+nfX0pnVdC01ZizrAnjKhDiUjwh4NrHMwe2i/wamEMuamoDQRGUuQMhxHL48vwwyeRAOy1/D48K7ohcGsQoTWW1znFTmwYMHkEgkiI2N1ZumLkCo6TK6b98+fv/9+/dhb29fIyCMiFAvxqvpMlpW9mxa8M2bN1cLhO3atcPatWsF2wYPHoyJEyfy/1YqlWjfvn21QKjpMnr79rNuRQkJCQIgjImJgaurK+7fv683T6GhoRg8eLDovzkBoTgiIBTXFEvLieXUuCz4xyjwZZbuukzjmk4+Y8xY1rULqCo/D+WzxqiBMP6Q6L+BKcSypiYgNFGZExDeL7mDrfJBkMmDkPzlO6IXBrEKE1ltcwRCjuOwePFiNGvWDFu2bMGVK1eQmpqKjRs3YsuWLeC4ugEhx6knlWnTpg2+/PJLpKWlYfjw4XB0dMSsWbPAcequogsXLsTp06dx9epVpKSkYNKkSZBIJEhLUw+kf/jwIZo3b4433ngDFy9exKeffgofH59qgbBfv34YOnQobty4wU9QM2vWLLRq1QpJSUm4dOkSJk+eDCcnp2qBsLy8HB06dMCAAQPw9ddfIykpiZ9U5tAh9YNXM6lMYGAgTp48iezsbJw4cQLh4eG4fl3dHentt99G69atkZ6ejvz8fMG4SGOagFAcERCKa4ql5cRyxK7L8I9R4MLtoirT3bivXp7i1U/EW5xeZwthZSCsYRdQ5V6ZOv3qBaL/BqYQy5qagNBEZU5AyHEcjpydDJk8CPHn3zD7herFjqW521yBUKVS4b333kOHDh1gY2ODFi1aYMCAAUhMTATH1R0IdS070b17dyxcuBAcx6GkpASvvfYaPDw8YGtri5YtW2Lo0KGCSWU4jsPp06fh5+cHW1tb+Pn5Yf/+/dUC4enTp+Hr6wuJRMIvO1FQUIDg4GA4ODjAzc0NS5Yswfjx46sFQo57tuyEra0tOnbsiMOHD/NLcWjS5ObmYvz48WjevDkkEgnatm2L0NBQvtXwzp076NevHxwcHGjZCQtQfYdDcBzVyQ1piqVlxFKlUiHw4zT4xyhw60HVQygeVVjA/sEj3QvYixHL8jnjBTBY02UkVFnp6mPG9oGqTJwPjmKagLCRydyAMPP6ccjkQYiVByHnnHkvVC92LM3d5gqExnJRURGcnZ3x8ccf1/pYU4tlUlISGGO4cuWK6Hlp6FgSEBpGBITimmJpGbEsfFTOQ97jcmW16YNi1YvTXykQZ3F6nUA4aYi66+dne2o1OYxKqUT5hCB1F1NF45jssL6xrIkJCE1E5jqpDF8AVSrsSx4JmTwIx0+FiV4gxChMZLUJCIVOTU3Fzp07ceXKFaSkpCA4OFiwxmBtLHYsDxw4gPj4eOTk5CA+Ph7PP/88AgICRI9xXUxAKI4ICMU1xdIyYnmloAT+MQoExV6qUfqQ/Znwj1EgNjUPeUXGb1XT6jJ6987fs4sGQPWo9pCq3LBCDZNb3xf9txA7ljU1AaGJytyAkOM4fCWPhkwehJ3nR+PhbfHWsxGrMJHVJiAUOjU1Ff/4xz9gb28PV1dX9O3bFxcuVL2mkj6LHcutW7fCx8cHEokEHh4eCAkJqRPYmoIJCMURAaG4plhaRizPXH8A/xgFxu/LrFH68fsy+BbFgBgFDqcbd4JALSA8f0oNhLPG1ul8yqSEeh1vziYgbGQyRyB8XFaMJfs2oOdHcuzddkT0QmHswkRWm4DQcKZYGi+WBISGEQGhuKZYWkYsj1y+C/8YBeZ+nlNt2ryixzwMatzzI4VRWworx1K5+yN1C9+GFXU6n+rhfX4JCtUd822gaIhY1tQEhCYqcwRCjuOw59Ap+McoMPbdY1BWmCLfnGwqsTRXExAazhRL48WSgNAwIiAU1xRLy4jllpQ8+McosDLxRrVpk28+1AJC/xgFUm49FC2W5W/PVQPhp3vrfM7yxVPU5/jioOi/hzFNQNjIZK5A+OBhCfpsSsa4bSegOF6zGaFMzaYSS3M1AaHhTLE0XiwJCA0jAkJxTbG0jFi+c+om/GMUiDlffetYXtFjBJhQC6FKpUL5pMHq1r3LijqfU7lvy9/LT8wX/fcwpgkIG5nMFQg5jkPC8cU4IB+C+NOTRc+LMQsTWW0CQsOZYmm8WBIQGkYEhOKaYmkZsZz1abZ6khh5zWbnPJx+12TGEKoKKk4o86jO51RlXVafZ3QglLk3Rf9NxIhlbY4jIDQRmfssoxWdcmEzZPIg7JEPQVHBHdHzY6zCRFabgNBwplgaL5YEhIYRAaG4plg2/ljWFe7WnFS3Ki6KvypqLFXnEtUgN/uNep1TGX/o2TqGI/xrvI6huZuAsJHJnIGwrKwY2+WDIZMH4cyJaNHzY6zCRFabgNBwplgaL5YEhIYRAaG4plg27ljWp/unZizhoNhLUKpUosVSuStGPfZvY1Sdz6fKz0P5CP9nQPj34va1Wc/QXE1A2MhkzkDIcRw+PzsRMnkQPj8zQfS8GKswkdUmIDScKZbGiyUBoWFEQCiuKZaNO5b1mSCmTKlCX9lF+McocDGvWLRYlkfNUQPh5/vqfD6VIlkIg39blZYi+m9kzFjW5jgCQhOVuQPhxYux6jUJ5YNx77Z59d02tViamwkIDeeKsczJyQFjDKmpqaLnyxxNQCiOCAjFNcWycceyvhPELIq/Bv8YBT5KNu5SDZpYchyH8glBanjLSKvz+XS2EA5/mVoIqzABoYnK3IFQqXyMnalDIJMH4eSJ1aLnx5xjaW4mIKy9IyIi0KFDB9jZ2cHFxQV9+vTBmTNntNLVFggDAwPBGANjDFZWVnBzc8OIESNw9ar2GJF9+/ahd+/ecHFxgVQqRfv27TFhwoRGC5wEhOKIgFBcUywbfyxXHL8ugMHaTBCjWb9w4oErosSSK8h71r2ztO4TynAcB2VCHMpHBgjGEarybon++xgrlgSEjUTmDoQcxyH+bChk8iAcORsiel7MPZbmZALC2nvHjh2Ij49HVlYW0tLSMGnSJDg5OeHOHeGkTHUBwtDQUOTm5uLWrVs4ffo0unfvjp49ewrSzZ8/H02aNMHs2bNx8uRJZGdn48SJE4iKisLAgQNFj48hTEAojggIxTXFsvHHcl2SenKYpQnXar10REFxGQ+TBcXGW0taE0vV2b8nlJkzvkHOq8rPgyot5dmahJvNq4GiPrEkIGwkagxAmJlxBBsS52D0rv24c8t8mulNMZbmZHMFQpVKhTVr1qBNmzaQSqXw9fXF3r3PFsU9fvw4GGM4evQo/Pz8IJVK0bt3b+Tl5eGzzz5Dx44d4ejoiNGjR6O4+Nn4iwcPHmDs2LGws7PDc889h+joaAQGBiI8PFxvXu7fvw/GGBISEvhtZ8+eRZcuXSCRSPDSSy/hwIEDNQLCyteJjY2FnZ0d/+/Tp0+DMYb33ntPb1zE/m0MYQJCcURAKK4plo0/llPjsuAfo8DRzHt1On7CgSvwj1Hg08vGW3pCE0vlzg//nlDm7QY9v+rS12rQHPUKVHeM2x3W2CYgbGRqDEDIcRxCN3wB/xgFdu1JED0v5h5Lc7E+ICxTluh1ubJUcExVactqmLa2+V60aBE6duyIo0ePIisrCzKZDBKJBCdOnADHPQPCHj16ICkpCampqfDx8UFgYCD69++P1NRUnDx5Es2aNcPq1c++Qk6ePBleXl5ISEiAQqHAa6+9BkdHx//P3pdFRZGmadsX1Uv1nDo90xfTczEzPTNXM93/RR/T6q7q6lqydrWq1CqSRUREBUVF3CitchcFAkxXXEABRWRXIFkzCRbZ930zZcuIwH3fQX3+i4CEJJMtMzIjIjPec56jGRl83xdPvrE88b3f+04oCHt7e7Fjxw688847aGlpAcMw0Gq1+P3vf48FCxagoKAAMTEx+M///M8ZC8LW1lZ8/vnneP/99/Xbli9fjt/+9rfo77fd22AhQBKE/JgkCPmFxKV9c0nRo4lhKtrMC7kkshshI0j4JVTbnEtqy0pWEMZFct6HbuMyVhTu3QSazLHb9YSSILQzsxdBeOokKwhd90mC0FEwkSBUquUTIr58g8HfHNF8NeG+saW+BvuGk9+Z3G8mY9Zqtfj1r3+N9PR0g+2urq5YsGABGGZUEI6dtdu2bRtmzZqF8vJy/TZ3d3d8/PHHYBh2dvCtt94ymGns6OjAb37zGyNBGBMTg7fffhu/+MUv8Ic//AFZWVn670JCQvC73/0OfX2j4T9BQUHTEoRvvfUW3n77bfzmN7/BrFmz8N///d+orKzU7/PJJ5/gf//3fw3+bufOnXj77bf1mEgciRmSIOTHJEHILyQu7ZvL2s5eyAgS74UVQEeZF91R0HQNMoLEB8oCZNZenXHYqblcvnnzBrofPmIFYXYa533QBXmGSWbstDahJAhFbvZUmH4sWmpqcTzhZ6TkOqG6wvyMUWI4mSSwEKMgzMrKwqxZswxE0Ntvv4233noLf/nLX8Awo4Jw7Lo+pVJpEH7JMAw2bNiAP//5z2AYBmq1GrNmzUJ1teGb1j//+c9GglCr1aK0tBQZGRlwcXHBv//7v6OpiX2DuWLFCrz//vsG14WRtqcShAqFAqWlpSgtLUVaWhrkcjn+67/+C11dXWAYVhD+3//9n8Hftbe3o7S0FMeOHcOsWbPQ3m5e6JGQIQlCfkwShPxC4tK+uUyu6LA4KYyOovGBssCs4vaWcPkwOcaqYo1uaTIuRWGHtQklQWhnZi+CkKYoROaxD/cXUzbwPh4xcykWiDFkVKVSYdasWUhJSdGLpxGMiLkRQThWHCmVSrzzzjsGbW3cuFEvsPLy8kwKwj/96U+TriFkGAZ//OMfsXXrVjAMG9ZpriAc309DQwNmzZqF0NBQMAwDLy8v/Pa3vzWYfRyBqWO2F0iCcOZGEAQ8PT31Ly7NMUkQ8guJS/vmciTcc2tqrdltNGn7jOoYzqR0hVlobbZ6IXlHqU0oCUI7M3sRhAzD4JLKC0q1HFHZC3gfi9i5FAPEmFSmq6sLv/rVr3DkyJEJ9zFHEI6EjEZEjBbY7ezsxNtvvz0tQbhx40YwzGjI6Nh1fsHBwWYJwubmZsyaNQv79u0DwzBIT0/HrFmzsGfPnmkds71AEoQzt5aWFtTU1EiCUMSQuLRvLlfHVUFGkIgoaDW7DUuK25sLuiDX6mLNZG3CedIM4QgkQShQsydBWF9+AUq1HEfUn6K5VfjZnYTMpRggRkHIMAz8/Pzwz//8zzh06BDKysqQl5eH/fv349ChQ2AY8wQhw7BJZf7jP/4DSUlJKCgowNy5c/FP//RPWLFiBRiGDRVdu3YtMjIyUFVVhdzcXLi6uuJXv/oVCgrYMOurV6/iX/7lX7Bo0SIUFhbi/Pnz+OMf/zijshMNDQ1Qq9WYN28efv3rX6O4uFi/n4+Pj77sxOXLl1FVVYWMjAwsWrQIv/jFL9DZ2cn778M1JEFonrW2tkqCUMSQuLRvLr86XgwZQULToDW7DUuL25sDuq7aJuGcRrUJ17rx/ptxDUkQ2pnZkyCkKR1OqoeL1KcF8z4eMXMpBohVENI0jb179+J//ud/8NZbb+H3v/89Pv74Y6SmpoJhzBeEpspO/OUvf8HatWvBMAy6u7vx9ddf4w9/+AN++ctf4l//9V/xxRdfGCSVYRi2PMSf/vQn/PKXv8Sf/vQnREZGzqgw/axZs/C73/0O7733HpKSkoz2PXnyJN577z288847eOutt/Bv//ZvWLhwIVQqFe+/jTXgaIKwra0NQUFB8Pb2hpOTE6qqqoz2yc3Nha+vL9zc3BAQEID29najfSRBKG5IXNovlx09Or2A0/bpLGorprjNQBRaew0hnZZgJAatlfCFbmkCFRsB3Vx2tpCKj7KrrKOSILQzsydByDAM4rO+Z4vUp3nyPhaxcyl0iFUQ2gparRbvvPMOwsLCZvy3EpfcwdEEYX19PeLj41FZWWlSEJaVlcHFxQX5+fmgKArR0dFwd3fH7du3DfaTBKG4IXFpv1yOhHrOP3GF0/bmECSarZxplNq+lhWCoTtBF+TaRJxRx4LsMuuoJAjtzOxNEOao1kOpliMiZ77gC10LnUuhQxKEhsjLy0N4eDjKysqQm5uLL7/80qDG4Ezg6FxyCUcThGPNlCDctm0bIiIiDLb5+/sjLi7OYNt0BeHg4CCePn2qx7NnzwCw97aBgYEZ4fr16wCA69evz/hvJUhcOgqXJ8kWyAgS6+KrOWvTY7jI/aG8ZquNm+nphu6796CbOxsDlaU244turDMZpsq0Wu9YbQFz/VIwglCn00Gj0SAxMRGxsbFIS0tDbW0tnj59ylUXojJ7E4SdNWnsOsLcr1BVL+x1hELnUuiQBKEh8vLy8P/+3//D22+/jd/97nf4xz/+gfz8fLPacnQuuYQkCEcF4dDQEJydnY1EYlRUFHbu3GmwbbqCMDExEU5OTnoEBARwM3jJJJPMpO3KaoOMIBFZ1sNZm1mtA5ARJL45VYbXb95w1u5Ye1rM1gccWP4d3lipD1P2vLHGZCKb5001NhuDPZlFgvDx48e4fPky1qxZA4VCYRIuLi4ICQlBS0sLV2MWhdmbIGRoGt7BhyEj8nEmNpP/8YwB3dEKWqMCFXUcFLEd1w/649G5VXgQ5YkHUUtw/6wn7p1dibtRa3AnZiMGYkNBq5JBV5SAvmb+wm17hSQIrQeJS9tx6UiC8O7du3ByckJnZ6fBfqmpqfDz89N/DgwMhJeXFxYvXgwfHx9otdoJ+5BmCIUJiUv75fKHiFLICBKXq7o4a7NXR+Ojw4WQESQya69aZdwj4aL3Y47blEuTpS7mzgZVUsBmNxXpTKHoZgizsrKwbNkyeHl5ITIyEuXl5bh+/TqePXuGoaEh3L9/H11dXVCpVNi5cycUCgUCAwP1B2rvZneCkGGgDIlkwxmIRF7HQff3YSD7Au6d9cHj8wq8yHTFzbUf6S8GtzZ8gqFijwlx56fP9PsOLPs7Hp93xr2olbh1cQ8GNMmgtdZLzSwGSILQepC4tB2XjigIu7q6DPZLTU3F+vXrOe1bWkPILyQu7ZPLPh2Nv4WxCWAar3K73m9Xev1wKGoV5+Omu6/pw0VfXuuwOZdGWUftYE2huX7JmyBcu3YtioqKMDQ0NK39r1+/jvDwcKSmpprbpajMHgVhfnwqZEQ+FMpE6Cge1hGWqnEnaj2exCmMRN7dgz9At2MdqPAQ3Igj8Dx/Bx7k7sT9vH14kLMdD7O24FGmP55krsaNQ2uhW+MKnbMct7fIjdp6nu6Ch9HuuB27FUz+ZdC9Pbxzb0tIgtB6kLi0HZeOJAhnEjJqqUmCkF9IXNonlyUt3ZARJD4+XMh5noayVrbtdwkSyRUdnJafoEayiy6djzdv3vDCJd3SxNZBPH3QJqUvrA3RCcJXr16Z9XevX782t0tBW05ODvz9/fVrM+xREOqaK5Gm/g6n1J9BXaq2Wb90RxuuK/3xMm+xgXB7kumDu8XHcLOjCAPUqGibCZfXO6pwr/AIHmVuwPNsTwwWLTHo4+a6j6Bb8B50P/mCTjwDur2Z99/B2ujo6ABFUfrPkojhDhKXtuGSoiiHEoQAm1QmMjLSYJuppDIzNUvvbQwjjvubWCBxaZ9cnr/Crh9cdq7CKu3PO1GsL0Exh8MyFLoAb312Ub65pMkckzOFVHqiqMpSiE4QSja52aMgZGgasXlfQ6mWI+bSVuuf3JQO1IVI6H74ELr5s/EsRYHn2ctwjwzDQK+xMKNpGj39FCiaMftN1QDVg1t1l3E/eweeqrxAeX6pv6jcO/AVXmS74WGMO24mBoFuFXZyHXPR39+P9vZ2vSiURAx3kLi0PpcURaG9vR39/f0T/p0Y7fnz5+jt7UVvby+cnJygUqnQ29urLysxUnaCJEmDshNcH68kCPmFxKV9crlnOKxzT0Y9521bq1A9dSHCIDzzce5lfgVhS5PJNYViCyG1G0H44sULPH782AiOZnYpCBkGGSo3KNVynFa5WPfEbm3AveNu0M0fPrlXOWHgigoMzYqUFm0PIgovI1B1EDvSfsLPl1dhV4Yz9md9g32Z38EvcS3+Fkbiw0OF+D6iFD9fJhCUdRSnyCSoG+rRP82QV5qmQVeWgjp9EE9T3A1mD19ku+FBtAdupCpBX+3k/bfhEv39/ejo6EBHRwe6u7v1/5dgGSQubcPlRGKQYcQrCFtbWw2yfo7g+PHj+n1GCtO7uroiICAAbW1tnI9DEoT8QuLSPrlcHFUGGUHiRP7MyxlNhZF6hOORW29+vgST4mu45AOfPE66pnB4jFRJoaBnDEUtCF+8eIHo6Gh4eXlNmG3U0cxeBWFV5g4o1XIczfsc3X0TP3RZArqlAY/Pu7BrA4O+AXUhAjSlQ31XL45rmrEkuhzvH8xEWN5nUKrlJrEldfmYC18+iJwvDb7fo/oB29M2gcg+iYyasumtiaR1uNmQiYeZW/BCbRha+ixZAWrvRvYiMybcUuwQi1+KARKXwuBSrIJQKCYJQvH6vgRhchld3GqVcM4RmJohnENYNkM4UXgmXZDH+++qX1M4sr7RxEyhkGcMRS0Iw8PD4eLigtDQUFy8eBFJSUlGcDSzV0FItZXhmPpTKNVyXCpI4f5Ebq7Ti8FBcglu1SRD29uPkOyTWH0xwOCC9vPl9QjN3YgTZDDiSs9C3aBCbVcpajpK0H3rGpq1/ahq70F6VSuOaQJB5PghKNvZSDwGpC7Dx4cLsTmpBknlHdD26qYeK63DrdpLeKTagJf5S/Dw2DejF5ul83AzLhB0Ryvvv5elEItfigESl8LgUhKEMzNpDaGwIHFpX1xaK5xzPGKK2/Bu6Ggf8iNF6O43/+U11dI4YVF4vn/XEUwZQirQGUNRC0IvLy+kp6dz0ZTdmL0KQoZhkJg3H0q1HBGX/Lk9eZsMxeDNmku4UJKDXenuevG2+mI8IgpaJ71YTsVld38XsuvScYIMQVD2ciw7T+gvkh8o07EvcxH2ZAQivboU1HSyfem6MVCSBSr4J+gWfYCBZX9nQ0pVrrgXvQoDxRmcZw2zFcTkl0KHxKUwuJQEoWUmCULx+r4E4XFpjXDOidCk7UN6dSe+OFoEGUFia0otcuqumiU+6epyI2HF9xpCUzAMIZ1AHApsxlD0grC5uZmLpuzG7FkQ5mYsgVItx4nM7zlrk25txONzznox2FOZhB1pAXohSOR+hZgr4aDoqcNUZ8qljqKhadAiUNWAlRcOG8we7khfirDc02i8Or0C9nRPD26lh+JF3mhI6WCBOx7GLMb1tBOg+7l962dtiMkvhQ6JS2FwKQlCy0wShOL1fQnC47Kus8dIDFpjhnAsVDVdRuGjMw1TpaLDWRG1wVNfBJ5vLifCSAgpVVI4rRlDuqWJBU+zhqIWhKdPn0ZsbCwXTdmN2bMgbMgKxdGsRfg5cR16ddysl7sX7T0qBquSsTPdRy/KDuYGoL17+gutLeGyT9eD1Kp4BGevxsG8T/VjCMqehz0Z+0A2TjOzKNWHOyVn8FS13LBURrwzmGhCNEloxOSXQofEpTC4lAShZSYJQvH6vgThcZlQ1mEkBrleQzgeTdo+i0WobssKtqTDuROC4XI6mDLpzNzZ0C37xmjW0JYCUdSC8Pnz5wgMDERMTAyamprQ3d1tBEczexaEVEc7PtqvgowgUVLCgHY9AAAgAElEQVRvutbXTEBrsnBn22d4mbcY9JVo/Jy2Dkq1HKG5XyC7VmWzk2k82rubEVl4BEHZi4bH8xk+UKbBI7ocmbVXpx0GeqOlAI9U6zFYsAQvMl2hmz8buoUfgDq8D3SbcOLtrcmlBIlLoXDJlSC8efMmcnJycPjwYezcuRNbt27FgQMHcO7cOdTV1WFoaIiTfoRmkiAUr+9LEB6XXucqICNI7E6vQ269eeGbM4WlYap0Xy90373HJpGprhAMl9PFjGYMJwgrtaZAFLUgfPjwIfbs2TNhhlEpy6j1HICXk4mm4b4jGjKCRFxGsWVt9fdB5/UddHNno+vQbjifKcH8k5EIzp6HnPosm55ME4GidbhcnYRd6QT+OmZR9oakICSVq6ctDAeuNeFm2hHovL8fvrDMxpO4H3D33Fow9WW8/6624NKRIXEpDC4tvWm2trYiMDAQzs7OUCgUWLVqFQICArB9+3b4+/vD3d0dCoUCK1asQEJCAp4+fWpRf3yblFRGWJC4tB8uazp69SGbjVdtt5zE0qyjtCaLfYZx+1z//MM3l+bCYMZw/hzo9mziRCDO9PPYbeaG3wpCEAYHB8PDwwMxMTHQaDQoLCw0gqOZPQtChmGwf58SvqejER5r2QJc6uxR6ObORofr11BEXIGMIPHpkSJcaTE/ZMKaXDZr+7A3ox5fh8frw0l3pvsguUIzbWFI0zTo3HTcChldZ/gy1w33YlYLThiKzS+FDIlLYXBpyU2TIAi4uLggODgY5eXlePjwodE+r1+/Rk9PDxISErBmzRqsWLECTU1NZvcpNJMEoXh9X4KwuNyvaoCMIOFzodLmfY/POvoPZQHark2vlBh1JJANFw0MEAyXlmBkxnBk7eC0Zw0nEooBKw0Eo27flkk/UwnRrDAds82cBD2CEIRLliyBRqPhoim7MXsXhHnpi6FUy3EqY7H5J2FzLR7HfI+baz9C4PlD+PDQZXx6pAjlbT0Wjc0WXNZ01ILI2WKwznBn2nrk1JVPvx26H3eKTuB51tJRYZi3GPfOrQZTX8r7b2wrLh0FEpfC4NKSm+axY8dw48aNae//+vVru3spKglC8fq+BOFw2aejID/CZvu8VMVPToEmbR8ya7qw4FQJZASJZefKp5V1VOfzAysIL10UBJdcw2DWcJ7MMoFoLswo4SEIQejt7W1Xb0C5MHsXhEWXV0OpliM8+1uz27h3lk0kcyfPHSE5X4HI/QKplQUWj82WXNZ2VoPI2YyDeawoDMv7DLvSf0J52wxqENIU7lw5YyQMmbBNoBvreP2dxeaXQobEpTC4lJLKWGaSIBSv70sQDpeRBWwx+s+OFkFH8VuWqqKtx2A5zGRZR+mO1tEi9J3tguDSGhg7a8iXQKQL8mY0ZkEIwgsXLuDEiRNcNGU3Zu+CsDVzL5RqOQ7nfYruvumFGRgcb34yXmrcMVTsgeMqbyjVcuzPWgKatjxrKR9clrRcQVD26uHj+AYfKLMRqGqAtm8aRe5HQFO4feUsnmUuxZPY4XWG37wLKvgn0E0NvPzOYvNLIUPiUhhcSoLQMpMEoXh9X4IwuIwpbjNYv2ftrKJTwdSawomyjlKpceyzySqFILi0FawuEMe3IdYZwpKSEqxZswYEQSA/Px+VlZVGEKIRBAFPT0/9YvkRe/bsGbZu3YrNmzdj48aNZoXD2rsgZEqScGQ4VFLTUDHjv39wfgWGij3QlbkMYcNhl3kNuZyMjU8uc+uzsTEpRn9R/fRoIc4UZU6vwP0IaApMcSZ0m4fTOi96F88uOePOuY2gO2Yw8yhyLu0NEpfC4NIagnBoaAhJSUnYtm0btm7disTERAwODnLeDx8mJZURFiQuxc3lTMSXrTCTrKPUvs3sc8mxA7xzyScmFIjz57BrBGfwWb+GcMw20a4hnCy7qJCzjLa0tKCmpsZIEL5+/RovXrwAALx48QJr1qzBo0ePZtS23QtCbSvOa76EUi1HTP7MEsvQPd14rnLFYNEShKncoFTLEZztz9nYhMBlenUXvj1Zgu8jjrAF7tPWorCpdsbt0EUa3Ds5Gkr6/LILbsXtBH1teumh7YFLe4HEpTC4tIYgPHv2LDZv3ozs7GykpqZi5cqViIiI4LwfIZgkCMXr+xL459LSkg/WgCmRKiNIXCxtN1hTSLU0Quf0ERvOmJ/NO5dCwliBaM7nsdtEnWW0ra1tSgjVWltbjQThWHv8+DFWr15tMpvcZGb3gpBhkJb3DZRqOU6kb53R393IOYuhYg905jsN1/f7HDWdNZyNSyhc9uloHNUc1M+AhuR8hZDsYzMOsaWpPtwllXipHs1K+jTRGTeSD4LWzSAkVcRc2gMkLoXBpSU3zYleDHp7e+P+/fv6zxUVFfDy8jK7HyGbJAjF6/sS+OeytrPH4qLw1sD4rKPjS1JEn0kyCGuk4s7wzqW9wlwueROEM50xm6m1tbUhKCgI3t7ecHJyQlVVldE+ubm58PX1hZubGwICAtDe3j7jfiYShE+ePMHmzZuxePFi5OTkzLhdRxCE6kxW0B1L95zR390/vxovipYgPHculGo5DqkDBXEyWQtlrVdwIGupPhvprowl5iXP6dfifu5evMwfFYaPzrmCzsuw2tiFxqWYIXEpDC4tuWl6eXmhqKjIaPvq1asNso8WFRXB29vb7H6EbJIgFK/vS+Cfy7jSdiMxyPcawhE0afuQW38VWTVdxqI1RIP6hXKDNW5jZ7ckv+QOohOELi4u2Lt3L/Ly8gzejHJl9fX1iI+PR2VlpUlBWFZWBhcXF+Tn54OiKERHR8Pd3R23b9/W7xMQEICNGzca4e7du/p9ppohvH//PrZv3z7jY3QEQag5tw7Bl72wIXr6go7W6XBzy9fovLAMe5PXYX/WQlzt7RLEyWRNUDSFc1dOg8j5anhW9DPszTg+s6QzI8fX3YqHmVswWLgEDw7PZy/OAStB18x8LacYuRQrJC6FwaUlN83y8nJ4e3tjz549uH79un57YmIifH19cf78eURGRsLDwwNxcXFm9yNkkwSheH1fAv9cukeVQ0aQ2K+qR2791CUe+MBEYa3Zbk7jsmCO5n2Q/JI7iE4Q1tTU4Pjx4/D09ISzszN27NiBrKwsA0HGlZkShNu2bTNao+Hv7z/jm/BUghAAIiIiUF5ebvK7wcFBPH36VI9nz54BYG+aAwMD08bIw8X169dn9Hd8ovXsKTacICQfvTp6Wn9DkznQzZ2NJTuiISNIRBQ0cD4uIXPZeq0ZITnroFTLMfdEFOaGX0FOndastm60l4I5tgu6b/4K3dzZYDz+jjvnNoDp6nAILsUGiUthcGnpTfPp06c4ffo03N3dkZqailevXuHNmzcgSRIHDx5EWFgYNBoNXr9+bVE/QjVJEIrzYVEC/1yWtXZDRpD4ayiJ1mkWgecDJtcUhuRjz9odyHRTsDOF0gyh1WAul7yvIXz16hUaGhpw6tQpLF++HAqFAlu3bkVaWprBG1RLbLwgHBoagrOzs5FIjIqKws6dO2fUtilBeP/+fTx9+hQAe/PfsGED+vr6TP59YmIinJyc9AgICJhR/2K2p+WF+GR/BmQEiY4b0wshvnciBO3ffoB3iXzICBIDD55ZeZTCszdv3qCwqxrfni7TX2xD1Pl4+vKlWe0NMjrc3rsRj6MWYqjYA89SXfAk6zjeDA1xPHLJJJNsxDo7O/VRJ11dXXwPx2bm6IKwSds3reLdQntYlMA/lzsu10FGkFifUM37sU+FSdcUhmgQfSaJVy7tGaIVhGPt9evXaGlpQWRkJLy9vaFQKLB582YkJydDp9OZ3e54QXj37l04OTmhs7PTYL/U1FT4+flNu93AwEB4eXlh8eLF8PHxgVarBQB0d3dj8+bN2Lx5MzZt2oS8vLwJ23DkGUKmtgL7w0IQGBuMc8VlU+/PMLi16zukbF2MeSfOYtHpQsHNHtgS3f00dqbV45PDyQjJ+RK7MpaBbKwxrz2Gwd2CIwaJZx6dd8VAwSWH4FIMkLgUBpdcZhl99eoVUlJS4O7ujsjISP31355MKjsxipjiNv3MyWTFu4X4sCiBXy61fTp8oCyEjCCRU8dfRtGZYGRNIanSYE5I/qSJcCS/5A7mcikoQTjW3rx5g46ODkRHR2P16tUWlZ6YSBCOfyubmpqK9evXm90Pl+YIawjpvmvI1MxnC9Rn7pn6GEuyMVTsAU3+N8NZN3cI6mTiC4nlqSBy2bWFwTlf44g6ZmZ1C8eitxMPsrZisJAVhoP57rgX5QO6uc4huBQyJC6FwaWlN82bN29Co9EgKysL3d3d+nHs2bMH3t7eqKiosKh9oZujCkKh1I+zBy6FAltyeUTdBBlBYl74FdDm3t95Qtb2HSZnCmOvtOlnzJu1/ZJfcgTRCcKqqqoZrZEYmX0zx6wZMsqVWfoWVawX+cKcRWz2zPQVU+57Jy4Ag0VLcDKPFT+JFcmCOpn4RG1njUEm0h3pm9B41fxkOzfaruCpaqV+tvDO7q9BnT0Kun9m6xbEyKVQIXEpDC4tuWnW1NTAzc0Nfn5+CAgIgIuLC5KTk/XfFxYWwsvLC8HBwbhz547Z/QjZHFUQCqV+nD1wKRTYisvo4laDEg5CySo6HdDV5ahfKMecEI2R7793kDSYMU9rYiS/5ACiE4QKhQIrVqxAbGwsaJq2aBBT2URJZSIjIw22mZNUxlrmKIKwJm0xK2KyFk6575OLrrhV6AqlWo6w3M+g7bXOjVSsXOqofpwgQ3FwuG7hXpUTLlcVmt8mTeNOaRSeJC+B7pvh+kErF4EuVts9l0KExKUwuLTkpunv74+LFy/qP1dVVcHFxcUgVPTRo0c4duwYPDw8zO5HyOaoglCaIbQ/2IJLofiNOaBbmqDbtBy6ubMRtTdMv6bw3VASXx8vNllPsVkr3GQ5YoHoBKFGo8HPP/8MhUIBhUKB7du3o6CgAM+fP7doQCP2/Plz9Pb2ore3F05OTlCpVOjt7dVnMR0pO0GSpEHZCS7Xh1hijiIIryWvYgVh3meTF1yvKcZgwRLUkguhVMsRmLlScCeTUKBuyENQ9gIo1XKsuPAzlLlN5oeQMgxomgaVfB46xSfQzZ+Np3Hf427MOtBd7XbPpZAgcSkMLi25R3h4eKCpqUn/+cmTJ1AoFPrxjLWWlhaz+xGyOaogZBh2DeHYB2BpDaG4McJls7bfaomCsgUyszxTUAnRhoXow0P0awqbtH1QmahVKCNIJJV38Jp0yR4gOkE4YgzDIDY2Vp9ExsPDAydPnrQ461pra6tB9s4RHD9+XL/PSGF6V1dXBAQEoK2tzdLDMdscNWT01uXdODIc5phXXz7hfrfjd2Co2ANxeez6wRPkEcGdTEJCR3cb9qn2YA6hhowgseJ8BTq6LXvzRnd14Haknz6M9FmKC66nn5x0PYM9cCkUSFwKg0tLbpoHDhzAjh07oNVq0d/fj/DwcHh7e9ttiQlT5siCkGEY/P0g++DrcqZMdL4vwZjLtCbGqomCwjXNJmfShCyY6JYmAzFoqhC9ydIUBMl70iV7gGgF4Yi9fv0atbW1CA0NhZubGxQKBfz9/ZGenm6VwvVCN0cRhDfzoxCr+RJKtRxniuIn3O/RRU88L1oCZd5nUKrlKG0tFdzJJETElrTj/YMFmEOosTF5C8jGSsvapCncLQrXZyMdLFyC+1GeoBtMt2tPXPINiUthcGnJTfPOnTvYvXu3PjLG39/foUpOAI4tCLv7Kf2D78LTJaLzfQmGaNb2G5VW4FKsdfbo8OmRIgOh9G6o8IXSSL3o8RhbiJ5hJi9NIQbhK1SIXhCOtUePHiEzMxObN2+GQqGAq6urNboRtDmKIBxoLEFWPptplMgOmXC/pymL0V2ogFItR1D2Aqtm2RIrlxOhvK0Hfgk7h7mbh/NXLlnOUU8rHqnW62cLn2e44mZ8IGidzq655BMSl8LgkotlBS9fvtTXqrV3k8pOjKK+q1f/sPv50SLR+b4EQ+TWa00KmcTydoOwR3NrT24frju48HQJ6jp79eGWfB/3VKBbmowF4bgZwhGMhJGOD6eeiEsJU8OuBOHQ0BAqKiqwf/9+/ZtURzNHEYS0Toe0k+7YlrAZG2IvTLzf8s9xdN9OLDwRhQNZFwV5MgkZDVcbsT/Lg03Ik/cpiJyD0FGUxe3eqkrC8xxWFD6N+x46XxfQ1RV2zSVfkLgUBpdCWWcuVnNkQVjQdE3/oPu3sAJeygfYC5dCQLO232TY49jwx4DkmhmHQTZp+3CmcDSzqNDXC44H3dMN3YL3DcQglRA9JZeTzRZKIaTTh10Iwt7eXkRFRcHLywsKhQJeXl6Ijo5Gf38/l90I0hx1DSHDMMjxWwsZQWLuYY3J7+n+PvTNleHTwDSbFGUVM5eTobe/B0TOVn1pij0ZG9DV021527puPMj+GcxyOXvx//avoCIPgdbp7JZLPiBxKQwuLblppqWl4eXLlzP6m+7ubtTV1Zndp9DMkQXhpcpOg4fc7n7LX8o5KpdCwMDAALamt0woYswJg4wpbjMQmW5ny3g/zpmCOneSfRZY8jWo/GyTM4OmuExrYqQQUg4gWkH4+PFj5OTkICAgAAqFAs7OzggMDERZWRmGhoYsbV605kiCsHXHxklvkHRzIwoV30JGkPhAWYA+nXXfqoqZy6lA0zQiC08jbHgt5h7VElR3NHLTdmszdD/6QDd3Nu6HzcWjC4sxUE3aLZe2hj37pZi4tLTsxMqVKxEfHz9puaWXL1+ioqICBw4cgKurK0pKSszuU2jmyIIwelxYHB8Pt/bCpRAwMDCAyLIeyAgSqy9UIr6sfVqicPyM30hIaX6DVrQlJkZA9/dD5/4Vm1n04tkZcQmwGVtz669OyGVCmRRCOl0uRSMIGxsbcejQIX0CGV9fXyQlJenLQji6OZIgZE7uwf7zgfg5NhA1HZ3Gx1acgYxYN4SkLcPmZOuGi4qey2kivToLwTnzEJLzFRaeSkJR8zVO2qVpGkxqJAYL2KQzL3Lc8CQtBAwH4amODkfwSzFwaclN89WrV8jKysKqVaugUCiwcuVKHDhwAOHh4YiIiIBSqcSPP/4IV1dXKBQKBAcHQ6fTWXg3EZY5siA8lNtk8HBb1spBhIaDcikEDAwM4GgRu45wb0b9hJkzxyNc06wXNSfzW6b8GzGFjFIpF9jZQZdPQff1zojLsX45FZdSCOn0uZwueC1M7+bmhkOHDqGpqQlv3ryxaCD2Zo4kCG+nB+OE+rPh0hNXjJ00OxwZ+fOgVMtxLF8p2JNJbChrrYLXufOQESTeP1iA5IoOztq+0aTBM9VSfdKZR7HuYGqLeT9mMcNR/FLoXHKxhvD169eorKzEyZMnsWHDBnh6esLNzQ0+Pj7Yu3cvUlJScOPGDYv7EaI5siDcdSLN4KE2J8b6LzjtlUshYGBgAISGradHZLORNmMzZ74byq4hnCwM0tIQUyGBam6AbsnX7OzgmZmVBjPll9PJQlrYfE2aMZwGl9MBb4IwKysLjx8/tqhzezJHXkN4oygBUZovoFTLkVqZbfT97Uv7kKD5Ckq1HOeuTJx4hu+TSYzQ9umw6kIlZASJr8NjcFQzeV3BmYCm+nA/excGC4dnC7PdcCtpP2hpttAsOJJfCplLKamMZeaogpBuaYLfZqXBA23SUs9pra8Siu9LMOZydzYbBnxE3azfPrYA+9jP+Q2ms5Kamv0aETximQUzKkR//tSMuTTllyPcJZZ3TMmZWLiyhV+ac47zvoZQMtPmSIKQaavHheFahHFF542+v5ewCeeGv0+uSBPsySRW6Cgau9ILEJTNzsIGZe1D/7jyEZZwOdRfjWeZ7GzhS/ViUFuXgW7hZt2iI8HR/FKoXEqC0DJzWEFI5sBzW4TBQ2yU1yqj2mxC9n0JxlyOJJU5XdAy5f45dVenNSNY2HxNNCUmGGZ6heinw+VkfjmdcFwxzaZa2y/NOcd5E4QHDhxAT0/PtPcfHByESqVCTk6OuV2KyhxJENI0jXj1XCjVclzIDjL6/sFFH0SoP4dSLUdOXb5gTyYxg6IoHNGEjmYgVW2AtreHMy4Zqg8PMrfh9rYv2JvF9x+CunSRl7TrYoUj+qUQuZQEoWXmsIKwpQkLdl2EjCDxxb5UyAgSR1dtkmYIRYyBgQGsT2mEjCBx7sr0ykmMFzVzhoWM2GYEx2K6hein4nIqvxwbQjqROIwpbnP4EFLRCcLw8HA4Oztj+/btUKvVYBjGaJ9nz56hqakJZ8+ehZeXF9auXYvOzk6LBiwWcyRByDAMEnK+YwVhxmaj7x7FL8Ux9adQquUobakU7MlkD4gujtJnIN2dsQJt17o45ZKuq4ZujSt0c2fjhu+HuH/OB7TWOJGQhKm5lMAPl5IgtMwcVRAyDINPwtSQESS8tp2CjCBx4NQlUfm+BGMuvePrhrNfTm8N/vg1hjHFbUYhpmLDTArRT8bldPxyhKvC5mtS0hkLuRwPXkNGe3p6oFQq9RnV3N3d4evrC39/f6xYsQLOzs5QKBRYs2YNVCoVBgcHLRqsmMzhBGGmEysI07yNvnuU7KafuWrWWv8EFzuXluJSVTqInK/0ZSnqu8zn3BSXtE4H+jSBF5mubEH7FFdcL8ng/biFDkf3S6FwKQnCmZml6+Ptxff7KVr/sLrDby9kBImfLtWKyvclGHO55Hw1ZASJ9OrpvzwVuwAcD/pqF3TfvDujQvSmuJypX06VdGaOgyadEaUgHLEHDx6guLgYkZGRIAgCgYGBOHLkCFJSUtDR0eEQGUgdOakMwzCIT1/CriHMWGz03R2Vq14Q9un6BXsy2RPy6vMRPLymcEPST6jvmn766Olyebv4DF6q3dm1hZrFuJ28CzRHaxftEZJfCoNLSRBaZo4qCFuu9bOzQiEanFjpBxlBwi+2VFS+L8GYS6ezFaIrDcE1qOhwVgh6LQBF5pgVBm2uX0pJZ7jjUhCCUDJjczRBePbCfmyLD0Dgib0G2+m+XtR5fgWvk+fxzfFkQZ9M9oYrLWXYkroe74bm4Ytjxahon/mawqm4HOioxpO0ZaPlKS56gmmr5/3YhQjJL4XBJReC8OXLl9BoNKAoyuK2xGaOKghLWrohI0h8FpiGWM8VbOjoKeuvibdHLoWCgYEBzD9VChlBclbLV2ygKQq6pfPYzKLxURZxaYlfSklnLOdSEoQCNUcThFGxKsgIEqt3nTXYTjfWId95AVsW4bht6tiJnUsu0aztw6LTJZARJORHClHQVMc5lzSlwz3VTn15iucZbmBI62eTFRskvxQGl1zNELq5uaGtrY2TtsRkjioIM2vZDJPf74xFmrsbZAQJxWHbZhi1Fy6FgoGBAXx6tBgygjTrhak9gM7N0CeKo3u6LeLSUr+cTtKZsJxGNF7tteswUkkQ2pk5miBMzLwCGUHCY7vhGya6WIP0xa6QESScIm0TXiN2LrlGR48OLmdKseLCTwjKno+8+iKrcHmz4hKeZ7nj0YlvoZsnA3UyFDQlhZCaw6UE63HJlSD09/dHWVkZJ22JyRxVEF4oaWdnBbeehMZlIWQEibmhxnV3hez7Eoy5fP9gIWQEiYYu+xQXU0H3ow87O6jcYzGXXPjldJPO2HMYqSQI7cwcTRBmlNZi34UDiI3bAKZrtJ7PddUZXIl0wf5Ly7Ep6ZCgTyZ7Rnu3Fvsy3aFUyxGcPQ85dQXW4fJaK+jgLaPFbQOWgmm1feIFIULyS2FwyZUgVKvV2LJlC54+fcpJe2IxRxWE4fnNkBEkNm4KReX3X0JGkPgwRJohFDMomtGLi45u6+c3EBqo4vzREhP1NRa1ZQ2/HJ/Rdfn5cocII5UEoZ2ZownCrFoNlGo5ItWf40Z5un777ZQ9qC9YBKVajn2qtYI+mewdbdc6sFflAaVajpCcr5FZq7Yal1RqHHQLP8DjMwvwXOWGGwUXeT9+viH5pTC45EoQnj17FqtWrYKnpyfCwsJw9uxZA0RFRXHSj9DMUQXhflUDZASJPet2oXXpQv0DaT9l21qs9sClUHCtj9L/jj39FO/jsSWohGiDMhMzzSo6Htbyy7EZXXPqrpqcKUwsb7erEFJJENqZOZogLG6ugFItx3H1p7idc1y//V78RlSQC6BUyxGYGSDok8kR0N7dhb0qT70ozKqbPCmCJVwO1BTgeSabhXSQdMed5O0OnYVU8kthcMmVIFQoFFPCHs1RBWFASg1kBInDPpvQt3uT/mG03cYzS/bApVDQOpI5NpQETdtW2PMJuqUJunkyi+oOjoct/HKqxDP2EkIqCUKRm6OXnWi82qIvLXEzdYd++4M4HxSR3w6HKu4W9MnkKOjs0WKvyov9TXLmIq+h0HpcXmvF49QVo1lI45eBHhNS7EiQ/FIYXEplJywzRxWE3rGVkBEkor18QMVF4sMDmZARJKqapl+/jm/fl2CI2q4+yAgS/zg08T3QHkGTOcaF6OfOBl1gfgi0rfxyqtqF79pB7ULRC8LBwUGo1WocOnQIe/fu1R9QdXU1bty4wVU3ojFHE4Q9/X16QUgl+uq3P4r3QG7+N1Cq5TiYGyrok8mR0NHThb2qpTiYJ8eS6KMTZljjgkuaonA3Y7c+C+mzy264XmH7ZAx8Q/JLYXApCULLzFEFofMZtjzBZffFoAvzMHdvCmQECTLftrUI7YFLoaCsrQcygsTnR6efaM0eQLc0QTdXfDOEIxgJI00oa7fLpDOiFoQPHz7Exo0boVAo4O3tDYVCge7ubgBAeHg4IiMjuehGVOZogpBhGBzM/RxKtRw9KUv1254muyE9ny2QfkwTLuiTydHQeq0TPhei9DfE6g7j4vVccnmzNBEvctkQ0mcpzqAuxfHOgS0h+aUwuORaEDY3NyMuLg6nT5/GxYsX0dLSwmn7QjNHFYRfHmPLExQ4fwe6oQaKfQmQESTSElWi8Q6j9KgAACAASURBVH0JhiCbrkFGkJh/4grvY7E1dL4uBmJQqGsIJ8N0axeKbcZQ1IIwPDwcq1atQnd3N169emUgCIuKirBhwwYuuhGVOaIgDMueD6VaDm26q37b8wxXJGm+ZhPOFMYI+mRyRHT26PBDROnwTTEHFW2GGUG55nKgvRpPUpfi+vIP9KmuHWVdoeSXwuCSK0E4NDSEoKAg/XpBFxcX/f+Dg4MxNDTEST9CM0cUhDRN42/DYWr1C+Wge7rhdSAeMoJE7Ok40fi+BENkDdeW/CHCtrO8QoBuydfsPTg63KKZwRHw5ZfTqV04R2QzhqIWhMuXL0dBQQEA4PXr1waCsKWlBR4eHlx0IypzREEYnPkDlGo5uvKcQNM06O5rGCTdEav5Ekq1HHGlyYI+mRwV7d39UERmYleGAoFZP6Cxq9mqXNK6flDHgvRvJ28c8DIoVWKvkPxSGFxyJQjj4+Ph4uKC9PR0PHz4EAAbLZOeng5XV1fEx8dz0o/QzBEFYVePTv9wee2HT8AwDNaHJUJGkDgVGiEa35dgiNTKTsgIEu5R5byPxZag21vZ++88GehrWk7a5NMvZ1K7UAxlKkQtCN3c3PRhMuMFYUNDA9zd3bnoRlTmiIJwU+JBbIsPwOUtbqA72kDXV4N2/hs2Hz6Ojw+nIrfeNm9m7IFLW6O6owH7s4bLg2S6oaO7y+pc0qpkDHh/gsF8dzy7vBgD06yNKFZIfikMLrkShGvXrkViYqLJ7xITE7FmzRpO+hGaOaIgrOpg15p9eCATOq9vwTAMfgpPg4wgEbr7qGh8X4Ih4ko7ICNILD9fyftYbAk6K5UVhCsXcdamUPxyOjOGQi9TIWpB6O/vj9TUVADGgjA5ORkBAQFcdCMqc0RB6HNhNAsbXVIAujAPurmzIT+ggowgUdbaLeiTydFR2FyBoOxvhkWhF3r6e6zO5c2yFLzIWYyhYg+8yF2MmwXneefBWpD8UhhcciUIXV1d0dzcbPK75uZmuLq6ctIP32ZpBm178H11vRYygsQ3uxOg2+AJhmEQdC4XMoLErm0HReP7EgwRVdQGGUFizcUq3sdiS1DHDrDhokHbOGtTSH453RlDoYaQiloQpqSkwMPDA9XV1fo1hD09PdBqtfDy8oJKpeKiG1GZIwrCjUlsnaZwb39Ql+NBXY5H/9zZ+GuIBjLCdtP09sAlX8isLURIDrvmc3/WWuhondW5HGguw9PLw/UKC5bgjuoA7zxY5TglvxQEl1wJQh8fH2RmZpr8LjMzEz4+Ppz0IzRzREGYVMHOJC356Qyo3RvBMAyOXy6DjCCxafNBm9awEzuXQsKJ/Bb2N0yq4X0stoRugycnxejHQqh+OZ0yFUKbKRS1IBwaGsL+/fuhUCiwbNkyKBQKLF++HAqFAgcOHMDr16+56EbQ5uh1CBmGwc7LxVgfdRiaiBW4GbcXNy4GYyBsHvZdWomVF7ZB22ubAr72wCWfSCzPBJH7BZRqOQ6rd+DNmzdW55Lu7sSjpNF6hfeT/UHrbFvw2dqQ/FIYXHIlCCMjI+Hh4YHKykqD7dXV1Vi6dKndZtd2REEYUdAKGUFi7ZbDoA4HgmEYxBSyYsLnx+Og22y3BlrsXAoJytwmyAgSP1+q430stgKt00G38O9s3cHqCs7aFbJfjswYJpZ3mBSFufVXeR8jF1wKQhACwJs3b1BaWoqjR49i3759OHz4MEpKShxCDJoyRxSEoTnBUKrl0OR/gwfxvrgXvwF3i9ygVMtB5H5hs7eo9sAl3zhdEI+DeXLszVyIJpq2CZe0Tod7yZv1ovD2CR/QvabrI4oRkl8Kg0uuBOHjx4+xefNmKBQKeHh4wM/PDx4eHlAoFNi8eTOePHnCST9CM0cUhKE5jaxwWL8PVDRbPillOCHJ4p/Pgi7SiML3JRgiKIv9XfdlNPA+FluBrixl1w9+/w/QFMVZu2Lwy4nKVPglVCOrVjhrCkUvCCUzNEcUhIfVR6FUy5GZPw+P4pfiQZw3BgpdoFTLcSDrW8GfTBJGQdM0tqZG4gNlOr4/U4Eene1Com5nEHgUsQC6ebOhW+MKuqOVdz64gOSXwuCSyzqEg4ODUKvVOHToEPbu3YtDhw5Bo9FgcHCQsz6EZo4oCH++VAcZQYLw3QrqcjwYhkHe8LrC73bHg0qOFYXvSzDEzrR69nfNbuR9LLYCdfEsKwg3eXHarlj8crKkM0JZUygJQjszRxSEpwrOQqmWIzV/Lp6muOHRRQ/0FCqGk5Q4C/5kkmCIjh4dvjxerA+p6adsN1tHF+RC98NH7I3Lcx6u1+XzzoelkPxSGFxyIQhfvnwJjUYDiqIsbkts5oiCcG18FWQEiYgVa0AX5IFhGJS1dkNGkJAHpoM6GSoK35dgiB9TaiEjSBxVN/M+FluBCgxg1w+Gh3Darpj8crKkM3NsmO+Cay55E4S+vr5Ys2bNtOFo5oiC8NyVBCjVclzUfIUXOW54muSKjgInKNVy7FF5Cv5kkmCMnHot5hAknCIP4YhmITp76m3WN11XDZ3HXDw8/i1e5rnjZmkC73xYAskvhcElVzOEbm5uaGtr46QtMZkjCkL3qHI2Xb3HUtD1bAKSZm0fZASJvwarodu7WRS+L8EQ6xOqWaFfYB9RKNOBzus7dv1gThqn7YrRL3PqrppcU7gzrQ6NV3t5K00hOkF4/PhxhIeH67F69Wq4urpi9+7dOHToEHbv3g1XV1f4+voiPDzcokGK0RxRECZXqKBUyxGt+YItI5DthsaC71lBmLFa8CeTBNNcHi7oxM+XF0OpluN04WL06a7Zbgxt9XiawpalGCSX4LbmBO+cWMKl5Jf8c8mVIPT390dZWRknbYnJHFEQfhPORkrkuX4Pupu9/vX0U/oHyK710gtPMWLVcKmsc1faeR+LLUBf7WKjbubOBt3ZwWnbYvTLidYU8h1GKjpBONaKi4vh5+eH27dvG2y/desW/Pz8UFhYyEU3ojJHFITZdYVQquU4qf5cnxikilwIpVqOvapNgj+ZJJjmcvDVa7hH5SIw8zt2Brh8I2iau8XoU4G+1o7HCUtZUVi0BHezxFmWQvJLYXDJlSBUq9XYsmULnj59ykl7YjFHFIT/OMg+HFa6faffRtM03htei1Tr8b0ofF+CITxjKoYLlXfyPhZbgM5TDS/DmM9522L1y7FrCt8NJbEmror30hSiFoQbNmxASUmJye+uXLkCf39/LroRlTmiICxpqWFLFeR9qheEV0hWROzP3CH4k0nCxFzWdvVhwak4hOZ+DqVajpz6wzYdB93Xgwdx3qNlKVQ/g7Fh7S8uuZT8kl8uuRKEZ8+exapVq+Dp6YmwsDCcPXvWAFFRUZz0IzRzNEE4diawY/kPBt99frQQMoJEkeIb0D22WWMtZi6FBpczbC3J9Oou3sdiDdAtTaDJHNAtTWAYBtTJMHb94F7uX9CL2S9H1hQ2afsmDCO1ZWkKUQtCNzc31NXVmfyurq4Obm5uXHQjKnNEQdisvYrlsdvhH/cTdIvfB+P2N5wO3oUPD13Ggcwrgj+ZJEzOZU7dVThFHoZSLYdSLUdF+yWbjoXW6XAvzk8vCh+kbeGdH3O55HssYocQBKFCoZgS9miOJggbuti1gn8LzkP/BsPQ0EWnSyAjSGS5KTit6WavXAoNC4d/v7x6Le9j4RpUQjR082TsjOA8Gft5w1L2c4SS8/7sxS9NhZHaOtGMqAXh+vXrERoaavI7giCwfv16LroRlTmiIOzT0foTqP3bD6CbOxsBu09DRpA4kme7LF72wKVQMJ7LyMJWrLjwE5RqOQ6pv0Rnj20L+tI0jTsXf8Rgvjtu+H4I6tgBm9W35JpLCfxwyWXZCUc0RxOERc3XICNIfLU3BdTuDQbfeUSzyWYSlnqCijikn4kRqu9LMMRXw1m0i5u7eR8Ll6BbmkbFoCkMC0Qu+7QnvxwbRjoiCONK2myWZEbUgrCwsBAKhQJbt26FSqVCSUkJVCoVtm7dCoVCgYKCAi66EZU5oiBkGAZ/P1gAGUGiZtFn0M2djTVBcZARJM4U2i6Ll71wKQSY4nLH5RpsTlmJFbG7UdZqwwQzY3HuoP7mRhE7OC2wa0suJdieS67KTsTFxaG7u9vitsRmjiYI06q6ICNIOO84B+rwPoPvfC+y643OLPe12oO2PXEpNHxyuIhdG9rey/tYuASVHDuxGBzB/DmcvsCwN78cCR8dyURryyQzohaEACsKfXx8DMJlvL29QZIkV10I2nJycuDv74+wsDAAjisIvz2ZDtdTEbiR7IKhYg+EJvrB+8JWxJYU2WwM9sKlEGCKy36KxrJz7NqLb09egbZPx8vYqItnoZsnA+P+Hh6l+IDu50mcWsClBNtzKZWdsMwcTRCeu9IGGUHC+8fjoKKPG3wXEFcKGUHi8KpNVnvQticuhYb3wtgX2I1X+3kfC1egtV3QLZ03tSCcOxt0QS5n/dqrX9Z19tg8yYzoBSEAvHnzBjRNo6OjAzRN482bN1w2LypzVEG4R8WWJ9AWOGGo2ANHc+ZDqZYjqTzTZmOwFy6FgIm47Oju14fbrE8oQ21nDi/jo1Ji8fySM4aKPfA0fTmYPuGuBZH8UhhcciUIAwICpAzaDuD7R/KaISNIbNkQDOrSRYPv9saoISNIBK7dbrUHbXviUkjQUaNLXDp7+HmpyTWo5nrofF1YP3T6CLp5c/Qz10YhpNIM4bQwUZKZuJJ2q4WQ2oUglGzUHFUQ7s5YAaVajtaCHzBU7IFDuV9BqZYjq1aaIRQjJuOyoOka/qHMwvY0VyjVn6FRW8jLGG+oTuFl3uJhUbgMTI8wU4hLfikMLrkShFVVVVi3bh2uX7/OSXtiMUcThFtTayEjSGxfvw90QZ7Bd8rLbNmCrf4HDNZnSTOEwoe2T6d/uO/ViWMd+mSgEqINBB91gmCzjBbkgm5pYr+fP0cvBqU1hNPDVLUKrRFCKmpBWFRUNCUczRxVEO7JWAelWo66gkUYLFoCZd6nUKrluNJSa7Mx2AuXQsBUXJ7Mb4ZfIvubH8tfhO5+bovdThc3cs7iZe6wKMzwFKQolPxSGFxyJQiDg4Ph7e0NFxcX/PjjjwgKCkJwcLAeISEhnPQjNHMkQRhT3Db64BeSj2hVqcH3kYWtkBEk1m45Mvow/v2HoEqLDNL9C8X3JYyi5Vq/PvyP77FYCpNJZEzMAI4ViFyPwZ79cnySGWuHkIpaEDpq+u3JzFEF4T7Vj1Cq5SjO/wHPitz1JQqatLar82MvXAoBU3FJ0zTWJ5RgV4YTlGo5zpf62bRo/VjcyDuPFzluw6JwGZh+29UN4oJLCbbhkitB6OvrOynWrFnDST9CM0cRhKZmBsY/+MWXdUBGkPA8cwVUdhp0nvOlbI4iQW1nL2QEiY8OF4meS5rMsfoawalg7345UqswsbzDpChMLOcuhFTUgvDWrVtG6O7uRkJCAvz8/NDT08NFN6IyRxWEBzL3QKmW40juDrS35ECpliMs7zP09tsuRt9euBQCpsOltk8HlzMpIHK/gFItR2FzFG/jvaGOxcthUfgwwUdQ2UclvxQGl45cdqK2thZ+fn5Yt24d8vPzzWrDUQTh5arOKQtUZ9ay64ucItmZQ6o4X8rmKBKUtHRDRpD4MrxE9FxSBXlW97up4Ch+aYsQUlELwsksLi4Ox48ft3Y3gjNHFYRETiiUajmCs7fiSksJlGo5DmTPt+kY7IVLIWC6XJa1dsM9mhieEf4S7d1VvI35RtZZPI5ZBGrhHFDEdsHUKZT8UhhcOqogfPXqFfz8/HD37l08e/YM69atw+PHj2fcDp+CkG5p4jwUcyS9/Mib/SZtH1Q1XVBElE4ZGqavUXi8mB2fDWZqpOsIN8hv0EJGkFgQUS56LqljQUZi0NrlT8bDkfzS2iGkdisIm5qa4Onpae1uBGeOKggPq09AqZZjf5YfsmvZGcJ9mT/YdAz2wqUQMBMuT5HN2JyyEkq1HJFFq3gdN3Xpon5NBXU4EDTFfxY5yS+FwaUlN82ioiI8evTIYNvdu3fx+vVro20JCQlm92MN6+zsBEEQ+s9RUVEoKSmZcTvWFISTCT4qIXp0ndQMQjFNCb6RzzHFbfq3/XMIEgHJNQZv//8aSmIOkc8+5IVojN78j4QdfqAs0I/faC3XPGmGUIgYmd11ja4SNZc0pYPO7Qv2XnfxjNXWCE4FR/PLqUJIx0YS2IpLwQtCkiTh5eVl7W4EZ44qCCMLs7A8djsCUiKQXduBfxy6DNez6TYdg71wKQTMhEuapuF7UYMNSavhdjYTPf38hmuOZF27s+NzPM5aB4bmVxRKfikMLi25aSoUCmi1Wv3n169fQ6FQGBWov3r1Kudr59va2hAUFARvb284OTmhqqrKaJ/c3Fz4+vrCzc0NAQEBaG9v139XUVGBM2fO6D+np6cjPT19xuOwRBAyrc0Ggm+sOKMSolG/8FNkuSlQv/BTUAnReoGoKzIvFHO84NsyTvBNhTkECTLiDLLdnNCwZ5tR+109o5kq+4YzVRpkc5w7Gzq3z0H39gjC9yWMIqWSDQledqFG1FzSearhMhMfg+6zXm28qeCofjmdtca24lIQgrCtrc0ITU1NyMjIgJeXF5RKJRfdiMocVRAmDb8tWRpTjpQK9oLrHlVu0zHYC5dCwEy5bOvux2dHiyAjSOxKr+d9/EzsIQzmu2Oo2AOPcreA4SnhjTlcSrAOl2IVhPX19YiPj0dlZaVJQVhWVgYXFxfk5+eDoihER0fD3d0dt2/fBgCUl5cbCcKMjIwZj8NcQfg497LBDF9UZJKBWFPsiIEsJH84o6cGUV6rjGfbxqExN3fCRA5TrfWZLrKDQ9n+gowFIUXT+j7aro0WN6dbmkBlJEPn9DH7t/s2cxbqKl1HuEFcaTtkBAnfxHpRc0nt8mdnB0N38joOR/bL8SGkIdkNvHApCEE4WXbRffv24cGDB1x0IypzVEGYNRyG8UNEKc5dYVN2r7pQadMx2AuXQoA5XKpquvQXxoRyFfp013g9hpvRP2GwYAmbaEazCwxPawolvxQGl2IVhGPNlCDctm0bIiIiDLb5+/sjLi4OAL8ho0xrs4G4q18ox5xh8TfhOpwQDeoXyicUg1Feq/ThnKYSOUxUUHomeJfIHzMG02GqnxwuhIwgUdluPAtI52VwnnVUuo5wg7NFbMmQTZeaRMslfbULum//xq5TLb/C61gc3S+btH1YcZ6tS7o23rIcCqIWhK2trUYzhFevXsX9+/e5aF6U5qiCsLCpCx8fToEiMgFHNXFYeWEbfroUa9Mx2AuXQoC5XO5Jr4fnud1QquVIqQrk9Rhomsbt0+swWMSKwvtFoaLiUgK3XNqjIBwaGoKzs7ORSIyKisLOnTsBsEll1q1bZ5BUZvx6yLE2ODiIp0+f6vHs2TMA7L1tYGBgRmAKDbMgpixxn5Ygi/TyHQ4hlesF1aig1BiJt2Ztv77PwubuaYWEjrzZfzeUREBK7ehngkTU8tVGYapMa7PBsc0/cQUygkR+4zXj425tNhnqOr6NmeD69esAgOvXr5vdhoQBnMhvgYwgsV3VKlou6bgzrE+t/F5/XeQLkl8OoLK9V39tqWzvtTmXghCEkhmbowrCstYGKNVyHMyTIyx3J5tlNGufaB4WJXDDZa+Ogue5c/o6lI1XC3g9Dlqnw50TyzFU7IGhYg/cLY8UDZcSuOXSHgXh3bt34eTkhM7OToP9UlNT4efnp/9cU1MDPz8/rF27FhqNZtI+EhMT4eTkpEdAQIDZ4x26fcNghnDTBsJYoJmaMRwbQrpiNV50teJ5Uw3KiqtNCryq5tHfYbuq1Whdz66sNgMBmNbE4Maj56jtv4cbj54DgP5zf2WVyZnJ5001BsfmcZ4dS7H2ttFxP2+smVYbktneIst6ICNI7M/t4HsoZtnQ7RsY8F4E3dzZeHTpAt/DkWzYNl9uZpfMZLUZbL/x6DlqxlxnhGichYyOvUmOte7ubqkwvQM9LLZor+lFQHD2WijVcoTmKEXzsCiBOy7JxmtYE78BSrUcx/JdoaN6eT0WuqcH906w6wkHC5fgenOhaLiUwB2XlgrCtLQ0VFZWorKyEhUVFVAoFMjIyNBvq6ysxOXLl3kRhF1dXQb7paamYv369Wb1weUM4fXr19k1hPPngHT+Tj+7N/LvuyEabN4YgnfHzNzJCEOB+C6Rj6LmbuTWa7H/nNqkeGzMzcPAwACKx8wOZlR3Ia9eq589bKppQE5aHppqGlj/aW1my0KMm7Wj0hOnNbu3MrYSMoLEhdJ2o+MeHyo7MsspzRDyjwOZjZARJJTkVdFxSSfGGPgVFXOC9zFJfsli5NozhyARX9aBZm0/Yq60G6yXjrlifK3ggktBzBBOJgi1Wi2cnZ256IZzIwgCnp6eCAsLM/n9ixcvsHr1apw7d27GbTuqIOzT0QjN/RxKtRxB2W5QquU4qjktmodFCdxyuSu9DIFZ30KplkNVa9sXA6ZAd7TjYYQCd3d+AZ2vC+ge7rL/WZtLCdxwaakgnAmsZeaEjHJllmQZra+ux7dHWBG4cVMo6hfKke3mpA8JbczNnTSVu1HI58is4vC/sblVoGkaHtHlkBFsVtGxpSzGl67Q7VxvspQF3a2FbsnX06rttjGxBjKCxI60OpPJbYyyjjp9DLqnW7qO8IydaXWQESROlHSLikuTpU1sXITeFCS/HMWCUyWTr00OJVHYfG3ChFjmcil4QZiXl4dly5Zx0Q3n1tLSgpqamgkF4cWLF3Hw4EFJEM4Q+4cFAJH7FZRqOSIKL9i0f3vikm9YyuW1PgoeMaeGw4g/Q+u1Mt6Pia6rhk4xnP3vpzU2q1Eo+aUwuLTkpmkqo/ZksJZNlFQmMjLSYNvYpDLmWk5ODvz9/fX3SXMFYVoTY5D187i3/4QPtdPJEDqHyAfpsgDZbk7Yu3YHZASJD0I1CEoug4wg8V4oiYYL56fMVDq+ZiBVUgjd1lXs5yVfg6qtnLS2m2dMucGaxPHJbRhmOOtobjp0bp+z7Qb/ZHbWUek6wg0CUmohI0hEV/aKikuazDHpu3RBLq/jkvySxUyzG5u6ZohOEGZlZWHNmjVYs2YNFAoFvL299Z9HsHLlSigUCkGXnWhtbTUpCAcGBhAaGorCwkJJEM4Qe1UKfdioUi3HhZI0m/ZvT1zyDS64VNV0wT/JF0q1HCcLloLiuR4gwzCgizTQfftXUAvm4GHKWgzYIBOq5JfC4NLSmyZf9vz5c/T29qK3txdOTk5QqVTo7e3Vl5UYKTtBkqRB2Qmuj9ccQdis7TdIy67P4Lno0wln38amcp/oASunqAZ0QS56j4XA46dIoxDSKK9V0xeDJkAdmnz9e5O2z+Tb/4lqkNG56RZnHZWuI9xgfQK79jOhTicqLqUZQmHDnOzG468ZohOEJSUlCAoKQlBQEBQKBXbs2KH/PIKwsDAkJSXhyZMnM27f0iK807WJBGFISAgYhpEEoRnYnbHUQBBeqtLYtH974pJvcMVlQHIBdqYrsCruOPp0/AtChmFAXY7H46iFGCr2wJM8P6sXrpf8UhhcilUQtra2GiR5GcHx48f1+4zcE11dXREQEGCVWUpzBGFuvXZSQTfRw2yTtg+59VdR2Hxt0uLPNE2D3LndKDHNVKUrpsQUD9oTPfzl1l81uT/d0jTjPrj0fQmj8LnArv1Mb2ZExyV1ZP+Uocy2huSXLEzNEI7NZjzRy62x1wzRCcKxtnv3btA0zUVTerO0CC8ABAQEYOPGjUa4e/eufh9TgrC6uhrnz58HAEkQmoFd6asMBKGmQapDKFZwxWVHdz/kR9h6XafIFt6PawTXz+3HSw2baOZB4QFRcCnBMQWhUIyzGcJJZtJMYeyM4buhxmFW2alZJh+0st2cDGbkdPPm6B+kdfu2jK7vmyC0dLJQPFMPf5POEHIQ7iddR7jBSKhvXscN0XFJnT3K+s56D95nBkcg+eUoTF2rJnu5JSNIxF4ZvZ6JWhBa28wpwjtdMyUI4+LisGrVKvj6+sLLywseHh5ITk42+fdcZWKzpwxNASnHsTx2OxacDsenR+NR22Vcn8masCcu+QaXXEYVt0FGkPjoUCHaunt4P7aBAfZieyvcd7QcRU2cKLh0dFjCpSQIZ2ZcriGcTNBNByMPVaYEV2N1vXFtwhAN6hcahqXSLU0Gs5Ijn6mSQrNC8Y7kNRmIwcmOy2S439zZoFoaZ8QlID14WwqXM+xa05Jrt0XHpe7nNazfRB3nfSwjkPzSEJNdq8YKxhF8dKgQ6notcuquolnbbxaXvAnC27dvY2hoSP//qWCJWTOj2kQhoyM21Qwhl7Wa7MV+Hlf/6eHzQb6HJJkA7NXrN1gcU4UfIg/jYN73uPXoGt9DAgC8fv4cD08Nl6MoWIJXt62XDEQyycRulmQZbdb2T/iQxAWizyTh3TGlLKLPJBkJwMlgkBF0mqF4OorGX4cf7shG7cz6GEHIz9NOMiM9eHODkUyQtf33RMUlTdPQuX7GziwX2XY5zmSQ/HJmGBGMdZ29cI8qN3hmnkOwNVJFIwjHZha1dvptc4vwTmWBgYHw8vLC4sWL4ePjYzJT6lSCUJohNMbOtHoDx6aHLxZimD2QYF0uNY3X9Alm/j97Z/7WxPl28T/KaPd+36atbXHpJqCgEa2i4loX3JdaWjFgUFFcUEErIiogbkBggLDv+x4gMDORunZxq9J63h8GAoEEErI8M8l9rutcrUmYmZw8T5JPnuVOLFpj+SBhbXNLI54lB2LIoME/uUsxONAh+yz92TRCyE6uAOH9+57/sthYXY/sm7lorK6f1t87A5Aj/u6MBBe3a7qcOgefdMLpTWa8maUv+8sTBqi0HFrv/6WoLMXmBqmtzJvl1bJJU5na5fQ9tm7q2NkGzcYBp47DDAgLCwvxCfqkkAAAIABJREFU999/W/5/KrsibxThdbf8eQ1h9N1qzD2ViE3X1iDsotbr5/elLFnbE1luvZ4Lbc5s6PRqVLRnMH+OFhvu4WV6MP65vQiDvx1SRJb+aleyJCB0TXIHQhZen1o9rfXR0iYzEwvX8yUFE0YMR+opmlubfTpLb/mTo9Ka9t5HzxWVpXDzqtRO1gQzv5ax9vU+7knb25wqt37qGQdj7ZdrCL1ZhNdRubrOwpc60zH9GcuGMgduhXj9/L6UJWt7IsvG7n788NsB6PRqHMv7HgOCjH7lvJ4A4ftZ0tqhkgLZZ+mvljsQumOphFxFQDjR0XcbodJy2JNZ59Tf2dtkxmrEMPWCNM10ZO3hHBWe5WROyHIEGK3WRk6z1qE/+L3YAqi0HO7/+VJR7VLQ/SStH4zZx/xaxtrX+7gnbW9zKsWMEHpT3izC6y75MxCeKbhsAcL9WT94/fy+lCVreyrLIzk1+PnON9Dp1chtOMP8eY5YFEUI+4cLZi+dA7GnQ/ZZ+qPlDoQLFy5EcHCwx8/jDblrUxnAd9v+5ZJ2qLQcwi5W2Ly/ydiP7LqJayftbTLjSDkMc2uz5TjjgZE/uNXq33IoS+ApTwd8eUG0fPH+48VrRbVLfl2IBITpKcyvZax9vY972uN3JlXUGsKioiKn7KzkUoR3uvJnILxUnGkBwn1Z671+fl/KkrU9lWXfgIDll44Nt5N56PdCYXhHLRq7wC/5Co+2f4p/9GEY7Hd+N0RvZumPljsQJiQk4OTJkx4/DwsREE50YVMPVFoOn8cbJtyXbGiz/Po/UztxF1KrTWacgEMh65o0wnj7xsRppzYAUigpVMSIoTOANx6EHQVfYz9vAcJXb/5VTLsU+/vBz39H2lCmoZb59Yy1r/dxb3hkoxnF7TLqyEYyrmwqI5civI6KpoyOOr0yb8yU0e1eP78vZcnanswy2dCM/bcCodOrYWi5xvy5jrVoyMWLq9ImMy/y3VO0ntql+yx3IPRlERBOdLdpFDCM/aPvFY7WKZy09EWAanqjiBMs/xFDISVxSsAbAUZHy4TYAsyWngHLa/H27VvFtEuxpEB6nkGfQBRF5tcz1r7ex5WQJTMgfPjwoVP2N/kzEObWV1iA8OfbBxTTmcjezXJAELE46SK+OHEJ8XnNbj++y8/93C94nRsqFa2vPCbrLP3NBITsREBo258fL4JKy6GoeXS2g73NInLqu+0ex1bpC5ulKlyxA/UVvW2hvmbK67QaEbQ7cppmAUB7I4g1HSaotBw+iitUVLsULp6SnsuO1cyvZbz9oY/LPUu/WEOoRPkzEJa3tliA8NDdaMV0JrL3s7xULBWrVx8rQk+/wPz5jrXI83hwSALCN0VL8KB74nQwOWXpTyYgZCcCQtsOu1gBlZbD5ZJ2y22OjhCOt63SF5bbbqbZ3YBmBKL4n7eNAUjbACUW5Ez5nKaavumujWzEfhP4iOBJr3Pa6y1tAGZJi7TN/+fHixTVLkfWtwtn45hfy3j7Qx+Xe5ayAsLXr1+js7MTNTU16OzsxOvXr915eEXJn4GwrcdkAUJdDpWdULI9nSUviJh3qhgqLYdj+hL0DXQyf85jLTbW4a8T30j1CbmVuC/0yjZLfzIrIHRmmQRtKuNfbX93Rh1UWg6H7zZa3R6UWGpVl3f8GkKn3doMfs7MCaAjlBRaQeTk01BngE88OinMCZfOTDp9c8JGNns3OL2eT2xpgpB7G/zmZfYhroST1kpeiJ8chB20WJCD/AYjVFoOcxOKFdUu+SVfSc8h/x7zaxlvf+jjcs9SNkB4+/ZthIWFWX0gLl26FLdu3XLXKWQtWkM46n5exIar67HlejhOcZxiOhOZTZa/lbRj0fnDiMmZjcxq748oT2Xxymm8ylqIIYMGf5dPvz4htUv3mRUQpqWl4dq1aw7bF0VAaNunuBaotBzWp1ZbbhNFEepjRRYgDE4ss/m39nYhtZfls5zMCdNKJ/ubyaecqsBvWGINViGf2QAvCTqF/Hvgr5xzaDRuso1sJkz/nDcLwrEo56bGjgFhfqQ+32Seo4LY0oQ7tdJU3gVnSxXTLsX21tHn0ONcfTpv2B/6uNyzlAUQ3rt3D0FBQYiKikJhYSEaGhpQWFiIn3/+GUFBQbh79647TqMo+TMQms1mfHCkYML0Gbl3JjKbLHlBxNLkC8OjyrNh7HeuuLOnLQoCfv8lDG8KluCPsxqI/PQ2mKF26T7TlFF2IiC07VvVXVBpOXx/ptRyW0VbH1RaDu+M2U5+7KYzZvPUu5Day9Lc2jxhWulkHhkx5IvzXZt26ZRtjxjanP45DGuW6zwdO/mI4DgQtndMq9HUb96H2GPEjcpOqLQcFl8ol227nDBim5YkPYfl85lf22TtUo5ZKs2KBsJ169YhPj7e5n3Hjh3DunXr3HEaRcnfgXB2vAEqLYebVV2K6Uxkdln+VtyG7Rlh0OnVuFlzmPnzHm+xsQ7iko+l9Ru/Ta9uIrVL95mAkJ0ICG27vkvaqOS92ALwgrQDZGJhK1RaDssvVWBuQvGEz8TprDF0NUuRy3YM5tyys6n1aJ5leqqdaxi7rtHeY4SsNLsgbGtDHrGlCULePfBL50h/fyoWKaVS3cgfLlXKsl1OmI67Z511BjLcJdYf+rjcs5QFEIaEhKChocHmfQ0NDQgJCXHHaRQlfwdCTVI5VFoOFe19iulMZHZZ8oKIsIvnh0cJA2RVl3DEwuXE0V+Zp1EDitql+ywXIBRFEXFxcVi5ciUWLlyI3t5eAMC1a9fQ0tLitvPISQSEts0LIt6LlWbG1HWaYDabsfFqNVRaDrqcJssaw59u1Vv+Zjq7kLoMhI5szjJ3JoTz8VPXR5zmRjZiS9OkwGj3Oh3YHdXWhjxmsxnCyGY8332I87m1UGk5rL1SJbt26ejrI7ddYv2hj8s9S1kA4datW+0Wny8sLMTmzZvdcRpZi9YQWrvZ2I+8Bjbz3H0tS5b2ZpYXilosdQm5pnPMn/t4i4IA/scw3Ne8jxcZGjzq0ss2S1+3HIDQZDJhyZIlCA8PR1xcHIKCgixAeOnSJeh0OrecR24iILTv789KG8hkVXdBFEV8NlyKQl9vRFp5hzSl9OzolNImY79NICxstv+DmDuyHD+SZgVzY6ZiTroxjbMb2QxPCTWbzRArSiYey8aol60Rv+k+Z1EQwIcvAB8wA/GHT0Ol5bDlsu01hNPdLdUddnQE15FdYr1pf+njcs5SFkBYVVWFiIgIDAwMWN3e39+PiIgIVFVVueM0ipK/A6ESOxOZbZZ9AwKWJsdCp1fjeF4gBFFeZSjMZmnq6B8xc6VdRwvCcV9wfASc2qX7LAcgjIqKwp49e/DPP//g33//tQLC8vJyrF271i3nkZsICO17w/CIYEJ+C6o6+ixTSE28gI7eAQvwtfUMwGyWNmD7SFcwAQjnnSpGRmWnzamj7spy/EiavZG1ETsLZxM2sln0GUST9H7Jb10h3bZzzZTrIKe6Lqee872b4ANmIHbNdqi0HHZu+hXPcjKl9ZhT1C70lmmEkKxoIIyOjsbq1asRHByMrVu3IioqClu3bkVwcDBWr16N6Ohoiw8fPuyOU8peBITK60xk9llG3a7CoXtzEJs7Gy09pV45p7MWzsZadh39s+6UbLP0ZcsBCJcsWYLq6moAwH///WcFhG1tbQgNDXXLeViLyk44bu29Rqi0HHZn1OFCkVRjNSy53HJ/4HAJitSyDpjNZlwtk0YNPztehHu13eAae/C/uEKbZSpGdiJtNko/vDcbB6x2JnVmp9Lp2lk4E1uaINxNBx/8qQQyv+6AMFI+Yt47EBvrvPr6CC2N4ANmIGrdHmn67vr9U6+XZABf/I7VVue3N4IrJ/tLH5dzlrIAwoiICIftq7+ajhcBofI6E5l9lvVdJsxNOI8PdLdQxWD9qSMW+014sPub4YL1Gjzoc2w9IbVL91kOQLh48WLU19cDmAiE1dXVCAsLc8t55CYCQvse2awkLLkcm9NqoNJyiMkerUsYdbsBKi2HHenSe8ay4WL2I4+xtcmMSssh4kql1U6k++9a70waeb3GqZ1KvW0x59ZE0Nqy3PvXMTwdc+/Gg1BpOURH7JTl9Ex+V4R07pi9Do/gsra/9HE5ZykLICRNFAGh8joTWR5ZRlypgkrLYf9N7/567IzFnCz8ffZrDBk0eFGyDWYHprdSu3Sf5QCEu3fvxunTpwFMBMKzZ8/i4MGDbjmP3ERAaN9FzT2WEb+Rnbaz60Y3iBkpTRFw0mCZUjorhkNjtzSqZ2+TGWdta6fS8SOI3hhRHLHNTWTmeH/kbWQ65rYfo6HScji6eotTu6N6y5YC9DJbJziZ/aWPyzlLAkKZioBQeZ2JLI8s7w4XDf5QV4gmYwPzDOxZPLAKr/WhGDJo8KQtXZZZ+qrlAIRlZWUICgpCUlISent7ERQUhKqqKmRlZWHRokWoqalxy3nkJgJC+zb281Zg9m5sAfoGRn8s6ukX8G4sZ9nhUqXlsCal0nK/vRHC6fhqWbsF+MbXOvT2iKIjZSa8ZeFqEjZsi4NKy+HUyg22axeO+X+vryE0do/m093JvE07an/p43LOUlZA+OrVKwiCgN7e3gn2ddEuo/IxZansLEVRxHdnOGxPXwad/jN0m+Q5RUZsrMPj/V9Ko4TcRphFUXZZ+qrlAIQAkJ6ejoULFyIoKMjiRYsWITMz023nkJsICCf3yMigSstBk1Q+4f6Rkkwjzqq2rtWbbGjDrDGF7E/om90Gic6MKDpiR0cdp1tCwlNelSTVhLyYXYVnOZkT1ufx0bulfx/c6vVrE4vypHOHfsG8LTtjf+rjcs1SFkD4119/4fDhw1YfiuPtbyIgVF5nIssny7MFrcNAqMa9ujjmOdizcEqLh5s/Ab/4C8sOenLL0hctFyAEgMePH4PjOKSnpyMvL8/nC98TEE7ukXWB9qa9R6RUWcHYhaLWCY9pMvYjp34UrMZD4v671v+OvF5j+fd0PVntQ1seP+q4Oa160lFHd5aQcNVhyRKUp5V3AoC0y+iY9XlCxhXpOteHev3aLPVut69i3padsT/1cblmKQsgjImJgUajQXJyMvLy8lBYWDjB/iYCQuV1JrJ8sjT28wg+d3K4BMX3EGVYgsJsNkM09YFf/CX4gBkQkk7IMktftJyA0J7evn3rlfN4WwSEk3sENmyBka0poY6Ozo1A4thdRsdC48j9I/UOnXVle6/Dz9GRqa2zYqR6imNHDBur65F9MxeN1fVMX6Pgc2WW0Vlb7VKsrZSg7Jv3IQq8V69NiN4lfZ7EH2Lelp2xP/VxuWYpCyBcunQp8vLy3HEonxEBofI6E1leWe7PqkJ09lfQ6dVo6M5nnoU9C9cuDtfZ+j88bL0nyyx9zXIHwuLiYmzcuNHj52EhAkL7ngr47G0a48zo3FRZ2rqGmcPXMdmI4tLkctypdWyTmVP5LU7B5kwthw1XqyaMIHpzY5ux/uZ0CVRaDrn1RptZigIP/pv3pXV8tZVevTZ+XYgEhDevMm/Pzthf+rics5QFEIaHh6OxsdEdh/IZERAqrzOR5ZVlUXMPIlJ/hE6vxo3KA8yzsGeR5yGEz8erW1Jtwgd9NbLL0tfMEghfvHiBwsJC3Lx5E1VVVfjvv/8s91VWVmLz5s0ICgpCRESES+eRi6gOoeOeCvhcGSF0JsvxU0xH4MvWiOLN6k68G2MNb/Y2mWky9iP6bgNmuWntIqtSGV+ekNZ5Gpp77WbJrw+VwCzjiteuSxQE8N9+wAREXbW/9HE5ZykLILx8+TJOnTrljkP5jAgIldeZyPLKUhRFLE66CJ1ejTh9AHjBxDwPexZupuHvhPkYMmjwvGKf7LL0NbMCwsHBQaxcudJqffyBAwfw4sUL/PrrrwgKCsKyZcuQlZWFN2/euPIRIlsRENq3I8BnC9Y8keV4AHTlms1mM5IMrVaP+eqkwe6o43Q2wZnuxjbT8SdHC4enyZrsZilo90pAeDTKa+1HbKiRZpt8/R5E3rtTVV21v/RxOWcpCyD8999/ER8fD61Wi/z8fFRWVk6wr4t2GZWPKUvfyVJ7rx4/3/kGOr0ale0ZzPOwZ1EQYN4ciDfcYgwZNHhkLJRdlr5kVkAYFxeHxYsX4/r166ivr0d2djbCw8Mto4KnT5/G8+fPp318JYiAcHI7AnyOwpo3snRkGmuTsd/uGkFbo46FzT3TgkJnN7aZrt+LLYBKy6Gxe8BulsLVJAnOtiz3WtsRb12XzrkmmHk7Zt0u/dmKBsL79+9j48aNtMvoGBEQKq8zkeWXZUVbHxYkHkVgYjzae4zM85jMYvZN/BE7B0MGDV6Wbp1QrJ51lr5kVkAYHh6OjIwMq9saGhoQFBSExMTEaR9XSSIgnNquAJ+3s7S3QUx+w+j77fb0GqcBbjwYjx9BdHXq7HTNC6LlnJ19vN0sxXKDBGeB/wdxipJC7rKQoJVGJX/ZzqztyqVd+rMVDYQHDhzAypUrcffuXTQ1NaGtrW2C/U0EhMrrTGR5Zvn9mVKotBwuFntvjcl0LIoihC2hlmL1j7tyZZelr5gVEC5cuHDC59mrV68QFBTkN59zBITKbfv2PBbeRvz92VJUtfdhb2bdtKd42lu32GTstypbofLiGkJjP285p4kX7WYp9veDnzdLWs/X3OiVa+N3RUhAeOk083Ymh3bpr1Y0EIaGhqKkpMQdh/IZERAqrzOR5ZllXG4TVFoOKy5VMM9jKosFuXh66KvhUcJIq2L1csjSV8wKCIOCgmA0Gq1u+++//xAUFITe3t5pH1dJIiBUbtufzCOwVtTcg8+PF00AwMDEUpfWPtpyaUuv5fh9A94pLdQ8PP11VgwHs9k8aZZ8+PcSEN5J98q18Uu+ks5XkMO8nTlr6uPss5QFEEZERKC+vt4dh/IZERAqrzOR5ZllbacJ7x+5jbCLB3G1fA/zTKayuH0p3uQvxrPba3BfGC1WL4csfcUsgdBgMKC3t9dio9GIoKAglJSUWN3uq4BIQKjctu+o0ysm1jK0tWbQVYuiiP/FSRu8lLf1ueWYU7mmwwSVlsNHusIpsxR+3iaN2CXEePy6RGO3NEU1YAbE7k7m7cxZUx9nn6UsgPDmzZs4fPiwOw7lMyIgVF5nIss3S01SDmJzP4VOr0aXiW1R46ks3k2HGPiOtPbEREDoCbMEQmfsiyIgVG7bd9TuqJfoqBedl4rE36j0DgSVDI9Kfn68aGogvHhKgrSdazx+XWJRnnSu0C+Yt7HpmPo4+yxlAYRZWVlYu3YtIiMjkZqaitu3b0+wv4mAUHmdiSzfLBPyW7Dtxgro9GrkNsQzz2UyiwJvmfojpCXLLktfMCsgLCwsdMq+KAJC5bZ9R+2OeomOevM1acOa4/pmr+SX32CESsthbkLxlFmKhXqvQZpwOVE61/ZVzNvYdEx9nH2WsgBCf/2ldDIRECqvM5Hlm2WTsR+BiXHQ6dVILFrGPJfJ3DsgoPLsGRQFzUPRjgjUVqQjq7oLXFMPTI+fo72PBy94Z9c6XzUrICQRELK2t7J0tV6io47NboRKy2FHeq1X8rtTK41+LjhbOjUQ9hhHp3F2dnj0uoToXdKPiCeimbav6Zr6OPssZQGEDx8+nNK+LqpDKB9Tlr6Z5fKLudDp1dDpP0XfgDzWWPCCiJz6bsTlNGHD1WrMP1Vs+WV9ztEsvCwMw8sCDeYezYBKm2/5tX3m8C/Uqy9X4pfbDbhU3IaK9j6vbW+udBMQek+ufra5+nqR2WXpyfIZI75S2g6VlsPS5HKv5HejshMqLYfQC+UOZcmHzZWAMO+OR6+LXx0oweflRK+3KXeY+jj7LGUBhFPpr7/+8sZpZCUCQuV1JrK8szxb0Iq9WQuh06tRwbBIvSiKKG3tRdTtBnwRb7CaVhV07ggiUn/ErsylOJi1APG5s3FUr4ZOr8av977G/x0bfXzYxf1YfWUrFif9iq9Pn8LHcZlQHyvCutQqJOS3oLyNANGeCQjZiYBQuW1fji5q7rGs6fPG+VKGAfSHSxUOZSns3SCN3J0/7rFrElIvWEYi+TkqCFeTmL8uztrX2qUSs5QtEL59+xZ1dXWIiYnBokWLPHUa2YqAUHmdiSzvLKs6+hB+eQd0ejUyqn5icg259UYEnysdhrp8qI+l4X9HC7H5Wg1O5DXjBLd4eBRzos8WSlPnBdGMlp4BnLTx2Kg7X2PTtQiEJv0KlTYfcxOK8dOteuTUd9M0Uze1SwJC10RAqNy2L0d3mUbrAhr7eY+f71xhK1RaDosvlKHZODBllkLiUQnUfgyD2NLk9usRW5rAz1GNAmHADPBzZ3rkXJ60r7VLJWYpOyAcHBzElStXsGrVKgQFBSEkJARHjx5192lkLwJC5XUmsryzFEURC88nIvpeAC4WH/TquZuN/dicJm1+8MGRWwi7GIXD2d8jTj8HPf09lscVNJ1Ddn0cKtrTUd+lR/OehXh4/Ts8K1qMweZkqyzL2q4hu/4orlfuxHnDUsTpP7OA4a93Q/BebIHli9KXJy5i9olcHLxVj9LWXuavBWsTELITAaFy275c/ekxqe5hSYvn39s2Xq2ymrp/s8k8ORAeOeDR0TuRy7aGwZE1iwqrReiL7VJpWcoCCF+/fg2DwYD9+/dbbSRz6dIl/P333+44heJEQKi8zkSWf5brU8sxU5uHY7ne2ZFOEEWczG/Gh7pCqLT5WHReiyO5X1rALT5/LpqMBrt/L2al4fGuz6RC9eXbJs1yQOhFQ3c+8hvPwNByET39Am5UdiLyRjlicmZDmzMbG9PW4evTpxCUWITTXAu6TZ7/RV2OJiBkJwJC5bZ9uXrxhXKotByulnl245am4aL043dPbTYO2Hy8N0bvaISQ7K4smQKh0WjEmTNnsHTpUgQFBUGj0SAhIQF1dXUICgpCW1ubSxenZBEQKq8zkeWf5WmuBSoth1WXKz1+Ll4Qse26NCr4cVwm9t9aaQHBC8XLUNKaigFh8mLK4sAAhGVf4A23GEMGDd4+63c6y46+GpwpXGg1tfTQvTlYnbINASevYm9mHSravVPUWS4mIGQnAkLltn25OvJGLVRaDnE5noUge/UVc+uNNh9vb/SOv3gKIpdtF9rElqZJ7x9vfv9GKxikNYT+bcUB4ZYtWywjgXv27AHHcXj16hUA4MWLFwSEBISK60xk+WdZPFxU+OO4AnSbPLMNutksweCPadVQaTl8pLsFnX4OdHo1juV9gcLmCxBFweFjCScO4+8zX+M1p8F/D8qnlaUoimjpKcGd2sM4yX1rBYeLzh+2bJJwr67bLzaiISBkJwJC5bZ9ufpobhNUWg7brtd49DxuGSEcaxtTSIWrSaN/4+AUU+HYL9Lj96xX3MjgiH2xXSotS2ZAOAKD0dHREATB6j4CQgJCJXYmsvyz5AURX8Rn4OCdb3Esby4EJ8DMUQ8IItanSjD4TgyHGxWduFV7CBdLwtHR53ytLLG6AmLwOxC+exf//f2Xy1kKooC6rhxcq9iOOP1sbErLsdQL+zguA4sv5COjqhOCm8BQ7GiFmHcXQnIChNj9EA5sBr9tBfg1weCXfw1+5XfgVy0AH7EQ/IbF4HethRC9G0JCDISURIj6OxCbGyEK7nutCAjZiYCQrX0xy2vlHcOlIMo8fq7AxFIrGJxyDeHVJPBzZw5DoA04HDO9c7rTP4WDWzy+k6mn7YvtUmlZMgPCu3fvYtu2bRYw3LVrF/Lz8/Hy5UsCQhAQKrEzkZWRZfhvZYjO/go6vRotPSVuPXY/L2DtFWnTgXdjOWRWSfUOecHkEnzyK78DHzADz/RZbs3SxHfDbDajoasf+7PqsfXGahzO/hIrL+/EonN3caOy0+kRQ7GjVdoGfcdq8MFq+7+OO+tv3gO/agGEqEgJFMsNEAemV9+MgJCdCAjZ2hezLG2VZn58cszzpSfCkqX1itp7jQ7tMmo2D08BLciBcPOq7fe2Cyek6aW6g9PaIIbfslwCwvTLzF+L6doX26XSsmS+qUxPTw/Onj2LsLAwBAUFYfHixdDpdAgKCkJ7e7urh1esCAiV15nIysjyaG4Tfry+Gjq9GnmNp9x67IO36qV6gonxOF2wFv28e3a9E87GgQ+YgYd7IvD7QKtHchkQTEgsWmqZShqT8xlWX9mKsORsu2tkRiz2GCEkJ4BfHzrxF+45Koir5oPfsxZC/K8Qkk7g0a3DeM5txcuiH/GycBNeFm7Ey4INeMltwPOcjRg8fxBC9G7wO9dAXPctBld+DOHbmRO/LM1/F/yGJRBOxUIsyoM4YHvqljvbJQGhayIgZGtfzLJ3QLCM2nX2eXajrJERwts1XU5nOeUUUlt2YISQ/+EbCRzz7zF/LaZrX2yXSsuSORCOyNZOoxs2bMCtW7f8cqdRAkLldSayMrLUNxgRciEaOr0al0vXue24BU09mBXDYd6ps9ANl4Dgms665dhiQw2Eb2fi5Y0gvDEsw31+ckCb9nlEEbWd95BcsmYMGH6O8Ms7sOZyPsrGlawQu7sgnIoFv+B/1r9o/7gQT67twrPiffindAOGDBr8bmq0/N0fLSkYMmjs+kHv6KY/T9vSLLe/LlyB53fX4I/zYXi4/UuIwe9af3n69gPwO1ZDuJoEscM+OBMQshMBIVv7apaz4w1QaTkUNPV49DxfnZDOU9jUM60sp5xCOvwj2sj/O7KGkF/wsfS+W+v5zdI8ZV9tl0rKUjZAOFaDg4NISUmx1CIMDQ31xGlkLQJC5XUmsjKyNPECPo+/Dp1ejTj97Cl3+nTE/byI78+U4vP4y4jN/QI6vRo3Knc5tXnMVOYjgvHyWiCGDBo8bb/h8ZwauvNxoXiVBQxDLxzCrBgOuzPq0NreDSH+EPjvPhz94rJqPh5nHMTzsr14Y1g6AfIedektx/69rxZPOm7hUXceHnWk+r1IAAAgAElEQVRzo+7Kw5OOW7jPj36pe9p6Ff+UrrUPj+lHIOzbCD7oE/Bzxn2pWh8K4cIJiC2NVs+NgJCdCAjZ2lezDLtYAZWWQ0ppu0fP85GuECoth5oO07SznHIKqe4n6b8RC6c+lqlv9Ic4Yxfz12G69tV2qaQsZQmEI/rvv/9QW1uLmJgYT55GFsrOzsamTZsQGxsLgIBQiZ2JrJwsNUll+PnO19Dp1ajrcr2A75GcJqi0+dibtQQ6vRpXyjeCFxybvuioxaSTeLzvc6kmYeUur+QkiiLqunJwsWQzNl4ts0zLmh2bhmOrNqF3zizwK7+DcDMNT1qvWYHay8rd+KMlBQ+Nxbjvhqmz94U+POitxpP2TPzdEI+XlTvwungFzIK0jlAUBPxZosUr/Ur8cToEgys+tP6StUkDIfU8xO5OAkKGIiBka1/NcldGrWVtn6fOwQui5T2wvXfA5SztbSIjZKZK///DN1Mfo7HOssZayTtE+2q7VFKWsgZCfxYBofI6E1k5WUbfbcTa1E3Q6dW4Vxfr0rEq2vvwbiyH788eh06vxvH8r9DT74GSFq1NEBfMwpuCJcNTMBu8mpko8MhJOIugvcnYlxWEvTcX4cek48iuHf5VWhzA85qf8UfLZfze3+yd6xKt1wu9rNxhBaX/FPyAP5M1+H3VR6NfuOa9AzE5YdrtkoDQNREQsrWvZhmf1wyVlsOPadUeO0dH74AFCAcE0S1ZWk0hHa4hKFaWSv9e+OmUfy8a8qTHLp3D/DVwxb7aLpWUJQGhTEVAqLzORFZOlrdrujD/1Bn8eG0HGrrzp30cXhAReqEc78Tk4HD2d9Dp1dA3nvRYlr9v+wHPzn2DIYMGfzZ7r/iw2NYCfuMS8AEz0KiZhaPZn1umkq6/ugHbbxSirde9I6LTyojvwePOe3hWF4vXJSutRyz1q8CHL5CmVuXcmna7JCB0Tq7Ofhlp+4D83keUaF/NMqOyEyoth+BzZR47R02HCSothw91hW7NcmQKqVX5iYAZ4OfNmnLUT8xKkx67YQnz18AV+2q7VFKWBIQyFQGh8joTWTlZGvt5S+29ZuP0SheYzWacK2yFSssh4OQ1nCoIxqmCBW5Zk2gvy79vp+HR9k+9O220o02qFxgwA/y3H+Lx7V/xV5EGufnzLFAYfS8AYRdjkFTUIp9pS+IAHhqL8HfDcbwuCcffDSek51NdATPPT7tdEhC6JgJCtvbVLCva+6DScvhIV+ix96DCph6otBy+PGHwaJbW6wK7J32scOm0tIZ77wbmr4Er9tV2qaQsCQhlKgJC5XUmsrKyDD4nbR+eWtYxrb8XRRHfnSmBSsvhFNcCXuhHe6/npivdv38f/z59DDH4PQwZNHhjWGq1+YonLHZ3gg//XvpysuQL/FV+2DLi9qxWi5bufJwqGC1VsStzMVZd5tDYPX3I9oiFftznR79Y0RpCdiIgZGtfzdLEC5g5PJ2zrcf+bIUmYz+y67rRNI0fAm/XdEGl5bDgbKnHs+S/eV8Cwsa6SR8nHP9VAsIjB5i/Bq7YV9ulkrIkIJSpCAiV15nIysryp6x6fHDkNn7Kuo5u0+R1nmy5bLgY8ruxBegyebb21dgshS0/4OkvX+Lh1Z8sG6p4wqKxG/yaYOlLyfLZeFGyzQKif7RegXn4V3hBFFDQlAydPgA7M8Kg0ubjI10hkgxt8hkttJMlAaH3RUCo3LYvdwecLIZKyyGvwXZZnmRDmwUaZ2o5JBucW+udWtYBlZZDWHK5x7PkF38hvfeWFk76OOHnrRIQnjvOPH9X7MvtUilZEhDKVASEyutMZGVleb2iA5uuRUCnV6Og+bzTf//rnQZ8ejwVOzOOQRC9B4Ti8BQhfvsqj51L7OsFv2GxdJ6gT/CsdL9UB7BkFR512/6CYuxvgaGlBosvlA+DcjbWpaa7NCXX01kSEHpfBITKbfty94pLUumJX27XTxgBbDL2W2BwxLNiOKdGCkeWCKy9UuXxLPnVgZb1zpM+bstyCQiv/8Y8f1fsy+1SKVkSEMpUBITK60xkZWVZ3WHC8kt7odOrcbPmZ6f+VhBFfHnCgPVXN0CnV+N27WGvZWkuL5ZA7fuPIQruq3No9fwO/CidY8HHECuKMTjQgec1v1gVl7dnXhCRkN+CiCuRiM39FOEpP+F6hQd2XXVDlgSE3hcBoXLbvty97GK5BfbGjwBm13VbweCIc+onX6M31kdzm6DScth+o9bjWVpAL/3y5I9b8a0Ejnl3mefvin25XSolSwJCmYqAUHmdiaysLE28gAWJR6HTq5FcvMapv9U3GDFTm4foewHQ6dVo7C7wWpZmQQD/7YcYXPkh/ig9isH+VreeR8y9NVzYfSZEQ970jiEKuFSyfczawiXYk5mD3gHPAOx0syQg9L4ICJXb9uXsqUYA3TFCGHW7ASoth59v1Xs8S2H/JgkILyZM+jh+wf8kIKyuYP4auGJfbZdKypKAUKYiIFReZyIrL8sFZ1Og06sRn/+tU3+3J7MOAScvQKdX4wQ3H4LoedAZmyW/9Qc8v/w9hgwaPGnPcNs5xL4+8JoA8AEz8ChpGx533pn+sUQRxS3XcCRXgubD2V9g1eU4lLR4diMcZ7N09m8JCF0TAaFy276c7cgIYPhvlXZHEB3xjvRaqLQc4nKaPJ6lELNXAsKT9mefiP2m0d1Iu7uYvwau2FfbpZKyJCCUqQgIldeZyMrLckkSZxnFMvGOTR3q50V8crQQKy/vhE6vRkbVPq9nKZw+gqdRXw7v9um+6apCvLRj3eDmuXhjWIYhgwaPjJNvajCVu01NOF2w2pLzj9fW4UJRLdMNZwgI2YmAkK19NUtHRgBXXR4Fwh8uOT+itj61Cioth7MFrR7PUkiIkYBQu8fuY8TGOgkI578r2w28HLWvtkslZUlAKFMRECqvM5GVl+WmtGpE3ZkPnV6NZqPBob+5WdUFlTYfP99ZAJ1ejcr2TK9nKebfw+CKD4c3egmH2Q0jlGJVGfh5s8DPmYFXBWsxZNDgeW20e44tCrhbdxpHcj/DoXsB+DguA1uu1aCnn80UUgJCdiIgZGtfzjLZ0DbpCODseIPl/k+PFTkNUcsuSpvWXClt93iWjtQXFA35EhBqAphn76p9uV0qJUsCQpmKgFB5nYmsvCx/vdOAyPTl0OnVKG276tDfbE6rgfpYGnR6NY7mzUa/h2sB2spS7OsFP28mXueGYsigwYNe19aPiIIAfn2oNFX0ZLhlR1F31zls7DbgmD4Ns2KkL2XfnC5BZXuf1193AkJ2IiBka1/PcuVvErTF3LPeAKujd8ACg+/GSv+t6zQ5dezARKl27a3qLo9nKaSnSLC3eZndx4i3rkmPWR/KPHdX7evtUglZEhC6IK1Wi7CwMMTGxk64Lzg4GFu3bsXWrVtx6tQpp49NQKi8zkRWXpbnClvx7ZkT2JlxHF2m+ikf39Mv4IMjBQg6dwQ6vRpXyjcyy5KPWIhnZ7/BkEGDP1pc23JcuHJe+mIR+BH+KZFGB/9oTfXYc8lrMGJ2vAHzTp3Bj9c24Eal83Ug3ZmlMyYgdE0EhGzt61nqcqSdQLdeq7G6fWSN4ZyEYoScL4NKyyG1rMOpYweclEYYC5p6PJ6lmHtbek8OX2D3McKlM9Jj9qxnnrur9vV2qYQsCQhdUEtLC2pqamwC4fLly106NgGh8joTWXlZ3qrugkrLITCx1KHHp5S2Q6XlMO9UMbpMjWjrrWSWpaA7iMe7PsOQQYMXVfunfVxR4MGHzAYfMANPMvdiyKDBP2XrPVr03mw2o9nYjcPZ86DTq7EvKxhHcvIheGkdDAEhOxEQsrWvZylN6efw/Rnr9/TTXIulhuCBrHqotBwOZE39I+BYfxxXCJWWQ1VHn8ezFMuKJNgLmW33MSNrvoXY6b//y8W+3i6VkCUBoYtqbW0lIPQxU5b+k2VFex9UWg7/O+rYxilrUqRNBWKyp67H5+ksxdvXYQ55VwK40rUwi/y0jjtSZkII+QSvS1ZJO5d2ZHnlOdV1cYjN/Ro6vRrR2V9h2/WLMPZP73m4kqUzJiB0TQSEbO3rWTYb+y0byvSNKXOz/Uat5b07tawDKi2HRefLHD4uL4iWKadtvQMez9KyYczX79l9jPDzNum9O/Eo89xdta+3SyVk6bNA2NbWhkOHDiE8PByBgYGoqqqa8JicnBxEREQgJCQEkZGRaG9vd/o89oBw4cKFiIyMxJ49e9DW1ub0cQkIldeZyMrLsndAgEqbjy9PXATXmAJBHJj08V+eMEClzUd+g5F5lmJHK/iAGbi/7EOIXc5NfRprYd9Gy6/MD41FeFYX65aNZBx1T38rjuf9AJ1ejSO5amxM+wUNXc6t7XE1S2dMQOiaCAjZ2tezFEURnx4rgkrLoah5dA108Dlpmuj1ig7UdZqG1xIWoJ937L2uo4+3AOGAIHo8S7HXOFpSos/2Omt+6w/Se/f1S8xzd9W+3i6VkKXPAmF9fT1SU1NRWVlpEwjLysqwcOFC5OfnQxAEJCUlYfHixXj06JHlMZGRkdi8efMEP3nyxPIYe0A48piBgQGsWbMGL168cOr6CQiV15nIysxSfawQ2uwvoNOr0dlXa/dxfQMCVFoO669uwG+lG9FsLGaeJR82T/rCkDO9ET2xuxP8/HekY1R49/mMNS8M4GLJT5bSFFuub0Jhk+fqahEQshMBIVv7Q5YrhusNXiiSdhnlBRHvHymASsuhpsNkBY0j6wGncu0wRH6oK/BKlqIogp83/N7cYntGCr/yO+l+/fTrxcrF/tAu5Z6lzwLhWNkCwp07d+Ls2bNWt23atAkpKSlOHdseEI7VL7/8gp6eHpv3vXnzBi9evLD45cuXAKQPzfv37zvswcFBAMDg4KBTf0emLP09y4XnyrDn5iLo9GrUdN6x+7jytj7MismFNvtL6PRqtPSUMM9S+GW79AvxiehpHVO8nCj9Cr1qAfPX4f79+7hTm4QjuZ9hbeqPeP8Ih+sVnV7L0lETELomAkJlfllUkn+90wCVlsOezDqYzWZUDS8N+OBIgWWd8poUCRpPcy0OHbOouQcqLYcvToyWJ/J0lvyizyb9sY4P/D/p/upy5pm7an9ol3LP0i+BcGhoCMHBwRMg8cKFC9i3b59Tx7YFhM+ePcObN28AAI8fP8aaNWvw7Nkzm3+flpaGwMBAiyMjI506P4lEck2RN5uxIW2dBISmq3Yfx3U+wNxTidDp1ThTFIi3b//z4lXa1rPcmxAD38Hz1DD82xTt9N8Prg8FP1eF19xa/Gv8DW+HXnrgKp1Tz8NmbBxe76PScrhU1Y+3b9+yviySm0RAqMwvi0ry1eE1giHDawTTyq3/bTabEZcr7Ub6Y1q1Q8e8W9s9YbMajwPhyAhg3t0J94n9/aNTSl1YMiAX+0O7lHuWfgmET548QWBgIDo7O60el56ejg0bNjh83KioKCxfvhyhoaFYtWoVjEYjAKCzsxObN2+2lJ2wtX5xRDRCKD9Tlv6VZdTtBoRd3A+dXo3M6p/sPu64vhmrU7ZBp1fjZs3PssjSXF8D4ZuZGDJoMGTQYFDoc/h45qoy8AEz8PvaT6S6g6URuG8Wmb8e9+/fhyCa8VNWPWZq87Du6kZE3b4BcfiDzlNZOmp/HiGcrNSSoyIgVOaXRSW5qkMaEXz/SAF4QUT03UaotBx2ZYwuCRgBvICTjk2VH4FKTdLoaJyns+Q3LZVmgNycWCNXbKqXgHD+OxC9tDuzJ+0P7VLuWfo1EHZ1dVk9Lj09HRs3bvT25dkUrSFUXmciKzPLMwUt+O5sPHR6NS6WrLL7uO03arHnZgh0ejUqO27KIktRFMEHq/HP3UXDBeqrHD6eoPsJfMAM/PnbCgwZNPi7/jjz12K8j+Udg06vRkzO59iVcd7hDSCmk6Wj9mcgnKzUkqMiIGRrf8hSEEV8qJPWDFa091mmh54taLU8ptvEY+bIrqE9k28mZjabcb6oFSoth4gro++xHgfCPeskILx8dsJ9YjEnAeGSr5jn7Q77Q7uUe5Z+CYTunDLqLmVnZ2PTpk2WD1oCQuV1JrIys8ys6sRn8SnQ6dU4wc23+7glSeWIujt/eP2gY3ULvZElv3stnv/2PYYMGjzudGxzAXGgH3zQJ+ADZuCfwtUYMmjwqCuP+Wsx3gOCCSe5TZYdSLfe0LmlLAUB4fTlyLr5yURAyNb+kuXiC+VQaTmklLbjqxNSQXn9uN2hvz1TApWWQ0ZV55THO6ZvhkrLIfLGaMF7T2cpHNopAeHpIxPuE29dl4BwXQjzrN1hf2mXcs7SL4EQkDaVSUxMtLptOpvKeEoEhMrrTGRlZlnW2ot3YnIsO1z2Ddj+cvDpsQLE5n4KnV6Nnv5Wr17jZFkKF07gr2NzMWTQ4M/mZIeOJd6+IZWsiPgKQwYN3hQvh1nwbKmH6VoQBVwwjO5AuvX6fnT0Tv2L/nSydMRyBULWpZYcFQEhW/tLlvtu1kGl5bAjfXQ9cpfJ+sekkfs2X6tBk7F/0uONbFTz063RYvYeB8L4QxIQHjkw8b7fzkhAuGcd86zdYX9pl3LO0meB8NWrVzCZTDCZTAgMDMTt27dhMpksZSVGyk5wHGdVdkIuH/YEhMrrTGRlZtltkupLBZ07gor2bPA2wKjLxOPd2HvYkbEUZwoXQZhmEXhPZCnevoEn+2djyKCRagg6cCx+5xrwATPwNHUzhgwaPK/5hfnrMJlFUcTV8ngLFG67sQOtPdMHWF8EQtallhwVASFb+0uWyYY2qLQcPo4rhErL4asxu4OOeNv1GgssztRySDa02T3ergwJHo/kNHktSyHphASEBzZPvO9EtHRfzF7mWbvD/tIu5ZylzwJha2ur1e6dIz5x4oTlMSO/li5atAiRkZHTKiDvKREQKq8zkZWb5f+OFlrWm9i6f2TL8c/jJ36pYJ2lWFqIB+v/hyGDBi8rtk95HHFgwFJ78EXxNgwZNHjSnsH8NXDEN6uTcCRXDW3ObIScT0fzFL/qO5ulI5YrEI4V61JLgPs2THN1EyCyf2ZZ0tJrgT2VlsOalCqr+5uNA5Y1hCOeFcOh2Thg83gbrlZb1iF6K0vx+iUJ+ratnHCfEBUp7TCaeJR51u6wv7RLOWfps0CoNNEaQvmYsvS/LAMTS6HScsiqtl0MPaW0HSoth6XJ7Oo92ctSbG2GOfQ9vOEW42XlzimPI9ZUSFONvv0QfzUk4J+yDRgcaGf+Gjjqu7WpCLlwHioth3mnitHY7TwUutIulQiEni61ZEtUUonEUq+H/sM7sQUW2Esotq4FXTPw1AoGR1w78NTm8dZcrYdKy+Fu66A3Lh8A8LKqGHzADAxuXDLhvofDG84802d57XpIpMlEQOghERCyM2Xpf1muvVKFD3VZSOCSUNaWNuH+2Gxp2/Kd6bVev7apshR5HvycmeDnzIDYPvXaRjErTQLCtYuk2xS4ZXl9lwlzEoqh0nL47kwWajuNTv29vwGhp0st2RKNEMrT/pTlyA99Ki2H80VtVvc5O0IYfK7M8qOht7I0lxRI79Warybcx6/8Xhoh1N9hnrM77E/tUq5Z0gihTEVAyM6Upf9leSCrHgEJ56HTq3G2aNGE+zen1WBJUhR0+m+Q28CmPMNkWfKLPpe+HJRPPaVVSIiRpiH9uoN57q64ydiPhefS8cvdediVuQr1XY5DoSvtUslAKIdSS7SGkK39KcuRnUbtrREcWWfoyBrCkR+g8htH32c8naVYVy0B4XcfTrhvZJdoscr7O157wv7ULuWaJQGhTERTRuVjytL/skzIb8FHupvQ6dWI038KXrDexTL4XBlWpWyHTq9Gdr1Odlnya4KlLwd3p14LyO9ZB36+Cr9fjYaZweY47nRRcw5icmZDp1djV+Zqh6HQ34BQTqWWCAjZ2l+ybDL22xwBHL+b6IpLFVBpORzNbZr0eCPrzCvHrDP3OBB2d0pAGDAD4sDodYsD/ZbbhUoCQrJ7siQglKkICJXXmcjKzfJGRSdU2nzE5kpw0W1qtNwniiI+0hViY9pa6PRqFDZfkF2W/K4IPNyixsu8CPw5xfXxy+fjwTrHN6GRuwua7lmgcGfGGjR297iU5VRWIhAC7Eotufpjp6uvF9k/s8yu67a5RjCnvtvqcbszpPIUMdmNdo8liKIFLlvHFLH3OBAKgrQcIGAGxLaW0es5H28BQn6OCsLVJOZ5u2p/aZdyzpKAUKYiIFReZyIrN8vi4R3pfr6zADq9Gg3d+Zb72noGpHpWGUuh06tRwWhHzsmyFLR78WjHp8MlJH62ewyx3wR+zkz8eVSqW/hXw0nm2bvDXNMdxOR8PgyFEWgx2t4t1pEsp7JcgVAppZYICNnaX7J0dIQwZnh9+O6MOrvH6urjLcfo5wWvZskH/Z8EhNXShmZiSxP4OapRIAyYAX7uTIgtk49wyt3+0i7lnCUBoUxFQKi8zkRWbpYdwx/4kenLodOrrTaW0TcYodJyiBqGxcbuAtllKZyNw2D4RxgyaPBP2Ua7xxArSsAHzMA/d0IwZNDgEaPn4gnnN45C4Y6Mzejss7/7qC8CoVJKLREQsrU/ZZlsaMOsmFEYtLVG8FxhK1RaDmuvVNk9Tl2nCSoth/ePWL9fegUIl8+XgLAgF2azGSKXbQ2DI1NKC3KY5+2K/aldyjVLAkKZioBQeZ2JrNwsRVHEh7pCrE3dBJ1ejbzGBMt9IxsPxOZ8CZ1ejc4++78ks8pSuH4J4oJZGDJo8MawFGbBNgwJ6ZfBz1dhyKDBkEGD+/zU0yuV5Nz6TMTmfordmaEISy5A34Bg83G+CIRKEQEhW/tblk3GfuTUd08YGRyxtFyAQ+gF+yWFDM3SDJLZ4+rQegUI14dKwHfrOszm4RHC8UBII4RkN2RJQCgT0aYy8jFl6Z9ZfnemBMsu7YdOr8bNMdMuf73TgHdjs6HTq6HTq9HP98ouSzHvLviAGXidtwRDBg1+twOtQvyvMIe8J4Fj8Q+KLDkxle/UZEF9LGe4GHUlBoSJz5GA0HuiNYTyMmVp7fzhGSBzE4rtPuZubfdwiZsSr2fJ71wjbR6Ten70tsVfWMEgrSEkuyNLAkKZioBQeZ2JrOwsV1+uxCfHruFs4Q10m0Z/bV2XKtUoPKpfiQvFYbLMUqytBB8wAy/SgjFk0OBxV67NY/A7VuP3VdLU0lflPzLP3FPWNxjx/hGpKPWO9DsQxoEvASE7ERCyNWVp7drh6aAfHLE/ff5aeQdUWg6aJOtRRG9kKURFSkCYeBRmsxliXy/4udJGM8LNNMWPDHozS38xAaGPiYBQeZ2JrOws92ZKu81F37Xebe67MyVQaTncrumSbZZijxF8wAz8dXI+hgwa/NGSYvMY/JKv8HCLGkMGDV5UH2CeuSd9s6oTSy/+jCO5ahy+d9HhLKcyAaFrIiBka8rS2j39gmXDmJ5+21PMk4aXDaxJsV5n6BUgPBolwV/cQZjNZogFOdLIYMhs5tm509Qu2WdJQChTERAqrzORlZ1lfF4zVFoOW67VWG4TRBHvxUojTXWdJtlmKYoi+G8/wJOfvsCr4g14OmZTHMtjenvAB8yAefF7eFp/EY877zDP3JMWRREJnDQFWJszG+eK7jqU5VQmIHRNBIRsTVlO9AfDswlq7bzHx+ulz4Zt12usbvcKEJ47JgHhz9us/s3vXc88N3ea2iX7LAkIZSoCQuV1JrKys0wbnha04epJ6BtPop/vQUNXP1RaDu/GcuBtrEWTU5b8svmT7jYnlhZKXyQWfcY8a29ZEHnE5kj1I3+9Ow9Zw1u3ExB6T7SGUF6mLCd6TkIxVFoO+Y1Gm/cfutMAlZbDgax6r2cppF6Q3rd3rIbZPGZNYZJvlAzyZpb+YgJChYs2lZGPKUv/zLKwqQcqLYdD9+ZBp1ejtafcspnAxrQDSCj4FvlNZ2SbJf9jmPRFIT3F5v1CWrL0xWLLcuZZe9MmvgfR9xZDp1fjp9tBKGttISBkKAJCtqYsJzr0QhlUWg43Kjpt3j9SvD52XPF6b2Qp3r4hvW+vC5FmggSrpR/+ijnmubnT1C7ZZ0lAKFMRECqvM5GVnWVHH49ZMRx2Z4ZCp1ejsj0LZwukGlUHbm0bLkdxSrZZCge3SEB4/ri0e+i4jVQE3UHwATPw+5nteNBbBbPAdgqsN93e24To7G+g06ux5+ZytPX2TbtdEhC6JgJCtqYsJ3rtlSqotBzOFbbavH/j1WqotBxOcy1ez1IszJWAMGwexIZa6f/nvwOx336dVSWa2iX7LAkIZSoCQuV1JrLyszyZ34xN1yKg06uxMyMOO9NrodJyiMlZA51ejZLWVNlmKRz/FXzADDy7uRqvS8LxsKfM6n5+6wqpKH3BSgwZNHjQW8E8b2+6sr0E2pwvsezSPuy7WTftdklA6JoICNmaspxoeyOAI15xqQIqLYffStq9nqVYXSFBYOD/SXVkh0cLWWfmblO7ZJ8lAaFMRUCovM5E9o0szxREQadXY8XlXZbd547lLYFOr0ZtZ7ZssxQunQEfMAPPM5ZjyKDBk44sq/v5kM/BB8zAG8NSDBk0GOxvY561t13SWo+d6bUw8eK02yUBoWsiIGRrynKiY7IbodJy2JNpu37rwnPSlNLMKusppV4Bwo5WCQLnqCBE75ZmgegOMs/M3aZ2yT5LAkKZioBQeZ2J7BtZFjSfh06vxtYb68YA4deWdYVyzVK8fR18wAz8eW4xhgwa/Nl0bvS+7i5p3UngO1JResNSmEWeedZyzXIyExC6JgJC5bZ9X3ViobQ0YO2VKpv3zx3edCavwXrTGa8A4cDAaBH6xV9K7+NZE3eRVrqpXbLPkoBQpiIgVF5nIvtGllUdWdDp1UgsWoHvzpRg9vEC6PSfQqdXo3egndl1TQmEJQXgA2bg8aFvMWTQ4Hlt9Oh9RUD103UAACAASURBVHngA2bg/qa5GDJo8E/pWuY5szQBofdEu4zKy5TlRF+vkHaYDr1g+we/T44WQqXlUNHWxyRL/vuPRqEwYAbEFttTW5VsapfssyQglIlol1H5mLL07yxbekqh06uRUPAdBFFER28zdHo14vSfQhBtFy6WQ5ZiS5O0acy6/8OQQYNX5Vss9wkp58AHzMCDIyswZNDgZeVO5jmzNAEhOxEQKrft+6rzG4xQaTnMTSiecJ8gipgVI80UaTZab+TiNSDUBIwCoY8VpPd2lv5gAkIfEwGh8joT2TeyHBB6UduZjfbeapjNZnSbGvFbaQQula6WdZbiwAD4OSqIwe8OTwsNs0wLFWL2SqOHl7dOGD30RxMQshMBoXLbvq+6ttMElZbDh7qCCfd1m3jL0oG+AesfBL0GhBELR4HQxwrSeztLfzABoY+JgFB5nYlMWbLOkl/02fDGMdLGMr+bpKlF/Kal4ANm4I97+zFk0OCvxgTmz0fuWdozAaFrIiBUbtv3VRv7R6Gvp98a+uq7JFh8L3YiLHoNCCNXWoDQ1wrSeztLfzABoY+JgFB5nYlMWbLOkl8TLJWe4CLxvCYKv/fVS8WMg/5Pmk5aegNPW6/iodHA/PnIPUt7JiB0TQSEym37vmpRFPH+kQKotBxqO63rs5a09EKl5fD58SJmWQo/bR4FwjvpzPPyhKldss+SgFCmIiBUXmci+06W9V165DUmoMlYyPxanMmS37lG+tKQMmaH0eG1hfwcFcS+Po9fpxJMQMhOBITKbfu+7DnDO4nmN1rvJJpd1w2VlsM3p0uYZclvWT46ZXSOCsLVJOZ5udvULtlnSUAoUxEQKq8zkX0ny5vVB6HTq6FvOInbtYeQUPAtipqTZZ+loN0jAeHJw6O3DW8o44vFjD2ZpT0TEDon2mVUXqYsbTv0glRr8Ealda3BkR1IF9vYgdQbWVp+0BvruTMhtjQxz8ydpnbJPksCQpmKgFB5nYnsO1nqG05Cp1cjs/onpFVEQqdXKwMIz8ZJQPhLpPQ3Qh/4bSuk284dx6PufDzorfTrGoSOZmnPBISuiYBQuW3fl732ShVUWg7nClutbk8ytEGl5bD6ciWTLEUueyIQBsyAWJDDPDN3mtol+ywJCGUqAkLldSay72RZ2nYVOr0aKWUbcLEkHDq9GlUdN2WfpXDtEviAGTDv+wGvS9fgdfEq8PPekb5A1JZiyKDBkEGD+3wv84zlnqU9ExC6JgJC5bZ9X/bujDqotBxis61r/MXnNUOl5bD1Wg2TLMWWJvBzVDRCSPZ4lgSEMhHVIZSPKUvKsr47b7g4fSjOFAZBp1czX0/oSJZi3h3pC8Oqb/HGsBRDBg3E72eBX/41fjc1YsigweviFTCLIvOM5Z6lPRMQuiYCQuW2fV92THYjVFoOezLrrG7fd1MCxcgbtcyyFK4mgZ870wKDtIaQ7IksCQhlKgJC5XUmsu9k2WWqh06vxrG8L3A07wvo9Gp0mRpkn6VYUyF9aVjwP/xTthFDBg0Gwz+CcPxXPDQWTyhY768mIGQnAkLltn1fdmJhK1RaDutSqyy3JQ9PFx1xsqGNWZZiSxPEghyfGxlkkaWvm4DQx0RAqLzORPadLHmhHzr9p9Dp1RYPCCam1+QQEBq7LNOKnlcewJBBg0c7P4NYzOFx5x0MGTR4Uf0T83xZm4CQnQgIldv2fdnjN49pMvZj5hgYVGk5zIrh0GTspyw9YMqSfZYEhDIVAaHyOhPZt7JMKPjOAoPx+XOZX49DQCiK4L95H3zADPx5ZweGDBr8cfw7iIKAP1qvYMigwd/1ccyfC2sTELITAaFy274vO6/BCJWWw9yEYpjNo+Umxjunvpuy9IApS/ZZEhDKVASEyutMZN/KsslYhMr2TFwsWYXU8k3Mr8fRLPmweeADZuBxdCCGDBo8uxkOs9mMvxpPYcigwZ9N55k/F9YmIGQnAkLltn1fdm2nCSothw91BTCbaYTQ26Ys2WdJQChTERAqrzORKUs5ZMn/GAY+YAZ+X/WRtGawcA3MZjOe1R7GkEGDp23XmT8X1iYg9J6oDqG8TFnatrGft4Bf74AAs9mMLddqrGCQ5RpCXzdlyT5LAkKZioBQeZ2JTFnKIUvhp81SmYkFs/D80gL8VZ8AsyjiodGAp21p+L2vzivXK2cTELITAaFy274vWxRFvH+kACoth7pOab34xqvVUGk5bL9RYzUySFm635Ql+ywJCGUqAkLldSayb2XZ0VcDfeNJGFouMb8WZ7IUjv1i2VhG2LeR+XXL0QSE7ERAqNy27+sOOFkMlZYD19gDURTx6bEiqLQc9A1GytLDpizZZ0lAKFMRECqvM5F9K8vqjluWTWVKWlOZX4/DQHjp9CgQZlxhft1yNAEhOxEQKrft+7pDL5RBpeVwo7ITFW19UGk5vH+kAP28QFl62JQl+ywJCGUqAkLldSayb2XZ2lNmAcKytjTm1+NoluKt6xIQzpsF0diF+0Iffjc14VGXHg96q5k/DzmYgJCdCAiV2/Z93WuvVEGl5XCusBVnC6S6hMsvVVCWXjBlyT5LAkKZioBQeZ2J7FtZ9g10WoCwuvM28+txGAg7WsEHqyFE78bTtmvDxeg3Y8igwT+l65g/DzmYgJCdCAiV2/Z93bsz6qDScjiS3YT1qdL6wbhc+4XgKUv3mbJknyUBoUxFQKi8zkT2rSxFUbQAYZPRwPx6nM1SFEU86tJjyKCx+GXlLubPQw4mIGQnAkLltn1ft/ZeI1RaDrsz6qAeXj+Yb2f9IGXpXlOW7LMkIJSJXN2amzoT+85E9r0sK9pvQN9wAqIoMr+W6WT5u6nBCgif1R5m/jzkYAJCdiIgVG7b93UnFkrTROefkjaX+eBIAfp5++/9lKX7TFmyz5KAUKYiIFReZyJTlrLLUuTxxhBmAcK/Gk8xfx5yMAEhOxEQKrft+7qvV3RYFaJfMcn6QcrSvaYs2WdJQChTERAqrzORKUs5ZvmqfIsFCP9oTWH+PORgAkJ2IiBUbtv3dec1GK2A8Ogk6wcpS/easmSfJQGhTEVAqLzORKYs5Zjl89poCxA+6WC/OY4cTEDoPbm6HMLV14tMWTrqmg6TFRByjT2UpZdMWbLPkoBQpiIgVF5nIlOWcszyz6ZzFiB8aCxm/jzkYAJCdiIgVG7b93Ub+3kLDH5whMOAMPnaccrSfaYs2WdJQChTERAqrzORKUs5ZvmouwD/lK7Fi6r9uD/Qyfx5yMEEhOxEQKjctu/rTjK0Wo0QJhvaKEsvmbJknyUBoUxFQKi8zkSmLClLZZiAkJ0ICJXb9n3ZTcZ+zBwDgyoth1kxHJqM/ZSlF0xZss+SgFCmIiBUXmciU5aUpTJMQMhOBITKbfu+7Oy6bisYHHFOfTdl6QVTluyzJCCUqQgIldeZyJQlZakMExCyEwGhctu+L5tGCNmasmSfJQGhTEVAqLzORKYsKUtlmICQnQgIldv2fd3JhjbMihmFQVpD6D1TluyzJCCUqQgIldeZyJQlZakMExCyEwGhctu+P7jJ2I+c+u5JRwYpS/ebsmSfJQGhTEVAqLzORKYsKUtlmICQnQgIldv2yZQlZSl/ExD6mAgIldeZyJQlZakMExCyEwGhcts+mbKkLOVvAkIG0mq1CAsLQ2xs7IT7Hjx4gAMHDmDTpk3YvHkzXr165dSxCQiV15nIlCVlqQwTELITAaFy2z6ZsqQs5W8CQgZqaWlBTU2NTSDct28f2tvbAQDPnj3Dv//+69SxCQiV15nIlCVlqQwTELITAaFy2z6ZsqQs5W8CQkZqbW2dAIQ8z+PgwYMuHZeAUHmdiUxZUpbKMAEhOxEQKrftkylLylL+JiAcp7a2Nhw6dAjh4eEIDAxEVVXVhMfk5OQgIiICISEhiIyMtIzoOSNbQFhVVYXDhw8jOjoakZGRSE9Pd/q4BITK60xkypKyVIYJCNmJgFC5bZ9MWVKW8jcB4TjV19cjNTUVlZWVNoGwrKwMCxcuRH5+PgRBQFJSEhYvXoxHjx5ZHhMZGYnNmzdP8JMnTyyPsQWE5eXlWLZsGR49eoQ3b97gwIEDaGpqcur6CQiV15nIlCVlqQwTEHpP2dnZ2LRpk+VzkoBQuW2fTFlSlvI3AeEksgWEO3fuxNmzZ61u27RpE1JSUpw6ti0g7OrqQlRUlOXfWVlZyMrKcuq4BITK60xkypKyVIYJCNmJgFC5bZ9MWVKW8jcB4SQaD4RDQ0MIDg6eAIkXLlzAvn37nDq2LSD8999/sW3bNjx79gz//fcfDh06hNraWpt//+bNG7x48cLily9fApA+NO/fv++wBwcHAQCDg4NO/R2ZsqQslWHKUh5ZEhC6JgJCZX5ZJFOWlKUyPN0s/RIInzx5gsDAQHR2dlo9Lj09HRs2bHD4uFFRUVi+fDlCQ0OxatUqGI1Gy3319fWWKabJycl2j5GWlobAwECLd+/e7cQzI5FIJBJJOXr69CkePnzotAFM6+/IlCVlqQxTlmyzfPr0qUvv7YoGwq6uLqvHpaenY+PGjV69tvEjhM7WKySRSCQSiUQikUgkVlIkELpzyqhc9PLlS0RGRlqmnJKmL8rSfaIs3SfK0n2iLJUler3cJ8rSfaIs3SfK0n1ilaUigRCQNpVJTEy0um06m8rIRS9evEBgYCBevHjB+lIUL8rSfaIs3SfK0n2iLJUler3cJ8rSfaIs3SfK0n1ilaVsgfDVq1cwmUwwmUwIDAzE7du3YTKZLGUlRspOcBxnVXZiZO6t0kSdyX2iLN0nytJ9oizdJ8pSWaLXy32iLN0nytJ9oizdJwLCcWptbbXarGXEJ06csDxmpDD9okWLEBkZiba2NoZX7JqoM7lPlKX7RFm6T5Sl+0RZKkv0erlPlKX7RFm6T5Sl+0RA6Od68+YN0tLS8ObNG9aXonhRlu4TZek+UZbuE2WpLNHr5T5Rlu4TZek+UZbuE6ssCQhJJBKJRCKRSCQSyU9FQEgikUgkEolEIpFIfioCQhKJRCKRSCQSiUTyUxEQkkgkEolEIpFIJJKfioCQRCKRSCQSiUQikfxUBISMlZGRgR07dmDJkiX44YcfcPjwYZjNZtaX5RPKyMhAYGAgkpKSWF+KIvXkyRMcO3YMy5YtQ2hoKLZu3Yre3l7Wl6U4/fvvv0hNTUVERARCQkKwdu1aXL9+Hf/99x/rS5O92tracOjQIYSHhyMwMBBVVVVW9799+xZpaWkIDw9HSEgI9u/fD57nGV0taazos81zos8210Sfbe4RfbZNX3L8bCMgZKyoqCgUFhaC53mYTCYcOnQIa9aswatXr1hfmqJlNBoRERGBrVu30ofmNPTs2TNERETg5MmTMBqNePDgAZqbmzE4OMj60hSn9PR0LF++HHV1dXjw4AEqKiqwZMkS3L17l/WlyV719fVITU1FZWWlzQ/NzMxMaDQaVFZWYmBgAHFxcQgPD8fLly8ZXTFpRPTZ5hnRZ5tros8294k+26YvOX62ERDKTH/99RcCAwPR1tbG+lIUq1evXmHDhg1oamrC/v376UNzGrp8+TL27t3L+jJ8QocOHUJCQoLVbTExMTh+/DijK1Kmxn9ovn37FitXrkRmZqbltjdv3mDp0qXQ6/UsLpE0ieizzXXRZ5vros8294k+29wjuXy2ERDKTIODgwgMDMTAwADrS1Gs4uPjLR+U9KE5PW3atAlJSUk4cuQIfvjhB2zbtg15eXmsL0uRyszMREREhGW6nMlkwooVK1BSUsL4ypSl8R+av//+OwIDA9HX12f1uMOHDyM+Pt7bl0eaQvTZ5rros8110Web+0Sfbe6RXD7bCAhlpLdv3yI6Opp+vXJBpaWl2Lx5M16/fg2APjSnq5CQEISEhCAlJQV9fX3Q6/UICQlBUVER60tTnN6+fYuUlBQEBQVh4cKFCAoKQkZGBuvLUpzGf2h2dnYiMDAQT548sXrc6dOnERUV5e3LI00i+mxzXfTZ5h7RZ5v7RJ9t7pFcPtsICGWkxMRERERE4PHjx6wvRZF69OgRVqxYAZPJZLmNPjSnp4ULF2L37t1Wt50/fx67du1idEXKVWlpKVavXo3S0lIMDAzAYDBg2bJlKCwsZH1pipK9D82nT59aPe7UqVMEhDITfba5Jvpsc5/os819os8290gun20EhDLR+fPnsXr1ajx48ID1pShWVVVVCAwMRHBw8P+zd+ZxUVX9Hz8gMMOOCrmBmulTaE9aokWPmuEaaWo2yKYgKG6kiEiKJZoLhor7VimYj6G5ZG4sOiiLiooigiLJzlzNFq2n9afp5/fHNFcuM4OAszLf9+v1eSnnnjv3zHfuved87j0LL4lEAh8fH4wbN45mvmoE06ZNw5YtWwRpaWlpCAsL01OJjJepU6ciJSVFkLZ//37MmjVLTyUyTgylWw3ROKhue3qobtMcVLdpDqrbNIOh1G1kCPXMo0eP8PnnnyMsLAy3bt3Sd3GMmj/++ANVVVUCzZs3D+vXr6dxK41k7dq1St27EhMTlZ6sEk9m4sSJSEtLE6QdPHgQM2fO1FOJjBN1A+8PHTrEpz148IAmlTEQqG7THFS3aQ6q2zQH1W2awVDqNjKEeuazzz5DUFAQrl27hnv37vFSjBMgng7qVtM0bt68CV9fXxw4cAC3b99GdnY2AgMDkZWVpe+iGR0bN27ElClT+Km5z58/j5CQEOzatUvfRTN4/vzzT1RUVKCiogISiQRHjhxBRUUFfvjhBwDySQ2CgoJw/vx5VFVVYe3atbTshIFAdZt2obqtaVDdpjmobms6hli3kSHUMxKJRKWoD7ZmoEqz6eTl5SEyMhL+/v6IiIigmdiayB9//IHExERMmzYN/v7+CA8PR3JyMh48eKDvohk8RUVFKu+PGzduBPB48d7JkyfD398fCxcupDcmBgLVbdqF6ramQ3WbZqC6rekYYt1GhpAgCIIgCIIgCMJEIUNIEARBEARBEARhopAhJAiCIAiCIAiCMFHIEBIEQRAEQRAEQZgoZAgJgiAIgiAIgiBMFDKEBEEQBEEQBEEQJgoZQoIgCIIgCIIgCBOFDCFBEARBEARBEISJQoaQIAiCIAiCIAjCRCFDSBA6pqSkBKtXr8bkyZPh6+uLSZMmYdWqVSgpKVGZ/8yZM5g9ezb8/f0hkUhQUVEBADh+/DjCw8Ph6+sLiUSC3377TaPlzM7OxtGjRxu1z4MHDzBr1ix8/fXXfNrevXshkUjwyy+/aLR8dZFKpQgLC8Off/6p1eMQBEEQylDdph2obiN0ARlCgtAhx48fh4+PD2JiYpCZmYlr164hMzMTMTEx8PHxQUpKiiD/L7/8Al9fX6xYsQLXrl1DSUkJ/vrrL1RUVEAikWDLli0oLi5GSUkJHj58qNGyxsXFYfr06Y3a59ixY5g0aZKg4tJVpfn3339j5syZ2Lt3r1aPQxAEQQihuk17UN1G6AIyhAShI4qLi+Hj44O4uDj8/fffgm1///034uLi4OPjg+LiYsE+EokEZ86cEeTPysqCRCLBzZs3tVbexlaaf//9N6ZMmYLdu3cL0nVVaQLA4cOHERQUhL/++kvrxyIIgiCobqO6jWgOkCEkCB0RFxeHcePG4ccff1S5/YcffsC4ceMQFxcHANi4cSMkEolAsbGxiI2NVUrfuHEjAKC8vBxxcXEIDQ2Fn58fwsLCsHz5csExHz16hNTUVERFRcHf3x/BwcFYtWoVvvvuOz6PqmNIJJJ6v19ubi4kEglqamoE6aoqTZlMhhkzZmD+/Pn4+eef+WNGRkaipKQECxYsgL+/P6ZPn46MjAwAwKVLlxAdHY2AgABERkYiPz9fqQz37t2Dj48PpFJpvWUlCIIgNAPVbVS3EcYPGUKC0AEPHz5EYGAgYmJi6s03f/58BAYG4uHDh7h9+zZSU1MhkUhw8OBBlJSUoKamBjU1NUhOToZEIsGpU6dQUlKC27dv488//0RISAjmzZuHs2fP4tq1azhz5gw+/fRTQUW2detW+Pr6YufOncjPz0d2djYiIiIwadIk3Lt3DwBQU1ODDz/8EJMnT0ZJSQmv+ti8eTMmTZqklF630rx27RqCg4PxySefCJ52xsbGIiQkBLNmzYJUKsWVK1cQFxcHiUSCffv2Yc6cOcjJycHly5cRExMDf39//PTTT0rHi4iIwMqVK+stK0EQBPH0UN1GdRvRPCBDSBA64N69e5BIJFizZk29+dasWQOJRMI/WSwqKoJEIsG5c+cE+U6dOgWJRILS0lI+raysDBKJBBcuXFD7+SUlJZBIJDhy5Igg/ccff0RAQAB27drFpzW2W01ERASWLVumlF670szMzISfnx927NihNC5E8eS2rKyMT/v1118xbtw4BAQECCpIxTiT48ePKx1v3bp1KitvgiAIQrNQ3UZ1G9E8IENIEDqgoZVmQkKC4IljYyrN33//HcHBwZg1axbS0tKUurcAQHJyMnx8fPDzzz/j77//FigmJgbz58/n8za20gwKCsKGDRuU0hWVZmJiIsaNG6dUYSuIjY1FWFiYUnpYWBgWLFggSHvw4AEkEgl27typlD8pKQk+Pj5KY1kIgiAIzUJ1G9VtRPOADCFB6ICmdKsBGldpAkBVVRXWrFmD4OBgSCQShIWFYe/evXjw4AEAYMuWLSrHTygUHh7Of1ZjK00/Pz9s27ZNKV1RaYaEhGDatGm4e/euyv0V4yzqMn36dH7sSW0kEgk+//xzpfTdu3dDIpHQFN0EQRBahuo2qtuI5gEZQoLQEU8aeP/jjz8KBt4Dja80FTx69AgVFRVITEyERCLh107avXs3P9tbaWmpkqqqqgTlbUylGRYWhtWrVyulKyrNsrIyhIeHIzw8HN9//71SPk1Vmtu2bYO/v3+Dy00QBEE0HarbqG4jjB8yhAShIxRTc69YsUJpjMHDhw/5qblv3LjBpze10qxNcHAwX5mpm+pbFStXrmzUeIXFixcjOjpaKb32OIu7d+9i9uzZmDp1Km7duiXIp6lKc8mSJSrLQRAEQWgeqtuobiOMHzKEBKFDai/em5WVhevXryMrKwsLFiyAj4+P0kDyxlSaeXl5WL58OU6cOIGCggIUFBTg008/hUQiwYkTJ/h827ZtQ2BgIHbt2oW8vDwUFhYiOzsbn332GdLS0vh8isouLS0NN2/efGIFvW/fPvj6+iqtk1R3JrZffvkF0dHRmDx5suCprSYqzYcPHyIoKEjl+AuCIAhCO1DdRnUbYdyQISQIHVNSUoLVq1dj0qRJ8PX1xaRJk7By5UqVU183ptLkOA5r165FeHg4AgICEBQUhPnz5+PUqVNKn5uRkYGYmBgEBgYiICAA4eHh2LBhg9IsaKtXr0ZwcDB8fHyeuFbT7du34ePjg7NnzwrSVa3V9Pvvv+PDDz/ExIkT+e+giUqzsLBQaTY3giAIQvtQ3UZ1G2G8kCEkCEJjxMXFqZyeW1esX78eH374od6OTxAEQTQ/qG4jmjtkCAmC0BhVVVXw9fXFzZs3dX7s27dvw9fXF8XFxTo/NkEQBNF8obqNaO6QISQIQqNkZmbi4sWLOj9uYWGhYDwJQRAEQWgKqtuI5gwZQoIgCIIgCIIgCBOFDCFBEARBEARBEISJQoaQIAiCIAiCIAjCRCFDSBAEQRAEQRAEYaKQISQIgiAIgiAIgjBRyBASBEEQBEEQBEGYKGQICYIgCIIgCIIgTBQyhARBEARBEARBECYKGUKCIAiCIAiCIAgThQwhQRAEQRAEQRCEiUKGkCAIgiAIgiAIwkQhQ0gQBEEQBEEQBGGikCEkCIIgCIIgCIIwUcgQEgRBEARBEARBmChkCAmCIAiCIAiN0qlTJ3Tq1Emrx6ioqABjDEFBQVo9DkE0d8gQEnonNjYWjDFUVFTouyha44033gBjdLkRmuPUqVNgjCExMVHfRSEIwohgjAlkbm6OVq1aYeDAgfjiiy/w6NEjjRxHU4aQMYY33nhD5TYyhAShGaiFSugddYYwMTGx2dzoyRASmoYMIUEQTUFhBGNjYxEbG4uYmBj4+PjA0tISjDHMnDlTI8fRhSG8f/8+iouLcevWrac+DkGYMtRCJfSOKRjCqqoqFBcX67sYRDOCDCFBEE1BYQjrkpOTA3Nzc5iZmWmkx44uDCFBEJqBDCGhd0zBEBKEpiFDSBBEU1BnCAGgR48eYIxh3759KrenpqbirbfeQuvWrWFlZYUuXbogKioK9+7dU8qryhD+/PPPiI+Px5tvvokOHTrA0tISzs7OGDlyJM6cOSPIq2gDqFJsbCwA1V1Ghw4dCsYYrly5ovI77Nq1C4wxREVFCdJ/+uknzJs3Dy+88ALEYjEcHBzg5eWFtLQ0lZ9TH8XFxQgKCoKrqyusrKzwzDPPwM/PDzdu3FDKGxQUBMYYysrKsGbNGrz44osQi8W8CVbc62NjY3Hu3DkMHz4cTk5OSu2mixcvYsyYMXBxcYGVlRU6duyIqVOnguO4Rh+TMD3IEBJ6p7GGUJH/1KlT+PLLL/HKK6/A2toa7dq1w+zZs/HXX38BANLT0zFgwADY2dnByckJ48ePx08//aR0/IyMDEyePBnu7u6wt7eHWCxG9+7dsXDhQvzxxx8qy3zr1i0EBwfDxcUFYrEYPXv2RFJSkuDGXRtVXUZr583Pz4e3tzccHR1hbW2N/v37IycnR+m4HMdh8eLFeP3119GmTRtYWlqiXbt28PX1RVFRkVL+2pVlcXExxo4dC2dnZ5iZmeHUqVN49dVXYW5urvZp8MqVK8EYw6pVq1Rur03t32Xnzp3w8PCAjY2NUoMgNzcXY8eO5cvv6uqKsLAwlZXWzZs3ERoaii5dukAkEsHJyQkvvPACwsLC8OOPP/L5FOdKYmIijhw5Ak9PT9jY2MDJyQljx47Ft99+q7LMHMdh2rRp6NSpE98wGT16vVOHEgAAIABJREFUNC5cuKCUt/YxMjIy8MYbb8DOzg729vZ46623VMb/9u3bmD17Nv71r3/BxsYG9vb2eO655zB+/HiUlpYq5W9MY4sMIUEQTaE+Q+ju7g7GGL7++mulbYsXLwZjDK1bt8aECRMQFRXFm6/u3bvj559/FuRXZQjPnTsHS0tLDBo0CGFhYfjggw/g6+sLa2trtGjRAseOHePz5ufn8/VKp06d+C6usbGxOHXqFADVhvDLL78EYwyRkZEqv+OQIUPAGENhYSGfVllZic6dO4MxhgEDBmD27NmYPHky2rVrBzMzM2zbtq2+kApISUmBtbU1LC0tMWbMGMydOxd+fn4QiURwcHDApUuXBPkV5uztt9+Go6Mj/P39ER0djfnz5wN4fK8fMmQILC0t4eXlhTlz5mD8+PF8vXno0CFYWlrCysoK/v7+mDdvHgYPHgzGGNq1a4eysrJGHZMwPcgQEnqnqYbw3XffhbW1Nfz8/BAZGYkXX3wRjDEEBwdj7969sLKywpgxYxAVFYXXX38djDEMHz5c6fjDhg1Dp06d4Ofnh6ioKMyYMQO9evUCYwz9+/fHgwcPBPnv3LkjqDjmzZuH4OBg2NjYYPTo0Y02hG+//Tasra35m7xEIoG5uTlEIhGuX78u2Cc5ORnW1tbw9vbG9OnTMXfuXIwePRoWFhawsbFBfn6+IL+isvzPf/4DR0dH9O3bFxEREZg0aRLy8vKwc+dOMMYQExOjFJdHjx6hW7duEIlE+OGHH1T9dCp/l7fffhsikQjvvfceoqOjERYWxufZsWMHWrRoAVtbW/j5+fHlNzc3R7t27VBVVcXn5TgOLVu2hIWFBd555x1ER0dj5syZGDlyJGxsbASVueJcGTlyJCwsLCCRSDB//ny89dZbYIyhVatWSk9my8rK0K5dOzDGMGjQIMybNw8BAQGwsrKCpaUlDh06JMivOMbYsWNhYWGBkSNHIioqCt7e3mCMwdnZGd9//z2f//fff8ezzz7LV+Rz5sxBZGQkxo4dCycnJxw5ckTw+Y1tbJEhJAiiKagzhNnZ2TA3N4eVlZXSA7qMjAy+Lql7L1LcG2fNmiVIV/eGUFV9UllZiTZt2uD5559XWd7GTCrzxx9/wMHBAW3atFGqv2UyGczNzfHKK68I0t944w2YmZnhq6++EqTfu3cPPXv2hFgsxu3bt1WWoTZ3796Fk5MTnJ2dlYaJFBUVwdbWFr169RKkK8xZ+/btUV5ervSZins9Ywxbt25V2v7rr7+iVatWaNGihdJb1uXLl4MxhsGDBzfqmITpQYaQ0DtNNYQODg4Cw/TXX3+he/fuMDc3h5OTE06fPs1ve/ToEd+4rmuaysrKVM6qNn/+fDDGkJycLEgPCQkBYwzR0dGC9CtXrsDKyqrRhpAxhqSkJMG2rVu3gjGGqVOnCtLv3LmD//3vf0plvXTpEmxsbDBs2DBBuqKyZIypfPL3119/wdnZGW3btlWqOKVSKRhj8Pf3V9pPFYrfxcbGBpcvX1baXlJSAktLS3Tr1k1pAgCpVApzc3OMGjWKT1u3bh0YY1izZo3SZ/3222+Ct7e1uxbVNVpr164FYwxeXl6CdMVT4hUrVgjSFY2ili1bCmKtOEaLFi1w8uRJwT7z5s1T+qxvvvlGZSMJAP7v//5P8NlNaWyRISQIoimomlRm3LhxsLKygpmZGdauXau0j+Jh57Vr11R+Zq9eveDi4iJIa+wYwvDwcDDGBA8GFeVt7CyjkyZNAmMMR48eFaSvWLECjDGsW7eOT7ty5QoYY5BIJCqPcejQITDGsHHjxid+B0V9s2nTJpXbIyIiwBgT9ChRmDNVdR3w+F7fs2dPldsVXWADAgKUtt2/fx+dOnUCYwyVlZUNPiZhepAhJPROUw3hRx99pPRZircs48ePV9qmeBtW13yp48cffwRjDBMnTuTT/u///g/W1tZwdHRUacwUlVBjDGG/fv2UPuf+/fuwsLBA7969G1RWABgxYgREIhHu37/PpykqyzZt2vBdaesyd+5cMMZw4MABQbqPjw8YY8jKymrQ8RW/iyoDBDyuCGt3CaqN4k3hL7/8AgBYv349GGMN6qqjOFfqmj4A+Pvvv/Hcc88JKsSamhq+G1JdIwwA/v7+YIxh586dSscIDAxUyl9eXs6/PVRw+PBhtUZc1XdvbGOLDCFBEE1BYQjryszMTO39RNHFv3a3zdrq3r07GGOCrvzqDGFOTg4kEgk/vq5uOc6ePatU3sYawpycHJUmr0ePHrC0tBS8pdyyZQvfk0PVd5s+fToYYwgPD1cf1H8YN24cGGOYPHmyys9S9CjZv38/v4/CnNX93goU9/ravW1qExkZCcYYPv/8c5XbJ0yYAMaE3YCfdEzC9CBDSOidphrCul36AOCzzz4DY0zlE84TJ06AMYalS5cK0n/77TcsW7YMHh4ecHBwgJmZmaByGjp0KJ/36tWrfFdSVSQlJTXaEEZERKj8rA4dOqBLly5K6UePHsWIESPQtm1bWFhYKFWmtd++KSrL2t+hLmVlZTAzMxPkuXPnDqysrNC9e3e1+9VF8bt8+eWXKre/+uqrYIxh7ty5KitKRbfevLw8APIuRHZ2drCwsMB7772Hbdu2oaioSOXbXMW5snjxYpXHVlR+igpRYdZUmTtA3rW1rrlVHEPVufXgwQMlQ/rLL7+gQ4cOMDMzw/Dhw7F+/Xrk5eXh77//Vtq/KY0tMoQEQTQFRV2h4LfffkN6ejpcXV0hEokEvWsUqKprVKn2WyhVhvDgwYMwNzeHjY0NRo0ahcjISHz00UeIjY3l60nF+MDa5W3KOoRdu3aFSCTC3bt3AcgnXWGMYfTo0YJ8S5cubdB3Cw4OrieqchTj9p6k2g+mFfWTuq6binv9woULVW4PDQ1V+TZUwQcffNDoYxKmBxlCQu88zaQydak98UddVE34cv/+ffTt2xeMMbz44ouYPHkyYmJi+IZ43YooOzsbjDG89957Kr9LSkpKow1h3bwKVFWmim6ULVu2hI+PD6KiorBw4ULExsaiZ8+eSnFUVJYTJkxQeQwFw4cPh5mZGV85qOpW8yQU8crIyFC5vWvXrg2qKGs3Rq5fvw4fHx84ODjw293c3LBhwwbBZyt+d1XjKwDlClHdLHMKFL9j7QZAfecWoLrRUlNTg5CQEDg7O/Pld3FxwaJFiwRvJpvS2CJDSBBEU6hrCBVcuXIFLVq0gKurK37//XfBttatW6Nly5aNOo6qOqxHjx4Qi8VK4+MBICwsTKOG8OOPPwZjDFu2bAEAvP/++0pvygBgw4YNja7v1DF27FgwxlBQUNDgfRTmTN3kbk9qKzzNG0JNLC9CNA/IEBJ6R5+GcN++fWork1u3bilVRAUFBRp/Q9hQQ/jgwQM4OTmhbdu2KhfhVYyRVGUIn7R0R+3ujY8ePcJzzz0Ha2tr/slqQ6jvdwGA3r17gzHGdwltDA8ePEBeXh5WrFiBDh06gDGGHTt28Nt1+YawMYZQwaNHj1BUVIT169fj+eefV3ra25TGFhlCgiCagjpDCABTp04FY8o9ad5++20wxlTOpqwOVYZQJBIpTegCAA8fPuR7QtStQ8zNzVUOrQDqr+MqKythZmaG1157Dffv34ezszOcnZ0FwyqAx28O1T3obQyKmbkbMt5QwdMaQsUDTlX12YMHD9ClSxcwJhybSYaQqAsZQkLv6NMQxsXFgTGGw4cPK+VPTk5WauRrYwxhQw3h7du3wZh8dtW6/Prrr/xbqKYYwocPH6JTp05o06YNjh071qB96vIkQzhjxox6u7U0lKysLDAmn1FUQVPHEHbu3FnlGMLx48eDMdVjCJtiCGtTXV0Nxhj+/e9/82lNaWyRISQIoinUZwhlMhnEYjGcnJwEDwRPnjwJxhg8PT1VLhH022+/4dy5c4I0VYbw+eefh729PWQyGZ/26NEjLFy4kC9X3TrExcVF7eQ0T6rj3nzzTTDGEB8fD8YYZs6cqTJf//79YW5uju3bt6vcfvXqVdy5c0flttr8+OOPcHJygouLC86fP6+0/eHDh0rf72kNYe1ZRuv+BgqDqm6WUTKEhAIyhITe0achVJi+2bNnC/KWlZWhY8eOKhv5wcHBYExzs4w21BA+fPgQNjY26NixI3799Vc+/f79+/zMp001hMDj6akVSzHUrViexJMMYXFxMT/LaElJidL2//u//xNMYHP+/Hl89913SvkUb3VrTxbQkFlG33zzTUG6YpbRlStXCtJzc3PRokULtbOMNtQQFhYWqqxsFU+j+/Tpw6c1pbFFhpAgiKZQnyEEgFmzZoExhnnz5gnSV6xYATMzM9jY2OC9997D3LlzMW3aNHh7e8PW1lZplmtVhlAxg/YzzzyDadOmYebMmejduzesra0xcuRIlXWIr68vGGN45513EBsbiyVLliAzMxPAk+s4Ra8dS0tLMMaU1gBUUFNTg27duoEx+WyeYWFhiI6Ohr+/P7+kVUPrxJMnT8Le3h5mZmYYPHgwZs2ahdmzZ2Ps2LFo3749RCKRIP/TGkJAuA5hQEAA5s+fz/caatu2rdp1CMkQEgrIEBJ6R5+G8LfffuPHtg0ZMgTR0dHw9fWFnZ0dP1tYXUP43XffCczi/PnzldYhrNt1UVNjCBXLG3Tu3BmzZs3CtGnT8MILL+CZZ57hn4Q21RAqJpJhjOGll156Yv66PMkQAvKuLZaWlrCwsMCIESMQGRmJ999/H6NGjUKrVq0Ea1DNmjULFhYWGDRoEKZMmYJ58+ZBIpFALBZDJBIhJyeHz6v43d955x3BOoSKGd1atWqltCZUWVkZ2rZty//28+fPR2BgIEQiUb3rEDbUEK5ZswZmZmb4z3/+g9DQUMyfPx/jx4/nJy6qu95VYxtbZAgJgmgKTzKE3333HWxsbGBjY6P0UC47OxsSiQTt2rWDpaUlnJ2d0bNnT8yePRsXL14U5FU3y2hiYiJ69uwJGxsbtG7dGqNHj8bVq1fV1iF37tyBn58fnnnmGZibmwvqzSfVcb/++itsbW3BmHyegPr43//+h2XLluGVV16Bra0txGIxOnfuDG9vb2zbtg2//fZbvfvXpqKiAjNmzOAntrG3t8fzzz+PwMBApTGMmjCEAHDhwgWMHj0azs7OsLS0hJubG6ZOnaryISMZQqIuZAgJvaNPQwjIu/D5+/ujffv2EIvF6N69Oz755BN+5khV3QBlMhkmTJgAZ2dniMVi9OzZE0lJSfzbq7ozUWrKED548ACrV6+Gu7s7xGIx2rRpg8DAQFRWVqq8wTfGEALAmDFjwBjD5s2bG5S/Ng0xhIC8601QUBA6duwIKysrtGzZEj169EBYWBikUimfLzc3F1OnTsVLL72Eli1bQiwW47nnnkNwcLBgUXpA+LsfPXoUr732GmxsbODo6Ih3331X5RtJQP47Tp06FR07doSlpSVat26NUaNG4cKFC0p5G2sIr1+/jtmzZ6N3795wdnaGlZUVOnXqhLFjxyotHqygMY0tMoQEQRAEQWgCMoSE3lFnCI2RmJgYMMaQmpqq76I0mocPH6Jz586wtbVt0sQv+uRJZq05QoaQIAiCIAhNQIaQ0DvGaAhVdcG4evUq7Ozs0Lp1a/z55596KNXToRhPOW3aNH0XpdGQISQIgiAIgmgaZAgJvWOMhrBdu3bo378/pk2bhqioKIwaNQoWFhYwNzdHcnKyvovXKJYsWYLZs2fDwcEBdnZ2qK6u1neRGg0ZQoIgCIIgiKZBhpDQO8ZoCBcvXozevXujdevWsLCwQOvWrTFixAjBourGgmIGtl69egnG8BkTZAgJgiAIgiCaBhlCQu+cOnUKsbGxuHfvnr6LQhBGQ0VFBWJjY5Gfn6/vohAEQRAEYcSQISQIgiAIgiAIgjBRyBASBEEQBEEQBEGYKGQICYIgCIIgCIIgTBQyhARBEARBEARBECYKGUItcffuXXz//feNEoBG70OiWFIsjUcUS/3H8u7du3qoEZoPTanb6Nw3jHOfRLGkWBqHmhLLp63byBBqiJSUFERERGDVqlUA5D8kx3EN1q1btwAAt27datR+JIolxdI4RLE0jFgqKluiaTS2bqNz33DOfRLFkmJp+GpqLJ+2biNDqCXIEBrfxUSiWFIsjUNkCPUHGULjPfdJFEuKpeGLDGEzgwyh8V1MJIolxdI4RIZQf5AhNN5zn0SxpFgavsgQNjPIEBrfxUSiWFIsjUNkCPUHGULjPfdJFEuKpeGLDGEzgwyh8V1MJIolxdI4RIZQf5AhNN5zn0SxpFgavsgQNjPIEBrfxUR6rKqqKhQXF6O4uBhlZWX8/0lPJ4qlbmJZVVWl9twmQ/h01Fe31b5v0Lmvn3PfUFTfNWgoorYCxdIQRYawmUGG0PguJpJcVVVVuH79OmpqasBxXJPeCJBUi2Kp/VjW1NTg+vXrahukZAgbR0Nn0K5736BzX/fnvqHoSdegoYjaChRLQxQZQiOHlp0wHFEsn07FxcWCRp2hNz6MSRRL3cSypqYGxcXFavcjmo66uNe9b9C5r59z31BU3zVoKKK2AsXSEEWGsJlBhtD4LiaSXHUrcWNofBiLKJa6iyUZQu1QnyGkc98wzn1DERlC0xHFUv+xJENooJAhNL6LiSQXGULtiWKpu1iSIdQOZAj1L2OJJRlC05ExxbLgZiVSLn2LgpuVei+LJmNJhtBAaaohLKusQlV1td5PSGOWMd2YDFFkCLUniqXuYkmGUDuQIdS/jCWWZAhNR8YSy6TMa+gTL4VHvBR94qVIyrym9zJpKpZkCA2UphrCleu/xNp907E9dQSOpEfj29LLej85jU3GcmMyVBmrIZRIJGCMITAwUGnbhAkTwBiDRCJR2nbo0CGYm5tj4MCBSttyc3PBGONlb2+Pl19+GYmJiYJ8CQkJgnwKiUQiPs+8efPQq1cv2NraonXr1hg2bBgyMzMFn+Pq6srvKxaL4erqihEjRmDv3r31lqu2Dh8+LChT165dlb7Xli1bwBiDq6ur2ngqjpGWlqb337ZujBYtWkSGUE80N0Po6emJ0NBQvZdDocjISHTv3t0oY9nYc0LforaCacWy4GYlbwYV6rtSanBvCskQNjOaaggDPv4Sn6Z5IyHdCwnpXliT7oWk46ORemIxKiu/1fuJagwyhhuTIcuYDWH79u3h4OCA0tJSPr2srAyOjo7o0KGDSkPo5+eH0NBQ2NjY4MKFC4JtClO0Z88e5OfnIzMzEyEhIbC0tIRUKuXzJSQkwN7eHvn5+QJduXKFzzNw4ECsX78eGRkZSE9Px6BBg9ChQwfcvHmTz+Pq6oqoqCjk5+fjwoULOHDgAAICAmBmZobo6Gi15aqtyspKvkw2NjZwcnLCN998I/heAwcORIcOHXRmCBVl0oTIEOoXMoTKqqio0Fh5yBDqTtRWMK1Yplz6VmAGFUq9bFhtazKEzYymGsKqkhKkfrkZX+4PRWItY5iQ7oWN6YORfGQ8Lp690KjPNjUZw43JkGXMhnDYsGFwd3fHhg0b+PSNGzfC3d0dw4YNUzKEN2/ehJ2dHTIzM/HOO+8gKipKsF2VKSopKQFjDEuWLOHTEhIS4ODg8MQy1o7l1atXwRjDgQMH+DSF2am73+zZs2Fubs6/UWyIWVOUaeLEifD39+fTL168CJFIhPDw8HoNYd03j56enuA4DseOHUP//v3RsmVL2Nvb47XXXkNqaqrSvnFxcRg6dCisra0xe/ZscByHxMREdO7cGWKxGJ6enlizZg0YY7h+/Tq/76FDh/Dqq69CLBajXbt2CAkJ4U2zp6enUrkaeh7X/g2IpqMLQygrLIBMmgJZYYHW7xl1z6czZ87A19cXbm5uEIvF6NKlCxYvXqzyXjNv3jy0adOGv44uX74MLy8viMViuLm5YePGjUrXdHFxMQICAtC6dWvY2dnh9ddfR3p6On/N1i1PQkLCU8VSnyJDaDoyhljSG8L6IUOoJTQxqUzFtyVI2bcWu78ej63pQ5GQ7oXtx33gES9F0JLd+DJpP0q/LdL7yWtoMoYbkyGrbiV+584dyMrL9COZrMHlVjTSYmNj0a9fPz69X79+WLRokUpDuHr1avTs2RMcxyEpKQlubm6CY9Y1XpWVlVi4cCFveBT5mmIIc3JywBgTvGlUZwiLiopgZmaGBQsWqCyXKinKlJaWBjs7O/6t6dy5czFs2DAsWrSoXkN47NgxwVvIoiL5vWbv3r1Yv349Tp8+jdOnT8PPzw8uLi4oKSnh92WMwdnZGatXr8bZs2dx/vx55ObmwtLSElOnTkVmZiY2b96Mtm3bCgzhyZMnYWtri0WLFiE7OxuHDh3Ciy++CB8fHz4O7dq1Q1RUFAoLC5Gfn9/g87j2b0A0nYYaQplMJriW79RUN+iar/nvZ6h+2wPV3r1R/bYHav77mdbuG8XFxejduzcCAgL4N+zl5eWYPXs2jh07hnPnzmHDhg2wtrbGli1bBPcaW1tbjB07FhkZGfw13L9/f/To0QNHjhxBamoqPD09IRaL+WtaJpOhT58+GDJkCI4fP47s7GxMmTIFLVu2RFFREUpLSzFlyhQ8//zzfHlq93ZQdR8xZJEhNB0ZSyzXpRXwZpDGEAohQ6glND3L6LfXruLQ/oVYtmUp+n5yAh7xUvht2S5/a3jYH+dPfaH3k9hQZCw3JkOVkiGsqZY3zvQgWXlZg8utMISFhYUQiUTIzc3F+fPnIRaLUVhYqNIQenh48E//q6qq0KpVKyQnJ/PbFcZLLBbDxsYG5ubmYIzBzc2NN0gc9/jJvo2NjUADBgwQHE9xX5DJZBgyZAj69u0r2K7OEHIcBxcXF0yYMEFluWqr+p9JqWqb1B49emDt2rWQyWTo3LkzEhMTn2gIG9pltLq6GnZ2dkhKSuLTGGOYNGmSIN+MGTPwwgsvCNJmzpwpMIRjx45FQECAIM/XX38Nc3NzlJWVCWJEXUb1Q4MNYXmZUdw3GtJlNCgoCN7e3oJ7jYuLi6CraGZmJhhjOH78OJ+meOijuKb37t0Le3t7lJeXCz6/c+fO+OSTT8Bx1GVUl6K2gunF8nitbqPhX57Xe3k0GUsyhAaKNpeduHIxHyvX7cbGA5MEXUp3HR+NjCPLITPxWUqN5cZkqDJ2Q8hxHLy9vTFnzhxERkbyDbm6hjAzMxMWFhaCcX7BwcEYPXo0/7fCFCUmJiI7Oxs7d+5E586dsWfPHsGxExISYGdnh5ycHIHy8vIE+RT3haCgILi6uuLixYuC7fUZQmdnZwQFBSmVq+4xa5dJYQiXLFkCT09P7Nu3Dy4uLqiqqmqyISwoKEBgYCCeffZZ2Nvbw8bGBmZmZli2bBmfhzEm6LbLcRyGDx+OcePGCdISExMFhvBf//oXrKysBAbX2toajDGcPn1aECMyhPrBFAxhXFwcXnrpJbRq1Qo2NjawtLREr169BPea/v37C/bZsWMHLCwsUFNTI0h3cnLir+kPP/wQ5ubmSg9xzM3NMX36dHAcGUJditoKphfLpMxrvCEM2XlO7+XRZCzJEBoIKSkpiIiIwKpVqwDoZh3CyopyHD68FInHxwiMYVLq2zh2cA6qSm/o/cQ2pouJJJexdxnlOA47d+6Em5sb3Nzc8MUX8rfndQ3htGnTwBhDixYteJmbm0MkEuHaNXk3ElWmaP/+/WjZsiUKCh6Pb2pMl9GJEyeiXbt2OHdOuTJSZwgLCwthZmaGjz76SG256qp2mYqKivhxe9OmTQPHcU02hF5eXujZsye++OILZGRkICcnB61atRKUmzGG7du3C/YbNmwYfH19BWk7duwQGMKuXbsiJCREyeTm5OTwb2PIEOoXbXYZrbl04XF3UYVG9EHNpQtau2/UNYRbtmyBWCzGsmXLkJaWhpycHAQEBAhMWu17jULbt29XaQgdHR35ayMmJgZt27ZVeX4XFhaC48gQ6lLUVjC9WMYfv8IbwtFbs/VeHk3GkgyhgaLrhemzsvbgiyN+WFtrAprAuB3YtWknqr41rBmUDPViIsll7JPKcJy8G2Pbtm3Rtm1bvgtlbUNYVVUFFxcXLFy4EFKpVKAuXbpg6dKl4Dj1pqhuI7IhhlAmkyEkJARt27ZFdrbqikidIYyIiECLFi34N4CNNYQcx2HkyJFgjPET0zzJEF66dAmMMaSkpAjSbW1tsW7dOv7vCxcuCLrFcZxqQzhjxgy4u7sL0mbNmiUwhGPGjMF//vOfeuP47LPP4qOPPiJDqCe0PalMzZ5EVI/o89gM7kls0H5N1YABAzBx4kT+74kTJyqdg/369XuiIVR0Ga19vdTtMpqcnIwWLVogNzdXbXk++OADpa7VTY2lvkWG0HRkLLGM2neRN4RvrDml9/JoMpZkCA0UXRtChfKvZOO/34Ri7f4Z/En/9sd78c2eSFQWa3fGNkORsdyYDFXNwRByHIcbN27gxo3Hb8lrG8Lt27fDyspKZYPl/fffR48ePcBx6o1XYmIiRCIR3yVU3bIT+fn5/BuDCRMmwMHBAfv37xdsrz1pRH3LTsTExPD56lt2QjHWrq4hLC0t5d9CcNyTDWFVVRXEYjGio6Nx5coVPlY9evTAgAEDcPr0aRw5coSfEfRJhvDcuXOwtLTE9OnTkZWVhS1btqBdu3ZgjPGffeLECYjFYgQFBSEtLQ3Z2dlITEwUNNgHDBiAoUOHoqCgQPB9nnQe1z6fTZEffvgBsbGxiIiIwJw5c3D27NkmfY7OZhnNSNX6LKMcxyEgIAC9evVCbm4uCgsLsXjxYtjb22P37t3IysrCrFmzYG9v/0RDyHHySWX+/e9/4+jRo0hLS8Prr78OsVjMj1OWyWTo27cvunfvjt27dyM3NxeHDh3CzJkz+bGHGzduhI2NDdLS0lBYWKg03rCxsdSnyBCajowllhMSzwpmGS2vqtF7mTQVSzKEBoo1emTcAAAgAElEQVS+DKFCJTfLsfazr+G1/CiivliOhHQvJKYPR9q+CFR9a9g3aX1dTCS5moshrKvahnDw4MHw8vJSmS81NRWMMaSmpqo1hDKZDM899xw/yYu6hekZY/xMmOq2155WvvbC9FZWVujQoQNGjhyJr776SnD8+ham37x5M1+m+t5aPskQchyHlStXon379jA3N+eXnUhLS0PPnj0hEonw7LPPYtu2bUpvNlUZQo57vOyESCSCp6cn4uLiwBjjTSzHyWc3HTBgAGxtbWFjYwN3d3d88MEH/PbDhw/D3d0dIpGIlp1oBHfv3kVFRQUA4Oeff8aUKVPw559/Nvpzmts6hFlZWXjllVcgFov5N+g+Pj5wcHCAo6MjJkyYgPDw8AYZQsWyEyKRCK6urti0aROcnZ2xYsUKPk9JSQnfU8DS0hLt27fHu+++y6+BWl5eDm9vbzg6OtKyE1oWtRVML5bDN2YKDGHeDc2tIarvWJIhNFD0bQgVKq2oxH+/nof1aYP5MYb/TXsbp/Z9gJpy5emsm4OM5cZkqDJWQ2gMolgKFR0djXbt2mkllmQI62fOnDn44YcfGr1fczOE2tTFixf5N/ma/FxjiSUZQtORMcSyslqGvivlRrB/wil4xEtxIv+m3sulqViSITRQDMUQKlT0bT52HpnEjzFMSPfC3pRRuPB1PGQ1hvfKXB8XE0kuMoTak6nHctmyZTh27BjOnj2LdevWwcHBATNnztRKLJubIbx27Rri4uIQFhYGiUSC8+fPK+VJTU3F9OnT4e/vj+joaFy/fl3lZ5WWliIyMrJJ5SBDqF579+5FYmIizp07h0OHDqFPnz5wc3NDZaVmF742lliSITQdGUMsL92ogEe8FK+vzsD4f7qO7j1reOcoGcJmhqEZQoUuXzuHHUfG86Zwe9pb+PCjtShKS9X7RaDvi4kkFxlC7cnUYzlp0iS0adOG724aFRWFqqoqrcSyuRnCy5cvIzk5Gbm5uSoN4ZkzZ+Dr64uTJ0+ipqYGiYmJCAwMVHoL+L///Q8RERG4ceNGk8pBhlC9du/ejeeffx5isRjOzs4YPnw4zp/X/FpnxhJLMoSmI2OIZco/axCO2pqNWXsuwCNeiq1S9ePQjS2WZAgNFEM1hAplXjyGbcfeRcx/l8hfny8/ho0fr0bVxSy9Xwz6uphIcpEh1J4olrqLZXMzhLVRZQjnz5+PTz/9VJAWERGB3bt383/fv38fCxcuRGZm5hOPcf/+ffz++++8/vjjDwDyuu3WrVtKIkNoOOe+oai4uFjluWIoun37NgDg9u3bei+LscsYYrkz6zo84qWYvCsXiw9fhke8FMuPXtF7uTQVSzKEBoqhG0KOk0+MkZp9GePiD8EjXor4fRHYnf4Wzu6dA1mZ4fWrNuRYNieRIdSeKJa6i6UpGcIHDx5g3LhxSiZxx44dWLhwIQDg0aNHWLNmDfbu3dugY+zduxcSiYRXdHR0vfnLysrw/fffk0i8ysrKGnlmE4T22JJdJjeBacXYmVsJj3gpFh69pu9iGQxkCLXE998bviFUqEYmw45vpNiYNgwJ6V5Yk+6FIynv4vrhjY1a4NdQRIbw6USGUHuiWOoulqZkCH/66SdIJBKlbqAHDhzAzJkzAQDFxcXw8fFBVFQUr6qqKrXHoDeEhitjieWT3hByRVchy0gFV3TVqN7EkIwzlnP358EjXor16VexO6cYHvFShH5xTu/l0lQsn7ZuM2lDGB8fj+DgYKxatUqQron1mozJECp0+UYeth4N4McXbksfgtP7g1FzSfUi2oYqQ4ilMYsMofZEsdRdLE3REJaUlAjyHThwALNmzdLosdXFnQyh4Zz7hqL6zomaPYmoftsD1d69Uf22B2r2JOq8fNRWMK1YBiXJJ5JJPlOM4/+MJxyzzfDat02NJRnCp6CwsBAXL15UMoSaWK/JGA2hQkfPJmNDijdvDA+kj8Dl3Qsh0/BMaYZ2MZHkIkOoPVEsdRdLUzKEDeky2lRSUlIQERHB15NkCPUvY4mlunNCVljw2AwqNKIPZIUFOi0ftRVMK5Zv/bMGYUZBKc5dL4dHvBRvrD2l93JpKpZkCJ+SoqIiJUNYl6as12TMhpDjOFRUlePz43OwJk3ehTRs20asnrsUlaelei+bscXS2ESGUHuiWOoulqZkCAH5pDKfffaZIK3upDKagAyh/mUssVRrCKUpQjP4j2QZup3tnNoKphPLqprHaxAWllahpLyaX5y+vMqwll4jQ1gHTa65VB9PMoRNXa/J2A2hQllXT2HV3vn8hePz0U5c3roAstISvZfN2GJpLCJDqD1RLHUXy+ZmCP/8809UVFSgoqICEokER44cQUVFBf+wUrHshFQqFSw7oenvS4ZQ/zKWWNIbQtORocfycol8DULPVRmokckgk8nw+uoMeMRLcbmkQu/l00Qsm60h1MSaS9HR0YiMjFTSTz/9xOepzxA+zXpNzcUQcpx8NtLdJy9h4CcpCN62DZvTByP7qA9kaTv1XjZji6UxiAyh9kSx1F0sm5shLCoqEsz6qdDGjRv5PIqHpH5+foiOjsa1a08/g15z7zLq6emJ0NBQrX2+RCLBsGHDdHru60Kurq5YtGhRvXmeOIawliGkMYTGLUOPpWINwne2PB4z+PbmLHjES3Ey37Bm1SdDWA9NXXOpIagzhI1Zr0kVzckQKlRUWoW1h6bwYwv3nfBG8ZczICu9ofeyGVssDVnGagglEgkYYwgMDFTaNmHCBDDGIJFIlLYdOnQI5ubmGDhwoNK23NxcMMZ42dvb4+WXX0ZiorDxkpCQIMinkEgk4vPMmzcPvXr1gq2tLVq3bo1hw4YhMzNT8Dmurq78vmKxGK6urhgxYgT27t1bb7lq6/Dhw4Iyde3aVel7bdmyBYwxuLq61hvT2p/bokULtG/fHpMnT0Z5ebkgX0VFBT788EO8+OKLsLa2hr29Pdzd3TFz5kxcunRJK7+3qRlCQ4EMYePvS2QIVas6cLjcEPoO0st3oLaC6cRyZ9Y1eMRLMWlXLp82PlE+ycxX5+o/T40lliZpCDU5gF6VIWzMek2NnZpb09PM6lo1shokSldhTdpgJKR7YVP6IJw/Og63pHv0XjZji6WhypgNYfv27eHg4IDS0lI+vaysDI6OjujQoYNKQ+jn54fQ0FDY2NjgwoULgm0K47Vnzx7k5+cjMzMTISEhsLS0hFT6eDxtQkIC7O3tkZ+fL9CVK1f4PAMHDsT69euRkZGB9PR0DBo0CB06dMDNm4+fTrq6uiIqKgr5+fm4cOECDhw4gICAAJiZmSE6OlptuWqr8p/JnxISEmBjYwMnJyd88803gu81cOBAdOjQoUGGMCEhgS9PUlISWrVqhVmzZvF5ysvL8dprr8HR0REff/wxUlJSkJOTg927d2P8+PEIDw/Xyu/dEEOo6vwmQ/h0kCFs/H1J04bQEJaD0oghHNPvn1lG+0BWXa3z73DrlmGbGGOSocdyVcoVeMRLseDg4weUM/dcgEe8FFulhXovnyZiaZKGsCFrLjWEpUuXIiQkBAEBAZgyZQpu3rwJoHHrNTV28d7mws3vb2Lt8ff4t4VHToxA9b4oPPyrcbOxEoaHsS4wPW7cOAwfPhzu7u7YvHkzn75lyxa4u7tj+PDhGDdunGCfiooK2NnZ4ezZsxg9ejSio6MF2/Py8sAYg1Qq5dPKy8vBGMPy5cv5tPXr18PBwaFR5b1+/ToYY/jmm2/4NDc3NyxZskQp75w5c2Bubo6zZ8+qLVddKcoUGhrKjyX7/vvvceXKFYhEIsycORNubm71lpExhqSkJEGav78/Bg8ezP/94YcfwtzcXG1Z7ty5o5fzgRbF1gz66DJacLMSKZe+RcFN7c9s7enpieDgYAQHB8PBwQFOTk6YOXMmb7rWr1+Pl156Cba2tnBxccHo0aNRUCAc65aRkQEvLy/Y2dnB1tYWffv2xZkzZ8BxyoYwJSUFrVu3FjzgmTt3Llq3bg1bW1v4+flhxowZ6N69O79d8Rnz5s1DmzZt4ObmBo7jcO3aNYwdOxaOjo4Qi8V48803kZ39uEtcZGSk4HM4jsOiRYsED4IUn/3RRx/hmWeegZOTE4KCgvgHSxzHoaCgAIMHD4ZYLIabmxs2bNjw1IZQVlEunFBGx+MHn6bhTTK+WEbvvwiPeCnWpV3l0xZ9cxke8VLEHc3Xe/k0EcvvvzdhQ6iLNZeehKm9IaytGlkVtqZ/jIQ0uSlct28G9s6KBHcuS6/lMsZYGpLqVuJ37txBWVWNXtSYJ+GKhk1sbCz69evHp/fr1w+LFi3CsGHDlN4Qrl69Gj179gTHcUhKSoKbm5vgmIo3cWlpaeA4DpWVlVi4cCEYY4iLi+PzJSQkwMHBoUE3bMX/c3JyeFOnSFPXyCoqKoKZmRkWLFigslyqpChTWloa7Ozs+Lemc+fOxbBhw5QahqrEGMP27dv5v7OystCxY0dERUXxae7u7iq722pbTzIY9IZQOzTUEMpkMsG1XM1916Br/rNTRejzzyRmfeKl+OxUkVbvG56enrC1tUVoaCgyMzOxYcMGWFtbIz4+nr9H7Nq1C2fPnsXhw4fxyiuvwMvLi98/Ly8PTk5O8Pb2xvHjx5GVlYWEhAS+O3htQ7hv3z44ODhg+fLl/P4bNmyAWCxGQkICsrKyEBUVBXt7eyVDaGtri7FjxyIjI4P/7KFDh6Jbt244ePAg0tPTMXDgQHTu3Jk3cw01hPb29hg/fjwyMzORlJQk+P4cx8HLywsvvPACvvnmG6SkpMDDwwNisfjpDOHVK0JDmJ2h83vIrVuGbWKMSYYey+BaaxAq0talX4VHvBRz913Ue/k0EUuTNITaXHOpqTT0KaqxXkz16WT+Caz8ejL6xqfDI16K+bOXo/zzlZBVVRnVxUSSq24lXs19x88yq2uVNWI6aEXDq7CwECKRCLm5uTh//jzEYjEKCwtVGkIPDw8sXrwYHMehqqoKrVq1QnJyMr9dYbzEYjFsbGxgbm4Oxhjc3NxQVFTE51OM17OxsRFowIABguMp7gsymQxDhgxB3759Bdvre+ru4uKCCRMmqCxXbVX/0/Wqtknt0aMH1q5dC5lMhs6dOyMxMbHBhlBxDJFIBMYYBg8eLHh7IBaLlbrcDR8+nC9P7969tXKeNsQQqtuPaDoNNYRlVTVGcd/w9PREt27dBCZyxowZ6Natm8r8x44dA2MM3377LTiOQ3h4ODp27Ci4JlTdl3bs2AE7Ozts2rRJsP3ll19GcHCwIK1Pnz5KhtDFxQUVFRX8OZydnQ3GGA4dOsTnKywshFgsxtatW8FxDTeErq6u/H2D4ziMGDEC77zzDjhO/hCIMYYjR47w2zMzM8EYezpDeOa00BAe3qeV+0R9oraC6cTSe5N8DcKTVx4P0diVfV0+rvCLc3ovnyZiaZKGENDdmktNxZQMIcfJ13iJO3gefT45if6rj+JA6hiUfTUB3OVsnZfF2GOpbxm7IeQ4Dt7e3pgzZw4iIyPh7e0NjuOUDGFmZiYsLCwE4/yCg4MxevRo/m+F8UpMTER2djZ27tyJzp07Y8+ePYJjJyQkwM7ODjk5OQLl5eUJ8inuC0FBQXB1dcXFi8Ink/UZQmdnZwQFBSmVq+4xa5dJYQiXLFkCT09P7Nu3Dy4uLqiqqmqwIYyLi0NOTg4yMzOxc+dOdOnShW8scpxqQ3j58mXk5OQgJCREqUGqKZEh1A/N0RCOGzdOkLZjxw5YWFiguroaaWlpGDp0KDp06ABbW1tYW1uDMYZTp+QLWnt5eeG9996r9770zDPPoEWLFvj888+Vtjs6OmLt2rWCtMmTJysZwv79+wvO4cTERL6Mtfft0aMH/wa/oYaw9htPjuMQGhqK119/XSkWdcv9VIYw9bBwltEvtmrlPlGfqK1gGrGsrrUG4dVa3dCP5clnHn13m+7bqdqIZbM1hIay5lJTMTVD+PgCK8HH+8KRkO6FjemDUJAqwZ2jm3RahuYSS33J2LuMchyHnTt3ws3NDW5ubvjiiy/AccqGcNq0afzsmQqZm5tDJBLh2rVr4DjVXTP379+Pli1bCsYRNabL6MSJE9GuXTucO6f8VFKdISwsLISZmRk++ugjteWqq9plKioqglgshqenJ6ZNmwaOU24YqlLdLqMcx2Hz5s1gjPHms74uo6oapJoSGUL9oM0uoxeKy/nuogr1XSnFheJyrXYZVWcIb968iVatWmHMmDE4ePAgMjMz8eWXXwquPW9v7ycawj59+uD555/H4MGD+bd8Cjk6OmLdunWCtEmTJqkcQ1j7HFZn1Lp3784bwqioKLi7uwu2f/jhhyrHENbOExoaCk9PT3Ach+3bt8PCwgI1NTVK5X6qZSf2fSE0hOuWauU+UZ+orWAasVSsQfjaP2sQKtLPXSuHR7wUb649pfcyaiKWzdYQ6mvNpaZiyl1G6+rSjTysPObPTzhz8uQ7+G73DMjKvjXoi4kklzHPMqpo2FRXV6Nt27Zo27Yt32CqbQirqqrg4uKChQsXQiqVCtSlSxcsXSpvnKgzXnVnJmyIIZTJZAgJCUHbtm0FEz/UljpDGBERgRYtWvAmrLGGkOM4jBw5EowxfvxRUw3h1q1bwRjDyZMnwXHy5TTMzc1VloUMofGj60llkjKv8U/z+66UIinzmlbvG4ouo7XTwsPD0a1bN6SkpIAxJph9eP369YJrb86cOQ3qMlpQUIBu3bph+PDhgrwvv/wyJk6cKNinb9++TzSE9XUZ3bZtGziOw/Lly+Hs7CwwyGPGjGmUIVR0Dz169Ci/XRNdRmt2bBAawkWRWv2dVYnaCqYRy9TLijUIswTpN8qr+QdPFdUN71VgqLFstobQ2DFlQ8hxHKprKrE+bRFvCnefGIbqr/1x61yKwV5MJLmagyHkOA43btzAjRuP18isbQi3b98OKysrlQ2W999/Hz169ADHqTdeiYmJEIlEfJdQdctO5Ofn80/WJ0yYAAcHB+zfv1+wvfYSGfUtOxETE8Pnq2/ZibKyMr5MtQ1haWkpCgsfT6/dUEOoWHbi0qVL2L9/P1544QV06dIFVf+MES4rK0OfPn3g6OiIxYsXIzU1FefOncPu3bvx8ssv49///rdWfm8yhPpBV7OMpl7W3Syjtra2mDx5MjIzM7Fp0ybY2NhgxYoVuHr1KqysrDBt2jScPXsWiYmJ6NKli+CeUFhYiJYtW/KTymRnZ2PdunUqJ5XJz89H165d4e3tzV8/ikll1qxZg+zsbMydOxf29vb8PUjVvU0Ry2HDhuFf//oXvv76a6Snp+PNN98UTCpz+vRp/t5x5swZLFu2DE5OTo0yhBzH4c0334S7uzuOHDmClJQU9O3b96knlalZt1RuBv2HyP+NmKD137quqK1gGrH8QrEGYZ2xgjKZDJ6rMuARL8Xlkgq9lE2TsSRDaKCYuiFU6EDufqxOHc6vWXhhrx9qvtppkBcTSa7mYgjrqrYhHDx4sNK4GYVSU1PBGENqaqpaQyiTyfDcc8/xk7yoW5ieMYb8fPmU1uq2JyQk8J9be2F6KysrdOjQASNHjsRXX30lOH59C9Nv3ryZL1N9by0baggVMjMzQ5s2bfDOO+/g7Nmzgnzl5eWIiYlB9+7dIRaLIRKJ0LVrV0yePFlpbUdNiQyhftCFIdSlPD09ERQUhPHjx8Pe3h5OTk4IDw/n36pt2rQJbm5uEIlE6N27NxITE5XuCSdOnMAbb7wBa2tr2NnZ4dVXX+Wvkbr3pcuXL6NLly4YOXIk33shKioKrVq1gq2tLXx9fREaGopXXnmF30edIVQsO+Hg4ACxWIyBAwcq9T6Ii4tD+/btYWNjg/feew8ffPBBow1hfn4+Bg0aBJFIhA4dOmDdunVPvexEzZK5ciP4wRT5v0Fv6/y3p7aCacRydUoBPOKliDmYp7TNe1MWPOKlkF4p1Xm5NB1LMoQGAnUZVa9LNy5hxdEArEsbitEbd+OjWR+jfOkHkJWXG9TFRJLLWA2hMYhiqbtYkiHUDs3NEBqi+vfvj3fffdfoY1nfOVEdHSbvKrrpE7khfOe1Ro391ISorWAasVQsQL/syGWlbQE75MtR7DtX//3LGGJJhtBAIUMoVGV1JeKPHef7a/sv2IHytQG4deGEwVxMJLnIEGpPFEvdxZIMoXYgQ6hZlZaWYuHChfz6grNnzwZjTLD8jbHGsl5DOG3cP8tNfPV46YkS3TbKqa3Q/GOZlHmNb3f2iVcek/x+stwsfppRpJfyaTKWZAgNFDKEqnXofAneWH0SC/77MT5PH4Lq9HH4/vhGg7iYSHKRIdSeKJa6iyUZQs2g60llTE2lpaXo168fnJycYG1tjRdffBGfffZZs4hlvYYwcDi/IH31OC/5/3NzdFY2jqO2QnOPZcHNSpWzFtcemxx76BI84qVYceyK3sqpqViSITRQyBCqV15xOZYf8UNCuhfWpw9CkfQ93NszC7JKzQzqNaVYakNkCLUniqXuYkmGUDuQIdS/jCWW6s4JmUyG6tGechN45RKqp0rk/0/9Rqflo7ZC845lyqVvVa5Tmnr58Yz369KuwiNeiuj9yuMLjS2WZAgNBBpD2DjdKL+B5Uen8LOQZkpH4ee948EVPv0EFKYWS02LDKH2RLHUXSzJEGoHMoT6l7HEUq0hLCt93E20rBTV86bKxxMmb9dp+ait0Lxj2ZA3hPwMpLty9V7ep40lGUIDhQzhk1VdU4016Ut5U3jgpDfuHvXHd1n79XIxkeQiQ6g9USx1F0syhNqBDKH+ZSyxVGsIr1ySG8LRnpDJZKj5ZIHcEG5eqdPyUVuh+cdywcE8gRmsO4bwaJ78LeLYT3XbXVkbsSRDaKCQIWy4/pvzX6xKHYKEdC8knhiKKwvHPNXSFKYcS02IDKH2RLHUXSzJEGoHMoT6l7HEUq0hzM6QG8LA4eA4DjXbEuSGcNkHOi0ftRWafyxXpVyBR7wUYf/NVbmu6ZmiMnjES/HmutN6L+vTxpIMoYFChrBxkl45hRXHRyHu4GT0W34MB8cHomZVLGT/LN5LsdSdyBBqTxRL3cWSDKF2qM8Q1tTU0LlvAOe+Iaimpka9ITz+tdwQThsnz/vVF/K/507SaRmprdD8YznrnyUnNp8sVLm9uLyaf4NYWa3+/mUMsSRDaCDQGMKnV8G3hZiYlC2fHviTE9ge8T5+2TUBXHE+xVKHIkOoPVEsdRdLMoSaoaF1W1VVFa5fv67WFNK5r7tzX9+qqanB9evXUaXmgW7NnkR+UXqO4yA7cVT+92T1ay9qQ9RWaP6xfHebvE155GKJyu0ymQyvrcqAR7wU+SXKbxCNKZZkCA0UMoRNU1WNDDEH89AnPh3bU0YjUzoKvx/yw+0LaRRLHYkMofZEsdRdLMkQaof64l5VVYXi4mKVKisrU7uN1DgZQyzVmUGO41Dz+Tp5F9Elc8FxHGR5uXJD+N4And5DqK3QvGNZVSPDa6vkb/+ufKve7A3bcBoe8VIcPH9D72V+mliSITRQyBA2XTKZDBtPJvOTzRw9+TZ+T/XHnfQdFEsdqLiYDKG2VDuWubm5YIwhLa3hDztIqmOpSnXP49r7EU2nKfcDuidrTs0hljUJH8sN4bql4DgOstKbj2cdLS+jWBqhDDGW56+XwyNein4JGZDJZCrzPGnhemOKJRlCA4UM4dNrZ3YiVqUNRkK6F/aeeAu/SgPw44GPKZZaFhnCxisyMhLPPfccrK2t4ejoiH79+uHIkSNK+RprCF1dXbFo0SK9f7/akkgkGDZsmN7LQYZQP5Ah1K+aQyxrPp4jN4Q7NvJp1e/2kxvCS0+/9JQpxdJQZIix3H/uBjzipfD9/IzK7Q1ZlsKYYkmG0EAhQ6gZHbrwDeJThiMh3QtJJ4bi7ml/3NszE7Jq9RcsxfLpRIaw8dq4cSOSk5Nx9uxZZGRkwM/PD/b29rh69araWOrSEFZUVGjsu5IhNG3IEOpXzSGW1VGT5IZw3xeP00JGyQ2hNIViaYQyxFiuSS2AR7wUc/ddVLm9IQvXG1MsyRAaCDSpjPZ0qiATccdHISHdC9vSB6PikA9qPpoOWXk5xVILMlZDKJPJsGDBAnTs2BFisRju7u7Ytm0bv33fvn1gjGH37t3o0aMHxGIxXn/9dRQUFGDXrl3o2rUr7OzsMGrUKJSWlvL7lZSUYMyYMbC2tsYzzzyD2NhYeHp6IjQ0VG1Zbty4AcYY9uzZw6cdPXoUL774IkQiEV566SV8/vnn9RpCT09PMMYE4jgOhYWFGDVqFNq2bQuxWIwXXngBmzZtUto3ODgYkydPRsuWLfHaa6+B4zhkZmaiT58+EIlE6NatG5KTk8EYw/btjxeEzsvLw8iRI+Ho6AgnJycMHToUubnyRXsjIyOVyrRv3z69/N5kCPUDGUL9qjnEsnrKe3Lzl3r4cdqcELlJ3P9fiqURyhBjGfnVRXjES7E+/arK7fSGUAgZQi1BhlCzyiu5jKVHfbEqdSgmbN6MIwHjUD3DD7LiIoqlhqXWENZU1KM6N1BN5G1kud9//3107doVu3fvxtmzZ5GQkACRSIT9+/eD4x4bwldeeQX/z96ZR0VxpvufnBljYjKZZDLnTub+ZpLMZM49J2PmnpObNhmdLAYTE5fEJdICIjG4bwSJosZ9xwYbN1zABRdkEZQduqGBln2RfbVl6VrQxBjjrqD5/v4oaWjoht6rCt7nnO9J7K6uqv7yVld/+n3e54mLi4NCocDrr7+OkSNH4qOPPoJCocC5c+fw0ksv4fvvv9ft193dHX/5y18QGRkJlUqFcePG4fnnnzcKhC0tLVi3bh1eeOEFVFdzpa41Gg1efvllTJ48GZmZmQgLC8Nrr73WJxDW1NTgz3/+M5YvX47y8nKUl3PVdktLS7Fu3TooFArk5+djy5Yt+M1vfnFlgK4AACAASURBVKOXojpy5Eg899xzWLhwIdRqNdRqNWiaxhtvvIEPP/wQSqUS58+fx9tvv60HhJcvX8bf/vY3uLq6IiMjA9nZ2ZgyZQreeOMNtLS04NKlS/jiiy/w8ccf687JlrOP5ogAIT9BgJBfDQQvKfexHBDmdfV+o7es4IDwyB7ipQglRC9dQnMhkalwvo9iMWHqWrwb0AWEq2NLeT9vAoQDLAgQ2l61TXVYEhEFiUyFf/srEfH1N7i2eQqulKQTL20oY0DYofY0qjsl2/Re035httFt7xZt0Nv2Yc5Cg9uZc84ajQbPPPMM4uPj9R53c3PD5MmTwbJdQNh91m716tVwcnJCfn6+7jEPDw+MHj0aLMvNDg4ZMkRvprG+vh7PPvtsLyAMCwvDsGHD8NRTT+GVV15BcnKy7rmdO3fixRdfRGtrFwzv2LHDZimjzs7OmD9/vu7fI0eOxPDhw/W2OX36NH7729/qwJJl2V4zhLt27cIbb7yhtwC/paUFzzzzDM6cOQOWJSmjgy2szX5hWfKZbEuJ3UuGYUB98R4HhFUVusfp/f4cEAasI16KUELzkqIZjNrFtZMobej7R8tKTatuNvHbSMetYbW1lwQIBRoECO2jVorWNRpdERaAatVXeJA6A1dzYomXNpIYgTA5ORlOTk4YNmyYnoYMGYK3334bLNsFhN3X9cnlcjz77LN6+1q2bBneeustsCwLpVIJJycnFBfr3yTeeuutXkCo0WiQm5uLhIQEuLq64q9//SsqKyvBsizmzJmDUaNG6X0udO7bXCCkKAp+fn5488038eKLL2LYsGH47W9/i4kTJ+q2GTlyJNzd3fVet2nTJrz66qt6j3WmtnYC4ddff43f/OY3vXx86qmnsH37drAsAcLBHgQI+ZXYvWQ0jV0VRVu6ln3Q4aHc498vJl6KUELzsrShBRKZCqN2ZYKiDVcY7a6sysuQyFT4QJ7Fe4N6AoQDLAgQ2k8UzWB9nAr+KeMhVzrjYuZUPFR64Mf0UOKlDSTGlNHExEQ4OTkhJiYGubm5euqEuU4grKur071OLpfjhRde0NuXr68v/vnPf4JlWSgUCoNAOHz48D7XELIsi9dffx2rVq0Cy7KYPXu2zYBwzZo1eOmll7B3714olUrk5uZizJgxepBmaI3jxo0b8dprr+k91hMIZ86cibfffruXh7m5ubpxQYBwcAcBQn4ldi+ZshIO/Ka8r/948jnu8YXTiZcilNC8PF/EVRh1Cc01aXuaYfDpPnWfTeyF7iUBQoEGAUL7iqYpBKZt1PUqLFRNQXvmTFxLDCBeWikxFpVpbGzE0KFDsWeP8fUnlgBhZ8poSEiI7vmGhgYMGzbMJCD09fUFy3aljHZv1uzv798vEP7tb3/DunX6KVSffPIJXF1ddf+maRp///vf+wXCzpTRioquNK3IyEg9IJTJZHjxxRfR0GB8zcWMGTPwySef8P43J0DITxAg5Fdi95JRp3Pg9/UE/cfzL3CPu31KvBShhOblXmUVJDIVfKMMVxg1pDXnLkIiU2Hd+Yui9JIAoUCDAKH9xTAMdiv9dVCYo5qE9uyZuH5uA/HSCokRCFmWhbe3N1566SUEBQUhLy8PCoUC27ZtQ1BQEFjWMiBkWa6ozKuvvoro6GhkZmZi/PjxeP755zFnzhywLJcqumTJEiQkJKCoqAhpaWlwc3PD0KFDkZmZCZZlcenSJfzhD3/A1KlTkZWVhZMnT+L111/vFwg//PBDjB07FqWlpboCNXPmzMF///d/Iy4uDtnZ2XB3d8fvfve7foGQoii88cYbGD16NNLT0xEXF6crKnPs2DGwbFdRmZEjR+LcuXMoKChATEwMZs+ejZIS7sa6cuVK/L//9/+gVqtRXV2tty7SkSJAyE8QIORXYveSSYrlwG+Jfko7U1/DPT5BAqbbD2fES3FIaF6uOMutCQxKqzT5NfHFjZDIVPhsn9poI3she0mAUCBB2k7wp/0ZQToozFR9ifbsmbh1NoD38xKrxAqEDMNg8+bNeOONNzBkyBC8/PLLGD16NGJjufWllgKhobYTb7/9NpYsWQKWZdHU1IRx48bhlVdewdNPP40//elPGDt2rF5RGZZlkZCQgOHDh+Ppp5/G8OHDERoa2i8QJiQk4M0338TQoUN1bSdqamrw2Wef4bnnnsMf//hHfPvtt5g2bVq/QMiyXW0nnn76afzjH/9AWFiYrhVH5zbl5eWYNm0a/vCHP2Do0KF47bXXMGPGDN2sYVVVFT788EM899xzpO3EIAwChPxK7F7SEUc58Fu9UO9xhqa7is1UVzjkXMTupZAkNC/djuZBIlMhpsB4tktPtVA03pdzhWiyqy7b7dzs5SUBQoEGAULHKiTrgA4KoyNnomW8BPTuLbz+yiNWiRUIHSWNRoMXXngBgYGBZr9WaF7GxcXByckJeXl5vJ+Lrb0kQGifIEDIr8TuJX1YzlUT3b6q13PUzHEcEF5QES9FJiF5yTCMDuyK6gz3qzampRFc0UJZimN+lLCllwQIBRoECB2v4+oj8IudjfcCUrFimT8HhTtXgqH4bTIqNhEg1JdCoUBwcDDy8vKQlpaGzz77TK/HoDni28sjR44gIiIChYWFiIiIwP/8z/9gxIgRvHtsiQgQ8hMECPmV2L2kd23kgHDfjl7PUd4eHBAmRBMvRSYheVlxqZVrTxaogtaECqPddTqnDhKZCpMO5SD14iVemtQTIBxgQYCQH0XldTUZ/f677bh1YgpuRc8lUGiGCBDqS6FQ4F//+heGDRuGF198ER988AEyMjIs2hffXu7evRuvv/46hg4dildeeQUuLi4Wga0QRICQnyBAyK/E7iW9wYcDwrADvZ9b/y2XTipbC6ba9LVfg9VLIUlIXiaWcGsBpx7OMfu19c0URsi6GtWPkKkQpq4VhZcECAUaBAj5U3RBA94LUGF11DJkZDxZUxg9FwxlXiuDwSoChPYT8dJxXhIgtE8QIORXYveS+s6LA8LY8N7PLZ+t61FITZCAjjxOvBSJhOTlgYxqi5vMV2padTDYqXcDVA6dKSRAOMCCACF/amtrQ1xlflehmU4oPDsHjJZAYX8iQGg/ES8d5yUBQvsEAUJ+JXYvqblTubRQZZLe40x1Jajxki4gHP8OqIkj7DpTKHYvhSQhebkqthQSmQqBqeavA0y9eKkXEEpkKqSVXRK8lwQIBRoECPlTp5dH1aE6KMzNmIwOtSdun50NRmveIuPBJgKE9hPx0nFeEiC0TVhbQZtlyf3NlhK7l5TrGA4IC/TT+RhVqj4MPhGTmUa8FIGE5KU0lKsweiSrxuzXVmpa9VJGyQwhCauDACF/6u7locz9Xc3rM6Z0QSFJHzUqAoT2E/HScV4SILRPECDkV2L2kqFpUBNHcKBXW6X/XHUlqAlkhlCsEoqXx9U1Vq//C1PX6kFhcIZj19kTIBxgQYCQP/X0cl96gA4KyzK+wkPlDLQF+oChKd7PVYgiQGg/ES8d5yUBQvsEAUJ+JWYvmcb6rpk/be8ZFzryOFlDKFIJwUtbzu5VXGrB5EMXIJGpsObcRVF4SYBQoEGAkD/19JJhGMgVWyBXOmOXYgzObpyl64PE0DTv5ys0ESC0n4iXjvOSAKF9ggAhvxKzl0xpIQd70z40ug21ZQW3zbaVxEsRSQhe2nr9X3q5RgeVBbWOW2pEgFDkYe06CyFcTANFhrykaRqy1HVwOSLHu7IMnJvJ9Ttq272SQGEPESC0n4iXjvOSAKF9ggAhvxKzl0yWgoO9b740ug19PoLbZuF04qWIJAQv7VEhdGlEESQyFb45ke+wvoQECAdYECDkT8a8ZBgGfjFc9alRARko8v4KDxUz8Ev0UgKF3USA0H7q7mVhYSGcnJygUCh4Py8xigAhP0GAkF+J2UsmIZqDPW8P49tUV3StH2y6TLwUiYTgZbOWxnsB+jBobQ/BkvoWh/clJEA4wIIAIX/qy0stzWDeqUK8L4+HLN4D17Pd0aH2xM/nVvN+3kIRAULz5evrizfeeAPPPvssfv/73+P9999HYmJir+3MBcKRI0fCyckJTk5OeOqpp/DHP/4REyZMQFFRUa9tQ0JCMGrUKPz+97/HM888g7///e+YPn36gAVOAoT8BAFCfiVmL+nwUA721izuczvK4zNunWFGCvFSJBKCl/HFXEP6sXuzbTabx0dfQgKEAywIEPKn/ry83EpjXdwiyJXOCEr5EreyZ6BD7Ymfkvx5P3chiACh+dq/fz8iIiKQn5+PzMxMuLm54Xe/+x2qqvQr6VkChDNmzEB5eTnKysqQkJCAt99+G++++67edosWLcJvfvMbzJ07F+fOnUNBQQFiYmLg5+eHjz/+mHd/7CEChPwEAUJ+JWYvKflmDgg3fdfndvQGH26d/+FdxEuRSAherj1/ERKZCuvO264IDB99CQkQDrAgQMifTPHyYmM5tidPhlzpjODkKXiQPRPtWTNxLf0g7+fPt8QKhAzDYM2aNXj11VfxzDPP4M0338Thw4d1z589exZOTk4IDw/H8OHD8cwzz2DUqFGorKzEqVOn8I9//APPP/88Jk2ahMuXu1KVGhsbMWXKFDz77LP4r//6L2zYsAEjR47E7NmzjZ5LQ0MDnJycEBkZqXssKSkJb731FoYOHYr//d//xZEjR0wCwp7H2bNnD5599lndvxMSEuDk5ITNmzcb9YXvv409RICQnyBAyK/E6qVeBdHx7/RZQZQ+dZjbbsUc4qVIxLeXDMPg8/1qSGQqxBc32my/hiqXSmRkhpCEGUGAkD+Z6qW6Khc7U8dBrnTGiUQp2rNnol01Ez/kRPD+HviUMSDU0k1GRdH6fR372lZr4rbmnvfSpUvxj3/8A+Hh4cjPz4dcLsfQoUMRExMDlu0Cwv/7v/9DXFwcFAoFXn/9dYwcORIfffQRFAoFzp07h5deegnff/+9br/u7u74y1/+gsjISKhUKowbNw7PP/+8USBsaWnBunXr8MILL6C6mutfpNFo8PLLL2Py5MnIzMxEWFgYXnvtNbOBsKamBp9++ilGjRqle2z27Nl47rnnoNVqeR87jhQBQn6CACG/EqOX5vYYZAoucNt89YFd20OJ0Uuhim8v82qauPoQuzLRrLVtTYgwdS3eDdAHwj3KSrsVmSFAOMCCACF/MsfL2MJ4BCrGQK50RlKCK9e4/oQLmMoy3t8HXzIGhJ29HA0pIn+Z3mv2pH9udNtTuYv0tg1WTTK4nTnnrNFo8MwzzyA+Pl7vcTc3N0yePBks2wWE3WftVq9eDScnJ+Tn5+se8/DwwOjRo8Gy3OzgkCFD9GYa6+vr8eyzz/YCwrCwMAwbNgxPPfUUXnnlFSQnJ+ue27lzJ1588UW0tnbdPHbs2GESEA4ZMgTDhg3Ds88+CycnJ/z9739HYWGhbpuPP/4Yb775pt7r1q9fj2HDhulkDI7ELAKE/AQBQn4lRi8ZVao+DHb2IsxMM7w9RYGa8j63TVEu8VIE4tvLPYoqSGQqLAwvtMv+KzWtSCu7hG2JZXpgaI8iMwQIB1gQIORP5np5OPOgDkIUJz1BfSEBNXcqmMsa3t8LHxIjECYnJ8PJyUkPgoYNG4YhQ4bg7bffBst2AWH3dX1yuVwv/ZJlWSxbtgxvvfUWWJaFUqmEk5MTiouL9bZ56623egGhRqNBbm4uEhIS4Orqir/+9a+orOR+AZ8zZw5GjRql97nQue/+gFAqlSI3Nxe5ubmIi4uDs7Mz/va3v6GxkUuL+fjjj/HPf/5T73V1dXXIzc3Fvn374OTkhLq6Ot7Hla1FgJCfIEDIr8TopbkzhCzLgvrOi0stDQ8lXopAfHvpcSwfEpkKR7Jq7Hqcikstdi8yQ4BwgAUBQv5kiZc7U9dhQ4ILPt17FqrZntwNy28eGK39e84ITWJMGU1MTISTkxNiYmJ08NSpTpjrBMLucCSXy/HCCy/o7cvX11cHWAqFwiAQDh8+vM81hCzL4vXXX8eqVavAslxap6VA2PM45eXlcHJyQkBAAFiWhZeXF5577jm92cdOGXrPA0UECPkJAoT8Sqxe0gdkejDY1xpClmVBHwzggHCTL/FSBOLTy/omrW6dX8Ul+35nM1ZkJjKvzmYppAQIB1gQIORPlnjZSmmxKDybK1m8W4Uql09wQzYet+MWgGUGV49CMRaVaWxsxNChQ7Fnzx6j21gChJ0poyEhIbrnGxoaMGzYMJOA0NeX+zLTmTLafZ2fv7+/RUBYVVUFJycnbNmyBSzLIj4+Hk5OTti0aZNJ73mgiACh+SGTyTBr1iwEBgZavA8ChPxKrF7SUWEcDM536XNmsFNMehK3ved44qUIxKeX4bl1kMhUcAm1X3pxp4wVmbFlCikBQh7C2M3x3r17WLVqFZYvXw5fX1+kp6ebvW8ChPzJUi81rRSmHM7hyhaf2IcHGVw7il8SB1ePQjECIcuy8Pb2xksvvYSgoCDk5eVBoVBg27ZtCAoKAstaBoQsyxWVefXVVxEdHY3MzEyMHz8ezz//PObM4SrgaTQaLFmyBAkJCSgqKkJaWhrc3NwwdOhQZGZmgmVZXLp0CX/4wx8wdepUZGVl4eTJk3j99dfNajtRXl4OpVKJCRMm4JlnnoFardZtN3/+fF3bifPnz6OoqAgJCQmYOnUqnnrqKTQ0NPD+97G1CBCaH9XV1SgpKSFAKGKJ1Us6YB0347d7i0nbM5c1oCaM4NYR1lYTLwUuPr1cGF4IiUyF9XG2azfRlwwVmbFlCikBQh7C2M3x8ePHePDgAQDgwYMHWLx4MW7dumXWvgkQ8idrvCyqa4Zn2E4EKsbgyLm56FB7cj0KswZPOwqxAiHDMNi8eTPeeOMNDBkyBC+//DJGjx6N2NhYsKzlQGio7cTbb7+NJUuWgGVZNDU1Ydy4cXjllVfw9NNP409/+hPGjh2rV1SGZbn2EMOHD8fTTz+N4cOHIzQ01KzG9E5OTnjxxRcxcuRIREdH99r24MGDGDlyJF544QUMGTIEf/7znzFlyhQkJiby/rexhwgQWhY1NTUECEUssXpJLZ3BAeG5M6a/Zt407jUHA0yaVRwsXgpRfHl5NLvGrgVejKmzyExUfr1BKDx1odbiFFIChDxFfzfH27dvY+HChbh586ZZ+yVAyJ+s9fLEhdO6wiaq6NnoUHuiXTUTV8uVvL83R0isQOgoaTQavPDCCwgMDDT7tcRL22mwAWFtbS127NiBefPmwcXFBUVFRb22SUtLw6JFi+Du7g4/Pz/U1dX12oYAobglRi8ZmgI1eRQ321daZPLrqGWzutYdTpD0u+5wMHgpVPHhpaH0TVsXeLHkHKxNISVA2CNsdfPrL4zdHO/cuYPly5djxowZSE1NNXu/BAj5ky283JmyEXKlM3amfo6maHd0qD1xP2Um2CbH/PrEpwgQ6kuhUCA4OBh5eXlIS0vDZ599ptdj0BwNdi9tqcEGhGVlZYiIiEBhYaHBe2JeXh5cXV2RkZEBmqZx/PhxeHh44Nq1a3rbESAUt8ToJVNayEHdlP+Y3FfQksqkg8FLoYoPL40VeEkru+TQ995fCukImQqZlRqTZwxFD4QURSE9PR1RUVE4deoU4uLiUFpairt371q0P1vc/Pz8/ODr69tL169f123T383xxo0bWLt2LW7cuGHW+RMg5E+28LKVasXmRC/Ilc7YliTFzXg3dKg9cSdh7oAvMkOAUF8KhQL/+te/MGzYMLz44ov44IMPkJGRYdG+BruXttRgA8LuYeieuHr1aoSEhOg95uPjg/DwcL3HTAXC9vZ23L17V6d79+4B4O5tbW1tZunKlSsAgCtXrpj9WiLxe8mcO8MB3VIP01+TmWakd6FiUHspVPHhZYWm1eD6vSqN1uHvv0qjhaJMYzSFVG/G8EIdqjRapJVpDJ6rpV7yCoS3b9/G+fPnsXjxYkilUoNydXXFzp07UV1dbfFxrLn59Rem3BxDQkKQn59v8Dlb3TTJB5PtZCsvyxqrsD3lSy59NG4JHqS448fvPgYTeZz392hPESDkD2KIbOdlfX29wfE9EIGwo6MD06dP73WfPHbsGNavX6/3mKlAGBUVBRcXF538/Pxsc/IkBl3cCA0CNf4d/Hxgp8mv6bh2VVdUpvsMYce1q3Y8UxJiimr2l14wGFfJ8npOV2/d73O2sDsY9jznq7fuo0T7M67eus/LuVsMhMnJyfjmm2/g5eWF0NBQ5Ofn48qVK7h37x46Ojpw48YNNDY2IjExEevXr4dUKsXWrVt1X9bNCWtufv2FoZvjjRs3dDObd+/exbJly9Da2mrw9eSmObAjrSZTt57wfHggd1P68t94eLme71OzWzQ1NeHHH38kIhK1mpqa+L6U7BY974nXr1+Hi4sLGhoa9LaLjY2Ft7e37t9bt26Fl5cXZsyYgfnz50Oj0Rg9BpkhFKbE6CXt+w2o8e+g4sxpo7MihsREhemljdLhoYPeS6GKDy+3J1VAIlNhUXgRFGaMK3sr7EKdDgr7Wl/YHQ43x5fpth0h4yBRNDOES5YsQXZ2Njo6Okza/sqVKwgODkZsbKzZx7L05tdfGLs5NjU1Yfny5Vi+fDm+++47KBQKo/sgM4TCk6293JmyHW5Hd8J5TxYq1/hyaSsLJ+GKppz392oPkRlC/ma1iGzn5WCaIey8JzY2NuptFxsbi2+//damx7ZkDLe1kSURtpLYvGQYBtRXH+KY1wK9L7ymFtqgq8pBuX3CAWH0yUHtpZDlaC8ZhsHEAxcgkakQnW94eQCf6qxCmlV12SQoNJb6as4xeQPCR48eWfS6x48fm/0aPm9+loa5N03ywWQ72drLVoqGS2guJDIV5hzPBeszAQ+S3HA3cTZYxrQF8mISAUL7iXjpOC8H0xpCW2bN9IzU1FT4+PjoMmkIEPIrsXnJVFxE2RRnjNiZbnE1SDp095M1iDMGtZdClqO9zKtpgkSmwqhdmbjcKuy6Dt2LzowwcdZQIlNBUaYx6zi8AaEjw5E3P0vD2psm+WCynezhZWFdM0btysS/A1NwJHYXHiq4pvU3FJt4f7+2Vn19PWi66wOWQIztRLx0jJc0TQ8qIAS4dfWhoaF6j1myrr6/IEDIr8TmJZNwFsnuUoNfeFMvmlYNkqmtBjXxSZP64oJB66WQ5WgvA1K5dNHFZ0xvY8KnOmcMKzWteoD4boAK+5VVBttniGaG0Fg8ePAAt2/f7iVrgs+bn6VBgJA/2cvLkMwCbE6cikDFJygK/47rT5g9Ez9ejOP9PdtSWq0WdXV1OigkEGM7ES/t7yVN06irq4NWa/hmKlYgvH//PlpaWtDS0gIXFxckJiaipaVFV1m7s/K2SqXSq7xt6/dLgJBfic1Ler//kxnCjF5AuCi8ECkmluKnvl/MpY3KbfcjrNi8FLIc7eVXh7msrdM5dby/d0vUHRBZlu0FiXGVrNleCgIIHzx4gOPHj8PLy8totVFzQyg3P0uDACF/speXNE1jU+ISyJXO2JLkgZ+Pcf0JH6TNBNsizg8lY9Jqtaivr0d9fT2ampp0/09knYiXjvHSGAyyrHiBsKamRq+AWaf279+v26azN6+bmxv8/PxQW1tr9XFJyqiwJDYvKb+5oMa/g6OHI3tVWDSneTeTFs+ljX71IWhFok36EYrNSyHLkV4W17dAIlPhvQAVGpsHzrKdTkis0mgt8lIQQBgcHAxXV1cEBATgzJkziI6O7iVzg6+bn6VBUkaFI3t6WVRXAf/U8ZArnXEgeTvuxbqiQ+2J20nevL9vsXk52ES8FIaXYgVCoQQBQvGOfUeLYRhQ051BjX8HVaou+FNVaAymyPU1U8jQFKhpH3W1oJggAR15fNB4KXQ50ss9yipIZCrMPVXI+/sWkpeCAEIvLy/Ex8fbYlcDJggQiu9iMlXBGUcgVzojIG0sSmNlaM+aiQ61J64VRfD+3sXm5WAS8VIYXhIgtC4IEIp37DtaTG3VE3gbgeyLXMPusfvUSL14yeCawrQy42sKmerK3o3qJ46waqZQTF4KXY70cloIly66V1nF+/sWkpeCAcKqqipb7GrABAFC8V1MpoqiaWxMmAu50hlbk2bjeujXuLn/S9DLZ4KhhV3tSmheDiYRL4XhJQFC84KkjApLYvKSSYnjwG3OFJwvaoBEpsL0I3mo1LSaP0OoSu0NhOPfAZOZZrWXbE0VGFWqTdJQB6scNS73PZkdNLd9iZgkaiA8fPgwTp06ZYtdiTZIyqhw5Agvs6qKIEv7DHKlM46m7QU15T+6xrl8v3+xeTlYRLwUhpcECK0LAoTiHfuOFrV7Cwdua5bgWHYtJDIVFoZzaX7di2hIZCrs62e2h6mu1GtS3znzaO0M4e208137tUEa6mCVI8ZlpabVYL8+U9uXiEWiBsL79+9j69atCAsLQ2VlJZqamnppsAUBQvFdTOZqV9pefBu1GOP2J6E2LJS7oUx5H22VObx7IDYvB4OIl8LwkgChdUGAULxj35GiI493gzcJAg/HQiJTYc25i7ptKjWt+PIg11w8yoTm4nTkcV37CWr8O6AWu1p3njVVoCaMsGka6mCVI8Zlcqn5qcZilKiB8ObNm9i0aZPRCqOWVBkVexAgFN/FZK5atDSmHs6BRKbCuvOloL+bgdtHp+CBwhOsdmB8QJFxSbwUooQAhD/88ANSU1Oxe/durF+/HqtWrcL27dtx4sQJXLx4ER0dHTY5jtCCAKF4x76jZGg2b/Wy7ZDIVAhMrdDbdt35i5DIVNicUGbyvukzR3T7ZxJjLD/PzDSDaah0fBRJITVTjhiXR7NryAxhHxIEEPr7+8PT0xNhYWFIT09HVlZWLw22IEAovovJEqWVXdJ9KOVkZeD+uenoUHvipmIN7z6IzcuBLuKlMLy09qZZU1ODrVu3Yvr06ZBKpViwYAH8/Pywdu1a+Pj4blFfswAAIABJREFUwMPDA1KpFHPmzEFkZCTu3r1r1fGEFgQIxTv2HSVD6/0W+O2DRKbC0ewavW1P59RBIlPB41i+Wceg9+3g9i39GHRSrGXwVlNlEAhJCqn5csS49Dyer9e25N0AsoawuwQBhDNnzkR6erotdiXaIGsIhSNHe+kTmY5voxZja9LXaDuxUdew/mpDHu9eiM3LgSzipTC8tOamKZPJ4OrqCn9/f+Tn5+PmzZu9tnn8+DGam5sRGRmJxYsXY86cOaisrLT4mHwHKSojLInBS0MVQaevC4NEpsL5oga9bTt7yo0MzEQrxZh+jNZWUG6fWNeGoqrcMBCSFFKzZe9xmVPdpOs9mF11Wa+p+0CTqIFw3rx5or7h2SMIEIrvYrJU+bXl2Jn6OeRKZ5xUH8OtY1J0qD1xN3UhWMb0G5wQRcYl8VKI4gsI9+3bh6tXr5q8/ePHjwdclgwBQvGOfUeK8vhcD6rG7lJAIuO+zHffjmEYjN6dZfC5vmSwyIyZ8MaEHXiyFtGdSx+NOm4QCkkKKf/jcmVMKSQyFXyjSnh/r0L1UhBAePr0aRw4cMAWuxowQYBQfBeTNZKlBEKudMb2lEloigtBe4YH15vwYjTvfojNy4Eq4qUwvCRFZawLAoTiHfuOEtMtFZNOjIG2qkJXUbTKwKzOvFOFkMhUCMmsMf0YRtpQUGdPmwxv1FIPbh3iqcPcPg1BZo9UUpJC6vhxWdekxcjATEhkKmRWmv6jgVglaiDMycnB4sWLIZPJkJGRgcLCwl4abEGAUHwXkzVqaL6MrUlTIVc6Y7dyJ34+wAHhA8UssHQL756IycuBKuKlMLwkQGhdECAU79h3lOgzRzmAWuIOlmVRe1mrW/tF0b2zZnalVkIiU+G7aNNnf6yFN6a2C1rZ2uquc+9ZyZSkkJoke43LSk0rVsaWQCJTwe1oHhiRZ13Z00tBAGFf1UUHS5VRsoZQOOLLy5Cs05ArnSFLG4tSVSzuJ7jiftx0XMk9z7snYvNyIIp4KQwv7QGEHR0diI6OxurVq7Fq1SpERUWhvb3d5sfhI8gaQmFJDF5SK+dzs4MhQWDZrvVfn+zNNrh9YkkjJDIVvjh4wazjWANv9OkQUOPfwVXfb3p5yVRXgslMAx0XaXi/Jw6SFNIesse4DFPX6grISGQqrI4t5f19CtlLQQBhbW1tvxpsQYBQfBeTtaJoGhsTvoFc6Qz/lFVoC/QB9YUElLeHaH/VIuOSeClECQ0Ijx49iuXLlyMlJQWxsbGYO3cuQkJCbH4cIQQBQvGOfUeIaW4GNWkkl4pZlAeWZRFfzAGfS2iuwdc0NlO6L/31TVrzjtcPvDGZaQZfRy2bBWr8O7gZe8qol6bOQjLVlYMeEG09Lis1rXowOFBbTNjSS96A8NatW1YdeKAHAULxXUy2UExBGuRKZ2xNnoTKkmJQk5/cGOOjePdFbF4ONBEvheGlNTdNY/e9efPm4caNG7p/FxQUwMvLy+LjCDkIEIp37DtCTPI5DpZmjtP9EBqmroVEpsL804VGXzfpENfTN7640bLjGoM3+SbQGSl6sMY01Oka0ndcYfr0Un8W0ggckjYVNh+XqRcHRxN6W3rJGxC6urpi8+bNUCgUejdCElwQIBTfxWQLMQyDJRGH8F5gKrYnlYM+GABqogTXdrjgSnM5796IycuBJuKlMLy05qbp5eWF7OzsXo8vXLhQr/podnY25s2bZ/FxhBwECMU79h0hesdqLl00YL3usaA0bo3gqj5S/vxiuHVispQKy4/dVwppN1ijI45xjy2cDgCo0miRetF4G4N+U0h7pKnSOVmDbsaQzBDy7yVvQFhSUoL9+/dj1qxZmD59OtatW4fk5GRcu3bNqhMaKEGAUHwXk63UmR7zvjwLdbUNuHlgCjrUnriVLr5m9Xx7OZBEvBSGl9bcNPPz8zFv3jxs2rQJV65c0T0eFRWFRYsW4eTJkwgNDYWnpyfCw8MtPo6QgwCheMe+vcXQtK43IKNM0j2+7vzFfmHvSFYNJDIV5pwssO4cnsAblZPZe0ZvggR0dQUoH08OWvduR1wlqwOPEbK+G533m0Kq0+CbMbT1uKRoBp/uy9aDwYHYhN6WXvK+hvDRo0coLy/HoUOHMHv2bEilUqxatQpxcXF6N8yBHqSojHDEt5cMw8AlNBcSWQYCUlNx9egGrll91kxcaani3R8xeTmQRLwUhpfW3jTv3r2Lw4cPw8PDA7GxsXj06BF+/fVXqFQq7Nq1C4GBgUhPT8fjx4+tOo5QgwCheMe+vUWnxHEgNPk/YFq7ZnIWnymCRKZCaJbxthKdhWc+CsoCbYM190bbUsz4TPf/ZVPGYIQsw6xZKL1ZyAmS/gHxSWGbgb7O0NbjMiKvHhKZCh8EZeJ8UcOgmBm01kvegbB7PH78GNXV1QgNDcW8efMglUqxfPlynD17FhRF2fJQgg8ChOK7mGypyLxirI+fDlnaWFRVFOD2ia/QofbEzayNvPsjNi8HioiXwvDSVkVlGhoa4OvrC19fXzQ2Ntpkn0IMUmVUWBKql3SkflP37jNj7kfzIJGpEFvYYPT1FM1g1C6u19zJC7VWA4Aps3nJ7lKL1ql1zkIy1ZWmrTH0+XrArzO05bikaAaTn6wplacNTIC2h5eCAsLu8euvv6K+vh7Hjx/HwoULB0Xrie5BgFB8F5MtxVUcnQm50hlBim24emjlk1lCT1xpNb35Lt8SgpcDRcRLYXhpyyqjjx49QkxMDDw8PBAaGop79+7ZbN9CDQKE4h379pJB+OrW8mHcfrVJTcW/OHBBB2X9pW+aIj1YmzgC1JP1jV0zhM6Q7DRvhtDY+2cy00DnZJmWUjoA1xnaalxWalrhn1wOiUyF0buzoGmleH9vYvGSNyAsKioyKyVGo9FYeihRBgFC8V1MttbR7EiuL2Hq56grzcOdk09mCS9s590jsXk5EES8FIaX1t40f/jhB6SnpyM5ORlNTU2689i0aRPmzZuHgoICq/Yv9CBAKN6xby8ZS89kMtPAMAz+HcjBVsUl46BVqWntNVNniyIi3WfzDIHrjDVH9Y7pG1Vi1fF6Qeh3s/uGwwHSusIW47Jn38ElZ4p4f19i8pI3IJRKpZgzZw5OnToFhmGsOomBGAQIxXcx2VpaisamRDfIlc7Ymy7DD3u/7Zol1Nbx7pOYvBwIIl4Kw0trbpolJSVwd3eHt7c3/Pz84OrqirNnz+qez8rKgpeXF/z9/fHTTz9ZfBwhBwFC8Y59e4mprjS6dq6+Sav7gt9KGV8b6Kg2Az2Bbd4BBSQyFaYeztEVg+sLXE31oy8INaV1hdgA0dpxOZiritrKS96AMD09HWvWrIFUKoVUKsXatWuRmZmJ+/fvW3VCAyUIEIrvYrKHDmeehFzpjB0pk6AtysGd01/hTthUXClK4f3cxOal2EW8FIaX1tw0fXx8cObMGd2/i4qK4OrqqpcqeuvWLezbtw+enp4WH0fIQYBQvGPfXmIYBtR0Z/2UyCfr5PJquGIxznuy+9yHI4GgE9jYmipMDS2ARKZC6sVL8DiWD4lMBa8T+X22oTBXJq0z7AmHIltzaO24HMx9B23lJe9rCFmWxalTp3RFZDw9PXHw4MEBvcjelCBAKL6LyR5qbG7GjpSJkCudEVt4DvT6RdyC+y0reD83sXkpdhEvheGlNTdNT09PVFZW6v59584dSKVS3fl0j+rqaouPI+QgQCjesW8vMblZHLxMGgk6LV5vViuxhGvD9FVIbr/76Zky6Ig2A/+RZ0EiU6G0oQWFdc14N6Dr+LZYx6jzyNx1hiJbc0hmCPn3kncg7IzHjx+jtLQUAQEBcHd3h1QqhY+PD+Lj4wdF43rSdkI4EpqXW5M2QK50xpbE78HkZOpunIxG+L98Cc1LMYt4KQwvrblpbt++HevWrYNGo4FWq0VwcDDmzZs3YFtMGAoChOId+/YSHbiB+6Fzo2+v505eqIVEpsLcU4Um7Su9XKMDAmtTN/tTQwvVLZ2Vtts6xl5+9WxdYcqsYfc1hxHHBJdSaotx+c2J/EHZd9BWXgoGCLvHrVu3kJSUhOXLl0MqlcLNzc0ehxF0ECAU38VkL6WWFWJc8HGM2qVCY7MW1OzJYKTv4ad44ReX6e4lQ9Ng6mvA5F8Ak3IedMRR0MeDQYcEgd7vD1q+GfTuLaD37QB9MIB7/OQh0DGnwaTEgVGng7lYDKa5mff3xbeXfJ+L2MUXEP7000/YuHGjbqmEj4/PoMuGIUAo3rFvDzFarS5dlEmL7/X8HkUVJDIV/GJKTd7nrBNcGmeQnVsO5NY0QyJTYcxeLp3VkWmLRltXmDt72C2llE9ItHZcNmtpfLSbm63dn141KGcGrfVSkEDY0dGBgoICbNu2TXfjHGxBgFB8F5O9xDAMpjxZsH40uwbMMTnaVR7oUHviaksF7+fXpxga94pzQK9ZAurLf+vfiL6QgHF5D22eo3Bl7gf4YelH+NFnNH70Ga23HesxEldmv4+2maPASN8DNeEdUNM+AjV3KqiV80HLN4EODwWTkQKmugKMDZoSC1FCG5diFt9tJx4+fIi7d+9avR8xBOlDKCwJzUsmLYH7rJ/uDEar7fX8hriLkMhU8E82/V53OqcOEpkKEw9csEmTemOKK+bSWV2P5IFlDactcjOVLfb30VpANNDrsCcg2hMYrR2XnY3ox+1X2/VvLgYNCCBsaWnBsWPH4OXlBalUCi8vLxw/fhxardaWhxFFECAU38VkTx3IqIZEpoL70WzQVRW4fXQKOtSe+KVgD+/nZkhMeSno4J2gZowFNVGCNs9RXTcnt09w+/QMdKg9jYoOWAt6+yrQ67/FzdNf6z3Xnj0TD1LdcS9GijthU8FMe7erTLnLe6BdPwLlPRO0bC0HirlZYFrF/2uhEMelWMU3EA7mIEAo3rFvD9Ebfbl00cANBp9fGlEMiUyFw5nVJu+zWUvjgydr++xZVORIVg3X3iCiq71BmLpWbx1hZ3N0WxaZMUVWrTnUqQsQqS0r7FqoxtpxOfskNyscmCrwH8kdINEC4e3bt5Gamgo/Pz9IpVJMnz4dW7duRV5eHjo6OqzdvWiDAKH4LiZ7qrZJizmn1mJHyniklaXhh8Bv0KH2xAOVF1hGGI1XmYY60KcOg1rijjbPUfh5y2e4c2Iq2jM80J71NZjCbDAUd663yvZwcKf+Gg9z5uN+/jLcK1yNu0XrcLdovd57ullxCA/yvsXDnAVoV3/dGx53bwS1agEory9xc/+X6FB74n6CK24fn4IbAeNxbbkz2mZ9AGrRdNA714A+exJMeYnoZhKFOC7FKr6AMC4uDg8fPjTrNU1NTbh48aLFxxRaECAU79i3tZgmDajJI7kf83KzDG7TWbnzbEG9Wftec46bWVwebV1fwL60I7kCEpkKG+PL9B6v1LQirewSAlIq9MDQlkVmzFGvGUOLAbF3WxAhjMuyxhadv2WN9p+NFbpEB4QVFRUICgrSFZBZtGgRoqOjce3aNatOaKAEAULxXUz21qaElZArnbEzdQXo2JN4kOyGDrUnrjUqeT0vprQQ9Dpv0NPew09rPsXdSJde0PYwdyGutnTdNK9o69FGNYJlaPOOx9Bo0zbiavNF/KjJxk8NyXrP3y7ebnTW8WG6B+hJI3Q3NNrjE1BrFoEOOwCmIAcMbea5kHEpWvHZdmLu3LmIiIjos//uw4cPUVBQgO3bt8PNzQ05OTkWH1NoQYBQvGPf1qJOHOQ+j2eON/oD3YQDFyCRqZBRoTFr35mVlyGRqTAyUIXzRQ12mZ37LrrkyZo1w7OXFZdaHFJkxhQZTSmdOALU0X0WQSIdH2WzFFJrxmVgKgfes08W8DqehSLRAaFUKoW7uzuCgoJQWVmJX3/91aoTGWhBgFB8F5O9FZWfBrnSGQFpY9HYUIUbu75Ah9oTd9Tf83I+TFUF6G1+oCZwN5aft37eldapnoU7pf74uS4Wv96h0cY6bjaujbqEHy/n4XrdedysOIi7RevwUD0b9zMXgN69BdTSGaC+fA93TkzFg2Q33Do8CT+t/RRtCz7lAPF0CJhy+/2qPNDGpRjFFxA+evQIycnJWLBgAaRSKebOnYvt27cjODgYISEhkMvlWLlyJdzc3CCVSuHv7w+Koqy8mwgrCBCKd+zbUnTkcX24MJCCyDAMRgZmQmLBzA/DMPh0X7ZdZ+e+DuPSFCPzDc9eCrk3XndA1P09LJ1FNLLm0BHjsvxSC8bs4dKDI/LMm0UeqBIdECYnJ+P27dtWHXwgBwFC8V1M9paWorEp0RVypTOOqQ+jLXDZkzV1nriiddwHIaNtBb1nK+ip/+aKvIx/B9T3i9FWmIZ7BSvxc200tC0NKKxrRnRBAw7kNGHxmSK4hOZiYXghNieU4UhWDZJLL6G2qXcRAbuIoXFF29D1Hlqa8VA9t/csYpo7boVMxrWVY0B9PQG0bC2Y5HNgmpt4//sLdVyKUXyvIXz8+DEKCwtx8OBBLFu2DLNmzYK7uzvmz5+PzZs3IyYmBlevXrX6OEIMAoTiHfu2ElNd2Rs4DKQgNjZ3tXVooczL4HBEX7qu2cvLJp+DRKbCloSLDl9TaOrfxegs4pYVXf/uAwwtLUpjybjs2XfySFYN7x4KQaIDQhJ9BwFC8V1MjlBA6h7Ilc7YluwJJiMFd09/hfYMD/xYl+aQ4zO1VaCWzsCP3znjQZIbbp79GomJGfBPrsDC8ELMPx2C5TFLsPr8TGxK/EpPGxJc8Pn+E7oP7zF7IzA1ZB+mhZzF4jN5CFJwC++btQ5K3aRb8ePlXNyoPo07RZvQrvbSgeGdk1/p3eiuLnYG/f080OFHeOvbJORxKTbxDYSDOQgQinfs20qMKtUgUDCZ+vexgjqurcPo3YbXF/Yle8/O0QyDfwdy+6zUGP9h01CRGb7XFJr8d+oxi6grVBMXafLMoe6/m77rsyiNueOSNKI3LtEB4fbt29Hc3Gzy9u3t7UhMTERqaqqlhxRVECAU38XkCF2oLoNc6YxdCmfUXa4Bu3g86MkjwMRH2f3YjDodlPsn+CFoMq5kuSFfNRmnlBMxcW+E7gPZM2wL5EpnozqcmYItCWVYGF6ExRE7dI8HKj7BhgQplkZ+C/djgZh/Og6BqWXIKNeAoh2UbspQ+OFyPm5Un8K16vNcr8SAdWDmjedmYlUeuH18Cn5a+ymY5a6gj+wBc7GYjEsRigAhf0GAULxj31ai89QmFSlJLuWgbsrhHLOPYW9gqLms1e2TZvo/l7SyS8is1PQ6pxEyFbKqLgtyxtCYDM7wmqsJ3N+7c9aQrakya1wKOR2Xb4kOCIODgzF9+nSsXbsWSiVXFKNn3Lt3D5WVlTh69Ci8vLywZMkSNDQ0WHXCQg1rezUJ4UN+oEjIXjIMo0sbPZN3GvR+f+7DddUCux6XDg9Fscd4XDg3E2fTx+lBnteJvfCLKcGRrBpE5ysRU3gYmVWxKG9UoVKTDeZGNSo12Si/lAEt3ZV6WVAXg+MX5mJ3+niD8Ng5m+i8JxsrzhbhbEE9NK2Or6j6w+V8PLiwuFd66d1IF/y85TOwPlNBHwwEU1o4aMel2ESAkL8gQCjesW8r0fJNvWDQ0BrCzn6ClhYL6ZlSeNSGKYXZVVzRmvEHcq2GGLHMGOr9DW1RudTbQ2/W8HbaebA1VSalmZYLqGCP0CQ6IASA5uZmyOVy3QJ6Dw8PLFq0CD4+PpgzZw6mT58OqVSKxYsXIzExEe3t7VadrJiCAKH4LiZHaVPCHnidXIetiWlgSou4D9Mv3gXTZJ9fxipPHcfWvT44ougCt12KMdiX8S0SS49C01pllZcMw0DTWoWShmSkle3BUfUiBCknwicqHx/t5haLzw9fiXXxrph7eh18o+MRnV/nuNRSlgXLMLjaUoYbNRG4m/892rO7wPDaCueuG9y8qaAPy8FUXBx041JMIkDIXxAgFO/Yt4WYuhpQk7hWE3RsuF5KYk/tU1ZxrSPOWl7kq6yxBc5Pio5E2rDoSExBAyQyFb45VWJVmqMhqBHLjKHRNYdWtbbolma6ebnRdYnHUop6+SYWmLa3RAmEnfHLL79ArVYjNDQUMpkMW7duxZ49exATE4P6+vpBWYGUAKH4LiZHKTKvHhKZCl+F5IJhGFz1+wL3zkpxO8v21UaTz6VimjwEcgUHgnuUY3BGvRqNzeV29ZJ50o5CSzNIK7uEwLTperOHGxJcMPf0RqyKTYaiTOPwnoJt2kZcr4/DncLNYGKOglo5H9QX7+H6hrG4FyvFz1s/B/v9TO4GprENqAt9XIpJBAj5CwKE4h37thC9ewv3BX+xW7+f25sSyiCRqbA9ybT7jTHJUmzfluCQqhoSmQqr4qvNLoTSuaawPzjsPmNYqWkVPCSaXJRm4gjunmkhMNZ++QFGb0uARKZCQGw+UuPSUFFcxvv7F4pEDYQkegcBQvFdTI5SfZNWdyOpvqzFVflSrkJm5iybNamnaAY7o3MwYmcGJDIV1p7+DorsuWhtNu+XWlt52axtQF5NFI6pfbFLMVYPDr87OxdfHryAvcoq1Fx2UNVSA2IuNeJuurd+WmnENPy04XMw274Fk55sVa9DoY9LMYkAoePC2uUQZOwLZ+xbK7ogB9SX/+YKyKQl9Lv9gtOFkMhUCEytsOq4nY3LLWlfYUxbE8shkakgz7xktpedawqzqi73C4USmQozj+XrtuuExJ6AKERgNFaUpnPtoCWziGVTnDHfbx/3o/j602iZ8K7FlU0HqggQDrAgQCi+i8mRcj+qwqRDB3E0+zzoiCN4qJiBDrUnfmiy/hfQhmYKPhH7MHr3WUhkKqxetx9Nly1ru2APL1upy8itiUJo9rfYpfgEiyNW6m6c7wVkwC8mHpmVhsuA233sUE24Xp+AO3nr9NJK2zNn4tbhSaA8Pgd9IABMpfm/ZophXIpFfAPhw4cPkZ6eDpqmrd6X2IIAoXjHvjUype9gd4Wpa226tm72Sa5noCzFOrjs1LeRxZDIVAgv0VrlpTkzht396A6IfmdLegEj3+PM5DFhRprpMa8FGLEzXefD1sVr+65sumVFn5VNB6oIEA6wIEAovovJkZI9aT+xPXk+mNIi3AqZjA61J25Uhlm1Xy3N4LtoOVf5M2kKFJsWgKmzfCG+vb283FqLmstVOHGhFjOO5WFc8HHIlc5YfX4mFp85iDO5NdA6qkppD13RNuB6TRTuqX3RofbEraNf6d202PWzwSSeBaM1bVZTDONSLOIbCAHA3d0dtbW1NtmXmIIAoXjHvqUyte9gp+xRITTiyVKLT/ZmI7nU+pm0GcfyuR6EDT9Y7aW5M4b9SUzFVTpnDdmaKtxOO280zbRsyhg9GJTIVHh3ZzrKpjj3CZF9VTYdqLOGBAgHWBAgFN/F5EjFF2dDrnRGQNpYtGib8NPmL7gURfUKi/fJMAw2xEUjUPEJ5EpnqNO/xMNsT/zQVCQaL2MKQ7FL8akunXRr0pdYeGYbDqtKHFuEpoeutpThSmMB6HNnQH3nBdZ9JPf3OjMN1zZ8AWb/NjBVfa+TEcO4FIuEAIQ+Pj7Iy8uzyb7EFAQIxTv2LZWpfQc7ZY+WAi0Ujf/syrTZrOPn+9Xcsg32F5t62X3G8N0AFfYrqyyCxKj8OsGlkJoyLtmaKoNppqnZJQbfZ4q7i3lppwumD/hZQwKEAywIEIrvYnKkWrQUtiZPglzpjOTSBLDbF3Kpidlfg6UtWyNxIEON7ckTIVc6Iz5tAtqzZ+L2xQCwVhRs4cPLy621iC/Zi12KSTow9E8dh0Vn1kGeVozGFse3ruipn4pO6aWUPlTOwC/yCWjbOh9MWoLBtYZiGJdikRCAUKlUYsWKFbh7965N9ieWIEAo3rFvqejy0t5fzB08Q1ipabXZTBpFMzpo+/H2A5t72Tlj2HluPdNKTQFEsaWQ9jcuDf79ZBkomzrGusqmE0eAzskaUDOGBAgHWBAgFN/F5Gitj18BudIZu5WbQZ84gPsJrlwbhEuZZu8rvrgaa+PcuObxCeNwL9sD9/N90UY3i9ZLitYiuyoCu5XukCudsSnxK4yQpeOj3VkIUlTicit/M4Ysy6WU/lx5CvezFugVorlzcirYxV+APnkITJNGEF4ONAkBCI8ePYoFCxZg1qxZCAwMxNGjR/V07NgxmxxHaEGAULxj31LR0Sd6fwnvZ2ams6CMrVoK2HLWsRNO3gtQ4dHjXx3iZXdI7DmL6He2RPdvYymkQm9lYcq47JyV7T4mzKpsunZpPymlA2PGkADhAAsChOK7mByt/eknn6wjdANTcAE3/Mfh5qEpuHrZvMIyhXVNWBbt/WTd4Ge4ppz+BCwzBoSXDEOjsC4Bx7IjMfVwzpObiRILwjciOD2P11RSlmXBMjR+1GTjdu4GtGd74qFiBqgvn/zS+dWHoHdvAVNVIQgvB4qEAIRSqbRfDcQgQCjesW+JGIYBNXcq93m237/PvoPdNecUB4Sb4stsAjHGegBmlGvMBiVVxZOm9MEXeBuXPWcRO/99Jreu35lDQ1VKO/fBFzT2Ny47K8WOkKlwrrDB6DlaXdm0j5lrsUj0QNje3g6lUomgoCBs3rxZ94aKi4tx9epVWx3GpiGTyXS/7hqKBw8eYOHChThx4oTZ+yZAKL6LydG6UF2FXU/6A9ZoykF99QG3LqPggln78Y7MxMaEadilGIOqoLHoUHvifv5ysIz1oCQ0LymawZncOsw7zRXOkaWNxdLItThxoRQUT8VnuutKay1+rE0DHX4E1Ddfghr/Du5GTMMve79Am8wbDy/XC8ZLMUsIQDhYgwCheMe+JWLSk7kv2lP+A0bTaNJraIbBR0FcM/mcassqXBtS95k1Y6Bkyn6i8rkCNZ7H8wU3Lo2Bb38ppWHqWl7TTPsblwcyuL6P35ywrpJ6r8qmhqAwLlIQ/qNmAAAgAElEQVTUKaSiBsKbN2/C19cXUqkU8+bNg1QqRVNTEwAgODgYoaGhtjiMzaO6uholJSVGgfDMmTPYtWsXAUKRSSxeMgyD9fEzIVc6IyIvXNfolT5x0OR9FNU1Y4RMhZG7kpH8/Rd4kOSGDrUnrtf33yNKzF6WNmZgT7qXbo3hjpTx8I7ciYQS4ay1YGgaPyiO6qWT3j4yGVe3zQKjiO+3qTORcREg5C8IEIp37Fsiym8ed18K3GDyawrrmiGRqTBqV6bNf6jrnEk7mF5l8ZrC4Azutb7RJYIcl5a0suC7Uml/49LzOFfV9ZCq2upjdc4a0jlZfc8YijSFVNRAGBwcjAULFqCpqQmPHj3SA8Ls7GwsW7bMFoexS9TU1BgEwra2NgQEBCArK4sAocgkJi/XnD+H0bvPYmtiOegje0GNfwdtOxfiaotpfe5WxZZCIlNh8a4YUBPewY/bXHCneAtYxjYN3oXsJcMwKKhLhPzJGkO50hmbk6bgu7OHUFhru1+lrRLD4MfLubh9Qb+v4d2Iafhxy3TQcRFgaP6L5IhNQgLCqqoqhIeH4/Dhwzhz5gyqq6ttun+hBQFC8Y59c8WUFOi+WDPlpSa/7uSFWt0MnL3OzdiaQlOqc26KL4NEpsL2pArBjktbtLKwpqqrLcdlbZNW9x4qLtkWUvVmDAdICqmogXD27NnIzMwEADx+/FgPCKurq+Hp6Wn2Pmtra7Fjxw7MmzcPLi4uKCoq6rVNWloaFi1aBHd3d/j5+aGurs7s4xgDwp07d4JlWQKEIpSYvOzsrTQtJBeMOgM/7xjH9bwr293vays1rfjy4FGMkKUj3dOVSzdNODvovKQZGukV4QhUTIFc6YyV52bhvYAMbE0sh6ZVOLD1Q2slOioPoD3rax0YXl30IahZE0FHHgejFWaxACFKCEDY0dGBHTt26NYLurq66v7f398fHR0dNjmO0IIAoXjHvrmiNi7jvlSvmGPW69acuwiJTIVtiX234rFG/aVW9pU2uTSiCBKZCiFZNaIYl6ZUKbV1VVdbjsswNfcDwfQjeXY5tm7GMC7SrPYoQpWogdDd3V33q2hPICwvL4eHh4fZ+ywrK0NERAQKCwsNAmFeXh5cXV2RkZEBmqZx/PhxeHh44Nq1a7pt/Pz84Ovr20vXr1/XbWMICIuLi3Hy5EkAIEAoQonJy+rLWt2HeUtzC64uHYMOtSceXFjYb7uI7UlxXFGahGlonfgOqAUuBtsdDBYvtXQzzhXtx7eRcbqb4uf703DiQqEg0jM7vbxCXcKNi4dxJ2UhKJfRupvWFZ8JoMMPgWklYGiql3wCYUREBFxdXREfH4+bN28C4JZPxMfHw83NDRERETY5jtCCAKF4x745osMOWJx65xKaC4lMhZiCBrueY19rCvuCIrejeZDIVDhX1CiacdlXldKeawglMhWCFI6dFetrXHZWnA1Ks+85GS46IyEzhCaGTYDQx8cHsbGxAHoD4dmzZ+Hn52fV/g0B4erVqxESEtLrPMLDw83atyEgDA8Px4IFC7Bo0SJ4eXnB09MTZ8+eNfj69vZ23L17V6d79+4B4G6abW1tJuvKlSsAgCtXrpj1OiJxe8myLL4O2wHvqKXIrsoBvXwW2lUe3OyRttbo6y610PA9uxhypTNCTkzHrUOT8HP8RrQxrYPWy+5KLLmESQdzMO/0KvinjoNvtBwFtU28nlMvL1kWbHMTmLADoGeOxcNUdzxIdsN1/ylgwg+B1dr2bzmQZM24tBUQLlmyBFFRUQafi4qKwuLFi21yHKEFAUJxflk0R0x1pcWpd01aWgcrVQ6YoeoEpc5CMaakTX66N1tX8Eas47JnlVKWZVFxqQXuT2B31okCh/4QamxcalopjAzMhESmQkGtdW2wTJHBFNKTB0VVZMbSa1wQQBgTEwNPT08UFxfr1hA2NzdDo9HAy8sLiYmJVu2/JxB2dHRg+vTpvSDx2LFjWL9+vVn7NpYy2hn9zRBGRUXBxcVFJ2vhl8Tgi42JXHGUhMo4/HL6EO6f59pGPL5hPAU6NLcYgYoxkCudUb/KmWtqr/4Gvz684cAzF3Y8aG/HXtVc3frCtXEzsDdLhfvtj/g+tV7x+OdLeJg1T5dK+iDFHT/LpuJ2SjR+HaCph2IPNzc3VFVVGXyuqqoKbm5uDj6j/qO0tBTe3t5YunQpMjIyLNoHAUJxflk0R3TKOYtT7xRlGkhkKny2X+1QX4ylkPZcU9hKMbrt6pupATcuyxpbMGoXB2AHMqod1obC2Lg8mlUDiUyFCcFqhwFqZwopJVsnyiIzogbCjo4ObNu2DVKpFN988w2kUilmz54NqVSK7du34/Hjx1btvycQXr9+HS4uLmhoaNDbLjY2Ft7e3ibvd+vWrfDy8sKMGTMwf/58aDSaXtv0B4RkhlB4EpuXG580qA9W7QKjSsWdE1OfVApNNLh9C8VgaeQqyJXO2J8yB7/smvBk3eGeQe9lTzEsg+SLxxGQNo7r06gYA5+o1UgqrXb4ufTrJUvhek0sHqjmdoFhkht+8v8KzLnTYBmadz+FIiHMEM6fPx9JSUkGn0tKSsL8+fNtchxbxaNHj+Dt7Y3r16/j3r17WLp0KW7fvm32fggQivPLojmi5JssniHcq+QqeHpHFjvcG1PaUnT2wxu1KxMsyw7Icdn5NzCld6GtZGhcdq4d7JSjW2HQVeWiLDJj6TUuCCAEgF9//RW5ubnYu3cvtmzZgt27dyMnJ8dqGASMA2FjY6PedrGxsfj222+tPp4tgqwhFN/FxJe2J8u4nnqpfmCam3Fz35foUHviRvFhg9uHZhZjZ+pnkCudUXRqDe6emYYOtSd+akge9F4a0+XWWhzO9NPNFm5JmoyN8XG43Oq4pvYme8lQ+KkmFg9Uc7iZ3+yZaPMcBWruVDCpcYJYD8m3rBmXtgLC0NBQeHp6orCwUO/x4uJifP3114Jrt9TQ0ACZTKb797Fjx5CTk2P2fggQinfsmyJG0wjqqw/1+7xNHGHyzMqSJwVb9qdX8eJPZyplUnGjwTWFZ5+klo7dl40qjXZAjstO6O2vd6Etj9lzXBqasXV0oRtGlSrKIjOiB0J7hj1TRm0Vqamp8PHx0aWfEiAU38XEl/aln+CKwyR7gWVZ/LyLmyG8eWGrwe1Xxu6EXOmMIKUHtCvndK05bLH9r15i87I/5demICDtK/injsMH8jhMPHABynKNQ45ttpeMFtcrI/BL3HJQ0z7U3cza1nuCUWfw7iWfEgIQ3r59G8uXL4dUKoWnpye8vb3h6ekJqVSK5cuX486dOzY5TmdYW3m7oKAAR44c0f07Pj4e8fHxZp8HAULxjn1TRB8M5D5r5kwBXVUOJjPN5BkVhmF06/PSHfS5akzG2lKM2ZOtB0hxleyAG5fG3rs94aznuDR2Do5shWGwyMwE4ReZIUDYRxgrKtPzF1hLisrYKwgQiu9i4ksReWmQK53hnzIJLMviyhpXXN8wFj+kHem1LcMwWHluHuRKZ5wtOIK2Bdz6wYfquf1WJR0MXpqiVqoJMQVpGLdfrftSsC0pBS2UfWcLrfGSudQIes82sJ4foD1zJu6emYargQvBlJfw7icfEgIQAtySAaVSiaCgIGzevBlBQUFIT09He3u7zY7RGdZW3s7Pz+8FhAkJCWafhzVAWFlSjtS4NFQUm9Znlci2Y78/dZ8dZOKjzH59xaVWSGQqvBegQrPWcdkXhtRfW4ruYFSlsU3fXqHI1PduSzgzNENobwg1Rb2KzEyQgLmg4v1vZI6Xpoo3IFy0aBEWL15sssyN+/fvo6WlBS0tLXBxcUFiYiJaWlp0N7fOm59KpdK7+dm66bClQYBQfBcTX8quqtSlMmrpZlB+80CNf8dgig53w83Ax7tj0ZiTip++/wQdak/cKd5MvDRTmlYKq2JL8eWhQ5ArnbEiZiWyK+vsdjxbeHntYjQeZnb1Mbx9ZAragteCuWTf8u5CE99A+PDhQ6Snp4Omaav3ZUlYUnnb3JRRW62P71zzGXniPEbsTOd+hNmZjuNHzvK2BlXMste6bramCvRWP93sIMswZu8juqBB12+Ob5/a2toQdqFOrz3Dd9ElBsFIWX6Z93O153s31LuwE4Rtdbye47L6slbvmO8GqBB2oY4XL9iaKq7IzPeLuPHtPhZMynmwNVW8/50MnWdbbbVF1zhvQLh//34EBwfrtHDhQri5uWHjxo0ICgrCxo0b4ebmhkWLFiE4ONjs/dfU1OhV7+zU/v37ddt0pse4ubnBz88PtbW1lr4dq4OkjApHYvNS00LBP3UcAhVjUNZYpLsp0yFBvbaNLeRuuC6huaCPB+P6xrF4mOGJX6rsUzlLbF5aouPqIB2Qb0r8CnuUCaBo4c62XtHW42auP9qzOChsz5yJX/ZOBnNqDxjtwPql2x5e2upHQ3d3d97uOZYso3j06BGWLl2qV1Tm1q1bRo9hywrabCutg0HdF8Sd6WBb+QFqEvpxO+08qAldsyjX922zaD9b0+ogkamwPom/72I94+qt+yjV/oyrt+7j6q37vYrOvBugwtVb9/k+TbtE9/ceV8nqvXdZemP/O7Aijhdy6xg9TxbrzoHveHz3NmjXMd1mC0fgdtp5dFy7ivsVJei4dpW3c+OuQYneeTk6bJIyqlar4e3trdcUHuCgyNvbG1lZWbY4jKiCAKE4vyzypfHBCRghUyKnugn03u1o++Y/+PHoCrTR+n17ZCkVkMhUWBVbqvu1iz62FyxtnzQMMXppiXKqU7EzdYquEunK2K2o1Ni2Z5KtvbzaUoY7mSu7KpImuoGaOxlMehLvftpbQgBCHx8f5OXl2WRf5oallbdLSkrg7e2NJUuWID09vc9j2HKGMDVeaXBmJjVOyfuv8mKTrWcI2Zqq3uusJo4we/Yk7EKdflVLnmaDTDnP7rOGcZWsaKtom6sqjRa+T2ZJpx7OBc2wNtt393HJsiy+PJgDiUyFUznCGQcGx3r34kkTJGCiwhx2Lp3rc+m4SIPVUNtqzauGLog1hMuWLTOaenLhwgX4+PjY4jCiCgKE4vyyyJfcnjSTjSlsAB12AA+S3NCh9sQPTUV6262M3Q2f6EU4kBEBymU0t9YjJ5N4aQM1axuxP2OlbrZwTdwsxBUVCt7LHxszcS9jIa77T+q6maxZDKZy4K7TssZLWwGhUqnEihUrcPfuXZvsz5zgs/K2JWsIK0vKDc4QkrWEjh37hmSLSoxCqChpjjorkQ7UKqN9qbGZwke7s3SwZqv9dh+Xyie9KN+XZzm0knd/MjbWHd2Wgo48bhhMe12DCrP2KwggdHd3x8WLFw0+d/HiRbi7u9viMIIOkjIqHInRS5+o/8/edb9Fce3vP4rVJDf5pmw6lsQYVgVUJIq9oWJX7DDoil1RAXtBkCJtYajSe+9t5gzexOu9uUmMN5rk/f5wdmdZdylbZ2b3fJ7nfXSnnJl5mTNz3jmfUi8XkhUz7uLlPXMpiT7bB0JM+joYTXrcM8XTh8aC973qJqhFLt1FTuNdJBR8BaNJj++unAeX1+oRF1KvcklEkP5uiKdiIcydhX+u+RC/Xv8O48nHQUbVOShTiktPCcLr169jzZo1iIiIwMmTJ3H9+nUbJCcne+Q4jsyXmbfdfbdZ/l737z6BLr54QgzhQ8XvIy3C44LQUSZGJwfFasgoqQYutYJz5jqFX54rw5OGPo8I94lcxjyis5C70xsVv9aJcHiv+7gsxUzPwTJL70zbqhCEmzdvRkJCgsN1HMeppjagL40JQuWgRS7jcnOw8f4mHM+NBSnOwy+Xv8Gb8mX4d5c101vH4BgO53wHo0mPmntH8CL2C7zKs92GcekZtA804kjOKXlgs+JGDbqG3BPevuKS1FXh9yxzfGFxOP59OgSkOEdxTj0JNQhCg8EwLbxlSmbedifL6J7T96HjeKyPva74PaRVeOM5IkTMtRmIzrTmoAVqySipBi61gOExER+dLrUrXO8JLgdHRbx/qgQ6jgffOqj4tb4Nm6yjc3RufwxxFpPNUopnjlrPay6NIXT2vlSFICwtLYXBYMCuXbuQk5ODyspK5OTkYNeuXTAYDCgpKfHEYTRlTBAqBy1yeaUk01x6IgSkrgr/4YJpLcJWa3H6jNoW2Z1x6NB6/HKFisb/dN5lXHoJ96p68IGxBB8Ys7AzfT0Km10v8+BLLp8NN+G34i1yfOGrjEX48cx6kO5OxTn1BNQgCH1tasm87Y4gLOKroeN4fHT8CUYHhxS/j7QIj88Q1j61CsHshy4PhudfrrTNKOnhwuda4FIr8IaLr4XLs+bZx7kXK0C8UArLEyAdbdb4vbfKUohnj3v92JOJUMt5SZ3tLt2XqhCEABWFq1evtvk6GhUVBZ7nPXUITRkThMpBi1xm1tWaE5p8DqG7A//a9zktK9AQJ29jLMiA0aQHV7AQwiI9XmUvxpvyZfhpoIJx6UXUdA9jz2Pqqns8by6ulGS49KLzOZeE4F9tGfhf0QpZGP5ydSGkR0mqfVH7gktPlZ24c+cOhoaG3G5rpqZU5m1PuYwCgCAS6I/n0IQy6bmK30dahKefI+KJ3XQgfGCzy22MCCLeO1lijkvrUv3MoLe41Aq84eI7Pj6OzDbJo7OOvgLpaIOwLZL2g9PeKeE1EcLBrVPOyLt6X6pGEALA33//DUIIenp6QAjB33//7cnmVW0shlA90CKXDT1DSCj4HEaTHr2DTfhx8yd4U74Mv1ftkLfZk2GE0aRHYt46kO9nU5fA8uUYF733pV2LXHoDrf2NOJEXJmchPZyTgBEnCy8rxeW4MISfK+PxunQp/YCwXQ9hWyRIa5PivLp8TSqYIVSy7ISS5o4gHB8fR0zcTeg4HodO3lL8PtIiPPkcIf19EL79B42bKjW53I5FYHx+rlxTH5sC9f3mjRnC9oExzHJQ0kMrHwdIziMq0JZ+7fV72CI+hfi9Dmfk/UIQMrMaE4TKQYtcjokEsdkhMJr0KOvIx/j6r/CmfBn+KI+St9mWFg2jSY+bj6Lx44aPqCtg9XbGpY8wKgzjtGmX7La7P3MLOgZm/kVVaS7/OVCDn7N2QPjmPfoy+vY9iKnnQURBcW6dhRoEYUxMDCup5MLfK+1WBnQcjznHXJtpD3R48jkipl6kz4JV37n1t7CUQ4p+6LpLvda51BpSy7ts6hKuu1PnVnsF5syiWkssZAEZHrZ+HKku995xBvohzJtNjzNJCIqmBWFZWdm0CDRjglA5aJXL3RmrYTTp8aDmOsR1C/Hi4Bd4XnQJEiHoHhrDxvubkFDwOcrPr8a/j1PB+EtzIuPShyCE4FblVZws/AxGkx6x2WEoa59Z6ny1cElaGiBsjYC4YBZeZS7Gi3NLQZrdGwz4GmoQhHV1ddi4caNcf8tfzZMuo+Pj4xjo7MJ7cQXQcTyqalsUv5e0Bk89R8T2Fgihn1M3uXvuJflZlkJjQ1M04iLoaS61iraBUSSYxfyHp0vRN+z6x8H2gTFNJhaaCGHfRtofzsd57Rji43v0I0zkN5Nuo2lBqGS2NbUaE4TKQatc7nm8F0aTHpdLjkPY/gN9MKVRt6onjdQl55tLpRjdHIZfUxbQLKTdjxiXCqCwpQBx+XNxMDsEHyfmI7OuT1NcEkLwIjtWji18ed+A8XunQUT11IzyFpeeEoRxcXGIiorC4sWLsXPnTpw4cQJxcXEy4uPjPXIctZm7glCSJKyOTYaO43H66mPF7yWtwRPPkbfroIm3r7rc1vCYiHdPUgHQ2DuiOD++5lLrEAnBwqSn0HE84nNbXW5nRCB4J8FWDGolhlDmIuOuWazN95r3ghi7ddrkNZoWhD/99JMdhoaGcP/+fWzatAnDw8OeOIyqjcUQqgda5TI2OxEnCz+FsXAfxEPR9KGRfA6SJOGMOXPXlgf1EEK/wH/ivsbv5Vvx41A941IhNPU1Ier2E/nld7V06gyequOSEPyr/ib+KDaXqODD8eJSBEi7+mdt1CAI161bNyXWr1/vkeOozTwhCK8nXoeO4xEa590PWv4Id58jnqg7OBG55o+VX53XVvygJ7j0Fzyq7YWO4/H+qRKk1/S4NKuXY74PvjhXhvymfk3NDFpABgcgzH+HunPW13i+/bExCAs/pO2XF0+6naYF4VR2584dmyxogWJMECoHrXJpLGjALM6EnY8aaaarsPfwU8pO/HO4CZvv08L1Zwta5Jc56e1mXCqMUYFgd3ojdByPJdfjEZ+XAGGSWTa1cjk+0olfCzZbZwvvGTCenaz4eU15zioQhIFqnhCE7QUF8kzCg6puTQ4etXjvS9LkddBcLcYdl0tdDnekaSt+0BNc+gsIIfjqfLlbGUKPZLdAx/HYk6HdZGWSJEHYuZp+jL9yyvM8F+fR/rZIP2Xsvt8Kwra2NkRERHj7MKozJgiVg1a5vPO0GzqOR8SNGojXz+LnxLl4U74MP7enYPOD/diftRj3ii7JCUF88TVWq1z6EoQQGAsK6eyuSY8jOTsxPGY/wFU1l4TgX7U38EcxzUT63/PzIB7eBuJE0hxfgglC35mnYwglSQIZHUXw4TRNpqhXGmqbIQxLroKO43GzQnt/P1U/k30IT2QdtbidPqzpVfx63IH48IbZbfRbED7fo0XqxVOxVGwei5lyO78VhDzPIzIy0tuHUZ0xQagctMplcSvN0hV8oRxi2k28OPwljRNsMCImfQWMJj1MBfEgi96BsOpbxqXKcKXkkiwKD+VEoWfYNq5QC1yOD7Xhl+yNEBe+Y03BPYVri2LnqZAgLCsrwy+//GKz7MWLF/jrr7/slt2/f9/l46jZPCEI2wZGoYsvth2AcsVsptDL974FQsyqKeugzRQDo4KcqbKlT3t/Oy08k30Bd+sSdgyOyR92eke0l7V6Ikhfj+0Hkzk6l/uHTbuEQFgWTGfjn6RPua2mBWFXV5cd2trakJ2djcjISBiNRk8cRlPGBKFy0CqXHYNjWHV7N3Y/XoaWgmv4aZseb8qX4ceKvTiRFwyjSY+OtFP4/ZEBr0uX4Xl/CeNSZbhXlQau4EsYTXoczlmK9gHrV3MtcUnKiyEsnwMhOAg/J87Fj7diVVWeQilBaDAYMDAwIP/+66+/YDAY7ArU9/f3+20yNU8IwvyyBocD0Pwy7bkdaunet0BYu4gOds+dcGsGJLu+j5YQuVihOC9KcekPcHeG8FYl9W5aeqNe81ySjjZ7l2o3ZtAtEItyaVvz3gEZnrp2tKYF4VTZRY8cOYKff/7ZE4dRtbGkMuqBVrkkhGB3RgSMJj1yi8/h2aoP8KZ8GcZKaTmKU4WfQbxKM0O+Ll2GcWGQcalC5DUVIy5vDowmPY4+MaCht1mTXJLBATy/uF6OLfzPtWUgMyyx4W0wQaiceUIQtubnY1Z8ke0ANL4IrQWuxbEFEtx2Ge21zoCQrg63zmVfRpOc7ExpXpTg0p+QWt5lIwqnS5I2Edsf0g8858sHNc+lp2NsJcmc1XdCW9PNOGpaEHZ2dtrNEPb39+M///mPJ5rXpDFBqBy0zGXMo20wmvS4wZ8AWfQO3pQvQ0+JgRZDLwzHi+S1eFO+DL/lr2dcqhglrVU4lvstjCY91t7l0No/qk0uxVH8XLBXFoW/py3Gs8K7ip8XE4TKmUdiCDvakPzDWtltdFZ8EZJXrvVovI6/wt3niJh5Xy5G7855pJZ3aT4GVJPPZC+itX8En58tg47j8WiGsYCEEHxm3qdh7N+a59LTMbautKdpQcjM3pggVA5a5nJ3RhyMJj3OFe2EMCcIr0uXooqnwuJa2R68TKdJP/6VH8+4VDmedjZh7Z3j0HHF+Op8OZr6RjXL5fP6B/jDRO+9P0xh+On2fkVrFjJB6DvzRlIZSaJfzbdGJ0DH8di3+YhH4nQCAW4LwuO76CzF6cPTbts2MOqwhIAnkpCoAez9Zo/D2c3QcTy2zzBrbFXnkFyy4o83f/kFl7RO5yyrgNux0uW2XJlx1LQgfPslOdGGhob85sXojDFBqBy0zOWRnOswmvQ4WfADhgx6jBdE4VHR1zCa9OCbL+B1GR2Uj1fkMC41gNb+Ucy/VEnjbC4UoZN0aZbL8cE2vMxZKc8W/jtpBUi/MhnlmCBUzjwlCCVJwrXt+6HjePxw5oni97cFpKPN49kF1XLvE0IghH9JB6SFU79Dkss6ZdH39gygu0lI1AL2frOHqYUmt/sosRRj4vSZzC8U0xrJUbdr/YpL0tEGMcloTSyTed+l50LAzRBOJQgHBgawaNEiTxxGU8YEoXLQMpfnip7AaNIjoWA+juxKwK5r13Do9gkYTXr0Np8314n7HqRn5v79gcqlWtA5OIbvrvCISY8El/81Stt5xc/JZRAB/8nbj9elS/HP1f9Hs5DW+D6hhJKCMDMzE7W1taitrUVNTQ0MBgOys7PlZbW1tXj8+DEThDP4e5UdioWO4/FxQqEqiprTmQGdR7MLquneJ0119NrmvwMyPDzpdtPNADb0DNuJQTZD6B8QRIJPz1AX0NzG6QX+8tRq6DgeJ/Nb/ZJL8cRuWzHnwnNB2L3Oqay+fisICwsLsWLFCk8cRlPGBKFy0DKXD2uaabygSY93TuZTX/6b6WjtL8FvVQfwpnwZXhwL9tngSctcqgmt/X04mEVLh3D5X4FvMyl+Tu7gWU2enIVU+PY9kMc3fHp8JQWhM/BH86QgHL5wEu/GFULH8WjqHVH0nvZ07JAa733x1hV6Xdsip9xuuhnApJJOOzHIYgj9B7vSG6HjeBzInLrI/LVS630wi+OR2Sb5HZdiY53bWUeFbZF0P2PsjPbTnCDMzc3F+vXrsX79ehgMBkRFRcm/LVi1ahUMBgMrOzEDsAeT56BlLis7hrAvcwni87/EuyfzsHfzEYg3LkKSJDwrfoAXsV9A2uybGoRa51JtaBsYwMHslVQUFpFYcX0AACAASURBVHyJklZti0Iy0AchJgpS+Hv4oyAUL+5Eg4yN+eTYSglCRyWWpoI/micFoZh2C4v236AFrat7lLmPO9ogFudBOHXIYaxPa0GBwzg6peDOvS/sXkvjB6+dnXK7toHRSWcARwWCr86XQ8fxOF3YhoJm9XDjSy79GVnmciJfniuHOMnH58lmkdsHfPMO8BXczTpKxkYhfPse3ae+Zkb7aE4QVlZW4sSJEzhx4gQMBgP2798v/7bg5MmTePjwIX777Te3TlILxspOqAda5rJ/RMC7J/PwgTELC7hsDIX8Az/d2I3nfUUQH6S6HeAcSFyqDePj43j288/Yn7XKLAq/wtPOUsXPyx0QUcB/0qLluMJf7iwH6W33CZeu3pfuvjQD3TwpCEl5MXZvOQYdx+P4kxaf3LMTE6WI91PQvOBT5IYa0LxADyE4CM0L9PLv5JVr7eLoJku04iu4eu+TsTEICz6gA9MZuHl/klhqM9iPfkCTjNyooNlFPztbhhFBucRSSnLp7xgRRHxgpH//0jbH5a0mm0UubB5Q/Pw9CXc9B0gFT/cxfDzjRGyaE4QTLTY2FoQQTzTlN8YEoXLQOpcLk57i/4yleHrpIv65/kO8KV+GVzUxEC8m0K+7cXsZlxqEhcv2gUHsz4yE0aRHfP4c1PdUK35uboEQ/LsgDq9LacKjl49CMd7gXaHLBKHvzFtZRiVJAunpwrUf1tHEMje83w8m1lqbxRVjxa5LmDWh9EV0NGetjxhfJJfFcASlSi24eu+L2Wl0YBoy/cB0VBBlniw15j5OLEXX0BjmX6qAjuNxrsj7H37UymUgYPP9evoh4GGDw48fk80i+9sMoSS9VUfQyRhC8fpZut+emZcK07QgZGZvTBAqB61zOTQmomdoDOL9FIwvfU+efXl+ZT2EuUEQr51hXGoQE7ms7+nGgaylOJEXjJW3HmBgVFD8/NzFj9Vp+KMwHG/Kl+F/uaH4Z4n36hUyQaiceVQQEoLSpSFUcBh5r8ZGUxe3yQUeFYHTrH97AMwVuzRT6M4soyv3vrOFsevMSWM+MJZAEAkWJj2FjuPxjTlj8oenS/zimcXeb5NjtzmOcKqPH0uuVdmIQX+MIbRAOH2E9p9da5zbb+dq2udSL854H80JwufPn+PNmzfy/6dDoBkThMrBX7gkeY8hfqOTBeFrfhl9sDy+x7jUIN7msrKzA/MuPYSO4xF5o0bz7leSJOFZRyVe5ZjrFRaH41nGOZ9w6QyYIHTPPCkIJUnC0GqDnFimuc/ziWUs4iv6VoVTYm+myC+bWb02C2xnKZ2fZXT23nfF5c0SQ/Z90lNIkoSCZnv3QC0mkXGXy0DBTOtM/nCzBjqOx4knLWgfGPNrLkmpifadRfoZf7giogBh4YfUTbu6fMbH0pwgnJhZNFCzrU1lTBAqB3/hkjwthRAcJAvClw+X0AdLhe/KFvgLl2qAIy7L2gflWI1tadkYHFUmsYZHMdSF3zIi8duthRDm6iBeiPf4zA8ThMqZpwWhsH+jNbFMjWfv/4nia2Yzgra/Z8UXYbbZhXQWVyy7l07cv7VgZsklJMkzBd2dFoQuJMW4UtIBHcdjw706+bwnSzLjs+eKF8Deb44x0zqTIVfpzHF2fZ/fc0lGRyF8Y04O01g7s31qKml/++4DEGHmM+qaE4SlpaX45Zdf5P9Ph0AzJgiVg79wSdpbIAQH4eUDA96UL8OPWz6hD6OuDsalBjEZl/lN/Zh3KRnx+V/hZMEPGBOVTb/vCRBhFOTScauL2oldIGOeGzwyQaiceVoQiueOy4llTngwsYxDF9EJAm92fBG2R8djtuz+ySMmrQGzE6yCJzl6N5oX6JEXGkKTzESukffXcTxmx5nQ+tQ+9nEyl1BPFHT3xQxhbFYzdByPozktHjtvNYK93xxjph8uvjhHs82WtQ8GBJeW8hHi7asz2l68eZn2t5hVTh1Hc4KQ2dTGBKFy8BcuycgwFYCGd/Bj1UNzQeF3Z5ypinGpLkzFZUo5j7j8r2E06XG+OBqEaN99VJIkiPeuQ5gzC/9JCMYvD1aADPd5ncvpwAShe+ZxQXg/RU4ss/LmzNKyzwS5hZUORcy95ZFU4H33KcT7KWgbGLUpnWD53VrfbC+kgoPMWUlDsGRvMnQcj4NxtvF4U7mEVnUOuT3T5lIM4b3kCUkxpi+MveZ2LXQcj+tlnTIn7s5sqhHs/TY5pnNtJoTg3ZN0fUvfaEBwKV4xUkG4f9OMthf2baTbX0106jhMEPqZMUGoHPyJS+E7miZcvHudvsxXzGdcahTTcZmQ/wgJBZ/DaNIjteKo4ufrKTzLvYHXPE0289ujpZB6W73O5VTwhSD059h5TwtCUlKAUsN86DgenySWuu1eTDraIOakIXnDdnvxFV+ElkdpICUF06aNn8zVUsx6AFJSgNx7j6HjeLwXV4DW+3dB+Hw01jVNKfhOF7bZrd/5qNHr9z4ZGrCef9P0MY/fXqZiOrfROgOYWt5lM3vKYgj9H0ez6Uzx1gf1duv6RwT5Hh4eEwOCS1JWNOM4QkIIhEW0lA0pK3LqOJoThGVlZU4h0IwJQuXgT1wKEXPpS/zYTvog2v4D41KjmI5LQgj2ZiTBaNLDaNLjcf0Vxc/ZU/ix9Ab+MFFR+HtWGMY7ZxaD4SqXU8EXgnDx4sVYtGiR14+jhHlcEHa0YWjObI8klhHvp0CYo0P/3Hfx1eFH5jjAYlkMJq9cO/P6YdO4WhJCsJTLgI7jsWPrCdyK+AFzYh9M6lo5PCbi07Nl0HE8rvAd2JVOyzksTZlZuQ2LG6oryTtIT5c1dnAaDxNBJHj3ZInDv8Xbs6laB3u/TY3kMlpzcu2dOrt1jb0j0HE83j9VEjBcOlNknjTVWb26Rp3rL5oThDNJJMOSynj/BmDwby6FTeH0obLqO3Ms1m7GpUYxEy5HBBHRD+NhNOlxqvBTPO3MVfy8PYV/1mTjf3lhtK5mThjG2yq9yuVk8IUgvHjxIi5cuOD14/jCvFmHUJIkEFGE8M17bieWmSjgjm3YBx3H4+vDaahd+JWNi6gzbYr3UyDMnSWLwbf3zy2qsU9O81bimVkcnSG8XtYJHccj+EI5xkSC9oFRecatpmt4yvN4233P2fT+lnh04Zv3pt22tZ8mkHn3JA9B9F4ZEDWAvd+mRnptL3Qcj9DrVXbrStoG5fs5kLi0xhEmTb3dNXP9wfWhTh9Dc4Lwp59+cgr+bu6+NAOlM/kC/sSlsG+DXAxVCA6CmHSacalRzJTLrsFRbEuLhtGkx+GcDRC9WJvN13jWVIxXT8LMtQrD8Ky52KtcOkIgvI+8aZ4WhJIkQVi5QE4ss/VBvUszUKIpB0JwEHKXhGB2nAk6jkf60nDZxXOmM4Nvg3S0Tbp/S16+vQCML8LsCcls/s9YgvaBUcwzF3S/zFuTgq2/Wwcdx+NQVvOkx58sfs+ZAuCk0Txb8f1H025rSSAz/1KFos8KX4C936YG32oRffb3wuM6KhYXX6sKKC7FpNO0L+3bOPk2Ttb9fBuaE4TMpjYmCJWDP3EpcvtsHywZ3iv27e9cKg1nuCxt68Xy1OOYxZlwKt+1gaxaMd5aiVdZoXhdthQ/7Z8P0mQfn+JJLt8GE4TumVcE4e612BbN2cyqORujJiQcwPXINVaBFl/slIuoK8gva3DoInrfVIechj65kPsXZlfRj06XYGjM6rKZba7393FiKYbHHLty5jY6zvD5sLpnxsXtSU0FfYeEfj7ttinl1E0w6pZ7bt1aAHu/TQ2LW+g/TpXYxczdqKD3yerbtQHFJSkvpn3J8IlD92tXsvq+Db8QhH/88Qd6e3vR0NCA3t5e/PHHH55sXlPGBKFy8CcuxUsJtrWjyl2bUWFcKg9nubxV2S0P/jLr+jxey09RtNfip0ML6X295DOQZueKezNBqJx5QxC2GOMwa0I5B2ezWJLyYjQv+NSuTuBsrtir8W6OSltMPGZN1zDeSZi8oLsgEnx9nqbuj8ttcXiuljiut/F2BsjJSl1Y+BGCgyAsnzPtNZ140gIdx+NAZpPyzwkvg73fpsbgqCjfb4OjtuLnrKmdxs+mNQQUlzSO8B/0A/2DVDuh50rdz7eheUGYk5ODiIgIm7jB5cuXIzs721OH0JQxQagc/IlL8c5V24dKZzvjUqNwhcv9j5swiyvC+nu78KjWt+7C3gbp6ZJjY8nKr/GsqdCrXFrgzkvTmbh5llRm5n+vvORbkyZjmfIe6miDmJ8JIfwrHNwY61Ib7iK1vMtayzC+CCnpJfK6mZRrsLiNTjYzGnGjxkYATgeHJQKKcuVY9OmuZ9P9eug4Hpd439W7VQrs/TY1CCH4xymaYKix1zbB0NEc+uHAUqsykLgUflgwoYyLzsYlNOBnCPPy8mAwGHD06FGUlpaipaUFpaWlOHLkCAwGA3Jzcz1xGE0ZE4TKwZ+4JDlp1ofKvHd8WoPQ37hUGq5wOSoQrLubKmcere3OU/w6PAnS0wWyfgFeZSzC/wrC8axlZjPgSgnCBw8e4OHDhzOGP5o3BGHrkydOzxCK91PMNQENOLl2h0Nx5KtaeW0Do8g/ehzNC/QQj8XIy6cr6D6dYKzuGpaXlbQNoqC538ZzYDLMTuBR2j4ozxiSvMf0HbJu8bTXYrj6FDqOx+O6XsWfD94Ge79Nj+ALNPaVbx20Wb4jjbpLnzW1BxSXpKPNfgbwLcEnpl6wWRdQMYQbNmzAuXPnHK47c+YMNmzY4InDaMqYIFQO/sQlKS+yPlgi5jIuNQxXuewYHMPmBztgNOlxsnA+Bke1X//LBh0N+D09lCaaKQjHs9aSafdhLqPKmTcEIWltQvKE+L/pYghJRxuSf1hrJyKjkisUq5Unu2V++x5Ifx8kaXrBN51g3PeY1jXceM8aZ9s+MCZf43QzhZZ/U25k0nPbsmzqayAEH54uhY7jUdM9deZTfwB7v02P0OtV0HE80mttPxCsvl1r4wIdKFzOxCVUTuL07fsuxTBrWhCGhoaipaXF4bqWlhaEhoZ64jCaMiYIlYM/cUlaGqwPnW2RjEsNwx0uH9d14EDWIhhNelwuWQ9CfDtT7HW01eL3jAmisHPq2mxMECpnXhGEggBh3mys2nkeOo7HqYLWKdtrzc+3E4Oz4ovRkl+gWK08QgiEKBoXK968LC9PufYQs83nOju+CCnXHsrrHAlGHUcFY9+IgPfN7noT3V7Hx8eR2SbJonDWDNxJZ3PFaF6gh7Bj5ZTX0DM0Ju8zWZIbfwJ7v02PtXeoS3Nyme3HlcXXqFDMMM8kBwqXM3EJJRU8Xb70a5eOoWlBuH379kmLz5eWliI6OtoTh9GUMUGoHPyJSzI0YM0wenwX41LDcJfLg5l54Aq+gNGkx5OmS4pfj8fRUoXfM5bQOoVPlmJ8YPIvq2oRhIQQnD59GqtWrcLixYsxNDQEAHj48CE6Ojo8dhw1mTcEoSRJECLm4dDGgzZxSZNhsuye+WXOJSfyNOSY74i5EIvzIFaWQpijQ/MCPa2FuEBvN3hMLe+ym/G7+7Qbl/kO6Dge312ptEkoZeGyfWBMFr4T25hMHOaFhkDYs37K87fUlvvyfLnyzwMfgL3fpsfu9EboOB7GAtvncfCFchtX0kDiUrx5ZUqXUFKQRdetCXGpfU0Lwrq6Oqxbtw5jY2M2y0dHR7Fu3TrU1dV54jCaMiYIlYM/cUkIgfDNe1QQXjEyLjUMd7kcHhOx+k6iuWj9Z2jtL1X8mjzOUT2PV9lUFP6evRzSqOOEIGoQhCMjI1i6dCmioqJw+vRpGAwGWRDevHkTRqPRI8dRm3lNEMaswqVVm+xcJB2htXvQPubQyxlFZwIyOABh/jsOXcqmyjhomdU8nE1dRD8wluDTM9R181pp54y4tLRR2j5o76ZqniEUY7dOef53ntL4xIjUqWfo/QXs/TY94nJboePss86+/1aymUDikhBirQ1dVWa3Xsy4S/t69AqX2te0IIyLi8OaNWuwaNEibN++HUePHsX27duxaNEirFmzBnFxcTLi4+M9cUjVGxOEysHfuBSWfk0fPOm3GZcahie4LGsbxOYHGxCf/xWuld1X/Jq8gWflGfhfXiheZS7G+MltDsttqEEQHj16FPv27cP//vc//PnnnzaCsLq6GuvXr/fIcdRm3hKEYsIBPFq6FDrOWux60m1vXnEq5tBXcJhw4m3M0U0aVySIBPMuVtiIuaSSmQnCiUgtt5aqmMXxSDmXSt8hx3ZOef6nCtqg43jsSm9UnEtfgL3fpodlpnrzfetHmuExazmK/hEhILkUDJ/Qjzv19h9PxNtJ0xavnwqaFoTr1q2bMdT0kuQ4DhERETh58qTdOou43b59Oy5duuR020wQKgd/41I8uIW6GTlZq41xqS54isvThXX4OPER/s9YitZ+ZWdEvIV/5qeAGN6lg9iLnEe59JQgXLp0Kerr6wEAf/31l40g7OrqQlhYmEeOo7Tl5+djy5Yt8nvSa4LwxiWUh8yFjuPxaeLkiYXI8BCExZ9iYO678qC0umtI8XtWkiZPOGETczRvNsScRyB8viwMSUcbCJ+P1vrmactUzPTe35tBZxtjHjVCvBBP+1LC/in3sWSOTCx0PhGGFsHeb9PjXlUPdByP5RNmjVv7R6HjeLx7kpc/2AUal8KK+VQQltqXSxKvGGl/O7HbpbY1LQi1ah0dHWhoaHAoCCMjI91qmwlC5eBvXBJBAOntYVxqHJ7iUhAJwpKroeN4rL9bp/h1eQvivevyIHr80WWbdWoQhOHh4WhubgZgLwjr6+sRERHhkeOozbwmCE8eRM/892UhNHg31W4b0tEG4cRuCMFBqF0ZKrtXOppFVgKTJZwQK0shFuVCiF5hLxT3bZD3yQ0zTJl11Jl7/2YFnSWMuFEDMfEIHaAmHp1yn3Dzc+VBtTLvG1+Dvd+mhyUT7reXK+Vl5e1D0HE8vjhnjTUNNC6FDaFUED5Jt1snnj5M+9uZqfvbZGCCUCHr7OxkgtDPwLhkXKoRnuSyumsY7yTwmHcpCRf4NRgT1TFD4mmIF+LxfNeneF2yFP8qtybSUYMg3Lt3Ly5fvgzAXhAmJSXh8OHDHjmO2swrWUYnCKkPjz+hs34hwba1ve6n2Iit3OPxdgNVNUC8nwJh7iyHCSfEpgbHM4hmNC/QT1uPcab3vmXQ/smZMgjc/kln2yfis7Nl0HE8ytv983ni7H3JIKHGXAvz40RrzHpOQx90HI+Qq08Dlkth52rapx7Yf7gSj8XQdUmnXWrbLwThq1evIIoihoaG7OCsdXV14cSJE4iKikJISIjDxDQFBQVYt24dQkNDERMTg+7ubqePM5kgXLx4MWJiYrBv3z50dXU53S4ThMqBccm4VCM8zWVcbiOOPPkWRpMej+vjFL8+b4CIIv6dvApvypfhdclS/NiU4zaXnhKEVVVVMBgMSElJwdDQEAwGA+rq6pCVlYUlS5agoaHBI8dRm3lFEE5wtVx44DZ0HI/M8CVy8hVHM283IldDx/H44WaN4vep3fV0tIGUFNjFCk7qUjoByZFrMJsrnrSW4kzv/eExUXY/bT+2jw5QryZOuv3gqDUurM8cF+bvYO+36TGxFMmoQGfib1d22/W9QONSPLyd9qnk83brhD3r6bo7V11qW9OC8L///S/i4+NhMBgmhbPW3NyMe/fuoba21qEgrKqqwuLFi1FcXAxRFJGSkoLw8HA8f/5c3iYmJgbR0dF2ePHihbzNZILQss3Y2BjWrl2Lly9fOnX+TBAqB8Yl41KN8DSXw2MilqVeN2cd/RRtA/bZzvwBZGgQv9wKozUKC5divK9BFYIQANLT07F48WKbd92SJUvw+PFjjx1DbebtGcI1Meeg43hcXbXeGmPnQEgZ12yXY+SUvkdnfC87cil9G3NnobW+edJaipNxaYlDnChC51+iCWqeHDpKB6g3Lk56blWd5hnFRP/LXuzqfckgQSRELmnSbr4fLxbTRDPRD6x5DQKNS/FULO1T507YrRO2LKfrHt9zqW1NC8KEhAQsW7YMqampKCoqQmlpqR3cMUeCcPfu3UhKSrJZtmXLFty5c8epticThBPt2LFjGBwcdLju9evXePnypYzff/8dAH1pjo+PzxjPnj0DADx79syp/RgYl4xLbcAbXOY1DWD9vS0wmvS4yIdCJKOKX6c3IHU04mXaYlqO4kkExsmYy1x6ujD9v/71L/A8j/T0dBQVFfl94XuvxRDeT4EQHIT9mw5Dx/E4fjlDXudISO3dchQ6jgeXN3URe7XhbZdS4cgOCHNmWevNXpjardMRlzbutHN0spvqhnu0qPiFQ+YkF3evT9puchmNOfw+6anb16gVzOS+ZJDwudmVuLKDuhKfeNICHccjNqs5YLkUL5+ifSpur906IWohjS8szHGpbVe5VIUgXL58OYqKijzRlEN7WxC+efMGixYtshOJycnJOHDggFNtOxKEv/76K16/fg2AvuzXrl2LX3/91eH+Dx48QEhIiIyYmBinjs+MGTNm7tiBJ/U4ljsPRpMeNUPOfRDTkv2vsxqvspfg+a5P8fNN5zM/+9r+/vtvpU/BK+YtQShJtMTO2dVb7WYfJMkqGKnomYU1Z2msYXKZ8uUmnMXbLqWko806qzBNIoq3uZwskQ3paENCPq0ht/MQTYMvpt1y2GZqeZdNdlM1lPDwBQJNxLiKkKtPoeN45DT0QZIk7DIXqz89oVh9oHEp3qLF6cX9m+zWCeFfUkH41LXZdk0LwqioKLS2tnqiKYf2tiB88eIFQkJC0Nvba7Ndeno6Nm3aNON2jx49isjISISFhWH16tUYGBgAAPT29iI6OlouO+EoftFibIZQfWBcMi7VCG9x2T0sIDzlLIwmPYymrzE01q34tXoLJOMWhGVfY7y+2mUufTGDV1FRgc2bN3v9OEqYVwXh+iW4vzwCOo5HeLJtfS8yMmydRWuslQepWfV9ig8OPQGSk0avL/yrKbOmvs3lZHGJpKQAD6tpyYAlR+/TZVkP7NprGxidttSFv2Km92WgY+XNGug4HrcquyFJEtbdpTPP10qtNTIDjUu5+Py2SLt1woIPaH9zsVSYq1yqQhDevn3bpVp9M7XJBGFfX5/Ndunp6ap5CbMYQuXAuGRcqhHe5PIy34Y9j8NhNOlhanEtkF0rIMPDbnHp7kvz5cuXKC0tRWZmJurq6vDXX3/J62praxEdHQ2DwYB169a5dRy1mlcFYcwq8Iu+gY7j8eWElPaSNKHo+7zZIIRAf4a6sVV1+kdGTDIyDOE780Cygp8xl1PNENZ10wyRH8QVYDRYB5KXadeepazAVKUu/BXs/TYzbH9I61OeK2qHJEly2aO0Gmt5kkDjkhRm0762JsR2+diY9aOMi+XCNC0I//zzT5w7dw4cx6G4uBi1tbV2cMe86TLqKXO3eG+gdSZvgnHJuFQjvMnlqECw+Po9zL90JSCKSislCJ89e4ZVq1bZJJGJjY3Fy5cvcfz4cRgMBqxYsQJZWVly2IG/mTcFoRi7Fe3ffAgdx2PWhKyGkiSB1D2lA61FeowI1oyYvcP+kxFTzlx4KtYpLoXD22zrHppjCAWR4L2TJdBxPOq/+xykOM+uPTZDyN5v0+FIdjN0HI+jOS2QJAnzzMmKTM0DAcsleVpK+9vSr22X93ZbBaHg2rNJ04JwfHwcmzdv9miW0Yk2WVKZq1ev2ixzJamMt4wJQuXAuGRcqhHe5vLOU5oK/KPTpX41SPY0l+68NE+fPo3w8HCkpaWhubkZ+fn5iIqKkmcFL1++jN9++82dV4fqzauCMGE/xoKD8A+O1uJr7B2R15GSAjrQivwGjb0j0HE83j+lnqL0ngDJz6TXuOQzENFxH3bEpXjtjNWdtr3ZZnuLa+2jpUtByosdtpla3iWLwVkOSl34K9j7bWY4Z2qHjuOxPY26QH6UWAodx6O2ezhguSQt5pqi331gu7yp3uFyZ6BpQRgbG4tVq1YhNzcXbW1t6OrqsoOz9urVK4yMjGBkZAQhISHIycnByMiIXFbCUnaC53mbshNqyfDGBKFyYFwyLtUIb3MpEiIP/o7mVKJzsErxa1Yjl+68I6KiopCRkWGzrKWlBQaDwe4Dpb+aVwXhRQ5CcBC+4XKg43jkN1ndFuUYu41hKGimbo7zL6mrKL27IGOjEL7/SI4BnCmX4tnj1lmJoQGb7S3ufsY1O0BqHPM1sQZhbbd/uODOBOz9NjPcMtcdXHmrFmMike+VnqGxgOWS9Pda+9yYlQdSWSLHArvatqYFYVhYGCorKz3RlGydnZ022TstOH/+vLyNpTD9kiVLEBMT45Lw9JQxl1H1gHHJuFQjfMFldn0fvr6Qirj8r3GpZDEE0T/dvpQShIsXL7Z7z7x69QoGg0HR989MjeM4RERETFtqaSrzqiBMvQghOAirTtyDjuNxo8I6UyVnGd29Vp4Nj7yhvqL07kI8sZte5641doXtJ+NSjNtjHZx2d9psb5nd2bz9FEhTncNj1ppjDT86HTg1CCfjksEe2fV90HE8Qq4+RcfgmOxWLE6YnQ80LokoyLG7pMf6nJJjC6O+d7ltTQvCdevWobm52RNN+Y0xQagcGJeMSzXCF1wSQhB5o1QuQ1HanqL4dauNS3demgaDQc5GbbG//voLBoMBQ0NDLrfrK+vo6EBDQ4N6BWHaTQjBQdh96Ap0HI+EfGuNQfHaWeoSeWQHzphFzo4017L4qRni6SMTymtYawpOxaV4YLNVELbYcpJlHswvOHgHpN1xzcbcRjrj+t0V/5pxnQ7s/TYzVHQMQcfx+PxcOao66f8/PVMW8FzKs/mN1g8tcvbRrREut6tpQZiZmYn4+HhPNOU3xgShcmBcMi7VCF9xWdI2iMXXORhNepznv4dIxrx6PK1x6a4gLC8vx9DQkIyBhpRa1wAAIABJREFUgQEYDAZUVlbaLFerQHRUe9cZ86YgJHmPIQQHwbg7DjqOR8wjq7gRzxyjgtB4GAcym6DjeMTlaqso/XSYKmPoVFwK23+wCsJa28LyLb10AP9OnAkj3Y5jA29U0BjCqFu1inPgS7D328zQNjAqzwpaPh4seOvjQSByKSyfQ/vchNhc8fZV2hf3rne5XU0LwqysLKxfvx4xMTG4d+8ecnJy7ODvxlxG1QPGJeNSjfAllxvuPpVnCZ923Vf82tXEpbuC0Bk4Y11dXThx4gSioqIcJlIDrGESoaGhiImJQXd3t9PXoGpBWFYEITgItzZth47jETHBJdTiSileMTqsg+YPmKymoJj1kBaz5/MhdbbbcSmsXWQVhG8ljhGHBvHh8SfQcTyeNnc7PO6p/DboOB670xsV58CXYO+3mWFUsMYNJpV00r6ZalsnNBC5FNYtpn1uQjkXMek07bPHdrrcrqYFoadfjP5gTBAqB8Yl41KN8CWXxa0DWJp6BEaTHldKw0GIqPj1q4VLd16apaWlTsEZa25uxr1791BbW+tQEFoSqRUXF9skUrMkWgOAmJgYREdH2+HFixfyNqoWhA01EIKDUBgZDh3HI/hChbxO2LeBDrRuX8WiazR5UmadfxSll6/f0QxhcBCEb96zLp+jw68Fj20FYcRcqyAsemLbZm8PwvZeh47jcafSsYDend4IHcfjVIH/l6yZCPZ+mzksmUX3P6az85vu1wc8l8KOlfSZlHZLXiYmUpdvMfGIy+1qWhD+9NNP0yLQjAlC5cC4ZFyqEb7kkhCC0Os84vK/htGkR11PluLXrxYutfA+mqzUUlJSks0yV0otqVoQdndCCA5C84JPqZtjAg9BpIkrhK0RdKD1+B4+PUuL0ld2+F9GTPF+CoS5s8zibxaE0M/tBeLcWZA62+V9BMPHVkH4JN2W045W7NlybEoX21W3au2S+AQC2Ptt5vjmciV0HI9lKdWyMAx0LsXYrfSZdOOiddmxnXRZ0mmX29W0IJzO/vvf//riMKoyJgiVA+OScalG+JrLW5XdiLy5HycLP4Op5ZLi168WLrUoCN+8eYNFixbZicTk5GQcOHDAqbZnKghfv36Nly9fyvj9998B0Hfb+Pi4U3j27BkA4NmzZ1NuJ42NQQgOwkiwDu8kUBe1toExjI+PQ1i1kK4rzLUpSu/suWgBUmc7SEkh/bfoiUM3Uqm0UL7/ZQEZHASSfse2reYGXF61ETqOR1hyFdrNfE7Egit0sJ/b1K/4tfsSM70vGcaxLJUKwU8S6ceYk/ltAc+lmHCAir+LnHXZXurJQO5cdbldV7lUrSD8+++/0dTUhISEBCxZssRbh1GNsRhC9YBxybhUI3zN5Ygg4qtz+fjwdAbSanoUv361cKlFQfjixQuEhISgt7fXZrv09HRs2rRpxu0ePXoUkZGRCAsLw+rVq+0ypk60Bw8e2JR8iomJcf5CXDBxwfsQgoMw/2IFdByPFvE/AABp6ddUELbQDKPvG0vx999/++SclLQ3z//pMNHMm+f/BAD89fI3m3W/5j2y2f+PoT4cXb9fFtGzE3hktkk223xkHuSP/Os3n10XM23Zzsx2+R7ScTzuNwlKn5Li9vPNSxCCg/Dvi3Hysh93RkEIDsJvpfkKnplr5nFB+OzZM9y9exerV6+GwWBAaGgoEhMTPX0Y1RsThNocLDIwLv2JSy6vFTqOx/K3EgBoHYEqCPv6+my2S09Px+bNm71yDkrMEI6Pj0MI+xJCcBBWXCmhcW9Pe+jyb9+DEBwEUylNKDPvUoXiMwQ+m4mI22sjBn8teCxzKXW02ghCknrRZt+2Yh6z4otsBvOzE3h5pnBiUfqhMaL4tfoSgTir5SoOZDbbCsLqnoDnktygglCM3SovE1Z/T/uhKcfldjU9Q/jHH3+gvLwcBw8etEkkc/PmTfzyyy+eOITmjAlCbQ4WGRiX/sRl+8Co7Hpnaq5Az7B/1G1zh0stCkJPuow6a+56vzj79xKiqGtozLVC6DgepwvbQEaGZcFzp6TFLgOpv4MU59HrX/q1XZZRUvvUNiPplVM2++ZnFtgM5C0oaO6HJEmo6TIXpU8MrKL0ksTeb87gVEGbzf2T19Qf8FyKabdov9uxUl4mhH9FBWFlicvtusqlooJwYGAAV65cwfLly2EwGLBs2TJcvHgRTU1NMBgM6OrqcuvktGxMECoHxiXjUo1QisstD+oRmhwHo0mPR3V7FedBaS61KAgBmlTm6tWrNstcSSrjrnldEG5ZDiE4CAnXcqDjeOzJaATpbJdnx86a6MB0+0P/+LgxE8iib/GndlySkkJbQXj2uM2+rdnZDmcI2wZGIUkSchpo4fqFSU99ek1qAHu/zRzXyzpt7qGqTtuEToHIJcnLpP1u3WJ5mfDdB1QQNtW73K7mBOG2bdvkmcB9+/aB53m8evUKAPDy5cuAE4QshlA9YFwyLtUIpbgsbh3Al+dvwmjSI7HoK4wKg4pzoSSXahWEr169wsjICEZGRhASEoKcnByMjIzIZSUsZSd4nrcpO+Hr6/G6INy7HkJwEFKT7kPH8Vh5swakrooOvBbpcTCLuq6deNKi+H3oK0wUxJIk2XBJnqTbCsKE/bb7ZqchOXINZsUXQ8fxmMXxSC23ZhNNKadF6VffDqyi9JLE3m/O4FFtr40g7BgcC3guSXkx7XfL59DfgmB13e51XPNzJtCcILSIwbi4OIiiaLMuEAXh28YEoXJgXDIu1QiluCSEYNG1p4jN/t5vCtX7oyDs7Oy0SeJiwfnz5+VtLIXplyxZgpiYGEXesd4WhOLxXRCCg5B7IQk6jseX58rRWmCeBYucj/XmovRX/awo/VQgoyPW7KKD/TZcig9v2grCozG2fKbfhhAchPP7ODnT6MT1Cfk0znhvRpNPrkVNYO+3maO4dcBGEI6Zy8EEMpekqY72u+8/pL97e6yCcGzM5XY1Jwhzc3OxY8cOWRju2bMHxcXF+P3335kgBBOESoJxybhUI5TkMqW8C8tvHILRpMftqg2Kc/E2BJGgumsYtyq7cTK/FfsfN2Hz/XpE3KjBkutVCE+uRkRqNVberMHq27XIbuh3mUu1CkK1mq9jCMXEoxCCg5B46ro8+JzFFSM5cg2EjWFYfK0KOo5HRl2v4vetT/vIAuqKJrU02nAppl60FYT7N9nyeS8ZQnAQSvftpmUDzpSBEOtgfpe5KP3pACtKL0ns/eYMGntH5P7oKN40ELkkvd1WASgKIM0N9PeCD9xqV3OC0GKDg4NISkpCREQEDAYDwsPDYTQaYTAY0N3d7W7zmjUmCJUD45JxqUYoyWX30Bg+SUyH0aSH0fQpBkY7FOWCEILy9iEczWlBWHIV/nGqxGHii8mQUt7lMpdMELpnXheEVxPRvEAvuzjKcW/xRWjevQWfm4vSV/hhUfqpIJjLbkiVJTZcihc5Ogg1Z2EVdq625fPmZQjBQRg8sBWzzQmmOie4+628WQMdx+NmgBWllyT2fnMGA6OC3BfnX6pgXEpvuYj29YA8LaW/w790q13NCkKLOco0umnTJmRnZwdkplEmCJUD45JxqUYozWXEjRrszIiA0aRHcesVRc6htX8UZwrb8e3lSjuR93/GUixPrca+x+Xg8rJwvvg2rpVdRHL5KVwrO4mrpSdxpSQBqeWtqO8ZcZlLJgjdM68LwrvXkRtqcPghIOfoCcwy/79ryHWXLC1CWLuIDjzzM224FE8epIPQiLn03y3LbPm8dpbOHB7ZgfmXzAXoG60ZIi198e2skYEApZ/JWgIhRP5wF55sX8YoULkUFv4f7ZfNDSCmJ7QPrvrOrTY1Lwgn2rNnz3Dnzh25FmFYWJg3DqMqY0ll1APGJeNSjVCay6SSThiunYLRpMeNyiifHrt/RMCh7GZ5hkLH8fjHqRJseVCLGxU86ntGIJrd2B7U7DDPZDrGqDDolzGEajWfu4xmPjDPEBbZzRAWnzwFHcfj3ZMlNm6PgQAhZhUdeKbdtOFSPLyNDkK3RtB/1y6y5fNSAhWE8Xux8V49dByPi8XUQ4AQgg+MdJBf3zOi+DX6Gko/k7WGL8+VyYmeGJcUlpl7UlkCMfO+w48yzsKvBKHF/vrrLzQ2NiIhIcGbh1GlMUGoHBiXjEs1Qmku2wZG8d7JXHyXdA6t/QM+OaZICG5VduNTs5ufjuOxPLUayWU1yGk8gwv8tzCa9Bges8aD5TTG41LJQqRWrsLD2p3IajiK7IZjMsbEESYIFTRvC0JSlAshOAjJ2/bafEBYsjcZhRcuQ8fxmHvR3mXN3yEe3k4HninnbbgUdq6mgs8iDCO/sd3vzDG63ngYJ80JZGIeNUKSJPSNWN0Ah8dExa/R11D6mawlpJqz0VowMVNtIHMprDHQflmQDfHONdoH96xzq02/FISBbEwQKgfGJeNSjVADl6HXaUKOaz7I0NjcN4Kw5Gp5APHN5Uo8qi1Cet1+JBZ9Ls/4nSueg9YBa5KCmcz8MEGonHldEFZX0EFV6BdoGxhFyoT6ZycuPpI/Kijdn30N0XhYrjM4kUthUzhdfu64w/gli0upeD4OaTU90HE8Fl2rgiRJqOocoolmArAovSSp45msBbQNjMqu2o5qWQYyl8K2SNq/0u9AvJroMNOvs2CC0M+MCULlwLhkXKoRauDynKkdOo7HDzdrQAgBId6ZFajuGsbn58qh43h8YCzFWVMjMuuP27h+3qhcjZruDIjE+VgwJgiVM68LwrZmOUmKZdmmQzTj6DscTTQTHUBF6S0Qk07TwebxXTZcCj98S5ffTqK8hXxsu9+J3XT9FSPqe0Zkd21BJMiup0XpQ64GXlF6SVLHM1kLyG/qdxjTW9BsjTsNVC7FA5tp/7p5BeKZo/JsvDttMkHoZ8YEoXJgXDIu1Qg1cGlJHR6aHI8rpaFo6M31+DFK2gbxcWIpdByPhVeeorV/FD3DDThT9CWMJj0e1e1Fx2ClYlwyQeic+TqGkAz0WzP3jdK4tur1P2D2hJjCfY8Dr2aeeO86HWzuWWfDpbDkc8qVpUD9/Hds9zsUTfdLOQ9BtCYGaegZQXIZdQNcE4BF6SVJHc9kLYDNEE4OMX4v7V+XTk74+HLKrTaZIPQzY4JQOTAuGZdqhFq4/D7pKVbf2SmLM0+2ndvYLyepCEuuQu+wIK972nkPTX0FinPJBKF75nVBSAiEubOoyOmiyU+EsC8RvufahLqE9jFM/g6Sk0YF38ZwGy4t5SZIVZlVSAvWfifsXU8HqXeuQpIkLDLXcXxU0wsurzVgBbYkqeeZrAWklnfJMb2zE1gMoQXi+Tjav07FQti3kf7/lntZvJkg9DNjglA5MC4Zl2qEWrg8ld+Gry+kwGjS42LJAo9la8xp6MO7J0vMWeiqkd14Cl1D3pl5YIJQOfO2IJQkCYLhEyps6mk2w2bDV/ZZR9+aofB3kNJCKvhWzJe5JKOjVhHY02n9/4DVlc+SnVR8eBOSJCHmES1Efyq/Tf5/YmHgFaWXJPU8k7WCtoFRFDT3O+x3gcqlmHLemtQpeoUcT+hOm0wQatxY2Qn1gHHJuFQj1MJlVecQZicUIqGAJnbpHW50u83eYQGfmTOJbrxXj9ym0zCa9DjPz8eI4PmMpkwQKmc+EYQR86iwKSsCGR2ZtC7hxBgmfwdpqJFjBC1cyiJwjg5EFCbMrLZbudyynA5SM+9DkiRcLO6Q+2mkuSj9rcpuxa9PCajlmewPCFQuxYc3aB/cuRrCmhBzxtEsRbhkglClxgShcmBcMi7VCLVwSQjB/EsVcpH6is47bre5I60BOo7Ht5crwbelyIljPNG2p7lkgtA984kgXL+EDqxyM0C6OhzXJQy0GcKeLln8/f3nGyoIG+vosoUfUt4sRbKb6h1yKUkSnjT2y1l/LYXq8wOwKL0kqeeZ7A8IVC5Jrjl2d0OotSZhBa8Il0wQqtSYIFQOjEvGpRqhJi6PP2lB5M39MJr0SK/b71ZbWeZMhbM4Hhl1D2A0fQqjSQ9TywVVcskEoXPm66QykiRBiImS3RxJfTWtS7huy5QxTP4OIgiyS+ifP/+bCsIKni5b+jXlLdScYKbGWqdRWPUdXVb0BJIkoWNwTOZwYoIZpa9PCajpmax1BCqXpNQku3ILCz+kfa2xThEumSBUqTFBqBwYl4xLNUJNXOY39WPupaswmvS4UmpwuZ3+EQFfnqflJY7lpCOx6AsYTXpkNRz1WGyip7lkgtA984UgFGMtmTEvWGPnIuZNGcMUCBC+/whCcBBejw1TQViQTblZ/T1dP8HVVt4nYq7NMkIIPjlTZjPbGohF6SVJXc9krSNQuST1Zlduc9+0xPMqwSUThCo1JgiVA+OScalGqInLnmEB757Mw+Gc7/CgZi8E0bUZgr0ZTdBxPOZeLMO1smUwmvR4ULMDopfqG3qCSyYI3TOfCMKEA1QQXoi3llPYEKp4v1EaFsH3v45mjI+PQ8y4S7nZGkHXr/6eDkgLc6z7hH5Bl1WXy8sibtTIYlB/pkzx61IKanomax2ByiXp6pCFoCwIx9z7YMUEoZ8ZE4TKgXHJuFQj1MalZWavqMW1pC95E4oV5zY24/bT9ThXPBcjgvfjkZggVM58IggvJlBByO2H+CBVTtqgdJ9RGsLGMAjBQXhZVUIFoaUY/b4NdP2mcDogzUmz7hPyMV3WUCMvO5jVLPfdQC1KL0nqeyZrGYHKJRkdsRWE3/5DMS6ZIFSpMUGoHBiXjEs1Qm1crrldCx3HI6nENfeWJddpPbP95hpmhBD0j/gmfT0ThMqZTwThjYtUEB7cAjHZmtZd6T6jNITdayEEB+HX/AwqCJNOU26O7aTrt6+kv9NuWfex1ClstdYaTCnvkgXh2jvuxTtpGWp7JmsZgcyl8O0/rIIw9AvFuGSCUKXGBKFyYFwyLtUItXEZn0uLUu/JqEfPcINT+zb0jMiJKbqGxjTFJROE7plPBGHaLTq42rES4rnjcuFnpfuM0hCP7YQQHIT/PkimgjDxCOUm8QgkSYKwb4NNEXpCiNWNrdv64aekbVAWhNsfOtf3/QlqeyZrGYHMpRD2pVUQrvpOMS6ZIFSpMUGoHBiXjEs1Qm1cPqzuwTsJBYjPnwujSY+hsZnXIjuZT8Xk6tvlKGg+g+GxXs1wyQShc6ZEllGSl0kHV+sWQ4zbQ0XOpZOK9xmlIZ45BiE4CP++aqSC8Pguyk3Sabr+8Hb6O/k85XGCO9vEYvVJJR02SWUCLWOrBWp7JmsZgcylELXQKgg3L1WMSyYIVWpMECoHxiXjUo1QG5eWWb6D2YtgNOlR15M5o/0IIZh3qQI6jseV0lMwmvRIqfjBq1lFPcklE4TumU8EYXkxHVwtnwNh/0Yqcm5eUbzPKA0x+RyE4CD869QBjI+PW2cEbyfR9fF7bcQz6e+zS3TRNjCKWRPEYCDWdLRAbc9kLSOQuRS2RlgF4e61inHJBKFKzN2vqIHcmTwNxiXjUo1QG5ciIfjAWII1d3bAaNIjpzF+RvuVmt3NPkl8grPFwTCa9KjpTtcMl0wQumc+EYSNtXRwFfIxhOgVVORk3FW8zygN8eENCMFB+OnAJioIzQNRCzeyC+mZo5THrnZzMftZ8geb/AnJoCaioDnwitOr7ZmsZQQyl5YPM0JwEMQjOxTjkglClRoThMqBccm4VCPUyGVYchW+SzpnnuWLnNE+seYMhYeyD8Fo0iO5YgWIl8tMeJJLJgjdM58Iwp5Os5DRye5YpDBb8f6iNEjeYwjBQfjnlmVUEK4JodwUUG7ES5bsrPvo9i2NlMcF78ttsBlCK9T4TNYqAplL8cRuqyA0HlKMSyYIVWpMECoHxiXjUo1QI5f7Hjfh/4yZMJr0OG36FCPC1CUoBJHg07Nl0HHFOFv0PYwmPep7fD9QZ4JQOfOJIBwbtbpgffcBFT1PSxXvL0qDVPAQgoMgRc6ngnDp15SbCh6SZHUpFY9sp9vXV1MODZ/YtJNa3oXZCVYxyGII1fNM1ioCmUvx7HGrILx8SjEumSBUqTFBqBwYl4xLNUKNXF4r7YSO4xGXR8VdY2/elNvnNPSZC9E/htGkR2LR5xgThzTFJROE7pkvBKEkSRAWvG9b7LklcLNhWkCaG+igc+GHVBAu/JBy00hLR4i3r5rrEm6k2z8to7/Dv7Rrq21gFAXN/QE5M2iBGp/JWkUgcyleP2sVhB6IdWaC0M+MCULlwLhkXKoRauTSkn4++uFWGE165Dcbp9x+e1oDdByPIzkXYTTpceup+wH0vuaSCUL3zGeCMPxLW0HY79tMtmrExCQx0ugIdamdUFJCTLtJ18esotuXFtLfK+Yrfu5qhBqfyVpFIHMp3k+xCsL024pxyQShSo0JQuXAuGRcqhFq5HJoTMQsjse8S1eQWW9E+0D5pNsOj4n4wFgKHcfjXvUFnCueg8KWs5rjkglC98xngjDqe6sgnKMDEX0bp6pGEFGEMHcWFYQNNVaxPEpn+Uj2Q7psE019Twpz6O+o7xU/dzVCjc9krSKQuSQ5ada+mD+zbN3e4JIJQpUaE4TKgXHJuFQj1Mrl/EuV0HE8njROnWXwflUPdByP4AsVIIRAJAJGBd+7i7rLJROEzpkSdQgl6a1U7iEfK95P1AJh0ad04JmbTrn59j15HSnMpsvWhNDfT8zbbAhV/LzVCLU+k7WIQOaSlBRYZwhzHinGJROEKjUmCJUD45JxqUaolcvN9+uh43icK2qfcru1d+povGFuq+LnzAShcuYzQTghlbsQMVfxe04tEFYuoIIw5TzlZsnn8jpSarJxERUz7tLfWyMUP281Qq3PZC0ikLm0ZPe1eDOI91MU4ZIJQpUaE4TKgXHJuFQj1MplYmEbdByPnY+y0dxnwpg44nC7jxKpu2hZu/KxXEwQKme+EoQTU7kL65cofs+pBcKW5XQmIm4P5eaHb+V1pLqCLgv9gnJoiW3auVrx81Yj1PpM1iIClUvS0SbH8sqYOwuko83nXDJBqFJjglA5MC4Zl2qEWrnMqqeZQ+Pz58Bo0qN7qM5um8FRUa5Zdrd6B66ULkZDb64muWSC0D3zmSA8c9Q6wDInSWGQIO7fSAVh9ApzvGC4vI401dFl339It711xSbrKIMt1PpM1iIClUvC59uKQUssYUmBz7lkgtAN4zgOERERcmzERPvxxx8RGxuLLVu2IDo6Gq9evXKqbSYIlQPjknGpRqiVy7aBUeg4HrHZITCa9GjpL7bbprZ7GDqOx4enS3CenwejSY+2KRLQqJlLJgjdM58JwmtnrHE5h6IV7ydqgRi/Vy4l8fbsH+lsp8vmzabbWuoSHt6m+HmrEWp9JmsRgcolmyH0E+vo6EBDQ4NDQXjgwAF0d3cDAH799Vf8+eefTrXNBKFyYFwyLtUItXJJCMEnZ8qwK2M5jCY9arsz7LbJbeyHjuOxNOUhjCY9zhYHQySCJrlkgtA985kgvHfdKghPHlS8n6gF4oV4m8HnRLFHBqxlKcjYKMTLp2T3UqXPW41Q6zNZiwhkLsX7KXL2X2HuLBZDqFXr7Oy0E4SCIODw4cNutcsEoXJgXDIu1Qg1cxl5owZbH66F0aRHWccNu/U3KrponGF6AowmPe5Vb9Usl0wQume+EoRyCYXgIIiXEhTvI2oBuXHJVhAmHLCuGxubULexD+K543SbU7GKn7caoeZnstYQ6FySjjaQkgK3Zgbd5dJvBWFXVxdOnDiBqKgohISEoK6uzm6bgoICrFu3DqGhoYiJiZFn9JwxR4Kwrq4O8fHxiIuLQ0xMDNLT051ulwlC5cC4ZFyqEWrm8kh2M9be3QajSY/ClnN260/l08QzJ/LWwWjSg2+7qlkumSB0z3wmCIvzrKLn5mXF+4haQNLv2ArCi5zNemHeO1QQdrRBPBVLtzl3XPHzViPU/EzWGhiXynPpt4KwubkZ9+7dQ21trUNBWFVVhcWLF6O4uBiiKCIlJQXh4eF4/vy5vE1MTAyio6Pt8OLFC3kbR4KwuroaK1aswPPnz/H69WvExsaira3NqfNnglB7nYmBcRmoXN6q7Ebkzf0wmvTIbrQfPO5Ob8QszoRThV/DaNKjc7BKs1wyQeie+UwQ1lRYRU/6bcX7iFpATDm2gjD1os164fuPqCBsrJMzkYqXTyl+3mqEmp/JWgPjUnku/VYQTjRHgnD37t1ISkqyWbZlyxbcuXPHqbYdCcK+vj4cPXpU/p2VlYWsrCyH+79+/RovX76U8fvvvwOgL83x8fEZ49mzZwCAZ8+eObUfA+OScakNqJnLp53DCEs+AaNJj7Ta3XbrV92qxZfnb8Jo0uM8Pw+SRDTLJROE7pnPBGF7i1X0pN1SfJCmGlSX2wrCh7Yu3kIYTTZDqsshHt5Ot0m2n/VnYCKGcalOuMplQArCN2/eYNGiRXYiMTk5GQcOHHCqbUeC8M8//8SOHTvw66+/4q+//sKJEyfQ2NjocP8HDx4gJCRERkxMjFPHZ8aMGTOl7bc/3uDzc7exLPUwuqSndutDrtdCf+YB7tbFo7T3ggJnyEwt5rOkMpaSCR4q9uw3mCCUheAgkCfpNuuFyPl0eWkhBEuJiltXlD9vFYKJGMalGsEE4RT2tiB88eIFQkJC0Nvba7Ndeno6Nm3aNON2jx49isjISISFhWH16tUYGBiQ1zU3N8supqmpqZO2wWYI1QfGJeNSjVA7l5+dLYOO41HRMWS37sPTtCh9fc+I4ufpLpdshtA5y8/Px5YtW+QPp74QhN5I5e43GB56q95Zoc16YY2BLi/IhrBzNRWETEw7hLP3JQPjUs1cBrQg7Ovrs9kuPT0dmzdv9vXpOTQWQ6i9zsTAuAxkLsOTq6HjeNyv6rFZPjAqyEXpB0dFxc/TXS6ZIHTPfCIIvVDs2Z8gfPOelZPap7brNi+ly7MfQjBsbVcKAAAcMUlEQVQXrxcz7ip+zmqE2p/JWgLjUnkuA1IQetJl1FPm7ldU1pmU70wMjMtA5nJHWh0+O3sXpwsfghCr8KvpGsYszoR5l+5jeKwXhBDFz5UJQuWMzRAqf+9Ly4KtgrC91Wa9EBNlji28CWFjGN0m55Hi561GqP2ZrCUwLpXnMiAFIUCTyly9etVmmStJZbxlTBBqrzMxMC4DmctTBU0wmvQwmvQYHuuTl+c09OGTxDS5ID0ThIFtPosh9HCxZ3/B+Pg4nm1YYhWEgwM26+W4wdtJEFZ/T7cpzFb8vNUItT+TtQTGpfJc+q0gfPXqFUZGRjAyMoKQkBDk5ORgZGRELithKTvB87xN2Qm1vOyZINReZ2JgXAYyl3efdiMun5aV6BlulJenlHch+OJ1GE16XC0LU/w83eVSLe8IrZqvBKEkebbYs79gfHwcP+5Za501fesDjXhkBxWE189aE8y8FWfIYOXSlfuSgXGpRi79VhB2dnbaZO+04Pz58/I2lsL0S5YsQUxMDLq6uhQ7X+Yyqh4wLhmXaoTauSxpG8ShnIUwmvRo7S+Rlyfkt+L7q4kwmvS4U7VR8fN0l0smCN0zXwpCBsdc/ituNxWEho/t1ovcfnPB+gQI4V9RQfi0VPHzViPYfcm4VCOYIPQzY4JQe52JgXEZyFz2DI1hz+OlMJr0eNr5WF6+81EjlqUegdGkR0b9AcXP010umSB0z5ggVP7e//fFeCoIl3xmN3sqnjlGBWHiEQiL9FQQ1lUpft5qBLsvGZdqBBOEfmZMEGqvMzEwLgOZS0IIdjxaDaNJj8x6a7zWyps1WHNnB4wmPfKbExU/T3e5ZILQPWOCUPl7/6dDWyet0ShePkUFYdxeCAs+oIKwpUHx81Yj2H3JuFQjmCDUuDGXUfWAccm4VCO0wOWujO0wmvRILj8tL/v2ciW2PlwDo0mP8o4bip+ju1wyQeieMUGoMDrbp8zAKqacp4Lw8DY5KQ/pbFf+vFUIdl8yLtUIJgj9zJgg1F5nYmBcBjqXB7MOwWjS44zpECSJzhp+YCzB3swwGE161PfmKH6O7nLJBKF7xgShsiAlBVPWaBTvXKPLdq2xruvvVfy81Qh2XzIu1QgmCP3MmCDUXmdiYFwGOpfxeY+wLPUIDmU9gCRJ6BuhRemXXI/H4/rj6BtpVvwc3eWSCUL3jAlChdHZDmHOrMlnCB/dpsvWTyhNMTKs/HmrEOy+ZFyqEUwQ+pkxQai9zsTAuAx0Lm9UdEHH8Vh5qxaSJKGqcwg6jscnZ8oUPzdPcckEoXvGBKHy9/6vBY8nrdFIch7R5cvnWAWhCmqHqhHsvmRcqhFMEGrcWAyhesC4ZFyqEVrgsrB5ADqOx9yLFZAkCVn1fdBxPEKuPlX83DzFZaAKwufPn+PgwYPYsmULtm3bhurqapfaYYJQHfe+1NnusEYjMT2hQnDhh/Tfb977//bu/SmqO83j+B+12a3a+WF3i5qd2dpMzU7NzmnaBgMitIqsEOIwgkZmKsbWIOCF0XglxhDCGjET4wUMEm4REu/aYge104ASo6JORsjI5bM/sDRpmxiBI+d7ut+vqueXkwYeH78njx+gux3v2dTiXDJLE4tAmGAIhO67mShmmeyzPP/Vdf18W41e3r5Pkb5+7fssqJ+Vf6L8Dw7rZiTkeH92zDJZA+Hg4KDC4bAk6eHDh1qyZImGh4dn/HkIhGaf/f6Wk7G/Tvq7f3W8Z1OLc8ksTSwCYYIhELrvZqKYZbLP8trNC6poTFHZiZ/ry+6beuvYBf121zZVNKbo3faljvdnxyyTNRA+7bXXXtPdu3dn/HEEQrPPfv/ptthA6Pt3x3s2tTiXzNLEIhAmGAKh+24milkm+ywjfTdV0Zjy/+9FeEFFH36p9Or1qmhM0YedJY73Z8csTQ2EwWBQgUBAfr9flmWpq6sr7jEnTpxQbm6ufD6fioqKdPXq1Vl9revXr6uwsHBWH0sgNPvs95//MjYQZrzseM+mFueSWZpYBEKX4zmE5hSzZJYmlltmuanhl6poTNHOU83Keve0ct4rVkVjio6e3eR4b3bM0tRAeO7cOdXW1qqzs3PaQNjR0SGv16tPP/1UfX19qq6uVnp6esxP+YqKilRYWBhX9+/fjz7mr3/9qwoKCnTt2rVZ9UkgNPvs9wcvxwbCnN863rOpxblkliYWgTDBEAjddzNRzJJZ3tKmE/+tisYUrTtSr1/vaFN+3XJVNKbo1KW9jvdmxyxNDYQ/NF0gLC4u1q5du2KuFRQUqKam5rk/75MnT7R27Vq1tLQ812MfP34craGhIUkTu+327dszqoGBAUnSwMDAjD+Wmtksb924HhsIly9wvGdTi3PJLE2s2c6SQGgoAqE7/7FIMctkn+Xmk5mqaEzRyoPV+ln5KRXXv6KKxhR1Xj3seG92zNKNgXBkZESpqalxIXHfvn1au3btc33O8fFxVVZWqq6u7rkeX1dXJ8uyolVUVPT8fwA4Znx0NCYQ3vlDjtMtAXABAuELQiB05z8WKWaZ7LN8+9QKVTSmaNH+Sr30VpPe+OQ3qmhM0cWeFsd7s2OWbgyE9+/fl2VZcb/meejQIa1YseK5Pmd3d7c8Ho9WrlwZrUgk8qOP5yeEZtbzzLL3v/45Ggj7inIc79nU4lwySxOLnxAmGAKhO/+xSDHLZJ9lTccaVTSmaOE7b+iltz7Vpoafq6IxRT1fX3K8Nztm6eZAGAqFYh536NAh5efnz2tvPIfQ/LPf6/m3qZ8Srl7ueM+mFueSWZpYs50lgdAQvKiMOcUsmaWJ5ZZZnr56RBn739Avq97VP5U1qKAuoI/PBNTXH3G8Nztm6cZAaMevjM7WXHebm86+G+q5AmH6f0z9hPCPhY73bGpxLpmliUUgTDAEQvfdTBSzZJYT9avtrXrprSa99FaTlr/f5Xg/ds7SjYFQmnhRmd27d8dcm+mLytiBQGj+2e9d/JupQLh+leM9m1qcS2ZpYhEIEwyB0H03E8UsmeVEZR/ojAbCtR+ddbwfO2dpaiAcHh5WOBxWOByWZVk6cuSIwuFw9G0lJt92oqmpKeZtJ+b7z0MgNP/s9y5LnQqEZWsd79nU4lwySxOLQJhgCITuu5koZsksJ96c/o9/+Vi/rHpX/1JZry2NxxXu/crxvuyapamB8MqVKzGv6jlZVVVV0cdMvjH9ggULVFRUpGAw+ML74ldGzarnCoSvZk4Fwi3rHO/Z1OJcMksTi0CYYAiE7ruZKGbJLG/pYk+LKhpT9MYnv9HiAxMvMHPkTMDxvuyapamB0C0IhOaf/d4/LJkKhNvLHO/Z1OJcMksTi0CYYAiE7ruZKGbJLG8pFD6nisYUvXXiF/qfD/JV0ZiiTy/sdLwvu2ZJIJwbAqH5Z79vbf5UINyzxfGeTS3OJbM0sQiECYZA6L6biWKWzPKWvu69rorGFFU0pqjkcLoqGlPUEfxfx/uya5YEwrkhEJp/9vveLJoKhPt3ON6zqcW5ZJYmFoHQ5XjbCXOKWTJLE8sts+zv79fmk79QRWNK9D0Iz3/1qeN92TVLAuHM8BxCs+q5AuGm0qlAWLvX8Z5NLc4lszSxCIQJhkDovpuJYpbMcqJ2nPpd9KeEFY0punbznOM92TVLAuHcEAjNP/t929ZPBcIP33O8Z1OLc8ksTSwCYYIhELrvZqKYJbOcqH2ti2ICYaQv7HhPds2SQDg3BELzz37fzopoIOz/2Kxf9zapOJfM0sQiECYYAqH7biaKWTLLiXq/Iz8aBquafu14P3bOkkA4NwRC889+3zvbpwLh8Y8c79nU4lwySxOLQJhgCITuu5koZsksJ+rz4EEdObNRh7pe18kL5r0oBYFw/vAcQrPquQJh7d6pQPjpMcd7NrU4l8zSxCIQJhgCoftuJopZMkt3FIHQOQRC889+X/37U4Gwtcnxnk0tziWzNLEIhAmGQOi+m4lilszSHUUgdA6B0Pyz33/00FQgPN3meM+mFueSWZpYBMIEQyB0381EMUtmOVFf915Xe/ADtV2p1de91x3vx85ZEgjnhkBo/tnvP3l0KhCe7XK8Z1OLc8ksTSwCYYIhELrvZqKYJbOcqK7uv0RfVKbtyvuO92PnLAmEc0MgNP/s97c2TQXCSxcc79nU4lwySxOLQJhgCITuu5koZsksJ+pCT3M0EJ4NnXC8HztnSSCcGV5Uxqx6rkDY1TH1PoSd7Y73bGpxLpmliUUgdLm5Lk1uJudvJopZMsuJunbzTDQQXr3xheP92DlLAuHcEAjNP/t9e7ZEA2Hvyy+p74Nqx/s2sTiXzNLEIhAmGAKh+24milkyy4kK934VDYTh3q8c78fOWRII54ZAaPbZ7798Ub0vvzQVCP/zH9T7q39U/+WLjvduWnEumaWJRSBMMARC991MFLNkllPVfGmfPrv0juN92D1LAuHcEAjNPvv9Tcdjw+DkcwlPmfer304X55JZmlgEwgRDIHTfzUQxS2bpjiIQOodAaPbZ5yeE9s2SYpZumiWB0FAEQvfdTBSzZJbuKAKhcwiE5p/9vg+q1furf4yGQZ5DOPtZUszSLbMkEBqKQOi+m4lilszSHUUgnD+8yqhZ9byz7L98Uf2nTvCTQRtmSTFLN8ySQGgoAqH7biaKWTJLdxSB0DkEQveefYpZMkvzi0CYYAiE7ruZKGbJLN1RBELnEAjde/YpZskszS8CYYIhELrvZqKYJbN0RxEInUMgdO/Zp5glszS/CIQJhkDovpuJYpbM0h1FIHQOgdC9Z59ilszS/CIQJhgCoftuJopZMkt3FIHQOQRC9559ilkyS/OLQJhgCITuu5koZsks3VEEQucQCN179ilmySzNLwJhgiEQuu9mopgls3RHEQidQyB079mnmCWzNL8IhAmGQOi+m4lilszSHUUgnD+8D6FZxSyZpYnFLJ2fJYHQUARC991MFLNklu4oAqFzCITuPfsUs2SW5heBMMEMDg7q22+/nVFJmvHHUMySWbqnmKXzsxwcHHRgIySO2ew2zr4ZZ59ilszSHTWbWc51txEIAQAAACBJEQgNMTQ0pKKiIg0NDTndiusxS/swS/swS/swS3fh78s+zNI+zNI+zNI+Ts2SQGiIx48fy7IsPX782OlWXI9Z2odZ2odZ2odZugt/X/ZhlvZhlvZhlvZxapYEQkNwM9mHWdqHWdqHWdqHWboLf1/2YZb2YZb2YZb2IRAmOW4m+zBL+zBL+zBL+zBLd+Hvyz7M0j7M0j7M0j4EwiT35MkT1dXV6cmTJ0634nrM0j7M0j7M0j7M0l34+7IPs7QPs7QPs7SPU7MkEAIAAABAkiIQAgAAAECSIhACAAAAQJIiEAIAAABAkiIQOqy+vl6rVq3SwoULtXjxYm3cuFG3bt1yuq2EUF9fL8uyVF1d7XQrrnT//n1t2bJFixYtUlpamlauXKkbN2443ZbrjI6Oqra2Vrm5ufL5fFq+fLkOHjyosbExp1szXjAYVCAQkN/vl2VZ6urqivnv4+Pjqqurk9/vl8/nU2lpqXp7ex3qFj/Ebntx2G1zw26zB7tt9kzcbQRCh61bt07Nzc3q7e1VOBxWIBDQsmXLNDw87HRrrtbT06Pc3FytXLmSpTkL3333nXJzc/XnP/9ZPT09unPnji5duqSBgQGnW3OdQ4cOKSsrS2fPntWdO3d0+vRpLVy4UEePHnW6NeOdO3dOtbW16uzsnHZpHj58WK+88oo6OzsViURUWVkpv9+voaEhhzrGJHbbi8Fumxt2m33YbbNn4m4jEBrm0aNHsixLwWDQ6VZca3h4WCtWrNDFixdVWlrK0pyF9957T6+//rrTbSSEQCCg7du3x1wrKyvT1q1bHerInZ5emuPj48rJydHhw4ej1548eaKMjAw1NjY60SKegd02d+y2uWO32YfdZg9TdhuB0DADAwOyLEuRSMTpVlxr27Zt0UXJ0pydgoICVVdXq7y8XIsXL9bvf/97nTx50um2XOnw4cPKzc2N/rpcOBxWdna22traHO7MXZ5emt98840sy9LNmzdjHrdx40Zt27ZtvtvDT2C3zR27be7YbfZht9nDlN1GIDTI+Pi4NmzYwHev5qC9vV2FhYX6+9//LomlOVs+n08+n081NTW6efOmGhsb5fP59NlnnzndmuuMj4+rpqZGHo9HXq9XHo9H9fX1TrflOk8vzWvXrsmyLN2/fz/mcW+//bbWrVs33+3hGdhtc8duswe7zT7sNnuYstsIhAbZvXu3cnNzde/ePadbcaW7d+8qOztb4XA4eo2lOTter1erV6+OubZ3716VlJQ41JF7tbe3a+nSpWpvb1ckElFLS4sWLVqk5uZmp1tzlR9bmoODgzGP27FjB4HQMOy2uWG32YfdZh92mz1M2W0EQkPs3btXS5cu1Z07d5xuxbW6urpkWZZSU1OjZVmWPB6PUlNTeeWrGVi2bJl27NgRc62hoUF+v9+hjtxr6dKlOn78eMy1Dz/8UPn5+Q515E6m/FoNZobdNnfsNvuw2+zDbrOHKbuNQOiw8fFx7dmzR36/X7dv33a6HVcbGhpSJBKJqVWrVmnr1q08b2WGNm/eHPfrXdXV1XHfWcVPW7RokRoaGmKu1dfXa8WKFQ515E4/9sT7jz76KHptZGSEF5UxBLvNPuw2+7Db7MNus4cpu41A6LDdu3crIyNDwWBQDx48iNbk8wQwN/xazez09PTI6/Xq0KFDGhgYUFtbm9LT09Xa2up0a65TVVWlJUuWRF+au6urS1lZWTpw4IDTrRlveHhY4XBY4XBYlmXpyJEjCofDunv3rqSJFzXIyMhQV1eXIpGINm/ezNtOGILd9mKx22aH3WYfdtvsmbjbCIQOsyxr2uJ3sO3B0py9M2fOqLCwUD6fTwUFBbwS2ywNDQ2purpay5Ytk8/nU15enmprazUyMuJ0a8a7cuXKtP9/rKqqkjT15r05OTny+Xxau3YtPzExBLvtxWK3zR67zR7sttkzcbcRCAEAAAAgSREIAQAAACBJEQgBAAAAIEkRCAEAAAAgSREIAQAAACBJEQgBAAAAIEkRCAEAAAAgSREIAQAAACBJEQgBAAAAIEkRCIF5FgqFVF5erpycHHm9XmVnZ2vTpk0KhULTPr6jo0OvvvqqfD6fLMtSOByWJB07dkx5eXnyer2yLEt/+9vfbO2zra1Nn3zyyYw+ZmRkRPn5+Tp8+HD0Wl1dnSzL0qNHj2zt72lNTU3y+/0aHh5+oV8HABCP3fZisNswHwiEwDw6duyYPB6PSkpK1NLSomAwqJaWFpWUlMjj8ej48eMxj3/06JG8Xq82bNigYDCoUCik77//XuFwWJZlaceOHeru7lYoFNLY2JitvQYCAeXm5s7oY44ePars7OyYxTVfS3N0dFQrVqxQXV3dC/06AIBY7LYXh92G+UAgBOZJd3e3PB6PAoGARkdHY/7b6OioAoGAPB6Puru7Yz7Gsix1dHTEPL61tVWWZamnp+eF9TvTpTk6OqolS5aopqYm5vp8LU1J+vjjj5WRkaHvv//+hX8tAAC7jd2GREAgBOZJIBBQamqq7t27N+1/v3v3rlJTUxUIBCRJVVVVsiwrpkpLS1VaWhp3vaqqSpJ08+ZNBQIBLV68WAsWLJDf79f69etjvub4+LhOnDihlStXyufzKTMzU5s2bdI333wTfcx0X8OyrGf++To7O2VZlvr6+mKuT7c0+/v7tXz5chUXF+vhw4fRr1lYWKhQKKTVq1fL5/MpNzdXp06dkiSdPXtWRUVFSktLU2Fhoc6fPx/Xw4MHD+TxeNTU1PTMXgEA9mC3sdvgfgRCYB6MjY0pPT1dJSUlz3xccXGx0tPTNTY2poGBAZ04cUKWZam+vl6hUEh9fX3q6+tTbW2tLMtSc3OzQqGQBgYGNDw8rKysLK1atUqff/65gsGgOjo6tGvXrphF9vbbb8vr9Wr//v06f/682traVFBQoOzsbD148ECS1NfXpzVr1ignJ0ehUChaz7J9+3ZlZ2fHXX96aQaDQWVmZmrjxo0x3+0sLS1VVlaW8vPz1dTUpAsXLigQCMiyLB08eFCvvfaa2tvbde7cOZWUlMjn8+n+/ftxX6+goEBlZWXP7BUAMHfsNnYbEgOBEJgHDx48kGVZqqysfObjKisrZVlW9DuLV65ckWVZOn36dMzjmpubZVmWrl+/Hr1248YNWZalL7744kc/fygUkmVZOnLkSMz1e/fuKS0tTQcOHIhem+mv1RQUFOjNN9+Mu/7DpdnS0qIFCxZo3759cc8LmfzO7Y0bN6LXvvvuO6WmpiotLS1mQU4+z+TYsWNxX2/Lli3TLm8AgL3Ybew2JAYCITAPnndpVlRUxHzHcSZL8/Hjx8rMzFR+fr4aGhrifr1Fkmpra+XxePTw4UONjo7GVElJiYqLi6OPnenSzMjI0LZt2+KuTy7N6upqpaamxi3sSaWlpfL7/XHX/X6/Vq9eHXNtZGRElmVp//79cY9/55135PF44p7LAgCwF7uN3YbEQCAE5sFsfq1GmtnSlKRIJKLKykplZmbKsiz5/X7V1dVpZGREkrRjx45pnz8xWXl5edHPNdOluWDBAu3cuTPu+uTSzMrK0rJlyzQ4ODjtx08+z+Jpubm50eee/JBlWdqzZ0/c9ZqaGlmWxUt0A8ALxm5jtyExEAiBefJTT7y/d+9ezBPvpZkvzUnj4+MKh8Oqrq6WZVnR906qqamJvtrb9evX4yoSicT0O5Ol6ff7VV5eHnd9cmneuHFDeXl5ysvL07fffhv3OLuW5s6dO+Xz+Z67bwDA7LHb2G1wPwIhME8mX5p7w4YNcc8xGBsbi74097Vr16LXZ7s0fygzMzO6zH7spb6nU1ZWNqPnK/zpT39SUVFR3PUfPs9icHBQr776qpYuXarbt2/HPM6upfnGG29M2wcAwH7sNnYb3I9ACMyjH755b2trq65evarW1latXr1aHo8n7onkM1maZ86c0fr163Xy5EldvHhRFy9e1K5du2RZlk6ePBl93M6dO5Wenq4DBw7ozJkzunz5stra2rR79241NDREHze57BoaGtTT0/OTC/rgwYPyer1x75P09CuxPXr0SEVFRcrJyYn5rq0dS3NsbEwZGRnTPv8CAPBisNvYbXA3AiEwz0KhkMrLy5WdnS2v16vs7GyVlZVN+9LXM1mat27d0ubNm5WXl6e0tDRlZGSouLhYzc3NcZ/31KlTKikpUXp6utLS0pSXl6dt27bFvQpaeXm5MjMz5fF4fvK9mgYGBuTxePT555/HXJ/uvZoeP36sNWvWaNGiRdE/gx1L8/Lly3Gv5gYAePHYbew2uBeBEIBtAoHAtC/PPV+2bt2qNWvWOPb1AQCJh92GREcgBGCbSCQir9ernp6eef/aAwMD8nq96u7unvevDQBIXOw2JDoCIQBbtbS06Msvv5z3r3v58uWY55MAAGAXdhsSGYEQAAAAAJIUgRAAAAAAkhSBEAAAAACSFIEQAAAAAJIUgRAAAAAAkhSBEAAAAACSFIEQAAAAAJIUgRAAAAAAkhSBEAAAAACSFIEQAAAAAJIUgRAAAAAAkhSBEAAAAACSFIEQAAAAAJIUgRAAAAAAkhSBEAAAAACS1P8B7dzc5YsVXzoAAAAASUVORK5CYII=\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 8))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.title(r'|Real response|')\n",
    "\n",
    "plt.plot(x, np.abs(mare.real), '-', label='MARE2DEM target')\n",
    "plt.plot(x, np.abs(bgmare.real), '-', label='MARE2DEM BG')\n",
    "\n",
    "plt.plot(xx, np.abs(em3_tg.real), 'C4--', label='emg3d target')\n",
    "plt.plot(xx, np.abs(em3_bg.real), 'C5--', label='emg3d BG')\n",
    "\n",
    "#plt.yscale('symlog', linthreshy=5e-16, linscaley=0.5)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Amplitude (V/m)')\n",
    "plt.xlabel('Offset (km)')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.title(r'Relative error')\n",
    "\n",
    "plt.semilogy(x, 100*np.abs((mare.real-em3_tg.real)/mare.real), '.-', label='target')\n",
    "plt.semilogy(x, 100*np.abs((bgmare.real-em3_bg.real)/bgmare.real), '.-', label='background')\n",
    "\n",
    "\n",
    "plt.ylabel('Rel. Error (%)')\n",
    "plt.xlabel('Offset (km)')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.title(r'|Imaginary response|')\n",
    "\n",
    "plt.plot(x, np.abs(mare.imag), '-', label='MARE2DEM target')\n",
    "plt.plot(x, np.abs(bgmare.imag), '-', label='MARE2DEM BG')\n",
    "\n",
    "plt.plot(xx, np.abs(em3_tg.imag), 'C4--', label='emg3d target')\n",
    "plt.plot(xx, np.abs(em3_bg.imag), 'C5--', label='emg3d BG')\n",
    "\n",
    "#plt.yscale('symlog', linthreshy=5e-16, linscaley=0.5)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Amplitude (V/m)')\n",
    "plt.xlabel('Offset (km)')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.title(r'Relative error')\n",
    "\n",
    "plt.semilogy(x, 100*np.abs((mare.imag-em3_tg.imag)/mare.imag), '.-', label='target')\n",
    "plt.semilogy(x, 100*np.abs((bgmare.imag-em3_bg.imag)/bgmare.imag), '.-', label='background')\n",
    "\n",
    "\n",
    "plt.ylabel('Rel. Error (%)')\n",
    "plt.xlabel('Offset (km)')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='border: 3px solid #ddd;'>\n",
       "  <tr>\n",
       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='6'>Thu Nov 07 20:57:44 2019 CET</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Linux</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>4</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>x86_64</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Machine</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>64bit</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Architecture</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>15.5 GB</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>RAM</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Jupyter</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Environment</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='6'>Python 3.7.4 (default, Aug 13 2019, 20:35:49) \n",
       "[GCC 7.3.0]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.17.3</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numpy</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.3.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>scipy</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.46.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numba</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.9.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>emg3d</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>7.9.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>IPython</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>3.1.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>matplotlib</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; background-color: #ddd;border: 2px solid #fff;' colspan='6'>Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "\n",
       "--------------------------------------------------------------------------------\n",
       "  Date: Thu Nov 07 20:57:44 2019 CET\n",
       "\n",
       "             Linux : OS\n",
       "                 4 : CPU(s)\n",
       "            x86_64 : Machine\n",
       "             64bit : Architecture\n",
       "           15.5 GB : RAM\n",
       "           Jupyter : Environment\n",
       "\n",
       "  Python 3.7.4 (default, Aug 13 2019, 20:35:49)  [GCC 7.3.0]\n",
       "\n",
       "            1.17.3 : numpy\n",
       "             1.3.1 : scipy\n",
       "            0.46.0 : numba\n",
       "             0.9.0 : emg3d\n",
       "             7.9.0 : IPython\n",
       "             3.1.1 : matplotlib\n",
       "\n",
       "  Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for\n",
       "  Intel(R) 64 architecture applications\n",
       "--------------------------------------------------------------------------------"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emg3d.Report()"
   ]
  }
 ],
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