spott/ContourPlot.ipynb

## ContourPlot.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from bokeh.plotting import figure, output_notebook, show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
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       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n",
       "  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\"];\n  var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct some test data\n",
    "x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2))).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.models.renderers.GlyphRenderer\">GlyphRenderer</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'1039', <span id=\"1042\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">data_source&nbsp;=&nbsp;ColumnDataSource(id='1034', ...),</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">glyph&nbsp;=&nbsp;Image(id='1035', ...),</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hover_glyph&nbsp;=&nbsp;None,</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">level&nbsp;=&nbsp;'glyph',</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted&nbsp;=&nbsp;False,</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted_glyph&nbsp;=&nbsp;None,</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">nonselection_glyph&nbsp;=&nbsp;Image(id='1037', ...),</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">selection_glyph&nbsp;=&nbsp;None,</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;[],</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">view&nbsp;=&nbsp;CDSView(id='1040', ...),</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible&nbsp;=&nbsp;True,</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range_name&nbsp;=&nbsp;'default',</div></div><div class=\"1041\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range_name&nbsp;=&nbsp;'default')</div></div></div>\n",
       "<script>\n",
       "(function() {\n",
       "  var expanded = false;\n",
       "  var ellipsis = document.getElementById(\"1042\");\n",
       "  ellipsis.addEventListener(\"click\", function() {\n",
       "    var rows = document.getElementsByClassName(\"1041\");\n",
       "    for (var i = 0; i < rows.length; i++) {\n",
       "      var el = rows[i];\n",
       "      el.style.display = expanded ? \"none\" : \"table-row\";\n",
       "    }\n",
       "    ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n",
       "    expanded = !expanded;\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "GlyphRenderer(id='1039', ...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = figure()\n",
    "f.image(image=[r], x=0, y=0, dw=1, dh=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"7377fe9a-e6b3-4cd4-9a99-421e7b4aa510\" data-root-id=\"1002\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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g5i+otDOm/FKrx3lH24r9WbRFwFCLZv4AImN7jQsq/DvYXJaGFpb9sgb7fMZS3P+IguLE2RMk/i2D+M5ex0T8Pad5ef0/VP1u3o/S+u9c/8Ntmf/A02T93PgmMrffZP+QTo87gP9o/3zEhnD1K2j/qzrff2OzaPx9ejmAc99o/HuFJW0w+2z92khcRhfvbP5jgQ9DcXt0/9MimfhqJ3z9fATqfqELhPyLFqj3eIeM/fajX881L5T9H6WPY75znP18WrwjY6Ok/vt83YAgB7D+ffWwG1rztP0sCBmk5AO8/wT9gmJy/7z/dEemT2P/vPyJlDr/x0u8/QJsB7BBT7z942sPBXJ3uPxc1Tvbeze0/UxORXPD87D9hN2d1FT7sPyPT6Ernn+s/z9iZ+JIs6z9Dj5dUjOrqP3tOOz4u3eo/SIjZNh8F6z++C0ZqZmDrP7NpP3st6us/eZ57ZTqa7D/Ix7w3P2TtP8T6sVk2N+4/Ic4/QyD97j+ervQBkpvvPz9aRJqH9e8/6hLvQsDu7z/Og1uPeXDvPzhZ+cW1bu4/N0aR6Jfs7D9pveIELP7qP2YjVzl+xug/rdwo7Rty5j+v/JF1cS/kPyJEmLQvJ+I/rdQpTKN24D+wMq6E2VnePyMR1f3wldw/g2BRD96K2z8jDiZdYA/bP1KTJeQh7to/4KA41Y/r2j+DRjY+QMraP6R1ji9qTdo/o4uCY2A52T+f6l9JflLXPzjm8zOBW9Q/0gHp+tcU0D//fnGNKH7EP/Mkkp3CGqU/UdqjfaBru78ivcJ8AiDSv2y2dY1Vrt6/Ev8OOl285b8uvBNsNWPrv3pxO/X7Je+/S9lhQUHP77+LAdseS4Psv7CzF77uN+W/4n+1kCHp1b+okKQipeeJP6grLyHkqdU//6zpTqnn4j9p2EwurDDoP0ytwWV/Des/87JRxngf7D9je64JG8frPwU76qZV4uk//nrvDO3i5T/QA7q5LW3eP0cp2MiGYcc/BQ9/RhEexb8H48LXuVrgvyL0s3+AU+m/PbzI8bKu7r9PgCW6Hu/vvyIUlNMcle2/SjWCdKO46L+Psq50vZHivzc8bzj9Sti/duyN3zaTyL95hQ2KAvedvztIrF2Vsro/6m/LUES/yj9JbL+aKWbSPyw07yrw/NU/AnNUZRBk2D/aBWI1AdrZP0XohcIFm9o/6Mey25Pi2j/qzrff2OzaPzpF9rtLydw/wD26nkfT3D/qsXg5hRjdP7sXhSmD0N0/EEWLBtEp3z+rT2386KHgP5tNo5g4FOI/TC5V8pXj4z8MRemHlvnlP/82flwbM+g/F8YpmqFk6j8KgZ+oAWHsPw4Erg8hAe4/slO+4nsq7z+rv/klsdLvPzEJyVJX/+8/t5KAY7HC7z820itlvDbvPz/RMywseO4/64rHWHWi7T/L748GV83sPyS+a8HKC+w/U1TGbfdr6z8fQgNCtPfqPyFotTY7teo/jEFhy8en6j+ELl9O+s/qPywGUpjsK+s/yXoltvS26z9uJQpdIGnsP5a9P8SFNu0/oiYqpp4O7j91V26a/9vuP+OLPinYhO8/u2wvlKzs7z9jCqEkkPfvP/vDWdq6ju8/4zQJB7Kl7j8CpWVunD7tPxM81ccibOs/QxcoEr5P6T9I8/sbgBTnP76Qv+Gw5+Q/VxupJFjx4j9c0Yhblk7hP8+ohQXFDuA/Fx3cSZtm3j9iaKmn+2LdPzjnuwzi6tw/pzxtyYvK3D950FqoC8jcP1boORGkp9w/GUgL3y0u3D8bwWQoeyHbP7e87kYkR9k/UuoWyLJi1j+JY+s5qDTSP+Lja2j0+Mg/wzIdYC8BtD9Fbi1NLomxv4fjQJT7Ms+/WGBoimM23L+RVIfJjZ/kv5R3qJbhh+q/nZkWREO27r9HLh3fFvXvvzKr5mIvW+2/OILXQ1zC5r8w6oeqBRzav29/anmG9q+/Q1jppo/W0D/lRF/8ebbgPxJ9173kUeY/6M4SNS136T9h/dd0Ia3qP8ugwfJwSOo/1gkpyf8q6D+iMB8QDtrjP0gn+o42x9k/UFw6m8s4uz//54iag0TOvwx41o1OM+K/5jPIwM2G6r8WGhskcSvvv0sXX3Nqxe+/cKIikxPq7L8ApTGmrLfnv7iTJF6zYeG/VDtC6xTH1b/JwNMmC4/Dv2zUeKvbioE/woicm7jzwT8fYNnEgBvPP9McJwbxeNQ/7VHERxX61z8JnhcrulHaP05zIrjbvds/EDUrcLF53D9GmUJRTr/cPzpF9rtLydw/aacj4eS93z+kikYSacffP07K4/6yBOA//iwlmlRc4D/I0SzEngDhPzarpTIjAOI/0kyfJ/Be4z9LermjOhTlPzgGkEmdCec/oHatZrkc6T84iQBuliPrP2aHrTA+8+w/jiEjgBln7j8NW8pgCWfvP986syaN6u8/SNv6+ln47z9+G82xB6PvP+B9LV1BBO8/+pRBH/s37j8SfiSbtljtP/sC2iA7few/ineCrqO36z8oj7EgYRXrPwiYaR3Cn+o/ewhnzqRc6j/d45z8Ek/qPzMgLiWjd+o/Tnw1tYzU6j9hed0jbGHrP3vYpg3BFuw/URWsH0Hp7D9IRBKFMcntP1G2N8EUou4/wh9FjRRb7z/hwX1EldjvP7aFpqo6/+8/Mm7ZnEG47z+sbJzAd/buP0/59AeAuu0/1sdMytUU7D9VStSbbyTqP4ekIc/3Eeg//OB2vdAI5j9GrHfr2y/kP2m0p2jbo+I/xynOTXF04T/mQxszxaPgP3OlW/ErKOA/SNE5KOjd3z8LFuvyFb/fP4kNIcmzvN8/KB8GANCd3z9Infym/infP4HuW4GXKd4/+nk4i1Nk3D+2j/GKM5/ZP3ehRFsZm9U/0n4Z3w8W0D/pxMwB1KPBP0xTO2bzIXe/FvHe2yzlxr+oHe8uZhjYv0Xv2PJ3ueK/Ww7ufHYA6b+YfUdZT9Htv7CJRJ7d+O+/qkyKbA587r/I/nK7Mwvpv0PAszNiUeC/HANSHTh3x7/CT1ROBZjBP/C+iLw319k/z/uxkmPy4j9C2V7ZZnzmP5/PAIur5ec/2rMdIVNv5z/1qU2H1QHlPyoL5HZUQuA/aBLGA+Lj0T9+IGDpTB+Tv5SYuepKYNa/cSL9T6sC5b/pn9ZmrD3svweZfApLue+/By21UJpM77/Zag3g4Krrv/9XYQMq+eW/HmCTFwzD3r/mXoJ3nJ3Rv2uvlSlGtra/SW6TVCnlsT/VSObOi17JP187y8TMDNM//LYkSB/J1z/kDFc7lyXbP+t2pJ0qY90/vgP0X9i+3j9BX8aQAXLfP573aSJftN8/aacj4eS93z+aq6kqHcrhP20HYNKLzuE/ZfQrBkbt4T+zzp4/1D7iP++O59aB1+I/pJRpFmDE4z9QrG9vVwjlPwUJlmADmuY/Xe976i1j6D8KOmahokLqP4MFVweXEOw/sV38lw+l7T9flY+px97uP1mURmy4qO8/i191oL787z93kWnC6eLvP1sDY8gkbu8/hTHusJi37j9iglaULtrtPxWbCRsg7+w//JqtGesL7D8B5V1NhUHrP1YGdSxxnOo/het1R0sl6j+pMJ5reuHpP7ef9N7H0+k/G4bqY7386T9TP+nEuFrqP81wJOyx6eo/7fgjwLyi6z/t1x5IX3vsP/V8Pd3pZO0/BkMhyBlM7j+F/26RZxnvP8OQ1Mtpsu8/wU5O1JT87z8ajHOXReHvPzgT0HB2Uu8/dZnpu+lO7j8k/BvVUuTsPxYdEbBgLus/oiSzjYVS6T8qxdQ8hHnnP8OOx4CPyOU/siRa/8Bb5D8BePh490LjP6U6OWVBgeI/46RM3UsO4j8NPVPfBdnhP5GugkGryuE/UZCrEI/J4T/NE8WTK7vhP+fTJhwzheE/30sZI5gN4T81hdZXwDngP1xgu6Kg2t0/lZqX9m0S2j9N6vFFxdfUP9oAY1h0z8s/Ak3IrfwytD93iFPjNCS3vzURst76XtK/PKJsiCsB4L/QyRhWmbDmv/6MUdgMQ+y/h/tQvJKN77+OZGHIXoTvvznk+YLnp+u/7zzxzX5c5L/ld6Dn+tjVv320j/rxzaG/2OqmF0bdzT+hPCT4vLPbPy10ifabyOE/GuVZDftn4z/6y7/y2d7iP9XjtguWHuA/L0Ik/hvt1T94M65PR0W7PzhxbKBN0Me/UBJo/X+b37+xe0zEC2Dov1WeW+QwEO6/1zmvbf7/778P9BlW/kPuvxSpg2+1r+m/Co3HoBhr478mFGd7DyDZv7tgYuKex8e/LQGJ058aYL8Z0PgstHPDP9JnXUtOmtE/eMYv2bOn1z+D/8KojSDcP2cGGlL1SN8/dFSjXOuw4D9V/i/UPFPhP0l16r7CpuE/Uq9Iuq3F4T+aq6kqHcrhP/KBc4JaAuQ/SY7l0l8G5D8QJZPIPSLkP7RkVd4jbOQ/+XMIrTn25D/DIw7LwsvlP3DDMbVc7uY/dgwTWhZU6D9wFywnLefpP363/8QOiOs/XcoPYbwR7T8R6/c24V/uP/wIjy4qVe8/S8zQjSLg7z/cKjIaS/3vP81iPTwftu8/GwfqgsId7z+gk67Ro0zuP1CgagxbXO0/SVqFIJJk7D+y3BJfPHnrPxSCNTX0qeo/TkDz3SMC6j/4gKK0l4npP+XhzoArRek/eM5vk1036T8jnIboqGDpP0xTOtaXv+k/1TCZpIxQ6j/2eVQ9SA3rP2FKmNpE7Os/2GRA+g3g7D+8I2CG19btPwjNlP+tuu4/1tbpdaJy7z+N3frFOuXvPzo2jS8f/O8/1SH91Hyo7z8Y45fsCefuP6e6i5dQwu0/bU9RKylS7D+yVmzdJLjqPz+2x8G+Gek/sTZVve2Z5z9OplsOxFPmP6oiu8o0V+U/fM35ZkKo5D8ZGb7dLEDkPyBev5fgD+Q/aM9DadsC5D+GN4CY2QHkP34b4JDL9OM/jpamSM3D4z9g2RZjFVfjP2659NgMluI/xp36BvRl4T8fPnYCV1HfP5qZCAnkeNo//b/qpG/70z+gwTEuAzLHP/A9Tk7e3pI/42/ZDRVXxr8cpBCmaO/YvyAXs4i/guO/9NtHyJLY6b9HnDGpvF7uv5BUU3Km/++/ebTcqtkN7r+iFSDwoqDovyYVcWPZruC/xvqF/fIYz7/8F3njJCGQPzl5+QBNB8w/m9MUo7pm1j+WwXVLHvjZPzg75F2LyNg/GX40ZpLP0j8uGaoJARnAP+OF4zF2w7u/xSGap7l82L+g2IFcgOHkvy8Gh0sevOu/T6U+tQR9778hnXuSZJrvv0Msvd4iaOy/a2g3ABnV5r+gAUKkHwTgvy5MG7/Q7dG/UjvXLOoYs7/QAO51WpO6P1yfMUYRCtA/w3SY9iRz1z/pqthkSxXdP0WMlA7+muA/JgnYnK0N4j8xQJL2tQLjP3zQXlZpluM/O3BIzUXi4z+dQ91pVP7jP/KBc4JaAuQ/XPtZo09e5j8IVPAf2WHmP2IK9LRaeuY/xVVGyUS75j/Dw2pgRzTnP/YEcluj7uc/F4X4iaDq6D9U4eCH5x3qP1F8RvB+c+s/3GeCevDN7D9MwXZSkwvuP/rLLTs8DO8/lwdIj++27z+4ORmUBv7vPx9UgLe14O8/HkQ8ps5p7z8ndW7LfqzuP35YtSNAwO0/me0gNR297D8XjHq3+bjrP9m8izQRxuo/Jd0jon/y6T+93F9EfUjpP9+npI30zug/wo5aGiuK6D8oJhl/TnzoP4A95NLJpeg/84+jXFcF6T+cR4UZzJfpP8pQyzakV+o/te2JqGM86z+xR/Zc7znsP6MxYBIaQO0/wwf/YLY67j97OpnNiRLvP5/onE1or+8/xZzgwHv77z+ajbDuR+fvP6PqyDl7be8/G+vAH1GV7j9xmCF6eXLtPzfa+9wtIuw/ngiDFxjG6j/DdvIobn7pPwYfoAXRZOg/rZvjUAmJ5z+uPaseAPDmP1LajmCplOY/40IB6zRq5j81QXgJwV7mPxz6KDreXeY/Y7RWrWFS5j9mhvXPPifmP91rGrJdx+U/LQFwhJsc5T/uZ1nQTw7kP3hL6gHaf+I/lohL9uxP4D+2ID40LbPaP1g5cb258NI/fLUqIixEwj+UOQxPGfilv1XQFeDDX9C/9S6LYlzo3r+i/SMlsHDmvwkddqXZJOy/0sp1Wo5877+XQsuYHKbvv8nnlbzlbuy/zzcIFsxy5r8Vulw2l+3dv1CiA0TT6c2/2Gex6lj+o7961+Ahtpi3P4eAIYfiJMM/xy2nEjGwwD+mPQYVlh+iP9zLwPj9lcC/rpfCL+9D1r9+DNAXocziv9Faqv4NuOm/VZIe47Zx7r/3aIz35/7vv0DpuGYXLO6/0Frmj3qF6b9Sc8arMgrjv5xQ5ca9mte/PJTwQa3Cwr+lR3BAVoqrPyjofVDEmcw/rqwR98oQ1z+SKfvQ2d/dPxtDHitTfuE/QhQwIRNZ4z8ys4qBlaPkP0MJj1/PfOU/G9LF1z3/5T96GuQiFELmPzGyrGLFWuY/XPtZo09e5j+4YKtFPbPoP0C/eps9tug/6EiMoAbL6D9a4nKaAQLpP/X7xA4waOk/IF4UgLYE6j9vZ3FzlNbqP7l5iaRD0+s/oTPTfe7m7D/F+Efjp/btP5UCUyOG5O4/HlPnYNeU7z+fITmIIPPvP/KE8yCJ9e8/ru8Jjdid7z/2o+xZAvjuP59shy0DF+4/xRKPTykR7T/q9Ht7xPzrP/ZVRdjP7eo/QU/VErf06T9f0YXjBx7pP1A1IOu+cug/A8t6Kt/45z9aVX0rFbTnP1vBbns8puc/eKQREbDP5z8nEMDcWi/oP1eRCJGGwug/CP5Orm6E6T8cBPlEqG3qP1K1Nyh+c+s/AZSTkXWH7D+NPz82RJftP/gkMfmLje4/fOigcqFT7z+mYk7UadTvP+4o6pL4/+8/stRlkSDP7z9wfnZW4EXvP5ghyP+qc+4//pb+BCVx7T8GH3gkyFvsPwtN1WybUOs/OxBzEmhn6j8uq1C2ga/pP+JQfG6VLuk/RAALLVLh6D/m+Qr1VL3oP8zt23+ds+g/f76ICN2y6D8tCypEHanoPwj8FeF4hOg/MXa5ddsy6D9pIMX18aDnPxZGmjCbuOY/13S4M0hf5T8aM5045XTjP+7bu9Yj1OA/5ZP7NLGq2j9XGXt746rRP+6wJYDxE7o/z3hnfxKPur+rLKwZpznVv5Z9qy44CeK/FgYVN9a46L9aLq/UFKbtv6UXIeYv6u+/bmqn8loU77/3WjVqK2frvwuiOqXV0uW//oRw//JO37/MR6mmOlXUv63RS/MvK8m/QDSviiM1wr/+9C7NMYzEv391MIq48c+/baUXppOD2b99GrB5RrrivzZncavfxei/OdQnAh6N7b+kR4BK4uLvv5qYNyQPKO+/+Fw2DO13678YYeDhtojlv+5lcBlztty/HVPosf+fy7/geZy6M2VlP67eObVUucg/ZrQb031u1j8tIO4e1GXeP2wy2ZvLPeI/zikju3J+5D+pB0LZ7xvmP1x0oTYeOec/f2f7WTzz5z8e2eC5cmLoP8+meX5Fm+g/8lLoMzyw6D+4YKtFPbPoPwxCjyGw2eo/txCgbyDc6j+WsYGAAe3qP7bXbx2SGes/0+oeog9s6z9rpTohhOnrP78QGkvHj+w/O31eXGJU7T9h46pJ9STuP7y5ilNu6e4/IZ8QPuCH7z/nACgYK+nvP21szH9D/e8/cHK1bOy97z+fUPfDOi/vP9LJN338Xe4/wIDGd8Bc7T8Ra5PafEDsPy8u4TqrHes/2b3+5FAG6j8NSSktBgnpP9mvsNLMMOg//WQCrGqF5z+YWzWIAAznP8b9cyumx+Y/s+Tis+e55j84Bf+AEOPmP+W/tJU6Quc/nx6p1iDV5z/vW70wuZfoPxbJLI2kg+k/nwdbwI+P6j9UxS6lsq7rP0HQSuWt0Ow/GxLtlBPi7T/xtQWc3M3uPwvu0JPef+8/ldIqBwDo7z+pTgm2gP3vP582eXhgwe8/nCxlG/k+7z+gJIFdVoruP0XmWZ2VvO0/TQd0tUTv7D/zgs5zADjsP2fmxnldpes/1MFMxZw96z85KLSTF//qP+DiKSDj4eo/SS8mU/7Z6j/63DbtYdnqP2hYJlp10eo/bgVYgqSz6j9W/JH0DHHqP4tXTPZm+ek/7qmXjm456T+Vgx7YKBnoPyY4nJmBeuY/jkNA5/M45D+P8Tt3RCvhP3eX4AZjUdo/5GXAlzgm0D/h9HdRPnCuP8dfAZVhwMS/+AzIV1iJ2b+OJ43DiCDkv82dAQOFWOq//1uyHPyB7r92QrYErP/vv33aRJRexe6/v1GsaQNr679cMazA0v7mv4VAh/lyruK/HWDdhgDt3r/mjAJJsPLbvzAibmtz9Ny/svaAfPri4L91Av3ng8Hkv/rMmP/wQ+m/LaIiWydN7b9x25uKorPvv4QG0VYBm++/Ks3pp6a77L9T06C8iHDnv1dXH12ch+C//2CyDeno0b+X4GBAo8SovzFuRokLesQ/EuV9brGC1T9vTQiow5XeP3lyo8BGzuI/VzylP5lw5T/sll/mxFvnP5ruVCsPt+g/xpM6gtOj6T/Ieq/T+DzqP1S6NFbpl+o/7KJzVDPG6j9RtagjP9fqPwxCjyGw2eo/7mpjCMax7D9tIKZTprPsP/nB/uyfwOw/WX3jecri7D890hT/qiHtP5pYbd9LgO0/k8ob74n77T+DDbRZOYnuPzUwaz6tGO8/kR0KE92U7z9AvIgR5efvP+YmiKoa/+8/qkiC8p/O7z+p8Sk9elPvPwf3j5yvk+4/Wflx5pmc7T96keE9I4DsP99z3BXMUes/H3o4rC8k6j87hD0DZAfpP/77Tb88COg/4LRcG0Ew5z+K/GXSEIbmP+rAnlb4DeY/K786loTK5T/nA7ja97zlP5TcfQyP5eU/Zneq4Y9D5j+O2fWjHtXmP+KzAbXfluc/waDeZXCD6D8ExHIizpLpP5CrlsrSueo/9Xtym/7p6z/g2BvK0xHtP57BvrMBHu4/YeKPZ3j77j9L8036NJrvP3WW+Dc78O8/hvWscOn77z/bOSwd0MTvP+n5DeySWu8/fdeWSvrR7j/v6sau+0DuP4rfoWPPuu0/z4digQxN7T/ec2a3Uv7sP47uh72Bzuw/GejlcxS47D8KB9ExArLsP9sfctyJsew/HRKagnCr7D+HD/eGdJTsP+cbt3XsYOw/Kz2oYrED7D8LvY25jWzrP68vgmt6huo/rTvhwQs26T+0nttVnFjnP+Qu4F0QxeQ/4GFXu2pP4T+KJ6zXWaHZP4k2qf+i0Mw/ARb8BLs6kT/Z/AygyZjLv6ENeZiYH92/60C6EMaq5b++7LdBWVjrvw7nIqG44u6/N6yKwtD/778sq7kR5PTuv238KegfhOy/QOgbS2Cs6b+P2u25T13nv8/QzhipQOa/nYG+uaWh5r9A2fNvF2fov3YvlqH9FOu/7tEO9szZ7b/m1+D2jLXvv1XFjlkGvu+//OR15b1p7b99IGVEaLzov2q4VBYpPeK/wQS4LGF+1b/Mxow5xeS4v49PExTm7b8/SexzMnpM1D/MFRUbM2beP04HrGMvKeM/WbnUyFko5j/5EYBuZVvoP8XJkCCN7ek/MCBpiu4E6z8T0STrisDrPwrhAw9xOOw/WAQtPAd/7D8RmlaDxKLsP/BAsRzlr+w/7mpjCMax7D/LcUq6HybuPyqEQ3J2J+4/AZKajbUw7j8ZoSd49EjuP72esUwpde4/ZLrdDo227j8HjHdwNAnvP4pC745vY+8/Xvpx3We27z8WcpafJPDvP09Hf6Gq/u8/5mN3U3vT7z/stKBjf2bvP1d9x7iNt+4/E3hxxDfO7T+jYZ+xF7jsP6OVMc9Hhus/nHSC68VK6j/0co2kWBbpP2OPuAc+9+c/ul+AAKL45j+57g4ariLmP4h8Eib1euU/iBg0CQMF5T8neqOg58LkP1BXIv+iteQ/a2OoGmXd5D/B3h7NmjnlP3kGqRTHyOU/OKIT9CuI5j8AlNOATHPnP8f52mpbg+g/NTX8Hreu6T9ISOVopOjqP9yLlfyDIew/pYd8LLxH7T8HK0rOcknuPxaKogD3Fu8/rte0pWql7z+Fjfg1+PDvPzqxA2DV/e8/SBBJ75fX7z9Wjwon6Y7vPyB4z2IyNu8/IGDmxDDe7j9kUm/4UZPuP5dyIlNhXO4/8iikJJQ67j8qdNFFnyruP903sKlKJu4/C5enyPQl7j9gxezbmSHuP+Ae0/ElEe4/MPq8+Qns7T/+sBsGL6jtP3nJS0J2OO0/O1LPC/+K7D8g+N7OfIfrP/fKaWkKDuo/SPlU1yr35z8vujdbAhblP6pqed8kPuE/u1UymIuc2D9zU2BJNwDJP2lQvpskqJi/r79237K70L/pnG1XGtHfv6n9hTk6nua/8JTHBfjC678nZ8MajeLuv9pWte5f/O+/hfhU+QKG779i6PIrOUHuvzNeSk0D++y/azGBg7hQ7L+3aUj6kYvsv5aJOnbLku2/YoR1Rhfw7r+web/LKujvvxYo4UFMq++/sUQ4J2yW7b9X8c2OVGzpv4iiVOu8auO/W+EEieth2L8GbM47qB/Cv54uPhmZvLY/dt58IZPT0j9sQdfb59TdP0Qtu1VrS+M/Gq1SacKi5j8WVvTOARnpP5/QX/3O2+o/Xtp7Cs4V7D8eb9M8zOrsP8KzdJTKdu0/lLwlEK/O7T83qBImwQHuP9jrOZ9mG+4/Wdt6c8gk7j/LcUq6HybuP09V2pv4LO8/NHKWWNEt7z+Fl8OppjPvP0OiuOvSQu8/EeE3Sf1d7z+eM4CfyITvP1uu9iK0su8/3J0bZK7e7z/FQ5favvvvP5WH10bf+u8/fq9Bb7bN7z8MBNjOh2nvPyuVJouFye4/Xc2Fft3v7T8456dLSOXsP7oqletet+s/MMiGXE926j8aB+S0lzLpPwJ6GndC++c/KyMAkNnc5j/8jCb3B+HlPxI9E529DuU/x7Bcs55q5D++TGfBjffjPxhPg2ktt+M/WSqQMkSq4z+ADVzY9tDjP+tYKizUKuQ/ktFJqLG25D+6cV1vW3LlP7aB3YkfWuY/nWTWekRo5z+sBLdGh5ToP0pRABrL0+k/DKiVxy8Y6z9TfACKw1HsP8f9ILXtb+0/M1agK35j7j/kQY6pCCHvPwtg01L1ou8/reHzsp3q7z86EXrE///vP8MA8Ff/7+8/y/IyravJ7z+R9iGFUJvvPyqgEJkbcO8/65KLIdhO7z99QYFl2TnvP3GJOlPQL+8/ys27xBMt7z/zSwZx3SzvP4O9IGQbKu8/WTpZy6Mf7z+0D75cxAfvP4gcnD1E2+4/yNUzjf2P7j+bwoXWORfuP5Pm3wUHXO0/+G84jNZB7D9dIMNQ8KPqPx9n8vKWVug/v2+PcBMr5T9JVpG1+ffgPwe+fIe4TNc/X9p2xfgKxT/tVa6iNS2vvxE7zvbS/9K/x6/yncS74L8qtg4ty/Lmv3xl8VmOmeu/U8lNJ2B67r/ohO162cXvvyHHWzk//O+/T4k7AtO677+HLgPbWYTvv2IrF3FYmO+/g8IZUyPh77+flswI2fnvv26yRyQfTO+/DltGPv1B7b/P4ciC9H3pvwKoCEEqAuS/dpVniQ1n2r8iJw5ix+DGv8upkrk/vqs/tjlQWVAo0T/0Nw8le+bcP5ORxxOHNOM/zcEXa3Lf5j/lxCliHpbpP1TEjoPzhes/8m9mqenc7D81wyNvhsPtP/o/bUxwWu4/Qr2jm0S67j80dcx3pPTuPydbfKrHFe8/GQeK3Csm7z8SRNpiHyzvP09V2pv4LO8/D9GiP2bH7z+y1lUN0MfvP6ApQyukyu8/9tkIqtXR7z+bFeS0Ct7vP55NuU2R7e8/PKFBd4H77z9xxfNje//vPzRb8yZU7u8/pTSNib677z9CWJCGolzvP3zp4ciIye4/7JrhJ2cA7j+FatltSQXtPy47EK204es/0hP02Ayj6j/A9arHhVjpP42CywMuEeg/yPD+JXfa5j/3YhasYr/lP+iG1rtGyOQ/kg+fKAH74z8/3iTRZ1vjP5NaHCrM6+I/Xkd7/XOt4j9KiT0s9aDiPywcjfpqxuI/MsNF5IAd4z+2GZ5JUqXjP2qu3SEhXOQ/mO9xFeo+5T/Dj3Nk40jmP+oWdmX+cuc/Uk1F6o+z6D88H6VCTv7pP7GIEHnUROs/hSJm7ch37D96vJl2nIjtP/+Hgmmca+4/EnH0K9oZ7z9YPYAjVZLvP3DzD4D32e8/bfQrbVD67z91fWn3Yv/vPxLdGxou9e8/YACoQZ3l7z8TIsWzWdfvP3/tlPmbze8/vcusysjI7z8fI0OEc8fvP7bKWflYx+8/l2rkBP/F7z+1djavzcDvPwyGqWOctO8/XJEZJ8ac7z+loCbc4XHvPyNY9xY+KO8/+RvK2kWu7j+Vu9fXCOvtP7mPKzVQvew/OXp7YvP76j+4njqDeHjoP+VM/d1BBeU/lXsgzjyA4D+FwV7YZMPVP5pXwUitMME/AyeIh+5It7/nFBxUn2vUv94RUa4A+OC/Svr2gLac5r/JDPi3isrqvzawQXLyee2/HnRpm9Tt7r9oKDUKtY/vv7nTMeSPwO+/YrEiw7mx779I8K5wOFLvv735xycXVe6/YOLRHkxP7L+TG3769OTov5cJbthm9eO/l0nCOZdk2794jF2n5VnKv1lMYDZ6vpc/yfa+YYzGzj81+0o2UabbPwLorixE5uI/UVkhXGPf5j85i2vUctXpPw4xjlbr8es/y3p7F+Nj7T8A6GaXsFfuP2etKJ+r8u4/wxqhHtlR7z96l49XUorvP8YGNoeHqu8/asrj9dK77z+4C6ouDsTvPwS6FAj8xu8/D9GiP2bH7z96hWg6Nv/vPykSANFB/+8/7m8i14j/7z8aYMd79f/vPyTRlFmA/+8/SOgv9mP77z/+MNqJeO7vP0XuOWUV0e8/4bKAsLqZ7z+STq1viD7vPy0LshU5t+4/c5rGcxf/7T8Uo8LfWRbtPxM/Jdp6Auw/UAyEBn/N6j/XQZgfcITpP+sDTs6GNeg/TMPX83vu5j+ENQXYUbvlPyNVG9GwpeQ/dB903se04z+3Bdpyiu3iPyFE3fkfU+I/CpTidmDn4T8ON3eiRKvhP3Xl9Do6n+E/cAapulTD4T84EIQ4VxfiP9mPZueWmuI/hZB/8bdL4z/XDujiSSjkP88ny8pOLOU/d3quPsFR5j8NCTGEOJDnP1d5wt3T3Og/5T5dBpgq6j+9H2OdXGvrP14VzqZFkew/zfoiKpOQ7T8YOXEfX2HuP9KCK0PKAO8/eY4cwzBx7z90sBlQSLnvP3iVDl5m4u8/OlZ6kXT27z92BIsFKv7vPwo3Zqv2/+8/nV4L5sL/7z83ZkXdW//vP2Buo7I3/+8/qXzKwDT/7z88lmOsDP/vP+cFk+lW/u8/o65EYRb87z8y70Gv4fXvPyXWMDi85u8/hU2CJrbF7z88Mnnna4TvP7zLR4CNDe8/E5zcF7xD7j9jGaCiTAHtP1TP26HRGes/VYbVTJFe6D/sy7Ib9qbkP981me9Hut8/e1KQQZoZ1D9fIFlpL3u7P9xNjT10jLy/zOztnvPU1L+3qyxNHYzgv7PTrVC+iOW/HHLFB2wv6b/Lo1jTYY/rv7L2DPyv4ey/uh1QL6Fk7b9rMVZ4/jntv/nXFUkFVuy/d4FsU0GF6r83eKHXOYfnv3G6ajm3M+O/9yA1JIU0279WXWeNTzHMv/oRb58Ws1m/aoUhxsJIyz8NKSMxuSbaP11+JyaOZOI/FFgwBUmk5j+0U2JYednpP0FdFUEiJew/iEe/Xz+07T/ANu3MLbXuP16FYIFSUe8/NVuq6kCq7z/P2p8uNtnvP/JtFuvf7+8/R1W5/bb57z/FBKbqgv3vPxel6MDN/u8/pI9NSyr/7z96hWg6Nv/vP7Yi8hEa5O8/xc2RRNjj7z+eMcf6BuLvP2ZPxu3l3O8/jcLS+BHS7z9AsLYP773vP7BNYxo1m+8/xvV0SO1i7z9oB2L5FA3vP78Gfd3kke4/FuaW/Xfr7T8xZ4hGXBftP6bJt+OPF+w/tig8eZvy6j+eVxTrxbLpP2fUXiynZOg/fMCFxIUV5z//HwDk5NHlPy+XP7uDpOQ/vSbA4OCV4z+3znt5MKziP3t600ih6+E/qiD2BMpW4T9N31MGH+/gPz+gdltateA/k+KZWcmp4D9d7iI2eczgP3Fwlg1AHeE/jxAK6aGb4T8lMb4lk0biP9tfbX4cHOM/9IPsCOoY5D8iP3tS1jflP8xOqKCMceY/fEgrX2a85z+JoSGZqgzpPwC6GlJMVeo/9Pg7+SiJ6z90TrB/nZzsPwZeKz8ch+0/JWwkzVVE7j+DR+KjltTuP6JTq8c1PO8/R5/58D+C7z8fxq9Ty67vPyAW3Rp0ye8/uWwSBGPY7z/Y1d4RAeDvP+d6+mk74+8/3fcT2RHk7z9XCKxJIuTvP5i6ToD15O8/49VyDe/n7z+zr3w33e3vP47UOWA19u8/rPO9kQ3+7z8pZTnc4f7vP0s4bmg07e8/W6SjnyK37z9WWkOqKEPvP1QBdr2Db+4/G2vpWe8S7T+33OTlwf/qPz8nmy14Cug/TfXSZC4U5D9abDvG8S/eP098khIhb9I/F2Ble94Ktj9Nn0OOsrK+v7QXz7/xFdS/ZqzPOBvN3r9L+GRkvpzjv9haT+Qslea/skAXfTNg6L+508ko8RzpvwroVuqf3ui/TrjWO2ue57/Jbe9AWUDlv6ILwhwIqeG/4kJg3byz2b/693RrgBTMv76qLaST0ZG/dKVKxnoiyD9FghsL34DYP3UYeRu8teE/jrRZ6Ycw5j8lM5ZvwKPpP1MqlHILI+w/v5PueCbV7T+kLul5uOfuPyI3BFHNhu8/fIH5hR/Y7z/lHtwDT/nvPw/A1+rt/+8/3aG1HoD67z+PZWn42PHvP3oHoFmF6u8/pUnsKhvm7z86mHmWW+TvP7Yi8hEa5O8/0tPcxMqI7z+FfwFVS4jvP+Cb8gvPhO8/Oj4fLUN77z9ol1LrM2jvPz+obTpkR+8/jBFuU4MT7z9zZG+eP8buP5R0U6vgWO4/wn86fGLF7T8Vhrff0wftP9IMS8+RH+w/sOjS4PgP6z8AiTX7T+DpP4qmoSjymug/7y+joflL5z/cQIX6yv/lPxF9A5vWweQ/m1D7isSb4z8GiCG1FpXiPws15WQvs+E/BzLG55r54D8W1KzZeWrgP3T29yfwBuA/gRe/qguf3z/k2YEP3YjfP1GvpgJly98/JvlO6DYz4D9hJ8QEpazgPxnff5ExUeE/nZpznUcf4j9xtO3VHhTjP4q1qO5LK+Q/57vtpmJe5T/4sTA/yaTmP6niQYXg8+c/4kiUE5w/6T/6bdObj3vqPxrgw5VRnOs/4nGDQuyY7D9omHE4/mvtP6KbcyM1FO4/qMOoB/yT7j9R7+G4efDuP4DoUhs2MO8/tqi8VtJZ7z8zF3zjKXPvP033UNH+gO8/Ec3h1ByH7z8PWvTTuojvP3De17TaiO8/8YsjFnaK7z8Voch2X5DvPxPHkofVnO8/32AKstKw7z/7exUIJcvvP58tKJxX5+8/DXk/8XT87z/9C+l7svvvP/43fTkuz+8/6Yq6SxZZ7z+rqGMh1nPuP5odu5Is9Ow/SzeuRi6u6j94jeBz4X3nP92rTTP6UuM/lgCuumZ63D/OjBei8+jQP5r2+stBurI/zccZb3Yfvb/hNK2Wsw7Sv+Z3R4nT7dq/YW7ecIy+4L+RytOzWc/ivzYhz+cVr+O/n7gnE8Bk47/Sdi/mru3hv89sftHAgt6/6xI7VrfD1r+BBEle37zJvyBFysC5SJa/f+d9yFulxT80weBmMtTWP+ET2qTv4uA/vkPc/KaH5T+l46828zTpP+6qbH+H7Os/s4yoax7K7T+WvMcJ+PbuP+fYV6yCn+8/j8l315Ts7z8L9Rjp///vP/tOMZSO8+8/daH8rXLZ7z8gBJZmYb3vPxgyYUDmpe8/ZVZ4L6+V7z8ySc7ft4zvP/w2ivlJie8/0tPcxMqI7z/XGqnEnADvPzy1uKTt/+4/vR9fFyf77j87PrV/Me7uP3rxm73J1O4/wFgMmz6q7j9vYJ4uR2nuPwAVGhEmDO4/HNZtpTmN7T+UJ3/B8OfsP6BYhIjrGew/y5GP0/Aj6z9jwLViYwrqP4fUAiYJ1eg/AH+q3TCO5z/i74ZLdEHmPwh6GcNq+uQ/zD0zUpXD4z9/KkaDrqXiP5QXTNpkp+E/zkBldW3N4D/btXA/0BrgP1NcH4ydIt8/R0Q5C5pj3j+p131PV/ndPyrfd5oU5N0/Ws5+wdoj3j/sDqVxi7jeP9c774Szod8/wEj7GRFv4D8MJE52pTXhP6Yl35A/IuI/bt99wQwx4z9o43foYVzkPwaCGPWHnOU/dFsv8tHn5j8xpevfFjPoP07IXLaWcuk/xHVlhTKb6j/zTvWQvKPrPxzmk1UOhuw/9GziOZc/7T9aRcoXPNHtP1M66w2ZPu4/oJ4hquqM7j/+5mmF+cHuP0+wc6RT4+4/eZHElPT17j/J+SN4Tv7uP5YLzdqGAO8/JTLGrbIA7z/Herv16ALvP5lrMIMYC+8/FbYlP58c7z9Y2YwUqznvP8vHkiRtYu8/TbTQAySU7z/tzSx0/8fvP5SHIADm8e8/Y+AZRjn/7z8/aY2c3dXvP/4riIYFVO8/C7AjA4ZR7j9tBqA1pKPsP8YRY/weJOo/UYlc4F675j/7XVqfDWziPznDF5mPuNo/+LkRo7Rdzz/FdtQUwieyPzo5VSguVbe/f+3yP5NRzb/cLOidWGXVvwR/Gz7xwNm/3ZPQYR6h2791gJ3i/gDbvzQkHl644de/vUUAhPhP0r/FNvJRmvXEv+oevzEXfIm/q58bsz4jxD83NRWt/ETVP8HmrUGs8N8/JE2bDQ2v5D9RQaHVeo3oP97TQVEKgOs/TeYHepuS7T87LV7Ih+XuPyJL0FZlou8/YVo+fFHz7z/3BYTblP3vP47pe6Qw3+8/87FUO2uu7z+GhbMRe3rvP1tp9pWsTO8/6/9a2KIp7z/Pij3LhBLvP6muRrAGBu8/F2MAtUsB7z/XGqnEnADvP/Yywlu/Xe4/Yfq5Vexc7j+qb/9ALVfuP1xrzfamR+4/bBimXH0p7j/XaZ/1rvftP+J0ykUFre0/bi38DkRE7T8GHPYRsLjsPwNrtbTiBuw/JDRACLgt6z97a1nYCy/qP+NdF9v/D+k//W8oGKnY5z/4Q/QhNZPmP7NV/m7BSuU/OxavMy8K5D8RY715MtviP/W+ape+xeE/5Ksgc9PP4D9ypcj6MfvfP/Q7qhZZo94/wmV+MQib3T/T6VrlouPcP/qhZIGtfdw/jT4SiEhp3D+1Gl3vdqbcP47gnEksNd0/Zh5YZSIV3j89lIjZd0XfPzAmqysOYuA/GE0UG4tG4T9JTfK720ziP++uA2Pzb+M/FBqpmdeo5D9/4yWIqO7lP2qWr90CN+c/MuKURcR26D+0IM00IKPpP1ZQrnPTsuo/RuqZ0y6f6z+GTpkEs2TsP9Jks6MVA+0/Hwt/hbh87T/FgDlKytXtP+23kBRdE+4/zWBMZrU67j993PNS8FDuP4HKylr4Wu4/7GHg8qRd7j/McwvE2V3uPzmSw62EYO4/3tBB42pq7j//KmxbxH/uP8QVMf6ro+4/4sCRgmrX7j+lbfaUnRnvP+oyov45Ze8/COMwx2uw7z8ntnQXaOvvP8k+C69m/+8/TccViy3O7z9CupBe2TLvP1IHQ4O+BO4/IpDSKysd7D9vyTThNGDpPzF0s+1hx+U/lTFVvitr4T/aoDroBw7ZP6krr3Sk0M0/4F1UIwvYtD/OfeFB6AWqv03tszuvusO/KIRwAARyzL+l8FJq2B7Qv5rESGTf+M6/jIuZIjGvyL88Ym97N0i7v5gJdcDsAIk/6rcbhyXnwz/nWiwKEfrTP79ouZYmCt4/2YrDacuu4z/pqEnhjq7nP7ZR/CJl2uo/1NvCQUIq7T95V2KesrDuP1op6SxIkO8/QL+oxJzx7z9ACFJc/vvvP3FQT8Uw0e8/XXBopLWL7z/1gqWbuz7vP+nIYP3s9u4/U+COvJO77j9LVaORxI/uP4nEEOduc+4/NvwcEkhk7j+K08M7kl7uP/Yywlu/Xe4/9QyltC2w7T8QhsJdQK/tPwKybS3LqO0/5XvgXGSX7T89ghZMwnXtP10tsbuuPu0/Bi/VWwvt7D/eOji5CXzsPylaxjSp5+s/AIKlMG8t6z8w9aPxK03qPwTnDhqLSek/lE1rwDUo6D9E2eZJbfHmP0RW3Icyr+U/iPMguy9s5D/CSN8KqDLjPyvJScmiC+I/3+V+jW7+4D/j4u/XfBDgPy9hC8QHi94/fX6eYJg/3T9R0de8IEHcPx6ld6G2kNs/GyPTq68u2z/vqx0WFRvbP1zG6GnlVds/IamnuiHf2z+wlXgLprbcPwRV6xPO290/7FjQgupM3z/SftFdSIPgP+BGl2FsgeE/70p2ClWc4j+S6qq3os3jP6QpOKMgDeU/uWxjJhRR5j9tt6E4547nP+Jmz2MgvOg/Z+G8EX7P6T+0ti8u9sHqP4qr6Txaj+s/Q3dphng27D8FMqIjvbjsP2jeBux+Ge0/FTOgfTld7T+Zfs917ojtPxQxzS7Loe0/7QXLOg6t7T/HjPX/D7DtP9UJ22hLsO0/CRI/10uz7T+HFg/zc77tP5EWXRmd1u0/Ew5bj5T/7T/2s2VWejvuP+5pDOwAiu4/LkoGEYrn7j/rfOy+H0zvPz6myn5Wqu8/LAAusUPu7z8B8ogk4fzvP7qM/4JytO8/fvbJJLTu7j/J+b+qmYXtP/xdTrPtWus/rWCDSQli6D/6tDNDPankPyry0hgrX+A/6XPPBt6g1z8429FIEXLNPxBIXfTbJ7s/A4R7ERWrfj/5/Hj8kuitv7dgFZYpXba/cn/MY+/hs7+NpFKD5Zmev3iQWsUzLas/BJ4/3f4txT8OZaeUuhnTP2fDODVaNdw/3oBw/C+S4j/jWfxUfJvmPwt9QggX+Ok/nNqeW8iJ7D9oWQhawFDuPz2SXLuyY+8/z9s3Bd/l7z9dNBGsM/7vP+Zq8IIb0e8/WCPkARx97z/4V9vupRnvP5l2czFHt+4/oRkGnIxg7j9ic250HRvuP3If8eLQ6O0/riuLiaPI7T9sEs/AiLftP/01Pu0ase0/9QyltC2w7T/KUdI1NgXtP834kBU2BO0/kXWL9D797D8qpw/TgersP84UmxBpxuw/FyeD2Z2L7D/B0fMOHDXsP8jKI1hwvus/OA/mYC4k6z/fpn62kmTqPyrCBQcngOk/el7+8y966D/dY8Dfs1jnP9XIHL4IJOY/5fTiR/7l5D9yXczy16jjPyD0L+FSduI/MjOniOdW4T8vNb5oXlHgP7U/lx1t1d4/bDe5sZdM3T/WjN1aSAzcP3SoHjKYFts/Kmq7cWFs2j+6Cqt11g3aP/UXlhbv+tk/DVuFlaYz2j/YQxURCLjaP/JeWjcJiNs/cHogNjSj3D+i3KF5JAjeP8QxvhHfs98/KlMrOozQ4D+OwHCrwuPhP4ZESW+wDeM/cn5yBa1G5D+ludrLioXlP364YBMtwOY/N0nm6Gjs5z8IoS5MDAHpP0t4HaLT9uk/CpOwpxLJ6j+Zoe5c6nXrP0rsSyIK/us/bpfBfB9k7D8r2KFsLKzsP6x2w+L32uw/s/r45rP17D9isY2V1wHtPyhuuCYWBe0/RUaLRFYF7T9CC0XEkwjtP/HEkPWhFO0/WbPjuM4u7T9nPyODalvtP0le+Jg3ne0/X6qhJL707T+1f1Nyf1/uP44prtQE1+4/H7xVpdJP7z/6u7yHYbjvP59pc+tp+O8/9Xp/bgTx7z8oUNXDVH7vP9mTIfR+e+4/iHh8i0/J7D9gyjemFFfqP+R1yP+4K+c/XYyCz9pr4z8aCSno1bLeP1eD+oaDltY/V5PGyol6zj/RT2Q6LJ/CP2dQvAjgN7U/EHUMYKlErD97TL4xeIKwP04Jdad0LLw/ff1RqLMfyD9U16xqO8bSPzS6GMLdl9o/+Irgg95n4T/A/3LA21rlP/opxv371ug/WIQf7mqo6z+H+6Vc0bntP/H9MF8cEe8/AVkiNG7H7z9pMueHzf/vP9vmptU/3+8/YHcN3XyH7z8AZtAYNBTvP2Cun/8/mu4/Chfr/QMo7j9u09fLVsbtP0v7+cuSee0/GkARZZdC7T/PFPH5px/tP0Z4aH8nDe0/Z5jqPTYG7T/KUdI1NgXtP7+Ct51uZ+w/AajDcWFm7D+S650iEF/sP+56ZOFlS+w/zY0iCpol7D/wKdRFQ+jrP6NNw+J2jus/bkOHIwsU6z9xmvL8BnbqP3gR0Eoys+k/C7Pmv6DM6D8YRb9PBcbnP/W8ybmkpeY/RtvyYNpz5T/b5q/9RTrkP6Zfn8rSAuM/nN9bVc/W4T9VvPyWQL7gP6M510gIf98/MSiMlX2+3T+BTcXEC0HcP/wA0sFXCts/E8qAWjQc2j8rS/buTnfZPxuVbqq/G9k/3iaDonEJ2T8ASc2yXUDZP4cHVVKWwNk/tmXlISSK2j/aJlkPtJzbP7IkpDEa99w/nIuEVbGW3j9yiJezUzvgP9UhOg6pR+E/Vl0vj9lq4j8hKTxFrZ3jP/XVeZRp1+Q/XW04hlcO5j9A5IpXkjjnPyLJLnb+TOg/nBFCDzZE6T9Ye02gNBnqPwOY3Oqbyeo/mmc9PY1V6z+dvdUANL/rP3xkAjgyCuw/27vgOB077D/88SmtJFfsP3BTLiXkY+w/WVFD7Exn7D9dyN1OkGfsP08oJDr4auw/SxPpa6Z37D8Oz8m3OpPsP2sXEcZjwuw/HoowbFsI7T8euoYUS2btPzlZsEaR2u0/ESHDAeNf7j+szSf9TezuP2zmgMI3cO8/2cahdJvV7z/2niHK+v/vP3Lc/82oze8/VGKUTiYb7z/MFq2kBcntP+b8deAExOs/AVFAf8cN6T8bh4YfTcPlPyDk3UDiHeI/wZHTFmPZ3D/urw+OSxDWP/IoijiWhtA/SdW1pnWAyT930ybevCXGPypwoo/+Rcc///pldvjIzD++ZbsLRRrTP80KJV+5Vtk/YiSsyEZB4D/d7wUwZvfjP9YDyBkdeOc/GsSOfu196j++ak7LXN3sPy0imcuhh+4/MqRnBZOG7z9zBd5lo/TvP7OT/nUu9O8/47IAJF2o7z/Llq5sizDvP8It/D4Kpu4/2d2M0Jgb7j+UgKA3353tP2v/uChbNO0/NpHnyGTi7D/irlmbHqjsP2aGd8xBg+w/A1jGTMhv7D9Fdz+2e2jsP7+Ct51uZ+w/b/qB3Ore6z/4SAHp1N3rP9mviPNG1us/1QgYkP3B6z+cDyweD5vrPz3jSvQIXOs/2ZCEBRcA6z/T5SsKSYPqP1lwKwP94uk/CGQf6WEe6T8v9pOM7zboP13O2GimMOc/TGGSgfMR5j8FaS81L+PkP69bRkrOreM/hsiznnN74j+FUQfDFVXhPyf2L01fQuA/HZWSLbaS3j+O1tF70dzcP3Bm6gzQaNs/+0AKXwk62j8iu0vqG1LZP/Ff5RKUsdg/KnG2AXdY2D9QejykpkbYP/JylyAafNg/eSGuLen42D8nMUkXKr3ZPwFFmS6jyNo/dN2delIa3D+8oa/40q/dP2RPI3CuhN8/YvHkutrI4D8WKchIQebhPzrhMYCqE+M/Bu6WdLZI5D+MCtsgAHzlP8YdVgfeo+Y/K+c5g0y35z/cv7n80q7oP3Xr97Ewhek/tzdzT6w36j9araL5/sXqP6sEGuPyMes/I7yhmeB+6z8Iy+1JNbHrP8iv6Lsazus/1j6740Lb6z8Okn0RyN7rPx++RqcN3+s/lRKBIZLi6z8eK14trO/rP09GEZAzDOw//8Cusxg97D91ZDEh64XsP+EC2otK6Ow/KbFBrTpj7T+gqbNrU/LtP589tATQjO4/I4zetJQk7z/JWz4rZaXvP077H5K19O8/EzDbt7Dy7z+2CUObI33vP2qXevDKdO4/zuHY3NLE7D9V2biCMWvqP8y9JFZGf+c/Fu1YkKgz5D8qYIHvJNHgP52WMqIjV9s/3Vw14u0n1j+SJcvDF5nSP3KOozjNAtE/mnHitkuL0T8a0JSf0CXUP9MZEvHYktg/sWzoMuZi3j96P6MyH4DiP3wkNOJK4eU/Nhi39/gE6T84bE4XZ6zrP/yoxy1ssu0/C5X74PUM7z89lrs9dcjvP/Q7EJT//+8/zTVf9SDV7z9D8aR4X2nvP+VY6rgm2u4/rwKaIeQ+7j8dOgIdsajtP0ni68/fIu0/tUOzEuez7D/0XFjMXF7sP+vfMJ7XIew/yvdIa6z76z+O03TaiufrP6nJJcAA4Os/b/qB3Ore6z/Ka2ywk3HrPxlx8xF4cOs/8bAN/MJo6z9HeMorE1TrP32l36JqLOs/6G9r3lLs6j8o5/5DCo/qP6w7WiLLEOo/FN2SPzJv6T+tm2qst6noP+2+a+4Ywuc/hNFN6Iq85j/wN8l/k5/lPxNiaouEc+Q/JNGiua9B4z/uOwoogBPiP1FtIiCq8eA/wB3p/inH3z8TYoVMHd7dP2Dtwi+IMNw/sSdeYdzD2j9KXneZOZvZP9Hn0vUYuNg/Lo6rkO8a2D9mFTFPtcPXPwTiccxFstc/OXw5Apfm1z9tbtlDxGDYP4/u3FjtINk/CN6XZOom2j9bMeA013HbP5cm8898/9w/AVXKbafL3j+5uLOXwGfgP8Wi8GuKgOE//3WioJGp4j9IxNUcutrjP0HTEyndCuU/opq75n8w5j+3DTw8tELnP/Vxmlj4Oeg/cWjD6eMQ6T8L4o32fsTpPzqkKWo5VOo/uIIB/ZfB6j/QwxqsvQ/rPwdMvwT5Qus/LkhDC21g6z8dUBmt2G3rP7UBny9wces/624EMbdx6z8aw+UcTnXrP8I6++etgus/QPZeAdOf6z9uiPvn2dHrP56S7xSNHOw/pUFDUt+B7D9ze7Y1SgHtPwFlMG4Jl+0/x558BjQ77j/YBpj6x+DuP55r0BbddO8/ZOC4IWHe7z933mrV7v7vPzdWdINpte8/ReORVN3i7j933sYkinHtP+Mdz5HtXOs/fquy1m246D8v2jjztLHlP5Rvog+pjOI/co0rs50y3z/Zs+FgLE3aPxpuHMmC7NY/NGR+3qtp1T/daNCBtevVP8H3fk+PZdg/Krrv66mW3D+IkhUaqAfhPz96am4bHuQ/x+8nl4c95z8L276SaRrqP1kyX1IMe+w/6LaHw9M+7j9nA5uN717vPwZwv4196e8/RYkQ5NP57z/3AUXVvLDvP3B+GUVvLu8/0/YWLdqO7j8Q9CwQAOjtP6kHZhjCSe0/K7l28Xe+7D/y+bfB2UvsP1hV3P7x8+s/7o92M/S16z/uLTGB747rP+NJKxtheus/4viVQa9y6z/Ka2ywk3HrP15mnXODI+s/YQUtbWQi6z8jT+7XlxrrP7qjw6eqBes/4OCtKpPd6j8ZwsvO2ZzqPyRxEkfKPuo/WFnQqLu/6T+BPXVDdB3pPy9CgOCbV+g/hyu33xxw5z84y4YFTWvmP0llLY3AT+U/9o3RvMIl5D8ODTjni/biP/O+mGlgy+E/NL2GNces4D9MN0wv9EPfPwENdsI7Yd0/vX2OZnu52z8NDa0E8VHaP8TmgWuXLdk/jfbLLc9N2D+SJvI5/rLXP9pcOOYTXdc/AfGcwudL1z/YyMMJb3/XPwelXFzH99c/0aoImRa12D+DbPVwQLfZP4Ke/y10/do/0SUEM5mF3D9I7ELUp0veP0lXMf+CJOA/ZvHc5wQ64T9tacvX5V/iP8f34uA3juM/xYQjNP+75D/LjkzQ4d/lPxg9HBQC8eY/2fFsS9rn5z87I5Xz6b7oP+1ZoywRc+k/MQRxZZAD6j/pdvSuvnHqP69qiUWbwOo/F2tSl1/06j/3qJbgKBLrP7Auf/+8H+s/UE+PhV8j6z+F1XxhpyPrP+1p8GNJJ+s/2wL2KtM06z+EgS5/V1LrP3c/UE4Ohes/lbylkufQ6z87mrjmCzjsP/Hp5ypCuuw/lCqZbzJU7T9KMmZGhv7tP04CNdf4rO4/CVKPMohN7z/Te7C2JMjvP3mE1ulp/+8/IgZw/AXT7z+NRfRYUSTvPwTY4pwJ3e0/+fz4yiD36z8qr2BEZ4PpP7NSfao6rOY/OBfn1h2y4z9K0+YdT+LgPxmIE3wYFN0/KMDK+PXU2T/NUfYnXGDYPwCSEcWp3dg/1K2QVa4/2z9nww2IhEXfP2v581TxP+I/JSmpS94u5T+dWPdFpiDoP82Z8KzvzOo/exKKS3T97D81DiYkmZTuP8AN83Kjje8/s761Z7P37z/JJsmhMO7vP07Mgs9Pke8/NKeXDFoA7z8Q5l6NN1buP17ZYN3zp+0/ztsfrZ0E7T8RqkAY5XXsP7cpyZkDAew/lkdpxKen6z+4XIaFxGjrP5figS48Qes/YPH+L2ws6z9Jz2xuoiTrP15mnXODI+s/I8mWTVj36j9aHK+QN/bqP/RjPyxf7uo/hU10MVPZ6j9LMWZCBLHqP98nLcT6b+o/lt7/+okR6j/xhX23GJLpP/18mK+D7+g/YuwOLowp6D8DZ21ONELnP0k8keziPeY/D9GBOzMj5T9glGopbPrjP59yl5e3zOI/qIxZUUSj4T/h0g9KgIbgP39CEKwY+94/om+rve0b3T/Rr2sycXfbPxnk6+HEEto/Y/s/sM/w2D+vOUcC5RLYP0uw76xiedc/uDF+fDMk1z+0lVbfLBPXP5vosKtDRtc/QcU5uJa91z/1kcYbT3nYPwJshq1Wedk/+DNlJue82j+waolB+EHcP07A4CyaBN4/NOAXSVP+3z/PJ/9f0RLhP1s48GvoNuI/Pz7sN5pj4z+HlYq0ApDkPwtunFPasuU/1QUhIU3D5j8fdFUb1LnnP9SVBmrikOg/1LeKNkNF6T+ZcXKsHdbpPwSQFFuwROo/lqqnGueT6j+lPWqm78fqP2FBRqTj5eo/oqP3GIzz6j8G55coNPfqP8ijanJ89+o/3mKpCCT76j9N00rzwgjrP7OwE1h3Jus/TCiWT4dZ6z8LYViD9qXrP1HofiYKDuw/PAEihbKR7D90ZMwd0i3tP6dmzOFh2+0/CfrX6YOO7j+zOt05tDXvP89BKJ5xue8/QVX+p+b87z9d1gpVNeDvPyDEKE7nRO8/Z1go8nwU7j8n6hXnGkjsP4ghIrQw7+k/MHwUj1Ey5z+0FAUoIFDkP7Ni7SxGlOE/hnnV0/eV3j/6C3TC7mnbP/HE7bt6/dk/+I5eIhJ42j8fJ3P0eczcP7J++UDfXOA/JVK49oDo4j8zgwBCU8HlP1VI+1jFmeg/DnXPYEMr6z+5jI6waEHtP0bzfyk4wO4/8drIWBCk7z92QDnyk/zvPxxRwEsr5e8/15zWGLN97z/D4RDE7uTuP9MV6BM1Ne4/ehouEQqD7T/hGUCcCd3sP4GDmNeITOw/vti2yHzW6z/fJ6abYnzrP+6ONr8KPes/nybV1j8V6z9D4MjITgDrP7nmxP94+Oo/I8mWTVj36j85ZonFce7qP9431bRQ7eo/82/CDnbl6j/c2SIzZNDqP3RWprMKqOo/QkiI//Fm6j8jyTXibgjqP0CLIUzriOk/ZulfX0jm6D/nPJdqTCDoPx/Tvi7+OOc/NO5N2cc05j9ckYzsRRrlPxHTRGm+8eM/ZNm1RVjE4j9O4A/8PZvhP6uOeITYfuA/O1SteIfs3j+9qek1Ew7dP0TKHV0+ats/1rBaOCUG2j9X5sq8quTYP1ooabYfB9g/0l+RYuBt1z9yH8yp1hjXP4zE2o/XB9c/g6Zc19c61z+qtoST9rHXP7MldHxdbdg/KVlMnvds2T+OULe/ALDaP03TX9VzNNw/UggZxGX23T+rWHxZY+/fPzLSevH5CuE/O619lLQu4j+YmdARElvjP1wa4hczh+Q/ElkfzdOp5T8ug0I4IrrmP9JSRieXsOc/3SNdYqOH6D80haT7DTzpP+aeiyn5zOk/a2ljp5476j998uV25orqP7qdNf37vuo/2yl8IPjc6j/nr4N3pOrqP7NmCpZN7uo/TxXe9JXu6j/dmQecPvLqPw/HkpHh/+o/6U0/Kp8d6z9/r608wFDrP3nGK0lMnes/9t+XHo4F7D+SpYQwfonsP7il8IUKJu0/Lug0wzvU7T/3DX5nSIjuPyt7gHrFMO8/kk04J0627z9uxtCeKfzvP5Wh9EWR4u8/6JgfLhlL7z8qFgxiNh/uP3Qx7qrlV+w/76SFQVEE6j/m9w6csUznPxd8ZpVJb+Q/BqKPJ3G34T+ontFhVeLeP3jPvqAnuts/2ajBiFlP2j9X/d5yZMnaP6mMFt4LG90/0x97/62B4D94w7xAyAnjPxHJU2gs3uU/LlRXiY6x6D9M28nOtT3rP+KtDEKdTu0/jylSY5nI7j80jz+8P6jvP+WRpQVM/e8/NuZn/CXj7z8F9UhXmHnvP0Q9AKZL3+4/csGbqHku7j9ZWAF1i3vtP5jbvCQG1ew/sY931yxE7D8MKcML583rP/tIfDCoc+s/dj/97Dk06z8hAdZCYgzrPwCvb+Fq9+o/PKSxy5Lv6j85ZonFce7qP7Bw7VoUCes/5WCBSfQH6z98x0eDIADrP0UArJEg6+o/y5mESefC6j/ZyYoH/YHqP6/MRAWyI+o/8X8Fzmak6T9rO7kw7wHpP/7tZFoCPOg/K/UPAplU5z9gv/dGE1DmP5y5MqgJNeU/awDD38QL5D/nRoyddN3iP0H5aU5Qs+E/KiGc4M+V4D+tS587PBjfP4p4A7qkN90/UEddUdmR2z+j6eIDByzaP89YQ6AcCdk/NX0Cx3Iq2D8CdU2DapDXPwYjiG3wOtc/M9zVy9op1z8lkxycHl3XP1NPhDra1Nc/0BHCdzWR2D+ruUm7F5LZPxpfGJq21to/LXz7UANd3D+JAeCRBCHePzIreekZDuA/QFN28n8i4T+E1nn8TkfiP6Ms11eodOM/0Zldd56h5D8K55pn4sTlPz+fUUqc1eY/jkupkEXM5z+c3Xk5VqPoP1M8m7ahV+k/k28pNlno6T8n+TQCxVbqPwT8RKrYpeo/GvuylsbZ6j88pt3kqffqP9cgLWZKBes/A1ZrS/AI6z8tE0NqOAnrP6GkD9LdDOs/5HaRd3Qa6z+7eMsLFjjrPy9m7hMDa+s/Hnbqcje36z/ZihMI7R7sP66/TK8Douw/gEcIr0Y97T/EyYdlj+ntP3hlUu7Wmu4/CamUGGc/7z+15PrUhb/vP0MMQ3gk/u8/q8bebjbb7z/hoD8eMTjvP5kSZXes/u0/qNViDh4o7D8GKmFKgcTpP0BS1tAf/eY/lYovMlwR5D/9LLvCgk3hPxAs0RZs/N0/1JYOobXI2j8epINt+ljZP5c6cC+p1Nk/M5sif4ou3D854EDR1hLgP+55zcyCpeI/zAb5Hy+H5T/M2L+AwmnoPw1MlUvzBes/sPwa1Jwm7T+7g0mWHa/uP4SpX+Bkm+8/LuBTSOL67z+jwTia/ujvP9GdKiC6he8/D7ah3Q3w7j/dTiW2ikLuP60G/7/qke0/ZPIDmffs7D8LEXCYKl3sP3b81PuT5+s/FLC/jcSN6z+sqo1qmk7rPzrO45PpJus/K766dQUS6z9YCkRhNArrP7Bw7VoUCes/S0BLv3BG6z/08iwvU0XrP00dwa+QPes/6zs8070o6z84Y+rd1QDrP5umjI1hwOo/tTpenqZi6j/u+i9u7+PpP/ymAQjwQek/dZyhSTt86D9uIML6p5TnPykOQpl9j+Y/Kjv1Skpz5T9W8HJ0XEjkPzAVLMj3F+M/MqW+33Hr4T9KlYHFZcvgP23sptpCft8/4ueclLeY3T/p+mzeXe7bP7B3gXOKhNo/mczZXkhe2T89rhT6An3YP0Hgftkm4dc/j7A0EaeK1z+oPMD0XHnXP91kEBI+rdc/36nqCGcm2D+7tBkP++TYP1gXI8fY6Nk/OPzK7iYx2z9lFu6Qv7vcP1w7PZeHhN4/DP+nSWVC4D97MOOwXlnhP4xWZNyogOI/qeXXbUGw4z8524+KGd/kPziYnofIA+Y/TpDASmkV5z8mBc+adwzoPyJG7WN94+g/2IVyKWyX6T9MCdkimSfqP5CSoGxvleo/MvOFBP7j6j/s8W+5hxfrPz9cOnUsNes/DRZkNK9C6z/s4SMATUbrPw4AQX6URus/WgkIvjFK6z9G+hx9qVfrPwtYt+IEdes/SEY++m+n6z8HgmVnyvLrPxaSPzklWew/MI1RZSXa7D9/NcFSQ3LtP7yLZAfoGe4/NWOCM3vE7j/tda4dlV/vPyjowIqz0u8/igv4uv//7z8QeBDGzMbvPwa7witPCO8/T8YyL3uu7T+R51Z67rPrP4R6l1SbKuk/TFJIpFM+5j8JzOVdGzHjP2ED2pNvUeA/YWBZ5qna2z9ywj8ufIzYP/XorBl8Edc/hPRGpeqQ1z+kWdp5o/3ZP7al78DdFt4/Dyhb+4i24T933A5HE7fkP3KOMj4kvec/4W4KPvl+6j+3OftNx8TsP4/B5XChb+4/RHp8GOR57z9MnURrQPLvP4lJ4+QY9O8/I/XaIeaf7z+RAcZqWxXvP5pFwpvVb+4/qfoKjM3E7T+gevzeriPtPy4zeI9zluw/3L802Ywi7D9nlRmt0cnrP22laklRi+s/JiD7kAFk6z+VtNLATU/rP0R7C0OOR+s/S0BLv3BG6z9o4X5DjqTrP+d7SyZ1o+s/MKBjWtGb6z9TAODETofrPyiSHUr4X+s/ylyM1Vgg6z9WGGErpsPqP/t0gxgHRuo/URv30vuk6T8Dw45M29/oP9v4QZlD+Oc/lfusJlLy5j/QQd39gdTlP5r7Emoop+Q/gUyqh6dz4z84kPo4hEPiP1+qrUGRH+E/27QD0VQP4D+Q61RZcTHeP0lAeugBgNw/NQT+gPAP2z8Xapg7duTZP0BZIisd/9g/NKIw9WRg2D94ktB7SwjYPwfiqzKv9tc/UgOQmoYr2D871gln66bYP1bcSxn5aNk/5AImxn9x2j/5Hky7jb/bPxljlhLXUN0/irw+wQkh3z9WGxmGk5TgP3+K6TSDr+E/1Q4xrJba4j+DUY6vkg3kP32QhdEzP+U/gy6Vjell5j+9H0mCu3jnP2xBhpYrcOg/DDTECuNG6T8yUo0RBvrpP+p/Q7Ilieo/rhzyMOb16j8RLWj7gkPrP8/Lh71Ydus/IaOY142T6z9FYmeq26DrP8FxCRNrpOs/wUK6c7Gk6z9ISZc4QKjrP5/biSyBtes/iaQCeWDS6z+rO4Dt5gPsPxcdroPETew/tvJ0dsax7D+QcMHfMC/tPzudm+X0we0/8VCbT8Vh7j/+1PZIHwHvP8TvErJ+jO8/Eo6NjiPq7z/+v6s4/vrvP7C8+zdvne8/U7e0UFmy7j8ptCwhYiTtPyobnRAi8Oo/okb2z8sq6D+JyRaMSQTlP2K3HqDEwuE/vNBAYBlw3T9GeXQFLmjYP0LNNj/Z8dQ/+nOVDNtl0z9u75nx7urTPyGdKLIjdNY/dPmcAlDB2j/V9NMNXDHgP4EwvbPoYeM/S8z8krmf5j+OscG/Np3pP+gI3XVZHuw/xAhE03MA7j8ONQn7PTvvPzlv/EIY3O8/wmRR6UD+7z8+Qgcc38LvP8sHS9DFSu8/5N7znZOy7j93w7Wu8RDuP4/4CB5Qdu0/esyM+Xjt7D+F0H5Ke3zsP3UtK+agJew/HWL1OUro6z8dzYDbpcHrPwiToplHres/A+k5Uqel6z9o4X5DjqTrP90Lh84ZIOw/D1ylygcf7D/oE5sElRfsPx5GCOqSA+w/Rw5rdybd6z+ruH7I4J7rP0c8xfDiQ+s/1mUM3SHI6j8Rwx9L0yjqPwPFYr/2ZOk/U9SLNNZ96D88fcXzTnfnP6uTRSq8V+Y/0sSfbngn5T83nq0rDfDjP+pMU2M/u+I/MncT4C6S4T/yWIpnsHzgP+xMyKrqAd8/rDZ50fpG3T8TZvNygs7bP+5Zk9j8m9o/2S1UmyCx2T8DtClIiQ7ZPzj92bxEtNg/syBGFTmi2D9ghP4+XtjYP3Rd6YvJVtk/uezNB4sd2j/fpOpdXSzbP5IOCUcrgtw/Swq5HXIc3j/VhTYlkfbfPxsAO/mPBOE/38t9/bgk4j9sEwBvulTjP0TTQfsJjOQ/WtIiHBzB5T/sUM0uK+rmP2w1zq0n/uc/rB7V3qD16D8n73UhccvpP+RO0q8Ifeo/hFGBgFAK6z++r8aeP3XrPxQoCK9Qwes/T/zOwQPz6z/B1EbRhQ/sP0HXYA9/HOw/AEy+gfcf7D/1jgkbPCDsP9sbRcezI+w/eUEvc50w7D8cUOK0t0zsP61HyizVfOw/E/94SV7E7D8tsndxvCTtP0ambzCnnO0/9o0xbU0n7j/ty+5zXrvuP8pjpVkMSu8/qnhmfUW+7z9gjQbrk/zvP50sIsxC5O8/Nc+94X1S7z8SsqS65CfuP4S5ffhSUOw/HN5sOWnL6T+BUm8fHbPmP1UUtfAkPeM/Pw0BHJ5q3z/0rb8jVOPYP5qTEM+YiNM/1GJXDMS9zz9PW5SxvnrMP4o32X0qk80/5RqJYgR30T9CnPVqKAfWP0aC5RYoC9w/NltCgid24T8MSiirgP/kP8+bTdPyTug/av+tPZQi6z/641q1T1LtP+5GZJXr0e4/YdPAq66s7z+K15Rebf3vPxWcEVoc5u8/wYOE7wmJ7z/35VzFiwTvP9iBxLfxcO4/JqmP4Rng7T+7QgJX9V3tP8B6gcJ08ew/Fg7+x4ed7D/6KdpnBWLsP9FlQm1wPOw/eUyARZoo7D/rGefAKyHsP90Lh84ZIOw/FnvOER207D9SU4fNFbPsP5pHQ07tq+w/20HlTa6Y7D9zc3sDp3PsPzfn1ex1N+w/jwuK3iPf6z8qtednZGbrPy683KgIyuo/UuA5zpcI6j8hUC3R5SLpP7yz/yVzHOg/NFikoGj75j/AUWhaIMjlP4NooaFRjOQ/z6x24xFS4z/q9qIW5CLiP3VIpqwDB+E/XUE1fQAF4D83L0dKU0PeP385De5qwNw/CUy7yxyF2z8FicpfXJPaPwfpcdLr69k/cfwvZu+O2T9yDRoQWHzZP/x7nsUftNk/AoRYnVU22j/NZ4l8+QLbP9vEsj6oGdw/inBjrRt53T8OFXWohh7fP6mKhDFvguA/6l1SmBaS4T8FJc3Ei7jiP5kM+cZh7uM/C02QpKcq5T8Iw4EDdmPmP+LGokLGjuc/VJDoHHGj6D/Th797H5rpPwfXxfH0beo/zTiiyc0c6z/8EkAcDafrP7GCm+UaD+w/6RmOJsZY7D8X4jDYu4jsPxZBdnYtpOw/zs2Lcaaw7D9g+rcd/LPsP30yjgU+tOw/i6HpvJK37D+y0p56+MPsPweBzdbp3uw/66ZjbOoM7T+W5mgH/VDtP+4ZJF8ArO0/QfBG++sb7j8f/yTH6JruP0KtyvFLHu8/QaDn6JKV7z8dFkz6punvPxB4t7zj/O8/6GxC+42s7z/atLcOd9TuP5bnzL48Ve0/viIvgb4c6z+1FJapCC/oP9V/+C+krOQ/KmdMy+/S4D/xNE9GjunZP+zWyNkG2tI/es/CgM09yj8s+B6m1HXCP4HgwHvEBb4/hF9oriArwD+B2RugTtfFP3FmenBjkc8/MHcrMhBB1j+iLPxCe73dP8g9fhvkxeI/WG5O+zZ95j9yxZ42N7vpP7x6aNaCUOw/eg5GtEsr7j+kT+XipFPvP8atfs504+8/wNPFNnX97z9XUpJguMXvP57SB6W7XO8/J74j3+/c7j+RXmfFElruPyZAdMmc4e0/iCitL6177T8BdReYFCztP40RXEJN8+w/QbNrMFDP7D/A95Z9R7zsP4YQdEAktew/FnvOER207D/5rNdppVntPzvWwNutWO0/LVLDAPJR7T+1wNPy0D/tP8IsxHLXHO0/NEyr2rzj7D/tFkHkb4/sP+QqSu5RG+w/E4la8rCD6z9iC03sc8bqP8jtwG3O4+k/1Odgo77e6D/GFI3LIL3nP2jnzPRCh+Y/RZ9Yjg1H5T9nj8+99QbkPyfEHq740OI/i/whsNKt4T/6X3m0i6TgP7vFaIaxdN8/N0pmcYDl3T/Rx+Nvzp/cP9csg6jfpds/lVO/Ear42j/JmTdjcJjaP8disn8yhdo/MVhiLO2+2j/vr9rjpkXbP+L4eHBJGdw/5782Z0k53T/d9KlVH6TeP5mtCQJOK+A/Gt6mOJUl4T/uS8BVjzziP/vGVsgeauM/xXG4k1im5D9LtnHuy+flP5K9/OkhJOc/XJT1JwlR6D9kA96bRmXpPzoz/vCvWeo//r8TZ9Ip6z9K+V0tH9TrP+Ur0Y6dWew/iJNHY0q97D9xaqIRYQPtP0h4y8jEMO0/YbejXaZK7T8zLtCYY1btP9eOlW2GWe0/P1KtZcRZ7T+YoHUI5lztP4IdjDeLaO0//SncfsyB7T8vB+z0vaztP8ySi7DW6+0/lP0MOTs/7j+57L4O5qPuP9gR4QCsEu8/nZaH8SZ/7z+xxcwSrdbvPzJlWdmm/+8/M3NtuM7Z7z8Qze/rGEDvPzP00L0GDe4/2q89z8Mh7D96JADvam/pP9cvbXhMAOY/tEAC/rT94T8Kywm8U13bPx2hzG593NI/fyeewf5vxj9khgb8AmG0P8aA8vIPR48/vdjBC+jJir+B4j2xYFFtv02egxVAGaY/2WH4VUS6vz/jAeFAp8bNP8rK4ygeDdc/q2aPSLW03z8/Saj0ng3kP1SiUE6gzec/8m0uh4Th6j8EOqAJ1y/tPyAxq6Xqu+4/iEfOpNad7z9STSSm0vjvP6LwT+ja8u8/7oVdsfeu7z8CrSclNUrvP5udq1Wp2u4/H9Nifb5v7j/h6uQPGhPuPzePetmyye0/fjjnGtyU7T9pn05UL3PtPx7JZCVSYe0/Oz7l3Jxa7T/5rNdppVntP2C45YxnB+4/80T+TYYG7j8i4XDLZADuP32UPrHa7+0/MYkKWtDP7T9sq5DzJpvtP2/U2aGzTO0/znd6cXPf7D/Is+CVDU/sP+o7qKaYmOs//0uM73K76j8O6ELb57npP0HZ3tximeg/nDuAxRJi5z9TRfm5Dx7mP6hAsDU82OQ/+RRVYCeb4z9jRj8EK3DiP75QkwfkXuE/aqS5XQdt4D+iXWrjAT3fPxAgEGiC690/ejb4NT3o3D98XeN1bDTcP7BC6oR70Ns/V6M4bX682z8NvnzFdfjbP3o1O11chNw/tQODT/5f3T/UvmjOmoreP710qY4qAeA/qUqfs7/h4D9HvJEr6uPhP5h0NfrgAuM/sME1CPs35D+nFwQUsnrlPwPkN6/6wOY/R6Un9/r/5z+iU3czEC3pP0HnJ7n1Puo/8H6yAsou6z8jxvgnr/jrP9AiHwbfm+w/hmvwvzga7T8K2yHIeHftP2hycy5iuO0/6AiqDxfi7T/ay39pvvntPywfKLtwBO4/TJj+W0sH7j9xrES9gwfuP/Gw+t1cCu4/mlWI//AU7j9eRW8dzyvuP8JkYeVxUu4/zuR/BZWK7j/dzmsqadPuP4nixPijKO8/nXHQ5myB7z9GUDovN8/vP6Dn3ae4/O8/pXCP8l7t7z+Yo34S4n3vP278VcvFhu4/+VFjIJLh7D+6NLUXA3HqP1RxvtU3K+c/+Sh68DEj4z8H07anNhvdP623vwBMetM/oK/pWSNXxD98NTzeEVWfPyhpb/s+ErK/sNYXEBajwb8w23MdXGbFvzZshAcLJMS/vQKf/IHWu7948Rp+gHeYvyMdWkoniLc/aZ29kJR3zT8VAZo5Yk3YPz3J6NKF5eA/nXGux/w85T+eg74oWejoP8aBtg7ow+s/HmzGlhfM7T93afB7yhXvP9QTtVIPxO8/k4sI/rf+7z9CGcdTnuvvP4/9pYHbqu8/Rw450llV7z+bA62C6PzuP8pfXqkVre4/wzJDfE5s7j9eRVh+/zzuP2q3rtKWHu4/AqhG7GEO7j/wddypSAjuP2C45YxnB+4/sadxBHWx7j89t/CbsrDuPyrSaotmq+4/lsPlDxCd7j82QPVyGYHuP2T54WmlUu4/SW0ERHMM7j+byeicCKntP1FppNA8I+0/j3qPbhx37D+N5rhh76LrP/3eXZYQqOo/RXTjKEqL6T+G91KyjlToP9ZtPFYeDuc/i0v+0VPD5T+phghvaH/kP5HBI4tyTOM/EWXAicQy4j/8NOZQsTjhP9vvb7KjYuA/80EGr9Zm3z9fCkiYQ1neP8CNo8wgnt0/G/HuHw823T98m4lpPSHdP3jMPVuxX90/og7TMVbx3T+Gu9HA0NXeP+l/gDsNBuA/W4x+UvTI4D8QOA8udrHhP/pzklIBvOI/+c2O5D3j4z9ddN2d1x/lP/16DY2MaOY/rdUPO5qy5z84rEyCkfLoP8SIjil8Heo/zpG7Kh4q6z+NNUcWBhLsP//7Ng8j0uw/IJGdZ7hq7T/KOOnHu97tP9nohYjbMu4//4Po3oBs7j/2jshdFJHuP+lVBeOkpe4/wl7w4OWu7j8kOk6wXLHuP3RJ6leNse4/pOd7RQK07j/V+09mHb3uPxIP/IWx0O4/cNrLIGvx7j+EsbPjDyDvPxJZleWXWu8/8w7SBiGb7z9XH3nqwdbvP1h+xnpU/O8/A3nxtXTz7z/N/GqwKJzvP9xXhhnpz+4/m8atX/Bk7T+0fUeSlzTrP97xdufOJOg/12TSxEAz5D8fTbs4ev3eP4vL1X+LlNQ/x4pgyDjYwz+6jiahf0lhvw5olOh5nsG/W5Sv4a0Gz79Qv+OcwuTTvyR/Yb2BytW/VnQtTU8o1b8qwoxY3AHSv/bS8Sa53si/jvBgD+6/sr9oVDMBqBCzPy6P8A2Pdc4//w/6Qgfe2T+QefSG/+3hP9P0034uR+Y/ti7EHHbJ6T8VSLhrfWfsP578R/LZMO4/CFYhKj9H7z/MOtrf49PvP3R5AdaF/+8/NtCzjaXt7z/QeiL5d7rvP6MIpw2peu8/GYc9Gxs87z+bsC/TDAfvPyuViVtP3+4/WWqbl2TF7j/WSrf5eLfuP+CfYEI3su4/sadxBHWx7j8izWXHIUnvPw9ZqeKISO8/BsCmSFxE7z8RjpTY/TjvP1x7H56KIu8/OhY0bob87j+XueDFosHuPyXyp3Laa+4/H0nwuQb17T+6+LGh5lftP1lAYyldkew/y6rL3Yeh6z+gP6hbUIzqP6P3JPZDWek/7rM12roS6D8sYgXhjsTmP0qGhja7euU/L7myMTRA5D+L0/oXJh7jP7c/0T2kG+I/I+viDLY94T8ZjvEjoofgP8ifaeCt9t8/5bWdB60z3z/0REX6J8fePzSEALZwsd4/4I11p5Py3j/KO7CNZ4rfP5PNhPouPOA/HJtFJIvd4D9ivS3p3KfhP2KaGY6XmOI/VzRzAKCr4z/uO3Ax79rkPz8uu79hHuY/2ffiZ9Zr5z/iUdoutrfoP98qg37s9ek/NEFjmjMb6z9nB9WndB7sP9+JpCPj+ew/wWeKl4Kr7T9ZG7Ig7zTuP1hZyHJ/mu4/UE3bsQ3i7j8M2ZtnyhHvP/lCrQxqL+8/I9bn484/7z9Svlo8HkfvPyobUaYOSe8/lVWl5zRJ7z8Qykz4IkvvP0CwTQ9DUu8/fuLh5mxh7z+bcEY0PnrvPwYOFqhAnO8/wD0kLuTD7z+GiTpHUunvP3Ldjlwm/+8/Pl0BhC3x7z9+eN9ke6PvP0ussI5h8u4/VvoZCh207T+zxNqJNL3rP2z11oFB6Og/5guWOOIg5T810cMAu2/gP41vO0eCBtY/o5srDP6/xD8ewtYZymKTv2LJ2f6Wqce/8sUGHPW61L/5B7Zwu2zbv6lrWBUOtN+/sFpdb/TD4L+5casaE3bgv3Ywaifs3t2/peqKuzVf2L+SgLEYdojQv3OZ7Qx9x7q/l8PzQC4Qsj9dKpGh4D/QP1Kx0ZPhmNs/bhhdzJHj4j9hV1j/JCPnP4NoFl1NcOo/P63GuVXS7D8hP23LpGfuP+9RFEjcWe8/OHP/QVrU7z9GUdZTMv7vPwf8wHpG9+8/faCB8ZLX7z9jpUNIvq/vP7gd8VY8iu8/Uglirphs7z9erEY3s1jvP8HHsibaTe8/EAgBd7pJ7z8izWXHIUnvPz90kwVBve8/p7CLiN687z9F1nrIKrrvP5T0b9mxsu8/PGNob3+j7z/80GPToIjvPzXY8RjHXO8/0mBE908Z7z9CcHxk5LbuP7HcHjKpLu4/0eA3V8B77T9ERaPprJzsPyzlK2kolOs/ajjWhyNp6j+S+LAZ9CXpPyi0+xD01uc/k71Fn/aI5j91BTIf8UflP6orNcoRHuQ/Am8gdVMT4z/d0Xd5fC3iP2w6sVhmcOE/U4RToGne4D8nqTg30HjgPzNR1bA5QOA/hS91xOU04D/u+xEH31bgP4/nW48DpuA/x/kDIesh4T8zbWcdrMnhPzW5ehKDm+I/TqEHc2WU4z/4zLdXj6/kP1KI3HUl5uU/7mzLIg0v5z9ZAcRWH3/oP9GH/O3Syek/AOffWWEC6z+3JYyBQR3sPxzAM8GqEe0/tOmN/rna7T9BdAos33fuP+NXJrJt7O4/otfjXHM+7z+T+G+cNnXvP2K3Rt/Rl+8/K7cEmUes7z+nvk3EMbfvP5d2DXD0u+8/phnItDS97z+4/1ZVTb3vP/Mh5LGKvu8/8bPl4RLD7z94VVU3d8zvPyejiWP62u8/1z1fjJns7z+Hf0D04/vvPxhSHDes/u8/bwRv46fk7z8yWugDLpbvPwqqJRh48+4/5rTsoBDV7T87igj1Yg7sP4jBXQR3c+k/vgHRuXLi5T+7h07qJFDhP4XdSrU1qdc/AS0VQ5XJxj/gxblv86uVv2BeAWLXM8u/NGKW+z1r2L+Riois4JHgv9QHuCAbuuO/Qk6YftSt5b//Tv3vsn7mv2bTaoV8Oea/xCN2/47Z5L8A0/GDPU3iv9R/8Ua7Ed2/HhiY8Ag907/lUROn+2i+vwLI05tDFLQ/6bz5oAyl0T+7FByPxVjdP8bqKxHouOM/QbtVcxvL5z+MZO0W8t3qP8FFqH6dCe0/axyewHF27j+v3cAmqFHvP+WNmafExe8/Fb/uwo727z9QCRo7tP/vP/S316ML9e8/cD5aLKXj7z+Vy5E9JNPvP+fFa+Qax+8/IQsIBUfA7z9sX9pAo73vPz90kwVBve8/1XNUQeT67z818OkWx/rvP02h26vz+e8/gVFVbnP37z+BTI4yh/HvP0/7+AD35O8/rwEBe4fM7z9ktM4S6qDvPyRPFbxjWe8/GAW3mCvt7j8DXDp6OlXuP3R4axEMju0/hgQ008iY7D8bphlefXvrPzudAiZWQOo/fUj7Kyb06D909ndjq6TnP+MOaZb/XuY/d6HF730u5T/Wr4oSNBzkP6QbaTPOLuM/1Omw6Nhq4j/ts5y5L9PhP0PkGEF2aeE/itLOMIQu4T+nipIqtiLhP9KnrVgcRuE/AlJh3oOY4T+38QGkWhniP1rtogxvx+I/YI7uR5Cg4z9LROVwGaHkP+gOwxF6w+U/26HgGdn/5j8MvmCv+EvoP+PLFcSDm+k/0sdOxeLg6j94HUtLlg7sP++G/m/oGO0/FS81aJb37T8D61sG/KbuP1B6SZFeKO8/xgq9ZjWB7z8or8X3pLnvP7iQypmk2u8/c7PUP1rs7z8Ga9d0E/XvP9lEd13++O8/end80YD67z/KQAWf4PrvP53mYOLn+u8/A1t3IUT77z+9nrIIf/zvP6EE7gON/u8/ixhZof//7z/RuzkO7/zvP9prHc2s7e8/LjHrClPF7z+XRV1xUXDvP7f7E5060+4/Juf4JVHK7T8G6qSroirsP60mMAfGxek/tK2YrE1x5j/+i9lVSBLiP7TcaeK/Vtk//X2PXqmlyT8eMvVO/wuGv+tr3EcvZsy/v1yQhUag2r9ccduO0ojiv6kYzSVVg+a/8Lt+tI056b+AuqnKD8/qv776KZfuceu/eHXDpXA8679+wis7XSXqv1SApjpnBei/1Lny4IGv5L8YhJl0ohHgv/VFPxKnnNS/UfRrM5ENvr9feqEClIm4P2QwoN08QtM/BhTgaTj83j8fdJTyzmPkP67Luy2yO+g/rI0KjLYT6z+mjy59NRDtP29qzk/LXu4/pRLmZdMs7z+C9NIu7KHvPzJfm76v3e8/UeoAwX737z8iZm/JZ//vP7eULLWF/+8/q1VlRXf97z9Arjz4wPvvPzo4TRsB++8/1XNUQeT67z9pHSpKwe7vP9MrRdv57u8/gQVBpXjw7z9Yf6hMIPTvP+yqVru1+e8/7t41OvX+7z9w5F120v7vP4DkfUFJ8e8/8T26ZwnM7z93/neLBoTvP8GxfyegD+8/YlroTtBo7j86GbX7vI7tP3qm72M2huw/1qlmCApZ6z/TVUCCchTqPyQCxX4lx+g/VxBQAYN/5z/oXp+IP0rmP8KO+gGqMeU/uf8HUn895D8Wt/Y5I3PjP/M+D30Q1uI/5zhl+1ho4j+Bud8PGiviP+qdkYTUHuI/ArMxhZ9D4j+vjIT2MpniP8W1p5XIHuM/rrja0dXS4z8V5SQjpLLkP9XwBBbUueU/igDl8uDh5j93iPyvxiHoP+dX3u71bek/D/MxT8O46j8h3fMecPPrP+PxxlLFD+0/pTf5ZwMC7j+Sp3EQtcLuPytTWKDbT+8/596i1wmt7z/6ouSMUuLvP1fOk49V+u8/Angjav//7z+re/HLnvzvP7L4spvC9u8/r+ptpgHy7z/V7fE2fu/vP+aTWWTI7u8/PXR1Lrru7z8jL/oRAO7vPyTUvlom6+8//J87Sibk7z9JMfA2hNXvP5jK6iEYue8/LkN9RJuE7z9uNDgmGSjvP1A5HB91jO4/P2pqP1+S7T94GZ0SWRLsP6JJc6nA3uk/cSbWSRbJ5j9+zo28gaviP84MkHwI7do/weUgJKIBzT+1Qp3o9TGEP/Ad6mwyf8u/R9P88IV027+LdVEUXqzjv+7nTX3RUOi/4GcbloaJ6785B7Q/IH7tv/bEGS0mf+6/3yvy2wTc7r8Dd/knS77uv7S/C92YF+6/bGLwP8qn7L8AbXSXkRnqv/EVIAP8K+a/U9UONirY4L9ai1uyE8PUv91VTVK8Q7q/Q4z/AjfKvj8UgEXHtvDUP7YWmUuaM+A/8hxiD/bc5D9ZEQa543LoP98ffMT4Ees/jlvSfNrl7D+fS9Ybgh3uP9b6cmPt4+4/kgicvJ1c7z8oFRBPtKLvP+MJcYCOye8/ZMmY2Bfe7z+c8tXOVujvP4SrTvnx7O8/JokNWIju7z9pHSpKwe7vP11IIDtMh+8/8H6+eeuH7z/QUHEeMozvP/+nc2M7l+8/l1LhraOq7z8aQ5giW8XvP9viq/en4u8/B8IKE9D57z/SFnARxf7vP5hHYMjl4+8/OD7G14ac7z/2MeuWnh/vP32u1c3Rae4/OBZx+Ut+7T/bCa5/NmbsP2hZ32UVL+s/gV8ndZro6T+2o7tGiaLoP/atsgIba+c/a4Mylw9O5j8aWaDhYVTlP6Ha2L9zhOQ/1Ly4Kn7i4z9U8q0YGHHjPzbfg7ezMeM/C32IIf4k4z8shhofF0vjPyzxWn6co+M/EFl/O4gt5D+icIzp4+bkPxPuh2xYzOU/Hk+0tKjY5j9C+5hXMQToP1UFXCmUROk/AzegtsGM6j/DtYOdks3rP0F1JM8O9+w/vamVilP67T/MrDZDycvuP/QUa/MfZe8/paYkC3TG7z+e1njBKPbvP+UO0oxo/+8/Tz36h7Hv7z9Og/4cI9TvP6ugnQhJt+8/J99DG+Gf7z91TqM0uJDvP2p3zIpiie8/Ob/zMGCH7z+rhH9DOIfvPyFpX9Qwhe8/aODm5nR97z+r0N5comvvPyRYe7DUSe8/Ub3fak0P7z8Py8Vs367uPzke7g5KFe4/GJUimskn7T93t8WwVcPrPxRBDo9Vvek/536tGOvm5j9LpzM2IBTjP+Kgqa+iT9w/pfUUgS1H0D/6az4UYn6jP4TdfNMOzMi/BXQi5jYJ27+iArsYRBHkvyVKz2QXSOm/m/aIVXPf7L98S1wBE+ruv8VTajybyO+/6BXYfGr+778sujZr2v3vv4VKQuGx/++/9aHlI33w77+B3A+k1Xbvv/y73Xe7Ee6/ImWn7bRI67/ikMIRF97mv8HjZhzS7eC/rX2/BXzT078LDW+VVLKzvwzytRnuFcM/VY22ztmN1j8QQ8r5+8HgP6LnBPNoH+U/SaRgFxxv6D8b+35Oa9fqPwZ1IBLwhuw/LuQqzQWr7T/uRMxQtWruPw/dog3e5O4/0VnXflsw7z+q6ZssMV3vP1MuSA0gdu8/Qte28UmC7z8s7oeJrIbvP11IIDtMh+8/QdP99Ga37j9GkS36fLjuP3HqF1D7v+4/A9vCDpHT7j+z6znFA/fuP6+oJ3S5Ku8/CFY/63Vq7z9T3Dkq1azvP5m7iFDw4+8/Cr8/hE3/7z/pQNdOze7vPy3UrOTgpe8/jCMwKiYe7z+XHYj1rljuP4BhNGSsXe0/NSrLar867D8EmIjOjgDrP0pooDxXwOk/Mhu4b/2J6D/aiBFR4GrnP41faApmbeY/sAwHixaZ5T9nLV5aCfPkP3pkgqpyfuQ/QOSMryk95D/u/+aYEDDkPx3W64ZRV+Q/OuNZ+Wqy5D+NCoK/CkDlPx1UB3O6/eU/reylI2bn5j8wX2rWzvbnP9pPJV8HI+k/VkVAAylg6j+A866heJ/rPzJY6zoz0Ow/b0BW3R7h7T+2EHsYxsLuPyVy7+r5ae8/BIz7oPXR7z+6A4iIcP3vP+tGiJIk9u8/zyfwUMvK7z8bo8jQGIzvP1Aa1XGOSe8/oaxIY/sO7z+mHKjOL+PuP8jYOCP3x+4/mQmI1gy77j9/l2fKibfuP10MfB1Et+4/NlpUKbuz7j/s/CEJWqbuP+iXtlgJiO4/AJvRMSlQ7j+hkxoTHfPtP0ydvLybYO0/EMD9JwKC7D9DutWxCDnrP5Y+hYZ2X+k/9tO5XcfI5j9m10zIDUfjP2p9iorIaN0/jhKjk+MC0j/bmempRzqyPwRHyKN2o8S/cwbrQPGC2b+foPoZEMrjv1Nal7Vkiem/o9j81G197b9tDGfd0ojvvx2HOahN/e+/OmrirNd7779swcDwnbruv5/b9UUGS+6/tJ6Q3zRy7r9ycRSPrBjvv5pQYImvz++/rX0rphvt779H8V2FhL3uv8SbV5paweu/W0hvF/bc5r/QMn1j92Pgv/B2PJUI9tG//iJQJNcYpr8jeglyjQbHP6rjybwY/dc/elY7iIch4T+yRznY/yflP5+TyN2uLug/d5F36f5g6j/xf3sWB+3rP8A1VveF/Ow/eYjmfMqx7T/gWdnuKyfuP5ovBinub+4/ekBefsyZ7j/HeV2asa7uP2TqNGpQtu4/QdP99Ga37j8AnmstTHntPwUsNIXmeu0/G5ihw/qF7T/HqY6aGaPtP3buMdJ52O0/vKQ9WzYo7j9fufeDwY7uPxXioSooAu8/JAbW86Vy7z9IJQ3Mu8zvP4tHnuyA/O8/FfaWTGzx7z9nWdbjkaHvP2zutdNuC+8/IYH5tts17j/ResF+Wy7tP4viwuR0Buw/he+fnufQ6j/x0qxfYp/pPyyEbmELgeg/xDNtFN6B5z+3CJG0rqrmP8E7hPySAeY/TrfIWHSK5T8F3k9Ln0flPzL+OPgzOuU/1drQmWhi5T/J8BcXl7/lP+quQooUUOY/HsNPd9YQ5z8qrUU68PznP5EsssP9DOk/6cYei5826j9oWlJEPGzrP8jR/I1Inew/gzW2OlC37T/8PjPP26fuP30BZK4KX+8/lWHMpWTS7z81AgniH//vPyp85E8Q6+8/K3e1UsWj7z+VZZuB7jvvPwmy2i63x+4/6A2kyBpZ7j8qa2XaH/3tPwn7wOqBuu0/uUimKtKR7T+z5309r37tP6sDj5R/ee0/ewrnwxh57T81Oopk4nPtP+UTgms4YO0/Yl5DjQQ07T9XZHk1rOPsP/eF6qiGYOw/2UxnuRyX6z//yp0Rh23qP8QBtrJowug/oQ57Mkpt5j/RgnU3eEHjP5JnPbqjKd4/3VnMd4qX0z/zCrUWcVO7PwPpz029wb6/UW0swz0J179Qj/N+3+Xiv04xiCrbJ+m/mBVxnp6P7b/qGRTjhbvvv1rU+WX2y++/1ZQQN1Bc7r9PSo65DE7svyVE96i5geq/MLedbg6d6b+BM5JMaOvpvxGGoRBpU+u/SOh/xSZa7b81MTlW3jbvv7NEYw/n/++/gl9az0Hu7r8C5vzy4Z/rvweDk+DUN+a/GOp5qXKX3r9n2RxLGa3Ov6Ghq7iLTnC/zNegtWvwyj/JUVAUHinZPwbi3tJ9TeE/+8NjFQb15D8jnT/V2a/nP7AXzHCKquk/etLRKD8Q6z9wzhbbDQbsPzy7HWeIqew/Nq63/C8R7T83u4Eb3E3tP5oojRZ4bO0/RznNPbF37T8AnmstTHntP4wVzXUh0es/R1SgVUnT6z+SPImsM+LrPw8TZ+yICew/hWDTziZS7D/Ivj3YEsDsP+xL2iihUO0/HpWOinn57T8Aq/9kD6nuP/3XE0/SSO8/liZrKNvA7z9AOEJQS/zvP5p9zQU57e8/aHeFZBWP7z8V7ZRV+ubuP1sdFpUFAu4/CIy73nny7D8b1ykqlczrP0ref7rho+o/RKyVQWuJ6T8SpMXn5IroP7ol/SyQsuc/L6n+Jp0H5z/HlRdMwI7mP024JW/KSuY/HMho6iE95j8jSlK2CmbmP/b67zG1xOY/KMPOJBJX5z89aXOQcBnoP+xh2AXyBek/I3Xph+4T6j+TpgDvcTfrP5/OES4PYew/rMpcmFF+7T+YdtQsDHvuP+/DZ1ydQ+8/xnrW4O7H7z/8xe0rl/7vP3Yx5lA45+8/7QLLMTuL7z8jQIOLbvzuP2oFSz7BUe4/10OhDvai7T+rd9x/fATtP7x8bzNxhOw/zygcKksp7D+ZayhQLPLrP5uEEbtg2Os/Zyi0lWbR6z8fCUJT3NDrP8JNCrvayes/2wVpS3mv6z8HPUSmc3TrP61L6j0SCus/HFlBsJRe6j/zTuu4cFvpPwryOwvU4+c/yd8zXQnU5T8EvB79sALjP7VZMrM4it4/rU3Cbpvw1D/pwzZahD7CP94ILdAhvrK/UxbSSADH078GAM/CZ3Lhv0BuHNUBKui/kPiqcdUj7b8Kboa34bPvv5xfOlagrO+/xi62x3J77b+L4AvKNQnqv0LwpkaPbua/Jl9208qk4783a5ytp1Tiv03Kt4C0xuK/CcghY9Hi5L8Mpn2l2i/ov6NFL8gF2eu/jhBU09vM7r/BRtVg4//vv5i9Tsp/v+6/7fjaJarr6r+kPNLQ/ffkv8xnDpl5btu/vqe+SxNGyL8LFhvm/HujP84Ev4hqm84/aQKzN0UE2j9XAe/i8EPhP8wSIjtGhuQ/m0M7p3Dx5j+qG5skRLDoP6sNM42b6ek/NyM3xL296j+bnYLzMkbrPyvV7XPplus/lbO2oeW/6z94933t+M7rP4wVzXUh0es/5V+ni3zO6T8UjDVLNdHpP9/gZUMN5Ok/dbQCeNoV6j9FM7TrPHLqPwBM/QRK/+o/hpYVXmi76z8WHDWgEZzsP6MWxM4hju0/RhaUVRF47j+l1NQS8D3vPxvBgYBVxu8/QpdY2AP/7z+aOtht+d/vPw4twtUubO8/hfVBdw6w7j+kU1uAZb7tPxXza9varOw/W4TZaNWQ6z+VjkfAVX3qPy8OxRvdgek/oyTI5jGq6D8SXLbxs/7nPz7BR+TzhOc/fMpA91JA5z8JKll3hDLnP2Im9HPbW+c/6/CWuVq75z8BhQlRhU7oP0lImAv1EOk/EzxUQsb76T8Rlewx9gTrP7CVwIDkHuw/7hI1qDw47T/B4sxvlzzuP0QzHTIYFu8/n7U4Qxaw7z+6cmj8hfrvP4zdNWhm7e8/kUunpyyL7z8fhAqrN+HuP5+AUYncBe4/sm9yEWkU7T9f+6pmLijsP8Bmu4boV+s/J31jU5Sy6j+ypnlcLz7qP3mEdyNB+Ok/WrvRUaPX6T8OtErP087pP9tmKEUlzuk/4aa9sk3F6T+3zV2vDaTpP4ZTTnDmWek/U4z5bQXV6D9+BR10rwDoP3tHmRmRw+Y/3Czey37+5D+AVIgYaoziP4a1N+FRid4/HocTFwoC1j9MNG5RUaTGP5JNK4bqkJe/6/nZofDTz7+TDHvbEv7evwVbpXT8j+a/MX1qeyIu7L/eGUzupG7vv3+S7GwKyO+/tKAy1LdT7b8SBQnEmNXov4nQKzezfuO/f61gxsMX3b/W9NijcuzVv7T8waFjqNK/ZOMJjzvC079hGvFKVhPZv4PSkGQC5+C/ojaNd5Qw5r9sWZEgR0Trv6Ynmint3u6/POnk3ej677+VFcPH7iXuv+7bmNhfnem/GAZs17Yl47+QJqnWyXXXv1N3VWe6EsG/LLXmaESutD/+QktYR/DQP25zU7eyiNo/IKlqy8sF4T9Gcmcslt3jPyWyZyE39OU/m/l0wQZx5z9XyswCPnboP1pMVi8CIOk/dbdE0yeF6T/waDjJvrjpP4NAvRXDy+k/5V+ni3zO6T8P9LVXQIznP3RDq4mGj+c/M9XatTSm5z8Y4ZuEPuLnPwHFYZIBUug/PCDXBLL96D/ibU+E8OTpPxi1y59G/eo/5AR+xUEy7D8TPBsEpGftP9Ajf4GZfe4/aDBxIy1W7z9sTyototrvP7dgTXE8/+8/iwwFioDE7z+2ikwc2TXvP7A1KuNiZu4/KZV5Vght7T/vjmsx+2DsP51v2dwtV+s/UQ6to/Vg6j9dWfInqIvpP0wZFhzg4Og/RvwEMxRn6D/f92VhPSLoP3KYsx5gFOg/WCIHbN896D/OVa71jZ3oP/FyJjJ7MOk/3jd+D4Px6T+sknH6sdjqPyHFLlif2us/+Aev8/Xn7D8Q+sCrd+3tP9A9uHLU1O4/3K+HFJuG7z9ISj0ATO3vP6kPcMoq+e8/RpXSe/Cj7z/wjC/LNPPuPxSF6wuI+O0/ZD9GRt3O7D8QBzgPz5XrP7fuALIHbOo/UC3/Q0hq6T90mHR5KKDoP+dkr8X4Eug/zoEES4u+5z+AE+4jQ5fnP7TbDFGpjOc/gI5aW9eL5z+FglsFNYHnP71xg55BWec/cbZryl4A5z/sEsT0x2HmP8g5ppgLZuU/bz9KIIDx4z95priZW+PhP3nVm72zLN4/5WRuQW/I1j9870zoUbbKPwHuIAhT9Jw/zMS+IlJBx7990tHe+T3avxcJgPOaW+S/I5DEJN+V6r8YX9sXT7/uv37eOguU+e+/NmS+TQzx7b8XEn5tKBTpv7GyzAeEfuK/TJWeYVU117+x6E8+i7/GvwJC+O2Itqm/dTyGPRzAeT+9rqMR3uuJv4Sz5dVXDbu/aRqaOPbq0L9GgyvR2f/dv2RFDYs06uW/mUI9e6XO678WrZX9fVvvv6uwrZLEw++/+dxLItL87L9laJ3k/qnnvxeLc+0tzeC/tER/3vTa0r+8in6E2NCyvy0mLQogX78/KWjRKaVV0j+wIjqrWrjaPxQHQ7oll+A/j1L+XOD/4j9cSwfszLzkP25PWb8r8eU/q4v09Cu75j/De7pNLzTnP4JqIGsccuc/RVI2ZfmI5z8P9LVXQIznP83846iPLuU/9W4OFlky5T9m9mLjlkzlPxuxTBckkuU/kbpQUPUT5j9bnH+/UdzmP2X/J8kr7Oc/zBEnHVk56T+sDe1kcK7qP2bVHYLeLOw/d+q2G0CR7T9Dm0naR7nuPxl9QGzFie8/QfUTdyfz7z/GtWTfSfPvP1+nuhBYlO8/6PWxVoHp7j/uC8RkwQruP6nK7CLwEO0/AX0u/dkS7D+Mwy25miPrP61bd7QQUuo/7pITYA+p6T/Zk22R8y/pP01YH9dO6+g/7aKYknfd6D+lwnbi4AbpP3u96dQsZuk/Lox5avb36T+ufG8OW7bqPzJOqg5YmOs/PTdn+SKR7D8pnMFlu4/tPyyiBucLf+4/cM4zl/lG7z8H2oVLqs7vP0TfG+L//+8/z7OZw8zL7z9j/hrzvy3vP6EZYEu9Lu4/9oOTNI/k7D9kdZRspm7rP2uYqimk8Ok/KpBfZy6M6D9VOPsfrFvnP2N15Q4Eb+Y/bq9NIa/K5T9yXf7QxGjlP/wEcydMO+U/TwhQEAkv5T+pHFs+Fi7lP4stcl/KIeU/S48KEaHz5D/b4E55HY3kP1lMl5zV1uM/GqtkVga34j/qNeghPRDhP2GxaQK3gd0/hOKyawdJ1z8ANhJzZGPOP/jCjNiiObQ/yC35UhxcvL9Bbn+CKdPUv/FwqTa7luG/m+8NJEhF6L+ny6Bakmbtv04RVeIp5O+/PF0Uotr87r/JRixyA6Lqv9DbSWljneO/xgxdd5K01r/MOoj96zu7v38baX45JLg/rJ5lWzgCzT8qApevgyjSP/xcmj+v8NA/QkMg8+a3xT9jPCifMn5aPz1oSXo6Ac2/zTdATMMT37/p8o9YumDnv+yMJXUrNu2/k/5bsRLj77+TjfXuHwnvv1y5TXDFGeu/fr2T460N5b+coiLLdwLcvyjYtG0ZnMu/xIMS74f9iL9a068OXsXEPyqAjfjhfNM/zzACOCyd2j8uUaGtu//fP12Y3/KC9uE/A04z29xV4z+e+vmnez3kPwH236TXyOQ/lwtjYFcQ5T8mPR51xSrlP83846iPLuU/gp4YKhnf4j9IN2m3VePiP9p1kZqzAOM/RUdmFp1O4z/2YzGrV+DjP8ZxlwcWwuQ/QPxMHB725T99I5jHwXLnP5OxauD6Iek/NmVGk1bj6j+0EqRiXZDsP9Z6CPfTAu4/Fanh+F0b7z/5nT8/ssbvP1u9rj36/+8/g8JDzPvP7z8rJ6XrwknvP6w2ReAihu4/P7Pua2Sf7T9dbv5SAa7sP1OyOhu5xus/ZC2addn56j/vknL4XFPqP9aGKhl72+k/iKoKEFeX6T+YS4KslYnpP9svfYC5suk/CDnfAjYR6j//AA7YNqHqPyIvCvcjXOs/U9dC5/k37D+UFcWpoSbtPxhY7IuNFe4/et0WjPjt7j8D8Lo9LpbvP+/gBcMj9O8/gtRdcFXx7z90BUw9VH/vP5YHesjem+4/RlRBMxxT7T9cifpG3r7rP9pKae+4Auo/HUVuid5F6D/hKaVjdazmP1/rQEwgUuU/Sss/1c5G5D9jsGONCY7jP0hOozdBIOM/VPglMlnt4j+y9zn7oN/iPxaz0VWR3uI/EM13PtDQ4j94SjukNZ3iP+7JOCXHKuI/xbPzHN1f4T8UL+qB2iDgP+ezKD8tntw/loyzj62R1z9eIZYwD9fQP/MOiebvZsA//pwr5+aWo79su0YPgeLNv0p3fwPJq9y/VpQOnCc25b+6PbAzAyzrv9eVoWOeE++/0bygoxfb77/lHdgVLOvsv/n4KHrfgea/CCWaB3iC27+93tNl+FPBv/2LEOpKm8A/MtGYleNF1T/zjPve7HLdPyYsvnoZcOA/3Lw6hJG93z81vq2Z8ffZP0l2gAsgu84/D57SHXIZbD/7Sn5wNgnSv+4tk2hFU+K/eulWt1gY6r+qyQb529vuv/nqOWr86O+/7sz8LUh07b8b0SB3+V/ov30tA9ut0uG/h43UJGq21b/tu63hLvvAvzWs9OSiAKk/QqJ4rT+KyT8RN3SjQm7UP+rbXhltSdo/YHGR7bmY3j+bEFgbtdDgP5c8DaoL0uE/VGXVp2tt4j9eDOAhTb3iP9wB/9Pb2uI/gp4YKhnf4j/5LbesfMjgP8ggHO4XzeA/Fw0zQgjt4D9qNviF1UHhPzIihl224OE/YIUlSIfX4j8Dz3LqySnkP39cza5mzuU/4aQg5Qiv5z8jdTyt2KnpP5bXOibgles//lh81o5J7T8BJDWq36HuP0uiQLgsiO8/FgpUtRf17z81tkMc/u/vP1lvJSOji+8/aT8sa3fh7j+f+H7E8gzuP1ZT6br8J+0/bjokI8FI7D/xk3s50IDrPwfCstgp3eo/mhJcIMhm6j++QN6FUCPqP9pyDCiuFeo/eDqwdnE+6j+WBhAc5JvqP/C8uATOKes/Ls1COPXg6z9zLwo5crbsP8Y6rr0Im+0/Co4LDNB67j850tmPjz3vP3J06/46yO8/PdvCP9b/7z8IcyS9qc3vP5rGP8saJO8/HUnDpOQC7j+r4ymHLnnsP1K/wLRgpOo/JHqsWKGr6D9COvlxGbnmPyguwLzk8uQ/f+34zHt14z/A+j1kolDiPyGoRSXwhuE//SbANF0P4T92JPGJ+9fgP2FU5FgQyeA/7dVn/ejH4D+UG3T19LjgP3R5tXXhgOA/L3UgYa0E4D93U3jX1FHeP8zabb85ods/bHrMzNC41z8NHNnI/FXSP4hdqElecMY/0M1j1CB5oT8MxZ+r4pbBv8Jor96xbdW/nRP8md534b/5Iht/PvPnvwJu2hTAIu2/cRhfCnDW77+yTDETWQrvv9yKENIoV+q/BgFLielF4r9UFSIIGIvQv3aVK8AWB60/0PD9dtjL1D9Ew9yIaZ/gP2/0yGXbXeQ/LyXnXMPi5T9ysfwu2mLlP2hrE7PJyuI/RUGJwHmn2z/OlYIG3XfJP/bA6C0VA7m/NlG2XPTM2r9zsZqcyafmv8UqJKUsK+2/B3ABvsrr778by5rno97uv2IqoLBIx+q/Vd2OZP3M5L9P44hxWSXcv1Vvhic54c2/qkihk1+RqL9/LFxQVsu7P5eMT24wBM4/vPTaoOc61T+9tIAaG9jZP/Qxrvx2HN0/ENfdnwFJ3z9Mte6Q/EzgP34BuLS/o+A/3rVgo+DD4D/5LbesfMjgP2EqEYSFI94/Ld1PjUst3j8Gbed/EnHeP2orSqQiJd8/5PbXJHQ74D/SEiVpYELhP8K60E3hq+I/OU6FZnRv5D/ei9Y6rnbmP1ZW79jDnug/pS3hHOO86j+BgTGi4qTsP7WLXiHNMO4/ICvbiDdH7z9IyqQ0nt7vP7sG9PYc/e8/naFF1x+17z/z+C5KgCDvPzK9+d6bW+4/sv7//niB7T92K0n4ZansP382wM725es/HPXtPP9E6z8rzuAEGNDqP6W+KRtRjeo/CkF7PM9/6j9WmEw4LqjqP2TytnKVBOs/v65eNHuQ6z8Dq+1OIUTsP01A/N3mE+0/6IdRfqLv7T+15KA3VcLuP8/K80mgcu8/akIxTm3k7z+GQzH5D/zvP6/Q8mnAou8/zh+cpa3L7j9tHnsKS3jtP6UQ1zpBuus/hu/PneOx6T9NdbNVLInnP7xGz3GIbOU/XLYUAn2D4z8uBmEFA+vhPw8KUPKXsuA/x4gfLvu33z9GIn6s87neP/zyXIBmRN4/r8vk074k3j/Afg0uTCLeP/vALQ2TAt4/C8EuuqaL3T/NoekNe4TcP2QP3zebs9o/KmDKnTTd1z8e7XOY6sHTP8DaBED+QMw/aNcmpyn0uj8AMdBQrHekvzAu/AVnccu/LMcCL+Jb2r+m3aojW8bjv3eqwaZ22+m/L9UcI4dV7r/Ib+/Qpv/vv+Kc5sHv5+2/Ff0HsbvU579t/jrGBh/dv0cqEYUBGL6/XXxxWf5syj+UfSfdHQzeP9MLBAhT1uQ/zHdsLAAs6D/hGDcpYXrpP2iE4cdMDek/xq3/h5/J5j+XkHlNEkPiP1c6juc0PtY/DgLYQVBsqT/MFEFkiHTSv6PR2aaugeO/3YBfHIBX67/lN5lHOHXvvyBzNUSqlu+/j1942Glf7L/k8frtT/Hmv34cqe/ee+C/loX9Lyjm07+EjDXM+K6/vziXUhUMqKI/SlFf2GNUxT8cLtmF+iXRPx4BtGFB/NU/TryTPvls2T/JC7IE67jbP3DMpdynHd0/AsfmA5fV3T/+wAHwvRneP2EqEYSFI94/76isZW+12z+m3ZwYlb/bP8WpWqj1Bdw/ktBBEAHB3D8GlQiKKiDeP+BEJOvRIeA/q5PahwGb4T+i3kD8mHPjP55QLoEyleU/yqqV2HDc5z94zkHTSB3qP05bOoDLKew/sA/aKfbZ7T9G1LmdZBLvP2gtb50DyO8/HkhDxvz/7z/jSlexb8zvP+LtmH5zR+8/1gi6CP+N7j8CqAid3bvtP5fxKown6ew/vwFr8CYp7D8iGL/ORIrrP1qx7xmKFus/yswsSVLU6j/ICR8E68bqP/43cy/47uo/G5M81YhK6z92sLhN2dTrP4yKMcjNhew/U4rxeUJR7T9VsMNUZybuP7o6EQd77+4/gFMXYVOS7z/K/l1QJfLvP/ju9czS8u8/v8h9QpZ97z8qkdh7PIbuP47yeIV9D+0/mJSKZ88s6z+ZlW+mlADpP/HeuZS7tuY/7BKBsjZ95D/JpxoucXziP9MWaH2m0eA/GF94XyAY3z/VFt6zn1ndPwghxOSmUdw/2gqbhJLX2z8vEAqvtLbbP9JdNRYqtNs/jZ64CjuT2z+Rp+tszBfbP4SOwDbQBto/tfCQ2iQl2D9WOAqu2jXVP5mWfYZk+dA/7JComK5fxj+HZv0nXQitP+I3EA0pSLe/O60Lce4R0b96m6SLN6fdv0h3EaprRuW/rE6sDV4J67+szgwA6/nuv+4R2oNm4u+/WVTNGznh7L9aGxv/P+Dlv4FxWJpcr9e/6NAyf7hIk7+JZ1MH76fTP15mytBBAOI/XR+KgUlt5z9q1K1ZaWnqP6ba5fnZius/HWEMZDEt6z8nvT84BDDpP23uozI5DeU/C1ohLjaA3D+h4bmmw0zDP9J0aaYI+Mi/P5whWFAj4b8XbejTm9fpv2KXMvNo5u6/2JdAHrLg77+GhonW2k/tv4XCGGOvTui/tM5FzXET4r+tL6xmZj7Xv31/nFAaesa/GIpmpu12i78UMzxWoo++P2QfoG4wlMw/QW/VoFBF0z/1HKIYHtPWPyJbitfPM9k//HQ/bqyl2j+HvMP7iGTbP3cTcS1Iq9s/76isZW+12z+x9qgWEnPaP6eHKHFnfdo/o8lLqBPF2j/jpqZCmoPbPzjcMJJz6dw/8VL1mMAX3z/CiSRY8AzhP8Odw/og8OI/7Gb1XBYf5T/sLoK9L3bnPxs77IPHyOk/eN7xUQno6z/zPg2H5KrtP5Zr5kDu9O4/C0QaxkC67z8N/MuKg//vP0YMq9Gp1u8/eZ8lp+ZZ7z/jsFZVfabuP44dBjad2O0/ZmqkIcYI7T9h8TH8n0rsP0mfG/HgrOs/D56tD8w56z//K7v74/fqP9KVqJyL6uo/siAlCmsS6z9mUPqnhG3rP4RXD7v29us/FJXZ53Sm7D9tCX6ol2/tP2rDcu44Qe4/+/9thjMF7z85+Qhe9aDvP0vQ2TxY9+8/Ix5oswbs7z/oP7jRQGjvPz9OdvUpYO4/eIPfmibX7D+zcbcxnuHqP8djDbwDo+g/7F3wsURI5j+w+cl+FgDkPxgMYkZT8+E/DBZjFlE/4D+Mf9sHQebdPwSDBqUaH9w/BAwd1CsS2z/6o0mx1ZXaP+zjFVdddNo/yRUw0MZx2j99+2FnPVDaP/sOAx6Q0tk/1VPvgrK82D8rqxoustLWPz2AASQc19M/Jcy4GL8Uzz/bQiLZulrDP3TRFgomUKA/K3hLs7Pqvb/4qnmffcLSvzh5R5ECTN+/Ri8LCJcC5r8fHfWeAJjrvz+dtNmpPu+/Jq/HemXB77+tIS9CLUjsv5LIPco40OS/6mlfj1fU1L+sg0jfyTWgPz2oG3ds3dY/Qy2E26Zw4z+jVc8v4qLoP1kyxJEqbOs/PTYtXYl07D9/m9obdB/sP2At4oPeSeo/lEQElKpg5j+Syf4AppLfP1zeUAuj1ck/vXeC7v/Hwr/zHoy3wb/fv73IghZvAem/5kAWlqqK7r8/PsaTsfXvv3JAClw8ve2/d7ySLmb36L9QuLokN93iv095td5K7Ni/6gfOe8/Wyb8i2qBRHuKjv29gaFZiXLg/hkw+aU2jyT9Yo/mB597RPyGLSFUIe9U/+s0cmQDm1z92LrYyYl7ZP07FdjmxINo/l11XOrto2j+x9qgWEnPaP7H2qBYSc9o/p4cocWd92j+jyUuoE8XaP+OmpkKag9s/ONwwknPp3D/xUvWYwBffP8KJJFjwDOE/w53D+iDw4j/sZvVcFh/lP+wugr0vduc/Gzvsg8fI6T943vFRCejrP/M+DYfkqu0/lmvmQO707j8LRBrGQLrvPw38y4qD/+8/Rgyr0anW7z95nyWn5lnvP+OwVlV9pu4/jh0GNp3Y7T9maqQhxgjtP2HxMfyfSuw/SZ8b8eCs6z8Pnq0PzDnrP/8ru/vj9+o/0pWonIvq6j+yICUKaxLrP2ZQ+qeEbes/hFcPu/b26z8UldnndKbsP20JfqiXb+0/asNy7jhB7j/7/22GMwXvPzn5CF71oO8/S9DZPFj37z8jHmizBuzvP+g/uNFAaO8/P0529Slg7j94g9+aJtfsP7NxtzGe4eo/x2MNvAOj6D/sXfCxREjmP7D5yX4WAOQ/GAxiRlPz4T8MFmMWUT/gP4x/2wdB5t0/BIMGpRof3D8EDB3UKxLbP/qjSbHVldo/7OMVV1102j/JFTDQxnHaP337YWc9UNo/+w4DHpDS2T/VU++CsrzYPyurGi6y0tY/PYABJBzX0z8lzLgYvxTPP9tCItm6WsM/dNEWCiZQoD8reEuzs+q9v/iqeZ99wtK/OHlHkQJM379GLwsIlwLmvx8d9Z4AmOu/P5202ak+778mr8d6ZcHvv60hL0ItSOy/ksg9yjjQ5L/qaV+PV9TUv6yDSN/JNaA/Pagbd2zd1j9DLYTbpnDjP6NVzy/ioug/WTLEkSps6z89Ni1diXTsP3+b2ht0H+w/YC3ig95J6j+URASUqmDmP5LJ/gCmkt8/XN5QC6PVyT+9d4Lu/8fCv/MejLfBv9+/vciCFm8B6b/mQBaWqoruvz8+xpOx9e+/ckAKXDy97b93vJIuZvfov1C4uiQ33eK/T3m13krs2L/qB857z9bJvyLaoFEe4qO/b2BoVmJcuD+GTD5pTaPJP1ij+YHn3tE/IYtIVQh71T/6zRyZAObXP3YutjJiXtk/TsV2ObEg2j+XXVc6u2jaP7H2qBYSc9o/76isZW+12z+m3ZwYlb/bP8WpWqj1Bdw/ktBBEAHB3D8GlQiKKiDeP+BEJOvRIeA/q5PahwGb4T+i3kD8mHPjP55QLoEyleU/yqqV2HDc5z94zkHTSB3qP05bOoDLKew/sA/aKfbZ7T9G1LmdZBLvP2gtb50DyO8/HkhDxvz/7z/jSlexb8zvP+LtmH5zR+8/1gi6CP+N7j8CqAid3bvtP5fxKown6ew/vwFr8CYp7D8iGL/ORIrrP1qx7xmKFus/yswsSVLU6j/ICR8E68bqP/43cy/47uo/G5M81YhK6z92sLhN2dTrP4yKMcjNhew/U4rxeUJR7T9VsMNUZybuP7o6EQd77+4/gFMXYVOS7z/K/l1QJfLvP/ju9czS8u8/v8h9QpZ97z8qkdh7PIbuP47yeIV9D+0/mJSKZ88s6z+ZlW+mlADpP/HeuZS7tuY/7BKBsjZ95D/JpxoucXziP9MWaH2m0eA/GF94XyAY3z/VFt6zn1ndPwghxOSmUdw/2gqbhJLX2z8vEAqvtLbbP9JdNRYqtNs/jZ64CjuT2z+Rp+tszBfbP4SOwDbQBto/tfCQ2iQl2D9WOAqu2jXVP5mWfYZk+dA/7JComK5fxj+HZv0nXQitP+I3EA0pSLe/O60Lce4R0b96m6SLN6fdv0h3EaprRuW/rE6sDV4J67+szgwA6/nuv+4R2oNm4u+/WVTNGznh7L9aGxv/P+Dlv4FxWJpcr9e/6NAyf7hIk7+JZ1MH76fTP15mytBBAOI/XR+KgUlt5z9q1K1ZaWnqP6ba5fnZius/HWEMZDEt6z8nvT84BDDpP23uozI5DeU/C1ohLjaA3D+h4bmmw0zDP9J0aaYI+Mi/P5whWFAj4b8XbejTm9fpv2KXMvNo5u6/2JdAHrLg77+GhonW2k/tv4XCGGOvTui/tM5FzXET4r+tL6xmZj7Xv31/nFAaesa/GIpmpu12i78UMzxWoo++P2QfoG4wlMw/QW/VoFBF0z/1HKIYHtPWPyJbitfPM9k//HQ/bqyl2j+HvMP7iGTbP3cTcS1Iq9s/76isZW+12z9oKhGEhSPePzvdT41LLd4/DW3nfxJx3j9xK0qkIiXfP+f21yR0O+A/1hIlaWBC4T/FutBN4aviPz9OhWZ0b+Q/4IvWOq525j9ZVu/Yw57oP6ct4RzjvOo/g4ExouKk7D+2i14hzTDuPyEr24g3R+8/SMqkNJ7e7z+7BvT2HP3vP5yhRdcfte8/8vguSoAg7z8xvfnem1vuP7H+//54ge0/dStJ+GWp7D99NsDO9uXrPxv17Tz/ROs/Ks7gBBjQ6j+lvikbUY3qPwhBezzPf+o/VZhMOC6o6j9j8rZylQTrP76uXjR7kOs/AqvtTiFE7D9MQPzd5hPtP+iHUX6i7+0/teSgN1XC7j/PyvNJoHLvP2pCMU5t5O8/h0Mx+Q/87z+v0PJpwKLvP88fnKWty+4/bx57Ckt47T+nENc6QbrrP4nvz53jsek/UHWzVSyJ5z+/Rs9xiGzlP1+2FAJ9g+M/MQZhBQPr4T8SClDyl7LgP86IHy77t98/TSJ+rPO53j8K81yAZkTeP73L5NO+JN4/x34NLkwi3j8JwS0NkwLePxnBLrqmi90/1KHpDXuE3D9rD983m7PaPzFgyp003dc/Ju1zmOrB0z/Q2gRA/kDMP6jXJqcp9Lo/ADHQUKx3pL8gLvwFZ3HLvyzHAi/iW9q/oN2qI1vG479uqsGmdtvpvy/VHCOHVe6/yW/v0Kb/77/onObB7+ftvxX9B7G71Oe/bf46xgYf3b8FKxGFARi+v118cVn+bMo/hn0n3R0M3j/NCwQIU9bkP7d3bCwALOg/2Bg3KWF66T9VhOHHTA3pP8at/4efyeY/hJB5TRJD4j9XOo7nND7WPw8A2EFQbKk/zBRBZIh00r+q0dmmroHjv+WAXxyAV+u/5TeZRzh1778dczVEqpbvv4tfeNhpX+y/3vH67U/x5r93HKnv3nvgv4eF/S8o5tO/RIw1zPiuv7+4l1IVDKiiP2pRX9hjVMU/JC7Zhfol0T8uAbRhQfzVP1W8kz75bNk/1wuyBOu42z93zKXcpx3dPxDH5gOX1d0/BcEB8L0Z3j9oKhGEhSPeP/ktt6x8yOA/yCAc7hfN4D8XDTNCCO3gP2o2+IXVQeE/MiKGXbbg4T9ghSVIh9fiPwPPcurJKeQ/f1zNrmbO5T/hpCDlCK/nPyN1PK3Yqek/ltc6JuCV6z/+WHzWjkntPwEkNarfoe4/S6JAuCyI7z8WClS1F/XvPzW2Qxz+7+8/WW8lI6OL7z9pPyxrd+HuP5/4fsTyDO4/VlPpuvwn7T9uOiQjwUjsP/GTeznQgOs/B8Ky2Cnd6j+aElwgyGbqP75A3oVQI+o/2nIMKK4V6j94OrB2cT7qP5YGEBzkm+o/8Ly4BM4p6z8uzUI49eDrP3MvCjlytuw/xjquvQib7T8KjgsM0HruPznS2Y+PPe8/cnTr/jrI7z8928I/1v/vPwhzJL2pze8/msY/yxok7z8dScOk5ALuP6vjKYcueew/Ur/AtGCk6j8keqxYoavoP0I6+XEZueY/KC7AvOTy5D9/7fjMe3XjP8D6PWSiUOI/IahFJfCG4T/9JsA0XQ/hP3Yk8Yn71+A/YVTkWBDJ4D/t1Wf96MfgP5QbdPX0uOA/dHm1deGA4D8vdSBhrQTgP3dTeNfUUd4/zNptvzmh2z9seszM0LjXPw0c2cj8VdI/iF2oSV5wxj/QzWPUIHmhPwzFn6vilsG/wmiv3rFt1b+dE/yZ3nfhv/kiG38+8+e/Am7aFMAi7b9xGF8KcNbvv7JMMRNZCu+/3IoQ0ihX6r8GAUuJ6UXiv1QVIggYi9C/dpUrwBYHrT/Q8P122MvUP0TD3Ihpn+A/b/TIZdtd5D8vJedcw+LlP3Kx/C7aYuU/aGsTs8nK4j9FQYnAeafbP86Vggbdd8k/9sDoLRUDub82UbZc9Mzav3OxmpzJp+a/xSokpSwr7b8HcAG+yuvvvxvLmuej3u6/YiqgsEjH6r9V3Y5k/czkv0/jiHFZJdy/VW+GJznhzb+qSKGTX5Gov38sXFBWy7s/l4xPbjAEzj+89Nqg5zrVP720gBob2Nk/9DGu/HYc3T8Q192fAUnfP0y17pD8TOA/fgG4tL+j4D/etWCj4MPgP/ktt6x8yOA/hp4YKhnf4j9LN2m3VePiP911kZqzAOM/SEdmFp1O4z/8YzGrV+DjP8lxlwcWwuQ/RvxMHB725T+AI5jHwXLnP5axauD6Iek/OGVGk1bj6j+2EqRiXZDsP9d6CPfTAu4/Fqnh+F0b7z/6nT8/ssbvP1u9rj36/+8/gsJDzPvP7z8qJ6XrwknvP6s2ReAihu4/P7Pua2Sf7T9cbv5SAa7sP1KyOhu5xus/Yy2addn56j/tknL4XFPqP9WGKhl72+k/h6oKEFeX6T+XS4KslYnpP9ovfYC5suk/BznfAjYR6j/+AA7YNqHqPyEvCvcjXOs/UtdC5/k37D+TFcWpoSbtPxZY7IuNFe4/ed0WjPjt7j8C8Lo9LpbvP+/gBcMj9O8/gtRdcFXx7z90BUw9VH/vP5cHesjem+4/R1RBMxxT7T9eifpG3r7rP9xKae+4Auo/IEVuid5F6D/kKaVjdazmP2XrQEwgUuU/UMs/1c5G5D9msGONCY7jP0tOozdBIOM/WPglMlnt4j+19zn7oN/iPxmz0VWR3uI/Fs13PtDQ4j97SjukNZ3iP/HJOCXHKuI/zLPzHN1f4T8XL+qB2iDgP/WzKD8tntw/nYyzj62R1z9tIZYwD9fQPxMPiebvZsA/fpwr5+aWo79Nu0YPgeLNvzt3fwPJq9y/UJQOnCc25b+2PbAzAyzrv9aVoWOeE++/0rygoxfb77/sHdgVLOvsv/74KHrfgea/FiWaB3iC27/83tNl+FPBv56LEOpKm8A/MtGYleNF1T/ljPve7HLdPwosvnoZcOA/wLw6hJG93z8Yvq2Z8ffZPwt2gAsgu84/D5bSHXIZbD8KS35wNgnSv/stk2hFU+K/g+lWt1gY6r+uyQb529vuv/jqOWr86O+/7sz8LUh07b8W0SB3+V/ov3AtA9ut0uG/eI3UJGq21b/eu63hLvvAv7Ws9OSiAKk/YaJ4rT+KyT8YN3SjQm7UP/LbXhltSdo/Z3GR7bmY3j+fEFgbtdDgP5o8DaoL0uE/WGXVp2tt4j9kDOAhTb3iP98B/9Pb2uI/hp4YKhnf4j/N/OOojy7lP/VuDhZZMuU/ZvZi45ZM5T8bsUwXJJLlP5G6UFD1E+Y/W5x/v1Hc5j9l/yfJK+znP8wRJx1ZOek/rA3tZHCu6j9m1R2C3izsP3fqthtAke0/Q5tJ2ke57j8ZfUBsxYnvP0H1E3cn8+8/xrVk30nz7z9fp7oQWJTvP+j1sVaB6e4/7gvEZMEK7j+pyuwi8BDtPwF9Lv3ZEuw/jMMtuZoj6z+tW3e0EFLqP+6SE2APqek/2ZNtkfMv6T9NWB/XTuvoP+2imJJ33eg/pcJ24uAG6T97venULGbpPy6MeWr29+k/rnxvDlu26j8yTqoOWJjrPz03Z/kikew/KZzBZbuP7T8sogbnC3/uP3DOM5f5Ru8/B9qFS6rO7z9E3xvi///vP8+zmcPMy+8/Y/4a878t7z+hGWBLvS7uP/aDkzSP5Ow/ZHWUbKZu6z9rmKoppPDpPyqQX2cujOg/VTj7H6xb5z9jdeUOBG/mP26vTSGvyuU/cl3+0MRo5T/8BHMnTDvlP08IUBAJL+U/qRxbPhYu5T+LLXJfyiHlP0uPChGh8+Q/2+BOeR2N5D9ZTJec1dbjPxqrZFYGt+I/6jXoIT0Q4T9hsWkCt4HdP4TismsHSdc/ADYSc2Rjzj/4wozYojm0P8gt+VIcXLy/QW5/ginT1L/xcKk2u5bhv5vvDSRIRei/p8ugWpJm7b9OEVXiKeTvvzxdFKLa/O6/yUYscgOi6r/Q20lpY53jv8YMXXeStNa/zDqI/es7u79/G2l+OSS4P6yeZVs4As0/KgKXr4Mo0j/8XJo/r/DQP0JDIPPmt8U/YzwonzJ+Wj89aEl6OgHNv803QEzDE9+/6fKPWLpg57/sjCV1Kzbtv5P+W7ES4++/k4317h8J779cuU1wxRnrv369k+OtDeW/nKIiy3cC3L8o2LRtGZzLv8SDEu+H/Yi/WtOvDl7FxD8qgI344XzTP88wAjgsndo/LlGhrbv/3z9dmN/ygvbhPwNOM9vcVeM/nvr5p3s95D8B9t+k18jkP5cLY2BXEOU/Jj0edcUq5T/N/OOojy7lPxf0tVdAjOc/fEOriYaP5z851dq1NKbnPyDhm4Q+4uc/BsVhkgFS6D9BINcEsv3oP+dtT4Tw5Ok/HLXLn0b96j/nBH7FQTLsPxU8GwSkZ+0/0iN/gZl97j9qMHEjLVbvP2xPKi2i2u8/t2BNcTz/7z+LDAWKgMTvP7WKTBzZNe8/rjUq42Jm7j8olXlWCG3tP+2OazH7YOw/m2/Z3C1X6z9PDq2j9WDqP1xZ8ieoi+k/ShkWHODg6D9E/AQzFGfoP933ZWE9Iug/cJizHmAU6D9WIgds3z3oP8xVrvWNneg/73ImMnsw6T/cN34Pg/HpP6mScfqx2Oo/H8UuWJ/a6z/3B6/z9efsPw76wKt37e0/zj24ctTU7j/br4cUm4bvP0dKPQBM7e8/qQ9wyir57z9HldJ78KPvP/KML8s08+4/FoXrC4j47T9oP0ZG3c7sPxQHOA/Ples/u+4Asgds6j9VLf9DSGrpP3mYdHkooOg/72SvxfgS6D/WgQRLi77nP4gT7iNDl+c/vNsMUamM5z+Fjlpb14vnP46CWwU1gec/w3GDnkFZ5z93tmvKXgDnP/USxPTHYeY/0TmmmAtm5T95P0oggPHjP4OmuJlb4+E/jtWbvbMs3j/7ZG5Bb8jWP6vvTOhRtso/ge8gCFP0nD+cxL4iUkHHv2DS0d75Pdq/DgmA85pb5L8akMQk35XqvxRf2xdPv+6/f946C5T5778+ZL5NDPHtvyYSfm0oFOm/xbLMB4R+4r95lZ5hVTXXvw/pTz6Lv8a/AUT47Yi2qb91MIY9HMB5P720oxHe64m/Q7Tl1VcNu7+mGpo49urQv3GDK9HZ/92/dUUNizTq5b+kQj17pc7rvxutlf19W++/qbCtksTD77/y3Esi0vzsv1poneT+qee/CYtz7S3N4L+dRH/e9NrSv1yKfoTY0LK/jCYtCiBfvz9AaNEppVXSP74iOqtauNo/HgdDuiWX4D+ZUv5c4P/iP2ZLB+zMvOQ/dE9Zvyvx5T+xi/T0K7vmP8x7uk0vNOc/h2ogaxxy5z9LUjZl+YjnPxf0tVdAjOc/5V+ni3zO6T8UjDVLNdHpP9/gZUMN5Ok/dbQCeNoV6j9FM7TrPHLqPwBM/QRK/+o/hpYVXmi76z8WHDWgEZzsP6MWxM4hju0/RhaUVRF47j+l1NQS8D3vPxvBgYBVxu8/QpdY2AP/7z+aOtht+d/vPw4twtUubO8/hfVBdw6w7j+kU1uAZb7tPxXza9varOw/W4TZaNWQ6z+VjkfAVX3qPy8OxRvdgek/oyTI5jGq6D8SXLbxs/7nPz7BR+TzhOc/fMpA91JA5z8JKll3hDLnP2Im9HPbW+c/6/CWuVq75z8BhQlRhU7oP0lImAv1EOk/EzxUQsb76T8Rlewx9gTrP7CVwIDkHuw/7hI1qDw47T/B4sxvlzzuP0QzHTIYFu8/n7U4Qxaw7z+6cmj8hfrvP4zdNWhm7e8/kUunpyyL7z8fhAqrN+HuP5+AUYncBe4/sm9yEWkU7T9f+6pmLijsP8Bmu4boV+s/J31jU5Sy6j+ypnlcLz7qP3mEdyNB+Ok/WrvRUaPX6T8OtErP087pP9tmKEUlzuk/4aa9sk3F6T+3zV2vDaTpP4ZTTnDmWek/U4z5bQXV6D9+BR10rwDoP3tHmRmRw+Y/3Czey37+5D+AVIgYaoziP4a1N+FRid4/HocTFwoC1j9MNG5RUaTGP5JNK4bqkJe/6/nZofDTz7+TDHvbEv7evwVbpXT8j+a/MX1qeyIu7L/eGUzupG7vv3+S7GwKyO+/tKAy1LdT7b8SBQnEmNXov4nQKzezfuO/f61gxsMX3b/W9NijcuzVv7T8waFjqNK/ZOMJjzvC079hGvFKVhPZv4PSkGQC5+C/ojaNd5Qw5r9sWZEgR0Trv6Ynmint3u6/POnk3ej677+VFcPH7iXuv+7bmNhfnem/GAZs17Yl47+QJqnWyXXXv1N3VWe6EsG/LLXmaESutD/+QktYR/DQP25zU7eyiNo/IKlqy8sF4T9Gcmcslt3jPyWyZyE39OU/m/l0wQZx5z9XyswCPnboP1pMVi8CIOk/dbdE0yeF6T/waDjJvrjpP4NAvRXDy+k/5V+ni3zO6T+QFc11IdHrP0tUoFVJ0+s/ljyJrDPi6z8TE2fsiAnsP4lg084mUuw/y7492BLA7D/wS9oooVDtPyGVjop5+e0/Aqv/ZA+p7j/+1xNP0kjvP5cmayjbwO8/QDhCUEv87z+afc0FOe3vP2d3hWQVj+8/E+2UVfrm7j9aHRaVBQLuPwaMu9558uw/GdcpKpXM6z9I3n+64aPqP0OslUFriek/EKTF5+SK6D+4Jf0skLLnPyyp/iadB+c/xZUXTMCO5j9LuCVvykrmPxrIaOohPeY/IUpStgpm5j/0+u8xtcTmPybDziQSV+c/O2lzkHAZ6D/qYdgF8gXpPyF16YfuE+o/kaYA73E36z+dzhEuD2HsP6vKXJhRfu0/lnbULAx77j/uw2dcnUPvP8V61uDux+8//MXtK5f+7z92MeZQOOfvP+4CyzE7i+8/JUCDi2787j9tBUs+wVHuP9pDoQ72ou0/rnfcf3wE7T+/fG8zcYTsP9MoHCpLKew/nWsoUCzy6z+fhBG7YNjrP2sotJVm0es/IwlCU9zQ6z/GTQq72snrP98FaUt5r+s/Cz1EpnN06z+xS+o9EgrrPyBZQbCUXuo/+E7ruHBb6T8S8jsL1OPnP8/fM10J1OU/Drwe/bAC4z/DWTKzOIreP8RNwm6b8NQ/GMQ2WoQ+wj9/CC3QIb6yvzUW0kgAx9O//P/Owmdy4b82bhzVASrov4n4qnHVI+2/CG6Gt+Gz77+fXzpWoKzvv8wutsdye+2/meALyjUJ6r9T8KZGj27mvzNfdtPKpOO/S2ucradU4r9ayreAtMbivxvIIWPR4uS/G6Z9pdov6L+vRS/IBdnrv5UQVNPbzO6/wUbVYOP/77+SvU7Kf7/uv+T42iWq6+q/mDzS0P335L+vZw6ZeW7bv3+nvksTRsi/yhYb5vx7oz/9BL+IapvOP38CszdFBNo/YQHv4vBD4T/SEiI7RobkP6NDO6dw8eY/shubJESw6D+wDTONm+npPzsjN8S9veo/n52C8zJG6z8v1e1z6ZbrP5uztqHlv+s/fPd97fjO6z+QFc11IdHrPwCeay1Mee0/BSw0heZ67T8bmKHD+oXtP8epjpoZo+0/du4x0nnY7T+8pD1bNijuP1+594PBju4/FeKhKigC7z8kBtbzpXLvP0glDcy7zO8/i0ee7ID87z8V9pZMbPHvP2dZ1uORoe8/bO61024L7z8hgfm22zXuP9F6wX5bLu0/i+LC5HQG7D+F75+e59DqP/HSrF9in+k/LIRuYQuB6D/EM20U3oHnP7cIkbSuquY/wTuE/JIB5j9Ot8hYdIrlPwXeT0ufR+U/Mv44+DM65T/V2tCZaGLlP8nwFxeXv+U/6q5CihRQ5j8ew0931hDnPyqtRTrw/Oc/kSyyw/0M6T/pxh6LnzbqP2haUkQ8bOs/yNH8jUid7D+DNbY6ULftP/w+M8/bp+4/fQFkrgpf7z+VYcylZNLvPzUCCeIf/+8/KnzkTxDr7z8rd7VSxaPvP5Vlm4HuO+8/CbLaLrfH7j/oDaTIGlnuPyprZdof/e0/CfvA6oG67T+5SKYq0pHtP7PnfT2vfu0/qwOPlH957T97CufDGHntPzU6imTic+0/5ROCazhg7T9iXkONBDTtP1dkeTWs4+w/94XqqIZg7D/ZTGe5HJfrP//KnRGHbeo/xAG2smjC6D+hDnsySm3mP9GCdTd4QeM/kmc9uqMp3j/dWcx3ipfTP/MKtRZxU7s/A+nPTb3Bvr9RbSzDPQnXv1CP837f5eK/TjGIKtsn6b+YFXGeno/tv+oZFOOFu++/WtT5ZfbL77/VlBA3UFzuv09KjrkMTuy/JUT3qLmB6r8wt51uDp3pv4Ezkkxo6+m/EYahEGlT679I6H/FJlrtvzUxOVbeNu+/s0RjD+f/77+CX1rPQe7uvwLm/PLhn+u/B4OT4NQ35r8Y6nmpcpfev2fZHEsZrc6/oaGruItOcL/M16C1a/DKP8lRUBQeKdk/BuLe0n1N4T/7w2MVBvXkPyOdP9XZr+c/sBfMcIqq6T960tEoPxDrP3DOFtsNBuw/PLsdZ4ip7D82rrf8LxHtPze7gRvcTe0/miiNFnhs7T9HOc09sXftPwCeay1Mee0/Q9P99Ga37j9IkS36fLjuP3PqF1D7v+4/BNvCDpHT7j+16znFA/fuP7CoJ3S5Ku8/CVY/63Vq7z9U3Dkq1azvP5m7iFDw4+8/Cr8/hE3/7z/pQNdOze7vPyzUrOTgpe8/iyMwKiYe7z+WHYj1rljuP39hNGSsXe0/NCrLar867D8DmIjOjgDrP0hooDxXwOk/MRu4b/2J6D/YiBFR4GrnP4pfaApmbeY/rgwHixaZ5T9lLV5aCfPkP3hkgqpyfuQ/P+SMryk95D/t/+aYEDDkPxvW64ZRV+Q/OONZ+Wqy5D+MCoK/CkDlPxxUB3O6/eU/rOylI2bn5j8vX2rWzvbnP9hPJV8HI+k/VEVAAylg6j9+866heJ/rPzFY6zoz0Ow/bUBW3R7h7T+1EHsYxsLuPyRy7+r5ae8/BIz7oPXR7z+6A4iIcP3vP+tGiJIk9u8/0CfwUMvK7z8co8jQGIzvP1Ia1XGOSe8/oqxIY/sO7z+nHKjOL+PuP8rYOCP3x+4/mwmI1gy77j+Bl2fKibfuP14MfB1Et+4/OFpUKbuz7j/t/CEJWqbuP+mXtlgJiO4/AZvRMSlQ7j+ikxoTHfPtP06dvLybYO0/EsD9JwKC7D9HutWxCDnrP5o+hYZ2X+k/+dO5XcfI5j9s10zIDUfjP3h9iorIaN0/lhKjk+MC0j87mumpRzqyP+RGyKN2o8S/bAbrQPGC2b+ToPoZEMrjv0lal7Vkiem/oNj81G197b9rDGfd0ojvvx2HOahN/e+/PGrirNd7779xwcDwnbruv6bb9UUGS+6/tp6Q3zRy7r94cRSPrBjvv5xQYImvz++/q30rphvt779F8V2FhL3uv7ybV5paweu/UEhvF/bc5r/GMn1j92Pgv9F2PJUI9tG/viJQJNcYpr9SeglyjQbHP8HjybwY/dc/hFY7iIch4T+7RznY/yflP6STyN2uLug/fJF36f5g6j/1f3sWB+3rP8M1VveF/Ow/fIjmfMqx7T/iWdnuKyfuP5wvBinub+4/e0BefsyZ7j/JeV2asa7uP2XqNGpQtu4/Q9P99Ga37j9dSCA7TIfvP/B+vnnrh+8/0FBxHjKM7z//p3NjO5fvP5dS4a2jqu8/GkOYIlvF7z/b4qv3p+LvPwfCChPQ+e8/0hZwEcX+7z+YR2DI5ePvPzg+xteGnO8/9jHrlp4f7z99rtXN0WnuPzgWcflLfu0/2wmufzZm7D9oWd9lFS/rP4FfJ3Wa6Ok/tqO7Romi6D/2rbICG2vnP2uDMpcPTuY/Glmg4WFU5T+h2ti/c4TkP9S8uCp+4uM/VPKtGBhx4z8234O3szHjPwt9iCH+JOM/LIYaHxdL4z8s8Vp+nKPjPxBZfzuILeQ/onCM6ePm5D8T7odsWMzlPx5PtLSo2OY/QvuYVzEE6D9VBVwplETpPwM3oLbBjOo/w7WDnZLN6z9BdSTPDvfsP72plYpT+u0/zKw2Q8nL7j/0FGvzH2XvP6WmJAt0xu8/ntZ4wSj27z/lDtKMaP/vP089+oex7+8/ToP+HCPU7z+roJ0ISbfvPyffQxvhn+8/dU6jNLiQ7z9qd8yKYonvPzm/8zBgh+8/q4R/QziH7z8haV/UMIXvP2jg5uZ0fe8/q9DeXKJr7z8kWHuw1EnvP1G932pND+8/D8vFbN+u7j85Hu4OShXuPxiVIprJJ+0/d7fFsFXD6z8UQQ6PVb3pP+d+rRjr5uY/S6czNiAU4z/ioKmvok/cP6X1FIEtR9A/+ms+FGJ+oz+E3XzTDszIvwV0IuY2Cdu/ogK7GEQR5L8lSs9kF0jpv5v2iFVz3+y/fEtcARPq7r/FU2o8m8jvv+gV2Hxq/u+/LLo2a9r977+FSkLhsf/vv/Wh5SN98O+/gdwPpNV277/8u913uxHuvyJlp+20SOu/4pDCERfe5r/B42Yc0u3gv619vwV809O/Cw1vlVSys78M8rUZ7hXDP1WNts7ZjdY/EEPK+fvB4D+i5wTzaB/lP0mkYBccb+g/G/t+TmvX6j8GdSAS8IbsPy7kKs0Fq+0/7kTMULVq7j8P3aIN3uTuP9FZ135bMO8/qumbLDFd7z9TLkgNIHbvP0LXtvFJgu8/LO6HiayG7z9dSCA7TIfvP2kdKkrB7u8/0ytF2/nu7z+BBUGlePDvP1h/qEwg9O8/7KpWu7X57z/v3jU69f7vP3DkXXbS/u8/gOR9QUnx7z/wPbpnCczvP3b+d4sGhO8/wLF/J6AP7z9hWuhO0GjuPzgZtfu8ju0/eKbvYzaG7D/UqWYIClnrP9FVQIJyFOo/IgLFfiXH6D9VEFABg3/nP+Zen4g/SuY/wI76Aaox5T+2/wdSfz3kPxS39jkjc+M/8j4PfRDW4j/lOGX7WGjiP3+53w8aK+I/6J2RhNQe4j8AszGFn0PiP62MhPYymeI/w7Wnlcge4z+suNrR1dLjPxPlJCOksuQ/0/AEFtS55T+IAOXy4OHmP3WI/K/GIeg/5Vfe7vVt6T8N8zFPw7jqPx/d8x5w8+s/4fHGUsUP7T+jN/lnAwLuP5GncRC1wu4/KlNYoNtP7z/m3qLXCa3vP/mi5IxS4u8/V86Tj1X67z8CeCNq///vP6t78cue/O8/s/iym8L27z+v6m2mAfLvP9Xt8TZ+7+8/55NZZMju7z89dHUuuu7vPyMv+hEA7u8/JdS+Wibr7z/9nztKJuTvP0ox8DaE1e8/mMrqIRi57z8vQ31Em4TvP280OCYZKO8/UTkcH3WM7j9Bamo/X5LtP3wZnRJZEuw/p0lzqcDe6T96JtZJFsnmP4TOjbyBq+I/5AyQfAjt2j/w5SAkogHNP7VFnej1MYQ/sh3qbDJ/y78q0/zwhXTbv391URRerOO/5OdNfdFQ6L/YZxuWhonrvzMHtD8gfu2/7sQZLSZ/7r/bK/LbBNzuv/52+SdLvu6/rL8L3ZgX7r9lYvA/yqfsv/dsdJeRGeq/5hUgA/wr5r9G1Q42Ktjgv0OLW7ITw9S/fVVNUrxDur+ijP8CN8q+PyqARce28NQ/vRaZS5oz4D/4HGIP9tzkP18RBrnjcug/5B98xPgR6z+SW9J82uXsP6FL1huCHe4/2PpyY+3j7j+TCJy8nVzvPykVEE+0ou8/5AlxgI7J7z9lyZjYF97vP5zy1c5W6O8/hatO+fHs7z8miQ1YiO7vP2kdKkrB7u8/1XNUQeT67z818OkWx/rvP02h26vz+e8/gVFVbnP37z+BTI4yh/HvP0/7+AD35O8/rwEBe4fM7z9ktM4S6qDvPyRPFbxjWe8/GAW3mCvt7j8DXDp6OlXuP3R4axEMju0/hgQ008iY7D8bphlefXvrPzudAiZWQOo/fUj7Kyb06D909ndjq6TnP+MOaZb/XuY/d6HF730u5T/Wr4oSNBzkP6QbaTPOLuM/1Omw6Nhq4j/ts5y5L9PhP0PkGEF2aeE/itLOMIQu4T+nipIqtiLhP9KnrVgcRuE/AlJh3oOY4T+38QGkWhniP1rtogxvx+I/YI7uR5Cg4z9LROVwGaHkP+gOwxF6w+U/26HgGdn/5j8MvmCv+EvoP+PLFcSDm+k/0sdOxeLg6j94HUtLlg7sP++G/m/oGO0/FS81aJb37T8D61sG/KbuP1B6SZFeKO8/xgq9ZjWB7z8or8X3pLnvP7iQypmk2u8/c7PUP1rs7z8Ga9d0E/XvP9lEd13++O8/end80YD67z/KQAWf4PrvP53mYOLn+u8/A1t3IUT77z+9nrIIf/zvP6EE7gON/u8/ixhZof//7z/RuzkO7/zvP9prHc2s7e8/LjHrClPF7z+XRV1xUXDvP7f7E5060+4/Juf4JVHK7T8G6qSroirsP60mMAfGxek/tK2YrE1x5j/+i9lVSBLiP7TcaeK/Vtk//X2PXqmlyT8eMvVO/wuGv+tr3EcvZsy/v1yQhUag2r9ccduO0ojiv6kYzSVVg+a/8Lt+tI056b+AuqnKD8/qv776KZfuceu/eHXDpXA8679+wis7XSXqv1SApjpnBei/1Lny4IGv5L8YhJl0ohHgv/VFPxKnnNS/UfRrM5ENvr9feqEClIm4P2QwoN08QtM/BhTgaTj83j8fdJTyzmPkP67Luy2yO+g/rI0KjLYT6z+mjy59NRDtP29qzk/LXu4/pRLmZdMs7z+C9NIu7KHvPzJfm76v3e8/UeoAwX737z8iZm/JZ//vP7eULLWF/+8/q1VlRXf97z9Arjz4wPvvPzo4TRsB++8/1XNUQeT67z8/dJMFQb3vP6awi4jevO8/RdZ6yCq67z+T9G/ZsbLvPztjaG9/o+8/+9Bj06CI7z802PEYx1zvP9FgRPdPGe8/QXB8ZOS27j+w3B4yqS7uP8/gN1fAe+0/Q0Wj6ayc7D8q5StpKJTrP2g41ocjaeo/kPiwGfQl6T8mtPsQ9NbnP5G9RZ/2iOY/cwUyH/FH5T+oKzXKER7kPwBvIHVTE+M/29F3eXwt4j9qOrFYZnDhP1GEU6Bp3uA/Jak4N9B44D8zUdWwOUDgP4IvdcTlNOA/7PsRB99W4D+O51uPA6bgP8T5AyHrIeE/MW1nHazJ4T8yuXoSg5viP0yhB3NllOM/9cy3V4+v5D9QiNx1JeblP+xsyyINL+c/VwHEVh9/6D/Ph/zt0snpP/7m31lhAus/tSWMgUEd7D8bwDPBqhHtP7Hpjf652u0/P3QKLN937j/iVyaybezuP6HX41xzPu8/kvhvnDZ17z9ht0bf0ZfvPyq3BJlHrO8/p75NxDG37z+Wdg1w9LvvP6UZyLQ0ve8/t/9WVU297z/yIeSxir7vP/Cz5eESw+8/eFVVN3fM7z8no4lj+trvP9Y9X4yZ7O8/h39A9OP77z8YUhw3rP7vP28Eb+On5O8/M1roAy6W7z8LqiUYePPuP+i07KAQ1e0/QYoI9WIO7D+KwV0Ed3PpP8MB0bly4uU/wodO6iRQ4T+b3Uq1NanXPxEtFUOVycY/4MS5b/Orlb8iXgFi1zPLvyVilvs9a9i/hoqIrOCR4L/HB7ggG7rjvzlOmH7UreW/807977J+5r9b02qFfDnmv74jdv+O2eS/9tLxgz1N4r/Gf/FGuxHdv/8XmPAIPdO/pVETp/tovr9iyNObQxS0PwC9+aAMpdE/yRQcj8VY3T/M6isR6LjjP0a7VXMby+c/kGTtFvLd6j/FRah+nQntP20cnsBxdu4/sN3AJqhR7z/mjZmnxMXvPxW/7sKO9u8/UAkaO7T/7z/0t9ejC/XvP28+Wiyl4+8/lcuRPSTT7z/mxWvkGsfvPyELCAVHwO8/bF/aQKO97z8/dJMFQb3vPyLNZcchSe8/D1mp4ohI7z8GwKZIXETvPxGOlNj9OO8/XHsfnooi7z86FjRuhvzuP5e54MWiwe4/JfKnctpr7j8fSfC5BvXtP7r4saHmV+0/WUBjKV2R7D/Lqsvdh6HrP6A/qFtQjOo/o/ck9kNZ6T/uszXauhLoPyxiBeGOxOY/SoaGNrt65T8vubIxNEDkP4vT+hcmHuM/tz/RPaQb4j8j6+IMtj3hPxmO8SOih+A/yJ9p4K323z/ltZ0HrTPfP/RERfonx94/NIQAtnCx3j/gjXWnk/LeP8o7sI1nit8/k82E+i484D8cm0Uki93gP2K9Lencp+E/YpoZjpeY4j9XNHMAoKvjP+47cDHv2uQ/Py67v2Ee5j/Z9+Jn1mvnP+JR2i62t+g/3yqDfuz16T80QWOaMxvrP2cH1ad0Huw/34mkI+P57D/BZ4qXgqvtP1kbsiDvNO4/WFnIcn+a7j9QTduxDeLuPwzZm2fKEe8/+UKtDGov7z8j1ufjzj/vP1K+WjweR+8/KhtRpg5J7z+VVaXnNEnvPxDKTPgiS+8/QLBND0NS7z9+4uHmbGHvP5twRjQ+eu8/Bg4WqECc7z/APSQu5MPvP4aJOkdS6e8/ct2OXCb/7z8+XQGELfHvP35432R7o+8/S6ywjmHy7j9W+hkKHbTtP7PE2ok0ves/bPXWgUHo6D/mC5Y44iDlPzXRwwC7b+A/jW87R4IG1j+jmysM/r/EPx7C1hnKYpO/YsnZ/papx7/yxQYc9brUv/kHtnC7bNu/qWtYFQ6037+wWl1v9MPgv7lxqxoTduC/djBqJ+ze3b+l6oq7NV/Yv5KAsRh2iNC/c5ntDH3Hur+Xw/NALhCyP10qkaHgP9A/UrHRk+GY2z9uGF3MkePiP2FXWP8kI+c/g2gWXU1w6j8/rca5VdLsPyE/bcukZ+4/71EUSNxZ7z84c/9BWtTvP0ZR1lMy/u8/B/zAekb37z99oIHxktfvP2OlQ0i+r+8/uB3xVjyK7z9SCWKumGzvP16sRjezWO8/wceyJtpN7z8QCAF3uknvPyLNZcchSe8/r6dxBHWx7j86t/CbsrDuPyfSaotmq+4/k8PlDxCd7j8zQPVyGYHuP2D54WmlUu4/Rm0ERHMM7j+YyeicCKntP05ppNA8I+0/i3qPbhx37D+J5rhh76LrP/reXZYQqOo/QHTjKEqL6T+D91KyjlToP9JtPFYeDuc/hkv+0VPD5T+lhghvaH/kP43BI4tyTOM/DmXAicQy4j/5NOZQsTjhP9nvb7KjYuA/7EEGr9Zm3z9YCkiYQ1neP7mNo8wgnt0/FfHuHw823T92m4lpPSHdP3LMPVuxX90/mw7TMVbx3T9/u9HA0NXeP+V/gDsNBuA/V4x+UvTI4D8MOA8udrHhP/ZzklIBvOI/9c2O5D3j4z9ZdN2d1x/lP/l6DY2MaOY/qdUPO5qy5z8zrEyCkfLoP7+Ijil8Heo/y5G7Kh4q6z+JNUcWBhLsP/v7Ng8j0uw/HZGdZ7hq7T/HOOnHu97tP9bohYjbMu4//IPo3oBs7j/zjshdFJHuP+ZVBeOkpe4/v17w4OWu7j8hOk6wXLHuP3JJ6leNse4/oed7RQK07j/T+09mHb3uPw8P/IWx0O4/bdrLIGvx7j+CsbPjDyDvPxBZleWXWu8/8Q7SBiGb7z9WH3nqwdbvP1h+xnpU/O8/BHnxtXTz7z/P/GqwKJzvP99Xhhnpz+4/ocatX/Bk7T+8fUeSlzTrP+jxdufOJOg/42TSxEAz5D9CTbs4ev3eP7jL1X+LlNQ/B4tgyDjYwz+6diahf0lhv59nlOh5nsG/7pOv4a0Gz78qv+OcwuTTv+9+Yb2BytW/MXQtTU8o1b/1wYxY3AHSv6jS8Sa53si/z+9gD+6/sr/oVDMBqBCzP4uP8A2Pdc4/KxD6Qgfe2T+gefSG/+3hP970034uR+Y/wi7EHHbJ6T8cSLhrfWfsP6P8R/LZMO4/C1YhKj9H7z/NOtrf49PvP3R5AdaF/+8/NdCzjaXt7z/OeiL5d7rvP6IIpw2peu8/F4c9Gxs87z+ZsC/TDAfvPymViVtP3+4/Vmqbl2TF7j/TSrf5eLfuP92fYEI3su4/r6dxBHWx7j9fuOWMZwfuP/FE/k2GBu4/IeFwy2QA7j98lD6x2u/tPy+JClrQz+0/a6uQ8yab7T9t1Nmhs0ztP8x3enFz3+w/x7PglQ1P7D/oO6immJjrP/5LjO9yu+o/DOhC2+e56T8/2d7cYpnoP5k7gMUSYuc/UUX5uQ8e5j+mQLA1PNjkP/cUVWAnm+M/YkY/BCtw4j++UJMH5F7hP2qkuV0HbeA/n11q4wE93z8OIBBoguvdP3c2+DU96Nw/eV3jdWw03D+uQuqEe9DbP1OjOG1+vNs/C758xXX42z93NTtdXITcP7EDg0/+X90/0b5ozpqK3j+8dKmOKgHgP6dKn7O/4eA/RbyRK+rj4T+WdDX64ALjP63BNQj7N+Q/pRcEFLJ65T8B5Dev+sDmP0WlJ/f6/+c/oFN3MxAt6T8/5ye59T7qP+9+sgLKLus/Icb4J6/46z/OIh8G35vsP4Vr8L84Gu0/CdshyHh37T9mcnMuYrjtP+cIqg8X4u0/2Mt/ab757T8rHyi7cATuP0uY/ltLB+4/cKxEvYMH7j/wsPrdXAruP5lViP/wFO4/XUVvHc8r7j/BZGHlcVLuP83kfwWViu4/3M5rKmnT7j+I4sT4oyjvP5xx0OZsge8/RVA6LzfP7z+g592nuPzvP6Zwj/Je7e8/maN+EuJ97z9w/FXLxYbuP/pRYyCS4ew/vDS1FwNx6j9Xcb7VNyvnPwApevAxI+M/FdO2pzYb3T+0t78ATHrTP7Cv6VkjV8Q//DY83hFVnz/IaG/7PhKyv6HWFxAWo8G/AdtzHVxmxb8WbIQHCyTEv34Cn/yB1ru/+PAafoB3mL9jHVpKJ4i3P4idvZCUd80/JAGaOWJN2D9HyejSheXgP6Nxrsf8POU/oIO+KFno6D/KgbYO6MPrPyBsxpYXzO0/eGnwe8oV7z/VE7VSD8TvP5OLCP63/u8/QhnHU57r7z+O/aWB26rvP0YOOdJZVe8/mgOtguj87j/JX16pFa3uP8EyQ3xObO4/XUVYfv887j9pt67Slh7uPwGoRuxhDu4/7nXcqUgI7j9fuOWMZwfuP/is12mlWe0/O9bA261Y7T8sUsMA8lHtP7TA0/LQP+0/wSzEctcc7T8zTKvavOPsP+wWQeRvj+w/4ypK7lEb7D8SiVrysIPrP2ELTexzxuo/x+3Abc7j6T/T52Cjvt7oP8UUjcsgvec/aOfM9EKH5j9En1iODUflP2ePz731BuQ/JsQervjQ4j+K/CGw0q3hP/lfebSLpOA/ucVohrF03z81SmZxgOXdP9HH42/On9w/1yyDqN+l2z+UU78RqvjaP8iZN2NwmNo/xmKyfzKF2j8vWGIs7b7aP+6v2uOmRds/4vh4cEkZ3D/mvzZnSTndP9v0qVUfpN4/ma0JAk4r4D8a3qY4lSXhP+5LwFWPPOI/+8ZWyB5q4z/EcbiTWKbkP0q2ce7L5+U/kb386SEk5z9blPUnCVHoP2MD3ptGZek/OTP+8K9Z6j/9vxNn0inrP0n5XS0f1Os/5SvRjp1Z7D+Ik0djSr3sP3BqohFhA+0/R3jLyMQw7T9gt6NdpkrtPzIu0JhjVu0/146VbYZZ7T8/Uq1lxFntP5egdQjmXO0/gR2MN4to7T/8Kdx+zIHtPy4H7PS9rO0/y5KLsNbr7T+T/Qw5Oz/uP7jsvg7mo+4/2BHhAKwS7z+dlofxJn/vP7HFzBKt1u8/MmVZ2ab/7z8zc224ztnvPxHN7+sYQO8/NPTQvQYN7j/erz3PwyHsP30kAO9qb+k/2i9teEwA5j+3QAL+tP3hPxLLCbxTXds/LKHMbn3c0j9/J57B/m/GP4SGBvwCYbQ/xoLy8g9Hjz+92MEL6MmKv4HePbFgUW2/zZ6DFUAZpj/5YfhVRLq/PwIC4UCnxs0/2crjKB4N1z+yZo9ItbTfP0JJqPSeDeQ/V6JQTqDN5z/1bS6HhOHqPwU6oAnXL+0/IDGrpeq77j+IR86k1p3vP1JNJKbS+O8/ofBP6Nry7z/uhV2x967vPwKtJyU1Su8/mp2rVana7j8e02J9vm/uP+Hq5A8aE+4/No962bLJ7T99OOca3JTtP2ifTlQvc+0/HclkJVJh7T87PuXcnFrtP/is12mlWe0/FnvOER207D9SU4fNFbPsP5pHQ07tq+w/20HlTa6Y7D9zc3sDp3PsPzfn1ex1N+w/jwuK3iPf6z8qtednZGbrPy683KgIyuo/UuA5zpcI6j8hUC3R5SLpP7yz/yVzHOg/NFikoGj75j/AUWhaIMjlP4NooaFRjOQ/z6x24xFS4z/q9qIW5CLiP3VIpqwDB+E/XUE1fQAF4D83L0dKU0PeP385De5qwNw/CUy7yxyF2z8FicpfXJPaPwfpcdLr69k/cfwvZu+O2T9yDRoQWHzZP/x7nsUftNk/AoRYnVU22j/NZ4l8+QLbP9vEsj6oGdw/inBjrRt53T8OFXWohh7fP6mKhDFvguA/6l1SmBaS4T8FJc3Ei7jiP5kM+cZh7uM/C02QpKcq5T8Iw4EDdmPmP+LGokLGjuc/VJDoHHGj6D/Th797H5rpPwfXxfH0beo/zTiiyc0c6z/8EkAcDafrP7GCm+UaD+w/6RmOJsZY7D8X4jDYu4jsPxZBdnYtpOw/zs2Lcaaw7D9g+rcd/LPsP30yjgU+tOw/i6HpvJK37D+y0p56+MPsPweBzdbp3uw/66ZjbOoM7T+W5mgH/VDtP+4ZJF8ArO0/QfBG++sb7j8f/yTH6JruP0KtyvFLHu8/QaDn6JKV7z8dFkz6punvPxB4t7zj/O8/6GxC+42s7z/atLcOd9TuP5bnzL48Ve0/viIvgb4c6z+1FJapCC/oP9V/+C+krOQ/KmdMy+/S4D/xNE9GjunZP+zWyNkG2tI/es/CgM09yj8s+B6m1HXCP4HgwHvEBb4/hF9oriArwD+B2RugTtfFP3FmenBjkc8/MHcrMhBB1j+iLPxCe73dP8g9fhvkxeI/WG5O+zZ95j9yxZ42N7vpP7x6aNaCUOw/eg5GtEsr7j+kT+XipFPvP8atfs504+8/wNPFNnX97z9XUpJguMXvP57SB6W7XO8/J74j3+/c7j+RXmfFElruPyZAdMmc4e0/iCitL6177T8BdReYFCztP40RXEJN8+w/QbNrMFDP7D/A95Z9R7zsP4YQdEAktew/FnvOER207D/bC4fOGSDsPw5cpcoHH+w/5hObBJUX7D8cRgjqkgPsP0UOa3cm3es/qrh+yOCe6z9GPMXw4kPrP9VlDN0hyOo/D8MfS9Mo6j8CxWK/9mTpP1HUizTWfeg/On3F80535z+qk0UqvFfmP9DEn254J+U/Np6tKw3w4z/pTFNjP7viPzB3E+AukuE/8ViKZ7B84D/oTMiq6gHfP6k2edH6Rt0/EWbzcoLO2z/rWZPY/JvaP9ctVJsgsdk/ArQpSIkO2T82/dm8RLTYP7EgRhU5otg/XYT+Pl7Y2D9zXemLyVbZP7fszQeLHdo/3aTqXV0s2z+PDglHK4LcP0gKuR1yHN4/1IU2JZH23z8aADv5jwThP93Lff24JOI/axMAb7pU4z9D00H7CYzkP1nSIhwcweU/6lDNLivq5j9rNc6tJ/7nP6se1d6g9eg/Ju91IXHL6T/iTtKvCH3qP4NRgYBQCus/va/Gnj916z8SKAivUMHrP038zsED8+s/v9RG0YUP7D9A12APfxzsP/5LvoH3H+w/844JGzwg7D/ZG0XHsyPsP3hBL3OdMOw/GlDitLdM7D+rR8os1XzsPxL/eElexOw/LLJ3cbwk7T9Epm8wp5ztP/SNMW1NJ+4/7Mvuc1677j/KY6VZDErvP6l4Zn1Fvu8/YI0G65P87z+eLCLMQuTvPzbPveF9Uu8/FLKkuuQn7j+FuX34UlDsPyHebDlpy+k/h1JvHx2z5j9bFLXwJD3jP00NARyeat8/A66/I1Tj2D+ikxDPmIjTPwNjVwzEvc8/XluUsb56zD+5N9l9KpPNP/UaiWIEd9E/WJz1aigH1j9UguUWKAvcPz1bQoInduE/Ekooq4D/5D/Sm03T8k7oP27/rT2UIus//ONatU9S7T/vRmSV69HuP2HTwKuurO8/iteUXm397z8VnBFaHObvP8GDhO8Jie8/9uVcxYsE7z/XgcS38XDuPyapj+EZ4O0/uUICV/Vd7T+/eoHCdPHsPxQO/seHnew/+CnaZwVi7D/QZUJtcDzsP3dMgEWaKOw/6hnnwCsh7D/bC4fOGSDsP2fhfkOOpOs/5ntLJnWj6z8woGNa0ZvrP1IA4MROh+s/J5IdSvhf6z/JXIzVWCDrP1UYYSumw+o/+3SDGAdG6j9QG/fS+6TpPwLDjkzb3+g/2vhBmUP45z+U+6wmUvLmP89B3f2B1OU/mvsSaiin5D+BTKqHp3PjPzeQ+jiEQ+I/X6qtQZEf4T/atAPRVA/gP47rVFlxMd4/SUB66AGA3D8zBP6A8A/bPxZqmDt25Nk/P1kiKx3/2D8yojD1ZGDYP3eS0HtLCNg/BuKrMq/21z9QA5CahivYPzrWCWfrptg/VdxLGflo2T/iAibGf3HaP/geTLuNv9s/GGOWEtdQ3T+IvD7BCSHfP1UbGYaTlOA/forpNIOv4T/UDjGsltriP4JRjq+SDeQ/fJCF0TM/5T+CLpWN6WXmP70fSYK7eOc/bEGGlitw6D8KNMQK40bpPzFSjREG+uk/6X9DsiWJ6j+tHPIw5vXqPxAtaPuCQ+s/zsuHvVh26z8go5jXjZPrP0RiZ6rboOs/wHEJE2uk6z/AQrpzsaTrP0dJlzhAqOs/ntuJLIG16z+IpAJ5YNLrP6o7gO3mA+w/Fh2ug8RN7D+18nR2xrHsP49wwd8wL+0/O52b5fTB7T/xUJtPxWHuP/3U9kgfAe8/w+8Ssn6M7z8Sjo2OI+rvP/+/qzj++u8/sLz7N2+d7z9Tt7RQWbLuPyu0LCFiJO0/LBudECLw6j+kRvbPyyroP4zJFoxJBOU/abceoMTC4T/D0EBgGXDdP015dAUuaNg/Uc02P9nx1D8CdJUM22XTP3bvmfHu6tM/KZ0osiN01j98+ZwCUMHaP9n00w1cMeA/hDC9s+hh4z9QzPySuZ/mP5Gxwb82nek/6gjddVke7D/GCETTcwDuPw41Cfs9O+8/OW/8Qhjc7z/CZFHpQP7vPz5CBxzfwu8/ywdL0MVK7z/k3vOdk7LuP3bDta7xEO4/jvgIHlB27T95zIz5eO3sP4TQfkp7fOw/dC0r5qAl7D8cYvU5SujrPxzNgNulwes/B5OimUet6z8C6TlSp6XrP2fhfkOOpOs/SkBLv3BG6z/08iwvU0XrP00dwa+QPes/6zs8070o6z83Y+rd1QDrP5umjI1hwOo/tDpenqZi6j/u+i9u7+PpP/umAQjwQek/dZyhSTt86D9tIML6p5TnPykOQpl9j+Y/Kjv1Skpz5T9V8HJ0XEjkPzAVLMj3F+M/MqW+33Hr4T9JlYHFZcvgP2vsptpCft8/4eeclLeY3T/o+mzeXe7bP693gXOKhNo/mMzZXkhe2T88rhT6An3YP0Dgftkm4dc/jrA0EaeK1z+nPMD0XHnXP9xkEBI+rdc/36nqCGcm2D+7tBkP++TYP1cXI8fY6Nk/N/zK7iYx2z9kFu6Qv7vcP1s7PZeHhN4/C/+nSWVC4D96MOOwXlnhP4xWZNyogOI/qeXXbUGw4z8424+KGd/kPziYnofIA+Y/TpDASmkV5z8mBc+adwzoPyFG7WN94+g/14VyKWyX6T9MCdkimSfqP5CSoGxvleo/MfOFBP7j6j/s8W+5hxfrPz9cOnUsNes/DBZkNK9C6z/s4SMATUbrPw4AQX6URus/WgkIvjFK6z9G+hx9qVfrPwtYt+IEdes/SEY++m+n6z8HgmVnyvLrPxWSPzklWew/MI1RZSXa7D9/NcFSQ3LtP7uLZAfoGe4/NGOCM3vE7j/tda4dlV/vPyjowIqz0u8/igv4uv//7z8QeBDGzMbvPwa7witPCO8/UMYyL3uu7T+T51Z67rPrP4R6l1SbKuk/T1JIpFM+5j8NzOVdGzHjP2ED2pNvUeA/aGBZ5qna2z9ywj8ufIzYP/XorBl8Edc/hPRGpeqQ1z+kWdp5o/3ZP72l78DdFt4/Dyhb+4i24T933A5HE7fkP3KOMj4kvec/424KPvl+6j+5OftNx8TsP4/B5XChb+4/RXp8GOR57z9MnURrQPLvP4lJ4+QY9O8/I/XaIeaf7z+RAcZqWxXvP5lFwpvVb+4/qfoKjM3E7T+gevzeriPtPy4zeI9zluw/3L802Ywi7D9mlRmt0cnrP22laklRi+s/JiD7kAFk6z+UtNLATU/rP0N7C0OOR+s/SkBLv3BG6z+wcO1aFAnrP+VggUn0B+s/fMdHgyAA6z9FAKyRIOvqP8uZhEnnwuo/2cmKB/2B6j+vzEQFsiPqP/F/Bc5mpOk/azu5MO8B6T/+7WRaAjzoPyv1DwKZVOc/YL/3RhNQ5j+cuTKoCTXlP2sAw9/EC+Q/50aMnXTd4j9B+WlOULPhPyohnODPleA/rUufOzwY3z+KeAO6pDfdP1BHXVHZkds/o+niAwcs2j/PWEOgHAnZPzV9AsdyKtg/AnVNg2qQ1z8GI4ht8DrXPzPc1cvaKdc/JZMcnB5d1z9TT4Q62tTXP9ARwnc1kdg/q7lJuxeS2T8aXxiattbaPy18+1ADXdw/iQHgkQQh3j8yK3npGQ7gP0BTdvJ/IuE/hNZ5/E5H4j+jLNdXqHTjP9GZXXeeoeQ/CueaZ+LE5T8/n1FKnNXmP45LqZBFzOc/nN15OVaj6D9TPJu2oVfpP5NvKTZZ6Ok/J/k0AsVW6j8E/ESq2KXqPxr7spbG2eo/PKbd5Kn36j/XIC1mSgXrPwNWa0vwCOs/LRNDajgJ6z+hpA/S3QzrP+R2kXd0Gus/u3jLCxY46z8vZu4TA2vrPx526nI3t+s/2YoTCO0e7D+uv0yvA6LsP4BHCK9GPe0/xMmHZY/p7T94ZVLu1pruPwmplBhnP+8/teT61IW/7z9DDEN4JP7vP6vG3m422+8/4aA/HjE47z+ZEmV3rP7tP6jVYg4eKOw/BiphSoHE6T9AUtbQH/3mP5WKLzJcEeQ//Sy7woJN4T8QLNEWbPzdP9SWDqG1yNo/HqSDbfpY2T+XOnAvqdTZPzObIn+KLtw/OeBA0dYS4D/uec3MgqXiP8wG+R8vh+U/zNi/gMJp6D8NTJVL8wXrP7D8GtScJu0/u4NJlh2v7j+EqV/gZJvvPy7gU0ji+u8/o8E4mv7o7z/RnSoguoXvPw+2od0N8O4/3U4ltopC7j+tBv+/6pHtP2TyA5n37Ow/CxFwmCpd7D92/NT7k+frPxSwv43Ejes/rKqNappO6z86zuOT6SbrPyu+unUFEus/WApEYTQK6z+wcO1aFAnrPzlmicVx7uo/3jfVtFDt6j/zb8IOduXqP9zZIjNk0Oo/dFamswqo6j9CSIj/8WbqPyPJNeJuCOo/QIshTOuI6T9m6V9fSOboP+c8l2pMIOg/H9O+Lv445z807k3ZxzTmP1uRjOxFGuU/EdNEab7x4z9k2bVFWMTiP07gD/w9m+E/q454hNh+4D87VK14h+zeP72p6TUTDt0/RModXT5q2z/WsFo4JQbaP1fmyryq5Ng/Wihpth8H2D/SX5Fi4G3XP3IfzKnWGNc/jMTaj9cH1z+DplzX1zrXP6q2hJP2sdc/syV0fF1t2D8pWUye92zZP45Qt78AsNo/TdNf1XM03D9SCBnEZfbdP6tYfFlj798/MtJ68fkK4T87rX2UtC7iP5iZ0BESW+M/XBriFzOH5D8SWR/N06nlPy6DQjgiuuY/0lJGJ5ew5z/dI11io4foPzSFpPsNPOk/5Z6LKfnM6T9raWOnnjvqP33y5Xbmiuo/uZ01/fu+6j/aKXwg+NzqP+evg3ek6uo/s2YKlk3u6j9PFd70le7qP92ZB5w+8uo/D8eSkeH/6j/pTT8qnx3rP3+vrTzAUOs/eMYrSUyd6z/235cejgXsP5KlhDB+iew/uKXwhQom7T8u6DTDO9TtP/cNfmdIiO4/K3uAesUw7z+STTgnTrbvP27G0J4p/O8/laH0RZHi7z/omB8uGUvvPyoWDGI2H+4/dDHuquVX7D/vpIVBUQTqP+b3DpyxTOc/F3xmlUlv5D8Goo8ncbfhP6ie0WFV4t4/eM++oCe62z/ZqMGIWU/aP1f93nJkydo/qYwW3gsb3T/TH3v/rYHgP3jDvEDICeM/EclTaCze5T8uVFeJjrHoP0zbyc61Pes/4q0MQp1O7T+PKVJjmcjuPzSPP7w/qO8/5ZGlBUz97z825mf8JePvPwX1SFeYee8/RD0Apkvf7j9ywZuoeS7uP1lYAXWLe+0/mNu8JAbV7D+xj3fXLETsPwwpwwvnzes/+0h8MKhz6z92P/3sOTTrPyEB1kJiDOs/AK9v4Wr36j88pLHLku/qPzlmicVx7uo/I8mWTVj36j9aHK+QN/bqP/RjPyxf7uo/hU10MVPZ6j9MMWZCBLHqP98nLcT6b+o/lt7/+okR6j/xhX23GJLpP/18mK+D7+g/YuwOLowp6D8EZ21ONELnP0k8keziPeY/D9GBOzMj5T9glGopbPrjP59yl5e3zOI/qIxZUUSj4T/h0g9KgIbgP39CEKwY+94/om+rve0b3T/Rr2sycXfbPxnk6+HEEto/Y/s/sM/w2D+vOUcC5RLYP0uw76xiedc/uDF+fDMk1z+0lVbfLBPXP5vosKtDRtc/QcU5uJa91z/1kcYbT3nYPwJshq1Wedk/+DNlJue82j+waolB+EHcP07A4CyaBN4/NOAXSVP+3z/PJ/9f0RLhP1s48GvoNuI/QD7sN5pj4z+HlYq0ApDkPwtunFPasuU/1gUhIU3D5j8fdFUb1LnnP9SVBmrikOg/1LeKNkNF6T+ZcXKsHdbpPwSQFFuwROo/lqqnGueT6j+mPWqm78fqP2FBRqTj5eo/oqP3GIzz6j8G55coNPfqP8ijanJ89+o/3mKpCCT76j9N00rzwgjrP7OwE1h3Jus/TCiWT4dZ6z8LYViD9qXrP1HofiYKDuw/PAEihbKR7D90ZMwd0i3tP6dmzOFh2+0/CfrX6YOO7j+zOt05tDXvP89BKJ5xue8/QVX+p+b87z9d1gpVNeDvPyDEKE7nRO8/Z1go8nwU7j8n6hXnGkjsP4ghIrQw7+k/MHwUj1Ey5z+0FAUoIFDkP7Ni7SxGlOE/hnnV0/eV3j/6C3TC7mnbP/HE7bt6/dk/+I5eIhJ42j8fJ3P0eczcP7J++UDfXOA/JVK49oDo4j8zgwBCU8HlP1VI+1jFmeg/DnXPYEMr6z+5jI6waEHtP0bzfyk4wO4/8drIWBCk7z92QDnyk/zvPxxRwEsr5e8/15zWGLN97z/D4RDE7uTuP9MV6BM1Ne4/ehouEQqD7T/hGUCcCd3sP4KDmNeITOw/vti2yHzW6z/gJ6abYnzrP+6ONr8KPes/nybV1j8V6z9D4MjITgDrP7rmxP94+Oo/I8mWTVj36j9eZp1zgyPrP2EFLW1kIus/I0/u15ca6z+6o8OnqgXrP+DgrSqT3eo/GcLLztmc6j8kcRJHyj7qP1hZ0Ki7v+k/gT11Q3Qd6T8vQoDgm1foP4crt98ccOc/OMuGBU1r5j9JZS2NwE/lP/aN0bzCJeQ/Dg0454v24j/zvphpYMvhPzS9hjXHrOA/TDdML/RD3z8BDXbCO2HdP719jmZ7uds/DQ2tBPFR2j/E5oFrly3ZP432yy3PTdg/kibyOf6y1z/aXDjmE13XPwHxnMLnS9c/2MjDCW9/1z8HpVxcx/fXP9GqCJkWtdg/g2z1cEC32T+Cnv8tdP3aP9ElBDOZhdw/SOxC1KdL3j9JVzH/giTgP2bx3OcEOuE/bWnL1+Vf4j/H9+LgN47jP8WEIzT/u+Q/y45M0OHf5T8YPRwUAvHmP9nxbEva5+c/OyOV8+m+6D/tWaMsEXPpPzEEcWWQA+o/6Xb0rr5x6j+vaolFm8DqPxdrUpdf9Oo/96iW4CgS6z+wLn//vB/rP1BPj4VfI+s/hdV8Yacj6z/tafBjSSfrP9sC9irTNOs/hIEuf1dS6z93P1BODoXrP5W8pZLn0Os/O5q45gs47D/x6ecqQrrsP5QqmW8yVO0/SjJmRob+7T9OAjXX+KzuPwlSjzKITe8/03uwtiTI7z95hNbpaf/vPyIGcPwF0+8/jUX0WFEk7z8E2OKcCd3tP/n8+Mog9+s/Kq9gRGeD6T+zUn2qOqzmPzgX59YdsuM/StPmHU/i4D8ZiBN8GBTdPyjAyvj11Nk/zVH2J1xg2D8AkhHFqd3YP9StkFWuP9s/Z8MNiIRF3z9r+fNU8T/iPyUpqUveLuU/nVj3RaYg6D/NmfCs78zqP3sSikt0/ew/NQ4mJJmU7j/ADfNyo43vP7O+tWez9+8/ySbJoTDu7z9OzILPT5HvPzSnlwxaAO8/EOZejTdW7j9e2WDd86ftP87bH62dBO0/EapAGOV17D+3KcmZAwHsP5ZHacSnp+s/uFyGhcRo6z+X4oEuPEHrP2Dx/i9sLOs/Sc9sbqIk6z9eZp1zgyPrP8prbLCTces/GHHzEXhw6z/wsA38wmjrP0d4yisTVOs/fKXfomos6z/ob2veUuzqPyjn/kMKj+o/qztaIssQ6j8T3ZI/Mm/pP62baqy3qeg/7b5r7hjC5z+E0U3oirzmP/A3yX+Tn+U/E2Jqi4Rz5D8k0aK5r0HjP+07CiiAE+I/UW0iIKrx4D++Hen+KcffPxJihUwd3t0/YO3CL4gw3D+wJ15h3MPaP0led5k5m9k/0OfS9Ri42D8tjquQ7xrYP2YVMU+1w9c/A+JxzEWy1z85fDkCl+bXP2xu2UPEYNg/ju7cWO0g2T8I3pdk6ibaP1ox4DTXcds/libzz3z/3D8BVcptp8veP7i4s5fAZ+A/xaLwa4qA4T//daKgkaniP0jE1Ry62uM/QNMTKd0K5T+imrvmfzDmP7cNPDy0Quc/9XGaWPg56D9xaMPp4xDpPwrijfZ+xOk/OaQpajlU6j+3ggH9l8HqP8/DGqy9D+s/Bky/BPlC6z8uSEMLbWDrPxxQGa3Ybes/tAGfL3Bx6z/qbgQxt3HrPxrD5RxOdes/wTr7562C6z9A9l4B05/rP26I++fZ0es/npLvFI0c7D+kQUNS34HsP3J7tjVKAe0/AWUwbgmX7T/HnnwGNDvuP9cGmPrH4O4/nmvQFt107z9k4LghYd7vP3featXu/u8/OFZ0g2m17z9G45FU3eLuP3jexiSKce0/5R3Pke1c6z+Aq7LWbbjoPzLaOPO0seU/lG+iD6mM4j95jSuznTLfP9mz4WAsTdo/Gm4cyYLs1j88ZH7eq2nVP91o0IG169U/wfd+T49l2D8xuu/rqZbcP4uSFRqoB+E/Qnpqbhse5D/K7yeXhz3nPwvbvpJpGuo/WTJfUgx77D/ptofD0z7uP2cDm43vXu8/BnC/jX3p7z9FiRDk0/nvP/cBRdW8sO8/cH4ZRW8u7z/T9hYt2o7uPxD0LBAA6O0/qAdmGMJJ7T8quXbxd77sP/L5t8HZS+w/V1Xc/vHz6z/tj3Yz9LXrP+4tMYHvjus/40krG2F66z/i+JVBr3LrP8prbLCTces/cPqB3Ore6z/5SAHp1N3rP9qviPNG1us/1ggYkP3B6z+dDyweD5vrPz7jSvQIXOs/2pCEBRcA6z/U5SsKSYPqP1pwKwP94uk/CmQf6WEe6T8w9pOM7zboP13O2GimMOc/TWGSgfMR5j8GaS81L+PkP7BbRkrOreM/h8iznnN74j+GUQfDFVXhPyn2L01fQuA/H5WSLbaS3j+Q1tF70dzcP3Jm6gzQaNs//UAKXwk62j8ku0vqG1LZP/Jf5RKUsdg/LHG2AXdY2D9RejykpkbYP/RylyAafNg/eiGuLen42D8pMUkXKr3ZPwNFmS6jyNo/dd2delIa3D+9oa/40q/dP2VPI3CuhN8/Y/HkutrI4D8WKchIQebhPzvhMYCqE+M/B+6WdLZI5D+MCtsgAHzlP8cdVgfeo+Y/Lec5g0y35z/dv7n80q7oP3br97Ewhek/uDdzT6w36j9craL5/sXqP6wEGuPyMes/JbyhmeB+6z8Jy+1JNbHrP8mv6Lsazus/1z6740Lb6z8Pkn0RyN7rPyC+RqcN3+s/lhKBIZLi6z8fK14trO/rP1BGEZAzDOw/AMGusxg97D92ZDEh64XsP+IC2otK6Ow/KrFBrTpj7T+hqbNrU/LtP589tATQjO4/JIzetJQk7z/JWz4rZaXvP077H5K19O8/EzDbt7Dy7z+2CUObI33vP2mXevDKdO4/zeHY3NLE7D9T2biCMWvqP8q9JFZGf+c/E+1YkKgz5D8nYIHvJNHgP5aWMqIjV9s/zlw14u0n1j+DJcvDF5nSP2uOozjNAtE/knHitkuL0T8L0JSf0CXUP8wZEvHYktg/qmzoMuZi3j9zP6MyH4DiP3kkNOJK4eU/Mxi39/gE6T82bE4XZ6zrP/uoxy1ssu0/CpX74PUM7z89lrs9dcjvP/Q7EJT//+8/zTVf9SDV7z9E8aR4X2nvP+ZY6rgm2u4/sAKaIeQ+7j8eOgIdsajtP0ni68/fIu0/tkOzEuez7D/1XFjMXF7sP+zfMJ7XIew/y/dIa6z76z+P03TaiufrP6rJJcAA4Os/cPqB3Ore6z+/gredbmfsPwKow3FhZuw/k+udIhBf7D/uemThZUvsP86NIgqaJew/8SnURUPo6z+kTcPido7rP29DhyMLFOs/cpry/AZ26j94EdBKMrPpPwyz5r+gzOg/GUW/TwXG5z/1vMm5pKXmP0bb8mDac+U/2+av/UU65D+mX5/K0gLjP5zfW1XP1uE/Vbz8lkC+4D+lOddICH/fPzIojJV9vt0/gk3FxAtB3D/9ANLBVwrbPxTKgFo0HNo/LEv27k532T8clW6qvxvZP98mg6JxCdk/AEnNsl1A2T+IB1VSlsDZP7dl5SEkito/2yZZD7Sc2z+yJKQxGvfcP52LhFWxlt4/c4iXs1M74D/WIToOqUfhP1ZdL4/ZauI/Iik8Ra2d4z/11XmUadfkP15tOIZXDuY/QeSKV5I45z8iyS52/kzoP50RQg82ROk/WXtNoDQZ6j8DmNzqm8nqP5tnPT2NVes/nr3VADS/6z99ZAI4MgrsP9y74DgdO+w//fEprSRX7D9xUy4l5GPsP1pRQ+xMZ+w/XsjdTpBn7D9QKCQ6+GrsP0wT6Wumd+w/D8/JtzqT7D9rFxHGY8LsPx+KMGxbCO0/HrqGFEtm7T85WbBGkdrtPxEhwwHjX+4/rc0n/U3s7j9s5oDCN3DvP9nGoXSb1e8/9p4hyvr/7z9y3P/NqM3vP1NilE4mG+8/yxatpAXJ7T/m/HXgBMTrPwFRQH/HDek/G4eGH03D5T8g5N1A4h3iP7qR0xZj2dw/5q8PjksQ1j/yKIo4lobQPznVtaZ1gMk/WNMm3rwlxj8KcKKP/kXHP+D6ZXb4yMw/t2W7C0Ua0z/GCiVfuVbZP18krMhGQeA/2u8FMGb34z/TA8gZHXjnPxfEjn7tfeo/vGpOy1zd7D8sIpnLoYfuPzKkZwWThu8/cwXeZaP07z+zk/51LvTvP+OyACRdqO8/y5aubIsw7z/CLfw+CqbuP9rdjNCYG+4/lYCgN9+d7T9r/7goWzTtPzaR58hk4uw/4q5Zmx6o7D9nhnfMQYPsPwRYxkzIb+w/Rnc/tnto7D+/gredbmfsP8pR0jU2Be0/zfiQFTYE7T+RdYv0Pv3sPyqnD9OB6uw/zhSbEGnG7D8XJ4PZnYvsP8HR8w4cNew/yMojWHC+6z84D+ZgLiTrP9+mfraSZOo/KsIFByeA6T96Xv7zL3roP91jwN+zWOc/1cgcvggk5j/l9OJH/uXkP3JdzPLXqOM/IPQv4VJ24j8yM6eI51bhPy81vmheUeA/tT+XHW3V3j9sN7mxl0zdP9aM3VpIDNw/dKgeMpgW2z8qartxYWzaP7oKq3XWDdo/9ReWFu/62T8NW4WVpjPaP9hDFREIuNo/8l5aNwmI2z9weiA2NKPcP6LcoXkkCN4/xDG+Ed+z3z8qUys6jNDgP47AcKvC4+E/hkRJb7AN4z9yfnIFrUbkP6W52suKheU/frhgEy3A5j83SeboaOznPwihLkwMAek/S3gdotP26T8Kk7CnEsnqP5mh7lzqdes/SuxLIgr+6z9ul8F8H2TsPyvYoWwsrOw/rHbD4vfa7D+z+vjms/XsP2KxjZXXAe0/KG64JhYF7T9FRotEVgXtP0ILRcSTCO0/8cSQ9aEU7T9Zs+O4zi7tP2c/I4NqW+0/SV74mDed7T9fqqEkvvTtP7V/U3J/X+4/jimu1ATX7j8fvFWl0k/vP/q7vIdhuO8/n2lz62n47z/1en9uBPHvPyhQ1cNUfu8/2ZMh9H577j+IeHyLT8nsP2DKN6YUV+o/5HXI/7gr5z9djILP2mvjPxoJKejVst4/V4P6hoOW1j9Xk8bKiXrOP9FPZDosn8I/Z1C8COA3tT8QdQxgqUSsP3tMvjF4grA/Tgl1p3QsvD99/VGosx/IP1TXrGo7xtI/NLoYwt2X2j/4iuCD3mfhP8D/csDbWuU/+inG/fvW6D9YhB/uaqjrP4f7pVzRue0/8f0wXxwR7z8BWSI0bsfvP2ky54fN/+8/2+am1T/f7z9gdw3dfIfvPwBm0Bg0FO8/YK6f/z+a7j8KF+v9AyjuP27T18tWxu0/S/v5y5J57T8aQBFll0LtP88U8fmnH+0/RnhofycN7T9nmOo9NgbtP8pR0jU2Be0/+AyltC2w7T8ShsJdQK/tPwOybS3LqO0/53vgXGSX7T8/ghZMwnXtP14tsbuuPu0/CC/VWwvt7D/gOji5CXzsPyxaxjSp5+s/AoKlMG8t6z8z9aPxK03qPwbnDhqLSek/lk1rwDUo6D9G2eZJbfHmP0dW3Icyr+U/ivMguy9s5D/FSN8KqDLjPy3JScmiC+I/4eV+jW7+4D/l4u/XfBDgPzJhC8QHi94/gX6eYJg/3T9V0de8IEHcPyKld6G2kNs/HiPTq68u2z/yqx0WFRvbP1/G6GnlVds/JqmnuiHf2z+zlXgLprbcPwdV6xPO290/8FjQgupM3z/TftFdSIPgP+JGl2FsgeE/8Up2ClWc4j+V6qq3os3jP6YpOKMgDeU/vGxjJhRR5j9wt6E4547nP+Vmz2MgvOg/aeG8EX7P6T+2ti8u9sHqP42r6Txaj+s/RXdphng27D8IMqIjvbjsP2reBux+Ge0/FzOgfTld7T+cfs917ojtPxYxzS7Loe0/7gXLOg6t7T/IjPX/D7DtP9YJ22hLsO0/ChI/10uz7T+IFg/zc77tP5IWXRmd1u0/FQ5bj5T/7T/4s2VWejvuP/BpDOwAiu4/MEoGEYrn7j/tfOy+H0zvPz+myn5Wqu8/LAAusUPu7z8B8ogk4fzvP7mM/4JytO8/fPbJJLTu7j/G+b+qmYXtP/hdTrPtWus/pWCDSQli6D/wtDNDPankPyDy0hgrX+A/03PPBt6g1z8J29FIEXLNP3BHXfTbJ7s/A357ERWrfj+5/Xj8kuitv1ZhFZYpXba/8n/MY+/hs78NplKD5Zmev7iPWsUzLas/5Z0/3f4txT/3ZKeUuhnTP1HDODVaNdw/1YBw/C+S4j/bWfxUfJvmPwZ9QggX+Ok/l9qeW8iJ7D9lWQhawFDuPzuSXLuyY+8/zts3Bd/l7z9dNBGsM/7vP+dq8IIb0e8/WSPkARx97z/6V9vupRnvP5p2czFHt+4/oxkGnIxg7j9kc250HRvuP3Qf8eLQ6O0/sCuLiaPI7T9uEs/AiLftP/81Pu0ase0/+AyltC2w7T/3MsJbv13uP2L6uVXsXO4/rG//QC1X7j9da832pkfuP20Yplx9Ke4/2Gmf9a737T/kdMpFBa3tP28t/A5ERO0/CBz2EbC47D8Fa7W04gbsPyY0QAi4Les/fWtZ2Asv6j/lXRfb/w/pP/9vKBip2Oc/+kP0ITWT5j+0Vf5uwUrlPz0WrzMvCuQ/FGO9eTLb4j/2vmqXvsXhP+arIHPTz+A/c6XI+jH73z/4O6oWWaPeP8VlfjEIm90/1ula5aLj3D/9oWSBrX3cP5E+EohIadw/uBpd73am3D+S4JxJLDXdP2oeWGUiFd4/P5SI2XdF3z8xJqsrDmLgPxlNFBuLRuE/S03yu9tM4j/xrgNj82/jPxUaqZnXqOQ/geMliKju5T9slq/dAjfnPzTilEXEdug/tiDNNCCj6T9YUK5z07LqP0fqmdMun+s/h06ZBLNk7D/UZLOjFQPtPyELf4W4fO0/xoA5SsrV7T/ut5AUXRPuP85gTGa1Ou4/ftzzUvBQ7j+Cyspa+FruP+1h4PKkXe4/zXMLxNld7j86ksOthGDuP9/QQeNqau4/AStsW8R/7j/FFTH+q6PuP+PAkYJq1+4/pm32lJ0Z7z/qMqL+OWXvPwjjMMdrsO8/KLZ0F2jr7z/JPguvZv/vP0zHFYstzu8/QbqQXtky7z9QB0ODvgTuPyCQ0isrHew/bMk04TRg6T8udLPtYcflP44xVb4ra+E/0qA66AcO2T+aK690pNDNP8FdVCML2LQ/jn7hQegFqr+M7bM7r7rDv0iEcAAEcsy/vPBSatge0L+5xEhk3/jOv5uLmSIxr8i/e2JvezdIu7+YCHXA7ACJP8q3G4cl58M/31osChH60z+qaLmWJgreP8+Kw2nLruM/5KhJ4Y6u5z+xUfwiZdrqP9LbwkFCKu0/d1dinrKw7j9ZKeksSJDvPz+/qMSc8e8/QAhSXP777z9xUE/FMNHvP15waKS1i+8/9oKlm7s+7z/qyGD97PbuP1PgjryTu+4/TFWjkcSP7j+KxBDnbnPuPzf8HBJIZO4/i9PDO5Je7j/3MsJbv13uP9caqcScAO8/PLW4pO3/7j+9H18XJ/vuPzs+tX8x7u4/evGbvcnU7j/AWAybPqruP29gni5Hae4/ABUaESYM7j8c1m2lOY3tP5Qnf8Hw5+w/oFiEiOsZ7D/LkY/T8CPrP2PAtWJjCuo/h9QCJgnV6D8Af6rdMI7nP+Lvhkt0QeY/CHoZw2r65D/MPTNSlcPjP38qRoOupeI/lBdM2mSn4T/OQGV1bc3gP9u1cD/QGuA/U1wfjJ0i3z9HRDkLmmPeP6nXfU9X+d0/Kt93mhTk3T9azn7B2iPeP+wOpXGLuN4/1zvvhLOh3z/ASPsZEW/gPwwkTnalNeE/piXfkD8i4j9u333BDDHjP2jjd+hhXOQ/BoIY9Yec5T90Wy/y0efmPzGl698WM+g/TshctpZy6T/EdWWFMpvqP/NO9ZC8o+s/HOaTVQ6G7D/0bOI5lz/tP1pFyhc80e0/UzrrDZk+7j+gniGq6ozuP/7maYX5we4/T7BzpFPj7j95kcSU9PXuP8n5I3hO/u4/lgvN2oYA7z8lMsatsgDvP8d6u/XoAu8/mWswgxgL7z8VtiU/nxzvP1jZjBSrOe8/y8eSJG1i7z9NtNADJJTvP+3NLHT/x+8/lIcgAObx7z9j4BlGOf/vPz9pjZzd1e8//iuIhgVU7z8LsCMDhlHuP20GoDWko+w/xhFj/B4k6j9RiVzgXrvmP/tdWp8NbOI/OcMXmY+42j/4uRGjtF3PP8V21BTCJ7I/OjlVKC5Vt79/7fI/k1HNv9ws6J1YZdW/BH8bPvHA2b/dk9BhHqHbv3WAneL+ANu/NCQeXrjh17+9RQCE+E/Sv8U28lGa9cS/6h6/MRd8ib+rnxuzPiPEPzc1Fa38RNU/weatQazw3z8kTZsNDa/kP1FBodV6jeg/3tNBUQqA6z9N5gd6m5LtPzstXsiH5e4/IkvQVmWi7z9hWj58UfPvP/cFhNuU/e8/jul7pDDf7z/zsVQ7a67vP4aFsxF7eu8/W2n2laxM7z/r/1rYoinvP8+KPcuEEu8/qa5GsAYG7z8XYwC1SwHvP9caqcScAO8/0tPcxMqI7z+FfwFVS4jvP+Cb8gvPhO8/Oj4fLUN77z9ol1LrM2jvPz+obTpkR+8/jBFuU4MT7z9zZG+eP8buP5R0U6vgWO4/wn86fGLF7T8Vhrff0wftP9IMS8+RH+w/sOjS4PgP6z8AiTX7T+DpP4qmoSjymug/7y+joflL5z/cQIX6yv/lPxF9A5vWweQ/m1D7isSb4z8GiCG1FpXiPws15WQvs+E/BzLG55r54D8W1KzZeWrgP3T29yfwBuA/gRe/qguf3z/k2YEP3YjfP1GvpgJly98/JvlO6DYz4D9hJ8QEpazgPxnff5ExUeE/nZpznUcf4j9xtO3VHhTjP4q1qO5LK+Q/57vtpmJe5T/4sTA/yaTmP6niQYXg8+c/4kiUE5w/6T/6bdObj3vqPxrgw5VRnOs/4nGDQuyY7D9omHE4/mvtP6KbcyM1FO4/qMOoB/yT7j9R7+G4efDuP4DoUhs2MO8/tqi8VtJZ7z8zF3zjKXPvP033UNH+gO8/Ec3h1ByH7z8PWvTTuojvP3De17TaiO8/8YsjFnaK7z8Voch2X5DvPxPHkofVnO8/32AKstKw7z/7exUIJcvvP58tKJxX5+8/DXk/8XT87z/9C+l7svvvP/43fTkuz+8/6Yq6SxZZ7z+rqGMh1nPuP5odu5Is9Ow/SzeuRi6u6j94jeBz4X3nP92rTTP6UuM/lgCuumZ63D/OjBei8+jQP5r2+stBurI/zccZb3Yfvb/hNK2Wsw7Sv+Z3R4nT7dq/YW7ecIy+4L+RytOzWc/ivzYhz+cVr+O/n7gnE8Bk47/Sdi/mru3hv89sftHAgt6/6xI7VrfD1r+BBEle37zJvyBFysC5SJa/f+d9yFulxT80weBmMtTWP+ET2qTv4uA/vkPc/KaH5T+l46828zTpP+6qbH+H7Os/s4yoax7K7T+WvMcJ+PbuP+fYV6yCn+8/j8l315Ts7z8L9Rjp///vP/tOMZSO8+8/daH8rXLZ7z8gBJZmYb3vPxgyYUDmpe8/ZVZ4L6+V7z8ySc7ft4zvP/w2ivlJie8/0tPcxMqI7z+3IvIRGuTvP8bNkUTY4+8/nzHH+gbi7z9nT8bt5dzvP47C0vgR0u8/QbC2D++97z+xTWMaNZvvP8j1dEjtYu8/agdi+RQN7z/CBn3d5JHuPxrmlv136+0/NGeIRlwX7T+qybfjjxfsP7ooPHmb8uo/o1cU68Wy6T9r1F4sp2ToP4DAhcSFFec/AyAA5OTR5T8zlz+7g6TkP8EmwODgleM/u857eTCs4j9/etNIoevhP64g9gTKVuE/UN9TBh/v4D9CoHZbWrXgP5jimVnJqeA/Ye4iNnnM4D91cJYNQB3hP5MQCumhm+E/KTG+JZNG4j/fX21+HBzjP/eD7AjqGOQ/Jj97UtY35T/QTqigjHHmP4BIK19mvOc/jqEhmaoM6T8EuhpSTFXqP/j4O/koies/eE6wf52c7D8KXis/HIftPylsJM1VRO4/hUfio5bU7j+kU6vHNTzvP0mf+fA/gu8/IMavU8uu7z8hFt0adMnvP7psEgRj2O8/2dXeEQHg7z/oevppO+PvP973E9kR5O8/WAisSSLk7z+Zuk6A9eTvP+TVcg3v5+8/s698N93t7z+P1DlgNfbvP6zzvZEN/u8/KGU53OH+7z9KOG5oNO3vP1mko58it+8/U1pDqihD7z9RAXa9g2/uPxRr6VnvEu0/rNzk5cH/6j80J5steAroP0H10mQuFOQ/Pmw7xvEv3j8hfJISIW/SP3dfZXveCrY/7J9DjrKyvr/pF8+/8RXUv5Cszzgbzd6/YvhkZL6c47/uWk/kLJXmv8dAF30zYOi/0tPJKPEc6b8f6Fbqn97ov1641jtrnue/3m3vQFlA5b+2C8IcCKnhvxVDYN28s9m/SPh0a4AUzL++rS2kk9GRvyWlSsZ6Isg/KIIbC9+A2D9hGHkbvLXhP4O0WemHMOY/GTOWb8Cj6T9MKpRyCyPsP7mT7ngm1e0/oS7pebjn7j8fNwRRzYbvP3uB+YUf2O8/5B7cA0/57z8PwNfq7f/vP92htR6A+u8/kGVp+Njx7z97B6BZhervP6ZJ7Cob5u8/O5h5llvk7z+3IvIRGuTvP3qFaDo2/+8/KRIA0UH/7z/ubyLXiP/vPxpgx3v1/+8/JNGUWYD/7z9I6C/2Y/vvP/4w2ol47u8/Ru45ZRXR7z/isoCwupnvP5NOrW+IPu8/LguyFTm37j91msZzF//tPxajwt9ZFu0/FT8l2noC7D9TDIQGf83qP9lBmB9whOk/7QNOzoY16D9Ow9fze+7mP4Y1BdhRu+U/JlUb0bCl5D93H3Tex7TjP7kF2nKK7eI/I0Td+R9T4j8LlOJ2YOfhPxA3d6JEq+E/d+X0Ojqf4T9yBqm6VMPhPzoQhDhXF+I/249m55aa4j+HkH/xt0vjP9kO6OJJKOQ/0ifLyk4s5T95eq4+wVHmPw8JMYQ4kOc/WXnC3dPc6D/nPl0GmCrqP78fY51ca+s/YRXOpkWR7D/P+iIqk5DtPxo5cR9fYe4/04IrQ8oA7z96jhzDMHHvP3WwGVBIue8/eJUOXmbi7z86VnqRdPbvP3YEiwUq/u8/Cjdmq/b/7z+dXgvmwv/vPzdmRd1b/+8/YG6jsjf/7z+pfMrANP/vPzyWY6wM/+8/5wWT6Vb+7z+jrkRhFvzvPzLvQa/h9e8/JdYwOLzm7z+FTYImtsXvPzoyeedrhO8/ustHgI0N7z8RnNwXvEPuP2AZoKJMAe0/T8/bodEZ6z9NhtVMkV7oP+LLshv2puQ/yjWZ70e63z9kUpBBmhnUP98fWWkve7s/O06NPXSMvL/j7O2e89TUv8WrLE0djOC/v9OtUL6I5b8lcsUHbC/pv9OjWNNhj+u/uPYM/K/h7L/BHVAvoWTtv3IxVnj+Oe2/AdgVSQVW7L+AgWxTQYXqv0J4odc5h+e/frpqObcz478NITUkhTTbv5VdZ41PMcy/+ilvnxazWb87hSHGwkjLP/8oIzG5Jto/V34nJo5k4j8LWDAFSaTmP69TYlh52ek/PV0VQSIl7D+FR79fP7TtP7427cwtte4/XYVggVJR7z80W6rqQKrvP87any422e8/8W0W69/v7z9HVbn9tvnvP8UEpuqC/e8/F6XowM3+7z+kj01LKv/vP3qFaDo2/+8/D9GiP2bH7z+y1lUN0MfvP6ApQyukyu8/9tkIqtXR7z+bFeS0Ct7vP55NuU2R7e8/PKFBd4H77z9xxfNje//vPzRb8yZU7u8/pTSNib677z9CWJCGolzvP3zp4ciIye4/7JrhJ2cA7j+FatltSQXtPy47EK204es/0hP02Ayj6j/A9arHhVjpP42CywMuEeg/yPD+JXfa5j/3YhasYr/lP+iG1rtGyOQ/kg+fKAH74z8/3iTRZ1vjP5NaHCrM6+I/Xkd7/XOt4j9KiT0s9aDiPywcjfpqxuI/MsNF5IAd4z+2GZ5JUqXjP2qu3SEhXOQ/mO9xFeo+5T/Dj3Nk40jmP+oWdmX+cuc/Uk1F6o+z6D88H6VCTv7pP7GIEHnUROs/hSJm7ch37D96vJl2nIjtP/+Hgmmca+4/EnH0K9oZ7z9YPYAjVZLvP3DzD4D32e8/bfQrbVD67z91fWn3Yv/vPxLdGxou9e8/YACoQZ3l7z8TIsWzWdfvP3/tlPmbze8/vcusysjI7z8fI0OEc8fvP7bKWflYx+8/l2rkBP/F7z+1djavzcDvPwyGqWOctO8/XJEZJ8ac7z+loCbc4XHvPyNY9xY+KO8/+RvK2kWu7j+Vu9fXCOvtP7mPKzVQvew/OXp7YvP76j+4njqDeHjoP+VM/d1BBeU/lXsgzjyA4D+FwV7YZMPVP5pXwUitMME/AyeIh+5It7/nFBxUn2vUv94RUa4A+OC/Svr2gLac5r/JDPi3isrqvzawQXLyee2/HnRpm9Tt7r9oKDUKtY/vv7nTMeSPwO+/YrEiw7mx779I8K5wOFLvv735xycXVe6/YOLRHkxP7L+TG3769OTov5cJbthm9eO/l0nCOZdk2794jF2n5VnKv1lMYDZ6vpc/yfa+YYzGzj81+0o2UabbPwLorixE5uI/UVkhXGPf5j85i2vUctXpPw4xjlbr8es/y3p7F+Nj7T8A6GaXsFfuP2etKJ+r8u4/wxqhHtlR7z96l49XUorvP8YGNoeHqu8/asrj9dK77z+4C6ouDsTvPwS6FAj8xu8/D9GiP2bH7z9PVdqb+CzvPzRylljRLe8/hZfDqaYz7z9Dorjr0kLvPxHhN0n9Xe8/njOAn8iE7z9brvYitLLvP9ydG2Su3u8/xUOX2r777z+Vh9dG3/rvP36vQW+2ze8/DATYzodp7z8rlSaLhcnuP13NhX7d7+0/OOenS0jl7D+6KpXrXrfrPzDIhlxPduo/GgfktJcy6T8Cehp3QvvnPysjAJDZ3OY//Iwm9wfh5T8SPROdvQ7lP8ewXLOeauQ/vkxnwY334z8YT4NpLbfjP1kqkDJEquM/gA1c2PbQ4z/rWCos1CrkP5LRSaixtuQ/unFdb1ty5T+2gd2JH1rmP51k1npEaOc/rAS3RoeU6D9KUQAay9PpPwyolccvGOs/U3wAisNR7D/H/SC17W/tPzNWoCt+Y+4/5EGOqQgh7z8LYNNS9aLvP63h87Kd6u8/OhF6xP//7z/DAPBX/+/vP8vyMq2rye8/kfYhhVCb7z8qoBCZG3DvP+uSiyHYTu8/fUGBZdk57z9xiTpT0C/vP8rNu8QTLe8/80sGcd0s7z+DvSBkGyrvP1k6WcujH+8/tA++XMQH7z+IHJw9RNvuP8jVM439j+4/m8KF1jkX7j+T5t8FB1ztP/hvOIzWQew/XSDDUPCj6j8fZ/LyllboP79vj3ATK+U/SVaRtfn34D8HvnyHuEzXP1/adsX4CsU/7VWuojUtr78RO8720v/Sv8ev8p3Eu+C/KrYOLcvy5r98ZfFZjpnrv1PJTSdgeu6/6ITtetnF778hx1s5P/zvv0+JOwLTuu+/hy4D21mE779iKxdxWJjvv4PCGVMj4e+/n5bMCNn5779uskckH0zvvw5bRj79Qe2/z+HIgvR96b8CqAhBKgLkv3aVZ4kNZ9q/IicOYsfgxr/LqZK5P76rP7Y5UFlQKNE/9DcPJXvm3D+TkccThzTjP83BF2ty3+Y/5cQpYh6W6T9UxI6D84XrP/JvZqnp3Ow/NcMjb4bD7T/6P21McFruP0K9o5tEuu4/NHXMd6T07j8nW3yqxxXvPxkHitwrJu8/EkTaYh8s7z9PVdqb+CzvP8lxSrofJu4/KIRDcnYn7j//kZqNtTDuPxihJ3j0SO4/vJ6xTCl17j9jut0OjbbuPwaMd3A0Ce8/iULvjm9j7z9e+nHdZ7bvPxZylp8k8O8/T0d/oar+7z/mY3dTe9PvP+20oGN/Zu8/WH3HuI237j8VeHHEN87tP6Rhn7EXuOw/pJUxz0eG6z+edILrxUrqP/VyjaRYFuk/ZY+4Bz735z+7X4AAovjmP7ruDhquIuY/iXwSJvV65T+JGDQJAwXlPyl6o6DnwuQ/Ulci/6K15D9tY6gaZd3kP8LeHs2aOeU/egapFMfI5T86ohP0K4jmPwGU04BMc+c/yfnaaluD6D83Nfwet67pP0lI5Wik6Oo/3YuV/IMh7D+nh3wsvEftPwkrSs5ySe4/F4qiAPcW7z+v17SlaqXvP4aN+DX48O8/OrEDYNX97z9IEEnvl9fvP1WPCifpju8/H3jPYjI27z8fYObEMN7uP2JSb/hRk+4/lXIiU2Fc7j/wKKQklDruPyl00UWfKu4/2zewqUom7j8Jl6fI9CXuP17F7NuZIe4/3h7T8SUR7j8u+rz5CeztP/ywGwYvqO0/dslLQnY47T85Us8L/4rsPx743s58h+s/8sppaQoO6j9D+VTXKvfnPym6N1sCFuU/pGp53yQ+4T+lVTKYi5zYP0RTYEk3AMk/aVG+mySomL/Ov3bfsrvQvwSdbVca0d+/rv2FOTqe5r/0lMcF+MLrvytnwxqN4u6/2la17l/877+B+FT5Aobvv2Do8is5Qe6/LV5KTQP77L9gMYGDuFDsv6xpSPqRi+y/k4k6dsuS7b9ehHVGF/Duv7B5v8sq6O+/GSjhQUyr77+0RDgnbJbtv2HxzY5UbOm/laJU67xq479i4QSJ62HYvyZszjuoH8K/Xi4+GZm8tj9f3nwhk9PSP15B19vn1N0/Pi27VWtL4z8UrVJpwqLmPxFW9M4BGek/mtBf/c7b6j9c2nsKzhXsPxxv0zzM6uw/v7N0lMp27T+SvCUQr87tPzWoEibBAe4/1us5n2Yb7j9X23pzyCTuP8lxSrofJu4/62pjCMax7D9nIKZTprPsP/bB/uyfwOw/Vn3jecri7D850hT/qiHtP5dYbd9LgO0/kMob74n77T+BDbRZOYnuPzMwaz6tGO8/kB0KE92U7z8/vIgR5efvP+cmiKoa/+8/qkiC8p/O7z+q8Sk9elPvPwn3j5yvk+4/W/lx5pmc7T98keE9I4DsP+Jz3BXMUes/Ino4rC8k6j88hD0DZAfpPwH8Tb88COg/4rRcG0Ew5z+N/GXSEIbmP+zAnlb4DeY/Lb86loTK5T/pA7ja97zlP5bcfQyP5eU/aHeq4Y9D5j+R2fWjHtXmP+SzAbXfluc/w6DeZXCD6D8HxHIizpLpP5OrlsrSueo/93tym/7p6z/i2BvK0xHtP5/BvrMBHu4/YuKPZ3j77j9M8036NJrvP3WW+Dc78O8/hvWscOn77z/aOSwd0MTvP+j5DeySWu8/e9eWSvrR7j/s6sau+0DuP4ffoWPPuu0/zIdigQxN7T/bc2a3Uv7sP4vuh72Bzuw/FujlcxS47D8GB9ExArLsP9gfctyJsew/GRKagnCr7D+DD/eGdJTsP+Qbt3XsYOw/Jz2oYrED7D8HvY25jWzrP6svgmt6huo/qDvhwQs26T+snttVnFjnP9su4F0QxeQ/2WFXu2pP4T90J6zXWaHZP1o2qf+i0Mw/gRT8BLs6kT8H/QygyZjLv7YNeZiYH92/90C6EMaq5b/G7LdBWVjrvxLnIqG44u6/N6yKwtD/778oq7kR5PTuv2b8KegfhOy/N+gbS2Cs6b+E2u25T13nv8PQzhipQOa/jIG+uaWh5r8x2fNvF2fov2kvlqH9FOu/5tEO9szZ7b/i1+D2jLXvv1jFjlkGvu+/AuV15b1p7b+HIGVEaLzov3S4VBYpPeK/3wS4LGF+1b8sx4w5xeS4vy9PExTm7b8/M+xzMnpM1D+3FRUbM2beP0gHrGMvKeM/ULnUyFko5j/0EYBuZVvoP8HJkCCN7ek/KyBpiu4E6z8P0STrisDrPwbhAw9xOOw/VAQtPAd/7D8OmlaDxKLsP+xAsRzlr+w/62pjCMax7D8MQo8hsNnqP7cQoG8g3Oo/lrGBgAHt6j+2128dkhnrP9PqHqIPbOs/a6U6IYTp6z+/EBpLx4/sPzt9XlxiVO0/YeOqSfUk7j+8uYpTbunuPyGfED7gh+8/5wAoGCvp7z9tbMx/Q/3vP3BytWzsve8/n1D3wzov7z/SyTd9/F3uP8CAxnfAXO0/EWuT2nxA7D8vLuE6qx3rP9m9/uRQBuo/DUkpLQYJ6T/Zr7DSzDDoP/1kAqxqhec/mFs1iAAM5z/G/XMrpsfmP7Pk4rPnueY/OAX/gBDj5j/lv7SVOkLnP58eqdYg1ec/71u9MLmX6D8WySyNpIPpP58HW8CPj+o/VMUupbKu6z9B0ErlrdDsPxsS7ZQT4u0/8bUFnNzN7j8L7tCT3n/vP5XSKgcA6O8/qU4JtoD97z+fNnl4YMHvP5wsZRv5Pu8/oCSBXVaK7j9F5lmdlbztP00HdLVE7+w/84LOcwA47D9n5sZ5XaXrP9TBTMWcPes/OSi0kxf/6j/g4ikg4+HqP0kvJlP+2eo/+tw27WHZ6j9oWCZaddHqP24FWIKks+o/VvyR9Axx6j+LV0z2ZvnpP+6pl45uOek/lYMe2CgZ6D8mOJyZgXrmP45DQOfzOOQ/j/E7d0Qr4T93l+AGY1HaP+RlwJc4JtA/4fR3UT5wrj/HXwGVYcDEv/gMyFdYidm/jieNw4gg5L/NnQEDhVjqv/9bshz8ge6/dkK2BKz/77992kSUXsXuv79RrGkDa+u/XDGswNL+5r+FQIf5cq7ivx1g3YYA7d6/5owCSbDy278wIm5rc/Tcv7L2gHz64uC/dQL954PB5L/6zJj/8EPpvy2iIlsnTe2/cdubiqKz77+EBtFWAZvvvyrN6aemu+y/U9OgvIhw579XVx9dnIfgv/9gsg3p6NG/l+BgQKPEqL8xbkaJC3rEPxLlfW6xgtU/b00IqMOV3j95cqPARs7iP1c8pT+ZcOU/7JZf5sRb5z+a7lQrD7foP8aTOoLTo+k/yHqv0/g86j9UujRW6ZfqP+yic1Qzxuo/UbWoIz/X6j8MQo8hsNnqP7hgq0U9s+g/QL96mz226D/oSIygBsvoP1ricpoBAuk/9fvEDjBo6T8gXhSAtgTqP29ncXOU1uo/uXmJpEPT6z+hM9N97ubsP8X4R+On9u0/lQJTI4bk7j8eU+dg15TvP58hOYgg8+8/8oTzIIn17z+u7wmN2J3vP/aj7FkC+O4/n2yHLQMX7j/FEo9PKRHtP+r0e3vE/Os/9lVF2M/t6j9BT9USt/TpP1/RheMHHuk/UDUg675y6D8Dy3oq3/jnP1pVfSsVtOc/W8Fuezym5z94pBERsM/nPycQwNxaL+g/V5EIkYbC6D8I/k6uboTpPxwE+USobeo/UrU3KH5z6z8BlJORdYfsP40/PzZEl+0/+CQx+YuN7j986KByoVPvP6ZiTtRp1O8/7ijqkvj/7z+y1GWRIM/vP3B+dlbgRe8/mCHI/6pz7j/+lv4EJXHtPwYfeCTIW+w/C03VbJtQ6z87EHMSaGfqPy6rULaBr+k/4lB8bpUu6T9EAAstUuHoP+b5CvVUveg/zO3bf52z6D9/vogI3bLoPy0LKkQdqeg/CPwV4XiE6D8xdrl12zLoP2kgxfXxoOc/FkaaMJu45j/XdLgzSF/lPxoznTjldOM/7tu71iPU4D/lk/s0saraP1cZe3vjqtE/7rAlgPETuj/PeGd/Eo+6v6ssrBmnOdW/ln2rLjgJ4r8WBhU31rjov1our9QUpu2/pRch5i/q779uaqfyWhTvv/daNWorZ+u/C6I6pdXS5b/+hHD/8k7fv8xHqaY6VdS/rdFL8y8ryb9ANK+KIzXCv/70Ls0xjMS/f3Uwirjxz79tpRemk4PZv30asHlGuuK/Nmdxq9/F6L851CcCHo3tv6RHgEri4u+/mpg3JA8o77/4XDYM7Xfrvxhh4OG2iOW/7mVwGXO23L8dU+ix/5/Lv+B5nLozZWU/rt45tVS5yD9mtBvTfW7WPy0g7h7UZd4/bDLZm8s94j/OKSO7cn7kP6kHQtnvG+Y/XHShNh455z9/Z/tZPPPnPx7Z4LlyYug/z6Z5fkWb6D/yUugzPLDoP7hgq0U9s+g/VvtZo09e5j8CVPAf2WHmP1wK9LRaeuY/v1VGyUS75j++w2pgRzTnP/AEcluj7uc/EoX4iaDq6D9R4eCH5x3qP098RvB+c+s/2meCevDN7D9KwXZSkwvuP/nLLTs8DO8/lwdIj++27z+4ORmUBv7vPyBUgLe14O8/H0Q8ps5p7z8odW7LfqzuP39YtSNAwO0/mu0gNR297D8ZjHq3+bjrP9u8izQRxuo/J90jon/y6T+/3F9EfUjpP+GnpI30zug/xI5aGiuK6D8qJhl/TnzoP4I95NLJpeg/9I+jXFcF6T+eR4UZzJfpP8xQyzakV+o/tu2JqGM86z+yR/Zc7znsP6QxYBIaQO0/xAf/YLY67j98OpnNiRLvP6DonE1or+8/xZzgwHv77z+ajbDuR+fvP6LqyDl7be8/GuvAH1GV7j9umCF6eXLtPzPa+9wtIuw/mwiDFxjG6j++dvIobn7pPwEfoAXRZOg/p5vjUAmJ5z+rPaseAPDmP0zajmCplOY/3kIB6zRq5j8wQXgJwV7mPxf6KDreXeY/XbRWrWFS5j9ghvXPPifmP9prGrJdx+U/JwFwhJsc5T/rZ1nQTw7kP3JL6gHaf+I/j4hL9uxP4D+oID40LbPaP0k5cb258NI/TbUqIixEwj/UOQxPGfilv2TQFeDDX9C/Ci+LYlzo3r+o/SMlsHDmvxEddqXZJOy/1cp1Wo5877+WQsuYHKbvv8bnlbzlbuy/xDcIFsxy5r/5uVw2l+3dvxKiA0TT6c2/WWax6lj+o7852OAhtpi3P8aAIYfiJMM/Ji6nEjGwwD8mPgYVlh+iP3zLwPj9lcC/n5fCL+9D1r9rDNAXocziv8xaqv4NuOm/UpIe47Zx7r/4aIz35/7vv0PpuGYXLO6/2lrmj3qF6b9fc8arMgrjv6tQ5ca9mte/a5TwQa3Cwr/lRnBAVoqrPwnofVDEmcw/p6wR98oQ1z+EKfvQ2d/dPxRDHitTfuE/PxQwIRNZ4z8vs4qBlaPkP0AJj1/PfOU/FdLF1z3/5T90GuQiFELmPyyyrGLFWuY/VvtZo09e5j/vgXOCWgLkP0WO5dJfBuQ/DSWTyD0i5D+xZFXeI2zkP/NzCK059uQ/wCMOy8LL5T9twzG1XO7mP3MME1oWVOg/bhcsJy3n6T98t//EDojrP1vKD2G8Ee0/EOv3NuFf7j/7CI8uKlXvP0vM0I0i4O8/3SoyGkv97z/NYj08H7bvPxwH6oLCHe8/oZOu0aNM7j9RoGoMW1ztP0pahSCSZOw/s9wSXzx56z8VgjU19KnqP09A890jAuo/+ICitJeJ6T/l4c6AK0XpP3nOb5NdN+k/JJyG6Khg6T9NUzrWl7/pP9YwmaSMUOo/93lUPUgN6z9jSpjaROzrP9lkQPoN4Ow/viNghtfW7T8IzZT/rbruP9bW6XWicu8/jt36xTrl7z86No0vH/zvP9Uh/dR8qO8/F+OX7Ann7j+muouXUMLtP2tPUSspUuw/sFZs3SS46j89tsfBvhnpP6s2Vb3tmec/S6ZbDsRT5j+kIrvKNFflP3nN+WZCqOQ/Fhm+3SxA5D8dXr+X4A/kP2LPQ2nbAuQ/gzeAmNkB5D97G+CQy/TjP4uWpkjNw+M/XdkWYxVX4z9rufTYDJbiP8Od+gb0ZeE/GD52AldR3z+TmQgJ5HjaP/a/6qRv+9M/kMExLgMyxz9wPU5O3t6SP/Nv2Q0VV8a/KqQQpmjv2L8mF7OIv4Ljv/nbR8iS2Om/TJwxqbxe7r+QVFNypv/vv3O03KrZDe6/mBUg8KKg6L8fFXFj2a7gv4j6hf3yGM+//Bd54yQhkD+XefkATQfMP7nTFKO6ZtY/lsF1Sx742T9WO+Rdi8jYPzd+NGaSz9I/bhmqCQEZwD9kheMxdsO7v6ghmqe5fNi/mtiBXIDh5L8vBodLHrzrv0+lPrUEfe+/Ip17kmSa779KLL3eImjsv3FoNwAZ1ea/oAFCpB8E4L89TBu/0O3Rv5I71yzqGLO/kADudVqTuj9UnzFGEQrQP7t0mPYkc9c/2qrYZEsV3T9CjJQO/prgPyMJ2JytDeI/LUCS9rUC4z950F5WaZbjPzhwSM1F4uM/mkPdaVT+4z/vgXOCWgLkP5qrqSodyuE/bQdg0ovO4T9l9CsGRu3hP7POnj/UPuI/747n1oHX4j+klGkWYMTjP1Csb29XCOU/BQmWYAOa5j9d73vqLWPoPwo6ZqGiQuo/gwVXB5cQ7D+xXfyXD6XtP1+Vj6nH3u4/WZRGbLio7z+LX3WgvvzvP3eRacLp4u8/WwNjyCRu7z+FMe6wmLfuP2KCVpQu2u0/FZsJGyDv7D/8mq0Z6wvsPwHlXU2FQes/VgZ1LHGc6j+F63VHSyXqP6kwnmt64ek/t5/03sfT6T8bhupjvfzpP1M/6cS4Wuo/zXAk7LHp6j/t+CPAvKLrP+3XHkhfe+w/9Xw93elk7T8GQyHIGUzuP4X/bpFnGe8/w5DUy2my7z/BTk7UlPzvPxqMc5dF4e8/OBPQcHZS7z91mem76U7uPyT8G9VS5Ow/Fh0RsGAu6z+iJLONhVLpPyrF1DyEeec/w47HgI/I5T+yJFr/wFvkPwF4+Hj3QuM/pTo5ZUGB4j/jpEzdSw7iPw09U98F2eE/ka6CQavK4T9RkKsQj8nhP80TxZMru+E/59MmHDOF4T/fSxkjmA3hPzWF1lfAOeA/XGC7oqDa3T+Vmpf2bRLaP03q8UXF19Q/2gBjWHTPyz8CTcit/DK0P3eIU+M0JLe/NRGy3vpe0r88omyIKwHgv9DJGFaZsOa//oxR2AxD7L+H+1C8ko3vv45kYchehO+/OeT5guen67/vPPHNflzkv+V3oOf62NW/fbSP+vHNob/Y6qYXRt3NP6E8JPi8s9s/LXSJ9pvI4T8a5VkN+2fjP/rLv/LZ3uI/1eO2C5Ye4D8vQiT+G+3VP3gzrk9HRbs/OHFsoE3Qx79QEmj9f5vfv7F7TMQLYOi/VZ5b5DAQ7r/XOa9t/v/vvw/0GVb+Q+6/FKmDb7Wv6b8KjcegGGvjvyYUZ3sPINm/u2Bi4p7Hx78tAYnTnxpgvxnQ+Cy0c8M/0mddS06a0T94xi/Zs6fXP4P/wqiNINw/ZwYaUvVI3z90VKNc67DgP1X+L9Q8U+E/SXXqvsKm4T9Sr0i6rcXhP5qrqSodyuE/XKcj4eS93z+PikYSacffP0TK4/6yBOA/9ywlmlRc4D/B0SzEngDhPyyrpTIjAOI/y0yfJ/Be4z9CermjOhTlPy8GkEmdCec/m3atZrkc6T80iQBuliPrP2KHrTA+8+w/jCEjgBln7j8MW8pgCWfvP986syaN6u8/SNv6+ln47z9/G82xB6PvP+F9LV1BBO8/+5RBH/s37j8UfiSbtljtP/0C2iA7few/jXeCrqO36z8qj7EgYRXrPwqYaR3Cn+o/fghnzqRc6j/g45z8Ek/qPzUgLiWjd+o/UHw1tYzU6j9jed0jbGHrP33Ypg3BFuw/UxWsH0Hp7D9JRBKFMcntP1O2N8EUou4/wx9FjRRb7z/iwX1EldjvP7aFpqo6/+8/Mm7ZnEG47z+qbJzAd/buP0z59AeAuu0/08dMytUU7D9QStSbbyTqP3+kIc/3Eeg/9uB2vdAI5j8/rHfr2y/kP1+0p2jbo+I/vSnOTXF04T/cQxszxaPgP2ylW/ErKOA/M9E5KOjd3z/9FevyFb/fP3QNIcmzvN8/Gh8GANCd3z86nfym/infP2zuW4GXKd4/7Hk4i1Nk3D+nj/GKM5/ZP2GhRFsZm9U/un4Z3w8W0D+5xMwB1KPBP0xZO2bzIXe/VfHe2yzlxr/GHe8uZhjYv1Lv2PJ3ueK/ZQ7ufHYA6b+efUdZT9Htv7GJRJ7d+O+/okyKbA587r+5/nK7Mwvpvy7AszNiUeC/vgJSHTh3x78iUFROBZjBPxy/iLw319k/6fuxkmPy4j9T2V7ZZnzmP7TPAIur5ec/77MdIVNv5z8Nqk2H1QHlPz8L5HZUQuA/lhLGA+Lj0T9+HWDpTB+Tv2eYuepKYNa/XyL9T6sC5b/en9ZmrD3svwWZfApLue+/DC21UJpM77/hag3g4KrrvwtYYQMq+eW/O2CTFwzD3r/9XoJ3nJ3Rv8uvlSlGtra/6W2TVCnlsT+mSObOi17JP1A7y8TMDNM/5rYkSB/J1z/ODFc7lyXbP9Z2pJ0qY90/sAP0X9i+3j8sX8aQAXLfP4n3aSJftN8/XKcj4eS93z86Rfa7S8ncP8A9up5H09w/6rF4OYUY3T+7F4Upg9DdPxBFiwbRKd8/q09t/Oih4D+bTaOYOBTiP0wuVfKV4+M/DEXph5b55T//Nn5cGzPoPxfGKZqhZOo/CoGfqAFh7D8OBK4PIQHuP7JTvuJ7Ku8/q7/5JbHS7z8xCclSV//vP7eSgGOxwu8/NtIrZbw27z8/0TMsLHjuP+uKx1h1ou0/y++PBlfN7D8kvmvBygvsP1NUxm33a+s/H0IDQrT36j8haLU2O7XqP4xBYcvHp+o/hC5fTvrP6j8sBlKY7CvrP8l6Jbb0tus/biUKXSBp7D+WvT/EhTbtP6ImKqaeDu4/dVdumv/b7j/jiz4p2ITvP7tsL5Ss7O8/YwqhJJD37z/7w1nauo7vP+M0CQeype4/AqVlbpw+7T8TPNXHImzrP0MXKBK+T+k/SPP7G4AU5z++kL/hsOfkP1cbqSRY8eI/XNGIW5ZO4T/PqIUFxQ7gPxcd3EmbZt4/Ymipp/ti3T8457sM4urcP6c8bcmLytw/edBaqAvI3D9W6DkRpKfcPxlIC98tLtw/G8FkKHsh2z+3vO5GJEfZP1LqFsiyYtY/iWPrOag00j/i42to9PjIP8MyHWAvAbQ/RW4tTS6Jsb+H40CU+zLPv1hgaIpjNty/kVSHyY2f5L+Ud6iW4Yfqv52ZFkRDtu6/Ry4d3xb1778yq+ZiL1vtvziC10Ncwua/MOqHqgUc2r9vf2p5hvavv0NY6aaP1tA/5URf/Hm24D8Sfde95FHmP+jOEjUtd+k/Yf3XdCGt6j/LoMHycEjqP9YJKcn/Kug/ojAfEA7a4z9IJ/qONsfZP1BcOpvLOLs//+eImoNEzr8MeNaNTjPiv+YzyMDNhuq/FhobJHEr779LF19zasXvv3CiIpMT6uy/AKUxpqy357+4kyRes2Hhv1Q7QusUx9W/ycDTJguPw79s1Hir24qBP8KInJu488E/H2DZxIAbzz/THCcG8XjUP+1RxEcV+tc/CZ4XK7pR2j9OcyK4273bPxA1K3Cxedw/RplCUU6/3D86Rfa7S8ncP+POt9/Y7No/GF6OYBz32j8X4UlbTD7bP2+SFxGF+9s/iuBD0Nxe3T/myKZ+GonfP1wBOp+oQuE/H8WqPd4h4z96qNfzzUvlP0TpY9jvnOc/XBavCNjo6T+83zdgCAHsP559bAbWvO0/SgIGaTkA7z/AP2CYnL/vP90R6ZPY/+8/ImUOv/HS7z9BmwHsEFPvP3naw8Fcne4/GDVO9t7N7T9UE5Fc8PzsP2I3Z3UVPuw/JNPoSuef6z/R2Jn4kizrP0SPl1SM6uo/fE47Pi7d6j9JiNk2HwXrP78LRmpmYOs/tGk/ey3q6z96nntlOprsP8nHvDc/ZO0/xfqxWTY37j8hzj9DIP3uP5+u9AGSm+8/P1pEmof17z/qEu9CwO7vP86DW495cO8/Nln5xbVu7j81RpHol+zsP2e94gQs/uo/YyNXOX7G6D+q3CjtG3LmP6z8kXVxL+Q/H0SYtC8n4j+n1ClMo3bgP6kyroTZWd4/HBHV/fCV3D97YFEP3orbPxwOJl1gD9s/RJMl5CHu2j/RoDjVj+vaP3tGNj5Ayto/nHWOL2pN2j+bi4JjYDnZP5DqX0l+Utc/MObzM4Fb1D/CAen61xTQP+B+cY0ofsQ/sySSncIapT9R2qN9oGu7vzG9wnwCINK/bLZ1jVWu3r8Y/w46Xbzlvze8E2w1Y+u/enE79fsl779J2WFBQc/vv4AB2x5Lg+y/sLMXvu435b/if7WQIenVv6iWpCKl54k/qCsvIeSp1T8MrelOqefiP3TYTC6sMOg/Wa3BZX8N6z/7slHGeB/sP3N7rgkbx+s/BTvqplXi6T8We+8M7eLlP9ADurktbd4/pSnYyIZhxz8FD39GER7Fv/riwte5WuC/HfSzf4BT6b89vMjxsq7uv0+AJboe7++/JRSU0xyV7b9PNYJ0o7jov5KyrnS9keK/PzxvOP1K2L+W7I3fNpPIv3mGDYoC952/G0isXZWyuj/Lb8tQRL/KP0Fsv5opZtI/JTTvKvD81T/zclRlEGTYP9IFYjUB2tk/PuiFwgWb2j/Zx7Lbk+LaP+POt9/Y7No/3zEhnD1K2j8PfEXomFTaPw2ZWn5unNo/NmVtTGRb2z+OU7BWE8LcPxv57cK68d4/FLPiwun64D8w7gt2bt/iP5PvJTwREOU/rpFmLSlp5z8G/c+C/b3pP2X8uhOd3+s/NOT8K9Wk7T8b3C6XGfHuPzqGEshpuO8/nIEkfFz/7z8KTpQd39fvP1wqZhAoXO8/Rf5UKISp7j9sL0dTL9ztP7k9AHm2DO0/zHUWWM1O7D9YlWjfM7HrP74XkXo0Pus/uhBrxFb86j+p9AtWAO/qPxTzdM/ZFus/ZP7Q+eNx6z8+xgBKOfvrP19t7T2Hquw/az9jvF1z7T9+bULWi0TuP4gfRrvfB+8/zg/4Qrqi7z80LpvH6ffvP+AOvAAS6+8/lpuC7HNl7z9fjtQbPlvuP8tYzRbrz+w/NG05/gDY6j9Ri2BQFZfoPw3X+qQ1OuY/7Zbkwi/w4z/e82p16+HhP6u9fMfBLOA/DzXRq3u/3T8G1NfFQPfbP+iaQHKz6do/Krk+OxVt2j8HwgWbiUvaP0SQMpfxSNo/kbGv7VQn2j+d4w77X6nZP8Xifabmktg/cmGgt9yn1j93NcFav6rTP4J1qJD8t84/GRQFfCv5wj9WnKt2omudPyZzF/hHwL6/E3GSC7j40r8w6bmkh4Dfv5lc+3znGea/WRGg8Wip67+up2I7l0bvv0+uqWBgvO+/v+5gHAI07L8K1eHnMq3kv3abXONhd9S/J+myKup2oz+rOejtwkPXP/isO7P8neM/lGI/A27I6D/1ays0EovrP9kZwKovkOw/dsQEJzc87D9V0UmnyGvqPzihYFwliuY/Gtqe4gz03z/XEbTAKKfKP04LOfyY/8G/J2+hcght37+Xxm/vm+Xov+kaSXk3fu6/lLeYS4j377+WRDCzXcrtvxg5i+otDOm/FKrx3lH24r9WbRFwFCLZv4AImN7jQsq/DvYXJaGFpb9sgb7fMZS3P+IguLE2RMk/i2D+M5ex0T8Pad5ef0/VP1u3o/S+u9c/8Ntmf/A02T93PgmMrffZP+QTo87gP9o/3zEhnD1K2j8=\",\"dtype\":\"float64\",\"shape\":[100,100]}]},\"selected\":{\"id\":\"1050\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1051\",\"type\":\"UnionRenderers\"}},\"id\":\"1034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"HelpTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1049\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"callback\":null},\"id\":\"1003\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1007\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"LinearScale\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"603e4917-e0ec-4359-8625-d655c19f7480\",\"roots\":{\"1002\":\"7377fe9a-e6b3-4cd4-9a99-421e7b4aa510\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Image:\n",
    "    def __init__(self, image, x_range=None, y_range=None):\n",
    "        import scipy.interpolate\n",
    "        \n",
    "        self.image = image\n",
    "        self._width = image.shape[0] - 1\n",
    "        self._height = image.shape[1] - 1\n",
    "\n",
    "        self._x_range = x_range if x_range is not None else (0, self._width)\n",
    "        self._y_range = y_range if y_range is not None else (0, self._height)\n",
    "#         x = np.arange(self._width+1).reshape(-1,1).repeat(self._height+1, axis=1)\n",
    "#         y = np.arange(self._height+1).reshape(1,-1).repeat(self._width+1, axis=0)\n",
    "        \n",
    "#         self.spline = scipy.interpolate.SmoothBivariateSpline(x.ravel(), y.ravel(), self.image.ravel())\n",
    "\n",
    "    def scale_contour(self, contour):\n",
    "        x_width = (self._x_range[1] - self._x_range[0]) / self._width\n",
    "        y_width = (self._y_range[1] - self._y_range[0]) / self._height\n",
    "        \n",
    "        mult = np.array([[x_width, y_width]])\n",
    "        add = np.array([[self._x_range[0], self._y_range[0]]])\n",
    "        \n",
    "        contour = contour.contour * mult + add\n",
    "        return contour\n",
    "\n",
    "    def corners(self, f='bottom', direction='counterclockwise'):\n",
    "        if f == 'bottom':\n",
    "            corners = [(self.width,0), (self.width, self.height), (0,self.height), (0,0)]\n",
    "        elif f == 'right':\n",
    "            corners = [(self.width, self.height), (0,self.height), (0,0), (self.width, 0)]\n",
    "        elif f == 'top':\n",
    "            corners = [(0,self.height), (0,0), (self.width, 0), (self.width, self.height)]\n",
    "        elif f == 'left':\n",
    "            corners = [(0,0), (self.width, 0), (self.width, self.height), (0,self.height)]\n",
    "        \n",
    "        corners = np.array(corners)\n",
    "        \n",
    "        if direction == 'clockwise':\n",
    "            corners = np.flip(corners, axis=0)\n",
    "        \n",
    "        return corners\n",
    "\n",
    "    @property\n",
    "    def x_min(self):\n",
    "        return self._x_range[0]\n",
    "    @property\n",
    "    def x_max(self):\n",
    "        return self._x_range[1]\n",
    "    @property\n",
    "    def y_min(self):\n",
    "        return self._y_range[0]\n",
    "    @property\n",
    "    def y_max(self):\n",
    "        return self._y_range[1]\n",
    "\n",
    "    \n",
    "    @property\n",
    "    def width(self):\n",
    "        return self._width\n",
    "    \n",
    "    @property\n",
    "    def height(self):\n",
    "        return self._height\n",
    "    \n",
    "    def __call__(self, x, y):\n",
    "        self.spline(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Contour:\n",
    "    def __init__(self, contour, image, threshold = None):\n",
    "        self.contour = contour\n",
    "        self.image = image\n",
    "        self._closed = None\n",
    "        self._direction = None\n",
    "        self.threshold = threshold\n",
    "        \n",
    "    @property\n",
    "    def closed(self):\n",
    "        if self._closed is None:\n",
    "            if all(self.contour[0] == self.contour[-1]):\n",
    "                self._closed = True\n",
    "            else:\n",
    "                self._closed = False\n",
    "\n",
    "        return self._closed\n",
    "    \n",
    "    @property\n",
    "    def direction(self):\n",
    "        if self._direction is None:\n",
    "            self._direction = self.find_direction()\n",
    "        \n",
    "        return self._direction\n",
    "    \n",
    "    def find_direction(self):\n",
    "        if not self.closed:\n",
    "            self.close()\n",
    "        curls = [(x2 - x1)*(y2 + y1) for (x1, y1), (x2, y2) in zip(self.contour, self.contour[1:])]\n",
    "        #print(curls)\n",
    "        return np.sign(np.sum(curls))\n",
    "    \n",
    "    def close(self):\n",
    "        if self.closed:\n",
    "            return self\n",
    "        \n",
    "        x0,y0 = self.contour[0]\n",
    "        x1,y1 = self.contour[-1]\n",
    "        \n",
    "        # bottom:\n",
    "        if x0 != x1 and y0 == y1 == 0:\n",
    "#            print(\"bottom edge is open, connect via shortest path\")\n",
    "            # bottom edge is open, connect via shortest path\n",
    "            self.contour = np.r_[self.contour, self.contour[[0],:]]\n",
    "        \n",
    "        # left:\n",
    "        elif x0 == x1 == 0 and y0 != y1:\n",
    "#            print(\"left edge is open, connect via shortest path\")\n",
    "            # left edge is open, connect via shortest path\n",
    "            self.contour = np.r_[self.contour, self.contour[[0],:]]\n",
    "            \n",
    "        # top:\n",
    "        elif x0 != x1 and y0 == y1 == self.image.height:\n",
    "#            print(\"top edge is open, connect via outside path\")\n",
    "            # top edge is open, connect via outside path\n",
    "            self.contour = np.r_[self.contour, self.contour[[0],:]]\n",
    "            \n",
    "        # right:\n",
    "        elif x0 == x1 == self.image.width and y0 != y1:\n",
    "            # right edge is open, connect via \n",
    "            self.contour = np.r_[self.contour, self.contour[[0],:]]\n",
    "            \n",
    "        # split left to right:\n",
    "        elif x0 == 0 and x1 == self.image.width:\n",
    "            # wrap around the top:\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='right', direction='clockwise')[:2], self.contour[[0],:]]\n",
    "        \n",
    "        elif x0 == self.image.width and x1 == 0:\n",
    "            # wrap around the bottom:\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='left', direction='clockwise')[:2], self.contour[[0],:]]\n",
    "        \n",
    "        # split top to bottom:\n",
    "        elif y0 == 0 and y1 == self.image.height:\n",
    "            # wrap around the left:\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='top', direction='clockwise')[:2], self.contour[[0],:]]\n",
    "        \n",
    "        elif y0 == self.image.height and y1 == 0:\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='bottom')[:2], self.contour[[0],:]]\n",
    "            \n",
    "        # bottom left corner:\n",
    "        elif (y0 == 0 and x1 == 0):\n",
    "            # connect via shortest path\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='left')[:1], self.contour[[0],:]]\n",
    "            \n",
    "        elif (y1 == 0 and x0 == 0):\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='bottom', direction='clockwise')[:1], self.contour[[0],:]]\n",
    "            \n",
    "        # bottom right corner:\n",
    "        elif (y0 == 0 and x1 == self.image.width):\n",
    "            # connect via shortest path\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='right', direction='clockwise')[:1], self.contour[[0],:]]\n",
    "            \n",
    "        elif (y1 == 0 and x0 == self.image.width):\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='bottom')[:1], self.contour[[0],:]]\n",
    "            \n",
    "        # top right corner:\n",
    "        elif (y0 == self.image.height and x1 == self.image.width):\n",
    "            # connect via shortest path\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='right')[:1], self.contour[[0],:]]\n",
    "        elif (y1 == self.image.height and x0 == self.image.width):\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='top', direction='clockwise')[:1], self.contour[[0],:]]\n",
    "            \n",
    "        # top left corner:\n",
    "        elif (y0 == self.image.height and x1 == 0):\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='left', direction='clockwise')[:1], self.contour[[0],:]]\n",
    "\n",
    "        elif (y1 == self.image.height and x0 == 0):\n",
    "            self.contour = np.r_[self.contour, self.image.corners(f='top')[:1], self.contour[[0],:]]\n",
    "        \n",
    "        assert all(self.contour[0] == self.contour[-1]), f\"first: {self.contour[0]}, second: {self.contour[-1]}\"\n",
    "        self._closed = True\n",
    "        \n",
    "        return self\n",
    "    \n",
    "    def __contains__(self, other):\n",
    "        from skimage.measure import points_in_poly\n",
    "        if not self.closed:\n",
    "            self.close()\n",
    "        if not other.closed:\n",
    "            other.close()\n",
    "        assert self.closed and other.closed\n",
    "        contained = points_in_poly(other.contour, self.contour)\n",
    "        if np.all(contained):\n",
    "            return True\n",
    "        else:\n",
    "            not_contained = other.contour[~contained]\n",
    "        \n",
    "            edge_xs = []\n",
    "            edge_ys = []\n",
    "            for x,y in self.contour:\n",
    "                if x in [0, self.image.width] or y in [0, self.image.height]:\n",
    "                    edge_xs += [x]\n",
    "                    edge_ys += [y]\n",
    "                \n",
    "            if len(edge_xs) == len(edge_ys) == 0:\n",
    "                return False\n",
    "            for x,y in not_contained:\n",
    "                if x in [0, self.image.width] and min(edge_ys) <= y <= max(edge_ys):\n",
    "                    continue\n",
    "                elif min(edge_xs) <= x <= max(edge_xs) and y in [0, self.image.height]:\n",
    "                    continue\n",
    "                else:\n",
    "                    return False\n",
    "            return True\n",
    "        \n",
    "    def polygon(self):\n",
    "        contour = self.image.scale_contour(self)\n",
    "        return (contour[:,0], contour[:,1])\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f\"Contour(start = {self.contour[0]}, mean = {self.contour.mean(axis=0)}, thresh = {self.threshold}, closed = {self.closed}, direction = {self.direction})\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_contours(image, thresholds, orientation='low'):\n",
    "#    image = Image(values)\n",
    "    contours = defaultdict(list)\n",
    "    thresholds = sorted(thresholds)\n",
    "    base_contour = np.r_[image.corners(direction='clockwise'),image.corners(direction='clockwise')[[0],:]]\n",
    "    if orientation == 'high':\n",
    "        base_contour = np.flip(base_contour, axis=0)\n",
    "    contours[-float('inf')] = [Contour(base_contour, image, thresholds[0])]\n",
    "    for i, t in enumerate(thresholds[1:], 1):\n",
    "        for contour in measure.find_contours(image.image, t, positive_orientation=orientation):\n",
    "            c = Contour(contour, image, t)\n",
    "            c.close()\n",
    "            if c.direction < 0 and orientation == 'low':\n",
    "                c.threshold = thresholds[i+1]\n",
    "                contours[thresholds[i+1]] += [c]\n",
    "            elif c.direction < 0 and orientation == 'high':\n",
    "                c.threshold = thresholds[i-1]\n",
    "                contours[thresholds[i-1]] += [c]\n",
    "            else:\n",
    "                contours[t] += [c]\n",
    "    contours_list = []\n",
    "    for k in contours:\n",
    "        contours_list += [contours[k]]\n",
    "    return contours_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = create_contours(Image(r, x_range=(x.min(), x.max()), y_range=(y.min(), y.max())), np.linspace(r.min(), r.max(), 13, endpoint=False), orientation='high')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tree:\n",
    "    def __init__(self, node, children=None):\n",
    "        if isinstance(node, Tree):\n",
    "            self.node = node.node\n",
    "            self.children = node.children\n",
    "        else:\n",
    "            self.children = []\n",
    "            if children:\n",
    "                for child in children:\n",
    "                    self.add(child)\n",
    "            self.node = node\n",
    "        \n",
    "    def add(self, child):\n",
    "        child_node = Tree(child)\n",
    "        self.children += [child_node]\n",
    "        #self.children = sorted(self.children)\n",
    "        return child_node\n",
    "    \n",
    "    def __lt__(self, other):\n",
    "        return self.node < other.node\n",
    "    \n",
    "    def _tostring(self, prefix=\"\"):\n",
    "#        print(\"in _tostring\", self.node, self.children)\n",
    "        r = repr(self.node)\n",
    "        r = prefix + prefix.join(r.splitlines(keepends=True))\n",
    "        s = r + \"\\n\"\n",
    "        for child in self.children:\n",
    "            s += child._tostring(prefix=prefix + \"•\")\n",
    "            \n",
    "        return s\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f\"Node({self.node}, children={[child.node for child in self.children]})\"\n",
    "    \n",
    "    def find(self, element):\n",
    "        if element == self.node:\n",
    "            return self\n",
    "        else:\n",
    "            for child in self.children:\n",
    "                r = child.find(element)\n",
    "                if r is not None:\n",
    "                    return r\n",
    "    \n",
    "    def get_level(self, target, current_level=0):\n",
    "        from functools import reduce\n",
    "        if current_level + 1 == target:\n",
    "            return self.children\n",
    "        elif current_level == target:\n",
    "            return [self]\n",
    "        else:\n",
    "            return reduce(lambda x,y: x+y, [child.get_level(target, current_level+1) for child in self.children], [])\n",
    "    \n",
    "    def num_elem(self):\n",
    "        return 1 + sum([c.num_elem() for c in self.children])\n",
    "\n",
    "    def insert(self, child, func=lambda x,y: x in y if y is not None else True):\n",
    "        if func(child, self.node):\n",
    "            # it belongs beneath this node.\n",
    "            if len(self.children) > 0:\n",
    "                for c in self.children:\n",
    "                    node = c.insert(child, func)\n",
    "                    if node:\n",
    "                        return node\n",
    "                else:\n",
    "                    # we didn't add it to a child, so we add it here, and then re-look at the children to see if any of them should be inside this\n",
    "                    node_children = []\n",
    "                    for i, c in enumerate(self.children):\n",
    "                        if func(c.node, child):\n",
    "                            node_children += [i]\n",
    "                    node = self.add(child)\n",
    "                    #print(node_children)\n",
    "                    for i in reversed(node_children):\n",
    "                        node.add(self.children.pop(i))\n",
    "                    \n",
    "#                     print(\"moving node: \")\n",
    "#                     print(node._tostring())\n",
    "#                     print()\n",
    "                    return node\n",
    "            else:\n",
    "                return self.add(child)\n",
    "        else:\n",
    "            return False\n",
    "        \n",
    "    def depth(self):\n",
    "        i = 1\n",
    "        while self.get_level(i):\n",
    "            i += 1\n",
    "        return i\n",
    "    \n",
    "    def mask(self, mask):\n",
    "        from functools import reduce\n",
    "        results = tuple()\n",
    "        if mask.node:\n",
    "             results += (self,)\n",
    "        results += reduce(lambda x,y: x+ y[0].mask(y[1]), zip(self.children, mask.children), tuple())\n",
    "        return results\n",
    "    \n",
    "    def map(self, func):\n",
    "        return Tree(func(self), [c.map(func) for c in self.children])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_contours_to_tree(contour, tree):\n",
    "    for c in contour:\n",
    "        n = tree.insert(c, lambda x,y: x in y)\n",
    "        assert n\n",
    "    return tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def order_contours(contours):\n",
    "    head = Tree(contours[0][0])\n",
    "    \n",
    "    for cs in contours[1:]:\n",
    "        add_contours_to_tree(cs, head)\n",
    "        \n",
    "    return head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = order_contours(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_contour_polygon(tree):\n",
    "    exterior_contour_xs, exterior_contour_ys = tree.node.polygon()\n",
    "    if tree.children:\n",
    "        holes_contours_xs, holes_contours_ys = list(zip(*[c.node.polygon() for c in tree.children]))\n",
    "    else:\n",
    "        holes_contours_xs, holes_contours_ys = [], []\n",
    "    \n",
    "    xs = [exterior_contour_xs, *holes_contours_xs]\n",
    "    ys = [exterior_contour_ys, *holes_contours_ys]\n",
    "    level = tree.node.threshold\n",
    "    \n",
    "    return (xs, ys, level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_contour_multipolygon(tree):\n",
    "    polygon_tree = tree.map(create_contour_polygon)\n",
    "#    return polygon_tree\n",
    "    multipolygon_xs = defaultdict(list)\n",
    "    multipolygon_ys = defaultdict(list)\n",
    "    for level in range(polygon_tree.depth()):\n",
    "        polygons = [n.node for n in polygon_tree.get_level(level)]\n",
    "        for xs, ys, thresh in polygons:\n",
    "            multipolygon_xs[thresh] += [xs]\n",
    "            multipolygon_ys[thresh] += [ys]\n",
    "    \n",
    "    xs = []\n",
    "    ys = []\n",
    "    labels = []\n",
    "    for k in sorted(multipolygon_xs):\n",
    "        labels += [k]\n",
    "        xs += [multipolygon_xs[k]]\n",
    "        ys += [multipolygon_ys[k]]\n",
    "    return xs,ys, labels        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data_for_contour(image, x_range, y_range, thresholds):\n",
    "    image = Image(image, x_range, y_range)\n",
    "    C = create_contours(image, thresholds, orientation='high')\n",
    "    t = order_contours(C)\n",
    "    xs, ys, thresh = create_contour_multipolygon(t)\n",
    "    return dict(xs = xs, ys = ys, level = thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs, ys, thresh = create_contour_multipolygon(t)#.children[3].children[0].children[3])#.children[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict(xs = xs, ys = ys, level = thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.palettes import Viridis256 as palette\n",
    "from bokeh.models import LinearColorMapper\n",
    "\n",
    "palette.reverse()\n",
    "color_mapper =LinearColorMapper(palette=palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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C\",\"#462D7C\",\"#472C7B\",\"#472B7A\",\"#472A79\",\"#472878\",\"#472777\",\"#472676\",\"#472575\",\"#482374\",\"#482273\",\"#482172\",\"#482071\",\"#481E70\",\"#481D6F\",\"#481C6E\",\"#481A6C\",\"#48196B\",\"#47186A\",\"#471669\",\"#471567\",\"#471466\",\"#471265\",\"#471163\",\"#470F62\",\"#460E61\",\"#460C5F\",\"#460B5E\",\"#46095C\",\"#45085B\",\"#45065A\",\"#450558\",\"#440357\",\"#440255\",\"#440154\"]},\"id\":\"1106\",\"type\":\"LinearColorMapper\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1124\",\"type\":\"BasicTicker\"}},\"id\":\"1127\",\"type\":\"Grid\"},{\"attributes\":{\"fill_color\":{\"field\":\"level\",\"transform\":{\"id\":\"1106\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"value\":\"#1f77b4\"},\"xs\":{\"field\":\"xs\"},\"ys\":{\"field\":\"ys\"}},\"id\":\"1139\",\"type\":\"MultiPolygons\"},{\"attributes\":{\"data_source\":{\"id\":\"1137\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1139\",\"type\":\"MultiPolygons\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1140\",\"type\":\"MultiPolygons\"},\"selection_glyph\":null,\"view\":{\"id\":\"1142\",\"type\":\"CDSView\"}},\"id\":\"1141\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"tooltips\":[[\"Level\",\"@level\"],[\"(y, x)\",\"($x, $y)\"]]},\"id\":\"1128\",\"type\":\"HoverTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1129\",\"type\":\"TapTool\"},{\"attributes\":{\"text\":\"multi_polygons\"},\"id\":\"1108\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null},\"id\":\"1112\",\"type\":\"DataRange1d\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"xs\":{\"field\":\"xs\"},\"ys\":{\"field\":\"ys\"}},\"id\":\"1140\",\"type\":\"MultiPolygons\"},{\"attributes\":{},\"id\":\"1156\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1157\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"1137\",\"type\":\"ColumnDataSource\"}},\"id\":\"1142\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1158\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null},\"id\":\"1110\",\"type\":\"DataRange1d\"},{\"attributes\":{\"formatter\":{\"id\":\"1154\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1119\",\"type\":\"BasicTicker\"}},\"id\":\"1118\",\"type\":\"LinearAxis\"},{\"attributes\":{\"ticker\":{\"id\":\"1119\",\"type\":\"BasicTicker\"}},\"id\":\"1122\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1114\",\"type\":\"LinearScale\"}],\"root_ids\":[\"1107\"]},\"title\":\"Bokeh Application\",\"version\":\"1.2.0\"}};\n",
       "  var render_items = [{\"docid\":\"d2c86a46-810e-42ae-8fcd-de288372f325\",\"roots\":{\"1107\":\"1c9e39fb-e429-447c-9cb6-35b7d624d527\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1107"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(title=\"multi_polygons\", tools='hover,tap,wheel_zoom,reset', tooltips=[\n",
    "        (\"Level\", \"@level\"), (\"(y, x)\", \"($x, $y)\")\n",
    "    ])\n",
    "p.multi_polygons('xs', 'ys', source=data, fill_color={'field': 'level', 'transform': color_mapper})\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = create_data_for_contour(r, x_range=(x.min(), x.max()), y_range=(y.min(), y.max()), thresholds=np.linspace(r.min(), r.max(), 50, endpoint=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"b5539977-59c2-44e9-a3ba-d2bdbfe31e8c\" data-root-id=\"1201\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"3f5183c3-42b6-45be-805e-df5c75e7830f\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1212\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1216\",\"type\":\"Grid\"},{\"id\":\"1221\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"1217\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"1235\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1202\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1226\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1204\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1208\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1206\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1210\",\"type\":\"LinearScale\"}},\"id\":\"1201\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"fill_color\":{\"field\":\"level\",\"transform\":{\"id\":\"1106\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"value\":\"#1f77b4\"},\"xs\":{\"field\":\"xs\"},\"ys\":{\"field\":\"ys\"}},\"id\":\"1233\",\"type\":\"MultiPolygons\"},{\"attributes\":{},\"id\":\"1259\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null},\"id\":\"1223\",\"type\":\"TapTool\"},{\"attributes\":{\"ticker\":{\"id\":\"1213\",\"type\":\"BasicTicker\"}},\"id\":\"1216\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1258\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"1231\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1233\",\"type\":\"MultiPolygons\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1234\",\"type\":\"MultiPolygons\"},\"selection_glyph\":null,\"view\":{\"id\":\"1236\",\"type\":\"CDSView\"}},\"id\":\"1235\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1210\",\"type\":\"LinearScale\"},{\"attributes\":{\"text\":\"multi_polygons\"},\"id\":\"1202\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null,\"tooltips\":[[\"Level\",\"@level\"],[\"(y, x)\",\"($x, 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