bd_pr_daily_eraintrim.ipynb

{
 "cells": [
  {
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   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing some conda package or module\n",
    "#!pip install netCDF4\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#!pip install cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.mpl.ticker as cticker\n",
    "from cartopy.util import add_cyclic_point\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing my file directory(path...don't copy paste instead write the path by your own)\n",
    "#my_example_nc_file = '/scratch/mkibria/data/EIN75/pr/v20190502/pr_WAS-22_ECMWF-ERAINT_evaluation_r1i1p1_ICTP-RegCM4-7_v0_mon_197901-198012.nc\n",
    "#fh = Dataset(my_example_nc_file) #defining my file's dataset.please notice the capital leters or small leters used in it.\n",
    "#print(fh)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 241, longitude: 480, time: 13149)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2\n",
       "  * time       (time) datetime64[ns] 1979-01-02 1979-01-03 ... 2015-01-01\n",
       "Data variables:\n",
       "    tp         (time, latitude, longitude) float32 dask.array&lt;chunksize=(365, 241, 480), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.0\n",
       "    history:      Wed Jun 29 12:05:53 2016: ncks -O -7 -L 1 -v tp -d time,7,,...\n",
       "    NCO:          20160629</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-de48823e-b2c6-43dc-ba55-f07041712db6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de48823e-b2c6-43dc-ba55-f07041712db6' 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'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li><li><span class='xr-has-index'>time</span>: 13149</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ba9e33d4-145e-495e-bdef-5df615db705c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba9e33d4-145e-495e-bdef-5df615db705c' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-74c64e8b-f811-47e6-8519-7c88f8fcc1ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74c64e8b-f811-47e6-8519-7c88f8fcc1ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30dd4ddd-9e7b-42d0-a8a4-c5ed67a32ef5' class='xr-var-data-in' type='checkbox'><label for='data-30dd4ddd-9e7b-42d0-a8a4-c5ed67a32ef5' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><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'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-6cdd6254-ab7c-40e7-b127-4690b06712f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cdd6254-ab7c-40e7-b127-4690b06712f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f8f0ff3-7c75-44fc-a91d-9251a3cf973a' class='xr-var-data-in' type='checkbox'><label for='data-3f8f0ff3-7c75-44fc-a91d-9251a3cf973a' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], 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'>1979-01-02 ... 2015-01-01</div><input id='attrs-1fbcf63b-358b-4a06-9729-05ea355e0cf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fbcf63b-358b-4a06-9729-05ea355e0cf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b87c6832-65b5-40f5-ae8c-51e791c5dbb0' class='xr-var-data-in' type='checkbox'><label for='data-b87c6832-65b5-40f5-ae8c-51e791c5dbb0' 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;1979-01-02T00:00:00.000000000&#x27;, &#x27;1979-01-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-04T00:00:00.000000000&#x27;, ..., &#x27;2014-12-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8f23c96-d831-45af-a1a9-ac16a500e485' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8f23c96-d831-45af-a1a9-ac16a500e485' 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>tp</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'>dask.array&lt;chunksize=(365, 241, 480), meta=np.ndarray&gt;</div><input id='attrs-5df68599-d2ae-4b62-9012-41193e8cf904' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5df68599-d2ae-4b62-9012-41193e8cf904' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5eb92f-ac88-4c04-9860-2fc75c3c7e07' class='xr-var-data-in' type='checkbox'><label for='data-aa5eb92f-ac88-4c04-9860-2fc75c3c7e07' 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>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><table>\n",
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      "text/plain": [
       "<xarray.Dataset>\n",
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     "metadata": {},
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   "source": [
    "data = xr.open_mfdataset('/home/mkibria/Academic/pr_of_thesis/reanalysis/Eraintrim/daily/ERAIN_daily_precip*.nc')\n",
    "\n",
    "\n",
    "data"
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   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
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    "#data.variables "
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  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for var in data.variables.values():\n",
    " #   print(var)"
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  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
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       "\n",
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       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 241, longitude: 480, time: 12769)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2\n",
       "  * time       (time) datetime64[ns] 1980-01-17 1980-01-18 ... 2015-01-01\n",
       "Data variables:\n",
       "    tp         (time, latitude, longitude) float32 dask.array&lt;chunksize=(351, 241, 480), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.0\n",
       "    history:      Wed Jun 29 12:05:53 2016: ncks -O -7 -L 1 -v tp -d time,7,,...\n",
       "    NCO:          20160629</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-51fb34ea-a750-4c2c-a6d3-694e948e0195' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-51fb34ea-a750-4c2c-a6d3-694e948e0195' 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'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li><li><span class='xr-has-index'>time</span>: 12769</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-30e24d13-c1ed-4ac3-9d25-46498a259f82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-30e24d13-c1ed-4ac3-9d25-46498a259f82' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-8801b88f-9a1a-436b-b509-a7a0d9a309b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8801b88f-9a1a-436b-b509-a7a0d9a309b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7a124c3-b40d-4fa2-b4b1-b81c8be5e5dd' class='xr-var-data-in' type='checkbox'><label for='data-a7a124c3-b40d-4fa2-b4b1-b81c8be5e5dd' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><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'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-b2405e7c-390a-4a60-a82d-06bdd5c7648b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2405e7c-390a-4a60-a82d-06bdd5c7648b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c9e7940-0fe7-437c-a73f-3385f177ddf0' class='xr-var-data-in' type='checkbox'><label for='data-5c9e7940-0fe7-437c-a73f-3385f177ddf0' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], 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'>1980-01-17 ... 2015-01-01</div><input id='attrs-5102d61f-af00-4718-8051-33033e1dc64a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5102d61f-af00-4718-8051-33033e1dc64a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fd53c23-9418-4af9-91fc-3dd7453f37ee' class='xr-var-data-in' type='checkbox'><label for='data-0fd53c23-9418-4af9-91fc-3dd7453f37ee' 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;1980-01-17T00:00:00.000000000&#x27;, &#x27;1980-01-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-19T00:00:00.000000000&#x27;, ..., &#x27;2014-12-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91b5ff82-321d-4361-bd46-e88dee9027bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91b5ff82-321d-4361-bd46-e88dee9027bc' 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>tp</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'>dask.array&lt;chunksize=(351, 241, 480), meta=np.ndarray&gt;</div><input id='attrs-d9974053-4f88-4a8d-9ea8-cea84e010aa0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9974053-4f88-4a8d-9ea8-cea84e010aa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0903975-9d12-430d-8519-e1f7d6cadd09' class='xr-var-data-in' type='checkbox'><label for='data-f0903975-9d12-430d-8519-e1f7d6cadd09' 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>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><table>\n",
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       "2015-03-19 14:25:04 GMT by grib_to_netcdf-1.12.3: grib_to_netcdf -o 3hr/erai_precip_19790101_19791231_00_0.75.nc 3hr/erai_precip_19790101_19791231_00_0.75.grib</dd><dt><span>NCO :</span></dt><dd>20160629</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 241, longitude: 480, time: 12769)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2\n",
       "  * time       (time) datetime64[ns] 1980-01-17 1980-01-18 ... 2015-01-01\n",
       "Data variables:\n",
       "    tp         (time, latitude, longitude) float32 dask.array<chunksize=(351, 241, 480), meta=np.ndarray>\n",
       "Attributes:\n",
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       "    history:      Wed Jun 29 12:05:53 2016: ncks -O -7 -L 1 -v tp -d time,7,,...\n",
       "    NCO:          20160629"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to select the year from 1980 to 2015\n",
    "data = data.sel(time=slice('1980-01-16T12:00:00.000000000','2015-12-16T00:00:00.000000000'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 241, longitude: 480, time: 4270)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2\n",
       "  * time       (time) datetime64[ns] 1980-06-01 1980-06-02 ... 2014-09-30\n",
       "Data variables:\n",
       "    tp         (time, latitude, longitude) float32 dask.array&lt;chunksize=(122, 241, 480), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.0\n",
       "    history:      Wed Jun 29 12:05:53 2016: ncks -O -7 -L 1 -v tp -d time,7,,...\n",
       "    NCO:          20160629</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-5b91bf90-a433-4b4d-969b-11cdb5ec80cb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b91bf90-a433-4b4d-969b-11cdb5ec80cb' 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'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li><li><span class='xr-has-index'>time</span>: 4270</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-89cfb27d-0f67-4117-94e3-f8a861b0b887' class='xr-section-summary-in' type='checkbox'  checked><label for='section-89cfb27d-0f67-4117-94e3-f8a861b0b887' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-66f65835-f10c-4e14-b70a-f43383ed2f39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66f65835-f10c-4e14-b70a-f43383ed2f39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8cd2452-3cdb-449c-905b-578cd4f0367c' class='xr-var-data-in' type='checkbox'><label for='data-c8cd2452-3cdb-449c-905b-578cd4f0367c' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><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'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-5ed9b43f-26e8-4902-b0ce-2e73ccd00c2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ed9b43f-26e8-4902-b0ce-2e73ccd00c2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d579837d-70a6-450e-89ed-8ae239a80a9b' class='xr-var-data-in' type='checkbox'><label for='data-d579837d-70a6-450e-89ed-8ae239a80a9b' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], 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'>1980-06-01 ... 2014-09-30</div><input id='attrs-5d1d64e7-2a87-498c-9d0d-dc3a2c4dbfb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d1d64e7-2a87-498c-9d0d-dc3a2c4dbfb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e323825-e78c-4ae0-8742-731fbb207123' class='xr-var-data-in' type='checkbox'><label for='data-8e323825-e78c-4ae0-8742-731fbb207123' 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;1980-06-01T00:00:00.000000000&#x27;, &#x27;1980-06-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-06-03T00:00:00.000000000&#x27;, ..., &#x27;2014-09-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-09-29T00:00:00.000000000&#x27;, &#x27;2014-09-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5df03467-91b2-4181-a1d5-44673b1ba67d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5df03467-91b2-4181-a1d5-44673b1ba67d' 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>tp</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'>dask.array&lt;chunksize=(122, 241, 480), meta=np.ndarray&gt;</div><input id='attrs-6c35b0e0-820e-4909-a371-f590ea0ed5cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c35b0e0-820e-4909-a371-f590ea0ed5cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd2640a-738f-4d32-8256-e8de9f4a87b9' class='xr-var-data-in' type='checkbox'><label for='data-bfd2640a-738f-4d32-8256-e8de9f4a87b9' 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>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.84 GiB </td>\n",
       "                        <td> 53.84 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (4270, 241, 480) </td>\n",
       "                        <td> (122, 241, 480) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 178 Tasks </td>\n",
       "                        <td> 35 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "2015-03-19 14:25:04 GMT by grib_to_netcdf-1.12.3: grib_to_netcdf -o 3hr/erai_precip_19790101_19791231_00_0.75.nc 3hr/erai_precip_19790101_19791231_00_0.75.grib</dd><dt><span>NCO :</span></dt><dd>20160629</dd></dl></div></li></ul></div></div>"
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      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 241, longitude: 480, time: 4270)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2\n",
       "  * time       (time) datetime64[ns] 1980-06-01 1980-06-02 ... 2014-09-30\n",
       "Data variables:\n",
       "    tp         (time, latitude, longitude) float32 dask.array<chunksize=(122, 241, 480), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.0\n",
       "    history:      Wed Jun 29 12:05:53 2016: ncks -O -7 -L 1 -v tp -d time,7,,...\n",
       "    NCO:          20160629"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting only JJAS months\n",
    "\n",
    "def is_jjas(month):\n",
    "    return (month >= 6) & (month <= 9)\n",
    "\n",
    "jjas_data = data.sel(time=is_jjas(data['time.month']))\n",
    "\n",
    "jjas_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#jjas_data = jjas_data.sel(longitude = slice(86.,94.), latitude = slice(18,27))\n",
    "#jjas_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[\"pr\"][\"x\"][379:429].values\n",
    "#data[\"pr\"][\"y\"][237:337].values\n",
    "#data[\"pr\"][\"time\"].values\n",
    "#jjas_data[\"pr\"][\"lat\"][:].values\n",
    "\n",
    "#jjas_data[\"pr\"][\"lon\"][:].values\n",
    "\n",
    "\n",
    "#data[\"pr\"][\"x\"].values\n",
    "\n",
    "#data.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[\"pr\"][\"y\"][287:337].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to select the x and y coordinates\n",
    "#jjas_data = jjas_data.sel(x=slice(4125000.,5350000.),y=slice(2975000.,4200000.))\n",
    "#jjas_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (latitude: 241, longitude: 480)&gt;\n",
       "dask.array&lt;mean_agg-aggregate, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray&gt;\n",
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       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1e9a8da8-586c-4d2f-9531-fe54cdcfc923' class='xr-array-in' type='checkbox' checked><label for='section-1e9a8da8-586c-4d2f-9531-fe54cdcfc923' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(241, 480), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'tp' (latitude: 241, longitude: 480)>\n",
       "dask.array<mean_agg-aggregate, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the mean for JJAS data\n",
    "jjas_mean = jjas_data.tp.mean('time')\n",
    " \n",
    "jjas_mean "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pr is given in kg m $^{-2}$  s $^{-1}$ so we have to convert it into mm/day.\n",
    "\n",
    "The formula is 1 kgm$^{-2}$ s$^{-1}$  = 86400 mm/day. \n",
    "\n",
    "so we have to divide pr value by 86400\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (latitude: 241, longitude: 480)&gt;\n",
       "dask.array&lt;mul, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b434c61a-91b1-4bae-b59f-6c17a429d0d7' class='xr-array-in' type='checkbox' checked><label for='section-b434c61a-91b1-4bae-b59f-6c17a429d0d7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(241, 480), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 451.88 kiB </td>\n",
       "                        <td> 451.88 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (241, 480) </td>\n",
       "                        <td> (241, 480) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 227 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.25 0.0,60.25\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"80.250000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >480</text>\n",
       "  <text x=\"140.000000\" y=\"30.125000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.125000)\">241</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-64a10e01-6e4e-4a9c-ba0b-fffe96f39037' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64a10e01-6e4e-4a9c-ba0b-fffe96f39037' class='xr-section-summary' >Coordinates: <span>(2)</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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-cd9860dd-5fd4-4383-807b-1d0074cd5826' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd9860dd-5fd4-4383-807b-1d0074cd5826' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b4a65f8-e4e8-49d2-8715-0fc83b25b362' class='xr-var-data-in' type='checkbox'><label for='data-6b4a65f8-e4e8-49d2-8715-0fc83b25b362' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><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'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-b25d1ae1-8a3b-4a2a-be77-add4adaf8341' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b25d1ae1-8a3b-4a2a-be77-add4adaf8341' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b37583b-d609-4228-b98d-10b8bdb5a2b5' class='xr-var-data-in' type='checkbox'><label for='data-5b37583b-d609-4228-b98d-10b8bdb5a2b5' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3a380eb-91cd-486a-8094-5b5ebe6caf3f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e3a380eb-91cd-486a-8094-5b5ebe6caf3f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tp' (latitude: 241, longitude: 480)>\n",
       "dask.array<mul, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_mm = jjas_mean*1000\n",
    "pr_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VDo+gxmblfo8+iNbBHgC9TkfC6bOcWP4v+35awNm1GyjIzS2tG4nEZmnWt5d6HLlzjwUtMcdaxagy03H9MOyiPmrihv2SoigrgVsoNhVXSd7GkIbAapkWlmXx6bmgrp3xD29Fwumz6PLyOLJ4Gb3uvbPiCy1Is+t64BsWSkpUFH0fvp9OU27k+N8rSIk0PKcsf9x8mdDR3Z3uM6bReepkNNranzaQSOobFx9v9Tg3I8OClpROoRDVxTRddahQhIzxycoKqDmzMjdRFCUCk5hHiqIcxgqn44pjaSESQtB56k2se+MdAA4u+IOwIQPxaxlmMZsqQuvgwNSfvyM/KwsHV1cAQvr35dBvpUcjyk1PZ8fcr7m4dTtj33sLJ4/SAnlLTIk9doIji5cRc+QY9s7OeDYJpsWAvrQdNxqhsfqvlc2i1+lIiYzC0d0dVz/fMtslnrugHgeEVzUmcP0xtK3OKoRIRkyogMKpOUuJUasRQzm8cIlxNJTPmtfeYsq8r7B3qkmc0bpFCKEKEECve+8iLz2DU6vWoC8owCM4iOCunbl84JDqBRhz+CgrnnyOG7/6FK1D3XtD2iKKonBwwUJ2ff296vABkBxxiYhtOzi5YiVDXnxGXUtsqCiKQuyx4ySeu4DQaHBwdcXV14eAdm2xc6ybz05K9GX+efpFUqMMI/pmva9j8HNPllgDzUpK5vTqoqAYjTu2rxN7agtrGBVZfey4+vSOqwhLCVFSxCUW3/UgBTmGkGnX3TOTnndNt4gtNaEgN4+CnGycPD0B0BUUsH/+r2ZOF/0ff4jOU2+qUr+6ggKunjlLyqUomvTsXu5Tqq2gy8sj/tQZslNS0dhpQVE4tXI15zeWFhS8CI2dHd1nTKP7Hbc2SDHPSkpm3RvvELVnX4lzbo0CGPveW/i3blmjeyScOcflAwdJiYrGzc+PdhPHcWbNerZ/9pVZOydPD4a98jxBXbuQduUKqVGX2fHlt6RdNmS00NjZMW3hz3gENi7tNlZJZcXo5N2D9iuKUmaK9KogRaiKWEqIjv25nM0ffAIY9g3N+GshBp8R22ffTwvY/c0PgOGLPeOvReU+0Sp6PUeX/sWZNevJy8wiPTZOFWh7F2d63XsXHW+aZJNrTIqicG7dRnZ8+S0ZcfFltgvs3JE+D96L1sGBC5u3cnDBQvQFRXkTvVs0Z+gLz1j9k3hVUPR6/n7sGS4fOFRmG69mTbjl13lo7ao+yZORkMCOL77l7Jr1NbDSgNBoGPzck7SbMK7GfdU3lREiKUIWxhJCpC/Q8f3oieRnGaYHZy5f3CCe+MHw1P/r1Onqj26HGyZw3b134uzladZOURRSLkWx69sfuLCp/Kj1/m1aM/i5pwhoY71z8qWx/q33OLVydblt2o4fy6CnHzMb6SSev8jGdz8k7vjJooZCEDZ4AN1uv5WAtuF1ZXK9cWHzVla9qKZdInRQfxzd3clJS+PilqIMB4Ofe4r2kyrvfKvLy+PQH0vY99OvFGRXHKA9uHtXUi5Fknk1sdTzWgcHRr7xKqED+1XaBmukPDGSImQl1LcY/TH9HnXR8+Yfv8bfihc9q8r+n39j19ffm9U5urtj7+KMg4sLCgpZVxPJTS/d28itUQAardYs0oTQaAgfPYIut96Mk5cnmQlXyUxIID87B4+gQLyaNcHJoy7y1VWPuBMnWXLPw2rZ0d2Nxh3aoygKurw8PJs2ofWIoQR361Lq9XqdjmPL/mbn19+X+DEN7t6VbrffQtPretTrCFpRFOKOnyT+5Cn0Oh12jo64+vvh5OmBUqBDp9OhLyhAIPBu0Ry3AP9S7dMX6Fh09wMknj0PQOepk+n/eNF7deCX39n5lSH5rIuvDzd8+QmxR48TsX0n6XHxOLq6EtStC017dsOzSRNyUlPJiIsn9tgJjv25XI1yUEhI/744uLpwZnXJTBV3/bsMRVHY8/18zqxZhy4vH4+gxngEBeHVtAntJozDN6xFbb6NFqU0MZIiZEXUpxD9/dgzRO87AMD1H71L897X1du965qC3FyWP/EcMYePVvqadhOvp8MNE3D28sQtwB9dfj4Hf/2DfT/9ii6v1JxuJfBu3oywYYNpPWIo3s2bVXxBHbLuzXc5vWoNAM36XMfI2a/g6OZW5X7SYmLZ+vFcIrbtKHHOr3VLuk27hbAhgwxrTXWArqCAnNQ0Lm7dzrE/l6vCURlc/HxpM2Yk3W6/FUd3w2vPiE9g84efqq/HztGR2xf9gqt/0ab9/OxsfrnpdrKTk2tku29YKAOefITgbl1QFIX5E6aQlZhU1EAI7l2zXHW8URQFRa+3yanfqlBciKQIWRn1JUSmUzXVWcC3dvKzszm8cCmXdu4m/oThybk4ju5uBHbqQPtJ4wnp16fUflKiL7Plw09LXbwuj85TJ9P7gXvrzMOqPLJTUvlp0s2qeN70/Zc0atemRn1ePXueAwv+4Nz6jSg6vdk5BzdX3AICcPXzNf7zw9XfcOzi44Neryc/K4u8zCzD/6bHal02eZmZ5Gdlk5eVRX6moZ0uL69GdgM4enjQtGc3clLTuHLwsNlnoc/D99Ft2i0lrjFdN60qzj7e9Jh5Ox0mTTAT55P//seGt99XyyH9+zLu/aokkG1YFIqRFCErpa7F6OjSv9nyv08BaDlsMKPefK1O72dJdAUF5GVkqj+AKODs7YWLj3el98LEHD3GwV8XErl7L3ZOTrj5++Hq74edowOpl6+QEhlVYsQU2LkjN3z5Sb1OWWUlJbNu9hyi9u4HIKBtOFN++KqCqypPWkwsh35fxMkVqywSocLO0ZEWg/rj7OVJflY2GfEJ5GVmorGzQ6PVorW3Jz8nh6tnz5GflV1uX51vmUK/R+4v9TOgKApHFv/Jod8XkZeZiU+LEFoM6Efjju1JvxJD9P6DXDl0hOyUFBzd3XFvFIBbowCa976OlsMGl+lNeG7DJqJ278PB3Y0eM2+v1ui0ofFF36FShKyVuhSihNNnWHTnAwB4Nm3C7QttI56cpVEUpVRRyc/J4dKOXRz/6x91mhNg2sKf8WrapNJ96wsK0Nrbl3k+8+pVspOSyUpOITs5hezkZLKTU8hKSubqufOGdT6T7+HI2a/QasTQKr7KislOTuHI0r84tuxvcuowBJTQaLB3ccYjMJA2Y0cRPmZkpTYh63U6zm/cwq6vvzdb2wPDw0HPu2fQtEe3ujJbUgVqU4TkZtVapi43t7r4Fc2B58pAoJWmrFGNvZMTLYcOJrBzJ+aPL5raPLliJemxcegKCvAIbEz46JH4tSoZpSJ6/0HWv/kueZlZtL1+DM379sI7pBkZ8VeJPXKMK4ePEHPkWJV+8HvcNZ2Ww4dU/UVWAmdvL3rdM5Pr7ppOdkoKmQmJZF5NJPPqVeP/iWQlJpKVmITGzg4HFxeDY4irq+ogYu/qYqx3waHYsb2LCw4uztg5OVVrJKnRamk1fAhhgwdy5fAR0mNisXdxoVG7NuUGxpXYNlKE6oi6EKP8zEz12N7FetJO2DpHl/xpVj7w6x9m5UO/LyagXRvajhuNb8tQtHb2KIqef597WfVCO7xwCYcXLqnW/YVWQ0CbNnSffistBtS9W6/QaHDx8cHFxwf/8FZ1fr+qorHT0qR7V0ubIaknpAjVMbUpRvGnzqjHnsFBNe5PYsB0k2dZxJ84RfyJU9Xq39HdDbeAAJy9vdR/Lj4+OHt74RbgT+MO7XFwlQ8VkmsTKUL1RG2I0dm1RTu5Azt3rLFNEgM9Zt5O2uUYLmzZhqLX49WsCa1HjcAjsDGXdu7m/Kat6PPLd/nuOHkilw8eITs5BScvD/xatSSoSyeCOnfEu3kzGVhUIikDKUL1THXFKPNqIpd2FeUmCR81vFbtupZxcHVl9JxZ5GVmoej1OLi5qmsa4aNHkJ2SypnVa7ly+CgZsfEoGPaGJJ2/iHdoCCNnvdzgg4ZKJHWFFCELUVUxOrhgobrXI6hrZzybBNeZbdcqZU2JOXt50nnqTSX2ZekKCtBotQ0mhp9EYgnkHIGFqUwm15SoaI4u/Ustd5pyQ5XuEbVnHzu/+o6rVdi5LqkYrZ2dFCCJpIbIkZCVUN7IaMfcb9TF88DOHQkdNKDS/R77awWb3/8YMHhwTfnh6wYV10oikdg2NjES0uv1WPum2tqi+Mgoet8BLm41RggWgv6PP1zpp+/ECxfZ+tHnalmXl0/0fqvOqi6RSK4xrF6EoiNiGNRkErf0vZ/LEbGWNqfemBaWxW2hmWz7vCh8S5sxI6uUmmD7Z1+VcD8uTAUhkUgk1oDVi1BGaiY6nZ6oC1dY8sMKS5tTrxzbd0qNQGzn5ETv+++u9LUp0ZdLD+B5jYwoJRKJbWD1ImTKpXPRljahXln/d1HitjGTB3Jf78q7dV/YXHrStz3zfmLbZ18Rte8Ail5fahuJRCKpL2xKhFKSrp14aXq9ng0rtqnlYRMNzgiV8aYDuLCp6Noed96BxpjuWNHpOfzHYpY/9gx/TL+X1OjLtWy5RCKRVB6rF6HgkMbq8bnjF8nNrv9Q9Jbg6N6TXI01JNPy8vWgW79OZufLE6P02Djijp8ADHHJOk+dzKg3X8PBzdWsXdKFi/z16NPEVTMcjUQikdQUqxchd083mrU0hNUvyC9gz+ZDljWonjh3IkI97jmwK3ZlZMEsTYy2z/1aPVZ0hqyPoYP6M+OvRYz7cA4dbpyoZoLMiIvn78eeNktlIJFIJPWF1YsQQI8BndXjNcs2Wc6QGqAoSpVGcW7uRes/6akZFbYvFKPECxc5v3GL2bkfx09h1Uuvc/iPxejz8+l6282M+3COOjLKz8rm78eeYd0b7xC97wAJp8+QnZxSaVslEomkutjEZtX23cNZ9uO/AJw8eAa9Xo/GSgNCpiSmcuHUJZqGBePf2BedTsfPny5i8fcrSE1Kp9/I63jzm+dwdHYst5/4mET1uHET/0rfPzThcAkPuIKcHC5s2sqFTUXOCo07tKPHzNs59PtishIN036n/1vL6f/WAoZw/x0nT6L/4w/J4JsSiaTOsAkRGjZhAB+9+DWZ6VlciYxjy6pdDB7X19JmlWDD8m3MfvhD8vMMe3O69+9E8lWDKBWybfVuru94B9/88wGhbZqX2df+bYfV49YdW1baBi9fz0q1iz12gthjJ+j32INcPnCIiG07zc4rej1HFi/Dt2Uo7caPrfT9S0NRFCJ37+XKoSN4NQmm9egRaO1s4qMnkUjqGJv4JXBwtGfSjDEsmLsUgO/e+5U+Q7tXOJooi9SkNNYv38a+rYc4c/QCGWmZFOQXENy8Ma06hnHD9DG07x5ebh+nj5xj/d/byMzIYsCoXvQa0o0fPvxNFSCA/duOlHptZnoWsx78gJ82fF5q9IPta/awZ1NRZIMufdpX+rX1HNiFRsH+xF1OAMDR2ZGP/5jNkt2JJJ6/yNVz57ly6LAaDPXAz78zbdHPJF+8xMmVq0k4fYa0yzHkpqcDsO3TL8hJTaNJ9674t2ld5VhpeVnZrHntTS7t2KXWnVm7gWEvP4dbQOVHeBKJpGEirD0cTtsurZR5az4h+Woqk3vcRY5xXWXI+H488+5DuHu5kZyQTGx0Age2H+XkoTN079+Zm+6+vsQPZkF+AQu+WMavc5eQmV6+m3Pf4T259/lphHcyH4VkZWYzd9YP/PXzf2b1fo18uBqXVGpfQgjufOoWPH08+Pjlb9T6jxe+Qa/B3czaZmfmMG3QQ8RGxQMw7pbhvPzpE+XaWpxzxy8y76PfiYtOYMYTUxk4prd6bsF5F1IvX2Hp/Y+SnZQMwKBnn6DDDRPUNvnZ2Sy4ZQaZCVfN+g0bMpDRb8+qtB2KorDqhdeKwg6ZoLG3Z8CTj9Bh0vgqvTaJRGJ5vug7dL+iKD1qoy+bESGApT/+y/9eKApjo9FoUBSl1Lhy9z5/O3c+dYtaVhSFtx77mFWLNlTp/gPH9GbSjDG0bNeCbat38+vcpVy5VH74oFE3DeGup29h0787KcjPp8+wnrTpbBCzl+6aw6Z/dwCGUcr0x6Zwy/2TcHZ1IjszhxdmvsXeLYcA8PTx4PdtX1V6iq0qPPfOf2z7ZC4Ajdq346bv5pqdTzh9lpUvvEpGXLxZ/QObV6O1t6/UPc6u28ia195Uyx5BgaRdiTFr0/mWKbQcNpiA8NZoyvAAlEgk1sU1K0KKovC/F79WnRTKw8XVmV82f0Fg0wAAtv63m+dnFP0gNm/VhEnTx9CtX0f8GvkiBFw8E8XfP69i7Z9bKhUwtefALmi0GnZvLHJv9mvsw9fL3yeoeeNSr4mNjmfm8MdJS05X67x8PZkwbSSHdh3nyJ4Tav0rnz3J2KnDKrSjOiRfTWVC5+noCnQA3Pb7fLybNzNrk5+Tw8Z3PuTs2iLh7nTzjQx44pFK3WPZg48Tc/goYMg8OvDpx7ly8DCbPvyE5IuXzNp6NWvKuA/n4CXzJEkkVs81K0JgiCSwcuF6VixYw9G9JxFC4OXriV8jb5qFNWH98iIPMFd3F8bdMpwJt4/itfvfVx0Ehk8ayGtfPF3m3psLpy7xw4e/sXFFyWkkADcPV5565wFGTR6MEIIje05wePcJPLzcGDZxAG4erqVeV8ilc9G8fPc7Zg4Lxbn/xenMeOLmcvupKtERMbx+//skJaQw/IaB6hobQP8nHqHzzTeWuCYvK5vvho8zq7vll+/xDQut8H4/TphC1lWDl9/Ql5+j7bjRAORmZLDyuVe4csh8zaxJj25M/OzDKr8uiURSv1zTImRKdmYO9g522NkX+Vfs3XyIJ6a+WuZIxtnFicV7vsfH36vCe585doF/f1/LwR1HuXg6kuatmjLh9lGMnjIUDy+36rwclYICHasWruert+eTkmgejujxN+9l6n0Ta9R/cZISUnjq1tc5c7T0xHbdpt9GnwfuKfVc/KkzLL7rAbUsNBp6P3AP3W6/pdT2hax48nkid+81XiRo3KEd4WNG0mbMKDRaLec3b+Xk8n+J2rtfvab3A/fQcuggmTlWIrFipAhVwPY1e/js9e+JunClxLk7n7qFe5+/vcp2KIpSJ1k0r1yK5dt3fyEnOxf/QF+Gju9P174da63/vNx8Fn+/nPkfLyzXGePJt+9nyj3jy0w3vuWjzzm65M+iCiEMI6LQshPkJZ6/yN+PPk12SopZvau/HxM+eR+fFiFkXk1k/oQpZuc19vZM/PQDgrqYhyqSSCTWgRShSqDX69m39TB//byKHWv3kpebz6Tpo3lqzgNmI6eGzKVz0bx459tEnIkyq2/VvgXXDe6Ki5sLugIdnj4e3DBzrDo9WZYQXTl4mD8fflItj54zi7DBA8u1IflSJFs++pzL+w+aRe0OHzOSIS88jdbenm2ffcXhPxabXddiYD/Gvvtm8e4kEokVIEWoihQU6MjJyqlwraYhERMVz92jnjCb6mvWsgmPzb6bvsN7Vni9qRDlpKVzbNnfHFnyp+rWDeDbKgxXP19a9OtD2NDBOHuV7cWXnZLK8b9WsPvbeWb1bcePpdXwISx//Fmz+ma9ejL+4/cqtFMikdQ/tSlC18SQwM5Oe00JkKIozHrwA1WAHJ0deeCl6Uy+c1ylR4HTwrJYcN6FjPgElt73CBnxCSXaJJ49T+LZ80Tu3MP2ud8w5p3ZNOvVE11eHof+WMKZ1esQdloC2oTT7Y5b6T5jGif/WWXmpn1yxUq8mjUx69fJy5Nu02+rwTsgkUhsBRkUrAESdeEKR/eeBAwbZT9Z+AZT75tY5WnIaWFZnPxnVakCVJwCozu3Li+PlS+8xq6vvyfpYgSJZ89zcsVKltzzMHEnTjHw6cdKXLvzi2/NyhM/+5Dgrp1LtJNIJA0PKUINkPgrRZEO2ncPp3Ovyof9Kc6dE9qqaR9UhCCwc0cGPPkI/R9/WK3OiE9g3ZvvEblrT4l+ctPS+PvRp1H0eu5Y+lu599z03kfodbpq2yyRSGyHa2I67lpDa7L/Sa+rWQrvzr3ac9P3X7Lrmx/Q63S0GNCXsMEDcfXzVdvkpKezb97PAJxbv1GtD+7elc4338iGdz4kJyWVgpwc/n32ZVz9/Uq9l8bODn1BAXHHT3J2zXrCx4yske1VoSA3j2N//k3E9l0U5OTQuEM7et49A0e3mrniSySS8pEi1IDISMtkx7p9/PJZkaeZvUPN/8T+4a0Y/9G7ZZ7vdNMNHF+23MwVW2g0DH3xGTyCApncvBnLn3iO9Ng4gBIx6TT29gx94WlSoqLZN/9XAE6uXF2vIrTqhVeL9jQBccdPougVBjxZuegQEomkekgRsmH0ej1H957kyJ6T7Nt6mIM7jlKQX2DWZtRNQ2p0j7LctU1x9vJkwqcfsPnDT4g9ehy3AH963jMTj6BAwBCSZ/I3n7Pji2+5uG07+VnZxvomBHXpTJdbp+DdvBkpkVGqCKVERpV5v9omIyHBTIAKubBlmxQhiaSOkSJko6SnZvD8jLc4tPNYqee1Wg033zeRCbePqhd7/FqFMfmbz9HrdAghSiTCc/X3Y8Ssl9Dl5ZEUEYmbvx/O3l5mbdyDAhFaDYpOT2bCVQpyc7FzrF66jsqiKEqJXEqF6HLz6vTeEolEipBNcuLAad579gvOHrtQ4lybzi0ZMLoXwycNpGlozULfVGYUVJwSTgzF0Do44N+69CR9Wjs73Bs1Ul24Uy9fKTciQ01RFIU1r73JufWbSj2fnZLC5g8/xcnTg3bjx+LeuFGd2SKRXKtIEbIh8vPy+d+LX7P819Vm9SNvHETXvh3pM6wHAUGlL/pXleoIUG3g1bypKkIn/v6X/k88XCfhkgDOrF5XpgAVcmzZ3wAcXfInN37zOT4hZWfDlUgkVUe6aNsQP370h5kA2TvY8dwHDzPrq2eZeMfoWhMgS9JqWNEa1pHFy/jt1hkcWPAHWSaRGmqLM2vWl7z/iKEElbJHKTc9g70//FzrNkgk1zpShGwEQyDSFWq599DuLNj6FZOmj6n1e1lqFATQauQws8ClKZHR7PziW36aeDNrXnuzRFK8mhDQrk2JOlc/XyZ+9iHXf/Quve6/m46Ti6KZX9q5iwK5TiSR1CpShGyEnev3qVGwg5o35sMFr9MkJNDCVtU+Wjs7xn0wh4433YC9S5EY6nU6zq7byMIZ95Fw5lyt3Mu/Vcm1KXtnZzRaLc17X0ePGdMY8NRjqpdfflY2e3+YXyv3lkgkBqQI2Qj//rFOPR4wqhcaTd386Sw5CirEwdWFgU89yp0rFjP05ecI7NRBPZeXmcn6t2snsGnciZMl6nLTM8zKQgi63FqUXPDAr3+QcPpMrdxfIpFIEbIZMtIy1eODO49VKv14VbEGATLF3tmZtuNGc+PXn3HDFx+rnneJZ8+z65sfyElLq6CH8vErZSR0bNnf5Bn3MRXSftL1+Jl49F0+eLhG95VIJEVIEbIRhk8qyttz5uh5zp24aEFr6p+grp0JGzpILe//aQHzJ9zM4UXLqt1ny2GD6XXfXWZ1Du5u2Dk4mNVptFp8QkLUsr2Tc7XvKZFIzKlQhIQQTYUQG4UQJ4UQx4UQj5uce1QIcdpY/75J/QdCiH1CiEHGcogQQhFCPGrSZq4QYmYtv54GS7Mw8z0/gtp1W7a2UVBp9H34frO037q8PLZ9Mpcji82FKOliBJcPHq7QiSB63wEO/rbQrK7HzDvQ2JXc62Tn7KQeR+3dVycjUYnkWqQy+4QKgKcVRTkghHAH9gsh1gKNgIlAJ0VRcoUQAQBCiEKXo4HAfGCzsRwPPC6E+EZRFOliVAXW/72V1+5XNZ7gkEDC2oXUWv+2IEAAbgH+TP35O86u3cDhP5aQdDECgK0fzyV6/0FaDRvC+U1bOL9xCwCBnTow9v23yU1PJyc1lZzUNJw8PQhoE46+oIA1r71JXoZhmlNjZ0ffh++n05QbSr13i/59OPH3PwCc37iFg7/+QfjYUbh4e5WIDiGRSCpPhSKkKEoMEGM8ThdCnASCgXuBdxVFyTWeizdeogX0gAJmj+sJwHZgBvBdbb2Ahk5GWib/e/Fr9cnby9eD1+Y+VWcbOK0deycn2o0fS8thhmysccdPAHBxy3Yubtlu1jbmyDF+GD2xRB/B3bvSacqN5KQa1pScvDwZ98EcGrdvW+Z9m/ftTZtxozn1738A7PzqO3Z+9R0+oS2Y8Mn7ZlHFJRJJ5anSI5wQIgToCuwGWgMDhBC7hRCbhRA9ARRFOQ64ANuAr4p18S7wtBCi3NguQoj7jNN5+5ITU6tiYoPjp08XkWJ8DxoF+/Pzxrl07Fn2j2VVWHDexWZGQcVxcHFm/Mfv0mZs1WPjXd5/kNWvvqGWQwcNKFeAwOAlN/i5J2nUvp1ZfdKFi/z38iw5PSeRVJNKh+0RQrgBS4EnFEVJE0LYAd5Ab6AnsEgIEaoYeLS0PhRFuSiE2AOUm7tZUZRvgW8B2nZpdc1+uwsKdPzz21q1/NCrM/Fr5FPjfm1VeIrj6ObGsFeep9PNkzmx/F8STp/B0d2djjdNInLXXo4t+xtFb8in5N+mNQXZOSRfigRAn5+v9uPk6VGp+2nt7RnywtP8Mf0eMBGd2KPHycvMlLmHJJJqUCkREkLYYxCgBYqiFK4CRwPLFMMj4B4hhB7wwzDtVh5zgCXAluqZfO1waOcxUpMMU0Z+jX0YNnFAjfprKOJTHP/WLRn0zONmdSF9e9PnoXsRQqiRuPU6HX8+9ASxR4+btW3SrUul7+Ub1gJnLy+yk83DCDm4ulbPeInkGqcy3nEC+AE4qSjKRyan/gKGGtu0BhyAqyU6KIaiKKeAE8D11bD3mmLD8q3q8eBx/aq9QdWWp91qgr2Tk1kqCI1Wy+i3XlfD9QiNhm6330KTnt0r3aeiKOjySvrVXD1ztuYGSyTXIJUZCfUD7gCOCiEOGeteAuYB84QQx4A8YIZS+Ynxt4GDVbT1muLgjqMs/3WNWh5yfd8qXX8tik5lcPX346bvviA9Ng57Z2ecvTyrdL0QgpFvvsqqF14zE6OE02fxD29d2+ZKJA2eynjHbYMyN6XcXpmbKIoSAXQwKR9GbpQtl38XrkdvXM/w9vOiU692FVxhQIpPxQgh8AhsXO3rm/e+jmkLf+bnG25R67yaNqkN0ySSaw6ZT8hKsbcv+tOMuGEg2gqSxUnxqV/2fD9fPXZrFECjDpV7SJBIJOZIEbJSwtoWJU9LT80os50Un/rn8oFD6n4hgEHPPI7W3t6CFkkktosUISulVYdQ9Xjb6t0U5BdgZzI6kuJjGXR5eWx6/2O1HDZ4ICH9+ljQIonEtpEiZKV06FGUcC09NZMLpyNp3SFUio+FubB1BymRUQDYu7jQ/8mHLWyRRGLbSOcAK0Wr1RLWNkQtP/PgZ/xypsjdODsllXMbNrHt0y/Y9+MvZCZU6B0vqQUSz55Xj9uNH4Obv78FrZFIbB85ErJi7n/xDp6/820UnZ6rZ84RsW0njh7u7PvxF6L3HzTbtb93/q+0nziOfo88gLZYKgJJ7ZGfXZRryNHd3YKWSCQNAylCVsqC8y7Qcgidp57m0G+LAFj14mtlttfn53N0yV9kXk1k1BuvlZqOQFJzfFsWrdVdPXe+nJYSiaQyyOk4K6IwsoHpuk/7SePRlOJ5JbQaGndsT7c7bqWxiXvwhU1b2fDOB2rMNEntEtAmXD2OP3nagpZIJA0DORKyAspzNvBqEsyYObNYO2sOeZmZ2Lu40GrEULpOm4qXMcGboihs/+wrDi9cAsDpVWtwcHFmwFOPXbMpH+oKnxYhaB0c0OXlkREXT1ZSMi4+3pY2SyKxWaQIWZDKerqF9OvDXf8uJS8rG0c3txJTbUII+j32IPlZWZxYsRKAo0v/RpdfQL9HH5DBNWsRjZ0W/9YtiT1myGMUf+o0IX17W9gqicR2kdNxFqA6AUW1Dg44e3mWudYjhGDQc0/SasRQte7E8n9ZcOtMzq7bKPPd1CL+JlNyEdt3WtASicT2kSJUj9R1NGuNVsuwV18gbOggtS7raiJrXnuTFU88R0pUdJ3d+1oidFB/9fj0yjXkpF7biRclkpogRageqM9UClo7O0a9+Roj33gVF5OU01F797Nwxn1cPnCoXuxoyAR364Jfq5YAFOTmcmzZcgtbJJHYLlKE6hBL5fERQtBq+BBu++1HOk25EWHMQ1SQk8M/z75UIqmbpGoIIehy6xS1fHLlagtaI5HYNlKE6gBrSSLn6ObGgCcfYcoPX+JqHBUVZOew7bOvLGyZ7dNy6CA1YV7a5SukxcRa2CKJxDaRIlSLWIv4FMc/vDXjP35PLSddjLCcMQ0ErYMDgZ07quWYQ0csaI1EYrtIF+1awBqFpzg+LUIQGg2KXk9+Vha6ggK0dvLPXxOcvb3UY11+vuUMkUhsGDkSqiG2IEAAQqPB0d1NLafHxlnQmoZBuskUnEdQoAUtkUhsFylC1cRap97Kw9XPTz1eNPN+9s3/lZy0dAtaZNukXolRjz2CgyxoiURiu0gRqiK2KD6FmHp05WdlsfvbeSy55yG5z6Ua5OfkkHU1ETDsz5IpHSSS6iFFqArYqvgU0mbsKEbMehm3gKIfzNToy2x87yMKcvMsaJntkZNSJNzOPt4yarlEUk2kCFUCWx79FKf1yGHcvvhXuk6bqtZd2LSVJfc8RHZySqX7URSFlKhocjMy6sBK60dXUKAey/xNEkn1ke5R5dBQhKc4Wnt7+jx4L+mxcZxbvwmAxPMXWPXS6/SYeTv2Ls7YOTji2bQJDi7OJa7X63Ts+X4++39agMbenpC+veh2x200atemRNuGit7EG05bSqoNiURSOaQIlUFDFaBChEbDyDdexb9Na3Z+8S0AMYePsuLJ59U2ds5OtBs/lh4z78DZyxNdfj47v/yO43+toCA3FzD8GF/YvI2I7bsY/fbrtBjQzyKvp77R5UkRkkhqAylC1zBCCLpNu4WUyGhOGlNAmFKQncORRcs4vWotnW+5idgjx4jcvbfUvvQFBfz38myGvvQs4aNH1LXpFic3vcirUOsgRUgiqS5ShCQMef4pgrt1IXrvftJj4yjIzSMzIYGM+ATA8IO757sfS1xn5+REs949uXr2PGmXr6AvKGDDnA/wa90S39AW9f0y6pWovfvV44b+WiWSukSKUBlMC8tq8FNyhQiNhvBRwwkfNVytUxSFi1t3sP2zL0kz2Q8D0PGmG+j78H1otHZo7LSkx8bx16NPq0J0bt1GfO9r2D/MEdt3qcch/ftY0BKJxLaRIiQpFSEEoQP70axXT07/t4Yrh46gy82jxYC+tB49wixtuHvjRoQO6s+h3xYBkJOaZimz64W0mFiSLlwEDFNxTXp0s7BFEontIkWoHK6l0VBZ2Dk60H7i9bSfeH2ZbXQFBaqXHYBvy9B6sMxyRO7crR4Hd+2CvXNJD0KJRFI55D4hSY2J3LWHjLh4tRw+ZqQFral74k6cUo+b9e5pQUskEttHilAFTAvLsrQJVo8+v8Cs3NCT5pmmSfeRTgkSSY2QIiSpMSH9++AbVjQFF7FtpwWtqXsKvQbBsB4mkUiqjxShSiBHQ+Wjtben+/Tb1PKRJX9y1mSNqKFhmhIjLyPTgpZIJLaPFKFKIoWofEKHDKRxh3aGgqKw5tU3+GboWP565CmSIyIta1wtYxoxO/XyFQtaIpHYPtI7TlIraO3sGPv+2yy9/1FSjWsmBTk5XD5wiIUz76XViGFoHezJz8pGX6AjbMhAWg4dZGGrq4d/eCsuGT3krhw6QqvhQ0i7EkNKZDQFublo7LQEdemEg6urhS2VSKwfoSiKpW0ol7ZdWinz1nxiaTNUrnWX7YrIiE9g84efELl7n1mQz9KY8NmHNLXBPTZRe/ez/PFn1bJPaAt131Ahdk5OtBw6iLbXjyGwc0ezfVUSia3zRd+h+xVF6VEbfcmRkKRWcQvwZ9z7b6Po9cQcPsqql2eZ5d4x5eya9TYpQkGdO+Lq50umMaldcQECwyjw1MrVnFq5Gs+mTWg7bjRtxozE1d+vRFuJ5FpGrglVEbk2VDmERkNQ187csXgBY959kwFPPkJQl05mbeycnCxkXc3QOjgw4MlHzeo0WsMUXEj/vvi0CDE7lxoVza6vv+enG27h32df4sLmrWb5iCSSaxk5EpLUKQ6uLoT0682GOR9w5dARtV7rYE+HG8Zb0LKaETZkIDd+8xkxh4/h6O5G6MD+OHt7AYa4e/EnT3Pyn1WcXbuBvEyDB52i1xOxfRcR23fh7O1N+OjhtL1+TAnRkkiuJeSaUDWRa0OV59ify9n8wSdq2a1RAMNffYHgbl0sZlN9kZ+Tw4VNWzn5zyouHzhUaptG7dvR9vrRhPTvi6uvT/0aKJFUA7kmZAXYely57JRU0q5cwcXXF/dGASXOF+TmkpWYhMbeDgcXVxxcy3+tiqIQe+w4+ZnZBHXthJ2jo3ouas8+9bjpdT0Y/fbr14znmL2TE+GjRxA+egSp0Zc5+e9/nFq5msyEq2qbuOMniDt+At77CFd/P/zDWxPQpjX+bQz/u/g0PGHS63QIjUZ12MhISOD43/+SGn2Zxh3a0XbcaBmT7xpBitA1xsWt29n8wSfqojqAd4vmhI8aQYsBffEOac7hhUvY/c08NXsqGBwOAtqGEzp4IC369zUTJUVRWDt7DmfXrAfAycuT0AH9CB0yEIALm7epbcMGD7xmBKg4nk2C6X3/3Vx3z0yi9u7n5IqVXNy6A73J+lBmwlUyE64SsW2HWucW4K8KU2DnjgR16YTQWP9yrr5AR3psLClR0aREXSY1Ksrwf/Rl0mPjUPR6tA72aB0czDb9nl2znr0//ETnmyfTdsI4qxgd6gt0ZF69Sm56Bm6N/HHy8AAgLzOT6H0HSDhzDo1Wi4OrK/6tW+LfNhx7G13zrG/kdFwNsaXRUE5aOr/efAe5aWWnWrBzdqIgO6fcfrQODjTv24uANuG4+HiTcOYsR5f8VSkbxr7/Fi36962K2Q2a7JRUzqxex/lNW0g4fZaCnPLfezCECmozbjRtx40uNWxQTmoqCWfOkXD6LInnLyCEwMnLC2cvT5y9vXBwdSXx/AVijhwjKykJ90aNCBsykFYjhmLv5ETq5SscXLCQnNRUGrVvR/tJ43FwKX9UoigKSRcucn7TVhJOnSYlKpq0K7FmAltdfFuF0ey6njTr1YPATh3QOjhUeE1+Tg4plyJx8vQsN7SSoiic27CZi1u2kZ+dg7O34X1S9HrSY+PJiI8nIy6ezKuJKHq9ep2juxvO3l6kXY5Br9OV6FdjZ0fzPr1oN3EcIX17V++FWzG1OR0nRaiG2JIInduwidWvvKGWvZs3M2ZSzS21vZ2jIw7ubuSkplW456eyPLh1LRqttlb6amjodTqSL0WScPos8SdPk3D6DFfPnCvz74MQNO3ZHb9WYQiNhpTIKBJOnyU9Nq5a9xcaDe6BjclOTiY/K1utd2sUQO/77sK/bTj6ggLSY2JJi4kjPTaO9NhYtVzew03FNxcEdupAYMf2nF2/ifSY2FKb2Tk5EdytC8169aBZr554Nm1itgcrPzubM2vWs/PLb8lNzwAgfPQIhr78nPq50+t07P3hJ+JOnCL22Anys+rW47Xb9Nvo88A9dXqP+kaKkJVhK0J0du0G1rz+FgAtBvZj7LtvkpeVzcUt2zi/aQtXDh01/JAIQasRQxnywtPYOzmhKygg5VIkF7ft5Nz6jSSeu1Bq//YuLty6YB4Z8fFc2LSV85u2mv2Y+LVuyc3zvraJqSRrQV9gEKb4U6eJO36Scxs21+zHvp5x8fPFq2kTvJoE49m0CV5Nm+DZNBjP4GC09nbo8vMpyM3FztEJO0fDCEdfoOPsug2c/GcVMUeOlTuacmsUgFfTJjj7eJMaGU38qdOltus4eSI97pyOg6srB39bWGq6+opw9vHGyd2d9Lh4sxGrX6uWNOnZDTsHBzKvJhJ7/ATJFy+ZXRvYqQNezZvh4u1N6OABBLRpXeX7WxNShKwMWxGhiO07+ffZlwFo3qcX1//vHbPzep2OlMgonL29cfbyLLOf5IhIInfvISPhKtlJyWQlJuHg7kaXW2+mcfu2ajtFUUg8f4G0y1dQFIUm3buZBf+UVB1dXh4Xtu7g5IqVRO3dD6V8fzX29viFheIX3gr/1i3R2tuTnZJKdkoK2ckp5Kal4d64EYGdO+HVtAnR+w9y8t9Vhhh/xv6cPD1oPXI4Z9asq3SmXHsXF5r36UVIv974tAjBs0lwhQ4tFZGXmcXlg4eI2r2XyN37SI2+XKP+ysLZ25sBTzxMbmYmOckpIARujQJwbxSAW4A/bgH+6jSgoihkJSaRHhuHe6OAUjcgp0RFs+m9j0r1iBQaDSPfeIWWQwfXyWupD6QIWSG2IESXDxzir0eeAiCwc0du/OpTC1skqQlpMbFE7tpDXkYmuvx8XAP8CQhvjXeL5mjtqu5zVJCbS0pkNHlZWfi3bom9szPZKakcXfoXlw8cIiM+AY1Wg3vjxrgHNsYjsDHujRup/7v4+tT5KDf18hVVkKL3HywxlSa0GjyDg2netzc9ZtzGfy/PLtM1Hgzems5eXvS67048ggJr1dbslFT+ffZlg+djMbQO9ox4/WXCjM47toYUISvEFkTowpbtrHrhVQCa9erJ+I/fs7BFEkn1KZwmzki4SlZiEk4e7gR16Ww22tYVFHD498Wc27iZ1KjLFOTk4OjpQUB4a/o8dK9ZHqy6oNBho3AUd2HzNrKTk9XzTXt2p1nv6/Bq1kQV94qcQKwBuU/ICrGFfUPpMTHqsXtQYwtaIpHUHK2dHb5hoeUKidbOjm533Eq3O26tR8uKEEKY2dj1tptZ/sRzpF0xfBej9u43TKsWXUDLoYMZ8uIzNiFGtYFcIa5FrD2uXNqVIicBj8DanXqQSCQV49kkmCnzviJ89AgoLbK6onBu/UbWv/kO+ZVw128IyJHQNURSRJHHjnugHAlJJJbAycOD4a+9yHX33knkrj0Gt/oYw6beQvf6C5u3MX/8FNpNGEv36bfh5Fm2o5CtU+FISAjRVAixUQhxUghxXAjxuLF+lhDishDikPHfWJNrPhBC7BNCDDKWQ4QQihDiUZM2c4UQM+vgNVkUax0NpcfGcXn/QbXcqG24Ba2RSCQegY3pcMMEhrzwNBM+/YDpy36n402T1PN5mZkc+n0xv958B7HHT1rO0DqmMtNxBcDTiqK0BXoDDwshjHmc+VhRlC7GfysBhBBtjOcGAg+b9BMPPC6EqHi7s40zLSzLqsSoIDeX1a++qe74Du7etdY9gSQSSc3p//jD9H/8YTybNlHrctMz2PXVdxa0qm6pUIQURYlRFOWA8TgdOAkEl3OJFtADCmA66ZkArAdmVNtaG8NahGjXN/OK3ESFoPuMaZY1SCKRlIpGq6Xz1MlM++Mnrrv3TrX+8oFDrHjqBdWhoSFRJccEIUQI0BXYbax6RAhxRAgxTwjhDaAoynHABdgGfFWsi3eBp4UQ5cZtEULcZ5zO25ecWHpWTlvBGoQoYvtO9bj/Yw/aZDZTieRaQghBtztuxdHdXa2L3LWHX6fewaWdu7H2rTVVodIiJIRwA5YCTyiKkoZBYMKALkAM8L/CtoqiPKooSndFUTaY9qEoykVgD3BbefdSFOVbRVF6KIrSw9vX9hfkalOIdAUFRO87wPG//uHi1u0VetBkJSWZhc5pe/2YWrNFIpHUHVo7O0a9/TpaB3u1TtHp+efpF/nv5VmWM6yWqZR3nBDCHoMALVAUZRmAoihxJue/A/6p5D3nAEuALVUz1bap6T6igtw8Tiz/hwO//mGWi8atUQDDX3uR4K6dS1yjKArbPv1Sjb3lH97qmk2jIJHYIk17dOOBTas5t2Ezq1+ZrdZf2LSVNa+9yfDXXkJjZ9sBgSvjHSeAH4CTiqJ8ZFJvurJ9A3CsMjdUFOUUcAK4vmqm2j7VHRGdWrmaX6dMY+vHc80ECCAjLp5/n32JxPMlg4oe+OV3zq4tGoxed8/Mat1fIpFYlpZDBzHlB/PVjbPrNrLji68tZFHtUZnpuH7AHcDQYu7Y7wshjgohjgBDgCercN+3gSYVtmqAVFWIrp49z/q33jNLQufsY4jE6+BmGNXkZ2Wbpc8GQ4ieXV9/r5bbjh9L8waY10QisVVSIqPYPvdrVjz1Aju++IYrh46UmpuokIC24YyeMwvv5s3UuqPLlpOVlFzmNbZAhdNxiqJsw9zLrZCVlb2JoigRQAeT8mGu4WgNVZmaK55LJnz0CAY//xR2jo4knr/IojvvR19QQMyRYxxd+hftxo8lPyeHTe+qS3QEde3MoGceN8u7IpFILMfhRcvY9slctRy5aw8HFyzEs0kwfR68l9DBA0r9voYNHkjooAEsnHEviecuoM/PJ/bYCUIH9qtP82uVa1YILE1lR0SN2relhckHzM7JCTtHRwB8w1oQZLIWtOV/n/HdyPHMG3sj2SkpgCE19Jg5s9Da2yORSCyPXqdj//xfSz2XGn2Z/16exbIHHiP2WMno24peXyJxYWWy8VozUoQsSGWESAhBl1umqOWz6zagM8lyGtS5o1l7XV6+WRriAU8+2qBDfkgktkZueob6kAgw+PmnaDfxerPo37FHj7P0vkdY/9Z75KZncG7DJtbOnsO8cTey+K4HyMvIBAzZj5v06FrfL6FWkbHjLExlpuYCTYQmLyOTlKhofENbANDltptx8vQk/uQpYo4eJzUqWm3beepNZqMoiURieZw8PXD28lKFyLNJMO0nXk+fB+9h3/wFHF3yp+rRemrlak6tXF1mX/0efQAXH5/6MLvOkCJkBRSOiMoSoxIhO0z2qdk7OdFx8kRgIgCZVxNJvXwFtwB/PGSQUonE6hBCEDZ0EMeW/Q3AsT+X06R7V5w8POj/2IN0nDyRRTPvJy8zs9Trnb28COzSkfBRwwkdNKA+Ta8TpAhZEcVHRZkJV1ly78NkxCeoda1GDsM3rEWZfbj6+eLq51undkokkprRftL1qghF7ztods4zOIjbF/3C6lffULPC+rVuSYv+fWkxoC9+rVs1KCcjKUJWhqkQbXjnQzMBanpdD4a++KylTJNIJLVE4XQ6QG5aGopeb5Ya3dnbi0lzPyIrKQkQuPh4W8DK+kE6Jlgh08KyyIhPIHLXHrXOydODIS88jZ1jgw9CLpE0eHLS0tVjO0dHMwEyxcXHp0ELEEgRskp++2oZP02aalZ364IfcW/cyEIWSSSS2uTCpq3qsW/LstOTXwtIEbIydDod375rvoeg+4xpDf5pSCK5ljix4l/1uNWIYRa0xPLINSErIykhhbycPLX8yOt3UTDsFgtaJJFIapOrZ88Tf/I0AFoHe8JHD7ewRZZFipCVcfLgWfW4VYdQbnvoRiAPyKtRFG6JRGIdnFhRFPEsdGB/nDw8LGiN5ZEiZEUUFOj46u35ajm0TXOz86YRFqQgSSS2R0FuLmdWr1XLbSeMs6A11oFcE7IitqzcyaWzhogHzi5OzHxyapltp4VlWUXWVolEUnkubN5GbnoGAB5BgTTp1sWyBlkBUoSsiN0bD6jHN941juYtK852IcVIIrEdTiwvckhoe/2YMl2zryXkO2BFnD8ZoR4vmLuUpfMqm6xWipFEYu2kxcSqERCERkObcaMsa5CVINeErAgvX/No1/978WsCmzWi7/Cele5DrhtJJNZJ9P6i8DxNunfFzd/fgtZYD3IkZEW89Mnj3DBjDBqTIfp7z8zlyqXYavUnR0cSifVw5eBh9bhJj24WtMS6kCJkRfj4e/Hs+w+zdN8PODgaktAlxCTy4QtfVXBl+Ugxkkgsz9Wz59TjwGJ5wK5lpAhZIY2C/XnlsyfV8ukj52ulXylGEollUBSFtCsxatm7eVMLWmNdSBGyUrr1rbsnJSlGEkn9EnP4KPlZ2QA4urvjeI1vUDVFOibYABlpGXXSr3RikEjqFkVRuLBpK5s/+EStC+nXu0HlA6opciRkpTg4FaVsyM8rYMWCNXV6Pzk6kkhqF32BjrWz5/Dfy7PUVN72Ls70vGu6ZQ2zMqQIWSluHq6MummIWn7nqc/4+OVvOH8iok7vWyhGUpAkkppx4p+VnF2zXi07e3sz7v238WwSbEGrrA8pQlbM8x88TLOwog/s4u9XcMeQR3j29tkc23eqzu8vxUgiqT4RW3eoxyH9enPbb/MIlmF6SiBFyIpxcnHioVfvNNs3BLB97V7uG/cMj9z4Evu2HEJRlDq1Q46OJJKqY5o9tcutN+Pk6VlO62sX6Zhg5Qwc05tFu77l0K7jbFuzm83/7lRF58D2IxzYfoT23cKZ/vjN9B91XZ0veBYKkXRkkEjKx9m7SHRy09PLaXltI0XIBghq3pig5o0ZO3UYEWei+OXzxaxZugmdTg/A8QOneX7Gm4S1DWH641MYOqE/Wq22Tm2SnnUSSfk4exdlQ85KSragJdaNnI6zMUJaN+XVz59i4c5vuWHmWDWyAhgCoL7+wAfc2u8BVixYQ35efr3YJKfqJJKSmCary0lJtaAl1o0UIRslqHljnn3vIZbs/YFbH7wBZxcn9Vz0xRjeeeozbu59H4u/X0Fudm692CTXjiSSIrKTi0Y/cnNq2UgRsnH8Gvnw6Ky7WbZ/Hnc9fSvunq7qubjLCXz88jfc2ONufv5sMZnp9ScOUowkDRldQQGxx06QcOYcGfEJFOTmqef0Oh1n12/izOp1ap1Xs4pzg12riLr2rKopbbu0Uuat+cTSZtgMmelZLJu/kj++/ovkqylm59w9XXnwlZlMvGO0RXZsy7UjSUNA0etZfPeDJJw+a1Zv5+yEvbMzuekZ6POLpsL9w1tx03dforGr23Xa+uSLvkP3K4rSozb6kiOhBoaruwt3PHoTy/b9wFNz7qdRcFHOkvTUTN5/9gtevvsd0pKr5q2jKEqNXcHl6Ehi66REX2b3tz+WECCAguwcspOSzQTI2dub0XNmNygBqm3kSKiBk5+Xz+qlm/j500VEXyyK4tso2J/Xv3yaLr07VNjHjnV7ef2BDxBC0Om6dtz51C207x5eK/bJ0ZHEFlAUhbWz55hFQDBFo9Wi1+nUsrO3N23GjqLzzTfi6u9XX2bWG7U5EpIidI2Qm53L3DfmsXReUY57jUbDqJsG065bOC6uTji5OOHs4kTjpgE0b9kEIQQF+QVM6XUvcZcTzPobOXkwz77/EK5utSMiUowk1kxyRCS/3Taz9JNCcO+a5SiKQn52No7u7tg5OjboIKW1KUJyn9A1gqOzI0+/8yA9B3ZlzpOfkpacjl6vZ9WiDaxatKFE+yYtAuk/qhcnDpwpIUAAa5ZuIiM1k/d+fqVW9iTJfUcSayYtJqbsk4pCelw8vqEtcHRzqz+jGghyTegaY+CY3vy84XO69Cl/Gi76Ygx/fP0XR/acUOvGTh3G4Ov7quUd6/by86eLa91GuXYksTa8mzdTjzX29lx37504eRkiIrg3boRXU+n9Vl3kSOgaJCDIj8+Xvs2BbUc5tv8k8VcSyc7KIScrh8z0LE4ePEtWZrbZNe27h/PCR49hZ6flyzfn8+vcJQAsnfcPdz51S53YKUMESawFj6BAPIICSbsSgz4/n6Y9utHtjltJvngJ98aN0NrbV9yJpFSkCF2jaLVaeg7qQs9BXUqcy83J48D2I+xct4/MjCz8G/sy9f5J2Bk9fO5/6Q4Wffc3ebn5JCWkkJGWiZuHa4l+ags5VSexBtwbN1JTdOfn5KC1s8OvVZiFrbJ9pAhJSuDo5ECfYT3oM6z0dUetVktwSCAXT0cChnBBnXu1rxfb5OhIUtsc+n0x5zZswiOwMf5tWuMf3hrf0BY4eXmqzgWKopB08ZJ6jYuPj6XMbXBIEZJUi5btWqgidGzfqXoToUKkGEmqyrE/l5Nw5hxhgwbQrHdPAC5u3c72z78CIO74Sc6u26i292vdkkmff4SjuxtJFyPUMDxOnh74tGhe/y+ggSIdEyRVRlEUju47qZZjo+MtZot0YpBUhks7d7P5g0848fc/rHjqeWKPGz6/F00SzxXn6plznF69FoDLBw6p9UFdOyM08qeztpDvpKTSKIrC9rV7efTGl4iNKhKegaP7WNAqA1KIJOVxYct2s/Kh3xYC4OBatJZp5+hI2/FjzdolRxhG+6YiJLOj1i5ShCSVQqfT8ep97/Hs7bM5sOOoWj96ypBSnRssgRwVScoi6cJFs/KFzdtIuxJDUJeOap1PaAhDX3yG4a++oNadWLGSiB27uHzgsFonRah2kWtCkkqx9s8tbFi+zayuW9+OPPzaXRayqGzkepHEFEVRSLoQYV6n17PiqRfwDina/5OTmgZA8359cPb2JjvZEAfu32deUtu4+Pni0yKkPsy+ZpAjIUml2LflkHrcvX8nft7wOXP/fAffAO+yL7IwcmQkAchMuEpeZmaJ+pTIKC6aTNM16tAOACcPdyZ98RFuAf4lrml3/ZgGHY7HEkgRklQKd8+icCQde7alZfsWFrSmakghura5tGO3ehzUpRN9Hr4PTbFQU26NAuj38P1q2SekOTd+8znNevVEaA0/k2FDB9F9xu31Y/Q1hJyOk1SKxk0D1OOrsUkWtKR6yCk620ZRFPb/tICz6zaSnZJCbmoazj7e+Ie3JsC4t8evdRjO3t5o7ezIy8omIy6OK4eOsOvr79V+QgcNoPPUyYQO7M+5DZvJTLiKT4sQWo8chqO7edw390YBjP/4PfKzDdFD7J2d6/U1XytIEZJUisO7j6vHzVoGW9CSmiHFyDaJ2rOP3d/OM6vLTLhKZsJVIraZu1nbOTlRkJNTog9nb2/Cx4wAwKtpE3rMmFape0vxqVvkdJykUhRuTAWws7P9Zxe5XmRbHPxtUaXbliZATp4ejHrzVZw8PGrTLEktYPu/JpJ6oUV4My6djQbgs9e/x97BjhtmjkVj45v2poVlyVGRDRB/4pR6fMOXn+DfpjVpl2NIOH2G+FNnSDh9hpTIKHLS0kFR0Njb4+bvh1ezpjTr1YPwMSOlAFkpFYqQEKIp8DPQGNAD3yqK8qnJ+WeADwB/RVGuGus+AIYATyuKslkIEQJcBB5TFOVzY5u5wD5FUebX6iuS1AkPvDSDA9uPqmnB//fi12z6dwefLXnb5r2F5BSddaMoCvk5RVHdG3Voh9bODt+wFviGtaDN2FFFbfV68rOzsXdxsfnP5bVCZR5jCzCISVugN/CwEKIdqAI1AlDnaoQQbYyHA4GHTfqJBx4XQjjUhuGS+qVZWDDvzn/ZrG7/tiNEXbhiIYtqHzlFZ53kpKah6PQAaB3s0ZYzHSw0GhxcXaUA2RAVjoQURYkBYozH6UKIk0AwcAL4GHgO+NvkEi2GEZMCmH4SEoDtwAzgu9owXlK/dOndAd8AbxLjDYEcXd1daBRcci+FrSNHRpYnOzmF85u3knU1kUSTaAf+4a0taJWkLqjSmpBxWq0rsFsIMQG4rCjKYdOnDkVRjgshXIBtwLPFungXWCWEmIfE5oiJilcFCODZ9x7C0anhDmzlepFlOL16HZve+6hUB4Pgrl3q3yBJnVJpERJCuAFLgScwTNG9DIwsra2iKI+WUX9RCLEHuK2Ce90H3AfQqEnDe9K2Vewd7NBoNOj1hqmRkPBmFVxh+0ghql8ubN7KutlzSj2nsbOj5bDB9WuQpM6plGuTEMIegwAtUBRlGRAGtAAOCyEigCbAASFE40p0Nwd4vrx7K4ryraIoPRRF6eHt61kZEyX1gF8jH4ZO6K+Wd67bZ0FrJA2N7OQUNsz5UC17BAfRftJ4/Fq3JKhLJ8a8M1tmMm2AVMY7TgA/ACcVRfkIQFGUo0CASZsIoEehd1x5KIpySghxArge2FNNuyUWomvfDqz7awsAURcuW9gaSUNixxffkJtu8L50b9yIKd9/gZOnfAht6FRmJNQPuAMYKoQ4ZPw3tqKLKuBtDKMniY0Rea5IeBRFsaAlkobElUNHOLVytVoe+MzjUoCuESrjHbcNcy+30tqEVHA+AuhgUj6MjNZgUyiKwi+fL2Hht0WOkO26hVvQIklDQVEUtn4yVy2HDh5ASN/eFrRIUp/IiAmSCinIL+Cz139gyQ8r1Lr23cK5/pbhFrSqfpBOCXVP6uUrXD1zDgCtgwP9H3+4giskDQkpQpISbF+7lx8+/I2YyDi0Wg05WblkZRbtWO/WtyPv/vQKjs6OFrRS0lDIS89Qjx3cXHHz97OgNZL6RoqQxIyN/2zn5bvfKfP80An9eW3u0zg42tejVZKGjHdIMxxcXcnLzCQ7KZmLW7cTOmiApc2S1BNyXUZixqJv/y613sPbnYdenckb3zwnBUhSq9g7O9N+0vVq+fzGrRa0RlLfyJGQxIyCfJ16/MRb9zF4XF/0ej2Ngv1lPC5JneERHKQe6wryLWiJpL6RIiRRKcgvID0tUy0HBPkRECTn5yV1z6XtO9Vjv1YtLWiJpL6R03ESlV8+X0zkOUPOIAcnBzr3am9hiyyL9IyrH7KSkrm0q2jfeqvhQyxojaS+kSIkASA3O5cFc5ep5XuevQ1vP7lZUFL3nP5vjZqqIbBzRzxNpuYkDR85HScBICEuycwN+6a7ri+ntURSM5IjIjm7fiNJ5y9yftMWtT589AgLWiWxBFKEJAA0bhKAj78XSQkpABzafZzeQ7pb1ihJgyTm6DH+fOgJdfRTiEdwkNWLkKLXE3/qNNnJqQR26oCju1u57XV5eUTvP8iFzdvIiIun+/TbCOrauZ6stQ2kCEkAsLPTEtiskSpCBXkFljVI0mDZ+/1PJQTIp0UII994FTtH694Avfu7H9n/0wIA3AMbc+PXn+LmX3q6mYgdu1j/5rvkpKapdbHHTjD1p29x9vbiwC+/c3bdRgLahtPzzul4hzT81CilIUVIAkBebj5nj11Qy227ygyWkroh/vRZ9Ti4Wxeuu/dOAju2R2ise4k6LyubQ78vVsvpMbH899Jsbvz6UzRarVnbywcOserF19Hnm7ub52Vmsv7t93HycOfC5m0ApEZf5uKW7Uz9+Tu8ml57cZ2t+68uqTdOHzlHXq7hCxMcEohvgLeFLbIs0jOu7vAIKko7dvnAIfbO+1lNlGjNpEZfRpeXZ1YXd/wEO+Z+bRZRPj0untWvvKEKkIObK+1vGI/QGn5urxw8rApQIQW5uVzef6huX4CVIkVIAsDRvSfV407XtbWgJZKGTodJ483K0Xv3l/hRtkbyMzNLrT+8cCl7vp8PGNaAVr8ym+yUFABcfH2Y+tN3DH72SZr3vq7c/gtzKV1ryOk4CQCHd59Qjzv2bGdBSyQNndI2oyaeO08rK0/drTVZr/Jr1RKP4EAubDKEGNr34y8knrtAZsJV4k+dBkBoNYx68zU8Ag0jvyY9uhOxfVeZ/fu1vjY36cqRkASAC6cuqcc/f7aIlMRUC1ojacic/Pc/8wohbGKDqr2Ls3qcm57OyNmv0LxPL7Xu4tbtqgAB9H34foK6dFLLLYcNxsHVtdS+tQ4OZm2vJaQISQBoZBKeJzYqnh8/+sOC1kjqEl1eHnEnThK9/yDZKfX/sJEWE2NWHvzck/iGhda7HVXFMygQrYMheG96bBy56emMnjOLpj2LbWUQgt4P3EPnqTeZVbv6+TJi1stoHRywc3Kix513qOfcGgVYvWdgXSGn4yQAPP7WvcwY+phaXvvnZrr27Uj3/p1w9yx/L0RDo6E6JaRERXNk8Z+cWbOe3LQit+EeM2+n13131ZsdLt5FTi+dptxI+4m2sTFa6+BAQNs2xBw+CkDs0eOEDhrA9f97l7PrNhBz5BhujQJo0b8vvmEtSu0jpF9vZi5fBAgUnY59P/4CQHZyMoqiXJNBguVISAJAq/ah3HR30YJxSmIaL901h6l97uP0kXMWtExSHbZ9+iW/T7uLA7/8ji4/n6ykJBbOuI+jS/40EyCAffN/JTMxqd5sCx1clCvo2LK/SY6IrLd715TGHYriKcYcPQ6Axk5L+OgRDH7uSXrMmFamABXi5OGBk4c7Tp4eOLq7A5CXkUn8yVN1Z7gVI0VIonLjnWNxcXM2q0tJTOPJW14j+apcI7IVMhISOLxwCUkXI9j51Xd8PWgUi+96kIKcnDKv0drbc+XgYaL2HSA7OaVO7Qvp10dd/9DrdGz7/Ms6vV9t0rhjkQjFGkWougiNhtBB/dXyufWbatSfrSJFSKIS0qop3638H+NuGU63vh2xszfM1qYkpvH12z9Z2DpJZXHx9ilRlxGfUGrbtuPHMvPvRSy97xH+fPhJlj/2DD/dcItZPLfaRghB/yceUTenRu7cw5WDh+vsfrVJoIkIxZ86Q0FuXjmtKyZs6CD1+NyGzWb7ja4VpAhJzGgR3oyXP32CuX++wzs/vqzWr/htDXs2H7SgZZLKorHTMva9Nyts12JgP4a++AwZCVdJiYxS63V5eWz79EuUOtxA6t+6JW3GjlLLx/7+p87uVZs4e3vhaYxqoM/PJ+HMmRr116RHN3VKLiMunrjjJyu4ouEhRUhSJv1G9GTA6N5q+fkZb/HnTyttYnf7tU6LAf2Y+vN3tBjYr8w2Dq6uXD17nqNL/ypxLiMunqw6XifqOHmiehy9Z7/NjAIC2oarx0kXImrUl9bOzmyN7FqckpMiJCmXp995AP9AX8CQc+iD577kntFPcfrIOX77chkzhj3GN+/8Qn5ew0jJ3JA84/xahjH23Td5aNs6Zvy1kDHvvGF2/vSqNSyccS+nV60pvYM69tTya9USJ08PALJTUojed6BO71dbmMaJ09jV3MG4pcmU3PmNm+t0BGqNSBGSlEtAkB9zl82hWViwWnfq8DnuHPEEc2fP4+yxC/z0yUI+eukbC1opKQ+h0eAW4I9vy6rtxdE6ONSRRQaERkNI/75qedWLr3F44VKrHhEpikLypSJvPjsnpxr3Gdy9K44eBjHOiE8g4cy15Y0qRUhSIU1Dg/lpw+fMfHJqmW2O77823Uvrmpijx/jlpmn8+dATxB47UfEF5aC1t69025B+vXHycK/R/SpDz7umq1EE8rOy2fbpF5z5b22d37e6nFj+L/EnDJ91odXQqF2bGveptbMjqEtHtZwaFV3jPm0JKUKSSuHo5MB9L9zBix89Vup530YlPbIkNefw70tIuxLDlUNHWHrfI6ydNYeoPfvQF+iq3FdZU0feIc3xbt4Mr2ZNCezUgeGvv8SYdyt2bKgNPAIbM/Hz/+HVrCiFwbE/l9fLvatKxPadbPvkC7Xc9bapaly4muLeuJF6nB4XXyt92goyYoKkSoy7dThnj11gz+aDRJ6/rNb3GdbDglY1XBTMp6bOrFnHmTXrcHBzxa9VS3zDQvENa4FvyzB8Q0Owd3YuoyeIPXqsRF2/Rx+ky61Tat3uqhDQpjWT5n7M/AkGOxLOnENfoENjp63gyrqnIDeP85u2cOLvf7hy6Iha79m0CT3vml5r9zEVIVNPxWsBKUKSKqHRaHjqnQc4uvck91//LACOzo6MuXmohS1rmFx3z0w1UrMpeRmZXDl42Hx/jRB4Bgfh2zIU39AWhv9bhuEZFGjYFPrZVyX6sZZU065+vrg1CiAjLh5dXh6xx08Q1LljxRfWEYpez8EFCznw6x8lUiy4+vtx/YdzajXWW0Dbomm9M2vW0WJgP1qYrJc1ZKQISarFnz+tVI9HTBrYIOLLWaNnnG9oC25f9As7vvy2VDEyQ1FIjb5MavRls7Z2Tk5lRkuwhtFGIc16X8cJ436hC5u3WUyEFL2etbPncHbtBrN6jVZLqxFD6ffogzh7e9XqPQM7tse3VRiJZ8+jy8tn7ay3uX3RL7j4NPxpbrkmJKkyqUlpbFhelITshpljLWhNw8ezSTBj5szmhq8+JaC8hfAyPKrLC9dTGy7GtYV5CJuNJbKY1hf7flpgJkDugY3p/cA9TP/zD4a/9mKtCxAYPAXHvvMGrn6G7RD5WdlE77s2NodLEZJUmX8XrlNTgbfp0oq2XVpZ2KJrg6DOHbnp27mMnP2K2RqCSjU8m3PTrCebZ5PuXXH28gIgM+Eqi+58gIjtO+vVhsjde9UsqQDtxo9l2sKf6T79NlUg6gqPoECzAKkRO3ZdE3uGpAhJqoRer+fvn4uSkt0wfYwFrbn2EBoNrUYM5bbff6LPw/fh4FZ6krRSrzW6FLc0yWAavd96nra19vZ0m36rWk66GMGqF1+vtwjf+dnZbHrvIzDuUwru1oVBzz6Jth5Hi36twtTjs2vWlxrNoqEhRUhSJZb88A9RF64A4ObhyvBJAy1s0bWJnaMD3abdwu2LfiV89IhKXaPo9MSdOEWGiQvwnu9+5M+HnuC/l2aRX860XX3R+ebJamBTAH1BAdnJyfVy773zfiY9Ng4AJ08PRr7xar2vmXW+dYpZWKBLO3bX6/0tgRQhSaWJPH+Zr0yiad987wScXWu+Y1xSfZy9PBn26gs063Ndpa+JPXbC7If+yqEjnN+0hb8ffbouTKwSQqMxS6Md3K1LvWRdTYmM4tAfi9Vy30cewMXHu5wr6gZ7Jyc63FgUU8/0vWioSBGSVJov3/yR3OxcAFq2C2H64zdb2KLawxo94yqLEIKBTz6qrqdUhtLWGkxjolkSJ09P9bhJ9671km30+N//oOgM70lQl05mEb7rG31BgcXubQmkCEkqRVJCCltW7VLLL3/6BA6OlQ8DI6lbPJsEc8uv39P7gXsIHTygUiF6ut1xm1nZq1nTujKvSnS9rejhJqaGieMqgy4/n9OrikIFdZt+m0XTbDdq11Y9vrRjN3mZmRazpT6wHv9MiVWTllzkReXt50V4p5YWtEZSGi4+PnSfbhCW7OQUjv+1gnMbN5MSfRldTm6J9q6+3vR/4hG2fTIXgEs7d6PLz69SjLm6ILBT0f6gFJM4aulx8ez86jsy4uJx9fOl2+234h9ec8/MqD37yE5JAcAtwJ+mPbvXuM+a4NsyFAdXV/IyMynIzSUl6jIBbVpb1Ka6RIqQpFL4+HshhDBEEb6awm9fLmP8tJFmm1Qjz18mJiqO4OaNCQ4JtOjT5LWOs7cXPe68gx533oGiKORnZZGRcJV9837m7LqNAOz48jvGvf+Wek1WYhIZcfF4Ngkuq9t6wbNJkHqcHhuLrqAA9Hr+ffYlEs9dUM9d2LKdYS8/R+uRw2p0P1NHjeBuXSw+LRl/8pQ6+tHY2+MZHFTBFbaNFCFJpfDwdmfYxAGs+8uQ9nnu7Hl89dZ8OvdqT6+h3Tm06xg71+1T2wc2bcSgcX0YPK4vHXq0QaOx3plfW14PqgxCCBxcXfFxdWXoy88Tf/osqVHR6PLyOPbXCrO2rv7+FrKyCDtHRzWEj6LTk34lhqvnLpgJEBgym66d9Tb5Wdm0n3R9te/nbhKEtKw06PXJ7m/mqccthwzC0d32o5GUhxQhSaV5dPbdnDl6Xg1cqtPpObDjKAd2HC3RNiYqjj++/os/vv4L3wBvhk7oT6Ngf1zdXRhyfT88vOs+TUB5NHThKQshzFMFmIb38W0Zip1j3eYQqiyeTYLVEUpK1GWz4KseQYEIjYbUaMPncNP7H+Hg7kYrk/1PVb1XIfGnTpOTll4vaSxKI+H0WaL27gcMnoI97rzdInbUJ9b7eCqxOvwb+/L9fx9x3wt3lBkloXXHMFzdzX/gE+OTWfz9CubOnsd7z8zl7tFPqV529c2C8y7XrAABZCWnlHmu2+23lnmuvvFqWpTaITU6muzkVLXc8+4ZjPvgbbP2G9/5sNr38mwSjHeL5oAhXM7xYqPD+iRq3371OGzIILybN7OYLfWFHAlJqoSbhyszn5zKzCenkhifzI61ezmw4ygajWD0TUPpOagL+Xn57N9+hM3/7mTLql0kX00x6+NyRAxRF67Qsn2LerP7WhYeU9z8/Uqt927RnFYjrCcSuunoJCXqcom4eN7NmxE2dBDnN2wGID8rixPL/6XdhHFVvpcQgm7TbmH9W+8BcG7DJtXBo77JTc9Qj33D6u/7YUmkCEmqjW+AN+OnjWT8tJFm9fYO9vQe0p3eQ7rzzHsP8uf8lWbpv11cnQkOCaxz+6TwlERoNHi3aE7yxUtm9YEdO1iVI4lX0yIRSo2KxsUkbpsu1xDYdOSsV1h48RJJFyMA2P/L79USIYAWA/sZ5ioVhcTzF8jPzi43N1NdocvLV4+1DtfGFgg5HSepU04fPse37/5qVvfgqzPrNNLCtT7lVhEu3iUjAdi7WNf75WkyHZcSfdlsZJRw9ixgSEMx4dMP1Pq0y1fISqpenDlHNze8QwxTX4pOT8KpM9Xqp6bYORStyeVYUXDZukSKkKTOOLr3JI/f/CoZaQZ3UwdHe57/8BEm31m9p9WKkOJTOZw8PErU2TvVXoK22sDT6HwAkB4bZxa6J/bYCfXY1c+XQJO8Q2fWrK/2PU0jWMceP1ntfmqCT2iIepx0/kLZDRsQUoQkdcKRPSd4cuprZKZnAeDl68FXy99n4h2ja/U+hcIjxad8FEXh8KJl/PfKbM5v2lLivOnIwxrQOjjgHWJwFkBRyMvIMEyXAUnnL5KXmaW2NQ3gemHzNqpL4/ZFkQoitu2odj81wVRsr56TIiSRVIvzJyJ4cuprZGVmA+Dl68nnS+fUat4hKTxVY9c3P7Dtk7nqQn5xTFMIWAstBhSlt47YsQvfUMNCvaLXE3/ylHoudGB/ddQUc+RYtafkmvfro25UjTlyjIQz56prerXxat5UTTSYERdv5qjQUJGOCZJa59PXviM7y5AWwMffi8+XzqFFeM1dTaXoVIyiKFzcuoOLW7ZRkJuHxk5LcsQlEk6fLfMajb190ajDiggbPID9Py0AMIinieNEwplzNOnRDTBEhwjs1IErh46A8fW3n1j1zauuvj4Edu7I5QOHALiwaQv+res3PJXWzg7vFs1JPHsegMTzFwjq0qlebahvpAhJapUzxy6wb+thALRaDZ8sfLNGAiSFp2qcXLGSje/+r0rXeAQ2qtfEbZXFr3UrQgf1L5piU4pSxxZfwwod1N8gQsCFLduqLELJEZGsmfUWV01GP8JCUT78wkKvKRGS03GSWiX64hX1uNeQ7tXaCyTXearP0WV/V9jGzsmJcR/OUctpV2LJTkkt5wrLIIRg2CvPl7ph062ReXrzkP5FU3eRO/dUOUHfzq+/MxMgR3c32oyr3fXLyuLb8tpaF6pQhIQQTYUQG4UQJ4UQx4UQjxvr3xRCHBFCHBJCrBFCBJlc84EQYp8QYpCxHCKEUIQQj5q0mSuEmFkHr0liQbIystVjL9+SXljlIYWn5igmo4XiNO5o8P4qyMlhw1vv42nci6MvKODQ74vqxb6q4uDqysTPPqTN2FHqXiGP4CCCu3U2a+cRFIiziev58seeqfQ9UqKiidi+06xuwqcf4mESU64+8WtZtD5XPF5eQ6QyY/AC4GlFUQ4IIdyB/UKItcAHiqK8CiCEeAx4DXhACNHGeN1AYD5QuBIaDzwuhPhGUZS82nwREushI7Uo94mLa8Wb/aTo1C6N27dTp3JMsXNyItYkN092SoqavgDgwC+/07hje1qYjCisBVd/P4a98jyKopCVmISju3uJGHdCCFoNH8KRxcsAgxt3ZWLApURf5p9nXlIT2nk2CebWX39A62C5GHo+oUWzBymRkRazo76ocCSkKEqMoigHjMfpwEkgWFGUNJNmrkDhI5gW0BvLpluwE4D1wIxasFtipRzYfkQ9btwsoNQ2crqt7mjWq0eJOmGnpaAS01PrZr9DSmRUXZhVKwghcPXzLTPIar/HHjQrZycll9lXdkoq5zduYdl9j6oBXTV2dox68zWLChCAvXPRRm69rmQG3IZGlVYjhRAhQFdgt7H8NjAdSAWGACiKclwI4QJsA54t1sW7wCohxDzKQQhxH3AfQKMmlg8tL6kcer2ePVsOqeWBY/qox1Jw6oeQ/n1x9HAn17jb3t7ZmfxswxSps5cXw19/Ef/w1qx46vmiqAACUCAvM5OVL7zGTd99gYOr7f29NFotgZ06EHPEEHH7n2depMXA/rS9fjQpkVHs/PI78rOzcfRwLxG2SOvgwMjZL9dKkryaYio8Gm3DX7av9CsUQrgBS4EnCkdBiqK8rChKU2AB8EhhW0VRHlUUpbuiKBtM+1AU5SKwByg3OqCiKN8qitJDUZQe3r6e5TWVWBH5eQXk5RTNtK7PaiJHPPWMRqul1313q+VCAQK47t6ZAGz+8BMzTzNMDpMjLrHh7ffLXVuyZjrdfKN6nHYlhsN/LOaP2+/mv5dmkRp9mazEpBIC5ODmysTPPyR00ID6NrdUFH2RCFnKQ68+qdQrFELYYxCgBYqiLCulyW/A5Erecw7wfGXvLbEdHBztzQJNbnz3fzb7Y2bLdJh0PY1Mdv8XsvmDT1jx5POc37C53H1D5zdt4cAvv9eliXVGy6GDGfDkIxUG/9RotQS0a0PnW6Zw64J5BHbsUE8WVoypCCn6hv/9qXA6ThhC6/4AnFQU5SOT+laKohR+kicAp0q7vjiKopwSQpwArscwKpLYOKYjne7Tb2PrR58DcGb1OoK7dq52ZGNJ9RAaDUNefIYl9z5MQXblXZV9w0JJNMYr2/3tPEIH9bfJfDadptxI+OiRXNy2g/Vvvmt2rv/jD+HTIgT/NuEWS1xXEU4eHtg5OVGQk0NuejoZ8Qm4BTTcZYnKjEb6AXcAQ43u2IeEEGOBd4UQx4QQR4CRwONVuO/bgHUFq5JUmdKm2jpOnkTb8WPV8s6vvqvyng1JzfENbUHPu6ab1XkEBeIf3oqwwQPV8DSmpMfF0bhDO8DwNH527YYSbayV3IwMzq7fpI7wHN3daDNmJO2Mn0UnTw+GvfI8nafeRNPrelitAIEhOnijdm3U8vHl/1rQmrqnwpGQoijbKJFSCoCVlb2JoigRQAeT8mHkdJxNUtH6jhCCAU8+QvTe/aTHxpGTmsbplavpcOPEerJQAob9QsX3uaTHJzDi9Zfxb9OKhNNn+fuxZ8y85vIyMmkxaIAapTrpQkR9mlxtFEVh+WPPEn/qNEKjYfK3n9OonWE6cvALT9N12lTcGgVg52hdkcLLI2zIQDV80P75v5J8MYION06kSfeuljWsDrC+WB0Sq6OqjgX2Tk50vmUK2z6ZC8DB3xbRdvxYtPbXRpIuS5OdnMKKp18okRNHKShg6f0G/yGtgwNBXTvhHtiYrKtJ5KanE9K/j9mPXMSOXaTHxePeqHRXe2shPSaW+FOnAcMILuH0WVWEhBB4NWtqSfOqRftJ4zm7biMxh4+i6PWc37iF8xu3MPDpx+g4eZKlzatV5GhEUio13cvT9voxOLq7AQYvpZ8n38aur38wC8EvqX10BQX888yLFSZl0+XlEbV7H9F79nPd3TO48atP6TbtFvzDWxHQJlxts/ubH+rD7BoRuWefWdmWRjxlodFqGfXmawR1NY8MsfXjuaREX7aQVXWDFCGJSm1uInVwcabHzDvUctbVRPb/vIC/H38GfYGuxv1LSufilu3Enzxdbhthsvck7UoMG+Z8wLkNm1AUBSGE2abPs2s3kFXOpk9r4OjiP83KgZ2sx9OtJrj6+XLDFx9zyy/fq16nil7PlYOHLWxZ7SJF6BqnLqMXdL7lJnrcNd3syTT+xCmi9u4r5ypJTYjcZe5w6ujujr2rC3bOTjTp2Z27Vv7JQ1vXMeqt19W8NVfPnmP1K2+wf74hDXtQl04EGBfG9TqdWbgfa0LR64nYvoukixFqnUdwkFkq8IaAb1gobcaOUssZcfEWtKb2kWtC1zB1vYlUCEGve2bS/Y5bWfbAY6rnUmbC1Tq977WKLi+PiG3mgThnLl9k9hCQHhvHP8+8R8yRo+gLCsza7v7uRwI7dyS4WxeCOnck/oRh18WVw0cJHdS/7l9AFcjLzOSfp19UoyMU0u32WyxkUd3ibuKindHAvj9yJHQNUt9RDOwcHXH0KIqo7eTlVW/3vhZQ9Hp2fz+fn2681SwoKYAwumIrisKlnbtZcu/DXNqxi7yMzFJ6gr3zfgYguFsXte7UytVW52a/8d3/lRAggKDODTP3jqupCMmRkMRWsVT4nPzsbOKOn1TLvmFVzzEkKR1FUTi4YCH7jOJRnKyriTh5evDfy7NLTNWVhqu/HwDNel+He2Bj0mNiyU1LI3LnHsKGDKxV26vChS3bubhlG3ZOTjh5eXJu/aYSbbybN8OrWcPcfugWUOShmB4ba0FLah8pQtcIlhIgRVHY9tlX5GcZvOLcAxvjERRoEVsaGrqCAla/MpuLW7aX2ebAL79TkJtbKQEKHz2CIS8ZYg5rtFpCBw3g8B+LAcMIqXnfXhbxPMu8msiqF18zj3dXCsmXIslKTFKFtCHh1awJGq0WvU5HSmQ0WUnJuPh4V3yhDSCn4xo4lg4gemb1Ok78/Y9a7jFjGoZIUJKaErlrT7kCBHDsz+WcWrlaLZt6xoliEZpTL18xS/PdfsI4NQZb4vkL9RpPTlEUInfvZfWrb7Llf59WKECFbP7fp3VsmWWwd3LCr3VLtbzlf59SkNsw0rLJkVADxVoiVx9dWpRuOmzoILOQPpKa4deqJY4eHuSmpVXc2Ehh8ja3AH9u+uErNFotP14/GUWvJ/bYCbKSknDx8QHAO6QZve67ix1zvwEMo6GctHQGPPlIrT1IXNy6neN//4s+Px+NnR2u/n6E9OuNotez6sXXK7w+oF0b0mPiyE42uJFf3LKdE/+sot31Y2rFPmuiw40T2fD2+wCc37iFjPgExr3/Ns7eXpY1rIZIEWpgWIv4FJKXWbQA3n7i9XIUVIu4Nwpgyg9fsuOLb4jYtrOEt1t5hA0ZhKuvQWwCO3XgyqEjoChc2LTVLMRS+4nXc3jhUtWj8eiSP2nWqych/XrX2H69Tse6N941+4wAnFj+b7l5fbrPmEbjDu2JOXyE9jdMwMHVlR/GTFJHSxvnfEDMoSO0n3i9mtK8IdBm7CjiT57m2DLDg13c8ZOsnfU24z9536a/V3I6rgFhbQIEmE0hXNxa/tSRpOp4BgcxZs5s7ln9N0Nffo5mvXrS4YYJdLl1Som2prlpmppkYA0dXORwcGTJX2apBBxcXZk09yN8W4aqdWtnv83p1etIrWDnfk5qarkbk1OjL5cQoELKSjVxy68/0Pv+uwnp15s+D92HR2BjnDzcuXfNCrO02KdWrmbp/Y+y9eO55dpoSwghGPj0Y/R//GEwik7U3v2legnaElKEGgCWXvcpD9NpkTOr18loCXWEvbMzbceNZvzH7zHo2SfoeNMNRSeFIXFbobho7O0J6lLkytxm7EjsXQyfn+SIS5zftNWsb6+mTRj28vNqOS8jk3Wz57Dglhmc/m9tCVuSIi7xx4x7+WHMDfwyZRqZiUml2rzt0y+r9BrDR4/AN7R0z0oHVxdu+PJjmvTsblZ/ZPEyTqyodKxlq0cIQeepk2k9cphaZ+sRFKQI2TDWLD6FBHfvqnor5aZnqPlqJHWLm79/UboGBXrcWRRCKbBje+ydnNSyo5sbHW6YoJZ3f/tDiYcFv9YtzUZDYNifdPD3RSXufWzpXySePQ8Y9rRs/fjzUm2syhRS6OAB9H/i4XLbOHl4MOGT97nx689o1qunWr/lf5+Wm8TPFimM7wcG70FbRoqQjWLt4lOIEMIsN0rEjl0WtObaQWOnJbBzR7UcvWe/euzXqmWJ9t3uuAUHN1cAUiKjObrsb7PzQgjGzJlN2NBBZkE1E8+eLxGrrlnvXmbl8xs2k5ueUeKefR6+r9QMsIWEjx7BxM//x10r/2TMnNk4mWx4LgshBIGdOjB6zix1ek6Xl89/L88iJy29wuttBVP37Gwrj+1XEVKEbAxbGP0Up1mv69Tjgwv+KPUHSVL7tB03Wj1ONImvVlpCNycPD7pPn6aWd3/7Q4nRg2eTYEa/9To3fPExTU2mvdbMehu9rmjkFNKvNz3vnmF27aFSRky+oS246bsveGDzaiZ/O5c+D96LnckILS0mlibdu+Ls5VmJV2uOvbMzY+bMUqcZ067EsP6td83Wu2wZZxMRykqWIiSpB2xRfAppc/1oNU10flY2Z9fZTsZOW8Z0lJGfla0ep16+Umr7zlMnq7l38rOyWfn8K2U+MAx48lFVMFKjolk7622zkUbX2242a79v/q9lLqBr7e1p3KEd3e64le7Tb1Prdfn55b28CvFq1pRhLz+nliO27eTAr3/UqE9rwcXo2QiQVcaam60gRcjKsWXxKURrZ0eHG4vWHCraYCmpGYqikHDmHEcWL1Pr8jKKxOTUytVs+/TLEvHgtPb2jJ4zS80DlRGfwI4vvin1Ht4hzcyE5tz6TSy55yFVtOydnWnSo5vZNYcXLa3QdlNHh9YjhlbYHgypvcsibMhAM0/B3d/OI3rfgUr1a804uBb9JuRl2XaOLilCVoyti48pTXsWuQSbht6X1C6KXs+a195i0cz7zDYKF+fwwiUsnH6vYX+QCb6hLRjywjNq+dSqNejK2H/UfcY0wkePUMup0Zf5fdqdrHrpdRbf81CJH/srBw6XOx2WEhVNSmQUYAh6227i9WW/0MLXsWgZ8yfeTNSefWz79ItScx/1fvBedX2s8P3JSEiosG9rRmcSLcHWk/hJEbJCGsLopzhJERHqcUFuruUMaeBc2rmbc+s3lt3AxCMtNfoyfz78JNs//8rMGy5syEDcjCm99fn5pJUxfae1t2f4ay/S67671LrMq4lc2LRVTQNhSnZKSpmjkMzEJNa8/pZabtKzm5kHX2lcOXSEbZ/MpSA7h+VPPMfhhUv57+VZJabxtHZ2jHrjVXUdJTslhdWvvFGmuNoCpt8hOycpQpJaoiGKD0Dkrr3899IstewsUznUGaVFlzYL6Kko2Dk5Fk3nKAqHfl/MmllvoZjEZ/M2rg0BJF+KKvee3WdMM9+XZIJvy1DVOQBg/VvvEbF9J4peT2ZiEpd27mbnV9/xxx13m6Ukbz9xfLn3BDj4W0lnh5jDR9nyv8/MXgsY3oNRb7yqbtiNPXqcnWVMNdoCuvwiARXCtn/GZdgeK6Ehik8h5zZsMisnX4okctdemvXuWfoFkmoTVWyk8dD29QghiNq7nw1vv09GfAIFObmEDh5giK690xBd+/yGzcRMPqK6X3u3aE7UXoNbd+TuPYQO7FfmPYUQDHzqUTpNuYGUyCjys3Nw8fHGq1lTXHx9yLqayB/T7yEnNY3Mq4n8++zL2Ls4mzlLqH1pNAx65okKwwIlX4okYtsOtdxm7Cg1UOuJ5f/i2zKUTsWEMbhbF3o/cA87v/wWgMMLl9KkRzdC+vUp917WiEdgI/U45VKkTUfVtm0JbQA01NGPKR0nT1SndwrZUsYGRkn1KcjNI6vYxkW9cWqqac/uDC3mKTb81RcIHTxArTPdGxQ6qKj+zOp1JJ6/WOH9vZo2IaRfH1oNH0Jwty64+vkihMDV34/hr7+E1sFBbVuaAGkdHLj+4/doP6nitaDzG7eox8379mboy8/ReuRwtW7bp1+UEGSArtOm0sJEULd//pVNTss5eXrSuEM7wBCD7+Q/qyxsUfWRImQhrgXxKcQ/vDV3LF7AuA/eVutSo6JJb2AZIsvj9Ko17Pr6hwrjrdWEq+fOm5U9goPMfvib9uxOgHHjsL6ggPMbNtNjRtHeINPEg0FdOuEd0hwwCMaSex7iyuGj1batee/rmLbwZzrdPBmNfekTMLq8PPLLiCVXHNM4eO6NGyGEYMiLTxPQ1hBJQNHpWf3y7BLvtxCCIS88g4Nr0cbckzYa1sfUcSNi+85yWlo3UoQk9YLGTktIvz4Ed++q1l3auduCFtUP2SmpfNF3KOvefJf9Py9gzWtvVXxRNcnPNh9dDHnh6RJtwkxGODu+/I7MxCJvsvTYOHJSUwHDj/XQF58xrOcIQUFuLn8/9kypseIqi3ujADybBKHPL3vkUTxuXVn4tQpTj68cPIyiKNg5OjLm3Tdw8fMFIDc9nX+fe6VE3h1nL0+63XGrWt7z/U/kZdqem3Ngpw7qcXpsnAUtqRlShCT1Skjforn+w38saTA72EsjIz6BRTPvM6tzqsbu/8oS1KUT3i0Mo5dBzz1JExPBL8R0Q2l+VlaJRHWxJqOhxh3bM+nz/6mZcPX5+ax74x12fzuvxMJ/Zalo2qhR2/Byz5vaVrhZNuliBDHGUZqbvz9j331DTcaXHHGp1OjtnadOxi3AH4Ds5ORSIzpYO+6BjdEYkxBmJly12UgkUoQk9Urb8WNMpkKibHoaoTxSL19h2YOPkxFvvh9l0DOP19k9tXZ2TP3pO+7+7y86TCrduyy7WIiXuOMnzMoHFyw0Kwe0DWfCJ++r4gaG6AfV+dHWF+i4euZcmeedvb1pM3Z0medNcXRzo/WoojWgvT/+oj7QNGrXlk5TblTPFZ+mBMPemuvuvVMtH/x9kc0FAtXa2Zn9XRIvVLxuZ41IEapnrlyK5c1HP2Ll86+y+/v55e72bog4urnRbkJRdtXofQctaE3tkZ2SStyJk+h1OnJSU1nx5POkx8Satel1313qqKKu0NrZlRvo09HdPG5c8UR4Vw4dKZHjxzM4iJu+nUuz3kUxAHfM/cZs1FQZhFZD6KD+pZ4LaBvOxM8+VKM1VAZT77fovfs59MditWwapDWpjB/n8NEj8A0zRAYvyM5hzw8/lXmvpIsRrH/7fVa99DoHFvxR7ZFgbWOa2iLxnG1GqJcu2vXMl2/NZ8PybYAhydulHbuY/PVnZgvIDR23RkXupWXFMbMl8rOzWTB1Ornp6TTu2B5nb68SC+Iuvj5moYssRejgARxeuKTM8xqtttTPooOrK2PffYOFM+8nOeISAMkXI2hcThTs4gghGPXW65xcsYrTq9eiL9DhFuBH61EjCOnXuyj1RCXxDWtBl9tu5pBxv9Cur75H0Su0HDbYbARaVg4rjVZLn4fv45+nXgDg5IqVdJ46GZ+Q5iXabv7gEzW6xIVNW2nUtg3B3bpUyd66wLdlKBg80202TYoUoXrGwdHerJxw6gznN201S1LV0Dn5T5E3kk+Lkl94WyPpYgS56Ya1ltijx0s2EIIRr79UqVQEdU1gpw74hIaQdCGi1PMaOzszzzNTtA4O6PKKFvlNnQPKQ1EUzq3byKWdu7FzcqLNuNHcWAk37MrQ+/67uXLoCPEnTqHX6dj55bfqPqBCykvx3axXT5r07E703v0oej3Hli1n4FOPmrXR5ecTc9Q8+Kq9s3Ot2F9TCkdyYLsiJKfj6pnpj91cIpnXZRvPjFgVdHl5RdMGQtD1tqmWNagWcPYuf5NgjzvvKBHM01IIIcyypBanICeHhNNnSj2XnZxC2pUYALQO9viEhZbarvhUVfTe/ax5/S1O/7eW43+tYOm9D/P3Y8+QfCmymq+iCK29PaPefE2N/l0azU2mEYsjhKDb7beo5Us7dpWwP+7EKRRdkQONW4A//m1a18Dq2sPPTIQuWs00YVWQIlTPhLRuyh2PTTGrK75Y3JDR2NkV5YxRFHQFNQvXbw24N26EZ5PgUs8FdelEz5l3lHrOUgS0DS/3R7SstYW4k0Xx4PxatUJrZz6RkpOWztL7HuGHMTdwZs16tT7+VElRi953gCX3PMzVsyWdBqqKR2Bjbvn1BwY9+wTeIc1x9vbGP7wVIf37MOyV59W9Q2UR1KWTWd6h4lOpxZ01woYMrFJW2LrExc8XJ0/DCDs/K8smXbWlCFmAAaPMM08mnrtgk08w1UFoNGY/CrFHT5TTun7JSU3lwuat5KSlVek6IQR+LUtOTTl5ejBi9sto7Kq21lEfeAQ2LvOcZ9MmpdabBiU1zZZbyM6vviP22Aly09LYMOd9MhOuAqh7jwoRWsPPjsbejojtO2ucNwiM6UJumMBtv/3IXf8u5eYfv2Hc+2/TZuyoiq+1tzdb20q6aFjz0ut0nF69ziw8EEDo4IE1tre2EEKYT8mV4glo7UgRsgBtu7ZS/fvB8PSVUMrTYkOlMNwIQOzR0hOd1TcxR4/xw5gbWPXi6/z96DNVeigoyM0j9nhJMR326gu4+fvXppm1Rk5q2UJb1tSW6TRdQDER0uXnc8ZkI6suL59zxtA6pvca8sLTTPn+S0N9Siq7v53H0SV/Vdn+2saredFrTomMInL3Xr4aMIJ1s+eUaBtYzhqTJfAtNiVna0gRsgAajabEk/Pp1dXfiW5rFOZ2ATi9el2VRx61TX52Nsvuf0wtZ6ekVGm65ciSZepTfyFdbrvZbGOutZESFV3mubJeumlUATfTyNxAwplzJVJ0xB0zOGmY/n2dPD3wD29N56k3qXU7vvzG4ovqXiajv51ffsuKJ0tfNxs5+5UyHTcshW+YiZu2DTonWNe7eQ1R/GmztPwrDZWm1/VQ57FzUlLZ9fUPdX7PY8v+Zt71k1ly78NkJZmnQy7+JN68j/l0aXlkJ6ewf/4CszpHDw+uu2dmdU2tMlfPnWf/z79xfuOWMt2RzdqfPV9CNEUl3KNNf3yLjxQzSokDWJhgznQnf+E+pe7Tb8PVGF5H0ek5s9ayKd/Lc2wAg2fgkBeeplUls73WJ74t5UhIUg1Mh/8A8afPlvhhaKho7ezQ64p+LM+sWVen99PrdGz/4huyk5KJO36SPd8XbUqMPXaC3d/9aNa+593TK9333nk/ldjc2X7iuAoTstUWiqLw1yNPs+vr7/nv5Vmsnf12hdeYRsvW2NuDxnzoY+dUhvuxyRCpuAgVX/cB0Doa9huZbojV2hu2KDh7e9H/iYfV+qjdeyu0uy4pT4SGv/4S96xeTrsJ4+rRosrj0yJE/dukREWViJVn7UgRshDexURIn5/Pmllv29wHqLoEtClyTigtrH9tknk1kYLsHLV8+r+1avj+XV9/b/Yj6ejhUel1nIz4BI79taJEffsyQubUBXqdjjyTqBvn1m8y28tTnNyMDM6sLhL9Me/MZsxbs1CMDwUeQYE4uJQuQmbTUMVi/sWfPF2ivU+LEAAz9+ZCpwSA4G5Fse3Kmx6sD9wbBZS5YXzd7DlqLDprxN7ZGc/gIMDwXhduJrYVpAhZiKAunUrMLV85eJjVr75Bfk4OGfEJbJ/7NXu+n1+pKRZbo5XJ5lyh0RBZh0/C6ca9LYUU5OSQa1ynKJwyKqT4Wkd5ZCUlm/3AAti7OOPeuFEZV9Q+Go2mZBDYctazTq9aQ0GOQZB9QlvQvE8vTpgEFW1WwZ6aQhR90Ugo8cJFTq1aXaJ9zGGD04leX/T5FZqiaT9TsSvIzbVoMFuh0eDVrHSvQICrZ87WozVVx5bXhaQIWQgXHx/ajisZrDFi2w5+vH4yv958B4d+W8TeeT83SKeF8NEjaNTe4CWn6PWsee1NUiLLTyNdXdJK2TshNFoSTp8t8dRY0Z4SU/zDW5mF0wfDqO5KPW4+Lr7h07tFc3XKqzh5mVkc+r0ovlrHyRNJOH2WSzt2GSqEoONNk8q8l/maUJFgnF2zoYQYgyGSBGCWusG0D62Dg5rqXdHpLZ5fqtXwstd7rH2GwpYjJ0gRsiD9HnvILApuIflZ2WZTKhvefp/jf/1DXh1PW5VFbkYG+378hW2ffsGZNetrZU+T1s6OMe/MxtU48shNz+Df516pk9eYlWjuiKB1sEdoBP8882KJtlWJ4CCEoPMtN5Wo3/jeR+VOiVWFnLS0cr0Hi28EzUlOLXXfTUZCAiuff0XdzOjo4UHrUSPYO+9ntU3LoYNLjZumYjLAMh0JJZfx8KDRatEX6Ei9XLT5szB9AhiiZ5iuR+WklFxXqk+63HZzCddzMLxX/uHWESGhLMz3CkkRklQSB1cXJnz8fonU16Wx6f2P+G74OFY+/yqnV6+rtR+5yrDr6x/Y/d2PHF64lLWz3ubi1h0VX1QJXP18GfveW9g5OgKG/RlHFi+rlb5NsXc2dxJw9vbm5D+rSojT0JeexTukWZX6btqzh9meLzBkjb20q+bTi5cPHubHcZP5+cZbSSpjnr941ILslBSzv09aTCzr33qPn2+4lcsHDqn1A596lNSoaLONmD3uvL1ce4QoORJSFIWEMqaqPJsGkxwZiS7PIIpuAf44m+RTOvbncrKN06FOXp6lPpDVJ1o7O8Z/9J6Zd6S9iwuTv/kMO0frDjBs5iFnYykdpAhZANO03m4B/kz87EOzJ5nyuLh1O+tmz2HBrTM5tWpNnUdaUBSlRFKw6H0Haq3/gDat6ff4Q2q5LlItF09fYIiBFluiXfiYkVXu28HVxWw+vpCKphYVRakwUsBfDz+JXqcjPyubU//+V2qb5n17q67OhRROg8UeP8nCGfdyauVqs/WWsCEDadKjG7u+/t6szjQtQGkITck1oatnzpVYcyukRf++ZlOTpgFPM68msvu7+Wq5x4xp9eZRWB5OHu5meYqa9e6Jd/OqPZhYAo+gQPVhLisxiezklDq7V23/5kgRsgK8mjZhyryvGPbqC7QZN5pmfa4joG14UYy1UkiPiWX9m+9ybNnyOrXt5L//lXAdL/7kX1PajhuNnXG0knYlpkQiuJpSPEeNLi+P7JSUEu0OL1xarf59SvnxzogrPYaXrqCAffN/5YcxN/D1oFFsfPd/pbYrPq+ffKl0UXNwdaHlsMFmdYWitOXDT8jLKHIfD+zckT4P3Yu9iws/Xj/ZzBmkx50Vx7czjXxQKEiXDx4q0a5xh3Y4e3nRasRQ9v1UtIeq6XU91OO9834mP8uw+dWrWVM63GD5NBeFOLgWPSRmJlwl9vjJUhPjWRMarRaf0BC1bPr5URSFnLQ04k6cJGrv/hqHSartuHkylYOVoLW3p82YkbQxeRrXF+hIioggYvsuzqxZR/LFklMyh/5YTMfJE+vEJl1BAfvn/1qivuXQQYbzeXnkpKXj7OVV7fhoeVnZnFr5nzplA5B59arZ2kFNKT4SAsOGzRYD+pmN8vZ89yNtx43CybNqKbhNv/yFxB0vffPx2tff4rwxnA3AxS3bGfLC0yXamToQAPi1LH2kfGrl6hLiGdKvD9kpqSScLpomm/jZhyiKwvInnoNiT7JezZqWGvvOlOSISGKPGUMTCaHGjks4VXIqLqhbF8Z9+A4b5rxPljFbqbOXlzrSjNy9lxMr/lXb93/iYavKpxXQtmhdKPbocZbea9jPNPDpx+g4eZKFrCobRVE4t36TmZu8abLMQ78v4sTylXiHNOfilm14BAUy7JXnCerSyRLmlkCOhKwYjZ0Wv5Zh9Jgxjanzvy01GGPa5St1NvS+vO+gGrq/kLbjx+IR1JgN73zI14NHM3/CFOZPupm0mJLTW5Vh2ydz2frR5+o+FQA7x9qdliktW2dqVDRNenQ1qyvIzeXUqjVV7t8nJKRE3dWzJcPYJJw+ayZAYD46yExMYvOHnzJ/whROrTR3eW7as3uJe+jy8tgw5wOzujbjRuPq58uRJX+qdY07tCMrKZnljz9bQoAMlD+9oisoYN0b76jXBnfrgouPDwB5mSUzA2u0WnZ/O4+LW4oEfvALT5N5NZEN73zIiqdeUL3pmvToRrNePcu9f33j4uNNo1KS9R1eVPvrlbXBtk++YM1rb5rVFT54KYrC/p9/JyUyiks7dwOG2YY/H36SHV98Uy2vv9p2pZciZCNo7e3pff/dpZ6rC5dMRVFKpDtu1uc6ut52Mwtn3m+2dpOdlEzcsepFw85IMJ96azGwX6lrLDWhrJTR5zZsLjGVFnuklKR0FeDkVXLkpNfpSuxB2vWNeXiikH69GfiMIWbd6dXrmD/+Jo4t+5tM4+ihkIC24Wbx9gpJvXylxA9C7/vvJiUqmgO//K7WuTUKYN2b75Rpf0pkNCmRUZz+b22pDxP75v9K/CnDU7bG3p7+Jmt4Dq4l31t9QQHHTKIydL5lCs5eniyaeZ/hc2MUMxc/XwY987jVpEUwpfPNk0vU6Yo9VFgLx//+x6zsE9pCDRKs6PVqwkV9fr6asgJF4eCChSy++8EqTTXmZ2ebpVGvDaQI2RAO7m6lTlsknDlX6/dKjYomrlhk6M5TJrPy+VfUKZZC/MNbETKgb5XvoSgKV4u5k7YY2L/qxlZAadNxADGHj5JUzJMoqxq5ncpKS13cgzFy1x6z8rgP5uDo5saplatLjdZcyPDXXiw1aGZGXClrZ0Kw5cNP0Rvn/YVGw7n1m0rdx6Pab2fHgltmsO6Nd/jr4SfNFp5jj59k/09FU7K97rvTbOqu4+SJZptjPYICS8Shiz16nD8ffsJsyrXpdT24+cdvKozZZinChg4qkXNpwJOPWMia8vFr1VI9bt63Nzf/+LXqpKDRas2m3a7/3ztmo+qkCxdZ9sBjldosnpeVzbIHHmfH3G9q0Xq5JmRT7Jv/a6mu2adXr6XLrVNq9Yky3yTMTSFRe/eREmkeXqXlsMEMffm5ank2pcfGkV3MTdqtmKdXbaC1t/9/e/cd31TVxgH89yRtujcdtEAHe+89yhYREQcOVFDcE5BXRXBvxNdXREVx4cKJAwd7Ccjeo+xZKKOD7pFx3j+S3N7bJG1a0t4kfb6fTz/ce3Nzcw5p8+Sce85z0KB5M2QeqTpYO7r3Incx7RDWv/chMo8cRUyrljYtFyv5DeADFUb9dbj5BgDAwcXLsPLVmXafH9euDQZPf8rh6Kxja9fZHJt/rXLekjNdJ/K0RfnnL4CIcPyfDdgwZy7yzp6THovv1AGdblUuyBjXvi2ufv0lnFi3ARFJiWh59XCU5OZi59ffSfkBK36ZGfr8M2hx1VC3bAFZabRajJz5Cr68rnzeWMXffXfRbEiq9H9s0usVk5XXvfs+/MNCERwbg4ILFxESG4Nr330L+35ZhH/f/wiG0lLoi4rx13+mY/CMp9ByxDCHr7Prux+d+huqLm4JeYiDi5dhx1cLpP2Ugf2l7awjxxSjoFzBmuVaruLNctJqMPzl52o8tFaexNQq1JIDy9WufuMldL9nQnl3hAPNhgyq8lpbv/gaGbv3Ql9UjLM7djkcjm39wrDnp1+w+o23FY8ZSstwaPEyrHzlTcVxjVaLCb/9gLv/XIgbPp7jcN7S6c1bcaBCN4wraHx8cOCPv7Fk+guKAOQbGIghz02z2+pLSe2HIc8+jS533IqgqEhEpSRj+CvPITAqUnFeaEI8rp87Gy1HDHPrAGQVHB2NwTOekvbl86zcSbPBA6Xt9B07FfeIj63+B8fXrEPBhYtI6tsLIXGxICK0v/E63PTZh9IcRZPRiBUvv4Ed33xvdwh2UXYOdi74wea4K3AQcnPCZMK2+d8oPqwadmyv+FbWsEM7h/c9aio4NsZm/olN2Ywm/PboEzaDF5wVFt8QfqHKrjJ7SwK4Qmh8Q3Qed7PNB6NcsyGDnBox5OsgwWerkVcpnl+UnYOT/27Cuv+9b3Nu7pl0rKgQgPxCQ3Hbgi8QHBONwMgIhx/UWcdPKG5E+/j7Y8DUSYoh/TXt5mo6OBWr33hb2YIiwsCnplS6GqvNdQYOwF2//4hbv/kMo2fPwvUf/A/jvv0c8XbubbmzhC6dpO1ze/ZKiW/dSXBMtJQ+ShhNOLN1u/SYfODBwGn/UTwvKiUZN348R3FfdOOH87Dl0/k2r3F681YpCXBEZVk1aqDKIEREjYloNRGlEdF+IppkOT6LiA4S0R4i+pWIwmXPmUVE24go1bKfRESCiB6TnfM+Ed3l0tp4ieNr12H97A+x7t338cNd92PzvM+lx8KbNIYwmaR7GRofH6Q+NcXlZTDp9TZ/cNZ+ZrlzO3fjz/9Mr9GIGdJo0L3C/JRlL7yKkrz8al+rKkII/HzvI8itJFtz38cfdOpaA6Y8andia6+H7lPknju6Yg2Orlxjc56Pv7/NrPaopim448evFIurOfLvnI8Ua/QYSkqw9YuvpMSkAFBcg3tbce3b4ujK1YpjfiHBuOatV9FClnDWWaTRIColGY27d0V8545uNQzbWaEN46SEtIbiErddAVkeLK0r4BrLyhS5bO0tFRMcE40b5s5WfHna9sXXOLbmH5QVFmLPz7+itLBI8ftanS8jznCmJWQAMFUI0RpALwCPEFEbAMsBtBNCdABwGMAzAEBE1kH2AwA8IrvORQCTiMjzfhPr0OFlK7H4mRew+4efsefHXxR5oBK6dkZcuzY4v7d8BNfAp6ZUOdO9Jv79YJ5NLq/g2GiMnPmKzbk5J0/VuKui7RjlJMWirGxsszM36UoVZWZJmQTsadixvdNLOPiHhWHoc9Mw4fcfFCmXzm7fiRbDy2fbn1i/wabLMTQhHn0eecDm//aGj+fAP9S2C7Si4su5dieIFlcYiScPUhU56pLMPn5SGsAQ3aoF7l22CPcuXYSkvr2rLJc3k3/Au2uXnDy33YX9aVj+4uuYN3SUYoKxvS52wPxF49r/vaUYKr/+vbn4/fH/YN07c7D5o0+w69vyrrhTFQbYXKkqg5AQIkMIscOynQ8gDUCCEGKZEML6VXkTAOtXOC0AE8yTD+T9CZcArAQwwUVl90r28j75+Puj28TxaDv6GsX8kS533IrWo652eRnyMs4r5plY6YKCoAsKsvscR3OVhMmEA3/8jU0ff4YzW7bZPO7jp0NIhW9Wh5etcHlqkIDIiEpz9DmbNkkuODoaTQcOkPYvn0lHdMvmUlJWfVExTlfIIZd39hz+/UA5uqhx964O1/CpaPtXCxSjzGqi+dBBdrtUrIvzaXW+GP7iDPgFu7aL11PJg9D5vfvUK0glols2l7bP7z+Aw8tWKAacJPXrXWkLxsdPh2EvPSv9fRecvyBNfj3wx2LlyWqm7SGiJACdAWyu8NBEAIsBQAixH0AggPUA5lY4700AU4mo0un1RHS/pTtvW06Wupl161rFLMYDn34Cd/z0DbrffadinklKaj/0evDeWinD+b377f6iNR822O79DdJoEFdhSQOr7V8twOo33sb2L7/FoilP48KBNJtzIircvyjOzrE7Oq8yQgjs/uFnLH/pdZyxk9tOo9Wi8+2OM2SH1XBAhHwpCGuWh+T+faVjpXYyYMu7zQDzB4QzinMuY98vv1V5XqdxNzt8LDAqEkdWrMY1s17FtbNn2T2nzyMPuO3QaTXIs3dU9/eyrgTHxiiWxbAKiIxA2+uvxbAXZlR5Df/QEDTq3sXmuPXeZG0t7Od0ECKiYAALAUwWQuTJjs+AuctOShIlhHhMCNFVCKFYOF4IcQLAFgDjKnstIcQ8IUQ3IUS3iKjqpVDxZEIIxYz6hC6d0Gb0NTCUlCB9xy5pAIBfSAgGT3/K7tyR6r7eyQ0bcWbbDkXLw2Tn5mtUsxREJDaxOzG2+z0TEOKglbH7h5/lL4hDdjIShMTbfkPTVjMN0NFVa7F+9oc4vHQFFk97XpG2xKr1qKsREBlh9/mhdsrgjLyM8kEZ1nsHXcePQ6CTQ80jU5KdXjY6ffvOKltBGl9f9Hn4fkVLJzAyEgER4dDqdCjKyoa+qAjfjL0Ty194FT3uvQsarRak1SAltR9unPc+Ooy9wany1EcVl3J3F0SEliOHK+Y2dZ1wOyb+uRADn5yiyIdXGfmKx1aDpj+JiMQmSLG0+itb/K8mnJonRES+MAegb4UQv8iOTwAwCsAQ4Xz/yesAfgbwT1Un1jenN29V5DJrO2YUlj77kjkwye4wxrVr45LRcEeWr8LyF18DYO4SGvmWeVkFH3/bAQhZR4/jz6kV1t8hQurUx9HWQfJJk9Fosz7QgT/+RtsxoxHVNBllhYUoyspGfoYy2WdYo4Rq38Q+IZszoy8qwvG1620WDfT190f7G8dgyydf2Dy/pgE9qmmKNFJx87zPkdClk5QZffUbb+P83v3w8ffHoGlToS8uxpqZ70jPTUnth9Qnp4A0GmSfPIXwxo0cTnwFYDOx1p7G3bogfccuRQstPLGx3YX2Si7nIrFPL3QadzOIyO7AE2Ze+sPq0qEjSPtrid0FKdVGRNLAicY9e6Dn/ROrfQ1rZg5dUBDKCgvhHx6Gpqn9kNSnF4ov56JJj25IGTgAnwx17ouTM6oMQmRui30GIE0I8Y7s+AgATwNIFUIUOfuCQoiDRHQA5uDl2jtcHk4+D6j1tSNxMe1QectIFuMvp5+t+NQaOb+vfIDDma3bsWvBj05lU7ZK6tMT7W5wnDxVo9Uirm1rnNu1RzpmLNPjh7vuQ2RKMrKO2E8X0qibbZdAVXwDlPdUtnw6H82HDrZZB6bi2kJW1nlWmUeOYf3sDxDdsjl6P3R/lYlZu919J06s+xcmgwEX9qfh5Pp/kZLaH5FJibjx4zkozMyCVqeDf2iIYq5Qq2tGYMiMp1CYmYVPhl0LY1kZ2t80BgOeeNzhaymyixPZ7TINjovBosfLh+L6hQRXutLrifX/IqZCZgBWrjArG8teeFVxbPtXC9wyCDXs2B47LQMISvPzajQXK75je3S+/Ra0HDEMh5YsR+PuXaHV6aDV6aALCkRYQkNXF9up7ri+AO4EMJiIdll+RgJ4H0AIgOWWYx9V43VfQ/lABgYgY88+6cNao9WizbUjsecn28EBgHmOSWGFTAPVVZKXhyPLFb2lKLAM4czY7dzNV39LH3RlOt9+q80xYTQ5DEAAbJbMdoZ8DRjAPN/o8PKVNufJMw3LlRUVwWQwYulzL+Hsjl3Y9d1P2PrFV3bPlWvQrCk63lKeZ+yfd+YoRqYFNYiCv2UulHyF1Ca9egAAvh9/rzSp9eyOypcFVyzl7aDjYf+vf0jbWp3Obl47ufBGCZU+Xp+V5hdg0aQnbVqguWfSr3g5hNog/7vJPHQE+uKarVLc55EHENU0BX0eeUCRYLe2ODM6br0QgoQQHYQQnSw/fwshmgkhGsuOOZxkIYQ4KYRoJ9vfLYTQCCHmu6geHm+7rBXUYsQwnNm63e69GatMB6tZOmvfL4sUwzcBICA8DIbSUhxa4jiTtE42YspRq0IuqW+vat9jqDiB1Rn2BkZkyFpgALDvtz8U83bkc3pKCwpxZPlKxSTg7V99i5LcqgfGyOtXeCkTB/+2vwCdXtY1qQsKRGFWtmKodmXdbWVFxVIS0Yo0vrY3jBs0b4bbvv0CuWcct5oHT3+y0jQt9d3meZ85fE8OLPoLR1ashr7EfQYq+IeGIswyz8xkNNpduNEdccYEN5B59BhO/bvJvEOELnfcWuXqpZUFKGdUHC5NWg1aXTMCR1etrXSOibx7ytl1d/pNfgQjZ76CxD69nDq/uhMCTQYjls54yea4/EN/87zPsfat/0mTaiMSmyhyshERtn7xteL5wmiyuadV0elNW7HwgccUx4ocDFcvyS+fhKvRarHunfcUj1fWAkzfus1hEtJmQwYq7hlGJCXi2v/NrHTYd+9H7q+V4f3ewmQ0Vrqsxz//fQ/Lnn/FJh2T2uRZToqyr6y3pK5wAlM3IE+73zS1PyISmyC3ivs+B/9eqhgKXF0Vb/wHx8QgqEEDm3xkpNUoPvzKCstv/yU5GVSICMn9+yK5f1+c27kby158ze7sbavj6zag+8TxTl0bADZ9/KnNEuQAEGwZrZaxZ59iAmxkSjK63H4LVsk+QErzC2z+z0Nks+UB82jC4pzL8A8NhcZHiz0//4r1736gyBZBGo3d1oW+uFgx8XjRpCdtznF0f600vwBbP//a7mMA0GPieLS/cQyOrliFyJRktBg+BD5+ftj/m21uOdJocNs3nzvMScfMCi9lSl9itDpfh6MSs45VPVikLsnLqXXxCsi1xTNK6cVOb9qq6CLqMt48el1rp4tF7vja9Ti9aSua9KrZgmBx7dsqWkP5Geex9LmXkbFHeT8osXdPnFy/0VwmnU66fxHWKMEm1b0z4jt3xE2ffIDv7pjoMOlq5qEjKLyUKU36rEzeuQyH65skW+bfyDNYJ3TphIFPP4Gf731Yak3GtG6Jxj26Yue33yueX5ydg93f/4z4Th2Qf+ECtnwyH9knTkIXHIQ2o6/BrgU/SudqtFokdOmEdjeOsZnrBZjn+FTVerWXGifr+AksnvZ8eYCsMCAhIjkRYY0SENYoAXEVFmKztyzFsJee5QDkBPm9tMqGxbe/aUwdlMZ5BRfLcy9WNjnbnXB3nEoKL2Vi8fQX8MfUadI36Sa9e0gjlVoMH4KIpESHyTIB4J//zbG7tIMz2o6+xmY48Mn1/yr2tTodGnUtX300UDZUNbFPzxpnQg6OiUb3uytv6ZzcsEnazj17TnlTHubVPjd/8gW+vul2u91UkclJiO/cEQBQJpsz1HLEMOz67iepyzEgMgLDX3oWjbp1QaMKq5caSkuxYc5c/HTPQ1gy/UUp7U9ZQaEiAMW0aYXxv/2A0bNnIWWA/dZpSMM4dKywDEJF8uW4AXMG5J/ve0TRQpPP3geArnc6nnLXetQIae2Y0PiGuOHj99B8yMBKy8DMfP39q1xiPqFrZ7Qbc20dlahqRoMBRdYBS0ROfYlzB9wSqmNGoxEH/16K9bM/lFY8BMzfvAY9PVXa7z5xAvxCQ7HhvQ8BKFshVrln0rHzu5/QbcLtyDl1GkVZ2Yjr0M6pZrguOMjuUgqKspaVIX37LmnfLywU+RfMc3qudOmIqrJVp/21GM2HDcKWT+dj9w8LAQBDnpuGVpbEoUdXrsG2CvdwIpITkXPCPD8mZdAAKUiGy+795J49p8jaMPDJyQizjBAbMuMp/HjXAyi+fNnpegTHxmDkGy8jqJLs3IC5S7Lf4w+h6523YfePC5F19Jgi0ALAj3c/gAYtmqFJj27IyzivaCH7+Ptj8IynENOyOZbMeAlF2dnofs+ESgcWBEdHY7SDrAisasEx0cph8TJanS963HsXhBAwlpW5xRyrwkuZ0hfawMiIKntT3AUHoTpUWlKGSWOfxZ4tykW+Env3RL/Jjyi+eZFWo1giufMdtyJj916c3b5T8dwdXy9AZFIiFk9/ARACoQnxuO69t6vMdLt+9odOlVneOgpLaCiNynM01NlZEcmJDue6AOYkjJ8MU37L3PfL71IQqpgANDI5CdEtm0tBSH4vR57NoSg7RzFgQT4RMTgmGsNfeQ5/PDFNWpnUKqxRAhp374qinBykb9uBkLhYNB86GO2uH12ticMBEeHSMu27vvsJm+d9DoNs2ejMw0eRWWGl3NCEeIx882Upv90tX86DEMIj1uTxVEII5Kafc/i4j58/Fj/zAkosX1hi27bGmPffUTUYyQNmVa04d8JBqA4t/3WtIgCFNIzDkGefRoKl20guY/deqRtGFxSEzuNugaG42CYI6YuKsf2rb6UP87yz5/DHlKdxy5ef2EzUtDKWleHwMts5NFWRf7OqLC2NobQMpzdthi4oCAldO9v9sPT190doQjzyqjHxNvvEKRhKS+Hj5wffCmlIUp+aorin4y9b0rtIlmHaN8AfsW1aS/+3Z3fuRsMO7WAyGJF79iy0vr4Y+uzTWPnaTMW9gP5THkVi755Ol9UZnW4bixZXDcWZLdtwatMWHF/zj839h+QBfTF4+lPSXCMrDkC1qygzq9IWsbwXAzB/abp48LCq6yXJ1+IKiY2t5Ez3wkGojhzYeRgfvFyeLia6VQuMmfOOw5xOaX+WZ65tPmwwdIEBinTtcrFtWytaJpdPn8GBRX86nJ+zf9FfNkk0q0QEH9kSAPZuvluteesdKUfcoGf+gzbXjrR7XkzL5tUKQtZ0PC2GD0HDdm2l4z4B/ohr10bRvWgdSl5WWKjIPB7VrCk0Gg0OL1sBADi3YxcOxcVi7ax3oS8yj/zz8fOD1lenCAjy1oorBUZGoOWIYWg5YhhK8h7HqY2bkXPyFHz8/BDfqQMadmzPAUcFlyq0Rp1RMRFvXZMHoeBYz2kJ8cCEWqYv0+OztxfgwWufQm62eXKoj78/hj43zWEAKisswtHVa6X91qPMKULkXWzyXGdRKbbLEFhHtNmz9+ff7B53tN4IALS5diQMsm6siGT7QchkNCqSlFZcElwurl0bh49ZkUaDTrLs12l/mSeChic2hp+ltWMoLkFu+lnF0gOZR4+jKDsHf/5nOvIzzJP2/MNC0WxQKgIjy+/fXEg7iA3vzZUCEGAOOPJElWGNGyGpX58qy3ql/END0PKqoej1wD3odtcdiO/UgQOQSmoyGbzYiYnNtUkxMi7GM0bGARyEak1ZqR6/fbUEt/V7CJ/NWgCD3jw8NyQ8GNfNnoXI5CSHzz26eq20lG5kchJiWpvXCZSn4dDKutr8QkMQWmEpgjNbt+PAor9sBh+U5hfg8ukzAMwz7TWyQQzXf/guBj71BDreOhZDnpuGbnfdUf5EImSfPCntOip/xZvtl8+cgclgfwBE08GpikmW9giTyTxCz3Je+rYdyD9/ASa9XtE68Q8LUwS1zR9/hi9G3YiM3XulY/0mPwpdUCAiU5KkIF5WUFjpKqRhjRvh6tdf8pg5F8w1CjIdz2Ozp0GLZgiMjKjxaFVXyL8guyfELaH6q7iwBD98/DvG9rwXbz35Ps6dKk+d0bZrS8z7621MHVP5SqgHZV1xrUddLX0bLpEv6Sxb1ySuXRukpPazuc7qN/+Lfz+Yh9L8ApzbtcecJkaWvywwMgKB8lFdRGg7ZhTajB6J0xs3KyZ4hjdOQM7J8mHSkQ7mmuxb+JtiXxhNKLhkf4RRcHS0zZDjinTBQYhr20YaagwhcHDxMqT9tUT6gzevpRKGViNHSCPdFIjQ97GH0NKSXy44Jhpd7rzN7us16dkd18+djV4P3ot+kx7BrV99gqimrl+5lrm3hh2qd2+nSY9u+GzEGHw0cATyMtRJlyMPgBUT+rozDkIuIoTAX9+vwI3d78Hs5z/BpYws6bHQiBBMfvV+fPTHW0hsVnne1vwLF5F71jwqh7QaKTHn6U1bsX72BzbnN+ndA8Ex0Wia2t/u9XZ//xM+vWo0fn14Mr6/Y6Jidn9hZqZi7k/61u0AgA1z5uLIitWK60QkJUq/5AGRETYpey4ePIzvJ9yHM5ZryBWcv2BzzGrA1MelxbgqIo0GA5+eCr+QYEVX4fk9+7Dr+/J1itpeNwqAOR/b6NmzFCODdMFBuGbWa+h0m3KOTvd7JthdOyUgMgLxHduj6/hx6HjLjW4x9JbVveZDB1VrWfMd35QPilky/YXaKFKVdMHlqx5f6RSKusRByAUuZ+Vi+sTX8dqkd3FZthJsVEwEHn1hIn7Z/jluvm80tLLJobc3tb/6xelNW6QJZ1FNUxAYGYHCzCwsfuZ5FGVmKc7V6nyROnUSAPPgBJ8qEoqW5OZBX1wiZToQRpMU8ADg0OLlAACTXjmzv+ngVEWwsrfs87/vf+QwM3ZeJUEoMikRYz78n90knAOffgLNhwyEyWBULJMdntgYBRfKr7nn51+lEXChDeNw/dzZ6HDzDegyfhxumT/PbnohrY8PrnrtBTRo0Uw6FtOqJfpNethhWVn9odFqa7QeD2A76biuyP8u7S3q6Cqr0qq34GRVuKPbSSaTCUcPnMTeLQeQfekyhBAICApAUUExFn2zFDmZl6VzYxtF445Hb8Ko24bBz796i7Ndkt0QbT50sHlDCLujs+Lat0NovHl9D9Jo0GxQqmIkWEUBkRGISklG1zvHYcmMFwFAkZL+4sFDyD5xUpHuwzcwEENmPGW+r2OZ13P59BlkHTuBqKbJMOr12PTxZzi7Y5fD1z22ei2iWzRDeONGdheri0xKRKuRV9nkrdu78De0vmYE8s+fVwyJTRnQD3t+lNZWRHF2Di4dOiwNoQ5tGIf+kx91WB6r0IZxGPvZXBxbsw5FWdloPerqSpN+svolKLoBSKNR9B4AwKj/voGNcz9F1lHHy5GoMY9LPl+tsiTENeXq4GPFQcgBIQROHUnH9g17sH39buzYsBd5OflVPu/6u0bi0ecnIiCo6mUO7L6uLAXNnh8XIvdMOsITmyA4NkYxBBMActPPKn7ZrWncHWnQNAUaH61i0biKwS19+05FEOow9nqpfzmpb29p8uq2+V/jqleex+7vf1aksLHn5PqNOLl+I3RBQeh+z3h0GHuDlDLo0uGjKMnNRXK/PlIQIiIIIZB17DguHTqC8CaNofH1lSaQ/vboE4rrRyQnIqFLZ9SERqvlVDbMroDwMMS1bysNbolu2RxDnpuGqJRk+AUH22RPlzOW6R3O06st8mVWylzYEqqt4GPFQUjm3Knz2L7eHHS2r9+DrIuOR01VFBkdjunvTkKfoc4nFL29aRG+PaYcpp3YuwcOLPoLAFCYmaVIvmllzepbcOEi0v5cjJYjhkHr64tzlbRGAPPw6eKcy/j3g4/Lr+XrK93rIY0GYY0SsO6dOdLj8mzX3SeOl4LQ0VVr0X3iSaRXmDxbmbLCQmx4by6Or1mHQc/8B5dPn8HiZ16AMJmQMrD8npZfWCgad++KHvfdLS261ubakYoMElYJXTph2Isz6vwPntUPKQP6SUEoMCoKUSnmQSpx7duiUbcuDpdcUeP30U92T6i08MrvCdV28LGq90GoIK8Qy39diz8WLMfBXZX35UY0CEeXvu2R2KwRNFoNiguLYTAY0bpjc/Qd3gNBIfbn/VRH8oB+aD50kM3AALmkfn1xbNUaAMDqN97Glk/no/+UR+0OCpA7u2MXvhh9k6K1Fd+pg5RNO75zR5yV5YoDzN1xVjGtWiCpby/zMGwhsO2LrytNsKrV6dB53M3IPHIUlw4dQaHlnlbGnn1YMO5uRcqe42vWSdvhjRth+EvPKq7Vb9LDCImLxf7f/4RJr0dk02S0GTUSKan9FHOmGHOllNR+2DBnLgAgfdt2GA0Gabh+i+FD7AahmqwM7AqKltAVdMfVVfCxqpdBSAiBXZv2Y+3f/+L3r5eitNj+bPiQsCB06dsBXft1QJe+HZDcsonL+3krtoaICMNeehbtb7oeOSfNaWpOb9qKs7t2w9fPHz3unYDEvr1x4p/10tIAhZcysWT6i3avH5oQj9L8ApRahmbLA1BCl04IjCwfcBAYGWEzZya2tXIEWZvR10hzgU5v3oYe992lCCByxrIy7F34G/o+/jCueu1F7PhqAbZ/+a157pKDnHEA0GzwQJtjWl9fdLnjVnS5w3a5cMZqS2h8QwQ1iEJhZpbU+xBmmZPnKINJrwfvrcsiSpQDE6rXEqrrwCPntUGorFQPvV6PoGDzB7wQAhmnL2D5r//gr++XI/1EBtp3b60IQDo/X3Tp2wHd+ndE134d0KxtsmJEW10hIjTs0E76RtVh7A02Nzrb3TBacXPentCEeIyc+Qr2//Yn9v78q3Q8qnlTdLhxDLS+vljxypvS8SPLV9kMHGg2dJBi//ja9dJ2RFITtLr6KkX3nZxW54vS/AKseu0t5J09h573T0Ry/77Y8P5HNjnwAPN8n/Y3jUH7G8dUWi/GHFmVpsXg1pVnh6+u0PiGUis+L+O8FIQcpb6q6t5sbanJwAQ1g4+V1wShQ3uO4q/vVuDs6fMoKSrB7k0HIIRAUovGCI8KxbG0UzYDCw7vPYaAIH80SmqIUeOG46obByI0IsTBK9Qee/eGKqrYAuvz6INI6NIZB/9eghP/2K4qCgBjP/sQ/qGh6PXAPQiOboDccxmIa9saLa8eDo1Wi0WTn7J5jnzC2+AZT9mkg8+UjQg6v3c/vr/zHodlDmrQAHnnMgAA2778Fi1HXoXols0xZs5/cenQYeRlnEdkUhIikprAZDTarG/EWE3IP1hdEZACIsKl7dK88s+QiwdtM8l3vHVslct61BblPKHKg5A7BB8rjw9Ch/Yew2ezFmD90s12Hz9x6LTd48GhQRh2QypG3z4cLdo39bgcXVofH6QM6AvSkN0gFNU0Bf6h5gmeuqBAuxkCLuxPszkm16B5M5tjXcePw7LnX5WGrebL5gD5BgZKOdgadmyPAU88jh8m3Gd+UAic37tfGmgQ3bKFojuDAxC7UvY+WK80IAkhkC8blSpPc3V+n3JJluQBfdHn4fur/RquouiOczAwwZ2Cj5XHBqFDe4/h87e/w7olm6o+2SI4NAhturTA1WMHI3Vkb/gH1mwYdW1wpjVkT6OunRGZnCSt+gmYb6amPjmlyucGNYhSJOqUazXyKkS3sA1CzQYPhMbHF+vffV8KQBqtFk0Hp6LZoFTzukYwL0UhBSALzwrzzNtU/AB2Jigd+P0vXDp42LxDhAbNm0qPyf/mAKD5sCFS9nY1VDYwwR2Dj5XHBaHsS5fxvxkfY+XvtjfDB4/uh2HXD4CvzhdNmiYgKiYSB3cfQXFRCZJbJiKuUbTHtXiq4hsQgJu/+Ajn9uyDvrAIEYlNEOEgr1tFKan9zWsRVXDtOzPRpJfjoeYpA/oiuX8flBUUouDiJQQ1iIR/WBiMZWXwCwm22x+t8fFBox7dnK8YY9VQkw9ZZ1pJh5fL1t0SAj/e9QD0JSUIjIywmbe34+sFaDY4VbXPGN8Af5BWA2E0wVBaihV7jND4uv/UBY8KQit/X4e3p82VlkSwGjy6H+6eciuatkmyeU7nPuotMlVXtDodGssmoDqr+z3jofXTYe/Pv6GssBBNenZHuxtGo0nPquc6ERH8QoIVN0O1Oh2ufuNlbPl0Ps7t2gOtToeQuFgERISj4803qtZXzlhVHAWkiiuUWjN3VAxAgHlV3O1ffqvMPl+HiAh+wcEoyTV/PpqKCzkIuYpBb8Brk2dj6c/KuTMDR/XBPVPH2Q0+nqimXXI1pfX1Rfe770T3u+90WZqRhC6dcP2H70JfXAyNVms3TQ9jruTqrib59Zr06oHDS1c4/dxDS5a7JAjJ5yM5a1WaFka/EADmIGQsyIVPaETlT3IDbh+EhBB44aFZWP1H+c332IRoTHvnMfQcWP1v/8w+V3cheFIqecYcORM3EH7x36H03AnpWGKfnvDx88Ox1f8ozg2OjUHncTfX+LWEENj3yyI07tkNP939IJr06oEud95m996snDxo+oRFQn/RvFqx4XIm/OKTalyeuuL2QejcqQs4tKd8WPDIW4Zg8qv3Izg0qJJnea66bg0xxhzT6PzQ5OnZyPp7AUrPnURgs3bw6T0cx54qDzZ+IcG448evbZY3qa7N8z7H9i+/RWh8Q5QVFuLoytU4uWEjbvx4jmJAhJW9FqAuphGKj5jTDJWcPoqgNu5/H9btg1B+bvlN7pvvG41Jr9zndYMLGGM1UxejvnyCwxB780PSvjAolzpp0rN7jQJQbvpZHFyyHLrAQBxb8w8uWIZ8W+fWAeYJsf+88x5umDsbQNX1DUhpjdwN5kUxi09UPgXDXXhM0q2b7rm23gQgR2sNMcbURz4+CO5UvpLxqQMnsHTdJaxK0zodFMsKC/Hrw5Ox7fOvcHrLVlzYd0AaYt2kd0+MfOtV6dzze/dj+Y4Sp67tn9xa2i4+us9mGQp35BFB6Ia7r8GU1+6vFwGIMeYcNee+RF1V3h1Xdu4kTs2aBGOReeScNRhVVr6d3/0kpQIqtizI2OPeCWhx1VCMePV5JPfrg4hE81QLYTKh9OwJh9eS809IgTbIPEndcDlT6ppzZ24fhMKjQjH1jQfrXQDi1hBj7iuwRUfE3PQAQOaPUH3meWQu+tLmPHsBSQiBw0uWS/sRiU0wePqTSOrbG8NemA7fgADzSLeG5cmDz332OrJX/oKiI3tRmnEaxScPIW/rahQd3Yeio/sgjOYuQvLxQUi3gdLzcjc7P7JPLW5/Tyg2IabeBSDGWOXcIQNA1NXj4BMRjXOfmLvOslf+gvABoxyOSLOWueTMUem+j29gIIY+N02ayiCvV8SgMcjbZA5W+otncWHB7PKLkQYQJgS27ISiQ7vg1ygFSdM/hMYvAGG9huLy2kUAgPxtaxA37nGQjzIHpDtx+5YQxx/GmLsK7TkUgS07mXdMRpxf8B5EJcuUCCFw6ddPpf2A9r2x9liA3e67wGbtEDd+KkjnZ3MdXYw5B2PxiYMAgNL048hZ+4f5ms3awycyFgBgLMxDwb4tNa5fXXD7IFSfcZccY+6NiBA77nGpW64obTvyd9pfXwsAspf/hILdG6X98AGjKr1+ROpoNHvrR8Td+QRCuvSHf3Jr+EbFIahddwS17wVNQPl0juylP8CkLzWvkNxrqHQ8b5N7d8m5fXccY4zJuUNXnJx/o6aIGHQdclaZ1+y6+MMHCG7XE5oKLRhDbjYuLZwn7UcOG4ugVlVPuPcJCUfEwOsQMfA6m8dM+lIce/o2GHKzYLiciYLdGxHabSBCew5F1t/mvJD5uzfApC+Fxte2ReUOuCXk5rg1xJj7i75uIrTB5rlC+szzyFnzu805BXs2Qhj0AAC/+GTzwIYrpPH1Q1ifq6T9kpPmNY78G6VAE2Ce0C/KSlF27tQVv1Zt4SDEGPMY7tYKstIGh6LB6AnSfsEu2zW+9DmXpO3gTn1cNljAP7F8Xa6SU+ZlJ3I3r4CpuHyZFmtAckcchDwAt4YYc3/BHftI26UZtotpyrvnjEXOLb/tDP+kVtJ24cGdOPP+DGnEHgAEte8lDWRwRxyEGGPMBbRB5al7THaCjF98srRdeva4y15XF90Qfk2aAwBIo0HBzvWAZYSeX3wy4idOc9lr1QYOQowxj+CuXXFWhtwsaVsbGm7zuC4+Udouu3DWpa8dc9MD8AlvgNDew6VjwR16o8lT77r9cg48Os5DcHZtxtybPrM88aiuQUObx+WJTzUuXmcruG13NH1jAYoO7oTGV4fgDr0Q1K6nR0z05yDEGHN77t4KApRByNdOEJLnf/NtEO/y19fo/BDcoReCO/Ry+bVrE3fHeRAeoMCY+zLk5kjbPhENbB63jlwDAP8mlS9UV59wEGKMuTVPaAUBgMa/fDXhsgvpNo8bci7KznXfIdN1jYOQh+HWEGPuKbB5e2k7f9sa5P67VPG4fK2fy+v+hD7zfJ2VzZ1xEGKMMRfwT26NoHY9pP1z82eiYO9maT+s51BoAs0L1xlyLuHk6w+jTHYfqb7iIOSBuDXE6gtP6YoDzMlMEx54Hn6NUswHjEakf/g8ik+aM11rg0LQ8O6npUwJhtwsZP72uVrFdRschBhjzEW0gSFoPHkWfKPiAACirARnP3oJphLzF8fQLgOQ8NBL0vm5G5eh6Og+VcrqLjgIMcbckie1guR8Ixqg8ROzpHxt+kvnkLX0B+nxkE594Z9Uvmrq6VmTkbl4QZ2X011wEPJQ3CXHmPvyi2uC2FsekfazlnyvuP8Td/sU6f6QMOhx6eePkb36t7ouplvgIMQYY7UgrO8I+DVqCsDcLXf67SdQkn4MABCQ0hrJz82DT0S0dP6Fb/4HYTKpUlY1VRmEiKgxEa0mojQi2k9EkyzHx1r2TUTUrcJzZhHRNiJKtewnEZEgosdk57xPRHe5uD71CreGmLfy1K44OdJo0XD8VEBrrov+0jmcfO1h5G1dBcC8RHdI11TFc4z5OTbX8XbOtIQMAKYKIVoD6AXgESJqA2AfgBsA/CM/mYisecUHAHhE9tBFAJOIyLVJkxhjzE0FNG2LRg+9DI2feSKrdaBC5l9fAwAM2RcV5+uzLtpcw9tVGYSEEBlCiB2W7XwAaQAShBBpQohDdp6iBWACIADIs+ddArASwAQ7z2E1xK0h5m28oRUkF9K5H5Ke/Qi62EbSsUu/fIqSU4elUXNW+uwLdV081VXrnhARJQHoDGCzo3OEEPsBBAJYD2BuhYffBDCViCr9LSOi+y3dedtysnKrU0TGGHM7fvFJSHr2IwSktJGO5W3/ByZ9meI8fRYHIYeIKBjAQgCThRB5lZ0rhHhMCNFVCLGqwvETALYAGFfF8+cJIboJIbpFRIVVdmq9x8s7MG8zuLVR7SLUCm1gCCKvukXaL0rbDtIqFzLQZ9W/VD5OBSEi8oU5AH0rhPjlCl/zdQBPO/vajDHmLQJbdZa2i08egiZA+SWyYM8mm9aRt3NmdBwB+AxAmhDinSt9QSHEQQAHAIy60mvVd9wKYt7KW1tDPsFh8GtsWcbBZFSsMQSYR9Bl/vm1CiVTjzOtkb4A7gQwmIh2WX5GEtH1RJQOoDeAv4hoaeWXUXgNQKMqz2KMMS8jX3ROf9F2me+sxd9CfznL5ri3qnJlVSHEeihHucn96syLCCFOAmgn298N7o67ItwKYswzRQ4bi5zVv8FUVGD/BKMReZtXIEp2/8ibcSBgjLklr+2SCwlH9Jh7Kj0nd+OyOiqN+jgIeSBuBTHm2SIGjoZfQrLDx0vPHEXJmWN1WCL1cBBijLktb20NkdYHseMer/Sc+tIa4iDkYbgVxJh3CGrVBSHdBjp8PHfTMgijoe4KpBIOQowxppLYsQ+BdH52HzPmZqMwbUcdl6jucRDyINwKYvWRt3bJAYBvgzhEjbjN4eO5/1Zn5otn4iDEGGMqihx6E0jnb/ex/J3rYCwurOMS1S23D0JCqF0C98CtIFafeXNrSBsUgvB+V9t9TJSVomDXhjouUd1y+yCUm11prlTGGPN4kUNvAsj+x3HRkb11XJq65fZBKPNCFoxG7/0W5AxuBTHm3a0hXWwjxN7ysN3Hio/tr+PS1C23D0IGvRF/fbcCf363HKUl9Su7LGOs/ogcNhYxNz8MTUAQdLGNpeOl6cdqNEqu+ORBZK9cCEOue+ehI+HmN12ISCrgg9PHY/ykm9UsTp3jVhBj5bxt1VV7rJ/J6XOeQcHujdLx4I69EXf7FPhGxVZ5DWNBHo48ORairAQAEH//cwjrOdRlZUy7J3W7EKKbK67l9i0hOa2P9/8CMsYc8+YuOSsiAhGhwXUTQb466XjB7o04Nv0OnHzzMeTv/rfyi2h9pAAEAOfmvYLczStqq8hXxKOC0PV3jVS7CHWKW0GM1V8BiS2QOG0Ogtp2l44JQxmKj+xB+pwZKDl9xOFztQGBCGjWXnHswoL3YCzMr7Xy1pTHBKE/932DwKAAtYvBGFNZfWgNWQUktUKTJ95G4jMflC+GBwDChHOfvwlD/mWHz2308MuKfWNBLi7+Mq+WSlpzbh+EAoMCsPTw94iMDle7KHWKW0GMMavAZu2Q8uJnaDzlbWkod+mZozg18zHosy/afY5PWCSib7hXcezymkVuN9rO7YNQ46bxCAkLVrsYjDGmuuB23dHwrqcAMq8zWpZxGqfemgSjgwXyokbchoCmbRXHzrz/rFt1y7l9ECJytKir9+JWEGOVq09dchWF97saCQ+8AGjNC2PrL53DxYX2u9lI64P4+55VHDPmZSNj/lu1Xk5nuX0QYowxphTafRDiJ06T9nP/Xepw2QdddDwa3j1NcSx/xz8oPLizVsvoLA5CboZbQYw5pz63hgAgtOdQaMMiAQCirARl5884PDes7wj4NWqqOJb5x5e1Wj5ncRBijDEPREQISG4t7Rce2lXpubrYRopjxcfTIEym2iqe0zgIuRFuBTFWPfW9NSQfdJD52+coOrrP4bk+EdGKfVFWAkP2RQiDAcKg3gquHIQYY8xDRQy4Fj5hUQAAY2EeTr89BXnb1to91zcyxuZYxpezcOixkTj4wBAce3a8Kiu5chByE9wKYoxVlzY4FI0eew3a4DAAgNCX4exHLyB7+U825/pWaAkBQOGBbRBlpQCAsoxTOPPeM9BnXajdQlfAQYgx5tHqfZdccmskzZhbfs9HCFz4/n2cX/AehKn8/8Y/sUWV1xJlJcjbbr8lVVs4CLkBbgUxxq6ELiYBic98gIBm7aRjOSsXIv3D52EqNScy1cU2spm4ak9ZxqlaK6c9HIQYYx6vvreGAMAnJBxNpr6DkK6p0rGCneuRMX+mtDxEWJ8RVV6nqvWHTHrXruvGQUhl3ApijLmKRueHhAdfRORVt0rH8rasQu6GJQCAkM79FOf7REQjPHU0mkz9r3TMcLnyIGTMy3FhiQEfl16NMcaYqkijQezND8FYlI/cdX8BAC4unIewXkMhX8RUExCM5m//DACKJKj6y5mVXt9UUuTS8nJLSEXcCmLMdbhLTinutsfgE94AgDlfXP6uDYr7PbqGTaRtn9BIaduYmw1h0Du8rjY41KXl5CDEGGNeSOMXgPD+10j7l9f+gbIL6dK+X1xjaZt8fBSTWTP//FrRapKzDgd3WTldejXmNG4FMeZ63BpSCu9/jbTsQ+GBbdBnnS9/UKNVnBvWe7i0nfnHlzg79wWYLHOI5Ejr2rs4HIQYY16FA1E536hYBLXrKe3n71gnbRsLchXnNhg1HoEtO5Wfu30tTrx0L3LWLkLRkb1Sy8iQm+3SMnIQYowxLxYxcLS0XXb+tLRdMQhp/PzReMosRAy+XnH++a/+i1NvPoqzH78EYTKh+ORBl5aPg5AKuCuOsdrFraFywe17SgMU5Iz5uTbHDNkXYci/bPc6+VtXw5CXg+wl37u0fByEGGNeiQORGWl9EN5/pM1xeUtIGA3I/HsBjr9wN/K3rrZ7nbDew1F8ZDeKDu92afl4nlAd41YQY6yuRQy9CTmrf1cEHmNhHoTJiNIzx3Bu/lsoPX1E8RyNfyD8E1sguGMfhHTpD4Bw4uX7XF42DkKMMa81uLURq9K0VZ/o5XyCw9Dw7qeRPme64njB3s1I/+A5QLY0uH9iC8TdPhn+ya1BGnNnmam0BCffeASmonyXl4274+oQt4IYq3vcLWcW0qkvokbeLu2Hdh+Msx+9KAUg8tUh5qYHkDRjLgKatpUCkBAC57/+L0rPHK2VcnFLqI5wAGKMqS16zERo/AJgKi2GITdbWkvINzoejSe/pZjAapWz+jfkblxWa2XilhBjzOtxa8iMtD5oMOpOxNx4P4oO7pSONxz/H7sBqOziWVz4/v1aLRMHoTrArSDGmLuRTzot2LMRwmCwOSfzr2+k7jq/Js2hCQhyeTk4CNUyDkCMuQduDSkFdyjPpJC9/CecmvkYyjIzpGNllzKQu3GptN/g6nEwFRe6vBwchBhjrB6Kv2c6gtr1kPaLjx/AiRfvRd62NQCArL+/AYzmwB3YshOC2veqlXJwEKpF3ApizL1wa6icxj8QjSfNRMxNDwBa8zB2U3EBzs59Aee/fReX1y+Wzm0wegLKztfOst8chBhjrJ4ijQZRV49D0tNz4NsgTjqes+pXwGQO2AHN2yMguQ1OzZpSK2XgIFRLuBXEmHvi1pCtgKZtkfTsx/CNilM+4OuH6OsmAsIkDed2NQ5CtYADEGPM0/iEhKPJ1P8iqH1P+DZoiMCWnaD1D8Dpt6fgwg8fQBsWWfVFaoCDEGOs3uHWkH262EZoMvktNJv5PbTBYTBaMmpf/udPmBxk175SHIRcjFtBjDFvUHbxrGJfGG3nEbkCByEX4gDEGPMWwbLh27WpyiBERI2JaDURpRHRfiKaZDkeSUTLieiI5d8I2XNmEdE2Ikq17CcRkSCix2TnvE9Ed9VCnVTBAYgxz8JdcpVrcN1d0ASG2BzXBAa79HWcaQkZAEwVQrQG0AvAI0TUBsA0ACuFEM0BrLTsg4haWZ43AMAjsutcBDCJiHSuKry74ADEGPM2Gl8/hPcdYXNcG1DHQUgIkSGE2GHZzgeQBiABwHUAvrSc9iWAMdYyAjABEABIdqlLMAerCa4ouLvgAMSY5+LWUOWiRt1p0/LRZ5136WtUaykHIkoC0BnAZgCxQogMwByoiCjGsr2fiAIBrAfwZIVLvAlgMRF9XsXr3A/gfstudp/YUSeqU04P1gBAptqFqENcX+9Wn+pbn+oKAP6uupDTQYiIggEsBDBZCJFHRA7PFUI85uD4CSLaAmBcZa8lhJgHYJ7ldbcJIbo5W05PVp/qCnB9vV19qm99qitgrq+rruXU6Dgi8oU5AH0rhPjFcvgCETW0PN4Q5ns+zngdwNPOvjZjjDHv5czoOALwGYA0IcQ7socWofz+zgQAvzvzgkKIgwAOABhVvaIyxhjzNs50x/UFcCeAvUS0y3JsOsz3d34konsAnAYwthqv+xqAnVWeZTavGtf1dPWprgDX19vVp/rWp7oCLqwvCSFcdS3GGGOsWvi+DGOMMdVwEGKMMaYaVYIQEU2xpADaR0TfEZE/Ef1ARLssPydl958cpQEqlp2/i4jGq1EXZziobyci2mQp+zYi6iE73xvr25GINhLRXiL6g4hCZed7bH2JaJKlnvuJaLLlWHVTWnlEXQGH9R1r2TcRUbcK53tjfWcR0UEi2kNEvxJRuOx8b6zvK5a67iKiZUQULzv/yusrhKjTH5izLZwAEGDZ/xHAXRXO+S+A5y3brQDMAhAI4EfLsSQA++q67K6sL4BlAK62HBsJYI2X13crgFTLsYkAXvH0+gJoB2Cfpew+AFYAaA7gLQDTLOdMAzDT0+taRX1bA2gJYA2AbrLzvbW+wwH4WM6ZWQ/e31DZOY8D+MiV9VWrO84HQAAR+VgqcM76ABERgJsBfGc55CgNkCexV18BwNoaCEP5/4G31rclgH8sjy8HcKNl25Pr2xrAJiFEkRDCAGAtgOtR/ZRWnsJufYUQaUKIQ3bO99b6LrPsA8AmAI0s295a3zzZOUEw1w9wUX3rPAgJIc4CeBvmYd0ZAHKFEMtkp/QHcEEIccRy/n6YP8jWA5grO69phSZf/7qpQfVUUt/JAGYR0RnL489YzvfW+u4DMNpy2lgAjS3ne3J99wEYQERRZE5VNRLmeilSWgGQUlrBc+sKOK6vXfWkvhMBLAa8u75E9Jrls+p2AM8DrqtvtXLHuYKlf/w6AMkALgP4iYjuEEJ8YznlNpS3ggA4TAN0TAjRqRaL6hKO6gugB4ApQoiFRHQzzBOChwJeW9+JAN4joudhnuhcZn2Op9ZXCJFGRDNhbtkVANgNc9b5yp7jkXUFuL6oUF8immHZ/1b2HK+srxBiBoAZRPQMgEcBvGA5fsX1VaM7biiAE0KIS0IIPYBfAPQBAEv3zQ0AflChXLXFUX0nWLYB4CeYg5I3sFtfIcRBIcRwIURXmL9kHFO1lC4ihPhMCNFFCDEAQDaAI6h5Siu356C+XstRfYloAsxZX24Xlpsh3sCJ93cByrvSXUKNIHQaQC8iCrTc/xkC8/IQgPkD7KAQIl2FctUWR/U9ByDVcs5geM8fs936kiXLOhFpADwL4CMVy+gysno1gfkL1HeoYUorT+Cgvl7LXn2JaATM+S9HCyGK1Cyfqzmob3PZKaMBHHTla9Z5d5wQYjMR/QxgB8xNvZ0oTwFxK5z/pW5KsmHcAD4XQrznsoK6SCX13QlgtqX1V4LypSsc8fT6PkhE1kUOfwHwRRWX8oj6AlhIRFEA9AAeEULkEFF1U1p5Sl0B+/W9HsAcANEA/iKiXUKIqyq5hqfX930AfgCWm79nYZMQ4sFKruHp9f2UiFrCPAjhFIDK6gpUs76ctocxxphqOGMCY4wx1XAQYowxphoOQowxxlTDQYgxxphqOAgxxhhTDQchxhhjquEgxBhjTDUchBhjjKmGgxBjjDHVcBBijDGmGg5CjDHGVMNBiDHGmGo4CDHGGFMNByHGGGOq4SDEGGNMNRyEGGOMqYaDEGOMMdVwEGKMMaYaDkKMMcZUw0GIMcaYajgIMcYYUw0HIcYYY6rhIMQYY0w1HIQYY4yphoMQY4wx1XAQYowxphoOQowxxlTDQYgxxphqOAgxxhhTDQchxhhjquEgxBhjTDUchBhjjKmGgxBjjDHVcBBijDGmGg5CjDHGVMNBiDHGmGo4CDHGGFMNByHGGGOq4SDEGGNMNRyEGGOMqYaDEGOMMdVwEGKMMaYaDkKMMcZUw0GIMcaYajgIMcYYUw0HIcYYY6rhIMQYY0w1HIQYY4yphoMQY4wx1XAQYowxphoOQowxxlTDQYgxxphqOAgxxhhTDQchxhhjquEgxBhjTDUchBhjjKmGgxBjjDHVcBBijDGmGg5CjDHGVOOjdgHcBRG1BhCjdjkYY6yG0oQQF9UuRHWREELtMqiKiJqOHTviaH5+IZq2jFW7OC6RWaJXuwguc/Cyr9pFcJkz2aR2EVzGmF6odhFcwnAxW+0iuExR3iFoAxJgyNsXIoQoULs8zqq3LSEiavD443de6t27Mx5+eBwGDuyJAv05tYvlEntzvOMDAgC+PRaodhFcZlWaVu0iuEzpmgy1i+ASpTsPql0ElzEENIGx4CjINzzfN7IHDDlbfYUQBrXLVZV6d0+IiAJmzvyP6NSp9aVevTph/foFGDiwp9rFYoyxK0KkgU9IC+hiBkMYCkG+kXpd9ABBRG7dBK83LSEi0nz55Uxjx46t4OPjg02bfoSfn07tYjHGmEuRRgff8A4QhmYw5O6Fxi/WRETdhBDb1S6bPfUiCK1YMV/07NkRe/YcwqpVXyIyMlztIjHGWK0in0D4RvWEqewyDLm7t2mDkmAqOpUshDipdtnkvDoIEVGHESP6746ICMN33/0XycmN1S4SY4zVKY0uHLroVBhLzkMYCk74hLaCMf9QpBAiR+2yAV4ahIgo4e67b0gfPLgXXn75cXTv3kHtIjHGmKq0/nHQ+MXAVHQKJt/wbN+IzjBc3uUvhChVs1xeFYSIKHT69Adzu3Zti+uvH4ZRowbBze/JMcZYnSHSQBuUDE1AYxgLDoN8w0t0UX2gz96oFUKY1CiTVwWhkSNTc3fuPICNG3+Ar6/3zC9hjDFXIo0PfELbQBPYGGUXVsInopsRgCrf2L1qiPbff69t0KJFEgYMuAOrVm1UuziMMeaWhDDCkH8Y+swN8AlrD0PONtW+tXtVS0gIkQWAiKjpRx99f/Sdd+Zj5sz/oG3b5moXjTHGVCeEgKk4HYa8A9AGJEDoLwfrs7eoOrvdq4KQlRDiGADavPkn8eijL6NZs0S89NJjiI/3jrQ8jDFWXabSTOhzd0PjEwahz4nTl2VfULtMgJd1x1XUs+dYWrNmi+aaawbi2msfwvPPz0Z+vsekVGKMsStm0uehLHM9DPlpEKWXWhsKjpAQwi0CEODlQQgAhBBizJiHaceO/bqGDaPRr984fPTRdzAY3D6lEmOM1ZgwlkCfsx367C0wFaenGovSSQjhdsnyvD4IWQkh9A899CLt2XMo9OzZC+jZ82b8/vsK1Pcs4owx7yJMBhjy9qPs0hpo/BpAlGVqhRD/qF0uR+pNELISQuS/8sqHtGPH/kZ//LEaQ4ZMwJYte9QuFmOMXREhTDAUHEfZxRUA+UDoL/uXZf5Las3/cRavJ0TUfuTI1D0BAf5o1tI71rS76EXrCe3N8Z75XiczvWfitOGkd9xbNZ7PVLsILlOQuw8avxgY8w+6TUoeZ9T7IGRFRN0BjAewRO2yuMAIeEc9AK6Lu/KWunhLPQCgvRDiTbULUV0chGSIaJsQopva5bhS3lIPgOvirrylLt5SD8Bz61Lv7gkxxhhzHxyEGGOMqYaDkNI8tQvgIt5SD4Dr4q68pS7eUg/AQ+vC94QYY4yphltCjDHGVMNBiDHGmGo4CAEgos+J6CIR7VO7LFeKiMKJ6GciOkhEaUTUW+0yOcve+0BEY4loPxGZiMhjhp86+p0ioseI6JClTm+pVT5nEVFjIlpt+V3aT0STLMc97n1xVBfLYx7zvhCRPxFtIaLdlvK+ZDnuce8J4KVLOdTAfADvA/hK5XK4wmwAS4QQNxGRDkCg2gWqhvmwfR/2AbgBwMdqFOgKzEeFuhDRIADXAegghCglIk9I0WEAMFUIsYOIQgBsJ6Ll8Mz3xVFdYuFZ70spgMFCiAIi8gWwnogWwzPfEw5CACCE+IeIktQux5UiolAAAwDcBQBCiDIAZWqWqTrsvQ9CiDQAIPKslDcOfqceAvCmEKLUcs7FOi9YNQkhMgBkWLbziSgNQIIQYjngWe+Lo7oAuA8e9L4I82gya94kX8uP8NS/Fe6O8y4pAC4B+IKIdhLRp0QUpHahmKQFgP5EtJmI1lpSRXkMS1DtDGCzykW5YhXq4nHvCxFpiWgXgIsAlgshPPY94SDkXXwAdAEwVwjRGUAhgGnqFonJ+ACIANALwJMAfiQP+dpKRMEAFgKYLITIU7s8V8JOXTzufRFCGIUQnQA0AtCDiNqpXKQa4yDkXdIBpMu+Ff0Mc1Bi7iEdwC/CbAsAE4AGKpepSpb7DgsBfCuE+EXt8lwJB3XxyPcFAIQQlwGsgTkRq0fiIORFhBDnAZwhopaWQ0MAHFCxSEzpNwCDAYCIWgDQAXDrtQQsLYLPAKQJId5RuzxXopK6/AYPel+IKJqIwi3bAQCGAnC7FVOdJoSo9z8AvoP5hqUe5m9F96hdpiuoSycA2wDsgfmPK0LtMl3J+wDgest2KYALAJaqXc4rqIsOwDcwj2LaAfMIJ9XLWkU9+gEQlt+nXZafkZ74vlRSF496XwB0ALDTUo99AJ63HPe490QIwWl7GGOMqYe74xhjjKmGgxBjjDHVcBBijDGmGg5CjDHGVMNBiDHGmGo4CDHGGFMNByHm9Sw59NrU4vU7EdHI2rp+NcrxDBHdbud4gb3zGXMHHISYxyMzh7/LQoh7hRBXlDmCiCrLON8J5kmPahsOYJnahWCsOjgIMY9EREmWxck+hHmWe2MimktE2+QLfVnOXWNd5IuICojoNcuCYJuIKLaS15hPRO8Q0WoAM4moBxH9a8lQ/i8RtbSs2fQygFuIaBcR3UJEQZZF7bZazr3OzrUHWjI2/0hEh4noTSK63bJY2V4iaiorw1zLYmzHiSjVcu00Ipovu14oAJ0Q4hIRJRPRRsvrvyI7J5iIVhLRDstrXGc5/gopF3h7jYger/Gbw1h1qJ2ygX/4pyY/AJJgTjTZS3Ys0vKvFuakjh0s+2sAdLNsCwDXWrbfAvBsJa8xH8CfALSW/VAAPpbtoQAWWrbvAvC+7HmvA7jDsh0O4DCAoArXHgjgMoCGAPwAnAXwkuWxSQDelZXhewAE88JreQDaw/wFcjuATpbzbgDwsmV7EYDxlu1HABRYtn0AhFq2GwA4arluEoAdluMaAMcARKn9HvNP/fjhlhDzZKeEEJtk+zcT0Q6Y82q1BWDvPlAZzIEFMH+IJ1XxGj8JIYyW7TAAP5F5ye7/WV7DnuEAplnWe1kDwB9AEzvnbRVCZAjzYmrHUN6VtrdCuf4QQgjL8QtCiL1CCBOA/bLzRgBYbNnuC3PuOgD4WnYdAvA6Ee0BsALmBd1ihRAnAWQRUWdL2XcKIbIc1I0xl+KVVZknK7RuEFEygP8A6C6EyLF0VfnbeY7e8oEOAEZU/TdQKNt+BcBqIcT1lkXR1jh4DgG4UQhxqIprl8q2TbJ9U4Vyldo5p+J5PWBeudXKXlLI2wFEA+gqhNAT0UmU/x99CnOLLg7A51WUmzGX4ZYQ8xahMAeMXMt9nqtr4TXCYO42AyxLqFvkAwiR7S8F8Jh1YTRLC6PWEFFbAAdlLbYNAG61bMtHy4UBuGgJQIMAJMoe+xXm1lR3S/kZqxMchJhXEELshrkbbj/M3+Q31MLLvAXgDSLaAPN9J6vVANpYBybA3GLyBbDH0nX3iu2lXOpqAEtk+5MAPEJEW2EOPFbfAuhGRNtgDk7SGjRCiDKY6/GjLJgxVut4KQfGPBwRLYd5IELGFVxDA/Mow7FCiCMuKxxjVeCWEGMeTggx7AoDUBuYR8qt5ADE6hq3hFi9R0QzAIytcPgnIcRrapSHsfqEgxBjjDHVcHccY4wx1XAQYowxphoOQowxxlTDQYgxxphq/g8To/uQyzxkjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting with cartopy jjas_pr_mean\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,9.5))\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([88,93,20,27])\n",
    "\n",
    "\n",
    " \n",
    "m1 = ax.contourf(pr_mm['longitude'][:], pr_mm['latitude'][:],pr_mm,\n",
    "                 levels=np.arange(1,35+1,5),transform = ccrs.PlateCarree(),cmap='YlGnBu',extend='both')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Define the xticks for longitude\n",
    "ax.set_xticks(np.arange(87,94,1), crs=ccrs.PlateCarree())\n",
    "lon_formatter = cticker.LongitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "\n",
    "# Define the yticks for latitude\n",
    "ax.set_yticks(np.arange(20,28,1), crs=ccrs.PlateCarree())\n",
    "lat_formatter = cticker.LatitudeFormatter()\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.colorbar(m1, orientation = 'horizontal',shrink=0.5, fraction = 0.04 , label = 'rain_rate mm/day')\n",
    "ax.coastlines(linewidth=3)\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=3)\n",
    "#ax.add_feature(cfeature.STATES)\n",
    "#ax.add_feature(cfeature.LAND)\n",
    "#ax.add_feature(cfeature.LAKES)\n",
    "#ax.add_feature(cfeature.OCEAN)\n",
    "#ax.add_feature(cfeature.RIVERS)\n",
    "\n",
    "\n",
    "\n",
    "plt.title('Reanalysis(Eraint) Mean jjas precip rate in mm/day')\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/bd_daily_pr_re_eraintrin_jjas_mean.png\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will plot the annual precipitation rate in mm/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (latitude: 241, longitude: 480)&gt;\n",
       "dask.array&lt;mean_agg-aggregate, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-db3b05e6-b931-49c3-b21c-872a0aef064f' class='xr-array-in' type='checkbox' checked><label for='section-db3b05e6-b931-49c3-b21c-872a0aef064f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(241, 480), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 451.88 kiB </td>\n",
       "                        <td> 451.88 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (241, 480) </td>\n",
       "                        <td> (241, 480) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 191 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.25 0.0,60.25\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"80.250000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >480</text>\n",
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       "</svg>\n",
       "        </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-6e4417be-5404-4282-b957-a665f4a74164' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e4417be-5404-4282-b957-a665f4a74164' class='xr-section-summary' >Coordinates: <span>(2)</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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-9f594f77-efdd-41f2-89c9-baa171c235c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f594f77-efdd-41f2-89c9-baa171c235c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b047a61-2b91-4cfd-bb38-28424945c391' class='xr-var-data-in' type='checkbox'><label for='data-3b047a61-2b91-4cfd-bb38-28424945c391' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><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'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-0cf34df0-6380-4c46-bce3-415771718086' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cf34df0-6380-4c46-bce3-415771718086' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2f49887-34b2-4ecb-b043-384eaeb7a07c' class='xr-var-data-in' type='checkbox'><label for='data-c2f49887-34b2-4ecb-b043-384eaeb7a07c' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6cf8d418-f95b-41cd-866a-22efa85c2868' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6cf8d418-f95b-41cd-866a-22efa85c2868' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tp' (latitude: 241, longitude: 480)>\n",
       "dask.array<mean_agg-aggregate, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_mean = data.tp.mean('time')\n",
    "annual_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (latitude: 241, longitude: 480)&gt;\n",
       "dask.array&lt;mul, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 241</li><li><span class='xr-has-index'>longitude</span>: 480</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-204819a5-a04a-488a-adc5-2d0884d8f283' class='xr-array-in' type='checkbox' checked><label for='section-204819a5-a04a-488a-adc5-2d0884d8f283' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(241, 480), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 451.88 kiB </td>\n",
       "                        <td> 451.88 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (241, 480) </td>\n",
       "                        <td> (241, 480) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 192 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.25 0.0,60.25\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"80.250000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >480</text>\n",
       "  <text x=\"140.000000\" y=\"30.125000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.125000)\">241</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-c41b5939-6976-4bcd-bdfb-7be26e805e20' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c41b5939-6976-4bcd-bdfb-7be26e805e20' class='xr-section-summary' >Coordinates: <span>(2)</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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.25 88.5 ... -89.25 -90.0</div><input id='attrs-f8b66ef8-e617-4bb3-9a39-47fdbc2ef44d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8b66ef8-e617-4bb3-9a39-47fdbc2ef44d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a95326f-5d17-40a2-959f-abd8472b8573' class='xr-var-data-in' type='checkbox'><label for='data-3a95326f-5d17-40a2-959f-abd8472b8573' 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([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)</pre></div></li><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'>0.0 0.75 1.5 ... 357.8 358.5 359.2</div><input id='attrs-d62e9ebd-efba-477a-b42b-2276ebb5e3fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d62e9ebd-efba-477a-b42b-2276ebb5e3fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed96969d-e963-4e79-bd8d-63b631eaaf8b' class='xr-var-data-in' type='checkbox'><label for='data-ed96969d-e963-4e79-bd8d-63b631eaaf8b' 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([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75491a6c-4347-4ad6-9419-b311d94ebbc3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75491a6c-4347-4ad6-9419-b311d94ebbc3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tp' (latitude: 241, longitude: 480)>\n",
       "dask.array<mul, shape=(241, 480), dtype=float32, chunksize=(241, 480), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting from kgm-2s-2 to mm/day\n",
    "\n",
    "annual_pr_mm = annual_mean*1000\n",
    "annual_pr_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6JNj12vRsQqFS2rRBgQGBLJmxNsGuHRsyZEkX4b7Ny3bzyf1zwhkjkSQwNrbWlK9RUi2f2m8WkXHMGmO84wTahWV3FEWZFWp3bbTB/V6GPTJ8FEW5izaqcOPoGGouJJQQWVhY8MvkkF7DkV2ncH/7MUGuHRsyZ89oUJ6ybAQpU2kXbiuKwpXTN1g2619mjVjE+kXb8fjgaQozJZJ4o1zNUur2+aOXTWhJ4sCY3kgltKvXa+q5ZDfU7fuOUENxRjIJbXqFRElC9YoKFM9DsfKFAAgKDGLt/NgELEgYSlctTplqJXBJ7cy4BUOp1rAirmlSqvtH95rG4mmr2bRkJ3PGLKZ5ia7M+30pfr5+kZxVEh5mtv5YosM5ZUi8Uj+/ABNakjiIck5IF88sogCc3Yy5iC5ERGG98jUS2XBceAQLUXzOF33XuznXzt4CYNOSndRoUomiZQvG2/Vii5WVJXM2TECj0WBhof2Kazevyj/T14TbPsA/kLX/28KZQxf5c/140maMs6HmJMvJfef4d9F2bly4TQr7FLjlyETleuVo37cFtimMyrsniQHeX324c+UBzq5O5CmUI0Inm3s3HqnbBYrnSSjzEi0yYkIcUMTVId6EqGqD8pSoWJgrp28SFKRhbN8ZrDz8F056b1vmSLAAAXQZ2JbAgEC2rdzLF8+vFC6Vj2LlC3Ph2BXuXNUGinhy7zn9W49i2f4/sXOITczVpItGo+GvcUtYv2i7Whfg/5XbV+5z+8p99m46wvA/fqJ4+ViF1TN7AgODOLnvHA9vP8HS0gJ7R3vSpE9F6SrF1KHfuObRnacMaD0Kjw+fAchTOCdj/vqFXAWzG7R79fQtu9cdVMtFysigIFFh9rHjzMU7LjrEtSC9ffmerrUG8OXzVwA69mtJvzE94vQaCYWiKOobpKIobFu5h1kjFhEUqA3+3XNYR7r/Er1MDV4eX7h27jbPH72kUp2yZM/rFvVBZo6XxxcunbqO5ycvrKws0WgU9mw8rPaKI6NF1wb0HdUNR+eEc6RJKN48f8eI7ydz7/qjMPucUjowdcVoSlSIuQgrisK5o5e5dPI6Lx69Im3G1LT+vgnH95xlwcTlBm1t7WwZ8Pv3lKhYhHev3Hn5+DVL//hXFSpbO1s2nltMmvSpYmyPuVIxfeNLiqKUjotzSRFKIGIrTPu3HGNc3xkAuKROye5bq5PMmpsty/9j5rD/AWDvaMeO6yuxd7CLsL2/XwBL/1jH/i1H8f7io4ozgKWVJR36tqD7L9+Rwj7x9agCAwLZvHQ3S2au5atXxL+Zqg3KM2hiLywtLTj23xkWTl6Jz1dfdX+aDKkYMvVHqjYonxBmJwiBgUH0ajiYu9cijoOaNmNq1p9eFKPv/vHdZ8weuYhLJ6/HxkxA6yX3+8JfqdYwTC7KJIEUoSSEseKk0WhoUKCD+sDdenkZ6TOnjeKoxEFQUBAdq/bj+UOtk2XNppXpMbg9bjkzYW1jrbbz++bPrUt3WTJzLVdO34z0nJmypmfItB8pr+epZO4oisKvncdz6sCFCNtYWFjQeUAbfvi1A5aWlmr9+9cfmDnsf5wM5RJcrFxBOv3Umgq1SxsMkSZG9m46wvh+f6jlFt0a4pTSkS+fv7B1xR61/qdxPejQt6XR5/X6/JUlM9awZdlugoxYmF63VXXuXnuo/l5D4+TiyPSVoylWrpDRNiQ2pAglcSISpq61BvDg5mMAluybnaQmPTf+s5PZI8N+z9Y2Vtg72qMoikEkBn0sLS3IVzQ3iqKoc0zBlK9ZivZ9WpAytTPubz7i/uYjvj6+ZMqagay5MuOWKzNWVpbhnjehuXzqOj+1HKGWM7ilo3TlYmgUhQC/ANxyZqJm08rkzJ8t3OMVReHwjpPMGrFIHRIKJke+rHT6qRV1WlTDyjrhpoIDA4M4feACt6/cJygoCNsUNqTNkJqUqZwJCgwiMCCQwMAghBBkz+tG7oI5sLG1DnMeP18/Otf4iZdP3gDQY0h7fhgakgpp87Ld/DF8AQD2DnZMXjaCe9cfcfrAed6+csfRyYESFYtQpmpx3HJm4vMnT96/+sDNi3f5b/0hfLxDepGWlhY07lAXR2d7g4XXwey99y+WVhasW7CVfZuO4u/nT+bsGcmULQNZc2aifpuapMuUtB1spAglQ254eDOo3WjOH70CwIzVY6lUxyyzNMSIwIBARvSYHOZNPjJ6DG5Pqx6NsXdIga2dLRqNhl1rDzB/wjKDIbrIcEntTPVGFanbqjrFyhUy6RDnyB+mcGTnKUDrUTj6r58NeoLG4uXxhQWTVrD734MEBgQa7EufOS3f9W5O4451cHC0j+AMMSMoKAhf72/4fPXl8ycvTuw9y47V+3B/Y/z6Nhtba2q3qEbPXzuqPf0Htx7zx/CFXD9/GwBHZwc2nlts4ITg7xdAu4q9ePfSPVafoVTlogya2ItcBbKjKAqty/zAmxfv1P2Wlhbse7A+0uHi5IAUoWTKT31msmKpNuX8+Mm9+HlI+wQPJxSfBAYEsmfDYc4cusj187fx/ORlMDwihMAtV2aKlStIs071KFgyX7jn+eT+mfnjl7J345ForaWp3bwqQ6f/aBLPQ/c3H2hZqof6eVcfmx9hj8dY3r/+wL+LtrFj1T6DN/1gXFI7kyZ9KtJkSK37X7udOp0LQUEafL764v3FB5+vvvh4++LzxQfvr9r/g+vU/V998fWJu9yENrbWFCyZD89PXjy599xg3/A/+tO0U70wxxzYeoyxfWbE6Ho58mXl+6EdqNG4ksGLyPE9ZxnebaJarte6BmPny8wrUoSSKcuW7GJAX+2YeOOmlVm3aUK47ZKKMCmKgv83f7y/+oKi4JzKOVpDZy+fvGbt/7Zw9shl7OxTkCZDatJmTI1tChtePX3Dw1tPwoQRypk/GysOzzWYb4lPFEXh8d1nTBo0h7u6ocQSFQszf+vUOLuG1+evbFm2mw2Ld/D5Y8JHqHBN40K91tVxSZ0Sn6++uL/5wFcvH6ysLbGyssLaxopvvn7cvfaQ18/eRngeCwsL+o7qSocfW0bYYz20/QRr/7cF76++5MyXlSr1y1GkTAFeP3/HxePXuHr2Jp8/euLs4kS6zGlInzktFWqVpkzV4hGe88rpG1w4fhXHlA607tEk3OHC5IYUoWTKzeuPqFD6BwBy5MzE9bvhLwCNiKQiTnGFRqPh+vnbbFuxh/1bjqn1a08sMNrN2++bP998vkW4PuWbzzeePnjJJ3cPPD544vHhs/r/J/fPPLr9lI/vPQyOia+hVj9fP3avP8Tmpbt49uAlGk3cR4e3d7DD3skOe0d7MmVNT8N2tajWsILRw4qXTl5nwcTl3L5yX62zsraiaoPydBvUjtyFcsS5zZLoI0UomfL+3SdyubUCIFVqZ5692R7FEVEjhUm79qRVme/VcpMOdXn19A0BAYFkzJqe+m1qULZaiTDeZcf+O834frPw9/OnZtMqVKhdmux53Hj/+gM3Ltzm6tlb3Lv+KMy8TERYWlkyaGIvWnVvFKefLzyCgoLw+ODJh7ef+PD2Ix/efdJuv/vEp/ceWFlbYe9opxMVe+wd7XBw1G076G3r1dvZp4gzD7wn957z9uV77B3tyFM4Z7KfgzE3pAglU+7eeUaZYt2AmPWE4oKkKFqh00+ER0a39DRoV5PcBXNgbW2FRqMwrGv4w6HRxSml1nOrY79WcoW9JFEQlyIkw/YkIi5eCEk5nzuPaeK/Rjdwa2IQLVu7qJMmvnnxjqUzYxKrF7LmzkL6zGlxTZNS98+FVGldcE3jQvpMaciRP2uCzUFJJOaGFKFExPq1B9TtSlWKmdAS40kMotW5fxs8P3mxZ8NhAgMCKVgyH/VaVSdT1gycPnSBPRsOR7hGCbRRHjr2a8XVMzfx+PCZlKmcyVM4J8XLF6Zo2QK4pE4Z4bESSXJHDsclEp49fUuRfB1QFAULCwtuP/yXzFmSRsSEmJCQYuX3zZ/je85w7dxt3r18j6KAomi4d/0ReQrnZNjMnyJN5ieRJDXkcFwyZPqUVeqal1p1SidrAYL4jVweGtsUNtRpUY06LaolyPUkkuRE4g4mlUy4fvUhq5brxcYa2CZax69fe4A+P0zj2JGkleUxIdOtSySS+EGKkJmjKAq//fo/tRdUt0E5atY2vhc8Y8pqfug2mTUr99K0wVAuXbwbX6aaBClEEkniJlGIkPt7D/z9k2ea3N07T3FcFy/O0tKCSVP7Gn3shfO3mfj7MrWs0Wg4G0X06cRIQqVbl0gkcY/Zi9D9e8/JmaUlubO25vbNJ6Y2J0EJCgpi1PAQp4yefZqRv4Bx8cQURWH4kP+FWRXv4x138b3MDSlEEkniw+xFyFv30PT45MWSxTtMbE3CcurEdR7pcpa4uDgyfFRXo4+9e/sp58PJwpnUe5RSiCSSxIXZi5A+jyJIIpVU2bzxiLrdrkMdUkdjvcmObSfCrZ86aSXfd5nI+rUH+PbNP9Y2miNSiCSSxEOiEiH3959NbUKCERgYxPatx9Vyi9bVo3W8vghNmtYXB4eQdMcb/j3ED90mU6xAR65fjThVcmJGCpFEkjgwexHKk9dNDbF+6+Zjvn4NmxclKXLy+FU+ftCG3c+YKQ0VKhY2+tj7955z/ZpWXGxsrOn2fSM27ZhKFjfDBZWvX32gcf3BHIoknXRiRjosSCTmj9mLkKOjHQV14duDgjTs2n7SxBYlDPf1EnlVrVbc6OjEWoeE+WrZ3z8Az89fqVylGLcerOP42YWMGttd7Rl5fPKiTfMRbFp/OG4/gBkhhUgiMV/MXoQAatYJWRez978zJrQk5vj4fOP5s7dG53BJqZfd82M0EpFdvXKfA/sMU2QXytOe2tV+YvDAOVy6cJfqtUqxc98fZMqcBoCAgEC6d55As4ZD2bDuIPv2nOXu7adGXzMxIIVIIjFPEkXYnpJ6aZzPnL5BYGBQtDJsJiT37z3n+tWH5M2XlSLFcvHtmz8jhy1g+ZLdBAQEUqp0frbuno6rq1Ok53n2NCTDZK7cxkfMvnH9UZg6RVE4d+YW586EeMvlzJWJ7t835t+1B1WHj8MHL3L44EW1TcvW1VmycpTZ3uvokpChfiQSiXEkip5Qs5ZVSZPWBdDOY2xYdzBOzx8QEIi3d+znmpb+s5NSRbrSvfMEKpXtSdni3SlWoBOLF24nQJfY7NLFu2RN35Rz4bhPB6MoCgf15mmKlchjtA0ZM6Yxqt3jR6+ZNH45XXs0pNv3jcJNbbxl01EWRpFnxxj8/QNYvXIvfXtOY+a0NXFyr2OK7BFJJOZFoomiPW3SSnX1f/YcGTl5/m+DIavo8OTxa9au2sfRI5e5fvUhPj7atUjpM6SiaLHc9OjZhEZNKkWYc15RFPbvPceWTUf54uVDwyYVad+xDiUKdeHJ49dG2ZArd2Yu31wZ7lzPhnUH+b7rJAAsLCy4dmc12XNkNOq8AQGBlCvRgwf3XwCQMqUDx88u4uGDl9y6+Zib1x/x367TqoOHs7MDV++s5ouXN+tW7+fKlfs8vP9S7R1ZW1vR68fmVKtektp1y2BtHb3Os7v7Z9q2GMHF8yG5kIqXyMOCxcMoXDRXtM4V18hekUQSM5JlZlVPz68UztOez5+/AlC9ZklmzOpPShdH3r79yPNn7zh5/CqXLt6jWvUSjBzbPcwwkre3L+NGLeafRTsIDAyK6rqMHNudOvXKGoiR+3sP+vf9g907Txm0t7KyNDinpaUFQUHa+R8bG2tmzP6J1KlT0um7cWqb5atH06ptTYPzfPrkRemi3XB/7wFAvwGtmTqzn5F3S8uzp2+ZO3s9L56/Z+DgdlSqXNRg/8ePnlSv2JenT94AMGXGjwZBUf38/Clf8gcePnhhcFyVasXZvX9WhOIcmsDAIJrUH8zJ49fC3T9yTLdoLcCND6QQSSTRJ1mKEMCm9Yfp3tm4lMo/DWrDlOk/qmWNRkO7lqPCdWywsLDA0tJCHTLTp2z5QvT4oTGFi+Tiv12nWbRgq+o6HRHdf2jM6N+/Z/+es/gHBFKzVmmyZc8AwKCfZrPkb23kB0tLC77v1ZRhIzqTLn0qPn70pEWjX7ly+T4AmTKn4eL1FTg52Rv1maPDsiW7GND3DwCKFsvNqQuLDfY/eviKzt+NDTPH9ObTfzg62hl1jRXLdvNT75kACCEoU64gly/eNRDrtt/VokXr6lSvWcro88Y1UogkkuiRbEUIYMqEFUyesDzK42xtrTl2eiGFiuQEtOkMfug2Wd1fsXJRevVpRpVqxUmbzhWNRsPjR69Z+s9O/lm43ahoAt91qIOFpQXrVu9Xo1zny5+N3ftnkT5DqnCPcX/vQdUKfXj54r1aZ2dnS/NW1bh6+T539LzS1m2aQOOmlaO0IzpoNBoCA4Pw9fUjt1sr9XOeufhPmOGxoKAgRo/4m79mb1DruvZoxLyFQ4y6Vo1Kfbl4QRu1e+jwTowZ/z23bz5h4E+zwgRSTZfelW27plOkWO7YfLwYI4VIIjGeZC1CAPv3nmPF0t0cOXQJG1trMmZMTYaMqclfMDvz/tyotrO2tqJp8yp06lqfgf1m8fzZOwB69W3OzD8HRDis9Ob1B/6YvpZl/+wKN9ZaFrd0zFs4hFp1ygDw8MFLzp25iWsqZ2rXLYONjXWkn+nN6w/80G2yGh07NEII5i4YTLcejSI9T3S5fvUh7duM4oO7J81aVmXd6v3qvtBDcsF8++ZPWud6BnW79v1BtRolo7xe3uxtePP6AwDT/uhH359aIYTg2zd/fug6ySAiBECZcgU5fGJ+eKdKEKQQSSTGkexFKDIunL9NvRoDwx1aA3B1deLG/bVGOTW8fPGef9ce4OTxq9y5/ZR8+bPRrUcjGjerHKXQRIWiKOzbc5Yhg+YauGNbWlqweNkI2nxXK1bnD83DBy9p3ey3COPv/TykPeMn9wp33+NHryhWoJNBXf+f2zJ5WuRpJdq3Hs2uHSGLi3PkzET7jnXp9WNzUqVy5tTJ6yxbvJMN/x5S2/T9qSVt29emdJkCxn60OEeKkUQSOVKEouDc2VuMGr4w3Nw5k6b1ZcDPbePKvFjj/t6DWTPX4evjR6ZMaWjSrDIFdBEi4oLPn78ybfJKFs3fGqEwA8xbNISu3SPuec2YspqJvy8zWGx7/OxCSuit4QrN0ydvaFzvFwORBa1H3vY9MyhdpgDu7z3InbW1wXmFEGzcNpl6Dcob8xHjBSlEEknESBEykpvXH7FsyS527TjJZ4+vDPylHcNHdTE6BE5i5+KFO3RoM0YdEgum7Xe1KF4yL07ODgQGBJI6dUqat6oWpdfbpYt3qV4xpPezduMEmjSLfM7q9St3xo5czO6dp/jyxUetb9m6OlNn9iN9hlT8MW0t48cuMTiuboNybN4+1diPGi9IIZJIwkeKkCRK7tx6Qs0q/QwCvpavWJipM/tRqnT+aJ3r5Yv3/PnHv6xavkddUwXgljU9qVI506BRBdq2r02evG4RnsPPz59/1x5QveWCadSkEj/0bkqLxsMM6us1LM+mbVOiZWd8kJyFKHhhb3K+B5LwiUsRShRheyTRQ6PR0L3zRFWAXFM5M3vuQFq2qWH0Gp9gHj54Sc3KP+Lh8SXMvhfP3/Hi+TuuXX3AzGlr+Gf5CFq1rYmn51cm/b6M9esOYmVlSYlS+Rg5phtdujVk/pxNBh6Au3eeIlcew7BEefK6MXpsj+h/8HggOT2II4omEVF9crgnkvhH9oSSIHduPaFsCe1D3MrKkpPn/lZd1aPLpN+XMXXSSqPapk6Tkhv31tKs4VAunLttsM/ePgWr/h2Hc0oH6lTrH+l5jp5eEO3eWkKQFB+6cR3GKCneI0lY4rInlDwmR5IZ7u6f1e3SZQvEWIAAGjetHGYRqb19Clq0qsbKtWNZvnq0Wv/xgycNag0KI0CgjSLeruVI7t55xuNXWyO9Zt8fpuHnZ35ZX5NK3LngPEvx8Xn0zy3zOUmMQYpQEkS/dxvgH7FHnDEUK5GHs5eX0rFLfb7rUIdV68bx5PVWVq4bR4vW1WnVtia/Twpx7b529YG63aFzPY6fXUjWbOkBbRif/n1mUjBXu3CvZW+vzXF05/ZTVi3fEyu7o4uHxxdG/7aIMsW6USjPd3TvNIH37z6FaZfYH6qmsF+KkSQy5HBcEuLJ49fs3nmKBfM2qwtzq1Qrzn8HZsfrdb988aF8yR7qNUEbL+/m/bVkzJSGN68/0KLxMG7dfBzu8SlTOvDPipHcuf2UMSP+BrQRLfYdnhOvdutTs0q/MD24Lt0bMn/RUIO6pDTcFF/CkJTukSR8pGOCBNB6nO3ZdYazZ25y9PDlcB/y3b9vHO92ODnZs3v/bEYOW8Cpk9fJmjU9w0Z2IWMmbVqJjJnSsPfwHKZPWcW2zcd48fwdKVLYUKpMfipXKcYPvZuRIWNq8uXPpopQ6OCp8cmL5+/CHUIMnRwwqT1cQ3+emIhSUrsnkoRHilAi5d3bTzSpP9jA00wfe/sUjBjTjdbtaoa7P67JniMjazaMj3C/i4sjk6f1ZdLUPrx/54FrKqcwUSeyZkuPtbUVAQGBvH/nwdevvvEe1NTfP4DVK/aGuy+qSOtJDX1BCS1IUmwk8YUUoUSGoijs/e8sv/7yl5qKIRhbW2uq1ypFoyaVaNy0Mml1iQDNCSFEhMFdLS0tyZotgxpa6MmjV/Ea0DQwMIiWTYZx7Ej4Mfzc33vQse0YUqV2planBuQqmD3ebDE3pOhIEgopQokIb29fenSeyH+7Tqt1lpYW9O3fiipVi1O1egmTpUOIK/Llz6qK0NzZG5i3aAi2tjbxcq3lS3ZFKEDB7Nh2AoC1q/ezYMd0ChQ3PsutRCKJGukdl4gYN2qxgQClTOnAirVjmTL9Rxo2rpjoBQigfce66va/aw+QL0dbfvv1f9y98yzOrxU6MSFoI6y3alMjTL2/XwDLZv0b5zZIJMkdKUKJBG9vX1YuC3Fb7tC5Hldur6ZZi6omtCruadqiCo2aVFLLHz94Mu/PjZQp1o2GdX7m6pX7cXatqtVLhKkTQrB8zRhOnV/M/L+H0nNYSPTw88eu4P3VJ8wxEokk5kgRSiTs3nFKjduWv0A2Fv4zzCznfGKLhYUFK9aOYcz478nils5g34ljV6lV5SdOngg/XXh0yZYtQ5i6DBlTA1C0eG66dGtI91++I7suJp7/N3/+ChVoVSKRxA4pQomEf9ceULdr1S0T7RhwiQlbWxuGDu/Ezftr2bJzGs1aVMXKyhLQerP91HsmcbG+7drVh2HqHj4IybcUPDnfZWBI6o8dq/dx/XxYd26JRBIzpAglEvQzvB7Ye94g/05SxdLSkjr1yrJ6/e+cvrAYOztbAB49fEn/vn/w+NGrWJ2/QqXCYerWrd6Pl5e3gXdYvVbVKaKXZO/W5Xuxuq5EIglBilAioUu3hur2/XvPDcLjJAcKFMphkG12xdLdFC/YmXGjFse4V1S/YQUmhcoOmyFjqjAOHkIIMuoNDdo7JH4HEInEXIhShIQQbkKII0KIO0KIW0KIgXr7+gsh7unqp+vVzxBCXBRCVNOVswshFCFEf70284QQ3eL48yRZQifis7NLYSJLTMf4yb0oWSokk6uiKPwxfS3jxyxRhSgoKIhjRy6zecNhPn/+Gun5tm46yvgx/xjUjZvQk1uevmHaOuqlgz+y6xRBQclrIatEEl8Ys04oEBisKMplIYQTcEkIcQBIDzQDiiqK4ieESAcghAiOwV8VWA4c05XfAwOFEIsURTG/EMlmzNLFOxjYLyT+W6HCOcmXP6sJLTINqVOn5PDJ+RzYd57ZM//l9MnrAMyctobjx67SsnV1dm4/wakT2voCBbOzct04Pn/+wscPnnz65EWqVM7UqlMGjUbDj72m4+enHeZ0dLTjrwVDaN2uZrgLNWs0rsSWZbsBOH/0Cv+bsJwGbWriljMTtrphQolEEn2iFCFFUd4Ab3TbX4QQd4DMQE9gqqIofrp973WHWAIaQAH0Z8/dgVNAV2BxXH2ApI77ew9G//a3Ws6eIyNLV45K0o4JkWFpaUn9hhWoWbs07VuPZv/ecwCcP3uL82dvGbS9c/spZYp1C3OOosVy039QWzXpX+YsadmxZyZ582WNMFJAqcpF6divJWvmbwFg3YKtrFuwlTQZUvG/7dPIkj1jHH5KiST5EK05ISFEdqAEcA7IC1QRQpwTQhwTQpQBUBTlFmAPnAQWhDrFVGCwEMIyiuv00g3nXfzwwTM6JiY5pkxcgZeX9sGYJ68bpy4spmDhHCa2yvTY2FizduN4fh7SXvWcM5br1x7Ss/tktdygUYVIBSiYvqO6Uaaa4dqiD28/8Vu3SXJ4TiKJIUaLkBDCEdgMDFIUxQttL8oVKA8MBTYI3eu5oij9FUUppSjKYf1zKIryBDgPdIjsWoqi/K0oSmlFUUqnSZMyWh8oKeHn58+GdQfV8pQZP+LsLPOyBGNra8P4yb24cW8to8Z2p1GTSnTsUp/jZxcyZvz3uLg4qkFS6zUsT6UqRcM9T+rUxv3GLCws+HV6PywtDf9sHt15yjcfv9h9GIkkmWJU7DghhDVaAVqjKMoWXfVLYIuinRE+L4TQAGnQDrtFxmRgE3A8ZiYnH44evoynp/btPHuOjNStX87EFpknWdzSMWxkF4O6EiXzMXR4J4M6RVFo3ew3dQgvmFp1yxgdsDNz9gw4uzrj8eGzQb2Dk330DZdIJEZ5xwlgCXBHUZRZeru2ATV1bfICNsCHqM6nKMpd4DYQ/4luEjmbN4R0JJu2qJps54HiCiEE/6wYSYNGFQFwcEjB1Jn9cCxgfPpzRVHCdQmXC1glkphhzHBcJaAzUFMIcVX3ryGwFMgphLgJ/At0VYxfsDEJyBIji5MJe3afYd2akCgJzVsmrRhxpsLV1YkNWyfx3nMvL97vpN+A1tE6XgjB9JWjSRMqHcUzvUgLEonEeIzxjjuJoZebPp0iqA99jqdAYb3yNeRC2UjZuumIup05S1pKlc4fSWtJdAmOvhCTvDmFS+fn790zaVmqh1oXHF9OIpFEDykEZoqj3hxDq7Y1wyxWlZiWuXqLXLPlyULBknlNaI1EkniRTzYzpVDhkHmKN6+i8vWQxISYZg89sfccR3eH5HX6dcZPWFpGz01cIpFokSJkphQrEZLBc/fO0/j5ySATcUlMBcjH25dZIxaq5cbt61CiQthAqBKJxDikCJkp+nNAPj7fuHI57pK5SWLO8T1neafrmbqkdqbfmO4mtkgiSdxIETJThBAGQtT3h+lqOgdFUbh7+ykL52+he6cJDB44h9s3n5jK1ERHTHtBoF2YGkzj9nVJmco5DiySSJIvRi1WlZiGidP60KLRr3z75s/DBy9Yu3o/To52TJu8iju3nxq0/XvBNpq3rMb/Fv+Kk1w4GW8EBgSq2za21ia0RCJJGkgRMmMqVynGz0PaM2XiCgD695kZafttW47x6ZMXm7ZPUV2QJYbEphcEkFfPYeT+jUexNUciSfbI4Tgzp2uPRuH2bBwcUtC0eRWm/dGPJs2qqPXHj16hc/txBplYJVpiK0AA+YuFOIzcvRY2PbhEIokeUoTMnMxZ0rJl1zSyZkuPpaUF2XNkZPjILty4v441G8bzY//WrN04nt8n9VKP2fffWXp2mywjO8cDWXNnxs5em1Dww7tPuL+JMlKVRCKJBDkclwgoX6Ewtx78i6IoEcaP+2Voe75+8WHG1NUAbNl0FD+/AGb/NYiMmdIkpLlmSVz0gkCbzyhfsdxcPXMTgDtXH5I2o7y/EklMkT2hRERUAUxH/96DPv1aqOXdO09RqkhX/vfXJgIDk2+vKK4EKJgCxUOG5I79dzqSlhKJJCqkCCUhhBBM++Mnev8YIkRfvvgwbPB8qlXowwUZ6TlOqNmksrp9YOtx3N9+NKE1EkniRopQEsPCwoKZfw5g9/5Z5M2XVa2/fu0hdasPYPvW5JXGKa57QQCFSuWjaNmCgNZle/OSXXF+DYkkuSBFKIlStXoJzlz6h7ETflDdtQMDg+jWcTwH9p03sXWJn+/6NFe3920+Gm6OIYlEEjVShJIwNjbWDBnWkfNXl5E7jzbVQGBgEONGLTaxZQlDfPSCgqlcrxz2jnYAvHvlzqunb+LtWhJJUkaKUDIge46MbN01TS0/epj0E7DFpwABWFlZGgQuvXr2VrxeTyJJqkgRSiZky54BS0vt1+3t/U1G5Y4DHPQXEcvhOIkkRkgRSiYIIXDVC7YZOvZcUiK+e0HBvHr2Vt3OnD1jglxTIklqSBFKRri5pVO3a1T6kSGD5vLyxXsTWhT3JJQAAbx+9k7dzpQtQ4JdVyJJSkgRSkYM/a2zmiY8MDCIRf/bSvVKfXnzWoaeiS4+3r54fPgMgLWNFWkypDKtQRJJIkWKUDKiSbPKbN01jSJFc6l1795+4oduk/Hw+GJCy+KGhOwFeX4KuV+uaVxkem+JJIZIEUpm1KxdmlMXFjN5el+17vjRK1Qq05MXz99FcqQhGo2GUyev8+zp26gbJ0ECA0KilMu8QhJJzJEBTJMhQgh+GtiG58/esnD+VgBePH9Hm+YjGPBzWxwc7bCzs6VwkZxkypw2zPGfPnkxqN8stm4+BkDZ8oUYPrILdeqVTdDPoU9C9oIAAvxDkttZ20gRkkhiihShZIoQghmzB1CiZD56fz8VgFs3H6vboA0B1KJVNcZO+IEcOTPx9asvA/rOZOP6wwbnOn/2Fi2bDGP+30Pp0q1hgn4OSHgBAgjQy7BqZS3/jCSSmCL/epI5HTrX497d58yasTbMPo1Gw+aNR9i14yRdujfk8sV7XLp4N8Jz9es1Ay9Pb34a2CY+TTYLPn/0VLflcJxEEnOkCEkYN/EHatQqyeGDl3j54h2+vv68fuXO5Uv3APDzC2Dxwu0Gx1hbW5EhY2o6dKrLnt1nuK7LMjri1wVUrFyUkqXyJZj9RVwdErw3dPbwJXU7f7HcCXptiSQpIUVIghCC6jVLUb1mKYP68+duM3TQXFWMghnz+/cM/a2TWu7bvxWN6/7CzRuPURSF7VuOJ6gIQcIL0ekDF9TtSnVNNxcmkSR2pAhJIqRsuYIcOfU/9v53llMnr/PN149GTSpRs3Zpg3apU6ekWs2S3LzxGIAvXxJ+jiYhefH4FS8evwYghZ0tJSoUMbFFEkniRYqQJFIsLCxo2LgiDRtXjLCNr68fG9YdUsulSudPCNPCkFC9odMHLqrbZaqVwDaFTbxfUyJJqsh1QpJYs3/PWdzfe6jl1u1qmsyWIq4O8X6Nu9cfqttlqxWP9+tJJEkZKUKSWBN6ncwhvZ5CUuTFo1fqdva8WSNpKZFIokKKkCTW1G9YnnIVCqnlXTtOmtCa+O8NfdTr9aXPnCZeryWRJHWkCElijYWFBYMGf6eWVy3fw/y5m/jyxSdJpr1OqZcSwysJxNyTSEyJFCFJnNCwcUXq1i+nlocPmU+m1I0oU6w7F87fTnB74rM3lC5janX7uc5LTiKRxAzpHSeJEywsLFixdiwNaw/iyuX7av29u8+oWbkfdeuXw8bGGm9vXwICAmnRqhq9+raIV5viy1suf/E8nNx/HoArp29Qr1V1bl26x/NHr/D75oelpSXlapQkfThx9yQSiSFShCRxhqOjHdv3zGTMiEUc3H/BIGHe/r3nDNqePH6NjJnS0qRZ5YQ2M9aUqBiyLmjnmv2c2HuWzx+9DNoIIShbvQRNOtSlcr1yMrSPRBIBcjhOEqe4ujrx14Ih3Hm0nks3VpArd+YI2yaEA0N8DMsVKVOALDlC0nmHFiAARVE4d+Qyo3pOpVnxrvw56m8e3noS57ZIJIkdKUKSeCNvvqxcvL6Cwyfns3z1aCpVKWqwP3WalCayLHZYWVkydFo/NUstgFNKB2o1rUKzzvUpXaWYQXvPT15sWLyDLjX706Pez2xZ/h9fPL8mtNkSiVkizN17qWSpfMrxs4tMbYYkFvj7B9Cu5UgO7g+Jt+bs7MDpi/+QLXuGBLEhPuaGXjx+xc2Ld3FK6RgmcsKb5+/Yvf4gu/89yLuX7mGOtUlhQ/VGFWnSoS4lKhY2EDSJxNypmL7xJUVRSkfdMmqkCEninbmzNzBy2AK1XKhwThYv+40iCRx92hR5hzQaDRdPXGPX2gMc33MGf7+AMG0yZU1Po/a1qVS3HDnzZZX5iSRmjxQhSazw8/Pn0YNXPHnymgwZU1OseB6srCwN2rx985Fnz95iY2OFk5MD2XNkDNNGn6CgIHbtOMXXLz7Ua1iBNHpDba2aDWf/Hq1jQotW1fhnxUhsTJCN1BQipI+Xxxf2bznKrnUHuX/jUbhtbGytyV0wB/mK5aZAsdzkK5abHHmTljBpNBo8PmjzMdnYWmNja8PTBy/YtmIPL5+8pnDpArT5vjGp0rma2FJJREgRksSY1Sv38kv/P/H19VPr0qZzpXXbGjRqUoky5QoyZcIK5sxab7DQ1NbWmmIl8tKsRVVatq5OFrd06r6goCBaNhnO4YMX1bY1apWmaYsqaDQafuo9U20753+/0OOHJgnwScPH1EIUzL0bj9i99gD7thzly+fI54dsUtiQp2AO8hfPTdGyBalSrxwp7FMkkKUxQ1EUPD548vLJa54/esXLx695/lj7/8snb/im9/sLD5sUNjTpUIemneqRu2AOhBAJZHlYgkXz3cv3eHl+JX2mtGTKmh5bO1vePH/HmcOXuH/jEZZWljg62ZO3SC6KlClAukxJN5qGFCFJjHj/7hNF83fE2/tbrM9VrkIhSpbKR7r0qbhy6R47tp0w6rjt/80IkwoiITEXEQrG75s/J/ae5ciuU9y9+pA3L95FeYyDkz11W1ajcYe65C+W2+ABHRQUxPOHr7h34xH3rj/k8Z1nIASuqZ1xSZ0Sl9QpcXS25+Htp9y4cIeP7z1InzktNRpXol7rGmTIkpb7Nx+zdOY6vDy8KFQqPx36toiyV+LvF8DVszc5tvs0d6495MWjV3h/8Yn1/QFInc6VstVLUK56ScpUK4GrEQ4tn957cPf6Q1xSp6RA8TwRiphGo2HTkl0c33OWbz7ftPcoTUo0QUG8e+XOu1cfcH/zgQD/wDDHOjjZR/oZC5XMR7PO9Wj4Xe0kN+cnRUgSIzatP0z3zhPUcpVqxbl/7znv3n4Kt33mLGlJk8aF9+89ePP6Q5zY4PntkMn/IM1NiPT5/NGTezcecffqQ+5df8idaw/CdWwIJnfB7OQulAMLS0ueP3zJw1tPouxlRIa9gx3ffP3QaDRqnVNKB7oMbEv+YnkIDAzkzfP3vH3xjrcv3/PmxXvevHjHhwh+QxHhlNIBaxtr/L754/fNHzuHFJSuUoyiZQqyf8tR7lx9EO5xQgjyFc1F2eolKVejJEVK51eHKhVF4c3zd+zbfITls9erwlGmWglmrBqjrtUK8A9g5vAF3Llyn4e3n8bgLkWPJh3r8tusAfF+nYREipAkRmzecJhunbQi1LR5FdZsGE9QUBDHj15l+9bjHD96hQf3X2Bvn4IevZowYXJvdR7I3f0ze3afZsvGIxw9fJmgIE2Y86dN58r5q8vw8vzKzu0n2bHtBOfP3lL3V65ajP8OzDbp0Eow5ixEofH44Mm96w+5eeku+zcf5eWTN6Y2ySjsHezIkjMTbsH/cmXGLYd2Wz/+XmgUReHi8WvsXLufC8ev4vkp7DqsYOzsU5DBLR2p0rrw8skb3r0KX7BrNatC14FtcXCyZ/W8TWxdsSdan8XJxZEMmdPilNKRt6/cefviPRqNBhtba0pWKkrpqsWwtbXB/e0nbl26y7VztwkMCOk9Zc6ekWy5s5AqrQs1m1WmfI1SkVzN/JEiJIkRe/87Q5vmIwCoW78cm3dMDdPm40dPnJ0dsI5kItzd/TPHDl/m9esPvH/3iffvPpHSxZGefZqTN59haoN3bz/x9OkbFEWhVOn8kZ43IUlMIqSPoihcPXuLXWv3c3jnKfzC6fWkyZCKfEVyka9obvIUzomNrTWfP3ri8dETjw+eeHl8IUOWdBQvXwi3XJm5dOIau9Yd4P6NR3zx1N6XzNkz0qRjXbav3GvUEKGFhQUZs6anUp0yVKpTlhz5s5I6nWusXziCgoK4d/0R545c5vzRy9y8eDfcF6DYkjl7RkbMHsAXT28+f/RECEG6zGlInzkt6TOlxc7BcA4uwD+AD28/4ZomZbjzc56fvJjyy1yO7zkb7vWGTv+RFl0bxvnnSCikCElixMnjV2lQ+2cAKlYuyr7Dc0xskWlJrEIUzFcvby6dvI73Fx/8/QJIlyk1+YrmJnUMvcoURcHD/TM+3r5kypYBCwsLvvl8Y++mI1w6eR33Nx+xsLQgo1s6MrqlJ4Pu/4xu6UiXKU2CePBpP/M1zh25zLkjV8IIpKOzA3kK56Bi7TK0/r4JE/vP5tCOiOcrm3Ssi6OzA51+am3UXFN08PH25fcf/+DE3rBCZGlpwaBJvWnZraFZjAxEFylCkhixfetxOrUbC0DdBuXYvD1sTyi5kdiFKDmjKAqfP3rh/vYjn9574OziSN6iuQ2WEiiKwp4Nhzm84yQvn7zmm68fKV2dyFc0N90HtyejnpdnfOHxwZMLx67w/PEr9m06yqunIcOpuQpkp3zNkrjlzExGt/Rkzp6BTNkSZgF3bIhLETKPsRFJgvDs6Vt1O3v2jJG0lEjMHyEErmlSRtqDEULQsF0tGrarlYCWGeKaJiV1W1UHoEXXhgzuME5dJ/bozlMe3Xlq0L5kxSJM/Gc4LqkTZ1ir6JK0/AYlkfJM7w0smxQhIP6zsEok+qRO58qindPp2K8ltna24ba5fPoGo3pOM8jgm5SRPaFkxM0bj9XtbNnSm9ASiST5YmtnS78xPeg6qB2XTl7n/o1HvHnxjhePXnPr8j0ALp+6TvMS3ajVrAq9hnVKFEN0MSXKnpAQwk0IcUQIcUcIcUsIMVBXP04I8UoIcVX3r6HeMTOEEBeFENV05exCCEUI0V+vzTwhRLd4+EyScLh39zmnT14HtJ5MZcoVNLFF5oPsDUlMgaOzA9UaVqDnsE6MmTeYxXv+4PuhHdT9QYFB7N98lA5V+nLh2FXTGRrPGDMcFwgMVhSlAFAe6CeECH6CzVYUpbju338AQoj8un1VgX5653kPDBRC2CBJUD5//kqndmPUct365cgks34aIIVIYg70GNye8Yt+pVj5Qmqdv18Af4xYiLk7kcWUKEVIUZQ3iqJc1m1/Ae4AEWcqA0tAAyiAvu+hO3AI6BpjayUxYvRvC7l75xkANjbWDBvZ2cQWSSSS8BBCULt5VRZsn8awmT+p9c8fvqRb7YHcu/7QhNbFD9FyTBBCZAdKAMG5mn8SQlwXQiwVQrgCKIpyC7AHTgILQp1iKjBYCBFxOGbtdXrphvMuftBF25XEnGNHrqjbc//3C6XLFDChNeaL7A1JzInGHeoYrPl6cPMx3esMYte6AwQFBZnQsrjFaBESQjgCm4FBiqJ4oRWYXEBx4A3wR3BbRVH6K4pSSlGUw/rnUBTlCXAe6EAkKIryt6IopRVFKZ0mkWbfjC88Pb+yYd1Bpk5cwdJ/duIehQfNs6dvef4sxDW7Wctq8W2iRCKJAywtLZm5ZqxBKnmAyYPm0L32QBNZFfcYJUJCCGu0ArRGUZQtAIqivFMUJUhRFA2wGChr5DUnA8OMvbZEy8ePnvw++h/y52jL910nMWn8cgb+OIuShbtEGME6KCiIXwb8qYY5qVi5KI6OdglpdqJD9oYk5kS+ornZcHYx/9s+VQ3ACvDw9lP6NhvGN5/YR8Q3NcZ4xwlgCXBHUZRZevX68twCuGnMBRVFuQvcBhpHz9TkiaIozJqxjsJ52jNz2hq+fvU12P/581e6dRzP2TNhb//oEX+zf++5kPK47vFub1JACpHE3ChevjCrjs4zqLt29hZ//LbQRBbFHcb0RioBnYGaodyxpwshbgghrgM1gJ+jcd1JQJbom5v8uHTxLmNH/m0gPnnzZaVnn2ZkyqxNmhUQEMjgAXMMvGfWrNzLX7M3qOWfh7SnctXiCWa3RCKJnKtnb/J7vz/oWmsA43/6g8M7TuIbSa4vt5yZmb9tKiUrFlHr9mw4jPubuEmzYipk7Dgz5/rVh1Qq21Mt9+rbnBmz+2NhYcGzp28pU6ybmiV14C/tGPBzW4KCNJQu2g0vL21ctMZNK7Nmw+8mz+OT2JBx5STxxT8z1rB05row9S6pnfl+SAeada4faUDYng0GqwtbJy35jRqNK8WbreERl7Hj5FPJzClaPDftO9VVy1+++Khiki17BipXLabumzNrPbncWpE3extVgHLncWPx8hFSgGKAHJaTxAeBAYFsXLwj3H2fP3rxx28L6VStH8f3nA2zNsjH25cTe8/x4PYTtU7RmHdHIirkkykR0KdfS3V7+5bjfPvmr5ar1SgZ6bF/zhsknREkEjPC+4uPmrcJYPrK0fQY3J70WUIWkD9/9Irh3SYyuMM4Xjx+xcZ/djKwzSjq5f2OYV0n4K97BjildKB0Ih9ml7HjEgHFS+RRt318vvHg3nOKFMsNQL8BrcmeIyOXL97j7JmbXDx/B3//AKysLBn9+/dRipQkcoq4OshhOUmc4uzqRNqMqXF/8xEAYWHBD792pPOANmxaspMVf27gq24k4+zhS7Sr0Dvc8wghGDq9H84ujglme3wgRcjMURSFfr1nGNSl0Iu+a2VlSbMWVWnWoioA37758+qlO+nSu+LkZJ+gtkokkqgRQlCnRTXW/m8LANtW/EelOmWwTWFDx36taPRdbX6o/wuvn4ef0TZXgewUK1+Iui2rUbRs4o8BKUXIjHn44CWli3Y1SGfcp18L8uR1i/CYFClsyJU7sqhKkugie0OSuKZpp3qqCF09e8tgn0vqlKw48hfTh8zn8M6TWFgISlcpRuV65ahUpyzpMqUxhcnxhhQhM6Zf7xkGAtSydXWmzuwXyRGS+EIKkSQuccuZSd32/uKDRqMxcB5ycLTn94VDGf3XzyCEQbbYpIYUITPlyuV7auoFALes6Zn55wAsLZPuj1EiSS7oJ6yzd7CL0Hs1MjftpIL0jjNDxo5cTNXyfdSyhYUFl26sIK1eMENJwiNdtiVxxSG9UFt5iuQ0oSWmR4qQmREYGMRff24wqBs74QfsIkgFLJFIEheKorBjzT61XL91DRNaY3qkCJkZb998JCAgUC0vXjaCn4d8Z0KLJPrI3pAktty+fJ8n954DYGefgto6z9bkihQhM+PMqRvqdomSefmuYx20MWQl5oIUIkls0O8F1WpWBQfH5L2UQoqQGeHn58/YUYvVctHieSJpLTElUogkMcH7qw8Htx5Xy0061o2kdfJAipAZsXnDEV7oFqi5pnLmt1EyE7o5I4VIEl0Obz+Jry4HUPa8bhQund/EFpkeKUJmxLGjIWm4O3drQGa9WFIS80QKkSQ66A/FNelYVw61I0XIrLh7+6m6PXfWeiaMXRomiq7E/JBCJDGGF49fceuSNv2ClbUV9VvXNLFF5oEUITMia7YMBuXpU1axZeMRE1kjiQ5SiCRRcfHENXW7XPUSuKZJaUJrzAcpQmbEgn+GMWJ0N1z0ouL+PGAOt248NqFVEmORQiSJjKtnQmLElapcLJKWyQspQmaEo6Mdv43uytU7q1Uh8vjkxZCf55rYMomxSCGSRMRDvUR0RcsWMKEl5oUUITMkdeqULF01Wi3fu/vchNZIoosUIkloNBqNQWoGt1wy0n0wUoTMlGLFc5vaBEkskEIk0efMoUv4+foB2lQNTikTdyK6uESKUCLg4wdP6SWXCJFCJAkMDGLz0l2M6TVNravWsIIJLTI/pAiZKXb2KdTw7hqNhrmzN0RxhMQckUKUfPH75s+QjuP447eF6gLVlKmc6TGkvYktMy+kCJkpTk729OrbTC2PGr6QDm3GcOTQJdkrSmQUcXWQYpQM2br8P87rLUDPkiMjczdNJG2G1Ca0yvyQImTGTJ7+I8X04sft3H6Cpg2GUL1iX3ZuP4lGo4nkaIm5IYUoeXHm8CV1u0Hbmqw6Mo88hZJ37qDwkCJkxlhbWzF5et8wuYQuX7pHhzajKV/ye9avPUBgYJCJLJREFylEyQdfb191u9F3tbGVOcHCRYqQmVO1egnuPd3I1l3T6NW3OSlS2Kj77tx+yg/dJlOycBeW/rMTPz9/E1oqMRY5PJc8cE3jom57enwxnSFmjhShRICrqxO165bljzkDufVgHYMGf4ejo526/8nj1wz8cRZF83dk/txNeOu9gUnMFylESRv9sDyf3D+bzhAzR4pQIiNd+lRMmNKbWw//ZeSYbrimclb3vX71geFD5lMoT3tmTFnN589fTWipxBikECVdnF2c1G0v2ROKEClCiZRUqZwZPqortx6sY+LUPqTPkErd9/GDJ+PHLqFQ7u/4ffQ/uMu3MLNGClHS5O3L9+q2DFYaMVKEEjlOTvYM/KUdN++vY/Zfg8iaLb26z8vLm5nT1lAo93cMGzyPVy/dTWipJDLkPFHiwuvzV479d4azhy9x5+oD3jx/h4+3LxqNBs9PXmxYvIND20+o7bPlcTOhteaNMPc1JyVL5VOOn11kajMSDQEBgWxYd5A/pq/lwf0XBvusra0YOrwTQ3/rhJWVpYkslETFDQ9vU5sgiYTAwCA6VunLi8evjWpfsmIR5m6epC4+TwpUTN/4kqIopePiXEnnrkgArdB07FKfC9eWsWrdOIoWC4lBFxAQyOQJy2lU9xdevngfyVnC4u3tKx0eEgjZIzJPNBoN187dYtrgv4wWoMzZMzJh8fAkJUBxjbwzSRRLS0uat6rGyfN/s3nHVEqXCcllf/rkdSqW/oEd205EcoYQ1q89QMZUjciYqhFVy/dm985TMmpDPCOH58wLRVEY1HY0fZsOY/e/B8PsT5cpDTa21gDYO9qRu2B2Bvz+A8sO/Cnng6JADsclE4KCgvhj2lomT1hOUFBIpIVadUpTukwBHBztcHBIgb2DHdmyZaB8xcJYW1vh6+tHwdzf8SGUc0P1miVZumo0adO6JOwHSYbI4TnT8/DWE7rU7B/h/r131+Hk4khQkCZZDHXH5XCcVVycRGL+WFpa8uuIzlSpXpzvu0zihS63yaEDFzl04GKY9i4ujtStX44L5++EESCAo4cv06HNaHbvn4WNjXV8m5+sKeLqIIXIxLi//Rjp/o/vPXB2dUoWAhTXyOG4ZEaFikU4dWExLVpVi7Td589f2fDvIZ7ojX2PGtudXn2bI4QA4Ozpm/w+Zkm82ivRIofnTEvugtnVbWsbK4ZO/5GsubOo+2SSupgje0LJEFdXJ1auG8etG485f/42r1+64+3zDR/vb3zx8ub0qRthHBdq1y3D0N86YWFhgVvW9Iz+TTtEumr5HiZN7WOKj5Eskb0i05A2Yxqy5srM80evCPAPJEe+rKw7uQAP9884pnSUPaBYIEUoGVOoSE4KFQkb1VdRFK5ffcj+vefw8vImU+Y0dO7WUPXwGfhLO6ZMWIGPzzc8Pnnx6ZMXqfQiN0jiFylEpiF1eleeP3oFQKB/IEIIUqVzNbFViR8pQpIwCCEoViIPxUrkiXB/jpyZuHXzMQA3rz+iavUSCWlisid4aE6KUexQFIX/TVjGoR0nyeSWnnzFcpOvaG5y5c9GtjxZsLLWPiI1Gg0Pbz9Vj0ubKY2JLE56SBGSxIjCRXKqInTu7C0pQiZC9oqMQ1EUlv6xjnvXH1GjSSUatKkJwIEtx1gzfwsAb1+85/LpG+ox6bOkZcne2aRK68KDW0/4oovFmDqdK1nlHFCcIR0TJNFGURT27zuvlt3fe5jQGol0WIiaI7tOsWTGWk7uO8eEn2Zx8fhVAM4fuxLhMe9eurN/81EALp+6rtaXqFhEdc6RxB4pQhKjCQgIZMWy3ZQu2g2PT15qfau2NU1olQSk91xUnD10yaC8buFWwDCwaPosaek6qJ1Buyf3ngNw+WSICJWsVDS+zEyWSBGSGIW/fwAtGv/KT71ncl/3hwnQq29zypUvZELLJPpIIQqfx3efGZTPHLrE47vPKFGxiFrn7OJE79868/vCoWrd7n8Psm3lHi6fChmmK1W5CJK4Q84JSYxi9cq9HDsSMnRhYWFBy9bVGf379ya0ShIecp7IEI1Go/Zo9BnUdjSZsmVQyz5ftbERK9ctR6as6Xn9/B0ajYbpQ+erbTK6pSdLjkzxb7QZE9cvOrInJDGKs3pvgo2bVubu4/UsWz0aFxdHE1oliQg5PBfC2xfv8fX5ppaD53M+vPvE9fO31fpSlbXDbHYOKZi/bSo58mUNc67mXRsk2/mg+PpNSRGSGEXa9CFJ8/LkdSOjdFFNFCRFIQoKCsLzkxeBAYFGtT+x95y6XbpKMUbOGYS9o51Bm1wFsvPjqG5qOX3mtCzYPo2G7Wrh5OKIg5M9rb9vQocfW8TJZ0hsxOfvSA7HSYwig37m1o+eJrREEl2SwvCcRqPhz5F/c3D7cTw/fUFRFGxsrcldMAf5iuUmf9Hc5Cmck9TpXLB3ssfD/TNvX7pz9exNVs/brJ6nZtPKNGxXi/I1S3H6wHnc334kR75sVKxdRo2CHYyzqxOj5v6sRoxPjj2ghHiJkSIkMYoTx66p2wUL5TChJZKYkNgXtx7fc5ZNS3cZ1Pn7BXD7yn1uX7lv1DkyuKWjbqvqAKRK60LjDnWNOk6KT/wih+MkRvHo4Ut1+4tX4nyQSRLv8Ny/C7cZlEMPp0VFpqzpmbJsJPYO0TsuuWGKuUTZE5IYRclS+VTX7Enjl/P581d+n9QTW1sbE1smiS6JcXju4a0n6vbmC0vImDU9Hh88uXfjIfeuPeLe9Yc8e/gSL48vfP3ig0tqZ9JmSE3W3FkoV70EVRtUwDaF/K1GhqleUKJMaieEcANWAhkADfC3oihz9PYPAWYAaRVF+aCrmwHUAAYrinJMCJEdeAIMUBTlL12becBFRVGWR3Z9mdTOPHj9yp2aVfrx6qW7WlekaC5Onv9bpi5OxCQGMVIUhSqZmqHRaJMxHn+1XUatjkNiIj5ONjXiLKmdMU+PQLRiUgAoD/QTQhQEVaDqAKoTvhAiOI90VaCf3nneAwOFEPJ1JBGSKXNaNm2bYlB34/ojHtx/GcERksRAYhie+/jeQxWgFHa2UoDiCHNx449yOE5RlDfAG932FyHEHSAzcBuYDfwKbNc7xBJtj0kB9Gf03IFTQFdgcVwYL0lYChfNhVvW9GpW1jRpXciZK3kv3EsKmOPw3IvHr9i/5Rgf333i7vVHan3BEnlNaFXSwRzEJ5hozQnphtVKAOeEEE2BV4qiXNP3HlEU5ZYQwh44CQwNdYqpwB4hxNJYWS0xCY8evlIFCGDhP8OwtpbTikkBc/Ke+3fhNuaPX0pQkCbMvhKVZMic2GBO4hOM0U8QIYQjsBkYhHaIbiQQro+joij9I6h/IoQ4D3SI4lq9gF4AblnTG2uiJJ5J6eKIjY01/v4BALjKRHaSOGbX2v3MHftPuPtS2NlSt2XkaeklEWOOAgRGumgLIazRCtAaRVG2ALmAHMA1IcRTIAtwWQiRIeKzqEwGhkV2bUVR/lYUpbSiKKXT6EW5lZiWNGlS0rZ9LbV8aP8FE1ojiQ9M+aB698qd2SP/Vsv5i+Wmx5D2VKhVilpNqzBn40Tccso8PjHBXAUIjOgJCe1Y2xLgjqIoswAURbkBpNNr8xQoHewdFxmKotwVQtwGGgPno2ovMS/Kli/E6hV7AXisS3UskcQFc0YvVmO8Zc/rxvytU7FzSGFiqxI/5ixAYFxPqBLQGagphLiq+9cwltedhLb3JElEKIrChXMhAR+trKWXUlLEFA+t0wcvcHT3abU8bOZPUoDiAHMXIDDOO+4khl5u4bXJHsX+p0BhvfI1ZLSGRIVGo2HErwtYtXyPWlexskzuJYk9iqIwd+wStdzou9oUKydzVMWWxCBAICMmSCIhMDAIIcDLy4eff5rN5o1H1H116pWlfUfjYm9JJJHx7OFLnuvCQtk72NFvTHcTW5T4SSwCBFKEJKFQFIVVK/YwefxyXr10RwhB6KgazVpU5Z8VI+WiwSRMQq4d8vP1U7dtUljj6Jx4HqDmSGISIJBDYpJQrFqxh369ZqjheUILUM8+zVixdgwpZBwuSRyRPW9WXFJr3f0/f/Ti0PYTJrYo8ZLYBAikCElCsXzJ7jB1Qgjy5c/GoiXD+WPOQCwtZQ8oOZBQDzTbFDa07NZILZ/cL51mY0JiFCCQw3GSUFhYhPigrFgzhibNqxAUpJE9H0m84prWRd1Ojvl7YktiFSCQPSGJHr6+frx981Et29haY21tJQUoGZNQD7djeu7Z+YvnTpBrJhUSswCBFCGJHhPGLeXZ07cAODs7UKVacdMaJDEL4vsh9+6VO5dOXge0vaA6zavG6/WSCuYSBTu2SBGSAPD1qy9//2+rWp4wtTcpUzqa0CJJcmHH6n2qA0zpqsVJmzGNiS0yf5KC+AQj54QkALi/98DPL0Att/2utgmtkZgbce2yffXsTQ5sOcajO8+4fj4kCkfDtjXj7BpJlaQkQCBFSKIjW/YMBrmCTh6/Sv2GFUxslcSciCshOnf0Mj+3GxOmPleB7NRqViXW549PAvwDOHv4Mp8/elKuRknSZYq81/b5oycn95/n+H9nePfKnR5DOlBN7+9KUZRoOWIkNQECKUISHRYWFqRL56qKkPRQksQXy2f9G6auVOWijPhzEFZmnp9q7th/2LxUu4zBJbUzC3ZMJ1vu8MNg7tlwiGlD5+P/zV+tG9tnOkv3/0mqtC4smLSCQ9tOkL94Hnr/1pkiZQpEeu2kKEAAIvRiRHOjZKl8yvGzi0xtRpLHx+cbWdI2ISAgEICnb7aROrVMoyEJS2x7Q/Xzt8fL4wsAJSoWZui0fmTP6xYXpsUrX728aVCgA0GBQWpdjnxZWXZgDja21gZtj/13hhE9JodZ7A2Qr2gunF2cuHD8qlpnaWnB8oNzyVUwe7jXNjcBcrKpcUlRlNJxcS7pmCAB4PLFu6oA5c2XVQqQJEJi+0DMmiskJ9CV0zcZ23eGQegec+XV0zcGAgTw5N5zpvwyF40mJAvss4cvmdB/lipA6TOn5fuhHbDRLXW4d/2RgQABBAVpuKY3N6aPuQlQXCNFSALAmdM31e0KMoWyJB75rk9zg/KDm485tOOkaYyJBj7e38Kt37fpCDOH/Q9FUfD+6sOIHpPx+eoLQKZsGVh24E++H9KBirUi7zj4evuGqUvqAgRyTkii4+yZEBEqX7FwJC0lktg5KbiG08t+/extbE2Kd1LY2arbeYvkokDxPGxfpU3wuG3lXh7ceoLHB0/1s9iksGHy0hG46D5v6arFDXImhaZgibwG5eQgQCB7QhIdD+69ULd//fkvXr54b0JrJEmZfZuPGpQtrSypnwhcs+0d7dRtjw+fGTr9R+q3qaHW3bp0z0BMf53ej7yFc6rl2s2q4JrGJfxzO9hRuHR+tZxcBAikCEl05ModMk7/5YsPM6etMaE1kvjk0ycvdu88xaoVe7h25UGMzxPTB+W7V+4G5YmLh5Mle8YY25FQZM6eUe0Nub/5iPubj4z4cxB1W1YzaGdja82IPwfSsF0tg3pnVyemLBuBs6sTrmlc+GVyb3VfqnQuWNtonRuSkwCBHI6T6Jg1dxBF83dUy6uW76FQ4Rw0aFSRLG7pTGiZJK64eOEOc2etZ/fO0/j7hyxM7vZ9I/5aMCRG54zJsFyGLCG/p8bt6xismzFnrKwsKVgyH5dPaUMM3bx4l1rNqjD2f0No9F0drp2/RYbMaSlXsxRpM6QO9xxFyxZk183VoCh4f/Fh1git5++n958JCgqieBrnBPs85oLsCUkAyJEzE/1/bquW/f0D+GXAHEoW7sLRw5dMaJkkuiiKQo/OE8iWsRm/DPgTLy9vXr54T+2qP7F18zEDAQJt+o43rz8kmH36C1J3/3uQe9cfJti1Y4v+Wp7gSA9CCMpUK84PQzvSuEPdCAUoGCsrS6ysrXB2dSKVLnq4j7cvX+88iTe7zRkpQhKVPj+2IG06V4M6X18/2rceLeeIEhGvX31g4/rDfProxeKF28mcpjGF87YnKEgT6XFbNh5h/doDPLj/ItJ2oYnu8FHpKsWoVKcMoBXM2SP/Dnc9jTlStGyICN24eDdW5xJCUL1xJbW8ddPRWJ0vsSJFSKKSNVsGTpxdxOBfO9C6bU1cXLQBTL9+9eW3of8zsXUSY8mYKeybeEQCNHFqH5683kb1Sn3p2nE8P3SbTKkiXVmxLGxyw8iIrhD1//0HNTrC9fO3OZtIetuFSoU4Dzy48QjfCNy2jaVmkxAR2rblGEFBQZG0TppIEZIYkDlLWsZN7Mmy1aP5d8sktX7blmNsWn/YhJZJjMXCwoJ9R+ZG2a5VmxoM/KUdz5+95fWrkOE4RVH4ffSSeH0gZs2VmRZdG6jlnWv2x9u14hJnF0dy5MsKaIX97rWYO3YAdGpQlnTptaMP7995cPrkjVjbmNiQIiSJkEqVi9K+U121/EO3SUwevxw/P/9IjpKYAxUrFeH2w3/p068FVlbhp2MPCAjk4P7zTBy3NMw+9/cevHv7KVrXjG5vqFnn+ur2pVPXE00vIF/RXOr2s4evYnyeIq4OWFpa0lzPu27LpiOxsi0xIkVIEimTp/9IHl1cr6AgDVMmrqBUka4cOnCBEcMWkDtrK/r1nsGXLz4mtlQSGres6ZkxewAePgf5+HU/h0/ON9i/Y9sJWjQexoF958M93sIi+o+H6AhRjnxZSa2bg/zy+SvH95yN9vVMTeiYccaif59atK6ubm/feoLAwMQhxnGFFCFJpKRJk5L/DsymTLmCat2zp29p3uhX/pq9gXdvP7Fy2X8M6jfLhFYmbeLioWRjY03atK5RN9TDzt426kaxQAhBDb2J+TG9p7Ng4nKDOGzmRmBgEA9uhnixpYjBPQot1BUqFiZ9hlSAtgd6+dK92BmZyJAiJImSDBlTc+DoXP6YMyDCNrdvJU/30vjm4P7zZE3flBKFurB756lYncvGxvhlgR271I9xZt3o9Ia6/fKd6qYcFBjEqr82sXX5fzG6bkKwftE2Ht15Cmh7QUXLFoz8AD0iSsdtaWlJhYoh8RqfPn4dazsTE1KEJEZhaWlJr74tWLNhfLj7M8sFrfHC4oXb+fLFh4cPXvBdq1E0qT+Ydav34+UV/bhtwSvyQ1O1egmqVCtO5arFaNaiKjv3zmTB4l9jZbexQpQqrQsLd043iJu2+9+Dsbp2fKAoCjvX7GfhpBVqXY8hHUiTPpVRx0d1P7JkDfn7ea7L6ZVckBETJNGiSbPKjPn9ew4euMDpk9fV+sZNK5vQqqSLvX0Kg/LRw5c5evgyFhYW5MqdmcJFclKocE4KFclJ4SK5yJotfYRzOfvDmXOZ//dQunRrGC+2G0uWHJmY9e946uf7DoCHt58S4B8QoWgmJB4fPNmz4RA7Vu/j+aMQJ4R8RXPRoW8Lo85hjCBnzZpe3b57+2m07UzMyKR2khhx9PAlmtTXhnpxdnbg/rONODjYRXGUJLo8e/qWUkW64OcXEHVjwMnJnoKFc1CocE6tQBXJRaHCObC2tqJIvg68f+dh0P7EuUUUDxW9OS6JTkifNuV68urpGwDmbpxI6arF48mqqAnwD+B/E5azZfluAvwDDfZlz+vG3E2TjOoFGdsjvHzpHtUq9AG0DiHz/x5Kpy71ozjKdMikdhKTs+Tvnep2+051pQDFE9myZ+DO4w307NPMqPZfvvhw7swtli7eyS8D5lCvxgCypG1CepcGYQQIwNoqfgdDojM/VEEv386xPWfiwxyjCAwIZEin8az/e7uBADk42dOxX0uW7J0dpwIEUKJkXjWPl0ajYeCPs3iRTIblpAhJos3bNx/ZpZeE7PueTUxoTdInbVoXZs0dxMXrK2jQqGKE7WxiMHxlGcEaIlNQvVFIINND20/gbSK3/3m/L+XCsStqOX/xPIz4cyA7rq2k35ge2DmkiOToiB0QIkMIwZr1v5MjZyZAG7vx1InrURyVNJAiJIk2K5buVt2GK1YuSoFCOUxsUfIgX/6sbNg6id37Z1G8RJ4w+0MHJjWGD+5he0dxjbEP5KLlCpE+c1oAPn/0on3lPuxadyA+TQvDoe0n2LB4h1ruMqANS/bOonH7OlGKD8QuDUPadK4ULZZbLW/ccChG32liQ4qQJFoEBASyfGlIXLEfejU1oTXJk6rVS3DszEL+XvobmbOkNfo4CwsLKlQqwncd66h1hw8mTMw2Yx7OVlaWfD+kvVr+8PYTU3/5K0z+ofjC6/NXNbUCQPVGFek9ogtCCKOOj4s8QPpZjffvOcfc2RtifU5zR4qQJFr8MW2NGlE7TVoXmraoEsURkvjAwsKC9p3qcvnmSgb80s6oYzQaDWdO3eDa1ZB4ZzOmriZv9jbUqvoTHz96xpe5RtOofR1sUtioZY1Gg69P7IKEGsvCScvx+PAZgLQZUzNyzqAEFSCA3j+2oEatUmr5xLGrcXJec0aKkMRorl15wLTJq9Tyz0PaY2trE8kRkvjG3j4Fk6b2oXU00mPfufXUwI37zesPnD97i8Z1f4kPE1WMeVALIbDUs61SnTJkz+MWn2YB8ODWY7at3KuWf5ncGwcne6OOjctMqNbWVrRtX1stp0qV9JPcSRGSGM2YkX+rc0HlKxam34BWJrZIEsyUGT+SPYfxKbI1Gg2WloZ//s4xjJAQHYx5YGfQW/icr2juSFrGHfoCVLF2Gao2iDrba0wcEIxBP5Crr69fnJ/f3JAiJDGKN68/cPjgRbU8f9FQLC3Nx7MquZMhY2rOXFrCgn+G0bNPM1xTOUV5zLgJPQ3KufNkiS/zokWnn0Jebm5cuBPv1/Pz9ePAlmNquWO/llEOw8WH+ARTvkJICJ/9e8+ZxTBpfCJFSGIUnp4hiw7TpHUhry6nisR8cHS0o1OX+syaO4h7Tzby18Ih1KlXlpy5MpEiRdhhUz9/f/5aOEQt79x2IkHevKN6gBcoHrJ49sWTkDhqT++/oH+rEbSr0Isfmw/nQhzNl5w5fImvujBImbNnpHiFwpG2j08BAsibzw1nZ+01AgICef7sbbxez9RIEZIYRcZMqdW8NB/cPzNi2AIe6eVSCQoK4uSJa6xdtY/Tp27InEMmxs7Olm49GrFl5zSu3VnDe8+9PH+3g74/tVTbTJ24kvR6iy49PL5EO4dQfJApWwZ1zurdS3f8vvnj6/2NIZ1+59LJ67x4/JqrZ24yqN1oNv6zM4qzRc1nvZ5GkTL5I+0FxbcAARw/ekWNDWhvn4I8eZP2C5+MHScxipQpHenUtT7Ll2jds/+avYG/Zm8gb76s1KlXljOnbhiEoHd2dqB+w/I0bV6F2vXKyogKJkYIgaurE5On/8iBfRd4+OAFgYFBLPrfFoN24aUGjw+KuDpEGNLHxtaa9JnT8ubFOxRF4fWzt9y/8YjXoXoEiqIwe+Qi3r9258fR3Y32ZAtNRr24bW90np8R2RzfKIrC+LEhSQbbdaiNo2PS/tuRIiQxmvGTe3Pz+iMuXrir1t2/95z7956Haevl5c2Gfw+x4d9DpEhhQ8PGFcnslo6Uzg6071SXrNkyJKTpEh3+/gE8fPBCLR86EDLPV6ZcwQTzdowqplyWnJl480IbtubF49fc0nvBKVGxMD5ffbl3/REAa+ZvIUuOTAaZWqND1pyZ1e07V+7z6b0HqdKFzb10w8M73oXo/LnbnD97C9BGwBg6vFO8Xs8ckMNxEqNxdXVi7+E5zFs0hAaNKmJnZ5jQy9LSgrr1y4Xx0vr2zZ8tm47y1+wNTPx9GTWr9OPrV9+ENF2i47PHlwj3JdQDz5igpm668DUAL5+8xsvzq1pu3KEu01aMNmg/bci8GNuTwS0d+YtrI1D4+wWwccmuCNtGJyBrTDh5/Jq63ea7Wrjp9dKSKlKEJNHC1taGrt0bsWHrJJ693c7GbZMZ8Es7Bg3+juNnFrF5x1Su313D6QuLGT6yCwXDCenz7u2nZJe4y1zImClNuPXFS+ShfsPy8X59Yx/ibjlCROjF49eGQ22KQrpMaWgfKpXCmvmbY2STEIJO/UI88o5HETw1PoXIS88ByFy8FeMbKUKSGGNnZ0v9hhWYNLUPE6b0pmhx7ZoOIQRFiuVm5NjunLuy1MADC8A1lTO588b/AkRJWIQQFCqcM0x9ydKRT8jHBdF5eGfJaShC+oFWfX20Hnz9xnSnUKl8av2mpRH3YKKiQq3SqjPE0/svogyeGl9C5B8QEivO2jp5zJZIEZLEKwf3n2fIwDkGdbPmDAzXZViSMKRJmzJMnUM8T35H96Edejgua66QeZt71x4C2tBFs/8NyfT77qV7jOPM2TmkIFfB7IDWOeDOlftRHhMfQuSgl8Twg/vnOD+/OSJFSBJv7NtzlnYtR6kJ2VKmdODfzRNp3c74EDOSuMfVNWwoGAf7qCNEx5SYPKwzZU2vRnR4//oDOfTclG9cDFnA6ujsQOkqxdTyvk1HYmxnYb1e1Y2LdyNpGUJcC5F+L/XWzcdxem5zRYqQJF74b9dpOrQZo4aid8uanmNnFtGoSSUTW5Y8CQoKYuzIxdSrOZBtetEBgomvxccxfUhbWVuRR++B/PmjpypKT++/wOtziKNCg3a11O2j/8U8GV7h0vnV7WP/ncHYrNNxKUSFioR85ps3pAhJJDHi3NlbdGo3VhWg7Dkysufgn+TKnTmKIyXxQVBQEL17TGXWjLWcPhl+orQixeI+RltsH85V6oc4ShzdfYrcek4uty+HuGxXrltWnTO6e/VBjIfkKtUpq0bwvn/jETeN7A1B3AlRrtyZ1aHqN68/8OFD0g7ZA1KEJHGMoigMHzyPgABtWuScuTLx34HZZMsu1wUlBEFBQSxdvIMWjX+lQe1BNKk/mEJ52rN+3cEIj7GzsyVP3rj1xIqLh3J1vSyyZw5dUtcFATy681TddkrpSMlKRdXyib1nY3Q9B2d7suUOuQ8n952L0Xlig6WlpYFH6e1kMCQnRUgSp1y8cEddzGpra8223TOSxVoHc+F/f21mYL/ZHNx/gZPHr3H08GVevYy8Z5A5S7o4DUYbV72CHPmy0qRD3XD3OTgZLhqt1jAk6vWxGAzJ3b5yn1alv+eB3kPfLppRPuLqcxsOyT2KpGXSQIqQJE558eydul27blly6Hk5SeKfNSv3RdnGNZUTO/bOVMtPHr/idRxlL43rifpfpvShQPGwqcwzuBlmlK1ct6y6fenkdYN4cMawZMYa3r/+oJbTZEhF047hC2BkxMXnL5zM5oWiFCEhhJsQ4ogQ4o4Q4pYQYqCufoIQ4roQ4qoQYr8QIpPeMTOEEBeFENV05exCCEUI0V+vzTwhRLd4+EwSE/LVOyQSgotr1OkEJHGLjU34a0ssLS0oUCg7AB6fvtCm2W/k0a3VCgrSMH3K6lhfOz5clm1T2DBn40Q6929N3iK5EEKQv1huSlYsatAuXaY0pM0YEveuX4vfjHYsuH/zMWcOhaQ5T2Fny/ytU8IN3WMMsb0PhYvkUrdvSRECIBAYrChKAaA80E8IURCYoShKUUVRigO7gDEAQohgF5OqQD+987wHBgoh5AKRJIzHp5CwME5OSTvwojlStnzBcOutrCy5c+upWvbzC+DB/ZAYckv+3sHaVVH3oiIiPqMIODo70HdUN5YfnMOxl9tYsm82NrbWYdrpD909ufccz09eUZ777rWHDOkwTi3nL56HA4824JYzdk40sbkfBXTrlQAePngZKzsSA1GKkKIobxRFuazb/gLcATIriqL/DTsAwa8dloBGV9Zfgu0OHAK6xoHdEjNFP/Fd7gRIyywxpH7DsBlBLSws1LVakTGw3yyu6HmdGUt8x1PTx8rKMsLIDt8P7WBQdn/70aCsKAqBgUFoNBoe333G5qW7+KnFb3x4p01fYWefgnH/GxJn82MxvS+2egu5je3NJWaiFRdCCJEdKAGc05UnAV0AT6AGgKIot4QQ9sBJYGioU0wF9gghlhIJQoheQC9ATmonIjQaDSf0Eo01blrZdMYkU2rWLk36DKnUvEAODinw9v4GaF3lV64bS958WWnXciTHjlwxOPbbN386th3LsTMLSZvWxajrJaQARYUQgpKVinL5lNYNvX+rkZSvUZJmnevz7OFL5v2+FL9v/jg6O4TpJTmldGDq8lEGkRnigphE3g4K0qjboVOwJ0WM/oRCCEdgMzAouBekKMpIRVHcgDXAT8FtFUXpryhKKUVRDuufQ1GUJ8B5wPCVJRSKovytKEppRVFKp0kTNsSIxDzx9w9UXbMBnJztTWhN8sTCwoLJ0/qq5WABAujaoxF3bz+jZZPhvHj+LrzDefH8Hd07jScwMCjKa5mTAAXTqX8rtafk5fGF/VuO0a/Fb0wfOh+fr74EBQaFEaC0GVOzcOcMSlQsEt4pY01075O+CFlIEdIihLBGK0BrFEXZEk6TtUCrcOrDYzIwzNhrSxIPKVLYGKwH6t55AkFBUT/MJHFLm+9qUaNWqTD1v4/+h149pnD65HUeP4o4ivmxI1cYO2pxpNcwRwECKF+jFL8vHIpL6rChifRxcnGkcr1y9BvTnTXH/0eOeE5XH537pWhCRCgoMCjJD8kZ4x0ngCXAHUVRZunV6/tNNgWMWl6sKMpd4DbQOHqmShIDo8f1ULf37znHvDmbTGhN8kQIwV8LhpAqigdxaMqUK6Buz521nutXH4bbzlwFKJjazauy49pK/twwwaDe0tKCGavHsurIPHbfXM30laPp2K8Vjs7xnzEVjL9vqVI746yzydPTm2dP30ZxROLGmN5IJaAzUFPnjn1VCNEQmCqEuCmEuA7UBQZG47qTgOSRLCOZ0a5DHQYN/k4tT5u0Ek+9hGSShCFb9gyMGtvdoK5w0VzUbVCOXn2bhytQt248MehBbd18NEwbcxWg968/sGHxDs4dvQxoY8+VrVaCLgPaAJA5e0amrhhNpTplyFUwO1YmSpNgzP2ztLQ0eCFYMC9meZISC8Z4x51UFEUEu2Pr/v2nKEorRVEK6+qbKIryKpJzPFUUpbBe+ZqiKBaKoiyPo88hMSPGjP9eDYj55YsPy5fsNrFFyQ9//4Awk9o3rz+iY6d6DBr8Heu3TCJ1qPlWX18/6tUvF9I+1BoVcxUgjUbDoHaj+XPU3/zcbgxXTt9Q9/UZ2ZX9D9az4ezfVKpTxoRWhmDMfWyjF5T1f39tpm6NAWxafzhJDs0lj6xJkgTF2tqKAb+05afe2lX58+ZspPsPjdUhBkn88vzZWxrW+TncYZyuHUPy75Qqk59OXevj5emNx6cv1G9YnhIlQ9IZHNx/nvv3npM3X1azFSCAl0/e8FRvzdPTBy8MnAwSargtOkTlNdehcz22bz3Bnt2nAThz6gZnTt3g/r3njBjTLYGsTBikc4AkXmjXvg7p0mtXnL9985Hcbq34sdd03uvWZEjih2/f/Gne6Fej5hEuXbjLutX7adKsCqv+HUfHLvUpWDgHVaoVByAwMIjfR/9j1gIEcObQRYNyinjMjRSXRHZfhRAsWjqcVm1qGPRop0xcwe2bTxLCvARDipAkXkiRwoZxE3uqZV9fP1Yt30P9WoPw8/M3oWVJm22bjxlEQgiPTJnTqNvv33kwefwyFs7fonoyTpn+o7p/5/aTuL/5EOYc5sS6BVsNyqUqFY2gpfkRmRC5ujqxfM0Y7j7eQM5cITEYz569mRCmJRhShCTxRueuDZj91yAyZgp56D24/4ID+86b0KqkzcH9hvc2T143cudxI2euTLTvVJc3n/7j3pONbP9vBg4O2h7DxQt3GfrzX4z4dQEAxUrkoVIV7YNcURRuXDA+r05C4vfNn51r9hsEHs1XNBfp9H5viYGoepoZMqamdduQOaLXUURFT2xIEZLEKz/0bsa9JxvUhxrAx2hGOJYYh5eXN3tDpTE4c+kfrtxaybU7a/h76W+8ePaWquV706zhUIOFrKCdAN+14yQA5cqrfkRc1pvoNxc+uX+me52BTPllrkF9255NTWRR7IhKiDLrRQ1/KUVIIokeQgiDeF/p0qUyoTVJj6CgIAb8+Ac5MrXA0zP8h1lAQCCrVuyhdrX+XLl8P8JzzZm1HoBsZUICoe7deJivXuY1LzSh/ywDZ4RgipQNP4BrYiAyIcqcOUSE4irthrkgveMk8Y6Hxxcu6Q3pFCmaK5LWkugQFBTE8CHzWfbPrnD3v3n9EUcne1o1GcblS1EHJy1aLDc3PLwpVaUo2fJk4dmDl/h89eXU/vPUa10jrs03mu2r9nJo+wmsbaxInS4V545cDtMmX9FcZM6WuDP4RuQ1lyVLOnX76ZM3CWlSvCNFSBKvaDQa+veZia+vHwB582Ulc5a0URwlMQYfn280aziUs6cjnqge/dsi/P0DjBKgbt83ZuafA7j52QcLCwuqNajAygcbAVg8fQ2V65XDwSnh4wG+e+XOtCHzomx37/ojXj55Hes0DOZI7rxZsLGxxt8/gCePX/PyxXuyuKWL+sBEgByOk8QrSxfvZPvW42p51NjuEYbil0SPwwcvRipAANu2HOO/XafVsoVFyJ986HVbVy/f4+ZnH7XcrEsD7B21OaFeP3vLwskrEmyxZFBQEHs2HKJrrQH80n6s0cfN+PV/iX5BZ3jDcra2NpQsHbKGq2/PaUkmEokUIUm8smJpSLSEbt83okXr6qYzJolRrkLhaPcqNbrgmLlyZ+b+s428dN+JtS6EzdUrD3inN9+Q0S0dP40NiQW4eeluRveapp4jLti1dj896g6iU7V+dK8zkF87j2fbyr3s33KMCf1n8+DmY57cex7h8aUqFyVLjoxq+eKJa6z6K/HHKwxPiPoPaqtuHz18meoVf+T5s8QfV06KkCRe8fcPSe3QvGU1E1qS9Eib1oUT5/6mZ59m6sJgY2nWohoODnakTOmoLk4FOLzzpEG7+q1rkDV3SJjHwztOclSvZxUbgoKCmDViEXevPeTx3Wfcu/6Ik/vPM33oPBZPCz/duJW1FYMm9uKfvbMYOL4nExcPZ/Wx/5E9b0gCxYWTVvDzd2M49t/pOBXMhCa0EDVtXoWRetESHj54QdeO4xN9pHopQpJ4RX8IYfPGIya0JGmSNq0Ls+YO4sGzTWzZOY0Onesxckw3JkzpHaatlVVIxtCatUMClTZvFfJysOmfXQa5hFLYp2D+1ikUrxDisj2q51TWLtjCrUjmmQIDg3h89xk+3r4Rtrl/4zHfdHOFoXn74n249VsvL6Ntz6YULJGXdr2bkTKVMza21iw/OJdi5UI8484ducxv3Sczts+MRD08F1qIho/qyrJVo9Xv8uL5Oxw/etUElsUdUoQk8Ur3H5qo21s2HsHfP+o005LoY2FhQZ16ZVm0ZDjDR3U1CIBpYSHIlDmNKi52draUrxgiKoXrVyBlKm1U7Tcv3rFvk+HLQup0rvw6vZ9B3bxxS+nZcDAb/9kZxpbbV+7TslR3OlXrR/187XkVQQihBROXR+sztuzeiNTpwu/x2dhaM/vf8dRtVd2g/tD2E6z9X3gp0BIPoYWodbuadOpaXy2fO5O4IyhIEZLEK2XKFiC7bsze2/sbN64/MrFFyYMMGVNhY2MNgEaj8GP/1uq+ylWLYWtro5ZT2Kegbc+Ql4V/pq/B75thaKXsed0oUqYAodm64r8wdf+tP8QHXXrxwIBAZgybH66NxnrapbCzpWX3RvQb3T3ydvYpGPe/Iaw/s4i6ekO/CyetMIisnRgJLUSFCudUt9++TdzxGKUISeIVIQQlSuZVy9u3HI+ktSSusLS0pHqtkmr5oF6opPwFs6vbwQ+3dr2b45Jam9rh3St31v4vbA6byUtH0Pr7JgYP+Kf3X3DxxDWDdrWaVTEonz96BY8PYaNkDJzQk2oNK0T4GVp0bcDaEwvY/2A9Q6b2xc7BuMCkbjkzM3LOIAqXzg9o02WP7jWND4k8eK6+EKXT6xG+f+dhCnPiDClCkninUZNK6vb//tqE+/vE/UeTWOjUOWTI5p6eh5mrixNg+FCzd7Cj57COann57PVcPnXd4Hyp07nyy+TejFswlCr1y6v1E/rPwt8vZJi1RIXCDBzf0+DYZX+sC2NfhizpmLJsJKfe7mTzhSWMmvuzwXDbg1tPyJ7XLUYJ6KxtrJm4eLgqrJ/cPzO61zQCAwKjODJxoO+I8v594hZXKUKSeKfNd7UoVlybDd7PL4D16w6a2KLkQVHdPQfw1nMQiCgKc9NO9chfLDcAAf6BjPxhKp8ieGH4ZXJvnFwcAXB/85HBHcby9mWIM0GLbg0N2m9auouzhy+Fey4hBBmzpqdhu1o06VjXoD42pMuUhvELh6pro66dvcXCyStjdU5TE/zikC59SOgr2ROSSKLAwsKCH/o0U8uhg2xK4pagoCAO7DtP/74z1TovvZhy+/eco2W7cXwOFUjW0tKSSUtGkEb3gPP85MXsUX+He430mdPS7ed2avnSyev0ajhEFS0bW2uKhorjtnXFnkjtVhSFfZuOquVG39WOtH0wb168j9ADrnTV4vQc1kktr/3fFo7ujhsXc1Nxw8MbR90iYtBGzkjMSBGSJAiVqxRTt+/eeWZCS5I2AQGB1K81iJZNhnHi2NUI2x3YeoyOVfuFWReU0S0do//6RS0f3XXKYKhNn3a9mtGxX0u1/OHdJ1qX/YHejYfSolR3rp+/bdD+6pkbka5peXznGW9evAO0TgsN2taMsC1oRWvhpBW0KfsDB7YeZ0zv6bx5/i5Mu84DWhuk9p44YDbPH72K9NzmzrV3n9VtOztb0xkSB0gRkiQIx4+GBJzUaBLvug1zZ9f2k5GG8tEf4fL48JlRP0xl3I8z+ab3Nl2mWnEyZU0PaCf1Xz55He65LCws6DemB8Nm/qTWffP148aFO7wLJ93AF09vju85G+65Xjx+xW89JqnlcjVKYq3z7ouIc0cus3LuRjQaDeP6zuDgtuMM6zohzNokCwsLRs8brH4mn6++jOgxGV/vxNuD8PMN8V5MkcImkpbmjxQhSbyzecNhBvabrZYT+x+NObN189EwdZWrFlPnRRQFrG2sSKXnALB/81F+7TLBYEgrW56QKAlPH0SeqbVZ5/r0/q1LGAcCS0sLylYvQa4C2dW6qYP/Yuea/Xh/9eH+zcfsWneAiQP/pEvNAbzURYe2sLCgVY/GUX7W8Nb/PLz9lIkDZoeJlODs4sikJb9hY6sVtsd3nzFj2PxEu5A1QM/BIpF+BBUpQpJ458hhw7D7L56/Y/OGwyayJmlz4rihu7SX32H2HPyTk+cWqeF3AvwDKVAsNw315lwunrjGmUMX1XKOfNnU7VP7o86E23VQW3bdWMWSfbOZv3UKa08u4PDTzfy5fgLzt05Rs51++fyVKb/MpU6utnSrNYDJg+bw378H8dNFTrCxtWbSkt8ooRehITzu33ysuoZbWFgYzE8d3XWapeF44+UrmpvBU/qq5b0bj3Bgy7EoP5s5kiVHRtVx48H9F7yMIMJEYkCKkCTe+XlIe9U7LpiRwxcm2rdQc8Xb25cP7p8N6oIjVBQplpvRc39W688cukTfkV1p3L6OWrd91T51u2bTyur2oe0nwszvhIezqxMFiuehRMUiZM/jpg6nObs6MeHvYTi7OkV6fAp7WxbsnB7p2qFgTu47p25Xb1yRXsM700YvOsfSmevCzHcBNOlY18DhYcGkFWEW5iYGHJ0dKFVZm61Yo9Ewff5WE1sUc6QISeKdXLkzc+LcIo6eXqDWvXrpnuSSc0WERqNh7MjF/NhrulF5fWLKtasPDcoFC+VQIyPc8PCmUKl8agw4jUbD/i1Hadc7xGvxztWQjKsFiuchv+7Fwd8vgD5Nfo1wPscYipQpwKbz/9D7t87YOWg9u0K7YH/z8eNpJBGz9UmhNxnvqEtJ0f/3HyhTtbhaP7H/bO7ffBzm2EETexkszN20JGzoocRA00711O3zR69EmSLcXJEiJEkQhBCUKp2fOvXKqnX/xVE0ZnPm2dO3pExRi1kz1rJq+R5+6j0jwa69aOnwMHUVa5dWt/8au4Qn90Me+h/efuKTriclhGDUnEGkSuuizif91n0SaxdsiXEP1tHZAQtLS3x1jgPhnefs4bAZU8Mjd6Ec6valk9fRaDRYWVky4e9hamqHb75+DOs6Ae+vPgbHOjjZ8/2Q9mp5xZ8b8PzkFe3PY2oKFA+JRBK8RuuGh3eiEyMpQpIERT96wvy5mwwmWJMa9+4+p2xxw3hn2XNkirfrlStfkMpVi2Fvn4KVa8dSvETeMG1Ch8/5d+E2g7L+sFvO/NlYsGMaOfJlBbSiMW/cUqYNmRejFAmKovDf+sgXKpeoWMSocxUpXUDtAb16+obzx64A2qG/6SvHqMn43r10D7cH16xzfdxyar+Lr17erJizwejPYS5kcEuLjc7J5+N7D4N1X8FilBgESYqQJEFp36kuadK6AFoHhR1bk2YsuZvXH1G/1sAwCwnnLhgcb9e0tLTkvwOzefVhV4TJAz09vhiU714zHMJbNXejQdktZ2bmbppksPB0x+p9LJwU/cgDQUEanj14GeH+jG7pqRcqCnZE2DmkoFH7kLmdJTPWqiF5sud1o23Ppuq+x3eehjneytqKPiO7quXNS3fxOpEliLO0tCSn7gUB4FEE6+/MXYykCCUwN68/onmjodSo/CMDfvyDd4k8Am50sbdPQQ+9CeRTJ69H0jrx8PjRK3bvPIWPzzdev3KnRZNhYZwEpsz4kTRpUsarHUIIg7xBYBgjzjW1s8E+TZBhj+bO1Qd8CZU22jVNSuZumkS91jXUutXzNnEpmt+dpaUFzbvUD3dfhdqlmb9titFBSgFa9WiMpaX2EXbr0j3+mb5G3Ze7YMhw3eMI5pmqN6qoBjkN8A9k0ZRVEV7r1qV7DO4wlt6NhzJ37D9mk0hO3/39UThiq4+59o6iHxlQEism/r6MQwe0rrDahFRXOHl+sUEYjqSOc0oHdft5OCvcExuenl8pV6IH3775kzdfVpyd7Xn75qNBmxw5M9Ht+6jXvsQ3tZtXZc38iPPrWNtYq44D+tjYWjNq7iAe3X7Cw9tPAQxSgRuDEIKh0/tRoVYZ9m0+QmBAIOkypqFe6xoUKpUv6hOEIkv2jPQc1kmNB7dy7kb8/Pyp1bSKQQ/PIoIYdEIIfhrbgz5NfgW0USS+69OcAqE8OQFmjVjInasPALhx4Q7la5aibLUS0bY5rjEQId33Ygz6QlTE1SGSlvGPFKEEJm06F4Pyo4ev2LblGJ0ieENMiixfslvdLhHOvEVi48H9F3zTufneD+et29raiuVrxpjFi0a+orkpXDo/Ny/eDXe/sBARrn60tLTE63NILylfkVxGXTMoKIhN/+zi9KGLpLCzpdF3tRm/6FfV4SE2dOrfmkunbnBBNye0ftF21i/abtCmaLlCER5ftGxBqjWswDFdPMPtq/aGESE/Xz9VgIJJlcYl1rbHBbn10nJE1ROKiGBBMpUYyeG4BGbo8E5hYj2dOZW4E25Fh2/f/HmoW4EvhOCnQW1MbFHsyaRbiBkRE6b0pmQM3vTjixGzB2JhEX7vwP+bP7cuh+9G7v72I+9ffwC0LtLZ8rqFaRMUFBQmbM7pAxeZM2YxF45d4cTeswzvNpEuNfpHmh7cWCwsLBg7f3CYYKn6+/XjxoWHvpv66YMXwnjt3Qgl2FlyZCSX3sPflOTU6wk9vvssRg4jwZhquE6KUAKTNVsGBv/awaDO/f1n0xhjAmxsrHDSZdRUFIXPHl+jOML8yZQ5LQUieCg1bFyRH/u3SliDoiB7XjcKloxYFCNyHrhzJaQ3kK9Y7jBzTx/efaJ9pT40yN/eIO13eG/oj+8+48fmw8I4RsSEVGldWLBjGlOWjaRkpaLkLpidCrVL07RTPWasHkPO/NkiPb5w6QLqQtoPbz/x9L5hmCL9uSaA6o0qxTrNRFyRKq0LqXSOPt98/eLMuSIhBUmKkAlo2LiiQfna1fuxeoNJTFhYWFC6bEia6PNnb5nQGkOePnnDP4u28+xp9P+Q8+p5KQWTxS0dC/4ZZjYPLH3SZkwd4b4c4XwWgNtXQhazFgxnGPWvsUt4+eQNAf6BzB2zWH0genoYrsGxs9c6H1hZWbF342E1ZE9sEEJQrWEF5m2ZzMoj8/hjzTiG/9GfCrVKR3mslZUlhfSy/wbHyvPx9mXtgi1hokXU0FtmYA7ozws9jMa8kLHEtyBJETIBhYvmMpgfeP3qA2ciiXyc1ChbLmTo5PQp8/CO27P7DEXydeDn/n/SovGv0VqQ+fWrbxgvP0tLC5auGkWqVM4RHGVaIkpWB5ApW4Zw6x/cfKRuhzdvcnT3KbUcFKThyC5t2UvPLXzE7AGsPDIPAF+fb2xYvIPlf5p+jY5bzszq9rMHL/lv/SFq52zDvHFLDdo5Ojuoif/MhVxxMC9kLPEhRlKETIAQggJ6K74BNv57yETWJDyVq4bkFlq/9mAYT7L44ts3/3DFxcPjC21bjFDLfn7+0eq9/DlzXRh37FHjelDByIWXCY2iKLx4HH56BoCIPvk3vR5L8BBQMHevPyTA33DhcXDPSV+EnFycyJw9A10GtlXrVvy5Pk7mh2JDcHBXgL+nrmLigNnhtvt94VCz69nG1EPOXJAiZCLyhJrUvXr5fgQtkx5Vq5dQF6x++eLDqOEL4/2a0yevInOaxhTJ1yFMzLq/Zhu+iTdtUdXo87566c7cUMdnyJiaAT+3jeCIuOfk8av8+stf/L1gK1+/+kbZ/tale2p4nmBCz++Eh9CTp9A5oT6Es97N86N2GO6rV8ibs7MuJXjn/q3ViAVAuMFGE5KsuTJHuj9T1vRMWzHaqOG9hCYuPORMiRQhExFGhK7c53Eiz/ZoLBYWFgTpLZLcujl+w+kHBgYxbfIq/P0DePb0LZN+X6buO3TgAjOmrjZo/8vQDqFPESG/j/kH31BzGj/0aopNFAnZ4gqNRkPrZr+xYN4WBg+cS5f246I8ZsvyEBd5Owc7rG2tVVERQqghb0Ij9D3qQvUoP4cTey144WlgQMjCzuCcQw5O9gz4/Qe1/vzRK1HaHZ9EJkIzVo1hw7nFVKlfLgEtMp7sedxUd/eXT94kumR9UoRMROiJ7KAgDV3a/46nZ+L3FjOGUqVDvLPiO37cq5fuakoDgC2bjuLnp13XMzVU+Jn0GVKRNtRQU0Q8efyadav3h6nv2qNRzI2NJkFBGvz00m8f2HdeXbMUHh4fPDm0/YRanrNhApP+Ga46xrjlykwK+/CjFuiv69GEEqFr4TiYZMujfdEK0nO6CY5wAFBEz63a1CFz0mZMrTpMhGZo5/EJbE30sLWzxS2XtlepKApP7oUfvsdckSJkIipXLYZ1qEyU164+oG2LkXz44Mntm0/o2W0yA/vNivShkljRX5yrKEq8Jrl79tRw+M3fPwCPT9p5itDu8en0Mo5GhU84b5wODilIl974c8QWIQSBgYYhZPQf9KEnkXetO6DO3RQskZfCpfOzbkFILhr9KNthrmWhPxwXIix3rj7g4LawMQAvHL8KQJCefRaWIcN+dvYh6+X8/fzDfI6ERAhhkE02NPru6eZI7gIhc8wRxZAzV6QImYhUqZzpG876kdMnr5MjU3PKlezBv2sPsHTxTpYu3mECC+OXFq2rG6R16NtzOlciWCQZW56F85Ztm8KGE8eu8uih4ZqYCpWMdyYoVCQndUMN0Xh7f2PvfzHPuxNdbt98YmhT4ZxhXm6C8fzkxfpF29Ryqx6NuHL6Bld0npmWVpa0+b5JuMeCYU9I0ZsTOrzjRHjNeaWbewvQ64VaWoWcw9rGmtQ60Q8K0qjpCEyFfrK7xEbOAiFroR7efhJJS/NDipAJGfN7D8qWjzikSDDDBs9n6sQVvHoZvVhdccW7t58YPHAO3TtNYN6cjXESvNHCwoKlq0arc2O+vn60bz2aT/GQ1yW055qdnS1+3/xp13JkmLYDf2kXpi4yevZpFqbu5/6z8faO2kHAGB7cfxFp8r9r1wzf0F+9fB9mjgq06bD7tfhNdUhIkyEVNZtWMUiD3bBtLTJmTR/htQx7QiEi9PxR+J52NimsCfAP4KWe/RndQs7v4+1r0Pvx9jJtYM0W3RpStnrYeHBZcmQkX1HjQhSZCv2ArbInJDEaW1sbNm6bHOFqe30mjV9O/pxtqVH5R+bO3sDnzwk3dzRu9GL+XrCNTRsO89vQ/7Fl49E4Oa+LiyPrt0wipS6g6auX7mE81eKC4AgNwaRJ68Kq5Xv48sUw2dm6TRPIGsEamYioVqMkKXQ5XYJ5/eoD+/eci+AI4/lv12lKFu5C8YKdIuwlOof6bJ8/fzVIj/Hw1hN+aT+WbrUG8PhuyMNp+B/9uXvtgRoJ29LSwsBtOjz0veMU3XCcRqPh7rXwh6qy5srCk/sv1BQLmbKmV3MAgTaXUXAyuXSZ0pAjX+SRDeIbCwsLpi0fRf02IdHCU6dzZf62qapDhbmSS68n9OjO0xgnHjQFUoRMTKpUzuzYM5Mq1Yob1f7i+TuMHLaAovk6MHvmuniPtKDRaNi5zXC45czpuIt1lyevG7P/+lktr129L87/gFxcnAzKb9985PXrsL1K/eFBY7Gzs6VwkZxh6h9Hsg4HtGuRXjx/F6lTRnBPLShIw5qV+8JtU69hBXLlNpzLePRQ62V5+OBFfmjwC2cPXzLY37xLfTJkScd8PS/Bem1qkjl75AJsEU5P6Pbl+7hHsM6rWqMKXNTNC4FhNtRXT9+yUi+R3PdDOmBjmzAehZFha2dL+Zoh82LFyhcibYaIo0uYCxnc0qlejZ6fvPgYyWLk2BLXf59ShMyADBlTs2vfH2zbPZ3Bv3agU9f6NGhUkazZIh4a8fD4wpgRf/PH9LXxatvC+Vvw9DQcJnF0tI+gdcxo0bq62ht6/eoDz5/FbXoHF1dHg3JAQCAfQ2UYBZg+eXWYOmMIvfAY4HkE3l7e3r4MGTSXNE71KJj7O7p3mhBuu0uhgmZGtKA3RQobA09DgGw6Mfn1l3n46zznLCwsqNm0MsNnDeD96w90qtaPm5dCrtF1YNSBZD/oPdisrLUOBlfOhI30UalOWfIXz0OVeuUMMpZWrB0SSHTxtFWqbfmK5qJBu1pRXj+hCF7LBPDi0SuO7znLuaOXzbp3YWFhYRAjT3+9kEaj4c2L91w8fpW9m46EyRcVXeJ6sa559zGTERYWFtSqU4ZaoSL+vn/3ib17zrJm5T5Oh5NEbOninQwZ1jFeVnH7+voxe+a/YerbtKup2vbm9Uey58xIypSOYdoZw4cPnsyfs9FA6N69+6Q+SOOClC5hbbtw/g7t2tdm/bqQdNOzZ66jZ59mZIgkrlp4FAxHhC6cux2mTlEUmtQfYrAvol7lrFAvF0UimJOYM2s9G0JF26jfqAKvX7lzTzf8ZmFhwYpDc3l05ynjfpwZ5hw58mU1CFsTHneuPuCuLp2BpZUlhUtr4/89uPk4TNvchXLwe/+h/Np5PF90w8YZ3NJRt2U1APZuOsL+LSFrw36e1NuoxbIJRcGS+RBCoCgKD249YXi3iQD0Gt6Zbj9Hb84wIQgMCGTzst0G6Tm+6WX0/XvKKvZuOkLO/Nk4e/gSKVM58+uMftRobB4x8GRPyMxJlz4VXbo1ZM/B2fQb2DrM/pcv3sdbdtYD+86HeQMfOrwTDo52tGo2nFxurahcrhf5srfh3t3ws1dGxa8/z2XmNMMoxa6uThG0jhmhh+NAe99KlMpn8PALCAhk9cq90T5//gJh5zJu3XwSJnrB2TM3w4hT0+ZV1O17d59Tp3p/nGxqsCPUEGitumHTEfj6+oWJNjHgl3akTp2SuXrx2IpXKMylU9fDFSDQxn2L7C3/m883xv/0h1quULOU2lvwCSdCg5W1JVN+nstlncAKIRg992fu33zMkI6/M75fyLmqNigfYRoGU+Hs4kiJcEIubV8V/d9GQjBx4J/MGb3YoC6lLmahoihsXrab968/qC7znp+8GPn9FMb/9EeMekVxPQUgRSiRYGFhweBfO4abCOzmjUfhHBE7NBqNQWQB0PaAGjerTPmS3xtMvHt7f+POrZi5hYYeFmvfqU6YaBKxxSUCUVu3ej/5QoX5v3j+TpycX6PRhPlso39bZFDu2acZU2b0A2DenI2ULtqVs+EEsq1esySldGmo9XkSjlfa4F87cP3qQxbOC8meau9ox5+j/o7Q/tfP33H32kPW/m9LuLHH/jdxhZrewc4+BQMm9FT3uYaTrtzX5xuH9Ny2e//WGZ+vvvRtOozTBy+o9VlzZeaXyX0itMuUtO/TPEydOcxZhUdwQr5gSlQsTOFS2t+LRqNRe0VBgUGqSzzA3o1H6Fz9J65EY47380dP5o9fFnXDaCBFKBFha2sd7rDF1XhYSHf/7nNuhxKWLt0b0rbFiDAuwDVqlaJR0+h37TUaDVdC2V6jZqnoGxsFLuEMx4F2cfCtUMNJnz9/CbdtTAiOygDaN9JzZ0KiCri4ODJr7iBSpLBhzqz1/Db0f+Gew9bWmgWLw08H8fx52Hmnl8/fM7DfH2pYJCEEJ/dF7qlnaWXJ9/V+Zt7vS+nfeqRBr+j8sStsWhKSG2jghJ5kyZ5RLX/Xu7mB51jugtmxD5UefMfqfQztPN7gvA3a1mTJ/tmkiyIhoKmoWKcMZaoWN6gbML5n+I1NTCG9tBp1W1Vn3pYp6ndiYWFBlfrl1f3TV4028P57//oDA9uOZv/mo1Fex+vzV36o/4vB4ua4QIpQImLU8IUG4WeCWbMy7j3K/P3Dem1t2nAkzNBfn34t2LxjaoQLJCPj6ZM3eIRaF5Q5S7ponycqrK2tKF+xsFFtixuRbvz40SsUL9gZJ5saFCvQkbYtwq43AvDTi3QR2oGkU7cGAMydvSHCAK7NW1bj5v11ZHEL/55s0JvPCqZS2Z5cvBAyN2DM70I/osHnj54IIdixeh8V0zdmUNvR6r7KdcvSpGNdg2NzFczOX5sn0bl/a0b/9TMLd82gbqvq2OplD379PMTRxNrGir82T2b0/9u77/CmqjeA49/TTcssLcjesvcespEpCqIyZKOoIFMERTYiMmSp/EA2AiKCOBAEEQRUtuwhouy9oYyu8/sj6W3SJB2Q9na8n+fhMffmJjnHQt/cM953xgAC3LzAxZ2UUoz5Yojdub8PPnkBvsTQwGZI9/K5K3ZfWEb1msyt67d5yvrvKuiprAz/dCAfzn3PGLILDwtn5FuTWDpzFbFZOHW53c/SXSQIpRDTPlnO/Dk/GsdRiwMA/jlxljtu3ugXFOw4zLJw3hqHc+Mn9XqsAAQ4HVrMZ/Mt252WfTOWaZ/1d7qIIIpSilfax71rfurkr4xMC/+evMBVF8thHz6ylI4YNWwOo4bNsXsu5N4Dpk/5mqGDZ9qdz5Y9C6curub2w40s/mqky0USq1ZsYsVy96c6eipPNhZMWc74gTPszmfOmokhk992ekdWtmpJ3vygC01fboB/QDpy58/B5CUj7LI7gyVN0Lz1U6lYq4zb250YMmZOz8jP3zGO92xz39YEd6rTrIbxb2n/jiNGCXaAPVv3s2/7YS6du0KLdo2M5eb1WtRkwS/T7AoYfjpyHtOGf+F0zufKhWt2d8TuJEEomXv4MJR3+k23+7bcoFEldtnMXTzbpOpjr05zJUfOIPLEsns+SuWyXTl04PHmpPLlf4qnngq0O3c0kVKOBAVl4rnnn+FqjOwJtjp3a0b5WMpeR8mZK9jp+QGD2lOnXvSO+wvnrrJqxSaHhRcAhw7+6xCAihXPx+87vyBr1kxOA3SUnTuO8EaPj41j/wA/Zs0bQjrr3YeXlyclS7kOtrEpUrIgs8cvtjvn7ePFsBn9CUxAXr0KNcuwaNOn/HRkCYs3f8pXf8zii7WT7WrfpATlbe6gD+05Znd3m1wEBme2a+eebfuNx7bZTV4b8qrd67LnCmbm9xMoa5O1Zfms75g8xP7vJcDOzXuNnIPuzh4RZxBSSuVRSm1SSh1VSh1WSvW1np+olDqmlDqglPpWKZXZ5jUTlVK7lVJ1rMf5lVJaKfW2zTWfKqW6uLU3qYDWmjmzvqNbxzF06TCaciVeZdbn0WOw5Ss8zfVrt41ULgEBfkye1tft7QgJecjdGHdXzuZWTvx9lpdbD32sTNhKKYaN7mF37rUu4xKlyF1kZCS1q7/h8q4FYOTY+I35T/ikN+8M7oBvjInqHj1bUtlmpdfyZRtZu+bPmC8nS2BG/vn7rN25Ro2r8Nuf/4vX8vDh78+ym5e7H/KQd/pON86Fh0c8VonyirXKsHWdfd67IqUK8umqjx67jk7mrJkoVDw/eQvlSnbF4OIjOEeQUfco9GGoXYnz5KRc9eggdGy/Zdjw2uUbhD6MHr53VvMpY+b0TF0+hrrNaxjnvl24lh+WrOfqpevM+mgxt67f5ui+6LnbDG7+whufO6FwYKDWujhQDeillCoBbABKaa3LAH8D7wEopaKW8dQGetm8zxWgr1LKPseJsLNg3hr6vz2VFct/ZeWKTXb54lq9WIf8BXIYCxGUUsxd9AH5C7h/CGvA21McUgNlDcrExq2fOVx79sxlftu097E+p12HRnbHt27dcyiv4A6XLl7n3FnXCTLrNahI1qyOQ5DO+Pv7MWJMD06eW2W3km/rb/toazNnsuHnHXZ1kwBKlS7IiNHduWlTbVQpxbJvxuDvopSArfPnrtotcIgSMwVRbMXtMrpYLXhwV/TddcVaZdh85lsWbpxO6crF42xXalahZvTwYUJWkiWlYmWiS44f3nOcPm2G0rJMJ+7b5DAMcpHd3dfPhzFfDKbhC9HFHGePX0zfNh+wcOpyZoycy7cL1xrP7d6639nbPLY4g5DW+qLWeq/18V3gKJBLa71eax319Xc7EJU7xBOIBDT2lYKvAhuBzm5qe6p01km2gOBsWZgyox8Nnq1sVwBu7Pg3aP6c+zec/XPiHMuWbHA4ny17oEMy0Ch379x3ej40NIyJH31Jr54TWb7U8T29vb0oVNh+o+TKFZvcvhfhqRxZKVzE9dJvZ3t94pIpU3oaN4teeXT61CWKFstLAes354cPQ/kmRomKQwf/pV9v+9LRjZpUwdc3ft/Nxn+48IlLHjRqVZtSTpZ8R2Uw8E+fjmEzBiTbJclJrbzNXcbhPcdiudI8RctGB6Gj+084BIomL9UjOIfrlYienp68P6WPsVjh+pWbnDphuVvfYLOxODEkaE5IKZUfKA/EXPPZDVgLoLU+DPgD24CYg4vjgYFKqVi3RyulXrcO5+2+5iS9SmpWtJh9sbvlqz7kwLEldO7WnA9HLTDOd+rajLf7xZ1q5XE42+0P8HLbBrw3yPFOyMfHm5rPOJ9sHj18LqNHzGXR/J/o0WUcW5xU0CwUo47LrZt3uX/fMRN0bCIiIhj+/mxaNn2HVSs2OTzv4eHBgHfbuXz9495NHtgXvWIq6j1aPF8rQe/x3PPPxH0RcOH8VRYvWBvndWPHu957kzlrJlbOW0Ov4V2Z/dMkh8Scnl6eDPr4rWS7dNoMtnNhjx46rk5NDoKyBxJknV+1LbNRpGQBug9qz5DJfeJ8Dz9/P4dl6WCpvgu4LPr3pOIdhJRS6YGVQD+t9R2b80OxDNkZs69a67e11hW11nZfA7XW/wE7gVjrJ2utZ2utK2mtKwU52QyXWkVGRrJw/k/GcYNGlWjctCqHDp5k9bdbuGhd9ZL9qUAmTnG+UikhIiIiWDBvDcuXbrCbwHS2rLdS5WJkzBjAv042SE74pDfZsgc6nNda263oAxxSzADkd7IizscnYSvuliz+mSmTlrFp4x5e7/YR1687fnl5pV1Dl4stHndV3oXz0SuRArNavkUOGNTe4e7OlarVS9KhY+N4Xbtl8z6H4b2Y0qXzpU//lylZKjqpar7CuXkqdzY8vT25Zf3/8mbLwfR/ZTh9R/cgY5YMZArMSKsuzVi2bSaN29Rz9fZp3q3rt5NtDrnmbRtS3GaLwYBxPVn464wEJYctZnNHFWXYjAGUqlSMBtbhupLxWLyTEPEKQkopbywBaInWepXN+c5AC6CDjv9PZhwwOL6fnZb8sHorW3/bB1jmCV5/4wWaPzuARnXepptNosuy5YvEa/4gLosXruPtNybRo8s4KpbubJQWd/beu3cdo3vnD+3OeXl58uXyUXR/vaXT94+MjHRYsLBw3hqjj2dOX2Lb1v0OWQKKFc+Hj0/ChoJ++Xmn8fjRozCnlVp9fLyd1v+Bx88MXKly9LDWB0P+R1hYOEFBmfjx509o+cIzeHt7kS17FlavmcDMOYPtXtujZ0tWr5nIgweP2LL5L6d1gGwds0lK6UrjptX4Zvmvdptwffx8uHTuChFh9sN4IXfvU6hEftYdW8bao0sZ9PFb5C6QMx69TluyBGU2Hv977DTLZ31nXmNipThqXThRq3FV2sRSoNCVqPmvqMwK2XIGUa1+BT5fPZ433uvIJ1+NYuYPE9zXZOK3Ok4Bc4GjWutPbM43wRJMWmqtnU8IOKG1PgYcwRK8hJXW2m4pb6++bfhpzZ/8vtUxaekxJ6lVHsdem4SHJ/85z7TJywHiXbSuafMaPN+qtsvnPT09qVHLMQdXs0b9yZr+WUoWaUfTBv04sN9+E2C9BgnPmhAUnNnuePyHix0m68F1BuA71gSq27bup1KZLvTo/GG8yqq/N6yzEbSPHT3NN9a9O7nzZGPJ16O5eGMNR08up0GjyvxuM07/Zu/WTJnRn4sXrpErqAXNnx3gMFcUk+0iFdsS3rbS+fvSrdNY4zhTYEanSUaj7NjkODwqop07dZEB7Ybbnft6TvKsdFy2WvTKzMvnH68AZrGyhek9shuz10yi98hujJv3Pl7eXnh5exGYLQvV6lV0e7LZ+NyN1AQ6AvWVUvusf5oBnwIZgA3Wc863fTv3IdELGQSW2i9Rq978/Hx4sU09FtkMzdk6c/oyZ59w5/LFC9dYEWNoLGoIa9Ov8VvpFrNEgjPvxNibEMVZ5ocoVarGXW02pg6dmtgdX71yk6+WrHe4bud25/Ndd+6E8OhRKK93/Yjjx06zfNkvLjMZ2CpYKBf9BrY1jge/85ndMnBfXx/jru7WzejVhjWtGzYrl+1inNsex8or2zshV8Nyy76M7nOmwIx2ReScKV6uSKzPp2XXr9ykT5uhDvWSLp29kiz3C5WuVNzYX/bP4f8IcfIlLD7av9maHHmz0/7N1pSIRwaRJxWf1XHbtNZKa11Ga13O+ucnrXVhrXUem3MuZ0O11qe01qVsjvdrrT201gvc1I8UzzatS+duzVnzw++xDhHFzHmWUHNmfe+wjDdnziDu3Anhm+WO8zZRouY9wLJHKS61ninL8FHdE9S2gPTp4r4oBmer22wDTmRkJB+NWcgP30Un1qxeM/ou7c7tEBYvWGsX3Gd9/i1XLsedofzVztEB8OaNOyxysXjAtuR3+gz+nDl9yS6YOJtvi3Lr1j3273OeNiadTYqcKOVrlOLLzZ9x/pTr0uBTvx5D7abVXD6f1n0+Zj6XXCzrnz95Gctnf2dUhk0OAjL4k6eQZThVa83FREixkxhkXiYZ2LH9sDFP4uXlSZ/+L7MtjrX4znK7JcSW3+yHYXx8vOnUtRnfLN9ISMhDF68CD5vhrGzZHBcjODPovVf5ddtndOraLF4pfnZud9wHE5sHDx7R+rkhDudtE4j26z2FcWMWGMcVKxUjp80KME8vTyaOdyxqF9dCgK+WbKBaBfsg6+pbsm1W7UePwnjzNfux9diq665fu93ll5JXOzexm0OrWr0kExYNt98gEcMH0/tTpU551xekceFh4Wz8fpvL5xdNX8G0YV/wYd+pSdeoeMhq82/yxrVb5jUkASQIJQOTP46+C3qpbQPy5nvKyIjgypxZ3z3RKp2MMYZpgrNlJiB9OofVbDEXCNy2qT+SkD1KlauU4LNZg9i2Y7ZD+YSY1vzwe7zfF+DdATOcFvyLWvH2y/qddv2q+UwZur7WglXfbDbO3b51z26lG0ChwrntMhiEhoZx6MBJY9hy7Mh5vNZ1nF3ePh8fb9o7We12504Ihw5G372+0nqow3L1Pv1fdtq/C+evMm7MQqfPgWU13s598xn14eusWD2O9ZumE5DBn43fbXW41i+dL6v2zKNZMqpkmhxZsg1YvkzEzApu63HnXhLLA5u7bV+/lJEXQIKQyZYv3cDaNX8YxwPesexlCYjlLz7Apo17+G7Vlsf+3Jjfus+fu0qrFoMdykI0fy46nYd/gB9h1hVWJUoWoMRj5CcrUaoA6zdPJziWPGRHj5yKMwhHOXbkFAvmOiZWBXjhRUslT9ts081a1GDqpwMY1G+GEcRr1y1PM5t+Rjl96iLD3pvF2jV/8sX/VlOicFuqV+pB/hwv0LHtSD4eF51jLUtgRtp1aMSP6yc73XN053ZInBtwGzsZGvvj94M8U62nkTA15qRwufJFyJ0nG4UK52LAoHY0aVbdmBdwdkc2fuEHRkZl4VpgcBZjEYtt1oGYOvV1/sXBLLbJS7O7yHGY3EgQMsmxo6dpWKc3PbqMM869+FI9ilkzD3ft0YJKVYqTPpb5kSGDPos1PUtsOnZpil+Mb0oxh8HSp09HDZuMx7Z7tuo3erxcYgCBgRl5f1jsiTOiArPWmh3bD7Nt6367O7/79x/y9puTqFyuq9PXV6hY1CgEZ7vxtXnLmnwycamxHLpAwZzMW/QB1aqX4gVr+eko4eERTPtkOS+3ep8BfabZlbFYbbOLvH7DShz6eymz579PdScVOcGyWm70uNcd7kBtbfh5p7EyUWvNF/9bTfNG/bly2bLQwdPTg0aNq9i95v3hzvsP8Fz7RjR5qR7ePl5Ub1iJpVtnyhBcPPn4epM9d+y/xOu2qEGDeG40TgphoWHcsGY08fDwIOiphJWpN4sEoSR2//5DpkxaRq0qr9nlAMubLzuTp0cnIu3T/2VaPl/LCDLOfnmdP3eVj6151n7fdoBvlv8a75IOHh4ecS5BvnfvAUsX/2wcB9nsl7jyhCXF41oBt2DuGs6euUy3jmNpWLs3TRv043+fRdc7WfXNZoc7oHLlo1d62Q4V2i5aOHniPAdtloRPntaX7Nad5lM/6x+vzOG2SpQswPwvh8UaXKL0f6cdx0+tYNLUPvTo6bi36sWWQyhZpB09u4+nacN+DOgzzUjRkzUoE9+vncT0zwfSqHEVypQtzJKvR9O0eXWXn5c5ayaGfzqQ386uZvKSkeR3c8Xa1C5rLHfr6TMG0LnfK9y5eZcLpy8R/hgJfN3t6sXrxhe1rNmzuH0pdWJ5vEIw4rHcvXuf+rXe4tjR08Y5b28v2nZoxIjRPewSaEZGRjL7f9Gb4t59vyObNu5m44bddu8589OVFC2ejzetqf2zBmXip/VT4hwq6x/HnpQof+2Nzhqcv0AO9u45DsDBxyzfEKVo8bx4enq4nPg/cvg/ShRua3du6eKfebP3iwAOGb4rVipGkafzGMOJOW2+xebMFb0A4cqVGzy0Sb2SLl303WDWrJlY8vUomjca4LDHqGr1kjR6tgp3797npx//IG++7LR5pQEvt22QoI216dOno+dbrQBLIH5/8Ey7fHznz121C/xgCa5LV4wxAuSqHz5GJC6tNWdOnnf5fFhYOF0bRn9pzF0gBws2To91/iix2c5PpaS0S3InlISWLv7ZLgCVKVuYrdtn8fnsd41v41E2/7rXyPqcNSgTb/RqRdlyjmv2Hz0KY5bNHcL1a7dp9dy7TjdqRrl//yE/xrLyxxUv7+hvVq6qfQLcvHmXObO+4+tlv7jc+Orr6xNrQlFnjh87Y/QrZv2kGTMHctOmNLdt2YlzZ6P/cWYJzEj1mtEJKX+zbta8efMu27bs48ypS0z/fCCZMtnf2fTp/wqDh3Zi7Pg32HtoEavXTOTVTk0SnNnBVrtXn+XwiWV8v3YSvfu9RFYnKaq6v96S9ZtnJPgOTTyZS+eucjdGFnlbj2Jktzj330X+PvhkX8ye1OUUOB8EcieUZDb8vJPh7882juvUK8+qHz52+UvMNlFl2/aN8PX1sRtuslW3fkW7BQUXzl/ji/99x4BBzhN2fj5jJY8eJSwRo4+Pt92QU9FYsk737jmR71dbVmZduXKT3n2dJ1otW64wx4+ddvqcMw8ePGLVN5vo3LW53R6fgAA/ihbPZzdnFDU5f+XyDRbOjx62K1u2MEop4//vH78fZOanK3lv0OfGXZmHh4fDUEbM2kHu4u/vR70GFanXoCKjxr7GH9sOcPzYGfzS+VCjZhm7UhEi6TxOQClYLL/7G5IAV2zuhFJSEJI7oUR2+/Y9+vWeQuvnBnP/vmX/TZbAjHw2612XAejGjTv88F30nUrHzk0B+yEm21+SBZ0ky/xl/U6Hc1EWzP3R6fnYyhl07tqMmzfuxnltaGiYEYAAZs9c7fI9a8SjzLOHh+KDkdGT718uXAdYFhRE3TmEhDzk35MXyJI5uk7Orh1H+O/fCzzX5B1jYj9HziCeb13H7o5j986jfDhqvt2wYGRkpF1Gh7LlijgsCEgMPj7e1K1fkZ5vtaJz1+YSgEwUW6ojV27EUjAxKchwnLBz8+Zdxo9dSOmn2zN3dnSuqVy5g1m/aTr58j/l8rXfLN9o/BKsULEoJUtbMiLbzoPYfjMPDMxIceuquihbf9vHhHGLHRYf3Lhxx6i6mS6dr937fLXyQ75aOZYxH/Vk9ZoJdpkOIrW2G0osXsL5nNNqm3pHYKmx46rqassXnnGZA8343EhNlaoljKC7/Y9DnPj7LPfuPTDyvYEls3gtm2Xnkz5eQpliHThy2FIu3MPDgxkzB+Ln50OZsoWNLwD37z/k9m3XizkqVSnOkq9Hx1puW6Q+txKYCaFCzTJ4+3hz08TSMylxeTZIEHK7K5dvMPz92ZQs3JYPRy+wq6DZrEUNNm37PM4CarZDcR27NDUe37ge/Q8jKquBUooaNUs7rUkzZuQ8Bvadxqn/LvLtN5s5fuwMt23GubMGZSKnzV/Whw8e0fy5mtR8pgyTJyxl9Ii5xnNFns7DCZuS1DHrHkWZM9s+uWNkZKRd4k1bwdmy8GyT2NPGZMuehSrVStrtoVmyaB1zZ39vBLdChXOTJUsG2rZvRFknudB8fLz535zBxnsEZ8vCiDE9HK4DS/mMfUe+ZOacwSz4chi//DYj1i8MInVyVlcnNk/lycZLVXvQvGQH/jt+JnEaFQfbYofeTzBXmdQkCLlJREQEn0xcRqmn2zNl0jK7hQEFCuZk4ZLhfLVyLDniuE0++c95Lth8o2lj3dn+9bJfnGZZbt2mLsHZsvDcC84LqS2a/xOli7anU/tR1Kne0+6u5Py5q3YT8Ot/ttQqHDNinpFGKEqx4vmMu7OcuYIcFgZs/W0fBXO35s/fHZNwnjl9yWV/J0/r47KgnL+/L7PnvUdAQDq74cf9+04w6/NvjeOoejx+fj58u2YCTxeNDpA5cgax7teptHs1uuw2QO++bahTz3HPTP4COSlUOBevdmrCiy/Xx9MzZSxzFe5Vu2k1WrRrGO/rf/oqekP0wA4jE6FFcbNNVhsSz60ayYEEITc49d9Fmjbsz4ihs+1qwhQrno8v5r/P3kOLaP1SvXgVodu4Yacxh1GrdlkyZ07PubNX6Nl9vMOKt4AAPz6e3BuwzFtkyZLB4f1shYQ8JCw03JiL0VrbJc1cuvhntNZ4eNi3s9trz9nNozhLmDn8/Vl22aNtnY4lCOXJm511G6c5TYY6YUofGjSqTGhomN0cV9lyReyWNU/75Cvjbis4ODM/b5rOyLGvMeGT3vyxew6Vq5SI+dZ4eHgw/8vhNLDZdFu/YSXGTXjTZVtF2qGUok0P53Wy4uIq6Wliy2DzhTLmFobkTFbHxdO9ew/48/eDbP/jEFeu3EBry56Pu3fvs/LrX+2SfpYuU4jBQzvx3PO1EjyXYJtfrFkLSyoZX19vp/tpqlQraSztVkrxfOvaLlPYgGVZdfGS+Rk0pAOtWlhyrdneGR07epr9+06QyyatS4GCOZk45W0ePQoz9vWc/Oc8u3cdpVLl4oSEPKBfryns3nXM4fOiLF38M7lyBVOmXBGcVcrNlTuYzt2a8/mMlXbnp03+ig4dG3P2zBW7/7+lSheyC/a3b4dw9Mh/5LIu3AgKysTAd2Mt3gtYAta3P07g920HuHLpBs9Zi9AJAZAjTzY8vTyJsBvm8mLmdx8zY9Q89seSaDciIiLJ76IDUuidkPyLc+Hhw1B2bj/Mb5v+Ystvf7F751G7MVdnPD09GDK0E+8MefWxdyvbBq1xoxewZfM+ni6Wl3TpfI3VdVEOH/rX7i973nyxz10ULZYXDw8PytmU541Z12fHn4fJY7MHqNWLdfHx8cbHx5vWbeqywlq07aOxC1n53XimTvqKr5ZuiPVzt23Zz7Yt+/Hy8qR335cYOqKrkTJo08Y9XL50gwaNKhtBKCrYXbxwjcMH/6VI0Tz4+/sZ/e/acYzd+1epVpI69SrE2gZXlFLUeqbsY71WpG4ZMqWnQo3S7NqyD4BKz5Rl2Iz+BOcIYuC4N+hU/22Xr40Ij0zyIJQho9wJpWiRkZHs2nmULZv+4rfNe9n+x6EE7aUpXCQPXyx4j0qViz9RO557vpaxmu7evQes++lP1v30p901/gF+3A95yJXLNxkzYh59B7Ylc+b0rPtpe6zvHRoazt/Hz/DBkFnGOR8fb+OuwsfHmwIFc/JOv+nG8xcvRC8qePf9Tnzz9Sa01qxfu4M9u4+xc4fzInHOhIdHMHXyV6xd8yefznqHf/85T8/u4wFL+psogVkz8nLbhgwc3IFga8XUXn3aOC210Obl+kz5tL/cwYhEUbtZdSMIAQTnsMzpFi5ZgNpNq7FlrfN/cz6JtK8sNult5mnvxbLiM7lJ8/9yz5y+xJcL1/HlonVxVistVbogteuWp8jTefDw9CDk3gPCwyOoULEoNWqVccsvwvoNK/HW2y86DE1F8fDwoM1L9YzCaZMnLGX2zNWMHd8zzjo8W3/bR8XS9olDGzWuYuzrqVW7LL/+Yp8WKNBm+KxY8Xy0blOXlSs2ATBh3OJY56EyZQrgveFdOHr4FHt2HTWGGo8fO02jOvbfIqOWUls+Jz/jJ/Wye/6DkV0pXCQ38+f+SOijMEqUKkCnrs1cJgwVwh1qN6nK5CEzAfjrz0OEPgozAkzdFjWdBqHKJiWJtV2YIEEomXv0KJQ13//O+nU7WPrlepd1eYo8nYc69cpTp24FatYua3wrT0xKKT6e3JvX32zF38fP8PDBIzas38nvW/eTLp0f733QiRrPlGXF8l+NO5i7d+/Tt5fzXHBlyhbmypWbXIpRohgs+3Rs55qCgzNzK0aqkqoxEo2+1LaBEYT+/P0goz/qaRzHdPt2CMOGzGLoiK5M/bQfC+f9xNDBM2MtmgfwSnvHVUkeHh6079jYaa0eIRJLcI4gsucK5vL5q0SER3D5/BXyFLRsDi/iYq9cz/c6JmUTDekz+huP792VIGS68+eu8uhRKAUK5kQpxYMHjziw/x++Wb6R5cs2cvPGHSpXKW4XgAKzZqRp8xrUrVeB2nXL2e2hSWqFCueikDUTQqs2dR2e7/9OO7tKoc5UqlyMhUtHMO+LH+zKh9dvWIk3erXm4sVr9H3rE+P8cpu6O2DZR9Sylf3+oxVfRZf+LlQ4F6+0a8jbb0xy+vnp06fj3r0HjPzgC86dvcyUGf1p+GwVRg+fw4rlv9r9vw/OloV8+bLz+putaNuhUaz9EiIp5cib3chGcOHMZSMI3b3jmFvO08uTnCbl+csgw3Hm0Vqz4eedLF6wllP/XSAk5KGxsTJrUCYCAzNy8p/zDkXF9v11gsCsGalQsSidujSj2XM18PVNGdUIBw/tSMPGlVn25Xq+sMm2bev7dZPJkMGf94Z1psjTeTj130UqVy1Bo8ZVUErRpEFfp6+L8uH4NxwmV22Lze3edYx8Tz3v9LXZnwoke/ZADljLJsyZ9T2vv/ECxUsWYO6iDxg9ridnz16mcJE8TlfMCZFcZA7MaDwOsdkmceSvvx2u7f5OezJnNefvc3pZmJD0ooLPR2MWuFwifP3aba47SaWRL/9TvNqpCe07NSFvCsxQ7OHhQeUqJbh1857TIFS1ekkyZLDcnvv6+tChUxOHa/bHqKIaUyUn+2uGjerGK62HGos2bJdKZw3KZPy/zpAhgI8mvkXzZwcYz+/Zc5zi1gUIuXIHG0uqhUiuIiIiOG2tagv25e4PW8uaRGnWtiGd+5lXaTV9Jts5IdcZwJObFLlZNSr41H+mFy+2HBLrHpUoSimKPJ2Hdq8+yw/rJnHg2BKGfNA5RQYgW7XrlrNLBOrp6UGPni1ZvWZinK91lakAoN/Atk5T8zRoVJnv102mUpXiRt65DBn86dW3DQuXDDeu++fEWbsABOCbglKJCAGwfPZ3RhoeT08PChXPzx3rvOkpmzRWAFXqlI/XhvTEYrtE+57cCSWek/+cp/cbE9m2Zb/deV9fb7r2aEGbVxrg6+tN/gI5yZQpgBN/n+X+/Yc8XTQv/v6Ou/JTOl9fH9ZtnMqxI6e4e+8BBQvlivfwVptXGthtjo2yfc9cI2mqMzVqlmbTts/RWnP9+h0yZQrA29uLsLBwsmXPYmR8sBUQ4EeDZyvHv2NCJANb1+0wHkdERNKmSg+01nh5ezlUU500+HMaPF/LtFRPvul8jc21oY/CePQwFF+/5D+9kGKCUGRkJP/7bBUjP5hjNwTk42MJPgMGtXO6kMA2j1hqpZQyhrkSov87bQnMmpHZn39LSMhD6jWoSNceLWINQDE/1zbgeXt7sfrHCUz6eAm/rN+Ff4AfhYvkJjg4M6+98QKBNmPrQqQEeQrktMuMELWYxlk573t3Qvh8zALeHtnd4bmkoJQiQ6YAblkTHYfcCZEg5C4hIQ/o8PJwu9LWnp4edH+9JQMGtZe5hcfk4eFB1+4t6Nq9hdves3TZwixcOsJt7yeEmao3rMSPy2LPCGLrj192uSUI3bl1j4yZ08d9YQwZM2cwgtCtG3cIzJbliduS2JL9nFBkpOblVkPtAlDJUgXZ/MdMJk/rKwFICJFonmlcldIxsqA837EJVeo6bkgtXq4IPd/r9NifFR4ewZwJSzhx6F+al2hPr1ZD2L11f9wvtGEbdK5cvBbLlclHsr8T+vfkebvs0f0GtmXYqG4uq5IKIYS7eHl7Me3rMaxa+BOnjp+ldOXiVKtfkefLRWceyRKUmRU7v8A/IN1jf47WmkmDP+f7L39m9eK1RERE8tcfhziwczifrhpH2Ribxl3JVzg3+/48BMDfB05SrV7Fx25TUkn2Qcg2AI0c+1q8siMLIYS7+Pn70f7N1sZxRIR9IuNq9Ss8VgA6fuAfVi9aR5agTPz6/TbOnDwPwI0rt6I/KzyCacPnMO9n5xlRYipR/mm+W7wOcL6PKTlK9sNxUYaP6i4BSAhhOk9PTxq+UNs43vvHQU4eOZWg97h1/TZ9XvqA7xav4/Ce45w5ed4YSmvyUj3m/zLNuPbYvhPGsvC4lCj/tPH4QDwy/ycHKSIIDR3ehUHvvWp2M4QQAoAOvVrj6Wn59Xn53FXebjOUGy6KOjqzeMY33LUGlquXLHkdu7/TjvZvtea9T/pQtHQhCtis7D1xyHErhTP5i+YhqzWY3bp+mz3bEjanZIZkH4SeypGVIR90jvtCIYRIIkXLFGbw5LfxsS6BvnX9Nv8btyher42MjOSX1VuM44rPlGXy0pHUalyV3iO64W2d7y5VqZhxzYg3JjDro8Vs+vF39v5+gK3rtvPN3B/4c+NuNq/5g5B7lmmLmHdp61duftKuJjrlKoN0clGhYlG9ZfusuC8UQhgO3kw5O+ZTsj9+2cU7HUYZx3PWTqaETdFIZw7vOc5rzQYCkDFLBn489KXTIph/H/qX7s/2c1pVOWpTaunKxTi46xjZcwWzaNMMMmRKz9F9J+jeuD8A/gHp+PHQYvzcvFG/RvYWe7TWldzxXsn+TkgIIZKrGg0rU6txVeN4ytDZDomSbYWHR/Dp6HnGca3GVV1WYX66VEHGzhlClqDMDs/lKZgTgGP7TwJw+fxVvpn7IwDFyhYmbyFLpu/7IQ/4fcOuhHUqiUkQEkKIJ9BndA+8fSwLjQ/vPc46F/W1AOZPXmZkYPDw8KB156axvnedZjVYtXsuH80fSqsuzajVuColKxSl/nO1aN6uIbnzP2Vcu3z2d9wPeYBSikat6xjnf07mQ3LJfom2EEIkZ7nz56Ddm61ZNO1rAD4fM5/aTavZlVYAy91K1DUA3Qe1j3PoDiw54eo0q06dZtUdngsPj6Bt9de5cOYyd27eZdu6HTz7Yl0av1iXuRMtNcS2/7qHh/cfun1Izl3kTkgIIZ5Qp74vEZwjKwA3rt5i5bw1Dtf8uXG3Mb9TvFwROvV96Yk/18vLk8Zt6hnHxw5Y6nflLpDT2LsUHhbOqRPnnL4+OZAgJIQQT8g/IB2vvdvBOP7jF8d5mBtXbxmPK9cp77Zs20/bJBw+bg1CUUNzUTJnTb7Jg2U4TohUqHSWAFkhl8Qq14nOJ3fOpgJxlHQB0cNht2/ccdvn2m5Q3ffnYV5v/g6HdkfXWGvUqg5P5c7mts9zN7kTEkIIN7CdA4rat2OrYLF8xuN/jvznts8NzhFkJFn19fOxC0BlqpTg3Ym93PZZiUGCkBBCuMHVi9eNx0FOSijkK5zbeHzh9GW3fnafUT0oWCwfLdo1Ms491/5ZPvlqFAEZ/N36We4mw3FCCOEGF85cMh4/lSe7w/MP70cX47QdmnOHkhWLsvDX6RzYeZTAbFmo3qAiRcsUdutnJBYJQkII4QYXz0Tf3eTM6xiETh49ZTyO2mzqTp6enpSvXory1Uu5/b0TkwzHCSGEG9yyWWzgrKJp1PJpgCIlCzo8n1ZJEBJCCDewXZhw4rBj1uub124bj7283bM8OzWQICSEEG5QsWYZ4/Gfv+zmq/+ttnu+bJUSxuNvF65NcA2i1EqCkBBCuEHhkgXsyihMHzGHn7+JziPX4IVnCAzODFj2Cb3R8l3+PXY6qZuZ7EgQEkIINxk6tS9lq0bf8YztO5XdWy2F5fwD0jF61rv4p7ek0wm5e585E5aY0s7kRIKQEEK4iW86Xz5eNNyoihoRHsGotyZx5+ZdACrULMOkJSOM6zev+YNtP+8wpa3JhQQhIYRwo4yZ0zN1+WijDtD1KzdZMHW58Xy5aqUoZ7OMenDnsXz8zgwiIiKSuqnJggQhIYRws+AcQQz86A3jeOW8H+3mfwZ9/JaRdVtrzXeLf+bLT1cmeTuTAwlCQgiRCOo9V9PI6RYWGk7v1u/z1x8HAShQNC/zN0wjj7UCKsCscYvS5N1QnEFIKZVHKbVJKXVUKXVYKdXXev4l63GkUqpSjNdMVErtVkrVsR7nV0pppdTbNtd8qpTq4ub+CCFEsqCUYvCk3vil8wXg1vXb9HnpA1bM+QGtNYHBmaneoKLda25cuWlGU00VnzuhcGCg1ro4UA3opZQqARwCWgNbbC9WShWzPqwN2KZvvQL0VUr5PHGrhRAiBShYLB+ffDXKmB+KCI9gytBZTBv2BQCXzl6xu/7SuatJ3UTTxRmEtNYXtdZ7rY/vAkeBXFrro1rr405e4glEAhpQNuevAhuBzk/caiGESCHKVSvFvPVTKF6uiHHu6y++58DOIzywSWoKlhLgaU2C5oSUUvmB8oDLNYVa68OAP7ANmBnj6fHAQKVUrDkrlFKvW4fzdl+zSXUhhBApUfZcwXz+3cd2he+2rttO6EMJQvEOQkqp9MBKoJ/WOtaygFrrt7XWFbXWv8Y4/x+wE2gfx+tna60raa0rBQVlim8ThRAi2fL186FNtxbG8e6t+/HxtZ+duHDGvXWGUoJ4BSGllDeWALREa73qCT9zHDA4vp8thHg8pbMExH2RSFLla5TCw8Pyq+/vg//ia120EGXbzzu4H/LAjKaZJj6r4xQwFziqtf7kST9Qa30MOAK0iOtaIYRITdJnDDDmhrTWnDhkn2376sXrzBy7wISWmSc+dyM1gY5AfaXUPuufZkqpVkqpc0B1YI1S6ucEfO6HQO44rxJCiFSmRqPKxmNnc0Cr5v/ExRir5lKz+KyO26a1VlrrMlrrctY/P2mtv9Va59Za+2qts2utG8fyHqe01qVsjvdrrT201gvc1A8hhBMyJJf8vPxaS7I6KXoXRWttl307tZN5GSGESEIBGfx5a1iXWK9Zt+JXtNZJ0yCTSRASQogk1rhNPUpWLOry+TMnz3P0r7+TsEXmkSAkRConQ3LJj4eHB/0/7Ill3Zdza1f86vK51ESCkBBCmKBE+adp0a6Ry+c3fLuFsNCwJGyROSQICSGESXq+34mADP5On7tz8y5//LI7iVuU9CQICZEGyJBc8hQYnJmuA9u6fH5dGhiSkyAkhBAmeqFTUzJkcv4l4fcNu7h9I9YsaSlesg9CaWSVohAijfIPSEfLjk2cPhceFs7WdduTuEVJK9kHoatX016RJyESgwzJJV9turXA28fL6XMHdh5N4tYkrWQfhC5dukFYWLjZzRBCiESTPVcw703p6/S5g7slCJkqIjyCyR8vYdLHS7hzJ8Ts5gghRKJo0qYewz4dQN5CuahQs4xx/vSJc6xf9VuCMyhs37SH/41bxOl/zrm7qW6lkntqCKWU0cAhQzsxdERXM5sjRIp28KZ8kUsphr3+MRu/22ocFy9XhCGfvE2RkgXjfO3Na7d5oXxnwkIto0h9RvXglZ7Px7o5NiFqZG+xR2tdyR3vlezvhGwFBWc2uwlCpGgyL5RydB/UnvQZo39eR/edoHP9PrxS/XV+XLYh1tem8/c1AhDA9BFz+Hbh2kRr65NIUUGoS/fmZjdBCCGSRP4ieZi3YSpNX66Pl3f0ooWz/15gXL9pHNh5xOVr/fz9qN20mt256SPmcP1K8lvolSKCkL+/Hxeur8E3RilcIYRIzXLnz8GwGQNYtGkGVetVsHtudO9PuBhLOfCh0/rhZ1O5NfRhKNOHz0m0tj6uZB+EMmdJz6mLq8ngIrWFECJhZEgu5clfJA9TvhrNl799ho+vNwAXTl/ijZbvcurvs05fkyFTenqN6GZ3bsO3v7Fj895Eb29CJPsgVKBATtLFqMMuhBBpUcFi+Rj7xRBjT9HVi9fp3fo9bly95fT61l2aOdxBvdtxNNcu30jspsZbsg9CQgghotVqXJXJS0eRzt8PgBtXbzF9hPNhNqUUQ6f1szsXFhrO6F6Tk03RPAlCQqRBMiSXslV6pixj5wwxjjf9sM1l2Yeg7IF8NH+o3bndW/fzx4ZdidrG+JIgJIQQKVD1BpXInjsYsNzd/OdibgigTrPqFCtXxO7cwmlfJ2r74kuCkBBCpFDFy0YHll2//RXrtbnyPmV3/PfBk0RERCRKuxJCgpAQaZQMyaV8ZauWNB7PnbiU39fvdHltcM6sdsehj8K4eOYyt2/c4cqFa4SblKNTgpAQQqRQLV9tTK78OQB4+OARgzuPZdWCn5xemy1HkMO5wZ3H0rR4e14o34WWZTvz6/fbErW9zkgQEkKIFCpdgB+TlowgR57sAERGRjJp8Od8NmY+kZGRdtdmy+kYhP47fsZ4fOv6bUa+NYmz/55P3EbHIEFIiDRMhuRSvnyFc/PFT5PsFh4s+XQlI96YyKOHoca5khWLxpnANDwsnM1r/ky0tjojQUgIIVK4wGxZ+GzVR9R6topxbuN3W+n38gfcuXUPsNQsqlCzdJzvde7fC4nWTmckCAkhRCqQLsCPjxYMpXXX6ETP+3ccYeSbE42huWavNHD6Wl+brDTXLl+P9XNs767cQYKQEEKkEp6engz86A162+SM2/7rHr6e/T0ANRtVsbu+WNnCdHunHVOXjzbOXbsUe0qfG27OxC1BSIg0TuaFUhelFO3fak2HXq2Nc/M/WcajB494cP+hcS5jlgzMWz+VHoM6kDNf9B6ia5djDzIPHzxya3slCAkhRCr0+pCORnC5ezuEX3/8nVMnorMq5Cuc23icJSizsWjh5rVbPIol0GQKzOjWdkoQEkKIVMjbx5vnOzYxjr9btI5z/100jvMWymU89vLy5Knc2Yzj6SPnEh7uPJtCpsAMbm2nBCEhhAzJpVLNX2mAp5cnAAd2HuHcf9Er32Lm0H6+Y2Pj8bcLfqJPm6HcvX3P4T09PNwbNiQICSFEKhWYLYtdme8tP203Ht++ftvu2vZvtaZuixrG8b4/D/FK9deZN3kZm3783Ujrc+1S7KvnEkqCkBBCpGK2Q3IXz0aXA791447ddV7eXoz9YgivDX41+prrd5gzYQlDu39E/7bDCQ8L59Ce425tnwQhIQQgQ3KpVaVnypIzb3aH83du3nU4d/SvE+zass/p++zZdoDbN++yyM0lICQICSFEKubh4UFLm7uhKLZ3Qg9CHjJt2Be83vwd9v15yOn7dOjVmt/X7+T4gZNubZ+XW99NCCFEsvNit+asnPcjVy9Gz+fcvXWPiIgI9m47yPiBM+yG6gByF8hBsbJFqNmoMjUaVebK+Wu81myg29smQUgIYSidJYCDN0PMboZws4D0/oycOYheL0SXBPf28eLnbzbzYd+paB29Vq5K3fK8O6GX3QbWu7fv8V63D92+URVkOE4IIdKE8tVL8dawLsZxvRY1+WjAdCMAZcicnmEz+jPlq9F2ASgyMpLRvSbb7TFyJ7kTEkKINKJDrxfJmDkDD+4/5PiBf4iwbkgtXCI/U5aPIWu2LA6vWTBlOb9v2JVobZIgJIQQaYRSipavWjalPl+us3F+0IReTgPQySOnmDtxaaK2SYbjhBAiDbpts0R75bwfuR/ywOGaBVOWG8N1ZauWICCDv9vbIUFICGFH9gulDY1eqG08Xr/qN7o925+/D/1rnPvv+Bl+/WGbcfxq7zaE3L3v9nZIEBJCiDTo3Ym9aPJSPeP4zD/neK3pAFbO+xGttd1dUM1GlalQs0yitEPmhIQQIg3y9vFm+KcDqVy7HJMGz+TB/YeEhYYz+b3/sWfbATav+cO4tuuAtuzfeThR2iF3QkIIkYY1fbkB8zZMpUjJAsY52wBUrX5F8hfNy7sdxyTK50sQEkKINC5f4dz874eJFCia1+58hswZeKn7c451H9xIhuOEEEKQLsCPaSvGMmvcIk7/c47A4Mzs2rKPgR1GUuvZKmQJymSX9sddJAgJIYQAICh7IEOn9QNg0KujeBDyEIBt63fi6Zk4A2cyHCeEcCDLtMXVSzfsjiMiIhPlcyQICSGEcFC3eY24L3KDOIOQUiqPUmqTUuqoUuqwUqqv9XygUmqDUuqE9b9ZbF4zUSm1WylVx3qcXymllVJv21zzqVKqSyL0SQghxBPq2KcNgcGZHc5nzprRrZ8TnzuhcGCg1ro4UA3opZQqAQwBNmqtiwAbrccopYpZX1cb6GXzPleAvkopH3c1XgghROLw9PQ08szZ8vJ271KCOIOQ1vqi1nqv9fFd4CiQC3geWGi9bCHwgvWxJxCJZVGfsnmrq1iCVWeEEEIkex16v+hwN3QtxlzRk0pQSFNK5QfKAzuA7Frri2AJVEqpbNbHh5VS/sA2YFCMtxgPrFVKzYvjc14HXrce3sjgU++/hLQzBQsCrpndiCQk/U3d0lJ/01JfAfzc9UbxDkJKqfTASqCf1vqOUsrltVrrt12c/08ptRNoH9tnaa1nA7Otn7tba10pvu1MydJSX0H6m9qlpf6mpb6Cpb/ueq94rY5TSnljCUBLtNarrKcvK6VyWJ/PgWXOJz7GAYPj+9lCCCFSr/isjlPAXOCo1voTm6e+J3p+pzPwXXw+UGt9DDgCtEhYU4UQQqQ28RmOqwl0BA4qpfZZz72PZX7na6VUd+AM8FICPvdD4K94Xjs7Ae+b0qWlvoL0N7VLS/1NS30FN/ZXRdWLEEIIIZKazMsIIYQwjQQhIYQQpjElCCml+ltTAB1SSi1TSvkppZYrpfZZ/5yymX9ylQbogc31+5RSnczoS3y46G85pdR2a9t3K6Wq2FyfGvtbVin1p1LqoFLqB6VURpvrU2x/lVJ9rf08rJTqZz2X0JRWKaKv4LK/L1mPI5VSlWJcnxr7O1EpdUwpdUAp9a1SKrPN9amxv2Osfd2nlFqvlMppc/2T91drnaR/sGRb+A9IZz3+GugS45rJwHDr42LARMAf+Np6Lj9wKKnb7s7+AuuBptZzzYDNqby/u4A61nPdgDEpvb9AKeCQte1ewC9AEWACMMR6zRDg45Te1zj6WxwoCmwGKtlcn1r7+yzgZb3m4zTw881oc00f4H/u7K9Zw3FeQDqllJe1AxeinlBKKeBlYJn1lKs0QCmJs/5qIOpuIBPR/w9Sa3+LAlusz28AXrQ+Tsn9LQ5s11rf11qHA78BrUh4SquUwml/tdZHtdbHnVyfWvu73noMsB3IbX2cWvt7x+aaAKLrrLqlv0kehLTW54FJWJZ1XwRua63X21zyDHBZa33Cev1hLL/ItgEzba4rFOOW75mk6UHCxNLffsBEpdRZ6/PvWa9Prf09BLS0XvYSkMd6fUru7yGgtlIqq7KkqmqGpV92Ka0AI6UVKbev4Lq/TqWR/nYD1kLq7q9S6kPr76oOwHBwX3+TvLKqdXz8eaAAcAtYoZR6VWv9pfWSdkTfBQEu0wCd1FqXS8SmuoWr/gJVgP5a65VKqZexbAhuCKm2v92A6Uqp4Vg2OodGvSal9ldrfVQp9TGWO7t7wH4sWedje02K7CtIf4nRX6XUUOvxEpvXpMr+aq2HAkOVUu8BvYER1vNP3F8zhuMaAv9pra9qrcOAVUANAOvwTWtguQntSiyu+tvZ+hhgBZaglBo47a/W+pjW+lmtdUUsXzJOmtpKN9Faz9VaV9Ba1wZuACd4/JRWyZ6L/qZarvqrlOqMJetLB22dDEkN4vHzXUr0ULpbmBGEzgDVlFL+1vmfBljKQ4DlF9gxrfU5E9qVWFz19wJQx3pNfVLPP2an/VXWLOtKKQ/gA+B/JrbRbWz6lRfLF6hlPGZKq5TARX9TLWf9VUo1wZL/sqXW+r6Z7XM3F/0tYnNJS+CYOz8zyYfjtNY7lFLfAHux3Or9RXQKiLbE/y91IWWzjBuYp7We7raGukks/f0LmGa9+3tIdOkKV1J6f99QSkUVOVwFzI/jrVJEf4GVSqmsQBjQS2t9UymV0JRWKaWv4Ly/rYAZQDCwRim1T2vtWA0tWkrv76eAL7DB8j2L7VrrN2J5j5Te3zlKqaJYFiGcBmLrKySwv5K2RwghhGkkY4IQQgjTSBASQghhGglCQgghTCNBSAghhGkkCAkhhDCNBCEhhBCmkSAkhBDCNBKEhBBCmEaCkBBCCNNIEBJCCGEaCUJCCCFMI0FICCGEaSQICSGEMI0EISGEEKaRICSEEMI0EoSEEEKYRoKQEEII00gQEkIIYRoJQkIIIUwjQUgIIYRpJAgJIYQwjQQhIYQQppEgJIQQwjQShIQQQphGgpAQQgjTSBASQghhGglCQgghTCNBSAghhGkkCAkhhDCNBCEhhBCmkSAkhBDCNBKEhBBCmEaCkBBCCNNIEBJCCGEaCUJCCCFMI0FICCGEaSQICSGEMI0EISGEEKaRICSEEMI0EoSEEEKYRoKQEEII00gQEkIIYRoJQkIIIUwjQUgIIYRpJAgJIYQwjQQhIYQQppEgJIQQwjQShIQQQphGgpAQQgjTSBASQghhGglCQgghTCNBSAghhGkkCAkhhDCNBCEhhBCmkSAkhBDCNBKEhBBCmEaCkBBCCNNIEBJCCGEaCUJCCCFM42V2A5IDpZQC6gLZTG6KEEI8rr+01n+b3YiESvNBaMeOFbpu3Srkz5+bgk8Hmd0ct7j0INTsJrjNX9d9zG6C25y8osxugtuE/3vX7Ca4Rfj5K2Y3wW3u3tqPV/oiRIT885TW+rLZ7YmvNBuElFIFX3656cmQkAfMmDGMUqWe5l7YBbOb5RYHb4aY3QS3USf9zW6C29w66ml2E9zm0eaLZjfBLR6FHjO7CW7zyDsrkQ/OERl6/ZJ3ptKE3zmUXmud7H8ZpLkgpJTK2q9f52vVq5enZ89XqF+/utlNEkKIJ6aUwtM/Dx7pchJx7yTKO/M978AqhN/c5aW1jjC7fa6kmYUJSim/CRMG6bJli12rVKkU27YtlQAkhEh1lPLEK8PT+GSrjw6/h/IODPcJrqOtc9/JTqq/E1JKeSxePCGibNliKKXYvv1r/Px8zW6WEEIkKuXhg3fmsujwEMJuH8TDN3ukUqqi1nqv2W2zlaqD0MaNC3W1auXYu/cIGzcuIGvWLGY3SQghkpTyCsAnazUiQ28Sfnv/Hs+A/ETeP51fa33a7LZBKg1CSqlSzZrVOZghQwBLlkyiYME8ZjdJCCFM5eGTBZ/gukQ8uIgOv3vKK2NxIu4eC9Ra3zSzXakqCCmlcnXv3uZcvXpVGTGiN1WqlDG7SUIIkax4psuBh192Iu6fQnlnvuGdpQLht/7y01o/MqM9qSoItWhR79z585f56acvZN5HCCFcUMoDr4CCePgEE3plI15ZKj8ETFm4kKpWx/344yb/OnWqUK3ayyxd+gORkZFmN0kIIZIdHRlG2K0DhF3/E+/AyoTf3GVaLEhVQUhr/WDIkElq//5jwTt27KdWrfZs3rzD7GYJIUSyoHUk4Xf/JvTKRpRXADrshnfo1S1Ka63NalOqGo6LorW+hiUlXOHPPlty4pNP5jN+/DuUKFHY7KYJIUSS01oT+eAc4XeO4JkuFzrsVoawGzvvmd0uSKVBKIrW+h9A/fnncv3mmyMpWrQAo0a9TY4ckqdUCJE2RD66Rtjt/Xh4ZUSH3cwZFnojWeVcSlXDca5Ur/6K2rJll0fTprVp3rwnI0fO4N69ZJ9SSQghHltk2F1Cr20j/M4R9KOrJcPv/aO01skqAEEaCUIAWmvdqlUv9ddfR3yCgwOpWbMds2cvJzw83OymCSGE2+iIR4Td3EPYjR1EPjhXL+LBeaW1PmJ2u1xJM0EoitY6rFevUerAgeOZTp++QLVqL/PDD79i4rycEEI8MR0ZTvidI4Re3YSHTxA69Jqn1nqz2e2Ki0rrv3yVUrm7dm199uTJsxQskjrmim48Sj13d//cST3TlhduJcv8kY8l4sJ9s5vgFhHXb5ndBLd5cO9fPP3zE357n2kbTx9Hmg9CUZRS+YEewLcmN8UdWpE6+gHSl+QqtfQltfQDoJbWeprZjUgoCUI2lFK7tdaVzG7Hk0ot/QDpS3KVWvqSWvoBKbcvaW5OSAghRPIhQUgIIYRpJAjZm212A9wktfQDpC/JVWrpS2rpB6TQvsickBBCCNPInZAQQgjTSBASQghhGglCgFJqnlLqilLqkNlteVJKqcxKqW+UUseUUkeVUtXNblN8Ofs5KKVeUkodVkpFKqVSzPJTV3+nlFJvK6WOW/s0waz2xZdSKo9SapP179JhpVRf6/kU93Nx1Rfrcynm56KU8lNK7VRK7be2d5T1fIr7mUAqz6KdAAuAT4FFJrfDHaYB67TWbZRSPoC/2Q1KgAU4/hwOAa2BWWY06AksIEZflFL1gOeBMlrrR0qplJCiIxwYqLXeq5TKAOxRSm0gZf5cXPUlOynr5/IIqK+1vqeU8ga2KaXWkjJ/JhKEALTWW6wZE1I0pVRGoDbQBUBrHQqEmtmmhHD2c9BaHwVQKmWlvHHxd+pNYHxUShWt9ZUkb1gCWbMuX7Q+vquUOgrk0lpvgJT1c3HVF+A1UtDPxVqALqoWkLf1j06p/1ZkOC51KQhcBeYrpf5SSs1RSgWY3ShheBp4Rim1Qyn1m1KqstkNSghrUC0PpPhyxTH6kuJ+LkopT6XUPuAKsEFrnWJ/JhKEUhcvoAIwU2tdHggBhpjbJGHDC8gCVAMGAV+rFPK1VSmVHlgJ9NNa3zG7PU/CSV9S3M9Fax2htS4H5AaqKKVKmdykxyZBKHU5B5yz+Vb0DZagJJKHc8AqbbETiASCTG5TnKzzDiuBJVrrVWa350m46EuK/LkAaK1vAZuBJua25PFJEEpFtNaXgLNKqaLWUw2AZFvMKg1aDdQHUEo9DfgA18xsUFysdwRzgaNa60/Mbs+TiKUvq0lBPxelVLBSKrP1cTqgIXDM1EY9Ca11mv8DLMMyYRmG5VtRd7Pb9AR9KQfsBg5g+ceVxew2PcnPAUuq/XNYVgRdBn42u51P0Bcf4Essq5j2YlnhZHpb4+hHLUBb/z7ts/5plhJ/LrH0JUX9XIAywF/WfhwChlvPp7ifidZa0vYIIYQwjwzHCSGEMI0EISGEEKaRICSEEMI0EoSEEEKYRoKQEEII00gQEiKJKKXaKaWGOjl/SimVIjZHCuFuEoSESDpNgHVmN0KI5ESyaAvhgjXJ5TpgG5a8YvuB+cAoIBvQActmxwJADiyJMAdYr20KnAee01qHWXfrlwP2KqWyYtnMGgzsBJTNZ64G8gB+wDSt9WylVHeglNa6v/Wa14DiWusBidh9IZKE3AkJEbvCWGo0lQGKAe2x7Lx/B3jfek0hoDmWmjRfApu01qWBB9bzYMnYvF9bdoePALZpS5LZ74G8Np/XTWtdEagE9LEGrK+Alta8ZwBdsQRDIVI8CUJCxO4/rfVBrXUkcBjYaA0kB4H81mvWaq3DrOc8iR5ys72mCbDW+rg2lmCF1noNcNPm8/oopfYD27HcERXRWocAvwItlFLFAG+t9UF3d1QIM8hwnBCxe2TzONLmOJLofz9RxdAilVJhOjoXlu01zwIv2ryXQ74spVRdLMkoq2ut7yulNmMZlgOYg+XO6xhyFyRSEbkTEiKRKaUyAV5a6+vWU1uwzCehlGqKpZYNQCbgpjUAFcMytwSAtpTnyINlOHBZUrVdiMQmQUiIxNcI+MXmeBRQWym1F8sd0hnr+XWAl1LqADAGy5Ccra+B37XWNxEilZAs2kIkMqXUHGCO1jpmUEno+/wITNFab3RPy4QwnwQhIZI5awGznVhW171kcnOEcCsJQkIIIUwjc0JCCCFMI0FICCGEaSQICSGEMI0EISGEEKaRICSEEMI0/wdINxDNZLeslwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting annual_pr_mean\n",
    "\n",
    "#plotting with cartopy\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,9.5))\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([88,93,20,27])\n",
    "\n",
    "\n",
    "m1 = ax.contourf(annual_pr_mm['longitude'], annual_pr_mm['latitude'],annual_pr_mm,\n",
    "                 np.arange(1,35+1,5),transform = ccrs.PlateCarree(),cmap='YlGnBu',extend='both')\n",
    "\n",
    " \n",
    "# Define the xticks for longitude\n",
    "ax.set_xticks(np.arange(87,94,1), crs=ccrs.PlateCarree())\n",
    "lon_formatter = cticker.LongitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "\n",
    "# Define the yticks for latitude\n",
    "ax.set_yticks(np.arange(20,28,1), crs=ccrs.PlateCarree())\n",
    "lat_formatter = cticker.LatitudeFormatter()\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "plt.colorbar(m1, orientation = 'horizontal',shrink=0.5, fraction = 0.04 , label = 'mm/day')\n",
    "ax.coastlines(linewidth=3)\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=3)\n",
    "#ax.add_feature(cfeature.STATES)\n",
    "plt.title('Reanalysis(Eraint) Annual mean precip rate in mm/day')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#to save and overwrite\n",
    "strFile =\"/home/mkibria/Academic/pr_of_thesis/figures/bd_daily_pr_re_eraintrin_annual_mean.png\"\n",
    "\n",
    "if os.path.isfile(strFile):\n",
    "   os.remove(strFile)   # Opt.: os.system(\"rm \"+strFile)\n",
    "plt.savefig(strFile)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}