Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfdcbbfb-f67e-40d6-9c2f-d02403ede7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\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, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls.length === 0 && js_modules.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'tabulator': 'https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator', 'moment': 'https://cdn.jsdelivr.net/npm/luxon/build/global/luxon.min', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n",
       "      require([\"tabulator\"], function(Tabulator) {\n",
       "\twindow.Tabulator = Tabulator\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"moment\"], function(moment) {\n",
       "\twindow.moment = moment\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"gridstack\"], function(GridStack) {\n",
       "\twindow.GridStack = GridStack\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"notyf\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      root._bokeh_is_loading = css_urls.length + 4;\n",
       "    } else {\n",
       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n",
       "    }    if (((window['Tabulator'] !== undefined) && (!(window['Tabulator'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['moment'] !== undefined) && (!(window['moment'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.jsdelivr.net/npm/luxon/build/global/luxon.min.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) >= 0) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\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",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) >= 0) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\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",
       "    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",
       "  var js_urls = [\"https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator.js\", \"https://cdn.jsdelivr.net/npm/luxon/build/global/luxon.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.js\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var css_urls = [\"https://unpkg.com/tabulator-tables@4.9.3/dist/css/tabulator_simple.min.css\", \"https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.css\", \"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/all.min.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/debugger.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/markdown.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/dataframe.css\"];\n",
       "  var inline_js = [    function(Bokeh) {\n",
       "      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n      background-size: auto calc(min(50%, 400px));\\n    }\\n    \");\n",
       "    },    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 (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\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",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, js_modules, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\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, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'tabulator': 'https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator', 'moment': 'https://cdn.jsdelivr.net/npm/luxon/build/global/luxon.min', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n      require([\"tabulator\"], function(Tabulator) {\n\twindow.Tabulator = Tabulator\n\ton_load()\n      })\n      require([\"moment\"], function(moment) {\n\twindow.moment = moment\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 4;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n    }    if (((window['Tabulator'] !== undefined) && (!(window['Tabulator'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['moment'] !== undefined) && (!(window['moment'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.jsdelivr.net/npm/luxon/build/global/luxon.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\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      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\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    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  var js_urls = [\"https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator.js\", \"https://cdn.jsdelivr.net/npm/luxon/build/global/luxon.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.js\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var css_urls = [\"https://unpkg.com/tabulator-tables@4.9.3/dist/css/tabulator_simple.min.css\", \"https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.css\", \"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/all.min.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/debugger.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/markdown.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/dataframe.css\"];\n  var inline_js = [    function(Bokeh) {\n      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n      background-size: auto calc(min(50%, 400px));\\n    }\\n    \");\n    },    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 (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\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  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, js_modules, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import panel as pn\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pn.extension(\"tabulator\")\n",
    "\n",
    "import hvplot.pandas\n",
    "\n",
    "#Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c0f2fb8-cc17-4062-957e-2f938630c5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"survey.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0814c8e8-3b71-4f7d-803f-50549cf1ce6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop BPM column and all NaN values\n",
    "df.drop(columns=[\"BPM\"], inplace=True)\n",
    "df = df.dropna()\n",
    "# DF now includes a total of 718 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4270f45-6efe-4c57-a4b0-60241320f347",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change Age, Anxiety, Depression, Insomnia, and OCD columns to int from float\n",
    "df[\"Age\"] = df[\"Age\"].astype(\"int\")\n",
    "df[\"Anxiety\"] = df[\"Anxiety\"].astype(\"int\")\n",
    "df[\"Depression\"] = df[\"Depression\"].astype(\"int\")\n",
    "df[\"Insomnia\"] = df[\"Insomnia\"].astype(\"int\")\n",
    "df[\"OCD\"] = df[\"OCD\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec376ae8-ffa0-4e20-956b-73bcf632f6b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make DataFrame Pipeline Interactive\n",
    "idf = df.interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f2b3d3-9cb5-41e6-b573-3edb503183db",
   "metadata": {},
   "source": [
    "1. Checking Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db9858b-0153-444b-851f-92e3f0824a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Timestamp', 'Age', 'Primary streaming service', 'Hours per day', 'While working', 'Instrumentalist', 'Composer', 'Fav genre', 'Exploratory', 'Foreign languages', 'Frequency [Classical]', 'Frequency [Country]', 'Frequency [EDM]', 'Frequency [Folk]', 'Frequency [Gospel]', 'Frequency [Hip hop]', 'Frequency [Jazz]', 'Frequency [K pop]', 'Frequency [Latin]', 'Frequency [Lofi]', 'Frequency [Metal]', 'Frequency [Pop]', 'Frequency [R&B]', 'Frequency [Rap]', 'Frequency [Rock]', 'Frequency [Video game music]', 'Anxiety', 'Depression', 'Insomnia', 'OCD', 'Music effects', 'Permissions']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d843c1d3-9885-44f5-9073-7b4b4cb95e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, y=\"While working\", x=\"Hours per day\", alpha = 1, hue = \"Music effects\")\n",
    "plt.title('Correlation between listening to music at work and effects');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1ddf10-8328-488d-b281-eccae532eef5",
   "metadata": {},
   "source": [
    "'Genre and Hours Correlation with music effects'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6ee94ce-fbd1-4390-b438-af5cfc3d359d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y+/Y3G1vTCa5du8Yvv/xSaF916tSxua/iqFWrFk2bNi308PDwsCqXlJSESqUqtDBBkiQCAwPvaXjJ1jGvXLmS4cOH88UXX9CqVSu8vb0ZNmwYCQkJDtUZGBho9bdKpcLHx8fSzoLnsFmzZoXO68qVKwudUxcXlyKHhgukpKRgMpkK7d9Wm+Li4mjbti1Xrlzhvffe488//2T//v189NFHgPW10ahRI9q2bWvJW7duHbGxsTz33HNFtqdgXu2tK7xttVmWZZvPQ8G86tufX3vTXuxdv0eOHCl0nt3c3JBlucjrd/Xq1QwYMICKFSvyzTffsHv3bvbv38/IkSPvOK/WnoJjsXe8JpOJlJQUq3RHX7+369SpE3FxcZw9e5atW7fSqFEj/P396dChA1u3biUnJ4ddu3bZHcZ31O3tK2ijo8GMt7e31d8Fc+3tpd/NuZ80aRLz589nz549dOvWDR8fHzp27FjkrfgcuX7tuV/vm47Wm5iYSIUKFay+/BTH+++/z8SJE1mzZg2RkZF4e3vTp08fzp49e1f1lSdijqkgFEGpVNKhQwc2b95MfHy81YdVwRzD2yfi+/r6AuY32r59+9qst0aNGnfVHlu9Ar6+vtSvX59Zs2bZ3Ob2RVn3g4+PDwaDgcTERKvgVJZlEhISaNasmSVNo9Gg1+sL1WEveLV3zAsXLmThwoXExcWxdu1aXnvtNa5fv87GjRvv2N6EhAQqVqxo+dtgMJCUlGT5AC94Dn/88UdCQkLuWJ+jvTVeXl5IkmQzgL49bc2aNWRlZbF69WqrNkRHR9us+4UXXqB///4cPHiQDz/8kOrVq9O5c+ci21PwXF2+fLnINisUCuLj4wvlFSzQKzhfBeydD3vPpbOzs935mLfXfatvvvmGsLAwVq5caVW3revLUQXXgL3jVSgUeHl53XX9typYJLd161a2bNlieb46duzI5MmT+eOPP9Dr9fccmJYlrVZr8/m4ceOG1XOrUql4+eWXefnll0lNTWXr1q3873//o0uXLly6dMnm3RscuX7tuV/vm47W6+fnx86dOzGZTHcVnLq6ujJ9+nSmT5/OtWvXLL2nPXv25NSpU3fV9vJC9JgKwh1MmjQJo9HIM88849CQcY0aNahWrRqHDx+22dvYtGlT3NzcSqx9PXr04NixY1SpUsXmvkojMC34gP3mm2+s0letWkVWVpbVKvXQ0FCOHDliVe63334jMzPzrvZduXJlnnvuOTp37szBgwcd2ubbb7+1+vv777/HYDBYVrx26dIFlUrF+fPn7T6Hd8PV1ZXmzZuzevVqq16ljIwMfvnlF6uyBYHWrYvGZFnm888/t1n3o48+SuXKlRk/fjxbt251aHizevXqVKlShcWLF9sN5lxdXWnRogWrV6+26mEzmUx88803VKpUqdD0kuLo0aMH58+fx8fHx+Z5LmoqgiRJqNXqQiuib1+VD473ENaoUYOKFSuyfPlyq0V+WVlZrFq1yrJSvyQEBQVRu3ZtVq1axYEDByyBaefOnUlMTGTBggW4u7tbfbGzxdEe2rJg6/V+5syZQtM/buXp6cljjz3G2LFjSU5OtntD/datW+Ph4cEnn3xSaEHmndyv901H6+3WrRu5ubmF7p5wO0eu24CAAKKionj88cc5ffp0oakmDxrRYyoId9CmTRs++ugjnn/+eRo3bszTTz9NnTp1LL1Iq1atAqxX+X766ad069aNLl26EBUVRcWKFUlOTubkyZMcPHjQMhewJMyYMYMtW7bQunVrXnjhBWrUqEFubi6xsbGsX7+eTz75xDLUFRUVxbJly4iJiSnRm0F37tyZLl26MHHiRNLT02nTpo1lVX6jRo0YOnSopezQoUN54403mDJlChEREZw4cYIPP/yw0PQAe9LS0oiMjOSJJ56gZs2auLm5sX//fjZu3Gi3h/p2q1evRqVS0blzZ8uq/AYNGjBgwADA/GE6Y8YMXn/9dS5cuEDXrl3x8vLi2rVr7Nu3z9JbcTdmzpxJ165d6dy5M+PHj8doNDJ37lxcXV1JTk62lOvcuTNqtZrHH3+cCRMmkJuby6JFiwoNIxdQKpWMHTuWiRMn4urq6vAvJH300Uf07NmTli1b8tJLL1G5cmXi4uLYtGmTJYCfPXs2nTt3JjIykldeeQW1Ws3HH3/MsWPH+O677+7pvrLjxo1j1apVtGvXjpdeeon69etjMpmIi4tj8+bNjB8/nhYtWtjctuD2aWPGjOGxxx7j0qVLzJw5k6CgoEJDmvXq1WPHjh388ssvBAUF4ebmZnPkQqFQMG/ePAYPHkyPHj0YPXo0er2et99+m9TUVObMmXPXx2pLx44d+eCDD3B2dqZNmzaAedV3WFgYmzdvplevXqhURX9UF/yy02effYabmxtarZawsDCbQ/ilbejQoQwZMoQxY8bQr18/Ll68yLx58wpN++nZsyd169aladOm+Pn5cfHiRRYuXEhISAjVqlWzWbdOp+Odd97hqaeeolOnTowaNYqAgADOnTvH4cOH+fDDD+22qzjvm8XhaL2PP/44S5Ys4ZlnnuH06dNERkZiMpnYu3cvtWrVYtCgQYD967ZFixb06NGD+vXr4+XlxcmTJ/n6669L9ItTmSnLlVeC8CCJjo6WR4wYIYeFhckajUbWarVy1apV5WHDhhVaaSzLsnz48GF5wIABsr+/v+zk5CQHBgbKHTp0kD/55BNLmYJVprevSL59pbAs218ZKsuynJiYKL/wwgtyWFiY7OTkJHt7e8tNmjSRX3/9davVtP369ZOdnZ3llJSUIo/VXrsKdO/e3WpVvizLck5Ojjxx4kQ5JCREdnJykoOCguRnn3220L70er08YcIEOTg4WHZ2dpYjIiLk6Ohou6vyb29Dbm6u/Mwzz8j169eX3d3dZWdnZ7lGjRry1KlT5aysrCKPq2AV9YEDB+SePXvKOp1OdnNzkx9//HH52rVrhcqvWbNGjoyMlN3d3WWNRiOHhITIjz32mLx161ZLmeHDh8uurq5F7vd2a9eulevXry+r1Wq5cuXK8pw5cyxtu9Uvv/wiN2jQQNZqtXLFihXlV199Vd6wYYPdFdixsbEyID/zzDPFas/u3bvlbt26yR4eHrJGo5GrVKlS6M4Ff/75p9yhQwfZ1dVVdnZ2llu2bCn/8ssvVmWKum4iIiLkOnXq2Nx/ZmamPHnyZLlGjRqyWq2WPTw85Hr16skvvfSSnJCQYClna4X3nDlz5NDQUFmj0ci1atWSP//8c5vnMjo6Wm7Tpo3s4uIiA5YV4bZea7Jsfu5btGgha7Va2dXVVe7YsaP8119/WZUp2E9iYqLN83CnOyLIsiz//PPPMiB37tzZKn3UqFEyIL///vuFtsHGnS0WLlwoh4WFyUqlUgbkJUuWyLJs/7wPHz680GvYFmysPI+JiZEB+e2337ZKLziXP/zwgyXNZDLJ8+bNk8PDw2WtVis3bdpU/u233wqtyn/nnXfk1q1by76+vpbXxZNPPinHxsZaytg7r+vXr5cjIiJkV1dX2cXFRa5du7Y8d+7cOx6bo++bxVmVX5x6c3Jy5ClTpsjVqlWT1Wq17OPjI3fo0EHetWuXpYy96/a1116TmzZtKnt5eckajUYODw+XX3rpJfnGjRt3PO7yTpLlYvZ/C4LwwAoMDGTo0KG8/fbbZd2UMjFt2jSmT59OYmJikXMXH1QffPABL7zwAseOHbMsthAEQXiQiKF8QfiPOH78ONnZ2UycOLGsmyKUsEOHDhETE8OMGTPo3bu3CEoFQXhgicBUEP4j6tSp49C9AYUHz6OPPkpCQgJt27blk08+KevmCIIg3DUxlC8IgiAIgiCUC+J2UYIgCIIgCEK5IAJTQRAEQRAEoVwQgakgCIIgCIJQLojFT0K5ZDKZuHr1Km5ubvd0825BEARBEEqPLMtkZGRQoUKFu/q5VRGYCuXS1atXCQ4OLutmCIIgCIJwFy5dunRXv54lAlOhXCr4LflLly5Z/dSnIAiCIAjlV3p6OsHBwZbP8eISgalQLhUM37u7u4vAVBAEQRAeMHc7DU8sfhIEQRAEQRDKBRGYCoIgCIIgCOWCCEwFQRAEQRCEckHMMRUEQRAEoUwYjUby8/PLuhlCManV6ru6FZQjRGAqCIIgCEKpkmWZhIQEUlNTy7opwl1QKBSEhYWhVqtLvG4RmAqCIAiCUKoKglJ/f39cXFzED6k8QAp+ACc+Pp7KlSuX+HMnAlNBEARBEEqN0Wi0BKU+Pj5l3RzhLvj5+XH16lUMBgNOTk4lWrdY/CQIgiAIQqkpmFPq4uJSxi0R7lbBEL7RaCzxukVgKgiCIAhCqRPD9w+u+/nclfvAdNq0aTRs2LDIMlFRUfTp06dU2iPYFxsbiyRJREdHl3VT7Mo3mriekUtihh6TSQYgS2/gWnouKVl5NwumX4XkGMhOtltXVtZ1rqdcIDn1ot0ysslEbtolclNjyc/LslvOmHYF042zGFMvFf+gbpOZnEBW4iWy01PuqR7ZYEBOOo984xzGnPR7qivPYORaei6JGbnIsnxPdemzM8hNvkJuWuI91QOQlpPPtfRcMnLvfVVwbuo1cpOvkJeTec91lRh9hvk6To0r65YIgiA4pMzmmPbs2ZOcnBy2bt1aKG/37t20bt2aAwcO8Morr/D888+XQQuF4goODiY+Ph5fX9+ybopNl1Oy+W7fJX6OvoJKITGwWTBd6wTy0fZz7DyXRKCHlm8HVMLl2gGk3R9ARgJUaAztXgXvcNCafxo1NzuZq7mJfHF0MfuuH8BT48nImo/TPKAZvh6VLfvLS72IfPIXtAeWQX4OeTW6kt98NCrvKkj/3GYjP/UyqpQYFH/OR7pxBtk7HPmh8Rh9qqHyCi7W8WUnx6O4tBvdnvcg6zr5lVqS1/YVjO7BOLsW72dd5eQYOP4TUvS3YMxDUb0bcvNRSL7VilUPQFxyNl/timXDsQS0TgqGtAyhW90gAj20xaonX58DyRdQ/PE2Tlf2gi6A3JbjIKQVWo+AYtWVlpPPiatpvLftLLE3sqke6Ma4jtWoHqBDpy3efCl9ajzyhR1o930E2cnkh7Qj/6GXkLzDUDmV/IpVhxgNkHwedn8E57eBWgdNRkDNR8Cz8p23F4T/uNDQUMaNG8e4cePu2z4+++wzZs6cyZUrV1iwYAHjxo2zmfZfI8n32n1xl9asWUPfvn2JiYkhJCTEKm/UqFH8/fffHDp0yKG6oqKiSE1NZc2aNfehpUJZSE9Px8PDg7S0NNzdixdU2XI5JZvHFu0mIT3XKr2qv45xHavx3HeH+HZQGK2vLEba/7n1xgoVDFkN4REAnEo8wpBNI9Eb9VbFulfuzMTG4/DyqIw+NQ7VDyNQXvnbui5nL/Kf3IKTbzXyc9JRnfwZae1zhdord52LoW5/nHSOLQzITLmO0x+z0Bxaap2hdCJv6K+oQ1s4VA+AnHwBfohCij9sneHqhzxiQ7GC07jkbPp89BfJt/ZGAw2DPfl0aBMC3B0PTvNidqP+ugeYDFbp+sZPIUe+jtbN26F69PlGvv/7Em/8fLxQ3gePN6Jb3UBUSscGk3LTrqPc8CpOp9ZYZ6i05I3YgrpifYfqKXHXT8KXD4P+tp7ukIfg0UUiOBXKVG5uLjExMYSFhaHVOvYeEBUVxbJlyxg9ejSffPKJVd6YMWNYtGgRw4cPZ+nSpSXSxsTERFxdXe/bPNj09HR8fX1ZsGAB/fr1w8PDA4PBUCjtXve/dOlSxo0bV+K35SrqObzXz+8yG8rv0aMH/v7+hS6i7OxsVq5cyZNPPgkUHso3Go28/PLLeHp64uPjw4QJEwoNDcqyzLx58wgPD8fZ2ZkGDRrw448/WpX5/fffad68ORqNhqCgIF577TUMBusPvNt9/vnnBAcH4+LiwqOPPsqCBQvw9PS05J8/f57evXsTEBCATqejWbNmhXqEQ0NDefPNNxk2bBg6nY6QkBB+/vlnEhMT6d27Nzqdjnr16vH339YBza5du2jXrh3Ozs4EBwfzwgsvkJVlf2i44LwtXryYypUro9PpePbZZzEajcybN4/AwED8/f2ZNWuWZRtbQ/GpqalIksSOHTsASElJYfDgwfj5+eHs7Ey1atVYsmSJ3e2PHz9O9+7dcXd3x83NjbZt23L+/Pkiz3NJMxpN/PD35UJBKcC565nEp+dSJ8iN5gFy4aAUzIHQ+lcg9RKpaZd5a//bhYJSgF/jtnBNn2r+I+Fo4aAUICcF+a+F5OszUGVdR9r8us02S9umocq1P43gdurcG4WDUgBjPuqN48lKjne4Lq4cLByUAmQlwp5PMOakOVRNnsHEF39eKBSUAkRfSuXoFcfqAchNTUC94eVCQSmA5uAXKLMdH9a/nqnnzV9P2sx74+djXM8o/Nzao0i/UjgoBTDkoto8idyMJIfrKjHZKbD9rcJBKcDFnXDjXOm3SRBKQHBwMCtWrCAnJ8eSlpuby3fffUflyiX7ZcvPz+++Ls6Ki4sjPz+f7t27ExQUhIuLi820/6IyC0xVKhXDhg1j6dKlVoHlDz/8QF5eHoMHD7a53TvvvMPixYv58ssv2blzJ8nJyfz0009WZSZPnsySJUtYtGgRx48f56WXXmLIkCH8/vvvAFy5coVHHnmEZs2acfjwYRYtWsSXX37Jm2++abe9f/31F8888wwvvvgi0dHRdO7c2SqoA8jMzOSRRx5h69atHDp0iC5dutCzZ0/i4qznd7377ru0adOGQ4cO0b17d4YOHcqwYcMYMmQIBw8epGrVqgwbNsxyXo4ePUqXLl3o27cvR44cYeXKlezcuZPnnivc03ar8+fPs2HDBjZu3Mh3333H4sWL6d69O5cvX+b3339n7ty5TJ48mT179hRZz63eeOMNTpw4wYYNGzh58iSLFi2yO3R/5coV2rVrh1ar5bfffuPAgQOMHDnS5hcAvV5Penq61aOkJGfn8cvhq3bzfzt5nUcbV0JxZZ/9Sm6cgdxUMo25HEqMtlvsr8t/YjIacDryvd0y6lO/Ysq6AdlJkGsnOMvPgcxr9ttzG2PsLvuZCUdR5mc4VI8pJx3p6A9286XT61BkXXeoruSsPNYftR8Q//D3JQxGk0N1Sfo0uH7Cbr7h4m6H6gG4lpaL3mB7v6nZ+STZCKTtMZ3ZZDdPcXEnClvB4f2WmwJn7beLY6tKry2CUIIaN25M5cqVWb16tSVt9erVBAcH06hRI6uyoaGhLFy40CqtYcOGTJs2zfL3tGnTqFy5MhqNhgoVKvDCCy/Y3T41NZWnn36agIAAtFotdevWZd26dXbbmpaWxtNPP42/vz/u7u506NCBw4fNX/iXLl1KvXr1AAgPD0eSJJtpsbGxAPzyyy80adIErVZLeHg406dPt/octde2HTt2MGLECNLS0pAkCUmSLMf/8ccfU61aNbRaLQEBATz22GNFn/xSVKb3MR05ciRvv/02O3bsIDIyEoDFixfTt29fvLy8bG6zcOFCJk2aRL9+/QD45JNP2LTp5ptwVlYWCxYs4LfffqNVq1aA+UneuXMnn376KREREXz88ccEBwfz4YcfIkkSNWvW5OrVq0ycOJEpU6bY/JmtDz74gG7duvHKK68AUL16dXbt2mV1YTZo0IAGDRpY/n7zzTf56aefWLt2rVUQ+cgjjzB69GgApkyZwqJFi2jWrBn9+/cHYOLEibRq1Ypr164RGBjI22+/zRNPPGGZa1KtWjXef/99IiIiWLRokd2hEJPJxOLFi3Fzc6N27dpERkZy+vRp1q9fj0KhoEaNGsydO5cdO3bQsmXLIp6pm+Li4mjUqBFNmzYFzC9eez766CM8PDxYsWKF5T5n1atXt1l29uzZTJ8+3aE2FJdCknAqYmhWrVKQZzCCUnOHipRIkoRCUmCSbQc2GpUGSVJgUhVRl1KNLEmgUN5hf8V4eRa1P0CSHPwOKimQlWrsrrdUasB+rhWFRJHnXaNS4ujCTkkq+lxJKsenBNxpmF6pKMZq06L2q1Di8AGWKAmUajDY6fl1Kt7cXkEoT0aMGMGSJUssnVeLFy9m5MiRllE9R/3444+8++67rFixgjp16pCQkGAJHG9nMpno1q0bGRkZfPPNN1SpUoUTJ06gVNp+X5Jlme7du+Pt7c369evx8PDg008/pWPHjpw5c4aBAwcSHBxMp06d2LdvH8HBwbi5uRVK8/PzY9OmTQwZMoT333/fMuL49NNPAzB16tQi29a6dWsWLlzIlClTOH36NAA6nY6///6bF154ga+//prWrVuTnJzMn3/+Wazzdz+V6ar8mjVr0rp1axYvXgyYe/j+/PNPRo4cabN8Wloa8fHxloATzD2vBUESwIkTJ8jNzaVz587odDrL46uvvrIMIZ88eZJWrVpZ3e6gTZs2ZGZmcvnyZZv7Pn36NM2bN7dKu/3vrKwsJkyYQO3atfH09ESn03Hq1KlCPab169+cdxYQYF60UfBN6da069fNPVMHDhxg6dKlVsfTpUsXTCYTMTExNtsL5qDRzc3Nqt7atWtbBd4BAQGW/Tji2WefZcWKFTRs2JAJEyawa5f9nrro6Gjatm3r0M13J02aRFpamuVx6dK9r04v4O2qZnBL+8M8XesG8u3eSxiDGtsPFis1A60n7kpn2ldsa7euNhXbIikUGBvZ7vEH0DcYhJNbELLWE9yCbBdy9kJ2cXwRmTKkpd0gyBTaDoPaw6F6FFodNB5qN19uMAiTu2OLsnxc1QxqZr/s4BaVUTr4W8smrSemym1sZ0oKlMHNHKoHwM9Ng7uz7aC/gocWHxfHFywpa3Sxm2eo3h1Z69i81xKl84O6RfR+1B9Qem0RhBI2dOhQdu7cSWxsLBcvXuSvv/5iyJAhxa4nLi6OwMBAOnXqROXKlWnevDmjRo2yWXbr1q3s27eP1atX07lzZ8LDw+nRowfdunWzWX779u0cPXqUH374gaZNm1KtWjXmz5+Pp6cnP/74I87OzpYfFvDz8yMwMBBXV9dCaUqlklmzZvHaa68xfPhwwsPD6dy5MzNnzuTTTz+9Y9vUajUeHh5IkkRgYCCBgYHodDri4uJwdXWlR48ehISE0KhRI6ve4rJW5reLevLJJ1m1ahXp6eksWbKEkJAQOnbseNf1mUzmnqxff/2V6Ohoy+PEiROWeaayLBe6B1fBsLm9e3MVtU2BV199lVWrVjFr1iz+/PNPoqOjqVevHnl51kODtwZqBXXaSis4FpPJxOjRo62O5/Dhw5w9e5YqVarYPRe3B4SSJNlMK9hPQcB663EV3Ai5QLdu3bh48SLjxo3j6tWrdOzY0dKLfDtnZ2e7bbudRqPB3d3d6lFSJEni4dqB1K9YuM6HqvpiMslcSc1h1ek8TF1mF65A6wHd3wH3Cri5V+DlxuPw0RZelPR8vafxUbkCIPtUJa+ejeDApyqKpiNRqjTIHiHIfT4G5W2Bu0KJ3PtjTDo7QasN+Vofcju+VTjD2QtD13m4evo5XBc+1ZBr9iiULPvXggaPo1TfoWf5H0qlggFNg6nipyuU17tBBar6F063R+vui/GR+aD1LJSn7zwXk4vjxxfgpuG9gY0K9YyqlQreG9SIgGLcLcDoGoD+oYmFM3T+yB2noSnm3RBKhFoHbV4Az5DCeY2GgoNfLAShPPL19aV79+4sW7aMJUuW0L1797u6E0z//v3JyckhPDycUaNG8dNPP9ldZxIdHU2lSpXsjvjd7sCBA2RmZuLj42PVoRQTE1PsNRYHDhxgxowZVvWMGjWK+Ph4srOzi902gM6dOxMSEkJ4eDhDhw7l22+/JTs7u1jtup/K/CdJBwwYwIsvvsjy5ctZtmwZo0aNshscenh4EBQUxJ49e2jXrh0ABoOBAwcO0LhxYwBq166NRqMhLi6OiIgIm/XUrl2bVatWWQWbu3btws3NjYoVK9rcpmbNmuzbZz0H8fYFSn/++SdRUVE8+uijgHnOacEckXvRuHFjjh8/TtWqVe+5rqL4+Zk/3OPj4y3zdWzdk9TPz4+oqCiioqJo27Ytr776KvPnzy9Urn79+ixbtoz8/PwS/8my4gr00PL58GYcvJjCyv2XUCklhrQMIczXlQ1H43moqi+Hb0D3iJ64PdUE6e/FkH4FKreGuv3AK9RSV4hXVZZ3Xcr2uG38Hr8XX60Xj1fvT7BzAO4elQDQuFdE32k6+oaDcdq/GEV+Nvq6jyKFtkX9T11KJyfyAurj9PSfcOgrpOsnkH2qQ5PhGFwDcNK6Onx8ru7eZNXpT15IKxT7P0OVGU9uSCSKOr1QeRYvEJG8w5AffhO58TA4uAzJkItcpy9Ubo3kHVasuoI8nfnmqebsi0lm1YHLOKuVDG8VSvVAN3x0jgW4BVT+Ncl/agem42vQXPwdg1tFTE2fQvYKRVuMAFClVNCqig+bxrVj+d6LnL6WQYNKnvRvGkxFr+INc2vdvMlt/jR51Tqh3P85yuxE9FW7ItXohtrHRmBYWrzDYdgaOL8dTq0DtRs0HQG+1cGjQtm1SxBKwMiRIy3T4z766CObZRQKRaHOo1s7WoKDgzl9+jRbtmxh69atjBkzhrfffpvff/+90OdVcTpZwNyZFBQUZHN6wa0Lph2ta/r06fTt27dQnlarLXbbANzc3Dh48CA7duxg8+bNTJkyhWnTprF///5it+9+KPPAVKfTMXDgQP73v/+RlpZGVFRUkeVffPFF5syZQ7Vq1ahVqxYLFiywug2Cm5sbr7zyCi+99BImk4mHHnqI9PR0du3ahU6nY/jw4YwZM4aFCxfy/PPP89xzz3H69GmmTp3Kyy+/bHN+KcDzzz9Pu3btWLBgAT179uS3335jw4YNVkF01apVWb16NT179kSSJN544w1Lb+S9mDhxIi1btmTs2LGMGjUKV1dXTp48yZYtW/jggw/uuf4Czs7OtGzZkjlz5hAaGsqNGzeYPHmyVZkpU6bQpEkT6tSpg16vZ926ddSqVctmfc899xwffPABgwYNYtKkSXh4eLBnzx6aN29OjRo1Sqzdjgpw19KtXhCRNf2QkNA4mYftn25XhWGtQ3FSKv6ZE1kBAutBfrb5A11Z+GVSwSOUgbWG0ye8F05KLWqtW6EyGo9K4FEJQ+WWGIz5aLSFh9PVOl/Q+WLsOBU5Jx1Jq0OpduFuwnhXLz/w8iPP/11y8vVotK4oVHf3Epe8w8A7DFOlFsiyAYXr3d+bNsjDmd4NK/JwnQAUkoRGdYe5tfbapFDg5BuG3PZF8pqPQumkRq26u/uEap2UVPXX8fojtdAbTWhUyuLNLb21LndfcPfFENSAPGM+ao2L5T61Zco73Pyo+5i5V17931zhK/z7dO3a1TIS2aWL7ek0fn5+xMffXHyZnp5eaOqbs7MzvXr1olevXowdO5aaNWty9OhRS0dXgfr163P58mXOnDnjUM9k48aNSUhIQKVSFbkOwxGNGzfm9OnTdjum7tQ2tVpt82dDVSoVnTp1olOnTkydOhVPT09+++03mwFwaSvzwBTMw/lffvklDz/88B1v+TB+/Hji4+OJiopCoVAwcuRIHn30UdLSbq5unjlzJv7+/syePZsLFy7g6elJ48aN+d///gdAxYoVWb9+Pa+++ioNGjTA29ubJ598slAQdqs2bdrwySefMH36dCZPnkyXLl146aWX+PDDDy1l3n33XUaOHEnr1q3x9fVl4sSJJbK6vH79+vz++++8/vrrtG3bFlmWqVKlCgMHDrznum9XMJG8adOm1KhRg3nz5vHwww9b8tVqNZMmTSI2NhZnZ2fatm3LihUrbNbl4+PDb7/9xquvvkpERARKpZKGDRvSpo2duYKlROtkfdkrFBIu6tteCirNHRcUqVQqVKo7DyGrnFy4U6SpdNKW2KIUtUYLmpKpS+HiWSL1ADg7lczbjaRQoHZ2fBpAUZRKBS4O3rP0TlRqDVC8XuBS4ezY/GJBeFAolUpOnjxp+b8tHTp0YOnSpfTs2RMvLy/eeOMNq7JLly7FaDTSokULXFxc+Prrr3F2di50X3WAiIgI2rVrR79+/ViwYAFVq1bl1KlTSJJE165dC5Xv1KkTrVq1ok+fPsydO5caNWpw9epV1q9fT58+fazWxdzJlClT6NGjB8HBwfTv3x+FQsGRI0c4evQob7755h3bFhoaSmZmJtu2baNBgwa4uLjw22+/ceHCBdq1a4eXlxfr16/HZDKVSYeRTbJw15566in5oYceKutm/CulpaXJgJyWllbWTREEQRBKUE5OjnzixAk5JyfH4W2GDx8u9+7d225+79695eHDh1v+TktLkwcMGCC7u7vLwcHB8tKlS+UGDRrIU6dOlWVZln/66Se5RYsWsru7u+zq6iq3bNlS3rp1q2X7kJAQ+d1337X8nZSUJI8YMUL28fGRtVqtXLduXXndunV225Oeni4///zzcoUKFWQnJyc5ODhYHjx4sBwXFyfLsiwfOnRIBuSYmBjLNrbSZFmWN27cKLdu3Vp2dnaW3d3d5ebNm8ufffaZw2175plnZB8fHxmQp06dKv/5559yRESE7OXlJTs7O8v169eXV65cafdYbCnqObzXz+8y++WnB9H8+fPp3Lkzrq6ubNiwgfHjx/Pxxx/z1FNPlXXT/nVK+pefBEEQhPLhbn75SShf7ucvP5WLofwHxb59+5g3bx4ZGRmEh4fz/vvvi6BUEARBEAShhIjAtBi+/97+r/kIgiAIgiAI96YcLB0VBEEQBEEQBBGYCoIgCIIgCOWECEwFQRAEQRCEckEEpoIgCIIgCEK5IAJTQRAEQRAEoVwQgakgCIIgCIJQLojAVBAEQRAEQSgXRGAqCIIgCILwAEhISLD8AqWnp6fdtAeZuMG+YCUqKorU1FTWrFlT1k0p126kppGQnsfhuCR8dVpqV/QkwN0ZtUZT7LribmQSk5zN2WsZVPHTEe7rQoivm3WhnFTIvAaX9oJSA8HNQOcPat3NMgY9pF2ChGOQfgUqNAaPSuAZXPwDzMuCzOtweT8YciG4Bbj6g4tX8etKvQw5yXBxF6hdzHU5e4PO7y7qioO0K3D1ILhXhMC64BEMquKfd9KuQNJZSLoAfjXAOwzcKxS7mry8PK6n5XDsSgpJmXoaVPYmyF2Dj2cZ/5RudjJkxMOl/aB1h4qNQRcITmX4E5CybL42E09BShwE1AavUHALLLs2lWfp8ZASA9dPma9P32rm17RQZqKioli2bBmzZ8/mtddes6SvWbOGRx99lPv9K+/vvvsu8fHxREdH4+HhYTftXoWGhjJu3DjGjRtXIvUVhwhMBaGYriWn89IPR9kVk2ZJ0zopWDykIU3DfFCr1Q7XdfZaBiOW7udySo4lLcBdw1cjmlMj6J/AJisRdsyB/V/c3FBSwCNvQ70B5qDDoIdL++C7QZCXebNcxcbw2BLzh7+jcjPg+Cr49WUwGW+mNxkBHV4H12IElCkXYftbcGTFzTSFErrNgxrdwT3I8bqSY+HHKLh66Gaaxg0eXwGVmhUvOE08DV/1goyEm2leoTB0jTkAcFCeXs++mCSe/OYweoPJkt4m3JMF/esR4FVGwWnGNdgwAU6suZmmdIK+X0C1zqB2Lf02yTJcOwpf9TYHzQV8q8OQVeBZufTbVJ4lx8A3fSH5ws00nT8MWwv+tcquXeWM0SSzLyaZ6xm5+LtpaR7mjVIh3dd9arVa5s6dy+jRo/Hyuosv6/fg/PnzNGnShGrVqhWZ9iATQ/mCXRs3buShhx7C09MTHx8fevTowfnz5y3506ZNQ5KkQo+lS5cSGxtrM699+/Zld0AlIE+v54udF6yCUoDcfBMjvo4mIS3b4bquJmcybmW0VVAKcC1dz7PfHuRSUoY5IXaXdVAKIJvg1/Hm3hQw95TeHpQCXDloDmpz0h1uF6kX4ZcXrYNSgANL4MIfjtcDcGG7dVAK5np/HQ+ZCba3sSUnFba/aR2UAugzzMeddsnxujISzNtk3Lb/lFhYNRKykhyuKiEth5FfWwelAH9dSGXpXzHk5+c73q6SYjLB8dXWQSmAMR9+HGHusSwL6Vfhm8esg1KAG2fgl3GQk2Zzs/+k7GT4abR1UArmUYzlA809qQIbj8Xz0NzfePzzPby4IprHP9/DQ3N/Y+Ox+3t+OnXqRGBgILNnzy6y3KpVq6hTpw4ajYbQ0FDeeeedO9b9yy+/0KRJE7RaLeHh4UyfPh2DwQCYezFXrVrFV199hSRJREVF2UwDSEtL4+mnn8bf3x93d3c6dOjA4cOHrfa1du1amjZtilarxdfXl759+wLQvn17Ll68yEsvvWT57C5NIjAV7MrKyuLll19m//79bNu2DYVCwaOPPorJZP4QfuWVV4iPj7c85s+fj4uLC02bNiU4ONgq79ChQ/j4+NCuXTub+9Lr9aSnp1s9yqPEjBy+/dt2QKU3mPg75obDdaXkGDh+1fZxXriRRUqOAbKTYOcC+5Xs+8IccMQfLhyUFji2CrKuOdYokxH2f2k/f+cCyHLwGJNjYc8i+/kHvgKjwbG6sq4XDrQK6DMg4bhj9YD5w/32D/wCVw5CtuPP4e7zieQZTTbzvtoXT2JaluPtKimZ12DX+7bzZBMc/7l021Mg7bK5bbac3wbZiaXbnvIs64Z52o4tqRfNr4f/uI3H4nn2m4PEp+VapSek5fLsNwfva3CqVCp56623+OCDD7h8+bLNMgcOHGDAgAEMGjSIo0ePMm3aNN544w2WLl1qt95NmzYxZMgQXnjhBU6cOMGnn37K0qVLmTVrFgD79++na9euDBgwgPj4eN577z2babIs0717dxISEli/fj0HDhygcePGdOzYkeRk8xfDX3/9lb59+9K9e3cOHTrEtm3baNq0KQCrV6+mUqVKzJgxw/IZXprEUL5gV79+/az+/vLLL/H39+fEiRPUrVsXnU6HTmee47hnzx4mT57MsmXLqFu3LgCBgeZ5Y7m5ufTp04dWrVoxbdo0m/uaPXs206dPv38HU0IMRpnsPKPd/KupuXbzbpdTRD0AWXqjOei092EOkH7ZHEwW1QtmzDM/HGHMh7Q4+/mZ18xlHCEbi257xlXzFASlA29DBn3R+8246libwH4AXyC/GL3eRTzfWXlGDKb7O9/MJtlU9HlPiS21pljJvkNPdH5O0fn/JXe6BnP/273LRpPM9F9OYOvVJQMSMP2XE3SuHXjfhvUfffRRGjZsyNSpU/nyy8Jf5hcsWEDHjh154403AKhevTonTpzg7bfftvRq3m7WrFm89tprDB8+HIDw8HBmzpzJhAkTmDp1Kn5+fmg0GpydnS2fr0ChtN9++42jR49y/fp1NP+se5g/fz5r1qzhxx9/5Omnn2bWrFkMGjTI6nO3QYMGAHh7e6NUKnFzc7PaT2kRPaaCXefPn+eJJ54gPDwcd3d3wsLMc+/i4qwDl7i4OPr06cMrr7zCgAEDCtXz5JNPkpGRwfLly1EobF9ykyZNIi0tzfK4dKkYQ7OlyNlJQYiPi938JiHeDtfl5apGZedNUyGBr04NancIbmm/kvBI89zKCk3sl3ELAidnxxrlpIWqneznB7cwz+t0hFoHFZvazw9tCxoH5zqqdeb5dfYENXSsHgBdANgbmlKqwdnxOWPNQu0/3+G+rjirSncIDDA/10VdD1U7lF5bbuUdbj9PrQOtZ6k1pdxz9ip6zrRb8Rfp/Zvsi0ku1FN6KxmIT8tlX0yy3TIlYe7cuSxbtowTJ04Uyjt58iRt2rSxSmvTpg1nz57FaLTdKXHgwAFmzJhh6fTR6XSMGjWK+Ph4srMd/8J84MABMjMz8fHxsaorJibGMh0vOjqajh07FuNoS48ITAW7evbsSVJSEp9//jl79+5l717z0FJe3s3et6ysLHr16kWrVq2YMWNGoTrefPNNNm7cyNq1a3Fzsx/QaDQa3N3drR7lkb+3B5O7VLGZV9VfR7if44tKfF2UPN7c9or5Pg0r4u2sNAduERNAYaNX0dkLavc2B1mewfaDs8j/gWeow+2iRndwsRFwKZTQfhJodIXzbHELMJdXKAvnufhA9a6Ot8kj2FyXLQV3H3CUqy/UG2Q7r8Uz4BrgcFXVAnSE+9p+zl/vWgU/b0/H21VSXLzh4TdtB9/uFcxfLsqCzh+q2XnO274iVubfShcALZ+znVe3v/ka/g+7nuHYyJSj5e5Wu3bt6NKlC//73/8K5cmyXGhu5p1W7JtMJqZPn050dLTlcfToUc6ePYtW6/jdNEwmE0FBQVb1REdHc/r0aV599VUAnJ0d7KwoAyIwFWxKSkri5MmTTJ48mY4dO1KrVi1SUlKsysiyzJAhQzCZTHz99deFXoSrVq1ixowZfP/991SpYjuYexC1CPXio4F1qOBhfqNQKiR61vVn2fBGBHg7fqsOD50Lz0SE8XyHqrhpzIGnq1rJqLZhjO9cDR/3fwIe73AYsR78a9/cOOQhGLHx5kpmz8owYBnU629efQ3mD/qe75t7QO30VNvkGQwjNkHYLfOB/WvB8F/Bp6rj9YB5hfuQ1eBX82ZaWDsY/gv4FqMupQqqd4MeC80f2mA+zvoDzHcdKM4tsbQe0Hk6tHnp5up0rQd0nAKtnwe142/YAd4efB3VmO51/CxDhhU8tHzyeF2ahXg63qaSFlAHBq++2UspSVC9C0StL7vbDbl4Q8+F0Hw0qLQ307rOgcZDb163gnnkouWz0HHazZ5kJxfz9dnlTXD2LMPGlT1/N8eCNEfL3Ys5c+bwyy+/sGvXLqv02rVrs3PnTqu0Xbt2Ub16dZRKG1/WgcaNG3P69GmqVq1a6GFvtNFePQkJCahUqkL1+Pqav9TUr1+fbdu22a1DrVbb7dm938QcU8EmLy8vfHx8+OyzzwgKCiIuLs7qnm1gXpW/detWNm/eTGZmJpmZ5rl7Hh4enD9/nmHDhjFx4kTq1KlDQoJ5wZBarcbb2/Hh7vLI3d2N7o3caFrZi8w8A05KBT46Da4u9of47anopWNsRDh9G1UgJ9+I1klJkE6Ns/Mtw3hOWnMv17C1kJtq7oF09io85OwVCo/Mh4iJ5nuPqnXgGVK8oBTMQYxfdRjwtfn+oyaj+cPxbu476uIN4e3hiZWgzzS3XeMBHncxFOkeBI2GmuvLyzQHN67+4HwX9+1zCzD3JDd70jy30cnZPOXBkfmut6no6868vnWYmJ1PvsGIq1pJoI9n8dtUkjQ685D9iI2gTwOFk7mXWlvGIxHuQfDwDGj9HOTnmu9r6xZku1f9v07nZw5E6z8GeTnm9wFdwN3ds/dfpnmYN0EeWhLScm3OM5WAQA/zraPut3r16jF48GA++OADq/Tx48fTrFkzZs6cycCBA9m9ezcffvghH3/8sd26pkyZQo8ePQgODqZ///4oFAqOHDnC0aNHefPNNx1uU6dOnWjVqhV9+vRh7ty51KhRg6tXr7J+/Xr69OlD06ZNmTp1Kh07dqRKlSoMGjQIg8HAhg0bmDBhAmC+A8Aff/zBoEGD0Gg0loC2NIjAVLBiMplQqVQoFApWrFjBCy+8QN26dalRowbvv/++1e2efv/9dzIzM2ndurVVHUuWLAEgOzubN9980+oFFRERwY4dO0rjUO67AB8PHB/0tU+rcSLMz4HeIp3fnYNDZ8+S600pybqKcx/VoihVxbrPaJFU6rv78QEbXF1dcS2DW4PekVuA+VGeqLTinqWOUqrM01gEK0qFxNSetXn2m4NIYBWcFozbTe1Z+77fz7TAzJkz+f77763SGjduzPfff8+UKVOYOXMmQUFBzJgxw+7CJ4AuXbqwbt06ZsyYwbx583BycqJmzZo89dRTxWqPJEmsX7+e119/nZEjR5KYmEhgYCDt2rUjIMD8ftC+fXt++OEHZs6cyZw5c3B3d7e6a86MGTMYPXo0VapUQa/X3/cfDrBqv1yaexPKva5du1K1alU+/PDDMm1Heno6Hh4epKWlldv5poIgCELx5ebmEhMTQ1hYWLHmTt5u47F4pv9ywmohVJCHlqk9a9O1bjF+vEMotqKew3v9/BY9pgIAKSkp7Nq1ix07dvDMM8+UdXMEQRAEoUhd6wbRuXZgqf/yk3B/icBUAGDkyJHs37+f8ePH07t377JujiAIgiDckVIh0aqKT1k3QyhBIjAVAPjpp5/KugmCIAiCIPzHidtFCYIgCIIgCOWCCEwFQRAEQRCEckEEpoIgCIIgCEK5IAJTQRAEQRAEoVwQgakgCIIgCIJQLojAVBAEQRAEQSgXRGAqCIIgCIIglAsiMBUEQRAEQXBAVFQUffr0Ketm/KuJwFQgKioKSZKQJAmVSkXlypV59tlnSUlJsSr3zTffULNmTbRaLaGhocycObNQXbGxsZa6JElCrVZTtWpV3nzzTWRZLq1DKhW5yZfRXzuDPjGGvOz0u67HmJeD8cY5TNdOYLpxHoM+y3bB7GRIjoGUWMjNsFkkLzsd/Y0Y9NfOkHPj0l23CSA1LZW46ynEXEsmMTnlzhsUJe0qXD8FN85CVtI9VWVMivnnXJ3DmJt5b+0qj3LSzM9xcgxk3+N5z0qE5AuQchHy7FxXgvAgMxkh5k84+qP5X5OxrFtU4oxGIyaTqaybUWpEYCoA0LVrV+Lj44mNjeWLL77gl19+YcyYMZb82NhYhg0bRp8+fTh58iTff/89YWFhduvbunUr8fHxnD17lunTpzNr1iwWL15cGody3+Wm3yDv5Ea03/ZCs6gZmk9bwObJ5CfHFbsuY3Icin2folz8MIpFrVB82RHl7g8wptwSVBrz4Go0fNsf3m9ofqx6EpLOWdWVl3wJtk5H82krNIua4fxtD/RH15Cdeq3Y7Yq5lsqrP52i/bu7iHx3N08uP87h2ERyMtOKV5E+E+L2wMon4OMW8FFz+HksJJ4udpvy0+KRj3yP8ute5nP16UMotr6BKeVisesql2QZEs/AjyPgvQbm53nFE5BwDIyG4tWVlw0Xd8FXveH9RvBBY1j7vDlAFYR/ixNrYWFdWNbD/J64rIf57xNrS2X37du35/nnn2fcuHF4eXkREBDAZ599RlZWFiNGjMDNzY0qVaqwYcMGyzY7duxAkiR+/fVXGjRogFarpUWLFhw9etRSZunSpXh6erJu3Tpq166NRqPh4sWLpKSkMGzYMLy8vHBxcaFbt26cPXsWgLS0NJydndm4caNVG1evXo2rqyuZmeYv8VeuXGHgwIF4eXnh4+ND7969iY2Nvf8nqxhEYCoAoNFoCAwMpFKlSjz88MMMHDiQzZs3W/ILekBHjhxJWFgYzZs3Z8iQIXbr8/HxITAwkJCQEAYPHkzr1q05ePBgaRzKfae4egD1yoGQdN6cYNCjjl6G4vvB6JMvO1xPfsZ1FPs/Rdo6FbL/6UXMSUHaMRvFn/PJT4s3pyXHwJed4crf5r9lGc5ugsVdINUcDOckX0Gx+inUB7+A/BxzuZRYNKuGo7y4s1jHd/lGGgM+/5stp5Iw/dPJfeRyGo99vp+49GL2RiSdM39YXD30T9tNcGYDLOt58/w5QDYaUcVsR1o9ynLM5Ocg/b0Y6afRGFLurXe4XEiNMz/P57fdTIvbBV92gtTY4tWVeBKWdodrx81/mwxwbJX5vKddKbEmC0KZObEWvh8G6Vet09PjzemlFJwuW7YMX19f9u3bx/PPP8+zzz5L//79LZ95Xbp0YejQoWRnZ1tt9+qrrzJ//nz279+Pv78/vXr1Ij8/35KfnZ3N7Nmz+eKLLzh+/Dj+/v5ERUXx999/s3btWnbv3o0syzzyyCPk5+fj4eFB9+7d+fbbb632s3z5cnr37o1OpyM7O5vIyEh0Oh1//PEHO3fuRKfT0bVrV/Ly8krlfDlCBKZCIRcuXGDjxo04OTlZ0ipWrEjTpk157rnnyM3NLVZ9f//9NwcPHqRFixYl3dRSl5t8GfXW123mKROOQEqMw3Upc1OR9n1mM0869BVKfZp5+PWPd8y9prfLugGn14Mso8y4iuryHpt1aX6bQu4Nx3tzt59KIDFTXyg93yjz/rZzpKWlOlZRdhL8Pg+M+YXzMq/BuW2F0+0wpV5E+q3w1BEAKW43yox4h+sql0xGOPI95KYWzsvPgd2LwODg6y4nBTZPMX8JuF3qRbj67/iCKPyHmYywcSJga3rYP2kbXyuVYf0GDRowefJkqlWrxqRJk3B2dsbX15dRo0ZRrVo1pkyZQlJSEkeOHLHaburUqXTu3Jl69eqxbNkyrl27xk8//WTJz8/P5+OPP6Z169bUqFGDq1evsnbtWr744gvatm1LgwYN+Pbbb7ly5Qpr1qwBYPDgwaxZs8YSBKenp/Prr79aOpFWrFiBQqHgiy++oF69etSqVYslS5YQFxfHjh077vu5cpQITAUA1q1bh06nw9nZmSpVqnDixAkmTpxoyR81ahSyLBMeHk7Xrl1JT785p7JHjx48//zzVvW1bt0anU6HWq2mWbNmDBgwgGHDhtndv16vJz093epRHkn5OeZ5knbIsX85Xld2su2AE8xBRdZ10KdD7B/2KzmzCQz5mC7/bb9M2mUkQ7b9/FtkZ2ey7Yz94fq9F9PIynNwrlNuurnHz54Lv0FejkNVSXlZhXtGbnXlgGNtKq/0mXBus/38mB3m8+mIvGy4tNt+/umN9vME4UFwcVfR7wfIkH7FXO4+q1+/vuX/SqUSHx8f6tWrZ0kLCAgA4Pr161bbtWrVyvJ/b29vatSowcmTJy1parXaqu6TJ0+iUqmsOnh8fHystuvevTsqlYq1a829xatWrcLNzY2HH34YgAMHDnDu3Dnc3NzQ6XTodDq8vb3Jzc3l/HnHR7DuN1VZN0AoHyIjI1m0aBHZ2dl88cUXnDlzxhJsnjhxgqVLl3L8+HFq1arFiBEjaN++PRs3bsTf35/jx48zdOhQq/pWrlxJrVq1yM/P5+jRo7zwwgt4eXkxZ84cm/ufPXs206dPv+/Hec+UTuDkfHO4/HZuQY7X5eRcZLbk5AoKJ3D1BXs9grpAUEigC7BfkdIJlGrHmqRUEeBm/23B21WNUnJwEZtCaW57jp0FPK5+DrcLlQYUKvOQtC1uRRz/g0ClBtcijsHV1/FzJSnAxQcyr9vO96hU/PYJQnmS6eC8eUfL3YNbRxbBPO3t1jRJkgAcWrxUUBbA2dnZ6m97i4dlWbaUU6vVPPbYYyxfvpxBgwaxfPlyBg4ciEqlsrShSZMmhYb7Afz8/O7YvtIiekwFAFxdXalatSr169fn/fffR6/XWwLFI0eOoFarqV27NpIk8eWXXxIeHk6bNm347LPPyMjIoFevXlb1BQcHU7VqVWrVqsWAAQMYN24c77zzjt1pAJMmTSItLc3yuHSpfM4ZVOp8yas32E6mE8qwhxyuy6j1BP/atjO9wjA5e5sDkjbj7FfSfBQonVBUaGQ30M2v1RdZ6+lQm5w0Woa2CLabP7pNRQJ8fRyqC8/K0Hy0/fzGUaBUOlSV0dkHuWZ325lOLsiB9W3nPSicnKHVWPv5bcaBs6djdekCoOVztvMkCer2LW7rBKF8KeqL+N2UKwN79tycepWSksKZM2eoWbOm3fK1a9fGYDCwd+9eS1pSUhJnzpyhVq1alrTBgwezceNGjh8/zvbt2xk8+ObnVePGjTl79iz+/v5UrVrV6uHh4VHCR3j3RGAq2DR16lTmz5/P1atXqVixInl5eZYXhFKpZPny5VStWpXRo0fz+uuv4+xcdO+fUqnEYDDYnWCt0Whwd3e3epRHKq0OHhqHsUJj6wylE3mPfY3s6u94Xd4hyH0/L/zm6eKD3H8ZSp9/7noQFgH1BxWuoNMM8A4HQHb1JW/Ad+aexVuY/OtA5Oto3bwdblcFnYLJXcILpfeu58tDVYv5rbp6V6jZwzpNkqDzDPB0vOfOSecDHach+9eyzlBpkQd+g8mtQvHaVR751YR2EwqnNx0JlZo5Xo9CAQ0GQpVO1umSAnovAveK99ZOQShrIa3BvQIg2Skgma/zkNal2apimTFjBtu2bePYsWNERUXh6+tb5P1Rq1WrRu/evRk1ahQ7d+7k8OHDDBkyhIoVK9K7d29LuYiICAICAhg8eDChoaG0bNnSkjd48GB8fX3p3bs3f/75JzExMfz++++8+OKLXL7s+MLd+00M5Qs2tW/fnjp16vDWW2/xwQcf0Lp1awYOHMjChQupV68eR48e5cKFC7i6urJ8+XJGjx6Ni4uLZfukpCQSEhIwGAwcPXqU9957j8jIyHIbcBaH2juY3Me+wZAWh3xxF+gCUIS0QtL54+TsVqy6pMC6mKI2ICWeQE44huRfCzmgHgrfKjcL6fyg62xo/Txc2AEqLYRHmIfxteb9aVzcyK3QlLzRuzFd2g9pV5AqN0f2DEPrU7yhW29vH/o1hMhagew6e53cfANtqgXg56LAz7uY36o9g6HbPGj7Mpz7DdQuUKWDeRjf1bdYVUk+4RgHrUBKiYFLe5E8KiFXaoHJvSIqjcudKyjvXLyg1XNQr7/5eTYZoEqk+Xl28SpeXW6B0PcTSLsMMX+A1hPC2pq/BKld70frBaH0KJTQda559T0S1oug/glWu84xlyun5syZw4svvsjZs2dp0KABa9euRa0uerrOkiVLePHFF+nRowd5eXm0a9eO9evXF5o68Pjjj/P2228zZcoUq+1dXFz4448/mDhxIn379iUjI4OKFSvSsWPHcvXZLMn/trueC8UWFRVFamqqZWVfgeXLlzNixAjOnTuHp6cn06ZN46effuLKlStUrVqVUaNGMWDAAFq0aEHTpk1ZtWoVcXFxVvc3VSqVBAUF0a1bN2bNmuXwPJb09HQ8PDxIS0srVy8YQRAE4d7k5uYSExNDWFgYWq327is6sda8Ov/WhVDuFc1Bae1e9rcrQzt27CAyMpKUlBQ8PT3Lujl3rajn8F4/v0VgKpRLIjAVBEH4dyqxwBTMt4S6uMu80EkXYB6+L8c9pSIwvTMxlC8IgiAIwoNJoTRPUxH+NURgKgiCIAiCUArat29v99ZPgplYlS8IgiAIgiCUCyIwFQRBEARBEMoFEZgKgiAIgiAI5YIITAVBEARBEIRyQQSmgiAIgiAIQrkgAlNBEARBEAShXBCBqSAIgiAIglAuiMBUEARBEAThDj755BPc3NwwGAyWtMzMTJycnGjb1vom/3/++SeSJHHmzJnSbuYDTwSmgiAIgiA8kIwmI/sT9rP+wnr2J+zHaDLet31FRkaSmZnJ33//bUn7888/CQwMZP/+/WRnZ1vSd+zYQYUKFahevXqx9mE0GjGZTCXW5geRCEwF4R4Y83KRjSXzRmjSZ5ZIPbLRiCkv+84FHWEymX+LuiQY86GEzpUh33DnQqVNlsFUDttlMpifR0H4l9l6cStdVnVh5KaRTPxzIiM3jaTLqi5svbj1vuyvRo0aVKhQgR07dljSduzYQe/evalSpQq7du2ySo+MjCQlJYVhw4bh5eWFi4sL3bp14+zZs5ZyS5cuxdPTk3Xr1lG7dm00Gg0XL15kx44dNG/eHFdXVzw9PWnTpg0XL160bPfLL7/QpEkTtFot4eHhTJ8+3aonV5IkvvjiCx599FFcXFyoVq0aa9euvS/npaSJwPRfLCoqij59+lil/fjjj2i1WubNm1c2jfqXyEu6SO7+r1CuGoHh11fIuxxNbuq1YtdjyMlATjyNvP0tFKtHIW+djnz9JMbs1OLXlXYV+eoh2PAqilVPIu9ZhHzj7J03tCXrBsTtgTXPwg9RcHoDpMffXV0psXBsNfw4An5+FmL+gLTLd1XVlRtpfPvXGZ79LpoZa6I5fSWJrKysu2tXSclJhfgj8Osr8P0wiP7uro+vRKVdgeM/wffD4ZcX4MoByE4u61YJQonYenErL+94mWvZ1u+717Ov8/KOl+9bcNq+fXu2b99u+Xv79u20b9+eiIgIS3peXh67d+8mMjKSqKgo/v77b9auXcvu3buRZZlHHnmE/Px8Sx3Z2dnMnj2bL774guPHj+Pt7U2fPn2IiIjgyJEj7N69m6effhpJkgDYtGkTQ4YM4YUXXuDEiRN8+umnLF26lFmzZlm1dfr06QwYMIAjR47wyCOPMHjwYJKTy/97gCSLH23914qKiiI1NZU1a9YA8MUXXzB27Fg++ugjnnrqqbJt3B2kp6fj4eFBWloa7u7uZd0cK/k3zuP0VQ9Iv2qVru/0FnK9/mg9/B2uS47difRNPzDk3kxUqpEfX4Ec8hAKJ41D9RjSr6E89QvS+vHWGa5+yMN+QQqo5XCbyLoBW6fBoa+t0ys0gkHLwb2C43WlxMLKoZBwxDq94RMQ+Tp4VHK4qgvXUuj/2d8kZeVZpc9/tCbd6wXh7OLieLtKSm4GHFwGm1+3TveoBFG/gldo6bcJzIHx133g9i8mbcZBmxfBxbssWiUIAOTm5hITE0NYWBharbbY2xtNRrqs6lIoKC0gIRHgEsDGfhtRKpT32lwrn3/+OS+99BKpqank5OTg7e3NlStX2L59O++//z5//fUXf/zxBxEREZw6dYqaNWvy119/0bp1awCSkpIIDg5m2bJl9O/fn6VLlzJixAiio6Np0KABAMnJyfj4+LBjxw4iIiIKtaFdu3Z069aNSZMmWdK++eYbJkyYwNWr5s8lSZKYPHkyM2fOBCArKws3NzfWr19P165d7/k8FPUc3uvnt+gx/Y+YN28ezz33HMuXLy8yKN2xYweSJPHrr7/SoEEDtFotLVq04OjRo1blVq1aRZ06ddBoNISGhvLOO+9Y5YeGhjJz5kyeeOIJdDodFSpU4IMPPrgvx1aa9BlJSNumFwpKATRb/4cyN9XhuuSk80g/jbYOSgGMeUg/PY2UfsnhupT6VKQNrxbOyEqETZMwpl1xuC5unCkclAJcPQTHVjk+LGzMN/ce3h6UAkQvh+QLDjcpLSODN34+WSgoBZj482kSMwunl4rM+MJBKZgDw99mQV4Z9OYa8mDPosJBKcBfC8tHb64g3IOD1w/aDUoBZGQSshM4eP1gie87MjKSrKws9u/fz59//kn16tXx9/cnIiKC/fv3k5WVxY4dO6hcuTKnT59GpVLRokULy/Y+Pj7UqFGDkydPWtLUajX169e3/O3t7U1UVBRdunShZ8+evPfee8TH3xyxOnDgADNmzECn01keo0aNIj4+3mqe6611urq64ubmxvXr10v8nJQ0EZj+B7z22mvMnDmTdevW0a9fP4e2efXVV5k/fz779+/H39+fXr16WYYeDhw4wIABAxg0aBBHjx5l2rRpvPHGGyxdutSqjrfffpv69etz8OBBJk2axEsvvcSWLVts7k+v15Oenm71KI8kfRqq0+vs5hvPbXO8stxU+0FC1g3ISnK8rou7QLYdMEoxO1DoMxyrx2iAfZ/bz9//pTnYdUT6VYj+1n7+gWXm/TkgNdvAXxdSbOYZTTLRcWU0PHXW9vUMwPHVZTN0nn0DDn5lP//IitJriyDcB4nZjr0HOVquOKpWrUqlSpXYvn0727dvt/RoBgYGEhYWxl9//cX27dvp0KED9gakZVm2DMsDODs7W/0NsGTJEnbv3k3r1q1ZuXIl1atXZ8+ePQCYTCamT59OdHS05XH06FHOnj1r1Xvp5ORkVackSQ/EwipVWTdAuL82bNjAzz//zLZt2+jQoYPD202dOpXOnTsDsGzZMipVqsRPP/3EgAEDWLBgAR07duSNN94AoHr16pw4cYK3336bqKgoSx1t2rThtddes5T566+/ePfddy313mr27NlMnz79Ho60lNxpMVBxFjDdKSgz5Redf6uiFjvJMsgOLsqRjZBfRC9ffrbdALhwXSYw5NjPN+SYF+Yo7/w2ZDQVPeMoO6+MFh0V1SNqMpjPZ2mT5cK98Ldy9EuKIJRTfi5+JVquuCIjI9mxYwcpKSm8+urNkaqIiAg2bdrEnj17GDFiBLVr18ZgMLB3716rofwzZ85Qq9adp1c1atSIRo0aMWnSJFq1asXy5ctp2bIljRs35vTp01StWvW+HF9ZEz2m/3L169cnNDSUKVOmkJHh+AdSq1atLP/39va2Gno4efIkbdq0sSrfpk0bzp49i/GWVde31lHw963DF7eaNGkSaWlplselS44PY5cmk5MLpopN7eYrqnV0vDIXH9C42c5TaUEX4HhdoW3s5/nXRnZydawelQYaPG4/v1Yvx+cnuvhCtS728+v0BSfH5pe5aZVU8dPZzW8S6utYm0pa1U7284JbgqYM5kdrPaB6Eee97mOl1xZBuA8a+zcmwCUACclmvoREoEsgjf0b35f9R0ZGsnPnTqKjo63mgEZERPD555+Tm5tLZGQk1apVo3fv3owaNYqdO3dy+PBhhgwZQsWKFendu7fd+mNiYpg0aRK7d+/m4sWLbN682SqYnTJlCl999RXTpk3j+PHjnDx5kpUrVzJ58uT7crylTQSm/3IVK1bk999/Jz4+nq5duxYrOL1dwVDD7cMQBWnFqeN2Go0Gd3d3q0d5pPWqgLHLXFA6FcrLr/YIJl2gw3WZXPyQO061mSdHTMSkdXyBisnZG7lO38IZCiVy19kovMMcrovgFuBfu3C6sxe0GmsOXh2hdTMvtNF6Fs4LqANFBPi38/PyYFav6ihsXD4DGvnjpyujwR/PYKhuYyGBUg3d5pbNIiONDjq8AWobX0aCW4JfjdJvkyCUIKVCyWvNzaNxtwenBX9PbD6xxBc+FYiMjCQnJ4eqVasSEHCzAyEiIoKMjAyqVKlCcHAwYB6Sb9KkCT169KBVq1bIssz69esLDbPfysXFhVOnTtGvXz+qV6/O008/zXPPPcfo0aMB6NKlC+vWrWPLli00a9aMli1bsmDBAkJCQu7L8ZY2sSr/X+zWVfmXL18mMjISX19fNm3aZDfwK7j32sqVKxkwYAAAKSkpVKpUiSVLljBgwAAGDx5MYmIimzdvtmw3YcIE1q9fz7FjxwDz4qfatWuzfv16S5nHH3+ctLQ0qzR7yvWq/KxUSLuEtGMOqkt/gbMXuc3HoqjRDbVXxWLVZUq9hJR4Eun3uXDjHHiHIbd9BTmwPgqvysWrK+Ui0vltSHs+hqwbyBWbQPtJGD1CULkVc0gr7Yp5fuiBJeZh4Vq9oc0L4BUGdr5c2G6UCZLOws6FcHYjqJzNK/IbDQWv4r2J5uTkcD4xi/lbznHwchp+Oi1j2lYiorofvp5leI1kXINT62D3h5CTAqEPQfv/gU9VUKnLpk1GA6TEwO9vw7nN5p755k+be0vdg8qmTYLwj3tdlV9g68WtzNk3x2ohVKBLIBObT6RTSBGjGcI9u5+r8kVg+i92++2irly5QmRkJN7e3mzatAkPD49C2xQEpnXq1OG9994jICCA119/nejoaM6ePYtarebgwYM0a9aMadOmMXDgQHbv3s2zzz7Lxx9/bJljGhoaSkpKCq+//jp9+vRhy5YtvPjii/z666906VLEMOM/ynNgWkCfnoiclwWSEq1P8D3VZUq5iGTMA6UaqZgBW6G6ki6AbEJWuaD0LMatnW5nNP6z0MkEzt4OD7vblJsO2UnmoNatwj0FbBkZmWTlGVApJHy9Cl/DZSbzmnn+scbd3GtZHuRlmxfZSUpw9QOFGCQTyl5JBaZgvnXUwesHScxOxM/Fj8b+je9bT6lw0/0MTMXip/+QgmH9yMhIOnfuzObNm/H09LRZds6cObz44oucPXuWBg0asHbtWtRqczDRuHFjvv/+e6ZMmcLMmTMJCgpixowZVgufAMaPH8+BAweYPn06bm5uvPPOOw4FpQ8KjbsfUDKT6xX3GIxa1eUTXjIVKZXg7vjUhCJp3c2PEuDmpsPOzNyyVZw5waVF7WJ+CMK/lFKhpFlgs7JuhlCCRGD6L3b77ZsAgoKCOHXq1B23feihhyzD8rb069fvjreecnd3Z+XKlXfclyAIgiAIAojFT4IgCIIgCEI5IQJTQRAEQRBKnVji8uC6n8+dGMoXrLRv375ELrjY2Nh7b4wgCILwr1Nwq6Ts7GycnZ3LuDXC3cjLM/8MtFJZ8gvNRGAqCIIgCEKpUSqVeHp6Wn633cXFxe49roXyx2QykZiYiIuLCypVyYeRIjAVBEEQBKFUBQaa7/hREJwKDxaFQkHlypXvyxcKEZgKgiAIglCqJEkiKCgIf39/8vPzy7o5QjGp1WoU9+m+yCIwFQRBEAShTCiVyvsyT1F4cIlV+YIgCIIgCEK5IAJTQRAEQRAEoVwQgakgCIIgCIJQLojAVLgvpk2bRsOGDcu6GYIgCIIgPEDE4qf/qKioKJYtWwaYJ59XqFCB7t2789Zbb+Hl5VXGrbs/9IZ84tOySc7KQ5IkvF2dqOihQ3XbxPukpCSS9ZCdZ8DTRY2v1oRr9hUwGcHFF9wCyctORZWbgpSdBE7OGDWeqHxCC+3z2o1kknKMGIwmfFzV+LlpUWutbyidnpqIVp+CnJuGpHVHr/bGzcvPuqJ8PaRfhuwbICnBxQc8Q+C2VZFycizo0yAvG1y8ze1yDyj2uZLz8yEtDnJTwGQAFx9Mrr4ona2vjbScfJIy9aTnGnDXqvDVaXB3drIqY8xKQpGTDNlJoFCBsxdG98qonKzLOcKQdg1lXqq5LrUrssYDhXdosesxN/4y5KZBbjo4e4LWA9wr3FVVpqQYJH0q5OeCiw9GrRcqN787bnc/5WelosxONF+jKjUmrRdKn7AybROAMfUiitx00GciO3th1Hri5B5Y1s0qMbn5RhIz9KRk56FWKvDWqfF305Z1s/71cvIMJGbqScnKx9lJibdOja9OU9bNKp8MeZCZAFlJoHQyf564B5V1qyxEYPof1rVrV5YsWYLBYODEiROMHDmS1NRUvvvuu7JuWolLzs7ijzM3mPrzadJyzLcm8XPT8Hb/OjQN8UKnMX9wXLyeynMrT3L0ShoAKoXEE02DeD4iBL8Pq4HaBcPov3D6+wukvYvAaP71C5VfTeR+XyIF1gXAoNdz9Goqz608zpXUHABc1Uomdw3nkTp+eHiYAzx98mVcNk5EdfZX+OcXt5RVOpHXfSFq72Bz4zOvw9nNsOl/5kAKwC0I+iyC4OagdgVAvn4SafUoSDhqLqN0Qtl4BHLr55C8Qhw+V8bMRBTXTyKtGQ3pV82JGncUnWdgqtIRhVdlAOLTcnj9p2P8durmfQg71/ZnRu+6BHmYg29j8kUU57cgbZ0G+gxzIfeKKB/9FENAbVQuPg63y5RyEeVf7yEdXGYOlgEpqAHyo58h+dd0uB4AkmNg3UtwYbv5b0mCGt3h4VlQzEBXjj+KYvWTkHjanKDSoGw5BlOTESiKcd5LUn7KJVTHVyP9PgfyswFQeoUh9/0cg39dnDRl82s7ctI5FD+PRYrbA4AkKZDq9sPU4Y0yO1clKTlLz7d74vhw+zn0BhMAVfxc+XhwE6oH6MRN5O+TG5l6Pv/jAov/iiHfaH4frRXkxkdPNCbcT1fGrStnclLg2GrYMgXyMs1pnpXhsSUQ1BCUZR8WiqH8/zCNRkNgYCCVKlXi4YcfZuDAgWzevBkw/7LDjBkzqFSpEhqNhoYNG7Jx40ar7S9fvsygQYPw9vbG1dWVpk2bsnfvXpv7iomJoWrVqjz77LOYTKb7fmy3u5CYzbgVxyxBKUBihp6nlh7icoo5cEy4kcywZYcsQSmAwSTz1b6rfLX3CnnD12N4+TTKU2uRdr1nCUrNlZ1C+roPpqTzAFxJy+HxxQctQSlAVp6RSWvPcvSqOVBIT01Euek1VGfWWYJSAOX5rSh/GUtGyj8BX+Jp+HnszaAUICMelg+A1DgA5KTzSN8+djMoBTDmI+3/DKK/xZCb4fC5UmTfQFr+2M2gFECfjrRuHFKy+fjSsvOYtOqoVVAKsOXEdd5Yc4y0HPO5USSdRfp1/M2gFCD9CtK3j6HMvOFwmww56UgHv0L6+0tLUApA/GGk5f0xJV1wuC5S4+DnMTeDUjCf/1PrYMsbkJ7gcFXyjXNIX/e+GZQCGPRIO99FOr0Bk8Fgf+P7SHVlP9LWKZagFICUGKSv+6BMv1wmbTImxyD9EGUJSgGQTUhHf0D6Yz6GLMevh/JIlmW2nrzOO1vOWIJSgPOJWQz6bDdXU3PLsHX/XkajidUHLvPpHxcsQSnAyfgMnvh8L/G3vAcLwJUD8OvLN4NSML8nLutpHkUqB0RgKgBw4cIFNm7caPkN4/fee4933nmH+fPnc+TIEbp06UKvXr04e/YsAJmZmURERHD16lXWrl3L4cOHmTBhgs2g89ixY7Rp04b+/fuzaNGi+3ZTXntSsrP5aLvtwMVgkvlmz0X0+flcStVzMSnbZrnFe66Q6FINKSPeHJTakpUICUcAWH/kCrn5tgPwt7fFkJKSjFafYg5KbVDG/I5Gn2TuLf3jbdv7M+bBwW/AmA9J5+2+qUh7P0WZ6ViwJRuNcHgFGPS2C/wxD1PKJW5k5bHjTKLNIltPXicpMw9T8kX4Y57tegy5cHSlQ20CUGZdQ9r3me3M1DiklBiH6yI3DS7usp136hfz9AVHxUebpxXYIP31LqTGOl5XCTEkxyL9Ptd2Zl4m0rktpdugfyiyblh/cbqFdOQ7FHbO44Pieoaed7ecsZmXkp1P9KViXFeCw65l6Pn49/M28xLSczmXmGkz7z8pKwm2zbSdl59tfv8rB8q+z1YoM+vWrUOn02E0GsnNNX+bX7BgAQDz589n4sSJDBo0CIC5c+eyfft2Fi5cyEcffcTy5ctJTExk//79eHt7A1C1atVC+9i9ezc9evRg0qRJvPLKK3bbotfr0etvBkPp6ekldpyZ+nzOXc+ym386IZvMvDxirtvfZ1aekex8I5Iqzxws2iElHMNQoyfR8fa/pV9IzCLXCC656VY9pbeTs1NArYUbtj/sALh+HEzA9RP2y+SmmgNBB8g5qSiuHbObL904A4Zcq55nW9Jz8pFc8s3l7dV17TimnDQUzh53blh+DuiLuCYST0HVjneuByDTdkANmJ+PW3um7yT+iP28jAQkU+n3mCpkU9HXTPzh0mvMrdLi7OcZ85H0jvfql0d5BhPxafZfZ8eupNO9/t3NYRbs0xuMpGbbfz86nZBB22plO9+73DDkFv3ecHk/mEyF1i6UNtFj+h8WGRlJdHQ0e/fu5fnnn6dLly48//zzpKenc/XqVdq0aWNVvk2bNpw8eRKA6OhoGjVqZAlKbYmLi6NTp05Mnjy5yKAUYPbs2Xh4eFgewcHB936A/3BRqwjzdbGbX8XfGRe1mhBfN7tltE4KnJ2UyEq1eaK4PX61UKlU1A2wv9ihso8LGiVIWvci2y05e4KTM3iH2y/kW938Kvarbr+Mxg2UDi6+0Loh+9awmy17hyMr1bhri1645KZ1Qlaoi2y77FvdsaAUzOfhn7m0NvkWcfy3cy3i+ZMk0BT9vFgJqF3EfvyQFcVf4HWvTJKi6GumqDbfT+6V7OcpVMiaB3suoFqpwN/N/mKbWkH231+Eu6dRKXHX2u9jq+b/YF9XJUqlKfq9oUKjMg9KQQSm/2murq5UrVqV+vXr8/7776PX65k+fbol//aJ+rIsW9Kcne+8eMLPz4/mzZuzYsWKO/aATpo0ibS0NMvj0qVLd3FEtvm4ujI20vaLUSHBsFYhODs5EeKtpaKn7eMa1qwC/rkxyK5ByC3H2t6RsxdyhUYA9GxQEbXS9svrlQ4heHt5o1d7YazS2WYZY3Ar8jTe4BYI7ewE9QoVNBluXlXpW91c1ga56VMYdY6tzFeo1NBoiLlOW9q+isI7BB+dmlbhtr+UtK3mi49OjcK7MnJbO21XOkGDxx1qE4DR1Q+5yQjbme4Vin6zvZ3WEyo1tZ1X9WHzCn1HVWxqrs8GufXzmDyKCMbuE5V3qP3z7uQM1buVboP+YdL5gZ/tLz1ynb4Ytfa/5D4I/N01vNCxms08d62KxiH/zrudlDV/Nw2j2tp+/fvq1FQLEF8ILFx9ocPrtvNUGqjdu3TbY4cITAWLqVOnMn/+fDIzM6lQoQI7d+60yt+1axe1atUCoH79+kRHR5OcnGy3PmdnZ9atW4dWq6VLly5kZNgfqtNoNLi7u1s9SlJVP1dm9a2Ji/rmraE8nJ34eEgDKnmZg9FAX2++jmpE1Vu+YUsS9GsYwFNtKqP+6hFUC8Ix1e2P3PRJUNxymymvUOShazC4m3t6K3ho+TqqIb469c1jVCmY3CWcRhXNb5RuXn4Yuy/AGBZh1VZjcCuMfT5F5/1PoBlQB7rONQcVBZy9YOA34GFeIS/5VEUevAp8qtwsIymQGzwOTaNQOTt+Pk0ufsgDv7XuGXZyRn54lqU31ctFzTsDGtIizDqYaF3Fh7n96uPpYj5u2b82cucZoLqlx9bVF3nQCoeDZQCVsydys6eR6w80PykFfKshD/4R6dbjvhOvEHj0M6jUzDo9vD10m1usW0bJHiHIQ38yr2otoFAiNxuFXLsPKqeyuV2NMeQh5HavgvLm9YdbIPLgVRiK6rm8j5Te4cgDv4HA+lbpco1HkDu8jpObf5m0q6RIkkS3uoGMbheOSnHzGq3o6cx3T7ekgkfZ3Anh306lVDCoeWWGtKjMLaedUB8XvhvVkgp2Ohv+s4JbQueZ5kC0gM4fhv4M7iU3UnkvJFkuYpKb8K8VFRVFamoqa9assUpv2rQpLVu2pGrVqkydOpXPPvuMhg0bsmTJEhYsWMDx48epVq0aeXl51KtXj4CAAGbPnk1QUBCHDh2iQoUKtGrVimnTprFmzRqio6PJzMykW7duyLLMxo0b0enuPLSSnp6Oh4cHaWlpJRakZufpuZ6Ry/UMPQpJwlenJsjDBY3Kunfw+o0kknJlsvT5+Oi0+GiMuGdfAqMB3ALANQB9VjJOeemQdQ3JydV8j0hf62/tpvx8rqVlciMrn3yjCT83DX4uTmhdrY8/I+U6Gn0yck4KktaDPI33zaC0gD7TfN+5jGvmnlKdn/lN5La2y0nnzXMk8zLB1Q+T2h2lZ8VinytjbiaKzATzwh5TPugCMGk9Ueqs52qlZOWRlKUnLScfD2cnfHQavFzUVmUM6ddR5qVB5jVQOIGrDwaXAJyci9+TYUi9gjIv3bzQTK1D1nqi8ClGb+mtUuMgJ9V8vpy9zD2fnncXtJmSLiDlJJsXELgGYNS4o/Io2/sC5mUkoco1L6CTVBpMzj5IHsEo7uL+sSXJmBKLIifVfKcGF2+MWg9UZdCzfL9k6w3cyNSTmJmHs5MCH52GAHdxH9P7LTM3n6SsPJIy83BWK/FxVeMvzrtt+TnmtRKZ18xfXl39zLcgLKFh/Hv9/BaB6X+UvcB0+fLljBgxgjNnzrBs2TI+++wzrl+/Tu3atZkzZw5du3a1lL148SLjx49ny5YtGAwGateuzUcffUTz5s2tAlMwr+Lv0qULSqWSDRs24OpaxHxB7k9gKgiCIAjC/SUCU+FfSQSmgiAIgvDgudfPbzHHVBAEQRAEQSgXRGAqCIIgCIIglAsiMBUEQRAEQRDKBRGYCoIgCIIgCOWCCEwFQRAEQRCEckEEpoIgCIIgCEK5IAJTQRAEQRAEoVwQgakgCIIgCIJQLojAVBAEQRAEQSgXRGAqCIIgCIIglAsiMBUEQRAEQRDKBVVZN0AQHlQ5Gamk5+bjpJDw9vG1XUiWISsRZBM4e4FKY7OYPv0Gcl42OGnRevjfU7tMybFIpnxMajeU7oF3XY9sNKJPjQdkVDpfVBrnu2+UIQ9ykkFSgKsfSNLd15WdArmpoNSAR4W7rwcgJxUMueDkAtri/6bzrXJTr4HJgMLZA7Wz7t7aJQiC8B8lAtMHQEJCArNmzeLXX3/lypUr+Pv707BhQ8aNG0fHjh1LrR2SJPHTTz/Rp0+fUttneZSfnc7FNAOLfr/AXxfS8HZVM7pNBVqHe+Hn7X2zYHo8nPoF9n0O+TlQqyc0fxq8Qi2BWW76DRRJZ3H6cy6KG6cxeVch76EJmPxqFjtAlVMuQtweFHs+hqzrKCo1Q247HpNbJZQ6n2LVlZd8CdPxn9FGLwFjHnnVe5Lf/CmcfMOLVQ8AKbGw9zM4uRactND0SajdG9yLGVTqMyH5PPwxH678DboAaDkWQlqDR8Xi1ZWTCglHYcccSLkAfrWg/WvgXws0bsVrVmo88oUdaPd9BNnJ5Ie0I/+hl5C8w1A5qYvXLkEQhP84SZZluawbIdgXGxtLmzZt8PT0ZPr06dSvX5/8/Hw2bdrEZ599xqlTp0qtLY4Epvn5+Tg5Od3zvtLT0/Hw8CAtLQ1393vrySppxy/doO+n+9EbTFbpfer5MaVbON7evpCRACuHwuV91hs7e8Go7eAdhjEvF8OxNWjWji60D33XBSgaDMTJwZ43U0oc0h9zkQ59Y52hdEIe9jNSSBuHj0+ffAnVD0NQxkdbZ7j6kT9ic/GC0+QY+KIjZCdZp1doAoO+Bfcgx+uK+RO+7gMmg3V64+EQ+T9wc7B3OD8XDn0D68cXzntsCdTqBUrHvrPnpl1HueFVnE6tsc5QackbsQV1xfqOtUkQBOFf4l4/v8Uc03JuzJgxSJLEvn37eOyxx6hevTp16tTh5ZdfZs+ePQDExcXRu3dvdDod7u7uDBgwgGvXrlnqiIqKKhRMjhs3jvbt21v+bt++PS+88AITJkzA29ubwMBApk2bZskPDQ0F4NFHH0WSJMvf06ZNo2HDhixevJjw8HA0Gg3Lli3Dx8cHvV5vtc9+/foxbNiwEjs3ZSE1JYmp604XCkoB1hxNJCHrn+95CUcLB6UAOSnw13uQn4shPQHN5gk296PZ9jpyVqLD7ZJyUwsHpQDGfKQNE5GTYx2ui8sHCwelAFmJmPZ8Sn5ulmP1GPSw56PCQSnA1QMQf9jxNqVegg0TCgelAAeX2d6HPZnXYPPrtvN+fRkyExyuSpF+pXBQCmDIRbV5ErkZxWiXIAiCIALT8iw5OZmNGzcyduxYXF1dC+V7enoiyzJ9+vQhOTmZ33//nS1btnD+/HkGDhxY7P0tW7YMV1dX9u7dy7x585gxYwZbtmwBYP/+/QAsWbKE+Ph4y98A586d4/vvv2fVqlVER0czYMAAjEYja9eutZS5ceMG69atY8SIETb3rdfrSU9Pt3qUR+n5En9fTLWb//vpa2AymXvk7DmxBnKSkbKTIDfNdpn8HMi4ZjvPltid9vMSjkK+Y8GkQZ+N5thyu/ma02swZjoYbGUnw/E19vMPfQ1GG4GmLfp0uH7Cfv7FXY7VA5ARb55XaktOCmTdcLgq05lNdvMUF3ei0JfP61gQBKG8EnNMy7Fz584hyzI1a9a0W2br1q0cOXKEmJgYgoODAfj666+pU6cO+/fvp1mzZg7vr379+kydOhWAatWq8eGHH7Jt2zY6d+6Mn58fYA6GAwOth0zz8vL4+uuvLWUAnnjiCZYsWUL//v0B+Pbbb6lUqZJVL+2tZs+ezfTp0x1ua1mRAIUEJjsTYLQqhXn+qKqIhUJKtbkmhbLonTk4nAzYXVRlId1hX5ZyCkxKjf1vrEqN4wuXJOmfY7VDpS1GXXdov0rrWD0Aijuc1zs9L47uV6G8t0VegiAI/0Gix7QcK5j+KxXx4Xby5EmCg4MtQSlA7dq18fT05OTJk8XaX/361vPhgoKCuH79+h23CwkJsQpKAUaNGsXmzZu5cuUKYO5pjYqKsnsskyZNIi0tzfK4dOlSsdpeWjw10KmmnRX4QLsageZgpMlw+5U0GgaufsjO3uBmZ46lsxeyi/39FBLS2m4QJIe2RVYX7nG3RaXWkt/Ydq82gL7+UJw8HJzL6eJnnv9pT9ORjgeBWg+o3Mp2nqSA4OaO1QPgFgBaT9t5HpWgGOddWaOL3TxD9e7IWm+7+YIgCEJhIjAtx6pVq4YkSUUGmLIs2wz2bk1XKBTcvsYtPz+/0Da3L1qSJAmTqfBcytvZmmbQqFEjGjRowFdffcXBgwc5evQoUVFRduvQaDS4u7tbPcojNw9vJnWtjq+ucE/gKx1C8NcazX/4VoMGjxeuwKcqNI0CpQq1Z0Xy+nwGytsWiymU5PX+DFUxboVk0ngid7LR4+zsBV1no/AMLpxnh+xTnfyafQrvw782UsNBKFUOLm5TKqHREPCtXjivXn/ws5Fuj3sQPPK27YCy85vgWowgXhcEfT8vHBQr1dD3i2ItyDK6BqB/aKKNffgjd5yGxrV8XseCIAjllRjKL8e8vb3p0qULH330ES+88EKhADA1NZXatWsTFxfHpUuXLL2mJ06cIC0tjVq1agHg5+fHsWPHrLaNjo4u9up5JycnjEajw+Wfeuop3n33Xa5cuUKnTp2senUfZGEBXqx5phlbj8ez9Ww6fjoVw1tUItRDgZvXPz3Hrr7w8ExoOBj2fQp52dBgEIS0sdzaSFIqkSo0Jv/pXciHvkZ9/Qh5PrWQmkQhuVdAWYxbDSk9gjDV7guVW8P+L5AyryGHPmS+LZNHaLGOT+tdkdyH38LUeDhOB79EYcght85AFJVbofYu5nPoURGG/QwX/4Lo78DJ2XzLLP/a5vuZFod/HXhqGxxfbZ5T6hYIzZ4EzxBwKcbtsJQqCGsLz+6GA0vNc1crNIZGg8GzcrGapHXzJrf50+RV64Ry/+cosxPRV+2KVKMbap+Q4h2fIAiCIG4XVd7FxMTQunVrvL29mTFjBvXr18dgMLBlyxYWLVrEiRMnaNKkCTqdjoULF2IwGBgzZgw6nY4dO3YAsGnTJrp168bSpUtp1aoV33zzDQsXLqRRo0aWMu3bt6dhw4YsXLjQsu8+ffrg6enJ0qVLAahevTqdOnViypQpaDQavLy8mDZtGmvWrCE6OrpQ29PT0wkKCsJgMPDVV18Va0FWeb5dVAHZYCA3JxOVkxonrYv9ggY9mIygtl/GaMjHmJuJUuNarIDUFlNuJhhykLReSKp7++6Zn5OJLJtQu5TAc5CfbZ4reqf5sHdiMoE+zXxT/Huty2gEY655rmhx5pbaYMjTYzLm46RxQVKIwShBEP6bxO2i/uXCwsI4ePAgkZGRjB8/nrp169K5c2e2bdvGokWLkCSJNWvW4OXlRbt27ejUqRPh4eGsXLnSUkeXLl144403mDBhAs2aNSMjI+Oubtv0zjvvsGXLFoKDg2nUqNEdy7u7u9OvXz90Ot2/8qb8kkqFs5tn0UEpmIOnIoJSAKXKCbXO656DUgCFVodC53fPQSmAk7OuZIJSKJlAEkChKPJXtIpFqQS16z0HpQAqtQa1s04EpYIgCPdA9JgK91Xnzp2pVasW77//frG2exB6TAVBEARBsHavn99ijqlwXyQnJ7N582Z+++03Pvzww7JujiAIgiAIDwARmAr3RePGjUlJSWHu3LnUqFGjrJsjCIIgCMIDQASmwn0RGxtb1k0QBEEQBOEBI2bpC4IgCIIgCOWCCEwFQRAEQRCEckEEpoIgCIIgCEK5IAJTQRAEQRAEoVwQgakgCIIgCIJQLojAVBAEQRAEQSgXRGAqCIIgCIIglAsiMBUEQRAEQRDKBXGD/f+4qKgoli1bVii9S5cubNy4kdDQUC5evAiAVqslICCA5s2b88wzz9ChQwdL+djYWMLCwlAqlVy8eJGKFSta8uLj4wkODsZoNBITE0NoaOh9P677TU6JhczrcHkfuAZAUEOMWi9Ubr7Friv+RgoXk7M5dy2DMD8d4b4uBPl6F79ReVmQfhWuHYP0K1ChCbgFgndY8evKvA45KRC3Bwy5ENIatJ7gGVzsqlLT07meaeBAzA1c1Eoahvjgp3PCxcW12HUZkuNQZlyBqweR3StAQD2MbhVx0jgXu64Sk5sBmQlw9RBkJUKlZqALAK+QsmsTQOolyLphfg61HlCpKbj6gYtX2bbr3y7rhvl1eOUAuPhAUANwCwKVuqxbVjLyss3vD1f+Nr/nBDc3X+8ud/GeJQg2iMBUoGvXrixZssQqTaPRWP4/Y8YMRo0aRV5eHrGxsXzzzTd06tSJmTNn8vrrr1ttV6FCBb766ismTZpkSVu2bBkVK1YkLi7u/h5IKZGTY+HnMUgX/7qZ6OSMcsDXGKUmKHWOv0HHXEth6NJDXE7JsaQFuGtYPqIJVYKKEUDkZcGVg/DdIMjLvJlesTH0W1y84DQ9Hk6uhU2TwGS8md5wMLR/DTwrO1zVjdQ03t50lpWHrlnSlAqJ2b2q80jdQHQ6ncN1mZJjUf4YhXT1EAASgMYN6fEV5Ac1KZvgNDfNHPh9P8wcwBcIbQu9Pyq74DTlImycBKd/vZmmdILeH0N4JOj8yqZd/3YZCbBmDJzfdjNNpYVB35qvCZXG/rYPAn0mnFoHP4+xfm9o8AR0ng46/7Jrm/CvIYbyBTQaDYGBgVYPL6+bQZGbmxuBgYFUrlyZdu3a8dlnn/HGG28wZcoUTp8+bVXX8OHDCwW5S5cuZfjw4aVyLPebMSsF9nxsHZQC5OcgrRyMIueGw3XdSEriuZXHrIJSgGvpekZ9e5jrScmONyz9auGgFMzB6u9zIdPxdpERDxsmWH/wAER/CzF/Ol4P8MeZG1ZBKYDRJDNhzWkup+kdrkefkYy0/U1LUHpLBtJ3g8y9qGUhIwFWDrEOSgFi/4S9n5g/yEubIR+O/2QdlAIY8+Gn0ebeLqHkGfNh/5fWQSmYr43vBplHMR50aZfM19Dt7w2Hl8PZLWXTJuFfRwSmwl158cUXkWWZn3/+2Sq9V69epKSksHPnTgB27txJcnIyPXv2LLI+vV5Penq61aM8UuTcQIr+xnamQQ+3B6xFSM6F41dtH+eFG1kk5Zgcb1j84cJBaYFjqyDHwSDXkAd/L7Gfv+djc2+cA26kpLFo52W7+d/tu4TJaLSbfyun3BtIJ9bYztRnmKcvlIWY38GYZzvv4FfmwLW0pV2CfZ/azpNNcHx16bbnvyLzuvnLiC3GfLiwo1Sbc18c+tp+3s4F4kuPUCJEYCqwbt06dDqd1WPmzJlFbuPt7Y2/vz+xsbFW6U5OTgwZMoTFixcDsHjxYoYMGYKTk1OR9c2ePRsPDw/LIzi4+HMZS4Ux3zxsbk+a470iOfmGIvOz9PkO10X6Vft5xjwwOtg7mZ8DGUXUlXkNTI61y2CSScy0v9/L6fkYHAxMMejN594OKT3esXpKWpr9wJu8TDAV/RzfH7L5ebLn39BzVx6ZDKAv4gt16qXSa8v9YDIW/aU083oZXe/Cv81dBaYGg4GtW7fy6aefkpGRAcDVq1fJzCyDYSvhnkVGRhIdHW31GDt27B23k2UZSZIKpT/55JP88MMPJCQk8MMPPzBy5Mg71jVp0iTS0tIsj0uXyumbuEoD3uH28yu3cLgqTxc1KkXh8wegkMBXp3W8XRUb289zCwKVg/MvnT0g9CH7+ZWagcbNoap0WieaBLvbzW8f7o5a7diCENnJtej5axUaOlRPiavc2n6eT9WymVOoVEOFIq6H0Lal15b/Eidn8KtpPz+0Tem15X5QKKHaw/bzKzUDdfEXNArC7YodmF68eJF69erRu3dvxo4dS2JiIgDz5s3jlVdeKfEGCvefq6srVatWtXp4exe9gCcpKYnExETCwgovqqlbty41a9bk8ccfp1atWtStW/eObdBoNLi7u1s9yiPJpwpyhym2M/1qgE8Vh+vycZYY1jzIZt5jDQPx0siON8y9IgQ1tJ3XfhJ4hjpeV82etlfYKpQQMcG8AtcBOldXXu5UFaWN4NvbVU1krUCHmyR7BiO3n2Q7r0JjjG4Vbebdd77VzQGoLR2n3N0dEe6VZzB0nAo2vjTiXqFYX56EYtD5Q5dZtvO8w8G/dum2536oEmm+s8PtJIX5etd6lH6bhH+dYgemL774Ik2bNiUlJQVn55u9MI8++ijbtm0rYkvh3+S9995DoVDQp08fm/kjR45kx44dDvWWPmjkio2RH/0MPCqZExRK5Nq9kQetQCqqN/U2OjdPnm0XyssdQnDTmG+Q4apWMrZtMK90roKHZzFuv+IdBv2XQt3+5tXXYL5VVM/3ILw9qIpxAw6vMBj+i3XPmn8tGPITeBZvlXkVXxe+G9mYav43V98/VMWLH0Y1pZKv4x9iKpUT+VW6IPdYeDMwVjoh1xuA/NhiVN6O3ymgRPmEwxPfQ62e5sAdzNdFvy+gYtOyaROAT3UY9N3N3n1JgqqdYega+4G0cO8qNYeB3968c4WkgFq9YdjP5i8FDzrPyv9v787jm6jWx49/JmmaLmm6lxZaoKUUKPuqLC6o7HJFUUFQKbj+RL2IonBVFjfUK+5f8boBrlxU4KpwES6ICiiyFRAQsFAKpaV0S/d0yfz+GCmEJiUphQR43q/XvDRzTp48mU7JkzNnTmH8Ckg4uVQgEUkw7hvtv0I0AreXi1q3bh3r16+vcwmuRYsWZGbK3KULkdVqJTvb/iYNHx8fIiK0NTmLi4vJzs6mqqqKgwcP8umnn/LBBx8we/ZsEhMdf8jdc8893HLLLYSEhJzr9M87XWgLCG2BGttdW9NP74vqF4zO7Hj0sz6RYaHcd4UvI7s2pbzKhp+PjoggX/z8XV9GqVZYPAyeDVdN0eZkGgK0kVJ3ilIAvR6adIAb/wUVRaDWgNEEoS3dTsnPP4BeiQF8Md6foooa9DqFkAAfgoNcmw5wKt/QZlR3uR0l/iqUylLwMVIdEIlvoIfX5QxvBUNfhWue1o67b6BbI+fnhLkJmIdol5atJdqXFb8QMLs+Si0awM8M7a6H2O7aTXl6XwiI0H5/LhYRidqX4PJ8bU6pn9nlqyhCuMLtwtRms1Hj4IaFI0eOENSADxvheStWrCAmxr6oatOmDX/88QcA06dPZ/r06fj6+hIdHc3ll1/O6tWr6d+/v9OYpxa2FyvllJEnxzNFXWP0D6SZfyPNzTJFNt4alcHNtK0RRIQG0xhng4+PQRul/IvXLFkeFKVt3sYTUwmENq87yP0vqhcM/2BtE+IcUFRVdWMiG4waNYrg4GDee+89goKC2LFjB5GRkdxwww00b968zhqWQjREUVERwcHBWCwWr51vKoQQQgh7Z/v57XZhmpmZyTXXXINer2f//v306NGD/fv3ExERwU8//URUlBeOGogLjhSmQgghxIXnbD+/3b6U36xZM1JTU1m4cCFbtmzBZrNx1113MXbsWLuboYQQQgghhHCHWyOmVVVVtGnThu+++47k5Itg6QvhtWTEVAghhLjwnO3nt1vLRRkMBqxWq8NF1YUQQgghhDgbbq9j+tBDD/HSSy9RXS1/ekwIIYQQQjQet+eYbty4kdWrV7Ny5Uo6duxIYKD9MjeLFy9utOSEEEIIIcSlw+3CNCQkhJEjR56LXIQQQgghxCXM7cJU1ikVQgghhBDngttzTIUQQgghhDgX3C5Mjx07xh133EHTpk3x8fFBr9fbbUIIIYQQQjSE25fyU1JSyMjI4OmnnyYmJkaWjhJCCCGEEI3C7cJ03bp1/Pzzz3Tp0uUcpCO8RUpKCgsWLADAx8eHuLg4brrpJmbNmlVnJYaLTVW5BV1pDkpNFaqPEX14K4f9rPkHUKorQGcAvxB8TZF1+pQUF1FQXoNNVQnyVQgLDa3Tp7LSSo6ljMoaFV+9QkSQH35+DfsratVlBejLcqGmClVvRBfhOHeXYlmy0FcWgWrDZghEH9q8wbEoPAyVJaDowS8Ygpo0PFZeGlRXgN4XAiIgoO4xdUllORQdgZoqLVZoC9AbGhSqvLSEvLIqqmtsBBoUIsPDGpYTQLkFKgpAVcEvpOHvD6D0OFiLteMeGAG+nv/draqp4XhxJZXVNvwMepqYjTLAIYSo5XZhGhcXhxt/LEpcwAYPHsy8efOoqqri559/5u6776a0tJS5c+d6OrVzxlZwCJ8Nb6GkfgZVZRAch9r/Sapa9sM3JA6AisLD6LJ3YFz1tFYk+Rip6ngzVVdOwRAaXxvrUE4hs1fsZeWeXGwqdGhm5tnrk0iOCsQYaALgWH4hy3Zm8/ZPh8kvrSTY38B9fZsxslssTcKC3cpdzU9Hv/51lB0LoaocJbQl6jVPU9OsFz5h7hWVas4f6FdNR/lzFag29E27oQ5+kaqwVviaIlwPVF4Ix/fAimlwdBsoOmg9EAY8A5Ft3MqJggw4/CuseRYKM8DgD51vg75/h9CWbsY6BKmfw8Z3oaJQK9r6/h3a3aAVqG7IyrPw+uo/WbI9h8oaGwkRgcwYmkj3WBMmc4jrgVQVcvfDiqmQtlrb17wPDP0nRLYFvRv/XFeWQVYqLH8Mju0CnQ8k3wDXznD7/TWmnOIKPt6QzvwNhyixVtPEbOTRgW0Y0K4JoYG+HstLCOE93J5j+vrrrzN16lTS09PPQTrCmxiNRqKjo4mLi2PMmDGMHTuWpUuXYrVaefjhh4mKisLPz49+/fqxadOm2uetXbsWRVFYtmwZnTt3xs/Pj8suu4ydO3d68N2cWVVBBsp/JqJsel8rSgEsh1GW3o8hbQ22v/6ohO7YTnwXjtGKUoBqK4Ztn6H/9x1YC9IBOJpbyKgPtrBit1aUAvyeWcQt728hLb9CC11sYcEvGTzz3zTySyu1feVVvPy/dP5vbRr5lkKXc7flp8Pie1C2zIOqcm1nQTrK13ehP7zBreOg5v2J8vFwlP3fg2rTdh7dirJgGIbio27FoiAdFgzXilLQ4u1boe3L/dP1ONXVcGgdLL5HK0pBe5+bP4Il92uv46qio/Djy/Dji1pRClCaCyufhs3zoDTf5VA5eQWM/ziVf2/NprJGO1YHcksZ9/F2tmWWup4TaO/rwwEni1KAjA3w4XVQmO5erON7YP4wrSgFsFXD719rx92S6V6sRlJYVsnMb3bx9g9plFi136VjRVYe/2oHS1Izqfrr+AkhLm1uF6ajRo1i7dq1tGrViqCgIMLCwuw2cfHy9/enqqqKxx9/nK+//poFCxawdetWEhMTGTRoEPn59h/oU6ZM4ZVXXmHTpk1ERUXxt7/9jaqqKg9lf2Y+pTko6T87bFN+eB7Vcghr/kF8V0132EeXvRPlrwLpl7TjZBdV1OlTbVOZ878/KS4qpLDcxgcbHBcJn27KoqjC9SsTSnEWypHfHLeteRZbbprLsdj7PZTk1N1fUwU/vUy1Jdu1OCXHtAKwxsHPvOSYfQF2JpZ0baTUkYxfoPiY67EqLLD9c8dtG9+B8lyXQx3IK+OPYyUO257575/k5rtY5NpqYMeik4XyqarK4Ze52vQFV5QXwMrpJ79UnKrwEBzd6lqcRpZbUsnynY7PnddW7iOn2HqeMxJCeCO3L+W//vrr5yAN4e1+++03Pv/8c/r378/cuXOZP38+Q4YMAeD9999n1apVfPjhh0yZMqX2OTNmzGDAgAEALFiwgNjYWJYsWcKtt95aJ77VasVqPfnBVFRUdI7fkQMnRvUcKTmGUlmCovfVLrc6YTu0jprm/Vi5z+K0z6ZDFkorbRSWVdeOstWJo0JuiZWWrk7FPLLJeZvlCEp1mUthqssK8Dmwxmm7kvEruirHhVgd1hJtxM+ZAz9Al9vB6MK8x8oybaTTmcyt0Pwy1/IqznJctAFUW6HM9RHTX9LynLbtzymhzNW/3GwtgT9XOm8/uBYqisDkd+ZYlWVw+Bfn7XtXQLvhLibWeA7mOj9viq3VFJVX0SykYXOrhRAXD7cL03Hjxp2LPIQX+u677zCZTFRXV1NVVcUNN9zAQw89xFdffUXfvn1r+xkMBnr16sWePXvsnt+7d+/a/w8LC6NNmzZ1+pwwe/ZsZs2adW7eiItUUxOc3oKh6EBvRNXrtbmNJy6Xn94tKAa9jw8xQc5/tUIDfdErKn6G+pdX8/d149fTVE8FqzeA3uhSGEXvhxoY5fw4BEag4uKycIpOm7tZXuAkViT4uJYXPkZtnqTNSaXnzs1UxqD62w2uF0fRZuf5+xv0+Lh6U4+PLwTW8x4CI7QbtFyh6CAg3PGoN0BwrGtxGllIQP35G31kWW0hRAMX2E9LS+Opp57itttuIydH+8dvxYoV7Nq1q1GTE57Vv39/UlNT2bt3LxUVFSxevJjgYO2GnNPvolVV1aU7a531mTZtGhaLpXY7fPjw2b8Bd0W1A1+Twya1zRCq/UNR/MOp6jza8fP1BnQtrwBgVA/nH/739m5KZHg4wX562jRxXCQ1Dwsg1N+NdYGbdnNaUKntbqDGL8SlMHqjP/SY4LRd7XU/+oh4p+12wuKh133O27uNc/2GHmMwtB3muM0QANEdXYsD4B8GIU5uBovupL2Wi/q0jsJH5/icHtO9CREmFwtvgz/0nui8ve8k8A9xLZapCVz+oOM2RYEON7kWp5HFhvgT5uQGp8sTwpy2CSEuLW4Xpj/++CMdO3Zk48aNLF68mJIS7fLMjh07mDFjRqMnKDwnMDCQxMREWrRogcGgLaOTmJiIr68v69atq+1XVVXF5s2badeund3zf/3119r/LygoYN++fbRt29bhaxmNRsxms912vlWbmqKO+qxugReRhDrgWXyDmuAbEAZ9J1HTtKt9H72BypvnYzNqeccG6Xn2+kROr8MHtw1jYLI2MhYdHsLbozsQGWRfvIQF+vLe2E40jXB9maCqwHDUWz+pMwKpRiVD/yfxCYpyOVZ1YDTqtXXn0arJI1Bb9Xc5DqDdgd/2evt9iqLdlR8U43occ7R2R3mU/TmGjx+M+lQbfXVVeCu45WPwP+34BsXAje9CWEuXQzUx+fDubR3qFKddY83cfUUCvv4BrucV2RaufLzu/h4TILan63F0Oug8ClpdZ79f0cENc8HczPVYjaiJ2Y95KT0xGe2/jMSG+vPyzZ3POKIqhLg0KKqbaz/17t2bW265hcmTJxMUFMT27dtJSEhg06ZNjBgxgsxMz9zxKRpXSkoKhYWFLF26tE7bpEmT+PLLL/nwww9p3rw5L7/8Mt988w1paWmEhoaydu1a+vfvT/v27XnjjTdo0qQJTz75JKmpqezfvx9f3zN/ABUVFREcHIzFYjmvRWqVtRR9USbKkU2ohRkQ1xM1rBX6MPtRQmvBQRTLEdRDG1BNTdC36IPqG4Kv+WQBWFpUSE65yi/7j1FiraFv6yhigvSEhdrfJHgkt5D9x4rZk2UhqYmZttFmYiND3M69sjgXQ0U+HN4IRZkQdxlqSEt04S6OcJ56HAqO4FNZBGlroLocEq7BFhCO3o2irVZBhraeZtpqMARC4jXa+pxmNwrTE/IPaHfgH/5NuyQd2wtMUa6PJp5QXa3dUJW9A3L2QnQHregNT3Q7JWtpCcdKq9l08DjHiyvplRBOXIiRyLAGrD9abtFuDDuwVpu20Ko/mKIbtpZp6XGwHIGDP2nHO/4KbTTVg2uZ2mwqRy3l7My0kJ5bSsfYEBIjTUQHuzB3VghxQTjbz2+3C1OTycTOnTuJj4+3K0zT09Np27YtFRUu3jkqvFp9hWlFRQWPP/44X3zxBcXFxfTo0YPXXnuNnj21UZ0Them3337L1KlT2b9/P507d+b999+nc+fOLr2+pwpTIYQQQjTc2X5+u33zU0hICFlZWcTH24/CbNu2jWbNPHOJSDS++fPnO23z8/PjzTff5M0336w3Rr9+/fj9998bOTMhhBBCXKzcnmM6ZswYnnjiCbKzs1EUBZvNxvr163nssce48847z0WOQgghhBDiEuB2Yfr888/TvHlzmjVrRklJCcnJyVx55ZX06dOHp5566lzkKIQQQgghLgFuzzE9IS0tjW3btmGz2ejatSutW7du7NzEJUzmmAohhBAXnvM+x/SEVq1a0apVq4Y+XQghhBBCCDtuF6aTJ092uF9RFPz8/EhMTOSGG24gLCzMYT8hhBBCCCEccftSfv/+/dm6dSs1NTW0adMGVVXZv38/er2etm3bsnfvXhRFYd26dSQnJ5+rvMVFTi7lCyGEEBees/38dvvmpxtuuIHrrruOo0ePsmXLFrZu3UpmZiYDBgzgtttuIzMzkyuvvJJHHnnE7WSEEEIIIcSly+0R02bNmrFq1ao6o6G7du1i4MCBZGZmsnXrVgYOHEhubm6jJisuHTJiKoQQQlx4zvuIqcViIScnp87+48ePU1RUBGiL8FdWVrqdjBBCCCGEuHQ16FL+hAkTWLJkCUeOHCEzM5MlS5Zw1113MWLECAB+++03kpKSGjtXIYQQQghxEXP7Un5JSQmPPPIIH3/8MdXV1QD4+Pgwbtw4XnvtNQIDA0lNTQWgS5cujZ2vuETIpXwhhBDiwnO2n98NXmC/pKSEAwcOoKoqrVq1wmQyNSSMaETz589n0qRJFBYWNjjG2rVr6d+/PwUFBYSEhDRabu7yhsK0proSvY9v/Z2qKkBnAL3eeR+bDVBBV08fAGsJGM/+90itqUGtqUDnG3jWsWzVVaDWoDP4nXUsaqoAXf3HyhOqreBjPPs4NhvYquFM54zQ1FSDvsFLaZ8TNpv2cajTKR7ORIgLl8cW2DeZTHTq1KmhTxduSElJobCwkKVLl9rtP72IHDVqFEOHDvVMkheJitIi9EVHYNsnGCzpVLToj67NQHzDW9p3zEuDA2vhz/9BYCR0uxOCYiC42ck+pXmQnwZb5oG1GDqNhmbdwNz0ZJ/yIijJgt+/huydENkWOt0KpmgICHUrd5vlCErpcdj6CbriLNT4KyHxOpQI9/8qmy0/HaXwEMq2T1GqK1Db34ga0wVdeILbsShIh8ytsGsxGAKg6+0QlgDBse7HaizWErAchu3/hty9EN0JOoyE0BbuF6nF2WDJhK0LoCwXEgdAwlXaexT2qsqh8DCkfq4d9xZ9od31ENwcdG7PLGs0x4sr+CO7mIW/ZaBTFG7r1ZzWTYKIDGqELyxCCLc0eMRUnD+uFqaN4VIeMbWWl8Ce7zB+ez+c+msREEbVuP9iaNJWe5y7Dz6+AYqO2gcY8Ax0vBXMMVCWBz+8AJs+sO/TpAOM/fJkcZq+Dj4dCdUVJ/vofeG2hdCiHxhc+2CssRxFt3c5yvJH7RsCI1Hv/BalSTuX4gDY8g6irJ2NsvPfdvvVmC5wywKUsJYux6IgHf59B2TvsN/fZQz0f9IzxWl1FRz8ERaO/msU9y+GALhjMTTv7XqskuOw9WNY84z9/uBYuGMpNOBLwUWrphL+XA0Lx4BqO7nfaIbx/4XoDh5JK6eogsmLtrPuT/tVZAa0i+KFmzoSGdQIVwuEuISc97vyhfeaP3++XTE5c+ZMunTpwr/+9S/i4uIICAjglltucelS/5YtW+jRowcBAQH06dOHvXv32rXPnTuXVq1a4evrS5s2bfjkk0/s2hVFYe7cuQwZMgR/f3/i4+P58ssvG+NtnjNKSQ7GZQ/aF6UAZfnolz9KRVGuVoisfqZuUQqwajpUFGj/n3egblEKcOx32PYp1NRoo65L7rMvSkH7AF9yLxQddjl3XWUxyn+n1G0oPQ7fT8NmOeJyLKXwYJ2iFEDJSoXfv8JWZXUtUE0VpH5RtygFbcQs/4DLOTUqSwYsuce+KAWoKoPF92nFtKtKc+oWpQCWI9oXk7KCs0r1olKcDV9NsC9KAaxF2vleetwjaW1Iy6tTlAKs2pND6mGLBzIS4tImhelF7s8//2TRokV8++23rFixgtTUVCZOnHjG5z355JPMmTOHzZs34+Pjw4QJE2rblixZwt///nceffRRfv/9d+677z7Gjx/PDz/8YBfj6aefZuTIkWzfvp3bb7+d2267jT179jh8PavVSlFRkd12vtmOptYtVv6iO7QOXUUBVBTC3uXOg+xfpRW2W+Y577P5IyjL0WI5KxhLc7WpAK46tKHuB/5flINrUawlLoWxWUtgywKn7cq2T53nfLqio5D6mfP2LQu0eYbnW0kOlOU7bis85LzNkX0rnLft+QbK3fgZXuzyD2rFvyPHdmlXGc6zwrJK5q0/6LT9o/UHKbF64BwV4hLmdmFaVubkHxZxTn333XeYTCa7bciQIWd8XkVFBQsWLKBLly5ceeWVvPXWWyxcuJDs7Ox6n/f8889z1VVXkZyczNSpU9mwYQMVFdrI3iuvvEJKSgoPPPAASUlJTJ48mZtuuolXXnnFLsYtt9zC3XffTVJSEs8++yw9evTgrbfecvh6s2fPJjg4uHaLi4tz8cg0Imcfmn9Ra6rBVqNtzlhLtMK0sp5CsLpC63OmoszmuEh2qLKe3FUVVBc/XG01KNXlzturygDHBXDd17VBfbGqy7Wbhc63mjOssexOTpWl9cdx8mXhklTfuQAeORdqbCoVVc5/RhWVNdTYZLabEOeT24VpVFQUd9xxB99//z02m/yje77079+f1NRUu+2DDxxcKj5N8+bNiY09OY+vd+/e2Gy2OpfmT3fqjW0xMTEAtX9YYc+ePfTt29euf9++feuMhvbu3bvOY2cjptOmTcNisdRuhw+7fhm7sehjuztvDG+F6hcMvoHQrJ5+rQdoN3F0Hu28T5th4B8KAeFgDHLcx8cPTE1cSxygZV/nbVHJYHDtDn2dfzBq8o1O29WkwRAQ4VpOARHQepDz9vY3QWPc7e8uczNtHq8jfsHaz8VVrQc6b4u7DHyd/HwvRRFJoDj5yAmKBv+w85sPEBLgy/WdYpy2j+jaDLOfd60cIMTFzu3C9OOPP6aiooIbb7yRpk2b8ve//51Nmzadi9zEKQIDA0lMTLTbmjVrduYnnkZRFLv/OmMwGOo859QvIqc/X1XVM8as73WNRiNms9luO9+qA6Ko7DKuboOio3LwHPxCm0JIHAyaDXpD3X6tB2l30wPEdIWYznX7GM1wxWQw+GuF27UzHCdz1RPg5/pd+ap/KGr7m+o26PSog2ejhMW7HIu4y1CjHNws5R8KvR9E5+pqAX5B0Pfv4BdSt61Je2jWw/WcGlNgBFz1uOO262aB2Y0bssyxkHhd3f16Xxj4vHYjnNAERkLfSY7bhs7RVrU4z/Q6hRu7NaOJue5NhrGh/gxIbuLSv2tCiMbjdmF600038eWXX3Ls2DFmz57Nnj176NOnD0lJSTzzjIObAIRHZWRkcPToyRt1fvnlF3Q63Vn9Za527dqxbt06u30bNmygXTv7YubXX3+t87ht27YNft1zzd8cjnr1k1QMfxci24BfCDUJ11A1YTVq7ClFVGQbuOt/0GaoVqyFJcDgF2HoPyG0udbHHKPdWX/tDO0O7YAw6HoH3LsWQv8qEgNCIGmQdpd+bA+tgGvaFUZ9pi1dZHJxZBLQhbaA62agXv+adie4fyhq4nWoE1Zii3DvmCvhCTD6c9QrHtVWDwgIQ+0yFnXC9xDc0q1YhLeGu1ZC5zHaMTA3gyunwOgvwJ27+xuTfwh0GQujPtWOt18IxPWC2xdrP1MXV0IAICQWrn9d+7ISlqCdD22Gwd3/gybJ5+gNXKCMQdB7Itz6sbY6hV8ItOwHd62ChKvBQwVgbGgAX9/fh7v6xRNh8iUyyMj9VyXw73svp2mIv0dyEuKSpjaCXbt2qV26dFF1Ol1jhBOnGTdunHrDDTfU2f/DDz+ogFpQUKCqqqrOmzdPDQ4Orm2fMWOGGhgYqF533XVqamqq+tNPP6lJSUnq6NGjnb7W6TFVVVW3bdumAurBgwdVVVXVJUuWqAaDQZ07d666b98+dc6cOaper1d/+OGH2ucAakREhPrhhx+qe/fuVadPn67qdDp1165dLr1ni8WiAqrFYnGpf2Mrz89Sy3Mz1PKifOedirJU9fh+Vc1Lc96nplrrZ8lUVWuZ83756ap6fJ+q5h9scM4n2HL/VG3H96s1BUfOKk51RYlac3y/WnN8v1pdcvzskiq3qGreAe39VVnPLlZjKjyi/fwsR88+VkG6Fqs09+xjXexKjmu/E2UFns6klrWqWs0qLFezCsvVyuoaT6cjxAXrbD+/Gzx5pqKigm+++YbPP/+cFStWEBUVxWOPPdY41bJoNImJidx0000MHTqU/Px8hg4dyjvvvHNWMUeMGMEbb7zBP//5Tx5++GHi4+OZN28eV199tV2/WbNmsXDhQh544AGio6P57LPPSE6+MEaR/EKjz9wpKBrONIVQp9f6nUloC5fycoUS3kr771nG0RsDwZh49gkB+Jm1zdsEuz8dxqmQxvsZXvQCXb8acL74+uiJDvayv0omxCXI7QX2V65cyWeffcbSpUvR6/XcfPPNjB07lquuuupc5SgaaObMmSxdupTU1NTz/tqKorBkyRJGjBjRoOd7w58kFUIIIYR7zvufJB0xYgTDhg1jwYIFDBs2zO4mGSGEEEIIIRrK7cI0OztbRrCEEEIIIUSjc/tS/qnKy8upqrJfBFyKVtEY5FK+EEIIceE5289vt5eLKi0t5cEHHyQqKgqTyURoaKjdJoQQQgghREO4XZg+/vjjrFmzhnfeeQej0cgHH3zArFmzaNq0KR9//PG5yFEIIYQQQlwC3J5j+u233/Lxxx9z9dVXM2HCBK644goSExNp0aIFn332GWPHjj0XeQohhBBCiIuc2yOm+fn5xMdrf7nGbDaTn58PQL9+/fjpp58aNzshhBBCCHHJcLswTUhIID09HYDk5GQWLVoEaCOpISEhjZmbEEIIIYS4hLhdmI4fP57t27cDMG3atNq5po888ghTpkxp9ASFEEIIIcSlweXlog4cOEB8fDyKYv+HDjMyMti8eTOtWrWic+fO5yRJcemR5aKEEEKIC895Wy6qdevWHD9+vPbxqFGjOHbsGM2bN+emm26SolQIIYQQQpwVlwvT0wdWly9fTmlpaaMnJDwrJSUFRVFQFAUfHx+aN2/O//t//4+CggJPp+ZVqksLsB3fh3roF9SjqVTnZzQ4Vo0lE/XYbi3WsV3UFB5ueGL56ZC1HQ79Asf3QklOw+LYbJB/EI5shoyNkJcGVeUNz6uRVJXkYDu+F/XQBtSs7dQUpHs6Je9VbYXCDMjcCtm/Q3G2pzMSQniBSquVI7mF7EjPYc/hXLLzCj2dkh23l4sSF7/Bgwczb948qqur2b17NxMmTKCwsJAvvvjC06l5hZqCw+g3vY+ycS7UVALgE9kWdeSHKNEd3Iql5h9E9/0/UPb9F/768qdrdQ3qsFdRwuLdSyxnDyy+B7J3ao/1Bug2Hvo8CKEtXI9TWQaZm2HJfVB0VNtnNMPAZ6HNEDA1cS+vRmIrOITP+jdQti4AWzUA+pjOqDe+hxLV1iM5ea2yfNjxb1j9DFSVaftC4+GW+RDdEXR6j6YnhPCMwqIivtuZzezvD1BaWQNAbKg/b4/qQIdmofgYDB7O0I0R0xOjaKfvExcfo9FIdHQ0sbGxDBw4kFGjRrFy5UoAampquOuuu4iPj8ff3582bdrwxhtv2D0/JSWFESNGMGvWLKKiojCbzdx3331UVlZ64u00Klt1Nbo9/0HZ8EZtUQrA8T9QPhmBLe+Ay7FqLJnw/ZMoe5fXFqUAStoa+PZhbIVujMLmpcFnN58sSgFqqmDTe5D6GVjLXI9VmKHFOlGUAliL4Nu/a8WvB9RYS1C2foyy+cPaohSArO0on9/i1nG/JBzaACumnixKAQoOwvxhYDniubyEEB61/UgRT327v7YoBThSUM5tH27laKEbnxPnkMsjpqqqkpKSgtFoBKCiooL777+fwMBAu36LFy9u3AyFRx04cIAVK1Zg+OtblM1mIzY2lkWLFhEREcGGDRu49957iYmJ4dZbb6193urVq/Hz8+OHH34gPT2d8ePHExERwfPPP++pt9IoVEsGug1vOG4sPQ7ZOyA8waVYugoLyr7lDtuUgz9BRZHrieWlOS84Nv4LOt4CxtZnjmOzwfYvtMvAjvz4EkS2g6DzO2qqFGeh/Pae48bCDK3ocvG4X/RKjsOaZx23VZbAn6uh54Tzm5MQwuPyC4v45+qDDtvKq2r4/vdM7ukffJ6zqsvlwnTcuHF2j2+//fZGT0Z4h++++w6TyURNTQ0VFRUAvPrqqwAYDAZmzZpV2zc+Pp4NGzawaNEiu8LU19eXjz76iICAANq3b88zzzzDlClTePbZZ9Hp6g7UW61WrNaTxVBRkRtF2Xmk1FjrnbepZO+E9iNcC1ZhsRspraMsz/XEju+u53UKobrCtTiVpXDsd+ftufugygNzy6vKtVFbJ5Tjf0DitecxIS9WU6n9nJzJ3CSFqRCXIGuNjbQc5/9+b80sx1ZjQ6d3eyXRRuVyYTpv3rxzmYfwIv3792fu3LmUlZXxwQcfsG/fPh566KHa9nfffZcPPviAQ4cOUV5eTmVlJV26dLGL0blzZwICAmof9+7dm5KSEg4fPkyLFnXnO86ePduu4PVael8ICHdeNEa1cz2W3xmW0fAPdT1WRJLzNmMQ+Pi5FscQABFt4c//OW4PSwAff9fzaiwGf/AN1ApnB9SIJGRi0V/0Bu3nlPen4/aYLuc1HSGEd/DVK7SMCGBPVrHD9k7Rfh4vSqEBC+yLi19gYCCJiYl06tSJN998E6vVWls0Llq0iEceeYQJEyawcuVKUlNTGT9+vMvzR53NS542bRoWi6V2O3z4LO5MP4fU4OaovSc6bvQPRW3a1eVYNmMwaivHo3xq3GWofm5cUolIgqBox2097oagGNfi6PXQ7XatuHHkyilgdjFWI1JNTVC7j3fcaG6qFWJCY4qC/k86bjP4Q+uB5zcfIYRXCA8JZvI1LR22GX10DO3U7Pwm5IQUpuKMZsyYwSuvvMLRo0f5+eef6dOnDw888ABdu3YlMTGRtLS0Os/Zvn075eUnlxf69ddfMZlMxMbGOnwNo9GI2Wy227yR3seAreOtqD3usr+zObQl6h1LUUJauh4rJBaGvYIaf6XdfjXuMhgxF507d9KHJ8LYryG81cl9ig463wY9UsBocj1WcByM+lwbGT7B4A8Dn4foTq7HaUR6PzNqr3tRO42CU7/cRLRGHfsVulPft4D4q+Cap7QR/hOCouHOb7WfrxDiktSjeQj/GBiP0edk+RdpMvJpSleaBgfU88zzx+W//CQuDSkpKRQWFrJ06VK7/T169ODyyy+ndevWTJ8+nUWLFhEfH88nn3zCm2++SXx8PKmpqbUxvv76a4YPH85TTz3FoUOHGD9+POPHj2f27Nku5eHtf/mpqjgXfXkelB5DMQRi8w9DH+7m8k5/sRVmoFRYoLwQ/IJR/YLdK0pPlXdAm1NaWQKBkeAXrI0ouqu6CooOQ2mudnd/UBMwRbtX4J4D1UVH0VdYUEuPo/iasPmHoJfRUscqy7Qb8kqOgY9ROx+CYuwLeyHEJaeivJzjJVaOF1sx+OiICDTQJDgInU/jLCN3tp/fso6pcMnkyZMZP348+/btIzU1lVGjRqEoCrfddhsPPPAA//3vf+36X3vttbRu3Zorr7wSq9XK6NGjmTlzpmeSPwcMQREQFAG0AeBsfp11Ic3tHp9V2dBYd6b7/DVP0cuKPh9zUzA3RUGbyyurcdbDNwB8W7i3hq0Q4qLn5+9PnL8/cZGezsQxGTEVjc7ZqKs7vH3EVAghhBB1ne3nt8wxFUIIIYQQXkEKUyGEEEII4RVkjqlodPPnz/d0CkIIIYS4AMmIqRBCCCGE8ApSmAohhBBCCK8ghakQQgghhPAKUpgKIYQQQgivIIWpEEIIIYTwClKYCiGEEEIIryCFqRBCCCGE8ApSmAohhBBCCK8ghekFRlGUs/ob9K5au3YtiqJQWFjYKPHS09NRFIXU1NRGiddgNVVQfAxKjoHNpu2zlkBxNpTl13arLjhCde4BqoqPOw1VWFzCoeMWMvOKnPax2Wwczi/jUF4pZdYq5/0sR1Bz92MrzHD/PZ0mNy+P7OO5FBUVnlUcW3U1x3NzOZabi7W8+OySshZDbhrkHYCamrMKVVZs4djxXAry884up8ZWeATyD0K58/NBCCFE/eQvP3mZ7Oxsnn/+eZYtW0ZmZiZRUVF06dKFSZMmce211563PPr06UNWVhbBwcHn7TXPucIM2LIAdn4JOj30fxIiWsNPr0DmZgiKQe03Gcwx+Cx/DIqzUZt2Q71yCtXm5hhMYQAUl5WRaali7o9pbDxQQFigLyl9W9AnIZTYsKDalzucX8Z/f8/i35uOUFFVwzVto0jp05L4iAB0Ou07oa0wA6XgEMrPr6Dk7oOwBNR+j6JGJKILae7W2zuen8+mQxbeWXeE3OJKejYP4qFrWtHMpCPQHOpWrOy8QpbvPMqnm3OorLExtF0Yt/duSfPIELfiAJC7H377F+xdAQY/6DIWkkdAWLxbYSpLizhYWMVbP6SxOaOYyCAjE69oRs8WwYSHhrmfV2MpPAwH1mrvsSwfWvSFfpMgLBEMRs/lJYQQFyBFVVXV00kITXp6On379iUkJIRZs2bRqVMnqqqq+P7773nvvff4448/UBSFJUuWMGLECE+n65b09HTi4+PZtm0bXbp0OWP/oqIigoODsVgsmM3ms0+gMAM+GgRFR7XHEUlw9VRYfA/YThvB63EXKAps+kB7rPNBvf1rlISrAdh+uIBb//Ur1mqb3dOGdYzhycGJNA03czi/jIe+2Ebq4UK7PiEBBr6+vw+tokzUlFvQ/fENyn8erJOuOuQlbO1vQW8Kd+nt5efnMWf1QT7bcsxuv0GvsOju7nSNb+JSHIDsvALu+WwnO4/aj5JGmHxZfF9P94rT3H3w0WAoO210s2lXGPkRhCe4HGpLWjajPtxKtc3+n6xxvaKZ3L8lwaGuHatGZcmE/z4Of3xnv9/HD8b/F5p1O/85CSGEB53t57dcyvciDzzwAIqi8Ntvv3HzzTeTlJRE+/btmTx5Mr/++qvD5zzxxBMkJSUREBBAQkICTz/9NFVVJy8Zb9++nf79+xMUFITZbKZ79+5s3rwZgEOHDjF8+HBCQ0MJDAykffv2LF++HHB8KX/9+vVcddVVBAQEEBoayqBBgygoKABgxYoV9OvXj5CQEMLDw7n++utJS0s7R0fKTTU1sO3Tk0UpQK974Ifn6xalAFs+gqRBWnEKYKtGWT6FmvxDZBcU88x3e+oUpQDLdmZxvEyLtzurqE5RClBYVsW7P6ZRXFGFrvQ4yvdPOkxZ+d9MdBX5Dtscya2gTlEKUFWj8uQ3+8jNcz3W9sOWOkUpQG5JJQvWH3T9sn5FEWz4v7pFKcDRbZC9w+WccvPy+cc3++oUpQALfssmt0JxOVajKjpatygFqK6AlU9p00aEEEK4TApTL5Gfn8+KFSuYOHEigYGBddpDQkIcPi8oKIj58+eze/du3njjDd5//31ee+212vaxY8cSGxvLpk2b2LJlC1OnTsVgMAAwceJErFYrP/30Ezt37uSll17CZDI5fJ3U1FSuvfZa2rdvzy+//MK6desYPnw4NX/NFywtLWXy5Mls2rSJ1atXo9PpuPHGG7HZ6hZwjlitVoqKiuy2RlOeB79/bb/PFA15TgpnVdUuP5ubndyXuw/FaqGkUmXLoQKnL/XDHzlU19j4T2qm0z6r9hwjr6RSK9gqLI47VZVr82BdtPmA87mwu7OKKK5y7cKItbyERdudx/p2Vx4FJVbXkirJgb3LnLfv/FJ7ny4oqrSx95jzgnjzQec5n1P7vnfedmg9WGW+qRBCuEPmmHqJP//8E1VVadu2rVvPe+qpp2r/v2XLljz66KP8+9//5vHHHwcgIyODKVOm1MZt3bp1bf+MjAxGjhxJx44dAUhIcH5Z9eWXX6ZHjx688847tfvat29f+/8jR4606//hhx8SFRXF7t276dChwxnfx+zZs5k1a9YZ+zWIogO9r/0+3Rm+k+l9wVZ92nN8UBTQKeBg4A4Ao0GPTgFfH+fxDXqdNhir09efg871X09fn/pj6RTXRhQVRYdR77yvr48OF0M5Pu6n0vuCcoZj8BedUv/Py8/gWpxG51PP+9Ppcf1gCSGEABkx9Ronpvoqbn6QffXVV/Tr14/o6GhMJhNPP/00GRkn7+yePHkyd999N9dddx0vvvii3eX1hx9+mOeee46+ffsyY8YMduxwfmn1xIipM2lpaYwZM4aEhATMZjPx8dqNLafmUp9p06ZhsVhqt8OHD7v0PJcEhEOPCfb7cvZAUyfz/3Q+EBKn3an/FzW2JzZfM2ajQv+2UU5fqn+bSHQ6Hbd0j3Pa58auzYg2+4F/CATFOO7kHwoBEU5jnK5HfITTGqhPQijmeuqnU/n6BXB7Tyc5AWO6RRFmdjyqXoe5KXQa7by96+31F3anCDHCZS1DHLbpFOjSwgPzSwHaDHHeljQE/Dx4U5YQQlyApDD1Eq1bt0ZRFPbs2ePyc3799VdGjx7NkCFD+O6779i2bRtPPvkklZWVtX1mzpzJrl27GDZsGGvWrCE5OZklS5YAcPfdd3PgwAHuuOMOdu7cSY8ePXjrrbccvpa/v3+9uQwfPpy8vDzef/99Nm7cyMaNGwHscqmP0WjEbDbbbY1GUaDtMO2GmxM2fajd/GR08DrXztDmpJ7gFwxD5+ATGktkcBBTB7clwlS3oHr4mkTCjFp1GB8RyN861y3wEiICuf2y5hgNejC3QB3xDugN9p10etQb3sFmcl4gni7MaGP64FZ19gf7G3hmeFtC3bhrPSnCj+vb1y302jQxcWO3OAy+fq4FMvhBtzu0G81OlzwCwuvm60xIaBjP/a0dZv+6o8izhiYS6eehezgDo6Df5Lr7TVHaeRQohakQQrhD7sr3IkOGDGHnzp3s3bu3zjzTwsJCQkJC7O7KnzNnDu+8847dKOjdd9/NV1995XT90dtuu43S0lK++eabOm3Tpk1j2bJl7Nixg7Vr19K/f38KCgoICQlh/Pjx7N+/n3Xr1tV5Xl5eHhEREfz0009cccUVAKxbt44rrriiNleP35UPUJQFRzbB1gWgM0C/v2uFxe7/wMGfIKQ5ao8J2iXmX95CKTqK2rwPdLgJNbgFOsPJYjT9eDHf7z7Gz/vzCDf5cvtlzYkLNhAddjLXo4Vl7M8p5fONhyivrGFYpxguTwinRfjJn21NSS66suOw9WOUnN2o4UnQfRy2wAj0QdFuvb2C/FwyS1QW/HKYrOIqrkoIYnDHpjQNDkDv6+KQ6V+O5+Wx73g5H/+WRUW1yshO4fRsGUZMhHvLTgGQfwDS18OuJX8VqykQ2QZCW7gVRq2u5khBCct3ZPLzwRKamg3ceXkcLcw6gkI8NGIK2nlVkK6t4lCWB4nXQpuhbhXeQghxsTjbz2+ZY+pF3nnnHfr06UOvXr145pln6NSpE9XV1axatYq5c+fWGU1NTEwkIyODhQsX0rNnT5YtW1Y7GgpQXl7OlClTuPnmm4mPj+fIkSNs2rSpdj7opEmTGDJkCElJSRQUFLBmzRratWvnMLdp06bRsWNHHnjgAe6//358fX354YcfuOWWWwgLCyM8PJz33nuPmJgYMjIymDp16rk7UA1ljoHkv0HrAYCiFUkAfSfBZfeB3hflr9FL29BXqbGWovMPRudj4PSr5C0jg7irrz+3do3G6GsgwK/uepVNQwJoGhJAzxahVNXYCA6oWxzqTRFgiqDm2hko5YWo/mb0hgAaMmMyNCyC0DB4NiqQ6soq/AMC0fs07Fc8MjycyHDo3jwYm81GgOks1rMNS9C2tsNA8QH/hn3RUHx8iIsM4d6rTNx5WRkGX198XB29PZfMMdoW0xlqrGAMPvMcZiGEEA7Jv55eJD4+nq1bt9K/f38effRROnTowIABA1i9ejVz586t0/+GG27gkUce4cEHH6RLly5s2LCBp59+urZdr9eTl5fHnXfeSVJSErfeeitDhgypvcmopqaGiRMn0q5dOwYPHkybNm3sbm46VVJSEitXrmT79u306tWL3r1785///AcfHx90Oh0LFy5ky5YtdOjQgUceeYR//vOf5+YgNQaD/8miFLQiwjfQ7pK6ztcfn6AIdD4GBwE0Pj4+hJpNDovSUwUYfRwWpafSG/zQmaPRGwJcew/18PMLwGQObnBRahcrIOjsitJTBYQ1uCg9leLjg7/J7B1F6al8A7S5wVKUCiFEg8mlfOGVzsmlfCGEEEKcU7LAvhBCCCGEuChIYSqEEEIIIbyCFKZCCCGEEMIrSGEqhBBCCCG8ghSmQgghhBDCK0hhKoQQQgghvIIUpkIIIYQQwitIYSqEEEIIIbyCFKZCCCGEEMIrSGEqhBBCCCG8ghSm4rxIT09HURRSU1M9nYoQQgghvJSPpxMQ515KSgoLFizgvvvu491337Vre+CBB5g7dy7jxo1j/vz5Z4y1du1a+vfvT0FBASEhIecm4XOlvBBKcyBjI+h9IO4yCIwCo+lkH1sNFB2FY7u0/8Z0gpDmYIqyC5WZV8Sxkmq2ZRQSGWSkQzMzUX4qpqBT/i5wZRmU5EDmZqgshbheYGoCAWF2sdS8NCg4CDl/QEQShLdCCW9VN/eSY3B4I+iNENdTy8n3ZO4V5SXkllTxR1YRxyzldIgNpanZQGS4/eu5wlZ4BKWqFDJ+heoKaNEH1WhGF9rC/Vj56SjWIji0AXwDIO4ybAYT+pBmbseiIF37uRzdCuZm0KQDBMeCwd/9WI2ksrKSHEs5v2cWkFdipXPzMGLMRsJD3P8b0VhLTv6cVdvJc9Q/2O1Q2flFHC+rYXN6AUF+Bro2DyHKH4KCgtzPqzgbCg9B9i4IiYOodtrxVxT3YwkhRD2kML1ExMXFsXDhQl577TX8/bUP8YqKCr744guaN2/u4ezOg9JcWPca/PL2yX2KDgY+B13Ggn+IVpQe3Qaf3AjWopP9YrrA6M+0Agg4nF/CY1/tYuPB/NoufgYd797enZ7NFAJNQVqB8cd38J8HtLgndB4DA2bVFrpqzh8on98ChRkn+wTFoN7+NUqT9n/lfhzWvgibPrDPfeg/oeOt4GemoryE34+WMm7BNkorT75e11gz79zWiZhw1wsbW2EGyt7/onw/zT73LmNRr3oCxY3iVC04hLJ2NsqOhSd36vTohryMLXEgulA3zr38g/DVeO1ndIIxCG5bCM26e6Q4rbRa+e1gHnd9uh1rta12f9+EEF69pSNNQt0oTssLYevH8L/poKon9/edBH0fhoBwl0Nl5pcw67u9rNydU7vPoFf4582duDrBRkiwG4Vu4WH4/BbI2XNyn38o3PkfiO4kxakQolHJpfxLRLdu3WjevDmLFy+u3bd48WLi4uLo2rVr7T5VVXn55ZdJSEjA39+fzp0789VXXwHa5fj+/fsDEBoaiqIopKSkALBixQr69etHSEgI4eHhXH/99aSlpZ2/N3gmmVvsi1LQRqS+/wfk/ak9LjoKn95kX5QCZKXC/2aCtZTS0hI+XJduV5QCVFTZuO+TLWSX/bXDchiW3Gdf2AFs/xz2r9JePu8gypJ77YtSgOIslEV3ouYd0B6nb7AvSk/kvuxRbaQVyC2pqlOUAmw7UsTrq/+ktLjQ+bE5jVKai/Lfx+vkrqR+BunrXI4DwIEf7ItSAFsNyrJHUcryXI9Tmgc/PG9flAJYi+GL0WA54l5ejSTbUs6ET+yLUoD1BwqZv/4gVVVVrgfL3Q+rnrYvSgHWvw6Z2xw+xZHqyiq+3ZFtV5QCVNWoTF60/eQ56oqKIlj+mH1RClBeAJ+O1H5nhBCiEUlhegkZP3488+bNq3380UcfMWHCBLs+Tz31FPPmzWPu3Lns2rWLRx55hNtvv50ff/yRuLg4vv76awD27t1LVlYWb7zxBgClpaVMnjyZTZs2sXr1anQ6HTfeeCM2m/0HtjNWq5WioiK7rdGUF8LPc5y3b3wXqq2QuxcqLI777FoCZcfJKVNZtNlxEWSttvHbiYJ12yfOX2/dq9ol/soiyNruuE/en1BRCGV5Wn9nfvsAaqrYnWmpU5SesGR7Dvnlrv0caqqssHme03bl13dQ8w+6FEvNO4Dy61znHbZ+TI2rhVtZLuxe6rjNWqxNvfCAX9KOU1nj+Nh+/FsWxy2lrgWqKq/7xelU616Dcifn5mmOWsqZv+GQwzabCst2ZrmWE2ij9fu/d95W6Ph1hBCioeRS/iXkjjvuYNq0abU3Iq1fv56FCxeydu1aQCsuX331VdasWUPv3r0BSEhIYN26dfzrX//iqquuIixMm68YFRVlN8d05MiRdq/14YcfEhUVxe7du+nQocMZc5s9ezazZs1qnDd6umqrVgg6U5QJNZVQlO28j60aqq1U19goc1IAAmQWlmsjjQX1fGCX5GjxKs8wdFVZouVVcqye3I+ADbKLyp2HqbE5LZ5Op1SVohTXMwpWcqzuKLAzak29uSvFR1Gqy8FgOHOsaivU1FPEFrlRbDWio4UVTttKK2uotqlO2+1UV0K9xz0baqwuhVIVheMlzvtm1pNz3bwq6o7gnqo01/VYQgjhAhkxvYREREQwbNgwFixYwLx58xg2bBgRERG17bt376aiooIBAwZgMplqt48//viMl+XT0tIYM2YMCQkJmM1m4uPjAcjIyKj3eSdMmzYNi8VSux0+fLjhb/R0RjM0v9x5e/zVYAiA6HoK6MAI8DXh56PQIjzAabceLUNBp4fWA53Hiu0JvoHaTVA6J98NFR0ERoKvGeLqyT2hP/j40DE21GmXJmYjAT6uzQPUBYShtuzntF2N7YnqG+hSLNUQgNqsh/P2lleg83dxDqZvYJ0b0Ow07eJanEbWs6XzG8sSIgLxd/G442vSzkNnmvfVzmMXGHUqnWOdzyHtk+D6XFWM5vpfNzzR9VhCCOECKUwvMRMmTGD+/PksWLCgzmX8E5fdly1bRmpqau22e/fu2nmmzgwfPpy8vDzef/99Nm7cyMaNGwHtjmVXGI1GzGaz3dZofP2h3yOg93XwwmboNEorJs3NnBeB/Z+GoBjiIsw8PqiNwy6JUSYSIv4qWlv11wrL0yk6uHY6+AWj+oWgdktxGEvteCuqbxAYA+Gqxx0XsP6hkHwDKArNgn3p1Mzx3dZTr4snMtSNm13aDq+zcgCgHaOrHkdnjnEpjC64GVw9TXve6QLCIWmw6zkFN9diOdK0G7iYU2Nr3cREQoTjQv3Jwa2IDAtxLZBeD13GaDdznc7HCH0fBIOfS6Giw7Rz1NE9SdFmP3q0dDEngKBouOoJx22J12mrTAghRCOSwvQSM3jwYCorK6msrGTQoEF2bcnJyRiNRjIyMkhMTLTb4uLiAPD11Yq7mpqTl3Pz8vLYs2cPTz31FNdeey3t2rWjoKDg/L0pV4TFw/gVEN3x5L7mvWHC99pyUACmSLhlHnS982QRa4qCv70FyX8Dnfbr0iM2gNdv7UzTYK1Q0OsUhnaI5sM7u9M8/K/CIqS59noJ15x8vYgkGPeN9l9AF9QE+j6MesWUk6NSvibU3g9B/3+gC4n9K/cEGL8copJPxmrRT4v/V+5R4WH8a0xnRnZpgkGvVSRRQUZevaktV7UKxsfH9Vk7qrk56rhvUVtecXJnVDvU25dgC3KvALSZY1DHLobItifjx1+JOu5blAg3Rtt8DJA4AK5//WQxpDdoqxLc/BGEtnQrr8bSJCyYT1K6Max9JHqddtybBvvx7m0d6NkixL1gIS208zHuspP7Yjr/9XNu6VaopHAD79/ZnZZ/je4rClydFMmnd/eiZYQby0XpDdB5tLYCxIlVAXz8oOc98Le3IdCN0VchhHCBoqr1TSASF4OUlBQKCwtZunQpQO2NRSdGJUeMGEFISAjz58/nqaee4t1332XOnDn069ePoqIiNmzYgMlkYty4cWRmZhIXF8e8efMYOnQo/v7+BAQEEBUVxZAhQ5gxYwYZGRlMnTqVTZs2sWTJEkaMGEF6ejrx8fFs27aNLl26nDHnoqIigoODsVgsjTt6Wpqr3QylKNqIo6ORwcpybb3Taqu2xqkpurYoPVVGbhEllTZ8fXSEGyE02EGe5RYoz9fmlPqZHY4w1ZQXoSs9pt0A4+OHzRSJ3t/BpfmS49oNUTq9lruDPiXFhRSU26isthFg0BEZanarKD2VrfAwirUE1GrwNaGExTcoDmgrEFBVAooPqq8JXWhcwwJVV4ElQ1sX1seojUo7+hmeZ6WlpeSVVVFVXUOgr57o8JCGByvL1+56V1VtGbPAiDM+xZnDucWUVNZg0OsINSqEhzRgDVPQ5hUXZ2nzog1+2tqqLo7gCiEuLWf7+S03P12C6jtRnn32WaKiopg9ezYHDhwgJCSEbt268Y9//AOAZs2aMWvWLKZOncr48eO58847mT9/PgsXLuThhx+mQ4cOtGnThjfffJOrr776PL0jNwRGnPmD3tcffM+8VmfzCBd+4fyDz7g4ut7fDKfMtXRw4VtjitS2epiCQjA1sPY4nS6kgcWjA0r4yaL2rFa99DHA6X98wAsEBgYS6NrU2zMLCGu0YjvOndHR+uj0tev4CiHEuSQjpsIrnbMRUyGEEEKcM2f7+S1zTIUQQgghhFeQwlQIIYQQQngFKUyFEEIIIYRXkMJUCCGEEEJ4BSlMhRBCCCGEV5DCVAghhBBCeAUpTIUQQgghhFeQwlQIIYQQQngFKUyFEEIIIYRXkMJUCCGEEEJ4BSlMRaO4+uqrmTRpUu3jli1b8vrrr3ssHyGEEEJceHw8nYDwHikpKSxYsKDO/v3795OYmOiBjM6dvBIrxdZq9IpCuMmXAN9TfhWqrVCSA7YqMPhDUIzjIPkHoKoc9AbwD4fA8Lp9rCVQlgdqDRiDHfapqaxAV3QYaqpA74vN3BS9b0DdWGX5UGEBRdFezy+oTpfKijKOF1upqrERYFCICg9z9ZDUVZoL5QVa7r5mCG7a4FD5BfkUV6roFIVQPwWTObTBscrzDqOr1o67EhiJr7+pYYGqK8FyGGoqwccIwS1Ar29wXkIIIc6eFKbCzuDBg5k3b57dvsjISA9l0/jKK6vZmWlh+n928Ud2MT46hSEdonlicFtiwwKgKAs2vAlb5kNVGQTHwYBnoFV/8P+rmLJkwtFt8L/pkJemFTUdboYrp0BY/MkXyz8Iq6bDH9+BaoOYLjD0FYjuCAY/ANSCQ+h2LUHZ8KZWwPqHorvsPtQuY1BCWmhxairh2G5Y9ihkbtYK08SBMPgFCD/5hSE738I7aw+waFs2FVU24sL8eXJQK/q0NGMOdrMQPL4XVj4Nf67Scm/aDYa8CE06gG+gy2EqS4vYc7yCp77dx85MCzoFrmsbwT+GJNEyyr2cygqPoT+0Hv8fZkBhBhj8qex8B5V9/o5vWKx7789yBLZ+AhvfhYpCCIyAvpOg/U0Q3My9WEIIIRqNXMoXdoxGI9HR0XabXq/nxx9/pFevXhiNRmJiYpg6dSrV1dUux503bx7BwcGsWrXqHGZ/ZvuOlTD6vV/5I7sYgGqbyrc7shj9/q9k5RfDkvvh13e0ohS0EbWvxsO+70FVtX1ZqfDvsVpRCtoIa+pnsOgOrRgFrfCZPxT2fKMVdieeN28w5O0HwFacA7+9h/K/GVpRClBegLL2RfjpVWyWo9q+/IPw4QCtKAUtj/3fw0eDtAINyM0v4KGFO/j4t6NUVGmvdzi/nPu/+J2f0wrdO0h5B2DB9dprnMj96FaYP0xrc8OBwipufn8zOzMt2ntWYeWeXG55fwuZx13Py1ZdjT79R4xLxte+Z6rK8d38Hrql91KRn+l6UqW5sHY2/PiiVpSe2LfyKfjtfagodj2WEEKIRiWFqTijzMxMhg4dSs+ePdm+fTtz587lww8/5LnnnnPp+a+88gqPPfYY33//PQMGDDjH2TpXWFbJC8v3YFPrth0pKGf7kUIoPOT4yaumQ3GWdvn+fzMc98neCQXp2v8f/BmKjtbtY6uGNc9BRRFKRQHKb+85DKVs+xjFWgSVpfDTHG3U9HSlubB3OagqmZZKNmUUOYz1/PcHOJab7zhnR/at0KYynK6mCn56SZtS4IKSogJeX/0nVTV1D/jxYis/73PwGk5UFh7FuMbxcffJWI+uJNvlWJTmQurnjts2vgPuxBJCCNGo5FK+sPPdd99hMp2cszdkyBCSkpKIi4vj7bffRlEU2rZty9GjR3niiSeYPn06Op3z7zfTpk1jwYIFrF27lo4dOzrtZ7VasVqttY+LihwXWWejvLKGzYcKnLav/iOXwVHtoOBg3caSY2At1uZb5u53/iLp6yH+Su3yvTMZv0BliVbgOSo4QRupLM0B/2BI/8l5rH3fQ9c72X7YebF41FJBaZWDatwRawmkra4n91+1ea4BZ567WmK18Wu6xWn7//60cFP3cnz9/M8YS6kqdVzo/6Umcxs0737GOID2BePESPDpqq0uF95CCCEanxSmwk7//v2ZO3du7ePAwEAmTpxI7969URSldn/fvn0pKSnhyJEjNG/e3GGsOXPmUFpayubNm0lISKj3dWfPns2sWbMa5004oVMUwgJ8OV5iddjeNNgIWU4KYkUHel9txNPgr9305Ig5GnR6MNcz5zEgXOtjOENBZggAnUGb/1ic5biPKRr0BiKD/JyH0SsY9C5eHNEbITDKeXtgBOhc+2fDRwfhgb4UllU5bI82+eDjY3AtLx9f7XVtjqePKKZ6cj6dse5NY3YMzo+lEEKIc0su5Qs7gYGBJCYm1m4xMTGoqmpXlAKof823PH3/qa644gpqampYtGjRGV932rRpWCyW2u3w4cNn90YciAwycvcV8Q7bFAWGd46BrJ2On9x2OARGalun0Y776A3Q8krt/7vd4TyRPg+BqQn4BUNUsuM+ofHazVYnbspxptc9oDfQITYEP4PjX+fhHSIJD3TxO6iPAXpOcN5+2f0QEudSqIjwcO7r4/xO/jG9YtH5uJaXzRhKVdu/OW40BKCL6eBSHEA7piGOv0wR3Un74iCEEMIjpDAVZ5ScnMyGDRtqi1GADRs2EBQURLNmzu9g7tWrFytWrOCFF17gn//8Z72vYTQaMZvNdltj0+kUbuzajKuS7FcZ0Ckw55bOxAQHwJiFdUcyI5Jg0HNgNIF/CPT9OzTtat9Hb4Cb550sakKaw7A5WsV7qnbDoe0wAJSweNSb3teK1FMFhKPeMh8lvJX2OP4qx8Xwdc9AmDYSHR3kx0e3d8boY/8r3baJiUcHJhEQeIZRwlMFN4drHcznTB4Bra51PQ7Qv00k17e3L/QUBWYMaUVckOv//Pibw+DamdhOL+R9/KgctRA1MNr1pEJbwq0fn1xl4YSgGLjxXxDs5h3+QgghGo2inlptiEtaSkoKhYWFLF261G5/ZmYmSUlJjB8/ngcffJC9e/dy9913M3HiRGbOnAloC+x36dKldlH9li1bMmnSJCZNmsT69esZPHgwzzzzDI888ohLuRQVFREcHIzFYmn0IjWvxEpmYTm/pOUR7G/g8oRwosxGbS3TmmooytTugC/MgNieEN6q7lqm+Qe1O/YPbdAKyxZ9ISACAk+Ze2kt0eamHvwJKosh/mowN9VG7E6h5qVBzm7I/h2i2kGTDigRp60bW5avzbE8sBZ8/CDhKu0y/ilrmVaWl3Gs2MrWQ/lkWSro1iKUlmH+RIU3YM3Q0lwoPQ5pa7RpC62u0d5nA5ZSys/PI7vUxvr9Ofj76umbGEWkn4opxP2RyYr8wyj56aiHf4PgWHRxPVFN0Rj9Haz7Wh+bTZtLnL0Dcv7QlvBq0t5+uS8hhBBuO9vPb5ljKs6oWbNmLF++nClTptC5c2fCwsK46667eOqpp1x6ft++fVm2bBlDhw5Fr9fz8MMPn+OM6xduMhJuMtIpNqRuo94HQltoW33C4rUt/krnfYwmbTsx8umEEt5K69NuuPNOAWHaFu38krWvfwBx/gHEubk+qEOBEdoW1e6sQ4WFhRMWBslxZ78erl9YHITFQeIVZxdIp9OOeXgraH/WaQkhhGgkMmIqvNK5HDEVQgghxLlxtp/fMsdUCCGEEEJ4BSlMhRBCCCGEV5DCVAghhBBCeAUpTIUQQgghhFeQwlQIIYQQQngFKUyFEEIIIYRXkMJUCCGEEEJ4BSlMhRBCCCGEV5DCVAghhBBCeAUpTIUQQgghhFeQwlQIIYQQQngFKUxFo1NVlXvvvZewsDAURSE1NZWrr76aSZMmeTq1xmerBlVtnFg1VY0TR1WhprpxYtVUQbW1cWLZasBma5xYjamxjpWqaueDEEKIBvPxdALCO6WkpFBYWMjSpUvdfu6KFSuYP38+a9euJSEhgYiICBYvXozBYGj8RD3FcgQO/gR7voWgptA9BUKbg1+we3FqqsGSAbuWwJHNENMZOt4CIXGg93UvlrUECg/B1o+hMAOSBkGrayCkuXtxAArSoeAQbPsUqiugw40Q0wXCEtyPVZQFR7fB9s/BYIIeKRCeCIER7sdqLNVWsByGHYsgeyfE9oL2IyA4DvRu/rNYXqgd7y0LoCQL2g6H+CsgOPZcZC6EEBc1KUxFo0tLSyMmJoY+ffrU7gsLC/NgRo2sIB3mDYGioyf3bf4ABr8EXceCMcj1WEe3woLhWvEHsHc5/DwHbl8MLfqCzsWLGpWlWnH7zYMn9+1dDoGRMH4FRCS6nlP+QfhhNuz898l9e77RCtNbFkBYS9djFR2FL0ZD1vaT+3Z8oRXy1zztmeLUVgOHf4VPR54cpd67HH56GcZ9C7E9XI9VUawV7yufPLnvj2VaUZqyDEJbNmrqQghxsZNL+cJtP/74I7169cJoNBITE8PUqVOprtYuYaakpPDQQw+RkZGBoii0bNkS4OK5lF9ZCqtm2helJ6x4AoqPuR6rKAu+Gn+yKD2hphK+SoHiLNdjleTAtw/X3V96XMurwuJ6rIKD9kXpCVmp8PtXUF3pWhxbDWxfaF+UnrBlPuT96XpOjak4C75MqTt1oqoMvpoAxdmuxyrJsi9KT7AcgTXPa+eLEEIIl0lhKtySmZnJ0KFD6dmzJ9u3b2fu3Ll8+OGHPPfccwC88cYbPPPMM8TGxpKVlcWmTZtcimu1WikqKrLbvFJZHvzxjfP2tDXuxbIccdxWmgulOa7HOvwrqE7mb6athrIC1+JUlmmXpJ3Z9ql2CdwVpbmw+SPn7Zs+9Myc05JjUJbvuK3wkJa3q/avct62a7Hz1xFCCOGQXMoXbnnnnXeIi4vj7bffRlEU2rZty9GjR3niiSeYPn06wcHBBAUFodfriY6Odjnu7NmzmTVr1jnMvJHYarTNmcoSN2Kd4WYnd26Gqix33qaqoLp4U46tCqrriVVV5rwArvO6tjPEKgW1hvP+/bjmDCO+7tzAVN+IqK36r/cnhBDCVTJiKtyyZ88eevfujaIotfv69u1LSUkJR444Gf1zwbRp07BYLLXb4cMujsqdb37B0KyeOYitrnE9VkCE8/moPn4Q5HphT/PLnbdFJYPR7Focv2BIvtF5e9Jgbd6qK/xDoc31zts73wZ6D9wQF9TU+Y1lfsEQEO56rMTrnLfFXe76cRdCCAFIYSrcpKqqXVF6Yh9QZ787jEYjZrPZbvNKAWEw9J+OC6o217t3J3ZQExj0guO2a6dDYJQbsaKh0+i6+3V6GDYHTG7EirsMotrV3e8fCr0fBP8Q1+IY/KDf38HPQf8mHaBZd9dzakyBkdqNV44MfhGCYlyPFRKnFeun0/vCkJe080UIIYTLpDAVbklOTmbDhg21xSjAhg0bCAoKolmzZh7M7Dxq0h7u/RHaXq8Va2EJcP3rcP2r7t1lrveF5Bvgzm+0O8H9QqBpVxj7FXQZoxV2rgoIg4HPwoi5ENFay6v1QLhnrRbTHeEJMPpzuOJRMDfVYncZCxO+h1A3l4sKjYd7f4BuKVocczPo/xSM/VKL7Qm+AdD1dhjzpXZs/EK0Ynzcd9BmmHvLRQVGwvA3Ydir2nngHwrthmvnR1TyOXsLQghxsZI5psIpi8VCamqq3b57772X119/nYceeogHH3yQvXv3MmPGDCZPnozO1aWNLnQ+Rq04vfFdsBaDotdGPxvCLxgSroLoL7W78338Gj7KZorSCtrE67T5jb4m8GvgyHNYAlwxRYunAv5hENiAvBRFizXkRbj6CVB0EBAJen3D8mosAWGQNBBiu2trmvr4Q0Bow2IFNYGed0G767X5x0YzGE2Nm68QQlwipDAVTq1du5auXe1H28aNG8fy5cuZMmUKnTt3JiwsjLvuuounnnrKQ1l6kDHIvTVL69OYl3zduWxfH19/bSH8xmDw1zZv48580jMxNfDLiRBCiFqKqjbW31MUovEUFRURHByMxWLx3vmmQgghhLBztp/fl8i1VyGEEEII4e2kMBVCCCGEEF5BClMhhBBCCOEVpDAVQgghhBBeQQpTIYQQQgjhFaQwFUIIIYQQXkEKUyGEEEII4RWkMBVCCCGEEF5BClMhhBBCCOEVpDAVQgghhBBeQQpTcd4oisLSpUs9nYYQQgghvJSPpxMQZy87O5vZs2ezbNkyjhw5QnBwMK1bt+b222/nzjvvJCAgwNMpeq/KUijJgfIC8A2AgEgIDD/z88otUJoDpblg8IeAMAhp3rAcSnKgLA8qLGA0Q0A4BDVpWCzhGSXHoDQPqiu0n58pSjsvhBBCuEUK0wvcgQMH6Nu3LyEhIbzwwgt07NiR6upq9u3bx0cffUTTpk3529/+5uk0vVPJMVj7EmydD7YabV+zbjDyIwiLd/48yxHY+B5snAs1ldq+yLYw8kOI7uBeDgWHYMVU2PdfUFVtX6tr4PrXILSlu+9IeELOH/DlnXB8r/bYxwh9H4Fe90BghGdzE0KIC4xcyr/APfDAA/j4+LB582ZuvfVW2rVrR8eOHRk5ciTLli1j+PDhAGRkZHDDDTdgMpkwm83ceuutHDt2rDbO9u3b6d+/P0FBQZjNZrp3787mzZsBmD9/PiEhISxdupSkpCT8/PwYMGAAhw8ftsvl22+/pXv37vj5+ZGQkMCsWbOorq4+fwfDHdVW+GUubP7wZFEKkLkVPhsJxdmOn1dTDbuWwoY3ThalAMf/gE9GQP4B13MoyYEV02Dv8pNFKUDaGvjmISjKcucdCU8oPAzzh54sSkE7t358EfZ8a/9zFUIIcUZSmF7A8vLyWLlyJRMnTiQwMNBhH0VRUFWVESNGkJ+fz48//siqVatIS0tj1KhRtf3Gjh1LbGwsmzZtYsuWLUydOhWDwVDbXlZWxvPPP8+CBQtYv349RUVFjB49urb9+++/5/bbb+fhhx9m9+7d/Otf/2L+/Pk8//zz5+4AnI2SY/Dbvxy35aVBYYbjNsthrSh1pPQ4ZO1wPYeyPNi33HHbwZ+gLN/1WMIzsrZrP0dHfnwRiuXLhRBCuEMu5V/A/vzzT1RVpU2bNnb7IyIiqKioAGDixIlcd9117Nixg4MHDxIXFwfAJ598Qvv27dm0aRM9e/YkIyODKVOm0LZtWwBat25tF7Oqqoq3336byy67DIAFCxbQrl07fvvtN3r16sXzzz/P1KlTGTduHAAJCQk8++yzPP7448yYMeOM78VqtWK1WmsfFxUVNfCouKiyDKrKnLfnH4C4XnX3V1u1kU5nsndC+xGu5VBhqX9ErdxJwSO8R31fRIqzobrSebsQQog6ZMT0IqAoit3j3377jdTUVNq3b4/VamXPnj3ExcXVFqUAycnJhISEsGfPHgAmT57M3XffzXXXXceLL75IWlqaXUwfHx969OhR+7ht27Z2z9+yZQvPPPMMJpOpdrvnnnvIysqirKyeAvAvs2fPJjg4uHY7NddzwuAPPn7O20NbON7v46vd3OJMVDvXczCa62/3D3U9lvCMJsnO2wIjtfNFCCGEy6QwvYAlJiaiKAp//PGH3f6EhAQSExPx99fuClZVtU7xevr+mTNnsmvXLoYNG8aaNWtITk5myZIldv0dxTixz2azMWvWLFJTU2u3nTt3sn//fvz86ikA/zJt2jQsFkvtdvr81UYX1AS6j3fcFtICQlo6bguOg94THbf5h0LTrq7nEBAOra513BZ3Gfi7sDqA8Kxm3cEvxHHbFY+BKfq8piOEEBc6KUwvYOHh4QwYMIC3336b0tJSp/2Sk5PJyMiwK/Z2796NxWKhXbuTI3xJSUk88sgjrFy5kptuuol58+bVtlVXV9feDAWwd+9eCgsLay/9d+vWjb1795KYmFhn0+nOfJoZjUbMZrPddk75+EG/SdDxVji14I5sC3csAXOM4+fpDdBpFPS4C3T6k/tDW8IdSyG0nrv5TxfUBIbNgfgr7ffHXQY3vgvBTV2PJTwjOBZSltkvFabTw+UTocNIcOHcF0IIcZLMMb3AvfPOO/Tt25cePXowc+ZMOnXqhE6nY9OmTfzxxx90796d6667jk6dOjF27Fhef/11qqureeCBB7jqqqvo0aMH5eXlTJkyhZtvvpn4+HiOHDnCpk2bGDlyZO3rGAwGHnroId58800MBgMPPvggl19+Ob16afMwp0+fzvXXX09cXBy33HILOp2OHTt2sHPnTp577jlPHZ76BUVrheHVU6EsF3yDtOV9TFH1Py84Fvo/CZfdp803NQRoa582ZHmnsHi48T3tBpryAvAP0UZKpSi9MCiKtkTYXaug5Lg2b9kUBYFRYHR8Q6IQQgjnpDC9wLVq1Ypt27bxwgsvMG3aNI4cOYLRaCQ5OZnHHnuMBx54oPYvLj300ENceeWV6HQ6Bg8ezFtvvQWAXq8nLy+PO++8k2PHjhEREcFNN93ErFmzal8nICCAJ554gjFjxnDkyBH69evHRx99VNs+aNAgvvvuO5555hlefvllDAYDbdu25e677z7vx8QtfmZtC2/l3vMCw7Utss2Z+56JOcb5CK24MARFa5sQQoizoqiqLLQn6jd//nwmTZpEYWHheXvNoqIigoODsVgs5/6yvhBCCCEaxdl+fssEKCGEEEII4RWkMBVCCCGEEF5BClNxRikpKef1Mr4QQgghLk1SmAohhBBCCK8ghakQQgghhPAKUpgKIYQQQgivIIWpEEIIIYTwClKYCiGEEEIIryCFqRBCCCGE8ApSmAohhBBCCK8ghakQQgghhPAKUpiKRtGyZUtef/11T6chvFV1JRRnQ0kOqKqnsxEeVlhWybGiCkqsVZ5ORQjhZXw8nYA4/078JaelS5e6/dz58+czadKkOn8JatOmTQQGBjZOguLiUpAOG9+DPd+AwQ963AXJN4C5qaczE+dZQWklO44U8sbqPzlaWE6HZmYmXZdEQmQgAb7ycSSEkMJUNJLIyEhPpyC8Uf5B+OBaKMs7uW/FVNjxJYz+DMwxnstNnFel1mo+/fUQc1btq92XXVTB6j9ymJfSk6vbRHkwOyGEt5BL+cLOq6++SseOHQkMDCQuLo4HHniAkpISANauXcv48eOxWCwoioKiKMycOROoeylfURQ++OADbrzxRgICAmjdujXffPONB96R8JhqK/z6f/ZF6QlHt0DW9vOfk/CY3BIrr6/eX2e/qsLUr3dyrKjCA1kJIbyNFKbCjk6n48033+T3339nwYIFrFmzhscffxyAPn368Prrr2M2m8nKyiIrK4vHHnvMaaxZs2Zx6623smPHDoYOHcrYsWPJz8932NdqtVJUVGS3iQtcWT7sWuq8fdsnUFN93tIRnnXgeAk1Nsfzi7OLKigoqzzPGQkhvJEUpsLOpEmT6N+/P/Hx8VxzzTU8++yzLFq0CABfX1+Cg4NRFIXo6Giio6MxmUxOY6WkpHDbbbeRmJjICy+8QGlpKb/99pvDvrNnzyY4OLh2i4uLOyfvT5xHigJ6X+ftPn5aH3FJMOj19bbr5VwQQiCFqTjNDz/8wIABA2jWrBlBQUHceeed5OXlUVpa6nasTp061f5/YGAgQUFB5OTkOOw7bdo0LBZL7Xb48OEGvwfhJQIiods45+09JoCu/mJFXDxahAdg9HH8kZMYZSI0sJ4vMUKIS4YUpqLWoUOHGDp0KB06dODrr79my5Yt/N///R8AVVXuL+tiMBjsHiuKgs1mc9jXaDRiNpvtNnGB0+uh6+0QkVS3reMtEOlgv7hoNTEbmXNL5zqD5P4GPa/d2pkIk9EziQkhvIrclS9qbd68merqaubMmYNOp31nOXEZ/wRfX19qamo8kZ64EAU3gzv/A4fWQ+oXYPCHXvdCVDIEykoOlxJfHz3XtI1ixd+v4LONGaQdL6FnyzBu7NqMZiH+nk5PCOElpDC9RFksFlJTU+32RUZGUl1dzVtvvcXw4cNZv3497777rl2fli1bUlJSwurVq+ncuTMBAQEEBAScx8zFBcfcVBshbTsMFD34yMjYpSrA6EObaDPTr0+mssaGn48enU7mlgohTpJL+ZeotWvX0rVrV7vto48+4tVXX+Wll16iQ4cOfPbZZ8yePdvueX369OH+++9n1KhRREZG8vLLL3voHYgLjiFAilIBgI9eR4CvjxSlQog6FFWVvw8ovE9RURHBwcFYLBaZbyqEEEJcIM7281tGTIUQQgghhFeQwlQIIYQQQngFKUyFEEIIIYRXkMJUCCGEEEJ4BSlMhRBCCCGEV5DCVAghhBBCeAUpTIUQQgghhFeQwlQIIYQQQngFKUyFEEIIIYRX8PF0AkI4cuIPkhUVFXk4EyGEEEK46sTndkP/sKgUpsIrFRcXAxAXF+fhTIQQQgjhruLiYoKDg91+nqI2tKQV4hyy2WwcPXqUoKAgFEVp1NhFRUXExcVx+PDhBv0dX9Ewctw9Q467Z8hx9ww57p5x6nEPCgqiuLiYpk2botO5P2NURkyFV9LpdMTGxp7T1zCbzfIPlwfIcfcMOe6eIcfdM+S4e8aJ496QkdIT5OYnIYQQQgjhFaQwFUIIIYQQXkEKU3HJMRqNzJgxA6PR6OlULily3D1DjrtnyHH3DDnuntGYx11ufhJCCCGEEF5BRkyFEEIIIYRXkMJUCCGEEEJ4BSlMhRBCCCGEV5DCVAghhBBCeAUpTMUl5Z133iE+Ph4/Pz+6d+/Ozz//7OmULnozZ85EURS7LTo62tNpXXR++uknhg8fTtOmTVEUhaVLl9q1q6rKzJkzadq0Kf7+/lx99dXs2rXLM8leJM50zFNSUuqc+5dffrlnkr2IzJ49m549exIUFERUVBQjRoxg7969dn3kfG98rhz3xjjnpTAVl4x///vfTJo0iSeffJJt27ZxxRVXMGTIEDIyMjyd2kWvffv2ZGVl1W47d+70dEoXndLSUjp37szbb7/tsP3ll1/m1Vdf5e2332bTpk1ER0czYMAAiouLz3OmF48zHXOAwYMH2537y5cvP48ZXpx+/PFHJk6cyK+//sqqVauorq5m4MCBlJaW1vaR873xuXLcoRHOeVWIS0SvXr3U+++/325f27Zt1alTp3ooo0vDjBkz1M6dO3s6jUsKoC5ZsqT2sc1mU6Ojo9UXX3yxdl9FRYUaHBysvvvuux7I8OJz+jFXVVUdN26cesMNN3gkn0tJTk6OCqg//vijqqpyvp8vpx93VW2cc15GTMUlobKyki1btjBw4EC7/QMHDmTDhg0eyurSsX//fpo2bUp8fDyjR4/mwIEDnk7pknLw4EGys7Ptzn+j0chVV10l5/85tnbtWqKiokhKSuKee+4hJyfH0ylddCwWCwBhYWGAnO/ny+nH/YSzPeelMBWXhNzcXGpqamjSpInd/iZNmpCdne2hrC4Nl112GR9//DHff/8977//PtnZ2fTp04e8vDxPp3bJOHGOy/l/fg0ZMoTPPvuMNWvWMGfOHDZt2sQ111yD1Wr1dGoXDVVVmTx5Mv369aNDhw6AnO/ng6PjDo1zzvuci4SF8FaKotg9VlW1zj7RuIYMGVL7/x07dqR37960atWKBQsWMHnyZA9mdumR8//8GjVqVO3/d+jQgR49etCiRQuWLVvGTTfd5MHMLh4PPvggO3bsYN26dXXa5Hw/d5wd98Y452XEVFwSIiIi0Ov1db4t5+Tk1PlWLc6twMBAOnbsyP79+z2dyiXjxCoIcv57VkxMDC1atJBzv5E89NBDfPPNN/zwww/ExsbW7pfz/dxydtwdacg5L4WpuCT4+vrSvXt3Vq1aZbd/1apV9OnTx0NZXZqsVit79uwhJibG06lcMuLj44mOjrY7/ysrK/nxxx/l/D+P8vLyOHz4sJz7Z0lVVR588EEWL17MmjVriI+Pt2uX8/3cONNxd6Qh57xcyheXjMmTJ3PHHXfQo0cPevfuzXvvvUdGRgb333+/p1O7qD322GMMHz6c5s2bk5OTw3PPPUdRURHjxo3zdGoXlZKSEv7888/axwcPHiQ1NZWwsDCaN2/OpEmTeOGFF2jdujWtW7fmhRdeICAggDFjxngw6wtbfcc8LCyMmTNnMnLkSGJiYkhPT+cf//gHERER3HjjjR7M+sI3ceJEPv/8c/7zn/8QFBRUOzIaHByMv78/iqLI+X4OnOm4l5SUNM45f1b39Atxgfm///s/tUWLFqqvr6/arVs3u2UuxLkxatQoNSYmRjUYDGrTpk3Vm266Sd21a5en07ro/PDDDypQZxs3bpyqqtoSOjNmzFCjo6NVo9GoXnnllerOnTs9m/QFrr5jXlZWpg4cOFCNjIxUDQaD2rx5c3XcuHFqRkaGp9O+4Dk65oA6b9682j5yvje+Mx33xjrnlb9eTAghhBBCCI+SOaZCCCGEEMIrSGEqhBBCCCG8ghSmQgghhBDCK0hhKoQQQgghvIIUpkIIIYQQwitIYSqEEEIIIbyCFKZCCCGEEMIrSGEqhBDikjB//nxCQkI8nYYQoh5SmAohxAUoJSWFESNG1Nm/du1aFEWhsLDwvOckhBBnSwpTIYQQbquqqvJ0CoD35CGEaBxSmAohxEXu66+/pn379hiNRlq2bMmcOXPs2hVFYenSpXb7QkJCmD9/PgDp6ekoisKiRYu4+uqr8fPz49NPP+XQoUMMHz6c0NBQAgMDad++PcuXL3eaR8uWLXn22WcZM2YMJpOJpk2b8tZbb9n1sVgs3HvvvURFRWE2m7nmmmvYvn17bfvMmTPp0qULH330EQkJCRiNRpz9Ze358+fTvHlzAgICuPHGG8nLy7NrT0tL44YbbqBJkyaYTCZ69uzJ//73v9r2Z555ho4dO9aJ2717d6ZPn+70fQohGk4KUyGEuIht2bKFW2+9ldGjR7Nz505mzpzJ008/XVt0uuOJJ57g4YcfZs+ePQwaNIiJEyditVr56aef2LlzJy+99BImk6neGP/85z/p1KkTW7duZdq0aTzyyCOsWrUKAFVVGTZsGNnZ2SxfvpwtW7bQrVs3rr32WvLz82tj/PnnnyxatIivv/6a1NRUh6+zceNGJkyYwAMPPEBqair9+/fnueees+tTUlLC0KFD+d///se2bdsYNGgQw4cPJyMjA4AJEyawe/duNm3aVPucHTt2sG3bNlJSUtw+fkIIF6hCCCEuOOPGjVP1er0aGBhot/n5+amAWlBQoKqqqo4ZM0YdMGCA3XOnTJmiJicn1z4G1CVLltj1CQ4OVufNm6eqqqoePHhQBdTXX3/drk/Hjh3VmTNnupxzixYt1MGDB9vtGzVqlDpkyBBVVVV19erVqtlsVisqKuz6tGrVSv3Xv/6lqqqqzpgxQzUYDGpOTk69r3Xbbbc5fK3g4OB6n5ecnKy+9dZbtY+HDBmi/r//9/9qH0+aNEm9+uqr640hhGg4GTEVQogLVP/+/UlNTbXbPvjgA7s+e/bsoW/fvnb7+vbty/79+6mpqXHr9Xr06GH3+OGHH+a5556jb9++zJgxgx07dpwxRu/eves83rNnD6CN7paUlBAeHo7JZKrdDh48SFpaWu1zWrRoQWRkZL2vs2fPHoevdarS0lIef/xxkpOTCQkJwWQy8ccff9SOmALcc889fPHFF1RUVFBVVcVnn33GhAkTzvg+hRAN4+PpBIQQQjRMYGAgiYmJdvuOHDli91hVVRRFqbPvVIqi1Nnn6KaiwMBAu8d33303gwYNYtmyZaxcuZLZs2czZ84cHnroIbfex4n8bDYbMTExrF27tk6fU5d5Oj0PR05/P45MmTKF77//nldeeYXExET8/f25+eabqaysrO0zfPhwjEYjS5YswWg0YrVaGTly5JnflBCiQaQwFUKIi1hycjLr1q2z27dhwwaSkpLQ6/UAREZGkpWVVdu+f/9+ysrKXIofFxfH/fffz/3338+0adN4//336y1Mf/311zqP27ZtC0C3bt3Izs7Gx8eHli1buvT6ziQnJzt8rVP9/PPPpKSkcOONNwLanNP09HS7Pj4+PowbN4558+ZhNBoZPXo0AQEBZ5WbEMI5KUyFEOIi9uijj9KzZ0+effZZRo0axS+//MLbb7/NO++8U9vnmmuu4e233+byyy/HZrPxxBNPYDAYzhh70qRJDBkyhKSkJAoKClizZg3t2rWr9znr16/n5ZdfZsSIEaxatYovv/ySZcuWAXDdddfRu3dvRowYwUsvvUSbNm04evQoy5cvZ8SIEXWmEtTn4Ycfpk+fPrWvtXLlSlasWGHXJzExkcWLFzN8+HAUReHpp5/GZrPViXX33XfXvq/169e7nIMQwn0yx1QIIS5i3bp1Y9GiRSxcuJAOHTowffp0nnnmGbu7yufMmUNcXBxXXnklY8aM4bHHHnNpVLCmpoaJEyfSrl07Bg8eTJs2bewKXkceffRRtmzZQteuXXn22WeZM2cOgwYNArRL+suXL+fKK69kwoQJJCUlMXr0aNLT02nSpIlb7/vyyy/ngw8+4K233qJLly6sXLmSp556yq7Pa6+9RmhoKH369GH48OEMGjSIbt261YnVunVr+vTpQ5s2bbjsssvcykMI4R5FdWUijhBCCHGWWrZsyaRJk5g0aZKnU3GLqqq0bduW++67j8mTJ3s6HSEuanIpXwghhHAiJyeHTz75hMzMTMaPH+/pdIS46ElhKoQQQjjRpEkTIiIieO+99wgNDfV0OkJc9ORSvhBCCCGE8Apy85MQQgghhPAKUpgKIYQQQgivIIWpEEIIIYTwClKYCiGEEEIIryCFqRBCCCGE8ApSmAohhBBCCK8ghakQQgghhPAKUpgKIYQQQgivIIWpEEIIIYTwCv8fHvesoEHz/JIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, y=\"Fav genre\", x=\"Hours per day\", alpha = 1, hue = \"Music effects\")\n",
    "plt.title('Genre, Hours per day correlation with music effects');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "711dcc5e-fb09-4927-8720-f7aad7029806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, y=\"Age\", x=\"Hours per day\", alpha = 1, hue = \"Music effects\")\n",
    "plt.title('Age, Hours listened to and Music Effects');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8427434-69f4-420f-94b3-ec1ec5733ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'No'), Text(1, 0, 'Yes')])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.boxplot(x=df['While working'], y=df['Hours per day'])\n",
    "plt.xticks(rotation=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a0b6ce3-08ee-4cfd-83c0-d0ee180f1171",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Line2D' object has no property 'errorbar'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
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      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m    708\u001b[0m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_attach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 710\u001b[0;31m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    711\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    536\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    529\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn_datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m         result = (make_artist(x[:, j % ncx], y[:, j % ncy], kw,\n\u001b[0m\u001b[1;32m    532\u001b[0m                               {**kwargs, 'label': label})\n\u001b[1;32m    533\u001b[0m                   for j, label in enumerate(labels))\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_makeline\u001b[0;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0mdefault_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m         \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    352\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;31m# update kwargs before updating data to give the caller a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0;31m# chance to init axes (and hence unit support)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpickradius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickradius\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mind_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m   1062\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"set_{k}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1063\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1064\u001b[0;31m                         raise AttributeError(f\"{type(self).__name__!r} object \"\n\u001b[0m\u001b[1;32m   1065\u001b[0m                                              f\"has no property {k!r}\")\n\u001b[1;32m   1066\u001b[0m                     \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Line2D' object has no property 'errorbar'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.lineplot(x=df['Fav genre'], y=df['Hours per day'], errorbar=None)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0bdc0be0-b425-4f1d-8e10-c61e92459123",
   "metadata": {},
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "sns.barplot(x=df['Hours per day'], y=df['Insomnia'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e41aa927-c620-4b69-9083-0dd576383ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25]),\n",
       " [Text(0, 0, '0.0'),\n",
       "  Text(1, 0, '0.1'),\n",
       "  Text(2, 0, '0.25'),\n",
       "  Text(3, 0, '0.5'),\n",
       "  Text(4, 0, '0.7'),\n",
       "  Text(5, 0, '1.0'),\n",
       "  Text(6, 0, '1.5'),\n",
       "  Text(7, 0, '2.0'),\n",
       "  Text(8, 0, '2.5'),\n",
       "  Text(9, 0, '3.0'),\n",
       "  Text(10, 0, '4.0'),\n",
       "  Text(11, 0, '4.5'),\n",
       "  Text(12, 0, '5.0'),\n",
       "  Text(13, 0, '6.0'),\n",
       "  Text(14, 0, '7.0'),\n",
       "  Text(15, 0, '8.0'),\n",
       "  Text(16, 0, '9.0'),\n",
       "  Text(17, 0, '10.0'),\n",
       "  Text(18, 0, '11.0'),\n",
       "  Text(19, 0, '12.0'),\n",
       "  Text(20, 0, '14.0'),\n",
       "  Text(21, 0, '15.0'),\n",
       "  Text(22, 0, '16.0'),\n",
       "  Text(23, 0, '18.0'),\n",
       "  Text(24, 0, '20.0'),\n",
       "  Text(25, 0, '24.0')])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "sns.barplot(x=df['Hours per day'], y=df['Anxiety'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "946b0456-0471-4d2e-9ac7-c99ab031aa8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25]),\n",
       " [Text(0, 0, '0.0'),\n",
       "  Text(1, 0, '0.1'),\n",
       "  Text(2, 0, '0.25'),\n",
       "  Text(3, 0, '0.5'),\n",
       "  Text(4, 0, '0.7'),\n",
       "  Text(5, 0, '1.0'),\n",
       "  Text(6, 0, '1.5'),\n",
       "  Text(7, 0, '2.0'),\n",
       "  Text(8, 0, '2.5'),\n",
       "  Text(9, 0, '3.0'),\n",
       "  Text(10, 0, '4.0'),\n",
       "  Text(11, 0, '4.5'),\n",
       "  Text(12, 0, '5.0'),\n",
       "  Text(13, 0, '6.0'),\n",
       "  Text(14, 0, '7.0'),\n",
       "  Text(15, 0, '8.0'),\n",
       "  Text(16, 0, '9.0'),\n",
       "  Text(17, 0, '10.0'),\n",
       "  Text(18, 0, '11.0'),\n",
       "  Text(19, 0, '12.0'),\n",
       "  Text(20, 0, '14.0'),\n",
       "  Text(21, 0, '15.0'),\n",
       "  Text(22, 0, '16.0'),\n",
       "  Text(23, 0, '18.0'),\n",
       "  Text(24, 0, '20.0'),\n",
       "  Text(25, 0, '24.0')])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nRESkcuXK0rt3b5k2bZpUqlTJ6jUCAAAA+JNKVfh//vlnSUhIkCNHjkjfvn2lQYMGoqqyb98+WbRokXzwwQeyZcsWqVy5sqvWCwAAAKAUSlX4p0yZIoGBgXLw4EGJiIgoMtaxY0eZMmWKPPfcc5YuEgAAAMCfU6q35czIyJBnnnmmSNkXEalevbo89dRTkp6ebtniAAAAAJRNqQr/sWPHpGHDhiWON2rUSHJycsq8KAAAAADWKFXhr1Klihw+fLjE8UOHDkl4eHhZ1wQAAADAIqUq/J07d5YJEybI+fPni4zl5+fL448/Lp07d7ZscQAAAADKplR/tDt58mRp3ry51K1bV5KTk6V+/foiIrJ371554YUXJD8/X1577TWXLBQAAABA6ZWq8NeoUUO2bt0qw4cPl3Hjxjk+Zddms0mHDh1k3rx5Eh0d7ZKFAgAAACi9Un/wVmxsrKxatUpOnDghBw4cEBGROnXqyDXXXGP54gAAAACUTakLf6HKlSvLTTfdZOVaAAAAAFisVH+0CwAAAMC3UPgBAAAAg1H4AQAAAINR+AEAAACDUfgBAAAAg1H4AQAAAINR+AEAAACDUfgBAAAAg1H4AQAAAINR+AEAAACDUfgBAAAAg1H4AQAAAINR+AEAAACDUfgBAAAAg1H4AQAAAINR+AEAAACDUfgBAAAAg1H4AQAAAINR+AEAAACDUfgBAAAAg1H4AQAAAINR+AEAAACDBXh6AQAAoHi93vrU8jnf6nWT5XMC8G6c4QcAAAAMRuEHAAAADEbhBwAAAAzGNfwAAMBIb731k0vm7dWrikvmBVyFM/wAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBfKrwz5gxQ2w2m6SkpHh6KQAAAIBP8JnCv337dpk/f77ccMMNnl4KAAAA4DN8ovCfOnVK+vbtKwsWLJDKlSt7ejkAAACAz/CJwp+cnCxdu3aV9u3bX/Gx+fn5kpeX53QDAAAArlYBnl7Albzxxhvy2Wefyfbt2//Q42fMmCGTJ0928aoAwFpd337W8jlX/G205XMCAHyPV5/hz87OllGjRsmSJUskKCjoDz1n3Lhxkpub67hlZ2e7eJUAAACA9/LqM/yZmZly/PhxiY+Pd2y7ePGibNq0SebNmyf5+fni7+/v9By73S52u93dSwUAAAC8klcX/ttuu0327NnjtO3ee++V+vXry9ixY4uUfQAAAADOvLrwh4SESKNGjZy2VahQQcLDw4tsBwAAAFCUV1/DDwAAAKBsvPoMf3E2bNjg6SUAAAAAPoMz/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBAjy9AAAAfEnSf9a5ZN6MOzq4ZF4AoPADQAluzxhv+Zwrk1ItnxMAgMvhkh4AAADAYBR+AAAAwGAUfgAAAMBgXMMPAACk99uHLJ/zjb/FWj4ngNLjDD8AAABgMAo/AAAAYDAKPwAAAGAwruEHAPi8xP+ku2Te9+7o6ZJ5AcCdKPwAAJfo9p/XXTLvf+/o65J5AcBUXNIDAAAAGIzCDwAAABiMwg8AAAAYjMIPAAAAGIzCDwAAABiMwg8AAAAYjMIPAAAAGIzCDwAAABiMD94CUGYT/t3ZJfNOv3O1S+YFAOBqwhl+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYLwtJ2CoOUs7uWTeUX3WuGReAADgGpzhBwAAAAxG4QcAAAAMRuEHAAAADEbhBwAAAAxG4QcAAAAMRuEHAAAADEbhBwAAAAxG4QcAAAAMRuEHAAAADMYn7QLwKZ3fvd0l867uvtIl8wIA4Gmc4QcAAAAMRuEHAAAADEbhBwAAAAzGNfwAcJXp+tYCy+dc0WuI5XMCAKzBGX4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGBeXfhnzJghLVq0kJCQEKlWrZokJSXJV1995ellAQAAAD4jwNMLuJyNGzdKcnKytGjRQn799VeZMGGCdOzYUfbu3SsVKlTw9PKAP2XR4o6WzzlwwFrL5wQAAGbw6sK/evVqp/sLFy6UatWqSWZmptx8880eWtXVLSst0SXzNrn/PZfMCwAAcLXz6sL/e7m5uSIics0115T4mPz8fMnPz3fcz8vLc/m6AAAAAG/lM4VfVWX06NHSpk0badSoUYmPmzFjhkyePNmNK4OrrH+pq+Vzthu8otjt6a90sTyr56BVls8JAABQWl79R7uXGjFihOzevVuWLVt22ceNGzdOcnNzHbfs7Gw3rRAAAADwPj5xhv+BBx6Qd999VzZt2iQ1atS47GPtdrvY7XY3rQwAAADwbl5d+FVVHnjgAUlPT5cNGzZIbGysp5cEAAAA+BSvLvzJycmydOlSeeeddyQkJERycnJERCQsLEyCg4M9vDoAAADA+3n1NfxpaWmSm5srbdu2lcjISMdt+fLlnl4aAAAA4BO8+gy/qnp6CQAAAIBP8+oz/AAAAADKhsIPAAAAGMyrL+lxhR/Tllg+Z9X7+1k+JwAAAGAFzvADAAAABqPwAwAAAAaj8AMAAAAGo/ADAAAABrvq/mjXRN/PG+SSeWuMeMUl8wIAAMB9OMMPAAAAGIzCDwAAABiMwg8AAAAYjMIPAAAAGIzCDwAAABiMwg8AAAAYjMIPAAAAGIzCDwAAABiMwg8AAAAYjMIPAAAAGIzCDwAAABiMwg8AAAAYjMIPAAAAGIzCDwAAABgswNMLAAAAAEx0fN4Kl8xbbUTXUj2eM/wAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMEo/AAAAIDBKPwAAACAwSj8AAAAgMECPL0Akx1/8VnL56w2bLTlcwIAAN+xZ/5xl8zb+L5qLpkXnscZfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGAUfgAAAMBgFH4AAADAYBR+AAAAwGA+UfhfeOEFiY2NlaCgIImPj5ePPvrI00sCAAAAfILXF/7ly5dLSkqKTJgwQXbu3Cl//etfpUuXLvLdd995emkAAACA1/P6wv/ss8/KP/7xDxk8eLA0aNBAZs+eLdHR0ZKWlubppQEAAABeL8DTC7ic8+fPS2Zmpjz66KNO2zt27Chbtmwp9jn5+fmSn5/vuJ+bmysiInl5eSIicvLsWcvXaf+/uX/v5NlzlmcFFZN18ux5y3NE/v8xu9SpsxfclnXaBVnF5YiInDn7q9uyzrop69wZ63NKysp3Y9avZ9z3GrxwJr+YR1qf81uW9d8vSs6y/vtg8cfvjOU53pF12o1Zp9yS81vWSbdlucsZF+yTiEheXqBL5v2jTp111X4FFdl28pz1WRXyKhS7/eQ561/v5UvqaOes//84uNiO5prvTYV9sPD/MVW9/BPUix05ckRFRD/++GOn7dOnT9d69eoV+5yJEyeqiHDjxo0bN27cuHHjdlXcsrOzL9upvfoMfyGbzeZ0X1WLbCs0btw4GT16tON+QUGB/PzzzxIeHl7ic34vLy9PoqOjJTs7W0JDQ//8wskiy8tzyCLL0zlk+VaWiftElm9lmbhPZclSVTl58qRERUVd9nFeXfirVKki/v7+kpOT47T9+PHjEhERUexz7Ha72O12p22VKlX6U/mhoaEu/wKTRZY35JBFlqdzyPKtLBP3iSzfyjJxn/5sVlhY2BUf49V/tBsYGCjx8fGybt06p+3r1q2TVq1aeWhVAAAAgO/w6jP8IiKjR4+Wv//979K8eXNJSEiQ+fPny3fffSfDhg3z9NIAAAAAr+f1hf/uu++W//3vfzJlyhQ5duyYNGrUSFauXCm1atVyWabdbpeJEycWuTSILLLclWXiPpHlW1km7hNZvpNDFlmezjEty6Z6pffxAQAAAOCrvPoafgAAAABlQ+EHAAAADEbhBwAAAAxG4QcAAAAMRuEHAAAADOb1b8vpThcvXpSffvpJbDabhIeHi7+/v6eXBLgMr/ey4fiVnanH0J37ZeoxBGAtzvCLSHp6urRu3VrKly8vUVFREhkZKeXLl5fWrVtLRkaG29axb98+qV27tiVznT9/3un+wYMHJSUlRbp27SqDBw+WzMxMS3JERPbv3y+Xvrvr5s2bJSkpSRo2bCjt27eXd955x7Ksxo0by9SpUyU7O9uyOf+Iixcvyg8//CDHjx+XixcvujXbau56vbvzdXEpV3+tOH5l54nvuabtl7f83AJMsn37dunbt6/ExsZKcHCwlC9fXmJjY6Vv376yY8cOn80SERG9yr344osaGBiow4YN0/T0dN2yZYt+/PHHmp6ersOGDVO73a7z5893y1qysrLUz8/Pkrn8/Pz0hx9+UFXVnTt3avny5bVJkyY6ZMgQbdGihQYGBuonn3xiedb69evVz89PExMTdfr06dqrVy/18/PT1atXW5Jls9k0PDxc/f39tVOnTvqf//xHL1y4YMncxXn77be1VatWGhgYqH5+furn56eBgYHaqlUrTU9Ptyznq6++0oKCAsf9jz76SHv06KFxcXF62223aUZGhiU57ny9u/N1oeqerxXHr+zc/T3XxP1y9zH89NNPtU+fPhoTE6NBQUEaHBysMTEx2qdPH92+fbtlOWT5To6JWenp6VquXDnt3LmzPvfcc7p06VJ9/fXX9bnnntMuXbpoYGCgZT+L3ZlV6Kov/Nddd52+9NJLJY6//PLLWrt2bUuyHnzwwcve+vXrZ1nht9lsjrLQrVs3veOOO5wK5b333qudO3e2POu2227T4cOHO40/+uijevPNN1uWdeTIEU1PT9fExEQNCAjQqlWr6kMPPaR79+61JKOQieXOna93d74u3PW14viVnTuPoan75c4sU0uQiVkm7pM7sxo2bKgzZswocfzJJ5/UuLi4Mue4O6vQVV/4g4KC9MsvvyxxfN++fRoUFGRJlp+fnzZr1kzbtm1b7K158+YuKfw1atTQzZs3O41nZWVpRESE5VmRkZG6bds2p/EvvvhCw8PDLc9SVT127JimpqZq3bp11c/PTxMSEvTll1+2JMvEcufO17s7Xxfu+lpx/MrOncfQ1P1yZ5apJcjELBP3yZ1Zdrtdv/rqqxLHv/zyS7Xb7WXOcXdWoau+8MfHx+vo0aNLHB89erTGx8dbknX99dfra6+9VuL4zp07Lb2k5/jx46qqWqtWLd29e7fT+DfffGNpMfn66681NzdXa9eurTt37nQaP3DggJYvX96SrEvPhP/e+vXrtV+/flqhQgVLskwsd+58vbvzdeGurxXHr+zceQxN3S93ZplagkzMMnGf3JkVFxenM2fOLHF85syZ2qBBgzLnuDur0FX/Lj2zZs2Srl27yurVq6Vjx44SEREhNptNcnJyZN26dfLtt9/KypUrLcmKj4+XzMxM6devX7HjNpvN6Y/0ykJVpV69emKz2eTUqVOyZ88eady4sWP8wIEDUr16dUuyRETq1avnyM3MzJQmTZo4xr744gu59tprLcm53PFp27attG3bVvLy8izJatiwocyfP19mzZpV7PiCBQukYcOGlmSJiJw8eVKCgoIkODhY7Ha701hgYKCcPXu2zBnufL2LuO914a6vFcev7Nx5DE3dL3dmXXfddZKRkSGPPPJIsePvvPOOZW82QZZv5JiaNWXKFOndu7ds3Lix2P+v1q5dK2+88UaZc9ydVeiqL/y33HKLfP7555KWlibbtm2TnJwcERGpXr26dOvWTYYNGyYxMTGWZM2aNUvy8/NLHL/xxhuloKDAkqyFCxc63b/uuuuc7m/btk169uxpSdb69eud7kdGRjrdP3z4sAwZMsSSrAEDBkhwcPBlHxMaGmpJlonlzp2vd3e+Ltz1teL4lZ27v+eauF/uzDK1BJmYZeI+uTOrV69esmnTJpkzZ448++yzTv9fJSQkyMaNGyUhIaHMOe7OKmRTq04pAwY6fPhwsT9UExISLP2hunHjRqf7kZGRjl8ARETmzJkj58+flzFjxliSZyJ3fa1MZerxM3W/3Gnr1q0yZ84c2bp1a5FjOGrUKEuLCVm+kWNylqko/AAAAIDB+OCtKxgwYIDceuutPpm1a9cumTZtmrzwwgvy008/OY3l5eXJoEGDLMu6HF8+hlcbvlZlw/ErO/bLt7KAq8X48ePd1ptckUXhv4KoqCipVauWz2WtXbtWbrrpJnnjjTdk5syZ0qBBA6drgs+ePSuLFy+2JOtKfPUYXomJP8CvvfZatx0/d2Zx/MrGna919su3sny9BF1NWSbukzuzjhw5IocPH3Z5jquyrvo/2r2SGTNm+GTWpEmT5OGHH5bp06eLqsozzzwj3bt3l3//+9/SuXNny3L+CF89hlcSFRUlfn7u+Z3ZXVmpqakuz/BElquPn6qKzWZzyz65M6uQO1/r7tyva6+91sj9cmfWkSNHJDs7mywfyDJxn9yZ5a6TpK7K4hp+Efn+++8lLS1NtmzZIjk5OWKz2SQiIkJatWol999/v9SoUcPnssLCwuSzzz5zeneeZcuWyZAhQ2TZsmVy0003SVRUlFy8eNGSPBOPIfBHBQYGyq5du6RBgwZGZcF7HTt2TNLS0mTz5s1y7Ngx8ff3l9jYWElKSpKBAweKv7+/p5cIwItc9YV/8+bN0qVLF4mOjna83ZOqyvHjx2XdunWSnZ0tq1atktatW/tUVrVq1WTVqlUSHx/vtH358uUyaNAgmTVrliQnJ1tS+E09hleSnZ0tEydOlFdeecWnss6ePSuZmZlyzTXXSFxcnNPYuXPn5M0335T+/fuXOcfdWfv27ZNt27ZJQkKC1K9fX7788kuZM2eO5OfnS79+/Sy5dGP06NHFbp8zZ47069dPwsPDRUTk2Wef9ams3ztx4oQsXrxYDhw4IJGRkTJgwACJjo62ZO6dO3dKpUqVJDY2VkRElixZImlpafLdd99JrVq1ZMSIEdK7d29Lsh544AG566675K9//asl813J3LlzZceOHdK1a1e566675LXXXpMZM2ZIQUGB/O1vf5MpU6ZIQEDZ/2F9x44d0r59e4mNjZXg4GD55JNPpG/fvnL+/HlZs2aNNGjQQNasWSMhISEW7BVw9Th9+rQsXbq0yAnF1q1byz333CMVKlRwaX7t2rVlzZo1UrduXesnt/RjvHxQ8+bNNSUlpcTxlJQUbd68uc9ldejQQZ9++ulix5YuXarlypWz7FN9TT2GV5KVlWXZMXRX1ldffaW1atVSm82mfn5+esstt+jRo0cd4zk5OZbtkzuzVq1apYGBgXrNNddoUFCQrlq1SqtWrart27fX2267TQMCAvSDDz4oc47NZtMmTZpo27ZtnW42m01btGihbdu21Xbt2lmwR+7NioyM1J9++klVf/sU7urVq2v16tW1Q4cOWqNGDQ0LC9N9+/ZZktW0aVP98MMPVVV1wYIFGhwcrCNHjtS0tDRNSUnRihUr6ssvv2xJVuFrr27duvrkk0/qsWPHLJm3OFOmTNGQkBDt1auXVq9eXZ988kkNDw/XadOmaWpqqlatWlWfeOIJS7Jat26tkyZNctx/7bXXtGXLlqqq+vPPP2uTJk105MiRlmSpqp46dUrnz5+vAwcO1M6dO2uXLl104MCBumDBAj116pRlOVeSk5OjkydPtnTO7OxsPXnyZJHt58+f140bN1qa9XuxsbG6f/9+l81//vx5TU9P16eeekpfe+01S79W2dnZ+uOPPzrub9q0Sfv06aNt2rTRvn376pYtWyzLUlV999139YknnnDM+8EHH2iXLl20U6dO+q9//cuSjC+++EKjoqK0UqVK2qNHD73vvvt0yJAh2qNHD61UqZJee+21+sUXX1iSNWfOnGJv/v7+Om7cOMd9K131hd+dH73uzqy33377ssV46dKl2rZtW0uyTD2G77zzzmVvzz33nGWF1V1ZSUlJ2q1bN/3xxx/1wIEDmpiYqLGxsfrtt9+qqrUl3J1ZCQkJOmHCBFVVXbZsmVauXFnHjx/vGB8/frx26NChzDmpqakaGxtb5JeHgIAAy34QeCLLZrPpDz/8oKqqvXv31rZt2+rp06dVVfXcuXParVs3veOOOyzJKl++vOM10LRp0yI/rF9//XWNi4uzJMtms+n777+vo0aN0ipVqmi5cuW0e/fu+t577+nFixctyShUu3Ztfeutt1T1t1/Q/f39dcmSJY7xt99+W+vUqWNJVnBwsB48eNBx/+LFi1quXDnNyclRVdW1a9dqVFSUJVnuLEFXYuVJlqNHj2qLFi3Uz89P/f39tX///k7F38rvT+4qdwkJCXrixAlVVT1+/Lg2btxYAwMDtW7duhoUFKQ1a9bU77//vsw5hVkrV65UVdWMjAz18/PT7t2769ixY7Vnz55arlw5fe+99yzJSktL04CAAI2Pj9fQ0FBdsmSJhoSE6ODBg3Xo0KEaHByss2fPLnNO27ZttXfv3pqfn19kLD8/X++55x7LepPNZtMaNWpoTEyM081ms+m1116rMTExGhsba0lWoau+8MfGxuorr7xS4vgrr7xi2UF3Z5Y7mXoMC88O2my2Em9W/UBwV1a1atV09+7dTtuGDx+uNWvW1IMHD1r6Q86dWaGhoXrgwAFV/a38BAQEaGZmpmN8z549GhERYUnWp59+qvXq1dOHHnpIz58/r6quKeHuzLq08Bf3S8a2bdu0Ro0almSFh4frjh07VPW310hWVpbT+Ndff63BwcGWZF26X+fPn9fly5drp06d1N/fX6OionT8+PGO101ZBQcHO36RUVUtV66cfv755477hw8f1vLly1uSVatWLd28ebPj/tGjR9Vms+mZM2dUVfXQoUOWnfhwZwnatWvXZW/Lly+37HtG//799S9/+Ytu375d161bp82bN9f4+Hj9+eefVfW3wm+z2SzJcle5u/T1PmTIEG3SpInjX7V++uknbdWqlQ4aNKjMOaqqISEheujQIVVVbdmypT755JNO43PnztWmTZtaktWgQQOdP3++qqp++OGHGhQUpM8//7xjfOHChdqgQYMy5wQHB1/2e+uePXss+9503333aZMmTXTv3r1O2131s0SVwq/PP/+8BgYGanJysmZkZOjWrVt127ZtmpGRocnJyWq32zUtLc3nstzJ1GMYFRWl6enpJY7v3LnTsh8+7soKCQkp8g1GVXXEiBFao0YN3bRpk2X75M6sSwu/qmrFihWdzoAePnzYsgKkqnry5Ent37+/3nDDDbp7924tV66cy75JuyPLZrPp8ePHVfW31+KlRVX1twJpt9styerXr5/+4x//UFXVO++8Ux977DGn8dTUVG3cuLElWZcWoEt9++23OnHiRK1Vq5Zlr8HY2FhdtWqVqqru379f/fz89M0333SMr1ixQmNiYizJGjVqlDZq1EhXrVqlH374obZr186pdK9evVqvu+46S7LcWYIud+KjcLuV33M/+eQTx/1z585pjx49tEmTJvq///3P0hMS7ip3l77e69Wrp//973+dxtevX2/ZazAsLEx37dqlqr/94l7434W+/vpry37BLe6X6T179jjuHzp0yJKsqKgozcjIKHE8PT3dsn85K5wvOjpa586d69hG4XexN954Q1u2bKkBAQGOby4BAQHasmVLXb58uc9mXU7//v0tu/5X1cxjmJiYqI8//niJ41lZWZadAXJXVosWLfTVV18tdiw5OVkrVapk2Q85d2bdcMMNjrKl+lsJuXDhguP+Rx995JJ/PVu2bJlGRESon5+fyy9rcGWWzWbTxo0ba9OmTbVixYr69ttvO41v3LhRr732Wkuyjhw5ojExMXrzzTfr6NGjNTg4WNu0aaNDhgzRm2++WQMDA3XFihWWZJVU+AsVFBTo2rVrLcmaMGGCVq1aVQcPHqyxsbE6btw4rVmzpqalpemLL76o0dHR+uCDD1qSdfLkSb3rrrsc3wNbtWql33zzjWN8zZo1Tr9slIU7S1CVKlX05Zdf1sOHDxd7W7FihWXfMypUqFDk+vkLFy5oUlKS45drK/9Gyx3l7tJf3KtVq1Zk7sOHD1v2i3v37t310UcfVVXVTp06FbkkacGCBVq3bl1LsgpPEKn+9v3DZrM5fY/YsGGDJf8COXHiRA0LC9Onn35as7Ky9NixY5qTk6NZWVn69NNPa+XKlS3/G5Lvv/9eb731Vu3cubMeO3aMwu8u58+f16NHj+rRo0cd/3xuQlZxHn30UR04cKDl85p0DDdt2uRUIn/v1KlTumHDBp/KSk1N1S5dupQ4fv/991v2S4w7s9LS0oqczbrU+PHjHWeVrZadna0ZGRlu+eNFV2VNmjTJ6bZ69Wqn8Ycfflh79+5tWd6JEyd07NixGhcXp0FBQRoYGKi1atXSPn366Pbt2y3LiYmJcfwxsqv9+uuvOm3aNO3WrZvj8oZly5ZpdHS0hoeH68CBAy3/up09e7bYPzi1kjtLUKdOnXTq1Kkljlt5kqVx48b6n//8p8j2wtJfs2ZNy9+UwdXlzmaz6e233649e/bUypUrO66xL7R161bLLm3cu3evhoeHa//+/XXq1KlasWJF7devn06fPl379++vdrtdFy5caElWcnKy1q1bV6dNm6Y33XSTDhgwQOvXr6+rVq3S1atXa+PGjS27VOnJJ5/UyMhIx78mFf7LUmRkpM6cOdOSjN8rKCjQ1NRUrV69uvr7+7us8F/1b8sJAABKNnPmTJkzZ47jbQpFfvsAuOrVq0tKSoo88sgjluSkp6fL6dOnpV+/fsWOnzhxQt59910ZMGBAmbPGjh0rWVlZsmbNmiJjv/76q/Tq1Uvee+89KSgoKHPWpVRVnnzySfnnP/8pP/74o+zevbvIWxb/Wffee6/T/dtvv13uvPNOx/0xY8bInj17ZPXq1ZbkHTx4UB577DFZsWKFnDp1SkREAgICpEWLFjJmzBhJSkqyJOf06dOSkpIi27ZtkzZt2sg///lPmTNnjkyYMEEuXLggt9xyiyxfvlyqVatmSZ6IyKFDhyQnJ0dERKpXr+54G2FXyszMlM2bN0v//v2lcuXKls9P4TcYH1AFALCKJ0qQq/z6669y5swZCQ0NLXb84sWL8v3330utWrVcku/qclec06dPi7+/vwQFBVk6r/7fZ+EUFBRIlSpVpFy5cpbOX5Jz587JhQsX+LyJP8g9nykOt9u8ebM0aNBA0tPT5cYbb5T+/ftLv3795MYbb5SMjAyJi4uTjz/+2NPLBAD4iNjYWElISJCEhARH2c/OzpZBgwa5Jd/KrICAgBLLvojI0aNHZfLkyZZkFSc+Pl5GjRollStXdtsx/Pnnn2X48OGWz1t4MjEyMtJR9t2xT0FBQRISEmJp1tmzZ2Xz5s2yd+/eImPnzp2TV1991ZIcd2eJcIbfWC1atJA2bdrIc889V+z4gw8+KJs3b5bt27e7eWUAAFPs2rVLmjVrZsmntpPl2iwT98nKrP3790vHjh3lu+++E5vNJn/9619l2bJlEhkZKSIiP/zwg0RFRVmyT+7MKlT2z/iGV/r8889lyZIlJY4PHTpUXnzxRTeuCADga959993Ljn/zzTdkeUmWifvkzqyxY8dK48aNZceOHfLLL7/I6NGjpXXr1rJhwwapWbOmJRmeyCrEGX5D1a5dWx5//PEif8RTaOHChTJ16lRL/6cEAJjFz89PbDabXK4q2Gw2S85EklW2LBP3yZ1ZERER8v7770vjxo0d25KTk+W///2vrF+/XipUqGDZWXd3ZhXiGn5DPfzwwzJs2DAZMWKEvPPOO7Jt2zb55JNP5J133pERI0bI/fffb9k7KwAAzBQZGSlvvfWWFBQUFHv77LPPyPKSLBP3yZ1ZZ8+elYAA5wtfnn/+eenevbvccsstsn//fkty3J1ViMJvqOHDh8urr74qO3bskDvuuENatWolCQkJcscdd8iOHTvk1VdflWHDhnl6mQAALxYfH3/ZQnWlM69kuS/LxH1yZ1b9+vVlx44dRbbPnTtXevToId27dy9zhieyCnENv8Huvvtuufvuu+XChQvy008/iYi49S2zAAC+bcyYMXL69OkSx+vUqSPr168nywuyTNwnd2b17NlTli1bJn//+9+LjM2bN08KCgos+9tHd2YV4hp+AAAAwGBc0gMAAAAYjMIPAAAAGIzCDwAAABiMwg8A8HmLFi2SSpUqeXoZAOCVKPwA4GUGDhwoSUlJRbZv2LBBbDab/PLLL25fEwDAd1H4AQBOLly44OkliIj3rAMAfB2FHwB82FtvvSUNGzYUu90uMTExMmvWLKdxm80mGRkZTtsqVaokixYtEhGRw4cPi81mkzfffFPatm0rQUFBsmTJEvn2228lMTFRKleuLBUqVJCGDRvKypUrS1xHTEyMTJ06Vfr06SMVK1aUqKgomTt3rtNjcnNz5b777pNq1apJaGio3HrrrbJr1y7H+KRJk6RJkybyyiuvSO3atcVut5f4gTqLFi2SmjVrSvny5aVnz57yv//9z2n84MGD0qNHD4mIiJCKFStKixYt5P3333eMT5kyxelj7QvFx8fLE088UeJ+AoAvovADgI/KzMyUu+66S3r37i179uyRSZMmyeOPP+4o86UxduxYGTlypOzbt086deokycnJkp+fL5s2bZI9e/bIzJkzpWLFiped4+mnn5YbbrhBPvvsMxk3bpw8+OCDsm7dOhERUVXp2rWr5OTkyMqVKyUzM1OaNWsmt912m/z888+OOb7++mt588035a233pKsrKxicz755BMZNGiQDB8+XLKysqRdu3Yybdo0p8ecOnVKbr/9dnn//fdl586d0qlTJ0lMTJTvvvtOREQGDRoke/fule3btzues3v3btm5c6cMHDiw1McPALyaAgC8yoABA9Tf318rVKjgdAsKClIR0RMnTqiqap8+fbRDhw5Ozx0zZozGxcU57ouIpqenOz0mLCxMFy5cqKqqhw4dUhHR2bNnOz2mcePGOmnSpD+85lq1amnnzp2dtt19993apUsXVVX94IMPNDQ0VM+dO+f0mOuuu07/9a9/qarqxIkTtVy5cnr8+PHLZt1zzz3FZoWFhV32eXFxcTp37lzH/S5duuj999/vuJ+SkqJt27a97BwA4Is4ww8AXqhdu3aSlZXldHvppZecHrNv3z5p3bq107bWrVvLgQMH5OLFi6XKa968udP9kSNHyrRp06R169YyceJE2b179xXnSEhIKHJ/3759IvLbv0acOnVKwsPDpWLFio7boUOH5ODBg47n1KpVS6pWrXrZnH379hWbdanTp0/LI488InFxcVKpUiWpWLGifPnll44z/CIiQ4YMkWXLlsm5c+fkwoUL8vrrr8ugQYOuuJ8A4GsCPL0AAEBRFSpUkDp16jht+/77753uq6rYbLYi2y5ls9mKbCvuj2ErVKjgdH/w4MHSqVMnWbFihaxdu1ZmzJghs2bNkgceeKBU+1G4voKCAomMjJQNGzYUecylb6f5+3UU5/f7U5wxY8bImjVr5JlnnpE6depIcHCw3HHHHXL+/HnHYxITE8Vut0t6errY7XbJz8+XXr16XXmnAMDHUPgBwEfFxcXJ5s2bnbZt2bJF6tWrJ/7+/iIiUrVqVTl27Jhj/MCBA3LmzJk/NH90dLQMGzZMhg0bJuPGjZMFCxZctvBv27atyP369euLiEizZs0kJydHAgICJCYm5g/llyQuLq7YrEt99NFHMnDgQOnZs6eI/HZN/+HDh50eExAQIAMGDJCFCxeK3W6X3r17S/ny5cu0NgDwRhR+APBRDz30kLRo0UKmTp0qd999t2zdulXmzZsnL7zwguMxt956q8ybN0/+8pe/SEFBgYwdO1bKlSt3xblTUlKkS5cuUq9ePTlx4oR8+OGH0qBBg8s+5+OPP5annnpKkpKSZN26dfLvf/9bVqxYISIi7du3l4SEBElKSpKZM2fK9ddfL0ePHpWVK1dKUlJSkUuKLmfkyJHSqlUrR9batWtl9erVTo+pU6eOvP3225KYmCg2m00ef/xxKSgoKDLX4MGDHfv18ccf/+E1AIAv4Rp+APBRzZo1kzfffFPeeOMNadSokTzxxBMyZcoUp3eZmTVrlkRHR8vNN98sffr0kYcffvgPncW+ePGiJCcnS4MGDaRz585y/fXXO/0iUZyHHnpIMjMzpWnTpjJ16lSZNWuWdOrUSUR+u7Rn5cqVcvPNN8ugQYOkXr160rt3bzl8+LBERESUar//8pe/yEsvvSRz586VJk2ayNq1a+Wxxx5zesxzzz0nlStXllatWkliYqJ06tRJmjVrVmSuunXrSqtWreT666+Xli1blmodAOArbPpHLoYEAOAyYmJiJCUlRVJSUjy9lFJRValfv74MHTpURo8e7enlAIBLcEkPAOCqdPz4cXnttdfkyJEjcu+993p6OQDgMhR+AMBVKSIiQqpUqSLz58+XypUre3o5AOAyXNIDAAAAGIw/2gUAAAAMRuEHAAAADEbhBwAAAAxG4QcAAAAMRuEHAAAADEbhBwAAAAxG4QcAAAAMRuEHAAAADEbhBwAAAAz2/wA1+gSY8puQLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "sns.barplot(x=df['Hours per day'], y=df['OCD'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "be336439-4528-45f2-8e16-f96d909961d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25]),\n",
       " [Text(0, 0, '0.0'),\n",
       "  Text(1, 0, '0.1'),\n",
       "  Text(2, 0, '0.25'),\n",
       "  Text(3, 0, '0.5'),\n",
       "  Text(4, 0, '0.7'),\n",
       "  Text(5, 0, '1.0'),\n",
       "  Text(6, 0, '1.5'),\n",
       "  Text(7, 0, '2.0'),\n",
       "  Text(8, 0, '2.5'),\n",
       "  Text(9, 0, '3.0'),\n",
       "  Text(10, 0, '4.0'),\n",
       "  Text(11, 0, '4.5'),\n",
       "  Text(12, 0, '5.0'),\n",
       "  Text(13, 0, '6.0'),\n",
       "  Text(14, 0, '7.0'),\n",
       "  Text(15, 0, '8.0'),\n",
       "  Text(16, 0, '9.0'),\n",
       "  Text(17, 0, '10.0'),\n",
       "  Text(18, 0, '11.0'),\n",
       "  Text(19, 0, '12.0'),\n",
       "  Text(20, 0, '14.0'),\n",
       "  Text(21, 0, '15.0'),\n",
       "  Text(22, 0, '16.0'),\n",
       "  Text(23, 0, '18.0'),\n",
       "  Text(24, 0, '20.0'),\n",
       "  Text(25, 0, '24.0')])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "sns.barplot(x=df['Hours per day'], y=df['Depression'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c133fd8-00ec-41a6-83ed-dffcf06b65a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'No'), Text(1, 0, 'Yes')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.barplot(x=df['While working'], y=df['Depression'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7550190-52ad-464c-92df-d2b2ff0f3dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'No'), Text(1, 0, 'Yes')])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.barplot(x=df['While working'], y=df['OCD'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "640f19c4-2c5d-4782-9602-7a3806f739a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'No'), Text(1, 0, 'Yes')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.barplot(x=df['While working'], y=df['Anxiety'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "88e4ed43-5860-4a54-ae43-8e51fbd81971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'No'), Text(1, 0, 'Yes')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.barplot(x=df['While working'], y=df['Insomnia'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "22b7ad76-af8c-484c-a28a-19991ee04f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'No'), Text(1, 0, 'Yes')])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"All mental health\"] = df[\"OCD\"] + df[\"Anxiety\"] + df[\"Depression\"] + df[\"Insomnia\"]\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.barplot(x=df['While working'], y=df['All mental health'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "789e8193-22db-4f55-923f-f8b5ee83fb92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25]),\n",
       " [Text(0, 0, '0.0'),\n",
       "  Text(1, 0, '0.1'),\n",
       "  Text(2, 0, '0.25'),\n",
       "  Text(3, 0, '0.5'),\n",
       "  Text(4, 0, '0.7'),\n",
       "  Text(5, 0, '1.0'),\n",
       "  Text(6, 0, '1.5'),\n",
       "  Text(7, 0, '2.0'),\n",
       "  Text(8, 0, '2.5'),\n",
       "  Text(9, 0, '3.0'),\n",
       "  Text(10, 0, '4.0'),\n",
       "  Text(11, 0, '4.5'),\n",
       "  Text(12, 0, '5.0'),\n",
       "  Text(13, 0, '6.0'),\n",
       "  Text(14, 0, '7.0'),\n",
       "  Text(15, 0, '8.0'),\n",
       "  Text(16, 0, '9.0'),\n",
       "  Text(17, 0, '10.0'),\n",
       "  Text(18, 0, '11.0'),\n",
       "  Text(19, 0, '12.0'),\n",
       "  Text(20, 0, '14.0'),\n",
       "  Text(21, 0, '15.0'),\n",
       "  Text(22, 0, '16.0'),\n",
       "  Text(23, 0, '18.0'),\n",
       "  Text(24, 0, '20.0'),\n",
       "  Text(25, 0, '24.0')])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"All mental health\"] = df[\"OCD\"] + df[\"Anxiety\"] + df[\"Depression\"] + df[\"Insomnia\"]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(9,5))\n",
    "sns.barplot(x=df['Hours per day'], y=df['All mental health'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "081c78eb-668f-4385-8ce8-fcb8c99653e2",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Line2D' object has no property 'errorbar'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/f_/jy4yp3q137s36hxywd7y7s_c0000gp/T/ipykernel_82353/383002821.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fav genre'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Hours per day'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m    708\u001b[0m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_attach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 710\u001b[0;31m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    711\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    529\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn_datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m         result = (make_artist(x[:, j % ncx], y[:, j % ncy], kw,\n\u001b[0m\u001b[1;32m    532\u001b[0m                               {**kwargs, 'label': label})\n\u001b[1;32m    533\u001b[0m                   for j, label in enumerate(labels))\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_makeline\u001b[0;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0mdefault_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m         \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    352\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;31m# update kwargs before updating data to give the caller a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0;31m# chance to init axes (and hence unit support)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpickradius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickradius\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mind_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m   1062\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"set_{k}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1063\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1064\u001b[0;31m                         raise AttributeError(f\"{type(self).__name__!r} object \"\n\u001b[0m\u001b[1;32m   1065\u001b[0m                                              f\"has no property {k!r}\")\n\u001b[1;32m   1066\u001b[0m                     \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Line2D' object has no property 'errorbar'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.lineplot(x=df['Fav genre'], y=df['Hours per day'], errorbar=None)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f95d035-79bb-4875-8453-7f1f659c6f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25]),\n",
       " [Text(0, 0, '0.0'),\n",
       "  Text(1, 0, '0.1'),\n",
       "  Text(2, 0, '0.25'),\n",
       "  Text(3, 0, '0.5'),\n",
       "  Text(4, 0, '0.7'),\n",
       "  Text(5, 0, '1.0'),\n",
       "  Text(6, 0, '1.5'),\n",
       "  Text(7, 0, '2.0'),\n",
       "  Text(8, 0, '2.5'),\n",
       "  Text(9, 0, '3.0'),\n",
       "  Text(10, 0, '4.0'),\n",
       "  Text(11, 0, '4.5'),\n",
       "  Text(12, 0, '5.0'),\n",
       "  Text(13, 0, '6.0'),\n",
       "  Text(14, 0, '7.0'),\n",
       "  Text(15, 0, '8.0'),\n",
       "  Text(16, 0, '9.0'),\n",
       "  Text(17, 0, '10.0'),\n",
       "  Text(18, 0, '11.0'),\n",
       "  Text(19, 0, '12.0'),\n",
       "  Text(20, 0, '14.0'),\n",
       "  Text(21, 0, '15.0'),\n",
       "  Text(22, 0, '16.0'),\n",
       "  Text(23, 0, '18.0'),\n",
       "  Text(24, 0, '20.0'),\n",
       "  Text(25, 0, '24.0')])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"All mental health\"] = df[\"OCD\"] + df[\"Anxiety\"] + df[\"Depression\"] + df[\"Insomnia\"]\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x=df['Hours per day'], y=df['All mental health'], hue=df['Music effects'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7b1b230-a3cb-45f9-813b-a8bb32163f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'No'), Text(1, 0, 'Yes')])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"All mental health\"] = df[\"OCD\"] + df[\"Anxiety\"] + df[\"Depression\"] + df[\"Insomnia\"]\n",
    "plt.figure(figsize=(9,6))\n",
    "sns.barplot(x=df['While working'], y=df['All mental health'], hue=df['Music effects'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65ada520-26de-4403-b2eb-b9f64fe2dee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25]),\n",
       " [Text(0, 0, '0.0'),\n",
       "  Text(1, 0, '0.1'),\n",
       "  Text(2, 0, '0.25'),\n",
       "  Text(3, 0, '0.5'),\n",
       "  Text(4, 0, '0.7'),\n",
       "  Text(5, 0, '1.0'),\n",
       "  Text(6, 0, '1.5'),\n",
       "  Text(7, 0, '2.0'),\n",
       "  Text(8, 0, '2.5'),\n",
       "  Text(9, 0, '3.0'),\n",
       "  Text(10, 0, '4.0'),\n",
       "  Text(11, 0, '4.5'),\n",
       "  Text(12, 0, '5.0'),\n",
       "  Text(13, 0, '6.0'),\n",
       "  Text(14, 0, '7.0'),\n",
       "  Text(15, 0, '8.0'),\n",
       "  Text(16, 0, '9.0'),\n",
       "  Text(17, 0, '10.0'),\n",
       "  Text(18, 0, '11.0'),\n",
       "  Text(19, 0, '12.0'),\n",
       "  Text(20, 0, '14.0'),\n",
       "  Text(21, 0, '15.0'),\n",
       "  Text(22, 0, '16.0'),\n",
       "  Text(23, 0, '18.0'),\n",
       "  Text(24, 0, '20.0'),\n",
       "  Text(25, 0, '24.0')])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"All mental health\"] = df[\"OCD\"] + df[\"Anxiety\"] + df[\"Depression\"] + df[\"Insomnia\"]\n",
    "plt.figure(figsize=(9,6))\n",
    "sns.barplot(x=df['Hours per day'], y=df['All mental health'], hue=df['Music effects'], errwidth=0)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4c5afd0-f3f2-4578-8487-c212b6a2fde4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}