reanalysis-heatwave.ipynb

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    "![logo](./img/LogoLine_horizon_C3S.png)"
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    "<br>"
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    "# Analysis of September 2020 European Heatwave using ERA5 Climate Reanalysis Data from C3S\n",
    "\n",
    "In September 2020, a record-breaking heatwave occured in large parts of western Europe, ([see a description here](https://climate.copernicus.eu/september-brings-record-breaking-warm-temperatures-and-low-sea-ice)). The city of Lille in northern France for example experienced its hottest day in September 2020 since records began in 1945. In this tutorial we will analyse this event with data from the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S).\n",
    "\n",
    "The tutorial comprises the following steps:\n",
    "\n",
    "1. Search, download and view data\n",
    "2. View daily maximum 2m temperature for September 2020\n",
    "3. Compare maximum temperatures with climatology"
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    "![logo](./img/Temp_records_banner.png)"
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    "<table align=\"left\">\n",
    "  <tr>\n",
    "    <th>Run the tutorial via free cloud platforms: </th>\n",
    "    <th><a href=\"https://mybinder.org/v2/gh/ecmwf-projects/copernicus-training-c3s/main?labpath=reanalysis-heatwave.ipynb\">\n",
    "        <img src = \"https://mybinder.org/badge.svg\" alt = \"Binder\"></th>\n",
    "    <th><a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ecmwf-projects/copernicus-training-c3s/blob/main/reanalysis-heatwave.ipynb\">\n",
    "        <img src = \"https://kaggle.com/static/images/open-in-kaggle.svg\" alt = \"Kaggle\"></th>\n",
    "    <th><a href=\"https://colab.research.google.com/github/ecmwf-projects/copernicus-training-c3s/blob/main/reanalysis-heatwave.ipynb\">\n",
    "        <img src = \"https://colab.research.google.com/assets/colab-badge.svg\" alt = \"Colab\"></th>\n",
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    "## Search, download and view data\n",
    "\n",
    "Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries that we will need.\n",
    "\n",
    "#### Install CDS API\n",
    "\n",
    "To install the CDS API, run the following command. We use an exclamation mark to pass the command to the shell (not to the Python interpreter)."
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      "Requirement already satisfied: cdsapi in c:\\users\\cxcs\\anaconda3\\lib\\site-packages (0.3.1)\n",
      "Requirement already satisfied: tqdm in c:\\users\\cxcs\\anaconda3\\lib\\site-packages (from cdsapi) (4.47.0)\n",
      "Requirement already satisfied: requests>=2.5.0 in c:\\users\\cxcs\\anaconda3\\lib\\site-packages (from cdsapi) (2.24.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\cxcs\\anaconda3\\lib\\site-packages (from requests>=2.5.0->cdsapi) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\cxcs\\anaconda3\\lib\\site-packages (from requests>=2.5.0->cdsapi) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\cxcs\\anaconda3\\lib\\site-packages (from requests>=2.5.0->cdsapi) (1.25.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\cxcs\\anaconda3\\lib\\site-packages (from requests>=2.5.0->cdsapi) (2020.6.20)\n"
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   ],
   "source": [
    "!pip install cdsapi"
   ]
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   "source": [
    "#### Import libraries\n",
    "\n",
    "We will be working with data in NetCDF format. To best handle this data we will use libraries for working with multidimensional arrays, in particular Xarray. We will also need libraries for plotting and viewing data, in this case we will use Matplotlib and Cartopy."
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   "source": [
    "# CDS API\n",
    "import cdsapi\n",
    "\n",
    "# Libraries for working with multidimensional arrays\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "# Libraries for plotting and visualising data\n",
    "import matplotlib.path as mpath\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Disable warnings for data download via API\n",
    "import urllib3 \n",
    "urllib3.disable_warnings()"
   ]
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   "source": [
    "#### Enter your CDS API key\n",
    "\n",
    "We will request data from the Climate Data Store (CDS) programmatically with the help of the CDS API. Let us make use of the option to manually set the CDS API credentials. First, you have to define two variables: `URL` and `KEY` which build together your CDS API key. The string of characters that make up your KEY include your personal User ID and CDS API key. To obtain these, first register or login to the CDS (https://cds.climate.copernicus.eu), then visit https://cds.climate.copernicus.eu/how-to-api and copy the string of characters listed after \"key:\". Replace the `#########` below with this string."
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   "source": [
    "URL = 'https://cds.climate.copernicus.eu/api'\n",
    "KEY = '##################################'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we specify a data directory in which we will download our data and all output files that we will generate:"
   ]
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   "source": [
    "DATADIR = './'"
   ]
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   "source": [
    "#### Search for data\n",
    "\n",
    "To search for data, visit the CDS website: https://cds.climate.copernicus.eu. To facilitate your search you can use keywords, or apply various filters. The data we are going to use in this exercise is the `ERA5 reanalysis data on single levels from 1979 to present`."
   ]
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   "source": [
    "Having selected the correct dataset, we now need to specify what product type, variables, temporal and geographic coverage we are interested in. These can all be selected in the **\"Download data\"** tab. In this tab a form appears in which we will select the following parameters to download. We will choose a subset area of 1x1 degrees, corresponding to a region of around 111km North/South and 72km East/West in Belgium and Northern France, around the city of Lille:\n",
    "\n",
    "- Product type: `Reanalysis`\n",
    "- Variable: `2m temperature`\n",
    "- Year: `all`\n",
    "- Month: `September`\n",
    "- Day: `all`\n",
    "- Time: `all`\n",
    "- Geographical area: `North: 51`, `East: 4`, `South: 50`, `West: 3`\n",
    "- Format: `NetCDF`\n",
    "\n",
    "![logo](./img/Notebook3_data.png)\n",
    "\n",
    "At the end of the download form, select **\"Show API request\"**. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook (see cell below) ...\n",
    "\n",
    "#### Download data\n",
    "\n",
    "... having copied the API request into the cell below, running this will retrieve and download the data you requested into your local directory. However, before you run the cell below, the **terms and conditions** of this particular dataset need to have been accepted in the CDS. The option to view and accept these conditions is given at the end of the download form, just above the **\"Show API request\"** option."
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     "text": [
      "2022-02-03 08:30:09,321 INFO Welcome to the CDS\n",
      "2022-02-03 08:30:09,326 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels\n",
      "2022-02-03 08:30:09,507 INFO Request is queued\n",
      "2022-02-03 08:30:10,564 INFO Request is running\n",
      "2022-02-03 09:10:26,261 INFO Request is completed\n",
      "2022-02-03 09:10:26,263 INFO Downloading https://download-0009.copernicus-climate.eu/cache-compute-0009/cache/data0/adaptor.mars.internal-1643879144.3714578-9573-17-990f3656-2699-457b-9cf4-ff7a0baba8af.nc to ./records/NFrance_hourly_Sep.nc (1.6M)\n",
      "2022-02-03 09:10:26,719 INFO Download rate 3.4M/s                                                                      \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=1634068,content_type=application/x-netcdf,location=https://download-0009.copernicus-climate.eu/cache-compute-0009/cache/data0/adaptor.mars.internal-1643879144.3714578-9573-17-990f3656-2699-457b-9cf4-ff7a0baba8af.nc)"
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    "c = cdsapi.Client(url=URL, key=KEY)\n",
    "c.retrieve(\n",
    "    'reanalysis-era5-single-levels',\n",
    "    {\n",
    "        'product_type': 'reanalysis',\n",
    "        'data_format': 'netcdf_legacy',\n",
    "        'variable': '2m_temperature',\n",
    "        'year': [\n",
    "            '1979', '1980', '1981',\n",
    "            '1982', '1983', '1984',\n",
    "            '1985', '1986', '1987',\n",
    "            '1988', '1989', '1990',\n",
    "            '1991', '1992', '1993',\n",
    "            '1994', '1995', '1996',\n",
    "            '1997', '1998', '1999',\n",
    "            '2000', '2001', '2002',\n",
    "            '2003', '2004', '2005',\n",
    "            '2006', '2007', '2008',\n",
    "            '2009', '2010', '2011',\n",
    "            '2012', '2013', '2014',\n",
    "            '2015', '2016', '2017',\n",
    "            '2018', '2019', '2020',\n",
    "        ],\n",
    "        'month': '09',\n",
    "        'day': [\n",
    "            '01', '02', '03',\n",
    "            '04', '05', '06',\n",
    "            '07', '08', '09',\n",
    "            '10', '11', '12',\n",
    "            '13', '14', '15',\n",
    "            '16', '17', '18',\n",
    "            '19', '20', '21',\n",
    "            '22', '23', '24',\n",
    "            '25', '26', '27',\n",
    "            '28', '29', '30',\n",
    "        ],\n",
    "        'time': [\n",
    "            '00:00', '01:00', '02:00',\n",
    "            '03:00', '04:00', '05:00',\n",
    "            '06:00', '07:00', '08:00',\n",
    "            '09:00', '10:00', '11:00',\n",
    "            '12:00', '13:00', '14:00',\n",
    "            '15:00', '16:00', '17:00',\n",
    "            '18:00', '19:00', '20:00',\n",
    "            '21:00', '22:00', '23:00',\n",
    "        ],\n",
    "        'area': [\n",
    "            51, 3, 50,\n",
    "            4,\n",
    "        ],\n",
    "    },\n",
    "    f'{DATADIR}NFrance_hourly_Sep.nc')"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### Inspect Data\n",
    "\n",
    "We have requested the data in NetCDF format. This is a commonly used format for array-oriented scientific data. To read and process this data we will make use of the Xarray library. Xarray is an open source project and Python package that makes working with labelled multi-dimensional arrays simple and efficient. We will read the data from our NetCDF file into an [xarray.Dataset](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = f'{DATADIR}NFrance_hourly_Sep.nc'\n",
    "# Create Xarray Dataset\n",
    "ds = xr.open_dataset(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can query our newly created Xarray dataset ..."
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (longitude: 5, latitude: 5, time: 30240)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 3.0 3.25 3.5 3.75 4.0\n",
       "  * latitude   (latitude) float32 51.0 50.75 50.5 50.25 50.0\n",
       "  * time       (time) datetime64[ns] 1979-09-01 ... 2020-09-30T23:00:00\n",
       "Data variables:\n",
       "    t2m        (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2022-02-03 09:10:11 GMT by grib_to_netcdf-2.23.0: /opt/ecmw...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8eb39140-1c6c-4dc5-9a4e-edfc488792e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8eb39140-1c6c-4dc5-9a4e-edfc488792e6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>longitude</span>: 5</li><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>time</span>: 30240</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d574a404-b4d4-4bb9-94ce-e97adb73c72e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d574a404-b4d4-4bb9-94ce-e97adb73c72e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.0 3.25 3.5 3.75 4.0</div><input id='attrs-7d27a276-3c68-4b93-a401-dd026fa63034' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d27a276-3c68-4b93-a401-dd026fa63034' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25adb501-997f-43b5-86a6-c88588bc2c54' class='xr-var-data-in' type='checkbox'><label for='data-25adb501-997f-43b5-86a6-c88588bc2c54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([3.  , 3.25, 3.5 , 3.75, 4.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>51.0 50.75 50.5 50.25 50.0</div><input id='attrs-de58d2e5-4b09-4f76-b5b7-10a4be9a0421' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de58d2e5-4b09-4f76-b5b7-10a4be9a0421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c890427-dd83-4366-b446-5452632f8de1' class='xr-var-data-in' type='checkbox'><label for='data-1c890427-dd83-4366-b446-5452632f8de1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([51.  , 50.75, 50.5 , 50.25, 50.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-09-01 ... 2020-09-30T23:00:00</div><input id='attrs-437c56a5-0f29-4372-87a8-65dccf6c3b35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-437c56a5-0f29-4372-87a8-65dccf6c3b35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-654ee106-c7fd-48a7-8694-6700a2474177' class='xr-var-data-in' type='checkbox'><label for='data-654ee106-c7fd-48a7-8694-6700a2474177' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-09-01T00:00:00.000000000&#x27;, &#x27;1979-09-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1979-09-01T02:00:00.000000000&#x27;, ..., &#x27;2020-09-30T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-09-30T22:00:00.000000000&#x27;, &#x27;2020-09-30T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c1e1894-d440-4bc8-889c-b34c6eeed4df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c1e1894-d440-4bc8-889c-b34c6eeed4df' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3694f7c4-4dd1-46c7-8a8c-7072ab0db1e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3694f7c4-4dd1-46c7-8a8c-7072ab0db1e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b7560d7-5c3a-4271-bcc5-a9fbbafacfed' class='xr-var-data-in' type='checkbox'><label for='data-7b7560d7-5c3a-4271-bcc5-a9fbbafacfed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[756000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6146824e-c42e-4a1d-ad8a-30fd53458a5e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6146824e-c42e-4a1d-ad8a-30fd53458a5e' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>2022-02-03 09:10:11 GMT by grib_to_netcdf-2.23.0: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/data0/adaptor.mars.internal-1643879144.3714578-9573-17-990f3656-2699-457b-9cf4-ff7a0baba8af.nc /cache/tmp/990f3656-2699-457b-9cf4-ff7a0baba8af-adaptor.mars.internal-1643877008.2708144-9573-32-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (longitude: 5, latitude: 5, time: 30240)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 3.0 3.25 3.5 3.75 4.0\n",
       "  * latitude   (latitude) float32 51.0 50.75 50.5 50.25 50.0\n",
       "  * time       (time) datetime64[ns] 1979-09-01 ... 2020-09-30T23:00:00\n",
       "Data variables:\n",
       "    t2m        (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2022-02-03 09:10:11 GMT by grib_to_netcdf-2.23.0: /opt/ecmw..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the dataset has one variable called **\"t2m\"**, which stands for \"2 metre temperature\", and three coordinates of **longitude**, **latitude** and **time**.\n",
    "\n",
    "Select the icons to the right of the table above to expand the attributes of the coordinates and data variables. What are the units of the temperature data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While an Xarray **dataset** may contain multiple variables, an Xarray **data array** holds a single variable (which may still be multi-dimensional) and its coordinates. To make the processing of the **t2m** data easier, we convert in into an Xarray data array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = ds['t2m']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert the units of the 2m temperature data from Kelvin to degrees Celsius. The formula for this is simple: degrees Celsius = Kelvin - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "t2m_C = da - 273.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View daily maximum 2m temperature for September 2020\n",
    "\n",
    "As a next step, let us visualize the daily maximum 2m air temperature for September 2020. From the graph, we should be able to identify which day in September was hottest in the area around Lille."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we average over the subset area:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** The size covered by each data point varies as a function of latitude. We need to take this into account when averaging. One way to do this is to use the cosine of the latitude as a proxy for the varying sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = np.cos(np.deg2rad(t2m_C.latitude))\n",
    "weights.name = \"weights\"\n",
    "t2m_C_weighted = t2m_C.weighted(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_t2m = t2m_C_weighted.mean([\"longitude\", \"latitude\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we select only the data for 2020:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_2020 = Lille_t2m.sel(time='2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now calculate the max daily 2m temperature for each day in September 2020:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_2020_max = Lille_2020.groupby('time.day').max('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the results in a chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "\n",
    "ax.plot(Lille_2020_max.day, Lille_2020_max)\n",
    "ax.set_title('Max daily t2m for Sep 2020 in Lille region')\n",
    "ax.set_ylabel('° C')\n",
    "ax.set_xlabel('day')\n",
    "ax.grid(linestyle='--')\n",
    "for i,j in zip(Lille_2020_max.day, np.around(Lille_2020_max.values, 0).astype(int)):\n",
    "    ax.annotate(str(j),xy=(i,j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum temperature in September 2020 in this area was 32.1 degrees Celsius.\n"
     ]
    }
   ],
   "source": [
    "print('The maximum temperature in September 2020 in this area was', \n",
    "      np.around(Lille_2020_max.max().values, 1), 'degrees Celsius.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which day in September had the highest maximum temperature?\n",
    "\n",
    "Is this typical for Northern France? How does this compare with the long term average? We will seek to answer these questions in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare maximum temperatures with climatology\n",
    "We will now seek to discover just how high the temperature for Lille in mid September 2020 was when compared with typical values exptected in this region at this time of year. To do that we will calculate the climatology of maximum daily 2m temperature for each day in September for the period of 1979 to 2019, and compare these with our values for 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we select all data prior to 2020:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_past = Lille_t2m.loc['1979':'2019']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we calculate the climatology for this data, i.e. the average values for each of the days in September for a period of several decades (from 1979 to 2019)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this, we first have to extract the maximum daily value for each day in the time series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_max = Lille_past.resample(time='D').max().dropna('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then calculate various quantiles of the maximum daily 2m temperatures for the 40 year time series for each day in September:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_max_max = Lille_max.groupby('time.day').max()\n",
    "Lille_max_min = Lille_max.groupby('time.day').min()\n",
    "Lille_max_mid = Lille_max.groupby('time.day').quantile(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot this data. We will plot the, maximum, minimum and 50th quantile of the maximum daily temperature to have an idea of the expected range in this part of France in September, and compare this range with the values for 2020:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cxcs\\AppData\\Local\\Temp/ipykernel_5360/21074062.py:5: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"bo-\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(Lille_2020_max.day, Lille_2020_max, 'bo-', color='darkred', label='Daily max t2m Sep 2020')\n"
     ]
    },
    {
     "data": {
      "image/png": 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tlLoV96R2WmvPYnMz0LbYvoqcK8uitd6vtb5ca91Ya90fI5eCagkoIQKVFLNCBDmlVF2l1OUY47k+0loXLGtQB+OqtMU9bu6GSuz2Q2AMRkE7u5zHVkapMSmlYjEmIHkUYyxgc6XU3aXs5wRGd7PCcZJKqUSM1o17tdbfeTHmkhS0Xj+slIpWxpqgIzlzTF2JlFL13WPp2iulIpRSjTCK+D/cD3kbuNPdkq2UUrWUscSO5xepvyulEtzjDx/FmPCopGOV97rMBh53x9QZY9KbWe5t62KMnftNaz25lG1vU0p1dRe9jxdsW4LvgaZKqQeUsRxQHaVUf/d9n7hjaOx+LaZg5EJV9VZKXekem/oAxsQtfwC1ML7wnnA/v1soY9yau1X3beBVpVQT9zbNlTGLMxiF0s3u5x9HCWOui/kd48t4P4xutpsxCqb+GK2ZJTkGtFYVXHarBO8CtyilLnDnWnP3++xNdTCKswx3PhZ/HY7h8VnFmBito1LqBqVUlFLqWoxxp9+XsO9PgElKqTbKGHtfMC7SgdGKN1IZy7fUwOgaWvyC3WyMFtFRVC6nPsZ43ZLd412fBVZprfdVZGOl1BVKqevcnyvlPt8NxeiqWl5eFXhKKVVDKTUYYzKkzysRfyH3xbLfgeeUUrFKqe4Yk17Nqcr+inO/h7EYXagj3ceIct8Xq5RKdL8GLTHGXb/uvpBWsH1N4BpKOHcopXq6z7F1MSaTOqS1/rmUUIp8Vir7vJVS4zDe54u01ns873P3ZkkFnnTvawxG1+kvPbaPxZjYCyDG/XvBfV3c57waSqkbgeEY3c6FENUkxawQwes7pVQ2RsvLYxh/GG/xuP9uYJr7MVOoxLIQ7ivSazG++C8v5+GVUVZMz2F8UXnT3bXrRuBppVSHEuLLwxiv9JsyuukNwGgdagy8q/5as7BKS+mUR2ttw/hyfAlGK8v/ATdprbdVcBc2jHG/v2LMNroJo+C62b3/FIyicjrGGNddBfd5+BijxXSP++fpUo5V3uvyJEY31P0YrYMvamMyFTAuaPTF+FLvuRZkS3ec8zGWy1js3n4/pRR07q5+F2EU/WkYs8AWLE3xNMbY7A0Y46XXlvF8KmIexnjJ0xitOVdqre1a6y0Y3ddXYnzxTcKYEbgsj2C8/n8oowvtrxgFKVrrnzDGfS9yP2ZRWTtydxtdizFDq81980pgv9b6eCmbFRQw6UqpteXEWtIxV2OcF17FmDl4KWe2ilbXaxjjIU9iXDSYX+z+14GrlbGm8hvulvvLMXIzHWNyqcu11idL2Pd7GBfXlmEsbWLBmHAN98WAezEuIh3F6PJ5HOOzhPsxv2Fc+Fpb0ULUvd1CjNmRv3Tvux3GBHIVdRrjM7wT4zP+EcZnq6CQKjWv3NLc+ziCUXzdWYnzS0muxzjnHAG+Bp7UWi+oxv48PY5xMWMyxnk7n78mcIvFOFflYLRErsR4XT2NxsjNxSXs+2GMvDqIMRHXmDLiKOmzUpnn/TRGi+mfHue6/3ncfx3QB+N9eR5jyIbnBIX5/DV0Yxt/dc8GYx6APe5t78SYa6C0SRWFEJWgtK5uDyYhRChSSr0HHNFaV3SWXOEnSql9GBPk/FreY8ONUmoqxgRMN5b3WBFa3C23GRgTqe31uH0R8LHW+h2zYqsMZfT0+EhrnVDOQ4UQIuzJgvBCiDMopVpjzLra0+RQhBCiVEqpkRgzHiuMbqgb8Vh6SBlzCfTCmEhICCFEiJFuxkKIIpRS/8bo9vqiZ+uGEEIEoCswupAewVjr9zrt7nKmlPoAo/vuA8VmtBVCCBEipJuxEEIIIYQQQoigIy2zQgghhBBCCCGCjhSzQgghhBBCCCGCTlBMANWoUSPdunVrs8MQfmCz2ahRo4bZYYgQJ3km/EHyTPiD5JnwB8kz4Q9r1qw5qbVuXJltgqKYbd26NSkpKWaHIfxgyZIlDBs2zOwwRIiTPBP+IHkm/EHyTPiD5JnwB6XU/spuI92MhRBCCCGEEEIEnaCYzbhPnz5aWmbDg8vlIiJCrrEI35I8E/4geSb8QfJM+IPkmfAHpdQarXWfymwjWSkCyubNm80OQYQByTPhD5Jnwh8kz4Q/SJ6JQBUUY2ZF+EhPTzc7BBEGJM+EP0ieCX+oSJ7Z7XYOHTqExWLxQ0QiFDkcDrZu3Wp2GCJExMbGkpCQQHR0dLX3JcWsEEIIIUQIO3ToEHXq1KF169YopcwORwSh7Oxs6tSpY3YYIgRorUlPT+fQoUO0adOm2vuTbsYioPTo0cPsEEQYkDwT/iB5JvyhInlmsVho2LChFLKiymrWrGl2CCJEKKVo2LCh13qKSDErAkp2drbZIYgwIHkm/EHyTPhDRfNMCllRHU6n0+wQRAjx5vlIilkRUPbs2WN2CCIMSJ4Jf5A8E/4QLHkWGRlJcnIy3bp1o0ePHrzyyiu4XK4ytzly5AhXX301YKxzevnll/sj1AqbNWsWR44cKfx93LhxdOrUicTERG699VbsdnuV9z116lSaN29OcnIyycnJ/Pjjj4X3Pffcc7Rv355OnTrx888/F97+7LPPFv5/3759JCYmVvn4xdlstipvm5qaWiT+b7/9lueffx4wnudLL71U7fhE+JJiVgghhBBC+FTNmjVJTU1l8+bNLFiwgB9//JGnnnqqzG2aNWvGF1984acIK6+kYnbbtm1s3LiR/Px83nnnnWrtf9KkSaSmppKamsqll14KwJYtW5g7dy6bN29m/vz53H333YWtpp7FbCApXsyOGjWKyZMnmxiRCCVSzIqA0rp1a7NDEGFA8kz4g+SZ8IdgzLMmTZowc+ZMpk+fjtaaffv2MXjwYHr16kWvXr34/fffgZJbF10uFx06dODEiROFv7dv356TJ08WedzUqVOZMGECw4cPp3Xr1nz11Vc8/PDDJCUlMWLEiMJW02nTptG3b18SExOZOHEiWmscDgd9+/ZlyZIlAPzrX//iscceK7L/L774gpSUFMaNG0dycjL5+flceumlKKVQStGvXz8OHTpUqVgqYt68eVx33XXExMTQpk0b2rdvz+rVq5k8eTL5+fkkJyczbtw4wOgafMcdd9CtWzeGDx9Ofn7+Gfvbu3cvAwcOpG/fvjzxxBPUrl0bOLMl/OGHH2bWrFmlvmYAw4YN45FHHqFfv3507NiR5cuXY7PZmDJlCp9++inJycl8+umnzJo1i3vuueeMWHbv3s2IESPo3bs3gwcPZtu2bRV+XUT4ktmMRUBp0KCB2SGIMCB5JvxB8kz4Q2Xz7IH5D5CalurVGJKbJvPaiNcqtU3btm1xuVwcP36cJk2asGDBAmJjY9m5cyfXX389KSkpJW4XERHBjTfeyJw5c3jggQf49ddf6dGjB40aNTrjsbt372bx4sVs2bKFgQMH8uWXX/LCCy8wZswYfvjhB0aPHs0999zDlClTABg/fjzff/89I0eOZNasWVx99dW88cYbzJ8/n1WrVhXZ99VXX8306dN56aWX6NOnT5H77HY7H374Ia+//nqlYilu+vTpzJ49mz59+vDyyy9Tv359Dh8+zIABAwofk5CQwOHDh3n++eeZPn06qampgHEhYOfOnXzyySe8/fbbjB07li+//JIbb7yxyDHuv/9+7rrrLm666SZmzJhR6vsVEfFX+1dprxkYS/isXr26sOX9119/Zdq0aaSkpDB9+nSAwqK4uIkTJ/K///2PDh06sGrVKu6++24WLVpUakxCgLTMigCzdu1as0MQYUDyTPiD5Jnwh2DOs4IWPbvdzh133EFSUhLXXHMNW7ZsKXO7W2+9ldmzZwPw3nvvccstt5T4uEsuuYTo6GiSkpJwOp2MGDECgKSkJPbt2wfA4sWL6d+/P0lJSSxatIjNmzcD0K1bN8aPH8/IkSN57733qFGjRoWf1913382QIUMYPHhwpWLxdNddd7F7925SU1M5++yz+ec//wn89Zp5Km0ynTZt2pCcnAxA7969SzzOb7/9xvXXXw8YhWlpPFuPS3vNAK688soyj1eanJwcfv/9d6655hqSk5P529/+xtGjRyu8vQhf0jIrhBBCCBEmKtuC6it79uwhMjKSJk2a8NRTTxEfH8/69etxuVzExsaWuW2LFi2Ij49n0aJFrFq1ijlz5pT4uJiYGMBoVYyOji4s+iIiInA4HFgsFu6++25SUlJo0aIFU6dOLbJcyMaNG6lXrx7Hjh2r8PN66qmnOHHiBG+99ValYikuPj6+8P933HFHYZffhIQEDh48WHjfoUOHaNasWZnPH4wJuErqZgwlF8NRUVFFJuiyWq0A5b5mBceMjIws8XmVxuVyUa9evcKWZSEqSlpmRUCpX7++2SGIMCB5Jnxpy5w5zGzdmpTzz2dm69ZsKeWLthDeEIznsxMnTnDnnXdyzz33oJQiMzOTs88+m4iICD788MMKLQNz++23c+ONNzJ27FgiIyOrFEdBEdaoUSNycnKKTDb11VdfkZ6ezrJly7jvvvvIyMg4Y/s6deoUWRrpnXfe4eeff+aTTz4p0i23KjxbJb/++uvCscOjRo1i7ty5WK1W9u7dy86dO+nXrx8A0dHRlZ5B+dxzz2Xu3LkARS4KtGrVii1btmC1WsnMzGTp0qVA2a9ZaYq/TiWpW7cubdq04fPPPweMFuj169dX6rmI8CTFrAgoFVn8XYjqkjwTvrJlzhx+mTiRrP37QWuy9u/nl4kTpaAVPhMs57OCyYm6devGhRdeyPDhw3nyyScBo1vuBx98wIABA9ixYwe1atUqd3+jRo0iJyen1C7GFVGvXr3C7s2jR4+mb9++AJw8eZLJkyfz7rvv0rFjR+655x7uv//+M7a/+eabufPOOwsngLrzzjs5duwYAwcOJDk5mWnTplU5toIJorp3787ixYt59dVXAaP789ixY+natSsjRoxgxowZhcX8xIkT6d69e+EEUBXx+uuvM2PGDPr27UtmZmbh7S1atGDs2LGF++vVq1eZr1lZzjvvPLZs2VI4AVRp5syZw7vvvkuPHj3o1q0b8+bNq/DzEOFLldT3PtD06dNHlzYRgAgtS5cuZejQoWaHIUKc5JnwlZmtWxuFbDF1W7ViYiXGjwlRURU5n23dupUuXbr4KSL/SElJYdKkSSxfvtzsUEJK7dq1ycnJOeP27Oxs6tSpY0JEIlSVdF5SSq3RWvcpZZMSyZhZEVCC4eKKCH6SZ8IXtLsltiRZBw74ORoRLsLxfPb888/z5ptvljpWVggRPqSbsQgopc3IJ4Q3SZ4Jb7NlZ/NjGTOB1m3Z0o/RiHASjuezyZMns3//fgYNGmR2KCGnpFZZIQKZFLMioEjXT+EPkmfCm46npvJhnz5s++QTOlx1FVFxcUXuj4qLY9Azz5gUnQh1cj4T/iBdjEWgkmJWBBSZuU74g+SZ8AatNalvvsmcAQOw5+QwdtEirvjiC4bPnEndVq0AUBERXPS//9G1EhOyCFEZcj4T/pCXl2d2CEKUSIpZEVBOnz5tdggiDEieieqyZmby3dix/Hr33bQ47zxuSk2lhbuFrOu4cUzct4+2Tz6JdrkKC1shfEHOZ8IfKrJckhBmkGJWCCGEqISjf/7J7J492fn11wz5z3+46ocfiGvc+IzHndWvH5ExMez6+msTohRCCCFCnxSzIqAUrGMmhC9Jnomq0Fqz5rXX+OTcc3E5nVy3bBn9Hn4YFVHyn9K+gwbR6sIL2fnNN2E546zwj2A5n0VGRhauM9ujRw9eeeUVXC5XmdscOXKEq6++GoAlS5Zw+eWX+yPUCps1axZHjhwp/H3cuHF06tSJxMREbr31Vux2e5X3/ccff9C/f3+Sk5Pp0qULU6dOrXa8eXl5XHbZZXTu3Jlu3boxefLkwvusVivXXnst7du3p3///uxzLyWWmprKwIEDGTBgAN27dy+yTuzevXvp378/HTp04Nprr8Vms1U7RiEqS4pZEVBOnTpldggiDEieicrKP3WKb0aPZvGkSbS55BJuWreO5uecU+Y2p06dosOYMWTt28cJGdcofMQX57Mtc+Yws3VrXoqIYGbr1mzxwhI4NWvWJDU1lc2bN7NgwQJ+/PFHnnrqqTK3adasGV988UW1j+0rJRWz27ZtY+PGjeTn5/POO+9Ued8TJkxg5syZpKamsmnTJsaOHeuNkHnwwQfZtm0b69at47fffuOnn34C4N1336V+/frs2rWLSZMm8cgjjwAQFxfH7NmzWbt2LfPnz+eBBx4gIyMDgEceeYRJkyaxc+dO6tevz7vvvuuVGIWoDClmRUApuBIohC9JnonKOPz778xOTmbvTz9x3muvMfqbb6jZoEG52+3bt4+2I0eiIiLY+c03vg9UhCVvn8+2zJnDLxMnGmsmu9dO/mXiRK8UtAWaNGnCzJkzmT59Olpr9u3bx+DBg+nVqxe9evXi999/B4znlpiYWGRbl8tFhw4dOHHiROHv7du35+TJk0UeN3XqVCZMmMDw4cNp3bo1X331FQ8//DBJSUmMGDGisNV02rRp9O3bl8TERCZOnIjWGofDQd++fVmyZAkA//rXv3jssceK7P+LL74gJSWFcePGkZycTH5+PpdeeilKKZRS9OvXj0OHDlUqFk/Hjx/n7LPPBoxW7a5duwKQm5vLrbfeSt++fenZsyfz5s0DjML6iiuuYMSIEXTq1KnECwVxcXGcd955ANSoUYNevXoVxjhv3jwmTJgAwNVXX83ChQvRWtOxY0c6dOiAzWajWbNmNGnShBMnTqC1ZtGiRYUt5xMmTOAbOc8JE0SZHYAQQggRiLTLxeoXX2TFY49Rt1Urbvj9d5r26VOpfdRq0oTm557Lrq+/5lwvdBMUoroWPfAAx1NTS73/6B9/4LRai9zmyMvj59tuY8Pbb5e4TZPkZM5/7bVKxdG2bVtcLhfHjx+nSZMmLFiwgNjYWHbu3Mn1119PSkpKidtFRERw4403MmfOHB544AF+/fVXevToQaNGjc547O7du1m8eDFbtmxh4MCBfPnll7zwwguMGTOGH374gdGjR3PPPfcwZcoUAMaPH8/333/PyJEjmTVrFldffTVvvPEG8+fPZ9WqVUX2ffXVVzN9+nReeukl+hQ7L9jtdj788ENef/31SsXiadKkSXTq1Ilhw4YxYsQIJkyYQGxsLM888wznn38+7733HhkZGfTr148LL7wQgNWrV7Np0ybi4uLo27cvl1122RmxFcjIyOC7777j/vvvB+Dw4cO0aNECgKioKM466yzS09OLvK6rV6/GZrPRrl070tPTqVevHlFRRimRkJDA4cOHSzyWEL4kLbMioLRt29bsEEQYkDwT5ck9fpwvL72U5ZMn0+HKK7lp7dpKF7IFedZ+zBhObNhAxp49vghVhDlvn8+KF7Ll3V4dBWPJ7XY7d9xxB0lJSVxzzTVs2bKlzO1uvfVWZs+eDcB7773HLbfcUuLjLrnkEqKjo0lKSsLpdDJixAgAkpKSClu0Fy9eTP/+/UlKSmLRokVs3rwZgG7dujF+/HhGjhzJe++9R40aNSr8vO6++26GDBnC4MGDKxWLpylTppCSksLw4cP5+OOPCx//yy+/8Pzzz5OcnMywYcOwWCwcOHAAgIsuuoiGDRtSs2ZNrrzySlasWFFifA6Hg+uvv5777ruvMH9KGtevlCr8f3p6OuPHj+f9998nIiKi3McL4S/SMisCiizKLfxB8kyU5cCSJfxwww1YTp3iwjffpMff/lalL2kFedZ+9GiW/OMf7PrmG/r84x/eDleEucqez8prQZ3ZurXRxbiYuq1acZ2726037Nmzh8jISJo0acJTTz1FfHw869evx+VyERsbW+a2LVq0ID4+nkWLFrFq1SrmlNIFOiYmBjBac6Ojows/xxERETgcDiwWC3fffTcpKSm0aNGCqVOnYrFYCrffuHEj9erV49ixYxV+Xk899RQnTpzgrbfeqlQsJWnXrh133XUXd9xxB40bNyY9PR2tNV9++SWdOnUq8thVq1adcZ4q7bw1ceJEOnTowAMPPFB4W0JCAgcPHiQhIQGHw0FmZiYN3MMpsrKyGDNmDE8//TQDBgwAoFGjRmRkZOBwOIiKiuLQoUM0a9asgq+SEN4jLbMioMji78IfJM9ESVxOJ78/9RSfX3ABNerUYdyqVSTfeWeVWxsK8qxemzY07tGDnbJEj/ABb5/PBj3zDFFxcUVui4qLY9Azz3jtGCdOnODOO+/knnvuQSlFZmYmZ599NhEREXz44YcVWtP09ttv58Ybb2Ts2LFERkZWKY6CwrVRo0bk5OQUmWzqq6++Ij09nWXLlnHfffcVTnrkqU6dOmRnZxf+/s477/Dzzz/zySefEFHKLOcV9cMPPxS2fu7cuZPIyEjq1avHxRdfzH//+9/C+9atW1e4zYIFCzh16hT5+fl88803nHvuuWfs9/HHHyczM5PXil3UGDVqFB988AFgjAc+//zzUUphs9kYM2YM1157Lddcc03h45VSnHfeeYWv2QcffMAVV1xRrecsRFVIMSuEECLs5Rw9yucXXcTvU6fS5YYbGL9mDU169PDa/tuPHs3h334j9/hxr+1TCF/oOm4cw2fOpG6rVqAUdVu1YvjMmXQdN65a+83Pzy9cmufCCy9k+PDhPPnkk4DRLfeDDz5gwIAB7Nixg1q1apW7v1GjRpGTk1NqF+OKqFevXmH35tGjR9O3b18ATp48yeTJk3n33Xfp2LEj99xzT+HYUk8333wzd955Z+EEUHfeeSfHjh1j4MCBJCcnM23atCrH9uGHH9KpUyeSk5MZP348c+bMITIykieeeAK73U737t1JTEzkiSeeKNxm0KBBjB8/nuTkZK666qozxsseOnSIZ555hi1bttCrVy+Sk5MLZ1y+7bbbSE9Pp3379rzyyis8//zzAHz22WcsW7aMjz/+mOTkZJKTk0l1j7n+z3/+wyuvvEL79u1JT0/ntttuq/LzFaKqVDCsfdenTx9d2kQAIrRs3LiRpKQks8MQIU7yTHja98sv/HDjjdhzcrhgxgwSb77ZK2O/PPPs+Pr1zE5OZvjbb9P99turvW8hClTkfLZ161a6dOnip4j8IyUlhUmTJrF8+XKzQwkIs2bNIiUlhenTp/tk/3l5ecQVa7EXojpKOi8ppdZorSs1QYW0zIqA0q1bN7NDEGFA8kwAuBwOlj/2GF+MGEFckybcmJJC0i23eG0SE888a9y9O3Vbt2aXLF0hvCwcz2fPP/88V111Fc8995zZoYSNmjVrmh2CECWSYlYElGXLlpkdgggDkmci6+BBPh02jFXPPkvSbbdx4+rVNHKv4+gtnnmmlKLDmDHsX7AAm8cYOyGqKxzPZ5MnT2b//v0MGjTI7FACxs033+yzVlmAnJwcn+1biOqQYlYIIURY2f3998xOTub4+vVcNmcOF7/9NtF+6D7XfvRonDYbe+fP9/mxhBBCiHAgxawIKAWLbwvhS5Jn4clps7H4n//k65EjqduyJePXrKHLDTf47HjF86z5uedSs3FjmdVYeJWcz4QQ4UzOgCKgSJch4Q+SZ+EnY+9evr/uOtJWryb57rsZ9vLLRJWzlmV1Fc+ziMhI2o8axfbPP8dpsxFZo4ZPjy/Cg5zPhD/I+uwiUEnLrAgoa9euNTsEEQYkz/xHa02u1eH3426ZM4eZrVvzUkQEMxo35v1u3Ti1bRsjP/+cC2fM8HkhCyXnWfvRo7FlZXFg8WKfH1+EBzmfCX/Izc01OwQhSiTFrAgoWVlZZocgwoDkmf/k2pzkWB04nC6/HXPLnDn8MnEiWfv3g9bknzyJ02rlnKlT6XT11X6Lo6Q8a3XhhUTXqsUu6WosvCRYzmdKKcaPH1/4u8PhoHHjxlx++eUmRmVYsmRJteKYMmUKv/76qxcjKqq68VXViRMn6N+/Pz179mTFihV+PXZGRgb/93//V/h7amoqAwcOpFu3bnTv3p1PP/20Wvt/44036NKlC+OquX5yeaZPn0779u1RSnHy5MnC20+fPs2YMWPo3r07/fr1Y9OmTQBs3769cD3f5ORk6taty2uvvQbA+vXrGThwIElJSYwcObLEz/6+ffv4+OOPKxVjWa/t3r176d+/Px06dODaa6/FZrMBsG3bNgYOHEhMTAwvvfRSkf29/vrrJCYm0q1bt8LYfUmKWSGEED7hdGny3K2yuVan34674rHHcOTlFb3R5WLt66/7LYbSRMXG0uaSS9g1bx7a5b8CXwhPx7IsXv2piFq1arFp0yby8/MBWLBgAc2bN/fl0/SbadOmceGFF5odhtctXLiQzp07s27dOs455xy/Hrt4MRsXF8fs2bPZvHkz8+fP54EHHiAjI6PK+/+///s/fvzxR+bMmVPkdofDuz2Jzj33XH799VdatWpV5PZnn32W5ORkNmzYwOzZs7n//vsB6NSpE6mpqaSmprJmzRri4uIYM2YMALfffjvPP/88GzduZMyYMbz44otnHK8qxWxZr+0jjzzCpEmT2LlzJ/Xr1+fdd98FoEGDBrzxxhs8+OCDRfa1adMm3n77bVavXs369ev5/vvv2blzZ6XiqSwpZkVA6dOnUuskC1Elkmf+kW2xo93/tzic2P3UOpt14EClbveV0vKsw5gx5KalcXTVKr/GI0JTMJ3PLrnkEn744QcAPvnkE66//vrC+1avXs0555xDz549Oeecc9i+fTsAr7zyCrfeeisAGzduJDExkbxiF6v27dvH4MGD6dWrF7169eL3338HjBbNYcOGcfXVV9O5c2fGjRuH1sZZaf78+XTu3JlBgwbx1VdflRjvrFmzGD16NCNHjqRNmzZMnz6dV155hZ49ezJgwABOnToFGMvifPHFFwC0bt2aJ598kl69epGUlMS2bdvO2G///v3ZvHlz4e/Dhg1jzZo1pb4GnqZOnVqkJSwxMZF9+/YB8NFHH9GvXz+Sk5P529/+htPpxOl0cvPNN5OYmEhSUhKvvvrqGfvcv38/F1xwAd27d+eCCy7gwIEDpKam8vDDD/Pjjz+SnJx8xvrbrVu35tFHH2XgwIH06dOHtWvXcvHFF9OuXTv+97//AcZyPhdccEHhazFv3jwA/vzzT7p3747FYiE3N5du3boVtkwWmDx5Mrt37yY5OZmHHnqIjh070qFDBwCaNWtGkyZNOHHiRIVj8XTnnXeyZ88eRo0axauvvsrUqVOZOHEiw4cP56abbirx9Sh4n++66y7OO+882rZty9KlS7n11lvp0qULN9988xnHAejZsyetW7c+4/YtW7ZwwQUXANC5c2f27dvHsWPHijxm4cKFtGvXrrAQ3r59O0OGDAHgoosu4ssvvzxjv5MnT2b58uUkJyfz6quvYrFYuOWWW0hKSqJnz54sLmGIS2mvrdaaRYsWcbW7R9OECRP4xr1WepMmTejbty/R0dFF9rV161YGDBhAXFwcUVFRDB06lK993BNJilkRUIp/kIXwBckz37PYnVgdRYtXf42drduyZaVu95XS8qzNpZcSERXFTveXAiGqI5jOZ9dddx1z587FYrGwYcMG+vfvX3hf586dWbZsGevWrWPatGk8+uijADzwwAPs2rWLr7/+mltuuYW33nqLuGJLaTVp0oQFCxawdu1aPv30U+67777C+9atW8drr73Gli1b2LNnD7/99hsWi4U77riD7777juXLl5OWllZqzJs2beLjjz9m9erVPPbYY8TFxbFu3ToGDhzI7NmzS9ymUaNGrF27lrvuuuuMLpgFr8Nnn30GwNGjRzly5Ai9e/cu9TWoiK1bt/Lpp5/y22+/kZqaSmRkJHPmzCE1NZXDhw+zadMmNm7cyC233HLGtvfccw833XQTGzZsYNy4cdx3330kJyczbdo0rr32WlJTU0ucNbtFixasXLmSwYMHFxb0f/zxB1OmTAEgNjaWr7/+mrVr17J48WL++c9/orWmb9++jBo1iscff5yHH36YG2+8kcTExCL7fv7552nXrh2pqalntECuXr0am81Gu3btKhyLp//97380a9aMxYsXM2nSJADWrFnDvHnz+Pjjj0t8PQqcPn2aRYsW8eqrrzJy5EgmTZrE5s2b2bhxI6mpqRV+v3r06FF4EWX16tXs37+fQ4cOFXnM3Llzi1zwSUxM5NtvvwXg888/5+DBg2fs9/nnn2fw4MGkpqYyadIkZsyYARgXgj755BMmTJiAxVJ6bwrP1zY9PZ169eoVvvcJCQkcPny4zOeVmJjIsmXLSE9PJy8vjx9//LHEOL1JilkRUHyd8EKA5Jmvaa3JtpxZuFodLmwO37fODnrmGSKKzRQcFRfHoGee8fmxPZWWZ7H16tHy/PPZ9fXXha1EQlRVMJ3Punfvzr59+/jkk0+49NJLi9yXmZnJNddcQ2JiYmGBABAREcGsWbMYP348Q4cO5dxzzz1jv3a7nTvuuIOkpCSuueYatmzZUnhfv379SEhIICIiguTkZPbt28e2bdto06YNHTp0QCnFjTfeWGrM5513HnXq1KFx48acddZZjBw5EoCkpKTCFtHirrzySgB69+5d4mPGjh3L559/DsBnn33GNddcU+ZrUBELFy5kzZo19O3bl+TkZBYuXMiePXto27Yte/bs4d5772X+/PnUrVv3jG1XrlzJDe5lysaPH1/i+Fi73X7GbaNGjQKM16J///6Fr1NsbCwZGRlorXn00Ufp3r07F154IYcPHy68+DJlyhQWLFhASkoKDz/8cIWf59GjRxk/fjzvv/8+ERF/lTHlxVKeUaNGUbNmzXJfj5EjR6KUIikpifj4eJKSkoiIiKBbt26l5kNJJk+ezOnTp0lOTua///0vPXv2LHLBwGaz8e233xbmBsB7773HjBkz6N27N9nZ2dSowIz4K1asKByr3rlzZ1q1asWOHTtKfGzx17akv0/FW+iL69KlC4888ggXXXQRI0aMoEePHj5fPkyKWSGEEF6Va3PiKqVIy/FD62zXceNoNmAARESAUtRt1YrhM2fS1ccTfVRG+9GjOb1zJ+lbt5odihB+NWrUKB588MEiLU4ATzzxBOeddx6bNm3iu+++K9J6tHPnTmrXrs2RI0dK3Oerr75KfHw869evJyUlpXCSGoCYmJjC/0dGRhaOiSzvS3lJ20dERBT+HhERUer4yoLHeB7PU/PmzWnYsCEbNmzg008/5brrriv3NSgQFRWFy2O8fcFjtNZMmDChcLzl9u3bmTp1KvXr12f9+vUMGzaMGTNmcPvtt5f7nCv72ni+LgW/OxwO5syZw4kTJ1izZg2pqanEx8cXxnvq1ClycnLIzs4us6XQU1ZWFpdddhlPP/00AwYMqFQs5alVq1ap93m+HtU9ToG6devy/vvvk5qayuzZszlx4gRt2rQpvP+nn36iV69exMfHF97WuXNnfvnlF9asWcP1119fpGW6NBW9YFrSa9uoUSMyMjIKn9ehQ4do1qxZufu67bbbWLt2LcuWLaNBgwaFXZh9RYpZEVB8nfBCgOSZLzmcrsJJn0pid7qwOnw7GZTWmsx9++gwejQPulxM3LfPlEK2rDxrf8UVADKrsai2YDuf3XrrrUyZMoWkpKQit2dmZhZOCDVr1qwit99///2FXRcLxqYW3/bss88mIiKCDz/8EKez7HNM586d2bt3L7t37waM8bv+dt111/HCCy+QmZlZ+FqU9hp4at26deFyTGvXrmXv3r0AXHDBBXzxxRccP34cMIrF/fv3c/LkSVwuF1dddRX//ve/S1zK6ZxzzmHu3LkAzJkzp8S1iz0Lt4rKzMykSZMmREdHs3jxYvbv319438SJE/n3v//NuHHjeOSRR87Ytk6dOmRnZxf+brPZGDNmDDfddFOR1kpfqMjrUV0ZGRmFF13eeecdhgwZUqTVvPiYcqDwvXW5XDz99NPceeedZ+y3+Os2ZMiQwkmuduzYwYEDB+jUqVORbUp7bZVSnHfeeYWfuQ8++IAr3H+7ylIQ54EDB/jqq6/OeB7eJsWsCChVOVkKUVmSZ76TbXFQ3nXgnBK6IHtT5p49ZB84QMvzz/fpccpTVp7VbtaMs/v3l3GzotqC7XyWkJBQOHOrp4cffph//etfnHvuuUWK0UmTJnH33XfTsWNH3n33XSZPnlz4ZbnA3XffzQcffMCAAQPYsWNHma1sYIzlnDlzJpdddhmDBg06Y6ZZf7j66quZO3cuY8eOLbyttNfA01VXXcWpU6dITk7mzTffpGPHjgB07dqVp59+muHDh9O9e3cuuugijh49yuHDhxk2bBjJycncfPPNPPfcc2fs84033uD999+ne/fufPjhh7xewszvFW2t9TRu3DhSUlLo06cPc+bMoXPnzgDMnj2bqKgobrjhBiZPnsyff/7JokWLimzbsGFDzj33XBITE3nooYf47LPPWLZsGbNmzSpctqYyY1QroyKvR2X2lZCQwKFDh+jevXthy/jWrVvp1q0bnTt35qeffipyjLy8PBYsWFDYXb3AJ598QseOHencuTPNmjUrcfxz9+7diYqKokePHrz66qvcfffdOJ1OkpKSuPbaa5k1a9YZ54yyXtv//Oc/vPLKK7Rv35709HRuu+02ANLS0khISOCVV17h6aefJiEhoXCpoKuuuoquXbsycuRIZsyYQf369av8+lWECobxOn369NEpKSlmhyH8oGDmQSF8SfLMNyx2J5n5Z46rKslZNaOJjY70SRwb3n6bXyZO5JatW2no/vJkhvLybNV//sPyyZOZeOAAdVu08F9gIqRU5Hy2detWunTp4p+AREjKzs6mTp06ZochQkhJ5yWl1BqtdaWmaJeWWSGEENVW2qRPpfHl2NkDixZR6+yzaVCsK1Wg6eBeO3CXtM4KIYQQVSLFrAgoTZo0MTsEEQYkz7wvx+ooddKnkjhdmnyb98fOaq05sGgRLc8/v0rd4rypvDxr0LEjDbp0kWJWVIucz4Q/+HpGWiGqSopZEVAKxn4I4UuSZ97lcLrIq0JhmmN1eH1pmpObN5N3/Dgt3YvRm6kiedZhzBgOLl1Kfnq6HyISoUjOZ8IfYmNjzQ5BiBJJMSsCSklrmwnhbZJn3lWZ7sWeXFqTb/du6+yBhQsBTJ/8CSqWZ+1Hj0Y7nez54Qc/RCRCUUXPZ8EwR4oIXDk5OWaHIEKIN89HUswKIYSoMovdic3pKv+BpfB26+yBRYuo164dZ5kwO2lVNO3Th9rNm7NTlugRPhQbG0t6eroUtEII02mtSU9P91prv886wCulYoFlQIz7OF9orZ9USjUAPgVaA/uAsVrr076KQwQPq8NJVHQNs8MQYSDYlrIIVJWd9KnkfUCezUmtmOr/OXI5HBxcsoTO115b7X15Q0XyTClF+9Gj2fTee9jz8oiOi/NDZCKUVCTPCpYGOXHihB8iEqHIarXK307hNbGxsSQkJHhlX74czW0Fztda5yilooEVSqmfgCuBhVrr55VSk4HJwJmrJYuworUmK99B5x69zQ5FhIGBAweaHUJIqOykT6XJtTmoGR1JRET1Jmw6tnYttqysgOhiDBXPsw5jxpA6Ywb7fvmFDqNH+zYoEXIqkmfR0dG0adPGD9EIIYR/+aybsTYUdLCPdv9o4ArgA/ftHwCjfRWDCB7Z7i/F2zam+mSGUyE8ybrV1VfVSZ9KorVR0FbXgUWLAGhx3nnV3pc3VDTPEoYMIbZ+fZnVWFSJnM+EP0ieiUDl0zGzSqlIpVQqcBxYoLVeBcRrrY8CuP+VOeXDnM3hKixgLfl5XvlSK0RZZCKL6suqZvfi4vJtTlyu6rXyHli4kEaJidSKj/dSVNVT0TyLjI6m7eWXs/u773A55PwnKkfOZ8IfJM9EoPLpolFaayeQrJSqB3ytlEqs6LZKqYnARIBmzZqxZMkSANq2bUudOnVYv349AA0bNqRbt24sW7YMMNbBGjRoEGvXriUrKwuAPn36cOzYMQ4ePAhAhw4diImJYdOmTYCxRlvHjh0LZwSMiYlh4MCBpKSkFH54+/fvz6FDhzh8+DAAnTp1IjIyki1btgDQtGlT2rRpw8qVKwGoWbMm/fv3Z9WqVeTn5wNGV6C9e/eSlpYGQNeuXXE6nWzfvh2A5s2bk5CQwKpVqwCoXbs2ffr0YeXKlVitVgAGDRrEjh07OH78OACJiYlYrVZ27twJQIsWLYiPjy+8gla3bl169erFihUrcLi/JA0ZMoTNmzeT7l4KokePHmRnZ7Nnzx4AWrduTYMGDVi7di0A9evXp0ePHixduhStNUophg4dyvr16zl92hju3KtXL06dOsW+ffsq/T6dysgEoH3X7thtVlJX/06kUnTsKO9TIL1PofR5ysnJYcmSJfI+VfF92rx5C06tqd+wMfHNW7JtwxoAasTE0impJ9s3rsNmtQDQuXtvjh0+wOl0Y6xei7YdcLlcHN6323gujZvSML4pOzalEqGgbp06Vfo8NapXjwPLltF45EjWrl0bEO9Tfn4+LperQu+TtX17LKdOse6rr2h1/vlB9XmS856575Pdbi/8jiTvU+C+T8H+eSr4uynvU2C/T8H+eaoK5a+Z7ZRSTwK5wB3AMK31UaXU2cASrXWnsrbt06ePlu4NoSnH6iDX+ldLhNWST0xsTSIjFI1qy0QDwjfy8/OpWbOm2WEEJZdLczLXii/+dCigYe0YIqswdvbg0qV8OmwYo+fNo/2oUd4Prgoqk2e23Fz+r1Ejku64gwveeMPHkYlQIucz4Q+SZ8IflFJrtNZ9KrONz7oZK6Uau1tkUUrVBC4EtgHfAhPcD5sAzPNVDCKw2Z2uIoUsQPox4+qY06WxeHn9SSEKHDp0yOwQglaOzeGTQhaMSRVyrFXrZrt/4UJURAQJQ4Z4N6hqqEye1ahVi9YXX8yub76R5VNEpcj5TPiD5JkIVL4cM3s2sFgptQH4E2PM7PfA88BFSqmdwEXu30UYKmlJj/QTaYX/r+qXWiHKU9DdRVSO3eny+QRtFrsTRxXWrT24aBHxffoQW6+e94OqosrmWfvRo8k+eJBj7q5kQlSEnM+EP0ieiUDlszGzWusNQM8Sbk8HLvDVcUVwyLU6sJfzhbWgdTY2OtJPUQkhylLdNWUrKsfqoF5cxdectuXkcHTVKvo8+KAPo/K9diNHoiIi2PX11zTtLcuUCSGEEOXx6WzGQpTE6dJndC8u0Lx1uyK/S+us8IVOncocpi9KkG9zlnsBylusDleljnVo+XJcDgetLgis66SVzbOaDRuSMGQIO2WJHlEJcj4T/iB5JgKVFLPC77Ly7ZQ2IiwiomhKythZ4QuRkdLaXxkulybbavfrMXMq0Qp8YNEiImvUoNk55/gwosqrSp51GDOG9M2bOe2eCVOI8sj5TPiD5JkIVFLMCr/KtzmxldHicnDPmV/gpHVWeFvBlPCiYnw56VNpbE4XVkfFLmQdWLiQswcOJDouzsdRVU5V8qzdFVcASOusqDA5nwl/kDwTgUqKWeE3VW3dkdZZIczjj0mfSpNrLf+4+enpHE9NDbguxlV1VqtWxPfqxa6vvzY7FCGEECLgSTEr/CbbUn7rTv2GjUu8XVpnhTc1bdrU7BCCRla+f7sXe7I7XeVeyDq4ZAloTcvzz/dPUJVQ1TxrP3o0R/74g5yjR70ckQhFcj4T/iB5JgKVFLPCLyx2J5YKdBmMb96yxNuldVZ4U5s2bcwOISjk2Rw4XOaueVraZHEFDixaRHStWjTt189PEVVcVfOs/ZgxoDW7v/3WyxGJUCTnM+EPkmciUEkxK3zO5dIVXtJj24Y1pd5X3pdaISpq5cqVZocQ8FwuHRA9IhzlXMg6sGgRCUOGEBkd7ceoKqaqedaoWzfqtWvHTulqLCpAzmfCHyTPRKCSYlb4XLbVgcsLs8eU96VWCOE92Vb/T/pUmhyrA11CMNmHD3Nq27aA7GJcHUop2o8Zw4FFi7BmZpodjhBCCBGwpJgVPmV1OCtVgNaIiS3zfmmdFd5Qs2ZNs0MIaDZH+WNV/ckYZnDmLOgHFy8GoGWATv5UnTzrMGYMLrudPT/+6MWIRCiS85nwB8kzEaikmBU+o3XFuxcX6JTUs8z7pXVWeEP//v3NDiGgZVvMm/SpNCW1zu5fuJDYBg1o0qOHSVGVrTp51mzAAOLi49klS/SIcsj5TPiD5JkIVFLMCp/JsTpwVnLymO0b15X7GGmdFdW1atUqs0MIWIEw6VNJXFqT57FEkNaaA4sW0eK881ARgfmnrDp5piIiaH/FFez58UccFosXoxKhRs5nwh8kz0SgCsxvACLo2Z2uIl88K8pmLf9Lm7TOiurKz883O4SA5HJpcirZm8Kfcm1/tc5m7tlD9oEDAT1etrp51n70aOw5ORxYtMhLEYlQJOcz4Q+SZyJQSTErvE5r7fO1KaV1VgQji71yY8j9LdviIPDaZP+iNeS6L5LtX7gQIKCL2epqef751KhTR2Y1FkIIIUohxazwujybs8rdFDt3712hx0nrrKiOgQMH+v2YFruTzHw7mfl2TuZYybc5S5yh1yw2h6tCa0GbLc/qwOUyuhjXbtaMBp06mR1SqaqbZ1ExMbS59FJ2f/stLmfgvzfCHGacz0T4kTwTgUqKWeFVDqerWq2mxw4fqPBjpXVWVNXevXv9eryCQraA06XJstg5mWMjz1bysjP+pLURTzDQQI7FxoFFi2h5/vkopcwOqVTeyLMOY8aQd/w4R2SNR1EKf5/PRHiSPBOBSopZ4VVZ1eymeDr9RIUf63BprEHQkiQCT1pamt+OVbyQ9eRyz/h9IsdKbilrqfpDns1Z6cnazHR0/UbyT5wI2CV5Cngjz9pccgmRNWpIV2NRKn+ez0T4kjwTgUqKWeE1eTYHdueZa0H6Uq5VilkRuMoqZD1pbcz+fSLHSo67G62/OF066Ho5HF22BIAW551nbiB+EFO3Li0vuIBd33xjegu+EEIIEWikmBVe4fTSLKgt2nao1OPtTpe0zopK69q1q8+PUdFC1pPWRvf5kzlWsix2v7SW5gT4pE8lSVu6hDpt2lIroYXZoZTJW3nWfvRoMvfs4eTGjV7Znwgt/jifCSF5JgKVFLPCK7Itdq98IXa5Kt+yK62zorKcPp5Mx2J3VmtGbw3k25yk51jJzLfj8FGPB6vDGRSTPnlyORwc+205TYeeF/CffW/lWfsrrgCl2PnNN17Znwgtvj6fCQGSZyJwSTErqs1id2J1eOfL9uF9uyu9jbTOisravn27z/ZtdRiFrDcu7miMz1d6ro3MPLtXu/Fr93jdYJOeuhZ7VhZNhwzF4nD6fWhDZXgrz2rFx9P8nHPYJeNmRQl8eT4TooDkmQhUUsyKanG5AmMW1EBvoRHhwepwkpnnnUK2OIvDyalcGxl5NmxeuHgUbJM+FUhbuhSA+CFDgfCZ1bz96NEcT00lc98+s0MRQgghAoYUs6Jasi0OvDknScPGTau0nbTOVo3V3bIVbhPLNG/e3Ov79GUhW/Q4Lk7n2Tida6tyzgfjpE8F0pYtoV63RGo2bgIYr4c3intf8GaetR89GoBd0tVYFOOL85kQxUmeiUAlxayoMl+Mt2sYX7ViFqR1trLsThcZeXZO5do4kW3lVK6NbIsdiz04W+wqIyEhwav781ch68nmfv/Sc6xY7JXLfW+Ncfc3p8XCiT9+p+nQYUVuD9TC3Jt5Vr99exolJsq4WXEGb5/PhCiJ5JkIVFLMiirRWpOV7/0vkDs2pVZ5W2mdrTjj/fure7jGeP3ybMYMvCdzrBzPtpCRZyPX6sDqcIZU6+2qVau8ti+bw+X3QtaTw6UL37OKFLVWh/fGuPvbiT9X47RYaDpkWJHbbQH62fdmngG0HzOGw8uXk3ei4utxi9Dn7TwToiSSZyJQSTErqiTb6sAVgMWNtM5WTK7NiaOc1letjS6cOVYHGXl2jmdbOemeXTffFtgT7/iLzeEiI88WEK2cTndReyLbSp7NUeLFh2Cd9KlA2tLFqIgI4s8ZdMZ94fDZ7zB6NNrlYvf335sdihBCCBEQpJgVlWZzuMi3+eaLY2zNuGptb3cG7vi5QGF3uqrcLdPp0sayMxaje/LxLAung7B7cu3atau9j0AqZD253AXriRwrudaiRW1ukE76VCBt2RIa9upNjbPOOuM+u9NV6e7WvuaNPPPUpGdP6rRsKbMaiyK8nWdClETyTAQqKWZFpWjt29mLO3TrUe19BOr4uUCgtdF657X9YXTx9OyefCLbSmaenVyrA5sjMCeX6tOnT7W2D9RC1pPWkGM1itocq8PoRh7Enw17djYn16Sc0cXYU06APb/q5llxSik6jB7Nvl9+wZaT49V9i+Dl7TwToiSSZyJQSTEboPJsjoBsYfR1y87W9SnV3odNWmdLlWN1+LxlzqU1FoeTHKuD03k2jmdbSc+xkmUxuic7AqB78sqVK6u8bTAUsp60Ni7wnMoNnphLcnzlb2iHg6ZDzyv1MQU9BwJFdfKsNO3HjMFptbLv55+9vm8RnHyRZ0IUJ3kmApUUswFGa01mnp1si1EInHS3qgRCAVCd7qkV5bB7p9VQWmfPZHMYLahmcLg0+Taje3J6ro3j2RYy8+ym5bXVaq3SdsYM0MFdFAartKVLiKhRg8b9B5T5OGO5sMB4h6qaZ2VJGDSImg0bslO6Goe9gp42efmWgJwATYQWX5zPhPCGKLMDEH/RWpORZ8fm8QW/YD3IXCtER0YQGx1BbFQkERHK7/FlebF7qq8VtM7WiJLrNeD77uGVpTVY3Es7xUZFUismkqjIwH6v7E5jfdfAKJPCz9GlS2jcfwBRNWuW+TiX1uTbncTVCM0/bxFRUbQdOZJdX3+N024nMjra7JCECWwOF5n5dlxao4GMPDsRykFsdAQ1owP/fCqEEN4iZ7sA4XJpTuXaihSyxdmdLrItDk7mWMnIs2Gx+2+5lFyro9zZb72ha3Jfr+1LWmf/4o/uxVVlcThJz7WRmWf32wzJgwadORtuWQoL2cB8CUOe9VQ6pzeuL7OLsadca2AsJVXZPKuoDmPGYM3M5OCSJT7Zvwhcxozkdk7n2QpXFCj4u+nSmjybcT49lWsjz+bAFaDnfRF8fHU+E6K6pJgNAA6ni/RcW4WLRY2xZEpmvp0T7rGIvhwjWtA67A+H9+/x2r5k7KzBzO7FlWFxODmVayMjz+bzonbHjh0VfqwUsuZLW74MgKZDhlbo8QVf6s1WmTyrjFYXXURUXBy7vvnGJ/sXgcnu/q5QPLdL+rvpefE7M88eUGPJRXDy1flMiOqSYtZkdqeLUx5XWCtLa8i3OYuMr/V2C1xWvt1vXSszT6d7dX/h3jrr7dmL/cHqcPm8qD1+/HiFHieFbGBIW7qEqNq1adSr4rNp5gZAq1RF86yyomvWpM2IEez65hu0Sy7YhYMc9yRuJf19L+vvpsa4UJiZb+d4toVsi/96wIjQ4qvzmRDVJcWsiawOJ6dzvfdFuaAF9WSOlVO5NvJtzmp/mcu3Ocvs+hzowr11NtvqqPKFErP5o6gti0MK2YCRtmwJ8ecMIqIS40O1hrwQbo1qP3o0OUeOkJZS/RngReByOI3zoDcuzGoNeTajB0x6jlW6IQshQoIUsyax2J1k5vmuxdPudJFlsRfpYlTZMWROlybb6t9WvVbtO3l9n+HaOmt1OMkPgK6W1eWLojYxMbHM+x3uHhNSyJov78hhsnbuqHAX4yLbmjxWvLw8q452l1+OioyUWY1DWJ7NaI0t77xXlb+bDpcm22KsQ+3vOThEcPLl+UyI6pBi1gR5NgeZfuq669nFqLLja7Mtdr9/mbf5YOr3cGyd1VqTlR9aRbw3i9qylhiQQjawpC1bClDhyZ88aTAm1jPp8+/LpSxi69enxbBh7JJiNuQ4XZrTuTZjmakKPL66fzeLz8Eh3ZBFSWRpHhGopJj1s2yLsYasGSozvtZid2I14Qvg0YP7fLLfPFtoFXblCebuxeXxRlG7c+fOEm83uhb7/yKOKF3asiXENGhI/cSkKm3v0prTeTZTzgGl5Zm3dBgzhlPbt5O+bZtPjyP8x2J3kp5jrdTwHm/93Sz4jnAq1/iOkBvAs+AL//P1+UyIqpJi1k8KFzcPkG6fZY2vdbm7H4USq8MVNlebQ6V7cXk8i1pvtLwVFLKhehEgGGmtObp0MfGDh6AiqvfnKtviMIZ2hND72/6KKwCkdTYEuFyajDyb33ptlcfp0uS4vyOczpVuyEKIwCXFrB8UFLKBOjV+8fG1WRbzvtA3atrMZ/sOh7GzLlfodS8uj9VhTNR0uhLdSVu0aFHkdylkA1P2nj3kHTpE0yHDvLK/gjWNHX66sFU8z7ytTkICTfv2Zacs0RPULHYnJ3OtVe4N5cu/m2AM1fHXUoAicPn6fCZEVUkx62Mul+Z0nt2ULruVVTC+1sxY6zVo5LN9h0PrbCh3Ly6PzVnxojY+Pr7w/073ZzRcX7dAlrZ0MQBNhw7z2j6dLs0pd0uTr3nmma90GDOGtNWryT582OfHEt5VcKE7M796Qxt8+XfTU/GhSuE2fCfc+eN8JkRVSDHrQ06X5pRJy4oEq11bNvh0/6HcOmuxOwO29d+fKlLUpriXMykobKSQDUxpy5YQ16wZddt38Op+NZCZb3dPcue79z7FD8vmtB89GoBd8+b5/FjCe6wOJydzvHNRxdd/N0vidA9Hsjrkb0648Mf5TIiqkGLWR+zuteFk8oTAEqqts6E4zrm6yitqpZANbNrlIm35MpoOGYZSyifHyLM5OZ1nD+rzdMMuXWjQqZOMmw0SWmuyLHYyQqQ3SLbFIWNphRCmkmLWB2zuMXyh8IfK32rWqu3zY4Ri62y2JXy7F5enpKK2Tp26UsgGuIwtm7GePFGlJXkqw+50kZ5r9ck4wLp163p9nyVpP3o0B5cswXL6tF+OJ6rGyDWb1yfo88ffzdI4XZrcMJhwUPjvfCZEZUkx62UWu5MMWaOyytp3qdryG5URaq2zFrsTi3T1KpdnUduqUzcpZANc2rIlADQdPMTnx9IaTufZvH6hq1evXl7dX2k6jBmDy+Fgzw8/+OV4onK0NmYG9lVvLX/83SxLntXht0nVhHn8dT4TorKkmPWifJszYKbVD1ab1672y3FCpXXW5TK6rImKszldbEhZZXYYohxHly6hTtt21GrR0m/HzLE63BcjvXMWX7FihVf2U56mfftS6+yz2SldjQOOwz3kyJd/c/z1d7M0GmSYSxjw1/lMiMqSYtZLcqwOKSq8wOXyTwtjqLTOGuOVzI4i+Pgrz0TVuBwOjv+23OddjEtidbi8tnyPw+GfL/gqIoL2o0ezd/587Pn5fjmmKF+ezWiNdfh4THYgnM9sTldYrG8ezvx1PhOisqSY9YIsiz1kWvrCSZ41uP/wSvdiEarSU9diz86m6ZChphzfn8v3eEuH0aNx5OWxf8ECs0MJe06X5nSuzbjYaHYwfpRtteMK4snUhBDBSYrZatBak5Hn/ckcwllir/5+O5bF4QzacT7Svbh6/JlnovLSliwBMK2Yhb+W78mqxvI9Q4b4frxvgRbDhhFz1lns+uYbvx1TnCnf5iQ9x4rNj39bAuV8prWx1rkITf48nwlRGVLMVpHLpTmdZ8fqgxkww9n+3Tv8erzcIG2dNb5gmx1F8PJ3nonKSVu2hPqJScQ2amx2KORXY/mezZs3+yCikkXWqEHbyy5j97ff4pLugH7nchkXt7Ms/p83I5DOZxa7U9aeDVH+PJ8JURlSzFaB06U5nWcLiTGXgSY7079LSwRj66zxZSG4Yg40/s4zUXFOi4UTq1YSb2KrbHEFy/dU9kt6enq6jyIqWfsxY8hPT+fwb7/59bjhQmuNw+nC6nBisTvJdc+VkZln52Su1bTzcqCdz2Tt2dDk7/OZEBUVZXYAwcbhdHE6RBY7F4Zcq5Oz4oLjuo5TuheLEHdi9SqcFgtnmzD5U1m0how8O7VjNLViAvNPZ5sRI4iMiWHn11/TYmjgXAwIdFprXNo4v7q08WP832hxdWqNy6XDavxrdRSsPVs7QD8nQojQEhTf4DUExKQCNoeLU3k2KWR9qE3HLn4/ZjC1zmZL92KvMCPPRMWkLVuCioykyTmDzA6lRAXL91Tkb1KPHj38ENFfatSuTYMuXVg3fTovRUQws3VrtsyZ49cYAo3LpbG7W1Pzbc7ClQcy8myk51g5nm3heLaVkzlWTufZyMy3k21xkGczWl9tThfOAC9kA/F8JmvPhh5/n8+EqKiguGzmcGpO5FhRgFKKyAhFpFJEREBkhCKiyG3KJzFYHU4y82QNWV/Lz82ldt16fj9uMLTO5tuke7G3mJVnonxpSxfTsFdvatSta3YopSpYvqdeXDTRkaWfN7Kzs6lfv77f4toyZw7pW7agnUZ36Kz9+/ll4kQAuo4b57c4zGB1OLE5XLhc4HS3rGod2EWotwTi+UwDWRYHDWrVMDsU4SX+Pp8JUVGB/e29GA24tHGV1eJwkmdzkm1xkJlv51SujRM5Vo5nWYwrrLnGFdYcq8NdBBitb1UZx5Fvc5IhhaxfpB0+YMpxLQ4np3NtATtxhdOlybZK92JvMSvPRNns2dmcXJNC0yHDzA6lXC5tLL9S1mz2e/bs8WNEsOKxx3DZbEVuc+TlseKxx/wah7/YHC6yLHaOZ1vIyLMbrakOJ3anC1eYFLIQuOczu6w9G1L8fT4ToqKComW2MjTGF38nGko5hyoFke7W3Ah3i65nC2+EMlqAAXKtDnJkqvmwYHO6sOW5iIpwUCsmitjoSLNDKpSVL92LReg79vsKtNMZFMUsFLQ+2bE5XdSNjSr8u2GWrAMlFzWl3R6MbI6CCZhcMuQnCGRb7cRERfis15wQQoRcMVsRWoNDaxxljHlSCiKUqtJyDKLqmjRLMDsEHC5d2Kpfq0YUsdERpn5Jzbc5/bpmYTgIhDwTZ0pbtoSImBga9x9gdiiVYrEbPX/qxdUg0uNLe+vWrf0aR92WLcnav7/E24OZ3enCYpcCtjSBfD4rWHv2rJrRZociqsnf5zMhKiqouhn7k3bPbCj8q04AjfspmDn4RI6VXKvDlEnInC5Ntsxe7HWBlGfiL2lLl9K43wCiatY0O5RKc7j0Gcv3NGjQwK8xDHrmGaLi4orcFhUXx6BnnvFrHN7gcLrIsTo4mWPlVK6NPJtTCtlSBPr5TNaeDQ3+Pp8JUVFSzIqAsnvbJrNDOIPWFH6pyrbY/XqRIytfxmr7QiDmWbizpJ/k9Mb1AbckT2UULN9TMDRl7dq1fj1+13HjGD5zJnUSjJa6mLPOYvjMmUEz+ZPTpcm1OkjPsZKeayPX6pCLyhUQDOczWXs2+Pn7fCZERYVlN2MhqkIDeTZjeYeY6Ehq1YgkqoyZTKsrz+aQ7sUibBxbvgyApiGwPmqu1YHd4UJrY5bdCGXMyaDA52MHu44bR9dx4/iwd2+i69QJ+ELW6dLuLsTOMof+iOAma88KIXxFzioioNSqc5bZIZRLQ+GXr5ioCOJqRFEjyrtFrdOlybHIxGO+Egx5Fm7Sli4hqnZtGvbsbXYoXmFzuoitXZeMvDOHCSgFCmOywYJCF0WR35X6a+6GqhTCCcOGkTpjBg6LhajYWC8+s+pzuTQW9yROdrlgV23Bcj7LszqIjYrw6UVg4TuyLI8IVFLMioDStlNXs0OoFKvDhdVhIzoygloxkcREeWcG5EzpXuxTwZZn4SBt2RLizx1MRHToTBRTWp5pDRqN0RBZuU96RQvhs4cMYc0rr3Do95W0Om+Y6TMtu1waq8OYyEl6nHhXsJzPZO3Z4NajRw+zQxCiRHJ5TASUjSl/mB1CldidLjLy7KTnWLHYqzfRRZ7NIa0VPhaseRaqcg8fImvXTpoOCf4uxp58kWdaG2vcOlwam3vNdYvdWHc9x+ogy2InM99OrZ79URER7FiwkOPZxhrsJ7KtpBesw55nJ8tijO/NszkKJ+mxOVw4nC6vTHjncmnybcYa3idyrIXLGAnvCqbzmaw9G7yWLl1qdghClEhaZkWACe72yIJlfbItDmrFRFIzOrJSLSIOp0u6F/tFcOdZqElbZnxJahrEkz+VzLw8q1GvHvW7J3NsxbLCSLSufGuwZ1fngjXYi7cCR3jcBkYXa4vdKIzlk+YPwfUqy9qzwUkm8Ap9DqcLm9NFhFLERJm7LGVlSDErAkxwfHDK49KabIuDHKuDuBpRxEVHVugPd5bFEWRfS4JVaORZqEhbtoSYBg2p3y3R7FC8zNw8azpoMNve/h9Oi4XIKo6b1RqcWuOUM1MAC67zmdbG7MZnxYXOkIJwECyFjag4l7uHj83pwlpsHW8F1IiKIDY6MuALW+lmLAJKUp8BZofgVVobM5uedHexK2uZiVyrdC/2l1DLs2CmtSZt6WLiBw9BRYTWnySz8yx+8BBcVisn/lxtahzCt8zOs6qwOGTt2WAzNARmmhdgcxhreJ9yD//IzLeTX8I63hpjXpjMfDsnsq1k5tmx2J0B2UIfWt8cRNDbs32L2SH4hAbybU5O5hgnhOJFq8PpItcq3Yv9JVTzLBhl79lN3uHDQb2+bGnMzrMmA85BRUQUdjUWocnsPKsqWXs2uKxfv97sEEQVON1zF2Tk2TiebeF0nq3SjSca4wJUoBa2UsyKgJKbnWl2CD5ncTg5lWsjI8+GzWGcTKR7sX+FQ54Fi7SlSwBoOnSYqXH4gtl5VnzcrPC+PZ/N5avEjnxYL46vEjuy57O5fo/B7DyrqoK1Z0VwOH36tNkhiArQ2li7O8ti52SOtbBnoNW99nm1949HYetu2bU6zC1sZcysECYpWNYnQqkzuncIES7Sli4mrnlz6rRrb3YoIckb42ZFyfZ8Npc/7vs7zvw8AHIPHuSP+/4OQNux15kZWtCQtWeFqD6704XN4cLqno3eX98otQaL3ZhRXymIiYokNjrCa8tUVpScPURAadc51CaAKZ8Usv4XjnkWiLTLRdryZTQdYv46qL4QCHkm42Z9J3XalMJCtoAzP4/UaVP8Gkcg5FlVFaw9KwJfr169zA5BuBV0Hc7Ms3M828KpXBs57q7DZn2jLChsM9wxZVnshb0PfU2KWRFQsrMyzA5BhAHJs8BwevMmrOknQ3BJHkMg5FnhuNnlskakt+UeOlSp230lEPKsOmTt2eBw6tQps0MIW1prrI4zuw5bHE6vdB32Nq2NeWJO59k4kW0l28eFrRSzIqAcP+LfLwEiPEmeBYa0ZUsAaDp4iLmB+Egg5FnBuNk0GTfrdTWbnl3i7VFxceQeOui3OAIhz6or22rHVcZs/8J8+/btMzuEsGJ3Twx6OtcoCDPyjFmHy1oVIxC5tCavWGHr7ZU7pJgVQghhirRlS6nTrj21ElqYHUpIazp4CCf/XI0jP9/sUEJG/rE0tPPM1kQVFYXDYuGbXkmsefxfWKU1q0IK1p4VQoDVPVFojtWBzcSuw95WUNieyrVxMsda2DW6uqSYFQGlafOWZocgwoDkmflcDgfHf1sekkvyFAiUPIsfPASXzcZJGTfrFZb0kyy44jIcuTkkPfIvarVoAUpRq0ULznnzbcas20zrq65hy/TX+Tq5KxtffhFHXl75O66iQMmz6pK1ZwNb27ZtzQ4hLDhdmsx8u9lh+JzTpcl1r3dbUNg6qljYSjErAkrNWrXMDkGEAckz86WvW4M9O5umQ4aaHYrPBEqeyXqz3mM9fZqFo0eSs3cPwz75guRHp3Dlph2Mz8jjyk07aDv2Omq3asW5b77N5b//Sfw5g0idNoVvenZjx7tv47J7/0tqoOSZN8jas4GrTp06ZocQFjLz7QE5DtaXCgrbqk4GJ8WsCCh7d2w1OwQRBiTPzJe2ZAkA8SFczAZKntU46ywa9JBxs9Vlz85m0dVXkLF1M0PnfMrZ5ayNXL9rN86b+wUXz/+V2q3bsOof9/Ft/17s++oLtMt7Y8YCJc+8QdaeDVzr1683O4SQ54vxpOFAilkhhBB+l7ZsCfWTuhPbsJHZoYSF+EEybrY6HHl5LLr2StLXrWXwrI9ofuHwCm/bZOC5XDx/Ied9+iWRMTEsv2U8P543mKOLF/kw4uCVV43uhkIEK6vDSZ5cyKkSKWZFQKlzVn2zQxBhQPLMXE6LheOrVoZ0F2MIrDyTcbNV57RYWHz9NZxY+TuD3n6flpePqvQ+lFIkjLiUy1as4pz/vYP11El+HX0ZC0ZdSvraNdWKL5DyzBtk7dnA1LBhQ7NDCFnhMk7WV6SYFQGlVbuOZocgwoDkmbmOr/oDl9UasuvLFgikPJNxs1XjtNlYetMNpC1ZxMAZb9H6qmuqtb+IyEjaXT+OK1I20Oe5Fzi9aSM/njeIZRPGkbVrZ5X2GUh55i2y9mzg6datm9khhKxwHCfrTVLMioCyae0qs0MQYUDyzFxpy5agIiNpMvBcs0PxqUDKMxk3W3kuh4MVt03g8M8/0f+VN2h3w41e23dkTAxd7r6X0amb6f7Ioxxe8DPf9uvJH/ffQ97RI5XaVyDlmTfJ2rOBZdkyOXf4greWpwlnUswKIYTwq7Sli2nUuw816tY1O5SwIuNmK87ldPL7XXdw4Ntv6PPcC3S87Q6fHKdG3br0ePQJRqdupuPtE9k9Zzbf9Exk7dQnsGVk+OSYwULWnhWhzupwkmuVHK8uKWZFQImIiDQ7BBEGJM/MY8vKIn3tGuKHDDM7FJ8LtDwrGDd7YnVotuR5i3a5WPXAvez9bC7JTzxFl7vv9fkxazaJp98LrzDqz/W0HHkFm197ma97dGXz66+Ue/Eh0PLMm2Tt2cARFRVldgghRcbJeo8UsyKgdOvVz+wQRBiQPDPP8ZW/oZ1OmoZBMRtoeSbjZsuntebPR/7Jrtnvk/TQZJIefNivx6/Tpg2D3n6fy5b/QaO+/Vg75THm9Upk5wfv43KU3IITaHnmbbL2bGAYNGiQ2SGEFBkn6z1SzIqAsmvrRrNDEGFA8sw8aUsXExETQ5P+A8wOxecCLc8Kxs1KMVsyrTVrpzzG9pn/o+s999PjsSmmxdIgqTsXfPENw3/4hbjmCfxx3918N7APB7795ozCLtDyzNucLk2OdMU03dq1a80OIWTIOFnvkmJWBJT83ByzQxBhQPLMPGnLltKk/0AiY2PNDsXnAjHP4gcP5WTKnzjy8swOJeBseP4ZtrzxKh1v/xu9nn4OpZTZIRE/aDAjFixh6JxPUUqxdPz1/HTBENKWLWXPZ3P5KrEjKwf256vEjuz5bK7Z4fpMns0pa8+aLCsry+wQQoKMk/U+KWaFEEL4heXkCU5v3BDyS/IEsqYF42ZlvdkiNr36Ehuef4Z2N06g34uvBEQhW0ApRcvLR3H5738ycPr/yE87yoKRI/j9b7eRe/AgaE3uwYP8cd/fQ7qglbVnRbCTcbK+4bNiVinVQim1WCm1VSm1WSl1v/v2qUqpw0qpVPfPpb6KQQSf9l27mx2CCAOSZ+Y4ttzo3tp06FCTI/GPQMyzxjJu9gxb35zOuqlP0PrqsQx4YwYqIjCv80dERdF+/ARGr91EjbPqoV1FWyqd+XmkTjOva7Svydqz5urTp4/ZIQQ9GSfrG748YzuAf2qtuwADgL8rpbq673tVa53s/vnRhzGIIJNx6qTZIYgwIHlmjrRlS4muU4eGPXubHYpfBGKe1ahblwbJPaWYddvx/rukTH6IFpeP4tz/vUNEZODPDBwZG4stK7PE+3IPHfJzNP4la8+a59ixY2aHENRknKzv+KyY1Vof1Vqvdf8/G9gKNPfV8URoOJlWucXihagKyTNzHF26mCbnDiYiTJZ4CNQ8ix80RMbNAnvmfsyqSffSfPgIBr//IRHR0WaHVGG1EhJKvD2uecm3hwpZe9Y8Bw8eNDuEoCXjZH3LL31plFKtgZ5AweJ29yilNiil3lNK1fdHDEIIIcyTe+gg2bt30XRIeHQxDmQybhb2f/0lv991B02HDGPoh58QWaOG2SFVSvKUaUTWjDvj9hp1zwr5ixSy9qwIJjJO1vd8fnlcKVUb+BJ4QGudpZR6E/g3oN3/vgzcWsJ2E4GJAGc3a8bGlJUANG3ekpq1arF3x1YA6pxVn1btOrJprVEnR0RE0q1XP3Zt3Vg4k2T7rt3JOHWy8Cr52S1aUyMmhv27tgNwVv2GNG/Vli2pfwIQFR1Nlx592Ll5PZZ8449Cx8Rk0o+lkX4iDYDmrdsRERHBwT07AajfsDHxzVuybcMaAGrExNIpqSfbN67DZrUA0Ll7b44dPsDp9BMAtGjbAZfLxeF9uwFo2LgpDeObsmNTKgCxNePo0K0HW9en4LAbH4SuyX05vH8PmafTAWjVvhM2q5WjB/cB0KhpM+o1aMSuLRsAqFmrNu27JLF57WpcLuPkn9irP/t37yA78zQAbTp2IT83l7TDBwBo0iyBOnXrsXvbJgBq1TmLtp26sjHlD/fbpkjqM4A927eQm210dWrXOZHsrAyOHzlUrfepZlztwvda3qfAfZ9C4fO0MWWlvE9+fJ92f/wRALWTe3Lq5PGw+DxFRddAu1wB9z5F1q2Niohgy1efcbJWTJH3KRzOexHbd7DuvnuI69qNxg89yMGD+4Lu85TbthXNH3iAE7M/IP/IEaIbN6ZOn76cmv8TC0ZfxtmPPkpk7dpB/T6Vd96LiY5m8OBBrF27tnCm3T59+nDs2LHCVsQOHToQExPDpk1GHE2aNKFjx46sWLHC2EdMDAMHDiQlJYWcHOO97t+/P4cOHeLw4cMAdOrUicjISLZs2WLE1bQpbdq0YeVK47tKzZo16d+/P6tWrSI/Px+AgQMHsnfvXtLSjPe6a9euOJ1Otm838qV58+YkJCSwapXxXGrXrk2fPn1YuXIlVqsVMNZ13bFjB8ePHzde88RErFYrO3ca+dKiRQvi4+NJSUkBoG7duvTq1YsVK1bgcK9JPGTIEDZv3kx6uvFe9+jRg+zsbPbs2QNA69atadCgQeGyO/Xr16dHjx4sXboUrTVKKYYOHcr69eux2+0sWbKEXr16cerUKfbtM/Klbdu21KlTh/Xr1wPQsGFDunXrxrJlxlCGqKgoBg0K3/epYXwz6tRvGPCfp0A471V12j3ly4WolVLRwPfAz1rrV0q4vzXwvdY6saz99OjZW/+y9DffBCkCSlbGKerWa2B2GCLESZ7532933s7hX37mml37A3aCHW8L5Dz78bxBRMbGcvFPv5odil8d+XUBi6+/mvqJSVw470dq1K1rdkjV5pln+7/+khV33EK9rolc8PW3xDZsZHJ0vhMdGUGDWsHVoh7MTp48SaNGoZtPvpBjdUj34kqIjoygYe2YNVrrSs025svZjBXwLrDVs5BVSp3t8bAxwCZfxSCCT8HVHiF8SfLMv7TWpC1bQtPBQ8KmkIXAzrP4QUM4+efqkO+S6ilt+TKWjBvLWZ06c8GX34ZEIQtF86zVmKsY9snnZG7fyi+XDifvaGCO2/YGu9NFtkW6b/pLQaupqBgZJ+s/vvxWcS4wHji/2DI8LyilNiqlNgDnAZN8GIMQQgiTZe/eRd7hw7K+bABpOngILrudE6tXlf/gEHBi9R8svvZKarduw4XffE9Mg8BsMfeG5hddzAVffkve4UP8fPEFZO/da3ZIPpNnc2Kxh8f42Xybk8w8mc05GMg4Wf/y5WzGK7TWSmvd3XMZHq31eK11kvv2UVrro76KQQSfs+o3NDsEEQYkz/wrbekSAJoOHWZqHP4WyHnWeMA5qMjIsFiiJ33dWhZedQU145ty4bwfiG3U2OyQvKqkPIsfNJgL5/2ILSuLny+5gIxtW02IzD+yLHYcIb7kic1htEJbHE7Sc22mTIDVpEkTvx8zWMl6sv4VPv29RFBo3qqt2SGIMCB55h97PpvLV4kdWfWP+1CRkZxc86fZIflVIOdZuKw3e3rzJn4dM5Ia9epx0bc/Etf07PI3CjKl5Vmj3n0Y/uMv4NL8culw0lPX+Tky/9C6oHgIzerB4XSRkW+j4Nm5tCYjz06Wxb/PuWPHjn47VjCT9WT9T4pZEVAKZkQTwpckz3xvz2dz+eO+v5Prnq1SO538cd897PlsrsmR+U+g51nTEF9vNnPHdn694jKiasZy0bfzqdWipdkh+URZeVa/azeG//QrUbXiWDByBMdXhuZkmg6XJisE1591uTQZpbTy5duMVlqbwz+FU8HMwqJ0Mk7WHFLMCiGE8LrUaVNw5hctkpz5eaROm2JSRKK4+BAeN5u9Zw8LRl0CwIXf/kSdNm1Mjsg8ddu14+KffqVmk3h+HTOSIwtDcwZri91Jvi20xs9m5ttxljFG1unSnM6zkWN1hGzLdLCQcbLmkWJWBJSo6GizQxBhQPLM93IPHarU7aEo0POscf+BITluNvfgARZccQlOi5UL5/3AWR1Cu3tkRfKsVkILhs//lbrtO7D4uqs48N08P0Tmf9kWe8h08cyy2LFV8LnkWh2cyrX5dOxwTEyMz/YdCrJknKxppJgVAaVLj0otLSVElUie+V6thIRK3R6KAj3PQmXcbMHY7A/rxfFFl3b8cN5gbJmZXPjN99TvVuYy9iGhonlWs3ETLvpuPg2Te7Jswjj2zP3Yx5H5nwYyQmDG3zybo9KtzA6X5lSujTybb7q5Dhw40Cf7DQU5VkeFLzwI75NiVgSUnZvXmx2CCAOSZ76XPGUaKiqqyG2RNeNInjLNpIj8LxjyLNjHzRYZm601+UeOYD1xnC5330PD5J5mh+cXlcmzmPr1ueDr74kfNITf/nYb299+y4eRmcOlNVlBvP6s1eEku4rjfzWQbXFwOtdWZvfkqkhJSfHq/kKFjJM1nxSzIqBY8oPzC5UILpJnvpdw8SWo6Ggia9YEpajVogUD3phB27HXmR2a3wRDngX7uNmSxmYD7P5otgnRmKOyeRZduzbnf/YVCZddzuoHH2Djyy/6KDLzWB2uoCwwHE6XV8Zd2pwu0nOtXl2DNycnx2v7ChUyTjYwRJX/ECGEEKJytr/9Fq78fC5btpIGPZLNDkeUwnPc7NnDzjM7nEqTsdlVExkby9APPua3u+4gddoU7NlZ9HxyGkops0Pzmhyrg+jICGpEBUe7TVkzF1dFwZJFVruLOrFRRESEznsbKGScbGCQYlYElI6JyWaHIMKA5Jlv2XNz2TrjDZoPHxHWhWww5FnBuNm05UvNDqVKaiUkFC7/VPz2cFHVPIuIjmbQzPeIrl2Hza++hD07m34vvoKKCI7iryIy8m00rBVDZIAXclrrcmcuriqLw4kt10XdmlHEREVWeT/9+/f3YlTBT8bJBo7QOWOJkJB+LM3sEEQYkDzzrZ2z3sV6Kp2khx4xOxRTBUueNR00hPQ1Kdhzc80OpdKSp0wjokaNIreF29js6uSZioig/6tv0PW+Sex45y1+v+sOXI7g655bmoLWyUCX7ePCyKU1GXl2siz2Ki/hc0h6OxSScbKBRYpZEVDSTwTHlz8R3CTPfMdpsbDljVeJHzyUxv0GmB2OqYIlzwrGzZ4MwnGzbcdeR1zzBGOysTAdm13dPFNK0WvaMyQ/PpU9cz9m2YRxOK1WL0VnPrvTRXYATwhVlZmLqyrf5iQ911al5YsOHz7sg4iCj0vGyQYcKWaFEEJ4za6PZpOflkbSQ5PNDkVUUJMB56AiI0kLwiV6Tm1YT87ePfSa9gzjM/K4ctOOsCpkvUUpRdJDj9DnPy9x8PtvWXzd1UHZUl+aPJvTq5MheUt1Zi6uKqd7CR9pWayaTBknG3CkmBUBpXnrdmaHIHzMc03IrxI7suezuX6PQfLMN1x2O5tff5nG/frTdMhQs8MxXbDkWXSdOjTs2Sso15vd8d47RMbG0u76G80OxTTezLMud/6dgTPeIm3JIhZeORJbRobX9m22LIsdRwCNcfTWzMVVlWN1cCrXVuHXpFOnTj6OKPDJONnAJMWsCCgRITTxhDhT8TUhcw8e5I/7/u73glbyzDf2fjaX3AMHSHzwkZCaFbWqginP4oNw3KwtK4u9n31C66uuIaZBA7PDMY2386z9jTcx+L3ZpK9JYcHIS7CcPOHV/ZulYPxsVceMepO3Zy6uKrvTxalcW4W6OUdGVn3yqFAg42QDV/D8pRVh4eCenWaHIHyopDUhnfl5pE6b4tc4JM+8z+V0svHlF6if1IPmw0eYHU5ACKY8C8Zxs3s//QRHbi4db7vD7FBM5Ys8azXmKoZ98jmZO7bx8yUXkXckNMZLOlyaLD936y3OlzMXV4XGaLXOyLPhKiOmLVu2+C+oACPjZAObFLNCCL+RNSFD14FvviJ79y6SHnxYWmWDUBP3erPBMm5Wa82O996mQY+eNOzVx+xwQlLziy7mgi+/Jf/oEX4ecSHZe/eaHZJXWOxOv024VJIsS2B2VbU6XJzMtQbk2GKzyTjZwCbFrAgo9Rs2NjsE4UOlrf3o7zUhJc+8S7tcbHz5Bc7q1JmWo0abHU7ACKY8C7Zxsyf++J2MLZvpeNsdYX/xxJd5Fj9oMBd9+xO2rCx+HnE+Gdu2+uxY/pRtsVdpRt/qyrM5ArpYLOiKXVJ37KZNm5oUlblknGzgk2JWBJT45i3NDkH4UPITT0HxL55K0f1R/3YzljzzrkPzfyRj8yYS//kwKojGifpasOVZMI2b3f7u20TXrUvrq8eaHYrpfJ1nDXv1ZviPv4CGXy65iPR1a316PH/QQEaevcxutd5mxszFVWWxOzmZY8Pm+KuIa9OmjYkRmUPGyQYH+dYhAsq2DWvMDkH4kIqMBK2JadAQlCKmUSPQmiw/X+2XPPMerTUbX3ye2q3b0Pqqa8wOJ6AEW54Fy7hZy8kTHJj3NW2vu4HoWrXMDsd0/siz+l27MfynX4mqXYsFoy5h3dNPmT4rfXW5tCbLT+vPOpwuMvOCa8ylS2tO59nIthittCtXrjQ7JL9wOF3kumd6zgiy9yxcSTErhPALl93O+memUa9bItfsPsD4jDzG7j5Ix1vvYPPrr3B08SKzQxRVcHTRQtLXriFx0oNEREWZHY6ohsJxs8uXmh1KmXZ/NBuXzUbHW8N74id/q9uuHRfPX0hkzTg2vfi86bPSe4PV4fJ5y5vLpTmdZydYh1zm2Zyk59pwaUzpmu1rWmt3q7mdkzlW0nNt5FgdIflcQ5UUsyKg1IiJNTsE4SO7P/6I7D27SX5iapGuqL2feZ6zOnXmtztvw5J+0i+xSJ55z8aX/0Ncs2a0vX6c2aEEnGDLs2AYN6tdLna8/y5Nzh1EvS5dzQ4nIPgzz2o1TyCihCVanPl5rHvqCb/F4U05VkeR7rTepLWxBI8ryGcPcro0UTViOJVr43i2hYw8G3m24C34XC6Nxe4kM8/OiWwrGXl28mzOgJlhWlSOFLMioHRK6ml2CMIHnBYLG/7zDI369iNhxKVF7ouKi2PQux9gPXWKlffc5Zc1ACXPvOPY7ys4/tsKut7/DyJjYswOJ+AEY57FDxrCyQAeN3tk0a/k7NsrrbIe/J1neUePlHz7oUN8P2gAf9x/Dztnz+L05k24nIE72ZGnjHybTwqZLEvwFnzFFeSZ1kaLdrbFcUZx6wjg5+rZffhEjpXMfDsWhzNoW8zFX6SYFQFl+8Z1ZocgfGD7uzPJO3yYnlOmlTjzaIOk7vSa9gyHfvyeHe++7ft4JM+8YtNLLxDbuAkdbrrF7FACUjDmWfzgIWiHgxOr/jA7lBLtePdtYhs3kVmzPfg7z0qbfT66bl1iGzVi39df8Me9d/H9OX35tEU8P196EWse/xf7v/6SnAP7/XLBsrIKZvH1plxrYM9cXFml5ZlncZvuLm4z8+ymF7cF3YezLEbrq3QfDl0ywEkEFJvVYnYIwsvs2dlsevlFmg47n6ZDhpb6uM53/p0jC39lzWOPEO/jLoSSZ9V3ck0KRxYuoOfUfxMVF2d2OAEpGPOsyYBzUJGRHFuxjGbnX2B2OEXkHjzA4fk/0u2BfxJZo4bZ4QQMf+dZ8pRp/HHf33Hm5xXeFlkzjn4vv07bsdehXS6y9+zm5JoUTq75k/Q1KWx76/9w2WwAxDZuQsPefWjUuw8Ne/WmUa/exqSAJrM7XWRb7NSJja72vix2JzkhNgtuRfNMa7A4nBRM3KwUxERGUiMqguhIRVSk79rRXC6N1eHC6nBic7ik1TVMSDErhPCprW9Ox5p+kp5TnirzcUopzvm/t/j+nH4sv20Cly5aTmRscI05DCcbX/4PNerVp+NtE80ORXhRdO3aNOzVOyDHze6cPQutNR1uvs3sUMJa27HXAZA6bQq5hw5RKyGB5CnTCm9XERHUbd+Buu070Pba6wFw2mxkbNrIybVrSF+bwsk1KRz++Sej8gHqtGn7V4Hbuw8NuicTVbPmGcfe89ncUo/rDXk2J9GREcRGnzkuuKLsThdZXm7lDWZ/FbdGK3WEUtSIjKBGlPETGVG9daLtThdWhwubwyWtrmFKBWJ3j+J69Oytf1n6m9lhCD+w22xEyxX3kGE9lc7XPbrSdMgwhs35tELbHP71FxZddQWd77ybvv952SdxSZ5Vz+nNm/j+nL50/9fj9Jj8mNnhBKxgzbO1U59gy39f49oDaQGz9I3LbuerxI406J7M+Z9/bXY4ASVY88yWlcWp1HWFrbcn16aQd/gwACoqivpdE4sUuKc2rGfVA/ee0SI84I0ZXi1olYIGcTWq1ILocmn3zL+B/926snyVZxFKGYVtZMWKW6P7sAub04XV7grJ1zpcRUdG0LB2zBqtdZ/KbCctsyKgHDt8gIQ27c0OQ3jJpldfxp6dTfLjT1Z4m+YXDqfz3few7f+mc/b5F5Jw8SVej0vyrHo2vfwCUbVr03niXWaHEtCCNc+aDhrC5ldf4sSqPwKmq/HBH74jPy2Njq/LxE/FBWue1ahbl6ZDhhYZfpJ39Ajpa9e4uyinsO+rz9n5/jvGnUoVtuQWcObnkTptileL2YLxsw1q1ShxjofStwuNmYtL46s8c2ljZuGC8cUFxW1MVATRkUZx63RpbNJ9WJRCJoASAeV0+gmzQxBeknf0CNtnvknba6+v9PjXXlOfpn5Sd36/+2/kH0vzemySZ1WXtWsn+776gk63/42YBg3MDiegBWueNR4wsHDcbKDY8e7b1GrZkmYXXWx2KAEnWPOsJHFnN6PFZSPpOeUpLpr3A9fuO8IVazZw7sz3zihkC+QeOuT1OBwuTZalcmNes/JDe3Ihf+VZQXGbmW+s+3oi28rJHCtZFjtWKWRFCaSYFUL4xMYX/4N2Oun+r8qvPRgZE8Ogd2bhyM3htzvvQLtC9wtCsNn06ktExsTQ5e/3mh2K8JFAGzebuXMHacuW0GHCbSWucSpCV8H427bXXk+tFi1KfExpsytXl8XuJN9WsdmIc62OwjGhwrtCtaVbeI8UsyKgtGjbwewQhBdk79nDzg/eo/2EW6nTunWV9lGvcxf6PPsCRxf9ytY3p3s1Psmzqsk5sJ89cz+mw823UrNJvNnhBLxgzrNAWm92x3tvExEdTfubJpgdSkAK5jyrjOQp04isWXTmdBUVRfKUaT47ZrbFXm5rayjOXFyScMkzEXwqVMwqpfoopSYppV5USk1TSo1VSkn/MuF1LmmBCwnrn3+aiOhokh56pFr76XDLbbS4fCTrnnycU+tTvRMckmdVtfn1V1FK0fW+SWaHEhSCOc+aDnKvN/vHSlPjcOTlsWfOR7QYeYVcQClFMOdZZbQdex0D3phhtNAqRWTNmqAU8ecO8tkxNcb4WZer5NbBcJq5OFzyTASfMotZpdTNSqm1wL+AmsB24DgwCFiglPpAKdXS92GKcHF4326zQxDVdHrLZvZ+NpfOE+8irunZ1dqXUoqB/32TmEaNWH7bBK+1EkmeVV7e0SPs+nAWbW8YT63mvunWF2qCOc8CZdzs/q+/xJaZQSdZAqpUwZxnldV27HVcuWkH4zPyGLVqHSoigtRpFZ9gsCqcLk2W5cyC1eXSZOTZw2YMZzjlmQgu5bXM1gLO1VpfpbV+Vmv9jtZ6utb6Pq11b+BVQPodCCEKrX/mKaLr1KHbA//0yv5iGjTk3LfeI2vXTlL+9bBX9ikqb8v019EOB4mTHjQ7FOEHgTJudvu7MzmrU2ea+LD1TQSn2q1a0eXue9kz92NOrvnTp8eyOlzkenQlDvWZi4UIJmUWs1rrGVrr/DLuT9VaL/R+WCJcNWzc1OwQRDWcSFnNwe+/o+t9k7w60+3ZQ4fR7YF/suuD99g/r/prTEqeVY4l/SQ73nuHNtdcS502bcwOJ2gEe57FDxrCybVrsOfkmHL89NR1pK9JocOtt1dqiZRwE+x5Vh2J/3iI2CbxpDz6CNrHhWWO1YHNYXS1DfWZi0sSznkmAltFx8y+oJSqq5SKVkotVEqdVErd6OvgRPhpGC8ny2CWOm0qMY0a0+XOv3t938mPTaFhr978cd/fyT10sFr7kjyrnK3/Nx1nfj6J/3jI7FCCSrDnWeG42VV/mHL8He++TWRcHO2uG2fK8YNFsOdZddSoW5fkx5/kxB8r2f/1lz4/Xka+jWyLPSxnLg7nPBOBraKzGQ/XWmcBlwOHgI6AfKsRXrdjU6rZIYgqOrpkMWlLF5P0z4eIrlPH6/uPiI5m0Dsf4LLb+O1vt+FyVv3LhORZxdkyMtg+801ajhrNWZ06mx1OUAn2PGs8YCAqKsqUrsa2zEz2fvEpba4aS4169fx+/GAS7HlWXe1uvIn6Sd1Z++RjOC0Wnx5La8ir4HI9oSbc80wErooWs9Hufy8FPtFan/JRPEKIIKS1JvXfTxKXkEDHW+/w2XHqtmtHv5de49iK5Wx+9WWfHccX9nw2l68SO/JhvTi+SuzIns/mmh1ShWx/+y3sWVkk/VPGK4eb6Nq1aWTSuNk9c+fgzMuj422+O5+I0BARGUmfZ18g98ABtsz4r9nhCCH8rKLF7HdKqW1AH2ChUqox4NvLXyIsxRZbQ04Eh0M/fs/JlD/p/shjRMbG+vRYba8fR+urrmH9s9M4kbK6Svvwd57t+Wyu0T364EHQmtyDB/njvr8HfEFrz8lh6//9l+YXX0KDHslmhxN0QuF8Zsa4Wa01O957h4a9etOwZy+/HTdYhUKeVVfTIUNJuOxyNr3yAvnH0swOJyRJnolAVaFiVms9GRgI9NFa24Fc4ApfBibCU4duPcwOQVSSy+kk9emnqNOuPe1u8P1QeqUU/V95g7jmzVlx283YsrIqvQ9/51nqtCk48/OK3ObMzyN12hS/xlFZO2e9i/VUOkkPSqtsVYTC+SzehHGzx3//jcxtW+koy/FUSCjkmTf0/vdzuKxWUv/9lNmhhCTJMxGoKjoB1E3ASGCc+/9XA8N9GZgIT1vXp5gdgqikfV9+RsaWzSQ/NoWIqCi/HLNGvXoMevt9cg/s58+HJlV6e3/nWe6hQ5W6PRA4LRa2vPEaTYcMo3G/AWaHE5RC4XzWuP8Av4+b3fHuTGqcVY/WV17tt2MGs1DIM2+o2649nf52F7s++oBTG9abHU7IkTwTgaqi3Yz7evwMBqYCo3wUkwhjDvuZC5OLwOWy21n/zL+pn9SDVmOu8uuxmww4h6RHHmXP3I/Z89knldrWX3nmcjjY+n+lj+GqlZDglziqYteHH5B/LI2khyabHUrQCoXzmb/HzeYfP8aBb7+h7Q3jiIqTbo0VEQp55i3dH/oXMfUbkPLowz5fqifcSJ6JQFXRbsb3evzcAfQEavg2NCFEoNs1exY5+/bSc8pUVERFr415T9KDj9B4wEBW/+N+svfu9fvxy3J85W/8MGQgKf96mHpdu5U4lrjVldeYEFn5XHY7m19/mcb9BxA/eIjZ4QiT+XPc7K4PZ+Oy2306kZwIXTXq1aPHo49zbPkyDv7wndnhCCH8oKrfPvOADt4MRAiArsl9zQ5BVJAjP58NLzxL4wEDaXbRxabEEBEVxaC334eICFbccTMuh6NC2/kyz/KPH+O3O2/n5xEXYs/KZOhHc7n8t9UM+O+b1GrRApSiZrNmxCW0YOuMN9gz92OfxVJVez79hNyDB0l68BGUUmaHE7RC5XxWOG72j5U+PY7L6WTnrHeIHzyUszp28umxQkmo5Jm3dLjlds7q3IW1TzyK02YzO5yQIXkmAlVFx8x+p5T61v3zPbAdmOfb0EQ4Orx/j9khiAra/vb/yE9Lo+eUaaYWPLVbtqL/K29w8s/VbPjPsxXaxhd55nI62TbzTeb16cG+Lz4j8R8PMXLVOlqOvAKlFG3HXseVm3YwPiOPq7fuZuTKFOLPGcRvf7uNza+9HDBd4lxOJ5teeZEG3ZNNu0gRKkLlfFYwbjbNx12Nj/z6C7kHDtBJluOplFDJM2+JiIqi9zPPk71nN9tnvml2OCFD8kwEqorO1vKSx/8dwH6tdeDOXCKCVubpdLNDEBVgy8pi0ysv0ezC4cSfO8jscGhz9ViOLvqVTS/9h7OHnV9uTN7OsxN/rmL1Px7g1IZUmg47n34vvcpZHTqWuU2NunU5/4tv+P2uO1j75OPkpR2lz7MvmNJd29P+r78ke/cuhsz+WFplqylUzmf+Gje74713qBnflBaXy5QclREqeeZNzS8cTrMLh7Phhedoe/04Yhs2MjukoCd5JgJVRcfMLvX4+U0KWSHC29bpr2M7fYrkx580O5RCff/zMrVbt2HFxFuwnj7tl2Na0k+y8p67mH/hMCwnjzNk1kdc+M335RayBSJjYhj0ziy6/P1etr05g+W33oTTavVx1KXTLhebXn6Bszp1puVIWX1N/CV+0BDSfThuNufAfg7//BPtb7qZiOhonxxDhJfezzyPIyeH9c8+bXYoQggfKrOYVUqtcP+brZTK8vjJVkpVfnFHIcrRqr2Mkwp0lpMn2DLjDVpeMYaGPXuZHU6h6Dp1GPTOLPLT0vjj/r+X2W23unnmcjrZ8d47zOvVnd2ffETX+yYx6s/1tBpzVaVbM1VEBH2efYHeTz/H/q+/ZOFVo7BlZlYrvqo69NMPZGzZTOKDD5veQhwKQul8Fj94KNrp9Nm42Z2z3kMpRYcJt/hk/6EslPLMm+p17kLHW29n5/vvkLFtq9nhBD3JMxGoyvy2orUe5P63jta6rsdPHa11Xf+EKMKJzcRWKVExm159CWdeHsmPTTE7lDM06t2H5Mef5MC8r9n90exSH1edPDu5JoX5Fwxh1aR7qZ+UxOUrVtH7388SXbt2lfcJ0PXeBzj37fc4vvJ3frn0IvKOHqnW/ipLa83GF5+ndus2tA7QWZaDTSidz3w5btZps7Fr9iyaX3wJtVq09Pr+Q10o5Zm3df/X40TVrs2ax2SJseqSPBOBqqITQLVTSsW4/z9MKXWfUqqeTyMTYenowX1mhyDKkHv4ENvffou2N9zIWZ06mx1Oibrd/w+aDhnG6of/QebOHSU+pip5Zj11ij8euJefLhhC3pEjDHrnfS76bj71unStZsR/aTv2es7//Buy9+1l/vDzyNyx3Wv7Ls/Rhb+Svm4tif94iIioik6nIMoSSuez6Fq1aNS7j0/GzR78bh6WE8fpKBM/VUko5Zm3xTZsRPeH/8WRX3/h8IKfzQ4nqEmeiUBV0X5kXwJOpVR74F2gDRB460kIIXxqw3+eA5eL7o88anYopVIREZz71jtExdZkxW03V3tpBu1ysXP2LOb17s6u2e/T+a6/MyplPW2uuc4nEyQ1O/8Chv/wC858C/OHn8+J1X94/Rgl2fjyf4hr3py214/zy/FE8PHVuNkd771N7VataXbBRV7drxAAnSbeRZ227Vjz2GRcdrvZ4QghvKyixaxLa+0AxgCvaa0nAWf7LiwRrho1bWZ2CKIUWbt3sfujD+hw6+3UbtnK7HDKFNesOQOnv8mp9etI/ffUM+6vaJ6dWp/Kzxefzx/33sVZnTpx2bKV9H3uRWrU9e0oi4bJPRmxYAkx9euzYNSlHJr/o0+Pd+y3FRz//Te63f8PImvU8Omxwkmonc/iBw3x+rjZzO3bOLZiOR1uuU3GaVdRqOWZt0XWqEHvp58jc/s2ds561+xwgpbkmQhUFf3LYVdKXQ9MAL533ybTDQqvq9dAps8PVOuf/TcRMTEkPfiI2aFUSIvLRtLxtolseeNVji5eVOS+8vLMlpHB6ocm8eOwc8nas4dz3nyb4T/9Sv3EJF+GXESdNm24+JdF1OvSlSXXX8POD9732bE2vvQfYhs3of1NMvmON4Xa+cwX42Z3vPc2ETVq0H78BK/tM9yEWp75QsKllxM/eCipz/zbb7PdhxrJMxGoKlrM3gIMBJ7RWu9VSrUBPvJdWCJc7dqywewQRAlObdzAvi8+o8td91CzSbzZ4VRY76ef46zOXfjtztuwnDxReHtpeaa1Zvcnc5jXpwc73plJx9vuYPSaDbS74UZT1lyt2bgJF303n7PPv5A/7rubDS88V+YszVVxcs2fHF30K13vuY+omjW9uu9wF2rns8Jxs8uXemV/9txcdn8yh5ZXjCG2UWOv7DMchVqe+YJSij7PvYAt4zQbX3zO7HCCkuSZCFQVXWd2i9b6Pq31J+7f92qtn/dtaOFrz2dz+SqxIx/Wi+OrxI7s+Wyu2SGJMJf69FPUOKseXe99wOxQKiUqLo7B736A9fRpVt5zV5mF4OnNm/jlkgv5/c7bqd2qFZcsXkG/l16jRr16/gu4BNG1a3Pe3C9oe8ONrH9mGqsm3YfL6fTa/je+9AI16tWn420TvbZPEbriBw0hfd1a7NnZ1d7Xvi8/x56ZScdbb/dCZEKUrUFSd9qPv5ltb71J1q6dZocjhPCS8taZ3aiU2lDCz0allFyi8YE9n83lj/v+Tu7Bg6A1uQcP8sd9fw+bgrZmreotbyK87/iqlRye/yNd7/8HMfXrmx1OpdVPTKLXtGc49NMP7HhnJlA0z2xZWfz5r4f4YfAAMrdvZ8B/32TEgiU0TO5pVshniIiO5pz/m0niPx5i5/vvsOym63Hk51d7v6c3beTQj9/T5e57iK5TxwuRCk+heD4rGDd7fFX1x83uePdtzurSlSYDz/VCZOErFPPMV5Ifn0JkbCxrngjcSQwDleSZCFSqrJYKpVSZs7xorfd7PaIS9OjZW/+y9Dd/HMp0XyV2NArZYmq1aMGVm0peZkQIX9Fas+Dyi8ncsYPRqZuJrlXL7JCqRGvNomvGcHTxQmIbNSL/2DFqJSTQ/OJLOPjdt+QfP0aHCbfS88mniGnQ0Oxwy7Rt5pv8+fA/adx/AOd98gUxDRpUeV/LbhnPkQU/M2bj9qC8UCH8z56by6ctm9L1nvvp9dTTVd7PyTUp/HT+YPq99Cqd7rjTixEKUbZNr7zIuqemcOG3P3H20GFmhyOEcIuOjKBh7Zg1Wus+ldmuzJZZrfX+gh/3TR3c/z8OnKpirKIMuYcOVer2ULN57WqzQxAeji5exLEVy0l66JGgLWTBGC+VcMmlaIeD/LS0wl4PO96ZSURMDJcsXMqA16cHfCEL0HniXQyZ9RHpa9fw84gLyD14oEr7ydy5g/1ff0nH2/8mhayPhOL5zFvrze54722iatWizbU3eCmy8BWKeeZLXe6+l1otW7Hm0Ye9OmQj1EmeiUBVoTGzSqk7gC+At9w3JQDf+CimsBYVF1fq7dZT6X6Oxv9cLvnDEii01qROm0Ktli3pMOFWs8Opts2vvlTi7drlolHvvn6Opnpajb6SC776jry0o8wffh6nt2yu9D42v/oSkbGxdPn7vT6IUEDons/iBw+t1rhZ6+nT7Pvyc9pcc53Pl7kKB6GaZ74SGRtLr6ee5vSmjez+8AOzwwkakmciUFV0NuO/A+cCWQBa651AE18FFa4O/vAdjtxcVFRUkdtVVBSOvDzm9Ulm98cfeX02UyFKcvC7eaSvW0uPyY8TGRNjdjjVVlrvhrzDwdnroengIVz8069orfl5xAUcW7G8wtvm7N/Pnrkf0+HmW6nZWE7lonKaVnPc7J65c3Dm59Pxtju8HJkQFdNqzFU0HjCQ1KefwpaVZXY4QohqqGgxa9Va2wp+UUpFAVJReVH+sTRW3ns39ZN6MOC/b1KrRQtQilotWnDOm29z+W+rqduuHb/fdYd7DON2s0P2icRe/c0OQQAup5PUp5+ibsdOtLn2erPD8YpaCQmVuj0Y1O+WyCULlhDX9Gx+vXIk++d9XaHtNr/+CioyMuhmpw42oXo+a9SvPxHR0RxbXvmuxlprdrz3Do369qNB9x4+iC78hGqe+ZJSij7PvoDlxHE2vfKi2eEEBckzEagqWswuVUo9CtRUSl0EfA5857uwwovWmt/v/huO3BwGvzuL9jfcyJWbdjA+I48rN+2g7djrqN8tkYt/XkT/16ZzeuNGvj+nL6lPP+WVGU0Dyf7dMslVINj72Vwyt28j+bEpRBTrKRCskqdMI7Jm0W78kTXjSJ4yzaSIvKNWi5ZcPH8hDZN7smzCOLa//b8yH5939Ai7PpxFuxvGU6t58BbywSBUz2fRtWrRsIrjZo8tX0bWju10vFVaZb0lVPPM1xr17kPb625g64w3yN63z+xwAp7kmQhUFS1mJwMngI3A34Afgcd9FVS42f72/zjy6y/0fvo5zurUudTHqYgIOt5yG6NSUml15dVsfPF5vh/YhyOLFvoxWt/Kzjxtdghhz2mzsf7Zf9MguRctrxhjdjhe03bsdQx4Y0aRXg8D3phB27HXmR1atcU0aMCF834k4ZLLWP3gJNZNe7LU4Qhb/vsa2umk2wP/9HOU4SeUz2dVXW92x7szqVGvPq3GXOWjyMJPKOeZryVPeQoVFcW6Jx8zO5SAJ3kmAlV568w2Vkp11Vq7tNZva62v0VpfDawEGvknxNCWsW0ra594lGYXXUzH2/9WoW1qNoln0Mz3uHDejxARwcIxl7P8tgnkH0vzcbQiHOz64D1yD+yn5xNTUUqZHY5XtR17HVdu2kHyrwsLez2EiqiaNRn64Sd0uPk2Nr38Aiv//jdcdnuRx1hOnmDH++/SZux11GnTxqRIRSioyrjZvLSjHPj+W9rdOJ6omjV9GJ0QFVOreQLd7v8H+7/5iuMrw2MJSCFCTXkts/8FGpdwe3Pgde+HE16cVisrbr+FqFq1OWfG/ypdOJw97DxG/v4n3Sc/xoFvv2Fe32S2vzMT7XL5KGLfa9Oxi9khhDV7bi4bXnieJucO4uwLLjQ7HJ8J1TyLiIqi/2v/pcejT7B7zocsvv4a7Lm5hfdv/b/pOPPzSfzHQyZGGT5CNc/AY9zssqUV3mb3h7PRDod0MfayUM4zf+h67wPENWvGn5MfDurvT74meSYCVXnFbJLW+oy/VFrrn4HuvgkpfKQ+/RSnN65n4Iz/UTO+aZX2ERkbS49/Pc7IlSk07NGT1f+8n/kXDePUxg1ejtY/8j2+eAv/2z7zTSzHj9FzylMh1yrrKZTzTClF90ceZcDrMzi68P/bu+/wqMrsgePfd3pJTyCEEEINvYN0QVAsKCoi9r62de3rrr91xdVd13XXXtfeBVFxVewNlN6lhB5KCIQASSCkZ+b+/phJpKfN3DvlfJ4nD2Eyc+9J5uRmzrzve97v+O7s01n/2st83L0zqx//N2aHg30rlhsdZlSI5DyrXTeb38B1s16Ph41vvUar0WOI69gpyNFFl0jOMz1Y3W76/e0fFK5YRs4HU40OJ2RJnolQVV8xa23i10Q98n+eTfazT9H5mt+Rceb4Zh8vrlNnTv3sS4a//DolW7fy5ahhLL3vXqoPHgxAtPrJz9tudAhRq6q4mDVPPUH66WfScsgwo8MJqmjIs85XX8uo9z6gcOWvLLr7jrotiDzl5Sy47RZypk8zOMLIF+l5ljriZApXLG/Q1iZ5335NaW4uXWQ7noCL9DzTQ/sLLyK5/wCWP3j/YbNZxG8kz0Soqq+Y3aiUOuvIG5VSZwI5wQkp8lUWFTH3puuI69iJAQ//K2DHVUrR4aJLOHfxCjpefhXZzz3NZ4P7kfuFNJ4W9Vvz7FNUFRfR968PGB2KCJCMs87GnpQERzSD8pSXseKhKQZFJSJF7brZPQ1YN7vhtVdwpqXRJgBv3goRaMpkYuAj/6Z81y6yn37C6HCEEI1QXzF7J/CUUupNpdSt/o+38K2XvT344UUeTdNYeOetlO/ezYhX3sDqdgf8HPakJIY+8zynf/sjtrh4Zl06mZ8uvZDSHbkBP1egtWwtW4UYobxgN+tefI7MiZOiYu/HaMqzij17jnl76Y4dOkcSfSI9zxq632zJ1q3s/P5bOl95DSarTOoKtEjPM720HDKMzPMvYM0zT1KaJ9fHI0meiVB1wmJW07QNQC9gNtDO/zEb6O3/mmikLR9MZdsnH9Pn/+4nuf+AoJ6r5eChjP95Pv0f/Ae7fvyBz07qR/ZzT+OtqQnqeZsjNi7B6BCi0uon/oOnooK+90XHaF005Zm7zbFfgBzvdhE4kZ5nDV03u/GNV1EmE52uukanyKJLpOeZnvo/+DCa18vyB6Pjb2FjSJ6JUFXf1jxK07RKTdPe0DTtbv/H65qmVRx6n+CHGRlKtm5l0R/voOWw4fS4U589Hk1WKz3uuJsJC5eTOuJklt53L1+OHs6eJYt0OX9jbV632ugQdJUzfRozembxToKLGT2zDFnHeHD7Nja89godL7uSuE6ddT+/EaIpz/pOeQiz03XYbWani75THjIoougRDXnWauSoE66b9VRWsundt2lz5lm40+UNlGCIhjzTS0xmJt1vuY0tH0xl79LFRocTUiTPRKiqb5rxT/6pxW0PvVEpZVNKjfFPOb4qeOFFDm9NDXNvvBaUYvhLr2Eym3U9f0xmJqd88DGj3plK5b69fH3qaBbedTtVxcW6xiF+kzN9Ggtuu4XS3FzQNEpzcw1pzLPy0UcA6P3n/9P1vEIfHSZfzJBnnsedkQFK4c7IYMgzz0fUHrvCOKn1rJvd/vn/qNy7h6xrb9A5MiGapudd9+BomcqS//sT2hH9BoQQocdSz9fPAK4Fpiql2gPFgBNfEfwt8KSmaSuCGWCkWP3kY+xZMJ/hr7xOTNtMQ2JQStF2wnmknTKWFQ8/yPqXXiR35mcM+OejtLvgwpDYisUdG290CLpZ8dAUPOVlh93mKS9j/i03smX6NOyJidiTkrAlJh3xue/DlpiILT4eZarvPamj5UyfxoqHpvjWTWoaaaeehrtNRqC+tZAXTXkGvoJWilf9RUOetThk3Wz6aacf9fUNr71CbPsOpJ0yxoDookM05JmerLGx9L3/byy49Wa2zfiIdhdcaHRIIUHyTIQq1dB3nZRSViAFKNc0rTiYQR2pT78B2rez5+p5yoDau3QxX592CpnnX8DI194yOpw6+1YsZ+Edf2Df8mWkjTmV1mNPY91/n6N0xw7cbdrQd8pD8gI4iN5JcB3VZbZWUt/+VBUVUllcRPX+/cc9hjKZsCUkYvMXu7VF7lGfJ9R+nkz+7J9YfO8f8ZSX1x3H7HAy5NkX5PkWQjTaN2eMxVNVxVk//nLY7UXZa5g5dCD9//5Petx2p0HRCdF4Xo+HL0cNp6q4kAmLf8XidBodkhARz2o2kRxjX6pp2sDGPK7BxayRwrmYrT54kC9OHoqnsoJz5i7GlpBgdEiH8Xo8bHjtZZb+9f/wVlYe9jWz06X7dMRVSxbQa+AQ3c5npKmtU6g5xn527owMJq7+rb+at7qaqv3FVBYVUVm4j6qiIioLC6ksKjz+58VFVDdg78cTnTeSRVOeCeNES56t+MeDrH7iP0zeuhNbXFzd7Yv+eAcb336TC9ZuwpGcYmCEkS1a8kxv+T/P5rtzzqDv/Q/S649/Mjocw0meiWBrajFb3zRj0UxL/vJnSnI2M27mNyFXyAKYzGa63nAzq598jPKdOw/7Wu1elPqO1oX+myuBkDN9KjWlpSiLBe2Q7tLHasxjslpxpLTAkdKiUefwVldTWVzkL3L3UVnk+3zezdcf8/7RtVVLdOSZMFp05FnqiJNZ9Z9/sWfBPNLHnQH43sjNmfY+medNlEI26KIjz/TW6uRRZJx9Dquf/A8dL78CV6s0o0MymOSZCE2NX2wnGmz7zM/Y9Nbr9Lj9LlJHjDQ6nBMq37XrmLfrX+AYv2432A5s2sjCO2+j5dBhDH3+paA15jFZrThbtCQ+qwsthwwj48zxdLz0ct/5jiG6tmqJ/DwToSA68qx23Wz+IfvNbvnwA6pLSsi67thvnolAio48M0L/hx7BW1nJir8/aHQoIUDyTISmRk0zVkrFcchorqZphcEI6kjhOM24LH8XM4cOwp3RljO+n4XZZjM6pBOa0TPL11X3CNE09VQPnooKvjp1FGV5eYyfs8CQrSpquygf2nzKiCnlQojI8c0ZY/FUVnLWT3PQNI0vTx6G5vUwfs7CkGguKERTLbnvz6x9/lnGz55HUp++RocjRMRq6jTjBo3MKqVuVErtBlYCS/0fSxofZnTQNI35v7+RmvIyRrz6RsgXsnDsvSgB2p57vq5x5KzP1vV8elty370UrVrJsP++Ytiei7JVS+TnmQgN0ZRnqSNOrttvdu/SxRSuXEHWtddLIauDaMozI/S+5/+wJyax5C/RvVWP5JkIVQ2dZvxHoIemae00TWvv/+gQzMDC2fqXX2TnD98x4O+PEJ/VxehwGuTIAsfVpg3utplseuctDmzerFscpSXH79wb7rb9bwYbXn2J7n+4nTann2loLB0mX8zE1Ru4oriMias3RFUhC5GdZyJ0RFOepY44Gc3rZc+CeWx47RUsMTG0j7LrilGiKc+MYEtIoM9f7mf3nF/InfmZ0eEYRvJMhKqGFrObgbJ67yUoXpvN0vv/QvrpZ5L1u/DaJP7QAueCNRsZN/MblMnM7CsupvoYXXdFw5Vs2cL8W28mZeAg+j7wUP0PEEKIMFK7bnb7Z5+ybcZHdLjoUqyxsUaHJURAdL7mOpxprfn5qst5J8HFjJ5Z5EyfZnRYQggaXsz+HzBPKfWSUuqZ2o9gBhaOPJWVzPnd1Vhj4xj63IthP70qJjOTka+9RXH2Ghbcfosu02s6du0Z9HPozVNVxS/XXgFKMfL1d8Ji2nmki8Q8E6EnmvLM4nLhbteOTe+8iaeigtyZn8mLfZ1EU54ZZeuMj6gs3IfmqQFNozQ3lwW33RJVOS55JkJVQ4vZl4AfgQX8tmZ2abCCClcr/v43ilavYtjz/8XZMtXocAKi9dhT6XvfA2z98APWv/xi0M9XcqA46OfQ2/K//ZV9y5Yy7Ln/EpOZaXQ4gsjMMxF6oinPcqZP4+CWLXX/L9+dH3Uv9o0STXlmlBUPTcFbWXnYbbXbF0YLyTMRqhpazNZomnaXpmlvaJr2Vu1HUCMLM7tmzyL7uafJuu4G2pxxltHhBFTPu++hzZnjWfKXP1MwP7hdpQt2RtZep7lfzmTt88/S5YabaDvhPKPDEX6RlmciNEVTnq14aMphe2ZD9L3YN0o05ZlRjrdNYTTtzy55JkJVQ4vZn5RSNyil0pRSSbUfQY0sjFQWFjLvpuuI69SZAf94xOhwAk6ZTAz/76vEtM3k56supyz/2HvSisOV5m5n3s03kNSnHwP+8S+jwxFCiKCRF/sikh1vH/bo2p9diNDU0GL2UvzrZpGteQ6jaRoL77yV8oICRrzyBhbX0dvbRAJbQgKj3p1GVckBfrnmCrzV1UE5T6v0tkE5rt681dX8ct1VaJ4aRr7xDma73eiQxCEiJc9EaIumPJMX+8aJpjwzyrG2LzQ7nPSdEj0NHSXPRKhqUDF7yHY87WVrnsPlTH2Pbf+bQd/7ppDcr7/R4QRVYo+eDH32BQrmzWXplL8E5RxOtzsox9XbiocfYs/CBQx+6jniOnY0OhxxhEjJMxHaoinPjvli3+mKqhf7RommPDPKkdsXAmScMyGqtrWTPBOhqt5iVikVp5Q66tW4Uqp3PY/LUEr9pJRaq5Rao5S63X97klLqO6XURv+/iU0P31glW7aw6J47aTl8BN1vv8vocHTRftJFdL3p96x74Tm2fDQ94MffsmFtwI+pt53ff8eaJx+j01XX0n7SZKPDEccQCXkmQl805dmRL/bdGRkMeeb5qHqxb5RoyjMjHbp9YXL/Aexft87okHQleSZC1QmLWaXUZGAd8LG/IB10yJffrOfYNcDdmqZ1A4YAtyilugP3Aj9omtYZ+MH//7Djralh7o3XosxmRrz0Giaz2eiQdDPgH/+ixZChzL/1Zoqy1xgdTkgp27WTOTdeR0L3Hgx69DGjwxFCCN0c+mJ/4uoNUsiKiNXh4kspWvUrRWtWGx2KEFGvvpHZvwADNE3rC1wDvKOUmuj/2gk3UdU0bZemacv8n5cAa4F04FygthPyW8B5TYrcYKuf+I9vGunjT+HOCMw6Ak3T2LBvHe+sfI0HZv+ZdXuzA3LcQDNZrZz81nvYYuOYffnFVO3fH7Bjx8aH7UA9Xo+HOddfQ01ZKSe/+S4Wp9PokMRxhHOeifAheSb0IHmmv3YXXIiyWMiZ9r7RoehG8kyEKqVp2vG/qNQqTdN6HfL/NGAmviL0ak3TGrRIVCnVDvgZ6Als1zQt4ZCvFWmadsLfkD79Bmjfzg7uljCNsWfJIr4ZN4bMiZMY+eqbTT5OtaeaVXtWsDBvLgvz5rIobz6FFfsAMCkTJmXi5gF3cOfg/8NlDb3GUrvnzeG7c84k/fQzGP3uByhTQ/uJHZ/m9QbkOEb49ZF/sPJfDzPsxVfoeOnlRocjTiCc80yED8kzoQfJM2P8dPEk9q1YxsQ1G6Nidp7kmQg2q9lEcox9qaZpAxvzOEs9Xy9RSnXUNG0z+EZblVKjgf8BPRpyAqVUDPAxcIemaQeUOuGA7qGPuwG4ASCtdWtWLZkP+LqpOd3uurn7sfGJZHbMYvWyhQCYTGZ69D+JTWtXUV56EIBO3XtTXLiXvfk7AUjLaIfNbmfbpvUAxCcmk57ZgewViwGwWK106zOQjWt+paK8DICsnn3Ztzufgu1bWH/jDThSW9H1r/fXxZWY3ILU9LasW7kUAJvdQZde/Vi/ajlVlRUAZHbrxg9rvmB+7i+sOrCK9QfXUVbjO35rR2uGtxzJyA5jaHEwgRhzDK/nvsazix/jw1Xv8YeOtzI4aQjd+w4ib1sO+4t8RW9mpy5UVVayK3crACmtWpOQlMKm7JUAON0xdOrWizXLFuH1egDo2X8w2zZvoGR/EQDts7pRXlpKft52AFq2bkNsXAKb1/mmz7hj4+nQpTurliwANEDRa+AQSpOTaH3Djex44XmWP/pP0q+6qm4fsqY+T2tXLMZqszf7edq3Jx+A9HYdMZlM5OZsbPDz1LX3AHbnbado3x4AMjp0xuv1krd1MwDJLVqRnNqKDatXAOBwuojZW8TKR/9J4mnjKMvqiKemJmSep5z12ZSW+EbPO3btScmB4mY/T4H6fdL7eercow9rf11CSXERTndMSP0+yfN09PNU4++aHq7PU2VFBQOGjZLnKcSfp3D/fdqyIRuLxSrPk87Pk2XQAMq/+oIlb73KSdfeGPG/T3vz83C6Y8LueZLrXvg8Tw2rEI9W38hsH6BU07RNR9xuBSZrmvbeCQ/uu99M4BtN057w37YeGO0vjNOAWZqmdTnRcUJpZHb+rb9n0ztvMu6Lb0kdPuKE9y0s38einfNYmDePhXlzWVmwnBpvDQpF9xa9GJw+jMGthzM4fRitYlof8xjzdvzCn3+4jY2F6xjf6Tz+Pvo/tI4Nna0ONE1jznVXse2Tjxnz8We0HjO2WcdbtWQ+vQYODVB0+igv2M0XI4ZgjY/nrJ/mYI2JMTokUY9wzDMRfiTPhB4kz4zhqajgw6x2ZJw5nuEvvWZ0OEEneSaCLSgjs5qm/QqglHpU07Q/H3J7tb+b8XGLWeUbgn0NWFtbyPp9BlwF/Mv/76eNCdhI2z//lE1vv0HPu+45ZiG748B2/5TheSzIm8uGQt+7HzazjX6pA/n9gDsZnD6cgWmDiXckNOicw9qM5IfLF/Li0qd4csEjzNr2PfcOe4Br+t6ExVTfwHrwKaUY8uyLFK/NZs51V3HW7LnEtM1s8vFMpvCaqqN5vcy94Tqq9hczdsZnUsiGiXDLMxGeJM+EHiTPjGF2OMg87wK2fvQBJz3+dMT//Zc8E6HqhCOzdXdSatmR62OVUis1TTvu9jxKqRHAL8AqwOu/+S/AQmA60BbYDlyoaVrhic4fCiOzZbt2MnPYSbgz2nLG97MwWa1sKFxXt951Qd5c8kpyAYi1xTGw9RCGpA9ncOth9G01EIfF0ewYthVv4d6f7uCnrd/Su2U/Hh37DP1aNerNi6A5sGkjX54ygriOnTj96x8wO5r//YaDVY//hxUPTWHwU8+Rdc11RocT8kqrDjIndxY/bP2W3APb6Js6gIFpgxmQdhIJDmkuIYQQInwUzJ/LN2ecyvCXXqPDxZcaHY4QYa2pI7P1TTO+Gfg90AHYfMiXYoG5mqbp0uXG6GJW83r5fuI57J4/l4Mv3sx88yYW7/ytWVMLVyqD04f5itf04XRP6YU5SO9gaZrG5xtncP+sP1JQupur+9zA/w1/kDh7fFDO1xi5X3zOrEsn0+nKaxj67AtNOsamtavo1K1X/XcMAQXz5/Lt+NNpe+75jHz9bRq6HjyaaJpGTvEmftjyNT9s+Yb5eb9Q5anCbY0hIy6TDYVr8Wq+97qykroxqPUQBqQNZlDrIXRKzArazzSc8kyEL8kzoQfJM+Nomsb/+nQntkNHTv3fTKPDCSrJMxFswWoA9T7wFfAIh+8HW1LfaGq4q6ypZPGuBczf8Qt73vqAXj9t4uPxsGDbU7RP6Mi4juMZnD6cIenDaRffQbdCRinFhKwLGJ15Ko/Oe5DXV/yXLzZ+ykOj/825WZMMLagyxp9Dz7v/xOrH/03KwEF0vuqaRh+jdtF5qKss3Mcv111FTNtMhjz9vBSyhyivKWf+jl/qCtit+3MA6JzUlWv73szYdqdzUuth2C12SqsOsjx/CYt3LWDJroV8sfF/vLf6DQASHUkMSBvMwLTBDGw9hH6tBuK2ugMTY5jkmQhvkmdCD5JnxlFK0f6iS1j92KOU7czD1Trd6JCCRvJMhKr61szuB/YDl+gTjnE0TWNT0QZmb/uen7Z9T8nnP3Dqd9Wk7odUoKJ9Mtc88BQvpQ8nNSbN6HCJs8fz8ClPcGG3y/jTD7dy05dX8sGad3hkzFO0S+hgWFx97pvCvmVLWXTPnST16k1y/wGGxdIYHq+nwaPpmqYx96brqdizhzO+m4UtLi7I0YW+bfu3+orXrd8wL3c25TXlOC1OhmeM5sYBtzGm3elkxrc76nFuWwwj2o5mRNvRAHg1L5sKN7DEX9wu3rmA77d8BYBZmenRojcDW/sL3LShZMS1lTcShBBCGKbDxZey6t+PsOXDD+hx+11GhyNE1GnQmlmjBWuacXFFEXNyZzFr6/f8tO27ujWv4za1ZOyHhZgqa+rua3Y4GfLsC3SYfHHA42guj9fDG7/+l3/Ne5AaTzV3DL6Xmwfcgd1iNySein17+XLUMADOmj0PR3JKgx9bXlaK0xWY0bcT2X1wV91o4JKdC1hZsJxuKT35z9jn6J3a74SPzX7uaZbedy+D/v04XW/8fdBjDUWVNZUszJvLD1u/4cet37Cx0NemvV18B05tfwZj25/BkDYjcFqczT5XUUUhS3ctYqm/uF2Wv5iy6lIAUt2tGJg2hIGtBzMobQi9WvZrUN7rlWciukmeCT1Inhnvq1NHUVNaytnzFkfsG6ySZyLYgrJmNlQEqpit8dawIn8Js7b9wKxt37EsfzFezUusLY6RbUczOvM0RmWeytLh4yjNzT3q8e6MDCau3tDsOIJl18E8psz6E59vnEHnpC48OvZZhrUZaUgs+5Yt5eszxpI6bARjPv60wRuK79qxjbQ2Te+GfCzVnmqy965i8c4FvoJo1wJ2HPDt3WU32+mT2p9eLfvy+YZP2FtewPX9buFPQ6fgth3dmXDv0sV8PW4Mbc44i1HvTovYP1rHkleSy49bvuWHrV/z8/afKKsuxWa2MazNyYxtdzpj259Bh8ROQY+jxlvD2r2r696IWLxzAdsPbAV8ncN7t+zHwNZDGJQ2hIFpg485kyIYeSbEkSTPhB4kz4y3/tWXWXT37Yz/ZQFJvfsYHU5QSJ6JYJNi9jh2HNjOrG3fM2vb9/yy/Sf2VxajUPRtNYDRmadxSuap9Gs1CKvZWveYdxJccKyfi1JcUVzW1G9DN99v+Zr/+/EOcg9sY3L3y5ky8p+kuFroHsfGt99kwa030/PuP9FvyoMNekwg9jHbV76XpTsX1o28rshfQnlNOQBpMa0PG8nr0aJP3Uje/opi/jn3Ad5e+QqtY9P55ylPcnrHs+uOW1VczMyRQwCN8T8vwJ4Y2d13qz3VLNm1sG768Nq9vs2602MzOLX9mYxtN47hbUcHbB1rcxSU5tdNS16ycwG/FiyjylMFQEZcJoNaD2Fg2hAGtR5Ct5SerF22WPbLE0En+zIKPUieGa+ycB8fZbWny403M/DhR40OJygkz0SwBasBVNgprS5l/o5fmO0vYGunP6bFtOasTucyOvNURrY9hSRn8jEfX1lYiMlmx1tZcdTX3G3aBDX2QDm1/RkMu3IZTy38Fy8sfZLvcr7k/pEPc3GPKzEpk25xdL7yavYuXuRrCDVgIBnjzwn4OTxeDxsK17LYP0K3dNdCcoo3AWAxWejZog+X97q2rkNuemzGcY8V70jg0bFPc2G3S7jnh1u56rMLOavTBP4x+nHSYtKZ/4ebKNuZx+lffx+xhezug7v4cdt3/LDla37e/iMHKvdjMVkYnD6cKSP/ydj2Z5CV1DXkRqRbultxVqdzOavTuYBvGvTqPSvq8mJu7mxmrPsAAJfVTd+4vtya8mdGZY7V9XdCCCFE5LEnJZN++hls/XA6/R98GJMl4l5eCxGywn5kVtM0sveu4qet3zF72w8s3DmXKk8VDrODoW1GMrrdqYzOPK1BL8D3r1/HTxdPomTrFkxmC97qqrqvmZ0uhjzzfEiumT2RdXuzuffH21iQN5fBrYfx6Nhn6ZrSXbfzeyoq+OaMsRzYvImzfppDXKfOJ7z/3t27SEk9foOt/RXFLMtfXFe4Ls1fxMGqEgCSnS0Y1Hqwr3BNG0Lv1P64rK4mxV3tqea/y57miQX/xKTM3FdwGtanP6H/3/9Jj9vubNIxQ5HH62F5/mJ+2PoNP2z5hpUFywFo5U5jbPszGNv+dEZmnEKsPbybXGmaxo6S7SzZuYBFO+fz6fqPKKzYR/uEjlzd5wYu6n6F7HMrAq6+65kQgSB5Fhq2f/4psy+/mDEff0r6qeOMDifgJM9EsEXVNOM9ZQX8vO0Hftr2PT9v+5GCsnwAuqX0ZFTmWE7JPI3B6cNxWBwNPseOb75iznVXYXY4GfXuVA5u386Kh6ZQumMH7jZt6DvlobArZGt5NS8fZL/L33/+Cweq9nPzgDu4c/D/NbnQa6yD27fx5ajhOFJTOfOHn7G6jz8t9UBxIXEJScBvHaZr10Yu2bWQDfvWoqFhUia6pfT0TRn2j7pmxrcP+IjhtuIt/PONaxny9wXs7hbHmR99SZ+08OjQfCL5B3fy6vIXeH/1mxRW7MOkTAxKG1JXwHZP6RVyo6+BtHffLn7eM5s3VrzE4l0LcFqcTOx6Mdf0uZGeLSNzvZPQ36HXMyGCRfIsNHgqK/koqz2tTxvHyFffNDqcgJM8E8EW0cVs7379tL+9+yiztvlGX2tHj5KcKYxqO4ZRmacyOnMsrWJaN/rYmqax5qnHWf7gFJJ692X0+x/gbnP8qajhbF/5Xh76+S98kP0OGXGZPDLmKU5tf4Yu59754w/8eMEEMidOYsSrbx5VKGmaxr7yvXw170P2xZawZNdClu5aSFGFbzvjeHtC3VThgWmD6ddqIDG22KDHXXXgAF+OGsbBg4U8c7OFHaZ9XN/vD/xp6P3HbBAV6tbvW8uLS5/i47VT8Wgezuw4gQlZFzAqc2xUjUweuvZnVcEK3vz1ZWasm0Z5TTmD0oZwTd8bGd/pfMM6govIIGvMhB4kz0LHwrtuZ/P773Dhxm1YY4P/GkVPkmci2CK6mDWnmzXvDV4sJgsD04ZwSrvTGNV2LL1T+zVrvVtNeTnzb72ZrR9+QOb5FzDshZexuPQZrTTSvB2/8OcfbmNj4TrGdzqPf5zyGGkxwd3ou7ymnEX/+hvb//MM6vYL2XVmFnkluews2UFeSS55B3Kp8Py2TrlzUte6wnVg2hA6JWXpvrZR0zTmXHcV2z75mNO++AZH/548PHcKb698hfTYNjxyylOM6zhe15iaQtM0FuTN5YUlT/Ldli9xWpxc3ONKbux/m6F7EhvpWH+UiyuK+CD7Hd789WW2FG8mxdWSy3tewxW9rzvhWmshjkde/Ak9SJ6Fjj2LFvD1aacw7IWX6XjZFUaHE1CSZyLYIrqYTenUQnv8wxcY3mZUwNbule3MY9alk9m3fBl973+QnnffE9HTKo9U5anixaVP8eSCRzCbLNw77AGu6XsTFlPjmxZ4vB72lO0mrySXHQdyfcVpXaG6gx0luRSW70V54coPoNtGeOkqKO+eRnpcBumxGbSObUOb2Axiyp2c0f/8kBgl3PjWGyy47ff0/evf6HXPn+tuX7xzPvd8fyvr9q1hfKdz+ccpjwf9zYCm8Hg9fLnpU15Y+iTL85eQ5Ezh2j43cU3fG0l2Nnzv30i0ffMG2nbMOubXvJqX2dt+4I1fX+K7nC9RSnFGx7O5ps9NjMgYHVXXCdE8J8ozIQJF8ix0aJrGp/174W6TwWmff2V0OAEleSaCLaKL2UDtM1trz5JFzL7sIqoPHmTEy68HpctuuNhWvIV7f7qDn7Z+S++W/Xh07DP0a3V4DpVUHmCHv0DNO5DLzoM7yDuQW3fbroN51HhrDntMjC2WNrFtfcVqTJu6orUVSeRfcjtaWQXjf56Pq9XhzQQ8NTWYQ6ALYNGa1Xw1ZiQthgxj7IzPjtont9pTzX+XPsXjC/6JxWzl3mF/45o+N2I2NWw/3WAqqy7jg+x3eGnpM2zdn0O7+A7cNOB2Jne/XLd10qGuoXm2ff823l75St3a4s5JXbiq9w1M7n4ZcfZ4HSIV4SxUrmciskmehZaVj/6TXx/5BxNXr4+oZWuSZyLYpJhtoM1T32PB7bfgapXG6Gkfkdi9R0COG840TePzjTO4f9YfKSjdzVmdJlDhqSTPP8paUnXgsPtbTBbSYtJJj21DemzGbx9xGXW3neiFflH2Gr4aezJJvfswbuY3mKy/7fEbCtNYqktL+XL0cKqKizl77kKcLVOPe99txVv484+3MWvb9/RNHcBjpz5vWAOhvWV7eOPXl3jj15coLN9Lv1YDuWXgXZzZcUJIFNmhpLF5VlFTwWcbPuaNX//L8vwluKxuJnW7lGv63Ei3FLmGiGMLheuZiHySZ6GlZMsW/te3O/0eeIied91jdDgBI3kmgk32ma2H1+Nh+QN/JfvZp0gdOYpRb7+HPenYe81GG6UUE7IuYHTmqTw670G+2Pg/WrhTaZfQnuEZJx82DTg9LoOWrlbNKo4Su/dg6LMvMue6q1h6//8x6F+PBfC7ab7F99zJgY0bOPV/X5ywkAXITGjP1PM/49MNH/LXWfdw+vvDub7/H7hnyF91axC1tTiH/y57hg/WvE15TTmntT+L3w+8kyHpw2VKbIA4LA4md7+Myd0vY0X+Ut749SU+WPM2b698hSHpI7i6zw2M73QeVrO1/oMJIYSIWLHt29Ny6DBypr1Pjzv/KH+HhQiyqChmq4qL+eW6q9j5/bd0ueEmBv7z34eNBgqfOHs8D5/yBA+f8kTQz9V+0mT2Ll3MuheeI2XgINpPuggAi8HPy+ap77H5vXfo9af/I230KQ16jFKK87pMZnTmaTw8537+u/RpPt8wg0fGPMW4DmcFLdZl+Yt5YcmTfLnpUywmCxd0vYSbBtxOl+RuQTtnpGhOnvVtNYCnW73MAyc/wtQ1b/HWr69w05dX0tLVist7XcuVva9rUmd1EXmMvp6J6CB5FnraX3QpC+/4A4UrlpPcr7/R4QSE5JkIVRE/zfjApo38dPEkSrbkcNJjT5F1zXUBjk40lbe6mu/OOYN9v67gzO9nk9ijpyFx5EyfVrenMEBsp85MWLAUUxPXhizKm8c9P9zK+n3ZAe8W7dW8fL/la15Y8iQL8uYQZ4/nqt7X87u+vyc1RjYzN4LH6+HHrd/yxq8v8dPWbzEpE2d2msA1fW5iWJuR8q68EEJEmcqiIj7KakfWddeH3OwzIUKVrJk9hp3ff8fP116ByWJl1DtTSR0+IgjRieYoy9/FlycPw+v1YrbZKNuZh7tNG/pOeYgOky8O+vlzpk9jwW234Ckvq7vN7HAw5NkXm3X+Kk8V/136NE/4G0T93/AHubr3DU2enl1ZU8mM9R/w4pKn2FC4lvTYNlzf7w9c3utaXfbbjTQb1/xK5x6BX9u8tTiHN1e+zLTVb1NcWUSX5O5c3ecGLux2qTxPUShYeSbEoSTPQtPsKy9l99w5TFq3OSJmA0qeiWCTYvYQmqax9vlnWHb/X0jo3oPRUz8kpm1mECMUzbHi4YdY9e9HDrvN7HQx5JnnG1RQej0ePOXleCrK8ZSXU1NRUff/mvIKPOVlx7197X+fp6ak5KhjujMymLh6Q7O/t63FOfz5h9uYvf2HJjWI2l9RzNurXuXV5c+zuzSf7im9+P3AOzk3a5Ksz2yGYDeyKKsu43/rP+TNX19iZcFyYmyxXNjtUq7pcxNZyV2Ddl4RWqRhitCD5Floyv1yJrMuuZBTps+gzelnGh1Os0meiWCTBlB+nooKFtx5Kznvv0vbCecx7MVXsMbo04hHNE3O1HePus1TXsaC225h2/8+xlNe8VuhWl7uL0grqPHf5q2qCnhMtVOOm6tdQgemTfycT9Z/wJTZf/6tQdTQ+3Fb3cd9XF5JLi8ve473Vr/BwaoSRmacwtOnv8KotmNl2moYcFldXNrzKi7pcSXL8hfxxoqXeG/1G7zx60v0aNGbERmjGJ4xmqHpIwK2d7YQQojQ0frUcdiTU8iZ+l5EFLNChKqIGpkty9/F7MsvZu/iRfT+v7/S+0//hzKZdIhQNMc7CS44Th4m9uyF2eHA7HRidjixOJ2HfO7A7PD931J7H6friNudmGv/73Bicf12HJPdzie9ulCam3vUeQM1MnuooopCHv7lft5d/TrpsRn8a8zTnNbh8D9w2XtW8cKSJ/nfhg/RNI0JWRdw84A76J3aL6CxRLvKinLsDqeu59xTVsD07HeZtfV7Fu+cT4WnArMy0ye1PyMyRjMiYzQDWw+RvYAjiBF5JqKP5FnoWnTPnWx86w0u3LgNW3x4700ueSaCLeqnGe9btpRZl02msriYES+9RtsJ5+kTnGi2GT2zdCsoj3TMNbONmOLcFAvz5nLP97eyoXAtZ3c+n7+P/g+bCjfwwpIn+Wnbd7isbi7reTXX97uVtvEyPT4Ydm7bQuvM9oadv6KmgqW7FjEndxZzc2exLH8xNd4abGYbA9IG+4vbUfRrNQib2WZYnKJ5jM4zER0kz0LX3qWL+WrMyQx59kU6X3m10eE0i+SZCLaoLma3fDiN+X+4GUfLlox+/0OSevXWMTrRXEYUlEeev7absV7Np6o8Vby49CmeWPBParw1eDQPLVypXNf3Zq7qcz2JjqSgnj/ahdran9KqgyzcOY8522cxJ3c2qwqWo6HhtLgYkj6c4RmjGJ4xit4t+zVrj2ehr1DLMxGZ9M6zak81P279hm9yvqBbcg/O6zqZFq6Wup0/nGiaxmeD+uJo2ZLTv/zO6HCaRa5nItiics2s1+Nhxd//xponH6Pl8BGMevt9HCktjA5LNFJt4ah3QXno+fU6Vy2b2cbtJ/2JCVkX8NKyZ+nZojeTul2Kw+LQNQ4RGty2GMa0G8eYduMAKK4oYv6OX5iT6ytu/zHnr4BvL+ih6SMZkTGKEW1H0yW5OyYlSymEEMG3uuBXpme/y8frPmBf+R5cVjfvV7/J336+l1PajePCbpdyesez5e/YIZRSdLjoUlb8428c3LaNmEyZbSVEoIXtyGzVgQPMuf4a8r7+ks7X/I5B/34cs02m44W7wr0FJKXIO7wiuMItzwpK85m342fmbJ/NnNxZbN2fA0CyswXDM06uW3PbPqGjNAgLIeGWZyI8BTPP9pQVMGPdNKZnv8eaPSuxmqyM6zCei7pfzintxrG5aCMfrX2fj9dNZdfBncTZ45nQ+QIu7H4pJ7UeJtcj4OD2bXzSqyt9/voAve+51+hwmkyuZyLYomqa8YHNm5l1ySQObNrIoH8/QZff3WBgdCKQigv3kpCUYnQYIsKFe57lHtjG3NyffSO3238iv3QXAK1j0hnuL2yHZ5xMm7i2xgYa5cI9z0R4CHSeVdZU8v2Wr/gg+11+3PoNNd4a+qT256LuV3BelwtJciYf9RiP18Pc3Nl8uPZ9vtj0P8qqS2kb145J3S7lwu6X0j6hY8DiC0ffjh9HeX4+E5b8GrYFvlzPRLBFTTG766cf+fnqy1EmEye/9R6tTh5lcHQikGRNhtBDJOWZpmnkFG/yr7edxdwdP1NYvheA9gkdGd5mFCPajmJ4m1G0cKcaG2yUiaQ8E8eXU7SJbfu30K/VQBIcibqfPxB5pmkav+5exvS17/LJuukUVRSS6m7FBV0vYXL3y+ma0r3BxyqtOsiXmz7jw7Xv8cv2n9DQGJQ2hAu7X8aErAsM+RkZbePbb7Lg1ps588efSRkwyOhwmkSuZyLYIn7NrKZprHvpBZb+5c/Ed+nK6Pc/JLa9dFUTQkQ3pRQdEzvTMbEzV/W5Hq/mZd3eNXXrbT/d8BHvrn4dgBRXSzomdKJDYmc6JHaiY2JnOiR0pl1CB1nnJkQjbd+/jccXPMyHa9/Dq3lRKLokd+ek1kM5KX0Yg1oPpW1cZkiPxO0+uIuP1k1levZ7rN+Xjd1s54yO53BRjys4ue0YLKbGv0x022K4sLtvRHZnyQ4+XjeND7Pf508/3MpfZ93NuA7jubDbpYxpdzpWszUI31XoyTz3fBbfcyc5U98L22JWiFAVFiOzGUppd7vd1JSW0mb82Yx46XWssbFGhyWCYMeWTbRp38noMESEi6Y8q/HWsLJgOQt2zGFT0QZyijayuWgTe8p2191HoWgT19ZX3CZ2okNCZ//nnWkTmyEdlJsomvIsmhSU5vP0on/z9spXMSkTV/e5kTHtxrEsfzGLds5nyc4FlFQdAKCVO41BrYcy2F/c9mjRu0kF4ok0Ns8qair4evPnTM9+j1nbvsOreRmYNpjJ3S/n3KxJxDsSAhof+AYkVhYs56O17zNj3XT2le8hyZnC+V0u5MJul9EntX9IF/2B8PM1V5A/6ycuWJ8Tlj1e5Homgi2ipxlnKKXdDiiLhWEvvEyHiy4xOiQRJNVVVVjD8CIvwovkGRyo3E9O0SZyin3FbW2Rm1O8kYNVJXX3s5lttIvvSMdE34iubzTXN6qb4moZ8S9Am0PyLLIUVxTxwpIneWX581R5Krm059XcOfheWse2Oex+Hq+H9fuyWbhzHot3zmdh3jzySnx7qbusbga0OomT0odxUuuhDEg7iRhb896cb0ieaZrGsvxFfJD9Hp+u/5D9lcW0jknnwu6XcWG3y+iUlNWsGBqj2lPNT9u+48Ps9/g25wsqPZV0TurK5G6XMrHbxaTHZugWi57yvv2aHy88n9HvTydj/DlGh9Nocj0TwRYVxSyAOyODias3GBqPCB5ZkyH0IHl2fJqmsads9yEF7sa6gndr8WaqvdV19421xR02Xbm24O2Q0IlYe5yB30VokDyLDKVVB3l1xQs8v+QJSioPcH7Xydwz9P5GNTXKK8ll8c75LNo5n0V588neuwqv5sWkTPRo0ZuTWg+tG8FNi0lvVHwnyrOdJTv4aO1Upme/y6aiDTgtTs7qdC4Xdb+C4RmjDJ91UVxRxOcbZvDR2vdZuHMeCsXwjFFc2O0yxnc+t9mFfijx1tTwcdeOtBw6jFHvTDU6nEaT65kItqgpZlGKK4rLjAxHBJFcLIUeJM+axuP1sKMk97AiN6doE5uLNrLjwHY0fvt70tLVqq7Q7ZSURd/UgfRJ7Y/L6jLwO9CX5Fl4q6yp5J1Vr/LUon+zt6yA0zuczZ+HTaF7i17NPnZJ5QGW5i9iUd58Fu2cx9Jdiyiv8b22aRPXlsGth9UVt/XtJ31knpVVl/HVpk+Znv0eP2//EQ2NIenDmdz9cs7pPDFk32jaWpzDR2un8uHa99i2fwtOi4vxnc7lwu6XMSJjtOGFdyAsvvePbHjtFSZt2Io9MbwaYcn1TARb1BSzMjIb2davWk6XXv2MDkNEOMmzwKuoqWBrcY5/FNdX5OYUbWRz8Sb2lhUAYFZmerToTf+0kxjQ6iQGpJ0U0XvjSp6FpxpvDR+ufZ/H5v+DvJJchrU5mb8Mf5CBrYcE7ZzVnmrW7Fnpm5bsn568uzQfgDh7PAPThtQ1luqbOuCwN4XWr1pOVs++LNo5jw+y3+WzDR9zsKqEjLhMLux2GZO7X0a7hA5Biz3QNE1j8c75fLR2Kp9u+Ij9lcW0cqdxQbdLmNTtUrql9DA6xCbbt2I5X44axuAnnyXr2t8ZHU6jyPVMBFtUFLNmp4shzzxPh8kXGx2SEEKIBtpbtodl+YtZtmsRS3ctYvnuJXXrchMdSYcVt/1aDSTOHm9wxAJ8RUVZdSkHq0o4WH2QksoDHKw+SGlVCQerSijx/1tafbDu/9WeKk5qPYzTO46nVUxro7+FRvFqXmZu/IR/z3uITUUb6Js6gL8Mf4iRbU/R/Q0XTdPYvn+rb1ryznks2jmf9fuyAbCYLPRq2ZeTWg9jYNpgNhauZ3r2u2zdn4PL6uaczhOZ3P1yhrYZccIR3XBQUVPBdzlf8uHa9+v2vO3Vsi+Tu13GZb2uDbuZHpqm8fmQAdgSEjjjmx+NDkeIw+RMn8aKh6ZQumMH7jZt6DvlIV1rrogvZv+SkaH7D1XoT975E3qQPDOWx+thQ+E6X3Gb7ytwN+xbi4aGQtE5qSsD0k5iYNpg+qedRFZS17CcYmhUnpVWl1JcUUhJZQml1SUcrDp43OLzYFWJvzg9SEnVgbqv1T7m0Knjx6NQxNhiibXF4tW85JfuAqBPan/O6HgOp3cYT7eUniE7Aq9pGj9u/ZZ/zfsbqwpW0CW5O/cOe4AzOp4TUjEXVRSyZOdCFvlHbpfnL6HSUwnAiIzRTO5+OeM7nYvbFmNwpMGxp6yAT9d/yPTs91lZsIz02AweOPkRzuk8MaSep/qsfvIxlv/tfs5bvobYDuEzYi5/NyNbzvRpLLjtFjzlvy3l1HsQMaKL2T79Bmjfzp5rdBhCB7ImQ+hB8iz0HKjcz4r8pSzNX8SSXQtZtmsRRRWFAMTYYumXOpABaSfRP+0k+rcaRIqrhcER1y+YeVZSeYAtxZvZUryZrftzyPE36NpSnENBWX69j7earMTa4nDbYoixxRJjjSHGHuf71xZLjC2GGFuc71+r7/+++9d+zVe8um2xuCyuumJC0zQ2FK7jm80z+SZnJst2LUZDIyMuk9M7jOf0jmczJH1EyOwvumDHHB6Z+wALd86jbVw7/jRsCud3mRwWb55U1lSyZu9KCjft4NQR5xsdjq7m7fiF+2f9kTV7VjK0zUgeHv14QNYy66E0bwczemTR+9776HPvfUaH02DydzOyzeiZRWlu7lG3O9PSOHfJSqwxwX+TTIpZERHkYin0IHkW+jRNY0vxZpb6R2+X7VrEmj0r8WgeANrFd6grbge0OokeLXqHTIFUq7l5dqByv79g3cSW4hzf50Wb2LI/p24dcq1W7jTaJXSkQ2In2sV3INmZgttfgNYWn4cWp3aLvbnfXoMUlObzXc5XfJPzBT9v+4EKTwVx9njGtjud0zuezZh24wyZVr5y93Iemfc3ftr6LanuVtw5+P+4tOfV2Mzht/VItF7PPF4P7656nUfnPUhxZRFX9Podfxp2P8nOFKNDq9d355xJae52zl2+OmxGlfXOsy3Fm3ln5Wt8uelT2iV0YHibkxmeMYreqf0DvlezgHcSXHCCmtDiduNsmYojNRVny1Scqak4UlvVfX7o10zWxv8t9k1xfoB/5m4nV9Ma9UshxawIKbKPmdCD5Fl4KqsuY+XuZXXF7ZJdC+ua5DjMDnqn9qsrbge2HtzoLU4CrSF5VlxR5BtdLd5MTu1Iq//zwvK9h923dUw67RI60v6Ij8yEDrit7mB+KwFRWl3KL9t/5OvNM/ku5yv2le/BarIyrM3JjOs4ntM7jKdNXNugxrCxcD2PznuQmRs/IdGRxB8G3c01fW4Ku7WXh4r261lRRSGPzf8Hb/76MrG2OO4Zdj9X9b4+pAueze+/y7ybr+f0b3+k5eDweCNCjzyr9lTzbc4XvL3yVWZv/wGzMnNy2zHsPJhXt2Y8xhbLkPQRDM84mREZo+nRonfYrw0PBccbmbUnJdHj9rsoL9hN+W7fR4X/86riomMey56c4it2Dyl0fcVvKs6WrXy3paZiS0xCKXXYFOenQYpZEd52bNlEm/adjA5DRDjJs8igaRo7D+7wjd7uWsjSXYtYWbCcKk8V4Cv+uiR3x2l1Yjc7cFic2C0OHBYHjrr/233/mh04LQ7/12vvbz/kc9/XnP7/N2QUuDbPCsv3+acA+4rU2s+3FG+um0pdKz22De0TOtE+oSPtEjrQIaET7RI6khnfPqwLriN5vB6W7lrINzlf8M3mmWwq8u1S0LNFH8Z1GM8ZHc+mV8u+ARu1yj2wjccX/JPp2e/itLi4sf9t3DTgtohoNibXM5+1e9dw/6w/Mid3Fl2Te/CP0Y8xou1oo8M6puqSEj7snEnHSy5n8JPPGB1OgwQzz/JKcnl31Ru8v/oNdpfm0zomnct7XcelPa+qayS3p6yAebk/Mzd3NnN3zGZz0UYAEuyJDG0zguEZoxmeMYquyd3DZrQ7lORMn8bcG68Dr7futvrWzHoqKqjYU+ArdPPzfcVuwW4qdu+mvCC/rvgt352Pt7LyqMebrFYcLVtSUVCAt9q3h70UsyLsRet0KaEvybPIVbuOsLZz8pbizVR6KqmoKaeypoKKmgoqPBVU1JTj1bz1H/A4zMpcV/g6zPbDCmVfYexk577t7K4uoLjyt3evFYr0uIy6IrV9Qgf/CGsn2sa3w2lxBuLHEHY2FW7gW39hu3jXAryal9Yx6YzreDandxjPsDYnN2lqdEFpPk8v+jdvr3wVkzJxdZ8buXXQH8NizXVDyfXsN5qm8dXmz3hg9p/JPbCN8Z3OZcrJ/yIzvp3RoR1lzvXXkPftN0zasAWzXZ9p/80R6DzzeD38tO073l75Kt9v+QpN0xjT/nSu6vU7xrQ/vd6R9V0H85hbW9zmzmb7ga0AJDtbMKzNSEb4i9uOiZ2luG2AA5s382n/nljj46k+cCCg3Yw1TaP6wIHfCt3d+XUjvRUFu9n83jt195ViVoQ9+aMs9CB5JjRNo8ZbQ0VNeV1xW1lTSaX/84oafwF8zP9XUFlTQXlN+RGFcjkV/v9X1JSjKjR6Zw70FazxHWmf2Im2ce10W68arvaW7eH7LV/zbc5Mftr6PeU1ZcTYYjkl8zTO6Hg2Y9qfTqIj6YTHKK4o4oUlT/LK8uep8lRyac+ruXPwvbSObaPTd6EfuZ4draKmgv8ufZqnF/0br+bh9wPv5A+D/hhS0/F3fv8dP1wwgVHvTKXthPOMDqdegcqzgtJ8pq55m3dWvcaOA9tJcbXksp5Xc1nPa2kbn9nk427fv425O2b7i9tZ7Dq4E/D1ExieMcr30WYUbePbSXF7DMum3Ef2c08zcc0GXGn6bqt26BRnKWZF2Csu3EtCUug3bxDhTfJM6EHyrPnKa8qZs32Wf9T2CwrK8jErM4PTh3NGx7M5vcPZZCa0r7t/aXUpry1/geeXPMGByv2c33UyfxzyVzokRu40XMmz49tZsoO//3Ifn6yfTuuYdO4f+TDndZkcEsWMt6aGGd07kzJoEKPfm250OPVqTp5pmsbc3Nm8tfIVvtr8GTXeGkZkjObK3r/jjI7nBLzxWm0Dwbm5s5mTO4u5O36ua5rXJq4tw9v4i9uMk0mPzQjoucONpmnkFW5h9oChVHRPZ+mtvUlyJDOx28X0Sx2oy++KrJkVEaVwbwFJKS2NDkNEOMkzoQfJs8Dyal5W7F7q2/Zn8xes27cGgK7JPTi943gSHIk8v+RJ9pYVMK7DeO4d9kDYbNfSHJJn9VuYN5e/zvojqwpWMLj1MP5xyuP0atnX6LBYet+9rHvpBSZt2II9KdnocE6oKXlWVFHI9Ox3eXvlq2wu2kiCPZGLelzBFb2uo1NSVpAiPVrtlmG1o7bzdvxS16+gfUJHf3Hr65bc0t1Kt7j0Vlpdyvp92azds5rsvavI3ruatXtW03ZpEVd8BK9eBgf7t2Fv2R4qPZV0TOzMpG6XMrHrxUGfqi/djEXEkOlSQg+SZ0IPkmfBtbU4xzdim/MFC3bMwaN5GNbmZP4y/EEGth5idHi6kTxrGI/Xw9Q1b/HPuQ9QVL6Py3tdy5+HPWDo+unCVSv5YsRgTnrsKbpcf6NhcTREQ/NM0zSW7lrIWytf5bMNH1HpqWRQ2hCu7H09Z2edHxJ9Abyal7V7V/tGbXN/Zv6OXyipOgBA56SuDM8YxYiMUQxuPYwUV8uQGMlvDK/mJffAdtbuXUW2v3Bdu3c1OUWb0PDVfS6rm24pPeie0oue//wZ264DnLlkKQnuZPZXFDNz4yd8tG4q83f8AsDg1sOY1O1SzsmaSIIjMShxyz6zIiLIH2WhB8kzoQfJM/0UVRSSf3BXVHYylTxrnP0VxTy+4GFe//W/uKxu/jjkPq7pc5Nh+1TPHH4SZoeDM3/42ZDzN1R9eVZSeYCP103j7ZWvkr13FTG2WCZ1u5Qre10X8jMkarw1rCpY4e+U/DML8+ZSVl0KgM1so4UrlZbuVFq6WtHSnVr3/1R37f9b0cLd0pBCvaTyAGv3ribb/7F2zyrW7lvDwaqSuvu0i+9Ajxa96JbSi+4tetI9pRdt49thUqa6xk99/voAve+596jj5x7Yxox1H/DR2vfZWLgem9nGae3PYlK3Sxjb/oyAThGXYlZEhJ3bttA6s339dxSiGSTPhB4kz4QeJM+aZsO+dUyZfQ+ztn1P56Su/GP0Y4zKHKt7HGueeZJl9/+Fc5euJK5TZ93P31DHy7NVBSt4e+WrfLxuGmXVpfRq2Zcre/2OiV0vwm2LMSDS5qv2VLNi9xKW5y9hd2k+BaW72VO2m4LS3RSU7WZf2Z66Ec5DxdnjaelKpYU7lZZ1BXAqLfxFb+1tyc4WmE3mRsXk8XrYuj+H7D2rfFOE/SOuuQe2HXb+7ik96ZbiK1i7t+hJ1+QeJ3weGtr4SdM0VhYs56O17/PJ+g/ZW1ZAoiOJCVmTmNTtYgamDWn2G4lSzIqIUFlRjt1h/BQUEdkkz4QeJM+EHiTPmk7TNL7JmckDs//Mtv1bOKPj2fzt5Edpl9BBtxjKdu1kRvfO9Pzjn+l73xTdzttYh+ZZWXUZn274iLdXvsLy/CU4LU7O7XIhV/b+nW5Ng4xU461hX9keCsp2H1HsHl34HjpCWsukTKQ4W/pGdY8qfFNp6W6FV/OyrnbEdc8q1u/LprymvO7xHROz6J7Sk+4tevmL156kx2Y06mfvqari426daDlkSKOakFV7qpm9/Qc+XjuVrzd/TnlNOe3iO3BBt0uY1O0S2id0bPCxDiXFrIgIMl1K6EHyTOhB8kzoQfKs+SpqKnh52bM8tehRarzV3NT/dm4/6U+6jSx+f/45HNi0kfN/zUaZTLqcs7FWLZmPrX0C76x8lelr3+NA5X46J3Xlqt6/Y1K3S4O2jjLclVaXsre0gIKy/LoCt6D0t8K3oCyfPf7ba7w1Rz0+yZlCD/8oa7eUXnRP6Unn5K4BmdK8dcZH/HLNFYz56H+kn3Z6k45RUnmALzZ9ykdr32du7mw0NAakncSkbpdybtYkkpwNb2wmxayICPJHWehB8kzoQfJM6EHyLHB2Hczj4V/u56N1U2nlTuOvIx/mgq4XB32kMeeDqcy94VrGffUdqcNGBPVcjXGwqoQNhevI3rOKtxa9xKoDK7GarJzd+Xyu7H09Q9KHR/worF68mpfiiqK6kV2P5qFbcg9aulsF7Wf83TlnUrI1h/NWZGMyN27a87HsLNnhX187lXX71mA1WRnb/nQu6HoJp3U4C4fFccLHN7WYtTQraiECzOF0GR2CiAKSZ0IPkmdCD5JngZMWk85zZ77O1X1u4C8/3c0fvr6WN399iX+Mfpy+rQYE7bwZZ0/A4naTM/V9Q4rZoopCNuxby8bC9WzYt5YNhevYsG8tOw/m1d2ntTOdv474Bxf1uIIWLtkKKtBMykSSM5kkZzJd6R708x3YvIn8n2fR568PBKSQBWgd24Y/DLqbWwbexZo9K/lw7ft8sm46X2+eSbw9gXM6T2RSt0s4KX0YJhW4GQgyMiuEEEIIIcQhvJqXaWve4Z9zp7CvbA8X97iSvwx/kBbu1KCcb+5NvyP3y5lcuGErZseJR7CaQtM09pYVsN5fqG4sXOcvWtexp2x33f2cFiedk7qSldSVzsn+f5O60iGxU0ALEGGshjZ+ai6P18MvuT/xUfb7fLHpU8prysiIy+SCrhczqdulh+03LNOMRURY++sSuvVpVA4L0WiSZ0IPkmdCD5JnwXWgcj9PLHyEV5c/j9Pi4tq+N5ERl0m8PYFERxLxjgTi7YkkOhKJscU2eUrorp9+5PvzxnPym++Sef4FTY5X0zR2Htzx2yjrvnVsKPSNuhZVFNbdL9YWR+ekLmQldyMrqStZyd3onNSVjLi2xyxaJc8iR1MbPzVXadVBvtr8OR+tfZ+ft/+IV/PSN3WAb31tl0m0jm0l04xF+KuprjY6BBEFJM+EHiTPhB4kz4Irzh7P307+F5f3vJYps//E04v+fdz7mpWZeHsCCY5E4h0JJNgT/Z8nklB3e+3nSSQ4Evz3T6LlyJNxtW7N5mnvN6iY9WpecvdvY32hf5T1kKL10A66iY4kuiR345zOE38rXpO70srdulGFt+RZ5Mid+RmVe/fQ+Zrf6Xpety2GSf6Ox/kHd/LJ+ul8vHYaf511Nw/M/hNj249r0nGlmBVCCCGEEOIEOiVl8f75/6O06iDFlUUUVxRRXFHM/rrPi9hfWUxRRRH7/Z8XVuxjS/Fm9lcWU1xRdMy9SWvZzDYmZFkY9O2XTHp5BI6WLUnwF77xDt/I78Gqg3XTgzcVrq/bqgWgpasVWcldmdz9MrKSfKOsWcldSXG2kCZN4jAb33gNd9u2pJ2i/77KtVrFtObmAXdw84A7WLt3DR+tfZ8Z6z5o0rFkmrEIKZ6aGswWeY9FBJfkmdCD5JnQg+RZePBqXkoqD1BcWUxxRSH7K4oprizy/VtRSHFlMZUbttDlrk9YdUknlo+Moch/v5KqA3XHSY/N8E8N7kJWkm+UtXNS16BvjSN5FhkObN7Ep/170fevf6PXPX82OpzDmJRGyziXTDMW4S1vWw5tO2bVf0chmkHyTOhB8kzoQfIsPJiUybe+1pFAZny7Y99pJHzx5lBGrzPx7//+NohT461hf2UxdrOdGFusPgEfQfIsMmx883WU2UzHy68wOpSjmE1N66osbclESNlftM/oEEQUkDwTepA8E3qQPIss7S++hH3Ll7F//bq62ywmC8nOFMMKWZA8iwSeyko2v/cObc48K6gdjPUmxawQQgghhBAhoP2kySizmZxp7xsdiogwuV98TuW+vbo3fgo2KWZFSMns1MXoEEQUkDwTepA8E3qQPIssztRWpI05lZzpU9G8XqPDqSN5Fv5CofFTMEgxK0JKVWWl0SGIKCB5JvQgeSb0IHkWeTpcfCllO3awe84vRodSR/IsvB3YvIn8n2fR+cprMZmbtjY1VEkxK0LKrtytRocgooDkmdCD5JnQg+RZ5MkYfw7W2FhyPgidqcaSZ+EtlBs/NZcUs0IIIYQQQoQIi9NJ23PPZ/unn1BTVmZ0OCLM/db4aXxENX6qJcWsCCkprSLvl0yEHskzoQfJM6EHybPI1OHiy6guKSH3y8+NDgWQPAtnuTM/8zd+us7oUIJCilkRUhKSUowOQUQByTOhB8kzoQfJs8iUOnwE7owMcqZNNToUQPIsnG188/WIbPxUS4pZEVI2Za80OgQRBSTPhB4kz4QeJM8ikzKZaD/5Enb98B3lu/ONDkfyLExFcuOnWlLMCiGEEEIIEWI6XHwpmtfLlo+mGx2KCFOR3PiplhSzIqQ43TFGhyCigOSZ0IPkmdCD5Fnkis/qQnK//mwJganGkmfhJ9IbP9WSYlaElE7dehkdgogCkmdCD5JnQg+SZ5GtwyWXUbhyBUXZawyNQ/Is/ER646daUsyKkLJm2SKjQxBRQPJM6EHyTOhB8iyytbvgQpTFwpZpxu45K3kWfmobP7Uec6rRoQSVFLMipHi9HqNDEFFA8kzoQfJM6EHyLLI5UlqQ0L0n2c8+xTsJLmb0zCJn+jTd45A8Cy+HNn5Spsgu9yxGByCEEEIIIYQ4Ws70aexfl43m9QJQmpvLgttuAaDD5IuNDE2EsLrGT1dcaXQoQac0TTM6hnr16TdA+3b2XKPDEDrQvN6IfwdJGE/yTOhB8kzoQfIsss3omUVpbu5Rt7szMpi4eoNucUiehQ9PZSUfd+tEy6HDGP3eB0aH02BWs4nkGPtSTdMGNuZxkpUipGzbrN+FWUQvyTOhB8kzoQfJs8hWumNHo24PFsmz8BEtjZ9qSTErQkrJ/iKjQxBRQPJM6EHyTOhB8iyyudu0adTtwSJ5Fj6ipfFTLSlmhRBCCCGECEF9pzyE2ek67Daz00XfKQ8ZFJEIZdHU+KlWdHyXImy0z+pmdAgiCkieCT1Ingk9SJ5Ftg6TL2bIM8/jzsjw3WAyMfipZ3Vv/hRNeZYzfRozemYZ2j26qaKp8VMtKWZFSCkvLTU6BBEFJM+EHiTPhB4kzyJfh8kXM3H1Bka89hZ4vcRmZuoeQ7TkWc70aSy47RZf0y1Nq+seHQ4Fraeyks3vvUObM8fjapVmdDi6kWJWhJT8vO1GhyCigOSZ0IPkmdCD5Fn0SB93Biabje0zP9P93NGSZysemoKnvOyw2zzlZax4aIpBETVcbeOnrGt+Z3QoupJiVgghhBBCiBBni4sjbfQYcj//jHDYWjMchUr36KaobfyUNmas0aHoSopZEVJatta3O5+ITpJnQg+SZ0IPkmfRJePsCRzctpWi1at0PW+05FmodI9urGhs/FQrur7bRrKYFMroIKJMbFyC0SGIKCB5JvQgeSb0IHkWXdqcNR5lMpH7+ae6njda8qzvlIcwWW1H3d7xiqv1D6YRorHxUy0pZo/DaTOT5LaR4LJhUlLS6mXzutVGhyCigOSZ0IPkmdCD5Fl0cbZoSYuhw3RfNxstedbugguxJiZgslpBKVyt07GnpLD+pRc5sHmz0eEdU7Q2fqolxewRFBDvtBLnsKKUwmYxkey2YTXLj0oIIYQQQhir7dkTKF6zOmSLq3CW+9n/qCwoYMRrb3FFcRkXrN3EGd/8CJrGjxdMoHxPgdEhHiVaGz/VkgrtECalSHTbcFjNh99uUiS6rDht5uM8UgSKOzbe6BBEFJA8E3qQPBN6kDyLPhlnTwAgd6Z+U42jIc80TWPV4/8hrnMWbc85t+72uE6dOWX6x5Tl7+KnyROpDrFtinyNnzKjrvFTLSlm/WzmE4/AKqWIc1iJd1plHW0QdejS3egQRBSQPBN6kDwTepA8iz4xbTNJ6tOPXB2nGkdDnu384TuKVv1KjzvuPqqJUotBgxn5+tsUrljOL9dcgbemxqAoD1fX+Omq6Gv8VCs6v+sjuGxmEt02TKb6y1SH1X9fWUcbFKuWLDA6BBEFJM+EHiTPhB4kz6JTxjkT2LNoIWX5u3Q5XzTk2eon/oMrPZ32ky8+5tczzjqbkx57irxvvmLhXbeHxPZIdY2fLr/C6FAME9XFbO362FiHtVGPs/pHcW2yjjYIjL8wiGggeSb0IHkm9CB5Fo3a1k01/lynM0Z2nhUsnE/B3Dl0v/UOzLajuxnXyrruenre/Sc2vfU6q/7zLx0jPFq0N36qFbRqTCn1ulKqQCm1+pDb/qaUylNKrfB/nBWs89fneOtjG/x4k+/xLllHG2Ay4i30IHkm9CB5JvQgeRaN4rt2I65TZx2nGkd2nq1+4jHsScl0uvKaeu/b9/6/0eGSy/j14YfY9O7bOkR3bNHe+KlWMIcW3wTOOMbtT2qa1tf/8WUQz39c9a2PbYxYWUcbUL0GDjE6BBEFJM+EHiTPhB4kz6KTUoqMsyeQ/8tsKouKgn6+SM6zojWryfv6S7refAtWt7ve+yulGPLMC6SdMpYFt/2evO+/1SHKo21447WobvxUK2jFrKZpPwOFwTp+UzVmfWxDOay+PWnNATxmtMpZn210CCIKSJ4JPUieCT1InkWvjHMmoNXUkPft10E/VyTn2eonH8MSE0OX629q8GPMNhuj3plKQvee/HzlpexbviyIER7twKaN7P5ldlQ3fqplMeCcf1BKXQksAe7WNO2YbycppW4AbgBIa92aVUvmA9AqvS1Ot5stG9YCEBufSGbHLFYvWwiAyWSmR/+T2LR2FeWlBwHo1L03+wv3Urh7F0pB586dsdvtrF7tmwHdsmVLsrKymDNnDgB2u52hQ4eyZMkSDh70HWPw4MHs2LGDvLw8ALp06YLZbCY72/fL3apVKzIz2zFv0Xw0wGZ30KVXP9avWk5VZQUAXXsPYHfedor27QEgo0NnvF4veVt9+4Qlt2hFcmorNqxeAYDD6aJzjz6s/XUJNdXVAHTvO4i8bTnsL9oHQGanLlRVVrIrdysAKa1ak5CUwqbslQA43TF06taLNcsW4fV6AOjZfzDbNm+gZL/vR98+qxvlpaXk5233/TxatyE2LqFug2x3bDwdunT3L/7XAEWvgUPIWZ9Nacl+ADp27UnJgWIKdu5o1vNUvK+AVUt8x0zLaIfNbmfbpvUAxCcmk57ZgewViwGwWK106zOQjWt+paK8DICsnn3ZtzuffXvyAUhv1xGTyURuzkYAEpNbkJrelnUrl4I8T7TOyMTudDX+eSrcy978nWH7PJUUF1Fasj9snqcm/z6F+fMUbr9PRz5PlRUVtO/cVZ6nEH+ewv33aX/R3rrXSPI8he7zFIzfp/iu3bCmpLD67dfx9u4Z1Odpb34epSX7I+552rpwLls//pAWkyZRrnko3VvQqOdp9LSP+PLUUXx7/tl0fvY5skaO1uX3qejdd8FsprqX7/mKhN+npg4JqmB24lJKtQNmaprW0///VGAvvt+QvwNpmqZdW99x+vQboH07e26T4zApRYLLGpBpxQ1xsLKG0srQaNkdblYtmU+vgUONDiMqmE2KZLeN/eXVVNZ4jQ5HV5JnQg+SZ0IPkmfRbdEf72DTu28zOWcHFpcraOeJ1DxbeOdtbHr3Lc5fuRZXWusmHWP/+nV8PW4MjpQUTv/2RxzJKQGO8nCeyko+7taJ1GHDGfXutKCeS09Ws4nkGPtSTdMGNuZxuo5La5q2W9M0j6ZpXuAV4KRgnzOQ62MbKsZu8a2jlVnHjdaxa0+jQ4gaMXZL3f7J0TZFXvJM6EHyTOhB8iy6ZZx9Lp7ycnb++H1QzxOJeVa+O59N771Nx0uvaHIhCxDfpSunfPARB3O3M+viSdSUlQUwyqPVNn7qfPV1QT1PuNC1mFVKHdo3+nxg9fHuGwjBWB/bUA6rmSSXrKNtrJIDxUaHEBWsZlNdJ2+TSZEQZW++SJ4JPUieCT1InkW31OEjsCUkkvv5p0E9TyTm2drnn0WrrqbH7Xc1+1gthwxj5KtvsmfxIuZcfzVejycAER6bNH46XDC35pkKzAe6KKV2KKWuA/6tlFqllFoJnALcGZRz07T9YwPN4h8Vdlhk+56Gqp3bL4Irxn74cnmL2US809jfFz1Jngk9SJ4JPUieRTeT1Uqbs8az46sv8frXcQZDpOVZVXExG15/hczzLyC2Q4eAHLPthPMY9Ohj5M78nMV/uptgLOWUxk9HC1oDKE3TLjnGza8F63y19F4fWx+lFPEuK5ZKxUFZRytCgMNixmY5+vfDbjETY9ckT4UQQogw0vbsCeS8/y675/xC2iljjA4nLKx/5SWqS0rocecfA3rcrjf+ntIdO8h+5knc6en0vOuegB5/45uvoywWOl5+RUCPG85Co+ILECPWxzaU224hwRVdUzmbolV6W13PZzYp3HZL1GytpAC3/fgzBdx2S1TMJNA7z0R0kjwTepA8E2ljTsXscrE9iFONIynPasrKWPvic6SPO4OkXr0Dfvz+D/6DdpMms/zBKeRMez9gx/VUVrL5/XfJOHM8rlZp9T8gSoRe1ddERq6PbSi7xUyy244lhGM0mrMBm1U3l0kpXDbf3sApMXZi7BasZhNxBk9L14PTZsZSz5s9cU5LxOeoHnkmhOSZ0IPkmbA4naSfOo7cLz5D8wZnd4JIyrNN77xJ5b699LwrsKOytZTJxLAXXiZ15Cjm3XIju376MSDHlcZPxxb2xWyorI9tKLNJkeS21TXfEYer3f8q0GoL2ESXjRaxdmIdR09Ft1lMEf28KAVuW/0rC5RSJLhsET2LIFh5JsShJM+EHiTPBEDGORMoz89n79LFQTl+pOSZt7qa7GefouXQYbQcOjxo5zHb7Yx+7wPiu3Rl9hUXU7jy12YfUxo/HVtYF7MmpUgMw8JQKeUvwC1N3iBY1E8p30jkoQXssdaKHirWbonYIs5tszR45oLZpEhw2iQ/hRBCiDDQZtyZKIuF3M8/MzqUkLblww8ozc2l591/Cvq5bPHxjPnwE6xxcfx44Xkc3L6tyceSxk/HF7Y/jVBeH9tQLpsl4kfAGis2PrFZj1fKty1SgstKy1gHcQ0oYA9lMili7eExyt8YtSPTjWGzmMJmxkNjNTfPhGgIyTOhB8kzAWBLSKDVyaPZ/vmnQemiGwl5pnm9rH7yMRJ79aH1qeN0Oac7vQ1jP/6MmvJyfpx0HpWFhU06Tm3jp05XXBngCMNfWFaC4bA+tqFsFhPJbntYF+WBlNkxq9GPObSAbRFjJ95pxd6MJkZOmznino9YhwXVhHdNnDYzzkYWweGgKXkmRGNJngk9SJ6JWm3POZeSnM0Ur80O+LEjIc9yZ37GgQ3r6Xnn3U16TdRUCd26c8r7H1KyJYdZl03GU1HRqMcf2vjJmdoqSFGGr7B6xR5u62MbymxSJLqsEVk0NNbqZQsbdD+Fb4uZeOfhBWygLk5xjqDtWqU7q7l5a4Fj/Q2yIklD80yI5pA8E3qQPBO1MsafDUqROzPwU43DPc80TWP1E/8htkNH2p43Uffzp44YyfD/vkrBvLnMueHaRjXqksZPJxY2r1DDdX1sQymliHNYiXNYZZ3icRxWwMbaiXdZcVgDV8AeymI2NXpabqiKsTevMFdKkeC0YpL58EIIIUTIcqa2osVJg9n+WfC26AlX+bN+Yt/yZfS4/S5MZmNe37W74EIGPPwvtn/6CUvu+3ODHyeNn04sLIpZkyLs18c2lLN2CnWUFg4m0+EXGAXYLSZdCtgjxdgtYf882C2mRq0ZPh6TSfn2SQ5ATKHgyDwTIhgkz4QeJM/Eodqecy5Fq36lZOvWgB433PNs9RP/wZmWRodLLjM0ju5/uJ2uv/8D6154juznnq73/tL4qX5h8VMxm1RErI9tKKvZREqMjVhH+BdTjdWj/0lHFbAJLptuBeyhlFLEhvl04+aOyh7KajYR5wz/Kf4K6Nn/JKPDEFGgh+SZ0IHkmThUxtnnApD7RWCnGodznu1dupj8n2fR/ZbbMNvtRofDwIcfJfO8iSy97162fvzhCe8rjZ/qFxbFbDRSSuGyWXyjkU4rligo5q1mE9s3rCElxrgC9kgOqxl7AEY2jeC0mbEEeDaDw2oO6+nXFv8+z9s3romYUWYRujatXWV0CCIKSJ6JQ8W2b09iz17kfh7YqcbhnGern/gPtoTEkFlzqkwmhr/0Gi2HDWfuTb8j/5efj3k/afzUMOH5Kj3KOKxmkmPsJLis2CJsqrXCV3Qlu20kuW2UHiwJuVH42DBcx6yAGFtwRpVjHdawLPCdNjNJbhsWs4mDJSXER9C0aRGayksPGh2CiAKSZ+JIGeecS8GC+ZQX7A7YMcM1z4rXrSV35ud0vfFmrLGxRodTx+xwMPq96cS278CsyyZTlL3mqPts//xTX+Ona35nQIThI/xekUYxu8W3njbJbcPRjK1nQoHF5Gt41SLWTpzDGvARxEAymxTuAE7X1YPbbgnqmwJxDivmEHvT4XiU8nVBj3NYDxvpt1vMxDmloBVCCBFZ2p49ATSNHV9+YXQohlvz1OOYXS663vR7o0M5ij0pibEff4rF6eTHSedSmrfjsK9vfPN1YjLbkXbKGIMiDA+hW0GI47KaTcS7rKTE2HHZzGHzYlzhG2VOcttIjrHjtB09jXjgwIHGBFcPl80cNlO9TUoFfSqwyeTrcBzqS7qtZt8+zkd2Qa/NM4fVHBHrgEVo6tS9t9EhiCggeSaOlNCjJzHt2rM9gFv0hGOeHdy+jS0ffkDnq6/FnpRsdDjH5M5oy5iPPqX6wAF+vPB8qvbvB35r/NRJGj/VS346YcxsUsQ6fEVtjN0SsoWFL04LKf79YE/UlXr37sBNiQkkXzOo8Ch6Yh0WXdYaW8y+Jl2hym23kOS2HXME+dA8c1jNIf19iPBVXLjX6BBEFJA8E0dSStH27Ankz/qxrjhqrnDMs+xnn0IpRfc/3G50KCeU1Ks3o96Zxv716/jqtNF83COLTwf43jywxYXO1OhQJcVsBDD5p8G2iLGH1PRPh8VMQt0IcsOmvebm5uoQWdPYLKaQ3+fYYlK6xmi3mAPaMTkQTMq3jdCJ4joyzxxWM3Fh8maFCB9783caHYKIApJn4lgyJpyLt7qavO++Dsjxwi3PyvcUsOntN2l/0aW409sYHU690k4ZQ6erruHA+nWU7fjtNcrS++8jZ/o0AyMLfVLMRhClFE6buUEjoMFiUr8V1vEuK/YwX9t7pNgQHgEHiDFgKyG33RIya7htZhPJbluT8s5pM4f9VkxCCCEEQItBg3GmtiL388Bu0RMu1r34PJ7KSnrccZfRoTTYzm+PfuPBU17GioemGBBN+JBiNkLVrk1NdNl06Tx76L6wMc1oPtS5c+cARxZYJpMi1h6aI3h2i8mwNw/inBbD1xTH2C0kum0Nyr3j5ZnLZpGCVgRMWkY7o0MQOrCaTYZe/yTPxLEok4k2Z51N3nffUFNe3uzjhVOeVR04wPpXX6LthPOI75xldDgNVrpjR6NuFz5SzEY4m8VEgstGstu/b2sAj62UrzHSofvCNpc9BDazro/TZjZk1Ls+Rk73VUqR4LIZMmptUopEl61RHadPlGcumyXkpk6L8GQLg+uZaDqFr0dBkttm2PUPJM/E8bU951xqSkvZNevHZh8rnPJsw2svU71/Pz3vusfoUBrF3ebY06GPd7vwCb1X5CIoapv1pMTYcTdzqqzVf6wWMXZiA7xGd/Xq1QE7VjDFhdjondNmNnx7I7NJkeC06dpd22Hx7VFsa+Tsg/ryzG23hN12TOJotXtYGzVqtm3TekPOK4LPZjaR7O8HAb7rn1GN5CTPxPGkjjwZa3w8uQHoahwueVZTXs7aF54jbcypJPftZ3Q4jdJ3ykOYna7DbjM7XfSd8pBBEYUHKWajjMmkiPGvaY11WDA1sKpV6rcXhkm1o7yhvHg0yCxmU9C3v2koBcTYQqPwsllMuqzbVfj2uo13WYO2n26M3RIyz7FoPKfNXLeHdZLbJs+lCAilfNeexGN0Sg/FhngiupltNtqcfiY7vvwCb02N0eHoYvN771BRsDvsRmUBOky+mCHPPI87IwOUwp2RwZBnnqfD5IuNDi2kyVU3SimlcNksOK1mKmu8lFbWUOPVjrqfxeS7n8Nq0qV4bdmyZdDPESgxdgsV1V682tE/Nz25m7FGORhcNgs1Xo3yKk9Qjm/273HbnJHohuZZrMOKBkH7XkRw1BaytWq31rJZTBwor9HtdzY+MTT3NRRN47D4msSd6Hrrtluo9niprPHqFpfkmTiRjLMnsGX6NArmzaXVyaOafJxwyDNvTQ3ZzzxJi5MGkzpipNHhNEmHyRdL8dpIUsxGOaV8W7k4rGYqazyUVXqo9nixW824DFgbmpUVPgv1fS+QLewvrzYsBpNSITniFGu3UOPRqPYE9gWdbwud5u+j25g8i3NY0TSoqJaCNhwcWcgeym4xk+w2UVJRQ0VN8J/P9MwOQT+HCL7a0diG9oWIc1gpLKvCc4w3iINB8kycSOtTx2F2ONg+89NmFbPhkGdbZ3zIwW1bGfjoY1E9e7A5jtzi8dCrmHbIG8GH336MGwHtkBsOfQ/5WMfUoMnLFmWasahjt5hJdNtoEWvc1j5z5szR/ZzN4bCadekWfTwx9uYXdsGglG/0tKHT2Os9HhDvtBLvtAbk+21snsU7G/5CVhinIfsFm0yKeJeVOIc16Ou7s1csDvIZRLA5rGZS3PZG/f6b/Otn9boyS56JE7G63aSNPZXcmZ8dVow0Vqjnmeb1svqJx0jo3oM2p59pdDhhyWo21TXUrP2IOeQj1mGt+4g75KP29Vm86/CPBJet7iPR/dtH0iEfyTF2kmPsdduKNoUUs+IooVgchbJYHV4UH4vF5NtXOFSZTIoEV/N/NhaTIjmmcS8mgyHeaQ2Z/XTF0RxWc6P+EDptvu3LQrEzuTCeSfmuX/HOpq3Lt5pNxBnUEEqII7U9+1zK8vLYt2yp0aEEzY6vv2T/2mx63Hk3yiTX9cayW0wkBrEPSTDJsy1CSjhszXMks0kZ0vlWj0ZLzdXcF3Quf8ERyI7Z0PQ8i3NaDB2JF8fW2EK2Vm1zqGD9/lqsUsyEI9+Wc7Zm79vtsJp1ecNR8kzUp80ZZ6HM5mZ1NQ7lPNM0jdVP/IeYzHa0m3ih0eGEHYfFHLCZb0aQV2UipAwdOtToEJrEbbfouv2H3WJq9gstvTj8668bQynfSGisIzgX16bmmVK+6YM2Gc0LGU0tZA8VY7eQ6LIFbFp8rW59Bgb0eCK4zCbfntWBvO7E2i1BH/2XPBP1sSclkTriZLY3o5gN5TzbPecX9i5eRPfb7sRkCf03+kOJ02Ym3hW+hSxIMStCzJIlS4wOocli61mrF0jhtv1DrMPa4BFNm9lEciPXqDVWc/JM+acfSkFrvNp3kwPBZjGREmMLaN5tXPNrwI4lgkfhe0OyKXtW13ts/xtgwXydKHkmGqLtORM4sGE9+9eva9LjQznPVj/xHxwtU+l0+ZVGhxJW3HZLvX0mwoG8GhMh5eDBg0aH0GQ2i0mXdZ0Oq7lZ29IYJc5hrXe6sNtuOeb+jYHW3DyrLWhlvaVxHBbfu8mBVFt4BKr4qCgva/5BRFBZ/VPNg9lMz7edmC0oxwbJM9EwGePPAWD7500bnQ3VPNu3fBm7fvyebr//A2aHw+hwwkaswxJ2AyPHI6/EhAigWLslqO/AK/85wpHJvz/ssX48JuWb3hdOF1alFIlS0BoiGIXsYce3mkl22+W5jWAK3wyXJLdNlzcHbRZTWF3fRORxtU4nZeAgcmd+anQoAbX6ycewxseTdd0NRocSFmp3h3DZIud6JH+pRUgZPHiw0SE0i8mkiLUH70W2y24Jy05ztSzHaAhlt5iCMr3vRAKVZ7VbEOm5XjraBbuQrWU2qboRu6bK6tk3cAGJgLGZTSTH2HVv3Oe2W4LSEV3yTDRUxtkT2Ld8GaW52xv92FDMs/0bN7D9s//R5Xc3YouLMzqckKeAeFfkbTUoxawIKTt27DA6hGZz2sxBGdExKYU7hLfiaSiH1eyb0odvmkuCy6Z7gR7IPDP5m8YEe2q00K+QPZTbP3rXlOd33+78IEQkmkop33IHPZYyHE+c0xLwc0ueiYZqe865AGz/4vNGPzYU82zNU09gttvpevMtRocS8pSCBFfzu7SHIilmRUjJy8szOoSAiHNYAr73bDDXdOmttkAwappLoPPMZFIkSUEbVHaLiTinMfliNftmDzR2m5V9e0LvxV+08s0AsRu+N3ftbI5AXikkz0RDxXXqTHy37k3aoifU8qw0bwdbPnifTldejbNFS6PDCWkm5XuNoucMOD1F5nclhMEsZlNAXzRZTMrwF2GBFo5NrE6kdoQ20Nu7CF8hYvQeeEop4hyBaw4l9FG7zVdCCL3ZdKzlFkLope3ZEyiYO4eKfXsb/JjQ+M05XPZzT6NpGt1vvcPoUEJa7ZKZSHvNdajI/c5EWOrSpYvRIQRMjN0SsMImxhE5C/VDQbDyrPaPhhS0gRMKheyhHFYzKW57g7ZmSm/XUYeIxPHUPlehuD6sKftvH4/kmWiMjLMnoHm97Pjyiwbd32E1kxxjJysrK2TeyKvYt5eNb75O+wsvIqZtptHhhCxLlMwak2JWhBSzOfRedDSVUorYABShNrMpItc4GCmYeWY2+bocS0HbfKFWyNYymRSJbhux9SwnMJnkT6wRTP6ts+Kd1pBumBfrCMx+1ZJnojGS+vTF3bYt2+uZamzzL6+Id/q21XPYrCHz5tD6l17EU1ZGjzvuNjqUkGU1m3yzxUL4GhgocgUUISU7O9voEALKYTVjb+YaBRmVDbxg55nFbCLRJdNRmyNUC9lDuWz+rV2O82IhN2ejzhEJp81MSkz4NDmJdzb/jS/JM9EYSikyzp7Arp9+oLqk5KivW0y+N4MSj5iamp2djcmk/NP2jXvDtrqkhHUvvUjG2eeQ0LWbITGEOrvF9xokGgpZkGJWiKCLdTS92YfDGpzOyCL4LP53RUO4FgtZ4VDI1rKYTf5mZuFRPEUqBSS4rMQ5wiNvatUWB+ETsYgEbc8+F29lJXnffVN3m8nfFyA5xl7vm0F2i+9NIyN6eWx88zWqiovoedc9up87HDisZhJctrC6DjaXvEoWIaVVq1ZGhxBwZpNq0n6GCpq1x6U4Pr3yzCoFbaPZzOFTyNbyLSk4erQiMbmFgVFFD18hGz6jsUeyWUzNmoEjeSYaq8WQodhTWpA787O61xr1FadH/t2sbYqn59Z0nspKsp9/hlYnjyZlwCBdzhlOXDYz8VHYXE6KWRFS2rdvb3QIQeG2W447FfF4XPbA70cofPTMM6vZRILTJiMvDWAzm0hwhVcheyi7xUyy21a3tCA1va3BEUU+BcS7rGG/5YTLZsHRxGJc8kw0lslsJuOss8n79msSrL7XKPVdd4/3d9Nm8a2t1WN2Ss7U9yjftYued/8p6OcKNzF2C7GO6CtkQYpZEWLmz59vdAhB05iLjFLglmmLQaN3ntksJt+0H13PGl7CvZCtZTIpElw24hxW1q1canQ4ES/OaQ3bEdkjxTkb/6YnIHkmGs1uMdFj0kSqS0rYMeunBj3mRH83a2ennKiHQHN5a2pY89TjJPcfQKtRo4NyjnAV57A2aQZgpIje71wIndksvr1ny6s89d431h7+L+rF4WoL2sqa355/7QT31473xRM8SDvBFzUNqj3eE57TKJFSyB7KaTNj8a+HLKvyUO3xGh1SxIl3WkOis2qgKOXLl8KyquP//gvRDBaTr+i0WUzEjDsNW2wsG2fMoMOZZwbk+FZ/D4GyKg+llTUB/Xuz7X8zKNmSw6i/T4uovxXNofC9oRdJ18GmkGJWhBSn02l0CEEVY7NQUe054QsVs0kZ0lQhmhiVZzaLyfDpkFU1Xqo8XqpqvCFRYEViIVvL6XTisJpxWM1Ue7yUV3uoqPKE5BsK4SbSCtlaFrOJOIeV/eXVDX6Mze4IYkRHU8o3LdppNVNcVkWNVzI61Jn8WwUe+jtjsdvpMH48mz79lNP++19M9WxZ19C/m0r5+oTYLSYOVNQE5O+Mpmmseepx4rt0JWP8Oc0+XiSoXWIRKTNTmkOmGYuQMnjwYKNDCCqTydcw4USk6VPwRXqenYjNYiLG7ttSpmWsnXinFafNbMj6bGsEF7JweJ5Z/UVKi1g7sQ5ZD98ccY7ILGRrOazmRk0Z7NKrXxCj+Y1S/kZBbjsx/p4OCdLgLqTVPWcxtmP+znQ6/3zK9+xh57x59R6rsX83azu917cfd0Ps/O4bilatpMcdd6NkX2WUCu+md4EmGSFCysKFC40OIegcVjO242y3YzObIvpFWqiIhjxrCKUUDquZOIeVlBg7KTF2X6FgMQf9Baq1bi/eyH0lfKw8U0rhsllIibGT6LL5ftYGxBauYh2WqJi5EmO3HPfvxJHWr1oe1FhqR/VaxNhx2y2H7V1pNilfx/agRhBawuV7ddrMpLjtJ2zu1OHMMzHb7WycMaPe4zX176bLZiE5pgAQJwAAFf5JREFUxt7gfD6W1U/8B3dGBu0vvKjJx4gUJqVIctkMn+UVSuQnIUJKeXm50SHo4njvVDZnewbRcNGSZ41VO8U93mWlZayDJLet7kV1IF/ARUMhC/Xnmc1iIt7leyMhxm45bFsfcbQYuwWXLXqukfFOa4NyoqqyIijnry1iU2JsuGzHL4isZhNxUbIdiN1iokWsnWS3jXinFZfN9+Z0KP3q2v3dheMc1sPeeDgWW2wsmaeeysZPPkGrZ6F2c/5umk2KRH9MjflZ5UyfxoedMymYP4/qkoNs/eTjJscQCcwm5Wuy1Yw3BiJR9PxVECKEWMwmXHYLpZU1dbc5rGascoESIcRqNmE1m3DbfWuWqjxeqj1as9bbRksh2xgm/17UbrtvTX15lYeqEFjPHEpqfz7RxNcZ20pRaZWu66zNJoXbZsFhNTX499RhNVPj1Q77mxZprIfsgW0xKyxmDptJ5fFqVHt818Yaj0a116trIy+r2beEpLEjdp0nTiTniy8oWLGC1H7BnbLutJn9a2mrqaw58TUuZ/o0Ftx2C57yMgCqiotYcNstAHSYfHFQ4wxFFv8siPreoIhGqr53YkLBwIEDtSVLlhgdhtBBZWUldrvd6DB0oWkaew9W4dU0FJAcY5d1dDqJpjwLFq/XV9zWNpPyNKAJTLQVss3JM49Xo6yqhvJ6GsZFA5fNHLX7JwKUV3k4UHH8hlDVVVVYbbZmn8fif1OlOUtd9pdVU1FTf8f+cNPUQqK2wK3xatT43wz0BvgX+ljNnRqjbM8eXmzVisF/+Qsj/v73494v0H83K6p9eX28H8eMnlmU5uYedbs7I4OJqzcELI5wEMmNEo+klFqqadrAxjxGhoFESNmyZYvRIehGKUWc0zfSYFQDnmgVTXkWLCZT49bbWs0mEpzR8ce4VnPyzOzfQqNFjK9JV7TO2nBGeSELvp/BiQqV3Xnbm3X82hHH5Bh7s3s2xDktEZer5maMiJn918kYu4UEl40WsXZaxNhJcFmJsVtwWJr+t18p6qaBN+d5c7VoQfrIkWz85JMT3i/QfzcdVt+a3mPF7qmoOGYhC1C6Y0dA4wh1dkv0FLJNFVlXHBH28vPzjQ5BV3aLGafNLB2MdRZteaaHE623rS1ko216VCDyrLZJV5LbRpLb96I1Wn6KtW+WCIhzWLAc5/enaN+eJh2ztpt4bV4FglLK97seIS+8lSLgUztNJoXd4utYXbtmvjEFrsI3WyHFbT/hWubGyJo4kX1r1lC0ceNx7xOMv5sm/17cCa7fcqYsfxffnHXacR/jbtMm4HGEKofV7O8YHhm/T8EixawQBvM1RJALlYgsvrW2FhL9RVi0FbLBUDuClhIT+dv7OCxm4qOkqVBDKBW4bXBsZhOJLt/vZTC29jCZlG85QcCPrK/aQlaP37NjFbgtY30dz2unEFtMCofFTHKMndgGNHdqjE7nnQdQ7+hssNgtZlJibBxctYKvThnB/nVr6XrzHzA7XYfdz+x00XfKQ4bEqDeXTa6BDSXFrAgp3bt3NzoEEQUkz4QegpVnJtPR2/tEEofFN8IvDmf2j2IdKaND5wY93m7x7fuZ6A7+th4Wsymsn0MFJDhthk6ZVkphs5hw2Sx108DjXdagFNdxbduSOmDACbfoCfbfzfXTp/P5qadgtlo56/tZDPrXfxjyzPO4MzJAKdwZGQx55vmIb/5k9b/ZFO3LKxpD5jaKkOLxRF7jCBF6JM+EHvTIM5vFhM1iItZroczfCTnQDWb0ZLeY6noJiKP5Ru8O7xrs9Z64K6zDYsZtN+u+nYfdYibWoVFSEX4djuOc1qjbx7PzxInMue8+SvLyiE1PP+rrwbqeaV4vc6dMYcHDD5M+YgQTPv4YV4sWHKysocPkiyO+eK3V3EZe0Sy6flNFyFu/fr3RIYgoIHkm9KBnnplMihi7hRaxvoZRx1tfGcpsh2x9Io4vxm7Bfkihlbd18zHv57CaffuhuqyG7Uvpsllw2sLrxXm80xqVBUXn888HYNOnnx7z68G4nlUdPMinF1zAgocfptd11zH5hx9wt2yJUr4GeEluW1heyxpD4dt6rLmNvKKZFLNCCCFEBHFYfevq4p3BmZIYDNYo2noiEOIcx35uFb7uxyn+59+oIvZQcQ4rthCIoyFimrk1UThL7taNpC5dTjjVOJD2b93K1OHD2fzZZ4x5+mnGvfIK5iO2mLKaTXXXskjrkg2/Xatj7IFp5BWtIi8zRFhLP8bUFiECTfJM6MHoPHNYfytqQrmojbb9hwOhtgusApJbtKrrcJvs3yIr1J7vUM9B8P383FG+s0Cn888nd9YsygsLj/paIK9nO375hXcHDeLAtm1c8NVX9L/tthP+/td2dE9whc8bIydiNfvWr4fD70U4CP+MEBGlTRS1XBfGkTwTegiVPKstauMcobdlisWkom7/4UCxmk3EOa1kZLTxd7gO3RfGJv9eraH6NDussp8x+NbNah4POTNnHvW1QF3PVr76KtPHjsWRlMRlixbRbty4Bj/WbjHXdcgPx8Z3tU3cktzGNheLNPKTFCFl4cKFRocgooDkmdBDqOWZb/qpb6uPUChqzf4CR7ZtajqH1czqFUvD4mdoNikSnLaQ27LHbjHJFih+rQYOJLZNm2NONW7u9cxbU8OPd9zBt9dfT9sxY7hs4UKSsrKadCyrv1t2stuG0xb6e28r5ZvCnhzAPZ3Fb6SYFUIIIaKEUrXb+tj867SMicNsUiRJIRt1bBbfaHKoqG06JnyUUnQ67zy2fvMNVaWlATtuRVERH591FsuefpoBd97JxJkzcSQkNPu4FrOJOIdvX16XzRySI/9Om5kUtx23rIsNGilmRUiJiYkxOgQRBSTPhB5COc+UUrjtFlrUNR/R79wmJSOygRTKeXYsDmtorE21mJQ0HTuGzhMnUlNRwdZvvjns9qbmWeH69bw3eDC5s2Zx+muvccoTT2CyBPb5N5l83Y9rr2ehMPPEZjaR7Lb5lnfItS6olBYG+9ENHDhQW7JkidFhCCGEEBHJ69Uoq/ZQVllDMF8VmJQiyW0L2bWdQj/7y6qpqDFmz2+ZGXB83poaXmzVivZnnslZ77zTrGNt+eYbZl50ESabjXNnzKDNiBEBivLENE2jotpLaVUNHq++dY7Z5Nsv1h6Ga3pDgVJqqaZpAxvzGBmZFSFl/vz5RocgooDkmdBDOOVZ7T61ddP1gnAOpSDRFbpNisJVOOXZoeKcFkP2EJWZASdmsljocM45bP78czxVVXW3NybPNE1jyZNPMuOss4hr147LFy/WrZAF38yTw7ao0uG5VgpiHb5rqBSy+pJiVoSUyspKo0MQUUDyTOghHPOsdrpecow9oI1VlIIkly0k9j2NNOGYZ+ArOBJcNl2nhMobKg3T+fzzqdy/n9xZs+pua2ie1VRW8s111zHrrrvodN55XDJnDvGZmUGKtH61e7kGa1uf2m2xUtx2XDbjp89HI/mrIoQQQojDmE2KOH9R29zum74CQgpZcTRz7bpVHc6lgASn5GFDZJ52Gla3m42ffNKox5UWFPDh2LGsfuMNhk6ZwoQPP8QWImu6g7Gtj93i2y82VtbFGkrWzIqQUlNTgyXAjQGEOJLkmdBDJOVZjcdLaaWn0WscFZDgsmGzSAERLJGQZxXVHvaXVwf1HAkuq0z/bITPLryQvDlzuCkvD2Uy1ZtnBStW8Mm551K+Zw9nvPkmXSdP1jHaxqvxeCmr9lBR5Wl0nwCLSREj62KDQtbMirC3YcMGo0MQUUDyTOghkvLMcsi+jvYGFqZSyOojEvLMYTUTE8QOx/FOKWQbq/P551Oan8/OBQuAE+fZhhkzeH/4cPB6uWTOnJAvZOEY2/o04DFKUTdjRfIpdMhfGBFSCgoKjA5BRAHJM6GHSMwzi9lEgss3Ve9ERa0C4l1WKWR1ECl55rZbmj2l/VhiHcE5bqTrMH48Jqu1bqrxsfJM0zTm//3vfHbBBbTo3ZvLFy8mtX9/vUNtlto+ASkn2KZMQd1WZk6b5FKokb8yQgghhGgUq7+oTXTZjmqqooA4GQkTTRDnsAS0SY/bbpGmPE1kj4+n7dixbJwxg2MtSawuK+Pziy5i7pQpdL/ySi766SfcrVoZEGlgmEy/7b0d5/itSZjD4msg5St0ZV1sKJJiVoSUnj17Gh2CiAKSZ0IP0ZBnNouJRLeNBJcVq78IiXNaZSRMR5GUZ0op4p2B6TbstAV36nI06Hz++ezPyWHvqlWH5dmB3FymjhjBho8+YtR//sOZb76JxeEwMNLAOXRbn2S3jXjpfh3ypJgVISVctxgQ4UXyTOghmvLMbjGT5LaR7LZJIauzSMszk0mR4LQec7pnQzksZuIc1sAFFaU6nXsuKMXGTz6py7Od8+fz7qBBFG/axPmff86gP/4xYkcspfN1eJBnSYSUjRs3Gh2CiAKSZ0IP0Zhn8uJPf5GYZxaziXhn04pRm9lEnFNGZAPBnZpKQufOLHj4YaZmZPBcSgpTR47EFhPDpQsW0HH8eKNDFEKKWSGEEEIIEVrsTRhd9a3ltkbsSKHest97jwNbtuCtrgZNo2LfPtA0Bt1zDynduxsdnhCAFLMixGRkZBgdgogCkmdCD5JnQg+RnGdOmxlXA7vHmuumJ0shGyhz7rvPV8geQvN6WfjIIwZFJMTRpJgVISU1NdXoEEQUkDwTepA8E3qI9DyLdVjr3dvYpBSJLhsmadQTUAe2b2/U7UIYQYpZEVKWLFlidAgiCkieCT1Ingk9REOexTutWI5TqCoFidJxNiji2rZt1O1CGEGKWSGEEEIIEbKUUiS4bEd1OFZAossmjceCZMTDD2NxuQ67zeJyMeLhhw2KSIijyW+/CClxcXFGhyCigOSZ0IPkmdBDtOSZb02sjdp6VgHxh+xvLAKv+2WXMe7ll4nLzASliMvMZNzLL9P9ssuMDk2IOkrTNKNjqNfAgQO1aJhGI4QQQgghjq+i2sP+8mrinVbZ01iICKOUWqpp2sDGPEbezhIhZc6cOUaHIKKA5JnQg+SZ0EO05ZnDaibZbZNCVmfRlmcifEgxK0JKTU2N0SGIKCB5JvQgeSb0EI15Jmtk9ReNeSbCg1wNhBBCCCGEEEKEHVkzK0KK1+vFZJL3WERwSZ4JPUieCT1Ingk9SJ4JPciaWRH21qxZY3QIIgpIngk9SJ4JPUieCT1InolQJcWsCCn79u0zOgQRBSTPhB4kz4QeJM+EHiTPRKiSYlYIIYQQQgghRNgJWjGrlHpdKVWglFp9yG1JSqnvlFIb/f8mBuv8Ijz16dPH6BBEFJA8E3qQPBN6kDwTepA8E6EqmCOzbwJnHHHbvcAPmqZ1Bn7w/1+IOiUlJUaHIKKA5JnQg+SZ0IPkmdCD5JkIVUErZjVN+xkoPOLmc4G3/J+/BZwXrPOL8JSTk2N0CCIKSJ4JPUieCT1Ingk9SJ6JUKX3mtlUTdN2Afj/banz+YUQQgghhBBCRACL0QEcj1LqBuAG/38PKqXWGxmP0E0KsNfoIETEkzwTepA8E3qQPBN6kDwTeujS2AfoXczuVkqlaZq2SymVBhQc746apr0MvKxfaCIUKKWWNHazZCEaS/JM6EHyTOhB8kzoQfJM6EEptaSxj9F7mvFnwFX+z68CPtX5/EIIIYQQQgghIkAwt+aZCswHuiildiilrgP+BZymlNoInOb/vxBCCCGEEEII0ShBm2asadolx/nS2GCdU0QEmVou9CB5JvQgeSb0IHkm9CB5JvTQ6DxTmqYFIxAhhBBCCCGEECJo9F4zK4QQQgghhBBCNJsUsyJkKKW2KqVWKaVWNKWbmRDHopR6XSlVoJRafchtSUqp75RSG/3/JhoZowh/x8mzvyml8vzXtBVKqbOMjFGEN6VUhlLqJ6XUWqXUGqXU7f7b5XomAuYEeSbXMxEwSimHUmqRUupXf5496L+90dczmWYsQoZSaiswUNM02cdMBIxS6mTgIPC2pmk9/bf9GyjUNO1fSql7gURN0/5sZJwivB0nz/4GHNQ07TEjYxORwb+lYZqmacuUUrHAUuA84GrkeiYC5AR5Nhm5nokAUUopwK1p2kGllBWYA9wOTKSR1zMZmRVCRDRN034GCo+4+VzgLf/nb+H7Qy1Ekx0nz4QIGE3Tdmmatsz/eQmwFkhHrmcigE6QZ0IEjOZz0P9fq/9DownXMylmRSjRgG+VUkuVUjcYHYyIaKmapu0C3x9uoKXB8YjI9Qel1Er/NGSZ/ikCQinVDugHLESuZyJIjsgzkOuZCCCllFkptQIoAL7TNK1J1zMpZkUoGa5pWn/gTOAW/7Q9IYQIVy8CHYG+wC7gcUOjERFBKRUDfAzcoWnaAaPjEZHpGHkm1zMRUJqmeTRN6wu0AU5SSvVsynGkmBUhQ9O0nf5/C4BPgJOMjUhEsN3+dUG164MKDI5HRCBN03b7/1h7gVeQa5poJv/aso+B9zRNm+G/Wa5nIqCOlWdyPRPBomlaMTALOIMmXM+kmBUhQSnl9jcaQCnlBsYBq0/8KCGa7DPgKv/nVwGfGhiLiFC1f5D9zkeuaaIZ/A1TXgPWapr2xCFfkuuZCJjj5Zlcz0QgKaVaKKUS/J87gVOBdTTheibdjEVIUEp1wDcaC2AB3tc07WEDQxIRQik1FRgNpAC7gQeA/wHTgbbAduBCTdOkeY9osuPk2Wh8U/I0YCtwY+1aICEaSyk1AvgFWAV4/Tf/Bd96RrmeiYA4QZ5dglzPRIAopXrja/Bkxje4Ol3TtIeUUsk08nomxawQQgghhBBCiLAj04yFEEIIIYQQQoQdKWaFEEIIIYQQQoQdKWaFEEIIIYQQQoQdKWaFEEIIIYQQQoQdKWaFEEIIIYQQQoQdKWaFEEIIgyml/qaU+qPRcQghhBDhRIpZIYQQQgghhBBhR4pZIYQQwgBKqfuUUuuVUt8DXfy3Xa+UWqyU+lUp9bFSyqWUilVKbVFKWf33iVNKba39vxBCCBGtpJgVQgghdKaUGgBcDPQDJgKD/F+aoWnaIE3T+gBrges0TSsBZgHj/fe5GPhY07RqfaMWQgghQosUs0IIIYT+RgKfaJpWpmnaAeAz/+09lVK/KKVWAZcBPfy3vwpc4//8GuANXaMVQgghQpAUs0IIIYQxtGPc9ibwB03TegEPAg4ATdPmAu2UUqMAs6Zpq3WLUgghhAhRUswKIYQQ+vsZOF8p5VRKxQLn+G+PBXb518NedsRj3gamIqOyQgghBABK0471xrAQQgghgkkpdR9wJbAN2AFkA6XAn/y3rQJiNU272n//VsAWIE3TtGIDQhZCCCFCihSzQgghRBhQSk0CztU07QqjYxFCCCFCgcXoAIQQQghxYkqpZ4EzgbOMjkUIIYQIFTIyK4QQQgghhBAi7EgDKCGEEEIIIYQQYUeKWSGEEEIIIYQQYUeKWSGEEEIIIYQQYUeKWSGEEEIIIYQQYUeKWSGEEEIIIYQQYUeKWSGEEEIIIYQQYef/AWwCakha9InfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,8))\n",
    "ax = plt.subplot()\n",
    "\n",
    "ax.plot(Lille_2020_max.day, Lille_max_mid, color='green', label='Daily max t2m 50th quantile')\n",
    "ax.plot(Lille_2020_max.day, Lille_2020_max, 'bo-', color='darkred', label='Daily max t2m Sep 2020')\n",
    "ax.fill_between(Lille_2020_max.day, Lille_max_max, Lille_max_min, alpha=0.1, \n",
    "                label='Max and min values of max t2m from 1979 to 2019')\n",
    "\n",
    "ax.set_xlim(1,30)\n",
    "ax.set_ylim(10,33)\n",
    "ax.set_title('Daily max t2m for Sep 2020 compared with climatology for Sep from 1979 to 2019')\n",
    "ax.set_ylabel('t2m (Celsius)')\n",
    "ax.set_xlabel('day')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)\n",
    "ax.grid(linestyle='--')\n",
    "\n",
    "fig.savefig(f'{DATADIR}Max_t2m_clim_Sep_Lille.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, we see from this plot that while the temperatures from 14 to 16 Sep 2020 were the highest in the ERA5 dataset, on 25 September 2020, the lowest of the maximum temperatures was recorded for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now look more closely at the probability distribution of maximum temperatures for 15 September in this time period. To do this, we will first select only the max daily temperature for 15 September, for each year in the time series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_max = Lille_max.dropna('time', how='all')\n",
    "Lille_15 = Lille_max[14::30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then plot the histogram of this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  3., 11.,  6.,  7.,  5.,  1.,  2.,  3.,  1.]),\n",
       " array([12.54313564, 13.95039225, 15.35764885, 16.76490545, 18.17216206,\n",
       "        19.57941866, 20.98667526, 22.39393187, 23.80118847, 25.20844507,\n",
       "        26.61570168]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Lille_15.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the range of maximum temperatures for 15 September in the period from 1979 to 2019. Has the temperature in this period ever exceeded that of 15 September 2020?\n",
    "\n",
    "The histogram shows the distribution of maximum temperature of one day in each year of the time series, which corresponds to 41 samples. In order to increase the number of samples, let's plot the histogram of maximum temperatures on 15 September, plus or minus three days. This would increase our number of samples by a factor of seven.\n",
    "\n",
    "To do this, we first need to produce an index that takes the maximum 2m air temperature values from 12 to 18 September (15 September +/- three days) from every year in the time series. The first step is to initiate three numpy arrays:\n",
    "* `years`: with the number of years [0:40]\n",
    "* `days_in_sep`: index values of day range [11:17]\n",
    "* `index`: empty numpy array with 287 (41 years * 7) entries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = np.arange(41)\n",
    "days_in_sep = np.arange(11,18)\n",
    "index = np.zeros(287)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a next step, we then loop through each entry of the `years` array and fill the empty `index` array year by year with the correct indices of the day ranges for each year. The resulting array contains the index values of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in years:\n",
    "    index[i*7:(i*7)+7] = days_in_sep + (i*30)\n",
    "index = index.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then apply this index to filter the array of max daily temperature from 1979 to 2019. The resulting object is an array of values representing the maximum 2m air temperature in Lille between 12 and 18 September for each year from 1979 to 2019:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lille_7days = Lille_max.values[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the histogram of maximum daily temperatures in the days 12-18 September from 1979-2019:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "\n",
    "ax.hist(Lille_7days, bins = np.arange(10,32,1), color='lightgrey', ec='grey')\n",
    "ax.set_title('Histogram of maximum 2m temperature in the days from 12-18 Sep in the period 1979-2019')\n",
    "ax.set_xticks(np.arange(10,32,1))\n",
    "ax.set_ylabel('Accumulated days')\n",
    "ax.set_xlabel('Maximum 2m temperature (° C)')\n",
    "\n",
    "fig.savefig(f'{DATADIR}Hist_max_t2m_mid-Sep_1979-2019.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the histogram above, you see that even if we take an increased sample covering a wider temporal range, the maximum daily temperature still never reached that of 15 September 2020. To increase the sample even further, you could include data from a longer time period. The C3S reanalysis dataset now extends back to 1940 and is accessible here [ERA5 hourly data on single levels from 1940 to present](https://cds.climate.copernicus.eu/datasets?q=era5+hourly+single+levels)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}