build-som-plots.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "### **This notebook uses the outputs of the `inference` mode of `sade`. For score-norm outputs saved via `eval` mode, refer to the `down_syndrome_scorenorm_som` notebook**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install holoviews hvplot -q\n",
    "# !cd /codespace/ && git clone https://github.com/ahsanMah/simpsom\n",
    "# !cd /codespace/simpsom && python setup.py install --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34mabcd-test\u001b[0m/   \u001b[01;34mconte\u001b[0m/     \u001b[01;34mibis-asd\u001b[0m/       \u001b[01;34mibis-hr-inliers\u001b[0m/\n",
      "\u001b[01;34mabcd-val\u001b[0m/    \u001b[01;34mebds\u001b[0m/      \u001b[01;34mibis-atypical\u001b[0m/  \u001b[01;34mibis-inlier\u001b[0m/\n",
      "config.json  \u001b[01;34mebds-ood\u001b[0m/  \u001b[01;34mibis-ds-sa\u001b[0m/     model_states.json\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES']='0'\n",
    "\n",
    "workdir = \"/ASD/ahsan_projects/braintypicality/workdir/cuda_opt/learnable/experiments/reprod-correct/\"\n",
    "%ls $workdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
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       "9hcGIqmODLQAHCy2Xti6v/XNRivf43f4fFvIteu854+VHnR7q9tfBlwAAGz+pnndB9vM26UebAe8\n",
       "SLHujPOTPVW+rwY+sxskAAC2HrA8t2Vvc7ffP1r9o+vwR2dcr92InIAbKKC1FZ5tB1tf+/G8p8sv\n",
       "N/9Q5zd/XR34LYCwV5JdccMEAMDBk45DH243r/X4xGvqxFa/GNpS7n6rwOwNWwHVE26oAADYurf1\n",
       "zx/utOzt+DMKYM0p17YtZZ5VNzqfsB2HewG1WXE8PoZ7gOclbTIvynZf9JV+fqZtfgs/8F/Nu5rB\n",
       "EIBmJ+8QRMmpU7EzGRsf2FzuePqYRbzh/zE26EwdrT10f6r6o8HOYzCJB9Dpff8tbnGLG8L/A/WE\n",
       "roTBs2RqAAAAAElFTkSuQmCC'\n",
       "     style='height:25px; border-radius:12px; display: inline-block; float: left; vertical-align: middle'></img>\n",
       "\n",
       "\n",
       "  <img src='data:image/png;base64,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'\n",
       "       style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
       "  \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "</div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import re\n",
    "import holoviews as hv\n",
    "from holoviews import opts\n",
    "from holoviews import dim\n",
    "\n",
    "hv.extension(\"bokeh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "8Waieg4khAaw"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  const force = true;\n",
       "  const py_version = '3.6.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
       "  const reloading = false;\n",
       "  const Bokeh = root.Bokeh;\n",
       "\n",
       "  // Set a timeout for this load but only if we are not already initializing\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "    if (js_exports == null) js_exports = {};\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      // Don't load bokeh if it is still initializing\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
       "      // There is nothing to load\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\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",
       "    window._bokeh_on_load = on_load\n",
       "\n",
       "    function on_error(e) {\n",
       "      const src_el = e.srcElement\n",
       "      console.error(\"failed to load \" + (src_el.href || src_el.src));\n",
       "    }\n",
       "\n",
       "    const skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n",
       "      root._bokeh_is_loading = css_urls.length + 0;\n",
       "    } else {\n",
       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
       "    }\n",
       "\n",
       "    const existing_stylesheets = []\n",
       "    const links = document.getElementsByTagName('link')\n",
       "    for (let i = 0; i < links.length; i++) {\n",
       "      const link = links[i]\n",
       "      if (link.href != null) {\n",
       "        existing_stylesheets.push(link.href)\n",
       "      }\n",
       "    }\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const escaped = encodeURI(url)\n",
       "      if (existing_stylesheets.indexOf(escaped) !== -1) {\n",
       "        on_load()\n",
       "        continue;\n",
       "      }\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",
       "    }    var existing_scripts = []\n",
       "    const scripts = document.getElementsByTagName('script')\n",
       "    for (let i = 0; i < scripts.length; i++) {\n",
       "      var script = scripts[i]\n",
       "      if (script.src != null) {\n",
       "        existing_scripts.push(script.src)\n",
       "      }\n",
       "    }\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const escaped = encodeURI(url)\n",
       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
       "        if (!window.requirejs) {\n",
       "          on_load();\n",
       "        }\n",
       "        continue;\n",
       "      }\n",
       "      const 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",
       "    for (let i = 0; i < js_modules.length; i++) {\n",
       "      const url = js_modules[i];\n",
       "      const escaped = encodeURI(url)\n",
       "      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
       "        if (!window.requirejs) {\n",
       "          on_load();\n",
       "        }\n",
       "        continue;\n",
       "      }\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",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (const name in js_exports) {\n",
       "      const url = js_exports[name];\n",
       "      const escaped = encodeURI(url)\n",
       "      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n",
       "        if (!window.requirejs) {\n",
       "          on_load();\n",
       "        }\n",
       "        continue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      element.textContent = `\n",
       "      import ${name} from \"${url}\"\n",
       "      window.${name} = ${name}\n",
       "      window._bokeh_on_load()\n",
       "      `\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\n",
       "    }\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",
       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.6.1/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.3.min.js\", \"https://cdn.holoviz.org/panel/1.6.1/dist/panel.min.js\"];\n",
       "  const js_modules = [];\n",
       "  const js_exports = {};\n",
       "  const css_urls = [];\n",
       "  const inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (let i = 0; i < inline_js.length; i++) {\n",
       "        try {\n",
       "          inline_js[i].call(root, root.Bokeh);\n",
       "        } catch(e) {\n",
       "          if (!reloading) {\n",
       "            throw e;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "        var NewBokeh = root.Bokeh;\n",
       "        if (Bokeh.versions === undefined) {\n",
       "          Bokeh.versions = new Map();\n",
       "        }\n",
       "        if (NewBokeh.version !== Bokeh.version) {\n",
       "          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "        }\n",
       "        root.Bokeh = Bokeh;\n",
       "      }\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",
       "    }\n",
       "    root._bokeh_is_initializing = false\n",
       "  }\n",
       "\n",
       "  function load_or_wait() {\n",
       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
       "    // versions of Bokeh and its dependencies at the same time.\n",
       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
       "    // to determine whether there is an ongoing attempt to initialize\n",
       "    // bokeh, however for backward compatibility we also try to ensure\n",
       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
       "    // before older versions are fully initialized.\n",
       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
       "      // If the timeout and bokeh was not successfully loaded we reset\n",
       "      // everything and try loading again\n",
       "      root._bokeh_timeout = Date.now() + 5000;\n",
       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\n",
       "      root._bokeh_is_loading = 0\n",
       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
       "      load_or_wait();\n",
       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
       "      setTimeout(load_or_wait, 100);\n",
       "    } else {\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n",
       "      if (!reloading && !bokeh_loaded) {\n",
       "        if (root.Bokeh) {\n",
       "          root.Bokeh = undefined;\n",
       "        }\n",
       "        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }\n",
       "  // Give older versions of the autoload script a head-start to ensure\n",
       "  // they initialize before we start loading newer version.\n",
       "  setTimeout(load_or_wait, 100)\n",
       "}(window));"
      ],
      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.6.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\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    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\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    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const 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    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\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      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\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  const js_urls = [\"https://cdn.holoviz.org/panel/1.6.1/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.3.min.js\", \"https://cdn.holoviz.org/panel/1.6.1/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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     "metadata": {
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       "id": "1e819281-95c5-4b25-9c68-2572a456a1f8"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import hvplot.pandas\n",
    "import cupy\n",
    "import simpsom as sps\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "\n",
    "from bokeh.plotting import figure, output_file, show, save\n",
    "from bokeh.util.hex import axial_to_cartesian, cartesian_to_axial\n",
    "from bokeh.transform import linear_cmap, factor_cmap, factor_mark, log_cmap, jitter\n",
    "from bokeh.models import ColumnDataSource, CDSView, BooleanFilter, GroupFilter, IndexFilter, Legend\n",
    "from bokeh.models import ColorBar, FixedTicker\n",
    "from bokeh.palettes import Spectral5, Accent5, Spectral10\n",
    "from collections import OrderedDict, Counter\n",
    "\n",
    "import colorcet as cc\n",
    "from colorcet.plotting import swatch, swatches\n",
    "from bokeh.layouts import row, column\n",
    "\n",
    "scale = lambda x,a,b: (b-a)*(x-min(x))/(max(x)-min(x)) + a  \n",
    "mpl.rcParams['lines.markersize'] = 3\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sade.datasets.loaders import get_image_files_list\n",
    "extract_id = lambda f: re.match(\"(.*)(.nii.gz|.npz)\", os.path.basename(f)).group(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some plotting defaults\n",
    "\n",
    "# Change this for higher resolution plots\n",
    "SCALE_FACTOR = 1\n",
    "\n",
    "HEXSIZE = 0.99 / np.sqrt(3)\n",
    "background_fill_color = \"#fafafa\"\n",
    "header_text_size = \"18px\"\n",
    "MARKER_SIZE = 13\n",
    "\n",
    "\n",
    "def plot_hex_heatmap(\n",
    "    scattersource,\n",
    "    binsource,\n",
    "    heatmap_col=\"distance\",\n",
    "    cmap_upper_ratio=1.02,\n",
    "    cmap_lower_ratio=1,\n",
    "    width=800,\n",
    "    height=700,\n",
    "    cmap=\"Greys256\",\n",
    "    cbar_ticks=True,\n",
    "    scatter_jitter=True,\n",
    "    filter_groups=[],\n",
    "):\n",
    "\n",
    "    heatmap_min, heatmap_max = min(binsource.data[heatmap_col]), max(\n",
    "        binsource.data[heatmap_col]\n",
    "    )\n",
    "\n",
    "    plot = figure(\n",
    "        width=int(width * SCALE_FACTOR),\n",
    "        height=int(height * SCALE_FACTOR),\n",
    "        background_fill_color=background_fill_color,\n",
    "        active_scroll=\"wheel_zoom\",\n",
    "    )\n",
    "    r = plot.hex_tile(\n",
    "        source=binsource,\n",
    "        color=\"darkgray\",\n",
    "        fill_color=linear_cmap(\n",
    "            heatmap_col,\n",
    "            cmap,\n",
    "            low=cmap_lower_ratio * heatmap_min,\n",
    "            high=cmap_upper_ratio * heatmap_max,\n",
    "        ),\n",
    "        alpha=0.8,\n",
    "    )\n",
    "\n",
    "    if cbar_ticks:\n",
    "        cbar_font_size = f\"{12*SCALE_FACTOR}pt\"\n",
    "        cbar_tick_color = \"black\"\n",
    "    else:\n",
    "        cbar_font_size = \"0pt\"\n",
    "        cbar_tick_color = None\n",
    "\n",
    "    tick_values = np.arange(1, 6)\n",
    "    tick_offset = 0.04  # Adjust based on how much offset you need\n",
    "    offset_tick_values = [tick + tick_offset for tick in tick_values]\n",
    "    tick_labels = {k: str(int(k)) for k in offset_tick_values}\n",
    "\n",
    "    color_bar = r.construct_color_bar(\n",
    "        padding=1,\n",
    "        title=\"Percentage of Typicals\",\n",
    "        title_text_font_size=f\"{18*SCALE_FACTOR}px\",\n",
    "        major_label_text_font_size=cbar_font_size,\n",
    "        major_tick_line_color=cbar_tick_color,\n",
    "        ticker=FixedTicker(ticks=offset_tick_values),\n",
    "        major_tick_line_width=2 * SCALE_FACTOR,\n",
    "    )\n",
    "    color_bar.major_label_overrides = tick_labels\n",
    "    plot.add_layout(color_bar, \"right\")\n",
    "\n",
    "    group_filter = BooleanFilter(\n",
    "        [c not in filter_groups for c in scattersource.data[\"dataset\"]]\n",
    "    )\n",
    "    view = CDSView(filter=group_filter)\n",
    "    #     view1 = CDSView(filter=GroupFilter(column_name='dataset', group='HR-Typical'))\n",
    "\n",
    "    plot.scatter(\n",
    "        source=scattersource,\n",
    "        marker=factor_mark(\"dataset\", MARKERS, DATASETS),\n",
    "        color=\"color\",\n",
    "        x=jitter(\"x\", 0.8) if scatter_jitter else \"x\",\n",
    "        y=jitter(\"y\", 0.8) if scatter_jitter else \"y\",\n",
    "        size=\"scale\" if \"scale\" in scattersource.data else MARKER_SIZE * SCALE_FACTOR,\n",
    "        legend_group=\"dataset\",\n",
    "        alpha=0.9,\n",
    "        line_color=\"black\",\n",
    "        view=view,\n",
    "    )\n",
    "\n",
    "    legend = Legend(\n",
    "        items=plot.legend.items,\n",
    "        location=\"top_right\",\n",
    "        orientation=\"horizontal\",\n",
    "        title=\"Dataset\",\n",
    "        title_text_font_size=f\"{int(14*SCALE_FACTOR)}px\",\n",
    "        background_fill_color=background_fill_color,\n",
    "        label_text_font_size=f\"{int(13*SCALE_FACTOR)}px\",\n",
    "        glyph_height=int(32 * SCALE_FACTOR),\n",
    "        glyph_width=int(32 * SCALE_FACTOR),\n",
    "    )\n",
    "\n",
    "    # Remove original legend\n",
    "    plot.legend.destroy()\n",
    "    plot.add_layout(legend, \"above\")\n",
    "\n",
    "    plot.axis.visible = False\n",
    "    plot.grid.visible = False\n",
    "\n",
    "    return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_bmu_df(net, test_data, test_labels, min_cluster_size_ratio=0.1):\n",
    "\n",
    "    bmus_arr = net.find_bmu_ix(cupy.asarray(test_data)).get()\n",
    "    bmu_dists = [\n",
    "        np.linalg.norm(net.nodes_list[bmuidx].weights - x)\n",
    "        for bmuidx, x in zip(bmus_arr, test_data)\n",
    "    ]\n",
    "\n",
    "    bmus = pd.DataFrame([b.item() for b in bmus_arr], columns=[\"BMU\"])\n",
    "    bmus[\"dataset\"] = [dataset_id_to_name[i] for i in test_labels]\n",
    "    bmus[\"bmu-distance\"] = bmu_dists\n",
    "    bmu_df = bmus.set_index(\"BMU\")\n",
    "\n",
    "    for idx in bmu_df.index:\n",
    "        node = net.nodes_list[idx]\n",
    "        bmu_df.loc[idx, [\"x\", \"y\"]] = node.pos.get()\n",
    "\n",
    "    bmucounts = (\n",
    "        bmus[[\"BMU\", \"dataset\"]]\n",
    "        .groupby([\"BMU\"])\n",
    "        .value_counts()\n",
    "        .reset_index(level=-1, name=\"count\")\n",
    "    )\n",
    "    bmudists = bmus[[\"BMU\", \"bmu-distance\"]].groupby([\"BMU\"]).mean()\n",
    "    bmudists_median = bmus[[\"BMU\", \"bmu-distance\"]].groupby([\"BMU\"]).median()\n",
    "\n",
    "    for idx in bmucounts.index:\n",
    "        node = net.nodes_list[idx]\n",
    "        bmucounts.loc[idx, [\"x\", \"y\"]] = node.pos.get()\n",
    "        bmucounts.loc[idx, \"neighbourhood-distance\"] = node.difference\n",
    "        bmucounts.loc[idx, \"mean-matching-distance\"] = bmudists.loc[idx, \"bmu-distance\"]\n",
    "        bmucounts.loc[idx, \"mean-matching-distance\"] = bmudists.loc[idx, \"bmu-distance\"]\n",
    "\n",
    "    significant_bmus = []\n",
    "    dataset_counts = bmus[\"dataset\"].value_counts()\n",
    "\n",
    "    for name, g in bmucounts.groupby(\"dataset\"):\n",
    "        cohort_count = dataset_counts[name]\n",
    "        min_cohort_count = int(min_cluster_size_ratio * cohort_count)\n",
    "        significant_bmus.append(g[g[\"count\"] >= min_cohort_count])\n",
    "\n",
    "    significant_bmus = pd.concat(significant_bmus).sort_values(\"count\", ascending=False)\n",
    "\n",
    "    return bmu_df, significant_bmus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_hexbin_source(net, train_data, bmu_df, significant_bmus):\n",
    "    _x, _y = bmu_df.x, bmu_df.y\n",
    "    _q, _r = cartesian_to_axial(\n",
    "        _x, _y, size=HEXSIZE, orientation=\"pointytop\", aspect_scale=1\n",
    "    )\n",
    "    _sx, _sy = axial_to_cartesian(_q, _r, size=1, orientation=\"pointytop\")\n",
    "\n",
    "    scattersource = ColumnDataSource(\n",
    "        dict(\n",
    "            x=_sx,\n",
    "            y=_sy,\n",
    "            dataset=bmu_df[\"dataset\"],\n",
    "            color=[color_dict[d] for d in bmu_df[\"dataset\"]],\n",
    "        )\n",
    "    )\n",
    "\n",
    "    bmu_density = net.find_bmu_ix(cupy.asarray(train_data)).get()\n",
    "    bmu_density = Counter(bmu_density)\n",
    "\n",
    "    x, y, distance, sample_count, density = [], [], [], [], []\n",
    "    binned_density = []\n",
    "    for idx, n in enumerate(net.nodes_list):\n",
    "        x.append(n.pos[0].get())\n",
    "        y.append(n.pos[1].get())\n",
    "        distance.append(n.difference)\n",
    "        c = bmu_density.get(idx, 1e-5)\n",
    "        d = 100 * c / len(train_data)\n",
    "        density.append(d)\n",
    "        binned_density.append(np.ceil(d) if d > 1e-5 else 0)\n",
    "\n",
    "        # add an average of matching distances? tain distances or test distances?\n",
    "\n",
    "    # This defines the datapoints for the heatmap background\n",
    "    x, y, distance = np.asarray(x), np.asarray(y), np.asarray(distance)\n",
    "    q, r = cartesian_to_axial(x, y, size=HEXSIZE, orientation=\"pointytop\")\n",
    "    binsource = ColumnDataSource(\n",
    "        dict(\n",
    "            q=q, r=r, distance=distance, density=density, binned_density=binned_density\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # This defines the source of the scatter plots of test samples\n",
    "    # to be overlayed on the heatmap\n",
    "    _x, _y = bmu_df.x, bmu_df.y\n",
    "    _q, _r = cartesian_to_axial(\n",
    "        _x, _y, size=HEXSIZE, orientation=\"pointytop\", aspect_scale=1\n",
    "    )\n",
    "    sx, sy = axial_to_cartesian(_q, _r, size=1, orientation=\"pointytop\")\n",
    "    scattersource = ColumnDataSource(\n",
    "        dict(\n",
    "            x=_sx,\n",
    "            y=_sy,\n",
    "            dataset=bmu_df[\"dataset\"],\n",
    "            color=[color_dict[d] for d in bmu_df[\"dataset\"]],\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Optionally we can plot only the significant nodes (i.e nodes w/ >= 10% of the dataset)\n",
    "    x = significant_bmus.x\n",
    "    y = significant_bmus.y\n",
    "    dataset = significant_bmus[\"dataset\"]\n",
    "    count = significant_bmus[\"count\"]\n",
    "    q, r = cartesian_to_axial(\n",
    "        x, y, size=HEXSIZE, orientation=\"pointytop\", aspect_scale=1\n",
    "    )\n",
    "    sx, sy = axial_to_cartesian(q, r, size=1, orientation=\"pointytop\")\n",
    "    significantsource = ColumnDataSource(\n",
    "        dict(\n",
    "            x=sx,\n",
    "            y=sy,\n",
    "            dataset=dataset,\n",
    "            color=[color_dict[d] for d in dataset],\n",
    "            scale=scale(significant_bmus[\"count\"], 10, 50),\n",
    "        )\n",
    "    )\n",
    "\n",
    "    return binsource, scattersource, significantsource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(dataset_list, inlier_cutoff_idx):\n",
    "\n",
    "    X_data = np.concatenate(dataset_list, axis=0)\n",
    "    y_labels = np.concatenate([[i] * len(d) for i, d in enumerate(dataset_list)])\n",
    "    assert len(y_labels) == len(X_data)\n",
    "\n",
    "    mu, std = np.mean(X_data[y_labels <= inlier_cutoff_idx], axis=0), np.std(\n",
    "        X_data[y_labels <= 0], axis=0\n",
    "    )\n",
    "    X_data = (X_data - mu) / std\n",
    "\n",
    "    train_data = X_data[y_labels <= inlier_cutoff_idx]\n",
    "    train_labels = y_labels[y_labels <= inlier_cutoff_idx]\n",
    "\n",
    "    test_data = X_data[y_labels > inlier_cutoff_idx]\n",
    "    test_labels = y_labels[y_labels > inlier_cutoff_idx]\n",
    "\n",
    "    return (train_data, train_labels), (test_data, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(330, 20)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abcd_data = []\n",
    "for c in [\"abcd-test\", \"abcd-val\"]:\n",
    "    for f in glob.glob(f\"{workdir}/{c}/*\"):\n",
    "        with np.load(f) as data:\n",
    "            abcd_data.append(data[\"score_norms\"])\n",
    "\n",
    "abcd_data = np.concatenate(abcd_data, axis=0)\n",
    "\n",
    "abcd_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibis_typical = []\n",
    "for f in glob.glob(f\"{workdir}/ibis-inlier/*\"):\n",
    "    with np.load(f) as data:\n",
    "        ibis_typical.append(data[\"score_norms\"])\n",
    "\n",
    "ibis_typical = np.concatenate(ibis_typical, axis=0)\n",
    "ibis_typical.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibis_ds = []\n",
    "ibis_ds_fnames = []\n",
    "for f in glob.glob(f\"{workdir}/ibis-ds-sa/*\"):\n",
    "    ibis_ds_fnames.append(f)\n",
    "    with np.load(f) as data:\n",
    "        ibis_ds.append(data[\"score_norms\"])\n",
    "\n",
    "ibis_ds = np.concatenate(ibis_ds, axis=0)\n",
    "ibis_ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibis_asd = []\n",
    "ibis_asd_fnames = []\n",
    "for f in glob.glob(f\"{workdir}/ibis-asd/*\"):\n",
    "    ibis_asd_fnames.append(f)\n",
    "    with np.load(f) as data:\n",
    "        ibis_asd.append(data[\"score_norms\"])\n",
    "\n",
    "ibis_asd = np.concatenate(ibis_asd, axis=0)\n",
    "ibis_asd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106, 20)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibis_hr_typical = []\n",
    "for f in glob.glob(f\"{workdir}/ibis-hr-inliers/*\"):\n",
    "    with np.load(f) as data:\n",
    "        ibis_hr_typical.append(data[\"score_norms\"])\n",
    "\n",
    "ibis_hr_typical = np.concatenate(ibis_hr_typical, axis=0)\n",
    "ibis_hr_typical.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebds = []\n",
    "ebds_fnames = []\n",
    "for f in glob.glob(f\"{workdir}/ebds/*\"):\n",
    "    ebds_fnames.append(f)\n",
    "    with np.load(f) as data:\n",
    "        ebds.append(data[\"score_norms\"])\n",
    "\n",
    "ebds = np.concatenate(ebds, axis=0)\n",
    "ebds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 20)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebds_ood = []\n",
    "ebds_ood_fnames = []\n",
    "for f in glob.glob(f\"{workdir}/ebds-ood/*\"):\n",
    "    ebds_ood_fnames.append(f)\n",
    "    with np.load(f) as data:\n",
    "        ebds_ood.append(data[\"score_norms\"])\n",
    "\n",
    "ebds_ood = np.concatenate(ebds_ood, axis=0)\n",
    "ebds_ood.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ABCD-to-IBIS ratio: 4.024390243902439\n"
     ]
    }
   ],
   "source": [
    "print(\"ABCD-to-IBIS ratio:\", len(abcd_data) / len(ibis_typical))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((611, 20), (117, 20), (28, 20))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = OrderedDict(\n",
    "    {\n",
    "        # First we place inliers\n",
    "        \"ABCD\": abcd_data,\n",
    "        \"IBIS-Typical\": ibis_typical,\n",
    "        \"IBIS-HR-Typical\": ibis_hr_typical,\n",
    "        \"EBDS-Inlier\": ebds,\n",
    "        # Then we place outliers\n",
    "        \"Down Syndrome\": ibis_ds,\n",
    "        \"IBIS-ASD\": ibis_asd,\n",
    "        \"EBDS-Outlier\": ebds_ood,\n",
    "    }\n",
    ")\n",
    "\n",
    "marker_dict = OrderedDict(\n",
    "    {\n",
    "        \"Down Syndrome\": \"triangle\",\n",
    "        \"IBIS-ASD\": \"star\",\n",
    "        \"IBIS-HR-Typical\": \"circle\",\n",
    "        \"EBDS-Outlier\": \"inverted_triangle\",\n",
    "    }\n",
    ")\n",
    "DATASETS = list(marker_dict.keys())\n",
    "MARKERS = list(marker_dict.values())\n",
    "color_dict = OrderedDict(\n",
    "    {\n",
    "        \"Down Syndrome\": \"#f03b20\",  # red\n",
    "        \"IBIS-HR-Typical\": \"#7fc97f\",  # green\n",
    "        \"IBIS-ASD\": \"#fdc076\",  # orange\n",
    "        \"EBDS-Outlier\": \"green\",\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "dataset_id_to_name = {i: v for i, v in enumerate(datasets.keys())}\n",
    "dataset_name_to_id = {v: k for k, v in dataset_id_to_name.items()}\n",
    "\n",
    "dataset_list = [\n",
    "    abcd_data,\n",
    "    ibis_typical,\n",
    "    ibis_hr_typical,\n",
    "    ebds,\n",
    "    ibis_ds,\n",
    "    ibis_asd,\n",
    "    ebds_ood,\n",
    "]\n",
    "(train_data, train_labels), (test_data, test_labels) = build_dataset(\n",
    "    dataset_list, inlier_cutoff_idx=dataset_name_to_id[\"EBDS-Inlier\"]\n",
    ")\n",
    "\n",
    "dssamples = test_data[test_labels == dataset_name_to_id[\"Down Syndrome\"]]\n",
    "\n",
    "train_data.shape, test_data.shape, dssamples.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0dp76aC-7MVG",
    "outputId": "a39b801a-841f-4488-b5cf-1cea3264968f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7254293  0.13698138 0.09117246 0.0313309  0.0068531 ] 5.295824\n",
      "Suggested minimum number of neurons: 123\n",
      "Suggested height and width: 5x25\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=5).fit(train_data)\n",
    "print(\n",
    "    pca.explained_variance_ratio_,\n",
    "    pca.explained_variance_ratio_[0] / pca.explained_variance_ratio_[1],\n",
    ")\n",
    "\n",
    "num_neurons = 5 * np.sqrt(train_data.shape[0])\n",
    "grid_size = int(np.ceil(np.sqrt(num_neurons)))\n",
    "\n",
    "ratio = np.floor(pca.explained_variance_ratio_[0] / pca.explained_variance_ratio_[1])\n",
    "h = np.ceil(np.sqrt(num_neurons / ratio))\n",
    "w = h * ratio\n",
    "print(f\"Suggested minimum number of neurons: {int(num_neurons)}\")\n",
    "print(f\"Suggested height and width: {int(h)}x{int(w)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-02-25 16:39:15.298\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m106\u001b[0m - \u001b[1mPeriodic Boundary Conditions active.\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:15.301\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m115\u001b[0m - \u001b[1mHexagonal topology.\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:15.301\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36m_set_weights\u001b[0m:\u001b[36m179\u001b[0m - \u001b[33m\u001b[1mPlease make sure that the data have been standardized before using PCA.\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:15.302\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36m_set_weights\u001b[0m:\u001b[36m181\u001b[0m - \u001b[1mThe weights will be initialized with PCA.\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:15.390\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m381\u001b[0m - \u001b[1mThe map will be trained with the batch algorithm.\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:15.392\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m403\u001b[0m - \u001b[1mEarly stop active.\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:15.393\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m404\u001b[0m - \u001b[33m\u001b[1mEarly stop is an experimental feature, make sure to know what you are doing!\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:18.155\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36mplot_convergence\u001b[0m:\u001b[36m803\u001b[0m - \u001b[1mConvergence results will be saved to:\n",
      "/tmp/./som_convergence.png\u001b[0m\n",
      "\u001b[32m2025-02-25 16:39:18.166\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36mget_nodes_difference\u001b[0m:\u001b[36m586\u001b[0m - \u001b[1mWeights difference among neighboring nodes calculated.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dLS0Fo0f5MmqUL5qajesQVpZX8cdn29i0aCfVlY0/3m07WTNl0Xh8BzW+Q3j9Yjqr3vmN+AOJja4LEDjMm4mfPotlO7NG143adoKfP9xK9s38RtfV1tNi9PQBDJ3SG41GHu/WcvGG+pK0k61I0qp4pEm6uqaWXw+cZM3u45RXNf6L/E8yqYSnArvxyrCeGOk//AR+K7eIbzYf5tDplGbFBTDR1+GVp/wZ2rMrUunD1/PCr6TySXQUaYWFzY7taW3D+0EhdLN8+Aizur6KiOwdRGbvokbZvI0rUmT4m/Wln+Uz6MgfPh2cU5XOn+lruFhyqllxAQzkJvS3fo7uRkEqrZ/uuXKRT+OiuFlS/NCyD+Nv0473/UPobPrwDmF5ZTU/7jnG72Gnqa6ta1ZchVzG6FB3Xh7ki54KHcKraTksXxbOyVNpzYoLYGFhwORJIYSEOKtUPnx9ND/NWU/OTdVmDB7Ee6AHUxaNp13nh+/HKC0s49dPt7NrVRi1Nc073hpaCp5+fSCjZw1BS/fhxzsl8Tor524kKa755xNre3NeXvA0/k+4N7stdRNJuvU9siR96ORlvt14mFu5Da+DNpW+jiYvD/FjVKg7cvm9G47KK6v5eV8868P+3pCmLp1tLZg1Kojujg2PMC/n5fHx4Uiir19r8PWmkkokDHfuwlv+AZjr6jZY5nRBDLsz1lNY0/wT5z/pyPTpb/UMPUz7IpXcu55XWVdGWNZmYvP23tmQpi62Oo4MsXmRdjoNj26T87JZEHOIYxmNH7U/iEwi4VlnV970DsBY694OoVKpZFdsMsu2xZBbpN5d6qYGOrwytCdP+rs02CEsLq5g9Zpodu06fWeDlLp069aWaa/2wdHRqsHXL8ansHz6apJjL6k1rlwh48lXBjB2/jPoGd37911fX8+enyP55aMtFOWWNNBC05nZGDPhw5GEjvZvsENYmFPM2k93cOC3o2o/3h5BnZn88UjsOqsvMTZX8g31bV7tYnv//TfC31o9SafczOGr3yM5ceFGi8axtzZh5qgg/Lvd3jGqVCrZHXeepduP3HdDmrr09XRk+oheWJvcXs8rqqxkcdxR1p9NvO+GNHXQ09DgVW9fXvTojsb//8Hym+VX2H5rDWnlF1ssLoC1VjuetBmPo/7tDV63N6SFcyDzd8rqmj+CvR8JEtyNAhloPQYDhQkA+RXlfBl/hA0XWvbyNUNNLd7w7ME4Fw/k/3/DUWJqOl9tiORcWlaLxQVwtrNg1qhg3DvePmnW1dWzY+cpfll7hOKShjekqYNUKmHAAFdemtALY+PbCTM/s4Af56wn7JfDLXr5mqGZPuM/HM2gSX3ubPBKOHyeFbPXc+Vsy/7sobNvR6Z+OQYnzw4A1NbUseOHQ/y+aA9lxS13eaZUJmXQ+EDGvvMk+sYNd8Bb09kb6ttQ2M1WvZ3n/6pWTdIH4y/y3qo91Km5x/kg4wd689qIQGav/JOwU5dbLa6WQs73M57G2sqAgb/+QkFly504/62jiQkHxr7A8fwINt1YibIVb0AwwGokfSyf5ucrH3Op9EyrxdWQajHF4WNqa4wYvGUdxdWtd92vm4UVO4aNYcOhM3y5IaJJ+yqaauYzQTzftzuvv/ErSUmtd9LT1dXk++XjqS0q4w3/uZSXtN59BNxDu/Jl2Hw2fLWLn+dvarW4EomE15e8wBMTQng1+GOunGu9461vrMuyiLmYtzFptZgNSbhuq7a23Nq17EDtv6JV7ziWmVfSqgka4FbO7en0m2qeVn+Yyppa8orL0TfRatUEDXC96PZnLajObdUEDZBXlQ1AfnXLjiT/rbq+krLaIiqrtFo1QQPcKL59vNPzils1QQPcyrsdOyOjsFXjlpVVUVxcQU1ucasmaIDMK7f/tjKvte7NX5RK5Z2YGa0cu6SgjLLiCsxb7lYJKqmjZa6hF+7vsb/jmCAIgiD8XyXu3S0IgiCopE6M61qdSNKCIAiCSuqVYrq7tYkkLQiCIKhErEm3PjF3IQiCIAiPKTGSFgRBEFRSpxTjutYmkrQgCIKgknox+drqRJIWBEEQVCLWpFufSNKCIAiCSsR0d+sTR1wQBEEQHlNiJC0IgiCopF5Md7c6kaQFQRAElYg7jrU+kaQFQRAElYg16dYnjrggCIIgPKZEkhYEQRBUUo9UbY/GWLhwIT4+PhgaGmJqakq/fv2Ij49/YJ20tDQkEsk9j19//fWucsnJyYSGhqKtrY2VlRVvv/02dXV1jT42LaVVk3Qvdwc8ndq2Wjw7K2OeDnYFYHw/L8wMdVstdp/ujrg5WGOjr884N3fk0tY51HoaGszw8wfAzcgPe51OrRIXwErLFh/TEACCLYajKzNolbgSJLgbBWKj3YH2hiY829kVqaR1NrgYaGjyhmcPAAb6dqaLnWWrxAVwsrVgkJ8zAGPG+KOvr9UqcaVSCU8MdMXOzgwHN3v6jgtC0krH29BMn2fffRqAfmMDad/VtlXiAnT2diB4hC8AY98ejE5rHW+ZlMEvBmFlZ94q8R6kTilR26MxDh06xOTJk4mOjiY6Ohpra2v69OnDtWvXHlp39+7dZGRk3HmMGDHizmslJSX07dsXY2Njjh07xg8//MCPP/7I3LlzG31sWopEqWztn6mH8BOX+HbTYdJzi1ukfT1tTV4e4sfo3u5UK+uIunmVvnaOVFfX8tPe46wPO0V1bcv0lJxszZk1MhjPTm1JLc6lqKaC7qa2XMrL5aOoSGJuXG+RuBJguLMLs3sGYK6rS/ity7iZ2mCmpcupgiPszviNopq8FomtI9Ojv9VIepj2pbq+ktTSBJwNfKmuryAsaxOxefuoU9a2SOy22h0ZYvMidrpOpJWlU11fQyd9O87lZrPg6CGOZ9xskbgyiYTRzq686dUTYy1twlJT8W7TBkMtLXYdTea7bUfIKy5vkdgm+jq8MtSfp3p2pbSyihOXbxLi6kBxcSVr1kSz68/T1Ne3zNe6W9e2vPpqHzp1suLSxQykUikdHS25cPwyy6ev5nzc5RaJK5PLGDK1H+M+GImuoQ6xB8/h3qMjWrqa7Pk5gnUfb6Uot6RFYptaGzPhw5H0ftafouIKzl1Ip6dvRwpzilnzyQ4O/n60xY63W6ATUz4Zib1zG5KTbqGrq4Fd+0eXrDeleqqtrWccTja5bl1dHcbGxixatIiJEyc2WCYtLY327dsTGxuLn59fg2VWrFjBrFmzyMrKQlf39iBu2bJlvP322+Tk5KCtrd3k96gujyRJA1TV1PLr/pOs2XOciqoatbQplUh4KrArrwzviZGeNttTkvk8/jCZZaV0NDJlnl8IQbbtuZlTyDebDxNxJlUtcQGM9bV55Ul/hgV0o7S2iqXno1ifeoJaZT1PtO3C2936YK1jyMHUFD6NjuJaUZHaYnta2zAvKBhXSyuS8jP56NRB4nNuoKfQ5DWXnozv5A2SWg5l7yAqexc1ymq1xJUio4dpH/pbjURLpsPpgkOEZa2ntLYQKy17nrB+ifZ6XcmpvMWfGWu4WHJaLXEB9OXGDLB6nu7GQRTXlvFr2m72ZcSiREmohTcvtB+CiaYhf6Ze4LO4w9wqVV+H0M/Glvn+oTibmnM6PZ2PIiNJyMzESEuL13v04Hk3N6qqa/lp9zF+Cz9NjZo6hHKZlNGhHkwc7Iu2poLNR87y/e6jFJZV0qWdJbNHBOPewYarV3P4blkYp08/fJShKgsLAyZNDCY0tAv5+aX89EMkB/afBWDgE25MeDkIQ0Mdwn49zE9z1pOXXqC22F793Zjy9QvYObfl7PFUVny4gyvJ6Ria6jJu5kAGjPKhrKiCXz/Zxq4fwqlT0/HW0FLw9OsDGT1rCHJNOVt3nWLt70cpLauiW5c2TJsUSmdHa1ISrrNi7kbOHUtRS1wAK3szXv7gaXoO8iAnu5gfvz/EoYPnkMmkDBnWnXETeqFv0PoJZEOKt9raGmp7hKqqqrue09TURFNT86F1S0pKsLCw4KeffuK5555rsMxfSdrW1paKigocHByYOnUq48aNuzPzM27cOLKysti/f/+deqmpqXTs2PGByb01PbIk/ZecglKWbolmb9x5mvNOuju15c3RwTi1s+BMdgYLYsM5nZ1xT7lQ2w7M6xFKe0Njjl+4zlcbo0i5ldvkuHKZlFHB7kwa4oeOloKNV0+z+FwkBdV3j6K0ZHJe7uTPJCd/pEhZffoUy+KPUVrd9IRprafH7J6BPNXZmdzKMr5KiGTz1UTq/3Ug7fVNmOvRm95tHMmvzmF3+noSimKbHBegk54rT7YZh5WWLdfKktmT/hPplVfuKdfFwI8B1i9irGHBxeJT/Jmxhpyq9CbHlUsUBJgNJsRiOHKpBn+mR/PbtX2U1t59vLVlmoy07cuwtiHU1UtYlRDP92eOUVHb9BG9rb4h7/oFMbBDJ7JKS/kiOpod58/z7z9bR1NT3gsOJsDOjhvZhXyz6TBRCc3rEAa6tmfGM0HYWRpz/OJ1vtgcSUrGvTMjAzydmDE0EEtjfaKPXGTFiggyMgqbHFdLS8GoUb6MHuWLVCply+bjrF8XQ3n53X+3urqajB0fwLDhXtRU1fDHZ9vY/PUuqiub3gFv42jNlEXj8RvsSdatfH767E+i9yTeU65DFxumvP8U3XwcuH7hFive/o2TYWebHBcgYKg3Ez8djZWdOXEnrrDsh0Ncv5l/VxmJBPr37sqk8b0wNdEjcls8Py/YSs6tpndQtHU1GT1jIEOn9EYJbPwtjo3rY6n813E0MNRm3IReDB7aHZms9VYt1Zmkz/86iAULFtz13Pz58/nggw8eWnfy5MmEhYWRlJR039Fubm4ua9asISAgAA0NDfbu3cuHH37IwoULmTFjBgD9+vXDysqKX3755U69srIy9PT02Lp1K8OGDWv6B1STR56k/5J0JYMvf4vg3NXMRtWzNjXgjWd60ce7E9nlpSw8fphtl8/dc+L8J4VUygsu3Xmtuz+6cgVbDp9lxc7bI5LG6NnVnjefCcLeyoS47DQ+SdzPxaLsB79fbQPe6tabwbZdySkr48ujR9iS/OD3+29acjkTu3syxcsHmVTK2kvxLD0XQ2lN1QPr9bLqwHvd+9DR0IwrpefZkb6GWxVpjYgMZhpWDLEZi4uhF0XVuezLXEtS0ZEH1pFLNPA3G0KQxQhkEjlHc/cSnr2ZyrqyRsXuYuDDIOvxmGpacjL/PD9c2cqN8qwH1rHSMmVCh6H0NHMjo7SEhccOsyPlfKPi6sgVvOLhy8uuXkiAH06cYGV8POU1D05AvTt04N2gIOyNjTmWfI1FG6NITW/ckkN7axNmjgzC38Wem7mFLNp6mIjEByd8LQ05L/b1ZnxvL6TA5i3xrF8fS0VF4zqEISHOTJ4UgoWFATFHLrHy+3BuPSQB2dqaMOWVPvj16EhmWjar3vqF6C3HGhVXx0CbMe+NYNgbT1BbW8/G7w+x5YdIqqse3MEKfMKVl+YMxrKNCXF7T7Pqnd+4lfLgv49/69CtHVO+eB63Xs5cv5nHdz9EcOzEvZ3Pu96vtgZjR/XgmaFe1NfWsfm7A2z67gBV5aofb4lEQu+Rvrz43jBMrAyJCk/mh+8PkZX54Bk3+w7mTH29L9292qscqzl+S/FVW1tP2x5u0kh6wYIFLFmyhIiICFxdXRsVc968eaxatYqsrNt/FyJJN5JSqWR37Hm+2xxNbtGDT+BaGnJeeMKHsQO8QAo/nj3B8jNxlD3kxPlPZto6vOkVwCgnV0rLq1ixK5bNUYnU1tc/sJ69pTFvjgyiZ9f23Cgr4PPEMA6kX1A5LoCnqS3vufXHxdiaxKxMPoqK5GTGw0eYgxw78U5AL9oYGBB+6zKfnA4nrST/ofX+IpdIed6xO9O7BaKv0OR4fgR7M/+grPbB08GaUm36WA4j0GwQ9dQRnbOVmJztjZo615eb0NdqDO5GwZTVFXMg8w/i88NR8uDjbanVjiHWL9BR35VbFdn8kLqN+PxzKscFcDPqxCSH4djr2nAy8xYLjh4iMefBJ3AJMMyxC2/79sJSV4+9ly6x8PBhbharPnWuIZMx3sODaX5+aMvlbI5MZOWuWIoe0iHU19Fk0hA/Rga7U11byw/7jrE+4nSj9lJYG+szfWgg/T2dyMsr5ccfIzlwMOmhM1aOjpZMe7UP3brZknY1h2XfHeTUyTSV4wL4+Dow9ZXetLMzIyHyHMtnrOZKwoOn36VSCf1fDOXFT57F2MKQQ9tPsfrL3eRmqL40pKEp5+mJwYycGopcLmXbsgP89vkOyosrHljP0Eyf8e8/zYAXgqmorGHN+iNs232a2toH/23+UxtrI6a+FEJgD0dybuXz04KtRG078dB6nb3aM+XTUTh52JNyKZPl3x7gbMINleMC+Ad2Ysq0Pli3MW5UvcZad1l9079jHeMaXWfu3LmsWLGCsLAwPDw8Gl1/9+7dDB48mMLCQgwNDRuc7r5y5QoODg5iuvtByiurWb37OOsPnLznpCSRwADfzkx7OhBLE332Xb3EJ8ciuVHS9DVeF1ML5vfojY91W66k5/HVpijiku89oejraDJxkB+jQtyoVtax8sIRVl8+RlV906ZQpUgYbu/GTJcQzLT02HnxAp8fiSaj9N4NMC7mFswLCsanTVtSinL5+FQYhzMf3MN/EGMNbaa79uI5Bw+qlZUczNpMTO4+6pT/Ot5I8DYJZqDVaPQVRiQWRnMg8xeKapq+RNBWuxNP2LyErU4n0ivS+DN9NVfK7k26OjJ9+lqOwte0LxV11fx+fT+7bkVRq2zamqMUKQOs/RlrPwh9uQ6bL53ji+PR5JTf2yF0t7Bmvn8oHpbWnM/J4aOICI7dbPomNDMdHd7s2ZMRXbtSUl7Fyp23O4R1/+oQyqQShgV2Y8qT/hjpabHzWDLf7Ywhp7hxsw7/1N2hDbNHBNPZ1oILFzL4blkYycm37ilnbKzLyy/1on9/V0pLK1nz82F27TzV5E1RcrmUp4Z6Mm58IDq6Guz5IZw1835vcINXt0BnXln8Ih092nMx4TorP9rB+VNNX1M3szbkxbcGEfKUB0U5JaxesIkDvxy+57PIFTKGTOrDmHeHomOgw5/7E/hp3REKi5q+6c/T3Y7XJvWmvZ0ZSXEprHpvI5cT7t0wamptxIR5wwgZ4UNhYTmrV0Wyf3dCk4+3QkPG8Gd8eH58ANo6Gk1+/w+y9rK/2toa73i0UeXffPNN1q5dS1hYGO7u7k2K+fHHH/P5559TWFiITCa7s3EsOzsbHR0dAJYvX87s2bPFxjFV3MopYvHGKCJO3d6Q0aW9JbNGh+Da0YYL+TksOHqI2Az17ZYe1N6JOb5BtNU3JCohlW82H+Z6diFSiYShAV159Sn/2xvSrieyKCmC7Er17CbVlWvwSudAxjv6UldXz4oT8fxw6gSVtbWYauvwpr8/I126UVJTyeKz0ay/fIpapeo9/AdxMjRnXve++FvZk1OVzs5b6zhfcgoAex0nhrZ5gbY6HbhVkcqe9B+5Xt64GYP7kSDB1SiIflZjMVCYcLYwlj0Z6yioyUaKDD/TfvSxHIWWTIcDmXGsS9tNYY16jreeXIfn7AYw2CaQipo6vjsdx89nT1JdV4eljh6zfQMZ7tiF/IoKvo6JYWNS0j3r/E3V1dKSecHBeLVpQ2p6Los2RHHs/O2/YS8nW2aNCsaxrRkJV9L5Yksk5641brr2fqQSCU/1cOG1IT0x1tMmPDyZVT9EkptbgkIhY/gwL8aM8UdLU8HOnSdZu+YIJQ8ZfarK0FCbFycE8cRgdypKKli3YBM7l++jtqYOi3ZmTPx8DMGjepKfXczqL/cQvvUk6jotOXe3Y/K8p3Bya0fKmTS+n72epJiLAHj1dWXy58/RzsmG04nX+W7VIVKuPni5SlUyqYQnn3BnwpgA9HQ1Ofh7LGs/2UFBTvHtDWmv9GXk6/2Ra8rZtjGeX9ceobzswctVqjIx1eXFSSH0f8JV7ZfGPaokPW3aNNauXcuWLVvumuLW09NDT08PgDlz5nD8+HHCw8Nvv9e1a5HL5Xh4eCCXy9m/fz9vv/02b7zxBp999hlwewOak5MT/v7+zJ8/n+vXrzN+/HhefvllFi5cqLbP2hyPdZL+y4kL18nML2VQD2cKqipYdOIIf1xIpK4F3rqWTM4kV2+muvsiR8qW6LN079iGTrbmJOTf4qMz+0gsaPrGpwex0zXhHdc+9LZx4lZxMXsuX2JU127oKhT8lnqaxYmHKahWz4nz3/q17cS7Hr1pp2fMheIzVNSV4WHck9KaQg5mreN0QQTKRq2cq0ZDqkWg+XB6mj0FwLH8gzjquWKpZUtSYQorU7dwpezeUZ862OpYMrHDcDxNnLleXMiBtBSedXZFQyrjlzNnWBoXR0mVek6c/zbIyYl3evXCRl+fyDOp1NfXE9rdkazCUhZvj2bvCfV0hv5NT0uDiQN8eS7Eg9rqOv7cfQb/Ho60aWPMyRNXWf7dQdLSmj5L8iAdHCx4dVpf3D3suHHxFvF7zzBoch8kUhnbfopiw/eHqFBTovoniURC6LDuTJg9CBMLA6K2HENLVxPfAe5kZBXx/U8RRMVcUntcAAN9LV54ridPDfKgqryKfeuOEDDYA8t2ZsTFXGbld2HcvKH6clVjdOpszavT+9Glq/ruTfHzpQC1tTWh04P3svzT/Tob/9xo9sILLxAZGUlaWhoAv/zyC1988QVXr169falgx45MmTKFiRMnIv3HfSuSk5OZNm0asbGxGBgYMH78eD799FPk8sfjrtn/E0kaoK6+nrXJp/n21FGKqhq3wasprHX1ecenF0917EJ2RQlfJIWz6/rZFkhT9+pp0YG5bv3oaGBObFYaH548yMWinBaPqyGVMcHJh2lde6Ipk3I0dxdR2Zuoqm+ZjsE/GSss6W89HhfDHuRUFvDjlW0cyT3T4nEBvE1cmOQwHBttc6KuXuXjyEiuFKjvEqL70ZLLmeTlxRQfHyRKWBN2gtUHT1BRrZ5LEh/EzsKYN4f3olfXDqSnF/D9sjCOxrTMNc7/FtjLiamv9MHSypDYg0ms+mQXmddb5hr+f9LW1WTU1FCGvxxEXb2SXzfGsWFbPNXVLXMN/z/ZtzPltUm98fKw58b1PL7/9gDxx5q+XNUYoX1dmPhKKGbmzb+50I+XAtXwjm57uVO02tr6L/ufuS1oemkxH8YeapUEDZBRVsL0iN0AbLmWwM5WStAAMdlXWHHhdi9zQSslaIDq+jpWnI8lLus6OVW3OJD5S6skaICCmiy23fwOgD0ZR1otQQPE559j681DAMwLD2+VBA1QWVvLkrg4zmRkcP5GNst3x7ZKgga4ll3AR7+HAbBl0/FWS9AA0Ycvsm3r7Q1Vi9/Z2CoJGqCirIo1X+3l2qVMzp6/xboNsa2SoAHSruex+PuDAPzx69FWS9AAhw6eY++uM2ppq04pVdtDUM3jMZ4XBEEQHnvipypbnzjigiAIgvCYEiNpQRAEQSX1jfxhDKH5RJIWBEEQVCKmu1ufSNKCIAiCSurFhq9WJ464IAiCIDymxEhaEARBUEkdYk26tYkkLQiCIKhETHe3PpGkBUEQBJWIkXTrE0laEARBUIkYSbc+ccQFQRAE4TElRtKCIAiCSsQ9t1ufSNKCIAiCSurFmnSrE0laEARBUIkYSbc+ccQFQRAE4TH1PzOS1pTL0ZDJqK6ra7WY+hqat/9fodlqMf+ip9B6JLElgJ5CAw2JAplETp2ydX5vF0BLpgOAjlyr1WL+RUf2/4+3hkarxpVJJOhoaKAllSGTSqirb61fLQd97dt/W7q6rf/3rat3O6aOvjbFBeWtFlcml6Klo4FOtRSpVEJ9Kx7vv47zozne6vlOiR/YaH0SpVLZen+lzXS9uJCP4yI4cC2lReNIJRJGOXVjllcgptq3E0d4+kUWJoZxrSy/RWObaOowvUsII9t7IJVIqKitYcX5WH44H0dlXcsmTE+ztszr3hdXU2sAcqpusTf9Zy6XnmrRuFJk+JgOINRyNNoyPQCic07z85UdZFe18PHWMGC8/RB6W/ogkUgora5mWVwca06fbvEOoW/btrwfEkJnc3MAUtJz+XJLJMcu3mjRuAq5jGeD3Jk0wBc9bU2USiVhB5P4YVUEebmlLRrbwsKAiZNDCO3tAkBpcQW/Lj7An7/GUFdb36KxPXs5Mem9J2nX0RKAS6lZLF0ZTuK5my0aV0NDzqhh3jw/0hdtLQ3q65Xs35PA6lWRFOSXtWhsaxsjJk/rQ89eTmpp753EEWppB2Ch62a1tfVf9tgn6YLyCpZGxJJRXMLb/Xphb2rM0VvX+DDuEBfyc9Uez9falvd7hOBiakl6xUUislZjq9OVHmYjUKJg7eVjLL8QTVlttVrjKiRSxnT0YZpzIDoyDX67kMCWy+eY5u5HH7uO3CorYuGZQ+y+fl6tcQGsdfSZ7RbKU/Yu5JWUs2R3DCUVlcx8shdtTQ25VHKSvRmrya26pfbYHfXcGWg9AQstW07np7Hs0kH8zR0ZYx8AEiVbbxxi042DVNar+3jLGdY2hJHt+qGQKFifGs+eG8m87hJET8sOpBUU8Nnhw4Slpqo1LoCtoSHvBAYyoFMnskpK+So8mnolvNU7ACsDfQ4lpPDNtsPcyC1Se+xeXdvz5vAg7CyMiU+6xqrNRwnxcWRkPw9qaur4ff1RNm08TnW1ejuEWloKRo32Y9SzfkilUrZtOEZs9CVenByCa3c7rqdk8cMnOzkRdVGtcQHatDdn0twh+IR2ITO7iGW/RKGtqWDymF6YGuty6PB5VvwcRVZOsdpjB/XsxNSXQrC2NCTq0lWWRx3jSdfOjPZ2pbKihvVrjrB9czw1NertEGpra/DcuJ4MH+WDhob6JkxnJzyjtra+cNuktrb+yx7bJF1bX89v8Qksi4ylqLIKAIVUyhhfD14N8kVboeC3Cwl8fSKGgqqKZsdro2fAu77BDOrgRGlNPhHZq0kqOgTcPjwGcnOCLV/ExTCY3MpSvj4Xwda0BOpp/uELsurIu679aK9vytH063wYd4jz+Tl/v97Wnnm+oTgam3I8+zofnTrIuYKsZsfVksmZ6OzHFOceyJHy6+HT/HDwGKWVtxOipkLG2CBPXu7jg6aGhGN5e4jI2khlffN7/yYa1gy0fpHOBt5kVhTy7cV9HMg4e+f1NtrGTO88kFArF3KrCll9dSdR2SdRquF49zB15eUOw7DSNiU6M5VPEw6QWvJ3hy/U2pE5bv2w0zMh5to1PoqM5HJeXrPj6igUTPHx4WVPTwB+ij3JDzHxlNfUAKCtkPOyvzcv9/BCCvwacYof9x+nvKqm2bE7WJkwa3gQ/l3suZVdyLe/RnL45N8dEDtrY94YE4y/ewcyMwpZ8X040YfVkzBDQrswaUooFhYGHD18kVVLw0i/+fcMSWCIM5Ne64OltRHHDyWz6pNd3Lqa84AWVaOrr8Wzr/XhqfGB1NbXs27LMX7feeJOB0RHW4NxT/syaogX9fX1/L7lOL9vPk6lGo53x/YWTJsUiodrO67k5rNwXxSHL6fded3RwpR3BwbTo0M7bt7IZ+XSg8Qdbf4MoUQCfQa48tLkYEzN9Jvd3r/NShiltra+ctugtrb+yx7LJB2Teo3P9keSktPwVKeprg7TQ/152qMrJdVVfHMyhl+Tz1CrbPx0mbZcwVQ3Hya5+iCXKjmev5XY3I1U1zec+Ntqu9DXajJW2h1JKsjg44T9nMpr2vRkB31T5rj2I8iqI9dLCvnkWCT70i43WFYukTK2izszuvdEX0OTjalnWJQYRV5V09bzBrVz5h33UNroGhKZlMqinYe5llPYYFkLQ11eHxTAEC9nyuuKCcv6jZP5YShp/PHWlOoQbDECP9PB1CiV/HIlmrVXoqmqb/jE6G3agTedB+Gob8X54qusSt3CpZLrjY4LYK9rw6QOw3Ez7kRaSR6fJhwkMrPh462Qyhjf0YdXnAPRlilYn5DAt7GxFFZWNjquBHjK2ZnZgYFY6umxL/kSX4RHc6uw4ZGbjaE+b/UO5AkXJ3KKSlmyM4Y/jyfTlG+qgY4mU57owchAN6pralm9PY4N+05RfZ+RWw+39kwfE4ydjQkJZ66xbOlBUlOzGx8YcOxkxavT+tLN1Za0K9ms+PYgp45fabCshoacEc/5MXp8AHKZlB1ro/l9aRhlJY0/3lKphH7P+DD+zQEYmelzICqZFb9Gk51X0mD5ttZGvDo+mECfjmTnFLNidRThUU2bsTI00OblcYEM6udKeU0N30XE8lt8AjV1DX9Xend24J3+vbA1MSL+WCorloZxPa1pM4RdurbhlTf64eRs06T6qhBJuvU9Vkn6Wn4hC/dHEXGp4S/yv3WxsuDdAUF42bUlpSCPBbGHiL6VplJdCfBUR2fe9g7CWk+fi8UxHMr6icKaTBXqSulm1Idgi/Hoyo3580YSX54NJ6NCtekyA4UW05x78byDF9V1dSw7c4wfk05QpcKas7GmNjM9e/J8ZzfK62pYmnSEtZfiqalXLWG6GFsyr3tffCzacSUrjy+2R3H0wjWV6nazs+LtYcG42lmTUXGVvRk/c7UsSaW6EqR4GIfQ13IMegoj9qUnsOTifrIqHz6lK5NIGWbrxVTHvhgqtAnLOs4vabvIr1bxeMt1GWP/BAOse1JeW82y5GjWpRynRoVOnZmmLjO7hjLc3o3iykoWx8bye2IitSoeb3crK+aFhOBubc2FzBw+ORDJ8WuqrYF6tWvD3H7BdLG2IOlaJl9sjiTxaoZKdWVSCcN7duPVQf4Y6mqx+/A5Vmw8Qm7hw2dB5DIpI/q689LwHuhqa7L7z9Os/imKoiLVZqyMjXV5aWIw/Qe4UlpSybofo/hz20nq7pOo/snMwoCXXgkltF9XivJKWfv1Pg5sPK7yBq+u3u2Z/P5QOrq04fzlDL79OYKki+kq1fVyteP1CSF0aGfG2eSbLF0ZzsUU1Was5HIpwwZ5MP65nujqaLLx5FmWRBwlv+zhx0xDLmO8nwdTevmiJZOzc9tJ1v18mBIVOyhm5vq8PDWU0L4uSCQtu7FrxpnRamvrG/c/1NbWo1ZcXMzy5cuJiIggOzublStX4uPjQ35+PmvWrOHJJ5+kY8eOTWr7sUjSpVVVLI86xrrjZ6hpwmadgS6deKtvIDaGBoRdS+GTuEiuFhfct7ybuRXv9wjF07IN2ZVXCctcxbXyhEbH1ZBq09NsNN6mQ6mugx8uHeXHS0fvu8FLioSR7T2Y7hKCsYY2W1PO8Xl8NFnljd+s09nYjPd7hNLTxo4rxXl8cjqciPT7T5eZaurwpmsQIx3cKa2oYvm+WDbGqJ5s/iKRwCBPZ6YPDsDCUI+koqPsz1hLYc39R1vtdDrzhM3LtNF24HzRLb46v5szBap1DP7JQKHNxI6hjGznS42ylg3X97P9ZiQ199mBLpNIGWQdyHN2A9GVa7Pp6mkWn4skr6rx0/Vdja2Z69YfTzNbLuXm8nFkJDHX7z+it9TV5a3AQIY6O1NQUcniiBg2n06irpFfN6lEwtPuLswI6Ymprg6748/z7Y4jZBfe/2/Gp5Mtbz0djGMbM85eTuebXyJIvvLwzue/GRtoM2lET54KcaW8rIpffjnCjm0nqL3PBi+FQsawp70ZM7YnWloK/tx6kl9+jKKkuPHLUV26tmXqjP44dbEhNfkWKz7cQdJ9RuEAFjZGvDRnML0GuZNXUMaKXw+zL/Jco2cfZFIJT/V34+XRPdHX02LPwbP8uDaa/Ad0bnw82zNtYih2tqYcT7vJp3sjuZDZ+Ol6c31dZvTuyTD3LpQUV7Dmhyh27zpNfV3DH0JDQ84zz/oyaow/2tqtc1XCG6efVVtb33r8rra2HqWbN28SFBTEjRs3cHR05MKFCxw8eJDQ0FAAnJycGDBgAN9++22T2n+kSbpeqWTr6XMsPhRDblnzLsPQksuZ4O/JxABvZFIpa86dZOmpWEpq/t5wZK6ty2yfXoxwdKGiroTDOes4U7C3SdO2/2SsYUNvy5dx1PcjvbyIL86Gsedm8l1lfM3teNe1H85GVpzOTmdB7CFO56g2KnqQ/naOvOcXTDt9I6LSU/n4dBipxX+vnyqkUsZ38maaS0905RpsOnqW5fuOUljW+GnEf9LRVPBSbx/Gh3RHIq0nJncH0Tlbqa7/u11DhRn9rMbhahRIXlUp313cz5+3Tjd7Hb+9rjlvOg+ih7kjGRW5/HRlO7F5iXeV6W7cmYkOw2mnY0V8znU+SdhPcmHjE9W/DbJ1YXa33ljrGHIwJYVPo6K4XvT3bICmTMYET09e8fVFIZWyPj6BZdFxFP//fRVNpa+pySuBvoz1cae2tp6fD8bzS/gJqv4xbd3G1ICZw3rR292R7PwSvvv9MAdjLzRpmvyfHNuZM2NcCN2dbblxPY/ly8I4fuzuDXU9/B2Z+kpv2rQ14XT8Vb5fvJ+0K81bV5ZIoM9AV156pTcmpnoc3n2Gnz77k+z0wjtlNLUVPDM5hBGTQpDIJGzYdZJ1W45RXtG8jYYGelpMGOXP0AHuVFfVsPaPWLbsOElN7d/H27aNMa++HEoPHwduFhTxxYHDHEhu/rpytzaWvDswGA9bG66mZrN8yUHOnEy7q0xgcGcmT+uDpZVhs+M1xmunnldbW0u7r1dbW4/Ss88+S3h4OJGRkVhYWGBhYUFYWNidJP3222/z559/cu7cuSa1/8iS9Knrt/hkXyTnMpq23nU/VgZ6vNknkCHdOpNbUcaX8dHsSDnPi109edXdDx2FjJP5f3IkZz2V9eq93KS9bnf6WE3CTLMdJ3JvJ4ai6kredu1D/zbOZJWVsjA+im0pyWrY/vQ3TZmMl7p6Mc3dD02ZnHWXT/Dt2SN4mrdlrkdvOhiYcuzydb7YFsXlDPXuiG9jYsDMJ3vR182R4po8DmSuI7kojgDzoQSYD0OCjN/SjvJTSiRldc1LVP8WaO7ETOcnaKdrxpmCi6xK3UqNspaXOwzF17Qb6eVFfJ4Yxt5/dZiaS1um4GWnHkx08keKlNUnT7L8+HEC7Ox4p1cvbA0NOZySxmcHoriSp95LyOxNjHmnby9COnUgPb+Yb7YdJiY5jZf6+TA2tDtKJazfHc+6XfFUqGED1D+FeDvy2vNB2Jgbciwuhe+XhSGVSXnl1T54eXcg/ebtDVCxhy+pNa6OjgajxwcwfLQvynolm1dFsHlVJH59XJjw9iDMrY04fOwyy9ZGcSuzUK2x29ua8vqEULzd7LiZXsDyHyNISLrBuGf9eXpId6rr61kVfZzVR09SVau+HdoSCQzq2pm3+gViaaDHkagLrFoWjo6uJq+80Q9X93Zqi9UYIknfy9TUlBkzZvDee++Rl5eHubn5XUl65cqVvPXWWxQXN+3qgVZP0hlFJXwZFs2eJPVfavFP3W1teHdAMF1tLKmsrUFLruBK6SnCMleSV91y16FKkeFh/ASBFmPQlOpRW19HvRJ+OBvP8oRjlNeq98T5TxY6urzt1YunHV2oqqtFS67gZl4Ri3YcJvxsy15b7t3RlreHBdHJxpya+ioUUk2iss7zzYW93Chv/s7o+1FIZIyy78FEhxC05RrUK+uprVey6mIMP12KbdFry210DJnVLZTBtl2prKlBS6Hgal4Bnx2IIirlaovFBQh0sGdOvyAczEyorK5FS0NO+LGLLP3tMJm56r+U6C+aCjnPPuHJ+Cd9UcilSJBQVVXDb2ui2bbhuNovJfon6zbGTJzWh4DgzlRV1qCppeDKtRy+XR3BycSmbSZUVYC3A6+9GEIbKyOqqmrQ0JCzK/ECi8KOkFXccteW62goeLmnFy/19EKKBJns9k1YHpVXTo1RW1vLu/+qtrYeJR0dHb755hsmT57cYJL+6quv+PDDD/83knTEpSvM3LybiprWuYuVBJge2pPJgT7sTV/CmcJ9rRIXQFtmwOh2nyDHiqE713OjRP3XvN6Pp4UNW598ngNnLvHu+n1Uq7GH/yAyqYRZTwXxfC8P3j2zgf0ZiQ+vpCYmGrqs8n0ZLakmoyLWkKniJj51CLR04KfA59iWcI55f4apvImvueRSKR88EcozHt1486ttxJxWbcOlOpgb67Fy/miUlbW8OfUX8vNa9iYo/xQQ0pn3P32GrXtP8+1Ph1rtLm0KuYw50wbQr5cz41Zv4nhay94E5Z9sjAz44+VRWOjrtVrMhkw5OVZtba3wXKe2th4lLy8vnJycWL9+fYNJOiAgAJlMRlRUVJPab9Xbgl7JzW+1BA23r3A+n3l7Oj2zsmVHkv9WUVdMUU0W8nrjVk3QAEl5t3ekpmTmtVqCBqirV3Lx1u11yPNF6r/xyYPkV5eRVVmMnky3VRM0QFLB7b0Fl7LzWi1Bw+17CVzMur18cfFq86+bb4ycglLyC8uoL6lp1QQNcPnC7b0FKWk5rXob1ZraOq5ev3281b1M9zDphcUUllc+8iRdL35g4x7Tp09n/PjxuLq68swzt2/2Ul9fT0pKCgsWLCA2NpYtW7Y0uf3/mXt3C4IgCI+W+KnKe40ZM4Zr167x3nvvMXfuXAAGDBiAUqlEKpXy6aefMnTo0Ca3L5K0IAiCIDTD3LlzGTt2LFu2bCElJYX6+nocHBwYPnw4HTp0aFbbIkkLgiAIKqkTv4J1X+3atWPGjBlqb1csMAiCIAgqqVdK1fZojIULF+Lj44OhoSGmpqb069eP+Pj4B9aJjIxk6NCh2NjYoKOjg4uLC0uWLLmnjEQiuedx5MiRRh+bliJG0oIgCIJKHtXvSR86dIjJkyfj7e2NXC7n888/p0+fPiQmJmJnZ9dgnaNHj+Ls7MysWbOwsbEhOjqaKVOmUF9fz/Tp0+8qm5iYiPn//8lYuH3ts6qkUqlKt2Ota+JP34okLQiCIDzWDhw4cNd///zzz2zbto0DBw4wceLEBuu8++67d/13hw4dOHbsGJs2bbonSZubm2NlZdWk9/b+++/fk6Tr6upIS0tj+/btODk5MXjw4Ca1DSJJC4IgCCpS5+7uqqoqqqruvguhpqYmmpqaD61bXl5OTU0Nurq6jYpZXFzcYB0/Pz8qKytxcnLirbfealRS/eCDD+77WkZGBn5+fnTq1KlR7/OfxJq0IAiCoJJ6pURtj88++wxDQ8O7Hp999plK7+OvKexhw4ap/N6jo6PZsGEDb7755p3nrK2tWb58OZs2bWLHjh14eXkxZMgQtm3b1uhj0xBra2umTJnCRx991OQ2xEhaEARBUIk6b2YyZ84cZs6ceddzqoyiFyxYwObNm4mIiEBbW1ulWKdOnWLo0KF88MEH9O/f/87zTk5OODk53flvX19fsrOzWbhwYaM6AA+iq6vL1atNv02wSNKCIAiCStS5cUzVqe1/mjt3LitWrCAsLAxXV1eV6hw/fpz+/fszffr0OzcbeRA/Pz/+/PPPRr2v+0lKSmLJkiXNmu4WSVoQBEF47L355pusXbuW8PBw3N3dVaoTExPDE088waxZs5g3b55KdU6fPo21tbXK76t9+/YN7u4uLCykqKgIHR0dtm/frnJ7/yaStCAIgqCSR3Vb0GnTprF27Vq2bNmClZUVmZm379+up6eHnt7t+5nPmTOH48ePEx4eDkBUVBSDBg3i5ZdfZuLEiXfqyGSyO5dbLV68GHt7e7p06UJtbS2bN29m9erVLF++XOX3FhQUdE+SlkgkGBsb4+DgwOjRozExMWnyZxdJWhAEQVDJo7pOetmyZQB3rScDzJ8//87u6oyMDFJTU++8tnr1asrKyvj222/59ttv7zxvZ2dHWloaANXV1cyePZsbN26gpaVF586d2bBhAyNGjFD5va1Zs6ZpH0pFIkkLgiAIKnlUSVqVX1T+d7Jcs2bNQxPo7NmzmT17djPeWcsTSVoQBEEQVPThhx82uo5EIlF5TfzfWjVJ+3doh6OFKZez81olnrmeDoO6dgbA1agf+dm3qK6vaJXYtjpdsdLuiBw9BrV3YvfVi60SVyGVMq6LBwABzvbsO32RtOyCVoltaaRHX3dHAIbZerMyJZzKuppWie1r6oCDngUaUgW9rTsRnnGpVeJqSGU85+AJQGinDhy8mMKNgtb5/fA2Rgb0dnIAYHgfN9buOE5VK/1ee0+PDthaGVNvWo+XrwMnjqU+vJIaaGrKGTS0OwDBPToRd/oq2bklrRK7rbUx/l63f9HoOW831sSeoqaJt3psrH5dOmJjqN8qsR7kUY2kHycPunnJ/TQnSUuUqswjqFFdfT0bTp5lScRRCisqWySGQiZjvK8HU3r5oqWQcyozHR+btpTVFhCZvYbEwjCgZT62ocKCEIsJOBv2Iru8lJKaKhwMTTmWcYMP4g6RnNdyPxYf3LY97/uF4mBkwpmcDByNTNGQyvgjOoEV++Moqax6eCNNoKWQMz7Ekwm9vdFQQEbFRdrqdCWrooglF/ezLyOhReICtNUxYUbngQRbdkFZlwHKaiRyO2KyrvBpwgEuF+e0WOw+Nk6849qXdnrG5FUmYKThRF29nDXHTvH9keOUV7dMB0VHoWBST28m9PAECZwryKC7mS3pOUUsXR9FRPzlFokLYG9jwvSxIfi52nMzpxCpRIKNmSFxRy6xcslBbt3Ib7HYQX26MHFaHywsDTl77iZOjlYolUp+3Xac37bHU1XdMh0UXR0Nxo/owTODulOrrOdyZi6u7axJyytg4b4oIi81/RrYh3GyNOPdgcH4trclLTMfHU0NLIz1Wizewww6/Lra2trda8nDCwmtn6T/UlRRydLIWP44kUhtfb3a2u3t5MDsfr2wMzEi+kYaH0ZHcrkgDy9rG+YHhNLNwpKMisuEZa7kZkWy2uIqJJr4mT2Dn+kI6pRSfjx/nO+TYqmqq+X5Th7McA1EX0OLDRcT+fLEEfIqy9UW28HQhHl+IYTYduB6SSGfHI9k37VLWOro8bZnL4Z3dKGgrILv9hxla1wS9Wr8J+/v3okZQwKxMTEgpSSOiKwfKai+RTsdN3pbTcZcqz0JBdf46vxukotuqS2urkyTlzoG85y9P3JJDcrSVVD2E1AHOuOQ6L1KHTr8fuUkS5OjKKxW3wyKk6EF77r1o4dFe0qqr5GY9xVZ5UfQllvRzXQ6bfX6k11SyteHYtiemKy27qAEeLKbM2/2DsBSX4+9N87zeWIYN8uKCLJy4F33vjgYmHEy+QaL10Vw+br6OigGulq8NLwHT/d1p7qmlh/3HOO38NNIJDCmjycTBvqgkEnZvvE461dHU1aqvg5hRycrXpnRn65u7bh6LZfvVoZz8vQ1bKyNmPpyCIH+jmTlFrP8l8OEH7mgtrhSqYRBoV2Z9FwAxka6/HnqPN/sPUJWUSmhLg7MGtyLdqZGRKeksXBfFKk56uugGOto80ZoD57x7EZ5ZTWrdsWxMTIBhVzGCwO8GdvPE01F669WDjz8htra2tvr24cXEh5dkv5LSk4en+2PIib1WrPacTQ3Zc6AIPw72JFWWMDHMZGEpV25q4xUIuFppy7M7hGIuY4u54oiicxaTXFtc05mElwMgwm2eBEDhRn7rl/k01OHuFFaeFcpY01tZrgG8pyjB2W1NXx76ihrk09R04wOioGGJm94+DPexYPqujqWJcTx47kTVNXdPaLwMLdmvm9vPCxsuHgrhy+2RxKfcrPJcQE6t7Hg7WFBeDq0JbfqGocyV5FWduquMhKkuBkPJNB8HFoyfXbdOs13l/aTV1Xa5LhSJAxp251XO/XDVFMPZcWfKEu+hPqMfxU0Q6I3A7SfpqimiiXnovjjyklqlU0/3kYa2kx3CWZUh+7U15dxvmAVqUV/oOTu422q5YGb2WyMNDuTmJ7Jp/sjOX0z4z6tqsatjRVz+wfj1saa84VZfHR6P8dzrt9VRiGV8ryDF6+79EJPrsmOiERWboqhsKTpHRSZVMJToa5MHtETAz0tdsUm8932GHKLyu4qZ2Gkx2vDAnjCtzOFBeWsWRXB/l1nqK9v+unF2ESXF6eE0m+QG6Vllfy87gg7d9/bpqeHHdMm96a9nRkJ52+y5OcILqZmNTkugFuXtrwxIYROHSw5eyOThTsjSbh297+hhlzG2AAPJoX6oqmQ81t8AssiYymqaHoHRSGT8py3G68G+6GnqcnW6ES+3xFLYend/4Y2pga8MSKQPp5Nv0lGU/SPmq62tvYHLVZbW/9ljzxJ/+XQxVQ+P3CYa/mFjapnqK3Fa8E9eNbLlfLaGpbGx7Em8TTV9fdfK9JTaDDNy5cJbp5IJDXE5W4mLm8LtcrGfbmstTrRx2oybXWcuVCQzYcnwojNenBnw8nInPe9+uBvZc+Vonw+iovg0I0rD6zzb1KJhGedXJnlFYCxpjZbU87x+cnDZJXfP/lJgKEOXZjjHYSljj4Hzlzi613RpOcXNyq2iZ4O057wZ7hvV6qVpRzJ+ZUz+bup5/7HW0uqh7/583Q3GUJFXS0/p0byW9pRqusbNz3pZmzHW86DcDZsg7ImCWXxx1Bz6sGV5F2RGMxFouHJ5aJsPkk4wNHsxk1PyiVSnnPw4rUuvTBQaJJWso3kvGVU1T9orV+Kvf5TdDGZhpbchJ1nz/NV+BGyShrXQbHQ1+XNkACecnWmoLqCr89GsPHqmQfOhhhr6jDDJYjRDt0pK6/ip62xbD54htq6xnVQvFzaMX1MMB3bmZOQms5XGyM5l/bg5OfawZq3RgXjYm9F6qVMln+zn7Nnrj+wzr8pFDKGjvThuRcD0dRSsPPP06xZH0Nxyf2Xx6RSCU8OcmfC2AD0dLXYE5HEqvXR5Bc2bsbK0kyfV8YH0btnZ3JLyvhmzxF2nkrmQWdJM30d3hgQwFCvLhRWVLLk0FE2njxLXSM7KIEd7ZkzIIgO5iacuHiDRRuiuHTzwQMIz05tmTUqmE625g8spy4iSTcsMTGRpUuXcurUKYqKiqj/1+BLIpHcdXlYYzw2SRqguq6OdcdO8/3hY5RWVT+wrFwqZZSnK6+F9MBAS5NN55P4Mu4IuRWqfyntDY2Y2zOIvu07UlSTTUTWz5wvPvzQenpyE4IsxtPNsA+F1RUsOnOYP1LOUNeIQ9nfthNzPXtjq2dE5I2rfBh3iNSih0+X+VnbMt8vlC6mFpzOTmfBsXBO56g+StORK3jF1Y+JXb2RKGFt5El+Coun4iHrp3KZlOcCPZjS3xcdTTkJBXs5krOOijrVk7yphi2hVpNpr+fJzfJ8vrmwh8is8w+tZ6llyOtO/Rlg44ayLhdl6SKo2AY0IuloDUKiPxuJzJqw9IssTDjI9bKHb6gLtHRgjltfOhqYk1txkoTcLymqVn0ToFyqR2fjiXQ0fJbKGvjhaDw/x56ksvbBHRRNuYwXfT2ZHOCDQi5l3eUTfJccTXGN6vs4nI0smeveFz8Le66l57P410hiEx7eQWljYchrzwUR7O1IdkEp326NZl/8hQcmqn+SSGCQrzOvDQ/A3FCPqLBz/LgsnKzMh2+o6xHYiUmv9aWNrQknTqfx3cpDpF3LVS0wYKCvxQvP9+TJwR5UVdWwdnMcm/48RU3tgzd4aWnKeX6YD8895Y1EJuGX6FP8cCiesoech/7Jpa0lc54Kxt3OhotZuXy2N5K4qzceWq+9mTHv9A8iqFN70nOLWLz5MOGnUlSOK5VIGBrQlVeG9sRYX7V7WTdV38gZamvrYPA3amvrUYqMjGTAgAEYGxvj5eXF7t27CQ0NpbKyktjYWFxcXPD09GT16tVNav+xStJ/ySsr55vwGLaeOdfgiMG/Qzvm9A/C0cKMExm3WBAdwdmcpk9vBdraMa9nMJ1MzbhRfo6wzJVkVt77JZFJFPiYDMXffDRSNFl36STfJh6hqLppG+A0pDJecvbh1a7+aMrkrE0+xeJTRymuvndE31bPkLm+QTzR3oms8hIWxh9mW+q5Jq932uoZ8q5PME/YO5FdVMriXdHsPtXwibhXl/bMeioIewtjrpWd4VDmSnKq0poYGRz0fAixnISJZhuO56by1fndpJbe+++nKVUwvkMg4zoEoiUFytaiLFsOyrJ7G1WFRBt0XkKiN4kapYI1l+L4/sIRymrvPRHb65kwx7UvITadKK9J52zeYm6VHWxaXEBPYUc305lY6/biVmExX4QdZt/5hjd49evckdl9emFrbEhURgqfnDnIlZKmXxHRv21n5rj1oa2uEUfPXOHbXyO5lnFvB0VHS8H4J3159glPlMAvB06wZv8JKqqatgFOR1PBhIE+jOnTHWWdkk2/xbJh3VGqKu9tz669GVOm98fTpwO30gtY/kMEMXGqJ6p/s7czY9rkULw87LmZUcB3ayI5Et/wSKZvoDNTx/bCwkyf8KQUvtp9mBt5Td+hP8i9MzOeCMDKSJ+D51P4Yv/hBnf862tp8kqQL2N83amtrWf13uP8evAkVTVN2zGur6PJxEF+jAxxQyGXNfn9P0jviJkPL6Si8JCv1dbWo9SrVy9yc3OJi4ujuroaCwsLwsLCCA0N5dixYwwcOJD169czcODAJrX/WCbpv5zLyOLTfZGcvJ4OgJ2JEW/360WokwPpJcV8evQwf6ao59ImuVTK8y6uzPTpiYGmBomFB4nK/oWyutsns076PQi1fBljDWui0q/w8ckwUorUcymZhbYes92DGN6hGwVVFSw6cYTfLyZSp1TeHvm6+TKxmzcAPyTFszwxjvJa9ewc9rOy5QO/3jibWJCQlsHn2yJJun779nkdLE14a2gQPTvbU1idQUTWD1wuiVVLXJlETnfjJ/E3fw65VJst14+z4nI4RTW3Z0L6WXfjdacBWGsboawMR1myEOqat2/hDqk1Ev23kGgPJqeylK+TDrE1LQEloCfX5FXnQMY6+iClmkuFq7lcuI46pXquRLDQ9sfV7E0MNDoQf+0mn+yP5HzW7SlNJwsz5vYPxtfelivFeXxy5gBRmeq5tElTJmdCJ1+mOvdEAxmbDp7hp62xlJZXIZHAwAAXXhkVgJmxHgdOXOTbrUfIyGvcUsj9tDUzZPqIXoR6dCQnu5gfl4UTcSAJAH19LcZODGLIMC+qqmtY93ssm7effOjIV1U9/TryysQQ2tgYE38mjSWrI7h64/b3trODJW+8FEq3zm24nJnL5zsjiUt5+MhXFdoKOROCvXkx2AupBNbEnmLF4ds7/qUSCSO6d2V6b3+MdbTZE3eepduOkFPYxM7nv9hZGvPmyCB6dmuvlvb+KeTQmw8vpKKI0EVqa+tR0tPTY8GCBbz55psUFBRgamrK/v376du3L3D7dqUHDx7kxIkTTWr/sU7Sf9mTdJGM4hLG+npQr6zn+1PxrDwd/9Apw6Yw1tJiho8/z7u4Uaus5FjeFtrpdMNez52rxfl8fDKMQ7da5ppQN1Nr5nv1xcO8DefzstmWksxLXb2w1NVjT9pFPj0eyY1S9V+DK5NIGN3JlVmegRhrarPrxHlKKqoY1dONekkVsTl/cCJ/G3VK9V9SpCMzItBiPK5G/SiprWLd1Wj8zTrhYWKPsjYFZfEnUB2j9rgAKDyRGLyHROFCUkEGe28mM8HRD1MtXa6X7OFc3hIq6pq3AakhEuR0MHwGZ+OpyKV6bD6dRJ2ynpEe3SivrWZJcjS/psQ3a1Ph/Vhq6zOrWwhD7bpRWFLB73tOEuztSBcHKy7eyObLDZGcuqy+Xfj/5NPZllkjg+nYxozkszeJO3KJEc/5oW+gzd4DZ/lxbTT5BepJVP+kkMsYMdSTsc/2QENTwY79Z9DSVPBEaFeKK6pYuv8om44lNnoNWRU2xgbMfCKAAW5OZJeU8kvsaQZ1c8LZ2oKkq5l8tSGSs1eat6nwfnp2tWfmyCDsrZp+3+h/E0n6XkZGRnzxxRdMmjQJpVKJlpYWP//8M88//zwAP/zwA2+88Qbl5U27oud/Ikn/Zcel83weG016acvfvMDJxIz3A4LpaWtHSXUVS84e4ZeLJx+4IU0dJMBT7V142yMEKx19zudn80FcOHGZ6unhP4iBhibTPXoyztkDuUTKuaIworLXUFbbcte+/sVSqyO9rSbTVqcryvoilKXfQvkfQEvfnEMK2sOR6M1EIjOjoPIcCXlfkl/Zctd2/0VDakQXk6m0N3gaJVI2XjnN10mR5Fep7/K8+3E3bcM89364mbahoKSc77bHsCOm4eUldZJJJQwP7MbUJ/0x0tMmKfkWS1eEc/FyZovGBTAx1uXl8YEM7NeNunolG+ISWH4wjqLylrlfwz95tW/DO08F09nGgpzCUpZuO8KeuPMqr/M3lVwmZVSIOxMH+6Gv07ifhWxIcPgsNbyr2yJ7f6W2th4lLy8vQkJC+PLLLwFwc3PD0dGRzZs3AzB06FASExO5cqVxG4T/or5f8G5hN4qLeOPgnlZJ0AAX83MZu2sLAOsuneTH88dbPEHD7VusbL96js9PRwAwPWp3qyRogOLqKj48dojoW2nkVl1jT/rXrZKgAbIqU9hy/QMAlGU/QfmvtHyCBqiHis0oS5cCcCxrdqskaIDq+kLO5H5GXuUZEvJv8d7JPa2SoAHO5N3ilaO3TyI/7D7GtiPqvX7+furqlWyKSmT1vngA5i7Y2ioJGiC/oIwvFu8jJTWb+Cs3+GxHZKskaIATV2/x1vo9ACzffpTdsS2foAFq6+pZH3aK3w+dVkt79UjU9viveOKJJ/j999+p/f8zuzNnzmTr1q04Ojri6OjIzp07mTx5cpPbF/fufoC/JhkexVTDX19g5SOI/qg+9d+f9ZEc8UcWW4myVU7Y943/KGM/kpiP5ng/yu+0uj6wuC3ovebNm8cbb7yBTHZ7s9748eORyWRs2bIFmUzG3LlzeeGFF5rcvkjSgiAIgkqUIknfQ6FQYGpqetdzY8aMYcyYMWpp/39mulsQBEEQHjezZ8/m9Gn1LCc0RCRpQRAEQSX1SonaHv8VS5cuxcvLC0dHR+bNm8fZs2fV2r5I0oIgCIJKlEqJ2h7/FdnZ2axevZpOnTrxxRdf4O7ujouLCx999BEXLzb/Ph4iSQuCIAgqESPpe+nr6zNu3Dh2795NVlYWq1atom3btnz00Ud06dIFd3d3Fi5c2OT2RZIWBEEQBDUwMjLipZdeYv/+/WRkZLBo0SKuXr3K3Llzm9ym2N0tCIIgqOR/59ZXj05NTQ179+5lw4YN7Nq1i9LSUmxtbZvcnkjSgiAIgkr+SzchUafa2loOHDjAhg0b2LFjB8XFxVhbW/Piiy8yatQo/P39m9y2SNKCIAiCSv5LG77U5aWXXmL79u0UFBRgZmbGs88+y+jRo+nVqxcSSfOPl0jSgiAIgkr+Sxu+1GX79u0MGzaMUaNGERoaeufOY+oikrQgCIIgNFFWVhZyeculUpGkBUEQBJWIjWP3askEDSJJC4IgCCoSa9KtTyRpQRAEQSUiSbe+/5kkLZdKkdC6P22nIbt9eDSk6t0IoArN/7/54JHElsqQSlr/T0MuUQAgkWi0/o/5STQAkP7//29NMonGI/l3/iumhqL1Yyvkjy62XC5Do/4Rfmb5o4st/O/5n7njmLWePjueeR5PK5tWidfH3oH9o8cB8JKzD5/6DsRUU6fF42rLFMxwDeRDn/4ArO33DM85uSFVw1b+h3EwNGFN3xEEtLHHVLMtz9svwkrLscXjAjgZ9GJchyW3/0P3FST680Bi1PKBJbpI9N5Coj8bgECbH2inPwRa4XpQA42OBFivwETLla4m1vwaPAZnI8sWjysBhtp144/Q8QBMG9aT6U8HoqfV8h0UfR1NZo0MYsqQHgAs/2YMoUGdWzwuQKeOliz58jk62Jvj1aEtK18aRgcLkxaPK5VIeNqnKz9OehqAN0cF88pT/mhrKlo8tpGeNu8+35tx/b3U0p64LWjrkyiVj/dWgMqaWn6MPUFmcQnTg/0x09Nlx6XzfB4bTXppidrjdTIx5f2AEAJs7UgrLOCrYzH42rTlORdXymqrWXI2hl8unqCmvl6tcSXAk/YuvO0RjLWuAfuuXmLzpXO86uGLh4UNyXnZLDgWTlzmDbXGBTDQ0OQNd3/Gd+lOrbKW36+FUVpbwQvtB6Iv1yGpKIzD2aspqy1Qe2wLLQd6W07BVrcraWVZ/Jh6AD9TJ56w8UKiLEZZugTK/wBq1RxZCtrDkejNRCIzIyY3npjckzzTdhDt9WzJrzxHYu4X5FclqjkuaEiNcDaZSgeDpymtqWVJfCz1KHndqwf6GppsuHKKr5OiKKgqV3tsN5M2zPPoh7tpG85n5fBddBz9O3fkya7O5BeXs2xHDDtizlGv5tOCTCpheGA3pjzpj5GuFjtOJxNx4QrTevfA0dKMxKSbfLcqnEuXs9QaF8DYWJeJ4wMZ0LcbxVVVfBsTi7ZCzqs9fNGUyfkjNoHlB2MprqhSe2zP9m1458lgnNtYcLYgnRUXohnSrhsD2nQhp7CUpVuPsOfYebVvyJLLpIwMdmPSED/0dbTU1m6X7R+ora3koepr63GRkZFBdnY2HTt2RFdXVy1tPtZJes+5i3wZHk160e1krKepwdQAX8b5elBXX8/3p46z6swJKmubfwI30tRiho8/Y7q6UVFby9ITsaxOOE11fR0AnU3NeD8gBP+27bhSnMcnJ8M5dCu12XEBXE2tme/Vh+7mbbmQn8OHRw9xNP06cDt5D3Pswts+QVjq6rH76kU+jY/kZmlRs+NKJRJGd3JlVvdATLS0Ccs6wU9XdpNXfbttfbkOY+3786RNT2qVVcTl/sGJ/O3UKWuaHVtHZkSgxThcjfpTUlvJz1cOsu1mLHXK250fJ/02vOH0FK5G9ihrLqMs+QSqjzY7LgCK7kgM3kOi6Epq6TXWXN3EhZLb/5YSJIRa+PNsuycxUOhzo3QvSXnfUlmX3eywEuR0MByBs/FU5FJ9Npw/y6JjR8ituJ2MTbS0menTk+dc3CitqWLJucOsT1VPh9BSW59Z3UIYateNgopKvomMYVNC0p1k7NHGmrl9g3G1seLC9Wy+3BDJ6ZRbzY4L4OVky1ujgnFsY8aZ6+l89mckZ2/dTsYyqYRR3q5M6+OPgZYm+w6e5Ye10RQUlDU7rkIu4+mhnowd3QNNbQW/nUlkyZFYCisrAbDQ1eXNXj0Z3rULReWVLD1wlM3HzlJX3/xTorWRPjOfCGSguxM5laV8nRTO9usJd5ZxvM3smOvan85GVpy9ksFXGyJJuprZ7LgA/i72zBwZRHtr9c8SOG9boLa2zg+br7a2HrUdO3bw9ttvc/nyZQAOHjxIaGgoubm59O3bl/nz5zN06NAmtf1YJunkjGw+ORDJiesNnyTsTIx4u08vejs5cKukmM+OHubPlKb9JJhMIuH5rm7M9PHHQFOLTeeT+DLu7xPnv/Xv0JH3egZja2BIVHoqH50IJ7U4r0mxzbV0me0RzNMdulFYVclX8dH8cSGRugb+SXQVCl5x9+NlVy+USliVdJzvE49RXtu0hOlnZct83950MbXgfPE1ll/exoWSaw2WbadjydSOQ/Ey6UxBdToRWT+SUhLbpLhS5HiaPIm/+XPIpdrsuBnHT1cOUFTT8PHubenGK46DsNQyQlkZhrJkIdRdb1JspNZI9N9Coj2Ywupifru+ncjsOJQNrIDryLQZ0fYJBlqHANVcLPyZy4XrqFc2bbRlod0DV7M3MdBw4Hj6TRYcOcS53IYTv7OpOe8HhNCjTTtSi3P55MxBDmc2rUOoIZUxoZMvU50D0JDK+PVkAsuOxFFcee/nkABDu3VhVkgA5nq6HDhxkcVbosnMb9qMVRszA6Y/3Yve3R3JKirlq/3R7Em80OCo0VBbi1d7+zHax42qyhrW/RHLlu0nqamta1Jsf18HXpkUSlsbY2LSrvHxoSgu5zb8PXW1suS93sF0b2PDpYxcPt8VybGUps1YaSvkvBjsxYRgb6RSWJMSx8qLRyirrb6nrBQJI+w9mOESipGGNrvjzvPdtiPkFDatg2JnacyMZ3oR6NqhSfVV0Xnrh2pr68Lw91Uuu3DhQrZu3crFixeRy+V4enryySef4O3t/cB6ycnJTJs2jdjYWAwNDRk/fjyffvrpXTcciYmJYcaMGSQmJmJhYcHrr7/OrFmzVH5vu3btYujQofTo0YN+/frxwQcfEBYWRmhoKACDBw9GJpOxY8cOldv8p8cqSeeVlfNNRAxbzqg23RbQwY45/YLoaG5KfPpNFhyJIClH9RFPQNt2zAsIwcnUjBMZt1gQHcHZnIdPt2nIZLzk5sk0T1805XJ+uXiCb8/GUFxdqVJcDamMl5x9eKVrD7RkCn45d5pvTx2lqOrh9W31DXnXN4iBHZzILCth4Ykotqcmq7zRqq2eIe96BzOovRO5VUX8dOVPwrNONpio/s3PtAuTHYbSVseca6WnCc9aRW5VmoqRoYOeD6GWEzHRbMvJ/BSWXNpJaunDRw9aUgXP2gUxxj4EDakSytagLPselKqezLRA92UkehOpVSrYk3GILTf3UlH38ONtrWXBePsReJp0o6wmnaS8b7hVFqZiXNBTtKOb6UysdYO4VVLMp0ej2J2qWodyYAdH3vW/3SGMzEjh0zMHuVKieoewf5vOvOPWG1s9Y6JSr/LpwSiu5j98yUJXQ4Mp/t686NOd+nr45cAJ1uyPp7JatRkrHU0FLw7wYWzf7iiBn6NP8GP0CSqqH96h7GhhyjuDgvDvaMfN9AK+/yGCmLgUleIC2Lcz5dVJoXh7tudaQSGfRkQRnnLlofUkwBDnzswODsRKX4+wpBS++vMwN/NVn7F6wt2JmU8EYmWkT1j6Bb44e5DrZQ8/3gYKLV7p3IvnHbyprann5z3HWR92kqoa1TooetqaTBzsy6gQ9xbfIPaoknS/fv0YNWoU3t7eyOVyPv/8c7Zv305iYiJ2dnYN1ikpKaFz5874+fkxf/58rl27xgsvvMDEiRPv/HTktWvXcHFxYdy4cUybNo2TJ08yceJEFi9ezJQpU1R6b97e3ujp6REREUFeXh7m5uZ3JelPPvmElStXcv160wYXj0WSrqmr45fjp1kefYzSqnt7nA8il0oZ7enK60E90NfSZOP5s3wVF3PfkTCAnYERc3sG0a9DR9JLivks9jC7Ljd+JG6ho8vsHoE87dSFgqoKvk44zB8pZxocCf+ln20n5nYPpZ2+MVE3rvJRbAQphY0fiftZ2zLfPxRnUwtOZaezIC6cM7kZ9y2vI1cw1dWXSV19kEqUbLoZyR/Xwqisb9zxVkhkPNUmkDH2/dGWKThTsIcjOeuorLv/aMtEw5ZQq0l00PPiVnkeyy7v5nBOUqPiAlhoGvGK4xP0sXJHWZeDsvRrqNjKA/f8az2BRH82EpkN8fkJ/JK2hczKnEbHdjfqwgv2z9BGx4rcipMk5H5JUfX9/2bkUj06G0+ko+GzVNXC96ePNWlpRlMm52U3T1719EUhk/HL5Xi+S46mpOb+I/rOhhbM9ehHDwt7ruTl82lYFIdT0xoVF8DWyJC3e/ein1NHsgpKWLL1CHuPX7hveYkEBvk689rwAMwN9dh39hJf7YsmvbC40bFDOnfg7SeCaGdqxIlTaXy3Mpy06/f/nujrafHimJ48OdiDytpalsceY/XJ09TUNW4krqNQMMnXi5e9vZBKYO3hU/xw6DjlD+hgdGljwTtPBdPdvg2XirL5LHE/sTlXGxUXoL2eKXNc+9HLypFbuUUs3nSYQ6fv30GRSiQ8FeDCq0N7Yqzf8ptaAZzUmKQvNiJJ/1tdXR3GxsYsWrSIiRMnNlhmxYoVzJo1i6ysrDvrw8uWLePtt98mJycHbW1t3nnnHTZu3Ehqauqd+2y/9dZbbN26ldRU1WavtLW1+frrr5k6dWqDSfrHH39k2rRpVFaqNoj7t0eepCMuXWHhwcOkqdDDfxAjbS1eD+rBaE9XymtrWBofx5rEv9eUAfQUGrzq5ctLbp7UK+v5/lQ8q07HU9HMNW03CyvmB4bQ3cqG8wXZfHjiIHFZd/eanIzMmefZh57W9lwtKuCj2EMcuv7wHv6DyCQSRnV2ZZZ3AMaa2mxJOcfnJ6LIrvh7hCkBnnLowhyvIKx09YnOSWBV6k4yK/ObFdtIoceL7QcxwNqHqvoyYrLXcaZgD/X8fbw1pXr0NH+O7iZPUllXyy9ph9h4PZrq+uYdb1cje97o9CROBm1R1pxFWfwJ1Jy6u5DcBYnBXCQaXtwoT2fN1c0kFp1vVlyZREp/qyBG2g5GR6ZFWsk2kvOWUVX/z79dKXb6T+Ji8hpachN2XDrPwtjDZJQ1b5Ojpa4eb/sFMqxTF/Kryvk6KZJNV8/cNeNkrKnDdJcgRnfwoLy6hqVH4lh/4kyz17R72Nsyt08wnSzMSEhN58sNkSRfu3vGqWt7K2aPCqFreyvOp2fz2e5ITqQ1b01bIZMx1t+dqSF+aMrl7PzzNKt/jaGk9O+TnVQq4ckn3HlxbAAG+lpsPnuOr6NjyClr3qa7NgYGzA4OYFBnJ3KKS/lm7xF2nbp7g5epng5vDOzJMC8XCqsrWHo+kg1XTz6wk66KXpYdece1Hx30zYi/cINFGyO5fDP3rjLdHdswa3QwTrYWzYrVWJ22fKS2ts4Onk1V1d2dTU1NTTQ1NR9at6SkBAsLC3766Seee+65BsuMGzeOrKws9u/ff+e51NRUOnbsSGxsLH5+fvTq1QtnZ2dWrlx5p0x4eDh9+vQhIyMDKyurh74XU1NT5syZw6xZsxpM0u+++y5r167l1q2mfR8eWZJOzcnj04NRHElteB20qTqZm/Ju/2B6tG/H1cICPo6J5FDaFUZ0duEtv0AsdHXZefkCC48eVuvucAnwVCdn3ukRiJWePnuvX+CzUxGU1lQx3TWQ5x09KK+tYcmpo6xJOqXW3eEGGpq83t2f8V09qK6rY1lCHD+ei8fZ2IL5fr3pbmHDldJ0lqdsI6FQ9alDVXTUa8MrHYfRzciB3MprHMpaybWyBNyMBxBgPg5tmQF7M06yMmUvedXqO95SJAy08WKywwBMNPVRVuxCWfIlUINEbwZoj6CsroIN1//kYNbhOxvS1EFfrseodoPpYxlIfX0Z5wtWklq0AROtbriavYWxpjOJ2ZksOHKIk5npaosL4G5pzfyAUDwsrUkuyOTjMwc4lXuTMR29eN2lF3oKTTaePsviw0fJL69QW1yZRMIoj2680csfI20tdh5NZtn2I0ikEl4bFsAgX2cKyiv49kAMW06qd3e4qZ4O0/v2ZLinC8Ullaxed4Sde87g7tqOaZND6WBvzqlb6XwUHsnZTPXuDvdu24b3egfjYmnB2euZLNwZSfKtbMYGejA51BdNDTm/XYln2fkoimqaNlJqiEIi5TkHb6Y5B6Ej02Bb9Fm+3xGLtqaCN54OpK9XJ7XFaoxOm9WXpJ9LqmPBgrs3os2fP58PPvjgoXUnT55MWFgYSUlJaGtrN1imX79+WFlZ8csvv9x5rqysDD09PbZu3cqwYcPo1KkTo0eP5sMP/54hOHfuHF27duXUqVN4eHg89L2MGDGCixcvcvr0aYqKiu5K0pmZmXTr1o3BgwezevXqh7bVkFZP0kUVlSw9HMvvJxKpVfNlTP/Uu5MD7/TtRTsTI7LLSrHQ1SMpJ4sF0RHEZ6hn12pDdOQKpnr6MNHdC4kEqupq0VNosuFCIotO3H9Dmjo4GJkwr0cowbbtyakow0xLh+KaMlZf3cPejGPU03LHO8jcnUkOT2KhZUxpbT56chOSCq/x7aWdnC9W/2Vjf9GVaTG+fSjPtAtALqkBZR31Eh0OZB5m440/Ka1t/i7h+7HTacML7Z+hq6ETlbW5aMnNyC4v44u4w2y5cK7FbsgiAYZ16sLbPXphqatHTkUp5tp6HL92k0/CIjmf1fjpfFUZamnyWmAPnvN0o6q6FqlEglwuZX3sGb6POEZJAxvS1MXFxoI5g4PpbteGvPxSTE30SC8u4cuoaHadb9rGUVVIJRJGdHPhzcCemOhok1dajpm+LkeyUlmYeICUkpY73iaaOrzuHMLI9t0pq6hCQyFHU/Ho7kGlziR9dkjTRtILFixgyZIlRERE4Orqet9yrZWkL168iJ+fH/b29jzzzDPMmzePWbNmoVAoWLlyJUqlkhMnTmBvb//QthrSqkk69up1pm/ZTWGF+nqcD6Ihk/FOvyCe93Ljs6OH+eHMCbVf/3k/bfUNWD14OOY6Ojy/eyPn8pp/CY+q+th15Mf+wziWl8zC879SWqu+EdWDaEoVTHN8mgHWviy6sI3tNxveOd0S2mqbssjjZXTkcj5KXsKNcvWOYB8kwMybNzpNYG/qJd46tI/Smsat8zeVjlzBwpD+POnYmbd37Wfb2eRWiQvQ0cyEn0cPp66unolrtpGWq/5r6O/nGe9uLBjah23nknn/QDgVNeq+hr5hehoafDloAH0dHXg9bhMH0pu3fNIYnQ0t+TlgDCaa6rn2tqkcN32strYuP/Neo+vMnTuXFStWEBYW9tAE2tB095UrV3BwcHjgdPehQ4fo3bu3ytPdcDuxv/HGG0RERPDPlBocHMyyZctwdnZu5Cf9W6vecSw5M7vVEjRAdV0dx9Nuj+Jibl5rtQQNcLOkmNTCfPIqK1o1QQMcvnl708rF4uutlqABquprSCq6vc4en3+51RI0wM2KPNIr8sivLmzVBA2QUHj7ZH0qM73VEjRAeW0NpzJvzwrFXFXvstHDpOTmk1lSSnphSasmaICjKbc/68mb6a2WoAFKq6tJyLi9OTMmWz33SFDVhaIscitbblZIVUql+h6N9eabb7Jy5UrCw8NVGuH6+/sTExNDefnfs5f79u1DV1cXNze3O2XCwu6+WmPfvn106NBB5QQN4OLiQlhYGLm5uRw7dozY2FiysrI4dOhQsxI0/A/dFlQQBEF4tJRKidoejTFt2jRWrVrFb7/9hpWVFZmZmWRmZlJaWnqnzJw5c+jdu/ed/37++ecxMDBg3LhxnD17lt27d/P+++8zbdq0O+vYU6dOJSsri1dffZXz58+zfv16li5dyltvvdWk42NsbIy3tze+vr6Ym5s3qY1/E0laEARBUI1Sor5HIyxbtozS0lL69++PtbX1ncdXX311p0xGRsZdl03p6+sTFhZGfn4+Pj4+TJgwgQkTJvDxx39P2dvZ2bFv3z6OHz+Ou7s777zzDh9++KHK10gDLFmyhP79+9/39YEDB/L999836vP+0//Mr2AJgiAI/zepsnVqzZo19zzXpUsXDh069MB6AQEBxMfHN/Wt8dNPP9253KohXbp0YdWqVUydOrVJ7YuRtCAIgqCSR7km/bhKTU194Lpz586dVb4xSkPESFoQBEFQzX8ouaqLhoYGmZn3v71xRkYGUmnTx8NiJC0IgiCo5FFtHHuc+fn5sWbNGkpK7r1ZU1FREatXr8bPz6/J7YuRtCAIgiA00fz58wkKCsLd3Z3p06fj4uICQFJSEosXLyYjI4Pffvutye2LJC0IgiCoRkx338PX15ddu3YxefJk3njjjTs/1KFUKmnfvj07d+6kR48eTW5fJGlBEARBJf+laWp16tu3LykpKZw+ffrOJjEHBwe6d+9+J2k3lUjSgiAIgmrESPq+pFIpnp6eeHp6qrVdkaQFQRAEFYmR9P0kJydz5coVCgoKGryue9y4cU1qVyRpQRAEQWii1NRUxowZw/Hjx+970xWJRCKStCAIgtDCxHT3PSZPnszZs2dZvHgxgYGBGBsbq7V9kaQFQRAE1YgkfY+YmBjeffddXnvttRZpXyRpQRAEQTVid/c9zMzMMDQ0bLH2W/WOYx5tbbA20G+1eLoaGgQ7tgegfwdHFM24NVtjdTIxxcnEDDNtHfysbVstrgQY3KEzAN2MHDDRMGi12HpybbxMbscOseiGTNKKx1vfBlsdc8w0jOmk36HV4kqQ0MO0OwD+bdth+v9/Aq81GGtp07OtHQADOjsibealHo3hZmNFG0MDbE0McbGxaLW4MqmEfi6OAATY22Gopdlqsc10dfC1vf1dHtjGpVW3UHmZtsNCu/XOnYLqpkyZwq+//kpdXV2LtC9RqvLzImpUUVPDj0dP8OPRE1TWtswPtkuAYW5dmBkagLmeLldL8mivb8qVwnw+PhLFoWtXWiQugJGmFjN8/BnT1Y2K2lrKa6qx0NVjz5WLfHosipslRS0W283civf9Q/G0bMP1wkJsDAyoqa/ht+sH2XIjihplyxxvKRIGWvvxYvsnMFDoklZcSHtDY66UZrLk0i5O5F9ukbgAxhp6THToz2Abb0qqqqmpr8dMR4fonOP8em0b+dWFLRbbSd+BF9s/g4OeHddzCmlrakhpTTXfnjjKL2dPU1Nf3yJx5VIpY1zcmeHtj76GJjfzimhnbkRyVjafHIgk/satFokLYKGny6yQAJ7q6kxhWSVSiQQDHU22njzH4oMx5JWWt1hs3w62zBkUTCcrM67lFNDOzIjCykq+iT7KhoSz1LXQqUwhk/GCpzuv9PBFR6HgZkkxdoZGJObf4pOEfSQUtNzxttE25K1ufRjY1oWc0jK05HL0W7Fj8m92P3+htrauTZittrYepU2bNrFw4UKqqqqYMGECtra2yGSye8oNHz68Se23epL+S3pRMV+GRbMn+ZJa2/Voa83c/sF0s7EiuSCDjxMOcCL3On1tnHjHrS+2usZEXrvKRzGRpBbkqy2uXCrleRdXZvj4Y6CpxaZzSSw6GkNFTQ1TvH14ubsnSuCHxHi+P3OM8toatcW20NHlLe9ejOjk8vdJ68xZ7E2Mea93MIHt7cioyGVl6k5ics+qLS6Am1FHpnYcioNeG05np7Mg9hCnczJ4skNn5vgEYaNnQHT2Ob67/Ce3KvLUFlcukTHCticvdOiNtlST3xMTWRxzlFqlkml+voz38KCOWrbf2s/O9DBq6tV3vE01jBlrP4yeZt7klZTx7a4YdsUn42htxlvDgvB2tCW1IJ+PYiKIvH5VbXEBAm3tmd8zhI4mppy8cpPPt0VxIT2bIV5dmDEoADMDXfYkX+SLQ9GkF997L+Gm0pTLeNHHkyn+PihkUv6ITmDFgTikEglT+/sxsqcbVbW1fB8Rx7qjZ6hR46iirbEBbw3sRV8XR7KKSvl6dzR7Ei7g0taSd54Mxt3Ohos5uXwcHkns9RtqiwvQu2MH5oQEYW9sRMzNa3x4NILLBXmM6tyNt3wCMNbSZueNsyxKCie7Un3HW1um4OVO/rzUyR+JUsKa+FN8fzQeLbmcGUH+POPWtVVnTv5i95Mak/RL/40krcqPZ0gkkiaPtB9Zkv7Lieu3+HR/JOcys5vVjpWBHrNCAxnSrTN5lWV8fS6CLVfPUP+PnQ4aUhkvOPoy1TkQTamcX86eYXH8UYqrqpoVO6BtO94PCKGTqRkn0m/xYWQkSdlZd5WxNTDkncBABjp2IrOshM+PHWZ7SnKz9mFoyGS81M2LaR5+aMrkrD+VwJKYWIoq7/48IQ7tmRsahL2JMacKLvF9ynbSyjKaERmstEyY5PAkgeZuZJWVsjA+im3/+jzacgVTXL2Z4uqDTCphw/Vofrl6iPK65h1vf7POTHMcQjtdc45ev87HEZFczM29q0x7Y2PeDQoi1KEDOZV5rLu2ldi8U82KqyFV8JRNP55q0w+pUsavUaf44cBxyqqq7yrXx60jM5/sRRtTQyKuXeGjmEiuFDavQ9je0Ji5PYPpY+9ARkExi3ZFs//M3R1cXU0NXu7jzbig7tQr4ce4E/wQF09FTfNmUPo7dWR2717YGhly5HwaX26P4mr23Z/HwcqUt4cG4edkx7W8Qr7YE0XEhebNWOloKJgU7MMLPbujVMLqqBP8FHmCiuq/O1wSCTzh1pmZgwKxNNTjwKUUPos8zI3C5s1YdTQ14b3ewQTY23GtqJCPYyM5mJZyVxkDDU1e9+zB+K4e1CjrWHXxCKsvx1FV37zjPdi2K7Nc+mClY8DBSyksDD/M9X99ni6WFrzXNwhv27bNitVYdj9+qba2rr38ltraepSioqJUKhcUFNSk9h95kgaoVyrZcuYciyNiyC1r3HSZllzOhB6eTOrpjVwm5ZfLx1l2PprS2vsnAgstPWZ2DWWYnSsFlRUsOnaUP5ITGz1dZmdgxNyeQfTr0JH0khIWRh/mz0sXH1inR1tb3gsKxtncnFNZ6Sw4eoiEnMYnzH72HXnPL4R2BkZEX73Gx+GRpObdPxEoZDLGdXdnWk9fdDQU7E4/ytqr+yiuLWtUXC2pBqPt+vBM22DqlRJ+OBvP8oQHzwy00TPgHe9ePOngTF5VCStT9rI34yTKRnZR7HQseK3TEPzMnLhRVMSnkVEcSEl5YJ1e9va8FxyMg6kJyUWXWZ22kbSym42KC+Bv6slY++GYaZoQmZTKou2HuZ5beN/ymgoZ44I9mdDHB4Vcytqk03wbH0tJdeM6KPoaGrzm2YMXXLtTV1fPT+HxrI08SeUDEm9bU0NmDelFb9eOZBaX8GXEEXadu9CouABOFma81zcYXztbrmbn89X2w0Sff/DMQHDXDrz1VBC2ZkYcTbnGwt1RpGQ3bgZFIoEn3Z2Z2S8AcwM99ideYtHuaNILiu9bR1tDwUvBXrwY5AUS+PnEKVbEHqespnEzKIZamrzRswfPebhRWVvL0lOxrE48RXX9/UdBHYxMmOcfTEi7DtwsK+SLswc5kH6+UXEBuhrZ8K5bf7qb2nIpJ5dPwiI5mvbgmYGBnR15J7QXNoats/fE/gf1Jem0if+NJN3SHosk/ZfSqiqWRR9j3XHVpssGODsyu08v2hgZEJF+iYWJB7laqvqIxdXYhvfc++Nu2pbzuTl8eCSC2FsPny7TU2jwqpcvL7l5Uq+sZ0V8PD+cPEGFimvsMomEUV27MdO/J8ZaWmy5fI4vjh8mu/zhCdPJ2Iz3/UPp2caOtPwCPjkURUSq6lOqpjo6zOzlzzOuXSmrreCXtH3sSo+hTvng9VMJEkItu/NyhyGYaRqy5+pFPj0exY1GrLF7W7Zhfo/edDOz5ELxDb69uJOzRdceWk9Prs2EDn0Y3tafqto6lh87xs8nT1Gt4vSRXCpljLsbb/TogZ6mBuFZMfxxfSfFtaUPrdte15YX24/E2aAjKRm5fLk9iriL11WKC2BuqMsbgwMY7OVMfmUFXx07wobzZ6l/yNdOKpHwTOeuvOUbgJmOLrtPXuCb3dFkFT78Pf/F19GWt4cG42htxqmb6Xx8MJKkjKyH1jPW1mZ6UA9GunejvKqGFfvj+P3IGWrrVFtjV8hkPN/Lncn9/NBUyPnjeALfhcdSXPHwDoqbrTVzBgXjamvFhfRsFu6I5MRV1dd8bYwNmPlEAAPcnMguLeXLqCNsP3f+od1BmUTCaHdXpgf0wEhLi00Xk/jy+BFyVPhO/iXYtj3zeobgYGTCsZw0Pk3cz8Wihx9vc009pruEMtzOjcKKSr6NjuWP06oPGrTkcl7y9WRyD2+0FQqV329TiCR9f1VVVZw6dYrs7Gx69uyJmZmZWtp9rJL0X9LyClh4MIqIyw0nH2dLc+b2D8bbri0pxTl8lnCQ6KzUJsWSAEPadWVWt95YaRuwL/UynxyN4kbxvclHAozo7MJbfoFY6Oqy6+IFFh6JJqOB3xFVhYGmJq/79mCcuztVdbUsOx3Hj2dPNJh8jDW1menVk+ec3SivqeG7mGP8cqrpa39dLC2Y1/v2dNm1sky+T9nOyYKGZwE667djasdhdDG053xeNh/EHSIuo2lrf1KJhGccu/KWVyDmOroczDzN95f3kl1VeE9ZmUTKEBsfXnboj6FCh63JyXwVfYTsssaN/v9ioq3NdH9/Rrt2o7K+is03drM3M5I65b3H0FBhwLPtniTEogelFdUs2xvL5phEapu4GczVzprZw4PpZmfFudxsPjxyiGPpDY/ova3bMD8glK7mlpy7kcnCbZGcSWva8oRMKmGEXzemDfTHUEeLrYnJfB15hJwGZqwUUinPe7oxLdAPPQ1NtsadZeneoxSUVjQptqm+Dq8P6slQHxeKKqr4LuwoG+ITqau/95RjaaDHjP4BDHHrTEFZBUv2xbA1/txDOzP349W+De88FUxnGwsSMjL5KDySM+kNH0N/O1veCw2mk7kZJzNvsSDmEIk5D0+uDVFIpYx18WC6lz+6Ggo2XT3Ft8mRFFQ3dLxljO/oyxSnQLRkCtafTGDpkXuXq1Rlpa/HrODbm/paiv0qNSbpSf+dJL1kyRI++OADiopu542DBw8SGhpKbm4unTt35osvvmDChAlNavuxTNJ/iU5N47MDUaTm3h4dm+hoMyOkJyM8ulJaU8WS5Ch+Sz1B7UNGgarQkSn4f+ydd3hUVde375lJ7z0hhfRAEhLSe6f3poj0XhQEERWVRx99fQQVkCJFijRBpUnvkF4hIQQIvSShJCSQ3tv3RwTpTJJJUL9zX9dcMHP22WvmZOb81l5r7b0ntvdnnF3DlmJrTiezPCXxUbjM3ciYLwJDcTYw4ty9HL6KiODUHdlUddro6DAnKIQgCwuyigr4OiGCwzcbKqLlRGKGO7jwvoc/6gqKbEs7x8KoOO6XyaaKtmd7O2aHBmKsoUF83jlWXtvNnfKG/K6OggbjrXrT2dCD/Mpy5p+K4bdLjU8LPA91eQWmuvoy1tGdOmrZfDOCLRmRVP5Z4OWmbc17dn2xUW/D6Tt3+L/wCM5kZzfbLkB7fT3mhITg27Ytd8pzWH9jO6cLzgEgJ5KjR5sQ3jDtiaJYkW2xaaw4FE9BaUWz7YpE0Mvdnhl9AtDXVOPAtUt8ExfJreKGMK6JmjqzfYPpY9uevKJSFu2PYc+pdGTxC9VQVuSdbr685d8Qxl0Rl8iGpNOPHMJAKws+7RyMtZ4Op67e4rtdEVy8ndt8w4CjmSEfDwjBxdKYKzl5zN0fQcK1BidPUU7C6AB3JgQ3FKRtiU1l5bFEipsoVI8jFokY6OnIe9390VZVZnf6Rb6PjCGnpCEa0VZLk09Cg+hia8PdkmLmJkSy52rj0wLPQ1dJmZleAbxt70xJdSXLLkay5dpJqv+8V3Vq046PnbrQVk2HmBsZ/O9YBFfzZFPI6mbShjldQnBqYyST/h7H4qf5Muvr5qRZMuvrdbJu3TrGjRvHkCFD6Nq1K2PHjuXYsWOEhYUBMHjwYAoKCjhy5EiT+v9bizRATV0dW06d4W5RMe8EeKOiKM/v11NYfD7yud5pczFV0eIj5050N3Ugp7SERUnx+JiY0s/OntzSUubHxbAjPb3JHv7LCLW05LOgEKy0tYm7ncH2y+eZ4uKFrbYep27d5v+ORXA+p3kFds9DSU6O8V7uTPLxRE4i4o9bUZTUlPN2287IieXYmH6aRSlxFDUynyoNlhrazPEOobO5DdkV+ay/fgwf3faEGDqRXVzM99Ex7L7w6nBlU+hma8MnwcGYaWpyOv88cXnJDDTtThtlAxIvZ/LdHxFcvSu7ivSHqCjKM66zFyND3agDVqeepLa+nsmunkgQszEyhdXHni1IkwXWhjp81D8Ev3bmZOYXsCI2ia7tbQi1seLOgyIW7Ini6JmWmTLXw60d7/cOxEhbnePp1wi/eI0pod6YaGsSeeE68/dFcSM3X+Z21ZUUmdzZm6H+LlTX1vFT4kmU5eUY4+FGPfX8lHqSlalJUqerGoODrgGf+4fiY2zG9eI8Vl2KpW9bJ/wMrMh4UMA3xyM5cVX2U0JFwAAnB2aFNExDlRUWK2Uo0pP/HSLdoUMHbG1t+eOPP7h//z76+vpPiPS3337LkiVLuH27aYO6v71IP07CvZv878xhLhXKXqiexkvPnM9cumKvZURVbS3rT6fwY1IiJVWyv3E+jrxYzEgXV6Z5+6ChqMidoiLmhUdz4KJsp6o9jzbq6nwUEkAfh4YFSSKybvBVwgmuFcpuqtqLCDa14D/eYdhq61JZU8PqU6f4KekkZY0s/GksChIJ49zdmeLthaqCAll5BSzYFUX4uaalTxqDia4GM/sG0bljw+Icx89eZcGeKLLut9xc+oeEOFrxYd9g2uprUVZZzdrjSWyMSKayumUWZHiIkoIcY0I9GBPmiZKCHNfvPeD7vZFEX7rZonYBLPS0+bBPEMH2DYvd7L16kXkJkdwukd3UqRfRw8qWT31CMNPQpLiykmWxiWw8Jdupas9DVUGBKX6ejPZ0Q1Gu+QtMCiL9LEpKSixZsoSJEyc+V6RXr17NtGnTqKhoWjSuVVccaw53ygoZGbWpVQQaICkvg0HH1wLwc0oy82KiW1ygAarr6libksxXEeEATNqxp1UEGuBucTHv7z1I5PWbXM7PY9Th7a0i0ACRt24yaN8WAJYlJvJDbFyLCzRAVW0tK5KS+C4qGoDJK3a2ikAD3L5fxAfr9pFy/TZnM7KZsW5vqwg0QMT564xbsR2ApQdiWX00qcUFGqCiqoYVhxNYcTgegNErt7WKQAPczMvn3XW7uXgnl7jbmUw7tq9VBBrg4PUrjDm4A4C5x6NYm5jc4gINUFpVxfyIWNYkJsumw3oZPv4laGlpkffUFNDHSU9Px8io6amHf4xIv6r6uCVtttRKRlLZbqFVq15GXX1di4TzX2n3z89a95zCopbm4d/4ddiuq6t/Ld/v13q96x5e79fzuV/L7+rPz/x6bMvIpiDSz9CzZ09WrVpFQUHBM8fOnz/P6tWr6du3b5P7/8eItICAgIDAa6ZeJLvHv4Svv/6a2tpaOnTowJw5cxCJRGzYsIHhw4fj4eGBgYEBn3/+eZP7F0RaQEBAQEAqRPWye/xbMDY2Jjk5me7du/P7779TX1/Ppk2b2Lt3L2+//TYJCQnNmjMtbFUpICAgICDQBCorKzl8+DAWFhasWbOGNWvWkJubS11dHfr6+lKt6/0qhJG0gICAgIB0CDnpJ1BQUODNN98kLi7u0Wv6+voYGhrKRKBBEGkBAQEBAYEmIRKJsLW1fWl1d3MRRFpAQEBAQCqEnPSzfPrpp/z4449cuvTyzZWaipCTFhAQEBAQaCIJCQno6urSoUMHQkJCsLCwQFlZ+Yk2IpGIxYsXN6l/QaQFBAQEBKTjNU6dioqKYv78+SQnJ3Pnzh3WrVvH6NGjX9j+v//9L19++eVzj+Xk5GBgYMDNmzextLR85vimTZsYPny4VO/rxx9/fPT/48ePP7eNINICAgICAi3PawxTl5SU4OTkxKhRoxg6dOgr28+aNYvJkyc/8dqQIUMQiUQYGBg88fr+/ftxc3N79FxLS0vq99XSC/IIIi0gICAgIB2vUaR79uxJz549gYaR6atQU1NDTU3t0fOsrCyio6PZtGnTM211dHSatXRnSyIUjgkICAgISIUsC8cqKyspKip64lFZKfud9h6ydu1atLW1GTRo0DPHBg8ejL6+Pj4+PmzYsIGm7DuVkJDA3Llzef/997lypWEnubKyMlJSUij5c3vUpiCItICAgIBAqzN37lw0NTWfeMydO7dFbNXW1vLzzz8zYsQIFBUVH72upqbG999/z9atWzl8+DB9+vRh4sSJLFq0SOq+q6qqGDhwIP7+/nz22WcsWbKErKyG/dLFYjFdu3Ztcj4ahHC3gICAgIC0yDDc/cknnzBz5swnXntcQGXJoUOHyMrKYsKECU+8rqenx6xZf22Z6ebmRkVFBfPmzeP999+Xqu///Oc/7Nu3jxUrVhAaGkq7du0eHVNSUuLNN99k9+7dfPbZZ01678JIWkBAQEBAOmS44piioiIaGhpPPFpKpFetWoWfnx8ODg6vbOvj48O9e/coLJRu29hff/2VKVOmMHHiRHR0dJ45bm9vz/Xr1xv9nh/yjxlJi2n90n/xn8UJYimKFP5VthG9FrsPi0GkKQqRNQ8/r+g1uK1i0ev5O4vED693q5vmT9Ov5/stFiGue33f79f522p2P//ARUju3LnD/v37WbNmjVTtT58+/UzR2cu4d+8eTk5OLzwukUgoKyuTqq/n8Y8ZSZuoarEm4G2s1HVbxZ6rjilbQ8cCMNbVjQ/8/FGRl29xu3JiMSM7uvCf4BAAlg/sQxdb6xa3C2Cgpsp3PbsSZGWBnbYeq7v0x1xDq1Vs+7QxY2uvIQC84+3FVB9vlORa3oeUF4sZ5+7Oh4EBACyfOIAAe4sWtwtgpKXOvJE9cbM2xdm8Dd+P7Imxtkar2PZvZ86qSQMBmNbTn9Gh7shLJC1uV1FewvjOXkzu5gvA2klv4G1j1uJ2AUx1NFk0sg/tjQ3wM2nLwrAeGKpIdyNuLp0trFnbYwAAs8OCGOnhgpyM1nZ+GcryckwP9GW8t0eL22ppSkpKSE1NJTU1lfr6ejIzM0lNTSUzMxNoCJ936tTpmfN+/vlnVFVVGTx48DPHNmzYwObNm0lPT+fy5cssXbqUb775hqlTpyKR8vdgZmbGxYsXX3g8NjYWGxsbKT/ls4jqm1LG1orU1tWxI+Es2QUljO/kiYK8HJuvnWRpehRF1RUyt2ekrMGHTmH0aevE/ZIyVkQl4mFuQndHO3JKSvguNppdFy60yEyEgLbmzAkOxk5Xj9NXb7M3MZ1RXTwwN9AmPiOTr49HcilX9mvEKkgkjPF04x1fLxQlcmxJSqWoopLxAZ6IxSJ+PneKpakJlFRXydy2mbomn3oF09OyHTklJfyYnEBwW0s6W1pzu6iIeZFRHLh8WeZ2AcKsrPg0OBhLHW3ibmWy98ol3nH3wkxDk+j0G8zfFcnNe/kyt6skL8foMA9Gd/JAIhGz/mQK9fX1jPZ0g3pYF36Kn0+cpLyqRua2zfW1+LBvMMGOVtx5UMSa40l06WiLr505mbkFzN8TScS5pofmXkZnZxtm9g3CVFeT+NQbRCdfY3R/bwx01Tl27irz90Vx64F0IcbGoKIgz8ROXowMdKO+tp4te06hrCTPGz1cqaqvZfnpJFafOUVlreyvt522Lv/xCyXQzIJbOQVs3HeSHv72uLY35Wrefb45Fkn0jQyZ2wXo49CeD0MDaKOhLrM+bb77QWZ9Xf1IupzvQyIiIggNDX3m9VGjRrF+/XpGjx5NREQEN2/efHSsvr4eKysrevTowfLly585d+PGjXz33XfcuHEDsViMjY0NkydPZsKECVJvkPHFF1+wcOFCjhw5gp2dHfr6+hw/fpzQ0FBWr17NlClTmDdv3hO578bwtxbpk1ezmLcrgst3G4RJX0OV6T396evhQH5VOYvPR7D1Rgq1MvgIShI5xtn5MrGdP3IiMRsTTrMiMpGSygZh8rIw5bMeIbQz0if17l2+jAjnTE52s+0CmGtp8VlgMJ2trcnOL+aHP6I4ktwgTPJyEoYEuzCxhzfKivL8mnqWxTFx5JfLxkHpYmvNJ2FBtNXSIvrqTeYdiuRa7gMA2miqM6tLIL2c2pFbVsq3p6LYfvmcTBwUFTl53unozQQnT0TA6tRTrEhOoqy6GoCgthb8JyAEGx1dTt66xf+FR3D+3j0ZWAYbHR0+Cw0hyMKCzMICvo6N5MiNqwAoSuQY7+LOu+7eyIvF/BZ9hp8OJVBcIZupId1c7ZjZNwgjbXWOX7nG3ONRZOQXAGCmpcnssCC6trMhu6CYH/bFcCDlxR56Y1BTUmBSF2+GBblSU1vH2uMn2RCRTEV1gzCFOFrxUb9gzPS0iL+UwXe7IrmWfV8mtu2M9fh4QAieNmZk3HnA4k0RxJ+5AYCSohzD+3gxrLcHYrGIjdEprDqeRFlVdbPtikTQ182BGT380ddQ43jcRZZviiI7twiAtsY6vDc6BF83K7KKCvkmIYKD16802y6ApqIS73v4MdzRhcrKGtbtTuC3w6eprqkFoJOXHdPeDqKNngYnrlxn7vFIbv75PWguTkaGzOkSgpupsUz6exybb2Uo0h83TqT/rlRVVdGnTx9OnDiBvb0958+fx8nJiQcPHnDr1i169uzJ7t27pR6ZP83fUqRvPyhk/p4ojp29+tzjTm2N+Lh/CB3N23CxIIdvzhwhIfdmk+31MLXnI6fOmKhqEX7pOt8ejuLm/WdHUBKxiDfcOjAjzB8tFSX+uHCB72KjuVda2iS7agoKvOvlzRhXN+pq61l/9CQbjp56dON8HB11Fd7t40d/3w4UV1WyOCaeLafTqGniajd2errM6RSCn0Vbbt7PZ+6hSCIv33huW/e2xnzaIwRHY0PScrP5b/xxku/daZJdETDAxoHZnsEYqqpx8Opl5sZFkVX07AhKTixmeIeOvO/th5qCIlvPnmVBTCwPysubZFtDUZH3/HwZ4eJCZU0NPyYnsvZMMlW1tc+0NVRV42PfQAbY2ZNfWs6P++P4I+EcdU38udibGvDRwBDcrEy4knef/x2NIPZm5nPb+lmY8VnnEOz09Ui9cYd5uyI4n5XTJLtikYgBXo5M6+mPrroK+5IvsGh/DDkFz87blJdIGB7kyqQu3ijKy7EtLo3lh+IpLGuaQ6itqsy7PXwZ5OtEeUU1a3fEs/3IaWpqn/3OGulp8O7QIDr7tCO3qIRFB2PZk5JOU+9OHdu2YXa/EJzMjLh84x6L150gNf3Wc9v6uFry3uhQzE10iL+dyVdx4Vy4n9skuxKRiKEOHZnp6Y+WohJ7o86zclss9wufvUcoKsgxrKc7o3p7IZETs+HUaZbF/jUwaCz6qirMDAlgkJNDi9V12M6TnUhfmf3vEGloGLFv3ryZ7du3c+XKFerq6rC2tmbw4MGMGDGiWX+Pv5VIl1VWs+Z4Ehsjk6msefbG+TgiEfRya8+MXoEYaqpx+PYFvk07xq3SAqnt2WsZMqdjNzz1zbmWe5+5hyKJufrq0JOGkiLvhvgw1KsjVbW1LD+ZxNqU59/sn4dYJGKQgwOz/ALQV1Xl4KmLLNkVQ3Z+8SvPbW9mwIdvBONmY9pwsz8eScxN6cNl2spKTA/w420XJ8qrqlkWmcgvialUv+K9i0UiBrg48H5nf/TUVNl97QLzkiK5U/rq9/wQV/02fOEbhquBMRfycvkqOpyE21mvPE9HSZn3vf1429GZsupqlsYnsPH0aaqldFAkIhFDnJ2Z4eeHtrISOy6l8118NPfKXu1cuRq24YvAUFwM23Dx1j2+/yOSU9eef7N/HrrqKkzt5U9/L0eKKytZHB3Pr1I4VxKRiLdcnJgR5IeWshK7T6azeH8MecXSF6C4WZkwu38I9qYGnM/KZt4fEaTevPvK8/TUVXivZ8N7LiqrZNmhOLbFpVFbJ92tQk4iZoh/RyZ390FNUZE94WdZtS2G/KJXO1cu7U15f2QodhYGnM3KZt6eCM5kvPo9P8RQU40ZPQLo7dqegqJyVv0aw74TZ6l7xXuXkxMzsJsr4wb7oaKiwK8X0liQFMODCukdQn+TtnzuH0o7HX3SLt9h4S/hXLjxaufKQEeNdwcH0t3fnvulZSyMjGV72nmpHUIFiYTRnq5M8fNGTVFB6vfbFGznylCkP/lnivTMmTMZMWIErq6uAGRmZqKvr//Mphqy4m8h0vX19exNvsDi/THcK2rcqFRZQZ5xYZ6MDnFHJIa1lxNYdSmW0poXe6M6iiq87xjKm5aulFRUsjQ8nl9PNn5UaqWnwyfdgwm0tSCrsJBvoiM5fPX5o/+HuBsb83lwKE6GhlzIzOH77RGcvtb4UWlXNztmDAikjY4Gx69eY+6JqJeGy+TEYoa6OjM9wBd1BUV2nD7HouNx3C9tXNWhmqICU4K8GeHjSi11rDiTyE9pJ6l4ST7PUEWNjz0DGWjjSH5FOQsSYvk9/Wyj0xTtdPX4IjAUX9O23HiQz/8iIwi//vzR/0N8zMz4PDSEdvr6pGTf4cvocM7ca1yaQgQMaOfAbN9ADFTVOJp6mYV7ornzoOiF58hLJAwLdmFCV2+UFeT59XQaS6LjG52m0FRSZFqAL8PcO1JZVcOqY4n8EnWaqpc4sW201ZnZO5Duru3IKypl0f4Y9pxq/KjU0cyQ2f1DcLE05srdPL7fFUnC5eeP/h/i396Cj/oHY2moQ8qFLBZtDOdKRuNGpWKRiN4hHZj8VgBa6srsP32RHw7GkFP44lWbFOUkjA72YFyoJwpiMdsOpLB+ewIlZY1LU2hpKDNhSAB9OztTXF3J4lPxbDr/cofQXEOLT32D6WZpS86DYn78LYoj8Y3fttDZ1piZw0OwtzLifPY9vj4Wwams2y89p7OtNbM7BWGurdVoe01BEOmGBUp++eWXR+uHSyQSNm3aJNV64k3htYt0WsZdvt0VQVpm8/K7JjoazOwdSNeOdtwrL2b+uRPszkh7In8qLxIz3MaLqQ6BqEgU2HrqLEvC48kva1r49CHBtpbM7h6MpZ428VmZfBUZwaWnNgFvo67Ox/6B9G3fnvtFpSzdE8vehPQmh0+hoQBpZGd3Rnf1RCL5M1wWl0hJ1ZMOSoCFOZ91CsZWT5dTGbf45mAk6Xebl98119Hi425BhLW35nZJEd8kRrDvxpM3JkWJhPEdPHjXxQcFsYSNZ1NZcjKeomYu/dfNyobPAkIw09Ak8sYN/hcRybUHD55oY6apySfBQXSztSW7pJh58dHsvty8gj9VeXnecfNmvKs71MGG8GR+PnaS8qfyp8GOVszqH0xbfS3ibmbyv2MRXM5tXn7XRk+HTzuFEGhlTlZeAd/viSL83LUn2igryDEm1IMxoZ6IxbAxMoU1x05S2sTw6UN6uLZjZu9AjLTVCT97jfl7IsnKezI9Ya6vzYf9gghytOJubhFLN0cSntS8gj81FUXGDPBhcDdXqurqWBt+kvWRp56JsnV1suWDXoGY6GgSl3yNJRsiyLrTvII/Gwt9ZowJw9XRjKv59/m/uHAis24+0UZVXp533XwY5+xOfW09v+w7xab9J6loRsGfSAQ9Axx5d3AAulqq7E+/xHfh0dwpejJiZauny2edg/G3NG+yraZg943sRPryp/9MkW7Tpg3vvvsuc+bMAZ4VbVnz2kT6XmEJi/bHsC/lQpPzTs/Dw9qU2f1DaGesz5kHt/lf6mFSH9wmxMiWTzp2wVJdl4QbWXxzMILLObKrlJaXiBnm5cK7IT6oKMjz69mzLEqIo6y6monuHkzy8EROJGZzeAprDiVRWiG7SmlDLTWm9w+kh2d78kpLWRAVx46z52mrpcknYUF0srHmTkER3x+J5uB52VZK+1ub80n3YGwMdEnKvsWX8Sc4dz+H7ha2fOYdQlt1LSIzbvB/MRFcy3/w6g6lREEiYVxHd9718EZRTo5Np1NZEh9PbV0dk729GOfuTj2w6vRJVqacpKym+YVIDzHT0ORTvyB6WNuRU1DCor3RHEy5iKWhDh/2D8GvvTkZ+QXMOx7FsSvXXt1hIwi1seTTTsFY6GiTcDmTb3dFcDX7foOQ9gnESEud42evsmBPFFn3ZVcp/bgDIBGL2BSZwuqjSYhFIiZ29WZokAs11XVs3JPIlv3JVD2nrqKptG2jzbRhwQS4WXP7QSEL9kdz5OwV2rXRZ3bfEDytTcm4fZ8l68JJSL0pM7sAIT62TB0ZQhsDTcIzrvN/8RHcKHjAoHaOfOQdhIGKKkcTLvHjb1Fk35c+9fMqVJQUGN3Xi7e7u1EHrE48xeqEkyhK5Jge5Mvbrs5IWmEK19PY/U+GIv3ZP1Okx48fz8aNG/Hx8UFLS4t9+/bh6uqKiYnJC88RiUTs3r27SfZaXaSramrYEJHCmhNJlFXK7sb5OGKRiIHeHXivhz9aqkpcLrpHO01DbuUX8u3hKI5eeHlIujnoqCozPcyPN92cKKmqpKS6GmN1dSLSrrFwZxRZuQUtZtvFypgP3wzBoa0h1+4/wExLk9raOlbHnOTnuOTnFqTJAjmxmCGezkwL9UVdSZEr+Xm009Hnev4Dvo6JIDzj5SHp5mCgospHvoEMbO9AfnkFNXW1GKipsf/qJebGRXGr+MUh6ebia2LGF4GhtNfV51r2fcz1tamsrWF5bCLrT56WukahschLJIz0cOFdf29U5OW5eS8fayNdrmbn8a0UIenm8HQoXSQSoaOmzOHYCyz/LZrcB03fSOBV+HS0YPrwECxMGj6rlYEupeWVrN0ax85DqdQ+pyBNFigoyPF2Hw9GDPBCTl5CRlEBNtq6XLp5j4W/hJN66eUh6eZgaqDJe28HE+xhw92iYpTl5dFSVmoxe6/C7msZivScf6ZIl5aW8r///Y/w8HDu3bvHzZs30dXVRVVV9YXniESiJq861qoinZZxl49+OcDtl+TyZIm6siKfDgilt7s9a2JOsiQ8/qW5PFlib6TP4rd6o62szMdr9xN/oWXmQj6NSAT9fTvw+bAuJN28xcc7D3G3UHYe/svQUlHiyz6d6eZgyw+JsaxITpK6uKu5dDQ0Ynn3PijJyTHl0F4S70hf3NUcJCIRI51c+SIwlOjrGczef5h7JU2r9m8seqoqzO3ZlRAbSxbsiWJTVIrUxV3Nxc3KhAWjelFfU8+ni/Zw7or0xV3NQSIRM7y3J5PfCiA66SrzVh6mQIqCNFmgr6PGnKk98HA257v1x/njRFqz0lWNwdOxLd9M642G6usTaBBE+nm0dLi7VeMlKddvt5pAAxSXVxJ+viHceODc5VYTaIAL2blcysnjflFpqwk0QH097E+6AEDijaxWE2iAgrKKR9O49ly+1GoCDXAmJ5tr+Q+4XVzcagINUFtfz+7LDdc79kZGqwk0QF5pGVHXbwKwP+Viqwk0/Plbvl9EVnZ+qwk0QG1tHUfiGq53/OnrrSbQALkPSjh1tiFKcTD2QqsJNMDJ85ktGqWQFlluVflPZeDAgURHRz96Hh4eTpcuXVrM3j9mWVABAQEBAYHXze7dux8tRQoQFhbG0aNHW8yeINICAgICAtIhw12w/qmYmJhw+vTpR8/r6+tbdFOgf8wuWAICAgICr5d/cphaVgwZMoT58+ezdetWtLS0AJg9ezZz58594TkikYgzZ840yZ4g0gICAgICAlIyd+5cbGxsHlV3i0QiVFVV0dVtmR0aBZEWEBAQEJAOYSSNRCJh4sSJTJw4EWio7p4zZ06LVXcLIi0gICAgIB2CSD/DjRs30NfXb7H+BZEWEBAQEJAKISf9LObmLbs0qyDSAgICAgICUiIWixGLxZSVlaGgoIBYLH5ldbdIJKKmpmkrPgoiLSAgICAgHcJIms8//xyRSIScnNwTz1sKQaQFBAQEBKRCCHfDf//735c+lzWCSAsICAgISIcg0q2OINICAgICAtIhiDQbN25s0nkjR45s0nmCSAsICAgICEjJ6NGjn3ntYU766U0lH89VCyItICAgINCiCDnphnnRj1NQUMCoUaPQ1NRk2rRptGvXDoCLFy+ydOlSiouL2bBhQ5PtCSItICAgICAdgkg/My/6v//9L/r6+hw5cuSJkbOTkxODBg2ia9eu/PDDD6xbt65J9lp1Fyw7Y33UlRVbzZ6cRIyrhTEAnhamtGCV/DMYaqhhrqOFlqoyNsYts6bri/CwNQXA0dgAVUWFVrOrICfBxbQNAD4mpq1mF8BUXQNTDU0MVFWx0tJuVds+JmYAOBsboizfen6vkpwcHY2NAPCwbt3rba6vhaGmGka6GpgYaLaqbTf7huvtaNsGBYXWu97KSvK0tzYEwN2+da+3pYku2hoqrWrzuQi7YD3Drl27GDBgwHOnYYnFYgYOHMju3bub3L+o/ukgeguTX1LOj4fi2JF4tkU3qQ9ob8FH/YKxNNChoLIcLUVlUrPu8s3BCNJuZ7eYXUU5CWP9PJgQ6ImCWExldS1KinJsj05jxf54CksrWsy2mZ4m7w8MJrSjNUWFZWhoqpBbXMoPx2P5I/U8LfmX7tzemo+7BWGmo0VRcTka6sok3r7FV9EnSM/LbTG7KvLyTHbzYqKrB6J6qK2rR05OzIazp1l8MoHiqsoWs22trcN//EMIMbeksLQCTVUl7hYV8314DHvTL7aYXYBe9nZ8FBqIsaYGhSXlaKopE3cpg+92RXAt50GL2VVTUmBSF2+GBbpSW1uHSCRCJIJfDySzcXciZRXVLWbbpq0+M0aE4O7YlqKicjQ0lLmTU8CPGyOJTLzSYnZFIugW5MCU4UHoaas9+lvHnL7Ooi0RZGUXtJhtDVVFJgz0Y2CnjlRX1iAnL0FeXtJi9l5Fh49+kFlf5757X2Z9vU60tLQYOXIkS5Ysee7xqVOnsnnzZvLz85vUf6uL9EMu3cnl210RnLx2S6b9Wuhr82G/YILsLckqLuR/J8M5knGFt9t15AO3ALQVldl1Jp2Fx2LJLS6Vqe1uDrZ81C0IEy0NYpOvsXRTJCVllUwaEkCvkA4Ul1eyYn88O6LTqKmrk5ldVSUFxnXzYniYG3U1dWzbGMvWjbHYtG/DlFndsW1vzNnb2XxzMILTWXdlZhfA1kCXT3uE4GvVlqw7+SxdH07SmZsM6ObC2Lf8UFVR5LfzZ1mYGMP98nKZ2RUBfe3sme0XiJGaOidOXmbJ1mhqamp5540AevjZ86CinPkJMfx+4Rx1MvyaaygqMt3Tl1FOrlRV1bD2QBJbjqXgYmPMB2+FYGOiR8qtO3x9NIKz2TkyswvgaGTAnM4heJiZcP3OfRb+HkHypVu83dmVcb19UJSX4/e4Myw/FE9RuewcFLFIRH8vR97r6Y+uugpHI9NZuSEKiUTE5FHBhAW0Jy+/hOW/RXMoJl2mDqGWujIT3vCjXydnysur2fBLDLv2pODuZsE7E8Noa6ZL8rlMlqwL52qGbB1CR7s2zBgThoNtGy5n5TL/twjO3cxmRFd3Rnf3RE4i5vfDp/l5dwKl5VUysysRi+gf6sykQX5oqClx+GAaP6+OQFlZgUnvdsLP305mthqDINLPMnr0aDZv3sy3337L5MmTUVFpiHiUlZWxYsUKZs+ezbBhw1i/fn2T+n9tIv2Qo2lXWLA3itsPiprVj7qSIpO7+vB2QEeq62pZlpbI6nMnqaz9ayk2DQVFZrj6M8rejcrqGn6KTmJ9fApVNbXNsm1vpM+nPULwtDDl5u37LNkYQULqzSfatLM0YMboUDq2N+XanTzm74gk4WJms+yKRNDX25Gpff3R01Ql4vBZ1i49xr3swkdtxGIRXfu4MPqdTmjrqrE37SILjkaTXVTSLNtaKkpMC/FliKczFRXVrNsWz44Dp6l+7FpqaSgzfog/fTo7U1pTzZKkeDaePU11Mx2UjoZGfBEYiquRMVeyclm4JYLkC1lPtOlg3YYPhoXgaNWG87n3+ComnMQ7zXMIJSIRQxyc+cDbD20lZfbFp/PjH7HkFf7l7EnEIgYGOTO5ry+aqkrsOJvOwogYckvLmmVbV0WFmcF+vNGxAyVllazcHceOyDRqa/+6lrqaqrwzwJ++/o4UllXw46F4tsenNTti5WZpzMcDQnAwNeTilWyWrj3B2Qu3n2jT0dGU98aHYWtlyPmrd1m0MZxzV5vnEEokYgZ1cWH8IF9UlRXZf+gMP6+PpqDwr2spJyemf183Rg0PQFlJnj3H0ljzeywFRc1zCPV01JgyLJBuQQ4UlJSz7I9Ydsecf8LZM9RWY+rAAHr62POgsIwV22LYF3W+2Q6hp2Nb3h8WgrWZHufP3WLZkqNcvvTktXT3sOSdaZ0xt2i5jR2eR4cPZSjS3/87RLqwsJC+ffsSHR2NvLw8bdo0pPzu3r1LdXU1/v7+7N2799He043ltYs0QGV1DRsik1lz/CTlVY0Ll4lFIgZ6d2BaDz+0VZX541o6805FklP2YhGy0dLlP16hhJhacSu/kG8PR3H0wtVGv28dVWWmh/nxppsTpeWVrN0Wz44jqU/cOJ+mk287pg4PwlBPg4i0ayzcGUVWbkGjbXe0asOHb4TgaG7ElYt3WDH/EOdTXyz6KqqKDB0XRP+3vampr2d1zEl+jkumorpx68nKicUM8XRmWqgv6oqK7D9xllVbYsgvfLEIWZvrM31MKG5ObbmW/4D/xUQQnnHjhe1fhIGKKh/5BjKwvQOFJRWs3BnL7sgXp01EIujh58DUNwPQ01Jj/9VLfBMXxe3ixjuEviZmfB4Qir2ePmev3+X73yI4f/PFaRMNFUUm9vHlzZCOVNTUsCIukfUnT1NV2ziHUF4iYaSHC+/6e6MiL8+OiDR+2hNHYcmL0yYOFoZ8MCSUjjbGXLmbx7e7Iki8kvXC9i+ijbY6M3sH0t21HffzS1i1MZpD4edeOEoWi0X06uzEhOGBaGooczj2Ast/iyb3QeMdQm9nC2aMCMHCRJfUtEyWrTzO1Wv3XtheS1OFsaMD6dW9Y8NvcWscOw+9/Lf4PBQU5Hi7jwcjBnghJyfh9xOprN6XSMlLohIdrdswa0gIDhZGXLp5j4W/hJN66fYL278IEwNNpr8dTLCHDbn3ili18gThx9Nf2F4iEdO7ryujxwWhrq7caHtNwWmW7ET67Px/h0g/ZPfu3Rw8eJCMjAygocCsZ8+e9OnTp1nLhv4tRPoh9wpLWLQ/hn0pF6QKl3lYm/Jxv2DamxiQmnuX/yYc53TuHanthZpa8bl3GFaaOiRcz+SbQ5Fczsl75XnyEjHDvFx4N8QHFQV5dh9LY83WOAqKpfPeFRXkGNbHg+H9vJBIxGwOT2HNoSRKK14dLjPUUmN6/0B6eLYn/34J65Yd5+i+VOqkHC0Zm+kwcUZXfIPbc6egiO+PRHPw/GWpzvW3NueT7sHYGOhyJv0Wi38+weUbL75xPk2wty3vjgrG2FCLiIwbfB0TwbX8V+dPFSQSxru48467N4piCduOn2HN7niKy6QL56ooyTOqlxfDurtTJ4LVqadYkZxEWc2rHUIzDU0+8wumu7Ut9/JLWLIzmkNJF6UO51q10WHm4BB8Hc3JyC9g3vEojl25JtW5oTaWfNopGAsdbZIuZLLgt3Cu3b4vnWGgm1c7pr0RhJGOOsfPXmXBniiy7he+8jxlBTnGhHowJtQTiUjE1j2n2LQtgTIpw7lqqoqMHOzLG73dqK6pY+OeJLbsP0WVFA5h2zbavDc8BH9XK7JzClm5OpzI6EtS2QWwsTbg3cmdcHFuy81b91m6PvyZqNaLCPGxZerIENoYaBKddp0ftkWRkS1dHlEkgt6+DkwdGICepipHEy7x429RZN8vfuW5KkryjO7rzdvd3aivq+f3XxPY+msCFVLm99U1lBk1JpA+/dyQSFq2FtjpAxmK9IJ/l0i3FH8rkX5IWsZdvt0VQVrm80cqJjoazOwdSNeOduSUFTPvZBR/XDvfpIJBebGYUfZuTHf1R1VOnq2nzrI4PI6CsuePVIJtLZndPRhLPW2Sz2WyaEM41zJfLezPw1BXnSnDAunqb8/9olKW7ollb0L6c8NlSvJyjOzszuiunsiJROz6NZEta6MoK21a3tHdx5pJM7thbmXAqYxbfHMwkvS7zxdccx0tPu4eRFg7a7Jzi1i+MZITcdLfOB9HQV7CW308GDHQG3kFCZvOprL4ZDxFlc//HN2sbPgsIAQzDU3i027ww6+R3LzbtMIoY31N3hscSJinHdklxcyLj2b35QvP/d6oysvzjps3413doQ42Hj7FhsOnKK9sWmFUkLMV778ZRFtDbWJvZPC/Y5FcyXu+4Frr6vBZ52ACrSy4da+AH7ZGEpkqnbA/jZKCHCO7ezKyuwdisYiNkSmsPpZE2Qs+Rw/XdszsHYiRtjrRCVdYvi6C200sjDIz1ubdsaH4eVpzN7eQpZujCE96vkOopqLImAE+DO7mSnVNLVt+S2DrjpNUVTVt56CgADumTAjDyEiTuORrLNkQQdad5wuujYU+M8aE4epoxo27D/hhaySx5242ya6qkgJjenoyrLMbdfX1/LLvFJv2n6TiOZ9DJIKe/g6881ZDpCf8RDqrV57gXk7TUn8Wlvq8M60zbu6WTTpfGgSRbn3+liINDSu37E2+wOL9Mdwrasj5KSvIMy7Mk9Eh7iCG1edOsvxMAqVSjIheha6SCrPcAhjSriPFFZX8GB7Pryf/KvCy0tNhdvcggmwtuXOvkKUbI4g82fgQ+fNwbmfMjNFhtLcyJD0zh++3RZB6/a+IQFc3O2YMCKSNjgbxkRdZtegId7KaX8ErkYjp/YYHIyaFoqqmxPaUcyw+Ecf9P/OnaooKTAnyZoSPK3W1dWz+I4ktu09S2cQb5+Po6agxaVgg3YMdyK8oZ0FCLL+ln33koLTT1ePzwFD8TNuSkZ3Poi0RxKY1PkT+PNztzZg5NARbM32Ss+/wVXQ4Z+41OIQiYEA7Bz72DcRQVY2jpy6zZEc0d+43r2YCQF5OwpAwF8b38kZZUZ4tKWksiY6noKLBIdRUUmRagC/D3DtSWVXDz/sS+fVY82smAIx01HnvjSC6erUjt6iERftj2XvqrwIvB1MDZg8IwdXShBuZeSxdc4JTZzKabRfA282SqeNCMTfVJeVCFos2hnPlzwIvsUhE75AOTBocgLaGMsdOpLPq5wjy8ppXMwENoevBgzwZOsQHeTkJ2w6ksH57AiV/RmC0NJSZMCSAvp2dKa2oZNXeBLaGn6GmkSHy52Gqr8mMN4MIdbUh534xP/4exZH4vxxbJ5s2zBwRioOVEVcuZ7N86VHOpjU+JfE8/ALsmPxOJ4xNZD8V0WmmDEV6oSDS0vC3FemHlFVWs+Z4EtkFxUzvFYChphoHbl7im6QIskpeHbprLA46BvzXpxPeRmZcy73PwmOxeJqbMsy7I9XVtWzYmcjvB5Kpqm7+jfNxRCLoGezIlLcD0dFS5eCpi+xLSGdcdy/cbEzJuH6PnxYeJjmhaSOql6GhqcyISaH0GuRBeXU1yyITKa6oZEYnP/TUVDkSfYGVm6K4J0XorrHY2xoxY2wnHO3akJ53jwUJsYSYWzLU0ZnyimrW7E5g67HTMrlxPo5ELKJfsBOTB/qjqabEjkvp7LtyiemevrgateFS1j3m/xZBypXG5xZfha6GCu/096evnyNFlZUsiY6ntq6OGUF+aCkrsTf2PMv+iOV+oWxnHwC42Jowa0gI7c0NOZeZzfLDCXRxtqGfpyMlpRWs3RLLnkOpMp8eKZGIGdDTlTFDGir+94SfJTrlGhPf9KOdhSEXL93lxxXHOH9B+nSVtOjpqTFxbAidwxwoKCpj1a+xKCnKM3ZwQ0Hazqg0VuyOp6BEdrMPHuJlb/ao4v/M5dus251Idz97uvm1p6CgjJ9XR3D4YJrU6SppkZeXMPANT4aN9EdFRXZrUzjLUKTTGinSUVFRzJ8/n+TkZO7cucO6deueu0TnQ27evIml5bNRhU2bNjF8+PBHz9PT05k6dSrx8fFoamoyatQovvnmGySS1zfV7XH+9iL9OBce3OO/CcdJyJaNx/kyelm041OvEEzVNKmvr+dgVDorf40mL1/2N87HUVFWYPQAb97q5Y68nITionI2/RTOvu2nGl0E01gsrA2Y/EF3XL2sALh4NZvF68I5e1H2QvU4IhF0DXRgyogg9HTUqKurZ3fUWVbuiCVfyjx/U1FXUWR8P1/e7OyCnERMfnEZy3Y9W8nbErRva8Cst0JwtTUB4MzVOyz4LZz0m7KdtvU0YpGIPv6OvDswAB0NFWpq69h9MJV1v8VSVNxy8/gBNDWUGTfUnz5dOyKRiLl/v4RVP0dy9PiLC9JkhYO9MVMnd8K+fcMCR6cuZbHgt0gu32q5efzwWMV/P1+01JSprq7ljx0n+WVjbJPTVdKio6PK2AkhdOvhLJM9j53fl6FI/9A4kT5w4ACxsbG4ubkxdOhQfvrpJ6lEev/+/bi5uT16XUtLCyUlJQCKi4tp3749Pj4+fPHFF2RkZDB69GgmTJjAvHnzmvS5ZM0/RqSzS4vx27qS2lZ8u8py8lwc+T6/7U9mycaIVrML0CfMiU8mdWXayFVcTpf96OJlfLtiJDomWgx77+cWv3E+jo6WCnvWvsPa3Qn89Edc6xkGhnR1Y+bQEAZ9voGb2S23GMjzWD97CPJyEoZ/9Uur2jXV12TX3HGsWB/Br3+cbFXbIwf7Mn5YAIOHLSc3T/YRmhchEsHalWMpqKpk/PfbWs0ugJ2pPr9+MZzFCw+xd3dKq9qeMCmUt4b6NrufjjNkJ9JnFjU93K2kpMTKlSulEun4+Hh8fHye22blypXMmjWLnJwcVFVVAVi2bBkff/wxubm5KCu3TtX8y2jVZUGbQ1VdbasKNEDFn7luWeRgG8tDm1WVr8F2ZQ3V1bWtKtDw12eubOS0sH+67arqWqkqn2XNw8/6er7fr+e3VV8P1dW1VMogz99YHl7v1/Kbfg1/41dRWVlJUVHRE4/KFxSQNofBgwejr6+Pj48PGzZseGKnqri4OPz9/R8JNED37t0pLS3lzJkzzbJ7/fp1Lly40Kw+4B8k0gICAgICrxkZrt09d+5cNDU1n3jMnTtXZm9VTU2N77//nq1bt3L48GH69OnDxIkTWbRo0aM22dnZGBoaPnGekVHDevh370q3GM+SJUsYMmTIE6+NGTMGW1tbOnTogIeHB/fuST9V9WmEXbAEBAQEBKRDhtG1Tz75hJkzZz7xmqKi7Irc9PT0mDVr1qPnbm5uVFRUMG/ePN5/X3aV5WvWrCE0NPTR88OHD7NhwwYmTZqEk5MTc+bM4csvv2TZsmVN6l8QaQEBAQEBqZDlftKKiooyFWVp8PHx4euvv6awsBBNTU2MjIzIyXmyUPPh84fLe76KjIwM7O3tHz3funUrlpaWrFixAmgYrW/atKnJ71kIdwsICAgI/H/B6dOnUVNTQ01NDQA/Pz9iY2MpK/trWeNDhw6hqqpKx44dperz6drrI0eO0KNHj0fPLSwsyM5u+s6LgkgLCAgICEjHa9xPuqSkhNTUVFJTU6mvryczM5PU1FQyMxv2LPjkk0/o1KnTo/YbNmxg8+bNpKenc/nyZZYuXco333zD1KlTH82BHjZsGBoaGowcOZKzZ8+yf/9+Pv/8c6ZOnSp1ZbednR1//PEH0BDqvnPnzhMifevWrSZvrgFCuFtAQEBAQEpkGe5uLKdOnXoi9/vFF1/wxRdfMGrUKNavX8/du3e5du2vxZ5EIhFz587lxo0biMVibGxs+OGHH5gwYcKjNurq6hw7doypU6fi5eWFhoYGY8eO5euvv5b6fc2aNYuhQ4eira1NaWkp9vb2dOvW7dHxEydO4OLi0uTPLYi0gICAgIB0vEaRDgkJeSa0/DhP79c8cuRIRo4c+cp+HRwcOHHiRJPf15AhQ9DV1eXAgQNoaWnxzjvvICfXIK0PHjxAR0eHESNGNLl/QaQFBAQEBKTidY6k/8506dKFLl26PPO6jo4OO3fubFbfgkgLCAgICAg0k9u3bxMVFcW9e/cYNGgQpqam1NbWPqokb+pa4ELhmICAgICAdLzGwrG/K/X19cycORNLS0uGDRvGzJkzuXy5YUvWkpISLCwsWLp0aZP7F0RaQEBAQEA6BJF+hu+//57Fixcza9Ysjh49+kTeXFNTk4EDB7Jjx44m9y+EuwUEBAQEpELIST/L6tWrGTlyJN988w33799/5rizszMHDx5scv/CSFpAQEBAQKCJZGVl4efn98LjqqqqFBUVNbl/YSQtICAgICAdwkj6GQwMDMjKynrh8eTkZNq2bdvk/oWRtICAgICAVIjq62X2+LcwcOBAVq5cyfXr1x+9JhKJgIYlQtevX8+bb77Z5P7/MSJtpqbJwsCeGCirvrqxDLDW1OHnLoMAGNClI290d0UiaZ3LFeZjx6QhAQDM+m9/HJzNWsWuiqoCY6d2xs3bCkszPT6c1AUtjdbZ9NyqrR7/+7AfAG93dWNAiBPiP7/oLYlIBD187RnXzxuA/43vgYO54SvOkg3qKop88FYwHW2McbAw4sOhoWiotM6GA+3bGjB3Um8Ahr/hQ/cwR1rhciMWi+jV2YmhA7wA+HJOf6ws9VveMKCpqcz707pibWWAu50pM94MQlVZoVVsO1m14atx3QEYNS6I0DCHVrErlojo29+NfgPcZdOhUDj2DF9++SVt2rTBxcWFkSNHIhKJ+PbbbwkICKBHjx44Ozvz6aefNrl/Uf3LlnD5m3A0/SrZRcW87dmRyroalp1JYM35k1TWyn7jdg0FRaa7+DHKwY3q6jo2R6bgbm2Cm7UpN27dZ/GGcJLSMmRuF8DWQp8Zo8JwdTDl5u37HI69wJvdXNHRVCX80FnWLj1Kbk7TcxsvQiSCLr1dGPNuJ3T01DmcdInisgoGBDlRXl7Num1x7Dh4mpqaOpnb1lRXZtwQP/p16Uh5dTWbok7j384cZ/M2XM64x8ItEaRcuiVzuwCOVkbMHBqCk40xl2/ncuz0FYaGuKKpqsTeuHR+/COG+0Vlr+6okUjEIvoHOjGlrx9aakrsj02nvq6e3oGOFJVWsHJ3HDsj06itk/1PU1dDhSkD/Onr34HSskq27k0myMcWW0sDLly5y5LVxzl/Sbp9dBuLs4MJ08Z3op21IZcu3iEx9iqD3vJCSVmB/QfP8PPGaAoLy2VuV05OTP8+bowa7o+KiiL7jqahpChP1xAHHhSVsWxXLHtizlPXArdCAy01pg0KoId3e/JLy9kSlUp3Vzts2uhxNi2L5UuPcuVy0zdfeBmu7ha8M7UzllYGMuvTc+xCmfV18ueZr270D6G8vJwFCxawfft2rly5Ql1dHdbW1gwePJgPP/xQ6nXAn8ffWqQv5+Qx92AECdcb4v3W+jrM7h5MgK0FmcUFfJ0UzuGMKzKxJRaJGGLnzCy3QHSUlNl36gKL98aQW1gKQFdXO2b2DaSNjgYxyddYsjGCW9kFMrGtpaHMpCEB9Al1orS8kjXb49hx7Ay1tXWoKiswur8Pb/Vwo7amlq0bYtm+MZbKyhqZ2HZwNmPKrO7YOZhwISOH+b9FkHr1DgC2pnp88FYInu3NyLzzgKXrwolPuSETuxKJmAHdOjL2LX/UVBT5I+kcSw7G8aCkDJEIervZ837vAPQ11Dh+8jJLfo/ibp5sHBQ9LVXefTOQnn72FJRWsGxvLLvizlFbV4+GiiKTevoyOKgjldU1rD2QyK/HTlNVIxuH0KOdGbPeCsbWVJ+zV++wcHME56833KTtLQ35YFgozrbGXL2dx8LfIki6kCkTu/JyEoZ0cmFcbx+UFeTZffgMP/8WR2FxecPotpMTE4YFoKWhzNHIdH7aGEXu/RKZ2DbU12DyqGA6Bbbnwf0S1v4UztGDadTXg5aWCqPGB9OzrytlZZVs+CWWXXtTZOYQenlY8u6kTrRtq0vquSyWrjnBlRv3AOjQ3pj3xneiva0RFzPvMf+3CE5fuS0Tu4ryEoZ3cWdMTy/k5MT8GpXKqsOJFFdUIicW84a/M+/28EVNSZHDB8/w85pI8h+UysS2sYk2k6aE4R/YTib9PY4g0q3P31Kk88vKWXI8jm3JZ587mgi2s+Tj7sFY6mkTeyeDrxJPcDE/t8n2fIzM+Nw7DEddQ85mZPPdzgjSbj47mlCSl2NUmDtjOnsiJxaz9UAK63cmUFpe1SS7chIxb3R3ZewbvigrybP7eBqrt8dRUPzsaMLMSJtpw4IJdLfmXnYBqxcfJero+SbZBdAz1GDctM6EdXfmflEpy3bGsjcu/bmjiTA3G2a8EYSJvibxKdf5cX0EGbcfNNm2V0cLpo0JwdJMj5Trt5m3O4ILt+49005FUZ7xYV6MCnGjvg5+OXSKjftPUl5Z3SS7CnIShnZ3Z3RvL+TlJWyNPMNPBxIoLq98pq2VkQ6z3gjB196crHsF/LAtisgz157Tq3SY6Gkw440gwtxsufegmGXbYjgUf4Hn/fq6+rRj2uAgDHXViTh9lUVbI7mVW9hk24HOVrz/VjBtDbU5lZbB0rXhXM/Me6admooiowb78kYvV6pratm8I4nfdp2kqqppDqGSojxvD/Rk6AAvxGIxO35P5NeNsZSVPft7sbY1ZMp7Xejoak5m5n2W/XScpFNNdwjNTHWYMjEMX29rsu8VsnxdBBFxl59pJxJB99AOTBwZiK62GkdOXmLx9miyHxQ32XYnNxtmvBmEsZ4mUeevs2B3FDfv5T/TTlNFiSk9fBns70xFeTW/bIxh145TVFc3zSFUVlZg6Ag/Br3phYJCy9QEe46RoUivE0RaGv5WIl1TW8evJ8+wLDyewufcOB9HXiJmmLcL74T4oKIgz5ZLZ1iQEkN+pfThMlM1DT71DKGXZXtyC0tYtDeG/aeef+N8HCMtdWb0DaCHe3seFJSy8rcYDkQ0Llzm52bFeyOCaWusQ/L5TH7YGM61rGdvnE/j7WzBjBEhWJjocvZ0BisXHOLqRenDk4qKcrwx0p/Bo/yRyEn49fhp1uxPfKWjoSAnYVgXN8b29EJBTsLOQ6n8vDWOktKX/50ex7SNNlNHhRDgac3d/CIW7I3m8Jlnb5xPY6KjwQd9gujibMu9/GJ+3BrN4YSLr/w7PU6ouw3vDQnGRF+T2PM3WbAjkhs5r3Y0gpys+GBgMG0NtEhMz2DB1kiu3Xl2LuSLUFGUZ0wPL4Z3caO+Hn45KJ2joaQgx4ienozo5YFYJGLLsRTW7U+itEJ6h9DKWJeZbwXj42jB7bsF/Lg+gpikq688z8xYm6ljQvDzsOZuTiEr1j9f4F5Gp8D2TB4VjKG+BrFRl1i17Dh3bj8rVE8TGNKeie92wqiNFvGJ11ix6gRZt6R3CFVVFRk51I+B/d2prqnll+2J/L771CsdDRVlBYa/4cPgfu7U1cPGw6fYcOgkFY1wUOxM9Zk1JAT3dqbcyHnA/F2RxFy4+crzbNro8tGAELzt2nL71gNWLjtOfJz0EUKRCLp2c2bcxBB0dNWkPq8peI2WnUgnrf9nivTYsWMbfY5IJGLt2rVNsve3EenYqxnMPRjBtdzGjdB0VVV4r5Mfb7h1oLi6kh9Ox7Lpwmlq6l8cLlORk2eKszcTO3ghBjaeSGHtsSTKGjlCc7E05uNBITiYGXLxeg6L1p8g7dKdl55jbqzD9FEh+LhYcudeIUs2RxB58tU3zseRSMQM7NyR8YP8UFNR4PCe06xffoKCV4TLgro4MmF6FwyMtIg6c40ftkaRea+gUbb1tVSZOiCAXr72FBaXs/rXWPYeS6PuJflTVRUFRg3y4c1e7tTU1/HziZOsj0imorpxIzQvGzM+7h+CXRs90q7eYeHmcNJv5Lz0HBtTPWYOC8HDvi0ZOfnM3xFJzPnGjdDk5SS8HezChB7eKCvKsz0ijZ/2xlNYWvHCc0Qi6Oljz7QBAehr/Rmy/63xIXsjXXWmvRVEF+925BWWsmxHDPviz7/UQdFQUWRiX1/eCHWhqqqGjdsS2LYvmapGjtB83CyZOiYEc9OGUPGSNce5euPlEat21oa8NyEMJ3tTbl7PZcWSo6Q0ckSsoCDHG0O8GTLCD3l5CTt3J7NxcxylL3EIxWIRPbo5M350EJqaTQ/Zmxhp8c6YEAJ9bMl5UMziHdEcTrr00nO01JR5p78fAwKdKKmo5KfDCfwWc4aa2saF7EM7WPNB/yDM9LQ4dfI6K348RsbNlzvuDo4mvPteV9q1b9MoW03Fa5QMRXrDP1OkLSwsHlVvP6SsrIzc3Ibfhra2NgD5+Q1Oqb6+Pqqqqk9UfzeG1y7SN+/n892hKMIvNe0DPMS+jQGf9gjGw8KUKwX3+b/EE0TefvLmIAL6WTvwiUcwRqrqHDtzhYW7o7h9v+m5TrFIRB8vB6b39kdXQ5WjsRdZvjmKnPtPhsvUVRUZO8iXQd1dqaquYcOuRH472Pgb5+Noqikx4U1/+ndypqKsii1ro9j9W+Iz4TKb9m2Y/EF3nFzNuX7nPgt+jyQhvXnFbx0sjZg1JAQnqzZcuXmPJT+Hc/r8k3MFxWIRPUM7MHFoADpaqhxIucgP+2PILmh6KFEiFjHI24mp3f3QUm0oulq+PYa8gicdFC11ZSYO8GNAiBNlldWsOpDAb5Gpjb5xPo6uugrv9vWnn48jxeWV/LQnnu2RadTWPdlnB0sjPhwSSgdLIy5n5rJwczgpF5tX/OZiZ8LMYSG0tzAk/WY283+NIO3akw6hRCxiYLAzk/r5oamqxIET51i1OYYH+U3PdcrJiRnQw5Uxb/miqtxQdLVmcwwFhU8W1OloqTBhRBA9wjpQUlLBhjWR7NudQl1t028vevrqjJscSqeuHSgsLGfN+igOHn7WIXR2MmPalE7YWBv+Wfx2gvOvcJZfhUdHc6aND8OyrR6pV2+z4LdI0jOedAjlJGLeCnVhQh9vVJUU2R6fxvID8eSXNr34TUFOwrBgVyZ08UZJTo49u5PZuC6a4uInHUJ9fXXGTwqjUxfHJttqCoJIP0t6ejpdu3Zl9OjRzJgxAz09PQDy8vL44Ycf2LhxI0eOHMHe3r5J/b82kS6pqGRFZCKbElKplmGVdndHWz7sFoSxlgbHM6/yf0nh3CjKp6OeEV/4dMLdwIQrd/L4dmcEJ6+8eAJ6Y1FVVGBCNy+GB7tRV1fHpt0n2bznJDU1tfTt5MyEwX5oqitzIDqdlb9FPyMqzcHaTI8ZI0PxcGzL7cz7/PTDYRKjL6Olo8rod8Lo1teVkvIqVj4UlWYI1eOIRNDDuz3vDQpEX0uNiPjLLNsYwd17RTjbmzB9bBjtrAw5n5XDt7siOH2zeTfOx9FQVmRKVx+G+LtQVV3Dur2J/Ho4hdr6et4I68iE/r6oKSvyR9xZlu2NI79EdlXD9m0N+OiNEFysTbh2J48Fv0eSeCETfS1Vpg0IoKePPYUlFazYHsPuyHMyqxoWi0T0DnTknTcD0NFQ4VDiBZZujyYnvwTP9mZ88HYoNiZ6nLt4h8Vrj3Px6sujDI1BU0OZ8W/707drR8rKq1j/Wyw7D5xGhIg3+rgxarAfigpy7N2VzMafoykukt31duhgwjvTu9LO3pir13JYuuI4aWezMDTUYNK4EEKD7cl7UMJPG6M4EvHyKENjkIhF9O3uwrih/qirPVbxX1iGfwcLZr4VjIWRDievZPH9H5FcutP0upin0dNQYVqvAPp5OVBUVM6Gn6PYt/c08nIS3nzLmyHD/FBSkpeZPWnxHik7kU7c+O8Q6U6dOmFlZcXq1aufe3zChAncuHGDY8eONan/Vhfpurp6dp4+z+LjseSVyH56CzQUeI3xc2dCoCdycmIS72bhb2xOYVkFP+6PY2f88wvSZEFbPS0+6B9EiJM1OXlFFJdWYmOuz/mrd1m44QTp11pmugVAkIcN7w0LxsRQi3OpGVjaGKKkosCOyLOs3BNHYcmLw7PNQVlRnjE9PBne1R3q4dylO7g7tSWvuJTF+2PZfUp2N86nsTLU4aO+wfi3t+D2vQKqamqxNNYl+cotvt8ewaVbsrtxPk0393bMGBCIkbY6py5l4WhhhLxEzNZjqazdnUBxmfT5+sagqqzAuL4+DOnqSk1tHeduZOPR3ozc+8Ws2BjF0agLLWIXwMZCn/fGheLaoS2Ztx4glogwbaNNyskbrFhylJuvCIc3FZEIOndzYtzkUHT11ElJzcDR3hiRWMTW3afYtC2B8oqmFRS+Cg11JcYM8adfDxcqq2u4lHkPNztTbt8vZOGeKI6daVy6qjE4mhny8cAQOloac+P6PVRUFDE00mwxe6/Ce4QMRXrTv0OkVVVVmT9/PlOmTHnu8eXLl/PRRx9RUtK02RKtKtKXc/L49I/DnL/zbCVvS2CkocbnfToR2s6KnfFn+WFPNEUtdON8Gp92bflqaFc0lJT47uejHI59dUGaLFCQl/B2Tw8mvxXAhYwc/rvuCFdvv7ogTRaY6Gnw2YjOeDuYszn6NEsPxlFa2bTK98YSZG/Jl291QVEi4f+2HOPoadlMzXsVSgpyjOvmxfju3qRducPXa49w827TK98bg5mhFnPGdcW1nSmbdyayoQWF6mmCfW35aEpX6mvrWTBvH/ExrXO9lZUVGDU+iEFveZOclsH3yw5zJ7vple+NwcJMl9nv9cDBrg0rDsaz7sRJKpuRrmoMPd3bM+fNTqgqtc7iKy/CZ7jsRDrhl3+HSJuZmeHq6sqePXuee7x3796kpqZy61bTUl6tuuJYzJWbrSbQANlFJexJTQdga0xaqwk0QMKlTM5l5HDvQTGHYlpHoAGqqmv59cApAKLOXG81gQa4nVfEoT+LbH6NSW01gQaIunCDy3fyyMorbDWBBqioqmFz+GkAwk9daTWBBsjKKeD4yYaq6237UlpNoAEi46+QdSefmzdyW02gAcrLq9i1/SQAJ2IutppAA9zMuk9MYsNn3RSR0moCDXAg+SJ38mW/kFGjqa+X3eNfwqRJk9i3bx/9+vXj2LFj3Lx5k5s3b3L06FH69u3LwYMHmTx5cpP7FzbYEBAQEBAQaCJz5syhsrKS77//nn379j1xTE5OjtmzZzNnzpwm9y+ItICAgICAVAj7ST+f//u//2P69OkcPXqUzMyGVQLNzc3p3Lnzo2rvpiKItICAgICAdAgi/UL09PR4++23Zd6vINICAgICAlIhkv0eO/8aIiMj2b9/PxkZDWtQmJub06tXL4KDg5vVryDSAgICAgICTaSqqoq3336bXbt2UV9fj5aWFgAFBQUsWLCAAQMG8OuvvyIv37R57f+Y/aQFBAQEBF4zwn7Sz/Dll1/yxx9/8MEHH3D37l0ePHjAgwcPyM7OZtasWezcuZOvvvqqyf0LIi0gICAgIBWietk9/i1s2bKFUaNG8d1332FoaPjodQMDA7799ltGjhzJpk2bmty/INICAgICAtIhzJN+hrt37+Lt7f3C497e3mRnN32lSUGkBQQEBASkQhhJP4upqSkREREvPB4ZGYmpqWmT+xdEWkBAQEBAoImMGjWKrVu3MnnyZC5dukRtbS11dXVcunSJKVOmsG3bNkaPHt3k/oXqbgEBAQEB6fgXjYBlxaeffsq1a9dYtWoVq1evRixuGPvW1dVRX1/PqFGj+PTTT5vcvyDSAgICAgJS8W8KU8sKiUTC+vXrmTlzJgcOHHhinnTPnj1xdnZuVv9CuFtAQEBAQDpeY+FYVFQUffv2xcTEBJFIxPr161/aPiIigv79+2NsbIyKigqOjo4sWbLkmTYikeiZR0xMTKPfn7OzM7Nnz2bFihWsWLGC2bNnN1ugQRhJCwgICAj8AygpKcHJyYlRo0YxdOjQV7aPi4vD3t6eWbNmYWxsTHR0NJMnT6auro4ZM2Y80TYtLQ19ff1Hz3V1dWX99puMINICAgICAlLxOsPdPXv2pGfPng3vQyR6Zfun88BWVlYkJiaybdu2Z0RaX18fIyMjqd9LY0fIIpGIM2fONOqch7SqSJvraqMgJ6GqpnX2YRWJwNawYQcSW2M9Ltxqvb2s1ZUVaaOjjrqqIoa66uTcL24129ZmDZ/ZwkgHOYmYmtrWWXBXLBJhbdzggdq20SMjr6BV7AJoqShhoKmKqoICehoq5BWVtZptW+OG621pootEIqa2la63RCzC8s/rbWWuz/380laxC6CjpYK2pgq1yrVoaqlQWNB619vCygAASzM9xGIRdXWtoxwSiRhz0z+/38a6pN642yp2AQw01dBUUWo1ey9Ehpe6srKSysrKJ15TVFREUVFRdkaeoqioCFVV1Wde9/HxoaKignbt2vHhhx/Su3fvl/ajo6MjlaOQnZ3NpUuXpGr7IkT19a07q/xWfiHfHY7iaPrVFrXjZGLIJz1CcG1rTFVtLQoSCRHnrrFgVxSZuQUtZlcsEjHAtwNTe/mhrapMTV0dtTV1bNqbxOZ9p6isqmkx29oaykwaHECfECdq6upQkJOQkZ3Pwq2RxJy90WJ2ATraGPPhkBDszQ2pqqlFQU7CkTOXWbgvmtsPWm6zejmxmMF+zrzTzRd1JUXq6uuprK5hzaFEtoSfblGHUF9TlWn9AujtZU9NbR3ychKu3crjhy0RJJ3PbDG7AO72ZnwwLAQbM32qq2uRkxNzNOoCKzdFkXu/pMXsystJeKO3G6Pe9EFJUR6RSERZaSWb1kWzZ+cpampazkExMNRgwjudCOnkQHV1LfLyEi5fz2HJ6hOkpd9qMbsA3m6WTB0XirmpLtU1tUjEYnYnnWfp/ljuF7ecg6IoL2FkqAdjO3miICdBIhY164bfXIL7fC+zvkLdS/nyyy+feO2LL77gv//97yvPVVJSYuXKlY2a2hQdHU1YWBj79u2jW7duAFy6dIkTJ07g4eFBXV0dW7duZeHChezcuZMBAwY05uM8QXZ2Nt9++y0//fQT1dXVjBgxgp9//rlJfbW6SD8k8XoW3xyM4HJOnkz71VNTYWaXAPq7OFBQUcGiqDh2n7/IGE9XJvp4IicSsznyNKuPJFJSUSVT2x42pnw0MIR2JvqcuX2Xr49GUFBewexOQXSysyY7r4gft0RyPOGyTO3KScS82c2VsQN9UVaSZ+upsyw9EY+7uQkfdw/CVFuTuHM3WfB7JDezH8jUtqGOOu8NDKC7d3vyiktZfCSWI2evMC7Yk9GBbtTXw/qIZNaeOEl5VbVMbfvZmfNRv2CsjXRJvn6Lb/+IpLqmhg/7h+DXzpys3AIW7owiIu2aTO0qyEkYHubGuG5eKMhJ2HY0lXW7EvBxtuDdIUEY6qoTmXKVxb9GcuteoUxtG+trMn1IEKEetuTeL2bFL1HEJV9n1Bs+vNnTjeqaWjbvTOLX3aeokrFD6O9pzdQxIZi20Sb59E2WrTiOWCxm6pROuHRsS2ZGHiuXHOVk4nWZ2lVSkmfwUB8GD/NFJBax7Y9TbN6WQKCfHRNHBaGro8bx6Ius3BBJTq5sHUIzY22mjgvF18OaOw+KmL83ipNXs5jSzZfBvs5UVFez+kgimyNTqa6VrUPYpaMt7/cNxERXk4jL15l3KAp1JQU+7RGCq5mxTG1JS3Cv72TW15Gd05s8km6sSKekpNClSxdmzpzJZ5999tK2I0aM4PLlyyQmJkrV9+Pk5OQwb948Vq1aRXV1NcOHD+ezzz7D2tq60X095LWJNEBtXd0jQckvK29WXwpyEkb5ujEpyAtFeTk2p5xhaUw8hRV/fQmM1NX4MCSQvo7tuV9UytL9cexOPE9dMy+BsY4GM/sF0sXFjpziEuaHx7Dn3IUnIkMBluZ82jkYG31dUi/eYtHGcC7dbH743c/ViunDg2nbRue5js8T10VOjq3hqazam0BxWeVLen01igpyjOrmwchuHkgkYjbFpvBTeBKllX85PqbaGnzQM4iuHWzJKSzhh33RHDh9sdkrAprraTGrbxAhjtbczS9i4d5oDqc+6fgEOVjyYb9gzPW1SbyYyfwdEVy9c795hoEwFxtmDgjCRE+T+DM3WLQ5kow7fzk+SopyDO/lyfDeHkjEYrYcTmb9niRKm+kQqijJM6q3F8O6u1NfD7/uPsnmXUmUV/zl+JgZazN1VAj+HtbcvVfI8vWRRMQ33yG0MNNl2thQvFwsuH0nnxU/hRMbf+WJNkEBdkyZGIaRkSaJ8Vf5aekxsjKbf71DOzsy4Z0w9A00iI6/woq14dy+W/DouIqyAsMH+/DmAA/q6+r59Y+TbNmZREVl8xxCNVVFRr3ly6BeblTV1rHmRBIbI5KpfCwyY2Oky0f9gvG1Myczt4AFuyOJONd8B6WdsT4fDQzBw8aU67kPmHsokuirNx8dF4mgV4f2fNg1EEMNtWbbawyyFOnI/R81+dzGiHRSUhLdunVjxowZfPHFF69sv2zZMubMmUN+fr7U7+fhyPlxcZ4zZw5WVlZS9/EiXqtIP6SwvIJl4fH8mpRGTV3jw2Wd7K35uFsQZjpaRN/I4JvjEVzNe/GI0d3UmDmdQ+hgZMiFrBy+3RnB6et3Gm1XWUGesZ09GRXmjkgEaxNTWBWfROkLRoxyYjFvuznzXqAv6oqK7Is8y8rfY8lvQv7U3FiH6SNC8O1oya38Qr49FMWxCy9OIeirqzKzsz/9XBwoKKlgxe44dkWfpbYJ+byunna8NyiQNroanEi/xvcHosi8X/DC9l5WZnzSJwQ7Iz3OZNzl210RnM1s/Fq2akoKTOzszfBAV2rq6vj5xEk2hCdTUf38EaO8RMLQQBcmdfVGWUGe7dFprNgfT2FpRaNt25ro8eEbIXjamZF5N59Fv0QQd+bFKQQjXXXefTuILj7tyCsoZcX2GPbFnG+0gyISQQ8/B6YODkBPS40TcZdYsSmSu/dePGL0drXgvdENodnU81ksWRvOlRuNdwjV1ZQYO8SP/t1dqKqs5pdf49m+8xTV1c8fMSooyDF4kCdDh/ggLy9h1/ZT/LI+mtKSxjuEtu2MeGd6Vzo4m3EjI4+lq46TnJrxwvYmbbSYMi6UQF9b7uUVs3JDJMeiLjTarlgsoldnJyYMD0RTQ5l9KRdZtD+Ge4UvTiGEOFrxUd9gzPS0iL+UwXd/RHC9CRErbTVlpvb0Y6CPEyWVlfwYkcCWpDMvvCeqKMgz3t+Dcf4eKMq3TnlRcE8ZivSBlhfp2NhYevbsyaxZs/jPf/4jVd/jx48nLi6O9PT0V7bNzs5m3rx5rF69+lFYe86cOVhaWkplSxr+FiL9kGu595l3MJKYqy/+MT6OrYEun/QIwde6LTcf5PPN8UjCr0mXexUBA50c+SDYH301VQ6mXGLR7miyC15d4CUSQQ+39szoG4ihlhqHL17h2xNR3CqQLtSmpazE9CA/hrg6UVFRzdo/4tl26LRUBV7qKoqMHeTLG11dqaypYVVUEuvjU6TOvTqbGPFJzxBczNpw5VYu3/8WQfIl6fJ57dsaMGtICK62JlzNuc+3+yOJuyLd30oiFvGGpxPTuvihpaLEnlPpLD4QS27Rq4udxCIR/TwdeK+nP3rqquxPvsii/dHkFEiXe9VVV2FaD3/6ezlSXF7Jiv3x7IiWziHUUlNmSi9fBgU4UV5RzZqd8Ww/mip1MV5HOxPeHxFCe0tD0m9ks/CXCNKuSucQdrA24oNhoThat+HKzXss/vkEqeel+1tJJGIGdndh7Ft+qCorsu9YGqs3x1BQ9OqIlUQsom/Xjox72x8NdSUOHTnLmnVRPHggXWGanp4aE8YG06WTI4UFZaxbFcGh/WekKvDS1lFl7MQQuvXqSHFJBT//EsOeA6lSO5PuHc2ZOjEMKwt9zl64xZLVJ7h0LUeqczs6mvLe+DBsrQw5l5nNvF0RnMmQrjhMXiJheJArkzp7oygvx9bYM6w4FE+RFBErOYmYIQEuTO7ug4qiPNuSz7K4EdFFEy0NPuwaSHdHO6naN4eQHrIT6YiDjRPpkpISrl5tGIh4e3vz2Wef0bdvX3R0dGjbti2ffPIJSUlJHD9+HGhYM7tXr16MHz+e2bNnP+pHIpE8mm61aNEiLCwscHBwoKamhu3bt/Pll1+yfPlyJk2a9ML3cvfu3UfiXFNTw8iRI/nss89kKs4P+VuJ9EPCL13n20ORZLxgdKalosTUUF+GeDpTVl3NsthENiY3LSekqqDAFD8vxni6UVdbz/rjJ1l34hQVL8jndTA34uOBIThbtOHivVy+ORpJQkZWo+0C2Onr8lmXEHwt2pJ59wFLfokk9vTzw2USsYi+oc5MfNMPTXVldqems/BYLLnFja/oFYmgt3N7ZnUJxEBDjePJV1i8PYrbec93MnTUVXhngB/9/DtQXFnJj0fj2ZrYtKiHhrIi73Ty4W0fF6pqalh9PImNkS92Mtwsjfm4fwgOpoakZ+Xw7a4ITt9ofNQDwN7UgI/7h+BmZcK1O3nM3xFJwsXnF3jJicUMDu7I5J4+qCopsifiLD9tjyVfCpF7GrFIRK8gR6YMDkBHU4VD8Rf4cWs09x4838nQ11Ll3cGB9PCzp6ConFVbYth/4myTqpi1NJQZP8SfPp2dKa+oZt3vcew8ePqFBV7uzm15b2woVub6nDt/ix9XHOfS5abt4ONgb8zUKZ2wb2/M1SvZrFh8lLTU519veXkJA970YthofxQU5dm9/zTrt8RSVNz4qIdELKJPDxfGjQhAXU2Jg8fPseqXaB68oPrdyECDKaNDCPVvR25RCYv2x7A3+UKT0jJ66iq896dDWFhWwfKD8WyPS3uhkxFgb8GH/YOxMNQh8UZDuupSE+t0PMxN+KxHCPZtDJp0vjSEdP9WZn1FHPq4ce0jIggNDX3m9VGjRrF+/XpGjx5NREQEN2/eBGD06NFs2LDhmfbm5uaP2nz33XesWbOGrKwslJSUaN++PR988AFvvPHGS9+LiooKlZWVuLi48Omnn0olzm5ubq/+kM/hbynSANW1tWyKP82KyERK/sxzyonFDPF0ZmpYQyXv9rRzLIyM435Z86srzbW1mB0aRGc7a7Lzi/lhTzSHUi49Oq6vocp7ffzp4+lAfnkFiyPj2Jp6lloZXL7OdtbM7hREW20tEs7cZPEv4dy8/Ve4zN3BjBkjQ7Fpq8+ZrLt8cyCCtNtN3/rsISoK8owP8GSsvzsAvxxJZt3Bk5T/mc+Tl5MwJMyF8b29UVaU5/fENJYdi6egrPE3zqexMtDh417BBNhZcOt+IfP3RnH87F/heiMtdWb2DqSHazvuF5eyeH8su082Plz8PLq72DGzTxBG2upEpF1j4c4osh6r+PezN+eDN4KxMtLl9MVb/LApnMsZuc22q6qswJh+3rzV3Y3a2jo27j/JpoN/VfwryksY2t2D0X28kJeI2X7gNOu3xVPSzPoBAGtzfaaPDcWtQ1sybz9g6c/hJKT8FXUyNtTk3TEhBHnbkptbxMo1EZwIb3y4+GlEIujSqQMTxwWjq6tG5IkLrF5+nJzsvwrqfANsmTS1MyamOpw6fZMfV5/gRkbzC0o11JUYPdSffr1cqaqsYcO2eLbvSab6T4dQSVGeYYO8GDLAC5EYNkamsPp4EmXNzGcDOJoZMrtfCC6Wxly5m8f3f0SQePkvZ97CQJsP+wcT4PBnuupwFEdfkq6SFrFIxCA3R97v5I+Oqkqz+3ua1ynSfycers8Nr56zXV9fj0gkoraJhYV/W5F+yP2SMhYdjyW7sJiPuwdjY6DLqazbfH0sgvM5sp/37GfRljmdgrHV1yPl2m0W7YnG3caUCV29UJCX45dTqfwYk0BRRfNvnI+jIJEw2suNKf5eKErk2HEklQPR5xnd35tQLzvuFZUw/2g0+9KaX3j1NCZaGnzYLZBujnbkFpSwdEcMxeWVvP9mEG0NtUm4msm8fRFcyWl+IdDTBLez5OPewZjraZN4JZPFB2IJaG/B2DBPJCIRm6JSWH30yYI0WaAkL8foUA/GhHkgJxazJfw0h1MuMbmnL0FOVtzNK+LHLVEcT5JtJT6AmZEW7w0NJtDNmuz7RSz5LYq6unreGxKEsb4m8cnXWbo+gsw7sq3EBwj2tuXdUcEYG2qRkHydtb/FEuxjx+C+7tTX1fPbtiR++z2x2YVXT6OsrMCwIT68OcgT6mHbrwnEx15mzMQQ3D2tuHUnn+VrwolNlP3UTEtzPd6dEIanqwW37uazfF0ESoryTB4VjIGeOsfPXmX+3ihu3ZdtJT5AD9d2zOwdiJGWOifSrrLqSCK9POwZEtiR6to6fopKYn38kwVpskBdSZF3gr0Z7u2CvEQis35Du8lOpMMP/3NF+nkj9FcxatSoJtn624v049wpLOLb8GgOXJT9jfNxJCIRb7s6Mz3QDy3lhgUEoq/f5JujkVy7L/sb5+MYqKkyM8SfAU4OiEQiKqtr+Dk2mTUxJymT8RSmp/GyMOWTniG0N2rI12Q9KOC7/VGcSJftFKankZOIGe7nypQwb9SUGqZfhJ+7xoI9UWS28IIoRlrqzOgdQE+39gBUVFazYU8SWw6covIFBVKywsfJnOnDQ7A0aVggI/P2A5asCyfhdMvOaVdQkOOt3u6MHOSNspICACciLrBqTQQ5LylIkwVtjDSZPDGUoIB2AJSVVbLht3h27P5rhNtS+HlZ8+6EMEyNtQG4cjePb3dHkHilaekqaVFWkGNMqCdjQj1Qkpejvr6e3WcusPBYDPeakK5qDJZ62szuFkywnWxypaFdZSjSR/65It2a/GNEOreklLCVP1NR03KLgTyNtrIySdMn82vKGb44dKLV7AKM9HBhTtdQRqzdyqmM261mVywSsXHsmxioq9L3h42ttjocNEzZOvzRONaHn2Lh3uhWswswvrMX7/X0Z+Rnv3A5o/VWppNIxPz85VDkxSLGfripRRcDeRrrtnps+GE06zZGs/GXuFazCzB+TBDD3vZl2ITV3Loj/VSX5iIvJ2Hd8jEU1FYxYulvTZrd0FQ6mrfhl/eGMO9QJOvjU1rNLsCcnqEM93Zpdj9hXeY1/838yYmjs1/dSOCfs3Z3RU1Nqwo0QEF5+Z//yja0LZ3thrxvYXnz87+Noa6+nuKKStQVFVtVoIFHue5CGeS8G0thacPfuqgJ07OaQ21tHaXllSiIJa0q0ACFJQ2ftbgJxVnNpai44XoXl7Su7eqaWsrLqyiurWxVgYa/vtet/ZsGKGjmOhSPaN2vqADCVpUCAgICAgJ/W/4xI2kBAQEBgdeL6J+RHf1XIYi0gICAgIB0CBrd6ggiLSAgICAgHcJIutURctICAgICAgJ/U4SRtICAgICAVIiEgXSrI4i0gICAgIB0COHuVkcQaQEBAQEBqRAJ86RbHUGkBQQEBASkQxhJtzpC4ZiAgICAgMDfFGEkLSAgICAgHcJAutURRFpAQEBAQCqEFcdaH0GkBQQEBASkQxDpVkfISQsICAgICPxNEUbSAgICAgLSIUzBanX+MSPpNhrqfBQaiJqiQqvY01dV4ZueXQEY4GRPD3u7VrEL4NXWlPG+HgB83D0YKz2dVrErLxEz2s8NTwtTLPS0mdbFD2UF+VaxbaSpzhcDOgMwyMeJ0A7WrWIXwK+dOcOD3ACYOSIUUwPNVrGrIC9hVB8vHKyMsLXQZ9QbPigotI7f3MZAk5njOwEwoJ87Xh6WrWIXwNfbmr69XQF4/50uGOprtIpdJUV5xgzzx8JcDxcLY8aEuCMvkbSKbQt9bT7sFwzAWH93vC3NWsUuQDcHWwa6OsqkL1F9vcweAtIhqq//+1+tlKw73C0qppdjO/JKS1kQGcvOs+nUtcBbV5BIGO3pyhRfb5TkJey7nYKzdlus1Aw5mXmL/x2NJD3nnsztAphqavBRWCDd7e3IrSjm8O10Bpq7oiCSY3NiKssjEiiqqGwR20G2lszuEYylnjbxtzIpqqykm7UtOYUlLDwUzf4zF1skHaUkL8eYQHfGBXsiJxazK+k83rZtaauvRcLlDL7dFcm17PuyNwy01dNiVr8gQhytuZdbRGzCVXp2dUIkFvHbwRTW70mkrKK6RWwHe9gwfWgwxgaanIq5Ql1dHV5B7bh7r5BlGyOJiL/cInaVleQZMdCbIX09oK6eI1uT8Ay1x9BUh/jEa6z46QRZtx60iG3ztrq8MzkMLw8r7t7K51TCVbr1daW+vp5fdyTx6/YkKipb5np3CrZn8phgDPQ1OHH+GioK8vjYtiUzr4Dvd0cSkX69ReyqKykyqYs3QwNdqKmvZWfmaTq3scdQWYMj6Vf47kgUt/KLWsR2eyN9Pu0RgpeFqcz67ObxX5n1dfiU7Pr6N/O3Fum7hcV8fyKafecvAeBuZsxnXUPo0MaQc9k5fH0sguRbd2Rmr7OtNbPDgjDX1iIh7wo/XNzPjZJ7SERiBpl5M9G2M2pySmxLPceiyDjul5XJxK6KvDwTfT0Z5+OOSATrrsax+koMZTVVGCip875DJ/qadaSgrILFx2PZlnxOZg6KlZ4OH3cPIsjOkqyiQr6JieTQ9SsA+Jm25T8BIbTX0yc14w5z90Vw7laOTOwCdHey44OegRhraRBx7hoL9kSRkVuAvETC0CAXJnX1RllBnq2xaSw/HE9hWYVM7KopKTChszfDg1ypraljy/ZEftuRRGVlDYb6GkweG0xYsD15+SUs3xrDwZh0mTko1mZ6vD88BA/Htty6mceq7w6QFNnw/fYMtGPiRz0xs9Ln9PksFv98gqs3c2ViVySCrkEOTBkehJ6OGtEHzrB27l5ybj1AQVGOQRNCGTwlDDkFOXbuSmbj5jhKS2XjEKqrKzFquD/9+7pRVVnDlrVR/PFbAtVVtbQx1Wbi9K74hbTnXm4RK9dFcjzygkzsArSzMWTapE44OZhyNTuPeXsjSbiSCUCYozWzegfRVleLuEsZfLc7gms5snFQxCIRA7wcmdbDHx01ZfbdOssP6cfILi9CWSLPBLsAxtj4IaoX8XNcCquikyirko2Doq2izPQwPwa7OyEWi2TS50O6uX8hs74OJ38ps77+zfwtRbqiuobV8SdZE3+K8uqaJ46JRSIGODvwQag/emqq7Eu/xHcR0dwtKm6yPVs9XT7rHIy/hTlZpfdZdHE/0bkXn2mnKa/CJNvODDDzoryqmh9jEtl08jTVdU1L1IiAPo7tmRUWiJG6GkdupzP//BFulRU809ZZ24RPnHrQUceUi9m5zD0QQdLNW02yC6ChpMg7IT4M9e5IVW0ty5MTWZOaTFVt7RPtJCIRQxyd+cDbHy0lJXalpLP4cCy5xaVNtu1gbMDs3sG4W5pyLfs+3++KJO5SxjPtdNVVmNbTn/5ejhSXV7L8cDzb4tKoaeL1FotE9PN04L1e/uiqq3IsIp2f1kVyL/fZ746zoynTJnfCztqQ9GvZ/PBLOGev3G2SXQBNNSUmvuFP/zAnKkqr2LziBHs2J1BT89T1lhPTZ4gPw98NQ0VNiT3H0ljzawwFReVNtu1g24bpY8NwtGvD9fTbrPxqF2cTrz3TTtdIk7Ef9SK0vzuFheWsWRfFwcNp1NU17RYhFovo3dOFsaMD0VBX4ui+M6xbfpwHeSXPtHX1smLyzG5YWBtwNv0WS386zqWrTXcIdbRUGT8qkB6dnSiuqOTHI3FsTUij9qnPoiAnYUSAKxPDvFGUl+P3uDMsPxxPUXnTHRQ3KxNm9wvB3tSAs/m3mXv2IKkPnv2tmqho8YFjF7qbOJJTVMLCYzHsSbvQZIdQXiJmqJcL7wZ7o6Gs1OT3/zIEkW59/nYivf/8Jb4/Hs2dV4iuqoIC7wR4Mcrbjdq6OlYnnGJ14ikqampeet7jaCkp8V6gL0NdnSmvq+Lnq+H8nhFHdX3tS8+zUTPiffteeOpac+N+PnOPRxJx9YbUdgGc2hgyp0sIrqbGXCrMZt7ZQyTm3XzpOSKgt6kzHzh2xkBZgyPnr/Dd4ShuF0gfLhOLRLzp3oHpnfzRUlFi56V0vo+PIaf02Rvn42gqKjHdy5cRTi5UVtewKjyJjbEpVNW8/Fo9jq6aCu919WOgewdKKipZfiierbGvFl17UwM+HhCCm5UJV7Pz+G5XJAmXM6W2C+BqaczH/UNwMDPk0pVslv50nLPpt196jlgsokcXJyaMCkRbS5VDsRdY9ls0ufkvv1aPI5GIGdSpI+MH+aKmrMDhHclsWHqUgvsvd3I0tVUYOa0z3d/wpLyimnVb49hx6DQ1NdI7KHo6akweFki3YAcKH5SyccEBDv+e+ErRtXezYNLn/WnXsS1Xr+WwdPlx0s5mSW0XwNXFnKlTOmFlqU96WhYrFhzicvrLo15iiYheAz0YOSkUdQ0lDhw9y5oN0TwokN4hlJeTMKifO6OG+KKgKM/vCWdYfjThlVEYPXUVpncPoL+HA4VlFfx4KJ7tzxH1l9FGW52ZvQPp7tKO3Ipifjh/jN1ZZ165/oenrjmfOPegvaYRabey+d/BcM7cypbaLkCQrQWzW6F+pZurDEX6tCDS0vC3Eenzd3P4+kgEyVmNC1+ba2sxu0sQneysuVtUzLfhUey/8PJ8npxYzNsuzrwX6IuGkgJ7b6ew4vIRHlRJf/MFCDZwYEb7npio6BB17SbfHIvk+v2Xh8sM1FSZGeLPACcHCqrKWHIhnO0ZydQ24s/QEC4LZIyNL9SJWBeXzOrok68Ml3lZmPJJzxDaG+lzOvsuX0af4ExO424GNto6zAkIJdjcgqwHBXy/P4rj6c+Oyh5HTiJmuJ8rU8Iawtfb4s6y/FAcBaWNC193d7VjZp8gjLTVCT93jfm7I8m6X/jSc4y01Hm/TyA9XNvxIL+UVeujOHTsbKNGK6oqCox82483+rlTXVPHxr1JbDlwisrqlzsoPk7mTB8egqWJLmdP3WDl3P1cu9i40bilnRGTP+lFRy8rMm8/YMm6cBJOv9whVFCQ463e7owc5I28RMKeDdFsWXKE0mLpr7dIJCJsgDtjP+6NjoEG4ZEX+GlNBDk5L3cI2xhpMnliKEEB7ci7V8SaJccIP3xWarsA6prKjJgQTO9BnlRWVbPht3h27E6m+hUOob+3De+MD8XUWJv4Kxl8uyeSqzmNq2dwNDVkdt8QXC2MuXI3j293RZB49eUOirKCHGNCPRkT6oFYDOuvxbPqcjRlNVVS2xUjYpC5K9MdOqGtoMKetAssOBrDvVdErCz1tJndLZhgu9Yp/Ovu8rnM+jqU+pXM+vo389pF+n5pGQvCY9l55nyz8qz+lm35tGswtvp6nMq6zdfHIjj/nAIvP4u2zOnU0C41/yYLL+zjYlHT89oKYjneNvdnjHUICmJ5Np9KZWnMswVeChIJo73cmOLvhaKchC3Xk1h+KZKi6qbnWU1UtJjl2IVuJo7cKyphwdEY9j4nXGaipcFH3YLo6mhLTkkJ8+Kj2H3pQrNW+Aszt2JOYAiWWtokXM1k3r4Irjznhhjc3pKPegZjoa9NwuVMvtsVwdW7TS8EU5KXY3SYB2PCPJATi9kUlcLqo0mUVlY92y70z3YiMdt2nWLTb/GUlUt/43waMxNt3hkfip+3DXfzivhxSxTHk551CM2MtJg+LIQAVyty7uSzZv4hog+fa7JdAP/Ojkz4sDtGpjrEJ19n6foIMu886xCG+NjyzshgjA21SApPZ/XXe7h1vemFjsqqirz1TicGjAsBEfy2NZFff098psBLWVmBYUN8eHOQJ/V19Wz/JY6tG2OpKG96ntXcSp9JM7vh7m3NrTv5LF8TTmzi1WfaWZrr8e6EMDxdLRoKwfZFEt7MQrCeLu2Y2TMQIy11jp+9yvy9Udx6jkPY07U97/cOwEhLnWN3LvD9uSNkleU32a66nCJT2gczzMqb6po6VkUnsS4umcqnHBR1JUXeDfZhmHfHVqtQB+je8T8y6+vQmf+TWV//Zl6bSFfV1rIx6TTLYxIpqWz6jfNx5MRihrg5816wLxpKiuxIO8/CqFjySstoq6XJJ2HBdLazJqe8gCWXDnI0u3Ee/svQU1TnHduu9DJxI7+8gsWRcWxNPUttfT2d7ayZ3SmIttpaROdc4dtzh7lenCcz2156Fsx26k57TSPOZN3lmwMRpN3ORkVBngmBnozxcwcRrDp9ipUpSZRVy6ZARV4sZpSzK9M8fVGVl2dr4ll+PBZHQVkFVgY6fNwrmAA7C27lFfD97ijCz718xN0YjLTUmdEngJ5u7blfXMri/bHsPnme+nro7vLXiDsm/grL14Zz+06BzGx7uVvy7oQwLNrqcvriLX7YFM7ljFxUlRUY29+Ht7q5UlNdy++rI9mxPoaqSulTMC9DQVGOgaP8eWt8MPKKcuw4eJp1W+MpKavE2lyf6WNDcevQlqxrOaz6ejenIp6tq2gqRm11Gf9JH/y7O5ObW8RPayI5Hp6OSARdOnVg4rhgdHXViDx6nrVLj5Jz9+URjsbgE2jHxPe7YWKmw8nTN1m2+gQ3MvJQV1NizDB/+vVypbK6hpXHE/glJpXqWulTMC9DWV6OsSGejAnxQCyCjZEprD6eRFllNY5mhszuF4KLpTGXC3OYe/YQiXmNS3m9DEs1XT7q0I1gIztu5Rfy3ZEojqRfRSwS8YZbB2Z08kNHVUVm9qRFEOnW57WI9InL15h3LIqbDwpapH8tZSWmBfky1N2ZsupqTly9To/2dtRRy8brUWy6EU1lXctM9XDQNGVm+944a7fl0r08HpSV4WvRlpslecw7e5ionCstYleMiEEWbsywD0NLQYWj6VdxMWuDgYYaB65eZm5sJLeKW2aqh66yCh/4+POWQ0ORTsylm3RzsqOqpoZVRxL5Jep0o3LXjcHV0piPBzTkmtOzcqiorsHNyoQbGXks/ek4yanPFqTJAolETL9eLowdFoCqqiLHky7jbm+GjqYKJ/al8vMPR8jLlp1QPY6ugTpjZnSjU18XCorKSTmXSYiPHeWllWxefJh9m2KpeUUovql09LNl0n/6YdnemPPptxGLRdi3N+bqpbusWHCIc6cbVysgLfLyEvq95cXQccEoKssTEX0RTzdL1NWU+OPUeZYciuV+iWxmWzxNGy11ZvYMpIdLO3KLSki5cYeuzrYUVpez5MIJtt1sXLqqMQQa2vBxh25YqeuTeCMLDSVF7NsYtIgtaejuPEdmfR1K+1pmff2baVWRznxQwH8PnSDmesvcOJ/GTl+XL3qE4dnWlPDscyy8uJ+cipa5cT6OCBFd2zgz074XKhJFFqWfYPP1RGrqW365Hg15Jd5tH8IIax+u5d9nTsRxEm43ruinqTjqG/B/wZ1xNWrD/uQLLNwTTW5R06vApUUsEtHX04FZ/YKQF4v56edI9hxIbVTRT1PR1FBm/KhA+vZw4fqlbH78ajfpqS0jVE/T3tmMqZ/3xcbemMO/J7Du+wMU3m9cXUVTEEvE9Hjbh7Ef96YeWLXoCEf2pja5CrwxaOuoMu69LnTp1ZFzWdl8tfM46bdbZt2Cp3G3NOE/A8KwMdJjy/Ukllw40ax0lbTIicQMtfJihkMnlCSts7jQi+ju9JnM+jp09n8y6+vfTKuuOHb00tVWE2iAy7n3+eVkKgDrrke0ikAD1FPP4btnSM3P4E5ZIRuuxbeKQAMUVVew4PxRAPZevtRqAg1wPvcev54/A8DKwwmtItAAdfX17Eo6z/msHDKzHvDHvtOtItAAhUXlrNkQDcDxPadbTaABLqZlceSPFAA2LjzUKgINUFdbx/5f4si4ks21S9kc2n26VQQaIP9BKZtWRQCwI+lcqwk0QPKN2+w73ZBC+CH9WKsINEBNfR0bryWQVdoyi8w0ijoZPgSkQli7W0BAQEBAKoTlPFuff8za3QICAgIC//8SFRVF3759MTExQSQSsX79+leek56eTlhYGMrKyhgZGfHxxx9T+1RhYWxsLF5eXigpKdG2bVvmz5/fQp+gaQgiLSAgICAgHfX1sns0kpKSEpycnFiyZAkKCq/eaKm4uJguXbqgra1NYmIiq1evZs2aNXz22V959YyMDLp164aHhwcpKSn873//Y86cOaxcubLR76+lEMLdAgICAgLS0Uq1B8+jZ8+e9OzZE2hYbOdVbN68mcLCQjZu3IiqqirOzs589dVXfPzxx3zxxRcoKyuzYsUKDAwMWLZsGSKRCAcHB9LS0vj++++ZPHlyS38kqRBG0gICAgIC0iHDkXRlZSVFRUVPPCorZbfLX1xcHP7+/qiqqj56rXv37pSWlnLmzJlHbbp06fKE6Hfv3p3r16+Tnd241RhbCkGkBQQEBARanblz56KpqfnEY+7cuTLrPzs7G0NDwydeMzIyAuDu3btSt3ndCOFuAQEBAQHpkGF19yeffMLMmTOfeE1RUVFm/f9bEERaQEBAQEA6ZCjSioqKLSrKRkZG5OQ8ud3pw+dt2rSRus3rRgh3CwgICAhIR1297B4tjJ+fH7GxsZSV/bVc7KFDh1BVVaVjx46P2hw7duyJ8w4dOoSVldWjsPfrRhBpAQEBAQHpqK+T3aORlJSUkJqaSmpqKvX19WRmZpKamkpmZsMqf5988gmdOnV61H7YsGFoaGgwcuRIzp49y/79+/n888+ZOnUqysrKAEyZMoWcnBzeffddLly4wObNm1m6dCkffvihbK6XDBBEWkBAQEDgb8+pU6dwdXXF1dWVqqoqvvjiC1xdXfn884Y9ru/evcu1a3/ttKeurs6xY8d48OABXl5ejB07lrFjx/L1139t7GFubs6hQ4dISkrCxcWF2bNn89VXX/1tpl+BkJMWEBAQEJCW17gsaEhICC/bD+p5K5A5ODhw4sSJl/YbEBDAyZMnm/v2WgxBpAUEBAQEpOM1Lmby/yuCSAsICAgISIewwUarI+SkBQQEBAQE/qa0qkgbqqshlmLNVVlipKEOgIGSZqvaVRDLoaOghqqcAhrySq1q20i54bMaqanRulcbjNU0ADDUUm9Vu0rycmipKqOmpoiK8qsX35clBvoNn1XfqHW/Y4/b1G+j1ap2lVUVUddQQUNTBUUl+Va1rW/Y8B0zauXv2OM2H/7GWgt1OUVU5f4GC328xg02/n9FVP+yTHwLcO5uDl8fjiDl1p0WtdNWW5PZnYPp3M4agLr6OvbcSmbllSM8qCptUdtBBvbMaN8TUxVdAPIrS1l84QTbb6ZQR8tdbmWJPBPsAhhj44eipOHGmXL3Dl9Gh5N2r2XXobXW1uE/ASEEm1sCUFtXx9bYNJYfiqewrKJFbXdzsWNm30DaaDfcvO8/KGHV+igOHz/XovcCVRUFRgzx5Y1+HsjLSwA4e+oGK+fu59rFll1S0MLOkCmze9PR2wqAmupa9myIZsuSI5QWt9z1FolEhA1wZ+zHvdExaLjeuTlFrFl6lIjD51rMLoCahhIjJoTQ5w1PJHIN44vYyxl8tzeCazkPWtS2g4kBs/uF4GZhAkBVbQ3rr8Wz6nI0ZTVVLWZXjIhB5q5Md+iEjqLqq09oYXqYTJNZXwdvL5VZX/9mWl2kH7Lv/CW+Px7N3aJimfarqqDAlAAvRnu7oSCRPHO8pKaCn6+G83tGHNX1tc/poelYqxnyvn0vvHRtnnv8YmE2884eIinvpkztioBepk584NgFQ2WNZ47X19ez82I638VHc69Mtg6KhqIi0z19Gensipz42cBMYWkFyw/Fsy0ujZq6xs+NfBn2pgZ81D8Yd2vT5x6/ePkuS386wbkLt2VqVywW0aNzB8aPCkJH+9kbZ11dHYe2n2LD0mMUPpDt9dbUVmHE1M70eNMTieTZ611wv4QN8w9wZGsidTIu8mnvas7kz/vTzsX8ucfPn8lk5YLDXL4gWwdcLBHRa4A7IyeFoqGl8szxmto6fk84w7Ij8RSVy26DBgBdNRWm9/Cnv7sjYvGzcancimIWnj/GnqwzMne/PXTN+cSpO/Zaf4+VrwB6tHlXZn0dvLtMZn39m3ltIg1QUV3DqriTrIk/RUVNTbP6EgEDOzoyM9QffbVXe5yZpXksuniAmNyLzbILoCGvzCSbzgxs641E9OoMwpHb6Xx//gi3ywqabdtJy5hPnHvgomP2yralVVUsO5XI2jPJVNU2z0GRiEQMcXRmprcfOsrP3jif5urdPL7bFUnC5cxm2QXQUVNhWi8/Bnh1eO6N82mOhqfz07pIcvOa7xA6OZry3qQw7GxevRpRaXEFvyw/wd4tCdTUNPN6y4npPcSb4e90Ql1T+ZXtr6XfZuWXf3Au6Xqz7ALoGmow5qPehA1wf+UWgfX19RzZm8r65Sd4cL+k2bZdPCyZ8kF3LGwMXtm2oLScH4/Esy0xjdpmOijyEgnDA1yY3MkHVaVXp0/S8m8xN+0QZ/JvNcsugLGyJh906EIPkw7N7kvW9DB6R2Z9HcxeLrO+/s28VpF+yN3CYr49HsWB9MtNOt/NtA2fdQ3Bybjxy7jF515m0cX93CjNbfS5EpGYgWZeTLTpjKbCq4XqcSprq1l3NZ7Vl6Mpr61utG19JTXed+hMP7OOUu2t+jiZhQX8LzaSI9evNtougI+JGZ8HhmKvp9/oc8PPXWPB7igy8woafa6cRMzQQFcmd/NGTalx+bmKimo2b0vg950nqaxsvENooK/O5LEh/L/27jUmiisMA/DLZVlAvAGKV4qKCChUQ1UEARFF8NJqY7VqU7UVEbXBxKo1Jv6waau9YKtpysWaqilNWkkVUSiirG5RAaWCggJeqqsVQZG7LJfd/mgUo6zMsrPTiXmf32fOSU4m851zvm9mwkK8jL5Wc7MKiTuOIV/dvfvbL3AkojfNhOuIrgPV806nXcSP24+g8u4jo6+1UVrj7RVTsHB1GGztjZvvpkYtkveexqFfctHaavwCZeDgvoiKnY7AUOPnu7ziAbanqpB7TWP0tQAQ6j0cG2aHwNW5j1HX6fV6HNEUIa4kC5XNxi8I7awUWDFyMpaPDICtlbR5fqEYpKUniyD9xPnbd/FZpgrFFZWC2g/o6YANYUGYM8bTpHHbdO04ePsc9lw/ibrWx4KumejkjnWeszCip0vXjV+i8nEd4kqycERTJOi4zMbSCu+PmIToUUEmF5KcuXMb29TZKH34QFD7ob16Y3NAMCLdPUwat7WtHQdOFSDpeB4atcLyecHew/DxWyFw69/XpLEr7tcifq8K2epSQe1tlQosmj8Bi+ZPgFJp2oMzX12KxC/TobkhbEE4+DUnrNw4ExOnmHZ/t2hbcTAhG7/Gn4T2sbD5DozwRdSWN+EyxNGkse9qqpH0XSbOnhI233b2Nli0PAjzFvvDxsa0N0SzLl/D12mncae6VlB7dxcnbJwTggCPzo/zhWpqa0FSmRo/XTsLrU7YgvC/dNU0yQvSjBXpEiNaX+n3fxCtr1eZrII0AOj0eqRcdyUnqQAABFtJREFULMZOVQ4eNDZ12sbW2hofTvLDyoDxsFOIt+KsaWlCQvlxHLqTj3YD35Ydau+EWM+ZCO5v/Ar/ZQqr7+DzS+m49Mhw/nTaQE9sGBOOoT1Me3A+q12nQ3JxEXbm5uBRc+cFR/YKBWL8JiBq7BtQWov3av3D+kbsOpqDw3kl0Bm4DYe7OGLD3BAEerqJNi4AFF7WYHfCCZRfN7wgnBriiZgPpqB/vxfz/N3V1tqO1ORzSI4/iYY6A/PtoMTiVaGY+14ArBUv1lV0V9W9GuzdkQbV4QKDbYZ7DUL01rnw9e+8rqK7CnJvID4uA7cMLFAsLIBpM1/H8rVhcHIWr2q7pa0N+9UFSDyRh6aWzk+setvbYs30SVjg7wvrTvL83XW38RG+Ks5E5j9XDLYZ02cQNvtEYJyTq2jjmlNkP/E+l5leFS9aX68y2QXpJxq0WnyvzsX+/ItofSZ/GuntgU1hQRjUW7wH5/Ou1VfgmytpuFDdkc/rYaXE8hGhWOQWAIWleb4Bo9frkaopRFxJFqqaO/J5Hr364xOfCPj3G26WcQGgtrkZO/PO4OfLhU8LvCwAzB3ljU2TguDi4GC2sUs097HjdxX+utlRcNTTTomYGf54d/JYUR+cz9Lp9DiaWYQ9+9Soqe1YEHq4u+Cj6DD4ju68IE0MNdWN2LfrOP5IOf+0wMvS0gLh8/ywLHY6+jiZcb4v3ET8tkMoL+o4Du7l2ANL10dixkL/TgvSxNDerkNaynkcSFShvrbjxMrLZwhi1kdg1OjBZhkXAKrqGvBteg5SC0qeVvxbWVpggb8v1oYHoLe9+V6TzKu6iS8uZaC0ruOXiM5KB6zzDsM817FGp6v+TxHOK0XrK+NBomh9vcpkG6SfuFVdgy+On0JFfQO2hIdgvKv5HpzPy664jN1lGRjXdxhWe4TDSSnNe5mNbVoklqmRersQ0aOC8Y6bn6CCNDGUVz/Ep+ps1Le0YGtQKMYNkK6y9FjBVew6moPJXm5YExGAvg5dF0iJoaFRi33JOVD9WYplSwIROc1HUEGaGG5cvYf47Ueh0+mxavMsuHsNkmRcvV6PrJR8HNiZgcAIXyyJnQGHXtLMd13tYxxIzEauugxLV03F1EgfyQLVJU0FtqeqYGejwKY5IRg5wFmScXV6HX77uwAJZacxe4gPoj2C0UMhg/eejcQgLT3ZB2kiIpKHCMco0frKqE4Sra9XGb/dTUREwnBPJzkGaSIiEkbkDxJR1xikiYhIGO6kJce/YBEREckUd9JERCSInsfdkmOQJiIiYXjcLTkGaSIiEkbkP6tR15iTJiIikinupImISBgD/zQg82GQJiIiQfQ87pYcgzQREQnDnbTkGKSJiEgQ7qSlx8IxIiIimeJOmoiIhOFxt+T4q0oiIiKZ4nE3ERGRTDFIExERyRSDNBERkUwxSBMREckUgzQREZFMMUgTERHJFIM0ERGRTDFIExERyRSDNBERkUz9C+LxmuiXL53XAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.39 s, sys: 1.37 s, total: 4.75 s\n",
      "Wall time: 3.82 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "som_height, som_width = 11, 11\n",
    "net = sps.SOMNet(som_height, som_width, train_data, topology='hexagonal', \n",
    "                init='PCA', metric='euclidean',\n",
    "                neighborhood_fun='gaussian', PBC=True,\n",
    "                random_seed=42, GPU=True, CUML=False,\n",
    "                output_path=\"/tmp/\")\n",
    "\n",
    "net.train(train_algo='batch', start_learning_rate=0.1, epochs=1000, batch_size=-1,\n",
    "           early_stop='mapdiff', early_stop_tolerance=1e-5, early_stop_patience=10\n",
    ")\n",
    "net.plot_convergence(figsize=(11, 5), logy=True)\n",
    "net.get_nodes_difference()\n",
    "net.plot_map_by_difference(show=True, print_out=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-02-25 16:39:18.819\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimpsom.network\u001b[0m:\u001b[36mplot_convergence\u001b[0m:\u001b[36m803\u001b[0m - \u001b[1mConvergence results will be saved to:\n",
      "/tmp/./som_convergence.png\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.plot_convergence(\n",
    "    figsize=(11, 5),\n",
    "    logy=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bmu</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>IBIS105477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>IBIS110925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>IBIS123451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>IBIS146443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>IBIS241887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bmu          ID\n",
       "0   30  IBIS105477\n",
       "1   30  IBIS110925\n",
       "2   11  IBIS123451\n",
       "3   30  IBIS146443\n",
       "4   30  IBIS241887"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmus_arr = net.find_bmu_ix(cupy.asarray(dssamples)).get()\n",
    "ibis_ds_IDs = [extract_id(f) for f in ibis_ds_fnames]\n",
    "df = pd.DataFrame({\"bmu\": bmus_arr, \"ID\": ibis_ds_IDs})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncomment the cell below to save the IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv(f'data/ds_prototypes_{som_height}x{som_width}.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>count</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>neighbourhood-distance</th>\n",
       "      <th>mean-matching-distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMU</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Down Syndrome</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.928203</td>\n",
       "      <td>1.863155</td>\n",
       "      <td>5.749036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>IBIS-ASD</td>\n",
       "      <td>6</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.330127</td>\n",
       "      <td>1.531191</td>\n",
       "      <td>2.917103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>EBDS-Outlier</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.229335</td>\n",
       "      <td>2.110394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Down Syndrome</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.101627</td>\n",
       "      <td>1.462361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Down Syndrome</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.660254</td>\n",
       "      <td>1.774890</td>\n",
       "      <td>0.890358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>EBDS-Outlier</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.928203</td>\n",
       "      <td>1.863155</td>\n",
       "      <td>5.749036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>EBDS-Outlier</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.512009</td>\n",
       "      <td>1.569548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>EBDS-Outlier</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.660254</td>\n",
       "      <td>2.351751</td>\n",
       "      <td>2.232406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>EBDS-Outlier</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.330127</td>\n",
       "      <td>1.509200</td>\n",
       "      <td>2.538659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>EBDS-Outlier</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.660254</td>\n",
       "      <td>1.431167</td>\n",
       "      <td>0.957679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dataset  count    x         y  neighbourhood-distance  \\\n",
       "BMU                                                                \n",
       "30   Down Syndrome     16  2.0  6.928203                1.863155   \n",
       "71        IBIS-ASD      6  6.5  4.330127                1.531191   \n",
       "55    EBDS-Outlier      4  5.0  0.000000                1.229335   \n",
       "11   Down Syndrome      2  1.0  0.000000                2.101627   \n",
       "32   Down Syndrome      2  2.0  8.660254                1.774890   \n",
       "30    EBDS-Outlier      2  2.0  6.928203                1.863155   \n",
       "44    EBDS-Outlier      2  4.0  0.000000                1.512009   \n",
       "54    EBDS-Outlier      2  4.0  8.660254                2.351751   \n",
       "60    EBDS-Outlier      2  5.5  4.330127                1.509200   \n",
       "76    EBDS-Outlier      2  6.0  8.660254                1.431167   \n",
       "\n",
       "     mean-matching-distance  \n",
       "BMU                          \n",
       "30                 5.749036  \n",
       "71                 2.917103  \n",
       "55                 2.110394  \n",
       "11                 1.462361  \n",
       "32                 0.890358  \n",
       "30                 5.749036  \n",
       "44                 1.569548  \n",
       "54                 2.232406  \n",
       "60                 2.538659  \n",
       "76                 0.957679  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmu_df, significant_bmus = build_bmu_df(\n",
    "    net,\n",
    "    test_data,\n",
    "    test_labels,\n",
    "    min_cluster_size_ratio=0.1,  # Select BMU that match >=10% of the data\n",
    ")\n",
    "significant_bmus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experimental plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = significant_bmus.x\n",
    "y = significant_bmus.y\n",
    "dataset = significant_bmus[\"dataset\"]\n",
    "count = significant_bmus[\"count\"]\n",
    "\n",
    "q, r = cartesian_to_axial(x, y, size=HEXSIZE, orientation=\"pointytop\", aspect_scale=1)\n",
    "sx, sy = axial_to_cartesian(q, r, size=1, orientation=\"pointytop\")\n",
    "\n",
    "hexsource = ColumnDataSource(\n",
    "    dict(q=q, r=r, dataset=dataset, scale=scale(count, 0.1, 1.1))\n",
    ")\n",
    "scattersource = ColumnDataSource(dict(x=sx, y=sy, dataset=dataset, scale=count + 10))\n",
    "group_filter = BooleanFilter([c != \"LR-Typical\" for c in dataset])\n",
    "view = CDSView(filter=group_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"cfbcbe45-ce14-461a-8ddb-b5a5d6a74bd4\" data-root-id=\"567457c5-3dd3-4af8-bf40-53fb8ff86550\" style=\"display: contents;\"></div>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
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Syndrome\"},\"renderers\":[{\"id\":\"6da88244-c650-4513-aa01-0c3e2122a5fe\"}],\"index\":0}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"f3d91785-2678-43ab-9c45-578acec9b16c\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"EBDS-Outlier\"},\"renderers\":[{\"id\":\"6da88244-c650-4513-aa01-0c3e2122a5fe\"}],\"index\":2}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"41a36004-0734-42d7-a2e8-bcee05bca81c\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"IBIS-ASD\"},\"renderers\":[{\"id\":\"6da88244-c650-4513-aa01-0c3e2122a5fe\"}],\"index\":1}}]}}]}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"4e3c00a5-3108-4f05-a729-a118d3e3c8ae\",\"roots\":{\"567457c5-3dd3-4af8-bf40-53fb8ff86550\":\"cfbcbe45-ce14-461a-8ddb-b5a5d6a74bd4\"},\"root_ids\":[\"567457c5-3dd3-4af8-bf40-53fb8ff86550\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "567457c5-3dd3-4af8-bf40-53fb8ff86550"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = figure(width=550, height=600)\n",
    "renderer = plot.hex_tile(\n",
    "    source=hexsource,\n",
    "    view=view,\n",
    "    color=factor_cmap(\"dataset\", \"Category10_7\", DATASETS),\n",
    "    scale=\"scale\",\n",
    "    legend_group=\"dataset\",\n",
    "    #     tool=\"wheel_zoom\"\n",
    ")\n",
    "\n",
    "plot.legend.location = \"top_left\"\n",
    "plot.legend.orientation = \"horizontal\"\n",
    "# plot.legend.title = \"Dataset\"\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "binsource, scattersource, significantsource = build_hexbin_source(\n",
    "    net, train_data, bmu_df, significant_bmus\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance_plot = plot_hex_heatmap(scattersource, binsource, cbar_ticks=False)\n",
    "score_norm_density_plot = plot_hex_heatmap(\n",
    "    scattersource,\n",
    "    binsource,\n",
    "    filter_groups=[\"IBIS-HR-Typical\", \"EBDS-Outlier\"],\n",
    "    heatmap_col=\"binned_density\",\n",
    "    cmap=cc.blues,\n",
    "    cbar_ticks=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"cbed561d-1d75-45cd-85ec-79aa6a2023af\" data-root-id=\"d13fddc9-80ac-4615-a594-67ab86e2fa5b\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"901dfb4b-9829-4192-87a7-67ae3c0243fe\",\"roots\":{\"d13fddc9-80ac-4615-a594-67ab86e2fa5b\":\"cbed561d-1d75-45cd-85ec-79aa6a2023af\"},\"root_ids\":[\"d13fddc9-80ac-4615-a594-67ab86e2fa5b\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "d13fddc9-80ac-4615-a594-67ab86e2fa5b"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = row(score_norm_density_plot, distance_plot)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"a75a0764-3a12-4e24-90ee-f977a5173d26\" data-root-id=\"f7d64d8a-a756-4930-88b3-f2facd0c28f8\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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of Typicals\",\"title_text_font_size\":\"18px\",\"ticker\":{\"type\":\"object\",\"name\":\"FixedTicker\",\"id\":\"0b636423-108d-4c3b-b00e-3c8ecaca1606\",\"attributes\":{\"ticks\":[1.04,2.04,3.04,4.04,5.04],\"minor_ticks\":[]}},\"major_label_overrides\":{\"type\":\"map\",\"entries\":[[1.04,\"1\"],[2.04,\"2\"],[3.04,\"3\"],[4.04,\"4\"],[5.04,\"5\"]]},\"major_label_policy\":{\"type\":\"object\",\"name\":\"NoOverlap\",\"id\":\"5e9cc564-bc81-450b-8ae7-94eca7f622a5\"},\"padding\":1,\"major_label_text_font_size\":\"12pt\",\"major_tick_line_color\":\"black\",\"major_tick_line_width\":2,\"color_mapper\":{\"id\":\"93c78c0d-1beb-4b90-98a4-992046c11e56\"}}}],\"above\":[{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"68ae6caa-f364-4793-ad17-08539e414f96\",\"attributes\":{\"orientation\":\"horizontal\",\"title\":\"Dataset\",\"title_text_font_size\":\"14px\",\"background_fill_color\":\"#fafafa\",\"glyph_height\":32,\"glyph_width\":32,\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"88a35dc6-b850-4e88-911d-883ce4cabfc1\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Down 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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"860bcce4-c367-455c-be32-11fe4fa6f433\",\"roots\":{\"f7d64d8a-a756-4930-88b3-f2facd0c28f8\":\"a75a0764-3a12-4e24-90ee-f977a5173d26\"},\"root_ids\":[\"f7d64d8a-a756-4930-88b3-f2facd0c28f8\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "f7d64d8a-a756-4930-88b3-f2facd0c28f8"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(\n",
    "    plot_hex_heatmap(\n",
    "        significantsource,\n",
    "        binsource,\n",
    "        cbar_ticks=True,\n",
    "        scatter_jitter=False,\n",
    "        heatmap_col=\"binned_density\",\n",
    "        cmap=cc.blues,\n",
    "        filter_groups=[\"IBIS-HR-Typical\", \"Down Syndrome\"],\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"fd07b8e5-769c-4c73-9c12-11108cba184d\" data-root-id=\"6c7a2ed8-f38a-494a-bed9-77ee7b031504\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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Syndrome\",\"IBIS-ASD\",\"IBIS-HR-Typical\",\"EBDS-Outlier\"],\"markers\":[\"triangle\",\"star\",\"circle\",\"inverted_triangle\"]}}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"4e57179f-2bdb-4b5e-816f-7e54f3ce8721\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"field\",\"field\":\"scale\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1},\"marker\":{\"type\":\"field\",\"field\":\"dataset\",\"transform\":{\"id\":\"61444df0-73b6-4d4b-90eb-3c4571ba5b2b\"}}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"79e18668-d995-4c3a-9156-4d0f313f32e9\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"field\",\"field\":\"scale\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2},\"marker\":{\"type\":\"field\",\"field\":\"dataset\",\"transform\":{\"id\":\"61444df0-73b6-4d4b-90eb-3c4571ba5b2b\"}}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"4b2ee608-0b7f-4aa8-8743-d4496509d7a2\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"9658f9ab-a0d7-4876-b685-703162501602\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"f207b047-c8a3-4fb2-ab09-f34ce21244c0\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"48dba902-0c54-46e3-9119-b15d67b6c327\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"b0c192ca-5168-474b-82e8-fb7bf70d2a56\",\"attributes\":{\"syncable\":false,\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"handles\":{\"type\":\"object\",\"name\":\"BoxInteractionHandles\",\"id\":\"725559fa-63c2-424a-9602-06848175aa5d\",\"attributes\":{\"all\":{\"type\":\"object\",\"name\":\"AreaVisuals\",\"id\":\"e986e403-7f64-4695-a2d0-651eee8794bb\",\"attributes\":{\"fill_color\":\"white\",\"hover_fill_color\":\"lightgray\"}}}}}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"8c3c6ff6-2b8d-4534-9291-1a67763953cb\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"29839b8e-d049-4cd2-816c-707454e60c11\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"594d7078-3a25-48f0-bd42-8e036c2f80a2\"}],\"active_scroll\":{\"id\":\"f207b047-c8a3-4fb2-ab09-f34ce21244c0\"}}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"d848ab36-10b8-45c8-aef1-3f8935eec334\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"57634541-e429-4ecb-a0d4-49c8f6229aef\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"3061fbcb-4232-4861-a3ea-4593e35effad\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"ce78f969-c2b5-4a9a-8e7b-366c4bf1247b\"}}}],\"right\":[{\"type\":\"object\",\"name\":\"ColorBar\",\"id\":\"9211c2a1-d923-4f9e-9f42-ea94bc7a6ba6\",\"attributes\":{\"title\":\"Percentage of Typicals\",\"title_text_font_size\":\"18px\",\"ticker\":{\"type\":\"object\",\"name\":\"FixedTicker\",\"id\":\"0711b26b-1abf-4f45-9283-2465354c22f8\",\"attributes\":{\"ticks\":[1.04,2.04,3.04,4.04,5.04],\"minor_ticks\":[]}},\"major_label_overrides\":{\"type\":\"map\",\"entries\":[[1.04,\"1\"],[2.04,\"2\"],[3.04,\"3\"],[4.04,\"4\"],[5.04,\"5\"]]},\"major_label_policy\":{\"type\":\"object\",\"name\":\"NoOverlap\",\"id\":\"10b97161-c55f-4167-815c-e0ead9bf954d\"},\"padding\":1,\"major_label_text_font_size\":\"0pt\",\"major_tick_line_color\":null,\"major_tick_line_width\":2,\"color_mapper\":{\"id\":\"798dd00d-e654-4391-a83b-58887ab428b3\"}}}],\"above\":[{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"2d5e7f15-f8ff-4e75-9f93-c5ef38501b6b\",\"attributes\":{\"orientation\":\"horizontal\",\"title\":\"Dataset\",\"title_text_font_size\":\"14px\",\"background_fill_color\":\"#fafafa\",\"glyph_height\":32,\"glyph_width\":32,\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"1cf14151-bb02-4b26-ad83-7d44e37ac908\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Down Syndrome\"},\"renderers\":[{\"id\":\"6de870f7-0ce7-4eba-94fc-19ac938f977b\"}],\"index\":0}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"b5d22095-3804-46c6-b640-a7c9ba7ea7eb\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"EBDS-Outlier\"},\"renderers\":[{\"id\":\"6de870f7-0ce7-4eba-94fc-19ac938f977b\"}],\"index\":2}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"e8a5aa09-c24f-406b-9a26-fec9e9acc581\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"IBIS-ASD\"},\"renderers\":[{\"id\":\"6de870f7-0ce7-4eba-94fc-19ac938f977b\"}],\"index\":1}}]}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"14601e81-728d-41f5-83c9-69406465fc89\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"b5adb63c-d6be-4d80-b409-39f6f2d55e08\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"fbe74af8-09b7-48f9-87e8-779e7b80e1bf\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"86666839-d8a6-4b17-880d-aa07490d4df8\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"23e7b59c-2f15-4107-9e6c-5ea64c36465e\",\"attributes\":{\"visible\":false,\"axis\":{\"id\":\"14601e81-728d-41f5-83c9-69406465fc89\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"cf493e94-cc57-4f8a-a22d-beff3e05a0ee\",\"attributes\":{\"visible\":false,\"dimension\":1,\"axis\":{\"id\":\"d848ab36-10b8-45c8-aef1-3f8935eec334\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"b861f04e-55b4-417b-b495-285bd07aaaa4\"}],\"background_fill_color\":\"#fafafa\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"df210dcf-c4dc-4acb-b833-a4bb53f3d145\",\"roots\":{\"6c7a2ed8-f38a-494a-bed9-77ee7b031504\":\"fd07b8e5-769c-4c73-9c12-11108cba184d\"},\"root_ids\":[\"6c7a2ed8-f38a-494a-bed9-77ee7b031504\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "6c7a2ed8-f38a-494a-bed9-77ee7b031504"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(\n",
    "    plot_hex_heatmap(\n",
    "        significantsource,\n",
    "        binsource,\n",
    "        heatmap_col=\"binned_density\",\n",
    "        filter_groups=[\"IBIS-HR-Typical\", \"EBDS-Outlier\"],\n",
    "        cbar_ticks=False,\n",
    "        scatter_jitter=False,\n",
    "    )\n",
    ")\n",
    "# save(score_norm_density_plot, filename=\"ds-som.html\", title=\"Down Syndrome SOM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image -> Network -> score-norms -> SOM ---> Gather **IDs** of samples matching prototype\n",
    "#                 |__-> score-images(B, 20, H,W,D)\n",
    "#                       | --> Likelihood Model -> Heatmaps (1, H,W,D)\n",
    "\n",
    "# Correlation Analysis: Behavior Scores and Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# p = swatch(\"coolwarm\")\n",
    "# p.title.text = \"Title With Options\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"bf776df0-4467-4908-a15a-d18445162eb7\" data-root-id=\"bf505be9-39f2-424c-a8ce-1002e4def0c9\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"6cb084c8-22a2-41d9-a22b-bf8aada2f083\":{\"version\":\"3.6.3\",\"title\":\"Bokeh 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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"6cb084c8-22a2-41d9-a22b-bf8aada2f083\",\"roots\":{\"bf505be9-39f2-424c-a8ce-1002e4def0c9\":\"bf776df0-4467-4908-a15a-d18445162eb7\"},\"root_ids\":[\"bf505be9-39f2-424c-a8ce-1002e4def0c9\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "bf505be9-39f2-424c-a8ce-1002e4def0c9"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict[\"EBDS\"] = \"#ffff99\"\n",
    "color_dict[\"EBDS-Outlier\"] = \"red\"\n",
    "\n",
    "SCALE_FACTOR = 1\n",
    "binsource, scattersource, significantsource = build_hexbin_source(\n",
    "    net, train_data, bmu_df, significant_bmus\n",
    ")\n",
    "ebds_density_plot = plot_hex_heatmap(\n",
    "    scattersource,\n",
    "    binsource,\n",
    "    heatmap_col=\"binned_density\",\n",
    "    filter_groups=[\"IBIS-ASD\", \"IBIS-HR-Typical\", \"Down Syndrome\"],\n",
    "    cmap=cc.blues,\n",
    "    cbar_ticks=True,\n",
    "    cmap_upper_ratio=0.999,\n",
    "    cmap_lower_ratio=1.1e2,\n",
    ")\n",
    "show(ebds_density_plot)\n",
    "# save(ebds_density_plot, filename=\"ebds.html\", title=\"EBDS Plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"c5c216c2-3d00-4496-b8d7-397090f61a21\" data-root-id=\"941c09eb-9503-4c28-a085-7dd6725160e9\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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       "  const render_items = [{\"docid\":\"d79b3733-9802-4291-a746-0978982b7f7a\",\"roots\":{\"941c09eb-9503-4c28-a085-7dd6725160e9\":\"c5c216c2-3d00-4496-b8d7-397090f61a21\"},\"root_ids\":[\"941c09eb-9503-4c28-a085-7dd6725160e9\"]}];\n",
       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "941c09eb-9503-4c28-a085-7dd6725160e9"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SCALE_FACTOR = 1.2\n",
    "\n",
    "color_dict[\"EBDS\"] = \"#ffff99\"\n",
    "color_dict[\"EBDS-Outlier\"] = \"red\"\n",
    "\n",
    "colors = [\"#f1eef6\", \"#f0f9e8\", \"#a8ddb5\", \"#7bccc4\", \"#43a2ca\", \"#0868ac\"]\n",
    "binsource, scattersource, significantsource = build_hexbin_source(\n",
    "    net, train_data, bmu_df, significant_bmus\n",
    ")\n",
    "\n",
    "ebds_density_plot = plot_hex_heatmap(\n",
    "    scattersource,\n",
    "    binsource,\n",
    "    heatmap_col=\"binned_density\",\n",
    "    cmap=colors[1:],\n",
    "    cbar_ticks=True,\n",
    "    filter_groups=[\"Down Syndrome\", \"IBIS-HR-Typical\", \"IBIS-ASD\"],\n",
    "    cmap_upper_ratio=1.25,\n",
    "    cmap_lower_ratio=0.9,\n",
    ")\n",
    "\n",
    "show(ebds_density_plot)"
   ]
  }
 ],
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