bike_counters_starting_kit.ipynb

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    "# RAMP on predicting cyclist traffic in Paris\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The dataset was collected with cyclist counters installed by Paris city council in multiple locations. It contains hourly information about cyclist traffic, as well as the following features,\n",
    " - counter name\n",
    " - counter site name\n",
    " - date\n",
    " - counter installation date\n",
    " - latitude and longitude\n",
    " \n",
    "Available features are quite scarce. However, **we can also use any external data that can help us to predict the target variable.** "
   ]
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   "source": [
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn"
   ]
  },
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   "source": [
    "# Loading the data with pandas\n",
    "\n",
    "First, download the data files from Kaggle, and put them to into the data folder.\n",
    "\n",
    "\n",
    "Data is stored in [Parquet format](https://parquet.apache.org/), an efficient columnar data format. We can load the train set with pandas,"
   ]
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   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counter_id</th>\n",
       "      <th>counter_name</th>\n",
       "      <th>site_id</th>\n",
       "      <th>site_name</th>\n",
       "      <th>bike_count</th>\n",
       "      <th>date</th>\n",
       "      <th>counter_installation_date</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>counter_technical_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>log_bike_count</th>\n",
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       "      <td>100007049</td>\n",
       "      <td>28 boulevard Diderot</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2020-09-01 18:00:00</td>\n",
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      "text/plain": [
       "                counter_id              counter_name    site_id  \\\n",
       "48321  100007049-102007049  28 boulevard Diderot E-O  100007049   \n",
       "48324  100007049-102007049  28 boulevard Diderot E-O  100007049   \n",
       "48327  100007049-102007049  28 boulevard Diderot E-O  100007049   \n",
       "48330  100007049-102007049  28 boulevard Diderot E-O  100007049   \n",
       "48333  100007049-102007049  28 boulevard Diderot E-O  100007049   \n",
       "\n",
       "                  site_name  bike_count                date  \\\n",
       "48321  28 boulevard Diderot         0.0 2020-09-01 02:00:00   \n",
       "48324  28 boulevard Diderot         1.0 2020-09-01 03:00:00   \n",
       "48327  28 boulevard Diderot         0.0 2020-09-01 04:00:00   \n",
       "48330  28 boulevard Diderot         4.0 2020-09-01 15:00:00   \n",
       "48333  28 boulevard Diderot         9.0 2020-09-01 18:00:00   \n",
       "\n",
       "      counter_installation_date         coordinates counter_technical_id  \\\n",
       "48321                2013-01-18  48.846028,2.375429          Y2H15027244   \n",
       "48324                2013-01-18  48.846028,2.375429          Y2H15027244   \n",
       "48327                2013-01-18  48.846028,2.375429          Y2H15027244   \n",
       "48330                2013-01-18  48.846028,2.375429          Y2H15027244   \n",
       "48333                2013-01-18  48.846028,2.375429          Y2H15027244   \n",
       "\n",
       "        latitude  longitude  log_bike_count  \n",
       "48321  48.846028   2.375429        0.000000  \n",
       "48324  48.846028   2.375429        0.693147  \n",
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   "source": [
    "data = pd.read_parquet(Path(\"data\") / \"train.parquet\")\n",
    "data.head()"
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   "source": [
    "We can check general information about different columns:"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 496827 entries, 48321 to 929187\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count   Dtype         \n",
      "---  ------                     --------------   -----         \n",
      " 0   counter_id                 496827 non-null  category      \n",
      " 1   counter_name               496827 non-null  category      \n",
      " 2   site_id                    496827 non-null  int64         \n",
      " 3   site_name                  496827 non-null  category      \n",
      " 4   bike_count                 496827 non-null  float64       \n",
      " 5   date                       496827 non-null  datetime64[us]\n",
      " 6   counter_installation_date  496827 non-null  datetime64[us]\n",
      " 7   coordinates                496827 non-null  category      \n",
      " 8   counter_technical_id       496827 non-null  category      \n",
      " 9   latitude                   496827 non-null  float64       \n",
      " 10  longitude                  496827 non-null  float64       \n",
      " 11  log_bike_count             496827 non-null  float64       \n",
      "dtypes: category(5), datetime64[us](2), float64(4), int64(1)\n",
      "memory usage: 32.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "586ebce3-c013-4c5e-ab78-03a8d8d86d7e",
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   "source": [
    "and in particular the number of unique entries in each column,"
   ]
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   "id": "0a88d812-daf9-451d-baa6-67bf735ae368",
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    {
     "data": {
      "text/plain": [
       "counter_id                     56\n",
       "counter_name                   56\n",
       "site_id                        30\n",
       "site_name                      30\n",
       "bike_count                    998\n",
       "date                         8974\n",
       "counter_installation_date      22\n",
       "coordinates                    30\n",
       "counter_technical_id           30\n",
       "latitude                       30\n",
       "longitude                      30\n",
       "log_bike_count                998\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.nunique(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7504b6-d301-4d37-aa55-4d73b0dcedbd",
   "metadata": {},
   "source": [
    "We have a 30 counting sites where sometimes multiple counters are installed per location.  Let's look at the most frequented stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd1c75a9-1976-4a97-bf11-76d0026b591b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5w/hyfqlrq101nd83437kh5nlm00000gn/T/ipykernel_32692/1192055722.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([\"site_name\", \"counter_name\"])[\"bike_count\"].sum()\n"
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       "      <th>Totem 73 boulevard de Sébastopol</th>\n",
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       "      <td>1992227.0</td>\n",
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       "      <td>1551730.0</td>\n",
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       "      <th>Totem 73 boulevard de Sébastopol</th>\n",
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       "      <td>1497912.0</td>\n",
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       "      <th>67 boulevard Voltaire SE-NO</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Totem 64 Rue de Rivoli</th>\n",
       "      <th>Totem 64 Rue de Rivoli E-O</th>\n",
       "      <td>1015875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27 quai de la Tournelle</th>\n",
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       "      <td>984914.0</td>\n",
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       "    <tr>\n",
       "      <th>Quai d'Orsay</th>\n",
       "      <th>Quai d'Orsay E-O</th>\n",
       "      <td>948357.0</td>\n",
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       "    <tr>\n",
       "      <th>Totem Cours la Reine</th>\n",
       "      <th>Totem Cours la Reine O-E</th>\n",
       "      <td>894937.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Face au 48 quai de la marne</th>\n",
       "      <th>Face au 48 quai de la marne SO-NE</th>\n",
       "      <td>884907.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Face au 48 quai de la marne NE-SO</th>\n",
       "      <td>833657.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                                                       bike_count\n",
       "site_name                        counter_name                                    \n",
       "Totem 73 boulevard de Sébastopol Totem 73 boulevard de Sébastopol S-N   1992227.0\n",
       "Totem 64 Rue de Rivoli           Totem 64 Rue de Rivoli O-E             1551730.0\n",
       "Totem 73 boulevard de Sébastopol Totem 73 boulevard de Sébastopol N-S   1497912.0\n",
       "67 boulevard Voltaire SE-NO      67 boulevard Voltaire SE-NO            1130565.0\n",
       "Totem 64 Rue de Rivoli           Totem 64 Rue de Rivoli E-O             1015875.0\n",
       "27 quai de la Tournelle          27 quai de la Tournelle SE-NO           984914.0\n",
       "Quai d'Orsay                     Quai d'Orsay E-O                        948357.0\n",
       "Totem Cours la Reine             Totem Cours la Reine O-E                894937.0\n",
       "Face au 48 quai de la marne      Face au 48 quai de la marne SO-NE       884907.0\n",
       "                                 Face au 48 quai de la marne NE-SO       833657.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    data.groupby([\"site_name\", \"counter_name\"])[\"bike_count\"].sum()\n",
    "    .sort_values(ascending=False)\n",
    "    .head(10)\n",
    "    .to_frame()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1dc688-2649-4f7f-9326-157b08ea4bae",
   "metadata": {},
   "source": [
    "# Visualizing the data\n",
    "\n",
    "\n",
    "Let's visualize the data, starting from the spatial distribution of counters on the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a1fe386-fb15-4d60-b9f8-8b4b2bc175b7",
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     "text": [
      "/Users/vincentmaladiere/mambaforge/envs/bike_counters/lib/python3.10/site-packages/folium/utilities.py:86: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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       "                &quot;map_ffe67163f8f8d1bd0fe1ce6c762b14c9&quot;,\n",
       "                {\n",
       "                    center: [48.854343481348636, 2.3454791459683153],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 13,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_8d85ba18f3d850c1bcb4aa50f79cf20e = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            );\n",
       "        \n",
       "    \n",
       "                tile_layer_8d85ba18f3d850c1bcb4aa50f79cf20e.addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "    \n",
       "            var marker_aa304b2a70ebc08dc4c5b3e85f70e043 = L.marker(\n",
       "                [48.846028, 2.375429],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_7a6626f9e45ed78207ca57de00ffed94 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_f204e5eac4e706b7cd0a9e9b9df9f0ea = $(`&lt;div id=&quot;html_f204e5eac4e706b7cd0a9e9b9df9f0ea&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;28 boulevard Diderot E-O&lt;/div&gt;`)[0];\n",
       "                popup_7a6626f9e45ed78207ca57de00ffed94.setContent(html_f204e5eac4e706b7cd0a9e9b9df9f0ea);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_aa304b2a70ebc08dc4c5b3e85f70e043.bindPopup(popup_7a6626f9e45ed78207ca57de00ffed94)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_6f35d2ea7eeed19b30c312eb4779eb91 = L.marker(\n",
       "                [48.846028, 2.375429],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_3dda15f4b23f46380335cd3916421d12 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ae9654289be9fe37db86953d8c3a975d = $(`&lt;div id=&quot;html_ae9654289be9fe37db86953d8c3a975d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;28 boulevard Diderot O-E&lt;/div&gt;`)[0];\n",
       "                popup_3dda15f4b23f46380335cd3916421d12.setContent(html_ae9654289be9fe37db86953d8c3a975d);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_6f35d2ea7eeed19b30c312eb4779eb91.bindPopup(popup_3dda15f4b23f46380335cd3916421d12)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_23b0484acf62c8613719cc65a623c4a5 = L.marker(\n",
       "                [48.83436, 2.377],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_9a06a0f31d218931c3028b2af4d9686c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_49caff0742391e0c808e892a2ea6ebcb = $(`&lt;div id=&quot;html_49caff0742391e0c808e892a2ea6ebcb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;39 quai François Mauriac NO-SE&lt;/div&gt;`)[0];\n",
       "                popup_9a06a0f31d218931c3028b2af4d9686c.setContent(html_49caff0742391e0c808e892a2ea6ebcb);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_23b0484acf62c8613719cc65a623c4a5.bindPopup(popup_9a06a0f31d218931c3028b2af4d9686c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_f5cf80e5dcf794bed7db67b077fe68ad = L.marker(\n",
       "                [48.83436, 2.377],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_2443a5843e4699a2159a6f3ebcc24157 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_27997c1bc36fe5f05f394a3df67547b2 = $(`&lt;div id=&quot;html_27997c1bc36fe5f05f394a3df67547b2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;39 quai François Mauriac SE-NO&lt;/div&gt;`)[0];\n",
       "                popup_2443a5843e4699a2159a6f3ebcc24157.setContent(html_27997c1bc36fe5f05f394a3df67547b2);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_f5cf80e5dcf794bed7db67b077fe68ad.bindPopup(popup_2443a5843e4699a2159a6f3ebcc24157)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7f13902d70fb23b4f040cb233cca3d13 = L.marker(\n",
       "                [48.85372, 2.35702],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_ada66ec23716551d1396300cf7c38863 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ee5790d7d8debe6649fc29a6fc2d3f55 = $(`&lt;div id=&quot;html_ee5790d7d8debe6649fc29a6fc2d3f55&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;18 quai de l&#x27;Hôtel de Ville NO-SE&lt;/div&gt;`)[0];\n",
       "                popup_ada66ec23716551d1396300cf7c38863.setContent(html_ee5790d7d8debe6649fc29a6fc2d3f55);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_7f13902d70fb23b4f040cb233cca3d13.bindPopup(popup_ada66ec23716551d1396300cf7c38863)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_d540d7c67c951f140c3170bbb863767a = L.marker(\n",
       "                [48.85372, 2.35702],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_e84453b240a04bdf026ce55e7c4f35e9 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_722aa68eeafb714dd685c716d896a0d0 = $(`&lt;div id=&quot;html_722aa68eeafb714dd685c716d896a0d0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;18 quai de l&#x27;Hôtel de Ville SE-NO&lt;/div&gt;`)[0];\n",
       "                popup_e84453b240a04bdf026ce55e7c4f35e9.setContent(html_722aa68eeafb714dd685c716d896a0d0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d540d7c67c951f140c3170bbb863767a.bindPopup(popup_e84453b240a04bdf026ce55e7c4f35e9)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_5d58d16cb8ed74b1173f40b4c5b24070 = L.marker(\n",
       "                [48.8484, 2.27586],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_e30063cec4fd1d7ecb0d1397876dd0b0 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_a869ff9f537d23dea188d614d4de4ec3 = $(`&lt;div id=&quot;html_a869ff9f537d23dea188d614d4de4ec3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Voie Georges Pompidou NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_e30063cec4fd1d7ecb0d1397876dd0b0.setContent(html_a869ff9f537d23dea188d614d4de4ec3);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_5d58d16cb8ed74b1173f40b4c5b24070.bindPopup(popup_e30063cec4fd1d7ecb0d1397876dd0b0)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_d080078d6044cc96441b9d1034cc6e16 = L.marker(\n",
       "                [48.8484, 2.27586],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_888b0e10105913796ad7808245816fbe = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d7a66c368a5792de58e55538d15a5089 = $(`&lt;div id=&quot;html_d7a66c368a5792de58e55538d15a5089&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Voie Georges Pompidou SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_888b0e10105913796ad7808245816fbe.setContent(html_d7a66c368a5792de58e55538d15a5089);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d080078d6044cc96441b9d1034cc6e16.bindPopup(popup_888b0e10105913796ad7808245816fbe)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_8cc983b91aa31bb95090f55b307ff2be = L.marker(\n",
       "                [48.86149, 2.37376],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_e87ef9902caf45b22187d7a53c60344c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_681fe1edabab96ad5d72568656d3a999 = $(`&lt;div id=&quot;html_681fe1edabab96ad5d72568656d3a999&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;67 boulevard Voltaire SE-NO&lt;/div&gt;`)[0];\n",
       "                popup_e87ef9902caf45b22187d7a53c60344c.setContent(html_681fe1edabab96ad5d72568656d3a999);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_8cc983b91aa31bb95090f55b307ff2be.bindPopup(popup_e87ef9902caf45b22187d7a53c60344c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_0d1a95529626b2b81b09c25e2a106399 = L.marker(\n",
       "                [48.89172, 2.38531],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_f889ea664e74eaf96c8b891c7714733e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_80b6dd37887f9627cd738c45df1263f1 = $(`&lt;div id=&quot;html_80b6dd37887f9627cd738c45df1263f1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 48 quai de la marne NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_f889ea664e74eaf96c8b891c7714733e.setContent(html_80b6dd37887f9627cd738c45df1263f1);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_0d1a95529626b2b81b09c25e2a106399.bindPopup(popup_f889ea664e74eaf96c8b891c7714733e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_825eab8a097e76fd79611435b62087aa = L.marker(\n",
       "                [48.89172, 2.38531],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_8759414471c72d772aed9e5f60d5ae29 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_90c78a5c196936753076d77ca8c3a213 = $(`&lt;div id=&quot;html_90c78a5c196936753076d77ca8c3a213&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 48 quai de la marne SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_8759414471c72d772aed9e5f60d5ae29.setContent(html_90c78a5c196936753076d77ca8c3a213);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_825eab8a097e76fd79611435b62087aa.bindPopup(popup_8759414471c72d772aed9e5f60d5ae29)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_9589e0bdfc684773b7dc267a6f3d76ea = L.marker(\n",
       "                [48.890457, 2.368852],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_e6fedef697f8f59f1a0eda0302d1b40c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_31c51f90564e60e4a45adde066f7c0ce = $(`&lt;div id=&quot;html_31c51f90564e60e4a45adde066f7c0ce&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face 104 rue d&#x27;Aubervilliers N-S&lt;/div&gt;`)[0];\n",
       "                popup_e6fedef697f8f59f1a0eda0302d1b40c.setContent(html_31c51f90564e60e4a45adde066f7c0ce);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_9589e0bdfc684773b7dc267a6f3d76ea.bindPopup(popup_e6fedef697f8f59f1a0eda0302d1b40c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_adef77ae984ca1fd7ee39346056ed9e8 = L.marker(\n",
       "                [48.890457, 2.368852],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_2ee10ee247a3072b3c5dfc76c624328f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_59192d7d8dd0f1b96d10f6477e993bf3 = $(`&lt;div id=&quot;html_59192d7d8dd0f1b96d10f6477e993bf3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face 104 rue d&#x27;Aubervilliers S-N&lt;/div&gt;`)[0];\n",
       "                popup_2ee10ee247a3072b3c5dfc76c624328f.setContent(html_59192d7d8dd0f1b96d10f6477e993bf3);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_adef77ae984ca1fd7ee39346056ed9e8.bindPopup(popup_2ee10ee247a3072b3c5dfc76c624328f)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_480e01a8538fce8785ea2f7e4a3a6ef8 = L.marker(\n",
       "                [48.829523, 2.38699],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_f41c75dac2289a7666f0d9e3d8305237 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_039abbff1d0356e2bd8ddaebf783373c = $(`&lt;div id=&quot;html_039abbff1d0356e2bd8ddaebf783373c&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 70 quai de Bercy N-S&lt;/div&gt;`)[0];\n",
       "                popup_f41c75dac2289a7666f0d9e3d8305237.setContent(html_039abbff1d0356e2bd8ddaebf783373c);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_480e01a8538fce8785ea2f7e4a3a6ef8.bindPopup(popup_f41c75dac2289a7666f0d9e3d8305237)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_69418e53ece9b73732259d59bd513501 = L.marker(\n",
       "                [48.829523, 2.38699],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_ae143d8f570b79fc06932f16d9bf5d44 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d9a5d5c7ef8cb10389560138cb0e42ad = $(`&lt;div id=&quot;html_d9a5d5c7ef8cb10389560138cb0e42ad&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 70 quai de Bercy S-N&lt;/div&gt;`)[0];\n",
       "                popup_ae143d8f570b79fc06932f16d9bf5d44.setContent(html_d9a5d5c7ef8cb10389560138cb0e42ad);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_69418e53ece9b73732259d59bd513501.bindPopup(popup_ae143d8f570b79fc06932f16d9bf5d44)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_5783c3b2fc7bfd2b4138105ddcc9bea1 = L.marker(\n",
       "                [48.82636, 2.30303],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_e0421126ca27c1b0d59f0b440e24c719 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_815bc362c70f8faca961a7dd4847d3f8 = $(`&lt;div id=&quot;html_815bc362c70f8faca961a7dd4847d3f8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;6 rue Julia Bartet NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_e0421126ca27c1b0d59f0b440e24c719.setContent(html_815bc362c70f8faca961a7dd4847d3f8);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_5783c3b2fc7bfd2b4138105ddcc9bea1.bindPopup(popup_e0421126ca27c1b0d59f0b440e24c719)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_6189bcd0db54208a5bc2792f3fb1c5a6 = L.marker(\n",
       "                [48.82636, 2.30303],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_f5a11d8d5ca80f869879a75c5204ba5a = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_3cae94dcc2613a22131028c30eb1b3b8 = $(`&lt;div id=&quot;html_3cae94dcc2613a22131028c30eb1b3b8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;6 rue Julia Bartet SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_f5a11d8d5ca80f869879a75c5204ba5a.setContent(html_3cae94dcc2613a22131028c30eb1b3b8);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_6189bcd0db54208a5bc2792f3fb1c5a6.bindPopup(popup_f5a11d8d5ca80f869879a75c5204ba5a)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_b973393ff86b6c6ce19ba8284ce84d31 = L.marker(\n",
       "                [48.89141, 2.38482],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_b4d3b946fff24e2527a0cdb0a445bb64 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e934c42b35b234b1f8cc90fd89a0f48e = $(`&lt;div id=&quot;html_e934c42b35b234b1f8cc90fd89a0f48e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 25 quai de l&#x27;Oise NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_b4d3b946fff24e2527a0cdb0a445bb64.setContent(html_e934c42b35b234b1f8cc90fd89a0f48e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_b973393ff86b6c6ce19ba8284ce84d31.bindPopup(popup_b4d3b946fff24e2527a0cdb0a445bb64)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_4365ffe57aaf1c0a2963a9d16343844b = L.marker(\n",
       "                [48.89141, 2.38482],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_f0c63a4984986cfc1f023ff78010132f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_42c11bc352568fe01f31a79e9faa0db3 = $(`&lt;div id=&quot;html_42c11bc352568fe01f31a79e9faa0db3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 25 quai de l&#x27;Oise SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_f0c63a4984986cfc1f023ff78010132f.setContent(html_42c11bc352568fe01f31a79e9faa0db3);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_4365ffe57aaf1c0a2963a9d16343844b.bindPopup(popup_f0c63a4984986cfc1f023ff78010132f)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_96b9da32dcfc86625cba358200ce0595 = L.marker(\n",
       "                [48.840801, 2.333233],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_3f0cabaa4e8c9b361d1f300af82e92a9 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e36c5de799d42cba772bcaa6097017d4 = $(`&lt;div id=&quot;html_e36c5de799d42cba772bcaa6097017d4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;152 boulevard du Montparnasse E-O&lt;/div&gt;`)[0];\n",
       "                popup_3f0cabaa4e8c9b361d1f300af82e92a9.setContent(html_e36c5de799d42cba772bcaa6097017d4);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_96b9da32dcfc86625cba358200ce0595.bindPopup(popup_3f0cabaa4e8c9b361d1f300af82e92a9)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_27295ae160456869add4c04271a2f517 = L.marker(\n",
       "                [48.840801, 2.333233],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_41659a332c774d690af55388d60aa2d1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_42de5142b20439d90fe163f8f4963ee1 = $(`&lt;div id=&quot;html_42de5142b20439d90fe163f8f4963ee1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;152 boulevard du Montparnasse O-E&lt;/div&gt;`)[0];\n",
       "                popup_41659a332c774d690af55388d60aa2d1.setContent(html_42de5142b20439d90fe163f8f4963ee1);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_27295ae160456869add4c04271a2f517.bindPopup(popup_41659a332c774d690af55388d60aa2d1)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_094ace48884cbbe5fa75fea0c8ac88fd = L.marker(\n",
       "                [48.85735, 2.35211],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_001917eca523717c5081a02eabe3499e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_041362c0d49751591e2f23dc3f63e390 = $(`&lt;div id=&quot;html_041362c0d49751591e2f23dc3f63e390&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem 64 Rue de Rivoli E-O&lt;/div&gt;`)[0];\n",
       "                popup_001917eca523717c5081a02eabe3499e.setContent(html_041362c0d49751591e2f23dc3f63e390);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_094ace48884cbbe5fa75fea0c8ac88fd.bindPopup(popup_001917eca523717c5081a02eabe3499e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_bd0c1de3d9209653af7cb3c57cfe34f9 = L.marker(\n",
       "                [48.85735, 2.35211],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_b1bd068328f09dde2173ea09edb4643c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_20a8d1fb0979e5dd8bb4cff7ff59cdf1 = $(`&lt;div id=&quot;html_20a8d1fb0979e5dd8bb4cff7ff59cdf1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem 64 Rue de Rivoli O-E&lt;/div&gt;`)[0];\n",
       "                popup_b1bd068328f09dde2173ea09edb4643c.setContent(html_20a8d1fb0979e5dd8bb4cff7ff59cdf1);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_bd0c1de3d9209653af7cb3c57cfe34f9.bindPopup(popup_b1bd068328f09dde2173ea09edb4643c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_60068106d54e5ba20bbddecf03a24787 = L.marker(\n",
       "                [48.86282, 2.31061],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_77ad393d63991a7d311f2c6a44cd83d2 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_66caac0df462bf60cd117786c0dacf41 = $(`&lt;div id=&quot;html_66caac0df462bf60cd117786c0dacf41&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Pont des Invalides S-N&lt;/div&gt;`)[0];\n",
       "                popup_77ad393d63991a7d311f2c6a44cd83d2.setContent(html_66caac0df462bf60cd117786c0dacf41);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_60068106d54e5ba20bbddecf03a24787.bindPopup(popup_77ad393d63991a7d311f2c6a44cd83d2)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_affd8d9497c7e50b99b2b99154b86274 = L.marker(\n",
       "                [48.86378, 2.32003],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_a4b5a1a97431925115b9b508f8f643ef = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_2f61705d212c68ccb45d254e7ff48e89 = $(`&lt;div id=&quot;html_2f61705d212c68ccb45d254e7ff48e89&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Pont de la Concorde S-N&lt;/div&gt;`)[0];\n",
       "                popup_a4b5a1a97431925115b9b508f8f643ef.setContent(html_2f61705d212c68ccb45d254e7ff48e89);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_affd8d9497c7e50b99b2b99154b86274.bindPopup(popup_a4b5a1a97431925115b9b508f8f643ef)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_0d52086cc6c2dde8713be086bd45c11d = L.marker(\n",
       "                [48.86284, 2.310345],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_c15c7aef2ec4e044f821cfa2dd5a5cd7 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_c9af8cfca62526ca0ed016c37fd99b45 = $(`&lt;div id=&quot;html_c9af8cfca62526ca0ed016c37fd99b45&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Pont des Invalides N-S&lt;/div&gt;`)[0];\n",
       "                popup_c15c7aef2ec4e044f821cfa2dd5a5cd7.setContent(html_c9af8cfca62526ca0ed016c37fd99b45);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_0d52086cc6c2dde8713be086bd45c11d.bindPopup(popup_c15c7aef2ec4e044f821cfa2dd5a5cd7)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_c576e6a3fd5fad9fa73213902dfbcae3 = L.marker(\n",
       "                [48.830331, 2.400551],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_e44bfd9358d9a38a3bfbe3e7c5983f57 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_fff08d67171a3fc96ca572ffcde5506b = $(`&lt;div id=&quot;html_fff08d67171a3fc96ca572ffcde5506b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 8 avenue de la porte de Charenton NO-SE&lt;/div&gt;`)[0];\n",
       "                popup_e44bfd9358d9a38a3bfbe3e7c5983f57.setContent(html_fff08d67171a3fc96ca572ffcde5506b);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_c576e6a3fd5fad9fa73213902dfbcae3.bindPopup(popup_e44bfd9358d9a38a3bfbe3e7c5983f57)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_8e9ada9056da252b28740e78cd88c264 = L.marker(\n",
       "                [48.830331, 2.400551],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_c2bbda22ca315ba29d7d277d1cc00a3d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_96fd44de62cd2d5f65b8cf39ff819940 = $(`&lt;div id=&quot;html_96fd44de62cd2d5f65b8cf39ff819940&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 8 avenue de la porte de Charenton SE-NO&lt;/div&gt;`)[0];\n",
       "                popup_c2bbda22ca315ba29d7d277d1cc00a3d.setContent(html_96fd44de62cd2d5f65b8cf39ff819940);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_8e9ada9056da252b28740e78cd88c264.bindPopup(popup_c2bbda22ca315ba29d7d277d1cc00a3d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_90a435e30bb7b8768e7489411d6e1587 = L.marker(\n",
       "                [48.86461, 2.40969],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_68cc6609b54be337e4dd3aa5d7532cdd = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_150f714f93dee2fc08b81c0e20142894 = $(`&lt;div id=&quot;html_150f714f93dee2fc08b81c0e20142894&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 4 avenue de la porte de Bagnolet E-O&lt;/div&gt;`)[0];\n",
       "                popup_68cc6609b54be337e4dd3aa5d7532cdd.setContent(html_150f714f93dee2fc08b81c0e20142894);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_90a435e30bb7b8768e7489411d6e1587.bindPopup(popup_68cc6609b54be337e4dd3aa5d7532cdd)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e358b44863d43ab7c00c128629ef8cfd = L.marker(\n",
       "                [48.86461, 2.40969],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_06569c639763fe9c33f60b2986c93e1e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d3281aab7bb32a7e6bcecf109f0d3439 = $(`&lt;div id=&quot;html_d3281aab7bb32a7e6bcecf109f0d3439&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 4 avenue de la porte de Bagnolet O-E&lt;/div&gt;`)[0];\n",
       "                popup_06569c639763fe9c33f60b2986c93e1e.setContent(html_d3281aab7bb32a7e6bcecf109f0d3439);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e358b44863d43ab7c00c128629ef8cfd.bindPopup(popup_06569c639763fe9c33f60b2986c93e1e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7b55b62ba65c508279f12b12917a13c6 = L.marker(\n",
       "                [48.84223, 2.36811],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_50ba466ba9ccae1efeb6e8e53e210730 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_1b00d36e3a5628d87ef45b33272e5d22 = $(`&lt;div id=&quot;html_1b00d36e3a5628d87ef45b33272e5d22&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Pont Charles De Gaulle NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_50ba466ba9ccae1efeb6e8e53e210730.setContent(html_1b00d36e3a5628d87ef45b33272e5d22);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_7b55b62ba65c508279f12b12917a13c6.bindPopup(popup_50ba466ba9ccae1efeb6e8e53e210730)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_2fde10f1817628bcc96e477342e40565 = L.marker(\n",
       "                [48.84223, 2.36811],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_5149f5ca16880ba65c00dd7cfc0f4220 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e308a8cf01216c38dc6b4ea88bb00f16 = $(`&lt;div id=&quot;html_e308a8cf01216c38dc6b4ea88bb00f16&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Pont Charles De Gaulle SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_5149f5ca16880ba65c00dd7cfc0f4220.setContent(html_e308a8cf01216c38dc6b4ea88bb00f16);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_2fde10f1817628bcc96e477342e40565.bindPopup(popup_5149f5ca16880ba65c00dd7cfc0f4220)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_8240afecc97c6980da5815aef6eb50f2 = L.marker(\n",
       "                [48.85209, 2.28508],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_aba6b6bbfb9aa0a7544a462c7d0590e6 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_115feed1de47beb694737942d6737384 = $(`&lt;div id=&quot;html_115feed1de47beb694737942d6737384&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;36 quai de Grenelle NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_aba6b6bbfb9aa0a7544a462c7d0590e6.setContent(html_115feed1de47beb694737942d6737384);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_8240afecc97c6980da5815aef6eb50f2.bindPopup(popup_aba6b6bbfb9aa0a7544a462c7d0590e6)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_0a476ea66fee0da6e76d9392f4504a3b = L.marker(\n",
       "                [48.85209, 2.28508],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_91a8d5b15587f7af1923a9432fab0aca = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_8895c41d26a0f94e4e9126eeedd5c630 = $(`&lt;div id=&quot;html_8895c41d26a0f94e4e9126eeedd5c630&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;36 quai de Grenelle SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_91a8d5b15587f7af1923a9432fab0aca.setContent(html_8895c41d26a0f94e4e9126eeedd5c630);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_0a476ea66fee0da6e76d9392f4504a3b.bindPopup(popup_91a8d5b15587f7af1923a9432fab0aca)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_d26de082770384ee599da7a165d63d3c = L.marker(\n",
       "                [48.83421, 2.26542],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_87a569f44a2f38a691f0cbab3b241248 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_1fee092777bd1fe764efb9e1aaa360ac = $(`&lt;div id=&quot;html_1fee092777bd1fe764efb9e1aaa360ac&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 40 quai D&#x27;Issy NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_87a569f44a2f38a691f0cbab3b241248.setContent(html_1fee092777bd1fe764efb9e1aaa360ac);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d26de082770384ee599da7a165d63d3c.bindPopup(popup_87a569f44a2f38a691f0cbab3b241248)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_56b66aaac1205cfe6dc861b3f03aaffc = L.marker(\n",
       "                [48.83421, 2.26542],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_c92562147b66e849aea7ffe39e3f6587 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_9de26a9b55c42d5553bf8a6725bfd8b4 = $(`&lt;div id=&quot;html_9de26a9b55c42d5553bf8a6725bfd8b4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Face au 40 quai D&#x27;Issy SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_c92562147b66e849aea7ffe39e3f6587.setContent(html_9de26a9b55c42d5553bf8a6725bfd8b4);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_56b66aaac1205cfe6dc861b3f03aaffc.bindPopup(popup_c92562147b66e849aea7ffe39e3f6587)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_a41b76166bd859655c5c5b33e0aeb396 = L.marker(\n",
       "                [48.83848, 2.37587],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_4c89434e134bc3feb20a9497e065f109 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e2dff65c6fe14178bbc990e87c60bb96 = $(`&lt;div id=&quot;html_e2dff65c6fe14178bbc990e87c60bb96&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Pont de Bercy NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_4c89434e134bc3feb20a9497e065f109.setContent(html_e2dff65c6fe14178bbc990e87c60bb96);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_a41b76166bd859655c5c5b33e0aeb396.bindPopup(popup_4c89434e134bc3feb20a9497e065f109)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_3e26a09d4ade3ce7b225ee3b01bda7ee = L.marker(\n",
       "                [48.83848, 2.37587],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_6bb3f3415c38a6cd6e388903a5d96748 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d45f2f9603ef0d9ddbb61391610f6cb0 = $(`&lt;div id=&quot;html_d45f2f9603ef0d9ddbb61391610f6cb0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Pont de Bercy SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_6bb3f3415c38a6cd6e388903a5d96748.setContent(html_d45f2f9603ef0d9ddbb61391610f6cb0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_3e26a09d4ade3ce7b225ee3b01bda7ee.bindPopup(popup_6bb3f3415c38a6cd6e388903a5d96748)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_a3bdace726a6e9f20f2777921da7a691 = L.marker(\n",
       "                [48.86502, 2.35387],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_8e985ddc65ff566743b1f4b4abdeaa24 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_7e482c66ab08f756e69ccc18b0ff10ef = $(`&lt;div id=&quot;html_7e482c66ab08f756e69ccc18b0ff10ef&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;38 rue Turbigo NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_8e985ddc65ff566743b1f4b4abdeaa24.setContent(html_7e482c66ab08f756e69ccc18b0ff10ef);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_a3bdace726a6e9f20f2777921da7a691.bindPopup(popup_8e985ddc65ff566743b1f4b4abdeaa24)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_1cb2ce877ba167010d77c8d7aa7bf6cf = L.marker(\n",
       "                [48.86502, 2.35387],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_58108af8a8f72ece9df8afab89248eb5 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_3c7673a1cfde5a32cc515918f666001e = $(`&lt;div id=&quot;html_3c7673a1cfde5a32cc515918f666001e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;38 rue Turbigo SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_58108af8a8f72ece9df8afab89248eb5.setContent(html_3c7673a1cfde5a32cc515918f666001e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_1cb2ce877ba167010d77c8d7aa7bf6cf.bindPopup(popup_58108af8a8f72ece9df8afab89248eb5)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e6d04845b862e441a894b6abc47c8c1c = L.marker(\n",
       "                [48.86288, 2.31179],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_f7f458bc8b9cb1f0d13a2e4a5dbe8fc3 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_c4698610c7b31e5f7c5c14af785ca0e1 = $(`&lt;div id=&quot;html_c4698610c7b31e5f7c5c14af785ca0e1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Quai d&#x27;Orsay E-O&lt;/div&gt;`)[0];\n",
       "                popup_f7f458bc8b9cb1f0d13a2e4a5dbe8fc3.setContent(html_c4698610c7b31e5f7c5c14af785ca0e1);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e6d04845b862e441a894b6abc47c8c1c.bindPopup(popup_f7f458bc8b9cb1f0d13a2e4a5dbe8fc3)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e870fe4e12d18fbe3c95cc60bbfc0ff0 = L.marker(\n",
       "                [48.86288, 2.31179],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_d8d3aa0b94daa348051401ea14f246d3 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_6da58b8486fe25fa00827bd1ccfd01c9 = $(`&lt;div id=&quot;html_6da58b8486fe25fa00827bd1ccfd01c9&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Quai d&#x27;Orsay O-E&lt;/div&gt;`)[0];\n",
       "                popup_d8d3aa0b94daa348051401ea14f246d3.setContent(html_6da58b8486fe25fa00827bd1ccfd01c9);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e870fe4e12d18fbe3c95cc60bbfc0ff0.bindPopup(popup_d8d3aa0b94daa348051401ea14f246d3)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_f1a04a18003f27ea1606572666893bdd = L.marker(\n",
       "                [48.85013, 2.35423],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_59d50e4906c5000c8b45dffb101aa95a = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_b494ac69ff905980739993392a9c3597 = $(`&lt;div id=&quot;html_b494ac69ff905980739993392a9c3597&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;27 quai de la Tournelle NO-SE&lt;/div&gt;`)[0];\n",
       "                popup_59d50e4906c5000c8b45dffb101aa95a.setContent(html_b494ac69ff905980739993392a9c3597);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_f1a04a18003f27ea1606572666893bdd.bindPopup(popup_59d50e4906c5000c8b45dffb101aa95a)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_701ff4e3f132aada462fafc117d58e10 = L.marker(\n",
       "                [48.85013, 2.35423],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_2acf1ecce1276035216e1f753f87af22 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_818008b71bd27ce33628fca6ca4dacce = $(`&lt;div id=&quot;html_818008b71bd27ce33628fca6ca4dacce&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;27 quai de la Tournelle SE-NO&lt;/div&gt;`)[0];\n",
       "                popup_2acf1ecce1276035216e1f753f87af22.setContent(html_818008b71bd27ce33628fca6ca4dacce);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_701ff4e3f132aada462fafc117d58e10.bindPopup(popup_2acf1ecce1276035216e1f753f87af22)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_1bcec1ba7d9c21d3e5710d45b05220c8 = L.marker(\n",
       "                [48.84201, 2.36729],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_4e36437f83edcd5e07d7305c124a1226 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e6de7bbc1079187b83e12fef565f0143 = $(`&lt;div id=&quot;html_e6de7bbc1079187b83e12fef565f0143&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem 85 quai d&#x27;Austerlitz NO-SE&lt;/div&gt;`)[0];\n",
       "                popup_4e36437f83edcd5e07d7305c124a1226.setContent(html_e6de7bbc1079187b83e12fef565f0143);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_1bcec1ba7d9c21d3e5710d45b05220c8.bindPopup(popup_4e36437f83edcd5e07d7305c124a1226)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_9cd0c181ef9bec4ff1fe38301214e232 = L.marker(\n",
       "                [48.84201, 2.36729],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_11e6e4cf17682a59c9a1573ec5e1102b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_36b19d4222b593d25c17a055046ca48b = $(`&lt;div id=&quot;html_36b19d4222b593d25c17a055046ca48b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem 85 quai d&#x27;Austerlitz SE-NO&lt;/div&gt;`)[0];\n",
       "                popup_11e6e4cf17682a59c9a1573ec5e1102b.setContent(html_36b19d4222b593d25c17a055046ca48b);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_9cd0c181ef9bec4ff1fe38301214e232.bindPopup(popup_11e6e4cf17682a59c9a1573ec5e1102b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_8a24ae6a2e5038f85f3ef315d7f1d20b = L.marker(\n",
       "                [48.86462, 2.31444],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_ad9483db930330db0c3d6161b0113ca6 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_2158635bc0801dc3a05289616309e024 = $(`&lt;div id=&quot;html_2158635bc0801dc3a05289616309e024&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem Cours la Reine E-O&lt;/div&gt;`)[0];\n",
       "                popup_ad9483db930330db0c3d6161b0113ca6.setContent(html_2158635bc0801dc3a05289616309e024);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_8a24ae6a2e5038f85f3ef315d7f1d20b.bindPopup(popup_ad9483db930330db0c3d6161b0113ca6)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7801e8ac09f02e33f3bdca24b3b17bbe = L.marker(\n",
       "                [48.86462, 2.31444],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_dbc39e9cdbfe680de1802f8312bd27be = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e8ce0eb4658a3b0beadcd55a14070b6f = $(`&lt;div id=&quot;html_e8ce0eb4658a3b0beadcd55a14070b6f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem Cours la Reine O-E&lt;/div&gt;`)[0];\n",
       "                popup_dbc39e9cdbfe680de1802f8312bd27be.setContent(html_e8ce0eb4658a3b0beadcd55a14070b6f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_7801e8ac09f02e33f3bdca24b3b17bbe.bindPopup(popup_dbc39e9cdbfe680de1802f8312bd27be)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_32c7a5ca8113965f123e02fa08cbdc9a = L.marker(\n",
       "                [48.86377, 2.35096],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_6ee850ab4e03f1f2a35b1d8baa0f4071 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_1631191346d17b607ce9df09fceac61b = $(`&lt;div id=&quot;html_1631191346d17b607ce9df09fceac61b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem 73 boulevard de Sébastopol N-S&lt;/div&gt;`)[0];\n",
       "                popup_6ee850ab4e03f1f2a35b1d8baa0f4071.setContent(html_1631191346d17b607ce9df09fceac61b);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_32c7a5ca8113965f123e02fa08cbdc9a.bindPopup(popup_6ee850ab4e03f1f2a35b1d8baa0f4071)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7d9564d30b18ed6f36e27aca1dd7fcf6 = L.marker(\n",
       "                [48.86377, 2.35096],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_3ad74b54f3fbb8b36118d605437514a5 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_93c9343d7f672a639bde6fe98aab51fc = $(`&lt;div id=&quot;html_93c9343d7f672a639bde6fe98aab51fc&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Totem 73 boulevard de Sébastopol S-N&lt;/div&gt;`)[0];\n",
       "                popup_3ad74b54f3fbb8b36118d605437514a5.setContent(html_93c9343d7f672a639bde6fe98aab51fc);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_7d9564d30b18ed6f36e27aca1dd7fcf6.bindPopup(popup_3ad74b54f3fbb8b36118d605437514a5)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_53dc601d7a9a9da21c851875dbf0c77c = L.marker(\n",
       "                [48.84638, 2.31529],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_339c2fc94fc1950ce67694691622881c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_f3d95c4faa6606d03d75320ace09d816 = $(`&lt;div id=&quot;html_f3d95c4faa6606d03d75320ace09d816&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;90 Rue De Sèvres NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_339c2fc94fc1950ce67694691622881c.setContent(html_f3d95c4faa6606d03d75320ace09d816);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_53dc601d7a9a9da21c851875dbf0c77c.bindPopup(popup_339c2fc94fc1950ce67694691622881c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_a8c8a5f50981590e152e2e64c321f53a = L.marker(\n",
       "                [48.84638, 2.31529],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_435eaaf4ee4afd9926990a228091eb7e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_1398dfac434dcd4a639bf91529280d7f = $(`&lt;div id=&quot;html_1398dfac434dcd4a639bf91529280d7f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;90 Rue De Sèvres SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_435eaaf4ee4afd9926990a228091eb7e.setContent(html_1398dfac434dcd4a639bf91529280d7f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_a8c8a5f50981590e152e2e64c321f53a.bindPopup(popup_435eaaf4ee4afd9926990a228091eb7e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_be4763a364e607ae29dd539876bf4ea9 = L.marker(\n",
       "                [48.88529, 2.32666],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_5f56c911a001cc9d651115b2415d13c2 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_c1cb4d8e2fc500c753b342453abc0ece = $(`&lt;div id=&quot;html_c1cb4d8e2fc500c753b342453abc0ece&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;20 Avenue de Clichy NO-SE&lt;/div&gt;`)[0];\n",
       "                popup_5f56c911a001cc9d651115b2415d13c2.setContent(html_c1cb4d8e2fc500c753b342453abc0ece);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_be4763a364e607ae29dd539876bf4ea9.bindPopup(popup_5f56c911a001cc9d651115b2415d13c2)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_2169767f9e5a8e9a219d00be9d2024d1 = L.marker(\n",
       "                [48.88529, 2.32666],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_a2a8c8cf10016bbf5e061ddf9ff49d9a = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d4728dfc456f4ebc7ace5a829e7ce198 = $(`&lt;div id=&quot;html_d4728dfc456f4ebc7ace5a829e7ce198&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;20 Avenue de Clichy SE-NO&lt;/div&gt;`)[0];\n",
       "                popup_a2a8c8cf10016bbf5e061ddf9ff49d9a.setContent(html_d4728dfc456f4ebc7ace5a829e7ce198);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_2169767f9e5a8e9a219d00be9d2024d1.bindPopup(popup_a2a8c8cf10016bbf5e061ddf9ff49d9a)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_c3d26527181a72932944eb271be43034 = L.marker(\n",
       "                [48.83977, 2.30198],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_e7197a9be3b0956124fd43450e037d0d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_a81360086f7dc9f5d2093fdde50ca1dd = $(`&lt;div id=&quot;html_a81360086f7dc9f5d2093fdde50ca1dd&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;254 rue de Vaugirard NE-SO&lt;/div&gt;`)[0];\n",
       "                popup_e7197a9be3b0956124fd43450e037d0d.setContent(html_a81360086f7dc9f5d2093fdde50ca1dd);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_c3d26527181a72932944eb271be43034.bindPopup(popup_e7197a9be3b0956124fd43450e037d0d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_958be54fc144e96aa547010c19f32a55 = L.marker(\n",
       "                [48.83977, 2.30198],\n",
       "                {}\n",
       "            ).addTo(map_ffe67163f8f8d1bd0fe1ce6c762b14c9);\n",
       "        \n",
       "    \n",
       "        var popup_59792f7fa787e4c70943a6abfc3bddfb = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_82850d1f9797a19e4d75dc1cbeeb61d6 = $(`&lt;div id=&quot;html_82850d1f9797a19e4d75dc1cbeeb61d6&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;254 rue de Vaugirard SO-NE&lt;/div&gt;`)[0];\n",
       "                popup_59792f7fa787e4c70943a6abfc3bddfb.setContent(html_82850d1f9797a19e4d75dc1cbeeb61d6);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_958be54fc144e96aa547010c19f32a55.bindPopup(popup_59792f7fa787e4c70943a6abfc3bddfb)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x12a65ee30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "m = folium.Map(location=data[[\"latitude\", \"longitude\"]].mean(axis=0), zoom_start=13)\n",
    "\n",
    "for _, row in (\n",
    "    data[[\"counter_name\", \"latitude\", \"longitude\"]]\n",
    "    .drop_duplicates(\"counter_name\")\n",
    "    .iterrows()\n",
    "):\n",
    "    folium.Marker(\n",
    "        row[[\"latitude\", \"longitude\"]].values.tolist(), popup=row[\"counter_name\"]\n",
    "    ).add_to(m)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a58f27-b2c3-4eee-8991-b206471eb723",
   "metadata": {},
   "source": [
    "Note that in this challenge, we consider only the 30 most frequented counting sites, to limit data size.\n",
    "\n",
    "Next we will look into the temporal distribution of the most frequented bike counter. If we plot it directly we will not see much because there are half a million data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fe0c1a0-e661-4dd9-bf14-be1ac5a1a34c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = data[\"counter_name\"] == \"Totem 73 boulevard de Sébastopol S-N\"\n",
    "\n",
    "data[mask].plot(x=\"date\", y=\"bike_count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6782d258-580c-4db4-9b96-fa1e16d89b2f",
   "metadata": {},
   "source": [
    "Instead we aggregate the data, for instance, by week to have a clearer overall picture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "349fbd40-c57e-40cc-bd99-608d9e2e89ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = (data[\"counter_name\"] == \"Totem 73 boulevard de Sébastopol S-N\")\n",
    "\n",
    "data[mask].groupby(\n",
    "    pd.Grouper(freq=\"1w\", key=\"date\")\n",
    ")[[\"bike_count\"]].sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c262f5c5-3a54-4017-a99b-fed24eee385f",
   "metadata": {},
   "source": [
    "While at the same time, we can zoom on a week in particular for a more short-term visualization,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f8b5840-53ba-43ca-8966-f594975696c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "\n",
    "mask = (\n",
    "    (data[\"counter_name\"] == \"Totem 73 boulevard de Sébastopol S-N\")\n",
    "    & (data[\"date\"] > pd.to_datetime(\"2021/03/01\"))\n",
    "    & (data[\"date\"] < pd.to_datetime(\"2021/03/08\"))\n",
    ")\n",
    "\n",
    "data[mask].plot(x=\"date\", y=\"bike_count\", ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca56052b-51f7-4c67-b135-2cc14698448f",
   "metadata": {},
   "source": [
    "The hourly pattern has a clear variation between work days and weekends (7 and 8 March 2021).\n",
    "\n",
    "If we look at the distribution of the target variable it skewed and non normal, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99893b87-cc1c-4c3e-b0cc-2069eb0e6874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "ax = sns.histplot(data, x=\"bike_count\", kde=True, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0055ae-ea52-4198-bc31-5089e73aaf9e",
   "metadata": {},
   "source": [
    "Least square loss would not be appropriate to model it since it is designed for normal error distributions. One way to precede would be to transform the variable with a logarithmic transformation,\n",
    "```py\n",
    "data['log_bike_count'] = np.log(1 + data['bike_count'])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6414fb09-93e7-431a-ac04-237ca8ccea2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data, x=\"log_bike_count\", kde=True, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ce873c-aa3d-40a8-9f07-ef1692df281d",
   "metadata": {},
   "source": [
    "which has a more pronounced central mode, but is still non symmetric. In the following, **we use `log_bike_count` as the target variable** as otherwise `bike_count` ranges over 3 orders of magnitude and least square loss would be dominated by the few large values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51fc59f-e77f-4c82-bfe8-35a93ef2db09",
   "metadata": {},
   "source": [
    "## Feature extraction\n",
    "\n",
    "To account for the temporal aspects of the data, we cannot input the `date` field directly into the model. Instead we extract the features on different time-scales from the `date` field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d13d9bfd-7852-4206-b16f-e09e7c83d6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _encode_dates(X):\n",
    "    X = X.copy()  # modify a copy of X\n",
    "    # Encode the date information from the DateOfDeparture columns\n",
    "    X[\"year\"] = X[\"date\"].dt.year\n",
    "    X[\"month\"] = X[\"date\"].dt.month\n",
    "    X[\"day\"] = X[\"date\"].dt.day\n",
    "    X[\"weekday\"] = X[\"date\"].dt.weekday\n",
    "    X[\"hour\"] = X[\"date\"].dt.hour\n",
    "\n",
    "    # Finally we can drop the original columns from the dataframe\n",
    "    return X.drop(columns=[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "df5a9c16-69fb-4d05-aa77-5ef1bc28fe78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48321   2020-09-01 02:00:00\n",
       "48324   2020-09-01 03:00:00\n",
       "48327   2020-09-01 04:00:00\n",
       "48330   2020-09-01 15:00:00\n",
       "48333   2020-09-01 18:00:00\n",
       "Name: date, dtype: datetime64[us]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"date\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8dd6cfea-6fc8-4707-a0c9-404b62a7833d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48321</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48324</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48327</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48330</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48333</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day  weekday  hour\n",
       "48321  2020      9    1        1     2\n",
       "48324  2020      9    1        1     3\n",
       "48327  2020      9    1        1     4\n",
       "48330  2020      9    1        1    15\n",
       "48333  2020      9    1        1    18"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_encode_dates(data[[\"date\"]].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4eb15a9-421b-4f2a-a913-26df51449092",
   "metadata": {},
   "source": [
    "To use this function with scikit-learn estimators we wrap it with [FunctionTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4676f76-1bdb-4fdd-94b3-061a9a7c691e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48321</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48324</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48327</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48330</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48333</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day  weekday  hour\n",
       "48321  2020      9    1        1     2\n",
       "48324  2020      9    1        1     3\n",
       "48327  2020      9    1        1     4\n",
       "48330  2020      9    1        1    15\n",
       "48333  2020      9    1        1    18"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "date_encoder = FunctionTransformer(_encode_dates, validate=False)\n",
    "sample_encoded = date_encoder.fit_transform(data[[\"date\"]]).head()\n",
    "sample_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c85678-2403-4a31-90ae-ac68f9565859",
   "metadata": {},
   "source": [
    "Since it is unlikely that, for instance, that `hour` is linearly correlated with the target variable, we would need to additionally encode categorical features for linear models. This is classically done with [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html), though other encoding strategies exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46e5f3cd-e177-4b73-8058-1472a46bb34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "enc = OneHotEncoder(sparse_output=False)\n",
    "\n",
    "enc.fit_transform(sample_encoded[[\"hour\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5feb47c-c05f-442b-8009-4b7272bdf84f",
   "metadata": {},
   "source": [
    "## Linear model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e8fb001e-3446-492c-b887-6ed485448495",
   "metadata": {},
   "source": [
    "Let's now construct our first linear model with [Ridge](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html). We use a few helper functions defined in `utils.py` of the starting kit to load the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "809fa5fa-099f-4b98-b47c-98f4e11ea0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counter_id</th>\n",
       "      <th>counter_name</th>\n",
       "      <th>site_id</th>\n",
       "      <th>site_name</th>\n",
       "      <th>date</th>\n",
       "      <th>counter_installation_date</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>counter_technical_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>400125</th>\n",
       "      <td>100049407-353255860</td>\n",
       "      <td>152 boulevard du Montparnasse E-O</td>\n",
       "      <td>100049407</td>\n",
       "      <td>152 boulevard du Montparnasse</td>\n",
       "      <td>2020-09-01 01:00:00</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>48.840801,2.333233</td>\n",
       "      <td>Y2H19070373</td>\n",
       "      <td>48.840801</td>\n",
       "      <td>2.333233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408305</th>\n",
       "      <td>100049407-353255859</td>\n",
       "      <td>152 boulevard du Montparnasse O-E</td>\n",
       "      <td>100049407</td>\n",
       "      <td>152 boulevard du Montparnasse</td>\n",
       "      <td>2020-09-01 01:00:00</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>48.840801,2.333233</td>\n",
       "      <td>Y2H19070373</td>\n",
       "      <td>48.840801</td>\n",
       "      <td>2.333233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 counter_id                       counter_name    site_id  \\\n",
       "400125  100049407-353255860  152 boulevard du Montparnasse E-O  100049407   \n",
       "408305  100049407-353255859  152 boulevard du Montparnasse O-E  100049407   \n",
       "\n",
       "                            site_name                date  \\\n",
       "400125  152 boulevard du Montparnasse 2020-09-01 01:00:00   \n",
       "408305  152 boulevard du Montparnasse 2020-09-01 01:00:00   \n",
       "\n",
       "       counter_installation_date         coordinates counter_technical_id  \\\n",
       "400125                2018-12-07  48.840801,2.333233          Y2H19070373   \n",
       "408305                2018-12-07  48.840801,2.333233          Y2H19070373   \n",
       "\n",
       "         latitude  longitude  \n",
       "400125  48.840801   2.333233  \n",
       "408305  48.840801   2.333233  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import utils\n",
    "\n",
    "X, y = utils.get_train_data()\n",
    "X.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ea2f21-1d7b-48d0-87ce-e55440a99360",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c396114-de58-40b4-bfd3-10c0c7f4f83a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.60943791, 1.38629436, 0.        , ..., 3.49650756, 2.30258509,\n",
       "       2.07944154])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937ebcf8-8769-46b4-93f8-d54a30194f87",
   "metadata": {},
   "source": [
    "Where `y` contains the `log_bike_count` variable. \n",
    "\n",
    "To test our baseline estimator, we create a validations set from the training set. Note that, during modeling and exploring the data, we always recommend using cross validation rather than a single train / validation split to evaluate your first results, because this will bring more stability and generalization.\n",
    "\n",
    "We create the validation set by simply slicing the last 30 days of the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e4b119e-f871-4a5c-9ef8-833cddcd1c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split_temporal(X, y, delta_threshold=\"30 days\"):\n",
    "    \n",
    "    cutoff_date = X[\"date\"].max() - pd.Timedelta(delta_threshold)\n",
    "    mask = (X[\"date\"] <= cutoff_date)\n",
    "    X_train, X_valid = X.loc[mask], X.loc[~mask]\n",
    "    y_train, y_valid = y[mask], y[~mask]\n",
    "\n",
    "    return X_train, y_train, X_valid, y_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "252bf7ea-2def-46e1-b4fd-583c0e2c3f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: n_samples=456507,  2020-09-01 01:00:00 to 2021-08-10 23:00:00\n",
      "Valid: n_samples=40320,  2021-08-11 00:00:00 to 2021-09-09 23:00:00\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train, X_valid, y_valid = train_test_split_temporal(X, y)\n",
    "\n",
    "print(\n",
    "    f'Train: n_samples={X_train.shape[0]},  {X_train[\"date\"].min()} to {X_train[\"date\"].max()}'\n",
    ")\n",
    "print(\n",
    "    f'Valid: n_samples={X_valid.shape[0]},  {X_valid[\"date\"].min()} to {X_valid[\"date\"].max()}'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3d1c151-da75-4dae-bbe6-07bf06e2b1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['year', 'month', 'day', 'weekday', 'hour']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_encode_dates(X_train[[\"date\"]]).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "413e7450-5529-47e5-a311-c78e364048ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
       "                 FunctionTransformer(func=&lt;function _encode_dates at 0x12dc1c280&gt;)),\n",
       "                (&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;date&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;year&#x27;, &#x27;month&#x27;, &#x27;day&#x27;,\n",
       "                                                   &#x27;weekday&#x27;, &#x27;hour&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;counter_name&#x27;,\n",
       "                                                   &#x27;site_name&#x27;])])),\n",
       "                (&#x27;ridge&#x27;, Ridge())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
       "                 FunctionTransformer(func=&lt;function _encode_dates at 0x12dc1c280&gt;)),\n",
       "                (&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;date&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;year&#x27;, &#x27;month&#x27;, &#x27;day&#x27;,\n",
       "                                                   &#x27;weekday&#x27;, &#x27;hour&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;counter_name&#x27;,\n",
       "                                                   &#x27;site_name&#x27;])])),\n",
       "                (&#x27;ridge&#x27;, Ridge())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function _encode_dates at 0x12dc1c280&gt;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;date&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;year&#x27;, &#x27;month&#x27;, &#x27;day&#x27;, &#x27;weekday&#x27;, &#x27;hour&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;counter_name&#x27;, &#x27;site_name&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">date</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year&#x27;, &#x27;month&#x27;, &#x27;day&#x27;, &#x27;weekday&#x27;, &#x27;hour&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;counter_name&#x27;, &#x27;site_name&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('functiontransformer',\n",
       "                 FunctionTransformer(func=<function _encode_dates at 0x12dc1c280>)),\n",
       "                ('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('date',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['year', 'month', 'day',\n",
       "                                                   'weekday', 'hour']),\n",
       "                                                 ('cat',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['counter_name',\n",
       "                                                   'site_name'])])),\n",
       "                ('ridge', Ridge())])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "date_encoder = FunctionTransformer(_encode_dates)\n",
    "date_cols = _encode_dates(X_train[[\"date\"]]).columns.tolist()\n",
    "\n",
    "categorical_encoder = OneHotEncoder(handle_unknown=\"ignore\")\n",
    "categorical_cols = [\"counter_name\", \"site_name\"]\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    [\n",
    "        (\"date\", OneHotEncoder(handle_unknown=\"ignore\"), date_cols),\n",
    "        (\"cat\", categorical_encoder, categorical_cols),\n",
    "    ]\n",
    ")\n",
    "\n",
    "regressor = Ridge()\n",
    "\n",
    "pipe = make_pipeline(date_encoder, preprocessor, regressor)\n",
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772e16fe-50fd-4229-af39-a6ebefad0a1e",
   "metadata": {},
   "source": [
    "We then evaluate this model with the RMSE metric,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d4d803c-80ea-4423-af83-5666c799838e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set, RMSE=0.80\n",
      "Valid set, RMSE=0.73\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print(\n",
    "    f\"Train set, RMSE={mean_squared_error(y_train, pipe.predict(X_train), squared=False):.2f}\"\n",
    ")\n",
    "print(\n",
    "    f\"Valid set, RMSE={mean_squared_error(y_valid, pipe.predict(X_valid), squared=False):.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2298cbed-ff41-49f4-9e51-6925410fc083",
   "metadata": {},
   "source": [
    "The model doesn't have enough capacity to generalize on the train set, since we have lots of data with relatively few parameters. However it happened to work somewhat better on the test set. We can compare these results with the baseline predicting the mean value,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f20e09d6-cbe8-4911-bfe1-ef5e939cacb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline mean prediction.\n",
      "Train set, RMSE=1.67\n",
      "Test set, RMSE=1.44\n"
     ]
    }
   ],
   "source": [
    "print(\"Baseline mean prediction.\")\n",
    "print(\n",
    "    f\"Train set, RMSE={mean_squared_error(y_train, np.full(y_train.shape, y_train.mean()), squared=False):.2f}\"\n",
    ")\n",
    "print(\n",
    "    f\"Test set, RMSE={mean_squared_error(y_valid, np.full(y_valid.shape, y_valid.mean()), squared=False):.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c753fe34-a94b-4d18-9497-e516c1ccc4e2",
   "metadata": {},
   "source": [
    "which illustrates that we are performing better than the baseline.\n",
    "\n",
    "Let's visualize the predictions for one of the stations (we transform the logarithmic bike counts back to their original unit):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3a2509f6-7cba-4671-8363-d4abda33ef30",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (\n",
    "    (X_valid[\"counter_name\"] == \"Totem 73 boulevard de Sébastopol S-N\")\n",
    "    & (X_valid[\"date\"] > pd.to_datetime(\"2021/09/01\"))\n",
    "    & (X_valid[\"date\"] < pd.to_datetime(\"2021/09/08\"))\n",
    ")\n",
    "\n",
    "df_viz = X_valid.loc[mask].copy()\n",
    "df_viz[\"bike_count\"] = np.exp(y_valid[mask.values]) - 1\n",
    "df_viz[\"bike_count (predicted)\"] = np.exp(pipe.predict(X_valid[mask])) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3fab3196-90bd-4918-a509-01cd38b1adfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'bike_count')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ECGIS2AshhBBCCCGEEEFM1tj7yel0UlhYSHx8PIqidPfhCCGEEEIIIYTo4VRVpaamhpycHEym5vPyEtj7qbCwkLy8vO4+DCGEEEIIIYQQIaagoIBevXo1e7sE9n6Kj48HtH/QhISEbj4aIYQQQgghhBA9XXV1NXl5eUY82hwJ7P2kl98nJCRIYC+EEEIIIYQQosu0thxcmucJIYQQQgghhBBBTAJ7IYQQQgghhBAiiEkpvhBCiB7F4XBgs9m6+zCECHjh4eGYzebuPgwhhBCdQAJ7IYQQPYKqqhQVFVFZWdndhyJE0EhKSiIrK0tG+QohRJCTwF4IIUSPoAf1GRkZxMTESKAiRAtUVaWuro6SkhIAsrOzu/mIhBBCdIQE9kIIIYKew+EwgvrU1NTuPhwhgkJ0dDQAJSUlZGRkSFm+EEIEMWmeJ4QQIujpa+pjYmK6+UiECC7634z0pRBCiOAmgb0QQogeQ8rvhWgb+ZsRQoieoVsD+2+++YYLLriAnJwcFEVh2bJlxm02m4077riDESNGEBsbS05ODjNmzKCwsNDrMcrLy5k+fToJCQkkJSUxa9YsLBaL1z6bNm1iwoQJREVFkZeXx4IFC7ri1xNCCCGEEEIIIY65bg3sa2trGTVqFE899VST2+rq6li3bh333nsv69at47333mPnzp1ceOGFXvtNnz6drVu3smLFCpYvX84333zDddddZ9xeXV3N2WefTZ8+ffjpp5947LHHuP/++3nmmWeO+e8nhBBCCCGEECL4fLatmDX55d19GH5TVFVVu/sgQCsFe//997n44oub3efHH3/k5JNPZv/+/fTu3Zvt27czbNgwfvzxR8aOHQvAxx9/zNSpUzl48CA5OTksXbqUe+65h6KiIiIiIgC48847WbZsGTt27PD7+Kqrq0lMTKSqqoqEhIQO/a5CCCE6V0NDA/n5+fTr14+oqKjuPpw2mTRpEqNHj2bRokU+b+/bty8333wzN998M+Df+6UQ/grmvx0hhDhWCivrOe1vX5AYHc6GeWd367H4G4cG1Rr7qqoqFEUhKSkJgO+//56kpCQjqAeYPHkyJpOJ1atXG/ucfvrpRlAPMGXKFHbu3ElFRUWzP8tqtVJdXe31IYQQQnS1H3/80asSraf76quvUBSFysrK7j4UIYQQIWpjQSWqCpV1Nqx2R3cfjl+CJrBvaGjgjjvu4IorrjCuVBQVFZGRkeG1X1hYGCkpKRQVFRn7ZGZmeu2jf6/v48ujjz5KYmKi8ZGXl9eZv44QQgjhl/T0dOn2L4QQQnShzYeqjK9rrRLYdxqbzcavf/1rVFVl6dKlXfIz77rrLqqqqoyPgoKCLvm5QgghOoeqqtQ12rvlo62r3Ox2O3PmzCExMZG0tDTuvfde4zH69u3bbJk+wH333Ud2djabNm0C4Ntvv2XChAlER0eTl5fH3Llzqa2t9es4rFYrd9xxB3l5eURGRjJgwAD+7//+z7j966+/5uSTTyYyMpLs7GzuvPNO7Ha7cbuvYx09ejT333+/8b2iKDz33HNccsklxMTEMHDgQD788EMA9u3bxy9/+UsAkpOTURSFq666yq9jF0IIITrLlkJ3tbalwd7CnoEjrLsPoDV6UL9//36++OILr3UFWVlZlJSUeO1vt9spLy8nKyvL2Ke4uNhrH/17fR9fIiMjiYyM7KxfQwghRBertzkYNu+TbvnZ2x6YQkyE/2+xL730ErNmzWLNmjWsXbuW6667jt69ezN79uxm76OqKnPnzmX58uWsXLmSAQMGsHfvXs455xweeughnn/+eUpLS5kzZw5z5szhhRdeaPU4ZsyYwffff8/ixYsZNWoU+fn5lJWVAXDo0CGmTp3KVVddxcsvv8yOHTuYPXs2UVFRXoG7P+bPn8+CBQt47LHHWLJkCdOnT2f//v3k5eXx7rvvctlll7Fz504SEhKIjo5u02MLIYQQHaGqKls8MvY1Vls3Ho3/Ajqw14P63bt38+WXX5Kamup1+7hx46isrOSnn37ixBNPBOCLL77A6XRyyimnGPvcc8892Gw2wsPDAVixYgWDBw8mOTm5a38h0aKnv97Lqj1lPDtjLFHh5u4+HCGE6DJ5eXksXLgQRVEYPHgwmzdvZuHChc0G9na7nd/+9resX7+eb7/9ltzcXEBbRjZ9+nSj0d7AgQNZvHgxEydOZOnSpS02R9u1axdvvfUWK1asYPLkyQAcd9xxxu3//Oc/ycvL48knn0RRFIYMGUJhYSF33HEH8+bNw2Tyvwjwqquu4oorrgDgkUceYfHixaxZs4ZzzjmHlJQUADIyMoyeOkIIIURXOVzVQHlto/F9sJTid2tgb7FY2LNnj/F9fn4+GzZsICUlhezsbC6//HLWrVvH8uXLcTgcxpr4lJQUIiIiGDp0KOeccw6zZ8/m6aefxmazMWfOHKZNm0ZOTg4AV155JfPnz2fWrFnccccdbNmyhX/84x8sXLiwW35n4Zuqqvzzyz1UN9jZdLCKk/uldPchCSGCXHS4mW0PTOm2n90Wp556KoqiGN+PGzeOJ554AofD98nELbfcQmRkJD/88ANpaWnG9o0bN7Jp0yZeffVVY5uqqjidTvLz8xk6dGizx7BhwwbMZjMTJ070efv27dsZN26c13GedtppWCwWDh48SO/evf3+fUeOHGl8HRsbS0JCQpMKPCGEEKI7eK6vB7BIxr51a9euNdbSAdx6660AzJw5k/vvv99Yczd69Giv+3355ZdMmjQJgFdffZU5c+Zw5plnYjKZuOyyy1i8eLGxb2JiIp9++il//OMfOfHEE0lLS2PevHkh1WE4GJTWWKl2rV+pbQyOdSxCiMCmKEqbyuGDyVlnncXrr7/OJ598wvTp043tFouF66+/nrlz5za5T2uBd2eUvJtMpib9BWy2pidEegWdTlEUnE5nh3++EEII0VFbjwrsa2SNfesmTZrUYoMhf5oPpaSk8Nprr7W4z8iRI1m5cmWbj090nd0lFuPruiApdxFCiM6ij2jV/fDDDwwcOBCz2Xfm/8ILL+SCCy7gyiuvxGw2M23aNADGjBnDtm3bGDBgQJuPYcSIETidTr7++mujFN/T0KFDeffdd1FV1cjar1q1ivj4eHr16gVoHfwPHz5s3Ke6upr8/Pw2HYc+nra5agUhhBDiWGqasQ+OwD4ouuKLnm93cY3xdW2Q/PEIIURnOXDgALfeeis7d+7k9ddfZ8mSJdx0000t3ueSSy7hlVde4eqrr+add94B4I477uC7775jzpw5bNiwgd27d/PBBx8wZ86cVo+hb9++zJw5k2uuuYZly5aRn5/PV199xVtvvQXADTfcQEFBATfeeCM7duzggw8+4L777uPWW2811tefccYZvPLKK6xcuZLNmzczc+bMZi9ONKdPnz4oisLy5cspLS3FYrG0fichhBCiE6iqyuZDWkf849JiAemKL0SbeGbspRRfCBFqZsyYQX19PSeffDJms5mbbrrJryVjl19+OU6nk9/97neYTCYuvfRSvv76a+655x4mTJiAqqr079+f3/zmN34dx9KlS7n77ru54YYbOHLkCL179+buu+8GIDc3l48++ojbbruNUaNGkZKSwqxZs/jLX/5i3P+uu+4iPz+f888/n8TERB588ME2Z+xzc3OZP38+d955J1dffTUzZszgxRdfbNNjCCGEEO1RUmOlzGLFpMDYvsn8XFYbNBl7RW3rsN0QVV1dTWJiIlVVVV4j90Tn+PW/vmdNfjkAt00ZzB9/2fYyUiFE6GpoaCA/P59+/fq12PldCOFN/naEEMLts23FXPvyWgZnxjN5WAZPfbmXq37Rl/svPL7bjsnfOFRK8UW3U1VVSvGFEEIIIYQQ3WpLoba+/vjcBOIitUavwZKxl1J80e2O1DZSUefumlzXKA2ThBCis61cuZJzzz232dtlLbsQQohQt8XVOG9EbiJhZi0HLmvshfDT7mLvk0nJ2AshROcbO3YsGzZs6O7DEEKIgLKnpIZnvvmZuWcOpFdyTHcfjuhmW1yN84bnJnKooh6QjL0QfttTUuP1vTTPE0KIzhcdHd2uMXhCCNFTqarKn9/exIaCSrISorj17MHdfUiiG5XWWCmqbkBRYFh2AlWuiuKaIAnsZY296HZ6R/y0uEgAamWOvRBCCCGEOMZW7TnChoJKAKqDpNxaHDv6+vrj0mKJjQwjLkrLgVsabC3dLWBIYC+6nV6KPzovEYA6ydgLIYQQQohjbPEXu42vZSmo2HLQvb4eIC7SFdgHyXNDAnvR7Xa7SvFH9UoCJGMvhBBCCCGOrdU/HzFGLYM0bxbujP1wV2Afb2TsJbAXolXltY2UWRoBGJmXBEjGXgghhBBCHFtLvtgDQGpsBBA8WVlx7Hg2zgN3xr620YHDqXbbcflLAnvRrfa41tfnJkWTrq+xlyumQgghhBDiGFl3oIJv95QRZlL4/cT+gCSWQl15bSOHKrUu+MNyEgCMNfYQHM29JbAX3Uovwx+YGee+KiZXTIUQIWTSpEncfPPNLe7Tt29fFi1aZHyvKArLli07pscVTH73u9/xyCOPdNvP9/z/2LdvH4qidMtowauuuoqLL77Y+H7atGk88cQTXX4cQgS6J13Z+ktOyGVQVjwgS0FDnT6/vl9aLAlR4QBEhpmJCKJZ9hLYi26lN84bmBFHTKQZ0NY4OYOg3EUIIbrKjz/+yHXXXdfdh9FlvvrqKxRFobKystV9N27cyEcffcTcuXOP/YH5IS8vj8OHDzN8+HC/9j86GO9Mf/nLX3j44Yepqqo6Jo8vRDDacqiKL3aUYFLgj78cQGyEdv4ZDBlZcezo6+uPd2XrdbGu+CQYlmpIYC+6lV6KPzAjntgId7lLvU2umgohhC49PZ2YmJjuPoyAtGTJEn71q18RFxfXocex2TpnnJHZbCYrK4uwsLDWdz7Ghg8fTv/+/fn3v//d3YciRMBY4uqEf+GoHPq6xpqBZOxDnZ6x1zvi6/Ry/BrJ2AvRsl3FWin+gMw4osJNKIq2Xa6aCiE6TWNt8x+2hjbsW+/fvu1gt9uZM2cOiYmJpKWlce+996Kq7sqlo0vxj3bfffeRnZ3Npk2bAPj222+ZMGEC0dHR5OXlMXfuXGpr/Ts2q9XKHXfcQV5eHpGRkQwYMID/+7//M27/+uuvOfnkk4mMjCQ7O5s777wTu939mu3rWEePHs39999vfK8oCs899xyXXHIJMTExDBw4kA8//BDQStl/+ctfApCcnIyiKFx11VU+j9XhcPDOO+9wwQUXeG3v27cvDz74IFdccQWxsbHk5uby1FNPee2jKApLly7lwgsvJDY2locffhiADz74gDFjxhAVFcVxxx3H/PnzvX6/3bt3c/rppxMVFcWwYcNYsWKF1+P6KsXfunUr559/PgkJCcTHxzNhwgT27t3L/fffz0svvcQHH3yAoigoisJXX30FQEFBAb/+9a9JSkoiJSWFiy66iH379nn97rfeeitJSUmkpqZy++23ez1ndBdccAFvvPGGz38/IULNjqJqPtlajKLAnDMGABiJJVljH9o2H/LuiK+Li9TK8oMhY9/9l5NFyKqqs1FSYwW0UnxFUYiNCMNitVNndUB8Nx+gEKJneCSn+dsGng3T33Z//9gAsNX53rfPeLj6v+7vF42AuiNN97u/7WXPL730ErNmzWLNmjWsXbuW6667jt69ezN79uwW76eqKnPnzmX58uWsXLmSAQMGsHfvXs455xweeughnn/+eUpLS5kzZw5z5szhhRdeaPVYZsyYwffff8/ixYsZNWoU+fn5lJWVAXDo0CGmTp3KVVddxcsvv8yOHTuYPXs2UVFRXoG7P+bPn8+CBQt47LHHWLJkCdOnT2f//v3k5eXx7rvvctlll7Fz504SEhKIjo72+RibNm2iqqqKsWPHNrntscce4+6772b+/Pl88skn3HTTTQwaNIizzjrL2Of+++/nr3/9K4sWLSIsLIyVK1cyY8YMFi9ebATf+hKI++67D6fTyaWXXkpmZiarV6+mqqqq1f4Ihw4d4vTTT2fSpEl88cUXJCQksGrVKux2O3/+85/Zvn071dXVxv9NSkoKNpuNKVOmMG7cOFauXElYWBgPPfQQ55xzDps2bSIiIoInnniCF198keeff56hQ4fyxBNP8P7773PGGWd4/fyTTz6Zhx9+GKvVSmRkZFv+i4TocfS19VOHZzMgQzvRPHopqMmkdNvxie5RVWejoFy7eD88xzuwj48MnpF3EtiLbrOnVMvWZydGEe9qUhETYcZitUvGXggRUvLy8li4cCGKojB48GA2b97MwoULWwzs7XY7v/3tb1m/fj3ffvstubm5ADz66KNMnz7dCDgHDhzI4sWLmThxIkuXLiUqKqrZx9y1axdvvfUWK1asYPLkyQAcd9xxxu3//Oc/ycvL48knn0RRFIYMGUJhYSF33HEH8+bNw2TyvxDwqquu4oorrgDgkUceYfHixaxZs4ZzzjmHlJQUADIyMkhKSmr2Mfbv34/ZbCYjI6PJbaeddhp33nknAIMGDWLVqlUsXLjQK7C/8sorufrqq43vr7nmGu68805mzpxp/O4PPvggt99+O/fddx+fffYZO3bs4JNPPiEnJ8c49nPPPbfZY3zqqadITEzkjTfeIDw83DgeXXR0NFarlaysLGPbv//9b5xOJ8899xyKq5TthRdeICkpia+++oqzzz6bRYsWcdddd3HppZcC8PTTT/PJJ580+fk5OTk0NjZSVFREnz59mj1OIXq64uoG/rv5MKCtrdfpzZsB6mwOr+9FaNDX1+elRJMYE+51m16Kb7F2znKtY0meuaLb6I3zBmS410XGRYZRUmOlTkbeCSE6y92Fzd+mmL2/v21PC/seFbTevLn9x3SUU0891QjgAMaNG8cTTzyBw+HAbDb7vM8tt9xCZGQkP/zwA2lpacb2jRs3smnTJl599VVjm6qqOJ1O8vPzGTp0aLPHsWHDBsxmMxMnTvR5+/bt2xk3bpzXsZ522mlYLBYOHjxI7969/f6dR44caXwdGxtLQkICJSUlft8foL6+nsjISK/j0Y0bN67J90cvETg6079x40ZWrVpllOWDVvLe0NBAXV0d27dvJy8vzwjqff2co23YsIEJEyYYQb0/Nm7cyJ49e4iP9y5da2hoYO/evVRVVXH48GFOOeUU47awsDDGjh3bpBxfr3aoq2umEkWIEHGwoh5VhV7J0cY4M4DIMBMmBZwq1FntEtiHoObW14P7wk8wrLGXZ67oNrs9GufpYoKo86QQIkhExHb/vsfAWWedxeuvv84nn3zC9OnTje0Wi4Xrr7/eZ5f41gLv5kre28JkMjUJLn01pjs60FUUBafT2aaflZaWRl1dHY2NjURERLT5WGNjvf8PLRYL8+fPN7LgnlqqdGhJe/5NLRYLJ554otfFGV16enqbHqu8vLxd9xOip6lu0F6HEqObvvbERoRRY7VjsdppWv8jerothdUAHJ/jI7A3MvaBH5tIYC+6jRHYZ7oz9jF6AxPpTCpcGu1O/ru5kF/0TyMzoX0n1kIEutWrV3t9/8MPPzBw4MBms/UAF154IRdccAFXXnklZrOZadOmATBmzBi2bdvGgAEDmr1vc0aMGIHT6eTrr782SvE9DR06lHfffRdVVY0s+apVq4iPj6dXr16AFkAePnzYuE91dTX5+fltOg49SHc4Wn4vGD16NADbtm0zvtb98MMPTb5vqVoBtH+7nTt3NvtvN3ToUAoKCjh8+DDZ2dk+f87RRo4cyUsvvYTNZvOZtY+IiGjye44ZM4Y333yTjIwMEhISmtwHIDs7m9WrV3P66acD2tKMn376iTFjxnjtt2XLFnr16uVV1SFEKKqu1wJ7fUa5p9hILbCXitHQVFylNdLtk9p0+kwwrbGXrvii2+x2dcQf6FGKL7NExdEeXL6NW97cyIKPd3b3oQhxzBw4cIBbb72VnTt38vrrr7NkyRJuuummVu93ySWX8Morr3D11VfzzjvvAHDHHXfw3XffMWfOHDZs2MDu3bv54IMPmDNnTquP17dvX2bOnMk111zDsmXLyM/P56uvvuKtt94C4IYbbqCgoIAbb7yRHTt28MEHH3Dfffdx6623GuvrzzjjDF555RVWrlzJ5s2bmTlzZosXKHzp06cPiqKwfPlySktLsVgsPvdLT09nzJgxfPvtt01uW7VqFQsWLGDXrl089dRTvP32263+m86bN4+XX36Z+fPns3XrVrZv384bb7zBX/7yFwAmT57MoEGDmDlzJhs3bmTlypXcc889LT7mnDlzqK6uZtq0aaxdu5bdu3fzyiuvsHOn9prWt29fNm3axM6dOykrK8NmszF9+nTS0tK46KKLWLlypfH/MHfuXA4ePAjATTfdxF//+leWLVvGjh07uOGGG6isrGzy81euXMnZZ5/d4jEKEQqqXYFZQnTTvKZeMVobBFlZ0fmqXBd9kqKbVn7ppfjBkLGXwF50i5oGG4ddV8e8S/H1jH3g//GIY2/l7lJe+WE/AIer6lvZW4jgNWPGDOrr6zn55JP54x//yE033WR0Y2/N5ZdfzksvvcTvfvc73nvvPUaOHMnXX3/Nrl27mDBhAieccALz5s3zWhfekqVLl3L55Zdzww03MGTIEGbPnm2MysvNzeWjjz5izZo1jBo1it///vfMmjXLCHwB7rrrLiZOnMj555/Peeedx8UXX0z//v3b9O+Rm5vL/PnzufPOO8nMzGzxosS1117rs2T9T3/6E2vXruWEE07goYce4u9//ztTpkxp8edOmTKF5cuX8+mnn3LSSSdx6qmnsnDhQqPpnMlk4v333zf+r6699lqv9fi+pKam8sUXX2CxWJg4cSInnngizz77rJG9nz17NoMHD2bs2LGkp6ezatUqYmJi+Oabb+jduzeXXnopQ4cOZdasWTQ0NBgZ/D/96U/87ne/Y+bMmYwbN474+HguueQSr5/d0NDAsmXLWp2uIEQoaDFj76oYlcRSaNID+6OXaYDHHPsgiE0U1dfQU9FEdXU1iYmJVFVVNVsWJ/y3/kAFl/zzOzLiI1lzj7vc8/Z3NvLW2oPcNmWwV8dSEXqq6m1MWfgNRdXaBaARuYn858bx3XxUIlA1NDSQn59Pv3792r0WWgSn+vp6Bg8ezJtvvmk0suvbty8333xzq6PoerqlS5fy/vvv8+mnnza7j/ztiFDx6P+286+vf2bW+H7ce/4wr9umPfM9P/xczpIrTuCCUf5dBBU9x9B7P6be5uCb235J76PK8d9eW8Bt72xi0uB0Xrz65G45Pn/jUMnYi27ha309aGucAOrkimnIm/+frRRVNxAVrr1MBUMJlBCi60VHR/Pyyy9TVlbW3YcScMLDw1myZEl3H4YQAaG63lWK30LGXs4/Q0+j3Um9Teut4CtjHx8VPGvspXme6BZ7fHTEB49SKGmeF9I+2VrEe+sOYVLg3vOHcc/7W6hpCPz5oUIEupUrV7Y4c725teyBbtKkSd19CAHp2muv7e5DECJguLvi+1pjr6+jlvPPUKOX4SuKO4j3FBepBfvBkGCSwF50C71xnucMe5DmJQLKLFbufk+bD379xP5MHKSNaKoOgiulQgS6sWPHsmHDhu4+jGNu37593X0IQogAY6yx97WO2nX+KT2eQk9VfSOgdb83mZQmtxtr7IPgPFQCe9EtdhXrGfujSvGNUii5YhqKVFXlnvc3c6S2kSFZ8dw8eSANNm2udaPdidXuIDKsbd21hRBu0dHR7RqDJ4QQwc7oiu+jFD/GaJ4n55+hxmicF9P0eQHSFV+IFtVa7Ryq1DqcD8r0LsWPkXF3IW3ZhkN8srWYcLPCE78eRWSY2XhBheC4Wiq6l/SDFaJt5G9GhIqaFjL2xrjlIAjeROdqqSM+eKyxt9oD/vVSAnvR5faWatn6tLgIkmO950UazfNkjVPIUVWVh5ZvB+CmMwdyfE4iAGaT4r5aKoG9aIY+Oqyurq6bj0SI4KL/zeh/Q0L0VPoae19z7PXzT0kshZ7WAnv9HNThVI0q0kAlpfiiy+12leEfvb4eJGMfymqsdo7UauucZo0/zuu2uMgwLFa7ZOxFs8xmM0lJSZSUlAAQExODojRdKyeE0KiqSl1dHSUlJSQlJWE2yzIn0XOpqtpiV/wYSSyFrKo6LbBPio7weXtMhBlFAVWFGquN6IjAfa2UwF50Ob0Mv09KbJPb9KtiUgoVevQX1qhwU5MXzfioMIqqkc74okVZWVkARnAvhGhdUlKS8bcjRE9ltTtpdGjZ1hZL8SWxFHKq9As+zWTsFUWrHK1psGNpsHPUQK+AIoG96HJ6cOarSYU0LwldeimUryum+vom6YwvWqIoCtnZ2WRkZGCzyUUgIVoTHh4umXoREvSO+CbFHcR7ipXEUshqrRQftI75NQ32gG+gJ4G96HLuUihfa5xk3Eioqqxr/oU13lU2Jxl74Q+z2SzBihBCCIN7fX24z2VaMpUpdPkT2MdFhUFV4Pd6kuZ5ost5vrgeTc/Y19kcOJ2B3XlSdK5K1xxRX5Ucnh1JhRBCCCHaQg/efK2vB4hxJZbkPCP0+BXYuyo6agL8+SGBvehyRmDv48VVz9irKjTY5appKKk0mpc0H9hL8zwhhBBCtJVRLeqjIz64AzfJ2Ieear8y9tptkrFvwTfffMMFF1xATk4OiqKwbNkyr9tVVWXevHlkZ2cTHR3N5MmT2b17t9c+5eXlTJ8+nYSEBJKSkpg1axYWi8Vrn02bNjFhwgSioqLIy8tjwYIFx/pXEy3Qg7N4H6X40eFa50mAWulMGlKMNfY+M/ZSii+EEEKI9mkpqQQeU5kCPCMrOp+/a+wh8Cs6ujWwr62tZdSoUTz11FM+b1+wYAGLFy/m6aefZvXq1cTGxjJlyhQaGhqMfaZPn87WrVtZsWIFy5cv55tvvuG6664zbq+urubss8+mT58+/PTTTzz22GPcf//9PPPMM8f89xO+6VfGfJXiK4risc4psP94ROdyB/Y+mudFSsZeCCGEEO1T3Uopvn7uabU7sTsCe1a56FxtKcUP9MC+W5vnnXvuuZx77rk+b1NVlUWLFvGXv/yFiy66CICXX36ZzMxMli1bxrRp09i+fTsff/wxP/74I2PHjgVgyZIlTJ06lccff5ycnBxeffVVGhsbef7554mIiOD4449nw4YN/P3vf/e6ACC6jt7ZvKWrphZr4HeeFJ2rss61xl5K8YUQQgjRiYxzz2ZK8fWu+KBNZkqMltXKocLo8dRa8zwC/zw0YJ+1+fn5FBUVMXnyZGNbYmIip5xyCt9//z0A33//PUlJSUZQDzB58mRMJhOrV6829jn99NOJiHBnAadMmcLOnTupqKho9udbrVaqq6u9PkTHqapqXDX1VYoP7hdXWecUWvzqii8Xe4QQQgjRRq1l7CPCTISbtbWgUjEaOqx2Bw02rULDv4x9YC8JDdjAvqioCIDMzEyv7ZmZmcZtRUVFZGRkeN0eFhZGSkqK1z6+HsPzZ/jy6KOPkpiYaHzk5eV17BcSADTYnNhd3e59leKDrHMKVZUtrLF3XykN7BdUIYQQQgSeliYy6fTJTHL+GTr0MnxFaT7hCB7TmSRjH5zuuusuqqqqjI+CgoLuPqQeQX9hNSkQG+F7zrTMEg1N+tX0pGgfa+yDpARKCCGEEIHH6IrfQvCmZ2WleXPo8KzkMJmUZvcLljX2ARvYZ2VlAVBcXOy1vbi42LgtKyuLkpISr9vtdjvl5eVe+/h6DM+f4UtkZCQJCQleH6LjPBvnKYrvPyB9lqhcMQ0txrg7Hxn7BOmKH9J++PkI5yz6htU/H+nuQxFCCBGE/MvYu84/pRQ/ZPjTOA9kjX2H9evXj6ysLD7//HNjW3V1NatXr2bcuHEAjBs3jsrKSn766Sdjny+++AKn08kpp5xi7PPNN99gs7kDghUrVjB48GCSk5O76LcRuuoWRt3pZI19aGqpeYlk7EPb8k2F7Ciq4fU1B7r7UIQQQgSh1tbYA8RIxj7k+B3YS8a+dRaLhQ0bNrBhwwZAa5i3YcMGDhw4gKIo3HzzzTz00EN8+OGHbN68mRkzZpCTk8PFF18MwNChQznnnHOYPXs2a9asYdWqVcyZM4dp06aRk5MDwJVXXklERASzZs1i69atvPnmm/zjH//g1ltv7abfOrS1NkcU3CX6gf7HIzpPg82jeUkLc+zrGh0yhiYEHbFoF33WHajs3gMRQggRlPTEkq9zDF2cq2JUmueFDn8De2ONfYDHJt067m7t2rX88pe/NL7Xg+2ZM2fy4osvcvvtt1NbW8t1111HZWUl48eP5+OPPyYqKsq4z6uvvsqcOXM488wzMZlMXHbZZSxevNi4PTExkU8//ZQ//vGPnHjiiaSlpTFv3jwZdddN/LpiKnPsQ47+wmo2KcbMek9xnmNorA4SYwK22EgcA3pgf6C8jjKLlbS4yG4+IiGEEMGkLeefkrEPHVUtTGTyFBep3R7ozfO6NbCfNGkSqqo2e7uiKDzwwAM88MADze6TkpLCa6+91uLPGTlyJCtXrmz3cYrO418pvr7GXl5YQ4XnFVNfvRciwkxEhpmw2p1UN9havOIuep6yWqvx9foDlZw1LLOFvYUQQgg3VVU91ti3cP4pU5lCTpXeVLGVwF6PTQJ97LKkvUSXqmnDuBHJ2IeOlmbY64xZ9gF+tVR0Pj1jD7DuQEU3HokQQohg02BzYnO4Ri23tBRUX2Mv558hw+9SfFfGvtHuxGoP3MSjBPaiS7nHjbS+xr5WmueFjMq65hvn6RJkln1IarQ7jTdegPUS2AshhGgDPVtvNilG53tfpHlz6PE3sNcz9hDYFcUS2Isu5VcplP7CGuDlLqLzVNY3P+pOJ53xQ1N5baPX9xsLqqSBohBCCL+519eHNTtqGdzj7gK9QZroPP4G9mFmE9Hhgb9UQwJ70aVqjDX2fpRCBfAVMdG59OYlSS28sMYFSUdS0bnKLNr6+rS4COIjw6i3OdhRVNPNRyWEECJY+DPDHtyNeiWxFDqqWhi1fLRgmGUvgb3oUp5XTZsTY5TiB+4fjuhcVUbGPqLZffT1TVKKH1qOuDL26fFRjO6dBMD6gsruOyAhhBBBxZ9loODRFV9K8UOGvxl7wJjaFMgJJgnsRZfy56qprHEKPZWuK6YtPS/0UvzqAL5SKjrfEY+M/Qm9kwFYv1/W2QshhPCPP8tAwXMqk5xnhIoqP5aC6tyVo4GbYJLAXnQp/+aIygtrqKn0oxRfuuKHJr0jfmpsBGMkYy+EEKKNqvw49wSIlYx9yGlLxl5fqhHI56ES2IsuVePPHPsIydiHGn+umMZLV/yQpM+wT42L5IQ8LWOfX1bbpKmeEEII4Ys/SSWAGFfGXtbYhwar3UGDTWvG21r/BXAH9lKKL4SLXg7V0pUxzzmiqqp2yXGJ7uUV2DsdYG8atMVL87yQVFbjytjHRZAYE07/9FhAxt4JIYTwj76Er9VSfD1jL+cZIUE/91QU9/r5lhil+JKxF+KoK2MtdsXXrpiqKsb+omfTS/FHrJ8Pj+bBjuVN9pFxd6HpiCtjnxYbCcAYfZ39gcruOiQhhBBBxN+MvTuxJBWjocDzeWEyNT8GUSfN84Tw4BmQxbVQih8VZkYfMxrIfzyi81TWNRJJIxEmJ9hq4dBPTfZxr7GXUvxQoq+xT4vXJiboDfTWScZeCCGEH/wdd6cnlupkKlNIaMv6epBxd0J40f8Q4iLDMLdwZcxkUogJlxfXUOFwqlQ32LnE/C2J21/XNh5c22Q/ydiHJr0rfqqese+TBMDGgkocTlmqI4QQomXGuLtWSvH1cXc2h4rVLln7nq7Ngb1r7HIgJx0lsBddxp8Z9roYvRzKKi+sPZ3+vOillLo3Ht4ADu/MvHTFDz2qqlJW615jDzAwI564yDBqGx3sKq7pzsMTQggRBIyMfatd8c3G13Vy/tnjtTdjL2vshcD/Uihwv7hKxr7n019Y+5qPuDfaG6B4q9d+0hU/9FisdhrtWp8NPWNvNimMyksEpBxfCCFE64zEUivnn2FmE5FhWmhUK+efPV5VXdsCe1ljL4QHf0bd6WJklmjIqHS94eaZjnjfcNQ6e88XVJmWEBrKXOvrYyPMRHtkUqSBnhBCCH8ZXfFbydiDRwM9ydj3eFXGEg1/S/FdCSYJ7IXwvyspuP94ZJZoz1dZpwVvObhK8fufoX0+OrB3PW+cqlzwCRXG+vq4SK/tY6SBnhBCCD+oquqRsW89saQ30JOMfc9XWa+df7a9FD9wK0dbf4YL0UnaUoof43phDeRyF9E5qupthGEnVS3XNoy6AhQz9BrrtV9UuIkwk4LdqVLTYDMu/oieS8/Y6+vrdaPzkgD4ubSWyrpGkmIijr6rEEIIQb3Ngd3VaNWfAE6fZS9r7Hs+fSloUkzbMvaBHJtIxl50GaMrqR+l+MYLq2Rme7yqehtZSjkmnBAWBSN+Bb99B8Ze47WfoijSGT/EGDPsj8rYJ8dGcFxaLADrCyq7+rCEEEIECf3cM8ykEB1ubmVviImQxFKoqG5j87x4aZ4nhJve9Czej1J8/YVVSqF6vso6G6DwU/K5MPRCUJofhSid8UOLPsM+O8YJW98Hu9W4TZ9nv36/lOMLIYTwzbNaVGnh/EKnr7GX5s09X9vH3bn7fwXquF0J7EWXMZqX+LXGSUqhQkVlnY2DajorBt0Plz3rvqH6MJTt8drXaFwSwOubROfR19hfUvovePsq+HCucdsJvZMAydgLIYRoXltGLYO7YlR6+fR87R13B4GbeJTAXnSZtjTPk4x96NCbl3itcVr7PPx9CHz6F699pRQ/tOhr7E8ofkfbsOkN4za9gd6GA5U4A/TKuRBCiO7Vlv5O4O7xVCul+D1eWwP7yDAzEWYtdA7UcnwJ7EWXqW5DKb5k7ENHVZ2NFKpJifAIzjKHa58PrQWP0XZSih9aylwZe1t4vLZh0LnGbYOz4okKN1FjtXOgvK47Dk8IIUSAc/d3alu5tUxl6vnaGtiDR2f8AH1+SGAvukxNW0rx9eYlkrHv8arqbTwZvphffTIWti7TNmaNAFMY1JZC5QFj34QoKcUPJUdqtYy9sS7yzHnGbWaTQnp8pNd+QgghhCd3xt6/UvwYKcUPCVa7gwabE/C/mgM8l4QGZnwigb3oMnopfoqzAt6dDUWbm903Rq6YhozKehu9lFIUVIjL1DaGR3tn7V2kFD+0HLFYSaCWsMZqbUNSb6/bU1xj7ioksBdCCOFDW5aBgjuxJKX4PZuerVcUiG/D+ORAH3kngb3oMnrzvL6r58Hmt+Dp8c3uK81LQkd1bQPZimuGvWfgps+xP7TO2BToJVCi89gdTirqbOQppdqGmFSoOgh7vzT2SY51BfZ1EtgLIYRoyt242c/APlLOP0OB5wUfk6n1aQm6uAAfeSeBvegSDqdqBGORlbvdNzidPvfXm5fIuJGeTVVVIhtKCFccqKYwiM9y35jrCuwPembstTfmainF7/HKXcF6Ptk4rv4ExsyEf54Cy/5g9F0wMvYS2AshhPChzV3x9fNPSSD0aO1ZXw/u7L7FGpjnoRLYiy7heWXLFJXgvuHIbh97uzP20jyvZ6u3Och0lgCgJvQCk9l9o56xP7wBHHrjRSnFDxVlNVqwHhMbj7nPqXD6bVrfhZrDUFUAQJIrsC+vDcw3WCGEEN1LD+D87oofIZWBoaBJYF9bBlveg+3LW7xfXICfh0pgL7qEnmGNCjdhqi5031BT5HN//YqpjLvr2SrrbPRSygBQkvK8b0zpD+PmwAWLQdUqO9xd8SWQ6+mO1God8VNjtQZ5RMRoTRUBCtYAkBKrPR9kjb0QQghfjOZ5be2KL6X4PVplnfa8MEYt7/kM3rkavl3Y4v0CfY29/90ChOgA/cpYfFQ4/PZdKNkGg6dCZJzP/Y019pKx79Eq62zkGoF9H+8bTSaY8rDXJsnYh44jrhn2lymfw5qdMOgcyDsFCtdDwWoYcbmxxr5cSvGFEEL4YIy787srviSWQoFXJUddOXz5iHbD4Q3QWKclE3zQM/aB2lxRMvaiSxij7qLCIGs4jPx1s0E9uNfY1zbaUT3mmIuepbK+kV1qLz4N/yX0Pa3V/eMD/Eqp6Dz6DPsL696Dj/4M5Xsh72TtxoLVgHTFF0II0bK2ZuyN5nlyntGjeZXil+dD5X7tBqcdCtc1e79APw+VwF50Cfcc0aNeWJ1OcDT949Az9qqKMWdS9DxVdTZWOMfyTMrtMPrKpjvYG2H/d/DTS4BnKX5gvqCKznOkthEFJ6k213KdpD5axh6gaAs01krGXgghRIuq27jGPjZSejyFAq/AviLf+8YDPzR7P5ljLwTuF9aT2QrfLtLWyH50OyzoB7v+12T/6HB3EzUph+q59BdWY43T0azV8MK58J+boKHKoxTfJpUcPdwRi5V0qghXG0ExQWIv7SMhF1QHHFpHiiuw19fKCSGEEDpVVd3j7to6x14qRnu09gb2sZKxF8J9ZeuUxtXw2X2w/T/gsEJDpc8/IJNJMdY5yVXTnquyzkpvpZiUqGZmiMamaZlaVDi0zgjsbQ4Vq10qOXqyI5ZG8hRtYgIJvcDsOik76wG48m3IGW1cEKqsa8ThlBMwIYQQbnWNDuO9we819q7AzSkVoz1atVdgv0/bOOR87XPBmmbHccfLHHsh3KX4WU5XWW1yX+g9TvvatV72aMY6J8nY91i2qmK+ibyFR3dM8bkkA4DcE7XPh9YSGxGG4roGILPse7Yyi5U8pVT7JtmjseKIy2HQ2RAZT7Jrjb1Tdb9JCyGEEOA+TwgzKV6VoC2JkYrRkOCdsXetrx9yPoTHgrUKynb6vF9cpJZQkIy9CGl6V9I022FtQ3Jf93rZwg1gq29yH70cqk5eWHssc7U2j7wuIhXMzVxN1wP7w5swmRTiIgL7aqnoHGWeGfujJya4hJtNxtVzWWcvhBDCk7sjfjiK0kxl4FGkYjQ0VPnK2KcOgCteg5u3QMZQn/eTOfZCoF81VUm2HtI2JPfVPuIywWnTRlgdJUYP4OSFtceKrD0IQF1MbvM7JfbSPlu0IE9G3vV8qqpypLaZjD3Ans/h8wegYp+xzl464wshhPCkZ+wT/Wycp3Off8p5Rk9lBPaRJq0TPmhxyXGTICmv2fsF+hz7gA7sHQ4H9957L/369SM6Opr+/fvz4IMPejWzUFWVefPmkZ2dTXR0NJMnT2b37t1ej1NeXs706dNJSEggKSmJWbNmYbFYuvrXCWk1DTbSqCbc2QAokJgHiuLO2vtYZx8bqV8xDcw/HtFxMXWFADTGthDYx6Zpn+u0effSGb/nq2t00GBz8jf7NOp/95E2HtPTN4/Dyicgf6VRjl8ugb0QQggPRkf8KP/W1+viIqVitKczAvvYKPjTDri70H2+2QJjjb01MJsrBnRg/7e//Y2lS5fy5JNPsn37dv72t7+xYMEClixZYuyzYMECFi9ezNNPP83q1auJjY1lypQpNDQ0GPtMnz6drVu3smLFCpYvX84333zDdddd1x2/UsiqrrfTWynWvknsBWHayXhL6+z1K6a1jZKx76kSGrSlGc7E3s3vFJuufa7VsreenfFFz3TEogXpdeEpRPc/TbuK7kmfZ39wjTtjL6X4QgghPDQ7arkVcv7ZszXYHEZjROO5ERGL0cTpuyXw2m+g6lCT++oZe4dTDcjmim27hNXFvvvuOy666CLOO+88APr27cvrr7/OmjVrAC1bv2jRIv7yl79w0UUXAfDyyy+TmZnJsmXLmDZtGtu3b+fjjz/mxx9/ZOzYsQAsWbKEqVOn8vjjj5OTk9M9v1yIqW6w0d8oq+3rvqHPL6DPadD71Cb3iZUrpj1eil272KMkN1/2RGIeXPZ/RoAvpfg9X1mtFYDUuAjfO+iVPgVrSE6/FoDyWrnQI4QQws1YY+/nqDudfv5ZKxWjPZJeyWFSID7SRyi8+R04vEGrFky8zOummAgzigKqCjVWG9ER/jVl7CoBnbH/xS9+weeff86uXbsA2LhxI99++y3nnnsuAPn5+RQVFTF58mTjPomJiZxyyil8//33AHz//fckJSUZQT3A5MmTMZlMrF7tuxs7gNVqpbq62utDtF91g41PnWPZdtF/4eyH3DfkjIarP4LxtzS5T6x+xVTW2PdYGQ4tsI9I8d0cTbsxRuuEftxEAOL0Unx5w+2xymqspFPBn/g3rHu56Q69TtI+l+4gO0JrvFkpGXshhBAejFJ8P0fd6YypTHKe0SNV1bsrOUzfLYKXLoSt77t30JONPpYJK4riXmcfgAmmgA7s77zzTqZNm8aQIUMIDw/nhBNO4Oabb2b69OkAFBVpo9MyMzO97peZmWncVlRUREZGhtftYWFhpKSkGPv48uijj5KYmGh85OW1kFEUrappsNNAJOE5o7Rg3g/6C6tk7Hsmm8PJu/bxvOuYQHTOEL/vJ6X4Pd+R2kYGmQ5ySf27Wknc0eLSIeU4AAY7tAu/ssZeCCGEJ6MUv60Z+wj9/FMSSz2RV0f8g2sh/2uoLXPvYAT23/u8f3wAN9AL6MD+rbfe4tVXX+W1115j3bp1vPTSSzz++OO89NJLx/xn33XXXVRVVRkfBQUFx/xn9lSqqnpcNW3mxbW+Aoo2e23Sx40E4h+O6Liqehv/dFzEn+1/IC6jX8s7//w1rH0ByvOlFD8EHPGcYd/MqDu9HL9f/RZA1tgLIYTw5jnuri3k/LNn8znD3nOZcJ4rsC/eCg1NK7b1kXeSsW+j2267zcjajxgxgt/97nfccsstPProowBkZWUBUFxc7HW/4uJi47asrCxKSkq8brfb7ZSXlxv7+BIZGUlCQoLXh2if2kYHThVuD3uDlA1Pa0G8p33fwt/6wpu/89psZOylFL9HqqzTXljjI8Mwm1qZL/vNY7D8Zji41rjyLhn7nstrhv3Ro+50rgZ66bXaFBTJ2AshhPDkzti3rxRfKkZ7JiOwjwqDinxto2dgn5CtJRVUJxz8scn99VL8QFwSGtCBfV1dHSaT9yGazWacTq0LYb9+/cjKyuLzzz83bq+urmb16tWMG6d1Wx83bhyVlZX89NNPxj5ffPEFTqeTU045pQt+C1HTYCMCG783/4fwL+4D51GBeuZwQNH+uCzuizD6FdNaeWHtkWorDtNbKSYtupWgHrw640vGvuc7UtvoMcO+r++dhl4Ef/yR/Wc9C0BFnVzoEUII4dbervju5nmSWOqJ9MA+J6IOGi0YY7g9tbDOXu/1JBn7Nrrgggt4+OGH+e9//8u+fft4//33+fvf/84ll1wCaA0Mbr75Zh566CE+/PBDNm/ezIwZM8jJyeHiiy8GYOjQoZxzzjnMnj2bNWvWsGrVKubMmcO0adOkI34Xqa63k6uUYVJUCI+FmFTvHaKTIGOY9rXHH5CscerZYra/zTeRt3C/w8ca6qN5zLKXwL7n86sUPzYV0geREhcJSMZeCCGEt/Z2xTfG3QVgRlZ0nB7Y9zG5kokJORAe5b1T71MhLAoaa5vcP5DX2Af0uLslS5Zw7733csMNN1BSUkJOTg7XX3898+bNM/a5/fbbqa2t5brrrqOyspLx48fz8ccfExXl/g969dVXmTNnDmeeeSYmk4nLLruMxYsXd8evFJKqG2z0Nspq+7rnRHrqfQqUbNXm2Q+7EIAYGTfSoylVBwGoisxufWePjH1ctnTF7+nKLFZ6tVaK75Ico43Eq6q3YXc4CTMH9PVqIYQQXaSqnV3x4yIlsdST6c+LXriWcvuqDBw5DUb/FsKajt2Nk8C+feLj41m0aBGLFi1qdh9FUXjggQd44IEHmt0nJSWF11577RgcofBHdb3NY71sX9875Z0Ka5/3ztjLC2uPFlGjBfZ10X5UzuhVHrVHpCt+CKitqSZdcTWsaS5jD7D6GZIL1nCyMoQ16lCq6m2kujL4QgghQlt7u+LLUtCeTQ/s48McWuIo2UcD54iYZu8fF8CVo5LaEMdcTYPdO2Pvi76W5fBGsGlzqWOlFKpHi64rBKAhzo/AXi/FlzX2PZ7DqVJYr3BKw5NUTPuPtlSnOflfY9ryNqMjteeSdMYXQggBfk5kaobMse/Z9OdF0XGXw2174MJWqriP6g2mZ+yrAzDBJIG9OOaqG/zI2Cf1hrhMcNq08RLIFdOeLs56GABnfF4re+Iuxa8rk674PVxFXSOqqlBMCvEDx7e8c1wGAL3CawAor5XnhBBCCPdEJvDI2Nvq4YXz4H93tHhfd2AvFaM9kZ6xT9Iv+JjMvnfc/Rk8dSq8c43X5qSYcK/HCSQBXYoveobqehsnthbYKwpMugucdkjIBWTcXY9WX0m0wwKAkty79f3Th8Dlz0N8tpGxb7A5sTmchMua6h7liEXLuifHhLe+Xj5WC+wzzXpgLxl7IYQQ7qxsuFkhKtz1XpK/EvZ/q31MuhOik33eN9aVWJJxdz2T1xz7lkTEQOn2JmO69ftVS2AvQlF1g51pjfcyd0w4s3u3MGJw7NVe38Z6ZOxVVUXx1XRPBKeqAgCOqPHExiW0vn9MCgy/DIBYh9PYbGmwkxzbtLGJCF5HLFYuM33DyWElUJBkzKv3ybVEI92krceXUnwhhBDgvb7eOH+sPujewdx8Pxa9K75FEks9UmWdNob7xA/PhLS+MO01iIhtumPGUO2zpQhsDUbnfD1jXxmAY3bbleq65pprqKmpabK9traWa665xsc9RCirabBRQwz1qcdDVKLf94txZeydKljtzlb2FkElKom3Yqbxb8fk1q+YHiXcbCI6XLvoI+vse55Si5Up5h/5TeN7Ws+NlrhK8VPUKkAy9kIIITTGqDvPc4wje7XPp/6x5eZoRvNmOcfoiarqbeQqZURW74OCHyG8medCVBKYXcmj2lJjs37eGoil+O0K7F966SXq6+ubbK+vr+fll1/u8EGJnsU9R7SVApHaMtj/nXEyHxPuXvMiDUx6mKQ8lpquYKH9VyTF+Jlx3/2ZNjmhpsgoxw/ExiWiY45YGlufYa9zleInOisBqJDAXgghBO4yaa9zz7Ld2ufU/i3eVx+3XNfowKkv1Bc9QoPNgdXubH0MN2jbXecZWEqMzYnR7jG7gaZNgX11dTVVVVWoqkpNTQ3V1dXGR0VFBR999BEZGRnH6lhFkMqu/IkHwl5gaPlnLe+49X144Vz45jEATCbFaKAnI+96HqN5SYyfGfsV82D5LVC8VTrj92BHLA300gP7VmbY600VY+yVAFQEYFmcEEKIrmeU4ntl7F2BfUMVfL0Aqg/7vK8+lQmgzibnnz2JfsGnT2u9v3RxrubNtZ6BvWuNfYMt4C78tGmNfVJSEoqioCgKgwYNanK7oijMnz+/0w5O9Ay96zYzI2wFhWWxwOzmd9Q7n1vc5S4xEWHUNTqwSMa+R3EeXE9c3UEqSXN3JW2NMfKujPioXoB0xu+J6qvKiFdcFWFJrTRWTO4Lt+7gg+118P4OWWMvhBAC8MzYu84x7I1QsV/7+sfnoPoQJPaC0Vc2uW9UuAmToi0FrbPajdJ8Efz0pNKAcD2B0LflO/jM2GvPKVXVEkyJ/iaoukCbnqlffvklqqpyxhln8O6775KSkmLcFhERQZ8+fcjJ8WMmtQgpqVZtxrSztbLauEzts6XY2BQbaabMIuucehr13Wv4JvJnrmy8m4Toqf7dSQ/s68qIj+oLIBd8eiBTlXbiVReZTkx4dMs7m8MgIZukuCJA1tgLIYTQVDcctcZeUWD6W3DkZ6g5DN/+HX7+ymdgrygKsRFh1Fjt1ErFaI+iB/b9zKXgoPXAPn2w9nzxaK4XEWYiJsJMXaODyvrG4A3sJ06cCEB+fj55eXmYTDJmSrQu3aGVOikp/Vre0dUIy7NBhd6ZVGaJ9iB2K6ZKLXg7aO5FVHgz80OPFqNn7EulFL8Hi7RoExOscb1ovrWRtxTXZATJ2AshhACPjH20K9Qxh8OAyTAA+PlrV2D/tZZ29bHGOibSrAX2kkBoltXu4A//Xkf/9FjuOW9Ydx+OX/TAvhd+luKf/aDPzUnR4dQ1OgJunX27akv69OlDZWUla9asoaSkBKfTu2P5jBkzOuXgRM+Q7SwGBSLSj2t5R70Uv9ECjXUQESOzRHui8nwU1UGNGk1jVKb/99OfH7VlxEdqV0elFL/niat3VfgktlKGr/v+nwz+eQ1jlFHsrg2OEwshhBDHlue4uybyToGwKG2MWdkuLSt7lNjIMMAqgX0Lvt1dxhc7Svh2tyloAvsjFi0BUBeeBJFprQf2zUiIDqewqiHgRt61K7D/z3/+w/Tp07FYLCQkJHjNF1cURQJ7YWhoaCCHMgCiMloJ7CPjtRdae4PWpCKir+uFVTL2PUrZLgD2qtkktWUGfWyq9rm2jPgEydj3VE9az+WlxpN4e/wppPpzh5+/JH73pwwwpbGuYRA2h5Nws1SThQRVhS8f1t47Trupu49GCBFAmoy72/ERNNZCn19AYi70PlUrxf/5K9+BfYQ+8k7OP5vz5U4t693ocGK1O4gM87MCsxuVWqwAvNj/Hzz2q1Htfhy98XOgZezbdfbzpz/9iWuuuQaLxUJlZSUVFRXGR3l5eWcfowhitaX7MCsqDWo4scm5Le/sNVZCK8ePjZSMfY9jBPa5bZthr2fs68qIj9I7ksrzoiepa7RT26hSRCpJOa1cCNS5XjMyFG2WvZTjh5Dyn7UpKivmNdvdWggRmtwZe1cO87sl8N612lhlgH7a8mJ+/trn/fWpTNLLxzdVVflyh3vpbLAkWkprtMA+PT7Svzsc3gSLx8C/Jnpt1s9fK3tCYH/o0CHmzp1LTIy/KyBFqGoozQfgoJKJyZ8s2oRb4bwntE6luNfYWyRj33O45sjudeb4P+oOIHcsXP4CTHmUuCj9eREcbyTCP3qJXESYyf8uxK5RNDlhNQABVxYnjiHXRUIA9n7efcchhAg4+vhTI4Ggj7pLG6B9Pm6S9vnwRq365yj6e5AklnzbVWzhUGW98b0lWAJ7V8Y+Lc7PwD4sCsr3aheSPRgj7wIssG9XKf6UKVNYu3Ytxx3nZ0ZFhKzitFM5t+EZhiTYecufO4y92utbWWPfAxkZ+5y2ZewTsmH4pQDEF2sN1mSNfc9SWFHL4vAl1ERlo9hO9+pC2yxXJUdWmAWQzvghpXSn++s9n8MJv+2+YxFCBJTyWi2AS42NhPpKd2PmVFdgnz0KrvkUcsc00zxPloK2RC/D1wVLxr6sxsrvzR/y6+/vBGU2nDa35Tvojb2t1WCrB9e0nqQYbSlpZYBVCbYrsD/vvPO47bbb2LZtGyNGjCA83Pvk/MILL+yUgxPBr6bBTjVx1MQmtOv+8sLaA/3iRlat+orN+f04P6YNa+w9JEhX/B6p7PB+LjR/j91mhrDn/LuTqxQ/XakGoEIC+9DhmbHPO6X7jkMIEVBUVTUu8qbGRcCRbdoNcVlaTw4Akxl6N/+6EedaCirN83z7YsfRgX1wJFpKLVaOUw4TV3tA6+nVmqhEMEeAo1GbZZ+sje7WE1OBtsa+XYH97NmzAXjggQea3KYoCg6HBGHHwidbi9h6qIpbzhrk1bAwkBnjRqL8fKpZSqFsJ0TEQc5oydj3RMMv5f3t/SnMP9i2jD1ozW8sRSTFTAKC541E+Kfh8HYAKiJySDf52YTHVYqfSiUA5QF29VwcQydeBRnDtGZYuWO6+2iEEAGiusGOzaGV16fERsB+vQx/oN+PYYxbluZ5TVTV2fhpfwUAmQmRFFdbqQmSCyClNVZ6m/RRd62M4QatmiMuE6oKtKqPowL7QFv+16419k6ns9kPCeqPnXkfbGHxF3vYdri6uw/FbwPXPcj8sBc4Lqy09Z0Btr4HL54HK58AcHfFlxfWHqXE1bwkw9/mJbr/3grLbyG18RAgGfuexuTKwFYnDPD/Tq5S/ASnq3meZOxDR97J8Is5EtQLIbwcca2jjosMIyrcbPT2IbW/9462evjPzfDUKdqYZQ+SWGreyj2lOJwqAzLiGJylVeQGw/lYg81BTYOdPMXPGfY6vXmzxV2lEKgZe5kJFCQabA6Kq7UXqqoAuzrUkn6H/sPMsBUkh/v5B2/MKnd1xdfHjQTJlUDRiuKtsPdLrJVFAGQmRLXt/jFpAMTZK4HgadYi/BNXo5182VMG+X+ntEHwp508deJ/ASivDZ7XR9GJ6sq1dfZCiE73302HGfvQCn7aHxyTr/Qy/BR9pO6RPdrn1KMy9mFRsOsTKN0BBT943aQnlqRJb1N6Gf4ZQzKI15sZB0EFZZnFSgQ2snE9j/0N7PV19pZiY1OgjrtrVym+rxJ8T/PmzWvXwYjmHa5yrwMJlnIX6sqJtGudqm3xvf27T1ym9tl1VSwmUsaN9Cg/vQhrnuFc5SJW8xsyEtqYsY/VAvtYRyWQgqXRjtOpYjIFx9IU0bL0+n0AhGcN8/9O5nCIzyI5rhaQcXcho/xnOLQOskZCdBI84ZpDffvPEJ3crYcmRE/zr2/2UmZp5IsdJZzYJ6W7D6dVZRaP9fUAZz0Ao66A9KMuGiuK1h1/42va2Lv+Zxg36T2e6qTHkxenU+XrnVrybdLgdD7cUAgER8a+tMZKrlKGSVEhPNY4p2xV+mCoOawtE3YJ1Ix9uwL7999/3+t7m81Gfn4+YWFh9O/fXwL7Y6DQY6RE0DTycHUsPqSmEh3nZ/M846qYFtgbGXspxe8ZXKXWWxu1CziZ8W3M2LtehKMby4EUVBUsjXYSotq4Vl8EnEabg77OA6BAUp8Rbb6/3qFWAvsQsfsz+N9tMHgqXPG6lokr2wk/fwXHX9LdRydEj1FU1cCmg9pSp2AI3sCdsU/VM/bJfYy10U0cN1EL7PO959nrpfi1UorvZdOhKo7UNhIXGcZJfVP40pW9D4akY2mNlT6KK+ue3NfnNASfzmqa0E6K1rvi94DAfv369U22VVdXc9VVV3HJJfKGeiwcCsbAvkTrQrrL2cv/5nl6KX5jDdjqjXnl0iSth/CYYR9hNrVtjj0Yz4/whnIizCYaHU5qGiSw7wmKSw6Thg2HqpCcN7Rtd161mNN3rmaEcioVtYnH5gBFYClzjbpLc2XgBkzWtu35XAJ7ITrRZ9vd5cfBE9h7jLprTb+J2ufCDdqSnhitIsHo8RQs59xdRC/DnzAwjXCziXjX+VcwPDfKLI04Ufg5cgjH5Yzu0GPpGft6mwOr3UFkmJ8Nf4+xTltjn5CQwPz587n33ns76yGFB8+MfTBcFQO0NUvALrWX/4GXPlYCwFJCsivwqwiwK2KiHaw1UK01vdur5pAeH9n26Q4xqdrn2jJjXZdc9OkZ9tdFMcz6PL+NexYlIqZtd97zGVkHltNfKZSu+KFCn2Gf7irBH+Aqod3zOahq9xyTED2Qd2AfHO+3eil+SlyE1tvn6wValY8vCdmQNhhQYd+3xmapGPXtK9f8+l8O0Sps4yKD51ystMbKN85RPDv4Obj4nx16rPioMCPhH0jl+J3aPK+qqoqqqqrOfEjhcqgiGDP22uiq3WovEqL9zNgrijGXmtpSEl2lLtUNNhxOOVkLaq7mNdbIVKqIa/v6enBXdNSVeVRzBMnfg2hRQUUdKiYi05opl2yJawlPmlJFhTTPCw36DPs0V2Df5zStEVZNofHeI4ToGIvVznd7jhjfV9cHx/utVyn+/u/gy4dh7fPN36HvadrnQz8Zm/QeT1KK71ZS416WMWmwdj4WH0TnYqUWrV9Zut57wV+HN8HiE+BfpxubTCbFSFpWB1Bg365S/MWLF3t9r6oqhw8f5pVXXuHcc8/tlAMT3gqrPAP7ILl6WFsGwE5nHpe2pVT69D+D6oTEXkaptqpqfzjJsW38YxSBw1WGXxnTF6raMeoOtLVwl78AKf1IeFdrlhZMUyJE8w5WaKOG8pLbmK0H42JgmlKFxWoPqLI4cQzUV7q7E+tzqcOjteB+7+faR2YbGjAKIXxauauURofT+L46CLKyAEf0Uvy4CDjczKg7TxnDIHOEuyoQdyY6aM65u8BXrqZ5I3ITyXD1SDK64gdB0rGs2ko4dtLbev4ZHq01bI307heWFBNOVb0toNbZtyuwX7hwodf3JpOJ9PR0Zs6cyV133dUpBya8FVZ6dMUPgqtiAPzxB859+F12N4QZa3D8MvZq48twID4yjBqrnYq6Rgnsg5krw1YUrk1IaPOoO9CanbjGk6TFrQHcb+AiuI3fNp8h4eU0hN8CDG/bnV1NFdOVakBrZpOZIIF9j6Vn6+NzIMrjRGvAZC2o3/MZ/OLG7jk2IXqQFdu0C2hj+ySzdn9F0Jx/HtFL8WMj3aPu0gY2f4eTZ2sfHmL05nlBELB2laPL8AGPNfaBE9w2q7qAbZGzqFs9GE79zv/meXq1qLUabPVaoE9gdsZvV2Cfn5/f2cchWuB0qsHZPA/Yb43FjsP/UnwfEmPCqbHaqQygPxzRDiN+DUl9+G6zdvW/XRl7D2lx2v31tXQiuA2r/pZEczXfxrfjtcJVip8VVg02rQyzXReORHAw1tcfNbpq6PkQGQf9z+z6YxKih7E7nHzhCuQuGZPrCuyD4zzsiGcp/hE9Yz+gTY+hr7G32p3YHU7CzJ26ejno2BxOVu7SKnHP8Arsg6cUP7FmN+GKg3DF6X9QD67+X5HgsGpTu1wTFvTAPpAy9h1+lh48eJCDBw92xrGIZhypbaTR7i6FCpb1PjaH02g60qau5ZYSyF+pdSgFkmP0kRISwPlSVWfj4f9uY0dRdXcfSsvSB8GY3/GDQ+t4ntGewMvphG0fwo//R2as9qJcWiMZ+6BXW0aiWo1TVUjq3cZsPRil+BmujL2MvOvhBp4N016D027y3p7UG8bMgMTc7jkuIXqQtfsrqKyzkRQTzi8Ha6+xFqsdNcCbUzqdKhV6YB/lhMoC7YbUFjL2Puhd8QFqpYEea/dVUGO1kxobwchc9/QZfcmCJQgC+4z6nwFQ09s4eUdR3OO4a0uNzYGYsW9XYO90OnnggQdITEykT58+9OnTh6SkJB588EGcTmfrDyDaxLMjPgTHVTG+fBTnvy9nkmk9iuK+oueXLe/BS+fDt9qSD32dvTTF8u2ddQd5dmU+//xyb3cfil9KXIF4uzL2igLvzoL/3kqvCAsAZRYJ7IOd9bA2GrNATadXRkrbH8BVip+MK7CX14qeLT4ThpwH/c/o7iMRosf6zFWGf8aQDOM8zKkGfpBb3WDD7mq2nGo9CKgQmWi8T/hka4AnT4ZH88CqnVtEhJkIN2sJhGCqlD1WvnRVb0wcnI7J5M5266X4lkY7zgBucl1rtdNP1S7yhGcf3/YH0MvxLSXGJv3vIpAqitsV2N9zzz08+eST/PWvf2X9+vWsX7+eRx55hCVLlsi4u2NAD+z1qpGgeIHZt5LI/M9Jopa0uMi2lTDpV8Vcfzx6xl6ycL7tK9OayAX0WnNLidaR9sAPlFRr/SLaVSqtKBCjvTlnm2sAydj3BFUHNgOQr/QyroC3SeZw+PNu7u37GoCMvAtlDVXww1L4UNbYC9FeqqqywjXm7qyhmUSHmzG7grlAL8fXy/DjI8OIqNQytKQNaLn0OjwKqgu1NdQ1RcbmJNf5p95lP5RtOlgJwC/6e18g0RN3qhrYFcWlNVYGK1pgH9GewD4uU/tscY9/1M9Xgr4r/ksvvcRzzz3HhRdeaGwbOXIkubm53HDDDTz88MOddoACY31975QY9h+pC/zAXlWhRMvA7VZ7kZPYxgDOKHfRAnv9ilgglboEEr2beED/+xxaB8tvQc0czpHau4EOrLGPTYOaQtJNNUC0ZOx7gMbD2niykqh+KG1Z96YLi4C4DJLitBOyCjkJ67lsDfD9Em3M3ZDzweTjovEn94DqgAl/NtZCCiH8t6fEwv4jdUSYTUwYlI6iKMRHhVFZZ6OmwU52YuuP0V30xnmpcREw6JfwxzVgq2v9jvFZcKRGG5mZpq3HT42NoLTGKoE97mrh1KNGxUW6KhtsDpWaBnvbmmV3odLqOkYqhdo3GW0sxQdIHww1hyEy3tjkXmMfOM+PdmXsy8vLGTJkSJPtQ4YMoby8vMMHJbzpgf2gTO3JVBPogb2lBOorcGJij5pDdmJ02+6vz7G3aOtYkiRj36KCCu35EdCBvauLdUOiNm4m3KwYlRht5iqnS3GVXUvzvOAXdmQHAJaEFsYR+SFZsis935E98MVDWkbe10WgqETIO1n7eu/nXXtsQvQQerb+FwNSjTXU7iZpAXyuAZS7qhdTYiO0i77pgyHnhNbvmJCtffbI2KfEynuKTk8qxkV654S1iz56Z/zAjU/qinYTqdhoIBKS2nHB96z5cP3XMOJyY1NStPb8CKTz73YF9qNGjeLJJ59ssv3JJ59k1KhRHT4o4U0vxR/sCuxrA715SamWfauMzMFKBFltztjrYyWqwNZAsr7GPoC6TgYKVVXdGftA/vdxBfZVsf0ASI+L9Fqj1SaudU4JzirtMettXs0lRfBpsDlpVM04Uge1vnNzVj7BZfnzGKrsl4uAPVmZ3hF/cLOltdVZ4wCo2vNDVx2VED2KPuZu8tBMY1t8pKvsOICDN3Bf7E+Na2NVYLwrsK8uNDbpgf0RCeyxWLXeCvq0AE/uWfaBex5aXtfIMscv2BQ33nelVzskBGDzvHaV4i9YsIDzzjuPzz77jHHjtDfQ77//noKCAj766KNOPUDhnmE/MDMO0JqXNNicREcE6JzmEi37djBcuyKWk9TGwD4qCcwR4GiE2lJ3KX4gB67dpNRipcGmBbXVDXYcTtVYBxdQyrRxM8UR2gz7dnXE17nW2Ec3lhNmUrA7VY7UWtteGSICxt8yH2NF6UHuyW3Hujfd7s/oX/wd/ZTBkl3pyUpdM+zTmr8I9PqeMK4Higv2EsAVw6ITVNQ28qt/fc/4AWncf2EHXj+EoaSmgQ0FlYB3YK+PLQ6k9cS+6K//qTHhsPwWSOwFJ82GqISW7xifpX32yNinGhl7WfLXXMbec1sgX/T52ZnNEtscfjegDyd30mP2mOZ5EydOZOfOnVxyySVUVlZSWVnJpZdeys6dO5kwYUJnH2PI00vxB2TEGQmKmgC+KqZn7PeovQDaHnApilf3SSnFb15B+dETEwL0eeHK2BeYtedEh2bYu0rxlfojxlqvshp5bgSzgxX12AmjV2p86zs3x/W8SFOqAmqmrOhkesa+mcD+u71lfFWknWzFWot97iN6jpV7ythTYuHfP+wPqKxZMPtyRwmqCiN7JXpVXAZDuTW4A/vcyHqtae/nD4DJjzxmfI72ucYzYx/p9ZihyuFUqbe5MvaRTZOKwTDLXm+0nN7e88+izfCP0fD0eGNTj2meB5CbmytN8rpAfaPDeEHplRxDbEQYFqudWqsDOnAOfEwpJohMYHOj9iKZ3dZSfICJt4PqhMReJKv6HPvA+cMJFHoZvq6q3mZcCAkYtWVQXw4o7HVkAQfb1xFfN+Q8SDkO0gaSdqCc4morpZYGkNxc0CpwPY/zUjpQdeFqupmmVIX8SViPpmfs0wc3uUlVVRZ8vJMqVRuZmGwv1Zq5tqchowgKWwu1JVl2p8pXO0u4aHRuNx9R8PNVhg/BEbyBewRuX71RWkIviIhp/Y4p/SBzBCTmuTe5kgdHQryXj2e3+zgf46vjXMs0AnmWvbPiACbU9gf2YdFQkQ8R7uDLyNjX2VBVtX3NfztZuzL2L7zwAm+//XaT7W+//TYvvfRShw/K06FDh/jtb39Lamoq0dHRjBgxgrVr1xq3q6rKvHnzyM7OJjo6msmTJ7N7926vxygvL2f69OkkJCSQlJTErFmzsFgsnXqcx0phlZaRjYsMIyEqzLhSFtCd8c9fiPP2/bxedyIA2UntOFk/8SoYew3EZ5IUgF0nA0VBeR39lMO8G3EfZ5nWBmbGwpWtJymPwlrtRa9DGfv0wXD8xZB5PGmuNXSSsQ9e1o/n8bL9di4wfUevZD9OvprjarqZRrVU9/RUTofWPA98Zuw/3VbMhoJKyhQtsI+hAUdDdVceoehi2wrd/796QCrar77RwcrdZUDTwD7ByNgH4HmGByNjbz+obXB1uG/VoCnwh29hijtpmSrN8wB3wB5uVogMa5qxTwj0xor2Rh45OINtkVeTE1bTvsfQ+3811kCjlozQM/Z2p0pdo6MzjrTD2hXYP/roo6SlpTXZnpGRwSOPPNLhg9JVVFRw2mmnER4ezv/+9z+2bdvGE088QXJysrHPggULWLx4MU8//TSrV68mNjaWKVOm0NDQYOwzffp0tm7dyooVK1i+fDnffPMN1113Xacd57F0yNXxPDcpGkVRjHUsAX/FtLaRBocJkwKZHQnicHe6rm10SJO0oxSU13Nn2OucaNrNsxF/D8zAPmskXPVfmPo4xTUdmGHvg37ltVRG3gUtW8E6RprySYl0+ly75zePUvy6RgcNtsB4kxWdqGIfOKwQFgVJvb1ucjhVHvtEK9P/7enDOL1xMYMaXqKkMcAqmESnUVXVK7D/emepnCN00K7iGqx2J2lxEQzN9i4LDZaMvZ5dT2/UZpaTOrDdjyVd8TV6MjG2mfdod/O8AH1uHNlDGA4aCSMxLad9jxGZoL33gDGOOzrcTIRZC6UDZZ19u86iDhw4QL9+/Zps79OnDwcOHOjwQen+9re/kZeXxwsvvGBs8/y5qqqyaNEi/vKXv3DRRRcB8PLLL5OZmcmyZcuYNm0a27dv5+OPP+bHH39k7NixACxZsoSpU6fy+OOPk5Pj+z/YarVitbqDherq7rnqr3fE1xvQ6Se+AZ2xBw5XaQFcRnwUYeZ2XD+ylEDpDohMID5rFCZFaxpYWdfYscZrPczByjoSlVrj+6q6hhb27iaRcdBXW5NU8r+VAKQndOBij60Bdn0M9eWkxf0CQGbZB7GwI1owVtvBUXd6KX66or1WV9Q1SkPFniapN/zhe20NrMk7a/TeuoPsKbGQFBPOHyb158MNhTRW1nOool6eBz1USY2VI7WNmBRtLG55bSNr8ssZP7Bp4kn4p9qVcU2Li2xSVhws4+70DvaJdfu1Dal+Zuw9uZbwpEpXfMAdsPvqiA/u8vxAveijlmxDAXarvchqbwyhKFplYNUBbRx3cl8URSEhOpwyi5WqOhu57alQ7mTtythnZGSwadOmJts3btxIampqhw9K9+GHHzJ27Fh+9atfkZGRwQknnMCzzz5r3J6fn09RURGTJ082tiUmJnLKKafw/fffA1q3/qSkJCOoB5g8eTImk4nVq1c3+7MfffRREhMTjY+8vLxm9z2W3IG99mTRr5Z5rncJKKv/BYvHELVmCUDbR93ptrwLL10A3y7EZFKMchcZeeetoLyeGtX9QqKW7m5h7+5Xomfs4ztwccZpg7dnwvJbyIrWsrIyyz5I1VcS1aBd+VZTm66ZbhNXw800k1ZmF+oZlh7JHA6Zw2DAZK/NDTYHiz7TXvtumNSfhKhwcpO110W9+azoefT19f3T4zjLVTa+YltRS3cRrdBLrn1VTwVD8zynUzWWYsVUus6H0tqQsf+/KfBoHhRvBdwZ+6p6GzZH6FaD1LpG3TVXVac/N6oD9KKPtXAbALucvYyLNe2il+O7Mvbg2Rk/MM452hXYX3HFFcydO5cvv/wSh8OBw+Hgiy++4KabbmLatGmddnA///wzS5cuZeDAgXzyySf84Q9/YO7cucY6/qIi7QU8M9N7HVBmZqZxW1FRERkZGV63h4WFkZKSYuzjy1133UVVVZXxUVBQ0Gm/V1scco260wN7/Y8qYMtdijZD+V7qLFrWrM2j7nR6V/zaUsBdji/r7N0cTtW48KMzVe7rnoNpyTePw7pXsNVWGFe9MzqSsY+IA7N2/9xwrVdGWY1k7INSqZatL1RTfC7vapOcE+DPu5mdsBSAitrAPMEQne/V1Qc4VFlPVkIUM8b1BeAc81oeD3+a2B3vdu/BiWNm6yHtPOP4nATOGqadB362vQRVVbvzsIJajT7SzEeDtGAoxa+qt+FwqkRhxVzjWmOfPcr/B2i0gLXaGHmXFBNh9N4M5d4tRsbeR0d88CjFD9Dnhr1oCwAHwvoQFd6BUeFxrnjT4u7nEWid8dtViv/ggw+yb98+zjzzTMLCtIdwOp3MmDGjU9fYO51Oxo4dazzmCSecwJYtW3j66aeZOXNmp/0cXyIjI4mM7Nja8M5wqFJr0NAr+ajAPkD/eCjRRt3lm7QKh3aXQLrKarFoV8X0K2KSsXc7XFWP3alyg3Ibvx8dwzM/VnBVzBDO6+4D82RrgC8eAlTKr5uIqkKYSSGlI5379XGI1QfJMGuBvayxD1KlOwDY48ylV0oHGucBhEVCXAaJsdFAfUifhPVYXz8GYREw8jfGzOmaBhtPfak11Lt58kDjpG2wcoDTzN/wY4lMy+ipth3WA/tEThuQRlS4iUOV9Ww7XM3xOfL/3h76uaWegfUU6FlZgCOuefMRUbEodx3ULh7HtuGicXw2FG8xRt6ZTQrJrmUe5bWNZHSk2jCItbbGPtD7f5ldY1JLo4/r2AOlD4Gaw9p6exd3g+/A+LtoV8Y+IiKCN998k507d/Lqq6/y3nvvsXfvXp5//nkiIjqvUU12djbDhg3z2jZ06FBjHX9WlvbGXlzs3Qm1uLjYuC0rK4uSkhKv2+12O+Xl5cY+gazwqIx9bCCvsVdVIwO3za6NnGnXqDswOlzr5S6SsW9Kn2GfmxRNeFIuViICr3leyTZAhchEiuxaI570+EhMpg6OBInVlvykutZTyxr7IOV6vdil9iIvuXPWpumlkxLY9zCqCt8thhXzoK7c2PzcynzKaxs5Li2Wy0/sZWwPS9K+jqiT0uyeaqurcd6wnASiI8xMGKhV+n22raSlu4kW1LRYih/YwRu4G+elxkVqF3uzR7btAVwXDPWMPXg00AvhJX/68t/mSvGNiQnWADsHBbDVE1Wj9Vuojm9HvwVPk++D676CEZcbm/SMfaCcf7crsNcNHDiQX/3qV5x//vn06dOnye0JCQn8/PPP7X780047jZ07d3pt27Vrl/Gz+vXrR1ZWFp9//rlxe3V1NatXr2bcuHEAjBs3jsrKSn766Sdjny+++AKn08kpp5zS7mPrCk6nyuEq7zX2cUbnyQDs+Fx1UBsDYQpnc712hbTDGfuGKrBbSTTWsATGH04gcM/+jgm4FxbDpre0z/0mUOwql++U5oeupRpJqrbGsrIutNe/NWvPZ7Cp6WjSQKFGJbBPzWanmtexUXe6rxfwh7KHGaAclDX2PU1NkVYiq5ggVWu0WN1g47mV2jnGn6cM9mrUGpOuVY3FN0qQ1xNVN9g4UK69Bw7L1rJnxjr77XIxp70srsAs3kcpfkJQZOxdgX1711EnuBpq1xw2NqVIAz2jFL+5wD4ukEvxHY1s6Deb9xzjiUjs/IRuoMUnHZgt1LqOrnO65ZZb+MUvfsEjjzzCr3/9a9asWcMzzzzDM888A4CiKNx888089NBDDBw4kH79+nHvvfeSk5PDxRdfDGgZ/nPOOYfZs2fz9NNPY7PZmDNnDtOmTWu2I36gKLVYsTlUzCbFGBnnXmMfGE8gL64yfFIHUFCl/XFnt3eNfVQSmMLAaYfaUiNjL1k4t4PldQxT9vHQkUdw7BrBPWFVTNm3DQpf1NYbdzdbA2x6Q/v6xKsoOeIK7Ds4/hCAGO3CUYytErMpA4dT5Yilsf3NGnsipxP+fZn2df8zjCqHQFJx0q1M+ngEAA92RsZ+18eMrPyJvspoKkL4JKxHcpVSktxPy8QByzceprbRwcCMOM4d7n3ClpTZF4BURxmqqjbp8C2C23ZXtj4nMYpkV+D1yyEZKApsOVTN4SqZhtAeLQVwCR4jzZxOteOVd8eAHnz/ufZx+LAXTPgTJDdNPDZLz9hXuwN7mWXvDthbG3cXkNUcUYl8kjGLp7fv5epjMFUr0BJrHcrYH2snnXQS77//Pq+//jrDhw/nwQcfZNGiRUyfPt3Y5/bbb+fGG2/kuuuu46STTsJisfDxxx8TFeX+z3v11VcZMmQIZ555JlOnTmX8+PHGxYFApnfzzUpwj4yLjdDWD9YGYsa+VAvsnelDjOxsTnvfWE0mdzm+pZhk/YqYNMQyFFTUk6eU0KduC6k12xmoHKK3fR8cXNvdh6bZsRzqKyChF/Q/g5JqfYZ9JwT2rjVzprpS401XyvGPUlfm+kKBqIQWd+0uB11VJxnxkR1raKPTO+MrVZQHyHo30UlKd2mf093TE97+SWtq++uxeU0C9/RcbTRuolJLZWVllxyi6Dr6+vphHmvp0+MjGdM7GdCa6Im2qzbW2DffFV9VA3cyU7mlkQhsnGT5Cta9pFX4tEW8ZOx9qW0lY29MTAjEZcK4zw/TO5pYKtoM/xgFS8cbm/Q19lUBcs5xTDP2neH888/n/PPPb/Z2RVF44IEHeOCBB5rdJyUlhddee+1YHN4xdfQMe3BfLQvIrvhRiZA9itqU4TicWqVBh/6IJt4OqJDQi8QYLSgMlHESgeBgRR3HK9paU2d8NhuLo5jERji0rpuPzOWnF7XPJ/wWTGZK9FL8zmg+M/LX0OskyBhG2o5iSmqs0kDvaFWujsDxWdqYsEDjdFBwxL2cpFPogT1VFNTK86FH0TP2aYMA2FNSw/oDlZhNChefkNtk96i4ZCxEE0c9pYX7SE5O7sqjFceY5/p6T5OHZvLT/gpWbCvmd6e2IVMrgJbH3UWFmwgzKdidKjUNdp8N9rrbkVorA5WDmHFAdDIk9mr9Tp6S8iBrBGQMNTa5M/ah+56iL/9trXleo92J1e4gMqwTLtR3lsL1OCoOAZAW18HAPiwaKvZBRLyxSS/Fl4y9aNXRM+zBY6REIAb2J14F13/D7kGzAMiMj8TckVKtsVfD2GsgPtPI2EtXfLeC8nqylAoAlIQcNjq1dacUBkBgb7dqV8oVsxbYgxHYd0rGPnsUHH8xpA8izXXxqFRG3nmr1t7ISMjxagQUMDa8xuT/nMy8sJeNqR8d5urNkapUc7iqoXMeUwSGYm0OMelDAHh7rXbh6peDM5q9gFxh1ip7Kkq6Z1ytOHa2FbpH3XnSx94NzP839eve1Pr0CL/p55a+MvaKogR2yTVaVv140z7tm6yR0NYlOJnHw++/hUueNjalSCm+R8beR8C+ewVxR7YY3wbcc+OtGSwsnM7Jyo6OZ+z1/l+NNdCoJSb0UvxASTwe08Be1rR1zKEKd9dznE6or/QoxQ+wPxwPh12d/LOTOm99m3TF92a1OyiuaSDTlbEPT8plkyuwV0t3grWmOw9PWwM780O4dZt2BRwodpXid/a4mHTXFVgpxT9KtTauh0M/wd+HGW9CAaN0B5H2GhRU8jqjcR4YGft0pYrCynqZZ91TOJ2uCRtAzgnYHU7eW69duPrV2OYzck/2XsjAhpfZGj6iK45SdJFGu5PdJdp7nN44T9c/PZYBqZHcZnqN6A+vg9oyrXZc+KWlcXee22sCtIHeEYuV4co+7Zu2dsRvRorrHOOIdMVvmrGvPACvXo75uUkkRGohZUA10LNatGNEm76T3tGMfWQ8hLnOYV1TuxKjtfgkJDL2clLVMYc8R91VH4K/9WHCawO5I+z1wMvYO2zg0I5J7+Tf4UZmNcXw89dQuMF9RUwy9oB20UdVIcekZewjU3pRRiIH1TQUVCjc0L0HqIt3N7QySvE7I2PfUA1b34cNr5EW71pjXxO6b7o+6aX4AKoDDm/otkPxqWgT4Bp1l9JJFwE91tg32JxS4dNTmEzwp51w7eeQNpCvd5VSWmMlNTaCM4ZkNHu3hLRcbIQZ/WpEz7C7pAabQyUhKqxJtY+iKPymby1Riutvf9kfYPW/uuEog5MesDe/ljqwM/blXhn7UR17MFcMI83z3JUcTQL7cvfks5HhWjIhoJ4brpG6JWoSlcR3PGOvKB79v0oBAi4+6VBg39jYyM6dO7Hbff8n/u9//yM3t+naN+EfvRQ/Vw/sXS4yrwq8wH7PZ/BINrw10yiBzeloYL/lHXj5Qlj1D6PrbWWdTS4YoTXOA+hl1gL7sKRcYiLMbHIep+3QneX4Zbu1izIe7A6nkVHvlIx9fTm8fRUsv4V0aZ7nm8drBgAFa7rnOHyp2A/5KwH4wTmsc0bdgRHYZ5i0bF6hBHQ9R3gU9BoLJrNRhn/xCbmEm5s/jcl1VY3p1W+iZ/BcX++rMvTMRHfjMwpWw+a3uurQgp7e/CzORyk+uAP7QB15V1FTz1BFy9C2O2P/9lXwSC/Y8V9ASvGhheZ5HtMDkiK1kcMBVc3hqvTa5cxFUdz/lx0S527sDZAUo1ex2HE4uz8+aVdgX1dXx6xZs4iJieH444/nwAHtj+jGG2/kr3/9q7Hf+PHjiYzshOxciCr0nGHvkX3LUcqJtxY3d7fuUbIdHI1gCjMy9h0eNROnrZXTxt1pfziNDid1jQE4EaCLFbjm9yrmcDCFQ3w2idHhbHD2pyGxv9bgo7t8cjf8fShscDesPFLbiKqC2aS0f76sJ9e4O+wNZEVpzwcJ7I8SGQ/x2ai5Y7XvD/7Yvcfj6acXAJVVzhHkq9mdV4qfdwr8eQ9/SlsKIJnaHqi8tpHPd2jvfy2V4QMMde7mifClTCoM/Ck4wn/u9fWJPm/va9WydO86JqAqZm050pG9XXZ8wUpV1RbX2Gvb3UFMoHE4Vcz1ZZSqiTjDYyB1QDsfyKatoXZ1xtfPWSrqGnEGQODWHfRJXE0Cez02Gf1bCmOHAQHWGb90BwC71DxSYiJavBDsNz2wN0rx3ctWqgOgHL9dv+Fdd93Fxo0b+eqrr7zGyk2ePJk333yz0w4ulNVa7UZZR05SlHdZLTBS3YXVHkABbuF67XPGEHfGvr0z7HWu7BuWEqLDzUSEaU9XmWUPBa4xYc+MeAP+UgIpx5EYHc4zjvNZc94ncMp13XNgVQe16g3VoQVZLvr6+vS4yM6ZfRsRa1y8yAyzABLYN3HBP5iV+gp/rrpc+/7gj4Gx1tRuhXUvA/CSfTImBbI7+lqhC4+CuHSyk2MBydT2GB/8EZbfAuU/s2z9IWwOlRG5iQzJanmMY3Z4LZeZVzKqfnUXHajoCs01ztOZXMuOvnSM5gdc/RW2vNsVhxbUahsdxltEfGRza+wDtxS/sq6RIjWZiY2LcNy6C0zt7Mwen619dgX2esWoU4XKAAjcuoOehW9Sil/tik0SexEXiBd9XBn7nWpexzvi69KHQM4YiNRef8LNJqP/WSCss29XYL9s2TKefPJJxo8f71UGdfzxx7N3r1wV7Qx6CWlCVJh2hfSowP5E067AaVBhKYGd/9O+HjDZaJ6X1eGMvfuqmKIoxqzIQFnH0p0OlrtK8ZOjtfWniuK6aqh07wvL+n+D6oS+EyC1v7G5pLoTO+KDa52TlrVPN2snedIV39uhyno+31HCf8syUU1hWtlYVQB0B9/2AdQdoTEmm8+dY8hOjO6cq+ge9BJsKcXvARw22PwOrH0e1engrbXac7i1bD1AcnZfANLVI9QF6Nxt0TZOp+oxw95HYG9vhOKtANSkDOdt66kA2Da8GRgXNgOYfk5pNilEhft+TU5wBW+BWIqvl8onRocTHh3fyt4t0HsDuabJhJtNJLguaITiyDtVValtbCZjX68tByUxl6zwWqKwYgmU54bDDoe1Xj67nbkdX1+vm3wfXPcljLjc2OTujN/9v3u7zqZKS0vJyGjasKa2tlY64XeSQ0ePutPXy/YeB8AY026jNKbb/fQiOG3Q6yTsmaMoqemkNfZ6g4r6CrA3enTG7/4/nO6mZ+w953/rLyxV9TZwOqCxtmsPyunQAnuAMTO9bip2PSfSO7MjviuwT1ErAW0Uos3h7LzHD3I/5msTExqIxJKkjQgLiHL8AZNhyiNsHfh7HJg7b9Sd7ouHufLAffRVDhvLmUQQK90B9gaITGRrQxo7imqIMJu4cFROq3eNT9fmmKcp1RwuqzjWRyq6QEFFHRarnYgwE/3T45ruULoDHFaISuSx2RexI+l0GtRwwiv2ULH3p64/4CBisbob5zV3Lp9gZOwD7zyszNW1PjWug8v9ElyvLfpkGSA1hDvjW+1OY+147NHj7n79MtxdCFveZcHeizndtClwMvYmM1z+PLsyz2OL2q/zAnsfEmMCpzN+uwL7sWPH8t///tf4Xn8BeO655xg3blznHFmI0wN746RXz9gPvwyA45V91NZ280gz0LIpa5/Xvj75eoprrDhVCDMpHS97iU7W5qAD1JYaDSqkFB8OVtQz1fQDE765ElY+AbgD+/47/wWP5sGqf3Tshzid8MJUWDgcvvpbk4Z4Tez9UssIRyXB0Au8bur0jD1AnHZVPa7xCGZXeX8oN7fxUrie0/93Jk+GLwbg5+zz4NQ/tn/NYWeKSYFxf2RV4nmA98WpTrHzf/Qr/pTeSokxWUQEsUOuRqA5o3j7J+0C91nHZ5IU48fJe3QyDWj7lR7ef6yOUHQhvXHe4Mx435U+WSPg5s0w7XUyEqN59rozWGU+CYDP3nqSCnmPaFZNQ8vr67XbArDc2qXcYuWriFtYbJ3n1dStzY7K2ENoN9DzbNYdG+HjuRERC8l9AS3pGDBr7BUFjpvIW3l/oZHwYxvYR2v/LoEwkrtdgf0jjzzC3XffzR/+8Afsdjv/+Mc/OPvss3nhhRd4+OGHO/sYQ1Lh0Rn7/mfAwClw3C8pU5IpVFOxlQdAWe2O/2rrkGIzYNhFFLkyZJkJUR1fS20yeZXj64F9IJS6dKdaq53y2kYGKIXEFP8EFfsAd2Bf7YwGW637hLi9TCY46wGthPurR2Dh8fDutVp39aNLGhuq4MM52tejpmlrnT3oVRydOsPe9eZrqi023nSlHN+l8gApjYfJVo4AsCr913DOI5DdwfE/najAtZyk0xrn6VyVHGlUSSl+T+Dq32LPGs0HG7UM2q9ObL0MHwBFoTJM69VSU3LgmBye6Fqtra9HUSCpN/Q9DdCW5Qw/9zq+VcbwieU4Zr6wJiDLyAOBHqw3N+oOAnuNvfXIPvqaihnauEW7gNxe8a6MfY374oB+jnEkBAN7vSN+TIS5+fN6V0+lE027Au65ofdfSutoJYeuaDMsGglLTzM2Jblm2Qdt87zx48ezYcMG7HY7I0aM4NNPPyUjI4Pvv/+eE088sbOPMSQVes6wBzhrPkx/C9IG8IfkfzGxcRFHonp34xG6DDoHLl4KZ94LYREex91JAdzEO+D8RZCQ6y7FD8EXVk96GX7v8Eptg+tNSA/sd5kHadsP/dTxNYVZI7X/314na8stNr8N/3cWLP0FfHije7+oRDCFQXgMnHRtk4c5Jhn7E36nlYGN/I1RHSIN9DSWUi07eVjVTm70f/9uVV8JL54PG14Hp4P95dpSkU4vxXddDExTqiitsQZWk1HRdq7AfqPzOCrrbGQlRDFhYLrfd6+L0qarWI8EwIVw0WFbC6uAZtbXNyPzpEvI/P2HrIsex6aDVfz+lZ9kbK4PrXXE127TM/bdH8AcLaxkCwDFUf0grAPnGvFZ2rlP3inaOm1Ce5Z9szPsy/bAK5fAJ/cYgf1IJZ/6ui5eBurLJ/doH1WHKHWdF3Zaxj48Bir3G0k1CKxZ9s3/9bZgy5YtDB8+nGeffbbJbcuWLePiiy/u6HGFvCZr7D2YIhOA8sCYZR8eBaOvNL7ttFF3urFXG18mxWhrhisC4A+nO+mZzt7hVWADErQOromuioad9NZG4NWXay8+rhKpNmmsdXWej9AahIy4XDvBXvOcFtyXbNPWn53/Dy2zDzDtVUjqA9FJTR5OX2Of0ZmBfa8TAe1CYlpcGSAZe13pwb3EAYfVVMA1lcBqgcJ1kHIcJPqZ8exMG9+AfSuhtgzniN+w9ZCWeRua7f8Jul9c0zQyzTXggKKqBvqkxnbuzxBdw241GqG9e1j7f710TK6x9MYftthssEBjdekxOUTRtfTGeT4z9vZGePcayBoFp831Cu4GZsbz8jUnc/FTq/hu7xEOVtR3/jKgIGcxSvF9d8TXbgvcjH18hdYBvTx+CK134GhBdBL8fqXXplAuxW921F35Xtj7BdSWwdkPYY1IJrKxgpSaHcCpXX+gurpy+PE5rTfL0AuM88L0uE5KOOqVxI0W41xZrygO2jX2U6ZMIT8/v8n2d999l+nTp3f4oIR7TFNuUrT2xGmoMm7TX1hrG6wB1+VVH3XXaeOrPLhL8UPvhdWTPsM+W9EudBydsT/SoEDWcO22Q+1oFtRQBf8YDf+5GawefRxyToCLn4Jbt8Ml/4LfvAJ4PP+yR/kM6sGdMe7UUnwP6UbGPrSfG7raMi07aYnUspUlNVZ4bza8dAFs+7DrD0hVtTdagJOvZW9ZLTVWO9HhZgZl+miA1RGuwD4vQhuDKCPvglh1ISTkoMak8lGB9vp21rDMNj3Ezyfdx8CGl3ndNPVYHKHoQmUWK8XVVhQF36MOS7bB9v/A90+CuWnZ7fDYKm5P+JRw7OwptXTBEQcXfYlCsJbip1q0meW1KcM6/bGlFN9H4zx9yk5iL23ZU+oJAPSu3dKVh9fUupe0oN5VdaGfF6bFd1IpfkScMW4ZizbLPiE6yAP7a6+9lsmTJ1NU5G4s8eabbzJjxgxefPHFzjq2kOVwqhS55n7nJkXD1vfhr73hdS0zHhth5tnwx7n00/FQ0fQCS5eoK4el4+GHpUapEmCMustO6KQArqYYfv4aCjeQHBM4pS7dSS/FT3Vq66f1Dq5eLyy5riUx7Vln//1TUFsC+1e5X7w8xaZq6+j7ne7XnFiHUzVK5Ds1Y99YC1uXwU8vGSVWUoqvMddoTcZy+wwEXBn7XmO1G7ujM37+N3Bkt/aGOPI3rC+oBGBEr0TCOnnUnX41PStMuyh1SNbZB6+UfnDzJnZOW0VlvZ2YCDPDcxPb9BCZGRnYCJMLPD2A3jivX2ps07JgMJZtkDNaW2vvyemE/5vCdQ0vcLppI7uLA6D5cIDRq0Dj/CjFD8Q+Bbn1uwGwZYzovAd1apN29E77oTjuTm+G1+SCT5VrWperArA+SzvH6N+wtcuOrQmHXassBTjl99icqlFlkd5Zc+wVBeJcy8FqtUqwQOoB1q4zqvnz5zN16lQmT55MeXk5r732GldffTUvv/wyv/rVrzr7GENOSU0DDqdKmEnRAhb9jydWK6uNjQonRakhwmHRGpl1h/WvQPFm2PCqV3BnlOL7WELQLpvfhpcvhO8WG12QQ70rfkF5PZE0EmOv1Da4Avskz8A+Z4x2W1sD+9oyLbAH+OU9YG7Xah0vRyzapASzSSE1thMD+4ZqeHsmLL+F9FjtOCWw1064Em3aVeSRw4YCWsWEmtuJgf2RvVpFx+Z3tHLp1ujZ+lHTIDKeDa7A/oS8pI4fy9HitIxuGloQUCid8YPeDwXa/+GJfZJ9d0JvQS/Xe1FRdYOMwwxyeuO8oc2trz+8Qfucc0LT20wmOP4SAC4yf8fuYsnYH80oxW8hY6+Pu7NY7TidAVQxWltGqlNbkheWM7Ljj/fx3fBILvzwTwBSYkN33F1ts4G9a1qXK7C39Tmd1+2/5GN+0ZWH523nR1B9EGJSYfhlxv+X2aQYfbo6hes8A4s2LSox2DP2AEuWLGHUqFGceuqpzJ49m9dff53LLrusM48tZOmdnLMSo7S1hHq5S4L2xxMfGcY6p5aJ65bA3unwKKu9zuvKeGGVPsO+kwJ7fS2LpUTm2LscrKgjgVrqY3MhMkEbC4hHV/x6G+SdrE1RGHxO2x7824XauqHs0TDsok453uJqd0fStqyNbVVsOqCA6iDHVXYtgT38lF/OITWNUiWFfv21RoqNDieVySNAMWmvJx0ZBQTaWvmNr8O7s+DvQ+HTv2jBvi/rXtamZ4DRWHHDgUoARh+LwL7PL+DPe3hn7KsA0hk/mLmWmq3O15YdnXpcapsfIk0tZ2HE0ywJW0RRlVzkCWZ647xmO+LrGfvs0b5vH3E5AGeZfiK/uKKTjy74tdg8z2GH0l1G0K+qUNsYQOX4DVV8z0h+cg4kKbkDHfF1iqKdC7k644dy87za5prn6YF9Qi4AYb3GcJd9Nh/aTu7Kw/O2+l/a5xOvgvAoY319amxExyd1eYp1xybgEdgHQHzidzruww+brsu89NJLWblyJVdccQWKohj7XHjhhZ13hCHooOf6eoBqvdxF++OJjQzjJ+cgZvNR9wT2uz6BygNaQDnCXaHRaHcagVVWYic3qbB4jLsL4Yy9qqocrKjHQjKHZq5hQHqscWFFf2GpsdqxJ/cnbPpbbXvwqkOwxtUQ88x7m5YytpM+6i6zs5Zn6MxhWnBfW0KWSTvhK6sJ3eeGbs3+CpY23s+vTuzFY8m9SIndTnltI8XWMJIzhkHxFji0FhIuaP8POfEqsDXAinuh7gh8t0T76DtB67Vw9kPu58+ez0B1aLdlDKWu0c6OIi3zNrp3Uod/3ybCoyE8mpxk7bWosEoC+6DUWAeLRqBmDWfjvmsBE6f0a/sJu8kcxiWmb3CqCj+WV0vDtCCml+IP89Vw026FYq15ms+MvWu7IzyOaJuFxtK9qOpElE56n+sJWhx39/2T8MWDRF3xBuFmBZtDpbrB3mKjva7kSD6OK613au1cOqMyMMF75J2+xr6irhFVVUPqedNsV/xqPWOfB7ifN5ZGrZqjUwNpfxRtgf3fgmKGsbMAd7Kn02fYZwwFS5E2EQr3uLtAyNj7Hdi31On++eef5/nnnwdAURQcDhkv1BF6AzqjI/5R61hiPTP2JVu1BmeR8e3/gXs+0+aT54yBMTNg8FStG3pz1jyjfR4zQzuJdimubkBVIcJsMq5udli81vGdmiKvrpPd8qIRACrrbMaLbK/kaK/gW19jD1DdYDfeiPz29d/AYYU+p0H/MzvleMGdsc/o7BdW0MbS1JaQRiUQbow1CWVrXNnNk11BUEZ8JOW1jZRUWxnS6yQtsC9YA0M7ENgDnPp7LQO/+1P46QXYvULL5O9bCWOvgdT+2n6jrtBeW0ZdAcDmg1U4VW30YadNz/BBH7kpa+yDVNFmqCvDUbSNwjqFqHATI3sltf1xYtKwE0aYYudIUQEMyOr0QxXHXkVtI/ll2hitUb6eB8VbtZGs0SnaHHtfFAUlpR8UbybNVkhRdcMxfQ0KNsZaal/B+o7l4LSjVBUQH5VHeW2ja+RdYPz7aQG39rXej6lD4l2vEzVaLzH9fEq/oJEYHRgXNLqCz1J8VXUvw3MlHeOjwgjDzlAO0LD7S2IGn9G1BxqTAqf+ERprjGMyOuJ39vnnmfdqHy7GuLsAaO7tdym+0+n060OC+o6rcJX6pMZGaH88RrmLuxS/hGTKzJmgOtvX+dxTeT7UV8Dez7U1y3ppbdlu7/2cDnhuMvz8pVbS67oiptMvSGQlRnVe0K1fNbVWkWTW/l2camA2bukKeuO89PhIosK9G9eFm03ERmjbjKuGlhLY9kHrD2ytge2uqpwz53Vath7cGfuMzs7Yg/Hmm+zQRyE2Yg/hdbT1jQ42HawE4JR+WtmyXimhNdA7Sdvx4Nr2/YCty7yrhMxhMGQqTH8bbt4Ek+7Sgn3P58/gc2H8zRCvrUnT19cfkzJ83TePMeq7uQxT9lFYWS8zq4ORq6y6KHYIoHBin2QiwtqxetBkojo8DYDa0v2deICiK21wva4dlxZLsq+L1lUFEBblu3GeB1NKPwD6KMWyzv4oFtd5lc9SfH1CTlKfgOyMX3HE3cSsUxqyGkklLWMfFW42zq9CrRzf4hp3Fxvh8bxQFPjzLri70JjMFBlm4tywdfwn8i+Ef3avr4dqmz2feTXnblVCDpzzCFy4xNhkzLDvrMZ5zdDHTTfYnDTYujcO7uR2xKIz6EFZYnQ4NFSCTbtK7VmKD7AjXGuM1eFy/JNnw1UfwSl/gLgsqCvTymqfHAsvnu/ez2TWRkgAnPBbSO7j9TDuGfadGMBFxkOEVo0QUVdsvLCG6jp7fYb9LRHLtIssG9/wut2rgUddOTwxBN6a4a76aMZne+u41LyEQ6cvgN6dO3/0mGbsXQ1MYhtLMSnadbBQe9P1tL6ggiv4mFVRt5C36R+A+9+9pMYKx02Ecx+Dc//W9gevr4D/3AT/d5Y2u/ZoSb1h0p1w3hOQclyzD+MO7JPbfgz+2vMFsXv/S3+lkAabk4oQfb0Iaq7AfqNTey7pF6raoz5auwDYWH6w48clusV6vS9Hc8t3hl0Edx2ES55p+YE8Avtd0hnfS01zzfNUFSpcF8WS+5IYqbj2D5DXVWsNA14YzqrIG8mN6aSgSg/sqw8bvT5SQrQzfrPj7gAiYrXGlGgV2zsjtFGDYWXbvMclt1XBGvj3ZfCvCdqyP4C1z8P/7tBK7muKYftyWHEfvHAe/O9Onw+jZ+zTjsX5JxjPjfjIMON6YnU3l+P7XYq/ePFirrvuOqKioli8eHGL+86dO7fDBxbKjMA+JlzLko+dpc0Wd5W9639ca00jGX+cAsl9O/5D+56mfZz9kFZau+5l2P0JFKzWym3CXH8UFy7Ruk261tR4MmbYd2ZgD5CQDWU1UF1IUkwEtY31VNQ10pfYzv05QUDP2A81FWjdzYd7N6xMiA6nsKpBew7FpGsjzgpWa83LTrmu2cd9d91B1pWZWHjkVB7v5GMuPVZr7MHI2JssxaTEnkiZxUqpxXpsqgOCwJr8cvooJeRSbFwQ9MrYJw5o8XnQopVPaBca04dCv4ntPsYuydgnaCdlx0XVQJ02y77NS1PEMVdmsVLTYKdfmo/Xcldg/2mFlg1qz/p6nSMuG6o3QE3LFzhF4Fp/QGt2d0LvFi4ImsPdY6iac8IM3qwaxuK1KmeXSMbeU7Pj7iwlYHctaXrpAq5Uz2ITZwdOxr5oCwoqJlRi45I65zH1Unx7vXb+HZ1ESmwkBeX1IdcZv9mu+D5YozM4aEmjF2VaNfFxk9r+A51O+Pgu7evcMRAepQXQ3/9TG5u7+umm96n33QzzmGXsy3+GVy7RKgpu3YrJpJAYHU5lnY2qelu3noP6HdgvXLiQ6dOnExUVxcKFC5vdT1EUCew7yCtjH5sG5//d63a9DOp95QxunvFg+3/Qd09qfzR9PEZT6KW1Q6Zqa4uqD2mNKHTNNaUBDld28qg73fhbwdEIaQNJjt3Locr6EM7Ya4F9BlrpuXFV2cWzDwEAQ853Bfb/aT6gs9UbF2W+3lXa6f0LjmnG/vhLIWskpA8hbU8RZRYrZSH2puvpx33lTFeOaN+4lu5kJmj/7sXVHegIXrHP3W327Ae9Rly2RXF1A4erGjApMLJX2+aRt4nr76JfZLUW2FfWM+JY/jzRJk6nyis/7Oev/9uBw6ny4Y2nMSTLoyGatQbKdgGwqi6PiDATozpwIciclAuFEFFb1MEjF93B6VQ7b0Rm2gCiB8ZQvnY9uyWw92KMuzt6jX2lxxKW6kOMit4EnE11oAT2ruWGm5zHGfPmOyw8GvqMh8g4Yy15qHbGr/F1wWfDa9o46mEXac10XeIiw/ipehC9zGVa1r09gf2Wd7QGvxFxcIZHSf+5f3VN2fkInHbIGKYlr3qdpE2C8uGYrbGPTNTOi8BIfuqBfXfPsvc7sM/Pz/f5tb52MZQ6RB5relCW0ExzDr0U39KRF9XibVpHa9UJN6yGjCFN94nPcl+19IN71F0nX6kafYXxZVK0NvovEBpUdIcC18SEJLs2r9XoQeDSZJbm0PO1/+d9q7TS/BgfWa9Xf8UDZSXcpcxga00/th2uZnhu5wVBx6wrPkDmMO0DSI+vYEdRDWU1oVUmp2u0O/lpfwV/VlwXfVxLd/QrxyX6v0tNsVaNozq93pBb9PmD2sW1fhNhwOR2H6NeTjsoM75ph93O5Arse5m1nycj745SV65dqDlplnvySBcpKK/jtnc28sPP5ca251bm8/ivRrl3OrwRUKmNyqKsIZFT8pKa9BRpi5jU3jhVBaw1IdfRuif4ucxCTYOdqHATQ7J8NAo+tA4++CP0PwOmPNzq4w3MiANgd7E8H3ROp4qlsZnMrB7AxKbD/7N31uFtnecbvo/QzBxyOHGYqaE2bZqmzMywwkpbu3XrOvpt7bquuHK7Mq1tunKTNA00zJw4HMfMsi2D6Pz++M6RDLIs2bIt2bqvK5cMR0cniXTOeb/3eZ/HXMKQ+r0YsAaGFL+mGLa9DcB79jMZ4E9l1k3fNvlWVX2V9bLC3m3cXcEuMZKXNrbJttFhOrY5hnKBdr1oKvmKxSzk9QCzH3TVIJIk7j2GLHBJ/L0wDVfvB5P83bGPSACtURhOVxdAfCZx4XpO4n3kncMhU9oJCtN2z9i/+eabjB49mrCwMMLCwhg9ejRvvPGGP4+t19KkY19TImRAjVANLFTZFDUlUHnKtxdZ9qi4sR95nvuivh0UOs3zOs8lVe1IV5gD4ILSDeSW1yLhILxBGMU079g3ybIHMeucOlrEjWV/33KHFSfhxM+MdhyiQhYnydWHSvx2vHaH7Fwx7ZSOfSPUE3dvdcbfm2+i3uqgj0bt2IvCXl1QKVaUE5Qdga9+CcsfA5sXNyh528QKOlLTGLt24Oy6dUbMXWMUKX4KQp4XKuyb8f3DsPqJph4qnYzapV/47Bo2HisnXK/l5lli3vmrnfnOBUBAmLMOmsceo1CITWtHfn1jImfeygjLOzxkua1XK3qCle0nKwEY2zfOvTFa/nYo3g8lB73a35C8//GI7kMM9WXO61Nvx2yxOV3lW5jnqfP1Q8+CyBT0soUJ0pHAkOJveBFsdZyKGMlax2gSO9Ekrbd27N1K8VVTbyWtSyU6TM82xzDxzaktQlbvC+tfgOp8iO0vHO7dYYz2qqg/WFjFiTIxktg33s91iSQ57zOoEgaLMU5n/Lbrk+Lqei5+eT1T/76CTcfK/Hpo7SrsH3vsMe677z7OO+88Pv30Uz799FPOO+88HnjgAR577DG/HmBvpElhv/R38ER/IZtXUE+6DTYH9jVPw1NDxE2atxxeLhzwtQY48y9+O+5OMc8D0V06tgqOryE+QpxYe2OWvcMhk1tZRyLVaBxWQGqhqGjRsQdXrNmBr1vudNdHAKxzjCIf4Ry9Ott/hX2ZuQGHDBqJzrng2iyw7wvY+ApJkeJz0Vs79puPl6PDpkT/4SzsXeZ59TgcsjBHjEoVC4bHVnneqSzDMkUKN+5KSB/refs22HlKFNqdOl8PTpfeOLtQtoSy7JtxeLl4LM12mv90JqZaK9e+uYk//G8vtRY7Uwcm8MP9s3nsvCwm9o/DYnfw/oZGct8BM5Gv+x/3190GwPQOzNcDGMIjSYgWXdpQ/GHwseOUOl8f534DxY/B06hgY/QbnucO3TcM1eRyKOSMD7gaRTqNhLF5+sSUW+CW5TDjHhg4G4CZ2n3d37GvLYctoqH4dew1gOS/qGUVWXY6syf00sLerSt+a4W9UcdBuT9WTRg0mLxebBP7zIO1z4qvz/yzmK1vJ7Is8+ev9uOQYdHoNPolRLR7X62i3GNRnQ9AXIR3Wfb78k1c+O91zkbHnjyTx+19pV2F/csvv8zrr7/O448/zvnnn8/555/P448/zmuvvcZLL73k1wPsbdgdsnMVNDZcL2bcoUkB11gOUxenrIwdW+3dDZrdCkt/L76edodH92pfaLDZnZ2QDH/P2OdsgHcvgOV/dHXse+GMvanOisXmIFWVWkcmC7OgRjizNBsvfIxQunLH17jcRUGspO78EIDP7HMIV6Su23Iq2jwxecv+/CpAdNO1fpzbb8KnN8IPv6FfmPi7lfbSjv3m4+WkUoEGGTR68f7ANVtmtctU1FrEfHzWBeJJ+77wvFOHTSwMpY+H0x9t8xie/OEg9360gwZbS2diu0NmT664gHWqIz44z5fh1kpAJq+yA/4CPZH7d7u+Lj7Q6S/30qojrD9aRphew5/Oy+Lj26YzIFEY5t06W1yD3t+U0yQmKKe8lsKqevRaybNhmpf0UTo2eRWhwj7YUEd4Jrb2PsjfJR7Tx3u3w8aRd8UhZ3xoPF+vazmaEJEgZphTs2DgHACma/ZTVdfNHfv9X4KlBlLHsMoxCcB/M/YgGmp/7wM/CMf13i7Fb9KxV2sTtbhViA7TYUfL8sGPwI3f+lZj2OrFzHy/6TDqog4d8w97C9lwrAyjTsPvzhnZoX21ijM5QRT2seHi38fkofG4dF8hl768wTm6DP5/P7WrsLdarUyePLnFzydNmoTNFgDSnCCm8QpobLheZLNCk1UxvVbjzPM1pU0XBhOmU95lU297W3RpIhJh9q/9dtxFJlFMGXUa4iPcewO0G3WOvLrAuSLW3eYU3YH64U802kW0mJs0BLcd+9RRcMmb8MDepiugOeuh8iRWXRRLHVPIyohhUHIkdofMuiOlHT5em93BE9+L1dqzR3vv1eATOoN4LwMZWlE09kaprd0hs+VEOUbJijl5guhcKRE0eq2GJOVmRzUydF40D37rNAZyi1YP0++EO1a3WJlvTm5FLS+tOspXu/L5aFNOi98fLq7GbLETadAyRJlx7TTi+sNDR8m+cS8ghaT4zQmLhcFniK+PrWzXLoqr61m6r9Dps+OJldnFAPzjkrHcOGtgE3POs7JS6RMXTrnZwv925In3Y205m5QZ/HF94wg3tH++HgBZ5qG65/hQ/39UFIci74KJmgYb2UosnVvjPGudkOGD1x174kVhnykVhQz0FFQjvBaO+M1RCvsJ0mEaaqs6+7A8M+lGuOFrOPtxypRiyq/pJzqjSJdRsuwTe2Hcnd0hU6csuDrj7qz1YFaUnW6k+ACbo8+EzNN867onDhb/n1d/0qGRv3qrnf/7VixY3zFnUOd066GFFD8uvPWOvSzLvLTqCHe8t406q53ZQ5O4Y45Y9PC3yrRdhf11113Hyy+/3OLnr732Gtdcc02HD6o3o74hIgxa9BLON0zzVTF15azGYYTh54gf7v3c884tZlj5d/H1vEcgPM5PR+2SuqbHhvnfiEaR1VJTREKY2HdvlOKr8q+TkWPh/j1wy7IW28S4K+wlCcZc2tI4b8cHABxNPYt6jKTFhjFvmDDSWqXciHeEjzbncLCwmthwPQ8sGNbh/bVKlFg0SJUqgd7Zsc8urKa63kaRvh/GX/wEty5v8vuUaCXyTp1j7jdd/Ls1mOBo+wq75vyw1+U4/sJPR1weIApq121s37jOU2+oaLQQmURGvOgKl1Q3uFUR9GoGny4ej/7Urqf/3zcHuOO9bXy2zXOhnF9Zx6GiGjQSzB3WMopMp9Vw06xMAN5cexz50FJ4ciATV14LwLRBHZPhAyBJZNVvZ6Z2P7UlJzq+vxBdxu5Tlcgy9IkLd28yVbBbeMhEJrcwk20VpWPfXyriSEiKDzSKujM2a8zYLELlufl1ofiMH0hp8gw+sc/HWm/uhiNthCSJhYaBs52ND7+apKkdWaWwT4gU+y7vRc0Ds8V1HXeqhdVuvT4SwpuqaNSFoar2jmlIUodrk1dXHyOvso6M2DDunDekQ/vySPII6DPZWeDHtjJjb7U7+NWnu3jyh2wArp8xgLdunMKgZHF/0m0d+wcffND5R5Ik3njjDUaPHs2tt97KrbfeypgxY3j99dfRaNrtxxeCZvP15mJwWIWRUDOTNGdh32B1ZZnv+0Lk3reGpIUFfxTS7Ek3+fW4XfP1nWCcF5kMGh3IDlI1oitb0YsLe+eKtJsFFFfHvg3lTEONMyJmU8zZAKTHhDFvuLjxXn2oxKtOXGtU1lr413IRV/Wrs4YR35kZ4orsOkEWc5i9sbDffFyYr0zKTHBrLqVG3pWoHXuNBkZdKL5uTY6//0vY8b5YEPSC75XCXpLEheo/a483+f1OpbAf39nGeY2Ij9ATphf/HgUhOb6gaL9wEK9Q/n9OrPOs2miFvcpc4De7Czxut0Yx4xzXL86puGrO5VP6EWnQcri4mprlwi9mY4MovqYN7JhxnkpDeCoA1opQln0wsaMtw80TP4vH/tO97/LFu6T4h4qrO3St6yk0luI3wXQKNvxbeK1odCBJHDjrPf5gu5k8Sycrr1rDWtckt9xmdzgjkP3asVc7stXi2pbYSIrfW94zZnfeC3WVEJ4gkneafebU909NvU2oiL/6Jexd4vlFDi0VufWtZNH7Ql5lHS+vPgLAI+eM7LjayxMTroXbVsDMXwIQ2zxuWuHPX+9jyfY8tBqJv1wwir9cMBqdVkOislDk73tWr6vwHTt2OP/s2bOHSZMmkZyczNGjRzl69ChJSUlMnDiRffv2+fUAextNCnvVnCI6XeTLN8IZeddgF52XsFioKYST61vfuT5MSJeu/KDF/jpKvnLTnB7XCZFmGo2zK5vgEAVMb8yxVwt7T+Yw6o1zlbtRhe3vwZsL4fCPwjjxwhdh4vVstQ8FIC02jKkDEwjXaymqauBAQftnD59efojKWisj0qK5emr/du/HK5TCPlYxSiszW7DZfXRiDXIOKXLSca1ktTs79o2z7EddBEiu6JjGyDKsekIUgIoPgycKTfVsOykuyr9X5tleW3OsicmQahTT6cZ5KlveRPrv9VwcJa5JITm+QsEusWBTkg0XvgK/3Cokpz5gszvIKa8FYP3RUo8mWmsOi8LeXbdeJSZMzxVT+rNQs5Xoin049JH8y7wQrUZi0gD/+DHISjdXoxgdhQgOduSoxnmtvA8MUZA4RERxeosy99tfKqay1tLrZqbdUdMgPsPRzaPu1Az7+AHOIk6VW3ebed72d+GZMbBBeHqVK40eScJpsOwXohsV9g67c9Ggweag1tI7FGDqgk+ksZH3Qt9J8Jvj8It1LbZXm47V9TahBtv+Lux4z/OLbHoFNr4kHPE7yOPfHaDe6mDqwATOHZve9hP8iMvjyvW5eG/jSd7fmIMkwUvXTOT6GZnO3yUp/kdlflaAeF3drVzpH7lmCM80ybBXC/tmMnyAKGXWxdxgE3PGI88XH559S5yupV2JGnXnd0d8lZgMqMol3l4GRPTSwl6s6t1Z/iS8UQFn/LHF/7XbGXuV/B1waiMc+BKGLhCF3aiLKHxFLAalx4YTptcyY3AiPx0sZvWhErIyYnw+zoOFVby/UdwMPHZelvt4In8SJTpxEQ2laCRwyOJCrxazvYEK5cb04mOPwbMHRCxd1vnO36sd+6LGkWJ9p8KvsiE6teUOc7dC8X5kXRi/2DWItIK9/PmC0a2+/g97Rdd20oB4bp41kC925LEvv4qXVh7h0XOzqGmwcajYw5xsZ5C/HQ58xcSYeD5kZMgNXUXt1CcMhPFXtWsXeZV12ByiY2W1y6zMLuH8cS1l0Da7g58PiwU3T4U9wE0z+1O75TMANiRfRkV1DBP6xjbNTu4A+ri+cAqMdZ4VBiECB1mWnSM8rXbsp/9C/PElVit+ACARLdWRSBWHiqr9n3MdZFS3NmOvZtjHDXD+KDpMhx4bg+v3QsM0MHZh597WAOueA0u1c35bXUCOjzD4d8wrMkUoZmU7mEuIiErFqNPQYHNQbrb47dwUyNS4M85T0bVcRIlRF31UNfHKvwlz75oSiHJzDSg/5hoHm3Bdh45107EyvtldgEaCP56X5f+x4NZQ1BtxzeKm1x8t5U9ficbCwwtHsHBUU68ptUlXUtOALMt+O96Qbj7AaNKxj+sHU26FEYtbbOeU4qs5olNuhYtehQV/dr/jn/8lZqTq/RuroOLKiuxck4poSxEgTjYWW+/qypabxXujv+Uw5G4RjuXNUAv7mgYb1uZdazX27uB3TUY2CpRFmTRlUUaV47dnzr55xMjMwUk+78NnlI69pqbIuaJeWt27OjDqaEpcfa7osGiays/U2VSneR4IJYy7oh5g+9sAFPdbxNKj9byz4SS7lI67O1QZ/qLRaWg0Eg+fPQKAdzeeJK+yjt25Yk42IzbM/ZxsZ6B4c/TVVQIuVVGvp1wt7NufiHK8tOl4xrJ9hW6323mqkup6G3EResb2jfO4z34FSxmuOUWVHMFdx2cC/pPhA0QkC+VQgt2zwiBE4HCqvI4yswWDVsOothaZfRkD1RnhlmX8qu/HlBPNkZCBnrOwbzXDvpFZb3SYjq8Mj/Ke9BiO4z937IXNZfDmWfCfs2HTa1Bd5Hn7XR+JGe/odBgvPL3UjqdfZfgglK1q196UiyRJTeT4vQGzEnXntrB3QxMpfuJgYWgp22H//9w/Yetb4nHwGU7vi/Zgd8j86Wthonnl1P6MynCvXvQrDgc8PxH+LxXMJU4pfmWdlZNlZu76YDt2h8xFE/rwi7ktr7dqYpHF5qC6oY3xWR8IFfYBRpPCvs8kWPwvOO3+Ftu5pPjKmyFjvMiZDnNz8auvgjX/gu9+DYV7/X7Msiw7b/rH9OmkD9PE6+H8FzAOP9M50uOvSLZgQe3Yx1gVN1I3So6YRhflFnL8zNMgLA5qS+GDS6EqH4dDdsqzVbWF2lnbdrLC5xvg77siYqQ5Q8+EK96HeY84uy69bc6+Qln0iaxXboqavTdSlWK6uKqV4ramxDVnXV/lnIlbF+taVHxtzTG3Ty2pbmDzCeFgvmiMuAmaMzSJ6YMSsNgcPPfjIZcMvwvn69UFnxSElDckxVcoV/4f4weKTsPGl+H9S8V7wEtOKIV9HyXadFV2iVtzwtXKfP3socmeO2l2G6x8HIDXbedgkkUH0C/GeQrGeOHenC6VO8cIQgQ2an59VkYMRp2bWdmqfGHo1h76TSW5z0BkNBwOGei1bp6nduzjXR37mDA9OxyDAbAeXdWxF176CJzaJGKNv38Inh4B75wP2d+7tjm+RoyFvTIbvv2V+NnMe50jROr13u8Z9gCD5guDaiVaOKGXOeOr7wunIz7A1/fBexdBzsYW26uKD3WhyOkB5s7c29YgxsIAptzS7mOUZZl/Ls3mQEEVMWE6fn3W8Hbvyyc0GuFBZG+Aqrwmitlb39lKZa2Vcf3iePziMW678WF6rXPBxJ9y/FBhH2A0Kew9oL4ZzN6s8uz5r4jsSBoOA2Z2+Bibc6Kslqp6GwadhuFp0X7fPwBDFsDE69GmDHef1d4LKDNbiKAeo025CYlpOT+k02qc740WCx9aPQxbKL4++hOcXE+Z2YLVLqORXKuHAxIjGZgUic3H2Lt6q52/qREjcwd3XsRIcxIGCTVCalbvLexrLeixoa9X/r+aRdCoUvxid7EqS+6Afw2Dw4qT/p5PwVoLScP5wZTp3Oz7vQWcLGtppCciz4Q5mlroSZKra//Ztly+2inmmrtsvh6cDtlxduHLoSZ39HoqGnXsJUmkYxxZDsdXe72LE2WiMD53bDqpMUZqGmysP1rWYju1sG9Lho8pB2wNyOHxbEm9AgCNBJP9NF8PQEwfHEgYsLFdkXeHCGzalOF/djP8I9N17vKRoUrsZijL3jUv36JjX9myY2/UadiMGM2STqxt/4se/hF2fwJIIn657xSQHeJcVN5oIbkkWxSAhbuFUrHvVJh0g/PX+/JF7F5mYmT7j6U1LnwRrvoI0scBLmd8f89FBypmZ2Hf6H1xcoO4h7S1bBQ4/RfU2mTUxYAkFm7U8WKV/V9CXbloRAxd2K7js9odPPTZbl5ZfRQQhnl+V254olHknRp3Z3fIHC6uITXGyGvXTSJM37qBnxqh6M971lBhH2BUNS7sy44K90k37ptRzTv2ILIl1z0Hb50jvgbx3C3/EV9PvqlD2ZCtoXbrR2XEoO/seWpc5igVvWzOvtxsIU0SnVEM0WB0v4jicc5+2Nmur0csdnojJEcbm/zfqTfiq7K97+I1iRiZO9jr5/mTpE44SQY6sixTUWshVSpHQgatESKaSphVv4Hi6gYcjmbnk8gkcTOluuNve1s8TrqB3XnihiktJgyHDG/83NTpHkTBD0KG35iJ/eM5KysVhwwHC5X5+tYMsDoDpWMfZREjJaEZe8QoVq1SgKuyx8HzxKMPsYfHlI79wKRIzswS4xzN5filNQ3szhWjX3OGtjGSkzAIfrkN6fqvuHaeKBgmDYh33iT6hb5TeHHmei6y/IXNx8v9t98QnYZqnDfR3XnDYhYjaZYaYZ7nKwW7mX3y31yvXRqS4uNhltrNjL0kSew1iELXULIXatv5eUoeJpo20++EM/4At/4I9+0S/kGjLnJtN3AOzHlIKPPu2y2ifg2uIn7jMXFOmz7Yfwqf1lBVAeW9RYpvafa+kGVXgR7br8X26sKQxeYQKq7YPq6GYnN3/C1viseJN7TL0Lu63srNb2/hs225aDUSf79oDFd1tllzc1R1ZHU+YXoNBuU+2qjT8Np1k51qydZQm1FlocK+59KkY//WIvjHAOFi3IwWUnwQTucbX4GT6+DIj+JnuVugeB/owoRUvxPYlVsJwLg2Zig7hKUWjq2C/V86C9feFnlXYbaQKilxIG669Spus+xVsi6EMx6Daz4HfbgzpjCt2cnH19i7kuoGXl3jWjHt1IiR5siyKEo3vkx6uN15PI0xN9hYebCYL3fm8cmWHN5ad5wXVx7hX8uyncZvwYrZYsdql8lAKdhiMlos4CVFGZAksZLcYjYw60LxeOgHMJeKRQFdGMWDLqKwqh6NBH+7SBRb/916qskFqNxsYeMxRYbfrLAH+PXC4agKbK1GYnRXzL2pKDP2+rpStNjJr6zrNRFFrVJ5SjxGJrsWBhvn2Xv576NK8TOTIp2GQMv3F2FvtGi0VjHNy0qP8c5XQWeA9LEsHpPOWzdO4dkrJ3h1LF6j1TF5sFiE2HSsLPReCHDqrXZnJ9Ztxz5ng+jexvZr0k32mvKjpOx+mfO1GyitsfSaQq01Wp2xv3MD3Ly0xeKJLTyJQw6lqDnRzjn7uP5wzWdw5l9cP4vPhNkPOhVXACQPh9MfFcq8Ru78IPLS1ejN6YP858nRBFl2xr4m9LLCvqZ5x76+UiiAoen/kUKkwfX+ccnxLxb/r42bUXYrpGaJ8dCJ1/t8XIWmei57ZQM/Hy4lwqDljesnc/W0Li7qweXBUJWPJElkJgml6pOXjmWcFwpFl4FeL5XiP/HEE0iSxP333+/8WX19PXfffTeJiYlERUVxySWXUFTU1HwjJyeHxYsXExERQUpKCg899BA2m/+MCvyJWozFGWWoUczLmslqoRUpvkYjPkAg3PEBtird+tGXQHjndMvUjv24fp14015TCO9eAEvuID5ckZr3oo69LIuCLA1lZdzNCVUlzlNhr9HA7F8JV3ygsKqpcZ7K9EGJGHUaCkz1HPJi/vDFlUeotdgZ1ze2yyNGkCT4+n744bdk6tUse4vy2MC/lmUz84mfuOntLdz38U5+8/ke/vz1fv65NJsXfjrCXR9sb7EQEEyojvj9dMqij5vzhU6rca4MFzWfs+87WdwcW2rEzfL1/4MHD7CzVFwehqREcfqIFMb2jaXB5uDdDSedT12+vxC7Q2ZURgwD3Mggh6VGc/FEcTwj0qK7dsEnMhkkLWh0JFBFvdXRa27GWiVtNPy+UHTGVPrPECqP6nwoPdTmLiw2B7kVQoo/MCmSaQMTiQ7TUVpjER1WixkstS4Z/nAPMnxbA+z6RMzYK0iSxPwRKc6xDn8yoX8cBq2G4uoGTpaF5uwDmb15JmwOmeRoo/v3gmraNnBO+5SISpb9II24Xzxc1Lvl+K127GPSof90pwO9SnSYnvWOUeIbXw30LI1GuiTJOb/eHrYcL8chQ2ZiBOmx/j9ncHI9/L0PvHEm4Crse495XrP3hdqtj0gCfct/b61GamnuPfEGuHenUA07N9TDuc/Arw95bFS5I7uwmoteWsfBQpFm8cntM5g/IsWnffgN9V68SjSI/nPjFL68exYXjG/pgeUOV+RdL+zYb9myhVdffZWxY8c2+fkDDzzA119/zaeffsrq1avJz8/n4osvdv7ebrezePFiLBYL69ev55133uHtt9/mscce6+q/gleoxViKXA7IotMe0XIVUjWoqGloZlikFvbZ34sPoCp9mXxzpxyv1e5wrqp3asdeXRWz1ZERJk6ovaljX2ux06CkADjiBnjsUMQ2i9zwRIEzprDpCVqNvYO23fFPldfywSZR7P3m7BFdFzHSGEV2na4RK/eHi6v5/Rd7mPXET7zw0xFMdVb6xIUzc3AiC0amcO7YdC6f3Je4CD0OGY6WBK8UU/0caI2R0GcypI1xu51rzr5ZYS9JkHWB+FqV40ckOGXU4/rGIUkSd8wR4xXvbjhBrSLP+26PkF+fM6b1C/Nvzh7BuWPTu87QRkWjgYeOIP2hBEl5f4Sc8RE3Y43PH/pwl1RSjR3ywKmKWhwyRBi0pEQbMeg0nKHcVG3YsRueG4/8j0wuOPArZmt2tz5fX1sOa5+FL24Xi7ZdQNiGp/ki4m/M0ewKyfEDHOd8fb8499eU42vE48A57XsBZRQlAROR1HG4l8vxW427a4XoMB0bnIX9Gu9fyNYAr58BX94jRk07iCrDV+9X/E5EouhQm4TaqbdJ8V059sqivClPPMa2Xrg2ybIHUcS3dl+o8y1mstxs4YrXNlBgqmdQciRf3DWTMX27UAnYHGdhL/5d+sZHeNWpV0mK7KUz9jU1NVxzzTW8/vrrxMe7us4mk4k333yTp59+mtNPP51Jkybx1ltvsX79ejZuFG6Ny5YtY//+/bz//vuMHz+eRYsW8de//pUXX3wRi6X1D2ZDQwNVVVVN/nQFamGfYFeKqZg+bj8QLil+s+ItY6K4abPWClOSIQsgfbxw2O8EsgurabA5iA7TdY5xiYo+3Kk4UOOretOMvXoR+VYzF+m+XXDec61u63HGvhmFJvcde/B+zv6Z5Yew2mVmD01i5pAuiLdzh5Jln6yMKuzNq+KDTTk02ByM6xfHK9dOZM3D8/nwtum8ccMU/n31RJ68dJwzgqt5fFcwoX4O9kTPhttWwNmPu90uVZ2zr3JzARmlLAju/VyMveAasRmrXKTOHp1G/4QIKmqtfLo1F1Ot1WmueLYbGb5KcrSRf189sXtW1CMSQJLIUDp+oTn7VnDK8dues1dl+AMSI50F11mj0pBwMGPPY2AuRrI3MI+tZOkLXfPRVfmw4i/wweXwdBY8ORBW/V38btSF/v4buaf4AKOsexgm5bLxeEuzvxCBg+qI79aXo64SCnaKrzNnt+8FwmIhXMxk95eKe/2cvVrAxTT2tcj+AZY96nbBLzpMx0bHSHYMvRcufMn7F/r5aSg5IJpPcscjizeo8/WdJcNXFXANVVBX2es69jXOuDvlfaEscLibr1eJdjrjN7sHtTWIMeHjP8OpLc7RrzqLnUeW7GHNobY9nT7anENlrZUhKVEsuXNm15k0t0bCINFQSclq19PVjr0/I5qDorC/++67Wbx4MQsWLGjy823btmG1Wpv8fMSIEfTv358NGzYAsGHDBsaMGUNqqiuveeHChVRVVbFv375WX/Pxxx8nNjbW+adfv9bfxP5ElZfHWlQZvvtVsShl9czcvGMvSa54ibztcNWHcMvyTjHNg6bz9RpPcUb+QDGpSNeIC76prnecWMFV2CdGGtrsiKtZmt4U9uqMfbqbwn7ecFGIbTlRznbFxKg5Bwur+GKnWKl8aGEXd2Qbo3Rk06RK549OH5HCJ7dP5393zeTs0elu47YGJYnFqBPBXNgr7434CM9yxpQYVYrvprDvM9H19Z5PkWWZPXlqx16shms1ErfNFl2uN9YeY+m+QmwOmeGp0QxOjuroX6NTUaW8vT7y7offwVe/hOIDTX8+eL6Q42t0bc7ZH3ca57luqOYOS+ZG/QomO3bh0IXxzehneM52MRUDzsagU24zDi+Hn/8Fh5c6uxvE9YdpvxBSza5AMQDrJxWHOvYBjkdH/JPrRVGYMNhj57BNEkS2dH+pqNc747uV4h/5Eda/4LYjHx2mx0QU6zNuEONc3lB8QJwDAM55Uiy8dgBTndWpGO20wt4Q6VLNmk45Xcx7S9ydS4qvdOwdNrEg5iZuWcVZ2DceFbZb4dkx8P4l8Pkt8OYCZ9Td17vy+WhzDvd/stOpBnSH1e7gPWUU8K55g4mL6EL3+9boN1U0VBY90a6nJ6opC358P/luQ9jFfPzxx2zfvp0tW7a0+F1hYSEGg4G4uLgmP09NTaWwsNC5TeOiXv29+rvWeOSRR3jwwQed31dVVXV6ce9wyM4PQnSDcmwxLedlwbV65jbubtTFyg3UMuGCHNZ5MpXdp5Sb/86cr1eJToeivaTKpUCiM7u7N6AW9t7EeLjiAH3o2LsxtxqYFMmCkSn8eKCYm9/ewn/vmMGw1KZO/E8tzUaWYfGYdGf3u1tQOvbxjnKW3DWTmDAdQ1Lajl7MTBTFSXB37L0s7JWOfVFzKT6Ihb/rvxIGemMuJae8lspaKwathhFpMc7NLp3Uj2d+PMyp8jqe+OEgAIvGtN6t73b2fwl7PmOxZQTfMj5U2O/7QszST2hmVpQ6Gn5zAgxtdz9OKJGHjRVakTUn+a3uQ5BhTf97eLd0JJttqfxtVKMuRk2RMElKHSNm/VNHdeq1yS1KFnd/TQm5FXXkVdZ1yix/iI5RYKqjwCSMO8e6k9mmjBBmaoYOLigmDIS8rWRKRXzRi7Ps7Q7ZVdg3luK7ibpTiW6eV+4N3z0EDisMW+RSiXWALcfLkWWxQN+W+3iHiO0n0kQqT5GQJBaDyntL3J2lmXne9DvFH0fraosoNfKu8XtDqxdF8IGvxbVAa4QRiwGcTYRys4UPN+Vw6+xBbvf7/d5CCqvqSYoysrirvZw6CVeSUy/p2J86dYr77ruPDz74gLCwTvzQusFoNBITE9PkT2dTXW9zNkvCahWn7lZWo9V5l2p3hX3qKCG/H36OKOw7EadctyuKOsVgI94hpFe9acZelX29UP0gvH5604zXZnh0xW+ELMutztirPH/VBMb3i6Oy1sr1b252mmYBbDtZzo8HitFqJB48a5hPfx+/o3owVAvprzdFPQhXb3AVK8GIKsX/+4kr4dmxIibTDeqNT3Fz8zyVQXOFjN8QyS5lvn5kRoyr4wqEG7RcP0MUR+pik6f5+m6n7Cgc+Iphlv1AL8+yt9aJoh6cnUonkuRVUQ9wolScA9TPDgAbXsQoN7DOPoq/Fs9im6LwmTO00Xz93Ifh/Bdg2u1ipr+ri3pwduyH6MU1ZHNIjh+QbD9ZCcCItBgiDG76TwmDRPzZ9Ds79kKKgV4/qZji6oZeZcjbGHOjLmmTjr2bqDsVZ155vVV09D+5zpXG5I7crcI9X6OHc/7pFxWpKsOf1lndepU4palnOuVsrpgtduqtdg9P6hm0cMVX0bRePqqLPjXNpfiqmhhEnKGi2Nib76pTXl1zrNV/17fWibjda6f3x6jrQiNeb3A4wOH7+8Epxe8tM/bbtm2juLiYiRMnotPp0Ol0rF69mueffx6dTkdqaioWi4XKysomzysqKiItTZHmpqW1cMlXv1e3CRTUQixcr0U7eB5MvkW4kbrBrSu+iiTBbSvh8neE1LGTqLXYOKQ4yY73wSyi3SjxVbFWMdfrjdS8p1BhtqDFTqb1CORtA33rN+HezthX1lqdhnyqTLs5EQYdb904hSEpURRW1XP9m5spq2lAlmX+8X02AJdP7tv9UuxoRZVT3boKxx0DnYV9bct89yChwmzBiIVYa7HosLSSfpHqSYrfDGfShZtu2fUzMgnTi0vHoORIhqYEsAxfMbaJswvZdV5vNs9Tb9KNMZ4lsHXux25UVHXLoMaF/aInMc9+lN/YbudoaR12h8yg5Mjun39sjtKxT5OLAZlNx0Jy/EDk4y05QCcaoqlMvQ0e2MfLEb8A6LVyfHW+3qDVEKZXJdcOqBT/D+469jGNO/bZ38OBr2DrW62/yDrFF2js5a5CuYN0unGeSqxyH12ZQ0yYDr1WLEr0hjn7Fq74XhDTmppj2Nni+gMw5RYAbHYHBwrEOEV0mI6S6gY+2XKqxT535FSwI6cSg1bDNdNaLjR1K+9dDH9LheOrfX5qkiLFr663+W2hKKAL+zPOOIM9e/awc+dO55/JkydzzTXXOL/W6/WsWLHC+Zzs7GxycnKYMWMGADNmzGDPnj0UF7ucvZcvX05MTAxZWe0zO+gsmmTYj7oQzn1amN+5Qf2Q1VrsTbKDnXhYTfMXe/OqcMhCxt2pMiiV4Yvg/H9jHnMd0Ps69kmY0OAASSOivFrB28JejbpLjDS4LuZuiI808N4tU+kTF86xUjM3vrWFb3YXsPlEOUadhnvPGNqOv5Gf6T8DrnhfzO35QJ+4cPRaCYvNEbTd3IpaC6mKaSC6cA+FvdKxdyfFb8ZuD0qchEgDV08VF9aLJ/TpnhQEb1GUHFGKZ0mvluKXi24HCQPdd8usdfDKafDkIDCXut1FvdXu/Jw06dhrdUSe8RAZmS6fjVbd8LuTmL4gadA7GkjGFJqzD0B2nark58OlaDUSN87MbLlB/g6R9lPTttFWm0SlQGxfBqWKBcze6ozvVoZfUwS2enG/4SZCtYlB2gRxT8ahH6C6qMW21JtcWfcz7/XLMVfWWtivFITTB3ZsVr9N+kwU4wPJIvUnXpnt7g1yfNXHK9KoEx3pF6fDexd5VAM7XfGbNx714XDtErjiAyHLB46Vmqm3Oog0aJ3JOa+sPkqDrWmR+9a6EwCcNy6D5GjfnPS7BLvFGXnnCzHhroUifyUtBHRhHx0dzejRo5v8iYyMJDExkdGjRxMbG8stt9zCgw8+yMqVK9m2bRs33XQTM2bMYPp00ek+66yzyMrK4rrrrmPXrl0sXbqURx99lLvvvhujMbDeHE0K+zZoLIsxezCb6AjPrzjMre9scZ70m6N29dzOwHUGGeNh4nWE9RdGXxW1VuQ2jJ56CuXmBlJUY7ioVNC0XojHeRl358kRvznpseG8d8tUEiIN7Mkz8cuPdgBw48zMzsmO9ZWYDBh5XqtRb62h02qcXUVVYhxsVNRayJAUSXFMRqsSxxTlYlhS3eB+MVDBZnewN0+NsHT/2f7dOSP48NZp/GLu4A4ceRegFPbGWnGzWVLd0Cvkk25Rx3cU+XEL9OEgaYUp2a6P3G5yqrwWWYZoo47EMISxltW1UHRWlsvPJiALe50BYvriiEojQVPNsVKzVwtdIbqOl1YdAeCC8RnuFR/b34PPboKfn/Lbaw5VRrcO9dIse9W9vElXVp2vj+3rNmc+uvEcdWoW9J0ijNV2fdjyBcJi4f49cPl7wh/BD2xW5+uTI0np7MbSmEvh6o9holjAcDnj93wDvZrG5nnVhSLR4Pgaj/4W0e5m7FX6TYGR5zq/3avM14/KiOWKKf1IiTZSYKrn8215zm0KTfV8t0cUzTfNyuzoX8n/KGPCVOX7/FRJkpwGev6S4wd0Ye8NzzzzDOeeey6XXHIJc+bMIS0tjSVLljh/r9Vq+eabb9BqtcyYMYNrr72W66+/nr/85S/deNTucUbdhQGFez1mfBp1GnSKy7dbOX4H2XaynKeXH+LHA8V8vDnH7TZOR/yukOE3QnXCtNgc1Fs7HpcSDJSbLaSoXdmoVI/betuxd83Xe3dRHJQcxTs3TSXSIBYVosN03DkvwAs7LxiomIAdLw3Obk2F2Uo6SmHvwSE6McqIRgKHDGUeLiBHSmqos9qJNGgZ1MqIhU6rYeaQJHTaAL+EKBdcyVJNol58HtQFrV5Hhdqxd29MBMDkm8XjljfdmiOpMvzMpEiktc+IKKz3L3E66S8clYZOIxFt1HWeS3VHuXcHml9no00VGdyhrn3gcKiomqX7ipAk4Xrtlo7m1zdn9T+5Kf9P9JVKnE78vQ21AIsO826+HlyxeFXqHLWabLH9XffJGsZoyDrfH4cLwEZljGZGN5xnXM74PbtjL8uys76INOpcaSbRGR6bS63G3blBbSKM6hNDmF7LHUqz4KVVR7DaxTXo/Y0nsTlkpmYmMLpPN2bWt4YyJuz0sPGRpGj/ZtkH+F1ZS1atWsWzzz7r/D4sLIwXX3yR8vJyzGYzS5YsaTE7P2DAAL777jtqa2spKSnhqaeeQqcLvEAAtRAbqiuGV2bBc2Nb3VaSJKdsypfCvsBU12aHwuGQ+dNX+53fv7PhhNsOn1rYd8l8vTgwOPoTkfs/JkIrum69RY4vCvtK8U20Z28ItbA3W+zOE6M7ChVJrTcde5UxfWN5/YbJDE6O5NHFIwMjbkTlwDew4SWoKW5720aoc/bHg7hjn+7s2LtP0QARV6dK2DzN2atJF2P6xrqNCAwqjNHOzsLoGFGU9tos+1qlgE1opWMPojNljBWLAMdaZlerJpNZ8TbRrQeYfJNTJdIvIYIPbp3GR7dP9zje061oxXVzqiLfDRX2jWioFiMbHhyvO5OXVwnjz7NHpbk3QK3Kh7LDQh4+YJZ/XnTfF/QrWMoQKZfduZW90kDPbdTdmMvhwQNw3nNun9PCFX/UReJcW34MTq5zbVhxos0IzfbQ6fn1zZFlcQ512ElQOqw9vbBvsDmwKff+UUZdowz71u8znNviXWKC2rEfnSEK9qun9icx0kBuRR1f7syn3mrnQ6W5GJDdenB6+bSnYw806tj3Ail+b0Mt7PtqlRsNDzfpAJEG3+JGai02zn72Z87412qOlrTenfx02yn25JmINuqIDddzqryOHw80nZsqq2ngVLm4Qe6yFTRJgg+vRPryboaGC8lcryrsqRTftNGxj2k0yuFJjt+WI35rzBycxIpfzeOKKZ1nzNgufvwjLH0ESrJ9elqwO+OLwl49Z2R43Fadsy9qzRmfRkqc7owv9CfR6aA1MDJWnCeX7fPNYLHHcNlb8Lv8ps7EzTFEwvirxdeb32jxa7Vjf1H9V2CpEdF1zfY3bVBiYHZVmjFNKexDBnqNOLoSnh8Pby3q8pfOKavlq13ixviueUPcb3RcmdNOHwfhcf55YWWha1J0JQ4Z1h917y/Rk3HbsddoxPUk0b1yokVhb4xynQu2vyseG6rh1TniTzvmj1ujstbCwcJOzq9vztMj4cmBUH6MRKcUX9x/yrLM4aJq3ttwgldXH8XmoaESTDQew4006MCUK75po7BXpfitjfGqOBwy+xRHfPWaEW7QOuPuXlp5hC925FFuttAnLpwzszzf+3YbzsI+z/N2rZAUFZLi91jUwt4lq/VuVUw1t2iLg4XVmOqsVNfbuOO9bW4/dKY6K0/+IAqj+xYM5epponhTYyZUdiurbIOSI73yBPALkuT8AA0yipO6N1ntPYEys4V6DFhj+reZdKBVpLAAlR4Ke9U8r0uMD7uCKEXJUOPGvMcDTmf8IMyyr7PYqbc6yJWTsWdMhuThHrdXs+yLq1u/gHRphGVXcPtKeLSYOWeIzNyPNp/qvSZ6hkjxxxOKWzGHfoCKk01+dbzUTAxmJhV+In4w9yG/xFZ1KTmb4O1zOf3AHwDILqqmood33rymWFHqndoIby4Ee+f497jjlTVHsTtk5g5LZkxrvj0nFBl+5mz/vbDi+D4lRtzTrDnc+wr7Gmdh7/29XOPizanonHgDJI+EftPE99veESZr1to2GxK+sEmZrx+SEtV1RmphceKxMsc5Y7/+SCl3f7idKX/7kTOfWcMfvtzH498fdC5QBTuqGjjCoEWjkcCkFK4eRv7Aeyn+iTIzZoudML2Gwcmu69J1MwYQF6HnWKmZv34jzkk3zBwQuKN/zsK+fYtXqhS/LNSx73mohX2KrFxY2ui+qVL8tlbFVI4Uubr0R4pr+PV/d7Uwn3t+xWHKzBYGJUdy/YxMrps+AK1GYuOxcvbnVzm3c8VhxXn12n5D+TcZoBOv3xsKe6vdQXW9jVft51FzxzaY8+s2n+NNlr2vM/YBjzqiUO3byVXt2OeU1wbdSruqWPkP56O57UcRJeQBV+Sd+459vdXOwQKhhukyU8zOxhgNksTMwUlMH5SAxe7gxZVHuvuoApekoTBwLiC3MNE7UVrLDdql6G014gZ+xHndc4wdwWGDEz9jLNjCECWqcfOJUNcegKJ9rq9PbXQ5mXf2y1bV89lW0Q28e34r3fqqAtj/tfh60Fz/vbjSsR+sFy77a4/4wW0/yKh2J8X/+n5Y9odW0wcad/ed96B9JsJdG8TioM0CG18SP595r1+TmjYcVWX4neyG35hGWfbqjP2uXBPf7i6gtMaCUaehT5xQP6444Ns4YKDSIsPe6469d2rivUpNMTI9pknRHmXUcfMs8bmstdgJ12u5YnKAKUQbE9NHmEdmntauMaakkHlez0WVTcfb1cLe86qY+mHztrBXHV+nDkxAr5X4YV8hLykzbQBHiqt5Z/0JAB47NwuDTkNGXDhnjxYFU+Ou/e5csbrdmmt2p6G4XPfRCiO5giCNKPMFtZukkbxLTADvDPR8ccUPCpyFvW9S6/SYMIw6DTaHTG5FcL2f1MI+LsLgVeycq2PvvrA/UFCFzSGTEGmgb3wApB34mQcWDAPgv1tPcaq8dU8FVSLoKT0gqDi5Ht45D1b9w7vt5/4GLn0LTnvQ+aM6i53qqgpu0X0vfjDn110Sq+p3lCx7TLlMzxTXr9CcvULxAfGoqsL2ft4lL/vGz8ew2B1MyYx3eh+0IG8b2BsgYwIMnOe/F1fMJBMbctFrJU6V13EySMey2ovasXfG3dkssO1tWP98q88J02sxKMWYszMrSS4Fz97PhTQ5KhXGXuHX43Xm1w9K8ut+PRKrFPaVpzhjRCqj+8Qwc3AiD545jP/eMYPdfzqLF66eAMCawyUe/Y2CBVUN7Fzw0YdBeEKbY8JONUcbhf2+ZvP1jblhZqZTeXrJpD7ERnSRMrg9RCTArT/C5e+065oY6tj3YJxxdzZlhVSNUGiFKKMwJ/LWPO+QktF60YQ+/OWC0QA8tSybVdnFyLLMX745gM0hs2BkCvOGpzifd7NiWPHlrnzKahqQZdnVse9iR3z132RklLjwfrg5B0dPufluBXWOKz7CIORQXhAX4Tnyrrre6lwQSusxUnxF6uejFF+jkchUnfGD7IauwmwFZOLDvTMDdXXs3a8MN16wC+h8el84sRY+vgZW/IVpgxI5bUgSVrvcatdelmUe+O9OFj+/lhvf2twpqSNdTtE+4Saev8O77TNnweiLRTycwokyM9HUsl3KgqRhwiwrGIlOB40eHDbmpIlz66bjZd18UAGAtQ7KlYX+0x8Tjwe+FkWeH/jpYBGXv7KBv327n/VHS52FT4XZwgebhDlWq916EBFZd66Hi151GiD6BSX+UVOZw8R+osDobXL8FnF3plOADPoIiGy9eG61M9tQDf/7hfh62i9EQegFsiyTX1nH6kMlvPHzMZ74/qDTXE2l3GzhYKFoUk3rpo59WmwY3/xyNh/eNp17zxjK1IEJGHVaxvWNIyHSQHW9jW0nK7ru2DoJlyO+YoR66X/gN8dh+Nken6e+j2osNo/353ud8/UxLX4XG67nzxeMYubgRM/nhR6Av+PuAs8avhejFvaRDYqMJ7qtwt63jv1hpWM/LDWKSQMS2J1r4qPNOdz70Q7uXzCMNYdKMGg1PLo4q8nzJvaPZ2zfWHbnmvhwUw4XTuhDmdmCTiMxMr3lB7JTUVQMwyNriA7TcazEzIqDxYFrquEHys0WJBx847gbXusD134uVgg90FbHXu3Wx4TpXDKrYEf9vPjYsQfITIogu6hazNl7HlMPKCpqhanit1U3wnN94d4dHmee2zLP63Hz9QC1ZXDwGzCLBdMHzhzK2iOlfLotlzvnDWZAYtOZ82d+PMyXO8WM5M+HS7n6jU28feMU4iMDKAHCV8q9iLprDYcDNBpOlJopJJHnkv/M6beO8xh3FNBotOImvfwYk2PENXF/fhVV9VZnhFevpCQbZAeEx4tFnWW/F4ukx1bCsIUd2nV1vZWHP9tNaY2FzSfKef3n40QbdcweJorGWoudURkxzB2W7HlHrRi5dYjYfqDRgexg4QANm07A2sMlXDfdfcxbT0S9h4xRO/aNo+48XE+iw3SUmS0tC/tPb3J9rUZoukGWZXblmvjfjjx25VZypKjGORag8srqo5yZlcr9C4YyKiOWzcoi3LDUKKfpWJfQqGPfGlqNxNxhyXyxI4+V2cWBG/npJU4pvsG3e0R1wUeWwWyxufVukGXZFXXnpmMPcPHEvlw80bM6IKBwOMSol863e4WQeV4PRi3CKoZfBVNuE7OOHvBFil9db3XOVKsxMn86P4sJ/eOoqrfxF8Wg4ubTBjpnjlUkSXLOu7y38aRzJXJkekzXRxopxZu+poBrlQvvq6uPenpG0FNutpBANelykei4GdteTHEW9q14ELTXET+giVYWd9pR2A9MErO2x4PMQK+i1kKaVI4eK9ga2jQyS1E69q2Z57mUOD1kvh5cCz6Ksc2kAQnMHZaM3SHzwk9Nu/ZLtufy/IrDANwxdxBxEXp2narkslc3BPfYjzPD3kPUnTvWPQ/PjYOC3U41y8DECOGAHcwo2dwJ1gIGJEbgkOkRHbYOocrwU0aJxY+sC8X3e5d0eNcvrjxKaY2FAYkRXDKxL4mRBqobbHy3p5Dv9ojz9d3zh7hXCa1+UhgedhZaHdy3Gx4tYuLokQCsP1IWdH4rHcEZd9e8sFeMBVtDLdhamKQt+JOQa5/9hNv0guKqelGwP7OGC19cx9vrT7Ajp5LqBhtajcTg5EgWjU5j0eg0JAmW7y9i8fNruf3drXy+XRi4dXnRrI6nmFov7AHmjxBq15UHg3/O3uzOe8ELmo5puK9PcivqMNVZ0WslhqW6ibYMNr57GP6WCptf9fmpSYpnQ7nZ4pfxvx7SqusZqIV9w+TbwV2GazNcrvhtF/aHFRl+aozRWfQZdVpeuXYS576wlpLqBlKijdxzunvJyzlj0vn7dwcorm7gmR8PAd108993Mpz/b0gczE3xmbz583G2nqxg28lyJg3oQllWF9Ikwz4yySsZorcd+x4zXw+QOhqu+KBNx1Z3DEyKAIKwsDdbSZWUgqSN0R1wdexLaxqw2R1NDGuq660cU/7+Papj71RyFIgWgiTx4JnDWH2ohCXbc7lr3mAGJUex6VgZv/l8NwC/mDuY3y4awWWT+nLdm5s5UlzDpS9v4N1bpjI4OQiL2vJj4tHXwj5/B5hyYMOLjMqHDGaS2caCc1CgztlXnGTawDGcLKtl07Fy5jcaQet1pGbB7F+5OpOjLxY3qQe/FTJ9ffsWgXPKavnPWrGw9IfFWSzISsXhkNmdZ+Kng8WsPlRCv/hwzh6V1vLJx1bByr/BqieEGim+k7royjVjTJ9YYsP1mOqs7Mo1MWlAfOe8XoBRpc7YG5XOaqWShtHGv3erUvy00fDgvhbb786t5Jnlh1h9qAS1fgnTa1g0Op35I1IYnhrNwKRIDDrXdelIcQ0v/HSYr3bls2y/a8yuywv7+EwYtkg8KtcRd8wdmoxGgkNFNeRW1NI3PqJLD9OfNDHPy9sGX9wpPC4ubrt4jQrTUe5OzaGgjlgMT4tu8v8dtOgMYLe0K8teTVlwyKJZ01ElSg/41+wZOBwyVcqqZ4yXBmm+dOxdMvymCwapMWG8fv1kpmTG8+SlY1tdmTPoNM4O+ckyYTrVLTf/sX1h4nUwYCYpMWFcNEFckF9dfazrj6WLKGtc2Ee5uflxQ1uu+D3OER/EeMLIc8WFx0fUGftgy7KvqLW4Cvs2RncAEiIM6DQSsgylzYxa9uSZkGXoExfetRLHzkY1VXRYhSwf4Q2yYGQKDlkkgRwvNXPH+9uw2mUWjU7j4YViHmNISjSf3TmTQcmR5FXWcdkrG9iTa2rtlQITh6NRB87Hwn7KreJx98fMLf2YDwx/Ex37YCc+U5xLNTqmDhQFwubePmefPg7OeAwmKzLqvlPFYumoC6ChxvNzPfDEDwew2B2cNiSJM0aKhRONRmJ8vzgePHMYX949i39fPbGlf4zFDF/dK76efHPnFfWN0GokZg0R74efD/ced/wa5d4z2p0U3wPq6EpVG7FmKr/5fA8rs0VRP2lAPE9cPIbNv1/AM1eM5/xxGW6LvCEpUTx35QSWPzCH88dlIEniOGd0dWEflQJXfwyLnvCojIuN0DsXhFZmB/d7qImSozIHSrNd7402iHamdrl/bzjn61uR4Qcdqtl5Owp7nVZDvOKL5Q8DvVBhHyBUN9iQZYilhrjqw1BX2eZznDP2bThPglg9BBjqRgkwvl8cn/5iZhPDPHdcPa2/U16jPq+7uW2OmBldfqCII8Xtv/kIZMrNDaQ4izfvvATUjn1rOfaFVUJW3KM69h1goJKhmldRR4PN3s1H4z2+FvYajeTM/W0+Z+80zutJMnwArR4ildndRhfd+xWH/C935XPtG5uorLUyrm8sT18+vkmR0ScunE/vmMGYPrGUmy1c/cbG4Mo9ry4AWz1IWpec1FsGzBSxdgpv2BczMBgVC82ZdT/8Ohvm/cZ5E743v6pHOFn7DY0GfrEWLngRotqYfW+FTcfK+G5PIRoJ/jQvDmnTKyKhwRtDvpV/F53jmL6w4I/ten2vydkEn90MP/6Z2UPF33VtLzLQq2kuuVbzytuU4nsXawZCeXigQMxUf3fvbD6/cyZXTu3vta/FkJRonr9qAmsems93984OaM+TniLHbyLFVzPavVAGguu9UdVqx16Zr+/TQ+43nCN/vhf24N85+1BhHyCo7uVnGnZjeO00+OTaNp/jlOJbvJfiD01t/01ZUpSR88eLHPlIg7b7JKkn18OOD6C6kCEpUZyZlYosi8icnkiF2UoKleIbLzv23krxe1THHuDQMtjwossszEuSo4xEGrQ4ZDzGoAUaFbVW0lCiuqK9e2+kuDHQK66q57s94sLdo2T4Km6MFUf3iWXhKHHuyKuso09cOK/fMJlwQ0vfkMQoIx/dPp1BSZFU19tYmR1EN2y1pWJhI66fWOTwBUkSmdRAgZzAp/a5LTxYgpJGHbfMxAhiwnRYbA6yFbftXkdDDRxZ0dKfpAPJGA6HzF+/Fd49N02MZch3V8IPv4W3FsE/BsB7F8HaZ4TE19FoMfXHP8MHl7ky0M99BoydPIPbUCXi2Q79wGlDhKHfjlOVXneigx21OeTs2N+yHB7YD4PmenxetA8dezVScmhKFFkZ7Tdd7pcQQb+EblINyTLUlkOdZz+O05XCfv3RUuqtwdMoaE6NEncXadCJ6EKA6AyvnqvWJ+4WfYRxnmgkjOkphb3asa8uaNfTQ4V9D0QtwDL1leIHMW1/eFxS/LZPHI0d8TvCHXMGEW3UsWhMOlovo9f8zg+PwJd3OaOb7lC69ku257Wazx3MlDXp2PtW2LcWd1fgnLHvQeZ5AGufhqW/g4KdPj1NkiRnwXK8NIgKe7NvHXuA1GiXgZ7DIfP+xpOc8fRqdueaMGg1LBjZA+eMYzJAa4T6pjL6+xcMQ6uRiDLqePPGyaREt77QFWXUcfZo8flbfSiIJJbp4+ChI3BXOw3IJt1E4dTfcbvlQaIjI3ucc7wkSc7YVjUVotdRsAvevxjeOLPl72RZFN/5O33a5efbc9mbV0W0UccDyVuFz0NEkvhjrYWjP8GPf4I3FwrjT5VDS+HwMuHQP/5aGHZWh/5qXpEk1DuUHqZfrIGBSZHYHTIbj/b88Qy7Q8ZsaZZXrtEI3wGD50U8Xzr2aqRkl0bU+ZtvHoAnB8LmNzxuNjw1mvTYMOqtDjYcC973UJO4O7Vg9aI2Ac9Z9kVVDZSZLWg1EiPSeoBxHriUDNUFYvzNRxIVA73mI5LtIWSeFyCohX1fbSXY8eom3VvzvCo3jvjtZWhqNFv/sKCJJL/LickQhZsieZmcmcCkAfFsO1nB2+tO8PDZI7rv2DqBcrOFaiKoi+pPuJql2gZqjn2rHfuqHtqxVxc+2hV5F8m+/CoReRckVNRaOCT3ZWKKnigvo8xUA72Nx8pYsj2X7TmVgMiu//vFYzp8jghILn1LmH8160COTI/hy7tnER2maxF75465w5J5adVR1hwqwe6Qu29xsz14mSXdAq2OLX2uY4+8g0k9oVuv8uGVULwPrv2CcX3j+PlwKbtOVXLNtN4Tc+akWHTWSc1q+bt1z4oCfPhiuOpDr3ZnbrDx5NJsAH55xhCiZp8FBi0MOROShwsH/uOr4fgasXBgaNSBnXmPGB1JGi5GQbqC2H4is91aCxUnOG1IEsdLzfx8uJSz3Jn69SAaezQ5XfG9xKfC/pjo2E8bGMQRcOr9hSnH42aSJDF/RAofbsph1cHioDXl7JAU39mxb3kPqnbrh6ZEdX2yVmcRlQqSRsTdmUu8HptVCXXseyBqAZbmdLhue1XM2xn7w8p8fVpMmLOT2xGMOq37WJquorHLtcLtStf+vY0nvTITDCbKzVaesl3B8avXwaQbvXqOJyl+ncVOpRKD1+Nm7KPaX9gPUoqWY8FU2Jst/J/tOkou/wYGzPDqOSlKx/6b3QVsz6kk0qDlT+dlseSuWa3myQY9hohWZcWj+8R6VdQDTBwQT7RRR0Wtld29qLurLnZlevnvFBRUnhSGUJUnGNtXvO93nQoyY0R/oRb2KSNb/m6o0jE/sryF4qU1Xll9lJLqBgYkhHPDzEzx2Zv5S0gZIb5OzYLpd8JVHwlDssaMv1qY5WXO6tAogE9oNK544ZKDzB4q5Phrj/T8OXv1fsmg02DUaeHEOvj0Rtj8epvPjWkt7q4ZplorBwrFTHVQd+y9yLJXUYv5n7KLkeWOR5h1B01c8VUpvio5bwN10afMjR/NHqWw71H3G1q9OFdmXSCKex9RI+/KQoV9z0EtwJJRZDtedOwjjWKlq62O/ZFiIcPvyHx9QKEuelS5CvszR6Y6518/3ux5NTWYcDhkKmrFiTHBB7MYtbCvtdhbGEKp3foIg9a5qtpjUFdJa4o8b+cGpzN+kBT2FpvDKaFMiPD+vZER5xq/WDgqlR9/NZcbZw0Mru5zN6HXajhNuelfFSyOxx9dDe9eCIV7270LZ4Z9Ug9wxFdRHb8rTjqNYA8XV3sVH9vjKFIL+1Etf5eSBckjRJTTwW/b3FWhqZ7X1hzjUu1qPo15FqM9SEabkhWlX2k20wcnotVIHC81B5XnSntQi3LnvUDhbtj3BZz4uc3netux33KiHFkWi+eexp0CHlUx2UaWPcCsIYkYdBpOlddxtCQ47imao/p3RRl1wqclItHrkb/hacJH4Z31J1okyexTHfH7tN9rISC5+hO4/N12RS67OvYhV/weg1rYJ9iVwt6bjr0aJ2GxeVwR9OSIH5Q4C/s85480GsnZtX/i+4PM++dKrn1jE7/9fDf/VjJQ6yzBZ2JSVW/FrgS+xkd6r7aIbjQH27xrX2ByOeJ3q/KiM+hAx16dsQ+WyLvKWgsaHGglh8v0yAsWjk7jjjmDeOP6ybx63WTSe5rPgjuKD8LH18CS2zu8q3nDhWv2qmCYs5dlOLEWjq0ETfslj86OfU+S4qsJAZUnSYkJIz02DIfskon2GmRZSOPBvRRfkmD0JeLrTa+K7T3wyZZTTHTs4Qn9G6QUrhZGt8GAOmdfkk1MmJ4JymJPT+/atzDOU7vRsW2P/UV72bHvEfP14Po3MeW2+TmIMOiYrkTyBas7vvreiDTq4LYV8PAxr2Mnr5jSj/nDk2mwObjt3a0UNzLrVR3xR/cU4zw/kKgU9qGOfQ/CVGdFg4NYm+pw7f2MvSyLzmxrHPKTcV7A4EaKD3DhhD4MS43C5pA5UVbL2iOlfLzlFE8tO8S9H+3gHz8c7IaD7RhlZgsx1LDJeA/G/ywAu3fdJK1Gcl6oVdm9So91xAdXx74DUvwCU31QLAKV11qYLGVz0HgjmnfO9fp5UUYdj5wzkgVZvs2ABTUOKxz8Rjh/d5C5w4TEcnduJeWBHntXWw4NSqHaRnSVJ06Uia5lj5Lix7s69oBLjt+LRiwAsUDeYAKNDhKHut9m8s1giBLeNge+bnVXdofM6i3beEX/DDrsMOpimNrxxbQuIXmE+Dewieujqszp6bF31Y2zysE1P+5FNKa3HftNx3vAfD0oMnRJvEfMbS/szlcWgX8K1sK+oZmpog9oNRLPXTWBISlRFFbVc/t726i32impbqCwqh5JEh43PQ6HAyy+q3yS/GieFyrsAwRTnRUddjYMuAOm3CaMGNogXK9FVc96kg+qM/ZDU3tKx16RuVQ1LezD9Fq+v28O6357Op/cPp2nLhvH/QuGOjts2056jigJRITreSWpUrlwFdZ6f4JV5fjN88qdjvgxPbBT65TXnvB6EUQlPtLg/DcLhq59hdlKmlSBnl4oHfYVNaKntrSpA3c7SIsNY0RaNLIMPx8O8K595QnxGJ0uzAPbganO6lzA6Fkde+VcUSkKe5czfi/r2Ksy/MShoGtlpCcyCabfJb7+6f+axtM1Yu2RUu6sfZVYqRZHxkS48GUxvx4MDD0Lfl8opLTQZM5eVc31RNSurLN4c3bs+7b5XG8K++p6q1MFE/Qde53B1VjyYc5+y4nyNlUNgUgT87x2EBOm543rJxMbrmfnqUp+t2QPexUZ/sCkyHbvN2DZ/Dr8Xwp8+6DPT21sntdRT4YgOeP2fEx1VizoOTTsdlj8lFcFnCRJjSLv3J9Yq+qtzpnqISk9pGMf1w8ueBEue6uFHEqrkegTF860QYlcOqkv9y8Yxp/PF3OD2UXVLebNA50ys6VR1J13s00qo5Ss2EeW7KGk2lXM9OiOffxAuPh1uH2VcCj1EaccPwjm7CtqLT7HIPZaIhJAqxQt7fBfaM5cVY4f6HP2SjfaWcS2A/WzkBxt7Fk3YmrHvlJ0KMf3jQNg16nK7jme7iJtjLienna/5+1m3gNhcYDcZAyuMQdWfsSZ2u3YJR2ai15pfxJDd6AzCAMshXF944g26jDVWdlyorwbD6xzqXZK8ZW/u8l7KX6MshBe02BrdfFj68kKHDL0T4joGWNfYy4VzbewtrvNmUmRDEqKxOaQg075YXfI1FnFAl78kc/g31PEop6PZCZF8tI1E9FqJJbsyOP/vhELiT0mv74xxhihDmzl/OgJtbBvsDk6bAAeKuwDBDVv3FfX+qg2Cnt/O+IHBPpwmHAtDDnDK9fcfvERRBl1WGwOjgWZiUm52UIKleIbH+Mz/u/CMfRLCCenvJZb3tlCrWKE4sqwD6KbLm/RaGDs5cJ9uR2dooGJwhzseDB07GstpEnKDaeXETS9FklyLX40U/q0h3mKHH/NoRIcgdzNq1QL+7Zlta2hqhKG9xTFl0rcAIhMgYRBYLMwWpHi51bU+WXOMWiISRfX03FXet4uLBZu+h7u3OD2/VRaXsb5+c8CUDHuDhFrF8TotBoWjxXn1ZdWHe3mo+k8ahoamedZzFCr+Dx5Ea3b2Nulsta9hNgVcxfk3XqVs/4qmm9JrYytNGP+CHGtWJkdXHJ81TgPILz6JJQecr03fGTWkCQeO1f4d6hGgqN7kiO+inof1o57jHCDlkiD8MHpqBw/VNgHCKY6KxmU0qfhqNeRMkCbHfvDRT3MEb8daDQSI9PFTen+guCSWZabLaSqXdko37qyydFG3rlpKvERenbnmrjnwx3Y7A4Kq4R5Xo/s2HeQgUnicxIUHfvG743ots02ez3qv5EXjsZtMTkzniijjjKzxRndE5CoHXsvDY+aY3fIfLhJdLQvnui7029AExYDDx2GW38EnYGYMD2Dk4ViZ3dvk+N7S2pWq2rClRu34ECiSJNK0jmPdvGB+YnNr8Nr82HLmwDcNW8IWo3EmkMlbM8JvlE+b3BK8cN0wptGowNDtKLO8IxRp3V+Zv67NdftNi7jvCCfr28npyuF/epDJUEVe6fK8HUaCW2N4lnkhal3a1w/YwBXTXUtFo3qaY744LrHqMpv01zRHf4y0AsV9gGCqc7KNbofmbb0fFjxV6+fp3bszQ3uZ95UR/xhPa3bkr8DdrzvdYSTatJxoKC6M4/K75SbLaRIleKbdsitByVH8cYNUzDqNPx0sJg/fLnXKcXvkR17EDcn6/8Na/7p81MzlTiv48FQ2NdaGxX2ISl+m6gZ3YV7OrwrvVbDrCHiRnV1ILvj64wQkdRuKf5PB4vJN9UTH6HnnDE9XxWiztnv7C1yfLtNFLMn1wvTJ2+x1ovnKSZRsizz8oEwzmx4kq2zXgFDkMYi1hRD/nZhEgj0T4zg4gliQeu5Hw9344F1HlWNZ+wTB8OjxXDvdq/UkCAWPwBeW3O0hddTrcXmjDrrMR17WRampBUnvNp80oB4tBqJoqoG51hsMGBulGEvVeeLH3aggSBJEn8+fzSLRqcxvl8cE/vH++MwAwu1Y281Q0OVz093GeiFCvsegalOGGEBPslqXVJ898Ych9UM+54yX6+y9S348m7Y+7lXm2cphf3+fN8/bN1JRwt7EBeW56+agEaCjzafcsp8esS8mzvMJbDs97DuBZ9XTQcqM/bHSwM/u7ii1kIq6jkj1LFvk4wJoAsDq3/+b+cpxkirAlliuegf8PBRIbVuB+9tFB3/yyf3I0zf/ri8YGGcOmffW5zxy4/Cd7+GDy7z7XnvXyyet+V1QMxRHysxIxkimXvanE440C5CHR8oOeT80T2ni6796kMl7OiBXXtV7emcsddoISrF6+dfMD6DzMQIKmqtzvOFyraTFdgcMn3iwumXEKSLPc05ugKeHCjiU70gTK91jjEFk39HE0d8VVrewZE/g07Dy9dO4n93z+qZ1xNDpEvp4oW5YnMS/ZRlHyrsAwCHQ6aqzkoqatSd9zfpkUbx4ahppWPf4xzxVTLGi8f87V5tnqUYye0vqAoqOVSZ2UKpHENNRD+vzGxaY+GoNP6kmAiCOMHGR/QQz4XmJI8QRmkNJq9X1VVU87zSmoaAd7GtMFvYKg+jNH68Vw7GvZ6xV8AjuXCO70oOd8wdJgz0dp6qbHW+NGDwsvvWmJNlZtYoaoSrp7V/Rj+g2fofeHYsLBPScbVjvzvX1Op14mBhFWsPl2KqDezzg1cU7ROPyT56kqgLRWufgc2vc3zpv5FwcO7Y9OA2WHQW9gedi8IDEiO5SO3ar+h5XfsmUvx2oNNquOd0MW/+2ppjTbr2PW6+HiBGudb6ULiN66dGaQbPiI8rw14Lasc+poeNY3UGqcp9dsEun5/a2Bm/I4QK+wCgxmLDIdOoY+99YR9lFMWZu7g7U53LEb/Hzdj3mSQe83d41ZUdlhqNViNRbrZQVBU8xkgVZgt/st3I5vNXwEjvs8rdcf2MTH4xdzAgMtuldtzsBwVaPaQIoxYKd/v01JgwvVMOdSLAu/YVtVZ+Zb2L7Qs+CRX23qAPa+J63VEy4sIZlhqFQ4Y1QeZ47A3qbP3cYckM6En59Y1x2IXBYJkwRxuZHo1eK64TuRV1LTY/WWbm/BfWce2bmxj3l2XMf2oV9328g/+sPc72nIqgWjQGoFiJukvN8u15Y6+ApGFQVwHf/ZrLC/7FBZr1XDElyBeAEoeINJX6SiHLV7hnvujar8ou6XFjGs6OvVEHq/4Bn94Ix3/2aR8Xjs9gQGIE5WYL7zfq2rvm63tQYa+aCjaYvPbDUpVAu4NICaS+LxIMNtff08dkpl7J0LNg1EXtUlGq955loY598KOu/LevsFc69m5yRI8oMvz02DBiwnpYdzYlS8hq600i370NwvQuk5dgMtBT86PjI1rJF/aRhxcO5/mrJvDsleP9sr+AJX2seGzHqmmmUsQEujN+hdIljo/0z3ujV+GnAiyg5fintsDzE+CLO31+ar3Vzn+3io7UtdPbH5UX8MRnikfFZNCo0zr9WNzJ8Z/98TAWu4MIxb34eKmZL3fm85dv9nPxS+uDzz29+IB4TBnlebvmaLQw//fOb/c5BnAw6Uwm9o/z37F1B/pwlx9Fabbzx5lJkVw4XnQrn+9hXXtVmRYdpoNjK2HfF2D27Xym02q4Z746a3+MWouNequdXafU+foeZJxniIRwZaHCy679WLWwP2UK7BSVRqjNwmR9g4jEjOvvVcRfr+e0++Gyt2HwfJ+fGurY9yBMdVaiqCVKUjoEPqyKeXLFV43zekx+fWO0enGyAcjb5tVTgtFAr8wsPuCJkUa/7E+jkTh/XAYj0nr4CTpNLex969iDa84+0J3xq821SDj8tujTK9j9X3hpJvz4R7/sbp4ix19zqDTwbtjKj4k/7UgB+G5PARW1VvrEhTtdnXskahFXedK52DOulTz7Q0XV/G+nyCf+7x0z2PGHM3nn5qn86sxhTBogjKCCLavaKcVXjSV9YeT50H8GDRj4vfUWLps6sGeowJxy/OwmP/6lMmv/08HioJqVbovqhkbmeWqhGuu78uKiCX3onxBBmdK1355TgcXuIDXGyIDEHjJfr6J27b08tw5LjSJMr6G6wRbwDQMVNe7OFpEKv1gL93fcdDaEZ0KFfQ+iqq6Ru7UxBozeF+LqXJQ7Kf7hnuqIr5IxUTzmeTlnH2QGerUWG2m2PDYa7ybjSx/NjXo76ePFo49SfHDN2QeyM77N7uAM6yqyjTfQb9X93X04wYPdCsX7INe7xcC2mJyZQIRBS2lNA/sLAuy84syw973jrppgXTW1H1pNDyjWWkPNY7fUCFk5MFbJs1e7jSpPLzuELMOi0WmM7hNLfKSBucOS+eUZQ/mz4l9ysDCIPFwsZpcHSaqPHXsAjYZ9Z7zDzPrn2K8Z5pxDD3pSRgolR7NFisykSC4YL9SUPalr75yxN8iuWWovMuyb07xrvzpb+HNMG5jYMxZ8GqP6HXnZsddpNc7c9mBZFKpp5IofwkdkWYx3mX1b6E0MSfF7DqY6K1VyJO9H3QQz7/XpuVEeOvaqI/6wnjZfr+Kcs/fdQC8YKDdbSJMqSJMq0JoLu/twgovUUWJW0lzi88lVHdk4WlLTGUfmFyrrrKRSgUGyozf00NjCziBjgngs2OlbvFcrGHQaZg5OAgJQjt/ODPu9eSZ25FSi10pcPqX9hp1BgT4MopS0EaXIHa8Y6O3JM2Gzi/fInlwTP+wrRJLgwTOHtdjNkJQotBqJilpr8Hi4lBwEZIhMhsikdu3iox3FlBHLWaNSSegpI0Fn/BHu2wVTbm3xq1+ePhSNBCsOFgfVvLQnqpXCPt5WBrJDGM9Gtk+lc9HEPvRLCKe0xsKba48DPWy+XkVdEDTleP0Upxw/SAz0zI2VHCF849Mb4YWJXqd2qagd+5JQxz74MdVZKSGOlUnXwNyHfHpupMGTFF+JuuupHfvBp8O1S+Cqj73aXJXinygzu1U4BBrlZgspSpyZFDIt8Q1DBNy+Gh7J8/mmdUiK+LwcKa4JPHm1QmWthTRJOA5rYkNRd16TNAz0EaJDW3bEL7ucN1zI8X86GGCFfTs79h9sEs9bOCqNlOhesGgU30iODwxKjiLKqKPOaueIsrj31DIhy75ofB+319MwvZZBitLnQGFwLByTPBJu+h7Oe65dT7c7ZL7dLWKwruhJC0AeussDm8za++f80Z3Y7A7qrCJRKbq+kfO5LwkJjdA36trblGtnj5qvV8mcDVNugwGzvH6Kyxm/spMOyr+YlaStRYUvwwuTYfu73XxEQYQ62uSlmlhFNc+rrrfRYHOfdOYNocI+ADDVCfOS2HDfDe5UKf7hohr25btWAk11rs5Bj5yxB4hKhiFnQIR3K8JJUUZSoo3IMhwsDPw5+7LGGfZRqd16LEFJ+lhR4PtIZmIEeq1ErcVOvqmlM3YgUG5uNL4Tnda9BxNMaHWQPk58nb/DL7tcMFJ8NrfnVFKspJAEBJW+d+yr6q38b4e4wb+uJ5vmNSZ9HPSdIsxYAa1GYnQfxUDvVCVbTpSz+lAJOo3EfQuGtrqbEcrC8cFg8XAxRMCAmTBicbuevju3kopaK9FGHdMH9cDiTZbdmmzepRSuPx0sotAUQJ/3dmBuFJMcXqtklbdDht+Yiyf2pW98OCDuuVQFXI9ixDmw+CkYvsjrp6jeHfvyq7DYOq4W62zUZmGy5RSUHQZbkCiRAgFVTeyl/5dKbLgenTL61hE5fqiwDwBMdVaGSLkMlU9Cg2/y36z0GCINWgqr6jnvhbU8+r89VJgtPdsRvwMEkxy/onFhHyreugydVsOgJLEYdrg4MOX4FbWWRoV9SM3hE6oc30+FfVpsmFO+vWx/kV/22WHsNjAJozdfOvZLtuVSZ7UzLDWKqT0pe9oT5/wTbv2xyU26mme/85SJfy4V3frLJvfzGPs3Ik108g8EwbXFH6w+JGaoTxuahF7bw24lP7kOnhwIpza3+NWQlCimZMbjkOGLHXndcHD+o7pBNJWMOg06SxVodO0yzmuMXqtxjqucmZXS8+br28mAxAhiw/VYbA6nmjaQUb0XYizic96e+LZei+r/VXYY6iq9fpokSX6Zs+9hZ+PgxFRn5WHdJ9x58HrY7Z2sXKVfQgTLH5zLuWPTccjw/sYc5j21ipeV2J0eK8NXKdoHyx+DDS96tXkwGeiVmy2kqMVbqGPvO9WF8M2D8PE1Pj91iOJLcThAL8AViv8CECrsfcXPhT3A2aPFwtvSfQHihVFvEp3o6Ayfzh0fbXZF3PXmG/LxSnft6135bD5ejkGn4d4zhnh8jnptORgsUvxV/4Dt70FD+85xqxRztLlKMkSPoqFamCmWZrv99SUT+wLw+fbc4DFLdIM6Xx8dpofpv4BHi2HRPzq834sn9uWH+2fz2LntMGUMFmwWyNnUIj2hNSRJchpz7gwCAz3VFT+yQRkxCxX23hOZ6FpQL9jp01P94YwfKuwDAFOd1TkvS7TvH56MuHD+ffVEPr59OiPSojHVWfnxgPgwDuupMnyVsqOw7jnY9ZFXmwdTx77MbCGFSvFNqGPvOzojbH0TDn7jdLz2lqEpamEfmB37ypo6klBGb0KFvW9kTBCz9ikj/LbLhaPE53PD0TJMtVa/7bfdRCbC7SvhVwe8npc9UlxDdlE1eq3EBeN7iMO5L9SWO6XXY5WOvSpHvXbaANJjwz0+fUS6WEQ/WmLu0Hxkl1BfBav+Dl/dAw7f/WbKzRbnrPDc4T2wsG8l8k7lnLHphOk1HCmuYVeQmKG5Q31/RysjnWi0PqUyeWJEWgzhBq1f9hWQ/PhH+M9ZsPl1r58yzmmgV9k5x+RHahpsaLET1lAmftCO2qRX0045vj8M9EKFfQAgCnul8Ihp/0369EGJfPPL0/jrBaOc8/rj+8f54QgDmD6K5KVoP1hq29xc7apkF1ZhD1BjNJXyGgsFJFAZ1g9i+3b34QQf4fEu99pC3zJYhyoGeoEqxa8xV/OdYyo5UeOEq3UI70kaCvdsabdpmDsGJkUyPDUam0NmxcEAkeP7iKo2mDk4qV1+L0GLLMMbZ8KTg5yGihmxYc4brAiDlrvmD25zN2kxYcSG67E75IBdEHRSqsS1RaaI86SP/Hy4BFkW4wdtLXgEJW0U9jFheudi3ufbcrvqqPyOM+ou5HzuO/2micecDV4/RR3xaR6lGYiYG2wkU4mEQ4xohO4zfEOtTXw00AtJ8XsI5tpG3beYjnVKdFoN183IZPVD8/j49umcM7qHd/Ni+gipqWz3qngbkBhJuF5LvdUR0DnlAOW1Fn5lvYtv538rTI5C+I5qlFawy6enqRGRR4prAlJqWVSv5x7rfXw96T/tdjAO4V8WjhKS9x/2Bogc30eWKYW9WrD0GiRJqHuQ4fga5UcSUzJFwXvTrExnke95NxIjla59wJuzqhJztYD1EacMvyd26wGSlH+XVqT4AJdOEovtX+3KD3yFRitU1Qt1UbRRC/9ZJGK6fJgJ7tX0nyEei/Z5/W82TpHiHy6uptYS2MlM5gY76U4lcXroPsNXBs2H0x6EyTf79LTk3iDFf/zxx5kyZQrR0dGkpKRw4YUXkp3d9GRbX1/P3XffTWJiIlFRUVxyySUUFTXtmuTk5LB48WIiIiJISUnhoYcewmYLjA+WtrYYjSTj0Oghon15ss2JizAwfVAiGk0Pn5OUJJdRhRd59lqN5JRMBrocv9wsVuwSe0o+cHeQphb2u3162oDESHQaiZoGG4WB5HSuUKHIveMjQu+NduOwQ02J33a3UJmzX3O4pPtv2r68B573Pke3wFTHrlwTkgRnZvVCP4/M2eLxxFrnj/5wbhaPXzyG+85omVvfGiPSVGf8wL62ODvR7SjsHQ6ZNYpx3rxh7cs7D3iSlTGdyhywuG8AzBycRFpMGKY6KysOBFjUpZeoUvx0XTXkrId9/xNxoCHaJjoVEgYBsluTRXekxISRHhuGQ4a9eYF9jqhpsCEh05A0ClJ7sFdCZ5E2Ghb8USR3+YCrY9+DC/vVq1dz9913s3HjRpYvX47VauWss87CbHadbB944AG+/vprPv30U1avXk1+fj4XX3yx8/d2u53FixdjsVhYv34977zzDm+//TaPPfZYd/yVWhBeLy4K9oiU0KpYe3BKXrybZQkWAz21sA8Vbx0gfax4LPStsDfoNGQqudSHAlBWW2OuQcJBfEQvkkz7k2Or4In+8NEVfttlVnoMfePDqbc6nIVPt1F6CMqPAt4t7C7bJxbCJ/WPJzm67e50jyPzNPF4Yq1zzj4jLpyrpvbHoPP+mqx27AM+y770kHhM9t1nYm++iTKzhSijjkkDfJfxBwWRia4mizq20AytRuLiiUJh+VmQyvFVKX4/rTpHnQ660P2G1/RXlJQ5671+imqgtyuADfRkWcbcYGO7PIzy61bA1Z909yH1GlzmeT1Yiv/DDz9w4403MmrUKMaNG8fbb79NTk4O27aJIs5kMvHmm2/y9NNPc/rppzNp0iTeeust1q9fz8aNGwFYtmwZ+/fv5/3332f8+PEsWrSIv/71r7z44otYLO7/8RoaGqiqqmrypzOQZZkoJU5CDplgtY8M32ZZVAO9QI8l6luzm03Guxj1853dfSjBiyrFLz3klQdDY1wGeoEnqz2v8j0OGW9gzKF/d/ehBCdx/cFSA4V7hbuxH5AkibNHqe743TxnX5kjHr3MsF/aW2X4Kn0nixx7c7Gr6G0HI9PVa0t1QI7wOCk5KB6TvFcjqKgy/JmDE31a9Ag6Bs6BwacDrf8/XqLI8VcfKqG4OvCUXW3h7NjLykJkBzPsex0DFDl+zkavn+Kcsw9gA70GmwOb4kEVGfJfaD91lXD4Rzi22uunJPYGKX5zTCYxi56QIDJ2t23bhtVqZcGCBc5tRowYQf/+/dmwQZhabNiwgTFjxpCa6pIYLly4kKqqKvbt2+f2dR5//HFiY2Odf/r165wTXk2DjWxHH/5hvRJp4vWd8ho9HrVjX1fhVfGm3nwFshTfancQYykhVarEaKns7sMJXqLThEFUdAZU+ZY5rBb2RwLQQC/GUoJesmOM6OGpF51F/EAIiwV7A5Qc8NtuVTn+jweKsNgcftuvT1jrobpAfB2X2ebmFWYLm46LWcpeW9jrjNBvqvj6xM/t3s3QlGg0klBbdcTVuFOx1kPFCfF1O6T4q7KFwnDe8B4qw1e57C247gtXPKYbBidHMaF/HHaHzJc78rvw4PyDGneXqhb2saHC3ifUOfu8beJz5QWqM34gF/bqgg9ApCFU2LebfV/AB5fA2qe9fkqSIsXv0R37xjgcDu6//35mzZrF6NGjASgsLMRgMBAXF9dk29TUVAoLC53bNC7q1d+rv3PHI488gslkcv45deqUn/82AlOdlaNyH96ULkQ/5YZOeY0eT0QC3L8HHj4Ghrbnw0akRSNJUFLdELCr7BW1rgx7bWxIydEh7t0BD+4Tbug+MCQ1MJ3xHQ6ZeIeQThrje2EsmT+QpE7Js5/YP56kKCPV9TY2Hivz2359wqRcqwxR4tzYBisOFmN3yIxIi6Z/Yi+er82cIx6Pt7+wDzdonSM8BwoCT+kDiEWMB/bD9V8K41kfqKy1ODO45/VU4zwfUU30PtsWfJn2amGfaFMURqGOvW8kDIKFj8ON34LWu7G40X2EFP9UeZ1z3DLQMCuF/dvGp9C+OMWnjnOIRqhNx/wd4PBuoT81JgyAMnNDu716gqqwv/vuu9m7dy8ff/xxp7+W0WgkJiamyZ/OwFQnTLB6VbxQZxDXX9yse0GEQcfAAL/5KjdbSJEqAdCEMuw7RjtzeVVn/MNFgSWrraq3kopY9AlPDN2ItZtOKOy1GomzVHf8fd3kjl9xUjx6eU7s9TJ8lSFnwNgrIeuCDu1GVYQFrIGeJIlY3UHzvL5mqvx8uBSHLM6NGXE9MObOHfWe/x/PHZuBQachu6iafQHu29OcmgbFhNWinKtCHXvfkCSYcZdQ+2i0Xj0lNlzPoGRx/xmoefZqx36wlA9lh71etAjRjJQsMeJVb4LyY149JSnKSGqMEVmm3eeToCns77nnHr755htWrlxJ376uTO+0tDQsFguVlZVNti8qKiItLc25TXOXfPV7dZvuwlRnZYJ0mAmGXLDWdeux9Cay0gN7zr5xYe9rVyWEfxiYFIlGgqp6G8XVgSOrLTdbSFXUHPq4jG4+miCmEwp7cBXIy/YVYXd0w4JQ5QnxGNf2fH2txeY0+uv1hX2fiXDxqzD64ra39cDItCCJvGsH6nx9j5fhA9SWw1PD4MmBHn04YsP1nKUkSQSbiZ7asddpZJFVHirsbTF8ewAARgxJREFUuwSnHD9A8+zNDXZAJoVGcXchfEerhzTFwNlLc2+Asc73R2W7XjbgC3tZlrnnnnv44osv+Omnnxg4cGCT30+aNAm9Xs+KFSucP8vOziYnJ4cZM8T8y4wZM9izZw/Fxa5IkuXLlxMTE0NWVlbX/EVaoarOytP6l3jNfJ/X5m8h3FCVD59cB28saHtbXAZ6geqMX262kEyl+CbUse8Y9SZ490J4OgvsVq+fZtRpyUwUK+uHA8gZ32QyESMpXhKhC277UQv7on1ez0d6w4xBiUSH6SitaWBHToXf9us1hmhIHy/idtpgzaESGmwO+iWEOx3dQ3QMNfIuUBeNWfc8/PhnKD7o09McDpnVzpi7XiDDD48Xnj0Om5Iw0Tqqid6XO/O6z1ujHaiK0f3z34BHixWzwBA+YbPA7k/h+9+ICFUvUPPsA7Vjb26wEUcNRpQFrdB9RvvpM0k8ehHHreJ6f7Rv4SfgC/u7776b999/nw8//JDo6GgKCwspLCykrk50t2NjY7nlllt48MEHWblyJdu2beOmm25ixowZTJ8+HYCzzjqLrKwsrrvuOnbt2sXSpUt59NFHufvuuzEauzfax1RrIU3pvhET+vC0G2MMHPwGcrdAddsS2EA30GvSsQ8V9h3DGCMWzaryXG7QXjJEdcYvDpzuW22Z6ArVSuEQ1jkjQr2C2H4w6mKY/WthoucnDDoNZ4wQHc2l3SHHH3cF3LEaTn+0zU1V9/6FWWlIPsqyeyQOh0hKOLS03bsYoSyQHCmuCcwib9dHwsxJTU7wkv0FVZTWNBBh0DIps4fG3DVGklxxqSfWetx09pAkUqKNVNRaWZkdHJn2DofM8VIRG90/IVJIybUhozSfkTTwzf2w6RUo9s6IdWwjZ/xAGvNTqaxrVJdEJII+rHsPKJjxMY4bXB379i78BHxh//LLL2MymZg3bx7p6enOP5984spVfOaZZzj33HO55JJLmDNnDmlpaSxZssT5e61WyzfffINWq2XGjBlce+21XH/99fzlL3/pjr9SE+qqywmXQqtiHcYYBUmKw68XyodRSmF/rKSGeqt3q6xdSVmNhVNyCqWGvhATMkjrEI1v0Ap2+fTUoeqcfQAZ6JnqbXxjn8bu8GndfSjBjSQJ5+v5jwiHfD9ytuKO/8O+woC8cQORvLHigFLYjw4tHgJiYfiVWfC/O5159r7SJy6c6DAdNofM0ZLAOW8AYLdB2RHxdbJvUXdqt37m4CSMOu/miYOeYWeLxwNfedxMp9Vw3jgxFvXTgeAo7E9V1FJrsWPQacjszaaZHUWrg75TxNc5G7x6SlZ6DDqNRGmNhXxT4Bk478urIk1SzF+jQ+N+HULt2Bfs9loxOlbp2J8oq6Wy1neDxYAv7GVZdvvnxhtvdG4TFhbGiy++SHl5OWazmSVLlrSYnR8wYADfffcdtbW1lJSU8NRTT6HTdf/qpFwlIlJqtbGg7yVmNJ2FD5KX5GgjSVEGHHJgdu0rai3cZv0V70xe0q5IohDN6DtZPB5e5tPThinO+EcCSIqfK6Vxj/U+Pu7/p+4+lBCtMGdYMkadhlPldV1/fvHSfXfjsTKq6m0kRRmY2L8XdGC9IWMC6COgtszr7ltzJEliZKDK8StPgt0CunCI7e/TU10xd71Ahq8y8lzxeGKdmLn3wOyhSQCsPVIasIt5jVE9IC6NP4LurbNgRfc3uoKWATPF48n1Xm0eptc6lT3tnaPuTHbnmkJKYn+RMAgueBFuXwmSdwuicREGBiiLbe2R4wd8Yd/T0VSLwt5s7AVmNJ1NH2Vm1gvJiyRJTFBuZjcd83zB7g5yysUMtRp9EaKDjL5EPGb/AHWVXj9NleIfKg4cZ/yKWrHqGxdh6OYj6SFUF/o9zifCoHPe6P98uNSv+/ZIfRX8LQ1emNSmb4A6JnBmVipaTUiGD4DOAP0UJUwb8mtPqDftAWegp44iJQ0Fjfe3f6Y6K9tzKoFeVtgnDIKUUSDb4dAPHjedOjABvVYir7KOk2W1XXSA7SdbeW9OiiwVSpWS7G4+oiBGzbPP2eC10mdsgObZ2x0ye/NN1MpG6pNGQ/KI7j6k4EaSYMK1kDrKp3PuuA7I8UOFfTejMwspZH14qLDvMBnqLMt2r7pWMwYlArChu/KmPbA3T3R6VJO/EB0kdbSIHrE3wP4vvX7a4OQoJAkqa62U1gRG5mxtdSUaHCREhgr7DlNTDP8aAe+eD+XH/brrGYNFYd+lefaVOeI9XlvucS7S4ZBZpszXn9Xb3fCbk3maeDzR/jz7kYGauqIWbz6qwH4+XILdITMkJYq+8b1Mtq127Q9843GzCIPOqXxZe6QLF/PaiVrYDzEojY2QI3776TMJNHqoLhCqGC8YrxZuAeaMf6S4hlqLnRW6OejvWgtn/bW7D6lXosrxd4U69sFHeL24ubJGhm6uOkzqaDDGQn0lHG+7AzdjsCjst54ox2oPHJOj4qp6xteuZ7PxLsZu+W13H07PQJJg7BXi692feN62EWF6Lf0TxI1soBjonXfi7xwyXs+U8q+7+1CCn6gUGDRXfL3rI7/uevqgBAC2HC/H1lXnF/WmMs6zzHpnbiXF1Q1EGXXMVM6DIRQGzhGPJ9Z6PdbQnBFK5N2BgsA4ZzgpPSQek3wr7JdszwNgwcheGL2adSFMvgWm/6LNTU8bIhbz1gVBYX+wUCw6paMca1yosG83hgjIGC++PundnP3YfqJw25NnwtEdsaitoI4GjOkbG1Jy+QubBXZ+BJ9cK3xOvGCcYrAY6tgHIRuk8TxhvZLK/gu7+1CCH50Bxl4mVk+9cHgenhpNfISeWos9oGJH9uabSJPKSZEq0VkDZ7Y76BlzmZDZjr7EJ2OsoYoc/0iAGOhFWUrQSQ4MUaG5aL8w/lrxuPPDdhdy7hiZFkNsuB6zxc6+rorVVJ3O4z1n2Ksy/PkjUnqPEZq3qHP2deVQ0r45++Fp0UgSlNY0UFLtv8SFDqOqUnzo2OdX1jnn6y+f3LczjiqwSc2Cc592Lfh4YJYyfrP+aBn2ACrWmlNvtXNCGReIsyjJHaGOfcdQ5fhenjOGJEcRrtdS02DjWGlg3FuAazRAlYKH8AcyLP0dHPgajq5oe3NgVEYMGgmKqhoo9NFgMVTYdzPbbIN5xX4+1kHe5a+HaIOzn4DbfoJB89rcVKORmK7K8Y8Gjhx/b14VKapxSVQv7JB0FrF94JZlMOUWrxZ+VIYqBnqBkmUfaxMdFn1cyK3WL4w8Vyh9TKe8Uvp4i0YjMSVTdO27TI5foXbsWy/sZdklw184KnR+aYFWD/1FVG575+wjDDoyEyMBl+Q5ILjpe7hvNwye7/VT/rv1FA5ZjK4NSo7qxIMLfsb2iSXaqMNUZ2VffmBJrBtzpLgGu0MmLkKPvkaoMUId+w4y/S548CCc6Z0JoU6rYUwf0bXfGUByfNWs7b49FwuvlgrvRgtCeEBnhPFXi6+3vePVUyIMOqd5s68+DKHCvpsx1QkjrNgIfTcfSQ9B69u/oyrHD6Q5+335Jvo4o0ZCIxrdjdqxP1QUADfoskyiLGYiwxNCN2J+QR8OYy4VX+/8wK+7VuX4XVbYeyHFP1xcw/FSMwadhnnDQ94ubpn9K7juC2F61E5ccvwAmrPXaISawxjt1eZ2h8wnW04BcNU031z0exQOh3A8X/6Yx8gqnVbDdOWeIpDn7NXFptEpRqQascjna0pCiGbEpPvsIK/OUQeKYrTBZudgYRVGLETU5YtoTD9HwfZaJlwnHg/9IAx7vaC9749QYd+NyLLM2PqtjJROEhvywfIvdRWw6+M2Jdeqgd7WExU02AIjz35vrolpGkXOlT6uew+mJ1JbDlve8DrOamiKEnkXAFJ8ubYMA2JGKyq5TzcfTQ9iwjXi8cDXPqUmtMX0RueXLpmzd0rxM1vdZOlecVNx2pAkoozdH/kakGSeBoNPB0Nku3cxQo28Kwygwt5HVh8qpsBUT3yEvperO2T45DpY91ybKo5gmLPPVhapxyc6xCKgMRYiErr5qHoQ1jqvNlPnqNtjkNYZHCioxmqXGRGhNDH0EaHC3l+kjBCjoLLd6waCa87et/dHqLDvRmpra3lL/wTfGx8hVuPbDEUID9ga4Lnx8MUdwiHfA0NSokiKMtJgc7BTifPpTirMFoxVx+grlSJrDS6H5hD+47uH4NtfwfZ3vdp8cIq4uS8zWyir6d552doC4WpdIscSHx2SxfqNjIkiNcFhg9ytftvtyPQYosN0VDfYuibPPn0cpI8XMV2tsHS/KOx7d6HW+YxUI+8CxUBv96fw6Y2wd4nXT/lwk+jWXzKxb+/2YtBoYcQ54uuDnt3xZymF/ZYTFdRbA6NZ0Bw1hjGj/2C4fw/85oRP42khWqHsKLx3MfznbK98fNQ59gP5VVhs3W/grHaGpycr9zkxGaH3hT+ZeIN43P6eV34+rsg7k09xy6HCvhvJO7ARgCoiCY9J6uaj6UHojDBMMSPc7nmeRZIkp1w2EOT4+/KrmKPZDYDUf3qHOkYhWmHMZeJxz2deOZRGGHT0jQ8Hur9rb8teBsBWOYswfS++0fY3kgQXvgy/yoah/vM70Wokpg3sQjn+hS/BHashcbDbX+dW1LI3rwqN1Esdzn0hdxss/T1sfr1dT1cj744U1wRG6srJtbDvCyja59XmhaZ6fjooZNpXTg3JtBlxnng8+K3Hm/LByZGkxYRhsTnYdrKiiw7ON7IVFclwZVzEl3ztEB4Ii4OT66BgpxjdaIN+CeHER+ix2B3OlILuZKfiiD81UlGbxPZCs8zOZNSFYIiGiuNexakOT4vGoNNgqrNyUjG79IbQp7kbqd4t4qoORk5FCp1Y/cvE68Xj3s+hwXMx5pyzDwADvX35JvLkJPZHTIHhi7v7cHomQ86AiEQwF8OxVV49RZ2zP9zNhf1h/XC+tU9la+Tsbj2OHknGeIj0/wKrKsffeKzc7/v2FdU0b3JmAolRxm4+mgCnKhc2/Bt++j9o8L3r3icunCijDovdwQX/Xsdb645TbrZ0woF6SYkSdeelI75qmjd1YAJDUkLqIAbNFTfl1QWQ37oSUJIkZ9c+EOfsK2stFFWJjqyzsA/hHyITYdyV4usNL7a5uSRJjFW6smrMXHeiSr7H1YumozdJECF8wBApkrsGzhENyDbQazVkKQvEvhjoharJbiS5YBUA9YPO7N4D6YkMmCXkqJYa2P8/j5uqc/Y7ciq7XTq3N7+K5Y7JrJr6ile5uSHagVYvIu8Adn/s1VOGOZ3xu1dW+3nNGO623o995IXdehw9nnr/zTyqhf2W4+WdG4Fls7Qp71Nj7haOCplytsmIcyFxCNRXwra3fX66RiNx2+xB6LUS+wuq+PPX+5n29x/5xXvbWHGgqGs8FxpTKsZ4vCnsG5vmXR3q1gt0Rhiq3Ksd+NrjpqcNFZ/5QJyzV2X4fePDiVr1R3hjARzwPF4Qwgem3yUes78T0vw2GKcYpHX3nH1Ng42jJTWE0UBisVLYD1vUrcfUIznnKbjha1fyShs43x8+JCeECvtuoqH0BP2tx7HLEn0mn9fdh9PzkCRX176NWeqBSZGkxhix2LtfOrcvT3x4R2WEDEs6lbHKqvqBb7zqxg0JgI69LMusOVQCwJxhodGdTqEqH946B16Y7NH92heazNl3Zp79xpfgb2lCPu6GspoGtpwQqoGzskIy/DbRaGHW/eLrDS8K7xYfuW/BUDb9bgF/Oi+LURkxWO0yP+wr5JZ3tvKrT3f593g9YS6F2jJAgsShbW6+5nAJeZV1xIbrOXt0aBHIychzxePBbzzOUM8aLM7Pe/JMVNZ2o0rDDaoj/oi0aMjfCblbwOq9zDdEGyQPh6FnATJseqXNzV0GaZWdelhtsSfXhCzD4FiQxl0F/aZDyshuPaYeica3Ecqxzjn7Su9fwqdXCOE38jd9AcAuzQgG9Q/FVnUK464GSQunNkHxwVY3kyTJ2bXvTjl+TYONqPI9pFLOqIyYbjuOXkGfiZAwGGx1XnUrnFn23VjYF21egtF0FL3W1QUO4WciU6D0sBjTOLzML7vUaiSmdkWefeVJsDeALsztr1ccKMYhw6iMGPolRHTecfQkxl4O0RlCfr3LO3VPcxIiDdw4ayDf3jub7+6dzc2zBqKR4Mud+V2nACpRuvVx/cDQ9v/9R5tEusIlE/uGvDwaM+RM0BrAYhaLJa2QEhPGsNQoZDkwRvwao3bsxyRJoqgHSB3djUfUA1G79js+EAlNHlALt8PFNdQ0tO3501mohWP/fgPgvGfhlqUh47zOpLpQpDO1YYqnLvzszTd5rfIKFfbdhHx4OQCnkuYihT48nUN0Kgw7WxT3uZs9bhoIefYHCqr4h+41NoXdQ1Luim47jl6BJMHYK8R7o/xYm5urHfuS6obu6cDYGkhcdjc/GX/NJRmVRBhCMWWdglbnmpHc4b9Me3UhZtPxTjy/FO0Xj61E3YVk+O1AZ4QZd4uv1z0Hjo6NamVlxPDYeVlO48K31p/o4AF6SYmysJ3Utgy/qKqeFQeLAbhqaqjp0ISwGLhzPTywH6KSPW4aqHP2qnHebMcWsRCYNDzUmfU3g+ZByiiwmtu8jiRHG8mIDUOWYW9e98nx1RludaEhRCdirYd/TxHpTG2k8AxKiiTaqKPe6uBoiXeNpVBh3038Qf8r7rA8gHbMJd19KD2bs/4KD+53yfJbYcYgcRHedaoSczetmh49doSRmhwcSNBvarccQ69i6m3wq4NwunvpcmOijDoyYkUntEtiy5pzcj16ex3Fchz9s6Z0/ev3JiZcKx4P/QA1xX7Z5TQleWNTZ83ZV5yEUxsBSeSvN6OmwcbPSoERKux9ZNKNwu26/Cgc+Movu7z5tIEALNme2zULhXUVoNF7NV//6dZT2B0yUzLjnUqlEI1IGuqVi3wg5tnLssyhIlEcDCtRFEmjLw51Zv2NJMHpj8L5L8CUW9vc3Jln340GertOmegrlXCa8XCHFzBDtIE+DEYo5tjb3/a4qUYjMbqPGM31duEnVNh3AzUNNjblNbDUMYVxo0MSqE4lcTBEt30j2y8hnD5x4dgcMlu7ac5ePvITACWRIzrFnTtEMyISICrF682nK6qOt9ed6KQDah179lIAVtrHM2dYaD66U0keDn2ngGyHbZ7jMr0lKz2GaKOO6nobBzpjYWjPp+Jx4GyI7dPi16uzS7DYHGQmRjAsNeRw7hPGKJj9IJz2APSf6ZddThuYwMj0GOqtDj5WTOo6lTm/ht8XwrzfetzMZnfw0WZxPFeFTPM8U18FOZta/fW0QYloNRInymo5VR4YM+y5FXXUNNhI0pqJzF0jfjjq4u49qJ7KiHNEQ0nvfjSqMWMb5ZV3B2U1DeRV1nGldiVjll4BX/2yW46jV+FM7loiziUeGNtPFPZ78ry7dwgV9t3AluPl2Bwy/RMiQrOOXcmJta0aYkmS1O2xd2kl6wAw95/bLa/fq8nfId4fHrhr3mAkCZbtL+pcEzQ3WA58D8BWw2Rn/EmITmTq7eJx7dNQ2fHCS6fVMKWz8uxlGXZ/Ir4ee4XbTRrL8EOjX+1g1n2w4E9ivMsPSJLETbMyAXh3/YmuccjX6sDouQP/0eYc8irrSIg0cM6Y9M4/pmCl9DD8ezJ8dAXUuo+xjDLqmKB0YtcfDYyuvWqcd03MbiSHTczWJw/r5qPqBTgcHmepxymF285u6tirCwqLDDvFD0Ixd51P/xnCyNRaC5tf9bjpOGXhZ29+qGMfmDgcDPj2ah7Qfcr8zLZX8kL4ie8ehrcXi1ziVnAa6HXDnH29xcpYyw4AYkcv7PLX79Uc+Bpemw//u0vMPrXCkJRozh2bAcDzKw531dFB2VHCq09gkbVIg+ej0YQKs05n9KXiwmutha3/8csupzkLez/n2RfshNJDwjRv5Pktfm2xOVipzEyfFZLhBwznj8sgMdJAvqmepfuKuvtwMNVaeXq5yLp/YMHQkGmeJ+IHQkSSGHFY8ZdWN5vpnLMPDAO9bMWsUZM8THTqx1/TzUfUC9j+Lrw4BXI2trrJmD6xSBLkVdZRVuN7+kZH2ZVbSR9KGOw4AZJGcfUP0alIEsx5SHy9+p8eoxHHKpF3R4pCM/aBSf4OBlVv4SbtD0weGloR7zIyxovHVf+A8uNuN1E79nvzTFTX+yfqylty9m4gUaqmhnAShp/Wpa/d6xk0X4xrVJ70uPADcO/pQ5Ak+GFfYedIqt1xSMjwtzhGMG1EZte8Zm9Ho4HFT8N5z8Ppf/DLLlUDvc3Hy/w7Zx8/EM59Bk57UJh7NWP90VKqG2ykRBudHcQQ7eT4z/DuBW0aHnlDmF7L1dOE3P2tde6vSX7h1GZ4dQ5871mG//xPh6motTI0JSokw28LrQ7O+af4etvbIjbODeqc/fojpV2jymgD1RHfMGgWXPYWzLirm4+oF5C7FcqOwPoXWt0kOkzPoKRIoHvk+LtzTZyuFY0l+k0TY4ohOp+xl4v7T3sDfH1fq6qOPnHhJEUZsHl53xAq7LuYun0iWmuNYyzTh4QK+y5j3FVCXmSrg28fdPsByogLZ0BiBHaH7Mx77ipqD4jiLTt8PJLO0KWv3esxRsGZStfl56dFlnkrDE2NZrEiUe2qrr3l0I8A/OQYz+yhIe+FLiM1Cybd4JVRljeMyoghyqijyt9z9uFxMPlmmPcbt79Wu8FnZqWG1B4dZddHcGyVOE/4gWunD0Cnkdh6soI9nXVDv/l1KNgFta3LwY+V1PCO4tD/6LlZ6LShW8M2yZwFYy4DZPjuISG3bsb4fnHER+gpM1v4qCu8FNpAdcQfkRYyRewyZtwNSJD9Lez7X6ubqQZ6XS3Hl2WZXacqWaDZLn4w7Owuff1ejSSJRXljLAyY2appoSRJPqUVhM7eXYxlv5iVPRA1k+RoYzcfTS9CkmDxM6A1wtGfYM9nbjfrrjz7bwxnc6/lHg5lXtulrxtCYcxlYqXaaoblf/S46b1nDEWS4Pu9hRws7Pyu/fLR/+Rmy6/JTlxASkxofKdbaKgBJaK0vei0GqZkxgPCHb8rsDtklu8XhX3IDd8PzLof50363iUd3l1qTBjnjhULhZ3Sta88BXs/F1+rsX1u+Pt3B7E5ZOYPT2buMM8xbiEaceZfQB8p4nR3f9zi1wadhvsXiBn2fy3L7p6oVAWLzcGxEjOXa1cySt/64nUIP5M8XBhvAnx9L5hy3W42zmmgV9k1x6WQV1lHvdnEDI0Slzp8UZe+fq8nYSA8sAfm/04ogVpBleN7Q6iw70pMucSaDmCXJeShZ3b30fQ+koYId2CAH37r1vRGleMv21+ExdZ10rktRRJfOWYSM/KMLnvNEI2QJFj0D0CCPf/1OA83LDXaaSzVFV37Vcdq+MkxkawRoazhbqGmBF6cBh9dBcUHO7SracrCod8M9FY/KTqyde6TPJZsz6W0poHYcL1zFCBEB0ge5rpJ/+peKD3S4V3eNEtE3329O5/iqtY9PtrFpldEukPmbMiY4HaTdUdK+fFAEVqNxO8Xh84xPhGTAXMfFl8vfwzqW6ourpnWn+Gp0VTWWnlG8TDoDo6W1JDgKOcJ/RukvDfXozIthJ+Z/zvImCjeH0vucNuZdUbe5ZqQPRjt+ZvduSZmafZikGxirCspZKbY5YQ1KtrdKH9AKO5+c3bbcaUQKuy7FmVWdrs8lPHDB3fzwfRSZt0PScOFLHH5Yy1+PX9ECklRBk6W1fLq6tbNLPyJ1e7ggDL7Nioj5HjebWRMcGWYf/+wxyzXe08fCsB3ewqdTsOdgSzL/HxYSGhDMvxuIioZ0saAwwrf/sqju3FbqMX1mkMlrD5U0rHjaqgWkvDvfg1lx1r8us5i56ll2QDcPX8wBl3ocu8X5v8eBpwGlmr47/VgrevQ7sb1i2Ni/zisdpn3N+X46SCBukox/w3C1d8NdofMX78Rnbrrpg9gSEpIou0z0+8S7taD5oPd1uLXOq2GP56XBcB7G092icrLHdmF1Zyj3YQGWajTYjK65Th6JVo9XPIGGKLg5FpY92yLTUamR6PXSpSbLeRWdOyc4gu7citZ7pjEy0NeEc2NUGpK95G3HV6bC0d+bPGrURmxXDcj06vdhK70XUj9/m8BWOmY6OzchOhidAY47zmIzgA3qomYMD1/OFdchF9YeYRjJd65UHaE8hXPcau8hBHGcvqH4g+7lzP+KFatJ93ocbPhadGcM0ZIm5//qZO69rJM7ZvncX3t2yTp6piSGTK06TYW/QN04eKmbFdLya23jO0Ty/zhyTTYHNz6zha+3tWBrtmBb4RnSMJg6DOxxa9f//kYRVUN9I0P53ovbwhCeIFWB5e+CZHJULxPzFd3ELVr/+GmkzTYWl9Q9Iltb4OlBlKyYMgCt5v8d+spDhZWExuu574zhvrndXsbOgPcvgoueR0i3d/XzRySxNmj0nDI8Oev9ndpR1blYGE152k3iG9C2fVdT+JgWPSk+LqmuMWvjTotI5Uo26400Nt9yoSMhoThs2BYKJGpW9n9XyjcDd88ABZzu3cTKuy7kAJ7HJVyJDlJc4gN13f34fReBsyA+3ZC1gVuf33+uAxmD03CYnPw+y/2du5FWJaJ3PkGD+v/y/yE0pC5VXcTlQy/3CbMyDSe457uPUPt2hdwqKgTuvaFu4nM/ZkbtUsZNzAtFD/VncQPcJnTLfs9lGS3azcajcSr103m3LHpWO0y9368g/c2nmzfMakzveOubNFlKa6u5xVFcfTw2SNC7x1/E50Gl7wJSELSbOtYRNXZo9NIiwmjtMbC/R/vxFTbwVQWhx02KdnIM+5x24Wrqrfy1FLxPr7vjKHER4ZMW9uNMcr1ta1BeBs04/eLR2LUadhwrIwf9hZ24cEJSnKPMElzGBmp1XufEJ3M+Kvhtp+Usb+WqHPUu7pozr6kuoEdp8QY17hQYkr3c/rvIaYvVObAyr+3ezehwr4LeSHqXiY1vEK/4S27KyG6GF0j48Jjq6CqwPmtJEn87cIxhOnFRfjz7Xmddxy5W4mqzcUia5EzZ3fe64TwnsYFfUk2bHzZ7WYj0mJYNDoNWYbnfuyErv3hZQCsc4xmxrCQbLLbmX43pI6B2jJ48yw4uaFduzHoNDx35QSund4fWYY//G8vz6847NsCYlUBHFstvh5zWYtfP7P8MLUWO+P6xXHe2FD6SqcwaC7c9B1c81nT60k70Gs1PHLOCHQaie/3FnL2c2s6ZuCq0cK1n8PU292+PwCe/OEgZWYLg5IjuW7GgPa/VggXFjN8eAW8tahFcd8vIYI75gwC4P++PUC91U/KDC/JLBSjoNWpUyEmdE7oFiQJ+kxyfd/snK86n+/qImf8F1ce4TrHV7wa+w7D7V0zehrCA8ZoOFdJXNnwb2Hk3MrMvSdChX0XIcsyG46WYUfLzCGhWdmAIW+7MMV64wwo2uf8cf/ECO47Q5iI/O3b/ZSbO8HNtrpQzGgCyxxTGNYv5FodUNRVwLsXCqPFH//kdrZadcj/dk8Bazo6M92YqgLkLf8B1Ji7kFN1t6MzwPVfQt8pUF8JP/+r3bvSaiT+esFop+rj6eWH+PPX+zHVedmp3fsZIEO/6cJVtxHZhdV8skXMaj+6eCRSaGay8xgws2kcopsZa2+5YHwfPrtzJpmJERSY6rn6jY3844eD7TdxTc0SWetu4lPfWX+C9zeK98hj52ahD8Xb+QdLLZhOiT/vni+u8Y34xbzBpMeGkVdZx6urW/pidBamOitzrGsB0I+7pMteN4QHTHnwznmw9lnnvcV4pWu+J8+Ezd655s2nymtZsimba7QrWNiwFKk8VNgHBMMWCh8XEF4Mn93os49L6GzeFfz4J0rWvk2BqR6DVsPkAaFZ2YAhPB5i+0JVHry5EI6scP7q1tkDGZEWTUWtlf/7dr9/X9daBx9fDdX5HJUzeMR6K6P7eB9nEaILCI+H6b8QX699Br59sIWh3sj0GG5Q5pcfWbIHc0P7b+ydNFTDh5chVedz1JHOpoh5DEuNavt5ITqfyES4/ishb77kjQ7tSpIkHjxzmNNY6+31Jxj352WM/dNSznnuZ25/dyt/+Xo/n23Lbdnd2/WJeBx7eYv9Pv79ARwynD0qLeTL0FVY6+DLe8QCcQec8sf3i+Pbe2dzxeR+yDK8vOool76yngMFVd4rOtro8Kw8WMyfvxaL2A+fPZx5w1PafbwhmhGVLM4Pcf2h/JhYGDa7lBcRBh2PnCOSB15efYS8yq4xSTt6ModMqRA7GsLHhubrA4JdH8GJn+HHP4oGT72JwclRxEXoqbXYeWTJHhyOzhsDfW7Zfp7VPEempkjc64SSugKHuQ/DRa+CRg/7v/S5iRAq7DubDS/C2mdIXvEAQ6VcJvSPI9wQmncMGBIGwi3LXC7HH1wG294BhDzy8YvHIEmwZHse646U+uc1ZVncBOZtw2qI42bLr7HqoxiUFOmf/YfwH7PuE2aLSLD1P7DkNpFp3oiHFg6nT1w4eZV1ThfydmO34vjvDVC4hyptPDdaH2bisAGhrmsgYYiAhX+D8DjXz7K/b5dkDoRx2nNXjictJgyAqnob+wuqWLa/iP+sO86vP93Faf/4iX//dFjkYFtqIbaPyM8edVGTff18uIRV2SXoNBK/WTSivX/DEL5SchAOfgMFO+HVObD9vXanJ0Qadfzj0rG8fM1EYsP17M41sei5n5n5xE888MlOPtmSQ05ZbeuF/oeXwxd3ijnNZhwoqOKeD7fjkOHyyX25c24oncfvxPYRxX10BpQcgPcvEgkFCueNTWdqZgL1Vgf3f7zDP4vBHmiw2Xl2fRlTGl7mH2nPiMWHEN3P7F/Bon+K4u3AV/DaPLRFe3ji4rFoNRKfbsvlT1/v6xSPp+yCKqbv+zOna3fi0IbBVZ80jVwL0f2MuxKu/x8MOVO8V3xAkrvDnjMIqaqqIjY2FpPJREyMl5Fkez6Dz28B4DXDDfy9aiEPnjnMKb8MEUDYGuCrX8JupRM27mo47X5IHs5jX+7l3Q0nyUyM4Pv75nR8YaamBN44A4cpjxtsj/CzdSSLx6Tz4jUh74WAZe/nsOR2cNggKg3O+muTbunqQyXc8J/NSBJ89ouZTBoQ7/Wu7Q6ZZfsK2XKigtyTh3ms5FckUMWVlkfZLQ/m+asmcP640Ix9wLLhRVj6Oxi+GC74N0S0v0tea7GRV1HHqYpacivqyCmr5fu9hc7OXoRByxVT+nHLaQPpG+FoYtpld8gsfv5nDhZWc+PMTP50/qgO/9VC+IApD764Q3ThALIuhPOeFd2wdlJgquOxL/exKrsYq73prVqfuHBumz2Q62dkukxX83eKuCRJKwxi4/o7ty+uqufCF9eRb6pnxqBE3rl5aigCsTMpOSRm7WtLIX0cXPaOc2wmu7CaS19eT3WDjWkDE3jrpilEGHR+PwSbuYK7Pz/C0v3FhOk1fHjbdCb2b//7MUQnkLsNPr1BjG9ojXDOk3whncGDn+5GluGOuYP47dkj/Lq4//0zd7DI9DF2NGiv+hCGL/LbvkN0Ht7WoaHC3kt8LuyPrYL3LwWHlU+1i3nIfDUp0WF8ec8s0mPDO/14Q7QDWRZOlGuUSJIrPoCR51Jdb2XB06spqmpgQGIEj52bxekjUjp0on1vxTZ+/ukbltknc2ZWKs9fOSGk5Ah0jq2Gr++FihMw4TpRxDXiwf/uZMn2PIamRPHNvadh1LX9/1lcXc/9H+9kfSOjrGQqGRNWiKXvaUwbmMAv5g0OzcAGMrs+Fgoch1V06S56RRir+Qmr3cF3ewrYvfx9Pi3LpIootBqJPnHhRBi0hOm1RBi02Bwym4+XEx2mY81D80Mu592Bww7rnoOVfxOLgDF94eLXIHNWh3ZbZ7GzPaeCjcfK2HC0jF25lc5Cf9rABP556Tj6x0jwybUiA3nMZU1GRWotNq54dSN78kwMSo7kiztnERsRSubpdAr3wtuLxb3FvTuaxOHtyKng+jc3U91gY/qgBP5zo3+Le7upgJIXF/JD7XD+Lt/EmzdOCXm1BCq15fDFL+CwMDhk4d/5UHMev/tiD4BfG4Inl/2bAevFDHfx/H+RMvdWv+w3ROcTKuz9jE+FfcEueGsxWKpZJs3kjrq7GJAYxXu3TKNfKKc88Dm5QURJnfMUaMXNT85Xf2Pfjo182TCRdY7RTBo+gD+cm8XgZC9nn2UZaoqwR6byf9/u5611JwC4fsYA/njeKLShmLvgwNYgXPLHX+OSNJpyQXZQoU9jwdOrKTNbuO+MoTxw5jCPu1p/pJR7P95JaU0DfQxmFkzKYnz/OMb1jSMzMTIUfRhM5O8U6qyyI4AkRjjm/96tcVm72PASLH0EU9JE7jP+iVVHa1rd9HfnjOD2OSGJdbeStw0+v1XMWScOgbs3txmf6Qu1Fhufbs3lie8PUme1M9aQzzsxrxBfc0R06+9YDWljsDtkDhRU8czyQ6w4WEx8hJ7/3T2LAYmhsa8uo/IUFB+AYWeJ72VZqDlHnsf2gjquf3MzNQ02ZgxK5D83TvHLAr9syqP0xYUkW05RICdw6IJvmTsxq8P7DdGJOByw/jkx8nfbKohM5M21x9n93WskStWMmHMJly+c36GXkGWZu176mgeLfsvR9MWcfec//XPsIbqEUGHvZ7wu7GUZXjkNivayWR7FtQ0PMzQjkbdvmkpydMcicUJ0Ew47PDdOSKUAq6xlq2M4PzOO+HHncuXis4gOd3MD77DDqU1w8FvI/g65Mof3Eu/nsVNCcv+7c0Zw2+xBofnpYOeDy8VKe9JwjsVO5S8H0tguZfHpL89keFp0i83tDpkXVx7hkx83MFezk/PDdzPNsQPpghfFXFWI4MRihh8ege3Co4OMCSLrPLEDRbYsi+7vGuUGbPpdcNbfyKmop6SmnjqLg1qLjTqrnTqLnXCDlnPHZoQWCgOBhmqRqDHyAldRZ7eJyCs/Ffk5pWZ+eO8Jrqt8hXDJQqUmnrz5z7KBsWw8Vs7m42VU1YsZboNWwwe3TQsZKnY3h5YKH4S4/pA5hyKrkU/3VlNiCyclOYWbr7+F8IT2x9HJlTlUvHw2CQ155MpJZJ/9EWfMmOrHv0CITsVaB3qXqrfoqRmk1gjz5kpjBlEjF6AbMh8Gzm2iAPG4v5PrIC6TlWUx3PTWFmJ1Fn749ULS40KNxmAiVNj7Gec/6Hd/ISYxTRhNhMVBdYGYqbvwZWd399R7d1JzZB2XN/yBrIF9ef2GycSEhWRvQYssi1i8vZ/B4eVQ1jSzfC3j+X7cv7lscj/G9Y1F+t+d4mR64meRea1gQcdy+0R+67iHv18+hfNCc9PBT32ViEvMWQ+yyzytQdZxxDCcjPFnYpr2sPPnhgOfs23zWoaY1jNS0zTnmHFXw4UviRv/EMHL/q+EX0dDFdz0PfSfLn5etA8ikiA61fPzHXYo3AM5G8T55qiS1HH6H4SJTuj9EbxsfBl2fiBMswbM6Pj+7FbkN85AKtjFz/I4Hmj4BaU0NcGKNuqYOjCBm08byKxQ1G73k/0DfPMAVOe7/fVjCU8xY/65TB2YQKL5iDBm7D8DYtq4X9j4MrV7vkKfvxW9bOGkI4W9C95n8ZxpnfCXCNElyDLyxpc4ueELMkzbMUjN0lEGzRMRrCp7l4hFAWO0uN4cXg4n1oKtDnnm/ZxzYAEHCqq4fc4gfqekM4QIHkKFvZ9x/oP+NpoYY8sbqwOLPuGrikxWHiymovAkJiKZndWfF66aQJg+NDvdoyg/Bod/pHTnN0QXbGCFfTx3We8HYGhyJEtrLkYjixNwjRTFMtt4ltsnscYxlrDIWF66ZiLTBnmx0hoieKgth+Nr4NhK7IdXoK0SRftq+1husP7Wudlu4y3ESMIITUaD1G+KiJkZuhDSxoSKtp6CKU/cUI27wvWz108XEu24/pAwWNx8qX/i+sOMu8V29VXwjwGNFookOPdpmHxzl/81QvgRuw1emAiVJ8X3GRMgdRSkjBKZ8ymjPDuW261Qekgs+sT1hwEzxc9Lj8Ch7zkx9Eb+8NV+9uaZGN8vjumDEpkxOJGs9Bh0IY+OwMJiFjFW1YVQXwn1JsrLSjhw/BS/s9zISTkNgH/Efs4VDZ8DCPdyYzRSWDSSMUacNy5+jWMNMXy7u4CJG+9hlnUjAAcc/dk//w0umR8q6nsCsizz/fYjrFz2P4aZt3OaZi8jNTkU9ltE6s0fCdWnLMNfk4XXSzPMxhS+CzuHh4rOItqoY83DIQ+WYCRU2LvhxRdf5J///CeFhYWMGzeOF154galTvZMoqf+gb/56EWlhVhK0daQZGrBrDSyty+KD+lnkIS7KGgmumtqfP58/KnRB7eHYLXVsyT7FR/vM/LC3EKvNxlXan4ikjj3yILY4hmOXdJw2JInLJvfjrKzU0EJPT0eW+XHdelav+I5SR9T/t3enwVGV+R7Hf03Ihp3FQBaWDqsiuSAYhBjQhDUBUeGK4gxaILKMGnCAGabIAEOQmYnjMAUWSjl1i4EZEQeDxaII6oTlAgaUJbEAiSQSY5mVAIEkkA7pc1/k0hJISEDpQ9PfT1W/6LPxO+FfJ/n3c/o52q3LTzswtFyvy+IXqPse/m9FRD/WvFvp4P4cDul/BtfNv6IGfuWG95Je2vPj+9WPSS396kZ170mo+9AH7q+yTNr+6v8/UvWqOogcIL2w9cf3KxMkLx/Ju5VUUVz3Pe3a6rp1fZ6tu7sHd5TjRef03v58ZXxbpm+KKzTR6xON9fpf/ZclT16Wa68bz1hXa/+pugYtvkWWIluU6lLkQA0aMFCJPW/+dn7cnqov1erdfflavv2EvKpOqZXlogLa3qP2wf6yeht68Ydk3eWolJ+jQqUK0ccXe+qT6p7KNmyS6gYNFj4epUkDO5t7IrgpNPZXWbdunSZMmKC3335bMTExWrZsmdLS0pSdna2wsLAm97/8A31uxXZlFtl1/qpnjwa38lb8vaEacl+Y4u4J5dMwD3TuYo22fFWotAPf61D+WXVuc5ee6ttBT0a350kIAOpG4wsO143UVZ+T7BV138Vu1UaKfdnsdHCVM99JBYek4mNSyf+/ug6VRi2pW19bIy1u4LZ538C6D3nuHSENfMW1meFSpyqq9cXJ09r3bZkycwt0sbxEFvt5WXVBAZYqBeiCtjn6y2jhrYHd2mjU/W2VEBWu4Fb87XmnO3exRm/vzNXKPSdVfclx3W3v8vFSv84heqhLaw3s2ka9OvC8endFY3+VmJgY9evXT2++WfeIKofDIZvNphkzZmju3LlN7F3/B9rqLquOFZ5TRm6Zquy1euSeNupjC2Z0Hk5V9kvy9/ZiYjwAQNMM48ev4tReknI+k2qqJHtV3Zw+Eb2kuzvxdR0PVn2pVqcr7SqrsKus0q6LNbWK6RxCM++his9dVEZumSrtl3TBXqsqe61zItXwQD891CVEvdoH0ZvcIWjsr2C329WqVSutX79eY8aMcS6fOHGizp49q02bNl2zT3V1taqrq53vz507J5vN1vzn2AMAAAAA8BM0t7H3iI9xTp06pdraWoWH15+NODw8XEVFRQ3uk5qaqqCgIOfLZrO5IioAAAAAADfEIxr7m5GcnKzy8nLn6/vvv296JwAAAAAAXKyl2QFcoU2bNvLy8lJxcXG95cXFxYqIiGhwH19fX/n6+roiHgAAAAAAN80jRux9fHzUt29fpaenO5c5HA6lp6crNjbWxGQAAAAAAPw0HjFiL0mzZ8/WxIkT9eCDD6p///5atmyZKisrNWnSJLOjAQAAAABw0zymsX/mmWdUWlqqP/zhDyoqKlKfPn20bdu2aybUAwAAAADAnXjE4+5+Ds19zAAAAAAAAD8HHncHAAAAAIAHoLEHAAAAAMCN0dgDAAAAAODGaOwBAAAAAHBjHjMr/k91eY7Bc+fOmZwEAAAAAOAJLvefTc15T2PfTGVlZZIkm81mchIAAAAAgCcpKytTUFBQo+tp7JspJCREkpSfn3/dHyjuXP369dOXX35pdgzchqgNNIS6QGOoDTSEukBjqA3PVl5ersjISGc/2hga+2Zq0aJuOoKgoCCeY++hvLy8+L9Hg6gNNIS6QGOoDTSEukBjqA1IP/ajja53UQ7A7SUlJZkdAbcpagMNoS7QGGoDDaEu0BhqA81hMZr6Fj4k1U1aEBQUpPLycj4xAwAAAADccs3tQxmxbyZfX18tXLhQvr6+ZkcBAAAAAHiA5vahjNgDAAAAAODGGLEHAAAAAMCN0dgDAAAAAODGaOyBBrz11lvq1KmT/Pz8FBMToy+++EKSdPr0ac2YMUPdu3eXv7+/IiMj9corr6i8vNzkxHCFxupCkn71q1+pa9eu8vf3V2hoqEaPHq3jx4+bmBaucr26uMwwDI0cOVIWi0UbN250fUiY4nq1MWjQIFkslnqvF1980cS0cJWmrhkZGRkaMmSI7rrrLgUGBiouLk4XLlwwKS1cqbHayMvLu+Z6cfmVlpZmcmrcLmjsgausW7dOs2fP1sKFC3Xo0CH17t1biYmJKikpUUFBgQoKCrRkyRIdOXJEq1ev1rZt2zR58mSzY+MWu15dSFLfvn21atUqff311/rkk09kGIYSEhJUW1trcnLcSk3VxWXLli2TxWIxKSXM0JzamDp1qgoLC52v119/3cTEcIWm6iIjI0MjRoxQQkKCvvjiC3355ZeaPn16k8+vhvu7Xm3YbLZ614rCwkItWrRIVqtVI0eONDs6bhcGgHr69+9vJCUlOd/X1tYa7dq1M1JTUxvc/v333zd8fHyMmpoaV0WECW60LrKysgxJRk5OjqsiwgTNqYvDhw8b7du3NwoLCw1JxoYNG0xICldrqjbi4+ONX//61yalg1maqouYmBhj/vz5ZsWDiW7074w+ffoYL7zwgqviwQ3w8R9wBbvdroMHD2rYsGHOZS1atNCwYcOUkZHR4D6XnynZsmVLV8WEi91oXVRWVmrVqlXq3LmzbDabK6PChZpTF1VVVRo/frzeeustRUREmBUVLtbca8a7776rNm3aqGfPnkpOTlZVVZUZceEiTdVFSUmJ9u/fr7CwMA0YMEDh4eGKj4/Xnj17TEwNV7jRvzMOHjyozMxM7hhFPTT2wBVOnTql2tpahYeH11seHh6uoqKiBrdfvHixpk2b5qqIMEFz62LFihWyWq2yWq3aunWrPvvsM/n4+Lg6LlykOXUxa9YsDRgwQKNHjzYjIkzSnNoYP3681qxZox07dig5OVnvvPOOnnvuOTPiwkWaqotvv/1WkpSSkqKpU6dq27Ztio6O1tChQ3XixAkzIsNFbvTvz5UrV6pHjx4aMGCAqyLCDTDECNykc+fOadSoUYqKilJKSorZcXAbePbZZzV8+HAVFhZqyZIlGjdunPbu3Ss/Pz+zo8EEmzdv1vbt23X48GGzo+A2dOUHwr169VLbtm01dOhQ5ebmqmvXriYmg1kcDoekuslYJ02aJEl64IEHlJ6ern/84x9KTU01Mx5uExcuXNDatWu1YMECs6PgNsOIPXCFNm3ayMvLS8XFxfWWFxcX17uN9vz58xoxYoQCAgK0YcMGeXt7uzoqXKi5dREUFKR77rlHcXFxWr9+vY4fP64NGza4Oi5cpKm62L59u3JzcxUcHKyWLVs6v64zduxYDRo0yITEcJXmXjOuFBMTI0nKycm55flgjqbqom3btpKkqKioeut79Oih/Px8l+WE693INWP9+vWqqqrShAkTXBkRboDGHriCj4+P+vbtq/T0dOcyh8Oh9PR0xcbGSqobqU9ISJCPj482b97MaKwHaE5dXM0wDBmGoerqalfFhIs1VRdz587VV199pczMTOdLkpYuXapVq1aZlBqucDPXjMv1cbm5w52nqbro1KmT2rVrp+zs7Hr7ffPNN+rYsaOr48KFbuSasXLlSj3xxBMKDQ11dUzc7syevQ+43fz73/82fH19jdWrVxvHjh0zpk2bZgQHBxtFRUVGeXm5ERMTY/Tq1cvIyckxCgsLna9Lly6ZHR230PXqIjc31/jzn/9sHDhwwPjuu++MvXv3Go8//rgREhJiFBcXmx0dt9D16qIhYlZ8j3G92sjJyTFeffVV48CBA8bJkyeNTZs2GV26dDHi4uLMjo1brKlrxtKlS43AwEAjLS3NOHHihDF//nzDz8+PJ6x4gOb8Pjlx4oRhsViMrVu3mpgUtysae6ABy5cvNyIjIw0fHx+jf//+xr59+wzDMIwdO3YYkhp8nTx50tzQuOUaq4sffvjBGDlypBEWFmZ4e3sbHTp0MMaPH28cP37c5MRwhcbqoiE09p6lsdrIz8834uLijJCQEMPX19fo1q2bMWfOHKO8vNzkxHCFpq4ZqampRocOHYxWrVoZsbGxxu7du01KCldrqjaSk5MNm81m1NbWmpQQtzOLYRiGefcLAAAAAACAn4Lv2AMAAAAA4MZo7AEAAAAAcGM09gAAAAAAuDEaewAAAAAA3BiNPQAAAAAAbozGHh6ttLRUL730kiIjI+Xr66uIiAglJiZq7969ZkcDAAAAgGZpaXYAwExjx46V3W7XP//5T3Xp0kXFxcVKT09XWVmZ2dEAAAAAoFkYsYfHOnv2rHbv3q2//OUvGjx4sDp27Kj+/fsrOTlZTzzxhHObKVOmKDQ0VIGBgRoyZIiysrKcx0hJSVGfPn3097//XTabTa1atdK4ceNUXl5u1mkBAAAA8DA09vBYVqtVVqtVGzduVHV1dYPbPP300yopKdHWrVt18OBBRUdHa+jQoTp9+rRzm5ycHL3//vv68MMPtW3bNh0+fFgvv/yyq04DAAAAgIezGIZhmB0CMMsHH3ygqVOn6sKFC4qOjlZ8fLx+8Ytf6P7779eePXs0atQolZSUyNfX17lPt27d9Lvf/U7Tpk1TSkqK/vjHP+q7775T+/btJUnbtm3TqFGj9MMPPygiIsKsUwMAAADgIRixh0cbO3asCgoKtHnzZo0YMUI7d+5UdHS0Vq9eraysLFVUVKh169bO0X2r1aqTJ08qNzfXeYzIyEhnUy9JsbGxcjgcys7ONuOUAAAAAHgYJs+Dx/Pz89Pw4cM1fPhwLViwQFOmTNHChQv18ssvq23bttq5c+c1+wQHB7s8JwAAAAA0hMYeuEpUVJQ2btyo6OhoFRUVqWXLlurUqVOj2+fn56ugoEDt2rWTJO3bt08tWrRQ9+7dXZQYAAAAgCfjVnx4rLKyMg0ZMkRr1qzRV199pZMnTyotLU2vv/66Ro8erWHDhik2NlZjxozRp59+qry8PH3++eeaN2+eDhw44DyOn5+fJk6cqKysLO3evVuvvPKKxo0bx/frAQAAALgEI/bwWFarVTExMVq6dKlyc3NVU1Mjm82mqVOn6ve//70sFos+/vhjzZs3T5MmTVJpaakiIiIUFxen8PBw53G6deumJ598Uo8++qhOnz6txx57TCtWrDDxzAAAAAB4EmbFB36ClJQUbdy4UZmZmWZHAQAAAOChuBUfAAAAAAA3RmMPAAAAAIAb41Z8AAAAAADcGCP2AAAAAAC4MRp7AAAAAADcGI09PEZqaqr69eungIAAhYWFacyYMcrOzq63zcWLF5WUlKTWrVvLarVq7NixKi4udq7PysrSL3/5S9lsNvn7+6tHjx5644036h2jsLBQ48eP17333qsWLVpo5syZrjg9AAAAAB6Kxh4eY9euXUpKStK+ffv02WefqaamRgkJCaqsrHRuM2vWLH344YdKS0vTrl27VFBQoCeffNK5/uDBgwoLC9OaNWt09OhRzZs3T8nJyXrzzTed21RXVys0NFTz589X7969XXqOAAAAADwPk+fBY5WWliosLEy7du1SXFycysvLFRoaqrVr1+qpp56SJB0/flw9evRQRkaGHnrooQaPk5SUpK+//lrbt2+/Zt2gQYPUp08fLVu27FaeCgAAAAAPxog9PFZ5ebkkKSQkRFLdaHxNTY2GDRvm3Oa+++5TZGSkMjIyrnucy8cAAAAAAFdraXYAwAwOh0MzZ87UwIED1bNnT0lSUVGRfHx8FBwcXG/b8PBwFRUVNXiczz//XOvWrdOWLVtudWQAAAAAaBCNPTxSUlKSjhw5oj179tz0MY4cOaLRo0dr4cKFSkhI+BnTAQAAAEDzcSs+PM706dP10UcfaceOHerQoYNzeUREhOx2u86ePVtv++LiYkVERNRbduzYMQ0dOlTTpk3T/PnzXREbAAAAABpEYw+PYRiGpk+frg0bNmj79u3q3LlzvfV9+/aVt7e30tPTncuys7OVn5+v2NhY57KjR49q8ODBmjhxov70pz+5LD8AAAAANIRb8eExkpKStHbtWm3atEkBAQHO780HBQXJ399fQUFBmjx5smbPnq2QkBAFBgZqxowZio2Ndc6If+TIEQ0ZMkSJiYmaPXu28xheXl4KDQ11/luZmZmSpIqKCpWWliozM1M+Pj6Kiopy7UkDAAAAuOPxuDt4DIvF0uDyVatW6fnnn5ckXbx4Ub/5zW/03nvvqbq6WomJiVqxYoXzVvyUlBQtWrTommN07NhReXl51/23rt4GAAAAAH4ONPYAAAAAALgxvmMPAAAAAIAbo7EHAAAAAMCN0dgDAAAAAODGaOwBAAAAAHBjNPYAAAAAALgxGnsAAAAAANwYjT0AAAAAAG6Mxh4AAAAAADdGYw8AAH6SQYMGaebMmWbHAADAY9HYAwAAl9m5c6csFovOnj1rdhQAAO4YNPYAAAAAALgxGnsAANBslZWVmjBhgqxWq9q2bau//e1v9da/8847evDBBxUQEKCIiAiNHz9eJSUlkqS8vDwNHjxYknT33XfLYrHo+eeflyQ5HA6lpqaqc+fO8vf3V+/evbV+/XqXnhsAAO6Kxh4AADTbnDlztGvXLm3atEmffvqpdu7cqUOHDjnX19TUaPHixcrKytLGjRuVl5fnbN5tNps++OADSVJ2drYKCwv1xhtvSJJSU1P1r3/9S2+//baOHj2qWbNm6bnnntOuXbtcfo4AALgbi2EYhtkhAADA7a+iokKtW7fWmjVr9PTTT0uSTp8+rQ4dOmjatGlatmzZNfscOHBA/fr10/nz52W1WrVz504NHjxYZ86cUXBwsCSpurpaISEh+s9//qPY2FjnvlOmTFFVVZXWrl3ritMDAMBttTQ7AAAAcA+5ubmy2+2KiYlxLgsJCVH37t2d7w8ePKiUlBRlZWXpzJkzcjgckqT8/HxFRUU1eNycnBxVVVVp+PDh9Zbb7XY98MADt+BMAAC4s9DYAwCAn0VlZaUSExOVmJiod999V6GhocrPz1diYqLsdnuj+1VUVEiStmzZovbt29db5+vre0szAwBwJ6CxBwAAzdK1a1d5e3tr//79ioyMlCSdOXNG33zzjeLj43X8+HGVlZXptddek81mk1R3K/6VfHx8JEm1tbXOZVFRUfL19VV+fr7i4+NddDYAANw5aOwBAECzWK1WTZ48WXPmzFHr1q0VFhamefPmqUWLurl4IyMj5ePjo+XLl+vFF1/UkSNHtHjx4nrH6NixoywWiz766CM9+uij8vf3V0BAgH77299q1qxZcjgcevjhh1VeXq69e/cqMDBQEydONON0AQBwG8yKDwAAmu2vf/2rHnnkET3++OMaNmyYHn74YfXt21eSFBoaqtWrVystLU1RUVF67bXXtGTJknr7t2/fXosWLdLcuXMVHh6u6dOnS5IWL16sBQsWKDU1VT169NCIESO0ZcsWde7c2eXnCACAu2FWfAAAAAAA3Bgj9gAAAAAAuDEaewAAAAAA3BiNPQAAAAAAbozGHgAAAAAAN0ZjDwAAAACAG6OxBwAAAADAjdHYAwAAAADgxmjsAQAAAABwYzT2AAAAAAC4MRp7AAAAAADcGI09AAAAAABu7P8AcIb2aU9M6koAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "\n",
    "df_viz.plot(x=\"date\", y=\"bike_count\", ax=ax)\n",
    "df_viz.plot(x=\"date\", y=\"bike_count (predicted)\", ax=ax, ls=\"--\")\n",
    "ax.set_title(\"Predictions with Ridge\")\n",
    "ax.set_ylabel(\"bike_count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187d342e-ba15-4741-a457-d4ae31d07c9a",
   "metadata": {},
   "source": [
    "So we start to see the daily trend, and some of the week day differences are accounted for, however we still miss the details and the spikes in the evening are under-estimated.\n",
    "\n",
    "A useful way to visualize the error is to plot `y_pred` as a function of `y_true`,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9ef6c8cd-56ce-4294-8d98-da217262aa22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='y_true', ylabel='y_pred'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "df_viz = pd.DataFrame({\"y_true\": y_valid, \"y_pred\": pipe.predict(X_valid)}).sample(\n",
    "    10000, random_state=0\n",
    ")\n",
    "\n",
    "df_viz.plot.scatter(x=\"y_true\", y=\"y_pred\", s=8, alpha=0.1, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d82e36-4033-4d96-a501-d2f56578c894",
   "metadata": {},
   "source": [
    "It is recommended to use cross-validation for hyper-parameter tuning with [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) or more reliable model evaluation with [cross_val_score](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score). In this case, because we want the test data to always be in the future as compared to the train data, we can use [TimeSeriesSplit](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html):\n",
    "\n",
    "<img src=\"https://i.stack.imgur.com/Q37Bn.png\" />\n",
    "\n",
    "The disadvantage, is that we can either have the training set size be different for each fold which is not ideal for hyper-parameter tuning (current figure), or have constant sized small training set which is also not ideal given the data periodicity. This explains that generally we will have worse cross-validation scores than test scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4781077b-9da4-4db4-af9a-c130cb9b2182",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  [-0.96370992 -0.87155645 -0.85104595 -0.87402962 -1.0638737  -0.96757066]\n",
      "RMSE (all folds): 0.932 ± 0.0744\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit, cross_val_score\n",
    "\n",
    "cv = TimeSeriesSplit(n_splits=6)\n",
    "\n",
    "# When using a scorer in scikit-learn it always needs to be better when smaller, hence the minus sign.\n",
    "scores = cross_val_score(\n",
    "    pipe, X_train, y_train, cv=cv, scoring=\"neg_root_mean_squared_error\"\n",
    ")\n",
    "print(\"RMSE: \", scores)\n",
    "print(f\"RMSE (all folds): {-scores.mean():.3} ± {(-scores).std():.3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d24838c-eb08-45c7-a49e-e85d71418cf6",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}